@proceedings{gwc-2021-global,
    title = "Proceedings of the 11th Global Wordnet Conference",
    editor = "Vossen, Piek  and
      Fellbaum, Christiane",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.0",
}
@inproceedings{bao-etal-2021-universal,
    title = "On Universal Colexifications",
    author = "Bao, Hongchang  and
      Hauer, Bradley  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.1",
    pages = "1--7",
    abstract = "Colexification occurs when two distinct concepts are lexified by the same word. The term covers both polysemy and homonymy. We posit and investigate the hypothesis that no pair of concepts are colexified in every language. We test our hypothesis by analyzing colexification data from BabelNet, Open Multilingual WordNet, and CLICS. The results show that our hypothesis is supported by over 99.9{\%} of colexified concept pairs in these three lexical resources.",
}
@inproceedings{agostini-etal-2021-uzwordnet,
    title = "{UZWORDNET}: A Lexical-Semantic Database for the {U}zbek Language",
    author = "Agostini, Alessandro  and
      Usmanov, Timur  and
      Khamdamov, Ulugbek  and
      Abdurakhmonova, Nilufar  and
      Mamasaidov, Mukhammadsaid",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.2",
    pages = "8--19",
    abstract = "The results reported in this paper aim to increase the presence of the Uzbek language in the Internet and its usability within IT applications. We describe the initial development of a {``}word-net{''} for the Uzbek language compatible to Princeton WordNet. We called it UZWORDNET. In the current version, UZWORDNET contains 28140 synsets, 64389 sense and 20683 words; its estimated accuracy is 75.98{\%}. To the best of our knowledge, it is the largest wordnet for Uzbek existing to date, and the second wordnet developed overall.",
}
@inproceedings{sefara-etal-2021-practical,
    title = "Practical Approach on Implementation of {W}ord{N}ets for {S}outh {A}frican Languages",
    author = "Sefara, Tshephisho Joseph  and
      Mokgonyane, Tumisho Billson  and
      Marivate, Vukosi",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.3",
    pages = "20--25",
    abstract = "This paper proposes the implementation of WordNets for five South African languages, namely, Sepedi, Setswana, Tshivenda, isiZulu and isiXhosa to be added to open multilingual WordNets (OMW) on natural language toolkit (NLTK). The African WordNets are converted from Princeton WordNet (PWN) 2.0 to 3.0 to match the synsets in PWN 3.0. After conversion, there were 7157, 11972, 1288, 6380, and 9460 lemmas for Sepedi, Setswana, Tshivenda, isiZulu and isiX- hosa respectively. Setswana, isiXhosa, Sepedi contains more lemmas compared to 8 languages in OMW and isiZulu contains more lemmas compared to 7 languages in OMW. A library has been published for continuous development of African WordNets in OMW using NLTK.",
}
@inproceedings{habibi-etal-2021-homonymy,
    title = "Homonymy and Polysemy Detection with Multilingual Information",
    author = "Habibi, Amir Ahmad  and
      Hauer, Bradley  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.4",
    pages = "26--35",
    abstract = "Deciding whether a semantically ambiguous word is homonymous or polysemous is equivalent to establishing whether it has any pair of senses that are semantically unrelated. We present novel methods for this task that leverage information from multilingual lexical resources. We formally prove the theoretical properties that provide the foundation for our methods. In particular, we show how the One Homonym Per Translation hypothesis of Hauer and Kondrak (2020a) follows from the synset properties formulated by Hauer and Kondrak (2020b). Experimental evaluation shows that our approach sets a new state of the art for homonymy detection.",
}
@inproceedings{bond-choo-2021-taboo,
    title = "Taboo {W}ordnet",
    author = "Bond, Francis  and
      Choo, Merrick Yeu Herng",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.5",
    pages = "36--43",
    abstract = "This paper describes the development of an online lexical resource to help detection systems regulate and curb the use of offensive words online. With the growing prevalence of social media platforms, many conversations are now conducted on- line. The increase of online conversations for leisure, work and socializing has led to an increase in harassment. In particular, we create a specialized sense-based vocabulary of Japanese offensive words for the Open Multilingual Wordnet. This vocabulary expands on an existing list of Japanese offen- sive words and provides categorization and proper linking to synsets within the multilingual wordnet. This paper then discusses the evaluation of the vocabulary as a resource for representing and classifying offensive words and as a possible resource for offensive word use detection in social media.",
}
@inproceedings{sainz-rigau-2021-ask2transformers,
    title = "{A}sk2{T}ransformers: Zero-Shot Domain labelling with Pretrained Language Models",
    author = "Sainz, Oscar  and
      Rigau, German",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.6",
    pages = "44--52",
    abstract = "In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels. We exploit the knowledge encoded within different off-the-shelf pre-trained Language Models and task formulations to infer the domain label of a particular WordNet definition. The proposed zero-shot system achieves a new state-of-the-art on the English dataset used in the evaluation.",
}
@inproceedings{janz-maziarz-2021-discriminating,
    title = "Discriminating Homonymy from Polysemy in Wordnets: {E}nglish, {S}panish and {P}olish Nouns",
    author = "Janz, Arkadiusz  and
      Maziarz, Marek",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.7",
    pages = "53--62",
    abstract = "We propose a novel method of homonymy-polysemy discrimination for three Indo-European Languages (English, Spanish and Polish). Support vector machines and LASSO logistic regression were successfully used in this task, outperforming baselines. The feature set utilised lemma properties, gloss similarities, graph distances and polysemy patterns. The proposed ML models performed equally well for English and the other two languages (constituting testing data sets). The algorithms not only ruled out most cases of homonymy but also were efficacious in distinguishing between closer and indirect semantic relatedness.",
}
@inproceedings{lualdi-etal-2021-implementing,
    title = "Implementing {ASLN}et V1.0: Progress and Plans",
    author = "Lualdi, Colin  and
      Wright, Elaine  and
      Hudson, Jack  and
      Caselli, Naomi  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.8",
    pages = "63--72",
    abstract = "We report on the development of ASLNet, a wordnet for American Sign Language (ASL). ASLNet V1.0 is currently under construction by mapping easy-to-translate ASL lexical nouns to Princeton WordNet synsets. We describe our data model and mapping approach, which can be extended to any sign language. Analysis of the 390 synsets processed to date indicates the success of our procedure yet also highlights the need to supplement our mapping with the {``}merge{''} method. We outline our plans for upcoming work to remedy this, which include use of ASL free-association data.",
}
@inproceedings{ahmadi-mccrae-2021-monolingual,
    title = "Monolingual Word Sense Alignment as a Classification Problem",
    author = "Ahmadi, Sina  and
      McCrae, John P.",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.9",
    pages = "73--80",
    abstract = "Words are defined based on their meanings in various ways in different resources. Aligning word senses across monolingual lexicographic resources increases domain coverage and enables integration and incorporation of data. In this paper, we explore the application of classification methods using manually-extracted features along with representation learning techniques in the task of word sense alignment and semantic relationship detection. We demonstrate that the performance of classification methods dramatically varies based on the type of semantic relationships due to the nature of the task but outperforms the previous experiments.",
}
@inproceedings{losing-etal-2021-extraction,
    title = "Extraction of Common-Sense Relations from Procedural Task Instructions using {BERT}",
    author = {Losing, Viktor  and
      Fischer, Lydia  and
      Deigm{\"o}ller, J{\"o}rg},
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.10",
    pages = "81--90",
    abstract = "Manipulation-relevant common-sense knowledge is crucial to support action-planning for complex tasks. In particular, instrumentality information of what can be done with certain tools can be used to limit the search space which is growing exponentially with the number of viable options. Typical sources for such knowledge, structured common-sense knowledge bases such as ConceptNet or WebChild, provide a limited amount of information which also varies drastically across different domains. Considering the recent success of pre-trained language models such as BERT, we investigate whether common-sense information can directly be extracted from semi-structured text with an acceptable annotation effort. Concretely, we compare the common-sense relations obtained from ConceptNet versus those extracted with BERT from large recipe databases. In this context, we propose a scoring function, based on the WordNet taxonomy to match specific terms to more general ones, enabling a rich evaluation against a set of ground-truth relations.",
}
@inproceedings{mccrae-etal-2021-globalwordnet,
    title = "The {G}lobal{W}ord{N}et Formats: Updates for 2020",
    author = "McCrae, John P.  and
      Goodman, Michael Wayne  and
      Bond, Francis  and
      Rademaker, Alexandre  and
      Rudnicka, Ewa  and
      Costa, Luis Morgado Da",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.11",
    pages = "91--99",
    abstract = "The Global Wordnet Formats have been introduced to enable wordnets to have a common representation that can be integrated through the Global WordNet Grid. As a result of their adoption, a number of shortcomings of the format were identified, and in this paper we describe the extensions to the formats that address these issues. These include: ordering of senses, dependencies between wordnets, pronunciation, syntactic modelling, relations, sense keys, metadata and RDF support. Furthermore, we provide some perspectives on how these changes help in the integration of wordnets.",
}
@inproceedings{goodman-bond-2021-intrinsically,
    title = "Intrinsically Interlingual: The Wn Python Library for Wordnets",
    author = "Goodman, Michael Wayne  and
      Bond, Francis",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.12",
    pages = "100--107",
    abstract = "This paper introduces Wn, a new Python library for working with wordnets. Unlike previous libraries, Wn is built from the beginning to accommodate multiple wordnets {---} for multiple languages or multiple versions of the same wordnet {---} while retaining the ability to query and traverse them independently. It is also able to download and incorporate wordnets published online. These features are made possible through Wn{'}s adoption of standard formats and methods for interoperability, namely the WN-LMF schema (Vossen et al., 2013; Bond et al., 2020) and the Collaborative Interlingual Index (Bond et al., 2016). Wn is open-source, easily available, and well-documented.",
}
@inproceedings{mititelu-etal-2021-semantic,
    title = "Semantic Analysis of Verb-Noun Derivation in {P}rinceton {W}ord{N}et",
    author = "Mititelu, Verginica  and
      Leseva, Svetlozara  and
      Stoyanova, Ivelina",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.13",
    pages = "108--117",
    abstract = "We present here the results of a morphosemantic analysis of the verb-noun pairs in the Princeton WordNet as reflected in the standoff file containing pairs annotated with a set of 14 semantic relations. We have automatically distinguished between zero-derivation and affixal derivation in the data and identified the affixes and manually checked the results. The data show that for each semantic relation an affix prevails in creating new words, although we cannot talk about their specificity with respect to such a relation. Moreover, certain pairs of verb-noun semantic primes are better represented for each semantic relation, and some semantic clusters (in the form of WordNet subtrees) take shape as a result. We thus employ a large-scale data-driven linguistically motivated analysis afforded by the rich derivational and morphosemantic description in WordNet to the end of capturing finer regularities in the process of derivation as represented in the semantic properties of the words involved and as reflected in the structure of the lexicon.",
}
@inproceedings{marsan-etal-2021-building,
    title = "Building the {T}urkish {F}rame{N}et",
    author = {Mar{\c{s}}an, B{\"u}{\c{s}}ra  and
      Kara, Neslihan  and
      {\"O}z{\c{c}}elik, Merve  and
      Ar{\i}can, Bilge Nas  and
      Cesur, Neslihan  and
      Kuzgun, Asl{\i}  and
      San{\i}yar, Ezgi  and
      Kuyruk{\c{c}}u, O{\u{g}}uzhan  and
      Yildiz, Olcay Taner},
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.14",
    pages = "118--125",
    abstract = "FrameNet (Lowe, 1997; Baker et al., 1998; Fillmore and Atkins, 1998; Johnson et al., 2001) is a computational lexicography project that aims to offer insight into the semantic relationships between predicate and arguments. Having uses in many NLP applications, FrameNet has proven itself as a valuable resource. The main goal of this study is laying the foundation for building a comprehensive and cohesive Turkish FrameNet that is compatible with other resources like PropBank (Kara et al., 2020) or WordNet (Bakay et al., 2019; Ehsani, 2018; Ehsani et al., 2018; Parlar et al., 2019; Bakay et al., 2020) in the Turkish language.",
}
@inproceedings{nikishina-etal-2021-evaluation,
    title = "Evaluation of Taxonomy Enrichment on Diachronic {W}ord{N}et Versions",
    author = "Nikishina, Irina  and
      Loukachevitch, Natalia  and
      Logacheva, Varvara  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.15",
    pages = "126--136",
    abstract = "The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy. On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures. Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations. In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings. We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.",
}
@inproceedings{rudnicka-etal-2021-non,
    title = "A (Non)-Perfect Match: Mapping pl{W}ord{N}et onto {P}rinceton{W}ord{N}et",
    author = "Rudnicka, Ewa  and
      Witkowski, Wojciech  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.16",
    pages = "137--146",
    abstract = "The paper reports on the methodology and final results of a large-scale synset mapping between plWordNet and Princeton WordNet. Dedicated manual and semi-automatic mapping procedures as well as interlingual relation types for nouns, verbs, adjectives and adverbs are described. The statistics of all types of interlingual relations are also provided.",
}
@inproceedings{rouhizadeh-etal-2021-persian,
    title = "{P}ersian {S}em{C}or: A Bag of Word Sense Annotated Corpus for the {P}ersian Language",
    author = "Rouhizadeh, Hossein  and
      Shamsfard, Mehrnoush  and
      Dehghan, Mahdi  and
      Rouhizadeh, Masoud",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.17",
    pages = "147--156",
    abstract = "Supervised approaches usually achieve the best performance in the Word Sense Disambiguation problem. However, the unavailability of large sense annotated corpora for many low-resource languages make these approaches inapplicable for them in practice. In this paper, we mitigate this issue for the Persian language by proposing a fully automatic approach for obtaining Persian SemCor (PerSemCor), as a Persian Bag-of-Word (BoW) sense-annotated corpus. We evaluated PerSemCor both intrinsically and extrinsically and showed that it can be effectively used as training sets for Persian supervised WSD systems. To encourage future research on Persian Word Sense Disambiguation, we release the PerSemCor in http://nlp.sbu.ac.ir.",
}
@inproceedings{ozcelik-etal-2021-hisnet,
    title = "{H}is{N}et: A Polarity Lexicon based on {W}ord{N}et for Emotion Analysis",
    author = {{\"O}z{\c{c}}elik, Merve  and
      Ar{\i}can, Bilge Nas  and
      Bakay, {\"O}zge  and
      Sarm{\i}{\c{s}}, Elif  and
      Ergelen, {\"O}zlem  and
      Bayezit, Nilg{\"u}n G{\"u}ler  and
      Y{\i}ld{\i}z, Olcay Taner},
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.18",
    pages = "157--165",
    abstract = "Dictionary-based methods in sentiment analysis have received scholarly attention recently, the most comprehensive examples of which can be found in English. However, many other languages lack polarity dictionaries, or the existing ones are small in size as in the case of SentiTurkNet, the first and only polarity dictionary in Turkish. Thus, this study aims to extend the content of SentiTurkNet by comparing the two available WordNets in Turkish, namely KeNet and TR-wordnet of BalkaNet. To this end, a current Turkish polarity dictionary has been created relying on 76,825 synsets matching KeNet, where each synset has been annotated with three polarity labels, which are positive, negative and neutral. Meanwhile, the comparison of KeNet and TR-wordnet of BalkaNet has revealed their weaknesses such as the repetition of the same senses, lack of necessary merges of the items belonging to the same synset and the presence of redundant narrower versions of synsets, which are discussed in light of their potential to the improvement of the current lexical databases of Turkish.",
}
@inproceedings{bakay-etal-2021-turkish,
    title = "{T}urkish {W}ord{N}et {K}e{N}et",
    author = {Bakay, {\"O}zge  and
      Ergelen, {\"O}zlem  and
      Sarm{\i}{\c{s}}, Elif  and
      Y{\i}ld{\i}r{\i}m, Selin  and
      Ar{\i}can, Bilge Nas  and
      Kocabalc{\i}o{\u{g}}lu, Atilla  and
      {\"O}z{\c{c}}elik, Merve  and
      San{\i}yar, Ezgi  and
      Kuyruk{\c{c}}u, O{\u{g}}uzhan  and
      Avar, Beg{\"u}m  and
      Y{\i}ld{\i}z, Olcay Taner},
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.19",
    pages = "166--174",
    abstract = "Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and is linked to PWN through interlingual relations. In this paper, we present the procedure adopted in creating KeNet, give details about our approach in annotating semantic relations such as hypernymy and discuss the language-specific problems encountered in these processes.",
}
@inproceedings{dziob-walentynowicz-2021-enriching,
    title = "Enriching pl{W}ord{N}et with morphology",
    author = "Dziob, Agnieszka  and
      Walentynowicz, Wiktor",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.20",
    pages = "175--181",
    abstract = "In the paper, we present the process of adding morphological information to the Polish WordNet (plWordNet). We describe the reasons for this connection and the intuitions behind it. We also draw attention to the specificity of the Polish morphology. We show in which tasks the morphological information is important and how the methods can be developed by extending them to include combined morphological information based on WordNet.",
}
@inproceedings{svetla-2021-towards,
    title = "Towards Expanding {W}ord{N}et with Conceptual Frames",
    author = "Svetla, Koeva",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.21",
    pages = "182--191",
    abstract = "The paper presents the project Semantic Network with a Wide Range of Semantic Relations and its main achievements. The ultimate objective of the project is to expand Princeton WordNet with conceptual frames that define the syntagmatic relations of verb synsets and the semantic classes of nouns felicitous to combine with particular verbs. At this stage of the work: a) over 5,000 WordNet verb synsets have been supplied with manually evaluated FrameNet semantic frames, b) 253 semantic types have been manually mapped to the appropriate WordNet concepts providing detailed ontological representation of the semantic classes of nouns.",
}
@inproceedings{siegel-bond-2021-odenet,
    title = "{O}de{N}et: Compiling a {G}erman{W}ord{N}et from other Resources",
    author = "Siegel, Melanie  and
      Bond, Francis",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.22",
    pages = "192--198",
    abstract = "The Princeton WordNet for the English language has been used worldwide in NLP projects for many years. With the OMW initiative, wordnets for different languages of the world are being linked via identifiers. The parallel development and linking allows new multilingual application perspectives. The development of a wordnet for the German language is also in this context. To save development time, existing resources were combined and recompiled. The result was then evaluated and improved. In a relatively short time a resource was created that can be used in projects and continuously improved and extended.",
}
@inproceedings{solovyev-loukachevitch-2021-comparing,
    title = "Comparing Similarity of Words Based on Psychosemantic Experiment and {R}u{W}ord{N}et",
    author = "Solovyev, Valery  and
      Loukachevitch, Natalia",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.23",
    pages = "199--206",
    abstract = "In the paper we compare the structure of the Russian language thesaurus RuWordNet with the data of a psychosemantic experiment to identify semantically close words. The aim of the study is to find out to what extent the structure of RuWordNet corresponds to the intuitive ideas of native speakers about the semantic proximity of words. The respondents were asked to list synonyms to a given word. As a result of the experiment, we found that the respondents mainly mentioned not only synonyms but words that are in paradigmatic relations with the stimuli. The words of the mental sphere were chosen for the experiment. In 95{\%} of cases, the words characterized in the experiment as semantically close were also close according to the thesaurus. In other cases, additions to the thesaurus were proposed.",
}
@inproceedings{marcinczuk-etal-2021-text,
    title = "Text Document Clustering: {W}ordnet vs. {TF}-{IDF} vs. Word Embeddings",
    author = "Marci{\'n}czuk, Micha{\l}  and
      Gniewkowski, Mateusz  and
      Walkowiak, Tomasz  and
      B{\k{e}}dkowski, Marcin",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.24",
    pages = "207--214",
    abstract = "In the paper, we deal with the problem of unsupervised text document clustering for the Polish language. Our goal is to compare the modern approaches based on language modeling (doc2vec and BERT) with the classical ones, i.e., TF-IDF and wordnet-based. The experiments are conducted on three datasets containing qualification descriptions. The experiments{'} results showed that wordnet-based similarity measures could compete and even outperform modern embedding-based approaches.",
}
@inproceedings{jarrar-etal-2021-extracting,
    title = "Extracting Synonyms from Bilingual Dictionaries",
    author = "Jarrar, Mustafa  and
      Naser, Eman  and
      Khalifa, Muhammad  and
      Shaalan, Khaled",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.25",
    pages = "215--222",
    abstract = "We present our progress in developing a novel algorithm to extract synonyms from bilingual dictionaries. Identification and usage of synonyms play a significant role in improving the performance of information access applications. The idea is to construct a translation graph from translation pairs, then to extract and consolidate cyclic paths to form bilingual sets of synonyms. The initial evaluation of this algorithm illustrates promising results in extracting Arabic-English bilingual synonyms. In the evaluation, we first converted the synsets in the Arabic WordNet into translation pairs (i.e., losing word-sense memberships). Next, we applied our algorithm to rebuild these synsets. We compared the original and extracted synsets obtaining an F-Measure of 82.3{\%} and 82.1{\%} for Arabic and English synsets extraction, respectively.",
}
@inproceedings{janz-etal-2021-neural,
    title = "Neural Language Models vs {W}ordnet-based Semantically Enriched Representation in {CST} Relation Recognition",
    author = "Janz, Arkadiusz  and
      Piasecki, Maciej  and
      W{\k{a}}torski, Piotr",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.26",
    pages = "223--233",
    abstract = "Neural language models, including transformer-based models, that are pre-trained on very large corpora became a common way to represent text in various tasks, including recognition of textual semantic relations, e.g. Cross-document Structure Theory. Pre-trained models are usually fine tuned to downstream tasks and the obtained vectors are used as an input for deep neural classifiers. No linguistic knowledge obtained from resources and tools is utilised. In this paper we compare such universal approaches with a combination of rich graph-based linguistically motivated sentence representation and a typical neural network classifier applied to a task of recognition of CST relation in Polish. The representation describes selected levels of the sentence structure including description of lexical meanings on the basis of the wordnet (plWordNet) synsets and connected SUMO concepts. The obtained results show that in the case of difficult relations and medium size training corpus semantically enriched text representation leads to significantly better results.",
}
@inproceedings{domingo-etal-2021-social,
    title = "What is on Social Media that is not in {W}ord{N}et? A Preliminary Analysis on the {T}witter{AAE} Corpus",
    author = "Domingo, Cecilia  and
      Gonzalez-Ferrero, Tatiana  and
      Gonzalez-Dios, Itziar",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.27",
    pages = "234--242",
    abstract = "Natural Language Processing tools and resources have been so far mainly created and trained for standard varieties of language. Nowadays, with the use of large amounts of data gathered from social media, other varieties and registers need to be processed, which may present other challenges and difficulties. In this work, we focus on English and we present a preliminary analysis by comparing the TwitterAAE corpus, which is annotated for ethnicity, and WordNet by quantifying and explaining the online language that WordNet misses.",
}
@inproceedings{arican-etal-2021-creating,
    title = "Creating Domain Dependent {T}urkish {W}ord{N}et and {S}enti{N}et",
    author = {Ar{\i}can, Bilge Nas  and
      {\"O}z{\c{c}}elik, Merve  and
      Aslan, Deniz Baran  and
      Sarm{\i}{\c{s}}, Elif  and
      Parlar, Selen  and
      Y{\i}ld{\i}z, Olcay Taner},
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.28",
    pages = "243--251",
    abstract = "A WordNet is a thesaurus that has a structured list of words organized depending on their meanings. WordNet represents word senses, all meanings a single lemma may have, the relations between these senses, and their definitions. Another study within the domain of Natural Language Processing is sentiment analysis. With sentiment analysis, data sets can be scored according to the emotion they contain. In the sentiment analysis we did with the data we received on the Tourism WordNet, we performed a domain-specific sentiment analysis study by annotating the data. In this paper, we propose a method to facilitate Natural Language Processing tasks such as sentiment analysis performed in specific domains via creating a specific-domain subset of an original Turkish dictionary. As the preliminary study, we have created a WordNet for the tourism domain with 14,000 words and validated it on simple tasks.",
}
@inproceedings{mccrae-cillessen-2021-towards,
    title = "Towards a Linking between {W}ord{N}et and {W}ikidata",
    author = "McCrae, John P.  and
      Cillessen, David",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.29",
    pages = "252--257",
    abstract = "WordNet is the most widely used lexical resource for English, while Wikidata is one of the largest knowledge graphs of entity and concepts available. While, there is a clear difference in the focus of these two resources, there is also a significant overlap and as such a complete linking of these resources would have many uses. We propose the development of such a linking, first by means of the hapax legomenon links and secondly by the use of natural language processing techniques. We show that these can be done with high accuracy but that human validation is still necessary. This has resulted in over 9,000 links being added between these two resources.",
}
@inproceedings{biagetti-etal-2021-toward,
    title = "Toward the creation of {W}ord{N}ets for ancient {I}ndo-{E}uropean languages",
    author = "Biagetti, Erica  and
      Zanchi, Chiara  and
      Short, William Michael",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.30",
    pages = "258--266",
    abstract = "This paper presents the work in progress toward the creation of a family of WordNets for Sanskrit, Ancient Greek, and Latin. Building on previous attempts in the field, we elaborate these efforts bridging together WordNet relational semantics with theories of meaning from Cognitive Linguistics. We discuss some of the innovations we have introduced to the WordNet architecture, to better capture the polysemy of words, as well as Indo-European language family-specific features. We conclude the paper framing our work within the larger picture of resources available for ancient languages and showing that WordNet-backed search tools have the potential to re-define the kinds of questions that can be asked of ancient language corpora.",
}
@inproceedings{nimb-etal-2021-dannet2,
    title = "{D}an{N}et2: Extending the coverage of adjectives in {D}an{N}et based on thesaurus data (project presentation)",
    author = "Nimb, Sanni  and
      Pedersen, Bolette  and
      Olsen, Sussi",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.31",
    pages = "267--272",
    abstract = "The paper describes work in progress in the DanNet2 project financed by the Carlsberg Foundation. The project aim is to extend the original Danish wordnet, DanNet, in several ways. Main focus is on extension of the coverage and description of the adjectives, a part of speech that was rather sparsely described in the original wordnet. We describe the methodology and initial work of semi-automatically transferring adjectives from the Danish Thesaurus to the wordnet with the aim of easily enlarging the coverage from 3,000 to approx. 13,000 adjectival synsets. Transfer is performed by manually encoding all missing adjectival subsection headwords from the thesaurus and thereafter employing a semi-automatic procedure where adjectives from the same subsection are transferred to the wordnet as either 1) near synonyms to the section{'}s headword, 2) hyponyms to the section{'}s headword, or 3) as members of the same synset as the headword. We also discuss how to deal with the problem of multiple representations of the same sense in the thesaurus, and present other types of information from the thesaurus that we plan to integrate, such as thematic and sentiment information.",
}
@inproceedings{bond-etal-2021-teaching,
    title = "Teaching Through Tagging {---} Interactive Lexical Semantics",
    author = "Bond, Francis  and
      Devadason, Andrew  and
      Teo, Melissa Rui Lin  and
      da Costa, Lu{\'\i}s Morgado",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.32",
    pages = "273--283",
    abstract = "In this paper we discuss an ongoing effort to enrich students{'} learning by involving them in sense tagging. The main goal is to lead students to discover how we can represent meaning and where the limits of our current theories lie. A subsidiary goal is to create sense tagged corpora and an accompanying linked lexicon (in our case wordnets). We present the results of tagging several texts and suggest some ways in which the tagging process could be improved. Two authors of this paper present their own experience as students. Overall, students reported that they found the tagging an enriching experience. The annotated corpora and changes to the wordnet are made available through the NTU multilingual corpus and associated wordnets (NTU-MC).",
}
@inproceedings{declerck-bajcetic-2021-towards,
    title = "Towards the Addition of Pronunciation Information to Lexical Semantic Resources",
    author = "Declerck, Thierry  and
      Baj{\v{c}}eti{\'c}, Lenka",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.33",
    pages = "284--291",
    abstract = "This paper describes ongoing work aiming at adding pronunciation information to lexical semantic resources, with a focus on open wordnets. Our goal is not only to add a new modality to those semantic networks, but also to mark heteronyms listed in them with the pronunciation information associated with their different meanings. This work could contribute in the longer term to the disambiguation of multi-modal resources, which are combining text and speech.",
}
@inproceedings{maziarz-etal-2021-testing,
    title = "Testing agreement between lexicographers: A case of homonymy and polysemy",
    author = "Maziarz, Marek  and
      Bond, Francis  and
      Rudnicka, Ewa",
    booktitle = "Proceedings of the 11th Global Wordnet Conference",
    month = jan,
    year = "2021",
    address = "University of South Africa (UNISA)",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2021.gwc-1.34",
    pages = "292--300",
    abstract = "In this paper we compare Oxford Lexico and Merriam Webster dictionaries with Princeton WordNet with respect to the description of semantic (dis)similarity between polysemous and homonymous senses that could be inferred from them. WordNet lacks any explicit description of polysemy or homonymy, but as a network of linked senses it may be used to compute semantic distances between word senses. To compare WordNet with the dictionaries, we transformed sample entry microstructures of the latter into graphs and cross-linked them with the equivalent senses of the former. We found that dictionaries are in high agreement with each other, if one considers polysemy and homonymy altogether, and in moderate concordance, if one focuses merely on polysemy descriptions. Measuring the shortest path lengths on WordNet gave results comparable to those on the dictionaries in predicting semantic dissimilarity between polysemous senses, but was less felicitous while recognising homonymy.",
}
@proceedings{wosp-2020-international,
    title = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    editor = "Knoth, Petr  and
      Stahl, Christopher  and
      Gyawali, Bikash  and
      Pride, David  and
      Kunnath, Suchetha N.  and
      Herrmannova, Drahomira",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.0",
}
@inproceedings{molloy-etal-2020-virtual,
    title = "Virtual Citation Proximity ({VCP}): Empowering Document Recommender Systems by Learning a Hypothetical In-Text Citation-Proximity Metric for Uncited Documents",
    author = "Molloy, Paul  and
      Beel, Joeran  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.1",
    pages = "1--8",
    abstract = "The relatedness of research articles, patents, court rulings, web pages, and other document types is often calculated with citation or hyperlink-based approaches like co-citation (proximity) analysis. The main limitation of citation-based approaches is that they cannot be used for documents that receive little or no citations. We propose Virtual Citation Proximity (VCP), a Siamese Neural Network architecture, which combines the advantages of co-citation proximity analysis (diverse notions of relatedness / high recommendation performance), with the advantage of content-based filtering (high coverage). VCP is trained on a corpus of documents with textual features, and with real citation proximity as ground truth. VCP then predicts for any two documents, based on their title and abstract, in what proximity the two documents would be co-cited, if they were indeed co-cited. The prediction can be used in the same way as real citation proximity to calculate document relatedness, even for uncited documents. In our evaluation with 2 million co-citations from Wikipedia articles, VCP achieves an MAE of 0.0055, i.e. an improvement of 20{\%} over the baseline, though the learning curve suggests that more work is needed.",
}
@inproceedings{matsubara-singh-2020-citations,
    title = "Citations Beyond Self Citations: Identifying Authors, Affiliations, and Nationalities in Scientific Papers",
    author = "Matsubara, Yoshitomo  and
      Singh, Sameer",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.2",
    pages = "9--20",
    abstract = "The question of the utility of the blind peer-review system is fundamental to scientific research. Some studies investigate exactly how {``}blind{''} the papers are in the double-blind review system by manually or automatically identifying the true authors, mainly suggesting the number of self-citations in the submitted manuscripts as the primary signal for identity. However, related work on the automated approaches are limited by the sizes of their datasets and the restricted experimental setup, thus they lack practical insights into the blind review process. In this work, we train models that identify the authors, their affiliations, and their nationalities through real-world, large-scale experiments on the Microsoft Academic Graph, including the cold start scenario. Our models are accurate; we identify at least one of authors, affiliations, and nationalities of held-out papers with 40.3{\%}, 47.9{\%} and 86.0{\%} accuracy respectively, from the top-10 guesses of our models. However, through insights from the model, we demonstrate that these entities are identifiable with a small number of guesses primarily by using a combination of self-citations, social, and common citations. Moreover, our further analysis on the results leads to interesting findings, such as that prominent affiliations are easily identifiable (e.g. 93.8{\%} of test papers written by Microsoft are identified with top-10 guesses). The experimental results show, against conventional belief, that the self-citations are no more informative than looking at the common citations, thus suggesting that removing self-citations is not sufficient for authors to maintain their anonymity.",
}
@inproceedings{guo-etal-2020-smartcitecon,
    title = "{S}mart{C}ite{C}on: Implicit Citation Context Extraction from Academic Literature Using Supervised Learning",
    author = "Guo, Chenrui  and
      Cui, Haoran  and
      Zhang, Li  and
      Wang, Jiamin  and
      Lu, Wei  and
      Wu, Jian",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.3",
    pages = "21--26",
    abstract = "We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers from the ACL Anthology. The model with 19 features achieves F1=85.6{\%}. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12{--}45 seconds on average depending on the format to process a document on a dedicated server with 6 multithreaded cores. Using SCC, we extracted 11.8 million citation context sentences from {\textasciitilde}33.3k PMC papers in the CORD-19 dataset, released on June 13, 2020. We will provide continuous supplementary data contribution to the CORD-19 and other datasets. The source code is released at https://gitee.com/irlab/SmartCiteCon.",
}
@inproceedings{grennan-beel-2020-synthetic,
    title = "Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with {GROBID}, {GIANT} and {CORA}",
    author = "Grennan, Mark  and
      Beel, Joeran",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.4",
    pages = "27--35",
    abstract = "Citation parsing, particularly with deep neural networks, suffers from a lack of training data as available datasets typically contain only a few thousand training instances. Manually labelling citation strings is very time-consuming, hence synthetically created training data could be a solution. However, as of now, it is unknown if synthetically created reference-strings are suitable to train machine learning algorithms for citation parsing. To find out, we train Grobid, which uses Conditional Random Fields, with a) human-labelled reference strings from {`}real{'} bibliographies and b) synthetically created reference strings from the GIANT dataset. We find that both synthetic and organic reference strings are equally suited for training Grobid (F1 = 0.74). We additionally find that retraining Grobid has a notable impact on its performance, for both synthetic and real data (+30{\%} in F1). Having as many types of labelled fields as possible during training also improves effectiveness, even if these fields are not available in the evaluation data (+13.5{\%} F1). We conclude that synthetic data is suitable for training (deep) citation parsing models. We further suggest that in future evaluations of reference parsers both evaluation data similar and dissimilar to the training data should be used for more meaningful evaluations.",
}
@inproceedings{marwah-beel-2020-term,
    title = "Term-Recency for {TF}-{IDF}, {BM}25 and {USE} Term Weighting",
    author = "Marwah, Divyanshu  and
      Beel, Joeran",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.5",
    pages = "36--41",
    abstract = "Effectiveness of a recommendation in an Information Retrieval (IR) system is determined by relevancy scores of retrieved results. Term weighting is responsible for computing the relevance scores and consequently differentiating between the terms in a document. However, the current term weighting formula (TF-IDF, for instance), weighs terms only based on term frequency and inverse document frequency irrespective of other important factors. This results in ambiguity in cases when both TF and IDF values the same for more than one document, hence resulting in same TF-IDF values. In this paper, we propose a modification of TF-IDF and other term-weighting schemes that weighs the terms based on the recency and the usage in the corpus. We have tested the performance of our algorithm with existing term weighting schemes; TF-IDF, BM25 and USE text embedding model. We have indexed three different datasets with different domains to validate the premises for our algorithm. On evaluating the algorithms using Precision, Recall, F1 score, and NDCG, we found that time normalized TF-IDF outperformed the classic TF-IDF with a significant difference in all the metrics and datasets. Time-based USE model performed better than the standard USE model in two out of three datasets. But the time-based BM25 model did not perform well in some of the input queries as compared to standard BM25 model.",
}
@inproceedings{ikeda-etal-2020-normalized,
    title = "The Normalized Impact Index for Keywords in Scholarly Papers to Detect Subtle Research Topics",
    author = "Ikeda, Daisuke  and
      Taniguchi, Yuta  and
      Koga, Kazunori",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.6",
    pages = "42--47",
    abstract = "Mainly due to the open access movement, the number of scholarly papers we can freely access is drastically increasing. A huge amount of papers is a promising resource for text mining and machine learning. Given a set of papers, for example, we can grasp past or current trends in a research community. Compared to the trend detection, it is more difficult to forecast trends in the near future, since the number of occurrences of some features, which are major cues for automatic detection, such as the word frequency, is quite small before such a trend will emerge. As a first step toward trend forecasting, this paper is devoted to finding subtle trends. To do this, the authors propose an index for keywords, called normalized impact index, and visualize keywords and their indices as a heat map. The authors have conducted case studies using some keywords already known as popular, and we found some keywords whose frequencies are not so large but whose indices are large.",
}
@inproceedings{chen-zhang-2020-representing,
    title = "Representing and Reconstructing {P}hy{SH}: Which Embedding Competent?",
    author = "Chen, Xiaoli  and
      Zhang, Zhixiong",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.7",
    pages = "48--53",
    abstract = "Recent advances in natural language processing make embedding representations dominate the computing language world. Though it is taken for granted, we actually have limited knowledge of how these embeddings perform in representing the complex hierarchy of domain scientific knowledge. In this paper, we conduct a comprehensive comparison of well-known embeddings{'} capability in capturing the hierarchical Physics knowledge. Several key findings are: i, Poincar{\'e} embeddings do outperform if trained on PhySH taxonomy, but it fails if trained on co-occurrence pairs which are extracted from raw text. ii, No algorithm can properly learn hierarchies from the more realistic case of co-occurrence pairs, which contains more noisy relations other than hierarchical relations. iii, Our statistic analysis of Poincar{\'e} embedding{'}s representation of PhySH shows successful hierarchical representation share two characteristics: firstly, upper-level terms have a smaller semantic distance to root; secondly, upper-level hypernym-hyponym pairs should be further apart than lower-level hypernym-hyponym pairs.",
}
@inproceedings{de-andrade-goncalves-2020-combining,
    title = "Combining Representations For Effective Citation Classification",
    author = "de Andrade, Claudio Mois{\'e}s Valiense  and
      Gon{\c{c}}alves, Marcos Andr{\'e}",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.8",
    pages = "54--58",
    abstract = "In this paper, we describe our participation in two tasks organized by WOSP 2020, consisting of classifying the context of a citation (e.g., background, motivational, extension) and whether a citation is influential in the work (or not). Classifying the context of an article citation or its influence/importance in an automated way presents a challenge for machine learning algorithms due to the shortage of information and inherently ambiguity of the task. Its solution, on the other hand, may allow enhanced bibliometric studies. Several text representations have already been proposed in the literature, but their combination has been underexploited in the two tasks described above. Our solution relies exactly on combining different, potentially complementary, text representations in order to enhance the final obtained results. We evaluate the combination of various strategies for text representation, achieving the best results with a combination of TF-IDF (capturing statistical information), LDA (capturing topical information) and Glove word embeddings (capturing contextual information) for the task of classifying the context of the citation. Our solution ranked first in the task of classifying the citation context and third in classifying its influence.",
}
@inproceedings{mishra-mishra-2020-scubed,
    title = "Scubed at 3{C} task A - A simple baseline for citation context purpose classification",
    author = "Mishra, Shubhanshu  and
      Mishra, Sudhanshu",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.9",
    pages = "59--64",
    abstract = "We present our team Scubed{'}s approach in the {`}3C{'} Citation Context Classification Task, Subtask A, citation context purpose classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of models which are capable of capturing non-linear features. Our best model on the leaderboard is a multi-layer perceptron which also performs best during our rerun. Our submission code for replicating experiments is at: https://github.com/napsternxg/Citation{\_}Context{\_}Classification.",
}
@inproceedings{mishra-mishra-2020-scubed-3c,
    title = "Scubed at 3{C} task {B} - A simple baseline for citation context influence classification",
    author = "Mishra, Shubhanshu  and
      Mishra, Sudhanshu",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.10",
    pages = "65--70",
    abstract = "We present our team Scubed{'}s approach in the 3C Citation Context Classification Task, Subtask B, citation context influence classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of simple models resulting in a strong baseline. Our best model on the leaderboard is a random forest classifier using only the citation context text. A replication of our analysis finds logistic regression and gradient boosted tree classifier to be the best performing model. Our submission code can be found at: https://github.com/napsternxg/Citation{\_}Context{\_}Classification.",
}
@inproceedings{b-kp-2020-amrita,
    title = "{A}mrita{\_}{CEN}{\_}{NLP} @ {WOSP} 3{C} Citation Context Classification Task",
    author = "B, Premjith  and
      KP, Soman",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.11",
    pages = "71--74",
    abstract = "Identification of the purpose and influence of citation is significant in assessing the impact of a publication. {`}3C{'} Citation Context Classification Task in Workshop on Mining Scientific Publication is a shared task to address the abovementioned problems. This working note describes the submissions of Amrita{\_}CEN{\_}NLP team to the shared task. We used Random Forest with cost-sensitive learning for classification of sentences encoded into a vector of dimension 300.",
}
@inproceedings{kunnath-etal-2020-overview,
    title = "Overview of the 2020 {WOSP} 3{C} Citation Context Classification Task",
    author = "Kunnath, Suchetha Nambanoor  and
      Pride, David  and
      Gyawali, Bikash  and
      Knoth, Petr",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wosp-1.12",
    pages = "75--83",
    abstract = "The 3C Citation Context Classification task is the first shared task addressing citation context classification. The two subtasks, A and B, associated with this shared task, involves the classification of citations based on their purpose and influence, respectively. Both tasks use a portion of the new ACT dataset, developed by the researchers at The Open University, UK. The tasks were hosted on Kaggle, and the participated systems were evaluated using the macro f-score. Three teams participated in subtask A and four teams participated in subtask B. The best performing systems obtained an overall score of 0.2056 for subtask A and 0.5556 for subtask B, outperforming the simple majority class baseline models, which scored 0.11489 and 0.32249, respectively. In this paper we provide a report specifying the shared task, the dataset used, a short description of the participating systems and the final results obtained by the teams based on the evaluation criteria. The shared task has been organised as part of the 8th International Workshop on Mining Scientific Publications (WOSP 2020) workshop.",
}
@proceedings{wnut-2020-noisy,
    title = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.0",
}
@inproceedings{kaplan-2020-may,
    title = "May {I} Ask Who{'}s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance",
    author = "Kaplan, Micaela",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.1",
    doi = "10.18653/v1/2020.wnut-1.1",
    pages = "1--6",
    abstract = "We investigate using Named Entity Recognition on a new type of user-generated text: a call center conversation. These conversations combine problems from spontaneous speech with problems novel to conversational Automated Speech Recognition, including incorrect recognition, alongside other common problems from noisy user-generated text. Using our own corpus with new annotations, training custom contextual string embeddings, and applying a BiLSTM-CRF, we match state-of- the-art results on our novel task.",
}
@inproceedings{aggarwal-etal-2020-really,
    title = "{``}Did you really mean what you said?{''} : Sarcasm Detection in {H}indi-{E}nglish Code-Mixed Data using Bilingual Word Embeddings",
    author = "Aggarwal, Akshita  and
      Wadhawan, Anshul  and
      Chaudhary, Anshima  and
      Maurya, Kavita",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.2",
    doi = "10.18653/v1/2020.wnut-1.2",
    pages = "7--15",
    abstract = "With the increased use of social media platforms by people across the world, many new interesting NLP problems have come into existence. One such being the detection of sarcasm in the social media texts. We present a corpus of tweets for training custom word embeddings and a Hinglish dataset labelled for sarcasm detection. We propose a deep learning based approach to address the issue of sarcasm detection in Hindi-English code mixed tweets using bilingual word embeddings derived from FastText and Word2Vec approaches. We experimented with various deep learning models, including CNNs, LSTMs, Bi-directional LSTMs (with and without attention). We were able to outperform all state-of-the-art performances with our deep learning models, with attention based Bi-directional LSTMs giving the best performance exhibiting an accuracy of 78.49{\%}.",
}
@inproceedings{kumar-etal-2020-noisy,
    title = "Noisy Text Data: Achilles{'} Heel of {BERT}",
    author = "Kumar, Ankit  and
      Makhija, Piyush  and
      Gupta, Anuj",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.3",
    doi = "10.18653/v1/2020.wnut-1.3",
    pages = "16--21",
    abstract = "Owing to the phenomenal success of BERT on various NLP tasks and benchmark datasets, industry practitioners are actively experimenting with fine-tuning BERT to build NLP applications for solving industry use cases. For most datasets that are used by practitioners to build industrial NLP applications, it is hard to guarantee absence of any noise in the data. While BERT has performed exceedingly well for transferring the learnings from one use case to another, it remains unclear how BERT performs when fine-tuned on noisy text. In this work, we explore the sensitivity of BERT to noise in the data. We work with most commonly occurring noise (spelling mistakes, typos) and show that this results in significant degradation in the performance of BERT. We present experimental results to show that BERT{'}s performance on fundamental NLP tasks like sentiment analysis and textual similarity drops significantly in the presence of (simulated) noise on benchmark datasets viz. IMDB Movie Review, STS-B, SST-2. Further, we identify shortcomings in the existing BERT pipeline that are responsible for this drop in performance. Our findings suggest that practitioners need to be vary of presence of noise in their datasets while fine-tuning BERT to solve industry use cases.",
}
@inproceedings{gardner-etal-2020-determining,
    title = "Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning",
    author = "Gardner, Rachel  and
      Varma, Maya  and
      Zhu, Clare  and
      Krishna, Ranjay",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.4",
    doi = "10.18653/v1/2020.wnut-1.4",
    pages = "22--27",
    abstract = "Datasets extracted from social networks and online forums are often prone to the pitfalls of natural language, namely the presence of unstructured and noisy data. In this work, we seek to enable the collection of high-quality question-answer datasets from social media by proposing a novel task for automated quality analysis and data cleaning: question-answer (QA) plausibility. Given a machine or user-generated question and a crowd-sourced response from a social media user, we determine if the question and response are valid; if so, we identify the answer within the free-form response. We design BERT-based models to perform the QA plausibility task, and we evaluate the ability of our models to generate a clean, usable question-answer dataset. Our highest-performing approach consists of a single-task model which determines the plausibility of the question, followed by a multi-task model which evaluates the plausibility of the response as well as extracts answers (Question Plausibility AUROC=0.75, Response Plausibility AUROC=0.78, Answer Extraction F1=0.665).",
}
@inproceedings{alghanmi-etal-2020-combining,
    title = "Combining {BERT} with Static Word Embeddings for Categorizing Social Media",
    author = "Alghanmi, Israa  and
      Espinosa Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.5",
    doi = "10.18653/v1/2020.wnut-1.5",
    pages = "28--33",
    abstract = "Pre-trained neural language models (LMs) have achieved impressive results in various natural language processing tasks, across different languages. Surprisingly, this extends to the social media genre, despite the fact that social media often has very different characteristics from the language that LMs have seen during training. A particularly striking example is the performance of AraBERT, an LM for the Arabic language, which is successful in categorizing social media posts in Arabic dialects, despite only having been trained on Modern Standard Arabic. Our hypothesis in this paper is that the performance of LMs for social media can nonetheless be improved by incorporating static word vectors that have been specifically trained on social media. We show that a simple method for incorporating such word vectors is indeed successful in several Arabic and English benchmarks. Curiously, however, we also find that similar improvements are possible with word vectors that have been trained on traditional text sources (e.g. Wikipedia).",
}
@inproceedings{hu-etal-2020-enhanced,
    title = "Enhanced Sentence Alignment Network for Efficient Short Text Matching",
    author = "Hu, Zhe  and
      Fu, Zuohui  and
      Peng, Cheng  and
      Wang, Weiwei",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.6",
    doi = "10.18653/v1/2020.wnut-1.6",
    pages = "34--40",
    abstract = "Cross-sentence attention has been widely applied in text matching, in which model learns the aligned information between two intermediate sequence representations to capture their semantic relationship. However, commonly the intermediate representations are generated solely based on the preceding layers and the models may suffer from error propagation and unstable matching, especially when multiple attention layers are used. In this paper, we pro-pose an enhanced sentence alignment network with simple gated feature augmentation, where the model is able to flexibly integrate both original word and contextual features to improve the cross-sentence attention. Moreover, our model is less complex with fewer parameters compared to many state-of-the-art structures.Experiments on three benchmark datasets validate our model capacity for text matching.",
}
@inproceedings{srivastava-singh-2020-phinc,
    title = "{PHINC}: A Parallel {H}inglish Social Media Code-Mixed Corpus for Machine Translation",
    author = "Srivastava, Vivek  and
      Singh, Mayank",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.7",
    doi = "10.18653/v1/2020.wnut-1.7",
    pages = "41--49",
    abstract = "Code-mixing is the phenomenon of using more than one language in a sentence. In the multilingual communities, it is a very frequently observed pattern of communication on social media platforms. Flexibility to use multiple languages in one text message might help to communicate efficiently with the target audience. But, the noisy user-generated code-mixed text adds to the challenge of processing and understanding natural language to a much larger extent. Machine translation from monolingual source to the target language is a well-studied research problem. Here, we demonstrate that widely popular and sophisticated translation systems such as Google Translate fail at times to translate code-mixed text effectively. To address this challenge, we present a parallel corpus of the 13,738 code-mixed Hindi-English sentences and their corresponding human translation in English. In addition, we also propose a translation pipeline build on top of Google Translate. The evaluation of the proposed pipeline on $PHINC$ demonstrates an increase in the performance of the underlying system. With minimal effort, we can extend the dataset and the proposed approach to other code-mixing language pairs.",
}
@inproceedings{sazzed-2020-cross,
    title = "Cross-lingual sentiment classification in low-resource {B}engali language",
    author = "Sazzed, Salim",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.8",
    doi = "10.18653/v1/2020.wnut-1.8",
    pages = "50--60",
    abstract = "Sentiment analysis research in low-resource languages such as Bengali is still unexplored due to the scarcity of annotated data and the lack of text processing tools. Therefore, in this work, we focus on generating resources and showing the applicability of the cross-lingual sentiment analysis approach in Bengali. For benchmarking, we created and annotated a comprehensive corpus of around 12000 Bengali reviews. To address the lack of standard text-processing tools in Bengali, we leverage resources from English utilizing machine translation. We determine the performance of supervised machine learning (ML) classifiers in machine-translated English corpus and compare it with the original Bengali corpus. Besides, we examine sentiment preservation in the machine-translated corpus utilizing Cohen{'}s Kappa and Gwet{'}s AC1. To circumvent the laborious data labeling process, we explore lexicon-based methods and study the applicability of utilizing cross-domain labeled data from the resource-rich language. We find that supervised ML classifiers show comparable performances in Bengali and machine-translated English corpus. By utilizing labeled data, they achieve 15{\%}-20{\%} higher F1 scores compared to both lexicon-based and transfer learning-based methods. Besides, we observe that machine translation does not alter the sentiment polarity of the review for most of the cases. Our experimental results demonstrate that the machine translation based cross-lingual approach can be an effective way for sentiment classification in Bengali.",
}
@inproceedings{sarkar-etal-2020-non,
    title = "The Non-native Speaker Aspect: {I}ndian {E}nglish in Social Media",
    author = "Sarkar, Rupak  and
      Mahinder, Sayantan  and
      KhudaBukhsh, Ashiqur",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.9",
    doi = "10.18653/v1/2020.wnut-1.9",
    pages = "61--70",
    abstract = "As the largest institutionalized second language variety of English, Indian English has received a sustained focus from linguists for decades. However, to the best of our knowledge, no prior study has contrasted web-expressions of Indian English in noisy social media with English generated by a social media user base that are predominantly native speakers. In this paper, we address this gap in the literature through conducting a comprehensive analysis considering multiple structural and semantic aspects. In addition, we propose a novel application of language models to perform automatic linguistic quality assessment.",
}
@inproceedings{hayashibe-mitsuzawa-2020-sentence,
    title = "Sentence Boundary Detection on Line Breaks in {J}apanese",
    author = "Hayashibe, Yuta  and
      Mitsuzawa, Kensuke",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.10",
    doi = "10.18653/v1/2020.wnut-1.10",
    pages = "71--75",
    abstract = "For NLP, sentence boundary detection (SBD) is an essential task to decompose a text into sentences. Most of the previous studies have used a simple rule that uses only typical characters as sentence boundaries. However, some characters may or may not be sentence boundaries depending on the context. We focused on line breaks in them. We newly constructed annotated corpora, implemented sentence boundary detectors, and analyzed performance of SBD in several settings.",
}
@inproceedings{yamaguchi-etal-2020-non,
    title = "Non-ingredient Detection in User-generated Recipes using the Sequence Tagging Approach",
    author = "Yamaguchi, Yasuhiro  and
      Inuzuka, Shintaro  and
      Hiramatsu, Makoto  and
      Harashima, Jun",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.11",
    doi = "10.18653/v1/2020.wnut-1.11",
    pages = "76--80",
    abstract = "Recently, the number of user-generated recipes on the Internet has increased. In such recipes, users are generally supposed to write a title, an ingredient list, and steps to create a dish. However, some items in an ingredient list in a user-generated recipe are not actually edible ingredients. For example, headings, comments, and kitchenware sometimes appear in an ingredient list because users can freely write the list in their recipes. Such noise makes it difficult for computers to use recipes for a variety of tasks, such as calorie estimation. To address this issue, we propose a non-ingredient detection method inspired by a neural sequence tagging model. In our experiment, we annotated 6,675 ingredients in 600 user-generated recipes and showed that our proposed method achieved a 93.3 F1 score.",
}
@inproceedings{mishra-etal-2020-generating,
    title = "Generating Fact Checking Summaries for Web Claims",
    author = "Mishra, Rahul  and
      Gupta, Dhruv  and
      Leippold, Markus",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.12",
    doi = "10.18653/v1/2020.wnut-1.12",
    pages = "81--90",
    abstract = "We present SUMO, a neural attention-based approach that learns to establish correctness of textual claims based on evidence in the form of text documents (e.g., news articles or web documents). SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim. Prior approaches to address the problem of fact checking and evidence extraction have relied on simple concatenation of claim and document word embeddings as an input to claim driven attention weight computation. This is done so as to extract salient words and sentences from the documents that help establish the correctness of the claim. However this design of claim-driven attention fails to capture the contextual information in documents properly. We improve on the prior art by using improved claim and title guided hierarchical attention to model effective contextual cues. We show the efficacy of our approach on political, healthcare, and environmental datasets.",
}
@inproceedings{kicken-etal-2020-intelligent,
    title = "Intelligent Analyses on Storytelling for Impact Measurement",
    author = "Kicken, Koen  and
      De Maesschalck, Tessa  and
      Vanrumste, Bart  and
      De Keyser, Tom  and
      Shim, Hee Reen",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.13",
    doi = "10.18653/v1/2020.wnut-1.13",
    pages = "91--100",
    abstract = "This paper explores how Dutch diary fragments, written by family coaches in the social sector, can be analysed automatically using machine learning techniques to quantitatively measure the impact of social coaching. The focus lays on two tasks: determining which sentiment a fragment contains (sentiment analysis) and investigating which fundamental social rights (education, employment, legal aid, etc.) are addressed in the fragment. To train and test the new algorithms, a dataset consisting of 1715 Dutch diary fragments is used. These fragments are manually labelled on sentiment and on the applicable fundamental social rights. The sentiment analysis models were trained to classify the fragments into three classes: negative, neutral or positive. Fine-tuning the Dutch pre-trained Bidirectional Encoder Representations from Transformers (BERTje) (de Vries et al., 2019) language model surpassed the more classic algorithms by correctly classifying 79.6{\%} of the fragments on the sentiment analysis, which is considered as a good result. This technique also achieved the best results in the identification of the fundamental rights, where for every fragment the three most likely fundamental rights were given as output. In this way, 93{\%} of the present fundamental rights were correctly recognised. To our knowledge, we are the first to try to extract social rights from written text with the help of Natural Language Processing techniques.",
}
@inproceedings{ma-lalor-2020-empirical,
    title = "An Empirical Analysis of Human-Bot Interaction on {R}eddit",
    author = "Ma, Ming-Cheng  and
      Lalor, John P.",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.14",
    doi = "10.18653/v1/2020.wnut-1.14",
    pages = "101--106",
    abstract = "Automated agents ({``}bots{''}) have emerged as an ubiquitous and influential presence on social media. Bots engage on social media platforms by posting content and replying to other users on the platform. In this work we conduct an empirical analysis of the activity of a single bot on Reddit. Our goal is to determine whether bot activity (in the form of posted comments on the website) has an effect on how humans engage on Reddit. We find that (1) the sentiment of a bot comment has a significant, positive effect on the subsequent human reply, and (2) human Reddit users modify their comment behaviors to overlap with the text of the bot, similar to how humans modify their text to mimic other humans in conversation. Understanding human-bot interactions on social media with relatively simple bots is important for preparing for more advanced bots in the future.",
}
@inproceedings{hughes-etal-2020-detecting,
    title = "Detecting Trending Terms in Cybersecurity Forum Discussions",
    author = "Hughes, Jack  and
      Aycock, Seth  and
      Caines, Andrew  and
      Buttery, Paula  and
      Hutchings, Alice",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.15",
    doi = "10.18653/v1/2020.wnut-1.15",
    pages = "107--115",
    abstract = "We present a lightweight method for identifying currently trending terms in relation to a known prior of terms, using a weighted log-odds ratio with an informative prior. We apply this method to a dataset of posts from an English-language underground hacking forum, spanning over ten years of activity, with posts containing misspellings, orthographic variation, acronyms, and slang. Our statistical approach supports analysis of linguistic change and discussion topics over time, without a requirement to train a topic model for each time interval for analysis. We evaluate the approach by comparing the results to TF-IDF using the discounted cumulative gain metric with human annotations, finding our method outperforms TF-IDF on information retrieval.",
}
@inproceedings{molteni-etal-2020-service,
    title = "Service registration chatbot: collecting and comparing dialogues from {AMT} workers and service{'}s users",
    author = "Molteni, Luca  and
      Singh, Mittul  and
      Leinonen, Juho  and
      Leino, Katri  and
      Kurimo, Mikko  and
      Della Valle, Emanuele",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.16",
    doi = "10.18653/v1/2020.wnut-1.16",
    pages = "116--121",
    abstract = "Crowdsourcing is the go-to solution for data collection and annotation in the context of NLP tasks. Nevertheless, crowdsourced data is noisy by nature; the source is often unknown and additional validation work is performed to guarantee the dataset{'}s quality. In this article, we compare two crowdsourcing sources on a dialogue paraphrasing task revolving around a chatbot service. We observe that workers hired on crowdsourcing platforms produce lexically poorer and less diverse rewrites than service users engaged voluntarily. Notably enough, on dialogue clarity and optimality, the two paraphrase sources{'} human-perceived quality does not differ significantly. Furthermore, for the chatbot service, the combined crowdsourced data is enough to train a transformer-based Natural Language Generation (NLG) system. To enable similar services, we also release tools for collecting data and training the dialogue-act-based transformer-based NLG module.",
}
@inproceedings{agarwal-etal-2020-automated,
    title = "Automated Assessment of Noisy Crowdsourced Free-text Answers for {H}indi in Low Resource Setting",
    author = "Agarwal, Dolly  and
      Gupta, Somya  and
      Baghel, Nishant",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.17",
    doi = "10.18653/v1/2020.wnut-1.17",
    pages = "122--131",
    abstract = "The requirement of performing assessments continually on a larger scale necessitates the implementation of automated systems for evaluation of the learners{'} responses to free-text questions. We target children of age group 8-14 years and use an ASR integrated assessment app to crowdsource learners{'} responses to free text questions in Hindi. The app helped collect 39641 user answers to 35 different questions of Science topics. Since the users are young children from rural India and may not be well-equipped with technology, it brings in various noise types in the answers. We describe these noise types and propose a preprocessing pipeline to denoise user{'}s answers. We showcase the performance of different similarity metrics on the noisy and denoised versions of user and model answers. Our findings have large-scale applications for automated answer assessment for school children in India in low resource settings.",
}
@inproceedings{alam-etal-2020-punctuation,
    title = "Punctuation Restoration using Transformer Models for High-and Low-Resource Languages",
    author = "Alam, Tanvirul  and
      Khan, Akib  and
      Alam, Firoj",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.18",
    doi = "10.18653/v1/2020.wnut-1.18",
    pages = "132--142",
    abstract = "Punctuation restoration is a common post-processing problem for Automatic Speech Recognition (ASR) systems. It is important to improve the readability of the transcribed text for the human reader and facilitate NLP tasks. Current state-of-art address this problem using different deep learning models. Recently, transformer models have proven their success in downstream NLP tasks, and these models have been explored very little for the punctuation restoration problem. In this work, we explore different transformer based models and propose an augmentation strategy for this task, focusing on high-resource (English) and low-resource (Bangla) languages. For English, we obtain comparable state-of-the-art results, while for Bangla, it is the first reported work, which can serve as a strong baseline for future work. We have made our developed Bangla dataset publicly available for the research community.",
}
@inproceedings{grishina-etal-2020-truecasing,
    title = "Truecasing {G}erman user-generated conversational text",
    author = "Grishina, Yulia  and
      Gueudre, Thomas  and
      Winkler, Ralf",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.19",
    doi = "10.18653/v1/2020.wnut-1.19",
    pages = "143--148",
    abstract = "True-casing, the task of restoring proper case to (generally) lower case input, is important in downstream tasks and for screen display. In this paper, we investigate truecasing as an in- trinsic task and present several experiments on noisy user queries to a voice-controlled dia- log system. In particular, we compare a rule- based, an n-gram language model (LM) and a recurrent neural network (RNN) approaches, evaluating the results on a German Q{\&}A cor- pus and reporting accuracy for different case categories. We show that while RNNs reach higher accuracy especially on large datasets, character n-gram models with interpolation are still competitive, in particular on mixed- case words where their fall-back mechanisms come into play.",
}
@inproceedings{alam-anastasopoulos-2020-fine,
    title = "Fine-Tuning {MT} systems for Robustness to Second-Language Speaker Variations",
    author = "Alam, Md Mahfuz Ibn  and
      Anastasopoulos, Antonios",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.20",
    doi = "10.18653/v1/2020.wnut-1.20",
    pages = "149--158",
    abstract = "The performance of neural machine translation (NMT) systems only trained on a single language variant degrades when confronted with even slightly different language variations. With this work, we build upon previous work to explore how to mitigate this issue. We show that fine-tuning using naturally occurring noise along with pseudo-references (i.e. {``}corrected{''} non-native inputs translated using the baseline NMT system) is a promising solution towards systems robust to such type of input variations. We focus on four translation pairs, from English to Spanish, Italian, French, and Portuguese, with our system achieving improvements of up to 3.1 BLEU points compared to the baselines, establishing a new state-of-the-art on the JFLEG-ES dataset. All datasets and code are publicly available here: https://github.com/mahfuzibnalam/finetuning{\_}for{\_}robustness .",
}
@inproceedings{balagopalan-etal-2020-impact,
    title = "Impact of {ASR} on {A}lzheimer{'}s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others",
    author = "Balagopalan, Aparna  and
      Shkaruta, Ksenia  and
      Novikova, Jekaterina",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.21",
    doi = "10.18653/v1/2020.wnut-1.21",
    pages = "159--164",
    abstract = "Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, little is known about the impact of ASR accuracy on dementia detection. In this paper, we experiment with controlled amounts of artificially generated ASR errors and investigate their influence on dementia detection. We find that deletion errors affect detection performance the most, due to their impact on the features of syntactic complexity and discourse representation in speech. We show the trend to be generalisable across two different datasets for cognitive impairment detection. As a conclusion, we propose optimising the ASR to reflect a higher penalty for deletion errors in order to improve dementia detection performance.",
}
@inproceedings{chakravarthy-etal-2020-detecting,
    title = "Detecting Entailment in Code-Mixed {H}indi-{E}nglish Conversations",
    author = "Chakravarthy, Sharanya  and
      Umapathy, Anjana  and
      Black, Alan W",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.22",
    doi = "10.18653/v1/2020.wnut-1.22",
    pages = "165--170",
    abstract = "The presence of large-scale corpora for Natural Language Inference (NLI) has spurred deep learning research in this area, though much of this research has focused solely on monolingual data. Code-mixing is the intertwined usage of multiple languages, and is commonly seen in informal conversations among polyglots. Given the rising importance of dialogue agents, it is imperative that they understand code-mixing, but the scarcity of code-mixed Natural Language Understanding (NLU) datasets has precluded research in this area. The dataset by Khanuja et. al. for detecting conversational entailment in code-mixed Hindi-English text is the first of its kind. We investigate the effectiveness of language modeling, data augmentation, translation, and architectural approaches to address the code-mixed, conversational, and low-resource aspects of this dataset. We obtain an 8.09{\%} increase in test set accuracy over the current state of the art.",
}
@inproceedings{waller-gorman-2020-detecting,
    title = "Detecting Objectifying Language in Online Professor Reviews",
    author = "Waller, Angie  and
      Gorman, Kyle",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.23",
    doi = "10.18653/v1/2020.wnut-1.23",
    pages = "171--180",
    abstract = "Student reviews often make reference to professors{'} physical appearances. Until recently RateMyProfessors.com, the website of this study{'}s focus, used a design feature to encourage a {``}hot or not{''} rating of college professors. In the wake of recent {\#}MeToo and {\#}TimesUp movements, social awareness of the inappropriateness of these reviews has grown; however, objectifying comments remain and continue to be posted in this online context. We describe two supervised text classifiers for detecting objectifying commentary in professor reviews. We then ensemble these classifiers and use the resulting model to track objectifying commentary at scale. We measure correlations between objectifying commentary, changes to the review website interface, and teacher gender across a ten-year period.",
}
@inproceedings{palakodety-khudabukhsh-2020-annotation,
    title = "Annotation Efficient Language Identification from Weak Labels",
    author = "Palakodety, Shriphani  and
      KhudaBukhsh, Ashiqur",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.24",
    doi = "10.18653/v1/2020.wnut-1.24",
    pages = "181--192",
    abstract = "India is home to several languages with more than 30m speakers. These languages exhibit significant presence on social media platforms. However, several of these widely-used languages are under-addressed by current Natural Language Processing (NLP) models and resources. User generated social media content in these languages is also typically authored in the Roman script as opposed to the traditional native script further contributing to resource scarcity. In this paper, we leverage a minimally supervised NLP technique to obtain weak language labels from a large-scale Indian social media corpus leading to a robust and annotation-efficient language-identification technique spanning nine Romanized Indian languages. In fast-spreading pandemic situations such as the current COVID-19 situation, information processing objectives might be heavily tilted towards under-served languages in densely populated regions. We release our models to facilitate downstream analyses in these low-resource languages. Experiments across multiple social media corpora demonstrate the model{'}s robustness and provide several interesting insights on Indian language usage patterns on social media. We release an annotated data set of 1,000 comments in ten Romanized languages as a social media evaluation benchmark.",
}
@inproceedings{eyre-etal-2020-fantastic,
    title = "Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach",
    author = "Eyre, Ben  and
      Balagopalan, Aparna  and
      Novikova, Jekaterina",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.25",
    doi = "10.18653/v1/2020.wnut-1.25",
    pages = "193--199",
    abstract = "Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains common in fields such as cognitive impairment (CI) detection. Manually engineering features from noisy text is time and resource consuming, and can potentially result in features that do not enhance model performance. To combat this, we describe a new approach to feature engineering that leverages sequential machine learning models and domain knowledge to predict which features help enhance performance. We provide a concrete example of this method on a standard data set of CI speech and demonstrate that CI classification accuracy improves by 2.3{\%} over a strong baseline when using features produced by this method. This demonstration provides an example of how this method can be used to assist classification in fields where interpretability is important, such as health care.",
}
@inproceedings{kashefi-hwa-2020-quantifying,
    title = "Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation",
    author = "Kashefi, Omid  and
      Hwa, Rebecca",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.26",
    doi = "10.18653/v1/2020.wnut-1.26",
    pages = "200--208",
    abstract = "Data augmentation has been shown to be effective in providing more training data for machine learning and resulting in more robust classifiers. However, for some problems, there may be multiple augmentation heuristics, and the choices of which one to use may significantly impact the success of the training. In this work, we propose a metric for evaluating augmentation heuristics; specifically, we quantify the extent to which an example is {``}hard to distinguish{''} by considering the difference between the distribution of the augmented samples of different classes. Experimenting with multiple heuristics in two prediction tasks (positive/negative sentiment and verbosity/conciseness) validates our claims by revealing the connection between the distribution difference of different classes and the classification accuracy.",
}
@inproceedings{wang-etal-2020-empirical,
    title = "An Empirical Survey of Unsupervised Text Representation Methods on {T}witter Data",
    author = "Wang, Lili  and
      Gao, Chongyang  and
      Wei, Jason  and
      Ma, Weicheng  and
      Liu, Ruibo  and
      Vosoughi, Soroush",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.27",
    doi = "10.18653/v1/2020.wnut-1.27",
    pages = "209--214",
    abstract = "The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, there has been a noticeable improvement in text representation. It is, however, unclear whether these improvements translate to noisy user-generated text, such as tweets. In this paper, we present an experimental survey of a wide range of well-known text representation techniques for the task of text clustering on noisy Twitter data. Our results indicate that the more advanced models do not necessarily work best on tweets and that more exploration in this area is needed.",
}
@inproceedings{sech-etal-2020-civil,
    title = "Civil Unrest on {T}witter ({CUT}): A Dataset of Tweets to Support Research on Civil Unrest",
    author = "Sech, Justin  and
      DeLucia, Alexandra  and
      Buczak, Anna L.  and
      Dredze, Mark",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.28",
    doi = "10.18653/v1/2020.wnut-1.28",
    pages = "215--221",
    abstract = "We present CUT, a dataset for studying Civil Unrest on Twitter. Our dataset includes 4,381 tweets related to civil unrest, hand-annotated with information related to the study of civil unrest discussion and events. Our dataset is drawn from 42 countries from 2014 to 2019. We present baseline systems trained on this data for the identification of tweets related to civil unrest. We include a discussion of ethical issues related to research on this topic.",
}
@inproceedings{harandizadeh-singh-2020-tweeki,
    title = "Tweeki: Linking Named Entities on {T}witter to a Knowledge Graph",
    author = "Harandizadeh, Bahareh  and
      Singh, Sameer",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.29",
    doi = "10.18653/v1/2020.wnut-1.29",
    pages = "222--231",
    abstract = "To identify what entities are being talked about in tweets, we need to automatically link named entities that appear in tweets to structured KBs like WikiData. Existing approaches often struggle with such short, noisy texts, or their complex design and reliance on supervision make them brittle, difficult to use and maintain, and lose significance over time. Further, there is a lack of a large, linked corpus of tweets to aid researchers, along with lack of gold dataset to evaluate the accuracy of entity linking. In this paper, we introduce (1) Tweeki, an unsupervised, modular entity linking system for Twitter, (2) TweekiData, a large, automatically-annotated corpus of Tweets linked to entities in WikiData, and (3) TweekiGold, a gold dataset for entity linking evaluation. Through comprehensive analysis, we show that Tweeki is comparable to the performance of recent state-of-the-art entity linkers models, the dataset is of high quality, and a use case of how the dataset can be used to improve downstream tasks in social media analysis (geolocation prediction).",
}
@inproceedings{hay-etal-2020-representation,
    title = "Representation learning of writing style",
    author = "Hay, Julien  and
      Doan, Bich-Lien  and
      Popineau, Fabrice  and
      Ait Elhara, Ouassim",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.30",
    doi = "10.18653/v1/2020.wnut-1.30",
    pages = "232--243",
    abstract = "In this paper, we introduce a new method of representation learning that aims to embed documents in a stylometric space. Previous studies in the field of authorship analysis focused on feature engineering techniques in order to represent document styles and to enhance model performance in specific tasks. Instead, we directly embed documents in a stylometric space by relying on a reference set of authors and the intra-author consistency property which is one of two components in our definition of writing style. The main intuition of this paper is that we can define a general stylometric space from a set of reference authors such that, in this space, the coordinates of different documents will be close when the documents are by the same author, and spread away when they are by different authors, even for documents by authors who are not in the set of reference authors. The method we propose allows for the clustering of documents based on stylistic clues reflecting the authorship of documents. For the empirical validation of the method, we train a deep neural network model to predict authors of a large reference dataset consisting of news and blog articles. Albeit the learning process is supervised, it does not require a dedicated labeling of the data but it relies only on the metadata of the articles which are available in huge amounts. We evaluate the model on multiple datasets, on both the authorship clustering and the authorship attribution tasks.",
}
@inproceedings{radhakrishnan-etal-2020-little,
    title = "{``}A Little Birdie Told Me ... {''} - Inductive Biases for Rumour Stance Detection on Social Media",
    author = "Radhakrishnan, Karthik  and
      Kanakagiri, Tushar  and
      Chakravarthy, Sharanya  and
      Balachandran, Vidhisha",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.31",
    doi = "10.18653/v1/2020.wnut-1.31",
    pages = "244--248",
    abstract = "The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works, we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media fine-tuning of BERT allow for better language understanding, thus yielding an F1 score of 58.7 on the SemEval 2019 task on rumour stance detection.",
}
@inproceedings{vizcarra-ochoa-luna-2020-paraphrase,
    title = "Paraphrase Generation via Adversarial Penalizations",
    author = "Vizcarra, Gerson  and
      Ochoa-Luna, Jose",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.32",
    doi = "10.18653/v1/2020.wnut-1.32",
    pages = "249--259",
    abstract = "Paraphrase generation is an important problem in Natural Language Processing that has been addressed with neural network-based approaches recently. This paper presents an adversarial framework to address the paraphrase generation problem in English. Unlike previous methods, we employ the discriminator output as penalization instead of using policy gradients, and we propose a global discriminator to avoid the Monte-Carlo search. In addition, this work use and compare different settings of input representation. We compare our methods to some baselines in the Quora question pairs dataset. The results show that our framework is competitive against the previous benchmarks.",
}
@inproceedings{tabassum-etal-2020-wnut,
    title = "{WNUT}-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols",
    author = "Tabassum, Jeniya  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.33",
    doi = "10.18653/v1/2020.wnut-1.33",
    pages = "260--267",
    abstract = "This paper presents the results of the wet labinformation extraction task at WNUT 2020.This task consisted of two sub tasks- (1) anamed entity recognition task with 13 partic-ipants; and (2) a relation extraction task with2 participants. We outline the task, data an-notation process, corpus statistics, and providea high-level overview of the participating sys-tems for each sub task.",
}
@inproceedings{vaidhya-kaushal-2020-iitkgp,
    title = "{IITKGP} at {W}-{NUT} 2020 Shared Task-1: Domain specific {BERT} representation for Named Entity Recognition of lab protocol",
    author = "Vaidhya, Tejas  and
      Kaushal, Ayush",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.34",
    doi = "10.18653/v1/2020.wnut-1.34",
    pages = "268--272",
    abstract = "Supervised models trained to predict properties from representations have been achieving high accuracy on a variety of tasks.For in-stance, the BERT family seems to work exceptionally well on the downstream task from NER tagging to the range of other linguistictasks. But the vocabulary used in the medical field contains a lot of different tokens used only in the medical industry such as the name of different diseases, devices, organisms,medicines, etc. that makes it difficult for traditional BERT model to create contextualized embedding. In this paper, we are going to illustrate the System for Named Entity Tagging based on Bio-Bert. Experimental results show that our model gives substantial improvements over the baseline and stood the fourth runner up in terms of F1 score, and first runner up in terms of Recall with just 2.21 F1 score behind the best one.",
}
@inproceedings{singh-wadhawan-2020-publishincovid19,
    title = "{P}ublish{I}n{C}ovid19 at {WNUT} 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings",
    author = "Singh, Janvijay  and
      Wadhawan, Anshul",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.35",
    doi = "10.18653/v1/2020.wnut-1.35",
    pages = "273--280",
    abstract = "In this paper, we describe the approach that we employed to address the task of Entity Recognition over Wet Lab Protocols - a shared task in EMNLP WNUT-2020 Workshop. Our approach is composed of two phases. In the first phase, we experiment with various contextualised word embeddings (like Flair, BERT-based) and a BiLSTM-CRF model to arrive at the best-performing architecture. In the second phase, we create an ensemble composed of eleven BiLSTM-CRF models. The individual models are trained on random train-validation splits of the complete dataset. Here, we also experiment with different output merging schemes, including Majority Voting and Structured Learning Ensembling (SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for the partial and exact match of the entity spans, respectively. We were ranked first and second, in terms of partial and exact match, respectively.",
}
@inproceedings{miller-vosoughi-2020-big,
    title = "Big Green at {WNUT} 2020 Shared Task-1: Relation Extraction as Contextualized Sequence Classification",
    author = "Miller, Chris  and
      Vosoughi, Soroush",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.36",
    doi = "10.18653/v1/2020.wnut-1.36",
    pages = "281--285",
    abstract = "Relation and event extraction is an important task in natural language processing. We introduce a system which uses contextualized knowledge graph completion to classify relations and events between known entities in a noisy text environment. We report results which show that our system is able to effectively extract relations and events from a dataset of wet lab protocols.",
}
@inproceedings{acharya-2020-wnut,
    title = "{WNUT} 2020 Shared Task-1: Conditional Random Field({CRF}) based Named Entity Recognition({NER}) for Wet Lab Protocols",
    author = "Acharya, Kaushik",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.37",
    doi = "10.18653/v1/2020.wnut-1.37",
    pages = "286--289",
    abstract = "The paper describes how classifier model built using Conditional Random Field detects named entities in wet lab protocols.",
}
@inproceedings{sohrab-etal-2020-mgsohrab,
    title = "mgsohrab at {WNUT} 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols",
    author = "Sohrab, Mohammad Golam  and
      Duong Nguyen, Anh-Khoa  and
      Miwa, Makoto  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.38",
    doi = "10.18653/v1/2020.wnut-1.38",
    pages = "290--298",
    abstract = "We present a neural exhaustive approach that addresses named entity recognition (NER) and relation recognition (RE), for the entity and re- lation recognition over the wet-lab protocols shared task. We introduce BERT-based neural exhaustive approach that enumerates all pos- sible spans as potential entity mentions and classifies them into entity types or no entity with deep neural networks to address NER. To solve relation extraction task, based on the NER predictions or given gold mentions we create all possible trigger-argument pairs and classify them into relation types or no relation. In NER task, we achieved 76.60{\%} in terms of F-score as third rank system among the partic- ipated systems. In relation extraction task, we achieved 80.46{\%} in terms of F-score as the top system in the relation extraction or recognition task. Besides we compare our model based on the wet lab protocols corpus (WLPC) with the WLPC baseline and dynamic graph-based in- formation extraction (DyGIE) systems.",
}
@inproceedings{zeng-etal-2020-fancy,
    title = "Fancy Man Launches Zippo at {WNUT} 2020 Shared Task-1: A Bert Case Model for Wet Lab Entity Extraction",
    author = "Zeng, Qingcheng  and
      Fang, Xiaoyang  and
      Liang, Zhexin  and
      Meng, Haoding",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.39",
    doi = "10.18653/v1/2020.wnut-1.39",
    pages = "299--304",
    abstract = "Automatic or semi-automatic conversion of protocols specifying steps in performing a lab procedure into machine-readable format benefits biological research a lot. These noisy, dense, and domain-specific lab protocols processing draws more and more interests with the development of deep learning. This paper presents our teamwork on WNUT 2020 shared task-1: wet lab entity extract, that we conducted studies in several models, including a BiLSTM CRF model and a Bert case model which can be used to complete wet lab entity extraction. And we mainly discussed the performance differences of Bert case under different situations such as transformers versions, case sensitivity that may don{'}t get enough attention before.",
}
@inproceedings{knafou-etal-2020-bitem,
    title = "{B}i{T}e{M} at {WNUT} 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models",
    author = "Knafou, Julien  and
      Naderi, Nona  and
      Copara, Jenny  and
      Teodoro, Douglas  and
      Ruch, Patrick",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.40",
    doi = "10.18653/v1/2020.wnut-1.40",
    pages = "305--313",
    abstract = "Recent improvements in machine-reading technologies attracted much attention to automation problems and their possibilities. In this context, WNUT 2020 introduces a Name Entity Recognition (NER) task based on wet laboratory procedures. In this paper, we present a 3-step method based on deep neural language models that reported the best overall exact match F1-score (77.99{\%}) of the competition. By fine-tuning 10 times, 10 different pretrained language models, this work shows the advantage of having more models in an ensemble based on a majority of votes strategy. On top of that, having 100 different models allowed us to analyse the combinations of ensemble that demonstrated the impact of having multiple pretrained models versus fine-tuning a pretrained model multiple times.",
}
@inproceedings{nguyen-etal-2020-wnut,
    title = "{WNUT}-2020 Task 2: Identification of Informative {COVID}-19 {E}nglish Tweets",
    author = "Nguyen, Dat Quoc  and
      Vu, Thanh  and
      Rahimi, Afshin  and
      Dao, Mai Hoang  and
      Nguyen, Linh The  and
      Doan, Long",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.41",
    doi = "10.18653/v1/2020.wnut-1.41",
    pages = "314--318",
    abstract = "In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets. We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task. In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.",
}
@inproceedings{tuan-nguyen-2020-tatl,
    title = "{TATL} at {WNUT}-2020 Task 2: A Transformer-based Baseline System for Identification of Informative {COVID}-19 {E}nglish Tweets",
    author = "Tuan Nguyen, Anh",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.42",
    doi = "10.18653/v1/2020.wnut-1.42",
    pages = "319--323",
    abstract = "As the COVID-19 outbreak continues to spread throughout the world, more and more information about the pandemic has been shared publicly on social media. For example, there are a huge number of COVID-19 English Tweets daily on Twitter. However, the majority of those Tweets are uninformative, and hence it is important to be able to automatically select only the informative ones for downstream applications. In this short paper, we present our participation in the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective baseline for the task. Despite its simplicity, our proposed approach shows very competitive results in the leaderboard as we ranked 8 over 56 teams participated in total.",
}
@inproceedings{yasuda-etal-2020-nhk,
    title = "{NHK}{\_}{STRL} at {WNUT}-2020 Task 2: {GAT}s with Syntactic Dependencies as Edges and {CTC}-based Loss for Text Classification",
    author = "Yasuda, Yuki  and
      Ishiwatari, Taichi  and
      Miyazaki, Taro  and
      Goto, Jun",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.43",
    doi = "10.18653/v1/2020.wnut-1.43",
    pages = "324--330",
    abstract = "The outbreak of COVID-19 has greatly impacted our daily lives. In these circumstances, it is important to grasp the latest information to avoid causing too much fear and panic. To help grasp new information, extracting information from social networking sites is one of the effective ways. In this paper, we describe a method to identify whether a tweet related to COVID-19 is informative or not, which can help to grasp new information. The key features of our method are its use of graph attention networks to encode syntactic dependencies and word positions in the sentence, and a loss function based on connectionist temporal classification (CTC) that can learn a label for each token without reference data for each token. Experimental results show that the proposed method achieved an F1 score of 0.9175, out- performing baseline methods.",
}
@inproceedings{giovanni-moller-etal-2020-nlp,
    title = "{NLP} North at {WNUT}-2020 Task 2: Pre-training versus Ensembling for Detection of Informative {COVID}-19 {E}nglish Tweets",
    author = "Giovanni M{\o}ller, Anders  and
      van der Goot, Rob  and
      Plank, Barbara",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.44",
    doi = "10.18653/v1/2020.wnut-1.44",
    pages = "331--336",
    abstract = "With the COVID-19 pandemic raging world-wide since the beginning of the 2020 decade, the need for monitoring systems to track relevant information on social media is vitally important. This paper describes our submission to the WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. We investigate the effectiveness for a variety of classification models, and found that domain-specific pre-trained BERT models lead to the best performance. On top of this, we attempt a variety of ensembling strategies, but these attempts did not lead to further improvements. Our final best model, the standalone CT-BERT model, proved to be highly competitive, leading to a shared first place in the shared task. Our results emphasize the importance of domain and task-related pre-training.",
}
@inproceedings{sai-2020-siva,
    title = "Siva at {WNUT}-2020 Task 2: Fine-tuning Transformer Neural Networks for Identification of Informative Covid-19 Tweets",
    author = "Sai, Siva",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.45",
    doi = "10.18653/v1/2020.wnut-1.45",
    pages = "337--341",
    abstract = "Social media witnessed vast amounts of misinformation being circulated every day during the Covid-19 pandemic so much so that the WHO Director-General termed the phenomenon as {``}infodemic.{''} The ill-effects of such misinformation are multifarious. Thus, identifying and eliminating the sources of misinformation becomes very crucial, especially when mass panic can be controlled only through the right information. However, manual identification is arduous, with such large amounts of data being generated every day. This shows the importance of automatic identification of misinformative posts on social media. WNUT-2020 Task 2 aims at building systems for automatic identification of informative tweets. In this paper, I discuss my approach to WNUT-2020 Task 2. I fine-tuned eleven variants of four transformer networks -BERT, RoBERTa, XLM-RoBERTa, ELECTRA, on top of two different preprocessing techniques to reap good results. My top submission achieved an F1-score of 85.3{\%} in the final evaluation.",
}
@inproceedings{reddy-biswal-2020-iiitbh,
    title = "{IIITBH} at {WNUT}-2020 Task 2: Exploiting the best of both worlds",
    author = "Reddy, Saichethan  and
      Biswal, Pradeep",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.46",
    doi = "10.18653/v1/2020.wnut-1.46",
    pages = "342--346",
    abstract = "In this paper, we present IIITBH team{'}s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.",
}
@inproceedings{wadhawan-2020-phonemer,
    title = "Phonemer at {WNUT}-2020 Task 2: Sequence Classification Using {COVID} {T}witter {BERT} and Bagging Ensemble Technique based on Plurality Voting",
    author = "Wadhawan, Anshul",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.47",
    doi = "10.18653/v1/2020.wnut-1.47",
    pages = "347--351",
    abstract = "This paper presents the approach that we employed to tackle the EMNLP WNUT-2020 Shared Task 2 : Identification of informative COVID-19 English Tweets. The task is to develop a system that automatically identifies whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. We solve the task in three stages. The first stage involves pre-processing the dataset by filtering only relevant information. This is followed by experimenting with multiple deep learning models like CNNs, RNNs and Transformer based models. In the last stage, we propose an ensemble of the best model trained on different subsets of the provided dataset. Our final approach achieved an F1-score of 0.9037 and we were ranked sixth overall with F1-score as the evaluation criteria.",
}
@inproceedings{perrio-tayyar-madabushi-2020-cxp949,
    title = "{CXP}949 at {WNUT}-2020 Task 2: Extracting Informative {COVID}-19 Tweets - {R}o{BERT}a Ensembles and The Continued Relevance of Handcrafted Features",
    author = "Perrio, Calum  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.48",
    doi = "10.18653/v1/2020.wnut-1.48",
    pages = "352--358",
    abstract = "This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text. We explore improving the performance of a pre-trained transformer-based language model fine-tuned for text classification through an ensemble implementation that makes use of corpus level information and a handcrafted feature. We test the effectiveness of including the aforementioned features in accommodating the challenges of a noisy data set centred on a specific subject outside the remit of the pre-training data. We show that inclusion of additional features can improve classification results and achieve a score within 2 points of the top performing team.",
}
@inproceedings{hettiarachchi-ranasinghe-2020-infominer,
    title = "{I}nfo{M}iner at {WNUT}-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction",
    author = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.49",
    doi = "10.18653/v1/2020.wnut-1.49",
    pages = "359--365",
    abstract = "Identifying informative tweets is an important step when building information extraction systems based on social media. WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves 10th place in the final rankings scoring 0.9004 F1 score for the test set.",
}
@inproceedings{huynh-etal-2020-banana,
    title = "{BANANA} at {WNUT}-2020 Task 2: Identifying {COVID}-19 Information on {T}witter by Combining Deep Learning and Transfer Learning Models",
    author = "Huynh, Tin  and
      Thanh Luan, Luan  and
      Luu, Son T.",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.50",
    doi = "10.18653/v1/2020.wnut-1.50",
    pages = "366--370",
    abstract = "The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81{\%} on the test set.",
}
@inproceedings{sengupta-2020-datamafia,
    title = "{DATAMAFIA} at {WNUT}-2020 {T}ask 2: {A} {S}tudy of {P}re-trained {L}anguage {M}odels along with {R}egularization {T}echniques for {D}ownstream {T}asks",
    author = "Sengupta, Ayan",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.51",
    doi = "10.18653/v1/2020.wnut-1.51",
    pages = "371--377",
    abstract = "This document describes the system description developed by team datamafia at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. This paper contains a thorough study of pre-trained language models on downstream binary classification task over noisy user generated Twitter data. The solution submitted to final test leaderboard is a fine tuned RoBERTa model which achieves F1 score of 90.8{\%} and 89.4{\%} on the dev and test data respectively. In the later part, we explore several techniques for injecting regularization explicitly into language models to generalize predictions over noisy data. Our experiments show that adding regularizations to RoBERTa pre-trained model can be very robust to data and annotation noises and can improve overall performance by more than 1.2{\%}.",
}
@inproceedings{magge-etal-2020-upennhlp,
    title = "{UP}enn{HLP} at {WNUT}-2020 Task 2 : Transformer models for classification of {COVID}19 posts on {T}witter",
    author = "Magge, Arjun  and
      Pimpalkhute, Varad  and
      Rallapalli, Divya  and
      Siguenza, David  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.52",
    doi = "10.18653/v1/2020.wnut-1.52",
    pages = "378--382",
    abstract = "Increasing usage of social media presents new non-traditional avenues for monitoring disease outbreaks, virus transmissions and disease progressions through user posts describing test results or disease symptoms. However, the discussions on the topic of infectious diseases that are informative in nature also span various topics such as news, politics and humor which makes the data mining challenging. We present a system to identify tweets about the COVID19 disease outbreak that are deemed to be informative on Twitter for use in downstream applications. The system scored a F1-score of 0.8941, Precision of 0.9028, Recall of 0.8856 and Accuracy of 0.9010. In the shared task organized as part of the 6th Workshop of Noisy User-generated Text (WNUT), the system was ranked 18th by F1-score and 13th by Accuracy.",
}
@inproceedings{tran-etal-2020-uit,
    title = "{UIT}-{HSE} at {WNUT}-2020 Task 2: Exploiting {CT}-{BERT} for Identifying {COVID}-19 Information on the {T}witter Social Network",
    author = "Tran, Khiem  and
      Phan, Hao  and
      Nguyen, Kiet  and
      Thuy Nguyen, Ngan Luu",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.53",
    doi = "10.18653/v1/2020.wnut-1.53",
    pages = "383--387",
    abstract = "Recently, COVID-19 has affected a variety of real-life aspects of the world and led to dreadful consequences. More and more tweets about COVID-19 has been shared publicly on Twitter. However, the plurality of those Tweets are uninformative, which is challenging to build automatic systems to detect the informative ones for useful AI applications. In this paper, we present our results at the W-NUT 2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. In particular, we propose our simple but effective approach using the transformer-based models based on COVID-Twitter-BERT (CT-BERT) with different fine-tuning techniques. As a result, we achieve the F1-Score of 90.94{\%} with the third place on the leaderboard of this task which attracted 56 submitted teams in total.",
}
@inproceedings{guo-etal-2020-emory,
    title = "Emory at {WNUT}-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification",
    author = "Guo, Yuting  and
      Ali Al-Garadi, Mohammed  and
      Sarker, Abeed",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.54",
    doi = "10.18653/v1/2020.wnut-1.54",
    pages = "388--393",
    abstract = "This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: {``}Identifi- cation of Informative COVID-19 English Tweet{''}. Our system explores three recent Transformer- based deep learning models pretrained on large- scale data to encode documents. Moreover, we developed two feature enrichment methods to en- hance document embeddings by integrating emoji embeddings and syntactic features into deep learn- ing models. Our system achieved F1-score of 0.897 and accuracy of 90.1{\%} on the test set, and ranked in the top-third of all 55 teams.",
}
@inproceedings{tasneem-etal-2020-csecu,
    title = "{CSECU}-{DSG} at {WNUT}-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative {COVID}-19 {E}nglish Tweets",
    author = "Tasneem, Fareen  and
      Naim, Jannatun  and
      Tasnia, Radiathun  and
      Hossain, Tashin  and
      Chy, Abu Nowshed",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.55",
    doi = "10.18653/v1/2020.wnut-1.55",
    pages = "394--398",
    abstract = "COVID-19 pandemic has become the trending topic on twitter and people are interested in sharing diverse information ranging from new cases, healthcare guidelines, medicine, and vaccine news. Such information assists the people to be updated about the situation as well as beneficial for public safety personnel for decision making. However, the informal nature of twitter makes it challenging to refine the informative tweets from the huge tweet streams. To address these challenges WNUT-2020 introduced a shared task focusing on COVID-19 related informative tweet identification. In this paper, we describe our participation in this task. We propose a neural model that adopts the strength of transfer learning and hand-crafted features in a unified architecture. To extract the transfer learning features, we utilize the state-of-the-art pre-trained sentence embedding model BERT, RoBERTa, and InferSent, whereas various twitter characteristics are exploited to extract the hand-crafted features. Next, various feature combinations are utilized to train a set of multilayer perceptron (MLP) as the base-classifier. Finally, a majority voting based fusion approach is employed to determine the informative tweets. Our approach achieved competitive performance and outperformed the baseline by 7{\%} (approx.).",
}
@inproceedings{chanda-etal-2020-irlab,
    title = "{IRL}ab@{IITBHU} at {WNUT}-2020 Task 2: Identification of informative {COVID}-19 {E}nglish Tweets using {BERT}",
    author = "Chanda, Supriya  and
      Nandy, Eshita  and
      Pal, Sukomal",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.56",
    doi = "10.18653/v1/2020.wnut-1.56",
    pages = "399--403",
    abstract = "This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020. We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: DistilBERT and FastText. DistilBERT achieves a F1 score of 0.7508 on the test set, which is the best of our submissions.",
}
@inproceedings{kumar-singh-2020-nutcracker,
    title = "{N}ut{C}racker at {WNUT}-2020 Task 2: Robustly Identifying Informative {COVID}-19 Tweets using Ensembling and Adversarial Training",
    author = "Kumar, Priyanshu  and
      Singh, Aadarsh",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.57",
    doi = "10.18653/v1/2020.wnut-1.57",
    pages = "404--408",
    abstract = "We experiment with COVID-Twitter-BERT and RoBERTa models to identify informative COVID-19 tweets. We further experiment with adversarial training to make our models robust. The ensemble of COVID-Twitter-BERT and RoBERTa obtains a F1-score of 0.9096 (on the positive class) on the test data of WNUT-2020 Task 2 and ranks 1st on the leaderboard. The ensemble of the models trained using adversarial training also produces similar result.",
}
@inproceedings{dhana-laxmi-etal-2020-dsc,
    title = "{DSC}-{IIT} {ISM} at {WNUT}-2020 Task 2: Detection of {COVID}-19 informative tweets using {R}o{BERT}a",
    author = "Dhana Laxmi, Sirigireddy  and
      Agarwal, Rohit  and
      Sinha, Aman",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.58",
    doi = "10.18653/v1/2020.wnut-1.58",
    pages = "409--413",
    abstract = "Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.",
}
@inproceedings{awatramani-kumar-2020-linguist,
    title = "Linguist Geeks on {WNUT}-2020 Task 2: {COVID}-19 Informative Tweet Identification using Progressive Trained Language Models and Data Augmentation",
    author = "Awatramani, Vasudev  and
      Kumar, Anupam",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.59",
    doi = "10.18653/v1/2020.wnut-1.59",
    pages = "414--418",
    abstract = "Since the outbreak of COVID-19, there has been a surge of digital content on social media. The content ranges from news articles, academic reports, tweets, videos, and even memes. Among such an overabundance of data, it is crucial to distinguish which information is actually informative or merely sensational, redundant or false. This work focuses on developing such a language system that can differentiate between Informative or Uninformative tweets associated with COVID-19 for WNUT-2020 Shared Task 2. For this purpose, we employ deep transfer learning models such as BERT along other techniques such as Noisy Data Augmentation and Progress Training. The approach achieves a competitive F1-score of 0.8715 on the final testing dataset.",
}
@inproceedings{mundotiya-etal-2020-nlprl,
    title = "{NLPRL} at {WNUT}-2020 Task 2: {ELM}o-based System for Identification of {COVID}-19 Tweets",
    author = "Mundotiya, Rajesh Kumar  and
      Baruah, Rupjyoti  and
      Srivastava, Bhavana  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.60",
    doi = "10.18653/v1/2020.wnut-1.60",
    pages = "419--422",
    abstract = "The Coronavirus pandemic has been a dominating news on social media for the last many months. Efforts are being made to reduce its spread and reduce the casualties as well as new infections. For this purpose, the information about the infected people and their related symptoms, as available on social media, such as Twitter, can help in prevention and taking precautions. This is an example of using noisy text processing for disaster management. This paper discusses the NLPRL results in Shared Task-2 of WNUT-2020 workshop. We have considered this problem as a binary classification problem and have used a pre-trained ELMo embedding with GRU units. This approach helps classify the tweets with accuracy as 80.85{\%} and 78.54{\%} as F1-score on the provided test dataset. The experimental code is available online.",
}
@inproceedings{fayoumi-yeniterzi-2020-su,
    title = "{SU}-{NLP} at {WNUT}-2020 Task 2: The Ensemble Models",
    author = "Fayoumi, Kenan  and
      Yeniterzi, Reyyan",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.61",
    doi = "10.18653/v1/2020.wnut-1.61",
    pages = "423--427",
    abstract = "In this paper, we address the problem of identifying informative tweets related to COVID-19 in the form of a binary classification task as part of our submission for W-NUT 2020 Task 2. Specifically, we focus on ensembling methods to boost the classification performance of classification models such as BERT and CNN. We show that ensembling can reduce the variance in performance, specifically for BERT base models.",
}
@inproceedings{ohashi-etal-2020-idsou,
    title = "{IDSOU} at {WNUT}-2020 Task 2: Identification of Informative {COVID}-19 {E}nglish Tweets",
    author = "Ohashi, Sora  and
      Kajiwara, Tomoyuki  and
      Chu, Chenhui  and
      Takemura, Noriko  and
      Nakashima, Yuta  and
      Nagahara, Hajime",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.62",
    doi = "10.18653/v1/2020.wnut-1.62",
    pages = "428--433",
    abstract = "We introduce the IDSOU submission for the WNUT-2020 task 2: identification of informative COVID-19 English Tweets. Our system is an ensemble of pre-trained language models such as BERT. We ranked 16th in the F1 score.",
}
@inproceedings{pelrine-etal-2020-complexdatalab,
    title = "{C}omplex{D}ata{L}ab at {W}-{NUT} 2020 Task 2: Detecting Informative {COVID}-19 Tweets by Attending over Linked Documents",
    author = "Pelrine, Kellin  and
      Danovitch, Jacob  and
      Camacho, Albert Orozco  and
      Rabbany, Reihaneh",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.63",
    doi = "10.18653/v1/2020.wnut-1.63",
    pages = "434--439",
    abstract = "Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.",
}
@inproceedings{chauhan-2020-neu,
    title = "{NEU} at {WNUT}-2020 Task 2: Data Augmentation To Tell {BERT} That Death Is Not Necessarily Informative",
    author = "Chauhan, Kumud",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.64",
    doi = "10.18653/v1/2020.wnut-1.64",
    pages = "440--443",
    abstract = "Millions of people around the world are sharing COVID-19 related information on social media platforms. Since not all the information shared on the social media is useful, a machine learning system to identify informative posts can help users in finding relevant information. In this paper, we present a BERT classifier system for W-NUT2020 Shared Task 2: Identification of Informative COVID-19 English Tweets. Further, we show that BERT exploits some easy signals to identify informative tweets, and adding simple patterns to uninformative tweets drastically degrades BERT performance. In particular, simply adding {``}10 deaths{''} to tweets in dev set, reduces BERT F1- score from 92.63 to 7.28. We also propose a simple data augmentation technique that helps in improving the robustness and generalization ability of the BERT classifier.",
}
@inproceedings{sancheti-etal-2020-lynyrdskynyrd,
    title = "{L}ynyrd{S}kynyrd at {WNUT}-2020 Task 2: Semi-Supervised Learning for Identification of Informative {COVID}-19 {E}nglish Tweets",
    author = "Sancheti, Abhilasha  and
      Chawla, Kushal  and
      Verma, Gaurav",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.65",
    doi = "10.18653/v1/2020.wnut-1.65",
    pages = "444--449",
    abstract = "In this work, we describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets. Our system is an ensemble of various machine learning methods, leveraging both traditional feature-based classifiers as well as recent advances in pre-trained language models that help in capturing the syntactic, semantic, and contextual features from the tweets. We further employ pseudo-labelling to incorporate the unlabelled Twitter data released on the pandemic. Our best performing model achieves an F1-score of 0.9179 on the provided validation set and 0.8805 on the blind test-set.",
}
@inproceedings{m-s-p-j-a-2020-nit,
    title = "{NIT}{\_}{COVID}-19 at {WNUT}-2020 Task 2: Deep Learning Model {R}o{BERT}a for Identify Informative {COVID}-19 {E}nglish Tweets",
    author = "M S, Jagadeesh  and
      P J A, Alphonse",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.66",
    doi = "10.18653/v1/2020.wnut-1.66",
    pages = "450--454",
    abstract = "This paper presents the model submitted by NIT COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14{\%} in the F1-score metric.",
}
@inproceedings{maveli-2020-edinburghnlp,
    title = "{E}dinburgh{NLP} at {WNUT}-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in {COVID}-19 Tweets",
    author = "Maveli, Nickil",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.67",
    doi = "10.18653/v1/2020.wnut-1.67",
    pages = "455--461",
    abstract = "Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they{'}re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (disaster relief organizations and news agencies) and therefore recognizing the informativeness of a tweet can help filter noise from large volumes of data. In this paper, we present our submission for WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets. Our most successful model is an ensemble of transformers including RoBERTa, XLNet, and BERTweet trained in a Semi-Supervised Learning (SSL) setting. The proposed system achieves a F1 score of 0.9011 on the test set (ranking 7th on the leaderboard), and shows significant gains in performance compared to a baseline system using fasttext embeddings.",
}
@inproceedings{varachkina-etal-2020-gcdh,
    title = "{\#}{GCDH} at {WNUT}-2020 Task 2: {BERT}-Based Models for the Detection of Informativeness in {E}nglish {COVID}-19 Related Tweets",
    author = {Varachkina, Hanna  and
      Ziehe, Stefan  and
      D{\"o}nicke, Tillmann  and
      Pannach, Franziska},
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.68",
    doi = "10.18653/v1/2020.wnut-1.68",
    pages = "462--465",
    abstract = "In this system paper, we present a transformer-based approach to the detection of informativeness in English tweets on the topic of the current COVID-19 pandemic. Our models distinguish informative tweets, i.e. tweets containing statistics on recovery, suspected and confirmed cases and COVID-19 related deaths, from uninformative tweets. We present two transformer-based approaches as well as a Naive Bayes classifier and a support vector machine as baseline systems. The transformer models outperform the baselines by more than 0.1 in F1-score, with F1-scores of 0.9091 and 0.9036. Our models were submitted to the shared task Identification of informative COVID-19 English tweets WNUT-2020 Task 2.",
}
@inproceedings{hoang-vu-2020-nuts,
    title = "Not-{NUT}s at {WNUT}-2020 Task 2: A {BERT}-based System in Identifying Informative {COVID}-19 {E}nglish Tweets",
    author = "Hoang, Thai  and
      Vu, Phuong",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.69",
    doi = "10.18653/v1/2020.wnut-1.69",
    pages = "466--470",
    abstract = "As of 2020 when the COVID-19 pandemic is full-blown on a global scale, people{'}s need to have access to legitimate information regarding COVID-19 is more urgent than ever, especially via online media where the abundance of irrelevant information overshadows the more informative ones. In response to such, we proposed a model that, given an English tweet, automatically identifies whether that tweet bears informative content regarding COVID-19 or not. By ensembling different BERTweet model configurations, we have achieved competitive results that are only shy of those by top performing teams by roughly 1{\%} in terms of F1 score on the informative class. In the post-competition period, we have also experimented with various other approaches that potentially boost generalization to a new dataset.",
}
@inproceedings{prakash-babu-eswari-2020-cia,
    title = "{CIA}{\_}{NITT} at {WNUT}-2020 Task 2: Classification of {COVID}-19 Tweets Using Pre-trained Language Models",
    author = "Prakash Babu, Yandrapati  and
      Eswari, Rajagopal",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.70",
    doi = "10.18653/v1/2020.wnut-1.70",
    pages = "471--474",
    abstract = "This paper presents our models for WNUT2020 shared task2. The shared task2 involves identification of COVID-19 related informative tweets. We treat this as binary text clas-sification problem and experiment with pre-trained language models. Our first model which is based on CT-BERT achieves F1-scoreof 88.7{\%} and second model which is an ensemble of CT-BERT, RoBERTa and SVM achieves F1-score of 88.52{\%}.",
}
@inproceedings{dao-quang-nguyen-minh-2020-uet,
    title = "{UET} at {WNUT}-2020 Task 2: A Study of Combining Transfer Learning Methods for Text Classification with {R}o{BERT}a",
    author = "Dao Quang, Huy  and
      Nguyen Minh, Tam",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.71",
    doi = "10.18653/v1/2020.wnut-1.71",
    pages = "475--479",
    abstract = "This paper reports our approach and the results of our experiments for W-NUT task 2: Identification of Informative COVID-19 English Tweets. In this paper, we test out the effectiveness of transfer learning method with state of the art language models as RoBERTa on this text classification task. Moreover, we examine the benefit of applying additional fine-tuning and training techniques including fine-tuning discrimination, gradual unfreezing as well as our custom head for the classifier. Our best model results in a high F1-score of 89.89 on the task{'}s private test dataset and that of 90.96 on public test set without ensembling multiple models and additional data.",
}
@inproceedings{whang-vosoughi-2020-dartmouth,
    title = "{D}artmouth {CS} at {WNUT}-2020 Task 2: Informative {COVID}-19 Tweet Classification Using {BERT}",
    author = "Whang, Dylan  and
      Vosoughi, Soroush",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.72",
    doi = "10.18653/v1/2020.wnut-1.72",
    pages = "480--484",
    abstract = "We describe the systems developed for the WNUT-2020 shared task 2, identification of informative COVID-19 English Tweets. BERT is a highly performant model for Natural Language Processing tasks. We increased BERT{'}s performance in this classification task by fine-tuning BERT and concatenating its embeddings with Tweet-specific features and training a Support Vector Machine (SVM) for classification (henceforth called BERT+). We compared its performance to a suite of machine learning models. We used a Twitter specific data cleaning pipeline and word-level TF-IDF to extract features for the non-BERT models. BERT+ was the top performing model with an F1-score of 0.8713.",
}
@inproceedings{doan-bao-etal-2020-sunbear,
    title = "{S}un{B}ear at {WNUT}-2020 Task 2: Improving {BERT}-Based Noisy Text Classification with Knowledge of the Data domain",
    author = "Doan Bao, Linh  and
      Nguyen, Viet Anh  and
      Pham Huu, Quang",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.73",
    doi = "10.18653/v1/2020.wnut-1.73",
    pages = "485--490",
    abstract = "This paper proposes an improved custom model for WNUT task 2: Identification of Informative COVID-19 English Tweet. We improve experiment with the effectiveness of fine-tuning methodologies for state-of-the-art language model RoBERTa. We make a preliminary instantiation of this formal model for the text classification approaches. With appropriate training techniques, our model is able to achieve 0.9218 F1-score on public validation set and the ensemble version settles at top 9 F1-score (0.9005) and top 2 Recall (0.9301) on private test set.",
}
@inproceedings{putri-etal-2020-iswara,
    title = "{ISWARA} at {WNUT}-2020 Task 2: Identification of Informative {COVID}-19 {E}nglish Tweets using {BERT} and {F}ast{T}ext Embeddings",
    author = "Putri, Wava Carissa  and
      Hidayat, Rani Aulia  and
      Khasanah, Isnaini Nurul  and
      Mahendra, Rahmad",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.74",
    doi = "10.18653/v1/2020.wnut-1.74",
    pages = "491--494",
    abstract = "This paper presents Iswara{'}s participation in the WNUT-2020 Task 2 {``}Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings{''},which tries to classify whether a certain tweet is considered informative or not. We proposed a method that utilizes word embeddings and using word occurrence related to the topic for this task. We compare several models to get the best performance. Results show that pairing BERT with word occurrences outperforms fastText with F1-Score, precision, recall, and accuracy on test data of 76{\%}, 81{\%}, 72{\%}, and 79{\%}, respectively",
}
@inproceedings{hurriyetoglu-etal-2020-covcor20,
    title = "{COVCOR}20 at {WNUT}-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules",
    author = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Safaya, Ali  and
      Mutlu, Osman  and
      Oostdijk, Nelleke  and
      Y{\"o}r{\"u}k, Erdem},
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.75",
    doi = "10.18653/v1/2020.wnut-1.75",
    pages = "495--498",
    abstract = "In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules. While both of the deep learning systems outperformed the system using the linguistically informed rules, we found that through the integration of (the output of) the three systems a better performance could be achieved than the standalone performance of each approach in a cross-validation setting. However, on the test data the performance of the integration was slightly lower than our best performing deep learning model. These results hardly indicate any progress in line of integrating machine learning and expert rules driven systems. We expect that the release of the annotation manuals and gold labels of the test data after this workshop will shed light on these perplexing results.",
}
@inproceedings{chen-etal-2020-test,
    title = "{TEST}{\_}{POSITIVE} at {W}-{NUT} 2020 Shared Task-3: Cross-task modeling",
    author = "Chen, Chacha  and
      Huang, Chieh-Yang  and
      Hou, Yaqi  and
      Shi, Yang  and
      Dai, Enyan  and
      Wang, Jiaqi",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.76",
    doi = "10.18653/v1/2020.wnut-1.76",
    pages = "499--504",
    abstract = "The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2{\%} in micro F1.",
}
@inproceedings{yang-etal-2020-imec,
    title = "imec-{ETRO}-{VUB} at {W}-{NUT} 2020 Shared Task-3: A multilabel {BERT}-based system for predicting {COVID}-19 events",
    author = "Yang, Xiangyu  and
      Bekoulis, Giannis  and
      Deligiannis, Nikos",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.77",
    doi = "10.18653/v1/2020.wnut-1.77",
    pages = "505--513",
    abstract = "In this paper, we present our system designed to address the W-NUT 2020 shared task for COVID-19 Event Extraction from Twitter. To mitigate the noisy nature of the Twitter stream, our system makes use of the COVID-Twitter-BERT (CT-BERT), which is a language model pre-trained on a large corpus of COVID-19 related Twitter messages. Our system is trained on the COVID-19 Twitter Event Corpus and is able to identify relevant text spans that answer pre-defined questions (i.e., slot types) for five COVID-19 related events (i.e., TESTED POSITIVE, TESTED NEGATIVE, CAN-NOT-TEST, DEATH and CURE {\&} PREVENTION). We have experimented with different architectures; our best performing model relies on a multilabel classifier on top of the CT-BERT model that jointly trains all the slot types for a single event. Our experimental results indicate that our Multilabel-CT-BERT system outperforms the baseline methods by 7 percentage points in terms of micro average F1 score. Our model ranked as 4th in the shared task leaderboard.",
}
@inproceedings{wang-lillis-2020-ucd,
    title = "{UCD}-{CS} at {W}-{NUT} 2020 Shared Task-3: A Text to Text Approach for {COVID}-19 Event Extraction on Social Media",
    author = "Wang, Congcong  and
      Lillis, David",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.78",
    doi = "10.18653/v1/2020.wnut-1.78",
    pages = "514--521",
    abstract = "In this paper, we describe our approach in the shared task: COVID-19 event extraction from Twitter. The objective of this task is to extract answers from COVID-related tweets to a set of predefined slot-filling questions. Our approach treats the event extraction task as a question answering task by leveraging the transformer-based T5 text-to-text model. According to the official evaluation scores returned, namely F1, our submitted run achieves competitive performance compared to other participating runs (Top 3). However, we argue that this evaluation may underestimate the actual performance of runs based on text-generation. Although some such runs may answer the slot questions well, they may not be an exact string match for the gold standard answers. To measure the extent of this underestimation, we adopt a simple exact-answer transformation method aiming at converting the well-answered predictions to exactly-matched predictions. The results show that after this transformation our run overall reaches the same level of performance as the best participating run and state-of-the-art F1 scores in three of five COVID-related events. Our code is publicly available to aid reproducibility",
}
@inproceedings{kaushal-vaidhya-2020-winners,
    title = "Winners at {W}-{NUT} 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting {COVID} Entities from Tweets",
    author = "Kaushal, Ayush  and
      Vaidhya, Tejas",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.79",
    doi = "10.18653/v1/2020.wnut-1.79",
    pages = "522--529",
    abstract = "Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks, while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-BERT with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leaderboard with F1 of 0.6598, without using any ensembles or additional datasets.",
}
@inproceedings{weinzierl-harabagiu-2020-hltri,
    title = "{HLTRI} at {W}-{NUT} 2020 Shared Task-3: {COVID}-19 Event Extraction from {T}witter Using Multi-Task Hopfield Pooling",
    author = "Weinzierl, Maxwell  and
      Harabagiu, Sanda",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.80",
    doi = "10.18653/v1/2020.wnut-1.80",
    pages = "530--538",
    abstract = "Extracting structured knowledge involving self-reported events related to the COVID-19 pandemic from Twitter has the potential to inform surveillance systems that play a critical role in public health. The event extraction challenge presented by the W-NUT 2020 Shared Task 3 focused on the identification of five types of events relevant to the COVID-19 pandemic and their respective set of pre-defined slots encoding demographic, epidemiological, clinical as well as spatial, temporal or subjective knowledge. Our participation in the challenge led to the design of a neural architecture for jointly identifying all Event Slots expressed in a tweet relevant to an event of interest. This architecture uses COVID-Twitter-BERT as the pre-trained language model. In addition, to learn text span embeddings for each Event Slot, we relied on a special case of Hopfield Networks, namely Hopfield pooling. The results of the shared task evaluation indicate that our system performs best when it is trained on a larger dataset, while it remains competitive when training on smaller datasets.",
}
@proceedings{wmt-2020-machine,
    title = "Proceedings of the Fifth Conference on Machine Translation",
    editor = {Barrault, Lo{\"\i}c  and
      Bojar, Ond{\v{r}}ej  and
      Bougares, Fethi  and
      Chatterjee, Rajen  and
      Costa-juss{\`a}, Marta R.  and
      Federmann, Christian  and
      Fishel, Mark  and
      Fraser, Alexander  and
      Graham, Yvette  and
      Guzman, Paco  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Morishita, Makoto  and
      Monz, Christof  and
      Nagata, Masaaki  and
      Nakazawa, Toshiaki  and
      Negri, Matteo},
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.0",
}
@inproceedings{barrault-etal-2020-findings,
    title = "Findings of the 2020 Conference on Machine Translation ({WMT}20)",
    author = {Barrault, Lo{\"\i}c  and
      Biesialska, Magdalena  and
      Bojar, Ond{\v{r}}ej  and
      Costa-juss{\`a}, Marta R.  and
      Federmann, Christian  and
      Graham, Yvette  and
      Grundkiewicz, Roman  and
      Haddow, Barry  and
      Huck, Matthias  and
      Joanis, Eric  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Lo, Chi-kiu  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Monz, Christof  and
      Morishita, Makoto  and
      Nagata, Masaaki  and
      Nakazawa, Toshiaki  and
      Pal, Santanu  and
      Post, Matt  and
      Zampieri, Marcos},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.1",
    pages = "1--55",
    abstract = "This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.",
}
@inproceedings{barrault-etal-2020-findings-first,
    title = "Findings of the First Shared Task on Lifelong Learning Machine Translation",
    author = {Barrault, Lo{\"\i}c  and
      Biesialska, Magdalena  and
      Costa-juss{\`a}, Marta R.  and
      Bougares, Fethi  and
      Galibert, Olivier},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.2",
    pages = "56--64",
    abstract = "A lifelong learning system can adapt to new data without forgetting previously acquired knowledge. In this paper, we introduce the first benchmark for lifelong learning machine translation. For this purpose, we provide training, lifelong and test data sets for two language pairs: English-German and English-French. Additionally, we report the results of our baseline systems, which we make available to the public. The goal of this shared task is to encourage research on the emerging topic of lifelong learning machine translation.",
}
@inproceedings{farajian-etal-2020-findings,
    title = "Findings of the {WMT} 2020 Shared Task on Chat Translation",
    author = "Farajian, M. Amin  and
      Lopes, Ant{\'o}nio V.  and
      Martins, Andr{\'e} F. T.  and
      Maruf, Sameen  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.3",
    pages = "65--75",
    abstract = "We report the results of the first edition of the WMT shared task on chat translation. The task consisted of translating bilingual conversational text, in particular customer support chats for the English-German language pair (English agent, German customer). This task varies from the other translation shared tasks, i.e. news and biomedical, mainly due to the fact that the conversations are bilingual, less planned, more informal, and often ungrammatical. Furthermore, such conversations are usually characterized by shorter and simpler sentences and contain more pronouns. We received 14 submissions from 6 participating teams, all of them covering both directions, i.e. En-{\textgreater}De for agent utterances and De-{\textgreater}En for customer messages. We used automatic metrics (BLEU and TER) for evaluating the translations of both agent and customer messages and human document-level direct assessments (DDA) to evaluate the agent translations.",
}
@inproceedings{specia-etal-2020-findings,
    title = "Findings of the {WMT} 2020 Shared Task on Machine Translation Robustness",
    author = "Specia, Lucia  and
      Li, Zhenhao  and
      Pino, Juan  and
      Chaudhary, Vishrav  and
      Guzm{\'a}n, Francisco  and
      Neubig, Graham  and
      Durrani, Nadir  and
      Belinkov, Yonatan  and
      Koehn, Philipp  and
      Sajjad, Hassan  and
      Michel, Paul  and
      Li, Xian",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.4",
    pages = "76--91",
    abstract = "We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs {--} English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for {''}catastrophic errors{''}. We received 59 submissions by 11 participating teams from a variety of types of institutions.",
}
@inproceedings{bawden-etal-2020-university,
    title = "The {U}niversity of {E}dinburgh{'}s {E}nglish-{T}amil and {E}nglish-{I}nuktitut Submissions to the {WMT}20 News Translation Task",
    author = "Bawden, Rachel  and
      Birch, Alexandra  and
      Dobreva, Radina  and
      Oncevay, Arturo  and
      Miceli Barone, Antonio Valerio  and
      Williams, Philip",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.5",
    pages = "92--99",
    abstract = "We describe the University of Edinburgh{'}s submissions to the WMT20 news translation shared task for the low resource language pair English-Tamil and the mid-resource language pair English-Inuktitut. We use the neural machine translation transformer architecture for all submissions and explore a variety of techniques to improve translation quality to compensate for the lack of parallel training data. For the very low-resource English-Tamil, this involves exploring pretraining, using both language model objectives and translation using an unrelated high-resource language pair (German-English), and iterative backtranslation. For English-Inuktitut, we explore the use of multilingual systems, which, despite not being part of the primary submission, would have achieved the best results on the test set.",
}
@inproceedings{bei-etal-2020-gtcom,
    title = "{GTCOM} Neural Machine Translation Systems for {WMT}20",
    author = "Bei, Chao  and
      Zong, Hao  and
      Liu, Qingmin  and
      Yuan, Conghu",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.6",
    pages = "100--104",
    abstract = "This paper describes the Global Tone Communication Co., Ltd.{'}s submission of the WMT20 shared news translation task. We participate in four directions: English to (Khmer and Pashto) and (Khmer and Pashto) to English. Further, we get the best BLEU scores in the directions of English to Pashto, Pashto to English and Khmer to English (13.1, 23.1 and 25.5 respectively) among all the participants. Our submitted systems are unconstrained and focus on mBART (Multilingual Bidirectional and Auto-Regressive Transformers), back-translation and forward-translation. Also, we apply rules, language model and RoBERTa model to filter monolingual, parallel sentences and synthetic sentences. Besides, we validate the difference of the vocabulary built from monolingual data and parallel data.",
}
@inproceedings{chen-etal-2020-didis,
    title = "{D}i{D}i{'}s Machine Translation System for {WMT}2020",
    author = "Chen, Tanfang  and
      Wang, Weiwei  and
      Wei, Wenyang  and
      Shi, Xing  and
      Li, Xiangang  and
      Ye, Jieping  and
      Knight, Kevin",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.7",
    pages = "105--112",
    abstract = "This paper describes the DiDi AI Labs{'} submission to the WMT2020 news translation shared task. We participate in the translation direction of Chinese-{\textgreater}English. In this direction, we use the Transformer as our baseline model and integrate several techniques for model enhancement, including data filtering, data selection, back-translation, fine-tuning, model ensembling, and re-ranking. As a result, our submission achieves a BLEU score of 36.6 in Chinese-{\textgreater}English.",
}
@inproceedings{chen-etal-2020-facebook,
    title = "{F}acebook {AI}{'}s {WMT}20 News Translation Task Submission",
    author = "Chen, Peng-Jen  and
      Lee, Ann  and
      Wang, Changhan  and
      Goyal, Naman  and
      Fan, Angela  and
      Williamson, Mary  and
      Gu, Jiatao",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.8",
    pages = "113--125",
    abstract = "This paper describes Facebook AI{'}s submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil {\textless}-{\textgreater} English and Inuktitut {\textless}-{\textgreater} English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En-{\textgreater}Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that further improvement can be obtained. On the test set, our best submitted systems achieve 21.5 and 13.7 BLEU for Ta-{\textgreater}En and En-{\textgreater}Ta respectively, and 27.9 and 13.0 for Iu-{\textgreater}En and En-{\textgreater}Iu respectively.",
}
@inproceedings{dhar-etal-2020-linguistically,
    title = "Linguistically Motivated Subwords for {E}nglish-{T}amil Translation: {U}niversity of {G}roningen{'}s Submission to {WMT}-2020",
    author = "Dhar, Prajit  and
      Bisazza, Arianna  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.9",
    pages = "126--133",
    abstract = "This paper describes our submission for the English-Tamil news translation task of WMT-2020. The various techniques and Neural Machine Translation (NMT) models used by our team are presented and discussed, including back-translation, fine-tuning and word dropout. Additionally, our experiments show that using a linguistically motivated subword segmentation technique (Ataman et al., 2017) does not consistently outperform the more widely used, non-linguistically motivated SentencePiece algorithm (Kudo and Richardson, 2018), despite the agglutinative nature of Tamil morphology.",
}
@inproceedings{escolano-etal-2020-talp,
    title = "The {TALP}-{UPC} System Description for {WMT}20 News Translation Task: Multilingual Adaptation for Low Resource {MT}",
    author = "Escolano, Carlos  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.10",
    pages = "134--138",
    abstract = "In this article, we describe the TALP-UPC participation in the WMT20 news translation shared task for Tamil-English. Given the low amount of parallel training data, we resort to adapt the task to a multilingual system to benefit from the positive transfer from high resource languages. We use iterative backtranslation to fine-tune the system and benefit from the monolingual data available. In order to measure the effectivity of such methods, we compare our results to a bilingual baseline system.",
}
@inproceedings{kim-etal-2020-iterative,
    title = "An Iterative Knowledge Transfer {NMT} System for {WMT}20 News Translation Task",
    author = "Kim, Jiwan  and
      Park, Soyoon  and
      Kim, Sangha  and
      Choi, Yoonjung",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.11",
    pages = "139--144",
    abstract = "This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction. Our main approach is based on transferring knowledge of domain and linguistic characteristics by pre-training the encoder-decoder model with large amount of in-domain monolingual data through unsupervised and supervised prediction task. We then fine-tune the model with parallel data and in-domain synthetic data, generated with iterative back-translation. For additional gain, we generate final results with an ensemble model and re-rank them with averaged models and language models. Through these methods, we achieve +5.42 BLEU score compare to the baseline model.",
}
@inproceedings{kiyono-etal-2020-tohoku,
    title = "Tohoku-{AIP}-{NTT} at {WMT} 2020 News Translation Task",
    author = "Kiyono, Shun  and
      Ito, Takumi  and
      Konno, Ryuto  and
      Morishita, Makoto  and
      Suzuki, Jun",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.12",
    pages = "145--155",
    abstract = "In this paper, we describe the submission of Tohoku-AIP-NTT to the WMT{'}20 news translation task. We participated in this task in two language pairs and four language directions: English {\textless}{--}{\textgreater} German and English {\textless}{--}{\textgreater} Japanese. Our system consists of techniques such as back-translation and fine-tuning, which are already widely adopted in translation tasks. We attempted to develop new methods for both synthetic data filtering and reranking. However, the methods turned out to be ineffective, and they provided us with no significant improvement over the baseline. We analyze these negative results to provide insights for future studies.",
}
@inproceedings{knowles-etal-2020-nrc,
    title = "{NRC} Systems for the 2020 {I}nuktitut-{E}nglish News Translation Task",
    author = "Knowles, Rebecca  and
      Stewart, Darlene  and
      Larkin, Samuel  and
      Littell, Patrick",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.13",
    pages = "156--170",
    abstract = "We describe the National Research Council of Canada (NRC) submissions for the 2020 Inuktitut-English shared task on news translation at the Fifth Conference on Machine Translation (WMT20). Our submissions consist of ensembled domain-specific finetuned transformer models, trained using the Nunavut Hansard and news data and, in the case of Inuktitut-English, backtranslated news and parliamentary data. In this work we explore challenges related to the relatively small amount of parallel data, morphological complexity, and domain shifts.",
}
@inproceedings{kocmi-2020-cuni,
    title = "{CUNI} Submission for the {I}nuktitut Language in {WMT} News 2020",
    author = "Kocmi, Tom",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.14",
    pages = "171--174",
    abstract = "This paper describes CUNI submission to the WMT 2020 News Translation Shared Task for the low-resource scenario Inuktitut{--}English in both translation directions. Our system combines transfer learning from a Czech{--}English high-resource language pair and backtranslation. We notice surprising behaviour when using synthetic data, which can be possibly attributed to a narrow domain of training and test data. We are using the Transformer model in a constrained submission.",
}
@inproceedings{krislauks-pinnis-2020-tilde,
    title = "Tilde at {WMT} 2020: News Task Systems",
    author = "Kri{\v{s}}lauks, Rihards  and
      Pinnis, M{\=a}rcis",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.15",
    pages = "175--180",
    abstract = "This paper describes Tilde{'}s submission to the WMT2020 shared task on news translation for both directions of the English-Polish language pair in both the constrained and the unconstrained tracks. We follow our submissions form the previous years and build our baseline systems to be morphologically motivated sub-word unit-based Transformer base models that we train using the Marian machine translation toolkit. Additionally, we experiment with different parallel and monolingual data selection schemes, as well as sampled back-translation. Our final models are ensembles of Transformer base and Transformer big models which feature right-to-left re-ranking.",
}
@inproceedings{krubinski-etal-2020-samsung,
    title = "{S}amsung {R}{\&}{D} Institute {P}oland submission to {WMT}20 News Translation Task",
    author = "Krubi{\'n}ski, Mateusz  and
      Chochowski, Marcin  and
      Boczek, Bart{\l}omiej  and
      Koszowski, Miko{\l}aj  and
      Dobrowolski, Adam  and
      Szyma{\'n}ski, Marcin  and
      Przybysz, Pawe{\l}",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.16",
    pages = "181--190",
    abstract = "This paper describes the submission to the WMT20 shared news translation task by Samsung R{\&}D Institute Poland. We submitted systems for six language directions: English to Czech, Czech to English, English to Polish, Polish to English, English to Inuktitut and Inuktitut to English. For each, we trained a single-direction model. However, directions including English, Polish and Czech were derived from a common multilingual base, which was later fine-tuned on each particular direction. For all the translation directions, we used a similar training regime, with iterative training corpora improvement through back-translation and model ensembling. For the En → Cs direction, we additionally leveraged document-level information by re-ranking the beam output with a separate model.",
}
@inproceedings{germann-etal-2020-speed,
    title = "Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the {UEDIN}-{CUNI} Submission to the {WMT} 2020 News Translation Task",
    author = "Germann, Ulrich  and
      Grundkiewicz, Roman  and
      Popel, Martin  and
      Dobreva, Radina  and
      Bogoychev, Nikolay  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.17",
    pages = "191--196",
    abstract = "We describe the joint submission of the University of Edinburgh and Charles University, Prague, to the Czech/English track in the WMT 2020 Shared Task on News Translation. Our fast and compact student models distill knowledge from a larger, slower teacher. They are designed to offer a good trade-off between translation quality and inference efficiency. On the WMT 2020 Czech ↔ English test sets, they achieve translation speeds of over 700 whitespace-delimited source words per second on a single CPU thread, thus making neural translation feasible on consumer hardware without a GPU.",
}
@inproceedings{germann-2020-university,
    title = "The {U}niversity of {E}dinburgh{'}s submission to the {G}erman-to-{E}nglish and {E}nglish-to-{G}erman Tracks in the {WMT} 2020 News Translation and Zero-shot Translation Robustness Tasks",
    author = "Germann, Ulrich",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.18",
    pages = "197--201",
    abstract = "This paper describes the University of Edinburgh{'}s submission of German {\textless}-{\textgreater} English systems to the WMT2020 Shared Tasks on News Translation and Zero-shot Robustness.",
}
@inproceedings{goyal-etal-2020-contact,
    title = "Contact Relatedness can help improve multilingual {NMT}: {M}icrosoft {STCI}-{MT} @ {WMT}20",
    author = "Goyal, Vikrant  and
      Kunchukuttan, Anoop  and
      Kejriwal, Rahul  and
      Jain, Siddharth  and
      Bhagwat, Amit",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.19",
    pages = "202--206",
    abstract = "We describe our submission for the English→Tamil and Tamil→English news translation shared task. In this submission, we focus on exploring if a low-resource language (Tamil) can benefit from a high-resource language (Hindi) with which it shares contact relatedness. We show utilizing contact relatedness via multilingual NMT can significantly improve translation quality for English-Tamil translation.",
}
@inproceedings{gwinnup-anderson-2020-afrl,
    title = "The {AFRL} {WMT}20 News Translation Systems",
    author = "Gwinnup, Jeremy  and
      Anderson, Tim",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.20",
    pages = "207--212",
    abstract = "This report summarizes the Air Force Research Laboratory (AFRL) machine translation (MT) systems submitted to the news-translation task as part of the 2020 Conference on Machine Translation (WMT20) evaluation campaign. This year we largely repurpose strategies from previous years{'} efforts with larger datasets and also train models with precomputed word alignments under various settings in an effort to improve translation quality.",
}
@inproceedings{hernandez-nguyen-2020-ubiqus,
    title = "The Ubiqus {E}nglish-{I}nuktitut System for {WMT}20",
    author = "Hernandez, Fran{\c{c}}ois  and
      Nguyen, Vincent",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.21",
    pages = "213--217",
    abstract = "This paper describes Ubiqus{'} submission to the WMT20 English-Inuktitut shared news translation task. Our main system, and only submission, is based on a multilingual approach, jointly training a Transformer model on several agglutinative languages. The English-Inuktitut translation task is challenging at every step, from data selection, preparation and tokenization to quality evaluation down the line. Difficulties emerge both because of the peculiarities of the Inuktitut language as well as the low-resource context.",
}
@inproceedings{li-etal-2020-sjtu,
    title = "{SJTU}-{NICT}{'}s Supervised and Unsupervised Neural Machine Translation Systems for the {WMT}20 News Translation Task",
    author = "Li, Zuchao  and
      Zhao, Hai  and
      Wang, Rui  and
      Chen, Kehai  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.22",
    pages = "218--229",
    abstract = "In this paper, we introduced our joint team SJTU-NICT {`}s participation in the WMT 2020 machine translation shared task. In this shared task, we participated in four translation directions of three language pairs: English-Chinese, English-Polish on supervised machine translation track, German-Upper Sorbian on low-resource and unsupervised machine translation tracks. Based on different conditions of language pairs, we have experimented with diverse neural machine translation (NMT) techniques: document-enhanced NMT, XLM pre-trained language model enhanced NMT, bidirectional translation as a pre-training, reference language based UNMT, data-dependent gaussian prior objective, and BT-BLEU collaborative filtering self-training. We also used the TF-IDF algorithm to filter the training set to obtain a domain more similar set with the test set for finetuning. In our submissions, the primary systems won the first place on English to Chinese, Polish to English, and German to Upper Sorbian translation directions.",
}
@inproceedings{marie-etal-2020-combination,
    title = "Combination of Neural Machine Translation Systems at {WMT}20",
    author = "Marie, Benjamin  and
      Rubino, Raphael  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.23",
    pages = "230--238",
    abstract = "This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese-{\textgreater}English translation tasks. We show that using a Transformer Big architecture, additional training data synthesized from monolingual data, and combining many NMT systems through n-best list reranking improve translation quality. However, while we observed such improvements on the validation data, we did not observed similar improvements on the test data. Our analysis reveals that the presence of translationese texts in the validation data led us to take decisions in building NMT systems that were not optimal to obtain the best results on the test data.",
}
@inproceedings{meng-etal-2020-wechat,
    title = "{W}e{C}hat Neural Machine Translation Systems for {WMT}20",
    author = "Meng, Fandong  and
      Yan, Jianhao  and
      Liu, Yijin  and
      Gao, Yuan  and
      Zeng, Xianfeng  and
      Zeng, Qinsong  and
      Li, Peng  and
      Chen, Ming  and
      Zhou, Jie  and
      Liu, Sifan  and
      Zhou, Hao",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.24",
    pages = "239--247",
    abstract = "We participate in the WMT 2020 shared newstranslation task on Chinese→English. Our system is based on the Transformer (Vaswaniet al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese→English system achieves 36.9 case-sensitive BLEU score, which is thehighest among all submissions.",
}
@inproceedings{molchanov-2020-promt,
    title = "{PROMT} Systems for {WMT} 2020 Shared News Translation Task",
    author = "Molchanov, Alexander",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.25",
    pages = "248--253",
    abstract = "This paper describes the PROMT submissions for the WMT 2020 Shared News Translation Task. This year we participated in four language pairs and six directions: English-Russian, Russian-English, English-German, German-English, Polish-English and Czech-English. All our submissions are MarianNMT-based neural systems. We use more data compared to last year and update our back-translations with better models from the previous year. We show competitive results in terms of BLEU in most directions.",
}
@inproceedings{oravecz-etal-2020-etranslations,
    title = "e{T}ranslation{'}s Submissions to the {WMT} 2020 News Translation Task",
    author = "Oravecz, Csaba  and
      Bontcheva, Katina  and
      Tihanyi, L{\'a}szl{\'o}  and
      Kolovratnik, David  and
      Bhaskar, Bhavani  and
      Lardilleux, Adrien  and
      Klocek, Szymon  and
      Eisele, Andreas",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.26",
    pages = "254--261",
    abstract = "The paper describes the submissions of the eTranslation team to the WMT 2020 news translation shared task. Leveraging the experience from the team{'}s participation last year we developed systems for 5 language pairs with various strategies. Compared to last year, for some language pairs we dedicated a lot more resources to training, and tried to follow standard best practices to build competitive systems which can achieve good results in the rankings. By using deep and complex architectures we sacrificed direct re-usability of our systems in production environments but evaluation showed that this approach could result in better models that significantly outperform baseline architectures. We submitted two systems to the zero shot robustness task. These submissions are described briefly in this paper as well.",
}
@inproceedings{parthasarathy-etal-2020-adapt,
    title = "The {ADAPT} System Description for the {WMT}20 News Translation Task",
    author = "Parthasarathy, Venkatesh  and
      Ramesh, Akshai  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.27",
    pages = "262--268",
    abstract = "This paper describes the ADAPT Centre{'}s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. onolin- gual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks.",
}
@inproceedings{popel-2020-cuni,
    title = "{CUNI} {E}nglish-{C}zech and {E}nglish-{P}olish Systems in {WMT}20: Robust Document-Level Training",
    author = "Popel, Martin",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.28",
    pages = "269--273",
    abstract = "We describe our two NMT systems submitted to the WMT 2020 shared task in English{\textless}-{\textgreater}Czech and English{\textless}-{\textgreater}Polish news translation. One system is sentence level, translating each sentence independently. The second system is document level, translating multiple sentences, trained on multi-sentence sequences up to 3000 characters long.",
}
@inproceedings{roest-etal-2020-machine,
    title = "Machine Translation for {E}nglish{--}{I}nuktitut with Segmentation, Data Acquisition and Pre-Training",
    author = "Roest, Christian  and
      Edman, Lukas  and
      Minnema, Gosse  and
      Kelly, Kevin  and
      Spenader, Jennifer  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.29",
    pages = "274--281",
    abstract = "Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English{--}Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.",
}
@inproceedings{shi-etal-2020-oppos,
    title = "{OPPO}{'}s Machine Translation Systems for {WMT}20",
    author = "Shi, Tingxun  and
      Zhao, Shiyu  and
      Li, Xiaopu  and
      Wang, Xiaoxue  and
      Zhang, Qian  and
      Ai, Di  and
      Dang, Dawei  and
      Zhengshan, Xue  and
      Hao, Jie",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.30",
    pages = "282--292",
    abstract = "In this paper we demonstrate our (OPPO{'}s) machine translation systems for the WMT20 Shared Task on News Translation for all the 22 language pairs. We will give an overview of the common aspects across all the systems firstly, including two parts: the data preprocessing part will show how the data are preprocessed and filtered, and the system part will show our models architecture and the techniques we followed. Detailed information, such as training hyperparameters and the results generated by each technique will be depicted in the corresponding subsections. Our final submissions ranked top in 6 directions (English $\leftrightarrow$ Czech, English $\leftrightarrow$ Russian, French $\rightarrow$ German and Tamil $\rightarrow$ English), third in 2 directions (English $\rightarrow$ German, English $\rightarrow$ Japanese), and fourth in 2 directions (English $\rightarrow$ Pashto and and English $\rightarrow$ Tamil).",
}
@inproceedings{wei-etal-2020-hw,
    title = "{HW}-{TSC}{'}s Participation in the {WMT} 2020 News Translation Shared Task",
    author = "Wei, Daimeng  and
      Shang, Hengchao  and
      Wu, Zhanglin  and
      Yu, Zhengzhe  and
      Li, Liangyou  and
      Guo, Jiaxin  and
      Wang, Minghan  and
      Yang, Hao  and
      Lei, Lizhi  and
      Qin, Ying  and
      Sun, Shiliang",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.31",
    pages = "293--299",
    abstract = "This paper presents our work in the WMT 2020 News Translation Shared Task. We participate in 3 language pairs including Zh/En, Km/En, and Ps/En and in both directions under the constrained condition. We use the standard Transformer-Big model as the baseline and obtain the best performance via two variants with larger parameter sizes. We perform detailed pre-processing and filtering on the provided large-scale bilingual and monolingual dataset. Several commonly used strategies are used to train our models such as Back Translation, Ensemble Knowledge Distillation, etc. We also conduct experiment with similar language augmentation, which lead to positive results, although not used in our submission. Our submission obtains remarkable results in the final evaluation.",
}
@inproceedings{wei-etal-2020-iies,
    title = "{IIE}{'}s Neural Machine Translation Systems for {WMT}20",
    author = "Wei, Xiangpeng  and
      Guo, Ping  and
      Li, Yunpeng  and
      Zhang, Xingsheng  and
      Xing, Luxi  and
      Hu, Yue",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.32",
    pages = "300--304",
    abstract = "In this paper we introduce the systems IIE submitted for the WMT20 shared task on German-French news translation. Our systems are based on the Transformer architecture with some effective improvements. Multiscale collaborative deep architecture, data selection, back translation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments. Our German-to-French system achieved 35.0 BLEU and ranked the second among all anonymous submissions, and our French-to-German system achieved 36.6 BLEU and ranked the fourth in all anonymous submissions.",
}
@inproceedings{wu-etal-2020-volctrans,
    title = "The Volctrans Machine Translation System for {WMT}20",
    author = "Wu, Liwei  and
      Pan, Xiao  and
      Lin, Zehui  and
      Zhu, Yaoming  and
      Wang, Mingxuan  and
      Li, Lei",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.33",
    pages = "305--312",
    abstract = "This paper describes our submission systems for VolcTrans for WMT20 shared news translation task. We participated in 8 translation directions. Our basic systems are based on Transformer (CITATION), into which we also employed new architectures (bigger or deeper Transformers, dynamic convolution). The final systems include text pre-process, subword(a.k.a. BPE(CITATION)), baseline model training, iterative back-translation, model ensemble, knowledge distillation and multilingual pre-training.",
}
@inproceedings{wu-etal-2020-tencent,
    title = "Tencent Neural Machine Translation Systems for the {WMT}20 News Translation Task",
    author = "Wu, Shuangzhi  and
      Wang, Xing  and
      Wang, Longyue  and
      Liu, Fangxu  and
      Xie, Jun  and
      Tu, Zhaopeng  and
      Shi, Shuming  and
      Li, Mu",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.34",
    pages = "313--319",
    abstract = "This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks. We participate in the shared news translation task on English $\leftrightarrow$ Chinese and English $\rightarrow$ German language pairs. Our systems are built on deep Transformer and several data augmentation methods. We propose a boosted in-domain finetuning method to improve single models. Ensemble is used to combine single models and we propose an iterative transductive ensemble method which can further improve the translation performance based on the ensemble results. We achieve a BLEU score of 36.8 and the highest chrF score of 0.648 on Chinese $\rightarrow$ English task.",
}
@inproceedings{xv-2020-russian,
    title = "{R}ussian-{E}nglish Bidirectional Machine Translation System",
    author = "Xv, Ariel",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.35",
    pages = "320--325",
    abstract = "This review depicts our submission to the WMT20 shared news translation task. WMT is the conference to assess the level of machine translation capabilities of organizations in the word. We participated in one language pair and two language directions, from Russian to English and from English to Russian. We used official training data, 102 million parallel corpora and 10 million monolingual corpora. Our baseline systems are Transformer models trained with the Sockeye sequence modeling toolkit, supplemented by bi-text data filtering schemes, back-translations, reordering and other related processing methods. The BLEU value of our translation result from Russian to English is 35.7, ranking 5th, while from English to Russian is 39.8, ranking 2th.",
}
@inproceedings{yu-etal-2020-deepmind,
    title = "The {D}eep{M}ind {C}hinese{--}{E}nglish Document Translation System at {WMT}2020",
    author = "Yu, Lei  and
      Sartran, Laurent  and
      Huang, Po-Sen  and
      Stokowiec, Wojciech  and
      Donato, Domenic  and
      Srinivasan, Srivatsan  and
      Andreev, Alek  and
      Ling, Wang  and
      Mokra, Sona  and
      Dal Lago, Agustin  and
      Doron, Yotam  and
      Young, Susannah  and
      Blunsom, Phil  and
      Dyer, Chris",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.36",
    pages = "326--337",
    abstract = "This paper describes the DeepMind submission to the Chinese$\rightarrow$English constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019).",
}
@inproceedings{zhang-etal-2020-niutrans,
    title = "The {N}iu{T}rans Machine Translation Systems for {WMT}20",
    author = "Zhang, Yuhao  and
      Wang, Ziyang  and
      Cao, Runzhe  and
      Wei, Binghao  and
      Shan, Weiqiao  and
      Zhou, Shuhan  and
      Reheman, Abudurexiti  and
      Zhou, Tao  and
      Zeng, Xin  and
      Wang, Laohu  and
      Mu, Yongyu  and
      Zhang, Jingnan  and
      Liu, Xiaoqian  and
      Zhou, Xuanjun  and
      Li, Yinqiao  and
      Li, Bei  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.37",
    pages = "338--345",
    abstract = "This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks. We participated in Japanese{\textless}-{\textgreater}English, English-{\textgreater}Chinese, Inuktitut-{\textgreater}English and Tamil-{\textgreater}English total five tasks and rank first in Japanese{\textless}-{\textgreater}English both sides. We mainly utilized iterative back-translation, different depth and widen model architectures, iterative knowledge distillation and iterative fine-tuning. And we find that adequately widened and deepened the model simultaneously, the performance will significantly improve. Also, iterative fine-tuning strategy we implemented is effective during adapting domain. For Inuktitut-{\textgreater}English and Tamil-{\textgreater}English tasks, we built multilingual models separately and employed pretraining word embedding to obtain better performance.",
}
@inproceedings{avramidis-etal-2020-fine,
    title = "Fine-grained linguistic evaluation for state-of-the-art Machine Translation",
    author = {Avramidis, Eleftherios  and
      Macketanz, Vivien  and
      Strohriegel, Ursula  and
      Burchardt, Aljoscha  and
      M{\"o}ller, Sebastian},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.38",
    pages = "346--356",
    abstract = "This paper describes a test suite submission providing detailed statistics of linguistic performance for the state-of-the-art German-English systems of the Fifth Conference of Machine Translation (WMT20). The analysis covers 107 phenomena organized in 14 categories based on about 5,500 test items, including a manual annotation effort of 45 person hours. Two systems (Tohoku and Huoshan) appear to have significantly better test suite accuracy than the others, although the best system of WMT20 is not significantly better than the one from WMT19 in a macro-average. Additionally, we identify some linguistic phenomena where all systems suffer (such as idioms, resultative predicates and pluperfect), but we are also able to identify particular weaknesses for individual systems (such as quotation marks, lexical ambiguity and sluicing). Most of the systems of WMT19 which submitted new versions this year show improvements.",
}
@inproceedings{kocmi-etal-2020-gender,
    title = "Gender Coreference and Bias Evaluation at {WMT} 2020",
    author = "Kocmi, Tom  and
      Limisiewicz, Tomasz  and
      Stanovsky, Gabriel",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.39",
    pages = "357--364",
    abstract = "Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.",
}
@inproceedings{scherrer-etal-2020-mucow,
    title = "The {MUCOW} word sense disambiguation test suite at {WMT} 2020",
    author = {Scherrer, Yves  and
      Raganato, Alessandro  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.40",
    pages = "365--370",
    abstract = "This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using existing resources, and the evaluation process is also entirely automated. We evaluate all participating systems of the language pairs English -{\textgreater} Czech, English -{\textgreater} German, and English -{\textgreater} Russian and compare the results with those obtained at WMT 2019. While current NMT systems are fairly good at handling ambiguous source words, we could not identify any substantial progress - at least to the extent that it is measurable by the MUCOW method - in that area over the last year.",
}
@inproceedings{zouhar-etal-2020-wmt20,
    title = "{WMT}20 Document-Level Markable Error Exploration",
    author = "Zouhar, Vil{\'e}m  and
      Vojt{\v{e}}chov{\'a}, Tereza  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.41",
    pages = "371--380",
    abstract = "Even though sentence-centric metrics are used widely in machine translation evaluation, document-level performance is at least equally important for professional usage. In this paper, we bring attention to detailed document-level evaluation focused on markables (expressions bearing most of the document meaning) and the negative impact of various markable error phenomena on the translation. For an annotation experiment of two phases, we chose Czech and English documents translated by systems submitted to WMT20 News Translation Task. These documents are from the News, Audit and Lease domains. We show that the quality and also the kind of errors varies significantly among the domains. This systematic variance is in contrast to the automatic evaluation results. We inspect which specific markables are problematic for MT systems and conclude with an analysis of the effect of markable error types on the MT performance measured by humans and automatic evaluation tools.",
}
@inproceedings{adebara-etal-2020-translating,
    title = "Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers",
    author = "Adebara, Ife  and
      Nagoudi, El Moatez Billah  and
      Abdul Mageed, Muhammad",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.42",
    pages = "381--386",
    abstract = "In this work we investigate different approaches to translate between similar languages despite low resource limitations. This work is done as the participation of the UBC NLP research group in the WMT 2019 Similar Languages Translation Shared Task. We participated in all language pairs and performed various experiments. We used a transformer architecture for all the models and used back-translation for one of the language pairs. We explore both bilingual and multi-lingual approaches. We describe the pre-processing, training, translation and results for each model. We also investigate the role of mutual intelligibility in model performance.",
}
@inproceedings{dhanani-rafi-2020-attention,
    title = "Attention Transformer Model for Translation of Similar Languages",
    author = "Dhanani, Farhan  and
      Rafi, Muhammad",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.43",
    pages = "387--392",
    abstract = "This paper illustrates our approach to the shared task on similar language translation in the fifth conference on machine translation (WMT-20). Our motivation comes from the latest state of the art neural machine translation in which Transformers and Recurrent Attention models are effectively used. A typical sequence-sequence architecture consists of an encoder and a decoder Recurrent Neural Network (RNN). The encoder recursively processes a source sequence and reduces it into a fixed-length vector (context), and the decoder generates a target sequence, token by token, conditioned on the same context. In contrast, the advantage of transformers is to reduce the training time by offering a higher degree of parallelism at the cost of freedom for sequential order. With the introduction of Recurrent Attention, it allows the decoder to focus effectively on order of the source sequence at different decoding steps. In our approach, we have combined the recurrence based layered encoder-decoder model with the Transformer model. Our Attention Transformer model enjoys the benefits of both Recurrent Attention and Transformer to quickly learn the most probable sequence for decoding in the target language. The architecture is especially suited for similar languages (languages coming from the same family). We have submitted our system for both Indo-Aryan Language forward (Hindi to Marathi) and reverse (Marathi to Hindi) pair. Our system trains on the parallel corpus of the training dataset provided by the organizers and achieved an average BLEU point of 3.68 with 97.64 TER score for the Hindi-Marathi, along with 9.02 BLEU point and 88.6 TER score for Marathi-Hindi testing set.",
}
@inproceedings{kumar-etal-2020-transformer,
    title = "Transformer-based Neural Machine Translation System for {H}indi {--} {M}arathi: {WMT}20 Shared Task",
    author = "Kumar, Amit  and
      Baruah, Rupjyoti  and
      Mundotiya, Rajesh Kumar  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.44",
    pages = "393--395",
    abstract = "This paper reports the results for the Machine Translation (MT) system submitted by the NLPRL team for the Hindi {--} Marathi Similar Translation Task at WMT 2020. We apply the Transformer-based Neural Machine Translation (NMT) approach on both translation directions for this language pair. The trained model is evaluated on the corpus provided by shared task organizers, using BLEU, RIBES, and TER scores. There were a total of 23 systems submitted for Marathi to Hindi and 21 systems submitted for Hindi to Marathi in the shared task. Out of these, our submission ranked 6th and 9th, respectively.",
}
@inproceedings{laskar-etal-2020-hindi,
    title = "{H}indi-{M}arathi Cross Lingual Model",
    author = "Laskar, Sahinur Rahman  and
      Khilji, Abdullah Faiz Ur Rahman  and
      Pakray, Partha  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.45",
    pages = "396--401",
    abstract = "Machine Translation (MT) is a vital tool for aiding communication between linguistically separate groups of people. The neural machine translation (NMT) based approaches have gained widespread acceptance because of its outstanding performance. We have participated in WMT20 shared task of similar language translation on Hindi-Marathi pair. The main challenge of this task is by utilization of monolingual data and similarity features of similar language pair to overcome the limitation of available parallel data. In this work, we have implemented NMT based model that simultaneously learns bilingual embedding from both the source and target language pairs. Our model has achieved Hindi to Marathi bilingual evaluation understudy (BLEU) score of 11.59, rank-based intuitive bilingual evaluation score (RIBES) score of 57.76 and translation edit rate (TER) score of 79.07 and Marathi to Hindi BLEU score of 15.44, RIBES score of 61.13 and TER score of 75.96.",
}
@inproceedings{madaan-etal-2020-transfer,
    title = "Transfer Learning for Related Languages: Submissions to the {WMT}20 Similar Language Translation Task",
    author = "Madaan, Lovish  and
      Sharma, Soumya  and
      Singla, Parag",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.46",
    pages = "402--408",
    abstract = "In this paper, we describe IIT Delhi{'}s submissions to the WMT 2020 task on Similar Language Translation for four language directions: Hindi {\textless}-{\textgreater} Marathi and Spanish {\textless}-{\textgreater} Portuguese. We try out three different model settings for the translation task and select our primary and contrastive submissions on the basis of performance of these three models. For our best submissions, we fine-tune the mBART model on the parallel data provided for the task. The pre-training is done using self-supervised objectives on a large amount of monolingual data for many languages. Overall, our models are ranked in the top four of all systems for the submitted language pairs, with first rank in Spanish -{\textgreater} Portuguese.",
}
@inproceedings{menendez-salazar-etal-2020-ipn,
    title = "The {IPN}-{CIC} team system submission for the {WMT} 2020 similar language task",
    author = "Men{\'e}ndez-Salazar, Luis A.  and
      Sidorov, Grigori  and
      Costa-Juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.47",
    pages = "409--413",
    abstract = "This paper describes the participation of the NLP research team of the IPN Computer Research center in the WMT 2020 Similar Language Translation Task. We have submitted systems for the Spanish-Portuguese language pair (in both directions). The three submitted systems are based on the Transformer architecture and used fine tuning for domain Adaptation.",
}
@inproceedings{mujadia-sharma-2020-nmt,
    title = "{NMT} based Similar Language Translation for {H}indi - {M}arathi",
    author = "Mujadia, Vandan  and
      Sharma, Dipti",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.48",
    pages = "414--417",
    abstract = "This paper describes the participation of team F1toF6 (LTRC, IIIT-Hyderabad) for the WMT 2020 task, similar language translation. We experimented with attention based recurrent neural network architecture (seq2seq) for this task. We explored the use of different linguistic features like POS and Morph along with back translation for Hindi-Marathi and Marathi-Hindi machine translation.",
}
@inproceedings{ojha-etal-2020-nuig,
    title = "{NUIG}-Panlingua-{KMI} {H}indi-{M}arathi {MT} Systems for Similar Language Translation Task @ {WMT} 2020",
    author = "Ojha, Atul Kr.  and
      Rani, Priya  and
      Bansal, Akanksha  and
      Chakravarthi, Bharathi Raja  and
      Kumar, Ritesh  and
      McCrae, John P.",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.49",
    pages = "418--423",
    abstract = "NUIG-Panlingua-KMI submission to WMT 2020 seeks to push the state-of-the-art in Similar Language Translation Task for Hindi↔Marathi language pair. As part of these efforts, we conducteda series of experiments to address the challenges for translation between similar languages. Among the 4 MT systems prepared under this task, 1 PBSMT systems were prepared for Hindi↔Marathi each and 1 NMT systems were developed for Hindi↔Marathi using Byte PairEn-coding (BPE) into subwords. The results show that different architectures NMT could be an effective method for developing MT systems for closely related languages. Our Hindi-Marathi NMT system was ranked 8th among the 14 teams that participated and our Marathi-Hindi NMT system was ranked 8th among the 11 teams participated for the task.",
}
@inproceedings{pal-zampieri-2020-neural,
    title = "Neural Machine Translation for Similar Languages: The Case of {I}ndo-{A}ryan Languages",
    author = "Pal, Santanu  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.50",
    pages = "424--429",
    abstract = "In this paper we present the WIPRO-RIT systems submitted to the Similar Language Translation shared task at WMT 2020. The second edition of this shared task featured parallel data from pairs/groups of similar languages from three different language families: Indo-Aryan languages (Hindi and Marathi), Romance languages (Catalan, Portuguese, and Spanish), and South Slavic Languages (Croatian, Serbian, and Slovene). We report the results obtained by our systems in translating from Hindi to Marathi and from Marathi to Hindi. WIPRO-RIT achieved competitive performance ranking 1st in Marathi to Hindi and 2nd in Hindi to Marathi translation among 22 systems.",
}
@inproceedings{popovic-poncelas-2020-neural,
    title = "Neural Machine Translation between similar {S}outh-{S}lavic languages",
    author = "Popovi{\'c}, Maja  and
      Poncelas, Alberto",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.51",
    pages = "430--436",
    abstract = "This paper describes the ADAPT-DCU machine translation systems built for the WMT 2020 shared task on Similar Language Translation. We explored several set-ups for NMT for Croatian{--}Slovenian and Serbian{--}Slovenian language pairs in both translation directions. Our experiments focus on different amounts and types of training data: we first apply basic filtering on the OpenSubtitles training corpora, then we perform additional cleaning of remaining misaligned segments based on character n-gram matching. Finally, we make use of additional monolingual data by creating synthetic parallel data through back-translation. Automatic evaluation shows that multilingual systems with joint Serbian and Croatian data are better than bilingual, as well as that character-based cleaning leads to improved scores while using less data. The results also confirm once more that adding back-translated data further improves the performance, especially when the synthetic data is similar to the desired domain of the development and test set. This, however, might come at a price of prolonged training time, especially for multitarget systems.",
}
@inproceedings{rathinasamy-etal-2020-infosys,
    title = "Infosys Machine Translation System for {WMT}20 Similar Language Translation Task",
    author = "Rathinasamy, Kamalkumar  and
      Singh, Amanpreet  and
      Sivasambagupta, Balaguru  and
      Prasad Neerchal, Prajna  and
      Sivasankaran, Vani",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.52",
    pages = "437--441",
    abstract = "This paper describes Infosys{'}s submission to the WMT20 Similar Language Translation shared task. We participated in Indo-Aryan language pair in the language direction Hindi to Marathi. Our baseline system is byte-pair encoding based transformer model trained with the Fairseq sequence modeling toolkit. Our final system is an ensemble of two transformer models, which ranked first in WMT20 evaluation. One model is designed to learn the nuances of translation of this low resource language pair by taking advantage of the fact that the source and target languages are same alphabet languages. The other model is the result of experimentation with the proportion of back-translated data to the parallel data to improve translation fluency.",
}
@inproceedings{ul-haq-etal-2020-document,
    title = "Document Level {NMT} of Low-Resource Languages with Backtranslation",
    author = "Ul Haq, Sami  and
      Abdul Rauf, Sadaf  and
      Shaukat, Arsalan  and
      Saeed, Abdullah",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.53",
    pages = "442--446",
    abstract = "This paper describes our system submission to WMT20 shared task on similar language translation. We examined the use of documentlevel neural machine translation (NMT) systems for low-resource, similar language pair Marathi−Hindi. Our system is an extension of state-of-the-art Transformer architecture with hierarchical attention networks to incorporate contextual information. Since, NMT requires large amount of parallel data which is not available for this task, our approach is focused on utilizing monolingual data with back translation to train our models. Our experiments reveal that document-level NMT can be a reasonable alternative to sentence-level NMT for improving translation quality of low resourced languages even when used with synthetic data.",
}
@inproceedings{verges-boncompte-r-costa-jussa-2020-multilingual,
    title = "Multilingual Neural Machine Translation: Case-study for {C}atalan, {S}panish and {P}ortuguese {R}omance Languages",
    author = "Verg{\'e}s Boncompte, Pere  and
      R. Costa-juss{\`a}, Marta",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.54",
    pages = "447--450",
    abstract = "In this paper, we describe the TALP-UPC participation in the WMT Similar Language Translation task between Catalan, Spanish, and Portuguese, all of them, Romance languages. We made use of different techniques to improve the translation between these languages. The multilingual shared encoder/decoder has been used for all of them. Additionally, we applied back-translation to take advantage of the monolingual data. Finally, we have applied fine-tuning to improve the in-domain data. Each of these techniques brings improvements over the previous one. In the official evaluation, our system was ranked 1st in the Portuguese-to-Spanish direction, 2nd in the opposite direction, and 3rd in the Catalan-Spanish pair.",
}
@inproceedings{yadav-shrivastava-2020-a3,
    title = "A3-108 Machine Translation System for Similar Language Translation Shared Task 2020",
    author = "Yadav, Saumitra  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.55",
    pages = "451--455",
    abstract = "In this paper, we describe our submissions for Similar Language Translation Shared Task 2020. We built 12 systems in each direction for Hindi⇐⇒Marathi language pair. This paper outlines initial baseline experiments with various tokenization schemes to train statistical models. Using optimal tokenization scheme among these we created synthetic source side text with back translation. And prune synthetic text with language model scores. This synthetic data was then used along with training data in various settings to build translation models. We also report configuration of the submitted systems and results produced by them.",
}
@inproceedings{bao-etal-2020-university,
    title = "The {U}niversity of {M}aryland{'}s Submissions to the {WMT}20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation",
    author = "Bao, Calvin  and
      Shiue, Yow-Ting  and
      Song, Chujun  and
      Li, Jie  and
      Carpuat, Marine",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.56",
    pages = "456--461",
    abstract = "This paper describes the University of Maryland{'}s submissions to the WMT20 Shared Task on Chat Translation. We focus on translating agent-side utterances from English to German. We started from an off-the-shelf BPE-based standard transformer model trained with WMT17 news and fine-tuned it with the provided in-domain training data. In addition, we augment the training set with its best matches in the WMT19 news dataset. Our primary submission uses a standard Transformer, while our contrastive submissions use multi-encoder Transformers to attend to previous utterances. Our primary submission achieves 56.7 BLEU on the agent side (en→de), outperforming a baseline system provided by the task organizers by more than 13 BLEU points. Moreover, according to an evaluation on a set of carefully-designed examples, the multi-encoder architecture is able to generate more coherent translations.",
}
@inproceedings{berard-etal-2020-naver,
    title = "Naver Labs {E}urope{'}s Participation in the Robustness, Chat, and Biomedical Tasks at {WMT} 2020",
    author = "Berard, Alexandre  and
      Calapodescu, Ioan  and
      Nikoulina, Vassilina  and
      Philip, Jerin",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.57",
    pages = "462--472",
    abstract = "This paper describes Naver Labs Europe{'}s participation in the Robustness, Chat, and Biomedical Translation tasks at WMT 2020. We propose a bidirectional German-English model that is multi-domain, robust to noise, and which can translate entire documents (or bilingual dialogues) at once. We use the same ensemble of such models as our primary submission to all three tasks and achieve competitive results. We also experiment with language model pre-training techniques and evaluate their impact on robustness to noise and out-of-domain translation. For German, Spanish, Italian, and French to English translation in the Biomedical Task, we also submit our recently released multilingual Covid19NMT model.",
}
@inproceedings{moghe-etal-2020-university,
    title = "The {U}niversity of {E}dinburgh-{U}ppsala {U}niversity{'}s Submission to the {WMT} 2020 Chat Translation Task",
    author = "Moghe, Nikita  and
      Hardmeier, Christian  and
      Bawden, Rachel",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.58",
    pages = "473--478",
    abstract = "This paper describes the joint submission of the University of Edinburgh and Uppsala University to the WMT{'}20 chat translation task for both language directions (English-German). We use existing state-of-the-art machine translation models trained on news data and fine-tune them on in-domain and pseudo-in-domain web crawled data. Our baseline systems are transformer-big models that are pre-trained on the WMT{'}19 News Translation task and fine-tuned on pseudo-in-domain web crawled data and in-domain task data. We also experiment with (i) adaptation using speaker and domain tags and (ii) using different types and amounts of preceding context. We observe that contrarily to expectations, exploiting context degrades the results (and on analysis the data is not highly contextual). However using domain tags does improve scores according to the automatic evaluation. Our final primary systems use domain tags and are ensembles of 4 models, with noisy channel reranking of outputs. Our en-de system was ranked second in the shared task while our de-en system outperformed all the other systems.",
}
@inproceedings{mohammed-etal-2020-just,
    title = "{JUST} System for {WMT}20 Chat Translation Task",
    author = "Mohammed, Roweida  and
      Al-Ayyoub, Mahmoud  and
      Abdullah, Malak",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.59",
    pages = "479--482",
    abstract = "Machine Translation (MT) is a sub-field of Artificial Intelligence and Natural Language Processing that investigates and studies the ways of automatically translating a text from one language to another. In this paper, we present the details of our submission to the WMT20 Chat Translation Task, which consists of two language directions, English {--}{\textgreater} German and German {--}{\textgreater} English. The major feature of our system is applying a pre-trained BERT embedding with a bidirectional recurrent neural network. Our system ensembles three models, each with different hyperparameters. Despite being trained on a very small corpus, our model produces surprisingly good results.",
}
@inproceedings{wang-etal-2020-tencent,
    title = "Tencent {AI} Lab Machine Translation Systems for {WMT}20 Chat Translation Task",
    author = "Wang, Longyue  and
      Tu, Zhaopeng  and
      Wang, Xing  and
      Ding, Li  and
      Ding, Liang  and
      Shi, Shuming",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.60",
    pages = "483--491",
    abstract = "This paper describes the Tencent AI Lab{'}s submission of the WMT 2020 shared task on chat translation in English-German. Our neural machine translation (NMT) systems are built on sentence-level, document-level, non-autoregressive (NAT) and pretrained models. We integrate a number of advanced techniques into our systems, including data selection, back/forward translation, larger batch learning, model ensemble, finetuning as well as system combination. Specifically, we proposed a hybrid data selection method to select high-quality and in-domain sentences from out-of-domain data. To better capture the source contexts, we exploit to augment NAT models with evolved cross-attention. Furthermore, we explore to transfer general knowledge from four different pre-training language models to the downstream translation task. In general, we present extensive experimental results for this new translation task. Among all the participants, our German-to-English primary system is ranked the second in terms of BLEU scores.",
}
@inproceedings{dabre-fujita-2020-combining,
    title = "Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models",
    author = "Dabre, Raj  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.61",
    pages = "492--502",
    abstract = "In neural machine translation (NMT), sequence distillation (SD) through creation of distilled corpora leads to efficient (compact and fast) models. However, its effectiveness in extremely low-resource (ELR) settings has not been well-studied. On the other hand, transfer learning (TL) by leveraging larger helping corpora greatly improves translation quality in general. This paper investigates a combination of SD and TL for training efficient NMT models for ELR settings, where we utilize TL with helping corpora twice: once for distilling the ELR corpora and then during compact model training. We experimented with two ELR settings: Vietnamese{--}English and Hindi{--}English from the Asian Language Treebank dataset with 18k training sentence pairs. Using the compact models with 40{\%} smaller parameters trained on the distilled ELR corpora, greedy search achieved 3.6 BLEU points improvement in average while reducing 40{\%} of decoding time. We also confirmed that using both the distilled ELR and helping corpora in the second round of TL further improves translation quality. Our work highlights the importance of stage-wise application of SD and TL for efficient NMT modeling for ELR settings.",
}
@inproceedings{zhang-etal-2020-fast,
    title = "Fast Interleaved Bidirectional Sequence Generation",
    author = "Zhang, Biao  and
      Titov, Ivan  and
      Sennrich, Rico",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.62",
    pages = "503--515",
    abstract = "Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming independence between neighbouring tokens (semi-autoregressive decoding, SA), we take inspiration from bidirectional sequence generation and introduce a decoder that generates target words from the left-to-right and right-to-left directions simultaneously. We show that we can easily convert a standard architecture for unidirectional decoding into a bidirectional decoder by simply interleaving the two directions and adapting the word positions and selfattention masks. Our interleaved bidirectional decoder (IBDecoder) retains the model simplicity and training efficiency of the standard Transformer, and on five machine translation tasks and two document summarization tasks, achieves a decoding speedup of {\textasciitilde}2x compared to autoregressive decoding with comparable quality. Notably, it outperforms left-to-right SA because the independence assumptions in IBDecoder are more felicitous. To achieve even higher speedups, we explore hybrid models where we either simultaneously predict multiple neighbouring tokens per direction, or perform multi-directional decoding by partitioning the target sequence. These methods achieve speedups to 4x{--}11x across different tasks at the cost of {\textless}1 BLEU or {\textless}0.5 ROUGE (on average)",
}
@inproceedings{pham-etal-2020-priming,
    title = "Priming Neural Machine Translation",
    author = "Pham, Minh Quang  and
      Xu, Jitao  and
      Crego, Josep  and
      Yvon, Fran{\c{c}}ois  and
      Senellart, Jean",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.63",
    pages = "516--527",
    abstract = "Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response. In this paper, we propose a framework to mimic the process of priming in the context of neural machine translation (NMT). We evaluate the effect of using similar translations as priming cues on the NMT network. We propose a method to inject priming cues into the NMT network and compare our framework to other mechanisms that perform micro-adaptation during inference. Overall, experiments conducted in a multi-domain setting confirm that adding priming cues in the NMT decoder can go a long way towards improving the translation accuracy. Besides, we show the suitability of our framework to gather valuable information for an NMT network from monolingual resources.",
}
@inproceedings{rios-etal-2020-subword,
    title = "Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation",
    author = {Rios, Annette  and
      M{\"u}ller, Mathias  and
      Sennrich, Rico},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.64",
    pages = "528--537",
    abstract = "Zero-shot neural machine translation is an attractive goal because of the high cost of obtaining data and building translation systems for new translation directions. However, previous papers have reported mixed success in zero-shot translation. It is hard to predict in which settings it will be effective, and what limits performance compared to a fully supervised system. In this paper, we investigate zero-shot performance of a multilingual EN{\textless}-{\textgreater}FR,CS,DE,FI system trained on WMT data. We find that zero-shot performance is highly unstable and can vary by more than 6 BLEU between training runs, making it difficult to reliably track improvements. We observe a bias towards copying the source in zero-shot translation, and investigate how the choice of subword segmentation affects this bias. We find that language-specific subword segmentation results in less subword copying at training time, and leads to better zero-shot performance compared to jointly trained segmentation. A recent trend in multilingual models is to not train on parallel data between all language pairs, but have a single bridge language, e.g. English. We find that this negatively affects zero-shot translation and leads to a failure mode where the model ignores the language tag and instead produces English output in zero-shot directions. We show that this bias towards English can be effectively reduced with even a small amount of parallel data in some of the non-English pairs.",
}
@inproceedings{zhong-chiang-2020-look,
    title = "Look It Up: Bilingual and Monolingual Dictionaries Improve Neural Machine Translation",
    author = "Zhong, Xing Jie  and
      Chiang, David",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.65",
    pages = "538--549",
    abstract = "Despite advances in neural machine translation (NMT) quality, rare words continue to be problematic. For humans, the solution to the rare-word problem has long been dictionaries, but dictionaries cannot be straightforwardly incorporated into NMT. In this paper, we describe a new method for {``}attaching{''} dictionary definitions to rare words so that the network can learn the best way to use them. We demonstrate improvements of up to 3.1 BLEU using bilingual dictionaries and up to 0.7 BLEU using monolingual source-language dictionaries.",
}
@inproceedings{freitag-firat-2020-complete,
    title = "Complete Multilingual Neural Machine Translation",
    author = "Freitag, Markus  and
      Firat, Orhan",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.66",
    pages = "550--560",
    abstract = "Multilingual Neural Machine Translation (MNMT) models are commonly trained on a joint set of bilingual corpora which is acutely English-centric (i.e. English either as source or target language). While direct data between two languages that are non-English is explicitly available at times, its use is not common. In this paper, we first take a step back and look at the commonly used bilingual corpora (WMT), and resurface the existence and importance of implicit structure that existed in it: multi-way alignment across examples (the same sentence in more than two languages). We set out to study the use of multi-way aligned examples in order to enrich the original English-centric parallel corpora. We reintroduce this direct parallel data from multi-way aligned corpora between all source and target languages. By doing so, the English-centric graph expands into a complete graph, every language pair being connected. We call MNMT with such connectivity pattern complete Multilingual Neural Machine Translation (cMNMT) and demonstrate its utility and efficacy with a series of experiments and analysis. In combination with a novel training data sampling strategy that is conditioned on the target language only, cMNMT yields competitive translation quality for all language pairs. We further study the size effect of multi-way aligned data, its transfer learning capabilities and how it eases adding a new language in MNMT. Finally, we stress test cMNMT at scale and demonstrate that we can train a cMNMT model with up to 12,432 language pairs that provides competitive translation quality for all language pairs.",
}
@inproceedings{thompson-post-2020-paraphrase,
    title = "Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity",
    author = "Thompson, Brian  and
      Post, Matt",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.67",
    pages = "561--570",
    abstract = "Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.",
}
@inproceedings{marchisio-etal-2020-unsupervised,
    title = "When Does Unsupervised Machine Translation Work?",
    author = "Marchisio, Kelly  and
      Duh, Kevin  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.68",
    pages = "571--583",
    abstract = "Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which the methods succeed and fail. We conduct an extensive empirical evaluation using dissimilar language pairs, dissimilar domains, and diverse datasets. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that stochasticity during embedding training can dramatically affect downstream results. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms. We release our preprocessed dataset to encourage evaluations that stress-test systems under multiple data conditions.",
}
@inproceedings{bhosale-etal-2020-language,
    title = "Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling",
    author = "Bhosale, Shruti  and
      Yee, Kyra  and
      Edunov, Sergey  and
      Auli, Michael",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.69",
    pages = "584--593",
    abstract = "Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, na ̈{\i}ve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation.",
}
@inproceedings{imankulova-etal-2020-towards,
    title = "Towards Multimodal Simultaneous Neural Machine Translation",
    author = "Imankulova, Aizhan  and
      Kaneko, Masahiro  and
      Hirasawa, Tosho  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.70",
    pages = "594--603",
    abstract = "Simultaneous translation involves translating a sentence before the speaker{'}s utterance is completed in order to realize real-time understanding in multiple languages. This task is significantly more challenging than the general full sentence translation because of the shortage of input information during decoding. To alleviate this shortage, we propose multimodal simultaneous neural machine translation (MSNMT), which leverages visual information as an additional modality. Our experiments with the Multi30k dataset showed that MSNMT significantly outperforms its text-only counterpart in more timely translation situations with low latency. Furthermore, we verified the importance of visual information during decoding by performing an adversarial evaluation of MSNMT, where we studied how models behaved with incongruent input modality and analyzed the effect of different word order between source and target languages.",
}
@inproceedings{huo-etal-2020-diving,
    title = "Diving Deep into Context-Aware Neural Machine Translation",
    author = "Huo, Jingjing  and
      Herold, Christian  and
      Gao, Yingbo  and
      Dahlmann, Leonard  and
      Khadivi, Shahram  and
      Ney, Hermann",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.71",
    pages = "604--616",
    abstract = "Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts.",
}
@inproceedings{pham-etal-2020-study,
    title = "A Study of Residual Adapters for Multi-Domain Neural Machine Translation",
    author = "Pham, Minh Quang  and
      Crego, Josep Maria  and
      Yvon, Fran{\c{c}}ois  and
      Senellart, Jean",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.72",
    pages = "617--628",
    abstract = "Domain adaptation is an old and vexing problem for machine translation systems. The most common approach and successful to supervised adaptation is to fine-tune a baseline system with in-domain parallel data. Standard fine-tuning however modifies all the network parameters, which makes this approach computationally costly and prone to overfitting. A recent, lightweight approach, instead augments a baseline model with supplementary (small) adapter layers, keeping the rest of the mode unchanged. This has the additional merit to leave the baseline model intact, and adaptable to multiple domains. In this paper, we conduct a thorough analysis of the adapter model in the context of a multidomain machine translation task. We contrast multiple implementations of this idea on two language pairs. Our main conclusions are that residual adapters provide a fast and cheap method for supervised multi-domain adaptation; our two variants prove as effective as the original adapter model, and open perspective to also make adapted models more robust to label domain errors.",
}
@inproceedings{stafanovics-etal-2020-mitigating,
    title = "Mitigating Gender Bias in Machine Translation with Target Gender Annotations",
    author = "Stafanovi{\v{c}}s, Art{\=u}rs  and
      Pinnis, M{\=a}rcis  and
      Bergmanis, Toms",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.73",
    pages = "629--638",
    abstract = "When translating The secretary asked for details. to a language with grammatical gender, it might be necessary to determine the gender of the subject secretary. If the sentence does not contain the necessary information, it is not always possible to disambiguate. In such cases, machine translation systems select the most common translation option, which often corresponds to the stereotypical translations, thus potentially exacerbating prejudice and marginalisation of certain groups and people. We argue that the information necessary for an adequate translation can not always be deduced from the sentence being translated or even might depend on external knowledge. Therefore, in this work, we propose to decouple the task of acquiring the necessary information from the task of learning to translate correctly when such information is available. To that end, we present a method for training machine translation systems to use word-level annotations containing information about subject{'}s gender. To prepare training data, we annotate regular source language words with grammatical gender information of the corresponding target language words. Using such data to train machine translation systems reduces their reliance on gender stereotypes when information about the subject{'}s gender is available. Our experiments on five language pairs show that this allows improving accuracy on the WinoMT test set by up to 25.8 percentage points.",
}
@inproceedings{rikters-etal-2020-document,
    title = "Document-aligned {J}apanese-{E}nglish Conversation Parallel Corpus",
    author = "Rikters, Mat{\=\i}ss  and
      Ri, Ryokan  and
      Li, Tong  and
      Nakazawa, Toshiaki",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.74",
    pages = "639--645",
    abstract = "Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation. To address the first issue, we present a document-aligned Japanese-English conversation corpus, including balanced, high-quality business conversation data for tuning and testing. As for the second issue, we manually identify the main areas where SL MT fails to produce adequate translations in lack of context. We then create an evaluation set where these phenomena are annotated to alleviate automatic evaluation of DL systems. We train MT models using our corpus to demonstrate how using context leads to improvements.",
}
@inproceedings{chatterjee-etal-2020-findings,
    title = "Findings of the {WMT} 2020 Shared Task on Automatic Post-Editing",
    author = "Chatterjee, Rajen  and
      Freitag, Markus  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.75",
    pages = "646--659",
    abstract = "We present the results of the 6th round of the WMT task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a {``}black-box{''} machine translation system by learning from existing human corrections of different sentences. This year, the challenge consisted of fixing the errors present in English Wikipedia pages translated into German and Chinese by state-ofthe-art, not domain-adapted neural MT (NMT) systems unknown to participants. Six teams participated in the English-German task, submitting a total of 11 runs. Two teams participated in the English-Chinese task submitting 2 runs each. Due to i) the different source/domain of data compared to the past (Wikipedia vs Information Technology), ii) the different quality of the initial translations to be corrected and iii) the introduction of a new language pair (English-Chinese), this year{'}s results are not directly comparable with last year{'}s round. However, on both language directions, participants{'} submissions show considerable improvements over the baseline results. On English-German, the top ranked system improves over the baseline by -11.35 TER and +16.68 BLEU points, while on EnglishChinese the improvements are respectively up to -12.13 TER and +14.57 BLEU points. Overall, coherent gains are also highlighted by the outcomes of human evaluation, which confirms the effectiveness of APE to improve MT quality, especially in the new generic domain selected for this year{'}s round.",
}
@inproceedings{bawden-etal-2020-findings,
    title = "Findings of the {WMT} 2020 Biomedical Translation Shared Task: {B}asque, {I}talian and {R}ussian as New Additional Languages",
    author = "Bawden, Rachel  and
      Di Nunzio, Giorgio Maria  and
      Grozea, Cristian  and
      Jauregi Unanue, Inigo  and
      Jimeno Yepes, Antonio  and
      Mah, Nancy  and
      Martinez, David  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Oronoz, Maite  and
      Perez-de-Vi{\~n}aspre, Olatz  and
      Piccardi, Massimo  and
      Roller, Roland  and
      Siu, Amy  and
      Thomas, Philippe  and
      Vezzani, Federica  and
      Vicente Navarro, Maika  and
      Wiemann, Dina  and
      Yeganova, Lana",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.76",
    pages = "660--687",
    abstract = "Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.",
}
@inproceedings{mathur-etal-2020-results,
    title = "Results of the {WMT}20 Metrics Shared Task",
    author = "Mathur, Nitika  and
      Wei, Johnny  and
      Freitag, Markus  and
      Ma, Qingsong  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.77",
    pages = "688--725",
    abstract = "This paper presents the results of the WMT20 Metrics Shared Task. Participants were asked to score the outputs of the translation systems competing in the WMT20 News Translation Task with automatic metrics. Ten research groups submitted 27 metrics, four of which are reference-less {``}metrics{''}. In addition, we computed five baseline metrics, including sentBLEU, BLEU, TER and using the SacreBLEU scorer. All metrics were evaluated on how well they correlate at the system-, document- and segment-level with the WMT20 official human scores. We present an extensive analysis on influence of different reference translations on metric reliability, how well automatic metrics score human translations, and we also flag major discrepancies between metric and human scores when evaluating MT systems. Finally, we investigate whether we can use automatic metrics to flag incorrect human ratings.",
}
@inproceedings{koehn-etal-2020-findings,
    title = "Findings of the {WMT} 2020 Shared Task on Parallel Corpus Filtering and Alignment",
    author = "Koehn, Philipp  and
      Chaudhary, Vishrav  and
      El-Kishky, Ahmed  and
      Goyal, Naman  and
      Chen, Peng-Jen  and
      Guzm{\'a}n, Francisco",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.78",
    pages = "726--742",
    abstract = "Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto{--}English and Khmer{--}English and also included the challenge of sentence alignment from document pairs.",
}
@inproceedings{specia-etal-2020-findings-wmt,
    title = "Findings of the {WMT} 2020 Shared Task on Quality Estimation",
    author = "Specia, Lucia  and
      Blain, Fr{\'e}d{\'e}ric  and
      Fomicheva, Marina  and
      Fonseca, Erick  and
      Chaudhary, Vishrav  and
      Guzm{\'a}n, Francisco  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.79",
    pages = "743--764",
    abstract = "We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels. This edition included new data with open domain texts, direct assessment annotations, and multiple language pairs: English-German, English-Chinese, Russian-English, Romanian-English, Estonian-English, Sinhala-English and Nepali-English data for the sentence-level subtasks, English-German and English-Chinese for the word-level subtask, and English-French data for the document-level subtask. In addition, we made neural machine translation models available to participants. 19 participating teams from 27 institutions submitted altogether 1374 systems to different task variants and language pairs.",
}
@inproceedings{fraser-2020-findings,
    title = "Findings of the {WMT} 2020 Shared Tasks in Unsupervised {MT} and Very Low Resource Supervised {MT}",
    author = "Fraser, Alexander",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.80",
    pages = "765--771",
    abstract = "We describe the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT. In both tasks, the community studied German to Upper Sorbian and Upper Sorbian to German MT, which is a very realistic machine translation scenario (unlike the simulated scenarios used in particular in much of the unsupervised MT work in the past). We were able to obtain most of the digital data available for Upper Sorbian, a minority language of Germany, which was the original motivation for the Unsupervised MT shared task. As we were defining the task, we also obtained a small amount of parallel data (about 60000 parallel sentences), allowing us to offer a Very Low Resource Supervised MT task as well. Six primary systems participated in the unsupervised shared task, two of these systems used additional data beyond the data released by the organizers. Ten primary systems participated in the very low resource supervised task. The paper discusses the background, presents the tasks and results, and discusses best practices for the future.",
}
@inproceedings{lee-2020-cross,
    title = "Cross-Lingual Transformers for Neural Automatic Post-Editing",
    author = "Lee, Dongjun",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.81",
    pages = "772--776",
    abstract = "In this paper, we describe the Bering Lab{'}s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source sentence and a machine-translated (MT) sentence as an input to generate the post-edited (PE) output. For further improvement, we mask incorrect or missing words in the PE output based on word-level quality estimation and then predict the actual word for each mask based on the fine-tuned cross-lingual language model (XLM-RoBERTa). Finally, to address the over-correction problem, we select the final output among the PE outputs and the original MT sentence based on a sentence-level quality estimation. When evaluated on the WMT 2020 English-German APE test dataset, our system improves the NMT output by -3.95 and +4.50 in terms of TER and BLEU, respectively.",
}
@inproceedings{lee-etal-2020-postech,
    title = "{POSTECH}-{ETRI}{'}s Submission to the {WMT}2020 {APE} Shared Task: Automatic Post-Editing with Cross-lingual Language Model",
    author = "Lee, Jihyung  and
      Lee, WonKee  and
      Shin, Jaehun  and
      Jung, Baikjin  and
      Kim, Young-Kil  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.82",
    pages = "777--782",
    abstract = "This paper describes POSTECH-ETRI{'}s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cross-lingual language model, which jointly adopts translation language modeling (TLM) and masked language modeling (MLM) training objectives in the pre-training stage; the APE models then utilize jointly learned language representations between the source language and the target language. In addition, we created 19 million new sythetic triplets as additional training data for our final ensemble model. According to experimental results on the WMT2020 APE development data set, our models showed an improvement over the baseline by TER of -3.58 and a BLEU score of +5.3 for the En-De subtask; and TER of -5.29 and a BLEU score of +7.32 for the En-Zh subtask.",
}
@inproceedings{lee-etal-2020-noising,
    title = "Noising Scheme for Data Augmentation in Automatic Post-Editing",
    author = "Lee, WonKee  and
      Shin, Jaehun  and
      Jung, Baikjin  and
      Lee, Jihyung  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.83",
    pages = "783--788",
    abstract = "This paper describes POSTECH{'}s submission to WMT20 for the shared task on Automatic Post-Editing (APE). Our focus is on increasing the quantity of available APE data to overcome the shortage of human-crafted training data. In our experiment, we implemented a noising module that simulates four types of post-editing errors, and we introduced this module into a Transformer-based multi-source APE model. Our noising module implants errors into texts on the target side of parallel corpora during the training phase to make synthetic MT outputs, increasing the entire number of training samples. We also generated additional training data using the parallel corpora and NMT model that were released for the Quality Estimation task, and we used these data to train our APE model. Experimental results on the WMT20 English-German APE data set show improvements over the baseline in terms of both the TER and BLEU scores: our primary submission achieved an improvement of -3.15 TER and +4.01 BLEU, and our contrastive submission achieved an improvement of -3.34 TER and +4.30 BLEU.",
}
@inproceedings{wang-etal-2020-alibabas,
    title = "{A}libaba{'}s Submission for the {WMT} 2020 {APE} Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual {BERT}",
    author = "Wang, Jiayi  and
      Wang, Ke  and
      Fan, Kai  and
      Zhang, Yuqi  and
      Lu, Jun  and
      Ge, Xin  and
      Shi, Yangbin  and
      Zhao, Yu",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.84",
    pages = "789--796",
    abstract = "The goal of Automatic Post-Editing (APE) is basically to examine the automatic methods for correcting translation errors generated by an unknown machine translation (MT) system. This paper describes Alibaba{'}s submissions to the WMT 2020 APE Shared Task for the English-German language pair. We design a two-stage training pipeline. First, a BERT-like cross-lingual language model is pre-trained by randomly masking target sentences alone. Then, an additional neural decoder on the top of the pre-trained model is jointly fine-tuned for the APE task. We also apply an imitation learning strategy to augment a reasonable amount of pseudo APE training data, potentially preventing the model to overfit on the limited real training data and boosting the performance on held-out data. To verify our proposed model and data augmentation, we examine our approach with the well-known benchmarking English-German dataset from the WMT 2017 APE task. The experiment results demonstrate that our system significantly outperforms all other baselines and achieves the state-of-the-art performance. The final results on the WMT 2020 test dataset show that our submission can achieve +5.56 BLEU and -4.57 TER with respect to the official MT baseline.",
}
@inproceedings{yang-etal-2020-hw,
    title = "{HW}-{TSC}{'}s Participation at {WMT} 2020 Automatic Post Editing Shared Task",
    author = "Yang, Hao  and
      Wang, Minghan  and
      Wei, Daimeng  and
      Shang, Hengchao  and
      Guo, Jiaxin  and
      Li, Zongyao  and
      Lei, Lizhi  and
      Qin, Ying  and
      Tao, Shimin  and
      Sun, Shiliang  and
      Chen, Yimeng",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.85",
    pages = "797--802",
    abstract = "The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation.",
}
@inproceedings{abdul-rauf-etal-2020-limsi,
    title = "{LIMSI} @ {WMT} 2020",
    author = "Abdul Rauf, Sadaf  and
      Rosales N{\'u}{\~n}ez, Jos{\'e} Carlos  and
      Pham, Minh Quang  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.86",
    pages = "803--812",
    abstract = "This paper describes LIMSI{'}s submissions to the translation shared tasks at WMT{'}20. This year we have focused our efforts on the biomedical translation task, developing a resource-heavy system for the translation of medical abstracts from English into French, using back-translated texts, terminological resources as well as multiple pre-processing pipelines, including pre-trained representations. Systems were also prepared for the robustness task for translating from English into German; for this large-scale task we developed multi-domain, noise-robust, translation systems aim to handle the two test conditions: zero-shot and few-shot domain adaptation.",
}
@inproceedings{corral-saralegi-2020-elhuyar,
    title = "Elhuyar submission to the Biomedical Translation Task 2020 on terminology and abstracts translation",
    author = "Corral, Ander  and
      Saralegi, Xabier",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.87",
    pages = "813--819",
    abstract = "This article describes the systems submitted by Elhuyar to the 2020 Biomedical Translation Shared Task, specifically the systems presented in the subtasks of terminology translation for English-Basque and abstract translation for English-Basque and English-Spanish. In all cases a Transformer architecture was chosen and we studied different strategies to combine open domain data with biomedical domain data for building the training corpora. For the English-Basque pair, given the scarcity of parallel corpora in the biomedical domain, we set out to create domain training data in a synthetic way. The systems presented in the terminology and abstract translation subtasks for the English-Basque language pair ranked first in their respective tasks among four participants, achieving 0.78 accuracy for terminology translation and a BLEU of 0.1279 for the translation of abstracts. In the abstract translation task for the English-Spanish pair our team ranked second (BLEU=0.4498) in the case of OK sentences.",
}
@inproceedings{hambardzumyan-etal-2020-yerevanns,
    title = "{Y}ereva{NN}{'}s Systems for {WMT}20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs",
    author = "Hambardzumyan, Karen  and
      Tamoyan, Hovhannes  and
      Khachatrian, Hrant",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.88",
    pages = "820--825",
    abstract = "This report describes YerevaNN{'}s neural machine translation systems and data processing pipelines developed for WMT20 biomedical translation task. We provide systems for English-Russian and English-German language pairs. For the English-Russian pair, our submissions achieve the best BLEU scores, with en$\rightarrow$ru direction outperforming the other systems by a significant margin. We explain most of the improvements by our heavy data preprocessing pipeline which attempts to fix poorly aligned sentences in the parallel data.",
}
@inproceedings{jauregi-unanue-piccardi-2020-pretrained,
    title = "Pretrained Language Models and Backtranslation for {E}nglish-{B}asque Biomedical Neural Machine Translation",
    author = "Jauregi Unanue, Inigo  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.89",
    pages = "826--832",
    abstract = "This paper describes the machine translation systems proposed by the University of Technology Sydney Natural Language Processing (UTS{\_}NLP) team for the WMT20 English-Basque biomedical translation tasks. Due to the limited parallel corpora available, we have proposed to train a BERT-fused NMT model that leverages the use of pretrained language models. Furthermore, we have augmented the training corpus by backtranslating monolingual data. Our experiments show that NMT models in low-resource scenarios can benefit from combining these two training techniques, with improvements of up to 6.16 BLEU percentual points in the case of biomedical abstract translations.",
}
@inproceedings{lopes-etal-2020-lite,
    title = "Lite Training Strategies for {P}ortuguese-{E}nglish and {E}nglish-{P}ortuguese Translation",
    author = "Lopes, Alexandre  and
      Nogueira, Rodrigo  and
      Lotufo, Roberto  and
      Pedrini, Helio",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.90",
    pages = "833--840",
    abstract = "Despite the widespread adoption of deep learning for machine translation, it is still expensive to develop high-quality translation models. In this work, we investigate the use of pre-trained models, such as T5 for Portuguese-English and English-Portuguese translation tasks using low-cost hardware. We explore the use of Portuguese and English pre-trained language models and propose an adaptation of the English tokenizer to represent Portuguese characters, such as diaeresis, acute and grave accents. We compare our models to the Google Translate API and MarianMT on a subset of the ParaCrawl dataset, as well as to the winning submission to the WMT19 Biomedical Translation Shared Task. We also describe our submission to the WMT20 Biomedical Translation Shared Task. Our results show that our models have a competitive performance to state-of-the-art models while being trained on modest hardware (a single 8GB gaming GPU for nine days). Our data, models and code are available in our GitHub repository.",
}
@inproceedings{nayak-etal-2020-adapts,
    title = "The {ADAPT}{'}s Submissions to the {WMT}20 Biomedical Translation Task",
    author = "Nayak, Prashant  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.91",
    pages = "841--848",
    abstract = "This paper describes the ADAPT Centre{'}s submissions to the WMT20 Biomedical Translation Shared Task for English-to-Basque. We present the machine translation (MT) systems that were built to translate scientific abstracts and terms from biomedical terminologies, and using the state-of-the-art neural MT (NMT) model: Transformer. In order to improve our baseline NMT system, we employ a number of methods, e.g. {``}pseudo{''} parallel data selection, monolingual data selection for synthetic corpus creation, mining monolingual sentences for adapting our NMT systems to this task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that systematic addition of the aforementioned techniques to the baseline yields an excellent performance in the English-to-Basque translation task.",
}
@inproceedings{naz-etal-2020-fjwu,
    title = "{FJWU} participation for the {WMT}20 Biomedical Translation Task",
    author = "Naz, Sumbal  and
      Abdul Rauf, Sadaf  and
      Hira, Noor-e-  and
      Ul Haq, Sami",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.92",
    pages = "849--856",
    abstract = "This paper reports system descriptions for FJWU-NRPU team for participation in the WMT20 Biomedical shared translation task. We focused our submission on exploring the effects of adding in-domain corpora extracted from various out-of-domain sources. Systems were built for French to English using in-domain corpora through fine tuning and selective data training. We further explored BERT based models specifically with focus on effect of domain adaptive subword units.",
}
@inproceedings{peng-etal-2020-huaweis,
    title = "Huawei{'}s Submissions to the {WMT}20 Biomedical Translation Task",
    author = "Peng, Wei  and
      Liu, Jianfeng  and
      Wang, Minghan  and
      Li, Liangyou  and
      Meng, Xupeng  and
      Yang, Hao  and
      Liu, Qun",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.93",
    pages = "857--861",
    abstract = "This paper describes Huawei{'}s submissions to the WMT20 biomedical translation shared task. Apart from experimenting with finetuning on domain-specific bitexts, we explore effects of in-domain dictionaries on enhancing cross-domain neural machine translation performance. We utilize a transfer learning strategy through pre-trained machine translation models and extensive scope of engineering endeavors. Four of our ten submissions achieve state-of-the-art performance according to the official automatic evaluation results, namely translation directions on English{\textless}-{\textgreater}French, English-{\textgreater}German and English-{\textgreater}Italian.",
}
@inproceedings{saunders-byrne-2020-addressing,
    title = "Addressing Exposure Bias With Document Minimum Risk Training: {C}ambridge at the {WMT}20 Biomedical Translation Task",
    author = "Saunders, Danielle  and
      Byrne, Bill",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.94",
    pages = "862--869",
    abstract = "The 2020 WMT Biomedical translation task evaluated Medline abstract translations. This is a small-domain translation task, meaning limited relevant training data with very distinct style and vocabulary. Models trained on such data are susceptible to exposure bias effects, particularly when training sentence pairs are imperfect translations of each other. This can result in poor behaviour during inference if the model learns to neglect the source sentence. The UNICAM entry addresses this problem during fine-tuning using a robust variant on Minimum Risk Training. We contrast this approach with data-filtering to remove {`}problem{'} training examples. Under MRT fine-tuning we obtain good results for both directions of English-German and English-Spanish biomedical translation. In particular we achieve the best English-to-Spanish translation result and second-best Spanish-to-English result, despite using only single models with no ensembling.",
}
@inproceedings{soares-vaz-2020-uos,
    title = "{U}o{S} Participation in the {WMT}20 Translation of Biomedical Abstracts",
    author = "Soares, Felipe  and
      Vaz, Delton",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.95",
    pages = "870--874",
    abstract = "This paper describes the machine translation systems developed by the University of Sheffield (UoS) team for the biomedical translation shared task of WMT20. Our system is based on a Transformer model with TensorFlow Model Garden toolkit. We participated in ten translation directions for the English/Spanish, English/Portuguese, English/Russian, English/Italian, and English/French language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources.",
}
@inproceedings{soto-etal-2020-ixameds,
    title = "Ixamed{'}s submission description for {WMT}20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation",
    author = "Soto, Xabier  and
      Perez-de-Vi{\~n}aspre, Olatz  and
      Labaka, Gorka  and
      Oronoz, Maite",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.96",
    pages = "875--880",
    abstract = "In this paper we describe the systems developed at Ixa for our participation in WMT20 Biomedical shared task in three language pairs, en-eu, en-es and es-en. When defining our approach, we have put the focus on making an efficient use of corpora recently compiled for training Machine Translation (MT) systems to translate Covid-19 related text, as well as reusing previously compiled corpora and developed systems for biomedical or clinical domain. Regarding the techniques used, we base on the findings from our previous works for translating clinical texts into Basque, making use of clinical terminology for adapting the MT systems to the clinical domain. However, after manually inspecting some of the outputs generated by our systems, for most of the submissions we end up using the system trained only with the basic corpus, since the systems including the clinical terminologies generated outputs shorter in length than the corresponding references. Thus, we present simple baselines for translating abstracts between English and Spanish (en/es); while for translating abstracts and terms from English into Basque (en-eu), we concatenate the best en-es system for each kind of text with our es-eu system. We present automatic evaluation results in terms of BLEU scores, and analyse the effect of including clinical terminology on the average sentence length of the generated outputs. Following the recent recommendations for a responsible use of GPUs for NLP research, we include an estimation of the generated CO2 emissions, based on the power consumed for training the MT systems.",
}
@inproceedings{wang-etal-2020-tencent-ai,
    title = "Tencent {AI} Lab Machine Translation Systems for the {WMT}20 Biomedical Translation Task",
    author = "Wang, Xing  and
      Tu, Zhaopeng  and
      Wang, Longyue  and
      Shi, Shuming",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.97",
    pages = "881--886",
    abstract = "This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German{\textless}-{\textgreater}English, English{\textless}-{\textgreater}German, Chinese{\textless}-{\textgreater}English and English{\textless}-{\textgreater}Chinese. We implement our system with model ensemble technique on different transformer architectures (Deep, Hybrid, Big, Large Transformers). To enlarge the in-domain bilingual corpus, we use back-translation of monolingual in-domain data in the target language as additional in-domain training data. Our systems in German-{\textgreater}English and English-{\textgreater}German are ranked 1st and 3rd respectively according to the official evaluation results in terms of BLEU scores.",
}
@inproceedings{bawden-etal-2020-parbleu,
    title = "{P}ar{BLEU}: Augmenting Metrics with Automatic Paraphrases for the {WMT}{'}20 Metrics Shared Task",
    author = {Bawden, Rachel  and
      Zhang, Biao  and
      T{\"a}ttar, Andre  and
      Post, Matt},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.98",
    pages = "887--894",
    abstract = "We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references.",
}
@inproceedings{lo-2020-extended,
    title = "Extended Study on Using Pretrained Language Models and {Y}i{S}i-1 for Machine Translation Evaluation",
    author = "Lo, Chi-kiu",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.99",
    pages = "895--902",
    abstract = "We present an extended study on using pretrained language models and YiSi-1 for machine translation evaluation. Although the recently proposed contextual embedding based metrics, YiSi-1, significantly outperform BLEU and other metrics in correlating with human judgment on translation quality, we have yet to understand the full strength of using pretrained language models for machine translation evaluation. In this paper, we study YiSi-1{'}s correlation with human translation quality judgment by varying three major attributes (which architecture; which inter- mediate layer; whether it is monolingual or multilingual) of the pretrained language mod- els. Results of the study show further improvements over YiSi-1 on the WMT 2019 Metrics shared task. We also describe the pretrained language model we trained for evaluating Inuktitut machine translation output.",
}
@inproceedings{lo-larkin-2020-machine,
    title = "Machine Translation Reference-less Evaluation using {Y}i{S}i-2 with Bilingual Mappings of Massive Multilingual Language Model",
    author = "Lo, Chi-kiu  and
      Larkin, Samuel",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.100",
    pages = "903--910",
    abstract = "We present a study on using YiSi-2 with massive multilingual pretrained language models for machine translation (MT) reference-less evaluation. Aiming at finding better semantic representation for semantic MT evaluation, we first test YiSi-2 with contextual embed- dings extracted from different layers of two different pretrained models, multilingual BERT and XLM-RoBERTa. We also experiment with learning bilingual mappings that trans- form the vector subspace of the source language to be closer to that of the target language in the pretrained model to obtain more accurate cross-lingual semantic similarity representations. Our results show that YiSi-2{'}s correlation with human direct assessment on translation quality is greatly improved by replacing multilingual BERT with XLM-RoBERTa and projecting the source embeddings into the tar- get embedding space using a cross-lingual lin- ear projection (CLP) matrix learnt from a small development set.",
}
@inproceedings{rei-etal-2020-unbabels,
    title = "Unbabel{'}s Participation in the {WMT}20 Metrics Shared Task",
    author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.101",
    pages = "911--920",
    abstract = "We present the contribution of the Unbabel team to the WMT 2020 Shared Task on Metrics. We intend to participate on the segmentlevel, document-level and system-level tracks on all language pairs, as well as the {``}QE as a Metric{''} track. Accordingly, we illustrate results of our models in these tracks with reference to test sets from the previous year. Our submissions build upon the recently proposed COMET framework: we train several estimator models to regress on different humangenerated quality scores and a novel ranking model trained on relative ranks obtained from Direct Assessments. We also propose a simple technique for converting segment-level predictions into a document-level score. Overall, our systems achieve strong results for all language pairs on previous test sets and in many cases set a new state-of-the-art.",
}
@inproceedings{sellam-etal-2020-learning,
    title = "Learning to Evaluate Translation Beyond {E}nglish: {BLEURT} Submissions to the {WMT} Metrics 2020 Shared Task",
    author = "Sellam, Thibault  and
      Pu, Amy  and
      Chung, Hyung Won  and
      Gehrmann, Sebastian  and
      Tan, Qijun  and
      Freitag, Markus  and
      Das, Dipanjan  and
      Parikh, Ankur",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.102",
    pages = "921--927",
    abstract = "The quality of machine translation systems has dramatically improved over the last decade, and as a result, evaluation has become an increasingly challenging problem. This paper describes our contribution to the WMT 2020 Metrics Shared Task, the main benchmark for automatic evaluation of translation. We make several submissions based on BLEURT, a previously published which uses transfer learning. We extend the metric beyond English and evaluate it on 14 language pairs for which fine-tuning data is available, as well as 4 {``}zero-shot{''} language pairs, for which we have no labelled examples. Additionally, we focus on English to German and demonstrate how to combine BLEURT{'}s predictions with those of YiSi and use alternative reference translations to enhance the performance. Empirical results show that the models achieve competitive results on the WMT Metrics 2019 Shared Task, indicating their promise for the 2020 edition.",
}
@inproceedings{stanchev-etal-2020-towards,
    title = "Towards a Better Evaluation of Metrics for Machine Translation",
    author = "Stanchev, Peter  and
      Wang, Weiyue  and
      Ney, Hermann",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.103",
    pages = "928--933",
    abstract = "An important aspect of machine translation is its evaluation, which can be achieved through the use of a variety of metrics. To compare these metrics, the workshop on statistical machine translation annually evaluates metrics based on their correlation with human judgement. Over the years, methods for measuring correlation with humans have changed, but little research has been performed on what the optimal methods for acquiring human scores are and how human correlation can be measured. In this work, the methods for evaluating metrics at both system- and segment-level are analyzed in detail and their shortcomings are pointed out.",
}
@inproceedings{xu-etal-2020-incorporate,
    title = "Incorporate Semantic Structures into Machine Translation Evaluation via {UCCA}",
    author = "Xu, Jin  and
      Guo, Yinuo  and
      Hu, Junfeng",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.104",
    pages = "934--939",
    abstract = "Copying mechanism has been commonly used in neural paraphrasing networks and other text generation tasks, in which some important words in the input sequence are preserved in the output sequence. Similarly, in machine translation, we notice that there are certain words or phrases appearing in all good translations of one source text, and these words tend to convey important semantic information. Therefore, in this work, we define words carrying important semantic meanings in sentences as semantic core words. Moreover, we propose an MT evaluation approach named Semantically Weighted Sentence Similarity (SWSS). It leverages the power of UCCA to identify semantic core words, and then calculates sentence similarity scores on the overlap of semantic core words. Experimental results show that SWSS can consistently improve the performance of popular MT evaluation metrics which are based on lexical similarity.",
}
@inproceedings{acarcicek-etal-2020-filtering,
    title = "Filtering Noisy Parallel Corpus using Transformers with Proxy Task Learning",
    author = "A{\c{c}}ar{\c{c}}i{\c{c}}ek, Haluk  and
      {\c{C}}olako{\u{g}}lu, Talha  and
      Aktan Hatipo{\u{g}}lu, P{\i}nar Ece  and
      Huang, Chong Hsuan  and
      Peng, Wei",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.105",
    pages = "940--946",
    abstract = "This paper illustrates Huawei{'}s submission to the WMT20 low-resource parallel corpus filtering shared task. Our approach focuses on developing a proxy task learner on top of a transformer-based multilingual pre-trained language model to boost the filtering capability for noisy parallel corpora. Such a supervised task also helps us to iterate much more quickly than using an existing neural machine translation system to perform the same task. After performing empirical analyses of the finetuning task, we benchmark our approach by comparing the results with past years{'} state-of-theart records. This paper wraps up with a discussion of limitations and future work. The scripts for this study will be made publicly available.",
}
@inproceedings{elnokrashy-etal-2020-score,
    title = "Score Combination for Improved Parallel Corpus Filtering for Low Resource Conditions",
    author = "ElNokrashy, Muhammad  and
      Hendy, Amr  and
      Abdelghaffar, Mohamed  and
      Afify, Mohamed  and
      Tawfik, Ahmed  and
      Hassan Awadalla, Hany",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.106",
    pages = "947--951",
    abstract = "This paper presents the description of our submission to WMT20 sentence filtering task. We combine scores from custom LASER built for each source language, a classifier built to distinguish positive and negative pairs and the original scores provided with the task. For the mBART setup, provided by the organizers, our method shows 7{\%} and 5{\%} relative improvement, over the baseline, in sacreBLEU score on the test set for Pashto and Khmer respectively.",
}
@inproceedings{espla-gomis-etal-2020-bicleaner,
    title = "Bicleaner at {WMT} 2020: {U}niversitat d{'}Alacant-Prompsit{'}s submission to the parallel corpus filtering shared task",
    author = "Espl{\`a}-Gomis, Miquel  and
      S{\'a}nchez-Cartagena, V{\'\i}ctor M.  and
      Zaragoza-Bernabeu, Jaume  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.107",
    pages = "952--958",
    abstract = "This paper describes the joint submission of Universitat d{'}Alacant and Prompsit Language Engineering to the WMT 2020 shared task on parallel corpus filtering. Our submission, based on the free/open-source tool Bicleaner, enhances it with Extremely Randomised Trees and lexical similarity features that account for the frequency of the words in the parallel sentences to determine if two sentences are parallel. To train this classifier we used the clean corpora provided for the task and synthetic noisy parallel sentences. In addition we re-score the output of Bicleaner using character-level language models and n-gram saturation.",
}
@inproceedings{kejriwal-koehn-2020-exploratory,
    title = "An exploratory approach to the Parallel Corpus Filtering shared task {WMT}20",
    author = "Kejriwal, Ankur  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.108",
    pages = "959--965",
    abstract = "In this document we describe our submission to the parallel corpus filtering task using multilingual word embedding, language models and an ensemble of pre and post filtering rules. We use the norms of embedding and the perplexities of language models along with pre/post filtering rules to complement the LASER baseline scores and in the end get an improvement on the dev set in both language pairs.",
}
@inproceedings{koerner-koehn-2020-dual,
    title = "Dual Conditional Cross Entropy Scores and {LASER} Similarity Scores for the {WMT}20 Parallel Corpus Filtering Shared Task",
    author = "Koerner, Felicia  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.109",
    pages = "966--971",
    abstract = "This paper describes our submission to the WMT20 Parallel Corpus Filtering and Alignment for Low-Resource Conditions Shared Task. This year{'}s corpora are noisy Khmer-English and Pashto-English, with 58.3 million and 11.6 million words respectively (English token count). Our submission focuses on filtering Pashto-English, building on previously successful methods to produce two sets of scores: LASER{\_}LM, a combination of the LASER similarity scores provided in the shared task and perplexity scores from language models, and DCCEF{\_}DUP, dual conditional cross entropy scores combined with a duplication penalty. We improve slightly on the LASER similarity score and find that the provided clean data can successfully be supplemented with a subsampled set of the noisy data, effectively increasing the training data for the models used for dual conditional cross entropy scoring.",
}
@inproceedings{lo-joanis-2020-improving,
    title = "Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models",
    author = "Lo, Chi-kiu  and
      Joanis, Eric",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.110",
    pages = "972--978",
    abstract = "The National Research Council of Canada{'}s team submissions to the parallel corpus filtering task at the Fifth Conference on Machine Translation are based on two key components: (1) iteratively refined statistical sentence alignments for extracting sentence pairs from document pairs and (2) a crosslingual semantic textual similarity metric based on a pretrained multilingual language model, XLM-RoBERTa, with bilingual mappings learnt from a minimal amount of clean parallel data for scoring the parallelism of the extracted sentence pairs. The translation quality of the neural machine translation systems trained and fine-tuned on the parallel data extracted by our submissions improved significantly when compared to the organizers{'} LASER-based baseline, a sentence-embedding method that worked well last year. For re-aligning the sentences in the document pairs (component 1), our statistical approach has outperformed the current state-of-the-art neural approach in this low-resource context.",
}
@inproceedings{lu-etal-2020-alibaba,
    title = "{A}libaba Submission to the {WMT}20 Parallel Corpus Filtering Task",
    author = "Lu, Jun  and
      Ge, Xin  and
      Shi, Yangbin  and
      Zhang, Yuqi",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.111",
    pages = "979--984",
    abstract = "This paper describes the Alibaba Machine Translation Group submissions to the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment. In the filtering task, three main methods are applied to evaluate the quality of the parallel corpus, i.e. a) Dual Bilingual GPT-2 model, b) Dual Conditional Cross-Entropy Model and c) IBM word alignment model. The scores of these models are combined by using a positive-unlabeled (PU) learning model and a brute-force search to obtain additional gains. Besides, a few simple but efficient rules are adopted to evaluate the quality and the diversity of the corpus. In the alignment-filtering task, the extraction pipeline of bilingual sentence pairs includes the following steps: bilingual lexicon mining, language identification, sentence segmentation and sentence alignment. The final result shows that, in both filtering and alignment tasks, our system significantly outperforms the LASER-based system.",
}
@inproceedings{xu-etal-2020-volctrans,
    title = "Volctrans Parallel Corpus Filtering System for {WMT} 2020",
    author = "Xu, Runxin  and
      Zhi, Zhuo  and
      Cao, Jun  and
      Wang, Mingxuan  and
      Li, Lei",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.112",
    pages = "985--990",
    abstract = "In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions. The task requires the participants to align potential parallel sentence pairs out of the given document pairs, and score them so that low-quality pairs can be filtered. Our system, Volctrans, is made of two modules, i.e., a mining module and a scoring module. Based on the word alignment model, the mining mod- ule adopts an iterative mining strategy to extract latent parallel sentences. In the scoring module, an XLM-based scorer provides scores, followed by reranking mechanisms and ensemble. Our submissions outperform the baseline by 3.x/2.x and 2.x/2.x for km-en and ps-en on From Scratch/Fine-Tune conditions.",
}
@inproceedings{baek-etal-2020-patquest,
    title = "{PATQUEST}: Papago Translation Quality Estimation",
    author = "Baek, Yujin  and
      Kim, Zae Myung  and
      Moon, Jihyung  and
      Kim, Hyunjoong  and
      Park, Eunjeong",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.113",
    pages = "991--998",
    abstract = "This paper describes the system submitted by Papago team for the quality estimation task at WMT 2020. It proposes two key strategies for quality estimation: (1) task-specific pretraining scheme, and (2) task-specific data augmentation. The former focuses on devising learning signals for pretraining that are closely related to the downstream task. We also present data augmentation techniques that simulate the varying levels of errors that the downstream dataset may contain. Thus, our PATQUEST models are exposed to erroneous translations in both stages of task-specific pretraining and finetuning, effectively enhancing their generalization capability. Our submitted models achieve significant improvement over the baselines for Task 1 (Sentence-Level Direct Assessment; EN-DE only), and Task 3 (Document-Level Score).",
}
@inproceedings{bicici-2020-rtm,
    title = "{RTM} Ensemble Learning Results at Quality Estimation Task",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.114",
    pages = "999--1003",
    abstract = "We obtain new results using referential translation machines (RTMs) with predictions mixed and stacked to obtain a better mixture of experts prediction. We are able to achieve better results than the baseline model in Task 1 subtasks. Our stacking results significantly improve the results on the training sets but decrease the test set results. RTMs can achieve to become the 5th among 13 models in ru-en subtask and 5th in the multilingual track of sentence-level Task 1 based on MAE.",
}
@inproceedings{cui-etal-2020-njus,
    title = "{NJU}{'}s submission to the {WMT}20 {QE} Shared Task",
    author = "Cui, Qu  and
      Geng, Xiang  and
      Huang, Shujian  and
      Chen, Jiajun",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.115",
    pages = "1004--1009",
    abstract = "This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20. Our system is based on the QE Brain, and we simply enhance it by injecting noise at the target side. And to obtain the deep bi-directional information, we use a masked language model at the target side instead of two single directional decoders. Meanwhile, we try to use the extra QE data from the WMT17 and WMT19 to improve our system{'}s performance. Finally, we ensemble the features or the results from different models to get our best results. Our system finished fifth in the end at sentence-level on both EN-ZH and EN-DE language pairs.",
}
@inproceedings{fomicheva-etal-2020-bergamot,
    title = "{BERGAMOT}-{LATTE} Submissions for the {WMT}20 Quality Estimation Shared Task",
    author = "Fomicheva, Marina  and
      Sun, Shuo  and
      Yankovskaya, Lisa  and
      Blain, Fr{\'e}d{\'e}ric  and
      Chaudhary, Vishrav  and
      Fishel, Mark  and
      Guzm{\'a}n, Francisco  and
      Specia, Lucia",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.116",
    pages = "1010--1017",
    abstract = "This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE). We participate in Task and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems. In addition to training a feature-based regression model using glass-box quality indicators, we also test whether they can be used to predict MT quality directly with no supervision. We assess our systems in a multi-lingual setting and show that both types of approaches generalise well across languages. Our black-box QE models tied for the winning submission in four out of seven language pairs inTask 1, thus demonstrating very strong performance. The glass-box approaches also performed competitively, representing a light-weight alternative to the neural-based models.",
}
@inproceedings{hu-etal-2020-niutrans-system,
    title = "The {N}iu{T}rans System for the {WMT}20 Quality Estimation Shared Task",
    author = "Hu, Chi  and
      Liu, Hui  and
      Feng, Kai  and
      Xu, Chen  and
      Xu, Nuo  and
      Zhou, Zefan  and
      Yan, Shiqin  and
      Luo, Yingfeng  and
      Wang, Chenglong  and
      Meng, Xia  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.117",
    pages = "1018--1023",
    abstract = "This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task. We participated in all tasks and all language pairs. We explored the combination of transfer learning, multi-task learning and model ensemble. Results on multiple tasks show that deep transformer machine translation models and multilingual pretraining methods significantly improve translation quality estimation performance. Our system achieved remarkable results in multiple level tasks, e.g., our submissions obtained the best results on all tracks in the sentence-level Direct Assessment task.",
}
@inproceedings{lee-2020-two,
    title = "Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation",
    author = "Lee, Dongjun",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.118",
    pages = "1024--1028",
    abstract = "In this paper, we describe the Bering Lab{'}s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.",
}
@inproceedings{moura-etal-2020-ist,
    title = "{IST}-Unbabel Participation in the {WMT}20 Quality Estimation Shared Task",
    author = "Moura, Jo{\~a}o  and
      Vera, Miguel  and
      van Stigt, Daan  and
      Kepler, Fabio  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.119",
    pages = "1029--1036",
    abstract = "We present the joint contribution of IST and Unbabel to the WMT 2020 Shared Task on Quality Estimation. Our team participated on all tracks (Direct Assessment, Post-Editing Effort, Document-Level), encompassing a total of 14 submissions. Our submitted systems were developed by extending the OpenKiwi framework to a transformer-based predictor-estimator architecture, and to cope with glass-box, uncertainty-based features coming from neural machine translation systems.",
}
@inproceedings{nakamachi-etal-2020-tmuou,
    title = "{TMUOU} Submission for {WMT}20 Quality Estimation Shared Task",
    author = "Nakamachi, Akifumi  and
      Shimanaka, Hiroki  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.120",
    pages = "1037--1041",
    abstract = "We introduce the TMUOU submission for the WMT20 Quality Estimation Shared Task 1: Sentence-Level Direct Assessment. Our system is an ensemble model of four regression models based on XLM-RoBERTa with language tags. We ranked 4th in Pearson and 2nd in MAE and RMSE on a multilingual track.",
}
@inproceedings{rubino-2020-nict,
    title = "{NICT} {K}yoto Submission for the {WMT}{'}20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation",
    author = "Rubino, Raphael",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.121",
    pages = "1042--1048",
    abstract = "This paper describes the NICT Kyoto submission for the WMT{'}20 Quality Estimation (QE) shared task. We participated in Task 2: Word and Sentence-level Post-editing Effort, which involved Wikipedia data and two translation directions, namely English-to-German and English-to-Chinese. Our approach is based on multi-task fine-tuned cross-lingual language models (XLM), initially pre-trained and further domain-adapted through intermediate training using the translation language model (TLM) approach complemented with a novel self-supervised learning task which aim is to model errors inherent to machine translation outputs. Results obtained on both word and sentence-level QE show that the proposed intermediate training method is complementary to language model domain adaptation and outperforms the fine-tuning only approach.",
}
@inproceedings{ranasinghe-etal-2020-transquest,
    title = "{T}rans{Q}uest at {WMT}2020: Sentence-Level Direct Assessment",
    author = "Ranasinghe, Tharindu  and
      Orasan, Constantin  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.122",
    pages = "1049--1055",
    abstract = "This paper presents the team TransQuest{'}s participation in Sentence-Level Direct Assessment shared task in WMT 2020. We introduce a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. The proposed methods achieve state-of-the-art results surpassing the results obtained by OpenKiwi, the baseline used in the shared task. We further fine tune the QE framework by performing ensemble and data augmentation. Our approach is the winning solution in all of the language pairs according to the WMT 2020 official results.",
}
@inproceedings{wang-etal-2020-hw,
    title = "{HW}-{TSC}{'}s Participation at {WMT} 2020 Quality Estimation Shared Task",
    author = "Wang, Minghan  and
      Yang, Hao  and
      Shang, Hengchao  and
      Wei, Daimeng  and
      Guo, Jiaxin  and
      Lei, Lizhi  and
      Qin, Ying  and
      Tao, Shimin  and
      Sun, Shiliang  and
      Chen, Yimeng  and
      Li, Liangyou",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.123",
    pages = "1056--1061",
    abstract = "This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task. Our system follows standard Predictor-Estimator architecture, with a pre-trained Transformer as the Predictor, and specific classifiers and regressors as Estimators. We integrate Bottleneck Adapter Layers in the Predictor to improve the transfer learning efficiency and prevent from over-fitting. At the same time, we jointly train the word- and sentence-level tasks with a unified model with multitask learning. Pseudo-PE assisted QE (PEAQE) is proposed, resulting in significant improvements on the performance. Our submissions achieve competitive result in word/sentence-level sub-tasks for both of En-De/Zh language pairs.",
}
@inproceedings{wu-etal-2020-tencent-submission,
    title = "Tencent submission for {WMT}20 Quality Estimation Shared Task",
    author = "Wu, Haijiang  and
      Wang, Zixuan  and
      Ma, Qingsong  and
      Wen, Xinjie  and
      Wang, Ruichen  and
      Wang, Xiaoli  and
      Zhang, Yulin  and
      Yao, Zhipeng  and
      Peng, Siyao",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.124",
    pages = "1062--1067",
    abstract = "This paper presents Tencent{'}s submission to the WMT20 Quality Estimation (QE) Shared Task: Sentence-Level Post-editing Effort for English-Chinese in Task 2. Our system ensembles two architectures, XLM-based and Transformer-based Predictor-Estimator models. For the XLM-based Predictor-Estimator architecture, the predictor produces two types of contextualized token representations, i.e., masked XLM and non-masked XLM; the LSTM-estimator and Transformer-estimator employ two effective strategies, top-K and multi-head attention, to enhance the sentence feature representation. For Transformer-based Predictor-Estimator architecture, we improve a top-performing model by conducting three modifications: using multi-decoding in machine translation module, creating a new model by replacing the transformer-based predictor with XLM-based predictor, and finally integrating two models by a weighted average. Our submission achieves a Pearson correlation of 0.664, ranking first (tied) on English-Chinese.",
}
@inproceedings{zhou-etal-2020-zero,
    title = "Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns",
    author = "Zhou, Lei  and
      Ding, Liang  and
      Takeda, Koichi",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.125",
    pages = "1068--1074",
    abstract = "This paper describes our submission of the WMT 2020 Shared Task on Sentence Level Direct Assessment, Quality Estimation (QE). In this study, we empirically reveal the mismatching issue when directly adopting BERTScore (Zhang et al., 2020) to QE. Specifically, there exist lots of mismatching errors between source sentence and translated candidate sentence with token pairwise similarity. In response to this issue, we propose to expose explicit cross lingual patterns, e.g. word alignments and generation score, to our proposed zero-shot models. Experiments show that our proposed QE model with explicit cross-lingual patterns could alleviate the mismatching issue, thereby improving the performance. Encouragingly, our zero-shot QE method could achieve comparable performance with supervised QE method, and even outperforms the supervised counterpart on 2 out of 6 directions. We expect our work could shed light on the zero-shot QE model improvement.",
}
@inproceedings{baruah-etal-2020-nlprl,
    title = "{NLPRL} System for Very Low Resource Supervised Machine Translation",
    author = "Baruah, Rupjyoti  and
      Mundotiya, Rajesh Kumar  and
      Kumar, Amit  and
      Singh, Anil kumar",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.126",
    pages = "1075--1078",
    abstract = "This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task. For our experiments, we use a byte-level version of BPE, which requires a base vocabulary of size 256 only. BPE based models are a kind of sub-word models. Such models try to address the Out of Vocabulary (OOV) word problem by performing word segmentation so that segments correspond to morphological units. They are also reported to work across different languages, especially similar languages due to their sub-word nature. Based on BLEU cased score, our NLPRL systems ranked ninth for HSB to GER and tenth in GER to HSB translation scenario.",
}
@inproceedings{berckmann-hiziroglu-2020-low,
    title = "Low-Resource Translation as Language Modeling",
    author = "Berckmann, Tucker  and
      Hiziroglu, Berkan",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.127",
    pages = "1079--1083",
    abstract = "We present our submission to the very low resource supervised machine translation task at WMT20. We use a decoder-only transformer architecture and formulate the translation task as language modeling. To address the low-resource aspect of the problem, we pretrain over a similar language parallel corpus. Then, we employ an intermediate back-translation step before fine-tuning. Finally, we present an analysis of the system{'}s performance.",
}
@inproceedings{chronopoulou-etal-2020-lmu,
    title = "{T}he {LMU} {M}unich {S}ystem for the {WMT} 2020 {U}nsupervised {M}achine {T}ranslation {S}hared {T}ask",
    author = "Chronopoulou, Alexandra  and
      Stojanovski, Dario  and
      Hangya, Viktor  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.128",
    pages = "1084--1091",
    abstract = "This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German↔Upper Sorbian. Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. (2020), using a monolingual pretrained language generation model (on German) and fine-tuning it on both German and Upper Sorbian, before initializing a UNMT model, which is trained with online backtranslation. Pseudo-parallel data obtained from an unsupervised statistical machine translation (USMT) system is used to fine-tune the UNMT model. We also apply BPE-Dropout to the low resource (Upper Sorbian) data to obtain a more robust system. We additionally experiment with residual adapters and find them useful in the Upper Sorbian→German direction. We explore sampling during backtranslation and curriculum learning to use SMT translations in a more principled way. Finally, we ensemble our best-performing systems and reach a BLEU score of 32.4 on German→Upper Sorbian and 35.2 on Upper Sorbian→German.",
}
@inproceedings{dutta-etal-2020-uds,
    title = "{U}d{S}-{DFKI}@{WMT}20: Unsupervised {MT} and Very Low Resource Supervised {MT} for {G}erman-{U}pper {S}orbian",
    author = "Dutta, Sourav  and
      Alabi, Jesujoba  and
      Bandyopadhyay, Saptarashmi  and
      Ruiter, Dana  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.129",
    pages = "1092--1098",
    abstract = "This paper describes the UdS-DFKI submission to the shared task for unsupervised machine translation (MT) and very low-resource supervised MT between German (de) and Upper Sorbian (hsb) at the Fifth Conference of Machine Translation (WMT20). We submit systems for both the supervised and unsupervised tracks. Apart from various experimental approaches like bitext mining, model pre-training, and iterative back-translation, we employ a factored machine translation approach on a small BPE vocabulary.",
}
@inproceedings{edman-etal-2020-data,
    title = "Data Selection for Unsupervised Translation of {G}erman{--}{U}pper {S}orbian",
    author = "Edman, Lukas  and
      Toral, Antonio  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.130",
    pages = "1099--1103",
    abstract = "This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2020 Unsupervised Machine Translation task for German{--}Upper Sorbian. We investigate the usefulness of data selection in the unsupervised setting. We find that we can perform data selection using a pretrained model and show that the quality of a set of sentences or documents can have a great impact on the performance of the UNMT system trained on it. Furthermore, we show that document-level data selection should be preferred for training the XLM model when possible. Finally, we show that there is a trade-off between quality and quantity of the data used to train UNMT systems.",
}
@inproceedings{libovicky-etal-2020-lmu,
    title = "The {LMU} {M}unich System for the {WMT}20 Very Low Resource Supervised {MT} Task",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Hangya, Viktor  and
      Schmid, Helmut  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.131",
    pages = "1104--1111",
    abstract = "We present our systems for the WMT20 Very Low Resource MT Task for translation between German and Upper Sorbian. For training our systems, we generate synthetic data by both back- and forward-translation. Additionally, we enrich the training data with German-Czech translated from Czech to Upper Sorbian by an unsupervised statistical MT system incorporating orthographically similar word pairs and transliterations of OOV words. Our best translation system between German and Sorbian is based on transfer learning from a Czech-German system and scores 12 to 13 BLEU higher than a baseline system built using the available parallel data only.",
}
@inproceedings{knowles-etal-2020-nrc-systems,
    title = "{NRC} Systems for Low Resource {G}erman-{U}pper {S}orbian Machine Translation 2020: Transfer Learning with Lexical Modifications",
    author = "Knowles, Rebecca  and
      Larkin, Samuel  and
      Stewart, Darlene  and
      Littell, Patrick",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.132",
    pages = "1112--1122",
    abstract = "We describe the National Research Council of Canada (NRC) neural machine translation systems for the German-Upper Sorbian supervised track of the 2020 shared task on Unsupervised MT and Very Low Resource Supervised MT. Our models are ensembles of Transformer models, built using combinations of BPE-dropout, lexical modifications, and backtranslation.",
}
@inproceedings{kvapilikova-etal-2020-cuni,
    title = "{CUNI} Systems for the Unsupervised and Very Low Resource Translation Task in {WMT}20",
    author = "Kvapil{\'\i}kov{\'a}, Ivana  and
      Kocmi, Tom  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.133",
    pages = "1123--1128",
    abstract = "This paper presents a description of CUNI systems submitted to the WMT20 task on unsupervised and very low-resource supervised machine translation between German and Upper Sorbian. We experimented with training on synthetic data and pre-training on a related language pair. In the fully unsupervised scenario, we achieved 25.5 and 23.7 BLEU translating from and into Upper Sorbian, respectively. Our low-resource systems relied on transfer learning from German-Czech parallel data and achieved 57.4 BLEU and 56.1 BLEU, which is an improvement of 10 BLEU points over the baseline trained only on the available small German-Upper Sorbian parallel corpus.",
}
@inproceedings{scherrer-etal-2020-university,
    title = "The {U}niversity of {H}elsinki and Aalto University submissions to the {WMT} 2020 news and low-resource translation tasks",
    author = {Scherrer, Yves  and
      Gr{\"o}nroos, Stig-Arne  and
      Virpioja, Sami},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.134",
    pages = "1129--1138",
    abstract = "This paper describes the joint participation of University of Helsinki and Aalto University to two shared tasks of WMT 2020: the news translation between Inuktitut and English and the low-resource translation between German and Upper Sorbian. For both tasks, our efforts concentrate on efficient use of monolingual and related bilingual corpora with scheduled multi-task learning as well as an optimized subword segmentation with sampling. Our submission obtained the highest score for Upper Sorbian -{\textgreater} German and was ranked second for German -{\textgreater} Upper Sorbian according to BLEU scores. For English{--}Inuktitut, we reached ranks 8 and 10 out of 11 according to BLEU scores.",
}
@inproceedings{singh-etal-2020-nits,
    title = "The {NITS}-{CNLP} System for the Unsupervised {MT} Task at {WMT} 2020",
    author = "Singh, Salam Michael  and
      Singh, Thoudam Doren  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.135",
    pages = "1139--1143",
    abstract = "We describe NITS-CNLP{'}s submission to WMT 2020 unsupervised machine translation shared task for German language (de) to Upper Sorbian (hsb) in a constrained setting i.e, using only the data provided by the organizers. We train our unsupervised model using monolingual data from both the languages by jointly pre-training the encoder and decoder and fine-tune using backtranslation loss. The final model uses the source side (de) monolingual data and the target side (hsb) synthetic data as a pseudo-parallel data to train a pseudo-supervised system which is tuned using the provided development set(dev set).",
}
@inproceedings{singh-2020-adobe,
    title = "Adobe {AMPS}{'}s Submission for Very Low Resource Supervised Translation Task at {WMT}20",
    author = "Singh, Keshaw",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.136",
    pages = "1144--1149",
    abstract = "In this paper, we describe our systems submitted to the very low resource supervised translation task at WMT20. We participate in both translation directions for Upper Sorbian-German language pair. Our primary submission is a subword-level Transformer-based neural machine translation model trained on original training bitext. We also conduct several experiments with backtranslation using limited monolingual data in our post-submission work and include our results for the same. In one such experiment, we observe jumps of up to 2.6 BLEU points over the primary system by pretraining on a synthetic, backtranslated corpus followed by fine-tuning on the original parallel training data.",
}
@inproceedings{castilho-2020-page,
    title = "On the Same Page? Comparing Inter-Annotator Agreement in Sentence and Document Level Human Machine Translation Evaluation",
    author = "Castilho, Sheila",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.137",
    pages = "1150--1159",
    abstract = {Document-level evaluation of machine translation has raised interest in the community especially since responses to the claims of {``}human parity{''} (Toral et al., 2018; L{\"a}ubli et al., 2018) with document-level human evaluations have been published. Yet, little is known about best practices regarding human evaluation of machine translation at the document-level. This paper presents a comparison of the differences in inter-annotator agreement between quality assessments using sentence and document-level set-ups. We report results of the agreement between professional translators for fluency and adequacy scales, error annotation, and pair-wise ranking, along with the effort needed to perform the different tasks. To best of our knowledge, this is the first study of its kind.},
}
@inproceedings{hanneman-dinu-2020-markup,
    title = "How Should Markup Tags Be Translated?",
    author = "Hanneman, Greg  and
      Dinu, Georgiana",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.138",
    pages = "1160--1173",
    abstract = "The ability of machine translation (MT) models to correctly place markup is crucial to generating high-quality translations of formatted input. This paper compares two commonly used methods of representing markup tags and tests the ability of MT models to learn tag placement via training data augmentation. We study the interactions of tag representation, data augmentation size, tag complexity, and language pair to show the drawbacks and benefits of each method. We construct and release new test sets containing tagged data for three language pairs of varying difficulty.",
}
@inproceedings{tiedemann-2020-tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.139",
    pages = "1174--1182",
    abstract = "This paper describes the development of a new benchmark for machine translation that provides training and test data for thousands of language pairs covering over 500 languages and tools for creating state-of-the-art translation models from that collection. The main goal is to trigger the development of open translation tools and models with a much broader coverage of the World{'}s languages. Using the package it is possible to work on realistic low-resource scenarios avoiding artificially reduced setups that are common when demonstrating zero-shot or few-shot learning. For the first time, this package provides a comprehensive collection of diverse data sets in hundreds of languages with systematic language and script annotation and data splits to extend the narrow coverage of existing benchmarks. Together with the data release, we also provide a growing number of pre-trained baseline models for individual language pairs and selected language groups.",
}
@inproceedings{freitag-etal-2020-human,
    title = "Human-Paraphrased References Improve Neural Machine Translation",
    author = "Freitag, Markus  and
      Foster, George  and
      Grangier, David  and
      Cherry, Colin",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.140",
    pages = "1183--1192",
    abstract = "Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by freitag2020bleu. When used in place of original references, the paraphrased versions produce metric scores that correlate better with human judgment. This effect holds for a variety of different automatic metrics, and tends to favor natural formulations over more literal (translationese) ones. In this paper we compare the results of performing end-to-end system development using standard and paraphrased references. With state-of-the-art English-German NMT components, we show that tuning to paraphrased references produces a system that is ignificantly better according to human judgment, but 5 BLEU points worse when tested on standard references. Our work confirms the finding that paraphrased references yield metric scores that correlate better with human judgment, and demonstrates for the first time that using these scores for system development can lead to significant improvements.",
}
@inproceedings{wan-etal-2020-incorporating,
    title = "Incorporating Terminology Constraints in Automatic Post-Editing",
    author = "Wan, David  and
      Kedzie, Chris  and
      Ladhak, Faisal  and
      Carpuat, Marine  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wmt-1.141",
    pages = "1193--1204",
    abstract = "Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during inference for MT, current APE approaches cannot ensure that they will appear in the final translation. In this paper, we present both autoregressive and non-autoregressive models for lexically constrained APE, demonstrating that our approach enables preservation of 95{\%} of the terminologies and also improves translation quality on English-German benchmarks. Even when applied to lexically constrained MT output, our approach is able to improve preservation of the terminologies. However, we show that our models do not learn to copy constraints systematically and suggest a simple data augmentation technique that leads to improved performance and robustness.",
}
@proceedings{winlp-2020-widening,
    title = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    editor = "Cunha, Rossana  and
      Shaikh, Samira  and
      Varis, Erika  and
      Georgi, Ryan  and
      Tsai, Alicia  and
      Anastasopoulos, Antonios  and
      Chandu, Khyathi Raghavi",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.0",
}
@inproceedings{alemneh-etal-2020-corpus,
    title = "Corpus based {A}mharic sentiment lexicon generation",
    author = "Alemneh, Girma Neshir  and
      Rauber, Andreas  and
      Atnafu, Solomon",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.1",
    doi = "10.18653/v1/2020.winlp-1.1",
    pages = "1--3",
    abstract = "Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages. To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach. The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus. Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb. We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus. Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI). Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds. Finally, the lexicon generated in corpus based approach is evaluated.",
}
@inproceedings{alemneh-etal-2020-negation,
    title = "Negation handling for {A}mharic sentiment classification",
    author = "Alemneh, Girma Neshir  and
      Rauber, Andreas  and
      Atnafu, Solomon",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.2",
    doi = "10.18653/v1/2020.winlp-1.2",
    pages = "4--6",
    abstract = "User generated content is bringing new aspects of processing data on the web. Due to the advancement of World Wide Web technology, users are not only consumer of web contents but also they are producers of contents in the form of text, audio, video and picture. This study focuses on the analysis of textual contents with subjective information (referring to sentiment analysis). Most of conventional approaches of sentiment analysis do not effectively capture negation in languages where there are limited computational linguistic resources (e.g. Amharic). For this research, we proposed Amharic negation handling framework for Amharic sentiment classification. The proposed framework combines the lexicon based sentiment classification approach and character ngram based machine learning algorithms. Finally, the performance of framework is evaluated using the annotated Amharic news comments. The system is performing the best of all models and the baselines with accuracy of 98.0. The result is compared with the baselines (without negation handling and word level ngram model).",
}
@inproceedings{woldemariyam-getahun-2020-embedding,
    title = "Embedding Oriented Adaptable Semantic Annotation Framework for {A}mharic Web Documents",
    author = "Woldemariyam, Kidane  and
      Getahun, Dr. Fekade",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.3",
    doi = "10.18653/v1/2020.winlp-1.3",
    pages = "7",
    abstract = "The Web has become a source of information, where information is provided by humans for humans and its growth has increased necessity to get solutions that intelligently extract valuable knowledge from existing and newly added web documents with no (minimal) supervisions. However, due to the unstructured nature of existing data on the Web, effective extraction of this knowledge is limited for both human beings and software agents. Thus, this research work designed generic and embedding oriented framework that automatically annotates semantically Amharic web documents using ontology. This framework significantly reduces manual annotation and learning cost used for semantic annotation of Amharic web documents with its nature of adaptability with minimal modification. The results have also implied that neural network techniques are promising for semantic annotation, especially for less resourced languages like Amharic in comparison to language dependent techniques that have cost of speed and challenge of adaptation into new domains and languages. We experiment the feasibility of the proposed approach using Amharic news collected from WALTA news agency and Amharic Wikipedia. Our results show that the proposed solution exhibits 70.68{\%} of precision, 66.89{\%} of recall and 68.53{\%} of f-measure in semantic annotation for a morphologically complex Amharic language with limited size dataset.",
}
@inproceedings{eshetu-etal-2020-similarity,
    title = "Similarity and Farness Based Bidirectional Neural Co-Attention for {A}mharic Natural Language Inference",
    author = "Eshetu, Abebawu  and
      Teshome, Getenesh  and
      Alemayehu, Ribka",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.4",
    doi = "10.18653/v1/2020.winlp-1.4",
    pages = "8--12",
    abstract = "In natural language one idea can be conveyed using different sentences; higher Natural Language Processing applications get difficulties in capturing meaning of ideas stated in different expressions. To solve this difficulty, different scholars have conducted Natural Language Inference (NLI) researches using methods from traditional discrete models with hard logic to an end-to-end neural network for different languages. In context of Amharic language, even though there are number of research efforts in higher NLP applications, still they have limitation on understanding idea expressed in different ways due to an absence of NLI in Amharic language. Accordingly, we proposed deep learning based Natural Language Inference using similarity and farness aware bidirectional attentive matching for Amharic texts. The experiment on limited Amharic NLI dataset prepared also shows promising result that can be used as baseline for subsequent works.",
}
@inproceedings{abate-etal-2020-large-vocabulary,
    title = "Large Vocabulary Read Speech Corpora for Four {E}thiopian Languages: {A}mharic, {T}igrigna, {O}romo, and {W}olaytta",
    author = "Abate, Solomon Teferra  and
      Tachbelie, Martha Yifiru  and
      Melese, Michael  and
      Abera, Hafte  and
      Gebreselassie, Tewodros  and
      Mulugeta, Wondwossen  and
      Assabie, Yaregal  and
      Beyene, Million Meshesha  and
      Atinafu, Solomon  and
      Seyoum, Binyam Ephrem",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.5",
    doi = "10.18653/v1/2020.winlp-1.5",
    pages = "13--17",
    abstract = "Automatic Speech Recognition (ASR) is one of the most important technologies to help people live a better life in the 21st century. However, its development requires a big speech corpus for a language. The development of such a corpus is expensive especially for under-resourced Ethiopian languages. To address this problem we have developed four medium-sized (longer than 22 hours each) speech corpora for four Ethiopian languages: Amharic, Tigrigna, Oromo, and Wolaytta. In a way of checking the usability of the corpora and deliver a baseline ASR for each language. In this paper, we present the corpora and the baseline ASR systems for each language. The word error rates (WERs) we achieved show that the corpora are usable for further investigation and we recommend the collection of text corpora to train strong language models for Oromo and Wolaytta compared to others.",
}
@inproceedings{hartmann-etal-2020-simplex,
    title = "{SIMPLEX}-{PB} 2.0: A Reliable Dataset for Lexical Simplification in {B}razilian {P}ortuguese",
    author = "Hartmann, Nathan  and
      Paetzold, Gustavo Henrique  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.6",
    doi = "10.18653/v1/2020.winlp-1.6",
    pages = "18--22",
    abstract = "Most research on Lexical Simplification (LS) addresses non-native speakers of English, since they are numerous and easy to recruit. This makes it difficult to create LS solutions for other languages and target audiences. This paper presents SIMPLEX-PB 2.0, a dataset for LS in Brazilian Portuguese that, unlike its predecessor SIMPLEX-PB, accurately captures the needs of Brazilian underprivileged children. To create SIMPLEX-PB 2.0, we addressed all limitations of the old SIMPLEX-PB through multiple rounds of manual annotation. As a result, SIMPLEX-PB 2.0 features much more reliable and numerous candidate substitutions to complex words, as well as word complexity rankings produced by a group underprivileged children.",
}
@inproceedings{eshetu-etal-2020-bi,
    title = "Bi-directional Answer-to-Answer Co-attention for Short Answer Grading using Deep Learning",
    author = "Eshetu, Abebawu  and
      Teshome, Getenesh  and
      Alemahu, Ribka",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.8",
    doi = "10.18653/v1/2020.winlp-1.8",
    pages = "26--30",
    abstract = "So far different research works have been conducted to achieve short answer questions. Hence, due to the advancement of artificial intelligence and adaptability of deep learning models, we introduced a new model to score short answer subjective questions. Using bi-directional answer to answer co-attention, we have demonstrated the extent to which each words and sentences features of student answer detected by the model and shown prom-ising result on both Kaggle and Mohler{'}s dataset. The experiment on Amharic short an-swer dataset prepared for this research work also shows promising result that can be used as baseline for subsequent works.",
}
@inproceedings{mazuecos-etal-2020-effective,
    title = "Effective questions in referential visual dialogue",
    author = "Mazuecos, Mauricio  and
      Testoni, Alberto  and
      Bernardi, Raffaella  and
      Benotti, Luciana",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.9",
    doi = "10.18653/v1/2020.winlp-1.9",
    pages = "31--35",
    abstract = "An interesting challenge for situated dialogue systems is referential visual dialog: by asking questions, the system has to identify the referent to which the user refers to. Task success is the standard metric used to evaluate these systems. However, it does not consider how effective each question is, that is how much each question contributes to the goal. We propose a new metric, that measures question effectiveness. As a preliminary study, we report the new metric for state of the art publicly available models on GuessWhat?!. Surprisingly, successful dialogues do not have a higher percentage of effective questions than failed dialogues. This suggests that a system with high task success is not necessarily one that generates good questions.",
}
@inproceedings{gebreselassie-etal-2020-translation,
    title = "A Translation-Based Approach to Morphology Learning for Low Resource Languages",
    author = "Gebreselassie, Tewodros  and
      Mersha, Amanuel  and
      Gasser, Michael",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.10",
    doi = "10.18653/v1/2020.winlp-1.10",
    pages = "36--40",
    abstract = "{``}Low resource languages{''} usually refers to languages that lack corpora and basic tools such as part-of-speech taggers. But a significant number of such languages do benefit from the availability of relatively complex linguistic descriptions of phonology, morphology, and syntax, as well as dictionaries. A further category, probably the majority of the world{'}s languages, suffers from the lack of even these resources. In this paper, we investigate the possibility of learning the morphology of such a language by relying on its close relationship to a language with more resources. Specifically, we use a transfer-based approach to learn the morphology of the severely under-resourced language Gofa, starting with a neural morphological generator for the closely related language, Wolaytta. Both languages are members of the Omotic family, spoken and southwestern Ethiopia, and, like other Omotic languages, both are morphologically complex. We first create a finite- state transducer for morphological analysis and generation for Wolaytta, based on relatively complete linguistic descriptions and lexicons for the language. Next, we train an encoder-decoder neural network on the task of morphological generation for Wolaytta, using data generated by the FST. Such a network takes a root and a set of grammatical features as input and generates a word form as output. We then elicit Gofa translations of a small set of Wolaytta words from bilingual speakers. Finally, we retrain the decoder of the Wolaytta network, using a small set of Gofa target words that are translations of the Wolaytta outputs of the original network. The evaluation shows that the transfer network performs better than a separate encoder-decoder network trained on a larger set of Gofa words. We conclude with implications for the learning of morphology for severely under-resourced languages in regions where there are related languages with more resources.",
}
@inproceedings{abera-hailemariam-2020-tigrinya,
    title = "{T}igrinya Automatic Speech recognition with Morpheme based recognition units",
    author = "Abera, Hafte  and
      Hailemariam, Sebsibe",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.12",
    doi = "10.18653/v1/2020.winlp-1.12",
    pages = "46--50",
    abstract = "The Tigrinya language is agglutinative and has a large number of inflected and derived forms of words. Therefore a Tigrinya large vocabulary continuous speech recognition system often has a large number of different units and a high out-of-vocabulary (OOV) rate if a word is used as a recognition unit of a language model (LM) and lexicon. Therefore a morpheme-based approach has often been used and a morpheme is used as the recognition unit to reduce the high OOV rate. This paper presents an automatic speech recognition experiment conducted to see the effect of OOV words on the performance speech recognition system for Tigrinya. We tried to solve the OOV problem by using morphemes as lexicon and language model units. It has been found that the morpheme-based recognition system is better lexical and language modeling units than words. An absolute improvement (in word recognition accuracy) of 3.45 token and 8.36 types has been obtained as a result of using a morph-based vocabulary.",
}
@inproceedings{alipoormolabashi-schulte-im-walde-2020-variants,
    title = "Variants of Vector Space Reductions for Predicting the Compositionality of {E}nglish Noun Compounds",
    author = "Alipoormolabashi, Pegah  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.13",
    doi = "10.18653/v1/2020.winlp-1.13",
    pages = "51--54",
    abstract = "Predicting the degree of compositionality of noun compounds is a crucial ingredient for lexicography and NLP applications, to know whether the compound should be treated as a whole, or through its constituents. Computational approaches for an automatic prediction typically represent compounds and their constituents within a vector space to have a numeric relatedness measure for the words. This paper provides a systematic evaluation of using different vector-space reduction variants for the prediction. We demonstrate that Word2vec and nouns-only dimensionality reductions are the most successful and stable vector space reduction variants for our task.",
}
@inproceedings{vernetti-freitas-2020-assessment,
    title = "An Assessment of Language Identification Methods on Tweets and {W}ikipedia Articles",
    author = "Vernetti, Pedro  and
      Freitas, Larissa",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.15",
    doi = "10.18653/v1/2020.winlp-1.15",
    pages = "58--60",
    abstract = "Language identification is the task of determining the language which a given text is written. This task is important for Natural Language Processing and Information Retrieval activities. Two popular approaches for language identification are the N-grams and stopwords models. In this paper, these two models were tested on different types of documents such as short, irregular texts (tweets) and long, regular texts (Wikipedia articles).",
}
@inproceedings{guimaraes-etal-2020-comparison,
    title = "A Comparison of Identification Methods of {B}razilian Music Styles by Lyrics",
    author = "Guimar{\~a}es, Patrick  and
      Froes, Jader  and
      Costa, Douglas  and
      Freitas, Larissa",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.16",
    doi = "10.18653/v1/2020.winlp-1.16",
    pages = "61--63",
    abstract = "In our work, we applied different techniques for the task of genre classification using lyrics. Utilizing our dataset with lyrics of typical genres in Brazil divided into seven classes, we apply some models used in machine learning and deep learning classification tasks. We explore the performance of usual models for text classification using an input in the Portuguese language. We also compare the use of RNN and classic machine learning approaches for text classification, exploring the most used methods in the field.",
}
@inproceedings{steinmetz-harbusch-2020-enabling,
    title = "Enabling fast and correct typing in {`}Leichte Sprache{'} (Easy Language)",
    author = "Steinmetz, Ina  and
      Harbusch, Karin",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.17",
    doi = "10.18653/v1/2020.winlp-1.17",
    pages = "64--67",
    abstract = "Simplified languages are instruments for inclusion aiming to overcome language barriers. Leichte Sprache (LS), for instance, is a variety of German with reduced complexity (cf. Basic English). So far, LS is mainly provided for, but rarely written by, its target groups, e.g. people with cognitive impairments. One reason may be the lack of technical support during the process from message conceptualization to sentence realization. In the following, we present a system for assisted typing in LS whose accuracy and speed is largely due to the deployment of real time natural-language processing enabling efficient prediction and context-sensitive grammar support.",
}
@inproceedings{siminyu-freshia-2020-ai4d,
    title = "{AI}4{D} - {A}frican Language Dataset Challenge",
    author = "Siminyu, Kathleen  and
      Freshia, Sackey",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.18",
    doi = "10.18653/v1/2020.winlp-1.18",
    pages = "68--77",
    abstract = "As language and speech technologies become more advanced, the lack of fundamental digital resources for African languages, such as data, spell checkers and PoS taggers, means that the digital divide between these languages and others keeps growing. This work details the organisation of the AI4D - African Language Dataset Challenge, an effort to incentivize the creation, curation and uncovering to African language datasets through a competitive challenge, particularly datasets that are annotated or prepared for use in a downstream NLP task.",
}
@inproceedings{meghwal-etal-2020-wikipedia,
    title = "Can {W}ikipedia Categories Improve Masked Language Model Pretraining?",
    author = "Meghwal, Diksha  and
      Kann, Katharina  and
      Calixto, Iacer  and
      Jastrzebski, Stanislaw",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.19",
    doi = "10.18653/v1/2020.winlp-1.19",
    pages = "78",
    abstract = "Pretrained language models have obtained impressive results for a large set of natural language understanding tasks. However, training these models is computationally expensive and requires huge amounts of data. Thus, it would be desirable to automatically detect groups of more or less important examples. Here, we investigate if we can leverage sources of information which are commonly overlooked, Wikipedia categories as listed in DBPedia, to identify useful or harmful data points during pretraining. We define an experimental setup in which we analyze correlations between language model perplexity on specific clusters and downstream NLP task performances during pretraining. Our experiments show that Wikipedia categories are not a good indicator of the importance of specific sentences for pretraining.",
}
@inproceedings{emezue-dossou-2020-ffr,
    title = "{FFR} v1.1: {F}on-{F}rench Neural Machine Translation",
    author = "Emezue, Chris Chinenye  and
      Dossou, Femi Pancrace Bonaventure",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.21",
    doi = "10.18653/v1/2020.winlp-1.21",
    pages = "83--87",
    abstract = "All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages. However, the low-resourceness, diacritical, and tonal complexities of African languages are major issues being faced. The FFR project is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we introduce FFR Dataset, a corpus of Fon-to-French translations, describe the diacritical encoding process, and introduce our FFR v1.1 model, trained on the dataset. The dataset and model are made publicly available, to promote collaboration and reproducibility.",
}
@inproceedings{wein-2020-classification,
    title = "Classification and Analysis of Neologisms Produced by Learners of {S}panish: Effects of Proficiency and Task",
    author = "Wein, Shira",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.22",
    doi = "10.18653/v1/2020.winlp-1.22",
    pages = "88--91",
    abstract = "The Spanish Learner Language Oral Corpora (SPLLOC) of transcribed conversations between investigators and language learners contains a set of neologism tags. In this work, the utterances tagged as neologisms are broken down into three categories: true neologisms, loanwords, and errors. This work examines the relationships between neologism, loanword, and error production and both language learner level and conversation task. The results of this study suggest that loanwords and errors are produced most frequently by language learners with moderate experience, while neologisms are produced most frequently by native speakers. This study also indicates that tasks that require descriptions of images draw more neologism, loanword and error production. We ultimately present a unique analysis of the implications of neologism, loanword, and error production useful for further work in second language acquisition research, as well as for language educators.",
}
@inproceedings{anees-etal-2020-developing,
    title = "Developing a Monolingual Sentence Simplification Corpus for {U}rdu",
    author = "Anees, Yusra  and
      Abdul Rauf, Sadaf  and
      Iqbal, Nauman  and
      Siddiqi, Abdul Basit",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.23",
    doi = "10.18653/v1/2020.winlp-1.23",
    pages = "92--95",
    abstract = "Complex sentences are a hurdle in the learning process of language learners. Sentence simplification aims to convert a complex sentence into its simpler form such that it is easily comprehensible. To build such automated simplification systems, corpora of complex sentences and their simplified versions is the first step to understand sentence complexity and enable the development of automatic text simplification systems. No such corpus has yet been developed for Urdu and we fill this gap by developing one such corpus to help start readability and automatic sentence simplification research. We present a lexical and syntactically simplified Urdu simplification corpus and a detailed analysis of the various simplification operations. We further analyze our corpora using text readability measures and present a comparison of the original, lexical simplified, and syntactically simplified corpora.",
}
@inproceedings{cerda-mardini-etal-2020-translating,
    title = "Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism",
    author = "Cerda-Mardini, Patricio  and
      Araujo, Vladimir  and
      Soto, {\'A}lvaro",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.24",
    doi = "10.18653/v1/2020.winlp-1.24",
    pages = "96--98",
    abstract = "We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.",
}
@inproceedings{basta-etal-2020-towards,
    title = "Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information",
    author = "Basta, Christine  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.25",
    doi = "10.18653/v1/2020.winlp-1.25",
    pages = "99--102",
    abstract = "Gender bias negatively impacts many natural language processing applications, including machine translation (MT). The motivation behind this work is to study whether recent proposed MT techniques are significantly contributing to attenuate biases in document-level and gender-balanced data. For the study, we consider approaches of adding the previous sentence and the speaker information, implemented in a decoder-based neural MT system. We show improvements both in translation quality (+1 BLEU point) as well as in gender bias mitigation on WinoMT (+5{\%} accuracy).",
}
@inproceedings{babafemi-akinfaderin-2020-predicting,
    title = "Predicting and Analyzing Law-Making in {K}enya",
    author = "Babafemi, Oyinlola  and
      Akinfaderin, Adewale",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.26",
    doi = "10.18653/v1/2020.winlp-1.26",
    pages = "103--106",
    abstract = "Modelling and analyzing parliamentary legislation, roll-call votes and order of proceedings in developed countries has received significant attention in recent years. In this paper, we focused on understanding the bills introduced in a developing democracy, the Kenyan bicameral parliament. We developed and trained machine learning models on a combination of features extracted from the bills to predict the outcome - if a bill will be enacted or not. We observed that the texts in a bill are not as relevant as the year and month the bill was introduced and the category the bill belongs to.",
}
@inproceedings{yeo-chen-2020-defining,
    title = "Defining and Evaluating Fair Natural Language Generation",
    author = "Yeo, Catherine  and
      Chen, Alyssa",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.27",
    doi = "10.18653/v1/2020.winlp-1.27",
    pages = "107--109",
    abstract = "Our work focuses on the biases that emerge in the natural language generation (NLG) task of sentence completion. In this paper, we introduce a mathematical framework of fairness for NLG followed by an evaluation of gender biases in two state-of-the-art language models. Our analysis provides a theoretical formulation for biases in NLG and empirical evidence that existing language generation models embed gender bias.",
}
@inproceedings{augustyniak-etal-2020-political,
    title = "Political Advertising Dataset: the use case of the {P}olish 2020 Presidential Elections",
    author = "Augustyniak, Lukasz  and
      Rajda, Krzysztof  and
      Kajdanowicz, Tomasz  and
      Bernaczyk, Micha{\l}",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.28",
    doi = "10.18653/v1/2020.winlp-1.28",
    pages = "110--114",
    abstract = "Political campaigns are full of political ads posted by candidates on social media. Political advertisements constitute a basic form of campaigning, subjected to various social requirements. We present the first publicly open dataset for detecting specific text chunks and categories of political advertising in the Polish language. It contains 1,705 human-annotated tweets tagged with nine categories, which constitute campaigning under Polish electoral law. We achieved a 0.65 inter-annotator agreement (Cohen{'}s kappa score). An additional annotator resolved the mismatches between the first two annotators improving the consistency and complexity of the annotation process. We used the newly created dataset to train a well established neural tagger (achieving a 70{\%} percent points F1 score). We also present a possible direction of use cases for such datasets and models with an initial analysis of the Polish 2020 Presidential Elections on Twitter.",
}
@inproceedings{jacobs-mccarthy-2020-human,
    title = "The human unlikeness of neural language models in next-word prediction",
    author = "Jacobs, Cassandra L.  and
      McCarthy, Arya D.",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.29",
    doi = "10.18653/v1/2020.winlp-1.29",
    pages = "115",
    abstract = "The training objective of unidirectional language models (LMs) is similar to a psycholinguistic benchmark known as the cloze task, which measures next-word predictability. However, LMs lack the rich set of experiences that people do, and humans can be highly creative. To assess human parity in these models{'} training objective, we compare the predictions of three neural language models to those of human participants in a freely available behavioral dataset (Luke {\&} Christianson, 2016). Our results show that while neural models show a close correspondence to human productions, they nevertheless assign insufficient probability to how often speakers guess upcoming words, especially for open-class content words.",
}
@inproceedings{dudy-bedrick-2020-long,
    title = "Long-Tail Predictions with Continuous-Output Language Models",
    author = "Dudy, Shiran  and
      Bedrick, Steven",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.31",
    doi = "10.18653/v1/2020.winlp-1.31",
    pages = "119--122",
    abstract = "Neural language models typically employ a categorical approach to prediction and training, leading to well-known computational and numerical limitations. An under-explored alternative approach is to perform prediction directly against a continuous word embedding space, which according to recent research is more akin to how lexemes are represented in the brain. Choosing this method opens the door for for large-vocabulary, language models and enables substantially smaller and simpler computational complexities. In this research we explore a different important trait - the continuous output prediction models reach low-frequency vocabulary words which we show are often ignored by the categorical model. Such words are essential, as they can contribute to personalization and user vocabulary adaptation. In this work, we explore continuous-space language modeling in the context of a word prediction task over two different textual domains (newswire text and biomedical journal articles). We investigate both traditional and adversarial training approaches, and report results using several different embedding spaces and decoding mechanisms. We find that our continuous-prediction approach outperforms the standard categorical approach in terms of term diversity, in particular with rare words.",
}
@inproceedings{acken-demszky-2020-analyzing,
    title = "Analyzing the Framing of 2020 Presidential Candidates in the News",
    author = "Acken, Audrey  and
      Demszky, Dorottya",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.32",
    doi = "10.18653/v1/2020.winlp-1.32",
    pages = "123",
    abstract = "In this study, we apply NLP methods to learn about the framing of the 2020 Democratic Presidential candidates in news media. We use both a lexicon-based approach and word embeddings to analyze how candidates are discussed in news sources with different political leanings. Our results show significant differences in the framing of candidates across the news sources along several dimensions, such as sentiment and agency, paving the way for a deeper investigation.",
}
@inproceedings{santhanam-shaikh-2020-understanding,
    title = "Understanding the Impact of Experiment Design for Evaluating Dialogue System Output",
    author = "Santhanam, Sashank  and
      Shaikh, Samira",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.33",
    doi = "10.18653/v1/2020.winlp-1.33",
    pages = "124--127",
    abstract = "Evaluation of output from natural language generation (NLG) systems is typically conducted via crowdsourced human judgments. To understand the impact of how experiment design might affect the quality and consistency of such human judgments, we designed a between-subjects study with four experimental conditions. Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design. Additionally, we find that factors such as no prior experience of participating in similar studies of rating dialogue system output",
}
@inproceedings{mahajan-shaikh-2020-studying,
    title = "Studying The Effect of Emotional and Moral Language on Information Contagion during the Charlottesville Event",
    author = "Mahajan, Khyati  and
      Shaikh, Samira",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.34",
    doi = "10.18653/v1/2020.winlp-1.34",
    pages = "128--130",
    abstract = "We highlight the contribution of emotional and moral language towards information contagion online. We find that retweet count on Twitter is significantly predicted by the use of negative emotions with negative moral language. We find that a tweet is less likely to be retweeted (hence less engagement and less potential for contagion) when it has emotional language expressed as anger along with a specific type of moral language, known as authority-vice. Conversely, when sadness is expressed with authority-vice, the tweet is more likely to be retweeted. Our findings indicate how emotional and moral language can interact in predicting information contagion.",
}
@inproceedings{nkolele-2020-mapping,
    title = "Mapping of Narrative Text Fields To {ICD}-10 Codes Using Natural Language Processing and Machine Learning",
    author = "Nkolele, Risuna",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.35",
    doi = "10.18653/v1/2020.winlp-1.35",
    pages = "131--135",
    abstract = "The assignment of ICD-10 codes is done manually, which is laborious and prone to errors. The use of natural language processing and machine learning approaches have been receiving increasing attention on automating the task of assigning ICD-10 codes. In this study, we investigate the effect of different approaches on automating the task of assigning ICD-10 codes. To do this we use the South African clinical dataset containing three narrative text fields (Clinical Summary, Presenting Complaints, and Examination Findings). The following traditional machine learning algorithms, namely: Logistic Regression, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, RandomForest, and Extreme Gradient Boost were used as our classifiers. Our study results show the strong potential of automated ICD-10 coding from the narrative text fields. ExtremeGradient Boost outperformed other classifiers in automating the task of assigning ICD-10 codes based on the three narrative text fields with an accuracy of 79{\%}, precision of75{\%}, and recall of 78{\%}. While our worst classifier (Decision Tree) achieved the accuracy of 54{\%}, precision of 60{\%} and recall of 56{\%}.",
}
@inproceedings{agrawal-carpuat-2020-multitask,
    title = "Multitask Models for Controlling the Complexity of Neural Machine Translation",
    author = "Agrawal, Sweta  and
      Carpuat, Marine",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.36",
    doi = "10.18653/v1/2020.winlp-1.36",
    pages = "136--139",
    abstract = "We introduce a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a novel dataset of news articles available in English and Spanish and written for diverse reading grade levels. We leverage this dataset to train multitask sequence to sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that multitask models outperform pipeline approaches that translate and simplify text independently.",
}
@inproceedings{pawlicka-maule-johnson-2020-using,
    title = "Using Social Media For Bitcoin Day Trading Behavior Prediction",
    author = "Pawlicka Maule, Anna Paula  and
      Johnson, Kristen",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.37",
    doi = "10.18653/v1/2020.winlp-1.37",
    pages = "140--143",
    abstract = "This abstract presents preliminary work in the application of natural language processing techniques and social network modeling for the prediction of cryptocurrency trading and investment behavior. Specifically, we are building models to use language and social network behaviors to predict if the tweets of a 24-hour period can be used to buy or sell cryptocurrency to make a profit. In this paper we present our novel task and initial language modeling studies.",
}
@inproceedings{akinfaderin-2020-hausamt,
    title = "{H}ausa{MT} v1.0: Towards {E}nglish{--}{H}ausa Neural Machine Translation",
    author = "Akinfaderin, Adewale",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.38",
    doi = "10.18653/v1/2020.winlp-1.38",
    pages = "144--147",
    abstract = "Neural Machine Translation (NMT) for low-resource languages suffers from low performance because of the lack of large amounts of parallel data and language diversity. To contribute to ameliorating this problem, we built a baseline model for English{--}Hausa machine translation, which is considered a task for low{--}resource language. The Hausa language is the second largest Afro{--}Asiatic language in the world after Arabic and it is the third largest language for trading across a larger swath of West Africa countries, after English and French. In this paper, we curated different datasets containing Hausa{--}English parallel corpus for our translation. We trained baseline models and evaluated the performance of our models using the Recurrent and Transformer encoder{--}decoder architecture with two tokenization approaches: standard word{--}level tokenization and Byte Pair Encoding (BPE) subword tokenization.",
}
@inproceedings{ramesh-anand-2020-outcomes,
    title = "Outcomes of coming out: Analyzing stories of {LGBTQ}+",
    author = "Ramesh, Krithika  and
      Anand, Tanvi",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.39",
    doi = "10.18653/v1/2020.winlp-1.39",
    pages = "148--150",
    abstract = "The Internet is frequently used as a platform through which opinions and views on various topics can be expressed. One such topic that draws controversial attention is LGBTQ+ rights. This paper attempts to analyze the reaction that members of the LGBTQ+ community face when they reveal their gender or sexuality, or in other words, when they {`}come out of the closet{'}. We aim to classify the experiences shared by them as positive or negative. We collected data from various sources, primarily Twitter. We have applied deep learning techniques and compared the results to other classifiers, and the results obtained from applying classical sentiment analysis techniques to it.",
}
@inproceedings{richburg-etal-2020-evaluation,
    title = "An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages",
    author = "Richburg, Aquia  and
      Eskander, Ramy  and
      Muresan, Smaranda  and
      Carpuat, Marine",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.40",
    doi = "10.18653/v1/2020.winlp-1.40",
    pages = "151--155",
    abstract = "Byte-Pair Encoding (BPE) (Sennrich et al., 2016) has become a standard pre-processing step when building neural machine translation systems. However, it is not clear whether this is an optimal strategy in all settings. We conduct a controlled comparison of subword segmentation strategies for translating two low-resource morphologically rich languages (Swahili and Turkish) into English. We show that segmentations based on a unigram language model (Kudo, 2018) yield comparable BLEU and better recall for translating rare source words than BPE.",
}
@inproceedings{farhan-etal-2020-enhanced,
    title = "Enhanced {U}rdu Word Segmentation using Conditional Random Fields and Morphological Context Features",
    author = "Farhan, Aamir  and
      Islam, Mashrukh  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.41",
    doi = "10.18653/v1/2020.winlp-1.41",
    pages = "156--159",
    abstract = "Word segmentation is a fundamental task for most of the NLP applications. Urdu adopts Nastalique writing style which does not have a concept of space. Furthermore, the inherent non-joining attributes of certain characters in Urdu create spaces within a word while writing in digital format. Thus, Urdu not only has space omission but also space insertion issues which make the word segmentation task challenging. In this paper, we improve upon the results of Zia, Raza and Athar (2018) by using a manually annotated corpus of 19,651 sentences along with morphological context features. Using the Conditional Random Field sequence modeler, our model achieves F 1 score of 0.98 for word boundary identification and 0.92 for sub-word boundary identification tasks. The results demonstrated in this paper outperform the state-of-the-art methods.",
}
@proceedings{wildre-2020-wildre5,
    title = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    editor = "Jha, Girish Nath  and
      Bali, Kalika  and
      L., Sobha  and
      Agrawal, S. S.  and
      Ojha, Atul Kr.",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.0",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{rane-etal-2020-part,
    title = "Part-of-Speech Annotation Challenges in {M}arathi",
    author = "Rane, Gajanan  and
      Joshi, Nilesh  and
      Rane, Geetanjali  and
      Redkar, Hanumant  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.1",
    pages = "1--6",
    abstract = "Part of Speech (POS) annotation is a significant challenge in natural language processing. The paper discusses issues and challenges faced in the process of POS annotation of the Marathi data from four domains viz., tourism, health, entertainment and agriculture. During POS annotation, a lot of issues were encountered. Some of the major ones are discussed in detail in this paper. Also, the two approaches viz., the lexical (L approach) and the functional (F approach) of POS tagging have been discussed and presented with examples. Further, some ambiguous cases in POS annotation are presented in the paper.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{suryawanshi-etal-2020-dataset,
    title = "A Dataset for Troll Classification of {T}amil{M}emes",
    author = "Suryawanshi, Shardul  and
      Chakravarthi, Bharathi Raja  and
      Verma, Pranav  and
      Arcan, Mihael  and
      McCrae, John Philip  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.2",
    pages = "7--13",
    abstract = "Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{parida-etal-2020-odiencorp,
    title = "{O}di{E}n{C}orp 2.0: {O}dia-{E}nglish Parallel Corpus for Machine Translation",
    author = "Parida, Shantipriya  and
      Dash, Satya Ranjan  and
      Bojar, Ond{\v{r}}ej  and
      Motlicek, Petr  and
      Pattnaik, Priyanka  and
      Mallick, Debasish Kumar",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.3",
    pages = "14--19",
    abstract = "The preparation of parallel corpora is a challenging task, particularly for languages that suffer from under-representation in the digital world. In a multi-lingual country like India, the need for such parallel corpora is stringent for several low-resource languages. In this work, we provide an extended English-Odia parallel corpus, OdiEnCorp 2.0, aiming particularly at Neural Machine Translation (NMT) systems which will help translate English↔Odia. OdiEnCorp 2.0 includes existing English-Odia corpora and we extended the collection by several other methods of data acquisition: parallel data scraping from many websites, including Odia Wikipedia, but also optical character recognition (OCR) to extract parallel data from scanned images. Our OCR-based data extraction approach for building a parallel corpus is suitable for other low resource languages that lack in online content. The resulting OdiEnCorp 2.0 contains 98,302 sentences and 1.69 million English and 1.47 million Odia tokens. To the best of our knowledge, OdiEnCorp 2.0 is the largest Odia-English parallel corpus covering different domains and available freely for non-commercial and research purposes.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{sundar-ram-lalitha-devi-2020-handling,
    title = "Handling Noun-Noun Coreference in {T}amil",
    author = "Sundar Ram, Vijay  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.4",
    pages = "20--24",
    abstract = "Natural language understanding by automatic tools is the vital requirement for document processing tools. To achieve it, automatic system has to understand the coherence in the text. Co-reference chains bring coherence to the text. The commonly occurring reference markers which bring cohesiveness are Pronominal, Reflexives, Reciprocals, Distributives, One-anaphors, Noun{--}noun reference. Here in this paper, we deal with noun-noun reference in Tamil. We present the methodology to resolve these noun-noun anaphors and also present the challenges in handling the noun-noun anaphoric relations in Tamil.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{k-r-etal-2020-malayalam,
    title = "{M}alayalam Speech Corpus: Design and Development for {D}ravidian Language",
    author = "K R, Lekshmi  and
      V S, Jithesh  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.5",
    pages = "25--28",
    abstract = "To overpass the disparity between theory and applications in language-related technology in the text as well as speech and several other areas, a well-designed and well-developed corpus is essential. Several problems and issues encountered while developing a corpus, especially for low resource languages. The Malayalam Speech Corpus (MSC) is one of the first open speech corpora for Automatic Speech Recognition (ASR) research to the best of our knowledge. It consists of 250 hours of Agricultural speech data. We are providing a transcription file, lexicon and annotated speech along with the audio segment. It is available in future for public use upon request at {``}www.iiitmk.ac.in/vrclc/utilities/ml{\_}speechcorpus{''}. This paper details the development and collection process in the domain of agricultural speech corpora in the Malayalam Language.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{madaan-sadat-2020-multilingual,
    title = "Multilingual Neural Machine Translation involving {I}ndian Languages",
    author = "Madaan, Pulkit  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.6",
    pages = "29--32",
    abstract = "Neural Machine Translations (NMT) models are capable of translating a single bilingual pair and require a new model for each new language pair. Multilingual Neural Machine Translation models are capable of translating multiple language pairs, even pairs which it hasn{'}t seen before in training. Availability of parallel sentences is a known problem in machine translation. Multilingual NMT model leverages information from all the languages to improve itself and performs better. We propose a data augmentation technique that further improves this model profoundly. The technique helps achieve a jump of more than 15 points in BLEU score from the multilingual NMT model. A BLEU score of 36.2 was achieved for Sindhi{--}English translation, which is higher than any score on the leaderboard of the LoResMT SharedTask at MT Summit 2019, which provided the data for the experiments.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{ojha-zeman-2020-universal,
    title = "{U}niversal {D}ependency Treebanks for Low-Resource {I}ndian Languages: The Case of {B}hojpuri",
    author = "Ojha, Atul Kr.  and
      Zeman, Daniel",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.7",
    pages = "33--38",
    abstract = "This paper presents the first dependency treebank for Bhojpuri, a resource-poor language that belongs to the Indo-Aryan language family. The objective behind the Bhojpuri Treebank (BHTB) project is to create a substantial, syntactically annotated treebank which not only acts as a valuable resource in building language technological tools, also helps in cross-lingual learning and typological research. Currently, the treebank consists of 4,881 annotated tokens in accordance with the annotation scheme of Universal Dependencies (UD). A Bhojpuri tagger and parser were created using machine learning approach. The accuracy of the model is 57.49{\%} UAS, 45.50{\%} LAS, 79.69{\%} UPOS accuracy and 77.64{\%} XPOS accuracy. The paper describes the details of the project including a discussion on linguistic analysis and annotation process of the Bhojpuri UD treebank.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{nallani-etal-2020-fully,
    title = "A Fully Expanded Dependency Treebank for {T}elugu",
    author = "Nallani, Sneha  and
      Shrivastava, Manish  and
      Sharma, Dipti",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.8",
    pages = "39--44",
    abstract = "Treebanks are an essential resource for syntactic parsing. The available Paninian dependency treebank(s) for Telugu is annotated only with inter-chunk dependency relations and not all words of a sentence are part of the parse tree. In this paper, we automatically annotate the intra-chunk dependencies in the treebank using a Shift-Reduce parser based on Context Free Grammar rules for Telugu chunks. We also propose a few additional intra-chunk dependency relations for Telugu apart from the ones used in Hindi treebank. Annotating intra-chunk dependencies finally provides a complete parse tree for every sentence in the treebank. Having a fully expanded treebank is crucial for developing end to end parsers which produce complete trees. We present a fully expanded dependency treebank for Telugu consisting of 3220 sentences. In this paper, we also convert the treebank annotated with Anncorra part-of-speech tagset to the latest BIS tagset. The BIS tagset is a hierarchical tagset adopted as a unified part-of-speech standard across all Indian Languages. The final treebank is made publicly available.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{shaikh-2020-determination,
    title = "Determination of Idiomatic Sentences in Paragraphs Using Statement Classification and Generalization of Grammar Rules",
    author = "Shaikh, Naziya",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.9",
    pages = "45--50",
    abstract = "The translation systems are often not able to determine the presence of an idiom in a given paragraph. Due to this many systems tend to return the word-for-word translation of such statements leading to loss in the flavor of the idioms in the paragraph. This paper suggests a novel approach to efficiently determine probability of any statement in a given English paragraph to be an idiom. This approach combines the rule-based generalization of idioms in English language and classification of statements based on the context to determine the idioms in the sentence. The context based classification method can be used further for determination of idioms in regional Indian languages such as Marathi, Konkani and Hindi as the difference in the semantic context of the proverb as compared to the context in a paragraph is also evident in these other languages.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{vetulani-vetulani-2020-polish,
    title = "{P}olish Lexicon-Grammar Development Methodology as an Example for Application to other Languages",
    author = "Vetulani, Zygmunt  and
      Vetulani, Gra{\.z}yna",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.10",
    pages = "51--59",
    abstract = "In the paper we present our methodology with the intention to propose it as a reference for creating lexicon-grammars. We share our long-term experience gained during research projects (past and on-going) concerning the description of Polish using this approach. The above-mentioned methodology, linking semantics and syntax, has revealed useful for various IT applications. Among other, we address this paper to researchers working on {``}less{''} or {``}middle-resourced{''} Indo-European languages as a proposal of a long term academic cooperation in the field. We believe that the confrontation of our lexicon-grammar methodology with other languages {--} Indo-European, but also Non-Indo-European languages of India, Ugro-Finish or Turkic languages in Eurasia {--} will allow for better understanding of the level of versatility of our approach and, last but not least, will create opportunities to intensify comparative studies. The reason of presenting some our works on language resources within the Wildre workshop is the intention not only to take up the challenge thrown down in the CFP of this workshop which is: {``}To provide opportunity for researchers from India to collaborate with researchers from other parts of the world{''}, but also to generalize this challenge to other languages.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{sinha-jha-2020-abstractive,
    title = "Abstractive Text Summarization for {S}anskrit Prose: A Study of Methods and Approaches",
    author = "Sinha, Shagun  and
      Jha, Girish",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.11",
    pages = "60--65",
    abstract = "The authors present a work-in-progress in the field of Abstractive Text Summarization (ATS) for Sanskrit Prose {--} a first attempt at ATS for Sanskrit (SATS). We will evaluate recent approaches and methods used for ATS and argue for the ones to be adopted for Sanskrit prose considering the unique properties of the language. There are three goals of SATS - to make manuscript summaries, to enrich the semantic processing of Sanskrit, and to improve the information retrieval systems in the language. While Extractive Text Summarization (ETS) is an important method, the summaries it generates are not always coherent. For qualitative coherent summaries, ATS is considered a better option by scholars. This paper reviews various ATS/ETS approaches for Sanskrit and other Indian Languages done till date. In the preliminary overview, authors conclude that of the two available approaches - structure-based and semantic-based - the latter would be viable owing to the rich morphology of Sanskrit. Moreover, a graph-based method may also be suitable. The second suggested method is the supervised-learning method. The authors also suggest attempting cross-lingual summarization as an extension to this work in future.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@inproceedings{c-s-lalitha-devi-2020-deeper,
    title = "A Deeper Study on Features for Named Entity Recognition",
    author = "C S, Malarkodi  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the WILDRE5{--} 5th Workshop on Indian Language Data: Resources and Evaluation",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.wildre-1.12",
    pages = "66--72",
    abstract = "This paper deals with the various features used for the identification of named entities. The performance of the machine learning system heavily depends on the feature selection criteria. The intention to trace the essential features required for the development of named entity system across languages motivated us to conduct this study. The linguistic analysis was done to find out the part of speech patterns surrounding the context of named entities and from the observation linguistic oriented features are identified for both Indian and European languages. The Indian languages belongs to Dravidian language family such as Tamil, Telugu, Malayalam, Indo-Aryan language family such as Hindi, Punjabi, Bengali and Marathi, European languages such as English, Spanish, Dutch, German and Hungarian are used in this work. The machine learning technique CRFs was used for the system development. The experiments were conducted using the linguistic features and the results obtained for each languages are comparable with state-of-art systems.",
    language = "English",
    ISBN = "979-10-95546-67-2",
}
@proceedings{webnlg-2020-international,
    title = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    editor = "Castro Ferreira, Thiago  and
      Gardent, Claire  and
      Ilinykh, Nikolai  and
      van der Lee, Chris  and
      Mille, Simon  and
      Moussallem, Diego  and
      Shimorina, Anastasia",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.0",
}
@inproceedings{mille-etal-2020-case,
    title = "A Case Study of {NLG} from Multimedia Data Sources: Generating Architectural Landmark Descriptions",
    author = "Mille, Simon  and
      Symeonidis, Spyridon  and
      Rousi, Maria  and
      Marimon Felipe, Montserrat  and
      Stavrothanasopoulos, Klearchos  and
      Alvanitopoulos, Petros  and
      Carlini Salguero, Roberto  and
      Grivolla, Jens  and
      Meditskos, Georgios  and
      Vrochidis, Stefanos  and
      Wanner, Leo",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.1",
    pages = "2--14",
    abstract = "In this paper, we present a pipeline system that generates architectural landmark descriptions using textual, visual and structured data. The pipeline comprises five main components:(i) a textual analysis component, which extracts information from Wikipedia pages; (ii)a visual analysis component, which extracts information from copyright-free images; (iii) a retrieval component, which gathers relevant (property, subject, object) triples from DBpedia; (iv) a fusion component, which stores the contents from the different modalities in a Knowledge Base (KB) and resolves the conflicts that stem from using different sources of information; (v) an NLG component, which verbalises the resulting contents of the KB. We show that thanks to the addition of other modalities, we can make the verbalisation of DBpedia triples more relevant and/or inspirational.",
}
@inproceedings{mahlaza-keet-2020-owlsiz,
    title = "{OWLSIZ}: An isi{Z}ulu {CNL} for structured knowledge validation",
    author = "Mahlaza, Zola  and
      Keet, C. Maria",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.2",
    pages = "15--25",
    abstract = "In iterative knowledge elicitation, engineers are expected to be directly involved in validating the already captured knowledge and obtaining new knowledge increments, thus making the process time consuming. Languages such as English have controlled natural languages than can be repurposed to generate natural language questions from an ontology in order to allow a domain expert to independently validate the contents of an ontology without understanding a ontology authoring language such as OWL. IsiZulu, South Africa{'}s main L1 language by number speakers, does not have such a resource, hence, it is not possible to build a verbaliser to generate such questions. Therefore, we propose an isiZulu controlled natural language, called OWL Simplified isiZulu (OWLSIZ), for producing grammatical and fluent questions from an ontology. Human evaluation of the generated questions showed that participants{'} judgements agree that most (83{\%}) questions are positive for grammaticality or understandability.",
}
@inproceedings{moussallem-etal-2020-general,
    title = "A General Benchmarking Framework for Text Generation",
    author = {Moussallem, Diego  and
      Kaur, Paramjot  and
      Ferreira, Thiago  and
      van der Lee, Chris  and
      Shimorina, Anastasia  and
      Conrads, Felix  and
      R{\"o}der, Michael  and
      Speck, Ren{\'e}  and
      Gardent, Claire  and
      Mille, Simon  and
      Ilinykh, Nikolai  and
      Ngonga Ngomo, Axel-Cyrille},
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.3",
    pages = "27--33",
    abstract = "The RDF-to-text task has recently gained substantial attention due to the continuous growth of RDF knowledge graphs in number and size. Recent studies have focused on systematically comparing RDF-to-text approaches on benchmarking datasets such as WebNLG. Although some evaluation tools have already been proposed for text generation, none of the existing solutions abides by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles and involves RDF data for the knowledge extraction task. In this paper, we present BENG, a FAIR benchmarking platform for Natural Language Generation (NLG) and Knowledge Extraction systems with focus on RDF data. BENG builds upon the successful benchmarking platform GERBIL, is opensource and is publicly available along with the data it contains.",
}
@inproceedings{leng-etal-2020-controllable,
    title = "Controllable Neural Natural Language Generation: comparison of state-of-the-art control strategies",
    author = "Leng, Yuanmin  and
      Portet, Fran{\c{c}}ois  and
      Labb{\'e}, Cyril  and
      Qader, Raheel",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.4",
    pages = "34--39",
    abstract = "Most NLG systems target text fluency and grammatical correctness, disregarding control over text structure and length. However, control over the output plays an important part in industrial NLG applications. In this paper, we study different strategies of control in triple-totext generation systems particularly from the aspects of text structure and text length. Regarding text structure, we present an approach that relies on aligning the input entities with the facts in the target side. It makes sure that the order and the distribution of entities in both the input and the text are the same. As for control over text length, we show two different approaches. One is to supply length constraint as input while the other is to force the end-ofsentence tag to be included at each step when using top-k decoding strategy. Finally, we propose four metrics to assess the degree to which these methods will affect a NLG system{'}s ability to control text structure and length. Our analyses demonstrate that all the methods enhance the system{'}s ability with a slight decrease in text fluency. In addition, constraining length at the input level performs much better than control at decoding level.",
}
@inproceedings{domingo-etal-2020-enhancing,
    title = "Enhancing Sequence-to-Sequence Modelling for {RDF} triples to Natural Text",
    author = "Domingo, Oriol  and
      Berg{\'e}s, David  and
      Cantenys, Roser  and
      Creus, Roger  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.5",
    pages = "40--47",
    abstract = "establishes key guidelines on how, which and when Machine Translation (MT) techniques are worth applying to RDF-to-Text task. Not only do we apply and compare the most prominent MT architecture, the Transformer, but we also analyze state-of-the-art techniques such as Byte Pair Encoding or Back Translation to demonstrate an improvement in generalization. In addition, we empirically show how to tailor these techniques to enhance models relying on learned embeddings rather than using pretrained ones. Automatic metrics suggest that Back Translation can significantly improve model performance up to 7 BLEU points, hence, opening a window for surpassing state-of-the-art results with appropriate architectures.",
}
@inproceedings{pasricha-etal-2020-utilising,
    title = "Utilising Knowledge Graph Embeddings for Data-to-Text Generation",
    author = "Pasricha, Nivranshu  and
      Arcan, Mihael  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.6",
    pages = "48--53",
    abstract = "Data-to-text generation has recently seen a move away from modular and pipeline architectures towards end-to-end architectures based on neural networks. In this work, we employ knowledge graph embeddings and explore their utility for end-to-end approaches in a data-to-text generation task. Our experiments show that using knowledge graph embeddings can yield an improvement of up to 2 {--} 3 BLEU points for seen categories on the WebNLG corpus without modifying the underlying neural network architecture.",
}
@inproceedings{castro-ferreira-etal-2020-2020,
    title = "The 2020 Bilingual, Bi-Directional {W}eb{NLG}+ Shared Task: Overview and Evaluation Results ({W}eb{NLG}+ 2020)",
    author = "Castro Ferreira, Thiago  and
      Gardent, Claire  and
      Ilinykh, Nikolai  and
      van der Lee, Chris  and
      Mille, Simon  and
      Moussallem, Diego  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.7",
    pages = "55--76",
    abstract = "WebNLG+ offers two challenges: (i) mapping sets of RDF triples to English or Russian text (generation) and (ii) converting English or Russian text to sets of RDF triples (semantic parsing). Compared to the eponymous WebNLG challenge, WebNLG+ provides an extended dataset that enable the training, evaluation, and comparison of microplanners and semantic parsers. In this paper, we present the results of the generation and semantic parsing task for both English and Russian and provide a brief description of the participating systems.",
}
@inproceedings{guo-etal-2020-cyclegt,
    title = "{C}ycle{GT}: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training",
    author = "Guo, Qipeng  and
      Jin, Zhijing  and
      Qiu, Xipeng  and
      Zhang, Weinan  and
      Wipf, David  and
      Zhang, Zheng",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.8",
    pages = "77--88",
    abstract = "Two important tasks at the intersection of knowledge graphs and natural language processing are graph-to-text (G2T) and text-tograph (T2G) conversion. Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG 2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation. Consequently, deep learning models for G2T and T2G suffer largely from scarce training data. We present CycleGT, an unsupervised training method that can bootstrap from fully non-parallel graph and text data, and iteratively back translate between the two forms. Experiments on WebNLG datasets show that our unsupervised model trained on the same number of data achieves performance on par with several fully supervised models. Further experiments on the non-parallel GenWiki dataset verify that our method performs the best among unsupervised baselines. This validates our framework as an effective approach to overcome the data scarcity problem in the fields of G2T and T2G.",
}
@inproceedings{montella-etal-2020-denoising,
    title = "Denoising Pre-Training and Data Augmentation Strategies for Enhanced {RDF} Verbalization with Transformers",
    author = "Montella, Sebastien  and
      Fabre, Betty  and
      Urvoy, Tanguy  and
      Heinecke, Johannes  and
      Rojas-Barahona, Lina",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.9",
    pages = "89--99",
    abstract = "The task of verbalization of RDF triples has known a growth in popularity due to the rising ubiquity of Knowledge Bases (KBs). The formalism of RDF triples is a simple and efficient way to store facts at a large scale. However, its abstract representation makes it difficult for humans to interpret. For this purpose, the WebNLG challenge aims at promoting automated RDF-to-text generation. We propose to leverage pre-trainings from augmented data with the Transformer model using a data augmentation strategy. Our experiment results show a minimum relative increases of 3.73{\%}, 126.05{\%} and 88.16{\%} in BLEU score for seen categories, unseen entities and unseen categories respectively over the standard training.",
}
@inproceedings{guo-etal-2020-2,
    title = "${\mathcal{P}^2}$: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation",
    author = "Guo, Qipeng  and
      Jin, Zhijing  and
      Dai, Ning  and
      Qiu, Xipeng  and
      Xue, Xiangyang  and
      Wipf, David  and
      Zhang, Zheng",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.10",
    pages = "100--106",
    abstract = "Text verbalization of knowledge graphs is an important problem with wide application to natural language generation (NLG) systems. It is challenging because the generated text not only needs to be grammatically correct (fluency), but also has to contain the given structured knowledge input (relevance) and meet some other criteria. We develop a plan-and-pretrain approach, $\mathcal{P}^2$, which consists of a relational graph convolutional network (RGCN) planner and the pretrained sequence-tosequence (Seq2Seq) model T5. Specifically, the R-GCN planner first generates an order of the knowledge graph triplets, corresponding to the order that they will be mentioned in text, and then T5 produces the surface realization of the given plan. In the WebNLG+ 2020 Challenge, our submission ranked in 1st place on all automatic and human evaluation criteria of the English RDF-to-text generation task.",
}
@inproceedings{yang-etal-2020-improving-text,
    title = "Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task",
    author = "Yang, Zixiaofan  and
      Einolghozati, Arash  and
      Inan, Hakan  and
      Diedrick, Keith  and
      Fan, Angela  and
      Donmez, Pinar  and
      Gupta, Sonal",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.11",
    pages = "107--116",
    abstract = "Converting a knowledge graph or sub-graph to natural text is useful when answering questions based on a knowledge base. High-capacity language models pre-trained on large-scale text corpora have recently been shown to be powerful when fine-tuned for the knowledge-graph-to-text (KG-to-text) task. In this paper, we propose two classes of methods to improve such pre-trained models for this task. First, we improve the structure awareness of the model by organizing the input as well as learning optimal ordering via multitask learning. Second, we bridge the domain gap between text-to-text and KG-to-text tasks via a second-phase KG-to-text pre-training on similar datasets and extra lexicalization supervision to make the input more similar to natural text. We demonstrate the efficacy of our methods on the popular WebNLG dataset. Our best model achieves an almost 3 point BLEU improvement on a strong baseline while lowering the relative slot-error-rate by around 35{\%}. We also validate our results via human evaluation.",
}
@inproceedings{li-etal-2020-leveraging-large,
    title = "Leveraging Large Pretrained Models for {W}eb{NLG} 2020",
    author = "Li, Xintong  and
      Maskharashvili, Aleksandre  and
      Jory Stevens-Guille, Symon  and
      White, Michael",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.12",
    pages = "117--124",
    abstract = "In this paper, we report experiments on finetuning large pretrained models to realize resource description framework (RDF) triples to natural language. We provide the details of how to build one of the top-ranked English generation models in WebNLG Challenge 2020. We also show that there appears to be considerable potential for reranking to improve the current state of the art both in terms of statistical metrics and model-based metrics. Our human analyses of the generated texts show that for Russian, pretrained models showed some success, both in terms of lexical and morpho-syntactic choices for generation, as well as for content aggregation. Nevertheless, in a number of cases, the model can be unpredictable, both in terms of failure or success. Omissions of the content and hallucinations, which in many cases occurred at the same time, were major problems. By contrast, the models for English showed near perfect performance on the validation set.",
}
@inproceedings{agarwal-etal-2020-machine,
    title = "Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing",
    author = "Agarwal, Oshin  and
      Kale, Mihir  and
      Ge, Heming  and
      Shakeri, Siamak  and
      Al-Rfou, Rami",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.13",
    pages = "125--130",
    abstract = "We present a system for bilingual Data-ToText Generation and Semantic Parsing. We use a text-to-text generator to learn a single model that works for both languages on each of the tasks. The model is aided by machine translation during both pre-training and fine-tuning. We evaluate the system on WebNLG 2020 data 1 , which consists of RDF triples in English and natural language sentences in English and Russian for both the tasks. We achieve considerable gains over monolingual models, especially on unseen relations and Russian.",
}
@inproceedings{sobrevilla-cabezudo-pardo-2020-nilc-webnlg,
    title = "{NILC} at {W}eb{NLG}+: Pretrained Sequence-to-Sequence Models on {RDF}-to-Text Generation",
    author = "Sobrevilla Cabezudo, Marco Antonio  and
      Pardo, Thiago A. S.",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.14",
    pages = "131--136",
    abstract = "This paper describes the submission by the NILC Computational Linguistics research group of the University of S{\~a}o Paulo/Brazil to the RDF-to-Text task for English at the WebNLG+ challenge. The success of the current pretrained models like BERT or GPT-2 in text-to-text generation tasks is well-known, however, its application/success on data-totext generation has not been well-studied and proven. This way, we explore how good a pretrained model, in particular BART, performs on the data-to-text generation task. The results obtained were worse than the baseline and other systems in almost all automatic measures. However, the human evaluation shows better results for our system. Besides, results suggest that BART may generate paraphrases of reference texts.",
}
@inproceedings{pasricha-etal-2020-nuig,
    title = "{NUIG}-{DSI} at the {W}eb{NLG}+ challenge: Leveraging Transfer Learning for {RDF}-to-text generation",
    author = "Pasricha, Nivranshu  and
      Arcan, Mihael  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.15",
    pages = "137--143",
    abstract = "This paper describes the system submitted by NUIG-DSI to the WebNLG+ challenge 2020 in the RDF-to-text generation task for the English language. For this challenge, we leverage transfer learning by adopting the T5 model architecture for our submission and fine-tune the model on the WebNLG+ corpus. Our submission ranks among the top five systems for most of the automatic evaluation metrics achieving a BLEU score of 51.74 over all categories with scores of 58.23 and 45.57 across seen and unseen categories respectively.",
}
@inproceedings{lapalme-2020-rdfjsrealb,
    title = "{RDF}js{R}eal{B}: a Symbolic Approach for Generating Text from {RDF} Triples",
    author = "Lapalme, Guy",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.16",
    pages = "144--153",
    abstract = "This paper describes the Resource Description Framework (RDF) triples verbalizer developed for the WEB NLG CHALLENGE 2020 shared task. After reviewing representative works in Natural Language Generation in the context of the Semantic Web, the task is then described. We then sketch the symbolic approach we used for verbalizing RDF triples: once the triples are grouped by subject, each group is realized as one or more sentences using templates written in Python whose output is feed to an English realizer written in Javascript. The system was developed using the test data of the previous edition of the task and the train and development data of this year{'}s task. The automatic scores for this year{'}s test data are quite competitive. We conclude with a critical review of the data and discuss the suitability of this competition results in a wider Natural Language Generation setting.",
}
@inproceedings{blinov-2020-semantic,
    title = "Semantic Triples Verbalization with Generative Pre-Training Model",
    author = "Blinov, Pavel",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.17",
    pages = "154--158",
    abstract = "The paper devoted to the problem of automatic text generation from RDF triples. This problem was formalized and proposed as a part of the 2020 WebNLG challenge. We describe our approach to the RDF-to-text generation task based on a neural network model with the Generative Pre-Training (GPT-2) architecture. In particular, we outline a way of base GPT-2 model conversion to a model with language and classification heads and discuss the text generation methods. To research the parameters{'} influence on the end-task performance a series of experiments was carried out. We report the result metrics and conclude with possible improvement directions.",
}
@inproceedings{kertkeidkachorn-takamura-2020-text,
    title = "Text-to-Text Pre-Training Model with Plan Selection for {RDF}-to-Text Generation",
    author = "Kertkeidkachorn, Natthawut  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.18",
    pages = "159--166",
    abstract = "We report our system description for the RDFto-Text task in English on the WebNLG 2020 Challenge. Our approach consists of two parts: 1) RDF-to-Text Generation Pipeline and 2) Plan Selection. RDF-to-Text Generation Pipeline is built on the state-of-the-art pretraining model, while Plan Selection helps decide the proper plan into the pipeline.",
}
@inproceedings{berges-etal-2020-upc,
    title = "The {UPC} {RDF}-to-Text System at {W}eb{NLG} Challenge 2020",
    author = "Berg{\'e}s, David  and
      Cantenys, Roser  and
      Creus, Roger  and
      Domingo, Oriol  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.19",
    pages = "167--170",
    abstract = "This work describes the end-to-end system architecture presented at WebNLG Challenge 2020. The system follows the traditional Machine Translation (MT) pipeline, based on the Transformer model, applied in most text-totext problems. Our solution is enriched by means of a Back Translation step over the original corpus. Thus, the system directly relies on lexicalise format since the synthetic data limits the use of delexicalisation.",
}
@inproceedings{kasner-dusek-2020-train,
    title = "Train Hard, Finetune Easy: Multilingual Denoising for {RDF}-to-Text Generation",
    author = "Kasner, Zden{\v{e}}k  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.20",
    pages = "171--176",
    abstract = "We describe our system for the RDF-to-text generation task of the WebNLG Challenge 2020. We base our approach on the mBART model, which is pre-trained for multilingual denoising. This allows us to use a simple, identical, end-to-end setup for both English and Russian. Requiring minimal taskor languagespecific effort, our model placed in the first third of the leaderboard for English and first or second for Russian on automatic metrics, and it made it into the best or second-best system cluster on human evaluation.",
}
@inproceedings{tran-nguyen-2020-webnlg,
    title = "{W}eb{NLG} 2020 Challenge: Semantic Template Mining for Generating References from {RDF}",
    author = "Tran, Trung  and
      Nguyen, Dang Tuan",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.21",
    pages = "177--185",
    abstract = "We present in this paper our mining system for shared task WebNLG Challenge 2020. The general idea of the system is that we generate the semantic template of the output reference from the input RDF XML structure. In the training process, we perform the following subtasks: (i) extract the core information from input RDF; (ii) generate semantic templates from corresponding references. With new RDF XML data, we detect the core information, in turn add the new template into the warehouse and determine the output semantic template. We will evaluate the output natural language references in two processes: automatic and human evaluations. The results of the first tested process show that our system generates the high quality English descriptions from testing RDF XML structures and has a good contribution to the NLG state-of-the-art.",
}
@inproceedings{zhou-lampouras-2020-webnlg,
    title = "{W}eb{NLG} Challenge 2020: Language Agnostic Delexicalisation for Multilingual {RDF}-to-text generation",
    author = "Zhou, Giulio  and
      Lampouras, Gerasimos",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.webnlg-1.22",
    pages = "186--191",
    abstract = "This paper presents our submission to the WebNLG Challenge 2020 for the English and Russian RDF-to-text generation tasks. Our first of three submissions is based on Language Agnostic Delexicalisation, a novel delexicalisation method that match values in the input to their occurrences in the corresponding text through comparison of pretrained multilingual embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our second submission forfeits delexicalisation and uses SentencePiece subwords as basic units. Our third submission combines the previous two by alternating between the output of the delexicalisation-based system when the input contains unseen entities and/or properties and the output of the SentencePiece-based system when the input is seen during training.",
}
@proceedings{wat-2020-asian,
    title = "Proceedings of the 7th Workshop on Asian Translation",
    editor = "Nakazawa, Toshiaki  and
      Nakayama, Hideki  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Pa, Win Pa  and
      Bojar, Ond{\v{r}}ej  and
      Parida, Shantipriya  and
      Goto, Isao  and
      Mino, Hidaya  and
      Manabe, Hiroshi  and
      Sudoh, Katsuhito  and
      Kurohashi, Sadao  and
      Bhattacharyya, Pushpak",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.0",
}
@inproceedings{nakazawa-etal-2020-overview,
    title = "Overview of the 7th Workshop on {A}sian Translation",
    author = "Nakazawa, Toshiaki  and
      Nakayama, Hideki  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Higashiyama, Shohei  and
      Mino, Hideya  and
      Goto, Isao  and
      Pa Pa, Win  and
      Kunchukuttan, Anoop  and
      Parida, Shantipriya  and
      Bojar, Ond{\v{r}}ej  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.1",
    pages = "1--44",
    abstract = "This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.",
}
@inproceedings{ahmadnia-aranovich-2020-effective,
    title = "An Effective Optimization Method for Neural Machine Translation: The Case of {E}nglish-{P}ersian Bilingually Low-Resource Scenario",
    author = "Ahmadnia, Benyamin  and
      Aranovich, Raul",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.2",
    pages = "45--49",
    abstract = "In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms. Our results confirm that applying the proposed optimization method on English-Persian translation can exceed translation quality compared to the English-Persian Statistical Machine Translation (SMT) paradigm.",
}
@inproceedings{imamura-sumita-2020-transformer,
    title = "Transformer-based Double-token Bidirectional Autoregressive Decoding in Neural Machine Translation",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.3",
    pages = "50--57",
    abstract = "This paper presents a simple method that extends a standard Transformer-based autoregressive decoder, to speed up decoding. The proposed method generates a token from the head and tail of a sentence (two tokens in total) in each step. By simultaneously generating multiple tokens that rarely depend on each other, the decoding speed is increased while the degradation in translation quality is minimized. In our experiments, the proposed method increased the translation speed by around 113{\%}-155{\%} in comparison with a standard autoregressive decoder, while degrading the BLEU scores by no more than 1.03. It was faster than an iterative non-autoregressive decoder in many conditions.",
}
@inproceedings{zhang-etal-2020-translation,
    title = "Translation of New Named Entities from {E}nglish to {C}hinese",
    author = "Zhang, Zizheng  and
      Hirasawa, Tosho  and
      Houjing, Wei  and
      Kaneko, Masahiro  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.4",
    pages = "58--63",
    abstract = "New things are being created and new words are constantly being added to languages worldwide. However, it is not practical to translate them all manually into a new foreign language. When translating from an alphabetic language such as English to Chinese, appropriate Chinese characters must be assigned, which is particularly costly compared to other language pairs. Therefore, we propose a task of generating and evaluating new translations from English to Chinese focusing on named entities. We defined three criteria for human evaluation{---}fluency, adequacy of pronunciation, and adequacy of meaning{---}and constructed evaluation data based on these definitions. In addition, we built a baseline system and analyzed the output of the system.",
}
@inproceedings{mao-etal-2020-meta,
    title = "Meta Ensemble for {J}apanese-{C}hinese Neural Machine Translation: {K}yoto-{U}+{ECNU} Participation to {WAT} 2020",
    author = "Mao, Zhuoyuan  and
      Shen, Yibin  and
      Chu, Chenhui  and
      Kurohashi, Sadao  and
      Jin, Cheqing",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.5",
    pages = "64--71",
    abstract = "This paper describes the Japanese-Chinese Neural Machine Translation (NMT) system submitted by the joint team of Kyoto University and East China Normal University (Kyoto-U+ECNU) to WAT 2020 (Nakazawa et al.,2020). We participate in APSEC Japanese-Chinese translation task. We revisit several techniques for NMT including various architectures, different data selection and augmentation methods, denoising pre-training, and also some specific tricks for Japanese-Chinese translation. We eventually perform a meta ensemble to combine all of the models into a single model. BLEU results of this meta ensembled model rank the first both on 2 directions of ASPEC Japanese-Chinese translation.",
}
@inproceedings{goto-etal-2020-neural,
    title = "Neural Machine Translation Using Extracted Context Based on Deep Analysis for the {J}apanese-{E}nglish Newswire Task at {WAT} 2020",
    author = "Goto, Isao  and
      Mino, Hideya  and
      Ito, Hitoshi  and
      Kinugawa, Kazutaka  and
      Yamada, Ichiro  and
      Tanaka, Hideki",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.6",
    pages = "72--79",
    abstract = "This paper describes the system of the NHK-NES team for the WAT 2020 Japanese{--}English newswire task. There are two main problems in Japanese-English news translation: translation of dropped subjects and compatibility between equivalent translations and English news-style outputs. We address these problems by extracting subjects from the context based on predicate-argument structures and using them as additional inputs, and constructing parallel Japanese-English news sentences equivalently translated from English news sentences. The evaluation results confirm the effectiveness of our context-utilization method.",
}
@inproceedings{tamura-etal-2020-tmu,
    title = "{TMU} {J}apanese-{E}nglish Multimodal Machine Translation System for {WAT} 2020",
    author = "Tamura, Hiroto  and
      Hirasawa, Tosho  and
      Kaneko, Masahiro  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.7",
    pages = "80--91",
    abstract = "We introduce our TMU system submitted to the Japanese{\textless}-{\textgreater}English Multimodal Task (constrained) for WAT 2020 (Nakazawa et al., 2020). This task aims to improve translation performance with the help of another modality (images) associated with the input sentences. In a multimodal translation task, the dataset is, by its nature, a low-resource one. Our method used herein augments the data by generating noisy translations and adding noise to existing training images. Subsequently, we pretrain a translation model on the augmented noisy data, and then fine-tune it on the clean data. We also examine the probabilistic dropping of either the textual or visual context vector in the decoder. This aims to regularize the network to make use of both features while training. The experimental results indicate that translation performance can be improved using our method of textual data augmentation with noising on the target side and probabilistic dropping of either context vector.",
}
@inproceedings{yu-etal-2020-hw,
    title = "{HW}-{TSC}{'}s Participation in the {WAT} 2020 Indic Languages Multilingual Task",
    author = "Yu, Zhengzhe  and
      Wu, Zhanglin  and
      Chen, Xiaoyu  and
      Wei, Daimeng  and
      Shang, Hengchao  and
      Guo, Jiaxin  and
      Li, Zongyao  and
      Wang, Minghan  and
      Li, Liangyou  and
      Lei, Lizhi  and
      Yang, Hao  and
      Qin, Ying",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.8",
    pages = "92--97",
    abstract = "This paper describes our work in the WAT 2020 Indic Multilingual Translation Task. We participated in all 7 language pairs (En{\textless}-{\textgreater}Bn/Hi/Gu/Ml/Mr/Ta/Te) in both directions under the constrained condition{---}using only the officially provided data. Using transformer as a baseline, our Multi-{\textgreater}En and En-{\textgreater}Multi translation systems achieve the best performances. Detailed data filtering and data domain selection are the keys to performance enhancement in our experiment, with an average improvement of 2.6 BLEU scores for each language pair in the En-{\textgreater}Multi system and an average improvement of 4.6 BLEU scores regarding the Multi-{\textgreater}En. In addition, we employed language independent adapter to further improve the system performances. Our submission obtains competitive results in the final evaluation.",
}
@inproceedings{dabre-chakrabarty-2020-nicts,
    title = "{NICT}{`}s Submission To {WAT} 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?",
    author = "Dabre, Raj  and
      Chakrabarty, Abhisek",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.9",
    pages = "98--102",
    abstract = "In this paper we describe our team{`}s (NICT-5) Neural Machine Translation (NMT) models whose translations were submitted to shared tasks of the 7th Workshop on Asian Translation. We participated in the Indic language multilingual sub-task as well as the NICT-SAP multilingual multi-domain sub-task. We focused on naive many-to-many NMT models which gave reasonable translation quality despite their simplicity. Our observations are twofold: (a.) Many-to-many models suffer from a lack of consistency where the translation quality for some language pairs is very good but for some others it is terrible when compared against one-to-many and many-to-one baselines. (b.) Oversampling smaller corpora does not necessarily give the best translation quality for the language pair associated with that pair.",
}
@inproceedings{parida-etal-2020-odianlps,
    title = "{ODIANLP}{'}s Participation in {WAT}2020",
    author = "Parida, Shantipriya  and
      Motlicek, Petr  and
      Dash, Amulya Ratna  and
      Dash, Satya Ranjan  and
      Mallick, Debasish Kumar  and
      Biswal, Satya Prakash  and
      Pattnaik, Priyanka  and
      Nayak, Biranchi Narayan  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.10",
    pages = "103--108",
    abstract = "This paper describes the ODIANLP submission to WAT 2020. We have participated in the English-Hindi Multimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and InceptionResNetV2 for the Hindi Image Captioning task. Our submission tops in English-{\textgreater}Hindi Multimodal task in its track and Odia{\textless}-{\textgreater}English translation tasks. Also, our submissions performed well in the Indic Multilingual tasks.",
}
@inproceedings{laskar-etal-2020-multimodal,
    title = "Multimodal Neural Machine Translation for {E}nglish to {H}indi",
    author = "Laskar, Sahinur Rahman  and
      Khilji, Abdullah Faiz Ur Rahman  and
      Pakray, Partha  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.11",
    pages = "109--113",
    abstract = "Machine translation (MT) focuses on the automatic translation of text from one natural language to another natural language. Neural machine translation (NMT) achieves state-of-the-art results in the task of machine translation because of utilizing advanced deep learning techniques and handles issues like long-term dependency, and context-analysis. Nevertheless, NMT still suffers low translation quality for low resource languages. To encounter this challenge, the multi-modal concept comes in. The multi-modal concept combines textual and visual features to improve the translation quality of low resource languages. Moreover, the utilization of monolingual data in the pre-training step can improve the performance of the system for low resource language translations. Workshop on Asian Translation 2020 (WAT2020) organized a translation task for multimodal translation in English to Hindi. We have participated in the same in two-track submission, namely text-only and multi-modal translation with team name CNLP-NITS. The evaluated results are declared at the WAT2020 translation task, which reports that our multi-modal NMT system attained higher scores than our text-only NMT on both challenge and evaluation test set. For the challenge test data, our multi-modal neural machine translation system achieves Bilingual Evaluation Understudy (BLEU) score of 33.57, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.754141, Adequacy-Fluency Metrics (AMFM) score 0.787320 and for evaluation test data, BLEU, RIBES, and, AMFM score of 40.51, 0.803208, and 0.820980 for English to Hindi translation respectively.",
}
@inproceedings{nayak-etal-2020-adapt,
    title = "The {ADAPT} Centre{'}s Participation in {WAT} 2020 {E}nglish-to-{O}dia Translation Task",
    author = "Nayak, Prashanth  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.12",
    pages = "114--117",
    abstract = "This paper describes the ADAPT Centre sub-missions to WAT 2020 for the English-to-Odia translation task. We present the approaches that we followed to try to build competitive machine translation (MT) systems for English-to-Odia. Our approaches include monolingual data selection for creating synthetic data and identifying optimal sets of hyperparameters for the Transformer in a low-resource scenario. Our best MT system produces 4.96BLEU points on the evaluation test set in the English-to-Odia translation task.",
}
@inproceedings{baruah-mundotiya-2020-nlprl,
    title = "{NLPRL} {O}dia-{E}nglish: Indic Language Neural Machine Translation System",
    author = "Baruah, Rupjyoti  and
      Mundotiya, Rajesh Kumar",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.13",
    pages = "118--121",
    abstract = "In this manuscript, we (team name is NLPRL) describe systems description that was submitted to the translation shared tasks at WAT 2020. We describe our model as transformer based NMT by using byte-level based BPE (BBPE). We used the OdiEnCorp 2.0 parallel corpus provided by the shared task organizer where the training, validation, and test data contain 69370, 13544, and 14344 lines of parallel sentences, respectively. The evaluation results show the BLEU score of English-to-Oria below the Organizer (1.34) and Oria-to-English direction shows above the Organizer (11.33).",
}
@inproceedings{pal-2020-wt,
    title = "{WT}: Wipro {AI} Submissions to the {WAT} 2020",
    author = "Pal, Santanu",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.14",
    pages = "122--126",
    abstract = "In this paper we present an English{--}Hindi and Hindi{--}English neural machine translation (NMT) system, submitted to the Translation shared Task organized at WAT 2020. We trained a multilingual NMT system based on transformer architecture. In this paper we show: (i) how effective pre-processing helps to improve performance, (ii) how synthetic data through back-translation from available monolingual data can help in overall translation performance, (iii) how language similarity can aid more onto it. Our submissions ranked 1st in both English to Hindi and Hindi to English translation achieving BLEU 20.80 and 29.59 respectively.",
}
@inproceedings{kim-etal-2020-korean,
    title = "{K}orean-to-{J}apanese Neural Machine Translation System using Hanja Information",
    author = "Kim, Hwichan  and
      Hirasawa, Tosho  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.15",
    pages = "127--134",
    abstract = "In this paper, we describe our TMU neural machine translation (NMT) system submitted for the Patent task (Korean→Japanese) of the 7th Workshop on Asian Translation (WAT 2020, Nakazawa et al., 2020). We propose a novel method to train a Korean-to-Japanese translation model. Specifically, we focus on the vocabulary overlap of Korean Hanja words and Japanese Kanji words, and propose strategies to leverage Hanja information. Our experiment shows that Hanja information is effective within a specific domain, leading to an improvement in the BLEU scores by +1.09 points compared to the baseline.",
}
@inproceedings{wang-htun-2020-gokus,
    title = "Goku{'}s Participation in {WAT} 2020",
    author = "Wang, Dongzhe  and
      Htun, Ohnmar",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.16",
    pages = "135--141",
    abstract = "This paper introduces our neural machine translation systems{'} participation in the WAT 2020 (team ID: goku20). We participated in the (i) Patent, (ii) Business Scene Dialogue (BSD) document-level translation, (iii) Mixed-domain tasks. Regardless of simplicity, standard Transformer models have been proven to be very effective in many machine translation systems. Recently, some advanced pre-training generative models have been proposed on the basis of encoder-decoder framework. Our main focus of this work is to explore how robust Transformer models perform in translation from sentence-level to document-level, from resource-rich to low-resource languages. Additionally, we also investigated the improvement that fine-tuning on the top of pre-trained transformer-based models can achieve on various tasks.",
}
@inproceedings{jooste-etal-2020-adapt,
    title = "The {ADAPT} Centre{'}s Neural {MT} Systems for the {WAT} 2020 Document-Level Translation Task",
    author = "Jooste, Wandri  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.17",
    pages = "142--146",
    abstract = "In this paper we describe the ADAPT Centre{'}s submissions to the WAT 2020 document-level Business Scene Dialogue (BSD) Translation task. We only consider translating from Japanese to English for this task and we use the MarianNMT toolkit to train Transformer models. In order to improve the translation quality, we made use of both in-domain and out-of-domain data for training our Machine Translation (MT) systems, as well as various data augmentation techniques for fine-tuning the model parameters. This paper outlines the experiments we ran to train our systems and report the accuracy achieved through these various experiments.",
}
@inproceedings{rikters-etal-2020-university,
    title = "The {U}niversity of {T}okyo{'}s Submissions to the {WAT} 2020 Shared Task",
    author = "Rikters, Mat{\=\i}ss  and
      Nakazawa, Toshiaki  and
      Ri, Ryokan",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.18",
    pages = "147--153",
    abstract = "The paper describes the development process of the The University of Tokyo{'}s NMT systems that were submitted to the WAT 2020 Document-level Business Scene Dialogue Translation sub-task. We describe the data processing workflow, NMT system training architectures, and automatic evaluation results. For the WAT 2020 shared task, we submitted 12 systems (both constrained and unconstrained) for English-Japanese and Japanese-English translation directions. The submitted systems were trained using Transformer models and one was a SMT baseline.",
}
@inproceedings{jaiswal-etal-2020-improving,
    title = "Improving {NMT} via Filtered Back Translation",
    author = "Jaiswal, Nikhil  and
      Patidar, Mayur  and
      Kumari, Surabhi  and
      Patwardhan, Manasi  and
      Karande, Shirish  and
      Agarwal, Puneet  and
      Vig, Lovekesh",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.19",
    pages = "154--159",
    abstract = "Document-Level Machine Translation (MT) has become an active research area among the NLP community in recent years. Unlike sentence-level MT, which translates the sentences independently, document-level MT aims to utilize contextual information while translating a given source sentence. This paper demonstrates our submission (Team ID - DEEPNLP) to the Document-Level Translation task organized by WAT 2020. This task focuses on translating texts from a business dialog corpus while optionally utilizing the context present in the dialog. In our proposed approach, we utilize publicly available parallel corpus from different domains to train an open domain base NMT model. We then use monolingual target data to create filtered pseudo parallel data and employ Back-Translation to fine-tune the base model. This is further followed by fine-tuning on the domain-specific corpus. We also ensemble various models to improvise the translation performance. Our best models achieve a BLEU score of 26.59 and 22.83 in an unconstrained setting and 15.10 and 10.91 in the constrained settings for En-{\textgreater}Ja {\&} Ja-{\textgreater}En direction, respectively.",
}
@inproceedings{buschbeck-exel-2020-parallel,
    title = "A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation",
    author = "Buschbeck, Bianka  and
      Exel, Miriam",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.20",
    pages = "160--169",
    abstract = "This paper accompanies the software documentation data set for machine translation, a parallel evaluation data set of data originating from the SAP Help Portal, that we released to the machine translation community for research purposes. It offers the possibility to tune and evaluate machine translation systems in the domain of corporate software documentation and contributes to the availability of a wider range of evaluation scenarios. The data set comprises of the language pairs English to Hindi, Indonesian, Malay and Thai, and thus also increases the test coverage for the many low-resource language pairs. Unlike most evaluation data sets that consist of plain parallel text, the segments in this data set come with additional metadata that describes structural information of the document context. We provide insights into the origin and creation, the particularities and characteristics of the data set as well as machine translation results.",
}
@inproceedings{saunders-etal-2020-inference,
    title = "Inference-only sub-character decomposition improves translation of unseen logographic characters",
    author = "Saunders, Danielle  and
      Feely, Weston  and
      Byrne, Bill",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.21",
    pages = "170--177",
    abstract = "Neural Machine Translation (NMT) on logographic source languages struggles when translating {`}unseen{'} characters, which never appear in the training data. One possible approach to this problem uses sub-character decomposition for training and test sentences. However, this approach involves complete retraining, and its effectiveness for unseen character translation to non-logographic languages has not been fully explored. We investigate existing ideograph-based sub-character decomposition approaches for Chinese-to-English and Japanese-to-English NMT, for both high-resource and low-resource domains. For each language pair and domain we construct a test set where all source sentences contain at least one unseen logographic character. We find that complete sub-character decomposition often harms unseen character translation, and gives inconsistent results generally. We offer a simple alternative based on decomposition before inference for unseen characters only. Our approach allows flexible application, achieving translation adequacy improvements and requiring no additional models or training.",
}
@inproceedings{ramesh-etal-2020-error,
    title = "An Error-based Investigation of Statistical and Neural Machine Translation Performance on {H}indi-to-{T}amil and {E}nglish-to-{T}amil",
    author = "Ramesh, Akshai  and
      Balavadhani Parthasa, Venkatesh  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wat-1.22",
    pages = "178--188",
    abstract = "Statistical machine translation (SMT) was the state-of-the-art in machine translation (MT) research for more than two decades, but has since been superseded by neural MT (NMT). Despite producing state-of-the-art results in many translation tasks, neural models underperform in resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and NMT on two rarely-tested low-resource language-pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain (software localisation) into consideration. This paper demonstrates our findings including the identification of several issues of the current neural approaches to low-resource domain-specific text translation.",
}
@proceedings{wanlp-2020-arabic,
    title = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    editor = "Zitouni, Imed  and
      Abdul-Mageed, Muhammad  and
      Bouamor, Houda  and
      Bougares, Fethi  and
      El-Haj, Mahmoud  and
      Tomeh, Nadi  and
      Zaghouani, Wajdi",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.0",
}
@inproceedings{hussain-etal-2020-german,
    title = "{G}erman-{A}rabic Speech-to-Speech Translation for Psychiatric Diagnosis",
    author = {Hussain, Juan  and
      Mediani, Mohammed  and
      Behr, Moritz  and
      Cheragui, M. Amin  and
      St{\"u}ker, Sebastian  and
      Waibel, Alexander},
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.1",
    pages = "1--11",
    abstract = "In this paper we present the natural language processing components of our German-Arabic speech-to-speech translation system which is being deployed in the context of interpretation during psychiatric, diagnostic interviews. For this purpose we have built a pipe-lined speech-to-speech translation system consisting of automatic speech recognition, text post-processing/segmentation, machine translation and speech synthesis systems. We have implemented two pipe-lines, from German to Arabic and Arabic to German, in order to be able to conduct interpreted two-way dialogues between psychiatrists and potential patients. All systems in our pipeline have been realized as all-neural end-to-end systems, using different architectures suitable for the different components. The speech recognition systems use an encoder/decoder + attention architecture, the text segmentation component and the machine translation system are based on the Transformer architecture, and for the speech synthesis systems we use Tacotron 2 for generating spectrograms and WaveGlow as vocoder. The speech translation is deployed in a server-based speech translation application that implements a turn based translation between a German speaking psychiatrist administrating the Mini-International Neuropsychiatric Interview (M.I.N.I.) and an Arabic speaking person answering the interview. As this is a very specific domain, in addition to the linguistic challenges posed by translating between Arabic and German, we also focus in this paper on the methods we implemented for adapting our speech translation system to the domain of this psychiatric interview.",
}
@inproceedings{alshaalan-al-khalifa-2020-hate,
    title = "Hate Speech Detection in Saudi Twittersphere: A Deep Learning Approach",
    author = "Alshaalan, Raghad  and
      Al-Khalifa, Hend",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.2",
    pages = "12--23",
    abstract = "With the rise of hate speech phenomena in Twittersphere, significant research efforts have been undertaken to provide automatic solutions for detecting hate speech, varying from simple ma-chine learning models to more complex deep neural network models. Despite that, research works investigating hate speech problem in Arabic are still limited. This paper, therefore, aims to investigate several neural network models based on Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) to detect hate speech in Arabic tweets. It also evaluates the recent language representation model BERT on the task of Arabic hate speech detection. To conduct our experiments, we firstly built a new hate speech dataset that contains 9,316 annotated tweets. Then, we conducted a set of experiments on two datasets to evaluate four models: CNN, GRU, CNN+GRU and BERT. Our experimental results on our dataset and an out-domain dataset show that CNN model gives the best performance with an F1-score of 0.79 and AUROC of 0.89.",
}
@inproceedings{saadany-orasan-2020-great,
    title = "Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of {A}rabic Reviews",
    author = "Saadany, Hadeel  and
      Orasan, Constantin",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.3",
    pages = "24--37",
    abstract = "Since the advent of Neural Machine Translation (NMT) approaches there has been a tremendous improvement in the quality of automatic translation. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes major errors that need extensive postediting. This is particularly noticeable with texts that do not follow common lexico-grammatical standards, such as user generated content (UGC). In this paper we investigate the challenges involved in translating book reviews from Arabic into English, with particular focus on the errors that lead to incorrect translation of sentiment polarity. Our study points to the special characteristics of Arabic UGC, examines the sentiment transfer errors made by Google Translate of Arabic UGC to English, analyzes why the problem occurs, and proposes an error typology specific of the translation of Arabic UGC. Our analysis shows that the output of online translation tools of Arabic UGC can either fail to transfer the sentiment at all by producing a neutral target text, or completely flips the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. We address this problem by fine-tuning an NMT model with respect to sentiment polarity showing that this approach can significantly help with correcting sentiment errors detected in the online translation of Arabic UGC.",
}
@inproceedings{alkhamissi-etal-2020-deep,
    title = "Deep Diacritization: Efficient Hierarchical Recurrence for Improved {A}rabic Diacritization",
    author = "AlKhamissi, Badr  and
      ElNokrashy, Muhammad  and
      Gabr, Mohamed",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.4",
    pages = "38--48",
    abstract = "We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark. The core is a two-level recurrence hierarchy that operates on the word and character levels separately{---}enabling faster training and inference than comparable traditional models. A cross-level attention module further connects the two and opens the door for network interpretability. The task module is a softmax classifier that enumerates valid combinations of diacritics. This architecture can be extended with a recurrent decoder that optionally accepts priors from partially diacritized text, which improves results. We employ extra tricks such as sentence dropout and majority voting to further boost the final result. Our best model achieves a WER of 5.34{\%}, outperforming the previous state-of-the-art with a 30.56{\%} relative error reduction.",
}
@inproceedings{helwe-etal-2020-semi,
    title = "A Semi-Supervised {BERT} Approach for {A}rabic Named Entity Recognition",
    author = "Helwe, Chadi  and
      Dib, Ghassan  and
      Shamas, Mohsen  and
      Elbassuoni, Shady",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.5",
    pages = "49--57",
    abstract = "Named entity recognition (NER) plays a significant role in many applications such as information extraction, information retrieval, question answering, and even machine translation. Most of the work on NER using deep learning was done for non-Arabic languages like English and French, and only few studies focused on Arabic. This paper proposes a semi-supervised learning approach to train a BERT-based NER model using labeled and semi-labeled datasets. We compared our approach against various baselines, and state-of-the-art Arabic NER tools on three datasets: AQMAR, NEWS, and TWEETS. We report a significant improvement in F-measure for the AQMAR and the NEWS datasets, which are written in Modern Standard Arabic (MSA), and competitive results for the TWEETS dataset, which contains tweets that are mostly in the Egyptian dialect and contain many mistakes or misspellings.",
}
@inproceedings{naous-etal-2020-empathy,
    title = "Empathy-driven {A}rabic Conversational Chatbot",
    author = "Naous, Tarek  and
      Hokayem, Christian  and
      Hajj, Hazem",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.6",
    pages = "58--68",
    abstract = "Conversational models have witnessed a significant research interest in the last few years with the advancements in sequence generation models. A challenging aspect in developing human-like conversational models is enabling the sense of empathy in bots, making them infer emotions from the person they are interacting with. By learning to develop empathy, chatbot models are able to provide human-like, empathetic responses, thus making the human-machine interaction close to human-human interaction. Recent advances in English use complex encoder-decoder language models that require large amounts of empathetic conversational data. However, research has not produced empathetic bots for Arabic. Furthermore, there is a lack of Arabic conversational data labeled with empathy. To address these challenges, we create an Arabic conversational dataset that comprises empathetic responses. However, the dataset is not large enough to develop very complex encoder-decoder models. To address the limitation of data scale, we propose a special encoder-decoder composed of a Long Short-Term Memory (LSTM) Sequence-to-Sequence (Seq2Seq) with Attention. The experiments showed success of our proposed empathy-driven Arabic chatbot in generating empathetic responses with a perplexity of 38.6, an empathy score of 3.7, and a fluency score of 3.92.",
}
@inproceedings{nagoudi-etal-2020-machine,
    title = "Machine Generation and Detection of {A}rabic Manipulated and Fake News",
    author = "Nagoudi, El Moatez Billah  and
      Elmadany, AbdelRahim  and
      Abdul-Mageed, Muhammad  and
      Alhindi, Tariq",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.7",
    pages = "69--84",
    abstract = "Fake news and deceptive machine-generated text are serious problems threatening modern societies, including in the Arab world. This motivates work on detecting false and manipulated stories online. However, a bottleneck for this research is lack of sufficient data to train detection models. We present a novel method for automatically generating Arabic manipulated (and potentially fake) news stories. Our method is simple and only depends on availability of true stories, which are abundant online, and a part of speech tagger (POS). To facilitate future work, we dispense with both of these requirements altogether by providing AraNews, a novel and large POS-tagged news dataset that can be used off-the-shelf. Using stories generated based on AraNews, we carry out a human annotation study that casts light on the effects of machine manipulation on text veracity. The study also measures human ability to detect Arabic machine manipulated text generated by our method. Finally, we develop the first models for detecting manipulated Arabic news and achieve state-of-the-art results on Arabic fake news detection (macro F1=70.06). Our models and data are publicly available.",
}
@inproceedings{terner-etal-2020-transliteration,
    title = "Transliteration of {J}udeo-{A}rabic Texts into {A}rabic Script Using Recurrent Neural Networks",
    author = "Terner, Ori  and
      Bar, Kfir  and
      Dershowitz, Nachum",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.8",
    pages = "85--96",
    abstract = "We trained a model to automatically transliterate Judeo-Arabic texts into Arabic script, enabling Arabic readers to access those writings. We employ a recurrent neural network (RNN), combined with the connectionist temporal classification (CTC) loss to deal with unequal input/output lengths. This obligates adjustments in the training data to avoid input sequences that are shorter than their corresponding outputs. We also utilize a pretraining stage with a different loss function to improve network converge. Since only a single source of parallel text was available for training, we take advantage of the possibility of generating data synthetically. We train a model that has the capability to memorize words in the output language, and that also utilizes context for distinguishing ambiguities in the transliteration. We obtain an improvement over the baseline 9.5{\%} character error, achieving 2{\%} error with our best configuration. To measure the contribution of context to learning, we also tested word-shuffled data, for which the error rises to 2.5{\%}.",
}
@inproceedings{abdul-mageed-etal-2020-nadi,
    title = "{NADI} 2020: The First Nuanced {A}rabic Dialect Identification Shared Task",
    author = "Abdul-Mageed, Muhammad  and
      Zhang, Chiyu  and
      Bouamor, Houda  and
      Habash, Nizar",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.9",
    pages = "97--110",
    abstract = "We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identification (Subtask 1) and province-level sub-dialect identification (Subtask 2). The data for the shared task covers a total of 100 provinces from 21 Arab countries and is collected from the Twitter domain. As such, NADI is the first shared task to target naturally-occurring fine-grained dialectal text at the sub-country level. A total of 61 teams from 25 countries registered to participate in the tasks, thus reflecting the interest of the community in this area. We received 47 submissions for Subtask 1 from 18 teams and 9 submissions for Subtask 2 from 9 teams.",
}
@inproceedings{talafha-etal-2020-multi,
    title = "Multi-dialect {A}rabic {BERT} for Country-level Dialect Identification",
    author = "Talafha, Bashar  and
      Ali, Mohammad  and
      Za{'}ter, Muhy Eddin  and
      Seelawi, Haitham  and
      Tuffaha, Ibraheem  and
      Samir, Mostafa  and
      Farhan, Wael  and
      Al-Natsheh, Hussein",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.10",
    pages = "111--118",
    abstract = "Arabic dialect identification is a complex problem for a number of inherent properties of the language itself. In this paper, we present the experiments conducted, and the models developed by our competing team, Mawdoo3 AI, along the way to achieving our winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task. The dialect identification subtask provides 21,000 country-level labeled tweets covering all 21 Arab countries. An unlabeled corpus of 10M tweets from the same domain is also presented by the competition organizers for optional use. Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26.78{\%} on the subtask at hand. We publicly release the pre-trained language model component of our winning solution under the name of Multi-dialect-Arabic-BERT model, for any interested researcher out there.",
}
@inproceedings{alkaoud-syed-2020-importance,
    title = "On the Importance of Tokenization in {A}rabic Embedding Models",
    author = "Alkaoud, Mohamed  and
      Syed, Mairaj",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.11",
    pages = "119--129",
    abstract = "Arabic, like other highly inflected languages, encodes a large amount of information in its morphology and word structure. In this work, we propose two embedding strategies that modify the tokenization phase of traditional word embedding models (Word2Vec) and contextual word embedding models (BERT) to take into account Arabic{'}s relatively complex morphology. In Word2Vec, we segment words into subwords during training time and then compose word-level representations from the subwords during test time. We train our embeddings on Arabic Wikipedia and show that they perform better than a Word2Vec model on multiple Arabic natural language processing datasets while being approximately 60{\%} smaller in size. Moreover, we showcase our embeddings{'} ability to produce accurate representations of some out-of-vocabulary words that were not encountered before. In BERT, we modify the tokenization layer of Google{'}s pretrained multilingual BERT model by incorporating information on morphology. By doing so, we achieve state of the art performance on two Arabic NLP datasets without pretraining.",
}
@inproceedings{muther-smith-2020-tracing,
    title = "Tracing Traditions: Automatic Extraction of Isnads from Classical {A}rabic Texts",
    author = "Muther, Ryan  and
      Smith, David",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.12",
    pages = "130--138",
    abstract = "We present our work on automatically detecting isnads, the chains of authorities for a re-port that serve as citations in hadith and other classical Arabic texts. We experiment with both sequence labeling methods for identifying isnads in a single pass and a hybrid {``}retrieve-and-tag{''} approach, in which a retrieval model first identifies portions of the text that are likely to contain start points for isnads, then a sequence labeling model identifies the exact starting locations within these much smaller retrieved text chunks. We find that the usefulness of full-document sequence to sequence models is limited due to memory limitations and the ineffectiveness of such models at modeling very long documents. We conclude by sketching future improvements on the tagging task and more in-depth analysis of the people and relationships involved in the social network that influenced the evolution of the written tradition over time.",
}
@inproceedings{younes-weeds-2020-embed,
    title = "Embed More Ignore Less ({EMIL}): Exploiting Enriched Representations for {A}rabic {NLP}",
    author = "Younes, Ahmed  and
      Weeds, Julie",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.13",
    pages = "139--154",
    abstract = "Our research focuses on the potential improvements of exploiting language specific characteristics in the form of embeddings by neural networks. More specifically, we investigate the capability of neural techniques and embeddings to represent language specific characteristics in two sequence labeling tasks: named entity recognition (NER) and part of speech (POS) tagging. In both tasks, our preprocessing is designed to use enriched Arabic representation by adding diacritics to undiacritized text. In POS tagging, we test the ability of a neural model to capture syntactic characteristics encoded within these diacritics by incorporating an embedding layer for diacritics alongside embedding layers for words and characters. In NER, our architecture incorporates diacritic and POS embeddings alongside word and character embeddings. Our experiments are conducted on 7 datasets (4 NER and 3 POS). We show that embedding the information that is encoded in automatically acquired Arabic diacritics improves the performance across all datasets on both tasks. Embedding the information in automatically assigned POS tags further improves performance on the NER task.",
}
@inproceedings{zarnoufi-etal-2020-manorm,
    title = "{MAN}orm: A Normalization Dictionary for {M}oroccan {A}rabic Dialect Written in {L}atin Script",
    author = "Zarnoufi, Randa  and
      Jaafar, Hamid  and
      Bachri, Walid  and
      Abik, Mounia",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.14",
    pages = "155--166",
    abstract = "Social media user generated text is actually the main resource for many NLP tasks. This text, however, does not follow the standard rules of writing. Moreover, the use of dialect such as Moroccan Arabic in written communications increases further NLP tasks complexity. A dialect is a verbal language that does not have a standard orthography. The written dialect is based on the phonetic transliteration of spoken words which leads users to improvise spelling while writing. Thus, for the same word we can find multiple forms of transliterations. Subsequently, it is mandatory to normalize these different transliterations to one canonical word form. To reach this goal, we have exploited the powerfulness of word embedding models generated with a corpus of YouTube comments. Besides, using a Moroccan Arabic dialect dictionary that provides the canonical forms, we have built a normalization dictionary that we refer to as MANorm. We have conducted several experiments to demonstrate the efficiency of MANorm, which have shown its usefulness in dialect normalization. We made MANorm freely available online.",
}
@inproceedings{shazal-etal-2020-unified,
    title = "A Unified Model for {A}rabizi Detection and Transliteration using Sequence-to-Sequence Models",
    author = "Shazal, Ali  and
      Usman, Aiza  and
      Habash, Nizar",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.15",
    pages = "167--177",
    abstract = "While online Arabic is primarily written using the Arabic script, a Roman-script variety called Arabizi is often seen on social media. Although this representation captures the phonology of the language, it is not a one-to-one mapping with the Arabic script version. This issue is exacerbated by the fact that Arabizi on social media is Dialectal Arabic which does not have a standard orthography. Furthermore, Arabizi tends to include a lot of code mixing between Arabic and English (or French). To map Arabizi text to Arabic script in the context of complete utterances, previously published efforts have split Arabizi detection and Arabic script target in two separate tasks. In this paper, we present the first effort on a unified model for Arabizi detection and transliteration into a code-mixed output with consistent Arabic spelling conventions, using a sequence-to-sequence deep learning model. Our best system achieves 80.6{\%} word accuracy and 58.7{\%} BLEU on a blind test set.",
}
@inproceedings{gugliotta-etal-2020-multi,
    title = "Multi-Task Sequence Prediction For {T}unisian {A}rabizi Multi-Level Annotation",
    author = "Gugliotta, Elisa  and
      Dinarelli, Marco  and
      Kraif, Olivier",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.16",
    pages = "178--191",
    abstract = "In this paper we propose a multi-task sequence prediction system, based on recurrent neural networks and used to annotate on multiple levels an Arabizi Tunisian corpus. The annotation performed are text classification, tokenization, PoS tagging and encoding of Tunisian Arabizi into CODA* Arabic orthography. The system is learned to predict all the annotation levels in cascade, starting from Arabizi input. We evaluate the system on the TIGER German corpus, suitably converting data to have a multi-task problem, in order to show the effectiveness of our neural architecture. We show also how we used the system in order to annotate a Tunisian Arabizi corpus, which has been afterwards manually corrected and used to further evaluate sequence models on Tunisian data. Our system is developed for the Fairseq framework, which allows for a fast and easy use for any other sequence prediction problem.",
}
@inproceedings{lauscher-etal-2020-araweat,
    title = "{A}ra{WEAT}: Multidimensional Analysis of Biases in {A}rabic Word Embeddings",
    author = "Lauscher, Anne  and
      Takieddin, Rafik  and
      Ponzetto, Simone Paolo  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.17",
    pages = "192--199",
    abstract = "Recent work has shown that distributional word vector spaces often encode human biases like sexism or racism. In this work, we conduct an extensive analysis of biases in Arabic word embeddings by applying a range of recently introduced bias tests on a variety of embedding spaces induced from corpora in Arabic. We measure the presence of biases across several dimensions, namely: embedding models (Skip-Gram, CBOW, and FastText) and vector sizes, types of text (encyclopedic text, and news vs. user-generated content), dialects (Egyptian Arabic vs. Modern Standard Arabic), and time (diachronic analyses over corpora from different time periods). Our analysis yields several interesting findings, e.g., that implicit gender bias in embeddings trained on Arabic news corpora steadily increases over time (between 2007 and 2017). We make the Arabic bias specifications (AraWEAT) publicly available.",
}
@inproceedings{kchaou-etal-2020-parallel,
    title = "Parallel resources for {T}unisian {A}rabic Dialect Translation",
    author = "Kchaou, Sam{\'e}h  and
      Boujelbane, Rahma  and
      Hadrich-Belguith, Lamia",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.18",
    pages = "200--206",
    abstract = "The difficulty of processing dialects is clearly observed in the high cost of building representative corpus, in particular for machine translation. Indeed, all machine translation systems require a huge amount and good management of training data, which represents a challenge in a low-resource setting such as the Tunisian Arabic dialect. In this paper, we present a data augmentation technique to create a parallel corpus for Tunisian Arabic dialect written in social media and standard Arabic in order to build a Machine Translation (MT) model. The created corpus was used to build a sentence-based translation model. This model reached a BLEU score of 15.03{\%} on a test set, while it was limited to 13.27{\%} utilizing the corpus without augmentation.",
}
@inproceedings{harrag-etal-2020-bert,
    title = "Bert Transformer model for Detecting {A}rabic {GPT}2 Auto-Generated Tweets",
    author = "Harrag, Fouzi  and
      Dabbah, Maria  and
      Darwish, Kareem  and
      Abdelali, Ahmed",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.19",
    pages = "207--214",
    abstract = "During the last two decades, we have progressively turned to the Internet and social media to find news, entertain conversations and share opinion. Recently, OpenAI has developed a machine learning system called GPT-2 for Generative Pre-trained Transformer-2, which can produce deepfake texts. It can generate blocks of text based on brief writing prompts that look like they were written by humans, facilitating the spread false or auto-generated text. In line with this progress, and in order to counteract potential dangers, several methods have been proposed for detecting text written by these language models. In this paper, we propose a transfer learning based model that will be able to detect if an Arabic sentence is written by humans or automatically generated by bots. Our dataset is based on tweets from a previous work, which we have crawled and extended using the Twitter API. We used GPT2-Small-Arabic to generate fake Arabic Sentences. For evaluation, we compared different recurrent neural network (RNN) word embeddings based baseline models, namely: LSTM, BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new transfer-learning model has obtained an accuracy up to 98{\%}. To the best of our knowledge, this work is the first study where ARABERT and GPT2 were combined to detect and classify the Arabic auto-generated texts.",
}
@inproceedings{sabty-etal-2020-contextual,
    title = "Contextual Embeddings for {A}rabic-{E}nglish Code-Switched Data",
    author = "Sabty, Caroline  and
      Islam, Mohamed  and
      Abdennadher, Slim",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.20",
    pages = "215--225",
    abstract = "Globalization has caused the rise of the code-switching phenomenon among multilingual societies. In Arab countries, code-switching between Arabic and English has become frequent, especially through social media platforms. Consequently, research in Natural Language Processing (NLP) systems increased to tackle such a phenomenon. One of the significant challenges of developing code-switched NLP systems is the lack of data itself. In this paper, we propose an open source trained bilingual contextual word embedding models of FLAIR, BERT, and ELECTRA. We also propose a novel contextual word embedding model called KERMIT, which can efficiently map Arabic and English words inside one vector space in terms of data usage. We applied intrinsic and extrinsic evaluation methods to compare the performance of the models. Our results show that FLAIR and FastText achieve the highest results in the sentiment analysis task. However, KERMIT is the best-achieving model on the intrinsic evaluation and named entity recognition. Also, it outperforms the other transformer-based models on question answering task.",
}
@inproceedings{chowdhury-etal-2020-improving-arabic,
    title = "Improving {A}rabic Text Categorization Using Transformer Training Diversification",
    author = "Chowdhury, Shammur Absar  and
      Abdelali, Ahmed  and
      Darwish, Kareem  and
      Soon-Gyo, Jung  and
      Salminen, Joni  and
      Jansen, Bernard J.",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.21",
    pages = "226--236",
    abstract = "Automatic categorization of short texts, such as news headlines and social media posts, has many applications ranging from content analysis to recommendation systems. In this paper, we use such text categorization i.e., labeling the social media posts to categories like {`}sports{'}, {`}politics{'}, {`}human-rights{'} among others, to showcase the efficacy of models across different sources and varieties of Arabic. In doing so, we show that diversifying the training data, whether by using diverse training data for the specific task (an increase of 21{\%} macro F1) or using diverse data to pre-train a BERT model (26{\%} macro F1), leads to overall improvements in classification effectiveness. In our work, we also introduce two new Arabic text categorization datasets, where the first is composed of social media posts from a popular Arabic news channel that cover Twitter, Facebook, and YouTube, and the second is composed of tweets from popular Arabic accounts. The posts in the former are nearly exclusively authored in modern standard Arabic (MSA), while the tweets in the latter contain both MSA and dialectal Arabic.",
}
@inproceedings{younes-etal-2020-team,
    title = "Team {A}lexa at {NADI} Shared Task",
    author = "Younes, Mutaz  and
      Al-khdour, Nour  and
      AL-Smadi, Mohammad",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.22",
    pages = "237--242",
    abstract = "In this paper, we discuss our team{'}s work on the NADI Shared Task. The task requires classifying Arabic tweets among 21 dialects. We tested out different approaches, and the best one was the simplest one. Our best submission was using Multinational Naive Bayes (MNB) classifier (Small and Hsiao, 1985) with n-grams as features. Despite its simplicity, this classifier shows better results than complicated models such as BERT. Our best submitted score was 17{\%} F1-score and 35{\%} accuracy.",
}
@inproceedings{dhaou-lejeune-2020-comparison,
    title = "Comparison between Voting Classifier and Deep Learning methods for {A}rabic Dialect Identification",
    author = {Dhaou, Ghoul  and
      Lejeune, Ga{\"e}l},
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.23",
    pages = "243--249",
    abstract = "In this paper, we present three methods developed for the NADI shared task on Arabic Dialect Identification for tweets. The first and the second method use respectively a machine learning model based on a Voting Classifier with words and character level features and a deep learning model at word level. The third method uses only character-level features. We explored different text representation such as Tf-idf (first model) and word embeddings (second model). The Voting Classifier was the most powerful prediction model, achieving the best macro-average F1 score of 18.8{\%} and an accuracy of 36.54{\%} on the official test. Our model ranked 9 on the challenge and in conclusion we propose some ideas to improve its results.",
}
@inproceedings{lichouri-abbas-2020-simple,
    title = "Simple vs Oversampling-based Classification Methods for Fine Grained {A}rabic Dialect Identification in {T}witter",
    author = "Lichouri, Mohamed  and
      Abbas, Mourad",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.24",
    pages = "250--256",
    abstract = "In this paper, we present a description of our experiments on country-level Arabic dialect identification. A comparison study between a set of classifiers has been carried out. The best results were achieved using the Linear Support Vector Classification (LSVC) model by applying a Random Over Sampling (ROS) process yielding an F1-score of 18.74{\%} in the post-evaluation phase.In the evaluation phase, our best submitted system has achieved an F1-score of 18.27{\%}, very close to the average F1-score (18.80{\%}) obtained for all the submitted systems.",
}
@inproceedings{appiah-balaji-b-2020-semi,
    title = "Semi-supervised Fine-grained Approach for {A}rabic dialect detection task",
    author = "Appiah Balaji, Nitin Nikamanth  and
      B, Bharathi",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.25",
    pages = "257--261",
    abstract = "Arabic being a language with numerous different dialects, it becomes extremely important to device a technique to distinguish each dialect efficiently. This paper focuses on the fine-grained country level and province level classification of Arabic dialects. The experiments in this paper are submissions done to the NADI 2020 shared Dialect detection task. Various text feature extraction techniques such as TF-IDF, AraVec, multilingual BERT and Fasttext embedding models are studied. We thereby, propose an approach of text embedding based model with macro average F1 score of 0.2232 for task1 and 0.0483 for task2, with the help of semi supervised learning approach.",
}
@inproceedings{beltagy-etal-2020-arabic,
    title = "{A}rabic Dialect Identification Using {BERT}-Based Domain Adaptation",
    author = "Beltagy, Ahmad  and
      Abouelenin, Abdelrahman  and
      ElSherief, Omar",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.26",
    pages = "262--267",
    abstract = "Arabic is one of the most important and growing languages in the world. With the rise of the social media giants like Twitter, Arabic spoken dialects have become more in use. In this paper we describe our effort and simple approach on the NADI Shared Task 1 that requires us to build a system to differentiate between different 21 Arabic dialects, we introduce a deep learning semisupervised fashion approach along with pre-processing that was reported on NADI shared Task 1 Corpus. Our system ranks 4th in NADI{'}s shared task competition achieving 23.09{\%} F1 macro average score with a very simple yet an efficient approach on differentiating between 21 Arabic Dialects given tweets.",
}
@inproceedings{el-mekki-etal-2020-weighted,
    title = "Weighted combination of {BERT} and N-{GRAM} features for Nuanced {A}rabic Dialect Identification",
    author = "El Mekki, Abdellah  and
      Alami, Ahmed  and
      Alami, Hamza  and
      Khoumsi, Ahmed  and
      Berrada, Ismail",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.27",
    pages = "268--274",
    abstract = "Around the Arab world, different Arabic dialects are spoken by more than 300M persons, and are increasingly popular in social media texts. However, Arabic dialects are considered to be low-resource languages, limiting the development of machine-learning based systems for these dialects. In this paper, we investigate the Arabic dialect identification task, from two perspectives: country-level dialect identification from 21 Arab countries, and province-level dialect identification from 100 provinces. We introduce an unified pipeline of state-of-the-art models, that can handle the two subtasks. Our experimental studies applied to the NADI shared task, show promising results both at the country-level (F1-score of 25.99{\%}) and the province-level (F1-score of 6.39{\%}), and thus allow us to be ranked 2nd for the country-level subtask, and 1st in the province-level subtask.",
}
@inproceedings{gaanoun-benelallam-2020-arabic,
    title = "{A}rabic dialect identification: An {A}rabic-{BERT} model with data augmentation and ensembling strategy",
    author = "Gaanoun, Kamel  and
      Benelallam, Imade",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.28",
    pages = "275--281",
    abstract = "This paper presents the ArabicProcessors team{'}s deep learning system designed for the NADI 2020 Subtask 1 (country-level dialect identification) and Subtask 2 (province-level dialect identification). We used Arabic-Bert in combination with data augmentation and ensembling methods. Unlabeled data provided by task organizers (10 Million tweets) was split into multiple subparts, to which we applied semi-supervised learning method, and finally ran a specific ensembling process on the resulting models. This system ranked 3rd in Subtask 1 with 23.26{\%} F1-score and 2nd in Subtask 2 with 5.75{\%} F1-score.",
}
@inproceedings{alshenaifi-azmi-2020-faheem,
    title = "Faheem at {NADI} shared task: Identifying the dialect of {A}rabic tweet",
    author = "AlShenaifi, Nouf  and
      Azmi, Aqil",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.29",
    pages = "282--287",
    abstract = "This paper describes Faheem (adj. of understand), our submission to NADI (Nuanced Arabic Dialect Identification) shared task. With so many Arabic dialects being under-studied due to the scarcity of the resources, the objective is to identify the Arabic dialect used in the tweet, country wise. We propose a machine learning approach where we utilize word-level n-gram (n = 1 to 3) and tf-idf features and feed them to six different classifiers. We train the system using a data set of 21,000 tweets{---}provided by the organizers{---}covering twenty-one Arab countries. Our top performing classifiers are: Logistic Regression, Support Vector Machines, and Multinomial Na ̈{\i}ve Bayes.",
}
@inproceedings{tahssin-etal-2020-identifying,
    title = "Identifying Nuanced Dialect for {A}rabic Tweets with Deep Learning and Reverse Translation Corpus Extension System",
    author = "Tahssin, Rawan  and
      Kishk, Youssef  and
      Torki, Marwan",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.30",
    pages = "288--294",
    abstract = "In this paper, we present our work for the NADI Shared Task (Abdul-Mageed and Habash, 2020): Nuanced Arabic Dialect Identification for Subtask-1: country-level dialect identification. We introduce a Reverse Translation Corpus Extension Systems (RTCES) to handle data imbalance along with reported results on several experimented approaches of word and document representations and different models architectures. The top scoring model was based on AraBERT (Antoun et al., 2020), with our modified extended corpus based on reverse translation of the given Arabic tweets. The selected system achieved a macro average F1 score of 20.34{\%} on the test set, which places us as the 7th out of 18 teams in the final ranking Leaderboard.",
}
@inproceedings{aloraini-etal-2020-qmul,
    title = "The {QMUL}/{HRBDT} contribution to the {NADI} {A}rabic Dialect Identification Shared Task",
    author = "Aloraini, Abdulrahman  and
      Poesio, Massimo  and
      Alhelbawy, Ayman",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.31",
    pages = "295--301",
    abstract = "We present the Arabic dialect identification system that we used for the country-level subtask of the NADI challenge. Our model consists of three components: BiLSTM-CNN, character-level TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieved an F1-Macro score of 20.77{\%} and an accuracy of 34.32{\%} on the test set. The model was also evaluated on the Arabic Online Commentary dataset, achieving results better than the state-of-the-art.",
}
@inproceedings{aliwy-etal-2020-arabic,
    title = "{A}rabic Dialects Identification for All {A}rabic countries",
    author = "Aliwy, Ahmed  and
      Taher, Hawraa  and
      AboAltaheen, Zena",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.32",
    pages = "302--307",
    abstract = {Arabic dialects are among of three main variant of Arabic language (Classical Arabic, modern standard Arabic and dialectal Arabic). It has many variants according to the country, city (provinces) or town. In this paper, several techniques with multiple algorithms are applied for Arabic dialects identification starting from removing noise till classification task using all Arabic countries as 21 classes. Three types of classifiers (Na{\"\i}ve Bayes, Logistic Regression, and Decision Tree) are combined using voting with two different methodologies. Also clustering technique is used for decreasing the noise that result from the existing of MSA tweets in the data set for training phase. The results of f-measure were 27.17, 41.34 and 52.38 for first methodology without clustering, second methodology without clustering, and second methodology with clustering, the used data set is NADI shared task data set.},
}
@inproceedings{mansour-etal-2020-arabic,
    title = "{A}rabic Dialect Identification Using {BERT} Fine-Tuning",
    author = "Mansour, Moataz  and
      Tohamy, Moustafa  and
      Ezzat, Zeyad  and
      Torki, Marwan",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.33",
    pages = "308--312",
    abstract = "In the last few years, deep learning has proved to be a very effective paradigm to discover patterns in large data sets. Unfortunately, deep learning training on small data sets is not the best option because most of the time traditional machine learning algorithms could get better scores. Now, we can train the neural network on a large data set then fine-tune on a smaller data set using the transfer learning technique. In this paper, we present our system for NADI shared Task: Country-level Dialect Identification, Our system is based on fine-tuning of BERT and it achieves 22.85 F1-score on Test Set and our rank is 5th out of 18 teams.",
}
@inproceedings{touileb-2020-ltg,
    title = "{LTG}-{ST} at {NADI} Shared Task 1: {A}rabic Dialect Identification using a Stacking Classifier",
    author = "Touileb, Samia",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wanlp-1.34",
    pages = "313--319",
    abstract = "This paper presents our results for the Nuanced Arabic Dialect Identification (NADI) shared task of the Fifth Workshop for Arabic Natural Language Processing (WANLP 2020). We participated in the first sub-task for country-level Arabic dialect identification covering 21 Arab countries. Our contribution is based on a stacking classifier using Multinomial Naive Bayes, Linear SVC, and Logistic Regression classifiers as estimators; followed by a Logistic Regression as final estimator. Despite the fact that the results on the test set were low, with a macro F1 of 17.71, we were able to show that a simple approach can achieve comparable results to more sophisticated solutions. Moreover, the insights of our error analysis, and of the corpus content in general, can be used to develop and improve future systems.",
}
@proceedings{wac-2020-web,
    title = "Proceedings of the 12th Web as Corpus Workshop",
    editor = {Barbaresi, Adrien  and
      Bildhauer, Felix  and
      Sch{\"a}fer, Roland  and
      Stemle, Egon},
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.0",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{jakubicek-etal-2020-current,
    title = "Current Challenges in Web Corpus Building",
    author = "Jakub{\'\i}{\v{c}}ek, Milo{\v{s}}  and
      Kov{\'a}{\v{r}}, Vojt{\v{e}}ch  and
      Rychl{\'y}, Pavel  and
      Suchomel, Vit",
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.1",
    pages = "1--4",
    abstract = "In this paper we discuss some of the current challenges in web corpus building that we faced in the recent years when expanding the corpora in Sketch Engine. The purpose of the paper is to provide an overview and raise discussion on possible solutions, rather than bringing ready solutions to the readers. For every issue we try to assess its severity and briefly discuss possible mitigation options.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{barbaresi-lejeune-2020-box,
    title = "Out-of-the-Box and into the Ditch? Multilingual Evaluation of Generic Text Extraction Tools",
    author = {Barbaresi, Adrien  and
      Lejeune, Ga{\"e}l},
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.2",
    pages = "5--13",
    abstract = "This article examines extraction methods designed to retain the main text content of web pages and discusses how the extraction could be oriented and evaluated: can and should it be as generic as possible to ensure opportunistic corpus construction? The evaluation grounds on a comparative benchmark of open-source tools used on pages in five different languages (Chinese, English, Greek, Polish and Russian), it features several metrics to obtain more fine-grained differentiations. Our experiments highlight the diversity of web page layouts across languages or publishing countries. These discrepancies are reflected by diverging performances so that the right tool has to be chosen accordingly.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{laippala-etal-2020-web,
    title = "From Web Crawl to Clean Register-Annotated Corpora",
    author = {Laippala, Veronika  and
      R{\"o}nnqvist, Samuel  and
      Hellstr{\"o}m, Saara  and
      Luotolahti, Juhani  and
      Repo, Liina  and
      Salmela, Anna  and
      Skantsi, Valtteri  and
      Pyysalo, Sampo},
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.3",
    pages = "14--22",
    abstract = "The web presents unprecedented opportunities for large-scale collection of text in many languages. However, two critical steps in the development of web corpora remain challenging: the identification of clean text from source HTML and the assignment of genre or register information to the documents. In this paper, we evaluate a multilingual approach to this end. Our starting points are the Swedish and French Common Crawl datasets gathered for the 2017 CoNLL shared task, particularly the URLs. We 1) fetch HTML pages based on the URLs and run boilerplate removal, 2) train a classifier to further clean out undesired text fragments, and 3) annotate text registers. We compare boilerplate removal against the CoNLL texts, and find an improvement. For the further cleaning of undesired material, the best results are achieved using Multilingual BERT with monolingual fine-tuning. However, our results are promising also in a cross-lingual setting, without fine-tuning on the target language. Finally, the register annotations show that most of the documents belong to a relatively small set of registers, which are relatively similar in the two languages. A number of additional flags in the annotation are, however, necessary to reflect the wide range of linguistic variation associated with the documents.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{jauhiainen-etal-2020-building,
    title = "Building Web Corpora for Minority Languages",
    author = "Jauhiainen, Heidi  and
      Jauhiainen, Tommi  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.4",
    pages = "23--32",
    abstract = "Web corpora creation for minority languages that do not have their own top-level Internet domain is no trivial matter. Web pages in such minority languages often contain text and links to pages in the dominant language of the country. When building corpora in specific languages, one has to decide how and at which stage to make sure the texts gathered are in the desired language. In the {``}Finno-Ugric Languages and the Internet{''} (Suki) project, we created web corpora for Uralic minority languages using web crawling combined with a language identification system in order to identify the language while crawling. In addition, we used language set identification and crowdsourcing before making sentence corpora out of the downloaded texts. In this article, we describe a strategy for collecting textual material from the Internet for minority languages. The strategy is based on the experiences we gained during the Suki project.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{indig-etal-2020-elte,
    title = "The {ELTE}.{DH} Pilot Corpus {--} Creating a Handcrafted {G}igaword Web Corpus with Metadata",
    author = {Indig, Bal{\'a}zs  and
      Knap, {\'A}rp{\'a}d  and
      S{\'a}rk{\"o}zi-Lindner, Zs{\'o}fia  and
      Tim{\'a}ri, M{\'a}ria  and
      Palk{\'o}, G{\'a}bor},
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.5",
    pages = "33--41",
    abstract = "In this article, we present the method we used to create a middle-sized corpus using targeted web crawling. Our corpus contains news portal articles along with their metadata, that can be useful for diverse audiences, ranging from digital humanists to NLP users. The method presented in this paper applies rule-based components that allow the curation of the text and the metadata content. The curated data can thereon serve as a reference for various tasks and measurements. We designed our workflow to encourage modification and customisation. Our concept can also be applied to other genres of portals by using the discovered patterns in the architecture of the portals. We found that for a systematic creation or extension of a similar corpus, our method provides superior accuracy and ease of use compared to The Wayback Machine, while requiring minimal manpower and computational resources. Reproducing the corpus is possible if changes are introduced to the text-extraction process. The standard TEI format and Schema.org encoded metadata is used for the output format, but we stress that placing the corpus in a digital repository system is recommended in order to be able to define semantic relations between the segments and to add rich annotation.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{rawat-etal-2020-hypernym,
    title = "Hypernym-{LIB}re: A Free Web-based Corpus for Hypernym Detection",
    author = "Rawat, Shaurya  and
      Rico, Mariano  and
      Corcho, Oscar",
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.6",
    pages = "42--49",
    abstract = "In this paper, we describe a new web-based corpus for hypernym detection. It consists of 32 GB of high quality english paragraphs along with their part-of-speech tagged and dependency parsed versions. For hypernym detection, the current state-of-the-art uses a corpus which is not available freely. We evaluate the state-of-the-art methods on our corpus and achieve similar results. The advantage of this corpora is that it is available under an open license. Our main contribution is the corpus with POS-tags and dependency tags and the code to extract and simulate the results we have achieved using our corpus.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{behzad-zeldes-2020-cross,
    title = "A Cross-Genre Ensemble Approach to Robust {R}eddit Part of Speech Tagging",
    author = "Behzad, Shabnam  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.7",
    pages = "50--56",
    abstract = "Part of speech tagging is a fundamental NLP task often regarded as solved for high-resource languages such as English. Current state-of-the-art models have achieved high accuracy, especially on the news domain. However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance. In this work, we study how a state-of-the-art tagging model trained on different genres performs on Web content from unfiltered Reddit forum discussions. We report the results when training on different splits of the data, tested on Reddit. Our results show that even small amounts of in-domain data can outperform the contribution of data an order of magnitude larger coming from other Web domains. To make progress on out-of-domain tagging, we also evaluate an ensemble approach using multiple single-genre taggers as input features to a meta-classifier. We present state of the art performance on tagging Reddit data, as well as error analysis of the results of these models, and offer a typology of the most common error types among them, broken down by training corpus.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@inproceedings{kreutz-daelemans-2020-streaming,
    title = "Streaming Language-Specific {T}witter Data with Optimal Keywords",
    author = "Kreutz, Tim  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 12th Web as Corpus Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.wac-1.8",
    pages = "57--64",
    abstract = "The Twitter Streaming API has been used to create language-specific corpora with varying degrees of success. Selecting a filter of frequent yet distinct keywords for German resulted in a near-complete collection of German tweets. This method is promising as it keeps within Twitter endpoint limitations and could be applied to other languages besides German. But so far no research has compared methods for selecting optimal keywords for this task. This paper proposes a method for finding optimal key phrases based on a greedy solution to the maximum coverage problem. We generate candidate key phrases for the 50 most frequent languages on Twitter. Candidates are then iteratively selected based on a variety of scoring functions applied to their coverage of target tweets. Selecting candidates based on the scoring function that exponentiates the precision of a key phrase and weighs it by recall achieved the best results overall. Some target languages yield lower results than what could be expected from their prevalence on Twitter. Upon analyzing the errors, we find that these are languages that are very close to more prevalent languages. In these cases, key phrases that limit finding the competitive language are selected, and overall recall on the target language also decreases. We publish the resulting optimized lists for each language as a resource. The code to generate lists for other research objectives is also supplied.",
    language = "English",
    ISBN = "979-10-95546-68-9",
}
@proceedings{vlsp-2020-international,
    title = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    editor = "Nguyen, Huyen T M.  and
      Nguyen, Huy Van  and
      Ha, Thanh-Le  and
      Vu, Xuan-Son  and
      Nguyen, Trang Thi Thu  and
      Tran, Trung The  and
      Tran, Mai-Vu",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.0",
}
@inproceedings{hieu-etal-2020-reintel,
    title = "{R}e{INTEL} Challenge 2020: {V}ietnamese Fake News Detection using{E}nsemble Model with {P}ho{BERT} embeddings",
    author = "Hieu, Thuan Nguyen  and
      Minh, Hieu Cao Nguyen  and
      Van, Hung To  and
      Quoc, Bang Vo",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.1",
    pages = "1--5",
}
@inproceedings{trinh-etal-2020-reintel,
    title = "{R}e{INTEL} Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on {V}ietnamese {SNS}s",
    author = "Trinh, Hoang Viet  and
      Bui, Tung Tien  and
      Nguyen, Tam Minh  and
      Dao, Huy Quang  and
      Pham, Quang Huu  and
      Tran, Ngoc N.",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.2",
    pages = "6--12",
}
@inproceedings{pham-2020-empirical,
    title = "An Empirical Study of Using Pre-trained {BERT} Models for {V}ietnamese Relation Extraction Task at {VLSP} 2020",
    author = "Pham, Minh Quang Nhat",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.3",
    pages = "13--18",
}
@inproceedings{ngoc-etal-2020-improving,
    title = "Improving prosodic phrasing of {V}ietnamese text-to-speech systems",
    author = "Ngoc, Phuong Pham  and
      Quang, Chung Tran  and
      Nguyen, Quang Minh  and
      Do, Quoc Truong",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.4",
    pages = "19--23",
}
@inproceedings{trieu-etal-2020-development,
    title = "Development of Smartcall {V}ietnamese Text-to-Speech for {VLSP} 2020",
    author = "Trieu, Khuong Duy  and
      Dam, Ba Quyen  and
      Nguyen, Quoc Bao",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.5",
    pages = "24--29",
}
@inproceedings{nguyen-man-2020-vietnamese,
    title = "{V}ietnamese Relation Extraction with {BERT}-based Models at {VLSP} 2020",
    author = "Nguyễn, Thuật  and
      Mẫn, Hiếu",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.6",
    pages = "30--34",
}
@inproceedings{nguyen-etal-2020-vietnamese-text,
    title = "{V}ietnamese Text-To-Speech Shared Task {VLSP} 2020: Remaining problems with state-of-the-art techniques",
    author = "Nguyen, Thi Thu Trang  and
      Nguyen, Hoang Ky  and
      Pham, Quang Minh  and
      Vu, Duy Manh",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.7",
    pages = "35--39",
}
@inproceedings{nguyen-pham-2020-reintel,
    title = "{R}e{INTEL} Challenge 2020: A Multimodal Ensemble Model for Detecting Unreliable Information on {V}ietnamese {SNS}",
    author = "Nguyen, Manh Duc Tuan  and
      Pham, Quang Nhat Minh",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.8",
    pages = "40--44",
}
@inproceedings{thanh-van-2020-reintel,
    title = "{R}e{INTEL} Challenge 2020: Exploiting Transfer Learning Models for Reliable Intelligence Identification on {V}ietnamese Social Network Sites",
    author = "Thanh, Kim Nguyen Thi  and
      Van, Kiet Nguyen",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.9",
    pages = "45--48",
}
@inproceedings{doan-2020-joint,
    title = "A Joint Deep Contextualized Word Representation for Deep Biaffine Dependency Parsing",
    author = "Doan, Xuan-Dung",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.10",
    pages = "49--53",
}
@inproceedings{thien-etal-2020-applying,
    title = "Applying Graph Neural Networks for {V}ietnamese Dependency Parsing",
    author = "Thien, Nguyen Duc  and
      Trang, Nguyen Thi Thu  and
      Quang, Truong Dang",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.11",
    pages = "54--59",
}
@inproceedings{nguyen-2020-implementing,
    title = "Implementing {B}i-{LSTM}-based deep biaffine neural dependency parser for {V}ietnamese {U}niversal {D}ependency Parsing",
    author = "Nguyen, Lien",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.12",
    pages = "60--63",
}
@inproceedings{cuong-thu-2020-vietnamese,
    title = "{V}ietnamese-{E}nglish Translation with Transformer and Back Translation in {VLSP} 2020 Machine Translation Shared Task",
    author = "Cuong, Le Duc  and
      Thu, Trang Nguyen Thi",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.13",
    pages = "64--70",
}
@inproceedings{thi-vinh-etal-2020-uet,
    title = "The {UET}-{ICTU} Submissions to the {VLSP} 2020 News Translation Task",
    author = "Thi-Vinh, Ngo  and
      Minh-Thuan, Nguyen  and
      Cong, Nguyen Hoang Minh  and
      Hoang-Quan, Nguyen  and
      Phuong-Thai, Nguyen  and
      Van-Vinh, Nguyen",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.14",
    pages = "71--76",
}
@inproceedings{linh-etal-2020-vlsp,
    title = "{VLSP} 2020 Shared Task: {U}niversal {D}ependency Parsing for {V}ietnamese",
    author = "Linh, Ha My  and
      Huyen, Nguyen Thi Minh  and
      Luong, Vu Xuan  and
      Luong, Nguyen Thi  and
      Hue, Phan Thi  and
      Cuong, Le Van",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.15",
    pages = "77--83",
}
@inproceedings{le-etal-2020-reintel,
    title = "{R}e{INTEL}: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites",
    author = "Le, Duc-Trong  and
      Vu, Xuan-Son  and
      To, Nhu-Dung  and
      Nguyen, Huu-Quang  and
      Nguyen, Thuy-Trinh  and
      Le, Thi Khanh-Linh  and
      Nguyen, Anh-Tuan  and
      Hoang, Minh-Duc  and
      Le, Nghia  and
      Nguyen, Huyen  and
      Nguyen, Hoang D.",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.16",
    pages = "84--91",
}
@inproceedings{mai-etal-2020-overview,
    title = "Overview of {VLSP} {R}el{E}x shared task: A Data Challenge for Semantic Relation Extraction from {V}ietnamese News",
    author = "Mai, Vu Tran  and
      Le, Hoang-Quynh  and
      Can, Duy-Cat  and
      Nguyen, Thi Minh Huyen  and
      Nguyen, Tran Ngoc Linh  and
      Doan, Thanh Tam",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.17",
    pages = "92--98",
}
@inproceedings{ha-etal-2020-goals,
    title = "Goals, Challenges and Findings of the {VLSP} 2020 {E}nglish-{V}ietnamese News Translation Shared Task",
    author = "Ha, Thanh-Le  and
      Tran, Van-Khanh  and
      Nguyen, Kim-Anh",
    booktitle = "Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing",
    month = dec,
    year = "2020",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Lingustics",
    url = "https://www.aclweb.org/anthology/2020.vlsp-1.18",
    pages = "99--105",
}
@proceedings{vardial-2020-nlp,
    title = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    editor = {Zampieri, Marcos  and
      Nakov, Preslav  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Scherrer, Yves},
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.0",
}
@inproceedings{gaman-etal-2020-report,
    title = "A Report on the {V}ar{D}ial Evaluation Campaign 2020",
    author = "Gaman, Mihaela  and
      Hovy, Dirk  and
      Ionescu, Radu Tudor  and
      Jauhiainen, Heidi  and
      Jauhiainen, Tommi  and
      Lind{\'e}n, Krister  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Partanen, Niko  and
      Purschke, Christoph  and
      Scherrer, Yves  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.1",
    pages = "1--14",
    abstract = "This paper presents the results of the VarDial Evaluation Campaign 2020 organized as part of the seventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2020. The campaign included three shared tasks each focusing on a different challenge of language and dialect identification: Romanian Dialect Identification (RDI), Social Media Variety Geolocation (SMG), and Uralic Language Identification (ULI). The campaign attracted 30 teams who enrolled to participate in one or multiple shared tasks and 14 of them submitted runs across the three shared tasks. Finally, 11 papers describing participating systems are published in the VarDial proceedings and referred to in this report.",
}
@inproceedings{nigmatulina-etal-2020-asr,
    title = "{ASR} for Non-standardised Languages with Dialectal Variation: the case of {S}wiss {G}erman",
    author = "Nigmatulina, Iuliia  and
      Kew, Tannon  and
      Samardzic, Tanja",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.2",
    pages = "15--24",
    abstract = "Strong regional variation, together with the lack of standard orthography, makes Swiss German automatic speech recognition (ASR) particularly difficult in a multi-dialectal setting. This paper focuses on one of the many challenges, namely, the choice of the output text to represent non-standardised Swiss German. We investigate two potential options: a) dialectal writing {--} approximate phonemic transcriptions that provide close correspondence between grapheme labels and the acoustic signal but are highly inconsistent and b) normalised writing {--} transcriptions resembling standard German that are relatively consistent but distant from the acoustic signal. To find out which writing facilitates Swiss German ASR, we build several systems using the Kaldi toolkit and a dataset covering 14 regional varieties. A formal comparison shows that the system trained on the normalised transcriptions achieves better results in word error rate (WER) (29.39{\%}) but underperforms at the character level, suggesting dialectal transcriptions offer a viable solution for downstream applications where dialectal differences are important. To better assess word-level performance for dialectal transcriptions, we use a flexible WER measure (FlexWER). When evaluated with this metric, the system trained on dialectal transcriptions outperforms that trained on the normalised writing. Besides establishing a benchmark for Swiss German multi-dialectal ASR, our findings can be helpful in designing ASR systems for other languages without standard orthography.",
}
@inproceedings{siewert-etal-2020-lsdc,
    title = "{LSDC} - A comprehensive dataset for Low {S}axon Dialect Classification",
    author = {Siewert, Janine  and
      Scherrer, Yves  and
      Wieling, Martijn  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.3",
    pages = "25--35",
    abstract = "We present a new comprehensive dataset for the unstandardised West-Germanic language Low Saxon covering the last two centuries, the majority of modern dialects and various genres, which will be made openly available in connection with the final version of this paper. Since so far no such comprehensive dataset of contemporary Low Saxon exists, this provides a great contribution to NLP research on this language. We also test the use of this dataset for dialect classification by training a few baseline models comparing statistical and neural approaches. The performance of these models shows that in spite of an imbalance in the amount of data per dialect, enough features can be learned for a relatively high classification accuracy.",
}
@inproceedings{tebbifakhr-etal-2020-machine,
    title = "Machine-oriented {NMT} Adaptation for Zero-shot {NLP} tasks: Comparing the Usefulness of Close and Distant Languages",
    author = "Tebbifakhr, Amirhossein  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.4",
    pages = "36--46",
    abstract = "Neural Machine Translation (NMT) models are typically trained by considering humans as end-users and maximizing human-oriented objectives. However, in some scenarios, their output is consumed by automatic NLP components rather than by humans. In these scenarios, translations{'} quality is measured in terms of their {``}fitness for purpose{''} (i.e. maximizing performance of external NLP tools) rather than in terms of standard human fluency/adequacy criteria. Recently, reinforcement learning techniques exploiting the feedback from downstream NLP tools have been proposed for {``}machine-oriented{''} NMT adaptation. In this work, we tackle the problem in a multilingual setting where a single NMT model translates from multiple languages for downstream automatic processing in the target language. Knowledge sharing across close and distant languages allows to apply our machine-oriented approach in the zero-shot setting where no labeled data for the test language is seen at training time. Moreover, we incorporate multi-lingual BERT in the source side of our NMT system to benefit from the knowledge embedded in this model. Our experiments show coherent performance gains, for different language directions over both i) {``}generic{''} NMT models (trained for human consumption), and ii) fine-tuned multilingual BERT. This gain for zero-shot language directions (e.g. Spanish{--}English) is higher when the models are fine-tuned on a closely-related source language (Italian) than a distant one (German).",
}
@inproceedings{gasser-etal-2020-character,
    title = "Character Alignment in Morphologically Complex Translation Sets for Related Languages",
    author = "Gasser, Michael  and
      Seyoum, Binyam Ephrem  and
      Kifle, Nazareth Amlesom",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.5",
    pages = "47--56",
    abstract = "For languages with complex morphology, word-to-word translation is a task with various potential applications, for example, in information retrieval, language instruction, and dictionary creation, as well as in machine translation. In this paper, we confine ourselves to the subtask of character alignment for the particular case of families of related languages with very few resources for most or all members. There are many such families; we focus on the subgroup of Semitic languages spoken in Ethiopia and Eritrea. We begin with an adaptation of the familiar alignment algorithms behind statistical machine translation, modifying them as appropriate for our task. We show how character alignment can reveal morphological, phonological, and orthographic correspondences among related languages.",
}
@inproceedings{chakravarthi-etal-2020-bilingual,
    title = "Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced {D}ravidian Languages",
    author = "Chakravarthi, Bharathi Raja  and
      Rajasekaran, Navaneethan  and
      Arcan, Mihael  and
      McGuinness, Kevin  and
      E. O{'}Connor, Noel  and
      McCrae, John P.",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.6",
    pages = "57--69",
    abstract = "Bilingual lexicons are a vital tool for under-resourced languages and recent state-of-the-art approaches to this leverage pretrained monolingual word embeddings using supervised or semi-supervised approaches. However, these approaches require cross-lingual information such as seed dictionaries to train the model and find a linear transformation between the word embedding spaces. Especially in the case of low-resourced languages, seed dictionaries are not readily available, and as such, these methods produce extremely weak results on these languages. In this work, we focus on the Dravidian languages, namely Tamil, Telugu, Kannada, and Malayalam, which are even more challenging as they are written in unique scripts. To take advantage of orthographic information and cognates in these languages, we bring the related languages into a single script. Previous approaches have used linguistically sub-optimal measures such as the Levenshtein edit distance to detect cognates, whereby we demonstrate that the longest common sub-sequence is linguistically more sound and improves the performance of bilingual lexicon induction. We show that our approach can increase the accuracy of bilingual lexicon induction methods on these languages many times, making bilingual lexicon induction approaches feasible for such under-resourced languages.",
}
@inproceedings{ahmadi-2020-building,
    title = "Building a Corpus for the {Z}aza{--}Gorani Language Family",
    author = "Ahmadi, Sina",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.7",
    pages = "70--78",
    abstract = "Thanks to the growth of local communities and various news websites along with the increasing accessibility of the Web, some of the endangered and less-resourced languages have a chance to revive in the information era. Therefore, the Web is considered a huge resource that can be used to extract language corpora which enable researchers to carry out various studies in linguistics and language technology. The Zaza{--}Gorani language family is a linguistic subgroup of the Northwestern Iranian languages for which there is no significant corpus available. Motivated to create one, in this paper we present our endeavour to collect a corpus in Zazaki and Gorani languages containing over 1.6M and 194k word tokens, respectively. This corpus is publicly available.",
}
@inproceedings{estarrona-etal-2020-dealing,
    title = "Dealing with dialectal variation in the construction of the {B}asque historical corpus",
    author = "Estarrona, Ainara  and
      Etxeberria, Izaskun  and
      Etxepare, Ricardo  and
      Padilla-Moyano, Manuel  and
      Soraluze, Ander",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.8",
    pages = "79--89",
    abstract = "This paper analyses the challenge of working with dialectal variation when semi-automatically normalising and analysing historical Basque texts. This work is part of a more general ongoing project for the construction of a morphosyntactically annotated historical corpus of Basque called Basque in the Making (BIM): A Historical Look at a European Language Isolate, whose main objective is the systematic and diachronic study of a number of grammatical features. This will be not only the first tagged corpus of historical Basque, but also a means to improve language processing tools by analysing historical Basque varieties more or less distant from present-day standard Basque.",
}
@inproceedings{vidal-gorene-etal-2020-recycling,
    title = "Recycling and Comparing Morphological Annotation Models for {A}rmenian Diachronic-Variational Corpus Processing",
    author = {Vidal-Gor{\`e}ne, Chahan  and
      Khurshudyan, Victoria  and
      Donab{\'e}dian-Demopoulos, Ana{\"\i}d},
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.9",
    pages = "90--101",
    abstract = "Armenian is a language with significant variation and unevenly distributed NLP resources for different varieties. An attempt is made to process an RNN model for morphological annotation on the basis of different Armenian data (provided or not with morphologically annotated corpora), and to compare the annotation results of RNN and rule-based models. Different tests were carried out to evaluate the reuse of an unspecialized model of lemmatization and POS-tagging for under-resourced language varieties. The research focused on three dialects and further extended to Western Armenian with a mean accuracy of 94,00 {\%} in lemmatization and 97,02{\%} in POS-tagging, as well as a possible reusability of models to cover different other Armenian varieties. Interestingly, the comparison of an RNN model trained on Eastern Armenian with the Eastern Armenian National Corpus rule-based model applied to Western Armenian showed an enhancement of 19{\%} in parsing. This model covers 88,79{\%} of a short heterogeneous dataset in Western Armenian, and could be a baseline for a massive corpus annotation in that standard. It is argued that an RNN-based model can be a valid alternative to a rule-based one giving consideration to such factors as time-consumption, reusability for different varieties of a target language and significant qualitative results in morphological annotation.",
}
@inproceedings{popovic-etal-2020-neural,
    title = "Neural Machine Translation for translating into {C}roatian and {S}erbian",
    author = "Popovi{\'c}, Maja  and
      Poncelas, Alberto  and
      Brkic, Marija  and
      Way, Andy",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.10",
    pages = "102--113",
    abstract = "In this work, we systematically investigate different set-ups for training of neural machine translation (NMT) systems for translation into Croatian and Serbian, two closely related South Slavic languages. We explore English and German as source languages, different sizes and types of training corpora, as well as bilingual and multilingual systems. We also explore translation of English IMDb user movie reviews, a domain/genre where only monolingual data are available. First, our results confirm that multilingual systems with joint target languages perform better. Furthermore, translation performance from English is much better than from German, partly because German is morphologically more complex and partly because the corpus consists mostly of parallel human translations instead of original text and its human translation. The translation from German should be further investigated systematically. For translating user reviews, creating synthetic in-domain parallel data through back- and forward-translation and adding them to a small out-of-domain parallel corpus can yield performance comparable with a system trained on a full out-of-domain corpus. However, it is still not clear what is the optimal size of synthetic in-domain data, especially for forward-translated data where the target language is machine translated. More detailed research including manual evaluation and analysis is needed in this direction.",
}
@inproceedings{ahmadi-2020-tokenization,
    title = "A Tokenization System for the {K}urdish Language",
    author = "Ahmadi, Sina",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.11",
    pages = "114--127",
    abstract = "Tokenization is one of the essential and fundamental tasks in natural language processing. Despite the recent advances in applying unsupervised statistical methods for this task, every language with its writing system and orthography represents specific challenges that should be addressed individually. In this paper, as a preliminary study of its kind, we propose an approach for the tokenization of the Sorani and Kurmanji dialects of Kurdish using a lexicon and a morphological analyzer. We demonstrate how the morphological complexity of the language along with the lack of a unified orthography can be efficiently addressed in tokenization. We also develop an annotated dataset for which our approach outperforms the performance of unsupervised methods.",
}
@inproceedings{abdullah-etal-2020-rediscovering,
    title = "Rediscovering the {S}lavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification",
    author = {Abdullah, Badr M.  and
      Kudera, Jacek  and
      Avgustinova, Tania  and
      M{\"o}bius, Bernd  and
      Klakow, Dietrich},
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.12",
    pages = "128--139",
    abstract = "Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists{'} perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.",
}
@inproceedings{miletic-etal-2020-four,
    title = "A Four-Dialect Treebank for {O}ccitan: Building Process and Parsing Experiments",
    author = "Miletic, Aleksandra  and
      Bras, Myriam  and
      Vergez-Couret, Marianne  and
      Esher, Louise  and
      Poujade, Clamen{\c{c}}a  and
      Sibille, Jean",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.13",
    pages = "140--149",
    abstract = "Occitan is a Romance language spoken mainly in the south of France. It has no official status in the country, it is not standardized and displays important diatopic variation resulting in a rich system of dialects. Recently, a first treebank for this language was created. However, this corpus is based exclusively on texts in the Lengadocian dialect. Our paper describes the work aimed at extending the existing corpus with content in three new dialects, namely Gascon, Proven{\c{c}}au and Lemosin. We describe both the annotation of initial content in these new varieties of Occitan and experiments allowing us to identify the most efficient method for further enrichment of the corpus. We observe that parsing models trained on Occitan dialects achieve better results than a delexicalized model trained on other Romance languages despite the latter training corpus being much larger (20K vs 900K tokens). The results of the native Occitan models show an important impact of cross-dialectal lexical variation, whereas syntactic variation seems to affect the systems less. We hope that the resulting corpus, incorporating several Occitan varieties, will facilitate the training of robust NLP tools, capable of processing all kinds of Occitan texts.",
}
@inproceedings{zugarini-etal-2020-vulgaris,
    title = "Vulgaris: Analysis of a Corpus for Middle-Age Varieties of {I}talian Language",
    author = "Zugarini, Andrea  and
      Tiezzi, Matteo  and
      Maggini, Marco",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.14",
    pages = "150--159",
    abstract = "Italian is a Romance language that has its roots in Vulgar Latin. The birth of the modern Italian started in Tuscany around the 14th century, and it is mainly attributed to the works of Dante Alighieri, Francesco Petrarca and Giovanni Boccaccio, who are among the most acclaimed authors of the medieval age in Tuscany. However, Italy has been characterized by a high variety of dialects, which are often loosely related to each other, due to the past fragmentation of the territory. Italian has absorbed influences from many of these dialects, as also from other languages due to dominion of portions of the country by other nations, such as Spain and France. In this work we present Vulgaris, a project aimed at studying a corpus of Italian textual resources from authors of different regions, ranging in a time period between 1200 and 1600. Each composition is associated to its author, and authors are also grouped in families, i.e. sharing similar stylistic/chronological characteristics. Hence, the dataset is not only a valuable resource for studying the diachronic evolution of Italian and the differences between its dialects, but it is also useful to investigate stylistic aspects between single authors. We provide a detailed statistical analysis of the data, and a corpus-driven study in dialectology and diachronic varieties.",
}
@inproceedings{hwang-etal-2020-towards,
    title = "Towards Augmenting Lexical Resources for Slang and {A}frican {A}merican {E}nglish",
    author = "Hwang, Alyssa  and
      Frey, William R.  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.15",
    pages = "160--172",
    abstract = "Researchers in natural language processing have developed large, robust resources for understanding formal Standard American English (SAE), but we lack similar resources for variations of English, such as slang and African American English (AAE). In this work, we use word embeddings and clustering algorithms to group semantically similar words in three datasets, two of which contain high incidence of slang and AAE. Since high-quality clusters would contain related words, we could also infer the meaning of an unfamiliar word based on the meanings of words clustered with it. After clustering, we compute precision and recall scores using WordNet and ConceptNet as gold standards and show that these scores are unimportant when the given resources do not fully represent slang and AAE. Amazon Mechanical Turk and expert evaluations show that clusters with low precision can still be considered high quality, and we propose the new Cluster Split Score as a metric for machine-generated clusters. These contributions emphasize the gap in natural language processing research for variations of English and motivate further work to close it.",
}
@inproceedings{jauhiainen-etal-2020-uralic,
    title = "Uralic Language Identification ({ULI}) 2020 shared task dataset and the Wanca 2017 corpora",
    author = "Jauhiainen, Tommi  and
      Jauhiainen, Heidi  and
      Partanen, Niko  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.16",
    pages = "173--185",
    abstract = "This article introduces the Wanca 2017 web corpora from which the sentences written in minor Uralic languages were collected for the test set of the Uralic Language Identification (ULI) 2020 shared task. We describe the ULI shared task and how the test set was constructed using the Wanca 2017 corpora and texts in different languages from the Leipzig corpora collection. We also provide the results of a baseline language identification experiment conducted using the ULI 2020 dataset.",
}
@inproceedings{coltekin-2020-dialect,
    title = "Dialect Identification under Domain Shift: Experiments with Discriminating {R}omanian and {M}oldavian",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.17",
    pages = "186--192",
    abstract = "This paper describes a set of experiments for discriminating between two closely related language varieties, Moldavian and Romanian, under a substantial domain shift. The experiments were conducted as part of the Romanian dialect identification task in the VarDial 2020 evaluation campaign. Our best system based on linear SVM classifier obtained the first position in the shared task with an F1 score of 0.79, supporting the earlier results showing (unexpected) success of machine learning systems in this task. The additional experiments reported in this paper also show that adapting to the test set is useful when the training data comes from another domain. However, the benefit of adaptation becomes doubtful even when a small amount of data from the target domain is available.",
}
@inproceedings{popa-stefanescu-2020-applying,
    title = "Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification",
    author = "Popa, Cristian  and
      {\textcommabelow{S}}tef{\u{a}}nescu, Vlad",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.18",
    pages = "193--201",
    abstract = "We study the ability of large fine-tuned transformer models to solve a binary classification task of dialect identification, with a special interest in comparing the performance of multilingual to monolingual ones. The corpus analyzed contains Romanian and Moldavian samples from the news domain, as well as tweets for assessing the performance. We find that the monolingual models are superior to the multilingual ones and the best results are obtained using an SVM ensemble of 5 different transformer-based models. We provide our experimental results and an analysis of the attention mechanisms of the best-performing individual classifiers to explain their decisions. The code we used was released under an open-source license.",
}
@inproceedings{scherrer-ljubesic-2020-helju,
    title = "{H}e{L}ju@{V}ar{D}ial 2020: Social Media Variety Geolocation with {BERT} Models",
    author = "Scherrer, Yves  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.19",
    pages = "202--211",
    abstract = "This paper describes the Helsinki-Ljubljana contribution to the VarDial shared task on social media variety geolocation. Our solutions are based on the BERT Transformer models, the constrained versions of our models reaching 1st place in two subtasks and 3rd place in one subtask, while our unconstrained models outperform all the constrained systems by a large margin. We show in our analyses that Transformer-based models outperform traditional models by far, and that improvements obtained by pre-training models on large quantities of (mostly standard) text are significant, but not drastic, with single-language models also outperforming multilingual models. Our manual analysis shows that two types of signals are the most crucial for a (mis)prediction: named entities and dialectal features, both of which are handled well by our models.",
}
@inproceedings{rebeja-cristea-2020-dual,
    title = "A dual-encoding system for dialect classification",
    author = "Rebeja, Petru  and
      Cristea, Dan",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.20",
    pages = "212--219",
    abstract = "In this paper we present the architecture, processing pipeline and results of the ensemble model developed for Romanian Dialect Identification task. The ensemble model consists of two TF-IDF encoders and a deep learning model aimed together at classifying input samples based on the writing patterns which are specific to each of the two dialects. Although the model performs well on the training set, its performance degrades heavily on the evaluation set. The drop in performance is due to the design decision which makes the model put too much weight on presence/lack of textual marks when determining the sample label.",
}
@inproceedings{jauhiainen-etal-2020-experiments,
    title = "Experiments in Language Variety Geolocation and Dialect Identification",
    author = "Jauhiainen, Tommi  and
      Jauhiainen, Heidi  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.21",
    pages = "220--231",
    abstract = "In this paper we describe the systems we used when participating in the VarDial Evaluation Campaign organized as part of the 7th workshop on NLP for similar languages, varieties and dialects. The shared tasks we participated in were the second edition of the Romanian Dialect Identification (RDI) and the first edition of the Social Media Variety Geolocation (SMG). The submissions of our SUKI team used generative language models based on Naive Bayes and character n-grams.",
}
@inproceedings{zaharia-etal-2020-exploring,
    title = "Exploring the Power of {R}omanian {BERT} for Dialect Identification",
    author = "Zaharia, George-Eduard  and
      Avram, Andrei-Marius  and
      Cercel, Dumitru-Clementin  and
      Rebedea, Traian",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.22",
    pages = "232--241",
    abstract = "Dialect identification represents a key aspect for improving a series of tasks, for example, opinion mining, considering that the location of the speaker can greatly influence the attitude towards a subject. In this work, we describe the systems developed by our team for VarDial 2020: Romanian Dialect Identification, a task specifically created for challenging participants to solve the previously mentioned issue. More specifically, we introduce a series of neural systems based on Transformers, that combine a BERT model exclusively pre-trained on the Romanian language with techniques such as adversarial training or character-level embeddings. By using these approaches, we were able to obtain a 0.6475 macro F1 score on the test dataset, thus allowing us to be ranked 5th out of 8 participant teams.",
}
@inproceedings{gaman-ionescu-2020-combining,
    title = "Combining Deep Learning and String Kernels for the Localization of {S}wiss {G}erman Tweets",
    author = "Gaman, Mihaela  and
      Ionescu, Radu Tudor",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.23",
    pages = "242--253",
    abstract = "In this work, we introduce the methods proposed by the UnibucKernel team in solving the Social Media Variety Geolocation task featured in the 2020 VarDial Evaluation Campaign. We address only the second subtask, which targets a data set composed of nearly 30 thousand Swiss German Jodels. The dialect identification task is about accurately predicting the latitude and longitude of test samples. We frame the task as a double regression problem, employing a variety of machine learning approaches to predict both latitude and longitude. From simple models for regression, such as Support Vector Regression, to deep neural networks, such as Long Short-Term Memory networks and character-level convolutional neural networks, and, finally, to ensemble models based on meta-learners, such as XGBoost, our interest is focused on approaching the problem from a few different perspectives, in an attempt to minimize the prediction error. With the same goal in mind, we also considered many types of features, from high-level features, such as BERT embeddings, to low-level features, such as characters n-grams, which are known to provide good results in dialect identification. Our empirical results indicate that the handcrafted model based on string kernels outperforms the deep learning approaches. Nevertheless, our best performance is given by the ensemble model that combines both handcrafted and deep learning models.",
}
@inproceedings{benites-etal-2020-zhaw,
    title = "{ZHAW}-{I}n{IT} - Social Media Geolocation at {V}ar{D}ial 2020",
    author = {Benites, Fernando  and
      H{\"u}rlimann, Manuela  and
      von D{\"a}niken, Pius  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.24",
    pages = "254--264",
    abstract = "We describe our approaches for the Social Media Geolocation (SMG) task at the VarDial Evaluation Campaign 2020. The goal was to predict geographical location (latitudes and longitudes) given an input text. There were three subtasks corresponding to German-speaking Switzerland (CH), Germany and Austria (DE-AT), and Croatia, Bosnia and Herzegovina, Montenegro and Serbia (BCMS). We submitted solutions to all subtasks but focused our development efforts on the CH subtask, where we achieved third place out of 16 submissions with a median distance of 15.93 km and had the best result of 14 unconstrained systems. In the DE-AT subtask, we ranked sixth out of ten submissions (fourth of 8 unconstrained systems) and for BCMS we achieved fourth place out of 13 submissions (second of 11 unconstrained systems).",
}
@inproceedings{ceolin-zhang-2020-discriminating,
    title = "Discriminating between standard {R}omanian and {M}oldavian tweets using filtered character ngrams",
    author = "Ceolin, Andrea  and
      Zhang, Hong",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.25",
    pages = "265--272",
    abstract = "We applied word unigram models, character ngram models, and CNNs to the task of distinguishing tweets of two related dialects of Romanian (standard Romanian and Moldavian) for the VarDial 2020 RDI shared task (Gaman et al. 2020). The main challenge of the task was to perform cross-genre text classification: specifically, the models must be trained using text from news articles, and be used to predict tweets. Our best model was a Naive Bayes model trained on character ngrams, with the most common ngrams filtered out. We also applied SVMs and CNNs, but while they yielded the best performance on an evaluation dataset of news article, their accuracy significantly dropped when they were used to predict tweets. Our best model reached an F1 score of 0.715 on the evaluation dataset of tweets, and 0.667 on the held-out test dataset. The model ended up in the third place in the shared task.",
}
@inproceedings{bernier-colborne-goutte-2020-challenges,
    title = "Challenges in Neural Language Identification: {NRC} at {V}ar{D}ial 2020",
    author = "Bernier-Colborne, Gabriel  and
      Goutte, Cyril",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.26",
    pages = "273--282",
    abstract = "We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2020 VarDial evaluation campaign. Although our official results were well below the baseline, we show in this paper that this was not due to the neural approach to language identification in general, but to a flaw in the function we used to sample data for training and evaluation purposes. Preliminary experiments conducted after the evaluation period suggest that our neural approach to language identification can achieve state-of-the-art results on this task, although further experimentation is required.",
}
@inproceedings{mishra-2020-geolocation,
    title = "Geolocation of Tweets with a {B}i{LSTM} Regression Model",
    author = "Mishra, Piyush",
    booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.vardial-1.27",
    pages = "283--289",
    abstract = "Identifying a user{'}s location can be useful for recommendation systems, demographic analyses, and disaster outbreak monitoring. Although Twitter allows users to voluntarily reveal their location, such information isn{'}t universally available. Analyzing a tweet can provide a general estimation of a tweet location while giving insight into the dialect of the user and other linguistic markers. Such linguistic attributes can be used to provide a regional approximation of tweet origins. In this paper, we present a neural regression model that can identify the linguistic intricacies of a tweet to predict the location of the user. The final model identifies the dialect embedded in the tweet and predicts the location of the tweet.",
}
@proceedings{udw-2020-universal,
    title = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    editor = "de Marneffe, Marie-Catherine  and
      de Lhoneux, Miryam  and
      Nivre, Joakim  and
      Schuster, Sebastian",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.0",
}
@inproceedings{andersen-rozonoyer-2020-small,
    title = "A Small {U}niversal {D}ependencies Treebank for {H}ittite",
    author = "Andersen, Erik  and
      Rozonoyer, Benjamin",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.1",
    pages = "1--7",
    abstract = "We present the first Universal Dependencies treebank for Hittite. This paper expands on earlier efforts at Hittite corpus creation (Molina and Molin, 2016; Molina, 2016) and discussions of annotation guidelines for Hittite within the UD framework (Inglese, 2015; Inglese et al., 2018). We build on the expertise of the above works to create a small corpus which we hope will serve as a stepping-stone to more expansive UD treebanking for Hittite.",
}
@inproceedings{aquino-de-leon-2020-parsing,
    title = "Parsing in the absence of related languages: Evaluating low-resource dependency parsers on {T}agalog",
    author = "Aquino, Angelina  and
      de Leon, Franz",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.2",
    pages = "8--15",
    abstract = "Cross-lingual and multilingual methods have been widely suggested as options for dependency parsing of low-resource languages; however, these typically require the use of annotated data in related high-resource languages. In this paper, we evaluate the performance of these methods versus monolingual parsing of Tagalog, an Austronesian language which shares little typological similarity with any existing high-resource languages. We show that a monolingual model developed on minimal target language data consistently outperforms all cross-lingual and multilingual models when no closely-related sources exist for a low-resource language.",
}
@inproceedings{arnardottir-etal-2020-universal,
    title = "A {U}niversal {D}ependencies Conversion Pipeline for a {P}enn-format Constituency Treebank",
    author = "Arnard{\'o}ttir, {\TH}{\'o}runn  and
      Hafsteinsson, Hinrik  and
      Sigur{\dh}sson, Einar Freyr  and
      Bjarnad{\'o}ttir, Krist{\'\i}n  and
      Ingason, Anton Karl  and
      J{\'o}nsd{\'o}ttir, Hildur  and
      Steingr{\'\i}msson, Stein{\th}{\'o}r",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.3",
    pages = "16--25",
    abstract = "The topic of this paper is a rule-based pipeline for converting constituency treebanks based on the Penn Treebank format to Universal Dependencies (UD). We describe an Icelandic constituency treebank, its annotation scheme and the UD scheme. The conversion is discussed, the methods used to deliver a fully automated UD corpus and complications involved. To show its applicability to corpora in different languages, we extend the pipeline and convert a Faroese constituency treebank to a UD corpus. The result is an open-source conversion tool, published under an Apache 2.0 license, applicable to a Penn-style treebank for conversion to a UD corpus, along with the two new UD corpora.",
}
@inproceedings{berdicevskis-piperski-2020-corpus,
    title = "Corpus evidence for word order freezing in {R}ussian and {G}erman",
    author = "Berdicevskis, Aleksandrs  and
      Piperski, Alexander",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.4",
    pages = "26--33",
    abstract = "We use Universal Dependencies treebanks to test whether a well-known typological trade-off between word order freedom and richness of morphological marking of core arguments holds within individual languages. Using Russian and German treebank data, we show that the following phenomenon (sometimes dubbed word order freezing) does occur: those sentences where core arguments cannot be distinguished by morphological means (due to case syncretism or other kinds of ambiguity) have more rigid order of subject, verb and object than those where unambiguous morphological marking is present. In ambiguous clauses, word order is more often equal to the one which is default or dominant (most frequent) in the language. While Russian and German differ with respect to how exactly they mark core arguments, the effect of morphological ambiguity is significant in both languages. It is, however, small, suggesting that languages do adapt to the evolutionary pressure on communicative efficiency and avoidance of redundancy, but that the pressure is weak in this particular respect.",
}
@inproceedings{bondeelle-kahane-2020-subjecthood,
    title = "Subjecthood and annotation: The cases of {F}rench and {W}olof",
    author = "Bond{\'e}elle, Olivier  and
      Kahane, Sylvain",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.5",
    pages = "34--45",
    abstract = "This article considers the annotation of subjects in UD treebanks. The identification of the subject poses a particular problem in Wolof, due to pronominal indices whose status as a pronoun or a pronominal affix is uncertain. In the UD treebank available for Wolof (Dione, 2019), these have been annotated depending on the construction either as true subjects, or as morphosyntactic features agreeing with the verb. The study of this corpus of 40 000 words allows us to show that the problem is indeed difficult to solve, especially since Wolof has a rich system of auxiliaries and several basic constructions with different properties. Before addressing the case of Wolof, we will present the simpler, but partly comparable, case of French, where subject clitics also tend to behave like affixes, and subjecthood can move from the preverbal to the detached position. We will also make a several annotation recommendations that would avoid overwriting information regarding subjecthood.",
}
@inproceedings{coltekin-2020-verification,
    title = "Verification, Reproduction and Replication of {NLP} Experiments: a Case Study on Parsing {U}niversal {D}ependencies",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.6",
    pages = "46--56",
    abstract = "As in any field of inquiry that depends on experiments, the verifiability of experimental studies is important in computational linguistics. Despite increased attention to verification of empirical results, the practices in the field are unclear. Furthermore, we argue, certain traditions and practices that are seemingly useful for verification may in fact be counterproductive. We demonstrate this through a set of multi-lingual experiments on parsing Universal Dependencies treebanks. In particular, we show that emphasis on exact replication leads to practices (some of which are now well established) that hide the variation in experimental results, effectively hindering verifiability with a false sense of certainty. The purpose of the present paper is to highlight the magnitude of the issues resulting from these common practices with the hope of instigating further discussion. Once we, as a community, are convinced about the importance of the problems, the solutions are rather obvious, although not necessarily easy to implement.",
}
@inproceedings{dione-2020-lfg,
    title = "From {LFG} To {UD}: A Combined Approach",
    author = "Dione, Cheikh M. Bamba",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.7",
    pages = "57--66",
    abstract = "This paper reports on a systematic approach for deriving Universal Dependencies from LFG structures. The conversion starts with a step-wise transformation of the c-structure, combining part-of-speech (POS) information and the embedding path to determine the true head of dependency structures. The paper discusses several issues faced by existing algorithms when applied on Wolof and presents the strategies used to account for these issues. An experimental evaluation indicated that our approach was able to generate the correct output in more than 90{\%} of the cases, leading to a substantial improvement in conversion accuracy compared to the previous models.",
}
@inproceedings{donicke-etal-2020-identifying,
    title = "Identifying and Handling Cross-Treebank Inconsistencies in {UD}: A Pilot Study",
    author = {D{\"o}nicke, Tillmann  and
      Yu, Xiang  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.8",
    pages = "67--75",
    abstract = "The Universal Dependencies treebanks are a still-growing collection of treebanks for a wide range of languages, all annotated with a common inventory of dependency relations. Yet, the usages of the relations can be categorically different even for treebanks of the same language. We present a pilot study on identifying such inconsistencies in a language-independent way and conduct an experiment which illustrates that a proper handling of inconsistencies can improve parsing performance by several percentage points.",
}
@inproceedings{ek-bernardy-2020-composing,
    title = "Composing Byte-Pair Encodings for Morphological Sequence Classification",
    author = "Ek, Adam  and
      Bernardy, Jean-Philippe",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.9",
    pages = "76--86",
    abstract = "Byte-pair encodings is a method for splitting a word into sub-word tokens, a language model then assigns contextual representations separately to each of these tokens. In this paper, we evaluate four different methods of composing such sub-word representations into word representations. We evaluate the methods on morphological sequence classification, the task of predicting grammatical features of a word. Our experiments reveal that using an RNN to compute word representations is consistently more effective than the other methods tested across a sample of eight languages with different typology and varying numbers of byte-pair tokens per word.",
}
@inproceedings{evang-2020-configurable,
    title = "Configurable Dependency Tree Extraction from {CCG} Derivations",
    author = "Evang, Kilian",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.10",
    pages = "87--93",
    abstract = "We revisit the problem of extracting dependency structures from the derivation structures of Combinatory Categorial Grammar (CCG). Previous approaches are often restricted to a narrow subset of CCG or support only one flavor of dependency tree. Our approach is more general and easily configurable, so that multiple styles of dependency tree can be obtained. In an initial case study, we show promising results for converting English, German, Italian, and Dutch CCG derivations from the Parallel Meaning Bank into (unlabeled) UD-style dependency trees.",
}
@inproceedings{grunewald-friedrich-2020-unifying,
    title = "Unifying the Treatment of Preposition-Determiner Contractions in {G}erman {U}niversal {D}ependencies Treebanks",
    author = {Gr{\"u}newald, Stefan  and
      Friedrich, Annemarie},
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.11",
    pages = "94--98",
    abstract = "HDT-UD, the largest German UD treebank by a large margin, as well as the German-LIT treebank, currently do not analyze preposition-determiner contractions such as zum (= zu dem, {``}to the{''}) as multi-word tokens, which is inconsistent both with UD guidelines as well as other German UD corpora (GSD and PUD). In this paper, we show that harmonizing corpora with regard to this highly frequent phenomenon using a lookup-table based approach leads to a considerable increase in automatic parsing performance.",
}
@inproceedings{han-etal-2020-annotation,
    title = "Annotation Issues in {U}niversal {D}ependencies for {K}orean and {J}apanese",
    author = "Han, Ji Yoon  and
      Oh, Tae Hwan  and
      Jin, Lee  and
      Kim, Hansaem",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.12",
    pages = "99--108",
    abstract = "To investigate issues that arise in the process of developing a Universal Dependency (UD) treebank for Korean and Japanese, we begin by addressing the typological characteristics of Korean and Japanese. Both Korean and Japanese are agglutinative and head-final languages. And the principle of word segmentation for both languages is different from English, which makes it difficult to apply UD guidelines. Following the typological characteristics of the two languages and the issue of UD application, we review the application of UPOS and DEPREL schemes to the two languages. The annotation principles for AUX, ADJ, DET, ADP and PART are discussed for the UPOS scheme, and the annotation principles for case, aux, iobj, and obl are discussed for the DEPREL scheme.",
}
@inproceedings{juzek-etal-2020-exploring,
    title = "Exploring diachronic syntactic shifts with dependency length: the case of scientific {E}nglish",
    author = "Juzek, Tom S  and
      Krielke, Marie-Pauline  and
      Teich, Elke",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.13",
    pages = "109--119",
    abstract = "We report on an application of universal dependencies for the study of diachronic shifts in syntactic usage patterns. Our focus is on the evolution of Scientific English in the Late Modern English period (ca. 1700-1900). Our data set is the Royal Society Corpus (RSC), comprising the full set of publications of the Royal Society of London between 1665 and 1996. Our starting assumption is that over time, Scientific English develops specific syntactic choice preferences that increase efficiency in (expert-to-expert) communication. The specific hypothesis we pursue in this paper is that changing syntactic choice preferences lead to greater dependency locality/dependency length minimization, which is associated with positive effects for the efficiency of human as well as computational linguistic processing. As a basis for our measurements, we parsed the RSC using Stanford CoreNLP. Overall, we observe a decrease in dependency length, with long dependency structures becoming less frequent and short dependency structures becoming more frequent over time, notably pertaining to the nominal phrase, thus marking an overall push towards greater communicative efficiency.",
}
@inproceedings{kalpakchi-boye-2020-udon2,
    title = "{UD}on2: a library for manipulating {U}niversal {D}ependencies trees",
    author = "Kalpakchi, Dmytro  and
      Boye, Johan",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.14",
    pages = "120--125",
    abstract = "UDon2 is an open-source library for manipulating dependency trees represented in the CoNLL-U format. The library is compatible with the Universal Dependencies. UDon2 is aimed at developers of downstream Natural Language Processing applications that require manipulating dependency trees on the sentence level (in addition to other available tools geared towards working with treebanks).",
}
@inproceedings{mcguinness-etal-2020-annotating,
    title = "Annotating {MWE}s in the {I}rish {UD} Treebank",
    author = "McGuinness, Sarah  and
      Phelan, Jason  and
      Walsh, Abigail  and
      Lynn, Teresa",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.15",
    pages = "126--139",
    abstract = "This paper reports on the analysis and annotation of Multiword Expressions in the Irish Universal Dependency Treebank. We provide a linguistic discussion around decisions on how to appropri- ately label Irish MWEs using the compound, flat and fixed dependency relation labels within the framework of the Universal Dependencies annotation guidelines. We discuss some nuances of the Irish language that pose challenges for assigning these UD labels and provide this report in support of the Irish UD annotation guidelines. With this we hope to ensure consistency in annotation across the dataset and provide a basis for future MWE annotation for Irish.",
}
@inproceedings{ruppenhofer-rehbein-2020-ive,
    title = "{I}{'}ve got a construction looks funny {--} representing and recovering non-standard constructions in {UD}",
    author = "Ruppenhofer, Josef  and
      Rehbein, Ines",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.16",
    pages = "140--151",
    abstract = "The UD framework defines guidelines for a crosslingual syntactic analysis in the framework of dependency grammar, with the aim of providing a consistent treatment across languages that not only supports multilingual NLP applications but also facilitates typological studies. Until now, the UD framework has mostly focussed on bilexical grammatical relations. In the paper, we propose to add a constructional perspective and discuss several examples of spoken-language constructions that occur in multiple languages and challenge the current use of basic and enhanced UD relations. The examples include cases where the surface relations are deceptive, and syntactic amalgams that either involve unconnected subtrees or structures with multiply-headed dependents. We argue that a unified treatment of constructions across languages will increase the consistency of the UD annotations and thus the quality of the treebanks for linguistic analysis.",
}
@inproceedings{scannell-2020-universal,
    title = "{U}niversal {D}ependencies for {M}anx {G}aelic",
    author = "Scannell, Kevin",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.17",
    pages = "152--157",
    abstract = "Manx Gaelic is one of the three Q-Celtic languages, along with Irish and Scottish Gaelic. We present a new dependency treebank for Manx consisting of 291 sentences and about 6000 tokens, annotated according to the Universal Dependency (UD) guidelines. To the best of our knowledge, this is the first annotated corpus of any kind for Manx. Our annotations generally follow the conventions established by the existing UD treebanks for Irish and Scottish Gaelic, although we highlight some areas where the grammar of Manx diverges, requiring new analyses. We use 10-fold cross validation to evaluate the accuracy of dependency parsers trained on the corpus, and compare these results with delexicalised models transferred from Irish and Scottish Gaelic.",
}
@inproceedings{sinnemaki-haakana-2020-variation,
    title = "Variation in {U}niversal {D}ependencies annotation: A token-based typological case study on adpossessive constructions",
    author = {Sinnem{\"a}ki, Kaius  and
      Haakana, Viljami},
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.18",
    pages = "158--167",
    abstract = "In this paper we present a method for identifying and analyzing adnominal possessive constructions in 66 Universal Dependencies treebanks. We classify adpossessive constructions in terms of their morphological type (locus of marking) and present a workflow for detecting and analyzing them typologically. Based on a preliminary evaluation, the algorithm works fairly reliably in adpossessive constructions that are morphologically marked. However, it performs rather poorly in adpossessive constructions that are not marked morphologically, so-called zero-marked constructions, because of difficulties in identifying these constructions with the current annotation. We also discuss different types of variation in annotation in different treebanks for the same language and for treebanks of closely related languages. The research focuses on one well-circumscribed and universal construction in the hope of generating more interest in using UD for cross-linguistic comparison and for contributing towards developing yet more consistent annotation of constructions in the UD annotation scheme.",
}
@inproceedings{taji-habash-2020-palmyra,
    title = "{PALMYRA} 2.0: A Configurable Multilingual Platform Independent Tool for Morphology and Syntax Annotation",
    author = "Taji, Dima  and
      Habash, Nizar",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.19",
    pages = "168--177",
    abstract = "We present PALMYRA 2.0, a graphical dependency-tree visualization and editing software. PALMYRA 2.0 is designed to be highly configurable to any dependency parsing representation, and to enable the annotation of a multitude of linguistic features. It uses an intuitive interface that relies on drag-and-drop utilities as well as pop-up menus and keyboard shortcuts that can be easily specified.",
}
@inproceedings{toska-etal-2020-universal,
    title = "{U}niversal {D}ependencies for {A}lbanian",
    author = "Toska, Marsida  and
      Nivre, Joakim  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.20",
    pages = "178--188",
    abstract = "In this paper, we introduce the first Universal Dependencies (UD) treebank for standard Albanian, consisting of 60 sentences collected from the Albanian Wikipedia, annotated with lemmas, universal part-of-speech tags, morphological features and syntactic dependencies. In addition to presenting the treebank itself, we discuss a selection of linguistic constructions in Albanian whose analysis in UD is not self-evident, including core arguments and the status of indirect objects, pronominal clitics, genitive constructions, prearticulated adjectives, and modal verbs.",
}
@inproceedings{turk-etal-2020-first,
    title = "First Steps towards {U}niversal {D}ependencies for {L}az",
    author = {T{\"u}rk, Utku  and
      Bayar, Kaan  and
      {\"O}zercan, Ay{\c{s}}eg{\"u}l Dilara  and
      {\"O}zt{\"u}rk, G{\"o}rkem Yi{\u{g}}it  and
      {\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l},
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.21",
    pages = "189--194",
    abstract = "This paper presents the first treebank for the Laz language, which is also the first Universal Dependencies Treebank for a South Caucasian language. This treebank aims to create a syntactically and morphologically annotated resource for further research. We also aim to document an endangered language in a systematic fashion within an inherently cross-linguistic framework: the Universal Dependencies Project (UD). As of now, our treebank consists of 576 sentences and 2,306 tokens annotated in light with the UD guidelines. We evaluated the treebank on the dependency parsing task using a pretrained multilingual parsing model, and the results are comparable with other low-resourced treebanks with no training set. We aim to expand our treebank in the near future to include 1,500 sentences. The bigger goal for our project is to create a set of treebanks for minority languages in Anatolia.",
}
@inproceedings{tyers-mishchenkova-2020-dependency,
    title = "Dependency annotation of noun incorporation in polysynthetic languages",
    author = "Tyers, Francis  and
      Mishchenkova, Karina",
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.22",
    pages = "195--204",
    abstract = "This paper describes an approach to annotating noun incorporation in Universal Dependencies. It motivates the need to annotate this particular morphosyntactic phenomenon and justifies it with respect to frequency of the construction. A case study is presented in which the proposed annotation scheme is applied to Chukchi, a language that exhibits noun incorporation. We compare argument encoding in Chukchi, English and Russian and find that while in English and Russian discourse elements are primarily tracked through noun phrases and pronouns, in Chukchi they are tracked through agreement marking and incorporation, with a lesser role for noun phrases.",
}
@inproceedings{zhou-etal-2020-universal,
    title = "{U}niversal {D}ependency Treebank for {X}ibe",
    author = {Zhou, He  and
      Chung, Juyeon  and
      K{\"u}bler, Sandra  and
      Tyers, Francis},
    booktitle = "Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.udw-1.23",
    pages = "205--215",
    abstract = "We present our work of constructing the first treebank for the Xibe language following the Universal Dependencies (UD) annotation scheme. Xibe is a low-resourced and severely endangered Tungusic language spoken by the Xibe minority living in the Xinjiang Uygur Autonomous Region of China. We collected 810 sentences so far, including 544 sentences from a grammar book on written Xibe and 266 sentences from Cabcal News. We annotated those sentences manually from scratch. In this paper, we report the procedure of building this treebank and analyze several important annotation issues of our treebank. Finally, we propose our plans for future work.",
}
@proceedings{trac-2020-trolling,
    title = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    editor = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Lahiri, Bornini  and
      Zampieri, Marcos  and
      Malmasi, Shervin  and
      Murdock, Vanessa  and
      Kadar, Daniel",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.0",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{kumar-etal-2020-evaluating,
    title = "Evaluating Aggression Identification in Social Media",
    author = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.1",
    pages = "1--5",
    abstract = "In this paper, we present the report and findings of the Shared Task on Aggression and Gendered Aggression Identification organised as part of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC - 2) at LREC 2020. The task consisted of two sub-tasks - aggression identification (sub-task A) and gendered identification (sub-task B) - in three languages - Bangla, Hindi and English. For this task, the participants were provided with a dataset of approximately 5,000 instances from YouTube comments in each language. For testing, approximately 1,000 instances were provided in each language for each sub-task. A total of 70 teams registered to participate in the task and 19 teams submitted their test runs. The best system obtained a weighted F-score of approximately 0.80 in sub-task A for all the three languages. While approximately 0.87 in sub-task B for all the three languages.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{yang-lin-2020-tocp,
    title = "{TOCP}: A Dataset for {C}hinese Profanity Processing",
    author = "Yang, Hsu  and
      Lin, Chuan-Jie",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.2",
    pages = "6--12",
    abstract = "This paper introduced TOCP, a larger dataset of Chinese profanity. This dataset contains natural sentences collected from social media sites, the profane expressions appearing in the sentences, and their rephrasing suggestions which preserve their meanings in a less offensive way. We proposed several baseline systems using neural network models to test this benchmark. We trained embedding models on a profanity-related dataset and proposed several profanity-related features. Our baseline systems achieved an F1-score of 86.37{\%} in profanity detection and an accuracy of 77.32{\%} in profanity rephrasing.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{srivastava-etal-2020-multi,
    title = "A Multi-Dimensional View of Aggression when voicing Opinion",
    author = "Srivastava, Arjit  and
      Vajpayee, Avijit  and
      Akhtar, Syed Sarfaraz  and
      Jain, Naman  and
      Singh, Vinay  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.3",
    pages = "13--20",
    abstract = "The advent of social media has immensely proliferated the amount of opinions and arguments voiced on the internet. These virtual debates often present cases of aggression. While research has been focused largely on analyzing aggression and stance in isolation from each other, this work is the first attempt to gain an extensive and fine-grained understanding of patterns of aggression and figurative language use when voicing opinion. We present a Hindi-English code-mixed dataset of opinion on the politico-social issue of {`}2016 India banknote demonetisation{`} and annotate it across multiple dimensions such as aggression, hate speech, emotion arousal and figurative language usage (such as sarcasm/irony, metaphors/similes, puns/word-play).",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{dsa-etal-2020-towards,
    title = "Towards Non-Toxic Landscapes: Automatic Toxic Comment Detection Using {DNN}",
    author = "D{'}Sa, Ashwin Geet  and
      Illina, Irina  and
      Fohr, Dominique",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.4",
    pages = "21--25",
    abstract = "The spectacular expansion of the Internet has led to the development of a new research problem in the field of natural language processing: automatic toxic comment detection, since many countries prohibit hate speech in public media. There is no clear and formal definition of hate, offensive, toxic and abusive speeches. In this article, we put all these terms under the umbrella of {``}toxic speech{''}. The contribution of this paper is the design of binary classification and regression-based approaches aiming to predict whether a comment is toxic or not. We compare different unsupervised word representations and different DNN based classifiers. Moreover, we study the robustness of the proposed approaches to adversarial attacks by adding one (healthy or toxic) word. We evaluate the proposed methodology on the English Wikipedia Detox corpus. Our experiments show that using BERT fine-tuning outperforms feature-based BERT, Mikolov{'}s and fastText representations with different DNN classifiers.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{ramiandrisoa-mothe-2020-aggression,
    title = "Aggression Identification in Social Media: a Transfer Learning Based Approach",
    author = "Ramiandrisoa, Faneva  and
      Mothe, Josiane",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.5",
    pages = "26--31",
    abstract = "The way people communicate have changed in many ways with the outbreak of social media. One of the aspects of social media is the ability for their information producers to hide, fully or partially, their identity during a discussion; leading to cyber-aggression and interpersonal aggression. Automatically monitoring user-generated content in order to help moderating it is thus a very hot topic. In this paper, we propose to use the transformer based language model BERT (Bidirectional Encoder Representation from Transformer) (Devlin et al., 2019) to identify aggressive content. Our model is also used to predict the level of aggressiveness. The evaluation part of this paper is based on the dataset provided by the TRAC shared task (Kumar et al., 2018a). When compared to the other participants of this shared task, our model achieved the third best performance according to the weighted F1 measure on both Facebook and Twitter collections.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{suryawanshi-etal-2020-multimodal,
    title = "Multimodal Meme Dataset ({M}ulti{OFF}) for Identifying Offensive Content in Image and Text",
    author = "Suryawanshi, Shardul  and
      Chakravarthi, Bharathi Raja  and
      Arcan, Mihael  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.6",
    pages = "32--41",
    abstract = "A meme is a form of media that spreads an idea or emotion across the internet. As posting meme has become a new form of communication of the web, due to the multimodal nature of memes, postings of hateful memes or related events like trolling, cyberbullying are increasing day by day. Hate speech, offensive content and aggression content detection have been extensively explored in a single modality such as text or image. However, combining two modalities to detect offensive content is still a developing area. Memes make it even more challenging since they express humour and sarcasm in an implicit way, because of which the meme may not be offensive if we only consider the text or the image. Therefore, it is necessary to combine both modalities to identify whether a given meme is offensive or not. Since there was no publicly available dataset for multimodal offensive meme content detection, we leveraged the memes related to the 2016 U.S. presidential election and created the MultiOFF multimodal meme dataset for offensive content detection dataset. We subsequently developed a classifier for this task using the MultiOFF dataset. We use an early fusion technique to combine the image and text modality and compare it with a text- and an image-only baseline to investigate its effectiveness. Our results show improvements in terms of Precision, Recall, and F-Score. The code and dataset for this paper is published in \textit{ \url{https://github.com/bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text} }",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{rani-etal-2020-comparative,
    title = "A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in {H}indi-{E}nglish Code-Mixed Data",
    author = "Rani, Priya  and
      Suryawanshi, Shardul  and
      Goswami, Koustava  and
      Chakravarthi, Bharathi Raja  and
      Fransen, Theodorus  and
      McCrae, John Philip",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.7",
    pages = "42--48",
    abstract = "Hate speech detection in social media communication has become one of the primary concerns to avoid conflicts and curb undesired activities. In an environment where multilingual speakers switch among multiple languages, hate speech detection becomes a challenging task using methods that are designed for monolingual corpora. In our work, we attempt to analyze, detect and provide a comparative study of hate speech in a code-mixed social media text. We also provide a Hindi-English code-mixed data set consisting of Facebook and Twitter posts and comments. Our experiments show that deep learning models trained on this code-mixed corpus perform better.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{ramiandrisoa-mothe-2020-irit,
    title = "{IRIT} at {TRAC} 2020",
    author = "Ramiandrisoa, Faneva  and
      Mothe, Josiane",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.8",
    pages = "49--54",
    abstract = "This paper describes the participation of the IRIT team in the TRAC (Trolling, Aggression and Cyberbullying) 2020 shared task (Bhattacharya et al., 2020) on Aggression Identification and more precisely to the shared task in English language. The shared task was further divided into two sub-tasks: (a) aggression identification and (b) misogynistic aggression identification. We proposed to use the transformer based language model BERT (Bidirectional Encoder Representation from Transformer) for the two sub-tasks. Our team was qualified as twelfth out of sixteen participants on sub-task (a) and eleventh out of fifteen participants on sub-task (b).",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{risch-krestel-2020-bagging,
    title = "Bagging {BERT} Models for Robust Aggression Identification",
    author = "Risch, Julian  and
      Krestel, Ralf",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.9",
    pages = "55--61",
    abstract = "Modern transformer-based models with hundreds of millions of parameters, such as BERT, achieve impressive results at text classification tasks. This also holds for aggression identification and offensive language detection, where deep learning approaches consistently outperform less complex models, such as decision trees. While the complex models fit training data well (low bias), they also come with an unwanted high variance. Especially when fine-tuning them on small datasets, the classification performance varies significantly for slightly different training data. To overcome the high variance and provide more robust predictions, we propose an ensemble of multiple fine-tuned BERT models based on bootstrap aggregating (bagging). In this paper, we describe such an ensemble system and present our submission to the shared tasks on aggression identification 2020 (team name: Julian). Our submission is the best-performing system for five out of six subtasks. For example, we achieve a weighted F1-score of 80.3{\%} for task A on the test dataset of English social media posts. In our experiments, we compare different model configurations and vary the number of models used in the ensemble. We find that the F1-score drastically increases when ensembling up to 15 models, but the returns diminish for more models.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{liu-etal-2020-scmhl5,
    title = "Scmhl5 at {TRAC}-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach",
    author = "Liu, Han  and
      Burnap, Pete  and
      Alorainy, Wafa  and
      Williams, Matthew",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.10",
    pages = "62--68",
    abstract = "This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification. In particular, we participated in English Sub-task A on three-class classification ({`}Overtly Aggressive{'}, {`}Covertly Aggressive{'} and {`}Non-aggressive{'}) and English Sub-task B on binary classification for Misogynistic Aggression ({`}gendered{'} or {`}non-gendered{'}). For both sub-tasks, our method involves using the pre-trained Bert model for extracting the text of each instance into a 768-dimensional vector of embeddings, and then training an ensemble of classifiers on the embedding features. Our method obtained accuracy of 0.703 and weighted F-measure of 0.664 for Sub-task A, whereas for Sub-task B the accuracy was 0.869 and weighted F-measure was 0.851. In terms of the rankings, the weighted F-measure obtained using our method for Sub-task A is ranked in the 10th out of 16 teams, whereas for Sub-task B the weighted F-measure is ranked in the 8th out of 15 teams.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{pascucci-etal-2020-role,
    title = "The Role of Computational Stylometry in Identifying (Misogynistic) Aggression in {E}nglish Social Media Texts",
    author = "Pascucci, Antonio  and
      Manna, Raffaele  and
      Masucci, Vincenzo  and
      Monti, Johanna",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.11",
    pages = "69--75",
    abstract = "In this paper, we describe UniOr{\_}ExpSys team participation in TRAC-2 (Trolling, Aggression and Cyberbullying) shared task, a workshop organized as part of LREC 2020. TRAC-2 shared task is organized in two sub-tasks: Aggression Identification (a 3-way classification between {``}Overtly Aggressive{''}, {``}Covertly Aggressive{''} and {``}Non-aggressive{''} text data) and Misogynistic Aggression Identification (a binary classifier for classifying the texts as {``}gendered{''} or {``}non-gendered{''}). Our approach is based on linguistic rules, stylistic features extraction through stylometric analysis and Sequential Minimal Optimization algorithm in building the two classifiers.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{baruah-etal-2020-aggression,
    title = "Aggression Identification in {E}nglish, {H}indi and {B}angla Text using {BERT}, {R}o{BERT}a and {SVM}",
    author = "Baruah, Arup  and
      Das, Kaushik  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.12",
    pages = "76--82",
    abstract = "This paper presents the results of the classifiers we developed for the shared tasks in aggression identification and misogynistic aggression identification. These two shared tasks were held as part of the second workshop on Trolling, Aggression and Cyberbullying (TRAC). Both the subtasks were held for English, Hindi and Bangla language. In our study, we used English BERT (En-BERT), RoBERTa, DistilRoBERTa, and SVM based classifiers for English language. For Hindi and Bangla language, multilingual BERT (M-BERT), XLM-RoBERTa and SVM classifiers were used. Our best performing models are EN-BERT for English Subtask A (Weighted F1 score of 0.73, Rank 5/16), SVM for English Subtask B (Weighted F1 score of 0.87, Rank 2/15), SVM for Hindi Subtask A (Weighted F1 score of 0.79, Rank 2/10), XLMRoBERTa for Hindi Subtask B (Weighted F1 score of 0.87, Rank 2/10), SVM for Bangla Subtask A (Weighted F1 score of 0.81, Rank 2/10), and SVM for Bangla Subtask B (Weighted F1 score of 0.93, Rank 4/8). It is seen that the superior performance of the SVM classifier was achieved mainly because of its better prediction of the majority class. BERT based classifiers were found to predict the minority classes better.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{mut-altin-etal-2020-lastus,
    title = "{L}a{STUS}/{TALN} at {TRAC} - 2020 Trolling, Aggression and Cyberbullying",
    author = {Mut Alt{\i}n, L{\"u}tfiye Seda  and
      Bravo, Alex  and
      Saggion, Horacio},
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.13",
    pages = "83--86",
    abstract = "This paper presents the participation of the LaSTUS/TALN team at TRAC-2020 Trolling, Aggression and Cyberbullying shared task. The aim of the task is to determine whether a given text is aggressive and contains misogynistic content. Our approach is based on a bidirectional Long Short Term Memory network (bi-LSTM). Our system performed well at sub-task A, aggression detection; however underachieved at sub-task B, misogyny detection.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{datta-etal-2020-spyder,
    title = "{S}pyder: Aggression Detection on Multilingual Tweets",
    author = "Datta, Anisha  and
      Si, Shukrity  and
      Chakraborty, Urbi  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.14",
    pages = "87--92",
    abstract = "In the last few years, hate speech and aggressive comments have covered almost all the social media platforms like facebook, twitter etc. As a result hatred is increasing. This paper describes our (\textbf{Team name:} \textbf{Spyder}) participation in the Shared Task on Aggression Detection organised by TRAC-2, Second Workshop on Trolling, Aggression and Cyberbullying. The Organizers provided datasets in three languages {--} English, Hindi and Bengali. The task was to classify each instance of the test sets into three categories {--} {``}Overtly Aggressive{''} (OAG), {``}Covertly Aggressive{''} (CAG) and {``}Non-Aggressive{''} (NAG). In this paper, we propose three different models using Tf-Idf, sentiment polarity and machine learning based classifiers. We obtained f1 score of 43.10{\%}, 59.45{\%} and 44.84{\%} respectively for English, Hindi and Bengali.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{gordeev-lykova-2020-bert,
    title = "{BERT} of all trades, master of some",
    author = "Gordeev, Denis  and
      Lykova, Olga",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.15",
    pages = "93--98",
    abstract = "This paper describes our results for TRAC 2020 competition held together with the conference LREC 2020. Our team name was Ms8qQxMbnjJMgYcw. The competition consisted of 2 subtasks in 3 languages (Bengali, English and Hindi) where the participants{'} task was to classify aggression in short texts from social media and decide whether it is gendered or not. We used a single BERT-based system with two outputs for all tasks simultaneously. Our model placed first in English and second in Bengali gendered text classification competition tasks with 0.87 and 0.93 in F1-score respectively.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{tawalbeh-etal-2020-saja,
    title = "{SAJA} at {TRAC} 2020 Shared Task: Transfer Learning for Aggressive Identification with {XGB}oost",
    author = "Tawalbeh, Saja  and
      Hammad, Mahmoud  and
      AL-Smadi, Mohammad",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.16",
    pages = "99--105",
    abstract = "we have developed a system based on transfer learning technique depending on universal sentence encoder (USE) embedding that will be trained in our developed model using xgboost classifier to identify the aggressive text data from English content. A reference dataset has been provided from TRAC 2020 to evaluate the developed approach. The developed approach achieved in sub-task EN-A 60.75{\%} F1 (weighted) which ranked fourteenth out of sixteen teams and achieved 85.66{\%} F1 (weighted) in sub-task EN-B which ranked six out of fifteen teams.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{koufakou-etal-2020-florunito,
    title = "{F}lor{U}ni{T}o@{TRAC}-2: Retrofitting Word Embeddings on an Abusive Lexicon for Aggressive Language Detection",
    author = "Koufakou, Anna  and
      Basile, Valerio  and
      Patti, Viviana",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.17",
    pages = "106--112",
    abstract = "This paper describes our participation to the TRAC-2 Shared Tasks on Aggression Identification. Our team, FlorUniTo, investigated the applicability of using an abusive lexicon to enhance word embeddings towards improving detection of aggressive language. The embeddings used in our paper are word-aligned pre-trained vectors for English, Hindi, and Bengali, to reflect the languages in the shared task data sets. The embeddings are retrofitted to a multilingual abusive lexicon, HurtLex. We experimented with an LSTM model using the original as well as the transformed embeddings and different language and setting variations. Overall, our systems placed toward the middle of the official rankings based on weighted F1 score. However, the results on the development and test sets show promising improvements across languages, especially on the misogynistic aggression sub-task.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{kumari-singh-2020-ai,
    title = "{AI}{\_}{ML}{\_}{NIT}{\_}{P}atna @ {TRAC} - 2: Deep Learning Approach for Multi-lingual Aggression Identification",
    author = "Kumari, Kirti  and
      Singh, Jyoti Prakash",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.18",
    pages = "113--119",
    abstract = "This paper describes the details of developed models and results of team AI{\_}ML{\_}NIT{\_}Patna for the shared task of TRAC - 2. The main objective of the said task is to identify the level of aggression and whether the comment is gendered based or not. The aggression level of each comment can be marked as either Overtly aggressive or Covertly aggressive or Non-aggressive. We have proposed two deep learning systems: Convolutional Neural Network and Long Short Term Memory with two different input text representations, FastText and One-hot embeddings. We have found that the LSTM model with FastText embedding is performing better than other models for Hindi and Bangla datasets but for the English dataset, the CNN model with FastText embedding has performed better. We have also found that the performances of One-hot embedding and pre-trained FastText embedding are comparable. Our system got 11th and 10th positions for English Sub-task A and Sub-task B, respectively, 8th and 7th positions, respectively for Hindi Sub-task A and Sub-task B and 7th and 6th positions for Bangla Sub-task A and Sub-task B, respectively among the total submitted systems.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{mishra-etal-2020-multilingual,
    title = "Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at {TRAC} 2020",
    author = "Mishra, Sudhanshu  and
      Prasad, Shivangi  and
      Mishra, Shubhanshu",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.19",
    pages = "120--125",
    abstract = "We present our team {`}3Idiots{'} (referred as {`}sdhanshu{'} in the official rankings) approach for the Trolling, Aggression and Cyberbullying (TRAC) 2020 shared tasks. Our approach relies on fine-tuning various Transformer models on the different datasets. We also investigated the utility of task label marginalization, joint label classification, and joint training on multilingual datasets as possible improvements to our models. Our team came second in English sub-task A, a close fourth in the English sub-task B and third in the remaining 4 sub-tasks. We find the multilingual joint training approach to be the best trade-off between computational efficiency of model deployment and model{'}s evaluation performance. We open source our approach at https://github.com/socialmediaie/TRAC2020.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{safi-samghabadi-etal-2020-aggression,
    title = "Aggression and Misogyny Detection using {BERT}: A Multi-Task Approach",
    author = "Safi Samghabadi, Niloofar  and
      Patwa, Parth  and
      PYKL, Srinivas  and
      Mukherjee, Prerana  and
      Das, Amitava  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.20",
    pages = "126--131",
    abstract = "In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection.This paper presents our system for TRAC-2 shared task on {``}Aggression Identification{''} (sub-task A) and {``}Misogynistic Aggression Identification{''} (sub-task B). The data for this shared task is provided in three different languages - English, Hindi, and Bengali. Each data instance is annotated into one of the three aggression classes - Not Aggressive, Covertly Aggressive, Overtly Aggressive, as well as one of the two misogyny classes - Gendered and Non-Gendered. We propose an end-to-end neural model using attention on top of BERT that incorporates a multi-task learning paradigm to address both the sub-tasks simultaneously. Our team, {``}na14{''}, scored 0.8579 weighted F1-measure on the English sub-task B and secured 3rd rank out of 15 teams for the task. The code and the model weights are publicly available at https://github.com/NiloofarSafi/TRAC-2. Keywords: Aggression, Misogyny, Abusive Language, Hate-Speech Detection, BERT, NLP, Neural Networks, Social Media",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{diaz-torres-etal-2020-automatic,
    title = "Automatic Detection of Offensive Language in Social Media: Defining Linguistic Criteria to build a {M}exican {S}panish Dataset",
    author = "D{\'\i}az-Torres, Mar{\'\i}a Jos{\'e}  and
      Mor{\'a}n-M{\'e}ndez, Paulina Alejandra  and
      Villasenor-Pineda, Luis  and
      Montes-y-G{\'o}mez, Manuel  and
      Aguilera, Juan  and
      Meneses-Ler{\'\i}n, Luis",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.21",
    pages = "132--136",
    abstract = "Phenomena such as bullying, homophobia, sexism and racism have transcended to social networks, motivating the development of tools for their automatic detection. The challenge becomes greater for languages rich in popular sayings, colloquial expressions and idioms which may contain vulgar, profane or rude words, but not always have the intention of offending, as is the case of Mexican Spanish. Under these circumstances, the identification of the offense goes beyond the lexical and syntactic elements of the message. This first work aims to define the main linguistic features of aggressive, offensive and vulgar language in social networks in order to establish linguistic-based criteria to facilitate the identification of abusive language. For this purpose, a Mexican Spanish Twitter corpus was compiled and analyzed. The dataset included words that, despite being rude, need to be considered in context to determine they are part of an offense. Based on the analysis of this corpus, linguistic criteria were defined to determine whether a message is offensive. To simplify the application of these criteria, an easy-to-follow diagram was designed. The paper presents an example of the use of the diagram, as well as the basic statistics of the corpus.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{risch-etal-2020-offensive,
    title = "Offensive Language Detection Explained",
    author = "Risch, Julian  and
      Ruff, Robin  and
      Krestel, Ralf",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.22",
    pages = "137--143",
    abstract = "Many online discussion platforms use a content moderation process, where human moderators check user comments for offensive language and other rule violations. It is the moderator{'}s decision which comments to remove from the platform because of violations and which ones to keep. Research so far focused on automating this decision process in the form of supervised machine learning for a classification task. However, even with machine-learned models achieving better classification accuracy than human experts, there is still a reason why human moderators are preferred. In contrast to black-box models, such as neural networks, humans can give explanations for their decision to remove a comment. For example, they can point out which phrase in the comment is offensive or what subtype of offensiveness applies. In this paper, we analyze and compare four explanation methods for different offensive language classifiers: an interpretable machine learning model (naive Bayes), a model-agnostic explanation method (LIME), a model-based explanation method (LRP), and a self-explanatory model (LSTM with an attention mechanism). We evaluate these approaches with regard to their explanatory power and their ability to point out which words are most relevant for a classifier{'}s decision. We find that the more complex models achieve better classification accuracy while also providing better explanations than the simpler models.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{safi-samghabadi-etal-2020-detecting,
    title = "Detecting Early Signs of Cyberbullying in Social Media",
    author = "Safi Samghabadi, Niloofar  and
      L{\'o}pez Monroy, Adri{\'a}n Pastor  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.23",
    pages = "144--149",
    abstract = "Nowadays, the amount of users{'} activities on online social media is growing dramatically. These online environments provide excellent opportunities for communication and knowledge sharing. However, some people misuse them to harass and bully others online, a phenomenon called cyberbullying. Due to its harmful effects on people, especially youth, it is imperative to detect cyberbullying as early as possible before it causes irreparable damages to victims. Most of the relevant available resources are not explicitly designed to detect cyberbullying, but related content, such as hate speech and abusive language. In this paper, we propose a new approach to create a corpus suited for cyberbullying detection. We also investigate the possibility of designing a framework to monitor the streams of users{'} online messages and detects the signs of cyberbullying as early as possible.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{koufakou-scott-2020-lexicon,
    title = "Lexicon-Enhancement of Embedding-based Approaches Towards the Detection of Abusive Language",
    author = "Koufakou, Anna  and
      Scott, Jason",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.24",
    pages = "150--157",
    abstract = "Detecting abusive language is a significant research topic, which has received a lot of attention recently. Our work focuses on detecting personal attacks in online conversations. As previous research on this task has largely used deep learning based on embeddings, we explore the use of lexicons to enhance embedding-based methods in an effort to see how these methods apply in the particular task of detecting personal attacks. The methods implemented and experimented with in this paper are quite different from each other, not only in the type of lexicons they use (sentiment or semantic), but also in the way they use the knowledge from the lexicons, in order to construct or to change embeddings that are ultimately fed into the learning model. The sentiment lexicon approaches focus on integrating sentiment information (in the form of sentiment embeddings) into the learning model. The semantic lexicon approaches focus on transforming the original word embeddings so that they better represent relationships extracted from a semantic lexicon. Based on our experimental results, semantic lexicon methods are superior to the rest of the methods in this paper, with at least 4{\%} macro-averaged F1 improvement over the baseline.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@inproceedings{bhattacharya-etal-2020-developing,
    title = "Developing a Multilingual Annotated Corpus of Misogyny and Aggression",
    author = "Bhattacharya, Shiladitya  and
      Singh, Siddharth  and
      Kumar, Ritesh  and
      Bansal, Akanksha  and
      Bhagat, Akash  and
      Dawer, Yogesh  and
      Lahiri, Bornini  and
      Ojha, Atul Kr.",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.trac-1.25",
    pages = "158--168",
    abstract = "In this paper, we discuss the development of a multilingual annotated corpus of misogyny and aggression in Indian English, Hindi, and Indian Bangla as part of a project on studying and automatically identifying misogyny and communalism on social media (the ComMA Project). The dataset is collected from comments on YouTube videos and currently contains a total of over 20,000 comments. The comments are annotated at two levels - aggression (overtly aggressive, covertly aggressive, and non-aggressive) and misogyny (gendered and non-gendered). We describe the process of data collection, the tagset used for annotation, and issues and challenges faced during the process of annotation. Finally, we discuss the results of the baseline experiments conducted to develop a classifier for misogyny in the three languages.",
    language = "English",
    ISBN = "979-10-95546-56-6",
}
@proceedings{tlt-2020-international,
    title = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    editor = "Evang, Kilian  and
      Kallmeyer, Laura  and
      Ehren, Rafael  and
      Petitjean, Simon  and
      Seyffarth, Esther  and
      Seddah, Djam{\'e}",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.0",
}
@inproceedings{donicke-2020-clause,
    title = "Clause-Level Tense, Mood, Voice and Modality Tagging for {G}erman",
    author = {D{\"o}nicke, Tillmann},
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.1",
    doi = "10.18653/v1/2020.tlt-1.1",
    pages = "1--17",
}
@inproceedings{hu-etal-2020-building,
    title = "Building a Treebank for {C}hinese Literature for Translation Studies",
    author = "Hu, Hai  and
      Li, Yanting  and
      Patterson, Yina  and
      Tian, Zuoyu  and
      Zhang, Yiwen  and
      Zhou, He  and
      Kuebler, Sandra  and
      Lin, Chien-Jer Charles",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.2",
    doi = "10.18653/v1/2020.tlt-1.2",
    pages = "18--30",
}
@inproceedings{meelen-roux-2020-meta,
    title = "Meta-dating the {PA}rsed Corpus of {T}ibetan ({PACT}ib)",
    author = "Meelen, Marieke  and
      Roux, {\'E}lie",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.3",
    doi = "10.18653/v1/2020.tlt-1.3",
    pages = "31--42",
}
@inproceedings{steimel-etal-2020-fine,
    title = "Fine-Grained Morpho-Syntactic Analysis for the Under-Resourced Language Chaghatay",
    author = {Steimel, Kenneth  and
      Amat, Akbar  and
      Dwyer, Arienne  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.4",
    doi = "10.18653/v1/2020.tlt-1.4",
    pages = "43--54",
}
@inproceedings{bladier-etal-2020-automatic,
    title = "Automatic Extraction of Tree-Wrapping Grammars for Multiple Languages",
    author = "Bladier, Tatiana  and
      Kallmeyer, Laura  and
      Osswald, Rainer  and
      Waszczuk, Jakub",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.5",
    doi = "10.18653/v1/2020.tlt-1.5",
    pages = "55--61",
}
@inproceedings{stymne-2020-cross,
    title = "Cross-Lingual Domain Adaptation for Dependency Parsing",
    author = "Stymne, Sara",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.6",
    doi = "10.18653/v1/2020.tlt-1.6",
    pages = "62--69",
}
@inproceedings{levshina-2020-tight,
    title = "How tight is your language? A semantic typology based on Mutual Information",
    author = "Levshina, Natalia",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.7",
    doi = "10.18653/v1/2020.tlt-1.7",
    pages = "70--78",
}
@inproceedings{berdicevskis-etal-2020-subjects,
    title = "Subjects tend to be coded only once: Corpus-based and grammar-based evidence for an efficiency-driven trade-off",
    author = "Berdicevskis, Aleksandrs  and
      Schmidtke-Bode, Karsten  and
      Ser{\v{z}}ant, Ilja",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.8",
    doi = "10.18653/v1/2020.tlt-1.8",
    pages = "79--92",
}
@inproceedings{aggarwal-zeman-2020-estimating,
    title = "Estimating {POS} Annotation Consistency of Different Treebanks in a Language",
    author = "Aggarwal, Akshay  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.9",
    doi = "10.18653/v1/2020.tlt-1.9",
    pages = "93--110",
}
@inproceedings{vale-schulz-2020-intelligenti,
    title = "Intelligenti Pauca - Probing a Novel Alternative to {U}niversal {D}ependencies for Under-Resourced Languages on {L}atin",
    author = "Vale, Daniel Couto  and
      Schulz, Konstantin",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.10",
    doi = "10.18653/v1/2020.tlt-1.10",
    pages = "111--123",
}
@inproceedings{luukko-etal-2020-akkadian,
    title = "{A}kkadian Treebank for early Neo-Assyrian Royal Inscriptions",
    author = "Luukko, Mikko  and
      Sahala, Aleksi  and
      Hardwick, Sam  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.11",
    doi = "10.18653/v1/2020.tlt-1.11",
    pages = "124--134",
}
@inproceedings{kulkarni-etal-2020-dependency,
    title = "Dependency Relations for {S}anskrit Parsing and Treebank",
    author = "Kulkarni, Amba  and
      Satuluri, Pavankumar  and
      Panchal, Sanjeev  and
      Maity, Malay  and
      Malvade, Amruta",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.12",
    doi = "10.18653/v1/2020.tlt-1.12",
    pages = "135--150",
}
@inproceedings{kleiweg-van-noord-2020-alpinograph,
    title = "{A}lpino{G}raph: A Graph-based Search Engine for Flexible and Efficient Treebank Search",
    author = "Kleiweg, Peter  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.13",
    doi = "10.18653/v1/2020.tlt-1.13",
    pages = "151--161",
}
@inproceedings{popov-etal-2020-implementing,
    title = "Implementing an End-to-End Treebank-Informed Pipeline for {B}ulgarian",
    author = "Popov, Alexander  and
      Osenova, Petya  and
      Simov, Kiril",
    booktitle = "Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories",
    month = oct,
    year = "2020",
    address = {D{\"u}sseldorf, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.tlt-1.14",
    doi = "10.18653/v1/2020.tlt-1.14",
    pages = "162--167",
}
@proceedings{textgraphs-2020-graph,
    title = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Panchenko, Alexander  and
      Malliaros, Fragkiskos D.  and
      Hulpu{\textcommabelow{s}}, Ioana  and
      Jansen, Peter  and
      Jana, Abhik",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.0",
}
@inproceedings{nguyen-2020-survey,
    title = "A survey of embedding models of entities and relationships for knowledge graph completion",
    author = "Nguyen, Dat Quoc",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.1",
    pages = "1--14",
    abstract = "Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.",
}
@inproceedings{zhao-etal-2020-graph,
    title = "Graph-based Aspect Representation Learning for Entity Resolution",
    author = "Zhao, Zhenqi  and
      Guo, Yuchen  and
      Wang, Dingxian  and
      Huang, Yufan  and
      He, Xiangnan  and
      Gu, Bin",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.2",
    pages = "15--23",
    abstract = "Entity Resolution (ER) identifies records that refer to the same real-world entity. Deep learning approaches improved the generalization ability of entity matching models, but hardly overcame the impact of noisy or incomplete data sources. In real scenes, an entity usually consists of multiple semantic facets, called aspects. In this paper, we focus on entity augmentation, namely retrieving the values of missing aspects. The relationship between aspects is naturally suitable to be represented by a knowledge graph, where entity augmentation can be modeled as a link prediction problem. Our paper proposes a novel graph-based approach to solve entity augmentation. Specifically, we apply a dedicated random walk algorithm, which uses node types to limit the traversal length, and encodes graph structure into low-dimensional embeddings. Thus, the missing aspects could be retrieved by a link prediction model. Furthermore, the augmented aspects with fixed orders are served as the input of a deep Siamese BiLSTM network for entity matching. We compared our method with state-of-the-art methods through extensive experiments on downstream ER tasks. According to the experiment results, our model outperforms other methods on evaluation metrics (accuracy, precision, recall, and f1-score) to a large extent, which demonstrates the effectiveness of our method.",
}
@inproceedings{luo-etal-2020-merge,
    title = "Merge and Recognize: A Geometry and 2{D} Context Aware Graph Model for Named Entity Recognition from Visual Documents",
    author = "Luo, Chuwei  and
      Wang, Yongpan  and
      Zheng, Qi  and
      Li, Liangchen  and
      Gao, Feiyu  and
      Zhang, Shiyu",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.3",
    pages = "24--34",
    abstract = "Named entity recognition (NER) from visual documents, such as invoices, receipts or business cards, is a critical task for visual document understanding. Most classical approaches use a sequence-based model (typically BiLSTM-CRF framework) without considering document structure. Recent work on graph-based model using graph convolutional networks to encode visual and textual features have achieved promising performance on the task. However, few attempts take geometry information of text segments (text in bounding box) in visual documents into account. Meanwhile, existing methods do not consider that related text segments which need to be merged to form a complete entity in many real-world situations. In this paper, we present GraphNEMR, a graph-based model that uses graph convolutional networks to jointly merge text segments and recognize named entities. By incorporating geometry information from visual documents into our model, richer 2D context information is generated to improve document representations. To merge text segments, we introduce a novel mechanism that captures both geometry information as well as semantic information based on pre-trained language model. Experimental results show that the proposed GraphNEMR model outperforms both sequence-based and graph-based SOTA methods significantly.",
}
@inproceedings{vargas-vieyra-etal-2020-joint,
    title = "Joint Learning of the Graph and the Data Representation for Graph-Based Semi-Supervised Learning",
    author = "Vargas-Vieyra, Mariana  and
      Bellet, Aur{\'e}lien  and
      Denis, Pascal",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.4",
    pages = "35--45",
    abstract = "Graph-based semi-supervised learning is appealing when labels are scarce but large amounts of unlabeled data are available. These methods typically use a heuristic strategy to construct the graph based on some fixed data representation, independently of the available labels. In this pa- per, we propose to jointly learn a data representation and a graph from both labeled and unlabeled data such that (i) the learned representation indirectly encodes the label information injected into the graph, and (ii) the graph provides a smooth topology with respect to the transformed data. Plugging the resulting graph and representation into existing graph-based semi-supervised learn- ing algorithms like label spreading and graph convolutional networks, we show that our approach outperforms standard graph construction methods on both synthetic data and real datasets.",
}
@inproceedings{denk-peleteiro-ramallo-2020-contextual,
    title = "Contextual {BERT}: Conditioning the Language Model Using a Global State",
    author = "Denk, Timo I.  and
      Peleteiro Ramallo, Ana",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.5",
    pages = "46--50",
    abstract = "BERT is a popular language model whose main pre-training task is to fill in the blank, i.e., predicting a word that was masked out of a sentence, based on the remaining words. In some applications, however, having an additional context can help the model make the right prediction, e.g., by taking the domain or the time of writing into account. This motivates us to advance the BERT architecture by adding a global state for conditioning on a fixed-sized context. We present our two novel approaches and apply them to an industry use-case, where we complete fashion outfits with missing articles, conditioned on a specific customer. An experimental comparison to other methods from the literature shows that our methods improve personalization significantly.",
}
@inproceedings{yatabe-sasaki-2020-semi,
    title = "Semi-supervised Word Sense Disambiguation Using Example Similarity Graph",
    author = "Yatabe, Rie  and
      Sasaki, Minoru",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.6",
    pages = "51--59",
    abstract = "Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural net-works and machine learning techniques to solve WSD problems. However, these previ-ous supervised approaches often suffer from the lack of manually sense-tagged exam-ples. In this paper, to solve these problems, we propose a semi-supervised WSD method using graph embeddings based learning method in order to make effective use of labeled and unlabeled examples. The results of the experiments show that the proposed method performs better than the previous semi-supervised WSD method. Moreover, the graph structure between examples is effective for WSD and it is effective to utilize a graph structure obtained by fine-tuning BERT in the proposed method.",
}
@inproceedings{guillou-etal-2020-incorporating,
    title = "Incorporating Temporal Information in Entailment Graph Mining",
    author = "Guillou, Liane  and
      Bijl de Vroe, Sander  and
      Hosseini, Mohammad Javad  and
      Johnson, Mark  and
      Steedman, Mark",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.7",
    pages = "60--71",
    abstract = "We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win ̸→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.",
}
@inproceedings{al-ghezi-kurimo-2020-graph,
    title = "Graph-based Syntactic Word Embeddings",
    author = "Al-Ghezi, Ragheb  and
      Kurimo, Mikko",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.8",
    pages = "72--78",
    abstract = "We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings show a competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings.",
}
@inproceedings{shah-etal-2020-relation,
    title = "Relation Specific Transformations for Open World Knowledge Graph Completion",
    author = "Shah, Haseeb  and
      Villmow, Johannes  and
      Ulges, Adrian",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.9",
    pages = "79--84",
    abstract = "We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training. Our model learns relation-specific transformation functions from text-based to graph-based embedding space, where the closed-world link prediction model can be applied. We demonstrate state-of-the-art results on common open-world benchmarks and show that our approach benefits from relation-specific transformation functions (RST), giving substantial improvements over a relation-agnostic approach.",
}
@inproceedings{jansen-ustalov-2020-textgraphs,
    title = "{T}ext{G}raphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration",
    author = "Jansen, Peter  and
      Ustalov, Dmitry",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.10",
    pages = "85--97",
    abstract = "The 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating large detailed multi-fact explanations for standardized science exam questions. Given a question, correct answer, and knowledge base, models must rank each fact in the knowledge base such that facts most likely to appear in the explanation are ranked highest. Explanations consist of an average of 6 (and as many as 16) facts that span both core scientific knowledge and world knowledge, and form an explicit lexically-connected {``}explanation graph{''} describing how the facts interrelate. In this second iteration of the explanation regeneration shared task, participants are supplied with more than double the training and evaluation data of the first shared task, as well as a knowledge base nearly double in size, both of which expand into more challenging scientific topics that increase the difficulty of the task. In total 10 teams participated, and 5 teams submitted system description papers. The best-performing teams significantly increased state-of-the-art performance both in terms of ranking (mean average precision) and inference speed on this challenge task.",
}
@inproceedings{li-etal-2020-pgl,
    title = "{PGL} at {T}ext{G}raphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods",
    author = "Li, Weibin  and
      Lu, Yuxiang  and
      Huang, Zhengjie  and
      Su, Weiyue  and
      Liu, Jiaxiang  and
      Feng, Shikun  and
      Sun, Yu",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.11",
    pages = "98--102",
    abstract = "This paper describes the system designed by the Baidu PGL Team which achieved the first place in the TextGraphs 2020 Shared Task. The task focuses on generating explanations for elementary science questions. Given a question and its corresponding correct answer, we are asked to select the facts that can explain why the answer is correct for the question and answering (QA) from a large knowledge base. To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question. Then, we adopt a re-ranking approach based on a pre-trained language model to rank the candidate explanations. To further improve the rankings, we also develop an architecture consisting both powerful pre-trained transformers and GNNs to tackle the multi-hop inference problem. The official evaluation shows that, our system can outperform the second best system by 1.91 points.",
}
@inproceedings{pawate-etal-2020-chisquarex,
    title = "{C}hi{S}quare{X} at {T}ext{G}raphs 2020 Shared Task: Leveraging Pretrained Language Models for Explanation Regeneration",
    author = "Pawate, Aditya Girish  and
      Madhavan, Varun  and
      Chandak, Devansh",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.12",
    pages = "103--108",
    abstract = "In this work, we describe the system developed by a group of undergraduates from the Indian Institutes of Technology for the Shared Task at TextGraphs-14 on Multi-Hop Inference Explanation Regeneration (Jansen and Ustalov, 2020). The shared task required participants to develop methods to reconstruct gold explanations for elementary science questions from the WorldTreeCorpus (Xie et al., 2020). Although our research was not funded by any organization and all the models were trained on freely available tools like Google Colab, which restricted our computational capabilities, we have managed to achieve noteworthy results, placing ourselves in 4th place with a MAPscore of 0.49021in the evaluation leaderboard and 0.5062 MAPscore on the post-evaluation-phase leaderboard using RoBERTa. We incorporated some of the methods proposed in the previous edition of Textgraphs-13 (Chia et al., 2019), which proved to be very effective, improved upon them, and built a model on top of it using powerful state-of-the-art pre-trained language models like RoBERTa (Liu et al., 2019), BART (Lewis et al., 2020), SciB-ERT (Beltagy et al., 2019) among others. Further optimization of our work can be done with the availability of better computational resources.",
}
@inproceedings{gupta-srinivasaraghavan-2020-explanation,
    title = "Explanation Regeneration via Multi-Hop {ILP} Inference over Knowledge Base",
    author = "Gupta, Aayushee  and
      Srinivasaraghavan, Gopalakrishnan",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.13",
    pages = "109--114",
    abstract = "Textgraphs 2020 Workshop organized a shared task on {`}Explanation Regeneration{'} that required reconstructing gold explanations for elementary science questions. This work describes our submission to the task which is based on multiple components: a BERT baseline ranking, an Integer Linear Program (ILP) based re-scoring and a regression model for re-ranking the explanation facts. Our system achieved a Mean Average Precision score of 0.3659.",
}
@inproceedings{chia-etal-2020-red,
    title = "Red Dragon {AI} at {T}ext{G}raphs 2020 Shared Task : {LIT} : {LSTM}-Interleaved Transformer for Multi-Hop Explanation Ranking",
    author = "Chia, Yew Ken  and
      Witteveen, Sam  and
      Andrews, Martin",
    booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.textgraphs-1.14",
    pages = "115--120",
    abstract = "Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in isolation, we propose the LSTM-Interleaved Transformer which incorporates cross-document interactions for improved multi-hop ranking. The LIT architecture can leverage prior ranking positions in the re-ranking setting. Our model is competitive on the current leaderboard for the TextGraphs 2020 shared task, achieving a test-set MAP of 0.5607, and would have gained third place had we submitted before the competition deadline. Our code implementation is made available at [https://github.com/mdda/worldtree{\_}corpus/tree/textgraphs{\_}2020](https://github.com/mdda/worldtree{\_}corpus/tree/textgraphs{\_}2020).",
}
@article{tacl-2020-transactions,
    title = "Transactions of the Association for Computational Linguistics, Volume 8",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.0",
}
@article{pimentel-etal-2020-phonotactic,
    title = "Phonotactic Complexity and Its Trade-offs",
    author = "Pimentel, Tiago  and
      Roark, Brian  and
      Cotterell, Ryan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.1",
    doi = "10.1162/tacl_a_00296",
    pages = "1--18",
    abstract = "We present methods for calculating a measure of phonotactic complexity{---}bits per phoneme{---} that permits a straightforward cross-linguistic comparison. When given a word, represented as a sequence of phonemic segments such as symbols in the international phonetic alphabet, and a statistical model trained on a sample of word types from the language, we can approximately measure bits per phoneme using the negative log-probability of that word under the model. This simple measure allows us to compare the entropy across languages, giving insight into how complex a language{'}s phonotactics is. Using a collection of 1016 basic concept words across 106 languages, we demonstrate a very strong negative correlation of − 0.74 between bits per phoneme and the average length of words.",
}
@article{wang-etal-2020-amr,
    title = "{AMR}-To-Text Generation with Graph Transformer",
    author = "Wang, Tianming  and
      Wan, Xiaojun  and
      Jin, Hanqi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.2",
    doi = "10.1162/tacl_a_00297",
    pages = "19--33",
    abstract = "Abstract meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations. The current state-of-the-art methods use graph-to-sequence models; however, they still cannot significantly outperform the previous sequence-to-sequence models or statistical approaches. In this paper, we propose a novel graph-to-sequence model (Graph Transformer) to address this task. The model directly encodes the AMR graphs and learns the node representations. A pairwise interaction function is used for computing the semantic relations between the concepts. Moreover, attention mechanisms are used for aggregating the information from the incoming and outgoing neighbors, which help the model to capture the semantic information effectively. Our model outperforms the state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLEU points on LDC2017T10 and achieves new state-of-the-art performances.",
}
@article{ettinger-2020-bert,
    title = "What {BERT} Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models",
    author = "Ettinger, Allyson",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.3",
    doi = "10.1162/tacl_a_00298",
    pages = "34--48",
    abstract = "Pre-training by language modeling has become a popular and successful approach to NLP tasks, but we have yet to understand exactly what linguistic capacities these pre-training processes confer upon models. In this paper we introduce a suite of diagnostics drawn from human language experiments, which allow us to ask targeted questions about information used by language models for generating predictions in context. As a case study, we apply these diagnostics to the popular BERT model, finding that it can generally distinguish good from bad completions involving shared category or role reversal, albeit with less sensitivity than humans, and it robustly retrieves noun hypernyms, but it struggles with challenging inference and role-based event prediction{---} and, in particular, it shows clear insensitivity to the contextual impacts of negation.",
}
@article{hisamoto-etal-2020-membership,
    title = "Membership Inference Attacks on Sequence-to-Sequence Models: {I}s My Data In Your Machine Translation System?",
    author = "Hisamoto, Sorami  and
      Post, Matt  and
      Duh, Kevin",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.4",
    doi = "10.1162/tacl_a_00299",
    pages = "49--63",
    abstract = "Data privacy is an important issue for {``}machine learning as a service{''} providers. We focus on the problem of membership inference attacks: Given a data sample and black-box access to a model{'}s API, determine whether the sample existed in the model{'}s training data. Our contribution is an investigation of this problem in the context of sequence-to-sequence models, which are important in applications such as machine translation and video captioning. We define the membership inference problem for sequence generation, provide an open dataset based on state-of-the-art machine translation models, and report initial results on whether these models leak private information against several kinds of membership inference attacks.",
}
@article{joshi-etal-2020-spanbert,
    title = "{S}pan{BERT}: Improving Pre-training by Representing and Predicting Spans",
    author = "Joshi, Mandar  and
      Chen, Danqi  and
      Liu, Yinhan  and
      Weld, Daniel S.  and
      Zettlemoyer, Luke  and
      Levy, Omer",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.5",
    doi = "10.1162/tacl_a_00300",
    pages = "64--77",
    abstract = "We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6{\%} and 88.7{\%} F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6{\%} F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1",
}
@article{yan-etal-2020-graph,
    title = "A Graph-based Model for Joint {C}hinese Word Segmentation and Dependency Parsing",
    author = "Yan, Hang  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.6",
    doi = "10.1162/tacl_a_00301",
    pages = "78--92",
    abstract = "Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined at the word-level. Therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffer from error propagation and unable to directly make use of character-level pre-trained language models (such as BERT). In this paper, we propose a graph-based model to integrate Chinese word segmentation and dependency parsing. Different from previous transition-based joint models, our proposed model is more concise, which results in fewer efforts of feature engineering. Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. Additionally, when BERT is combined, our model can substantially reduce the performance gap of dependency parsing between joint models and gold-segmented word-based models. Our code is publicly available at https://github.com/fastnlp/JointCwsParser",
}
@article{guan-etal-2020-knowledge,
    title = "A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation",
    author = "Guan, Jian  and
      Huang, Fei  and
      Zhao, Zhihao  and
      Zhu, Xiaoyan  and
      Huang, Minlie",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.7",
    doi = "10.1162/tacl_a_00302",
    pages = "93--108",
    abstract = "Story generation, namely, generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) still suffer from repetition, logic conflicts, and lack of long-range coherence in generated stories. We conjecture that this is because of the difficulty of associating relevant commonsense knowledge, understanding the causal relationships, and planning entities and events with proper temporal order. In this paper, we devise a knowledge-enhanced pretraining model for commonsense story generation. We propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories. To further capture the causal and temporal dependencies between the sentences in a reasonable story, we use multi-task learning, which combines a discriminative objective to distinguish true and fake stories during fine-tuning. Automatic and manual evaluation shows that our model can generate more reasonable stories than state-of-the-art baselines, particularly in terms of logic and global coherence.",
}
@article{zhou-etal-2020-improving,
    title = "Improving Candidate Generation for Low-resource Cross-lingual Entity Linking",
    author = "Zhou, Shuyan  and
      Rijhwani, Shruti  and
      Wieting, John  and
      Carbonell, Jaime  and
      Neubig, Graham",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.8",
    doi = "10.1162/tacl_a_00303",
    pages = "109--124",
    abstract = "Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for resources in the low-resource languages by utilizing resources in closely related languages, but the performance still lags far behind their high-resource counterparts. In this paper, we first assess the problems faced by current entity candidate generation methods for low-resource XEL, then propose three improvements that (1) reduce the disconnect between entity mentions and KB entries, and (2) improve the robustness of the model to low-resource scenarios. The methods are simple, but effective: We experiment with our approach on seven XEL datasets and find that they yield an average gain of 16.9{\%} in Top-30 gold candidate recall, compared with state-of-the-art baselines. Our improved model also yields an average gain of 7.9{\%} in in-KB accuracy of end-to-end XEL.1",
}
@article{mccoy-etal-2020-syntax,
    title = "Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks",
    author = "McCoy, R. Thomas  and
      Frank, Robert  and
      Linzen, Tal",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.9",
    doi = "10.1162/tacl_a_00304",
    pages = "125--140",
    abstract = "Learners that are exposed to the same training data might generalize differently due to differing inductive biases. In neural network models, inductive biases could in theory arise from any aspect of the model architecture. We investigate which architectural factors affect the generalization behavior of neural sequence-to-sequence models trained on two syntactic tasks, English question formation and English tense reinflection. For both tasks, the training set is consistent with a generalization based on hierarchical structure and a generalization based on linear order. All architectural factors that we investigated qualitatively affected how models generalized, including factors with no clear connection to hierarchical structure. For example, LSTMs and GRUs displayed qualitatively different inductive biases. However, the only factor that consistently contributed a hierarchical bias across tasks was the use of a tree-structured model rather than a model with sequential recurrence, suggesting that human-like syntactic generalization requires architectural syntactic structure.",
}
@article{sun-etal-2020-investigating,
    title = "Investigating Prior Knowledge for Challenging {C}hinese Machine Reading Comprehension",
    author = "Sun, Kai  and
      Yu, Dian  and
      Yu, Dong  and
      Cardie, Claire",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.10",
    doi = "10.1162/tacl_a_00305",
    pages = "141--155",
    abstract = "Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5{\%}) and human readers (96.0{\%}), especiallyon problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C3 to present great challenges to existing systems as answering 86.8{\%} of questions requires both knowledge within and beyond the accompanying document, and we hope that C3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C3 is available at https://dataset.org/c3/.",
}
@article{hahn-2020-theoretical,
    title = "Theoretical Limitations of Self-Attention in Neural Sequence Models",
    author = "Hahn, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.11",
    doi = "10.1162/tacl_a_00306",
    pages = "156--171",
    abstract = "Transformers are emerging as the new workhorse of NLP, showing great success across tasks. Unlike LSTMs, transformers process input sequences entirely through self-attention. Previous work has suggested that the computational capabilities of self-attention to process hierarchical structures are limited. In this work, we mathematically investigate the computational power of self-attention to model formal languages. Across both soft and hard attention, we show strong theoretical limitations of the computational abilities of self-attention, finding that it cannot model periodic finite-state languages, nor hierarchical structure, unless the number of layers or heads increases with input length. These limitations seem surprising given the practical success of self-attention and the prominent role assigned to hierarchical structure in linguistics, suggesting that natural language can be approximated well with models that are too weak for the formal languages typically assumed in theoretical linguistics.",
}
@article{zhang-etal-2020-target,
    title = "Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis",
    author = "Zhang, Ji  and
      Chen, Chengyao  and
      Liu, Pengfei  and
      He, Chao  and
      Leung, Cane Wing-Ki",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.12",
    doi = "10.1162/tacl_a_00308",
    pages = "172--182",
    abstract = "Target-dependent sentiment analysis (TDSA) aims to classify the sentiment of a text towards a given target. The major challenge of this task lies in modeling the semantic relatedness between a target and its context sentence. This paper proposes a novel Target-Guided Structured Attention Network (TG-SAN), which captures target-related contexts for TDSA in a fine-to-coarse manner. Given a target and its context sentence, the proposed TG-SAN first identifies multiple semantic segments from the sentence using a target-guided structured attention mechanism. It then fuses the extracted segments based on their relatedness with the target for sentiment classification. We present comprehensive comparative experiments on three benchmarks with three major findings. First, TG-SAN outperforms the state-of-the-art by up to 1.61{\%} and 3.58{\%} in terms of accuracy and Marco-F1, respectively. Second, it shows a strong advantage in determining the sentiment of a target when the context sentence contains multiple semantic segments. Lastly, visualization results show that the attention scores produced by TG-SAN are highly interpretable",
}
@article{wolfson-etal-2020-break,
    title = "Break It Down: A Question Understanding Benchmark",
    author = "Wolfson, Tomer  and
      Geva, Mor  and
      Gupta, Ankit  and
      Gardner, Matt  and
      Goldberg, Yoav  and
      Deutch, Daniel  and
      Berant, Jonathan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.13",
    doi = "10.1162/tacl_a_00309",
    pages = "183--198",
    abstract = "Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.",
}
@article{chen-etal-2020-acoustic,
    title = "Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies",
    author = "Chen, Xi (Leslie)  and
      Levitan, Sarah Ita  and
      Levine, Michelle  and
      Mandic, Marko  and
      Hirschberg, Julia",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.14",
    doi = "10.1162/tacl_a_00311",
    pages = "199--214",
    abstract = "Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of deceptive and truthful interviews. We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality. With this data we built classifiers to automatically distinguish trusted from mistrusted speech, achieving an F1 of 66.1{\%}. We next evaluated whether the strategies raters said they used to discriminate between truthful and deceptive responses were in fact useful. Our results show that, although several prosodic and lexical features were consistently perceived as trustworthy, they were not reliable cues. Also, the strategies that judges reported using in deception detection were not helpful for the task. Our work sheds light on the nature of trusted language and provides insight into the challenging problem of human deception detection.",
}
@article{nishida-nakayama-2020-unsupervised,
    title = "Unsupervised Discourse Constituency Parsing Using {V}iterbi {EM}",
    author = "Nishida, Noriki  and
      Nakayama, Hideki",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.15",
    doi = "10.1162/tacl_a_00312",
    pages = "215--230",
    abstract = "In this paper, we introduce an unsupervised discourse constituency parsing algorithm. We use Viterbi EM with a margin-based criterion to train a span-based discourse parser in an unsupervised manner. We also propose initialization methods for Viterbi training of discourse constituents based on our prior knowledge of text structures. Experimental results demonstrate that our unsupervised parser achieves comparable or even superior performance to fully supervised parsers. We also investigate discourse constituents that are learned by our method.",
}
@article{djokic-etal-2020-decoding,
    title = "Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models",
    author = "Djokic, Vesna G.  and
      Maillard, Jean  and
      Bulat, Luana  and
      Shutova, Ekaterina",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.16",
    doi = "10.1162/tacl_a_00307",
    pages = "231--246",
    abstract = "Recent years have seen a growing interest within the natural language processing (NLP) community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to decode brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor processing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing support for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension.",
}
@article{settles-etal-2020-machine,
    title = "Machine Learning{--}Driven Language Assessment",
    author = "Settles, Burr  and
      T. LaFlair, Geoffrey  and
      Hagiwara, Masato",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.17",
    doi = "10.1162/tacl_a_00310",
    pages = "247--263",
    abstract = "We describe a method for rapidly creating language proficiency assessments, and provide experimental evidence that such tests can be valid, reliable, and secure. Our approach is the first to use machine learning and natural language processing to induce proficiency scales based on a given standard, and then use linguistic models to estimate item difficulty directly for computer-adaptive testing. This alleviates the need for expensive pilot testing with human subjects. We used these methods to develop an online proficiency exam called the Duolingo English Test, and demonstrate that its scores align significantly with other high-stakes English assessments. Furthermore, our approach produces test scores that are highly reliable, while generating item banks large enough to satisfy security requirements.",
}
@article{rothe-etal-2020-leveraging,
    title = "Leveraging Pre-trained Checkpoints for Sequence Generation Tasks",
    author = "Rothe, Sascha  and
      Narayan, Shashi  and
      Severyn, Aliaksei",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.18",
    doi = "10.1162/tacl_a_00313",
    pages = "264--280",
    abstract = "Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2, and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.",
}
@article{zhu-etal-2020-crosswoz,
    title = "{C}ross{WOZ}: A Large-Scale {C}hinese Cross-Domain Task-Oriented Dialogue Dataset",
    author = "Zhu, Qi  and
      Huang, Kaili  and
      Zhang, Zheng  and
      Zhu, Xiaoyan  and
      Huang, Minlie",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.19",
    doi = "10.1162/tacl_a_00314",
    pages = "281--295",
    abstract = "To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.",
}
@article{lau-etal-2020-furiously,
    title = "How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context",
    author = "Lau, Jey Han  and
      Armendariz, Carlos  and
      Lappin, Shalom  and
      Purver, Matthew  and
      Shu, Chang",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.20",
    doi = "10.1162/tacl_a_00315",
    pages = "296--310",
    abstract = "We study the influence of context on sentence acceptability. First we compare the acceptability ratings of sentences judged in isolation, with a relevant context, and with an irrelevant context. Our results show that context induces a cognitive load for humans, which compresses the distribution of ratings. Moreover, in relevant contexts we observe a discourse coherence effect that uniformly raises acceptability. Next, we test unidirectional and bidirectional language models in their ability to predict acceptability ratings. The bidirectional models show very promising results, with the best model achieving a new state-of-the-art for unsupervised acceptability prediction. The two sets of experiments provide insights into the cognitive aspects of sentence processing and central issues in the computational modeling of text and discourse.",
}
@article{arora-etal-2020-learning,
    title = "Learning Lexical Subspaces in a Distributional Vector Space",
    author = "Arora, Kushal  and
      Chakraborty, Aishik  and
      Cheung, Jackie C. K.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.21",
    doi = "10.1162/tacl_a_00316",
    pages = "311--329",
    abstract = "In this paper, we propose LexSub, a novel approach towards unifying lexical and distributional semantics. We inject knowledge about lexical-semantic relations into distributional word embeddings by defining subspaces of the distributional vector space in which a lexical relation should hold. Our framework can handle symmetric attract and repel relations (e.g., synonymy and antonymy, respectively), as well as asymmetric relations (e.g., hypernymy and meronomy). In a suite of intrinsic benchmarks, we show that our model outperforms previous approaches on relatedness tasks and on hypernymy classification and detection, while being competitive on word similarity tasks. It also outperforms previous systems on extrinsic classification tasks that benefit from exploiting lexical relational cues. We perform a series of analyses to understand the behaviors of our model.1Code available at https://github.com/aishikchakraborty/LexSub.",
}
@article{kumar-etal-2020-syntax,
    title = "Syntax-Guided Controlled Generation of Paraphrases",
    author = "Kumar, Ashutosh  and
      Ahuja, Kabir  and
      Vadapalli, Raghuram  and
      Talukdar, Partha",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.22",
    doi = "10.1162/tacl_a_00318",
    pages = "329--345",
    abstract = "Given a sentence (e.g., {``}I like mangoes{''}) and a constraint (e.g., sentiment flip), the goal of controlled text generation is to produce a sentence that adapts the input sentence to meet the requirements of the constraint (e.g., {``}I hate mangoes{''}). Going beyond such simple constraints, recent work has started exploring the incorporation of complex syntactic-guidance as constraints in the task of controlled paraphrase generation. In these methods, syntactic-guidance is sourced from a separate exemplar sentence. However, this prior work has only utilized limited syntactic information available in the parse tree of the exemplar sentence. We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser (SGCP), an end-to-end framework for syntactic paraphrase generation. We find that Sgcp can generate syntax-conforming sentences while not compromising on relevance. We perform extensive automated and human evaluations over multiple real-world English language datasets to demonstrate the efficacy of Sgcp over state-of-the-art baselines. To drive future research, we have made Sgcp{'}s source code available.1",
}
@article{yu-etal-2020-better,
    title = "Better Document-Level Machine Translation with {B}ayes{'} Rule",
    author = "Yu, Lei  and
      Sartran, Laurent  and
      Stokowiec, Wojciech  and
      Ling, Wang  and
      Kong, Lingpeng  and
      Blunsom, Phil  and
      Dyer, Chris",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.23",
    doi = "10.1162/tacl_a_00319",
    pages = "346--360",
    abstract = "We show that Bayes{'} rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents a compelling benefit because parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the {``}reverse translation probability{''} of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model{'}s independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches.",
}
@article{azpiazu-pera-2020-hierarchical,
    title = "Hierarchical Mapping for Crosslingual Word Embedding Alignment",
    author = "Azpiazu, Ion Madrazo  and
      Pera, Maria Soledad",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.24",
    doi = "10.1162/tacl_a_00320",
    pages = "361--376",
    abstract = "The alignment of word embedding spaces in different languages into a common crosslingual space has recently been in vogue. Strategies that do so compute pairwise alignments and then map multiple languages to a single pivot language (most often English). These strategies, however, are biased towards the choice of the pivot language, given that language proximity and the linguistic characteristics of the target language can strongly impact the resultant crosslingual space in detriment of topologically distant languages. We present a strategy that eliminates the need for a pivot language by learning the mappings across languages in a hierarchical way. Experiments demonstrate that our strategy significantly improves vocabulary induction scores in all existing benchmarks, as well as in a new non-English{--}centered benchmark we built, which we make publicly available.",
}
@article{warstadt-etal-2020-blimp-benchmark,
    title = "{BL}i{MP}: The Benchmark of Linguistic Minimal Pairs for {E}nglish",
    author = "Warstadt, Alex  and
      Parrish, Alicia  and
      Liu, Haokun  and
      Mohananey, Anhad  and
      Peng, Wei  and
      Wang, Sheng-Fu  and
      Bowman, Samuel R.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.25",
    doi = "10.1162/tacl_a_00321",
    pages = "377--392",
    abstract = "We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs{---}that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4{\%}. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands.",
}
@article{zhang-duh-2020-reproducible,
    title = "Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems",
    author = "Zhang, Xuan  and
      Duh, Kevin",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.26",
    doi = "10.1162/tacl_a_00322",
    pages = "393--408",
    abstract = "Hyperparameter selection is a crucial part of building neural machine translation (NMT) systems across both academia and industry. Fine-grained adjustments to a model{'}s architecture or training recipe can mean the difference between a positive and negative research result or between a state-of-the-art and underperforming system. While recent literature has proposed methods for automatic hyperparameter optimization (HPO), there has been limited work on applying these methods to neural machine translation (NMT), due in part to the high costs associated with experiments that train large numbers of model variants. To facilitate research in this space, we introduce a lookup-based approach that uses a library of pre-trained models for fast, low cost HPO experimentation. Our contributions include (1) the release of a large collection of trained NMT models covering a wide range of hyperparameters, (2) the proposal of targeted metrics for evaluating HPO methods on NMT, and (3) a reproducible benchmark of several HPO methods against our model library, including novel graph-based and multiobjective methods.",
}
@article{clark-fijalkow-2020-consistent,
    title = "Consistent Unsupervised Estimators for Anchored {PCFG}s",
    author = {Clark, Alexander  and
      Fijalkow, Nathana{\"e}l},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.27",
    doi = "10.1162/tacl_a_00323",
    pages = "409--422",
    abstract = "Learning probabilistic context-free grammars (PCFGs) from strings is a classic problem in computational linguistics since Horning (1969). Here we present an algorithm based on distributional learning that is a consistent estimator for a large class of PCFGs that satisfy certain natural conditions including being anchored (Stratos et al., 2016). We proceed via a reparameterization of (top--down) PCFGs that we call a bottom{--}up weighted context-free grammar. We show that if the grammar is anchored and satisfies additional restrictions on its ambiguity, then the parameters can be directly related to distributional properties of the anchoring strings; we show the asymptotic correctness of a naive estimator and present some simulations using synthetic data that show that algorithms based on this approach have good finite sample behavior.",
}
@article{jiang-etal-2020-know,
    title = "How Can We Know What Language Models Know?",
    author = "Jiang, Zhengbao  and
      Xu, Frank F.  and
      Araki, Jun  and
      Neubig, Graham",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.28",
    doi = "10.1162/tacl_a_00324",
    pages = "423--438",
    abstract = "Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as {``}Obama is a {\_}{\_} by profession{''}. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as {``}Obama worked as a {\_}{\_} {''} may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1{\%} to 39.6{\%}, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA.",
}
@article{dieng-etal-2020-topic,
    title = "Topic Modeling in Embedding Spaces",
    author = "Dieng, Adji B.  and
      Ruiz, Francisco J. R.  and
      Blei, David M.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.29",
    doi = "10.1162/tacl_a_00325",
    pages = "439--453",
    abstract = "Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the etm models each word with a categorical distribution whose natural parameter is the inner product between the word{'}s embedding and an embedding of its assigned topic. To fit the etm, we develop an efficient amortized variational inference algorithm. The etm discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation, in terms of both topic quality and predictive performance.",
}
@article{clark-etal-2020-tydi,
    title = "{T}y{D}i {QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages",
    author = "Clark, Jonathan H.  and
      Choi, Eunsol  and
      Collins, Michael  and
      Garrette, Dan  and
      Kwiatkowski, Tom  and
      Nikolaev, Vitaly  and
      Palomaki, Jennimaria",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.30",
    doi = "10.1162/tacl_a_00317",
    pages = "454--470",
    abstract = "Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations. We present TyDi QA{---}a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology{---}the set of linguistic features each language expresses{---}such that we expect models performing well on this set to generalize across a large number of the world{'}s languages. We present a quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don{'}t know the answer yet, and the data is collected directly in each language without the use of translation.",
}
@article{zhu-etal-2020-neural,
    title = "A Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings",
    author = "Zhu, Lixing  and
      He, Yulan  and
      Zhou, Deyu",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.31",
    doi = "10.1162/tacl_a_00326",
    pages = "471--485",
    abstract = "We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are shared across documents, a word is generated by a hidden semantic vector encoding its contextual semantic meaning, and its context words are generated conditional on both the hidden semantic vector and global latent topics. Topics are trained jointly with the word embeddings. The trained model maps words to topic-dependent embeddings, which naturally addresses the issue of word polysemy. Experimental results show that the proposed model outperforms the word-level embedding methods in both word similarity evaluation and word sense disambiguation. Furthermore, the model also extracts more coherent topics compared with existing neural topic models or other models for joint learning of topics and word embeddings. Finally, the model can be easily integrated with existing deep contextualized word embedding learning methods to further improve the performance of downstream tasks such as sentiment classification.",
}
@article{kumar-etal-2020-nurse,
    title = "Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings",
    author = "Kumar, Vaibhav  and
      Bhotia, Tenzin Singhay  and
      Kumar, Vaibhav  and
      Chakraborty, Tanmoy",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.32",
    doi = "10.1162/tacl_a_00327",
    pages = "486--503",
    abstract = "Word embeddings are the standard model for semantic and syntactic representations of words. Unfortunately, these models have been shown to exhibit undesirable word associations resulting from gender, racial, and religious biases. Existing post-processing methods for debiasing word embeddings are unable to mitigate gender bias hidden in the spatial arrangement of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing methodology that not only eliminates the bias present in a word vector but also alters the spatial distribution of its neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset. We also propose a new bias evaluation metric, Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of evaluation metrics show that RAN-Debias significantly outperforms the state-of-the-art in reducing proximity bias (GIPE) by at least 42.02{\%}. It also reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task (coreference resolution).",
}
@article{ben-david-etal-2020-perl,
    title = "{PERL}: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models",
    author = "Ben-David, Eyal  and
      Rabinovitz, Carmel  and
      Reichart, Roi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.33",
    doi = "10.1162/tacl_a_00328",
    pages = "504--521",
    abstract = "Pivot-based neural representation models have led to significant progress in domain adaptation for NLP. However, previous research following this approach utilize only labeled data from the source domain and unlabeled data from the source and target domains, but neglect to incorporate massive unlabeled corpora that are not necessarily drawn from these domains. To alleviate this, we propose PERL: A representation learning model that extends contextualized word embedding models such as BERT (Devlin et al., 2019) with pivot-based fine-tuning. PERL outperforms strong baselines across 22 sentiment classification domain adaptation setups, improves in-domain model performance, yields effective reduced-size models, and increases model stability.1",
}
@article{opitz-etal-2020-amr,
    title = "{AMR} Similarity Metrics from Principles",
    author = "Opitz, Juri  and
      Parcalabescu, Letitia  and
      Frank, Anette",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.34",
    doi = "10.1162/tacl_a_00329",
    pages = "522--538",
    abstract = "Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) and increases computational efficiency by ablating the variable-alignment. In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of Smatch and SemBleu where we show that the latter exhibits some undesirable properties. For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; and iii) we propose a novel metric S2 match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over Smatch and SemBleu.",
}
@article{fomicheva-etal-2020-unsupervised,
    title = "Unsupervised Quality Estimation for Neural Machine Translation",
    author = "Fomicheva, Marina  and
      Sun, Shuo  and
      Yankovskaya, Lisa  and
      Blain, Fr{\'e}d{\'e}ric  and
      Guzm{\'a}n, Francisco  and
      Fishel, Mark  and
      Aletras, Nikolaos  and
      Chaudhary, Vishrav  and
      Specia, Lucia",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.35",
    doi = "10.1162/tacl_a_00330",
    pages = "539--555",
    abstract = "Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation, and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By utilizing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivaling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.",
}
@article{andreas-etal-2020-task,
    title = "Task-Oriented Dialogue as Dataflow Synthesis",
    author = "Andreas, Jacob  and
      Bufe, John  and
      Burkett, David  and
      Chen, Charles  and
      Clausman, Josh  and
      Crawford, Jean  and
      Crim, Kate  and
      DeLoach, Jordan  and
      Dorner, Leah  and
      Eisner, Jason  and
      Fang, Hao  and
      Guo, Alan  and
      Hall, David  and
      Hayes, Kristin  and
      Hill, Kellie  and
      Ho, Diana  and
      Iwaszuk, Wendy  and
      Jha, Smriti  and
      Klein, Dan  and
      Krishnamurthy, Jayant  and
      Lanman, Theo  and
      Liang, Percy  and
      Lin, Christopher H.  and
      Lintsbakh, Ilya  and
      McGovern, Andy  and
      Nisnevich, Aleksandr  and
      Pauls, Adam  and
      Petters, Dmitrij  and
      Read, Brent  and
      Roth, Dan  and
      Roy, Subhro  and
      Rusak, Jesse  and
      Short, Beth  and
      Slomin, Div  and
      Snyder, Ben  and
      Striplin, Stephon  and
      Su, Yu  and
      Tellman, Zachary  and
      Thomson, Sam  and
      Vorobev, Andrei  and
      Witoszko, Izabela  and
      Wolfe, Jason  and
      Wray, Abby  and
      Zhang, Yuchen  and
      Zotov, Alexander",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.36",
    doi = "10.1162/tacl_a_00333",
    pages = "556--571",
    abstract = "We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people. Experiments show that dataflow graphs and metacomputation substantially improve representability and predictability in these natural dialogues. Additional experiments on the MultiWOZ dataset show that our dataflow representation enables an otherwise off-the-shelf sequence-to-sequence model to match the best existing task-specific state tracking model. The SMCalFlow dataset, code for replicating experiments, and a public leaderboard are available at https://www.microsoft.com/en-us/research/project/dataflow-based-dialogue-semantic-machines.",
}
@article{richardson-sabharwal-2020-qa,
    title = "What Does My {QA} Model Know? Devising Controlled Probes Using Expert Knowledge",
    author = "Richardson, Kyle  and
      Sabharwal, Ashish",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.37",
    doi = "10.1162/tacl_a_00331",
    pages = "572--588",
    abstract = "Open-domain question answering (QA) involves many knowledge and reasoning challenges, but are successful QA models actually learning such knowledge when trained on benchmark QA tasks? We investigate this via several new diagnostic tasks probing whether multiple-choice QA models know definitions and taxonomic reasoning{---}two skills widespread in existing benchmarks and fundamental to more complex reasoning. We introduce a methodology for automatically building probe datasets from expert knowledge sources, allowing for systematic control and a comprehensive evaluation. We include ways to carefully control for artifacts that may arise during this process. Our evaluation confirms that transformer-based multiple-choice QA models are already predisposed to recognize certain types of structural linguistic knowledge. However, it also reveals a more nuanced picture: their performance notably degrades even with a slight increase in the number of {``}hops{''} in the underlying taxonomic hierarchy, and with more challenging distractor candidates. Further, existing models are far from perfect when assessed at the level of clusters of semantically connected probes, such as all hypernym questions about a single concept.",
}
@article{ribeiro-etal-2020-modeling,
    title = "Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs",
    author = "Ribeiro, Leonardo F. R.  and
      Zhang, Yue  and
      Gardent, Claire  and
      Gurevych, Iryna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.38",
    doi = "10.1162/tacl_a_00332",
    pages = "589--604",
    abstract = "Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models that encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on the AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.1",
}
@article{shibuya-hovy-2020-nested,
    title = "Nested Named Entity Recognition via Second-best Sequence Learning and Decoding",
    author = "Shibuya, Takashi  and
      Hovy, Eduard",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.39",
    doi = "10.1162/tacl_a_00334",
    pages = "605--620",
    abstract = "When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82{\%}, 84.34{\%}, and 77.36{\%} on ACE-2004, ACE-2005, and GENIA datasets, respectively.",
}
@article{tu-etal-2020-empirical,
    title = "An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models",
    author = "Tu, Lifu  and
      Lalwani, Garima  and
      Gella, Spandana  and
      He, He",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.40",
    doi = "10.1162/tacl_a_00335",
    pages = "621--633",
    abstract = "Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations.1",
}
@article{lichtarge-etal-2020-data,
    title = "Data Weighted Training Strategies for Grammatical Error Correction",
    author = "Lichtarge, Jared  and
      Alberti, Chris  and
      Kumar, Shankar",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.41",
    doi = "10.1162/tacl_a_00336",
    pages = "634--646",
    abstract = "Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. Building upon recent work in Neural Machine Translation (NMT), we make use of both kinds of data by deriving example-level scores on our large pretraining data based on a smaller, higher-quality dataset. In this work, we perform an empirical study to discover how to best incorporate delta-log-perplexity, a type of example scoring, into a training schedule for GEC. In doing so, we perform experiments that shed light on the function and applicability of delta-log-perplexity. Models trained on scored data achieve state- of-the-art results on common GEC test sets.",
}
@article{zhu-etal-2020-return,
    title = "The Return of Lexical Dependencies: Neural Lexicalized {PCFG}s",
    author = "Zhu, Hao  and
      Bisk, Yonatan  and
      Neubig, Graham",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.42",
    doi = "10.1162/tacl_a_00337",
    pages = "647--661",
    abstract = "In this paper we demonstrate that context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies. This contrasts to the most popular current methods for grammar induction, which focus on discovering either constituents or dependencies. Previous approaches to marry these two disparate syntactic formalisms (e.g., lexicalized PCFGs) have been plagued by sparsity, making them unsuitable for unsupervised grammar induction. However, in this work, we present novel neural models of lexicalized PCFGs that allow us to overcome sparsity problems and effectively induce both constituents and dependencies within a single model. Experiments demonstrate that this unified framework results in stronger results on both representations than achieved when modeling either formalism alone.1",
}
@article{bartolo-etal-2020-beat,
    title = "Beat the {AI}: Investigating Adversarial Human Annotation for Reading Comprehension",
    author = "Bartolo, Max  and
      Roberts, Alastair  and
      Welbl, Johannes  and
      Riedel, Sebastian  and
      Stenetorp, Pontus",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.43",
    doi = "10.1162/tacl_a_00338",
    pages = "662--678",
    abstract = "Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD{---}only marginally lower than when trained on data collected using RoBERTa itself (41.0F1).",
}
@article{ye-etal-2020-sketch,
    title = "Sketch-Driven Regular Expression Generation from Natural Language and Examples",
    author = "Ye, Xi  and
      Chen, Qiaochu  and
      Wang, Xinyu  and
      Dillig, Isil  and
      Durrett, Greg",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.44",
    doi = "10.1162/tacl_a_00339",
    pages = "679--694",
    abstract = "Recent systems for converting natural language descriptions into regular expressions (regexes) have achieved some success, but typically deal with short, formulaic text and can only produce simple regexes. Real-world regexes are complex, hard to describe with brief sentences, and sometimes require examples to fully convey the user{'}s intent. We present a framework for regex synthesis in this setting where both natural language (NL) and examples are available. First, a semantic parser (either grammar-based or neural) maps the natural language description into an intermediate sketch, which is an incomplete regex containing holes to denote missing components. Then a program synthesizer searches over the regex space defined by the sketch and finds a regex that is consistent with the given string examples. Our semantic parser can be trained purely from weak supervision based on correctness of the synthesized regex, or it can leverage heuristically derived sketches. We evaluate on two prior datasets (Kushman and Barzilay 2013; Locascio et al. 2016) and a real-world dataset from Stack Overflow. Our system achieves state-of-the-art performance on the prior datasets and solves 57{\%} of the real-world dataset, which existing neural systems completely fail on.1",
}
@article{sperber-etal-2020-consistent,
    title = "Consistent Transcription and Translation of Speech",
    author = "Sperber, Matthias  and
      Setiawan, Hendra  and
      Gollan, Christian  and
      Nallasamy, Udhyakumar  and
      Paulik, Matthias",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.45",
    doi = "10.1162/tacl_a_00340",
    pages = "695--709",
    abstract = "The conventional paradigm in speech translation starts with a speech recognition step to generate transcripts, followed by a translation step with the automatic transcripts as input. To address various shortcomings of this paradigm, recent work explores end-to-end trainable direct models that translate without transcribing. However, transcripts can be an indispensable output in practical applications, which often display transcripts alongside the translations to users.We make this common requirement explicit and explore the task of jointly transcribing and translating speech. Although high accuracy of transcript and translation are crucial, even highly accurate systems can suffer from inconsistencies between both outputs that degrade the user experience. We introduce a methodology to evaluate consistency and compare several modeling approaches, including the traditional cascaded approach and end-to-end models. We find that direct models are poorly suited to the joint transcription/translation task, but that end-to-end models that feature a coupled inference procedure are able to achieve strong consistency. We further introduce simple techniques for directly optimizing for consistency, and analyze the resulting trade-offs between consistency, transcription accuracy, and translation accuracy.1",
}
@article{marie-fujita-2020-synthesizing,
    title = "Synthesizing Parallel Data of User-Generated Texts with Zero-Shot Neural Machine Translation",
    author = "Marie, Benjamin  and
      Fujita, Atsushi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.46",
    doi = "10.1162/tacl_a_00341",
    pages = "710--725",
    abstract = "Neural machine translation (NMT) systems are usually trained on clean parallel data. They can perform very well for translating clean in-domain texts. However, as demonstrated by previous work, the translation quality significantly worsens when translating noisy texts, such as user-generated texts (UGT) from online social media. Given the lack of parallel data of UGT that can be used to train or adapt NMT systems, we synthesize parallel data of UGT, exploiting monolingual data of UGT through crosslingual language model pre-training and zero-shot NMT systems. This paper presents two different but complementary approaches: One alters given clean parallel data into UGT-like parallel data whereas the other generates translations from monolingual data of UGT. On the MTNT translation tasks, we show that our synthesized parallel data can lead to better NMT systems for UGT while making them more robust in translating texts from various domains and styles.",
}
@article{liu-etal-2020-multilingual,
    title = "Multilingual Denoising Pre-training for Neural Machine Translation",
    author = "Liu, Yinhan  and
      Gu, Jiatao  and
      Goyal, Naman  and
      Li, Xian  and
      Edunov, Sergey  and
      Ghazvininejad, Marjan  and
      Lewis, Mike  and
      Zettlemoyer, Luke",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.47",
    doi = "10.1162/tacl_a_00343",
    pages = "726--742",
    abstract = "This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART{---}a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective (Lewis et al., 2019). mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, whereas previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine-tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task- specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show that it enables transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training.1",
}
@article{talmor-etal-2020-olmpics,
    title = "o{LM}pics-On What Language Model Pre-training Captures",
    author = "Talmor, Alon  and
      Elazar, Yanai  and
      Goldberg, Yoav  and
      Berant, Jonathan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.48",
    doi = "10.1162/tacl_a_00342",
    pages = "743--758",
    abstract = "Recent success of pre-trained language models (LMs) has spurred widespread interest in the language capabilities that they possess. However, efforts to understand whether LM representations are useful for symbolic reasoning tasks have been limited and scattered. In this work, we propose eight reasoning tasks, which conceptually require operations such as comparison, conjunction, and composition. A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data. To address this, we propose an evaluation protocol that includes both zero-shot evaluation (no fine-tuning), as well as comparing the learning curve of a fine-tuned LM to the learning curve of multiple controls, which paints a rich picture of the LM capabilities. Our main findings are that: (a) different LMs exhibit qualitatively different reasoning abilities, e.g., RoBERTa succeeds in reasoning tasks where BERT fails completely; (b) LMs do not reason in an abstract manner and are context-dependent, e.g., while RoBERTa can compare ages, it can do so only when the ages are in the typical range of human ages; (c) On half of our reasoning tasks all models fail completely. Our findings and infrastructure can help future work on designing new datasets, models, and objective functions for pre-training.",
}
@article{simpson-etal-2020-interactive,
    title = "Interactive Text Ranking with {B}ayesian Optimization: A Case Study on Community {QA} and Summarization",
    author = "Simpson, Edwin  and
      Gao, Yang  and
      Gurevych, Iryna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.49",
    doi = "10.1162/tacl_a_00344",
    pages = "759--775",
    abstract = "For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user{'}s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user{'}s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.",
}
@article{kuncoro-etal-2020-syntactic,
    title = "Syntactic Structure Distillation Pretraining for Bidirectional Encoders",
    author = "Kuncoro, Adhiguna  and
      Kong, Lingpeng  and
      Fried, Daniel  and
      Yogatama, Dani  and
      Rimell, Laura  and
      Dyer, Chris  and
      Blunsom, Phil",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.50",
    doi = "10.1162/tacl_a_00345",
    pages = "776--794",
    abstract = "Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests of syntactic competence. Hence, it remains an open question whether scalable learners like BERT can become fully proficient in the syntax of natural language by virtue of data scale alone, or whether they still benefit from more explicit syntactic biases. To answer this question, we introduce a knowledge distillation strategy for injecting syntactic biases into BERT pretraining, by distilling the syntactically informative predictions of a hierarchical{---}albeit harder to scale{---}syntactic language model. Since BERT models masked words in bidirectional context, we propose to distill the approximate marginal distribution over words in context from the syntactic LM. Our approach reduces relative error by 2{--}21{\%} on a diverse set of structured prediction tasks, although we obtain mixed results on the GLUE benchmark. Our findings demonstrate the benefits of syntactic biases, even for representation learners that exploit large amounts of data, and contribute to a better understanding of where syntactic biases are helpful in benchmarks of natural language understanding.",
}
@article{meister-etal-2020-best,
    title = "Best-First Beam Search",
    author = "Meister, Clara  and
      Vieira, Tim  and
      Cotterell, Ryan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.51",
    doi = "10.1162/tacl_a_00346",
    pages = "795--809",
    abstract = "Decoding for many NLP tasks requires an effective heuristic algorithm for approximating exact search because the problem of searching the full output space is often intractable, or impractical in many settings. The default algorithm for this job is beam search{---}a pruned version of breadth-first search. Quite surprisingly, beam search often returns better results than exact inference due to beneficial search bias for NLP tasks. In this work, we show that the standard implementation of beam search can be made up to 10x faster in practice. Our method assumes that the scoring function is monotonic in the sequence length, which allows us to safely prune hypotheses that cannot be in the final set of hypotheses early on. We devise effective monotonic approximations to popular nonmonontic scoring functions, including length normalization and mutual information decoding. Lastly, we propose a memory-reduced variant of best-first beam search, which has a similar beneficial search bias in terms of downstream performance, but runs in a fraction of the time.",
}
@article{sai-etal-2020-improving,
    title = "Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining",
    author = "Sai, Ananya B.  and
      Mohankumar, Akash Kumar  and
      Arora, Siddhartha  and
      Khapra, Mitesh M.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.52",
    doi = "10.1162/tacl_a_00347",
    pages = "810--827",
    abstract = "There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all relevant responses and a low score to all irrelevant responses. Ideally, such models should be trained using multiple relevant and irrelevant responses for any given context. However, no such data is publicly available, and hence existing models are usually trained using a single relevant response and multiple randomly selected responses from other contexts (random negatives). To allow for better training and robust evaluation of model-based metrics, we introduce the DailyDialog++ dataset, consisting of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context. Using this dataset, we first show that even in the presence of multiple correct references, n-gram based metrics and embedding based metrics do not perform well at separating relevant responses from even random negatives. While model-based metrics perform better than n-gram and embedding based metrics on random negatives, their performance drops substantially when evaluated on adversarial examples. To check if large scale pretraining could help, we propose a new BERT-based evaluation metric called DEB, which is pretrained on 727M Reddit conversations and then finetuned on our dataset. DEB significantly outperforms existing models, showing better correlation with human judgments and better performance on random negatives (88.27{\%} accuracy). However, its performance again drops substantially when evaluated on adversarial responses, thereby highlighting that even large-scale pretrained evaluation models are not robust to the adversarial examples in our dataset. The dataset1 and code2 are publicly available.",
}
@article{keung-etal-2020-unsupervised,
    title = "Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings",
    author = "Keung, Phillip  and
      Salazar, Julian  and
      Lu, Yichao  and
      Smith, Noah A.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.53",
    doi = "10.1162/tacl_a_00348",
    pages = "828--841",
    abstract = "We describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence embeddings for nearest-neighbor search and adapt the model via self-training. We validate our technique by extracting parallel sentence pairs on the BUCC 2017 bitext mining task and observe up to a 24.5 point increase (absolute) in F1 scores over previous unsupervised methods. We then improve an XLM-based unsupervised neural MT system pre-trained on Wikipedia by supplementing it with pseudo-parallel text mined from the same corpus, boosting unsupervised translation performance by up to 3.5 BLEU on the WMT{'}14 French-English and WMT{'}16 German-English tasks and outperforming the previous state-of-the-art. Finally, we enrich the IWSLT{'}15 English-Vietnamese corpus with pseudo-parallel Wikipedia sentence pairs, yielding a 1.2 BLEU improvement on the low-resource MT task. We demonstrate that unsupervised bitext mining is an effective way of augmenting MT datasets and complements existing techniques like initializing with pre-trained contextual embeddings.",
}
@article{rogers-etal-2020-primer,
    title = "A Primer in {BERT}ology: What We Know About How {BERT} Works",
    author = "Rogers, Anna  and
      Kovaleva, Olga  and
      Rumshisky, Anna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.tacl-1.54",
    doi = "10.1162/tacl_a_00349",
    pages = "842--866",
    abstract = "Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue, and approaches to compression. We then outline directions for future research.",
}
@proceedings{sustainlp-2020-sustainlp,
    title = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    editor = "Moosavi, Nafise Sadat  and
      Fan, Angela  and
      Shwartz, Vered  and
      Glava{\v{s}}, Goran  and
      Joty, Shafiq  and
      Wang, Alex  and
      Wolf, Thomas",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.0",
}
@inproceedings{septiandri-etal-2020-knowing,
    title = "Knowing Right from Wrong: Should We Use More Complex Models for Automatic Short-Answer Scoring in {B}ahasa {I}ndonesia?",
    author = "Septiandri, Ali Akbar  and
      Winatmoko, Yosef Ardhito  and
      Putra, Ilham Firdausi",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.1",
    doi = "10.18653/v1/2020.sustainlp-1.1",
    pages = "1--7",
    abstract = "We compare three solutions to UKARA 1.0 challenge on automated short-answer scoring: single classical, ensemble classical, and deep learning. The task is to classify given answers to two questions, whether they are right or wrong. While recent development shows increasing model complexity to push the benchmark performances, they tend to be resource-demanding with mundane improvement. For the UKARA task, we found that bag-of-words and classical machine learning approaches can compete with ensemble models and Bi-LSTM model with pre-trained word2vec embedding from 200 million words. In this case, the single classical machine learning achieved less than 2{\%} difference in F1 compared to the deep learning approach with 1/18 time for model training.",
}
@inproceedings{thakker-etal-2020-rank,
    title = "Rank and run-time aware compression of {NLP} Applications",
    author = "Thakker, Urmish  and
      Beu, Jesse  and
      Gope, Dibakar  and
      Dasika, Ganesh  and
      Mattina, Matthew",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.2",
    doi = "10.18653/v1/2020.sustainlp-1.2",
    pages = "8--18",
    abstract = "Sequence model based NLP applications canbe large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while still having run-time constraints.As a result, there is a need for a compression technique that can achieve significant compression without negatively impacting inference run-time and task accuracy. This paper proposes a new compression technique called Hybrid Matrix Factorization (HMF) that achieves this dual objective. HMF improves low-rank matrix factorization (LMF) techniques by doubling the rank of the matrix using an intelligent hybrid-structure leading to better accuracy than LMF. Further, by preserving dense matrices, it leads to faster inference run-timethan pruning or structure matrix based compression technique. We evaluate the impact of this technique on 5 NLP benchmarks across multiple tasks (Translation, Intent Detection,Language Modeling) and show that for similar accuracy values and compression factors, HMF can achieve more than 2.32x faster inference run-time than pruning and 16.77{\%} better accuracy than LMF.",
}
@inproceedings{shah-fauqueur-2020-learning,
    title = "Learning Informative Representations of Biomedical Relations with Latent Variable Models",
    author = "Shah, Harshil  and
      Fauqueur, Julien",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.3",
    doi = "10.18653/v1/2020.sustainlp-1.3",
    pages = "19--28",
    abstract = "Extracting biomedical relations from large corpora of scientific documents is a challenging natural language processing task. Existing approaches usually focus on identifying a relation either in a single sentence (mention-level) or across an entire corpus (pair-level). In both cases, recent methods have achieved strong results by learning a point estimate to represent the relation; this is then used as the input to a relation classifier. However, the relation expressed in text between a pair of biomedical entities is often more complex than can be captured by a point estimate. To address this issue, we propose a latent variable model with an arbitrarily flexible distribution to represent the relation between an entity pair. Additionally, our model provides a unified architecture for both mention-level and pair-level relation extraction. We demonstrate that our model achieves results competitive with strong baselines for both tasks while having fewer parameters and being significantly faster to train. We make our code publicly available.",
}
@inproceedings{shridhar-etal-2020-end,
    title = "End to End Binarized Neural Networks for Text Classification",
    author = "Shridhar, Kumar  and
      Jain, Harshil  and
      Agarwal, Akshat  and
      Kleyko, Denis",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.4",
    doi = "10.18653/v1/2020.sustainlp-1.4",
    pages = "29--34",
    abstract = "Deep neural networks have demonstrated their superior performance in almost every Natural Language Processing task, however, their increasing complexity raises concerns. A particular concern is that these networks pose high requirements for computing hardware and training budgets. The state-of-the-art transformer models are a vivid example. Simplifying the computations performed by a network is one way of addressing the issue of the increasing complexity. In this paper, we propose an end to end binarized neural network for the task of intent and text classification. In order to fully utilize the potential of end to end binarization, both the input representations (vector embeddings of tokens statistics) and the classifier are binarized. We demonstrate the efficiency of such a network on the intent classification of short texts over three datasets and text classification with a larger dataset. On the considered datasets, the proposed network achieves comparable to the state-of-the-art results while utilizing 20-40{\%} lesser memory and training time compared to the benchmarks.",
}
@inproceedings{wasserblat-etal-2020-exploring,
    title = "Exploring the Boundaries of Low-Resource {BERT} Distillation",
    author = "Wasserblat, Moshe  and
      Pereg, Oren  and
      Izsak, Peter",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.5",
    doi = "10.18653/v1/2020.sustainlp-1.5",
    pages = "35--40",
    abstract = "In recent years, large pre-trained models have demonstrated state-of-the-art performance in many of NLP tasks. However, the deployment of these models on devices with limited resources is challenging due to the models{'} large computational consumption and memory requirements. Moreover, the need for a considerable amount of labeled training data also hinders real-world deployment scenarios. Model distillation has shown promising results for reducing model size, computational load and data efficiency. In this paper we test the boundaries of BERT model distillation in terms of model compression, inference efficiency and data scarcity. We show that classification tasks that require the capturing of general lexical semantics can be successfully distilled by very simple and efficient models and require relatively small amount of labeled training data. We also show that the distillation of large pre-trained models is more effective in real-life scenarios where limited amounts of labeled training are available.",
}
@inproceedings{kobayashi-etal-2020-efficient,
    title = "Efficient Estimation of Influence of a Training Instance",
    author = "Kobayashi, Sosuke  and
      Yokoi, Sho  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.6",
    doi = "10.18653/v1/2020.sustainlp-1.6",
    pages = "41--47",
    abstract = "Understanding the influence of a training instance on a neural network model leads to improving interpretability. However, it is difficult and inefficient to evaluate the influence, which shows how a model{'}s prediction would be changed if a training instance were not used. In this paper, we propose an efficient method for estimating the influence. Our method is inspired by dropout, which zero-masks a sub-network and prevents the sub-network from learning each training instance. By switching between dropout masks, we can use sub-networks that learned or did not learn each training instance and estimate its influence. Through experiments with BERT and VGGNet on classification datasets, we demonstrate that the proposed method can capture training influences, enhance the interpretability of error predictions, and cleanse the training dataset for improving generalization.",
}
@inproceedings{hsu-etal-2020-efficient,
    title = "Efficient Inference For Neural Machine Translation",
    author = "Hsu, Yi-Te  and
      Garg, Sarthak  and
      Liao, Yi-Hsiu  and
      Chatsviorkin, Ilya",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.7",
    doi = "10.18653/v1/2020.sustainlp-1.7",
    pages = "48--53",
    abstract = "Large Transformer models have achieved state-of-the-art results in neural machine translation and have become standard in the field. In this work, we look for the optimal combination of known techniques to optimize inference speed without sacrificing translation quality. We conduct an empirical study that stacks various approaches and demonstrates that combination of replacing decoder self-attention with simplified recurrent units, adopting a deep encoder and a shallow decoder architecture and multi-head attention pruning can achieve up to 109{\%} and 84{\%} speedup on CPU and GPU respectively and reduce the number of parameters by 25{\%} while maintaining the same translation quality in terms of BLEU.",
}
@inproceedings{tsai-el-ghaoui-2020-sparse,
    title = "Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm",
    author = "Tsai, Alicia  and
      El Ghaoui, Laurent",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.8",
    doi = "10.18653/v1/2020.sustainlp-1.8",
    pages = "54--62",
    abstract = "We address the problem of unsupervised extractive document summarization, especially for long documents. We model the unsupervised problem as a sparse auto-regression one and approximate the resulting combinatorial problem via a convex, norm-constrained problem. We solve it using a dedicated Frank-Wolfe algorithm. To generate a summary with k sentences, the algorithm only needs to execute approximately k iterations, making it very efficient for a long document. We evaluate our approach against two other unsupervised methods using both lexical (standard) ROUGE scores, as well as semantic (embedding-based) ones. Our method achieves better results with both datasets and works especially well when combined with embeddings for highly paraphrased summaries.",
}
@inproceedings{wu-etal-2020-dont,
    title = "Don{'}t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering",
    author = "Wu, Yuxiang  and
      Minervini, Pasquale  and
      Stenetorp, Pontus  and
      Riedel, Sebastian",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.9",
    doi = "10.18653/v1/2020.sustainlp-1.9",
    pages = "63--72",
    abstract = "Most approaches to Open-Domain Question Answering consist of a light-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines the passages to identify the correct answer. Previous works have shown that as the number of retrieved passages increases, so does the performance of the reader. However, they assume all retrieved passages are of equal importance and allocate the same amount of computation to them, leading to a substantial increase in computational cost. To reduce this cost, we propose the use of adaptive computation to control the computational budget allocated for the passages to be read. We first introduce a technique operating on individual passages in isolation which relies on anytime prediction and a per-layer estimation of an early exit probability. We then introduce SKYLINEBUILDER, an approach for dynamically deciding on which passage to allocate computation at each step, based on a resource allocation policy trained via reinforcement learning. Our results on SQuAD-Open show that adaptive computation with global prioritisation improves over several strong static and adaptive methods, leading to a 4.3x reduction in computation while retaining 95{\%} performance of the full model.",
}
@inproceedings{oguz-vu-2020-two,
    title = "A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings",
    author = "Oguz, Cennet  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.10",
    doi = "10.18653/v1/2020.sustainlp-1.10",
    pages = "73--82",
    abstract = "Learning-based slot filling - a key component of spoken language understanding systems - typically requires a large amount of in-domain hand-labeled data for training. In this paper, we propose a novel two-stage model architecture that can be trained with only a few in-domain hand-labeled examples. The first step is designed to remove non-slot tokens (i.e., O labeled tokens), as they introduce noise in the input of slot filling models. This step is domain-agnostic and therefore, can be trained by exploiting out-of-domain data. The second step identifies slot names only for slot tokens by using state-of-the-art pretrained contextual embeddings such as ELMO and BERT. We show that our approach outperforms other state-of-art systems on the SNIPS benchmark dataset.",
}
@inproceedings{xin-etal-2020-early,
    title = "Early Exiting {BERT} for Efficient Document Ranking",
    author = "Xin, Ji  and
      Nogueira, Rodrigo  and
      Yu, Yaoliang  and
      Lin, Jimmy",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.11",
    doi = "10.18653/v1/2020.sustainlp-1.11",
    pages = "83--88",
    abstract = "Pre-trained language models such as BERT have shown their effectiveness in various tasks. Despite their power, they are known to be computationally intensive, which hinders real-world applications. In this paper, we introduce early exiting BERT for document ranking. With a slight modification, BERT becomes a model with multiple output paths, and each inference sample can exit early from these paths. In this way, computation can be effectively allocated among samples, and overall system latency is significantly reduced while the original quality is maintained. Our experiments on two document ranking datasets demonstrate up to 2.5x inference speedup with minimal quality degradation. The source code of our implementation can be found at https://github.com/castorini/earlyexiting-monobert.",
}
@inproceedings{lancioni-etal-2020-keyphrase,
    title = "Keyphrase Generation with {GAN}s in Low-Resources Scenarios",
    author = "Lancioni, Giuseppe  and
      S.Mohamed, Saida  and
      Portelli, Beatrice  and
      Serra, Giuseppe  and
      Tasso, Carlo",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.12",
    doi = "10.18653/v1/2020.sustainlp-1.12",
    pages = "89--96",
    abstract = "Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document. Several past studies provided diverse approaches to generate Keyphrases for an input document. However, all of these approaches still need to be trained on very large datasets. In this paper, we introduce BeGanKP, a new conditional GAN model to address the problem of Keyphrase Generation in a low-resource scenario. Our main contribution relies in the Discriminator{'}s architecture: a new BERT-based module which is able to distinguish between the generated and humancurated KPs reliably. Its characteristics allow us to use it in a low-resource scenario, where only a small amount of training data are available, obtaining an efficient Generator. The resulting architecture achieves, on five public datasets, competitive results with respect to the state-of-the-art approaches, using less than 1{\%} of the training data.",
}
@inproceedings{kis-szabo-berend-2020-quasi,
    title = "Quasi-Multitask Learning: an Efficient Surrogate for Obtaining Model Ensembles",
    author = "Kis-Szab{\'o}, Norbert  and
      Berend, G{\'a}bor",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.13",
    doi = "10.18653/v1/2020.sustainlp-1.13",
    pages = "97--106",
    abstract = "We propose the technique of quasi-multitask learning (Q-MTL), a simple and easy to implement modification of standard multitask learning, in which the tasks to be modeled are identical. With this easy modification of a standard neural classifier we can get benefits similar to an ensemble of classifiers with a fraction of the resources required.We illustrate it through a series of sequence labeling experiments over a diverse set of languages, that applying Q-MTL consistently increases the generalization ability of the applied models. The proposed architecture can be regarded as a new regularization technique that encourages the model to develop an internal representation of the problem at hand which is beneficial to multiple output units of the classifier at the same time. Our experiments corroborate that by relying on the proposed algorithm, we can approximate the quality of an ensemble of classifiers at a fraction of computational resources required. Additionally, our results suggest that Q-MTL handles the presence of noisy training labels better than ensembles.",
}
@inproceedings{zhang-etal-2020-little,
    title = "A Little Bit Is Worse Than None: Ranking with Limited Training Data",
    author = "Zhang, Xinyu  and
      Yates, Andrew  and
      Lin, Jimmy",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.14",
    doi = "10.18653/v1/2020.sustainlp-1.14",
    pages = "107--112",
    abstract = "Researchers have proposed simple yet effective techniques for the retrieval problem based on using BERT as a relevance classifier to rerank initial candidates from keyword search. In this work, we tackle the challenge of fine-tuning these models for specific domains in a data and computationally efficient manner. Typically, researchers fine-tune models using corpus-specific labeled data from sources such as TREC. We first answer the question: How much data of this type do we need? Recognizing that the most computationally efficient training is no training, we explore zero-shot ranking using BERT models that have already been fine-tuned with the large MS MARCO passage retrieval dataset. We arrive at the surprising and novel finding that {``}some{''} labeled in-domain data can be worse than none at all.",
}
@inproceedings{awasthy-etal-2020-predictive,
    title = "Predictive Model Selection for Transfer Learning in Sequence Labeling Tasks",
    author = "Awasthy, Parul  and
      Bhattacharjee, Bishwaranjan  and
      Kender, John  and
      Florian, Radu",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.15",
    doi = "10.18653/v1/2020.sustainlp-1.15",
    pages = "113--118",
    abstract = "Transfer learning is a popular technique to learn a task using less training data and fewer compute resources. However, selecting the correct source model for transfer learning is a challenging task. We demonstrate a novel predictive method that determines which existing source model would minimize error for transfer learning to a given target. This technique does not require learning for prediction, and avoids computational costs of trail-and-error. We have evaluated this technique on nine datasets across diverse domains, including newswire, user forums, air flight booking, cybersecurity news, etc. We show that it per-forms better than existing techniques such as fine-tuning over vanilla BERT, or curriculum learning over the largest dataset on top of BERT, resulting in average F1 score gains in excess of 3{\%}. Moreover, our technique consistently selects the best model using fewer tries.",
}
@inproceedings{abdaoui-etal-2020-load,
    title = "Load What You Need: Smaller Versions of Mutililingual {BERT}",
    author = "Abdaoui, Amine  and
      Pradel, Camille  and
      Sigel, Gr{\'e}goire",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.16",
    doi = "10.18653/v1/2020.sustainlp-1.16",
    pages = "119--123",
    abstract = "Pre-trained Transformer-based models are achieving state-of-the-art results on a variety of Natural Language Processing data sets. However, the size of these models is often a drawback for their deployment in real production applications. In the case of multilingual models, most of the parameters are located in the embeddings layer. Therefore, reducing the vocabulary size should have an important impact on the total number of parameters. In this paper, we propose to extract smaller models that handle fewer number of languages according to the targeted corpora. We present an evaluation of smaller versions of multilingual BERT on the XNLI data set, but we believe that this method may be applied to other multilingual transformers. The obtained results confirm that we can generate smaller models that keep comparable results, while reducing up to 45{\%} of the total number of parameters. We compared our models with DistilmBERT (a distilled version of multilingual BERT) and showed that unlike language reduction, distillation induced a 1.7{\%} to 6{\%} drop in the overall accuracy on the XNLI data set. The presented models and code are publicly available.",
}
@inproceedings{iandola-etal-2020-squeezebert,
    title = "{S}queeze{BERT}: What can computer vision teach {NLP} about efficient neural networks?",
    author = "Iandola, Forrest  and
      Shaw, Albert  and
      Krishna, Ravi  and
      Keutzer, Kurt",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.17",
    doi = "10.18653/v1/2020.sustainlp-1.17",
    pages = "124--135",
    abstract = "Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, natural language processing (NLP) technology has made significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. Toward this end, we consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today{'}s highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. To begin to address this problem, we draw inspiration from the computer vision community, where work such as MobileNet has demonstrated that grouped convolutions (e.g. depthwise convolutions) can enable speedups without sacrificing accuracy. We demonstrate how to replace several operations in self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test set. A PyTorch-based implementation of SqueezeBERT is available as part of the Hugging Face Transformers library: https://huggingface.co/squeezebert",
}
@inproceedings{pranesh-shekhar-2020-analysis,
    title = "Analysis of Resource-efficient Predictive Models for Natural Language Processing",
    author = "Pranesh, Raj  and
      Shekhar, Ambesh",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.18",
    doi = "10.18653/v1/2020.sustainlp-1.18",
    pages = "136--140",
    abstract = "In this paper, we presented an analyses of the resource efficient predictive models, namely Bonsai, Binary Neighbor Compression(BNC), ProtoNN, Random Forest, Naive Bayes and Support vector machine(SVM), in the machine learning field for resource constraint devices. These models try to minimize resource requirements like RAM and storage without hurting the accuracy much. We utilized these models on multiple benchmark natural language processing tasks, which were sentimental analysis, spam message detection, emotion analysis and fake news classification. The experiment results shows that the tree-based algorithm, Bonsai, surpassed the rest of the machine learning algorithms by achieve higher accuracy scores while having significantly lower memory usage.",
}
@inproceedings{cao-etal-2020-towards,
    title = "Towards Accurate and Reliable Energy Measurement of {NLP} Models",
    author = "Cao, Qingqing  and
      Balasubramanian, Aruna  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.19",
    doi = "10.18653/v1/2020.sustainlp-1.19",
    pages = "141--148",
    abstract = "Accurate and reliable measurement of energy consumption is critical for making well-informed design choices when choosing and training large scale NLP models. In this work, we show that existing software-based energy estimations are not accurate because they do not take into account hardware differences and how resource utilization affects energy consumption. We conduct energy measurement experiments with four different models for a question answering task. We quantify the error of existing software-based energy estimations by using a hardware power meter that provides highly accurate energy measurements. Our key takeaway is the need for a more accurate energy estimation model that takes into account hardware variabilities and the non-linear relationship between resource utilization and energy consumption. We release the code and data at https://github.com/csarron/sustainlp2020-energy.",
}
@inproceedings{kim-hassan-2020-fastformers,
    title = "{F}ast{F}ormers: Highly Efficient Transformer Models for Natural Language Understanding",
    author = "Kim, Young Jin  and
      Hassan, Hany",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.20",
    doi = "10.18653/v1/2020.sustainlp-1.20",
    pages = "149--158",
    abstract = "Transformer-based models are the state-of-the-art for Natural Language Understanding (NLU) applications. Models are getting bigger and better on various tasks. However, Transformer models remain computationally challenging since they are not efficient at inference-time compared to traditional approaches. In this paper, we present FastFormers, a set of recipes to achieve efficient inference-time performance for Transformer-based models on various NLU tasks. We show how carefully utilizing knowledge distillation, structured pruning and numerical optimization can lead to drastic improvements on inference efficiency. We provide effective recipes that can guide practitioners to choose the best settings for various NLU tasks and pretrained models. Applying the proposed recipes to the SuperGLUE benchmark, we achieve from 9.8x up to 233.9x speed-up compared to out-of-the-box models on CPU. On GPU, we also achieve up to 12.4x speed-up with the presented methods. We show that FastFormers can drastically reduce cost of serving 100 million requests from 4,223 USD to just 18 USD on an Azure F16s{\_}v2 instance. This translates to a sustainable runtime by reducing energy consumption 6.9x - 125.8x according to the metrics used in the SustaiNLP 2020 shared task.",
}
@inproceedings{quattoni-carreras-2020-comparison,
    title = "A comparison between {CNN}s and {WFA}s for Sequence Classification",
    author = "Quattoni, Ariadna  and
      Carreras, Xavier",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.21",
    doi = "10.18653/v1/2020.sustainlp-1.21",
    pages = "159--163",
    abstract = "We compare a classical CNN architecture for sequence classification involving several convolutional and max-pooling layers against a simple model based on weighted finite state automata (WFA). Each model has its advantages and disadvantages and it is possible that they could be combined. However, we believe that the first research goal should be to investigate and understand how do these two apparently dissimilar models compare in the context of specific natural language processing tasks. This paper is the first step towards that goal. Our experiments with five sequence classification datasets suggest that, despite the apparent simplicity of WFA models and training algorithms, the performance of WFAs is comparable to that of the CNNs.",
}
@inproceedings{choi-etal-2020-label,
    title = "Label-Efficient Training for Next Response Selection",
    author = "Choi, Seungtaek  and
      Jeong, Myeongho  and
      Yeo, Jinyoung  and
      Hwang, Seung-won",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.22",
    doi = "10.18653/v1/2020.sustainlp-1.22",
    pages = "164--168",
    abstract = "This paper studies label augmentation for training dialogue response selection. The existing model is trained by {``}observational{''} annotation, where one observed response is annotated as gold. In this paper, we propose {``}counterfactual augmentation{''} of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.",
}
@inproceedings{mishra-sachdeva-2020-need,
    title = "Do We Need to Create Big Datasets to Learn a Task?",
    author = "Mishra, Swaroop  and
      Sachdeva, Bhavdeep Singh",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.23",
    doi = "10.18653/v1/2020.sustainlp-1.23",
    pages = "169--173",
    abstract = "Deep Learning research has been largely accelerated by the development of huge datasets such as Imagenet. The general trend has been to create big datasets to make a deep neural network learn. A huge amount of resources is being spent in creating these big datasets, developing models, training them, and iterating this process to dominate leaderboards. We argue that the trend of creating bigger datasets needs to be revised by better leveraging the power of pre-trained language models. Since the language models have already been pre-trained with huge amount of data and have basic linguistic knowledge, there is no need to create big datasets to learn a task. Instead, we need to create a dataset that is sufficient for the model to learn various task-specific terminologies, such as {`}Entailment{'}, {`}Neutral{'}, and {`}Contradiction{'} for NLI. As evidence, we show that RoBERTA is able to achieve near-equal performance on 2{\%} data of SNLI. We also observe competitive zero-shot generalization on several OOD datasets. In this paper, we propose a baseline algorithm to find the optimal dataset for learning a task.",
}
@inproceedings{wang-wolf-2020-overview,
    title = "Overview of the {S}ustai{NLP} 2020 Shared Task",
    author = "Wang, Alex  and
      Wolf, Thomas",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sustainlp-1.24",
    doi = "10.18653/v1/2020.sustainlp-1.24",
    pages = "174--178",
    abstract = "We describe the SustaiNLP 2020 shared task: efficient inference on the SuperGLUE benchmark (Wang et al., 2019). Participants are evaluated based on performance on the benchmark as well as energy consumed in making predictions on the test sets. We describe the task, its organization, and the submitted systems. Across the six submissions to the shared task, participants achieved efficiency gains of 20{\mbox{$\times$}} over a standard BERT (Devlin et al., 2019) baseline, while losing less than an absolute point in performance.",
}
@proceedings{stoc-2020-international,
    title = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    editor = "Bhatia, Archna  and
      Shaikh, Samira",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.0",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{dalton-etal-2020-active,
    title = "Active Defense Against Social Engineering: The Case for Human Language Technology",
    author = "Dalton, Adam  and
      Aghaei, Ehsan  and
      Al-Shaer, Ehab  and
      Bhatia, Archna  and
      Castillo, Esteban  and
      Cheng, Zhuo  and
      Dhaduvai, Sreekar  and
      Duan, Qi  and
      Hebenstreit, Bryanna  and
      Islam, Md Mazharul  and
      Karimi, Younes  and
      Masoumzadeh, Amir  and
      Mather, Brodie  and
      Santhanam, Sashank  and
      Shaikh, Samira  and
      Zemel, Alan  and
      Strzalkowski, Tomek  and
      Dorr, Bonnie J.",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.1",
    pages = "1--8",
    abstract = "We describe a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. The system processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the system is that it uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker{'}s time and resources.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{bhatia-etal-2020-adaptation,
    title = "Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation",
    author = "Bhatia, Archna  and
      Dalton, Adam  and
      Mather, Brodie  and
      Santhanam, Sashank  and
      Shaikh, Samira  and
      Zemel, Alan  and
      Strzalkowski, Tomek  and
      Dorr, Bonnie J.",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.2",
    pages = "9--14",
    abstract = "We present a paradigm for extensible lexicon development based on Lexical Conceptual Structure to support social engineering detection and response generation. We leverage the central notions of ask (elicitation of behaviors such as providing access to money) and framing (risk/reward implied by the ask). We demonstrate improvements in ask/framing detection through refinements to our lexical organization and show that response generation qualitatively improves as ask/framing detection performance improves. The paradigm presents a systematic and efficient approach to resource adaptation for improved task-specific performance.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{kim-etal-2020-analysis,
    title = "Analysis of Online Conversations to Detect Cyberpredators Using Recurrent Neural Networks",
    author = "Kim, Jinhwa  and
      Kim, Yoon Jo  and
      Behzadi, Mitra  and
      Harris, Ian G.",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.3",
    pages = "15--20",
    abstract = "We present an automated approach to analyze the text of an online conversation and determine whether one of the participants is a cyberpredator who is preying on another participant. The task is divided into two stages, 1) the classification of each message, and 2) the classification of the entire conversation. Each stage uses a Recurrent Neural Network (RNN) to perform the classification task.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{abeywardana-thayasivam-2020-privacy,
    title = "A Privacy Preserving Data Publishing Middleware for Unstructured, Textual Social Media Data",
    author = "Abeywardana, Prasadi  and
      Thayasivam, Uthayasanker",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.4",
    pages = "21--28",
    abstract = "Privacy is going to be an integral part of data science and analytics in the coming years. The next hype of data experimentation is going to be heavily dependent on privacy preserving techniques mainly as it{'}s going to be a legal responsibility rather than a mere social responsibility. Privacy preservation becomes more challenging specially in the context of unstructured data. Social networks have become predominantly popular over the past couple of decades and they are creating a huge data lake at a high velocity. Social media profiles contain a wealth of personal and sensitive information, creating enormous opportunities for third parties to analyze them with different algorithms, draw conclusions and use in disinformation campaigns and micro targeting based dark advertising. This study provides a mitigation mechanism for disinformation campaigns that are done based on the insights extracted from personal/sensitive data analysis. Specifically, this research is aimed at building a privacy preserving data publishing middleware for unstructured social media data without compromising the true analytical value of those data. A novel way is proposed to apply traditional structured privacy preserving techniques on unstructured data. Creating a comprehensive twitter corpus annotated with privacy attributes is another objective of this research, especially because the research community is lacking one.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{krumbiegel-etal-2020-information,
    title = "Information Space Dashboard",
    author = "Krumbiegel, Theresa  and
      Pritzkau, Albert  and
      Schmitz, Hans-Christian",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.5",
    pages = "29--34",
    abstract = "The information space, where information is generated, stored, exchanged and discussed, is not idyllic but a space where campaigns of disinformation and destabilization are conducted. Such campaigns are subsumed under the terms {``}hybrid warfare{''} and {``}information warfare{''} (Woolley and Howard, 2017). In order to enable awareness of them, we propose an information state dashboard comprising various components/apps for data collection, analysis and visualization. The aim of the dashboard is to support an analyst in generating a common operational picture of the information space, link it with an operational picture of the physical space and, thus, contribute to overarching situational awareness. The dashboard is work in progress. However, a first prototype with components for exploiting elementary language statistics, keyword and metadata analysis, text classification and network analysis has been implemented. Further components, in particular, for event extraction and sentiment analysis are under development. As a demonstration case, we briefly discuss the analysis of historical data regarding violent anti-migrant protests and respective counter-protests that took place in Chemnitz in 2018.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{pascucci-etal-2020-hotel,
    title = "Is this hotel review truthful or deceptive? A platform for disinformation detection through computational stylometry",
    author = "Pascucci, Antonio  and
      Manna, Raffaele  and
      Caterino, Ciro  and
      Masucci, Vincenzo  and
      Monti, Johanna",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.6",
    pages = "35--40",
    abstract = "In this paper, we present a web service platform for disinformation detection in hotel reviews written in English. The platform relies on a hybrid approach of computational stylometry techniques, machine learning and linguistic rules written using COGITO, Expert System Corp.{'}s semantic intelligence software thanks to which it is possible to analyze texts and extract all their characteristics. We carried out a research experiment on the Deceptive Opinion Spam corpus, a balanced corpus composed of 1,600 hotel reviews of 20 Chicago hotels split into four datasets: positive truthful, negative truthful, positive deceptive and negative deceptive reviews. We investigated four different classifiers and we detected that Simple Logistic is the most performing algorithm for this type of classification.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{blackburn-etal-2020-corpus,
    title = "Corpus Development for Studying Online Disinformation Campaign: A Narrative + Stance Approach",
    author = "Blackburn, Mack  and
      Yu, Ning  and
      Berrie, John  and
      Gordon, Brian  and
      Longfellow, David  and
      Tirrell, William  and
      Williams, Mark",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.7",
    pages = "41--47",
    abstract = "Disinformation on social media is impacting our personal life and society. The outbreak of the new coronavirus is the most recent example for which a wealth of disinformation provoked fear, hate, and even social panic. While there are emerging interests in studying how disinformation campaigns form, spread, and influence target audiences, developing disinformation campaign corpora is challenging given the high volume, fast evolution, and wide variation of messages associated with each campaign. Disinformation cannot always be captured by simple factchecking, which makes it even more challenging to validate and create ground truth. This paper presents our approach to develop a corpus for studying disinformation campaigns targeting the White Helmets of Syria. We bypass directly classifying a piece of information as disinformation or not. Instead, we label the narrative and stance of tweets and YouTube comments about White Helmets. Narratives is defined as a recurring statement that is used to express a point of view. Stance is a high-level point of view on a topic. We demonstrate that narrative and stance together can provide a dynamic method for real world users, e.g., intelligence analysts, to quickly identify and counter disinformation campaigns based on their knowledge at the time.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@inproceedings{castillo-etal-2020-email,
    title = "Email Threat Detection Using Distinct Neural Network Approaches",
    author = "Castillo, Esteban  and
      Dhaduvai, Sreekar  and
      Liu, Peng  and
      Thakur, Kartik-Singh  and
      Dalton, Adam  and
      Strzalkowski, Tomek",
    booktitle = "Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.stoc-1.8",
    pages = "48--55",
    abstract = "This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools. Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email. The proposed approaches are trained and tested on distinct email collections, including datasets constructed from publicly available corpora (such as Enron, APWG, etc.) as well as several smaller, non-public datasets used in recent government evaluations. Experimental results show that back-propagation both with and without recurrent neural layers outperforms current state of the art techniques that include supervised learning algorithms with stylometric elements of texts as features. Our results also demonstrate that word embedding vectors are effective means for capturing certain aspects of text meaning that can be teased out through machine learning in non-linear/complex neural networks, in order to obtain highly accurate detection of malicious emails based on email text alone.",
    language = "English",
    ISBN = "979-10-95546-39-9",
}
@proceedings{sem-2020-joint,
    title = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    editor = "Gurevych, Iryna  and
      Apidianaki, Marianna  and
      Faruqui, Manaal",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.0",
}
@inproceedings{chowdhury-etal-2020-improving,
    title = "Improving Medical {NLI} Using Context-Aware Domain Knowledge",
    author = "Chowdhury, Shaika  and
      Yu, Philip  and
      Luo, Yuan",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.1",
    pages = "1--11",
    abstract = "Domain knowledge is important to understand both the lexical and relational associations of words in natural language text, especially for domain-specific tasks like Natural Language Inference (NLI) in the medical domain, where due to the lack of a large annotated dataset such knowledge cannot be implicitly learned during training. However, because of the linguistic idiosyncrasies of clinical texts (e.g., shorthand jargon), solely relying on domain knowledge from an external knowledge base (e.g., UMLS) can lead to wrong inference predictions as it disregards contextual information and, hence, does not return the most relevant mapping. To remedy this, we devise a knowledge adaptive approach for medical NLI that encodes the premise/hypothesis texts by leveraging supplementary external knowledge, alongside the UMLS, based on the word contexts. By incorporating refined domain knowledge at both the lexical and relational levels through a multi-source attention mechanism, it is able to align the token-level interactions between the premise and hypothesis more effectively. Comprehensive experiments and case study on the recently released MedNLI dataset are conducted to validate the effectiveness of the proposed approach.",
}
@inproceedings{mishra-etal-2020-reading,
    title = "Reading Comprehension as Natural Language Inference:A Semantic Analysis",
    author = "Mishra, Anshuman  and
      Patel, Dhruvesh  and
      Vijayakumar, Aparna  and
      Li, Xiang  and
      Kapanipathi, Pavan  and
      Talamadupula, Kartik",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.2",
    pages = "12--19",
    abstract = "In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.",
}
@inproceedings{abzianidze-2020-learning,
    title = "Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference",
    author = "Abzianidze, Lasha",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.3",
    pages = "20--31",
    abstract = "Tackling Natural Language Inference with a logic-based method is becoming less and less common. While this might have been counterintuitive several decades ago, nowadays it seems pretty obvious. The main reasons for such a conception are that (a) logic-based methods are usually brittle when it comes to processing wide-coverage texts, and (b) instead of automatically learning from data, they require much of manual effort for development. We make a step towards to overcome such shortcomings by modeling learning from data as abduction: reversing a theorem-proving procedure to abduce semantic relations that serve as the best explanation for the gold label of an inference problem. In other words, instead of proving sentence-level inference relations with the help of lexical relations, the lexical relations are proved taking into account the sentence-level inference relations. We implement the learning method in a tableau theorem prover for natural language and show that it improves the performance of the theorem prover on the SICK dataset by 1.4{\%} while still maintaining high precision ({\textgreater}94{\%}). The obtained results are competitive with the state of the art among logic-based systems.",
}
@inproceedings{chersoni-etal-2020-automatic,
    title = "Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings",
    author = "Chersoni, Emmanuele  and
      Xiang, Rong  and
      Lu, Qin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.4",
    pages = "32--38",
    abstract = "Collecting modality exclusivity norms for lexical items has recently become a common practice in psycholinguistics and cognitive research. However, these norms are available only for a relatively small number of languages and often involve a costly and time-consuming collection of ratings. In this work, we aim at learning a mapping between word embeddings and modality norms. Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one. We ran two experiments, one in a monolingual and the other one in a crosslingual setting. Results show that modality prediction using off-the-shelf crosslingual embeddings indeed has moderate-to-high correlations with human ratings even when regression algorithms are trained on an English resource and tested on a completely unseen language.",
}
@inproceedings{hakimi-parizi-cook-2020-joint,
    title = "Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora",
    author = "Hakimi Parizi, Ali  and
      Cook, Paul",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.5",
    pages = "39--49",
    abstract = "In this paper, we propose a novel method for learning cross-lingual word embeddings, that incorporates sub-word information during training, and is able to learn high-quality embeddings from modest amounts of monolingual data and a bilingual lexicon. This method could be particularly well-suited to learning cross-lingual embeddings for lower-resource, morphologically-rich languages, enabling knowledge to be transferred from rich- to lower-resource languages. We evaluate our proposed approach simulating lower-resource languages for bilingual lexicon induction, monolingual word similarity, and document classification. Our results indicate that incorporating sub-word information indeed leads to improvements, and in the case of document classification, performance better than, or on par with, strong benchmark approaches.",
}
@inproceedings{sulem-etal-2020-semantic,
    title = "Semantic Structural Decomposition for Neural Machine Translation",
    author = "Sulem, Elior  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.6",
    pages = "50--57",
    abstract = "Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation. We experiment with a Transformer model and evaluate using large-scale crowd-sourcing experiments. Results show a significant increase in fluency on long sentences on an English-to- French setting with a training corpus of 5M sentence pairs, while retaining comparable adequacy. We also perform a manual analysis which explores the tradeoff between adequacy and fluency in the case where all sentence lengths are considered.",
}
@inproceedings{oberlander-klinger-2020-token,
    title = "Token Sequence Labeling vs. Clause Classification for {E}nglish Emotion Stimulus Detection",
    author = {Oberl{\"a}nder, Laura Ana Maria  and
      Klinger, Roman},
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.7",
    pages = "58--70",
    abstract = "Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli ({``}Is the stimulus category A or B?{''}), (2) as sequence labeling of tokens ({``}Which tokens describe the stimulus?{''}), and (3) as clause classification ({``}Does this clause contain the emotion stimulus?{''}). So far, setting (3) has been evaluated broadly on Mandarin and (2) on English, but no comparison has been performed. Therefore, we analyze whether clause classification or token sequence labeling is better suited for emotion stimulus detection in English. We propose an integrated framework which enables us to evaluate the two different approaches comparably, implement models inspired by state-of-the-art approaches in Mandarin, and test them on four English data sets from different domains. Our results show that token sequence labeling is superior on three out of four datasets, in both clause-based and token sequence-based evaluation. The only case in which clause classification performs better is one data set with a high density of clause annotations. Our error analysis further confirms quantitatively and qualitatively that clauses are not the appropriate stimulus unit in English.",
}
@inproceedings{hulpus-etal-2020-knowledge,
    title = "Knowledge Graphs meet Moral Values",
    author = "Hulpu{\textcommabelow{s}}, Ioana  and
      Kobbe, Jonathan  and
      Stuckenschmidt, Heiner  and
      Hirst, Graeme",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.8",
    pages = "71--80",
    abstract = "Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core {--} such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.",
}
@inproceedings{gantt-etal-2020-natural,
    title = "Natural Language Inference with Mixed Effects",
    author = "Gantt, William  and
      Kane, Benjamin  and
      White, Aaron Steven",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.9",
    pages = "81--87",
    abstract = "There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating those annotations to a single label problematic. We propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without subjecting the model to unwanted noise that can arise from annotator response biases. We demonstrate that this method, which generalizes the notion of a mixed effects model by incorporating annotator random effects into any existing neural model, improves performance over models that do not incorporate such effects.",
}
@inproceedings{ravichander-etal-2020-systematicity,
    title = "On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in {BERT}",
    author = "Ravichander, Abhilasha  and
      Hovy, Eduard  and
      Suleman, Kaheer  and
      Trischler, Adam  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.10",
    pages = "88--102",
    abstract = "Contextualized word representations have become a driving force in NLP, motivating widespread interest in understanding their capabilities and the mechanisms by which they operate. Particularly intriguing is their ability to identify and encode conceptual abstractions. Past work has probed BERT representations for this competence, finding that BERT can correctly retrieve noun hypernyms in cloze tasks. In this work, we ask the question: \textit{do probing studies shed light on systematic knowledge in BERT representations?} As a case study, we examine hypernymy knowledge encoded in BERT representations. In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT. Our main conclusion is cautionary: even if BERT demonstrates high probing accuracy for a particular competence, it does not necessarily follow that BERT {`}understands{'} a concept, and it cannot be expected to systematically generalize across applicable contexts.",
}
@inproceedings{jakubowski-etal-2020-topology,
    title = "Topology of Word Embeddings: Singularities Reflect Polysemy",
    author = "Jakubowski, Alexander  and
      Gasic, Milica  and
      Zibrowius, Marcus",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.11",
    pages = "103--113",
    abstract = "The manifold hypothesis suggests that word vectors live on a submanifold within their ambient vector space. We argue that we should, more accurately, expect them to live on a {\textless}i{\textgreater}pinched{\textless}/i{\textgreater} manifold: a singular quotient of a manifold obtained by identifying some of its points. The identified, singular points correspond to polysemous words, i.e. words with multiple meanings. Our point of view suggests that monosemous and polysemous words can be distinguished based on the topology of their neighbourhoods. We present two kinds of empirical evidence to support this point of view: (1) We introduce a topological measure of polysemy based on persistent homology that correlates well with the actual number of meanings of a word. (2) We propose a simple, topologically motivated solution to the SemEval-2010 task on {\textless}i{\textgreater}Word Sense Induction {\&} Disambiguation{\textless}/i{\textgreater} that produces competitive results.",
}
@inproceedings{haber-poesio-2020-assessing,
    title = "Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance",
    author = "Haber, Janosch  and
      Poesio, Massimo",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.12",
    pages = "114--124",
    abstract = "Co-predication is one of the most frequently used linguistic tests to tell apart shifts in polysemic sense from changes in homonymic meaning. It is increasingly coming under criticism as evidence is accumulating that it tends to mis-classify specific cases of polysemic sense alteration as homonymy. In this paper, we collect empirical data to investigate these accusations. We asses how co-predication acceptability relates to explicit ratings of polyseme word sense similarity, and how well either measure can be predicted through the distance between target words{'} contextualised word embeddings. We find that sense similarity appears to be a major contributor in determining co-predication acceptability, but that co-predication judgements tend to rate especially less similar sense interpretations equally as unacceptable as homonym pairs, effectively mis-classifying these instances. The tested contextualised word embeddings fail to predict word sense similarity consistently, but the similarities between BERT embeddings show a significant correlation with co-predication ratings. We take this finding as evidence that BERT embeddings might be better representations of context than encodings of word meaning.",
}
@inproceedings{mulang-etal-2020-fine,
    title = "Fine-tuning {BERT} with Focus Words for Explanation Regeneration",
    author = {Mulang{'}, Isaiah Onando  and
      D{'}Souza, Jennifer  and
      Auer, S{\"o}ren},
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.13",
    pages = "125--130",
    abstract = "Explanation generation introduced as the world tree corpus (Jansen et al., 2018) is an emerging NLP task involving multi-hop inference for explaining the correct answer in multiple-choice QA. It is a challenging task evidenced by low state-of-the-art performances(below 60{\%} in F-score) demonstrated on the task. Of the state-of-the-art approaches, fine-tuned transformer-based (Vaswani et al., 2017) BERT models have shown great promise toward continued system performance improvements compared with approaches relying on surface-level cues alone that demonstrate performance saturation. In this work, we take a novel direction by addressing a particular linguistic characteristic of the data {---} we introduce a novel and lightweight focus feature in the transformer-based model and examine task improvements. Our evaluations reveal a significantly positive impact of this lightweight focus feature achieving the highest scores, second only to a significantly computationally intensive system.",
}
@inproceedings{cappelli-lenci-2020-pisa,
    title = "{PISA}: A measure of Preference In Selection of Arguments to model verb argument recoverability",
    author = "Cappelli, Giulia  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.14",
    pages = "131--136",
    abstract = "Our paper offers a computational model of the semantic recoverability of verb arguments, tested in particular on direct objects and Instruments. Our fully distributional model is intended to improve on older taxonomy-based models, which require a lexicon in addition to the training corpus. We computed the selectional preferences of 99 transitive verbs and 173 Instrument verbs as the mean value of the pairwise cosines between their arguments (a weighted mean between all the arguments, or an unweighted mean with the topmost k arguments). Results show that our model can predict the recoverability of objects and Instruments, providing a similar result to that of taxonomy-based models but at a much cheaper computational cost.",
}
@inproceedings{mckenna-steedman-2020-learning,
    title = "Learning Negation Scope from Syntactic Structure",
    author = "McKenna, Nick  and
      Steedman, Mark",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.15",
    pages = "137--142",
    abstract = "We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, though recent research relies on combining a variety of features including part-of-speech tags, word embeddings, and semantic representations to achieve high task performance. Our simplified model returns to syntactic theory and achieves state-of-the-art performance on the task of Negation Scope Detection while demonstrating the tight relationship between the syntax and semantics of negation.",
}
@inproceedings{ebert-pavlick-2020-visuospatial,
    title = "A Visuospatial Dataset for Naturalistic Verb Learning",
    author = "Ebert, Dylan  and
      Pavlick, Ellie",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.16",
    pages = "143--153",
    abstract = "We introduce a new dataset for training and evaluating grounded language models. Our data is collected within a virtual reality environment and is designed to emulate the quality of language data to which a pre-verbal child is likely to have access: That is, naturalistic, spontaneous speech paired with richly grounded visuospatial context. We use the collected data to compare several distributional semantics models for verb learning. We evaluate neural models based on 2D (pixel) features as well as feature-engineered models based on 3D (symbolic, spatial) features, and show that neither modeling approach achieves satisfactory performance. Our results are consistent with evidence from child language acquisition that emphasizes the difficulty of learning verbs from naive distributional data. We discuss avenues for future work on cognitively-inspired grounded language learning, and release our corpus with the intent of facilitating research on the topic.",
}
@inproceedings{zhang-etal-2020-find,
    title = "Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking",
    author = "Zhang, Jianguo  and
      Hashimoto, Kazuma  and
      Wu, Chien-Sheng  and
      Wang, Yao  and
      Yu, Philip  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.17",
    pages = "154--167",
    abstract = "Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and ontology-free methods. An ontology-based method selects a value from a candidate-value list for each target slot, while an ontology-free method extracts spans from dialog contexts. Recent work introduced a BERT-based model to strike a balance between the two methods by pre-defining categorical and non-categorical slots. However, it is not clear enough which slots are better handled by either of the two slot types, and the way to use the pre-trained model has not been well investigated. In this paper, we propose a simple yet effective dual-strategy model for DST, by adapting a single BERT-style reading comprehension model to jointly handle both the categorical and non-categorical slots. Our experiments on the MultiWOZ datasets show that our method significantly outperforms the BERT-based counterpart, finding that the key is a deep interaction between the domain-slot and context information. When evaluated on noisy (MultiWOZ 2.0) and cleaner (MultiWOZ 2.1) settings, our method performs competitively and robustly across the two different settings. Our method sets the new state of the art in the noisy setting, while performing more robustly than the best model in the cleaner setting. We also conduct a comprehensive error analysis on the dataset, including the effects of the dual strategy for each slot, to facilitate future research.",
}
@inproceedings{you-goldwasser-2020-relationship,
    title = "{``}where is this relationship going?{''}: Understanding Relationship Trajectories in Narrative Text",
    author = "You, Keen  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.18",
    pages = "168--178",
    abstract = "We examine a new commonsense reasoning task: given a narrative describing a social interaction that centers on two protagonists, systems make inferences about the underlying relationship trajectory. Specifically, we propose two evaluation tasks: Relationship Outlook Prediction MCQ and Resolution Prediction MCQ. In Relationship Outlook Prediction, a system maps an interaction to a relationship outlook that captures how the interaction is expected to change the relationship. In Resolution Prediction, a system attributes a given relationship outlook to a particular resolution that explains the outcome. These two tasks parallel two real-life questions that people frequently ponder upon as they navigate different social situations: {``}where is this relationship going?{''} and {``}how did we end up here?{''}. To facilitate the investigation of human social relationships through these two tasks, we construct a new dataset, Social Narrative Tree, which consists of 1250 stories documenting a variety of daily social interactions. The narratives encode a multitude of social elements that interweave to give rise to rich commonsense knowledge of how relationships evolve with respect to social interactions. We establish baseline performances using language models and the accuracies are significantly lower than human performance. The results demonstrate that models need to look beyond syntactic and semantic signals to comprehend complex human relationships.",
}
@inproceedings{saedi-dras-2020-large,
    title = "Large Scale Author Obfuscation Using {S}iamese Variational Auto-Encoder: The {S}iam{AO} System",
    author = "Saedi, Chakaveh  and
      Dras, Mark",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.starsem-1.19",
    pages = "179--189",
    abstract = "Author obfuscation is the task of masking the author of a piece of text, with applications in privacy. Recent advances in deep neural networks have boosted author identification performance making author obfuscation more challenging. Existing approaches to author obfuscation are largely heuristic. Obfuscation can, however, be thought of as the construction of adversarial examples to attack author identification, suggesting that the deep learning architectures used for adversarial attacks could have application here. Current architectures are proposed to construct adversarial examples against classification-based models, which in author identification would exclude the high-performing similarity-based models employed when facing large number of authorial classes. In this paper, we propose the first deep learning architecture for constructing adversarial examples against similarity-based learners, and explore its application to author obfuscation. We analyse the output from both success in obfuscation and language acceptability, as well as comparing the performance with some common baselines, and showing promising results in finding a balance between safety and soundness of the perturbed texts.",
}
@proceedings{spnlp-2020-structured,
    title = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    editor = "Agrawal, Priyanka  and
      Kozareva, Zornitsa  and
      Kreutzer, Julia  and
      Lampouras, Gerasimos  and
      Martins, Andr{\'e}  and
      Ravi, Sujith  and
      Vlachos, Andreas",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.0",
}
@inproceedings{casas-etal-2020-syntax,
    title = "Syntax-driven Iterative Expansion Language Models for Controllable Text Generation",
    author = "Casas, Noe  and
      Fonollosa, Jos{\'e} A. R.  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.1",
    doi = "10.18653/v1/2020.spnlp-1.1",
    pages = "1--10",
    abstract = "The dominant language modeling paradigm handles text as a sequence of discrete tokens. While that approach can capture the latent structure of the text, it is inherently constrained to sequential dynamics for text generation. We propose a new paradigm for introducing a syntactic inductive bias into neural text generation, where the dependency parse tree is used to drive the Transformer model to generate sentences iteratively. Our experiments show that this paradigm is effective at text generation, with quality between LSTMs and Transformers, and comparable diversity, requiring less than half their decoding steps, and its generation process allows direct control over the syntactic constructions of the generated text, enabling the induction of stylistic variations.",
}
@inproceedings{singh-etal-2020-copynext,
    title = "{C}opy{N}ext: Explicit Span Copying and Alignment in Sequence to Sequence Models",
    author = "Singh, Abhinav  and
      Xia, Patrick  and
      Qin, Guanghui  and
      Yarmohammadi, Mahsa  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.2",
    doi = "10.18653/v1/2020.spnlp-1.2",
    pages = "11--16",
    abstract = "Copy mechanisms are employed in sequence to sequence (seq2seq) models to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed.",
}
@inproceedings{tran-tan-2020-generating,
    title = "Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations",
    author = "Tran, Ke  and
      Tan, Ming",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.3",
    doi = "10.18653/v1/2020.spnlp-1.3",
    pages = "17--21",
    abstract = "Modern conversational AI systems support natural language understanding for a wide variety of capabilities. While a majority of these tasks can be accomplished using a simple and flat representation of intents and slots, more sophisticated capabilities require complex hierarchical representations supported by semantic parsing. State-of-the-art semantic parsers are trained using supervised learning with data labeled according to a hierarchical schema which might be costly to obtain or not readily available for a new domain. In this work, we explore the possibility of generating synthetic data for neural semantic parsing using a pretrained denoising sequence-to-sequence model (i.e., BART). Specifically, we first extract masked templates from the existing labeled utterances, and then fine-tune BART to generate synthetic utterances conditioning on the extracted templates. Finally, we use an auxiliary parser (AP) to filter the generated utterances. The AP guarantees the quality of the generated data. We show the potential of our approach when evaluating on the Facebook TOP dataset for navigation domain.",
}
@inproceedings{ter-horst-cimiano-2020-structured,
    title = "Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension",
    author = "ter Horst, Hendrik  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.4",
    doi = "10.18653/v1/2020.spnlp-1.4",
    pages = "22--32",
    abstract = "Model-complete text comprehension aims at interpreting a natural language text with respect to a semantic domain model describing the classes and their properties relevant for the domain in question. Solving this task can be approached as a structured prediction problem, consisting in inferring the most probable instance of the semantic model given the text. In this work, we focus on the challenging sub-problem of cardinality prediction that consists in predicting the number of distinct individuals of each class in the semantic model. We show that cardinality prediction can successfully be approached by modeling the overall task as a joint inference problem, predicting the number of individuals of certain classes while at the same time extracting their properties. We approach this task with probabilistic graphical models computing the maximum-a-posteriori instance of the semantic model. Our main contribution lies on the empirical investigation and analysis of different approximative inference strategies based on Gibbs sampling. We present and evaluate our models on the task of extracting key parameters from scientific full text articles describing pre-clinical studies in the domain of spinal cord injury.",
}
@inproceedings{trinh-etal-2020-energy,
    title = "Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking",
    author = "Trinh, Anh Duong  and
      Ross, Robert J.  and
      Kelleher, John D.",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.5",
    doi = "10.18653/v1/2020.spnlp-1.5",
    pages = "33--42",
    abstract = "Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their prediction process.",
}
@inproceedings{sage-etal-2020-end,
    title = "End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks",
    author = "Sage, Cl{\'e}ment  and
      Aussem, Alex  and
      Eglin, V{\'e}ronique  and
      Elghazel, Haytham  and
      Espinas, J{\'e}r{\'e}my",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.6",
    doi = "10.18653/v1/2020.spnlp-1.6",
    pages = "43--52",
    abstract = "The predominant approaches for extracting key information from documents resort to classifiers predicting the information type of each word. However, the word level ground truth used for learning is expensive to obtain since it is not naturally produced by the extraction task. In this paper, we discuss a new method for training extraction models directly from the textual value of information. The extracted information of a document is represented as a sequence of tokens in the XML language. We learn to output this representation with a pointer-generator network that alternately copies the document words carrying information and generates the XML tags delimiting the types of information. The ability of our end-to-end method to retrieve structured information is assessed on a large set of business documents. We show that it performs competitively with a standard word classifier without requiring costly word level supervision.",
}
@inproceedings{manginas-etal-2020-layer,
    title = "Layer-wise Guided Training for {BERT}: Learning Incrementally Refined Document Representations",
    author = "Manginas, Nikolaos  and
      Chalkidis, Ilias  and
      Malakasiotis, Prodromos",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.7",
    doi = "10.18653/v1/2020.spnlp-1.7",
    pages = "53--61",
    abstract = "Although BERT is widely used by the NLP community, little is known about its inner workings. Several attempts have been made to shed light on certain aspects of BERT, often with contradicting conclusions. A much raised concern focuses on BERT{'}s over-parameterization and under-utilization issues. To this end, we propose o novel approach to fine-tune BERT in a structured manner. Specifically, we focus on Large Scale Multilabel Text Classification (LMTC) where documents are assigned with one or more labels from a large predefined set of hierarchically organized labels. Our approach guides specific BERT layers to predict labels from specific hierarchy levels. Experimenting with two LMTC datasets we show that this structured fine-tuning approach not only yields better classification results but also leads to better parameter utilization.",
}
@inproceedings{tu-etal-2020-improving,
    title = "Improving Joint Training of Inference Networks and Structured Prediction Energy Networks",
    author = "Tu, Lifu  and
      Pang, Richard Yuanzhe  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.8",
    doi = "10.18653/v1/2020.spnlp-1.8",
    pages = "62--73",
    abstract = "Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger and McCallum, 2016). Tu and Gimpel (2018) developed an efficient framework for energy-based models by training {``}inference networks{''} to approximate structured inference instead of using gradient descent. However, their alternating optimization approach suffers from instabilities during training, requiring additional loss terms and careful hyperparameter tuning. In this paper, we contribute several strategies to stabilize and improve this joint training of energy functions and inference networks for structured prediction. We design a compound objective to jointly train both cost-augmented and test-time inference networks along with the energy function. We propose joint parameterizations for the inference networks that encourage them to capture complementary functionality during learning. We empirically validate our strategies on two sequence labeling tasks, showing easier paths to strong performance than prior work, as well as further improvements with global energy terms.",
}
@inproceedings{chen-etal-2020-reading,
    title = "Reading the Manual: Event Extraction as Definition Comprehension",
    author = "Chen, Yunmo  and
      Chen, Tongfei  and
      Ebner, Seth  and
      White, Aaron Steven  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.9",
    doi = "10.18653/v1/2020.spnlp-1.9",
    pages = "74--83",
    abstract = "We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, {``}Some person was born in some location at some time.{''} We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.",
}
@inproceedings{lee-etal-2020-discrepancy,
    title = "On the Discrepancy between Density Estimation and Sequence Generation",
    author = "Lee, Jason  and
      Tran, Dustin  and
      Firat, Orhan  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.10",
    doi = "10.18653/v1/2020.spnlp-1.10",
    pages = "84--94",
    abstract = "Many sequence-to-sequence generation tasks, including machine translation and text-to-speech, can be posed as estimating the density of the output y given the input x: p(y|x). Given this interpretation, it is natural to evaluate sequence-to-sequence models using conditional log-likelihood on a test set. However, the goal of sequence-to-sequence generation (or structured prediction) is to find the best output y{\^{}} given an input x, and each task has its own downstream metric R that scores a model output by comparing against a set of references y*: R(y{\^{}}, y* | x). While we hope that a model that excels in density estimation also performs well on the downstream metric, the exact correlation has not been studied for sequence generation tasks. In this paper, by comparing several density estimators on five machine translation tasks, we find that the correlation between rankings of models based on log-likelihood and BLEU varies significantly depending on the range of the model families being compared. First, log-likelihood is highly correlated with BLEU when we consider models within the same family (e.g. autoregressive models, or latent variable models with the same parameterization of the prior). However, we observe no correlation between rankings of models across different families: (1) among non-autoregressive latent variable models, a flexible prior distribution is better at density estimation but gives worse generation quality than a simple prior, and (2) autoregressive models offer the best translation performance overall, while latent variable models with a normalizing flow prior give the highest held-out log-likelihood across all datasets. Therefore, we recommend using a simple prior for the latent variable non-autoregressive model when fast generation speed is desired.",
}
@inproceedings{jean-cho-2020-log,
    title = "Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation",
    author = "Jean, S{\'e}bastien  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.11",
    doi = "10.18653/v1/2020.spnlp-1.11",
    pages = "95--101",
    abstract = "We seek to maximally use various data sources, such as parallel and monolingual data, to build an effective and efficient document-level translation system. In particular, we start by considering a noisy channel approach (CITATION) that combines a target-to-source translation model and a language model. By applying Bayes{'} rule strategically, we reformulate this approach as a log-linear combination of translation, sentence-level and document-level language model probabilities. In addition to using static coefficients for each term, this formulation alternatively allows for the learning of dynamic per-token weights to more finely control the impact of the language models. Using both static or dynamic coefficients leads to improvements over a context-agnostic baseline and a context-aware concatenation model.",
}
@inproceedings{mitra-etal-2020-deeply,
    title = "Deeply Embedded Knowledge Representation {\&} Reasoning For Natural Language Question Answering: A Practitioner{'}s Perspective",
    author = "Mitra, Arindam  and
      Narayana, Sanjay  and
      Baral, Chitta",
    booktitle = "Proceedings of the Fourth Workshop on Structured Prediction for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.spnlp-1.12",
    doi = "10.18653/v1/2020.spnlp-1.12",
    pages = "102--111",
    abstract = "Successful application of Knowledge Representation and Reasoning (KR) in Natural Language Understanding (NLU) is largely limited by the availability of a robust and general purpose natural language parser. Even though several projects have been launched in the pursuit of developing a universal meaning representation language, the existence of an accurate universal parser is far from reality. This has severely limited the application of knowledge representation and reasoning (KR) in the field of NLP and also prevented a proper evaluation of KR based NLU systems. Our goal is to build KR based systems for Natural Language Understanding without relying on a parser. Towards this we propose a method named Deeply Embedded Knowledge Representation {\&} Reasoning (DeepEKR) where we replace the parser by a neural network, soften the symbolic representation so that a deterministic mapping exists between the parser neural network and the interpretable logical form, and finally replace the symbolic solver by an equivalent neural network, so the model can be trained end-to-end. We evaluate our method with respect to the task of Qualitative Word Problem Solving on the two available datasets (QuaRTz and QuaRel). Our system achieves same accuracy as that of the state-of-the-art accuracy on QuaRTz, outperforms the state-of-the-art on QuaRel and severely outperforms a traditional KR based system. The results show that the bias introduced by a KR solution does not prevent it from doing a better job at the end task. Moreover, our method is interpretable due to the bias introduced by the KR approach.",
}
@proceedings{splu-2020-international,
    title = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    editor = "Kordjamshidi, Parisa  and
      Bhatia, Archna  and
      Alikhani, Malihe  and
      Baldridge, Jason  and
      Bansal, Mohit  and
      Moens, Marie-Francine",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.0",
}
@inproceedings{komada-inui-2020-element,
    title = "An Element-wise Visual-enhanced {B}i{LSTM}-{CRF} Model for Location Name Recognition",
    author = "Komada, Takuya  and
      Inui, Takashi",
    booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.1",
    doi = "10.18653/v1/2020.splu-1.1",
    pages = "1--9",
    abstract = "In recent years, previous studies have used visual information in named entity recognition (NER) for social media posts with attached images. However, these methods can only be applied to documents with attached images. In this paper, we propose a NER method that can use element-wise visual information for any documents by using image data corresponding to each word in the document. The proposed method obtains element-wise image data using an image retrieval engine, to be used as extra features in the neural NER model. Experimental results on the standard Japanese NER dataset show that the proposed method achieves a higher F1 value (89.67{\%}) than a baseline method, demonstrating the effectiveness of using element-wise visual information.",
}
@inproceedings{shin-etal-2020-bert,
    title = "{BERT}-based Spatial Information Extraction",
    author = "Shin, Hyeong Jin  and
      Park, Jeong Yeon  and
      Yuk, Dae Bum  and
      Lee, Jae Sung",
    booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.2",
    doi = "10.18653/v1/2020.splu-1.2",
    pages = "10--17",
    abstract = "Spatial information extraction is essential to understand geographical information in text. This task is largely divided to two subtasks: spatial element extraction and spatial relation extraction. In this paper, we utilize BERT (Devlin et al., 2018), which is very effective for many natural language processing applications. We propose a BERT-based spatial information extraction model, which uses BERT for spatial element extraction and R-BERT (Wu and He, 2019) for spatial relation extraction. The model was evaluated with the SemEval 2015 dataset. The result showed a 15.4{\%} point increase in spatial element extraction and an 8.2{\%} point increase in spatial relation extraction in comparison to the baseline model (Nichols and Botros, 2015).",
}
@inproceedings{xu-etal-2020-cognitively,
    title = "A Cognitively Motivated Approach to Spatial Information Extraction",
    author = "Xu, Chao  and
      Dietz Saldanha, Emmanuelle-Anna  and
      Gromann, Dagmar  and
      Zhou, Beihai",
    booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.3",
    doi = "10.18653/v1/2020.splu-1.3",
    pages = "18--28",
    abstract = "Automatic extraction of spatial information from natural language can boost human-centered applications that rely on spatial dynamics. The field of cognitive linguistics has provided theories and cognitive models to address this task. Yet, existing solutions tend to focus on specific word classes, subject areas, or machine learning techniques that cannot provide cognitively plausible explanations for their decisions. We propose an automated spatial semantic analysis (ASSA) framework building on grammar and cognitive linguistic theories to identify spatial entities and relations, bringing together methods of spatial information extraction and cognitive frameworks on spatial language. The proposed rule-based and explainable approach contributes constructions and preposition schemas and outperforms previous solutions on the CLEF-2017 standard dataset.",
}
@inproceedings{testoni-etal-2020-alike,
    title = "They Are Not All Alike: Answering Different Spatial Questions Requires Different Grounding Strategies",
    author = "Testoni, Alberto  and
      Greco, Claudio  and
      Bianchi, Tobias  and
      Mazuecos, Mauricio  and
      Marcante, Agata  and
      Benotti, Luciana  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.4",
    doi = "10.18653/v1/2020.splu-1.4",
    pages = "29--38",
    abstract = "In this paper, we study the grounding skills required to answer spatial questions asked by humans while playing the GuessWhat?! game. We propose a classification for spatial questions dividing them into absolute, relational, and group questions. We build a new answerer model based on the LXMERT multimodal transformer and we compare a baseline with and without visual features of the scene. We are interested in studying how the attention mechanisms of LXMERT are used to answer spatial questions since they require putting attention on more than one region simultaneously and spotting the relation holding among them. We show that our proposed model outperforms the baseline by a large extent (9.70{\%} on spatial questions and 6.27{\%} overall). By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.",
}
@inproceedings{richard-bollans-etal-2020-categorisation,
    title = "Categorisation, Typicality {\&} Object-Specific Features in Spatial Referring Expressions",
    author = "Richard-Bollans, Adam  and
      Cohn, Anthony  and
      G{\'o}mez {\'A}lvarez, Luc{\'\i}a",
    booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.5",
    doi = "10.18653/v1/2020.splu-1.5",
    pages = "39--49",
    abstract = "Various accounts of cognition and semantic representations have highlighted that, for some concepts, different factors may influence category and typicality judgements. In particular, some features may be more salient in categorisation tasks while other features are more salient when assessing typicality. In this paper we explore the extent to which this is the case for English spatial prepositions and discuss the implications for pragmatic strategies and semantic models. We hypothesise that object-specific features {---} related to object properties and affordances {---} are more salient in categorisation, while geometric and physical relationships between objects are more salient in typicality judgements. In order to test this hypothesis we conducted a study using virtual environments to collect both category and typicality judgements in 3D scenes. Based on the collected data we cannot verify the hypothesis and conclude that object-specific features appear to be salient in both category and typicality judgements, further evidencing the need to include these types of features in semantic models.",
}
@inproceedings{datta-roberts-2020-hybrid,
    title = "A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports",
    author = "Datta, Surabhi  and
      Roberts, Kirk",
    booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.6",
    doi = "10.18653/v1/2020.splu-1.6",
    pages = "50--55",
    abstract = "Radiology reports contain important clinical information about patients which are often tied through spatial expressions. Spatial expressions (or triggers) are mainly used to describe the positioning of radiographic findings or medical devices with respect to some anatomical structures. As the expressions result from the mental visualization of the radiologist{'}s interpretations, they are varied and complex. The focus of this work is to automatically identify the spatial expression terms from three different radiology sub-domains. We propose a hybrid deep learning-based NLP method that includes {--} 1) generating a set of candidate spatial triggers by exact match with the known trigger terms from the training data, 2) applying domain-specific constraints to filter the candidate triggers, and 3) utilizing a BERT-based classifier to predict whether a candidate trigger is a true spatial trigger or not. The results are promising, with an improvement of 24 points in the average F1 measure compared to a standard BERT-based sequence labeler.",
}
@inproceedings{mehta-etal-2020-retouchdown,
    title = "Retouchdown: Releasing Touchdown on {S}treet{L}earn as a Public Resource for Language Grounding Tasks in Street View",
    author = "Mehta, Harsh  and
      Artzi, Yoav  and
      Baldridge, Jason  and
      Ie, Eugene  and
      Mirowski, Piotr",
    booktitle = "Proceedings of the Third International Workshop on Spatial Language Understanding",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.splu-1.7",
    doi = "10.18653/v1/2020.splu-1.7",
    pages = "56--62",
    abstract = "The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given location. To enable the wider research community to work effectively with the Touchdown tasks, we are publicly releasing the 29k raw Street View panoramas needed for Touchdown. We follow the process used for the StreetLearn data release (Mirowski et al., 2019) to check panoramas for personally identifiable information and blur them as necessary. These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn. We also provide a reference implementation for both Touchdown tasks: vision and language navigation (VLN) and spatial description resolution (SDR). We compare our model results to those given in (Chen et al., 2019) and show that the panoramas we have added to StreetLearn support both Touchdown tasks and can be used effectively for further research and comparison.",
}
@proceedings{socialnlp-2020-international,
    title = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.0",
}
@inproceedings{kameswari-etal-2020-enhancing,
    title = "Enhancing Bias Detection in Political News Using Pragmatic Presupposition",
    author = "Kameswari, Lalitha  and
      Sravani, Dama  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.1",
    doi = "10.18653/v1/2020.socialnlp-1.1",
    pages = "1--6",
    abstract = "Usage of presuppositions in social media and news discourse can be a powerful way to influence the readers as they usually tend to not examine the truth value of the hidden or indirectly expressed information. Fairclough and Wodak (1997) discuss presupposition at a discourse level where some implicit claims are taken for granted in the explicit meaning of a text or utterance. From the Gricean perspective, the presuppositions of a sentence determine the class of contexts in which the sentence could be felicitously uttered. This paper aims to correlate the type of knowledge presupposed in a news article to the bias present in it. We propose a set of guidelines to identify various kinds of presuppositions in news articles and present a dataset consisting of 1050 articles which are annotated for bias (positive, negative or neutral) and the magnitude of presupposition. We introduce a supervised classification approach for detecting bias in political news which significantly outperforms the existing systems.",
}
@inproceedings{xia-etal-2020-demoting,
    title = "Demoting Racial Bias in Hate Speech Detection",
    author = "Xia, Mengzhou  and
      Field, Anjalie  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.2",
    doi = "10.18653/v1/2020.socialnlp-1.2",
    pages = "7--14",
    abstract = "In the task of hate speech detection, there exists a high correlation between African American English (AAE) and annotators{'} perceptions of toxicity in current datasets. This bias in annotated training data and the tendency of machine learning models to amplify it cause AAE text to often be mislabeled as abusive/offensive/hate speech (high false positive rate) by current hate speech classifiers. Here, we use adversarial training to mitigate this bias. Experimental results on one hate speech dataset and one AAE dataset suggest that our method is able to reduce the false positive rate for AAE text with only a minimal compromise on the performance of hate speech classification.",
}
@inproceedings{hiware-etal-2020-narmada,
    title = "{NARMADA}: Need and Available Resource Managing Assistant for Disasters and Adversities",
    author = "Hiware, Kaustubh  and
      Dutt, Ritam  and
      Sinha, Sayan  and
      Patro, Sohan  and
      Ghosh, Kripa  and
      Ghosh, Saptarshi",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.3",
    doi = "10.18653/v1/2020.socialnlp-1.3",
    pages = "15--24",
    abstract = "Although a lot of research has been done on utilising Online Social Media during disasters, there exists no system for a specific task that is critical in a post-disaster scenario {--} identifying resource-needs and resource-availabilities in the disaster-affected region, coupled with their subsequent matching. To this end, we present NARMADA, a semi-automated platform which leverages the crowd-sourced information from social media posts for assisting post-disaster relief coordination efforts. The system employs Natural Language Processing and Information Retrieval techniques for identifying resource-needs and resource-availabilities from microblogs, extracting resources from the posts, and also matching the needs to suitable availabilities. The system is thus capable of facilitating the judicious management of resources during post-disaster relief operations.",
}
@inproceedings{moon-etal-2020-beep,
    title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
    author = "Moon, Jihyung  and
      Cho, Won Ik  and
      Lee, Junbum",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.4",
    doi = "10.18653/v1/2020.socialnlp-1.4",
    pages = "25--31",
    abstract = "Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff{'}s alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks.",
}
@inproceedings{hosseinia-etal-2020-stance,
    title = "Stance Prediction for Contemporary Issues: Data and Experiments",
    author = "Hosseinia, Marjan  and
      Dragut, Eduard  and
      Mukherjee, Arjun",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.5",
    doi = "10.18653/v1/2020.socialnlp-1.5",
    pages = "32--40",
    abstract = "We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.",
}
@inproceedings{helbig-etal-2020-challenges,
    title = "Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline",
    author = "Helbig, David  and
      Troiano, Enrica  and
      Klinger, Roman",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.6",
    doi = "10.18653/v1/2020.socialnlp-1.6",
    pages = "41--50",
    abstract = "We propose the task of emotion style transfer, which is particularly challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise) are on the fence between content and style. To understand the particular difficulties of this task, we design a transparent emotion style transfer pipeline based on three steps: (1) select the words that are promising to be substituted to change the emotion (with a brute-force approach and selection based on the attention mechanism of an emotion classifier), (2) find sets of words as candidates for substituting the words (based on lexical and distributional semantics), and (3) select the most promising combination of substitutions with an objective function which consists of components for content (based on BERT sentence embeddings), emotion (based on an emotion classifier), and fluency (based on a neural language model). This comparably straight-forward setup enables us to explore the task and understand in what cases lexical substitution can vary the emotional load of texts, how changes in content and style interact and if they are at odds. We further evaluate our pipeline quantitatively in an automated and an annotation study based on Tweets and find, indeed, that simultaneous adjustments of content and emotion are conflicting objectives: as we show in a qualitative analysis motivated by Scherer{'}s emotion component model, this is particularly the case for implicit emotion expressions based on cognitive appraisal or descriptions of bodily reactions.",
}
@inproceedings{jia-etal-2020-incorporating,
    title = "Incorporating Uncertain Segmentation Information into {C}hinese {NER} for Social Media Text",
    author = "Jia, Shengbin  and
      Ding, Ling  and
      Chen, Xiaojun  and
      E, Shijia  and
      Xiang, Yang",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.7",
    doi = "10.18653/v1/2020.socialnlp-1.7",
    pages = "51--60",
    abstract = "Chinese word segmentation is necessary to provide word-level information for Chinese named entity recognition (NER) systems. However, segmentation error propagation is a challenge for Chinese NER while processing colloquial data like social media text. In this paper, we propose a model (UIcwsNN) that specializes in identifying entities from Chinese social media text, especially by leveraging uncertain information of word segmentation. Such ambiguous information contains all the potential segmentation states of a sentence that provides a channel for the model to infer deep word-level characteristics. We propose a trilogy (i.e., Candidate Position Embedding ={\textgreater} Position Selective Attention ={\textgreater} Adaptive Word Convolution) to encode uncertain word segmentation information and acquire appropriate word-level representation. Experimental results on the social media corpus show that our model alleviates the segmentation error cascading trouble effectively, and achieves a significant performance improvement of 2{\%} over previous state-of-the-art methods.",
}
@inproceedings{meftah-etal-2020-multi,
    title = "Multi-Task Supervised Pretraining for Neural Domain Adaptation",
    author = "Meftah, Sara  and
      Semmar, Nasredine  and
      Tahiri, Mohamed-Ayoub  and
      Tamaazousti, Youssef  and
      Essafi, Hassane  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.socialnlp-1.8",
    doi = "10.18653/v1/2020.socialnlp-1.8",
    pages = "61--71",
    abstract = "Two prevalent transfer learning approaches are used in recent works to improve neural networks performance for domains with small amounts of annotated data: Multi-task learning which involves training the task of interest with related auxiliary tasks to exploit their underlying similarities, and Mono-task fine-tuning, where the weights of the model are initialized with the pretrained weights of a large-scale labeled source domain and then fine-tuned with labeled data of the target domain (domain of interest). In this paper, we propose a new approach which takes advantage from both approaches by learning a hierarchical model trained across multiple tasks from a source domain, and is then fine-tuned on multiple tasks of the target domain. Our experiments on four tasks applied to the social media domain show that our proposed approach leads to significant improvements on all tasks compared to both approaches.",
}
@proceedings{smm4h-2020-social,
    title = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    editor = "Gonzalez-Hernandez, Graciela  and
      Klein, Ari Z.  and
      Flores, Ivan  and
      Weissenbacher, Davy  and
      Magge, Arjun  and
      O'Connor, Karen  and
      Sarker, Abeed  and
      Minard, Anne-Lyse  and
      Tutubalina, Elena  and
      Miftahutdinov, Zulfat  and
      Alimova, Ilseyar",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.0",
}
@inproceedings{cornelius-etal-2020-covid,
    title = "{COVID}-19 {T}witter Monitor: Aggregating and Visualizing {COVID}-19 Related Trends in Social Media",
    author = "Cornelius, Joseph  and
      Ellendorff, Tilia  and
      Furrer, Lenz  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.1",
    pages = "1--10",
    abstract = "Social media platforms offer extensive information about the development of the COVID-19 pandemic and the current state of public health. In recent years, the Natural Language Processing community has developed a variety of methods to extract health-related information from posts on social media platforms. In order for these techniques to be used by a broad public, they must be aggregated and presented in a user-friendly way. We have aggregated ten methods to analyze tweets related to the COVID-19 pandemic, and present interactive visualizations of the results on our online platform, the COVID-19 Twitter Monitor. In the current version of our platform, we offer distinct methods for the inspection of the dataset, at different levels: corpus-wide, single post, and spans within each post. Besides, we allow the combination of different methods to enable a more selective acquisition of knowledge. Through the visual and interactive combination of various methods, interconnections in the different outputs can be revealed.",
}
@inproceedings{dirkson-etal-2020-conversation,
    title = "Conversation-Aware Filtering of Online Patient Forum Messages",
    author = "Dirkson, Anne  and
      Verberne, Suzan  and
      Kraaij, Wessel",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.2",
    pages = "11--18",
    abstract = "Previous approaches to NLP tasks on online patient forums have been limited to single posts as units, thereby neglecting the overarching conversational structure. In this paper we explore the benefit of exploiting conversational context for filtering posts relevant to a specific medical topic. We experiment with two approaches to add conversational context to a BERT model: a sequential CRF layer and manually engineered features. Although neither approach can outperform the F1 score of the BERT baseline, we find that adding a sequential layer improves precision for all target classes whereas adding a non-sequential layer with manually engineered features leads to a higher recall for two out of three target classes. Thus, depending on the end goal, conversation-aware modelling may be beneficial for identifying relevant messages. We hope our findings encourage other researchers in this domain to move beyond studying messages in isolation towards more discourse-based data collection and classification. We release our code for the purpose of follow-up research.",
}
@inproceedings{romberg-etal-2020-annotating,
    title = "Annotating Patient Information Needs in Online Diabetes Forums",
    author = "Romberg, Julia  and
      Dyczmons, Jan  and
      Borgmann, Sandra Olivia  and
      Sommer, Jana  and
      Vomhof, Markus  and
      Brunoni, Cecilia  and
      Bruck-Ramisch, Ismael  and
      Enders, Luis  and
      Icks, Andrea  and
      Conrad, Stefan",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.3",
    pages = "19--26",
    abstract = "Identifying patient information needs is an important issue for health care services and implementation of patient-centered care. A relevant number of people with diabetes mellitus experience a need for information during the course of the disease. Health-related online forums are a promising option for researching relevant information needs closely related to everyday life. In this paper, we present a novel data corpus comprising 4,664 contributions from an online diabetes forum in German language. Two annotation tasks were implemented. First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0.89 (Krippendorff{'}s α). Moreover, the textual content of diabetes-specific information needs was segmented and labeled using a well-founded definition of health-related information needs, which achieved a promising agreement of 0.82 (Krippendorff{'}s αu). We further report a baseline for two sub-tasks of the information extraction system planned for the long term: contribution categorization and segment classification.",
}
@inproceedings{klein-etal-2020-overview,
    title = "Overview of the Fifth Social Media Mining for Health Applications ({\#}{SMM}4{H}) Shared Tasks at {COLING} 2020",
    author = "Klein, Ari  and
      Alimova, Ilseyar  and
      Flores, Ivan  and
      Magge, Arjun  and
      Miftahutdinov, Zulfat  and
      Minard, Anne-Lyse  and
      O{'}Connor, Karen  and
      Sarker, Abeed  and
      Tutubalina, Elena  and
      Weissenbacher, Davy  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.4",
    pages = "27--36",
    abstract = "The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics. The fifth iteration of the Social Media Mining for Health Applications ({\#}SMM4H) shared tasks sought to advance the use of Twitter data (tweets) for pharmacovigilance, toxicovigilance, and epidemiology of birth defects. In addition to re-runs of three tasks, {\#}SMM4H 2020 included new tasks for detecting adverse effects of medications in French and Russian tweets, characterizing chatter related to prescription medication abuse, and detecting self reports of birth defect pregnancy outcomes. The five tasks required methods for binary classification, multi-class classification, and named entity recognition (NER). With 29 teams and a total of 130 system submissions, participation in the {\#}SMM4H shared tasks continues to grow.",
}
@inproceedings{dang-etal-2020-ensemble,
    title = "Ensemble {BERT} for Classifying Medication-mentioning Tweets",
    author = {Dang, Huong  and
      Lee, Kahyun  and
      Henry, Sam  and
      Uzuner, {\"O}zlem},
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.5",
    pages = "37--41",
    abstract = "Twitter is a valuable source of patient-generated data that has been used in various population health studies. The first step in many of these studies is to identify and capture Twitter messages (tweets) containing medication mentions. In this article, we describe our submission to Task 1 of the Social Media Mining for Health Applications (SMM4H) Shared Task 2020. This task challenged participants to detect tweets that mention medications or dietary supplements in a natural, highly imbalance dataset. Our system combined a handcrafted preprocessing step with an ensemble of 20 BERT-based classifiers generated by dividing the training dataset into subsets using 10-fold cross validation and exploiting two BERT embedding models. Our system ranked first in this task, and improved the average F1 score across all participating teams by 19.07{\%} with a precision, recall, and F1 on the test set of 83.75{\%}, 87.01{\%}, and 85.35{\%} respectively.",
}
@inproceedings{wang-etal-2020-islab,
    title = "{ISL}ab System for {SMM}4{H} Shared Task 2020",
    author = "Wang, Chen-Kai  and
      Dai, Hong-Jie  and
      Zhang, You-Chen  and
      Xu, Bo-Chun  and
      Wang, Bo-Hong  and
      Xu, You-Ning  and
      Chen, Po-Hao  and
      Lee, Chung-Hong",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.6",
    pages = "42--45",
    abstract = "In this paper, we described our systems for the first and second subtasks of Social Media Mining for Health Applications (SMM4H) shared task in 2020. The two subtasks are automatic classi-fication of medication mentions and adverse effect in tweets. Our systems for both subtasks are based on Robustly optimized BERT approach (RoBERTa) and our previous work at SMM4H{'}19. The best F1-scores achieved by our systems for subtask 1 and 2 were 0.7974 and 0.64 respec-tively, which outperformed the average F1-scores among all teams{'} best runs by at least 0.13.",
}
@inproceedings{gusev-etal-2020-bert,
    title = "{BERT} Implementation for Detecting Adverse Drug Effects Mentions in {R}ussian",
    author = "Gusev, Andrey  and
      Kuznetsova, Anna  and
      Polyanskaya, Anna  and
      Yatsishin, Egor",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.7",
    pages = "46--50",
    abstract = "This paper describes a system developed for the Social Media Mining for Health 2020 shared task. Our team participated in the second subtask for Russian language creating a system to detect adverse drug reaction presence in a text. For our submission, we exploited an ensemble model architecture, combining BERT{'}s extension for Russian language, Logistic Regression and domain-specific preprocessing pipeline. Our system was ranked first among others, achieving F-score of 0.51.",
}
@inproceedings{miftahutdinov-etal-2020-kfu,
    title = "{KFU} {NLP} Team at {SMM}4{H} 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions",
    author = "Miftahutdinov, Zulfat  and
      Sakhovskiy, Andrey  and
      Tutubalina, Elena",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.8",
    pages = "51--56",
    abstract = "This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2020 shared tasks. Specifically, we participated in two tasks. We investigate the use of a language representation model BERT pretrained on a large-scale corpus of 5 million health-related user reviews in English and Russian. The ensemble of neural networks for extraction and normalization of adverse drug reactions ranked first among 7 teams at the SMM4H 2020 Task 3 and obtained a relaxed F1 of 46{\%}. The BERT-based multilingual model for classification of English and Russian tweets that report adverse reactions ranked second among 16 and 7 teams at two first subtasks of the SMM4H 2019 Task 2 and obtained a relaxed F1 of 58{\%} on English tweets and 51{\%} on Russian tweets.",
}
@inproceedings{metzger-etal-2020-smm4h,
    title = "{SMM}4{H} Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from {T}witter: Machine Learning, Deep Learning, and Post-Processing",
    author = "Metzger, Isabel  and
      Haskovic, Emir Y.  and
      Black, Allison  and
      Yi, Whitley M.  and
      Chandra, Rajat S.  and
      Rutledge, Mark T.  and
      McMahon, William  and
      Aphinyanaphongs, Yindalon",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.9",
    pages = "57--62",
    abstract = "This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques. Data augmentation increased our training and validation corpora from 13,172 tweets to 28,094 tweets. We also created word-embeddings on domain-specific social media and medical corpora. Our hybrid pipeline of an attention-based CNN with post-processing was the best performing system in task 4 of SMM4H 2020, with an F1 score of 0.51 for class A.",
}
@inproceedings{bai-zhou-2020-automatic,
    title = "Automatic Detecting for Health-related {T}witter Data with {B}io{BERT}",
    author = "Bai, Yang  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.10",
    pages = "63--69",
    abstract = "Social media used for health applications usually contains a large amount of data posted by users, which brings various challenges to NLP, such as spoken language, spelling errors, novel/creative phrases, etc. In this paper, we describe our system submitted to SMM4H 2020: Social Media Mining for Health Applications Shared Task which consists of five sub-tasks. We participate in subtask 1, subtask 2-English, and subtask 5. Our final submitted approach is an ensemble of various fine-tuned transformer-based models. We illustrate that these approaches perform well in imbalanced datasets (For example, the class ratio is 1:10 in subtask 2), but our model performance is not good in extremely imbalanced datasets (For example, the class ratio is 1:400 in subtask 1). Finally, in subtask 1, our result is lower than the average score, in subtask 2-English, our result is higher than the average score, and in subtask 5, our result achieves the highest score. The code is available online.",
}
@inproceedings{mossburger-etal-2020-exploring,
    title = "Exploring Online Depression Forums via Text Mining: A Comparison of {R}eddit and a Curated Online Forum",
    author = "Mo{\ss}burger, Luis  and
      Wende, Felix  and
      Brinkmann, Kay  and
      Schmidt, Thomas",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.11",
    pages = "70--81",
    abstract = "We present a study employing various techniques of text mining to explore and compare two different online forums focusing on depression: (1) the subreddit r/depression (over 60 million tokens), a large, open social media platform and (2) Beyond Blue (almost 5 million tokens), a professionally curated and moderated depression forum from Australia. We are interested in how the language and the content on these platforms differ from each other. We scrape both forums for a specific period. Next to general methods of computational text analysis, we focus on sentiment analysis, topic modeling and the distribution of word categories to analyze these forums. Our results indicate that Beyond Blue is generally more positive and that the users are more supportive to each other. Topic modeling shows that Beyond Blue{'}s users talk more about adult topics like finance and work while topics shaped by school or college terms are more prevalent on r/depression. Based on our findings we hypothesize that the professional curation and moderation of a depression forum is beneficial for the discussion in it.",
}
@inproceedings{owen-etal-2020-towards,
    title = "Towards Preemptive Detection of Depression and Anxiety in {T}witter",
    author = "Owen, David  and
      Camacho-Collados, Jose  and
      Espinosa Anke, Luis",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.12",
    pages = "82--89",
    abstract = "Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. For example, these disorders manifest themselves somewhat frequently in texts written by nondiagnosed users in social media. However, detecting users with these conditions is not a straightforward task as they may not explicitly talk about their mental state, and if they do, contextual cues such as immediacy must be taken into account. When available, linguistic flags pointing to probable anxiety or depression could be used by medical experts to write better guidelines and treatments. In this paper, we develop a dataset designed to foster research in depression and anxiety detection in Twitter, framing the detection task as a binary tweet classification problem. We then apply state-of-the-art classification models to this dataset, providing a competitive set of baselines alongside qualitative error analysis. Our results show that language models perform reasonably well, and better than more traditional baselines. Nonetheless, there is clear room for improvement, particularly with unbalanced training sets and in cases where seemingly obvious linguistic cues (keywords) are used counter-intuitively.",
}
@inproceedings{vydiswaran-etal-2020-identifying,
    title = "Identifying Medication Abuse and Adverse Effects from Tweets: {U}niversity of {M}ichigan at {\#}{SMM}4{H} 2020",
    author = "Vydiswaran, V.G.Vinod  and
      Yu, Deahan  and
      Zhao, Xinyan  and
      Carr, Ermioni  and
      Martindale, Jonathan  and
      Xiao, Jingcheng  and
      Ghannam, Noha  and
      Althoen, Matteo  and
      Castellanos, Alexis  and
      Patel, Neel  and
      Vasquez, Daniel",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.13",
    pages = "90--94",
    abstract = "The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications ({\#}SMM4H) 2020 shared tasks {--} on detecting mentions of adverse effects (Task 2), extracting and normalizing them (Task 3), and detecting mentions of medication abuse (Task 4). Our approaches relied on a combination of traditional machine learning and deep learning models. On Tasks 2 and 4, our submitted runs performed at or above the task average.",
}
@inproceedings{gattepaille-2020-far,
    title = "How Far Can We Go with Just Out-of-the-box {BERT} Models?",
    author = "Gattepaille, Lucie",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.14",
    pages = "95--100",
    abstract = "Social media have been seen as a promising data source for pharmacovigilance. Howev-er, methods for automatic extraction of Adverse Drug Reactions from social media plat-forms such as Twitter still need further development before they can be included reliably in routine pharmacovigilance practices. As the Bidirectional Encoder Representations from Transformer (BERT) models have shown great performance in many major NLP tasks recently, we decided to test its performance on the SMM4H Shared Tasks 1 to 3, by submitting results of pretrained and fine-tuned BERT models without more added knowledge than the one carried in the training datasets and additional datasets. Our three submissions all ended up above average over all teams{'} submissions: 0.766 F1 for task 1 (15{\%} above the average of 0.665), 0.47 F1 for task 2 (2{\%} above the average of 0.46) and 0.380 F1 score for task 3 (30{\%} above the average of 0.292). Used in many of the high-ranking submission in the 2019 edition of the SMM4H Shared Task, BERT contin-ues to be state-of-the-art in ADR extraction for Twitter data.",
}
@inproceedings{casola-lavelli-2020-fbk,
    title = "{FBK}@{SMM}4{H}2020: {R}o{BERT}a for Detecting Medications on {T}witter",
    author = "Casola, Silvia  and
      Lavelli, Alberto",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.15",
    pages = "101--103",
    abstract = "This paper describes a classifier for tweets that mention medications or supplements, based on a pretrained transformer. We developed such a system for our participation in Subtask 1 of the Social Media Mining for Health Application workshop, which featured an extremely unbalanced dataset. The model showed promising results, with an F1 of 0.8 (task mean: 0.66).",
}
@inproceedings{saha-etal-2020-autobots,
    title = "Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines",
    author = "Saha, Sougata  and
      Das, Souvik  and
      Khurana, Prashi  and
      Srihari, Rohini",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.16",
    pages = "104--109",
    abstract = "This paper details a system designed for Social Media Mining for Health Applications (SMM4H) Shared Task 2020. We specifically describe the systems designed to solve task 2: Automatic classification of multilingual tweets that report adverse effects, and task 3: Automatic extraction and normalization of adverse effects in English tweets. Fine tuning RoBERTa large for classifying English tweets enables us to achieve a F1 score of 56{\%}, which is an increase of +10{\%} compared to the average F1 score for all the submissions. Using BERT based NER and question answering, we are able to achieve a F1 score of 57.6{\%} for extracting adverse reaction mentions from tweets, which is an increase of +1.2{\%} compared to the average F1 score for all the submissions.",
}
@inproceedings{blinov-avetisian-2020-transformer,
    title = "Transformer Models for Drug Adverse Effects Detection from Tweets",
    author = "Blinov, Pavel  and
      Avetisian, Manvel",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.17",
    pages = "110--112",
    abstract = "In this paper we present the drug adverse effects detection system developed during our participation in the Social Media Mining for Health Applications Shared Task 2020. We experimented with transfer learning approach for English and Russian, BERT and RoBERTa architectures and several strategies for regression head composition. Our final submissions in both languages overcome average F1 by several percents margin.",
}
@inproceedings{khademi-etal-2020-adverse,
    title = "Adverse Drug Reaction Detection in {T}witter Using {R}o{BERT}a and Rules",
    author = "Khademi, Sedigh  and
      Delirhaghighi, Pari  and
      Burstein, Frada",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.18",
    pages = "113--117",
    abstract = "This paper describes the method we developed for the Task 2 English variation of the Social Media Mining for Health Applications (SMM4H) 2020 shared task. The task was to classify tweets containing adverse effects (AE) after medication intake. Our approach combined transfer learning using a RoBERTa Large Transformer model with a rule-based post-prediction correction to improve model precision. The model{'}s F1-Score of 0.56 on the test dataset was 10{\%} better than the mean of the F1-Score of the best submissions in the task.",
}
@inproceedings{lichouri-abbas-2020-speechtrans,
    title = "{S}peech{T}rans@{SMM}4{H}{'}20: Impact of Preprocessing and N-grams on Automatic Classification of Tweets That Mention Medications",
    author = "Lichouri, Mohamed  and
      Abbas, Mourad",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.19",
    pages = "118--120",
    abstract = "This paper describes our system developed for automatically classifying tweets that mention medications. We used the Decision Tree classifier for this task. We have shown that using some elementary preprocessing steps and TF-IDF n-grams led to acceptable classifier performance. Indeed, the F1-score recorded was 74.58{\%} in the development phase and 63.70{\%} in the test phase.",
}
@inproceedings{kalyan-sangeetha-2020-want,
    title = "Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use {R}o{BERT}a",
    author = "Kalyan, Katikapalli Subramanyam  and
      Sangeetha, Sivanesan",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.20",
    pages = "121--124",
    abstract = "This paper presents our approach for task 2 and task 3 of Social Media Mining for Health (SMM4H) 2020 shared tasks. In task 2, we have to differentiate adverse drug reaction (ADR) tweets from nonADR tweets and is treated as binary classification. Task 3 involves extracting ADR mentions and then mapping them to MedDRA codes. Extracting ADR mentions is treated as sequence labeling and normalizing ADR mentions is treated as multi-class classification. Our system is based on pre-trained language model RoBERTa and it achieves a) F1-score of 58{\%} in task 2 which is 12{\%} more than the average score b) relaxed F1-score of 70.1{\%} in ADR extraction of task 3 which is 13.7{\%} more than the average score and relaxed F1-score of 35{\%} in ADR extraction + normalization of task 3 which is 5.8{\%} more than the average score. Overall, our models achieve promising results in both the tasks with significant improvements over average scores.",
}
@inproceedings{reddy-2020-detecting,
    title = "Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View {CNN} {RNN} Based Architecture",
    author = "Reddy, Saichethan",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.21",
    pages = "125--127",
    abstract = "This research work addresses a new multi-class classification task (fifth task) provided at the fifth Social Media Mining for Health Applications (SMM4H) workshop. This automatic tweet classification task involves distinguishing three classes of tweets that mention birth defects. We propose a novel two view based CNN-BiGRU based architectures for this task. Experimental evaluation of our proposed architecture over the validation set gives encouraging results as it improves by approximately 7{\%} over our single view model for the fifth task. Code of our proposed framework is made available on Github",
}
@inproceedings{prakash-babu-eswari-2020-identification,
    title = "Identification of Medication Tweets Using Domain-specific Pre-trained Language Models",
    author = "Prakash Babu, Yandrapati  and
      Eswari, Rajagopal",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.22",
    pages = "128--130",
    abstract = "In this paper, we present our approach for task1 of SMM4H 2020. This task involves automatic classification of tweets mentioning medication or dietary supplements. For this task, we experiment with pre-trained models like Biomedical RoBERTa, Clinical BERT and Biomedical BERT. Our approach achieves F1-score of 73.56{\%}.",
}
@inproceedings{lee-etal-2020-medication,
    title = "Medication Mention Detection in Tweets Using {ELECTRA} Transformers and Decision Trees",
    author = "Lee, Lung-Hao  and
      Chen, Po-Han  and
      Kao, Hao-Chuan  and
      Hung, Ting-Chun  and
      Lee, Po-Lei  and
      Shyu, Kuo-Kai",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.23",
    pages = "131--133",
    abstract = "This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.",
}
@inproceedings{tanguy-etal-2020-litl,
    title = "{LITL} at {SMM}4{H}: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets",
    author = "Tanguy, Ludovic  and
      Ho-Dac, Lydia-Mai  and
      Fabre, C{\'e}cile  and
      Bois, Roxane  and
      Haddad, Touati Mohamed Yacine  and
      Ibarboure, Claire  and
      Joyau, Marie  and
      Le moal, Fran{\c{c}}ois  and
      Moiilic, Jade  and
      Roudaut, Laura  and
      Simounet, Mathilde  and
      Stankovic, Irena  and
      Vandewaetere, Mickaela",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.24",
    pages = "134--137",
    abstract = "This paper describes our participation to the SMM4H shared task 2. We designed a rule-based classifier that estimates whether a tweet mentions an adverse effect associated to a medication. Our system addresses English and French, and is based on a number of specific word lists and features. These cues were mostly obtained through an extensive corpus analysis of the provided training data. Different weighting schemes were tested (manually tuned or based on a logistic regression), the best one achieving a F1 score of 0.31 for English and 0.15 for French.",
}
@inproceedings{liza-2020-sentence,
    title = "Sentence Classification with Imbalanced Data for Health Applications",
    author = "Liza, Farhana Ferdousi",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.25",
    pages = "138--145",
    abstract = "Identifying and extracting reports of medications, their abuse or adverse effects from social media is a challenging task. In social media, relevant reports are very infrequent, causes imbalanced class distribution for machine learning algorithms. Learning algorithms typically designed to optimize the overall accuracy without considering the relative distribution of each class. Thus, imbalanced class distribution is problematic as learning algorithms have low predictive accuracy for the infrequent class. Moreover, social media represents natural linguistic variation in creative language expressions. In this paper, we have used a combination of data balancing and neural language representation techniques to address the challenges. Specifically, we participated the shared tasks 1, 2 (all languages), 4, and 3 (only the span detection, no normalization was attempted) in Social Media Mining for Health applications (SMM4H) 2020 (Klein et al., 2020). The results show that with the proposed methodology recall scores are better than the precision scores for the shared tasks. The recall score is also better compared to the mean score of the total submissions. However, the F1-score is worse than the mean score except for task 2 (French).",
}
@inproceedings{zhao-etal-2020-hitsz,
    title = "{HITSZ}-{ICRC}: A Report for {SMM}4{H} Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets",
    author = "Zhao, Xiaoyu  and
      Xiong, Ying  and
      Tang, Buzhou",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.26",
    pages = "146--149",
    abstract = "This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020. The first task is automatic classification of tweets that mention medications and the second task is automatic classification of tweets in English that report adverse effects. The system we propose for these tasks is based on bidirectional encoder representations from transformers (BERT) incorporating with knowledge graph and retrieving evidence from online information. Our system achieves an F1 of 0.7553 in task 1 and an F1 of 0.5455 in task 2.",
}
@inproceedings{mehnaz-2020-automatic,
    title = "Automatic Classification of Tweets Mentioning a Medication Using Pre-trained Sentence Encoders",
    author = "Mehnaz, Laiba",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.27",
    pages = "150--152",
    abstract = "This paper describes our submission to the 5th edition of the Social Media Mining for Health Applications (SMM4H) shared task 1. Task 1 aims at the automatic classification of tweets that mention a medication or a dietary supplement. This task is specifically challenging due to its highly imbalanced dataset, with only 0.2{\%} of the tweets mentioning a drug. For our submission, we particularly focused on several pretrained encoders for text classification. We achieve an F1 score of 0.75 for the positive class on the test set.",
}
@inproceedings{dima-etal-2020-approaching,
    title = "Approaching {SMM}4{H} 2020 with Ensembles of {BERT} Flavours",
    author = "Dima, George-Andrei  and
      Avram, Andrei-Marius  and
      Cercel, Dumitru-Clementin",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.28",
    pages = "153--157",
    abstract = "This paper describes our solutions submitted to the Social Media Mining for Health Applications ({\#}SMM4H) Shared Task 2020. We participated in the following tasks: Task 1 aimed at classifying if a tweet reports medications or not, Task 2 (only for the English dataset) aimed at discriminating if a tweet mentions adverse effects or not, and Task 5 aimed at recognizing if a tweet mentions birth defects or not. Our work focused on studying different neural network architectures based on various flavors of bidirectional Transformers (i.e., BERT), in the context of the previously mentioned classification tasks. For Task 1, we achieved an F1-score (70.5{\%}) above the mean performance of the best scores made by all teams, whereas for Task 2, we obtained an F1-score of 37{\%}. Also, we achieved a micro-averaged F1-score of 62{\%} for Task 5.",
}
@inproceedings{mahendran-etal-2020-nlp,
    title = "{NLP}@{VCU}: Identifying Adverse Effects in {E}nglish Tweets for Unbalanced Data",
    author = "Mahendran, Darshini  and
      Lewis, Cora  and
      McInnes, Bridget",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.29",
    pages = "158--160",
    abstract = "This paper describes our participation in the Social Media Mining for Health Application (SMM4H 2020) Challenge Track 2 for identifying tweets containing Adverse Effects (AEs). Our system uses Convolutional Neural Networks. We explore downsampling, oversampling, and adjusting the class weights to account for the imbalanced nature of the dataset. Our results showed downsampling outperformed oversampling and adjusting the class weights on the test set however all three obtained similar results on the development set.",
}
@inproceedings{gencoglu-2020-sentence,
    title = "Sentence Transformers and {B}ayesian Optimization for Adverse Drug Effect Detection from {T}witter",
    author = "Gencoglu, Oguzhan",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.30",
    pages = "161--164",
    abstract = "This paper describes our approach for detecting adverse drug effect mentions on Twitter as part of the Social Media Mining for Health Applications (SMM4H) 2020, Shared Task 2. Our approach utilizes multilingual sentence embeddings (sentence-BERT) for representing tweets and Bayesian hyperparameter optimization of sample weighting parameter for counterbalancing high class imbalance.",
}
@inproceedings{aduragba-etal-2020-sentence,
    title = "Sentence Contextual Encoder with {BERT} and {B}i{LSTM} for Automatic Classification with Imbalanced Medication Tweets",
    author = "Aduragba, Olanrewaju Tahir  and
      Yu, Jialin  and
      Senthilnathan, Gautham  and
      Crsitea, Alexandra",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.31",
    pages = "165--167",
    abstract = "This paper details the system description and approach used by our team for the SMM4H 2020 competition, Task 1. Task 1 targets the automatic classification of tweets that mention medication. We adapted the standard BERT pretrain-then-fine-tune approach to include an intermediate training stage with a biLSTM architecture neural network acting as a further fine-tuning stage. We were inspired by the effectiveness of within-task further pre-training and sentence encoders. We show that this approach works well for a highly imbalanced dataset. In this case, the positive class is only 0.2{\%} of the entire dataset. Our model performed better in both F1 and precision scores compared to the mean score for all participants in the competition and had a competitive recall score.",
}
@inproceedings{bagherzadeh-bergler-2020-clac,
    title = "{CL}a{C} at {SMM}4{H} 2020: Birth Defect Mention Detection",
    author = "Bagherzadeh, Parsa  and
      Bergler, Sabine",
    booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.smm4h-1.32",
    pages = "168--170",
    abstract = "For the detection of personal tweets, where a parent speaks of a child{'}s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture. To address the task, we compile several gazetteer lists from resources such as MeSH and GI. The proposed system obtains .69 for μF1 score in the SMM4H 2020 Task 5 where the competition average is .65.",
}
@proceedings{sltu-2020-joint,
    title = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    editor = "Beermann, Dorothee  and
      Besacier, Laurent  and
      Sakti, Sakriani  and
      Soria, Claudia",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.0",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{scannell-2020-neural,
    title = "Neural Models for Predicting Celtic Mutations",
    author = "Scannell, Kevin",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.1",
    pages = "1--8",
    abstract = "The Celtic languages share a common linguistic phenomenon known as initial mutations; these consist of pronunciation and spelling changes that occur at the beginning of some words, triggered in certain semantic or syntactic contexts. Initial mutations occur quite frequently and all non-trivial NLP systems for the Celtic languages must learn to handle them properly. In this paper we describe and evaluate neural network models for predicting mutations in two of the six Celtic languages: Irish and Scottish Gaelic. We also discuss applications of these models to grammatical error detection and language modeling.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{gutkin-2020-eidos,
    title = "{E}idos: An Open-Source Auditory Periphery Modeling Toolkit and Evaluation of Cross-Lingual Phonemic Contrasts",
    author = "Gutkin, Alexander",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.2",
    pages = "9--20",
    abstract = "Many analytical models that mimic, in varying degree of detail, the basic auditory processes involved in human hearing have been developed over the past decades. While the auditory periphery mechanisms responsible for transducing the sound pressure wave into the auditory nerve discharge are relatively well understood, the models that describe them are usually very complex because they try to faithfully simulate the behavior of several functionally distinct biological units involved in hearing. Because of this, there is a relative scarcity of toolkits that support combining publicly-available auditory models from multiple sources. We address this shortcoming by presenting an open-source auditory toolkit that integrates multiple models of various stages of human auditory processing into a simple and easily configurable pipeline, which supports easy switching between ten available models. The auditory representations that the pipeline produces can serve as machine learning features and provide analytical benchmark for comparing against auditory filters learned from the data. Given a low- and high-resource language pair, we evaluate several auditory representations on a simple multilingual phonemic contrast task to determine whether contrasts that are meaningful within a language are also empirically robust across languages.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{kjartansson-etal-2020-open,
    title = "Open-Source High Quality Speech Datasets for {B}asque, {C}atalan and {G}alician",
    author = "Kjartansson, Oddur  and
      Gutkin, Alexander  and
      Butryna, Alena  and
      Demirsahin, Isin  and
      Rivera, Clara",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.3",
    pages = "21--27",
    abstract = "This paper introduces new open speech datasets for three of the languages of Spain: Basque, Catalan and Galician. Catalan is furthermore the official language of the Principality of Andorra. The datasets consist of high-quality multi-speaker recordings of the three languages along with the associated transcriptions. The resulting corpora include over 33 hours of crowd-sourced recordings of 132 male and female native speakers. The recording scripts also include material for elicitation of global and local place names, personal and business names. The datasets are released under a permissive license and are available for free download for commercial, academic and personal use. The high-quality annotated speech datasets described in this paper can be used to, among other things, build text-to-speech systems, serve as adaptation data in automatic speech recognition and provide useful phonetic and phonological insights in corpus linguistics.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{hoenen-etal-2020-two,
    title = "Two {LRL} {\&} Distractor Corpora from Web Information Retrieval and a Small Case Study in Language Identification without Training Corpora",
    author = "Hoenen, Armin  and
      Koc, Cemre  and
      Rahn, Marc",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.4",
    pages = "28--35",
    abstract = "In recent years, low resource languages (LRLs) have seen a surge in interest after certain tasks have been solved for larger ones and as they present various challenges (data sparsity, sparsity of experts and expertise, unusual structural properties etc.). For a larger number of them in the wake of this interest resources and technologies have been created. However, there are very small languages for which this has not yet led to a significant change. We focus here one such language (Nogai) and one larger small language (Maori). Since especially smaller languages often face the situation of having very similar siblings or a larger small sister language which is more accessible, the rate of noise in data gathered on them so far is often high. Therefore, we present small corpora for our 2 case study languages which we obtained through web information retrieval and likewise for their noise inducing distractor languages and conduct a small language identification experiment where we identify documents in a boolean way as either belonging or not to the target language. We release our test corpora for two such scenarios in the format of the An Crubadan project (Scannell, 2007) and a tool for unsupervised language identification using alphabet and toponym information.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{hamalainen-wiechetek-2020-morphological,
    title = "Morphological Disambiguation of {S}outh {S}{\'a}mi with {FST}s and Neural Networks",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Wiechetek, Linda},
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.5",
    pages = "36--40",
    abstract = "We present a method for conducting morphological disambiguation for South S{\'a}mi, which is an endangered language. Our method uses an FST-based morphological analyzer to produce an ambiguous set of morphological readings for each word in a sentence. These readings are disambiguated with a Bi-RNN model trained on the related North S{\'a}mi UD Treebank and some synthetically generated South S{\'a}mi data. The disambiguation is done on the level of morphological tags ignoring word forms and lemmas; this makes it possible to use North S{\'a}mi training data for South S{\'a}mi without the need for a bilingual dictionary or aligned word embeddings. Our approach requires only minimal resources for South S{\'a}mi, which makes it usable and applicable in the contexts of any other endangered language as well.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{singh-etal-2020-effects,
    title = "Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models",
    author = "Singh, Mittul  and
      Smit, Peter  and
      Virpioja, Sami  and
      Kurimo, Mikko",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.6",
    pages = "41--45",
    abstract = "Character-based Neural Network Language Models (NNLM) have the advantage of smaller vocabulary and thus faster training times in comparison to NNLMs based on multi-character units. However, in low-resource scenarios, both the character and multi-character NNLMs suffer from data sparsity. In such scenarios, cross-lingual transfer has improved multi-character NNLM performance by allowing information transfer from a source to the target language. In the same vein, we propose to use cross-lingual transfer for character NNLMs applied to low-resource Automatic Speech Recognition (ASR). However, applying cross-lingual transfer to character NNLMs is not as straightforward. We observe that relatedness of the source language plays an important role in cross-lingual pretraining of character NNLMs. We evaluate this aspect on ASR tasks for two target languages: Finnish (with English and Estonian as source) and Swedish (with Danish, Norwegian, and English as source). Prior work has observed no difference between using the related or unrelated language for multi-character NNLMs. We, however, show that for character-based NNLMs, only pretraining with a related language improves the ASR performance, and using an unrelated language may deteriorate it. We also observe that the benefits are larger when there is much lesser target data than source data.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{liu-etal-2020-multilingual-graphemic,
    title = "Multilingual Graphemic Hybrid {ASR} with Massive Data Augmentation",
    author = "Liu, Chunxi  and
      Zhang, Qiaochu  and
      Zhang, Xiaohui  and
      Singh, Kritika  and
      Saraf, Yatharth  and
      Zweig, Geoffrey",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.7",
    pages = "46--52",
    abstract = "Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations. In this work we present a single grapheme-based ASR model learned on 7 geographically proximal languages, using standard hybrid BLSTM-HMM acoustic models with lattice-free MMI objective. We build the single ASR grapheme set via taking the union over each language-specific grapheme set, and we find such multilingual graphemic hybrid ASR model can perform language-independent recognition on all 7 languages, and substantially outperform each monolingual ASR model. Secondly, we evaluate the efficacy of multiple data augmentation alternatives within language, as well as their complementarity with multilingual modeling. Overall, we show that the proposed multilingual graphemic hybrid ASR with various data augmentation can not only recognize any within training set languages, but also provide large ASR performance improvements.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{corral-etal-2020-neural,
    title = "Neural Text-to-Speech Synthesis for an Under-Resourced Language in a Diglossic Environment: the Case of {G}ascon {O}ccitan",
    author = {Corral, Ander  and
      Leturia, Igor  and
      S{\'e}guier, Aure  and
      Barret, Mich{\"a}el  and
      Daz{\'e}as, Benaset  and
      Boula de Mare{\"u}il, Philippe  and
      Quint, Nicolas},
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.8",
    pages = "53--60",
    abstract = "Occitan is a minority language spoken in Southern France, some Alpine Valleys of Italy, and the Val d{'}Aran in Spain, which only very recently started developing language and speech technologies. This paper describes the first project for designing a Text-to-Speech synthesis system for one of its main regional varieties, namely Gascon. We used a state-of-the-art deep neural network approach, the Tacotron2-WaveGlow system. However, we faced two additional difficulties or challenges: on the one hand, we wanted to test if it was possible to obtain good quality results with fewer recording hours than is usually reported for such systems; on the other hand, we needed to achieve a standard, non-Occitan pronunciation of French proper names, therefore we needed to record French words and test phoneme-based approaches. The evaluation carried out over the various developed systems and approaches shows promising results with near production-ready quality. It has also allowed us to detect the phenomena for which some flaws or fall of quality occur, pointing at the direction of future work to improve the quality of the actual system and for new systems for other language varieties and voices.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{woldemariam-2020-transfer,
    title = "Transfer Learning for Less-Resourced {S}emitic Languages Speech Recognition: the Case of {A}mharic",
    author = "Woldemariam, Yonas",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.9",
    pages = "61--69",
    abstract = "While building automatic speech recognition (ASR) requires a large amount of speech and text data, the problem gets worse for less-resourced languages. In this paper, we investigate a model adaptation method, namely transfer learning for a less-resourced Semitic language i.e., Amharic, to solve resource scarcity problems in speech recognition development and improve the Amharic ASR model. In our experiments, we transfer acoustic models trained on two different source languages (English and Mandarin) to Amharic using very limited resources. The experimental results show that a significant WER (Word Error Rate) reduction has been achieved by transferring the hidden layers of the trained source languages neural networks. In the best case scenario, the Amharic ASR model adapted from English yields the best WER reduction from 38.72{\%} to 24.50{\%} (an improvement of 14.22{\%} absolute). Adapting the Mandarin model improves the baseline Amharic model with a WER reduction of 10.25{\%} (absolute). Our analysis also reveals that, the speech recognition performance of the adapted acoustic model is highly influenced by the relatedness (in a relative sense) between the source and the target languages than other considered factors (e.g. the quality of source models). Furthermore, other Semitic as well as Afro-Asiatic languages could benefit from the methodology presented in this study.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{wilkinson-etal-2020-semi,
    title = "Semi-supervised Acoustic Modelling for Five-lingual Code-switched {ASR} using Automatically-segmented Soap Opera Speech",
    author = "Wilkinson, Nick  and
      Biswas, Astik  and
      Yilmaz, Emre  and
      De Wet, Febe  and
      Van der westhuizen, Ewald  and
      Niesler, Thomas",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.10",
    pages = "70--78",
    abstract = "This paper considers the impact of automatic segmentation on the fully-automatic, semi-supervised training of automatic speech recog-nition (ASR) systems for five-lingual code-switched (CS) speech. Four automatic segmentation techniques were evaluated in terms ofthe recognition performance of an ASR system trained on the resulting segments in a semi-supervised manner. For comparative purposesa semi-supervised syste Three of these use a newly proposed convolutional neural network (CNN) model for framewise classification,and include a novel form of HMM smoothing of the CNN outputs. Automatic segmentation was applied in combination with automaticspeaker diarization. The best-performing segmentation technique was also evaluated without speaker diarization. An evaluation basedon 248 unsegmented soap opera episodes indicated that voice activity detection (VAD) based on a CNN followed by Gaussian mixturemodel-hidden Markov model smoothing (CNN-GMM-HMM) yields the best ASR performance. The semi-supervised system trainedwith the best automatic segmentation achieved an overall WER improvement of 1.1{\%} absolute over a semi-supervised system trainedwith manually created segments. Furthermore, we found that recognition rates improved even further when the automatic segmentationwas used in conjunction with speaker diarization.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{zanon-boito-etal-2020-investigating,
    title = "Investigating Language Impact in Bilingual Approaches for Computational Language Documentation",
    author = "Zanon Boito, Marcely  and
      Villavicencio, Aline  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.11",
    pages = "79--87",
    abstract = "For endangered languages, data collection campaigns have to accommodate the challenge that many of them are from oral tradition, and producing transcriptions is costly. Therefore, it is fundamental to translate them into a widely spoken language to ensure interpretability of the recordings. In this paper we investigate how the choice of translation language affects the posterior documentation work and potential automatic approaches which will work on top of the produced bilingual corpus. For answering this question, we use the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56 bilingual pairs that we apply to the task of low-resource unsupervised word segmentation and alignment. Our results highlight that the choice of language for translation influences the word segmentation performance, and that different lexicons are learned by using different aligned translations. Lastly, this paper proposes a hybrid approach for bilingual word segmentation, combining boundary clues extracted from a non-parametric Bayesian model (Goldwater et al., 2009a) with the attentional word segmentation neural model from Godard et al. (2018). Our results suggest that incorporating these clues into the neural models{'} input representation increases their translation and alignment quality, specially for challenging language pairs.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{santos-harrigan-2020-design,
    title = "Design and evaluation of a smartphone keyboard for {P}lains {C}ree syllabics",
    author = "Santos, Eddie  and
      Harrigan, Atticus",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.12",
    pages = "88--96",
    abstract = "Plains Cree is a less-resourced language in Canada. To promote its usage online, we describe previous keyboard layouts for typing Plains Cree syllabics on smartphones. We describe our own solution whose development was guided by ergonomics research and corpus statistics. We then describe a case study in which three participants used a previous layout and our own, and we collected quantitative and qualitative data. We conclude that, despite observing accuracy improvements in user testing, introducing a brand new paradigm for typing Plains Cree syllabics may not be ideal for the community.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{sarioglu-kayi-etal-2020-multiseg,
    title = "{M}ulti{S}eg: Parallel Data and Subword Information for Learning Bilingual Embeddings in Low Resource Scenarios",
    author = "Sarioglu Kayi, Efsun  and
      Anand, Vishal  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.13",
    pages = "97--105",
    abstract = "Distributed word embeddings have become ubiquitous in natural language processing as they have been shown to improve performance in many semantic and syntactic tasks. Popular models for learning cross-lingual word embeddings do not consider the morphology of words. We propose an approach to learn bilingual embeddings using parallel data and subword information that is expressed in various forms, i.e. character n-grams, morphemes obtained by unsupervised morphological segmentation and byte pair encoding. We report results for three low resource morphologically rich languages (Swahili, Tagalog, and Somali) and a high resource language (German) in a simulated a low-resource scenario. Our results show that our method that leverages subword information outperforms the model without subword information, both in intrinsic and extrinsic evaluations of the learned embeddings. Specifically, analogy reasoning results show that using subwords helps capture syntactic characteristics. Semantically, word similarity results and intrinsically, word translation scores demonstrate superior performance over existing methods. Finally, qualitative analysis also shows better-quality cross-lingual embeddings particularly for morphological variants in both languages.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{zamora-fernandez-etal-2020-poio,
    title = "Poio Text Prediction: Lessons on the Development and Sustainability of {LT}s for Endangered Languages",
    author = "Zamora Fern{\'a}ndez, Gema  and
      Ferreira, Vera  and
      Manha, Pedro",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.14",
    pages = "106--110",
    abstract = "2019, the International Year of Indigenous Languages (IYIL), marked a crucial milestone for a diverse community united by a strong sense of urgency. In this presentation, we evaluate the impact of IYIL{'}s outcomes in the development of LTs for endangered languages. We give a brief description of the field of Language Documentation, whose experts have led the research and data collection efforts surrounding endangered languages for the past 30 years. We introduce the work of the Interdisciplinary Centre for Social and Language Documentation and we look at Poio as an example of an LT developed specifically with speakers of endangered languages in mind. This example illustrates how the deeper systemic causes of language endangerment are reflected in the development of LTs. Additionally, we share some of the strategic decisions that have led the development of this project. Finally, we advocate the importance of bridging the divide between research and activism, pushing for the inclusion of threatened languages in the world of LTs, and doing so in close collaboration with the speaker community.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{millour-fort-2020-text,
    title = "Text Corpora and the Challenge of Newly Written Languages",
    author = {Millour, Alice  and
      Fort, Kar{\"e}n},
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.15",
    pages = "111--120",
    abstract = "Text corpora represent the foundation on which most natural language processing systems rely. However, for many languages, collecting or building a text corpus of a sufficient size still remains a complex issue, especially for corpora that are accessible and distributed under a clear license allowing modification (such as annotation) and further resharing. In this paper, we review the sources of text corpora usually called upon to fill the gap in low-resource contexts, and how crowdsourcing has been used to build linguistic resources. Then, we present our own experiments with crowdsourcing text corpora and an analysis of the obstacles we encountered. Although the results obtained in terms of participation are still unsatisfactory, we advocate that the effort towards a greater involvement of the speakers should be pursued, especially when the language of interest is newly written.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{buckeridge-foley-2020-scaling,
    title = "Scaling Language Data Import/Export with a Data Transformer Interface",
    author = "Buckeridge, Nicholas  and
      Foley, Ben",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.16",
    pages = "121--125",
    abstract = "This paper focuses on the technical improvement of Elpis, a language technology which assists people in the process of transcription, particularly for low-resource language documentation situations. To provide better support for the diversity of file formats encountered by people working to document the world{'}s languages, a Data Transformer interface has been developed to abstract the complexities of designing individual data import scripts. This work took place as part of a larger project of code quality improvement and the publication of template code that can be used for development of other language technologies.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{thai-etal-2020-fully,
    title = "Fully Convolutional {ASR} for Less-Resourced Endangered Languages",
    author = "Thai, Bao  and
      Jimerson, Robert  and
      Ptucha, Raymond  and
      Prud{'}hommeaux, Emily",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.17",
    pages = "126--130",
    abstract = "The application of deep learning to automatic speech recognition (ASR) has yielded dramatic accuracy increases for languages with abundant training data, but languages with limited training resources have yet to see accuracy improvements on this scale. In this paper, we compare a fully convolutional approach for acoustic modelling in ASR with a variety of established acoustic modeling approaches. We evaluate our method on Seneca, a low-resource endangered language spoken in North America. Our method yields word error rates up to 40{\%} lower than those reported using both standard GMM-HMM approaches and established deep neural methods, with a substantial reduction in training time. These results show particular promise for languages like Seneca that are both endangered and lack extensive documentation.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{novitasari-etal-2020-cross,
    title = "Cross-Lingual Machine Speech Chain for {J}avanese, {S}undanese, {B}alinese, and {B}ataks Speech Recognition and Synthesis",
    author = "Novitasari, Sashi  and
      Tjandra, Andros  and
      Sakti, Sakriani  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.18",
    pages = "131--138",
    abstract = "Even though over seven hundred ethnic languages are spoken in Indonesia, the available technology remains limited that could support communication within indigenous communities as well as with people outside the villages. As a result, indigenous communities still face isolation due to cultural barriers; languages continue to disappear. To accelerate communication, speech-to-speech translation (S2ST) technology is one approach that can overcome language barriers. However, S2ST systems require machine translation (MT), speech recognition (ASR), and synthesis (TTS) that rely heavily on supervised training and a broad set of language resources that can be difficult to collect from ethnic communities. Recently, a machine speech chain mechanism was proposed to enable ASR and TTS to assist each other in semi-supervised learning. The framework was initially implemented only for monolingual languages. In this study, we focus on developing speech recognition and synthesis for these Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. We first separately train ASR and TTS of standard Indonesian in supervised training. We then develop ASR and TTS of ethnic languages by utilizing Indonesian ASR and TTS in a cross-lingual machine speech chain framework with only text or only speech data removing the need for paired speech-text data of those ethnic languages.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{pa-pa-aung-etal-2020-automatic,
    title = "Automatic {M}yanmar Image Captioning using {CNN} and {LSTM}-Based Language Model",
    author = "Pa Pa Aung, San  and
      Pa Pa, Win  and
      Nwe, Tin Lay",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.19",
    pages = "139--143",
    abstract = "An image captioning system involves modules on computer vision as well as natural language processing. Computer vision module is for detecting salient objects or extracting features of images and Natural Language Processing (NLP) module is for generating correct syntactic and semantic image captions. Although many image caption datasets such as Flickr8k, Flickr30k and MSCOCO are publicly available, most of the datasets are captioned in English language. There is no image caption corpus for Myanmar language. Myanmar image caption corpus is manually built as part of the Flickr8k dataset in this current work. Furthermore, a generative merge model based on Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) is applied especially for Myanmar image captioning. Next, two conventional feature extraction models Visual Geometry Group (VGG) OxfordNet 16-layer and 19-layer are compared. The performance of this system is evaluated on Myanmar image caption corpus using BLEU scores and 10-fold cross validation.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{parabattina-das-2020-phoneme,
    title = "Phoneme Boundary Analysis using Multiway Geometric Properties of Waveform Trajectories",
    author = "Parabattina, Bhagath  and
      Das, Pradip K.",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.20",
    pages = "144--152",
    abstract = "Automatic phoneme segmentation is an important problem in speech processing. It helps in improving the recognition quality by providing a proper segmentation information for phonemes or phonetic units. Inappropriate segmentation may lead to recognition falloff. The problem is essential not only for recognition but also for annotation purpose also. In general, segmentation algorithms rely on training large data sets where data is observed to find the patterns among them. But this process is not straight forward for languages that are under resourced because of less availability of datasets. In this paper, we propose a method that uses geometrical properties of waveform trajectory where intra signal variations are studied and used for segmentation. The method does not rely on large datasets for training. The geometric properties are extracted as linear structural changes in a raw waveform. The methods and findings of the study are presented.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{alves-etal-2020-natural,
    title = "Natural Language Processing Chains Inside a Cross-lingual Event-Centric Knowledge Pipeline for {E}uropean {U}nion Under-resourced Languages",
    author = "Alves, Diego  and
      Thakkar, Gaurish  and
      Tadi{\'c}, Marko",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.21",
    pages = "153--158",
    abstract = "This article presents the strategy for developing a platform containing Language Processing Chains for European Union languages, consisting of Tokenization to Parsing, also including Named Entity recognition and with addition of Sentiment Analysis. These chains are part of the first step of an event-centric knowledge processing pipeline whose aim is to process multilingual media information about major events that can cause an impact in Europe and the rest of the world. Due to the differences in terms of availability of language resources for each language, we have built this strategy in three steps, starting with processing chains for the well-resourced languages and finishing with the development of new modules for the under-resourced ones. In order to classify all European Union official languages in terms of resources, we have analysed the size of annotated corpora as well as the existence of pre-trained models in mainstream Language Processing tools, and we have combined this information with the proposed classification published at META-NET whitepaper series.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{sirajzade-etal-2020-component,
    title = "Component Analysis of Adjectives in {L}uxembourgish for Detecting Sentiments",
    author = "Sirajzade, Joshgun  and
      Gierschek, Daniela  and
      Schommer, Christoph",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.22",
    pages = "159--166",
    abstract = "The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television Letzebuerg). Alongside many textual features or representations,adjectives could be used in order to detect sentiment, even on a sentence or comment level. In fact, they are also by themselves one of the best ways to describe a sentiment, despite the fact that other word classes such as nouns, verbs, adverbs or conjunctions can also be utilized for this purpose. The empirical part of this study focuses on a list of adjectives that were extracted from an annotated corpus. The corpus contains the part of speech tags of individual words and sentiment annotation on the adjective, sentence and comment level. Suffixes of Luxembourgish adjectives like -esch, -eg, -lech, -al, -el, -iv, -ent, -los, -barand the prefixon- were explicitly investigated, especially by paying attention to their role in regards to building a model by applying classical machine learning techniques. We also considered the interaction of adjectives with other grammatical means, especially other part of speeches, e.g. negations, which can completely reverse the meaning, thus the sentiment of an utterance.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{bansal-2020-acoustic,
    title = "Acoustic-Phonetic Approach for {ASR} of Less Resourced Languages Using Monolingual and Cross-Lingual Information",
    author = "Bansal, Shweta",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.23",
    pages = "167--171",
    abstract = "The exploration of speech processing for endangered languages has substantially increased in the past epoch of time. In this paper, we present the acoustic-phonetic approach for automatic speech recognition (ASR) using monolingual and cross-lingual information with application to under-resourced Indian languages, Punjabi, Nepali and Hindi. The challenging task while developing the ASR was the collection of the acoustic corpus for under-resourced languages. We have described here, in brief, the strategies used for designing the corpus and also highlighted the issues pertaining while collecting data for these languages. The bootstrap GMM-UBM based approach is used, which integrates pronunciation lexicon, language model and acoustic-phonetic model. Mel Frequency Cepstral Coefficients were used for extracting the acoustic signal features for training in monolingual and cross-lingual settings. The experimental result shows the overall performance of ASR for cross-lingual and monolingual. The phone substitution plays a key role in the cross-lingual as well as monolingual recognition. The result obtained by cross-lingual recognition compared with other baseline system and it has been found that the performance of the recognition system is based on phonemic units . The recognition rate of cross-lingual generally declines as compared with the monolingual.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{sirajzade-etal-2020-annotation,
    title = "An Annotation Framework for {L}uxembourgish Sentiment Analysis",
    author = "Sirajzade, Joshgun  and
      Gierschek, Daniela  and
      Schommer, Christoph",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.24",
    pages = "172--176",
    abstract = "The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and facilitates sentence-level annotation of several annotators in parallel. In the heart of our framework is an XML database, which serves as central part linking several components. The corpus in the database consists of news articles and user comments. One of the components is LuNa, a tool for linguistic preprocessing of the data set. It tokenizes the text, splits it into sentences and assigns POS-tags to the tokens. After that, the preprocessed text is stored in XML format into the database. The Sentiment Annotation Tool, which is a browser-based tool, then enables the annotation of split sentences from the database. The Sentiment Engine, a separate module, is trained with this material in order to annotate the whole data set and analyze the sentiment of the comments over time and in relationship to the news articles. The gained knowledge can again be used to improve the sentiment classification on the one hand and on the other hand to understand the sentiment phenomenon from the linguistic point of view.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{chakravarthi-etal-2020-sentiment,
    title = "A Sentiment Analysis Dataset for Code-Mixed {M}alayalam-{E}nglish",
    author = "Chakravarthi, Bharathi Raja  and
      Jose, Navya  and
      Suryawanshi, Shardul  and
      Sherly, Elizabeth  and
      McCrae, John Philip",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.25",
    pages = "177--184",
    abstract = "There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff{'}s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{fahmi-etal-2020-speech,
    title = "Speech-Emotion Detection in an {I}ndonesian Movie",
    author = "Fahmi, Fahmi  and
      Jiwanggi, Meganingrum Arista  and
      Adriani, Mirna",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.26",
    pages = "185--193",
    abstract = "The growing demand to develop an automatic emotion recognition system for the Human-Computer Interaction field had pushed some research in speech emotion detection. Although it is growing, there is still little research about automatic speech emotion detection in Bahasa Indonesia. Another issue is the lack of standard corpus for this research area in Bahasa Indonesia. This study proposed several approaches to detect speech-emotion in the dialogs of an Indonesian movie by classifying them into 4 different emotion classes i.e. happiness, sadness, anger, and neutral. There are two different speech data representations used in this study i.e. statistical and temporal/sequence representations. This study used Artificial Neural Network (ANN), Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) variation, word embedding, and also the hybrid of three to perform the classification task. The best accuracies given by one-vs-rest scenario for each emotion class with speech-transcript pairs using hybrid of non-temporal and embedding approach are 1) happiness: 76.31{\%}; 2) sadness: 86.46{\%}; 3) anger: 82.14{\%}; and 4) neutral: 68.51{\%}. The multiclass classification resulted in 64.66{\%} of precision, 66.79{\%} of recall, and 64.83{\%} of F1-score.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{jones-2020-macsen,
    title = "{M}acsen: A Voice Assistant for Speakers of a Lesser Resourced Language",
    author = "Jones, Dewi",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.27",
    pages = "194--201",
    abstract = "This paper reports on the development of a voice assistant mobile app for speakers of a lesser resourced language {--} Welsh. An assistant with a smaller set of effective but useful skills is both desirable and urgent for the wider Welsh speaking community. Descriptions of the app{'}s skills, architecture, design decisions and user interface is provided before elaborating on the most recent research and activities in open source speech technology for Welsh. The paper reports on the progress to date on crowdsourcing Welsh speech data in Mozilla Common Voice and of its suitability for training Mozilla{'}s DeepSpeech speech recognition for a voice assistant application according to conventional and transfer learning methods. We demonstrate that with smaller datasets of speech data, transfer learning and a domain specific language model, acceptable speech recognition is achievable that facilitates, as confirmed by beta users, a practical and useful voice assistant for Welsh speakers. We hope that this work informs and serves as a model to researchers and developers in other lesser-resourced linguistic communities and helps bring into being voice assistant apps for their languages.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{chakravarthi-etal-2020-corpus,
    title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
    author = "Chakravarthi, Bharathi Raja  and
      Muralidaran, Vigneshwaran  and
      Priyadharshini, Ruba  and
      McCrae, John Philip",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.28",
    pages = "202--210",
    abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{roy-etal-2020-gender,
    title = "Gender Detection from Human Voice Using Tensor Analysis",
    author = "Roy, Prasanta  and
      Bhagath, Parabattina  and
      Das, Pradip",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.29",
    pages = "211--217",
    abstract = "Speech-based communication is one of the most preferred modes of communication for humans. The human voice contains several important information and clues that help in interpreting the voice message. The gender of the speaker can be accurately guessed by a person based on the received voice of a speaker. The knowledge of the speaker{'}s gender can be a great aid to design accurate speech recognition systems. GMM based classifier is a popular choice used for gender detection. In this paper, we propose a Tensor-based approach for detecting the gender of a speaker and discuss its implementation details for low resourceful languages. Experiments were conducted using the TIMIT and SHRUTI dataset. An average gender detection accuracy of 91{\%} is recorded. Analysis of the results with the proposed method is presented in this paper.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{ritchie-etal-2020-data,
    title = "Data-Driven Parametric Text Normalization: Rapidly Scaling Finite-State Transduction Verbalizers to New Languages",
    author = "Ritchie, Sandy  and
      Mahon, Eoin  and
      Heiligenstein, Kim  and
      Bampounis, Nikos  and
      van Esch, Daan  and
      Schallhart, Christian  and
      Mortensen, Jonas  and
      Brard, Benoit",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.30",
    pages = "218--225",
    abstract = "This paper presents a methodology for rapidly generating FST-based verbalizers for ASR and TTS systems by efficiently sourcing language-specific data. We describe a questionnaire which collects the necessary data to bootstrap the number grammar induction system and parameterize the verbalizer templates described in Ritchie et al. (2019), and a machine-readable data store which allows the data collected through the questionnaire to be supplemented by additional data from other sources. This system allows us to rapidly scale technologies such as ASR and TTS to more languages, including low-resource languages.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{hutin-etal-2020-lenition,
    title = "Lenition and Fortition of Stop Codas in {R}omanian",
    author = "Hutin, Mathilde  and
      Niculescu, Oana  and
      Vasilescu, Ioana  and
      Lamel, Lori  and
      Adda-Decker, Martine",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.31",
    pages = "226--234",
    abstract = "The present paper aims at providing a first study of lenition- and fortition-type phenomena in coda position in Romanian, a language that can be considered as less-resourced. Our data show that there are two contexts for devoicing in Romanian: before a voiceless obstruent, which means that there is regressive voicelessness assimilation in the language, and before pause, which means that there is a tendency towards final devoicing proper. The data also show that non-canonical voicing is an instance of voicing assimilation, as it is observed mainly before voiced consonants (voiced obstruents and sonorants alike). Two conclusions can be drawn from our analyses. First, from a phonetic point of view, the two devoicing phenomena exhibit the same behavior regarding place of articulation of the coda, while voicing assimilation displays the reverse tendency. In particular, alveolars, which tend to devoice the most, also voice the least. Second, the two assimilation processes have similarities that could distinguish them from final devoicing as such. Final devoicing seems to be sensitive to speech style and gender of the speaker, while assimilation processes do not. This may indicate that the two kinds of processes are phonologized at two different degrees in the language, assimilation being more accepted and generalized than final devoicing.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{prys-2020-adapting,
    title = "Adapting a {W}elsh Terminology Tool to Develop a {C}ornish Dictionary",
    author = "Prys, Delyth",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.32",
    pages = "235--239",
    abstract = "Cornish and Welsh are closely related Celtic languages and this paper provides a brief description of a recent project to publish an online bilingual English/Cornish dictionary, the Gerlyver Kernewek, based on similar work previously undertaken for Welsh. Both languages are endangered, Cornish critically so, but both can benefit from the use of language technology. Welsh has previous experience of using language technologies for language revitalization, and this is now being used to help the Cornish language create new tools and resources, including lexicographical ones, helping a dispersed team of language specialists and editors, many of them in a voluntary capacity, to work collaboratively online. Details are given of the Maes T dictionary writing and publication platform, originally developed for Welsh, and of some of the adaptations that had to be made to accommodate the specific needs of Cornish, including their use of Middle and Late varieties due to its development as a revived language.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{poncelas-etal-2020-multiple,
    title = "Multiple Segmentations of {T}hai Sentences for Neural Machine Translation",
    author = "Poncelas, Alberto  and
      Pidchamook, Wichaya  and
      Liu, Chao-Hong  and
      Hadley, James  and
      Way, Andy",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.33",
    pages = "240--244",
    abstract = "Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English{--}Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{knyazeva-etal-2020-automatic,
    title = "Automatic Extraction of Verb Paradigms in Regional Languages: the case of the Linguistic Crescent varieties",
    author = {Knyazeva, Elena  and
      Adda, Gilles  and
      Boula de Mare{\"u}il, Philippe  and
      Gu{\'e}rin, Maximilien  and
      Quint, Nicolas},
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.34",
    pages = "245--249",
    abstract = "Language documentation is crucial for endangered varieties all over the world. Verb conjugation is a key aspect of this documentation for Romance varieties such as those spoken in central France, in the area of the Linguistic Crescent, which extends overs significant portions of the old provinces of Marche and Bourbonnais. We present a first methodological experiment using automatic speech processing tools for the extraction of verbal paradigms collected and recorded during fieldworks sessions made in situ. In order to prove the feasibility of the approach, we test it with different protocols, on good quality data, and we offer possible ways of extension for this research.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{rueter-hamalainen-2020-fst,
    title = "{FST} Morphology for the Endangered {S}kolt {S}ami Language",
    author = {Rueter, Jack  and
      H{\"a}m{\"a}l{\"a}inen, Mika},
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.35",
    pages = "250--257",
    abstract = "We present advances in the development of a FST-based morphological analyzer and generator for Skolt Sami. Like other minority Uralic languages, Skolt Sami exhibits a rich morphology, on the one hand, and there is little golden standard material for it, on the other. This makes NLP approaches for its study difficult without a solid morphological analysis. The language is severely endangered and the work presented in this paper forms a part of a greater whole in its revitalization efforts. Furthermore, we intersperse our description with facilitation and description practices not well documented in the infrastructure. Currently, the analyzer covers over 30,000 Skolt Sami words in 148 inflectional paradigms and over 12 derivational forms.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{tolegen-etal-2020-voted,
    title = "Voted-Perceptron Approach for {K}azakh Morphological Disambiguation",
    author = "Tolegen, Gulmira  and
      Toleu, Alymzhan  and
      Mussabayev, Rustam",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.36",
    pages = "258--264",
    abstract = "This paper presents an approach of voted perceptron for morphological disambiguation for the case of Kazakh language. Guided by the intuition that the feature value from the correct path of analyses must be higher than the feature value of non-correct path of analyses, we propose the voted perceptron algorithm with Viterbi decoding manner for disambiguation. The approach can use arbitrary features to learn the feature vector for a sequence of analyses, which plays a vital role for disambiguation. Experimental results show that our approach outperforms other statistical and rule-based models. Moreover, we manually annotated a new morphological disambiguation corpus for Kazakh language.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{tachbelie-etal-2020-dnn,
    title = "{DNN}-Based Multilingual Automatic Speech Recognition for {W}olaytta using {O}romo Speech",
    author = "Tachbelie, Martha Yifiru  and
      Abate, Solomon Teferra  and
      Schultz, Tanja",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.37",
    pages = "265--270",
    abstract = "It is known that Automatic Speech Recognition (ASR) is very useful for human-computer interaction in all the human languages. However, due to its requirement for a big speech corpus, which is very expensive, it has not been developed for most of the languages. Multilingual ASR (MLASR) has been suggested to share existing speech corpora among related languages to develop an ASR for languages which do not have the required speech corpora. Literature shows that phonetic relatedness goes across language families. We have, therefore, conducted experiments on MLASR taking two language families: one as source (Oromo from Cushitic) and the other as target (Wolaytta from Omotic). Using Oromo Deep Neural Network (DNN) based acoustic model, Wolaytta pronunciation dictionary and language model we have achieved Word Error Rate (WER) of 48.34{\%} for Wolaytta. Moreover, our experiments show that adding only 30 minutes of speech data from the target language (Wolaytta) to the whole training data (22.8 hours) of the source language (Oromo) results in a relative WER reduction of 32.77{\%}. Our results show the possibility of developing ASR system for a language, if we have pronunciation dictionary and language model, using an existing speech corpus of another language irrespective of their language family.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{abulimiti-schultz-2020-building,
    title = "Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of {U}yghur",
    author = "Abulimiti, Ayimunishagu  and
      Schultz, Tanja",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.38",
    pages = "271--276",
    abstract = "Huge amounts of data are needed to build reliable statistical language models. Automatic speech processing tasks in low-resource languages typically suffer from lower performances due to weak or unreliable language models. Furthermore, language modeling for agglutinative languages is very challenging, as the morphological richness results in higher Out Of Vocabulary (OOV) rate. In this work, we show our effort to build word-based as well as morpheme-based language models for Uyghur, a language that combines both challenges, i.e. it is a low-resource and agglutinative language. Fortunately, there exists a closely-related rich-resource language, namely Turkish. Here, we present our work on leveraging Turkish text data to improve Uyghur language models. To maximize the overlap between Uyghur and Turkish words, the Turkish data is pre-processed on the word surface level, which results in 7.76{\%} OOV-rate reduction on the Uyghur development set. To investigate various levels of low-resource conditions, different subsets of Uyghur data are generated. Morpheme-based language models trained with bilingual data achieved up to 40.91{\%} relative perplexity reduction over the language models trained only with Uyghur data.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{tracey-strassel-2020-basic,
    title = "Basic Language Resources for 31 Languages (Plus {E}nglish): The {LORELEI} Representative and Incident Language Packs",
    author = "Tracey, Jennifer  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.39",
    pages = "277--284",
    abstract = "This paper documents and describes the thirty-one basic language resource packs created for the DARPA LORELEI program for use in development and testing of systems capable of providing language-independent situational awareness in emerging scenarios in a low resource language context. Twenty-four Representative Language Packs cover a broad range of language families and typologies, providing large volumes of monolingual and parallel text, smaller volumes of entity and semantic annotations, and a variety of grammatical resources and tools designed to support research into language universals and cross-language transfer. Seven Incident Language Packs provide test data to evaluate system capabilities on a previously unseen low resource language. We discuss the makeup of Representative and Incident Language Packs, the methods used to produce them, and the evolution of their design and implementation over the course of the multi-year LORELEI program. We conclude with a summary of the final language packs including their low-cost publication in the LDC catalog.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{abdul-rauf-etal-2020-exploration,
    title = "On the Exploration of {E}nglish to {U}rdu Machine Translation",
    author = "Abdul Rauf, Sadaf  and
      Abida, Syeda  and
      Hira, Noor-e-  and
      Zahra, Syeda  and
      Parvez, Dania  and
      Bashir, Javeria  and
      Majid, Qurat-ul-ain",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.40",
    pages = "285--293",
    abstract = "Machine Translation is the inevitable technology to reduce communication barriers in today{'}s world. It has made substantial progress in recent years and is being widely used in commercial as well as non-profit sectors. Such is only the case for European and other high resource languages. For English-Urdu language pair, the technology is in its infancy stage due to scarcity of resources. Present research is an important milestone in English-Urdu machine translation, as we present results for four major domains including Biomedical, Religious, Technological and General using Statistical and Neural Machine Translation. We performed series of experiments in attempts to optimize the performance of each system and also to study the impact of data sources on the systems. Finally, we established a comparison of the data sources and the effect of language model size on statistical machine translation performance.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{beermann-etal-2020-developing,
    title = "Developing a {T}wi ({A}sante) Dictionary from {A}kan Interlinear Glossed Texts",
    author = "Beermann, Dorothee  and
      Hellan, Lars  and
      Mihaylov, Pavel  and
      Struck, Anna",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.41",
    pages = "294--297",
    abstract = "Traditionally, a lexicographer identifies the lexical items to be added to a dictionary. Here we present a corpus-based approach to dictionary compilation and describe a procedure that derives a Twi dictionary from a TypeCraft corpus of Interlinear Glossed Texts. We first extracted a list of unique words. We excluded words belonging to different dialects of Akan (mostly Fante and Abron). We corrected misspellings and distinguished English loan words to be integrated in our dictionary from instances of code switching. Next to the dictionary itself, one other resource arising from our work is a lexicographical model for Akan which represents the lexical resource itself, and the extended morphological and word class inventories that provide information to be aggregated. We also represent external resources such as the corpus that serves as the source and word level audio files. The Twi dictionary consists at present of 1367 words; it will be available online and from an open mobile app.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{woldemariam-dahlgren-2020-adapting,
    title = "Adapting Language Specific Components of Cross-Media Analysis Frameworks to Less-Resourced Languages: the Case of {A}mharic",
    author = "Woldemariam, Yonas  and
      Dahlgren, Adam",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.42",
    pages = "298--305",
    abstract = "We present an ASR based pipeline for Amharic that orchestrates NLP components within a cross media analysis framework (CMAF). One of the major challenges that are inherently associated with CMAFs is effectively addressing multi-lingual issues. As a result, many languages remain under-resourced and fail to leverage out of available media analysis solutions. Although spoken natively by over 22 million people and there is an ever-increasing amount of Amharic multimedia content on the Web, querying them with simple text search is difficult. Searching for, especially audio/video content with simple key words, is even hard as they exist in their raw form. In this study, we introduce a spoken and textual content processing workflow into a CMAF for Amharic. We design an ASR-named entity recognition (NER) pipeline that includes three main components: ASR, a transliterator and NER. We explore various acoustic modeling techniques and develop an OpenNLP-based NER extractor along with a transliterator that interfaces between ASR and NER. The designed ASR-NER pipeline for Amharic promotes the multi-lingual support of CMAFs. Also, the state-of-the art design principles and techniques employed in this study shed light for other less-resourced languages, particularly the Semitic ones.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{wisniewski-etal-2020-phonemic,
    title = "Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?",
    author = "Wisniewski, Guillaume  and
      Guillaume, S{\'e}verine  and
      Michaud, Alexis",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.43",
    pages = "306--315",
    abstract = "Automatic Speech Recognition for low-resource languages has been an active field of research for more than a decade. It holds promise for facilitating the urgent task of documenting the world{'}s dwindling linguistic diversity. Various methodological hurdles are encountered in the course of this exciting development, however. A well-identified difficulty is that data preprocessing is not at all trivial: data collected in classical fieldwork are usually tailored to the needs of the linguist who collects them, and there is baffling diversity in formats and annotation schema, even among fieldworkers who use the same software package (such as ELAN). The tests reported here (on Yongning Na and other languages from the Pangloss Collection, an open archive of endangered languages) explore some possibilities for automating the process of data preprocessing: assessing to what extent it is possible to bypass the involvement of language experts for menial tasks of data preparation for Natural Language Processing (NLP) purposes. What is at stake is the accessibility of language archive data for a range of NLP tasks and beyond.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{sigurgeirsson-etal-2020-manual,
    title = "Manual Speech Synthesis Data Acquisition - From Script Design to Recording Speech",
    author = {Sigurgeirsson, Atli  and
      {\"O}rn{\'o}lfsson, Gunnar  and
      Gu{\dh}nason, J{\'o}n},
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.44",
    pages = "316--320",
    abstract = {Atli {\TH}{\'o}r Sigurgeirsson, atlithors@ru.is, Reykjavik University Gunnar Thor {\"O}rn{\'o}lfsson, gunnarthor@hi.is, {\'A}rni Magn{\'u}sson institute of Icelandic studies Dr. J{\'o}n Gu{\dh}nason, jg@ru.is In this paper we present the work of collecting a large amount of high quality speech synthesis data for Icelandic. 8 speakers will be recorded for 20 hours each. A script design strategy is proposed and three scripts have been generated to maximize diphone coverage, varying in length. The largest reading script contains 14,400 prompts and includes 87.3{\%} of all Icelandic diphones at least once and 81{\%} of all Icelandic diphones at least twenty times. A recording client was developed to facilitate recording sessions. The client supports easily importing scripts and maintaining multiple collections in parallel. The recorded data can be downloaded straight from the client. Recording sessions are carried out in a professional studio under supervision and started October of 2019. As of writing, 58.7 hours of high quality speech data has been collected. The scripts, the recording software and the speech data will later be released under a CC-BY 4.0 license.},
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{ullrich-etal-2020-owoksape,
    title = "Ow{\'o}ksape - An Online Language Learning Platform for {L}akota",
    author = "Ullrich, Jan  and
      Thornton, Elliot  and
      Vieira, Peter  and
      Swango, Logan  and
      Kupiec, Marek",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.45",
    pages = "321--329",
    abstract = "This paper presents Ow{\'o}ksape, an online language learning platform for the under-resourced language Lakota. The Lakota language (Lakȟ{\'o}tiyapi) is a Siouan language native to the United States with fewer than 2000 fluent speakers. Ow{\'o}ksape was developed by The Language Conservancy to support revitalization efforts, including reaching younger generations and providing a tool to complement traditional teaching methods. This project grew out of various multimedia resources in order to combine their most effective aspects into a single, self-paced learning tool. The first section of this paper discusses the motivation for and background of Ow{\'o}ksape. Section two details the linguistic features and language documentation principles that form the backbone of the platform. Section three lays out the unique integration of cultural aspects of the Lakota people into the visual design of the application. Section four explains the pedagogical principles of Ow{\'o}ksape. Application features and exercise types are then discussed in detail with visual examples, followed by an overview of the software design, as well as the effort required to develop the platform. Finally, a description of future features and considerations is presented.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{ahmadi-etal-2020-corpus,
    title = "A Corpus of the {S}orani {K}urdish Folkloric Lyrics",
    author = "Ahmadi, Sina  and
      Hassani, Hossein  and
      Abedi, Kamaladdin",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.46",
    pages = "330--335",
    abstract = "Kurdish poetry and prose narratives were historically transmitted orally and less in a written form. Being an essential medium of oral narration and literature, Kurdish lyrics have had a unique attribute in becoming a vital resource for different types of studies, including Digital Humanities, Computational Folkloristics and Computational Linguistics. As an initial study of its kind for the Kurdish language, this paper presents our efforts in transcribing and collecting Kurdish folk lyrics as a corpus that covers various Kurdish musical genres, in particular Beyt, Gorani, Bend, and Heyran. We believe that this corpus contributes to Kurdish language processing in several ways, such as compensation for the lack of a long history of written text by incorporating oral literature, presenting an unexplored realm in Kurdish language processing, and assisting the initiation of Kurdish computational folkloristics. Our corpus contains 49,582 tokens in the Sorani dialect of Kurdish. The corpus is publicly available in the Text Encoding Initiative (TEI) format for non-commercial use.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{hjortnaes-etal-2020-improving,
    title = "Improving the Language Model for Low-Resource {ASR} with Online Text Corpora",
    author = "Hjortnaes, Nils  and
      Arkhangelskiy, Timofey  and
      Partanen, Niko  and
      Rie{\ss}ler, Michael  and
      Tyers, Francis",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.47",
    pages = "336--341",
    abstract = "In this paper, we expand on previous work on automatic speech recognition in a low-resource scenario typical of data collected by field linguists. We train DeepSpeech models on 35 hours of dialectal Komi speech recordings and correct the output using language models constructed from various sources. Previous experiments showed that transfer learning using DeepSpeech can improve the accuracy of a speech recognizer for Komi, though the error rate remained very high. In this paper we present further experiments with language models created using KenLM from text materials available online. These are constructed from two corpora, one containing literary texts, one for social media content, and another combining the two. We then trained the model using each language model to explore the impact of the language model data source on the speech recognition model. Our results show significant improvements of over 25{\%} in character error rate and nearly 20{\%} in word error rate. This offers important methodological insight into how ASR results can be improved under low-resource conditions: transfer learning can be used to compensate the lack of training data in the target language, and online texts are a very useful resource when developing language models in this context.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{neubig-etal-2020-summary,
    title = "A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization",
    author = "Neubig, Graham  and
      Rijhwani, Shruti  and
      Palmer, Alexis  and
      MacKenzie, Jordan  and
      Cruz, Hilaria  and
      Li, Xinjian  and
      Lee, Matthew  and
      Chaudhary, Aditi  and
      Gessler, Luke  and
      Abney, Steven  and
      Hayati, Shirley Anugrah  and
      Anastasopoulos, Antonios  and
      Zamaraeva, Olga  and
      Prud{'}hommeaux, Emily  and
      Child, Jennette  and
      Child, Sara  and
      Knowles, Rebecca  and
      Moeller, Sarah  and
      Micher, Jeffrey  and
      Li, Yiyuan  and
      Zink, Sydney  and
      Xia, Mengzhou  and
      Sharma, Roshan S  and
      Littell, Patrick",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.48",
    pages = "342--351",
    abstract = "Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited. In August 2019, a workshop was held at Carnegie Mellon University in Pittsburgh, PA, USA to attempt to bring together language community members, documentary linguists, and technologists to discuss how to bridge this gap and create prototypes of novel and practical language revitalization technologies. The workshop focused on developing technologies to aid language documentation and revitalization in four areas: 1) spoken language (speech transcription, phone to orthography decoding, text-to-speech and text-speech forced alignment), 2) dictionary extraction and management, 3) search tools for corpora, and 4) social media (language learning bots and social media analysis). This paper reports the results of this workshop, including issues discussed, and various conceived and implemented technologies for nine languages: Arapaho, Cayuga, Inuktitut, Irish Gaelic, Kidaw{'}ida, Kwak{'}wala, Ojibwe, San Juan Quiahije Chatino, and Seneca.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{kumar-etal-2020-passage,
    title = "{``}A Passage to {I}ndia{''}: Pre-trained Word Embeddings for {I}ndian Languages",
    author = "Kumar, Saurav  and
      Kumar, Saunack  and
      Kanojia, Diptesh  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.49",
    pages = "352--357",
    abstract = "Dense word vectors or {`}word embeddings{'} which encode semantic properties of words, have now become integral to NLP tasks like Machine Translation (MT), Question Answering (QA), Word Sense Disambiguation (WSD), and Information Retrieval (IR). In this paper, we use various existing approaches to create multiple word embeddings for 14 Indian languages. We place these embeddings for all these languages, \textit{viz.}, Assamese, Bengali, Gujarati, Hindi, Kannada, Konkani, Malayalam, Marathi, Nepali, Odiya, Punjabi, Sanskrit, Tamil, and Telugu in a single repository. Relatively newer approaches that emphasize catering to context (BERT, ELMo, \textit{etc.}) have shown significant improvements, but require a large amount of resources to generate usable models. We release pre-trained embeddings generated using both contextual and non-contextual approaches. We also use MUSE and XLM to train cross-lingual embeddings for all pairs of the aforementioned languages. To show the efficacy of our embeddings, we evaluate our embedding models on XPOS, UPOS and NER tasks for all these languages. We release a total of 436 models using 8 different approaches. We hope they are useful for the resource-constrained Indian language NLP. The title of this paper refers to the famous novel {``}A Passage to India{''} by E.M. Forster, published initially in 1924.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{lee-etal-2020-counselling,
    title = "A Counselling Corpus in {C}antonese",
    author = "Lee, John  and
      Cai, Tianyuan  and
      Xie, Wenxiu  and
      Xing, Lam",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.50",
    pages = "358--361",
    abstract = "Virtual agents are increasingly used for delivering health information in general, and mental health assistance in particular. This paper presents a corpus designed for training a virtual counsellor in Cantonese, a variety of Chinese. The corpus consists of a domain-independent subcorpus that supports small talk for rapport building with users, and a domain-specific subcorpus that provides material for a particular area of counselling. The former consists of ELIZA style responses, chitchat expressions, and a dataset of general dialog, all of which are reusable across counselling domains. The latter consists of example user inputs and appropriate chatbot replies relevant to the specific domain. In a case study, we created a chatbot with a domain-specific subcorpus that addressed 25 issues in test anxiety, with 436 inputs solicited from native speakers of Cantonese and 150 chatbot replies harvested from mental health websites. Preliminary evaluations show that Word Mover{'}s Distance achieved 56{\%} accuracy in identifying the issue in user input, outperforming a number of baselines.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{gupta-boulianne-2020-speech,
    title = "Speech Transcription Challenges for Resource Constrained Indigenous Language {C}ree",
    author = "Gupta, Vishwa  and
      Boulianne, Gilles",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.51",
    pages = "362--367",
    abstract = "Cree is one of the most spoken Indigenous languages in Canada. From a speech recognition perspective, it is a low-resource language, since very little data is available for either acoustic or language modeling. This has prevented development of speech technology that could help revitalize the language. We describe our experiments with available Cree data to improve automatic transcription both in speaker- independent and dependent scenarios. While it was difficult to get low speaker-independent word error rates with only six speakers, we were able to get low word and phoneme error rates in the speaker-dependent scenario. We compare our phoneme recognition with two state-of-the-art open-source phoneme recognition toolkits, which use end-to-end training and sequence-to-sequence modeling. Our phoneme error rate (8.7{\%}) is significantly lower than that achieved by the best of these systems (15.1{\%}). With these systems and varying amounts of transcribed and text data, we show that pre-training on other languages is important for speaker-independent recognition, and even small amounts of additional text-only documents are useful. These results can guide practical language documentation work, when deciding how much transcribed and text data is needed to achieve useful phoneme accuracies.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@inproceedings{canpolat-etal-2020-turkish,
    title = "{T}urkish Emotion Voice Database ({T}ur{EV}-{DB})",
    author = "Canpolat, Salih Firat  and
      Ormano{\u{g}}lu, Zuhal  and
      Zeyrek, Deniz",
    booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources association",
    url = "https://www.aclweb.org/anthology/2020.sltu-1.52",
    pages = "368--375",
    abstract = "We introduce the Turkish Emotion-Voice Database (TurEV-DB) which involves a corpus of over 1700 tokens based on 82 words uttered by human subjects in four different emotions (\textit{angry, calm, happy, sad}). Three machine learning experiments are run on the corpus data to classify the emotions using a convolutional neural network (CNN) model and a support vector machine (SVM) model. We report the performance of the machine learning models, and for evaluation, compare machine learning results with the judgements of humans.",
    language = "English",
    ISBN = "979-10-95546-35-1",
}
@proceedings{sigtyp-2020-research,
    title = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    editor = "Vylomova, Ekaterina  and
      Ponti, Edoardo M.  and
      Grossman, Eitan  and
      McCarthy, Arya D.  and
      Berzak, Yevgeni  and
      Dubossarsky, Haim  and
      Vuli{\'c}, Ivan  and
      Reichart, Roi  and
      Korhonen, Anna  and
      Cotterell, Ryan",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigtyp-1.0",
}
@inproceedings{bjerva-etal-2020-sigtyp,
    title = "{SIGTYP} 2020 Shared Task: Prediction of Typological Features",
    author = "Bjerva, Johannes  and
      Salesky, Elizabeth  and
      Mielke, Sabrina J.  and
      Chaudhary, Aditi  and
      Celano, Giuseppe G. A.  and
      Ponti, Edoardo Maria  and
      Vylomova, Ekaterina  and
      Cotterell, Ryan  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigtyp-1.1",
    doi = "10.18653/v1/2020.sigtyp-1.1",
    pages = "1--11",
    abstract = "Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world{'}s languages. They have been shown to be useful for downstream applications, including cross-lingual transfer learning and linguistic probing. A major drawback hampering broader adoption of typological KBs is that they are sparsely populated, in the sense that most languages only have annotations for some features, and skewed, in that few features have wide coverage. As typological features often correlate with one another, it is possible to predict them and thus automatically populate typological KBs, which is also the focus of this shared task. Overall, the task attracted 8 submissions from 5 teams, out of which the most successful methods make use of such feature correlations. However, our error analysis reveals that even the strongest submitted systems struggle with predicting feature values for languages where few features are known.",
}
@inproceedings{kumar-etal-2020-kmi,
    title = "{KMI}-Panlingua-{IITKGP} @{SIGTYP}2020: Exploring rules and hybrid systems for automatic prediction of typological features",
    author = "Kumar, Ritesh  and
      Alok, Deepak  and
      Bansal, Akanksha  and
      Lahiri, Bornini  and
      Ojha, Atul Kr.",
    booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigtyp-1.2",
    doi = "10.18653/v1/2020.sigtyp-1.2",
    pages = "12--16",
    abstract = "This paper enumerates SigTyP 2020 Shared Task on the prediction of typological features as performed by the KMI-Panlingua-IITKGP team. The task entailed the prediction of missing values in a particular language, provided, the name of the language family, its genus, location (in terms of latitude and longitude coordinates and name of the country where it is spoken) and a set of feature-value pair are available. As part of fulfillment of the aforementioned task, the team submitted 3 kinds of system - 2 rule-based and one hybrid system. Of these 3, one rule-based system generated the best performance on the test set. All the systems were {`}constrained{'} in the sense that no additional dataset or information, other than those provided by the organisers, was used for developing the systems.",
}
@inproceedings{gutkin-sproat-2020-nemo,
    title = "{NEMO}: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in {SIGTYP} 2020 Shared Task",
    author = "Gutkin, Alexander  and
      Sproat, Richard",
    booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigtyp-1.3",
    doi = "10.18653/v1/2020.sigtyp-1.3",
    pages = "17--28",
    abstract = "This paper describes the NEMO submission to SIGTYP 2020 shared task (Bjerva et al., 2020) which deals with prediction of linguistic typological features for multiple languages using the data derived from World Atlas of Language Structures (WALS). We employ frequentist inference to represent correlations between typological features and use this representation to train simple multi-class estimators that predict individual features. We describe two submitted ridge regression-based configurations which ranked second and third overall in the constrained task. Our best configuration achieved the microaveraged accuracy score of 0.66 on 149 test languages.",
}
@inproceedings{vastl-etal-2020-predicting,
    title = "Predicting Typological Features in {WALS} using Language Embeddings and Conditional Probabilities: {{\'U}FAL} Submission to the {SIGTYP} 2020 Shared Task",
    author = "Vastl, Martin  and
      Zeman, Daniel  and
      Rosa, Rudolf",
    booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigtyp-1.4",
    doi = "10.18653/v1/2020.sigtyp-1.4",
    pages = "29--35",
    abstract = "We present our submission to the SIGTYP 2020 Shared Task on the prediction of typological features. We submit a constrained system, predicting typological features only based on the WALS database. We investigate two approaches. The simpler of the two is a system based on estimating correlation of feature values within languages by computing conditional probabilities and mutual information. The second approach is to train a neural predictor operating on precomputed language embeddings based on WALS features. Our submitted system combines the two approaches based on their self-estimated confidence scores. We reach the accuracy of 70.7{\%} on the test data and rank first in the shared task.",
}
@inproceedings{jager-2020-imputing,
    title = "Imputing typological values via phylogenetic inference",
    author = {J{\"a}ger, Gerhard},
    booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigtyp-1.5",
    doi = "10.18653/v1/2020.sigtyp-1.5",
    pages = "36--42",
    abstract = "This paper describes a workflow to impute missing values in a typological database, a sub- set of the World Atlas of Language Structures (WALS). Using a world-wide phylogeny de- rived from lexical data, the model assumes a phylogenetic continuous time Markov chain governing the evolution of typological val- ues. Data imputation is performed via a Max- imum Likelihood estimation on the basis of this model. As back-off model for languages whose phylogenetic position is unknown, a k- nearest neighbor classification based on geo- graphic distance is performed.",
}
@inproceedings{choudhary-2020-nuig,
    title = "{NUIG}: Multitasking Self-attention based approach to {S}ig{T}yp 2020 Shared Task",
    author = "Choudhary, Chinmay",
    booktitle = "Proceedings of the Second Workshop on Computational Research in Linguistic Typology",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigtyp-1.6",
    doi = "10.18653/v1/2020.sigtyp-1.6",
    pages = "43--50",
    abstract = "The paper describes the \textit{Multitasking Self-attention based approach} to constrained sub-task within Sigtyp 2020 Shared task. Our model is simple neural network based architecture inspired by Transformers (CITATION) model. The model uses Multitasking to compute values of all WALS features for a given input language simultaneously.Results show that our approach performs at par with the baseline approaches, even though our proposed approach requires only phylogenetic and geographical attributes namely \textit{Longitude}, \textit{Latitude}, \textit{Genus-index}, \textit{Family-index} and \textit{Country-index} and do not use any of the known WALS features of the respective input language, to compute its missing WALS features.",
}
@proceedings{signlang-2020-lrec2020,
    title = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    editor = "Efthimiou, Eleni  and
      Fotinea, Stavroula-Evita  and
      Hanke, Thomas  and
      Hochgesang, Julie A.  and
      Kristoffersen, Jette  and
      Mesch, Johanna",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.0",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{becker-etal-2020-back,
    title = "Back and Forth between Theory and Application: Shared Phonological Coding Between {ASL} {S}ignbank and {ASL}-{LEX}",
    author = "Becker, Amelia  and
      Catt, Donovan  and
      Hochgesang, Julie A.",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.1",
    pages = "1--6",
    abstract = "The development of signed language lexical databases, digital organizations that describe different phonological features of and attempt to establish relationships between signs has resulted in a renewed interest in the phonological descriptions used to uniquely identify and organize the lexicons of respective sign languages (van der Kooij, 2002; Fenlon et al., 2016; Brentari et al., 2018). Throughout the mutually shared coding process involved in organizing two lexical databases, ASL Signbank (Hochgesang, Crasborn and Lillo-Martin, 2020) and ASL-LEX (Caselli et al., 2016), issues have arisen that require revisiting how phonological features and categories are to be applied and even decided upon, and which would adequately distinguish lexical contrast for respective sign languages. The paper concludes by exploring the inverse of the theory-to-database relationship. Examples are given of theoretical implications and research questions that arise from consequences of language resource building. These are presented as evidence that not only does theory impact organization of databases but that the process of database creation can also inform our theories.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{belissen-etal-2020-improving,
    title = "Improving and Extending Continuous Sign Language Recognition: Taking Iconicity and Spatial Language into account",
    author = "Belissen, Valentin  and
      Gouiff{\`e}s, Mich{\`e}le  and
      Braffort, Annelies",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.2",
    pages = "7--12",
    abstract = "In a lot of recent research, attention has been drawn to recognizing sequences of lexical signs in continuous Sign Language corpora, often artificial. However, as SLs are structured through the use of space and iconicity, focusing on lexicon only prevents the field of Continuous Sign Language Recognition (CSLR) from extending to Sign Language Understanding and Translation. In this article, we propose a new formulation of the CSLR problem and discuss the possibility of recognizing higher-level linguistic structures in SL videos, like classifier constructions. These structures show much more variability than lexical signs, and are fundamentally different than them in the sense that form and meaning can not be disentangled. Building on the recently published French Sign Language corpus Dicta-Sign-LSF-v2, we discuss the performance and relevance of a simple recurrent neural network trained to recognize illustrative structures.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{bono-etal-2020-utterance,
    title = "Utterance-Unit Annotation for the {JSL} Dialogue Corpus: Toward a Multimodal Approach to Corpus Linguistics",
    author = "Bono, Mayumi  and
      Sakaida, Rui  and
      Okada, Tomohiro  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.3",
    pages = "13--20",
    abstract = "This paper describes a method for annotating the Japanese Sign Language (JSL) dialogue corpus. We developed a way to identify interactional boundaries and define a {`}utterance unit{'} in sign language using various multimodal features accompanying signing. The utterance unit is an original concept for segmenting and annotating sign language dialogue referring to signer{'}s native sense from the perspectives of Conversation Analysis (CA) and Interaction Studies. First of all, we postulated that we should identify a fundamental concept of interaction-specific unit for understanding interactional mechanisms, such as turn-taking (Sacks et al. 1974), in sign-language social interactions. Obviously, it does should not relying on a spoken language writing system for storing signings in corpora and making translations. We believe that there are two kinds of possible applications for utterance units: one is to develop corpus linguistics research for both signed and spoken corpora; the other is to build an informatics system that includes, but is not limited to, a machine translation system for sign languages.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{borstell-etal-2020-measuring,
    title = "Measuring Lexical Similarity across Sign Languages in {G}lobal {S}ignbank",
    author = {B{\"o}rstell, Carl  and
      Crasborn, Onno  and
      Whynot, Lori},
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.4",
    pages = "21--26",
    abstract = "Lexicostatistics is the main method used in previous work measuring linguistic distances between sign languages. As a method, it disregards any possible structural/grammatical similarity, instead focusing exclusively on lexical items, but it is time consuming as it requires some comparable phonological coding (i.e. form description) as well as concept matching (i.e. meaning description) of signs across the sign languages to be compared. In this paper, we present a novel approach for measuring lexical similarity across any two sign languages using the Global Signbank platform, a lexical database of uniformly coded signs. The method involves a feature-by-feature comparison of all matched phonological features. This method can be used in two distinct ways: 1) automatically comparing the amount of lexical overlap between two sign languages (with a more detailed feature-description than previous lexicostatistical methods); 2) finding exact form-matches across languages that are either matched or mismatched in meaning (i.e. true or false friends). We show the feasability of this method by comparing three languages (datasets) in Global Signbank, and are currently expanding both the size of these three as well as the total number of datasets.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{brumm-grigat-2020-optimised,
    title = "Optimised Preprocessing for Automatic Mouth Gesture Classification",
    author = "Brumm, Maren  and
      Grigat, Rolf-Rainer",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.5",
    pages = "27--32",
    abstract = "Mouth gestures are facial expressions in sign language, that do not refer to lip patterns of a spoken language. Research on this topic has been limited so far. The aim of this work is to automatically classify mouth gestures from video material by training a neural network. This could render time-consuming manual annotation unnecessary and help advance the field of automatic sign language translation. However, it is a challenging task due to the little data available as training material and the similarity of different mouth gesture classes. In this paper we focus on the preprocessing of the data, such as finding the area of the face important for mouth gesture recognition. Furthermore we analyse the duration of mouth gestures and determine the optimal length of video clips for classification. Our experiments show, that this can improve the classification results significantly and helps to reach a near human accuracy.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{cabral-etal-2020-pe2lgp,
    title = "{PE}2{LGP} Animator: A Tool To Animate A {P}ortuguese {S}ign {L}anguage Avatar",
    author = "Cabral, Pedro  and
      Gon{\c{c}}alves, Matilde  and
      Nicolau, Hugo  and
      Coheur, Lu{\'\i}sa  and
      Santos, Ruben",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.6",
    pages = "33--38",
    abstract = "Software for the production of sign languages is much less common than for spoken languages. Such software usually relies on 3D humanoid avatars to produce signs which, inevitably, necessitates the use of animation. One barrier to the use of popular animation tools is their complexity and steep learning curve, which can be hard to master for inexperienced users. Here, we present PE2LGP, an authoring system that features a 3D avatar that signs Portuguese Sign Language. Our Animator is designed specifically to craft sign language animations using a key frame method, and is meant to be easy to use and learn to users without animation skills. We conducted a preliminary evaluation of the Animator, where we animated seven Portuguese Sign Language sentences and asked four sign language users to evaluate their quality. This evaluation revealed that the system, in spite of its simplicity, is indeed capable of producing comprehensible messages.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{cueto-etal-2020-translating,
    title = "Translating an {A}esop{'}s Fable to {F}ilipino {S}ign {L}anguage through 3{D} Animation",
    author = "Cueto, Mark  and
      He, Winnie  and
      Untiveros, Rei  and
      Zu{\~n}iga, Josh  and
      Rivera, Joanna Pauline",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.7",
    pages = "39--44",
    abstract = "According to the National Statistics Office (2003) in the 2000 Population Census, the deaf community in the Philippines numbered to about 121,000 deaf and hard of hearing Filipinos. Deaf and hard of hearing Filipinos in these communities use the Filipino Sign Language (FSL) as the main method of manual communication. Deaf and hard of hearing children experience difficulty in developing reading and writing skills through traditional methods of teaching used primarily for hearing children. This study aims to translate an Aesop{'}s fable to Filipino Sign Language with the use of 3D animation resulting to a video output. The video created contains a 3D animated avatar performing the sign translations to FSL (mainly focusing on hand gestures which includes hand shape, palm orientation, location, and movement) on screen beside their English text equivalent and related images. The final output was then evaluated by FSL deaf signers. Evaluation results showed that the final output can potentially be used as a learning material. In order to make it more effective as a learning material, it is very important to consider the animation{'}s appearance, speed, naturalness, and accuracy. In this paper, the common action units were also listed for easier construction of animations of the signs.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{docio-fernandez-etal-2020-lse,
    title = "{LSE}{\_}{UVIGO}: A Multi-source Database for {S}panish {S}ign {L}anguage Recognition",
    author = "Doc{\'\i}o-Fern{\'a}ndez, Laura  and
      Alba-Castro, Jos{\'e} Luis  and
      Torres-Guijarro, Soledad  and
      Rodr{\'\i}guez-Banga, Eduardo  and
      Rey-Area, Manuel  and
      P{\'e}rez-P{\'e}rez, Ania  and
      Rico-Alonso, Sonia  and
      Garc{\'\i}a-Mateo, Carmen",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.8",
    pages = "45--52",
    abstract = "This paper presents LSE{\_}UVIGO, a multi-source database designed to foster research on Sign Language Recognition. It is being recorded and compiled for Spanish Sign Language (LSE acronym in Spanish) and contains also spoken Galician language, so it is very well fitted to research on these languages, but also quite useful for fundamental research in any other sign language. LSE{\_}UVIGO is composed of two datasets: LSE{\_}Lex40{\_}UVIGO, a multi-sensor and multi-signer dataset acquired from scratch, designed as an incremental dataset, both in complexity of the visual content and in the variety of signers. It contains static and co-articulated sign recordings, fingerspelled and gloss-based isolated words, and sentences. Its acquisition is done in a controlled lab environment in order to obtain good quality videos with sharp video frames and RGB and depth information, making them suitable to try different approaches to automatic recognition. The second subset, LSE{\_}TVGWeather{\_}UVIGO is being populated from the regional television weather forecasts interpreted to LSE, as a faster way to acquire high quality, continuous LSE recordings with a domain-restricted vocabulary and with a correspondence to spoken sentences.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{filhol-2020-elicitation,
    title = "Elicitation and Corpus of Spontaneous Sign Language Discourse Representation Diagrams",
    author = "Filhol, Michael",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.9",
    pages = "53--60",
    abstract = "While Sign Languages have no standard written form, many signers do capture their language in some form of spontaneous graphical form. We list a few use cases (discourse preparation, deverbalising for translation, etc.) and give examples of diagrams. After hypothesising that they contain regular patterns of significant value, we propose to build a corpus of such productions. The main contribution of this paper is the specification of the elicitation protocol, explaining the variables that are likely to affect the diagrams collected. We conclude with a report on the current state of a collection following this protocol, and a few observations on the collected contents. A first prospect is the standardisation of a scheme to represent SL discourse in a way that would make them sharable. A subsequent longer-term prospect is for this scheme to be owned by users and with time be shaped into a script for their language.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{filhol-mcdonald-2020-synthesis,
    title = "The Synthesis of Complex Shape Deployments in Sign Language",
    author = "Filhol, Michael  and
      McDonald, John C.",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.10",
    pages = "61--68",
    abstract = "Proform constructs such as classifier predicates and size and shape specifiers are essential elements of Sign Language communication, but have remained a challenge for synthesis due to their highly variable nature. In contrast to frozen signs, which may be pre-animated or recorded, their variability necessitates a new approach both to their linguistic description and to their synthesis in animation. Though the specification and animation of classifier predicates was covered in previous works, size and shape specifiers have to this date remain unaddressed. This paper presents an efficient method for linguistically describing such specifiers using a small number of rules that cover a large range of possible constructs. It continues to show that with a small number of services in a signing avatar, these descriptions can be synthesized in a natural way that captures the essential gestural actions while also including the subtleties of human motion that make the signing legible.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{fragkiadakis-etal-2020-signing,
    title = "Signing as Input for a Dictionary Query: Matching Signs Based on Joint Positions of the Dominant Hand",
    author = "Fragkiadakis, Manolis  and
      Nyst, Victoria  and
      van der Putten, Peter",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.11",
    pages = "69--74",
    abstract = "This study presents a new methodology to search sign language lexica, using a full sign as input for a query. Thus, a dictionary user can look up information about a sign by signing the sign to a webcam. The recorded sign is then compared to potential matching signs in the lexicon. As such, it provides a new way of searching sign language dictionaries to complement existing methods based on (spoken language) glosses or phonological features, like handshape or location. The method utilizes OpenPose to extract the body and finger joint positions. Dynamic Time Warping (DTW) is used to quantify the variation of the trajectory of the dominant hand and the average trajectories of the fingers. Ten people with various degrees of sign language proficiency have participated in this study. Each subject viewed a set of 20 signs from the newly compiled Ghanaian sign language lexicon and was asked to replicate the signs. The results show that DTW can predict the matching sign with 87{\%} and 74{\%} accuracy at the Top-10 and Top-5 ranking level respectively by using only the trajectory of the dominant hand. Additionally, more proficient signers obtain 90{\%} accuracy at the Top-10 ranking. The methodology has the potential to be used also as a variation measurement tool to quantify the difference in signing between different signers or sign languages in general.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{hanke-etal-2020-extending,
    title = "Extending the {P}ublic {DGS} {C}orpus in Size and Depth",
    author = "Hanke, Thomas  and
      Schulder, Marc  and
      Konrad, Reiner  and
      Jahn, Elena",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.12",
    pages = "75--82",
    abstract = "In 2018 the DGS-Korpus project published the first full release of the Public DGS Corpus. This event marked a change of focus for the project. While before most attention had been on increasing the size of the corpus, now an increase in its depth became the priority. New data formats were added, corpus annotation conventions were released and OpenPose pose information was published for all transcripts. The community and research portal websites of the corpus also received upgrades, including persistent identifiers, archival copies of previous releases and improvements to their usability on mobile devices.The research portal was enhanced even further, improving its transcript web viewer, adding a KWIC concordance view, introducing cross-references to other linguistic resources of DGS and making its entire interface available in German in addition to English. This article provides an overview of these changes, chronicling the evolution of the Public DGS Corpus from its first release in 2018, through its second release in 2019 until its third release in 2020.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{hanke-etal-2020-signhunter,
    title = "{S}ign{H}unter {--} A Sign Elicitation Tool Suitable for Deaf Events",
    author = {Hanke, Thomas  and
      Jahn, Elena  and
      W{\"a}hl, Sabrina  and
      B{\"o}se, Oliver  and
      K{\"o}nig, Lutz},
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.13",
    pages = "83--88",
    abstract = "This paper presents SignHunter, a tool for collecting isolated signs, and discusses application possibilities. SignHunter is successfully used within the DGS-Korpus project to collect name signs for places and cities. The data adds to the content of a German Sign Language (DGS) {--} German dictionary which is currently being developed, as well as a freely accessible subset of the DGS Corpus, the Public DGS Corpus. We discuss reasons to complement a natural language corpus by eliciting concepts without context and present an application example of SignHunter.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{hassan-etal-2020-isolated,
    title = "An Isolated-Signing {RGBD} Dataset of 100 {A}merican {S}ign {L}anguage Signs Produced by Fluent {ASL} Signers",
    author = "Hassan, Saad  and
      Berke, Larwan  and
      Vahdani, Elahe  and
      Jing, Longlong  and
      Tian, Yingli  and
      Huenerfauth, Matt",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.14",
    pages = "89--94",
    abstract = "We have collected a new dataset consisting of color and depth videos of fluent American Sign Language (ASL) signers performing sequences of 100 ASL signs from a Kinect v2 sensor. This directed dataset had originally been collected as part of an ongoing collaborative project, to aid in the development of a sign-recognition system for identifying occurrences of these 100 signs in video. The set of words consist of vocabulary items that would commonly be learned in a first-year ASL course offered at a university, although the specific set of signs selected for inclusion in the dataset had been motivated by project-related factors. Given increasing interest among sign-recognition and other computer-vision researchers in red-green-blue-depth (RBGD) video, we release this dataset for use by the research community. In addition to the RGB video files, we share depth and HD face data as well as additional features of face, hands, and body produced through post-processing of this data.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{isard-2020-approaches,
    title = "Approaches to the Anonymisation of Sign Language Corpora",
    author = "Isard, Amy",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.15",
    pages = "95--100",
    abstract = "In this paper we survey the state of the art for the anonymisation of sign language corpora. We begin by exploring the motivations behind anonymisation and the close connection with the issue of ethics and informed consent for corpus participants. We detail how the the names which should be anonymised can be identified. We then describe the processes which can be used to anonymise both the video and the annotations belonging to a corpus, and the variety of ways in which these can be carried out. We provide examples for all of these processes from three sign language corpora in which anonymisation of the data has been performed.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{jedlicka-etal-2020-sign,
    title = "Sign Language Motion Capture Dataset for Data-driven Synthesis",
    author = "Jedli{\v{c}}ka, Pavel  and
      Kr{\v{n}}oul, Zden{\v{e}}k  and
      Kanis, Jakub  and
      {\v{Z}}elezn{\'y}, Milo{\v{s}}",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.16",
    pages = "101--106",
    abstract = "This paper presents a new 3D motion capture dataset of Czech Sign Language (CSE). Its main purpose is to provide the data for further analysis and data-based automatic synthesis of CSE utterances. The content of the data in the given limited domain of weather forecasts was carefully selected by the CSE linguists to provide the necessary utterances needed to produce any new weather forecast. The dataset was recorded using the state-of-the-art motion capture (MoCap) technology to provide the most precise trajectories of the motion. In general, MoCap is a device capable of accurate recording of motion directly in 3D space. The data contains trajectories of body, arms, hands and face markers recorded at once to provide consistent data without the need for the time alignment.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{johnson-wolfe-2020-survey,
    title = "A survey of Shading Techniques for Facial Deformations on Sign Language Avatars",
    author = "Johnson, Ronan  and
      Wolfe, Rosalee",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.17",
    pages = "107--112",
    abstract = "Of the five phonemic parameters in sign language (handshape, location, palm orientation, movement and nonmanual expressions), the one that still poses the most challenges for effective avatar display is nonmanual signals. Facial nonmanual signals carry a rich combination of linguistic and pragmatic information, but current techniques have yet to portray these in a satisfactory manner. Due to the complexity of facial movements, additional considerations must be taken into account for rendering in real time. Of particular interest is the shading areas of facial deformations to improve legibility. In contrast to more physically-based, compute-intensive techniques that more closely mimic nature, we propose using a simple, classic, Phong illumination model with a dynamically modified layered texture. To localize and control the desired shading, we utilize an opacity channel within the texture. The new approach, when applied to our avatar {``}Paula{''}, results in much quicker render times than more sophisticated, computationally intensive techniques.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{kaczmarek-filhol-2020-use,
    title = "Use Cases for a Sign Language Concordancer",
    author = "Kaczmarek, Marion  and
      Filhol, Michael",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.18",
    pages = "113--116",
    abstract = "This article treats about a Sign Language concordancer. In the past years, the need for content translated into Sign Language has been growing, and is still growing nowadays. Yet, unlike their text-to-text counterparts, Sign Language translators are not equipped with computer-assisted translation software. As we aim to provide them with such software, we explore the possibilities offered by a first tool: a Sign Language concordancer. It includes designing an alignments database as well as a search function to browse it. Testing sessions with professionals highlight relevant use cases for their professional practices. It can either comfort the translator when the results are identical, or show the importance of context when the results are different for a same expression. This concordancer is available online, and aim to be a collaborative tool. Though our current database is small, we hope for translators to invest themselves and help us to keep it expanding.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{kamal-hassani-2020-towards,
    title = "Towards {K}urdish Text to Sign Translation",
    author = "Kamal, Zina  and
      Hassani, Hossein",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.19",
    pages = "117--122",
    abstract = "The resources and technologies for Sign language processing of resourceful languages are emerging, while the low-resource languages are falling behind. Kurdish is a multi-dialect language, and it is considered a low-resource language. It is spoken by approximately 30 million people in several countries, which denotes that it has a large community with hearing-impairments as well. This paper reports on a project which aims to develop the necessary data and tools to process the Sign language for Sorani as one of the spoken Kurdish dialects. We present the results of developing a dataset in HamNoSys and its corresponding SiGML form for the Kurdish Sign lexicon. We use this dataset to implement a sign-supported Kurdish tool to check the accuracy of the Sign lexicon. We tested the tool by presenting it to hearing-impaired individuals. The experiment showed that 100{\%} of the translated letters were understandable by a hearing-impaired person. The percentages were 65{\%} for isolated words, and approximately 30{\%} for the words in sentences. The data is publicly available at \url{https://github.com/KurdishBLARK/KurdishSignLanguage} for non-commercial use under the CC BY-NC-SA 4.0 licence",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{koulierakis-etal-2020-recognition,
    title = "Recognition of Static Features in Sign Language Using Key-Points",
    author = "Koulierakis, Ioannis  and
      Siolas, Georgios  and
      Efthimiou, Eleni  and
      Fotinea, Evita  and
      Stafylopatis, Andreas-Georgios",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.20",
    pages = "123--126",
    abstract = "In this paper we report on a research effort focusing on recognition of static features of sign formation in single sign videos. Three sequential models have been developed for handshape, palm orientation and location of sign formation respectively, which make use of key-points extracted via OpenPose software. The models have been applied to a Danish and a Greek Sign Language dataset, providing results around 96{\%}. Moreover, during the reported research, a method has been developed for identifying the time-frame of real signing in the video, which allows to ignore transition frames during sign recognition processing.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{langer-schulder-2020-collocations,
    title = "Collocations in Sign Language Lexicography: Towards Semantic Abstractions for Word Sense Discrimination",
    author = "Langer, Gabriele  and
      Schulder, Marc",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.21",
    pages = "127--134",
    abstract = "In general monolingual lexicography a corpus-based approach to word sense discrimination (WSD) is the current standard. Automatically generated lexical profiles such as Word Sketches provide an overview on typical uses in the form of collocate lists grouped by their part of speech categories and their syntactic dependency relations to the base item. Collocates are sorted by their typicality according to frequency-based rankings. With the advancement of sign language (SL) corpora, SL lexicography can finally be based on actual language use as reflected in corpus data. In order to use such data effectively and gain new insights on sign usage, automatically generated collocation profiles need to be developed under the special conditions and circumstances of the SL data available. One of these conditions is that many of the prerequesites for the automatic syntactic parsing of corpora are not yet available for SL. In this article we describe a collocation summary generated from DGS Corpus data which is used for WSD as well as in entry-writing. The summary works based on the glosses used for lemmatisation. In addition, we explore how other resources can be utilised to add an additional layer of semantic grouping to the collocation analysis. For this experimental approach we use glosses, concepts, and wordnet supersenses.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{liang-etal-2020-machine,
    title = "Machine Learning for Enhancing Dementia Screening in Ageing Deaf Signers of {B}ritish {S}ign {L}anguage",
    author = "Liang, Xing  and
      Woll, Bencie  and
      Epaminondas, Kapetanios  and
      Angelopoulou, Anastasia  and
      Al-Batat, Reda",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.22",
    pages = "135--138",
    abstract = "Ageing trend in populations is correlated with increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. With this in mind, researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In this paper, we introduce an automatic dementia screening system for ageing Deaf signers of British Sign Language (BSL), using Convolutional Neural Networks (CNN), by analysing the sign space envelope and facial expression of BSL signers using normal 2D videos from BSL corpus. Our approach firstly establishes an accurate real-time hand trajectory tracking model together with a real-time landmark facial motion analysis model to identify differences in sign space envelope and facial movement as the keys to identifying language changes associated with dementia. Based on the differences in patterns obtained from facial and trajectory motion data, CNN models (ResNet50/VGG16) are fine-tuned using Keras deep learning models to incrementally identify and improve dementia recognition rates. We report the results for two methods using different modalities (sign trajectory and facial motion), together with the performance comparisons between different deep learning CNN models in ResNet50 and VGG16. The experiments show the effectiveness of our deep learning based approach in terms of sign space tracking, facial motion tracking and early stage dementia performance assessment tasks. The results are validated against cognitive assessment scores as of our ground truth data with a test set performance of 87.88{\%}. The proposed system has potential for economical, simple, flexible, and adaptable assessment of other acquired neurological impairments associated with motor changes, such as stroke and Parkinson{'}s disease in both hearing and Deaf people.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{miyazaki-etal-2020-machine,
    title = "Machine Translation from Spoken Language to Sign Language using Pre-trained Language Model as Encoder",
    author = "Miyazaki, Taro  and
      Morita, Yusuke  and
      Sano, Masanori",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.23",
    pages = "139--144",
    abstract = "Sign language is the first language for those who were born deaf or lost their hearing in early childhood, so such individuals require services provided with sign language. To achieve flexible open-domain services with sign language, machine translations into sign language are needed. Machine translations generally require large-scale training corpora, but there are only small corpora for sign language. To overcome this data-shortage scenario, we developed a method that involves using a pre-trained language model of spoken language as the initial model of the encoder of the machine translation model. We evaluated our method by comparing it to baseline methods, including phrase-based machine translation, using only 130,000 phrase pairs of training data. Our method outperformed the baseline method, and we found that one of the reasons of translation error is from pointing, which is a special feature used in sign language. We also conducted trials to improve the translation quality for pointing. The results are somewhat disappointing, so we believe that there is still room for improving translation quality, especially for pointing.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{mocialov-etal-2020-towards,
    title = "Towards Large-Scale Data Mining for Data-Driven Analysis of Sign Languages",
    author = "Mocialov, Boris  and
      Turner, Graham  and
      Hastie, Helen",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.24",
    pages = "145--150",
    abstract = "Access to sign language data is far from adequate. We show that it is possible to collect the data from social networking services such as TikTok, Instagram, and YouTube by applying data filtering to enforce quality standards and by discovering patterns in the filtered data, making it easier to analyse and model. Using our data collection pipeline, we collect and examine the interpretation of songs in both the American Sign Language (ASL) and the Brazilian Sign Language (Libras). We explore their differences and similarities by looking at the co-dependence of the orientation and location phonological parameters.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{moncrief-2020-extending,
    title = "Extending a Model for Animating Adverbs of Manner in {A}merican {S}ign {L}anguage",
    author = "Moncrief, Robyn",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.25",
    pages = "151--156",
    abstract = "The goal of this work is to show that a model produced to characterize adverbs of manner can be applied to a variety of neutral animated signs to be used towards avatar sign language synthesis. This case study presents the extension of a new approach that was first presented at SLTAT 2019 in Hamburg for modeling language processes that manifest themselves as modifications to the manual channel. This work discusses additions to the model to be effective for one-handed and two-handed signs, repeating and non-repeating signs, and signs with contact.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{muller-etal-2020-dictionary,
    title = "From Dictionary to Corpus and Back Again {--} Linking Heterogeneous Language Resources for {DGS}",
    author = {M{\"u}ller, Anke  and
      Hanke, Thomas  and
      Konrad, Reiner  and
      Langer, Gabriele  and
      W{\"a}hl, Sabrina},
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.26",
    pages = "157--164",
    abstract = "The Public DGS Corpus is published in two different formats, that is subtitled videos for lay persons and lemmatized and annotated transcripts and videos for experts. In addition, a draft version with the first set of preliminary entries of the DGS dictionary (DW-DGS) to be completed in 2023 is now online. The Public DGS Corpus and the DW-DGS are conceived of as stand-alone products, but are nevertheless closely interconnected to offer additional and complementary informative functions. In this paper we focus on linking the published products in order to provide users access to corpus and corpus-based dictionary in various, interrelated ways. We discuss which links are thought to be useful and what challenges the linking of the products poses. In addition we address the inclusion of links to other, older lexical resources (LSP dictionaries).",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{mukushev-etal-2020-automatic,
    title = "Automatic Classification of Handshapes in {R}ussian {S}ign {L}anguage",
    author = "Mukushev, Medet  and
      Imashev, Alfarabi  and
      Kimmelman, Vadim  and
      Sandygulova, Anara",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.27",
    pages = "165--170",
    abstract = "Handshapes are one of the basic parameters of signs, and any phonological or phonetic analysis of a sign language must account for handshapes. Many sign languages have been carefully analysed by sign language linguists to create handshape inventories. This has theoretical implications, but also applied use, as it is important due to the need of generating corpora for sign languages that can be searched, filtered, sorted by different sign components (such as handshapes, orientation, location, movement, etc.). However, it is a very time-consuming process, thus only a handful of sign languages have such inventories. This work proposes a process of automatically generating such inventories for sign languages by applying automatic hand detection, cropping, and clustering techniques. We applied our proposed method to a commonly used resource: the Spreadthesign online dictionary (www.spreadthesign.com), in particular to Russian Sign Language (RSL). We then manually verified the data to be able to perform classification. Thus, the proposed pipeline can serve as an alternative approach to manual annotation, and can help linguists in answering numerous research questions in relation to handshape frequencies in sign languages.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{nadal-collet-2020-design,
    title = "Design and Evaluation for a Prototype of an Online Tool to Access Mathematics Notions in Sign Language",
    author = "Nadal, Camille  and
      Collet, Christophe",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.28",
    pages = "171--176",
    abstract = "The Sign{'}Maths project aims at giving access to pedagogical resources in Sign Language (SL). It will provide Deaf students and teachers with mathematics vocabulary in SL, this in order to contribute to the standardisation of the vocabulary used at school. The work conducted led to Sign{'}Maths, an online interactive tool that gives Deaf students access to mathematics definitions in SL. A group of mathematics teachers for Deafs and teachers experts in SL collaborated to create signs to express mathematics concepts, and to produce videos of definitions, examples and illustrations for these concepts. In parallel, we are working on the conception and the design of Sign{'}Maths software and user interface. Our research work investigated ways to include SL in pedagogical resources in order to present information but also to navigate through the content. User tests revealed that users appreciate the use of SL in a pedagogical resource. However, they pointed out that SL content should be complemented with French to support bilingual education. Our final solution takes advantage of the complementarity of SL, French and visual content to provide an interface that will suit users no matter what their education background is. Future work will investigate a tool for text and signs{'} search within Sign{'}Maths.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{oqvist-etal-2020-sts,
    title = "{STS}-korpus: A Sign Language Web Corpus Tool for Teaching and Public Use",
    author = {{\"O}qvist, Zrajm  and
      Riemer Kankkonen, Nikolaus  and
      Mesch, Johanna},
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.29",
    pages = "177--180",
    abstract = "In this paper we describe STS-korpus, a web corpus tool for Swedish Sign Language (STS) which we have built during the past year, and which is now publicly available on the internet. STS-korpus uses the data of Swedish Sign Language Corpus (SSLC) and is primarily intended for teachers and students of sign language. As such it is created to be simple and user-friendly with no download or setup required. The user interface allows for searching {--} with search results displayed as a simple concordance {--} and viewing of videos with annotations. Each annotation also provides additional data and links to the corresponding entry in the online Swedish Sign Language Dictionary. We describe the corpus, its appearance and search syntax, as well as more advanced features like access control and dynamic content. Finally we say a word or two about the role we hope it will play in the classroom, and something about the development process and the software used. STS-korpus is available here: https://teckensprakskorpus.su.se",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{ozdemir-etal-2020-bosphorussign22k,
    title = "{B}osphorus{S}ign22k Sign Language Recognition Dataset",
    author = {{\"O}zdemir, O{\u{g}}ulcan  and
      K{\i}nd{\i}ro{\u{g}}lu, Ahmet Alp  and
      Cihan Camg{\"o}z, Necati  and
      Akarun, Lale},
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.30",
    pages = "181--188",
    abstract = "Sign Language Recognition is a challenging research domain. It has recently seen several advancements with the increased availability of data. In this paper, we introduce the BosphorusSign22k, a publicly available large scale sign language dataset aimed at computer vision, video recognition and deep learning research communities. The primary objective of this dataset is to serve as a new benchmark in Turkish Sign Language Recognition for its vast lexicon, the high number of repetitions by native signers, high recording quality, and the unique syntactic properties of the signs it encompasses. We also provide state-of-the-art human pose estimates to encourage other tasks such as Sign Language Production. We survey other publicly available datasets and expand on how BosphorusSign22k can contribute to future research that is being made possible through the widespread availability of similar Sign Language resources. We have conducted extensive experiments and present baseline results to underpin future research on our dataset.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{polat-saraclar-2020-unsupervised,
    title = "Unsupervised Term Discovery for Continuous Sign Language",
    author = "Polat, Korhan  and
      Sara{\c{c}}lar, Murat",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.31",
    pages = "189--196",
    abstract = "Most of the sign language recognition (SLR) systems rely on supervision for training and available annotated sign language resources are scarce due to the difficulties of manual labeling. Unsupervised discovery of lexical units would facilitate the annotation process and thus lead to better SLR systems. Inspired by the unsupervised spoken term discovery in speech processing field, we investigate whether a similar approach can be applied in sign language to discover repeating lexical units. We adapt an algorithm that is designed for spoken term discovery by using hand shape and pose features instead of speech features. The experiments are run on a large scale continuous sign corpus and the performance is evaluated using gloss level annotations. This work introduces a new task for sign language processing that has not been addressed before.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{salonen-etal-2020-corpus,
    title = "The Corpus of {F}innish {S}ign {L}anguage",
    author = "Salonen, Juhana  and
      Kronqvist, Antti  and
      Jantunen, Tommi",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.32",
    pages = "197--202",
    abstract = "This paper presents the Corpus of Finnish Sign Language (Corpus FinSL), a structured and annotated collection of Finnish Sign Language (FinSL) videos published in May 2019 in FIN-CLARIN{'}s Language Bank of Finland. The corpus is divided into two subcorpora, one of which comprises elicited narratives and the other conversations. All of the FinSL material has been annotated using ELAN and the lexical database Finnish Signbank. Basic annotation includes ID-glosses and translations into Finnish. The anonymized metadata of Corpus FinSL has been organized in accordance with the IMDI standard. Altogether, Corpus FinSL contains nearly 15 hours of video material from 21 FinSL users. Corpus FinSL has already been exploited in FinSL research and teaching, and it is predicted that in the future it will have a significant positive impact on these fields as well as on the status of the sign language community in Finland. Keywords: Corpus of Finnish Sign Language, Language Bank of Finland, Finnish Signbank, annotation, metadata, research, teaching",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{sevilla-etal-2020-tools,
    title = "Tools for the Use of {S}ign{W}riting as a Language Resource",
    author = "Sevilla, Antonio F. G.  and
      D{\'\i}az Esteban, Alberto  and
      Lahoz-Bengoechea, Jos{\'e} Mar{\'\i}a",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.33",
    pages = "203--208",
    abstract = "Representation of linguistic data is an issue of utmost importance when developing language resources, but the lack of a standard written form in sign languages presents a challenge. Different notation systems exist, but only SignWriting seems to have some use in the native signer community. It is, however, a difficult system to use computationally, not based on a linear sequence of characters. We present the project {``}VisSE{''}, which aims to develop tools for the effective use of SignWriting in the computer. The first of these is an application which uses computer vision to interpret SignWriting, understanding the meaning of new or existing transcriptions, or even hand-written images. Two additional tools will be able to consume the result of this recognizer: first, a textual description of the features of the transcription will make it understandable for non-signers. Second, a three-dimensional avatar will be able to reproduce the configurations and movements contained within the transcription, making it understandable for signers even if not familiar with SignWriting. Additionally, the project will result in a corpus of annotated SignWriting data which will also be of use to the computational linguistics community.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{skobov-lepage-2020-video,
    title = "Video-to-{H}am{N}o{S}ys Automated Annotation System",
    author = "Skobov, Victor  and
      Lepage, Yves",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.34",
    pages = "209--216",
    abstract = "The Hamburg Notation System (HamNoSys) was developed for movement annotation of any sign language (SL) and can be used to produce signing animations for a virtual avatar with the JASigning platform. This provides the potential to use HamNoSys, i.e., strings of characters, as a representation of an SL corpus instead of video material. Processing strings of characters instead of images can significantly contribute to sign language research. However, the complexity of HamNoSys makes it difficult to annotate without a lot of time and effort. Therefore annotation has to be automatized. This work proposes a conceptually new approach to this problem. It includes a new tree representation of the HamNoSys grammar that serves as a basis for the generation of grammatical training data and classification of complex movements using machine learning. Our automatic annotation system relies on HamNoSys grammar structure and can potentially be used on already existing SL corpora. It is retrainable for specific settings such as camera angles, speed, and gestures. Our approach is conceptually different from other SL recognition solutions and offers a developed methodology for future research.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{tamer-saraclar-2020-cross,
    title = "Cross-Lingual Keyword Search for Sign Language",
    author = "Tamer, Nazif Can  and
      Sara{\c{c}}lar, Murat",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.35",
    pages = "217--223",
    abstract = "Sign language research most often relies on exhaustively annotated and segmented data, which is scarce even for the most studied sign languages. However, parallel corpora consisting of sign language interpreting are rarely explored. By utilizing such data for the task of keyword search, this work aims to enable information retrieval from sign language with the queries from the translated written language. With the written language translations as labels, we train a weakly supervised keyword search model for sign language and further improve the retrieval performance with two context modeling strategies. In our experiments, we compare the gloss retrieval and cross language retrieval performance on RWTH-PHOENIX-Weather 2014T dataset.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@inproceedings{trevino-etal-2020-one,
    title = "One Side of the Coin: Development of an {ASL}-{E}nglish Parallel Corpus by Leveraging {SRT} Files",
    author = "Trevi{\~n}o, Rafael  and
      Hochgesang, Julie A.  and
      Shaw, Emily P.  and
      Willow, Nic",
    booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.signlang-1.36",
    pages = "224--230",
    abstract = "We report on a method used to develop a sizable parallel corpus of English and American Sign Language (ASL). The effort is part of the Gallaudet University Documentation of ASL (GUDA) project, which is currently coordinated by an interdisciplinary team from the Department of Linguistics and the Department of Interpretation and Translation at Gallaudet University. Creation of the parallel corpus makes use of the available SRT (SubRip Subtitle) files of ASL videos that have been interpreted into or from English, or captioned into English. The corpus allows for one-way searches based on the English translation or interpretation, which is useful for translators, interpreters, and those conducting comparative analyses. We conclude with a discussion of important considerations for this method of constructing a parallel corpus, as well as next steps that will help to refine the development and utility of this type of corpus.",
    language = "English",
    ISBN = "979-10-95546-54-2",
}
@proceedings{sigmorphon-2020-sigmorphon,
    title = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    editor = "Nicolai, Garrett  and
      Gorman, Kyle  and
      Cotterell, Ryan",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.0",
}
@inproceedings{vylomova-etal-2020-sigmorphon,
    title = "{SIGMORPHON} 2020 Shared Task 0: Typologically Diverse Morphological Inflection",
    author = "Vylomova, Ekaterina  and
      White, Jennifer  and
      Salesky, Elizabeth  and
      Mielke, Sabrina J.  and
      Wu, Shijie  and
      Ponti, Edoardo Maria  and
      Hall Maudslay, Rowan  and
      Zmigrod, Ran  and
      Valvoda, Josef  and
      Toldova, Svetlana  and
      Tyers, Francis  and
      Klyachko, Elena  and
      Yegorov, Ilya  and
      Krizhanovsky, Natalia  and
      Czarnowska, Paula  and
      Nikkarinen, Irene  and
      Krizhanovsky, Andrew  and
      Pimentel, Tiago  and
      Torroba Hennigen, Lucas  and
      Kirov, Christo  and
      Nicolai, Garrett  and
      Williams, Adina  and
      Anastasopoulos, Antonios  and
      Cruz, Hilaria  and
      Chodroff, Eleanor  and
      Cotterell, Ryan  and
      Silfverberg, Miikka  and
      Hulden, Mans",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.1",
    doi = "10.18653/v1/2020.sigmorphon-1.1",
    pages = "1--39",
    abstract = "A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems{'} ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90{\%} mean accuracy while others were more challenging.",
}
@inproceedings{gorman-etal-2020-sigmorphon,
    title = "The {SIGMORPHON} 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion",
    author = "Gorman, Kyle  and
      Ashby, Lucas F.E.  and
      Goyzueta, Aaron  and
      McCarthy, Arya  and
      Wu, Shijie  and
      You, Daniel",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.2",
    doi = "10.18653/v1/2020.sigmorphon-1.2",
    pages = "40--50",
    abstract = "We describe the design and findings of the SIGMORPHON 2020 shared task on multilingual grapheme-to-phoneme conversion. Participants were asked to submit systems which take in a sequence of graphemes in a given language as input, then output a sequence of phonemes representing the pronunciation of that grapheme sequence. Nine teams submitted a total of 23 systems, at best achieving a 18{\%} relative reduction in word error rate (macro-averaged over languages), versus strong neural sequence-to-sequence baselines. To facilitate error analysis, we publicly release the complete outputs for all systems{---}a first for the SIGMORPHON workshop.",
}
@inproceedings{kann-etal-2020-sigmorphon,
    title = "The {SIGMORPHON} 2020 Shared Task on Unsupervised Morphological Paradigm Completion",
    author = "Kann, Katharina  and
      McCarthy, Arya D.  and
      Nicolai, Garrett  and
      Hulden, Mans",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.3",
    doi = "10.18653/v1/2020.sigmorphon-1.3",
    pages = "51--62",
    abstract = "In this paper, we describe the findings of the SIGMORPHON 2020 shared task on unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel task in the field of inflectional morphology. Participants were asked to submit systems which take raw text and a list of lemmas as input, and output all inflected forms, i.e., the entire morphological paradigm, of each lemma. In order to simulate a realistic use case, we first released data for 5 development languages. However, systems were officially evaluated on 9 surprise languages, which were only revealed a few days before the submission deadline. We provided a modular baseline system, which is a pipeline of 4 components. 3 teams submitted a total of 7 systems, but, surprisingly, none of the submitted systems was able to improve over the baseline on average over all 9 test languages. Only on 3 languages did a submitted system obtain the best results. This shows that unsupervised morphological paradigm completion is still largely unsolved. We present an analysis here, so that this shared task will ground further research on the topic.",
}
@inproceedings{peters-martins-2020-one,
    title = "One-Size-Fits-All Multilingual Models",
    author = "Peters, Ben  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.4",
    doi = "10.18653/v1/2020.sigmorphon-1.4",
    pages = "63--69",
    abstract = "This paper presents DeepSPIN{'}s submissions to Tasks 0 and 1 of the SIGMORPHON 2020 Shared Task. For both tasks, we present multilingual models, training jointly on data in all languages. We perform no language-specific hyperparameter tuning {--} each of our submissions uses the same model for all languages. Our basic architecture is the sparse sequence-to-sequence model with entmax attention and loss, which allows our models to learn sparse, local alignments while still being trainable with gradient-based techniques. For Task 1, we achieve strong performance with both RNN- and transformer-based sparse models. For Task 0, we extend our RNN-based model to a multi-encoder set-up in which separate modules encode the lemma and inflection sequences. Despite our models{'} lack of language-specific tuning, they tie for first in Task 0 and place third in Task 1.",
}
@inproceedings{yu-etal-2020-ensemble,
    title = "Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection",
    author = "Yu, Xiang  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.5",
    doi = "10.18653/v1/2020.sigmorphon-1.5",
    pages = "70--78",
    abstract = "We present an iterative data augmentation framework, which trains and searches for an optimal ensemble and simultaneously annotates new training data in a self-training style. We apply this framework on two SIGMORPHON 2020 shared tasks: grapheme-to-phoneme conversion and morphological inflection. With very simple base models in the ensemble, we rank the first and the fourth in these two tasks. We show in the analysis that our system works especially well on low-resource languages.",
}
@inproceedings{murikinati-anastasopoulos-2020-cmu,
    title = "The {CMU}-{LTI} submission to the {SIGMORPHON} 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer",
    author = "Murikinati, Nikitha  and
      Anastasopoulos, Antonios",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.6",
    doi = "10.18653/v1/2020.sigmorphon-1.6",
    pages = "79--84",
    abstract = "This paper describes the CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0 on typologically diverse morphological inflection. The (unrestricted) submission uses the cross-lingual approach of our last year{'}s winning submission (Anastasopoulos and Neubig, 2019), but adapted to use specific transfer languages for each test language. Our system, with fixed non-tuned hyperparameters, achieved a macro-averaged accuracy of 80.65 ranking 20th among 31 systems, but it was still tied for best system in 25 of the 90 total languages.",
}
@inproceedings{elsaadany-suter-2020-grapheme,
    title = "Grapheme-to-Phoneme Conversion with a Multilingual Transformer Model",
    author = "ElSaadany, Omnia  and
      Suter, Benjamin",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.7",
    doi = "10.18653/v1/2020.sigmorphon-1.7",
    pages = "85--89",
    abstract = "In this paper, we describe our three submissions to the SIGMORPHON 2020 shared task 1 on grapheme-to-phoneme conversion for 15 languages. We experimented with a single multilingual transformer model. We observed that the multilingual model achieves results on par with our separately trained monolingual models and is even able to avoid a few of the errors made by the monolingual models.",
}
@inproceedings{singer-kann-2020-nyu,
    title = "The {NYU}-{CUB}oulder Systems for {SIGMORPHON} 2020 Task 0 and Task 2",
    author = "Singer, Assaf  and
      Kann, Katharina",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.8",
    doi = "10.18653/v1/2020.sigmorphon-1.8",
    pages = "90--98",
    abstract = "We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion. The former consists of generating morphological inflections from a lemma and a set of morphosyntactic features describing the target form. The latter requires generating entire paradigms for a set of given lemmas from raw text alone. We model morphological inflection as a sequence-to-sequence problem, where the input is the sequence of the lemma{'}s characters with morphological tags, and the output is the sequence of the inflected form{'}s characters. First, we apply a transformer model to the task. Second, as inflected forms share most characters with the lemma, we further propose a pointer-generator transformer model to allow easy copying of input characters.",
}
@inproceedings{mager-kann-2020-ims,
    title = "The {IMS}{--}{CUB}oulder System for the {SIGMORPHON} 2020 Shared Task on Unsupervised Morphological Paradigm Completion",
    author = "Mager, Manuel  and
      Kann, Katharina",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.9",
    doi = "10.18653/v1/2020.sigmorphon-1.9",
    pages = "99--105",
    abstract = "In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS--CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020). The task consists of generating the morphological paradigms of a set of lemmas, given only the lemmas themselves and unlabeled text. Our proposed system is a modified version of the baseline introduced together with the task. In particular, we experiment with substituting the inflection generation component with an LSTM sequence-to-sequence model and an LSTM pointer-generator network. Our pointer-generator system obtains the best score of all seven submitted systems on average over all languages, and outperforms the official baseline, which was best overall, on Bulgarian and Kannada.",
}
@inproceedings{forster-meister-2020-sigmorphon,
    title = {{SIGMORPHON} 2020 Task 0 System Description: {ETH} {Z}{\"u}rich Team},
    author = "Forster, Martina  and
      Meister, Clara",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.10",
    doi = "10.18653/v1/2020.sigmorphon-1.10",
    pages = "106--110",
    abstract = "This paper presents our system for the SIGMORPHON 2020 Shared Task. We build off of the baseline systems, performing exact inference on models trained on language family data. Our systems return the globally best solution under these models. Our two systems achieve 80.9{\%} and 75.6{\%} accuracy on the test set. We ultimately find that, in this setting, exact inference does not seem to help or hinder the performance of morphological inflection generators, which stands in contrast to its affect on Neural Machine Translation (NMT) models.",
}
@inproceedings{agirrezabal-wedekind-2020-ku,
    title = "{KU}-{CST} at the {SIGMORPHON} 2020 Task 2 on Unsupervised Morphological Paradigm Completion",
    author = {Agirrezabal, Manex  and
      Wedekind, J{\"u}rgen},
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.11",
    doi = "10.18653/v1/2020.sigmorphon-1.11",
    pages = "111--116",
    abstract = "We present a model for the unsupervised dis- covery of morphological paradigms. The goal of this model is to induce morphological paradigms from the bible (raw text) and a list of lemmas. We have created a model that splits each lemma in a stem and a suffix, and then we try to create a plausible suffix list by con- sidering lemma pairs. Our model was not able to outperform the official baseline, and there is still room for improvement, but we believe that the ideas presented here are worth considering.",
}
@inproceedings{hauer-etal-2020-low,
    title = "Low-Resource {G}2{P} and {P}2{G} Conversion with Synthetic Training Data",
    author = "Hauer, Bradley  and
      Habibi, Amir Ahmad  and
      Luan, Yixing  and
      Mallik, Arnob  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.12",
    doi = "10.18653/v1/2020.sigmorphon-1.12",
    pages = "117--122",
    abstract = "This paper presents the University of Alberta systems and results in the SIGMORPHON 2020 Task 1: Multilingual Grapheme-to-Phoneme Conversion. Following previous SIGMORPHON shared tasks, we define a low-resource setting with 100 training instances. We experiment with three transduction approaches in both standard and low-resource settings, as well as on the related task of phoneme-to-grapheme conversion. We propose a method for synthesizing training data using a combination of diverse models.",
}
@inproceedings{prabhu-kann-2020-frustratingly,
    title = "Frustratingly Easy Multilingual Grapheme-to-Phoneme Conversion",
    author = "Prabhu, Nikhil  and
      Kann, Katharina",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.13",
    doi = "10.18653/v1/2020.sigmorphon-1.13",
    pages = "123--127",
    abstract = "In this paper, we describe two CU-Boulder submissions to the SIGMORPHON 2020 Task 1 on multilingual grapheme-to-phoneme conversion (G2P). Inspired by the high performance of a standard transformer model (Vaswani et al., 2017) on the task, we improve over this approach by adding two modifications: (i) Instead of training exclusively on G2P, we additionally create examples for the opposite direction, phoneme-to-grapheme conversion (P2G). We then perform multi-task training on both tasks. (ii) We produce ensembles of our models via majority voting. Our approaches, though being conceptually simple, result in systems that place 6th and 8th amongst 23 submitted systems, and obtain the best results out of all systems on Lithuanian and Modern Greek, respectively.",
}
@inproceedings{jayarao-etal-2020-exploring,
    title = "Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection",
    author = "Jayarao, Pratik  and
      Pillay, Siddhanth  and
      Thombre, Pranav  and
      Chaudhary, Aditi",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.14",
    doi = "10.18653/v1/2020.sigmorphon-1.14",
    pages = "128--136",
    abstract = "Morphological inflection in low resource languages is critical to augment existing corpora in Low Resource Languages, which can help develop several applications in these languages with very good social impact. We describe our attention-based encoder-decoder approach that we implement using LSTMs and Transformers as the base units. We also describe the ancillary techniques that we experimented with, such as hallucination, language vector injection, sparsemax loss and adversarial language network alongside our approach to select the related language(s) for training. We present the results we generated on the constrained as well as unconstrained SIGMORPHON 2020 dataset (CITATION). One of the primary goals of our paper was to study the contribution varied components described above towards the performance of our system and perform an analysis on the same.",
}
@inproceedings{canby-etal-2020-university,
    title = "{U}niversity of {I}llinois Submission to the {SIGMORPHON} 2020 Shared Task 0: Typologically Diverse Morphological Inflection",
    author = "Canby, Marc  and
      Karipbayeva, Aidana  and
      Lunt, Bryan  and
      Mozaffari, Sahand  and
      Yoder, Charlotte  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.15",
    doi = "10.18653/v1/2020.sigmorphon-1.15",
    pages = "137--145",
    abstract = "The objective of this shared task is to produce an inflected form of a word, given its lemma and a set of tags describing the attributes of the desired form. In this paper, we describe a transformer-based model that uses a bidirectional decoder to perform this task, and evaluate its performance on the 90 languages and 18 language families used in this task.",
}
@inproceedings{vesik-etal-2020-one,
    title = "One Model to Pronounce Them All: Multilingual Grapheme-to-Phoneme Conversion With a Transformer Ensemble",
    author = "Vesik, Kaili  and
      Abdul-Mageed, Muhammad  and
      Silfverberg, Miikka",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.16",
    doi = "10.18653/v1/2020.sigmorphon-1.16",
    pages = "146--152",
    abstract = "The task of grapheme-to-phoneme (G2P) conversion is important for both speech recognition and synthesis. Similar to other speech and language processing tasks, in a scenario where only small-sized training data are available, learning G2P models is challenging. We describe a simple approach of exploiting model ensembles, based on multilingual Transformers and self-training, to develop a highly effective G2P solution for 15 languages. Our models are developed as part of our participation in the SIGMORPHON 2020 Shared Task 1 focused at G2P. Our best models achieve 14.99 word error rate (WER) and 3.30 phoneme error rate (PER), a sizeable improvement over the shared task competitive baselines.",
}
@inproceedings{liu-hulden-2020-leveraging,
    title = "Leveraging Principal Parts for Morphological Inflection",
    author = "Liu, Ling  and
      Hulden, Mans",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.17",
    doi = "10.18653/v1/2020.sigmorphon-1.17",
    pages = "153--161",
    abstract = "This paper presents the submission by the CU Ling team from the University of Colorado to SIGMORPHON 2020 shared task 0 on morphological inflection. The task is to generate the target inflected word form given a lemma form and a target morphosyntactic description. Our system uses the Transformer architecture. Our overall approach is to treat the morphological inflection task as a paradigm cell filling problem and to design the system to leverage principal parts information for better morphological inflection when the training data is limited. We train one model for each language separately without external data. The overall average performance of our submission ranks the first in both average accuracy and Levenshtein distance from the gold inflection among all submissions including those using external resources.",
}
@inproceedings{beemer-etal-2020-linguist,
    title = "Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars",
    author = "Beemer, Sarah  and
      Boston, Zak  and
      Bukoski, April  and
      Chen, Daniel  and
      Dickens, Princess  and
      Gerlach, Andrew  and
      Hopkins, Torin  and
      Anand Jawale, Parth  and
      Koski, Chris  and
      Malhotra, Akanksha  and
      Mishra, Piyush  and
      Muradoglu, Saliha  and
      Sang, Lan  and
      Short, Tyler  and
      Shreevastava, Sagarika  and
      Spaulding, Elizabeth  and
      Umada, Testumichi  and
      Xiang, Beilei  and
      Yang, Changbing  and
      Hulden, Mans",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.18",
    doi = "10.18653/v1/2020.sigmorphon-1.18",
    pages = "162--170",
    abstract = "Sequence-to-sequence models have proven to be highly successful in learning morphological inflection from examples as the series of SIGMORPHON/CoNLL shared tasks have shown. It is usually assumed, however, that a linguist working with inflectional examples could in principle develop a gold standard-level morphological analyzer and generator that would surpass a trained neural network model in accuracy of predictions, but that it may require significant amounts of human labor. In this paper, we discuss an experiment where a group of people with some linguistic training develop 25+ grammars as part of the shared task and weigh the cost/benefit ratio of developing grammars by hand. We also present tools that can help linguists triage difficult complex morphophonological phenomena within a language and hypothesize inflectional class membership. We conclude that a significant development effort by trained linguists to analyze and model morphophonological patterns are required in order to surpass the accuracy of neural models.",
}
@inproceedings{makarov-clematide-2020-cluzh,
    title = "{CLUZH} at {SIGMORPHON} 2020 Shared Task on Multilingual Grapheme-to-Phoneme Conversion",
    author = "Makarov, Peter  and
      Clematide, Simon",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.19",
    doi = "10.18653/v1/2020.sigmorphon-1.19",
    pages = "171--176",
    abstract = "This paper describes the submission by the team from the Institute of Computational Linguistics, Zurich University, to the Multilingual Grapheme-to-Phoneme Conversion (G2P) Task of the SIGMORPHON 2020 challenge. The submission adapts our system from the 2018 edition of the SIGMORPHON shared task. Our system is a neural transducer that operates over explicit edit actions and is trained with imitation learning. It is well-suited for morphological string transduction partly because it exploits the fact that the input and output character alphabets overlap. The challenge posed by G2P has been to adapt the model and the training procedure to work with disjoint alphabets. We adapt the model to use substitution edits and train it with a weighted finite-state transducer acting as the expert policy. An ensemble of such models produces competitive results on G2P. Our submission ranks second out of 23 submissions by a total of nine teams.",
}
@inproceedings{scherbakov-2020-unimelb,
    title = "The {U}ni{M}elb Submission to the {SIGMORPHON} 2020 Shared Task 0: Typologically Diverse Morphological Inflection",
    author = "Scherbakov, Andreas",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.20",
    doi = "10.18653/v1/2020.sigmorphon-1.20",
    pages = "177--183",
    abstract = "The paper describes the University of Melbourne{'}s submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection. Our team submitted three systems in total, two neural and one non-neural. Our analysis of systems{'} performance shows positive effects of newly introduced data hallucination technique that we employed in one of neural systems, especially in low-resource scenarios. A non-neural system based on observed inflection patterns shows optimistic results even in its simple implementation ({\textgreater}75{\%} accuracy for 50{\%} of languages). With possible improvement within the same modeling principle, accuracy might grow to values above 90{\%}.",
}
@inproceedings{ryan-hulden-2020-data,
    title = "Data Augmentation for Transformer-based {G}2{P}",
    author = "Ryan, Zach  and
      Hulden, Mans",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.21",
    doi = "10.18653/v1/2020.sigmorphon-1.21",
    pages = "184--188",
    abstract = "The Transformer model has been shown to outperform other neural seq2seq models in several character-level tasks. It is unclear, however, if the Transformer would benefit as much as other seq2seq models from data augmentation strategies in the low-resource setting. In this paper we explore strategies for data augmentation in the g2p task together with the Transformer model. Our results show that a relatively simple alignment-based strategy of identifying consistent input-output subsequences in grapheme-phoneme data coupled together with a subsequent splicing together of such pieces to generate hallucinated data works well in the low-resource setting, often delivering substantial performance improvement over a standard Transformer model.",
}
@inproceedings{murikinati-etal-2020-transliteration,
    title = "Transliteration for Cross-Lingual Morphological Inflection",
    author = "Murikinati, Nikitha  and
      Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.22",
    doi = "10.18653/v1/2020.sigmorphon-1.22",
    pages = "189--197",
    abstract = "Cross-lingual transfer between typologically related languages has been proven successful for the task of morphological inflection. However, if the languages do not share the same script, current methods yield more modest improvements. We explore the use of transliteration between related languages, as well as grapheme-to-phoneme conversion, as data preprocessing methods in order to alleviate this issue. We experimented with several diverse language pairs, finding that in most cases transliterating the transfer language data into the target one leads to accuracy improvements, even up to 9 percentage points. Converting both languages into a shared space like the International Phonetic Alphabet or the Latin alphabet is also beneficial, leading to improvements of up to 16 percentage points.",
}
@inproceedings{gupta-etal-2020-evaluating,
    title = "Evaluating Neural Morphological Taggers for {S}anskrit",
    author = "Gupta, Ashim  and
      Krishna, Amrith  and
      Goyal, Pawan  and
      Hellwig, Oliver",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.23",
    doi = "10.18653/v1/2020.sigmorphon-1.23",
    pages = "198--203",
    abstract = "Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training. We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism.",
}
@inproceedings{klein-tsarfaty-2020-getting,
    title = "Getting the {\#}{\#}life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?",
    author = "Klein, Stav  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.24",
    doi = "10.18653/v1/2020.sigmorphon-1.24",
    pages = "204--209",
    abstract = "This work investigates the most basic units that underlie contextualized word embeddings, such as BERT {---} the so-called word pieces. In Morphologically-Rich Languages (MRLs) which exhibit morphological fusion and non-concatenative morphology, the different units of meaning within a word may be fused, intertwined, and cannot be separated linearly. Therefore, when using word-pieces in MRLs, we must consider that: (1) a linear segmentation into sub-word units might not capture the full morphological complexity of words; and (2) representations that leave morphological knowledge on sub-word units inaccessible might negatively affect performance. Here we empirically examine the capacity of word-pieces to capture morphology by investigating the task of multi-tagging in Modern Hebrew, as a proxy to evaluate the underlying segmentation. Our results show that, while models trained to predict multi-tags for complete words outperform models tuned to predict the distinct tags of WPs, we can improve the WPs tag prediction by purposefully constraining the word-pieces to reflect their internal functions. We suggest that linguistically-informed word-pieces schemes, that make the morphological structure explicit, might boost performance for MRLs.",
}
@inproceedings{adams-etal-2020-induced,
    title = "Induced Inflection-Set Keyword Search in Speech",
    author = "Adams, Oliver  and
      Wiesner, Matthew  and
      Trmal, Jan  and
      Nicolai, Garrett  and
      Yarowsky, David",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.25",
    doi = "10.18653/v1/2020.sigmorphon-1.25",
    pages = "210--216",
    abstract = "We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance changes with the number of hypothesized inflections, while ablation experiments highlight the relative importance of different components in the lexeme-set search pipeline and the value of using curated inflectional paradigms. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches.",
}
@inproceedings{li-etal-2020-representation,
    title = "Representation Learning for Discovering Phonemic Tone Contours",
    author = "Li, Bai  and
      Xie, Jing Yi  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.26",
    doi = "10.18653/v1/2020.sigmorphon-1.26",
    pages = "217--223",
    abstract = "Tone is a prosodic feature used to distinguish words in many languages, some of which are endangered and scarcely documented. In this work, we use unsupervised representation learning to identify probable clusters of syllables that share the same phonemic tone. Our method extracts the pitch for each syllable, then trains a convolutional autoencoder to learn a low-dimensional representation for each contour. We then apply the mean shift algorithm to cluster tones in high-density regions of the latent space. Furthermore, by feeding the centers of each cluster into the decoder, we produce a prototypical contour that represents each cluster. We apply this method to spoken multi-syllable words in Mandarin Chinese and Cantonese and evaluate how closely our clusters match the ground truth tone categories. Finally, we discuss some difficulties with our approach, including contextual tone variation and allophony effects.",
}
@inproceedings{nelson-2020-joint,
    title = "Joint learning of constraint weights and gradient inputs in Gradient Symbolic Computation with constrained optimization",
    author = "Nelson, Max",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.27",
    doi = "10.18653/v1/2020.sigmorphon-1.27",
    pages = "224--232",
    abstract = "This paper proposes a method for the joint optimization of constraint weights and symbol activations within the Gradient Symbolic Computation (GSC) framework. The set of grammars representable in GSC is proven to be a subset of those representable with lexically-scaled faithfulness constraints. This fact is then used to recast the problem of learning constraint weights and symbol activations in GSC as a quadratically-constrained version of learning lexically-scaled faithfulness grammars. This results in an optimization problem that can be solved using Sequential Quadratic Programming.",
}
@inproceedings{cathcart-wandl-2020-search,
    title = "In search of isoglosses: continuous and discrete language embeddings in {S}lavic historical phonology",
    author = "Cathcart, Chundra  and
      Wandl, Florian",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.28",
    doi = "10.18653/v1/2020.sigmorphon-1.28",
    pages = "233--244",
    abstract = "This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in amultilingual setting. We employ models using three different types of language embedding (dense, sigmoid, and straight-through). We find that the Straight-Through model out-performs the other two in terms of accuracy, but the Sigmoid model{'}s language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages. We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research.",
}
@inproceedings{burness-mcmullin-2020-multi,
    title = "Multi-Tiered Strictly Local Functions",
    author = "Burness, Phillip  and
      McMullin, Kevin",
    booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigmorphon-1.29",
    doi = "10.18653/v1/2020.sigmorphon-1.29",
    pages = "245--255",
    abstract = "Tier-based Strictly Local functions, as they have so far been defined, are equipped with just a single tier. In light of this fact, they are currently incapable of modelling simultaneous phonological processes that would require different tiers. In this paper we consider whether and how we can allow a single function to operate over more than one tier. We conclude that multiple tiers can and should be permitted, but that the relationships between them must be restricted in some way to avoid overgeneration. The particular restriction that we propose comes in two parts. First, each input element is associated with a set of tiers that on their own can fully determine what the element is mapped to. Second, the set of tiers associated to a given input element must form a strict superset-subset hierarchy. In this way, we can track multiple, related sources of information when deciding how to process a particular input element. We demonstrate that doing so enables simple and intuitive analyses to otherwise challenging phonological phenomena.",
}
@proceedings{sigdial-2020-special,
    title = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    editor = "Pietquin, Olivier  and
      Muresan, Smaranda  and
      Chen, Vivian  and
      Kennington, Casey  and
      Vandyke, David  and
      Dethlefs, Nina  and
      Inoue, Koji  and
      Ekstedt, Erik  and
      Ultes, Stefan",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.0",
}
@inproceedings{hsueh-ma-2020-semantic,
    title = "Semantic Guidance of Dialogue Generation with Reinforcement Learning",
    author = "Hsueh, Cheng-Hsun  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.1",
    pages = "1--9",
    abstract = "Neural encoder-decoder models have shown promising performance for human-computer dialogue systems over the past few years. However, due to the maximum-likelihood objective for the decoder, the generated responses are often universal and safe to the point that they lack meaningful information and are no longer relevant to the post. To address this, in this paper, we propose semantic guidance using reinforcement learning to ensure that the generated responses indeed include the given or predicted semantics and that these semantics do not appear repeatedly in the response. Synsets, which comprise sets of manually defined synonyms, are used as the form of assigned semantics. For a given/assigned/predicted synset, only one of its synonyms should appear in the generated response; this constitutes a simple but effective semantic-control mechanism. We conduct both quantitative and qualitative evaluations, which show that the generated responses are not only higher-quality but also reflect the assigned semantic controls.",
}
@inproceedings{shen-etal-2020-counseling,
    title = "Counseling-Style Reflection Generation Using Generative Pretrained Transformers with Augmented Context",
    author = "Shen, Siqi  and
      Welch, Charles  and
      Mihalcea, Rada  and
      P{\'e}rez-Rosas, Ver{\'o}nica",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.2",
    pages = "10--20",
    abstract = "We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills. The system generates counselors{'}reflections {--} i.e., responses that reflect back on what the client has said given the dialogue history. Our method builds upon the new generative pretrained transformer architecture and enhances it with context augmentation techniques inspired by traditional strategies used during counselor training. Through a set of comparative experiments, we show that the system that incorporates these strategies performs better in the reflection generation task than a system that is just fine-tuned with counseling conversations. To confirm our findings, we present a human evaluation study that shows that our system generates naturally-looking reflections that are also stylistically and grammatically correct.",
}
@inproceedings{reed-etal-2020-learning,
    title = "Learning from Mistakes: Combining Ontologies via Self-Training for Dialogue Generation",
    author = "Reed, Lena  and
      Harrison, Vrindavan  and
      Oraby, Shereen  and
      Hakkani-Tur, Dilek  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.3",
    pages = "21--34",
    abstract = "Natural language generators (NLGs) for task-oriented dialogue typically take a meaning representation (MR) as input, and are trained end-to-end with a corpus of MR/utterance pairs, where the MRs cover a specific set of dialogue acts and domain attributes. Creation of such datasets is labor intensive and time consuming. Therefore, dialogue systems for new domain ontologies would benefit from using data for pre-existing ontologies. Here we explore, for the first time, whether it is possible to train an NLG for a new larger ontology using existing training sets for the restaurant domain, where each set is based on a different ontology. We create a new, larger combined ontology, and then train an NLG to produce utterances covering it. For example, if one dataset has attributes for family friendly and rating information, and the other has attributes for decor and service, our aim is an NLG for the combined ontology that can produce utterances that realize values for family friendly, rating, decor and service. Initial experiments with a baseline neural sequence-to-sequence model show that this task is surprisingly challenging. We then develop a novel self-training method that identifies (errorful) model outputs, automatically constructs a corrected MR input to form a new (MR, utterance) training pair, and then repeatedly adds these new instances back into the training data. We then test the resulting model on a new test set. The result is a self-trained model whose performance is an absolute 75.4{\%} improvement over the baseline model. We also report a human qualitative evaluation of the final model showing that it achieves high naturalness, semantic coherence and grammaticality.",
}
@inproceedings{heck-etal-2020-trippy,
    title = "{T}rip{P}y: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking",
    author = "Heck, Michael  and
      van Niekerk, Carel  and
      Lubis, Nurul  and
      Geishauser, Christian  and
      Lin, Hsien-Chin  and
      Moresi, Marco  and
      Gasic, Milica",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.4",
    pages = "35--44",
    abstract = "Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user{'}s goal during the course of an interaction. Multi-domain and open-vocabulary settings complicate the task considerably and demand scalable solutions. In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values. Our model has no need to maintain a list of candidate values. Instead, all values are extracted from the dialog context on-the-fly. A slot is filled by one of three copy mechanisms: (1) Span prediction may extract values directly from the user input; (2) a value may be copied from a system inform memory that keeps track of the system{'}s inform operations (3) a value may be copied over from a different slot that is already contained in the dialog state to resolve coreferences within and across domains. Our approach combines the advantages of span-based slot filling methods with memory methods to avoid the use of value picklists altogether. We argue that our strategy simplifies the DST task while at the same time achieving state of the art performance on various popular evaluation sets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55{\%}.",
}
@inproceedings{sieinska-etal-2020-conversational,
    title = "Conversational Agents for Intelligent Buildings",
    author = "Siei{\'n}ska, Weronika  and
      Dondrup, Christian  and
      Gunson, Nancie  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.5",
    pages = "45--48",
    abstract = "We will demonstrate a deployed conversational AI system that acts as a host of a smart-building on a university campus. The system combines open-domain social conversation with task-based conversation regarding navigation in the building, live resource updates (e.g. available computers) and events in the building. We are able to demonstrate the system on several platforms: Google Home devices, Android phones, and a Furhat robot.",
}
@inproceedings{michael-2020-retico,
    title = "Retico: An incremental framework for spoken dialogue systems",
    author = "Michael, Thilo",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.6",
    pages = "49--52",
    abstract = "In this paper we present the newest version of retico - a python-based incremental dialogue framework to create state-of-the-art spoken dialogue systems and simulations. Retico provides a range of incremental modules that are based on services like Google ASR, Google TTS and Rasa NLU. Incremental networks can be created either in code or with a graphical user interface. In this demo we present three use cases that are implemented in retico: a spoken translation tool that translates speech in real-time, a conversation simulation that models turn-taking and a spoken dialogue restaurant information service.",
}
@inproceedings{kohn-etal-2020-mc,
    title = "{MC}-Saar-Instruct: a Platform for {M}inecraft Instruction Giving Agents",
    author = {K{\"o}hn, Arne  and
      Wichlacz, Julia  and
      Sch{\"a}fer, Christine  and
      Torralba, {\'A}lvaro  and
      Hoffmann, Joerg  and
      Koller, Alexander},
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.7",
    pages = "53--56",
    abstract = "We present a comprehensive platform to run human-computer experiments where an agent instructs a human in Minecraft, a 3D blocksworld environment. This platform enables comparisons between different agents by matching users to agents. It performs extensive logging and takes care of all boilerplate, allowing to easily incorporate new agents to evaluate them. Our environment is prepared to evaluate any kind of instruction giving system, recording the interaction and all actions of the user. We provide example architects, a Wizard-of-Oz architect and set-up scripts to automatically download, build and start the platform.",
}
@inproceedings{chang-etal-2020-convokit,
    title = "{C}onvo{K}it: A Toolkit for the Analysis of Conversations",
    author = "Chang, Jonathan P.  and
      Chiam, Caleb  and
      Fu, Liye  and
      Wang, Andrew  and
      Zhang, Justine  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.8",
    pages = "57--60",
    abstract = "This paper describes the design and functionality of ConvoKit, an open-source toolkit for analyzing conversations and the social interactions embedded within. ConvoKit provides an unified framework for representing and manipulating conversational data, as well as a large and diverse collection of conversational datasets. By providing an intuitive interface for exploring and interacting with conversational data, this toolkit lowers the technical barriers for the broad adoption of computational methods for conversational analysis.",
}
@inproceedings{liu-etal-2020-commonsense,
    title = "Commonsense Evidence Generation and Injection in Reading Comprehension",
    author = "Liu, Ye  and
      Yang, Tao  and
      You, Zeyu  and
      Fan, Wei  and
      Yu, Philip S.",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.9",
    pages = "61--73",
    abstract = "Human tackle reading comprehension not only based on the given context itself but often rely on the commonsense beyond. To empower the machine with commonsense reasoning, in this paper, we propose a Commonsense Evidence Generation and Injection framework in reading comprehension, named CEGI. The framework injects two kinds of auxiliary commonsense evidence into comprehensive reading to equip the machine with the ability of rational thinking. Specifically, we build two evidence generators: one aims to generate textual evidence via a language model; the other aims to extract factual evidence (automatically aligned text-triples) from a commonsense knowledge graph after graph completion. Those evidences incorporate contextual commonsense and serve as the additional inputs to the reasoning model. Thereafter, we propose a deep contextual encoder to extract semantic relationships among the paragraph, question, option, and evidence. Finally, we employ a capsule network to extract different linguistic units (word and phrase) from the relations, and dynamically predict the optimal option based on the extracted units. Experiments on the CosmosQA dataset demonstrate that the proposed CEGI model outperforms the current state-of-the-art approaches and achieves the highest accuracy (83.6{\%}) on the leaderboard.",
}
@inproceedings{jain-etal-2020-identifying,
    title = "Identifying Collaborative Conversations using Latent Discourse Behaviors",
    author = "Jain, Ayush  and
      Pacheco, Maria Leonor  and
      Lancette, Steven  and
      Goindani, Mahak  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.10",
    pages = "74--78",
    abstract = "In this work, we study collaborative online conversations. Such conversations are rich in content, constructive and motivated by a shared goal. Automatically identifying such conversations requires modeling complex discourse behaviors, which characterize the flow of information, sentiment and community structure within discussions. To help capture these behaviors, we define a hybrid relational model in which relevant discourse behaviors are formulated as discrete latent variables and scored using neural networks. These variables provide the information needed for predicting the overall collaborative characterization of the entire conversational thread. We show that adding inductive bias in the form of latent variables results in performance improvement, while providing a natural way to explain the decision.",
}
@inproceedings{hewitt-beaver-2020-case,
    title = "A Case Study of User Communication Styles with Customer Service Agents versus Intelligent Virtual Agents",
    author = "Hewitt, Timothy  and
      Beaver, Ian",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.11",
    pages = "79--85",
    abstract = "We investigate differences in user communication with live chat agents versus a commercial Intelligent Virtual Agent (IVA). This case study compares the two types of interactions in the same domain for the same company filling the same purposes. We compared 16,794 human-to-human conversations and 27,674 conversations with the IVA. Of those IVA conversations, 8,324 escalated to human live chat agents. We then investigated how human-to-human communication strategies change when users first communicate with an IVA in the same conversation thread. We measured quantity, quality, and diversity of language, and analyzed complexity using numerous features. We find that while the complexity of language did not significantly change between modes, the quantity and some quality metrics did vary significantly. This fair comparison provides unique insight into how humans interact with commercial IVAs and how IVA and chatbot designers might better curate training data when automating customer service tasks.",
}
@inproceedings{gervits-etal-2020-time,
    title = "It{'}s About Time: Turn-Entry Timing For Situated Human-Robot Dialogue",
    author = "Gervits, Felix  and
      Thielstrom, Ravenna  and
      Roque, Antonio  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.12",
    pages = "86--96",
    abstract = "Turn-entry timing is an important requirement for conversation, and one that spoken dialogue systems largely fail at. In this paper, we introduce a computational framework based on work from Psycholinguistics, which is aimed at achieving proper turn-taking timing for situated agents. The approach involves incremental processing and lexical prediction of the turn in progress, which allows a situated dialogue system to start its turn and initiate actions earlier than would otherwise be possible. We evaluate the framework by integrating it within a cognitive robotic architecture and testing performance on a corpus of task-oriented human-robot directives. We demonstrate that: 1) the system is superior to a non-incremental system in terms of faster responses, reduced gap between turns, and the ability to perform actions early, 2) the system can time its turn to come in immediately at a transition point or earlier to produce several types of overlap, and 3) the system is robust to various forms of disfluency in the input. Overall, this domain-independent framework can be integrated into various dialogue systems to improve responsiveness, and is a step toward more natural, human-like turn-taking behavior.",
}
@inproceedings{mcneill-kennington-2020-learning,
    title = "Learning Word Groundings from Humans Facilitated by Robot Emotional Displays",
    author = "McNeill, David  and
      Kennington, Casey",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.13",
    pages = "97--106",
    abstract = "In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not{--}indeed, cannot{--}yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 participants that emotional displays improve the quantity and quality of the inputs provided to the robot.",
}
@inproceedings{lu-etal-2020-learning,
    title = "Learning and Reasoning for Robot Dialog and Navigation Tasks",
    author = "Lu, Keting  and
      Zhang, Shiqi  and
      Stone, Peter  and
      Chen, Xiaoping",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.14",
    pages = "107--117",
    abstract = "Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot{'}s performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.",
}
@inproceedings{inoue-etal-2020-attentive,
    title = "An Attentive Listening System with Android {ERICA}: Comparison of Autonomous and {WOZ} Interactions",
    author = "Inoue, Koji  and
      Lala, Divesh  and
      Yamamoto, Kenta  and
      Nakamura, Shizuka  and
      Takanashi, Katsuya  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.15",
    pages = "118--127",
    abstract = "We describe an attentive listening system for the autonomous android robot ERICA. The proposed system generates several types of listener responses: backchannels, repeats, elaborating questions, assessments, generic sentimental responses, and generic responses. In this paper, we report a subjective experiment with 20 elderly people. First, we evaluated each system utterance excluding backchannels and generic responses, in an offline manner. It was found that most of the system utterances were linguistically appropriate, and they elicited positive reactions from the subjects. Furthermore, 58.2{\%} of the responses were acknowledged as being appropriate listener responses. We also compared the proposed system with a WOZ system where a human operator was operating the robot. From the subjective evaluation, the proposed system achieved comparable scores in basic skills of attentive listening such as encouragement to talk, focused on the talk, and actively listening. It was also found that there is still a gap between the system and the WOZ for more sophisticated skills such as dialogue understanding, showing interest, and empathy towards the user.",
}
@inproceedings{platonov-etal-2020-spoken,
    title = "A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World",
    author = "Platonov, Georgiy  and
      Schubert, Lenhart  and
      Kane, Benjamin  and
      Gindi, Aaron",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.16",
    pages = "128--131",
    abstract = "A physical blocks world, despite its relative simplicity, requires (in fully interactive form) a rich set of functional capabilities, ranging from vision to natural language understanding. In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robustly interprets spatial queries, and a constraint solver that derives answers based on 3-D spatial modeling. The contributions of this work include a semantic parser that maps spatial questions into logical forms consistent with a general approach to meaning representation, a dialogue manager based on a schema representation, and a constraint solver for spatial questions that provides answers in agreement with human perception. These and other components are integrated into a multi-modal human-computer interaction pipeline.",
}
@inproceedings{kennington-etal-2020-rrsds,
    title = "rr{SDS}: Towards a Robot-ready Spoken Dialogue System",
    author = "Kennington, Casey  and
      Moro, Daniele  and
      Marchand, Lucas  and
      Carns, Jake  and
      McNeill, David",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.17",
    pages = "132--135",
    abstract = "Spoken interaction with a physical robot requires a dialogue system that is modular, multimodal, distributive, incremental and temporally aligned. In this demo paper, we make significant contributions towards fulfilling these requirements by expanding upon the ReTiCo incremental framework. We outline the incremental and multimodal modules and how their computation can be distributed. We demonstrate the power and flexibility of our robot-ready spoken dialogue system to be integrated with almost any robot.",
}
@inproceedings{ghosh-etal-2020-discovering,
    title = "Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog",
    author = "Ghosh, Subhasis  and
      Kundu, Arpita  and
      Pramanick, Aniket  and
      Bhattacharya, Indrajit",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.18",
    pages = "136--146",
    abstract = "We study the problem of schema discovery for knowledge graphs. We propose a solution where an agent engages in multi-turn dialog with an expert for this purpose. Each mini-dialog focuses on a short natural language statement, and looks to elicit the expert{'}s desired schema-based interpretation of that statement, taking into account possible augmentations to the schema. The overall schema evolves by performing dialog over a collection of such statements. We take into account the probability that the expert does not respond to a query, and model this probability as a function of the complexity of the query. For such mini-dialogs with response uncertainty, we propose a dialog strategy that looks to elicit the schema over as short a dialog as possible. By combining the notion of uncertainty sampling from active learning with generalized binary search, the strategy asks the query with the highest expected reduction of entropy. We show that this significantly reduces dialog complexity while engaging the expert in meaningful dialog.",
}
@inproceedings{komatani-nakano-2020-user,
    title = "User Impressions of Questions to Acquire Lexical Knowledge",
    author = "Komatani, Kazunori  and
      Nakano, Mikio",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.19",
    pages = "147--156",
    abstract = "For the acquisition of knowledge through dialogues, it is crucial for systems to ask questions that do not diminish the user{'}s willingness to talk, i.e., that do not degrade the user{'}s impression. This paper reports the results of our analysis on how user impression changes depending on the types of questions to acquire lexical knowledge, that is, explicit and implicit questions, and the correctness of the content of the questions. We also analyzed how sequences of the same type of questions affect user impression. User impression scores were collected from 104 participants recruited via crowdsourcing and then regression analysis was conducted. The results demonstrate that implicit questions give a good impression when their content is correct, but a bad impression otherwise. We also found that consecutive explicit questions are more annoying than implicit ones when the content of the questions is correct. Our findings reveal helpful insights for creating a strategy to avoid user impression deterioration during knowledge acquisition.",
}
@inproceedings{michael-moller-2020-simulating,
    title = "Simulating Turn-Taking in Conversations with Delayed Transmission",
    author = {Michael, Thilo  and
      M{\"o}ller, Sebastian},
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.20",
    pages = "157--161",
    abstract = "Conversations over the telephone require timely turn-taking cues that signal the participants when to speak and when to listen. When a two-way transmission delay is introduced into such conversations, the immediate feedback is delayed, and the interactivity of the conversation is impaired. With delayed speech on each side of the transmission, different conversation realities emerge on both ends, which alters the way the participants interact with each other. Simulating conversations can give insights on turn-taking and spoken interactions between humans but can also used for analyzing and even predicting human behavior in conversations. In this paper, we simulate two types of conversations with distinct levels of interactivity. We then introduce three levels of two-way transmission delay between the agents and compare the resulting interaction-patterns with human-to-human dialog from an empirical study. We show how the turn-taking mechanisms modeled for conversations without delay perform in scenarios with delay and identify to which extend the simulation is able to model the delayed turn-taking observed in human conversation.",
}
@inproceedings{cervone-riccardi-2020-dialogue,
    title = "Is this Dialogue Coherent? Learning from Dialogue Acts and Entities",
    author = "Cervone, Alessandra  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.21",
    pages = "162--174",
    abstract = "In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn candidates while considering the entire history of the dialogue. First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities previously introduced and the Dialogue Acts used. Second, we experiment with different architectures to model entities, Dialogue Acts and their combination and evaluate their performance in predicting human coherence ratings on SWBD-Coh. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.",
}
@inproceedings{mcmahan-stone-2020-analyzing,
    title = "Analyzing Speaker Strategy in Referential Communication",
    author = "McMahan, Brian  and
      Stone, Matthew",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.22",
    pages = "175--185",
    abstract = "We analyze a corpus of referential communication through the lens of quantitative models of speaker reasoning. Different models place different emphases on linguistic reasoning and collaborative reasoning. This leads models to make different assessments of the risks and rewards of using specific utterances in specific contexts. By fitting a latent variable model to the corpus, we can exhibit utterances that give systematic evidence of the diverse kinds of reasoning speakers employ, and build integrated models that recognize not only speaker reference but also speaker reasoning.",
}
@inproceedings{wang-etal-2020-contextualized,
    title = "Contextualized Emotion Recognition in Conversation as Sequence Tagging",
    author = "Wang, Yan  and
      Zhang, Jiayu  and
      Ma, Jun  and
      Wang, Shaojun  and
      Xiao, Jing",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.23",
    pages = "186--195",
    abstract = "Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on. In this paper, we propose a method to model ERC task as sequence tagging where a Conditional Random Field (CRF) layer is leveraged to learn the emotional consistency in the conversation. We employ LSTM-based encoders that capture self and inter-speaker dependency of interlocutors to generate contextualized utterance representations which are fed into the CRF layer. For capturing long-range global context, we use a multi-layer Transformer encoder to enhance the LSTM-based encoder. Experiments show that our method benefits from modeling the emotional consistency and outperforms the current state-of-the-art methods on multiple emotion classification datasets.",
}
@inproceedings{stiff-etal-2020-self,
    title = "How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent",
    author = "Stiff, Adam  and
      Song, Qi  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.24",
    pages = "196--202",
    abstract = "Contextualized language modeling using deep Transformer networks has been applied to a variety of natural language processing tasks with remarkable success. However, we find that these models are not a panacea for a question-answering dialogue agent corpus task, which has hundreds of classes in a long-tailed frequency distribution, with only thousands of data points. Instead, we find substantial improvements in recall and accuracy on rare classes from a simple one-layer RNN with multi-headed self-attention and static word embeddings as inputs. While much research has used attention weights to illustrate what input is important for a task, the complexities of our dialogue corpus offer a unique opportunity to examine how the model represents what it attends to, and we offer a detailed analysis of how that contributes to improved performance on rare classes. A particularly interesting phenomenon we observe is that the model picks up implicit meanings by splitting different aspects of the semantics of a single word across multiple attention heads.",
}
@inproceedings{fuscone-etal-2020-filtering,
    title = "Filtering conversations through dialogue acts labels for improving corpus-based convergence studies",
    author = "Fuscone, Simone  and
      Favre, Benoit  and
      Pr{\'e}vot, Laurent",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.25",
    pages = "203--208",
    abstract = "Cognitive models of conversation and research on user-adaptation in dialogue systems involves a better understanding of speakers convergence in conversation. Convergence effects have been established on controlled data sets, for various acoustic and linguistic variables. Tracking interpersonal dynamics on generic corpora has provided positive but more contrasted outcomes. We propose here to enrich large conversational corpora with dialogue act (DA) information. We use DA-labels as filters in order to create data sub sets featuring homogeneous conversational activity. Those data sets allow a more precise comparison between speakers{'} speech variables. Our experiences consist of comparing convergence on low level variables (Energy, Pitch, Speech Rate) measured on raw data sets, with human and automatically DA-labelled data sets. We found that such filtering does help in observing convergence suggesting that studies on interpersonal dynamics should consider such high level dialogue activity types and their related NLP topics as important ingredients of their toolboxes.",
}
@inproceedings{bergqvist-etal-2020-nontrivial,
    title = "Nontrivial Lexical Convergence in a Geography-Themed Game",
    author = "Bergqvist, Amanda  and
      Manuvinakurike, Ramesh  and
      Karkada, Deepthi  and
      Paetzel, Maike",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.26",
    pages = "209--214",
    abstract = "The present study aims to examine the prevalent notion that people entrain to the vocabulary of a dialogue system. Although previous research shows that people will replace their choice of words with simple substitutes, studies using more challenging substitutions are sparse. In this paper, we investigate whether people adapt their speech to the vocabulary of a dialogue system when the system{'}s suggested words are not direct synonyms. 32 participants played a geography-themed game with a remote-controlled agent and were primed by referencing strategies (rather than individual terms) introduced in follow-up questions. Our results suggest that context-appropriate substitutes support convergence and that the convergence has a lasting effect within a dialogue session if the system{'}s wording is more consistent with the norms of the domain than the original wording of the speaker.",
}
@inproceedings{galvez-etal-2020-unifying,
    title = "A unifying framework for modeling acoustic/prosodic entrainment: definition and evaluation on two large corpora",
    author = "G{\'a}lvez, Ramiro H.  and
      Gauder, Lara  and
      Luque, Jordi  and
      Gravano, Agust{\'\i}n",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.27",
    pages = "215--224",
    abstract = "Acoustic/prosodic (a/p) entrainment has been associated with multiple positive social aspects of human-human conversations. However, research on its effects is still preliminary, first because how to model it is far from standardized, and second because most of the reported findings rely on small corpora or on corpora collected in experimental setups. The present article has a twofold purpose: 1) it proposes a unifying statistical framework for modeling a/p entrainment, and 2) it tests on two large corpora of spontaneous telephone interactions whether three metrics derived from this framework predict positive social aspects of the conversations. The corpora differ in their spoken language, domain, and positive social outcome attached. To our knowledge, this is the first article studying relations between a/p entrainment and positive social outcomes in such large corpora of spontaneous dialog. Our results suggest that our metrics effectively predict, up to some extent, positive social aspects of conversations, which not only validates the methodology, but also provides further insights into the elusive topic of entrainment in human-human conversation.",
}
@inproceedings{mehri-eskenazi-2020-unsupervised,
    title = "Unsupervised Evaluation of Interactive Dialog with {D}ialo{GPT}",
    author = "Mehri, Shikib  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.28",
    pages = "225--235",
    abstract = "It is important to define meaningful and interpretable automatic evaluation metrics for open-domain dialog research. Standard language generation metrics have been shown to be ineffective for dialog. This paper introduces the FED metric (fine-grained evaluation of dialog), an automatic evaluation metric which uses DialoGPT, without any fine-tuning or supervision. It also introduces the FED dataset which is constructed by annotating a set of human-system and human-human conversations with eighteen fine-grained dialog qualities. The FED metric (1) does not rely on a ground-truth response, (2) does not require training data and (3) measures fine-grained dialog qualities at both the turn and whole dialog levels. FED attains moderate to strong correlation with human judgement at both levels.",
}
@inproceedings{finch-choi-2020-towards,
    title = "Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols",
    author = "Finch, Sarah E.  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.29",
    pages = "236--245",
    abstract = "As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing. The current state of affairs suggests various evaluation protocols to assess chat-oriented dialogue management systems, rendering it difficult to conduct fair comparative studies across different approaches and gain an insightful understanding of their values. To foster this research, a more robust evaluation protocol must be set in place. This paper presents a comprehensive synthesis of both automated and human evaluation methods on dialogue systems, identifying their shortcomings while accumulating evidence towards the most effective evaluation dimensions. A total of 20 papers from the last two years are surveyed to analyze three types of evaluation protocols: automated, static, and interactive. Finally, the evaluation dimensions used in these papers are compared against our expert evaluation on the system-user dialogue data collected from the Alexa Prize 2020.",
}
@inproceedings{gupta-etal-2020-human,
    title = "Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis",
    author = "Gupta, Itika  and
      Di Eugenio, Barbara  and
      Ziebart, Brian  and
      Baiju, Aiswarya  and
      Liu, Bing  and
      Gerber, Ben  and
      Sharp, Lisa  and
      Nabulsi, Nadia  and
      Smart, Mary",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.30",
    pages = "246--256",
    abstract = "Our goal is to develop and deploy a virtual assistant health coach that can help patients set realistic physical activity goals and live a more active lifestyle. Since there is no publicly shared dataset of health coaching dialogues, the first phase of our research focused on data collection. We hired a certified health coach and 28 patients to collect the first round of human-human health coaching interaction which took place via text messages. This resulted in 2853 messages. The data collection phase was followed by conversation analysis to gain insight into the way information exchange takes place between a health coach and a patient. This was formalized using two annotation schemas: one that focuses on the goals the patient is setting and another that models the higher-level structure of the interactions. In this paper, we discuss these schemas and briefly talk about their application for automatically extracting activity goals and annotating the second round of data, collected with different health coaches and patients. Given the resource-intensive nature of data annotation, successfully annotating a new dataset automatically is key to answer the need for high quality, large datasets.",
}
@inproceedings{wang-etal-2020-agent,
    title = "Agent-Based Dynamic Collaboration Support in a Smart Office Space",
    author = "Wang, Yansen  and
      Murray, R. Charles  and
      Bao, Haogang  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.31",
    pages = "257--260",
    abstract = "For the past 15 years, in computer-supported collaborative learning applications, conversational agents have been used to structure group interactions in online chat-based environments. A series of experimental studies has provided an empirical foundation for the design of chat-based conversational agents that significantly improve learning over no-support control conditions and static-support control conditions. In this demo, we expand upon this foundation, bringing conversational agents to structure group interaction into physical spaces, with the specific goal of facilitating collaboration and learning in workplace scenarios.",
}
@inproceedings{finch-choi-2020-emora,
    title = "Emora {STDM}: A Versatile Framework for Innovative Dialogue System Development",
    author = "Finch, James D.  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.32",
    pages = "261--264",
    abstract = "This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.",
}
@inproceedings{zhu-2020-boosting,
    title = "Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training",
    author = "Zhu, Chenguang",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.33",
    pages = "265--271",
    abstract = "The natural language generation (NLG) module in a task-oriented dialogue system produces user-facing utterances conveying required information. Thus, it is critical for the generated response to be natural and fluent. We propose to integrate adversarial training to produce more human-like responses. The model uses Straight-Through Gumbel-Softmax estimator for gradient computation. We also propose a two-stage training scheme to boost performance. Empirical results show that the adversarial training can effectively improve the quality of language generation in both automatic and human evaluations. For example, in the RNN-LG Restaurant dataset, our model AdvNLG outperforms the previous state-of-the-art result by 3.6{\%} in BLEU.",
}
@inproceedings{shi-2020-sequence,
    title = "A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems",
    author = "Shi, Hongjie",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.34",
    pages = "272--277",
    abstract = "Dialog systems capable of filling slots with numerical values have wide applicability to many task-oriented applications. In this paper, we perform a particular case study on the {``}number{\_}of{\_}guests{''} slot-filling in hotel reservation domain, and propose two methods to improve current dialog system model on 1. numerical reasoning performance by training the model to predict arithmetic expressions, and 2. multi-turn question generation by introducing additional context slots. Furthermore, because the proposed methods are all based on an end-to-end trainable sequence-to-sequence (seq2seq) neural model, it is possible to achieve further performance improvement on increasing dialog logs in the future.",
}
@inproceedings{kim-etal-2020-beyond,
    title = "Beyond Domain {API}s: Task-oriented Conversational Modeling with Unstructured Knowledge Access",
    author = "Kim, Seokhwan  and
      Eric, Mihail  and
      Gopalakrishnan, Karthik  and
      Hedayatnia, Behnam  and
      Liu, Yang  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.35",
    pages = "278--289",
    abstract = "Most prior work on task-oriented dialogue systems are restricted to a limited coverage of domain APIs, while users oftentimes have domain related requests that are not covered by the APIs. In this paper, we propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources. We define three sub-tasks: knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation, which can be modeled individually or jointly. We introduce an augmented version of MultiWOZ 2.1, which includes new out-of-API-coverage turns and responses grounded on external knowledge sources. We present baselines for each sub-task using both conventional and neural approaches. Our experimental results demonstrate the need for further research in this direction to enable more informative conversational systems.",
}
@inproceedings{jhunjhunwala-etal-2020-multi,
    title = "Multi-Action Dialog Policy Learning with Interactive Human Teaching",
    author = "Jhunjhunwala, Megha  and
      Bryant, Caleb  and
      Shah, Pararth",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.36",
    pages = "290--296",
    abstract = "We present a framework for improving task-oriented dialog systems through online interactive teaching with human trainers. A dialog policy trained with imitation learning on a limited corpus may not generalize well to novel dialog flows often uncovered in live interactions. This issue is magnified in multi-action dialog policies which have a more expressive action space. In our approach, a pre-trained dialog policy model interacts with human trainers, and at each turn the trainers choose the best output among N-best multi-action outputs. We present a novel multi-domain, multi-action dialog policy architecture trained on MultiWOZ, and show that small amounts of online supervision can lead to significant improvement in model performance. We also present transfer learning results which show that interactive learning in one domain improves policy model performance in related domains.",
}
@inproceedings{takanobu-etal-2020-goal,
    title = "Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation",
    author = "Takanobu, Ryuichi  and
      Zhu, Qi  and
      Li, Jinchao  and
      Peng, Baolin  and
      Gao, Jianfeng  and
      Huang, Minlie",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.37",
    pages = "297--310",
    abstract = "There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations. Although many methods are devised to evaluate and improve the performance of individual dialog components, there is a lack of comprehensive empirical study on how different components contribute to the overall performance of a dialog system. In this paper, we perform a system-wise evaluation and present an empirical analysis on different types of dialog systems which are composed of different modules in different settings. Our results show that (1) a pipeline dialog system trained using fine-grained supervision signals at different component levels often obtains better performance than the systems that use joint or end-to-end models trained on coarse-grained labels, (2) component-wise, single-turn evaluation results are not always consistent with the overall performance of a dialog system, and (3) despite the discrepancy between simulators and human users, simulated evaluation is still a valid alternative to the costly human evaluation especially in the early stage of development.",
}
@inproceedings{ultes-maier-2020-similarity,
    title = "Similarity Scoring for Dialogue Behaviour Comparison",
    author = "Ultes, Stefan  and
      Maier, Wolfgang",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.38",
    pages = "311--322",
    abstract = "The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.",
}
@inproceedings{arimoto-etal-2020-collection,
    title = "Collection and Analysis of Dialogues Provided by Two Speakers Acting as One",
    author = "Arimoto, Tsunehiro  and
      Higashinaka, Ryuichiro  and
      Tanaka, Kou  and
      Kawanishi, Takahito  and
      Sugiyama, Hiroaki  and
      Sawada, Hiroshi  and
      Ishiguro, Hiroshi",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.39",
    pages = "323--328",
    abstract = "We are studying a cooperation style where multiple speakers can provide both advanced dialogue services and operator education. We focus on a style in which two operators interact with a user by pretending to be a single operator. For two operators to effectively act as one, each must adjust his/her conversational content and timing to the other. In the process, we expect each operator to experience the conversational content of his/her partner as if it were his/her own, creating efficient and effective learning of the other{'}s skill. We analyzed this educational effect and examined whether dialogue services can be successfully provided by collecting travel guidance dialogue data from operators who give travel information to users. In this paper, we report our preliminary results on dialogue content and user satisfaction of operators and users.",
}
@inproceedings{cao-etal-2020-adaptive,
    title = "Adaptive Dialog Policy Learning with Hindsight and User Modeling",
    author = "Cao, Yan  and
      Lu, Keting  and
      Chen, Xiaoping  and
      Zhang, Shiqi",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.40",
    pages = "329--338",
    abstract = "Reinforcement learning (RL) methods have been widely used for learning dialog policies. Sample efficiency, i.e., the efficiency of learning from limited dialog experience, is particularly important in RL-based dialog policy learning, because interacting with people is costly and low-quality dialog policies produce very poor user experience. In this paper, we develop LHUA (Learning with Hindsight, User modeling, and Adaptation) that, for the first time, enables dialog agents to adaptively learn with hindsight from both simulated and real users. Simulation and hindsight provide the dialog agent with more experience and more (positive) reinforcement respectively. Experimental results suggest that LHUA outperforms competitive baselines from the literature, including its no-simulation, no-adaptation, and no-hindsight counterparts.",
}
@inproceedings{zare-etal-2020-dialogue,
    title = "Dialogue Policies for Learning Board Games through Multimodal Communication",
    author = "Zare, Maryam  and
      Ayub, Ali  and
      Liu, Aishan  and
      Sudhakara, Sweekar  and
      Wagner, Alan  and
      Passonneau, Rebecca",
    booktitle = "Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2020",
    address = "1st virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sigdial-1.41",
    pages = "339--351",
    abstract = "This paper presents MDP policy learning for agents to learn strategic behavior{--}how to play board games{--}during multimodal dialogues. Policies are trained offline in simulation, with dialogues carried out in a formal language. The agent has a temporary belief state for the dialogue, and a persistent knowledge store represented as an extensive-form game tree. How well the agent learns a new game from a dialogue with a simulated partner is evaluated by how well it plays the game, given its dialogue-final knowledge state. During policy training, we control for the simulated dialogue partner{'}s level of informativeness in responding to questions. The agent learns best when its trained policy matches the current dialogue partner{'}s informativeness. We also present a novel data collection for training natural language modules. Human subjects who engaged in dialogues with a baseline system rated the system{'}s language skills as above average. Further, results confirm that human dialogue partners also vary in their informativeness.",
}
@proceedings{semeval-2020-semantic,
    title = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.0",
}
@inproceedings{schlechtweg-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 1: Unsupervised Lexical Semantic Change Detection",
    author = "Schlechtweg, Dominik  and
      McGillivray, Barbara  and
      Hengchen, Simon  and
      Dubossarsky, Haim  and
      Tahmasebi, Nina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.1",
    pages = "1--23",
    abstract = "Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.",
}
@inproceedings{glavas-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment",
    author = "Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.2",
    pages = "24--35",
    abstract = "Lexical entailment (LE) is a fundamental asymmetric lexico-semantic relation, supporting the hierarchies in lexical resources (e.g., WordNet, ConceptNet) and applications like natural language inference and taxonomy induction. Multilingual and cross-lingual NLP applications warrant models for LE detection that go beyond language boundaries. As part of SemEval 2020, we carried out a shared task (Task 2) on multilingual and cross-lingual LE. The shared task spans three dimensions: (1) monolingual vs. cross-lingual LE, (2) binary vs. graded LE, and (3) a set of 6 diverse languages (and 15 corresponding language pairs). We offered two different evaluation tracks: (a) Dist: for unsupervised, fully distributional models that capture LE solely on the basis of unannotated corpora, and (b) Any: for externally informed models, allowed to leverage any resources, including lexico-semantic networks (e.g., WordNet or BabelNet). In the Any track, we recieved runs that push state-of-the-art across all languages and language pairs, for both binary LE detection and graded LE prediction.",
}
@inproceedings{armendariz-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 3: Graded Word Similarity in Context",
    author = "Armendariz, Carlos Santos  and
      Purver, Matthew  and
      Pollak, Senja  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Ul{\v{c}}ar, Matej  and
      Vuli{\'c}, Ivan  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.3",
    pages = "36--49",
    abstract = "This paper presents the Graded Word Similarity in Context (GWSC) task which asked participants to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish. We received 15 submissions and 11 system description papers. A new dataset (CoSimLex) was created for evaluation in this task: it contains pairs of words, each annotated within two different contexts. Systems beat the baselines by significant margins, but few did well in more than one language or subtask. Almost every system employed a Transformer model, but with many variations in the details: WordNet sense embeddings, translation of contexts, TF-IDF weightings, and the automatic creation of datasets for fine-tuning were all used to good effect.",
}
@inproceedings{beck-2020-diasense,
    title = "{D}ia{S}ense at {S}em{E}val-2020 Task 1: Modeling Sense Change via Pre-trained {BERT} Embeddings",
    author = "Beck, Christin",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.4",
    pages = "50--58",
    abstract = "This paper describes DiaSense, a system developed for Task 1 {`}Unsupervised Lexical Semantic Change Detection{'} of SemEval 2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This allows for the calculation of metrics which mimic human intuitions about the semantic relatedness between individual use pairs of a target word for the assessment of lexical semantic change. DiaSense is able to detect lexical semantic change in English, German, Latin and Swedish (accuracy = 0.728). Moreover, DiaSense differentiates between weak and strong change.",
}
@inproceedings{costella-pessutto-etal-2020-babelenconding,
    title = "{B}abel{E}nconding at {S}em{E}val-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models",
    author = "Costella Pessutto, Lucas Rafael  and
      de Melo, Tiago  and
      Moreira, Viviane P.  and
      da Silva, Altigran",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.5",
    pages = "59--66",
    abstract = "This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.",
}
@inproceedings{martinc-etal-2020-discovery,
    title = "Discovery Team at {S}em{E}val-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection",
    author = "Martinc, Matej  and
      Montariol, Syrielle  and
      Zosa, Elaine  and
      Pivovarova, Lidia",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.6",
    pages = "67--73",
    abstract = "This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection. The proposed method is based on clustering of BERT contextual embeddings, followed by a comparison of cluster distributions across time. The best results were obtained by an ensemble of this method and static Word2Vec embeddings. According to the official results, our approach proved the best for Latin in Subtask 2.",
}
@inproceedings{cassotti-etal-2020-gm,
    title = "{GM}-{CTSC} at {S}em{E}val-2020 Task 1: {G}aussian Mixtures Cross Temporal Similarity Clustering",
    author = "Cassotti, Pierluigi  and
      Caputo, Annalina  and
      Polignano, Marco  and
      Basile, Pierpaolo",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.7",
    pages = "74--80",
    abstract = "This paper describes the system proposed by the Random team for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focus our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or lost senses. To this end, we define a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compare the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system.",
}
@inproceedings{kaiser-etal-2020-ims,
    title = "{IMS} at {S}em{E}val-2020 Task 1: How Low Can You Go? Dimensionality in Lexical Semantic Change Detection",
    author = "Kaiser, Jens  and
      Schlechtweg, Dominik  and
      Papay, Sean  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.8",
    pages = "81--89",
    abstract = "We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical semantic change detection model based on Skip-Gram with Negative Sampling. Our system focuses on Vector Initialization (VI) alignment, compares VI to the currently top-ranking models for Subtask 2 and demonstrates that these can be outperformed if we optimize VI dimensionality. We demonstrate that differences in performance can largely be attributed to model-specific sources of noise, and we reveal a strong relationship between dimensionality and frequency-induced noise in VI alignment. Our results suggest that lexical semantic change models integrating vector space alignment should pay more attention to the role of the dimensionality parameter.",
}
@inproceedings{amar-liebeskind-2020-jct,
    title = "{JCT} at {S}em{E}val-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection",
    author = "Amar, Efrat  and
      Liebeskind, Chaya",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.9",
    pages = "90--97",
    abstract = "In this paper, we present our contribution in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, where we systematically combine existing models for unsupervised capturing of lexical semantic change across time in text corpora of German, English, Latin and Swedish. In particular, we analyze the score distribution of existing models. Then we define a general threshold, adjust it independently to each of the models and measure the models{'} score reliability. Finally, using both the threshold and score reliability, we aggregate the models for the two sub- tasks: binary classification and ranking.",
}
@inproceedings{iwamoto-yukawa-2020-rijp,
    title = "{RIJP} at {S}em{E}val-2020 Task 1: {G}aussian-based Embeddings for Semantic Change Detection",
    author = "Iwamoto, Ran  and
      Yukawa, Masahiro",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.10",
    pages = "98--104",
    abstract = "This paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection. In the model, words are represented by Gaussian distributions. For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively. The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning. The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational",
}
@inproceedings{gruppi-etal-2020-schme,
    title = "{SC}h{ME} at {S}em{E}val-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change",
    author = "Gruppi, Maur{\'\i}cio  and
      Adali, Sibel  and
      Chen, Pin-Yu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.11",
    pages = "105--111",
    abstract = "This paper describes SChME (Semantic Change Detection with Model Ensemble), a method used in SemEval-2020 Task 1 on unsupervised detection of lexical semantic change. SChME uses a model ensemble combining signals distributional models (word embeddings) and word frequency where each model casts a vote indicating the probability that a word suffered semantic change according to that feature. More specifically, we combine cosine distance of word vectors combined with a neighborhood-based metric we named Mapped Neighborhood Distance (MAP), and a word frequency differential metric as input signals to our model. Additionally, we explore alignment-based methods to investigate the importance of the landmarks used in this process. Our results show evidence that the number of landmarks used for alignment has a direct impact on the predictive performance of the model. Moreover, we show that languages that suffer less semantic change tend to benefit from using a large number of landmarks, whereas languages with more semantic change benefit from a more careful choice of landmark number for alignment.",
}
@inproceedings{cuba-gyllensten-etal-2020-sensecluster,
    title = "{S}ense{C}luster at {S}em{E}val-2020 Task 1: Unsupervised Lexical Semantic Change Detection",
    author = "Cuba Gyllensten, Amaru  and
      Gogoulou, Evangelia  and
      Ekgren, Ariel  and
      Sahlgren, Magnus",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.12",
    pages = "112--118",
    abstract = "We (Team Skurt) propose a simple method to detect lexical semantic change by clustering contextualized embeddings produced by XLM-R, using K-Means++. The basic idea is that contextualized embeddings that encode the same sense are located in close proximity in the embedding space. Our approach is both simple and generic, but yet performs relatively good in both sub-tasks of SemEval-2020 Task 1. We hypothesize that the main shortcoming of our method lies in the simplicity of the clustering method used.",
}
@inproceedings{nulty-lillis-2020-ucd,
    title = "The {UCD}-Net System at {S}em{E}val-2020 Task 1: Temporal Referencing with Semantic Network Distances",
    author = "Nulty, Paul  and
      Lillis, David",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.13",
    pages = "119--125",
    abstract = "This paper describes the UCD system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an implementation of a web application for the purpose of searching and visualising semantic networks. The results of the change measure used for this task were not among the best performing systems, but further calibration of the distance metric and backoff approaches may improve this method.",
}
@inproceedings{kutuzov-giulianelli-2020-uio,
    title = "{U}i{O}-{U}v{A} at {S}em{E}val-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection",
    author = "Kutuzov, Andrey  and
      Giulianelli, Mario",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.14",
    pages = "126--134",
    abstract = "We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic drift over time. We analyse the performance of two contextualising architectures (BERT and ELMo) and three change detection algorithms. We find that the most effective algorithms rely on the cosine similarity between averaged token embeddings and the pairwise distances between token embeddings. They outperform strong baselines by a large margin (in the post-evaluation phase, we have the best Subtask 2 submission for SemEval-2020 Task 1), but interestingly, the choice of a particular algorithm depends on the distribution of gold scores in the test set.",
}
@inproceedings{kovacs-etal-2020-bmeaut,
    title = "{BMEAUT} at {S}em{E}val-2020 Task 2: Lexical Entailment with Semantic Graphs",
    author = "Kov{\'a}cs, {\'A}d{\'a}m  and
      G{\'e}mes, Kinga  and
      Kornai, Andras  and
      Recski, G{\'a}bor",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.15",
    pages = "135--141",
    abstract = "In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions. Combined with a simple WordNet-based method our system achieves top scores on the English and Italian datasets of the Semeval-2020 task {``}Predicting Multilingual and Cross-lingual (graded) Lexical Entailment{''} (Glava{\v{s}} et al., 2020). A detailed error analysis of our output uncovers future di- rections for improving both the semantic parsing method and the inference process on semantic graphs.",
}
@inproceedings{hettiarachchi-ranasinghe-2020-brums,
    title = "{BRUMS} at {S}em{E}val-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similarity",
    author = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.16",
    pages = "142--149",
    abstract = "This paper presents the team BRUMS submission to SemEval-2020 Task 3: Graded Word Similarity in Context. The system utilises state-of-the-art contextualised word embeddings, which have some task-specific adaptations, including stacked embeddings and average embeddings. Overall, the approach achieves good evaluation scores across all the languages, while maintaining simplicity. Following the final rankings, our approach is ranked within the top 5 solutions of each language while preserving the 1st position of Finnish subtask 2.",
}
@inproceedings{gomez-adorno-etal-2020-mineriaunam,
    title = "{M}ineria{UNAM} at {S}em{E}val-2020 Task 3: Predicting Contextual {W}ord{S}imilarity Using a Centroid Based Approach and Word Embeddings",
    author = "Gomez-Adorno, Helena  and
      Bel-Enguix, Gemma  and
      Reyes-Maga{\~n}a, Jorge  and
      Moreno, Benjam{\'\i}n  and
      Casillas, Ram{\'o}n  and
      Vargas, Daniel",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.17",
    pages = "150--157",
    abstract = "This paper presents our systems to solve Task 3 of Semeval-2020, which aims to predict the effect that context has on human perception of similarity of words. The task consists of two subtasks in English, Croatian, Finnish, and Slovenian: (1) predicting the change of similarity and (2) predicting the human scores of similarity, both of them for a pair of words within two different contexts. We tackled the problem by developing two systems, the first one uses a centroid approach and word vectors. The second one uses the ELMo language model, which is trained for each pair of words with the given context. Our approach achieved the highest score in subtask 2 for the English language.",
}
@inproceedings{gari-soler-apidianaki-2020-multisem,
    title = "{MULTISEM} at {S}em{E}val-2020 Task 3: Fine-tuning {BERT} for Lexical Meaning",
    author = "Gar{\'\i} Soler, Aina  and
      Apidianaki, Marianna",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.18",
    pages = "158--165",
    abstract = "We present the MULTISEM systems submitted to SemEval 2020 Task 3: Graded Word Similarity in Context (GWSC). We experiment with injecting semantic knowledge into pre-trained BERT models through fine-tuning on lexical semantic tasks related to GWSC. We use existing semantically annotated datasets, and propose to approximate similarity through automatically generated lexical substitutes in context. We participate in both GWSC subtasks and address two languages, English and Finnish. Our best English models occupy the third and fourth positions in the ranking for the two subtasks. Performance is lower for the Finnish models which are mid-ranked in the respective subtasks, highlighting the important role of data availability for fine-tuning.",
}
@inproceedings{tang-2020-uzh,
    title = "{UZH} at {S}em{E}val-2020 Task 3: Combining {BERT} with {W}ord{N}et Sense Embeddings to Predict Graded Word Similarity Changes",
    author = "Tang, Li",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.19",
    pages = "166--170",
    abstract = "CoSimLex is a dataset that can be used to evaluate the ability of context-dependent word embed- dings for modeling subtle, graded changes of meaning, as perceived by humans during reading. At SemEval-2020, task 3, subtask 1 is about {''}predicting the (graded) effect of context in word similarity{''}, using CoSimLex to quantify such a change of similarity for a pair of words, from one context to another. Here, a meaning shift is composed of two aspects, a) discrete changes observed between different word senses, and b) more subtle changes of meaning representation that are not captured in those discrete changes. Therefore, this SemEval task was designed to allow the evaluation of systems that can deal with a mix of both situations of semantic shift, as they occur in the human perception of meaning. The described system was developed to improve the BERT baseline provided with the task, by reducing distortions in the BERT semantic space, compared to the human semantic space. To this end, complementarity between 768- and 1024-dimensional BERT embeddings, and average word sense vectors were used. With this system, after some fine-tuning, the baseline performance of 0.705 (uncentered Pearson correlation with human semantic shift data from 27 annotators) was enhanced by more than 6{\%}, to 0.7645. We hope that this work can make a contribution to further our understanding of the semantic vector space of human perception, as it can be modeled with context-dependent word embeddings in natural language processing systems.",
}
@inproceedings{arefyev-zhikov-2020-bos,
    title = "{BOS} at {S}em{E}val-2020 Task 1: Word Sense Induction via Lexical Substitution for Lexical Semantic Change Detection",
    author = "Arefyev, Nikolay  and
      Zhikov, Vasily",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.20",
    pages = "171--179",
    abstract = "SemEval-2020 Task 1 is devoted to detection of changes in word meaning over time. The first subtask raises a question if a particular word has acquired or lost any of its senses during the given time period. The second subtask requires estimating the change in frequencies of the word senses. We have submitted two solutions for both subtasks. The first solution performs word sense induction (WSI) first, then makes the decision based on the induced word senses. We extend the existing WSI method based on clustering of lexical substitutes generated with neural language models and adapt it to the task. The second solution exploits a well-known approach to semantic change detection, that includes building word2vec SGNS vectors, aligning them with Orthogonal Procrustes and calculating cosine distance between resulting vectors. While WSI-based solution performs better in Subtask 1, which requires binary decisions, the second solution outperforms it in Subtask 2 and obtains the 3rd best result in this subtask.",
}
@inproceedings{pomsl-lyapin-2020-circe,
    title = "{CIRCE} at {S}em{E}val-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations",
    author = {P{\"o}msl, Martin  and
      Lyapin, Roman},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.21",
    pages = "180--186",
    abstract = "This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) context-free word representations are a powerful and robust baseline, (2) a sentence classification objective can be used to obtain useful context-dependent word representations, and (3) combining those representations increases performance on some datasets while decreasing performance on others.",
}
@inproceedings{rother-etal-2020-cmce,
    title = "{CMCE} at {S}em{E}val-2020 Task 1: Clustering on Manifolds of Contextualized Embeddings to Detect Historical Meaning Shifts",
    author = "Rother, David  and
      Haider, Thomas  and
      Eger, Steffen",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.22",
    pages = "187--193",
    abstract = "This paper describes the system Clustering on Manifolds of Contextualized Embeddings (CMCE) submitted to the SemEval-2020 Task 1 on Unsupervised Lexical Semantic Change Detection. Subtask 1 asks to identify whether or not a word gained/lost a sense across two time periods. Subtask 2 is about computing a ranking of words according to the amount of change their senses underwent. Our system uses contextualized word embeddings from MBERT, whose dimensionality we reduce with an autoencoder and the UMAP algorithm, to be able to use a wider array of clustering algorithms that can automatically determine the number of clusters. We use Hierarchical Density Based Clustering (HDBSCAN) and compare it to Gaussian MixtureModels (GMMs) and other clustering algorithms. Remarkably, with only 10 dimensional MBERT embeddings (reduced from the original size of 768), our submitted model performs best on subtask 1 for English and ranks third in subtask 2 for English. In addition to describing our system, we discuss our hyperparameter configurations and examine why our system lags behind for the other languages involved in the shared task (German, Swedish, Latin). Our code is available at https://github.com/DavidRother/semeval2020-task1",
}
@inproceedings{zamora-reina-bravo-marquez-2020-dcc,
    title = "{DCC}-Uchile at {S}em{E}val-2020 Task 1: Temporal Referencing Word Embeddings",
    author = "Zamora-Reina, Frank D.  and
      Bravo-Marquez, Felipe",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.23",
    pages = "194--200",
    abstract = "We present a system for the task of unsupervised lexical change detection: given a target word and two corpora spanning different periods of time, automatically detects whether the word has lost or gained senses from one corpus to another. Our system employs the temporal referencing method to obtain compatible representations of target words in different periods of time. This is done by concatenating corpora of different periods and performing a temporal referencing of target words i.e., treating occurrences of target words in different periods as two independent tokens. Afterwards, we train word embeddings on the joint corpus and compare the referenced vectors of each target word using cosine similarity. Our submission was ranked 7th among 34 teams for subtask 1, obtaining an average accuracy of 0.637, only 0.050 points behind the first ranked system.",
}
@inproceedings{asgari-etal-2020-emblexchange,
    title = "{E}mb{L}ex{C}hange at {S}em{E}val-2020 Task 1: Unsupervised Embedding-based Detection of Lexical Semantic Changes",
    author = {Asgari, Ehsaneddin  and
      Ringlstetter, Christoph  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.24",
    pages = "201--207",
    abstract = "This paper describes EmbLexChange, a system introduced by the {``}Life-Language{''} team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes. EmbLexChange is defined as the divergence between the embedding based profiles of word w (calculated with respect to a set of reference words) in the source and the target domains (source and target domains can be simply two time frames t{\_}1 and t{\_}2). The underlying assumption is that the lexical-semantic change of word $w$ would affect its co-occurring words and subsequently alters the neighborhoods in the embedding spaces. We show that using a resampling framework for the selection of reference words (with conserved senses), we can more reliably detect lexical-semantic changes in English, German, Swedish, and Latin. EmbLexChange achieved second place in the binary detection of semantic changes in the SemEval-2020.",
}
@inproceedings{jain-2020-gloveinit,
    title = "{G}lo{V}e{I}nit at {S}em{E}val-2020 Task 1: Using {G}lo{V}e Vector Initialization for Unsupervised Lexical Semantic Change Detection",
    author = "Jain, Vaibhav",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.25",
    pages = "208--213",
    abstract = "This paper presents a vector initialization approach for the SemEval2020 Task 1: Unsupervised Lexical Semantic Change Detection. Given two corpora belonging to different time periods and a set of target words, this task requires us to classify whether a word gained or lost a sense over time (subtask 1) and to rank them on the basis of the changes in their word senses (subtask 2). The proposed approach is based on using Vector Initialization method to align GloVe embeddings. The idea is to consecutively train GloVe embeddings for both corpora, while using the first model to initialize the second one. This paper is based on the hypothesis that GloVe embeddings are more suited for the Vector Initialization method than SGNS embeddings. It presents an intuitive reasoning behind this hypothesis, and also talks about the impact of various factors and hyperparameters on the performance of the proposed approach. Our model ranks 12th and 10th among 33 teams in the two subtasks. The implementation has been shared publicly.",
}
@inproceedings{kanjirangat-etal-2020-sst,
    title = "{SST}-{BERT} at {S}em{E}val-2020 Task 1: Semantic Shift Tracing by Clustering in {BERT}-based Embedding Spaces",
    author = "Kanjirangat, Vani  and
      Mitrovic, Sandra  and
      Antonucci, Alessandro  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.26",
    pages = "214--221",
    abstract = "Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift tracing, focal point of SemEval2020, is particularly challenging. Given the unsupervised setup, in this work, we propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings. As such, disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages. To leverage this idea, clustering is performed on contextualized (BERT-based) embeddings of word occurrences. The obtained results show that our approach performs well both measured separately (per language) and overall, where we surpass all provided SemEval baselines.",
}
@inproceedings{zhou-li-2020-temporalteller,
    title = "{T}emporal{T}eller at {S}em{E}val-2020 Task 1: Unsupervised Lexical Semantic Change Detection with Temporal Referencing",
    author = "Zhou, Jinan  and
      Li, Jiaxin",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.27",
    pages = "222--231",
    abstract = "This paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning word embeddings, calculating vector distances and determining threshold. We also propose Gamma Quantile Threshold to distinguish between changed and stable words. Based on our system, we conduct a comprehensive comparison among BERT, Skip-gram, Temporal Referencing and alignment-based methods. Evaluation results show that Skip-gram with Temporal Referencing achieves the best performance of 66.5{\%} classification accuracy and 51.8{\%} Spearman{'}s Ranking Correlation.",
}
@inproceedings{karnysheva-schwarz-2020-tue,
    title = "{TUE} at {S}em{E}val-2020 Task 1: Detecting Semantic Change by Clustering Contextual Word Embeddings",
    author = "Karnysheva, Anna  and
      Schwarz, Pia",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.28",
    pages = "232--238",
    abstract = "This paper describes our system for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. Target words of corpora from two different time periods are classified according to their semantic change. The languages covered are English, German, Latin, and Swedish. Our approach involves clustering ELMo embeddings using DBSCAN and K-means. For a more fine grained detection of semantic change we take the Jensen-Shannon Distance metric and rank the target words from strongest to weakest change. The results show that this is a valid approach for the classification subtask where we rank 13th out of 33 groups with an accuracy score of 61.2{\%}. For the ranking subtask we score a Spearman{'}s rank-order correlation coefficient of 0.087 which places us on rank 29.",
}
@inproceedings{sarsfield-tayyar-madabushi-2020-uob,
    title = "{U}o{B} at {S}em{E}val-2020 Task 1: Automatic Identification of Novel Word Senses",
    author = "Sarsfield, Eleri  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.29",
    pages = "239--245",
    abstract = "Much as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users. Lexical semantic change analysis is a burgeoning field of semantic analysis which aims to trace changes in the meanings of words over time. This paper presents an approach to lexical semantic change detection based on Bayesian word sense induction suitable for novel word sense identification. This approach is used for a submission to SemEval-2020 Task 1, which shows the approach to be capable of the SemEval task. The same approach is also applied to a corpus gleaned from 15 years of Twitter data, the results of which are then used to identify words which may be instances of slang.",
}
@inproceedings{prazak-etal-2020-uwb,
    title = "{UWB} at {S}em{E}val-2020 Task 1: Lexical Semantic Change Detection",
    author = "Pra{\v{z}}{\'a}k, Ond{\v{r}}ej  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Taylor, Stephen  and
      Sido, Jakub",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.30",
    pages = "246--254",
    abstract = "In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.",
}
@inproceedings{wang-etal-2020-shikeblcu,
    title = "{SHIKEBLCU} at {S}em{E}val-2020 Task 2: An External Knowledge-enhanced Matrix for Multilingual and Cross-Lingual Lexical Entailment",
    author = "Wang, Shike  and
      Fan, Yuchen  and
      Luo, Xiangying  and
      Yu, Dong",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.31",
    pages = "255--262",
    abstract = "Lexical entailment recognition plays an important role in tasks like Question Answering and Machine Translation. As important branches of lexical entailment, predicting multilingual and cross-lingual lexical entailment (LE) are two subtasks of SemEval2020 Task2. In previous monolingual LE studies, researchers leverage external linguistic constraints to transform word embeddings for LE relation. In our system, we expand the number of external constraints in multiple languages to obtain more specialised multilingual word embeddings. For the cross-lingual subtask, we apply a bilingual word embeddings mapping method in the model. The mapping method takes specialised embeddings as inputs and is able to retain the embeddings{'} LE features after operations. Our results for multilingual subtask are about 20{\%} and 10{\%} higher than the baseline in graded and binary prediction respectively.",
}
@inproceedings{hauer-etal-2020-ualberta,
    title = "{UA}lberta at {S}em{E}val-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment",
    author = "Hauer, Bradley  and
      Habibi, Amir Ahmad  and
      Luan, Yixing  and
      Mallik, Arnob  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.32",
    pages = "263--269",
    abstract = "We investigate the hypothesis that translations can be used to identify cross-lingual lexical entailment. We propose novel methods that leverage parallel corpora, word embeddings, and multilingual lexical resources. Our results demonstrate that the implementation of these ideas leads to improvements in predicting entailment.",
}
@inproceedings{mahmoud-torki-2020-alexu,
    title = "{A}lex{U}-{AUX}-{BERT} at {S}em{E}val-2020 Task 3: Improving {BERT} Contextual Similarity Using Multiple Auxiliary Contexts",
    author = "Mahmoud, Somaia  and
      Torki, Marwan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.33",
    pages = "270--274",
    abstract = "This paper describes the system we built for SemEval-2020 task 3. That is predicting the scores of similarity for a pair of words within two different contexts. Our system is based on both BERT embeddings and WordNet. We simply use cosine similarity to find the closest synset of the target words. Our results show that using this simple approach greatly improves the system behavior. Our model is ranked 3rd in subtask-2 for SemEval-2020 task 3.",
}
@inproceedings{gamallo-2020-citiusnlp,
    title = "{C}itius{NLP} at {S}em{E}val-2020 Task 3: Comparing Two Approaches for Word Vector Contextualization",
    author = "Gamallo, Pablo",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.34",
    pages = "275--280",
    abstract = "This article describes some unsupervised strategies submitted to SemEval 2020 Task 3, a task which consists of considering the effect of context to compute word similarity. More precisely, given two words in context, the system must predict the degree of similarity of those words considering the context in which they occur, and the system score is compared against human prediction. We compare one approach based on pre-trained BERT models with other strategy relying on static word embeddings and syntactic dependencies. The BERT-based method clearly outperformed the dependency-based strategy.",
}
@inproceedings{chen-etal-2020-ferryman,
    title = "Ferryman at {S}em{E}val-2020 Task 3: Bert with {TFIDF}-Weighting for Predicting the Effect of Context in Word Similarity",
    author = "Chen, Weilong  and
      Yuan, Xin  and
      Zhang, Sai  and
      Wu, Jiehui  and
      Zhang, Yanru  and
      Wang, Yan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.35",
    pages = "281--285",
    abstract = "Word similarity is widely used in machine learning applications like searching engine and recommendation. Measuring the changing meaning of the same word between two different sentences is not only a way to handle complex features in word usage (such as sentence syntax and semantics), but also an important method for different word polysemy modeling. In this paper, we present the methodology proposed by team Ferryman. Our system is based on the Bidirectional Encoder Representations from Transformers (BERT) model combined with term frequency-inverse document frequency (TF-IDF), applying the method on the provided datasets called CoSimLex, which covers four different languages including English, Croatian, Slovene, and Finnish. Our team Ferryman wins the the first position for English task and the second position for Finnish in the subtask 1.",
}
@inproceedings{morishita-etal-2020-hitachi,
    title = "Hitachi at {S}em{E}val-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity",
    author = "Morishita, Terufumi  and
      Morio, Gaku  and
      Ozaki, Hiroaki  and
      Miyoshi, Toshinori",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.36",
    pages = "286--291",
    abstract = "In this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity. Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity measures induced by different layers of different pre-trained Transformer-based language models, which can be good approximations of the human sense of word similarity. Interestingly, our experiments reveal a language-independent characteristic: the middle to upper layers of Transformer-based language models can induce good approximate similarity measures. Finally, our system was ranked 1st on the Slovenian part of Subtask1 and 2nd on the Croatian part of both Subtask1 and Subtask2.",
}
@inproceedings{al-khdour-etal-2020-justmasters,
    title = "{JUSTM}asters at {S}em{E}val-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity",
    author = "Al-khdour, Nour  and
      Bni Younes, Mutaz  and
      Abdullah, Malak  and
      AL-Smadi, Mohammad",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.37",
    pages = "292--300",
    abstract = "There is a growing research interest in studying word similarity. Without a doubt, two similar words in a context may considered different in another context. Therefore, this paper investigates the effect of the context in word similarity. The SemEval-2020 workshop has provided a shared task (Task 3: Predicting the (Graded) Effect of Context in Word Similarity). In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish and Slovenian. Our team, JUSTMasters, has participated in this competition in the two subtasks: A and B. Our approach has used a weighted average ensembling method for different pretrained embeddings techniques for each of the four languages. Our proposed model outperformed the baseline models in both subtasks and acheived the best result for subtask 2 in English and Finnish, with score 0.725 and 0.68 respectively. We have been ranked the sixth for subtask 1, with scores for English, Croatian, Finnish, and Slovenian as follows: 0.738, 0.44, 0.546, 0.512.",
}
@inproceedings{bao-etal-2020-will,
    title = "{W}ill{\_}{G}o at {S}em{E}val-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on {BERT}",
    author = "Bao, Wei  and
      Che, Hongshu  and
      Zhang, Jiandong",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.38",
    pages = "301--306",
    abstract = "Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years. Our paper mainly discusses a methodology to analyze the effect that context has on human perception of similar words, which is the third task of SemEval 2020. We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT). Our team will go won the 1st place in Finnish language track of subtask1, the second place in English track of subtask1.",
}
@inproceedings{wang-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 4: Commonsense Validation and Explanation",
    author = "Wang, Cunxiang  and
      Liang, Shuailong  and
      Jin, Yili  and
      Wang, Yilong  and
      Zhu, Xiaodan  and
      Zhang, Yue",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.39",
    pages = "307--321",
    abstract = "In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to humans from one that does not, and provide the reasons. Specifically, in our first subtask, the participating systems are required to choose from two natural language statements of similar wording the one that makes sense and the one does not. The second subtask additionally asks a system to select the key reason from three options why a given statement does not make sense. In the third subtask, a participating system needs to generate the reason automatically. 39 teams submitted their valid systems to at least one subtask. For Subtask A and Subtask B, top-performing teams have achieved results closed to human performance. However, for Subtask C, there is still a considerable gap between system and human performance. The dataset used in our task can be found at https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation.",
}
@inproceedings{yang-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 5: Counterfactual Recognition",
    author = "Yang, Xiaoyu  and
      Obadinma, Stephen  and
      Zhao, Huasha  and
      Zhang, Qiong  and
      Matwin, Stan  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.40",
    pages = "322--335",
    abstract = "We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequent with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. Our data and baseline code are made publicly available at https://zenodo.org/record/3932442. The task website and leaderboard can be found at https://competitions.codalab.org/competitions/21691.",
}
@inproceedings{spala-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 6: Definition Extraction from Free Text with the {DEFT} Corpus",
    author = "Spala, Sasha  and
      Miller, Nicholas  and
      Dernoncourt, Franck  and
      Dockhorn, Carl",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.41",
    pages = "336--345",
    abstract = "Research on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered. In this work, we present DeftEval, a SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflects the complex reality of definitions in natural language. Definitions and glosses in free text often appear without explicit indicators, across sentences boundaries, or in an otherwise complex linguistic manner. DeftEval involved 3 distinct subtasks: 1) Sentence classification, 2) sequence labeling, and 3) relation extraction.",
}
@inproceedings{xing-etal-2020-iie,
    title = "{IIE}-{NLP}-{NUT} at {S}em{E}val-2020 Task 4: Guiding {PLM} with Prompt Template Reconstruction Strategy for {C}om{VE}",
    author = "Xing, Luxi  and
      Xie, Yuqiang  and
      Hu, Yue  and
      Peng, Wei",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.42",
    pages = "346--353",
    abstract = "This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation. To clarify the intention for judgment and inject contrastive information for selection, we propose the input reconstruction strategy with prompt templates. Specifically, we formalize the subtasks into the multiple-choice question answering format and construct the input with the prompt templates, then, the final prediction of question answering is considered as the result of subtasks. Experimental results show that our approaches achieve significant performance compared with the baseline systems. Our approaches secure the third rank on both official test sets of the first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 respectively.",
}
@inproceedings{ding-etal-2020-hit,
    title = "{HIT}-{SCIR} at {S}em{E}val-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection",
    author = "Ding, Xiao  and
      Hao, Dingkui  and
      Zhang, Yuewei  and
      Liao, Kuo  and
      Li, Zhongyang  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.43",
    pages = "354--360",
    abstract = "We describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly drives today{'}s AI to strong AI, as it lacks of causation, which is a fundamental concept in human thinking and reasoning. In this task, we dedicate to detecting causation, especially counterfactuals from texts. We explore multiple pre-trained models to learn basic features and then fine-tune models with counterfactual data and pseudo-labeling data. Our team HIT-SCIR wins the first place (1st) in Sub-task 1 {---} Detecting Counterfactual Statements and is ranked 4th in Sub-task 2 {---} Detecting Antecedent and Consequence. In this paper we provide a detailed description of the approach, as well as the results obtained in this task.",
}
@inproceedings{jeawak-etal-2020-cardiff,
    title = "{C}ardiff {U}niversity at {S}em{E}val-2020 Task 6: Fine-tuning {BERT} for Domain-Specific Definition Classification",
    author = "Jeawak, Shelan  and
      Espinosa-Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.44",
    pages = "361--366",
    abstract = "We describe the system submitted to SemEval-2020 Task 6, Subtask 1. The aim of this subtask is to predict whether a given sentence contains a definition or not. Unsurprisingly, we found that strong results can be achieved by fine-tuning a pre-trained BERT language model. In this paper, we analyze the performance of this strategy. Among others, we show that results can be improved by using a two-step fine-tuning process, in which the BERT model is first fine-tuned on the full training set, and then further specialized towards a target domain.",
}
@inproceedings{konar-etal-2020-ana,
    title = "{ANA} at {S}em{E}val-2020 Task 4: {MU}lti-task lear{NI}ng for c{O}mmonsense reaso{N}ing ({UNION})",
    author = "Konar, Anandh  and
      Huang, Chenyang  and
      Trabelsi, Amine  and
      Zaiane, Osmar",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.45",
    pages = "367--373",
    abstract = "In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement is non-sensical. However, we found in the early experiments that simple adaptations such as fine-tuning GPT2 often yield dull and non-informative generations (e.g. simple negations). In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning. In order to perform model selection efficiently, accurately, and promptly, we also propose a couple of auxiliary automatic evaluation metrics so that we can extensively compare the models from different perspectives. Our submitted system not only results in a good performance in the proposed metrics but also outperforms its competitors with the highest achieved score of 2.10 for human evaluation while remaining a BLEU score of 15.7. Our code is made publicly available.",
}
@inproceedings{jon-etal-2020-fit,
    title = "{BUT}-{FIT} at {S}em{E}val-2020 Task 4: Multilingual Commonsense",
    author = "Jon, Josef  and
      Fajcik, Martin  and
      Docekal, Martin  and
      Smrz, Pavel",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.46",
    pages = "374--390",
    abstract = "We participated in all three subtasks. In subtasks A and B, our submissions are based on pretrained language representation models (namely ALBERT) and data augmentation. We experimented with solving the task for another language, Czech, by means of multilingual models and machine translated dataset, or translated model inputs. We show that with a strong machine translation system, our system can be used in another language with a small accuracy loss. In subtask C, our submission, which is based on pretrained sequence-to-sequence model (BART), ranked 1st in BLEU score ranking, however, we show that the correlation between BLEU and human evaluation, in which our submission ended up 4th, is low. We analyse the metrics used in the evaluation and we propose an additional score based on model from subtask B, which correlates well with our manual ranking, as well as reranking method based on the same principle. We performed an error and dataset analysis for all subtasks and we present our findings.",
}
@inproceedings{wang-etal-2020-cuhk,
    title = "{CUHK} at {S}em{E}val-2020 Task 4: {C}ommon{S}ense Explanation, Reasoning and Prediction with Multi-task Learning",
    author = "Wang, Hongru  and
      Tang, Xiangru  and
      Lai, Sunny  and
      Leung, Kwong Sak  and
      Zhu, Jia  and
      Fung, Gabriel Pui Cheong  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.47",
    pages = "391--400",
    abstract = "This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks. The task is to directly validate the given sentence whether or not to make sense and require the model to explain it. Based on BERT architecture with the multi-task setting, we propose an effective and interpretable {``}Explain, Reason and Predict{''} (ERP) system to solve the three sub-tasks about commonsense: (a) Validation, (b) Reasoning, and (c) Explanation. Inspired by cognitive studies of common sense, our system first generates a reason or understanding of the sentences and then choose which one statement makes sense, which is achieved by multi-task learning. During the post-evaluation, our system has reached 92.9{\%} accuracy in subtask A (rank 11), 89.7{\%} accuracy in subtask B (rank 9), and BLEU score of 12.9 in subtask C (rank 8).",
}
@inproceedings{zhao-etal-2020-ecnu,
    title = "{ECNU}-{S}ense{M}aker at {S}em{E}val-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation",
    author = "Zhao, Qian  and
      Tao, Siyu  and
      Zhou, Jie  and
      Wang, Linlin  and
      Lin, Xin  and
      He, Liang",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.48",
    pages = "401--410",
    abstract = "This paper describes our system for SemEval-2020 Task 4: Commonsense Validation and Explanation (Wang et al., 2020). We propose a novel Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task, leveraging heterogeneous knowledge from both the structured knowledge base (i.e. ConceptNet) and unstructured text to better improve the ability of a machine in commonsense understanding. This model has a powerful commonsense inference capability via utilizing suitable commonsense incorporation methods and upgraded data augmentation techniques. Besides, an internal sharing mechanism is cooperated to prohibit our model from insufficient and excessive reasoning for commonsense. As a result, this model performs quite well in both validation and explanation. For instance, it achieves state-of-the-art accuracy in the subtask called Commonsense Explanation (Multi-Choice). We officially name the system as ECNU-SenseMaker. Code is publicly available at https://github.com/ECNU-ICA/ECNU-SenseMaker.",
}
@inproceedings{lu-2020-masked,
    title = "Masked Reasoner at {S}em{E}val-2020 Task 4: Fine-Tuning {R}o{BERT}a for Commonsense Reasoning",
    author = "Lu, Daming",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.49",
    pages = "411--414",
    abstract = "This paper describes the masked reasoner system that participated in SemEval-2020 Task 4: Commonsense Validation and Explanation. The system participated in the subtask B.We proposes a novel method to fine-tune RoBERTa by masking the most important word in the statement. We believe that the confidence of the system in recovering that word is positively correlated to the score the masked language model gives to the current statement-explanation pair. We evaluate the importance of each word using InferSent and do the masked fine-tuning on RoBERTa. Then we use the fine-tuned model to predict the most plausible explanation. Our system is fast in training and achieved 73.5{\%} accuracy.",
}
@inproceedings{pai-2020-qiaoning,
    title = "{Q}iao{N}ing at {S}em{E}val-2020 Task 4: Commonsense Validation and Explanation System Based on Ensemble of Language Model",
    author = "Pai, Liu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.50",
    pages = "415--421",
    abstract = "The ability of common sense validation and explanation is very important for most models. Most obviously, this will directly affect the rationality of the generated model output. The large amount and diversity of common sense poses great challenges to this task. In addition, many common sense expressions are obscure, thus we need to understand the meaning contained in the vocabulary in order to judge correctly, which further increases the model{'}s requirements for the accuracy of word representation. The current neural network models are often data-driven, while the annotated data is often limited and requires a lot of manual labeling. In such case, we proposed transfer learning to handle this challenge. From our experiments, we can draw the following three main conclusions: a) Neural language model fully qualified for commonsense validation and explanation. We attribute this to the powerful word and sentence representation capabilities of language models. b) The inconsistency of task of pre-training and fine-tuning will badly hurt the performance. c) A larger amount of corpus and more parameters will enhance the common sense of the model. At the same time, the content of the corpus is equally important.",
}
@inproceedings{ben-rim-okazaki-2020-swagex,
    title = "{SWAG}ex at {S}em{E}val-2020 Task 4: Commonsense Explanation as Next Event Prediction",
    author = "Ben Rim, Wiem  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.51",
    pages = "422--429",
    abstract = "We describe the system submitted by the SWAGex team to the SemEval-2020 Commonsense Validation and Explanation Task. We use multiple methods on the pre-trained language model BERT (Devlin et al., 2018) for tasks that require the system to recognize sentences against commonsense and justify the reasoning behind this decision. Our best performing model is BERT trained on SWAG and fine-tuned for the task. We investigate the ability to transfer commonsense knowledge from SWAG to SemEval-2020 by training a model for the Explanation task with Next Event Prediction data",
}
@inproceedings{markchom-etal-2020-uor,
    title = "{U}o{R} at {S}em{E}val-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation",
    author = "Markchom, Thanet  and
      Dhruva, Bhuvana  and
      Pravin, Chandresh  and
      Liang, Huizhi",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.52",
    pages = "430--436",
    abstract = "SemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense. Two subtasks, A and B, are focused in this work, i.e., detecting against-common-sense statements and selecting explanations of why they are false from the given options. Intuitively, commonsense validation requires additional knowledge beyond the given statements. Therefore, we propose a system utilising pre-trained sentence transformer models based on BERT, RoBERTa and DistillBERT architectures to embed the statements before classification. According to the results, these embeddings can improve the performance of the typical MLP and LSTM classifiers as downstream models of both subtasks compared to regular tokenised statements. These embedded statements are shown to comprise additional information from external resources which help validate common sense in natural language.",
}
@inproceedings{fajcik-etal-2020-fit,
    title = "{BUT}-{FIT} at {S}em{E}val-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models",
    author = "Fajcik, Martin  and
      Jon, Josef  and
      Docekal, Martin  and
      Smrz, Pavel",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.53",
    pages = "437--444",
    abstract = "This paper describes BUT-FIT{'}s submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a given statement contains a counterfactual (Subtask 1) and extracting both antecedent and consequent parts of the counterfactual from the text (Subtask 2). We experimented with various state-of-the-art language representation models (LRMs). We found RoBERTa LRM to perform the best in both subtasks. We achieved the first place in both exact match and F1 for Subtask 2 and ranked second for Subtask 1.",
}
@inproceedings{sung-etal-2020-clac,
    title = "{CL}a{C} at {S}em{E}val-2020 Task 5: Muli-task Stacked {B}i-{LSTM}s",
    author = "Sung, MinGyou  and
      Bagherzadeh, Parsa  and
      Bergler, Sabine",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.54",
    pages = "445--450",
    abstract = "We consider detection of the span of antecedents and consequents in argumentative prose a structural, grammatical task. Our system comprises a set of stacked Bi-LSTMs trained on two complementary linguistic annotations. We explore the effectiveness of grammatical features (POS and clause type) through ablation. The reported experiments suggest that a multi-task learning approach using this external, grammatical knowledge is useful for detecting the extent of antecedents and consequents and performs nearly as well without the use of word embeddings.",
}
@inproceedings{patil-baths-2020-cnrl,
    title = "{CNRL} at {S}em{E}val-2020 Task 5: Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights Based Counterfactual Detection",
    author = "Patil, Rajaswa  and
      Baths, Veeky",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.55",
    pages = "451--457",
    abstract = "In this paper, we describe an approach for modelling causal reasoning in natural language by detecting counterfactuals in text using multi-head self-attention weights. We use pre-trained transformer models to extract contextual embeddings and self-attention weights from the text. We show the use of convolutional layers to extract task-specific features from these self-attention weights. Further, we describe a fine-tuning approach with a common base model for knowledge sharing between the two closely related sub-tasks for counterfactual detection. We analyze and compare the performance of various transformer models in our experiments. Finally, we perform a qualitative analysis with the multi-head self-attention weights to interpret our models{'} dynamics.",
}
@inproceedings{ojha-etal-2020-iitk,
    title = "{IITK}-{RSA} at {S}em{E}val-2020 Task 5: Detecting Counterfactuals",
    author = "Ojha, Anirudh Anil  and
      Garg, Rohin  and
      Gupta, Shashank  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.56",
    pages = "458--467",
    abstract = "This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also explored various other approaches that involved classical methods, other neural architectures and incorporation of different linguistic features.",
}
@inproceedings{abi-akl-etal-2020-yseop,
    title = "Yseop at {S}em{E}val-2020 Task 5: Cascaded {BERT} Language Model for Counterfactual Statement Analysis",
    author = "Abi-Akl, Hanna  and
      Mariko, Dominique  and
      Labidurie, Estelle",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.57",
    pages = "468--478",
    abstract = "In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the sequence identification task. Our experiments show that while introducing syntactic and semantic features does little in improving the system in the classification task, using these types of features as cascaded linear inputs to fine-tune the sequence-delimiting ability of the model ensures it outperforms other similar-purpose complex systems like BiLSTM-CRF in the second task. Our system achieves an F1 score of 85.00{\%} in Task 1 and 83.90{\%} in Task 2.",
}
@inproceedings{caspani-etal-2020-acnlp,
    title = "{ACNLP} at {S}em{E}val-2020 Task 6: A Supervised Approach for Definition Extraction",
    author = "Caspani, Fabien  and
      Ratnamogan, Pirashanth  and
      Linger, Mathis  and
      Hajaiej, Mhamed",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.58",
    pages = "479--486",
    abstract = "We describe our contribution to two of the subtasks of SemEval 2020 Task 6, DeftEval: Extracting term-definition pairs in free text. The system for Subtask 1: Sentence Classification is based on a transformer architecture where we use transfer learning to fine-tune a pretrained model on the downstream task, and the one for Subtask 3: Relation Classification uses a Random Forest classifier with handcrafted dedicated features. Our systems respectively achieve 0.830 and 0.994 F1-scores on the official test set, and we believe that the insights derived from our study are potentially relevant to help advance the research on definition extraction.",
}
@inproceedings{davletov-etal-2020-gorynych,
    title = "Gorynych Transformer at {S}em{E}val-2020 Task 6: Multi-task Learning for Definition Extraction",
    author = "Davletov, Adis  and
      Arefyev, Nikolay  and
      Shatilov, Alexander  and
      Gordeev, Denis  and
      Rey, Alexey",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.59",
    pages = "487--493",
    abstract = "This paper describes our approach to {``}DeftEval: Extracting Definitions from Free Text in Textbooks{''} competition held as a part of Semeval 2020. The task was devoted to finding and labeling definitions in texts. DeftEval was split into three subtasks: sentence classification, sequence labeling and relation classification. Our solution ranked 5th in the first subtask and 23rd and 21st in the second and the third subtasks respectively. We applied simultaneous multi-task learning with Transformer-based models for subtasks 1 and 3 and a single BERT-based model for named entity recognition.",
}
@inproceedings{zhang-etal-2020-cn,
    title = "{CN}-{HIT}-{IT}.{NLP} at {S}em{E}val-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples",
    author = "Zhang, Yice  and
      Lin, Jiaxuan  and
      Fan, Yang  and
      Jin, Peng  and
      Liu, Yuanchao  and
      Liu, Bingquan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.60",
    pages = "494--500",
    abstract = "This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements. But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus. This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs. Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT. We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.",
}
@inproceedings{dash-etal-2020-cs,
    title = "{CS}-{NET} at {S}em{E}val-2020 Task 4: {S}iamese {BERT} for {C}om{VE}",
    author = "Dash, Soumya Ranjan  and
      Routray, Sandeep  and
      Varshney, Prateek  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.61",
    pages = "501--506",
    abstract = "In this paper, we describe our system for Task 4 of SemEval 2020, which involves differentiating between natural language statements that conform to common sense and those that do not. The organizers propose three subtasks - first, selecting between two sentences, the one which is against common sense. Second, identifying the most crucial reason why a statement does not make sense. Third, generating novel reasons for explaining the against common sense statement. Out of the three subtasks, this paper reports the system description of subtask A and subtask B. This paper proposes a model based on transformer neural network architecture for addressing the subtasks. The novelty in work lies in the architecture design, which handles the logical implication of contradicting statements and simultaneous information extraction from both sentences. We use a parallel instance of transformers, which is responsible for a boost in the performance. We achieved an accuracy of 94.8{\%} in subtask A and 89{\%} in subtask B on the test set.",
}
@inproceedings{saeedi-etal-2020-cs,
    title = "{CS}-{NLP} Team at {S}em{E}val-2020 Task 4: Evaluation of State-of-the-art {NLP} Deep Learning Architectures on Commonsense Reasoning Task",
    author = "Saeedi, Sirwe  and
      Panahi, Aliakbar  and
      Saeedi, Seyran  and
      C Fong, Alvis",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.62",
    pages = "507--515",
    abstract = "In this paper, we investigate a commonsense inference task that unifies natural language understanding and commonsense reasoning. We describe our attempt at SemEval-2020 Task 4 competition: Commonsense Validation and Explanation (ComVE) challenge. We discuss several state-of-the-art deep learning architectures for this challenge. Our system uses prepared labeled textual datasets that were manually curated for three different natural language inference subtasks. The goal of the first subtask is to test whether a model can distinguish between natural language statements that make sense and those that do not make sense. We compare the performance of several language models and fine-tuned classifiers. Then, we propose a method inspired by question/answering tasks to treat a classification problem as a multiple choice question task to boost the performance of our experimental results (96.06{\%}), which is significantly better than the baseline. For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93.7{\%}) among 27 participants with very competitive results. Our result for last subtask of generating reason against the nonsense statement shows many potentials for future researches as we applied the most powerful generative model of language (GPT-2) with 6.1732 BLEU score among first four teams. .",
}
@inproceedings{bai-zhou-2020-deepyang,
    title = "{DEEPYANG} at {S}em{E}val-2020 Task 4: Using the Hidden Layer State of {BERT} Model for Differentiating Common Sense",
    author = "Bai, Yang  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.63",
    pages = "516--520",
    abstract = "Introducing common sense to natural language understanding systems has received increasing research attention. To facilitate the researches on common sense reasoning, the SemEval-2020 Task 4 Commonsense Validation and Explanation(ComVE) is proposed. We participate in sub-task A and try various methods including traditional machine learning methods, deep learning methods, and also recent pre-trained language models. Finally, we concatenate the original output of BERT and the output vector of BERT hidden layer state to obtain more abundant semantic information features, and obtain competitive results. Our model achieves an accuracy of 0.8510 in the final test data and ranks 25th among all the teams.",
}
@inproceedings{al-jarrah-etal-2020-hr,
    title = "{HR}@{JUST} Team at {S}em{E}val-2020 Task 4: The Impact of {R}o{BERT}a Transformer for Evaluation Common Sense Understanding",
    author = "Al-Jarrah, Heba  and
      Al-Hamouri, Rahaf  and
      AL-Smadi, Mohammad",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.64",
    pages = "521--526",
    abstract = "This paper describes the results of our team HR@JUST participation at SemEval-2020 Task 4 - Commonsense Validation and Explanation (ComVE) for POST evaluation period. The provided task consists of three sub-tasks, we participate in task A. We considered a state-of-the-art approach for solving this task by performing RoBERTa model with no Next Sentences Prediction (NSP), dynamic masking, larger training data, and larger batch size. The achieved results show that we got the 11th rank on the final test set leaderboard with an accuracy of 91.3{\%}.",
}
@inproceedings{na-lee-2020-jbnu,
    title = "{JBNU} at {S}em{E}val-2020 Task 4: {BERT} and {U}ni{LM} for Commonsense Validation and Explanation",
    author = "Na, Seung-Hoon  and
      Lee, Jong-Hyeon",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.65",
    pages = "527--534",
    abstract = "This paper presents our contributions to the SemEval-2020 Task 4 Commonsense Validation and Explanation (ComVE) and includes the experimental results of the two Subtasks B and C of the SemEval-2020 Task 4. Our systems rely on pre-trained language models, i.e., BERT (including its variants) and UniLM, and rank 10th and 7th among 27 and 17 systems on Subtasks B and C, respectively. We analyze the commonsense ability of the existing pretrained language models by testing them on the SemEval-2020 Task 4 ComVE dataset, specifically for Subtasks B and C, the explanation subtasks with multi-choice and sentence generation, respectively.",
}
@inproceedings{fadel-etal-2020-justers,
    title = "{JUST}ers at {S}em{E}val-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation",
    author = "Fadel, Ali  and
      Al-Ayyoub, Mahmoud  and
      Cambria, Erik",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.66",
    pages = "535--542",
    abstract = "In this paper, we describe our team{'}s (JUSTers) effort in the Commonsense Validation and Explanation (ComVE) task, which is part of SemEval2020. We evaluate five pre-trained Transformer-based language models with various sizes against the three proposed subtasks. For the first two subtasks, the best accuracy levels achieved by our models are 92.90{\%} and 92.30{\%}, respectively, placing our team in the 12th and 9th places, respectively. As for the last subtask, our models reach 16.10 BLEU score and 1.94 human evaluation score placing our team in the 5th and 3rd places according to these two metrics, respectively. The latter is only 0.16 away from the 1st place human evaluation score.",
}
@inproceedings{wan-huang-2020-kalm,
    title = "{K}a{LM} at {S}em{E}val-2020 Task 4: Knowledge-aware Language Models for Comprehension and Generation",
    author = "Wan, Jiajing  and
      Huang, Xinting",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.67",
    pages = "543--550",
    abstract = "This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.",
}
@inproceedings{mendbayar-aono-2020-kde,
    title = "{KDE} {S}ense{F}orce at {S}em{E}val-2020 Task 4: Exploiting {BERT} for Commonsense Validation and Explanation",
    author = "Mendbayar, Khanddorj  and
      Aono, Masaki",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.68",
    pages = "551--555",
    abstract = "Using a natural language understanding system for commonsense comprehension is getting increasing attention from researchers. Current multi-purpose state-of-the-art models suffer on commonsense validation and explanation tasks. We have adopted one of the state-of-the-art models and proposing a method to boost the performance of the model in commonsense related tasks.",
}
@inproceedings{li-etal-2020-lijunyi,
    title = "Lijunyi at {S}em{E}val-2020 Task 4: An {ALBERT} Model Based Maximum Ensemble with Different Training Sizes and Depths for Commonsense Validation and Explanation",
    author = "Li, Junyi  and
      Wang, Bin  and
      Ding, Haiyan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.69",
    pages = "556--561",
    abstract = "This article describes the system submitted to SemEval 2020 Task 4: Commonsense Validation and Explanation. We only participated in the subtask A, which is mainly to distinguish whether the sentence has meaning. To solve this task, we mainly used ALBERT model-based maximum ensemble with different training sizes and depths. To prove the validity of the model to the task, we also used some other neural network models for comparison. Our model achieved the accuracy score of 0.938(ranked 10/41) in subtask A.",
}
@inproceedings{liu-etal-2020-lmve,
    title = "{LMVE} at {S}em{E}val-2020 Task 4: Commonsense Validation and Explanation Using Pretraining Language Model",
    author = "Liu, Shilei  and
      Guo, Yu  and
      Li, BoChao  and
      Ren, Feiliang",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.70",
    pages = "562--568",
    abstract = "This paper introduces our system for commonsense validation and explanation. For Sen-Making task, we use a novel pretraining language model based architecture to pick out one of the two given statements that is againstcommon sense. For Explanation task, we use a hint sentence mechanism to improve the performance greatly. In addition, we propose a subtask level transfer learning to share information between subtasks.",
}
@inproceedings{collins-etal-2020-mxgra,
    title = "Mxgra at {S}em{E}val-2020 Task 4: Common Sense Making with Next Token Prediction",
    author = "Collins, Kris  and
      Grathwohl, Max  and
      Ahmed, Heba",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.71",
    pages = "569--573",
    abstract = "In this paper, we explore solutions to a common sense making task in which a model must discern which of two sentences is against common sense. We used a pre-trained language model which we used to calculate complexity scores for input to discern which sentence contained an unlikely sequence of tokens. Other approaches we tested were word vector distances, which were used to find semantic outliers within a sentence, and siamese network. By using the pre-trained language model to calculate perplexity scores based on the sequence of tokens in input sentences, we achieved an accuracy of 75 percent.",
}
@inproceedings{al-bashabsheh-etal-2020-nlp,
    title = "{NLP}@{JUST} at {S}em{E}val-2020 Task 4: Ensemble Technique for {BERT} and Roberta to Evaluate Commonsense Validation",
    author = "Al-Bashabsheh, Emran  and
      Abu Aqouleh, Ayah  and
      AL-Smadi, Mohammad",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.72",
    pages = "574--579",
    abstract = "This paper presents the work of the NLP@JUST team at SemEval-2020 Task 4 competition that related to commonsense validation and explanation (ComVE) task. The team participates in sub-taskA (Validation) which related to validation that checks if the text is against common sense or not. Several models have trained (\textit{i.e. Bert, XLNet, and Roberta}), however, the main models used are the RoBERTa-large and BERT Whole word masking. As well as, we utilized the results from both models to generate final prediction by using the average Ensemble technique, that used to improve the overall performance. The evaluation result shows that the implemented model achieved an accuracy of 93.9{\%} obtained and published at the post-evaluation result on the leaderboard.",
}
@inproceedings{k-etal-2020-ssn,
    title = "{SSN}-{NLP} at {S}em{E}val-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks",
    author = "K., Rishivardhan  and
      S, Kayalvizhi  and
      D., Thenmozhi  and
      R., Raghav  and
      Sharma, Kshitij",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.73",
    pages = "580--584",
    abstract = "Common sense validation deals with testing whether a system can differentiate natural language statements that make sense from those that do not make sense. This paper describes the our approach to solve this challenge. For common sense validation with multi choice, we propose a stacking based approach to classify sentences that are more favourable in terms of common sense to the particular statement. We have used majority voting classifier methodology amongst three models such as Bidirectional Encoder Representations from Transformers (BERT), Micro Text Classification (Micro TC) and XLNet. For sentence generation, we used Neural Machine Translation (NMT) model to generate explanatory sentences.",
}
@inproceedings{srivastava-etal-2020-team,
    title = "Team {S}olomon at {S}em{E}val-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning",
    author = "Srivastava, Vertika  and
      Sahoo, Sudeep Kumar  and
      Kim, Yeon Hyang  and
      R.r, Rohit  and
      Raj, Mayank  and
      Jaiswal, Ajay",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.74",
    pages = "585--593",
    abstract = "In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don{'}t. ComVE comprises of three subtasks to challenge and test a system{'}s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96{\%} and 94{\%} accuracy respectively standing at 4th position in both the subtasks.",
}
@inproceedings{mohammed-abdullah-2020-teamjust,
    title = "{T}eam{JUST} at {S}em{E}val-2020 Task 4: Commonsense Validation and Explanation Using Ensembling Techniques",
    author = "Mohammed, Roweida  and
      Abdullah, Malak",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.75",
    pages = "594--600",
    abstract = "Common sense for natural language processing methods has been attracting a wide research interest, recently. Estimating automatically whether a sentence makes sense or not is considered an essential question. Task 4 in the International Workshop SemEval 2020 has provided three subtasks (A, B, and C) that challenges the participants to build systems for distinguishing the common sense statements from those that do not make sense. This paper describes TeamJUST{'}s approach for participating in subtask A to differentiate between two sentences in English and classify them into two classes: common sense and uncommon sense statements. Our approach depends on ensembling four different state-of-the-art pre-trained models (BERT, ALBERT, Roberta, and XLNet). Our baseline model which we used only the pre-trained model of BERT has scored 89.1, while the TeamJUST model outperformed the baseline model with an accuracy score of 96.2. We have improved the results in the post-evaluation period to achieve our best result, which would rank the 4th in the competition if we had the chance to use our latest experiment.",
}
@inproceedings{teo-2020-tr,
    title = "{TR} at {S}em{E}val-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation",
    author = "Teo, Don",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.76",
    pages = "601--608",
    abstract = "In this paper, we present our submission for subtask A of the Common Sense Validation and Explanation (ComVE) shared task. We examine the ability of large-scale pre-trained language models to distinguish commonsense from non-commonsense statements. We also explore the utility of external resources that aim to supplement the world knowledge inherent in such language models, including commonsense knowledge graph embedding models, word concreteness ratings, and text-to-image generation models. We find that such resources provide insignificant gains to the performance of fine-tuned language models. We also provide a qualitative analysis of the limitations of the language model fine-tuned to this task.",
}
@inproceedings{cusmuliuc-etal-2020-uaics,
    title = "{UAICS} at {S}em{E}val-2020 Task 4: Using a Bidirectional Transformer for Task a",
    author = "Cusmuliuc, Ciprian-Gabriel  and
      Coca, Lucia-Georgiana  and
      Iftene, Adrian",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.77",
    pages = "609--613",
    abstract = "Commonsense Validation and Explanation has been a difficult task for machines since the dawn of computing. Although very trivial to humans it poses a high complexity for machines due to the necessity of inference over a pre-existing knowledge base. In order to try and solve this problem the SemEval 2020 Task 4 - {''}Commonsense Validation and Explanation (ComVE){''} aims to evaluate systems capable of multiple stages of ComVE. The challenge includes 3 tasks (A, B and C), each with it{'}s own requirements. Our team participated only in task A which required selecting the statement that made the least sense. We choose to use a bidirectional transformer in order to solve the challenge, this paper presents the details of our method, runs and result.",
}
@inproceedings{doxolodeo-mahendra-2020-ui,
    title = "{UI} at {S}em{E}val-2020 Task 4: Commonsense Validation and Explanation by Exploiting Contradiction",
    author = "Doxolodeo, Kerenza  and
      Mahendra, Rahmad",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.78",
    pages = "614--619",
    abstract = "This paper describes our submissions into the ComVe challenge, the SemEval 2020 Task 4. This evaluation task consists of three sub-tasks that test commonsense comprehension by identifying sentences that do not make sense and explain why they do not. In subtask A, we use Roberta to find which sentence does not make sense. In subtask B, besides using BERT, we also experiment with replacing the dataset with MNLI when selecting the best explanation from the provided options why the given sentence does not make sense. In subtask C, we utilize the MNLI model from subtask B to evaluate the explanation generated by Roberta and GPT-2 by exploiting the contradiction of the sentence and their explanation. Our system submission records a performance of 88.2{\%}, 80.5{\%}, and BLEU 5.5 for those three subtasks, respectively.",
}
@inproceedings{wu-wu-2020-warren,
    title = "Warren at {S}em{E}val-2020 Task 4: {ALBERT} and Multi-Task Learning for Commonsense Validation",
    author = "Wu, Yuhang  and
      Wu, Hao",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.79",
    pages = "620--625",
    abstract = "This paper describes our system in subtask A of SemEval 2020 Shared Task 4. We propose a reinforcement learning model based on MTL(Multi-Task Learning) to enhance the prediction ability of commonsense validation. The experimental results demonstrate that our system outperforms the single-task text classification model. We combine MTL and ALBERT pretrain model to achieve an accuracy of 0.904 and our model is ranked 16th on the final leader board of the competition among the 45 teams.",
}
@inproceedings{ou-li-2020-ynu,
    title = "{YNU}-oxz at {S}em{E}val-2020 Task 4: Commonsense Validation Using {BERT} with Bidirectional {GRU}",
    author = "Ou, Xiaozhi  and
      Li, Hongling",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.80",
    pages = "626--632",
    abstract = "This paper describes the system and results of our team participated in SemEval-2020 Task4: Commonsense Validation and Explanation (ComVE), which aim to distinguish meaningful natural language statements from unreasonable natural language statements. This task contains three subtasks: Subtask A{--}Validation, Subtask B{--}Explanation (Multi-Choice), and Subtask C{--} Explanation (Generation). In these three subtasks, we only participated in Subtask A, which aims to distinguish whether a given two natural language statements with similar wording are meaningful. To solve this problem, we proposed a method using a combination of BERT with the Bidirectional Gated Recurrent Unit (Bi-GRU). Our model achieved an accuracy of 0.836 in Subtask A (ranked 27/45).",
}
@inproceedings{liu-yu-2020-blcu,
    title = "{BLCU}-{NLP} at {S}em{E}val-2020 Task 5: Data Augmentation for Efficient Counterfactual Detecting",
    author = "Liu, Chang  and
      Yu, Dong",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.81",
    pages = "633--639",
    abstract = "Counterfactuals describe events counter to facts and hence naturally involve common sense, knowledge, and reasoning. SemEval 2020 task 5 is focusing on this field. We participate in the subtask 1 and we use BERT as our system. Our Innovations are feature extraction and data augmentation. We extract and summarize features of counterfactual statements, augment counterfactual examples in training set with the help of these features, and two general methods of data augmentation is experimented in our work. We demonstrate the effectiveness of our approaches, which achieves 0.95 of subtask 1 in F1 while using only a subset of giving training set to fine-tune the BERT model, and our official submission achieves F1 0.802, which ranks us 16th in the competition.",
}
@inproceedings{bai-zhou-2020-byteam,
    title = "{BY}team at {S}em{E}val-2020 Task 5: Detecting Counterfactual Statements with {BERT} and Ensembles",
    author = "Bai, Yang  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.82",
    pages = "640--644",
    abstract = "We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals). This paper examines different approaches and models towards detecting counterfactual statements classification. We choose the Bert model. However, the output of Bert is not a good summary of semantic information, so in order to obtain more abundant semantic information features, we modify the upper layer structure of Bert. Finally, our system achieves an accuracy of 88.90{\%} and F1 score of 86.30{\%} by hard voting, which ranks 6th on the final leader board of the in subtask 1 competition.",
}
@inproceedings{yabloko-2020-ethan,
    title = "{ETHAN} at {S}em{E}val-2020 Task 5: Modelling Causal Reasoning in Language Using Neuro-symbolic Cloud Computing",
    author = "Yabloko, Len",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.83",
    pages = "645--652",
    abstract = "I present ETHAN: Experimental Testing of Hybrid AI Node implemented entirely on free cloud computing infrastructure. The ultimate goal of this research is to create modular reusable hybrid neuro-symbolic architecture for Artificial Intelligence. As a test case I model natural language comprehension of causal relations from open domain text corpus that combines semi-supervised language model (Huggingface Transformers) with constituency and dependency parsers (Allen Institute for Artificial Intelligence.)",
}
@inproceedings{chen-etal-2020-ferryman-semeval,
    title = "Ferryman as {S}em{E}val-2020 Task 5: Optimized {BERT} for Detecting Counterfactuals",
    author = "Chen, Weilong  and
      Zhuang, Yan  and
      Wang, Peng  and
      Hong, Feng  and
      Wang, Yan  and
      Zhang, Yanru",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.84",
    pages = "653--657",
    abstract = "The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge. Nowadays, counterfactual reasoning has been widely used in various fields. In the realm of natural language process(NLP), counterfactual reasoning has huge potential to improve the correctness of a sentence. In the shared Task 5 of detecting counterfactual in SemEval 2020, we pre-process the officially given dataset according to case conversion, extract stem and abbreviation replacement. We use last-5 bidirectional encoder representation from bidirectional encoder representation from transformer (BERT)and term frequency{--}inverse document frequency (TF-IDF) vectorizer for counterfactual detection. Meanwhile, multi-sample dropout and cross validation are used to improve versatility and prevent problems such as poor generosity caused by overfitting. Finally, our team Ferryman ranked the 8th place in the sub-task 1 of this competition.",
}
@inproceedings{lu-etal-2020-iscas,
    title = "{ISCAS} at {S}em{E}val-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling",
    author = "Lu, Yaojie  and
      Li, Annan  and
      Lin, Hongyu  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.85",
    pages = "658--663",
    abstract = "ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence. This paper describes our system which is based on pretrained transformers. For the first subtask, we train several transformer-based classifiers for detecting counterfactual statements. For the second subtask, we formulate antecedent and consequence extraction as a query-based question answering problem. The two subsystems both achieved third place in the evaluation. Our system is openly released at https://github.com/casnlu/ISCASSemEval2020Task5.",
}
@inproceedings{li-etal-2020-lee,
    title = "Lee at {S}em{E}val-2020 Task 5: {ALBERT} Model Based on the Maximum Ensemble Strategy and Different Data Sampling Methods for Detecting Counterfactual Statements",
    author = "Li, Junyi  and
      Wu, Yuhang  and
      Wang, Bin  and
      Ding, Haiyan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.86",
    pages = "664--669",
    abstract = "This article describes the system submitted to SemEval 2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. In this task, we only participate in the subtask A which is detecting counterfactual statements. In order to solve this sub-task, first of all, because of the problem of data balance, we use the undersampling and oversampling methods to process the data set. Second, we used the ALBERT model and the maximum ensemble method based on the ALBERT model. Our methods achieved a F1 score of 0.85 in subtask A.",
}
@inproceedings{mboning-tchiaze-nouvel-2020-nlu,
    title = "{NLU}-Co at {S}em{E}val-2020 Task 5: {NLU}/{SVM} Based Model Apply Tocharacterise and Extract Counterfactual Items on Raw Data",
    author = "Mboning Tchiaze, Elvis  and
      Nouvel, Damien",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.87",
    pages = "670--676",
    abstract = "In this article, we try to solve the problem of classification of counterfactual statements and extraction of antecedents/consequences in raw data, by mobilizing on one hand Support vector machine (SVMs) and on the other hand Natural Language Understanding (NLU) infrastructures available on the market for conversational agents. Our experiments allowed us to test different pipelines of two known platforms (Snips NLU and Rasa NLU). The results obtained show that a Rasa NLU pipeline, built with a well-preprocessed dataset and tuned algorithms, allows to model accurately the structure of a counterfactual event, in order to facilitate the identification and the extraction of its components.",
}
@inproceedings{babvey-etal-2020-pheonix,
    title = "Pheonix at {S}em{E}val-2020 Task 5: Masking the Labels Lubricates Models for Sequence Labeling",
    author = "Babvey, Pouria  and
      Borrelli, Dario  and
      Zhao, Yutong  and
      Lipizzi, Carlo",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.88",
    pages = "677--682",
    abstract = "This paper presents the deep-learning model that is submitted to the SemEval-2020 Task 5 competition: {``}Detecting Counterfactuals{''}. We participated in both Subtask1 and Subtask2. The model proposed in this paper ranked 2nd in Subtask2 {``}Detecting antecedent and consequence{''}. Our model approaches the task as a sequence labeling. The architecture is built on top of BERT, and a multi-head attention layer with label masking is used to benefit from the mutual information between nearby labels. Also, for prediction, a multi-stage algorithm is used in which the model finalize some predictions with higher certainty in each step and use them in the following. Our results show that masking the labels not only is an efficient regularization method but also improves the accuracy of the model compared with other alternatives like CRF. Label masking can be used as a regularization method in sequence labeling. Also, it improves the performance of the model by learning the specific patterns in the target variable.",
}
@inproceedings{ou-etal-2020-ynu,
    title = "{YNU}-oxz at {S}em{E}val-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons {LSTM} and Hierarchical Attention Network",
    author = "Ou, Xiaozhi  and
      Liu, Shengyan  and
      Li, Hongling",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.89",
    pages = "683--689",
    abstract = "This paper describes the system and results of our team{'}s participation in SemEval-2020 Task5: Modelling Causal Reasoning in Language: Detecting Counterfactuals, which aims to simulate counterfactual semantics and reasoning in natural language. This task contains two subtasks: Subtask1{--}Detecting counterfactual statements and Subtask2{--}Detecting antecedent and consequence. We only participated in Subtask1, aiming to determine whether a given sentence is counterfactual. In order to solve this task, we proposed a system based on Ordered Neurons LSTM (ON-LSTM) with Hierarchical Attention Network (HAN) and used Pooling operation for dimensionality reduction. Finally, we used the K-fold approach as the ensemble method. Our model achieved an F1 score of 0.7040 in Subtask1 (Ranked 16/27).",
}
@inproceedings{zhang-ren-2020-bertatde,
    title = "{BERT}at{DE} at {S}em{E}val-2020 Task 6: Extracting Term-definition Pairs in Free Text Using Pre-trained Model",
    author = "Zhang, Huihui  and
      Ren, Feiliang",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.90",
    pages = "690--696",
    abstract = "Definition extraction is an important task in Nature Language Processing, and it is used to identify the terms and definitions related to terms. The task contains sentence classification task (i.e., classify whether it contains definition) and sequence labeling task (i.e., find the boundary of terms and definitions). The paper describes our system BERTatDE1 in sentence classification task (subtask 1) and sequence labeling task (subtask 2) in the definition extraction (SemEval-2020 Task 6). We use BERT to solve the multi-domain problems including the uncertainty of term boundary that is, different areas have different ways to definite the domain related terms. We use BERT, BiLSTM and attention in subtask 1 and our best result achieved 79.71{\%} in F1 and the eighteenth place in subtask 1. For the subtask 2, we use BERT, BiLSTM and CRF to sequence labeling, and achieve 40.73{\%} in Macro-averaged F1.",
}
@inproceedings{kaparina-soboleva-2020-deftpunk,
    title = "{D}eft{P}unk at {S}em{E}val-2020 Task 6: Using {RNN}-ensemble for the Sentence Classification.",
    author = "Kaparina, Jekaterina  and
      Soboleva, Anna",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.91",
    pages = "697--703",
    abstract = "This paper describes participation in DeftEval 2020 (part of SemEval sharing task competition), and is focused on the sentence classification. Our approach to the task was to create an ensemble of several RNNs combined with fasttext and ELMo embeddings. Results show that various types of models in an ensemble give a performance boost in comparison to standard models. Our model achieved F1-score of 78{\%} for a positive class on the DeftEval dataset.",
}
@inproceedings{hubner-etal-2020-defx,
    title = "Defx at {S}em{E}val-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction",
    author = {H{\"u}bner, Marc  and
      Alt, Christoph  and
      Schwarzenberg, Robert  and
      Hennig, Leonhard},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.92",
    pages = "704--709",
    abstract = "Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definition concepts and the relations among them. Furthermore we provide an ablation study of our model variations and describe the results of an error analysis.",
}
@inproceedings{singh-etal-2020-dsc,
    title = "{DSC} {IIT}-{ISM} at {S}em{E}val-2020 Task 6: Boosting {BERT} with Dependencies for Definition Extraction",
    author = "Singh, Aadarsh  and
      Kumar, Priyanshu  and
      Sinha, Aman",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.93",
    pages = "710--716",
    abstract = "We explore the performance of Bidirectional Encoder Representations from Transformers (BERT) at definition extraction. We further propose a joint model of BERT and Text Level Graph Convolutional Network so as to incorporate dependencies into the model. Our proposed model produces better results than BERT and achieves comparable results to BERT with fine tuned language model in DeftEval (Task 6 of SemEval 2020), a shared task of classifying whether a sentence contains a definition or not (Subtask 1).",
}
@inproceedings{ranasinghe-etal-2020-rgcl,
    title = "{RGCL} at {S}em{E}val-2020 Task 6: Neural Approaches to {D}efinition{E}xtraction",
    author = "Ranasinghe, Tharindu  and
      Plum, Alistair  and
      Orasan, Constantin  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.94",
    pages = "717--723",
    abstract = "This paper presents the RGCL team submission to SemEval 2020 Task 6: DeftEval, subtasks 1 and 2. The system classifies definitions at the sentence and token levels. It utilises state-of-the-art neural network architectures, which have some task-specific adaptations, including an automatically extended training set. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility in architecture selection.",
}
@inproceedings{kannan-santhi-ponnusamy-2020-tukapo,
    title = {{T}{\"u}{K}a{P}o at {S}em{E}val-2020 Task 6: Def(n)tly Not {BERT}: Definition Extraction Using pre-{BERT} Methods in a post-{BERT} World},
    author = "Kannan, Madeeswaran  and
      Santhi Ponnusamy, Haemanth",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.95",
    pages = "724--729",
    abstract = {We describe our system (T{\"u}KaPo) submitted for Task 6: DeftEval, at SemEval 2020. We developed a hybrid approach that combined existing CNN and RNN methods and investigated the impact of purely-syntactic and semantic features on the task of definition extraction. Our final model achieved a F1-score of 0.6851 in subtask 1, i.e, sentence classification.},
}
@inproceedings{xie-etal-2020-unixlong,
    title = "{UNIXLONG} at {S}em{E}val-2020 Task 6: A Joint Model for Definition Extraction",
    author = "Xie, ShuYi  and
      Ma, Jian  and
      Yang, Haiqin  and
      Lianxin, Jiang  and
      Yang, Mo  and
      Shen, Jianping",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.96",
    pages = "730--736",
    abstract = "Definition Extraction is the task to automatically extract terms and their definitions from text. In recent years, it attracts wide interest from NLP researchers. This paper describes the unixlong team{'}s system for the SemEval 2020 task6: DeftEval: Extracting term-definition pairs in free text. The goal of this task is to extract definition, word level BIO tags and relations. This task is challenging due to the free style of the text, especially the definitions of the terms range across several sentences and lack explicit verb phrases. We propose a joint model to train the tasks of definition extraction and the word level BIO tagging simultaneously. We design a creative format input of BERT to capture the location information between entity and its definition. Then we adjust the result of BERT with some rules. Finally, we apply TAG{\_}ID, ROOT{\_}ID, BIO tag to predict the relation and achieve macro-averaged F1 score 1.0 which rank first on the official test set in the relation extraction subtask.",
}
@inproceedings{avram-etal-2020-upb,
    title = "{UPB} at {S}em{E}val-2020 Task 6: Pretrained Language Models for Definition Extraction",
    author = "Avram, Andrei-Marius  and
      Cercel, Dumitru-Clementin  and
      Chiru, Costin",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.97",
    pages = "737--745",
    abstract = "This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different levels of granularity: (1) classification of sentences as definitional or non-definitional, (2) labeling of definitional sentences, and (3) relation classification. We use various pretrained language models (i.e., BERT, XLNet, RoBERTa, SciBERT, and ALBERT) to solve each of the three subtasks of the competition. Specifically, for each language model variant, we experiment by both freezing its weights and fine-tuning them. We also explore a multi-task architecture that was trained to jointly predict the outputs for the second and the third subtasks. Our best performing model evaluated on the DeftEval dataset obtains the 32nd place for the first subtask and the 37th place for the second subtask. The code is available for further research at: \url{https://github.com/avramandrei/DeftEval}",
}
@inproceedings{hossain-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 7: Assessing Humor in Edited News Headlines",
    author = "Hossain, Nabil  and
      Krumm, John  and
      Gamon, Michael  and
      Kautz, Henry",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.98",
    pages = "746--758",
    abstract = "This paper describes the SemEval-2020 shared task {``}Assessing Humor in Edited News Headlines.{''} The task{'}s dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.",
}
@inproceedings{sharma-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!",
    author = {Sharma, Chhavi  and
      Bhageria, Deepesh  and
      Scott, William  and
      PYKL, Srinivas  and
      Das, Amitava  and
      Chakraborty, Tanmoy  and
      Pulabaigari, Viswanath  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.99",
    pages = "759--773",
    abstract = "Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twitter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes- with human annotated labels namely sentiment(positive, negative, neutral), type of emotion(sarcastic,funny,offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes,overall emotion (humor, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.",
}
@inproceedings{patwa-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets",
    author = {Patwa, Parth  and
      Aguilar, Gustavo  and
      Kar, Sudipta  and
      Pandey, Suraj  and
      PYKL, Srinivas  and
      Gamb{\"a}ck, Bj{\"o}rn  and
      Chakraborty, Tanmoy  and
      Solorio, Thamar  and
      Das, Amitava},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.100",
    pages = "774--790",
    abstract = "In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English)and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0{\%} F1 score for Hinglish and 80.6{\%} F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.",
}
@inproceedings{morishita-etal-2020-hitachi-semeval,
    title = "Hitachi at {S}em{E}val-2020 Task 7: Stacking at Scale with Heterogeneous Language Models for Humor Recognition",
    author = "Morishita, Terufumi  and
      Morio, Gaku  and
      Ozaki, Hiroaki  and
      Miyoshi, Toshinori",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.101",
    pages = "791--803",
    abstract = "This paper describes the winning system for SemEval-2020 task 7: Assessing Humor in Edited News Headlines. Our strategy is Stacking at Scale (SaS) with heterogeneous pre-trained language models (PLMs) such as BERT and GPT-2. SaS first performs fine-tuning on numbers of PLMs with various hyperparameters and then applies a powerful stacking ensemble on top of the fine-tuned PLMs. Our experimental results show that SaS outperforms a naive average ensemble, leveraging weaker PLMs as well as high-performing PLMs. Interestingly, the results show that SaS captured non-funny semantics. Consequently, the system was ranked 1st in all subtasks by significant margins compared with other systems.",
}
@inproceedings{bonheme-grzes-2020-sesam,
    title = "{SESAM} at {S}em{E}val-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes",
    author = "Bonheme, Lisa  and
      Grzes, Marek",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.102",
    pages = "804--816",
    abstract = "This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition. We explain the algorithms that were used to learn our models along with the process of tuning the algorithms and selecting the best model. Since meme analysis is a challenging task with two distinct modalities, we studied the impact of different multimodal representation strategies. The results of several approaches to dealing with multimodal data are therefore discussed in the paper. We found that alignment-based strategies did not perform well on memes. Our quantitative results also showed that images and text were uncorrelated. Fusion-based strategies did not show significant improvements and using one modality only (text or image) tends to lead to better results when applied with the predictive models that we used in our research.",
}
@inproceedings{liu-etal-2020-kk2018,
    title = "Kk2018 at {S}em{E}val-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification",
    author = "Liu, Jiaxiang  and
      Chen, Xuyi  and
      Feng, Shikun  and
      Wang, Shuohuan  and
      Ouyang, Xuan  and
      Sun, Yu  and
      Huang, Zhengjie  and
      Su, Weiyue",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.103",
    pages = "817--823",
    abstract = "Code switching is a linguistic phenomenon which may occur within a multilingual setting where speakers share more than one language. With the increasing communication between groups with different languages, this phenomenon is more and more popular. However, there are little research and data in this area, especially in code-mixing sentiment classification. In this work, the domain transfer learning from state-of-the-art uni-language model ERNIE is tested on the code-mixing dataset, and surprisingly, a strong baseline is achieved. And further more, the adversarial training with a multi-lingual model is used to achieved 1st place of SemEval-2020 Task9 Hindi-English sentiment classification competition.",
}
@inproceedings{jensen-etal-2020-buhscitu,
    title = "Buhscitu at {S}em{E}val-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases",
    author = "Jensen, Kristian N{\o}rgaard  and
      Rasmussen, Nicolaj Filrup  and
      Wang, Thai  and
      Placenti, Marco  and
      Plank, Barbara",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.104",
    pages = "824--832",
    abstract = "This paper describes a system that aims at assessing humour intensity in edited news headlines as part of the 7th task of SemEval-2020 on {``}Humor, Emphasis and Sentiment{''}. Various factors need to be accounted for in order to assess the funniness of an edited headline. We propose an architecture that uses hand-crafted features, knowledge bases and a language model to understand humour, and combines them in a regression model. Our system outperforms two baselines. In general, automatic humour assessment remains a difficult task.",
}
@inproceedings{desetty-etal-2020-hasyarasa,
    title = "Hasyarasa at {S}em{E}val-2020 Task 7: Quantifying Humor as Departure from Expectedness",
    author = "Desetty, Ravi Theja  and
      Chatterjee, Ranit  and
      Ghaisas, Smita",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.105",
    pages = "833--842",
    abstract = "This paper describes our system submission Hasyarasa for the SemEval-2020 Task-7: Assessing Humor in Edited News Headlines. This task has two subtasks. The goal of Subtask 1 is to predict the mean funniness of the edited headline given the original and the edited headline. In Subtask 2, given two edits on the original headline, the goal is to predict the funnier of the two. We observed that the departure from expected state/ actions of situations/ individuals is the cause of humor in the edited headlines. We propose two novel features: Contextual Semantic Distance and Contextual Neighborhood Distance to estimate this departure and thus capture the contextual absurdity and hence the humor in the edited headlines. We have used these features together with a Bi-LSTM Attention based model and have achieved 0.53310 RMSE for Subtask 1 and 60.19{\%} accuracy for Subtask 2.",
}
@inproceedings{docekal-etal-2020-jokemeter,
    title = "{J}oke{M}eter at {S}em{E}val-2020 Task 7: Convolutional Humor",
    author = "Docekal, Martin  and
      Fajcik, Martin  and
      Jon, Josef  and
      Smrz, Pavel",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.106",
    pages = "843--851",
    abstract = "This paper describes our system that was designed for Humor evaluation within the SemEval-2020 Task 7. The system is based on convolutional neural network architecture. We investigate the system on the official dataset, and we provide more insight to model itself to see how the learned inner features look.",
}
@inproceedings{miraj-aono-2020-kdehumor,
    title = "{KDE}humor at {S}em{E}val-2020 Task 7: A Neural Network Model for Detecting Funniness in Dataset Humicroedit",
    author = "Miraj, Rida  and
      Aono, Masaki",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.107",
    pages = "852--857",
    abstract = "This paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite some attention has been devoted to humor production and perception. Our team KDEhumor employs recurrent neural network models including Bi-Directional LSTMs (BiLSTMs). Moreover, we utilize the state-of-the-art pre-trained sentence embedding techniques. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.",
}
@inproceedings{mahurkar-patil-2020-lrg,
    title = "{LRG} at {S}em{E}val-2020 Task 7: Assessing the Ability of {BERT} and Derivative Models to Perform Short-Edits Based Humor Grading",
    author = "Mahurkar, Siddhant  and
      Patil, Rajaswa",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.108",
    pages = "858--864",
    abstract = "In this paper, we assess the ability of BERT and its derivative models (RoBERTa, DistilBERT, and ALBERT) for short-edits based humor grading. We test these models for humor grading and classification tasks on the Humicroedit and the FunLines dataset. We perform extensive experiments with these models to test their language modeling and generalization abilities via zero-shot inference and cross-dataset inference based approaches. Further, we also inspect the role of self-attention layers in humor-grading by performing a qualitative analysis over the self-attention weights from the final layer of the trained BERT model. Our experiments show that all the pre-trained BERT derivative models show significant generalization capabilities for humor-grading related tasks.",
}
@inproceedings{s-etal-2020-ssn,
    title = "{SSN}{\_}{NLP} at {S}em{E}val-2020 Task 7: Detecting Funniness Level Using Traditional Learning with Sentence Embeddings",
    author = "S, Kayalvizhi  and
      D., Thenmozhi  and
      Chandrabose, Aravindan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.109",
    pages = "865--870",
    abstract = "Assessing the funniness of edited news headlines task deals with estimating the humorness in the headlines edited with micro-edits. This task has two sub-tasks in which one has to calculate the mean predicted score of humor level and other deals with predicting the best funnier sentence among given two sentences. We have calculated the humorness level using microtc and predicted the funnier sentence using microtc, universal sentence encoder classifier, many other traditional classifiers that use the vectors formed with universal sentence encoder embeddings, sentence embeddings and majority algorithm within these approaches. Among these approaches, microtc with 6 folds, 24 processes and 3 folds, 36 processes achieve the least Root Mean Square Error for development and test set respectively for subtask 1. For subtask 2, Universal sentence encoder classifier achieves the highest accuracy for development set and Multi-Layer Perceptron applied on vectors vectorized using universal sentence encoder embeddings for the test set.",
}
@inproceedings{tomasulo-etal-2020-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2020 Task 7: Using an Ensemble {B}i{GRU} Model to Evaluate the Humor of Edited News Titles",
    author = "Tomasulo, Joseph  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.110",
    pages = "871--875",
    abstract = "This paper describes an ensemble model designed for Semeval-2020 Task 7. The task is based on the Humicroedit dataset that is comprised of news titles and one-word substitutions designed to make them humorous. We use BERT, FastText, Elmo, and Word2Vec to encode these titles then pass them to a bidirectional gated recurrent unit (BiGRU) with attention. Finally, we used XGBoost on the concatenation of the results of the different models to make predictions.",
}
@inproceedings{gupta-etal-2020-dsc,
    title = "{DSC} {IIT}-{ISM} at {S}em{E}val-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis",
    author = "Gupta, Pradyumna  and
      Gupta, Himanshu  and
      Sinha, Aman",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.111",
    pages = "876--884",
    abstract = "Memes have become an ubiquitous social media entity and the processing and analysis of such multimodal data is currently an active area of research. This paper presents our work on the Memotion Analysis shared task of SemEval 2020, which involves the sentiment and humor analysis of memes. We propose a system which uses different bimodal fusion techniques to leverage the inter-modal dependency for sentiment and humor classification tasks. Out of all our experiments, the best system improved the baseline with macro F1 scores of 0.357 on Sentiment Classification (Task A), 0.510 on Humor Classification (Task B) and 0.312 on Scales of Semantic Classes (Task C).",
}
@inproceedings{baruah-etal-2020-iiitg,
    title = "{IIITG}-{ADBU} at {S}em{E}val-2020 Task 8: A Multimodal Approach to Detect Offensive, Sarcastic and Humorous Memes",
    author = "Baruah, Arup  and
      Das, Kaushik  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.112",
    pages = "885--890",
    abstract = "In this paper, we present a multimodal architecture to determine the emotion expressed in a meme. This architecture utilizes both textual and visual information present in a meme. To extract image features we experimented with pre-trained VGG-16 and Inception-V3 classifiers and to extract text features we used LSTM and BERT classifiers. Both FastText and GloVe embeddings were experimented with for the LSTM classifier. The best F1 scores our classifier obtained on the official analysis results are 0.3309, 0.4752, and 0.2897 for Task A, B, and C respectively in the Memotion Analysis task (Task 8) organized as part of International Workshop on Semantic Evaluation 2020 (SemEval 2020). In our study, we found that combining both textual and visual information expressed in a meme improves the performance of the classifier as opposed to using standalone classifiers that use only text or visual data.",
}
@inproceedings{shrestha-rusert-2020-nlp,
    title = "{NLP}{\_}{UIOWA} at {S}em{E}val-2020 Task 8: You{'}re Not the Only One Cursed with Knowledge - Multi Branch Model Memotion Analysis",
    author = "Shrestha, Ingroj  and
      Rusert, Jonathan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.113",
    pages = "891--900",
    abstract = "We propose hybrid models (HybridE and HybridW) for meme analysis (SemEval 2020 Task 8), which involves sentiment classification (Subtask A), humor classification (Subtask B), and scale of semantic classes (Subtask C). The hybrid model consists of BLSTM and CNN for text and image processing respectively. HybridE provides equal weight to BLSTM and CNN performance, while HybridW provides weightage based on the performance of BLSTM and CNN on a validation set. The performances (macro F1) of our hybrid model on Subtask A are 0.329 (HybridE), 0.328 (HybridW), on Subtask B are 0.507 (HybridE), 0.512 (HybridW), and on Subtask C are 0.309 (HybridE), 0.311 (HybridW).",
}
@inproceedings{guo-etal-2020-nuaa,
    title = "{NUAA}-{QMUL} at {S}em{E}val-2020 Task 8: Utilizing {BERT} and {D}ense{N}et for {I}nternet Meme Emotion Analysis",
    author = "Guo, Xiaoyu  and
      Ma, Jing  and
      Zubiaga, Arkaitz",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.114",
    pages = "901--907",
    abstract = "This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis. Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation. We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet. Adding text features is however not always helpful for Memotion Analysis.",
}
@inproceedings{de-la-pena-sarracen-etal-2020-prhlt,
    title = "{PRHLT}-{UPV} at {S}em{E}val-2020 Task 8: Study of Multimodal Techniques for Memes Analysis",
    author = "De la Pe{\~n}a Sarrac{\'e}n, Gretel Liz  and
      Rosso, Paolo  and
      Giachanou, Anastasia",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.115",
    pages = "908--915",
    abstract = "This paper describes the system submitted by the PRHLT-UPV team for the task 8 of SemEval-2020: Memotion Analysis. We propose a multimodal model that combines pretrained models of the BERT and VGG architectures. The BERT model is used to process the textual information and VGG the images. The multimodal model is used to classify memes according to the presence of offensive, sarcastic, humorous and motivating content. Also, a sentiment analysis of memes is carried out with the proposed model. In the experiments, the model is compared with other approaches to analyze the relevance of the multimodal model. The results show encouraging performances on the final leaderboard of the competition, reaching good positions in the ranking of systems.",
}
@inproceedings{yuan-etal-2020-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis",
    author = "Yuan, Li  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.116",
    pages = "916--921",
    abstract = "this paper proposed a parallel-channel model to process the textual and visual information in memes and then analyze the sentiment polarity of memes. In the shared task of identifying and categorizing memes, we preprocess the dataset according to the language behaviors on social media. Then, we adapt and fine-tune the Bidirectional Encoder Representations from Transformers (BERT), and two types of convolutional neural network models (CNNs) were used to extract the features from the pictures. We applied an ensemble model that combined the BiLSTM, BIGRU, and Attention models to perform cross domain suggestion mining. The officially released results show that our system performs better than the baseline algorithm",
}
@inproceedings{laureano-de-leon-etal-2020-cs,
    title = "{CS}-Embed at {S}em{E}val-2020 Task 9: The Effectiveness of Code-switched Word Embeddings for Sentiment Analysis",
    author = "Laureano De Leon, Frances Adriana  and
      Gu{\'e}niat, Florimond  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.117",
    pages = "922--927",
    abstract = "The growing popularity and applications of sentiment analysis of social media posts has naturally led to sentiment analysis of posts written in multiple languages, a practice known as code-switching. While recent research into code-switched posts has focused on the use of multilingual word embeddings, these embeddings were not trained on code-switched data. In this work, we present word-embeddings trained on code-switched tweets, specifically those that make use of Spanish and English, known as Spanglish. We explore the embedding space to discover how they capture the meanings of words in both languages. We test the effectiveness of these embeddings by participating in SemEval 2020 Task 9: \textit{Sentiment Analysis on Code-Mixed Social Media Text}. We utilised them to train a sentiment classifier that achieves an F-1 score of 0.722. This is higher than the baseline for the competition of 0.656, with our team (codalab username francesita) ranking 14 out of 29 participating teams, beating the baseline.",
}
@inproceedings{aparaschivei-etal-2020-fii,
    title = "{FII}-{UAIC} at {S}em{E}val-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using {CNN}",
    author = "Aparaschivei, Lavinia  and
      Palihovici, Andrei  and
      G{\^\i}fu, Daniela",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.118",
    pages = "928--933",
    abstract = "The {``}Sentiment Analysis for Code-Mixed Social Media Text{''} task at the SemEval 2020 competition focuses on sentiment analysis in code-mixed social media text , specifically, on the combination of English with Spanish (Spanglish) and Hindi (Hinglish). In this paper, we present a system able to classify tweets, from Spanish and English languages, into positive, negative and neutral. Firstly, we built a classifier able to provide corresponding sentiment labels. Besides the sentiment labels, we provide the language labels at the word level. Secondly, we generate a word-level representation, using Convolutional Neural Network (CNN) architecture. Our solution indicates promising results for the Sentimix Spanglish-English task (0.744), the team, Lavinia{\_}Ap, occupied the 9th place. However, for the Sentimix Hindi-English task (0.324) the results have to be improved.",
}
@inproceedings{bhange-kasliwal-2020-hinglishnlp,
    title = "{H}inglish{NLP} at {S}em{E}val-2020 Task 9: Fine-tuned Language Models for {H}inglish Sentiment Detection",
    author = "Bhange, Meghana  and
      Kasliwal, Nirant",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.119",
    pages = "934--939",
    abstract = "Sentiment analysis for code-mixed social media text continues to be an under-explored area. This work adds two common approaches: fine-tuning large transformer models and sample efficient methods like ULMFiT. Prior work demonstrates the efficacy of classical ML methods for polarity detection. Fine-tuned general-purpose language representation models, such as those of the BERT family are benchmarked along with classical machine learning and ensemble methods. We show that NB-SVM beats RoBERTa by 6.2{\%} (relative) F1. The best performing model is a majority-vote ensemble which achieves an F1 of 0.707. The leaderboard submission was made under the codalab username nirantk, with F1 of 0.689.",
}
@inproceedings{kong-etal-2020-hpcc,
    title = "{HPCC}-{YNU} at {S}em{E}val-2020 Task 9: A Bilingual Vector Gating Mechanism for Sentiment Analysis of Code-Mixed Text",
    author = "Kong, Jun  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.120",
    pages = "940--945",
    abstract = "It is fairly common to use code-mixing on a social media platform to express opinions and emotions in multilingual societies. The purpose of this task is to detect the sentiment of code-mixed social media text. Code-mixed text poses a great challenge for the traditional NLP system, which currently uses monolingual resources to deal with the problem of multilingual mixing. This task has been solved in the past using lexicon lookup in respective sentiment dictionaries and using a long short-term memory (LSTM) neural network for monolingual resources. In this paper, we present a system that uses a bilingual vector gating mechanism for bilingual resources to complete the task. The model consists of two main parts: the vector gating mechanism, which combines the character and word levels, and the attention mechanism, which extracts the important emotional parts of the text. The results show that the proposed system outperforms the baseline algorithm. We achieved fifth place in Spanglish and 19th place in Hinglish.",
}
@inproceedings{baruah-etal-2020-iiitg-adbu,
    title = "{IIITG}-{ADBU} at {S}em{E}val-2020 Task 9: {SVM} for Sentiment Analysis of {E}nglish-{H}indi Code-Mixed Text",
    author = "Baruah, Arup  and
      Das, Kaushik  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.121",
    pages = "946--950",
    abstract = "In this paper, we present the results that the team IIITG-ADBU (codalab username {`}abaruah{'}) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342.",
}
@inproceedings{srinivasan-2020-msr,
    title = "{MSR} {I}ndia at {S}em{E}val-2020 Task 9: Multilingual Models Can Do Code-Mixing Too",
    author = "Srinivasan, Anirudh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.122",
    pages = "951--956",
    abstract = "In this paper, we present our system for the SemEval 2020 task on code-mixed sentiment analysis. Our system makes use of large transformer based multilingual embeddings like mBERT. Recent work has shown that these models posses the ability to solve code-mixed tasks in addition to their originally demonstrated cross-lingual abilities. We evaluate the stock versions of these models for the sentiment analysis task and also show that their performance can be improved by using unlabelled code-mixed data. Our submission (username Genius1237) achieved the second rank on the English-Hindi subtask with an F1 score of 0.726.",
}
@inproceedings{angel-etal-2020-nlp,
    title = "{NLP}-{CIC} at {S}em{E}val-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier",
    author = "Angel, Jason  and
      Aroyehun, Segun Taofeek  and
      Tamayo, Antonio  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.123",
    pages = "957--962",
    abstract = "Code-switching is a phenomenon in which two or more languages are used in the same message. Nowadays, it is quite common to find messages with languages mixed in social media. This phenomenon presents a challenge for sentiment analysis. In this paper, we use a standard convolutional neural network model to predict the sentiment of tweets in a blend of Spanish and English languages. Our simple approach achieved a F1-score of 0:71 on test set on the competition. We analyze our best model capabilities and perform error analysis to expose important difficulties for classifying sentiment in a code-switching setting.",
}
@inproceedings{palomino-ochoa-luna-2020-palomino,
    title = "Palomino-Ochoa at {S}em{E}val-2020 Task 9: Robust System Based on Transformer for Code-Mixed Sentiment Classification",
    author = "Palomino, Daniel  and
      Ochoa-Luna, Jos{\'e}",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.124",
    pages = "963--967",
    abstract = "We present a transfer learning system to perform a mixed Spanish-English sentiment classification task. Our proposal uses the state-of-the-art language model BERT and embed it within a ULMFiT transfer learning pipeline. This combination allows us to predict the polarity detection of code-mixed (English-Spanish) tweets. Thus, among 29 submitted systems, our approach (referred to as dplominop) is ranked 4th on the Sentimix Spanglish test set of SemEval 2020 Task 9. In fact, our system yields the weighted-F1 score value of 0.755 which can be easily reproduced {---} the source code and implementation details are made available.",
}
@inproceedings{goswami-etal-2020-uld,
    title = "{ULD}@{NUIG} at {S}em{E}val-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text",
    author = "Goswami, Koustava  and
      Rani, Priya  and
      Chakravarthi, Bharathi Raja  and
      Fransen, Theodorus  and
      McCrae, John P.",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.125",
    pages = "968--974",
    abstract = "Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed usage in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system contributed to SemEval 2020 Task 9 SentiMix. The system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags instead inferring this automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set. Our results can be found under the user name {``}koustava{''} on the {``}Sentimix Hindi English{''} page.",
}
@inproceedings{ma-etal-2020-xlp,
    title = "{XLP} at {S}em{E}val-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language",
    author = "Ma, Yili  and
      Zhao, Liang  and
      Hao, Jie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.126",
    pages = "975--980",
    abstract = "In this paper, we present an approach for sentiment analysis in code-mixed language on twitter defined in SemEval-2020 Task 9. Our team (referred as LiangZhao) employ different multilingual models with weighted loss focused on complexity of code-mixing in sentence, in which the best model achieved f1-score of 0.806 and ranked 1st of subtask- Sentimix Spanglish. The performance of method is analyzed and each component of our architecture is demonstrated.",
}
@inproceedings{rozental-etal-2020-amobee,
    title = "{A}mobee at {S}em{E}val-2020 Task 7: Regularization of Language Model Based Classifiers",
    author = "Rozental, Alon  and
      Biton, Dadi  and
      Blank, Ido",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.127",
    pages = "981--985",
    abstract = "This paper describes Amobee{'}s participation in SemEval-2020 task 7: {``}Assessing Humor in Edited News Headlines{''}, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of human modified news headlines. in this paper we present methods to fine-tune and ensemble various language models (LM) based classifiers to for this task. This technique used for both sub-tasks and reached the second place (out of 49) in sub-tasks 1 with RMSE score of 0.5, and the second (out of 32) place in sub-task 2 with accuracy of 66{\%} without using any additional data except the official training set.",
}
@inproceedings{jin-etal-2020-duluth,
    title = "{D}uluth at {S}em{E}val-2020 Task 7: Using Surprise as a Key to Unlock Humorous Headlines",
    author = "Jin, Shuning  and
      Yin, Yue  and
      Tang, XianE  and
      Pedersen, Ted",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.128",
    pages = "986--994",
    abstract = "We use pretrained transformer-based language models in SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines. Inspired by the incongruity theory of humor, we use a contrastive approach to capture the surprise in the edited headlines. In the official evaluation, our system gets 0.531 RMSE in Subtask 1, 11th among 49 submissions. In Subtask 2, our system gets 0.632 accuracy, 9th among 32 submissions.",
}
@inproceedings{zhang-etal-2020-ecnu,
    title = "{ECNU} at {S}em{E}val-2020 Task 7: Assessing Humor in Edited News Headlines Using {B}i{LSTM} with Attention",
    author = "Zhang, Tiantian  and
      Chen, Zhixuan  and
      Lan, Man",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.129",
    pages = "995--1000",
    abstract = "In this paper we describe our system submitted to SemEval 2020 Task 7: {``}Assessing Humor in Edited News Headlines{''}. We participated in all subtasks, in which the main goal is to predict the mean funniness of the edited headline given the original and the edited headline. Our system involves two similar sub-networks, which generate vector representations for the original and edited headlines respectively. And then we do a subtract operation of the outputs from two sub-networks to predict the funniness of the edited headline.",
}
@inproceedings{khwaileh-al-asad-2020-elmo,
    title = "{ELM}o-{NB} at {S}em{E}val-2020 Task 7: Assessing Sense of Humor in {E}dited{N}ews Headlines Using {ELM}o and {NB}",
    author = "Khwaileh, Enas  and
      Al-As{'}ad, Muntaha A.",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.130",
    pages = "1001--1007",
    abstract = "Our approach is constructed to improve on a couple of aspects; preprocessing with an emphasis on humor sense detection, using embeddings from state-of-the-art language model(Elmo), and ensembling the results came up with using machine learning model Na ̈{\i}ve Bayes(NB) with a deep learning pre-trained models. Elmo-NB participation has scored (0.5642) on the competition leader board, where results were measured by Root Mean Squared Error (RMSE).",
}
@inproceedings{chen-etal-2020-ferryman-semeval-2020,
    title = "Ferryman at {S}em{E}val-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines",
    author = "Chen, Weilong  and
      Li, Jipeng  and
      Huang, Chenghao  and
      Bai, Wei  and
      Zhang, Yanru  and
      Wang, Yan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.131",
    pages = "1008--1012",
    abstract = "Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis. In the shared task of assessing the funniness of edited news headlines, which is a part of the SemEval 2020 competition, we preprocess datasets by replacing abbreviation, stemming words, then merge three models including Light Gradient Boosting Machine (LightGBM), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representation from Transformer (BERT) by taking the average to perform the best. Our team Ferryman wins the 9th place in Sub-task 1 of Task 7 - Regression.",
}
@inproceedings{luo-tang-2020-funny3,
    title = "Funny3 at {S}em{E}val-2020 Task 7: Humor Detection of Edited Headlines with {LSTM} and {TFIDF} Neural Network System",
    author = "Luo, Xuefeng  and
      Tang, Kuan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.132",
    pages = "1013--1018",
    abstract = "This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 {``}Assessing the Funniness of Edited News Headlines{''}. Our target is to create to neural network model that can predict the funniness of edited headlines. We build our model using a combination of LSTM and TF-IDF, then a feed-forward neural network. The system manages to slightly improve RSME scores regarding our mean score baseline.",
}
@inproceedings{dick-etal-2020-humoraac,
    title = "{H}umor{AAC} at {S}em{E}val-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions",
    author = "Dick, Anna-Katharina  and
      Weirich, Charlotte  and
      Kutkina, Alla",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.133",
    pages = "1019--1025",
    abstract = "In this paper we describe our contribution to the Semeval-2020 Humor Assessment task. We essentially use three different features that are passed into a ridge regression to determine a funniness score for an edited news headline: statistical, count-based features, semantic features and contextual information. For deciding which one of two given edited headlines is funnier, we additionally use scoring information and logistic regression. Our work was mostly concentrated on investigating features, rather than improving prediction based on pre-trained language models. The resulting system is task-specific, lightweight and performs above the majority baseline. Our experiments indicate that features related to socio-cultural context, in our case mentions of Donald Trump, generally perform better than context-independent features like headline length.",
}
@inproceedings{ballapuram-2020-lmml,
    title = "{LMML} at {S}em{E}val-2020 Task 7: {S}iamese Transformers for Rating Humor in Edited News Headlines",
    author = "Ballapuram, Pramodith",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.134",
    pages = "1026--1032",
    abstract = "This paper contains a description of my solution to the problem statement of SemEval 2020: Assessing the Funniness of Edited News Headlines. I propose a Siamese Transformer based approach, coupled with a Global Attention mechanism that makes use of contextual embeddings and focus words, to generate important features that are fed to a 2 layer perceptron to rate the funniness of the edited headline. I detail various experiments to show the performance of the system. The proposed approach outperforms a baseline Bi-LSTM architecture and finished 5th (out of 49 teams) in sub-task 1 and 4th (out of 32 teams) in sub-task 2 of the competition and was the best non-ensemble model in both tasks.",
}
@inproceedings{vanroy-etal-2020-lt3,
    title = "{LT}3 at {S}em{E}val-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines",
    author = "Vanroy, Bram  and
      Labat, Sofie  and
      Kaminska, Olha  and
      Lefever, Els  and
      Hoste, Veronique",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.135",
    pages = "1033--1040",
    abstract = "This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines. Our first system is a feature-based machine learning system that combines different types of information (e.g. word embeddings, string similarity, part-of-speech tags, perplexity scores, named entity recognition) in a Nu Support Vector Regressor (NuSVR). The second system is a deep learning-based approach that uses the pre-trained language model RoBERTa to learn latent features in the news headlines that are useful to predict the funniness of each headline. The latter system was also our final submission to the competition and is ranked seventh among the 49 participating teams, with a root-mean-square error (RMSE) of 0.5253.",
}
@inproceedings{shatnawi-etal-2020-mlengineer,
    title = "{MLE}ngineer at {S}em{E}val-2020 Task 7: {BERT}-Flair Based Humor Detection Model ({BFH}umor)",
    author = "Shatnawi, Fara  and
      Abdullah, Malak  and
      Hammad, Mahmoud",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.136",
    pages = "1041--1048",
    abstract = "Task 7, Assessing the Funniness of Edited News Headlines, in the International Workshop SemEval2020 introduces two sub-tasks to predict the funniness values of edited news headlines from the Reddit website. This paper proposes the BFHumor model of the MLEngineer team that participates in both sub-tasks in this competition. The BFHumor{'}s model is defined as a BERT-Flair based humor detection model that is a combination of different pre-trained models with various Natural Language Processing (NLP) techniques. The Bidirectional Encoder Representations from Transformers (BERT) regressor is considered the primary pre-trained model in our approach, whereas Flair is the main NLP library. It is worth mentioning that the BFHumor model has been ranked 4th in sub-task1 with a root mean square error (RMSE) value of 0.51966, and it is 0.02 away from the first ranked model. Also, the team is ranked 12th in the sub-task2 with an accuracy of 0.62291, which is 0.05 away from the top-ranked model. Our results indicate that the BFHumor model is one of the top models for detecting humor in the text.",
}
@inproceedings{meaney-etal-2020-smash,
    title = "Smash at {S}em{E}val-2020 Task 7: Optimizing the Hyperparameters of {ERNIE} 2.0 for Humor Ranking and Rating",
    author = "Meaney, J. A.  and
      Wilson, Steven  and
      Magdy, Walid",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.137",
    pages = "1049--1054",
    abstract = "The use of pre-trained language models such as BERT and ULMFiT has become increasingly popular in shared tasks, due to their powerful language modelling capabilities. Our entry to SemEval uses ERNIE 2.0, a language model which is pre-trained on a large number of tasks to enrich the semantic and syntactic information learned. ERNIE{'}s knowledge masking pre-training task is a unique method for learning about named entities, and we hypothesise that it may be of use in a dataset which is built on news headlines and which contains many named entities. We optimize the hyperparameters in a regression and classification model and find that the hyperparameters we selected helped to make bigger gains in the classification model than the regression model.",
}
@inproceedings{soloveva-2020-semeval,
    title = "{SO} at {S}em{E}val-2020 Task 7: {D}eep{P}avlov Logistic Regression with {BERT} Embeddings vs {SVR} at Funniness Evaluation",
    author = "Soloveva, Anita",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.138",
    pages = "1055--1059",
    abstract = "This paper describes my efforts in evaluating how editing news headlines can make them funnier within the frames of SemEval 2020 Task 7. I participated in both of the sub-tasks: Sub-Task 1 {``}Regression{''} and Sub-task 2 {``}Predict the funnier of the two edited versions of an original headline{''}. I experimented with a number of different models, but ended up using DeepPavlov logistic regression (LR) with BERT English cased embeddings for the first sub-task and support vector regression model (SVR) for the second. RMSE score obtained for the first task was 0.65099 and accuracy for the second {--} 0.32915.",
}
@inproceedings{ammer-gruner-2020-unituebingencl,
    title = "{U}ni{T}uebingen{CL} at {S}em{E}val-2020 Task 7: Humor Detection in News Headlines",
    author = {Ammer, Charlotte  and
      Gr{\"u}ner, Lea},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.139",
    pages = "1060--1065",
    abstract = "This paper describes the work done by the team UniTuebingenCL for the SemEval 2020 Task 7: {``}Assessing the Funniness of Edited News Headlines{''}. We participated in both sub-tasks: sub-task A, given the original and the edited headline, predicting the mean funniness of the edited headline; and sub-task B, given the original headline and two edited versions, predicting which edited version is the funnier of the two. A Ridge Regression model using Elmo and Glove embeddings as well as Truncated Singular Value Decomposition was used as the final model. A long short term memory model recurrent network (LSTM) served as another approach for assessing the funniness of a headline. For the first sub-task, we experimented with the extraction of multiple features to achieve lower Root Mean Squared Error. The lowest Root Mean Squared Error achieved was 0.575 for sub-task A, and the highest Accuracy was 0.618 for sub-task B.",
}
@inproceedings{paetzold-2020-utfpr,
    title = "{UTFPR} at {S}em{E}val-2020 Task 7: Using Co-occurrence Frequencies to Capture Unexpectedness",
    author = "Paetzold, Gustavo Henrique",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.140",
    pages = "1066--1070",
    abstract = "We describe the UTFPR system for SemEval-2020{'}s Task 7: Assessing Humor in Edited News Headlines. Ours is a minimalist unsupervised system that uses word co-occurrence frequencies from large corpora to capture unexpectedness as a mean to capture funniness. Our system placed 22nd on the shared task{'}s Task 2. We found that our approach requires more text than we used to perform reliably, and that unexpectedness alone is not sufficient to gauge funniness for humorous content that targets a diverse target audience.",
}
@inproceedings{zhang-yamana-2020-wuy,
    title = "{WUY} at {S}em{E}val-2020 Task 7: Combining {BERT} and Naive {B}ayes-{SVM} for Humor Assessment in Edited News Headlines",
    author = "Zhang, Cheng  and
      Yamana, Hayato",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.141",
    pages = "1071--1076",
    abstract = "This paper describes our participation in SemEval 2020 Task 7 on assessment of humor in edited news headlines, which includes two subtasks, estimating the humor of micro-editd news headlines (subtask A) and predicting the more humorous of the two edited headlines (subtask B). To address these tasks, we propose two systems. The first system adopts a regression-based fine-tuned single-sequence bidirectional encoder representations from transformers (BERT) model with easy data augmentation (EDA), called {``}BERT+EDA{''}. The second system adopts a hybrid of a regression-based fine-tuned sequence-pair BERT model and a combined Naive Bayes and support vector machine (SVM) model estimated on term frequency{--}inverse document frequency (TFIDF) features, called {``}BERT+NB-SVM{''}. In this case, no additional training datasets were used, and the BERT+NB-SVM model outperformed BERT+EDA. The official root-mean-square deviation (RMSE) score for subtask A is 0.57369 and ranks 31st out of 48, whereas the best RMSE of BERT+NB-SVM is 0.52429, ranking 7th. For subtask B, we simply use a sequence-pair BERT model, the official accuracy of which is 0.53196 and ranks 25th out of 32.",
}
@inproceedings{ma-etal-2020-xsysigma,
    title = "{XSYSIGMA} at {S}em{E}val-2020 Task 7: Method for Predicting Headlines{'} Humor Based on Auxiliary Sentences with {EI}-{BERT}",
    author = "Ma, Jian  and
      Xie, ShuYi  and
      Jin, Meizhi  and
      Lianxin, Jiang  and
      Yang, Mo  and
      Shen, Jianping",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.142",
    pages = "1077--1084",
    abstract = "This paper describes xsysigma team{'}s system for SemEval 2020 Task 7: Assessing the Funniness of Edited News Headlines. The target of this task is to assess the funniness changes of news headlines after minor editing and is divided into two subtasks: Subtask 1 is a regression task to detect the humor intensity of the sentence after editing; and Subtask 2 is a classification task to predict funnier of the two edited versions of an original headline. In this paper, we only report our implement of Subtask 2. We first construct sentence pairs with different features for Enhancement Inference BERT(EI-BERT){'}s input. We then conduct data augmentation strategy and Pseudo-Label method. After that, we apply feature enhancement interaction on the encoding of each sentence for classification with EI-BERT. Finally, we apply weighted fusion algorithm to the logits results which obtained by different pre-trained models. We achieve 64.5{\%} accuracy in subtask2 and rank the first and the fifth in dev and test dataset 1 , respectively.",
}
@inproceedings{gupta-etal-2020-bennettnlp,
    title = "{B}ennett{NLP} at {S}em{E}val-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier",
    author = "Gupta, Ambuje  and
      Kataria, Harsh  and
      Mishra, Souvik  and
      Badal, Tapas  and
      Mishra, Vipul",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.143",
    pages = "1085--1093",
    abstract = {Memotion analysis is a very crucial and important subject in today{'}s world that is dominated by social media. This paper presents the results and analysis of the SemEval-2020 Task-8: Memotion analysis by team Kraken that qualified as winners for the task. This involved performing multimodal sentiment analysis on memes commonly posted over social media. The task comprised of 3 subtasks, Task A was to find the overall sentiment of a meme and classify it into positive, negative or neutral, Task B was to classify it into the different types which were namely humour, sarcasm, offensive or motivation where a meme could have more than one category, Task C was to further quantify the classifications achieved in task B. An imbalanced data of 6992 rows was utilized for this which contained images (memes), text (extracted OCR) and their annotations in 17 classes provided by the task organisers. In this paper, the authors proposed a hybrid neural Na{\"\i}ve-Bayes Support Vector Machine and logistic regression to solve a multilevel 17 class classification problem. It achieved the best result in Task B i.e 0.70 F1 score. The authors were ranked third in Task B.},
}
@inproceedings{avvaru-vobilisetty-2020-bert,
    title = "{BERT} at {S}em{E}val-2020 Task 8: Using {BERT} to Analyse Meme Emotions",
    author = "Avvaru, Adithya  and
      Vobilisetty, Sanath",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.144",
    pages = "1094--1099",
    abstract = "Sentiment analysis, being one of the most sought after research problems within Natural Language Processing (NLP) researchers. The range of problems being addressed by sentiment analysis is increasing. Till now, most of the research focuses on predicting sentiment, or sentiment categories like sarcasm, humor, offense and motivation on text data. But, there is very limited research that is focusing on predicting or analyzing the sentiment of internet memes. We try to address this problem as part of {``}Task 8 of SemEval 2020: Memotion Analysis{''}. We have participated in all the three tasks under Memotion Analysis. Our system built using state-of-the-art Transformer-based pre-trained Bidirectional Encoder Representations from Transformers (BERT) performed better compared to baseline models for the two tasks A and C and performed close to the baseline model for task B. In this paper, we present the data used, steps used by us for data cleaning and preparation, the fine-tuning process for BERT based model and finally predict the sentiment or sentiment categories. We found that the sequence models like Long Short Term Memory(LSTM) and its variants performed below par in predicting the sentiments. We also performed a comparative analysis with other Transformer based models like DistilBERT and XLNet.",
}
@inproceedings{li-etal-2020-cn,
    title = "{CN}-{HIT}-{MI}.{T} at {S}em{E}val-2020 Task 8: Memotion Analysis Based on {BERT}",
    author = "Li, Zhen  and
      Zhang, Yaojie  and
      Xu, Bing  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.145",
    pages = "1100--1105",
    abstract = "Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and data contains noise. We use RandAugment to enhance the data of the picture, and use Training Signal Annealing (TSA) to solve the impact of the imbalance of the label. At the same time, a new loss function is designed to ensure that the model is not affected by input noise which will improve the robustness of the model. We participated in sub-task a and our model based on BERT obtains 34.58{\%} macro F1 score, ranking 10/32.",
}
@inproceedings{chy-etal-2020-csecu,
    title = "{CSECU}{\_}{KDE}{\_}{MA} at {S}em{E}val-2020 Task 8: A Neural Attention Model for Memotion Analysis",
    author = "Chy, Abu Nowshed  and
      Siddiqua, Umme Aymun  and
      Aono, Masaki",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.146",
    pages = "1106--1111",
    abstract = "A meme is a pictorial representation of an idea or theme. In the age of emerging volume of social media platforms, memes are spreading rapidly from person to person and becoming a trending ways of opinion expression. However, due to the multimodal characteristics of meme contents, detecting and analyzing the underlying emotion of a meme is a formidable task. In this paper, we present our approach for detecting the emotion of a meme defined in the SemEval-2020 Task 8. Our team CSECU{\_}KDE{\_}MA employs an attention-based neural network model to tackle the problem. Upon extracting the text contents from a meme using an optical character reader (OCR), we represent it using the distributed representation of words. Next, we perform the convolution based on multiple kernel sizes to obtain the higher-level feature sequences. The feature sequences are then fed into the attentive time-distributed bidirectional LSTM model to learn the long-term dependencies effectively. Experimental results show that our proposed neural model obtained competitive performance among the participants{'} systems.",
}
@inproceedings{gundapu-mamidi-2020-gundapusunil,
    title = "Gundapusunil at {S}em{E}val-2020 Task 8: Multimodal Memotion Analysis",
    author = "Gundapu, Sunil  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.147",
    pages = "1112--1119",
    abstract = "Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication. These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes. Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions. In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing. Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed. Our system has been developed using CNN and LSTM and outperformed the baseline score.",
}
@inproceedings{guo-etal-2020-guoym,
    title = "Guoym at {S}em{E}val-2020 Task 8: Ensemble-based Classification of Visuo-Lingual Metaphor in Memes",
    author = "Guo, Yingmei  and
      Huang, Jinfa  and
      Dong, Yanlong  and
      Xu, Mingxing",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.148",
    pages = "1120--1125",
    abstract = "In this paper, we describe our ensemble-based system designed by guoym Team for the SemEval-2020 Task 8, Memotion Analysis. In our system, we utilize five types of representation of data as input of base classifiers to extract information from different aspects. We train five base classifiers for each type of representation using five-fold cross-validation. Then the outputs of these base classifiers are combined through data-based ensemble method and feature-based ensemble method to make full use of all data and representations from different aspects. Our method achieves the performance within the top 2 ranks in the final leaderboard of Memotion Analysis among 36 Teams.",
}
@inproceedings{morishita-etal-2020-hitachi-semeval-2020,
    title = "Hitachi at {S}em{E}val-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition",
    author = "Morishita, Terufumi  and
      Morio, Gaku  and
      Horiguchi, Shota  and
      Ozaki, Hiroaki  and
      Miyoshi, Toshinori",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.149",
    pages = "1126--1134",
    abstract = "Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them. SemEval-2020 task 8, Memotion Analysis, aims at automatically recognizing these emotions of so-called internet memes. In this paper, we propose a simple but effective Modality Ensemble that incorporates visual and textual deep-learning models, which are independently trained, rather than providing a single multi-modal joint network. To this end, we first fine-tune four pre-trained visual models (i.e., Inception-ResNet, PolyNet, SENet, and PNASNet) and four textual models (i.e., BERT, GPT-2, Transformer-XL, and XLNet). Then, we fuse their predictions with ensemble methods to effectively capture cross-modal correlations. The experiments performed on dev-set show that both visual and textual features aided each other, especially in subtask-C, and consequently, our system ranked 2nd on subtask-C.",
}
@inproceedings{keswani-etal-2020-iitk-semeval,
    title = "{IITK} at {S}em{E}val-2020 Task 8: Unimodal and Bimodal Sentiment Analysis of {I}nternet Memes",
    author = "Keswani, Vishal  and
      Singh, Sakshi  and
      Agarwal, Suryansh  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.150",
    pages = "1135--1140",
    abstract = "Social media is abundant in visual and textual information presented together or in isolation. Memes are the most popular form, belonging to the former class. In this paper, we present our approaches for the Memotion Analysis problem as posed in SemEval-2020 Task 8. The goal of this task is to classify memes based on their emotional content and sentiment. We leverage techniques from Natural Language Processing (NLP) and Computer Vision (CV) towards the sentiment classification of internet memes (Subtask A). We consider Bimodal (text and image) as well as Unimodal (text-only) techniques in our study ranging from the Na ̈{\i}ve Bayes classifier to Transformer-based approaches. Our results show that a text-only approach, a simple Feed Forward Neural Network (FFNN) with Word2vec embeddings as input, performs superior to all the others. We stand first in the Sentiment analysis task with a relative improvement of 63{\%} over the baseline macro-F1 score. Our work is relevant to any task concerned with the combination of different modalities.",
}
@inproceedings{ruiz-etal-2020-infotec,
    title = "Infotec + {C}entro{GEO} at {S}em{E}val-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification",
    author = "Ruiz, Guillermo  and
      Tellez, Eric S.  and
      Moctezuma, Daniela  and
      Miranda-Jim{\'e}nez, Sabino  and
      Ram{\'\i}rez-delReal, Tania  and
      Graff, Mario",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.151",
    pages = "1141--1147",
    abstract = "The information shared on social media is increasingly important; both images and text, and maybe the most popular combination of these two kinds of data are the memes. This manuscript describes our participation in Memotion task at SemEval 2020. This task is about to classify the memes in several categories related to the emotional content of them. For the proposed system construction, we used different strategies, and the best ones were based on deep neural networks and a text categorization algorithm. We obtained results analyzing the text and images separately, and also in combination. Our better performance was achieved in Task A, related to polarity classification.",
}
@inproceedings{das-etal-2020-kafk,
    title = "{KAFK} at {S}em{E}val-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify {I}nternet Memes",
    author = "Das, Kaushik Amar  and
      Baruah, Arup  and
      Barbhuiya, Ferdous Ahmed  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.152",
    pages = "1148--1154",
    abstract = "This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes. We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively. Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.",
}
@inproceedings{singh-etal-2020-lt3,
    title = "{LT}3 at {S}em{E}val-2020 Task 8: Multi-Modal Multi-Task Learning for Memotion Analysis",
    author = "Singh, Pranaydeep  and
      Bauwelinck, Nina  and
      Lefever, Els",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.153",
    pages = "1155--1162",
    abstract = "Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval-2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates {``}memebeddings{''}, viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition{'}s baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores.",
}
@inproceedings{sharma-etal-2020-memebusters,
    title = "Memebusters at {S}em{E}val-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning",
    author = "Sharma, Mayukh  and
      Kandasamy, Ilanthenral  and
      Vasantha, W.b.",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.154",
    pages = "1163--1171",
    abstract = "In this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis. We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: Humor classification, and Subtask C: Scales of semantic classes. Similar multimodal architecture was used for each subtask. The proposed architecture makes use of transfer learning for images and text feature extraction. The extracted features are then fused together using stacked bidirectional Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention mechanism for final predictions. We also propose a single model for predicting semantic classes (Subtask B) as well as their scales (Subtask C) by branching the final output of the post LSTM dense layers. Our model was ranked 5 in Subtask B and ranked 8 in Subtask C and performed nicely in Subtask A on the leader board. Our system makes use of transfer learning for feature extraction and fusion of image and text features for predictions.",
}
@inproceedings{bejan-2020-memosys,
    title = "{M}emo{SYS} at {S}em{E}val-2020 Task 8: Multimodal Emotion Analysis in Memes",
    author = "Bejan, Irina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.155",
    pages = "1172--1178",
    abstract = "Internet memes are one of the most viral types of content in social media and are equally used in promoting hate speech. Towards a more broad understanding of memes, this paper describes the MemoSys system submitted in Task 8 of SemEval 2020, which aims to classify the sentiment of Internet memes and provide a minimum description of the type of humor it depicts (sarcastic, humorous, offensive, motivational) and its semantic scale. The solution presented covers four deep model architectures which are based on a joint fusion between the VGG16 pre-trained model for extracting visual information and the canonical BERT model or TF-IDF for text understanding. The system placed 5th of 36 participating systems in the task A, offering promising prospects to the use of transfer learning to approach Internet memes understanding.",
}
@inproceedings{swamy-etal-2020-nit,
    title = "{NIT}-Agartala-{NLP}-Team at {S}em{E}val-2020 Task 8: Building Multimodal Classifiers to Tackle {I}nternet Humor",
    author = "Swamy, Steve Durairaj  and
      Laddha, Shubham  and
      Abdussalam, Basil  and
      Datta, Debayan  and
      Jamatia, Anupam",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.156",
    pages = "1179--1189",
    abstract = "The paper describes the systems submitted to SemEval-2020 Task 8: Memotion by the {`}NIT-Agartala-NLP-Team{'}. A dataset of 8879 memes was made available by the task organizers to train and test our models. Our systems include a Logistic Regression baseline, a BiLSTM +Attention-based learner and a transfer learning approach with BERT. For the three sub-tasks A, B and C, we attained ranks 24/33, 11/29 and 15/26, respectively. We highlight our difficulties in harnessing image information as well as some techniques and handcrafted features we employ to overcome these issues. We also discuss various modelling issues and theorize possible solutions and reasons as to why these problems persist.",
}
@inproceedings{boinepelli-etal-2020-sis,
    title = "{SIS}@{IIITH} at {S}em{E}val-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis",
    author = "Boinepelli, Sravani  and
      Shrivastava, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.157",
    pages = "1190--1194",
    abstract = "Memes are steadily taking over the feeds of the public on social media. There is always the threat of malicious users on the internet posting offensive content, even through memes. Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text. However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge. This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis. We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem. Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images. Our submitted model achieved an accuracy of 72.69{\%} and exceeded the existing baseline{'}s Macro F1 score by 8{\%} on the official test dataset. Apart from describing our official submission, we shall elucidate how different classification models respond to this task.",
}
@inproceedings{suciati-budi-2020-ui,
    title = "{UI} at {S}em{E}val-2020 Task 8: Text-Image Fusion for Sentiment Classification",
    author = "Suciati, Andi  and
      Budi, Indra",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.158",
    pages = "1195--1200",
    abstract = "This paper describes our system, UI, for task A: Sentiment Classification in SemEval-2020 Task 8 Memotion Analysis. We use a common traditional machine learning, which is SVM, by utilizing the combination of text and images features. The data consist text that extracted from memes and the images of memes. We employ n-gram language model for text features and pre-trained model,VGG-16,for image features. After obtaining both features from text and images in form of 2-dimensional arrays, we concatenate and classify the final features using SVM. The experiment results show SVM achieved 35{\%} for its F1 macro, which is 0.132 points or 13.2{\%} above the baseline model.",
}
@inproceedings{liu-etal-2020-uor,
    title = "{U}o{R} at {S}em{E}val-2020 Task 8: {G}aussian Mixture Modelling ({GMM}) Based Sampling Approach for Multi-modal Memotion Analysis",
    author = "Liu, Zehao  and
      Osei-Brefo, Emmanuel  and
      Chen, Siyuan  and
      Liang, Huizhi",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.159",
    pages = "1201--1207",
    abstract = "Memes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online communities. The provided memes data often face an imbalanced data problem, that is, some classes or labelled sentiment categories significantly outnumber other classes. This often results in difficulty in applying machine learning techniques where balanced labelled input data are required. In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classification task. To utilise both text and image data, a multi-modal CNN-LSTM model is proposed to jointly learn latent features for positive, negative and neutral category predictions. The experiments show that the re-sampling model can slightly improve the accuracy on the trial data of sub-task A of Task 8. The multi-modal CNN-LSTM model can achieve macro F1 score 0.329 on the test set.",
}
@inproceedings{vlad-etal-2020-upb,
    title = "{UPB} at {S}em{E}val-2020 Task 8: Joint Textual and Visual Modeling in a Multi-Task Learning Architecture for Memotion Analysis",
    author = "Vlad, George-Alexandru  and
      Zaharia, George-Eduard  and
      Cercel, Dumitru-Clementin  and
      Chiru, Costin  and
      Trausan-Matu, Stefan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.160",
    pages = "1208--1214",
    abstract = "Users from the online environment can create different ways of expressing their thoughts, opinions, or conception of amusement. Internet memes were created specifically for these situations. Their main purpose is to transmit ideas by using combinations of images and texts such that they will create a certain state for the receptor, depending on the message the meme has to send. These posts can be related to various situations or events, thus adding a funny side to any circumstance our world is situated in. In this paper, we describe the system developed by our team for SemEval-2020 Task 8: Memotion Analysis. More specifically, we introduce a novel system to analyze these posts, a multimodal multi-task learning architecture that combines ALBERT for text encoding with VGG-16 for image representation. In this manner, we show that the information behind them can be properly revealed. Our approach achieves good performance on each of the three subtasks of the current competition, ranking 11th for Subtask A (0.3453 macro F1-score), 1st for Subtask B (0.5183 macro F1-score), and 3rd for Subtask C (0.3171 macro F1-score) while exceeding the official baseline results by high margins.",
}
@inproceedings{walinska-potoniec-2020-urszula,
    title = "Urszula Wali{\'n}ska at {S}em{E}val-2020 Task 8: Fusion of Text and Image Features Using {LSTM} and {VGG}16 for Memotion Analysis",
    author = "Wali{\'n}ska, Urszula  and
      Potoniec, J{\k{e}}drzej",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.161",
    pages = "1215--1220",
    abstract = "In this paper, we describe the entry to the task of Memotion Analysis. The sentiment analysis of memes task, is motivated by a pervasive problem of offensive content spread in social media, up to the present time. In fact, memes are an important medium of expressing opinion and emotions, therefore they can be hateful at many times. In order to identify emotions expressed by memes we construct a tool based on neural networks and deep learning methods. It takes an advantage of a multi-modal nature of the task and performs fusion of image and text features extracted by models dedicated to this task. Moreover, we show that visual information might be more significant in the sentiment analysis of memes than textual one. Our solution achieved 0.346 macro F1-score in Task A {--} Sentiment Classification, which brought us to the 7th place in the official rank of the competition.",
}
@inproceedings{kumar-etal-2020-baksa,
    title = "{BAKSA} at {S}em{E}val-2020 Task 9: Bolstering {CNN} with Self-Attention for Sentiment Analysis of Code Mixed Text",
    author = "Kumar, Ayush  and
      Agarwal, Harsh  and
      Bansal, Keshav  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.162",
    pages = "1221--1226",
    abstract = "Sentiment Analysis of code-mixed text has diversified applications in opinion mining ranging from tagging user reviews to identifying social or political sentiments of a sub-population. In this paper, we present an ensemble architecture of convolutional neural net (CNN) and self-attention based LSTM for sentiment analysis of code-mixed tweets. While the CNN component helps in the classification of positive and negative tweets, the self-attention based LSTM, helps in the classification of neutral tweets, because of its ability to identify correct sentiment among multiple sentiment bearing units. We achieved F1 scores of 0.707 (ranked 5th) and 0.725 (ranked 13th) on Hindi-English (Hinglish) and Spanish-English (Spanglish) datasets, respectively. The submissions for Hinglish and Spanglish tasks were made under the usernames ayushk and harsh{\_}6 respectively.",
}
@inproceedings{advani-etal-2020-c1,
    title = "C1 at {S}em{E}val-2020 Task 9: {S}enti{M}ix: Sentiment Analysis for Code-Mixed Social Media Text Using Feature Engineering",
    author = "Advani, Laksh  and
      Lu, Clement  and
      Maharjan, Suraj",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.163",
    pages = "1227--1232",
    abstract = "In today{'}s interconnected and multilingual world, code-mixing of languages on social media is a common occurrence. While many Natural Language Processing (NLP) tasks like sentiment analysis are mature and well designed for monolingual text, techniques to apply these tasks to code-mixed text still warrant exploration. This paper describes our feature engineering approach to sentiment analysis in code-mixed social media text for SemEval-2020 Task 9: SentiMix. We tackle this problem by leveraging a set of hand-engineered lexical, sentiment, and metadata fea- tures to design a classifier that can disambiguate between {``}positive{''}, {``}negative{''} and {``}neutral{''} sentiment. With this model we are able to obtain a weighted F1 score of 0.65 for the {``}Hinglish{''} task and 0.63 for the {``}Spanglish{''} tasks.",
}
@inproceedings{verissimo-dos-santos-neto-etal-2020-deep,
    title = "Deep Learning Brasil - {NLP} at {S}em{E}val-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models",
    author = "Ver{\'\i}ssimo dos Santos Neto, Manoel  and
      Amaral, Ayrton  and
      Silva, N{\'a}dia  and
      da Silva Soares, Anderson",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.164",
    pages = "1233--1238",
    abstract = "In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7{\%} on the F1 score.",
}
@inproceedings{braaksma-etal-2020-fissa,
    title = "{F}i{SSA} at {S}em{E}val-2020 Task 9: Fine-tuned for Feelings",
    author = {Braaksma, Bertelt  and
      Scholtens, Richard  and
      van Suijlekom, Stan  and
      Wang, Remy  and
      {\"U}st{\"u}n, Ahmet},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.165",
    pages = "1239--1246",
    abstract = "In this paper, we present our approach for sentiment classification on Spanish-English code-mixed social media data in the SemEval-2020 Task 9. We investigate performance of various pre-trained Transformer models by using different fine-tuning strategies. We explore both monolingual and multilingual models with the standard fine-tuning method. Additionally, we propose a custom model that we fine-tune in two steps: once with a language modeling objective, and once with a task-specific objective. Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 on development data and 0.739 on test data. With this score, our team jupitter placed tenth overall in the competition.",
}
@inproceedings{gundapu-mamidi-2020-gundapusunil-semeval,
    title = "Gundapusunil at {S}em{E}val-2020 Task 9: Syntactic Semantic {LSTM} Architecture for {SENTI}ment Analysis of Code-{MIX}ed Data",
    author = "Gundapu, Sunil  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.166",
    pages = "1247--1252",
    abstract = "The phenomenon of mixing the vocabulary and syntax of multiple languages within the same utterance is called Code-Mixing. This is more evident in multilingual societies. In this paper, we have developed a system for SemEval 2020: Task 9 on Sentiment Analysis of Hindi-English code-mixed social media text. Our system first generates two types of embeddings for the social media text. In those, the first one is character level embeddings to encode the character level information and to handle the out-of-vocabulary entries and the second one is FastText word embeddings for capturing morphology and semantics. These two embeddings were passed to the LSTM network and the system outperformed the baseline model.",
}
@inproceedings{srivastava-vardhan-2020-hcms,
    title = "{HCMS} at {S}em{E}val-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts",
    author = "Srivastava, Aditya  and
      Vardhan, V. Harsha",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.167",
    pages = "1253--1258",
    abstract = "Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1{\%}, we demonstrate that simple convolution and attention may well produce reasonable results.",
}
@inproceedings{srivastava-singh-2020-iit,
    title = "{IIT} {G}andhinagar at {S}em{E}val-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection",
    author = "Srivastava, Vivek  and
      Singh, Mayank",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.168",
    pages = "1259--1264",
    abstract = "Code-mixing is the phenomenon of using multiple languages in the same utterance. It is a frequently used pattern of communication on social media sites such as Facebook, Twitter, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text on various platforms such as social media, online gaming, forums, product reviews, etc. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral. The proposed candidate sentence generation and selection based approach show an improvement in the system performance as compared to the Bi-LSTM based neural classifier. We can extend the proposed method to solve other problems with code-mixing in the textual data, such as humor-detection, intent classification, etc.",
}
@inproceedings{parikh-etal-2020-irlab,
    title = "{IRL}ab{\_}{DAIICT} at {S}em{E}val-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets",
    author = "Parikh, Apurva  and
      Bisht, Abhimanyu Singh  and
      Majumder, Prasenjit",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.169",
    pages = "1265--1269",
    abstract = "The paper describes systems that our team IRLab{\_}DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020. We conducted our experiments on a Hindi-English CodeMixed Tweet dataset which was annotated with sentiment labels. F1-score was the official evaluation metric and our best approach, an ensemble of Logistic Regression, Random Forest and BERT, achieved an F1-score of 0.693.",
}
@inproceedings{javdan-etal-2020-iust,
    title = "{IUST} at {S}em{E}val-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using Deep Neural Networks and Linear Baselines",
    author = "Javdan, Soroush  and
      Shangipour ataei, Taha  and
      Minaei-Bidgoli, Behrouz",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.170",
    pages = "1270--1275",
    abstract = "Sentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-established methods and tools. One of these challenges is code-mixing, which means using different languages to convey thoughts in social media texts. Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict the sentiment of a given code-mixed tweet. We used different preprocessing techniques and proposed to use different methods that vary from NBSVM to more complicated deep neural network models. Our best performing method obtains an F1 score of 0.751 for the Spanish-English sub-task and 0.706 over the Hindi-English sub-task.",
}
@inproceedings{garain-etal-2020-junlp,
    title = "{JUNLP} at {S}em{E}val-2020 Task 9: Sentiment Analysis of {H}indi-{E}nglish Code Mixed Data Using Grid Search Cross Validation",
    author = "Garain, Avishek  and
      Mahata, Sainik  and
      Das, Dipankar",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.171",
    pages = "1276--1280",
    abstract = "Code-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language. This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few. In this work, we focus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis. This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences. our username for the submission was {``}sainik.mahata{''} and team name was {``}JUNLP{''}. We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task. Our approach garnered an f1-score of 66.2{\%} when tested using metrics prepared by the organizers of the task.",
}
@inproceedings{banerjee-etal-2020-limsi,
    title = "{LIMSI}{\_}{UPV} at {S}em{E}val-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis",
    author = "Banerjee, Somnath  and
      Ghannay, Sahar  and
      Rosset, Sophie  and
      Vilnat, Anne  and
      Rosso, Paolo",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.172",
    pages = "1281--1287",
    abstract = "This paper describes the participation of LIMSI{\_}UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix HindiEnglish subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent Convolutional Neural Network that combines both the recurrent neural network and the convolutional network to better capture the semantics of the text, for code-mixed sentiment analysis. The proposed system obtained 0.69 (best run) in terms of F1 score on the given test data and achieved the 9th place (Codalab username: somban) in the SentiMix Hindi-English subtask.",
}
@inproceedings{singh-lefever-2020-lt3,
    title = "{LT}3 at {S}em{E}val-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of {H}inglish Social Media Text",
    author = "Singh, Pranaydeep  and
      Lefever, Els",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.173",
    pages = "1288--1293",
    abstract = "This paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. The first approach uses cross-lingual embeddings resulting from projecting Hinglish and pre-trained English FastText word embeddings in the same space. The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets. The results show that the second approach performs best, with an F1-score of 70.52{\%} on the held-out test data.",
}
@inproceedings{wu-etal-2020-meistermorxrc,
    title = "{M}eister{M}orxrc at {S}em{E}val-2020 Task 9: Fine-Tune Bert and Multitask Learning for Sentiment Analysis of Code-Mixed Tweets",
    author = "Wu, Qi  and
      Wang, Peng  and
      Huang, Chenghao",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.174",
    pages = "1294--1297",
    abstract = "Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis. In the shared task of sentiment analysis of code-mixed tweets, which is a part of the SemEval-2020 competition, we preprocess datasets by replacing emoji and deleting uncommon characters and so on, and then fine-tune the Bidirectional Encoder Representation from Transformers(BERT) to perform the best. After exhausting top3 submissions, Our team MeisterMorxrc achieves an averaged F1 score of 0.730 in this task, and and our codalab username is MeisterMorxrc",
}
@inproceedings{baroi-etal-2020-nits,
    title = "{NITS}-{H}inglish-{S}enti{M}ix at {S}em{E}val-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using an Ensemble Model",
    author = "Baroi, Subhra Jyoti  and
      Singh, Nivedita  and
      Das, Ringki  and
      Singh, Thoudam Doren",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.175",
    pages = "1298--1303",
    abstract = "Sentiment Analysis refers to the process of interpreting what a sentence emotes and classifying them as positive, negative, or neutral. The widespread popularity of social media has led to the generation of a lot of text data and specifically, in the Indian social media scenario, the code-mixed Hinglish text i.e, the words of Hindi language, written in the Roman script along with other English words is a common sight. The ability to effectively understand the sentiments in these texts is much needed. This paper proposes a system titled NITS-Hinglish to effectively carry out the sentiment analysis of such code-mixed Hinglish text. The system has fared well with a final F-Score of 0.617 on the test data.",
}
@inproceedings{gopalan-hopkins-2020-reed,
    title = "Reed at {S}em{E}val-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis",
    author = "Gopalan, Vinay  and
      Hopkins, Mark",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.176",
    pages = "1304--1309",
    abstract = "We explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task. We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations. During the evaluation phase of the competition, we obtained an F-score of 71.3{\%} with our best model, which placed 4th out of 62 entries in the official system rankings.",
}
@inproceedings{malte-etal-2020-team,
    title = "{T}eam{\_}{S}wift at {S}em{E}val-2020 Task 9: Tiny Data Specialists through Domain-Specific Pre-training on Code-Mixed Data",
    author = "Malte, Aditya  and
      Bhavsar, Pratik  and
      Rathi, Sushant",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.177",
    pages = "1310--1315",
    abstract = "Code-mixing is an interesting phenomenon where the speaker switches between two or more languages in the same text. In this paper, we describe an unconventional approach to tackling the SentiMix Hindi-English challenge (UID: aditya{\_}malte). Instead of directly fine-tuning large contemporary Transformer models, we train our own domain-specific embeddings and make use of them for downstream tasks. We also discuss how this technique provides comparable performance while making for a much more deployable and lightweight model. It should be noted that we have achieved the stated results without using any ensembling techniques, thus respecting a paradigm of efficient and production-ready NLP. All relevant source code shall be made publicly available to encourage the usage and reproduction of the results.",
}
@inproceedings{bear-etal-2020-tuemix,
    title = "{T}ue{M}ix at {S}em{E}val-2020 Task 9: Logistic Regression with Linguistic Feature Set",
    author = "Bear, Elizabeth  and
      Hoefels, Diana Constantina  and
      Manolescu, Mihai",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.178",
    pages = "1316--1321",
    abstract = "Commonly occurring in settings such as social media platforms, code-mixed content makes the task of identifying sentiment notably more challenging and complex due to the lack of structure and noise present in the data. SemEval-2020 Task 9, SentiMix, was organized with the purpose of detecting the sentiment of a given code-mixed tweet comprising Hindi and English. We tackled this task by comparing the performance of a system, TueMix - a logistic regression algorithm trained with three feature components: TF-IDF n-grams, monolingual sentiment lexicons, and surface features - with a neural network approach. Our results showed that TueMix outperformed the neural network approach and yielded a weighted F1-score of 0.685.",
}
@inproceedings{zaharia-etal-2020-upb,
    title = "{UPB} at {S}em{E}val-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts Using Transformers and Multi-Task Learning",
    author = "Zaharia, George-Eduard  and
      Vlad, George-Alexandru  and
      Cercel, Dumitru-Clementin  and
      Rebedea, Traian  and
      Chiru, Costin",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.179",
    pages = "1322--1330",
    abstract = "Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms. In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English. We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings. Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16 out of 62 participants. For the Spanish-English task, we obtained an average F1-score of 0.7064 ranking our team 17th out of 29 participants by using another multilingual Transformer-based model, XLM-RoBERTa.",
}
@inproceedings{singh-singh-parmar-2020-voice,
    title = "Voice@{SRIB} at {S}em{E}val-2020 Tasks 9 and 12: Stacked Ensemblingmethod for Sentiment and Offensiveness detection in Social Media",
    author = "Singh, Abhishek  and
      Singh Parmar, Surya Pratap",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.180",
    pages = "1331--1341",
    abstract = "In social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models we used, using the external dataset to train embeddings, ensembling methods for Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation. In this experiment, we have used our trained code-mixed embeddings and twitter pre-trained embeddings to SemEval tasks. We evaluate our models on macro F1-score, precision, accuracy, and recall on the datasets. We intend to show that hyper-parameter tuning and data pre-processing steps help a lot in improving the scores. In our experiments, we are able to achieve 0.886 F1-Macro on OffenEval Greek language subtask post-evaluation, whereas the highest is 0.852 during the Evaluation Period. We stood third in Spanglish competition with our best F1-score of 0.756. Codalab username is asking28.",
}
@inproceedings{sultan-etal-2020-wessa,
    title = "{WESSA} at {S}em{E}val-2020 Task 9: Code-Mixed Sentiment Analysis Using Transformers",
    author = "Sultan, Ahmed  and
      Salim, Mahmoud  and
      Gaber, Amina  and
      El Hosary, Islam",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.181",
    pages = "1342--1347",
    abstract = "In this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes XLM-RoBERTa, a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed data. Our system outperforms the official task baseline by achieving a 70.1{\%} average F1-Score on the official leaderboard using the test set. For later submissions, our system manages to achieve a 75.9{\%} average F1-Score on the test set using CodaLab username {``}ahmed0sultan{''}.",
}
@inproceedings{bao-etal-2020-will-go,
    title = "Will{\_}go at {S}em{E}val-2020 Task 9: An Accurate Approach for Sentiment Analysis on {H}indi-{E}nglish Tweets Based on Bert and Pesudo Label Strategy",
    author = "Bao, Wei  and
      Chen, Weilong  and
      Bai, Wei  and
      Zhuang, Yan  and
      Cheng, Mingyuan  and
      Ma, Xiangyu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.182",
    pages = "1348--1353",
    abstract = "Mixing languages are widely used in social media, especially in multilingual societies like India. Detecting the emotions contained in these languages, which is of great significance to the development of society and political trends. In this paper, we propose an ensemble of pesudo-label based Bert model and TFIDF based SGDClassifier model to identify the sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model combines the strengths of rich semantic information from the Bert model and word frequency information from the probabilistic ngram model to predict the sentiment of a given code-mixed tweet.Finally our team got an average F1 score of 0.731 on the final leaderboard,and our codalab username is will{\_}go.",
}
@inproceedings{zhu-etal-2020-zyy1510,
    title = "Zyy1510 Team at {S}em{E}val-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text with Sub-word Level Representations",
    author = "Zhu, Yueying  and
      Zhou, Xiaobing  and
      Li, Hongling  and
      Dong, Kunjie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.183",
    pages = "1354--1359",
    abstract = "This paper reports the zyy1510 team{'}s work in the International Workshop on Semantic Evaluation (SemEval-2020) shared task on Sentiment analysis for Code-Mixed (Hindi-English, English-Spanish) Social Media Text. The purpose of this task is to determine the polarity of the text, dividing it into one of the three labels positive, negative and neutral. To achieve this goal, we propose an ensemble model of word n-grams-based Multinomial Naive Bayes (MNB) and sub-word level representations in LSTM (Sub-word LSTM) to identify the sentiments of code-mixed data of Hindi-English and English-Spanish. This ensemble model combines the advantage of rich sequential patterns and the intermediate features after convolution from the LSTM model, and the polarity of keywords from the MNB model to obtain the final sentiment score. We have tested our system on Hindi-English and English-Spanish code-mixed social media data sets released for the task. Our model achieves the F1 score of 0.647 in the Hindi-English task and 0.682 in the English-Spanish task, respectively.",
}
@inproceedings{shirani-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 10: Emphasis Selection for Written Text in Visual Media",
    author = "Shirani, Amirreza  and
      Dernoncourt, Franck  and
      Lipka, Nedim  and
      Asente, Paul  and
      Echevarria, Jose  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.184",
    pages = "1360--1370",
    abstract = "In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring. The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes. Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations. SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task. The highest-ranked submission achieved 0.823 Matchm score. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature. Full results can be found on the task{'}s website.",
}
@inproceedings{shin-etal-2020-ids,
    title = "{IDS} at {S}em{E}val-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?",
    author = "Shin, Jaeyoul  and
      Kim, Taeuk  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.185",
    pages = "1371--1376",
    abstract = "We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs). With extensive experiments and analyses, we show that 1) our zero-shot approach is superior to a reasonable baseline that adopts TF-IDF and that 2) there exist several attention heads in PLMs specialized for emphasis selection, confirming that PLMs are capable of recognizing important words in sentences.",
}
@inproceedings{da-san-martino-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 11: Detection of Propaganda Techniques in News Articles",
    author = "Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Wachsmuth, Henning  and
      Petrov, Rostislav  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.186",
    pages = "1377--1414",
    abstract = "We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.",
}
@inproceedings{jurkiewicz-etal-2020-applicaai,
    title = "{A}pplica{AI} at {S}em{E}val-2020 Task 11: On {R}o{BERT}a-{CRF}, Span {CLS} and Whether Self-Training Helps Them",
    author = "Jurkiewicz, Dawid  and
      Borchmann, {\L}ukasz  and
      Kosmala, Izabela  and
      Grali{\'n}ski, Filip",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.187",
    pages = "1415--1424",
    abstract = "This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task. The purpose of TC task was to identify an applied propaganda technique given propaganda text fragment. The goal of SI task was to find specific text fragments which contain at least one propaganda technique. Both of the developed solutions used semi-supervised learning technique of self-training. Interestingly, although CRF is barely used with transformer-based language models, the SI task was approached with RoBERTa-CRF architecture. An ensemble of RoBERTa-based models was proposed for the TC task, with one of them making use of Span CLS layers we introduce in the present paper. In addition to describing the submitted systems, an impact of architectural decisions and training schemes is investigated along with remarks regarding training models of the same or better quality with lower computational budget. Finally, the results of error analysis are presented.",
}
@inproceedings{zampieri-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 12: Multilingual Offensive Language Identification in Social Media ({O}ffens{E}val 2020)",
    author = {Zampieri, Marcos  and
      Nakov, Preslav  and
      Rosenthal, Sara  and
      Atanasova, Pepa  and
      Karadzhov, Georgi  and
      Mubarak, Hamdy  and
      Derczynski, Leon  and
      Pitenis, Zeses  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.188",
    pages = "1425--1447",
    abstract = "We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.",
}
@inproceedings{wang-etal-2020-galileo,
    title = "Galileo at {S}em{E}val-2020 Task 12: Multi-lingual Learning for Offensive Language Identification Using Pre-trained Language Models",
    author = "Wang, Shuohuan  and
      Liu, Jiaxiang  and
      Ouyang, Xuan  and
      Sun, Yu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.189",
    pages = "1448--1455",
    abstract = "This paper describes Galileo{'}s performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media. For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R. For offensive language categorization, we proposed a knowledge distillation method trained on soft labels generated by several supervised models. Our team participated in all three sub-tasks. In Sub-task A - Offensive Language Identification, we ranked first in terms of average F1 scores in all languages. We are also the only team which ranked among the top three across all languages. We also took the first place in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offence Target Identification.",
}
@inproceedings{huang-etal-2020-ernie,
    title = "{ERNIE} at {S}em{E}val-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model",
    author = "Huang, Zhengjie  and
      Feng, Shikun  and
      Su, Weiyue  and
      Chen, Xuyi  and
      Wang, Shuohuan  and
      Liu, Jiaxiang  and
      Ouyang, Xuan  and
      Sun, Yu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.190",
    pages = "1456--1461",
    abstract = "This paper describes the system designed by ERNIE Team which achieved the first place in SemEval-2020 Task 10: Emphasis Selection For Written Text in Visual Media. Given a sentence, we are asked to find out the most important words as the suggestion for automated design. We leverage the unsupervised pre-training model and finetune these models on our task. After our investigation, we found that the following models achieved an excellent performance in this task: ERNIE 2.0, XLM-ROBERTA, ROBERTA and ALBERT. We combine a pointwise regression loss and a pairwise ranking loss which is more close to the final Match m metric to finetune our models. And we also find that additional feature engineering and data augmentation can help improve the performance. Our best model achieves the highest score of 0.823 and ranks first for all kinds of metrics.",
}
@inproceedings{chernyavskiy-etal-2020-aschern,
    title = "Aschern at {S}em{E}val-2020 Task 11: It Takes Three to Tango: {R}o{BERT}a, {CRF}, and Transfer Learning",
    author = "Chernyavskiy, Anton  and
      Ilvovsky, Dmitry  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.191",
    pages = "1462--1468",
    abstract = "We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task, the consistency between nested spans, repetitions, and labels from similar spans in training. We achieved sizable improvements over baseline fine-tuned RoBERTa models, and the official evaluation ranked our system 3rd (almost tied with the 2nd) out of 36 teams on the span identification subtask with an F1 score of 0.491, and 2nd (almost tied with the 1st) out of 31 teams on the technique classification subtask with an F1 score of 0.62.",
}
@inproceedings{blaschke-etal-2020-cyberwalle,
    title = "{C}yber{W}all{E} at {S}em{E}val-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection",
    author = "Blaschke, Verena  and
      Korniyenko, Maxim  and
      Tureski, Sam",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.192",
    pages = "1469--1480",
    abstract = "This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86{\%}) and 8 out of 31 teams in the TC subtask (F1-score: 57.37{\%}).",
}
@inproceedings{grigorev-ivanov-2020-inno,
    title = "Inno at {S}em{E}val-2020 Task 11: Leveraging Pure Transfomer for Multi-Class Propaganda Detection",
    author = "Grigorev, Dmitry  and
      Ivanov, Vladimir",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.193",
    pages = "1481--1487",
    abstract = "The paper presents the solution of team {''}Inno{''} to a SEMEVAL 2020 task 11 {''}Detection of propaganda techniques in news articles{''}. The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure Transformer-based model with an optimized learning scheme on the ability to distinguish propaganda techniques between each other. Our model showed 0:6 and 0:58 overall F1 score on validation set and test set accordingly and non-zero F1 score on each class on both sets.",
}
@inproceedings{dimov-etal-2020-nopropaganda,
    title = "{N}o{P}ropaganda at {S}em{E}val-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification",
    author = "Dimov, Ilya  and
      Korzun, Vladislav  and
      Smurov, Ivan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.194",
    pages = "1488--1494",
    abstract = "This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6{\%} and a micro-averaged F-score of 58.2{\%} for those tasks accordingly.",
}
@inproceedings{arsenos-siolas-2020-ntuaails,
    title = "{NTUAAILS} at {S}em{E}val-2020 Task 11: Propaganda Detection and Classification with bi{LSTM}s and {ELM}o",
    author = "Arsenos, Anastasios  and
      Siolas, Georgios",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.195",
    pages = "1495--1501",
    abstract = "This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles. This task comprises of two different sub-tasks, namely A: Span Identification (SI), B: Technique Classification (TC). The goal for the SI sub-task is to identify specific fragments, in a given plain text, containing at least one propaganda technique. The TC sub-task aims to identify the applied propaganda technique in a given text fragment. A different model was trained for each sub-task. Our best performing system for the SI task consists of pre-trained ELMo word embeddings followed by residual bidirectional LSTM network. For the TC sub-task pre-trained word embeddings from GloVe fed to a bidirectional LSTM neural network. The models achieved rank 28 among 36 teams with F1 score of 0.335 and rank 25 among 31 teams with 0.463 F1 score for SI and TC sub-tasks respectively. Our results indicate that the proposed deep learning models, although relatively simple in architecture and fast to train, achieve satisfactory results in the tasks on hand.",
}
@inproceedings{kranzlein-etal-2020-team,
    title = "Team {D}o{N}ot{D}istribute at {S}em{E}val-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles",
    author = "Kranzlein, Michael  and
      Behzad, Shabnam  and
      Goharian, Nazli",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.196",
    pages = "1502--1508",
    abstract = "This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques with the goal of aiding future studies in propaganda detection.",
}
@inproceedings{dao-etal-2020-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2020 Task 11: {LSTM} Network for Detection of Propaganda Techniques in News Articles",
    author = "Dao, Jiaxu  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.197",
    pages = "1509--1515",
    abstract = "This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our approach achieved good results for both the SI and TC subtasks. The macro- F 1 - score for the SI subtask is 0.406, and the micro- F 1 - score for the TC subtask is 0.505. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set. This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques.",
}
@inproceedings{herath-etal-2020-adelaidecyc,
    title = "{A}delaide{C}y{C} at {S}em{E}val-2020 Task 12: Ensemble of Classifiers for Offensive Language Detection in Social Media",
    author = "Herath, Mahen  and
      Atapattu, Thushari  and
      Dung, Hoang Anh  and
      Treude, Christoph  and
      Falkner, Katrina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.198",
    pages = "1516--1523",
    abstract = "This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks {--} offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C). Our team has participated in all the three subtasks. We have developed machine learning and deep learning-based ensembles of models. We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively. While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still remain challenging to classify.",
}
@inproceedings{perez-etal-2020-andes,
    title = "{ANDES} at {S}em{E}val-2020 Task 12: A Jointly-trained {BERT} Multilingual Model for Offensive Language Detection",
    author = "P{\'e}rez, Juan Manuel  and
      Arango, Aym{\'e}  and
      Luque, Franco",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.199",
    pages = "1524--1531",
    abstract = "This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research",
}
@inproceedings{alharbi-lee-2020-bhamnlp,
    title = "{B}ham{NLP} at {S}em{E}val-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for {A}rabic Offensive Language Identification in Social Media",
    author = "Alharbi, Abdullah I.  and
      Lee, Mark",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.200",
    pages = "1532--1538",
    abstract = "Social media platforms such as Twitter offer people an opportunity to publish short posts in which they can share their opinions and perspectives. While these applications can be valuable, they can also be exploited to promote negative opinions, insults, and hatred against a person, race, or group. These opinions can be spread to millions of people at the click of a mouse. As such, there is a need to develop mechanisms by which offensive language can be automatically detected in social media channels and managed in a timely manner. To help achieve this goal, SemEval 2020 offered a shared task (OffensEval 2020) that involved the detection of offensive text in Arabic. We propose an ensemble approach that combines different levels of word embedding models and transfers learning from other sources of emotion-related tasks. The proposed system ranked 9th out of the 52 entries within the Arabic Offensive language identification subtask.",
}
@inproceedings{casula-etal-2020-fbk,
    title = "{FBK}-{DH} at {S}em{E}val-2020 Task 12: Using Multi-channel {BERT} for Multilingual Offensive Language Detection",
    author = "Casula, Camilla  and
      Palmero Aprosio, Alessio  and
      Menini, Stefano  and
      Tonelli, Sara",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.201",
    pages = "1539--1545",
    abstract = "In this paper we present our submission to sub-task A at SemEval 2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval2). For Danish, Turkish, Arabic and Greek, we develop an architecture based on transfer learning and relying on a two-channel BERT model, in which the English BERT and the multilingual one are combined after creating a machine-translated parallel corpus for each language in the task. For English, instead, we adopt a more standard, single-channel approach. We find that, in a multilingual scenario, with some languages having small training data, using parallel BERT models with machine translated data can give systems more stability, especially when dealing with noisy data. The fact that machine translation on social media data may not be perfect does not hurt the overall classification performance.",
}
@inproceedings{colla-etal-2020-grupato,
    title = "{G}ru{P}a{T}o at {S}em{E}val-2020 Task 12: Retraining m{BERT} on Social Media and Fine-tuned Offensive Language Models",
    author = "Colla, Davide  and
      Caselli, Tommaso  and
      Basile, Valerio  and
      Mitrovi{\'c}, Jelena  and
      Granitzer, Michael",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.202",
    pages = "1546--1554",
    abstract = "We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model. We download extra training data from Twitter in English, Danish, and Turkish, and use it to re-train the model. We then fine-tuned the model on the provided training data and, in some configurations, implement transfer learning approach exploiting the typological relatedness between English and Danish. Our systems obtained good results across the three languages (.9036 for EN, .7619 for DA, and .7789 for TR).",
}
@inproceedings{sotudeh-etal-2020-guir,
    title = "{GUIR} at {S}em{E}val-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection",
    author = "Sotudeh, Sajad  and
      Xiang, Tong  and
      Yao, Hao-Ren  and
      MacAvaney, Sean  and
      Yang, Eugene  and
      Goharian, Nazli  and
      Frieder, Ophir",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.203",
    pages = "1555--1561",
    abstract = "Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes three English sub-tasks: identifying the presence of offensive language (Sub-task A), identifying the presence of target in offensive language (Sub-task B), and identifying the categories of the target (Sub-task C). Our experiments explore using a domain-tuned contextualized language model (namely, BERT) for this task. We also experiment with different components and configurations (e.g., a multi-view SVM) stacked upon BERT models for specific sub-tasks. Our submissions achieve F1 scores of 91.7{\%} in Sub-task A, 66.5{\%} in Sub-task B, and 63.2{\%} in Sub-task C. We perform an ablation study which reveals that domain tuning considerably improves the classification performance. Furthermore, error analysis shows common misclassification errors made by our model and outlines research directions for future.",
}
@inproceedings{baruah-etal-2020-iiitg-adbu-semeval,
    title = "{IIITG}-{ADBU} at {S}em{E}val-2020 Task 12: Comparison of {BERT} and {B}i{LSTM} in Detecting Offensive Language",
    author = "Baruah, Arup  and
      Das, Kaushik  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.204",
    pages = "1562--1568",
    abstract = "Task 12 of SemEval 2020 consisted of 3 subtasks, namely offensive language identification (Subtask A), categorization of offense type (Subtask B), and offense target identification (Subtask C). This paper presents the results our classifiers obtained for the English language in the 3 subtasks. The classifiers used by us were BERT and BiLSTM. On the test set, our BERT classifier obtained macro F1 score of 0.90707 for subtask A, and 0.65279 for subtask B. The BiLSTM classifier obtained macro F1 score of 0.57565 for subtask C. The paper also performs an analysis of the errors made by our classifiers. We conjecture that the presence of few misleading instances in the dataset is affecting the performance of the classifiers. Our analysis also discusses the need of temporal context and world knowledge to determine the offensiveness of few comments.",
}
@inproceedings{pamies-etal-2020-lt,
    title = "{LT}@{H}elsinki at {S}em{E}val-2020 Task 12: Multilingual or Language-specific {BERT}?",
    author = {P{\`a}mies, Marc  and
      {\"O}hman, Emily  and
      Kajava, Kaisla  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.205",
    pages = "1569--1575",
    abstract = "This paper presents the different models submitted by the LT@Helsinki team for the SemEval 2020 Shared Task 12. Our team participated in sub-tasks A and C; titled offensive language identification and offense target identification, respectively. In both cases we used the so-called Bidirectional Encoder Representation from Transformer (BERT), a model pre-trained by Google and fine-tuned by us on the OLID and SOLID datasets. The results show that offensive tweet classification is one of several language-based tasks where BERT can achieve state-of-the-art results.",
}
@inproceedings{ahn-etal-2020-nlpdove,
    title = "{NLPD}ove at {S}em{E}val-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer",
    author = "Ahn, Hwijeen  and
      Sun, Jimin  and
      Park, Chan Young  and
      Seo, Jungyun",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.206",
    pages = "1576--1586",
    abstract = "This paper describes our approach to the task of identifying offensive languages in a multilingual setting. We investigate two data augmentation strategies: using additional semi-supervised labels with different thresholds and cross-lingual transfer with data selection. Leveraging the semi-supervised dataset resulted in performance improvements compared to the baseline trained solely with the manually-annotated dataset. We propose a new metric, Translation Embedding Distance, to measure the transferability of instances for cross-lingual data selection. We also introduce various preprocessing steps tailored for social media text along with methods to fine-tune the pre-trained multilingual BERT (mBERT) for offensive language identification. Our multilingual systems achieved competitive results in Greek, Danish, and Turkish at OffensEval 2020.",
}
@inproceedings{wang-marinho-2020-nova,
    title = "Nova-Wang at {S}em{E}val-2020 Task 12: {O}ffens{E}mblert: An Ensemble of{O}ffensive Language Classifiers",
    author = "Wang, Susan  and
      Marinho, Zita",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.207",
    pages = "1587--1597",
    abstract = "This paper presents our contribution to the Offensive Language Classification Task (English SubTask A) of Semeval 2020. We propose different Bert models trained on several offensive language classification and profanity datasets, and combine their output predictions in an ensemble model. We experimented with different ensemble approaches, such as SVMs, Gradient boosting, AdaBoosting and Logistic Regression. We further propose an under-sampling approach of the current SOLID dataset, which removed the most uncertain partitions of the dataset, increasing the recall of the dataset. Our best model, an average ensemble of four different Bert models, achieved 11th place out of 82 participants with a macro F1 score of 0.91344 in the English SubTask A.",
}
@inproceedings{suryawanshi-etal-2020-nuig,
    title = "{NUIG} at {S}em{E}val-2020 Task 12: Pseudo Labelling for Offensive Content Classification",
    author = "Suryawanshi, Shardul  and
      Arcan, Mihael  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.208",
    pages = "1598--1604",
    abstract = "This work addresses the classification problem defined by sub-task A (English only) of the OffensEval 2020 challenge. We used a semi-supervised approach to classify given tweets into an offensive (OFF) or not-offensive (NOT) class. As the OffensEval 2020 dataset is loosely labelled with confidence scores given by unsupervised models, we used last year{'}s offensive language identification dataset (OLID) to label the OffensEval 2020 dataset. Our approach uses a pseudo-labelling method to annotate the current dataset. We trained four text classifiers on the OLID dataset and the classifier with the highest macro-averaged F1-score has been used to pseudo label the OffensEval 2020 dataset. The same model which performed best amongst four text classifiers on OLID dataset has been trained on the combined dataset of OLID and pseudo labelled OffensEval 2020. We evaluated the classifiers with precision, recall and macro-averaged F1-score as the primary evaluation metric on the OLID and OffensEval 2020 datasets. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: \url{http://creativecommons.org/licenses/by/4.0/}.",
}
@inproceedings{de-la-pena-sarracen-rosso-2020-prhlt,
    title = "{PRHLT}-{UPV} at {S}em{E}val-2020 Task 12: {BERT} for Multilingual Offensive Language Detection",
    author = "De la Pe{\~n}a Sarrac{\'e}n, Gretel Liz  and
      Rosso, Paolo",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.209",
    pages = "1605--1614",
    abstract = "The present paper describes the system submitted by the PRHLT-UPV team for the task 12 of SemEval-2020: OffensEval 2020. The official title of the task is Multilingual Offensive Language Identification in Social Media, and aims to identify offensive language in texts. The languages included in the task are English, Arabic, Danish, Greek and Turkish. We propose a model based on the BERT architecture for the analysis of texts in English. The approach leverages knowledge within a pre-trained model and performs fine-tuning for the particular task. In the analysis of the other languages the Multilingual BERT is used, which has been pre-trained for a large number of languages. In the experiments, the proposed method for English texts is compared with other approaches to analyze the relevance of the architecture used. Furthermore, simple models for the other languages are evaluated to compare them with the proposed one. The experimental results show that the model based on BERT outperforms other approaches. The main contribution of this work lies in this study, despite not obtaining the first positions in most cases of the competition ranking.",
}
@inproceedings{janiszewski-etal-2020-pum,
    title = "{PUM} at {S}em{E}val-2020 Task 12: Aggregation of Transformer-based Models{'} Features for Offensive Language Recognition",
    author = "Janiszewski, Piotr  and
      Skiba, Mateusz  and
      Wali{\'n}ska, Urszula",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.210",
    pages = "1615--1621",
    abstract = "In this paper, we describe the PUM team{'}s entry to the SemEval-2020 Task 12. Creating our solution involved leveraging two well-known pretrained models used in natural language processing: BERT and XLNet, which achieve state-of-the-art results in multiple NLP tasks. The models were fine-tuned for each subtask separately and features taken from their hidden layers were combinedand fed into a fully connected neural network. The model using aggregated Transformer featurescan serve as a powerful tool for offensive language identification problem. Our team was ranked7th out of 40 in Sub-task C - Offense target identification with 64.727{\%} macro F1-score and 64thout of 85 in Sub-task A - Offensive language identification (89.726{\%} F1-score).",
}
@inproceedings{plaza-del-arco-etal-2020-sinai,
    title = "{SINAI} at {S}em{E}val-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models",
    author = "Plaza del Arco, Flor Miriam  and
      Molina Gonz{\'a}lez, M. Dolores  and
      Ure{\~n}a-L{\'o}pez, Alfonso  and
      Martin, Maite",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.211",
    pages = "1622--1627",
    abstract = "This paper describes the participation of SINAI team at Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. In particular, the participation in Sub-task A in English which consists of identifying tweets as offensive or not offensive. We preprocess the dataset according to the language characteristics used on social media. Then, we select a small set from the training set provided by the organizers and fine-tune different Transformerbased models in order to test their effectiveness. Our team ranks 20th out of 85 participants in Subtask-A using the XLNet model.",
}
@inproceedings{jahan-2020-team,
    title = "Team Oulu at {S}em{E}val-2020 Task 12: Multilingual Identification of Offensive Language, Type and Target of {T}witter Post Using Translated Datasets",
    author = "Jahan, Md Saroar",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.212",
    pages = "1628--1637",
    abstract = "With the proliferation of social media platforms, anonymous discussions together with easy online access, reports on offensive content have caused serious concern to both authorities and research communities. Although there is extensive research in identifying textual offensive language from online content, the dynamic discourse of social media content, as well as the emergence of new forms of offensive language, especially in a multilingual setting, calls for future research in the issue. In this work, we tackled Task A, B, and C of Offensive Language Challenge at SemEval2020. We handled offensive language in five languages: English, Greek, Danish, Arabic, and Turkish. Specifically, we pre-processed all provided datasets and developed an appropriate strategy to handle Tasks (A, B, {\&} C) for identifying the presence/absence, type and the target of offensive language in social media. For this purpose, we used OLID2019, OLID2020 datasets, and generated new datasets, which we made publicly available. We used the provided unsupervised machine learning implementation for automated annotated datasets and the online Google translation tools to create new datasets as well. We discussed the limitations and the success of our machine learning-based approach for all the five different languages. Our results for identifying offensive posts (Task A) yielded satisfactory accuracy of 0.92 for English, 0.81 for Danish, 0.84 for Turkish, 0.85 for Greek, and 0.89 for Arabic. For the type detection (Task B), the results are significantly higher (.87 accuracy) compared to target detection (Task C), which yields .81 accuracy. Moreover, after using automated Google translation, the overall efficiency improved by 2{\%} for Greek, Turkish, and Danish.",
}
@inproceedings{wiedemann-etal-2020-uhh,
    title = "{UHH}-{LT} at {S}em{E}val-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection",
    author = "Wiedemann, Gregor  and
      Yimam, Seid Muhie  and
      Biemann, Chris",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.213",
    pages = "1638--1644",
    abstract = "Fine-tuning of pre-trained transformer networks such as BERT yield state-of-the-art results for text classification tasks. Typically, fine-tuning is performed on task-specific training datasets in a supervised manner. One can also fine-tune in unsupervised manner beforehand by further pre-training the masked language modeling (MLM) task. Hereby, in-domain data for unsupervised MLM resembling the actual classification target dataset allows for domain adaptation of the model. In this paper, we compare current pre-trained transformer networks with and without MLM fine-tuning on their performance for offensive language detection. Our MLM fine-tuned RoBERTa-based classifier officially ranks 1st in the SemEval 2020 Shared Task 12 for the English language. Further experiments with the ALBERT model even surpass this result.",
}
@inproceedings{kanani-etal-2020-el,
    title = "{EL}-{BERT} at {S}em{E}val-2020 Task 10: A Multi-Embedding Ensemble Based Approach for Emphasis Selection in Visual Media",
    author = "Kanani, Chandresh  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.214",
    pages = "1645--1651",
    abstract = "In visual media, text emphasis is the strengthening of words in a text to convey the intent of the author. Text emphasis in visual media is generally done by using different colors, backgrounds, or font for the text; it helps in conveying the actual meaning of the message to the readers. Emphasis selection is the task of choosing candidate words for emphasis, it helps in automatically designing posters and other media contents with written text. If we consider only the text and do not know the intent, then there can be multiple valid emphasis selections. We propose the use of ensembles for emphasis selection to improve over single emphasis selection models. We show that the use of multi-embedding helps in enhancing the results for base models. To show the efficacy of proposed approach we have also done a comparison of our results with state-of-the-art models.",
}
@inproceedings{guo-etal-2020-fpai,
    title = "{FPAI} at {S}em{E}val-2020 Task 10: A Query Enhanced Model with {R}o{BERT}a for Emphasis Selection",
    author = "Guo, Chenyang  and
      Hou, Xiaolong  and
      Ren, Junsong  and
      Jiang, Lianxin  and
      Mo, Yang  and
      Yang, Haiqin  and
      Shen, Jianping",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.215",
    pages = "1652--1657",
    abstract = "This paper describes the model we apply in the SemEval-2020 Task 10. We formalize the task of emphasis selection as a simplified query-based machine reading comprehension (MRC) task, i.e. answering a fixed question of {``}Find candidates for emphasis{''}. We propose our subword puzzle encoding mechanism and subword fusion layer to align and fuse subwords. By introducing the semantic prior knowledge of the informative query and some other techniques, we attain the 7th place during the evaluation phase and the first place during train phase.",
}
@inproceedings{morio-etal-2020-hitachi,
    title = "Hitachi at {S}em{E}val-2020 Task 10: Emphasis Distribution Fusion on Fine-Tuned Language Models",
    author = "Morio, Gaku  and
      Morishita, Terufumi  and
      Ozaki, Hiroaki  and
      Miyoshi, Toshinori",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.216",
    pages = "1658--1664",
    abstract = "This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams.",
}
@inproceedings{singhal-etal-2020-iitk,
    title = "{IITK} at {S}em{E}val-2020 Task 10: Transformers for Emphasis Selection",
    author = "Singhal, Vipul  and
      Dhull, Sahil  and
      Agarwal, Rishabh  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.217",
    pages = "1665--1670",
    abstract = "We propose an end-to-end model that takes as input the text and corresponding to each word gives the probability of the word to be emphasized. Our results show that transformer-based models are particularly effective in this task. We achieved an evaluation score of 0.810 and were ranked third on the leaderboard.",
}
@inproceedings{bestgen-2020-last,
    title = "{LAST} at {S}em{E}val-2020 Task 10: Finding Tokens to Emphasise in Short Written Texts with Precomputed Embedding Models and {L}ight{GBM}",
    author = "Bestgen, Yves",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.218",
    pages = "1671--1677",
    abstract = "To select tokens to be emphasised in short texts, a system mainly based on precomputed embedding models, such as BERT and ELMo, and LightGBM is proposed. Its performance is low. Additional analyzes suggest that its effectiveness is poor at predicting the highest emphasis scores while they are the most important for the challenge and that it is very sensitive to the specific instances provided during learning.",
}
@inproceedings{anand-etal-2020-midas,
    title = "{MIDAS} at {S}em{E}val-2020 Task 10: Emphasis Selection Using Label Distribution Learning and Contextual Embeddings",
    author = "Anand, Sarthak  and
      Gupta, Pradyumna  and
      Yadav, Hemant  and
      Mahata, Debanjan  and
      Gosangi, Rakesh  and
      Zhang, Haimin  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.219",
    pages = "1678--1684",
    abstract = "This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text. We approach this emphasis selection problem as a sequence labeling task where we represent the underlying text with various contextual embedding models. We also employ label distribution learning to account for annotator disagreements. We experiment with the choice of model architectures, trainability of layers, and different contextual embeddings. Our best performing architecture is an ensemble of different models, which achieved an overall matching score of 0.783, placing us 15th out of 31 participating teams. Lastly, we analyze the results in terms of parts of speech tags, sentence lengths, and word ordering.",
}
@inproceedings{shatilov-etal-2020-randomseed19,
    title = "Randomseed19 at {S}em{E}val-2020 Task 10: Emphasis Selection for Written Text in Visual Media",
    author = "Shatilov, Aleksandr  and
      Gordeev, Denis  and
      Rey, Alexey",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.220",
    pages = "1685--1690",
    abstract = "This paper describes our approach to emphasis selection for written text in visual media as a solution for SemEval 2020 Task 10. We used an ensemble of several different Transformer-based models and cast the task as a sequence labeling problem with two tags: {`}I{'} as {`}emphasized{'} and {`}O{'} as {`}non-emphasized{'} for each token in the text.",
}
@inproceedings{yang-etal-2020-textlearner,
    title = "{T}ext{L}earner at {S}em{E}val-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text",
    author = "Yang, Zhishen  and
      Wolfsteller, Lars  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.221",
    pages = "1691--1697",
    abstract = "This paper describes the emphasis selection system of the team TextLearner for SemEval 2020 Task 10: Emphasis Selection For Written Text in Visual Media. The system aims to learn the emphasis selection distribution using contextual representations extracted from pre-trained language models and a two-staged ranking model. The experimental results demonstrate the strong contextual representation power of the recent advanced transformer-based language model RoBERTa, which can be exploited using a simple but effective architecture on top.",
}
@inproceedings{glocker-markianos-wright-2020-textmarkers,
    title = {{T}{\"e}{X}tmarkers at {S}em{E}val-2020 Task 10: Emphasis Selection with Agreement Dependent Crowd Layers},
    author = "Glocker, Kevin  and
      Markianos Wright, Stefanos Andreas",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.222",
    pages = "1698--1703",
    abstract = "In visual communication, the ability of a short piece of text to catch someone{'}s eye in a single glance or from a distance is of paramount importance. In our approach to the SemEval-2020 task {``}Emphasis Selection For Written Text in Visual Media{''}, we use contextualized word representations from a pretrained model of the state-of-the-art BERT architecture together with a stacked bidirectional GRU network to predict token-level emphasis probabilities. For tackling low inter-annotator agreement in the dataset, we attempt to model multiple annotators jointly by introducing initialization with agreement dependent noise to a crowd layer architecture. We found our approach to both perform substantially better than initialization with identities for this purpose and to outperform a baseline trained with token level majority voting. Our submission system reaches substantially higher Match m on the development set than the task baseline (0.779), but only slightly outperforms the test set baseline (0.754) using a three model ensemble.",
}
@inproceedings{hossu-parde-2020-uic,
    title = "{UIC}-{NLP} at {S}em{E}val-2020 Task 10: Exploring an Alternate Perspective on Evaluation",
    author = "Hossu, Philip  and
      Parde, Natalie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.223",
    pages = "1704--1709",
    abstract = "In this work we describe and analyze a supervised learning system for word emphasis selection in phrases drawn from visual media as a part of the Semeval 2020 Shared Task 10. More specifically, we begin by briefly introducing the shared task problem and provide an analysis of interesting and relevant features present in the training dataset. We then introduce our LSTM-based model and describe its structure, input features, and limitations. Our model ultimately failed to beat the benchmark score, achieving an average match() score of 0.704 on the validation data (0.659 on the test data) but predicted 84.8{\%} of words correctly considering a 0.5 threshold. We conclude with a thorough analysis and discussion of erroneous predictions with many examples and visualizations.",
}
@inproceedings{liao-etal-2020-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2020 Task 10: Using a Multi-granularity Ordinal Classification of the {B}i{LSTM} Model for Emphasis Selection",
    author = "Liao, Dawei  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.224",
    pages = "1710--1715",
    abstract = "In this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection. In detail, the word embedding is learned from Embeddings from Language Model (ELMO) to extract feature vector representation. Then, the ordinal classifica-tions are implemented on four different multi-granularities to approximate the continuous em-phasize values. Comparative experiments were conducted to compare the model with baseline in which the problem is transformed to label distribution problem.",
}
@inproceedings{dewantara-etal-2020-3218ir,
    title = "3218{IR} at {S}em{E}val-2020 Task 11: {C}onv1{D} and Word Embedding in Propaganda Span Identification at News Articles",
    author = "Dewantara, Dimas Sony  and
      Budi, Indra  and
      Ibrohim, Muhammad Okky",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.225",
    pages = "1716--1721",
    abstract = "In this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value. We describe the system entered by the team of Information Retrieval Lab. Universitas Indonesia (3218IR) in the SemEval 2020 Task 11 Sub Task 1 about propaganda span identification in news articles. The best model obtained an F1 score of 0.24 in the development set and 0.23 in the test set. We show that there is a potential for performance improvement through the use of models with appropriate hyper-parameters. Our system uses a combination of Conv1D and GloVe as Word Embedding to detect propaganda in the fragment text level.",
}
@inproceedings{patil-etal-2020-bpgc,
    title = "{BPGC} at {S}em{E}val-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features Based Ensemble Learning",
    author = "Patil, Rajaswa  and
      Singh, Somesh  and
      Agarwal, Swati",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.226",
    pages = "1722--1731",
    abstract = "Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Task-11 consists of two sub-tasks, namely, Span Identification - given any news article, the system tags those specific fragments which contain at least one propaganda technique; and Technique Classification - correctly classify a given propagandist statement amongst 14 propaganda techniques. For sub-task 1, we use contextual embeddings extracted from pre-trained transformer models to represent the text data at various granularities and propose a multi-granularity knowledge sharing approach. For sub-task 2, we use an ensemble of BERT and logistic regression classifiers with linguistic features. Our results reveal that the linguistic features are the strong indicators for covering minority classes in a highly imbalanced dataset.",
}
@inproceedings{bairaktaris-etal-2020-duth,
    title = "{DUTH} at {S}em{E}val-2020 Task 11: {BERT} with Entity Mapping for Propaganda Classification",
    author = "Bairaktaris, Anastasios  and
      Symeonidis, Symeon  and
      Arampatzis, Avi",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.227",
    pages = "1732--1738",
    abstract = "This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. Our team dealt with Subtask 2: Technique Classification. We used shallow Natural Language Processing (NLP) preprocessing techniques to reduce the noise in the dataset, feature selection methods, and common supervised machine learning algorithms. Our final model is based on using the BERT system with entity mapping. To improve our model{'}s accuracy, we mapped certain words into five distinct categories by employing word-classes and entity recognition",
}
@inproceedings{morio-etal-2020-hitachi-semeval,
    title = "Hitachi at {S}em{E}val-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection",
    author = "Morio, Gaku  and
      Morishita, Terufumi  and
      Ozaki, Hiroaki  and
      Miyoshi, Toshinori",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.228",
    pages = "1739--1748",
    abstract = "In this paper, we show our system for SemEval-2020 task 11, where we tackle propaganda span identification (SI) and technique classification (TC). We investigate heterogeneous pre-trained language models (PLMs) such as BERT, GPT-2, XLNet, XLM, RoBERTa, and XLM-RoBERTa for SI and TC fine-tuning, respectively. In large-scale experiments, we found that each of the language models has a characteristic property, and using an ensemble model with them is promising. Finally, the ensemble model was ranked 1st amongst 35 teams for SI and 3rd amongst 31 teams for TC.",
}
@inproceedings{altiti-etal-2020-just,
    title = "{JUST} at {S}em{E}val-2020 Task 11: Detecting Propaganda Techniques Using {BERT} Pre-trained Model",
    author = "Altiti, Ola  and
      Abdullah, Malak  and
      Obiedat, Rasha",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.229",
    pages = "1749--1755",
    abstract = "This paper presents the submission to semeval-2020 task 11, Detection of Propaganda Techniques in News Articles. Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in a specific propaganda span. We have used and implemented various models to detect propaganda. Our proposed model is based on BERT uncased pre-trained language model as it has achieved state-of-the-art performance on multiple NLP benchmarks. The performance results of our proposed model have scored 0.55307 F1-Score, which outperforms the baseline model provided by the organizers with 0.2519 F1-Score, and our model is 0.07 away from the best performing team. Compared to other participating systems, our submission is ranked 15th out of 31 participants.",
}
@inproceedings{khosla-etal-2020-ltiatcmu,
    title = "{LTI}at{CMU} at {S}em{E}val-2020 Task 11: Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification",
    author = "Khosla, Sopan  and
      Joshi, Rishabh  and
      Dutt, Ritam  and
      Black, Alan W  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.230",
    pages = "1756--1763",
    abstract = "In this paper we describe our submission for the task of Propaganda Span Identification in news articles. We introduce a BERT-BiLSTM based span-level propaganda classification model that identifies which token spans within the sentence are indicative of propaganda. The {''}multi-granular{''} model incorporates linguistic knowledge at various levels of text granularity, including word, sentence and document level syntactic, semantic and pragmatic affect features, which significantly improve model performance, compared to its language-agnostic variant. To facilitate better representation learning, we also collect a corpus of 10k news articles, and use it for fine-tuning the model. The final model is a majority-voting ensemble which learns different propaganda class boundaries by leveraging different subsets of incorporated knowledge.",
}
@inproceedings{singh-etal-2020-newssweeper,
    title = "news{S}weeper at {S}em{E}val-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification",
    author = "Singh, Paramansh  and
      Sandhu, Siraj  and
      Kumar, Subham  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.231",
    pages = "1764--1770",
    abstract = "This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.",
}
@inproceedings{martinkovic-etal-2020-nlfiit,
    title = "{NLFIIT} at {S}em{E}val-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles",
    author = "Martinkovic, Matej  and
      Pecar, Samuel  and
      Simko, Marian",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.232",
    pages = "1771--1778",
    abstract = "Since propaganda became more common technique in news, it is very important to look for possibilities of its automatic detection. In this paper, we present neural model architecture submitted to the SemEval-2020 Task 11 competition: {``}Detection of Propaganda Techniques in News Articles{''}. We participated in both subtasks, propaganda span identification and propaganda technique classification. Our model utilizes recurrent Bi-LSTM layers with pre-trained word representations and also takes advantage of self-attention mechanism. Our model managed to achieve score 0.405 F1 for subtask 1 and 0.553 F1 for subtask 2 on test set resulting in 17th and 16th place in subtask 1 and subtask 2, respectively.",
}
@inproceedings{chauhan-diddee-2020-psuedoprop,
    title = "{P}suedo{P}rop at {S}em{E}val-2020 Task 11: Propaganda Span Detection Using {BERT}-{CRF} and Ensemble Sentence Level Classifier",
    author = "Chauhan, Aniruddha  and
      Diddee, Harshita",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.233",
    pages = "1779--1785",
    abstract = "This paper explains our teams{'} submission to the Shared Task of Fine-Grained Propaganda Detection in which we propose a sequential BERT-CRF based Span Identification model where the fine-grained detection is carried out only on the articles that are flagged as containing propaganda by an ensemble SLC model. We propose this setup bearing in mind the practicality of this approach in identifying propaganda spans in the exponentially increasing content base where the fine-tuned analysis of the entire data repository may not be the optimal choice due to its massive computational resource requirements. We present our analysis on different voting ensembles for the SLC model. Our system ranks 14th on the test set and 22nd on the development set and with an F1 score of 0.41 and 0.39 respectively.",
}
@inproceedings{dementieva-etal-2020-skoltechnlp,
    title = "{S}koltech{NLP} at {S}em{E}val-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection",
    author = "Dementieva, Daryna  and
      Markov, Igor  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.234",
    pages = "1786--1792",
    abstract = "This paper presents a solution for the Span Identification (SI) task in the {``}Detection of Propaganda Techniques in News Articles{''} competition at SemEval-2020. The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique. This is a binary sequence tagging task. We tested several approaches finally selecting a fine-tuned BERT model as our baseline model. Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger. We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.",
}
@inproceedings{krishnamurthy-etal-2020-soccogcom,
    title = "{S}oc{C}og{C}om at {S}em{E}val-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features",
    author = "Krishnamurthy, Gangeshwar  and
      Gupta, Raj Kumar  and
      Yang, Yinping",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.235",
    pages = "1793--1801",
    abstract = "This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the {``}loaded language{''} and {``}slogan{''} techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, {``}flag waving{''} and {``}appeal to fear-prejudice{''} have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting {``}loaded language{''} (F1 = 0.772), {``}name calling and labeling{''} (F1 = 0.673), {``}doubt{''} (F1 = 0.604) and {``}flag waving{''} (F1 = 0.543).",
}
@inproceedings{raj-etal-2020-solomon,
    title = "{S}olomon at {S}em{E}val-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles",
    author = "Raj, Mayank  and
      Jaiswal, Ajay  and
      R.R, Rohit  and
      Gupta, Ankita  and
      Sahoo, Sudeep Kumar  and
      Srivastava, Vertika  and
      Kim, Yeon Hyang",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.236",
    pages = "1802--1807",
    abstract = "This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 {''}Detection of Propaganda Techniques in News Articles{''}. We participated in Task {''}Technique Classification{''} (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.",
}
@inproceedings{li-xiao-2020-syrapropa,
    title = "syrapropa at {S}em{E}val-2020 Task 11: {BERT}-based Models Design for Propagandistic Technique and Span Detection",
    author = "Li, Jinfen  and
      Xiao, Lu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.237",
    pages = "1808--1816",
    abstract = "This paper describes the BERT-based models proposed for two subtasks in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. We first build the model for Span Identification (SI) based on SpanBERT, and facilitate the detection by a deeper model and a sentence-level representation. We then develop a hybrid model for the Technique Classification (TC). The hybrid model is composed of three submodels including two BERT models with different training methods, and a feature-based Logistic Regression model. We endeavor to deal with imbalanced dataset by adjusting cost function. We are in the seventh place in SI subtask (0.4711 of F1-measure), and in the third place in TC subtask (0.6783 of F1-measure) on the development set.",
}
@inproceedings{kaas-etal-2020-team,
    title = "Team {D}i{S}aster at {S}em{E}val-2020 Task 11: Combining {BERT} and Hand-crafted Features for Identifying Propaganda Techniques in News",
    author = "Kaas, Anders  and
      Thomsen, Viktor Torp  and
      Plank, Barbara",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.238",
    pages = "1817--1822",
    abstract = "The identification of communication techniques in news articles such as propaganda is important, as such techniques can influence the opinions of large numbers of people. Most work so far focused on the identification at the news article level. Recently, a new dataset and shared task has been proposed for the identification of propaganda techniques at the finer-grained span level. This paper describes our system submission to the subtask of technique classification (TC) for the SemEval 2020 shared task on detection of propaganda techniques in news articles. We propose a method of combining neural BERT representations with hand-crafted features via stacked generalization. Our model has the added advantage that it combines the power of contextual representations from BERT with simple span-based and article-based global features. We present an ablation study which shows that even though BERT representations are very powerful also for this task, BERT still benefits from being combined with carefully designed task-specific features.",
}
@inproceedings{verma-etal-2020-transformers,
    title = "Transformers at {S}em{E}val-2020 Task 11: Propaganda Fragment Detection Using Diversified {BERT} Architectures Based Ensemble Learning",
    author = "Verma, Ekansh  and
      Motupalli, Vinodh  and
      Chakraborty, Souradip",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.239",
    pages = "1823--1828",
    abstract = "In this paper, we present our approach for the {'}Detection of Propaganda Techniques in News Articles{'} task as a part of the 2020 edition of International Workshop on Semantic Evaluation. The specific objective of this task is to identify and extract the text segments in which propaganda techniques are used. We propose a multi-system deep learning framework that can be used to identify the presence of propaganda fragments in a news article and also deep dive into the diverse enhancements of BERT architecture which are part of the final solution. Our proposed final model gave an F1-score of 0.48 on the test dataset.",
}
@inproceedings{kim-bethard-2020-ttui,
    title = "{TTUI} at {S}em{E}val-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles",
    author = "Kim, Moonsung  and
      Bethard, Steven",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.240",
    pages = "1829--1834",
    abstract = "In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task.",
}
@inproceedings{ermurachi-gifu-2020-uaic1860,
    title = "{UAIC}1860 at {S}em{E}val-2020 Task 11: Detection of Propaganda Techniques in News Articles",
    author = "Ermurachi, Vlad  and
      Gifu, Daniela",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.241",
    pages = "1835--1840",
    abstract = "The {``}Detection of Propaganda Techniques in News Articles{''} task at the SemEval 2020 competition focuses on detecting and classifying propaganda, pervasive in news article. In this paper, we present a system able to evaluate on sentence level, three traditional text representation techniques for these study goals, using: tf*idf, word and character n-grams. Firstly, we built a binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Secondly, we build a multilabel multiclass model to identify applied propaganda.",
}
@inproceedings{jiang-etal-2020-umsiforeseer,
    title = "{UMSIF}oreseer at {S}em{E}val-2020 Task 11: Propaganda Detection by Fine-Tuning {BERT} with Resampling and Ensemble Learning",
    author = "Jiang, Yunzhe  and
      Garbacea, Cristina  and
      Mei, Qiaozhu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.242",
    pages = "1841--1846",
    abstract = "We describe our participation at the SemEval 2020 {``}Detection of Propaganda Techniques in News Articles{''} - Techniques Classification (TC) task, designed to categorize textual fragments into one of the 14 given propaganda techniques. Our solution leverages pre-trained BERT models. We present our model implementations, evaluation results and analysis of these results. We also investigate the potential of combining language models with resampling and ensemble learning methods to deal with data imbalance and improve performance.",
}
@inproceedings{petee-palmer-2020-untling,
    title = "{UNTL}ing at {S}em{E}val-2020 Task 11: Detection of Propaganda Techniques in {E}nglish News Articles",
    author = "Petee, Maia  and
      Palmer, Alexis",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.243",
    pages = "1847--1852",
    abstract = "Our system for the PropEval task explores the ability of semantic features to detect and label propagandistic rhetorical techniques in English news articles. For Subtask 2, labeling identified propagandistic fragments with one of fourteen technique labels, our system attains a micro-averaged F1 of 0.40; in this paper, we take a detailed look at the fourteen labels and how well our semantically-focused model detects each of them. We also propose strategies to fill the gaps.",
}
@inproceedings{paraschiv-etal-2020-upb,
    title = "{UPB} at {S}em{E}val-2020 Task 11: Propaganda Detection with Domain-Specific Trained {BERT}",
    author = "Paraschiv, Andrei  and
      Cercel, Dumitru-Clementin  and
      Dascalu, Mihai",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.244",
    pages = "1853--1857",
    abstract = "Manipulative and misleading news have become a commodity for some online news outlets and these news have gained a significant impact on the global mindset of people. Propaganda is a frequently employed manipulation method having as goal to influence readers by spreading ideas meant to distort or manipulate their opinions. This paper describes our participation in the SemEval-2020, Task 11: Detection of PropagandaTechniques in News Articles competition. Our approach considers specializing a pre-trained BERT model on propagandistic and hyperpartisan news articles, enabling it to create more adequate representations for the two subtasks, namely propaganda Span Identification (SI) and propaganda Technique Classification (TC). Our proposed system achieved a F1-score of 46.060{\%} in subtask SI, ranking 5th in the leaderboard from 36 teams and a micro-averaged F1 score of 54.302{\%} for subtask TC, ranking 19th from 32 teams.",
}
@inproceedings{mikhalkova-etal-2020-utmn,
    title = "{UTMN} at {S}em{E}val-2020 Task 11: A Kitchen Solution to Automatic Propaganda Detection",
    author = "Mikhalkova, Elena  and
      Ganzherli, Nadezhda  and
      Glazkova, Anna  and
      Bidulya, Yuliya",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.245",
    pages = "1858--1864",
    abstract = "The article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based on feature adjustment. We use per-token vectorization of features and a simple Logistic Regression classifier to quickly test different hypotheses about our data. We come up with what seems to us the best solution, however, we are unable to align it with the result of the metric suggested by the organizers of the task. We test how our system handles class and feature imbalance by varying the number of samples of two classes (Propaganda and None) in the training set, the size of a context window in which a token is vectorized and combination of vectorization means. The result of our system at SemEval2020 Task 11 is F-score=0.37.",
}
@inproceedings{daval-frerot-weis-2020-wmd,
    title = "{WMD} at {S}em{E}val-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation",
    author = "Daval-Frerot, Guillaume  and
      Weis, Yannick",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.246",
    pages = "1865--1874",
    abstract = "In this work, we combine the state-of-the-art BERT architecture with the semi-supervised learning technique UDA in order to exploit unlabeled raw data to assess humor and detect propaganda in the tasks 7 and 11 of the SemEval-2020 competition. The use of UDA shows promising results with a systematic improvement of the performances over the four different subtasks, and even outperforms supervised learning with the additional labels of the Funlines dataset.",
}
@inproceedings{tao-zhou-2020-ynutaoxin,
    title = "{YNU}taoxin at {S}em{E}val-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model",
    author = "Tao, Xin  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.247",
    pages = "1875--1880",
    abstract = "We only participated in the first subtask, and a neural sequence model was used to perform the sequence tagging task. We investigated the effects of different markup strategies on model performance. Bert that performed very well in NLP was used as a feature extractor.",
}
@inproceedings{ibrahim-etal-2020-alexu,
    title = "{A}lex{U}-{B}ack{T}ranslation-{TL} at {S}em{E}val-2020 Task 12: Improving Offensive Language Detection Using Data Augmentation and Transfer Learning",
    author = "Ibrahim, Mai  and
      Torki, Marwan  and
      El-Makky, Nagwa",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.248",
    pages = "1881--1890",
    abstract = "Social media platforms, online news commenting spaces, and many other public forums have become widely known for issues of abusive behavior such as cyber-bullying and personal attacks. In this paper, we use the annotated tweets of the Offensive Language Identification Dataset (OLID) to train three levels of deep learning classifiers to solve the three sub-tasks associated with the dataset. Sub-task A is to determine if the tweet is toxic or not. Then, for offensive tweets, sub-task B requires determining whether the toxicity is targeted. Finally, for sub-task C, we predict the target of the offense; i.e. a group, individual, or other entity. In our solution, we tackle the problem of class imbalance in the dataset by using back translation for data augmentation and utilizing the fine-tuned BERT model in an ensemble of deep learning classifiers. We used this solution to participate in the three English sub-tasks of SemEval-2020 task 12. The proposed solution achieved 0.91393, 0.6300, and 0.57607 macro F1-average in sub-tasks A, B, and C respectively. We achieved the 9th, 14th, and 22nd places for sub-tasks A, B and C respectively.",
}
@inproceedings{hassan-etal-2020-alt-semeval,
    title = "{ALT} at {S}em{E}val-2020 Task 12: {A}rabic and {E}nglish Offensive Language Identification in Social Media",
    author = "Hassan, Sabit  and
      Samih, Younes  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.249",
    pages = "1891--1897",
    abstract = "This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90{\%} macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT.",
}
@inproceedings{mosquera-2020-amsqr,
    title = "Amsqr at {S}em{E}val-2020 Task 12: Offensive Language Detection Using Neural Networks and Anti-adversarial Features",
    author = "Mosquera, Alejandro",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.250",
    pages = "1898--1905",
    abstract = "This paper describes a method and system to solve the problem of detecting offensive language in social media using anti-adversarial features. Our submission to the SemEval-2020 task 12 challenge was generated by an stacked ensemble of neural networks fine-tuned on the OLID dataset and additional external sources. For Task-A (English), text normalisation filters were applied at both graphical and lexical level. The normalisation step effectively mitigates not only the natural presence of lexical variants but also intentional attempts to bypass moderation by introducing out of vocabulary words. Our approach provides strong F1 scores for both 2020 (0.9134) and 2019 (0.8258) challenges.",
}
@inproceedings{ranasinghe-hettiarachchi-2020-brums,
    title = "{BRUMS} at {S}em{E}val-2020 Task 12: Transformer Based Multilingual Offensive Language Identification in Social Media",
    author = "Ranasinghe, Tharindu  and
      Hettiarachchi, Hansi",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.251",
    pages = "1906--1915",
    abstract = "In this paper, we describe the team \textit{BRUMS} entry to OffensEval 2: Multilingual Offensive Language Identification in Social Media in SemEval-2020. The OffensEval organizers provided participants with annotated datasets containing posts from social media in Arabic, Danish, English, Greek and Turkish. We present a multilingual deep learning model to identify offensive language in social media. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility between languages.",
}
@inproceedings{chapman-etal-2020-coli,
    title = "{C}o{L}i at {U}d{S} at {S}em{E}val-2020 Task 12: Offensive Tweet Detection with Ensembling",
    author = "Chapman, Kathryn  and
      Bernhard, Johannes  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.252",
    pages = "1916--1924",
    abstract = "We present our submission and results for SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) where we participated in offensive tweet classification tasks in English, Arabic, Greek, Turkish and Danish. Our approach included classical machine learning architectures such as support vector machines and logistic regression combined in an ensemble with a multilingual transformer-based model (XLM-R). The transformer model is trained on all languages combined in order to create a fully multilingual model which can leverage knowledge between languages. The machine learning model hyperparameters are fine-tuned and the statistically best performing ones included in the final ensemble.",
}
@inproceedings{paul-etal-2020-cybertronics,
    title = "{C}yber{T}ronics at {S}em{E}val-2020 Task 12: Multilingual Offensive Language Identification over Social Media",
    author = "Paul, Sayanta  and
      Saha, Sriparna  and
      Hasanuzzaman, Mohammed",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.253",
    pages = "1925--1931",
    abstract = "The SemEval-2020 Task 12 (OffensEval) challenge focuses on detection of signs of offensiveness using posts or comments over social media. This task has been organized for several languages, e.g., Arabic, Danish, English, Greek and Turkish. It has featured three related sub-tasks for English language: sub-task A was to discriminate between offensive and non-offensive posts, the focus of sub-task B was on the type of offensive content in the post and finally, in sub-task C, proposed systems had to identify the target of the offensive posts. The corpus for each of the languages is developed using the posts and comments over Twitter, a popular social media platform. We have participated in this challenge and submitted results for different languages. The current work presents different machine learning and deep learning techniques and analyzes their performance for offensiveness prediction which involves various classifiers and feature engineering schemes. The experimental analysis on the training set shows that SVM using language specific pre-trained word embedding (Fasttext) outperforms the other methods. Our system achieves a macro-averaged F1 score of 0.45 for Arabic language, 0.43 for Greek language and 0.54 for Turkish language.",
}
@inproceedings{orabe-etal-2020-dothemath,
    title = "{D}o{T}he{M}ath at {S}em{E}val-2020 Task 12 : Deep Neural Networks with Self Attention for {A}rabic Offensive Language Detection",
    author = "Orabe, Zoher  and
      Haddad, Bushr  and
      Ghneim, Nada  and
      Al-Abood, Anas",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.254",
    pages = "1932--1937",
    abstract = "This paper describes our team work and submission for the SemEval 2020 (Sub-Task A) {``}Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media{''}. Our two baseline models were based on different levels of representation: character vs. word level. In word level based representation we implemented a convolutional neural network model and a bi-directional GRU model. In character level based representation we implemented a hyper CNN and LSTM model. All of these models have been further augmented with attention layers for a better performance on our task. We also experimented with three types of static word embeddings: word2vec, FastText, and Glove, in addition to emoji embeddings, and compared the performance of the different deep learning models on the dataset provided by this task. The bi-directional GRU model with attention has achieved the highest score (0.85{\%} F1 score) among all other models.",
}
@inproceedings{pedersen-2020-duluth,
    title = "{D}uluth at {S}em{E}val-2020 Task 12: Offensive Tweet Identification in {E}nglish with Logistic Regression",
    author = "Pedersen, Ted",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.255",
    pages = "1938--1946",
    abstract = "This paper describes the Duluth systems that participated in SemEval{--}2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval{--}2020). We participated in the three English language tasks. Our systems provide a simple machine learning baseline using logistic regression. We trained our models on the distantly supervised training data made available by the task organizers and used no other resources. As might be expected we did not rank highly in the comparative evaluation: 79th of 85 in task A, 34th of 43 in task B, and 24th of 39 in task C. We carried out a qualitative analysis of our results and found that the class labels in the gold standard data are somewhat noisy. We hypothesize that the extremely high accuracy ({\textgreater}{\$} 90{\%}) of the top ranked systems may reflect methods that learn the training data very well but may not generalize to the task of identifying offensive language in English. This analysis includes examples of tweets that despite being mildly redacted are still offensive.",
}
@inproceedings{chen-etal-2020-ferryman-semeval-2020-task,
    title = "Ferryman at {S}em{E}val-2020 Task 12: {BERT}-Based Model with Advanced Improvement Methods for Multilingual Offensive Language Identification",
    author = "Chen, Weilong  and
      Wang, Peng  and
      Li, Jipeng  and
      Zheng, Yuanshuai  and
      Wang, Yan  and
      Zhang, Yanru",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.256",
    pages = "1947--1952",
    abstract = "Indiscriminately posting offensive remarks on social media may promote the occurrence of negative events such as violence, crime, and hatred. This paper examines different approaches and models for solving offensive tweet classification, which is a part of the OffensEval 2020 competition. The dataset is Offensive Language Identification Dataset (OLID), which draws 14,200 annotated English Tweet comments. The main challenge of data preprocessing is the unbalanced class distribution, abbreviation, and emoji. To overcome these issues, methods such as hashtag segmentation, abbreviation replacement, and emoji replacement have been adopted for data preprocessing approaches. The main task can be divided into three sub-tasks, and are solved by Term Frequency{--}Inverse Document Frequency(TF-IDF), Bidirectional Encoder Representation from Transformer (BERT), and Multi-dropout respectively. Meanwhile, we applied different learning rates for different languages and tasks based on BERT and non-BERTmodels in order to obtain better results. Our team Ferryman ranked the 18th, 8th, and 21st with F1-score of 0.91152 on the English Sub-task A, Sub-task B, and Sub-task C, respectively. Furthermore, our team also ranked in the top 20 on the Sub-task A of other languages.",
}
@inproceedings{garain-2020-garain,
    title = "Garain at {S}em{E}val-2020 Task 12: Sequence Based Deep Learning for Categorizing Offensive Language in Social Media",
    author = "Garain, Avishek",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.257",
    pages = "1953--1960",
    abstract = "SemEval-2020 Task 12 was OffenseEval: Multilingual Offensive Language Identification inSocial Media (Zampieri et al., 2020). The task was subdivided into multiple languages anddatasets were provided for each one. The task was further divided into three sub-tasks: offensivelanguage identification, automatic categorization of offense types, and offense target identification.I participated in the task-C, that is, offense target identification. For preparing the proposed system,I made use of Deep Learning networks like LSTMs and frameworks like Keras which combine thebag of words model with automatically generated sequence based features and manually extractedfeatures from the given dataset. My system on training on 25{\%} of the whole dataset achieves macro averaged f1 score of 47.763{\%}.",
}
@inproceedings{ravikiran-etal-2020-hitachi,
    title = "Hitachi at {S}em{E}val-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing",
    author = "Ravikiran, Manikandan  and
      Muljibhai, Amin Ekant  and
      Miyoshi, Toshinori  and
      Ozaki, Hiroaki  and
      Koreeda, Yuta  and
      Masayuki, Sakata",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.258",
    pages = "1961--1967",
    abstract = "In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system with the BERT classifier trained with tweets selected using Statistical Sampling Algorithm (SA) and Post-Processed (PP) using an offensive wordlist. Our developed system achieved 34th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over both offensive and non-offensive classes. We further show comprehensive results and error analysis to assist future research in offensive language identification with noisy labels.",
}
@inproceedings{pachon-alvarez-etal-2020-i2c,
    title = "{I}2{C} at {S}em{E}val-2020 Task 12: Simple but Effective Approaches to Offensive Speech Detection in {T}witter",
    author = "Pach{\'o}n {\'A}lvarez, Victoria  and
      Mata V{\'a}zquez, Jacinto  and
      L{\'o}pez Betanzos, Jos{\'e} Manuel  and
      Arjona Fern{\'a}ndez, Jos{\'e} Luis",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.259",
    pages = "1968--1977",
    abstract = "This paper describes the systems developed for I2C Group to participate on Subtasks A and B in English, and Subtask A in Turkish and Arabic in OffensEval (Task 12 of SemEval 2020). In our experiments we compare three architectures we have developed, two based on Transformer and the other based on classical machine learning algorithms. In this paper, the proposed architectures are described, and the results obtained by our systems are presented.",
}
@inproceedings{messaoudi-etal-2020-icompass,
    title = "i{C}ompass at {S}em{E}val-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model",
    author = "Messaoudi, Abir  and
      Haddad, Hatem  and
      Ben Haj Hmida, Moez",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.260",
    pages = "1978--1982",
    abstract = "We describe our submitted system to the SemEval 2020. We tackled Task 12 entitled {``}Multilingual Offensive Language Identification in Social Media{''}, specifically subtask 4A-Arabic. We propose three Arabic offensive language identification models: Tw-StAR, BERT and BERT+BiLSTM. Two Arabic abusive/hate datasets were added to the training dataset: L-HSAB and T-HSAB. The final submission was chosen based on the best performances which was achieved by the BERT+BiLSTM model.",
}
@inproceedings{ghosh-etal-2020-iitp,
    title = "{IITP}-{AINLPML} at {S}em{E}val-2020 Task 12: Offensive Tweet Identification and Target Categorization in a Multitask Environment",
    author = "Ghosh, Soumitra  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.261",
    pages = "1983--1991",
    abstract = "In this paper, we describe the participation of IITP-AINLPML team in the SemEval-2020 SharedTask 12 on Offensive Language Identification and Target Categorization in English Twitter data. Our proposed model learns to extract textual features using a BiGRU-based deep neural network supported by a Hierarchical Attention architecture to focus on the most relevant areas in the text. We leverage the effectiveness of multitask learning while building our models for sub-task A and B. We do necessary undersampling of the over-represented classes in the sub-tasks A and C.During training, we consider a threshold of 0.5 as the separation margin between the instances belonging to classes OFF and NOT in sub-task A and UNT and TIN in sub-task B. For sub-task C, the class corresponding to the maximum score among the given confidence scores of the classes(IND, GRP and OTH) is considered as the final label for an instance. Our proposed model obtains the macro F1-scores of 90.95{\%}, 55.69{\%} and 63.88{\%} in sub-task A, B and C, respectively.",
}
@inproceedings{miranda-jimenez-etal-2020-ingeotec,
    title = "{INGEOTEC} at {S}em{E}val-2020 Task 12: Multilingual Classification of Offensive Text",
    author = "Miranda-Jim{\'e}nez, Sabino  and
      Tellez, Eric S.  and
      Graff, Mario  and
      Moctezuma, Daniela",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.262",
    pages = "1992--1997",
    abstract = "This paper describes our participation in OffensEval challenges for English, Arabic, Danish, Turkish, and Greek languages. We used several approaches, such as μTC, TextCategorization, and EvoMSA. Best results were achieved with EvoMSA, which is a multilingual and domain-independent architecture that combines the prediction from different knowledge sources to solve text classification problems.",
}
@inproceedings{kurniawan-etal-2020-ir3218,
    title = "{IR}3218-{UI} at {S}em{E}val-2020 Task 12: Emoji Effects on Offensive Language {I}dentifi{C}ation",
    author = "Kurniawan, Sandy  and
      Budi, Indra  and
      Ibrohim, Muhammad Okky",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.263",
    pages = "1998--2005",
    abstract = "In this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020 namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (sub-task C). We participated in sub-task A of English OffensEval 2020. Our approach emphasizes on how the emoji affects offensive language identification. Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media. The best model obtained macro F1-score of 0.88428.",
}
@inproceedings{parikh-etal-2020-irlab-daiict,
    title = "{IRL}ab{\_}{DAIICT} at {S}em{E}val-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification",
    author = "Parikh, Apurva  and
      Bisht, Abhimanyu Singh  and
      Majumder, Prasenjit",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.264",
    pages = "2006--2011",
    abstract = "The paper describes systems that our team IRLab{\_}DAIICT employed for shared task OffensEval2020: Multilingual Offensive Language Identification in Social Media shared task. We conducted experiments on the English language dataset which contained weakly labelled data. There were three sub-tasks but we only participated in sub-tasks A and B. We employed Machine learning techniques like Logistic Regression, Support Vector Machine, Random Forest and Deep learning techniques like Convolutional Neural Network and BERT. Our best approach achieved a MacroF1 score of 0.91 for sub-task A and 0.64 for sub-task B.",
}
@inproceedings{saroj-etal-2020-irlab,
    title = "{IR}lab@{IITV} at {S}em{E}val-2020 Task 12: Multilingual Offensive Language Identification in Social Media Using {SVM}",
    author = "Saroj, Anita  and
      Chanda, Supriya  and
      Pal, Sukomal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.265",
    pages = "2012--2016",
    abstract = "This paper describes the IRlab@IIT-BHU system for the OffensEval 2020. We take the SVM with TF-IDF features to identify and categorize hate speech and offensive language in social media for two languages. In subtask A, we used a linear SVM classifier to detect abusive content in tweets, achieving a macro $F_1$ score of 0.779 and 0.718 for Arabic and Greek, respectively.",
}
@inproceedings{uzan-hacohen-kerner-2020-jct,
    title = "{JCT} at {S}em{E}val-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams",
    author = "Uzan, Moshe  and
      HaCohen-Kerner, Yaakov",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.266",
    pages = "2017--2022",
    abstract = "In this paper, we describe our submissions to SemEval-2020 contest. We tackled subtask 12 - {``}Multilingual Offensive Language Identification in Social Media{''}. We developed different models for four languages: Arabic, Danish, Greek, and Turkish. We applied three supervised machine learning methods using various combinations of character and word n-gram features. In addition, we applied various combinations of basic preprocessing methods. Our best submission was a model we built for offensive language identification in Danish using Random Forest. This model was ranked at the 6 position out of 39 submissions. Our result is lower by only 0.0025 than the result of the team that won the 4 place using entirely non-neural methods. Our experiments indicate that char ngram features are more helpful than word ngram features. This phenomenon probably occurs because tweets are more characterized by characters than by words, tweets are short, and contain various special sequences of characters, e.g., hashtags, shortcuts, slang words, and typos.",
}
@inproceedings{das-etal-2020-kafk-semeval,
    title = "{KAFK} at {S}em{E}val-2020 Task 12: Checkpoint Ensemble of Transformers for Hate Speech Classification",
    author = "Das, Kaushik Amar  and
      Baruah, Arup  and
      Barbhuiya, Ferdous Ahmed  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.267",
    pages = "2023--2029",
    abstract = "This paper presents the approach of Team KAFK for the English edition of SemEval-2020 Task 12. We use checkpoint ensembling to create ensembles of BERT-based transformers and show that it can improve the performance of classification systems. We explore attention mask dropout to mitigate for the poor constructs of social media texts. Our classifiers scored macro-f1 of 0.909, 0.551 and 0.616 for subtasks A, B and C respectively. The code is publicly released online.",
}
@inproceedings{hanahata-aono-2020-kdelab,
    title = "{KDELAB} at {S}em{E}val-2020 Task 12: A System for Estimating Aggression of Tweets Using Two Layers of {BERT} Features",
    author = "Hanahata, Keisuke  and
      Aono, Masaki",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.268",
    pages = "2030--2034",
    abstract = "In recent years, with the development of social network services and video distribution services, there has been a sharp increase in offensive posts. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval- 2020 Task 12. Our system precise classification by using features extracted from two different layers of a pre-trained model, the BERT-large, and ensemble them.",
}
@inproceedings{tawalbeh-etal-2020-keis,
    title = "{KEIS}@{JUST} at {S}em{E}val-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned {BERT}",
    author = "Tawalbeh, Saja  and
      Hammad, Mahmoud  and
      AL-Smadi, Mohammad",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.269",
    pages = "2035--2044",
    abstract = "This research presents our team KEIS@JUST participation at SemEval-2020 Task 12 which represents shared task on multilingual offensive language. We participated in all the provided languages for all subtasks except sub-task-A for the English language. Two main approaches have been developed the first is performed to tackle both languages Arabic and English, a weighted ensemble consists of Bi-GRU and CNN followed by Gaussian noise and global pooling layer multiplied by weights to improve the overall performance. The second is performed for other languages, a transfer learning from BERT beside the recurrent neural networks such as Bi-LSTM and Bi-GRU followed by a global average pooling layer. Word embedding and contextual embedding have been used as features, moreover, data augmentation has been used only for the Arabic language.",
}
@inproceedings{socha-2020-ks,
    title = "{KS}@{LTH} at {S}em{E}val-2020 Task 12: Fine-tuning Multi- and Monolingual Transformer Models for Offensive Language Detection",
    author = "Socha, Kasper",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.270",
    pages = "2045--2053",
    abstract = "This paper describes the KS@LTH system for SemEval-2020 Task 12 OffensEval2: Multilingual Offensive Language Identification in Social Media. We compare mono- and multilingual models based on fine-tuning pre-trained transformer models for offensive language identification in Arabic, Greek, English and Turkish. For Danish, we explore the possibility of fine-tuning a model pre-trained on a similar language, Swedish, and additionally also cross-lingual training together with English.",
}
@inproceedings{safaya-etal-2020-kuisail,
    title = "{KUISAIL} at {S}em{E}val-2020 Task 12: {BERT}-{CNN} for Offensive Speech Identification in Social Media",
    author = "Safaya, Ali  and
      Abdullatif, Moutasem  and
      Yuret, Deniz",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.271",
    pages = "2054--2059",
    abstract = "In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020. We show that combining CNN with BERT is better than using BERT on its own, and we emphasize the importance of utilizing pre-trained language models for downstream tasks. Our system, ranked 4th with macro averaged F1-Score of 0.897 in Arabic, 4th with score of 0.843 in Greek, and 3rd with score of 0.814 in Turkish. Additionally, we present ArabicBERT, a set of pre-trained transformer language models for Arabic that we share with the community.",
}
@inproceedings{dai-etal-2020-kungfupanda,
    title = "Kungfupanda at {S}em{E}val-2020 Task 12: {BERT}-Based Multi-{T}ask{L}earning for Offensive Language Detection",
    author = "Dai, Wenliang  and
      Yu, Tiezheng  and
      Liu, Zihan  and
      Fung, Pascale",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.272",
    pages = "2060--2066",
    abstract = "Nowadays, offensive content in social media has become a serious problem, and automatically detecting offensive language is an essential task. In this paper, we build an offensive language detection system, which combines multi-task learning with BERT-based models. Using a pre-trained language model such as BERT, we can effectively learn the representations for noisy text in social media. Besides, to boost the performance of offensive language detection, we leverage the supervision signals from other related tasks. In the OffensEval-2020 competition, our model achieves 91.51{\%} F1 score in English Sub-task A, which is comparable to the first place (92.23{\%}F1). An empirical analysis is provided to explain the effectiveness of our approaches.",
}
@inproceedings{li-etal-2020-lee-semeval,
    title = "Lee at {S}em{E}val-2020 Task 12: A {BERT} Model Based on the Maximum Self-ensemble Strategy for Identifying Offensive Language",
    author = "Li, Junyi  and
      Zhou, Xiaobing  and
      Zhang, Zichen",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.273",
    pages = "2067--2072",
    abstract = "This article describes the system submitted to SemEval 2020 Task 12: OffensEval 2020. This task aims to identify and classify offensive languages in different languages on social media. We only participate in the English part of subtask A, which aims to identify offensive languages in English. To solve this task, we propose a BERT model system based on the transform mechanism, and use the maximum self-ensemble to improve model performance. Our model achieved a macro F1 score of 0.913(ranked 13/82) in subtask A.",
}
@inproceedings{ghadery-moens-2020-liir,
    title = "{LIIR} at {S}em{E}val-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification",
    author = "Ghadery, Erfan  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.274",
    pages = "2073--2079",
    abstract = "This paper presents our system entitled {`}LIIR{'} for SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2). We have participated in sub-task A for English, Danish, Greek, Arabic, and Turkish languages. We adapt and fine-tune the BERT and Multilingual Bert models made available by Google AI for English and non-English languages respectively. For the English language, we use a combination of two fine-tuned BERT models. For other languages we propose a cross-lingual augmentation approach in order to enrich training data and we use Multilingual BERT to obtain sentence representations.",
}
@inproceedings{alami-etal-2020-lisac,
    title = "{LISAC} {FSDM}-{USMBA} Team at {S}em{E}val-2020 Task 12: Overcoming {A}ra{BERT}{'}s pretrain-finetune discrepancy for {A}rabic offensive language identification",
    author = "Alami, Hamza  and
      Ouatik El Alaoui, Said  and
      Benlahbib, Abdessamad  and
      En-nahnahi, Noureddine",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.275",
    pages = "2080--2085",
    abstract = "AraBERT is an Arabic version of the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) model. The latter has achieved good performance in a variety of Natural Language Processing (NLP) tasks. In this paper, we propose an effective AraBERT embeddings-based method for dealing with offensive Arabic language in Twitter. First, we pre-process tweets by handling emojis and including their Arabic meanings. To overcome the pretrain-finetune discrepancy, we substitute each detected emojis by the special token [MASK] into both fine tuning and inference phases. Then, we represent tweets tokens by applying AraBERT model. Finally, we feed the tweet representation into a sigmoid function to decide whether a tweet is offensive or not. The proposed method achieved the best results on OffensEval 2020: Arabic task and reached a macro F1 score equal to 90.17{\%}.",
}
@inproceedings{nayel-2020-nayel,
    title = "{NAYEL} at {S}em{E}val-2020 Task 12: {TF}/{IDF}-Based Approach for Automatic Offensive Language Detection in {A}rabic Tweets",
    author = "Nayel, Hamada",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.276",
    pages = "2086--2089",
    abstract = "In this paper, we present the system submitted to {``}SemEval-2020 Task 12{''}. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm. Our model reported 84.20{\%}, 81.82{\%} f1-score on development set and test set respectively. The best performed system and the system in the last rank reported 90.17{\%} and 44.51{\%} f1-score on test set respectively.",
}
@inproceedings{hussein-etal-2020-nlp,
    title = "{NLP}{\_}{P}assau at {S}em{E}val-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in {E}nglish, {D}anish and {T}urkish",
    author = "Hussein, Omar  and
      Sfar, Hachem  and
      Mitrovi{\'c}, Jelena  and
      Granitzer, Michael",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.277",
    pages = "2090--2097",
    abstract = "This paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop. The aim of this task is to identify offensive speech in social media, particularly in tweets. The model we used, C-BiGRU, is composed of a Convolutional Neural Network (CNN) along with a bidirectional Recurrent Neural Network (RNN). A multidimensional numerical representation (embedding) for each of the words in the tweets that were used by the model were determined using fastText. This allowed for using a dataset of labeled tweets to train the model on detecting combinations of words that may convey an offensive meaning. This model was used in the sub-task A of the English, Turkish and Danish competitions of the workshop, achieving F1 scores of 90.88{\%}, 76.76{\%} and 76.70{\%}, respectively.",
}
@inproceedings{hamdy-etal-2020-nlpup,
    title = "nlp{UP} at {S}em{E}val-2020 Task 12 : A Blazing Fast System for Offensive Language Detection",
    author = "Hamdy, Ehab  and
      Mitrovi{\'c}, Jelena  and
      Granitzer, Michael",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.278",
    pages = "2098--2104",
    abstract = "In this paper, we introduce our submission for the SemEval Task 12, sub-tasks A and B for offensive language identification and categorization in English tweets. This year the data set for Task A is significantly larger than in the previous year. Therefore, we have adapted the BlazingText algorithm to extract embedding representation and classify texts after filtering and sanitizing the dataset according to the conventional text patterns on social media. We have gained both advantages of a speedy training process and obtained a good F1 score of 90.88{\%} on the test set. For sub-task B, we opted to fine-tune a Bidirectional Encoder Representation from a Transformer (BERT) to accommodate the limited data for categorizing offensive tweets. We have achieved an F1 score of only 56.86{\%}, but after experimenting with various label assignment thresholds in the pre-processing steps, the F1 score improved to 64{\%}.",
}
@inproceedings{chen-etal-2020-ntu,
    title = "{NTU}{\_}{NLP} at {S}em{E}val-2020 Task 12: Identifying Offensive Tweets Using Hierarchical Multi-Task Learning Approach",
    author = "Chen, Po-Chun  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.279",
    pages = "2105--2110",
    abstract = "This paper presents our hierarchical multi-task learning (HMTL) and multi-task learning (MTL) approaches for improving the text encoder in Sub-tasks A, B, and C of Multilingual Offensive Language Identification in Social Media (SemEval-2020 Task 12). We show that using the MTL approach can greatly improve the performance of complex problems, i.e. Sub-tasks B and C. Coupled with a hierarchical approach, the performances are further improved. Overall, our best model, HMTL outperforms the baseline model by 3{\%} and 2{\%} of Macro F-score in Sub-tasks B and C of OffensEval 2020, respectively.",
}
@inproceedings{pham-hong-chokshi-2020-pgsg,
    title = "{PGSG} at {S}em{E}val-2020 Task 12: {BERT}-{LSTM} with Tweets{'} Pretrained Model and Noisy Student Training Method",
    author = "Pham-Hong, Bao-Tran  and
      Chokshi, Setu",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.280",
    pages = "2111--2116",
    abstract = "The paper presents a system developed for the SemEval-2020 competition Task 12 (OffensEval-2): Multilingual Offensive Language Identification in Social Media. We achieve the second place (2nd) in sub-task B: Automatic categorization of offense types and are ranked 55th with a macro F1-score of 90.59 in sub-task A: Offensive language identification. Our solution is using a stack of BERT and LSTM layers, training with the Noisy Student method. Since the tweets data contains a large number of noisy words and slang, we update the vocabulary of the BERT large model pre-trained by the Google AI Language team. We fine-tune the model with tweet sentences provided in the challenge.",
}
@inproceedings{arslan-2020-pin,
    title = "Pin{\_}cod{\_} at {S}em{E}val-2020 Task 12: Injecting Lexicons into Bidirectional Long Short-Term Memory Networks to Detect {T}urkish Offensive Tweets",
    author = "Arslan, Pinar",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.281",
    pages = "2117--2122",
    abstract = "This paper describes a system (pin{\_}cod{\_}) built for SemEval 2020 Task 12: OffensEval: Multilingual Offensive Language Identification in Social Media (Zampieri et al., 2020). I present the system based on the architecture of bidirectional long short-term memory networks (BiLSTM) concatenated with lexicon-based features and a social-network specific feature and then followed by two fully connected dense layers for detecting Turkish offensive tweets. The pin cod {'}s system achieved a macro F1-score of 0.7496 for Sub-task A - Offensive language identification in Turkish.",
}
@inproceedings{laud-etal-2020-problemconquero,
    title = "problem{C}onquero at {S}em{E}val-2020 Task 12: Transformer and Soft Label-based Approaches",
    author = "Laud, Karishma  and
      Singh, Jagriti  and
      Sahu, Randeep Kumar  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.282",
    pages = "2123--2132",
    abstract = "In this paper, we present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 {``}Multilingual Offensive Language Identification in Social Media{''}. We participated in all the three sub-tasks of OffensEval-2020, and our final submissions during the evaluation phase included transformer-based approaches and a soft label-based approach. BERT based fine-tuned models were submitted for each language of sub-task A (offensive tweet identification). RoBERTa based fine-tuned model for sub-task B (automatic categorization of offense types) was submitted. We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model. Our ranks for sub-task A were Greek-19 out of 37, Turkish-22 out of 46, Danish-26 out of 39, Arabic-39 out of 53, and English-20 out of 85. We achieved a rank of 28 out of 43 for sub-task B. Our best rank for sub-task C was 20 out of 39 using BERT based fine-tuned model.",
}
@inproceedings{husain-etal-2020-salamnet,
    title = "{S}alam{NET} at {S}em{E}val-2020 Task 12: Deep Learning Approach for {A}rabic Offensive Language Detection",
    author = "Husain, Fatemah  and
      Lee, Jooyeon  and
      Henry, Sam  and
      Uzuner, Ozlem",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.283",
    pages = "2133--2139",
    abstract = "This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83{\%}",
}
@inproceedings{henrichsen-rathje-2020-smatgrisene,
    title = "Smatgrisene at {S}em{E}val-2020 Task 12: Offense Detection by {AI} - with a Pinch of Real {I}",
    author = "Henrichsen, Peter Juel  and
      Rathje, Marianne",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.284",
    pages = "2140--2145",
    abstract = "This paper discusses how ML based classifiers can be enhanced disproportionately by adding small amounts of qualitative linguistic knowledge. As an example we present the Danish classifier Smatgrisene, our contribution to the recent OffensEval Challenge 2020. The classifier was trained on 3000 social media posts annotated for offensiveness, supplemented by rules extracted from the reference work on Danish offensive language (Rathje 2014b). Smatgrisene did surprisingly well in the competition in spite of its extremely simple design, showing an interesting trade-off between technological muscle and linguistic intelligence. Finally, we comment on the perspectives in combining qualitative and quantitative methods for NLP.",
}
@inproceedings{kumari-2020-sonal,
    title = "Sonal.kumari at {S}em{E}val-2020 Task 12: Social Media Multilingual Offensive Text Identification and Categorization Using Neural Network Models",
    author = "Kumari, Sonal",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.285",
    pages = "2146--2154",
    abstract = "In this paper, we present our approaches and results for SemEval-2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval 2020). The OffensEval 2020 had three subtasks: A) Identifying the tweets to be offensive (OFF) or non-offensive (NOT) for Arabic, Danish, English, Greek, and Turkish languages, B) Detecting if the offensive tweet is targeted (TIN) or untargeted (UNT) for the English language, and C) Categorizing the offensive targeted tweets into three classes, namely: individual (IND), Group (GRP), or Other (OTH) for the English language. We participate in all the subtasks A, B, and C. In our solution, first we use the pre-trained BERT model for all subtasks, A, B, and C and then we apply the BiLSTM model with attention mechanism (Attn-BiLSTM) for the same. Our result demonstrates that the pre-trained model is not giving good results for all types of languages and is compute and memory intensive whereas the Attn-BiLSTM model is fast and gives good accuracy with fewer resources. The Attn-BiLSTM model is giving better accuracy for Arabic and Greek where the pre-trained model is not able to capture the complete context of these languages due to lower vocab-size.",
}
@inproceedings{d-etal-2020-ssn,
    title = "Ssn{\_}nlp at {S}em{E}val 2020 Task 12: Offense Target Identification in Social Media Using Traditional and Deep Machine Learning Approaches",
    author = "D., Thenmozhi  and
      P.r., Nandhinee  and
      S., Arunima  and
      Sengupta, Amlan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.286",
    pages = "2155--2160",
    abstract = "Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that is addressed towards an individual or a group. Due to immense growth and usage of social media, it has an extensive reach and impact on the society. OLI is helpful for hate speech detection, flame detection and cyber bullying, hence it is used to avoid abuse and hurts. In this paper, we present state of the art machine learning approaches for OLI. We follow several approaches which include classifiers like Naive Bayes, Support Vector Machine(SVM) and deep learning approaches like Recurrent Neural Network(RNN) and Masked LM (MLM). The approaches are evaluated on the OffensEval@SemEval2020 dataset and our team ssn{\_}nlp submitted runs for the third task of OffensEval shared task. The best run of ssn{\_}nlp that uses BERT (Bidirectional Encoder Representations from Transformers) for the purpose of training the OLI model obtained F1 score as 0.61. The model performs with an accuracy of 0.80 and an evaluation loss of 1.0828. The model has a precision rate of 0.72 and a recall rate of 0.58.",
}
@inproceedings{kalaivani-d-2020-ssn,
    title = "{SSN}{\_}{NLP}{\_}{MLRG} at {S}em{E}val-2020 Task 12: Offensive Language Identification in {E}nglish, {D}anish, {G}reek Using {BERT} and Machine Learning Approach",
    author = "Kalaivani, A  and
      D., Thenmozhi",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.287",
    pages = "2161--2170",
    abstract = "Offensive language identification is to detect the hurtful tweets, derogatory comments, swear words on social media. As an emerging growth of social media communication, offensive language detection has received more attention in the last years; we focus to perform the task on English, Danish and Greek. We have investigated which can be effect more on pre-trained models BERT (Bidirectional Encoder Representation from Transformer) and Machine Learning Approaches. Our investigation shows the difference performance between the three languages and to identify the best performance is evaluated by the classification algorithms. In the shared task SemEval-2020, our team SSN{\_}NLP{\_}MLRG submitted for three languages that are Subtasks A, B, C in English, Subtask A in Danish and Subtask A in Greek. Our team SSN{\_}NLP{\_}MLRG obtained the F1 Scores as 0.90, 0.61, 0.52 for the Subtasks A, B, C in English, 0.56 for the Subtask A in Danish and 0.67 for the Subtask A in Greek respectively.",
}
@inproceedings{ozdemir-yeniterzi-2020-su,
    title = "{SU}-{NLP} at {S}em{E}val-2020 Task 12: Offensive Language {I}dentifi{C}ation in {T}urkish Tweets",
    author = "Ozdemir, Anil  and
      Yeniterzi, Reyyan",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.288",
    pages = "2171--2176",
    abstract = "This paper summarizes our group{'}s efforts in the offensive language identification shared task, which is organized as part of the International Workshop on Semantic Evaluation (Sem-Eval2020). Our final submission system is an ensemble of three different models, (1) CNN-LSTM, (2) BiLSTM-Attention and (3) BERT. Word embeddings, which were pre-trained on tweets, are used while training the first two models. BERTurk, which is the first BERT model for Turkish, is also explored. Our final submitted approach ranked as the second best model in the Turkish sub-task.",
}
@inproceedings{anwar-baig-2020-tac,
    title = "{TAC} at {S}em{E}val-2020 Task 12: Ensembling Approach for Multilingual Offensive Language Identification in Social Media",
    author = "Anwar, Talha  and
      Baig, Omer",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.289",
    pages = "2177--2182",
    abstract = "Usage of offensive language on social media is getting more common these days, and there is a need of a mechanism to detect it and control it. This paper deals with offensive language detection in five different languages; English, Arabic, Danish, Greek and Turkish. We presented an almost similar ensemble pipeline comprised of machine learning and deep learning models for all five languages. Three machine learning and four deep learning models were used in the ensemble. In the OffensEval-2020 competition our model achieved F1-score of 0.85, 0.74, 0.68, 0.81, and 0.9 for Arabic, Turkish, Danish, Greek and English language tasks respectively.",
}
@inproceedings{dadu-pant-2020-team,
    title = "Team Rouges at {S}em{E}val-2020 Task 12: Cross-lingual Inductive Transfer to Detect Offensive Language",
    author = "Dadu, Tanvi  and
      Pant, Kartikey",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.290",
    pages = "2183--2189",
    abstract = "With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains. This phenomenon has given rise to the growing need to detect the offensive language used in social media cross-lingually. In OffensEval 2020, the organizers have released the multilingual Offensive Language Identification Dataset (mOLID), which contains tweets in five different languages, to detect offensive language. In this work, we introduce a cross-lingual inductive approach to identify the offensive language in tweets using the contextual word embedding XLM-RoBERTa (XLM-R). We show that our model performs competitively on all five languages, obtaining the fourth position in the English task with an F1-score of 0.919 and eighth position in the Turkish task with an F1-score of 0.781. Further experimentation proves that our model works competitively in a zero-shot learning environment, and is extensible to other languages.",
}
@inproceedings{sivanaiah-etal-2020-techssn,
    title = "{TECHSSN} at {S}em{E}val-2020 Task 12: Offensive Language Detection Using {BERT} Embeddings",
    author = "Sivanaiah, Rajalakshmi  and
      Suseelan, Angel  and
      Rajendram, S Milton  and
      T.t., Mirnalinee",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.291",
    pages = "2190--2196",
    abstract = "This paper describes the work of identifying the presence of offensive language in social media posts and categorizing a post as targeted to a particular person or not. The work developed by team TECHSSN for solving the Multilingual Offensive Language Identification in Social Media (Task 12) in SemEval-2020 involves the use of deep learning models with BERT embeddings. The dataset is preprocessed and given to a Bidirectional Encoder Representations from Transformers (BERT) model with pretrained weight vectors. The model is retrained and the weights are learned for the offensive language dataset. We have developed a system with the English language dataset. The results are better when compared to the model we developed in SemEval-2019 Task6.",
}
@inproceedings{alonso-etal-2020-thenorth,
    title = "{T}he{N}orth at {S}em{E}val-2020 Task 12: Hate Speech Detection Using {R}o{BERT}a",
    author = {Alonso, Pedro  and
      Saini, Rajkumar  and
      Kovacs, Gy{\"o}rgy},
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.292",
    pages = "2197--2202",
    abstract = "Hate speech detection on social media platforms is crucial as it helps to avoid severe situations, and severe harm to marginalized people and groups. The application of Natural Language Processing(NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection. The expression of hate, however is varied and ever evolving. Thus, it is important for better detection systems to adapt to this variance. Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions. In this paper, we discuss our entry to one such competition, namely the English version of sub-task A for the OffensEval competition. Our contribution can be perceived through our results, which were first a F1-score of 0.9089, and with further refinements described here climb up to0.9166. It serves to give more support to our hypothesis that one of the variants of BERT (Devlin et al., 2018), namely RoBERTa can successfully differentiate between offensive and not-offensive tweets, given some preprocessing steps (also outlined here).",
}
@inproceedings{yao-etal-2020-ujnlp,
    title = "{UJNLP} at {S}em{E}val-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers",
    author = "Yao, Yinnan  and
      Su, Nan  and
      Ma, Kun",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.293",
    pages = "2203--2208",
    abstract = "In this paper, we built several pre-trained models to participate SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. In the common task of Offensive Language Identification in Social Media, pre-trained models such as Bidirectional Encoder Representation from Transformer (BERT) have achieved good results. We preprocess the dataset by the language habits of users in social network. Considering the data imbalance in OffensEval, we screened the newly provided machine annotation samples to construct a new dataset. We use the dataset to fine-tune the Robustly Optimized BERT Pretraining Approach (RoBERTa). For the English subtask B, we adopted the method of adding Auxiliary Sentences (AS) to transform the single-sentence classification task into a relationship recognition task between sentences. Our team UJNLP wins the ranking 16th of 85 in English subtask A (Offensive language identification).",
}
@inproceedings{fromknecht-palmer-2020-unt,
    title = "{UNT} Linguistics at {S}em{E}val-2020 Task 12: Linear {SVC} with Pre-trained Word Embeddings as Document Vectors and Targeted Linguistic Features",
    author = "Fromknecht, Jared  and
      Palmer, Alexis",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.294",
    pages = "2209--2215",
    abstract = "This paper outlines our approach to Tasks A {\&} B for the English Language track of SemEval-2020 Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. We use a Linear SVM with document vectors computed from pre-trained word embeddings, and we explore the effectiveness of lexical, part of speech, dependency, and named entity (NE) features. We manually annotate a subset of the training data, which we use for error analysis and to tune a threshold for mapping training confidence values to labels. While document vectors are consistently the most informative features for both tasks, testing on the development set suggests that dependency features are an effective addition for Task A, and NE features for Task B.",
}
@inproceedings{lim-tayyar-madabushi-2020-uob,
    title = "{U}o{B} at {S}em{E}val-2020 Task 12: Boosting {BERT} with Corpus Level Information",
    author = "Lim, Wah Meng  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.295",
    pages = "2216--2221",
    abstract = "Pre-trained language model word representation, such as BERT, have been extremely successful in several Natural Language Processing tasks significantly improving on the state-of-the-art. This can largely be attributed to their ability to better capture semantic information contained within a sentence. Several tasks, however, can benefit from information available at a corpus level, such as Term Frequency-Inverse Document Frequency (TF-IDF). In this work we test the effectiveness of integrating this information with BERT on the task of identifying abuse on social media and show that integrating this information with BERT does indeed significantly improve performance. We participate in Sub-Task A (abuse detection) wherein we achieve a score within two points of the top performing team and in Sub-Task B (target detection) wherein we are ranked 4 of the 44 participating teams.",
}
@inproceedings{tanase-etal-2020-upb,
    title = "{UPB} at {S}em{E}val-2020 Task 12: Multilingual Offensive Language Detection on Social Media by Fine-tuning a Variety of {BERT}-based Models",
    author = "Tanase, Mircea-Adrian  and
      Cercel, Dumitru-Clementin  and
      Chiru, Costin",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.296",
    pages = "2222--2231",
    abstract = "Offensive language detection is one of the most challenging problem in the natural language processing field, being imposed by the rising presence of this phenomenon in online social media. This paper describes our Transformer-based solutions for identifying offensive language on Twitter in five languages (i.e., English, Arabic, Danish, Greek, and Turkish), which was employed in Subtask A of the Offenseval 2020 shared task. Several neural architectures (i.e., BERT, mBERT, Roberta, XLM-Roberta, and ALBERT), pre-trained using both single-language and multilingual corpora, were fine-tuned and compared using multiple combinations of datasets. Finally, the highest-scoring models were used for our submissions in the competition, which ranked our team 21st of 85, 28th of 53, 19th of 39, 16th of 37, and 10th of 46 for English, Arabic, Danish, Greek, and Turkish, respectively.",
}
@inproceedings{boriola-paetzold-2020-utfpr,
    title = "{UTFPR} at {S}em{E}val 2020 Task 12: Identifying Offensive Tweets with Lightweight Ensembles",
    author = "Boriola, Marcos Aur{\'e}lio Hermogenes  and
      Paetzold, Gustavo Henrique",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.297",
    pages = "2232--2236",
    abstract = "Offensive language is a common issue on social media platforms nowadays. In an effort to address this issue, the SemEval 2020 event held the OffensEval 2020 shared task where the participants were challenged to develop systems that identify and classify offensive language in tweets. In this paper, we present a system that uses an Ensemble model stacking a BOW model and a CNN model that led us to place 29th in the ranking for English sub-task A.",
}
@inproceedings{otiefy-etal-2020-woli,
    title = "{WOLI} at {S}em{E}val-2020 Task 12: {A}rabic Offensive Language Identification on Different {T}witter Datasets",
    author = "Otiefy, Yasser  and
      Abdelmalek, Ahmed  and
      El Hosary, Islam",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.298",
    pages = "2237--2243",
    abstract = "Communicating through social platforms has become one of the principal means of personal communications and interactions. Unfortunately, healthy communication is often interfered by offensive language that can have damaging effects on the users. A key to fight offensive language on social media is the existence of an automatic offensive language detection system. This paper presents the results and the main findings of SemEval-2020, Task 12 OffensEval Sub-task A Zampieri et al. (2020), on Identifying and categorising Offensive Language in Social Media. The task was based on the Arabic OffensEval dataset Mubarak et al. (2020). In this paper, we describe the system submitted by WideBot AI Lab for the shared task which ranked 10th out of 52 participants with Macro-F1 86.9{\%} on the golden dataset under CodaLab username {``}yasserotiefy{''}. We experimented with various models and the best model is a linear SVM in which we use a combination of both character and word n-grams. We also introduced a neural network approach that enhanced the predictive ability of our system that includes CNN, highway network, Bi-LSTM, and attention layers.",
}
@inproceedings{dong-choi-2020-xd,
    title = "{XD} at {S}em{E}val-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders",
    author = "Dong, Xiangjue  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.299",
    pages = "2244--2250",
    abstract = "This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media. For the individual models, deep transformer layers are applied to perform multi-head attentions. For the ensemble model, the utterance representations taken from those individual models are concatenated and fed into a linear decoder to make the final decisions. Our ensemble model outperforms the individual models and shows up to 8.6{\%} improvement over the individual models on the development set. On the test set, it achieves macro-F1 of 90.9{\%} and becomes one of the high performing systems among 85 participants in the sub-task A of this shared task. Our analysis shows that although the ensemble model significantly improves the accuracy on the development set, the improvement is not as evident on the test set.",
}
@inproceedings{ou-li-2020-ynu-oxz,
    title = "{YNU}{\_}oxz at {S}em{E}val-2020 Task 12: Bidirectional {GRU} with Capsule for Identifying Multilingual Offensive Language",
    author = "Ou, Xiaozhi  and
      Li, Hongling",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.semeval-1.300",
    pages = "2251--2257",
    abstract = "This article describes the system submitted to SemEval-2020 Task 12 OffensEval 2: Multilingual Offensive Language Recognition in Social Media. The task is to classify offensive language in social media. The shared task contains five languages (English, Greek, Arabic, Danish, and Turkish) and three subtasks. We only participated in subtask A of English to identify offensive language. To solve this task, we proposed a system based on a Bidirectional Gated Recurrent Unit (Bi-GRU) with a Capsule model. Finally, we used the K-fold approach for ensemble. Our model achieved a Macro-average F1 score of 0.90969 (ranked 27/85) in subtask A.",
}
@proceedings{sdp-2020-scholarly,
    title = "Proceedings of the First Workshop on Scholarly Document Processing",
    editor = "Chandrasekaran, Muthu Kumar  and
      de Waard, Anita  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Hovy, Eduard  and
      Knoth, Petr  and
      Konopnicki, David  and
      Mayr, Philipp  and
      Patton, Robert M.  and
      Shmueli-Scheuer, Michal",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.0",
}
@inproceedings{chandrasekaran-etal-2020-overview,
    title = "Overview of the First Workshop on Scholarly Document Processing ({SDP})",
    author = "Chandrasekaran, Muthu Kumar  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Hovy, Eduard  and
      Mayr, Philipp  and
      Shmueli-Scheuer, Michal  and
      de Waard, Anita",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.1",
    doi = "10.18653/v1/2020.sdp-1.1",
    pages = "1--6",
    abstract = "Next to keeping up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. To address these challenges, computational work on enhancing search, summarization, and analysis of scholarly documents has flourished. However, the various strands of research on scholarly document processing remain fragmented. To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event. The SDP workshop consisted of a research track (including a poster session), two invited talks and three Shared Tasks (CL-SciSumm, Lay-Summ and LongSumm), geared towards easier access to scientific methods and results. \textbf{Website}: \url{https://ornlcda.github.io/SDProc}",
}
@inproceedings{sigurdsson-2020-future,
    title = "The future of ar{X}iv and knowledge discovery in open science",
    author = "Sigurdsson, Steinn",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.2",
    doi = "10.18653/v1/2020.sdp-1.2",
    pages = "7--9",
    abstract = "arXiv, the preprint server for the physical and mathematical sciences, is in its third decade of operation. As the flow of new, open access research increases inexorably, the challenges to keep up with and discover research content also become greater. I will discuss the status and future of arXiv, and possibilities and plans to make more effective use of the research database to enhance ongoing research efforts.",
}
@inproceedings{wu-etal-2020-acknowledgement,
    title = "Acknowledgement Entity Recognition in {CORD}-19 Papers",
    author = "Wu, Jian  and
      Wang, Pei  and
      Wei, Xin  and
      Rajtmajer, Sarah  and
      Giles, C. Lee  and
      Griffin, Christopher",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.3",
    doi = "10.18653/v1/2020.sdp-1.3",
    pages = "10--19",
    abstract = "Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, AckExtract based on open-source text mining software and evaluate our method using manually labeled data. AckExtract uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F{\_}1=0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50{--}60{\%} of named entities are actually acknowledged. We further analyze chronological trends of acknowledgement entities in CORD-19 papers. All codes and labeled data are publicly available at https://github.com/lamps-lab/ackextract.",
}
@inproceedings{bhambhoria-etal-2020-smart,
    title = "A Smart System to Generate and Validate Question Answer Pairs for {COVID}-19 Literature",
    author = "Bhambhoria, Rohan  and
      Feng, Luna  and
      Sepehr, Dawn  and
      Chen, John  and
      Cowling, Conner  and
      Kocak, Sedef  and
      Dolatabadi, Elham",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.4",
    doi = "10.18653/v1/2020.sdp-1.4",
    pages = "20--30",
    abstract = "Automatically generating question answer (QA) pairs from the rapidly growing coronavirus-related literature is of great value to the medical community. Creating high quality QA pairs would allow researchers to build models to address scientific queries for answers which are not readily available in support of the ongoing fight against the pandemic. QA pair generation is, however, a very tedious and time consuming task requiring domain expertise for annotation and evaluation. In this paper we present our contribution in addressing some of the challenges of building a QA system without gold data. We first present a method to create QA pairs from a large semi-structured dataset through the use of transformer and rule-based models. Next, we propose a means of engaging subject matter experts (SMEs) for annotating the QA pairs through the usage of a web application. Finally, we demonstrate some experiments showcasing the effectiveness of leveraging active learning in designing a high performing model with a substantially lower annotation effort from the domain experts.",
}
@inproceedings{zhang-etal-2020-covidex,
    title = "Covidex: Neural Ranking Models and Keyword Search Infrastructure for the {COVID}-19 Open Research Dataset",
    author = "Zhang, Edwin  and
      Gupta, Nikhil  and
      Tang, Raphael  and
      Han, Xiao  and
      Pradeep, Ronak  and
      Lu, Kuang  and
      Zhang, Yue  and
      Nogueira, Rodrigo  and
      Cho, Kyunghyun  and
      Fang, Hui  and
      Lin, Jimmy",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.5",
    doi = "10.18653/v1/2020.sdp-1.5",
    pages = "31--41",
    abstract = "We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the multi-round TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the best systems. In round 3, we submitted the highest-scoring run that took advantage of previous training data and the second-highest fully automatic run. In rounds 4 and 5, we submitted the highest-scoring fully automatic runs.",
}
@inproceedings{satish-etal-2020-impact,
    title = "The impact of preprint servers in the formation of novel ideas",
    author = "Satish, Swarup  and
      Yao, Zonghai  and
      Drozdov, Andrew  and
      Veytsman, Boris",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.6",
    doi = "10.18653/v1/2020.sdp-1.6",
    pages = "42--55",
    abstract = "We study whether novel ideas in biomedical literature appear first in preprints or traditional journals. We develop a Bayesian method to estimate the time of appearance for a phrase in the literature, and apply it to a number of phrases, both automatically extracted and suggested by experts. We see that presently most phrases appear first in the traditional journals, but there is a number of phrases with the first appearance on preprint servers. A comparison of the general composition of texts from bioRxiv and traditional journals shows a growing trend of bioRxiv being predictive of traditional journals. We discuss the application of the method for related problems.",
}
@inproceedings{berger-etal-2020-effective,
    title = "Effective distributed representations for academic expert search",
    author = "Berger, Mark  and
      Zavrel, Jakub  and
      Groth, Paul",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.7",
    doi = "10.18653/v1/2020.sdp-1.7",
    pages = "56--71",
    abstract = "Expert search aims to find and rank experts based on a user{'}s query. In academia, retrieving experts is an efficient way to navigate through a large amount of academic knowledge. Here, we study how different distributed representations of academic papers (i.e. embeddings) impact academic expert retrieval. We use the Microsoft Academic Graph dataset and experiment with different configurations of a document-centric voting model for retrieval. In particular, we explore the impact of the use of contextualized embeddings on search performance. We also present results for paper embeddings that incorporate citation information through retrofitting. Additionally, experiments are conducted using different techniques for assigning author weights based on author order. We observe that using contextual embeddings produced by a transformer model trained for sentence similarity tasks produces the most effective paper representations for document-centric expert retrieval. However, retrofitting the paper embeddings and using elaborate author contribution weighting strategies did not improve retrieval performance.",
}
@inproceedings{kim-etal-2020-learning,
    title = "Learning {CNF} Blocking for Large-scale Author Name Disambiguation",
    author = "Kim, Kunho  and
      Sefid, Athar  and
      Giles, C. Lee",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.8",
    doi = "10.18653/v1/2020.sdp-1.8",
    pages = "72--80",
    abstract = "Author name disambiguation (AND) algorithms identify a unique author entity record from all similar or same publication records in scholarly or similar databases. Typically, a clustering method is used that requires calculation of similarities between each possible record pair. However, the total number of pairs grows quadratically with the size of the author database making such clustering difficult for millions of records. One remedy is a blocking function that reduces the number of pairwise similarity calculations. Here, we introduce a new way of learning blocking schemes by using a conjunctive normal form (CNF) in contrast to the disjunctive normal form (DNF). We demonstrate on PubMed author records that CNF blocking reduces more pairs while preserving high pairs completeness compared to the previous methods that use a DNF and that the computation time is significantly reduced. In addition, we also show how to ensure that the method produces disjoint blocks so that much of the AND algorithm can be efficiently paralleled. Our CNF blocking method is tested on the entire PubMed database of 80 million author mentions and efficiently removes 82.17{\%} of all author record pairs in 10 minutes.",
}
@inproceedings{muller-etal-2020-reconstructing,
    title = "Reconstructing Manual Information Extraction with {DB}-to-Document Backprojection: Experiments in the Life Science Domain",
    author = {M{\"u}ller, Mark-Christoph  and
      Ghosh, Sucheta  and
      Rey, Maja  and
      Wittig, Ulrike  and
      M{\"u}ller, Wolfgang  and
      Strube, Michael},
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.9",
    doi = "10.18653/v1/2020.sdp-1.9",
    pages = "81--90",
    abstract = "We introduce a novel scientific document processing task for making previously inaccessible information in printed paper documents available to automatic processing. We describe our data set of scanned documents and data records from the biological database SABIO-RK, provide a definition of the task, and report findings from preliminary experiments. Rigorous evaluation proved challenging due to lack of gold-standard data and a difficult notion of correctness. Qualitative inspection of results, however, showed the feasibility and usefulness of the task",
}
@inproceedings{ling-chen-2020-deeppapercomposer,
    title = "{D}eep{P}aper{C}omposer: A Simple Solution for Training Data Preparation for Parsing Research Papers",
    author = "Ling, Meng  and
      Chen, Jian",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.10",
    doi = "10.18653/v1/2020.sdp-1.10",
    pages = "91--96",
    abstract = "We present DeepPaperComposer, a simple solution for preparing highly accurate (100{\%}) training data without manual labeling to extract content from scholarly articles using convolutional neural networks (CNNs). We used our approach to generate data and trained CNNs to extract eight categories of both textual (titles, abstracts, headers, figure and table captions, and other texts) and non-textural content (figures and tables) from 30 years of IEEE VIS conference papers, of which a third were scanned bitmap PDFs. We curated this dataset and named it VISpaper-3K. We then showed our initial benchmark performance using VISpaper-3K over itself and CS-150 using YOLOv3 and Faster-RCNN. We open-source DeepPaperComposer of our training data generation and released the resulting annotation data VISpaper-3K to promote re-producible research.",
}
@inproceedings{medic-snajder-2020-improved,
    title = "Improved Local Citation Recommendation Based on Context Enhanced with Global Information",
    author = "Medi{\'c}, Zoran  and
      Snajder, Jan",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.11",
    doi = "10.18653/v1/2020.sdp-1.11",
    pages = "97--103",
    abstract = "Local citation recommendation aims at finding articles relevant for given citation context. While most previous approaches represent context using solely text surrounding the citation, we propose enhancing context representation with global information. Specifically, we include citing article{'}s title and abstract into context representation. We evaluate our model on datasets with different citation context sizes and demonstrate improvements with globally-enhanced context representations when citation contexts are smaller.",
}
@inproceedings{ozyurt-2020-effectiveness,
    title = "On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining",
    author = "Ozyurt, Ibrahim Burak",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.12",
    doi = "10.18653/v1/2020.sdp-1.12",
    pages = "104--112",
    abstract = "Neural language representation models such as BERT have recently shown state of the art performance in downstream NLP tasks and bio-medical domain adaptation of BERT (Bio-BERT) has shown same behavior on biomedical text mining tasks. However, due to their large model size and resulting increased computational need, practical application of models such as BERT is challenging making smaller models with comparable performance desirable for real word applications. Recently, a new language transformers based language representation model named ELECTRA is introduced, that makes efficient usage of training data in a generative-discriminative neural model setting that shows performance gains over BERT. These gains are especially impressive for smaller models. Here, we introduce two small ELECTRA based model named Bio-ELECTRA and Bio-ELECTRA++ that are eight times smaller than BERT Base and Bio-BERT and achieves comparable or better performance on biomedical question answering, yes/no question answer classification, question answer candidate ranking and relation extraction tasks. Bio-ELECTRA is pre-trained from scratch on PubMed abstracts using a consumer grade GPU with only 8GB memory. Bio-ELECTRA++ is the further pre-trained version of Bio-ELECTRA trained on a corpus of open access full papers from PubMed Central. While, for biomedical named entity recognition, large BERT Base model outperforms Bio-ELECTRA++, Bio-ELECTRA and ELECTRA-Small++, with hyperparameter tuning Bio-ELECTRA++ achieves results comparable to BERT.",
}
@inproceedings{ramesh-kashyap-kan-2020-sciwing,
    title = "{S}ci{WING}{--} A Software Toolkit for Scientific Document Processing",
    author = "Ramesh Kashyap, Abhinav  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.13",
    doi = "10.18653/v1/2020.sdp-1.13",
    pages = "113--120",
    abstract = "We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from http://sciwing.io and the demos are available at http://rebrand.ly/sciwing-demo.",
}
@inproceedings{li-etal-2020-multi,
    title = "Multi-task Peer-Review Score Prediction",
    author = "Li, Jiyi  and
      Sato, Ayaka  and
      Shimura, Kazuya  and
      Fukumoto, Fumiyo",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.14",
    doi = "10.18653/v1/2020.sdp-1.14",
    pages = "121--126",
    abstract = "Automatic prediction on the peer-review aspect scores of academic papers can be a useful assistant tool for both reviewers and authors. To handle the small size of published datasets on the target aspect of scores, we propose a multi-task approach to leverage additional information from other aspects of scores for improving the performance of the target. Because one of the problems of building multi-task models is how to select the proper resources of auxiliary tasks and how to select the proper shared structures. We propose a multi-task shared structure encoding approach which automatically selects good shared network structures as well as good auxiliary resources. The experiments based on peer-review datasets show that our approach is effective and has better performance on the target scores than the single-task method and naive multi-task methods.",
}
@inproceedings{basu-etal-2020-erlkg,
    title = "{ERLKG}: Entity Representation Learning and Knowledge Graph based association analysis of {COVID}-19 through mining of unstructured biomedical corpora",
    author = "Basu, Sayantan  and
      Chakraborty, Sinchani  and
      Hassan, Atif  and
      Siddique, Sana  and
      Anand, Ashish",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.15",
    doi = "10.18653/v1/2020.sdp-1.15",
    pages = "127--137",
    abstract = "We introduce a generic, human-out-of-the-loop pipeline, ERLKG, to perform rapid association analysis of any biomedical entity with other existing entities from a corpora of the same domain. Our pipeline consists of a Knowledge Graph (KG) created from the Open Source CORD-19 dataset by fully automating the procedure of information extraction using SciBERT. The best latent entity representations are then found by benchnmarking different KG embedding techniques on the task of link prediction using a Graph Convolution Network Auto Encoder (GCN-AE). We demonstrate the utility of ERLKG with respect to COVID-19 through multiple qualitative evaluations. Due to the lack of a gold standard, we propose a relatively large intrinsic evaluation dataset for COVID-19 and use it for validating the top two performing KG embedding techniques. We find TransD to be the best performing KG embedding technique with Pearson and Spearman correlation scores of 0.4348 and 0.4570 respectively. We demonstrate that a considerable number of ERLKG{'}s top protein, chemical and disease predictions are currently in consideration for COVID-19 related research.",
}
@inproceedings{asakura-etal-2020-towards,
    title = "Towards Grounding of Formulae",
    author = "Asakura, Takuto  and
      Greiner-Petter, Andr{\'e}  and
      Aizawa, Akiko  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.16",
    doi = "10.18653/v1/2020.sdp-1.16",
    pages = "138--147",
    abstract = "A large amount of scientific knowledge is represented within mixed forms of natural language texts and mathematical formulae. Therefore, a collaboration of natural language processing and formula analyses, so-called mathematical language processing, is necessary to enable computers to understand and retrieve information from the documents. However, as we will show in this project, a mathematical notation can change its meaning even within the scope of a single paragraph. This flexibility makes it difficult to extract the exact meaning of a mathematical formula. In this project, we will propose a new task direction for grounding mathematical formulae. Particularly, we are addressing the widespread misconception of various research projects in mathematical information retrieval, which presume that mathematical notations have a fixed meaning within a single document. We manually annotated a long scientific paper to illustrate the task concept. Our high inter-annotator agreement shows that the task is well understood for humans. Our results indicate that it is worthwhile to grow the techniques for the proposed task to contribute to the further progress of mathematical language processing.",
}
@inproceedings{van-dongen-etal-2020-schubert,
    title = "{SC}hu{BERT}: Scholarly Document Chunks with {BERT}-encoding boost Citation Count Prediction.",
    author = "van Dongen, Thomas  and
      Maillette de Buy Wenniger, Gideon  and
      Schomaker, Lambert",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.17",
    doi = "10.18653/v1/2020.sdp-1.17",
    pages = "148--157",
    abstract = "Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider use as an imperfect proxy for quality which has the advantage of being cheaply available for large volumes of scholarly documents. Previous work has dealt with number of citations prediction with relatively small training data sets, or larger datasets but with short, incomplete input text. In this work we leverage the open access ACL Anthology collection in combination with the Semantic Scholar bibliometric database to create a large corpus of scholarly documents with associated citation information and we propose a new citation prediction model called SChuBERT. In our experiments we compare SChuBERT with several state-of-the-art citation prediction models and show that it outperforms previous methods by a large margin. We also show the merit of using more training data and longer input for number of citations prediction.",
}
@inproceedings{maillette-de-buy-wenniger-etal-2020-structure,
    title = "Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction",
    author = "Maillette de Buy Wenniger, Gideon  and
      van Dongen, Thomas  and
      Aedmaa, Eleri  and
      Kruitbosch, Herbert Teun  and
      Valentijn, Edwin A.  and
      Schomaker, Lambert",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.18",
    doi = "10.18653/v1/2020.sdp-1.18",
    pages = "158--167",
    abstract = "Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document. Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction. The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs. Compared to plain HANs, accuracy increases on all three domains.On the computation and language domain our new model works best overall, and increases accuracy 4.7{\%} over the best literature result. We also obtain improvements when introducing the tags for prediction of the number of citations for 88k scientific publications that we compiled from the Allen AI S2ORC dataset. For our HAN-system with structure-tags we reach 28.5{\%} explained variance, an improvement of 1.8{\%} over our reimplementation of the BiLSTM-based model as well as 1.0{\%} improvement over plain HANs.",
}
@inproceedings{ding-etal-2020-cydex,
    title = "Cydex: Neural Search Infrastructure for the Scholarly Literature",
    author = "Ding, Shane  and
      Zhang, Edwin  and
      Lin, Jimmy",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.19",
    doi = "10.18653/v1/2020.sdp-1.19",
    pages = "168--173",
    abstract = "Cydex is a platform that provides neural search infrastructure for domain-specific scholarly literature. The platform represents an abstraction of Covidex, our recently developed full-stack open-source search engine for the COVID-19 Open Research Dataset (CORD-19) from AI2. While Covidex takes advantage of the latest best practices for keyword search using the popular Lucene search library as well as state-of-the-art neural ranking models using T5, parts of the system were hard coded to only work with CORD-19. This paper describes our efforts to generalize Covidex into Cydex, which can be applied to scholarly literature in different domains. By decoupling corpus-specific configurations from the frontend implementation, we are able to demonstrate the generality of Cydex on two very different corpora: the ACL Anthology and a collection of hydrology abstracts. Our platform is entirely open source and available at cydex.ai.",
}
@inproceedings{patel-etal-2020-use,
    title = "On the Use of Web Search to Improve Scientific Collections",
    author = "Patel, Krutarth  and
      Caragea, Cornelia  and
      Gollapalli, Sujatha Das",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.20",
    doi = "10.18653/v1/2020.sdp-1.20",
    pages = "174--183",
    abstract = "Despite the advancements in search engine features, ranking methods, technologies, and the availability of programmable APIs, current-day open-access digital libraries still rely on crawl-based approaches for acquiring their underlying document collections. In this paper, we propose a novel search-driven framework for acquiring documents for such scientific portals. Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine. We were able to obtain {\textasciitilde}267,000 unique research papers through our fully-automated framework using {\textasciitilde}76,000 queries, resulting in almost 200,000 more papers than the number of queries. Moreover, through a combination of title and author name search, we were able to recover 78{\%} of the original searched titles.",
}
@inproceedings{goldfarb-tarrant-etal-2020-scaling,
    title = "Scaling Systematic Literature Reviews with Machine Learning Pipelines",
    author = "Goldfarb-Tarrant, Seraphina  and
      Robertson, Alexander  and
      Lazic, Jasmina  and
      Tsouloufi, Theodora  and
      Donnison, Louise  and
      Smyth, Karen",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.21",
    doi = "10.18653/v1/2020.sdp-1.21",
    pages = "184--195",
    abstract = "Systematic reviews, which entail the extraction of data from large numbers of scientific documents, are an ideal avenue for the application of machine learning. They are vital to many fields of science and philanthropy, but are very time-consuming and require experts. Yet the three main stages of a systematic review are easily done automatically: searching for documents can be done via APIs and scrapers, selection of relevant documents can be done via binary classification, and extraction of data can be done via sequence-labelling classification. Despite the promise of automation for this field, little research exists that examines the various ways to automate each of these tasks. We construct a pipeline that automates each of these aspects, and experiment with many human-time vs. system quality trade-offs. We test the ability of classifiers to work well on small amounts of data and to generalise to data from countries not represented in the training data. We test different types of data extraction with varying difficulty in annotation, and five different neural architectures to do the extraction. We find that we can get surprising accuracy and generalisability of the whole pipeline system with only 2 weeks of human-expert annotation, which is only 15{\%} of the time it takes to do the whole review manually and can be repeated and extended to new data with no additional effort.",
}
@inproceedings{kang-etal-2020-document,
    title = "Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions",
    author = "Kang, Dongyeop  and
      Head, Andrew  and
      Sidhu, Risham  and
      Lo, Kyle  and
      Weld, Daniel  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.22",
    doi = "10.18653/v1/2020.sdp-1.22",
    pages = "196--206",
    abstract = "The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition detection, current approaches are far from being accurate enough to use in realworld applications. In this paper, we first perform in-depth error analysis of the current best performing definition detection system and discover major causes of errors. Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark. Because current benchmarks evaluate randomly sampled sentences, we propose an alternative evaluation that assesses every sentence within a document. This allows for evaluating recall in addition to precision. HEDDEx outperforms the leading system on both the sentence-level and the document-level tasks, by 12.7 F1 points and 14.4 F1 points, respectively. We note that performance on the high-recall document-level task is much lower than in the standard evaluation approach, due to the necessity of incorporation of document structure as features. We discuss remaining challenges in document-level definition detection, ideas for improvements, and potential issues for the development of reading aid applications.",
}
@inproceedings{fadaee-etal-2020-new,
    title = "A New Neural Search and Insights Platform for Navigating and Organizing {AI} Research",
    author = "Fadaee, Marzieh  and
      Gureenkova, Olga  and
      Rejon Barrera, Fernando  and
      Schnober, Carsten  and
      Weerkamp, Wouter  and
      Zavrel, Jakub",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.23",
    doi = "10.18653/v1/2020.sdp-1.23",
    pages = "207--213",
    abstract = "To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.",
}
@inproceedings{chandrasekaran-etal-2020-overview-insights,
    title = "Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: {CL}-{S}ci{S}umm, {L}ay{S}umm and {L}ong{S}umm",
    author = "Chandrasekaran, Muthu Kumar  and
      Feigenblat, Guy  and
      Hovy, Eduard  and
      Ravichander, Abhilasha  and
      Shmueli-Scheuer, Michal  and
      de Waard, Anita",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.24",
    doi = "10.18653/v1/2020.sdp-1.24",
    pages = "214--224",
    abstract = "We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks. In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.",
}
@inproceedings{li-etal-2020-cist,
    title = "{CIST}@{CL}-{S}ci{S}umm 2020, {L}ong{S}umm 2020: Automatic Scientific Document Summarization",
    author = "Li, Lei  and
      Xie, Yang  and
      Liu, Wei  and
      Liu, Yinan  and
      Jiang, Yafei  and
      Qi, Siya  and
      Li, Xingyuan",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.25",
    doi = "10.18653/v1/2020.sdp-1.25",
    pages = "225--234",
    abstract = "Our system participates in two shared tasks, CL-SciSumm 2020 and LongSumm 2020. In the CL-SciSumm shared task, based on our previous work, we apply more machine learning methods on position features and content features for facet classification in Task1B. And GCN is introduced in Task2 to perform extractive summarization. In the LongSumm shared task, we integrate both the extractive and abstractive summarization ways. Three methods were tested which are T5 Fine-tuning, DPPs Sampling, and GRU-GCN/GAT.",
}
@inproceedings{chai-etal-2020-nlp,
    title = "{NLP}-{PINGAN}-{TECH} @ {CL}-{S}ci{S}umm 2020",
    author = "Chai, Ling  and
      Fu, Guizhen  and
      Ni, Yuan",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.26",
    doi = "10.18653/v1/2020.sdp-1.26",
    pages = "235--241",
    abstract = "We focus on systems for TASK1 (TASK 1A and TASK 1B) of CL-SciSumm Shared Task 2020 in this paper. Task 1A is regarded as a binary classification task of sentence pairs. The strategies of domain-specific embedding and special tokens based on language models are proposed. Fusion of contextualized embedding and extra information is further explored in this article. We leverage Sembert to capture the structured semantic information. The joint of BERT-based model and classifiers without neural networks is also exploited. For the Task 1B, a language model with different weights for classes is fine-tuned to accomplish a multi-label classification task. The results show that extra information can improve the identification of cited text spans. The end-to-end trained models outperform models trained with two stages, and the averaged prediction of multi-models is more accurate than an individual one.",
}
@inproceedings{reddy-etal-2020-iiitbh,
    title = "{IIITBH}-{IITP}@{CL}-{S}ci{S}umm20, {CL}-{L}ay{S}umm20, {L}ong{S}umm20",
    author = "Reddy, Saichethan  and
      Saini, Naveen  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.27",
    doi = "10.18653/v1/2020.sdp-1.27",
    pages = "242--250",
    abstract = "In this paper, we present the IIIT Bhagalpur and IIT Patna team{'}s effort to solve the three shared tasks namely, CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 at SDP 2020. The theme of these tasks is to generate medium-scale, lay and long summaries, respectively, for scientific articles. For the first two tasks, unsupervised systems are developed, while for the third one, we develop a supervised system.The performances of all the systems were evaluated on the associated datasets with the shared tasks in term of well-known ROUGE metric.",
}
@inproceedings{gidiotis-etal-2020-auth,
    title = "{AUTH} @ {CLS}ci{S}umm 20, {L}ay{S}umm 20, {L}ong{S}umm 20",
    author = "Gidiotis, Alexios  and
      Stefanidis, Stefanos  and
      Tsoumakas, Grigorios",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.28",
    doi = "10.18653/v1/2020.sdp-1.28",
    pages = "251--260",
    abstract = "We present the systems we submitted for the shared tasks of the Workshop on Scholarly Document Processing at EMNLP 2020. Our approaches to the tasks are focused on exploiting large Transformer models pre-trained on huge corpora and adapting them to the different shared tasks. For tasks 1A and 1B of CL-SciSumm we are using different variants of the BERT model to tackle the tasks of {``}cited text span{''} and {``}facet{''} identification. For the summarization tasks 2 of CL-SciSumm, LaySumm and LongSumm we make use of different variants of the PEGASUS model, with and without fine-tuning, adapted to the nuances of each one of those particular tasks.",
}
@inproceedings{aumiller-etal-2020-unihd,
    title = "{U}ni{HD}@{CL}-{S}ci{S}umm 2020: Citation Extraction as Search",
    author = "Aumiller, Dennis  and
      Almasian, Satya  and
      Hausner, Philip  and
      Gertz, Michael",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.29",
    doi = "10.18653/v1/2020.sdp-1.29",
    pages = "261--269",
    abstract = "This work presents the entry by the team from Heidelberg University in the CL-SciSumm 2020 shared task at the Scholarly Document Processing workshop at EMNLP 2020. As in its previous iterations, the task is to highlight relevant parts in a reference paper, depending on a citance text excerpt from a citing paper. We participated in tasks 1A (citation identification) and 1B (citation context classification). Contrary to most previous works, we frame Task 1A as a search relevance problem, and introduce a 2-step re-ranking approach, which consists of a preselection based on BM25 in addition to positional document features, and a top-k re-ranking with BERT. For Task 1B, we follow previous submissions in applying methods that deal well with low resources and imbalanced classes.",
}
@inproceedings{mishra-etal-2020-iitp,
    title = "{IITP}-{AI}-{NLP}-{ML}@ {CL}-{S}ci{S}umm 2020, {CL}-{L}ay{S}umm 2020, {L}ong{S}umm 2020",
    author = "Mishra, Santosh Kumar  and
      Kundarapu, Harshavardhan  and
      Saini, Naveen  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.30",
    doi = "10.18653/v1/2020.sdp-1.30",
    pages = "270--276",
    abstract = "The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art. Scientific document summarization solves this problem by summarizing the essential fact and findings of the document. In the current paper, we present the participation of IITP-AI-NLP-ML team in three shared tasks, namely, CL-SciSumm 2020, LaySumm 2020, LongSumm 2020, which aims to generate medium, lay, and long summaries of the scientific articles, respectively. To solve CL-SciSumm 2020 and LongSumm 2020 tasks, three well-known clustering techniques are used, and then various sentence scoring functions, including textual entailment, are used to extract the sentences from each cluster for a summary generation. For LaySumm 2020, an encoder-decoder based deep learning model has been utilized. Performances of our developed systems are evaluated in terms of ROUGE measures on the associated datasets with the shared task.",
}
@inproceedings{jurk-etal-2020-1a,
    title = {1{A}-Team / {M}artin-Luther-Universit{\"a}t Halle-Wittenberg@{CLS}ci{S}umm 20},
    author = "Jurk, Artur  and
      Boltze, Maik  and
      Keller, Georg  and
      Ulbrich, Lorna  and
      Fischer, Anja",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.31",
    doi = "10.18653/v1/2020.sdp-1.31",
    pages = "277--281",
    abstract = "This document demonstrates our groups approach to the CL-SciSumm shared task 2020. There are three tasks in CL-SciSumm 2020. In Task 1a, we apply a Siamese neural network to identify the spans of text in the reference paper best reflecting a citation. In Task 1b, we use a SVM to classify the facet of a citation.",
}
@inproceedings{huang-krylova-2020-team,
    title = "Team {MLU}@{CL}-{S}ci{S}umm20: Methods for Computational Linguistics Scientific Citation Linkage",
    author = "Huang, Rong  and
      Krylova, Kseniia",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.32",
    doi = "10.18653/v1/2020.sdp-1.32",
    pages = "282--287",
    abstract = "This paper describes our approach to the CL-SciSumm 2020 shared task toward the problem of identifying reference span of the citing article in the referred article. In Task 1a, we apply and compare different methods in combination with similarity scores to identify spans of the reference text for the given citance. In Task 1b, we use a logistic regression to classifying the discourse facets.",
}
@inproceedings{zhang-etal-2020-ir,
    title = "{IR}{\&}{TM}-{NJUST}@{CLS}ci{S}umm 20",
    author = "Zhang, Heng  and
      Liu, Lifan  and
      Wang, Ruping  and
      Hu, Shaohu  and
      Ma, Shutian  and
      Zhang, Chengzhi",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.33",
    doi = "10.18653/v1/2020.sdp-1.33",
    pages = "288--296",
    abstract = "This paper mainly introduces our methods for Task 1A and Task 1B of CL-SciSumm 2020. Task 1A is to identify reference text in reference paper. Traditional machine learning models and MLP model are used. We evaluate the performances of these models and submit the final results from the optimal model. Compared with previous work, we optimize the ratio of positive to negative examples after data sampling. In order to construct features for classification, we calculate similarities between reference text and candidate sentences based on sentence vectors. Accordingly, nine similarities are used, of which eight are chosen from what we used in CL-SciSumm 2019 and a new sentence similarity based on fastText is added. Task 1B is to classify the facets of reference text. Unlike the methods used in CL-SciSumm 2019, we construct inputs of models based on word vectors and add deep learning models for classification this year.",
}
@inproceedings{umapathy-etal-2020-citeqa,
    title = "{C}ite{QA}@{CLS}ci{S}umm 2020",
    author = "Umapathy, Anjana  and
      Radhakrishnan, Karthik  and
      Jain, Kinjal  and
      Singh, Rahul",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.34",
    doi = "10.18653/v1/2020.sdp-1.34",
    pages = "297--302",
    abstract = "In academic publications, citations are used to build context for a concept by highlighting relevant aspects from reference papers. Automatically identifying referenced snippets can help researchers swiftly isolate principal contributions of scientific works. In this paper, we exploit the underlying structure of scientific articles to predict reference paper spans and facets corresponding to a citation. We propose two methods to detect citation spans - keyphrase overlap, BERT along with structural priors. We fine-tune FastText embeddings and leverage textual, positional features to predict citation facets.",
}
@inproceedings{yu-etal-2020-dimsum,
    title = "Dimsum @{L}ay{S}umm 20",
    author = "Yu, Tiezheng  and
      Su, Dan  and
      Dai, Wenliang  and
      Fung, Pascale",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.35",
    doi = "10.18653/v1/2020.sdp-1.35",
    pages = "303--309",
    abstract = "Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on BART model. We leverage sentence labels as extra supervision signals to improve the performance of lay summarization. In the CL-LaySumm 2020 shared task, our model achieves 46.00 Rouge1-F1 score.",
}
@inproceedings{el-ebshihy-etal-2020-artu,
    title = "{ARTU} / {TU} {W}ien and Artificial Researcher@ {L}ong{S}umm 20",
    author = "El-Ebshihy, Alaa  and
      Ningtyas, Annisa Maulida  and
      Andersson, Linda  and
      Piroi, Florina  and
      Rauber, Andreas",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.36",
    doi = "10.18653/v1/2020.sdp-1.36",
    pages = "310--317",
    abstract = "In this paper, we present our approach to solve the LongSumm 2020 Shared Task, at the 1st Workshop on Scholarly Document Processing. The objective of the long summaries task is to generate long summaries that cover salient information in scientific articles. The task is to generate abstractive and extractive summaries of a given scientific article. In the proposed approach, we are inspired by the concept of Argumentative Zoning (AZ) that de- fines the main rhetorical structure in scientific articles. We define two aspects that should be covered in scientific paper summary, namely Claim/Method and Conclusion/Result aspects. We use Solr index to expand the sentences of the paper abstract. We formulate each abstract sentence in a given publication as query to retrieve similar sentences from the text body of the document itself. We utilize a sentence selection algorithm described in previous literature to select sentences for the final summary that covers the two aforementioned aspects.",
}
@inproceedings{ju-etal-2020-monash,
    title = "Monash-Summ@{L}ong{S}umm 20 {S}ci{S}umm{P}ip: An Unsupervised Scientific Paper Summarization Pipeline",
    author = "Ju, Jiaxin  and
      Liu, Ming  and
      Gao, Longxiang  and
      Pan, Shirui",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.37",
    doi = "10.18653/v1/2020.sdp-1.37",
    pages = "318--327",
    abstract = "The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task. It contains three shared tasks and we participate in the LongSumm shared task. In this paper, we describe our text summarization system, SciSummPip, inspired by SummPip (Zhao et al., 2020) that is an unsupervised text summarization system for multi-document in News domain. Our SciSummPip includes a transformer-based language model SciBERT (Beltagy et al., 2019) for contextual sentence representation, content selection with PageRank (Page et al., 1999), sentence graph construction with both deep and linguistic information, sentence graph clustering and within-graph summary generation. Our work differs from previous method in that content selection and a summary length constraint is applied to adapt to the scientific domain. The experiment results on both training dataset and blind test dataset show the effectiveness of our method, and we empirically verify the robustness of modules used in SciSummPip with BERTScore (Zhang et al., 2019a).",
}
@inproceedings{kim-2020-using,
    title = "Using Pre-Trained Transformer for Better Lay Summarization",
    author = "Kim, Seungwon",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.38",
    doi = "10.18653/v1/2020.sdp-1.38",
    pages = "328--335",
    abstract = "In this paper, we tack lay summarization tasks, which aim to automatically produce lay summaries for scientific papers, to participate in the first CL-LaySumm 2020 in SDP workshop at EMNLP 2020. We present our approach of using Pre-training with Extracted Gap-sentences for Abstractive Summarization (PEGASUS; Zhang et al., 2019b) to produce the lay summary and combining those with the extractive summarization model using Bidirectional Encoder Representations from Transformers (BERT; Devlin et al., 2018) and readability metrics that measure the readability of the sentence to further improve the quality of the summary. Our model achieves a remarkable performance on ROUGE metrics, demonstrating the produced summary is more readable while it summarizes the main points of the document.",
}
@inproceedings{ghosh-roy-etal-2020-summaformers,
    title = "Summaformers @ {L}ay{S}umm 20, {L}ong{S}umm 20",
    author = "Ghosh Roy, Sayar  and
      Pinnaparaju, Nikhil  and
      Jain, Risubh  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.39",
    doi = "10.18653/v1/2020.sdp-1.39",
    pages = "336--343",
    abstract = "Automatic text summarization has been widely studied as an important task in natural language processing. Traditionally, various feature engineering and machine learning based systems have been proposed for extractive as well as abstractive text summarization. Recently, deep learning based, specifically Transformer-based systems have been immensely popular. Summarization is a cognitively challenging task {--} extracting summary worthy sentences is laborious, and expressing semantics in brief when doing abstractive summarization is complicated. In this paper, we specifically look at the problem of summarizing scientific research papers from multiple domains. We differentiate between two types of summaries, namely, (a) LaySumm: A very short summary that captures the essence of the research paper in layman terms restricting overtly specific technical jargon and (b) LongSumm: A much longer detailed summary aimed at providing specific insights into various ideas touched upon in the paper. While leveraging latest Transformer-based models, our systems are simple, intuitive and based on how specific paper sections contribute to human summaries of the two types described above. Evaluations against gold standard summaries using ROUGE metrics prove the effectiveness of our approach. On blind test corpora, our system ranks first and third for the LongSumm and LaySumm tasks respectively.",
}
@inproceedings{chaturvedi-etal-2020-divide,
    title = "Divide and Conquer: From Complexity to Simplicity for Lay Summarization",
    author = "Chaturvedi, Rochana  and
      ., Saachi  and
      Dhani, Jaspreet Singh  and
      Joshi, Anurag  and
      Khanna, Ankush  and
      Tomar, Neha  and
      Duari, Swagata  and
      Khurana, Alka  and
      Bhatnagar, Vasudha",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.40",
    doi = "10.18653/v1/2020.sdp-1.40",
    pages = "344--355",
    abstract = "We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20. The task is to produce non-technical summaries of scholarly documents. The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article. We propose a two step divide-and-conquer approach. First, we judiciously select segments of the documents that are not overly pedantic and are likely to be of interest to the laity, and over-extract sentences from each segment using an unsupervised network based method. Next, we perform abstractive summarization on these extractions and systematically merge the abstractions. We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary. Our approach leverages state-of-the-art pre-trained deep neural network based models as zero-shot learners to achieve high scores on the task.",
}
@inproceedings{sotudeh-gharebagh-etal-2020-guir,
    title = "{GUIR} @ {L}ong{S}umm 2020: Learning to Generate Long Summaries from Scientific Documents",
    author = "Sotudeh Gharebagh, Sajad  and
      Cohan, Arman  and
      Goharian, Nazli",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.41",
    doi = "10.18653/v1/2020.sdp-1.41",
    pages = "356--361",
    abstract = "This paper presents our methods for the LongSumm 2020: Shared Task on Generating Long Summaries for Scientific Documents, where the task is to generatelong summaries given a set of scientific papers provided by the organizers. We explore 3 main approaches for this task: 1. An extractive approach using a BERT-based summarization model; 2. A two stage model that additionally includes an abstraction step using BART; and 3. A new multi-tasking approach on incorporating document structure into the summarizer. We found that our new multi-tasking approach outperforms the two other methods by large margins. Among 9 participants in the shared task, our best model ranks top according to Rouge-1 score (53.11{\%}) while staying competitive in terms of Rouge-2.",
}
@proceedings{scil-2020-society,
    title = "Proceedings of the Society for Computation in Linguistics 2020",
    editor = "Ettinger, Allyson  and
      Jarosz, Gaja  and
      Pater, Joe",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.0",
}
@inproceedings{chaves-2020-dont,
    title = "What Don{'}t {RNN} Language Models Learn About Filler-Gap Dependencies?",
    author = "Chaves, Rui",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.1",
    pages = "1--11",
}
@inproceedings{da-costa-chaves-2020-assessing,
    title = "Assessing the ability of Transformer-based Neural Models to represent structurally unbounded dependencies",
    author = "Da Costa, Jillian  and
      Chaves, Rui",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.2",
    pages = "12--21",
}
@inproceedings{forsythe-pearl-2020-immature,
    title = "Immature representation or immature deployment? Modeling child pronoun resolution",
    author = "Forsythe, Hannah  and
      Pearl, Lisa",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.3",
    pages = "22--26",
}
@inproceedings{elsner-etal-2020-stop,
    title = "Stop the Morphological Cycle, {I} Want to Get Off: Modeling the Development of Fusion",
    author = "Elsner, Micha  and
      Johnson, Martha  and
      Antetomaso, Stephanie  and
      Sims, Andrea",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.4",
    pages = "27--37",
}
@inproceedings{begus-2020-modeing,
    title = "Modeing unsupervised phonetic and phonological learning in Generative Adversarial Phonology",
    author = "Begus, Gasper",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.5",
    pages = "38--48",
}
@inproceedings{talmina-linzen-2020-neural,
    title = "Neural network learning of the {R}ussian genitive of negation: optionality and structure sensitivity",
    author = "Talmina, Natalia  and
      Linzen, Tal",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.6",
    pages = "49--58",
}
@inproceedings{de-santo-2020-mg,
    title = "{MG} Parsing as a Model of Gradient Acceptability in Syntactic Islands",
    author = "De Santo, Aniello",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.7",
    pages = "59--69",
}
@inproceedings{potter-2020-rhetorical,
    title = "The Rhetorical Structure of Modus Tollens: An Exploration in Logic-Mining",
    author = "Potter, Andrew",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.8",
    pages = "70--79",
}
@inproceedings{zaslavsky-etal-2020-semantic,
    title = "Semantic categories of artifacts and animals reflect efficient coding",
    author = "Zaslavsky, Noga  and
      Regier, Terry  and
      Tishby, Naftali  and
      Kemp, Charles",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.9",
    pages = "80--81",
}
@inproceedings{ghosh-etal-2020-interpreting,
    title = "Interpreting Verbal Irony: Linguistic Strategies and the Connection to the{T}ype of Semantic Incongruity",
    author = "Ghosh, Debanjan  and
      Musi, Elena  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.10",
    pages = "82--93",
}
@inproceedings{dai-futrell-2020-work,
    title = "Work in Progress: Information-theoretic characterization of the subregular hierarchy",
    author = "Dai, Huteng  and
      Futrell, Richard",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.11",
    pages = "94--97",
}
@inproceedings{hahn-futrell-2020-crosslinguistic,
    title = "Crosslinguistic Word Orders Enable an Efficient Tradeoff of Memory and Surprisal",
    author = "Hahn, Michael  and
      Futrell, Richard",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.12",
    pages = "98--99",
}
@inproceedings{moreton-2020-evolving,
    title = "Evolving constraints and rules in Harmonic Grammar",
    author = "Moreton, Elliott",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.13",
    pages = "100--111",
}
@inproceedings{anttila-etal-2020-equiprobable,
    title = "Equiprobable mappings in weighted constraint grammars",
    author = "Anttila, Arto  and
      Borgeson, Scott  and
      Magri, Giorgio",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.14",
    pages = "112--113",
}
@inproceedings{storme-magri-2020-constraint,
    title = "Constraint summation in phonological theory",
    author = "Storme, Benjamin  and
      Magri, Giorgio",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.15",
    pages = "114--115",
}
@inproceedings{newman-etal-2020-communication,
    title = "Communication-based Evaluation for Natural Language Generation",
    author = "Newman, Benjamin  and
      Cohn-Gordon, Reuben  and
      Potts, Christopher",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.16",
    pages = "116--126",
}
@inproceedings{zhu-2020-extending,
    title = "Extending the Autosegmental Input Strictly Local Framework: Metrical Dominance and Floating Tones",
    author = "Zhu, Yuhong",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.17",
    pages = "127--135",
}
@inproceedings{jiang-etal-2020-dialectgram,
    title = "{D}ialect{G}ram: Automatic Detection of Dialectal Changes with Multi-geographic Resolution Analysis",
    author = "Jiang, Hang  and
      Hong, Haoshen  and
      Chen, Yuxing  and
      Kulkarni, Vivek",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.18",
    pages = "136--143",
}
@inproceedings{kann-2020-acquisition,
    title = "Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge",
    author = "Kann, Katharina",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.19",
    pages = "144--154",
}
@inproceedings{ikawa-etal-2020-quantifier,
    title = "Quantifier-free tree transductions",
    author = "Ikawa, Shiori  and
      Ohtaka, Akane  and
      Jardine, Adam",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.20",
    pages = "155--158",
}
@inproceedings{lambert-rogers-2020-tier,
    title = "Tier-Based Strictly Local Stringsets: Perspectives from Model and Automata Theory",
    author = "Lambert, Dakotah  and
      Rogers, James",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.21",
    pages = "159--166",
}
@inproceedings{nelson-etal-2020-probing,
    title = "Probing {RNN} Encoder-Decoder Generalization of Subregular Functions using Reduplication",
    author = "Nelson, Max  and
      Dolatian, Hossep  and
      Rawski, Jonathan  and
      Prickett, Brandon",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.22",
    pages = "167--178",
}
@inproceedings{magri-2020-principled,
    title = "A principled derivation of Harmonic Grammar",
    author = "Magri, Giorgio",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.23",
    pages = "179--188",
}
@inproceedings{lemke-etal-2020-script,
    title = "Script knowledge constrains ellipses in fragments {--} Evidence from production data and language modeling",
    author = {Lemke, Robin  and
      Sch{\"a}fer, Lisa  and
      Drenhaus, Heiner  and
      Reich, Ingo},
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.24",
    pages = "189--192",
}
@inproceedings{dolatian-rawski-2020-multi,
    title = "Multi-Input Strictly Local Functions for Templatic Morphology",
    author = "Dolatian, Hossep  and
      Rawski, Jonathan",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.25",
    pages = "193--207",
}
@inproceedings{rawski-dolatian-2020-multi,
    title = "Multi-Input Strictly Local Functions for Tonal Phonology",
    author = "Rawski, Jonathan  and
      Dolatian, Hossep",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.26",
    pages = "208--223",
}
@inproceedings{graf-2020-curbing,
    title = "Curbing Feature Coding: Strictly Local Feature Assignment",
    author = "Graf, Thomas",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.27",
    pages = "224--233",
}
@inproceedings{kobele-etal-2020-role,
    title = "The role of information theory in gap-filler dependencies",
    author = "Kobele, Gregory  and
      He, Linyang  and
      Xiang, Ming",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.28",
    pages = "234--237",
}
@inproceedings{waldon-degen-2020-modeling,
    title = "Modeling Behavior in Truth Value Judgment Task Experiments",
    author = "Waldon, Brandon  and
      Degen, Judith",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.29",
    pages = "238--247",
}
@inproceedings{morgan-levy-2020-frequency,
    title = "Frequency-(in)dependent regularization in language production and cultural transmission",
    author = "Morgan, Emily  and
      Levy, Roger",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.30",
    pages = "248--249",
}
@inproceedings{abrams-etal-2020-graph,
    title = "Graph-to-Graph Meaning Representation Transformations for Human-Robot Dialogue",
    author = "Abrams, Mitchell  and
      Bonial, Claire  and
      Donatelli, Lucia",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.31",
    pages = "250--253",
}
@inproceedings{ahn-etal-2020-code,
    title = "What Code-Switching Strategies are Effective in Dialog Systems?",
    author = "Ahn, Emily  and
      Jimenez, Cecilia  and
      Tsvetkov, Yulia  and
      Black, Alan W",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.32",
    pages = "254--264",
}
@inproceedings{srivastava-etal-2020-role,
    title = "The Role of Linguistic Features in Domain Adaptation: {TAG} Parsing of Questions",
    author = "Srivastava, Aarohi  and
      Frank, Robert  and
      Widder, Sarah  and
      Chartash, David",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.33",
    pages = "265--276",
}
@inproceedings{mccoy-etal-2020-tensor,
    title = "Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks",
    author = "McCoy, R. Thomas  and
      Linzen, Tal  and
      Dunbar, Ewan  and
      Smolensky, Paul",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.34",
    pages = "277--278",
}
@inproceedings{mickus-etal-2020-mean,
    title = "What do you mean, {BERT}?",
    author = "Mickus, Timothee  and
      Paperno, Denis  and
      Constant, Mathieu  and
      van Deemter, Kees",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.35",
    pages = "279--290",
}
@inproceedings{mayer-nelson-2020-phonotactic,
    title = "Phonotactic learning with neural language models",
    author = "Mayer, Connor  and
      Nelson, Max",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.36",
    pages = "291--301",
}
@inproceedings{sims-2020-inflectional,
    title = "Inflectional networks: Graph-theoretic tools for inflectional typology",
    author = "Sims, Andrea D.",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.37",
    pages = "302--312",
}
@inproceedings{bhattasali-etal-2020-modeling,
    title = "Modeling conventionalization and predictability within {MWE}s at the brain level",
    author = "Bhattasali, Shohini  and
      Fabre, Murielle  and
      Pallier, Christophe  and
      Hale, John",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.38",
    pages = "313--322",
}
@inproceedings{hu-etal-2020-closer,
    title = "A closer look at the performance of neural language models on reflexive anaphor licensing",
    author = "Hu, Jennifer  and
      Chen, Sherry Yong  and
      Levy, Roger",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.39",
    pages = "323--333",
}
@inproceedings{hu-etal-2020-monalog,
    title = "{M}ona{L}og: a Lightweight System for Natural Language Inference Based on Monotonicity",
    author = "Hu, Hai  and
      Chen, Qi  and
      Richardson, Kyle  and
      Mukherjee, Atreyee  and
      Moss, Lawrence S.  and
      Kuebler, Sandra",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.40",
    pages = "334--344",
}
@inproceedings{liter-feldman-2020-modeling,
    title = "Modeling the learning of the Person Case Constraint",
    author = "Liter, Adam  and
      Feldman, Naomi",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.41",
    pages = "345--354",
}
@inproceedings{mcmillan-major-2020-automating,
    title = "Automating Gloss Generation in Interlinear Glossed Text",
    author = "McMillan-Major, Angelina",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.42",
    pages = "355--366",
}
@inproceedings{ryskina-etal-2020-new,
    title = "Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods",
    author = "Ryskina, Maria  and
      Rabinovich, Ella  and
      Berg-Kirkpatrick, Taylor  and
      Mortensen, David  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.43",
    pages = "367--376",
}
@inproceedings{priva-etal-2020-stability,
    title = "The stability of segmental properties across genre and corpus types in low-resource languages",
    author = "Priva, Uriel Cohen  and
      Yang, Shiying  and
      Strand, Emily",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.44",
    pages = "377--385",
}
@inproceedings{an-white-2020-lexical,
    title = "The lexical and grammatical sources of neg-raising inferences",
    author = "An, Hannah Youngeun  and
      White, Aaron Steven",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.45",
    pages = "386--399",
}
@inproceedings{collard-2020-unsupervised,
    title = "Unsupervised Formal Grammar Induction with Confidence",
    author = "Collard, Jacob",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.46",
    pages = "400--408",
}
@inproceedings{warstadt-etal-2020-blimp,
    title = "{BL}i{MP}: A Benchmark of Linguistic Minimal Pairs for {E}nglish",
    author = "Warstadt, Alex  and
      Parrish, Alicia  and
      Liu, Haokun  and
      Mohananey, Anhad  and
      Peng, Wei  and
      Wang, Sheng-Fu  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.47",
    pages = "409--410",
}
@inproceedings{karakas-2020-ibsp,
    title = "An {IBSP} Description of {S}anskrit /n/-Retroflexion",
    author = "Karaka{\c{s}}, Ayla",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.48",
    pages = "411--420",
}
@inproceedings{shafiei-graf-2020-subregular,
    title = "The Subregular Complexity of Syntactic Islands",
    author = "Shafiei, Nazila  and
      Graf, Thomas",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.49",
    pages = "421--430",
}
@inproceedings{oseki-marantz-2020-modeling,
    title = "Modeling morphological processing in human magnetoencephalography",
    author = "Oseki, Yohei  and
      Marantz, Alec",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.50",
    pages = "431--441",
}
@inproceedings{rhyne-2020-reconciling,
    title = "Reconciling historical data and modern computational models in corpus creation",
    author = "Rhyne, Joseph",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.51",
    pages = "442--445",
}
@inproceedings{malouf-etal-2020-lexical,
    title = "Lexical databases for computational analyses: A linguistic perspective",
    author = "Malouf, Robert  and
      Ackerman, Farrell  and
      Semenuks, Arturs",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.52",
    pages = "446--456",
}
@inproceedings{indurkhya-2020-inferring,
    title = "Inferring {M}inimalist {G}rammars with an {SMT}-Solver",
    author = "Indurkhya, Sagar",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.53",
    pages = "457--460",
}
@inproceedings{ohara-2020-frequency,
    title = "Frequency Matching Behavior in On-line {M}ax{E}nt Learners",
    author = "O{'}Hara, Charlie",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.54",
    pages = "461--463",
}
@inproceedings{paperno-2020-limitations,
    title = "Limitations in learning an interpreted language with recurrent models",
    author = "Paperno, Denis",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.55",
    pages = "464--465",
}
@inproceedings{hao-2020-metrical,
    title = "Metrical Grids and Generalized Tier Projection",
    author = "Hao, Yiding",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.56",
    pages = "466--476",
}
@inproceedings{breiss-wilson-2020-extending,
    title = "Extending adaptor grammars to learn phonological alternations",
    author = "Breiss, Canaan  and
      Wilson, Colin",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.57",
    pages = "477--480",
}
@inproceedings{king-etal-2020-interpreting,
    title = "Interpreting Sequence-to-Sequence Models for {R}ussian Inflectional Morphology",
    author = "King, David  and
      Sims, Andrea  and
      Elsner, Micha",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.58",
    pages = "481--490",
}
@inproceedings{meinhardt-etal-2020-questioning,
    title = "Questioning to Resolve Transduction Problems",
    author = "Meinhardt, Eric  and
      Mai, Anna  and
      Bakovi{\'c}, Eric  and
      McCollum, Adam",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.59",
    pages = "491--492",
}
@inproceedings{ermolaeva-2020-automatic,
    title = "Automatic Induction of {M}inimalist {G}rammars",
    author = "Ermolaeva, Marina",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.60",
    pages = "493--496",
}
@inproceedings{burness-mcmullin-2020-modelling,
    title = "Modelling non-local maps as Strictly Piecewise Functions",
    author = "Burness, Phillip  and
      McMullin, Kevin",
    booktitle = "Proceedings of the Society for Computation in Linguistics 2020",
    month = jan,
    year = "2020",
    address = "New York, New York",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scil-1.61",
    pages = "497--499",
}
@proceedings{scai-2020-international,
    title = "Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)",
    editor = "Dalton, Jeff  and
      Chuklin, Aleksandr  and
      Kiseleva, Julia  and
      Burtsev, Mikhail",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scai-1.0",
}
@inproceedings{penha-hauff-2020-slice,
    title = "Slice-Aware Neural Ranking",
    author = "Penha, Gustavo  and
      Hauff, Claudia",
    booktitle = "Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scai-1.1",
    doi = "10.18653/v1/2020.scai-1.1",
    pages = "1--6",
    abstract = "Understanding when and why neural ranking models fail for an IR task via error analysis is an important part of the research cycle. Here we focus on the challenges of (i) identifying categories of difficult instances (a pair of question and response candidates) for which a neural ranker is ineffective and (ii) improving neural ranking for such instances. To address both challenges we resort to slice-based learning for which the goal is to improve effectiveness of neural models for slices (subsets) of data. We address challenge (i) by proposing different slicing functions (SFs) that select slices of the dataset{---}based on prior work we heuristically capture different failures of neural rankers. Then, for challenge (ii) we adapt a neural ranking model to learn slice-aware representations, i.e. the adapted model learns to represent the question and responses differently based on the model{'}s prediction of which slices they belong to. Our experimental results (the source code and data are available at https://github.com/Guzpenha/slice{\_}based{\_}learning) across three different ranking tasks and four corpora show that slice-based learning improves the effectiveness by an average of 2{\%} over a neural ranker that is not slice-aware.",
}
@inproceedings{vakulenko-etal-2020-wrong,
    title = "A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering",
    author = "Vakulenko, Svitlana  and
      Longpre, Shayne  and
      Tu, Zhucheng  and
      Anantha, Raviteja",
    booktitle = "Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scai-1.2",
    doi = "10.18653/v1/2020.scai-1.2",
    pages = "7--16",
    abstract = "The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answer selection approaches. We introduce a simple framework that enables an automated analysis of the conversational question answering (QA) performance using question rewrites, and present the results of this analysis on the TREC CAsT and QuAC (CANARD) datasets. Our experiments uncover sensitivity to question formulation of the popular state-of-the-art question answering approaches. Our results demonstrate that the reading comprehension model is insensitive to question formulation, while the passage ranking changes dramatically with a little variation in the input question. The benefit of QR is that it allows us to pinpoint and group such cases automatically. We show how to use this methodology to verify whether QA models are really learning the task or just finding shortcuts in the dataset, and better understand the frequent types of error they make.",
}
@inproceedings{kongyoung-etal-2020-multi,
    title = "Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering",
    author = "Kongyoung, Sarawoot  and
      Macdonald, Craig  and
      Ounis, Iadh",
    booktitle = "Proceedings of the 5th International Workshop on Search-Oriented Conversational AI (SCAI)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.scai-1.3",
    doi = "10.18653/v1/2020.scai-1.3",
    pages = "17--26",
    abstract = "Conversational Question Answering (ConvQA) is a Conversational Search task in a simplified setting, where an answer must be extracted from a given passage. Neural language models, such as BERT, fine-tuned on large-scale ConvQA datasets such as CoQA and QuAC have been used to address this task. Recently, Multi-Task Learning (MTL) has emerged as a particularly interesting approach for developing ConvQA models, where the objective is to enhance the performance of a primary task by sharing the learned structure across several related auxiliary tasks. However, existing ConvQA models that leverage MTL have not investigated the dynamic adjustment of the relative importance of the different tasks during learning, nor the resulting impact on the performance of the learned models. In this paper, we first study the effectiveness and efficiency of dynamic MTL methods including Evolving Weighting, Uncertainty Weighting, and Loss-Balanced Task Weighting, compared to static MTL methods such as the uniform weighting of tasks. Furthermore, we propose a novel hybrid dynamic method combining Abridged Linear for the main task with a Loss-Balanced Task Weighting (LBTW) for the auxiliary tasks, so as to automatically fine-tune task weighting during learning, ensuring that each of the task{'}s weights is adjusted by the relative importance of the different tasks. We conduct experiments using QuAC, a large-scale ConvQA dataset. Our results demonstrate the effectiveness of our proposed method, which significantly outperforms both the single-task learning and static task weighting methods with improvements ranging from +2.72{\%} to +3.20{\%} in F1 scores. Finally, our findings show that the performance of using MTL in developing ConvQA model is sensitive to the correct selection of the auxiliary tasks as well as to an adequate balancing of the loss rates of these tasks during training by using LBTW.",
}
@proceedings{rocling-2020-linguistics,
    title = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    editor = "Wang, Jenq-Haur  and
      Lai, Ying-Hui",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.0",
}
@inproceedings{lin-etal-2020-analyzing,
    title = "Analyzing the Morphological Structures in Seediq Words",
    author = "Lin, Chuan-Jie  and
      Sung, Li-May  and
      You, Jing-Sheng  and
      Wang, Wei  and
      Lee, Cheng-Hsun  and
      Liao, Zih-Cyuan",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.1",
    pages = "1--3",
}
@inproceedings{lu-lee-2020-gated,
    title = "Gated Graph Sequence Neural Networks for {C}hinese Healthcare Named Entity Recognition",
    author = "Lu, Yi  and
      Lee, Lung-Hao",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.2",
    pages = "4--5",
}
@inproceedings{chen-etal-2020-improving-phrase,
    title = "Improving Phrase Translation Based on Sentence Alignment of {C}hinese-{E}nglish Parallel Corpus",
    author = "Chen, Yi-Jyun  and
      Yang, Ching-Yu Helen  and
      Chang, Jason S.",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.3",
    pages = "6--7",
}
@inproceedings{liao-hsieh-2020-mitigating,
    title = "Mitigating Impacts of Word Segmentation Errors on Collocation Extraction in {C}hinese",
    author = "Liao, Yongfu  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.4",
    pages = "8--20",
}
@inproceedings{yang-etal-2020-japanese,
    title = "{J}apanese Word Readability Assessment using Word Embeddings",
    author = "Yang, Cheng-Zen  and
      Lin, Shu-Chang  and
      Hsueh, Yun-Ju Hsueh  and
      Cheng, Ching-Chieh  and
      Wang, Yih-Chen  and
      Hwang, I-Shyan",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.5",
    pages = "21--34",
}
@inproceedings{huang-chang-2020-hierarchical,
    title = "A Hierarchical Decomposable Attention Model for News Stance Detection",
    author = "Huang, Chen-Yu  and
      Chang, Chia-Hui",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.6",
    pages = "35--49",
}
@inproceedings{chou-etal-2020-combining,
    title = "Combining Dependency Parser and {GNN} models for Text Classification",
    author = "Chou, Kuan-Hsun  and
      Wu, Yen-Tsang  and
      Wang, Jenq-Haur",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.7",
    pages = "50--58",
}
@inproceedings{lin-chen-2020-preliminary,
    title = "A Preliminary Study on Using Meta-learning Technique for Information Retrieval",
    author = "Lin, Chong-En  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.8",
    pages = "59--71",
}
@inproceedings{chung-etal-2020-nllp,
    title = "{NLLP} for the Understanding and Prediction of Construction Litigation Based on Multiple {BERT} Model",
    author = "Chung, Wen-Chieh  and
      Chen, Che-Wen  and
      Wang, Jhing-Fa  and
      Tseng, Shih-Pang  and
      Wang, Tzong-Song",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.9",
    pages = "72--86",
}
@inproceedings{wang-chen-2020-real,
    title = "Real-Time Single-Speaker {T}aiwanese-Accented {M}andarin Speech Synthesis System",
    author = "Wang, Yih-Wen  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.10",
    pages = "87--101",
}
@inproceedings{yeh-etal-2020-taiwanese,
    title = "{T}aiwanese Speech Recognition Based on Hybrid Deep Neural Network Architecture",
    author = "Yeh, Yu-Fu  and
      Su, Bo-Hao  and
      Ou, Yang-Yen  and
      Wang, Jhing-Fa  and
      Tsai, An-Chao",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.11",
    pages = "102--113",
}
@inproceedings{zhang-chen-2020-nsysu,
    title = "{NSYSU}+{CHT} Speaker Verification System for Far-Field Speaker Verification Challenge 2020",
    author = "Zhang, Yu-Jia  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.12",
    pages = "114--115",
}
@inproceedings{hsu-etal-2020-preliminary,
    title = "A Preliminary Study on Deep Learning-based {C}hinese Text to {T}aiwanese Speech Synthesis System",
    author = "Hsu, Wen-Han  and
      Tseng, Cheng-Jung  and
      Liao, Yuan-Fu  and
      Wang, Wern-Jun  and
      Pan, Chen-Ming",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.13",
    pages = "116--117",
}
@inproceedings{chang-hung-2020-preliminary,
    title = "The preliminary study of robust speech feature extraction based on maximizing the accuracy of states in deep acoustic models",
    author = "Chang, Li-chia  and
      Hung, Jeih-weih",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.14",
    pages = "118--119",
}
@inproceedings{chao-etal-2020-multi,
    title = "Multi-view Attention-based Speech Enhancement Model for Noise-robust Automatic Speech Recognition",
    author = "Chao, Fu-An  and
      Hung, Jeih-weih  and
      Chen, Berlin",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.15",
    pages = "120--135",
}
@inproceedings{yu-chen-2020-preliminary,
    title = "A Preliminary Study on Leveraging Meta Learning Technique for Code-switching Speech Recognition",
    author = "Yu, Fu-Hao  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.16",
    pages = "136--147",
}
@inproceedings{chiu-chen-2020-innovative,
    title = "Innovative Pretrained-based Reranking Language Models for N-best Speech Recognition Lists",
    author = "Chiu, Shih-Hsuan  and
      Chen, Berlin",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.17",
    pages = "148--162",
}
@inproceedings{shih-etal-2020-lectal,
    title = "Lectal Variation of the Two {C}hinese Causative Auxiliaries",
    author = "Shih, Cing-Fang  and
      Ku, Mao-Chang  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.18",
    pages = "163--177",
}
@inproceedings{lin-chung-2020-semantic,
    title = "The Semantic Features and Cognitive Concepts of Mang2 {`}Busy{'}: A Corpus-Based Study",
    author = "Lin, Hsin-Rou  and
      Chung, Siaw-Fong",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.19",
    pages = "178--192",
}
@inproceedings{chen-hsieh-2020-analysis,
    title = "An Analysis of Multimodal Document Intent in {I}nstagram Posts",
    author = "Chen, Ying-Yu  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.20",
    pages = "193--207",
}
@inproceedings{wang-etal-2020-chinese-math,
    title = "A {C}hinese Math Word Problem Solving System Based on Linguistic Theory and Non-statistical Approach",
    author = "Wang, Wen-jet  and
      Chen, Chia-Jung  and
      Lai, Chien-yu  and
      Lee, Chia-ming  and
      Lin, Hsin-hung",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.21",
    pages = "208--222",
}
@inproceedings{lin-yu-2020-adaptive,
    title = "An Adaptive Method for Building a {C}hinese Dimensional Sentiment Lexicon",
    author = "Lin, Ying-Lung  and
      Yu, Liang-Chih",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.22",
    pages = "223--237",
}
@inproceedings{bhatta-etal-2020-nepali,
    title = "{N}epali Speech Recognition Using {CNN}, {GRU} and {CTC}",
    author = "Bhatta, Bharat  and
      Joshi, Basanta  and
      Maharjhan, Ram Krishna",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.23",
    pages = "238--246",
}
@inproceedings{tseng-etal-2020-study,
    title = "A Study on Contextualized Language Modeling for {FAQ} Retrieval",
    author = "Tseng, Wen-Ting  and
      Hsu, Yung-Chang  and
      Chen, Berlin",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.24",
    pages = "247--259",
}
@inproceedings{zhang-2020-french,
    title = "{F}rench and {R}ussian students{'} production of {M}andarin tones",
    author = "Zhang, Felicia",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.25",
    pages = "260--274",
}
@inproceedings{peng-yu-2020-sentiment,
    title = "Sentiment Analysis for Investment Atmosphere Scoring",
    author = "Peng, Chih-Hsiang  and
      Yu, Liang-Chih",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.26",
    pages = "275--289",
}
@inproceedings{cheng-etal-2020-exploiting,
    title = "Exploiting Text Prompts for the Development of an End-to-End Computer-Assisted Pronunciation Training System",
    author = "Cheng, Yu-Sen  and
      Lo, Tien-Hong  and
      Chen, Berlin",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.27",
    pages = "290--303",
}
@inproceedings{liu-wang-2020-combining,
    title = "Combining Hybrid Attention Networks and {LSTM} for Stock Trend Prediction",
    author = "Liu, Hsin-Wen  and
      Wang, Jenq-Haur",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.28",
    pages = "304--318",
}
@inproceedings{shen-chang-2020-low,
    title = "Low False Alarm Rate {C}hinese Misspelling Detection Model Based on {BERT} Task Model",
    author = "Shen, Jyun-Yi  and
      Chang, Tao-Hsing",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.29",
    pages = "319--330",
}
@inproceedings{shih-etal-2020-analysis,
    title = "The Analysis and Annotation of Propaganda Techniques in {C}hinese News Texts",
    author = "Shih, Meng-Hsien  and
      Duann, Ren-feng  and
      Chung, Siaw-Fong",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.30",
    pages = "331--345",
}
@inproceedings{lin-chen-2020-exploring,
    title = "Exploring Disparate Language Model Combination Strategies for {M}andarin-{E}nglish Code-Switching {ASR}",
    author = "Lin, Wei-Ting  and
      Chen, Berlin",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.31",
    pages = "346--358",
}
@inproceedings{wang-etal-2020-scientific,
    title = "Scientific Writing Evaluation Using Ensemble Multi-channel Neural Networks",
    author = "Wang, Yuh-Shyang  and
      Lee, Lung-Hao  and
      Lin, Bo-Lin  and
      Yu, Liang-Chih",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.32",
    pages = "359--371",
}
@inproceedings{lin-wu-2020-building,
    title = "Building A Multi-Label Detection Model for Question classification of Auction Website",
    author = "Lin, I-Ju  and
      Wu, Jheng-Long",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.33",
    pages = "372--386",
}
@inproceedings{chang-etal-2020-email,
    title = "Email Writing Assistant System",
    author = "Chang, Jason S.  and
      Yang, Ching-Yu  and
      Peng, Guan-Fu",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.34",
    pages = "387--397",
}
@inproceedings{chen-chang-2020-aspect,
    title = "Aspect-Based Sentiment Analysis Based on {BERT}-{DAOA}",
    author = "Chen, Chen-Yu  and
      Chang, Chia-Hui",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.35",
    pages = "398--412",
}
@inproceedings{liu-etal-2020-natural,
    title = "Natural Language Processing for Digital Humanities",
    author = "Liu, Chao-Lin  and
      Hung, Jen-Joe  and
      Chang, Su-bing  and
      Wu, Wan-yi",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.36",
    pages = "413--414",
}
@inproceedings{hung-2020-opportunities,
    title = "The Opportunities and Challenges of Natural Language Processing Technology in the Field of Digital {H}umanities{---}{T}aking the Study of Buddhist Scriptures as an Example",
    author = "Hung, Jen-Joe",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.37",
    pages = "415--417",
}
@inproceedings{chang-2020-taiwan,
    title = "The {T}aiwan Biographical Database ({TBDB}): An Introduction",
    author = "Chang, Su-bing",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.38",
    pages = "418--420",
}
@inproceedings{wu-2020-analyze,
    title = "How to Analyze the Related Materials of Traditional {C}hinese Drama in the Early 20th Century (1900{--}1937) from the Perspective of Digital {H}umanities{---}{F}ocusing on Newspaper Databases, Record Databases, and Script Collections",
    author = "Wu, Wan-yi",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.39",
    pages = "421--422",
}
@inproceedings{liu-2020-optical,
    title = "Optical Character Recognition, Word Segmentation, Sentence Segmentation, and Information Extraction for Historical and Literature Texts in Classical {C}hinese",
    author = "Liu, Chao-Lin",
    booktitle = "Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing ({ROCLING} 2020)",
    month = sep,
    year = "2020",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/2020.rocling-1.40",
    pages = "423--425",
}
@proceedings{restup-2020-resources,
    title = "Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language",
    editor = "Monti, Johanna  and
      Basile, Valerio  and
      Buono, Maria Pia Di  and
      Manna, Raffaele  and
      Pascucci, Antonio  and
      Tonelli, Sara",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.restup-1.0",
    ISBN = "979-10-95546-49-8",
}
@inproceedings{rosso-2020-profiling,
    title = "Profiling Bots, Fake News Spreaders and Haters",
    author = "Rosso, Paolo",
    booktitle = "Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.restup-1.1",
    pages = "1",
    abstract = "Author profiling studies how language is shared by people. Stylometry techniques help in identifying aspects such as gender, age, native language, or even personality. Author profiling is a problem of growing importance, not only in marketing and forensics, but also in cybersecurity. The aim is not only to identify users whose messages are potential threats from a terrorism viewpoint but also those whose messages are a threat from a social exclusion perspective because containing hate speech, cyberbullying etc. Bots often play a key role in spreading hate speech, as well as fake news, with the purpose of polarizing the public opinion with respect to controversial issues like Brexit or the Catalan referendum. For instance, the authors of a recent study about the 1 Oct 2017 Catalan referendum, showed that in a dataset with 3.6 million tweets, about 23.6{\%} of tweets were produced by bots. The target of these bots were pro-independence influencers that were sent negative, emotional and aggressive hateful tweets with hashtags such as {\#}sonunesbesties (i.e. {\#}theyareanimals). Since 2013 at the PAN Lab at CLEF (https://pan.webis.de/) we have addressed several aspects of author profiling in social media. In 2019 we investigated the feasibility of distinguishing whether the author of a Twitter feed is a bot, while this year we are addressing the problem of profiling those authors that are more likely to spread fake news in Twitter because they did in the past. We aim at identifying possible fake news spreaders as a first step towards preventing fake news from being propagated among online users (fake news aim to polarize the public opinion and may contain hate speech). In 2021 we specifically aim at addressing the challenging problem of profiling haters in social media in order to monitor abusive language and prevent cases of social exclusion in order to combat, for instance, racism, xenophobia and misogyny. Although we already started addressing the problem of detecting hate speech when targets are immigrants or women at the HatEval shared task in SemEval-2019, and when targets are women also in the Automatic Misogyny Identification tasks at IberEval-2018, Evalita-2018 and Evalita-2020, it was not done from an author profiling perspective. At the end of the keynote, I will present some insights in order to stress the importance of monitoring abusive language in social media, for instance, in foreseeing sexual crimes. In fact, previous studies confirmed that a correlation might lay between the yearly per capita rate of rape and the misogynistic language used in Twitter.",
    language = "English",
    ISBN = "979-10-95546-49-8",
}
@inproceedings{saroj-pal-2020-indian,
    title = "An {I}ndian Language Social Media Collection for Hate and Offensive Speech",
    author = "Saroj, Anita  and
      Pal, Sukomal",
    booktitle = "Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.restup-1.2",
    pages = "2--8",
    abstract = "In social media, people express themselves every day on issues that affect their lives. During the parliamentary elections, people{'}s interaction with the candidates in social media posts reflects a lot of social trends in a charged atmosphere. People{'}s likes and dislikes on leaders, political parties and their stands often become subject of hate and offensive posts. We collected social media posts in Hindi and English from Facebook and Twitter during the run-up to the parliamentary election 2019 of India (PEI data-2019). We created a dataset for sentiment analysis into three categories: hate speech, offensive and not hate, or not offensive. We report here the initial results of sentiment classification for the dataset using different classifiers.",
    language = "English",
    ISBN = "979-10-95546-49-8",
}
@inproceedings{fersini-etal-2020-profiling,
    title = "Profiling {I}talian Misogynist: An Empirical Study",
    author = "Fersini, Elisabetta  and
      Nozza, Debora  and
      Boifava, Giulia",
    booktitle = "Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.restup-1.3",
    pages = "9--13",
    abstract = "Hate speech may take different forms in online social environments. In this paper, we address the problem of automatic detection of misogynous language on Italian tweets by focusing both on raw text and stylometric profiles. The proposed exploratory investigation about the adoption of stylometry for enhancing the recognition capabilities of machine learning models has demonstrated that profiling users can lead to good discrimination of misogynous and not misogynous contents.",
    language = "English",
    ISBN = "979-10-95546-49-8",
}
@inproceedings{van-rosendaal-etal-2020-lower,
    title = "Lower Bias, Higher Density Abusive Language Datasets: A Recipe",
    author = "van Rosendaal, Juliet  and
      Caselli, Tommaso  and
      Nissim, Malvina",
    booktitle = "Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.restup-1.4",
    pages = "14--19",
    abstract = "Datasets to train models for abusive language detection are at the same time necessary and still scarce. One the reasons for their limited availability is the cost of their creation. It is not only that manual annotation is expensive, it is also the case that the phenomenon is sparse, causing human annotators having to go through a large number of irrelevant examples in order to obtain some significant data. Strategies used until now to increase density of abusive language and obtain more meaningful data overall, include data filtering on the basis of pre-selected keywords and hate-rich sources of data. We suggest a recipe that at the same time can provide meaningful data with possibly higher density of abusive language and also reduce top-down biases imposed by corpus creators in the selection of the data to annotate. More specifically, we exploit the controversy channel on Reddit to obtain keywords that are used to filter a Twitter dataset. While the method needs further validation and refinement, our preliminary experiments show a higher density of abusive tweets in the filtered vs unfiltered dataset, and a more meaningful topic distribution after filtering.",
    language = "English",
    ISBN = "979-10-95546-49-8",
}
@proceedings{repl4nlp-2020-representation,
    title = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    editor = "Gella, Spandana  and
      Welbl, Johannes  and
      Rei, Marek  and
      Petroni, Fabio  and
      Lewis, Patrick  and
      Strubell, Emma  and
      Seo, Minjoon  and
      Hajishirzi, Hannaneh",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.0",
}
@inproceedings{liu-etal-2020-zero,
    title = "Zero-Resource Cross-Domain Named Entity Recognition",
    author = "Liu, Zihan  and
      Winata, Genta Indra  and
      Fung, Pascale",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.1",
    doi = "10.18653/v1/2020.repl4nlp-1.1",
    pages = "1--6",
    abstract = "Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains. However, collecting data for low-resource target domains is not only expensive but also time-consuming. Hence, we propose a cross-domain NER model that does not use any external resources. We first introduce a Multi-Task Learning (MTL) by adding a new objective function to detect whether tokens are named entities or not. We then introduce a framework called Mixture of Entity Experts (MoEE) to improve the robustness for zero-resource domain adaptation. Finally, experimental results show that our model outperforms strong unsupervised cross-domain sequence labeling models, and the performance of our model is close to that of the state-of-the-art model which leverages extensive resources.",
}
@inproceedings{chang-rafferty-2020-encodings,
    title = "Encodings of Source Syntax: Similarities in {NMT} Representations Across Target Languages",
    author = "Chang, Tyler A.  and
      Rafferty, Anna",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.2",
    doi = "10.18653/v1/2020.repl4nlp-1.2",
    pages = "7--16",
    abstract = "We train neural machine translation (NMT) models from English to six target languages, using NMT encoder representations to predict ancestor constituent labels of source language words. We find that NMT encoders learn similar source syntax regardless of NMT target language, relying on explicit morphosyntactic cues to extract syntactic features from source sentences. Furthermore, the NMT encoders outperform RNNs trained directly on several of the constituent label prediction tasks, suggesting that NMT encoder representations can be used effectively for natural language tasks involving syntax. However, both the NMT encoders and the directly-trained RNNs learn substantially different syntactic information from a probabilistic context-free grammar (PCFG) parser. Despite lower overall accuracy scores, the PCFG often performs well on sentences for which the RNN-based models perform poorly, suggesting that RNN architectures are constrained in the types of syntax they can learn.",
}
@inproceedings{chen-gimpel-2020-learning,
    title = "Learning Probabilistic Sentence Representations from Paraphrases",
    author = "Chen, Mingda  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.3",
    doi = "10.18653/v1/2020.repl4nlp-1.3",
    pages = "17--23",
    abstract = "Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation for sentences. In this paper we define probabilistic models that produce distributions for sentences. Our best-performing model treats each word as a linear transformation operator applied to a multivariate Gaussian distribution. We train our models on paraphrases and demonstrate that they naturally capture sentence specificity. While our proposed model achieves the best performance overall, we also show that specificity is represented by simpler architectures via the norm of the sentence vectors. Qualitative analysis shows that our probabilistic model captures sentential entailment and provides ways to analyze the specificity and preciseness of individual words.",
}
@inproceedings{bhat-etal-2020-word,
    title = "Word Embeddings as Tuples of Feature Probabilities",
    author = "Bhat, Siddharth  and
      Debnath, Alok  and
      Banerjee, Souvik  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.4",
    doi = "10.18653/v1/2020.repl4nlp-1.4",
    pages = "24--33",
    abstract = "In this paper, we provide an alternate perspective on word representations, by reinterpreting the dimensions of the vector space of a word embedding as a collection of features. In this reinterpretation, every component of the word vector is normalized against all the word vectors in the vocabulary. This idea now allows us to view each vector as an $n$-tuple (akin to a fuzzy set), where $n$ is the dimensionality of the word representation and each element represents the probability of the word possessing a feature. Indeed, this representation enables the use fuzzy set theoretic operations, such as union, intersection and difference. Unlike previous attempts, we show that this representation of words provides a notion of similarity which is inherently asymmetric and hence closer to human similarity judgements. We compare the performance of this representation with various benchmarks, and explore some of the unique properties including function word detection, detection of polysemous words, and some insight into the interpretability provided by set theoretic operations.",
}
@inproceedings{gupta-etal-2020-compositionality,
    title = "Compositionality and Capacity in Emergent Languages",
    author = "Gupta, Abhinav  and
      Resnick, Cinjon  and
      Foerster, Jakob  and
      Dai, Andrew  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.5",
    doi = "10.18653/v1/2020.repl4nlp-1.5",
    pages = "34--38",
    abstract = "Recent works have discussed the extent to which emergent languages can exhibit properties of natural languages particularly learning compositionality. In this paper, we investigate the learning biases that affect the efficacy and compositionality in multi-agent communication in addition to the communicative bandwidth. Our foremost contribution is to explore how the capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.",
}
@inproceedings{jawanpuria-etal-2020-learning,
    title = "Learning Geometric Word Meta-Embeddings",
    author = "Jawanpuria, Pratik  and
      N T V, Satya Dev  and
      Kunchukuttan, Anoop  and
      Mishra, Bamdev",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.6",
    doi = "10.18653/v1/2020.repl4nlp-1.6",
    pages = "39--44",
    abstract = "We propose a geometric framework for learning meta-embeddings of words from different embedding sources. Our framework transforms the embeddings into a common latent space, where, for example, simple averaging or concatenation of different embeddings (of a given word) is more amenable. The proposed latent space arises from two particular geometric transformations - source embedding specific orthogonal rotations and a common Mahalanobis metric scaling. Empirical results on several word similarity and word analogy benchmarks illustrate the efficacy of the proposed framework.",
}
@inproceedings{vulic-etal-2020-improving,
    title = "Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces",
    author = "Vuli{\'c}, Ivan  and
      Korhonen, Anna  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.7",
    doi = "10.18653/v1/2020.repl4nlp-1.7",
    pages = "45--54",
    abstract = "Work on projection-based induction of cross-lingual word embedding spaces (CLWEs) predominantly focuses on the improvement of the projection (i.e., mapping) mechanisms. In this work, in contrast, we show that a simple method for post-processing monolingual embedding spaces facilitates learning of the cross-lingual alignment and, in turn, substantially improves bilingual lexicon induction (BLI). The post-processing method we examine is grounded in the generalisation of first- and second-order monolingual similarities to the nth-order similarity. By post-processing monolingual spaces before the cross-lingual alignment, the method can be coupled with any projection-based method for inducing CLWE spaces. We demonstrate the effectiveness of this simple monolingual post-processing across a set of 15 typologically diverse languages (i.e., 15*14 BLI setups), and in combination with two different projection methods.",
}
@inproceedings{pereira-etal-2020-adversarial,
    title = "Adversarial Training for Commonsense Inference",
    author = "Pereira, Lis  and
      Liu, Xiaodong  and
      Cheng, Fei  and
      Asahara, Masayuki  and
      Kobayashi, Ichiro",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.8",
    doi = "10.18653/v1/2020.repl4nlp-1.8",
    pages = "55--60",
    abstract = "We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model. We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model boosts the fine-tuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference.",
}
@inproceedings{volpi-malago-2020-evaluating,
    title = "Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks",
    author = "Volpi, Riccardo  and
      Malag{\`o}, Luigi",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.9",
    doi = "10.18653/v1/2020.repl4nlp-1.9",
    pages = "61--71",
    abstract = "Skip-Gram is a simple, but effective, model to learn a word embedding mapping by estimating a conditional probability distribution for each word of the dictionary. In the context of Information Geometry, these distributions form a Riemannian statistical manifold, where word embeddings are interpreted as vectors in the tangent bundle of the manifold. In this paper we show how the choice of the geometry on the manifold allows impacts on the performances both on intrinsic and extrinsic tasks, in function of a deformation parameter alpha.",
}
@inproceedings{adhikari-etal-2020-exploring,
    title = "Exploring the Limits of Simple Learners in Knowledge Distillation for Document Classification with {D}oc{BERT}",
    author = "Adhikari, Ashutosh  and
      Ram, Achyudh  and
      Tang, Raphael  and
      Hamilton, William L.  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.10",
    doi = "10.18653/v1/2020.repl4nlp-1.10",
    pages = "72--77",
    abstract = "Fine-tuned variants of BERT are able to achieve state-of-the-art accuracy on many natural language processing tasks, although at significant computational costs. In this paper, we verify BERT{'}s effectiveness for document classification and investigate the extent to which BERT-level effectiveness can be obtained by different baselines, combined with knowledge distillation{---}a popular model compression method. The results show that BERT-level effectiveness can be achieved by a single-layer LSTM with at least $40\times$ fewer FLOPS and only ${\sim}3\%$ parameters. More importantly, this study analyzes the limits of knowledge distillation as we distill BERT{'}s knowledge all the way down to linear models{---}a relevant baseline for the task. We report substantial improvement in effectiveness for even the simplest models, as they capture the knowledge learnt by BERT.",
}
@inproceedings{cengiz-yuret-2020-joint,
    title = "Joint Training with Semantic Role Labeling for Better Generalization in Natural Language Inference",
    author = "Cengiz, Cemil  and
      Yuret, Deniz",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.11",
    doi = "10.18653/v1/2020.repl4nlp-1.11",
    pages = "78--88",
    abstract = "End-to-end models trained on natural language inference (NLI) datasets show low generalization on out-of-distribution evaluation sets. The models tend to learn shallow heuristics due to dataset biases. The performance decreases dramatically on diagnostic sets measuring compositionality or robustness against simple heuristics. Existing solutions for this problem employ dataset augmentation which has the drawbacks of being applicable to only a limited set of adversaries and at worst hurting the model performance on other adversaries not included in the augmentation set. Instead, our proposed solution is to improve sentence understanding (hence out-of-distribution generalization) with joint learning of explicit semantics. We show that a BERT based model trained jointly on English semantic role labeling (SRL) and NLI achieves significantly higher performance on external evaluation sets measuring generalization performance.",
}
@inproceedings{coria-etal-2020-metric,
    title = "A Metric Learning Approach to Misogyny Categorization",
    author = "Coria, Juan Manuel  and
      Ghannay, Sahar  and
      Rosset, Sophie  and
      Bredin, Herv{\'e}",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.12",
    doi = "10.18653/v1/2020.repl4nlp-1.12",
    pages = "89--94",
    abstract = "The task of automatic misogyny identification and categorization has not received as much attention as other natural language tasks have, even though it is crucial for identifying hate speech in social Internet interactions. In this work, we address this sentence classification task from a representation learning perspective, using both a bidirectional LSTM and BERT optimized with the following metric learning loss functions: contrastive loss, triplet loss, center loss, congenerous cosine loss and additive angular margin loss. We set new state-of-the-art for the task with our fine-tuned BERT, whose sentence embeddings can be compared with a simple cosine distance, and we release all our code as open source for easy reproducibility. Moreover, we find that almost every loss function performs equally well in this setting, matching the regular cross entropy loss.",
}
@inproceedings{lange-etal-2020-choice,
    title = "On the Choice of Auxiliary Languages for Improved Sequence Tagging",
    author = {Lange, Lukas  and
      Adel, Heike  and
      Str{\"o}tgen, Jannik},
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.13",
    doi = "10.18653/v1/2020.repl4nlp-1.13",
    pages = "95--102",
    abstract = "Recent work showed that embeddings from related languages can improve the performance of sequence tagging, even for monolingual models. In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language distances and show that the most related language is not always the best auxiliary language. Further, we show that attention-based meta-embeddings can effectively combine pre-trained embeddings from different languages for sequence tagging and set new state-of-the-art results for part-of-speech tagging in five languages.",
}
@inproceedings{lange-etal-2020-adversarial,
    title = "Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text",
    author = {Lange, Lukas  and
      Iurshina, Anastasiia  and
      Adel, Heike  and
      Str{\"o}tgen, Jannik},
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.14",
    doi = "10.18653/v1/2020.repl4nlp-1.14",
    pages = "103--109",
    abstract = "Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers. However, all of them focus on monolingual settings. In this paper, we explore multilingual methods for the extraction of temporal expressions from text and investigate adversarial training for aligning embedding spaces to one common space. With this, we create a single multilingual model that can also be transferred to unseen languages and set the new state of the art in those cross-lingual transfer experiments.",
}
@inproceedings{miaschi-dellorletta-2020-contextual,
    title = "Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation",
    author = "Miaschi, Alessio  and
      Dell{'}Orletta, Felice",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.15",
    doi = "10.18653/v1/2020.repl4nlp-1.15",
    pages = "110--119",
    abstract = "In this paper we present a comparison between the linguistic knowledge encoded in the internal representations of a contextual Language Model (BERT) and a contextual-independent one (Word2vec). We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that, although BERT is capable of understanding the full context of each word in an input sequence, the implicit knowledge encoded in its aggregated sentence representations is still comparable to that of a contextual-independent model. We also find that BERT is able to encode sentence-level properties even within single-word embeddings, obtaining comparable or even superior results than those obtained with sentence representations.",
}
@inproceedings{wu-dredze-2020-languages,
    title = "Are All Languages Created Equal in Multilingual {BERT}?",
    author = "Wu, Shijie  and
      Dredze, Mark",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.16",
    doi = "10.18653/v1/2020.repl4nlp-1.16",
    pages = "120--130",
    abstract = "Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals. However, these evaluations have focused on cross-lingual transfer with high-resource languages, covering only a third of the languages covered by mBERT. We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance. We consider three tasks: Named Entity Recognition (99 languages), Part-of-speech Tagging and Dependency Parsing (54 languages each). mBERT does better than or comparable to baselines on high resource languages but does much worse for low resource languages. Furthermore, monolingual BERT models for these languages do even worse. Paired with similar languages, the performance gap between monolingual BERT and mBERT can be narrowed. We find that better models for low resource languages require more efficient pretraining techniques or more data.",
}
@inproceedings{tutek-snajder-2020-staying,
    title = "Staying True to Your Word: (How) Can Attention Become Explanation?",
    author = "Tutek, Martin  and
      Snajder, Jan",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.17",
    doi = "10.18653/v1/2020.repl4nlp-1.17",
    pages = "131--142",
    abstract = "The attention mechanism has quickly become ubiquitous in NLP. In addition to improving performance of models, attention has been widely used as a glimpse into the inner workings of NLP models. The latter aspect has in the recent years become a common topic of discussion, most notably in recent work of Jain and Wallace; Wiegreffe and Pinter. With the shortcomings of using attention weights as a tool of transparency revealed, the attention mechanism has been stuck in a limbo without concrete proof when and whether it can be used as an explanation. In this paper, we provide an explanation as to why attention has seen rightful critique when used with recurrent networks in sequence classification tasks. We propose a remedy to these issues in the form of a word level objective and our findings give credibility for attention to provide faithful interpretations of recurrent models.",
}
@inproceedings{gordon-etal-2020-compressing,
    title = "Compressing {BERT}: Studying the Effects of Weight Pruning on Transfer Learning",
    author = "Gordon, Mitchell  and
      Duh, Kevin  and
      Andrews, Nicholas",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.18",
    doi = "10.18653/v1/2020.repl4nlp-1.18",
    pages = "143--155",
    abstract = "Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40{\%}) do not affect pre-training loss or transfer to downstream tasks at all. Medium levels of pruning increase the pre-training loss and prevent useful pre-training information from being transferred to downstream tasks. High levels of pruning additionally prevent models from fitting downstream datasets, leading to further degradation. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.",
}
@inproceedings{raunak-etal-2020-dimensional,
    title = "On Dimensional Linguistic Properties of the Word Embedding Space",
    author = "Raunak, Vikas  and
      Kumar, Vaibhav  and
      Gupta, Vivek  and
      Metze, Florian",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.19",
    doi = "10.18653/v1/2020.repl4nlp-1.19",
    pages = "156--165",
    abstract = "Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties. In this work, we analyze word embeddings in terms of their principal components and arrive at a number of novel and counterintuitive observations. In particular, we characterize the utility of variance explained by the principal components as a proxy for downstream performance. Furthermore, through syntactic probing of the principal embedding space, we show that the syntactic information captured by a principal component does not correlate with the amount of variance it explains. Consequently, we investigate the limitations of variance based embedding post-processing algorithms and demonstrate that such post-processing is counter-productive in sentence classification and machine translation tasks. Finally, we offer a few precautionary guidelines on applying variance based embedding post-processing and explain why non-isotropic geometry might be integral to word embedding performance.",
}
@inproceedings{toshniwal-etal-2020-cross,
    title = "A Cross-Task Analysis of Text Span Representations",
    author = "Toshniwal, Shubham  and
      Shi, Haoyue  and
      Shi, Bowen  and
      Gao, Lingyu  and
      Livescu, Karen  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.20",
    doi = "10.18653/v1/2020.repl4nlp-1.20",
    pages = "166--176",
    abstract = "Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution. While extensive research has focused on functional architectures for representing words and sentences, there is less work on representing arbitrary spans of text within sentences. In this paper, we conduct a comprehensive empirical evaluation of six span representation methods using eight pretrained language representation models across six tasks, including two tasks that we introduce. We find that, although some simple span representations are fairly reliable across tasks, in general the optimal span representation varies by task, and can also vary within different facets of individual tasks. We also find that the choice of span representation has a bigger impact with a fixed pretrained encoder than with a fine-tuned encoder.",
}
@inproceedings{zhang-etal-2020-enhancing-transformer,
    title = "Enhancing Transformer with Sememe Knowledge",
    author = "Zhang, Yuhui  and
      Yang, Chenghao  and
      Zhou, Zhengping  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.21",
    doi = "10.18653/v1/2020.repl4nlp-1.21",
    pages = "177--184",
    abstract = "While large-scale pretraining has achieved great success in many NLP tasks, it has not been fully studied whether external linguistic knowledge can improve data-driven models. In this work, we introduce sememe knowledge into Transformer and propose three sememe-enhanced Transformer models. Sememes, by linguistic definition, are the minimum semantic units of language, which can well represent implicit semantic meanings behind words. Our experiments demonstrate that introducing sememe knowledge into Transformer can consistently improve language modeling and downstream tasks. The adversarial test further demonstrates that sememe knowledge can substantially improve model robustness.",
}
@inproceedings{bhathena-etal-2020-evaluating,
    title = "Evaluating Compositionality of Sentence Representation Models",
    author = "Bhathena, Hanoz  and
      Willis, Angelica  and
      Dass, Nathan",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.22",
    doi = "10.18653/v1/2020.repl4nlp-1.22",
    pages = "185--193",
    abstract = "We evaluate the compositionality of general-purpose sentence encoders by proposing two different metrics to quantify compositional understanding capability of sentence encoders. We introduce a novel metric, Polarity Sensitivity Scoring (PSS), which utilizes sentiment perturbations as a proxy for measuring compositionality. We then compare results from PSS with those obtained via our proposed extension of a metric called Tree Reconstruction Error (TRE) (CITATION) where compositionality is evaluated by measuring how well a true representation producing model can be approximated by a model that explicitly combines representations of its primitives.",
}
@inproceedings{bhargava-penn-2020-supertagging,
    title = "Supertagging with {CCG} primitives",
    author = "Bhargava, Aditya  and
      Penn, Gerald",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.23",
    doi = "10.18653/v1/2020.repl4nlp-1.23",
    pages = "194--204",
    abstract = "In CCG and other highly lexicalized grammars, supertagging a sentence{'}s words with their lexical categories is a critical step for efficient parsing. Because of the high degree of lexicalization in these grammars, the lexical categories can be very complex. Existing approaches to supervised CCG supertagging treat the categories as atomic units, even when the categories are not simple; when they encounter words with categories unseen during training, their guesses are accordingly unsophisticated. In this paper, we make use of the primitives and operators that constitute the lexical categories of categorial grammars. Instead of opaque labels, we treat lexical categories themselves as linear sequences. We present an LSTM-based model that replaces standard word-level classification with prediction of a sequence of primitives, similarly to LSTM decoders. Our model obtains state-of-the-art word accuracy for single-task English CCG supertagging, increases parser coverage and F1, and is able to produce novel categories. Analysis shows a synergistic effect between this decomposed view and incorporation of prediction history.",
}
@inproceedings{balasubramanian-etal-2020-whats,
    title = "What{'}s in a Name? Are {BERT} Named Entity Representations just as Good for any other Name?",
    author = "Balasubramanian, Sriram  and
      Jain, Naman  and
      Jindal, Gaurav  and
      Awasthi, Abhijeet  and
      Sarawagi, Sunita",
    booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.repl4nlp-1.24",
    doi = "10.18653/v1/2020.repl4nlp-1.24",
    pages = "205--214",
    abstract = "We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input. We highlight that on several tasks while such perturbations are natural, state of the art trained models are surprisingly brittle. The brittleness continues even with the recent entity-aware BERT models. We also try to discern the cause of this non-robustness, considering factors such as tokenization and frequency of occurrence. Then we provide a simple method that ensembles predictions from multiple replacements while jointly modeling the uncertainty of type annotations and label predictions. Experiments on three NLP tasks shows that our method enhances robustness and increases accuracy on both natural and adversarial datasets.",
}
@proceedings{readi-2020-tools,
    title = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    editor = "Gala, N{\'u}ria  and
      Wilkens, Rodrigo",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.0",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{fridriksdottir-ingason-2020-disambiguating,
    title = "Disambiguating Confusion Sets as an Aid for Dyslexic Spelling",
    author = "Fri{\dh}riksd{\'o}ttir, Steinunn Rut  and
      Ingason, Anton Karl",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.1",
    pages = "1--5",
    abstract = "Spell checkers and other proofreading software are crucial tools for people with dyslexia and other reading disabilities. Most spell checkers automatically detect spelling mistakes by looking up individual words and seeing if they exist in the vocabulary. However, one of the biggest challenges of automatic spelling correction is how to deal with real-word errors, i.e. spelling mistakes which lead to a real but unintended word, such as when then is written in place of than. These errors account for 20{\%} of all spelling mistakes made by people with dyslexia. As both words exist in the vocabulary, a simple dictionary lookup will not detect the mistake. The only way to disambiguate which word was actually intended is to look at the context in which the word appears. This problem is particularly apparent in languages with rich morphology where there is often minimal orthographic difference between grammatical items. In this paper, we present our novel confusion set corpus for Icelandic and discuss how it could be used for context-sensitive spelling correction. We have collected word pairs from seven different categories, chosen for their homophonous properties, along with sentence examples and frequency information from said pairs. We present a small-scale machine learning experiment using a decision tree binary classification which results range from 73{\%} to 86{\%} average accuracy with 10-fold cross validation. While not intended as a finalized result, the method shows potential and will be improved in future research.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{rennes-2020-simpler,
    title = "Is it simpler? An Evaluation of an Aligned Corpus of Standard-Simple Sentences",
    author = "Rennes, Evelina",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.2",
    pages = "6--13",
    abstract = "Parallel monolingual resources are imperative for data-driven sentence simplification research. We present the work of aligning, at the sentence level, a corpus of all Swedish public authorities and municipalities web texts in standard and simple Swedish. We compare the performance of three alignment algorithms used for similar work in English (Average Alignment, Maximum Alignment, and Hungarian Alignment), and the best-performing algorithm is used to create a resource of 15,433 unique sentence pairs. We evaluate the resulting corpus using a set of features that has proven to predict text complexity of Swedish texts. The results show that the sentences of the simple sub-corpus are indeed less complex than the sentences of the standard part of the corpus, according to many of the text complexity measures.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{gooding-etal-2020-incorporating,
    title = "Incorporating Multiword Expressions in Phrase Complexity Estimation",
    author = "Gooding, Sian  and
      Taslimipoor, Shiva  and
      Kochmar, Ekaterina",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.3",
    pages = "14--19",
    abstract = "Multiword expressions (MWEs) were shown to be useful in a number of NLP tasks. However, research on the use of MWEs in lexical complexity assessment and simplification is still an under-explored area. In this paper, we propose a text complexity assessment system for English, which incorporates MWE identification. We show that detecting MWEs using state-of-the-art systems improves predicting complexity on an established lexical complexity dataset.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{mich-etal-2020-automatically,
    title = "Automatically Assess Children{'}s Reading Skills",
    author = "Mich, Ornella  and
      Mana, Nadia  and
      Gretter, Roberto  and
      Matassoni, Marco  and
      Falavigna, Daniele",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.4",
    pages = "20--26",
    abstract = "Assessing reading skills is an important task teachers have to perform at the beginning of a new scholastic year to evaluate the starting level of the class and properly plan next learning activities. Digital tools based on automatic speech recognition (ASR) may be really useful to support teachers in this task, currently very time consuming and prone to human errors. This paper presents a web application for automatically assessing fluency and accuracy of oral reading in children attending Italian primary and lower secondary schools. Our system, based on ASR technology, implements the Cornoldi{'}s MT battery, which is a well-known Italian test to assess reading skills. The front-end of the system has been designed following the participatory design approach by involving end users from the beginning of the creation process. Teachers may use our system to both test student{'}s reading skills and monitor their performance over time. In fact, the system offers an effective graphical visualization of the assessment results for both individual students and entire class. The paper also presents the results of a pilot study to evaluate the system usability with teachers.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{sauvan-etal-2020-text,
    title = "Text Simplification to Help Individuals with Low Vision Read More Fluently",
    author = "Sauvan, Lauren  and
      Stolowy, Natacha  and
      Aguilar, Carlos  and
      Fran{\c{c}}ois, Thomas  and
      Gala, N{\'u}ria  and
      Matonti, Fr{\'e}d{\'e}ric  and
      Castet, Eric  and
      Calabr{\`e}se, Aur{\'e}lie",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.5",
    pages = "27--32",
    abstract = "The objective of this work is to introduce text simplification as a potential reading aid to help improve the poor reading performance experienced by visually impaired individuals. As a first step, we explore what makes a text especially complex when read with low vision, by assessing the individual effect of three word properties (frequency, orthographic similarity and length) on reading speed in the presence of Central visual Field Loss (CFL). Individuals with bilateral CFL induced by macular diseases read pairs of French sentences displayed with the self-paced reading method. For each sentence pair, sentence n contained a target word matched with a synonym word of the same length included in sentence n+1. Reading time was recorded for each target word. Given the corpus we used, our results show that (1) word frequency has a significant effect on reading time (the more frequent the faster the reading speed) with larger amplitude (in the range of seconds) compared to normal vision; (2) word neighborhood size has a significant effect on reading time (the more neighbors the slower the reading speed), this effect being rather small in amplitude, but interestingly reversed compared to normal vision; (3) word length has no significant effect on reading time. Supporting the development of new and more effective assistive technology to help low vision is an important and timely issue, with massive potential implications for social and rehabilitation practices. The end goal of this project will be to use our findings to custom text simplification to this specific population and use it as an optimal and efficient reading aid.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{goriachun-gala-2020-identifying,
    title = "Identifying Abstract and Concrete Words in {F}rench to Better Address Reading Difficulties",
    author = "Goriachun, Daria  and
      Gala, N{\'u}ria",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.6",
    pages = "33--40",
    abstract = "Literature in psycholinguistics and neurosciences has showed that abstract and concrete concepts are perceived differently by our brain, and that the abstractness of a word can cause difficulties in reading. In order to integrate this parameter into an automatic text simplification (ATS) system for French readers, an annotated list with 7,898 abstract and concrete nouns has been semi-automatically developed. Our aim was to obtain abstract and concrete nouns from an initial manually annotated short list by using two distributional approaches: nearest neighbors and syntactic co-occurrences. The results of this experience have enabled to shed light on the different behaviors of concrete and abstract nouns in context. Besides, the final list, a resource per se in French available on demand, provides a valuable contribution since annotated resources based on cognitive variables such as concreteness or abstractness are scarce and very difficult to obtain. In future work, the list will be enlarged and integrated into an existing lexicon with ranked synonyms for the identification of complex words in text simplification applications.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{sauberli-etal-2020-benchmarking,
    title = "Benchmarking Data-driven Automatic Text Simplification for {G}erman",
    author = {S{\"a}uberli, Andreas  and
      Ebling, Sarah  and
      Volk, Martin},
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.7",
    pages = "41--48",
    abstract = "Automatic text simplification is an active research area, and there are first systems for English, Spanish, Portuguese, and Italian. For German, no data-driven approach exists to this date, due to a lack of training data. In this paper, we present a parallel corpus of news items in German with corresponding simplifications on two complexity levels. The simplifications have been produced according to a well-documented set of guidelines. We then report on experiments in automatically simplifying the German news items using state-of-the-art neural machine translation techniques. We demonstrate that despite our small parallel corpus, our neural models were able to learn essential features of simplified language, such as lexical substitutions, deletion of less relevant words and phrases, and sentence shortening.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{santini-etal-2020-visualizing,
    title = "Visualizing Facets of Text Complexity across Registers",
    author = "Santini, Marina  and
      Jonsson, Arne  and
      Rennes, Evelina",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.8",
    pages = "49--56",
    abstract = "In this paper, we propose visualizing results of a corpus-based study on text complexity using radar charts. We argue that the added value of this type of visualisation is the polygonal shape that provides an intuitive grasp of text complexity similarities across the registers of a corpus. The results that we visualize come from a study where we explored whether it is possible to automatically single out different facets of text complexity across the registers of a Swedish corpus. To this end, we used factor analysis as applied in Biber{'}s Multi-Dimensional Analysis framework. The visualization of text complexity facets with radar charts indicates that there is correspondence between linguistic similarity and similarity of shape across registers.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{shardlow-etal-2020-complex,
    title = "{C}omp{L}ex {---} A New Corpus for Lexical Complexity Prediction from {L}ikert {S}cale Data",
    author = "Shardlow, Matthew  and
      Cooper, Michael  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.9",
    pages = "57--62",
    abstract = "Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such astext simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studieshave approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) fora set of target words in a text. This choice is motivated by the fact that all CWI datasets compiled so far have been annotated using abinary annotation scheme. Our paper addresses this limitation by presenting the first English dataset for continuous lexical complexityprediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl,and biomedical texts. This resulted in a corpus of 9,476 sentences each annotated by around 7 annotators.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{bengoetxea-etal-2020-laguntest,
    title = "{L}agun{T}est: A {NLP} Based Application to Enhance Reading Comprehension",
    author = "Bengoetxea, Kepa  and
      Gonzalez-Dios, Itziar  and
      Aguirregoitia, Amaia",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.10",
    pages = "63--69",
    abstract = "The ability to read and understand written texts plays an important role in education, above all in the last years of primary education. This is especially pertinent in language immersion educational programmes, where some students have low linguistic competence in the languages of instruction. In this context, adapting the texts to the individual needs of each student requires a considerable effort by education professionals. However, language technologies can facilitate the laborious adaptation of materials in order to enhance reading comprehension. In this paper, we present LagunTest, a NLP based application that takes as input a text in Basque or English, and offers synonyms, definitions, examples of the words in different contexts and presents some linguistic characteristics as well as visualizations. LagunTest is based on reusable and open multilingual and multimodal tools, and it is also distributed with an open license. LagunTest is intended to ease the burden of education professionals in the task of adapting materials, and the output should always be supervised by them.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{zilio-etal-2020-lexical,
    title = "A Lexical Simplification Tool for Promoting Health Literacy",
    author = "Zilio, Leonardo  and
      Braga Paraguassu, Liana  and
      Leiva Hercules, Luis Antonio  and
      Ponomarenko, Gabriel  and
      Berwanger, Laura  and
      Bocorny Finatto, Maria Jos{\'e}",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.11",
    pages = "70--76",
    abstract = "This paper presents MedSimples, an authoring tool that combines Natural Language Processing, Corpus Linguistics and Terminology to help writers to convert health-related information into a more accessible version for people with low literacy skills. MedSimples applies parsing methods associated with lexical resources to automatically evaluate a text and present simplification suggestions that are more suitable for the target audience. Using the suggestions provided by the tool, the author can adapt the original text and make it more accessible. The focus of MedSimples lies on texts for special purposes, so that it not only deals with general vocabulary, but also with specialized terms. The tool is currently under development, but an online working prototype exists and can be tested freely. An assessment of MedSimples was carried out aiming at evaluating its current performance with some promising results, especially for informing the future developments that are planned for the tool.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{stodden-kallmeyer-2020-multi,
    title = "A multi-lingual and cross-domain analysis of features for text simplification",
    author = "Stodden, Regina  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.12",
    pages = "77--84",
    abstract = "In text simplification and readability research, several features have been proposed to estimate or simplify a complex text, e.g., readability scores, sentence length, or proportion of POS tags. These features are however mainly developed for English. In this paper, we investigate their relevance for Czech, German, English, Spanish, and Italian text simplification corpora. Our multi-lingual and multi-domain corpus analysis shows that the relevance of different features for text simplification is different per corpora, language, and domain. For example, the relevance of the lexical complexity is different across all languages, the BLEU score across all domains, and 14 features within the web domain corpora. Overall, the negative statistical tests regarding the other features across and within domains and languages lead to the assumption that text simplification models may be transferable between different domains or different languages.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{pintard-francois-2020-combining,
    title = "Combining Expert Knowledge with Frequency Information to Infer {CEFR} Levels for Words",
    author = "Pintard, Alice  and
      Fran{\c{c}}ois, Thomas",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.13",
    pages = "85--92",
    abstract = "Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29{\%} in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@inproceedings{wilkens-etal-2020-coreference,
    title = "Coreference-Based Text Simplification",
    author = "Wilkens, Rodrigo  and
      Oberle, Bruno  and
      Todirascu, Amalia",
    booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.readi-1.14",
    pages = "93--100",
    abstract = "Text simplification aims at adapting documents to make them easier to read by a given audience. Usually, simplification systems consider only lexical and syntactic levels, and, moreover, are often evaluated at the sentence level. Thus, studies on the impact of simplification in text cohesion are lacking. Some works add coreference resolution in their pipeline to address this issue. In this paper, we move forward in this direction and present a rule-based system for automatic text simplification, aiming at adapting French texts for dyslexic children. The architecture of our system takes into account not only lexical and syntactic but also discourse information, based on coreference chains. Our system has been manually evaluated in terms of grammaticality and cohesion. We have also built and used an evaluation corpus containing multiple simplification references for each sentence. It has been annotated by experts following a set of simplification guidelines, and can be used to run automatic evaluation of other simplification systems. Both the system and the evaluation corpus are freely available.",
    language = "English",
    ISBN = "979-10-95546-45-0",
}
@proceedings{rdsm-2020-international,
    title = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    editor = "Aker, Ahmet  and
      Zubiaga, Arkaitz",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.0",
}
@inproceedings{glenski-etal-2020-towards,
    title = "Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages",
    author = "Glenski, Maria  and
      Ayton, Ellyn  and
      Cosbey, Robin  and
      Arendt, Dustin  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.1",
    pages = "1--13",
    abstract = "Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop future models that are generalizable and robust across expected environments a model may encounter in deployment. In this paper, we present a framework for measuring model robustness for an important but difficult text classification task {--} deceptive news detection. We evaluate model robustness to out-of-domain data, modality-specific features, and languages other than English. Our investigation focuses on three type of models: LSTM models trained on multiple datasets (Cross-Domain), several fusion LSTM models trained with images and text and evaluated with three state-of-the-art embeddings, BERT ELMo, and GloVe (Cross-Modality), and character-level CNN models trained on multiple languages (Cross-Language). Our analyses reveal a significant drop in performance when testing neural models on out-of-domain data and non-English languages that may be mitigated using diverse training data. We find that with additional image content as input, ELMo embeddings yield significantly fewer errors compared to BERT or GLoVe. Most importantly, this work not only carefully analyzes deception model robustness but also provides a framework of these analyses that can be applied to new models or extended datasets in the future.",
}
@inproceedings{qiao-etal-2020-language,
    title = "A Language-Based Approach to Fake News Detection Through Interpretable Features and {BRNN}",
    author = "Qiao, Yu  and
      Wiechmann, Daniel  and
      Kerz, Elma",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.2",
    pages = "14--31",
    abstract = "{`}Fake news{'} {--} succinctly defined as false or misleading information masquerading as legitimate news {--} is a ubiquitous phenomenon and its dissemination weakens the fact-based reporting of the established news industry, making it harder for political actors, authorities, media and citizens to obtain a reliable picture. State-of-the art language-based approaches to fake news detection that reach high classification accuracy typically rely on black box models based on word embeddings. At the same time, there are increasing calls for moving away from black-box models towards white-box (explainable) models for critical industries such as healthcare, finances, military and news industry. In this paper we performed a series of experiments where bi-directional recurrent neural network classification models were trained on interpretable features derived from multi-disciplinary integrated approaches to language. We apply our approach to two benchmark datasets. We demonstrate that our approach is promising as it achieves similar results on these two datasets as the best performing black box models reported in the literature. In a second step we report on ablation experiments geared towards assessing the relative importance of the human-interpretable features in distinguishing fake news from real news.",
}
@inproceedings{ermakova-etal-2020-covid,
    title = "Covid or not Covid? Topic Shift in Information Cascades on {T}witter",
    author = "Ermakova, Liana  and
      Nurbakova, Diana  and
      Ovchinnikova, Irina",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.3",
    pages = "32--37",
    abstract = "Social media have become a valuable source of information. However, its power to shape public opinion can be dangerous, especially in the case of misinformation. The existing studies on misinformation detection hypothesise that the initial message is fake. In contrast, we focus on information distortion occurring in cascades as the initial message is quoted or receives a reply. We show a significant topic shift in information cascades on Twitter during the Covid-19 pandemic providing valuable insights for the automatic analysis of information distortion.",
}
@inproceedings{li-scarton-2020-revisiting,
    title = "Revisiting Rumour Stance Classification: Dealing with Imbalanced Data",
    author = "Li, Yue  and
      Scarton, Carolina",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.4",
    pages = "38--44",
    abstract = "Correctly classifying stances of replies can be significantly helpful for the automatic detection and classification of online rumours. One major challenge is that there are considerably more non-relevant replies (comments) than informative ones (supports and denies), making the task highly imbalanced. In this paper we revisit the task of rumour stance classification, aiming to improve the performance over the informative minority classes. We experiment with traditional methods for imbalanced data treatment with feature- and BERT-based classifiers. Our models outperform all systems in RumourEval 2017 shared task and rank second in RumourEval 2019.",
}
@inproceedings{kuzmin-etal-2020-fake,
    title = "Fake news detection for the {R}ussian language",
    author = "Kuzmin, Gleb  and
      Larionov, Daniil  and
      Pisarevskaya, Dina  and
      Smirnov, Ivan",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.5",
    pages = "45--57",
    abstract = "In this paper, we trained and compared different models for fake news detection in Russian. For this task, we used such language features as bag-of-n-grams and bag of Rhetorical Structure Theory features, and BERT embeddings. We also compared the score of our models with the human score on this task and showed that our models deal with fake news detection better. We investigated the nature of fake news by dividing it into two non-overlapping classes: satire and fake news. As a result, we obtained the set of models for fake news detection; the best of these models achieved 0.889 F1-score on the test set for 2 classes and 0.9076 F1-score on 3 classes task.",
}
@inproceedings{vincze-szabo-2020-automatic,
    title = "Automatic Detection of {H}ungarian Clickbait and Entertaining Fake News",
    author = "Vincze, Veronika  and
      Szab{\'o}, Martina Katalin",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.6",
    pages = "58--69",
    abstract = "Online news do not always come from reliable sources and they are not always even realistic. The constantly growing number of online textual data has raised the need for detecting deception and bias in texts from different domains recently. In this paper, we identify different types of unrealistic news (clickbait and fake news written for entertainment purposes) written in Hungarian on the basis of a rich feature set and with the help of machine learning methods. Our tool achieves competitive scores: it is able to classify clickbait, fake news written for entertainment purposes and real news with an accuracy of over 80{\%}. It is also highlighted that morphological features perform the best in this classification task.",
}
@inproceedings{saadany-etal-2020-fake,
    title = "Fake or Real? A Study of {A}rabic Satirical Fake News",
    author = "Saadany, Hadeel  and
      Orasan, Constantin  and
      Mohamed, Emad",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.rdsm-1.7",
    pages = "70--80",
    abstract = "One very common type of fake news is satire which comes in a form of a news website or an online platform that parodies reputable real news agencies to create a sarcastic version of reality. This type of fake news is often disseminated by individuals on their online platforms as it has a much stronger effect in delivering criticism than through a straightforward message. However, when the satirical text is disseminated via social media without mention of its source, it can be mistaken for real news. This study conducts several exploratory analyses to identify the linguistic properties of Arabic fake news with satirical content. It shows that although it parodies real news, Arabic satirical news has distinguishing features on the lexico-grammatical level. We exploit these features to build a number of machine learning models capable of identifying satirical fake news with an accuracy of up to 98.6{\%}. The study introduces a new dataset (3185 articles) scraped from two Arabic satirical news websites ({`}Al-Hudood{'} and {`}Al-Ahram Al-Mexici{'}) which consists of fake news. The real news dataset consists of 3710 articles collected from three official news sites: the {`}BBC-Arabic{'}, the {`}CNN-Arabic{'} and {`}Al-Jazeera news{'}. Both datasets are concerned with political issues related to the Middle East.",
}
@proceedings{rail-2020-resources,
    title = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    editor = "Mabuya, Rooweither  and
      Ramukhadi, Phathutshedzo  and
      Setaka, Mmasibidi  and
      Wagner, Valencia  and
      van Zaanen, Menno",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.0",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{jones-muftic-2020-endangered,
    title = "Endangered {A}frican Languages Featured in a Digital Collection: The Case of the ǂ{K}homani {S}an, {H}ugh {B}rody {C}ollection",
    author = "Jones, Kerry  and
      Muftic, Sanjin",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.1",
    pages = "1--8",
    abstract = "The ǂKhomani San, Hugh Brody Collection features the voices and history of indigenous hunter gatherer descendants in three endangered languages namely, N|uu, Kora and Khoekhoe as well as a regional dialect of Afrikaans. A large component of this collection is audio-visual (legacy media) recordings of interviews conducted with members of the community by Hugh Brody and his colleagues between 1997 and 2012, referring as far back as the 1800s. The Digital Library Services team at the University of Cape Town aim to showcase the collection digitally on the UCT-wide Digital Collections platform, Ibali which runs on Omeka-S. In this paper we highlight the importance of such a collection in the context of South Africa, and the ethical steps that were taken to ensure the respect of the ǂKhomani San as their stories get uploaded onto a repository and become accessible to all. We will also feature some of the completed collection on Ibali and guide the reader through the organisation of the collection on the Omeka-S backend. Finally, we will outline our development process, from digitisation to repository publishing as well as present some of the challenges in data clean-up, the curation of legacy media, multi-lingual support, and site organisation.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{eckart-etal-2020-usability,
    title = "Usability and Accessibility of {B}antu Language Dictionaries in the Digital Age: Mobile Access in an Open Environment",
    author = {Eckart, Thomas  and
      Bosch, Sonja  and
      Quasthoff, Uwe  and
      K{\"o}rner, Erik  and
      Goldhahn, Dirk  and
      Kaleschke, Simon},
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.2",
    pages = "9--14",
    abstract = "This contribution describes a free and open mobile dictionary app based on open dictionary data. A specific focus is on usability and user-adequate presentation of data. This includes, in addition to the alphabetical lemma ordering, other vocabulary selection, grouping, and access criteria. Beyond search functionality for stems or roots {--} required due to the morphological complexity of Bantu languages {--} grouping of lemmas by subject area of varying difficulty allows customization. A dictionary profile defines available presentation options of the dictionary data in the app and can be specified according to the needs of the respective user group. Word embeddings and similar approaches are used to link to semantically similar or related words. The underlying data structure is open for monolingual, bilingual or multilingual dictionaries and also supports the connection to complex external resources like Wordnets. The application in its current state focuses on Xhosa and Zulu dictionary data but more resources will be integrated soon.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{marivate-etal-2020-investigating,
    title = "Investigating an Approach for Low Resource Language Dataset Creation, Curation and Classification: Setswana and Sepedi",
    author = "Marivate, Vukosi  and
      Sefara, Tshephisho  and
      Modupe, Abiodun",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.3",
    pages = "15--20",
    abstract = "The recent advances in Natural Language Processing have only been a boon for well represented languages, negating research in lesser known global languages. This is in part due to the availability of curated data and research resources. One of the current challenges concerning low-resourced languages are clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creating two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and the creation of a news topic classification task from these datasets. In this study, we document our work, propose baselines for classification, and investigate an approach on data augmentation better suited to low-resourced languages in order to improve the performance of the classifiers.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{malema-etal-2020-complex,
    title = "Complex Setswana Parts of Speech Tagging",
    author = "Malema, Gabofetswe  and
      Okgetheng, Boago  and
      Tebalo, Bopaki  and
      Motlhanka, Moffat  and
      Rammidi, Goaletsa",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.4",
    pages = "21--24",
    abstract = "Setswana language is one of the Bantu languages written disjunctively. Some of its parts of speech such as qualificatives and some adverbs are made up of multiple words. That is, the part of speech is made up of a group of words. The disjunctive style of writing poses a challenge when a sentence is tokenized or when tagging. A few studies have been done on identification of multi-word parts of speech. In this study we go further to tokenize complex parts of speech which are formed by extending basic forms of multi-word parts of speech. The parts of speech are extended by recursively concatenating more parts of speech to a basic form of parts of speech. We developed rules for building complex relative parts of speech. A morphological analyzer and Python NLTK are used to tag individual words and basic forms of multi-word parts of speech. Developed rules are then used to identify complex parts of speech. Results from a 300 sentence text files give a performance of 74{\%}. The tagger fails when it encounters expansion rules not implemented and when tagging by the morphological analyzer is incorrect.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{seyoum-etal-2020-comparing,
    title = "Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: {A}mharic Dependency Parser",
    author = "Seyoum, Binyam Ephrem  and
      Miyao, Yusuke  and
      Mekonnen, Baye Yimam",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.5",
    pages = "25--30",
    abstract = "In this paper, we compare four state-of-the-art neural network dependency parsers for the Semitic language Amharic. As Amharic is a morphologically-rich and less-resourced language, the out-of-vocabulary (OOV) problem will be higher when we develop data-driven models. This fact limits researchers to develop neural network parsers because the neural network requires large quantities of data to train a model. We empirically evaluate neural network parsers when a small Amharic treebank is used for training. Based on our experiment, we obtain an 83.79 LAS score using the UDPipe system. Better accuracy is achieved when the neural parsing system uses external resources like word embedding. Using such resources, the LAS score for UDPipe improves to 85.26. Our experiment shows that the neural networks can learn dependency relations better from limited data while segmentation and POS tagging require much data.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{griscom-2020-mobilizing,
    title = "Mobilizing Metadata: Open Data Kit ({ODK}) for Language Resource Development in {E}ast {A}frica",
    author = "Griscom, Richard",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.6",
    pages = "31--35",
    abstract = "Linguistic fieldworkers collect and archive metadata as part of the language resources (LRs) that they create, but they often work in resource-constrained environments that prevent them from using computers for data entry. In such situations, linguists must complete time-consuming and error-prone digitization tasks that limit the quantity and quality of the resources and metadata that they produce (Thieberger {\&} Berez 2012; Margetts {\&} Margetts 2012). This paper describes a method for entering linguistic metadata into mobile devices using the Open Data Kit (ODK) platform, a suite of open source tools designed for mobile data collection.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{hellan-2020-computational,
    title = "A Computational Grammar of {G}a",
    author = "Hellan, Lars",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.7",
    pages = "36--44",
    abstract = "The paper describes aspects of an HPSG style computational grammar of the West African language Ga (a Kwa language spoken in the Accra area of Ghana). As a Volta Basin Kwa language, Ga features many types of multiverb expressions and other particular constructional patterns in the verbal and nominal domain. The paper highlights theoretical and formal features of the grammar motivated by these phenomena, some of them possibly innovative to the formal framework. As a so-called deep grammar of the language, it hosts a rich lexical structure, and we describe ways in which the grammar builds on previously available lexical resources. We outline an environment of current resources in which the grammar is part, and lines of research and development in which it and its environment can be used.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{griesel-bosch-2020-navigating,
    title = "Navigating Challenges of Multilingual Resource Development for Under-Resourced Languages: The Case of the {A}frican {W}ordnet Project",
    author = "Griesel, Marissa  and
      Bosch, Sonja",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.8",
    pages = "45--50",
    abstract = "Creating a new wordnet is by no means a trivial task and when the target language is under-resourced as is the case for the languages currently included in the multilingual African Wordnet (AfWN), developers need to rely heavily on human expertise. During the different phases of development of the AfWN, we incorporated various methods of fast-tracking to ease the tedious and time-consuming work. Some methods have proven effective while others seem to have little positive impact on the work rate. As in the case of many other under-resourced languages, the expand model was implemented throughout, thus depending on English source data such as the English Princeton Wordnet (PWN) which is then translated into the target language with the assumption that the new language shares an underlying structure with the PWN. The paper discusses some problems encountered along the way and points out various possibilities of (semi) automated quality assurance measures and further refinement of the AfWN to ensure accelerated growth. In this paper we aim to highlight some of the lessons learnt from hands-on experience in order to facilitate similar projects, in particular for languages from other African countries.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@inproceedings{mboning-tchiaze-etal-2020-building,
    title = "Building Collaboration-based Resources in Endowed {A}frican Languages: Case of {NT}e{AL}an Dictionaries Platform",
    author = "Mboning Tchiaze, Elvis  and
      Bassahak, Jean Marc  and
      Baleba, Daniel  and
      Wandji, Ornella  and
      Assoumou, Jules",
    booktitle = "Proceedings of the first workshop on Resources for African Indigenous Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.rail-1.9",
    pages = "51--56",
    abstract = "In a context where open-source NLP resources and tools in African languages are scarce and dispersed, it is difficult for researchers to truly fit African languages into current algorithms of artificial intelligence. Created in 2017, with the aim of building communities of voluntary contributors around African native and/or national languages, cultures, NLP technologies and artificial intelligence, the NTeALan association has set up a series of web collaborative platforms intended to allow the aforementioned communities to create and administer their own lexicographic resources. In this article, we present on the one hand the first versions of the three platforms: the REST API for saving lexicographical resources, the dictionary management platform and the collaborative dictionary platform; on the other hand, we describe the data format chosen and used to encapsulate our resources. After experimenting with a few dictionaries and some users feedback, we are convinced that only collaboration-based approach and platforms can effectively respond to the production of good resources in African native and/or national languages.",
    language = "English",
    ISBN = "979-10-95546-60-3",
}
@proceedings{privatenlp-2020-privacy,
    title = "Proceedings of the Second Workshop on Privacy in NLP",
    editor = "Feyisetan, Oluwaseyi  and
      Ghanavati, Sepideh  and
      Malmasi, Shervin  and
      Thaine, Patricia",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.privatenlp-1.0",
}
@inproceedings{aggarwal-etal-2020-log,
    title = "On Log-Loss Scores and (No) Privacy",
    author = "Aggarwal, Abhinav  and
      Xu, Zekun  and
      Feyisetan, Oluwaseyi  and
      Teissier, Nathanael",
    booktitle = "Proceedings of the Second Workshop on Privacy in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.privatenlp-1.1",
    doi = "10.18653/v1/2020.privatenlp-1.1",
    pages = "1--6",
    abstract = "A common metric for assessing the performance of binary classifiers is the Log-Loss score, which is a real number indicating the cross entropy distance between the predicted distribution over the labels and the true distribution (a point distribution defined by the ground truth labels). In this paper, we show that a malicious modeler, upon obtaining access to the Log-Loss scores on its predictions, can exploit this information to infer all the ground truth labels of arbitrary test datasets with full accuracy. We provide an efficient algorithm to perform this inference. A particularly interesting application where this attack can be exploited is to breach privacy in the setting of Membership Inference Attacks. These attacks exploit the vulnerabilities of exposing models trained on customer data to queries made by an adversary. Privacy auditing tools for measuring leakage from sensitive datasets assess the total privacy leakage based on the adversary{'}s predictions for datapoint membership. An instance of the proposed attack can hence, cause complete membership privacy breach, obviating any attack model training or access to side knowledge with the adversary. Moreover, our algorithm is agnostic to the model under attack and hence, enables perfect membership inference even for models that do not memorize or overfit. In particular, our observations provide insight into the extent of information leakage from statistical aggregates and how they can be exploited.",
}
@inproceedings{xu-etal-2020-differentially,
    title = "A Differentially Private Text Perturbation Method Using Regularized Mahalanobis Metric",
    author = "Xu, Zekun  and
      Aggarwal, Abhinav  and
      Feyisetan, Oluwaseyi  and
      Teissier, Nathanael",
    booktitle = "Proceedings of the Second Workshop on Privacy in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.privatenlp-1.2",
    doi = "10.18653/v1/2020.privatenlp-1.2",
    pages = "7--17",
    abstract = "Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data. A popular approach for privacy- preserving text analysis is noise injection, in which text data is first mapped into a continuous embedding space, perturbed by sampling a spherical noise from an appropriate distribution, and then projected back to the discrete vocabulary space. While this allows the perturbation to admit the required metric differential privacy, often the utility of downstream tasks modeled on this perturbed data is low because the spherical noise does not account for the variability in the density around different words in the embedding space. In particular, words in a sparse region are likely unchanged even when the noise scale is large. In this paper, we propose a text perturbation mechanism based on a carefully designed regularized variant of the Mahalanobis metric to overcome this problem. For any given noise scale, this metric adds an elliptical noise to account for the covariance structure in the embedding space. This heterogeneity in the noise scale along different directions helps ensure that the words in the sparse region have sufficient likelihood of replacement without sacrificing the overall utility. We provide a text-perturbation algorithm based on this metric and formally prove its privacy guarantees. Additionally, we empirically show that our mechanism improves the privacy statistics to achieve the same level of utility as compared to the state-of-the-art Laplace mechanism.",
}
@inproceedings{bokaie-hosseini-etal-2020-identifying,
    title = "Identifying and Classifying Third-party Entities in Natural Language Privacy Policies",
    author = "Bokaie Hosseini, Mitra  and
      K C, Pragyan  and
      Reyes, Irwin  and
      Egelman, Serge",
    booktitle = "Proceedings of the Second Workshop on Privacy in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.privatenlp-1.3",
    doi = "10.18653/v1/2020.privatenlp-1.3",
    pages = "18--27",
    abstract = "App developers often raise revenue by contracting with third party ad networks, which serve targeted ads to end-users. To this end, a free app may collect data about its users and share it with advertising companies for targeting purposes. Regulations such as General Data Protection Regulation (GDPR) require transparency with respect to the recipients (or categories of recipients) of user data. These regulations call for app developers to have privacy policies that disclose those third party recipients of user data. Privacy policies provide users transparency into what data an app will access, collect, shared, and retain. Given the size of app marketplaces, verifying compliance with such regulations is a tedious task. This paper aims to develop an automated approach to extract and categorize third party data recipients (i.e., entities) declared in privacy policies. We analyze 100 privacy policies associated with most downloaded apps in the Google Play Store. We crowdsource the collection and annotation of app privacy policies to establish the ground truth with respect to third party entities. From this, we train various models to extract third party entities automatically. Our best model achieves average F1 score of 66{\%} when compared to crowdsourced annotations.",
}
@inproceedings{khandelwal-etal-2020-surfacing,
    title = "Surfacing Privacy Settings Using Semantic Matching",
    author = "Khandelwal, Rishabh  and
      Nayak, Asmit  and
      Yao, Yao  and
      Fawaz, Kassem",
    booktitle = "Proceedings of the Second Workshop on Privacy in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.privatenlp-1.4",
    doi = "10.18653/v1/2020.privatenlp-1.4",
    pages = "28--38",
    abstract = "Online services utilize privacy settings to provide users with control over their data. However, these privacy settings are often hard to locate, causing the user to rely on provider-chosen default values. In this work, we train privacy-settings-centric encoders and leverage them to create an interface that allows users to search for privacy settings using free-form queries. In order to achieve this goal, we create a custom Semantic Similarity dataset, which consists of real user queries covering various privacy settings. We then use this dataset to fine-tune a state of the art encoder. Using this fine-tuned encoder, we perform semantic matching between the user queries and the privacy settings to retrieve the most relevant setting. Finally, we also use the encoder to generate embeddings of privacy settings from the top 100 websites and perform unsupervised clustering to learn about the online privacy settings types. We find that the most common type of privacy settings are {`}Personalization{'} and {`}Notifications{'}, with coverage of 35.8{\%} and 34.4{\%}, respectively, in our dataset.",
}
@inproceedings{kerrigan-etal-2020-differentially,
    title = "Differentially Private Language Models Benefit from Public Pre-training",
    author = "Kerrigan, Gavin  and
      Slack, Dylan  and
      Tuyls, Jens",
    booktitle = "Proceedings of the Second Workshop on Privacy in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.privatenlp-1.5",
    doi = "10.18653/v1/2020.privatenlp-1.5",
    pages = "39--45",
    abstract = "Language modeling is a keystone task in natural language processing. When training a language model on sensitive information, differential privacy (DP) allows us to quantify the degree to which our private data is protected. However, training algorithms which enforce differential privacy often lead to degradation in model quality. We study the feasibility of learning a language model which is simultaneously high-quality and privacy preserving by tuning a public base model on a private corpus. We find that DP fine-tuning boosts the performance of language models in the private domain, making the training of such models possible.",
}
@proceedings{peoples-2020-modeling,
    title = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    editor = "Nissim, Malvina  and
      Patti, Viviana  and
      Plank, Barbara  and
      Durmus, Esin",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.0",
}
@inproceedings{cornelisse-2020-inferring,
    title = "Inferring Neuroticism of {T}witter Users by Utilizing their Following Interests",
    author = "Cornelisse, Joran",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.1",
    pages = "1--10",
    abstract = "Twitter is a medium where, when used adequately, users{'} interests can be derived from what he follows. This characteristic can make it attractive for a source of personality derivation. We set out to test the hypothesis that, analogous to the Lexical hypothesis, which posits that word use should reveal personality, following behavior on social media should reveal personality aspects. We used a two-step approach, wherein the first stage, we selected accounts for whom it was possible to infer personality profiles to some extent using available literature on personality and interests. On these accounts, we trained a regression model and segmented the derived features using hierarchical cluster analysis. In the second stage, we obtained a small sample of users{'} personalities via a questionnaire and tested whether the model from stage 1 correlated with the users from step 2. The the explained variance for the neurotic and neutral neuroticism groups indicated significant results (R2 = .131, p = .0205; R2 = .22, p = .0044). Confirming the hypothesis that following behavior should be correlated with one{'}s interests and that interests are correlated with the neuroticism personality dimension.",
}
@inproceedings{bassignana-etal-2020-matching,
    title = "Matching Theory and Data with Personal-{ITY}: What a Corpus of {I}talian {Y}ou{T}ube Comments Reveals About Personality",
    author = "Bassignana, Elisa  and
      Nissim, Malvina  and
      Patti, Viviana",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.2",
    pages = "11--22",
    abstract = "As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, where authors are labelled with personality traits. The traits are derived from one of the mainstream personality theories in psychology research, named MBTI. Using personality prediction experiments, we (i) study the task of personality prediction in itself on our corpus as well as on TWISTY, a Twitter dataset also annotated with MBTI labels; (ii) carry out an extensive, in-depth analysis of the features used by the classifier, and view them specifically under the light of the original theory that we used to create the corpus in the first place. We observe that no single model is best at personality detection, and that while some traits are easier than others to detect, and also to match back to theory, for other, less frequent traits the picture is much more blurred.",
}
@inproceedings{branz-etal-2020-red,
    title = "Red Is Open-Minded, Blue Is Conscientious: Predicting User Traits From {I}nstagram Image Data",
    author = "Branz, Lisa  and
      Brockmann, Patricia  and
      Hinze, Annika",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.3",
    pages = "23--28",
    abstract = "Various studies have addressed the connection between a user{'}s traits and their social media content. This paper explores the relationship between gender, age and Big Five personality traits of 179 university students from Germany and their Instagram images. With regards to both image features and image content, significant differences between genders as well as preferences related to age and personality traits emerged. Gender, age and personality traits are predicted using machine learning classification and regression methods. This work is the first of its kind to focus on data from European Instagram users, as well as to predict age from Instagram image features and content on a fine-grained level.",
}
@inproceedings{el-baff-etal-2020-persuasiveness,
    title = "Persuasiveness of News Editorials depending on Ideology and Personality",
    author = "El Baff, Roxanne  and
      Al Khatib, Khalid  and
      Stein, Benno  and
      Wachsmuth, Henning",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.4",
    pages = "29--40",
    abstract = "News editorials aim to shape the opinions of their readership and the general public on timely controversial issues. The impact of an editorial on the reader{'}s opinion does not only depend on its content and style, but also on the reader{'}s profile. Previous work has studied the effect of editorial style depending on general political ideologies (liberals vs.conservatives). In our work, we dig deeper into the persuasiveness of both content and style, exploring the role of the intensity of an ideology (lean vs.extreme) and the reader{'}s personality traits (agreeableness, conscientiousness, extraversion, neuroticism, and openness). Concretely, we train content- and style-based models on New York Times editorials for different ideology- and personality-specific groups. Our results suggest that particularly readers with extreme ideology and non {``}role model{''} personalities are impacted by style. We further analyze the importance of various text features with respect to the editorials{'} impact, the readers{'} profile, and the editorials{'} geographical scope.",
}
@inproceedings{chakravarthi-2020-hopeedi,
    title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion",
    author = "Chakravarthi, Bharathi Raja",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.5",
    pages = "41--53",
    abstract = "Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff{'}s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.",
}
@inproceedings{hande-etal-2020-kancmd,
    title = "{K}an{CMD}: {K}annada {C}ode{M}ixed Dataset for Sentiment Analysis and Offensive Language Detection",
    author = "Hande, Adeep  and
      Priyadharshini, Ruba  and
      Chakravarthi, Bharathi Raja",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.6",
    pages = "54--63",
    abstract = "We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.",
}
@inproceedings{kim-etal-2020-contextual,
    title = "Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations",
    author = "Kim, Jonggu  and
      Ko, Hyeonmok  and
      Song, Seoha  and
      Jang, Saebom  and
      Hong, Jiyeon",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.7",
    pages = "64--73",
    abstract = "Since language model pretraining to learn contextualized word representations has been proposed, pretrained language models have made success in many natural language processing tasks. That is because it is helpful to use individual contextualized representations of self-attention layers as to initialize parameters for downstream tasks. Yet, unfortunately, use of pretrained language models for emotion recognition in conversations has not been studied enough. We firstly use ELECTRA which is a state-of-the-art pretrained language model and validate the performance on emotion recognition in conversations. Furthermore, we propose contextual augmentation of pretrained language models for emotion recognition in conversations, which is to consider not only previous utterances, but also conversation-related information such as speakers, speech acts and topics. We classify information based on what the information is related to, and propose position of words corresponding to the information in the entire input sequence. To validate the proposed method, we conduct experiments on the DailyDialog dataset which contains abundant annotated information of conversations. The experiments show that the proposed method achieves state-of-the-art F1 scores on the dataset and significantly improves the performance.",
}
@inproceedings{johannssen-biemann-2020-social,
    title = "Social Media Unrest Prediction during the {COVID}-19 Pandemic: Neural Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises",
    author = "Johann{\ss}en, Dirk  and
      Biemann, Chris",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.8",
    pages = "74--86",
    abstract = "The COVID-19 pandemic has caused international social tension and unrest. Besides the crisis itself, there are growing signs of rising conflict potential of societies around the world. Indicators of global mood changes are hard to detect and direct questionnaires suffer from social desirability biases. However, so-called implicit methods can reveal humans intrinsic desires from e.g. social media texts. We present psychologically validated social unrest predictors and replicate scalable and automated predictions, setting a new state of the art on a recent German shared task dataset. We employ this model to investigate a change of language towards social unrest during the COVID-19 pandemic by comparing established psychological predictors on samples of tweets from spring 2019 with spring 2020. The results show a significant increase of the conflict indicating psychometrics. With this work, we demonstrate the applicability of automated NLP-based approaches to quantitative psychological research.",
}
@inproceedings{marinov-etal-2020-topic,
    title = "Topic and Emotion Development among {D}utch {COVID}-19 {T}witter Communities in the early Pandemic",
    author = "Marinov, Boris  and
      Spenader, Jennifer  and
      Caselli, Tommaso",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.9",
    pages = "87--98",
    abstract = "The paper focuses on a large collection of Dutch tweets from the Netherlands to get an insight into the perception and reactions of users during the early months of the COVID-19 pandemic. We focused on five major user communities of users: government and health organizations, news media, politicians, the general public and conspiracy theory supporters, investigating differences among them in topic dominance and the expressions of emotions. Through topic modeling we monitor the evolution of the conversation about COVID-19 among these communities. Our results indicate that the national focus on COVID-19 shifted from the virus itself to its impact on the economy between February and April. Surprisingly, the overall emotional public response appears to be substantially positive and expressing trust, although differences can be observed in specific group of users.",
}
@inproceedings{ovchinnikova-etal-2020-sentiments,
    title = "Sentiments in {R}ussian Medical Professional Discourse during the Covid-19 Pandemic",
    author = "Ovchinnikova, Irina  and
      Ermakova, Liana  and
      Nurbakova, Diana",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.10",
    pages = "99--108",
    abstract = "Medical discourse within the professional community has undeservingly received very sparse researchers{'} attention. Medical professional discourse exists offline and online. We carried out sentiment analysis on titles and text descriptions of materials published on the Russian portal Mir Vracha (90,000 word forms approximately). The texts were generated by and for physicians. The materials include personal narratives describing participants{'} professional experience, participants{'} opinions about pandemic news and events in the professional sphere, and Russian reviews and discussion of papers published in international journals in English. We present the first results and discussion of the sentiment analysis of Russian online medical discourse. Based on the results of sentiment analysis and discourse analysis, we described the emotions expressed in the forum and the linguistic means the forum participants used to verbalise their attitudes and emotions while discussing the Covid-19 pandemic. The results showed prevalence of neutral texts in the publications since the medical professionals are interested in research materials and outcomes. In the discussions and personal narratives, the forum participants expressed negative sentiments by colloquial words and figurative language.",
}
@inproceedings{stoikos-izbicki-2020-multilingual,
    title = "Multilingual Emoticon Prediction of Tweets about {COVID}-19",
    author = "Stoikos, Stefanos  and
      Izbicki, Mike",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.11",
    pages = "109--118",
    abstract = "Emojis are a widely used tool for encoding emotional content in informal messages such as tweets,and predicting which emoji corresponds to a piece of text can be used as a proxy for measuring the emotional content in the text. This paper presents the first model for predicting emojis in highly multilingual text.Our BERTmoticon model is a fine-tuned version of the BERT model,and it can predict emojis for text written in 102 different languages.We trained our BERTmoticon model on 54.2 million geolocated tweets sent in the first 6 months of 2020,and we apply the model to a case study analyzing the emotional reaction of Twitter users to news about the coronavirus. Example findings include a spike in sadness when the World Health Organization (WHO) declared that coronavirus was a global pandemic, and a spike in anger and disgust when the number of COVID-19 related deaths in the United States surpassed one hundred thousand. We provide an easy-to-use and open source python library for predicting emojis with BERTmoticon so that the model can easily be applied to other data mining tasks.",
}
@inproceedings{oberlander-etal-2020-experiencers,
    title = "Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?",
    author = {Oberl{\"a}nder, Laura Ana Maria  and
      Reich, Kevin  and
      Klinger, Roman},
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.12",
    pages = "119--128",
    abstract = "Emotion recognition is predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory (e.g., fear, joy, anger, disgust, sadness, surprise, trust, anticipation). More recently, semantic role labeling approaches have been developed to extract structures from the text to answer questions like: {``}who is described to feel the emotion?{''} (experiencer), {``}what causes this emotion?{''} (stimulus), and at which entity is it directed?{''} (target). Though it has been shown that jointly modeling stimulus and emotion category prediction is beneficial for both subtasks, it remains unclear which of these semantic roles enables a classifier to infer the emotion. Is it the experiencer, because the identity of a person is biased towards a particular emotion (X is always happy)? Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)? We answer these questions by training emotion classification models on five available datasets annotated with at least one semantic role by masking the fillers of these roles in the text in a controlled manner and find that across multiple corpora, stimuli and targets carry emotion information, while the experiencer might be considered a confounder. Further, we analyze if informing the model about the position of the role improves the classification decision. Particularly on literature corpora we find that the role information improves the emotion classification.",
}
@inproceedings{buechel-etal-2020-learning-emotion,
    title = "Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations",
    author = "Buechel, Sven  and
      Sedoc, Jo{\~a}o  and
      Schwartz, H. Andrew  and
      Ungar, Lyle",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.13",
    pages = "129--139",
    abstract = "One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data is limited, such as less-resourced languages or emotion analysis, with its many nuanced and hard-to-acquire annotation formats. We conduct a questionnaire study indicating that indeed the vast majority of researchers in emotion analysis deems neural models inferior to traditional machine learning when training data is limited. In stark contrast to those survey results, we provide empirical evidence for English, Polish, and Portuguese that commonly used neural architectures can be trained on surprisingly few observations, outperforming n-gram based ridge regression on only 100 data points. Our analysis suggests that high-quality, pre-trained word embeddings are a main factor for achieving those results.",
}
@inproceedings{navas-alejo-etal-2020-cross,
    title = "Cross-lingual Emotion Intensity Prediction",
    author = "Navas Alejo, Irean  and
      Badia, Toni  and
      Barnes, Jeremy",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.14",
    pages = "140--152",
    abstract = "Emotion intensity prediction determines the degree or intensity of an emotion that the author expresses in a text, extending previous categorical approaches to emotion detection. While most previous work on this topic has concentrated on English texts, other languages would also benefit from fine-grained emotion classification, preferably without having to recreate the amount of annotated data available in English in each new language. Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets. To this end we annotate a test set of Spanish and Catalan tweets using Best-Worst scaling. We compare six cross-lingual approaches, e.g., machine translation and cross-lingual embeddings, which have varying requirements for parallel data {--} from millions of parallel sentences to completely unsupervised. The results show that on this data, methods with low parallel-data requirements perform surprisingly better than methods that use more parallel data, which we explain through an in-depth error analysis. We make the dataset and the code available at \url{https://github.com/jerbarnes/fine-grained_cross-lingual_emotion}.",
}
@inproceedings{ljubesic-etal-2020-lilah,
    title = "The {L}i{L}a{H} Emotion Lexicon of {C}roatian, {D}utch and {S}lovene",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Markov, Ilia  and
      Fi{\v{s}}er, Darja  and
      Daelemans, Walter",
    booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.peoples-1.15",
    pages = "153--157",
    abstract = "In this paper, we present emotion lexicons of Croatian, Dutch and Slovene, based on manually corrected automatic translations of the English NRC Emotion lexicon. We evaluate the impact of the translation changes by measuring the change in supervised classification results of socially unacceptable utterances when lexicon information is used for feature construction. We further showcase the usage of the lexicons by calculating the difference in emotion distributions in texts containing and not containing socially unacceptable discourse, comparing them across four languages (English, Croatian, Dutch, Slovene) and two topics (migrants and LGBT). We show significant and consistent improvements in automatic classification across all languages and topics, as well as consistent (and expected) emotion distributions across all languages and topics, proving for the manually corrected lexicons to be a useful addition to the severely lacking area of emotion lexicons, the crucial resource for emotive analysis of text.",
}
@proceedings{parlaclarin-2020-parlaclarin,
    title = "Proceedings of the Second ParlaCLARIN Workshop",
    editor = "Fi{\v{s}}er, Darja  and
      Eskevich, Maria  and
      de Jong, Franciska",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.0",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{ogrodniczuk-niton-2020-new,
    title = "New Developments in the {P}olish Parliamentary Corpus",
    author = "Ogrodniczuk, Maciej  and
      Nito{\'n}, Bart{\l}omiej",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.1",
    pages = "1--4",
    abstract = "This short paper presents the current (as of February 2020) state of preparation of the Polish Parliamentary Corpus (PPC){---}an extensive collection of transcripts of Polish parliamentary proceedings dating from 1919 to present. The most evident developments as compared to the 2018 version is harmonization of metadata, standardization of document identifiers, uploading contents of all documents and metadata to the database (to enable easier modification, maintenance and future development of the corpus), linking utterances to the political ontology, linking corpus texts to source data and processing historical documents.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{rodven-eide-2020-anforanden,
    title = {{A}nf{\"o}randen: Annotated and Augmented Parliamentary Debates from {S}weden},
    author = "R{\o}dven Eide, Stian",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.2",
    pages = "5--10",
    abstract = {The Swedish parliamentary debates have been available since 2010 through the parliament{'}s open data web site Riksdagens {\"o}ppna data. While fairly comprehensive, the structure of the data can be hard to understand and its content is somewhat noisy for use as a quality language resource. In order to make them easier to use and process {--} in particular for language technology research, but also for political science and other fields with an interest in parliamentary data {--} we have published a large selection of the debates in a cleaned and structured format, annotated with linguistic information and augmented with semantic links. Especially prevalent in the parliament{'}s data were end-line hyphenations {--} something that tokenisers generally are not equipped for {--} and a lot of the effort went into resolving these. In this paper, we provide detailed descriptions of the structure and contents of the resource, and explain how it differs from the parliament{'}s own version.},
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{steingrimsson-etal-2020-igc,
    title = "{IGC}-Parl: {I}celandic Corpus of Parliamentary Proceedings",
    author = {Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Barkarson, Starka{\dh}ur  and
      {\"O}rn{\'o}lfsson, Gunnar Thor},
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.3",
    pages = "11--17",
    abstract = "We describe the acquisition, annotation and encoding of the corpus of the Althingi parliamentary proceedings. The first version of the corpus includes speeches from 1911-2019. It comprises 406 thousand speeches and over 219 million words. The corpus has been automatically part-of-speech tagged and lemmatised. It is annotated with extensive metadata about the speeches, speakers and political parties, including speech topic, whether the speaker is in the government coalition or opposition, age and gender of speaker at the time of delivery, references to sound and video recordings and more. The corpus is encoded in accordance with the Text Encoding Initiative (TEI) Guidelines and conforms to the Parla-CLARIN schema. We plan to update the corpus annually and its major versions will be archived in the CLARIN.IS repository. It is available for download and search using the KORP concordance tool. Furthermore, information on word frequency are accessible in a custom made web application and an n-gram viewer.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{hladka-etal-2020-compiling,
    title = "Compiling {C}zech Parliamentary Stenographic Protocols into a Corpus",
    author = "Hladka, Barbora  and
      Kopp, Maty{\'a}{\v{s}}  and
      Stra{\v{n}}{\'a}k, Pavel",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.4",
    pages = "18--22",
    abstract = "The Parliament of the Czech Republic consists of two chambers: the Chamber of Deputies (Lower House) and the Senate (Upper House). In our work, we focus on agenda and documents that relate to the Chamber of Deputies exclusively. We pay particular attention to stenographic protocols that record the Chamber of Deputies{'} meetings. Our overall goal is to (1) compile the protocols into a ParlaCLARIN TEI encoded corpus, (2) make this corpus accessible and searchable in the TEITOK web-based platform, (3) annotate the corpus using the modules available in TEITOK, e.g. detect and recognize named entities, and (4) highlight the annotations in TEITOK. In addition, we add two more goals that we consider innovative: (5) update the corpus every time a new stenographic protocol is published online by the Chambers of Deputies and (6) expose the annotations as the linked open data in order to improve the protocols{'} interoperability with other existing linked open data. This paper is devoted to the goals (1) and (5).",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{coole-etal-2020-unfinished,
    title = "Unfinished Business: Construction and Maintenance of a Semantically Tagged Historical Parliamentary Corpus, {UK} Hansard from 1803 to the present day",
    author = "Coole, Matthew  and
      Rayson, Paul  and
      Mariani, John",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.5",
    pages = "23--27",
    abstract = "Creating, curating and maintaining modern political corpora is becoming an ever more involved task. As interest from various social bodies and the general public in political discourse grows so too does the need to enrich such datasets with metadata and linguistic annotations. Beyond this, such corpora must be easy to browse and search for linguists, social scientists, digital humanists and the general public. We present our efforts to compile a linguistically annotated and semantically tagged version of the Hansard corpus from 1803 right up to the present day. This involves combining multiple sources of documents and transcripts. We describe our toolchain for tagging; using several existing tools that provide tokenisation, part-of-speech tagging and semantic annotations. We also provide an overview of our bespoke web-based search interface built on LexiDB. In conclusion, we examine the completed corpus by looking at four case studies including semantic categories made available by our toolchain.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{pancur-erjavec-2020-siparl,
    title = "The si{P}arl corpus of {S}lovene parliamentary proceedings",
    author = "Pancur, Andrej  and
      Erjavec, Toma{\v{z}}",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.6",
    pages = "28--34",
    abstract = "The paper describes the process of acquisition, up-translation, encoding, annotation, and distribution of siParl, a collection of the parliamentary debates from the Assembly of the Republic of Slovenia from 1990{--}2018, covering the period from just before Slovenia became an independent country in 1991, and almost up to the present. The entire corpus, comprising over 8 thousand sessions, 1 million speeches and 200 million words was uniformly encoded in accordance with the TEI-based Parla-CLARIN schema for encoding corpora of parliamentary debates, and contains extensive meta-data about the speakers, a typology of sessions etc. and structural and editorial annotations. The corpus was also part-of-speech tagged and lemmatised using state-of-the-art tools. The corpus is maintained on GitHub with its major versions archived in the CLARIN.SI repository and is available for linguistic analysis in the scope of the on-line CLARIN.SI concordancers, thus offering an invaluable resource for scholars studying Slovenian political history.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{kerkvliet-etal-2020-mentions,
    title = "Who mentions whom? Recognizing political actors in proceedings",
    author = "Kerkvliet, Lennart  and
      Kamps, Jaap  and
      Marx, Maarten",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.7",
    pages = "35--39",
    abstract = "We show that it is straightforward to train a state of the art named entity tagger (spaCy) to recognize political actors in Dutch parliamentary proceedings with high accuracy. The tagger was trained on 3.4K manually labeled examples, which were created in a modest 2.5 days work. This resource is made available on github. Besides proper nouns of persons and political parties, the tagger can recognize quite complex definite descriptions referring to cabinet ministers, ministries, and parliamentary committees. We also provide a demo search engine which employs the tagged entities in its SERP and result summaries.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{de-vos-verberne-2020-challenges,
    title = "Challenges of Applying Automatic Speech Recognition for Transcribing {EU} Parliament Committee Meetings: A Pilot Study",
    author = "de Vos, Hugo  and
      Verberne, Suzan",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.8",
    pages = "40--43",
    abstract = "Challenges of Applying Automatic Speech Recognition for Transcribing EUParliament Committee Meetings: A Pilot StudyHugo de Vos and Suzan VerberneInstitute of Public Administration and Leiden Institute of Advanced Computer Science, Leiden Universityh.p.de.vos@fgga.leidenuniv.nl, s.verberne@liacs.leidenuniv.nlAbstractWe tested the feasibility of automatically transcribing committee meetings of the European Union parliament with the use of AutomaticSpeech Recognition techniques. These committee meetings contain more valuable information for political science scholars than theplenary meetings since these meetings showcase actual debates opposed to the more formal plenary meetings. However, since there areno transcriptions of those meetings, they are a lot less accessible for research than the plenary meetings, of which multiple corpora exist.We explored a freely available ASR application and analysed the output in order to identify the weaknesses of an out-of-the box system.We followed up on those weaknesses by proposing directions for optimizing the ASR for our goals. We found that, despite showcasingacceptable results in terms of Word Error Rate, the model did not yet suffice for the purpose of generating a data set for use in PoliticalScience. The application was unable to successfully recognize domain specific terms and names. To overcome this issue, future researchwill be directed at using domain specific language models in combination with off-the-shelf acoustic models.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{runarsson-sigurdsson-2020-parsing,
    title = "Parsing {I}celandic Al{\th}ingi Transcripts: Parliamentary Speeches as a Genre",
    author = "R{\'u}narsson, Kristj{\'a}n  and
      Sigur{\dh}sson, Einar Freyr",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.9",
    pages = "44--50",
    abstract = "We introduce a corpus of transcripts from Al{\th}ingi, the Icelandic parliament. The corpus is syntactically parsed for phrase structure according to the annotation scheme of the Icelandic Parsed Historical Corpus (IcePaHC). This addition to IcePaHC makes it more diverse with respect to text types and we argue that having a syntactically parsed corpus facilitates research on differt types of texts. We furthermore argue that the speech corpus can be treated somewhat like spoken language even though the transcripts differ in various ways from daily spoken language. We also compare this text type to other types and argue that this genre can shed light on their properties. Finally, we exhibit how this addition to IcePaHC has helped us in identifying and solving issues with our parsing scheme.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{navarretta-haltrup-hansen-2020-identifying,
    title = "Identifying Parties in Manifestos and Parliament Speeches",
    author = "Navarretta, Costanza  and
      Haltrup Hansen, Dorte",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.10",
    pages = "51--57",
    abstract = "This paper addresses differences in the word use of two left-winged and two right-winged Danish parties, and how these differences reflecting some of the basic stances of the parties can be used to automatically identify the party of politicians from their speeches. In the first study, the most frequent and characteristic lemmas in the manifestos of the political parties are analysed. The analysis shows that the most frequently occurring lemmas in the manifestos reflect either the ideology or the position of the parties towards specific subjects, confirming for Danish preceding studies of English and German manifestos. Successively, we scaled our analysis applying machine learning on different language models built on the transcribed speeches by members of the same parties in the Parliament (Hansards) in order to determine to what extent it is possible to predict the party of the politicians from the speeches. The speeches used are a subset of the Danish Parliament corpus 2009{--}2017. The best models resulted in a weighted F1-score of 0.57. These results are significantly better than the results obtained by the majority classifier (F1-score = 0.11) and by chance results (0.25) and show that building language models over the speeches used by politicians can be used to identify the politicians{'} party even if they debate about the same subjects and thus often use the same terminology in many cases. In the future, we will include the subject of the speeches in the prediction experiments",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{hofmann-etal-2020-comparing,
    title = "Comparing Lexical Usage in Political Discourse across Diachronic Corpora",
    author = "Hofmann, Klaus  and
      Marakasova, Anna  and
      Baumann, Andreas  and
      Neidhardt, Julia  and
      Wissik, Tanja",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.11",
    pages = "58--65",
    abstract = "Most diachronic studies on both lexico-semantic change and political language usage are based on individual or comparable corpora. In this paper, we explore ways of studying the stability (and changeability) of lexical usage in political discourse across two corpora which are substantially different in structure and size. We present a case study focusing on lexical items associated with political parties in two diachronic corpora of Austrian German, namely a diachronic media corpus (AMC) and a corpus of parliamentary records (ParlAT), and measure the cross-temporal stability of lexical usage over a period of 20 years. We conduct three sets of comparative analyses investigating a) the stability of sets of lexical items associated with the three major political parties over time, b) lexical similarity between parties, and c) the similarity between the lexical choices in parliamentary speeches by members of the parties vis-{`}a-vis the media{'}s reporting on the parties. We employ time series modeling using generalized additive models (GAMs) to compare the lexical similarities and differences between parties within and across corpora. The results show that changes observed in these measures can be meaningfully related to political events during that time.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{blaette-etal-2020-europeanization,
    title = "The {E}uropeanization of Parliamentary Debates on Migration in {A}ustria, {F}rance, {G}ermany, and the {N}etherlands",
    author = "Blaette, Andreas  and
      Gehlhar, Simon  and
      Leonhardt, Christoph",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.12",
    pages = "66--74",
    abstract = "Corpora of plenary debates in national parliaments are available for many European states. For comparative research on political discourse, a persisting problem is that the periods covered by corpora differ and that a lack of standardization of data formats inhibits the integration of corpora into a single analytical framework. The solution we pursue is a {`}Framework for Parsing Plenary Protocols{'} (frappp), which has been used to prepare corpora of the Assembl{\'e}e Nationale ({`}{`}ParisParl{''}), the German Bundestag ({`}{`}GermaParl{''}), the Tweede Kamer of the Netherlands ({`}{`}TweedeTwee{''}), and the Austrian Nationalrat ({`}{`}AustroParl{''}) for the first two decades of the 21st century (2000-2019). To demonstrate the usefulness of the data gained, we investigate the Europeanization of migration debates in these Western European countries of immigration, i.e. references to a European dimension of policy-making in speeches on migration and integration. Based on a segmentation of the corpora into speeches, the method we use is topic modeling, and the analysis of joint occurrences of topics indicating migration and European affairs, respectively. A major finding is that after 2015, we see an increasing Europeanization of migration debates in the small EU member states in our sample (Austria and the Netherlands), and a regression of respective Europeanization in France and {--} more notably {--} in Germany.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@inproceedings{diwersy-luxardo-2020-querying,
    title = "Querying a large annotated corpus of parliamentary debates",
    author = "Diwersy, Sascha  and
      Luxardo, Giancarlo",
    booktitle = "Proceedings of the Second ParlaCLARIN Workshop",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.parlaclarin-1.13",
    pages = "75--79",
    abstract = "The TAPS corpus makes it possible to share a large volume of French parliamentary data. The TEI-compliant approach behind its design choices facilitates the publishing and the interoperability of data, but also the implementation of exploratory data analysis techniques in order to process institutional or political discourse. We demonstrate its application to the debates occurred in the context of a specific legislative process, which generated a strong opposition.",
    language = "English",
    ISBN = "979-10-95546-47-4",
}
@proceedings{pam-2020-probability,
    title = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    editor = "Chatzikyriakidis, Stergios  and
      Howes, Christine",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.0",
}
@inproceedings{maguire-2020-practical,
    title = "{`}Practical{'}, if that{'}s the word",
    author = "Maguire, Eimear",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.1",
    pages = "1--7",
    abstract = "Certain conditionals have something other than a clause as their consequent: their antecedent if-clauses are {`}adverbial clauses{'} without a verb. We argue that they function in a way already seen for those with clausal consequents, despite lacking the content we might expect for the formation of a conditional. The use of the if-clause with sub-clausal consequents is feasible thanks to the fact that this function does not depend on the consequent content, and so is not impeded when the consequent does not provide a proposition, question or imperative. To support this we provide meaning rules for conditionals in terms of information state updates, letting the same construction play out in different ways depending on context and content.",
}
@inproceedings{noble-etal-2020-personae,
    title = "Personae under uncertainty: The case of topoi",
    author = "Noble, Bill  and
      Breitholz, Ellen  and
      Cooper, Robin",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.2",
    pages = "8--16",
    abstract = "In this paper, we propose a probabilistic model of social signalling which adopts a persona-based account of social meaning. We use this model to develop a socio-semantic theory of conventionalised reasoning patterns, known as topoi. On this account the social meaning of a topos, as conveyed in a argument, is based on the set of idealogically-related topoi it indicates in context. We draw a connection between the role of personae in social meaning and the category adjustment effect, a well-known psychological phenomenon in which the representation of a stimulus is biased in the direction of the category in which it falls. Finally, we situate the interpretation of social signals as an update to the information state of an agent in a formal TTR model of dialogue.",
}
@inproceedings{denigot-burnett-2020-dogwhistles,
    title = "Dogwhistles as Identity-based interpretative variation",
    author = "D{\'e}nigot, Quentin  and
      Burnett, Heather",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.3",
    pages = "17--25",
    abstract = "The following paper presents a formal model for the description of \textit{dogwhistles}. Dogwhistles are a class of terms or expressions often used in political discourse that are used with the goal of being interpreted in different ways by different communities. The model presented here describes this phenomenon using a variation on the Social Meaning Games framework that uses probability distributions over possible interpretation functions as well as RSA/IBR reasoning.",
}
@inproceedings{tellings-2020-conditional,
    title = "Conditional answers and the role of probabilistic epistemic representations",
    author = "Tellings, Jos",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.4",
    pages = "26--33",
    abstract = "Conditional utterances can be used in discourse as answers to regular, non-conditional questions in situations of partial knowledge of the answerer. We claim that the probabilities assigned to possible epistemic states of A are a measure of the utility of conditional answers. A second criterion that makes a conditional answer {`}if p, then q{'} relevant has to do with the dependency between p and q that is conveyed in the statement. A conditional answer counts as relevant when this dependency leads the question asker to shift from a decision problem about q to an alternative, easier, decision problem about p.",
}
@inproceedings{waldon-2020-linguistic,
    title = "Linguistic interpretation as inference under argument system uncertainty: the case of epistemic must",
    author = "Waldon, Brandon",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.5",
    pages = "34--40",
    abstract = "Modern semantic analyses of epistemic language (incl. the modals must and might) can be characterized by the following {`}credence assumption{'}: speakers have full certainty regarding the propositions that structure their epistemic state. Intuitively, however: a) speakers have graded, rather than categorical, commitment to these propositions, which are often never fully and explicitly articulated; b) listeners have higher-order uncertainty about this speaker uncertainty; c) must p is used to communicate speaker commitment to some conclusion p and to indicate speaker commitment to the premises that condition the conclusion. I explore the consequences of relaxing the credence assumption by extending the argument system semantic framework first proposed by Stone (1994) to a Bayesian probabilistic framework of modeling pragmatic interpretation (Goodman and Frank, 2016). The analysis makes desirable predictions regarding the behavior and interpretation of must, and it suggests a new way of considering the nature of context and communicative exchange.",
}
@inproceedings{emerson-2020-linguists,
    title = "Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics",
    author = "Emerson, Guy",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.6",
    pages = "41--52",
    abstract = "Functional Distributional Semantics provides a computationally tractable framework for learning truth-conditional semantics from a corpus. Previous work in this framework has provided a probabilistic version of first-order logic, recasting quantification as Bayesian inference. In this paper, I show how the previous formulation gives trivial truth values when a precise quantifier is used with vague predicates. I propose an improved account, avoiding this problem by treating a vague predicate as a distribution over precise predicates. I connect this account to recent work in the Rational Speech Acts framework on modelling generic quantification, and I extend this to modelling donkey sentences. Finally, I explain how the generic quantifier can be both pragmatically complex and yet computationally simpler than precise quantifiers.",
}
@inproceedings{cano-santin-etal-2020-fast,
    title = "Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot",
    author = "Cano Sant{\'\i}n, Jos{\'e} Miguel  and
      Dobnik, Simon  and
      Ghanimifard, Mehdi",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.7",
    pages = "53--61",
    abstract = "The major shortcomings of using neural networks with situated agents are that in incremental interaction very few learning examples are available and that their visual sensory representations are quite different from image caption datasets. In this work we adapt and evaluate a few-shot learning approach, Matching Networks (Vinyals et al., 2016), to conversational strategies of a robot interacting with a human tutor in order to efficiently learn to categorise objects that are presented to it and also investigate to what degree transfer learning from pre-trained models on images from different contexts can improve its performance. We discuss the implications of such learning on the nature of semantic representations the system has learned.",
}
@inproceedings{larsson-2020-discrete,
    title = "Discrete and Probabilistic Classifier-based Semantics",
    author = "Larsson, Staffan",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.8",
    pages = "62--68",
    abstract = "We present a formal semantics (a version of Type Theory with Records) which places classifiers of perceptual information at the core of semantics. Using this framework, we present an account of the interpretation and classification of utterances referring to perceptually available situations (such as visual scenes). The account improves on previous work by clarifying the role of classifiers in a hybrid semantics combining statistical/neural classifiers with logical/inferential aspects of meaning. The account covers both discrete and probabilistic classification, thereby enabling learning, vagueness and other non-discrete linguistic phenomena.",
}
@inproceedings{mccready-henderson-2020-social,
    title = "Social Meaning in Repeated Interactions",
    author = "McCready, Elin  and
      Henderson, Robert",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.9",
    pages = "69--72",
    abstract = "Judgements about communicative agents evolve over the course of interactions both in how individuals are judged for testimonial reliability and for (ideological) trustworthiness. This paper combines a theory of social meaning and persona with a theory of reliability within a game-theoretic view of communication, giving a formal model involving interactional histories, repeated game models and ways of evaluating social meaning and trustworthiness.",
}
@inproceedings{henderson-mccready-2020-towards,
    title = "Towards functional, agent-based models of dogwhistle communication",
    author = "Henderson, Robert  and
      McCready, Elin",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.10",
    pages = "73--77",
    abstract = "Henderson and McCready 2017, 2018, 2019 build a novel theory of so-called {`}dogwhistle{'} communication by extending the social meaning games of Burnett 2017. This work reports on an ongoing project to build systems to model the evolution of dogwhistle communication in a population based on probability monads (Erwig and Kollmansberger, 2006; Kidd, 2007). The ultimate results will be useful not just for dogwhistles, but modeling the diffusion and evolution of social meaning in populations in general. The initial results presented here is a computational implementation of Henderson and McCready 2018, which will serve as the basis for models with multiple speakers and repeated interactions.",
}
@inproceedings{schuster-etal-2020-stochastic,
    title = "Stochastic Frames",
    author = "Schuster, Annika  and
      Stroessner, Corina  and
      Sutton, Peter  and
      Zeevat, Henk",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.11",
    pages = "78--85",
    abstract = "In the frame hypothesis (CITATION), human concepts are equated with frames, which extend feature lists by a functional structure consisting of attributes and values. For example, a bachelor is represented by the attributes gender and marital status and their values {`}male{'} and {`}unwed{'}. This paper makes the point that for many applications of concepts in cognition, including for concepts to be associated with lexemes in natural languages, the right structures to assume are not merely frames but stochastic frames in which attributes are associated with probability distributions over values. The paper introduces the idea of stochastic frames and suggests three applications: vagueness, ambiguity, and typicality.",
}
@inproceedings{sadrzadeh-wijnholds-2020-toy,
    title = "A toy distributional model for fuzzy generalised quantifiers",
    author = "Sadrzadeh, Mehrnoosh  and
      Wijnholds, Gijs",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.12",
    pages = "86--94",
    abstract = "Recent work in compositional distributional semantics showed how bialgebras model generalised quantifiers of natural language. That technique requires working with vector space over power sets of bases, and therefore is computationally costly. It is possible to overcome the computational hurdles by working with fuzzy generalised quantifiers. In this paper, we show that the compositional notion of semantics of natural language, guided by a grammar, extends from a binary to a many valued setting and instantiate in it the fuzzy computations. We import vector representations of words and predicates, learnt from large scale compositional distributional semantics, interpret them as fuzzy sets, and analyse their performance on a toy inference dataset.",
}
@inproceedings{anwar-etal-2020-generating,
    title = "Generating Lexical Representations of Frames using Lexical Substitution",
    author = "Anwar, Saba  and
      Shelmanov, Artem  and
      Panchenko, Alexander  and
      Biemann, Chris",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.13",
    pages = "95--103",
    abstract = "Semantic frames are formal linguistic structures describing situations/actions/events, e.g. Commercial transfer of goods. Each frame provides a set of roles corresponding to the situation participants, e.g. Buyer and Goods, and lexical units (LUs) {--} words and phrases that can evoke this particular frame in texts, e.g. Sell. The scarcity of annotated resources hinders wider adoption of frame semantics across languages and domains. We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs. We evaluate the expansion quality using FrameNet. Contextualized models demonstrate overall superior performance compared to the non-contextualized ones on roles. However, the latter show comparable performance on the task of LU expansion.",
}
@inproceedings{coppock-etal-2020-informativity,
    title = "Informativity in Image Captions vs. Referring Expressions",
    author = "Coppock, Elizabeth  and
      Dionne, Danielle  and
      Graham, Nathanial  and
      Ganem, Elias  and
      Zhao, Shijie  and
      Lin, Shawn  and
      Liu, Wenxing  and
      Wijaya, Derry",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.14",
    pages = "104--108",
    abstract = "At the intersection between computer vision and natural language processing, there has been recent progress on two natural language generation tasks: Dense Image Captioning and Referring Expression Generation for objects in complex scenes. The former aims to provide a caption for a specified object in a complex scene for the benefit of an interlocutor who may not be able to see it. The latter aims to produce a referring expression that will serve to identify a given object in a scene that the interlocutor can see. The two tasks are designed for different assumptions about the common ground between the interlocutors, and serve very different purposes, although they both associate a linguistic description with an object in a complex scene. Despite these fundamental differences, the distinction between these two tasks is sometimes overlooked. Here, we undertake a side-by-side comparison between image captioning and reference game human datasets and show that they differ systematically with respect to informativity. We hope that an understanding of the systematic differences among these human datasets will ultimately allow them to be leveraged more effectively in the associated engineering tasks.",
}
@inproceedings{ek-etal-2020-punctuation,
    title = "How does Punctuation Affect Neural Models in Natural Language Inference",
    author = "Ek, Adam  and
      Bernardy, Jean-Philippe  and
      Chatzikyriakidis, Stergios",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.15",
    pages = "109--116",
    abstract = "Natural Language Inference models have reached almost human-level performance but their generalisation capabilities have not been yet fully characterized. In particular, sensitivity to small changes in the data is a current area of investigation. In this paper, we focus on the effect of punctuation on such models. Our findings can be broadly summarized as follows: (1) irrelevant changes in punctuation are correctly ignored by the recent transformer models (BERT) while older RNN-based models were sensitive to them. (2) All models, both transformers and RNN-based models, are incapable of taking into account small relevant changes in the punctuation.",
}
@inproceedings{von-essen-hesslow-2020-building,
    title = "Building a {S}wedish Question-Answering Model",
    author = "von Essen, Hannes  and
      Hesslow, Daniel",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.16",
    pages = "117--127",
    abstract = "High quality datasets for question answering exist in a few languages, but far from all. Producing such datasets for new languages requires extensive manual labour. In this work we look at different methods for using existing datasets to train question-answering models in languages lacking such datasets. We show that machine translation followed by cross-lingual projection is a viable way to create a full question-answering dataset in a new language. We introduce new methods both for bitext alignment, using optimal transport, and for direct cross-lingual projection, utilizing multilingual BERT. We show that our methods produce good Swedish question-answering models without any manual work. Finally, we apply our proposed methods on Spanish and evaluate it on the XQuAD and MLQA benchmarks where we achieve new state-of-the-art values of 80.4 F1 and 62.9 Exact Match (EM) points on the Spanish XQuAD corpus and 70.8 F1 and 53.0 EM on the Spanish MLQA corpus, showing that the technique is readily applicable to other languages.",
}
@inproceedings{haber-poesio-2020-word,
    title = "Word Sense Distance in Human Similarity Judgements and Contextualised Word Embeddings",
    author = "Haber, Janosch  and
      Poesio, Massimo",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.17",
    pages = "128--145",
    abstract = "Homonymy is often used to showcase one of the advantages of context-sensitive word embedding techniques such as ELMo and BERT. In this paper we want to shift the focus to the related but less exhaustively explored phenomenon of polysemy, where a word expresses various distinct but related senses in different contexts. Specifically, we aim to i) investigate a recent model of polyseme sense clustering proposed by Ortega-Andres {\&} Vicente (2019) through analysing empirical evidence of word sense grouping in human similarity judgements, ii) extend the evaluation of context-sensitive word embedding systems by examining whether they encode differences in word sense similarity and iii) compare the word sense similarities of both methods to assess their correlation and gain some intuition as to how well contextualised word embeddings could be used as surrogate word sense similarity judgements in linguistic experiments.",
}
@inproceedings{marakasova-neidhardt-2020-short,
    title = "Short-term Semantic Shifts and their Relation to Frequency Change",
    author = "Marakasova, Anna  and
      Neidhardt, Julia",
    booktitle = "Proceedings of the Probability and Meaning Conference (PaM 2020)",
    month = jun,
    year = "2020",
    address = "Gothenburg",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.pam-1.18",
    pages = "146--153",
    abstract = "We present ongoing research on the relationship between short-term semantic shifts and frequency change patterns by examining the case of the refugee crisis in Austria from 2015 to 2016. Our experiments are carried out on a diachronic corpus of Austrian German, namely a corpus of newspaper articles. We trace the evolution of the usage of words that represent concepts in the context of the refugee crisis by analyzing cosine similarities of word vectors over time as well as similarities based on the words{'} nearest neighbourhood sets. In order to investigate how exactly the contextual meanings have changed, we measure cosine similarity between the following pairs of words: words describing the refugee crisis, on the one hand, and words indicating the process of mediatization and politicization of the refugee crisis in Austria proposed by a domain expert, on the other hand. We evaluate our approach against the expert knowledge. The paper presents the current findings and outlines the directions of the future work.",
}
@proceedings{osact-2020-open,
    title = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    editor = "Al-Khalifa, Hend  and
      Magdy, Walid  and
      Darwish, Kareem  and
      Elsayed, Tamer  and
      Mubarak, Hamdy",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.0",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{abu-kwaik-etal-2020-arabic,
    title = "An {A}rabic Tweets Sentiment Analysis Dataset ({ATSAD}) using Distant Supervision and Self Training",
    author = "Abu Kwaik, Kathrein  and
      Chatzikyriakidis, Stergios  and
      Dobnik, Simon  and
      Saad, Motaz  and
      Johansson, Richard",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.1",
    pages = "1--8",
    abstract = "As the number of social media users increases, they express their thoughts, needs, socialise and publish their opinions reviews. For good social media sentiment analysis, good quality resources are needed, and the lack of these resources is particularly evident for languages other than English, in particular Arabic. The available Arabic resources lack of from either the size of the corpus or the quality of the annotation. In this paper, we present an Arabic Sentiment Analysis Corpus collected from Twitter, which contains 36K tweets labelled into positive and negative. We employed distant supervision and self-training approaches into the corpus to annotate it. Besides, we release an 8K tweets manually annotated as a gold standard. We evaluated the corpus intrinsically by comparing it to human classification and pre-trained sentiment analysis models, Moreover, we apply extrinsic evaluation methods exploiting sentiment analysis task and achieve an accuracy of 86{\%}.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{antoun-etal-2020-arabert,
    title = "{A}ra{BERT}: Transformer-based Model for {A}rabic Language Understanding",
    author = "Antoun, Wissam  and
      Baly, Fady  and
      Hajj, Hazem",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.2",
    pages = "9--15",
    abstract = "The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achieve state-of-the-art results for most NLP tasks. In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language. The performance of AraBERT is compared to multilingual BERT from Google and other state-of-the-art approaches. The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks. The pretrained araBERT models are publicly available on https://github.com/aub-mind/araBERT hoping to encourage research and applications for Arabic NLP.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{abdul-mageed-etal-2020-aranet,
    title = "{A}ra{N}et: A Deep Learning Toolkit for {A}rabic Social Media",
    author = "Abdul-Mageed, Muhammad  and
      Zhang, Chiyu  and
      Hashemi, Azadeh  and
      Nagoudi, El Moatez Billah",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.3",
    pages = "16--23",
    abstract = "We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of both publicly available and novel social media datasets to train bidirectional encoders from transformers (BERT) focused at social meaning extraction. AraNet models predict age, dialect, gender, emotion, irony, and sentiment. AraNet either delivers state-of-the-art performance on a number of these tasks and performs competitively on others. AraNet is exclusively based on a deep learning framework, giving it the advantage of being feature-engineering free. To the best of our knowledge, AraNet is the first to performs predictions across such a wide range of tasks for Arabic NLP. As such, AraNet has the potential to meet critical needs. We publicly release AraNet to accelerate research, and to facilitate model-based comparisons across the different tasks",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{al-mulla-zaghouani-2020-building,
    title = "Building a Corpus of Qatari {A}rabic Expressions",
    author = "Al-Mulla, Sara  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.4",
    pages = "24--31",
    abstract = "The current Arabic natural language processing resources are mainly build to address the Modern Standard Arabic (MSA), while we witnessed some scattered efforts to build resources for various Arabic dialects such as the Levantine and the Egyptian dialects. We observed a lack of resources for Gulf Arabic and especially the Qatari variety. In this paper, we present the first Qatari idioms and expression corpus of 1000 entries. The corpus was created from on-line and printed sources in addition to transcribed recorded interviews. The corpus covers various Qatari traditional expressions and idioms. To this end, audio recordings were collected from interviews and an online survey questionnaire was conducted to validate our data. This corpus aims to help advance the dialectal Arabic Speech and Natural Language Processing tools and applications for the Qatari dialect.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{abu-farha-magdy-2020-arabic,
    title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
    author = "Abu Farha, Ibrahim  and
      Magdy, Walid",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.5",
    pages = "32--39",
    abstract = "Sarcasm is one of the main challenges for sentiment analysis systems. Its complexity comes from the expression of opinion using implicit indirect phrasing. In this paper, we present ArSarcasm, an Arabic sarcasm detection dataset, which was created through the reannotation of available Arabic sentiment analysis datasets. The dataset contains 10,547 tweets, 16{\%} of which are sarcastic. In addition to sarcasm the data was annotated for sentiment and dialects. Our analysis shows the highly subjective nature of these tasks, which is demonstrated by the shift in sentiment labels based on annotators{'} biases. Experiments show the degradation of state-of-the-art sentiment analysers when faced with sarcastic content. Finally, we train a deep learning model for sarcasm detection using BiLSTM. The model achieves an F1 score of 0.46, which shows the challenging nature of the task, and should act as a basic baseline for future research on our dataset.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{alshehri-etal-2020-understanding,
    title = "Understanding and Detecting Dangerous Speech in Social Media",
    author = "Alshehri, Ali  and
      Nagoudi, El Moatez Billah  and
      Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.6",
    pages = "40--47",
    abstract = "Social media communication has become a significant part of daily activity in modern societies. For this reason, ensuring safety in social media platforms is a necessity. Use of dangerous language such as physical threats in online environments is a somewhat rare, yet remains highly important. Although several works have been performed on the related issue of detecting offensive and hateful language, dangerous speech has not previously been treated in any significant way. Motivated by these observations, we report our efforts to build a labeled dataset for dangerous speech. We also exploit our dataset to develop highly effective models to detect dangerous content. Our best model performs at 59.60{\%} macro F1, significantly outperforming a competitive baseline.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{mubarak-etal-2020-overview,
    title = "Overview of {OSACT}4 {A}rabic Offensive Language Detection Shared Task",
    author = "Mubarak, Hamdy  and
      Darwish, Kareem  and
      Magdy, Walid  and
      Elsayed, Tamer  and
      Al-Khalifa, Hend",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.7",
    pages = "48--52",
    abstract = "This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4). There were two subtasks, namely: Subtask A, involving the detection of offensive language, which contains unacceptable or vulgar content in addition to any kind of explicit or implicit insults or attacks against individuals or groups; and Subtask B, involving the detection of hate speech, which contains insults or threats targeting a group based on their nationality, ethnicity, race, gender, political or sport affiliation, religious belief, or other common characteristics. In total, 40 teams signed up to participate in Subtask A, and 14 of them submitted test runs. For Subtask B, 33 teams signed up to participate and 13 of them submitted runs. We present and analyze all submissions in this paper.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{husain-2020-osact4,
    title = "{OSACT}4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach",
    author = "Husain, Fatemah",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.8",
    pages = "53--60",
    abstract = "The preprocessing phase is one of the key phases within the text classification pipeline. This study aims at investigating the impact of the preprocessing phase on text classification, specifically on offensive language and hate speech classification for Arabic text. The Arabic language used in social media is informal and written using Arabic dialects, which makes the text classification task very complex. Preprocessing helps in dimensionality reduction and removing useless content. We apply intensive preprocessing techniques to the dataset before processing it further and feeding it into the classification model. An intensive preprocessing-based approach demonstrates its significant impact on offensive language detection and hate speech detection shared tasks of the fourth workshop on Open-Source Arabic Corpora and Corpora Processing Tools (OSACT). Our team wins the third place (3rd) in the Sub-Task A Offensive Language Detection division and wins the first place (1st) in the Sub-Task B Hate Speech Detection division, with an F1 score of 89{\%} and 95{\%}, respectively, by providing the state-of-the-art performance in terms of F1, accuracy, recall, and precision for Arabic hate speech detection.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{hassan-etal-2020-alt,
    title = "{ALT} Submission for {OSACT} Shared Task on Offensive Language Detection",
    author = "Hassan, Sabit  and
      Samih, Younes  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Rashed, Ammar  and
      Chowdhury, Shammur Absar",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.9",
    pages = "61--65",
    abstract = "In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51{\%} on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63{\%} on the test set.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{keleg-etal-2020-asu,
    title = "{ASU}{\_}{OPTO} at {OSACT}4 - Offensive Language Detection for {A}rabic text",
    author = "Keleg, Amr  and
      El-Beltagy, Samhaa R.  and
      Khalil, Mahmoud",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.10",
    pages = "66--70",
    abstract = "In the past years, toxic comments and offensive speech are polluting the internet and manual inspection of these comments is becoming a tiresome task to manage. Having a machine learning based model that is able to filter offensive Arabic content is of high need nowadays. In this paper, we describe the model that was submitted to the Shared Task on Offensive Language Detection that is organized by (The 4th Workshop on Open-Source Arabic Corpora and Processing Tools). Our model makes use transformer based model (BERT) to detect offensive content. We came in the fourth place in subtask A (detecting Offensive Speech) and in the third place in subtask B (detecting Hate Speech).",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{saeed-etal-2020-osact4,
    title = "{OSACT}4 Shared Tasks: Ensembled Stacked Classification for Offensive and Hate Speech in {A}rabic Tweets",
    author = "Saeed, Hafiz Hassaan  and
      Calders, Toon  and
      Kamiran, Faisal",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.11",
    pages = "71--75",
    abstract = "In this paper, we describe our submission for the OCAST4 2020 shared tasks on offensive language and hate speech detection in the Arabic language. Our solution builds upon combining a number of deep learning models using pre-trained word vectors. To improve the word representation and increase word coverage, we compare a number of existing pre-trained word embeddings and finally concatenate the two empirically best among them. To avoid under- as well as over-fitting, we train each deep model multiple times, and we include the optimization of the decision threshold into the training process. The predictions of the resulting models are then combined into a tuned ensemble by stacking a classifier on top of the predictions by these base models. We name our approach {``}ESOTP{''} (Ensembled Stacking classifier over Optimized Thresholded Predictions of multiple deep models). The resulting ESOTP-based system ranked 6th out of 35 on the shared task of Offensive Language detection (sub-task A) and 5th out of 30 on Hate Speech Detection (sub-task B).",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{haddad-etal-2020-arabic,
    title = "{A}rabic Offensive Language Detection with Attention-based Deep Neural Networks",
    author = "Haddad, Bushr  and
      Orabe, Zoher  and
      Al-Abood, Anas  and
      Ghneim, Nada",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.12",
    pages = "76--81",
    abstract = "In this paper, we tackle the problem of offensive language and hate speech detection. We proposed our methods for data preprocessing and balancing, and then we presented our Convolutional Neural Network (CNN) and bidirectional Gated Recurrent Unit (GRU) models used. After that, we augmented these models with attention layer. The best results achieved was using the Bidirectional Gated Recurrent Unit augmented with attention layer (Bi-GRU{\_}ATT). Keywords: Abusive Language, Text Mining, Arabic Language, Social Media Mining, Deep Learning, Convolutional Neural Network, Gated Recurrent Unit, Attention Mechanism, Machine Learning.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{abdellatif-elgammal-2020-offensive,
    title = "Offensive language detection in {A}rabic using {ULMF}i{T}",
    author = "Abdellatif, Mohamed  and
      Elgammal, Ahmed",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.13",
    pages = "82--85",
    abstract = "In this paper, we approach the shared task OffenseEval 2020 by Mubarak et al. (2020) using ULMFiT Howard and Ruder (2018) pre-trained on Arabic Wikipedia Khooli (2019) which we use as a starting point and use the target data-set to fine-tune it. The data set of the task is highly imbalanced. We train forward and backward models and ensemble the results. We report confusion matrix, accuracy, precision, recall and F1 of the development set and report summarized results of the test set. Transfer learning method using ULMFiT shows potential for Arabic text classification. Mubarak, K. Darwish,W. Magdy, T. Elsayed, and H. Al-Khalifa. Overview of osact4 arabic offensive language detection shared task. 4, 2020. Howard and S. Ruder. Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146, 2018. Khooli. Applied data science. https://github.com/abedkhooli/ds2, 2019.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{abu-farha-magdy-2020-multitask,
    title = "Multitask Learning for {A}rabic Offensive Language and Hate-Speech Detection",
    author = "Abu Farha, Ibrahim  and
      Magdy, Walid",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.14",
    pages = "86--90",
    abstract = "Offensive language and hate-speech are phenomena that spread with the rising popularity of social media. Detecting such content is crucial for understanding and predicting conflicts, understanding polarisation among communities and providing means and tools to filter or block inappropriate content. This paper describes the SMASH team submission to OSACT4{'}s shared task on hate-speech and offensive language detection, where we explore different approaches to perform these tasks. The experiments cover a variety of approaches that include deep learning, transfer learning and multitask learning. We also explore the utilisation of sentiment information to perform the previous task. Our best model is a multitask learning architecture, based on CNN-BiLSTM, that was trained to detect hate-speech and offensive language and predict sentiment.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{alharbi-lee-2020-combining,
    title = "Combining Character and Word Embeddings for the Detection of Offensive Language in {A}rabic",
    author = "Alharbi, Abdullah I.  and
      Lee, Mark",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.15",
    pages = "91--96",
    abstract = "Twitter and other social media platforms offer users the chance to share their ideas via short posts. While the easy exchange of ideas has value, these microblogs can be leveraged by people who want to share hatred. and such individuals can share negative views about an individual, race, or group with millions of people at the click of a button. There is thus an urgent need to establish a method that can automatically identify hate speech and offensive language. To contribute to this development, during the OSACT4 workshop, a shared task was undertaken to detect offensive language in Arabic. A key challenge was the uniqueness of the language used on social media, prompting the out-of-vocabulary (OOV) problem. In addition, the use of different dialects in Arabic exacerbates this problem. To deal with the issues associated with OOV, we generated a character-level embeddings model, which was trained on a massive data collected carefully. This level of embeddings can work effectively in resolving the problem of OOV words through its ability to learn the vectors of character n-grams or parts of words. The proposed systems were ranked 7th and 8th for Subtasks A and B, respectively.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{djandji-etal-2020-multi,
    title = "Multi-Task Learning using {A}ra{B}ert for Offensive Language Detection",
    author = "Djandji, Marc  and
      Baly, Fady  and
      Antoun, Wissam  and
      Hajj, Hazem",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.16",
    pages = "97--101",
    abstract = "The use of social media platforms has become more prevalent, which has provided tremendous opportunities for people to connect but has also opened the door for misuse with the spread of hate speech and offensive language. This phenomenon has been driving more and more people to more extreme reactions and online aggression, sometimes causing physical harm to individuals or groups of people. There is a need to control and prevent such misuse of online social media through automatic detection of profane language. The shared task on Offensive Language Detection at the OSACT4 has aimed at achieving state of art profane language detection methods for Arabic social media. Our team {``}BERTologists{''} tackled this problem by leveraging state of the art pretrained Arabic language model, AraBERT, that we augment with the addition of Multi-task learning to enable our model to learn efficiently from little data. Our Multitask AraBERT approach achieved the second place in both subtasks A {\&} B, which shows that the model performs consistently across different tasks.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{elmadany-etal-2020-leveraging,
    title = "Leveraging Affective Bidirectional Transformers for Offensive Language Detection",
    author = "Elmadany, AbdelRahim  and
      Zhang, Chiyu  and
      Abdul-Mageed, Muhammad  and
      Hashemi, Azadeh",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.17",
    pages = "102--108",
    abstract = "Social media are pervasive in our life, making it necessary to ensure safe online experiences by detecting and removing offensive and hate speech. In this work, we report our submission to the Offensive Language and hate-speech Detection shared task organized with the 4th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT4). We focus on developing purely deep learning systems, without a need for feature engineering. For that purpose, we develop an effective method for automatic data augmentation and show the utility of training both offensive and hate speech models off (i.e., by fine-tuning) previously trained affective models (i.e., sentiment and emotion). Our best models are significantly better than a vanilla BERT model, with 89.60{\%} acc (82.31{\%} macro F1) for hate speech and 95.20{\%} acc (70.51{\%} macro F1) on official TEST data.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@inproceedings{abuzayed-elsayed-2020-quick,
    title = "Quick and Simple Approach for Detecting Hate Speech in {A}rabic Tweets",
    author = "Abuzayed, Abeer  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://www.aclweb.org/anthology/2020.osact-1.18",
    pages = "109--114",
    abstract = "As the use of social media platforms increases extensively to freely communicate and share opinions, hate speech becomes an outstanding problem that requires urgent attention. This paper focuses on the problem of detecting hate speech in Arabic tweets. To tackle the problem efficiently, we adopt a {``}quick and simple{''} approach by which we investigate the effectiveness of 15 classical (e.g., SVM) and neural (e.g., CNN) learning models, while exploring two different term representations. Our experiments on 8k labelled dataset show that the best neural learning models outperform the classical ones, while distributed term representation is more effective than statistical bag-of-words representation. Overall, our best classifier (that combines both CNN and RNN in a joint architecture) achieved 0.73 macro-F1 score on the dev set, which significantly outperforms the majority-class baseline that achieves 0.49, proving the effectiveness of our {``}quick and simple{''} approach.",
    language = "English",
    ISBN = "979-10-95546-51-1",
}
@proceedings{onion-2020-lrec2020,
    title = "Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)",
    editor = "Paggio, Patrizia  and
      Gatt, Albert  and
      Klinger, Roman",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.onion-1.0",
    ISBN = "979-10-95546-70-2",
}
@inproceedings{lembke-etal-2020-prototypes,
    title = "Prototypes and Recognition of Self in Depictions of Christ",
    author = "Lembke, Carla Sophie  and
      Folger{\o}, Per Olav  and
      Andresen, Alf Edgar  and
      Johansson, Christer",
    booktitle = "Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.onion-1.1",
    pages = "1--6",
    abstract = "We present a study on prototype effects. We designed an experiment investigating the effect of adapting a prototypical image towards more human, male or female, prototypes, and additionally investigating the effect of self-recognition in a manipulated image. Results show that decisions are affected by prototypicality, but we find less evidence that self-recognition further enhances perceptions of attractiveness. This study has implications for the psychological perception of faces, and may contribute to the study of Christian imagery.",
    language = "English",
    ISBN = "979-10-95546-70-2",
}
@inproceedings{mori-etal-2020-analysis,
    title = "Analysis of Body Behaviours in Human-Human and Human-Robot Interactions",
    author = "Mori, Taiga  and
      Jokinen, Kristiina  and
      Den, Yasuharu",
    booktitle = "Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.onion-1.2",
    pages = "7--14",
    abstract = "We conducted preliminary comparison of human-robot (HR) interaction with human-human (HH) interaction conducted in English and in Japanese. As the result, body gestures increased in HR, while hand and head gestures decreased in HR. Concerning hand gesture, they were composed of more diverse and complex forms, trajectories and functions in HH than in HR. Moreover, English speakers produced 6 times more hand gestures than Japanese speakers in HH. Regarding head gesture, even though there was no difference in the frequency of head gestures between English speakers and Japanese speakers in HH, Japanese speakers produced slightly more nodding during the robot{'}s speaking than English speakers in HR. Furthermore, positions of nod were different depending on the language. Concerning body gesture, participants produced body gestures mostly to regulate appropriate distance with the robot in HR. Additionally, English speakers produced slightly more body gestures than Japanese speakers.",
    language = "English",
    ISBN = "979-10-95546-70-2",
}
@inproceedings{paggio-etal-2020-automatic,
    title = "Automatic Detection and Classification of Head Movements in Face-to-Face Conversations",
    author = "Paggio, Patrizia  and
      Agirrezabal, Manex  and
      Jongejan, Bart  and
      Navarretta, Costanza",
    booktitle = "Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.onion-1.3",
    pages = "15--21",
    abstract = "This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-to-face conversations in Danish involving 12 different speakers. A number of classifiers were trained with different combinations of visual, acoustic and word features and tested in a leave-one-out cross validation scenario. The visual movement features were extracted from the raw video data using OpenPose, and the acoustic ones using Praat. The best results were obtained by a Multilayer Perceptron classifier, which reached an average 0.68 F1 score across the 12 speakers for head movement detection, and 0.40 for head movement classification given four different classes. In both cases, the classifier outperformed a simple most frequent class baseline as well as a more advanced baseline only relying on velocity features.",
    language = "English",
    ISBN = "979-10-95546-70-2",
}
@inproceedings{anastasiou-etal-2020-move,
    title = "{``}You move {THIS}!{''}: Annotation of Pointing Gestures on Tabletop Interfaces in Low Awareness Situations",
    author = "Anastasiou, Dimitra  and
      Afkari, Hoorieh  and
      Maquil, Val{\'e}rie",
    booktitle = "Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.onion-1.4",
    pages = "22--27",
    abstract = "This paper analyses pointing gestures during low awareness situations occurring in a collaborative problem-solving activity implemented on an interactive tabletop interface. Awareness is considered as crucial requirement to support fluid and natural collaboration. We focus on pointing gestures as strategy to maintain awareness. We describe the results from a user study with five groups, each group consisting of three participants, who were asked to solve a task collaboratively on a tabletop interface. The ideal problem-solving solution would have been, if the three participants had been fully aware of what their personal area is depicting and had communicated this properly to the peers. However, often some participants are hesitant due to lack of awareness, some other want to take the lead work or expedite the process, and therefore pointing gestures to others{'} personal areas arise. Our results from analyzing a multimodal corpus of 168.68 minutes showed that in 95{\%} of the cases, one user pointed to the personal area of the other, while in a few cases (3{\%}) a user not only pointed, but also performed a touch gesture on the personal area of another user. In our study, the mean for such pointing gestures in low awareness situations per minute and for all groups was M=1.96, SD=0.58.",
    language = "English",
    ISBN = "979-10-95546-70-2",
}
@inproceedings{schlor-etal-2020-improving,
    title = "Improving Sentiment Analysis with Biofeedback Data",
    author = {Schl{\"o}r, Daniel  and
      Zehe, Albin  and
      Kobs, Konstantin  and
      Veseli, Blerta  and
      Westermeier, Franziska  and
      Br{\"u}bach, Larissa  and
      Roth, Daniel  and
      Latoschik, Marc Erich  and
      Hotho, Andreas},
    booktitle = "Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.onion-1.5",
    pages = "28--33",
    abstract = "Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.",
    language = "English",
    ISBN = "979-10-95546-70-2",
}
@proceedings{nuse-2020-joint,
    title = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    editor = "Bonial, Claire  and
      Caselli, Tommaso  and
      Chaturvedi, Snigdha  and
      Clark, Elizabeth  and
      Huang, Ruihong  and
      Iyyer, Mohit  and
      Jaimes, Alejandro  and
      Ji, Heng  and
      Martin, Lara J.  and
      Miller, Ben  and
      Mitamura, Teruko  and
      Peng, Nanyun  and
      Tetreault, Joel",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.0",
}
@inproceedings{cremisini-finlayson-2020-new,
    title = "New Insights into Cross-Document Event Coreference: Systematic Comparison and a Simplified Approach",
    author = "Cremisini, Andres  and
      Finlayson, Mark",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.1",
    doi = "10.18653/v1/2020.nuse-1.1",
    pages = "1--10",
    abstract = "Cross-Document Event Coreference (CDEC) is the task of finding coreference relationships between events in separate documents, most commonly assessed using the Event Coreference Bank+ corpus (ECB+). At least two different approaches have been proposed for CDEC on ECB+ that use only event triggers, and at least four have been proposed that use both triggers and entities. Comparing these approaches is complicated by variation in the systems{'} use of gold vs. computed labels, as well as variation in the document clustering pre-processing step. We present an approach that matches or slightly beats state-of-the-art performance on CDEC over ECB+ with only event trigger annotations, but with a significantly simpler framework and much smaller feature set relative to prior work. This study allows us to directly compare with prior systems and draw conclusions about the effectiveness of various strategies. Additionally, we provide the first cross-validated evaluation on the ECB+ dataset; the first explicit evaluation of the pairwise event coreference classification step; and the first quantification of the effect of document clustering on system performance. The last in particular reveals that while document clustering is a crucial pre-processing step, improvements can at most provide for a 3 point improvement in CDEC performance, though this might be attributable to ease of document clustering on ECB+.",
}
@inproceedings{chiu-etal-2020-screenplay,
    title = "Screenplay Quality Assessment: Can We Predict Who Gets Nominated?",
    author = "Chiu, Ming-Chang  and
      Feng, Tiantian  and
      Ren, Xiang  and
      Narayanan, Shrikanth",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.2",
    doi = "10.18653/v1/2020.nuse-1.2",
    pages = "11--16",
    abstract = "Deciding which scripts to turn into movies is a costly and time-consuming process for filmmakers. Thus, building a tool to aid script selection, an initial phase in movie production, can be very beneficial. Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues. We address this in a two-fold approach: (1) we define the task as predicting nominations of scripts at major film awards with the hypothesis that the peer-recognized scripts should have a greater chance to succeed. (2) based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques. We face two challenges (1) scripts are much longer than other document datasets (2) nominated scripts are limited and thus difficult to collect. However, with narratology-inspired modeling and domain features, our approach offers clear improvements over strong baselines. Our work provides a new approach for future work in screenplay analysis.",
}
@inproceedings{banisakher-etal-2020-improving,
    title = "Improving the Identification of the Discourse Function of News Article Paragraphs",
    author = "Banisakher, Deya  and
      Yarlott, W. Victor  and
      Aldawsari, Mohammed  and
      Rishe, Naphtali  and
      Finlayson, Mark",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.3",
    doi = "10.18653/v1/2020.nuse-1.3",
    pages = "17--25",
    abstract = "Identifying the discourse structure of documents is an important task in understanding written text. Building on prior work, we demonstrate an improved approach to automatically identifying the discourse function of paragraphs in news articles. We start with the hierarchical theory of news discourse developed by van Dijk (1988) which proposes how paragraphs function within news articles. This discourse information is a level intermediate between phrase- or sentence-sized discourse segments and document genre, characterizing how individual paragraphs convey information about the events in the storyline of the article. Specifically, the theory categorizes the relationships between narrated events and (1) the overall storyline (such as Main Events, Background, or Consequences) as well as (2) commentary (such as Verbal Reactions and Evaluations). We trained and tested a linear chain conditional random field (CRF) with new features to model van Dijk{'}s labels and compared it against several machine learning models presented in previous work. Our model significantly outperformed all baselines and prior approaches, achieving an average of 0.71 F1 score which represents a 31.5{\%} improvement over the previously best-performing support vector machine model.",
}
@inproceedings{zad-finlayson-2020-systematic,
    title = "Systematic Evaluation of a Framework for Unsupervised Emotion Recognition for Narrative Text",
    author = "Zad, Samira  and
      Finlayson, Mark",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.4",
    doi = "10.18653/v1/2020.nuse-1.4",
    pages = "26--37",
    abstract = "Identifying emotions as expressed in text (a.k.a. text emotion recognition) has received a lot of attention over the past decade. Narratives often involve a great deal of emotional expression, and so emotion recognition on narrative text is of great interest to computational approaches to narrative understanding. Prior work by Kim et al. 2010 was the work with the highest reported emotion detection performance, on a corpus of fairy tales texts. Close inspection of that work, however, revealed significant reproducibility problems, and we were unable to reimplement Kim{'}s approach as described. As a consequence, we implemented a framework inspired by Kim{'}s approach, where we carefully evaluated the major design choices. We identify the highest-performing combination, which outperforms Kim{'}s reported performance by 7.6 $F_1$ points on average. Close inspection of the annotated data revealed numerous missing and incorrect emotion terms in the relevant lexicon, WordNetAffect (WNA; Strapparava and Valitutti, 2004), which allowed us to augment it in a useful way. More generally, this showed that numerous clearly emotive words and phrases are missing from WNA, which suggests that effort invested in augmenting or refining emotion ontologies could be useful for improving the performance of emotion recognition systems. We release our code and data to definitely enable future reproducibility of this work.",
}
@inproceedings{lai-etal-2020-extensively,
    title = "Extensively Matching for Few-shot Learning Event Detection",
    author = "Lai, Viet Dac  and
      Nguyen, Thien Huu  and
      Dernoncourt, Franck",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.5",
    doi = "10.18653/v1/2020.nuse-1.5",
    pages = "38--45",
    abstract = "Current event detection models under supervised learning settings fail to transfer to new event types. Few-shot learning has not been explored in event detection even though it allows a model to perform well with high generalization on new event types. In this work, we formulate event detection as a few-shot learning problem to enable to extend event detection to new event types. We propose two novel loss factors that matching examples in the support set to provide more training signals to the model. Moreover, these training signals can be applied in many metric-based few-shot learning models. Our extensive experiments on the ACE-2005 dataset (under a few-shot learning setting) show that the proposed method can improve the performance of few-shot learning.",
}
@inproceedings{august-etal-2020-exploring,
    title = "Exploring the Effect of Author and Reader Identity in Online Story Writing: the {STORIESINTHEWILD} Corpus.",
    author = "August, Tal  and
      Sap, Maarten  and
      Clark, Elizabeth  and
      Reinecke, Katharina  and
      Smith, Noah A.",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.6",
    doi = "10.18653/v1/2020.nuse-1.6",
    pages = "46--54",
    abstract = "Current story writing or story editing systems rely on human judgments of story quality for evaluating performance, often ignoring the subjectivity in ratings. We analyze the effect of author and reader characteristics and story writing setup on the quality of stories in a short storytelling task. To study this effect, we create and release STORIESINTHEWILD, containing 1,630 stories collected on a volunteer-based crowdsourcing platform. Each story is rated by three different readers, and comes paired with the author{'}s and reader{'}s age, gender, and personality. Our findings show significant effects of authors{'} and readers{'} identities, as well as writing setup, on story writing and ratings. Notably, compared to younger readers, readers age 45 and older consider stories significantly less creative and less entertaining. Readers also prefer stories written all at once, rather than in chunks, finding them more coherent and creative. We also observe linguistic differences associated with authors{'} demographics (e.g., older authors wrote more vivid and emotional stories). Our findings suggest that reader and writer demographics, as well as writing setup, should be accounted for in story writing evaluations.",
}
@inproceedings{belyy-van-durme-2020-script,
    title = "Script Induction as Association Rule Mining",
    author = "Belyy, Anton  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.7",
    doi = "10.18653/v1/2020.nuse-1.7",
    pages = "55--62",
    abstract = "We show that the count-based Script Induction models of Chambers and Jurafsky (2008) and Jans et al. (2012) can be unified in a general framework of narrative chain likelihood maximization. We provide efficient algorithms based on Association Rule Mining (ARM) and weighted set cover that can discover interesting patterns in the training data and combine them in a reliable and explainable way to predict the missing event. The proposed method, unlike the prior work, does not assume full conditional independence and makes use of higher-order count statistics. We perform the ablation study and conclude that the inductive biases introduced by ARM are conducive to better performance on the narrative cloze test.",
}
@inproceedings{eisenberg-sheriff-2020-automatic,
    title = "Automatic extraction of personal events from dialogue",
    author = "Eisenberg, Joshua  and
      Sheriff, Michael",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.8",
    doi = "10.18653/v1/2020.nuse-1.8",
    pages = "63--71",
    abstract = "In this paper we introduce the problem of extracting events from dialogue. Previous work on event extraction focused on newswire, however we are interested in extracting events from spoken dialogue. To ground this study, we annotated dialogue transcripts from fourteen episodes of the podcast This American Life. This corpus contains 1,038 utterances, made up of 16,962 tokens, of which 3,664 represent events. The agreement for this corpus has a Cohen{'}s Kappa of 0.83. We have open-sourced this corpus for the NLP community. With this corpus in hand, we trained support vector machines (SVM) to correctly classify these phenomena with 0.68 F1, when using episode-fold cross-validation. This is nearly 100{\%} higher F1 than the baseline classifier. The SVM models achieved performance of over 0.75 F1 on some testing folds. We report the results for SVM classifiers trained with four different types of features (verb classes, part of speech tags, named entities, and semantic role labels), and different machine learning protocols (under-sampling and trigram context). This work is grounded in narratology and computational models of narrative. It is useful for extracting events, plot, and story content from spoken dialogue.",
}
@inproceedings{kearns-2020-annotating,
    title = "Annotating and quantifying narrative time disruptions in modernist and hypertext fiction",
    author = "Kearns, Edward",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.9",
    doi = "10.18653/v1/2020.nuse-1.9",
    pages = "72--77",
    abstract = "This paper outlines work in progress on a new method of annotating and quantitatively discussing narrative techniques related to time in fiction. Specifically those techniques are analepsis, prolepsis, narrative level changes, and stream-of-consciousness and free-indirect-discourse narration. By counting the frequency and extent of the usage of these techniques, the narrative characteristics of different works from different time periods and genres can be compared. This project uses modernist fiction and hypertext fiction as its case studies.",
}
@inproceedings{saldias-roy-2020-exploring,
    title = "Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types",
    author = "Saldias, Belen  and
      Roy, Deb",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.10",
    doi = "10.18653/v1/2020.nuse-1.10",
    pages = "78--86",
    abstract = "Sharing personal narratives is a fundamental aspect of human social behavior as it helps share our life experiences. We can tell stories and rely on our background to understand their context, similarities, and differences. A substantial effort has been made towards developing storytelling machines or inferring characters{'} features. However, we don{'}t usually find models that compare narratives. This task is remarkably challenging for machines since they, as sometimes we do, lack an understanding of what similarity means. To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. Second, we ask non-narrative experts to annotate those clauses under Labov{'}s sociolinguistic model of personal narratives (i.e., action, orientation, and evaluation clause types) and train a classifier that reaches 84.7{\%} F-score for the highest-agreed clauses. Finally, we match stories and explore whether people implicitly rely on Labov{'}s framework to compare narratives. We show that actions followed by the narrator{'}s evaluation of these are the aspects non-experts consider the most. Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.",
}
@inproceedings{hingmire-etal-2020-extracting,
    title = "Extracting Message Sequence Charts from {H}indi Narrative Text",
    author = "Hingmire, Swapnil  and
      Ramrakhiyani, Nitin  and
      Singh, Avinash Kumar  and
      Patil, Sangameshwar  and
      Palshikar, Girish  and
      Bhattacharyya, Pushpak  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.11",
    doi = "10.18653/v1/2020.nuse-1.11",
    pages = "87--96",
    abstract = "In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi. An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference. We propose an approach to extract MSC actors and interactions from a Hindi narrative. As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments. We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History. Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English. Moreover, there is relatively less work in the context of event processing in Hindi. Hence, we believe that this work is among the initial works for Hindi event processing.",
}
@inproceedings{somasundaran-etal-2020-emotion,
    title = "Emotion Arcs of Student Narratives",
    author = "Somasundaran, Swapna  and
      Chen, Xianyang  and
      Flor, Michael",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.12",
    doi = "10.18653/v1/2020.nuse-1.12",
    pages = "97--107",
    abstract = "This paper studies emotion arcs in student narratives. We construct emotion arcs based on event affect and implied sentiments, which correspond to plot elements in the story. We show that student narratives can show elements of plot structure in their emotion arcs and that properties of these arcs can be useful indicators of narrative quality. We build a system and perform analysis to show that our arc-based features are complementary to previously studied sentiment features in this area.",
}
@inproceedings{mou-etal-2020-frustratingly,
    title = "Frustratingly Hard Evidence Retrieval for {QA} Over Books",
    author = "Mou, Xiangyang  and
      Yu, Mo  and
      Yao, Bingsheng  and
      Yang, Chenghao  and
      Guo, Xiaoxiao  and
      Potdar, Saloni  and
      Su, Hui",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.13",
    doi = "10.18653/v1/2020.nuse-1.13",
    pages = "108--113",
    abstract = "A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth. We formulate BookQA as an open-domain QA task given its similar dependency on evidence retrieval. We further investigate how state-of-the-art open-domain QA approaches can help BookQA. Besides achieving state-of-the-art on the NarrativeQA benchmark, our study also reveals the difficulty of evidence retrieval in books with a wealth of experiments and analysis - which necessitates future effort on novel solutions for evidence retrieval in BookQA.",
}
@inproceedings{wang-mcallester-2020-fly,
    title = "On-The-Fly Information Retrieval Augmentation for Language Models",
    author = "Wang, Hai  and
      McAllester, David",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.14",
    doi = "10.18653/v1/2020.nuse-1.14",
    pages = "114--119",
    abstract = "Here we experiment with the use of information retrieval as an augmentation for pre-trained language models. The text corpus used in information retrieval can be viewed as form of episodic memory which grows over time. By augmenting GPT 2.0 with information retrieval we achieve a zero shot 15{\%} relative reduction in perplexity on Gigaword corpus without any re-training. We also validate our IR augmentation on an event co-reference task.",
}
@inproceedings{shahid-etal-2020-detecting,
    title = "Detecting and understanding moral biases in news",
    author = "Shahid, Usman  and
      Di Eugenio, Barbara  and
      Rojecki, Andrew  and
      Zheleva, Elena",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nuse-1.15",
    doi = "10.18653/v1/2020.nuse-1.15",
    pages = "120--125",
    abstract = "We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing. First we draw connections between issue-specific frames and moral frames that apply to all issues. Then we analyze the connection between moral frame presence and news source political leaning. We develop and test a simple classification model for detecting the presence of a moral frame, highlighting the need for more sophisticated models. We also discuss some of the annotation and frame detection challenges that can inform future research in this area.",
}
@proceedings{nlp-tea-2020-natural,
    title = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    editor = "YANG, Erhong  and
      XUN, Endong  and
      ZHANG, Baolin  and
      RAO, Gaoqi",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.0",
}
@inproceedings{homma-komachi-2020-non,
    title = "Non-Autoregressive Grammatical Error Correction Toward a Writing Support System",
    author = "Homma, Hiroki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.1",
    pages = "1--10",
    abstract = "There are several problems in applying grammatical error correction (GEC) to a writing support system. One of them is the handling of sentences in the middle of the input. Till date, the performance of GEC for incomplete sentences is not well-known. Hence, we analyze the performance of each model for incomplete sentences. Another problem is the correction speed. When the speed is slow, the usability of the system is limited, and the user experience is degraded. Therefore, in this study, we also focus on the non-autoregressive (NAR) model, which is a widely studied fast decoding method. We perform GEC in Japanese with traditional autoregressive and recent NAR models and analyze their accuracy and speed.",
}
@inproceedings{moslem-etal-2020-arabisc,
    title = "Arabisc: Context-Sensitive Neural Spelling Checker",
    author = "Moslem, Yasmin  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.2",
    pages = "11--19",
    abstract = "Traditional statistical approaches to spelling correction usually consist of two consecutive processes {---} error detection and correction {---} and they are generally computationally intensive. Current state-of-the-art neural spelling correction models usually attempt to correct spelling errors directly over an entire sentence, which, as a consequence, lacks control of the process, e.g. they are prone to overcorrection. In recent years, recurrent neural networks (RNNs), in particular long short-term memory (LSTM) hidden units, have proven increasingly popular and powerful models for many natural language processing (NLP) problems. Accordingly, we made use of a bidirectional LSTM language model (LM) for our context-sensitive spelling detection and correction model which is shown to have much control over the correction process. While the use of LMs for spelling checking and correction is not new to this line of NLP research, our proposed approach makes better use of the rich neighbouring context, not only from before the word to be corrected, but also after it, via a dual-input deep LSTM network. Although in theory our proposed approach can be applied to any language, we carried out our experiments on Arabic, which we believe adds additional value given the fact that there are limited linguistic resources readily available in Arabic in comparison to many languages. Our experimental results demonstrate that the proposed methods are effective in both improving the quality of correction suggestions and minimising overcorrection.",
}
@inproceedings{lee-lee-2020-lxper,
    title = "{LXPER} Index 2.0: Improving Text Readability Assessment Model for {L}2 {E}nglish Students in {K}orea",
    author = "Lee, Bruce W.  and
      Lee, Jason",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.3",
    pages = "20--24",
    abstract = "Developing a text readability assessment model specifically for texts in a foreign English Language Training (ELT) curriculum has never had much attention in the field of Natural Language Processing. Hence, most developed models show extremely low accuracy for L2 English texts, up to the point where not many even serve as a fair comparison. In this paper, we investigate a text readability assessment model for L2 English learners in Korea. In accordance, we improve and expand the Text Corpus of the Korean ELT curriculum (CoKEC-text). Each text is labeled with its target grade level. We train our model with CoKEC-text and significantly improve the accuracy of readability assessment for texts in the Korean ELT curriculum.",
}
@inproceedings{rao-etal-2020-overview,
    title = "Overview of {NLPTEA}-2020 Shared Task for {C}hinese Grammatical Error Diagnosis",
    author = "Rao, Gaoqi  and
      Yang, Erhong  and
      Zhang, Baolin",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.4",
    pages = "25--35",
    abstract = "This paper presents the NLPTEA 2020 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as a foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 30 teams registered for this shared task, 17 teams developed the system and submitted a total of 43 runs. System performances achieved a significant progress, reaching F1 of 91{\%} in detection level, 40{\%} in position level and 28{\%} in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers.",
}
@inproceedings{wang-etal-2020-combining,
    title = "Combining {R}es{N}et and Transformer for {C}hinese Grammatical Error Diagnosis",
    author = "Wang, Shaolei  and
      Wang, Baoxin  and
      Gong, Jiefu  and
      Wang, Zhongyuan  and
      Hu, Xiao  and
      Duan, Xingyi  and
      Shen, Zizhuo  and
      Yue, Gang  and
      Fu, Ruiji  and
      Wu, Dayong  and
      Che, Wanxiang  and
      Wang, Shijin  and
      Hu, Guoping  and
      Liu, Ting",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.5",
    pages = "36--43",
    abstract = "Grammatical error diagnosis is an important task in natural language processing. This paper introduces our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED). CGED aims to diagnose four types of grammatical errors which are missing words (M), redundant words (R), bad word selection (S) and disordered words (W). Our system is built on the model of multi-layer bidirectional transformer encoder and ResNet is integrated into the encoder to improve the performance. We also explore two ensemble strategies including weighted averaging and stepwise ensemble selection from libraries of models to improve the performance of single model. In official evaluation, our system obtains the highest F1 scores at identification level and position level. We also recommend error corrections for specific error types and achieve the second highest F1 score at correction level.",
}
@inproceedings{luo-etal-2020-chinese,
    title = "{C}hinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning",
    author = "Luo, Yikang  and
      Bao, Zuyi  and
      Li, Chen  and
      Wang, Rui",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.6",
    pages = "44--48",
    abstract = "This paper describes our participating system on the Chinese Grammatical Error Diagnosis (CGED) 2020 shared task. For the detection subtask, we propose two BERT-based approaches 1) with syntactic dependency trees enhancing the model performance and 2) under the multi-task learning framework to combine the sequence labeling and the sequence-to-sequence (seq2seq) models. For the correction subtask, we utilize the masked language model, the seq2seq model and the spelling check model to generate corrections based on the detection results. Finally, our system achieves the highest recall rate on the top-3 correction and the second best F1 score on identification level and position level.",
}
@inproceedings{cao-etal-2020-integrating,
    title = "Integrating {BERT} and Score-based Feature Gates for {C}hinese Grammatical Error Diagnosis",
    author = "Cao, Yongchang  and
      He, Liang  and
      Ridley, Robert  and
      Dai, Xinyu",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.7",
    pages = "49--56",
    abstract = "This paper describes our proposed model for the Chinese Grammatical Error Diagnosis (CGED) task in NLPTEA2020. The goal of CGED is to use natural language processing techniques to automatically diagnose Chinese grammatical errors in sentences. To this end, we design and implement a CGED model named BERT with Score-feature Gates Error Diagnoser (BSGED), which is based on the BERT model, Bidirectional Long Short-Term Memory (BiLSTM) and conditional random field (CRF). In order to address the problem of losing partial-order relationships when embedding continuous feature items as with previous works, we propose a gating mechanism for integrating continuous feature items, which effectively retains the partial-order relationships between feature items. We perform LSTM processing on the encoding result of the BERT model, and further extract the sequence features. In the final test-set evaluation, we obtained the highest F1 score at the detection level and are among the top 3 F1 scores at the identification level.",
}
@inproceedings{liang-etal-2020-bert,
    title = "{BERT} Enhanced Neural Machine Translation and Sequence Tagging Model for {C}hinese Grammatical Error Diagnosis",
    author = "Liang, Deng  and
      Zheng, Chen  and
      Guo, Lei  and
      Cui, Xin  and
      Xiong, Xiuzhang  and
      Rong, Hengqiao  and
      Dong, Jinpeng",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.8",
    pages = "57--66",
    abstract = "This paper presents the UNIPUS-Flaubert team{'}s hybrid system for the NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED). As a challenging NLP task, CGED has attracted increasing attention recently and has not yet fully benefited from the powerful pre-trained BERT-based models. We explore this by experimenting with three types of models. The position-tagging models and correction-tagging models are sequence tagging models fine-tuned on pre-trained BERT-based models, where the former focuses on detecting, positioning and classifying errors, and the latter aims at correcting errors. We also utilize rich representations from BERT-based models by transferring the BERT-fused models to the correction task, and further improve the performance by pre-training on a vast size of unsupervised synthetic data. To the best of our knowledge, we are the first to introduce and transfer the BERT-fused NMT model and sequence tagging model into the Chinese Grammatical Error Correction field. Our work achieved the second highest F1 score at the detecting errors, the best F1 score at correction top1 subtask and the second highest F1 score at correction top3 subtask.",
}
@inproceedings{fang-etal-2020-hybrid,
    title = "A Hybrid System for {NLPTEA}-2020 {CGED} Shared Task",
    author = "Fang, Meiyuan  and
      Fu, Kai  and
      Wang, Jiping  and
      Liu, Yang  and
      Huang, Jin  and
      Duan, Yitao",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.9",
    pages = "67--77",
    abstract = "This paper introduces our system at NLPTEA2020 shared task for CGED, which is able to detect, locate, identify and correct grammatical errors in Chinese writings. The system consists of three components: GED, GEC, and post processing. GED is an ensemble of multiple BERT-based sequence labeling models for handling GED tasks. GEC performs error correction. We exploit a collection of heterogenous models, including Seq2Seq, GECToR and a candidate generation module to obtain correction candidates. Finally in the post processing stage, results from GED and GEC are fused to form the final outputs. We tune our models to lean towards optimizing precision, which we believe is more crucial in practice. As a result, among the six tracks in the shared task, our system performs well in the correction tracks: measured in F1 score, we rank first, with the highest precision, in the TOP3 correction track and third in the TOP1 correction track, also with the highest precision. Ours are among the top 4 to 6 in other tracks, except for FPR where we rank 12. And our system achieves the highest precisions among the top 10 submissions at IDENTIFICATION and POSITION tracks.",
}
@inproceedings{wang-etal-2020-chinese-grammatical,
    title = "{C}hinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation",
    author = "Wang, Yi  and
      Yuan, Ruibin  and
      Luo, Yan{`}gen  and
      Qin, Yufang  and
      Zhu, NianYong  and
      Cheng, Peng  and
      Wang, Lihuan",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.10",
    pages = "78--86",
    abstract = "A better Chinese Grammatical Error Diagnosis (CGED) system for automatic Grammatical Error Correction (GEC) can benefit foreign Chinese learners and lower Chinese learning barriers. In this paper, we introduce our solution to the CGED2020 Shared Task Grammatical Error Correction in detail. The task aims to detect and correct grammatical errors that occur in essays written by foreign Chinese learners. Our solution combined data augmentation methods, spelling check methods, and generative grammatical correction methods, and achieved the best recall score in the Top 1 Correction track. Our final result ranked fourth among the participants.",
}
@inproceedings{wang-komachi-2020-tmu,
    title = "{TMU}-{NLP} System Using {BERT}-based Pre-trained Model to the {NLP}-{TEA} {CGED} Shared Task 2020",
    author = "Wang, Hongfei  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.11",
    pages = "87--90",
    abstract = "In this paper, we introduce our system for NLPTEA 2020 shared task of Chinese Grammatical Error Diagnosis (CGED). In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we treat the grammar error diagnosis (GED) task as a grammatical error correction (GEC) problem and propose a method that incorporates a pre-trained model into an encoder-decoder model to solve this problem.",
}
@inproceedings{wu-wang-2020-cyut,
    title = "{CYUT} Team {C}hinese Grammatical Error Diagnosis System Report in {NLPTEA}-2020 {CGED} Shared Task",
    author = "Wu, Shih-Hung  and
      Wang, Junwei",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.12",
    pages = "91--96",
    abstract = "This paper reports our Chinese Grammatical Error Diagnosis system in the NLPTEA-2020 CGED shared task. In 2020, we sent two runs with two approaches. The first one is a combination of conditional random fields (CRF) and a BERT model deep-learning approach. The second one is a BERT model deep-learning approach. The official results shows that our run1 achieved the highest precision rate 0.9875 with the lowest false positive rate 0.0163 on detection, while run2 gives a more balanced performance.",
}
@inproceedings{han-etal-2020-chinese,
    title = "{C}hinese Grammatical Error Diagnosis Based on {R}o{BERT}a-{B}i{LSTM}-{CRF} Model",
    author = "Han, Yingjie  and
      Yan, Yingjie  and
      Han, Yangchao  and
      Chao, Rui  and
      Zan, Hongying",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.13",
    pages = "97--101",
    abstract = "Chinese Grammatical Error Diagnosis (CGED) is a natural language processing task for the NLPTEA6 workshop. The goal of this task is to automatically diagnose grammatical errors in Chinese sentences written by L2 learners. This paper proposes a RoBERTa-BiLSTM-CRF model to detect grammatical errors in sentences. Firstly, RoBERTa model is used to obtain word vectors. Secondly, word vectors are input into BiLSTM layer to learn context features. Last, CRF layer without hand-craft features work for processing the output by BiLSTM. The optimal global sequences are obtained according to state transition matrix of CRF and adjacent labels of training data. In experiments, the result of RoBERTa-CRF model and ERNIE-BiLSTM-CRF model are compared, and the impacts of parameters of the models and the testing datasets are analyzed. In terms of evaluation results, our recall score of RoBERTa-BiLSTM-CRF ranks fourth at the detection level.",
}
@inproceedings{zan-etal-2020-chinese,
    title = "{C}hinese Grammatical Errors Diagnosis System Based on {BERT} at {NLPTEA}-2020 {CGED} Shared Task",
    author = "Zan, Hongying  and
      Han, Yangchao  and
      Huang, Haotian  and
      Yan, Yingjie  and
      Wang, Yuke  and
      Han, Yingjie",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.14",
    pages = "102--107",
    abstract = "In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation. The correction stage is a hybrid model based on the n-gram and Seq2Seq. Without adding additional features and external data, the BERT contextual word representation can effectively improve the performance metrics of Chinese grammatical error detection and correction.",
}
@inproceedings{cheng-duan-2020-chinese,
    title = "{C}hinese Grammatical Error Detection Based on {BERT} Model",
    author = "Cheng, Yong  and
      Duan, Mofan",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.15",
    pages = "108--113",
    abstract = "Automatic grammatical error correction is of great value in assisting second language writing. In 2020, the shared task for Chinese grammatical error diagnosis(CGED) was held in NLP-TEA. As the LDU team, we participated the competition and submitted the final results. Our work mainly focused on grammatical error detection, that is, to judge whether a sentence contains grammatical errors. We used the BERT pre-trained model for binary classification, and we achieve 0.0391 in FPR track, ranking the second in all teams. In error detection track, the accuracy, recall and F-1 of our submitted result are 0.9851, 0.7496 and 0.8514 respectively.",
}
@inproceedings{bade-shrestha-bal-2020-named,
    title = "Named-Entity Based Sentiment Analysis of {N}epali News Media Texts",
    author = "Bade Shrestha, Birat  and
      Bal, Bal Krishna",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.16",
    pages = "114--120",
    abstract = "Due to the general availability, relative abundance and wide diversity of opinions, news Media texts are very good sources for sentiment analysis. However, the major challenge with such texts is the difficulty in aligning the expressed opinions to the concerned political leaders as this entails a non-trivial task of named-entity recognition and anaphora resolution. In this work, our primary focus is on developing a Natural Language Processing (NLP) pipeline involving a robust Named-Entity Recognition followed by Anaphora Resolution and then after alignment of the recognized and resolved named-entities, in this case, political leaders to the correct class of opinions as expressed in the texts. We visualize the popularity of the politicians via the time series graph of positive and negative sentiments as an outcome of the pipeline. We have achieved the performance metrics of the individual components of the pipeline as follows: Part of speech tagging {--} 93.06{\%} (F1-score), Named-Entity Recognition {--} 86{\%} (F1-score), Anaphora Resolution {--} 87.45{\%} (Accuracy), Sentiment Analysis {--} 80.2{\%} (F1-score).",
}
@inproceedings{zhang-etal-2020-sema,
    title = "{SEMA}: Text Simplification Evaluation through Semantic Alignment",
    author = "Zhang, Xuan  and
      Zhao, Huizhou  and
      Zhang, KeXin  and
      Zhang, Yiyang",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.17",
    pages = "121--128",
    abstract = "Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.",
}
@inproceedings{chi-rao-2020-corpus,
    title = "A Corpus Linguistic Perspective on the Appropriateness of Pop Songs for Teaching {C}hinese as a Second Language",
    author = "Chi, Xiangyu  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlptea-1.18",
    pages = "129--137",
    abstract = "Language and music are closely related. Regarding the linguistic feature richness, pop songs are probably suitable to be used as extracurricular materials in language teaching. In order to prove this point, this paper presents the Contemporary Chinese Pop Lyrics (CCPL) corpus. Based on that, we investigated and evaluated the appropriateness of pop songs for Teaching Chinese as a Second Language (TCSL) with the assistance of Natural Language Processing methods from the perspective of Chinese character coverage, lexical coverage and the addressed topic similarity. Some suggestions in Chinese teaching with the aid of pop lyrics are provided.",
}
@proceedings{nlposs-2020-nlp,
    title = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    editor = "Park, Eunjeong L.  and
      Hagiwara, Masato  and
      Milajevs, Dmitrijs  and
      Liu, Nelson F.  and
      Chauhan, Geeticka  and
      Tan, Liling",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.0",
}
@inproceedings{madeira-etal-2020-framework,
    title = "A Framework to Assist Chat Operators of Mental Healthcare Services",
    author = "Madeira, Thiago  and
      Bernardino, Heder  and
      Francisco De Souza, Jairo  and
      Gomide, Henrique  and
      Munck Machado, Nath{\'a}lia  and
      Marcos Pinheiro da Silva, Bruno  and
      Vieira Pereira Pacelli, Alexandre",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.1",
    doi = "10.18653/v1/2020.nlposs-1.1",
    pages = "1--7",
    abstract = "Conversational agents can be used to make diagnoses, classify mental states, promote health education, and provide emotional support. The benefits of adopting conversational agents include widespread access, increased treatment engagement, and improved patient relationships with the intervention. We propose here a framework to assist chat operators of mental healthcare services, instead of a fully automated conversational agent. This design eases to avoid the adverse effects of applying chatbots in mental healthcare. The proposed framework is capable of improving the quality and reducing the time of interactions via chat between a user and a chat operator. We also present a case study in the context of health promotion on reducing tobacco use. The proposed framework uses artificial intelligence, specifically natural language processing (NLP) techniques, to classify messages from chat users. A list of suggestions is offered to the chat operator, with topics to be discussed in the session. These suggestions were created based on service protocols and the classification of previous chat sessions. The operator can also edit the suggested messages. Data collected can be used in the future to improve the quality of the suggestions offered.",
}
@inproceedings{alyafeai-al-shaibani-2020-arbml,
    title = "{ARBML}: Democritizing {A}rabic Natural Language Processing Tools",
    author = "Alyafeai, Zaid  and
      Al-Shaibani, Maged",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.2",
    doi = "10.18653/v1/2020.nlposs-1.2",
    pages = "8--13",
    abstract = "Automating natural language understanding is a lifelong quest addressed for decades. With the help of advances in machine learning and particularly, deep learning, we are able to produce state of the art models that can imitate human interactions with languages. Unfortunately, these advances are controlled by the availability of language resources. Arabic advances in this field , although it has a great potential, are still limited. This is apparent in both research and development. In this paper, we showcase some NLP models we trained for Arabic. We also present our methodology and pipeline to build such models from data collection, data preprocessing, tokenization and model deployment. These tools help in the advancement of the field and provide a systematic approach for extending NLP tools to many languages.",
}
@inproceedings{saqur-deshpande-2020-clevr,
    title = "{CLEVR} Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes",
    author = "Saqur, Raeid  and
      Deshpande, Ameet",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.3",
    doi = "10.18653/v1/2020.nlposs-1.3",
    pages = "14--19",
    abstract = "The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP). We present a graph parser library for CLEVR, that provides functionalities for object-centric attributes and relationships extraction, and construction of structural graph representations for dual modalities. Structural order-invariant representations enable geometric learning and can aid in downstream tasks like language grounding to vision, robotics, compositionality, interpretability, and computational grammar construction. We provide three extensible main components {--} parser, embedder, and visualizer that can be tailored to suit specific learning setups. We also provide out-of-the-box functionality for seamless integration with popular deep graph neural network (GNN) libraries. Additionally, we discuss downstream usage and applications of the library, and how it can accelerate research for the NLP community.",
}
@inproceedings{becquin-2020-end,
    title = "End-to-end {NLP} Pipelines in Rust",
    author = "Becquin, Guillaume",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.4",
    doi = "10.18653/v1/2020.nlposs-1.4",
    pages = "20--25",
    abstract = "The recent progress in natural language processing research has been supported by the development of a rich open source ecosystem in Python. Libraries allowing NLP practitioners but also non-specialists to leverage state-of-the-art models have been instrumental in the democratization of this technology. The maturity of the open-source NLP ecosystem however varies between languages. This work proposes a new open-source library aimed at bringing state-of-the-art NLP to Rust. Rust is a systems programming language for which the foundations required to build machine learning applications are available but still lacks ready-to-use, end-to-end NLP libraries. The proposed library, rust-bert, implements modern language models and ready-to-use pipelines (for example translation or summarization). This allows further development by the Rust community from both NLP experts and non-specialists. It is hoped that this library will accelerate the development of the NLP ecosystem in Rust. The library is under active development and available at https://github.com/guillaume-be/rust-bert.",
}
@inproceedings{kumar-etal-2020-fair,
    title = "Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings",
    author = "Kumar, Vaibhav  and
      Bhotia, Tenzin  and
      Kumar, Vaibhav",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.5",
    doi = "10.18653/v1/2020.nlposs-1.5",
    pages = "26--31",
    abstract = "Non-contextual word embedding models have been shown to inherit human-like stereotypical biases of gender, race and religion from the training corpora. To counter this issue, a large body of research has emerged which aims to mitigate these biases while keeping the syntactic and semantic utility of embeddings intact. This paper describes Fair Embedding Engine (FEE), a library for analysing and mitigating gender bias in word embeddings. FEE combines various state of the art techniques for quantifying, visualising and mitigating gender bias in word embeddings under a standard abstraction. FEE will aid practitioners in fast track analysis of existing debiasing methods on their embedding models. Further, it will allow rapid prototyping of new methods by evaluating their performance on a suite of standard metrics.",
}
@inproceedings{boytsov-nyberg-2020-flexible,
    title = "Flexible retrieval with {NMSLIB} and {F}lex{N}eu{ART}",
    author = "Boytsov, Leonid  and
      Nyberg, Eric",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.6",
    doi = "10.18653/v1/2020.nlposs-1.6",
    pages = "32--43",
    abstract = "Our objective is to introduce to the NLP community NMSLIB, describe a new retrieval toolkit FlexNeuART, as well as their integration capabilities. NMSLIB, while being one the fastest k-NN search libraries, is quite generic and supports a variety of distance/similarity functions. Because the library relies on the distance-based structure-agnostic algorithms, it can be further extended by adding new distances. FlexNeuART is a modular, extendible and flexible toolkit for candidate generation in IR and QA applications, which supports mixing of classic and neural ranking signals. FlexNeuART can efficiently retrieve mixed dense and sparse representations (with weights learned from training data), which is achieved by extending NMSLIB. In that, other retrieval systems work with purely sparse representations (e.g., Lucene), purely dense representations (e.g., FAISS and Annoy), or only perform mixing at the re-ranking stage.",
}
@inproceedings{mccann-2020-fugashi,
    title = "fugashi, a Tool for Tokenizing {J}apanese in Python",
    author = "McCann, Paul",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.7",
    doi = "10.18653/v1/2020.nlposs-1.7",
    pages = "44--51",
    abstract = "Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.",
}
@inproceedings{warmerdam-etal-2020-going,
    title = "Going Beyond {T}-{SNE}: Exposing whatlies in Text Embeddings",
    author = "Warmerdam, Vincent  and
      Kober, Thomas  and
      Tatman, Rachael",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.8",
    doi = "10.18653/v1/2020.nlposs-1.8",
    pages = "52--60",
    abstract = "We introduce whatlies, an open source toolkit for visually inspecting word and sentence embeddings. The project offers a unified and extensible API with current support for a range of popular embedding backends including spaCy, tfhub, huggingface transformers, gensim, fastText and BytePair embeddings. The package combines a domain specific language for vector arithmetic with visualisation tools that make exploring word embeddings more intuitive and concise. It offers support for many popular dimensionality reduction techniques as well as many interactive visualisations that can either be statically exported or shared via Jupyter notebooks. The project documentation is available from https://rasahq.github.io/whatlies/.",
}
@inproceedings{tang-etal-2020-howl,
    title = "Howl: A Deployed, Open-Source Wake Word Detection System",
    author = "Tang, Raphael  and
      Lee, Jaejun  and
      Razi, Afsaneh  and
      Cambre, Julia  and
      Bicking, Ian  and
      Kaye, Jofish  and
      Lin, Jimmy",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.9",
    doi = "10.18653/v1/2020.nlposs-1.9",
    pages = "61--65",
    abstract = "We describe Howl, an open-source wake word detection toolkit with native support for open speech datasets such as Mozilla Common Voice (MCV) and Google Speech Commands (GSC). We report benchmark results of various models supported by our toolkit on GSC and our own freely available wake word detection dataset, built from MCV. One of our models is deployed in Firefox Voice, a plugin enabling speech interactivity for the Firefox web browser. Howl represents, to the best of our knowledge, the first fully productionized, open-source wake word detection toolkit with a web browser deployment target. Our codebase is at howl.ai.",
}
@inproceedings{arora-2020-inltk,
    title = "i{NLTK}: Natural Language Toolkit for Indic Languages",
    author = "Arora, Gaurav",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.10",
    doi = "10.18653/v1/2020.nlposs-1.10",
    pages = "66--71",
    abstract = "We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages. By using pre-trained models from iNLTK for text classification on publicly available datasets, we significantly outperform previously reported results. On these datasets, we also show that by using pre-trained models and data augmentation from iNLTK, we can achieve more than 95{\%} of the previous best performance by using less than 10{\%} of the training data. iNLTK is already being widely used by the community and has 40,000+ downloads, 600+ stars and 100+ forks on GitHub. The library is available at https://github.com/goru001/inltk.",
}
@inproceedings{ahmadi-2020-klpt,
    title = "{KLPT} {--} {K}urdish Language Processing Toolkit",
    author = "Ahmadi, Sina",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.11",
    doi = "10.18653/v1/2020.nlposs-1.11",
    pages = "72--84",
    abstract = "Despite the recent advances in applying language-independent approaches to various natural language processing tasks thanks to artificial intelligence, some language-specific tools are still essential to process a language in a viable manner. Kurdish language is a less-resourced language with a remarkable diversity in dialects and scripts and lacks basic language processing tools. To address this issue, we introduce a language processing toolkit to handle such a diversity in an efficient way. Our toolkit is composed of fundamental components such as text preprocessing, stemming, tokenization, lemmatization and transliteration and is able to get further extended by future developers. The project is publicly available.",
}
@inproceedings{cho-etal-2020-open,
    title = "Open {K}orean Corpora: A Practical Report",
    author = "Cho, Won Ik  and
      Moon, Sangwhan  and
      Song, Youngsook",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.12",
    doi = "10.18653/v1/2020.nlposs-1.12",
    pages = "85--93",
    abstract = "Korean is often referred to as a low-resource language in the research community. While this claim is partially true, it is also because the availability of resources is inadequately advertised and curated. This work curates and reviews a list of Korean corpora, first describing institution-level resource development, then further iterate through a list of current open datasets for different types of tasks. We then propose a direction on how open-source dataset construction and releases should be done for less-resourced languages to promote research.",
}
@inproceedings{rueter-etal-2020-open,
    title = "Open-Source Morphology for Endangered Mordvinic Languages",
    author = {Rueter, Jack  and
      H{\"a}m{\"a}l{\"a}inen, Mika  and
      Partanen, Niko},
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.13",
    doi = "10.18653/v1/2020.nlposs-1.13",
    pages = "94--100",
    abstract = "This document describes shared development of finite-state description of two closely related but endangered minority languages, Erzya and Moksha. It touches upon morpholexical unity and diversity of the two languages and how this provides a motivation for shared open-source FST development. We describe how we have designed the transducers so that they can benefit from existing open-source infrastructures and are as reusable as possible.",
}
@inproceedings{granroth-wilding-2020-pimlico,
    title = "Pimlico: A toolkit for corpus-processing pipelines and reproducible experiments",
    author = "Granroth-Wilding, Mark",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.14",
    doi = "10.18653/v1/2020.nlposs-1.14",
    pages = "101--109",
    abstract = "We present Pimlico, an open source toolkit for building pipelines for processing large corpora. It is especially focused on processing linguistic corpora and provides wrappers around existing, widely used NLP tools. A particular goal is to ease distribution of reproducible and extensible experiments by making it easy to document and re-run all steps involved, including data loading, pre-processing, model training and evaluation. Once a pipeline is released, it is easy to adapt, for example, to run on a new dataset, or to re-run an experiment with different parameters. The toolkit takes care of many common challenges in writing and distributing corpus-processing code, such as managing data between the steps of a pipeline, installing required software and combining existing toolkits with new, task-specific code.",
}
@inproceedings{sadvilkar-neumann-2020-pysbd,
    title = "{P}y{SBD}: Pragmatic Sentence Boundary Disambiguation",
    author = "Sadvilkar, Nipun  and
      Neumann, Mark",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.15",
    doi = "10.18653/v1/2020.nlposs-1.15",
    pages = "110--114",
    abstract = "We present a rule-based sentence boundary disambiguation Python package that works out-of-the-box for 22 languages. We aim to provide a realistic segmenter which can provide logical sentences even when the format and domain of the input text is unknown. In our work, we adapt the Golden Rules Set (a language specific set of sentence boundary exemplars) originally implemented as a ruby gem pragmatic segmenter which we ported to Python with additional improvements and functionality. PySBD passes 97.92{\%} of the Golden Rule Set examplars for English, an improvement of 25{\%} over the next best open source Python tool.",
}
@inproceedings{lester-2020-iobes,
    title = "iobes: Library for Span Level Processing",
    author = "Lester, Brian",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.16",
    doi = "10.18653/v1/2020.nlposs-1.16",
    pages = "115--119",
    abstract = "Many tasks in natural language processing, such as named entity recognition and slot-filling, involve identifying and labeling specific spans of text. In order to leverage common models, these tasks are often recast as sequence labeling tasks. Each token is given a label and these labels are prefixed with special tokens such as B- or I-. After a model assigns labels to each token, these prefixes are used to group the tokens into spans. Properly parsing these annotations is critical for producing fair and comparable metrics; however, despite its importance, there is not an easy-to-use, standardized, programmatically integratable library to help work with span labeling. To remedy this, we introduce our open-source library, iobes. iobes is used for parsing, converting, and processing spans represented as token level decisions.",
}
@inproceedings{deutsch-roth-2020-sacrerouge,
    title = "{S}acre{ROUGE}: An Open-Source Library for Using and Developing Summarization Evaluation Metrics",
    author = "Deutsch, Daniel  and
      Roth, Dan",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.17",
    doi = "10.18653/v1/2020.nlposs-1.17",
    pages = "120--125",
    abstract = "We present SacreROUGE, an open-source library for using and developing summarization evaluation metrics. SacreROUGE removes many obstacles that researchers face when using or developing metrics: (1) The library provides Python wrappers around the official implementations of existing evaluation metrics so they share a common, easy-to-use interface; (2) it provides functionality to evaluate how well any metric implemented in the library correlates to human-annotated judgments, so no additional code needs to be written for a new evaluation metric; and (3) it includes scripts for loading datasets that contain human judgments so they can easily be used for evaluation. This work describes the design of the library, including the core Metric interface, the command-line API for evaluating summarization models and metrics, and the scripts to load and reformat publicly available datasets. The development of SacreROUGE is ongoing and open to contributions from the community.",
}
@inproceedings{morris-etal-2020-textattack,
    title = "{T}ext{A}ttack: Lessons learned in designing Python frameworks for {NLP}",
    author = "Morris, John  and
      Yoo, Jin Yong  and
      Qi, Yanjun",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.18",
    doi = "10.18653/v1/2020.nlposs-1.18",
    pages = "126--131",
    abstract = "TextAttack is an open-source Python toolkit for adversarial attacks, adversarial training, and data augmentation in NLP. TextAttack unites 15+ papers from the NLP adversarial attack literature into a single framework, with many components reused across attacks. This framework allows both researchers and developers to test and study the weaknesses of their NLP models. To build such an open-source NLP toolkit requires solving some common problems: How do we enable users to supply models from different deep learning frameworks? How can we build tools to support as many different datasets as possible? We share our insights into developing a well-written, well-documented NLP Python framework in hope that they can aid future development of similar packages.",
}
@inproceedings{lisena-etal-2020-tomodapi,
    title = "{TOMODAPI}: A Topic Modeling {API} to Train, Use and Compare Topic Models",
    author = "Lisena, Pasquale  and
      Harrando, Ismail  and
      Kandakji, Oussama  and
      Troncy, Raphael",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.19",
    doi = "10.18653/v1/2020.nlposs-1.19",
    pages = "132--140",
    abstract = "From LDA to neural models, different topic modeling approaches have been proposed in the literature. However, their suitability and performance is not easy to compare, particularly when the algorithms are being used in the wild on heterogeneous datasets. In this paper, we introduce ToModAPI (TOpic MOdeling API), a wrapper library to easily train, evaluate and infer using different topic modeling algorithms through a unified interface. The library is extensible and can be used in Python environments or through a Web API.",
}
@inproceedings{madnani-loukina-2020-user,
    title = "User-centered {\&} Robust {NLP} {OSS}: Lessons Learned from Developing {\&} Maintaining {RSMT}ool",
    author = "Madnani, Nitin  and
      Loukina, Anastassia",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.20",
    doi = "10.18653/v1/2020.nlposs-1.20",
    pages = "141--146",
    abstract = "For the last 5 years, we have developed and maintained RSMTool {--} an open-source tool for evaluating NLP systems that automatically score written and spoken responses. RSMTool is designed to be cross-disciplinary, borrowing heavily from NLP, machine learning, and educational measurement. Its cross-disciplinary nature has required us to learn a user-centered development approach in terms of both design and implementation. We share some of these lessons in this paper.",
}
@inproceedings{ekmekci-howald-2020-waffle,
    title = "{WAFFLE}: A Graph for {W}ord{N}et Applied to {F}ree{F}orm Linguistic Exploration",
    author = "Ekmekci, Berk  and
      Howald, Blake",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.21",
    doi = "10.18653/v1/2020.nlposs-1.21",
    pages = "147--157",
    abstract = "The WordNet database of English (Fellbaum, 1998) is a key source of semantic information for research and development of natural language processing applications. As the sophistication of these applications increases with the use of large datasets, deep learning, and graph-based methods, so should the use of WordNet. To this end, we introduce WAFFLE: WordNet Applied to FreeForm Linguistic Exploration which makes WordNet available in an open source graph data structure. The WAFFLE graph relies on platform agnostic formats for robust interrogation and flexibility. Where existing implementations of WordNet offer dictionary-like lookup, single degree neighborhood operations, and path based similarity-scoring, the WAFFLE graph makes all nodes (semantic relation sets) and relationships queryable at scale, enabling local and global analysis of all relationships without the need for custom code. We demonstrate WAFFLE{'}s ease of use, visualization capabilities, and scalable efficiency with common queries, operations, and interactions. WAFFLE is available at github.com/TRSS-NLP/WAFFLE.",
}
@proceedings{nlpmc-2020-natural,
    title = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    editor = "Bhatia, Parminder  and
      Lin, Steven  and
      Gangadharaiah, Rashmi  and
      Wallace, Byron  and
      Shafran, Izhak  and
      Shivade, Chaitanya  and
      Du, Nan  and
      Diab, Mona",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.0",
}
@inproceedings{ding-etal-2020-methods,
    title = "Methods for Extracting Information from Messages from Primary Care Providers to Specialists",
    author = "Ding, Xiyu  and
      Barnett, Michael  and
      Mehrotra, Ateev  and
      Miller, Timothy",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.1",
    doi = "10.18653/v1/2020.nlpmc-1.1",
    pages = "1--6",
    abstract = "Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems. Understanding the usage patterns of eConsult system is an important part of improving specialist efficiency. In this work, we develop and apply classifiers to a dataset of eConsult questions from primary care providers to specialists, classifying the messages for how they were triaged by the specialist office, and the underlying type of clinical question posed by the primary care provider. We show that pre-trained transformer models are strong baselines, with improving performance from domain-specific training and shared representations.",
}
@inproceedings{mani-etal-2020-towards,
    title = "Towards Understanding {ASR} Error Correction for Medical Conversations",
    author = "Mani, Anirudh  and
      Palaskar, Shruti  and
      Konam, Sandeep",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.2",
    doi = "10.18653/v1/2020.nlpmc-1.2",
    pages = "7--11",
    abstract = "Domain Adaptation for Automatic Speech Recognition (ASR) error correction via machine translation is a useful technique for improving out-of-domain outputs of pre-trained ASR systems to obtain optimal results for specific in-domain tasks. We use this technique on our dataset of Doctor-Patient conversations using two off-the-shelf ASR systems: Google ASR (commercial) and the ASPIRE model (open-source). We train a Sequence-to-Sequence Machine Translation model and evaluate it on seven specific UMLS Semantic types, including Pharmacological Substance, Sign or Symptom, and Diagnostic Procedure to name a few. Lastly, we breakdown, analyze and discuss the 7{\%} overall improvement in word error rate in view of each Semantic type.",
}
@inproceedings{wang-etal-2020-studying,
    title = "Studying Challenges in Medical Conversation with Structured Annotation",
    author = "Wang, Nan  and
      Song, Yan  and
      Xia, Fei",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.3",
    doi = "10.18653/v1/2020.nlpmc-1.3",
    pages = "12--21",
    abstract = "Medical conversation is a central part of medical care. Yet, the current state and quality of medical conversation is far from perfect. Therefore, a substantial amount of research has been done to obtain a better understanding of medical conversation and to address its practical challenges and dilemmas. In line with this stream of research, we have developed a multi-layer structure annotation scheme to analyze medical conversation, and are using the scheme to construct a corpus of naturally occurring medical conversation in Chinese pediatric primary care setting. Some of the preliminary findings are reported regarding 1) how a medical conversation starts, 2) where communication problems tend to occur, and 3) how physicians close a conversation. Challenges and opportunities for research on medical conversation with NLP techniques will be discussed.",
}
@inproceedings{enarvi-etal-2020-generating,
    title = "Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models",
    author = "Enarvi, Seppo  and
      Amoia, Marilisa  and
      Del-Agua Teba, Miguel  and
      Delaney, Brian  and
      Diehl, Frank  and
      Hahn, Stefan  and
      Harris, Kristina  and
      McGrath, Liam  and
      Pan, Yue  and
      Pinto, Joel  and
      Rubini, Luca  and
      Ruiz, Miguel  and
      Singh, Gagandeep  and
      Stemmer, Fabian  and
      Sun, Weiyi  and
      Vozila, Paul  and
      Lin, Thomas  and
      Ramamurthy, Ranjani",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.4",
    doi = "10.18653/v1/2020.nlpmc-1.4",
    pages = "22--30",
    abstract = "We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach. We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequence-to-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale.",
}
@inproceedings{amith-etal-2020-towards,
    title = "Towards an Ontology-based Medication Conversational Agent for {P}r{EP} and {PEP}",
    author = "Amith, Muhammad  and
      Cui, Licong  and
      Roberts, Kirk  and
      Tao, Cui",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.5",
    doi = "10.18653/v1/2020.nlpmc-1.5",
    pages = "31--40",
    abstract = "ABSTRACT: HIV (human immunodeficiency virus) can damage a human{'}s immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death. While HIV infections have decreased over the last decade, there is still a significant population where the infection permeates. PrEP and PEP are two proven preventive measures introduced that involve periodic dosage to stop the onset of HIV infection. However, the adherence rates for this medication is low in part due to the lack of information about the medication. There exist several communication barriers that prevent patient-provider communication from happening. In this work, we present our ontology-based method for automating the communication of this medication that can be deployed for live conversational agents for PrEP and PEP. This method facilitates a model of automated conversation between the machine and user can also answer relevant questions.",
}
@inproceedings{gupta-etal-2020-heart,
    title = "Heart Failure Education of {A}frican {A}merican and {H}ispanic/{L}atino Patients: Data Collection and Analysis",
    author = "Gupta, Itika  and
      Di Eugenio, Barbara  and
      Salunke, Devika  and
      Boyd, Andrew  and
      Allen-Meares, Paula  and
      Dickens, Carolyn  and
      Garcia, Olga",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.6",
    doi = "10.18653/v1/2020.nlpmc-1.6",
    pages = "41--46",
    abstract = "Heart failure is a global epidemic with debilitating effects. People with heart failure need to actively participate in home self-care regimens to maintain good health. However, these regimens are not as effective as they could be and are influenced by a variety of factors. Patients from minority communities like African American (AA) and Hispanic/Latino (H/L), often have poor outcomes compared to the average Caucasian population. In this paper, we lay the groundwork to develop an interactive dialogue agent that can assist AA and H/L patients in a culturally sensitive and linguistically accurate manner with their heart health care needs. This will be achieved by extracting relevant educational concepts from the interactions between health educators and patients. Thus far we have recorded and transcribed 20 such interactions. In this paper, we describe our data collection process, thematic and initiative analysis of the interactions, and outline our future steps.",
}
@inproceedings{neumann-etal-2020-utility,
    title = "On the Utility of Audiovisual Dialog Technologies and Signal Analytics for Real-time Remote Monitoring of Depression Biomarkers",
    author = "Neumann, Michael  and
      Roessler, Oliver  and
      Suendermann-Oeft, David  and
      Ramanarayanan, Vikram",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.7",
    doi = "10.18653/v1/2020.nlpmc-1.7",
    pages = "47--52",
    abstract = "We investigate the utility of audiovisual dialog systems combined with speech and video analytics for real-time remote monitoring of depression at scale in uncontrolled environment settings. We collected audiovisual conversational data from participants who interacted with a cloud-based multimodal dialog system, and automatically extracted a large set of speech and vision metrics based on the rich existing literature of laboratory studies. We report on the efficacy of various audio and video metrics in differentiating people with mild, moderate and severe depression, and discuss the implications of these results for the deployment of such technologies in real-world neurological diagnosis and monitoring applications.",
}
@inproceedings{sunkara-etal-2020-robust,
    title = "Robust Prediction of Punctuation and Truecasing for Medical {ASR}",
    author = "Sunkara, Monica  and
      Ronanki, Srikanth  and
      Dixit, Kalpit  and
      Bodapati, Sravan  and
      Kirchhoff, Katrin",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.8",
    doi = "10.18653/v1/2020.nlpmc-1.8",
    pages = "53--62",
    abstract = "Automatic speech recognition (ASR) systems in the medical domain that focus on transcribing clinical dictations and doctor-patient conversations often pose many challenges due to the complexity of the domain. ASR output typically undergoes automatic punctuation to enable users to speak naturally, without having to vocalize awkward and explicit punctuation commands, such as {``}period{''}, {``}add comma{''} or {``}exclamation point{''}, while truecasing enhances user readability and improves the performance of downstream NLP tasks. This paper proposes a conditional joint modeling framework for prediction of punctuation and truecasing using pretrained masked language models such as BERT, BioBERT and RoBERTa. We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data. Finally, we improve the robustness of the model against common errors made in ASR by performing data augmentation. Experiments performed on dictation and conversational style corpora show that our proposed model achieves 5{\%} absolute improvement on ground truth text and 10{\%} improvement on ASR outputs over baseline models under F1 metric.",
}
@inproceedings{asgari-etal-2020-topic,
    title = "Topic-Based Measures of Conversation for Detecting Mild {C}ognitive{I}mpairment",
    author = "Asgari, Meysam  and
      Chen, Liu  and
      Dodge, Hiroko",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpmc-1.9",
    doi = "10.18653/v1/2020.nlpmc-1.9",
    pages = "63--67",
    abstract = "Conversation is a complex cognitive task that engages multiple aspects of cognitive functions to remember the discussed topics, monitor the semantic and linguistic elements, and recognize others{'} emotions. In this paper, we propose a computational method based on the lexical coherence of consecutive utterances to quantify topical variations in semi-structured conversations of older adults with cognitive impairments. Extracting the lexical knowledge of conversational utterances, our method generate a set of novel conversational measures that indicate underlying cognitive deficits among subjects with mild cognitive impairment (MCI). Our preliminary results verifies the utility of the proposed conversation-based measures in distinguishing MCI from healthy controls.",
}
@proceedings{nlp-css-2020-natural,
    title = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    editor = "Bamman, David  and
      Hovy, Dirk  and
      Jurgens, David  and
      O'Connor, Brendan  and
      Volkova, Svitlana",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.0",
}
@inproceedings{dunn-etal-2020-measuring,
    title = "Measuring Linguistic Diversity During {COVID}-19",
    author = "Dunn, Jonathan  and
      Coupe, Tom  and
      Adams, Benjamin",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.1",
    doi = "10.18653/v1/2020.nlpcss-1.1",
    pages = "1--10",
    abstract = "Computational measures of linguistic diversity help us understand the linguistic landscape using digital language data. The contribution of this paper is to calibrate measures of linguistic diversity using restrictions on international travel resulting from the COVID-19 pandemic. Previous work has mapped the distribution of languages using geo-referenced social media and web data. The goal, however, has been to describe these corpora themselves rather than to make inferences about underlying populations. This paper shows that a difference-in-differences method based on the Herfindahl-Hirschman Index can identify the bias in digital corpora that is introduced by non-local populations. These methods tell us where significant changes have taken place and whether this leads to increased or decreased diversity. This is an important step in aligning digital corpora like social media with the real-world populations that have produced them.",
}
@inproceedings{grandeit-etal-2020-using,
    title = "Using {BERT} for Qualitative Content Analysis in Psychosocial Online Counseling",
    author = "Grandeit, Philipp  and
      Haberkern, Carolyn  and
      Lang, Maximiliane  and
      Albrecht, Jens  and
      Lehmann, Robert",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.2",
    doi = "10.18653/v1/2020.nlpcss-1.2",
    pages = "11--23",
    abstract = "Qualitative content analysis is a systematic method commonly used in the social sciences to analyze textual data from interviews or online discussions. However, this method usually requires high expertise and manual effort because human coders need to read, interpret, and manually annotate text passages. This is especially true if the system of categories used for annotation is complex and semantically rich. Therefore, qualitative content analysis could benefit greatly from automated coding. In this work, we investigate the usage of machine learning-based text classification models for automatic coding in the area of psycho-social online counseling. We developed a system of over 50 categories to analyze counseling conversations, labeled over 10.000 text passages manually, and evaluated the performance of different machine learning-based classifiers against human coders.",
}
@inproceedings{blokker-etal-2020-swimming,
    title = "Swimming with the Tide? Positional Claim Detection across Political Text Types",
    author = "Blokker, Nico  and
      Dayanik, Erenay  and
      Lapesa, Gabriella  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.3",
    doi = "10.18653/v1/2020.nlpcss-1.3",
    pages = "24--34",
    abstract = "Manifestos are official documents of political parties, providing a comprehensive topical overview of the electoral programs. Voters, however, seldom read them and often prefer other channels, such as newspaper articles, to understand the party positions on various policy issues. The natural question to ask is how compatible these two formats (manifesto and newspaper reports) are in their representation of party positioning. We address this question with an approach that combines political science (manual annotation and analysis) and natural language processing (supervised claim identification) in a cross-text type setting: we train a classifier on annotated newspaper data and test its performance on manifestos. Our findings show a) strong performance for supervised classification even across text types and b) a substantive overlap between the two formats in terms of party positioning, with differences regarding the salience of specific issues.",
}
@inproceedings{andy-guntuku-2020-social,
    title = "Does Social Support (Expressed in Post Titles) Elicit Comments in Online Substance Use Recovery Forums?",
    author = "Andy, Anietie  and
      Guntuku, Sharath Chandra",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.4",
    doi = "10.18653/v1/2020.nlpcss-1.4",
    pages = "35--40",
    abstract = "Individuals recovering from substance use often seek social support (emotional and informational) on online recovery forums, where they can both write and comment on posts, expressing their struggles and successes. A common challenge in these forums is that certain posts (some of which may be support seeking) receive no comments. In this work, we use data from two Reddit substance recovery forums: /r/Leaves and /r/OpiatesRecovery, to determine the relationship between the social supports expressed in the titles of posts and the number of comments they receive. We show that the types of social support expressed in post titles that elicit comments vary from one substance use recovery forum to the other.",
}
@inproceedings{ng-etal-2020-miss,
    title = "{I} miss you babe: Analyzing Emotion Dynamics During {COVID}-19 Pandemic",
    author = "Ng, Hui Xian Lynnette  and
      Lee, Roy Ka-Wei  and
      Awal, Md Rabiul",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.5",
    doi = "10.18653/v1/2020.nlpcss-1.5",
    pages = "41--49",
    abstract = "With the world on a lockdown due to the COVID-19 pandemic, this paper studies emotions expressed on Twitter. Using a combined strategy of time series analysis of emotions augmented by tweet topics, this study provides an insight into emotion transitions during the pandemic. After tweets are annotated with dominant emotions and topics, a time-series emotion analysis is used to identify disgust and anger as the most commonly identified emotions. Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states. Observing user patterns through clustering of user longitudinal analyses reveals emotional transitions fall into four main clusters: (1) erratic motion over short period of time, (2) disgust -{\textgreater} anger, (3) optimism -{\textgreater} joy. (4) erratic motion over a prolonged period. Finally, we propose a method for predicting users subsequent topic, and by consequence their emotions, through constructing an Emotion Topic Hidden Markov Model, augmenting emotion transition states with topic information. Results suggests that the predictions fare better than baselines, spurring directions of predicting emotional states based on Twitter posts.",
}
@inproceedings{fine-etal-2020-assessing,
    title = "Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using {NLP} applied to social media data",
    author = "Fine, Alex  and
      Crutchley, Patrick  and
      Blase, Jenny  and
      Carroll, Joshua  and
      Coppersmith, Glen",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.6",
    doi = "10.18653/v1/2020.nlpcss-1.6",
    pages = "50--54",
    abstract = "Prevailing methods for assessing population-level mental health require costly collection of large samples of data through instruments such as surveys, and are thus slow to reflect current, rapidly changing social conditions. This constrains how easily population-level mental health data can be integrated into health and policy decision-making. Here, we demonstrate that natural language processing applied to publicly-available social media data can provide real-time estimates of psychological distress in the population (specifically, English-speaking Twitter users in the US). We examine population-level changes in linguistic correlates of mental health symptoms in response to the COVID-19 pandemic and to the killing of George Floyd. As a case study, we focus on social media data from healthcare providers, compared to a control sample. Our results provide a concrete demonstration of how the tools of computational social science can be applied to provide real-time or near-real-time insight into the impact of public events on mental health.",
}
@inproceedings{gupta-etal-2020-viable,
    title = "Viable Threat on News Reading: Generating Biased News Using Natural Language Models",
    author = "Gupta, Saurabh  and
      Nguyen, Hong Huy  and
      Yamagishi, Junichi  and
      Echizen, Isao",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.7",
    doi = "10.18653/v1/2020.nlpcss-1.7",
    pages = "55--65",
    abstract = "Recent advancements in natural language generation has raised serious concerns. High-performance language models are widely used for language generation tasks because they are able to produce fluent and meaningful sentences. These models are already being used to create fake news. They can also be exploited to generate biased news, which can then be used to attack news aggregators to change their reader{'}s behavior and influence their bias. In this paper, we use a threat model to demonstrate that the publicly available language models can reliably generate biased news content based on an input original news. We also show that a large number of high-quality biased news articles can be generated using controllable text generation. A subjective evaluation with 80 participants demonstrated that the generated biased news is generally fluent, and a bias evaluation with 24 participants demonstrated that the bias (left or right) is usually evident in the generated articles and can be easily identified.",
}
@inproceedings{todd-etal-2020-unsupervised,
    title = "Unsupervised Anomaly Detection in Parole Hearings using Language Models",
    author = "Todd, Graham  and
      Voss, Catalin  and
      Hong, Jenny",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.8",
    doi = "10.18653/v1/2020.nlpcss-1.8",
    pages = "66--71",
    abstract = "Each year, thousands of roughly 150-page parole hearing transcripts in California go unread because legal experts lack the time to review them. Yet, reviewing transcripts is the only means of public oversight in the parole process. To assist reviewers, we present a simple unsupervised technique for using language models (LMs) to identify procedural anomalies in long-form legal text. Our technique highlights unusual passages that suggest further review could be necessary. We utilize a contrastive perplexity score to identify passages, defined as the scaled difference between its perplexities from two LMs, one fine-tuned on the target (parole) domain, and another pre-trained on out-of-domain text to normalize for grammatical or syntactic anomalies. We present quantitative analysis of the results and note that our method has identified some important cases for review. We are also excited about potential applications in unsupervised anomaly detection, and present a brief analysis of results for detecting fake TripAdvisor reviews.",
}
@inproceedings{verma-etal-2020-identifying,
    title = "Identifying Worry in {T}witter: Beyond Emotion Analysis",
    author = "Verma, Reyha  and
      von der Weth, Christian  and
      Vachery, Jithin  and
      Kankanhalli, Mohan",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.9",
    doi = "10.18653/v1/2020.nlpcss-1.9",
    pages = "72--82",
    abstract = "Identifying the worries of individuals and societies plays a crucial role in providing social support and enhancing policy decision-making. Due to the popularity of social media platforms such as Twitter, users share worries about personal issues (e.g., health, finances, relationships) and broader issues (e.g., changes in society, environmental concerns, terrorism) freely. In this paper, we explore and evaluate a wide range of machine learning models to predict worry on Twitter. While this task has been closely associated with emotion prediction, we argue and show that identifying worry needs to be addressed as a separate task given the unique challenges associated with it. We conduct a user study to provide evidence that social media posts express two basic kinds of worry {--} normative and pathological {--} as stated in psychology literature. In addition, we show that existing emotion detection techniques underperform, especially while capturing normative worry. Finally, we discuss the current limitations of our approach and propose future applications of the worry identification system.",
}
@inproceedings{gnehm-clematide-2020-text,
    title = "Text Zoning and Classification for Job Advertisements in {G}erman, {F}rench and {E}nglish",
    author = "Gnehm, Ann-Sophie  and
      Clematide, Simon",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.10",
    doi = "10.18653/v1/2020.nlpcss-1.10",
    pages = "83--93",
    abstract = "We present experiments to structure job ads into text zones and classify them into pro- fessions, industries and management functions, thereby facilitating social science analyses on labor marked demand. Our main contribution are empirical findings on the benefits of contextualized embeddings and the potential of multi-task models for this purpose. With contextualized in-domain embeddings in BiLSTM-CRF models, we reach an accuracy of 91{\%} for token-level text zoning and outperform previous approaches. A multi-tasking BERT model performs well for our classification tasks. We further compare transfer approaches for our multilingual data.",
}
@inproceedings{schmahl-etal-2020-wikipedia,
    title = "Is {W}ikipedia succeeding in reducing gender bias? Assessing changes in gender bias in {W}ikipedia using word embeddings",
    author = "Schmahl, Katja Geertruida  and
      Viering, Tom Julian  and
      Makrodimitris, Stavros  and
      Naseri Jahfari, Arman  and
      Tax, David  and
      Loog, Marco",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.11",
    doi = "10.18653/v1/2020.nlpcss-1.11",
    pages = "94--103",
    abstract = "Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain stereotypical gender biases. As a result, such unwanted biases will typically also be present in word embeddings derived from such corpora and downstream applications in the field of natural language processing (NLP). To minimize the effect of gender bias in these settings, more insight is needed when it comes to where and how biases manifest themselves in the text corpora employed. This paper contributes by showing how gender bias in word embeddings from Wikipedia has developed over time. Quantifying the gender bias over time shows that art related words have become more female biased. Family and science words have stereotypical biases towards respectively female and male words. These biases seem to have decreased since 2006, but these changes are not more extreme than those seen in random sets of words. Career related words are more strongly associated with male than with female, this difference has only become smaller in recently written articles. These developments provide additional understanding of what can be done to make Wikipedia more gender neutral and how important time of writing can be when considering biases in word embeddings trained from Wikipedia or from other text corpora.",
}
@inproceedings{xiao-xiao-2020-effects,
    title = "Effects of Anonymity on Comment Persuasiveness in {W}ikipedia Articles for Deletion Discussions",
    author = "Xiao, Yimin  and
      Xiao, Lu",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.12",
    doi = "10.18653/v1/2020.nlpcss-1.12",
    pages = "104--115",
    abstract = "It has been shown that anonymity affects various aspects of online communications such as message credibility, the trust among communicators, and the participants{'} accountability and reputation. Anonymity influences social interactions in online communities in these many ways, which can lead to influences on opinion change and the persuasiveness of a message. Prior studies also suggest that the effect of anonymity can vary in different online communication contexts and online communities. In this study, we focus on Wikipedia Articles for Deletion (AfD) discussions as an example of online collaborative communities to study the relationship between anonymity and persuasiveness in this context. We find that in Wikipedia AfD discussions, more identifiable users tend to be more persuasive. The higher persuasiveness can be related to multiple aspects, including linguistic features of the comments, the user{'}s motivation to participate, persuasive skills the user learns over time, and the user{'}s identity and credibility established in the community through participation.",
}
@inproceedings{keith-etal-2020-uncertainty,
    title = "Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty",
    author = "Keith, Katherine  and
      Teichmann, Christoph  and
      O{'}Connor, Brendan  and
      Meij, Edgar",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.13",
    doi = "10.18653/v1/2020.nlpcss-1.13",
    pages = "116--131",
    abstract = "Methods and applications are inextricably linked in science, and in particular in the domain of text-as-data. In this paper, we examine one such text-as-data application, an established economic index that measures economic policy uncertainty from keyword occurrences in news. This index, which is shown to correlate with firm investment, employment, and excess market returns, has had substantive impact in both the private sector and academia. Yet, as we revisit and extend the original authors{'} annotations and text measurements we find interesting text-as-data methodological research questions: (1) Are annotator disagreements a reflection of ambiguity in language? (2) Do alternative text measurements correlate with one another and with measures of external predictive validity? We find for this application (1) some annotator disagreements of economic policy uncertainty can be attributed to ambiguity in language, and (2) switching measurements from keyword-matching to supervised machine learning classifiers results in low correlation, a concerning implication for the validity of the index.",
}
@inproceedings{vidgen-etal-2020-recalibrating,
    title = "Recalibrating classifiers for interpretable abusive content detection",
    author = "Vidgen, Bertie  and
      Hale, Scott  and
      Staton, Sam  and
      Melham, Tom  and
      Margetts, Helen  and
      Kammar, Ohad  and
      Szymczak, Marcin",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.14",
    doi = "10.18653/v1/2020.nlpcss-1.14",
    pages = "132--138",
    abstract = "We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the {`}raw{'} scores are used) align poorly with human evaluations. This limits their use for understanding the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK{'}s 2017 general election. A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers.",
}
@inproceedings{maier-cimiano-2020-predicting,
    title = "Predicting independent living outcomes from written reports of social workers",
    author = "Maier, Angelika  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.15",
    doi = "10.18653/v1/2020.nlpcss-1.15",
    pages = "139--148",
    abstract = "In social care environments, the main goal of social workers is to foster independent living by their clients. An important task is thus to monitor progress towards reaching independence in different areas of their patients{'} life. To support this task, we present an approach that extracts indications of independence on different life aspects from the day-to-day documentation that social workers create. We describe the process of collecting and annotating a corresponding corpus created from data records of two social work institutions with a focus on disability care. We show that the agreement on the task of annotating the observations of social workers with respect to discrete independent levels yields a high agreement of .74 as measured by Fleiss{'} Kappa. We present a classification approach towards automatically classifying an observation into the discrete independence levels and present results for different types of classifiers. Against our original expectation, we show that we reach F-Measures (macro) of 95{\%} averaged across topics, showing that this task can be automatically solved.",
}
@inproceedings{chen-etal-2020-analyzing,
    title = "Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity",
    author = "Chen, Wei-Fan  and
      Al Khatib, Khalid  and
      Wachsmuth, Henning  and
      Stein, Benno",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.16",
    doi = "10.18653/v1/2020.nlpcss-1.16",
    pages = "149--154",
    abstract = "Media is an indispensable source of information and opinion, shaping the beliefs and attitudes of our society. Obviously, media portals can also provide overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such a form of unfair news coverage can be exposed. This paper addresses the automatic detection of bias, but it goes one step further in that it explores how political bias and unfairness are manifested linguistically. We utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com to develop a neural model for bias assessment. Analyzing the model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.",
}
@inproceedings{gupta-nishu-2020-mapping,
    title = "Mapping Local News Coverage: Precise location extraction in textual news content using fine-tuned {BERT} based language model",
    author = "Gupta, Sarang  and
      Nishu, Kumari",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.17",
    doi = "10.18653/v1/2020.nlpcss-1.17",
    pages = "155--162",
    abstract = "Mapping local news coverage from textual content is a challenging problem that requires extracting precise location mentions from news articles. While traditional named entity taggers are able to extract geo-political entities and certain non geo-political entities, they cannot recognize precise location mentions such as addresses, streets and intersections that are required to accurately map the news article. We fine-tune a BERT-based language model for achieving high level of granularity in location extraction. We incorporate the model into an end-to-end tool that further geocodes the extracted locations for the broader objective of mapping news coverage.",
}
@inproceedings{berdicevskis-2020-foreigner,
    title = "Foreigner-directed speech is simpler than native-directed: Evidence from social media",
    author = "Berdicevskis, Aleksandrs",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.18",
    doi = "10.18653/v1/2020.nlpcss-1.18",
    pages = "163--172",
    abstract = "I test two hypotheses that play an important role in modern sociolinguistics and language evolution studies: first, that non-native production is simpler than native; second, that production addressed to non-native speakers is simpler than that addressed to natives. The second hypothesis is particularly important for theories about contact-induced simplification, since the accommodation to non-natives may explain how the simplification can spread from adult learners to the whole community. To test the hypotheses, I create a very large corpus of native and non-native written speech in four languages (English, French, Italian, Spanish), extracting data from an internet forum where native languages of the participants are known and the structure of the interactions can be inferred. The corpus data yield inconsistent evidence with respect to the first hypothesis, but largely support the second one, suggesting that foreigner-directed speech is indeed simpler than native-directed. Importantly, when testing the first hypothesis, I contrast production of different speakers, which can introduce confounds and is a likely reason for the inconsistencies. When testing the second hypothesis, the comparison is always within the production of the same speaker (but with different addressees), which makes it more reliable.",
}
@inproceedings{hennig-wilson-2020-diachronic,
    title = "Diachronic Embeddings for People in the News",
    author = "Hennig, Felix  and
      Wilson, Steven",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.19",
    doi = "10.18653/v1/2020.nlpcss-1.19",
    pages = "173--183",
    abstract = "Previous English-language diachronic change models based on word embeddings have typically used single tokens to represent entities, including names of people. This leads to issues with both ambiguity (resulting in one embedding representing several distinct and unrelated people) and unlinked references (leading to several distinct embeddings which represent the same person). In this paper, we show that using named entity recognition and heuristic name linking steps before training a diachronic embedding model leads to more accurate representations of references to people, as compared to the token-only baseline. In large news corpus of articles from The Guardian, we provide examples of several types of analysis that can be performed using these new embeddings. Further, we show that real world events and context changes can be detected using our proposed model.",
}
@inproceedings{kelly-etal-2020-social,
    title = "Social media data as a lens onto care-seeking behavior among women veterans of the {US} armed forces",
    author = "Kelly, Kacie  and
      Fine, Alex  and
      Coppersmith, Glen",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.20",
    doi = "10.18653/v1/2020.nlpcss-1.20",
    pages = "184--192",
    abstract = "In this article, we examine social media data as a lens onto support-seeking among women veterans of the US armed forces. Social media data hold a great deal of promise as a source of information on needs and support-seeking among individuals who are excluded from or systematically prevented from accessing clinical or other institutions ostensibly designed to support them. We apply natural language processing (NLP) techniques to more than 3 million Tweets collected from 20,000 Twitter users. We find evidence that women veterans are more likely to use social media to seek social and community engagement and to discuss mental health and veterans{'} issues significantly more frequently than their male counterparts. By contrast, male veterans tend to use social media to amplify political ideologies or to engage in partisan debate. Our results have implications for how organizations can provide outreach and services to this uniquely vulnerable population, and illustrate the utility of non-traditional observational data sources such as social media to understand the needs of marginalized groups.",
}
@inproceedings{zamani-etal-2020-understanding,
    title = "Understanding Weekly {COVID}-19 Concerns through Dynamic Content-Specific {LDA} Topic Modeling",
    author = "Zamani, Mohammadzaman  and
      Schwartz, H. Andrew  and
      Eichstaedt, Johannes  and
      Guntuku, Sharath Chandra  and
      Virinchipuram Ganesan, Adithya  and
      Clouston, Sean  and
      Giorgi, Salvatore",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.21",
    doi = "10.18653/v1/2020.nlpcss-1.21",
    pages = "193--198",
    abstract = "The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media.We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.",
}
@inproceedings{li-etal-2020-emoji,
    title = "Emoji and Self-Identity in {T}witter Bios",
    author = "Li, Jinhang  and
      Longinos, Giorgos  and
      Wilson, Steven  and
      Magdy, Walid",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.22",
    doi = "10.18653/v1/2020.nlpcss-1.22",
    pages = "199--211",
    abstract = "Emoji are widely used to express emotions and concepts on social media, and prior work has shown that users{'} choice of emoji reflects the way that they wish to present themselves to the world. Emoji usage is typically studied in the context of posts made by users, and this view has provided important insights into phenomena such as emotional expression and self-representation. In addition to making posts, however, social media platforms like Twitter allow for users to provide a short bio, which is an opportunity to briefly describe their account as a whole. In this work, we focus on the use of emoji in these bio statements. We explore the ways in which users include emoji in these self-descriptions, finding different patterns than those observed around emoji usage in tweets. We examine the relationships between emoji used in bios and the content of users{'} tweets, showing that the topics and even the average sentiment of tweets varies for users with different emoji in their bios. Lastly, we confirm that homophily effects exist with respect to the types of emoji that are included in bios of users and their followers.",
}
@inproceedings{gala-etal-2020-analyzing,
    title = "Analyzing Gender Bias within Narrative Tropes",
    author = "Gala, Dhruvil  and
      Khursheed, Mohammad Omar  and
      Lerner, Hannah  and
      O{'}Connor, Brendan  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcss-1.23",
    doi = "10.18653/v1/2020.nlpcss-1.23",
    pages = "212--217",
    abstract = "Popular media reflects and reinforces societal biases through the use of tropes, which are narrative elements, such as archetypal characters and plot arcs, that occur frequently across media. In this paper, we specifically investigate gender bias within a large collection of tropes. To enable our study, we crawl tvtropes.org, an online user-created repository that contains 30K tropes associated with 1.9M examples of their occurrences across film, television, and literature. We automatically score the {``}genderedness{''} of each trope in our TVTROPES dataset, which enables an analysis of (1) highly-gendered topics within tropes, (2) the relationship between gender bias and popular reception, and (3) how the gender of a work{'}s creator correlates with the types of tropes that they use.",
}
@proceedings{nlp-covid19-2020-nlp,
    title = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    editor = "Verspoor, Karin  and
      Cohen, Kevin Bretonnel  and
      Dredze, Mark  and
      Ferrara, Emilio  and
      May, Jonathan  and
      Munro, Robert  and
      Paris, Cecile  and
      Wallace, Byron",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.0",
}
@inproceedings{wang-etal-2020-cord,
    title = "{CORD-19}: The {COVID-19} Open Research Dataset",
    author = "Wang, Lucy Lu  and
      Lo, Kyle  and
      Chandrasekhar, Yoganand  and
      Reas, Russell  and
      Yang, Jiangjiang  and
      Burdick, Doug  and
      Eide, Darrin  and
      Funk, Kathryn  and
      Katsis, Yannis  and
      Kinney, Rodney Michael  and
      Li, Yunyao  and
      Liu, Ziyang  and
      Merrill, William  and
      Mooney, Paul  and
      Murdick, Dewey A.  and
      Rishi, Devvret  and
      Sheehan, Jerry  and
      Shen, Zhihong  and
      Stilson, Brandon  and
      Wade, Alex D.  and
      Wang, Kuansan  and
      Wang, Nancy Xin Ru  and
      Wilhelm, Christopher  and
      Xie, Boya  and
      Raymond, Douglas M.  and
      Weld, Daniel S.  and
      Etzioni, Oren  and
      Kohlmeier, Sebastian",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1",
    abstract = "The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.",
}
@inproceedings{zhang-etal-2020-rapidly,
    title = "Rapidly Deploying a Neural Search Engine for the {COVID-19} {Open} {Research} {Dataset}",
    author = "Zhang, Edwin  and
      Gupta, Nikhil  and
      Nogueira, Rodrigo  and
      Cho, Kyunghyun  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.2",
    abstract = "The Neural Covidex is a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset (CORD-19) curated by the Allen Institute for AI. It exists as part of a suite of tools we have developed to help domain experts tackle the ongoing global pandemic. We hope that improved information access capabilities to the scientific literature can inform evidence-based decision making and insight generation.",
}
@inproceedings{jimenez-gutierrez-etal-2020-document,
    title = "Document Classification for {COVID-19} Literature",
    author = "Jim{\'e}nez Guti{\'e}rrez, Bernal  and
      Zeng, Juncheng  and
      Zhang, Dongdong  and
      Zhang, Ping  and
      Su, Yu",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.3",
    abstract = "The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset. We find that pre-trained language models outperform other models in both low and high data regimes, achieving a maximum F1 score of around 86{\%}. We note that even the highest performing models still struggle with label correlation, distraction from introductory text and CORD-19 generalization. Both data and code are available on GitHub.",
}
@inproceedings{spangher-etal-2020-enabling,
    title = "Enabling Low-Resource Transfer Learning across {COVID-19} Corpora by Combining Event-Extraction and Co-Training",
    author = "Spangher, Alexander  and
      Peng, Nanyun  and
      May, Jonathan  and
      Ferrara, Emilio",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.4",
}
@inproceedings{grujicic-etal-2020-self,
    title = "Self-supervised context-aware {COVID-19} document exploration through atlas grounding",
    author = "Grujicic, Dusan  and
      Radevski, Gorjan  and
      Tuytelaars, Tinne  and
      Blaschko, Matthew",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.5",
    abstract = "In this paper, we aim to develop a self-supervised grounding of Covid-related medical text based on the actual spatial relationships between the referred anatomical concepts. More specifically, we learn to project sentences into a physical space defined by a three-dimensional anatomical atlas, allowing for a visual approach to navigating Covid-related literature. We design a straightforward and empirically effective training objective to reduce the curated data dependency issue. We use BERT as the main building block of our model and perform a quantitative analysis that demonstrates that the model learns a context-aware mapping. We illustrate two potential use-cases for our approach, one in interactive, 3D data exploration, and the other in document retrieval. To accelerate research in this direction, we make public all trained models, codebase and the developed tools, which can be accessed at https://github.com/gorjanradevski/macchina/.",
}
@inproceedings{huang-etal-2020-coda,
    title = "{CODA-19}: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the {COVID-19} Open Research Dataset",
    author = "Huang, Ting-Hao Kenneth  and
      Huang, Chieh-Yang  and
      Ding, Chien-Kuang Cornelia  and
      Hsu, Yen-Chia  and
      Giles, C. Lee",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.6",
    abstract = "This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10,966 English abstracts in the COVID-19 Open Research Dataset. CODA-19 was created by 248 crowd workers from Amazon Mechanical Turk within 10 days, and achieved labeling quality comparable to that of experts. Each abstract was annotated by nine different workers, and the final labels were acquired by majority vote. The inter-annotator agreement (Cohen{'}s kappa) between the crowd and the biomedical expert (0.741) is comparable to inter-expert agreement (0.788). CODA-19{'}s labels have an accuracy of 82.2{\%} when compared to the biomedical expert{'}s labels, while the accuracy between experts was 85.0{\%}. Reliable human annotations help scientists access and integrate the rapidly accelerating coronavirus literature, and also serve as the battery of AI/NLP research, but obtaining expert annotations can be slow. We demonstrated that a non-expert crowd can be rapidly employed at scale to join the fight against COVID-19.",
}
@inproceedings{das-etal-2020-information,
    title = "Information Retrieval and Extraction on {COVID-19} Clinical Articles Using Graph Community Detection and {Bio-BERT} Embeddings",
    author = "Das, Debasmita  and
      Katyal, Yatin  and
      Verma, Janu  and
      Dubey, Shashank  and
      Singh, AakashDeep  and
      Agarwal, Kushagra  and
      Bhaduri, Sourojit  and
      Ranjan, RajeshKumar",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.7",
    abstract = "In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module.",
}
@inproceedings{wei-etal-2020-people,
    title = "What Are People Asking About {COVID-19}? A Question Classification Dataset",
    author = "Wei, Jerry  and
      Huang, Chengyu  and
      Vosoughi, Soroush  and
      Wei, Jason",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.8",
    abstract = "We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA. We post our dataset publicly at https://github.com/JerryWei03/COVID-Q. For classifying questions into 15 categories, a BERT baseline scored 58.1{\%} accuracy when trained on 20 examples per category, and for a question clustering task, a BERT + triplet loss baseline achieved 49.5{\%} accuracy. We hope COVID-Q can help either for direct use in developing applied systems or as a domain-specific resource for model evaluation.",
}
@inproceedings{li-etal-2020-jennifer,
    title = "Jennifer for {COVID-19}: An {NLP}-Powered Chatbot Built for the People and by the People to Combat Misinformation",
    author = "Li, Yunyao  and
      Grandison, Tyrone  and
      Silveyra, Patricia  and
      Douraghy, Ali  and
      Guan, Xinyu  and
      Kieselbach, Thomas  and
      Li, Chengkai  and
      Zhang, Haiqi",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.9",
    abstract = "Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread. With the goal of combating misinformation, we designed and built Jennifer{--}a chatbot maintained by a global group of volunteers. With Jennifer, we hope to learn whether public information from reputable sources could be more effectively organized and shared in the wake of a crisis as well as to understand issues that the public were most immediately curious about. In this paper, we introduce Jennifer and describe the design of this proof-of-principle system. We also present lessons learned and discuss open challenges. Finally, to facilitate future research, we release COVID-19 Question Bank, a dataset of 3,924 COVID-19-related questions in 944 groups, gathered from our users and volunteers.",
}
@inproceedings{chapman-etal-2020-natural,
    title = "A Natural Language Processing System for National {COVID-19} Surveillance in the {US Department of Veterans Affairs}",
    author = "Chapman, Alec  and
      Peterson, Kelly  and
      Turano, Augie  and
      Box, Tam{\'a}ra  and
      Wallace, Katherine  and
      Jones, Makoto",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.10",
    abstract = "Timely and accurate accounting of positive cases has been an important part of the response to the COVID-19 pandemic. While most positive cases within Veterans Affairs (VA) are identified through structured laboratory results, some patients are tested or diagnosed outside VA so their clinical status is documented only in free-text narratives. We developed a Natural Language Processing pipeline for identifying positively diagnosed COVID19 patients and deployed this system to accelerate chart review. As part of the VA national response to COVID-19, this process identified 6,360 positive cases which did not have corresponding laboratory data. These cases accounted for 36.1{\%} of total confirmed positive cases in VA to date. With available data, performance of the system is estimated as 82.4{\%} precision and 94.2{\%} recall. A public-facing implementation is released as open source and available to the community.",
}
@inproceedings{kleinberg-etal-2020-measuring,
    title = "Measuring {Emotions} in the {COVID}-19 {Real} {World} {Worry} {Dataset}",
    author = "Kleinberg, Bennett  and
      van der Vegt, Isabelle  and
      Mozes, Maximilian",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.11",
    abstract = "The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14{\%} of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem.",
}
@inproceedings{wolohan-2020-estimating,
    title = "Estimating the effect of {COVID-19} on mental health: Linguistic indicators of depression during a global pandemic",
    author = "Wolohan, JT",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.12",
    abstract = "This preliminary analysis uses a deep LSTM neural network with fastText embeddings to predict population rates of depression on Reddit in order to estimate the effect of COVID-19 on mental health. We find that year over year, depression rates on Reddit are up 50{\%} , suggesting a 15-million person increase in the number of depressed Americans and a {\$}7.5 billion increase in depression related spending. This finding suggests that utility in NLP approaches to longitudinal public-health surveillance.",
}
@inproceedings{aggarwal-etal-2020-exploration,
    title = "Exploration of Gender Differences in {COVID-19} Discourse on {R}eddit",
    author = "Aggarwal, Jai  and
      Rabinovich, Ella  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.13",
    abstract = "Decades of research on differences in the language of men and women have established postulates about the nature of lexical, topical, and emotional preferences between the two genders, along with their sociological underpinnings. Using a novel dataset of male and female linguistic productions collected from the Reddit discussion platform, we further confirm existing assumptions about gender-linked affective distinctions, and demonstrate that these distinctions are amplified in social media postings involving emotionally-charged discourse related to COVID-19. Our analysis also confirms considerable differences in topical preferences between male and female authors in pandemic-related discussions.",
}
@inproceedings{kruspe-etal-2020-cross,
    title = "Cross-language sentiment analysis of {European} {Twitter} messages during the {COVID-19} pandemic",
    author = {Kruspe, Anna  and
      H{\"a}berle, Matthias  and
      Kuhn, Iona  and
      Zhu, Xiao Xiang},
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.14",
    abstract = "In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment. This is implemented with a neural network for sentiment analysis using multilingual sentence embeddings. We separate the results by country of origin, and correlate their temporal development with events in those countries. This allows us to study the effect of the situation on people{'}s moods. We see, for example, that lockdown announcements correlate with a deterioration of mood in almost all surveyed countries, which recovers within a short time span.",
}
@inproceedings{levy-wang-2020-cross,
    title = "Cross-lingual Transfer Learning for {COVID-19} Outbreak Alignment",
    author = "Levy, Sharon  and
      Wang, William Yang",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.15",
    abstract = "The spread of COVID-19 has become a significant and troubling aspect of society in 2020. With millions of cases reported across countries, new outbreaks have occurred and followed patterns of previously affected areas. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. It is useful to ask, can we utilize this knowledge in one country to model the outbreak in another? To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro and micro text features, we train on Italy{'}s early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.",
}
@inproceedings{alsudias-rayson-2020-covid,
    title = "{COVID-19} and {Arabic} {Twitter}: How can {Arab} World Governments and Public Health Organizations Learn from Social Media?",
    author = "Alsudias, Lama  and
      Rayson, Paul",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.16",
    abstract = {In March 2020, the World Health Organization announced the COVID-19 outbreak as a pandemic. Most previous social media related research has been on English tweets and COVID-19. In this study, we collect approximately 1 million Arabic tweets from the Twitter streaming API related to COVID-19. Focussing on outcomes that we believe will be useful for Public Health Organizations, we analyse them in three different ways: identifying the topics discussed during the period, detecting rumours, and predicting the source of the tweets. We use the k-means algorithm for the first goal with k=5. The topics discussed can be grouped as follows: COVID-19 statistics, prayers for God, COVID-19 locations, advise and education for prevention, and advertising. We sample 2000 tweets and label them manually for false information, correct information, and unrelated. Then, we apply three different machine learning algorithms, Logistic Regression, Support Vector Classification, and Na{\"\i}ve Bayes with two sets of features, word frequency approach and word embeddings. We find that Machine Learning classifiers are able to correctly identify the rumour related tweets with 84{\%} accuracy. We also try to predict the source of the rumour related tweets depending on our previous model which is about classifying tweets into five categories: academic, media, government, health professional, and public. Around (60{\%}) of the rumour related tweets are classified as written by health professionals and academics.},
}
@inproceedings{medina-serrano-etal-2020-nlp,
    title = "{NLP}-based Feature Extraction for the Detection of {COVID}-19 Misinformation Videos on {Y}ou{T}ube",
    author = "Medina Serrano, Juan Carlos  and
      Papakyriakopoulos, Orestis  and
      Hegelich, Simon",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.17",
    abstract = "We present a simple NLP methodology for detecting COVID-19 misinformation videos on YouTube by leveraging user comments. We use transfer learning pre-trained models to generate a multi-label classifier that can categorize conspiratorial content. We use the percentage of misinformation comments on each video as a new feature for video classification.",
}
@inproceedings{moller-etal-2020-covid,
    title = "{COVID-QA}: A Question Answering Dataset for {COVID}-19",
    author = {M{\"o}ller, Timo  and
      Reina, Anthony  and
      Jayakumar, Raghavan  and
      Pietsch, Malte},
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.18",
    abstract = "We present COVID-QA, a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. To evaluate the dataset we compared a RoBERTa base model fine-tuned on SQuAD with the same model trained on SQuAD and our COVID-QA dataset. We found that the additional training on this domain-specific data leads to significant gains in performance. Both the trained model and the annotated dataset have been open-sourced at: https://github.com/deepset-ai/COVID-QA",
}
@proceedings{nlp-covid19-2020-nlp-covid,
    title = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    editor = "Verspoor, Karin  and
      Cohen, Kevin Bretonnel  and
      Conway, Michael  and
      de Bruijn, Berry  and
      Dredze, Mark  and
      Mihalcea, Rada  and
      Wallace, Byron",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.0",
}
@inproceedings{lee-etal-2020-answering,
    title = "Answering Questions on {COVID}-19 in Real-Time",
    author = "Lee, Jinhyuk  and
      Yi, Sean S.  and
      Jeong, Minbyul  and
      Sung, Mujeen  and
      Yoon, WonJin  and
      Choi, Yonghwa  and
      Ko, Miyoung  and
      Kang, Jaewoo",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.1",
    doi = "10.18653/v1/2020.nlpcovid19-2.1",
    abstract = "The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system also leverages information retrieval (IR) approaches to provide entity-level answers that are complementary to QA models. Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on information from various sources, including the CDC and the WHO. We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19, but for future pandemics as well.",
}
@inproceedings{afzal-etal-2020-cora,
    title = "{CORA}: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles",
    author = "Afzal, Zubair  and
      Yadav, Vikrant  and
      Fedorova, Olga  and
      Kandala, Vaishnavi  and
      van de Loo, Janneke  and
      Akhondi, Saber A.  and
      Coupet, Pascal  and
      Tsatsaronis, George",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.2",
    doi = "10.18653/v1/2020.nlpcovid19-2.2",
    abstract = "Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world have joined forces in an unprecedented manner to fight the threat. Clinicians, biologists, chemists, bioinformaticians, nurses, data scientists, and all of the affiliated relevant disciplines have been mobilized to help discover efficient treatments for the infected population, as well as a vaccine solution to prevent further the virus spread. In this combat against the virus responsible for the pandemic, key for any advancements is the timely, accurate, peer-reviewed, and efficient communication of any novel research findings. In this paper we present a novel framework to address the information need of filtering efficiently the scientific bibliography for relevant literature around COVID-19. The contributions of the paper are summarized in the following: we define and describe the information need that encompasses the major requirements for COVID-19 articles relevancy, we present and release an expert-curated benchmark set for the task, and we analyze the performance of several state-of-the-art machine learning classifiers that may distinguish the relevant from the non-relevant COVID-19 literature.",
}
@inproceedings{almeida-matos-2020-frugal,
    title = "Frugal neural reranking: evaluation on the Covid-19 literature",
    author = "Almeida, Tiago  and
      Matos, S{\'e}rgio",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.3",
    doi = "10.18653/v1/2020.nlpcovid19-2.3",
    abstract = "The Covid-19 pandemic urged the scientific community to join efforts at an unprecedented scale, leading to faster than ever dissemination of data and results, which in turn motivated more research works. This paper presents and discusses information retrieval models aimed at addressing the challenge of searching the large number of publications that stem from these studies. The model presented, based on classical baselines followed by an interaction based neural ranking model, was evaluated and evolved within the TREC Covid challenge setting. Results on this dataset show that, when starting with a strong baseline, our light neural ranking model can achieve results that are comparable to other model architectures that use very large number of parameters.",
}
@inproceedings{grotheer-etal-2020-covid,
    title = "{COVID}-19 Literature Topic-Based Search via Hierarchical {NMF}",
    author = "Grotheer, Rachel  and
      Huang, Longxiu  and
      Huang, Yihuan  and
      Kryshchenko, Alona  and
      Kryshchenko, Oleksandr  and
      Li, Pengyu  and
      Li, Xia  and
      Rebrova, Elizaveta  and
      Ha, Kyung  and
      Needell, Deanna",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.4",
    doi = "10.18653/v1/2020.nlpcovid19-2.4",
    abstract = "A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.",
}
@inproceedings{anastasopoulos-etal-2020-tico,
    title = "{TICO}-19: the Translation Initiative for {CO}vid-19",
    author = {Anastasopoulos, Antonios  and
      Cattelan, Alessandro  and
      Dou, Zi-Yi  and
      Federico, Marcello  and
      Federmann, Christian  and
      Genzel, Dmitriy  and
      Guzm{\'a}n, Franscisco  and
      Hu, Junjie  and
      Hughes, Macduff  and
      Koehn, Philipp  and
      Lazar, Rosie  and
      Lewis, Will  and
      Neubig, Graham  and
      Niu, Mengmeng  and
      {\"O}ktem, Alp  and
      Paquin, Eric  and
      Tang, Grace  and
      Tur, Sylwia},
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.5",
    doi = "10.18653/v1/2020.nlpcovid19-2.5",
    abstract = "The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages. In addition to 9 high-resourced, {''}pivot{''} languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus. The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set. Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.",
}
@inproceedings{welch-etal-2020-expressive,
    title = "Expressive Interviewing: A Conversational System for Coping with {COVID}-19",
    author = "Welch, Charles  and
      Lahnala, Allison  and
      Perez-Rosas, Veronica  and
      Shen, Siqi  and
      Seraj, Sarah  and
      An, Larry  and
      Resnicow, Kenneth  and
      Pennebaker, James  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.6",
    doi = "10.18653/v1/2020.nlpcovid19-2.6",
    abstract = "The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce Expressive Interviewing {--} an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the system{'}s design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.",
}
@inproceedings{tabak-purver-2020-temporal,
    title = "Temporal Mental Health Dynamics on Social Media",
    author = "Tabak, Tom  and
      Purver, Matthew",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.7",
    doi = "10.18653/v1/2020.nlpcovid19-2.7",
    abstract = "We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant- supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depres- sion, supported by the literature. We propose a methodology for providing insight into tem- poral mental health dynamics to be utilised for strategic decision-making.",
}
@inproceedings{biester-etal-2020-quantifying,
    title = "Quantifying the Effects of {COVID}-19 on Mental Health Support Forums",
    author = "Biester, Laura  and
      Matton, Katie  and
      Rajendran, Janarthanan  and
      Provost, Emily Mower  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.8",
    doi = "10.18653/v1/2020.nlpcovid19-2.8",
    abstract = "The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategies for mitigating negative consequences. In this work, we seek to better understand the effects of COVID-19 on mental health by examining discussions within mental health support communities on Reddit. First, we quantify the rate at which COVID-19 is discussed in each community, or subreddit, in order to understand levels of pandemic-related discussion. Next, we examine the volume of activity in order to determine whether the number of people discussing mental health has risen. Finally, we analyze how COVID-19 has influenced language use and topics of discussion within each subreddit.",
}
@inproceedings{lwowski-najafirad-2020-covid,
    title = "{COVID}-19 Surveillance through {T}witter using Self-Supervised and Few Shot Learning",
    author = "Lwowski, Brandon  and
      Najafirad, Peyman",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.9",
    doi = "10.18653/v1/2020.nlpcovid19-2.9",
    abstract = "Public health surveillance and tracking virus via social media can be a useful digital tool for contact tracing and preventing the spread of the virus. Nowadays, large volumes of COVID-19 tweets can quickly be processed in real-time to offer information to researchers. Nonetheless, due to the absence of labeled data for COVID-19, the preliminary supervised classifier or semi-supervised self-labeled methods will not handle non-spherical data with adequate accuracy. With the seasonal influenza and novel Coronavirus having many similar symptoms, we propose using few shot learning to fine-tune a semi-supervised model built on unlabeled COVID-19 and previously labeled influenza dataset that can provide in- sights into COVID-19 that have not been investigated. The experimental results show the efficacy of the proposed model with an accuracy of 86{\%}, identification of Covid-19 related discussion using recently collected tweets.",
}
@inproceedings{loon-etal-2020-explaining,
    title = "Explaining the Trump Gap in Social Distancing Using {COVID} Discourse",
    author = "Loon, Austin Van  and
      Stewart, Sheridan  and
      Waldon, Brandon  and
      Lakshmikanth, Shrinidhi K  and
      Shah, Ishan  and
      Guntuku, Sharath Chandra  and
      Sherman, Garrick  and
      Zou, James  and
      Eichstaedt, Johannes",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.10",
    doi = "10.18653/v1/2020.nlpcovid19-2.10",
    abstract = "Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities{'} response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the {``}{''}Trump Gap{''}, or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.",
}
@inproceedings{hossain-etal-2020-covidlies,
    title = "{COVIDL}ies: Detecting {COVID}-19 Misinformation on Social Media",
    author = "Hossain, Tamanna  and
      Logan IV, Robert L.  and
      Ugarte, Arjuna  and
      Matsubara, Yoshitomo  and
      Young, Sean  and
      Singh, Sameer",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.11",
    doi = "10.18653/v1/2020.nlpcovid19-2.11",
    abstract = "The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter. However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective for evaluating systems designed to detect misinformation on this topic. Misinformation detection can be divided into two sub-tasks: (i) retrieval of misconceptions relevant to posts being checked for veracity, and (ii) stance detection to identify whether the posts Agree, Disagree, or express No Stance towards the retrieved misconceptions. To facilitate research on this task, we release COVIDLies (https://ucinlp.github.io/covid19 ), a dataset of 6761 expert-annotated tweets to evaluate the performance of misinformation detection systems on 86 different pieces of COVID-19 related misinformation. We evaluate existing NLP systems on this dataset, providing initial benchmarks and identifying key challenges for future models to improve upon.",
}
@inproceedings{otmakhova-etal-2020-improved,
    title = "Improved Topic Representations of Medical Documents to Assist {COVID}-19 Literature Exploration",
    author = "Otmakhova, Yulia  and
      Verspoor, Karin  and
      Baldwin, Timothy  and
      {\v{S}}uster, Simon",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.12",
    doi = "10.18653/v1/2020.nlpcovid19-2.12",
    abstract = "Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and propose several ways to improve topic coherence and specificity.",
}
@inproceedings{aizawa-etal-2020-system,
    title = "A System for Worldwide {COVID}-19 Information Aggregation",
    author = "Aizawa, Akiko  and
      Bergeron, Frederic  and
      Chen, Junjie  and
      Cheng, Fei  and
      Hayashi, Katsuhiko  and
      Inui, Kentaro  and
      Ito, Hiroyoshi  and
      Kawahara, Daisuke  and
      Kitsuregawa, Masaru  and
      Kiyomaru, Hirokazu  and
      Kobayashi, Masaki  and
      Kodama, Takashi  and
      Kurohashi, Sadao  and
      Liu, Qianying  and
      Matsubara, Masaki  and
      Miyao, Yusuke  and
      Morishima, Atsuyuki  and
      Murawaki, Yugo  and
      Omura, Kazumasa  and
      Song, Haiyue  and
      Sumita, Eiichiro  and
      Suzuki, Shinji  and
      Tanaka, Ribeka  and
      Tanaka, Yu  and
      Toyoda, Masashi  and
      Ueda, Nobuhiro  and
      Ueoka, Honai  and
      Utiyama, Masao  and
      Zhong, Ying",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.13",
    doi = "10.18653/v1/2020.nlpcovid19-2.13",
    abstract = "The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.",
}
@inproceedings{su-etal-2020-caire,
    title = "{CA}i{RE}-{COVID}: A Question Answering and Query-focused Multi-Document Summarization System for {COVID}-19 Scholarly Information Management",
    author = "Su, Dan  and
      Xu, Yan  and
      Yu, Tiezheng  and
      Siddique, Farhad Bin  and
      Barezi, Elham  and
      Fung, Pascale",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.14",
    doi = "10.18653/v1/2020.nlpcovid19-2.14",
    abstract = "We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Challenge, judged by medical experts. Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 by answering high priority questions from the community and summarizing salient question-related information. It combines information extraction with state-of-the-art QA and query-focused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query. We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question. We further conduct quantitative experiments that show consistent improvements on various metrics for each module. We have launched our website CAiRE-COVID for broader use by the medical community, and have open-sourced the code for our system, to bootstrap further study by other researches.",
}
@inproceedings{otegi-etal-2020-automatic,
    title = "Automatic Evaluation vs. User Preference in Neural Textual {Q}uestion{A}nswering over {COVID}-19 Scientific Literature",
    author = "Otegi, Arantxa  and
      Campos, Jon Ander  and
      Azkune, Gorka  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.15",
    doi = "10.18653/v1/2020.nlpcovid19-2.15",
    abstract = "We present a Question Answering (QA) system that won one of the tasks of the Kaggle CORD-19 Challenge, according to the qualitative evaluation of experts. The system is a combination of an Information Retrieval module and a reading comprehension module that finds the answers in the retrieved passages. In this paper we present a quantitative and qualitative analysis of the system. The quantitative evaluation using manually annotated datasets contradicted some of our design choices, e.g. the fact that using QuAC for fine-tuning provided better answers over just using SQuAD. We analyzed this mismatch with an additional A/B test which showed that the system using QuAC was indeed preferred by users, confirming our intuition. Our analysis puts in question the suitability of automatic metrics and its correlation to user preferences. We also show that automatic metrics are highly dependent on the characteristics of the gold standard, such as the average length of the answers.",
}
@inproceedings{berard-etal-2020-multilingual,
    title = "A Multilingual Neural Machine Translation Model for Biomedical Data",
    author = "B{\'e}rard, Alexandre  and
      Kim, Zae Myung  and
      Nikoulina, Vassilina  and
      Park, Eunjeong Lucy  and
      Gall{\'e}, Matthias",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.16",
    doi = "10.18653/v1/2020.nlpcovid19-2.16",
    abstract = "We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English. It is trained with large amounts of generic and biomedical data, using domain tags. Our benchmarks show that it performs near state-of-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models. We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19 crisis and of its effects on society, economy, and healthcare policies. We also release a test set of biomedical text for Korean-English. It consists of 758 sentences from official guidelines and recent papers, all about COVID-19.",
}
@inproceedings{wang-etal-2020-public,
    title = "Public Sentiment on Governmental {COVID}-19 Measures in {D}utch Social Media",
    author = "Wang, Shihan  and
      Schraagen, Marijn  and
      Tjong Kim Sang, Erik  and
      Dastani, Mehdi",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.17",
    doi = "10.18653/v1/2020.nlpcovid19-2.17",
    abstract = "Public sentiment (the opinion, attitude or feeling that the public expresses) is a factor of interest for government, as it directly influences the implementation of policies. Given the unprecedented nature of the COVID-19 crisis, having an up-to-date representation of public sentiment on governmental measures and announcements is crucial. In this paper, we analyse Dutch public sentiment on governmental COVID-19 measures from text data collected across three online media sources (Twitter, Reddit and Nu.nl) from February to September 2020. We apply sentiment analysis methods to analyse polarity over time, as well as to identify stance towards two specific pandemic policies regarding social distancing and wearing face masks. The presented preliminary results provide valuable insights into the narratives shown in vast social media text data, which help understand the influence of COVID-19 measures on the general public.",
}
@inproceedings{jang-etal-2020-exploratory,
    title = "Exploratory Analysis of {COVID}-19 Related Tweets in {N}orth {A}merica to Inform Public Health Institutes",
    author = "Jang, Hyeju  and
      Rempel, Emily  and
      Carenini, Giuseppe  and
      Janjua, Naveed",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.18",
    doi = "10.18653/v1/2020.nlpcovid19-2.18",
    abstract = "Social media is a rich source where we can learn about people{'}s reactions to social issues. As COVID-19 has significantly impacted on people{'}s lives, it is essential to capture how people react to public health interventions and understand their concerns. In this paper, we aim to investigate people{'}s reactions and concerns about COVID-19 in North America, especially focusing on Canada. We analyze COVID-19 related tweets using topic modeling and aspect-based sentiment analysis, and interpret the results with public health experts. We compare timeline of topics discussed with timing of implementation of public health interventions for COVID-19. We also examine people{'}s sentiment about COVID-19 related issues. We discuss how the results can be helpful for public health agencies when designing a policy for new interventions. Our work shows how Natural Language Processing (NLP) techniques could be applied to public health questions with domain expert involvement.",
}
@inproceedings{miao-etal-2020-twitter,
    title = "{T}witter Data Augmentation for Monitoring Public Opinion on {COVID}-19 Intervention Measures",
    author = "Miao, Lin  and
      Last, Mark  and
      Litvak, Marina",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.19",
    doi = "10.18653/v1/2020.nlpcovid19-2.19",
    abstract = "The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.",
}
@inproceedings{barros-etal-2020-covid,
    title = "{COVID}-19: A Semantic-Based Pipeline for Recommending Biomedical Entities",
    author = "Barros, Marcia Afonso  and
      Lamurias, Andre  and
      Sousa, Diana  and
      Ruas, Pedro  and
      Couto, Francisco M.",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.20",
    doi = "10.18653/v1/2020.nlpcovid19-2.20",
    abstract = "With the increasing number of publications about COVID-19, it is a challenge to extract personalized knowledge suitable for each researcher. This work aims to build a new semantic-based pipeline for recommending biomedical entities to scientific researchers. To this end, we developed a pipeline that creates an implicit feedback matrix based on Named Entity Recognition (NER) on a corpus of documents, using multidisciplinary ontologies for recognizing and linking the entities. Our hypothesis is that by using ontologies from different fields in the NER phase, we can improve the results for state-of-the-art collaborative-filtering recommender systems applied to the dataset created. The tests performed using the COVID-19 Open Research Dataset (CORD-19) dataset show that when using four ontologies, the results for precision@k, for example, reach the 80{\%}, whereas when using only one ontology, the results for precision@k drops to 20{\%}, for the same users. Furthermore, the use of multi-fields entities may help in the discovery of new items, even if the researchers do not have items from that field in their set of preferences.",
}
@inproceedings{koksal-etal-2020-vapur,
    title = "Vapur: A Search Engine to Find Related Protein - Compound Pairs in {COVID}-19 Literature",
    author = {K{\"o}ksal, Abdullatif  and
      D{\"o}nmez, Hilal  and
      {\"O}z{\c{c}}elik, R{\i}za  and
      Ozkirimli, Elif  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.21",
    doi = "10.18653/v1/2020.nlpcovid19-2.21",
    abstract = "Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.",
}
@inproceedings{piad-morffis-etal-2020-knowledge,
    title = "Knowledge Discovery in {COVID}-19 Research Literature",
    author = "Piad-Morffis, Alejandro  and
      Estevez-Velarde, Suilan  and
      Estevanell-Valladares, Ernesto Luis  and
      Guti{\'e}rrez, Yoan  and
      Montoyo, Andr{\'e}s  and
      Mu{\~n}oz, Rafael  and
      Almeida-Cruz, Yudivi{\'a}n",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.22",
    doi = "10.18653/v1/2020.nlpcovid19-2.22",
    abstract = "This paper presents the preliminary results of an ongoing project that analyzes the growing body of scientific research published around the COVID-19 pandemic. In this research, a general-purpose semantic model is used to double annotate a batch of {\$}500{\$} sentences that were manually selected by the researchers from the CORD-19 corpus. Afterwards, a baseline text-mining pipeline is designed and evaluated via a large batch of {\$}100,959{\$} sentences. We present a qualitative analysis of the most interesting facts automatically extracted and highlight possible future lines of development. The preliminary results show that general-purpose semantic models are a useful tool for discovering fine-grained knowledge in large corpora of scientific documents.",
}
@inproceedings{thukral-etal-2020-identifying,
    title = "Identifying pandemic-related stress factors from social-media posts {--} {E}ffects on students and young-adults",
    author = "Thukral, Sachin  and
      Sangwan, Suyash  and
      Chatterjee, Arnab  and
      Dey, Lipika",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.23",
    doi = "10.18653/v1/2020.nlpcovid19-2.23",
    abstract = "The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to wide-spread gloom and depression across society but more so among the young and the elderly. There are currently more than 200 million college students in 186 countries worldwide, affected due to the pandemic. The mode of education has changed suddenly, with the rapid adaptation of e-learning, whereby teaching is undertaken remotely and on digital platforms. This study presents insights gathered from social media posts that were posted by students and young adults during the COVID times. Using statistical and NLP techniques, we analyzed the behavioural issues reported by users themselves in their posts in depression related communities on Reddit. We present methodologies to systematically analyze content using linguistic techniques to find out the stress-inducing factors. Online education, losing jobs, isolation from friends and abusive families emerge as key stress factors",
}
@inproceedings{addawood-etal-2020-tracking,
    title = "Tracking And Understanding Public Reaction During {COVID}-19: {S}audi {A}rabia As A Use Case",
    author = "Addawood, Aseel  and
      Alsuwailem, Alhanouf  and
      Alohali, Ali  and
      Alajaji, Dalal  and
      Alturki, Mashail  and
      Alsuhaibani, Jaida  and
      Aljabli, Fawziah",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.24",
    doi = "10.18653/v1/2020.nlpcovid19-2.24",
    abstract = "The coronavirus disease of 2019 (COVID-19) has a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are getting affected by these new measures. With restrictions like lockdown and social distancing, it became important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabia citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that were originated from Saudi Arabia. Our results showed that people had kept a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passes. Overall, the results showed that people were so supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.",
}
@inproceedings{tekumalla-banda-2020-characterizing,
    title = "Characterizing drug mentions in {COVID}-19 {T}witter Chatter",
    author = "Tekumalla, Ramya  and
      Banda, Juan M",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.25",
    doi = "10.18653/v1/2020.nlpcovid19-2.25",
    abstract = "Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task. By applying these complementary methods, we are able to recover almost 15{\%} additional data, making misspelling handling a needed task as a pre-processing step when dealing with social media data.",
}
@inproceedings{hosseini-etal-2020-content,
    title = "Content analysis of {P}ersian/{F}arsi Tweets during {COVID}-19 pandemic in {I}ran using {NLP}",
    author = "Hosseini, Pedram  and
      Hosseini, Poorya  and
      Broniatowski, David",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.26",
    doi = "10.18653/v1/2020.nlpcovid19-2.26",
    abstract = "Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread. Twitter is one of the widely-used online platforms by Iranians inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues. In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time. We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. We additionally categorized the broader content of tweets that shows satire, followed by news, is the dominant tweet type among Iranian users. While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.",
}
@inproceedings{colic-etal-2020-annotating,
    title = "Annotating the Pandemic: Named Entity Recognition and Normalisation in {COVID}-19 Literature",
    author = "Colic, Nico  and
      Furrer, Lenz  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.27",
    doi = "10.18653/v1/2020.nlpcovid19-2.27",
    abstract = "The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. We are presenting a publicly available pipeline to perform named entity recognition and normalisation in parallel to help find relevant publications and to aid in downstream NLP tasks such as text summarisation. In our approach, we are using a dictionary-based system for its high recall in conjunction with two models based on BioBERT for their accuracy. Their outputs are combined according to different strategies depending on the entity type. In addition, we are using a manually crafted dictionary to increase performance for new concepts related to COVID-19. We have previously evaluated our work on the CRAFT corpus, and make the output of our pipeline available on two visualisation platforms.",
}
@inproceedings{suderman-etal-2020-askme,
    title = "{A}sk{M}e: A {LAPPS} {G}rid-based {NLP} Query and Retrieval System for Covid-19 Literature",
    author = "Suderman, Keith  and
      Ide, Nancy  and
      Marc, Verhagen  and
      Cochran, Brent  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.28",
    doi = "10.18653/v1/2020.nlpcovid19-2.28",
    abstract = "In a recent project, the Language Application Grid was augmented to support the mining of scientific publications. The results of that ef- fort have now been repurposed to focus on Covid-19 literature, including modification of the LAPPS Grid {``}AskMe{''} query and retrieval engine. We describe the AskMe system and discuss its functionality as compared to other query engines available to search covid-related publications.",
}
@inproceedings{lymperopoulos-etal-2020-concept,
    title = "Concept Wikification for {COVID}-19",
    author = "Lymperopoulos, Panagiotis  and
      Qiu, Haoling  and
      Min, Bonan",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.29",
    doi = "10.18653/v1/2020.nlpcovid19-2.29",
    abstract = "Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19{\_}wikification.",
}
@inproceedings{resnik-etal-2020-developing,
    title = "Developing a Curated Topic Model for {COVID}-19 Medical Research Literature",
    author = "Resnik, Philip  and
      Goodman, Katherine E.  and
      Moran, Mike",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.30",
    doi = "10.18653/v1/2020.nlpcovid19-2.30",
    abstract = "Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.",
}
@inproceedings{poliak-etal-2020-collecting,
    title = "Collecting Verified {COVID}-19 Question Answer Pairs",
    author = "Poliak, Adam  and
      Fleming, Max  and
      Costello, Cash  and
      Murray, Kenton W  and
      Yarmohammadi, Mahsa  and
      Pandya, Shivani  and
      Irani, Darius  and
      Agarwal, Milind  and
      Sharma, Udit  and
      Sun, Shuo  and
      Ivanov, Nicola  and
      Shang, Lingxi  and
      Srinivasan, Kaushik  and
      Lee, Seolhwa  and
      Han, Xu  and
      Agarwal, Smisha  and
      Sedoc, Jo{\~a}o",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.31",
    doi = "10.18653/v1/2020.nlpcovid19-2.31",
    abstract = "We release a dataset of over 2,100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites. We include an additional 24, 000 questions pulled from online sources that have been aligned by experts with existing answered questions from our dataset. This paper describes our efforts in collecting the dataset and summarizes the resulting data. Our dataset is automatically updated daily and available at https://github.com/JHU-COVID-QA/ scraping-qas. So far, this data has been used to develop a chatbot providing users information about COVID-19. We encourage others to build analytics and tools upon this dataset as well.",
}
@inproceedings{leaman-lu-2020-comprehensive,
    title = "A Comprehensive Dictionary and Term Variation Analysis for {COVID}-19 and {SARS}-{C}o{V}-2",
    author = "Leaman, Robert  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.32",
    doi = "10.18653/v1/2020.nlpcovid19-2.32",
    abstract = "The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dictionary of terms used in the literature to refer to SARS-CoV-2 and COVID-19. We use a rule-based approach to iteratively generate new term variants, then locate these variants in a large text corpus. We compare our dictionary to an extensive collection of terminological resources, demonstrating that our resource provides a substantial number of additional terms. We use our dictionary to analyze the usage of SARS-CoV-2 and COVID-19 terms over time and show that the number of unique terms continues to grow rapidly. Our dictionary is freely available at https://github.com/ncbi-nlp/CovidTermVar.",
}
@inproceedings{lee-sedoc-2020-using,
    title = "Using the Poly-encoder for a {COVID}-19 Question Answering System",
    author = "Lee, Seolhwa  and
      Sedoc, Jo{\~a}o",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.33",
    doi = "10.18653/v1/2020.nlpcovid19-2.33",
    abstract = "To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19. We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs. We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score. We make our code publicly available for other researchers to use.",
}
@inproceedings{hu-etal-2020-weibo,
    title = "{W}eibo-{COV}: A Large-Scale {COVID}-19 Social Media Dataset from {W}eibo",
    author = "Hu, Yong  and
      Huang, Heyan  and
      Chen, Anfan  and
      Mao, Xian-Ling",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.34",
    doi = "10.18653/v1/2020.nlpcovid19-2.34",
    abstract = "With the rapid development of COVID-19 around the world, people are requested to maintain {``}social distance{''} and {``}stay at home{''}. In this scenario, extensive social interactions transfer to cyberspace, especially on social media platforms like Twitter and Sina Weibo. People generate posts to share information, express opinions and seek help during the pandemic outbreak, and these kinds of data on social media are valuable for studies to prevent COVID-19 transmissions, such as early warning and outbreaks detection. Therefore, in this paper, we release a novel and fine-grained large-scale COVID-19 social media dataset collected from Sina Weibo, named Weibo-COV, contains more than 40 million posts ranging from December 1, 2019 to April 30, 2020. Moreover, this dataset includes comprehensive information nuggets like post-level information, interactive information, location information, and repost network. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and rapid researches to suppress the spread of this pandemic.",
}
@inproceedings{santosh-etal-2020-detecting,
    title = "Detecting Emerging Symptoms of {COVID}-19 using Context-based {T}witter Embeddings",
    author = "Santosh, Roshan  and
      Schwartz, H.  and
      Eichstaedt, Johannes  and
      Ungar, Lyle  and
      Guntuku, Sharath Chandra",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.35",
    doi = "10.18653/v1/2020.nlpcovid19-2.35",
    abstract = "In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving. More generally, the method can be applied to finding context-specific words and texts (e.g. symptom mentions) in large imbalanced corpora (e.g. all tweets mentioning {\#}COVID-19). Given the novelty of COVID-19, we also test if the proposed approach generalizes to the problem of detecting Adverse Drug Reaction (ADR). We find that the approach applied to Twitter data can detect symptom mentions substantially before to their being reported by the Centers for Disease Control (CDC).",
}
@inproceedings{hardage-najafirad-2020-hate,
    title = "Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using {XAI}: Ongoing Applied Research",
    author = "Hardage, David  and
      Najafirad, Peyman",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.36",
    doi = "10.18653/v1/2020.nlpcovid19-2.36",
    abstract = "As social distancing, self-quarantines, and travel restrictions have shifted a lot of pandemic conversations to social media so does the spread of hate speech. While recent machine learning solutions for automated hate and offensive speech identification are available on Twitter, there are issues with their interpretability. We propose a novel use of learned feature importance which improves upon the performance of prior state-of-the-art text classification techniques, while producing more easily interpretable decisions. We also discuss both technical and practical challenges that remain for this task.",
}
@inproceedings{mircea-2020-real,
    title = "Real-time Classification, Geolocation and Interactive Visualization of {COVID}-19 Information Shared on Social Media to Better Understand Global Developments",
    author = "Mircea, Andrei",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-2.37",
    doi = "10.18653/v1/2020.nlpcovid19-2.37",
    abstract = "As people communicate on social media during COVID-19, it can be an invaluable source of useful and up-to-date information. However, the large volume and noise-to-signal ratio of social media can make this impractical. We present a prototype dashboard for the real-time classification, geolocation and interactive visualization of COVID-19 tweets that addresses these issues. We also describe a novel L2 classification layer that outperforms linear layers on a dataset of respiratory virus tweets.",
}
@proceedings{nlpbt-2020-international,
    title = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    editor = "Castellucci, Giuseppe  and
      Filice, Simone  and
      Poria, Soujanya  and
      Cambria, Erik  and
      Specia, Lucia",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.0",
}
@inproceedings{delbrouck-etal-2020-modulated,
    title = "Modulated Fusion using Transformer for Linguistic-Acoustic Emotion Recognition",
    author = "Delbrouck, Jean-Benoit  and
      Tits, No{\'e}  and
      Dupont, St{\'e}phane",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.1",
    doi = "10.18653/v1/2020.nlpbt-1.1",
    pages = "1--10",
    abstract = "This paper aims to bring a new lightweight yet powerful solution for the task of Emotion Recognition and Sentiment Analysis. Our motivation is to propose two architectures based on Transformers and modulation that combine the linguistic and acoustic inputs from a wide range of datasets to challenge, and sometimes surpass, the state-of-the-art in the field. To demonstrate the efficiency of our models, we carefully evaluate their performances on the IEMOCAP, MOSI, MOSEI and MELD dataset. The experiments can be directly replicated and the code is fully open for future researches.",
}
@inproceedings{srinivasan-etal-2020-multimodal,
    title = "Multimodal Speech Recognition with Unstructured Audio Masking",
    author = "Srinivasan, Tejas  and
      Sanabria, Ramon  and
      Metze, Florian  and
      Elliott, Desmond",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.2",
    doi = "10.18653/v1/2020.nlpbt-1.2",
    pages = "11--18",
    abstract = "Visual context has been shown to be useful for automatic speech recognition (ASR) systems when the speech signal is noisy or corrupted. Previous work, however, has only demonstrated the utility of visual context in an unrealistic setting, where a fixed set of words are systematically masked in the audio. In this paper, we simulate a more realistic masking scenario during model training, called RandWordMask, where the masking can occur for any word segment. Our experiments on the Flickr 8K Audio Captions Corpus show that multimodal ASR can generalize to recover different types of masked words in this unstructured masking setting. Moreover, our analysis shows that our models are capable of attending to the visual signal when the audio signal is corrupted. These results show that multimodal ASR systems can leverage the visual signal in more generalized noisy scenarios.",
}
@inproceedings{wang-etal-2020-building,
    title = "Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data",
    author = "Wang, Xinyu  and
      Sun, Xiaowen  and
      Yang, Tan  and
      Wang, Hongbo",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.3",
    doi = "10.18653/v1/2020.nlpbt-1.3",
    pages = "19--29",
    abstract = "Sarcasm detection in social media with text and image is becoming more challenging. Previous works of image-text sarcasm detection were mainly to fuse the summaries of text and image: different sub-models read the text and image respectively to get the summaries, and fuses the summaries. Recently, some multi-modal models based on the architecture of BERT are proposed such as ViLBERT. However, they can only be pretrained on the image-text data. In this paper, we propose an image-text model for sarcasm detection using the pretrained BERT and ResNet without any further pretraining. BERT and ResNet have been pretrained on much larger text or image data than image-text data. We connect the vector spaces of BERT and ResNet to utilize more data. We use the pretrained Multi-Head Attention of BERT to model the text and image. Besides, we propose a 2D-Intra-Attention to extract the relationships between words and images. In experiments, our model outperforms the state-of-the-art model.",
}
@inproceedings{xu-etal-2020-benchmark,
    title = "A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos",
    author = "Xu, Frank F.  and
      Ji, Lei  and
      Shi, Botian  and
      Du, Junyi  and
      Neubig, Graham  and
      Bisk, Yonatan  and
      Duan, Nan",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.4",
    doi = "10.18653/v1/2020.nlpbt-1.4",
    pages = "30--40",
    abstract = "Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned. We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos. This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples. Our manually annotated open-vocabulary resource includes 356 instructional cooking videos and 15,523 video clip/sentence-level annotations. Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.",
}
@inproceedings{della-corte-stymne-2020-iestac,
    title = "{IESTAC}: {E}nglish-{I}talian Parallel Corpus for End-to-End Speech-to-Text Machine Translation",
    author = "Della Corte, Giuseppe  and
      Stymne, Sara",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.5",
    doi = "10.18653/v1/2020.nlpbt-1.5",
    pages = "41--50",
    abstract = "We discuss a set of methods for the creation of IESTAC: a English-Italian speech and text parallel corpus designed for the training of end-to-end speech-to-text machine translation models and publicly released as part of this work. We first mapped English LibriVox audiobooks and their corresponding English Gutenberg Project e-books to Italian e-books with a set of three complementary methods. Then we aligned the English and the Italian texts using both traditional Gale-Church based alignment methods and a recently proposed tool to perform bilingual sentences alignment computing the cosine similarity of multilingual sentence embeddings. Finally, we forced the alignment between the English audiobooks and the English side of our textual parallel corpus with a text-to-speech and dynamic time warping based forced alignment tool. For each step, we provide the reader with a critical discussion based on detailed evaluation and comparison of the results of the different methods.",
}
@inproceedings{uehara-harada-2020-unsupervised,
    title = "Unsupervised Keyword Extraction for Full-Sentence {VQA}",
    author = "Uehara, Kohei  and
      Harada, Tatsuya",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.6",
    doi = "10.18653/v1/2020.nlpbt-1.6",
    pages = "51--59",
    abstract = "In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during dataset construction. This study envisions a VQA task for natural situations, where the answers are more likely to be sentences rather than single words. To bridge the gap between this natural VQA and existing VQA approaches, a novel unsupervised keyword extraction method is proposed. The method is based on the principle that the full-sentence answers can be decomposed into two parts: one that contains new information answering the question (i.e. keywords), and one that contains information already included in the question. Discriminative decoders were designed to achieve such decomposition, and the method was experimentally implemented on VQA datasets containing full-sentence answers. The results show that the proposed model can accurately extract the keywords without being given explicit annotations describing them.",
}
@inproceedings{khullar-arora-2020-mast,
    title = "{MAST}: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention",
    author = "Khullar, Aman  and
      Arora, Udit",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.7",
    doi = "10.18653/v1/2020.nlpbt-1.7",
    pages = "60--69",
    abstract = "This paper presents MAST, a new model for Multimodal Abstractive Text Summarization that utilizes information from all three modalities {--} text, audio and video {--} in a multimodal video. Prior work on multimodal abstractive text summarization only utilized information from the text and video modalities. We examine the usefulness and challenges of deriving information from the audio modality and present a sequence-to-sequence trimodal hierarchical attention-based model that overcomes these challenges by letting the model pay more attention to the text modality. MAST outperforms the current state of the art model (video-text) by 2.51 points in terms of Content F1 score and 1.00 points in terms of Rouge-L score on the How2 dataset for multimodal language understanding.",
}
@inproceedings{mansimov-etal-2020-towards,
    title = "Towards End-to-End In-Image Neural Machine Translation",
    author = "Mansimov, Elman  and
      Stern, Mitchell  and
      Chen, Mia  and
      Firat, Orhan  and
      Uszkoreit, Jakob  and
      Jain, Puneet",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.8",
    doi = "10.18653/v1/2020.nlpbt-1.8",
    pages = "70--74",
    abstract = "In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language. We propose an end-to-end neural model for this task inspired by recent approaches to neural machine translation, and demonstrate promising initial results based purely on pixel-level supervision. We then offer a quantitative and qualitative evaluation of our system outputs and discuss some common failure modes. Finally, we conclude with directions for future work.",
}
@inproceedings{shah-etal-2020-reasoning,
    title = "Reasoning Over History: Context Aware Visual Dialog",
    author = "Shah, Muhammad  and
      Mehri, Shikib  and
      Srinivasan, Tejas",
    booktitle = "Proceedings of the First International Workshop on Natural Language Processing Beyond Text",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlpbt-1.9",
    doi = "10.18653/v1/2020.nlpbt-1.9",
    pages = "75--83",
    abstract = "While neural models have been shown to exhibit strong performance on single-turn visual question answering (VQA) tasks, extending VQA to a multi-turn, conversational setting remains a challenge. One way to address this challenge is to augment existing strong neural VQA models with the mechanisms that allow them to retain information from previous dialog turns. One strong VQA model is the MAC network, which decomposes a task into a series of attention-based reasoning steps. However, since the MAC network is designed for single-turn question answering, it is not capable of referring to past dialog turns. More specifically, it struggles with tasks that require reasoning over the dialog history, particularly coreference resolution. We extend the MAC network architecture with Context-aware Attention and Memory (CAM), which attends over control states in past dialog turns to determine the necessary reasoning operations for the current question. MAC nets with CAM achieve up to 98.25{\%} accuracy on the CLEVR-Dialog dataset, beating the existing state-of-the-art by 30{\%} (absolute). Our error analysis indicates that with CAM, the model{'}s performance particularly improved on questions that required coreference resolution.",
}
@proceedings{nlp4musa-2020-nlp,
    title = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    editor = "Oramas, Sergio  and
      Espinosa-Anke, Luis  and
      Epure, Elena  and
      Jones, Rosie  and
      Sordo, Mohamed  and
      Quadrana, Massimo  and
      Watanabe, Kento",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.0",
}
@inproceedings{jiang-etal-2020-discovering,
    title = "Discovering Music Relations with Sequential Attention",
    author = "Jiang, Junyan  and
      Xia, Gus  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.1",
    pages = "1--5",
}
@inproceedings{watanabe-goto-2020-lyrics,
    title = "Lyrics Information Processing: Analysis, Generation, and Applications",
    author = "Watanabe, Kento  and
      Goto, Masataka",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.2",
    pages = "6--12",
}
@inproceedings{joshi-etal-2020-read,
    title = "Did You {``}Read{''} the Next Episode? Using Textual Cues for Predicting Podcast Popularity",
    author = "Joshi, Brihi  and
      Mittal, Shravika  and
      Chetan, Aditya",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.3",
    pages = "13--17",
}
@inproceedings{hao-downie-2020-using,
    title = "Using Latent Semantics of Playlist Titles and Descriptions to Enhance Music Recommendations",
    author = "Hao, Yun  and
      Downie, J. Stephen",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.4",
    pages = "18--22",
}
@inproceedings{choi-etal-2020-prediction,
    title = "Prediction of user listening contexts for music playlists",
    author = "Choi, Jeong  and
      Khlif, Anis  and
      Epure, Elena",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.5",
    pages = "23--27",
}
@inproceedings{crespo-sanchez-etal-2020-information,
    title = "An Information-based Model for Writing Style Analysis of Lyrics",
    author = "Crespo-Sanchez, Melesio  and
      Aldana-Bobadilla, Edwin  and
      Lopez-Arevalo, Ivan  and
      Molina-Villegas, Alejandro",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.6",
    pages = "28--32",
}
@inproceedings{vechtomova-etal-2020-generation,
    title = "Generation of lyrics lines conditioned on music audio clips",
    author = "Vechtomova, Olga  and
      Sahu, Gaurav  and
      Kumar, Dhruv",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.7",
    pages = "33--37",
}
@inproceedings{astefanoaei-collignon-2020-hyperbolic,
    title = "Hyperbolic Embeddings for Music Taxonomy",
    author = "Astefanoaei, Maria  and
      Collignon, Nicolas",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.8",
    pages = "38--42",
}
@inproceedings{kalonaris-etal-2020-computational,
    title = "Computational Linguistics Metrics for the Evaluation of Two-Part Counterpoint Generated with Neural Machine Translation",
    author = "Kalonaris, Stefano  and
      McLachlan, Thomas  and
      Aljanaki, Anna",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.9",
    pages = "43--48",
}
@inproceedings{geerlings-merono-penuela-2020-interacting,
    title = "Interacting with {GPT}-2 to Generate Controlled and Believable Musical Sequences in {ABC} Notation",
    author = "Geerlings, Cari{\~n}a  and
      Mero{\~n}o-Pe{\~n}uela, Albert",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.10",
    pages = "49--53",
}
@inproceedings{zhang-etal-2020-butter,
    title = "{BUTTER}: A Representation Learning Framework for Bi-directional Music-Sentence Retrieval and Generation",
    author = "Zhang, Yixiao  and
      Wang, Ziyu  and
      Wang, Dingsu  and
      Xia, Gus",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.11",
    pages = "54--58",
}
@inproceedings{sawada-etal-2020-unsupervised,
    title = "Unsupervised Melody Segmentation Based on a Nested {P}itman-{Y}or Language Model",
    author = "Sawada, Shun  and
      Yoshii, Kazuyoshi  and
      Hirata, Keiji",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.12",
    pages = "59--63",
}
@inproceedings{rossetto-dalton-2020-musicbert,
    title = "{M}usic{BERT} - learning multi-modal representations for music and text",
    author = "Rossetto, Federico  and
      Dalton, Jeff",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.13",
    pages = "64--66",
}
@inproceedings{cai-etal-2020-music,
    title = "Music autotagging as captioning",
    author = "Cai, Tian  and
      Mandel, Michael I  and
      He, Di",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.14",
    pages = "67--72",
}
@inproceedings{mayerl-etal-2020-comparing,
    title = "Comparing Lyrics Features for Genre Recognition",
    author = {Mayerl, Maximilian  and
      V{\"o}tter, Michael  and
      Moosleitner, Manfred  and
      Zangerle, Eva},
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.15",
    pages = "73--77",
}
@inproceedings{liew-etal-2020-classification,
    title = "Classification of Nostalgic Music Through {LDA} Topic Modeling and Sentiment Analysis of {Y}ou{T}ube Comments in {J}apanese Songs",
    author = "Liew, Kongmeng  and
      Uchida, Yukiko  and
      Maeura, Nao  and
      Aramaki, Eiji",
    booktitle = "Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)",
    month = "16 " # oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4musa-1.16",
    pages = "78--82",
}
@proceedings{nlp4if-2020-nlp4if,
    title = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    editor = "Da San Martino, Giovanni  and
      Brew, Chris  and
      Ciampaglia, Giovanni Luca  and
      Feldman, Anna  and
      Leberknight, Chris  and
      Nakov, Preslav",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.nlp4if-1.0",
}
@inproceedings{vijjali-etal-2020-two,
    title = "Two Stage Transformer Model for {COVID}-19 Fake News Detection and Fact Checking",
    author = "Vijjali, Rutvik  and
      Potluri, Prathyush  and
      Kumar, Siddharth  and
      Teki, Sundeep",
    booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.nlp4if-1.1",
    pages = "1--10",
    abstract = "The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user queries about particular COVID-19 claims. The second model verifies the level of {``}truth{''} in the queried claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.",
}
@inproceedings{niven-kao-2020-measuring,
    title = "Measuring Alignment to Authoritarian State Media as Framing Bias",
    author = "Niven, Timothy  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.nlp4if-1.2",
    pages = "11--21",
    abstract = "We introduce what is to the best of our knowledge a new task in natural language processing: measuring alignment to authoritarian state media. We operationalize alignment in terms of sociological definitions of media bias. We take as a case study the alignment of four Taiwanese media outlets to the Chinese Communist Party state media. We present the results of an initial investigation using the frequency of words in psychologically meaningful categories. Our findings suggest that the chosen word categories correlate with framing choices. We develop a calculation method that yields reasonable results for measuring alignment, agreeing well with the known labels. We confirm that our method does capture event selection bias, but whether it captures framing bias requires further investigation.",
}
@inproceedings{prakash-tayyar-madabushi-2020-incorporating,
    title = "Incorporating Count-Based Features into Pre-Trained Models for Improved Stance Detection",
    author = "Prakash, Anushka  and
      Tayyar Madabushi, Harish",
    booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.nlp4if-1.3",
    pages = "22--32",
    abstract = "The explosive growth and popularity of Social Media has revolutionised the way we communicate and collaborate. Unfortunately, this same ease of accessing and sharing information has led to an explosion of misinformation and propaganda. Given that stance detection can significantly aid in veracity prediction, this work focuses on boosting automated stance detection, a task on which pre-trained models have been extremely successful on, as on several other tasks. This work shows that the task of stance detection can benefit from feature based information, especially on certain under performing classes, however, integrating such features into pre-trained models using ensembling is challenging. We propose a novel architecture for integrating features with pre-trained models that address these challenges and test our method on the RumourEval 2019 dataset. This method achieves state-of-the-art results with an F1-score of 63.94 on the test set.",
}
@inproceedings{li-etal-2020-multi-modal,
    title = "A Multi-Modal Method for Satire Detection using Textual and Visual Cues",
    author = "Li, Lily  and
      Levi, Or  and
      Hosseini, Pedram  and
      Broniatowski, David",
    booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.nlp4if-1.4",
    pages = "33--38",
    abstract = "Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines.",
}
@inproceedings{shin-etal-2020-risk,
    title = "A Risk Communication Event Detection Model via Contrastive Learning",
    author = "Shin, Mingi  and
      Han, Sungwon  and
      Park, Sungkyu  and
      Cha, Meeyoung",
    booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.nlp4if-1.5",
    pages = "39--43",
    abstract = "This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries. The strength of our model is two-fold. First, it detects contextualized events based on topical and temporal information via contrastive learning. Second, it can be applied to multiple languages, enabling a comparison of risk communication across cultures. We present a case study and discuss future implications of the proposed model.",
}
@proceedings{nlp4convai-2020-natural,
    title = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    editor = "Wen, Tsung-Hsien  and
      Celikyilmaz, Asli  and
      Yu, Zhou  and
      Papangelis, Alexandros  and
      Eric, Mihail  and
      Kumar, Anuj  and
      Casanueva, I{\~n}igo  and
      Shah, Rushin",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.0",
}
@inproceedings{varada-etal-2020-using,
    title = "Using Alternate Representations of Text for Natural Language Understanding",
    author = "Varada, Venkat  and
      Peris, Charith  and
      Park, Yangsook  and
      Dipersio, Christopher",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.1",
    doi = "10.18653/v1/2020.nlp4convai-1.1",
    pages = "1--10",
    abstract = "One of the core components of voice assistants is the Natural Language Understanding (NLU) model. Its ability to accurately classify the user{'}s request (or {``}intent{''}) and recognize named entities in an utterance is pivotal to the success of these assistants. NLU models can be challenged in some languages by code-switching or morphological and orthographic variations. This work explores the possibility of improving the accuracy of NLU models for Indic languages via the use of alternate representations of input text for NLU, specifically ISO-15919 and IndicSOUNDEX, a custom SOUNDEX designed to work for Indic languages. We used a deep neural network based model to incorporate the information from alternate representations into the NLU model. We show that using alternate representations significantly improves the overall performance of NLU models when training data is limited.",
}
@inproceedings{moghe-etal-2020-incorporating,
    title = "On Incorporating Structural Information to improve Dialogue Response Generation",
    author = "Moghe, Nikita  and
      Vijayan, Priyesh  and
      Ravindran, Balaraman  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.2",
    doi = "10.18653/v1/2020.nlp4convai-1.2",
    pages = "11--24",
    abstract = "We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task is to generate the next response based on background knowledge about the movie such as the plot, review, Reddit comments etc. This requires capturing structural, sequential and semantic information from the conversation context and the background resources. We propose a new architecture that uses the ability of BERT to capture deep contextualized representations in conjunction with explicit structure and sequence information. More specifically, we use (i) Graph Convolutional Networks (GCNs) to capture structural information, (ii) LSTMs to capture sequential information and (iii) BERT for the deep contextualized representations that capture semantic information. We analyze the proposed architecture extensively. To this end, we propose a plug-and-play Semantics-Sequences-Structures (SSS) framework which allows us to effectively combine such linguistic information. Through a series of experiments we make some interesting observations. First, we observe that the popular adaptation of the GCN model for NLP tasks where structural information (GCNs) was added on top of sequential information (LSTMs) performs poorly on our task. This leads us to explore interesting ways of combining semantic and structural information to improve the performance. Second, we observe that while BERT already outperforms other deep contextualized representations such as ELMo, it still benefits from the additional structural information explicitly added using GCNs. This is a bit surprising given the recent claims that BERT already captures structural information. Lastly, the proposed SSS framework gives an improvement of 7.95{\%} on BLUE score over the baseline.",
}
@inproceedings{varanasi-etal-2020-copybert,
    title = "{C}opy{BERT}: A Unified Approach to Question Generation with Self-Attention",
    author = "Varanasi, Stalin  and
      Amin, Saadullah  and
      Neumann, Guenter",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.3",
    doi = "10.18653/v1/2020.nlp4convai-1.3",
    pages = "25--31",
    abstract = "Contextualized word embeddings provide better initialization for neural networks that deal with various natural language understanding (NLU) tasks including Question Answering (QA) and more recently, Question Generation(QG). Apart from providing meaningful word representations, pre-trained transformer models (Vaswani et al., 2017), such as BERT (Devlin et al., 2019) also provide self-attentions which encode syntactic information that can be probed for dependency parsing (Hewitt and Manning, 2019) and POStagging (Coenen et al., 2019). In this paper, we show that the information from selfattentions of BERT are useful for language modeling of questions conditioned on paragraph and answer phrases. To control the attention span, we use semi-diagonal mask and utilize a shared model for encoding and decoding, unlike sequence-to-sequence. We further employ copy-mechanism over self-attentions to acheive state-of-the-art results for Question Generation on SQuAD v1.1 (Rajpurkar et al., 2016).",
}
@inproceedings{summerville-etal-2020-tame,
    title = "How to Tame Your Data: Data Augmentation for Dialog State Tracking",
    author = "Summerville, Adam  and
      Hashemi, Jordan  and
      Ryan, James  and
      Ferguson, William",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.4",
    doi = "10.18653/v1/2020.nlp4convai-1.4",
    pages = "32--37",
    abstract = "Dialog State Tracking (DST) is a problem space in which the effective vocabulary is practically limitless. For example, the domain of possible movie titles or restaurant names is bound only by the limits of language. As such, DST systems often encounter out-of-vocabulary words at inference time that were never encountered during training. To combat this issue, we present a targeted data augmentation process, by which a practitioner observes the types of errors made on held-out evaluation data, and then modifies the training data with additional corpora to increase the vocabulary size at training time. Using this with a RoBERTa-based Transformer architecture, we achieve state-of-the-art results in comparison to systems that only mask trouble slots with special tokens. Additionally, we present a data-representation scheme for seamlessly retargeting DST architectures to new domains.",
}
@inproceedings{casanueva-etal-2020-efficient,
    title = "Efficient Intent Detection with Dual Sentence Encoders",
    author = "Casanueva, I{\~n}igo  and
      Tem{\v{c}}inas, Tadas  and
      Gerz, Daniela  and
      Henderson, Matthew  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.5",
    doi = "10.18653/v1/2020.nlp4convai-1.5",
    pages = "38--45",
    abstract = "Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups). Motivated by these requirements, we introduce intent detection methods backed by pretrained dual sentence encoders such as USE and ConveRT. We demonstrate the usefulness and wide applicability of the proposed intent detectors, showing that: 1) they outperform intent detectors based on fine-tuning the full BERT-Large model or using BERT as a fixed black-box encoder on three diverse intent detection data sets; 2) the gains are especially pronounced in few-shot setups (i.e., with only 10 or 30 annotated examples per intent); 3) our intent detectors can be trained in a matter of minutes on a single CPU; and 4) they are stable across different hyperparameter settings. In hope of facilitating and democratizing research focused on intention detection, we release our code, as well as a new challenging single-domain intent detection dataset comprising 13,083 annotated examples over 77 intents.",
}
@inproceedings{ahuja-desai-2020-accelerating,
    title = "Accelerating Natural Language Understanding in Task-Oriented Dialog",
    author = "Ahuja, Ojas  and
      Desai, Shrey",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.6",
    doi = "10.18653/v1/2020.nlp4convai-1.6",
    pages = "46--53",
    abstract = "Task-oriented dialog models typically leverage complex neural architectures and large-scale, pre-trained Transformers to achieve state-of-the-art performance on popular natural language understanding benchmarks. However, these models frequently have in excess of tens of millions of parameters, making them impossible to deploy on-device where resource-efficiency is a major concern. In this work, we show that a simple convolutional model compressed with structured pruning achieves largely comparable results to BERT on ATIS and Snips, with under 100K parameters. Moreover, we perform acceleration experiments on CPUs, where we observe our multi-task model predicts intents and slots nearly 63x faster than even DistilBERT.",
}
@inproceedings{oluwatobi-mueller-2020-dlgnet,
    title = "{DLGN}et: A Transformer-based Model for Dialogue Response Generation",
    author = "Oluwatobi, Olabiyi  and
      Mueller, Erik",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.7",
    doi = "10.18653/v1/2020.nlp4convai-1.7",
    pages = "54--62",
    abstract = "Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses. On the other hand, transformer-based models such as GPT-2 have demonstrated an excellent ability to capture long-range structures in language modeling tasks. In this paper, we present DLGNet, a transformer-based model for dialogue modeling. We specifically examine the use of DLGNet for multi-turn dialogue response generation. In our experiments, we evaluate DLGNet on the open-domain Movie Triples dataset and the closed-domain Ubuntu Dialogue dataset. DLGNet models, although trained with only the maximum likelihood objective, achieve significant improvements over state-of-the-art multi-turn dialogue models. They also produce best performance to date on the two datasets based on several metrics, including BLEU, ROUGE, and distinct n-gram. Our analysis shows that the performance improvement is mostly due to the combination of (1) the long-range transformer architecture with (2) the injection of random informative paddings. Other contributing factors include the joint modeling of dialogue context and response, and the 100{\%} tokenization coverage from the byte pair encoding (BPE).",
}
@inproceedings{wang-etal-2020-data,
    title = "Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors",
    author = "Wang, Longshaokan  and
      Fazel-Zarandi, Maryam  and
      Tiwari, Aditya  and
      Matsoukas, Spyros  and
      Polymenakos, Lazaros",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.8",
    doi = "10.18653/v1/2020.nlp4convai-1.8",
    pages = "63--70",
    abstract = "Speech-based virtual assistants, such as Amazon Alexa, Google assistant, and Apple Siri, typically convert users{'} audio signals to text data through automatic speech recognition (ASR) and feed the text to downstream dialog models for natural language understanding and response generation. The ASR output is error-prone; however, the downstream dialog models are often trained on error-free text data, making them sensitive to ASR errors during inference time. To bridge the gap and make dialog models more robust to ASR errors, we leverage an ASR error simulator to inject noise into the error-free text data, and subsequently train the dialog models with the augmented data. Compared to other approaches for handling ASR errors, such as using ASR lattice or end-to-end methods, our data augmentation approach does not require any modification to the ASR or downstream dialog models; our approach also does not introduce any additional latency during inference time. We perform extensive experiments on benchmark data and show that our approach improves the performance of downstream dialog models in the presence of ASR errors, and it is particularly effective in the low-resource situations where there are constraints on model size or the training data is scarce.",
}
@inproceedings{chen-etal-2020-automating,
    title = "Automating Template Creation for Ranking-Based Dialogue Models",
    author = "Chen, Jingxiang  and
      Elfardy, Heba  and
      Wang, Simi  and
      Kahn, Andrea  and
      Kramer, Jared",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.9",
    doi = "10.18653/v1/2020.nlp4convai-1.9",
    pages = "71--78",
    abstract = "Dialogue response generation models that use template ranking rather than direct sequence generation allow model developers to limit generated responses to pre-approved messages. However, manually creating templates is time-consuming and requires domain expertise. To alleviate this problem, we explore automating the process of creating dialogue templates by using unsupervised methods to cluster historical utterances and selecting representative utterances from each cluster. Specifically, we propose an end-to-end model called Deep Sentence Encoder Clustering (DSEC) that uses an auto-encoder structure to jointly learn the utterance representation and construct template clusters. We compare this method to a random baseline that randomly assigns templates to clusters as well as a strong baseline that performs the sentence encoding and the utterance clustering sequentially. To evaluate the performance of the proposed method, we perform an automatic evaluation with two annotated customer service datasets to assess clustering effectiveness, and a human-in-the-loop experiment using a live customer service application to measure the acceptance rate of the generated templates. DSEC performs best in the automatic evaluation, beats both the sequential and random baselines on most metrics in the human-in-the-loop experiment, and shows promising results when compared to gold/manually created templates.",
}
@inproceedings{gao-etal-2020-machine,
    title = "From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap",
    author = "Gao, Shuyang  and
      Agarwal, Sanchit  and
      Jin, Di  and
      Chung, Tagyoung  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.10",
    doi = "10.18653/v1/2020.nlp4convai-1.10",
    pages = "79--89",
    abstract = "Dialogue state tracking (DST) is at the heart of task-oriented dialogue systems. However, the scarcity of labeled data is an obstacle to building accurate and robust state tracking systems that work across a variety of domains. Existing approaches generally require some dialogue data with state information and their ability to generalize to unknown domains is limited. In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets. We divide the slot types in dialogue state into categorical or extractive to borrow the advantages from both multiple-choice and span-based reading comprehension models. Our method achieves near the current state-of-the-art in joint goal accuracy on MultiWOZ 2.1 given full training data. More importantly, by leveraging machine reading comprehension datasets, our method outperforms the existing approaches by many a large margin in few-shot scenarios when the availability of in-domain data is limited. Lastly, even without any state tracking data, i.e., zero-shot scenario, our proposed approach achieves greater than 90{\%} average slot accuracy in 12 out of 30 slots in MultiWOZ 2.1.",
}
@inproceedings{pouran-ben-veyseh-etal-2020-improving,
    title = "Improving Slot Filling by Utilizing Contextual Information",
    author = "Pouran Ben Veyseh, Amir  and
      Dernoncourt, Franck  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.11",
    doi = "10.18653/v1/2020.nlp4convai-1.11",
    pages = "90--95",
    abstract = "Slot Filling (SF) is one of the sub-tasks of Spoken Language Understanding (SLU) which aims to extract semantic constituents from a given natural language utterance. It is formulated as a sequence labeling task. Recently, it has been shown that contextual information is vital for this task. However, existing models employ contextual information in a restricted manner, e.g., using self-attention. Such methods fail to distinguish the effects of the context on the word representation and the word label. To address this issue, in this paper, we propose a novel method to incorporate the contextual information in two different levels, i.e., representation level and task-specific (i.e., label) level. Our extensive experiments on three benchmark datasets on SF show the effectiveness of our model leading to new state-of-the-art results on all three benchmark datasets for the task of SF.",
}
@inproceedings{krone-etal-2020-learning,
    title = "Learning to Classify Intents and Slot Labels Given a Handful of Examples",
    author = "Krone, Jason  and
      Zhang, Yi  and
      Diab, Mona",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.12",
    doi = "10.18653/v1/2020.nlp4convai-1.12",
    pages = "96--108",
    abstract = "Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training examples per class is small. We propose a new few-shot learning task, few-shot IC/SF, to study and improve the performance of IC and SF models on classes not seen at training time in ultra low resource scenarios. We establish a few-shot IC/SF benchmark by defining few-shot splits for three public IC/SF datasets, ATIS, TOP, and Snips. We show that two popular few-shot learning algorithms, model agnostic meta learning (MAML) and prototypical networks, outperform a fine-tuning baseline on this benchmark. Prototypical networks achieves significant gains in IC performance on the ATIS and TOP datasets, while both prototypical networks and MAML outperform the baseline with respect to SF on all three datasets. In addition, we demonstrate that joint training as well as the use of pre-trained language models, ELMo and BERT in our case, are complementary to these few-shot learning methods and yield further gains.",
}
@inproceedings{zang-etal-2020-multiwoz,
    title = "{M}ulti{WOZ} 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines",
    author = "Zang, Xiaoxue  and
      Rastogi, Abhinav  and
      Sunkara, Srinivas  and
      Gupta, Raghav  and
      Zhang, Jianguo  and
      Chen, Jindong",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.13",
    doi = "10.18653/v1/2020.nlp4convai-1.13",
    pages = "109--117",
    abstract = "MultiWOZ is a well-known task-oriented dialogue dataset containing over 10,000 annotated dialogues spanning 8 domains. It is extensively used as a benchmark for dialogue state tracking. However, recent works have reported presence of substantial noise in the dialogue state annotations. MultiWOZ 2.1 identified and fixed many of these erroneous annotations and user utterances, resulting in an improved version of this dataset. This work introduces MultiWOZ 2.2, which is a yet another improved version of this dataset. Firstly, we identify and fix dialogue state annotation errors across 17.3{\%} of the utterances on top of MultiWOZ 2.1. Secondly, we redefine the ontology by disallowing vocabularies of slots with a large number of possible values (e.g., restaurant name, time of booking). In addition, we introduce slot span annotations for these slots to standardize them across recent models, which previously used custom string matching heuristics to generate them. We also benchmark a few state of the art dialogue state tracking models on the corrected dataset to facilitate comparison for future work. In the end, we discuss best practices for dialogue data collection that can help avoid annotation errors.",
}
@inproceedings{shum-etal-2020-sketch,
    title = "Sketch-Fill-A-{R}: A Persona-Grounded Chit-Chat Generation Framework",
    author = "Shum, Michael  and
      Zheng, Stephan  and
      Kryscinski, Wojciech  and
      Xiong, Caiming  and
      Socher, Richard",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.14",
    doi = "10.18653/v1/2020.nlp4convai-1.14",
    pages = "118--131",
    abstract = "Human-like chit-chat conversation requires agents to generate responses that are fluent, engaging and consistent. We propose Sketch- Fill-A-R, a framework that uses a persona-memory to generate chit-chat responses in three phases. First, it generates dynamic sketch responses with open slots. Second, it generates candidate responses by filling slots with parts of its stored persona traits. Lastly, it ranks and selects the final response via a language model score. Sketch-Fill-A-R outperforms a state-of-the-art baseline both quantitatively (10-point lower perplexity) and qualitatively (preferred by 55{\%} in head-to-head single-turn studies and 20{\%} higher in consistency in multi-turn user studies) on the Persona-Chat dataset. Finally, we extensively analyze Sketch-Fill-A-R{'}s responses and human feedback, and show it is more consistent and engaging by using more relevant responses and questions.",
}
@inproceedings{saleh-etal-2020-probing,
    title = "Probing Neural Dialog Models for Conversational Understanding",
    author = "Saleh, Abdelrhman  and
      Deutsch, Tovly  and
      Casper, Stephen  and
      Belinkov, Yonatan  and
      Shieber, Stuart",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlp4convai-1.15",
    doi = "10.18653/v1/2020.nlp4convai-1.15",
    pages = "132--143",
    abstract = "The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets. However, this approach provides little insight as to what these models learn (or do not learn) about engaging in dialog. In this study, we analyze the internal representations learned by neural open-domain dialog systems and evaluate the quality of these representations for learning basic conversational skills. Our results suggest that standard open-domain dialog systems struggle with answering questions, inferring contradiction, and determining the topic of conversation, among other tasks. We also find that the dyadic, turn-taking nature of dialog is not fully leveraged by these models. By exploring these limitations, we highlight the need for additional research into architectures and training methods that can better capture high-level information about dialog.",
}
@proceedings{nlp4call-2020-nlp,
    title = "Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning",
    editor = "Alfter, David  and
      Volodina, Elena  and
      Pilan, Ildik{\'o}  and
      Lange, Herbert  and
      Borin, Lars",
    month = nov,
    year = "2020",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/2020.nlp4call-1.0",
}
@inproceedings{araneta-etal-2020-substituto,
    title = "Substituto {--} A Synchronous Educational Language Game for Simultaneous Teaching and Crowdsourcing",
    author = {Araneta, Marianne Grace  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      K{\"o}nig, Alexander  and
      Lee, Ji-Ung  and
      Lu{\'\i}s, Ana  and
      Lyding, Verena  and
      Nicolas, Lionel  and
      Rodosthenous, Christos  and
      Sangati, Federico},
    booktitle = "Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning",
    month = nov,
    year = "2020",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/2020.nlp4call-1.1",
    pages = "1--9",
}
@inproceedings{caines-etal-2020-teacher,
    title = "The Teacher-Student Chatroom Corpus",
    author = "Caines, Andrew  and
      Yannakoudakis, Helen  and
      Edmondson, Helena  and
      Allen, Helen  and
      P{\'e}rez-Paredes, Pascual  and
      Byrne, Bill  and
      Buttery, Paula",
    booktitle = "Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning",
    month = nov,
    year = "2020",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/2020.nlp4call-1.2",
    pages = "10--20",
}
@inproceedings{da-cruz-dalcol-poesio-2020-polygloss,
    title = "Polygloss - A conversational agent for language practice",
    author = "da Cruz Dalcol, Etiene  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning",
    month = nov,
    year = "2020",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/2020.nlp4call-1.3",
    pages = "21--36",
}
@inproceedings{robertson-2020-show,
    title = "Show, Don{'}t Tell: Visualising {F}innish Word Formation in a Browser-Based Reading Assistant",
    author = "Robertson, Frankie",
    booktitle = "Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning",
    month = nov,
    year = "2020",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/2020.nlp4call-1.4",
    pages = "37--45",
}
@proceedings{nli-2020-natural,
    title = "Proceedings of the First Workshop on Natural Language Interfaces",
    editor = "Awadallah, Ahmed Hassan  and
      Su, Yu  and
      Sun, Huan  and
      Yih, Scott Wen-tau",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nli-1.0",
}
@inproceedings{bhutani-etal-2020-answering,
    title = "Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases",
    author = "Bhutani, Nikita  and
      Zheng, Xinyi  and
      Qian, Kun  and
      Li, Yunyao  and
      Jagadish, H.",
    booktitle = "Proceedings of the First Workshop on Natural Language Interfaces",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nli-1.1",
    doi = "10.18653/v1/2020.nli-1.1",
    pages = "1--10",
    abstract = "Knowledge-based question answering (KB{\_}QA) has long focused on simple questions that can be answered from a single knowledge source, a manually curated or an automatically extracted KB. In this work, we look at answering complex questions which often require combining information from multiple sources. We present a novel KB-QA system, Multique, which can map a complex question to a complex query pattern using a sequence of simple queries each targeted at a specific KB. It finds simple queries using a neural-network based model capable of collective inference over textual relations in extracted KB and ontological relations in curated KB. Experiments show that our proposed system outperforms previous KB-QA systems on benchmark datasets, ComplexWebQuestions and WebQuestionsSP.",
}
@inproceedings{tarasov-2020-towards,
    title = "Towards Reversal-Based Textual Data Augmentation for {NLI} Problems with Opposable Classes",
    author = "Tarasov, Alexey",
    booktitle = "Proceedings of the First Workshop on Natural Language Interfaces",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nli-1.2",
    doi = "10.18653/v1/2020.nli-1.2",
    pages = "11--19",
    abstract = "Data augmentation methods are commonly used in computer vision and speech. However, in domains dealing with textual data, such techniques are not that common. Most of the existing methods rely on rephrasing, i.e. new sentences are generated by changing a source sentence, preserving its meaning. We argue that in tasks with opposable classes (such as Positive and Negative in sentiment analysis), it might be beneficial to also invert the source sentence, reversing its meaning, to generate examples of the opposing class. Methods that use somewhat similar intuition exist in the space of adversarial learning, but are not always applicable to text classification (in our experiments, some of them were even detrimental to the resulting classifier accuracy). We propose and evaluate two reversal-based methods on an NLI task of recognising a type of a simple logical expression from its description in plain-text form. After gathering a dataset on MTurk, we show that a simple heuristic using a notion of negating the main verb has a potential not only on its own, but that it can also boost existing state-of-the-art rephrasing-based approaches.",
}
@inproceedings{shao-etal-2020-examination,
    title = "Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation",
    author = "Shao, Liqun  and
      Mantravadi, Sahitya  and
      Manzini, Tom  and
      Buendia, Alejandro  and
      Knoertzer, Manon  and
      Srinivasan, Soundar  and
      Quirk, Chris",
    booktitle = "Proceedings of the First Workshop on Natural Language Interfaces",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nli-1.3",
    doi = "10.18653/v1/2020.nli-1.3",
    pages = "20--26",
    abstract = "In this paper, we detail novel strategies for interpolating personalized language models and methods to handle out-of-vocabulary (OOV) tokens to improve personalized language models. Using publicly available data from Reddit, we demonstrate improvements in offline metrics at the user level by interpolating a global LSTM-based authoring model with a user-personalized n-gram model. By optimizing this approach with a back-off to uniform OOV penalty and the interpolation coefficient, we observe that over 80{\%} of users receive a lift in perplexity, with an average of 5.4{\%} in perplexity lift per user. In doing this research we extend previous work in building NLIs and improve the robustness of metrics for downstream tasks.",
}
@inproceedings{colas-etal-2020-efficient,
    title = "Efficient Deployment of Conversational Natural Language Interfaces over Databases",
    author = "Colas, Anthony  and
      Bui, Trung  and
      Dernoncourt, Franck  and
      Sinha, Moumita  and
      Kim, Doo Soon",
    booktitle = "Proceedings of the First Workshop on Natural Language Interfaces",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nli-1.4",
    doi = "10.18653/v1/2020.nli-1.4",
    pages = "27--36",
    abstract = "Many users communicate with chatbots and AI assistants in order to help them with various tasks. A key component of the assistant is the ability to understand and answer a user{'}s natural language questions for question-answering (QA). Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language. However, in order to train most natural language-to-query-language state-of-the-art models, a large amount of training data is needed first. In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming. In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces. We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.",
}
@inproceedings{nguyen-cavallari-2020-neural,
    title = "Neural Multi-task Text Normalization and Sanitization with Pointer-Generator",
    author = "Nguyen, Hoang  and
      Cavallari, Sandro",
    booktitle = "Proceedings of the First Workshop on Natural Language Interfaces",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nli-1.5",
    doi = "10.18653/v1/2020.nli-1.5",
    pages = "37--47",
    abstract = "Text normalization and sanitization are intrinsic components of Natural Language Inferences. In Information Retrieval or Dialogue Generation, normalization of user queries or utterances enhances linguistic understanding by translating non-canonical text to its canonical form, on which many state-of-the-art language models are trained. On the other hand, text sanitization removes sensitive information to guarantee user privacy and anonymity. Existing approaches to normalization and sanitization mainly rely on hand-crafted heuristics and syntactic features of individual tokens while disregarding the linguistic context. Moreover, such context-unaware solutions cannot dynamically determine whether out-of-vocab tokens are misspelt or are entity names. In this work, we formulate text normalization and sanitization as a multi-task text generation approach and propose a neural hybrid pointer-generator network based on multi-head attention. Its generator effectively captures linguistic context during normalization and sanitization while its pointer dynamically preserves the entities that are generally missing in the vocabulary. Experiments show that our generation approach outperforms both token-based text normalization and sanitization, while the hybrid pointer-generator improves the generator-only baseline in terms of BLEU4 score, and classical attentional pointer networks in terms of pointing accuracy.",
}
@proceedings{nl4xai-2020-interactive,
    title = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    editor = "Alonso, Jose M.  and
      Catala, Alejandro",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.0",
}
@inproceedings{khramer-2020-automatically,
    title = "Automatically explaining health information",
    author = "Khramer, Emiel",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.1",
    pages = "1--2",
    abstract = "Modern AI systems automatically learn from data using sophisticated statistical models. Explaining how these systems work and how they make their predictions therefore increasingly involves producing descriptions of how different probabilities are weighted and which uncertainties underlie these numbers. But what is the best way to (automatically) present such probabilistic explanations, do people actually understand them, and what is the potential impact of such information on people{'}s wellbeing? In this talk, I adress these questions in the context of systems that automatically generate personalised health information. The emergence of large national health registeries, such as the Dutch cancer registry, now make it possible to automatically generate descriptions of treatment options for new cancer patients based on data of comparable patients, including health and quality of life predictions following different treatments. I describe a series of studies, in which our team has investigated to what extent this information is currently provided to people, and under which conditions people actually want to have access to these kind of data-driven explanations. Additionally, we have studied whether there are different profiles in information needs, and what the best way is to provide probabilistic information and the associated undertainties to people.",
}
@inproceedings{ntoutsi-2020-bias,
    title = "Bias in {AI}-systems: A multi-step approach",
    author = "Ntoutsi, Eirini",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.2",
    pages = "3--4",
    abstract = "Algorithmic-based decision making powered via AI and (big) data has already penetrated into almost all spheres of human life, from content recommendation and healthcare to predictive policing and autonomous driving, deeply affecting everyone, anywhere, anytime. While technology allows previously unthinkable optimizations in the automation of expensive human decision making, the risks that the technology can pose are also high, leading to an ever increasing public concern about the impact of the technology in our lives. The area of responsible AI has recently emerged in an attempt to put humans at the center of AI-based systems by considering aspects, such as fairness, reliability and privacy of decision-making systems. In this talk, we will focus on the fairness aspect. We will start with understanding the many sources of bias and how biases can enter at each step of the learning process and even get propagated/amplified from previous steps. We will continue with methods for mitigating bias which typically focus on some step of the pipeline (data, algorithms or results) and why it is important to target bias in each step and collectively, in the whole (machine) learning pipeline. We will conclude this talk by discussing accountability issues in connection to bias and in particular, proactive consideration via bias-aware data collection, processing and algorithmic selection and retroactive consideration via explanations.",
}
@inproceedings{anselma-etal-2020-content,
    title = "Content Selection for Explanation Requests in Customer-Care Domain",
    author = "Anselma, Luca  and
      Di Lascio, Mirko  and
      Mana, Dario  and
      Mazzei, Alessandro  and
      Sanguinetti, Manuela",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.3",
    pages = "5--10",
    abstract = "This paper describes a content selection module for the generation of explanations in a dialogue system designed for customer care domain. First we describe the construction of a corpus of a dialogues containing explanation requests from customers to a virtual agent of a telco, and second we study and formalize the importance of a specific information content for the generated message. In particular, we adapt the notions of importance and relevance in the case of schematic knowledge bases.",
}
@inproceedings{rawsthorne-etal-2020-extra,
    title = "{E}x{TRA}: Explainable Therapy-Related Annotations",
    author = "Rawsthorne, Mat  and
      Jilani, Tahseen  and
      Andrews, Jacob  and
      Long, Yunfei  and
      Clos, Jeremie  and
      Malins, Samuel  and
      Hunt, Daniel",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.4",
    pages = "11--15",
    abstract = "In this paper we report progress on a novel explainable artificial intelligence (XAI) initiative applying Natural Language Processing (NLP) with elements of codesign to develop a text classifier for application in psychotherapy training. The task is to produce a tool that will facilitate therapists to review their sessions by automatically labelling transcript text with levels of interaction for patient activation in known psychological processes, using XAI to increase their trust in the model{'}s suggestions and client trajectory predictions. After pre-processing of the language features extracted from professionally annotated therapy session transcripts, we apply a supervised machine learning approach (CHAID) to classify interaction labels (negative, neutral, positive). Weighted samples are used to overcome class imbalanced data. The results show this initial model can make useful distinctions among the three labels of patient activation with 74{\%} accuracy and provide insight into its reasoning. This ongoing project will additionally evaluate which XAI approaches can be used to increase the transparency of the tool to end users, exploring whether direct involvement of stakeholders improves usability of the XAI interface and therefore trust in the solution.",
}
@inproceedings{faille-etal-2020-natural,
    title = "The Natural Language Pipeline, Neural Text Generation and Explainability",
    author = "Faille, Juliette  and
      Gatt, Albert  and
      Gardent, Claire",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.5",
    pages = "16--21",
    abstract = "End-to-end encoder-decoder approaches to data-to-text generation are often black boxes whose predictions are difficult to explain. Breaking up the end-to-end model into sub-modules is a natural way to address this problem. The traditional pre-neural Natural Language Generation (NLG) pipeline provides a framework for breaking up the end-to-end encoder-decoder. We survey recent papers that integrate traditional NLG submodules in neural approaches and analyse their explainability. Our survey is a first step towards building explainable neural NLG models.",
}
@inproceedings{mariotti-etal-2020-towards,
    title = "Towards Harnessing Natural Language Generation to Explain Black-box Models",
    author = "Mariotti, Ettore  and
      Alonso, Jose M.  and
      Gatt, Albert",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.6",
    pages = "22--27",
    abstract = "The opaque nature of many machine learning techniques prevents the wide adoption of powerful information processing tools for high stakes scenarios. The emerging field eXplainable Artificial Intelligence (XAI) aims at providing justifications for automatic decision-making systems in order to ensure reliability and trustworthiness in the users. For achieving this vision, we emphasize the importance of a natural language textual modality as a key component for a future intelligent interactive agent. We outline the challenges of XAI and review a set of publications that work in this direction.",
}
@inproceedings{hennessy-etal-2020-explaining,
    title = "Explaining {B}ayesian Networks in Natural Language: State of the Art and Challenges",
    author = "Hennessy, Conor  and
      Bugar{\'\i}n, Alberto  and
      Reiter, Ehud",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.7",
    pages = "28--33",
    abstract = "In order to increase trust in the usage of Bayesian Networks and to cement their role as a model which can aid in critical decision making, the challenge of explainability must be faced. Previous attempts at explaining Bayesian Networks have largely focused on graphical or visual aids. In this paper we aim to highlight the importance of a natural language approach to explanation and to discuss some of the previous and state of the art attempts of the textual explanation of Bayesian Networks. We outline several challenges that remain to be addressed in the generation and validation of natural language explanations of Bayesian Networks. This can serve as a reference for future work on natural language explanations of Bayesian Networks.",
}
@inproceedings{sevilla-2020-explaining,
    title = "Explaining data using causal {B}ayesian networks",
    author = "Sevilla, Jaime",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.8",
    pages = "34--38",
    abstract = "I introduce Causal Bayesian Networks as a formalism for representing and explaining probabilistic causal relations, review the state of the art on learning Causal Bayesian Networks and suggest and illustrate a research avenue for studying pairwise identification of causal relations inspired by graphical causality criteria.",
}
@inproceedings{mayn-van-deemter-2020-towards,
    title = "Towards Generating Effective Explanations of Logical Formulas: Challenges and Strategies",
    author = "Mayn, Alexandra  and
      van Deemter, Kees",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.9",
    pages = "39--43",
    abstract = "While the problem of natural language generation from logical formulas has a long tradition, thus far little attention has been paid to ensuring that the generated explanations are optimally effective for the user. We discuss issues related to deciding what such output should look like and strategies for addressing those issues. We stress the importance of informing generation of NL explanations of logical formulas through reader studies and findings on the comprehension of logic from Pragmatics and Cognitive Science. We then illustrate the discussed issues and potential ways of addressing them using a simple demo system{'}s output generated from a propositional logic formula.",
}
@inproceedings{demollin-etal-2020-argumentation,
    title = "Argumentation Theoretical Frameworks for Explainable Artificial Intelligence",
    author = "Demollin, Martijn  and
      Shaheen, Qurat-Ul-Ain  and
      Budzynska, Katarzyna  and
      Sierra, Carles",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.10",
    pages = "44--49",
    abstract = "This paper discusses four major argumentation theoretical frameworks with respect to their use in support of explainable artificial intelligence (XAI). We consider these frameworks as useful tools for both system-centred and user-centred XAI. The former is concerned with the generation of explanations for decisions taken by AI systems, while the latter is concerned with the way explanations are given to users and received by them.",
}
@inproceedings{rieger-etal-2020-toward,
    title = "Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems",
    author = {Rieger, Alisa  and
      Theune, Mari{\"e}t  and
      Tintarev, Nava},
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.11",
    pages = "50--54",
    abstract = "Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and 2) preference elicitation. It highlights a number of promising directions for Natural Language Generation research for mitigating cognitive bias including: the need for personalization, as well as for transparency and control.",
}
@inproceedings{krause-vossen-2020-explain,
    title = "When to explain: Identifying explanation triggers in human-agent interaction",
    author = "Krause, Lea  and
      Vossen, Piek",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.12",
    pages = "55--60",
    abstract = "With more agents deployed than ever, users need to be able to interact and cooperate with them in an effective and comfortable manner. Explanations have been shown to increase the understanding and trust of a user in human-agent interaction. There have been numerous studies investigating this effect, but they rely on the user explicitly requesting an explanation. We propose a first overview of when an explanation should be triggered and show that there are many instances that would be missed if the agent solely relies on direct questions. For this, we differentiate between direct triggers such as commands or questions and introduce indirect triggers like confusion or uncertainty detection.",
}
@inproceedings{tulli-etal-2020-learning,
    title = "Learning from Explanations and Demonstrations: A Pilot Study",
    author = {Tulli, Silvia  and
      Wallk{\"o}tter, Sebastian  and
      Paiva, Ana  and
      Melo, Francisco S.  and
      Chetouani, Mohamed},
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.13",
    pages = "61--66",
    abstract = "AI has become prominent in a growing number of systems, and, as a direct consequence, the desire for explainability in such systems has become prominent as well. To build explainable systems, a large portion of existing research uses various kinds of natural language technologies, e.g., text-to-speech mechanisms, or string visualizations. Here, we provide an overview of the challenges associated with natural language explanations by reviewing existing literature. Additionally, we discuss the relationship between explainability and knowledge transfer in reinforcement learning. We argue that explainability methods, in particular methods that model the recipient of an explanation, might help increasing sample efficiency. For this, we present a computational approach to optimize the learner{'}s performance using explanations of another agent and discuss our results in light of effective natural language explanations for humans.",
}
@inproceedings{thielstrom-etal-2020-generating,
    title = "Generating Explanations of Action Failures in a Cognitive Robotic Architecture",
    author = "Thielstrom, Ravenna  and
      Roque, Antonio  and
      Chita-Tegmark, Meia  and
      Scheutz, Matthias",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nl4xai-1.14",
    pages = "67--72",
    abstract = "We describe an approach to generating explanations about why robot actions fail, focusing on the considerations of robots that are run by cognitive robotic architectures. We define a set of Failure Types and Explanation Templates, motivating them by the needs and constraints of cognitive architectures that use action scripts and interpretable belief states, and describe content realization and surface realization in this context. We then describe an evaluation that can be extended to further study the effects of varying the explanation templates.",
}
@proceedings{ngt-2020-neural,
    title = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    editor = "Birch, Alexandra  and
      Finch, Andrew  and
      Hayashi, Hiroaki  and
      Heafield, Kenneth  and
      Junczys-Dowmunt, Marcin  and
      Konstas, Ioannis  and
      Li, Xian  and
      Neubig, Graham  and
      Oda, Yusuke",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.0",
}
@inproceedings{heafield-etal-2020-findings,
    title = "Findings of the Fourth Workshop on Neural Generation and Translation",
    author = "Heafield, Kenneth  and
      Hayashi, Hiroaki  and
      Oda, Yusuke  and
      Konstas, Ioannis  and
      Finch, Andrew  and
      Neubig, Graham  and
      Li, Xian  and
      Birch, Alexandra",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.1",
    doi = "10.18653/v1/2020.ngt-1.1",
    pages = "1--9",
    abstract = "We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.",
}
@inproceedings{lin-etal-2020-learning,
    title = "Learning to Generate Multiple Style Transfer Outputs for an Input Sentence",
    author = "Lin, Kevin  and
      Liu, Ming-Yu  and
      Sun, Ming-Ting  and
      Kautz, Jan",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.2",
    doi = "10.18653/v1/2020.ngt-1.2",
    pages = "10--23",
    abstract = "Text style transfer refers to the task of rephrasing a given text in a different style. While various methods have been proposed to advance the state of the art, they often assume the transfer output follows a delta distribution, and thus their models cannot generate different style transfer results for a given input text. To address the limitation, we propose a one-to-many text style transfer framework. In contrast to prior works that learn a one-to-one mapping that converts an input sentence to one output sentence, our approach learns a one-to-many mapping that can convert an input sentence to multiple different output sentences, while preserving the input content. This is achieved by applying adversarial training with a latent decomposition scheme. Specifically, we decompose the latent representation of the input sentence to a style code that captures the language style variation and a content code that encodes the language style-independent content. We then combine the content code with the style code for generating a style transfer output. By combining the same content code with a different style code, we generate a different style transfer output. Extensive experimental results with comparisons to several text style transfer approaches on multiple public datasets using a diverse set of performance metrics validate effectiveness of the proposed approach.",
}
@inproceedings{dabre-etal-2020-balancing,
    title = "Balancing Cost and Benefit with Tied-Multi Transformers",
    author = "Dabre, Raj  and
      Rubino, Raphael  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.3",
    doi = "10.18653/v1/2020.ngt-1.3",
    pages = "24--34",
    abstract = "We propose a novel procedure for training multiple Transformers with tied parameters which compresses multiple models into one enabling the dynamic choice of the number of encoder and decoder layers during decoding. In training an encoder-decoder model, typically, the output of the last layer of the N-layer encoder is fed to the M-layer decoder, and the output of the last decoder layer is used to compute loss. Instead, our method computes a single loss consisting of NxM losses, where each loss is computed from the output of one of the M decoder layers connected to one of the N encoder layers. Such a model subsumes NxM models with different number of encoder and decoder layers, and can be used for decoding with fewer than the maximum number of encoder and decoder layers. Given our flexible tied model, we also address to a-priori selection of the number of encoder and decoder layers for faster decoding, and explore recurrent stacking of layers and knowledge distillation for model compression. We present a cost-benefit analysis of applying the proposed approaches for neural machine translation and show that they reduce decoding costs while preserving translation quality.",
}
@inproceedings{aji-heafield-2020-compressing,
    title = "Compressing Neural Machine Translation Models with 4-bit Precision",
    author = "Aji, Alham Fikri  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.4",
    doi = "10.18653/v1/2020.ngt-1.4",
    pages = "35--42",
    abstract = "Neural Machine Translation (NMT) is resource-intensive. We design a quantization procedure to compress fit NMT models better for devices with limited hardware capability. We use logarithmic quantization, instead of the more commonly used fixed-point quantization, based on the empirical fact that parameters distribution is not uniform. We find that biases do not take a lot of memory and show that biases can be left uncompressed to improve the overall quality without affecting the compression rate. We also propose to use an error-feedback mechanism during retraining, to preserve the compressed model as a stale gradient. We empirically show that NMT models based on Transformer or RNN architecture can be compressed up to 4-bit precision without any noticeable quality degradation. Models can be compressed up to binary precision, albeit with lower quality. RNN architecture seems to be more robust towards compression, compared to the Transformer.",
}
@inproceedings{sharaf-etal-2020-meta,
    title = "Meta-Learning for Few-Shot {NMT} Adaptation",
    author = "Sharaf, Amr  and
      Hassan, Hany  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.5",
    doi = "10.18653/v1/2020.ngt-1.5",
    pages = "43--53",
    abstract = "We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adaptable to many target do- mains with the minimal amount of in-domain data. We frame the adaptation of NMT systems as a meta-learning problem, where we learn to adapt to new unseen domains based on simulated offline meta-training domain adaptation tasks. We evaluate the proposed meta-learning strategy on ten domains with general large scale NMT systems. We show that META-MT significantly outperforms classical domain adaptation when very few in- domain examples are available. Our experiments shows that META-MT can outperform classical fine-tuning by up to 2.5 BLEU points after seeing only 4, 000 translated words (300 parallel sentences).",
}
@inproceedings{gudkov-etal-2020-automatically,
    title = "Automatically Ranked {R}ussian Paraphrase Corpus for Text Generation",
    author = "Gudkov, Vadim  and
      Mitrofanova, Olga  and
      Filippskikh, Elizaveta",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.6",
    doi = "10.18653/v1/2020.ngt-1.6",
    pages = "54--59",
    abstract = "The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics. Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag which are suitable for a set of NLP tasks, such as paraphrase and plagiarism detection, sentence similarity and relatedness estimation, etc. Due to size restrictions, these datasets can hardly be applied in end-to-end text generation solutions. Meanwhile, paraphrase generation requires a large amount of training data. In our study we propose a solution to the problem: we collect, rank and evaluate a new publicly available headline paraphrase corpus (ParaPhraser Plus), and then perform text generation experiments with manual evaluation on automatically ranked corpora using the Universal Transformer architecture.",
}
@inproceedings{dou-etal-2020-deep,
    title = "A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards",
    author = "Dou, Zi-Yi  and
      Kumar, Sachin  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.7",
    doi = "10.18653/v1/2020.ngt-1.7",
    pages = "60--68",
    abstract = "Cross-lingual text summarization aims at generating a document summary in one language given input in another language. It is a practically important but under-explored task, primarily due to the dearth of available data. Existing methods resort to machine translation to synthesize training data, but such pipeline approaches suffer from error propagation. In this work, we propose an end-to-end cross-lingual text summarization model. The model uses reinforcement learning to directly optimize a bilingual semantic similarity metric between the summaries generated in a target language and gold summaries in a source language. We also introduce techniques to pre-train the model leveraging monolingual summarization and machine translation objectives. Experimental results in both English{--}Chinese and English{--}German cross-lingual summarization settings demonstrate the effectiveness of our methods. In addition, we find that reinforcement learning models with bilingual semantic similarity as rewards generate more fluent sentences than strong baselines.",
}
@inproceedings{wu-etal-2020-question,
    title = "A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation",
    author = "Wu, Xiuyu  and
      Jiang, Nan  and
      Wu, Yunfang",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.8",
    doi = "10.18653/v1/2020.ngt-1.8",
    pages = "69--78",
    abstract = "The answer-agnostic question generation is a significant and challenging task, which aims to automatically generate questions for a given sentence but without an answer. In this paper, we propose two new strategies to deal with this task: question type prediction and copy loss mechanism. The question type module is to predict the types of questions that should be asked, which allows our model to generate multiple types of questions for the same source sentence. The new copy loss enhances the original copy mechanism to make sure that every important word in the source sentence has been copied when generating questions. Our integrated model outperforms the state-of-the-art approach in answer-agnostic question generation, achieving a BLEU-4 score of 13.9 on SQuAD. Human evaluation further validates the high quality of our generated questions. We will make our code public available for further research.",
}
@inproceedings{field-etal-2020-generative,
    title = "A Generative Approach to Titling and Clustering {W}ikipedia Sections",
    author = "Field, Anjalie  and
      Rothe, Sascha  and
      Baumgartner, Simon  and
      Yu, Cong  and
      Ittycheriah, Abe",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.9",
    doi = "10.18653/v1/2020.ngt-1.9",
    pages = "79--87",
    abstract = "We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles. Our analysis shows that decoders containing attention mechanisms over the encoder output achieve high-scoring results by generating extractive text. In contrast, a decoder without attention better facilitates semantic encoding and can be used to generate section embeddings. We additionally introduce a new loss function, which further encourages the decoder to generate high-quality embeddings.",
}
@inproceedings{fadaee-monz-2020-unreasonable,
    title = "The Unreasonable Volatility of Neural Machine Translation Models",
    author = "Fadaee, Marzieh  and
      Monz, Christof",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.10",
    doi = "10.18653/v1/2020.ngt-1.10",
    pages = "88--96",
    abstract = "Recent works have shown that Neural Machine Translation (NMT) models achieve impressive performance, however, questions about understanding the behavior of these models remain unanswered. We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct. We discover that with trivial modifications of source sentences, we can identify cases where \textit{unexpected changes} happen in the translation and in the worst case lead to mistranslations. This volatile behavior of translating extremely similar sentences in surprisingly different ways highlights the underlying generalization problem of current NMT models. We find that both RNN and Transformer models display volatile behavior in 26{\%} and 19{\%} of sentence variations, respectively.",
}
@inproceedings{borgeaud-emerson-2020-leveraging,
    title = "Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting",
    author = "Borgeaud, Sebastian  and
      Emerson, Guy",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.11",
    doi = "10.18653/v1/2020.ngt-1.11",
    pages = "97--109",
    abstract = "We propose a method for natural language generation, choosing the most representative output rather than the most likely output. By viewing the language generation process from the voting theory perspective, we define representativeness using range voting and a similarity measure. The proposed method can be applied when generating from any probabilistic language model, including n-gram models and neural network models. We evaluate different similarity measures on an image captioning task and a machine translation task, and show that our method generates longer and more diverse sentences, providing a solution to the common problem of short outputs being preferred over longer and more informative ones. The generated sentences obtain higher BLEU scores, particularly when the beam size is large. We also perform a human evaluation on both tasks and find that the outputs generated using our method are rated higher.",
}
@inproceedings{gordon-duh-2020-distill,
    title = "Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation",
    author = "Gordon, Mitchell  and
      Duh, Kevin",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.12",
    doi = "10.18653/v1/2020.ngt-1.12",
    pages = "110--118",
    abstract = "We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both domain adaptation and knowledge distillation are widely-used, their interaction remains little understood. Our large-scale empirical results in machine translation (on three language pairs with three domains each) suggest distilling twice for best performance: once using general-domain data and again using in-domain data with an adapted teacher.",
}
@inproceedings{yang-etal-2020-training,
    title = "Training and Inference Methods for High-Coverage Neural Machine Translation",
    author = "Yang, Michael  and
      Liu, Yixin  and
      Mayuranath, Rahul",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.13",
    doi = "10.18653/v1/2020.ngt-1.13",
    pages = "119--128",
    abstract = "In this paper, we introduce a system built for the Duolingo Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task at the 4th Workshop on Neural Generation and Translation (WNGT 2020). We participated in the English-to-Japanese track with a Transformer model pretrained on the JParaCrawl corpus and fine-tuned in two steps on the JESC corpus and then the (smaller) Duolingo training corpus. First, during training, we find it is essential to deliberately expose the model to higher-quality translations more often during training for optimal translation performance. For inference, encouraging a small amount of diversity with Diverse Beam Search to improve translation coverage yielded marginal improvement over regular Beam Search. Finally, using an auxiliary filtering model to filter out unlikely candidates from Beam Search improves performance further. We achieve a weighted F1 score of 27.56{\%} on our own test set, outperforming the STAPLE AWS translations baseline score of 4.31{\%}.",
}
@inproceedings{nomoto-2020-meeting,
    title = "Meeting the 2020 {D}uolingo Challenge on a Shoestring",
    author = "Nomoto, Tadashi",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.14",
    doi = "10.18653/v1/2020.ngt-1.14",
    pages = "129--133",
    abstract = "What is given below is a brief description of the two systems, called gFCONV and c-VAE, which we built in a response to the 2020 Duolingo Challenge. Both are neural models that aim at disrupting a sentence representation the encoder generates with an eye on increasing the diversity of sentences that emerge out of the process. Importantly, we decided not to turn to external sources for extra ammunition, curious to know how far we can go while confining ourselves to the data released by Duolingo. gFCONV works by taking over a pre-trained sequence model, and intercepting the output its encoder produces on its way to the decoder. c-VAE is a conditional variational auto-encoder, seeking the diversity by blurring the representation that the encoder derives. Experiments on a corpus constructed out of the public dataset from Duolingo, containing some 4 million pairs of sentences, found that gFCONV is a consistent winner over c-VAE though both suffered heavily from a low recall.",
}
@inproceedings{kaneko-etal-2020-english,
    title = "{E}nglish-to-{J}apanese Diverse Translation by Combining Forward and Backward Outputs",
    author = "Kaneko, Masahiro  and
      Imankulova, Aizhan  and
      Hirasawa, Tosho  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.15",
    doi = "10.18653/v1/2020.ngt-1.15",
    pages = "134--138",
    abstract = "We introduce our TMU system that is submitted to The 4th Workshop on Neural Generation and Translation (WNGT2020) to English-to-Japanese (En→Ja) track on Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task. In most cases machine translation systems generate a single output from the input sentence, however, in order to assist language learners in their journey with better and more diverse feedback, it is helpful to create a machine translation system that is able to produce diverse translations of each input sentence. However, creating such systems would require complex modifications in a model to ensure the diversity of outputs. In this paper, we investigated if it is possible to create such systems in a simple way and whether it can produce desired diverse outputs. In particular, we combined the outputs from forward and backward neural translation models (NMT). Our system achieved third place in En→Ja track, despite adopting only a simple approach.",
}
@inproceedings{park-etal-2020-postech,
    title = "{POSTECH} Submission on {D}uolingo Shared Task",
    author = "Park, Junsu  and
      Kwon, Hongseok  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.16",
    doi = "10.18653/v1/2020.ngt-1.16",
    pages = "139--143",
    abstract = "In this paper, we propose a transfer learning based simultaneous translation model by extending BART. We pre-trained BART with Korean Wikipedia and a Korean news dataset, and fine-tuned with an additional web-crawled parallel corpus and the 2020 Duolingo official training dataset. In our experiments on the 2020 Duolingo test dataset, our submission achieves 0.312 in weighted macro F1 score, and ranks second among the submitted En-Ko systems.",
}
@inproceedings{haque-etal-2020-adapt,
    title = "The {ADAPT} System Description for the {STAPLE} 2020 {E}nglish-to-{P}ortuguese Translation Task",
    author = "Haque, Rejwanul  and
      Moslem, Yasmin  and
      Way, Andy",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.17",
    doi = "10.18653/v1/2020.ngt-1.17",
    pages = "144--152",
    abstract = "This paper describes the ADAPT Centre{'}s submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task. In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences). We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g. data and sentence selection, monolingual MT for generating alternative translations, and combining multiple n-best translations. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task.",
}
@inproceedings{libovicky-etal-2020-expand,
    title = "Expand and Filter: {CUNI} and {LMU} Systems for the {WNGT} 2020 {D}uolingo Shared Task",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Kasner, Zden{\v{e}}k  and
      Helcl, Jind{\v{r}}ich  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.18",
    doi = "10.18653/v1/2020.ngt-1.18",
    pages = "153--160",
    abstract = "We present our submission to the Simultaneous Translation And Paraphrase for Language Education (STAPLE) challenge. We used a standard Transformer model for translation, with a crosslingual classifier predicting correct translations on the output n-best list. To increase the diversity of the outputs, we used additional data to train the translation model, and we trained a paraphrasing model based on the Levenshtein Transformer architecture to generate further synonymous translations. The paraphrasing results were again filtered using our classifier. While the use of additional data and our classifier filter were able to improve results, the paraphrasing model produced too many invalid outputs to further improve the output quality. Our model without the paraphrasing component finished in the middle of the field for the shared task, improving over the best baseline by a margin of 10-22 {\%} weighted F1 absolute.",
}
@inproceedings{li-etal-2020-exploring,
    title = "Exploring Model Consensus to Generate Translation Paraphrases",
    author = "Li, Zhenhao  and
      Fomicheva, Marina  and
      Specia, Lucia",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.19",
    doi = "10.18653/v1/2020.ngt-1.19",
    pages = "161--168",
    abstract = "This paper describes our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). This task focuses on improving the ability of neural MT systems to generate diverse translations. Our submission explores various methods, including N-best translation, Monte Carlo dropout, Diverse Beam Search, Mixture of Experts, Ensembling, and Lexical Substitution. Our main submission is based on the integration of multiple translations from multiple methods using Consensus Voting. Experiments show that the proposed approach achieves a considerable degree of diversity without introducing noisy translations. Our final submission achieves a 0.5510 weighted F1 score on the blind test set for the English-Portuguese track.",
}
@inproceedings{nagoudi-etal-2020-growing,
    title = "Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation",
    author = "Nagoudi, El Moatez Billah  and
      Abdul-Mageed, Muhammad  and
      Cavusoglu, Hasan",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.20",
    doi = "10.18653/v1/2020.ngt-1.20",
    pages = "169--177",
    abstract = "We describe our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). We view MT models at various training stages (i.e., checkpoints) as human learners at different levels. Hence, we employ an ensemble of multi-checkpoints from the same model to generate translation sequences with various levels of fluency. From each checkpoint, for our best model, we sample n-Best sequences (n=10) with a beam width =100. We achieve an 37.57 macro F1 with a 6 checkpoint model ensemble on the official shared task test data, outperforming a baseline Amazon translation system of 21.30 macro F1 and ultimately demonstrating the utility of our intuitive method.",
}
@inproceedings{agrawal-carpuat-2020-generating,
    title = "Generating Diverse Translations via Weighted Fine-tuning and Hypotheses Filtering for the {D}uolingo {STAPLE} Task",
    author = "Agrawal, Sweta  and
      Carpuat, Marine",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.21",
    doi = "10.18653/v1/2020.ngt-1.21",
    pages = "178--187",
    abstract = "This paper describes the University of Maryland{'}s submission to the Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). Unlike the standard machine translation task, STAPLE requires generating a set of outputs for a given input sequence, aiming to cover the space of translations produced by language learners. We adapt neural machine translation models to this requirement by (a) generating n-best translation hypotheses from a model fine-tuned on learner translations, oversampled to reflect the distribution of learner responses, and (b) filtering hypotheses using a feature-rich binary classifier that directly optimizes a close approximation of the official evaluation metric. Combination of systems that use these two strategies achieves F1 scores of 53.9{\%} and 52.5{\%} on Vietnamese and Portuguese, respectively ranking 2nd and 4th on the leaderboard.",
}
@inproceedings{khayrallah-etal-2020-jhu,
    title = "The {JHU} Submission to the 2020 {D}uolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education",
    author = "Khayrallah, Huda  and
      Bremerman, Jacob  and
      McCarthy, Arya D.  and
      Murray, Kenton  and
      Wu, Winston  and
      Post, Matt",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.22",
    doi = "10.18653/v1/2020.ngt-1.22",
    pages = "188--197",
    abstract = "This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing first in each. Our approach involved a language-agnostic pipeline of three components: (1) building strong machine translation systems on general-domain data, (2) fine-tuning on Duolingo-provided data, and (3) generating n-best lists which are then filtered with various score-based techniques. In addi- tion to the language-agnostic pipeline, we attempted a number of linguistically-motivated approaches, with, unfortunately, little success. We also find that improving BLEU performance of the beam-search generated translation does not necessarily improve on the task metric{---}weighted macro F1 of an n-best list.",
}
@inproceedings{chada-2020-simultaneous,
    title = "Simultaneous paraphrasing and translation by fine-tuning Transformer models",
    author = "Chada, Rakesh",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.23",
    doi = "10.18653/v1/2020.ngt-1.23",
    pages = "198--203",
    abstract = "This paper describes the third place submission to the shared task on simultaneous translation and paraphrasing for language education at the 4th workshop on Neural Generation and Translation (WNGT) for ACL 2020. The final system leverages pre-trained translation models and uses a Transformer architecture combined with an oversampling strategy to achieve a competitive performance. This system significantly outperforms the baseline on Hungarian (27{\%} absolute improvement in Weighted Macro F1 score) and Portuguese (33{\%} absolute improvement) languages.",
}
@inproceedings{hu-etal-2020-niutrans,
    title = "The {N}iu{T}rans System for {WNGT} 2020 Efficiency Task",
    author = "Hu, Chi  and
      Li, Bei  and
      Li, Yinqiao  and
      Lin, Ye  and
      Li, Yanyang  and
      Wang, Chenglong  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.24",
    doi = "10.18653/v1/2020.ngt-1.24",
    pages = "204--210",
    abstract = "This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models (Wang et al., 2019; Li et al., 2019) using NiuTensor, a flexible toolkit for NLP tasks. We explored the combination of deep encoder and shallow decoder in Transformer models via model compression and knowledge distillation. The neural machine translation decoding also benefits from FP16 inference, attention caching, dynamic batching, and batch pruning. Our systems achieve promising results in both translation quality and efficiency, e.g., our fastest system can translate more than 40,000 tokens per second with an RTX 2080 Ti while maintaining 42.9 BLEU on newstest2018.",
}
@inproceedings{klein-etal-2020-efficient,
    title = "Efficient and High-Quality Neural Machine Translation with {O}pen{NMT}",
    author = "Klein, Guillaume  and
      Zhang, Dakun  and
      Chouteau, Cl{\'e}ment  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.25",
    doi = "10.18653/v1/2020.ngt-1.25",
    pages = "211--217",
    abstract = "This paper describes the OpenNMT submissions to the WNGT 2020 efficiency shared task. We explore training and acceleration of Transformer models with various sizes that are trained in a teacher-student setup. We also present a custom and optimized C++ inference engine that enables fast CPU and GPU decoding with few dependencies. By combining additional optimizations and parallelization techniques, we create small, efficient, and high-quality neural machine translation models.",
}
@inproceedings{bogoychev-etal-2020-edinburghs,
    title = "{E}dinburgh{'}s Submissions to the 2020 Machine Translation Efficiency Task",
    author = "Bogoychev, Nikolay  and
      Grundkiewicz, Roman  and
      Aji, Alham Fikri  and
      Behnke, Maximiliana  and
      Heafield, Kenneth  and
      Kashyap, Sidharth  and
      Farsarakis, Emmanouil-Ioannis  and
      Chudyk, Mateusz",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.26",
    doi = "10.18653/v1/2020.ngt-1.26",
    pages = "218--224",
    abstract = "We participated in all tracks of the Workshop on Neural Generation and Translation 2020 Efficiency Shared Task: single-core CPU, multi-core CPU, and GPU. At the model level, we use teacher-student training with a variety of student sizes, tie embeddings and sometimes layers, use the Simpler Simple Recurrent Unit, and introduce head pruning. On GPUs, we used 16-bit floating-point tensor cores. On CPUs, we customized 8-bit quantization and multiple processes with affinity for the multi-core setting. To reduce model size, we experimented with 4-bit log quantization but use floats at runtime. In the shared task, most of our submissions were Pareto optimal with respect the trade-off between time and quality.",
}
@inproceedings{ul-haq-etal-2020-improving,
    title = "Improving Document-Level Neural Machine Translation with Domain Adaptation",
    author = "Ul Haq, Sami  and
      Abdul Rauf, Sadaf  and
      Shoukat, Arslan  and
      Hira, Noor-e-",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.27",
    doi = "10.18653/v1/2020.ngt-1.27",
    pages = "225--231",
    abstract = "Recent studies have shown that translation quality of NMT systems can be improved by providing document-level contextual information. In general sentence-based NMT models are extended to capture contextual information from large-scale document-level corpora which are difficult to acquire. Domain adaptation on the other hand promises adapting components of already developed systems by exploiting limited in-domain data. This paper presents FJWU{'}s system submission at WNGT, we specifically participated in Document level MT task for German-English translation. Our system is based on context-aware Transformer model developed on top of original NMT architecture by integrating contextual information using attention networks. Our experimental results show providing previous sentences as context significantly improves the BLEU score as compared to a strong NMT baseline. We also studied the impact of domain adaptation on document level translationand were able to improve results by adaptingthe systems according to the testing domain.",
}
@inproceedings{mayhew-etal-2020-simultaneous,
    title = "Simultaneous Translation and Paraphrase for Language Education",
    author = "Mayhew, Stephen  and
      Bicknell, Klinton  and
      Brust, Chris  and
      McDowell, Bill  and
      Monroe, Will  and
      Settles, Burr",
    booktitle = "Proceedings of the Fourth Workshop on Neural Generation and Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ngt-1.28",
    doi = "10.18653/v1/2020.ngt-1.28",
    pages = "232--243",
    abstract = "We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE). Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce. This is motivated by the need to create and maintain large, high-quality sets of acceptable translations for exercises in a language-learning application, and synthesizes work spanning machine translation, MT evaluation, automatic paraphrasing, and language education technology. We developed a novel corpus with unique properties for five languages (Hungarian, Japanese, Korean, Portuguese, and Vietnamese), and report on the results of a shared task challenge which attracted 20 teams to solve the task. In our meta-analysis, we focus on three aspects of the resulting systems: external training corpus selection, model architecture and training decisions, and decoding and filtering strategies. We find that strong systems start with a large amount of generic training data, and then fine-tune with in-domain data, sampled according to our provided learner response frequencies.",
}
@proceedings{mwe-2020-joint,
    title = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    editor = "Markantonatou, Stella  and
      McCrae, John  and
      Mitrovi{\'c}, Jelena  and
      Tiberius, Carole  and
      Ramisch, Carlos  and
      Vaidya, Ashwini  and
      Osenova, Petya  and
      Savary, Agata",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.0",
}
@inproceedings{fisas-etal-2020-collfren,
    title = "{C}oll{F}r{E}n: Rich Bilingual {E}nglish{--}{F}rench Collocation Resource",
    author = "Fisas, Beatriz  and
      Espinosa Anke, Luis  and
      Codina-Filb{\'a}, Joan  and
      Wanner, Leo",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.1",
    pages = "1--12",
    abstract = "Collocations in the sense of idiosyncratic lexical co-occurrences of two syntactically bound words traditionally pose a challenge to language learners and many Natural Language Processing (NLP) applications alike. Reliable ground truth (i.e., ideally manually compiled) resources are thus of high value. We present a manually compiled bilingual English{--}French collocation resource with 7,480 collocations in English and 6,733 in French. Each collocation is enriched with information that facilitates its downstream exploitation in NLP tasks such as machine translation, word sense disambiguation, natural language generation, relation classification, and so forth. Our proposed enrichment covers: the semantic category of the collocation (its lexical function), its vector space representation (for each individual word as well as their joint collocation embedding), a subcategorization pattern of both its elements, as well as their corresponding BabelNet id, and finally, indices of their occurrences in large scale reference corpora.",
}
@inproceedings{robertson-2020-filling,
    title = "Filling the {\_}{\_}{\_}-s in {F}innish {MWE} lexicons",
    author = "Robertson, Frankie",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.2",
    pages = "13--21",
    abstract = "This paper describes the automatic construction of FinnMWE: a lexicon of Finnish Multi-Word Expressions (MWEs). In focus here are syntactic frames: verbal constructions with arguments in a particular morphological form. The verbal frames are automatically extracted from FinnWordNet and English Wiktionary. The resulting lexicon interoperates with dependency tree searching software so that instances can be quickly found within dependency treebanks. The extraction and enrichment process is explained in detail. The resulting resource is evaluated in terms of its coverage of different types of MWEs. It is also compared with and evaluated against Finnish PropBank.",
}
@inproceedings{nabizadeh-etal-2020-hierarchy,
    title = "Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Constructed Taxonomies",
    author = "Nabizadeh, Nima  and
      Heckmann, Martin  and
      Kolossa, Dorothea",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.3",
    pages = "22--26",
    abstract = "When repairing a device, humans employ a series of tools that corresponds to the arrangement of the device components. Such sequences of tool usage can be learned from repair manuals, so that at each step, having observed the previously applied tools, a sequential model can predict the next required tool. In this paper, we improve the tool prediction performance of such methods by additionally taking the hierarchical relationships among the tools into account. To this aim, we build a taxonomy of tools with hyponymy and hypernymy relations from the data by decomposing all multi-word expressions of tool names. We then develop a sequential model that performs a binary prediction for each node in the taxonomy. The evaluation of the method on a dataset of repair manuals shows that encoding the tools with the constructed taxonomy and using a top-down beam search for decoding increases the prediction accuracy and yields an interpretable taxonomy as a potentially valuable byproduct.",
}
@inproceedings{vlaskova-docekal-2020-scalar,
    title = "Scalar vs. mereological conceptualizations of the N-{BY}-N and {NUM}-{BY}-{NUM} adverbials",
    author = "Vl{\'a}{\v{s}}kov{\'a}, Lucia  and
      Do{\v{c}}ekal, Mojm{\'\i}r",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.4",
    pages = "27--31",
    abstract = "The multiword adverbials N-BY-N and NUM-BY-NUM (as English {``}brick by brick{''} and {``}one by one{''}, respectively) are event modifiers which require temporal sequencing of the event they modify into a linearly ordered series of sub-events. Previous studies unified these two constructions under a single semantic analysis and adopted either a mereological or a scalar approach. However, based on a corpus study examining new Slavic language material and a binomial logistic regression modelling of the manually annotated data, we argue that two separate analyses are needed to account for these constructions, namely a scalar analysis for the N-BY-N construction and a mereological one for the NUM-BY-NUM construction.",
}
@inproceedings{savary-waszczuk-2020-polish,
    title = "{P}olish corpus of verbal multiword expressions",
    author = "Savary, Agata  and
      Waszczuk, Jakub",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.5",
    pages = "32--43",
    abstract = "This paper describes a manually annotated corpus of verbal multi-word expressions in Polish. It is among the 4 biggest datasets in release 1.2 of the PARSEME multiligual corpus. We describe the data sources, as well as the annotation process and its outcomes. We also present interesting phenomena encountered during the annotation task and put forward enhancements for the PARSEME annotation guidelines.",
}
@inproceedings{han-etal-2020-alphamwe,
    title = "{A}lpha{MWE}: Construction of Multilingual Parallel Corpora with {MWE} Annotations",
    author = "Han, Lifeng  and
      Jones, Gareth  and
      Smeaton, Alan",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.6",
    pages = "44--57",
    abstract = "In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually. The languages covered include English, Chinese, Polish, and German. Our original English corpus is taken from the PARSEME shared task in 2018. We performed machine translation of this source corpus followed by human post editing and annotation of target MWEs. Strict quality control was applied for error limitation, i.e., each MT output sentence received first manual post editing and annotation plus second manual quality rechecking. One of our findings during corpora preparation is that accurate translation of MWEs presents challenges to MT systems. To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation. To acquire a broader view of MT issues, we selected four popular state-of-the-art MT models for comparisons namely: Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the noise removal, translation post editing and MWE annotation by human professionals, we believe our AlphaMWE dataset will be an asset for cross-lingual and multilingual research, such as MT and information extraction. Our multilingual corpora are available as open access at github.com/poethan/AlphaMWE.",
}
@inproceedings{walsh-etal-2020-annotating,
    title = "Annotating Verbal {MWE}s in {I}rish for the {PARSEME} Shared Task 1.2",
    author = "Walsh, Abigail  and
      Lynn, Teresa  and
      Foster, Jennifer",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.7",
    pages = "58--65",
    abstract = "This paper describes the creation of two Irish corpora (labelled and unlabelled) for verbal MWEs for inclusion in the PARSEME Shared Task 1.2 on automatic identification of verbal MWEs, and the process of developing verbal MWE categories for Irish. A qualitative analysis on the two corpora is presented, along with discussion of Irish verbal MWEs.",
}
@inproceedings{stamou-etal-2020-vmwe,
    title = "{VMWE} discovery: a comparative analysis between Literature and {T}witter Corpora",
    author = "Stamou, Vivian  and
      Xylogianni, Artemis  and
      Malli, Marilena  and
      Takorou, Penny  and
      Markantonatou, Stella",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.8",
    pages = "66--72",
    abstract = "We evaluate manually five lexical association measurements as regards the discovery of Modern Greek verb multiword expressions with two or more lexicalised components usingmwetoolkit3 (Ramisch et al., 2010). We use Twitter corpora and compare our findings with previous work on fiction corpora. The results of LL, MLE and T-score were found to overlap significantly in both the fiction and the Twitter corpora, while the results of PMI and Dice do not.We find that MWEs with two lexicalised components are more frequent in Twitter than in fiction corpora and that lean syntactic patterns help retrieve them more efficiently than richer ones.Our work (i) supports the enrichment of the lexicographical database for Modern Greek MWEs{'} IDION{'} (Markantonatou et al., 2019) and (ii) highlights aspects of the usage of five association measurements on specific text genres for best MWE discovery results.",
}
@inproceedings{navigli-2020-invited,
    title = "Invited Talk: Generationary or: {``}How We Went beyond Sense Inventories and Learned to Gloss{''}",
    author = "Navigli, Roberto",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.9",
    pages = "73",
    abstract = "In this talk I present Generationary, an approach that goes beyond the mainstream assumption that word senses can be represented as discrete items of a predefined inventory, and put forward a unified model which produces contextualized definitions for arbitrary lexical items, from words to phrases and even sentences. Generationary employs a novel span-based encoding scheme to fine-tune an English pre-trained Encoder-Decoder system and generate new definitions. Our model outperforms previous approaches in the generative task of Definition Modeling in many settings, but it also matches or surpasses the state of the art in discriminative tasks such as Word Sense Disambiguation and Word-in-Context. I also show that Generationary benefits from training on definitions from multiple inventories, with strong gains across benchmarks, including a novel dataset of definitions for free adjective-noun phrases, and discuss interesting examples of generated definitions. Joint work with Michele Bevilacqua and Marco Maru.",
}
@inproceedings{stankovic-etal-2020-multi,
    title = "Multi-word Expressions for Abusive Speech Detection in {S}erbian",
    author = "Stankovi{\'c}, Ranka  and
      Mitrovi{\'c}, Jelena  and
      Joki{\'c}, Danka  and
      Krstev, Cvetana",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.10",
    pages = "74--84",
    abstract = "This paper presents our work on the refinement and improvement of the Serbian language part of Hurtlex, a multilingual lexicon of words to hurt. We pay special attention to adding Multi-word expressions that can be seen as abusive, as such lexical entries are very important in obtaining good results in a plethora of abusive language detection tasks. We use Serbian morphological dictionaries as a basis for data cleaning and MWE dictionary creation. A connection to other lexical and semantic resources in Serbian is outlined and building of abusive language detection systems based on that connection is foreseen.",
}
@inproceedings{kurfali-ostling-2020-disambiguation,
    title = "Disambiguation of Potentially Idiomatic Expressions with Contextual Embeddings",
    author = {Kurfal{\i}, Murathan  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.11",
    pages = "85--94",
    abstract = "The majority of multiword expressions can be interpreted as figuratively or literally in different contexts which pose challenges in a number of downstream tasks. Most previous work deals with this ambiguity following the observation that MWEs with different usages occur in distinctly different contexts. Following this insight, we explore the usefulness of contextual embeddings by means of both supervised and unsupervised classification. The results show that in the supervised setting, the state-of-the-art can be substantially improved for all expressions in the experiments. The unsupervised classification, similarly, yields very impressive results, comparing favorably to the supervised classifier for the majority of the expressions. We also show that multilingual contextual embeddings can also be employed for this task without leading to any significant loss in performance; hence, the proposed methodology has the potential to be extended to a number of languages.",
}
@inproceedings{pickard-2020-comparing,
    title = "Comparing word2vec and {G}lo{V}e for Automatic Measurement of {MWE} Compositionality",
    author = "Pickard, Thomas",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.12",
    pages = "95--100",
    abstract = "This paper explores the use of word2vec and GloVe embeddings for unsupervised measurement of the semantic compositionality of MWE candidates. Through comparison with several human-annotated reference sets, we find word2vec to be substantively superior to GloVe for this task. We also find Simple English Wikipedia to be a poor-quality resource for compositionality assessment, but demonstrate that a sample of 10{\%} of sentences in the English Wikipedia can provide a conveniently tractable corpus with only moderate reduction in the quality of outputs.",
}
@inproceedings{li-gaillat-2020-automatic,
    title = "Automatic detection of unexpected/erroneous collocations in learner corpus",
    author = "Li, Jen-Yu  and
      Gaillat, Thomas",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.13",
    pages = "101--106",
    abstract = "This research investigates the collocational errors made by English learners in a learner corpus. It focuses on the extraction of unexpected collocations. A system was proposed and implemented with open source toolkit. Firstly, the collocation extraction module was evaluated by a corpus with manually annotated collocations. Secondly, a standard collocation list was collected from a corpus of native speaker. Thirdly, a list of unexpected collocations was generated by extracting candidates from a learner corpus and discarding the standard collocations on the list. The overall performance was evaluated, and possible sources of error were pointed out for future improvement.",
}
@inproceedings{ramisch-etal-2020-edition,
    title = "Edition 1.2 of the {PARSEME} Shared Task on Semi-supervised Identification of Verbal Multiword Expressions",
    author = {Ramisch, Carlos  and
      Savary, Agata  and
      Guillaume, Bruno  and
      Waszczuk, Jakub  and
      Candito, Marie  and
      Vaidya, Ashwini  and
      Barbu Mititelu, Verginica  and
      Bhatia, Archna  and
      I{\~n}urrieta, Uxoa  and
      Giouli, Voula  and
      G{\"u}ng{\"o}r, Tunga  and
      Jiang, Menghan  and
      Lichte, Timm  and
      Liebeskind, Chaya  and
      Monti, Johanna  and
      Ramisch, Renata  and
      Stymne, Sara  and
      Walsh, Abigail  and
      Xu, Hongzhi},
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.14",
    pages = "107--118",
    abstract = "We present edition 1.2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs). Lessons learned from previous editions indicate that VMWEs have low ambiguity, and that the major challenge lies in identifying test instances never seen in the training data. Therefore, this edition focuses on unseen VMWEs. We have split annotated corpora so that the test corpora contain around 300 unseen VMWEs, and we provide non-annotated raw corpora to be used by complementary discovery methods. We released annotated and raw corpora in 14 languages, and this semi-supervised challenge attracted 7 teams who submitted 9 system results. This paper describes the effort of corpus creation, the task design, and the results obtained by the participating systems, especially their performance on unseen expressions.",
}
@inproceedings{colson-2020-hmsid,
    title = "{HMS}id and {HMS}id2 at {PARSEME} Shared Task 2020: Computational Corpus Linguistics and unseen-in-training {MWE}s",
    author = "Colson, Jean-Pierre",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.15",
    pages = "119--123",
    abstract = "This paper is a system description of HMSid, officially sent to the PARSEME Shared Task 2020 for one language (French), in the open track. It also describes HMSid2, sent to the organ-izers of the workshop after the deadline and using the same methodology but in the closed track. Both systems do not rely on machine learning, but on computational corpus linguistics. Their score for unseen MWEs is very promising, especially in the case of HMSid2, which would have received the best score for unseen MWEs in the French closed track.",
}
@inproceedings{pasquer-etal-2020-seen2unseen,
    title = "{S}een2{U}nseen at {PARSEME} Shared Task 2020: All Roads do not Lead to Unseen Verb-Noun {VMWE}s",
    author = "Pasquer, Caroline  and
      Savary, Agata  and
      Ramisch, Carlos  and
      Antoine, Jean-Yves",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.16",
    pages = "124--129",
    abstract = "We describe the Seen2Unseen system that participated in edition 1.2 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). The identification of VMWEs that do not appear in the provided training corpora (called unseen VMWEs) {--} with a focus here on verb-noun VMWEs {--} is based on mutual information and lexical substitution or translation of seen VMWEs. We present the architecture of the system, report results for 14 languages, and propose an error analysis.",
}
@inproceedings{yirmibesoglu-gungor-2020-ermi,
    title = "{ERMI} at {PARSEME} Shared Task 2020: Embedding-Rich Multiword Expression Identification",
    author = {Yirmibe{\c{s}}o{\u{g}}lu, Zeynep  and
      G{\"u}ng{\"o}r, Tunga},
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.17",
    pages = "130--135",
    abstract = "This paper describes the ERMI system submitted to the closed track of the PARSEME shared task 2020 on automatic identification of verbal multiword expressions (VMWEs). ERMI is an embedding-rich bidirectional LSTM-CRF model, which takes into account the embeddings of the word, its POS tag, dependency relation, and its head word. The results are reported for 14 languages, where the system is ranked 1st in the general cross-lingual ranking of the closed track systems, according to the Unseen MWE-based F1.",
}
@inproceedings{kurfali-2020-travis,
    title = "{TRAVIS} at {PARSEME} Shared Task 2020: How good is (m){BERT} at seeing the unseen?",
    author = "Kurfal{\i}, Murathan",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.18",
    pages = "136--141",
    abstract = "This paper describes the TRAVIS system built for the PARSEME Shared Task 2020 on semi-supervised identification of verbal multiword expressions. TRAVIS is a fully feature-independent model, relying only on the contextual embeddings. We have participated with two variants of TRAVIS, TRAVIS-multi and TRAVIS-mono, where the former employs multilingual contextual embeddings and the latter uses monolingual ones. Our systems are ranked second and third among seven submissions in the open track, respectively. Despite the strong performance of multilingual contextual embeddings across all languages, the results show that language-specific contextual embeddings have better generalization capabilities.",
}
@inproceedings{taslimipoor-etal-2020-mtlb,
    title = "{MTLB}-{STRUCT} @Parseme 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models",
    author = "Taslimipoor, Shiva  and
      Bahaadini, Sara  and
      Kochmar, Ekaterina",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.19",
    pages = "142--148",
    abstract = "This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task. The model benefits from pre-trained multilingual BERT. BERT hidden layers are shared among the two tasks and we introduce an additional linear layer to retrieve VMWE tags. The dependency parse tree prediction is modelled by a linear layer and a bilinear one plus a tree CRF architecture on top of the shared BERT. The system has participated in the open track of the PARSEME shared task 2020 and ranked first in terms of F1-score in identifying unseen VMWEs as well as VMWEs in general, averaged across all 14 languages.",
}
@inproceedings{gombert-bartsch-2020-multivitaminbooster,
    title = "{M}ulti{V}itamin{B}ooster at {PARSEME} Shared Task 2020: Combining Window- and Dependency-Based Features with Multilingual Contextualised Word Embeddings for {VMWE} Detection",
    author = "Gombert, Sebastian  and
      Bartsch, Sabine",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons",
    month = dec,
    year = "2020",
    address = "online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.mwe-1.20",
    pages = "149--155",
    abstract = "In this paper, we present MultiVitaminBooster, a system implemented for the PARSEME shared task on semi-supervised identification of verbal multiword expressions - edition 1.2. For our approach, we interpret detecting verbal multiword expressions as a token classification task aiming to decide whether a token is part of a verbal multiword expression or not. For this purpose, we train gradient boosting-based models. We encode tokens as feature vectors combining multilingual contextualized word embeddings provided by the XLM-RoBERTa language model with a more traditional linguistic feature set relying on context windows and dependency relations. Our system was ranked 7th in the official open track ranking of the shared task evaluations with an encoding-related bug distorting the results. For this reason we carry out further unofficial evaluations. Unofficial versions of our systems would have achieved higher ranks.",
}
@proceedings{multilingualbio-2020-lrec,
    title = "Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)",
    editor = "Melero, Maite",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.multilingualbio-1.0",
    ISBN = "979-10-95546-65-8",
}
@inproceedings{bampa-dalianis-2020-detecting,
    title = "Detecting Adverse Drug Events from {S}wedish Electronic Health Records using Text Mining",
    author = "Bampa, Maria  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.multilingualbio-1.1",
    pages = "1--8",
    abstract = "Electronic Health Records are a valuable source of patient information which can be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance. The overall aim of this study is to scrutinize text written by clinicians in the EHRs and build a model for ADE detection that produces medically relevant predictions. Natural Language Processing techniques will be exploited to create important predictors and incorporate them into the learning process. The study focuses on the 5 most frequent ADE cases found ina Swedish electronic patient record corpus. The results indicate that considering textual features, rather than the structured, can improve the classification performance by 15{\%} in some ADE cases. Additionally, variable patient history lengths are incorporated in the models, demonstrating the importance of the above decision rather than using an arbitrary number for a history length. The experimental findings suggest that the clinical text in EHRs includes information that can capture data beyond the ones that are found in a structured format.",
    language = "English",
    ISBN = "979-10-95546-65-8",
}
@inproceedings{pilan-etal-2020-building,
    title = "Building a {N}orwegian Lexical Resource for Medical Entity Recognition",
    author = "Pilan, Ildiko  and
      Brekke, P{\aa}l H.  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.multilingualbio-1.2",
    pages = "9--14",
    abstract = "We present a large Norwegian lexical resource of categorized medical terms. The resource, which merges information from large medical databases, contains over 56,000 entries, including automatically mapped terms from a Norwegian medical dictionary. We describe the methodology behind this automatic dictionary entry mapping based on keywords and suffixes and further present the results of a manual evaluation performed on a subset by a domain expert. The evaluation indicated that ca. 80{\%} of the mappings were correct.",
    language = "English",
    ISBN = "979-10-95546-65-8",
}
@inproceedings{schulz-etal-2020-localising,
    title = "Localising the Clinical Terminology {SNOMED} {CT} by Semi-automated Creation of a {G}erman Interface Vocabulary",
    author = "Schulz, Stefan  and
      Hammer, Larissa  and
      Hashemian-Nik, David  and
      Kreuzthaler, Markus",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.multilingualbio-1.3",
    pages = "15--20",
    abstract = "Medical language exhibits great variations regarding users, institutions and language registers. With large parts of clinical documents in free text, NLP is playing a more and more important role in unlocking re-usable and interoperable meaning from medical records. This study describes the architectural principles and the evolution of a German interface vocabulary, combining machine translation with human annotation and rule-based term generation, yielding a resource with 7.7 million raw entries, each of which linked to the reference terminology SNOMED CT, an international standard with about 350 thousand concepts. The purpose is to offer a high coverage of medical jargon in order to optimise terminology grounding of clinical texts by text mining systems. The core resource is a manually curated table of English-to-German word and chunk translations, supported by a set of language generation rules. The work describes a workflow consisting the enrichment and modification of this table with human and machine efforts, manually enriched by grammarspecific tags. Top-down and bottom-up methods for terminology population used in parallel. The final interface terms are produced by a term generator, which creates one-to-many German variants per SNOMED CT English description. Filtering against a large collection of domain terminologies and corpora drastically reduces the size of the vocabulary in favour of more realistic terms or terms that can reasonably be expected to match clinical text passages within a text-mining pipeline. An evaluation was performed by a comparison between the current version of the German interface vocabulary and the English description table of the SNOMED CT International release. An exact term matching was performed with a small parallel corpus constituted by text snippets from different clinical documents. With overall low retrieval parameters (with F-values around 30{\%}), the performance of the German language scenario reaches 80 {--} 90{\%} of the English one. Interestingly, annotations are slightly better with machine-translated (German {--} English) texts, using the International SNOMED CT resource only.",
    language = "English",
    ISBN = "979-10-95546-65-8",
}
@inproceedings{bouscarrat-etal-2020-multilingual,
    title = "Multilingual enrichment of disease biomedical ontologies",
    author = "Bouscarrat, L{\'e}o  and
      Bonnefoy, Antoine  and
      Capponi, C{\'e}cile  and
      Ramisch, Carlos",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.multilingualbio-1.4",
    pages = "21--28",
    abstract = "Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both, plus Arabic, Chinese and Russian for the second. We first use direct links between Wikidata and the studied ontologies and then use second-order links by going through other intermediate ontologies. We then compare the quality of the translations obtained thanks to Wikidata with a commercial machine translation tool, here Google Cloud Translation.",
    language = "English",
    ISBN = "979-10-95546-65-8",
}
@inproceedings{lopez-ubeda-etal-2020-transfer,
    title = "Transfer learning applied to text classification in {S}panish radiological reports",
    author = "L{\'o}pez {\'U}beda, Pilar  and
      D{\'\i}az-Galiano, Manuel Carlos  and
      Urena Lopez, L. Alfonso  and
      Martin, Maite  and
      Mart{\'\i}n-Noguerol, Teodoro  and
      Luna, Antonio",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.multilingualbio-1.5",
    pages = "29--32",
    abstract = "Pre-trained text encoders have rapidly advanced the state-of-the-art on many Natural Language Processing tasks. This paper presents the use of transfer learning methods applied to the automatic detection of codes in radiological reports in Spanish. Assigning codes to a clinical document is a popular task in NLP and in the biomedical domain. These codes can be of two types: standard classifications (e.g. ICD-10) or specific to each clinic or hospital. In this study we show a system using specific radiology clinic codes. The dataset is composed of 208,167 radiology reports labeled with 89 different codes. The corpus has been evaluated with three methods using the BERT model applied to Spanish: Multilingual BERT, BETO and XLM. The results are interesting obtaining 70{\%} of F1-score with a pre-trained multilingual model.",
    language = "English",
    ISBN = "979-10-95546-65-8",
}
@inproceedings{valentin-etal-2020-automated,
    title = "Automated Processing of Multilingual Online News for the Monitoring of Animal Infectious Diseases",
    author = "Valentin, Sarah  and
      Lancelot, Renaud  and
      Roche, Mathieu",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.multilingualbio-1.6",
    pages = "33--36",
    abstract = "The Platform for Automated extraction of animal Disease Information from the web (PADI-web) is an automated system which monitors the web for monitoring and detecting emerging animal infectious diseases. The tool automatically collects news via customised multilingual queries, classifies them and extracts epidemiological information. We detail the processing of multilingual online sources by PADI-web and analyse the translated outputs in a case study",
    language = "English",
    ISBN = "979-10-95546-65-8",
}
@proceedings{msr-2020-multilingual,
    title = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    editor = "Belz, Anya  and
      Bohnet, Bernd  and
      Ferreira, Thiago Castro  and
      Graham, Yvette  and
      Mille, Simon  and
      Wanner, Leo",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.0",
}
@inproceedings{mille-etal-2020-third,
    title = "The Third Multilingual Surface Realisation Shared Task ({SR}{'}20): Overview and Evaluation Results",
    author = "Mille, Simon  and
      Belz, Anya  and
      Bohnet, Bernd  and
      Castro Ferreira, Thiago  and
      Graham, Yvette  and
      Wanner, Leo",
    booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.1",
    pages = "1--20",
    abstract = "This paper presents results from the Third Shared Task on Multilingual Surface Realisation (SR{'}20) which was organised as part of the COLING{'}20 Workshop on Multilingual Surface Realisation. As in SR{'}18 and SR{'}19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed. Moreover, each track had two subtracks: (a) restricted-resource, where only the data provided or approved as part of a track could be used for training models, and (b) open-resource, where any data could be used. The Shallow Track was offered in 11 languages, whereas the Deep Track in 3 ones. Systems were evaluated using both automatic metrics and direct assessment by human evaluators in terms of Readability and Meaning Similarity to reference outputs. We present the evaluation results, along with descriptions of the SR{'}19 tracks, data and evaluation methods, as well as brief summaries of the participating systems. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.",
}
@inproceedings{recski-etal-2020-bme,
    title = "{BME}-{TUW} at {SR}{'}20: Lexical grammar induction for surface realization",
    author = "Recski, G{\'a}bor  and
      Kov{\'a}cs, {\'A}d{\'a}m  and
      G{\'e}mes, Kinga  and
      {\'A}cs, Judit  and
      Kornai, Andras",
    booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.2",
    pages = "21--29",
    abstract = "We present a system for mapping Universal Dependency structures to raw text which learns to restore word order by training an Interpreted Regular Tree Grammar (IRTG) that establishes a mapping between string and graph operations. The reinflection step is handled by a standard sequence-to-sequence architecture with a biLSTM encoder and an LSTM decoder with attention. We modify our 2019 system (Kov{\'a}cs et al., 2019) with a new grammar induction mechanism that allows IRTG rules to operate on lemmata in addition to part-of-speech tags and ensures that each word and its dependents are reordered using the most specific set of learned patterns. We also introduce a hierarchical approach to word order restoration that independently determines the word order of each clause in a sentence before arranging them with respect to the main clause, thereby improving overall readability and also making the IRTG parsing task tractable. We participated in the 2020 Surface Realization Shared task, subtrack T1a (shallow, closed). Human evaluation shows we achieve significant improvements on two of the three out-of-domain datasets compared to the 2019 system we modified. Both components of our system are available on GitHub under an MIT license.",
}
@inproceedings{elder-2020-adapt,
    title = "{ADAPT} at {SR}{'}20: How Preprocessing and Data Augmentation Help to Improve Surface Realization",
    author = "Elder, Henry",
    booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.3",
    pages = "30--34",
    abstract = "In this paper, we describe the ADAPT submission to the Surface Realization Shared Task 2020. We present a neural-based system trained on the English Web Treebank and an augmented dataset, automatically created from existing text corpora.",
}
@inproceedings{yu-etal-2020-imsurreal,
    title = "{IMS}ur{R}eal Too: {IMS} in the Surface Realization Shared Task 2020",
    author = "Yu, Xiang  and
      Tannert, Simon  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.4",
    pages = "35--41",
    abstract = "We introduce the IMS contribution to the Surface Realization Shared Task 2020. The new system achieves substantial improvement over the state-of-the-art system from last year, mainly due to a better token representation and a better linearizer, as well as a simple ensembling approach. We also experiment with data augmentation, which brings some additional performance gain. The system is available at https://github.com/EggplantElf/IMSurReal.",
}
@inproceedings{tobaili-2020-lexical,
    title = "Lexical Induction of Morphological and Orthographic Forms for Low-Resourced Languages",
    author = "Tobaili, Taha",
    booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.5",
    pages = "42--49",
    abstract = "In this work we address the issue of high-degree lexical sparsity for non-standard languages under severe circumstance of small resources that are considered insufficient to train recent powerful language models. We proposed a new rule-based approach and utilised word embeddings to connect words with their inflectional and orthographic forms from a given corpus. Our case example is the low-resourced Lebanese dialect Arabizi. Arabizi is the name given to a new social transcription of the spoken Arabic in Latin script. The term comes from the portmanteau of Araby (Arabic) and Englizi (English). It is an informal written language where Arabs transcribe their dialectal mother tongue in text using Latin alphanumeral instead of Arabic script. For example حبيبي Ḥab{\=\i}b{\=\i} my-love could be transcribed as 7abibi in Arabizi. We induced 175K forms from a list of 1.7K sentiment words. We evaluated this induction extrinsically on a sentiment-annotated dataset pushing its coverage by 13{\%} over the previous version. We named the new lexicon SenZi-Large and released it publicly.",
}
@inproceedings{sobrevilla-cabezudo-pardo-2020-nilc,
    title = "{NILC} at {SR}{'}20: Exploring Pre-Trained Models in Surface Realisation",
    author = "Sobrevilla Cabezudo, Marco Antonio  and
      Pardo, Thiago",
    booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.6",
    pages = "50--56",
    abstract = "This paper describes the submission by the NILC Computational Linguistics research group of the University of S ̃ao Paulo/Brazil to the English Track 2 (closed sub-track) at the Surface Realisation Shared Task 2020. The success of the current pre-trained models like BERT or GPT-2 in several tasks is well-known, however, this is not the case for data-to-text generation tasks and just recently some initiatives focused on it. This way, we explore how a pre-trained model (GPT-2) performs on the UD-to-text generation task. In general, the achieved results were poor, but there are some interesting ideas to explore. Among the learned lessons we may note that it is necessary to study strategies to represent UD inputs and to introduce structural knowledge into these pre-trained models.",
}
@inproceedings{farahnak-etal-2020-surface,
    title = "Surface Realization Using Pretrained Language Models",
    author = "Farahnak, Farhood  and
      Rafiee, Laya  and
      Kosseim, Leila  and
      Fevens, Thomas",
    booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.msr-1.7",
    pages = "57--63",
    abstract = "In the context of Natural Language Generation, surface realization is the task of generating the linear form of a text following a given grammar. Surface realization models usually consist of a cascade of complex sub-modules, either rule-based or neural network-based, each responsible for a specific sub-task. In this work, we show that a single encoder-decoder language model can be used in an end-to-end fashion for all sub-tasks of surface realization. The model is designed based on the BART language model that receives a linear representation of unordered and non-inflected tokens in a sentence along with their corresponding Universal Dependency information and produces the linear sequence of inflected tokens along with the missing words. The model was evaluated on the shallow and deep tracks of the 2020 Surface Realization Shared Task (SR{'}20) using both human and automatic evaluation. The results indicate that despite its simplicity, our model achieves competitive results among all participants in the shared task.",
}
@proceedings{mmw-2020-lrec,
    title = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    editor = "Declerk, Thierry  and
      Gonzalez-Dios, Itziar  and
      Rigau, German",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.0",
    ISBN = "979-10-95546-41-2",
}
@inproceedings{gonzalez-dios-etal-2020-towards,
    title = "Towards modelling {SUMO} attributes through {W}ord{N}et adjectives: a Case Study on Qualities",
    author = "Gonzalez-Dios, Itziar  and
      Alvez, Javier  and
      Rigau, German",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.1",
    pages = "1--6",
    abstract = "Previous studies have shown that the knowledge about attributes and properties in the SUMO ontology and its mapping to WordNet adjectives lacks of an accurate and complete characterization. A proper characterization of this type of knowledge is required to perform formal commonsense reasoning based on the SUMO properties, for instance to distinguish one concept from another based on their properties. In this context, we propose a new semi-automatic approach to model the knowledge about properties and attributes in SUMO by exploiting the information encoded in WordNet adjectives and its mapping to SUMO. To that end, we considered clusters of semantically related groups of WordNet adjectival and nominal synsets. Based on these clusters, we propose a new semi-automatic model for SUMO attributes and their mapping to WordNet, which also includes polarity information. In this paper, as an exploratory approach, we focus on qualities.",
    language = "English",
    ISBN = "979-10-95546-41-2",
}
@inproceedings{mohapatra-etal-2020-incorporating,
    title = "Incorporating Localised Context in {W}ordnet for Indic Languages",
    author = "Mohapatra, Soumya  and
      Agnihotri, Shikhar  and
      Garg, Apar  and
      Shah, Praveen  and
      Chakraverty, Shampa",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.2",
    pages = "7--13",
    abstract = "Due to rapid urbanization and a homogenized medium of instruction imposed in educational institutions, we have lost much of the golden literary offerings of the diverse languages and dialects that India once possessed. There is an urgent need to mitigate the paucity of online linguistic resources for several Hindi dialects. Given the corpus of a dialect, our system integrates the vocabulary of the dialect to the synsets of IndoWordnet along with their corresponding meta-data. Furthermore, we propose a systematic method for generating exemplary sentences for each newly integrated dialect word. The vocabulary thus integrated follows the schema of the wordnet and generates exemplary sentences to illustrate the meaning and usage of the word. We illustrate our methodology with the integration of words in the Awadhi dialect to the Hindi IndoWordnet to achieve an enrichment of 11.68 {\%} to the existing Hindi synsets. The BLEU metric for evaluating the quality of sentences yielded a 75th percentile score of 0.6351.",
    language = "English",
    ISBN = "979-10-95546-41-2",
}
@inproceedings{mccrae-etal-2020-english,
    title = "{E}nglish {W}ord{N}et 2020: Improving and Extending a {W}ord{N}et for {E}nglish using an Open-Source Methodology",
    author = "McCrae, John Philip  and
      Rademaker, Alexandre  and
      Rudnicka, Ewa  and
      Bond, Francis",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.3",
    pages = "14--19",
    abstract = "WordNet, while one of the most widely used resources for NLP, has not been updated for a long time, and as such a new project English WordNet has arisen to continue the development of the model under an open-source paradigm. In this paper, we detail the second release of this resource entitled {``}English WordNet 2020{''}. The work has focused firstly, on the introduction of new synsets and senses and developing guidelines for this and secondly, on the integration of contributions from other projects. We present the changes in this edition, which total over 15,000 changes over the previous release.",
    language = "English",
    ISBN = "979-10-95546-41-2",
}
@inproceedings{gonzalez-dios-alkorta-2020-exploring,
    title = "Exploring the Enrichment of {B}asque {W}ord{N}et with a Sentiment Lexicon",
    author = "Gonzalez-Dios, Itziar  and
      Alkorta, Jon",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.4",
    pages = "20--24",
    abstract = "Wordnets are lexical databases where the semantic relations of words and concepts are established. These resources are useful for manyNLP tasks, such as automatic text classification, word-sense disambiguation or machine translation. In comparison with other wordnets,the Basque version is smaller and some PoS are underrepresented or missing e.g. adjectives and adverbs. In this work, we explore anovel approach to enrich the Basque WordNet, focusing on the adjectives. We want to prove the use and and effectiveness of sentimentlexicons to enrich the resource without the need of starting from scratch. Using as complementary resources, one dictionary and thesentiment valences of the words, we check if the word of the lexicon matches with the meaning of the synset, and if it matches we addthe word as variant to the Basque WordNet. Following this methodology, we describe the most frequent adjectives with positive andnegative valence, the matches and the possible solutions for the non-matches.",
    language = "English",
    ISBN = "979-10-95546-41-2",
}
@inproceedings{marciniak-2020-wordnet,
    title = "{W}ordnet As a Backbone of Domain and Application Conceptualizations in Systems with Multimodal Data",
    author = "Marciniak, Jacek",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.5",
    pages = "25--32",
    abstract = "Information systems gathering big amounts of resources growing with time containing distinct modalities (text, audio, video, images, GIS) and aggregating content in various ways (modular e-learning modules, Web systems presenting cultural artefacts) require tools supporting content description. The subject of the description may be the topic and the characteristics of the content expressed by sets of attributes. To describe such resources one can just use some of existing indexing languages like thesauri, classification systems, domain and upper ontologies, terminologies or dictionaries. When appropriate language does not exist, it is necessary to build a new system, which will have to serve both experts who describe resources and non-experts who search through them. The solution presented in this paper used to resource description, allows experts to freely select words and expressions, which are organized in hierarchies of various nature, including that of domain and application character. This is based on the wordnet structure, which introduces a clear order for each of these groups due to its lexical nature. The paper presents two systems where such approach was applied: the E-archaeology.org e-learning content repository in which domain knowledge was integrated to describe content topics and the Hatch system gathering multimodal information about the archaeological site targeted at a wide audience, where application conceptualization was applied to describe the content by a set of attributes.",
    language = "English",
    ISBN = "979-10-95546-41-2",
}
@inproceedings{tessarollo-rademaker-2020-inclusion,
    title = "Inclusion of Lithological terms (rocks and minerals) in The Open {W}ordnet for {E}nglish",
    author = "Tessarollo, Alexandre  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.6",
    pages = "33--38",
    abstract = "We extend the Open WordNet for English (OWN-EN) with rock-related and other lithological terms using the authoritative source of GBA{'}s Thesaurus. Our aim is to improve WordNet to better function within Oil {\&} Gas domain, particularly geoscience texts. We use a three step approach: a proof of concept-level extension of WordNet, a major extension on which we evaluate the impact with positive results and a full extension encompassing all GBA{'}s lithological terms. We also build a mapping to GBA which also links to several other resources: WikiData, British Geological Survey, Inspire, GeoSciML and DBpedia.",
    language = "English",
    ISBN = "979-10-95546-41-2",
}
@inproceedings{declerck-etal-2020-adding,
    title = "Adding Pronunciation Information to Wordnets",
    author = "Declerck, Thierry  and
      Bajcetic, Lenka  and
      Siegel, Melanie",
    booktitle = "Proceedings of the LREC 2020 Workshop on Multimodal Wordnets (MMW2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "The European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.mmw-1.7",
    pages = "39--44",
    abstract = "We describe on-going work consisting in adding pronunciation information to wordnets, as such information can indicate specific senses of a word. Many wordnets associate with their senses only a lemma form and a part-of-speech tag. At the same time, we are aware that additional linguistic information can be useful for identifying a specific sense of a wordnet lemma when encountered in a corpus. While work already deals with the addition of grammatical number or grammatical gender information to wordnet lemmas,we are investigating the linking of wordnet lemmas to pronunciation information, adding thus a speech-related modality to wordnets",
    language = "English",
    ISBN = "979-10-95546-41-2",
}
@proceedings{lt4hala-2020-lt4hala,
    title = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.0",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{hellwig-2020-dating,
    title = "Dating and Stratifying a Historical Corpus with a {B}ayesian Mixture Model",
    author = "Hellwig, Oliver",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.1",
    pages = "1--9",
    abstract = "This paper introduces and evaluates a Bayesian mixture model that is designed for dating texts based on the distributions of linguistic features. The model is applied to the corpus of Vedic Sanskrit the historical structure of which is still unclear in many details. The evaluation concentrates on the interaction between time, genre and linguistic features, detecting those whose distributions are clearly coupled with the historical time. The evaluation also highlights the problems that arise when quantitative results need to be reconciled with philological insights.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{liebeskind-liebeskind-2020-automatic,
    title = "Automatic Construction of {A}ramaic-{H}ebrew Translation Lexicon",
    author = "Liebeskind, Chaya  and
      Liebeskind, Shmuel",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.2",
    pages = "10--16",
    abstract = "Aramaic is an ancient Semitic language with a 3,000 year history. However, since the number of Aramaic speakers in the world hasdeclined, Aramaic is in danger of extinction. In this paper, we suggest a methodology for automatic construction of Aramaic-Hebrew translation Lexicon. First, we generate an initial translation lexicon by a state-of-the-art word alignment translation model. Then,we filter the initial lexicon using string similarity measures of three types: similarity between terms in the target language, similarity between a source and a target term, and similarity between terms in the source language. In our experiments, we use a parallel corporaof Biblical Aramaic-Hebrew sentence pairs and evaluate various string similarity measures for each type of similarity. We illustratethe empirical benefit of our methodology and its effect on precision and F1. In particular, we demonstrate that our filtering methodsignificantly exceeds a filtering approach based on the probability scores given by a state-of-the-art word alignment translation model.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{baledent-etal-2020-dating,
    title = "Dating Ancient texts: an Approach for Noisy {F}rench Documents",
    author = {Baledent, Ana{\"e}lle  and
      Hiebel, Nicolas  and
      Lejeune, Ga{\"e}l},
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.3",
    pages = "17--21",
    abstract = "Automatic dating of ancient documents is a very important area of research for digital humanities applications. Many documents available via digital libraries do not have any dating or dating that is uncertain. Document dating is not only useful by itself but it also helps to choose the appropriate NLP tools (lemmatizer, POS tagger ) for subsequent analysis. This paper provides a dataset with thousands of ancient documents in French and present methods and evaluation metrics for this task. We compare character-level methods with token-level methods on two different datasets of two different time periods and two different text genres. Our results show that character-level models are more robust to noise than classical token-level models. The experiments presented in this article focused on documents written in French but we believe that the ability of character-level models to handle noise properly would help to achieve comparable results on other languages and more ancient languages in particular.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{vidal-gorene-kindt-2020-lemmatization,
    title = "Lemmatization and {POS}-tagging process by using joint learning approach. Experimental results on {C}lassical {A}rmenian, {O}ld {G}eorgian, and {S}yriac",
    author = "Vidal-Gor{\`e}ne, Chahan  and
      Kindt, Bastien",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.4",
    pages = "22--27",
    abstract = "Classical Armenian, Old Georgian and Syriac are under-resourced digital languages. Even though a lot of printed critical editions or dictionaries are available, there is currently a lack of fully tagged corpora that could be reused for automatic text analysis. In this paper, we introduce an ongoing project of lemmatization and POS-tagging for these languages, relying on a recurrent neural network (RNN), specific morphological tags and dedicated datasets. For this paper, we have combine different corpora previously processed by automatic out-of-context lemmatization and POS-tagging, and manual proofreading by the collaborators of the GREgORI Project (UCLouvain, Louvain-la-Neuve, Belgium). We intend to compare a rule based approach and a RNN approach by using PIE specialized by Calfa (Paris, France). We introduce here first results. We reach a mean accuracy of 91,63{\%} in lemmatization and of 92,56{\%} in POS-tagging. The datasets, which were constituted and used for this project, are not yet representative of the different variations of these languages through centuries, but they are homogenous and allow reaching tangible results, paving the way for further analysis of wider corpora.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{marr-mortensen-2020-computerized,
    title = "Computerized Forward Reconstruction for Analysis in Diachronic Phonology, and {L}atin to {F}rench Reflex Prediction",
    author = "Marr, Clayton  and
      Mortensen, David R.",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.5",
    pages = "28--36",
    abstract = "Traditionally, historical phonologists have relied on tedious manual derivations to calibrate the sequences of sound changes that shaped the phonological evolution of languages. However, humans are prone to errors, and cannot track thousands of parallel word derivations in any efficient manner. We propose to instead automatically derive each lexical item in parallel, and we demonstrate forward reconstruction as both a computational task with metrics to optimize, and as an empirical tool for inquiry. For this end we present DiaSim, a user-facing application that simulates {``}cascades{''} of diachronic developments over a language{'}s lexicon and provides diagnostics for {``}debugging{''} those cascades. We test our methodology on a Latin-to-French reflex prediction task, using a newly compiled dataset FLLex with 1368 paired Latin/French forms. We also present, FLLAPS, which maps 310 Latin reflexes through five stages until Modern French, derived from Pope (1934){'}s sound tables. Our publicly available rule cascades include the baselines BaseCLEF and BaseCLEF*, representing the received view of Latin to French development, and DiaCLEF, build by incremental corrections to BaseCLEF aided by DiaSim{'}s diagnostics. DiaCLEF vastly outperforms the baselines, improving final accuracy on FLLex from 3.2{\%}to 84.9{\%}, and similar improvements across FLLAPS{'} stages.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{pellegrini-2020-using,
    title = "Using {L}at{I}nf{L}exi for an Entropy-Based Assessment of Predictability in {L}atin Inflection",
    author = "Pellegrini, Matteo",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.6",
    pages = "37--46",
    abstract = "This paper presents LatInfLexi, a large inflected lexicon of Latin providing information on all the inflected wordforms of 3,348 verbs and 1,038 nouns. After a description of the structure of the resource and some data on its size, the procedure followed to obtain the lexicon from the database of the Lemlat 3.0 morphological analyzer is detailed, as well as the choices made regarding overabundant and defective cells. The way in which the data of LatInfLexi can be exploited in order to perform a quantitative assessment of predictability in Latin verb inflection is then illustrated: results obtained by computing the conditional entropy of guessing the content of a paradigm cell assuming knowledge of one wordform or multiple wordforms are presented in turn, highlighting the descriptive and theoretical relevance of the analysis. Lastly, the paper envisages the advantages of an inclusion of LatInfLexi into the LiLa knowledge base, both for the presented resource and for the knowledge base itself.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{poncelas-etal-2020-tool,
    title = "A Tool for Facilitating {OCR} Postediting in Historical Documents",
    author = "Poncelas, Alberto  and
      Aboomar, Mohammad  and
      Buts, Jan  and
      Hadley, James  and
      Way, Andy",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.7",
    pages = "47--51",
    abstract = "Optical character recognition (OCR) for historical documents is a complex procedure subject to a unique set of material issues, including inconsistencies in typefaces and low quality scanning. Consequently, even the most sophisticated OCR engines produce errors. This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents. The proposed tool suggests alternatives for word forms not found in a specified vocabulary. The assumed error is replaced by a presumably correct alternative in the post-edition based on the scores of a Language Model (LM). The tool is tested on a chapter of the book An Essay Towards Regulating the Trade and Employing the Poor of this Kingdom (Cary, 1719). As demonstrated below, the tool is successful in correcting a number of common errors. If sometimes unreliable, it is also transparent and subject to human intervention.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{cheng-etal-2020-integration,
    title = "Integration of Automatic Sentence Segmentation and Lexical Analysis of {A}ncient {C}hinese based on {B}i{LSTM}-{CRF} Model",
    author = "Cheng, Ning  and
      Li, Bin  and
      Xiao, Liming  and
      Xu, Changwei  and
      Ge, Sijia  and
      Hao, Xingyue  and
      Feng, Minxuan",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.8",
    pages = "52--58",
    abstract = "The basic tasks of ancient Chinese information processing include automatic sentence segmentation, word segmentation, part-of-speech tagging and named entity recognition. Tasks such as lexical analysis need to be based on sentence segmentation because of the reason that a plenty of ancient books are not punctuated. However, step-by-step processing is prone to cause multi-level diffusion of errors. This paper designs and implements an integrated annotation system of sentence segmentation and lexical analysis. The BiLSTM-CRF neural network model is used to verify the generalization ability and the effect of sentence segmentation and lexical analysis on different label levels on four cross-age test sets. Research shows that the integration method adopted in ancient Chinese improves the F1-score of sentence segmentation, word segmentation and part of speech tagging. Based on the experimental results of each test set, the F1-score of sentence segmentation reached 78.95, with an average increase of 3.5{\%}; the F1-score of word segmentation reached 85.73{\%}, with an average increase of 0.18{\%}; and the F1-score of part-of-speech tagging reached 72.65, with an average increase of 0.35{\%}.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{keersmaekers-2020-automatic,
    title = "Automatic semantic role labeling in {A}ncient {G}reek using distributional semantic modeling",
    author = "Keersmaekers, Alek",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.9",
    pages = "59--67",
    abstract = "This paper describes a first attempt to automatic semantic role labeling in Ancient Greek, using a supervised machine learning approach. A Random Forest classifier is trained on a small semantically annotated corpus of Ancient Greek, annotated with a large amount of linguistic features, including form of the construction, morphology, part-of-speech, lemmas, animacy, syntax and distributional vectors of Greek words. These vectors turned out to be more important in the model than any other features, likely because they are well suited to handle a low amount of training examples. Overall labeling accuracy was 0.757, with large differences with respect to the specific role that was labeled and with respect to text genre. Some ways to further improve these results include expanding the amount of training examples, improving the quality of the distributional vectors and increasing the consistency of the syntactic annotation.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{azar-etal-2020-thesaurus,
    title = "A Thesaurus for Biblical {H}ebrew",
    author = "Azar, Miriam  and
      Pahmer, Aliza  and
      Waxman, Joshua",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.10",
    pages = "68--73",
    abstract = "We built a thesaurus for Biblical Hebrew, with connections between roots based on phonetic, semantic, and distributional similarity. To this end, we apply established algorithms to find connections between headwords based on existing lexicons and other digital resources. For semantic similarity, we utilize the cosine-similarity of tf-idf vectors of English gloss text of Hebrew headwords from Ernest Klein{'}s A Comprehensive Etymological Dictionary of the Hebrew Language for Readers of English as well as to Brown-Driver-Brigg{'}s Hebrew Lexicon. For phonetic similarity, we digitize part of Matityahu Clark{'}s Etymological Dictionary of Biblical Hebrew, grouping Hebrew roots into phonemic classes, and establish phonetic relationships between headwords in Klein{'}s Dictionary. For distributional similarity, we consider the cosine similarity of PPMI vectors of Hebrew roots and also, in a somewhat novel approach, apply Word2Vec to a Biblical corpus reduced to its lexemes. The resulting resource is helpful to those trying to understand Biblical Hebrew, and also stands as a good basis for programs trying to process the Biblical text.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{layfield-etal-2020-word,
    title = "Word Probability Findings in the Voynich Manuscript",
    author = "Layfield, Colin  and
      van der Plas, Lonneke  and
      Rosner, Michael  and
      Abela, John",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.11",
    pages = "74--78",
    abstract = "The Voynich Manuscript has baffled scholars for centuries. Some believe the elaborate 15th century codex to be a hoax whilst others believe it is a real medieval manuscript whose contents are as yet unknown. In this paper, we provide additional evidence that the text of the manuscript displays the hallmarks of a proper natural language with respect to the relationship between word probabilities and (i) average information per subword segment and (ii) the relative positioning of consecutive subword segments necessary to uniquely identify words of different probabilities.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{fourrier-sagot-2020-comparing,
    title = "Comparing Statistical and Neural Models for Learning Sound Correspondences",
    author = "Fourrier, Cl{\'e}mentine  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.12",
    pages = "79--83",
    abstract = "Cognate prediction and proto-form reconstruction are key tasks in computational historical linguistics that rely on the study of sound change regularity. Solving these tasks appears to be very similar to machine translation, though methods from that field have barely been applied to historical linguistics. Therefore, in this paper, we investigate the learnability of sound correspondences between a proto-language and daughter languages for two machine-translation-inspired models, one statistical, the other neural. We first carry out our experiments on plausible artificial languages, without noise, in order to study the role of each parameter on the algorithms respective performance under almost perfect conditions. We then study real languages, namely Latin, Italian and Spanish, to see if those performances generalise well. We show that both model types manage to learn sound changes despite data scarcity, although the best performing model type depends on several parameters such as the size of the training data, the ambiguity, and the prediction direction.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{bloem-etal-2020-distributional,
    title = "Distributional Semantics for Neo-{L}atin",
    author = "Bloem, Jelke  and
      Parisi, Maria Chiara  and
      Reynaert, Martin  and
      Oortwijn, Yvette  and
      Betti, Arianna",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.13",
    pages = "84--93",
    abstract = "We address the problem of creating and evaluating quality Neo-Latin word embeddings for the purpose of philosophical research, adapting the Nonce2Vec tool to learn embeddings from Neo-Latin sentences. This distributional semantic modeling tool can learn from tiny data incrementally, using a larger background corpus for initialization. We conduct two evaluation tasks: definitional learning of Latin Wikipedia terms, and learning consistent embeddings from 18th century Neo-Latin sentences pertaining to the concept of mathematical method. Our results show that consistent Neo-Latin word embeddings can be learned from this type of data. While our evaluation results are promising, they do not reveal to what extent the learned models match domain expert knowledge of our Neo-Latin texts. Therefore, we propose an additional evaluation method, grounded in expert-annotated data, that would assess whether learned representations are conceptually sound in relation to the domain of study.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{martinez-garcia-garcia-tejedor-2020-latin,
    title = "{L}atin-{S}panish Neural Machine Translation: from the {B}ible to Saint Augustine",
    author = "Mart{\'\i}nez Garcia, Eva  and
      Garc{\'\i}a Tejedor, {\'A}lvaro",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.14",
    pages = "94--99",
    abstract = "Although there are several sources where to find historical texts, they usually are available in the original language that makes them generally inaccessible. This paper presents the development of state-of-the-art Neural Machine Systems for the low-resourced Latin-Spanish language pair. First, we build a Transformer-based Machine Translation system on the Bible parallel corpus. Then, we build a comparable corpus from Saint Augustine texts and their translations. We use this corpus to study the domain adaptation case from the Bible texts to Saint Augustine{'}s works. Results show the difficulties of handling a low-resourced language as Latin. First, we noticed the importance of having enough data, since the systems do not achieve high BLEU scores. Regarding domain adaptation, results show how using in-domain data helps systems to achieve a better quality translation. Also, we observed that it is needed a higher amount of data to perform an effective vocabulary extension that includes in-domain vocabulary.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{byszuk-etal-2020-detecting,
    title = "Detecting Direct Speech in Multilingual Collection of 19th-century Novels",
    author = "Byszuk, Joanna  and
      Wo{\'z}niak, Micha{\l}  and
      Kestemont, Mike  and
      Le{\'s}niak, Albert  and
      {\L}ukasik, Wojciech  and
      {\v{S}}e{\c{l}}a, Artjoms  and
      Eder, Maciej",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.15",
    pages = "100--104",
    abstract = "Fictional prose can be broadly divided into narrative and discursive forms with direct speech being central to any discourse representation (alongside indirect reported speech and free indirect discourse). This distinction is crucial in digital literary studies and enables interesting forms of narratological or stylistic analysis. The difficulty of automatically detecting direct speech, however, is currently under-estimated. Rule-based systems that work reasonably well for modern languages struggle with (the lack of) typographical conventions in 19th-century literature. While machine learning approaches to sequence modeling can be applied to solve the task, they typically face a severed skewness in the availability of training material, especially for lesser resourced languages. In this paper, we report the result of a multilingual approach to direct speech detection in a diverse corpus of 19th-century fiction in 9 European languages. The proposed method finetunes a transformer architecture with multilingual sentence embedder on a minimal amount of annotated training in each language, and improves performance across languages with ambiguous direct speech marking, in comparison to a carefully constructed regular expression baseline.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{sprugnoli-etal-2020-overview,
    title = "Overview of the {E}va{L}atin 2020 Evaluation Campaign",
    author = "Sprugnoli, Rachele  and
      Passarotti, Marco  and
      Cecchini, Flavio Massimiliano  and
      Pellegrini, Matteo",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.16",
    pages = "105--110",
    abstract = "This paper describes the first edition of EvaLatin, a campaign totally devoted to the evaluation of NLP tools for Latin. The two shared tasks proposed in EvaLatin 2020, i. e. Lemmatization and Part-of-Speech tagging, are aimed at fostering research in the field of language technologies for Classical languages. The shared dataset consists of texts taken from the Perseus Digital Library, processed with UDPipe models and then manually corrected by Latin experts. The training set includes only prose texts by Classical authors. The test set, alongside with prose texts by the same authors represented in the training set, also includes data relative to poetry and to the Medieval period. This also allows us to propose the Cross-genre and Cross-time subtasks for each task, in order to evaluate the portability of NLP tools for Latin across different genres and time periods. The results obtained by the participants for each task and subtask are presented and discussed.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{bacon-2020-data,
    title = "Data-driven Choices in Neural Part-of-Speech Tagging for {L}atin",
    author = "Bacon, Geoff",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.17",
    pages = "111--113",
    abstract = "Textual data in ancient and historical languages such as Latin is increasingly available in machine readable forms, yet computational tools to analyze and process this data are still lacking. We describe our system for part-of-speech tagging in Latin, an entry in the EvaLatin 2020 shared task. Based on a detailed analysis of the training data, we make targeted preprocessing decisions and design our model. We leverage existing large unlabelled resources to pre-train representations at both the grapheme and word level, which serve as the inputs to our LSTM-based models. We perform an extensive cross-validated hyperparameter search, achieving an accuracy score of up to 93 on in-domain texts. We publicly release all our code and trained models in the hope that our system will be of use to social scientists and digital humanists alike. The insights we draw from our inital analysis can also inform future NLP work modeling syntactic information in Latin.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{wu-nicolai-2020-jhubcs,
    title = "{JHUBC}{'}s Submission to {LT}4{HALA} {E}va{L}atin 2020",
    author = "Wu, Winston  and
      Nicolai, Garrett",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.18",
    pages = "114--118",
    abstract = "We describe the JHUBC submission to the EvaLatin Shared task on lemmatization and part-of-speech tagging for Latin. We modify a hard-attentional character-based encoder-decoder to produce lemmas and POS tags with separate decoders, and to incorporate contextual tagging cues. While our results show that the dual decoder approach fails to encode data as successfully as the single encoder, our simple context incorporation method does lead to modest improvements.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{celano-2020-gradient,
    title = "A Gradient Boosting-{S}eq2{S}eq System for {L}atin {POS} Tagging and Lemmatization",
    author = "Celano, Giuseppe G. A.",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.19",
    pages = "119--123",
    abstract = "The paper presents the system used in the EvaLatin shared task to POS tag and lemmatize Latin. It consists of two components. A gradient boosting machine (LightGBM) is used for POS tagging, mainly fed with pre-computed word embeddings of a window of seven contiguous tokens{---}the token at hand plus the three preceding and following ones{---}per target feature value. Word embeddings are trained on the texts of the Perseus Digital Library, Patrologia Latina, and Biblioteca Digitale di Testi Tardo Antichi, which together comprise a high number of texts of different genres from the Classical Age to Late Antiquity. Word forms plus the outputted POS labels are used to feed a seq2seq algorithm implemented in Keras to predict lemmas. The final shared-task accuracies measured for Classical Latin texts are in line with state-of-the-art POS taggers (∼0.96) and lemmatizers (∼0.95).",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{straka-strakova-2020-udpipe,
    title = "{UDP}ipe at {E}va{L}atin 2020: Contextualized Embeddings and Treebank Embeddings",
    author = "Straka, Milan  and
      Strakov{\'a}, Jana",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.20",
    pages = "124--129",
    abstract = "We present our contribution to the EvaLatin shared task, which is the first evaluation campaign devoted to the evaluation of NLP tools for Latin. We submitted a system based on UDPipe 2.0, one of the winners of the CoNLL 2018 Shared Task, The 2018 Shared Task on Extrinsic Parser Evaluation and SIGMORPHON 2019 Shared Task. Our system places first by a wide margin both in lemmatization and POS tagging in the open modality, where additional supervised data is allowed, in which case we utilize all Universal Dependency Latin treebanks. In the closed modality, where only the EvaLatin training data is allowed, our system achieves the best performance in lemmatization and in classical subtask of POS tagging, while reaching second place in cross-genre and cross-time settings. In the ablation experiments, we also evaluate the influence of BERT and XLM-RoBERTa contextualized embeddings, and the treebank encodings of the different flavors of Latin treebanks.",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@inproceedings{stoeckel-etal-2020-voting,
    title = "Voting for {POS} tagging of {L}atin texts: Using the flair of {FLAIR} to better Ensemble Classifiers by Example of {L}atin",
    author = "Stoeckel, Manuel  and
      Henlein, Alexander  and
      Hemati, Wahed  and
      Mehler, Alexander",
    booktitle = "Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.lt4hala-1.21",
    pages = "130--135",
    abstract = "Despite the great importance of the Latin language in the past, there are relatively few resources available today to develop modern NLP tools for this language. Therefore, the EvaLatin Shared Task for Lemmatization and Part-of-Speech (POS) tagging was published in the LT4HALA workshop. In our work, we dealt with the second EvaLatin task, that is, POS tagging. Since most of the available Latin word embeddings were trained on either few or inaccurate data, we trained several embeddings on better data in the first step. Based on these embeddings, we trained several state-of-the-art taggers and used them as input for an ensemble classifier called LSTMVoter. We were able to achieve the best results for both the cross-genre and the cross-time task (90.64{\%} and 87.00{\%}) without using additional annotated data (closed modality). In the meantime, we further improved the system and achieved even better results (96.91{\%} on classical, 90.87{\%} on cross-genre and 87.35{\%} on cross-time).",
    language = "English",
    ISBN = "979-10-95546-53-5",
}
@proceedings{lt4gov-2020-language,
    title = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
    editor = "Samy, Doaa  and
      P{\'e}rez-Fern{\'a}ndez, David  and
      Arenas-Garc{\'\i}a, Jer{\'o}nimo",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lt4gov-1.0",
    ISBN = "979-10-95546-62-7",
}
@inproceedings{desmet-etal-2020-development,
    title = "Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the {U}.{S}.",
    author = "Desmet, Bart  and
      Porcino, Julia  and
      Zirikly, Ayah  and
      Newman-Griffis, Denis  and
      Divita, Guy  and
      Rasch, Elizabeth",
    booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lt4gov-1.1",
    pages = "1--6",
    abstract = "The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA{'}s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain.",
    language = "English",
    ISBN = "979-10-95546-62-7",
}
@inproceedings{miliani-etal-2020-fraque,
    title = "{FRAQUE}: a {FRA}me-based {QUE}stion-answering system for the Public Administration domain",
    author = "Miliani, Martina  and
      Passaro, Lucia C.  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lt4gov-1.2",
    pages = "7--14",
    abstract = "In this paper, we propose FRAQUE, a question answering system for factoid questions in the Public administration domain. The system is based on semantic frames, here intended as collections of slots typed with their possible values. FRAQUE queries unstructured textual data and exploits the potential of different approaches: it extracts pattern elements from texts which are linguistically analyzed through statistical methods.FRAQUE allows Italian users to query vast document repositories related to the domain of Public Administration. Given the statistical nature of most of its components such as word embeddings, the system allows for a flexible domain and language adaptation process. FRAQUE{'}s goal is to associate questions with frames stored into a Knowledge Graph along with relevant document passages, which are returned as the answer.",
    language = "English",
    ISBN = "979-10-95546-62-7",
}
@inproceedings{sierra-martinez-etal-2020-enhancing,
    title = "Enhancing Job Searches in {M}exico City with Language Technologies",
    author = "Sierra Mart{\'\i}nez, Gerardo  and
      Bel-Enguix, Gemma  and
      G{\'o}mez-Adorno, Helena  and
      Torres Moreno, Juan Manuel  and
      Hern{\'a}ndez-Garc{\'\i}a, Tonatiuh  and
      Guadarrama-Olvera, Julio V  and
      Ortiz-Barajas, Jes{\'u}s-Germ{\'a}n  and
      Rojas, {\'A}ngela Mar{\'\i}a  and
      Damerau, Tomas  and
      Arag{\'o}n Mart{\'\i}nez, Soledad",
    booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lt4gov-1.3",
    pages = "15--21",
    abstract = {In this paper, we show the enhancing of the Demanded Skills Diagnosis (DiCoDe: Diagn{\'o}stico de Competencias Demandadas), a system developed by Mexico City{'}s Ministry of Labor and Employment Promotion (STyFE: Secretar{\'\i}a de Trabajo y Fomento del Empleo de la Ciudad de M{\'e}xico) that seeks to reduce information asymmetries between job seekers and employers. The project uses webscraping techniques to retrieve job vacancies posted on private job portals on a daily basis and with the purpose of informing training and individual case management policies as well as labor market monitoring. For this purpose, a collaboration project between STyFE and the Language Engineering Group (GIL: Grupo de Ingenier{\'\i}a Ling{\"u}{\'\i}stica) was established in order to enhance DiCoDe by applying NLP models and semantic analysis. By this collaboration, DiCoDe{'}s job vacancies system{'}s macro-structure and its geographic referencing at the city hall (municipality) level were improved. More specifically, dictionaries were created to identify demanded competencies, skills and abilities (CSA) and algorithms were developed for dynamic classifying of vacancies and identifying terms for searches on free text, in order to improve the results and processing time of queries.},
    language = "English",
    ISBN = "979-10-95546-62-7",
}
@inproceedings{grypari-etal-2020-research,
    title = "Research {\&} Innovation Activities{'} Impact Assessment: The {D}ata4{I}mpact System",
    author = "Grypari, Ioanna  and
      Pappas, Dimitris  and
      Manola, Natalia  and
      Papageorgiou, Haris",
    booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lt4gov-1.4",
    pages = "22--27",
    abstract = "Cat. 2 Show-case: We present the Data4Impact (D4I) platform, a novel end-to-end system for evidence-based, timely and accurate monitoring and evaluation of research and innovation (R{\&}I) activities. Using the latest technological advances in Human Language Technology (HLT) and our data-driven methodology, we build a novel set of indicators in order to track funded projects and their impact on science, the economy and the society as a whole, during and after the project life-cycle. We develop our methodology by targeting Health-related EC projects from 2007 to 2019 to produce solutions that meet the needs of stakeholders (mainly policy-makers and research funders). Various D4I text analytics workflows process datasets and their metadata, extract valuable insights and estimate intermediate results and metrics, culminating in a set of robust indicators that the users can interact with through our dashboard, the D4I Monitor (available at monitor.data4impact.eu). Therefore, our approach, which can be generalized to different contexts, is multidimensional (technology, tools, indicators, dashboard) and the resulting system can provide an innovative solution for public administrators in their policy-making needs related to RDI funding allocation.",
    language = "English",
    ISBN = "979-10-95546-62-7",
}
@inproceedings{heinisch-lusicky-2020-austrian,
    title = "The {A}ustrian Language Resource Portal for the Use and Provision of Language Resources in a Language Variety by Public Administration {--} a Showcase for Collaboration between Public Administration and a University",
    author = "Heinisch, Barbara  and
      Lu{\v{s}}icky, Vesna",
    booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lt4gov-1.5",
    pages = "28--31",
    abstract = {The Austrian Language Resource Portal (Sprachressourcenportal {\"O}sterreichs) is Austria{'}s central platform for language resources in the area of public administration. It focuses on language resources in the Austrian variety of the German language. As a product of the cooperation between a public administration body and a university, the Portal contains various language resources (terminological resources in the public administration domain, a language guide, named entities based on open public data, translation memories, etc.). German is a pluricentric language that considerably varies in the domain of public administration due to different public administration systems. Therefore, the Austrian Language Resource Portal stresses the importance of language resources specific to a language variety, thus paving the way for the re-use of variety-specific language data for human language technology, such as machine translation training, for the Austrian standard variety.},
    language = "English",
    ISBN = "979-10-95546-62-7",
}
@inproceedings{samy-etal-2020-legal,
    title = "Legal-{ES}: A Set of Large Scale Resources for {S}panish Legal Text Processing",
    author = "Samy, Doaa  and
      Arenas-Garc{\'\i}a, Jer{\'o}nimo  and
      P{\'e}rez-Fern{\'a}ndez, David",
    booktitle = "Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lt4gov-1.6",
    pages = "32--36",
    abstract = "Legal-ES is an open source resource kit for legal Spanish. It consists of a large scale Spanish corpus of open legal texts and different kinds of language models including word embeddings and topic models. The corpus includes over 1000 million words covering a collection of legislative and administrative open access documents in Spanish from different sources representing international, national and regional entities. The corpus is pre-processed and tokenized using Spacy. For the word embeddings, gensim was used on the collection of tokens, producing a representation space that is especially suited to reflect the inherent characteristics of the legal domain. We calculate also topic models to obtain a convenient tool to understand the main topics in the corpus and to navigate through the documents exploiting the semantic similarity among documents. We will analyse the time structure of a dynamic topic model to infer changes in the legal production of Spanish jurisdiction that have occurred over the analysed time framework.",
    language = "English",
    ISBN = "979-10-95546-62-7",
}
@proceedings{lrec-2020-language,
    title = "Proceedings of the 12th Language Resources and Evaluation Conference",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.0",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yu-etal-2020-neural,
    title = "Neural Mention Detection",
    author = "Yu, Juntao  and
      Bohnet, Bernd  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.1",
    pages = "1--10",
    abstract = "Mention detection is an important preprocessing step for annotation and interpretation in applications such as NER and coreference resolution, but few stand-alone neural models have been proposed able to handle the full range of mentions. In this work, we propose and compare three neural network-based approaches to mention detection. The first approach is based on the mention detection part of a state of the art coreference resolution system; the second uses ELMO embeddings together with a bidirectional LSTM and a biaffine classifier; the third approach uses the recently introduced BERT model. Our best model (using a biaffine classifier) achieves gains of up to 1.8 percentage points on mention recall when compared with a strong baseline in a HIGH RECALL coreference annotation setting. The same model achieves improvements of up to 5.3 and 6.2 p.p. when compared with the best-reported mention detection F1 on the CONLL and CRAC coreference data sets respectively in a HIGH F1 annotation setting. We then evaluate our models for coreference resolution by using mentions predicted by our best model in start-of-the-art coreference systems. The enhanced model achieved absolute improvements of up to 1.7 and 0.7 p.p. when compared with our strong baseline systems (pipeline system and end-to-end system) respectively. For nested NER, the evaluation of our model on the GENIA corpora shows that our model matches or outperforms state-of-the-art models despite not being specifically designed for this task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yu-etal-2020-cluster,
    title = "A Cluster Ranking Model for Full Anaphora Resolution",
    author = "Yu, Juntao  and
      Uma, Alexandra  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.2",
    pages = "11--20",
    abstract = "Anaphora resolution (coreference) systems designed for the CONLL 2012 dataset typically cannot handle key aspects of the full anaphora resolution task such as the identification of singletons and of certain types of non-referring expressions (e.g., expletives), as these aspects are not annotated in that corpus. However, the recently released dataset for the CRAC 2018 Shared Task can now be used for that purpose. In this paper, we introduce an architecture to simultaneously identify non-referring expressions (including expletives, predicative s, and other types) and build coreference chains, including singletons. Our cluster-ranking system uses an attention mechanism to determine the relative importance of the mentions in the same cluster. Additional classifiers are used to identify singletons and non-referring markables. Our contributions are as follows. First all, we report the first result on the CRAC data using system mentions; our result is 5.8{\%} better than the shared task baseline system, which used gold mentions. Second, we demonstrate that the availability of singleton clusters and non-referring expressions can lead to substantially improved performance on non-singleton clusters as well. Third, we show that despite our model not being designed specifically for the CONLL data, it achieves a score equivalent to that of the state-of-the-art system by Kantor and Globerson (2019) on that dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bernard-han-2020-mandarinograd,
    title = "{M}andarinograd: A {C}hinese Collection of {W}inograd Schemas",
    author = "Bernard, Timoth{\'e}e  and
      Han, Ting",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.3",
    pages = "21--26",
    abstract = "This article introduces Mandarinograd, a corpus of Winograd Schemas in Mandarin Chinese. Winograd Schemas are particularly challenging anaphora resolution problems, designed to involve common sense reasoning and to limit the biases and artefacts commonly found in natural language understanding datasets. Mandarinograd contains the schemas in their traditional form, but also as natural language inference instances (ENTAILMENT or NO ENTAILMENT pairs) as well as in their fully disambiguated candidate forms. These two alternative representations are often used by modern solvers but existing datasets present automatically converted items that sometimes contain syntactic or semantic anomalies. We detail the difficulties faced when building this corpus and explain how weavoided the anomalies just mentioned. We also show that Mandarinograd is resistant to a statistical method based on a measure of word association.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{henlein-mehler-2020-influence,
    title = "On the Influence of Coreference Resolution on Word Embeddings in Lexical-semantic Evaluation Tasks",
    author = "Henlein, Alexander  and
      Mehler, Alexander",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.4",
    pages = "27--33",
    abstract = "Coreference resolution (CR) aims to find all spans of a text that refer to the same entity. The F1-Scores on these task have been greatly improved by new developed End2End-approaches and transformer networks. The inclusion of CR as a pre-processing step is expected to lead to improvements in downstream tasks. The paper examines this effect with respect to word embeddings. That is, we analyze the effects of CR on six different embedding methods and evaluate them in the context of seven lexical-semantic evaluation tasks and instantiation/hypernymy detection. Especially in the last tasks we hoped for a significant increase in performance. We show that all word embedding approaches do not benefit significantly from pronoun substitution. The measurable improvements are only marginal (around 0.5{\%} in most test cases). We explain this result with the loss of contextual information, reduction of the relative occurrence of rare words and the lack of pronouns to be replaced.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khullar-etal-2020-noel,
    title = "{N}o{E}l: An Annotated Corpus for Noun Ellipsis in {E}nglish",
    author = "Khullar, Payal  and
      Majmundar, Kushal  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.5",
    pages = "34--43",
    abstract = "Ellipsis resolution has been identified as an important step to improve the accuracy of mainstream Natural Language Processing (NLP) tasks such as information retrieval, event extraction, dialog systems, etc. Previous computational work on ellipsis resolution has focused on one type of ellipsis, namely Verb Phrase Ellipsis (VPE) and a few other related phenomenon. We extend the study of ellipsis by presenting the No(oun)El(lipsis) corpus - an annotated corpus for noun ellipsis and closely related phenomenon using the first hundred movies of Cornell Movie Dialogs Dataset. The annotations are carried out in a standoff annotation scheme that encodes the position of the licensor, the antecedent boundary, and Part-of-Speech (POS) tags of the licensor and antecedent modifier. Our corpus has 946 instances of exophoric and endophoric noun ellipsis, making it the biggest resource of noun ellipsis in English, to the best of our knowledge. We present a statistical study of our corpus with novel insights on the distribution of noun ellipsis, its licensors and antecedents. Finally, we perform the tasks of detection and resolution of noun ellipsis with different classifiers trained on our corpus and report baseline results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bamman-etal-2020-annotated,
    title = "An Annotated Dataset of Coreference in {E}nglish Literature",
    author = "Bamman, David  and
      Lewke, Olivia  and
      Mansoor, Anya",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.6",
    pages = "44--54",
    abstract = "We present in this work a new dataset of coreference annotations for works of literature in English, covering 29,103 mentions in 210,532 tokens from 100 works of fiction published between 1719 and 1922. This dataset differs from previous coreference corpora in containing documents whose average length (2,105.3 words) is four times longer than other benchmark datasets (463.7 for OntoNotes), and contains examples of difficult coreference problems common in literature. This dataset allows for an evaluation of cross-domain performance for the task of coreference resolution, and analysis into the characteristics of long-distance within-document coreference.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{pagel-reiter-2020-gerdracor,
    title = "{G}er{D}ra{C}or-Coref: A Coreference Corpus for Dramatic Texts in {G}erman",
    author = "Pagel, Janis  and
      Reiter, Nils",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.7",
    pages = "55--64",
    abstract = "Dramatic texts are a highly structured literary text type. Their quantitative analysis so far has relied on analysing structural properties (e.g., in the form of networks). Resolving coreferences is crucial for an analysis of the content of the character speech, but developing automatic coreference resolution (CR) systems depends on the existence of annotated corpora. In this paper, we present an annotated corpus of German dramatic texts, a preliminary analysis of the corpus as well as some baseline experiments on automatic CR. The analysis shows that with respect to the reference structure, dramatic texts are very different from news texts, but more similar to other dialogical text types such as interviews. Baseline experiments show a performance of 28.8 CoNLL score achieved by the rule-based CR system CorZu. In the future, we plan to integrate the (partial) information given in the dramatis personae into the CR model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dakle-etal-2020-study,
    title = "A Study on Entity Resolution for Email Conversations",
    author = "Dakle, Parag Pravin  and
      Desai, Takshak  and
      Moldovan, Dan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.8",
    pages = "65--73",
    abstract = "This paper investigates the problem of entity resolution for email conversations and presents a seed annotated corpus of email threads labeled with entity coreference chains. Characteristics of email threads concerning reference resolution are first discussed, and then the creation of the corpus and annotation steps are explained. Finally, performance of the current state-of-the-art deep learning models on the seed corpus is evaluated and qualitative error analysis on the predictions obtained is presented.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aralikatte-sogaard-2020-model,
    title = "Model-based Annotation of Coreference",
    author = "Aralikatte, Rahul  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.9",
    pages = "74--79",
    abstract = "Humans do not make inferences over texts, but over models of what texts are about. When annotators are asked to annotate coreferent spans of text, it is therefore a somewhat unnatural task. This paper presents an alternative in which we preprocess documents, linking entities to a knowledge base, and turn the coreference annotation task {--} in our case limited to pronouns {--} into an annotation task where annotators are asked to assign pronouns to entities. Model-based annotation is shown to lead to faster annotation and higher inter-annotator agreement, and we argue that it also opens up an alternative approach to coreference resolution. We present two new coreference benchmark datasets, for English Wikipedia and English teacher-student dialogues, and evaluate state-of-the-art coreference resolvers on them.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wilkens-etal-2020-french,
    title = "{F}rench Coreference for Spoken and Written Language",
    author = "Wilkens, Rodrigo  and
      Oberle, Bruno  and
      Landragin, Fr{\'e}d{\'e}ric  and
      Todirascu, Amalia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.10",
    pages = "80--89",
    abstract = "Coreference resolution aims at identifying and grouping all mentions referring to the same entity. In French, most systems run different setups, making their comparison difficult. In this paper, we present an extensive comparison of several coreference resolution systems for French. The systems have been trained on two corpora (ANCOR for spoken language and Democrat for written language) annotated with coreference chains, and augmented with syntactic and semantic information. The models are compared with different configurations (e.g. with and without singletons). In addition, we evaluate mention detection and coreference resolution apart. We present a full-stack model that outperforms other approaches. This model allows us to study the impact of mention detection errors on coreference resolution. Our analysis shows that mention detection can be improved by focusing on boundary identification while advances in the pronoun-noun relation detection can help the coreference task. Another contribution of this work is the first end-to-end neural French coreference resolution model trained on Democrat (written texts), which compares to the state-of-the-art systems for oral French.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aloraini-poesio-2020-cross,
    title = "Cross-lingual Zero Pronoun Resolution",
    author = "Aloraini, Abdulrahman  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.11",
    pages = "90--98",
    abstract = "In languages like Arabic, Chinese, Italian, Japanese, Korean, Portuguese, Spanish, and many others, predicate arguments in certain syntactic positions are not realized instead of being realized as overt pronouns, and are thus called zero- or null-pronouns. Identifying and resolving such omitted arguments is crucial to machine translation, information extraction and other NLP tasks, but depends heavily on semantic coherence and lexical relationships. We propose a BERT-based cross-lingual model for zero pronoun resolution, and evaluate it on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, ours is the first neural model of zero-pronoun resolution for Arabic; and our model also outperforms the state-of-the-art for Chinese. In the paper we also evaluate BERT feature extraction and fine-tune models on the task, and compare them with our model. We also report on an investigation of BERT layers indicating which layer encodes the most suitable representation for the task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{loaiciga-etal-2020-exploiting,
    title = "Exploiting Cross-Lingual Hints to Discover Event Pronouns",
    author = "Lo{\'a}iciga, Sharid  and
      Hardmeier, Christian  and
      Sayeed, Asad",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.12",
    pages = "99--103",
    abstract = "Non-nominal co-reference is much less studied than nominal coreference, partly because of the lack of annotated corpora. We explore the possibility to exploit parallel multilingual corpora as a means of cheap supervision for the classification of three different readings of the English pronoun {`}it{'}: entity, event or pleonastic, from their translation in several languages. We found that the {`}event{'} reading is not very frequent, but can be easily predicted provided that the construction used to translate the {`}it{'} example is a pronoun as well. These cases, nevertheless, are not enough to generalize to other types of non-nominal reference.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{martin-etal-2020-mudoco,
    title = "{M}u{D}o{C}o: Corpus for Multidomain Coreference Resolution and Referring Expression Generation",
    author = "Martin, Scott  and
      Poddar, Shivani  and
      Upasani, Kartikeya",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.13",
    pages = "104--111",
    abstract = "This paper proposes a new dataset, MuDoCo, composed of authored dialogs between a fictional user and a system who are given tasks to perform within six task domains. These dialogs are given rich linguistic annotations by expert linguists for several types of reference mentions and named entity mentions, either of which can span multiple words, as well as for coreference links between mentions. The dialogs sometimes cross and blend domains, and the users exhibit complex task switching behavior such as re-initiating a previous task in the dialog by referencing the entities within it. The dataset contains a total of 8,429 dialogs with an average of 5.36 turns per dialog. We are releasing this dataset to encourage research in the field of coreference resolution, referring expression generation and identification within realistic, deep dialogs involving multiple domains. To demonstrate its utility, we also propose two baseline models for the downstream tasks: coreference resolution and referring expression generation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{xiang-etal-2020-affection,
    title = "Affection Driven Neural Networks for Sentiment Analysis",
    author = "Xiang, Rong  and
      Long, Yunfei  and
      Wan, Mingyu  and
      Gu, Jinghang  and
      Lu, Qin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.14",
    pages = "112--119",
    abstract = "Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0{\%} to 1.5{\%} in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bhattasali-etal-2020-alice,
    title = "The Alice Datasets: f{MRI} {\&} {EEG} Observations of Natural Language Comprehension",
    author = "Bhattasali, Shohini  and
      Brennan, Jonathan  and
      Luh, Wen-Ming  and
      Franzluebbers, Berta  and
      Hale, John",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.15",
    pages = "120--125",
    abstract = "The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mikhalkova-etal-2020-modelling,
    title = "Modelling Narrative Elements in a Short Story: A Study on Annotation Schemes and Guidelines",
    author = "Mikhalkova, Elena  and
      Protasov, Timofei  and
      Sokolova, Polina  and
      Bashmakova, Anastasiia  and
      Drozdova, Anastasiia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.16",
    pages = "126--132",
    abstract = "Text-processing algorithms that annotate main components of a story-line are presently in great need of corpora and well-agreed annotation schemes. The Text World Theory of cognitive linguistics offers a model that generalizes a narrative structure in the form of world building elements (characters, time and space) as well as text worlds themselves and switches between them. We have conducted a survey on how text worlds and their elements are annotated in different projects and proposed our own annotation scheme and instructions. We tested them, first, on the science fiction story {``}We Can Remember It for You Wholesale{''} by Philip K. Dick. Then we corrected the guidelines and added computer annotation of verb forms with the purpose to get a higher raters{'} agreement and tested them again on the short story {``}The Gift of the Magi{''} by O. Henry. As a result, the agreement among the three raters has risen. With due revision and tests, our annotation scheme and guidelines can be used for annotating narratives in corpora of literary texts, criminal evidence, teaching materials, quests, etc.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hoge-2020-cortical,
    title = "Cortical Speech Databases For Deciphering the Articulatory Code",
    author = {H{\"o}ge, Harald},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.17",
    pages = "133--137",
    abstract = "The paper relates to following {`}AC-hypotheses{'}: The articulatory code (AC) is a neural code exchanging multi-item messages between the short-term memory and cortical areas as the vSMC and STG. In these areas already neurons active in the presence of articulatory features have been measured. The AC codes the content of speech segmented in chunks and is the same for both modalities - speech perception and speech production. Each AC-message is related to a syllable. The items of each message relate to coordinated articulatory gestures composing the syllable. The mechanism to transport the AC and to segment the auditory signal is based on Ɵ/γ-oscillations, where a Ɵ-cycle has the duration of a Ɵ-syllable. The paper describes the findings from neuroscience, phonetics and the science of evolution leading to the AC-hypotheses. The paper proposes to verify the AC-hypotheses by measuring the activity of all ensembles of neurons coding and decoding the AC. Due to state of the art, the cortical measurements to be prepared, done and further processed need a high effort from scientists active in different areas. We propose to launch a project to produce cortical speech databases with cortical recordings synchronized with the speech signal allowing to decipher the articulatory code.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hollenstein-etal-2020-zuco,
    title = "{Z}u{C}o 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation",
    author = "Hollenstein, Nora  and
      Troendle, Marius  and
      Zhang, Ce  and
      Langer, Nicolas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.18",
    pages = "138--146",
    abstract = "We recorded and preprocessed ZuCo 2.0, a new dataset of simultaneous eye-tracking and electroencephalography during natural reading and during annotation. This corpus contains gaze and brain activity data of 739 English sentences, 349 in a normal reading paradigm and 390 in a task-specific paradigm, in which the 18 participants actively search for a semantic relation type in the given sentences as a linguistic annotation task. This new dataset complements ZuCo 1.0 by providing experiments designed to analyze the differences in cognitive processing between natural reading and annotation. The data is freely available here: https://osf.io/2urht/.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{reinboth-etal-2020-linguistic,
    title = "Linguistic, Kinematic and Gaze Information in Task Descriptions: The {LKG}-Corpus",
    author = "Reinboth, Tim  and
      Gross, Stephanie  and
      Bishop, Laura  and
      Krenn, Brigitte",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.19",
    pages = "147--155",
    abstract = "Data from neuroscience and psychology suggest that sensorimotor cognition may be of central importance to language. Specifically, the linguistic structure of utterances referring to concrete actions may reflect the structure of the sensorimotor processing underlying the same action. To investigate this, we present the Linguistic, Kinematic and Gaze information in task descriptions Corpus (LKG-Corpus), comprising multimodal data on 13 humans, conducting take, put, and push actions, and describing these actions with 350 utterances. Recorded are audio, video, motion and eye-tracking data while participants perform an action and describe what they do. The dataset is annotated with orthographic transcriptions of utterances and information on: (a) gaze behaviours, (b) when a participant touched an object, (c) when an object was moved, (d) when a participant looked at the location s/he would next move the object to, (e) when the participant{'}s gaze was stable on an area. With the exception of the annotation of stable gaze, all annotations were performed manually. With the LKG-Corpus, we present a dataset that integrates linguistic, kinematic and gaze data with an explicit focus on relations between action and language. On this basis, we outline applications of the dataset to both basic and applied research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jancso-etal-2020-acqdiv,
    title = "The {ACQDIV} Corpus Database and Aggregation Pipeline",
    author = "Jancso, Anna  and
      Moran, Steven  and
      Stoll, Sabine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.20",
    pages = "156--165",
    abstract = "We present the ACQDIV corpus database and aggregation pipeline, a tool developed as part of the European Research Council (ERC) funded project ACQDIV, which aims to identify the universal cognitive processes that allow children to acquire any language. The corpus database represents 15 corpora from 14 typologically maximally diverse languages. Here we give an overview of the project, database, and our extensible software package for adding more corpora to the current language sample. Lastly, we discuss how we use the corpus database to mine for universal patterns in child language acquisition corpora and we describe avenues for future research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schwab-etal-2020-providing,
    title = "Providing Semantic Knowledge to a Set of Pictograms for People with Disabilities: a Set of Links between {W}ord{N}et and Arasaac: Arasaac-{WN}",
    author = {Schwab, Didier  and
      Trial, Pauline  and
      Vaschalde, C{\'e}line  and
      Vial, Lo{\"\i}c  and
      Esperanca-Rodier, Emmanuelle  and
      Lecouteux, Benjamin},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.21",
    pages = "166--171",
    abstract = "This article presents a resource that links WordNet, the widely known lexical and semantic database, and Arasaac, the largest freely available database of pictograms. Pictograms are a tool that is more and more used by people with cognitive or communication disabilities. However, they are mainly used manually via workbooks, whereas caregivers and families would like to use more automated tools (use speech to generate pictograms, for example). In order to make it possible to use pictograms automatically in NLP applications, we propose a database that links them to semantic knowledge. This resource is particularly interesting for the creation of applications that help people with cognitive disabilities, such as text-to-picto, speech-to-picto, picto-to-speech... In this article, we explain the needs for this database and the problems that have been identified. Currently, this resource combines approximately 800 pictograms with their corresponding WordNet synsets and it is accessible both through a digital collection and via an SQL database. Finally, we propose a method with associated tools to make our resource language-independent: this method was applied to create a first text-to-picto prototype for the French language. Our resource is distributed freely under a Creative Commons license at the following URL: https://github.com/getalp/Arasaac-WN.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tulkens-etal-2020-orthographic,
    title = "Orthographic Codes and the Neighborhood Effect: Lessons from Information Theory",
    author = "Tulkens, St{\'e}phan  and
      Sandra, Dominiek  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.22",
    pages = "172--181",
    abstract = "We consider the orthographic neighborhood effect: the effect that words with more orthographic similarity to other words are read faster. The neighborhood effect serves as an important control variable in psycholinguistic studies of word reading, and explains variance in addition to word length and word frequency. Following previous work, we model the neighborhood effect as the average distance to neighbors in feature space for three feature sets: slots, character ngrams and skipgrams. We optimize each of these feature sets and find evidence for language-independent optima, across five megastudy corpora from five alphabetic languages. Additionally, we show that weighting features using the inverse of mutual information (MI) improves the neighborhood effect significantly for all languages. We analyze the inverse feature weighting, and show that, across languages, grammatical morphemes get the lowest weights. Finally, we perform the same experiments on Korean Hangul, a non-alphabetic writing system, where we find the opposite results: slower responses as a function of denser neighborhoods, and a negative effect of inverse feature weighting. This raises the question of whether this is a cognitive effect, or an effect of the way we represent Hangul orthography, and indicates more research is needed.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kerz-etal-2020-understanding,
    title = "Understanding the Dynamics of Second Language Writing through Keystroke Logging and Complexity Contours",
    author = {Kerz, Elma  and
      Pruneri, Fabio  and
      Wiechmann, Daniel  and
      Qiao, Yu  and
      Str{\"o}bel, Marcus},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.23",
    pages = "182--188",
    abstract = "The purpose of this paper is twofold: [1] to introduce, to our knowledge, the largest available resource of keystroke logging (KSL) data generated by Etherpad (https://etherpad.org/), an open-source, web-based collaborative real-time editor, that captures the dynamics of second language (L2) production and [2] to relate the behavioral data from KSL to indices of syntactic and lexical complexity of the texts produced obtained from a tool that implements a sliding window approach capturing the progression of complexity within a text. We present the procedures and measures developed to analyze a sample of 14,913,009 keystrokes in 3,454 texts produced by 512 university students (upper-intermediate to advanced L2 learners of English) (95,354 sentences and 18,32,027 words) aiming to achieve a better alignment between keystroke-logging measures and underlying cognitive processes, on the one hand, and L2 writing performance measures, on the other hand. The resource introduced in this paper is a reflection of increasing recognition of the urgent need to obtain ecologically valid data that have the potential to transform our current understanding of mechanisms underlying the development of literacy (reading and writing) skills.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{oseki-asahara-2020-design,
    title = "Design of {BCCWJ}-{EEG}: Balanced Corpus with Human Electroencephalography",
    author = "Oseki, Yohei  and
      Asahara, Masayuki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.24",
    pages = "189--194",
    abstract = "The past decade has witnessed the happy marriage between natural language processing (NLP) and the cognitive science of language. Moreover, given the historical relationship between biological and artificial neural networks, the advent of deep learning has re-sparked strong interests in the fusion of NLP and the neuroscience of language. Importantly, this inter-fertilization between NLP, on one hand, and the cognitive (neuro)science of language, on the other, has been driven by the language resources annotated with human language processing data. However, there remain several limitations with those language resources on annotations, genres, languages, etc. In this paper, we describe the design of a novel language resource called BCCWJ-EEG, the Balanced Corpus of Contemporary Written Japanese (BCCWJ) experimentally annotated with human electroencephalography (EEG). Specifically, after extensively reviewing the language resources currently available in the literature with special focus on eye-tracking and EEG, we summarize the details concerning (i) participants, (ii) stimuli, (iii) procedure, (iv) data preprocessing, (v) corpus evaluation, (vi) resource release, and (vii) compilation schedule. In addition, potential applications of BCCWJ-EEG to neuroscience and NLP will also be discussed.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{smirnova-etal-2020-using,
    title = "Using the {RUPEX} Multichannel Corpus in a Pilot f{MRI} Study on Speech Disfluencies",
    author = "Smirnova, Katerina  and
      Korotaev, Nikolay  and
      Panikratova, Yana  and
      Lebedeva, Irina  and
      Pechenkova, Ekaterina  and
      Fedorova, Olga",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.25",
    pages = "195--203",
    abstract = "In modern linguistics and psycholinguistics speech disfluencies in real fluent speech are a well-known phenomenon. But it{'}s not still clear which components of brain systems are involved into its comprehension in a listener{'}s brain. In this paper we provide a pilot neuroimaging study of the possible neural correlates of speech disfluencies perception, using a combination of the corpus and functional magnetic-resonance imaging (fMRI) methods. Special technical procedure of selecting stimulus material from Russian multichannel corpus RUPEX allowed to create fragments in terms of requirements for the fMRI BOLD temporal resolution. They contain isolated speech disfluencies and their clusters. Also, we used the referential task for participants fMRI scanning. As a result, it was demonstrated that annotated multichannel corpora like RUPEX can be an important resource for experimental research in interdisciplinary fields. Thus, different aspects of communication can be explored through the prism of brain activation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{koyama-etal-2020-construction,
    title = "Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of {J}apanese as a Second Language",
    author = "Koyama, Aomi  and
      Kiyuna, Tomoshige  and
      Kobayashi, Kenji  and
      Arai, Mio  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.26",
    pages = "204--211",
    abstract = "The NAIST Lang-8 Learner Corpora (Lang-8 corpus) is one of the largest second-language learner corpora. The Lang-8 corpus is suitable as a training dataset for machine translation-based grammatical error correction systems. However, it is not suitable as an evaluation dataset because the corrected sentences sometimes include inappropriate sentences. Therefore, we created and released an evaluation corpus for correcting grammatical errors made by learners of Japanese as a Second Language (JSL). As our corpus has less noise and its annotation scheme reflects the characteristics of the dataset, it is ideal as an evaluation corpus for correcting grammatical errors in sentences written by JSL learners. In addition, we applied neural machine translation (NMT) and statistical machine translation (SMT) techniques to correct the grammar of the JSL learners{'} sentences and evaluated their results using our corpus. We also compared the performance of the NMT system with that of the SMT system.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nam-etal-2020-effective,
    title = "Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction",
    author = "Nam, Sangha  and
      Lee, Minho  and
      Kim, Donghwan  and
      Han, Kijong  and
      Kim, Kuntae  and
      Yoon, Sooji  and
      Kim, Eun-kyung  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.27",
    pages = "212--219",
    abstract = "Information extraction from unstructured texts plays a vital role in the field of natural language processing. Although there has been extensive research into each information extraction task (i.e., entity linking, coreference resolution, and relation extraction), data are not available for a continuous and coherent evaluation of all information extraction tasks in a comprehensive framework. Given that each task is performed and evaluated with a different dataset, analyzing the effect of the previous task on the next task with a single dataset throughout the information extraction process is impossible. This paper aims to propose a Korean information extraction initiative point and promote research in this field by presenting crowdsourcing data collected for four information extraction tasks from the same corpus and the training and evaluation results for each task of a state-of-the-art model. These machine learning data for Korean information extraction are the first of their kind, and there are plans to continuously increase the data volume. The test results will serve as an initiative result for each Korean information extraction task and are expected to serve as a comparison target for various studies on Korean information extraction using the data collected in this study.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{roque-etal-2020-developing,
    title = "Developing a Corpus of Indirect Speech Act Schemas",
    author = "Roque, Antonio  and
      Tsuetaki, Alexander  and
      Sarathy, Vasanth  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.28",
    pages = "220--228",
    abstract = "Resolving Indirect Speech Acts (ISAs), in which the intended meaning of an utterance is not identical to its literal meaning, is essential to enabling the participation of intelligent systems in peoples{'} everyday lives. Especially challenging are those cases in which the interpretation of such ISAs depends on context. To test a system{'}s ability to perform ISA resolution we need a corpus, but developing such a corpus is difficult, especialy given the contex-dependent requirement. This paper addresses the difficult problems of constructing a corpus of ISAs, taking inspiration from relevant work in using corpora for reasoning tasks. We present a formal representation of ISA Schemas required for such testing, including a measure of the difficulty of a particular schema. We develop an approach to authoring these schemas using corpus analysis and crowdsourcing, to maximize realism and minimize the amount of expert authoring needed. Finally, we describe several characteristics of collected data, and potential future work.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sato-miyazawa-2020-quality,
    title = "Quality Estimation for Partially Subjective Classification Tasks via Crowdsourcing",
    author = "Sato, Yoshinao  and
      Miyazawa, Kouki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.29",
    pages = "229--235",
    abstract = "The quality estimation of artifacts generated by creators via crowdsourcing has great significance for the construction of a large-scale data resource. A common approach to this problem is to ask multiple reviewers to evaluate the same artifacts. However, the commonly used majority voting method to aggregate reviewers{'} evaluations does not work effectively for partially subjective or purely subjective tasks because reviewers{'} sensitivity and bias of evaluation tend to have a wide variety. To overcome this difficulty, we propose a probabilistic model for subjective classification tasks that incorporates the qualities of artifacts as well as the abilities and biases of creators and reviewers as latent variables to be jointly inferred. We applied this method to the partially subjective task of speech classification into the following four attitudes: agreement, disagreement, stalling, and question. The result shows that the proposed method estimates the quality of speech more effectively than a vote aggregation, measured by correlation with a fine-grained classification by experts.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hahm-etal-2020-crowdsourcing,
    title = "Crowdsourcing in the Development of a Multilingual {F}rame{N}et: A Case Study of {K}orean {F}rame{N}et",
    author = "Hahm, Younggyun  and
      Noh, Youngbin  and
      Han, Ji Yoon  and
      Oh, Tae Hwan  and
      Choe, Hyonsu  and
      Kim, Hansaem  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.30",
    pages = "236--244",
    abstract = "Using current methods, the construction of multilingual resources in FrameNet is an expensive and complex task. While crowdsourcing is a viable alternative, it is difficult to include non-native English speakers in such efforts as they often have difficulty with English-based FrameNet tools. In this work, we investigated cross-lingual issues in crowdsourcing approaches for multilingual FrameNets, specifically in the context of the newly constructed Korean FrameNet. To accomplish this, we evaluated the effectiveness of various crowdsourcing settings whereby certain types of information are provided to workers, such as English definitions in FrameNet or translated definitions. We then evaluated whether the crowdsourced results accurately captured the meaning of frames both cross-culturally and cross-linguistically, and found that by allowing the crowd workers to make intuitive choices, they achieved a quality comparable to that of trained FrameNet experts (F1 {\textgreater} 0.75). The outcomes of this work are now publicly available as a new release of Korean FrameNet 1.1.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{iskender-etal-2020-towards,
    title = "Towards a Reliable and Robust Methodology for Crowd-Based Subjective Quality Assessment of Query-Based Extractive Text Summarization",
    author = {Iskender, Neslihan  and
      Polzehl, Tim  and
      M{\"o}ller, Sebastian},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.31",
    pages = "245--253",
    abstract = "The intrinsic and extrinsic quality evaluation is an essential part of the summary evaluation methodology usually conducted in a traditional controlled laboratory environment. However, processing large text corpora using these methods reveals expensive from both the organizational and the financial perspective. For the first time, and as a fast, scalable, and cost-effective alternative, we propose micro-task crowdsourcing to evaluate both the intrinsic and extrinsic quality of query-based extractive text summaries. To investigate the appropriateness of crowdsourcing for this task, we conduct intensive comparative crowdsourcing and laboratory experiments, evaluating nine extrinsic and intrinsic quality measures on 5-point MOS scales. Correlating results of crowd and laboratory ratings reveals high applicability of crowdsourcing for the factors overall quality, grammaticality, non-redundancy, referential clarity, focus, structure {\&} coherence, summary usefulness, and summary informativeness. Further, we investigate the effect of the number of repetitions of assessments on the robustness of mean opinion score of crowd ratings, measured against the increase of correlation coefficients between crowd and laboratory. Our results suggest that the optimal number of repetitions in crowdsourcing setups, in which any additional repetitions do no longer cause an adequate increase of overall correlation coefficients, lies between seven and nine for intrinsic and extrinsic quality factors.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{radhakrishnan-2020-seed,
    title = "A Seed Corpus of {H}indu Temples in {I}ndia",
    author = "Radhakrishnan, Priya",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.32",
    pages = "254--258",
    abstract = "Temples are an integral part of culture and heritage of India and are centers of religious practice for practicing Hindus. A scientific study of temples can reveal valuable insights into Indian culture and heritage. However to the best of our knowledge, learning resources that aid such a study are either not publicly available or non-existent. In this endeavour we present our initial efforts to create a corpus of Hindu temples in India. In this paper, we present a simple, re-usable platform that creates temple corpus from web text on temples. Curation is improved using classifiers trained on textual data in Wikipedia articles on Hindu temples. The training data is verified by human volunteers. The temple corpus consists of 4933 high accuracy facts about 573 temples. We make the corpus and the platform freely available. We also test the re-usability of the platform by creating a corpus of museums in India. We believe the temple corpus will aid scientific study of temples and the platform will aid in construction of similar corpuses. We believe both these will significantly contribute in promoting research on culture and heritage of a region.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chang-hsieh-2020-believe,
    title = "Do You Believe It Happened? Assessing {C}hinese Readers{'} Veridicality Judgments",
    author = "Chang, Yu-Yun  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.33",
    pages = "259--267",
    abstract = "This work collects and studies Chinese readers{'} veridicality judgments to news events (whether an event is viewed as happening or not). For instance, in {``}The FBI alleged in court documents that Zazi had admitted having a handwritten recipe for explosives on his computer{''}, do people believe that Zazi had a handwritten recipe for explosives? The goal is to observe the pragmatic behaviors of linguistic features under context which affects readers in making veridicality judgments. Exploring from the datasets, it is found that features such as event-selecting predicates (ESP), modality markers, adverbs, temporal information, and statistics have an impact on readers{'} veridicality judgments. We further investigated that modality markers with high certainty do not necessarily trigger readers to have high confidence in believing an event happened. Additionally, the source of information introduced by an ESP presents low effects to veridicality judgments, even when an event is attributed to an authority (e.g. {``}The FBI{''}). A corpus annotated with Chinese readers{'} veridicality judgments is released as the Chinese PragBank for further analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nicolas-etal-2020-creating,
    title = "Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning",
    author = {Nicolas, Lionel  and
      Lyding, Verena  and
      Borg, Claudia  and
      Forascu, Corina  and
      Fort, Kar{\"e}n  and
      Zdravkova, Katerina  and
      Kosem, Iztok  and
      {\v{C}}ibej, Jaka  and
      Arhar Holdt, {\v{S}}pela  and
      Millour, Alice  and
      K{\"o}nig, Alexander  and
      Rodosthenous, Christos  and
      Sangati, Federico  and
      ul Hassan, Umair  and
      Katinskaia, Anisia  and
      Barreiro, Anabela  and
      Aparaschivei, Lavinia  and
      HaCohen-Kerner, Yaakov},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.34",
    pages = "268--278",
    abstract = "We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of this generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{haagsma-etal-2020-magpie,
    title = "{MAGPIE}: A Large Corpus of Potentially Idiomatic Expressions",
    author = "Haagsma, Hessel  and
      Bos, Johan  and
      Nissim, Malvina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.35",
    pages = "279--287",
    abstract = "Given the limited size of existing idiom corpora, we aim to enable progress in automatic idiom processing and linguistic analysis by creating the largest-to-date corpus of idioms for English. Using a fixed idiom list, automatic pre-extraction, and a strictly controlled crowdsourced annotation procedure, we show that it is feasible to build a high-quality corpus comprising more than 50K instances, an order of a magnitude larger than previous resources. Crucial ingredients of crowdsourcing were the selection of crowdworkers, clear and comprehensive instructions, and an interface that breaks down the task in small, manageable steps. Analysis of the resulting corpus revealed strong effects of genre on idiom distribution, providing new evidence for existing theories on what influences idiom usage. The corpus also contains rich metadata, and is made publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chiyah-garcia-etal-2020-crwiz,
    title = "{CRWIZ}: A Framework for Crowdsourcing Real-Time {W}izard-of-{O}z Dialogues",
    author = "Chiyah Garcia, Francisco Javier  and
      Lopes, Jos{\'e}  and
      Liu, Xingkun  and
      Hastie, Helen",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.36",
    pages = "288--297",
    abstract = "Large corpora of task-based and open-domain conversational dialogues are hugely valuable in the field of data-driven dialogue systems. Crowdsourcing platforms, such as Amazon Mechanical Turk, have been an effective method for collecting such large amounts of data. However, difficulties arise when task-based dialogues require expert domain knowledge or rapid access to domain-relevant information, such as databases for tourism. This will become even more prevalent as dialogue systems become increasingly ambitious, expanding into tasks with high levels of complexity that require collaboration and forward planning, such as in our domain of emergency response. In this paper, we propose CRWIZ: a framework for collecting real-time Wizard of Oz dialogues through crowdsourcing for collaborative, complex tasks. This framework uses semi-guided dialogue to avoid interactions that breach procedures and processes only known to experts, while enabling the capture of a wide variety of interactions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gomes-etal-2020-effort,
    title = "Effort Estimation in Named Entity Tagging Tasks",
    author = "Gomes, In{\^e}s  and
      Correia, Rui  and
      Ribeiro, Jorge  and
      Freitas, Jo{\~a}o",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.37",
    pages = "298--306",
    abstract = "Named Entity Recognition (NER) is an essential component of many Natural Language Processing pipelines. However, building these language dependent models requires large amounts of annotated data. Crowdsourcing emerged as a scalable solution to collect and enrich data in a more time-efficient manner. To manage these annotations at scale, it is important to predict completion timelines and compute fair pricing for workers in advance. To achieve these goals, we need to know how much effort will be taken to complete each task. In this paper, we investigate which variables influence the time spent on a named entity annotation task by a human. Our results are two-fold: first, the understanding of the effort-impacting factors which we divided into cognitive load and input length; and second, the performance of the prediction itself. On the latter, through model adaptation and feature engineering, we attained a Root Mean Squared Error (RMSE) of 25.68 words per minute with a Nearest Neighbors model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rodosthenous-etal-2020-using,
    title = "Using Crowdsourced Exercises for Vocabulary Training to Expand {C}oncept{N}et",
    author = {Rodosthenous, Christos  and
      Lyding, Verena  and
      Sangati, Federico  and
      K{\"o}nig, Alexander  and
      ul Hassan, Umair  and
      Nicolas, Lionel  and
      Horbacauskiene, Jolita  and
      Katinskaia, Anisia  and
      Aparaschivei, Lavinia},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.38",
    pages = "307--316",
    abstract = "In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet. V-TREL is built on top of a generic architecture implementing the implicit crowdsourding paradigm in order to offer vocabulary training exercises generated from the commonsense knowledge-base ConceptNet and {--} in the background {--} to collect and evaluate the learners{'} answers to extend ConceptNet with new words. In the experiment about 90 university students learning English at C1 level, based on Common European Framework of Reference for Languages (CEFR), trained their vocabulary with V-TREL over a period of 16 calendar days. The experiment allowed to gather more than 12,000 answers from learners on different question types. In this paper we present in detail the experimental setup and the outcome of the experiment, which indicates the potential of our approach for both crowdsourcing data as well as fostering vocabulary skills.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bailly-etal-2020-predicting,
    title = "Predicting Multidimensional Subjective Ratings of Children{'} Readings from the Speech Signals for the Automatic Assessment of Fluency",
    author = "Bailly, G{\'e}rard  and
      Godde, Erika  and
      Piat-Marchand, Anne-Laure  and
      Bosse, Marie-Line",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.39",
    pages = "317--322",
    abstract = "The objective of this research is to estimate multidimensional subjective ratings of the reading performance of young readers from signal-based objective measures. We here combine linguistic features (number of correct words, repetitions, deletions, insertions uttered per minute . . . ) with phonetic features. Expressivity is particularly difficult to predict since there is no unique golden standard. We here propose a novel framework for performing such an estimation that exploits multiple references performed by adults and demonstrate its efficiency using recordings of 273 pupils.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{akhlaghi-etal-2020-constructing,
    title = "Constructing Multimodal Language Learner Texts Using {LARA}: Experiences with Nine Languages",
    author = {Akhlaghi, Elham  and
      B{\'e}di, Branislav  and
      Bekta{\c{s}}, Fatih  and
      Berthelsen, Harald  and
      Butterweck, Matthias  and
      Chua, Cathy  and
      Cucchiarin, Catia  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Gerlach, Johanna  and
      Habibi, Hanieh  and
      N{\'\i} Chiar{\'a}in, Neasa  and
      Rayner, Manny  and
      Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Strik, Helmer},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.40",
    pages = "323--331",
    abstract = "LARA (Learning and Reading Assistant) is an open source platform whose purpose is to support easy conversion of plain texts into multimodal online versions suitable for use by language learners. This involves semi-automatically tagging the text, adding other annotations and recording audio. The platform is suitable for creating texts in multiple languages via crowdsourcing techniques that can be used for teaching a language via reading and listening. We present results of initial experiments by various collaborators where we measure the time required to produce substantial LARA resources, up to the length of short novels, in Dutch, English, Farsi, French, German, Icelandic, Irish, Swedish and Turkish. The first results are encouraging. Although there are some startup problems, the conversion task seems manageable for the languages tested so far. The resulting enriched texts are posted online and are freely available in both source and compiled form.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{pilan-etal-2020-dataset,
    title = "A Dataset for Investigating the Impact of Feedback on Student Revision Outcome",
    author = "Pilan, Ildiko  and
      Lee, John  and
      Yeung, Chak Yan  and
      Webster, Jonathan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.41",
    pages = "332--339",
    abstract = "We present an annotation scheme and a dataset of teacher feedback provided for texts written by non-native speakers of English. The dataset consists of student-written sentences in their original and revised versions with teacher feedback provided for the errors. Feedback appears both in the form of open-ended comments and error category tags. We focus on a specific error type, namely linking adverbial (e.g. however, moreover) errors. The dataset has been annotated for two aspects: (i) revision outcome establishing whether the re-written student sentence was correct and (ii) directness, indicating whether teachers provided explicitly the correction in their feedback. This dataset allows for studies around the characteristics of teacher feedback and how these influence students{'} revision outcome. We describe the data preparation process and we present initial statistical investigations regarding the effect of different feedback characteristics on revision outcome. These show that open-ended comments and mitigating expressions appear in a higher proportion of successful revisions than unsuccessful ones, while directness and metalinguistic terms have no effect. Given that the use of this type of data is relatively unexplored in natural language processing (NLP) applications, we also report some observations and challenges when working with feedback data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nagata-etal-2020-creating,
    title = "Creating Corpora for Research in Feedback Comment Generation",
    author = "Nagata, Ryo  and
      Inui, Kentaro  and
      Ishikawa, Shin{'}ichiro",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.42",
    pages = "340--345",
    abstract = "In this paper, we report on datasets that we created for research in feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning. There has been almost no such corpus open to the public and accordingly there has been a very limited amount of work on this task. In this paper, we first discuss the principle and guidelines for feedback comment annotation. Then, we describe two corpora that we have manually annotated with feedback comments (approximately 50,000 general comments and 6,700 on preposition use). A part of the annotation results is now available on the web, which will facilitate research in feedback comment generation",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{graen-etal-2020-using,
    title = "Using Multilingual Resources to Evaluate {CEFRL}ex for Learner Applications",
    author = {Gra{\"e}n, Johannes  and
      Alfter, David  and
      Schneider, Gerold},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.43",
    pages = "346--355",
    abstract = "The Common European Framework of Reference for Languages (CEFR) defines six levels of learner proficiency, and links them to particular communicative abilities. The CEFRLex project aims at compiling lexical resources that link single words and multi-word expressions to particular CEFR levels. The resources are thought to reflect second language learner needs as they are compiled from CEFR-graded textbooks and other learner-directed texts. In this work, we investigate the applicability of CEFRLex resources for building language learning applications. Our main concerns were that vocabulary in language learning materials might be sparse, i.e. that not all vocabulary items that belong to a particular level would also occur in materials for that level, and, on the other hand, that vocabulary items might be used on lower-level materials if required by the topic (e.g. with a simpler paraphrasing or translation). Our results indicate that the English CEFRLex resource is in accordance with external resources that we jointly employ as gold standard. Together with other values obtained from monolingual and parallel corpora, we can indicate which entries need to be adjusted to obtain values that are even more in line with this gold standard. We expect that this finding also holds for the other languages",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{platte-etal-2020-immersive,
    title = "Immersive Language Exploration with Object Recognition and Augmented Reality",
    author = "Platte, Benny  and
      Platte, Anett  and
      Roschke, Christian  and
      Thomanek, Rico  and
      Rolletschke, Thony  and
      Zimmer, Frank  and
      Ritter, Marc",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.44",
    pages = "356--362",
    abstract = "The use of Augmented Reality (AR) in teaching and learning contexts for language is still young. The ideas are endless, the concrete educational offers available emerge only gradually. Educational opportunities that were unthinkable a few years ago are now feasible. We present a concrete realization: an executable application for mobile devices with which users can explore their environment interactively in different languages. The software recognizes up to 1000 objects in the user{'}s environment using a deep learning method based on Convolutional Neural Networks and names this objects accordingly. Using Augmented Reality the objects are superimposed with 3D information in different languages. By switching the languages, the user is able to interactively discover his surrounding everyday items in all languages. The application is available as Open Source.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{conijn-etal-2020-process,
    title = "A Process-oriented Dataset of Revisions during Writing",
    author = "Conijn, Rianne  and
      Dux Speltz, Emily  and
      van Zaanen, Menno  and
      Van Waes, Luuk  and
      Chukharev-Hudilainen, Evgeny",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.45",
    pages = "363--368",
    abstract = "Revision plays a major role in writing and the analysis of writing processes. Revisions can be analyzed using a product-oriented approach (focusing on a finished product, the text that has been produced) or a process-oriented approach (focusing on the process that the writer followed to generate this product). Although several language resources exist for the product-oriented approach to revisions, there are hardly any resources available yet for an in-depth analysis of the process of revisions. Therefore, we provide an extensive dataset on revisions made during writing (accessible via https://hdl.handle.net/10411/VBDYGX). This dataset is based on keystroke data and eye tracking data of 65 students from a variety of backgrounds (undergraduate and graduate English as a first language and English as a second language students) and a variety of tasks (argumentative text and academic abstract). In total, 7,120 revisions were identified in the dataset. For each revision, 18 features have been manually annotated and 31 features have been automatically extracted. As a case study, we show two potential use cases of the dataset. In addition, future uses of the dataset are described.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{morgado-da-costa-etal-2020-automated,
    title = "Automated Writing Support Using Deep Linguistic Parsers",
    author = "Morgado da Costa, Lu{\'\i}s  and
      V P Winder, Roger  and
      Li, Shu Yun  and
      Lin Tzer Liang, Benedict Christopher  and
      Mackinnon, Joseph  and
      Bond, Francis",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.46",
    pages = "369--377",
    abstract = "This paper introduces a new web system that integrates English Grammatical Error Detection (GED) and course-specific stylistic guidelines to automatically review and provide feedback on student assignments. The system is being developed as a pedagogical tool for English Scientific Writing. It uses both general NLP methods and high precision parsers to check student assignments before they are submitted for grading. Instead of generalized error detection, our system aims to identify, with high precision, specific classes of problems that are known to be common among engineering students. Rather than correct the errors, our system generates constructive feedback to help students identify and correct them on their own. A preliminary evaluation of the system{'}s in-class performance has shown measurable improvements in the quality of student assignments.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gretter-etal-2020-tlt,
    title = "{TLT}-school: a Corpus of Non Native Children Speech",
    author = "Gretter, Roberto  and
      Matassoni, Marco  and
      Bann{\`o}, Stefano  and
      Daniele, Falavigna",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.47",
    pages = "378--385",
    abstract = "This paper describes {``}TLT-school{''} a corpus of speech utterances collected in schools of northern Italy for assessing the performance of students learning both English and German. The corpus was recorded in the years 2017 and 2018 from students aged between nine and sixteen years, attending primary, middle and high school. All utterances have been scored, in terms of some predefined proficiency indicators, by human experts. In addition, most of utterances recorded in 2017 have been manually transcribed carefully. Guidelines and procedures used for manual transcriptions of utterances will be described in detail, as well as results achieved by means of an automatic speech recognition system developed by us. Part of the corpus is going to be freely distributed to scientific community particularly interested both in non-native speech recognition and automatic assessment of second language proficiency.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{katinskaia-etal-2020-toward,
    title = "Toward a Paradigm Shift in Collection of Learner Corpora",
    author = "Katinskaia, Anisia  and
      Ivanova, Sardana  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.48",
    pages = "386--391",
    abstract = "We present the first version of the longitudinal Revita Learner Corpus (ReLCo), for Russian. In contrast to traditional learner corpora, ReLCo is collected and annotated fully automatically, while students perform exercises using the Revita language-learning platform. The corpus currently contains 8 422 sentences exhibiting several types of errors{---}grammatical, lexical, orthographic, etc.{---}which were committed by learners during practice and were automatically annotated by Revita. The corpus provides valuable information about patterns of learner errors and can be used as a language resource for a number of research tasks, while its creation is much cheaper and faster than for traditional learner corpora. A crucial advantage of ReLCo that it grows continually while learners practice with Revita, which opens the possibility of creating an unlimited learner resource with longitudinal data collected over time. We make the pilot version of the Russian ReLCo publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dargis-etal-2020-quality,
    title = "Quality Focused Approach to a Learner Corpus Development",
    author = "Dar{\c{g}}is, Roberts  and
      Auzi{\c{n}}a, Ilze  and
      Lev{\=a}ne-Petrova, Krist{\=\i}ne  and
      Kaija, Inga",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.49",
    pages = "392--396",
    abstract = "The paper presents quality focused approach to a learner corpus development. The methodology was developed with multiple design considerations put in place to make the annotation process easier and at the same time reduce the amount of mistakes that could be introduced due to inconsistent text correction or carelessness. The approach suggested in this paper consists of multiple parts: comparison of digitized texts by several annotators, text correction, automated morphological analysis, and manual review of annotations. The described approach is used to create Latvian Language Learner corpus (LaVA) which is part of a currently ongoing project Development of Learner corpus of Latvian: methods, tools and applications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{de-clercq-van-hoecke-2020-exploratory,
    title = "An Exploratory Study into Automated Pr{\'e}cis Grading",
    author = "De Clercq, Orphee  and
      Van Hoecke, Senne",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.50",
    pages = "397--404",
    abstract = "Automated writing evaluation is a popular research field, but the main focus has been on evaluating argumentative essays. In this paper, we consider a different genre, namely pr{\'e}cis texts. A pr{\'e}cis is a written text that provides a coherent summary of main points of a spoken or written text. We present a corpus of English pr{\'e}cis texts which all received a grade assigned by a highly-experienced English language teacher and were subsequently annotated following an exhaustive error typology. With this corpus we trained a machine learning model which relies on a number of linguistic, automatic summarization and AWE features. Our results reveal that this model is able to predict the grade of pr{\'e}cis texts with only a moderate error margin.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lin-etal-2020-adjusting,
    title = "Adjusting Image Attributes of Localized Regions with Low-level Dialogue",
    author = "Lin, Tzu-Hsiang  and
      Rudnicky, Alexander  and
      Bui, Trung  and
      Kim, Doo Soon  and
      Oh, Jean",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.51",
    pages = "405--412",
    abstract = "Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions are often ambiguous and contain high-level abstractions which require complex editing steps. Motivated by this inexperience aspect, we aim to smooth the learning curve by teaching the novices to edit images using low-level command terminologies. Towards this end, we develop a task-oriented dialogue system to investigate low-level instructions for NLIE. Our system grounds language on the level of edit operations, and suggests options for users to choose from. Though compelled to express in low-level terms, user evaluation shows that 25{\%} of users found our system easy-to-use, resonating with our motivation. Analysis shows that users generally adapt to utilizing the proposed low-level language interface. We also identified object segmentation as the key factor to user satisfaction. Our work demonstrates advantages of low-level, direct language-action mapping approach that can be applied to other problem domains beyond image editing such as audio editing or industrial design.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yim-etal-2020-alignment,
    title = "Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation",
    author = "Yim, Wen-wai  and
      Yetisgen, Meliha  and
      Huang, Jenny  and
      Grossman, Micah",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.52",
    pages = "413--421",
    abstract = "For every patient{'}s visit to a clinician, a clinical note is generated documenting their medical conversation, including complaints discussed, treatments, and medical plans. Despite advances in natural language processing, automating clinical note generation from a clinic visit conversation is a largely unexplored area of research. Due to the idiosyncrasies of the task, traditional methods of corpus creation are not effective enough approaches for this problem. In this paper, we present an annotation methodology that is content- and technique- agnostic while associating note sentences to sets of dialogue sentences. The sets can further be grouped with higher order tags to mark sets with related information. This direct linkage from input to output decouples the annotation from specific language understanding or generation strategies. Here we provide data statistics and qualitative analysis describing the unique annotation challenges. Given enough annotated data, such a resource would support multiple modeling methods including information extraction with template language generation, information retrieval type language generation, or sequence to sequence modeling.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{eric-etal-2020-multiwoz,
    title = "{M}ulti{WOZ} 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines",
    author = "Eric, Mihail  and
      Goel, Rahul  and
      Paul, Shachi  and
      Sethi, Abhishek  and
      Agarwal, Sanchit  and
      Gao, Shuyang  and
      Kumar, Adarsh  and
      Goyal, Anuj  and
      Ku, Peter  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.53",
    pages = "422--428",
    abstract = "MultiWOZ 2.0 (Budzianowski et al., 2018) is a recently released multi-domain dialogue dataset spanning 7 distinct domains and containing over 10,000 dialogues. Though immensely useful and one of the largest resources of its kind to-date, MultiWOZ 2.0 has a few shortcomings. Firstly, there are substantial noise in the dialogue state annotations and dialogue utterances which negatively impact the performance of state-tracking models. Secondly, follow-up work (Lee et al., 2019) has augmented the original dataset with user dialogue acts. This leads to multiple co-existent versions of the same dataset with minor modifications. In this work we tackle the aforementioned issues by introducing MultiWOZ 2.1. To fix the noisy state annotations, we use crowdsourced workers to re-annotate state and utterances based on the original utterances in the dataset. This correction process results in changes to over 32{\%} of state annotations across 40{\%} of the dialogue turns. In addition, we fix 146 dialogue utterances by canonicalizing slot values in the utterances to the values in the dataset ontology. To address the second problem, we combined the contributions of the follow-up works into MultiWOZ 2.1. Hence, our dataset also includes user dialogue acts as well as multiple slot descriptions per dialogue state slot. We then benchmark a number of state-of-the-art dialogue state tracking models on the MultiWOZ 2.1 dataset and show the joint state tracking performance on the corrected state annotations. We are publicly releasing MultiWOZ 2.1 to the community, hoping that this dataset resource will allow for more effective models across various dialogue subproblems to be built in the future.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kraus-etal-2020-comparison,
    title = "A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation",
    author = "Kraus, Matthias  and
      Fischbach, Fabian  and
      Jansen, Pascal  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.54",
    pages = "429--435",
    abstract = "Recommendation systems aim at facilitating information retrieval for users by taking into account their preferences. Based on previous user behaviour, such a system suggests items or provides information that a user might like or find useful. Nonetheless, how to provide suggestions is still an open question. Depending on the way a recommendation is communicated influences the user{'}s perception of the system. This paper presents an empirical study on the effects of proactive dialogue strategies on user acceptance. Therefore, an explicit strategy based on user preferences provided directly by the user, and an implicit proactive strategy, using autonomously gathered information, are compared. The results show that proactive dialogue systems significantly affect the perception of human-computer interaction. Although no significant differences are found between implicit and explicit strategies, proactivity significantly influences the user experience compared to reactive system behaviour. The study contributes new insights to the human-agent interaction and the voice user interface design. Furthermore, we discover interesting tendencies that motivate futurework.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{otegi-etal-2020-conversational,
    title = "Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for {B}asque",
    author = "Otegi, Arantxa  and
      Agirre, Aitor  and
      Campos, Jon Ander  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.55",
    pages = "436--442",
    abstract = "Conversational Question Answering (CQA) systems meet user information needs by having conversations with them, where answers to the questions are retrieved from text. There exist a variety of datasets for English, with tens of thousands of training examples, and pre-trained language models have allowed to obtain impressive results. The goal of our research is to test the performance of CQA systems under low-resource conditions which are common for most non-English languages: small amounts of native annotations and other limitations linked to low resource languages, like lack of crowdworkers or smaller wikipedias. We focus on the Basque language, and present the first non-English CQA dataset and results. Our experiments show that it is possible to obtain good results with low amounts of native data thanks to cross-lingual transfer, with quality comparable to those obtained for English. We also discovered that dialogue history models are not directly transferable to another language, calling for further research. The dataset is publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yamazaki-etal-2020-construction,
    title = "Construction and Analysis of a Multimodal Chat-talk Corpus for Dialog Systems Considering Interpersonal Closeness",
    author = "Yamazaki, Yoshihiro  and
      Chiba, Yuya  and
      Nose, Takashi  and
      Ito, Akinori",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.56",
    pages = "443--448",
    abstract = "There are high expectations for multimodal dialog systems that can make natural small talk with facial expressions, gestures, and gaze actions as next-generation dialog-based systems. Two important roles of the chat-talk system are keeping the user engaged and establishing rapport. Many studies have conducted user evaluations of such systems, some of which reported that considering the relationship with the user is an effective way to improve the subjective evaluation. To facilitate research of such dialog systems, we are currently constructing a large-scale multimodal dialog corpus focusing on the relationship between speakers. In this paper, we describe the data collection and annotation process, and analysis of the corpus collected in the early stage of the project. This corpus contains 19,303 utterances (10 hours) from 19 pairs of participants. A dialog act tag is annotated to each utterance by two annotators. We compare the frequency and the transition probability of the tags between different closeness levels to help construct a dialog system for establishing a relationship with the user.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{van-waterschoot-etal-2020-bliss,
    title = "{BLISS}: An Agent for Collecting Spoken Dialogue Data about Health and Well-being",
    author = {van Waterschoot, Jelte  and
      Hendrickx, Iris  and
      Khan, Arif  and
      Klabbers, Esther  and
      de Korte, Marcel  and
      Strik, Helmer  and
      Cucchiarini, Catia  and
      Theune, Mari{\"e}t},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.57",
    pages = "449--458",
    abstract = "An important objective in health-technology is the ability to gather information about people{'}s well-being. Structured interviews can be used to obtain this information, but are time-consuming and not scalable. Questionnaires provide an alternative way to extract such information, though typically lack depth. In this paper, we present our first prototype of the BLISS agent, an artificial intelligent agent which intends to automatically discover what makes people happy and healthy. The goal of Behaviour-based Language-Interactive Speaking Systems (BLISS) is to understand the motivations behind people{'}s happiness by conducting a personalized spoken dialogue based on a happiness model. We built our first prototype of the model to collect 55 spoken dialogues, in which the BLISS agent asked questions to users about their happiness and well-being. Apart from a description of the BLISS architecture, we also provide details about our dataset, which contains over 120 activities and 100 motivations and is made available for usage.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chen-etal-2020-jddc,
    title = "The {JDDC} Corpus: A Large-Scale Multi-Turn {C}hinese Dialogue Dataset for {E}-commerce Customer Service",
    author = "Chen, Meng  and
      Liu, Ruixue  and
      Shen, Lei  and
      Yuan, Shaozu  and
      Zhou, Jingyan  and
      Wu, Youzheng  and
      He, Xiaodong  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.58",
    pages = "459--466",
    abstract = "Human conversations are complicated and building a human-like dialogue agent is an extremely challenging task. With the rapid development of deep learning techniques, data-driven models become more and more prevalent which need a huge amount of real conversation data. In this paper, we construct a large-scale real scenario Chinese E-commerce conversation corpus, JDDC, with more than 1 million multi-turn dialogues, 20 million utterances, and 150 million words. The dataset reflects several characteristics of human-human conversations, e.g., goal-driven, and long-term dependency among the context. It also covers various dialogue types including task-oriented, chitchat and question-answering. Extra intent information and three well-annotated challenge sets are also provided. Then, we evaluate several retrieval-based and generative models to provide basic benchmark performance on the JDDC corpus. And we hope JDDC can serve as an effective testbed and benefit the development of fundamental research in dialogue task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{priego-valverde-etal-2020-cheese,
    title = "{``}Cheese!{''}: a Corpus of Face-to-face {F}rench Interactions. A Case Study for Analyzing Smiling and Conversational Humor",
    author = "Priego-Valverde, B{\'e}atrice  and
      Bigi, Brigitte  and
      Amoyal, Mary",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.59",
    pages = "467--475",
    abstract = "Cheese! is a conversational corpus. It consists of 11 French face-to-face conversations lasting around 15 minutes each. Cheese! is a duplication of an American corpus (ref) in order to conduct a cross-cultural comparison of participants{'} smiling behavior in humorous and non-humorous sequences in American English and French conversations. In this article, the methodology used to collect and enrich the corpus is presented: experimental protocol, technical choices, transcription, semi-automatic annotations, manual annotations of smiling and humor. An exploratory study investigating the links between smile and humor is then proposed. Based on the analysis of two interactions, two questions are asked: (1) Does smile frame humor? (2) Does smile has an impact on its success or failure? If the experimental design of Cheese! has been elaborated to study specifically smiles and humor in conversations, the high quality of the dataset obtained, and the methodology used are also replicable and can be applied to analyze many other conversational activities and other multimodal modalities.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chierici-etal-2020-margarita,
    title = "The Margarita Dialogue Corpus: A Data Set for Time-Offset Interactions and Unstructured Dialogue Systems",
    author = "Chierici, Alberto  and
      Habash, Nizar  and
      Bicec, Margarita",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.60",
    pages = "476--484",
    abstract = "Time-Offset Interaction Applications (TOIAs) are systems that simulate face-to-face conversations between humans and digital human avatars recorded in the past. Developing a well-functioning TOIA involves several research areas: artificial intelligence, human-computer interaction, natural language processing, question answering, and dialogue systems. The first challenges are to define a sensible methodology for data collection and to create useful data sets for training the system to retrieve the best answer to a user{'}s question. In this paper, we present three main contributions: a methodology for creating the knowledge base for a TOIA, a dialogue corpus, and baselines for single-turn answer retrieval. We develop the methodology using a two-step strategy. First, we let the avatar maker list pairs by intuition, guessing what possible questions a user may ask to the avatar. Second, we record actual dialogues between random individuals and the avatar-maker. We make the Margarita Dialogue Corpus available to the research community. This corpus comprises the knowledge base in text format, the video clips for each answer, and the annotated dialogues.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schmidt-etal-2020-users,
    title = "How Users React to Proactive Voice Assistant Behavior While Driving",
    author = "Schmidt, Maria  and
      Minker, Wolfgang  and
      Werner, Steffen",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.61",
    pages = "485--490",
    abstract = "Nowadays Personal Assistants (PAs) are available in multiple environments and become increasingly popular to use via voice. Therefore, we aim to provide proactive PA suggestions to car drivers via speech. These suggestions should be neither obtrusive nor increase the drivers{'} cognitive load, while enhancing user experience. To assess these factors, we conducted a usability study in which 42 participants perceive proactive voice output in a Wizard-of-Oz study in a driving simulator. Traffic density was varied during a highway drive and it included six in-car-specific use cases. The latter were presented by a proactive voice assistant and in a non-proactive control condition. We assessed the users{'} subjective cognitive load and their satisfaction in different questionnaires during the interaction with both PA variants. Furthermore, we analyze the user reactions: both regarding their content and the elapsed response times to PA actions. The results show that proactive assistant behavior is rated similarly positive as non-proactive behavior. Furthermore, the participants agreed to 73.8{\%} of proactive suggestions. In line with previous research, driving-relevant use cases receive the best ratings, here we reach 82.5{\%} acceptance. Finally, the users reacted significantly faster to proactive PA actions, which we interpret as less cognitive load compared to non-proactive behavior.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{asai-etal-2020-emotional,
    title = "Emotional Speech Corpus for Persuasive Dialogue System",
    author = "Asai, Sara  and
      Yoshino, Koichiro  and
      Shinagawa, Seitaro  and
      Sakti, Sakriani  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.62",
    pages = "491--497",
    abstract = "Expressing emotion is known as an efficient way to persuade one{'}s dialogue partner to accept one{'}s claim or proposal. Emotional expression in speech can express the speaker{'}s emotion more directly than using only emotion expression in the text, which will lead to a more persuasive dialogue. In this paper, we built a speech dialogue corpus in a persuasive scenario that uses emotional expressions to build a persuasive dialogue system with emotional expressions. We extended an existing text dialogue corpus by adding variations of emotional responses to cover different combinations of broad dialogue context and a variety of emotional states by crowd-sourcing. Then, we recorded emotional speech consisting of of collected emotional expressions spoken by a voice actor. The experimental results indicate that the collected emotional expressions with their speeches have higher emotional expressiveness for expressing the system{'}s emotion to users.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bangalore-kantharaju-etal-2020-multimodal,
    title = "Multimodal Analysis of Cohesion in Multi-party Interactions",
    author = "Bangalore Kantharaju, Reshmashree  and
      Langlet, Caroline  and
      Barange, Mukesh  and
      Clavel, Chlo{\'e}  and
      Pelachaud, Catherine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.63",
    pages = "498--507",
    abstract = "Group cohesion is an emergent phenomenon that describes the tendency of the group members{'} shared commitment to group tasks and the interpersonal attraction among them. This paper presents a multimodal analysis of group cohesion using a corpus of multi-party interactions. We utilize 16 two-minute segments annotated with cohesion from the AMI corpus. We define three layers of modalities: non-verbal social cues, dialogue acts and interruptions. The initial analysis is performed at the individual level and later, we combine the different modalities to observe their impact on perceived level of cohesion. Results indicate that occurrence of laughter and interruption are higher in high cohesive segments. We also observe that, dialogue acts and head nods did not have an impact on the level of cohesion by itself. However, when combined there was an impact on the perceived level of cohesion. Overall, the analysis shows that multimodal cues are crucial for accurate analysis of group cohesion.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nedelchev-etal-2020-treating,
    title = "Treating Dialogue Quality Evaluation as an Anomaly Detection Problem",
    author = "Nedelchev, Rostislav  and
      Usbeck, Ricardo  and
      Lehmann, Jens",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.64",
    pages = "508--512",
    abstract = "Dialogue systems for interaction with humans have been enjoying increased popularity in the research and industry fields. To this day, the best way to estimate their success is through means of human evaluation and not automated approaches, despite the abundance of work done in the field. In this paper, we investigate the effectiveness of perceiving dialogue evaluation as an anomaly detection task. The paper looks into four dialogue modeling approaches and how their objective functions correlate with human annotation scores. A high-level perspective exhibits negative results. However, a more in-depth look shows some potential for using anomaly detection for evaluating dialogues.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rach-etal-2020-evaluation,
    title = "Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems",
    author = "Rach, Niklas  and
      Matsuda, Yuki  and
      Daxenberger, Johannes  and
      Ultes, Stefan  and
      Yasumoto, Keiichi  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.65",
    pages = "513--522",
    abstract = "We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments. To this end, we introduce a dialogue system that presents arguments by means of a virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories (Interesting, Convincing, Comprehensible, Relation). The approach is applied in a user study in order to compare two state of the art argument search engines to each other and with a system based on traditional web search. The results show a significant advantage of the two search engines over the baseline. Moreover, the two search engines show significant advantages over each other in different categories, thereby reflecting strengths and weaknesses of the different underlying techniques.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zarcone-etal-2020-pate,
    title = "{PATE}: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain",
    author = "Zarcone, Alessandra  and
      Alam, Touhidul  and
      Kolagar, Zahra",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.66",
    pages = "523--530",
    abstract = "The recognition and automatic annotation of temporal expressions (e.g. {``}Add an event for tomorrow evening at eight to my calendar{''}) is a key module for AI voice assistants, in order to allow them to interact with apps (for example, a calendar app). However, in the NLP literature, research on temporal expressions has focused mostly on data from the news, from the clinical domain, and from social media. The voice assistant domain is very different than the typical domains that have been the focus of work on temporal expression identification, thus requiring a dedicated data collection. We present a crowdsourcing method for eliciting natural-language commands containing temporal expressions for an AI voice assistant, by using pictures and scenario descriptions. We annotated the elicited commands (480) as well as the commands in the Snips dataset following the TimeML/TIMEX3 annotation guidelines, reaching a total of 1188 annotated commands. The commands can be later used to train the NLU components of an AI voice assistant.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ribeiro-etal-2020-mapping,
    title = "Mapping the Dialog Act Annotations of the {LEGO} Corpus into {ISO} 24617-2 Communicative Functions",
    author = "Ribeiro, Eug{\'e}nio  and
      Ribeiro, Ricardo  and
      Martins de Matos, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.67",
    pages = "531--539",
    abstract = "ISO 24617-2, the ISO standard for dialog act annotation, sets the ground for more comparable research in the area. However, the amount of data annotated according to it is still reduced, which impairs the development of approaches for automatic recognition. In this paper, we describe a mapping of the original dialog act labels of the LEGO corpus, which have been neglected, into the communicative functions of the standard. Although this does not lead to a complete annotation according to the standard, the 347 dialogs provide a relevant amount of data that can be used in the development of automatic communicative function recognition approaches, which may lead to a wider adoption of the standard. Using the 17 English dialogs of the DialogBank as gold standard, our preliminary experiments have shown that including the mapped dialogs during the training phase leads to improved performance while recognizing communicative functions in the Task dimension.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{miehle-etal-2020-estimating,
    title = "Estimating User Communication Styles for Spoken Dialogue Systems",
    author = "Miehle, Juliana  and
      Feustel, Isabel  and
      Hornauer, Julia  and
      Minker, Wolfgang  and
      Ultes, Stefan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.68",
    pages = "540--548",
    abstract = "We present a neural network approach to estimate the communication style of spoken interaction, namely the stylistic variations elaborateness and directness, and investigate which type of input features to the estimator are necessary to achive good performance. First, we describe our annotated corpus of recordings in the health care domain and analyse the corpus statistics in terms of agreement, correlation and reliability of the ratings. We use this corpus to estimate the elaborateness and the directness of each utterance. We test different feature sets consisting of dialogue act features, grammatical features and linguistic features as input for our classifier and perform classification in two and three classes. Our classifiers use only features that can be automatically derived during an ongoing interaction in any spoken dialogue system without any prior annotation. Our results show that the elaborateness can be classified by only using the dialogue act and the amount of words contained in the corresponding utterance. The directness is a more difficult classification task and additional linguistic features in form of word embeddings improve the classification results. Afterwards, we run a comparison with a support vector machine and a recurrent neural network classifier.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bunt-etal-2020-iso,
    title = "The {ISO} Standard for Dialogue Act Annotation, Second Edition",
    author = "Bunt, Harry  and
      Petukhova, Volha  and
      Gilmartin, Emer  and
      Pelachaud, Catherine  and
      Fang, Alex  and
      Keizer, Simon  and
      Pr{\'e}vot, Laurent",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.69",
    pages = "549--558",
    abstract = "ISO standard 24617-2 for dialogue act annotation, established in 2012, has in the past few years been used both in corpus annotation and in the design of components for spoken and multimodal dialogue systems. This has brought some inaccuracies and undesirbale limitations of the standard to light, which are addressed in a proposed second edition. This second edition allows a more accurate annotation of dependence relations and rhetorical relations in dialogue. Following the ISO 24617-4 principles of semantic annotation, and borrowing ideas from EmotionML, a triple-layered plug-in mechanism is introduced which allows dialogue act descriptions to be enriched with information about their semantic content, about accompanying emotions, and other information, and allows the annotation scheme to be customised by adding application-specific dialogue act types.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jokinen-2020-aico,
    title = "The {AICO} Multimodal Corpus {--} Data Collection and Preliminary Analyses",
    author = "Jokinen, Kristiina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.70",
    pages = "559--564",
    abstract = "This paper describes data collection and the first explorative research on the AICO Multimodal Corpus. The corpus contains eye-gaze, Kinect, and video recordings of human-robot and human-human interactions, and was collected to study cooperation, engagement and attention of human participants in task-based as well as in chatty type interactive situations. In particular, the goal was to enable comparison between human-human and human-robot interactions, besides studying multimodal behaviour and attention in the different dialogue activities. The robot partner was a humanoid Nao robot, and it was expected that its agent-like behaviour would render humanrobot interactions similar to human-human interaction but also high-light important differences due to the robot{'}s limited conversational capabilities. The paper reports on the preliminary studies on the corpus, concerning the participants{'} eye-gaze and gesturing behaviours,which were chosen as objective measures to study differences in their multimodal behaviour patterns with a human and a robot partner.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{galetzka-etal-2020-corpus,
    title = "A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models",
    author = "Galetzka, Fabian  and
      Eneh, Chukwuemeka Uchenna  and
      Schlangen, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.71",
    pages = "565--573",
    abstract = "Fully data driven Chatbots for non-goal oriented dialogues are known to suffer from inconsistent behaviour across their turns, stemming from a general difficulty in controlling parameters like their assumed background personality and knowledge of facts. One reason for this is the relative lack of labeled data from which personality consistency and fact usage could be learned together with dialogue behaviour. To address this, we introduce a new labeled dialogue dataset in the domain of movie discussions, where every dialogue is based on pre-specified facts and opinions. We thoroughly validate the collected dialogue for adherence of the participants to their given fact and opinion profile, and find that the general quality in this respect is high. This process also gives us an additional layer of annotation that is potentially useful for training models. We introduce as a baseline an end-to-end trained self-attention decoder model trained on this data and show that it is able to generate opinionated responses that are judged to be natural and knowledgeable and show attentiveness.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{laleye-etal-2020-french,
    title = "A {F}rench Medical Conversations Corpus Annotated for a Virtual Patient Dialogue System",
    author = {Laleye, Fr{\'e}jus A. A.  and
      de Chalendar, Ga{\"e}l  and
      Blani{\'e}, Antonia  and
      Brouquet, Antoine  and
      Behnamou, Dan},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.72",
    pages = "574--580",
    abstract = "Data-driven approaches for creating virtual patient dialogue systems require the availability of large data specific to the language,domain and clinical cases studied. Based on the lack of dialogue corpora in French for medical education, we propose an annotatedcorpus of dialogues including medical consultation interactions between doctor and patient. In this work, we detail the building processof the proposed dialogue corpus, describe the annotation guidelines and also present the statistics of its contents. We then conducted aquestion categorization task to evaluate the benefits of the proposed corpus that is made publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wu-etal-2020-getting,
    title = "Getting To Know You: User Attribute Extraction from Dialogues",
    author = "Wu, Chien-Sheng  and
      Madotto, Andrea  and
      Lin, Zhaojiang  and
      Xu, Peng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.73",
    pages = "581--589",
    abstract = "User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated. In this paper, we leverage dialogues with conversational agents, which contain strong suggestions of user information, to automatically extract user attributes. Since no existing dataset is available for this purpose, we apply distant supervision to train our proposed two-stage attribute extractor, which surpasses several retrieval and generation baselines on human evaluation. Meanwhile, we discuss potential applications (e.g., personalized recommendation and dialogue systems) of such extracted user attributes, and point out current limitations to cast light on future work.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kumar-etal-2020-augmenting,
    title = "Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization",
    author = "Kumar, Abhinav  and
      Di Eugenio, Barbara  and
      Aurisano, Jillian  and
      Johnson, Andrew",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.74",
    pages = "590--599",
    abstract = "Our goal is to develop an intelligent assistant to support users explore data via visualizations. We have collected a new corpus of conversations, CHICAGO-CRIME-VIS, geared towards supporting data visualization exploration, and we have annotated it for a variety of features, including contextualized dialogue acts. In this paper, we describe our strategies and their evaluation for dialogue act classification. We highlight how thinking aloud affects interpretation of dialogue acts in our setting and how to best capture that information. A key component of our strategy is data augmentation as applied to the training data, since our corpus is inherently small. We ran experiments with the Balanced Bagging Classifier (BAGC), Condiontal Random Field (CRF), and several Long Short Term Memory (LSTM) networks, and found that all of them improved compared to the baseline (e.g., without the data augmentation pipeline). CRF outperformed the other classification algorithms, with the LSTM networks showing modest improvement, even after obtaining a performance boost from domain-trained word embeddings. This result is of note because training a CRF is far less resource-intensive than training deep learning models, hence given a similar if not better performance, traditional methods may still be preferable in order to lower resource consumption.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{paetzel-etal-2020-rdg,
    title = "{RDG}-Map: A Multimodal Corpus of Pedagogical Human-Agent Spoken Interactions.",
    author = "Paetzel, Maike  and
      Karkada, Deepthi  and
      Manuvinakurike, Ramesh",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.75",
    pages = "600--609",
    abstract = "This paper presents a multimodal corpus of 209 spoken game dialogues between a human and a remote-controlled artificial agent. The interactions involve people collaborating with the agent to identify countries on the world map as quickly as possible, which allows studying rapid and spontaneous dialogue with complex anaphoras, disfluent utterances and incorrect descriptions. The corpus consists of two parts: 8 hours of game interactions have been collected with a virtual unembodied agent online and 26.8 hours have been recorded with a physically embodied robot in a research lab. In addition to spoken audio recordings available for both parts, camera recordings and skeleton-, facial expression- and eye-gaze tracking data have been collected for the lab-based part of the corpus. In this paper, we introduce the pedagogical reference resolution game (RDG-Map) and the characteristics of the corpus collected. We also present an annotation scheme we developed in order to study the dialogue strategies utilized by the players. Based on a subset of 330 minutes of interactions annotated so far, we discuss initial insights into these strategies as well as the potential of the corpus for future research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chen-etal-2020-mpdd,
    title = "{MPDD}: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships",
    author = "Chen, Yi-Ting  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.76",
    pages = "610--614",
    abstract = "A dialogue dataset is an indispensable resource for building a dialogue system. Additional information like emotions and interpersonal relationships labeled on conversations enables the system to capture the emotion flow of the participants in the dialogue. However, there is no publicly available Chinese dialogue dataset with emotion and relation labels. In this paper, we collect the conversions from TV series scripts, and annotate emotion and interpersonal relationship labels on each utterance. This dataset contains 25,548 utterances from 4,142 dialogues. We also set up some experiments to observe the effects of the responded utterance on the current utterance, and the correlation between emotion and relation types in emotion and relation classification tasks.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{siegert-2020-alexa,
    title = "{``}{A}lexa in the wild{''} {--} Collecting Unconstrained Conversations with a Modern Voice Assistant in a Public Environment",
    author = "Siegert, Ingo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.77",
    pages = "615--619",
    abstract = "Datasets featuring modern voice assistants such as Alexa, Siri, Cortana and others allow an easy study of human-machine interactions. But data collections offering an unconstrained, unscripted public interaction are quite rare. Many studies so far have focused on private usage, short pre-defined task or specific domains. This contribution presents a dataset providing a large amount of unconstrained public interactions with a voice assistant. Up to now around 40 hours of device directed utterances were collected during a science exhibition touring through Germany. The data recording was part of an exhibit that engages visitors to interact with a commercial voice assistant system (Amazon{'}s ALEXA), but did not restrict them to a specific topic. A specifically developed quiz was starting point of the conversation, as the voice assistant was presented to the visitors as a possible joker for the quiz. But the visitors were not forced to solve the quiz with the help of the voice assistant and thus many visitors had an open conversation. The provided dataset {--} Voice Assistant Conversations in the wild (VACW) {--} includes the transcripts of both visitors requests and Alexa answers, identified topics and sessions as well as acoustic characteristics automatically extractable from the visitors{'} audio files.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bothe-etal-2020-eda,
    title = "{EDA}: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators",
    author = "Bothe, Chandrakant  and
      Weber, Cornelius  and
      Magg, Sven  and
      Wermter, Stefan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.78",
    pages = "620--627",
    abstract = "The recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and dialogue acts reflect the intentions and performative functions in the utterances. However, most of the textual and multi-modal conversational emotion corpora contain only emotion labels but not dialogue acts. To address this problem, we propose to use a pool of various recurrent neural models trained on a dialogue act corpus, with and without context. These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label. We annotated two accessible multi-modal emotion corpora: IEMOCAP and MELD. We analyzed the co-occurrence of emotion and dialogue act labels and discovered specific relations. For example, Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy. We make the Emotional Dialogue Acts (EDA) corpus publicly available to the research community for further study and analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{amoyal-etal-2020-paco,
    title = "{PACO}: a Corpus to Analyze the Impact of Common Ground in Spontaneous Face-to-Face Interaction",
    author = "Amoyal, Mary  and
      Priego-Valverde, B{\'e}atrice  and
      Rauzy, Stephane",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.79",
    pages = "628--633",
    abstract = "PAC0 is a French audio-video conversational corpus made of 15 face-to-face dyadic interactions, lasting around 20 min each. This compared corpus has been created in order to explore the impact of the lack of personal common ground (Clark, 1996) on participants collaboration during conversation and specifically on their smile during topic transitions. We have constituted this conversational corpus '' PACO{''} by replicating the experimental protocol of {``}Cheese!{''} (Priego-valverde {\&} al.,2018). The only difference that distinguishes these two corpora is the degree of CG of the interlocutors: in Cheese! interlocutors are friends, while in PACO they do not know each other. This experimental protocol allows to analyze how the participants are getting acquainted. This study brings two main contributions. First, the PACO conversational corpus enables to compare the impact of the interlocutors{'} common ground. Second, the semi-automatic smile annotation protocol allows to obtain reliable and reproducible smile annotations while reducing the annotation time by a factor 10. Keywords : Common ground, spontaneous interaction, smile, automatic detection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{navarretta-paggio-2020-dialogue,
    title = "Dialogue Act Annotation in a Multimodal Corpus of First Encounter Dialogues",
    author = "Navarretta, Costanza  and
      Paggio, Patrizia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.80",
    pages = "634--643",
    abstract = "This paper deals with the annotation of dialogue acts in a multimodal corpus of first encounter dialogues, i.e. face-to- face dialogues in which two people who meet for the first time talk with no particular purpose other than just talking. More specifically, we describe the method used to annotate dialogue acts in the corpus, including the evaluation of the annotations. Then, we present descriptive statistics of the annotation, particularly focusing on which dialogue acts often follow each other across speakers and which dialogue acts overlap with gestural behaviour. Finally, we discuss how feedback is expressed in the corpus by means of feedback dialogue acts with or without co-occurring gestural behaviour, i.e. multimodal vs. unimodal feedback.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{enomoto-etal-2020-conversation,
    title = "A Conversation-Analytic Annotation of Turn-Taking Behavior in {J}apanese Multi-Party Conversation and its Preliminary Analysis",
    author = "Enomoto, Mika  and
      Den, Yasuharu  and
      Ishimoto, Yuichi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.81",
    pages = "644--652",
    abstract = "In this study, we propose a conversation-analytic annotation scheme for turn-taking behavior in multi-party conversations. The annotation scheme is motivated by a proposal of a proper model of turn-taking incorporating various ideas developed in the literature of conversation analysis. Our annotation consists of two sets of tags: the beginning and the ending type of the utterance. Focusing on the ending-type tags, in some cases combined with the beginning-type tags, we emphasize the importance of the distinction among four selection types: i) selecting other participant as next speaker, ii) not selecting next speaker but followed by a switch of the speakership, iii) not selecting next speaker and followed by a continuation of the speakership, and iv)being inside a multi-unit turn. Based on the annotation of Japanese multi-party conversations, we analyze how syntactic and prosodic features of utterances vary across the four selection types. The results show that the above four-way distinction is essential to account for the distributions of the syntactic and prosodic features, suggesting the insufficiency of previous turn-taking models that do not consider the distinction between i) and ii) or between ii) or iii).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{asao-etal-2020-understanding,
    title = "Understanding User Utterances in a Dialog System for Caregiving",
    author = "Asao, Yoshihiko  and
      Kloetzer, Julien  and
      Mizuno, Junta  and
      Saiki, Dai  and
      Kadowaki, Kazuma  and
      Torisawa, Kentaro",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.82",
    pages = "653--661",
    abstract = "A dialog system that can monitor the health status of seniors has a huge potential for solving the labor force shortage in the caregiving industry in aging societies. As a part of efforts to create such a system, we are developing two modules that are aimed to correctly interpret user utterances: (i) a yes/no response classifier, which categorizes responses to health-related yes/no questions that the system asks; and (ii) an entailment recognizer, which detects users{'} voluntary mentions about their health status. To apply machine learning approaches to the development of the modules, we created large annotated datasets of 280,467 question-response pairs and 38,868 voluntary utterances. For question-response pairs, we asked annotators to avoid direct {``}yes{''} or {``}no{''} answers, so that our data could cover a wide range of possible natural language responses. The two modules were implemented by fine-tuning a BERT model, which is a recent successful neural network model. For the yes/no response classifier, the macro-average of the average precisions (APs) over all of our four categories (Yes/No/Unknown/Other) was 82.6{\%} (96.3{\%} for {``}yes{''} responses and 91.8{\%} for {``}no{''} responses), while for the entailment recognizer it was 89.9{\%}.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lin-etal-2020-designing,
    title = "Designing Multilingual Interactive Agents using Small Dialogue Corpora",
    author = "Lin, Donghui  and
      Otani, Masayuki  and
      Okuno, Ryosuke  and
      Ishida, Toru",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.83",
    pages = "662--667",
    abstract = "Interactive dialogue agents like smart speakers have become more and more popular in recent years. These agents are being developed on machine learning technologies that use huge amounts of language resources. However, many entities in specialized fields are struggling to develop their own interactive agents due to a lack of language resources such as dialogue corpora, especially when the end users need interactive agents that offer multilingual support. Therefore, we aim at providing a general design framework for multilingual interactive agents in specialized domains that, it is assumed, have small or non-existent dialogue corpora. To achieve our goal, we first integrate and customize external language services for supporting multilingual functions of interactive agents. Then, we realize context-aware dialogue generation under the situation of small corpora. Third, we develop a gradual design process for acquiring dialogue corpora and improving the interactive agents. We implement a multilingual interactive agent in the field of healthcare and conduct experiments to illustrate the effectiveness of the implemented agent.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rauchbauer-etal-2020-multimodal,
    title = "Multimodal Corpus of Bidirectional Conversation of Human-human and Human-robot Interaction during f{MRI} Scanning",
    author = "Rauchbauer, Birgit  and
      Hmamouche, Youssef  and
      Bigi, Brigitte  and
      Pr{\'e}vot, Laurent  and
      Ochs, Magalie  and
      Chaminade, Thierry",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.84",
    pages = "668--675",
    abstract = "In this paper we present investigation of real-life, bi-directional conversations. We introduce the multimodal corpus derived from these natural conversations alternating between human-human and human-robot interactions. The human-robot interactions were used as a control condition for the social nature of the human-human conversations. The experimental set up consisted of conversations between the participant in a functional magnetic resonance imaging (fMRI) scanner and a human confederate or conversational robot outside the scanner room, connected via bidirectional audio and unidirectional videoconferencing (from the outside to inside the scanner). A cover story provided a framework for natural, real-life conversations about images of an advertisement campaign. During the conversations we collected a multimodal corpus for a comprehensive characterization of bi-directional conversations. In this paper we introduce this multimodal corpus which includes neural data from functional magnetic resonance imaging (fMRI), physiological data (blood flow pulse and respiration), transcribed conversational data, as well as face and eye-tracking recordings. Thus, we present a unique corpus to study human conversations including neural, physiological and behavioral data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ochs-etal-2020-brain,
    title = "The Brain-{IHM} Dataset: a New Resource for Studying the Brain Basis of Human-Human and Human-Machine Conversations",
    author = "Ochs, Magalie  and
      Bertrand, Roxane  and
      Goujon, Aur{\'e}lie  and
      Bolger, Deirdre  and
      Dubarry, Anne-Sophie  and
      Blache, Philippe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.85",
    pages = "676--683",
    abstract = "This paper presents an original dataset of controlled interactions, focusing on the study of feedback items. It consists on recordings of different conversations between a doctor and a patient, played by actors. In this corpus, the patient is mainly a listener and produces different feedbacks, some of them being (voluntary) incongruent. Moreover, these conversations have been re-synthesized in a virtual reality context, in which the patient is played by an artificial agent. The final corpus is made of different movies of human-human conversations plus the same conversations replayed in a human-machine context, resulting in the first human-human/human-machine parallel corpus. The corpus is then enriched with different multimodal annotations at the verbal and non-verbal levels. Moreover, and this is the first dataset of this type, we have designed an experiment during which different participants had to watch the movies and give an evaluation of the interaction. During this task, we recorded participant{'}s brain signal. The Brain-IHM dataset is then conceived with a triple purpose: 1/ studying feedbacks by comparing congruent vs. incongruent feedbacks 2/ comparing human-human and human-machine production of feedbacks 3/ studying the brain basis of feedback perception.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bonial-etal-2020-dialogue,
    title = "Dialogue-{AMR}: {A}bstract {M}eaning {R}epresentation for Dialogue",
    author = "Bonial, Claire  and
      Donatelli, Lucia  and
      Abrams, Mitchell  and
      Lukin, Stephanie M.  and
      Tratz, Stephen  and
      Marge, Matthew  and
      Artstein, Ron  and
      Traum, David  and
      Voss, Clare",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.86",
    pages = "684--695",
    abstract = "This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker{'}s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present {``}Dialogue-AMR{''}, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the {``}DialAMR{''} corpus{---}a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ito-etal-2020-relation,
    title = "Relation between Degree of Empathy for Narrative Speech and Type of Responsive Utterance in Attentive Listening",
    author = "Ito, Koichiro  and
      Murata, Masaki  and
      Ohno, Tomohiro  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.87",
    pages = "696--701",
    abstract = "Nowadays, spoken dialogue agents such as communication robots and smart speakers listen to narratives of humans. In order for such an agent to be recognized as a listener of narratives and convey the attitude of attentive listening, it is necessary to generate responsive utterances. Moreover, responsive utterances can express empathy to narratives and showing an appropriate degree of empathy to narratives is significant for enhancing speaker{'}s motivation. The degree of empathy shown by responsive utterances is thought to depend on their type. However, the relation between responsive utterances and degrees of the empathy has not been explored yet. This paper describes the classification of responsive utterances based on the degree of empathy in order to explain that relation. In this research, responsive utterances are classified into five levels based on the effect of utterances and literature on attentive listening. Quantitative evaluations using 37,995 responsive utterances showed the appropriateness of the proposed classification.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rojowiec-etal-2020-intent,
    title = "Intent Recognition in Doctor-Patient Interviews",
    author = "Rojowiec, Robin  and
      Roth, Benjamin  and
      Fink, Maximilian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.88",
    pages = "702--709",
    abstract = "Learning to interview patients to find out their disease is an essential part of the training of medical students. The practical part of this training has traditionally relied on paid actors that play the role of a patient to be interviewed. This process is expensive and severely limits the amount of practice per student. In this work, we present a novel data set and methods based on Natural Language Processing, for making progress towards modern applications and e-learning tools that support this training by providing language-based user interfaces with virtual patients. A data set of german transcriptions from live doctor-patient interviews was collected. These transcriptions are based on audio recordings of exercise sessions within the university and only the doctor{'}s utterances could be transcribed. We annotated each utterance with an intent inventory characterizing the purpose of the question or statement. For some intent classes, the data only contains a few samples, and we apply Information Retrieval and Deep Learning methods that are robust with respect to small amounts of training data for recognizing the intent of an utterance and providing the correct response. Our results show that the models are effective and they provide baseline performance scores on the data set for further research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hmamouche-etal-2020-brainpredict,
    title = "{B}rain{P}redict: a Tool for Predicting and Visualising Local Brain Activity",
    author = "Hmamouche, Youssef  and
      Pr{\'e}vot, Laurent  and
      Ochs, Magalie  and
      Chaminade, Thierry",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.89",
    pages = "710--716",
    abstract = "In this paper, we present a tool allowing dynamic prediction and visualization of an individual{'}s local brain activity during a conversation. The prediction module of this tool is based on classifiers trained using a corpus of human-human and human-robot conversations including fMRI recordings. More precisely, the module takes as input behavioral features computed from raw data, mainly the participant and the interlocutor speech but also the participant{'}s visual input and eye movements. The visualisation module shows in real-time the dynamics of brain active areas synchronised with the behavioral raw data. In addition, it shows which integrated behavioral features are used to predict the activity in individual brain areas.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{senese-etal-2020-mtsi,
    title = "{MTSI}-{BERT}: A Session-aware Knowledge-based Conversational Agent",
    author = "Senese, Matteo Antonio  and
      Rizzo, Giuseppe  and
      Dragoni, Mauro  and
      Morisio, Maurizio",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.90",
    pages = "717--725",
    abstract = "In the last years, the state of the art of NLP research has made a huge step forward. Since the release of ELMo (Peters et al., 2018), a new race for the leading scoreboards of all the main linguistic tasks has begun. Several models have been published achieving promising results in all the major NLP applications, from question answering to text classification, passing through named entity recognition. These great research discoveries coincide with an increasing trend for voice-based technologies in the customer care market. One of the next biggest challenges in this scenario will be the handling of multi-turn conversations, a type of conversations that differs from single-turn by the presence of multiple related interactions. The proposed work is an attempt to exploit one of these new milestones to handle multi-turn conversations. MTSI-BERT is a BERT-based model achieving promising results in intent classification, knowledge base action prediction and end of dialogue session detection, to determine the right moment to fulfill the user request. The study about the realization of PuffBot, an intelligent chatbot to support and monitor people suffering from asthma, shows how this type of technique could be an important piece in the development of future chatbots.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{georgila-etal-2020-predicting,
    title = "Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers",
    author = "Georgila, Kallirroi  and
      Gordon, Carla  and
      Yanov, Volodymyr  and
      Traum, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.91",
    pages = "726--734",
    abstract = "We collected a corpus of dialogues in a Wizard of Oz (WOz) setting in the Internet of Things (IoT) domain. We asked users participating in these dialogues to rate the system on a number of aspects, namely, intelligence, naturalness, personality, friendliness, their enjoyment, overall quality, and whether they would recommend the system to others. Then we asked dialogue observers, i.e., Amazon Mechanical Turkers (MTurkers), to rate these dialogues on the same aspects. We also generated simulated dialogues between dialogue policies and simulated users and asked MTurkers to rate them again on the same aspects. Using linear regression, we developed dialogue evaluation functions based on features from the simulated dialogues and the MTurkers{'} ratings, the WOz dialogues and the MTurkers{'} ratings, and the WOz dialogues and the WOz participants{'} ratings. We applied all these dialogue evaluation functions to a held-out portion of our WOz dialogues, and we report results on the predictive power of these different types of dialogue evaluation functions. Our results suggest that for three conversational aspects (intelligence, naturalness, overall quality) just training evaluation functions on simulated data could be sufficient.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alavi-etal-2020-model,
    title = "Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?",
    author = "Alavi, Seyed Hossein  and
      Leuski, Anton  and
      Traum, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.92",
    pages = "735--742",
    abstract = "We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kontogiorgos-etal-2020-chinese,
    title = "{C}hinese Whispers: A Multimodal Dataset for Embodied Language Grounding",
    author = "Kontogiorgos, Dimosthenis  and
      Sibirtseva, Elena  and
      Gustafson, Joakim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.93",
    pages = "743--749",
    abstract = "In this paper, we introduce a multimodal dataset in which subjects are instructing each other how to assemble IKEA furniture. Using the concept of {`}Chinese Whispers{'}, an old children{'}s game, we employ a novel method to avoid implicit experimenter biases. We let subjects instruct each other on the nature of the task: the process of the furniture assembly. Uncertainty, hesitations, repairs and self-corrections are naturally introduced in the incremental process of establishing common ground. The corpus consists of 34 interactions, where each subject first assembles and then instructs. We collected speech, eye-gaze, pointing gestures, and object movements, as well as subjective interpretations of mutual understanding, collaboration and task recall. The corpus is of particular interest to researchers who are interested in multimodal signals in situated dialogue, especially in referential communication and the process of language grounding.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kumar-etal-2020-amused,
    title = "{AMUSED}: A Multi-Stream Vector Representation Method for Use in Natural Dialogue",
    author = "Kumar, Gaurav  and
      Joshi, Rishabh  and
      Singh, Jaspreet  and
      Yenigalla, Promod",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.94",
    pages = "750--758",
    abstract = "The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. To overcome this problem, we propose an end to end multi-stream deep learning architecture that learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse. A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighborhood of the entities from a Knowledge Base (KB). We benchmark these embeddings on the next sentence prediction task and significantly improve upon the existing techniques. Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{somashekarappa-etal-2020-annotation,
    title = "An Annotation Approach for Social and Referential Gaze in Dialogue",
    author = "Somashekarappa, Vidya  and
      Howes, Christine  and
      Sayeed, Asad",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.95",
    pages = "759--765",
    abstract = "This paper introduces an approach for annotating eye gaze considering both its social and the referential functions in multi-modal human-human dialogue. Detecting and interpreting the temporal patterns of gaze behavior cues is natural for humans and also mostly an unconscious process. However, these cues are difficult for conversational agents such as robots or avatars to process or generate. The key factor is to recognize these variants and carry out a successful conversation, as misinterpretation can lead to total failure of the given interaction. This paper introduces an annotation scheme for eye-gaze in human-human dyadic interactions that is intended to facilitate the learning of eye-gaze patterns in multi-modal natural dialogue.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{booth-etal-2020-penn,
    title = "A {P}enn-style Treebank of {M}iddle {L}ow {G}erman",
    author = "Booth, Hannah  and
      Breitbarth, Anne  and
      Ecay, Aaron  and
      Farasyn, Melissa",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.96",
    pages = "766--775",
    abstract = "We outline the issues and decisions involved in creating a Penn-style treebank of Middle Low German (MLG, 1200-1650), which will form part of the Corpus of Historical Low German (CHLG). The attestation for MLG is rich, but the syntax of the language remains relatively understudied. The development of a syntactically annotated corpus for the language will facilitate future studies with a strong empirical basis, building on recent work which indicates that, syntactically, MLG occupies a position in its own right within West Germanic. In this paper, we describe the background for the corpus and the process by which texts were selected to be included. In particular, we focus on the decisions involved in the syntactic annotation of the corpus, specifically, the practical and linguistic reasons for adopting the Penn annotation scheme, the stages of the annotation process itself, and how we have adapted the Penn scheme for syntactic features specific to MLG. We also discuss the issue of data uncertainty, which is a major issue when building a corpus of an under-researched language stage like MLG, and some novel ways in which we capture this uncertainty in the annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hazem-etal-2020-books,
    title = "Books of Hours. the First Liturgical Data Set for Text Segmentation.",
    author = "Hazem, Amir  and
      Daille, Beatrice  and
      Kermorvant, Christopher  and
      Stutzmann, Dominique  and
      Bonhomme, Marie-Laurence  and
      Maarand, Martin  and
      Boillet, M{\'e}lodie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.97",
    pages = "776--784",
    abstract = "The Book of Hours was the bestseller of the late Middle Ages and Renaissance. It is a historical invaluable treasure, documenting the devotional practices of Christians in the late Middle Ages. Up to now, its textual content has been scarcely studied because of its manuscript nature, its length and its complex content. At first glance, it looks too standardized. However, the study of book of hours raises important challenges: (i) in image analysis, its often lavish ornamentation (illegible painted initials, line-fillers, etc.), abbreviated words, multilingualism are difficult to address in Handwritten Text Recognition (HTR); (ii) its hierarchical entangled structure offers a new field of investigation for text segmentation; (iii) in digital humanities, its textual content gives opportunities for historical analysis. In this paper, we provide the first corpus of books of hours, which consists of Latin transcriptions of 300 books of hours generated by Handwritten Text Recognition (HTR) - that is like Optical Character Recognition (OCR) but for handwritten and not printed texts. We designed a structural scheme of the book of hours and annotated manually two books of hours according to this scheme. Lastly, we performed a systematic evaluation of the main state of the art text segmentation approaches.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zinin-xu-2020-corpus,
    title = "Corpus of {C}hinese Dynastic Histories: Gender Analysis over Two Millennia",
    author = "Zinin, Sergey  and
      Xu, Yang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.98",
    pages = "785--793",
    abstract = "Chinese dynastic histories form a large continuous linguistic space of approximately 2000 years, from the 3rd century BCE to the 18th century CE. The histories are documented in Classical (Literary) Chinese in a corpus of over 20 million characters, suitable for the computational analysis of historical lexicon and semantic change. However, there is no freely available open-source corpus of these histories, making Classical Chinese low-resource. This project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license. An original list of Classical Chinese gender-specific terms was developed as a case study for analyzing the historical linguistic use of male and female terms. The study demonstrates considerable stability in the usage of these terms, with dominance of male terms. Exploration of word meanings uses keyword analysis of focus corpora created for gender-specific terms. This method yields meaningful semantic representations that can be used for future studies of diachronic semantics.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fischer-etal-2020-royal,
    title = "The Royal Society Corpus 6.0: Providing 300+ Years of Scientific Writing for Humanistic Study",
    author = {Fischer, Stefan  and
      Knappen, J{\"o}rg  and
      Menzel, Katrin  and
      Teich, Elke},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.99",
    pages = "794--802",
    abstract = "We present a new, extended version of the Royal Society Corpus (RSC), a diachronic corpus of scientific English now covering 300+ years of scientific writing (1665--1996). The corpus comprises 47 837 texts, primarily scientific articles, and is based on publications of the Royal Society of London, mainly its Philosophical Transactions and Proceedings. The corpus has been built on the basis of the FAIR principles and is freely available under a Creative Commons license, excluding copy-righted parts. We provide information on how the corpus can be found, the file formats available for download as well as accessibility via a web-based corpus query platform. We show a number of analytic tools that we have implemented for better usability and provide an example of use of the corpus for linguistic analysis as well as examples of subsequent, external uses of earlier releases. We place the RSC against the background of existing English diachronic/scientific corpora, elaborating on its value for linguistic and humanistic study.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{brunner-etal-2020-corpus,
    title = "Corpus {REDEWIEDERGABE}",
    author = "Brunner, Annelen  and
      Engelberg, Stefan  and
      Jannidis, Fotis  and
      Tu, Ngoc Duyen Tanja  and
      Weimer, Lukas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.100",
    pages = "803--812",
    abstract = "This article presents corpus REDEWIEDERGABE, a German-language historical corpus with detailed annotations for speech, thought and writing representation (ST{\&}WR). With approximately 490,000 tokens, it is the largest resource of its kind. It can be used to answer literary and linguistic research questions and serve as training material for machine learning. This paper describes the composition of the corpus and the annotation structure, discusses some methodological decisions and gives basic statistics about the forms of ST{\&}WR found in this corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{egloff-picca-2020-wedh,
    title = "{W}e{DH} - a Friendly Tool for Building Literary Corpora Enriched with Encyclopedic Metadata",
    author = "Egloff, Mattia  and
      Picca, Davide",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.101",
    pages = "813--816",
    abstract = "In recent years the interest in the use of repositories of literary works has been successful. While many efforts related to Linked Open Data go in the right direction, the use of these repositories for the creation of text corpora enriched with metadata remains difficult and cumbersome. In fact, many of these repositories can be useful to the community not only for the automatic creation of textual corpora but also for retrieving crucial meta-information about texts. In particular, the use of metadata provides the reader with a wealth of information that is often not identifiable in the texts themselves. Our project aims to fill both the access to the textual resources available on the web and the possibility of combining these resources with sources of metadata that can enrich the texts with useful information lengthening the life and maintenance of the data itself. We introduce here a user-friendly web interface of the Digital Humanities toolkit named WeDH with which the user can leverage the encyclopedic knowledge provided by DBpedia, wikidata and VIAF in order to enrich the corpora with bibliographical and exegetical knowledge. WeDH is a collaborative project and we invite anyone who has ideas or suggestions regarding this procedure to reach out to us.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sabbatino-etal-2020-automatic,
    title = "Automatic Section Recognition in Obituaries",
    author = "Sabbatino, Valentino  and
      Bostan, Laura Ana Maria  and
      Klinger, Roman",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.102",
    pages = "817--825",
    abstract = "Obituaries contain information about people{'}s values across times and cultures, which makes them a useful resource for exploring cultural history. They are typically structured similarly, with sections corresponding to Personal Information, Biographical Sketch, Characteristics, Family, Gratitude, Tribute, Funeral Information and Other aspects of the person. To make this information available for further studies, we propose a statistical model which recognizes these sections. To achieve that, we collect a corpus of 20058 English obituaries from TheDaily Item, Remembering.CA and The London Free Press. The evaluation of our annotation guidelines with three annotators on 1008 obituaries shows a substantial agreement of Fleiss κ = 0.87. Formulated as an automatic segmentation task, a convolutional neural network outperforms bag-of-words and embedding-based BiLSTMs and BiLSTM-CRFs with a micro F1 = 0.81.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{stymne-ostman-2020-slanda,
    title = {{SL}{\"a}{ND}a: An Annotated Corpus of Narrative and Dialogue in {S}wedish Literary Fiction},
    author = {Stymne, Sara  and
      {\"O}stman, Carin},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.103",
    pages = "826--834",
    abstract = {We describe a new corpus, SL{\"a}NDa, the Swedish Literary corpus of Narrative and Dialogue. It contains Swedish literary fiction, which has been manually annotated for cited materials, with a focus on dialogue. The annotation covers excerpts from eight Swedish novels written between 1879{--}1940, a period of modernization of the Swedish language. SL{\"a}NDa contains annotations for all cited materials that are separate from the main narrative, like quotations and signs. The main focus is on dialogue, for which we annotate speech segments, speech tags, and speakers. In this paper we describe the annotation protocol and procedure and show that we can reach a high inter-annotator agreement. In total, SL{\"a}NDa contains annotations of 44 chapters with over 220K tokens. The annotation identified 4,733 instances of cited material and 1,143 named speaker{--}speech mappings. The corpus is useful for developing computational tools for different types of analysis of literary narrative and speech. We perform a small pilot study where we show how our annotation can help in analyzing language change in Swedish. We find that a number of common function words have their modern version appear earlier in speech than in narrative.},
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{papay-pado-2020-riqua,
    title = "{R}i{Q}u{A}: A Corpus of Rich Quotation Annotation for {E}nglish Literary Text",
    author = "Papay, Sean  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.104",
    pages = "835--841",
    abstract = "We introduce RiQuA (RIch QUotation Annotations), a corpus that provides quotations, including their interpersonal structure (speakers and addressees) for English literary text. The corpus comprises 11 works of 19th-century literature that were manually doubly annotated for direct and indirect quotations. For each quotation, its span, speaker, addressee, and cue are identified (if present). This provides a rich view of dialogue structures not available from other available corpora. We detail the process of creating this dataset, discuss the annotation guidelines, and analyze the resulting corpus in terms of inter-annotator agreement and its properties. RiQuA, along with its annotations guidelines and associated scripts, are publicly available for use, modification, and experimentation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schneider-2020-corpus,
    title = "A Corpus Linguistic Perspective on Contemporary {G}erman Pop Lyrics with the Multi-Layer Annotated {``}Songkorpus{''}",
    author = "Schneider, Roman",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.105",
    pages = "842--848",
    abstract = "Song lyrics can be considered as a text genre that has features of both written and spoken discourse, and potentially provides extensive linguistic and cultural information to scientists from various disciplines. However, pop songs play a rather subordinate role in empirical language research so far - most likely due to the absence of scientifically valid and sustainable resources. The present paper introduces a multiply annotated corpus of German lyrics as a publicly available basis for multidisciplinary research. The resource contains three types of data for the investigation and evaluation of quite distinct phenomena: TEI-compliant song lyrics as primary data, linguistically and literary motivated annotations, and extralinguistic metadata. It promotes empirically/statistically grounded analyses of genre-specific features, systemic-structural correlations and tendencies in the texts of contemporary pop music. The corpus has been stratified into thematic and author-specific archives; the paper presents some basic descriptive statistics, as well as the public online frontend with its built-in evaluation forms and live visualisations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{grilo-etal-2020-bdcamoes,
    title = "The {BDC}am{\~o}es Collection of {P}ortuguese Literary Documents: a Research Resource for Digital Humanities and Language Technology",
    author = "Grilo, Sara  and
      Bolrinha, M{\'a}rcia  and
      Silva, Jo{\~a}o  and
      Vaz, Rui  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.106",
    pages = "849--854",
    abstract = "This paper presents the BDCam{\~o}es Collection of Portuguese Literary Documents, a new corpus of literary texts written in Portuguese that in its inaugural version includes close to 4 million words from over 200 complete documents from 83 authors in 14 genres, covering a time span from the 16th to the 21st century, and adhering to different orthographic conventions. Many of the texts in the corpus have also been automatically parsed with state-of-the-art language processing tools, forming the BDCam{\~o}es Treebank subcorpus. This set of characteristics makes of BDCam{\~o}es an invaluable resource for research in language technology (e.g. authorship detection, genre classification, etc.) and in language science and digital humanities (e.g. comparative literature, diachronic linguistics, etc.).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{frossard-etal-2020-dataset,
    title = "Dataset for Temporal Analysis of {E}nglish-{F}rench Cognates",
    author = "Frossard, Esteban  and
      Coustaty, Mickael  and
      Doucet, Antoine  and
      Jatowt, Adam  and
      Hengchen, Simon",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.107",
    pages = "855--859",
    abstract = "Languages change over time and, thanks to the abundance of digital corpora, their evolutionary analysis using computational techniques has recently gained much research attention. In this paper, we focus on creating a dataset to support investigating the similarity in evolution between different languages. We look in particular into the similarities and differences between the use of corresponding words across time in English and French, two languages from different linguistic families yet with shared syntax and close contact. For this we select a set of cognates in both languages and study their frequency changes and correlations over time. We propose a new dataset for computational approaches of synchronized diachronic investigation of language pairs, and subsequently show novel findings stemming from the cognate-focused diachronic comparison of the two chosen languages. To the best of our knowledge, the present study is the first in the literature to use computational approaches and large data to make a cross-language diachronic analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{waldispuhl-etal-2020-material,
    title = "Material Philology Meets Digital Onomastic Lexicography: The {N}ordi{C}on Database of Medieval Nordic Personal Names in Continental Sources",
    author = {Waldisp{\"u}hl, Michelle  and
      Dannells, Dana  and
      Borin, Lars},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.108",
    pages = "860--867",
    abstract = "We present NordiCon, a database containing medieval Nordic personal names attested in Continental sources. The database combines formally interpreted and richly interlinked onomastic data with digitized versions of the medieval manuscripts from which the data originate and information on the tokens{'} context. The structure of NordiCon is inspired by other online historical given name dictionaries. It takes up challenges reported on in previous works, such as how to cover material properties of a name token and how to define lemmatization principles, and elaborates on possible solutions. The lemmatization principles for NordiCon are further developed in order to facilitate the connection to other name dictionaries and corpuses, and the integration of the database into Spr{\aa}kbanken Text, an infrastructure containing modern and historical written data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mohammad-2020-nlp,
    title = "{NLP} Scholar: A Dataset for Examining the State of {NLP} Research",
    author = "Mohammad, Saif M.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.109",
    pages = "868--877",
    abstract = "Google Scholar is the largest web search engine for academic literature that also provides access to rich metadata associated with the papers. The ACL Anthology (AA) is the largest repository of articles on Natural Language Processing (NLP). We extracted information from AA for about 44 thousand NLP papers and identified authors who published at least three papers there. We then extracted citation information from Google Scholar for all their papers (not just their AA papers). This resulted in a dataset of 1.1 million papers and associated Google Scholar information. We aligned the information in the AA and Google Scholar datasets to create the NLP Scholar Dataset {--} a single unified source of information (from both AA and Google Scholar) for tens of thousands of NLP papers. It can be used to identify broad trends in productivity, focus, and impact of NLP research. We present here initial work on analyzing the volume of research in NLP over the years and identifying the most cited papers in NLP. We also list a number of additional potential applications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{virk-etal-2020-dream,
    title = "The {DR}ea{M} Corpus: A Multilingual Annotated Corpus of Grammars for the World{'}s Languages",
    author = {Virk, Shafqat Mumtaz  and
      Hammarstr{\"o}m, Harald  and
      Forsberg, Markus  and
      Wichmann, S{\o}ren},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.110",
    pages = "878--884",
    abstract = "There exist as many as 7000 natural languages in the world, and a huge number of documents describing those languages have been produced over the years. Most of those documents are in paper format. Any attempts to use modern computational techniques and tools to process those documents will require them to be digitized first. In this paper, we report a multilingual digitized version of thousands of such documents searchable through some well-established corpus infrastructures. The corpus is annotated with various meta, word, and text level attributes to make searching and analysis easier and more useful.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{muller-etal-2020-livito,
    title = "{L}i{V}i{T}o: Linguistic and Visual Features Tool for Assisted Analysis of Historic Manuscripts",
    author = {M{\"u}ller, Klaus  and
      Tikhonov, Aleksej  and
      Meyer, Roland},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.111",
    pages = "885--890",
    abstract = "We propose a mixed methods approach to the identification of scribes and authors in handwritten documents, and present LiViTo, a software tool which combines linguistic insights and computer vision techniques in order to assist researchers in the analysis of handwritten historical documents. Our research shows that it is feasible to train neural networks for the automatic transcription of handwritten documents and to use these transcriptions as input for further learning processes. Hypotheses about scribes can be tested effectively by extracting visual handwriting features and clustering them appropriately. Methods from linguistics and from computer vision research integrate into a mixed methods system, with benefits on both sides. LiViTo was trained with historical Czech texts by 18th century immigrants to Berlin, a total of 564 pages from a corpus of about 5000 handwritten pages without indication of author or scribe. We provide an overview of the three-year development of LiViTo and an introduction into its methodology and its functions. We then present our findings concerning the corpus of Berlin Czech manuscripts and discuss possible further usage scenarios.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abrami-etal-2020-textannotator,
    title = "{T}ext{A}nnotator: A {UIMA} Based Tool for the Simultaneous and Collaborative Annotation of Texts",
    author = "Abrami, Giuseppe  and
      Stoeckel, Manuel  and
      Mehler, Alexander",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.112",
    pages = "891--900",
    abstract = "The annotation of texts and other material in the field of digital humanities and Natural Language Processing (NLP) is a common task of research projects. At the same time, the annotation of corpora is certainly the most time- and cost-intensive component in research projects and often requires a high level of expertise according to the research interest. However, for the annotation of texts, a wide range of tools is available, both for automatic and manual annotation. Since the automatic pre-processing methods are not error-free and there is an increasing demand for the generation of training data, also with regard to machine learning, suitable annotation tools are required. This paper defines criteria of flexibility and efficiency of complex annotations for the assessment of existing annotation tools. To extend this list of tools, the paper describes TextAnnotator, a browser-based, multi-annotation system, which has been developed to perform platform-independent multimodal annotations and annotate complex textual structures. The paper illustrates the current state of development of TextAnnotator and demonstrates its ability to evaluate annotation quality (inter-annotator agreement) at runtime. In addition, it will be shown how annotations of different users can be performed simultaneously and collaboratively on the same document from different platforms using UIMA as the basis for annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gyawali-etal-2020-deduplication,
    title = "Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings",
    author = "Gyawali, Bikash  and
      Anastasiou, Lucas  and
      Knoth, Petr",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.113",
    pages = "901--910",
    abstract = "Deduplication is the task of identifying near and exact duplicate data items in a collection. In this paper, we present a novel method for deduplication of scholarly documents. We develop a hybrid model which uses structural similarity (locality sensitive hashing) and meaning representation (word embeddings) of document texts to determine (near) duplicates. Our collection constitutes a subset of multidisciplinary scholarly documents aggregated from research repositories. We identify several issues causing data inaccuracies in such collections and motivate the need for deduplication. In lack of existing dataset suitable for study of deduplication of scholarly documents, we create a ground truth dataset of $100K$ scholarly documents and conduct a series of experiments to empirically establish optimal values for the parameters of our deduplication method. Experimental evaluation shows that our method achieves a macro F1-score of 0.90. We productionise our method as a publicly accessible web API service serving deduplication of scholarly documents in real time.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{boschetti-etal-2020-voices,
    title = "{``}Voices of the Great War{''}: A Richly Annotated Corpus of {I}talian Texts on the First World War",
    author = "Boschetti, Federico  and
      De Felice, Irene  and
      Dei Rossi, Stefano  and
      Dell{'}Orletta, Felice  and
      Di Giorgio, Michele  and
      Miliani, Martina  and
      Passaro, Lucia C.  and
      Puddu, Angelica  and
      Venturi, Giulia  and
      Labanca, Nicola  and
      Lenci, Alessandro  and
      Montemagni, Simonetta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.114",
    pages = "911--918",
    abstract = "{``}Voices of the Great War{''} is the first large corpus of Italian historical texts dating back to the period of First World War. This corpus differs from other existing resources in several respects. First, from the linguistic point of view it gives account of the wide range of varieties in which Italian was articulated in that period, namely from a diastratic (educated vs. uneducated writers), diaphasic (low/informal vs. high/formal registers) and diatopic (regional varieties, dialects) points of view. From the historical perspective, through a collection of texts belonging to different genres it represents different views on the war and the various styles of narrating war events and experiences. The final corpus is balanced along various dimensions, corresponding to the textual genre, the language variety used, the author type and the typology of conveyed contents. The corpus is fully annotated with lemmas, part-of-speech, terminology, and named entities. Significant corpus samples representative of the different {``}voices{''} have also been enriched with meta-linguistic and syntactic information. The layer of syntactic annotation forms the first nucleus of an Italian historical treebank complying with the Universal Dependencies standard. The paper illustrates the final resource, the methodology and tools used to build it, and the Web Interface for navigating it.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lapesa-etal-2020-debatenet,
    title = "{DE}bate{N}et-mig15:Tracing the 2015 Immigration Debate in {G}ermany Over Time",
    author = "Lapesa, Gabriella  and
      Blessing, Andre  and
      Blokker, Nico  and
      Dayanik, Erenay  and
      Haunss, Sebastian  and
      Kuhn, Jonas  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.115",
    pages = "919--927",
    abstract = "DEbateNet-migr15 is a manually annotated dataset for German which covers the public debate on immigration in 2015. The building block of our annotation is the political science notion of a claim, i.e., a statement made by a political actor (a politician, a party, or a group of citizens) that a specific action should be taken (e.g., vacant flats should be assigned to refugees). We identify claims in newspaper articles, assign them to actors and fine-grained categories and annotate their polarity and date. The aim of this paper is two-fold: first, we release the full DEbateNet-mig15 corpus and document it by means of a quantitative and qualitative analysis; second, we demonstrate its application in a discourse network analysis framework, which enables us to capture the temporal dynamics of the political debate",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alvarez-mellado-2020-corpus,
    title = "A Corpus of {S}panish Political Speeches from 1937 to 2019",
    author = "{\'A}lvarez-Mellado, Elena",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.116",
    pages = "928--932",
    abstract = "This paper documents a corpus of political speeches in Spanish. The documents in the corpus belong to the Christmas speeches that have been delivered yearly by the head of state of Spain since 1937. The historical period covered by these speeches ranges from the Spanish Civil War and the Francoist dictatorship up until today. As a result, the corpus reflects some of the most significant events and political changes in the recent history of Spain. Up until now, the speeches as a whole had not been collected into a single, systematic and reusable resource, as most of the texts were scattered among different sources. The paper describes: (1) the composition of the corpus; (2) the Python interface that facilitates querying and analyzing the corpus using the NLTK and spaCy libraries and (3) a set of HTML visualizations aimed at the general public to navigate the corpus and explore differences between TF-IDF frequencies.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cecchini-etal-2020-new,
    title = "A New {L}atin Treebank for {U}niversal {D}ependencies: Charters between {A}ncient {L}atin and {R}omance Languages",
    author = "Cecchini, Flavio Massimiliano  and
      Korkiakangas, Timo  and
      Passarotti, Marco",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.117",
    pages = "933--942",
    abstract = "The present work introduces a new Latin treebank that follows the Universal Dependencies (UD) annotation standard. The treebank is obtained from the automated conversion of the Late Latin Charter Treebank 2 (LLCT2), originally in the Prague Dependency Treebank (PDT) style. As this treebank consists of Early Medieval legal documents, its language variety differs considerably from both the Classical and Medieval learned varieties prevalent in the other currently available UD Latin treebanks. Consequently, besides significant phenomena from the perspective of diachronic linguistics, this treebank also poses several challenging technical issues for the current and future syntactic annotation of Latin in the UD framework. Some of the most relevant cases are discussed in depth, with comparisons between the original PDT and the resulting UD annotations. Additionally, an overview of the UD-style structure of the treebank is given, and some diachronic aspects of the transition from Latin to Romance languages are highlighted.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rocha-souza-etal-2020-identification,
    title = "Identification of Indigenous Knowledge Concepts through Semantic Networks, Spelling Tools and Word Embeddings",
    author = "Rocha Souza, Renato  and
      Dorn, Amelie  and
      Piringer, Barbara  and
      Wandl-Vogt, Eveline",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.118",
    pages = "943--947",
    abstract = {In order to access indigenous, regional knowledge contained in language corpora, semantic tools and network methods are most typically employed. In this paper we present an approach for the identification of dialectal variations of words, or words that do not pertain to High German, on the example of non-standard language legacy collection questionnaires of the Bavarian Dialects in Austria (DB{\"O}). Based on selected cultural categories relevant to the wider project context, common words from each of these cultural categories and their lemmas using GermaLemma were identified. Through word embedding models the semantic vicinity of each word was explored, followed by the use of German Wordnet (Germanet) and the Hunspell tool. Whilst none of these tools have a comprehensive coverage of standard German words, they serve as an indication of dialects in specific semantic hierarchies. Methods and tools applied in this study may serve as an example for other similar projects dealing with non-standard or endangered language collections, aiming to access, analyze and ultimately preserve native regional language heritage.},
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{saleva-2020-multi,
    title = "A Multi-Orthography Parallel Corpus of {Y}iddish Nouns",
    author = "Saleva, Jonne",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.119",
    pages = "948--952",
    abstract = "Yiddish is a low-resource language belonging to the Germanic language family and written using the Hebrew alphabet. As a language, Yiddish can be considered resource-poor as it lacks both public accessible corpora and a widely-used standard orthography, with various countries and organizations influencing the spellings speakers use. While existing corpora of Yiddish text do exist, they are often only written in a single, potentially non-standard orthography, with no parallel version with standard orthography available. In this work, we introduce the first multi-orthography parallel corpus of Yiddish nouns built by scraping word entries from Wiktionary. We also demonstrate how the corpus can be used to bootstrap a transliteration model using the Sequitur-G2P grapheme-to-phoneme conversion toolkit to map between various orthographies. Our trained system achieves error rates between 16.79{\%} and 28.47{\%} on the test set, depending on the orthographies considered. In addition to quantitative analysis, we also conduct qualitative error analysis of the trained system, concluding that non-phonetically spelled Hebrew words are the largest cause of error. We conclude with remarks regarding future work and release the corpus and associated code under a permissive license for the larger community to use.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gerhalter-etal-2020-annotated,
    title = "An Annotated Corpus of Adjective-Adverb Interfaces in {R}omance Languages",
    author = "Gerhalter, Katharina  and
      Schneider, Gerlinde  and
      Pollin, Christopher  and
      Hummel, Martin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.120",
    pages = "953--957",
    abstract = "The final outcome of the project Open Access Database: Adjective-Adverb Interfaces in Romance is an annotated and lemmatised corpus of various linguistic phenomena related to Romance adjectives with adverbial functions. The data is published under open-access and aims to serve linguistic research based on transparent and accessible corpus-based data. The annotation model was developed to offer a cross-linguistic categorization model for the heterogeneous word-class {``}adverb{''}, based on its diverse forms, functions and meanings. The project focuses on the interoperability and accessibility of data, with particular respect to reusability in the sense of the FAIR Data Principles. Topics presented by this paper include data compilation and creation, annotation in XML/TEI, data preservation and publication process by means of the GAMS repository and accessibility via a search interface. These aspects are tied together by semantic technologies, using an ontology-based approach.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ehrmann-etal-2020-language,
    title = "Language Resources for Historical Newspapers: the Impresso Collection",
    author = {Ehrmann, Maud  and
      Romanello, Matteo  and
      Clematide, Simon  and
      Str{\"o}bel, Phillip Benjamin  and
      Barman, Rapha{\"e}l},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.121",
    pages = "958--968",
    abstract = "Following decades of massive digitization, an unprecedented amount of historical document facsimiles can now be retrieved and accessed via cultural heritage online portals. If this represents a huge step forward in terms of preservation and accessibility, the next fundamental challenge{--} and real promise of digitization{--} is to exploit the contents of these digital assets, and therefore to adapt and develop appropriate language technologies to search and retrieve information from this {`}Big Data of the Past{'}. Yet, the application of text processing tools on historical documents in general, and historical newspapers in particular, poses new challenges, and crucially requires appropriate language resources. In this context, this paper presents a collection of historical newspaper data sets composed of text and image resources, curated and published within the context of the {`}impresso - Media Monitoring of the Past{'} project. With corpora, benchmarks, semantic annotations and language models in French, German and Luxembourgish covering ca. 200 years, the objective of the impresso resource collection is to contribute to historical language resources, and thereby strengthen the robustness of approaches to non-standard inputs and foster efficient processing of historical documents.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kampe-etal-2020-allgemeine,
    title = "Allgemeine Musikalische Zeitung as a Searchable Online Corpus",
    author = "Kampe, Bernd  and
      Duan, Tinghui  and
      Hahn, Udo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.122",
    pages = "969--976",
    abstract = "The massive digitization efforts related to historical newspapers over the past decades have focused on mass media sources and ordinary people as their primary recipients. Much less attention has been paid to newspapers published for a more specialized audience, e.g., those aiming at scholarly or cultural exchange within intellectual communities much narrower in scope, such as newspapers devoted to music criticism, arts or philosophy. Only some few of these specialized newspapers have been digitized up until now, but they are usually not well curated in terms of digitization quality, data formatting, completeness, redundancy (de-duplication), supply of metadata, and, hence, searchability. This paper describes our approach to eliminate these drawbacks for a major German-language newspaper resource of the Romantic Age, the Allgemeine Musikalische Zeitung (General Music Gazette). We here focus on a workflow that copes with a posteriori digitization problems, inconsistent OCRing and index building for searchability. In addition, we provide a user-friendly graphic interface to empower content-centric access to this (and other) digital resource(s) adopting open-source software for the purpose of Web presentation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cinkova-rybicki-2020-stylometry,
    title = "Stylometry in a Bilingual Setup",
    author = "Cinkova, Silvie  and
      Rybicki, Jan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.123",
    pages = "977--984",
    abstract = "The method of stylometry by most frequent words does not allow direct comparison of original texts and their translations, i.e. across languages. For instance, in a bilingual Czech-German text collection containing parallel texts (originals and translations in both directions, along with Czech and German translations from other languages), authors would not cluster across languages, since frequency word lists for any Czech texts are obviously going to be more similar to each other than to a German text, and the other way round. We have tried to come up with an interlingua that would remove the language-specific features and possibly keep the linguistically independent features of individual author signal, if they exist. We have tagged, lemmatized, and parsed each language counterpart with the corresponding language model in UDPipe, which provides a linguistic markup that is cross-lingual to a significant extent. We stripped the output of language-dependent items, but that alone did not help much. As a next step, we transformed the lemmas of both language counterparts into shared pseudolemmas based on a very crude Czech-German glossary, with a 95.6{\%} success. We show that, for stylometric methods based on the most frequent words, we can do without translations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sato-heffernan-2020-dialect,
    title = "Dialect Clustering with Character-Based Metrics: in Search of the Boundary of Language and Dialect",
    author = "Sato, Yo  and
      Heffernan, Kevin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.124",
    pages = "985--990",
    abstract = "We present in this work a universal, character-based method for representing sentences so that one can thereby calculate the distance between any two sentence pair. With a small alphabet, it can function as a proxy of phonemes, and as one of its main uses, we carry out dialect clustering: cluster a dialect/sub-language mixed corpus into sub-groups and see if they coincide with the conventional boundaries of dialects and sub-languages. By using data with multiple Japanese dialects and multiple Slavic languages, we report how well each group clusters, in a manner to partially respond to the question of what separates languages from dialects.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sileo-etal-2020-discsense,
    title = "{D}isc{S}ense: Automated Semantic Analysis of Discourse Markers",
    author = "Sileo, Damien  and
      Van de Cruys, Tim  and
      Pradel, Camille  and
      Muller, Philippe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.125",
    pages = "991--999",
    abstract = "Using a model trained to predict discourse markers between sentence pairs, we predict plausible markers between sentence pairs with a known semantic relation (provided by existing classification datasets). These predictions allow us to study the link between discourse markers and the semantic relations annotated in classification datasets. Handcrafted mappings have been proposed between markers and discourse relations on a limited set of markers and a limited set of categories, but there exists hundreds of discourse markers expressing a wide variety of relations, and there is no consensus on the taxonomy of relations between competing discourse theories (which are largely built in a top-down fashion). By using an automatic prediction method over existing semantically annotated datasets, we provide a bottom-up characterization of discourse markers in English. The resulting dataset, named DiscSense, is publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dominguez-etal-2020-themepro,
    title = "{T}heme{P}ro: A Toolkit for the Analysis of Thematic Progression",
    author = "Dominguez, Monica  and
      Soler, Juan  and
      Wanner, Leo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.126",
    pages = "1000--1007",
    abstract = "This paper introduces ThemePro, a toolkit for the automatic analysis of thematic progression. Thematic progression is relevant to natural language processing (NLP) applications dealing, among others, with discourse structure, argumentation structure, natural language generation, summarization and topic detection. A web platform demonstrates the potential of this toolkit and provides a visualization of the results including syntactic trees, hierarchical thematicity over propositions and thematic progression over whole texts.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jo-etal-2020-machine,
    title = "Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation",
    author = "Jo, Yohan  and
      Mayfield, Elijah  and
      Reed, Chris  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.127",
    pages = "1008--1018",
    abstract = "We introduce a corpus of the 2016 U.S. presidential debates and commentary, containing 4,648 argumentative propositions annotated with fine-grained proposition types. Modern machine learning pipelines for analyzing argument have difficulty distinguishing between types of propositions based on their factuality, rhetorical positioning, and speaker commitment. Inability to properly account for these facets leaves such systems inaccurate in understanding of fine-grained proposition types. In this paper, we demonstrate an approach to annotating for four complex proposition types, namely normative claims, desires, future possibility, and reported speech. We develop a hybrid machine learning and human workflow for annotation that allows for efficient and reliable annotation of complex linguistic phenomena, and demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates. This new dataset and method can support technical researchers seeking more nuanced representations of argument, as well as argumentation theorists developing new quantitative analyses.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chuan-an-etal-2020-chinese,
    title = "{C}hinese Discourse Parsing: Model and Evaluation",
    author = "Chuan-An, Lin  and
      Hung, Shyh-Shiun  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.128",
    pages = "1019--1024",
    abstract = "Chinese discourse parsing, which aims to identify the hierarchical relationships of Chinese elementary discourse units, has not yet a consistent evaluation metric. Although Parseval is commonly used, variations of evaluation differ from three aspects: micro vs. macro F1 scores, binary vs. multiway ground truth, and left-heavy vs. right-heavy binarization. In this paper, we first propose a neural network model that unifies a pre-trained transformer and CKY-like algorithm, and then compare it with the previous models with different evaluation scenarios. The experimental results show that our model outperforms the previous systems. We conclude that (1) the pre-trained context embedding provides effective solutions to deal with implicit semantics in Chinese texts, and (2) using multiway ground truth is helpful since different binarization approaches lead to significant differences in performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{long-etal-2020-shallow,
    title = "Shallow Discourse Annotation for {C}hinese {TED} Talks",
    author = "Long, Wanqiu  and
      Cai, Xinyi  and
      Reid, James  and
      Webber, Bonnie  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.129",
    pages = "1025--1032",
    abstract = "Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{olshefski-etal-2020-discussion,
    title = "The Discussion Tracker Corpus of Collaborative Argumentation",
    author = "Olshefski, Christopher  and
      Lugini, Luca  and
      Singh, Ravneet  and
      Litman, Diane  and
      Godley, Amanda",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.130",
    pages = "1033--1043",
    abstract = "Although NLP research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals. Few published corpora of synchronous, multi-party argumentation are available. The Discussion Tracker corpus, collected in high school English classes, is an annotated dataset of transcripts of spoken, multi-party argumentation. The corpus consists of 29 multi-party discussions of English literature transcribed from 985 minutes of audio. The transcripts were annotated for three dimensions of collaborative argumentation: argument moves (claims, evidence, and explanations), specificity (low, medium, high) and collaboration (e.g., extensions of and disagreements about others{'} ideas). In addition to providing descriptive statistics on the corpus, we provide performance benchmarks and associated code for predicting each dimension separately, illustrate the use of the multiple annotations in the corpus to improve performance via multi-task learning, and finally discuss other ways the corpus might be used to further NLP research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sluyter-gathje-etal-2020-shallow,
    title = "Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection",
    author = {Sluyter-G{\"a}thje, Henny  and
      Bourgonje, Peter  and
      Stede, Manfred},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.131",
    pages = "1044--1050",
    abstract = "Shallow Discourse Parsing (SDP), the identification of coherence relations between text spans, relies on large amounts of training data, which so far exists only for English - any other language is in this respect an under-resourced one. For those languages where machine translation from English is available with reasonable quality, MT in conjunction with annotation projection can be an option for producing an SDP resource. In our study, we translate the English Penn Discourse TreeBank into German and experiment with various methods of annotation projection to arrive at the German counterpart of the PDTB. We describe the key characteristics of the corpus as well as some typical sources of errors encountered during its creation. Then we evaluate the GermanPDTB by training components for selected sub-tasks of discourse parsing on this silver data and compare performance to the same components when trained on the gold, original PDTB corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rasmussen-schuler-2020-corpus,
    title = "A Corpus of Encyclopedia Articles with Logical Forms",
    author = "Rasmussen, Nathan  and
      Schuler, William",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.132",
    pages = "1051--1060",
    abstract = "People can extract precise, complex logical meanings from text in documents such as tax forms and game rules, but language processing systems lack adequate training and evaluation resources to do these kinds of tasks reliably. This paper describes a corpus of annotated typed lambda calculus translations for approximately 2,000 sentences in Simple English Wikipedia, which is assumed to constitute a broad-coverage domain for precise, complex descriptions. The corpus described in this paper contains a large number of quantifiers and interesting scoping configurations, and is presented specifically as a resource for quantifier scope disambiguation systems, but also more generally as an object of linguistic study.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bourgonje-stede-2020-potsdam,
    title = "The {P}otsdam Commentary Corpus 2.2: Extending Annotations for Shallow Discourse Parsing",
    author = "Bourgonje, Peter  and
      Stede, Manfred",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.133",
    pages = "1061--1066",
    abstract = "We present the Potsdam Commentary Corpus 2.2, a German corpus of news editorials annotated on several different levels. New in the 2.2 version of the corpus are two additional annotation layers for coherence relations following the Penn Discourse TreeBank framework. Specifically, we add relation senses to an already existing layer of discourse connectives and their arguments, and we introduce a new layer with additional coherence relation types, resulting in a German corpus that mirrors the PDTB. The aim of this is to increase usability of the corpus for the task of shallow discourse parsing. In this paper, we provide inter-annotator agreement figures for the new annotations and compare corpus statistics based on the new annotations to the equivalent statistics extracted from the PDTB.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mohammadi-etal-2020-creation,
    title = "On the Creation of a Corpus for Coherence Evaluation of Discursive Units",
    author = "Mohammadi, Elham  and
      Beiko, Timothe  and
      Kosseim, Leila",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.134",
    pages = "1067--1072",
    abstract = "In this paper, we report on our experiments towards the creation of a corpus for coherence evaluation. Most corpora for textual coherence evaluation are composed of randomly shuffled sentences that focus on sentence ordering, regardless of whether the sentences were originally related by a discourse relation. To the best of our knowledge, no publicly available corpus has been designed specifically for the evaluation of coherence of known discursive units. In this paper, we focus on coherence modeling at the intra-discursive level and describe our approach to build a corpus of incoherent pairs of sentences. We experimented with a variety of corruption strategies to create synthetic incoherent pairs of discourse arguments from coherent ones. Using discourse argument pairs from the Penn Discourse Tree Bank, we generate incoherent discourse argument pairs, by swapping either their discourse connective or a discourse argument. To evaluate how incoherent the generated corpora are, we use a convolutional neural network to try to distinguish the original pairs from the corrupted ones. Results of the classifier as well as a manual inspection of the corpora show that generating such corpora is still a challenge as the generated instances are clearly not {``}incoherent enough{''}, indicating that more effort should be spent on developing more robust ways of generating incoherent corpora.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{desai-etal-2020-joint,
    title = "Joint Learning of Syntactic Features Helps Discourse Segmentation",
    author = "Desai, Takshak  and
      Dakle, Parag Pravin  and
      Moldovan, Dan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.135",
    pages = "1073--1080",
    abstract = "This paper describes an accurate framework for carrying out multi-lingual discourse segmentation with BERT (Devlin et al., 2019). The model is trained to identify segments by casting the problem as a token classification problem and jointly learning syntactic features like part-of-speech tags and dependency relations. This leads to significant improvements in performance. Experiments are performed in different languages, such as English, Dutch, German, Portuguese Brazilian and Basque to highlight the cross-lingual effectiveness of the segmenter. In particular, the model achieves a state-of-the-art F-score of 96.7 for the RST-DT corpus (Carlson et al., 2003) improving on the previous best model by 7.2{\%}. Additionally, a qualitative explanation is provided for how proposed changes contribute to model performance by analyzing errors made on the test data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ruf-navarretta-2020-creating,
    title = "Creating a Corpus of Gestures and Predicting the Audience Response based on Gestures in Speeches of {D}onald Trump",
    author = "Ruf, Verena  and
      Navarretta, Costanza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.136",
    pages = "1081--1088",
    abstract = "Gestures are an important component of non{--}verbal communication. This has an increasing potential in human{--}computer interaction. For example, Navarretta (2017b) uses sequences of speech and pauses together with co{--}speech gestures produced by Barack Obama in order to predict audience response, such as applause. The aim of this study is to explore the role of speech pauses and gestures alone as predictors of audience reaction without other types of speech information. For this work, we created a corpus of speeches held by Donald Trump before and during his time as president between 2016 and 2019. The data were transcribed with pause information and co{--}speech gestures were annotated as well as audience responses. Gestures and long silent pauses of the duration of at least 0.5 seconds are the input of computational models to predict audience reaction. The results of this study indicate that especially head movements and facial expressions play an important role and they confirm that gestures can to some extent be used to predict audience reaction independently of speech.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{polakova-etal-2020-geczlex,
    title = "{G}e{C}z{L}ex: Lexicon of {C}zech and {G}erman Anaphoric Connectives",
    author = "Pol{\'a}kov{\'a}, Lucie  and
      Rysov{\'a}, Kate{\v{r}}ina  and
      Rysov{\'a}, Magdal{\'e}na  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.137",
    pages = "1089--1096",
    abstract = "We introduce the first version of GeCzLex, an online electronic resource for translation equivalents of Czech and German discourse connectives. The lexicon is one of the outcomes of the research on anaphoricity and long-distance relations in discourse, it contains at present anaphoric connectives (ACs) for Czech and German connectives, and further their possible translations documented in bilingual parallel corpora (not necessarily anaphoric). As a basis, we use two existing monolingual lexicons of connectives: the Lexicon of Czech Discourse Connectives (CzeDLex) and the Lexicon of Discourse Markers (DiMLex) for German, interlink their relevant entries via semantic annotation of the connectives (according to the PDTB 3 sense taxonomy) and statistical information of translation possibilities from the Czech and German parallel data of the InterCorp project. The lexicon is, as far as we know, the first bilingual inventory of connectives with linkage on the level of individual entries, and a first attempt to systematically describe devices engaged in long-distance, non-local discourse coherence. The lexicon is freely available under the Creative Commons License.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{das-etal-2020-dimlex,
    title = "{D}i{ML}ex-{B}angla: A Lexicon of {B}angla Discourse Connectives",
    author = "Das, Debopam  and
      Stede, Manfred  and
      Ghosh, Soumya Sankar  and
      Chatterjee, Lahari",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.138",
    pages = "1097--1102",
    abstract = "We present DiMLex-Bangla, a newly developed lexicon of discourse connectives in Bangla. The lexicon, upon completion of its first version, contains 123 Bangla connective entries, which are primarily compiled from the linguistic literature and translation of English discourse connectives. The lexicon compilation is later augmented by adding more connectives from a currently developed corpus, called the Bangla RST Discourse Treebank (Das and Stede, 2018). DiMLex-Bangla provides information on syntactic categories of Bangla connectives, their discourse semantics and non-connective uses (if any). It uses the format of the German connective lexicon DiMLex (Stede and Umbach, 1998), which provides a cross-linguistically applicable XML schema. The resource is the first of its kind in Bangla, and is freely available for use in studies on discourse structure and computational applications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{knaebel-stede-2020-semi,
    title = "Semi-Supervised Tri-Training for Explicit Discourse Argument Expansion",
    author = "Knaebel, Ren{\'e}  and
      Stede, Manfred",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.139",
    pages = "1103--1109",
    abstract = "This paper describes a novel application of semi-supervision for shallow discourse parsing. We use a neural approach for sequence tagging and focus on the extraction of explicit discourse arguments. First, additional unlabeled data is prepared for semi-supervised learning. From this data, weak annotations are generated in a first setting and later used in another setting to study performance differences. In our studies, we show an increase in the performance of our models that ranges between 2-10{\%} F1 score. Further, we give some insights to the generated discourse annotations and compare the developed additional relations with the training relations. We release this new dataset of explicit discourse arguments to enable the training of large statistical models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chinnappa-etal-2020-wikipossessions,
    title = "{W}iki{P}ossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts",
    author = "Chinnappa, Dhivya  and
      Palmer, Alexis  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.140",
    pages = "1110--1117",
    abstract = "This paper presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. We annotate Wikipedia articles for 90 different well-known artifacts paintings, diamonds, and archaeological artifacts), producing 799 artifact-possessor relations with associated attributes. For each article, we also produce a full possession timeline. The full version of the task combines straightforward entity-relation extraction with complex temporal reasoning, as well as verification of textual support for the relevant types of knowledge. Specifically, to complete the full TOP task for a given article, a system must do the following: a) identify possessors; b) anchor possessors to times/events; c) identify temporal relations between each temporal anchor and the possession relation it corresponds to; d) assign certainty scores to each possessor and each temporal relation; and e) assemble individual possession events into a global possession timeline. In addition to the corpus, we release evaluation scripts and a baseline model for the task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{westera-etal-2020-ted,
    title = "{TED}-{Q}: {TED} Talks and the Questions they Evoke",
    author = "Westera, Matthijs  and
      Mayol, Laia  and
      Rohde, Hannah",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.141",
    pages = "1118--1127",
    abstract = "We present a new dataset of TED-talks annotated with the questions they evoke and, where available, the answers to these questions. Evoked questions represent a hitherto mostly unexplored type of linguistic data, which promises to open up important new lines of research, especially related to the Question Under Discussion (QUD)-based approach to discourse structure. In this paper we introduce the method and open the first installment of our data to the public. We summarize and explore the current dataset, illustrate its potential by providing new evidence for the relation between predictability and implicitness {--} capitalizing on the already existing PDTB-style annotations for the texts we use {--} and outline its potential for future research. The dataset should be of interest, at its current scale, to researchers on formal and experimental pragmatics, discourse coherence, information structure, discourse expectations and processing. Our data-gathering procedure is designed to scale up, relying on crowdsourcing by non-expert annotators, with its utility for Natural Language Processing in mind (e.g., dialogue systems, conversational question answering).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mirovsky-etal-2020-czedlex,
    title = "{C}ze{DL}ex 0.6 and its Representation in the {PML}-{TQ}",
    author = "M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}  and
      Pol{\'a}kov{\'a}, Lucie  and
      Synkov{\'a}, Pavl{\'\i}na",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.142",
    pages = "1128--1134",
    abstract = "CzeDLex is an electronic lexicon of Czech discourse connectives with its data coming from a large treebank annotated with discourse relations. Its new version CzeDLex 0.6 (as compared with the previous version 0.5, which was published in 2017) is significantly larger with respect to manually processed entries. Also, its structure has been modified to allow for primary connectives to appear with multiple entries for a single discourse sense. The lexicon comes in several formats, being both human and machine readable, and is available for searching in PML Tree Query, a user-friendly and powerful search tool for all kinds of linguistically annotated treebanks. The main purpose of this paper/demo is to present the new version of the lexicon and to demonstrate possibilities of mining various types of information from the lexicon using PML Tree Query; we present several examples of search queries over the lexicon data along with their results. The new version of the lexicon, CzeDLex{\textasciitilde}0.6, is available on-line and was officially released in December 2019 under the Creative Commons License.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{egawa-etal-2020-corpus,
    title = "Corpus for Modeling User Interactions in Online Persuasive Discussions",
    author = "Egawa, Ryo  and
      Morio, Gaku  and
      Fujita, Katsuhide",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.143",
    pages = "1135--1141",
    abstract = "Persuasions are common in online arguments such as discussion forums. To analyze persuasive strategies, it is important to understand how individuals construct posts and comments based on the semantics of the argumentative components. In addition to understanding how we construct arguments, understanding how a user post interacts with other posts (i.e., argumentative inter-post relation) still remains a challenge. Therefore, in this study, we developed a novel annotation scheme and corpus that capture both user-generated inner-post arguments and inter-post relations between users in ChangeMyView, a persuasive forum. Our corpus consists of arguments with 4612 elementary units (EUs) (i.e., propositions), 2713 EU-to-EU argumentative relations, and 605 inter-post argumentative relations in 115 threads. We analyzed the annotated corpus to identify the characteristics of online persuasive arguments, and the results revealed persuasive documents have more claims than non-persuasive ones and different interaction patterns among persuasive and non-persuasive documents. Our corpus can be used as a resource for analyzing persuasiveness and training an argument mining system to identify and extract argument structures. The annotated corpus and annotation guidelines have been made publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wilkens-todirascu-2020-simplifying,
    title = "Simplifying Coreference Chains for Dyslexic Children",
    author = "Wilkens, Rodrigo  and
      Todirascu, Amalia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.144",
    pages = "1142--1151",
    abstract = "We present a work aiming to generate adapted content for dyslexic children for French, in the context of the ALECTOR project. Thus, we developed a system to transform the texts at the discourse level. This system modifies the coreference chains, which are markers of text cohesion, by using rules. These rules were designed following a careful study of coreference chains in both original texts and its simplified versions. Moreover, in order to define reliable transformation rules, we analysed several coreference properties as well as the concurrent simplification operations in the aligned texts. This information is coded together with a coreference resolution system and a text rewritten tool in the proposed system, which comprise a coreference module specialised in written text and seven text transformation operations. The evaluation of the system firstly focused on check the simplification by manual validation of three judges. These errors were grouped into five classes that combined can explain 93{\%} of the errors. The second evaluation step consisted of measuring the simplification perception by 23 judges, which allow us to measure the simplification impact of the proposed rules.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kishimoto-etal-2020-adapting,
    title = "Adapting {BERT} to Implicit Discourse Relation Classification with a Focus on Discourse Connectives",
    author = "Kishimoto, Yudai  and
      Murawaki, Yugo  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.145",
    pages = "1152--1158",
    abstract = "BERT, a neural network-based language model pre-trained on large corpora, is a breakthrough in natural language processing, significantly outperforming previous state-of-the-art models in numerous tasks. However, there have been few reports on its application to implicit discourse relation classification, and it is not clear how BERT is best adapted to the task. In this paper, we test three methods of adaptation. (1) We perform additional pre-training on text tailored to discourse classification. (2) In expectation of knowledge transfer from explicit discourse relations to implicit discourse relations, we add a task named explicit connective prediction at the additional pre-training step. (3) To exploit implicit connectives given by treebank annotators, we add a task named implicit connective prediction at the fine-tuning step. We demonstrate that these three techniques can be combined straightforwardly in a single training pipeline. Through comprehensive experiments, we found that the first and second techniques provide additional gain while the last one did not.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{barbedette-eshkol-taravella-2020-speakers,
    title = "What Speakers really Mean when they Ask Questions: Classification of Intentions with a Supervised Approach",
    author = "Barbedette, Ang{\`e}le  and
      Eshkol-Taravella, Iris",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.146",
    pages = "1159--1166",
    abstract = "This paper focuses on the automatic detection of hidden intentions of speakers in questions asked during meals. Our corpus is composed of a set of transcripts of spontaneous oral conversations from ESLO{'}s corpora. We suggest a typology of these intentions based on our research work and the exploration and annotation of the corpus, in which we define two {``}explicit{''} categories (request for agreement and request for information) and three {``}implicit{''} categories (opinion, will and doubt). We implement a supervised automatic classification model based on annotated data and selected linguistic features and we evaluate its results and performances. We finally try to interpret these results by looking more deeply and specifically into the predictions of the algorithm and the features it used. There are many motivations for this work which are part of ongoing challenges such as opinion analysis, irony detection or the development of conversational agents.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{farzana-etal-2020-modeling,
    title = "Modeling Dialogue in Conversational Cognitive Health Screening Interviews",
    author = "Farzana, Shahla  and
      Valizadeh, Mina  and
      Parde, Natalie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.147",
    pages = "1167--1177",
    abstract = "Automating straightforward clinical tasks can reduce workload for healthcare professionals, increase accessibility for geographically-isolated patients, and alleviate some of the economic burdens associated with healthcare. A variety of preliminary screening procedures are potentially suitable for automation, and one such domain that has remained underexplored to date is that of structured clinical interviews. A task-specific dialogue agent is needed to automate the collection of conversational speech for further (either manual or automated) analysis, and to build such an agent, a dialogue manager must be trained to respond to patient utterances in a manner similar to a human interviewer. To facilitate the development of such an agent, we propose an annotation schema for assigning dialogue act labels to utterances in patient-interviewer conversations collected as part of a clinically-validated cognitive health screening task. We build a labeled corpus using the schema, and show that it is characterized by high inter-annotator agreement. We establish a benchmark dialogue act classification model for the corpus, thereby providing a proof of concept for the proposed annotation schema. The resulting dialogue act corpus is the first such corpus specifically designed to facilitate automated cognitive health screening, and lays the groundwork for future exploration in this area.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{straton-etal-2020-stigma,
    title = "Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media",
    author = "Straton, Nadiya  and
      Jang, Hyeju  and
      Ng, Raymond",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.148",
    pages = "1178--1190",
    abstract = "Much research has been done within the social sciences on the interpretation and influence of stigma on human behaviour and health, which result in out-of-group exclusion, distancing, cognitive separation, status loss, discrimination, in-group pressure, and often lead to disengagement, non-adherence to treatment plan, and prescriptions by the doctor. However, little work has been conducted on computational identification of stigma in general and in social media discourse in particular. In this paper, we develop the annotation scheme and improve the annotation process for stigma identification, which can be applied to other health-care domains. The data from pro-vaccination and anti-vaccination discussion groups are annotated by trained annotators who have professional background in social science and health-care studies, therefore the group can be considered experts on the subject in comparison to non-expert crowd. Amazon MTurk annotators is another group of annotator with no knowledge on their education background, they are initially treated as non-expert crowd on the subject matter of stigma. We analyze the annotations with visualisation techniques, features from LIWC (Linguistic Inquiry and Word Count) list and make prediction based on bi-grams with traditional and deep learning models. Data augmentation method and application of CNN show high performance accuracy in comparison to other models. Success of the rigorous annotation process on identifying stigma is reconfirmed by achieving high prediction rate with CNN.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dhanwal-etal-2020-annotated,
    title = "An Annotated Dataset of Discourse Modes in {H}indi Stories",
    author = "Dhanwal, Swapnil  and
      Dutta, Hritwik  and
      Nankani, Hitesh  and
      Shrivastava, Nilay  and
      Kumar, Yaman  and
      Li, Junyi Jessy  and
      Mahata, Debanjan  and
      Gosangi, Rakesh  and
      Zhang, Haimin  and
      Shah, Rajiv Ratn  and
      Stent, Amanda",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.149",
    pages = "1191--1196",
    abstract = "In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. We present a detailed account of the entire data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.87 k-alpha). We analyze the data in terms of label distributions, part of speech tags, and sentence lengths. We characterize the performance of various classification algorithms on this dataset and perform ablation studies to understand the nature of the linguistic models suitable for capturing the nuances of the embedded discourse structures in the presented corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{shavarani-sekine-2020-multi,
    title = "Multi-class Multilingual Classification of {W}ikipedia Articles Using Extended Named Entity Tag Set",
    author = "Shavarani, Hassan S.  and
      Sekine, Satoshi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.150",
    pages = "1197--1201",
    abstract = "Wikipedia is a great source of general world knowledge which can guide NLP models better understand their motivation to make predictions. Structuring Wikipedia is the initial step towards this goal which can facilitate fine-grain classification of articles. In this work, we introduce the Shinra 5-Language Categorization Dataset (SHINRA-5LDS), a large multi-lingual and multi-labeled set of annotated Wikipedia articles in Japanese, English, French, German, and Farsi using Extended Named Entity (ENE) tag set. We evaluate the dataset using the best models provided for ENE label set classification and show that the currently available classification models struggle with large datasets using fine-grained tag sets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moudjari-etal-2020-algerian,
    title = "An {A}lgerian Corpus and an Annotation Platform for Opinion and Emotion Analysis",
    author = "Moudjari, Leila  and
      Akli-Astouati, Karima  and
      Benamara, Farah",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.151",
    pages = "1202--1210",
    abstract = "In this paper, we address the lack of resources for opinion and emotion analysis related to North African dialects, targeting Algerian dialect. We present TWIFIL (TWItter proFILing) a collaborative annotation platform for crowdsourcing annotation of tweets at different levels of granularity. The plateform allowed the creation of the largest Algerian dialect dataset annotated for both sentiment (9,000 tweets), emotion (about 5,000 tweets) and extra-linguistic information including author profiling (age and gender). The annotation resulted also in the creation of the largest Algerien dialect subjectivity lexicon of about 9,000 entries which can constitute a valuable resources for the development of future NLP applications for Algerian dialect. To test the validity of the dataset, a set of deep learning experiments were conducted to classify a given tweet as positive, negative or neutral. We discuss our results and provide an error analysis to better identify classification errors.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{slovikovskaya-attardi-2020-transfer,
    title = "Transfer Learning from Transformers to Fake News Challenge Stance Detection ({FNC}-1) Task",
    author = "Slovikovskaya, Valeriya  and
      Attardi, Giuseppe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.152",
    pages = "1211--1218",
    abstract = "Transformer models, trained and publicly released over the last couple of years, have proved effective in many NLP tasks. We wished to test their usefulness in particular on the stance detection task. We performed experiments on the data from the Fake News Challenge Stage 1 (FNC-1). We were indeed able to improve the reported SotA on the challenge, by exploiting the generalization power of large language models based on Transformer architecture. Specifically (1) we improved the FNC-1 best performing model adding BERT sentence embedding of input sequences as a model feature, (2) we fine-tuned BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained state-of-the-art results on FNC-1 task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ginev-miller-2020-scientific,
    title = "Scientific Statement Classification over ar{X}iv.org",
    author = "Ginev, Deyan  and
      Miller, Bruce R",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.153",
    pages = "1219--1226",
    abstract = "We introduce a new classification task for scientific statements and release a large-scale dataset for supervised learning. Our resource is derived from a machine-readable representation of the arXiv.org collection of preprint articles. We explore fifty author-annotated categories and empirically motivate a task design of grouping 10.5 million annotated paragraphs into thirteen classes. We demonstrate that the task setup aligns with known success rates from the state of the art, peaking at a 0.91 F1-score via a BiLSTM encoder-decoder model. Additionally, we introduce a lexeme serialization for mathematical formulas, and observe that context-aware models could improve when also trained on the symbolic modality. Finally, we discuss the limitations of both data and task design, and outline potential directions towards increasingly complex models of scientific discourse, beyond isolated statements.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dias-paraboni-2020-cross,
    title = "Cross-domain Author Gender Classification in {B}razilian {P}ortuguese",
    author = "Dias, Rafael  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.154",
    pages = "1227--1234",
    abstract = "Author profiling models predict demographic characteristics of a target author based on the text that they have written. Systems of this kind will often follow a single-domain approach, in which the model is trained from a corpus of labelled texts in a given domain, and it is subsequently validated against a test corpus built from precisely the same domain. Although single-domain settings are arguably ideal, this strategy gives rise to the question of how to proceed when no suitable training corpus (i.e., a corpus that matches the test domain) is available. To shed light on this issue, this paper discusses a cross-domain gender classification task based on four domains (Facebook, crowd sourced opinions, Blogs and E-gov requests) in the Brazilian Portuguese language. A number of simple gender classification models using word- and psycholinguistics-based features alike are introduced, and their results are compared in two kinds of cross-domain setting: first, by making use of a single text source as training data for each task, and subsequently by combining multiple sources. Results confirm previous findings related to the effects of corpus size and domain similarity in English, and pave the way for further studies in the field.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tuggener-etal-2020-ledgar,
    title = "{LEDGAR}: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts",
    author = {Tuggener, Don  and
      von D{\"a}niken, Pius  and
      Peetz, Thomas  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.155",
    pages = "1235--1241",
    abstract = "We present LEDGAR, a multilabel corpus of legal provisions in contracts. The corpus was crawled and scraped from the public domain (SEC filings) and is, to the best of our knowledge, the first freely available corpus of its kind. Since the corpus was constructed semi-automatically, we apply and discuss various approaches to noise removal. Due to the rather large labelset of over 12{'}000 labels annotated in almost 100{'}000 provisions in over 60{'}000 contracts, we believe the corpus to be of interest for research in the field of Legal NLP, (large-scale or extreme) text classification, as well as for legal studies. We discuss several methods to sample subcopora from the corpus and implement and evaluate different automatic classification approaches. Finally, we perform transfer experiments to evaluate how well the classifiers perform on contracts stemming from outside the corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rodier-carter-2020-online,
    title = "Online Near-Duplicate Detection of News Articles",
    author = "Rodier, Simon  and
      Carter, Dave",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.156",
    pages = "1242--1249",
    abstract = "Near-duplicate documents are particularly common in news media corpora. Editors often update wirefeed articles to address space constraints in print editions or to add local context; journalists often lightly modify previous articles with new information or minor corrections. Near-duplicate documents have potentially significant costs, including bloating corpora with redundant information (biasing techniques built upon such corpora) and requiring additional human and computational analytic resources for marginal benefit. Filtering near-duplicates out of a collection is thus important, and is particularly challenging in applications that require them to be filtered out in real-time with high precision. Previous near-duplicate detection methods typically work offline to identify all near-duplicate pairs in a set of documents. We propose an online system which flags a near-duplicate document by finding its most likely original. This system adapts the shingling algorithm proposed by Broder (1997), and we test it on a challenging dataset of web-based news articles. Our online system presents state-of-the-art F1-scores, and can be tuned to trade precision for recall and vice-versa. Given its performance and online nature, our method can be used in many real-world applications. We present one such application, filtering near-duplicates to improve productivity of human analysts in a situational awareness tool.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hirao-etal-2020-automated,
    title = "Automated Essay Scoring System for Nonnative {J}apanese Learners",
    author = "Hirao, Reo  and
      Arai, Mio  and
      Shimanaka, Hiroki  and
      Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.157",
    pages = "1250--1257",
    abstract = "In this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including content, organization, and language scores. In particular, we developed AES systems using two different approaches: a feature-based approach and a neural-network-based approach. In the former approach, we used Japanese-specific linguistic features, including character-type features such as {``}kanji{''} and {``}hiragana.{''} In the latter approach, we used two models: a long short-term memory (LSTM) model (Hochreiter and Schmidhuber, 1997) and a bidirectional encoder representations from transformers (BERT) model (Devlin et al., 2019), which achieved the highest accuracy in various natural language processing tasks in 2018. Overall, the BERT model achieved the best root mean squared error and quadratic weighted kappa scores. In addition, we analyzed the robustness of the outputs of the BERT model. We have released and shared this system to facilitate further research on AES for Japanese as a second language learners.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{neerbek-etal-2020-real,
    title = "A Real-World Data Resource of Complex Sensitive Sentences Based on Documents from the Monsanto Trial",
    author = "Neerbek, Jan  and
      Eskildsen, Morten  and
      Dolog, Peter  and
      Assent, Ira",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.158",
    pages = "1258--1267",
    abstract = "In this work we present a corpus for the evaluation of sensitive information detection approaches that addresses the need for real world sensitive information for empirical studies. Our sentence corpus contains different notions of complex sensitive information that correspond to different aspects of concern in a current trial of the Monsanto company. This paper describes the annotations process, where we both employ human annotators and furthermore create automatically inferred labels regarding technical, legal and informal communication within and with employees of Monsanto, drawing on a classification of documents by lawyers involved in the Monsanto court case. We release corpus of high quality sentences and parse trees with these two types of labels on sentence level. We characterize the sensitive information via several representative sensitive information detection models, in particular both keyword-based (n-gram) approaches and recent deep learning models, namely, recurrent neural networks (LSTM) and recursive neural networks (RecNN). Data and code are made publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lazaridou-etal-2020-discovering,
    title = "Discovering Biased News Articles Leveraging Multiple Human Annotations",
    author = {Lazaridou, Konstantina  and
      L{\"o}ser, Alexander  and
      Mestre, Maria  and
      Naumann, Felix},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.159",
    pages = "1268--1277",
    abstract = "Unbiased and fair reporting is an integral part of ethical journalism. Yet, political propaganda and one-sided views can be found in the news and can cause distrust in media. Both accidental and deliberate political bias affect the readers and shape their views. We contribute to a trustworthy media ecosystem by automatically identifying politically biased news articles. We introduce novel corpora annotated by two communities, i.e., domain experts and crowd workers, and we also consider automatic article labels inferred by the newspapers{'} ideologies. Our goal is to compare domain experts to crowd workers and also to prove that media bias can be detected automatically. We classify news articles with a neural network and we also improve our performance in a self-supervised manner.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{goncalo-oliveira-etal-2020-corpora,
    title = "Corpora and Baselines for Humour Recognition in {P}ortuguese",
    author = "Gon{\c{c}}alo Oliveira, Hugo  and
      Clem{\^e}ncio, Andr{\'e}  and
      Alves, Ana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.160",
    pages = "1278--1285",
    abstract = "Having in mind the lack of work on the automatic recognition of verbal humour in Portuguese, a topic connected with fluency in a natural language, we describe the creation of three corpora, covering two styles of humour and four sources of non-humorous text, that may be used for related studies. We then report on some experiments where the created corpora were used for training and testing computational models that exploit content and linguistic features for humour recognition. The obtained results helped us taking some conclusions about this challenge and may be seen as baselines for those willing to tackle it in the future, using the same corpora.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{van-der-meulen-reijnierse-2020-factcorp,
    title = "{F}act{C}orp: A Corpus of {D}utch Fact-checks and its Multiple Usages",
    author = "van der Meulen, Marten  and
      Reijnierse, W. Gudrun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.161",
    pages = "1286--1292",
    abstract = "Fact-checking information before publication has long been a core task for journalists, but recent times have seen the emergence of dedicated news items specifically aimed at fact-checking after publication. This relatively new form of fact-checking receives a fair amount of attention from academics, with current research focusing mostly on journalists{'} motivations for publishing post-hoc fact-checks, the effects of fact-checking on the perceived accuracy of false claims, and the creation of computational tools for automatic fact-checking. In this paper, we propose to study fact-checks from a corpus linguistic perspective. This will enable us to gain insight in the scope and contents of fact-checks, to investigate what fact-checks can teach us about the way in which science appears (incorrectly) in the news, and to see how fact-checks behave in the science communication landscape. We report on the creation of FactCorp, a 1,16 million-word corpus containing 1,974 fact-checks from three major Dutch newspapers. We also present results of several exploratory analyses, including a rhetorical moves analysis, a qualitative content elements analysis, and keyword analyses. Through these analyses, we aim to demonstrate the wealth of possible applications that FactCorp allows, thereby stressing the importance of creating such resources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ortmann-dipper-2020-automatic,
    title = "Automatic Orality Identification in Historical Texts",
    author = "Ortmann, Katrin  and
      Dipper, Stefanie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.162",
    pages = "1293--1302",
    abstract = "Independently of the medial representation (written/spoken), language can exhibit characteristics of conceptual orality or literacy, which mainly manifest themselves on the lexical or syntactic level. In this paper we aim at automatically identifying conceptually-oral historical texts, with the ultimate goal of gaining knowledge about spoken data of historical time stages. We apply a set of general linguistic features that have been proven to be effective for the classification of modern language data to historical German texts from various registers. Many of the features turn out to be equally useful in determining the conceptuality of historical data as they are for modern data, especially the frequency of different types of pronouns and the ratio of verbs to nouns. Other features like sentence length, particles or interjections point to peculiarities of the historical data and reveal problems with the adoption of a feature set that was developed on modern language data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{song-etal-2020-using,
    title = "Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour",
    author = "Song, Xingyi  and
      Downs, Johnny  and
      Velupillai, Sumithra  and
      Holden, Rachel  and
      Kikoler, Maxim  and
      Bontcheva, Kalina  and
      Dutta, Rina  and
      Roberts, Angus",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.163",
    pages = "1303--1310",
    abstract = "Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs. We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mohamed-ha-2020-first,
    title = "A First Dataset for Film Age Appropriateness Investigation",
    author = "Mohamed, Emad  and
      Ha, Le An",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.164",
    pages = "1311--1317",
    abstract = "Film age appropriateness classification is an important problem with a significant societal impact that has so far been out of the interest of Natural Language Processing and Machine Learning researchers. To this end, we have collected a corpus of 17000 films along with their age ratings. We use the textual contents in an experiment to predict the correct age classification for the United States (G, PG, PG-13, R and NC-17) and the United Kingdom (U, PG, 12A, 15, 18 and R18). Our experiments indicate that gradient boosting machines beat FastText and various Deep Learning architectures. We reach an overall accuracy of 79.3{\%} for the US ratings compared to a projected super human accuracy of 84{\%}. For the UK ratings, we reach an overall accuracy of 65.3{\%} (UK) compared to a projected super human accuracy of 80.0{\%}.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{el-haj-2020-habibi,
    title = "Habibi - a multi Dialect multi National {A}rabic Song Lyrics Corpus",
    author = "El-Haj, Mahmoud",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.165",
    pages = "1318--1326",
    abstract = "This paper introduces Habibi the first Arabic Song Lyrics corpus. The corpus comprises more than 30,000 Arabic song lyrics in 6 Arabic dialects for singers from 18 different Arabic countries. The lyrics are segmented into more than 500,000 sentences (song verses) with more than 3.5 million words. I provide the corpus in both comma separated value (csv) and annotated plain text (txt) file formats. In addition, I converted the csv version into JavaScript Object Notation (json) and eXtensible Markup Language (xml) file formats. To experiment with the corpus I run extensive binary and multi-class experiments for dialect and country-of-origin identification. The identification tasks include the use of several classical machine learning and deep learning models utilising different word embeddings. For the binary dialect identification task the best performing classifier achieved a testing accuracy of 93{\%}. This was achieved using a word-based Convolutional Neural Network (CNN) utilising a Continuous Bag of Words (CBOW) word embeddings model. The results overall show all classical and deep learning models to outperform our baseline, which demonstrates the suitability of the corpus for both dialect and country-of-origin identification tasks. I am making the corpus and the trained CBOW word embeddings freely available for research purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{shafaei-etal-2020-age,
    title = "Age Suitability Rating: Predicting the {MPAA} Rating Based on Movie Dialogues",
    author = "Shafaei, Mahsa  and
      Safi Samghabadi, Niloofar  and
      Kar, Sudipta  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.166",
    pages = "1327--1335",
    abstract = "Movies help us learn and inspire societal change. But they can also contain objectionable content that negatively affects viewers{'} behaviour, especially children. In this paper, our goal is to predict the suitability of movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We create a corpus for movie MPAA ratings and propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 81{\%} weighted F1-score for the classification model that outperforms the traditional machine learning method by 7{\%}.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alkhereyf-rambow-2020-email,
    title = "Email Classification Incorporating Social Networks and Thread Structure",
    author = "Alkhereyf, Sakhar  and
      Rambow, Owen",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.167",
    pages = "1336--1345",
    abstract = "Existing methods for different document classification tasks in the context of social networks typically only capture the semantics of texts, while ignoring the users who exchange the text and the network they form. However, some work has shown that incorporating the social network information in addition to information from language is effective for various NLP applications including sentiment analysis, inferring user attributes, and predicting inter-personal relations. In this paper, we present an empirical study of email classification into {``}Business{''} and {``}Personal{''} categories. We represent the email communication using various graph structures. As features, we use both the textual information from the email content and social network information from the communication graphs. We also model the thread structure for emails. We focus on detecting personal emails, and we evaluate our methods on two corpora, only one of which we train on. The experimental results reveal that incorporating social network information improves over the performance of an approach based on textual information only. The results also show that considering the thread structure of emails improves the performance further. Furthermore, our approach improves over a state-of-the-art baseline which uses node embeddings based on both lexical and social network information.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tseng-etal-2020-development,
    title = "Development and Validation of a Corpus for Machine Humor Comprehension",
    author = "Tseng, Yuen-Hsien  and
      Wu, Wun-Syuan  and
      Chang, Chia-Yueh  and
      Chen, Hsueh-Chih  and
      Hsu, Wei-Lun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.168",
    pages = "1346--1352",
    abstract = "This work developed a Chinese humor corpus containing 3,365 jokes collected from over 40 sources. Each joke was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. To validate the manual labels, we trained SVM (Support Vector Machine) and BERT (Bidirectional Encoder Representations from Transformers) with half of the corpus (labeled by one annotator) to predict the skill and intent labels of the other half (labeled by the other annotator). Based on two assumptions that a valid manually labeled corpus should follow, our results showed the validity for the skill and intent labels. As to the funniness label, the validation results showed that the correlation between the corpus label and user feedback rating is marginal, which implies that the funniness level is a harder annotation problem to be solved. The contribution of this work is two folds: 1) a Chinese humor corpus is developed with labels of humor skills, intents, and funniness, which allows machines to learn more intricate humor framing, effect, and amusing level to predict and respond in proper context (https://github.com/SamTseng/Chinese{\_}Humor{\_}MultiLabeled). 2) An approach to verify whether a minimum human labeled corpus is valid or not, which facilitates the validation of low-resource corpora.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gala-etal-2020-alector,
    title = "{A}lector: A Parallel Corpus of Simplified {F}rench Texts with Alignments of Misreadings by Poor and Dyslexic Readers",
    author = {Gala, N{\'u}ria  and
      Tack, Ana{\"\i}s  and
      Javourey-Drevet, Ludivine  and
      Fran{\c{c}}ois, Thomas  and
      Ziegler, Johannes C.},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.169",
    pages = "1353--1361",
    abstract = "In this paper, we present a new parallel corpus addressed to researchers, teachers, and speech therapists interested in text simplification as a means of alleviating difficulties in children learning to read. The corpus is composed of excerpts drawn from 79 authentic literary (tales, stories) and scientific (documentary) texts commonly used in French schools for children aged between 7 to 9 years old. The excerpts were manually simplified at the lexical, morpho-syntactic, and discourse levels in order to propose a parallel corpus for reading tests and for the development of automatic text simplification tools. A sample of 21 poor-reading and dyslexic children with an average reading delay of 2.5 years read a portion of the corpus. The transcripts of readings errors were integrated into the corpus with the goal of identifying lexical difficulty in the target population. By means of statistical testing, we provide evidence that the manual simplifications significantly reduced reading errors, highlighting that the words targeted for simplification were not only well-chosen but also substituted with substantially easier alternatives. The entire corpus is available for consultation through a web interface and available on demand for research purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moseley-etal-2020-corpus,
    title = "A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients",
    author = "Moseley, Edward T.  and
      Wu, Joy T.  and
      Welt, Jonathan  and
      Foote, John  and
      Tyler, Patrick D.  and
      Grant, David W.  and
      Carlson, Eric T.  and
      Gehrmann, Sebastian  and
      Dernoncourt, Franck  and
      Celi, Leo Anthony",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.170",
    pages = "1362--1367",
    abstract = "A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much of the information is only present in the patient notes. Therefore, it is critical to develop robust algorithms to infer patients{'} conditions and treatments from their written notes. In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note. Nursing Progress Notes and Discharge Summaries from the Intensive Care Unit of a large tertiary care hospital were manually annotated for the presence of several high-context phenotypes relevant to treatment and risk of re-hospitalization. This dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. Each Discharge Summary and Progress Note has been annotated by at least two expert human annotators (one clinical researcher and one resident physician). Annotated phenotypes include treatment non-adherence, chronic pain, advanced/metastatic cancer, as well as 10 other phenotypes. This dataset can be utilized for academic and industrial research in medicine and computer science, particularly within the field of medical natural language processing.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zotova-etal-2020-multilingual,
    title = "Multilingual Stance Detection in Tweets: The {C}atalonia Independence Corpus",
    author = "Zotova, Elena  and
      Agerri, Rodrigo  and
      Nu{\~n}ez, Manuel  and
      Rigau, German",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.171",
    pages = "1368--1375",
    abstract = "Stance detection aims to determine the attitude of a given text with respect to a specific topic or claim. While stance detection has been fairly well researched in the last years, most the work has been focused on English. This is mainly due to the relative lack of annotated data in other languages. The TW-10 referendum Dataset released at IberEval 2018 is a previous effort to provide multilingual stance-annotated data in Catalan and Spanish. Unfortunately, the TW-10 Catalan subset is extremely imbalanced. This paper addresses these issues by presenting a new multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages, with the aim of facilitating research on stance detection in multilingual and cross-lingual settings. The dataset is annotated with stance towards one topic, namely, the ndependence of Catalonia. We also provide a semi-automatic method to annotate the dataset based on a categorization of Twitter users. We experiment on the new corpus with a number of supervised approaches, including linear classifiers and deep learning methods. Comparison of our new corpus with the with the TW-1O dataset shows both the benefits and potential of a well balanced corpus for multilingual and cross-lingual research on stance detection. Finally, we establish new state-of-the-art results on the TW-10 dataset, both for Catalan and Spanish.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moeed-etal-2020-evaluation,
    title = "An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing",
    author = "Moeed, Abdul  and
      Hagerer, Gerhard  and
      Dugar, Sumit  and
      Gupta, Sarthak  and
      Ghosh, Mainak  and
      Danner, Hannah  and
      Mitevski, Oliver  and
      Nawroth, Andreas  and
      Groh, Georg",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.172",
    pages = "1376--1381",
    abstract = "A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cecillon-etal-2020-wac,
    title = "{WAC}: A Corpus of {W}ikipedia Conversations for Online Abuse Detection",
    author = "C{\'e}cillon, No{\'e}  and
      Labatut, Vincent  and
      Dufour, Richard  and
      Linar{\`e}s, Georges",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.173",
    pages = "1382--1390",
    abstract = "With the spread of online social networks, it is more and more difficult to monitor all the user-generated content. Automating the moderation process of the inappropriate exchange content on Internet has thus become a priority task. Methods have been proposed for this purpose, but it can be challenging to find a suitable dataset to train and develop them. This issue is especially true for approaches based on information derived from the structure and the dynamic of the conversation. In this work, we propose an original framework, based on the the Wikipedia Comment corpus, with comment-level abuse annotations of different types. The major contribution concerns the reconstruction of conversations, by comparison to existing corpora, which focus only on isolated messages (i.e. taken out of their conversational context). This large corpus of more than 380k annotated messages opens perspectives for online abuse detection and especially for context-based approaches. We also propose, in addition to this corpus, a complete benchmarking platform to stimulate and fairly compare scientific works around the problem of content abuse detection, trying to avoid the recurring problem of result replication. Finally, we apply two classification methods to our dataset to demonstrate its potential.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hamoui-etal-2020-flodusta,
    title = "{F}lo{D}us{TA}: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events",
    author = "Hamoui, Btool  and
      Mars, Mourad  and
      Almotairi, Khaled",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.174",
    pages = "1391--1396",
    abstract = "The rise of social media platforms makes it a valuable information source of recent events and users{'} perspective towards them. Twitter has been one of the most important communication platforms in recent years. Event detection, one of the information extraction aspects, involves identifying specified types of events in the text. Detecting events from tweets can help to predict real-world events precisely. A serious challenge that faces Arabic event detection is the lack of Arabic datasets that can be exploited in detecting events. This paper will describe FloDusTA, which is a dataset of tweets that we have built for the purpose of developing an event detection system. The dataset contains tweets written in both Modern Standard Arabic and Saudi dialect. The process of building the dataset starting from tweets collection to annotation by human annotators will be present. The tweets are labeled with four labels: flood, dust storm, traffic accident, and non-event. The dataset was tested for classification and the result was strongly encouraging.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chiril-etal-2020-annotated,
    title = "An Annotated Corpus for Sexism Detection in {F}rench Tweets",
    author = "Chiril, Patricia  and
      Moriceau, V{\'e}ronique  and
      Benamara, Farah  and
      Mari, Alda  and
      Origgi, Gloria  and
      Coulomb-Gully, Marl{\`e}ne",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.175",
    pages = "1397--1403",
    abstract = "Social media networks have become a space where users are free to relate their opinions and sentiments which may lead to a large spreading of hatred or abusive messages which have to be moderated. This paper presents the first French corpus annotated for sexism detection composed of about 12,000 tweets. In a context of offensive content mediation on social media now regulated by European laws, we think that it is important to be able to detect automatically not only sexist content but also to identify if a message with a sexist content is really sexist (i.e. addressed to a woman or describing a woman or women in general) or is a story of sexism experienced by a woman. This point is the novelty of our annotation scheme. We also propose some preliminary results for sexism detection obtained with a deep learning approach. Our experiments show encouraging results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{santos-etal-2020-measuring,
    title = "Measuring the Impact of Readability Features in Fake News Detection",
    author = "Santos, Roney  and
      Pedro, Gabriela  and
      Leal, Sidney  and
      Vale, Oto  and
      Pardo, Thiago  and
      Bontcheva, Kalina  and
      Scarton, Carolina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.176",
    pages = "1404--1413",
    abstract = "The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92{\%} classification accuracy) and may improve previous classification results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{stajner-hulpus-2020-shallow,
    title = "When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features",
    author = "Stajner, Sanja  and
      Hulpu{\textcommabelow{s}}, Ioana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.177",
    pages = "1414--1422",
    abstract = "According to psycholinguistic studies, the complexity of concepts used in a text and the relations between mentioned concepts play the most important role in text understanding and maintaining reader{'}s interest. However, the classical approaches to automatic assessment of text complexity, and their commercial applications, take into consideration mainly syntactic and lexical complexity. Recently, we introduced the task of automatic assessment of conceptual text complexity, proposing a set of graph-based deep semantic features using DBpedia as a proxy to human knowledge. Given that such graphs can be noisy, incomplete, and computationally expensive to deal with, in this paper, we propose the use of textual features and shallow semantic features that only require entity linking. We compare the results obtained with new features with those of the state-of-the-art deep semantic features on two tasks: (1) pairwise comparison of two versions of the same text; and (2) five-level classification of texts. We find that the shallow features achieve state-of-the-art results on both tasks, significantly outperforming performances of the deep semantic features on the five-level classification task. Interestingly, the combination of the shallow and deep semantic features lead to a significant improvement of the performances on that task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{capuozzo-etal-2020-decop,
    title = "{D}ec{O}p: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text",
    author = "Capuozzo, Pasquale  and
      Lauriola, Ivano  and
      Strapparava, Carlo  and
      Aiolli, Fabio  and
      Sartori, Giuseppe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.178",
    pages = "1423--1430",
    abstract = "In recent years, the increasing interest in the development of automatic approaches for unmasking deception in online sources led to promising results. Nonetheless, among the others, two major issues remain still unsolved: the stability of classifiers performances across different domains and languages. Tackling these issues is challenging since labelled corpora involving multiple domains and compiled in more than one language are few in the scientific literature. For filling this gap, in this paper we introduce DecOp (Deceptive Opinions), a new language resource developed for automatic deception detection in cross-domain and cross-language scenarios. DecOp is composed of 5000 examples of both truthful and deceitful first-person opinions balanced both across five different domains and two languages and, to the best of our knowledge, is the largest corpus allowing cross-domain and cross-language comparisons in deceit detection tasks. In this paper, we describe the collection procedure of the DecOp corpus and his main characteristics. Moreover, the human performance on the DecOp test-set and preliminary experiments by means of machine learning models based on Transformer architecture are shown.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{blandin-etal-2020-age,
    title = "Age Recommendation for Texts",
    author = "Blandin, Alexis  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Battistelli, Delphine  and
      {\'E}tienne, Aline",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.179",
    pages = "1431--1439",
    abstract = "The understanding of a text by a reader or listener is conditioned by the adequacy of the text{'}s characteristics with the person{'}s capacities and knowledge. This adequacy is critical in the case of a child since her/his cognitive and linguistic skills are still under development. Hence, in this paper, we present and study an original natural language processing (NLP) task which consists in predicting the age from which a text can be understood by someone. To do so, this paper first exhibits features derived from the psycholinguistic domain, as well as some coming from related NLP tasks. Then, we propose a set of neural network models and compare them on a dataset of French texts dedicated to young or adult audiences. To circumvent the lack of data, we study the idea to predict ages at the sentence level. The experiments first show that the sentence-based age recommendations can be efficiently merged to predict text-based recommendations. Then, we also demonstrate that the age predictions returned by our best model are better than those provided by psycholinguists. Finally, the paper investigates the impact of the various features used in these results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{huang-etal-2020-multilingual,
    title = "Multilingual {T}witter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition",
    author = "Huang, Xiaolei  and
      Xing, Linzi  and
      Dernoncourt, Franck  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.180",
    pages = "1440--1448",
    abstract = "Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this work, we assemble and publish a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. We evaluate the inferred demographic labels with a crowdsourcing platform, Figure Eight. To examine factors that can cause biases, we take an empirical analysis of demographic predictability on the English corpus. We measure the performance of four popular document classifiers and evaluate the fairness and bias of the baseline classifiers on the author-level demographic attributes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{luz-de-araujo-etal-2020-victor,
    title = "{VICTOR}: a Dataset for {B}razilian Legal Documents Classification",
    author = "Luz de Araujo, Pedro Henrique  and
      de Campos, Te{\'o}filo Em{\'\i}dio  and
      Ataides Braz, Fabricio  and
      Correia da Silva, Nilton",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.181",
    pages = "1449--1458",
    abstract = "This paper describes VICTOR, a novel dataset built from Brazil{'}s Supreme Court digitalized legal documents, composed of more than 45 thousand appeals, which includes roughly 692 thousand documents{---}about 4.6 million pages. The dataset contains labeled text data and supports two types of tasks: document type classification; and theme assignment, a multilabel problem. We present baseline results using bag-of-words models, convolutional neural networks, recurrent neural networks and boosting algorithms. We also experiment using linear-chain Conditional Random Fields to leverage the sequential nature of the lawsuits, which we find to lead to improvements on document type classification. Finally we compare a theme classification approach where we use domain knowledge to filter out the less informative document pages to the default one where we use all pages. Contrary to the Court experts{'} expectations, we find that using all available data is the better method. We make the dataset available in three versions of different sizes and contents to encourage explorations of better models and techniques.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{patel-etal-2020-dynamic,
    title = "Dynamic Classification in Web Archiving Collections",
    author = "Patel, Krutarth  and
      Caragea, Cornelia  and
      Phillips, Mark",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.182",
    pages = "1459--1468",
    abstract = "The Web archived data usually contains high-quality documents that are very useful for creating specialized collections of documents. To create such collections, there is a substantial need for automatic approaches that can distinguish the documents of interest for a collection out of the large collections (of millions in size) from Web Archiving institutions. However, the patterns of the documents of interest can differ substantially from one document to another, which makes the automatic classification task very challenging. In this paper, we explore dynamic fusion models to find, on the fly, the model or combination of models that performs best on a variety of document types. Our experimental results show that the approach that fuses different models outperforms individual models and other ensemble methods on three datasets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vasconcelos-etal-2020-aspect,
    title = "Aspect Flow Representation and Audio Inspired Analysis for Texts",
    author = "Vasconcelos, Larissa  and
      Campelo, Claudio  and
      Jeronimo, Caio",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.183",
    pages = "1469--1477",
    abstract = "For better understanding how people write texts, it is fundamental to examine how a particular aspect (e.g., subjectivity, sentiment, argumentation) is exploited in a text. Analysing such an aspect of a text as a whole (i.e., through a summarised single feature) can lead to significant information loss. In this paper, we propose a novel method of representing and analysing texts that consider how an aspect behaves throughout the text. We represent the texts by aspect flows for capturing all the aspect behaviour. Then, inspired by the resemblance between these flows format and a sound waveform, we fragment them into frames and calculate an adaptation of audio analysis features, named here Audio-Like Features, as a way of analysing the texts. The results of the conducted classification tasks reveal that our approach can surpass methods based on summarised features. We also show that a detailed examination of the Audio-Like Features can lead to a more profound knowledge about the represented texts.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lim-etal-2020-annotating,
    title = "Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing",
    author = {Lim, Sora  and
      Jatowt, Adam  and
      F{\"a}rber, Michael  and
      Yoshikawa, Masatoshi},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.184",
    pages = "1478--1484",
    abstract = "The spread of biased news and its consumption by the readers has become a considerable issue. Researchers from multiple domains including social science and media studies have made efforts to mitigate this media bias issue. Specifically, various techniques ranging from natural language processing to machine learning have been used to help determine news bias automatically. However, due to the lack of publicly available datasets in this field, especially ones containing labels concerning bias on a fine-grained level (e.g., on sentence level), it is still challenging to develop methods for effectively identifying bias embedded in new articles. In this paper, we propose a novel news bias dataset which facilitates the development and evaluation of approaches for detecting subtle bias in news articles and for understanding the characteristics of biased sentences. Our dataset consists of 966 sentences from 46 English-language news articles covering 4 different events and contains labels concerning bias on the sentence level. For scalability reasons, the labels were obtained based on crowd-sourcing. Our dataset can be used for analyzing news bias, as well as for developing and evaluating methods for news bias detection. It can also serve as resource for related researches including ones focusing on fake news detection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jayashree-srijith-2020-evaluation,
    title = "Evaluation of Deep {G}aussian Processes for Text Classification",
    author = "Jayashree, P.  and
      Srijith, P. K.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.185",
    pages = "1485--1491",
    abstract = "With the tremendous success of deep learning models on computer vision tasks, there are various emerging works on the Natural Language Processing (NLP) task of Text Classification using parametric models. However, it constrains the expressability limit of the function and demands enormous empirical efforts to come up with a robust model architecture. Also, the huge parameters involved in the model causes over-fitting when dealing with small datasets. Deep Gaussian Processes (DGP) offer a Bayesian non-parametric modelling framework with strong function compositionality, and helps in overcoming these limitations. In this paper, we propose DGP models for the task of Text Classification and an empirical comparison of the performance of shallow and Deep Gaussian Process models is made. Extensive experimentation is performed on the benchmark Text Classification datasets such as TREC (Text REtrieval Conference), SST (Stanford Sentiment Treebank), MR (Movie Reviews), R8 (Reuters-8), which demonstrate the effectiveness of DGP models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{plaza-del-arco-etal-2020-emoevent,
    title = "{E}mo{E}vent: A Multilingual Emotion Corpus based on different Events",
    author = "Plaza del Arco, Flor Miriam  and
      Strapparava, Carlo  and
      Urena Lopez, L. Alfonso  and
      Martin, Maite",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.186",
    pages = "1492--1498",
    abstract = "In recent years emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, and artificial intelligence, among others. While opinion mining is a well-established task with many standard data sets and well-defined methodologies, emotion mining has received less attention due to its complexity. In particular, the annotated gold standard resources available are not enough. In order to address this shortage, we present a multilingual emotion data set based on different events that took place in April 2019. We collected tweets from the Twitter platform. Then one of seven emotions, six Ekman{'}s basic emotions plus the {``}neutral or other emotions{''}, was labeled on each tweet by 3 Amazon MTurkers. A total of 8,409 in Spanish and 7,303 in English were labeled. In addition, each tweet was also labeled as offensive or no offensive. We report some linguistic statistics about the data set in order to observe the difference between English and Spanish speakers when they express emotions related to the same events. Moreover, in order to validate the effectiveness of the data set, we also propose a machine learning approach for automatically detecting emotions in tweets for both languages, English and Spanish.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jaiswal-etal-2020-muse,
    title = "{M}u{SE}: a Multimodal Dataset of Stressed Emotion",
    author = "Jaiswal, Mimansa  and
      Bara, Cristian-Paul  and
      Luo, Yuanhang  and
      Burzo, Mihai  and
      Mihalcea, Rada  and
      Provost, Emily Mower",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.187",
    pages = "1499--1510",
    abstract = "Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users{'} affective state. These affective states are impacted by a combination of emotion inducers, current psychological state, and various conversational factors. Although emotion classification in both singular and dyadic settings is an established area, the effects of these additional factors on the production and perception of emotion is understudied. This paper presents a new dataset, Multimodal Stressed Emotion (MuSE), to study the multimodal interplay between the presence of stress and expressions of affect. We describe the data collection protocol, the possible areas of use, and the annotations for the emotional content of the recordings. The paper also presents several baselines to measure the performance of multimodal features for emotion and stress classification.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zhang-etal-2020-affect,
    title = "Affect in{T}weets: A Transfer Learning Approach",
    author = "Zhang, Linrui  and
      Huang, Hsin-Lun  and
      Yu, Yang  and
      Moldovan, Dan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.188",
    pages = "1511--1516",
    abstract = "People convey sentiments and emotions through language. To understand these affectual states is an essential step towards understanding natural language. In this paper, we propose a transfer-learning based approach to inferring the affectual state of a person from their tweets. As opposed to the traditional machine learning models which require considerable effort in designing task specific features, our model can be well adapted to the proposed tasks with a very limited amount of fine-tuning, which significantly reduces the manual effort in feature engineering. We aim to show that by leveraging the pre-learned knowledge, transfer learning models can achieve competitive results in the affectual content analysis of tweets, compared to the traditional models. As shown by the experiments on SemEval-2018 Task 1: Affect in Tweets, our model ranking 2nd, 4th and 6th place in four of its subtasks proves the effectiveness of our idea.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tammewar-etal-2020-annotation,
    title = "Annotation of Emotion Carriers in Personal Narratives",
    author = "Tammewar, Aniruddha  and
      Cervone, Alessandra  and
      Messner, Eva-Maria  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.189",
    pages = "1517--1525",
    abstract = "We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts. For PNs, we define emotion carriers as the speech or text segments that best explain the emotional state of the narrator. Such segments may span from single to multiple words, containing for example verb or noun phrases. Advanced automatic understanding of PNs requires not only the prediction of the narrator{'}s emotional state but also to identify which events (e.g. the loss of a relative or the visit of grandpa) or people (e.g. the old group of high school mates) carry the emotion manifested during the personal recollection. This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives. Compared to other text genres such as news and microblogs, spoken PNs are particularly challenging because a narrative is usually unstructured, involving multiple sub-events and characters as well as thoughts and associated emotions perceived by the narrator. In this work, we experiment with annotating emotion carriers in speech transcriptions from the Ulm State-of-Mind in Speech (USoMS) corpus, a dataset of PNs in German. We believe this resource could be used for experiments in the automatic extraction of emotion carriers from PN, a task that could provide further advancements in narrative understanding.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wottawa-etal-2020-towards,
    title = "Towards Interactive Annotation for Hesitation in Conversational Speech",
    author = "Wottawa, Jane  and
      Tahon, Marie  and
      Marin, Apolline  and
      Audibert, Nicolas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.190",
    pages = "1526--1532",
    abstract = "Manual annotation of speech corpora is expensive in both human resources and time. Furthermore, recognizing affects in spontaneous, non acted speech presents a challenge for humans and machines. The aim of the present study is to automatize the labeling of hesitant speech as a marker of expressed uncertainty. That is why, the NCCFr-corpus was manually annotated for {`}degree of hesitation{'} on a continuous scale between -3 and 3 and the affective dimensions {`}activation, valence and control{'}. In total, 5834 chunks of the NCCFr-corpus were manually annotated. Acoustic analyses were carried out based on these annotations. Furthermore, regression models were trained in order to allow automatic prediction of hesitation for speech chunks that do not have a manual annotation. Preliminary results show that the number of filled pauses as well as vowel duration increase with the degree of hesitation, and that automatic prediction of the hesitation degree reaches encouraging RMSE results of 1.6.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{costa-jussa-etal-2020-abusive,
    title = "Abusive language in {S}panish children and young teenager{'}s conversations: data preparation and short text classification with contextual word embeddings",
    author = "Costa-juss{\`a}, Marta R.  and
      Gonz{\'a}lez, Esther  and
      Moreno, Asuncion  and
      Cumalat, Eudald",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.191",
    pages = "1533--1537",
    abstract = "Abusive texts are reaching the interests of the scientific and social community. How to automatically detect them is onequestion that is gaining interest in the natural language processing community. The main contribution of this paper is toevaluate the quality of the recently developed {''}Spanish Database for cyberbullying prevention{''} for the purpose of trainingclassifiers on detecting abusive short texts. We compare classical machine learning techniques to the use of a more ad-vanced model: the contextual word embeddings in the particular case of classification of abusive short-texts for the Spanishlanguage. As contextual word embeddings, we use Bidirectional Encoder Representation from Transformers (BERT), pro-posed at the end of 2018. We show that BERT mostly outperforms classical techniques. Far beyond the experimentalimpact of our research, this project aims at planting the seeds for an innovative technological tool with a high potentialsocial impact and aiming at being part of the initiatives in artificial intelligence for social good.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rambabu-etal-2020-iiit,
    title = "{IIIT}-{H} {TEMD} Semi-Natural Emotional Speech Database from Professional Actors and Non-Actors",
    author = "Rambabu, Banothu  and
      Botsa, Kishore Kumar  and
      Paidi, Gangamohan  and
      Gangashetty, Suryakanth V",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.192",
    pages = "1538--1545",
    abstract = "A fundamental essence for emotional speech analysis towards emotion recognition is a good database. Database collected from natural scenarios consists of spontaneous emotions, but there are several issues in collection of such database. Other than the privacy and legal related concerns, there is no control over environment at the background. As it is difficult to collect data from natural scenarios, many research groups have collected data from semi-natural or designed procedures. In this paper, a new emotional speech database named IIIT-H TEMD (International Institute of Information Technology-Hyderabad Telugu Emotional Database) is collected using designed drama situations from actors and non-actors. Utterances are manually annotated using a hybrid strategy by giving the context to one of the listeners. As some of the data collection studies in the literature recommend for actors, analysis of actors data versus non-actors data is carried out for their significance. The total size of the dataset is about 5 hours, which makes it an useful resource for the emotional speech analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{janssoone-etal-2020-potus,
    title = "The {POTUS} Corpus, a Database of Weekly Addresses for the Study of Stance in Politics and Virtual Agents",
    author = {Janssoone, Thomas  and
      Bailly, K{\'e}vin  and
      Richard, Ga{\"e}l  and
      Clavel, Chlo{\'e}},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.193",
    pages = "1546--1553",
    abstract = "One of the main challenges in the field of Embodied Conversational Agent (ECA) is to generate socially believable agents. The common strategy for agent behaviour synthesis is to rely on dedicated corpus analysis. Such a corpus is composed of multimedia files of socio-emotional behaviors which have been annotated by external observers. The underlying idea is to identify interaction information for the agent{'}s socio-emotional behavior by checking whether the intended socio-emotional behavior is actually perceived by humans. Then, the annotations can be used as learning classes for machine learning algorithms applied to the social signals. This paper introduces the POTUS Corpus composed of high-quality audio-video files of political addresses to the American people. Two protagonists are present in this database. First, it includes speeches of former president Barack Obama to the American people. Secondly, it provides videos of these same speeches given by a virtual agent named Rodrigue. The ECA reproduces the original address as closely as possible using social signals automatically extracted from the original one. Both are annotated for social attitudes, providing information about the stance observed in each file. It also provides the social signals automatically extracted from Obama{'}s addresses used to generate Rodrigue{'}s ones.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bostan-etal-2020-goodnewseveryone,
    title = "{G}ood{N}ews{E}veryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception",
    author = "Bostan, Laura Ana Maria  and
      Kim, Evgeny  and
      Klinger, Roman",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.194",
    pages = "1554--1566",
    abstract = "Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, as well as the reader{'}s perception of the emotion of the headline. This annotation task is comparably challenging, given the large number of classes and roles to be identified. We therefore propose a multiphase annotation procedure in which we first find relevant instances with emotional content and then annotate the more fine-grained aspects. Finally, we develop a baseline for the task of automatic prediction of semantic role structures and discuss the results. The corpus we release enables further research on emotion classification, emotion intensity prediction, emotion cause detection, and supports further qualitative studies.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kiritchenko-etal-2020-solo,
    title = "{SOLO}: A Corpus of Tweets for Examining the State of Being Alone",
    author = "Kiritchenko, Svetlana  and
      Hipson, Will  and
      Coplan, Robert  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.195",
    pages = "1567--1577",
    abstract = "The state of being alone can have a substantial impact on our lives, though experiences with time alone diverge significantly among individuals. Psychologists distinguish between the concept of solitude, a positive state of voluntary aloneness, and the concept of loneliness, a negative state of dissatisfaction with the quality of one{'}s social interactions. Here, for the first time, we conduct a large-scale computational analysis to explore how the terms associated with the state of being alone are used in online language. We present SOLO (State of Being Alone), a corpus of over 4 million tweets collected with query terms solitude, lonely, and loneliness. We use SOLO to analyze the language and emotions associated with the state of being alone. We show that the term solitude tends to co-occur with more positive, high-dominance words (e.g., enjoy, bliss) while the terms lonely and loneliness frequently co-occur with negative, low-dominance words (e.g., scared, depressed), which confirms the conceptual distinctions made in psychology. We also show that women are more likely to report on negative feelings of being lonely as compared to men, and there are more teenagers among the tweeters that use the word lonely than among the tweeters that use the word solitude.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hipson-mohammad-2020-poki,
    title = "{P}o{K}i: A Large Dataset of Poems by Children",
    author = "Hipson, Will  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.196",
    pages = "1578--1589",
    abstract = "Child language studies are crucial in improving our understanding of child well-being; especially in determining the factors that impact happiness, the sources of anxiety, techniques of emotion regulation, and the mechanisms to cope with stress. However, much of this research is stymied by the lack of availability of large child-written texts. We present a new corpus of child-written text, PoKi, which includes about 62 thousand poems written by children from grades 1 to 12. PoKi is especially useful in studying child language because it comes with information about the age of the child authors (their grade). We analyze the words in PoKi along several emotion dimensions (valence, arousal, dominance) and discrete emotions (anger, fear, sadness, joy). We use non-parametric regressions to model developmental differences from early childhood to late-adolescence. Results show decreases in valence that are especially pronounced during mid-adolescence, while arousal and dominance peaked during adolescence. Gender differences in the developmental trajectory of emotions are also observed. Our results support and extend the current state of emotion development research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{macary-etal-2020-allosat,
    title = "{A}llo{S}at: A New Call Center {F}rench Corpus for Satisfaction and Frustration Analysis",
    author = "Macary, Manon  and
      Tahon, Marie  and
      Est{\`e}ve, Yannick  and
      Rousseau, Anthony",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.197",
    pages = "1590--1597",
    abstract = "We present a new corpus, named AlloSat, composed of real-life call center conversations in French that is continuously annotated in frustration and satisfaction. This corpus has been set up to develop new systems able to model the continuous aspect of semantic and paralinguistic information at the conversation level. The present work focuses on the paralinguistic level, more precisely on the expression of emotions. In the call center industry, the conversation usually aims at solving the caller{'}s request. As far as we know, most emotional databases contain static annotations in discrete categories or in dimensions such as activation or valence. We hypothesize that these dimensions are not task-related enough. Moreover, static annotations do not enable to explore the temporal evolution of emotional states. To solve this issue, we propose a corpus with a rich annotation scheme enabling a real-time investigation of the axis frustration / satisfaction. AlloSat regroups 303 conversations with a total of approximately 37 hours of audio, all recorded in real-life environments collected by Allo-Media (an intelligent call tracking company). First regression experiments, with audio features, show that the evolution of frustration / satisfaction axis can be retrieved automatically at the conversation level.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wu-chien-2020-learning,
    title = "Learning the Human Judgment for the Automatic Evaluation of Chatbot",
    author = "Wu, Shih-Hung  and
      Chien, Sheng-Lun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.198",
    pages = "1598--1602",
    abstract = "It is hard to evaluate the quality of the generated text by a generative dialogue system. Currently, dialogue evaluation relies on human judges to label the quality of the generated text. It is not a reusable mechanism that can give consistent evaluation for system developers. We believe that it is easier to get consistent results on comparing two generated dialogue by two systems and it is hard to give a consistent quality score on only one system at a time. In this paper, we propose a machine learning approach to reduce the effort of human evaluation by learning the human judgment on comparing two dialogue systems. Training from the human labeling result, the evaluation model learns which generative models is better in each dialog context. Thus, it can be used for system developers to compare the fine-tuned models over and over again without the human labor. In our experiment we find the agreement between the learned model and human judge is 70{\%}. The experiment is conducted on comparing two attention based GRU-RNN generative models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lee-etal-2020-korean,
    title = "{K}orean-Specific Emotion Annotation Procedure Using {N}-Gram-Based Distant Supervision and {K}orean-Specific-Feature-Based Distant Supervision",
    author = "Lee, Young-Jun  and
      Lim, Chae-Gyun  and
      Choi, Ho-Jin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.199",
    pages = "1603--1610",
    abstract = "Detecting emotions from texts is considerably important in an NLP task, but it has the limitation of the scarcity of manually labeled data. To overcome this limitation, many researchers have annotated unlabeled data with certain frequently used annotation procedures. However, most of these studies are focused mainly on English and do not consider the characteristics of the Korean language. In this paper, we present a Korean-specific annotation procedure, which consists of two parts, namely n-gram-based distant supervision and Korean-specific-feature-based distant supervision. We leverage the distant supervision with the n-gram and Korean emotion lexicons. Then, we consider the Korean-specific emotion features. Through experiments, we showed the effectiveness of our procedure by comparing with the KTEA dataset. Additionally, we constructed a large-scale emotion-labeled dataset, Korean Movie Review Emotion (KMRE) Dataset, using our procedure. In order to construct our dataset, we used a large-scale sentiment movie review corpus as the unlabeled dataset. Moreover, we used a Korean emotion lexicon provided by KTEA. We also performed an emotion classification task and a human evaluation on the KMRE dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{xu-etal-2020-semi,
    title = "Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification",
    author = "Xu, Jiajun  and
      Masuda, Kyosuke  and
      Nishizaki, Hiromitsu  and
      Fukumoto, Fumiyo  and
      Suzuki, Yoshimi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.200",
    pages = "1611--1617",
    abstract = "In the case of using a deep learning (machine learning) framework for emotion classification, one significant difficulty faced is the requirement of building a large, emotion corpus in which each sentence is assigned emotion labels. As a result, there is a high cost in terms of time and money associated with the construction of such a corpus. Therefore, this paper proposes a method of creating a semi-automatically constructed emotion corpus. For the purpose of this study sentences were mined from Twitter using some emotional seed words that were selected from a dictionary in which the emotion words were well-defined. Tweets were retrieved by one emotional seed word, and the retrieved sentences were assigned emotion labels based on the emotion category of the seed word. It was evident from the findings that the deep learning-based emotion classification model could not achieve high levels of accuracy in emotion classification because the semi-automatically constructed corpus had many errors when assigning emotion labels. In this paper, therefore, an approach for improving the quality of the emotion labels by automatically correcting the errors of emotion labels is proposed and tested. The experimental results showed that the proposed method worked well, and the classification accuracy rate was improved to 55.1{\%} from 44.9{\%} on the Twitter emotion classification task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ghosh-etal-2020-cease,
    title = "{CEASE}, a Corpus of Emotion Annotated Suicide notes in {E}nglish",
    author = "Ghosh, Soumitra  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.201",
    pages = "1618--1626",
    abstract = "A suicide note is usually written shortly before the suicide and it provides a chance to comprehend the self-destructive state of mind of the deceased. From a psychological point of view, suicide notes have been utilized for recognizing the motive behind the suicide. To the best of our knowledge, there is no openly accessible suicide note corpus at present, making it challenging for the researchers and developers to deep dive into the area of mental health assessment and suicide prevention. In this paper, we create a fine-grained emotion annotated corpus (CEASE) of suicide notes in English and develop various deep learning models to perform emotion detection on the curated dataset. The corpus consists of 2393 sentences from around 205 suicide notes collected from various sources. Each sentence is annotated with a particular emotion class from a set of 15 fine-grained emotion labels, namely (forgiveness, happiness{\_}peacefulness, love, pride, hopefulness, thankfulness, blame, anger, fear, abuse, sorrow, hopelessness, guilt, information, instructions). For the evaluation, we develop an ensemble architecture, where the base models correspond to three supervised deep learning models, namely Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM). We obtain the highest test accuracy of 60.17{\%} and cross-validation accuracy of 60.32{\%}",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{guhr-etal-2020-training,
    title = "Training a Broad-Coverage {G}erman Sentiment Classification Model for Dialog Systems",
    author = {Guhr, Oliver  and
      Schumann, Anne-Kathrin  and
      Bahrmann, Frank  and
      B{\"o}hme, Hans Joachim},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.202",
    pages = "1627--1632",
    abstract = "This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lee-lau-2020-event,
    title = "An Event-comment Social Media Corpus for Implicit Emotion Analysis",
    author = "Lee, Sophia Yat Mei  and
      Lau, Helena Yan Ping",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.203",
    pages = "1633--1642",
    abstract = "The classification of implicit emotions in text has always been a great challenge to emotion processing. Even though the majority of emotion expressed implicitly, most previous attempts at emotions have focused on the examination of explicit emotions. The poor performance of existing emotion identification and classification models can partly be attributed to the disregard of implicit emotions. In view of this, this paper presents the development of a Chinese event-comment social media emotion corpus. The corpus deals with both explicit and implicit emotions with more emphasis being placed on the implicit ones. This paper specifically describes the data collection and annotation of the corpus. An annotation scheme has been proposed for the annotation of emotion-related information including the emotion type, the emotion cause, the emotion reaction, the use of rhetorical question, the opinion target (i.e. the semantic role in an event that triggers an emotion), etc. Corpus data shows that the annotated items are of great value to the identification of implicit emotions. We believe that the corpus will be a useful resource for both explicit and implicit emotion classification and detection as well as event classification.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{de-bruyne-etal-2020-emotional,
    title = "An Emotional Mess! Deciding on a Framework for Building a {D}utch Emotion-Annotated Corpus",
    author = "De Bruyne, Luna  and
      De Clercq, Orphee  and
      Hoste, Veronique",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.204",
    pages = "1643--1651",
    abstract = "Seeing the myriad of existing emotion models, with the categorical versus dimensional opposition the most important dividing line, building an emotion-annotated corpus requires some well thought-out strategies concerning framework choice. In our work on automatic emotion detection in Dutch texts, we investigate this problem by means of two case studies. We find that the labels joy, love, anger, sadness and fear are well-suited to annotate texts coming from various domains and topics, but that the connotation of the labels strongly depends on the origin of the texts. Moreover, it seems that information is lost when an emotional state is forcedly classified in a limited set of categories, indicating that a bi-representational format is desirable when creating an emotion corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{haider-etal-2020-po,
    title = "{PO}-{EMO}: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in {G}erman and {E}nglish Poetry",
    author = "Haider, Thomas  and
      Eger, Steffen  and
      Kim, Evgeny  and
      Klinger, Roman  and
      Menninghaus, Winfried",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.205",
    pages = "1652--1663",
    abstract = "Most approaches to emotion analysis of social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik. However, art (such as literature) enables engagement in a broader range of more complex and subtle emotions. These have been shown to also include mixed emotional responses. We consider emotions in poetry as they are elicited in the reader, rather than what is expressed in the text or intended by the author. Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context. We evaluate this novel setting in an annotation experiment both with carefully trained experts and via crowdsourcing. Our annotation with experts leads to an acceptable agreement of k = .70, resulting in a consistent dataset for future large scale analysis. Finally, we conduct first emotion classification experiments based on BERT, showing that identifying aesthetic emotions is challenging in our data, with up to .52 F1-micro on the German subset. Data and resources are available at https://github.com/tnhaider/poetry-emotion.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sedoc-etal-2020-learning,
    title = "Learning Word Ratings for Empathy and Distress from Document-Level User Responses",
    author = "Sedoc, Jo{\~a}o  and
      Buechel, Sven  and
      Nachmany, Yehonathan  and
      Buffone, Anneke  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.206",
    pages = "1664--1673",
    abstract = "Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different emotions are indispensable to the NLP community because they allow for interpretable and robust predictions. Emotion analysis of text is increasing in popularity in NLP; however, manually creating lexica for psychological constructs such as empathy has proven difficult. This paper automatically creates empathy word ratings from document-level ratings. The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods. We systematically compare a number of approaches to learning word ratings from higher-level supervision against a Mixed-Level Feed Forward Network (MLFFN), which we find performs best, and use the MLFFN to create the first-ever empathy lexicon. We then use Signed Spectral Clustering to gain insights into the resulting words. The empathy and distress lexica are publicly available at: http://www.wwbp.org/lexica.html.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dadas-etal-2020-evaluation,
    title = "Evaluation of Sentence Representations in {P}olish",
    author = "Dadas, Slawomir  and
      Pere{\l}kiewicz, Micha{\l}  and
      Po{\'s}wiata, Rafa{\l}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.207",
    pages = "1674--1680",
    abstract = "Methods for learning sentence representations have been actively developed in recent years. However, the lack of pre-trained models and datasets annotated at the sentence level has been a problem for low-resource languages such as Polish which led to less interest in applying these methods to language-specific tasks. In this study, we introduce two new Polish datasets for evaluating sentence embeddings and provide a comprehensive evaluation of eight sentence representation methods including Polish and multilingual models. We consider classic word embedding models, recently developed contextual embeddings and multilingual sentence encoders, showing strengths and weaknesses of specific approaches. We also examine different methods of aggregating word vectors into a single sentence vector.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{riad-etal-2020-identification,
    title = "Identification of Primary and Collateral Tracks in Stuttered Speech",
    author = "Riad, Rachid  and
      Bachoud-L{\'e}vi, Anne-Catherine  and
      Rudzicz, Frank  and
      Dupoux, Emmanuel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.208",
    pages = "1681--1688",
    abstract = "Disfluent speech has been previously addressed from two main perspectives: the clinical perspective focusing on diagnostic, and the Natural Language Processing (NLP) perspective aiming at modeling these events and detect them for downstream tasks. In addition, previous works often used different metrics depending on whether the input features are text or speech, making it difficult to compare the different contributions. Here, we introduce a new evaluation framework for disfluency detection inspired by the clinical and NLP perspective together with the theory of performance from (Clark, 1996) which distinguishes between primary and collateral tracks. We introduce a novel forced-aligned disfluency dataset from a corpus of semi-directed interviews, and present baseline results directly comparing the performance of text-based features (word and span information) and speech-based (acoustic-prosodic information). Finally, we introduce new audio features inspired by the word-based span features. We show experimentally that using these features outperformed the baselines for speech-based predictions on the present dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ghio-etal-2020-compare,
    title = "How to Compare Automatically Two Phonological Strings: Application to Intelligibility Measurement in the Case of Atypical Speech",
    author = "Ghio, Alain  and
      Lalain, Muriel  and
      Giusti, Laurence  and
      Fredouille, Corinne  and
      Woisard, Virginie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.209",
    pages = "1689--1694",
    abstract = "Atypical speech productions, regardless of their origins (accents, learning, pathology), need to be assessed with regard to {``}typical{''} or {``}expected{''} productions. Evaluation is necessarily based on comparisons between linguistic forms produced and linguistic forms expected. In the field of speech disorders, the intelligibility of a patient is evaluated in order to measure the functional impact of his/her pathology on his/her oral communication. The usual method is to transcribe orthographic linguistic forms perceived and to assign a global and imprecise rating based on their correctness or incorrect. To obtain a more precise evaluation of the production deviations, we propose a measurement method based on phonological transcriptions. An algorithm computes automatically and finely the distances between the phonological forms produced and expected from cost matrices based on the differences of features between phonemes. A first test of this method among a large population of healthy speakers and patients treated for cancer of the oral and pharyngeal cavities has proved its validity.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{liu-etal-2020-evaluating,
    title = "Evaluating Text Coherence at Sentence and Paragraph Levels",
    author = "Liu, Sennan  and
      Zeng, Shuang  and
      Li, Sujian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.210",
    pages = "1695--1703",
    abstract = "In this paper, to evaluate text coherence, we propose the paragraph ordering task as well as conducting sentence ordering. We collected four distinct corpora from different domains on which we investigate the adaptation of existing sentence ordering methods to a paragraph ordering task. We also compare the learnability and robustness of existing models by artificially creating mini datasets and noisy datasets respectively and verifying the efficiency of established models under these circumstances. Furthermore, we carry out human evaluation on the rearranged passages from two competitive models and confirm that WLCS-l is a better metric performing significantly higher correlations with human rating than τ , the most prevalent metric used before. Results from these evaluations show that except for certain extreme conditions, the recurrent graph neural network-based model is an optimal choice for coherence modeling.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bernier-colborne-langlais-2020-hardeval,
    title = "{H}ard{E}val: Focusing on Challenging Tokens to Assess Robustness of {NER}",
    author = "Bernier-Colborne, Gabriel  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.211",
    pages = "1704--1711",
    abstract = "To assess the robustness of NER systems, we propose an evaluation method that focuses on subsets of tokens that represent specific sources of errors: unknown words and label shift or ambiguity. These subsets provide a system-agnostic basis for evaluating specific sources of NER errors and assessing room for improvement in terms of robustness. We analyze these subsets of challenging tokens in two widely-used NER benchmarks, then exploit them to evaluate NER systems in both in-domain and out-of-domain settings. Results show that these challenging tokens explain the majority of errors made by modern NER systems, although they represent only a small fraction of test tokens. They also indicate that label shift is harder to deal with than unknown words, and that there is much more room for improvement than the standard NER evaluation procedure would suggest. We hope this work will encourage NLP researchers to adopt rigorous and meaningful evaluation methods, and will help them develop more robust models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{iwatsuki-etal-2020-evaluation,
    title = "An Evaluation Dataset for Identifying Communicative Functions of Sentences in {E}nglish Scholarly Papers",
    author = "Iwatsuki, Kenichi  and
      Boudin, Florian  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.212",
    pages = "1712--1720",
    abstract = "Formulaic expressions, such as {`}in this paper we propose{'}, are used by authors of scholarly papers to perform communicative functions; the communicative function of the present example is {`}stating the aim of the paper{'}. Collecting such expressions and pairing them with their communicative functions would be highly valuable for various tasks, particularly for writing assistance. However, such collection and paring in a principled and automated manner would require high-quality annotated data, which are not available. In this study, we address this shortcoming by creating a manually annotated dataset for detecting communicative functions in sentences. Starting from a seed list of labelled formulaic expressions, we retrieved new sentences from scholarly papers in the ACL Anthology and asked multiple human evaluators to label communicative functions. To show the usefulness of our dataset, we conducted a series of experiments that determined to what extent sentence representations acquired by recent models, such as word2vec and BERT, can be employed to detect communicative functions in sentences.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fassetti-fassetti-2020-automatic,
    title = "An Automatic Tool For Language Evaluation",
    author = "Fassetti, Fabio  and
      Fassetti, Ilaria",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.213",
    pages = "1721--1726",
    abstract = "The aim of evaluating children speech and language is to measure their communication skills. In particular, the speech language pathologist is interested in determining the child{'}s impairments in the areas of language, articulation, voice, fluency and swallowing. In literature some standardized tests have been proposed to assess and screen developmental language impairments but they require manual laborious transcription, annotation and calculation. This work is very time demanding and, also, may introduce several kinds of errors in the evaluation phase and non-uniform evaluations. In order to help therapists, a system performing automated evaluation is proposed. Providing as input the correct sentence and the sentence produced by patients, the technique evaluates the level of the verbal production and returns a score. The main phases of the method concern an ad-hoc transformation of the produced sentence in the reference sentence and in the evaluation of the cost of this transformation. Since the cost function is related to many weights, a learning phase is defined to automatically set such weights.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{boyd-graber-etal-2020-evaluations,
    title = "Which Evaluations Uncover Sense Representations that Actually Make Sense?",
    author = "Boyd-Graber, Jordan  and
      Guo, Fenfei  and
      Findlater, Leah  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.214",
    pages = "1727--1738",
    abstract = "Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word{'}s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language{'}s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lai-etal-2020-diversity,
    title = "Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections",
    author = "Lai, Yi-An  and
      Zhu, Xuan  and
      Zhang, Yi  and
      Diab, Mona",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.215",
    pages = "1739--1746",
    abstract = "Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, there are still insufficient characteristic metrics to describe a collection of texts in terms of the words, sentences, or paragraphs they comprise. In this work, we propose metrics of diversity, density, and homogeneity that quantitatively measure the dispersion, sparsity, and uniformity of a text collection. We conduct a series of simulations to verify that each metric holds desired properties and resonates with human intuitions. Experiments on real-world datasets demonstrate that the proposed characteristic metrics are highly correlated with text classification performance of a renowned model, BERT, which could inspire future applications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{min-etal-2020-towards,
    title = "Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A {S}iamese Network Approach",
    author = "Min, Bonan  and
      Chan, Yee Seng  and
      Zhao, Lingjun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.216",
    pages = "1747--1752",
    abstract = "Automatically analyzing events in a large amount of text is crucial for situation awareness and decision making. Previous approaches treat event extraction as {``}one size fits all{''} with an ontology defined a priori. The resulted extraction models are built just for extracting those types in the ontology. These approaches cannot be easily adapted to new event types nor new domains of interest. To accommodate personalized event-centric information needs, this paper introduces the few-shot Event Mention Retrieval (EMR) task: given a user-supplied query consisting of a handful of event mentions, return relevant event mentions found in a corpus. This formulation enables {``}query by example{''}, which drastically lowers the bar of specifying event-centric information needs. The retrieval setting also enables fuzzy search. We present an evaluation framework leveraging existing event datasets such as ACE. We also develop a Siamese Network approach, and show that it performs better than ad-hoc retrieval models in the few-shot EMR setting.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{horbach-etal-2020-linguistic,
    title = "Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions",
    author = "Horbach, Andrea  and
      Aldabe, Itziar  and
      Bexte, Marie  and
      Lopez de Lacalle, Oier  and
      Maritxalar, Montse",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.217",
    pages = "1753--1762",
    abstract = "Automatic generation of reading comprehension questions is a topic receiving growing interest in the NLP community, but there is currently no consensus on evaluation metrics and many approaches focus on linguistic quality only while ignoring the pedagogic value and appropriateness of questions. This paper overcomes such weaknesses by a new evaluation scheme where questions from the questionnaire are structured in a hierarchical way to avoid confronting human annotators with evaluation measures that do not make sense for a certain question. We show through an annotation study that our scheme can be applied, but that expert annotators with some level of expertise are needed. We also created and evaluated two new evaluation data sets from the biology domain for Basque and German, composed of questions written by people with an educational background, which will be publicly released. Results show that manually generated questions are in general both of higher linguistic as well as pedagogic quality and that among the human generated questions, teacher-generated ones tend to be most useful.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{born-etal-2020-dataset,
    title = "Dataset Reproducibility and {IR} Methods in Timeline Summarization",
    author = "Born, Leo  and
      Bacher, Maximilian  and
      Markert, Katja",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.218",
    pages = "1763--1771",
    abstract = "Timeline summarization (TLS) generates a dated overview of real-world events based on event-specific corpora. The two standard datasets for this task were collected using Google searches for news reports on given events. Not only is this IR method not reproducible at different search times, it also uses components (such as document popularity) that are not always available for any large news corpus. It is unclear how TLS algorithms fare when provided with event corpora collected with varying IR methods. We therefore construct event-specific corpora from a large static background corpus, the newsroom dataset, using differing, relatively simple IR methods based on raw text alone. We show that the choice of IR method plays a crucial role in the performance of various TLS algorithms. A weak TLS algorithm can even match a stronger one by employing a stronger IR method in the data collection phase. Furthermore, the results of TLS systems are often highly sensitive to additional sentence filtering. We consequently advocate for integrating IR into the development of TLS systems and having a common static background corpus for evaluation of TLS systems.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nadig-etal-2020-database,
    title = "Database Search vs. Information Retrieval: A Novel Method for Studying Natural Language Querying of Semi-Structured Data",
    author = "Nadig, Stefanie  and
      Braschler, Martin  and
      Stockinger, Kurt",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.219",
    pages = "1772--1779",
    abstract = "The traditional approach of querying a relational database is via a formal language, namely SQL. Recent developments in the design of natural language interfaces to databases show promising results for querying either with keywords or with full natural language queries and thus render relational databases more accessible to non-tech savvy users. Such enhanced relational databases basically use a search paradigm which is commonly used in the field of information retrieval. However, the way systems are evaluated in the database and the information retrieval communities often differs due to a lack of common benchmarks. In this paper, we provide an adapted benchmark data set that is based on a test collection originally used to evaluate information retrieval systems. The data set contains 45 information needs developed on the Internet Movie Database (IMDb), including corresponding relevance assessments. By mapping this benchmark data set to a relational database schema, we enable a novel way of directly comparing database search techniques with information retrieval. To demonstrate the feasibility of our approach, we present an experimental evaluation that compares SODA, a keyword-enabled relational database system, against the Terrier information retrieval system and thus lays the foundation for a future discussion of evaluating database systems that support natural language interfaces.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{grimsley-etal-2020-attention,
    title = "Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models",
    author = "Grimsley, Christopher  and
      Mayfield, Elijah  and
      R.S. Bursten, Julia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.220",
    pages = "1780--1790",
    abstract = "As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for NLP tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms. We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert the impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{marczyk-etal-2020-cake,
    title = "Have a Cake and Eat it Too: Assessing Discriminating Performance of an Intelligibility Index Obtained from a Reduced Sample Size",
    author = "Marczyk, Anna  and
      Ghio, Alain  and
      Lalain, Muriel  and
      Rebourg, Marie  and
      Fredouille, Corinne  and
      Woisard, Virginie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.221",
    pages = "1791--1795",
    abstract = "This paper investigates random vs. phonetically motivated reduction of linguistic material used in an intelligibility task in speech disordered populations and the subsequent impact on the discrimination classifier quantified by the area under the receiver operating characteristics curve (AUC of ROC). The comparison of obtained accuracy indexes shows that when the sample size is reduced based on a phonetic criterium{---}here, related to phonotactic complexity{---}, the classifier has a higher ranking ability than when the linguistic material is arbitrarily reduced. Crucially, downsizing the linguistic sample to about 30{\%} of the original dataset does not diminish the discriminatory performance of the classifier. This result is of significant interest to both clinicians and patients as it validates a tool that is both reliable and efficient.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moeed-etal-2020-evaluation-metrics,
    title = "Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings",
    author = "Moeed, Abdul  and
      An, Yang  and
      Hagerer, Gerhard  and
      Groh, Georg",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.222",
    pages = "1796--1802",
    abstract = "With the explosive growth in textual data, it is becoming increasingly important to summarize text automatically. Recently, generative language models have shown promise in abstractive text summarization tasks. Since these models rephrase text and thus use similar but different words as found in the summarized text, existing metrics such as ROUGE that use n-gram overlap may not be optimal. Therefore we evaluate two embedding-based evaluation metrics that are applicable to abstractive summarization: Fr ́echet embedding distance, which has been introduced recently, and angular embedding similarity, which is our proposed metric. To demonstrate the utility of both metrics, we analyze the headline generation capacity of two state-of-the-art language models: GPT-2 and ULMFiT. In particular, our proposed metric shows close relation with human judgments in our experiments and has overall better correlations with them. To provide reproducibility, the source code plus human assessments of our experiments is available on GitHub.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aguilar-etal-2020-lince,
    title = "{L}in{CE}: A Centralized Benchmark for Linguistic Code-switching Evaluation",
    author = "Aguilar, Gustavo  and
      Kar, Sudipta  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.223",
    pages = "1803--1813",
    abstract = "Recent trends in NLP research have raised an interest in linguistic code-switching (CS); modern approaches have been proposed to solve a wide range of NLP tasks on multiple language pairs. Unfortunately, these proposed methods are hardly generalizable to different code-switched languages. In addition, it is unclear whether a model architecture is applicable for a different task while still being compatible with the code-switching setting. This is mainly because of the lack of a centralized benchmark and the sparse corpora that researchers employ based on their specific needs and interests. To facilitate research in this direction, we propose a centralized benchmark for Linguistic Code-switching Evaluation (LinCE) that combines eleven corpora covering four different code-switched language pairs (i.e., Spanish-English, Nepali-English, Hindi-English, and Modern Standard Arabic-Egyptian Arabic) and four tasks (i.e., language identification, named entity recognition, part-of-speech tagging, and sentiment analysis). As part of the benchmark centralization effort, we provide an online platform where researchers can submit their results while comparing with others in real-time. In addition, we provide the scores of different popular models, including LSTM, ELMo, and multilingual BERT so that the NLP community can compare against state-of-the-art systems. LinCE is a continuous effort, and we will expand it with more low-resource languages and tasks.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sjoblom-etal-2020-paraphrase,
    title = "Paraphrase Generation and Evaluation on Colloquial-Style Sentences",
    author = {Sj{\"o}blom, Eetu  and
      Creutz, Mathias  and
      Scherrer, Yves},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.224",
    pages = "1814--1822",
    abstract = "In this paper, we investigate paraphrase generation in the colloquial domain. We use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages: German, English, Finnish, French, Russian, and Swedish. We perform experiments to understand how data selection and filtering for diverse paraphrase pairs affects the generated paraphrases. We compare two different model architectures, an RNN and a Transformer model, and find that the Transformer does not generally outperform the RNN. We also conduct human evaluation on five of the six languages and compare the results to the automatic evaluation metrics BLEU and the recently proposed BERTScore. The results advance our understanding of the trade-offs between the quality and novelty of generated paraphrases, affected by the data selection method. In addition, our comparison of the evaluation methods shows that while BLEU correlates well with human judgments at the corpus level, BERTScore outperforms BLEU in both corpus and sentence-level evaluation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{han-etal-2020-analyzing,
    title = "Analyzing Word Embedding Through Structural Equation Modeling",
    author = "Han, Namgi  and
      Hayashi, Katsuhiko  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.225",
    pages = "1823--1832",
    abstract = "Many researchers have tried to predict the accuracies of extrinsic evaluation by using intrinsic evaluation to evaluate word embedding. The relationship between intrinsic and extrinsic evaluation, however, has only been studied with simple correlation analysis, which has difficulty capturing complex cause-effect relationships and integrating external factors such as the hyperparameters of word embedding. To tackle this problem, we employ partial least squares path modeling (PLS-PM), a method of structural equation modeling developed for causal analysis. We propose a causal diagram consisting of the evaluation results on the BATS, VecEval, and SentEval datasets, with a causal hypothesis that linguistic knowledge encoded in word embedding contributes to solving downstream tasks. Our PLS-PM models are estimated with 600 word embeddings, and we prove the existence of causal relations between linguistic knowledge evaluated on BATS and the accuracies of downstream tasks evaluated on VecEval and SentEval in our PLS-PM models. Moreover, we show that the PLS-PM models are useful for analyzing the effect of hyperparameters, including the training algorithm, corpus, dimension, and context window, and for validating the effectiveness of intrinsic evaluation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{prokopalo-etal-2020-evaluation,
    title = "Evaluation of Lifelong Learning Systems",
    author = "Prokopalo, Yevhenii  and
      Meignier, Sylvain  and
      Galibert, Olivier  and
      Barrault, Loic  and
      Larcher, Anthony",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.226",
    pages = "1833--1841",
    abstract = "Current intelligent systems need the expensive support of machine learning experts to sustain their performance level when used on a daily basis. To reduce this cost, i.e. remaining free from any machine learning expert, it is reasonable to implement lifelong (or continuous) learning intelligent systems that will continuously adapt their model when facing changing execution conditions. In this work, the systems are allowed to refer to human domain experts who can provide the system with relevant knowledge about the task. Nowadays, the fast growth of lifelong learning systems development rises the question of their evaluation. In this article we propose a generic evaluation methodology for the specific case of lifelong learning systems. Two steps will be considered. First, the evaluation of human-assisted learning (including active and/or interactive learning) outside the context of lifelong learning. Second, the system evaluation across time, with propositions of how a lifelong learning intelligent system should be evaluated when including human assisted learning or not.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hajnicz-2020-interannotator,
    title = "Interannotator Agreement for Lexico-Semantic Annotation of a Corpus",
    author = "Hajnicz, El{\.z}bieta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.227",
    pages = "1842--1848",
    abstract = "This paper examines the procedure for lexico-semantic annotation of the Basic Corpus of Polish Metaphors that is the first step for annotating metaphoric expressions occurring in it. The procedure involves correcting the morphosyntactic annotation of part of the corpus that is automatically annotated on the morphosyntactic level. The main procedure concerns annotation of adjectives, adverbs, nouns and verbs (including gerunds and participles), including abbreviations of the words that belong to the above classes. It is composed of three steps: deciding whether a particular occurrence of a word is asemantic (e.g. anaphoric or strictly grammatical), whether we are dealing with a multi-word expression, reciprocal usages of the si{\k{e}} marker and pluralia tantum, which may involve annotation with two lexical units (having two different lemmas) for a single token. We propose an interannotator agreement statistics adequate for this procedure. Finally, we discuss the preliminary results of annotation of a fragment of the corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nather-2020-depth,
    title = "An In-Depth Comparison of 14 Spelling Correction Tools on a Common Benchmark",
    author = {N{\"a}ther, Markus},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.228",
    pages = "1849--1857",
    abstract = "Determining and correcting spelling and grammar errors in text is an important but surprisingly difficult task. There are several reasons why this remains challenging. Errors may consist of simple typing errors like deleted, substituted, or wrongly inserted letters, but may also consist of word confusions where a word was replaced by another one. In addition, words may be erroneously split into two parts or get concatenated. Some words can contain hyphens, because they were split at the end of a line or are compound words with a mandatory hyphen. In this paper, we provide an extensive evaluation of 14 spelling correction tools on a common benchmark. In particular, the evaluation provides a detailed comparison with respect to 12 error categories. The benchmark consists of sentences from the English Wikipedia, which were distorted using a realistic error model. Measuring the quality of an algorithm with respect to these error categories requires an alignment of the original text, the distorted text and the corrected text provided by the tool. We make our benchmark generation and evaluation tools publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yuan-sharoff-2020-sentence,
    title = "Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks",
    author = "Yuan, Yu  and
      Sharoff, Serge",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.229",
    pages = "1858--1865",
    abstract = "This paper explores the use of Deep Learning methods for automatic estimation of quality of human translations. Automatic estimation can provide useful feedback for translation teaching, examination and quality control. Conventional methods for solving this task rely on manually engineered features and external knowledge. This paper presents an end-to-end neural model without feature engineering, incorporating a cross attention mechanism to detect which parts in sentence pairs are most relevant for assessing quality. Another contribution concerns oprediction of fine-grained scores for measuring different aspects of translation quality, such as terminological accuracy or idiomatic writing. Empirical results on a large human annotated dataset show that the neural model outperforms feature-based methods significantly. The dataset and the tools are available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alves-etal-2020-evaluating,
    title = "Evaluating Language Tools for Fifteen {EU}-official Under-resourced Languages",
    author = "Alves, Diego  and
      Thakkar, Gaurish  and
      Tadi{\'c}, Marko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.230",
    pages = "1866--1873",
    abstract = "This article presents the results of the evaluation campaign of language tools available for fifteen EU-official under-resourced languages. The evaluation was conducted within the MSC ITN CLEOPATRA action that aims at building the cross-lingual event-centric knowledge processing on top of the application of linguistic processing chains (LPCs) for at least 24 EU-official languages. In this campaign, we concentrated on three existing NLP platforms (Stanford CoreNLP, NLP Cube, UDPipe) that all provide models for under-resourced languages and in this first run we covered 15 under-resourced languages for which the models were available. We present the design of the evaluation campaign and present the results as well as discuss them. We considered the difference between reported and our tested results within a single percentage point as being within the limits of acceptable tolerance and thus consider this result as reproducible. However, for a number of languages, the results are below what was reported in the literature, and in some cases, our testing results are even better than the ones reported previously. Particularly problematic was the evaluation of NERC systems. One of the reasons is the absence of universally or cross-lingually applicable named entities classification scheme that would serve the NERC task in different languages analogous to the Universal Dependency scheme in parsing task. To build such a scheme has become one of our the future research directions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lakmal-etal-2020-word,
    title = "Word Embedding Evaluation for {S}inhala",
    author = "Lakmal, Dimuthu  and
      Ranathunga, Surangika  and
      Peramuna, Saman  and
      Herath, Indu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.231",
    pages = "1874--1881",
    abstract = "This paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and Glove are evaluated under two types of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part-of-speech (POS) tagging were conducted as the extrinsic evaluation tasks. Benchmark datasets used for intrinsic evaluations were carefully crafted considering specific linguistic features of Sinhala. In general, FastText word embeddings with 300 dimensions reported the finest accuracies across all the evaluation tasks, while Glove reported the lowest results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aspillaga-etal-2020-stress,
    title = "Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks",
    author = "Aspillaga, Carlos  and
      Carvallo, Andr{\'e}s  and
      Araujo, Vladimir",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.232",
    pages = "1882--1894",
    abstract = "There has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture. Current state-of-the-art models, via a large number of parameters and pre-training on massive text corpus, have shown impressive results on several downstream tasks. Many researchers have studied previous (non-Transformer) models to understand their actual behavior under different scenarios, showing that these models are taking advantage of clues or failures of datasets and that slight perturbations on the input data can severely reduce their performance. In contrast, recent models have not been systematically tested with adversarial-examples in order to show their robustness under severe stress conditions. For that reason, this work evaluates three Transformer-based models (RoBERTa, XLNet, and BERT) in Natural Language Inference (NLI) and Question Answering (QA) tasks to know if they are more robust or if they have the same flaws as their predecessors. As a result, our experiments reveal that RoBERTa, XLNet and BERT are more robust than recurrent neural network models to stress tests for both NLI and QA tasks. Nevertheless, they are still very fragile and demonstrate various unexpected behaviors, thus revealing that there is still room for future improvement in this field.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{janz-etal-2020-brand,
    title = "Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations",
    author = "Janz, Arkadiusz  and
      Kopoci{\'n}ski, {\L}ukasz  and
      Piasecki, Maciej  and
      Pluwak, Agnieszka",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.233",
    pages = "1895--1901",
    abstract = "Relation Extraction is a fundamental NLP task. In this paper we investigate the impact of underlying text representation on the performance of neural classification models in the task of Brand-Product relation extraction. We also present the methodology of preparing annotated textual corpora for this task and we provide valuable insight into the properties of Brand-Product relations existing in textual corpora. The problem is approached from a practical angle of applications Relation Extraction in facilitating commercial Internet monitoring.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{buljan-etal-2020-tale,
    title = "A Tale of Three Parsers: Towards Diagnostic Evaluation for Meaning Representation Parsing",
    author = "Buljan, Maja  and
      Nivre, Joakim  and
      Oepen, Stephan  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.234",
    pages = "1902--1909",
    abstract = "We discuss methodological choices in contrastive and diagnostic evaluation in meaning representation parsing, i.e. mapping from natural language utterances to graph-based encodings of its semantic structure. Drawing inspiration from earlier work in syntactic dependency parsing, we transfer and refine several quantitative diagnosis techniques for use in the context of the 2019 shared task on Meaning Representation Parsing (MRP). As in parsing proper, moving evaluation from simple rooted trees to general graphs brings along its own range of challenges. Specifically, we seek to begin to shed light on relative strenghts and weaknesses in different broad families of parsing techniques. In addition to these theoretical reflections, we conduct a pilot experiment on a selection of top-performing MRP systems and one of the five meaning representation frameworks in the shared task. Empirical results suggest that the proposed methodology can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different systems that can inform future development and cross-fertilization across approaches.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yang-etal-2020-headword,
    title = "Headword-Oriented Entity Linking: A Special Entity Linking Task with Dataset and Baseline",
    author = "Yang, Mu  and
      Chen, Chi-Yen  and
      Lee, Yi-Hui  and
      Zeng, Qian-hui  and
      Ma, Wei-Yun  and
      Shih, Chen-Yang  and
      Chen, Wei-Jhih",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.235",
    pages = "1910--1917",
    abstract = "In this paper, we design headword-oriented entity linking (HEL), a specialized entity linking problem in which only the headwords of the entities are to be linked to knowledge bases; mention scopes of the entities do not need to be identified in the problem setting. This special task is motivated by the fact that in many articles referring to specific products, the complete full product names are rarely written; instead, they are often abbreviated to shorter, irregular versions or even just to their headwords, which are usually their product types, such as {``}stick{''} or {``}mask{''} in a cosmetic context. To fully design the special task, we construct a labeled cosmetic corpus as a public benchmark for this problem, and propose a product embedding model to address the task, where each product corresponds to a dense representation to encode the different information on products and their context jointly. Besides, to increase training data, we propose a special transfer learning framework in which distant supervision with heuristic patterns is first utilized, followed by supervised learning using a small amount of manually labeled data. The experimental results show that our model provides a strong benchmark performance on the special task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{li-etal-2020-tablebank,
    title = "{T}able{B}ank: Table Benchmark for Image-based Table Detection and Recognition",
    author = "Li, Minghao  and
      Cui, Lei  and
      Huang, Shaohan  and
      Wei, Furu  and
      Zhou, Ming  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.236",
    pages = "1918--1925",
    abstract = "We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models can be downloaded from https://github.com/doc-analysis/TableBank.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{frej-etal-2020-wikir,
    title = "{WIKIR}: A Python Toolkit for Building a Large-scale {W}ikipedia-based {E}nglish Information Retrieval Dataset",
    author = "Frej, Jibril  and
      Schwab, Didier  and
      Chevallet, Jean-Pierre",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.237",
    pages = "1926--1933",
    abstract = "Over the past years, deep learning methods allowed for new state-of-the-art results in ad-hoc information retrieval. However such methods usually require large amounts of annotated data to be effective. Since most standard ad-hoc information retrieval datasets publicly available for academic research (e.g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets. These models (e.g. DUET, Conv-KNRM) are trained and evaluated on data collected from commercial search engines not publicly available for academic research which is a problem for reproducibility and the advancement of research. In this paper, we propose WIKIR: an open-source toolkit to automatically build large-scale English information retrieval datasets based on Wikipedia. WIKIR is publicly available on GitHub. We also provide wikIR59k: a large-scale publicly available dataset that contains 59,252 queries and 2,617,003 (query, relevant documents) pairs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tanaka-etal-2020-constructing,
    title = "Constructing a Public Meeting Corpus",
    author = "Tanaka, Koji  and
      Chu, Chenhui  and
      Ren, Haolin  and
      Renoust, Benjamin  and
      Nakashima, Yuta  and
      Takemura, Noriko  and
      Nagahara, Hajime  and
      Fujikawa, Takao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.238",
    pages = "1934--1940",
    abstract = "In this paper, we propose a full pipeline of analysis of a large corpus about a century of public meeting in historical Australian news papers, from construction to visual exploration. The corpus construction method is based on image processing and OCR. We digitize and transcribe texts of the specific topic of public meeting. Experiments show that our proposed method achieves a F-score of 87.8{\%} for corpus construction. As a result, we built a content search tool for temporal and semantic content analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kuniyoshi-etal-2020-annotating,
    title = "Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature",
    author = "Kuniyoshi, Fusataka  and
      Makino, Kohei  and
      Ozawa, Jun  and
      Miwa, Makoto",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.239",
    pages = "1941--1950",
    abstract = "The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry. In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature. We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers. The automated machine-reading system is developed by a deep learning-based sequence tagger and simple heuristic rule-based relation extractor. Our experimental results demonstrate that the sequence tagger with the optimal setting can detect the entities with a macro-averaged F1 score of 0.826, while the rule-based relation extractor can achieve high performance with a macro-averaged F1 score of 0.887.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{strobl-etal-2020-wexea,
    title = "{WEXEA}: {W}ikipedia {EX}haustive Entity Annotation",
    author = "Strobl, Michael  and
      Trabelsi, Amine  and
      Zaiane, Osmar",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.240",
    pages = "1951--1958",
    abstract = "Building predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated text. Wikipedia is often used as a corpus for these tasks where the annotation is a named entity linked by a hyperlink to its article. However, editors on Wikipedia are only expected to link these mentions in order to help the reader to understand the content, but are discouraged from adding links that do not add any benefit for understanding an article. Therefore, many mentions of popular entities (such as countries or popular events in history), or previously linked articles, as well as the article{'}s entity itself, are not linked. In this paper, we discuss WEXEA, a Wikipedia EXhaustive Entity Annotation system, to create a text corpus based on Wikipedia with exhaustive annotations of entity mentions, i.e. linking all mentions of entities to their corresponding articles. This results in a huge potential for additional annotations that can be used for downstream NLP tasks, such as Relation Extraction. We show that our annotations are useful for creating distantly supervised datasets for this task. Furthermore, we publish all code necessary to derive a corpus from a raw Wikipedia dump, so that it can be reproduced by everyone.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ferre-etal-2020-handling,
    title = "Handling Entity Normalization with no Annotated Corpus: Weakly Supervised Methods Based on Distributional Representation and Ontological Information",
    author = "Ferr{\'e}, Arnaud  and
      Bossy, Robert  and
      Ba, Mouhamadou  and
      Del{\'e}ger, Louise  and
      Lavergne, Thomas  and
      Zweigenbaum, Pierre  and
      N{\'e}dellec, Claire",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.241",
    pages = "1959--1966",
    abstract = "Entity normalization (or entity linking) is an important subtask of information extraction that links entity mentions in text to categories or concepts in a reference vocabulary. Machine learning based normalization methods have good adaptability as long as they have enough training data per reference with a sufficient quality. Distributional representations are commonly used because of their capacity to handle different expressions with similar meanings. However, in specific technical and scientific domains, the small amount of training data and the relatively small size of specialized corpora remain major challenges. Recently, the machine learning-based CONTES method has addressed these challenges for reference vocabularies that are ontologies, as is often the case in life sciences and biomedical domains. And yet, its performance is dependent on manually annotated corpus. Furthermore, like other machine learning based methods, parametrization remains tricky. We propose a new approach to address the scarcity of training data that extends the CONTES method by corpus selection, pre-processing and weak supervision strategies, which can yield high-performance results without any manually annotated examples. We also study which hyperparameters are most influential, with sometimes different patterns compared to previous work. The results show that our approach significantly improves accuracy and outperforms previous state-of-the-art algorithms.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bonin-etal-2020-hbcp,
    title = "{HBCP} Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports",
    author = "Bonin, Francesca  and
      Gleize, Martin  and
      Finnerty, Ailbhe  and
      Moore, Candice  and
      Jochim, Charles  and
      Norris, Emma  and
      Hou, Yufang  and
      Wright, Alison J.  and
      Ganguly, Debasis  and
      Hayes, Emily  and
      Zink, Silje  and
      Pascale, Alessandra  and
      Mac Aonghusa, Pol  and
      Michie, Susan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.242",
    pages = "1967--1975",
    abstract = "Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports. Automatic extraction of the reports{'} intervention content, population, settings and their results etc. are essential in synthesising and summarising the literature. However, to the best of our knowledge, no unique resource exists at the moment to facilitate this synthesis. In this paper, we describe the construction of a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation. We also describe and release the annotation of 57 entities, that can be used as an off-the-shelf data resource for tasks such as entity recognition, etc. Both the corpus and the annotation dataset are being made available to the community.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lu-etal-2020-cross,
    title = "Cross-lingual Structure Transfer for Zero-resource Event Extraction",
    author = "Lu, Di  and
      Subburathinam, Ananya  and
      Ji, Heng  and
      May, Jonathan  and
      Chang, Shih-Fu  and
      Sil, Avi  and
      Voss, Clare",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.243",
    pages = "1976--1981",
    abstract = "Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging. To tackle more complex tasks such as event extraction we need to transfer graph structures (event trigger linked to multiple arguments with various roles) across languages. We develop a novel share-and-transfer framework to reach this goal with three steps: (1) Convert each sentence in any language to language-universal graph structures; in this paper we explore two approaches based on universal dependency parses and complete graphs, respectively. (2) Represent each node in the graph structure with a cross-lingual word embedding so that all sentences in multiple languages can be represented with one shared semantic space. (3) Using this common semantic space, train event extractors from English training data and apply them to languages that do not have any event annotations. Experimental results on three languages (Spanish, Russian and Ukrainian) without any annotations show this framework achieves comparable performance to a state-of-the-art supervised model trained from more than 1,500 manually annotated event mentions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ramponi-etal-2020-cross,
    title = "Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction",
    author = "Ramponi, Alan  and
      Plank, Barbara  and
      Lombardo, Rosario",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.244",
    pages = "1982--1989",
    abstract = "Biomedical event extraction is a crucial task in order to automatically extract information from the increasingly growing body of biomedical literature. Despite advances in the methods in recent years, most event extraction systems are still evaluated in-domain and on complete event structures only. This makes it hard to determine the performance of intermediate stages of the task, such as edge detection, across different corpora. Motivated by these limitations, we present the first cross-domain study of edge detection for biomedical event extraction. We analyze differences between five existing gold standard corpora, create a standardized benchmark corpus, and provide a strong baseline model for edge detection. Experiments show a large drop in performance when the baseline is applied on out-of-domain data, confirming the need for domain adaptation methods for the task. To encourage research efforts in this direction, we make both the data and the baseline available to the research community: https://www.cosbi.eu/cfx/9985.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{thompson-etal-2020-semantic,
    title = "Semantic Annotation for Improved Safety in Construction Work",
    author = "Thompson, Paul  and
      Yates, Tim  and
      Inan, Emrah  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.245",
    pages = "1990--1999",
    abstract = "Risk management is a vital activity to ensure employee safety in construction projects. Various documents provide important supporting evidence, including details of previous incidents, consequences and mitigation strategies. Potential hazards may depend on a complex set of project-specific attributes, including activities undertaken, location, equipment used, etc. However, finding evidence about previous projects with similar attributes can be problematic, since information about risks and mitigations is usually hidden within and may be dispersed across a range of different free text documents. Automatic named entity recognition (NER), which identifies mentions of concepts in free text documents, is the first stage in structuring knowledge contained within them. While developing NER methods generally relies on annotated corpora, we are not aware of any such corpus targeted at concepts relevant to construction safety. In response, we have designed a novel named entity annotation scheme and associated guidelines for this domain, which covers hazards, consequences, mitigation strategies and project attributes. Four health and safety experts used the guidelines to annotate a total of 600 sentences from accident reports; an average inter-annotator agreement rate of 0.79 F-Score shows that our work constitutes an important first step towards developing tools for detailed semantic analysis of construction safety documents.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tsekouras-etal-2020-social,
    title = "Social Web Observatory: A Platform and Method for Gathering Knowledge on Entities from Different Textual Sources",
    author = "Tsekouras, Leonidas  and
      Petasis, Georgios  and
      Giannakopoulos, George  and
      Kosmopoulos, Aris",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.246",
    pages = "2000--2008",
    abstract = "Within this work we describe a framework for the collection and summarization of information from the Web in an entity-driven manner. The framework consists of a set of appropriate workflows and the Social Web Observatory platform, which implements those workflows, supporting them through a language analysis pipeline. The pipeline includes text collection/crawling, identification of different entities, clustering of texts into events related to entities, entity-centric sentiment analysis, but also text analytics and visualization functionalities. The latter allow the user to take advantage of the gathered information as actionable knowledge: to understand the dynamics of the public opinion for a given entity over time and across real-world events. We describe the platform and the analysis functionality and evaluate the performance of the system, by allowing human users to score how the system fares in its intended purpose of summarizing entity-centered information from different sources in the Web.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chaturvedi-etal-2020-development,
    title = "Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records",
    author = "Chaturvedi, Jaya  and
      Viani, Natalia  and
      Sanyal, Jyoti  and
      Tytherleigh, Chloe  and
      Hasan, Idil  and
      Baird, Kate  and
      Velupillai, Sumithra  and
      Stewart, Robert  and
      Roberts, Angus",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.247",
    pages = "2009--2016",
    abstract = "Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. One such type of information is medications which are not always available in structured fields, especially in mental health records. Most use cases that require medication information also generally require the associated temporal information (e.g. current or past) and attributes (e.g. dose, route, frequency). The purpose of this study is to develop a corpus of medication annotations in mental health records. The aim is to provide a more complete picture behind the mention of medications in the health records, by including additional contextual information around them, and to create a resource for use when developing and evaluating applications for the extraction of medications from EHR text. Thus far, an analysis of temporal information related to medications mentioned in a sample of mental health records has been conducted. The purpose of this analysis was to understand the complexity of medication mentions and their associated temporal information in the free text of EHRs, with a specific focus on the mental health domain.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mandya-etal-2020-history,
    title = "Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering",
    author = "Mandya, Angrosh  and
      O{'} Neill, James  and
      Bollegala, Danushka  and
      Coenen, Frans",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.248",
    pages = "2017--2025",
    abstract = "The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph. Although existing approaches employ human-written ground-truth answers for answering conversational questions at test time, in a realistic scenario, the CoQA model will not have any access to ground-truth answers for the previous questions, compelling the model to rely upon its own previously predicted answers for answering the subsequent questions. In this paper, we find that compounding errors occur when using previously predicted answers at test time, significantly lowering the performance of CoQA systems. To solve this problem, we propose a sampling strategy that dynamically selects between target answers and model predictions during training, thereby closely simulating the situation at test time. Further, we analyse the severity of this phenomena as a function of the question type, conversation length and domain type.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zeng-etal-2020-cleek,
    title = "{CLEEK}: A {C}hinese Long-text Corpus for Entity Linking",
    author = "Zeng, Weixin  and
      Zhao, Xiang  and
      Tang, Jiuyang  and
      Tan, Zhen  and
      Huang, Xuqian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.249",
    pages = "2026--2035",
    abstract = "Entity linking, as one of the fundamental tasks in natural language processing, is crucial to knowledge fusion, knowledge base construction and update. Nevertheless, in contrast to the research on entity linking for English text, which undergoes continuous development, the Chinese counterpart is still in its infancy. One prominent issue lies in publicly available annotated datasets and evaluation benchmarks, which are lacking and deficient. In specific, existing Chinese corpora for entity linking were mainly constructed from noisy short texts, such as microblogs and news headings, where long texts were largely overlooked, which yet constitute a wider spectrum of real-life scenarios. To address the issue, in this work, we build CLEEK, a Chinese corpus of multi-domain long text for entity linking, in order to encourage advancement of entity linking in languages besides English. The corpus consists of 100 documents from diverse domains, and is publicly accessible. Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus. Additionally, the results of two baselines and seven state-of-the-art solutions on CLEEK are reported and compared. The empirical results validate the usefulness of CLEEK and the effectiveness of proposed difficulty measure.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{shafran-etal-2020-medical,
    title = "The Medical Scribe: Corpus Development and Model Performance Analyses",
    author = "Shafran, Izhak  and
      Du, Nan  and
      Tran, Linh  and
      Perry, Amanda  and
      Keyes, Lauren  and
      Knichel, Mark  and
      Domin, Ashley  and
      Huang, Lei  and
      Chen, Yu-hui  and
      Li, Gang  and
      Wang, Mingqiu  and
      El Shafey, Laurent  and
      Soltau, Hagen  and
      Paul, Justin Stuart",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.250",
    pages = "2036--2044",
    abstract = "There is a growing interest in creating tools to assist in clinical note generation using the audio of provider-patient encounters. Motivated by this goal and with the help of providers and medical scribes, we developed an annotation scheme to extract relevant clinical concepts. We used this annotation scheme to label a corpus of about 6k clinical encounters. This was used to train a state-of-the-art tagging model. We report ontologies, labeling results, model performances, and detailed analyses of the results. Our results show that the entities related to medications can be extracted with a relatively high accuracy of 0.90 F-score, followed by symptoms at 0.72 F-score, and conditions at 0.57 F-score. In our task, we not only identify where the symptoms are mentioned but also map them to canonical forms as they appear in the clinical notes. Of the different types of errors, in about 19-38{\%} of the cases, we find that the model output was correct, and about 17-32{\%} of the errors do not impact the clinical note. Taken together, the models developed in this work are more useful than the F-scores reflect, making it a promising approach for practical applications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{funaki-etal-2020-contract,
    title = "A Contract Corpus for Recognizing Rights and Obligations",
    author = "Funaki, Ruka  and
      Nagata, Yusuke  and
      Suenaga, Kohei  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.251",
    pages = "2045--2053",
    abstract = "A contract is a legal document executed by two or more parties. It is important for these parties to precisely understand their rights and obligations that are described in the contract. However, understanding the content of a contract is sometimes difficult and costly, particularly if the contract is long and complicated. Therefore, a language-processing system that can present information concerning rights and obligations found within a given contract document would help a contracting party to make better decisions. As a step toward the development of such a language-processing system, in this paper, we describe the annotated corpus of contract documents that we built. Our corpus is annotated so that a language-processing system can recognize a party{'}s rights and obligations. The annotated information includes the parties involved in the contract, the rights and obligations of the parties, the conditions and the exceptions under which these rights and obligations to take effect. The corpus was built based on 46 English contracts and 25 Japanese contracts drafted by lawyers. We explain how we annotated the corpus and the statistics of the corpus. We also report the results of the experiments for recognizing rights and obligations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{pezanowski-mitra-2020-recognition,
    title = "Recognition of Implicit Geographic Movement in Text",
    author = "Pezanowski, Scott  and
      Mitra, Prasenjit",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.252",
    pages = "2054--2063",
    abstract = "Analyzing the geographic movement of humans, animals, and other phenomena is a growing field of research. This research has benefited urban planning, logistics, animal migration understanding, and much more. Typically, the movement is captured as precise geographic coordinates and time stamps with Global Positioning Systems (GPS). Although some research uses computational techniques to take advantage of implicit movement in descriptions of route directions, hiking paths, and historical exploration routes, innovation would accelerate with a large and diverse corpus. We created a corpus of sentences labeled as describing geographic movement or not and including the type of entity moving. Creating this corpus proved difficult without any comparable corpora to start with, high human labeling costs, and since movement can at times be interpreted differently. To overcome these challenges, we developed an iterative process employing hand labeling, crowd voting for confirmation, and machine learning to predict more labels. By merging advances in word embeddings with traditional machine learning models and model ensembling, prediction accuracy is at an acceptable level to produce a large silver-standard corpus despite the small gold-standard corpus training set. Our corpus will likely benefit computational processing of geography in text and spatial cognition, in addition to detection of movement.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{takamaru-etal-2020-extraction,
    title = "Extraction of the Argument Structure of {T}okyo Metropolitan Assembly Minutes: Segmentation of Question-and-Answer Sets",
    author = "Takamaru, Keiichi  and
      Kimura, Yasutomo  and
      Shibuki, Hideyuki  and
      Ototake, Hokuto  and
      Uchida, Yuzu  and
      Sakamoto, Kotaro  and
      Ishioroshi, Madoka  and
      Mitamura, Teruko  and
      Kando, Noriko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.253",
    pages = "2064--2068",
    abstract = "In this study, we construct a corpus of Japanese local assembly minutes. All speeches in an assembly were transcribed into a local assembly minutes based on the local autonomy law. Therefore, the local assembly minutes form an extremely large amount of text data. Our ultimate objectives were to summarize and present the arguments in the assemblies, and to use the minutes as primary information for arguments in local politics. To achieve this, we structured all statements in assembly minutes. We focused on the structure of the discussion, i.e., the extraction of question and answer pairs. We organized the shared task {``}QA Lab-PoliInfo{''} in NTCIR 14. We conducted a {``}segmentation task{''} to identify the scope of one question and answer in the minutes as a sub task of the shared task. For the segmentation task, 24 runs from five teams were submitted. Based on the obtained results, the best recall was 1.000, best precision was 0.940, and best F-measure was 0.895.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{robin-etal-2020-term,
    title = "A Term Extraction Approach to Survey Analysis in Health Care",
    author = "Robin, C{\'e}cile  and
      Isazad Mashinchi, Mona  and
      Ahmadi Zeleti, Fatemeh  and
      Ojo, Adegboyega  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.254",
    pages = "2069--2077",
    abstract = "The voice of the customer has for a long time been a key focus of businesses in all domains. It has received a lot of attention from the research community in Natural Language Processing (NLP) resulting in many approaches to analyzing customers feedback ((aspect-based) sentiment analysis, topic modeling, etc.). In the health domain, public and private bodies are increasingly prioritizing patient engagement for assessing the quality of the service given at each stage of the care. Patient and customer satisfaction analysis relate in many ways. In the domain of health particularly, a more precise and insightful analysis is needed to help practitioners locate potential issues and plan actions accordingly. We introduce here an approach to patient experience with the analysis of free text questions from the 2017 Irish National Inpatient Survey campaign using term extraction as a means to highlight important and insightful subject matters raised by patients. We evaluate the results by mapping them to a manually constructed framework following the Activity, Resource, Context (ARC) methodology (Ordenes, 2014) and specific to the health care environment, and compare our results against manual annotations done on the full 2017 dataset based on those categories.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kruiper-etal-2020-scientific,
    title = "A Scientific Information Extraction Dataset for Nature Inspired Engineering",
    author = "Kruiper, Ruben  and
      Vincent, Julian F.V.  and
      Chen-Burger, Jessica  and
      Desmulliez, Marc P.Y.  and
      Konstas, Ioannis",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.255",
    pages = "2078--2085",
    abstract = "Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vanetik-etal-2020-automated,
    title = "Automated Discovery of Mathematical Definitions in Text",
    author = "Vanetik, Natalia  and
      Litvak, Marina  and
      Shevchuk, Sergey  and
      Reznik, Lior",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.256",
    pages = "2086--2094",
    abstract = "Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology generation, concept identification, and question answering. For definitions that are contained within a single sentence, this problem can be viewed as a binary classification of sentences into definitions and non-definitions. Definitions in scientific literature can be generic (Wikipedia) or more formal (mathematical articles). In this paper, we focus on automatic detection of one-sentence definitions in mathematical texts, which are difficult to separate from surrounding text. We experiment with several data representations, which include sentence syntactic structure and word embeddings, and apply deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN), in order to identify mathematical definitions. Our experiments demonstrate the superiority of CNN and its combination with RNN, applied on the syntactically-enriched input representation. We also present a new dataset for definition extraction from mathematical texts. We demonstrate that the use of this dataset for training learning models improves the quality of definition extraction when these models are then used for other definition datasets. Our experiments with different domains approve that mathematical definitions require special treatment, and that using cross-domain learning is inefficient.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wu-etal-2020-wn,
    title = "{WN}-Salience: A Corpus of News Articles with Entity Salience Annotations",
    author = "Wu, Chuan  and
      Kanoulas, Evangelos  and
      de Rijke, Maarten  and
      Lu, Wei",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.257",
    pages = "2095--2102",
    abstract = "Entities can be found in various text genres, ranging from tweets and web pages to user queries submitted to web search engines. Existing research either considers all entities in the text equally important, or heuristics are used to measure their salience. We believe that a key reason for the relatively limited work on entity salience is the lack of appropriate datasets. To support research on entity salience, we present a new dataset, the WikiNews Salience dataset (WN-Salience), which can be used to benchmark tasks such as entity salience detection and salient entity linking. WN-Salience is built on top of Wikinews, a Wikimedia project whose mission is to present reliable news articles. Entities in Wikinews articles are identified by the authors of the articles and are linked to Wikinews categories when they are salient or to Wikipedia pages otherwise. The dataset is built automatically, and consists of approximately 7,000 news articles, and 90,000 in-text entity annotations. We compare the WN-Salience dataset against existing datasets on the task and analyze their differences. Furthermore, we conduct experiments on entity salience detection; the results demonstrate that WN-Salience is a challenging testbed that is complementary to existing ones.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tadesse-etal-2020-event,
    title = "Event Extraction from Unstructured {A}mharic Text",
    author = "Tadesse, Ephrem  and
      Tsegaye, Rosa  and
      Qaqqabaa, Kuulaa",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.258",
    pages = "2103--2109",
    abstract = "In information extraction, event extraction is one of the types that extract the specific knowledge of certain incidents from texts. Event extraction has been done on different languages text but not on one of the Semitic language, Amharic. In this study, we present a system that extracts an event from unstructured Amharic text. The system has designed by the integration of supervised machine learning and rule-based approaches. We call this system a hybrid system. The system uses the supervised machine learning to detect events from the text and the handcrafted and the rule-based rules to extract the event from the text. For the event extraction, we have been using event arguments. Event arguments identify event triggering words or phrases that clearly express the occurrence of the event. The event argument attributes can be verbs, nouns, sometimes adjectives (such as ̃rg/wedding) and time as well. The hybrid system has compared with the standalone rule-based method that is well known for event extraction. The study has shown that the hybrid system has outperformed the standalone rule-based method.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{magnini-etal-2020-comparing,
    title = "Comparing Machine Learning and Deep Learning Approaches on {NLP} Tasks for the {I}talian Language",
    author = "Magnini, Bernardo  and
      Lavelli, Alberto  and
      Magnolini, Simone",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.259",
    pages = "2110--2119",
    abstract = "We present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian. We carried on experiments using available datasets (e.g., from the Evalita shared tasks) on two sequence tagging tasks (i.e., named entities recognition and nominal entities recognition) and four classification tasks (i.e., lexical relations among words, semantic relations among sentences, sentiment analysis and text classification). We show that deep learning approaches outperform traditional machine learning algorithms in sequence tagging, while for classification tasks that heavily rely on semantics approaches based on feature engineering are still competitive. We think that a similar analysis could be carried out for other languages to provide an assessment of machine learning / deep learning models across different languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nabizadeh-etal-2020-myfixit,
    title = "{M}y{F}ixit: An Annotated Dataset, Annotation Tool, and Baseline Methods for Information Extraction from Repair Manuals",
    author = "Nabizadeh, Nima  and
      Kolossa, Dorothea  and
      Heckmann, Martin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.260",
    pages = "2120--2128",
    abstract = "Text instructions are among the most widely used media for learning and teaching. Hence, to create assistance systems that are capable of supporting humans autonomously in new tasks, it would be immensely productive, if machines were enabled to extract task knowledge from such text instructions. In this paper, we, therefore, focus on information extraction (IE) from the instructional text in repair manuals. This brings with it the multiple challenges of information extraction from the situated and technical language in relatively long and often complex instructions. To tackle these challenges, we introduce a semi-structured dataset of repair manuals. The dataset is annotated in a large category of devices, with information that we consider most valuable for an automated repair assistant, including the required tools and the disassembled parts at each step of the repair progress. We then propose methods that can serve as baselines for this IE task: an unsupervised method based on a bags-of-n-grams similarity for extracting the needed tools in each repair step, and a deep-learning-based sequence labeling model for extracting the identity of disassembled parts. These baseline methods are integrated into a semi-automatic web-based annotator application that is also available along with the dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{van-erp-groth-2020-towards,
    title = "Towards Entity Spaces",
    author = "van Erp, Marieke  and
      Groth, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.261",
    pages = "2129--2137",
    abstract = "Entities are a central element of knowledge bases and are important input to many knowledge-centric tasks including text analysis. For example, they allow us to find documents relevant to a specific entity irrespective of the underlying syntactic expression within a document. However, the entities that are commonly represented in knowledge bases are often a simplification of what is truly being referred to in text. For example, in a knowledge base, we may have an entity for Germany as a country but not for the more fuzzy concept of Germany that covers notions of German Population, German Drivers, and the German Government. Inspired by recent advances in contextual word embeddings, we introduce the concept of entity spaces - specific representations of a set of associated entities with near-identity. Thus, these entity spaces provide a handle to an amorphous grouping of entities. We developed a proof-of-concept for English showing how, through the introduction of entity spaces in the form of disambiguation pages, the recall of entity linking can be improved.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fell-etal-2020-love,
    title = "Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the {WASABI} Song Corpus with Lyrics Annotations",
    author = "Fell, Michael  and
      Cabrio, Elena  and
      Korfed, Elmahdi  and
      Buffa, Michel  and
      Gandon, Fabien",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.262",
    pages = "2138--2147",
    abstract = "We present the WASABI Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, as their structure segmentation, their topic, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The creation of the resource is still ongoing: so far, the corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. Such corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and segmentation recommendation of songs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ocal-finlayson-2020-evaluating,
    title = "Evaluating Information Loss in Temporal Dependency Trees",
    author = "Ocal, Mustafa  and
      Finlayson, Mark",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.263",
    pages = "2148--2156",
    abstract = "Temporal Dependency Trees (TDTs) have emerged as an alternative to full temporal graphs for representing the temporal structure of texts, with a key advantage being that TDTs can be straightforwardly computed using adapted dependency parsers. Relative to temporal graphs, the tree form of TDTs naturally omits some fraction of temporal relationships, which intuitively should decrease the amount of temporal information available, potentially increasing temporal indeterminacy of the global ordering. We demonstrate a new method for quantifying this indeterminacy that relies on solving temporal constraint problems to extract timelines, and show that TDTs result in up to a 109{\%} increase in temporal indeterminacy over their corresponding temporal graphs for the three corpora we examine. On average, the increase in indeterminacy is 32{\%}, and we show that this increase is a result of the TDT representation eliminating on average only 2.4{\%} of total temporal relations. This result suggests that small differences can have big effects in temporal graphs, and the use of TDTs must be balanced against their deficiencies, with tasks requiring an accurate global temporal ordering potentially calling for use of the full temporal graph",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{humphreys-etal-2020-populating,
    title = "Populating Legal Ontologies using Semantic Role Labeling",
    author = "Humphreys, Llio  and
      Boella, Guido  and
      Di Caro, Luigi  and
      Robaldo, Livio  and
      van der Torre, Leon  and
      Ghanavati, Sepideh  and
      Muthuri, Robert",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.264",
    pages = "2157--2166",
    abstract = "This paper is concerned with the goal of maintaining legal information and compliance systems: the {`}resource consumption bottleneck{'} of creating semantic technologies manually. The use of automated information extraction techniques could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in legal document management systems such as Eunomos (Boella et al., 2016).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{marcinczuk-etal-2020-pst,
    title = "{PST} 2.0 {--} Corpus of {P}olish Spatial Texts",
    author = "Marci{\'n}czuk, Micha{\l}  and
      Oleksy, Marcin  and
      Wieczorek, Jan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.265",
    pages = "2167--2174",
    abstract = "In the paper, we focus on modeling spatial expressions in texts. We present the guidelines used to annotate the PST 2.0 (Corpus of Polish Spatial Texts) {---} a corpus designed for training and testing the tools for spatial expression recognition. The corpus contains a set of texts gathered from texts collected from travel blogs available under Creative Commons license. We have defined our guidelines based on three existing specifications for English (SpatialML, SpatialRole Labelling from SemEval-2013 Task 3 and ISO-Space1.4 from SpaceEval 2014). We briefly present the existing specifications and discuss what modifications have been made to adapt the guidelines to the characteristics of the Polish language. We also describe the process of data collection and manual annotation, including inter-annotator agreement calculation and corpus statistics. In the end, we present detailed statistics of the PST 2.0 corpus, which include the number of components, relations, expressions, and the most common values of spatial indicators, motion indicators, path indicators, distances, directions, and regions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ferreira-freitas-2020-natural,
    title = "Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text",
    author = "Ferreira, Deborah  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.266",
    pages = "2175--2182",
    abstract = "Mathematical text is written using a combination of words and mathematical expressions. This combination, along with a specific way of structuring sentences makes it challenging for state-of-art NLP tools to understand and reason on top of mathematical discourse. In this work, we propose a new NLP task, the natural premise selection, which is used to retrieve supporting definitions and supporting propositions that are useful for generating an informal mathematical proof for a particular statement. We also make available a dataset, NL-PS, which can be used to evaluate different approaches for the natural premise selection task. Using different baselines, we demonstrate the underlying interpretation challenges associated with the task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{valenzuela-escarcega-etal-2020-odinson,
    title = "{O}dinson: A Fast Rule-based Information Extraction Framework",
    author = "Valenzuela-Esc{\'a}rcega, Marco A.  and
      Hahn-Powell, Gus  and
      Bell, Dane",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.267",
    pages = "2183--2191",
    abstract = "We present Odinson, a rule-based information extraction framework, which couples a simple yet powerful pattern language that can operate over multiple representations of text, with a runtime system that operates in near real time. In the Odinson query language, a single pattern may combine regular expressions over surface tokens with regular expressions over graphs such as syntactic dependencies. To guarantee the rapid matching of these patterns, our framework indexes most of the necessary information for matching patterns, including directed graphs such as syntactic dependencies, into a custom Lucene index. Indexing minimizes the amount of expensive pattern matching that must take place at runtime. As a result, the runtime system matches a syntax-based graph traversal in 2.8 seconds in a corpus of over 134 million sentences, nearly 150,000 times faster than its predecessor.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dsouza-etal-2020-stem,
    title = "The {STEM}-{ECR} Dataset: Grounding Scientific Entity References in {STEM} Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources",
    author = {D{'}Souza, Jennifer  and
      Hoppe, Anett  and
      Brack, Arthur  and
      Jaradeh, Mohmad Yaser  and
      Auer, S{\"o}ren  and
      Ewerth, Ralph},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.268",
    pages = "2192--2203",
    abstract = "We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alexeeva-etal-2020-mathalign,
    title = "{M}ath{A}lign: Linking Formula Identifiers to their Contextual Natural Language Descriptions",
    author = "Alexeeva, Maria  and
      Sharp, Rebecca  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Kadowaki, Jennifer  and
      Pyarelal, Adarsh  and
      Morrison, Clayton",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.269",
    pages = "2204--2212",
    abstract = "Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired. This entails segmenting mathematical formulae into identifiers and linking them to their natural language descriptions. We propose a rule-based approach for this task, which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task, as well as the tool used to create it.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sainz-etal-2020-domain,
    title = "Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction",
    author = "Sainz, Oscar  and
      Lopez de Lacalle, Oier  and
      Aldabe, Itziar  and
      Maritxalar, Montse",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.270",
    pages = "2213--2222",
    abstract = "In this paper we present a relation extraction system that given a text extracts pedagogically motivated relation types, as a previous step to obtaining a semantic representation of the text which will make possible to automatically generate questions for reading comprehension. The system maps pedagogically motivated relations with relations from ConceptNet and deploys Distant Supervision for relation extraction. We run a study on a subset of those relationships in order to analyse the viability of our approach. For that, we build a domain-specific relation extraction system and explore two relation extraction models: a state-of-the-art model based on transfer learning and a discrete feature based machine learning model. Experiments show that the neural model obtains better results in terms of F-score and we yield promising results on the subset of relations suitable for pedagogical purposes. We thus consider that distant supervision for relation extraction is a valid approach in our target domain, i.e. biology.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{niu-etal-2020-temporal,
    title = "Temporal Histories of Epidemic Events ({THEE}): A Case Study in Temporal Annotation for Public Health",
    author = "Niu, Jingcheng  and
      Ng, Victoria  and
      Penn, Gerald  and
      Rees, Erin E.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.271",
    pages = "2223--2230",
    abstract = "We present a new temporal annotation standard, THEE-TimeML, and a corpus TheeBank enabling precise temporal information extraction (TIE) for event-based surveillance (EBS) systems in the public health domain. Current EBS must estimate the occurrence time of each event based on coarse document metadata such as document publication time. Because of the complicated language and narration style of news articles, estimated case outbreak times are often inaccurate or even erroneous. Thus, it is necessary to create annotation standards and corpora to facilitate the development of TIE systems in the public health domain to address this problem.We will discuss the adaptations that have proved necessary for this domain as we present THEE-TimeML and TheeBank. Finally, we document the corpus annotation process, and demonstrate the immediate benefit to public health applications brought by the annotations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khadka-etal-2020-exploiting,
    title = "Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications",
    author = "Khadka, Anita  and
      Cantador, Iv{\'a}n  and
      Fernandez, Miriam",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.272",
    pages = "2231--2240",
    abstract = "In this paper we address the problem of providing personalised recommendations of recent scientific publications to a particular user, and explore the use of citation knowledge to do so. For this purpose, we have generated a novel dataset that captures authors{'} publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, citation contexts, and citation types. Through a number of empirical experiments on such dataset, we show that the exploitation of the extracted knowledge, particularly the type of citation, is a promising approach for recommending recently published papers that may not be cited yet. The dataset, which we make publicly available, also represents a valuable resource for further investigation on academic information retrieval and filtering.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sahoo-etal-2020-platform,
    title = "A Platform for Event Extraction in {H}indi",
    author = "Sahoo, Sovan Kumar  and
      Saha, Saumajit  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.273",
    pages = "2241--2250",
    abstract = "Event Extraction is an important task in the widespread field of Natural Language Processing (NLP). Though this task is adequately addressed in English with sufficient resources, we are unaware of any benchmark setup in Indian languages. Hindi is one of the most widely spoken languages in the world. In this paper, we present an Event Extraction framework for Hindi language by creating an annotated resource for benchmarking, and then developing deep learning based models to set as the baselines. We crawl more than seventeen hundred disaster related Hindi news articles from the various news sources. We also develop deep learning based models for Event Trigger Detection and Classification, Argument Detection and Classification and Event-Argument Linking.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{datta-etal-2020-rad,
    title = "Rad-{S}patial{N}et: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports",
    author = "Datta, Surabhi  and
      Ulinski, Morgan  and
      Godfrey-Stovall, Jordan  and
      Khanpara, Shekhar  and
      Riascos-Castaneda, Roy F.  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.274",
    pages = "2251--2260",
    abstract = "This paper proposes a representation framework for encoding spatial language in radiology based on frame semantics. The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more accurate representations of spatial language used by radiologists. We describe Rad-SpatialNet in detail along with illustrating the importance of incorporating domain knowledge in understanding the varied linguistic expressions involved in different radiological spatial relations. This work also constructs a corpus of 400 radiology reports of three examination types (chest X-rays, brain MRIs, and babygrams) annotated with fine-grained contextual information according to this schema. Spatial trigger expressions and elements corresponding to a spatial frame are annotated. We apply BERT-based models (BERT-Base and BERT- Large) to first extract the trigger terms (lexical units for a spatial frame) and then to identify the related frame elements. The results of BERT- Large are decent, with F1 of 77.89 for spatial trigger extraction and an overall F1 of 81.61 and 66.25 across all frame elements using gold and predicted spatial triggers respectively. This frame-based resource can be used to develop and evaluate more advanced natural language processing (NLP) methods for extracting fine-grained spatial information from radiology text in the future.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{masson-paroubek-2020-nlp,
    title = "{NLP} Analytics in Finance with {D}o{R}e: A {F}rench 250{M} Tokens Corpus of Corporate Annual Reports",
    author = "Masson, Corentin  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.275",
    pages = "2261--2267",
    abstract = "Recent advances in neural computing and word embeddings for semantic processing open many new applications areas which had been left unaddressed so far because of inadequate language understanding capacity. But this new kind of approaches rely even more on training data to be operational. Corpora for financial applications exists, but most of them concern stock market prediction and are in English. To address this need for the French language and regulation oriented applications which require a deeper understanding of the text content, we hereby present {``}DoRe{''}, a French and dialectal French Corpus for NLP analytics in Finance, Regulation and Investment. This corpus is composed of: (a) 1769 Annual Reports from 336 companies among the most capitalized companies in: France (Euronext Paris) {\&} Belgium (Euronext Brussels), covering a time frame from 2009 to 2019, and (b) related MetaData containing information for each company about its ISIN code, capitalization and sector. This corpus is designed to be as modular as possible in order to allow for maximum reuse in different tasks pertaining to Economics, Finance and Regulation. After presenting existing resources, we relate the construction of the DoRe corpus and the rationale behind our choices, concluding on the spectrum of possible uses of this new resource for NLP applications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{maldonado-harabagiu-2020-language,
    title = "The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports",
    author = "Maldonado, Ramon  and
      Harabagiu, Sanda",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.276",
    pages = "2268--2275",
    abstract = "Brain signals are captured by clinical electroencephalography (EEG) which is an excellent tool for probing neural function. When EEG tests are performed, a textual EEG report is generated by the neurologist to document the findings, thus using language that describes the brain signals and its clinical correlations. Even with the impetus provided by the BRAIN initiative (brainitititive.nih.gov), there are no annotations available in texts that capture language describing the brain activities and their correlations with various pathologies. In this paper we describe an annotation effort carried out on a large corpus of EEG reports, providing examples of EEG-specific and clinically relevant concepts. In addition, we detail our annotation schema for brain signal attributes. We also discuss the resulting annotation of long-distance relations between concepts in EEG reports. By exemplifying a self-attention joint-learning to predict similar annotations in the EEG report corpus, we discuss the promising results, hoping that our effort will inform the design of novel knowledge capture techniques that will include the language of brain signals.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{shavrina-etal-2020-humans,
    title = "Humans Keep It One Hundred: an Overview of {AI} Journey",
    author = "Shavrina, Tatiana  and
      Emelyanov, Anton  and
      Fenogenova, Alena  and
      Fomin, Vadim  and
      Mikhailov, Vladislav  and
      Evlampiev, Andrey  and
      Malykh, Valentin  and
      Larin, Vladimir  and
      Natekin, Alex  and
      Vatulin, Aleksandr  and
      Romov, Peter  and
      Anastasiev, Daniil  and
      Zinov, Nikolai  and
      Chertok, Andrey",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.277",
    pages = "2276--2284",
    abstract = "Artificial General Intelligence (AGI) is showing growing performance in numerous applications - beating human performance in Chess and Go, using knowledge bases and text sources to answer questions (SQuAD) and even pass human examination (Aristo project). In this paper, we describe the results of AI Journey, a competition of AI-systems aimed to improve AI performance on knowledge bases, reasoning and text generation. Competing systems pass the final native language exam (in Russian), including versatile grammar tasks (test and open questions) and an essay, achieving a high score of 69{\%}, with 68{\%} being an average human result. During the competition, a baseline for the task and essay parts was proposed, and 80+ systems were submitted, showing different approaches to task understanding and reasoning. All the data and solutions can be found on github https://github.com/sberbank-ai/combined{\_}solution{\_}aij2019",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{de-boer-verhoosel-2020-towards,
    title = "Towards Data-driven Ontologies: a Filtering Approach using Keywords and Natural Language Constructs",
    author = "de Boer, Maaike  and
      Verhoosel, Jack P. C.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.278",
    pages = "2285--2292",
    abstract = "Creating ontologies is an expensive task. Our vision is that we can automatically generate ontologies based on a set of relevant documents to create a kick-start in ontology creating sessions. In this paper, we focus on enhancing two often used methods, OpenIE and co-occurrences. We evaluate the methods on two document sets, one about pizza and one about the agriculture domain. The methods are evaluated using two types of F1-score (objective, quantitative) and through a human assessment (subjective, qualitative). The results show that 1) Cooc performs both objectively and subjectively better than OpenIE; 2) the filtering methods based on keywords and on Word2vec perform similarly; 3) the filtering methods both perform better compared to OpenIE and similar to Cooc; 4) Cooc-NVP performs best, especially considering the subjective evaluation. Although, the investigated methods provide a good start for extracting an ontology out of a set of domain documents, various improvements are still possible, especially in the natural language based methods.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jabbari-etal-2020-french,
    title = "A {F}rench Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News",
    author = "Jabbari, Ali  and
      Sauvage, Olivier  and
      Zeine, Hamada  and
      Chergui, Hamza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.279",
    pages = "2293--2299",
    abstract = "In financial services industry, compliance involves a series of practices and controls in order to meet key regulatory standards which aim to reduce financial risk and crime, e.g. money laundering and financing of terrorism. Faced with the growing risks, it is imperative for financial institutions to seek automated information extraction techniques for monitoring financial activities of their customers. This work describes an ontology of compliance-related concepts and relationships along with a corpus annotated according to it. The presented corpus consists of financial news articles in French and allows for training and evaluating domain-specific named entity recognition and relation extraction algorithms. We present some of our experimental results on named entity recognition and relation extraction using our annotated corpus. We aim to furthermore use the the proposed ontology towards construction of a knowledge base of financial relations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bebeshina-lafourcade-2020-inferences,
    title = "Inferences for Lexical Semantic Resource Building with Less Supervision",
    author = "Bebeshina, Nadia  and
      Lafourcade, Mathieu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.280",
    pages = "2300--2305",
    abstract = "Lexical semantic resources may be built using various approaches such as extraction from corpora, integration of the relevant pieces of knowledge from the pre-existing knowledge resources, and endogenous inference. Each of these techniques needs human supervision in order to deal with the potential errors, mapping difficulties or inferred candidate validation. We detail how various inference processes can be employed for the less supervised lexical semantic resource building. Our experience is based on the combination of different inference techniques for multilingual resource building and evaluation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{iwai-etal-2020-acquiring,
    title = "Acquiring Social Knowledge about Personality and Driving-related Behavior",
    author = "Iwai, Ritsuko  and
      Kawahara, Daisuke  and
      Kumada, Takatsune  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.281",
    pages = "2306--2315",
    abstract = "In this paper, we introduce our psychological approach to collect human-specific social knowledge from a text corpus, using NLP techniques. It is often not explicitly described but shared among people, which we call social knowledge. We focus on the social knowledge, especially personality and driving. We used the language resources that were developed based on psychological research methods; a Japanese personality dictionary (317 words) and a driving experience corpus (8,080 sentences) annotated with behavior and subjectivity. Using them, we automatically extracted collocations between personality descriptors and driving-related behavior from a driving behavior and subjectivity corpus (1,803,328 sentences after filtering) and obtained unique 5,334 collocations. To evaluate the collocations as social knowledge, we designed four step-by-step crowdsourcing tasks. They resulted in 266 pieces of social knowledge. They include the knowledge that might be difficult to recall by themselves but easy to agree with. We discuss the acquired social knowledge and the contribution to implementations into systems.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{becker-etal-2020-implicit,
    title = "Implicit Knowledge in Argumentative Texts: An Annotated Corpus",
    author = "Becker, Maria  and
      Korfhage, Katharina  and
      Frank, Anette",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.282",
    pages = "2316--2324",
    abstract = "When speaking or writing, people omit information that seems clear and evident, such that only part of the message is expressed in words. Especially in argumentative texts it is very common that (important) parts of the argument are implied and omitted. We hypothesize that for argument analysis it will be beneficial to reconstruct this implied information. As a starting point for filling knowledge gaps, we build a corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. To learn more about the characteristics of both the argumentative texts and the added information, we further annotate the data with semantic clause types and commonsense knowledge relations. The outcome of our work is a carefully designed and richly annotated dataset, for which we then provide an in-depth analysis by investigating characteristic distributions and correlations of the assigned labels. We reveal interesting patterns and intersections between the annotation categories and properties of our dataset, which enable insights into the characteristics of both argumentative texts and implicit knowledge in terms of structural features and semantic information. The results of our analysis can help to assist automated argument analysis and can guide the process of revealing implicit information in argumentative texts automatically.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{faralli-etal-2020-multiple,
    title = "Multiple Knowledge {G}raph{DB} ({MKGDB})",
    author = "Faralli, Stefano  and
      Velardi, Paola  and
      Yusifli, Farid",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.283",
    pages = "2325--2331",
    abstract = "We present MKGDB, a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category hierarchy. MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of open-domain natural language processing applications relying on knowledge bases, such as information extraction, hypernymy discovery, topic clustering, and others. Our resource consists of a large hypernymy graph which counts more than 37 million nodes and more than 81 million hypernymy relations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moreno-schneider-etal-2020-orchestrating,
    title = "Orchestrating {NLP} Services for the Legal Domain",
    author = "Moreno-Schneider, Julian  and
      Rehm, Georg  and
      Montiel-Ponsoda, Elena  and
      Rodriguez-Doncel, V{\'\i}ctor  and
      Revenko, Artem  and
      Karampatakis, Sotirios  and
      Khvalchik, Maria  and
      Sageder, Christian  and
      Gracia, Jorge  and
      Maganza, Filippo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.284",
    pages = "2332--2340",
    abstract = "Legal technology is currently receiving a lot of attention from various angles. In this contribution we describe the main technical components of a system that is currently under development in the European innovation project Lynx, which includes partners from industry and research. The key contribution of this paper is a workflow manager that enables the flexible orchestration of workflows based on a portfolio of Natural Language Processing and Content Curation services as well as a Multilingual Legal Knowledge Graph that contains semantic information and meaningful references to legal documents. We also describe different use cases with which we experiment and develop prototypical solutions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bordea-etal-2020-evaluation,
    title = "Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the {W}ikipedia Knowledge Graph",
    author = "Bordea, Georgeta  and
      Faralli, Stefano  and
      Mougin, Fleur  and
      Buitelaar, Paul  and
      Diallo, Gayo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.285",
    pages = "2341--2347",
    abstract = "In this work, we address the task of extracting application-specific taxonomies from the category hierarchy of Wikipedia. Previous work on pruning the Wikipedia knowledge graph relied on silver standard taxonomies which can only be automatically extracted for a small subset of domains rooted in relatively focused nodes, placed at an intermediate level in the knowledge graphs. In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies. We employ an existing state of the art algorithm in an iterative manner and we propose several sampling strategies to reduce the amount of manual work needed for evaluation. A first gold standard dataset is released to the research community for this task along with a companion evaluation framework. This dataset addresses a real-world application from the medical domain, namely the extraction of food-drug and herb-drug interactions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{randria-etal-2020-subjective,
    title = "Subjective Evaluation of Comprehensibility in Movie Interactions",
    author = "Randria, Estelle  and
      Fontan, Lionel  and
      Le Coz, Maxime  and
      Ferran{\'e}, Isabelle  and
      Pinquier, Julien",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.286",
    pages = "2348--2357",
    abstract = "Various research works have dealt with the comprehensibility of textual, audio, or audiovisual documents, and showed that factors related to text (e.g. linguistic complexity), sound (e.g. speech intelligibility), image (e.g. presence of visual context), or even to cognition and emotion can play a major role in the ability of humans to understand the semantic and pragmatic contents of a given document. However, to date, no reference human data is available that could help investigating the role of the linguistic and extralinguistic information present at these different levels (i.e., linguistic, audio/phonetic, and visual) in multimodal documents (e.g., movies). The present work aimed at building a corpus of human annotations that would help to study further how much and in which way the human perception of comprehensibility (i.e., of the difficulty of comprehension, referred in this paper as overall difficulty) of audiovisual documents is affected (1) by lexical complexity, grammatical complexity, and speech intelligibility, and (2) by the modality/ies (text, audio, video) available to the human recipient.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{leon-arauz-etal-2020-representing,
    title = "Representing Multiword Term Variation in a Terminological Knowledge Base: a Corpus-Based Study",
    author = "Le{\'o}n-Ara{\'u}z, Pilar  and
      Reimerink, Arianne  and
      Cabezas-Garc{\'\i}a, Melania",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.287",
    pages = "2358--2367",
    abstract = "In scientific and technical communication, multiword terms are the most frequent type of lexical units. Rendering them in another language is not an easy task due to their cognitive complexity, the proliferation of different forms, and their unsystematic representation in terminographic resources. This often results in a broad spectrum of translations for multiword terms, which also foment term variation since they consist of two or more constituents. In this study we carried out a quantitative and qualitative analysis of Spanish translation variants of a set of environment-related concepts by evaluating equivalents in three parallel corpora, two comparable corpora and two terminological resources. Our results showed that MWTs exhibit a significant degree of term variation of different characteristics, which were used to establish a set of criteria according to which term variants should be selected, organized and described in terminological knowledge bases.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dan-etal-2020-understanding,
    title = "Understanding Spatial Relations through Multiple Modalities",
    author = "Dan, Soham  and
      He, Hangfeng  and
      Roth, Dan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.288",
    pages = "2368--2372",
    abstract = "Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general. Spatial relations between objects can either be explicit {--} expressed as spatial prepositions, or implicit {--} expressed by spatial verbs such as moving, walking, shifting, etc. Both these, but implicit relations in particular, require significant common sense understanding. In this paper, we introduce the task of inferring implicit and explicit spatial relations between two entities in an image. We design a model that uses both textual and visual information to predict the spatial relations, making use of both positional and size information of objects and image embeddings. We contrast our spatial model with powerful language models and show how our modeling complements the power of these, improving prediction accuracy and coverage and facilitates dealing with unseen subjects, objects and relations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{roy-etal-2020-topic,
    title = "A Topic-Aligned Multilingual Corpus of {W}ikipedia Articles for Studying Information Asymmetry in Low Resource Languages",
    author = "Roy, Dwaipayan  and
      Bhatia, Sumit  and
      Jain, Prateek",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.289",
    pages = "2373--2380",
    abstract = "Wikipedia is the largest web-based open encyclopedia covering more than three hundred languages. However, different language editions of Wikipedia differ significantly in terms of their information coverage. We present a systematic comparison of information coverage in English Wikipedia (most exhaustive) and Wikipedias in eight other widely spoken languages (Arabic, German, Hindi, Korean, Portuguese, Russian, Spanish and Turkish). We analyze the content present in the respective Wikipedias in terms of the coverage of topics as well as the depth of coverage of topics included in these Wikipedias. Our analysis quantifies and provides useful insights about the information gap that exists between different language editions of Wikipedia and offers a roadmap for the IR community to bridge this gap.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kmetty-etal-2020-partelet,
    title = "{P}{\'a}rt{\'e}let: A {H}ungarian Corpus of Propaganda Texts from the {H}ungarian Socialist Era",
    author = "Kmetty, Zolt{\'a}n  and
      Vincze, Veronika  and
      Demszky, Dorottya  and
      Ring, Orsolya  and
      Nagy, Bal{\'a}zs  and
      Szab{\'o}, Martina Katalin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.290",
    pages = "2381--2388",
    abstract = "In this paper, we present P{\'a}rt{\'e}let, a digitized Hungarian corpus of Communist propaganda texts. P{\'a}rt{\'e}let was the official journal of the governing party during the Hungarian socialism from 1956 to 1989, hence it represents the direct political agitation and propaganda of the dictatorial system in question. The paper has a dual purpose: first, to present a general review of the corpus compilation process and the basic statistical data of the corpus, and second, to demonstrate through two case studies what the dataset can be used for. We show that our corpus provides a unique opportunity for conducting research on Hungarian propaganda discourse, as well as analyzing changes of this discourse over a 35-year period of time with computer-assisted methods.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{noullet-etal-2020-kore,
    title = "{KORE} 50{\^{}}{DYWC}: An Evaluation Data Set for Entity Linking Based on {DB}pedia, {YAGO}, {W}ikidata, and Crunchbase",
    author = {Noullet, Kristian  and
      Mix, Rico  and
      F{\"a}rber, Michael},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.291",
    pages = "2389--2395",
    abstract = "A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). One of the main ways of attempting to achieve this goal is through use of Semantic Web technologies and its structured data formats. Due to the nature of structured data, information can be extracted more easily, therewith allowing for the creation of knowledge graphs. In order to properly evaluate a NERD system, gold standard data sets are required. A plethora of different evaluation data sets exists, mostly relying on either Wikipedia or DBpedia. Therefore, we have extended a widely-used gold standard data set, KORE 50, to not only accommodate NERD tasks for DBpedia, but also for YAGO, Wikidata and Crunchbase. As such, our data set, KORE 50{\^{}}DYWC, allows for a broader spectrum of evaluation. Among others, the knowledge graph agnosticity of NERD systems may be evaluated which, to the best of our knowledge, was not possible until now for this number of knowledge graphs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alacam-etal-2020-eye4ref,
    title = "{E}ye4{R}ef: A Multimodal Eye Movement Dataset of Referentially Complex Situations",
    author = {Alacam, {\"O}zge  and
      Ruppert, Eugen  and
      Salama, Amr Rekaby  and
      Staron, Tobias  and
      Menzel, Wolfgang},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.292",
    pages = "2396--2404",
    abstract = "Eye4Ref is a rich multimodal dataset of eye-movement recordings collected from referentially complex situated settings where the linguistic utterances and their visual referential world were available to the listener. It consists of not only fixation parameters but also saccadic movement parameters that are time-locked to accompanying German utterances (with English translations). Additionally, it also contains symbolic knowledge (contextual) representations of the images to map the referring expressions onto the objects in corresponding images. Overall, the data was collected from 62 participants in three different experimental setups (86 systematically controlled sentence{--}image pairs and 1844 eye-movement recordings). Referential complexity was controlled by visual manipulations (e.g. number of objects, visibility of the target items, etc.), and by linguistic manipulations (e.g., the position of the disambiguating word in a sentence). This multimodal dataset, in which the three different sources of information namely eye-tracking, language, and visual environment are aligned, offers a test of various research questions not from only language perspective but also computer vision.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chen-etal-2020-sibert,
    title = "{S}i{B}ert: Enhanced {C}hinese Pre-trained Language Model with Sentence Insertion",
    author = "Chen, Jiahao  and
      Cao, Chenjie  and
      Jiang, Xiuyan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.293",
    pages = "2405--2412",
    abstract = "Pre-trained models have achieved great success in learning unsupervised language representations by self-supervised tasks on large-scale corpora. Recent studies mainly focus on how to fine-tune different downstream tasks from a general pre-trained model. However, some studies show that customized self-supervised tasks for a particular type of downstream task can effectively help the pre-trained model to capture more corresponding knowledge and semantic information. Hence a new pre-training task called Sentence Insertion (SI) is proposed in this paper for Chinese query-passage pairs NLP tasks including answer span prediction, retrieval question answering and sentence level cloze test. The related experiment results indicate that the proposed SI can improve the performance of the Chinese Pre-trained models significantly. Moreover, a word segmentation method called SentencePiece is utilized to further enhance Chinese Bert performance for tasks with long texts. The complete source code is available at https://github.com/ewrfcas/SiBert{\_}tensorflow.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{roark-etal-2020-processing,
    title = "Processing {S}outh {A}sian Languages Written in the {L}atin Script: the Dakshina Dataset",
    author = "Roark, Brian  and
      Wolf-Sonkin, Lawrence  and
      Kirov, Christo  and
      Mielke, Sabrina J.  and
      Johny, Cibu  and
      Demirsahin, Isin  and
      Hall, Keith",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.294",
    pages = "2413--2423",
    abstract = "This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. The dataset includes, for each language: 1) native script Wikipedia text; 2) a romanization lexicon; and 3) full sentence parallel data in both a native script of the language and the basic Latin alphabet. We document the methods used for preparation and selection of the Wikipedia text in each language; collection of attested romanizations for sampled lexicons; and manual romanization of held-out sentences from the native script collections. We additionally provide baseline results on several tasks made possible by the dataset, including single word transliteration, full sentence transliteration, and language modeling of native script and romanized text.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{melli-etal-2020-gm,
    title = "{GM}-{RKB} {W}iki{T}ext Error Correction Task and Baselines",
    author = "Melli, Gabor  and
      Eldallal, Abdelrhman  and
      Lazem, Bassim  and
      Moreira, Olga",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.295",
    pages = "2424--2430",
    abstract = "We introduce the GM-RKB WikiText Error Correction Task for the automatic detection and correction of typographical errors in WikiText annotated pages. The included corpus is based on a snapshot of the GM-RKB domain-specific semantic wiki consisting of a large collection of concepts, personages, and publications primary centered on data mining and machine learning research topics. Numerous Wikipedia pages were also included as additional training data in the task{'}s evaluation process. The corpus was then automatically updated to synthetically include realistic errors to produce a training and evaluation ground truth comparison. We designed and evaluated two supervised baseline WikiFixer error correction methods: (1) a naive approach based on a maximum likelihood character-level language model; (2) and an advanced model based on a sequence-to-sequence (seq2seq) neural network architecture. Both error correction models operated at a character level. When compared against an off-the-shelf word-level spell checker these methods showed a significant improvement in the task{'}s performance {--} with the seq2seq-based model correcting a higher number of errors than it introduced. Finally, we published our data and code.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{beyer-etal-2020-embedding,
    title = "Embedding Space Correlation as a Measure of Domain Similarity",
    author = {Beyer, Anne  and
      Kauermann, G{\"o}ran  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.296",
    pages = "2431--2439",
    abstract = "Prior work has determined domain similarity using text-based features of a corpus. However, when using pre-trained word embeddings, the underlying text corpus might not be accessible anymore. Therefore, we propose the CCA measure, a new measure of domain similarity based directly on the dimension-wise correlations between corresponding embedding spaces. Our results suggest that an inherent notion of domain can be captured this way, as we are able to reproduce our findings for different domain comparisons for English, German, Spanish and Czech as well as in cross-lingual comparisons. We further find a threshold at which the CCA measure indicates that two corpora come from the same domain in a monolingual setting by applying permutation tests. By evaluating the usability of the CCA measure in a domain adaptation application, we also show that it can be used to determine which corpora are more similar to each other in a cross-domain sentiment detection task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{guo-etal-2020-wiki,
    title = "{W}iki-40{B}: Multilingual Language Model Dataset",
    author = "Guo, Mandy  and
      Dai, Zihang  and
      Vrande{\v{c}}i{\'c}, Denny  and
      Al-Rfou, Rami",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.297",
    pages = "2440--2452",
    abstract = "We propose a new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families. With around 40 billion characters, we hope this new resource will accelerate the research of multilingual modeling. We train monolingual causal language models using a state-of-the-art model (Transformer-XL) establishing baselines for many languages. We also introduce the task of multilingual causal language modeling where we train our model on the combined text of 40+ languages from Wikipedia with different vocabulary sizes and evaluate on the languages individually. We released the cleaned-up text of 40+ Wikipedia language editions, the corresponding trained monolingual language models, and several multilingual language models with different fixed vocabulary sizes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sharoff-2020-know,
    title = "Know thy Corpus! Robust Methods for Digital Curation of Web corpora",
    author = "Sharoff, Serge",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.298",
    pages = "2453--2460",
    abstract = "This paper proposes a novel framework for digital curation of Web corpora in order to provide robust estimation of their parameters, such as their composition and the lexicon. In recent years language models pre-trained on large corpora emerged as clear winners in numerous NLP tasks, but no proper analysis of the corpora which led to their success has been conducted. The paper presents a procedure for robust frequency estimation, which helps in establishing the core lexicon for a given corpus, as well as a procedure for estimating the corpus composition via unsupervised topic models and via supervised genre classification of Web pages. The results of the digital curation study applied to several Web-derived corpora demonstrate their considerable differences. First, this concerns different frequency bursts which impact the core lexicon obtained from each corpus. Second, this concerns the kinds of texts they contain. For example, OpenWebText contains considerably more topical news and political argumentation in comparison to ukWac or Wikipedia. The tools and the results of analysis have been released.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{king-cook-2020-evaluating,
    title = "Evaluating Approaches to Personalizing Language Models",
    author = "King, Milton  and
      Cook, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.299",
    pages = "2461--2469",
    abstract = "In this work, we consider the problem of personalizing language models, that is, building language models that are tailored to the writing style of an individual. Because training language models requires a large amount of text, and individuals do not necessarily possess a large corpus of their writing that could be used for training, approaches to personalizing language models must be able to rely on only a small amount of text from any one user. In this work, we compare three approaches to personalizing a language model that was trained on a large background corpus using a relatively small amount of text from an individual user. We evaluate these approaches using perplexity, as well as two measures based on next word prediction for smartphone soft keyboards. Our results show that when only a small amount of user-specific text is available, an approach based on priming gives the most improvement, while when larger amounts of user-specific text are available, an approach based on language model interpolation performs best. We carry out further experiments to show that these approaches to personalization outperform language model adaptation based on demographic factors.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kipyatkova-karpov-2020-class,
    title = "Class-based {LSTM} {R}ussian Language Model with Linguistic Information",
    author = "Kipyatkova, Irina  and
      Karpov, Alexey",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.300",
    pages = "2470--2474",
    abstract = "In the paper, we present class-based LSTM Russian language models (LMs) with classes generated with the use of both word frequency and linguistic information data, obtained with the help of the {``}VisualSynan{''} software from the AOT project. We have created LSTM LMs with various numbers of classes and compared them with word-based LM and class-based LM with word2vec class generation in terms of perplexity, training time, and WER. In addition, we performed a linear interpolation of LSTM language models with the baseline 3-gram language model. The LSTM language models were used for very large vocabulary continuous Russian speech recognition at an N-best list rescoring stage. We achieved significant progress in training time reduction with only slight degradation in recognition accuracy comparing to the word-based LM. In addition, our LM with classes generated using linguistic information outperformed LM with classes generated using word2vec. We achieved WER of 14.94 {\%} at our own speech corpus of continuous Russian speech that is 15 {\%} relative reduction with respect to the baseline 3-gram model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ralethe-2020-adaptation,
    title = "Adaptation of Deep Bidirectional Transformers for {A}frikaans Language",
    author = "Ralethe, Sello",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.301",
    pages = "2475--2478",
    abstract = "The recent success of pretrained language models in Natural Language Processing has sparked interest in training such models for languages other than English. Currently, training of these models can either be monolingual or multilingual based. In the case of multilingual models, such models are trained on concatenated data of multiple languages. We introduce AfriBERT, a language model for the Afrikaans language based on Bidirectional Encoder Representation from Transformers (BERT). We compare the performance of AfriBERT against multilingual BERT in multiple downstream tasks, namely part-of-speech tagging, named-entity recognition, and dependency parsing. Our results show that AfriBERT improves the current state-of-the-art in most of the tasks we considered, and that transfer learning from multilingual to monolingual model can have a significant performance improvement on downstream tasks. We release the pretrained model for AfriBERT.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{le-etal-2020-flaubert,
    title = "{F}lau{BERT}: Unsupervised Language Model Pre-training for {F}rench",
    author = {Le, Hang  and
      Vial, Lo{\"\i}c  and
      Frej, Jibril  and
      Segonne, Vincent  and
      Coavoux, Maximin  and
      Lecouteux, Benjamin  and
      Allauzen, Alexandre  and
      Crabb{\'e}, Benoit  and
      Besacier, Laurent  and
      Schwab, Didier},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.302",
    pages = "2479--2490",
    abstract = "Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ciosici-etal-2020-accelerated,
    title = "Accelerated High-Quality Mutual-Information Based Word Clustering",
    author = "Ciosici, Manuel R.  and
      Assent, Ira  and
      Derczynski, Leon",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.303",
    pages = "2491--2496",
    abstract = "Word clustering groups words that exhibit similar properties. One popular method for this is Brown clustering, which uses short-range distributional information to construct clusters. Specifically, this is a hard hierarchical clustering with a fixed-width beam that employs bi-grams and greedily minimizes global mutual information loss. The result is word clusters that tend to outperform or complement other word representations, especially when constrained by small datasets. However, Brown clustering has high computational complexity and does not lend itself to parallel computation. This, together with the lack of efficient implementations, limits their applicability in NLP. We present efficient implementations of Brown clustering and the alternative Exchange clustering as well as a number of methods to accelerate the computation of both hierarchical and flat clusters. We show empirically that clusters obtained with the accelerated method match the performance of clusters computed using the original methods.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{coulange-rossato-2020-rhythmic,
    title = "Rhythmic Proximity Between Natives And Learners Of {F}rench - Evaluation of a metric based on the {CEFC} corpus",
    author = "Coulange, Sylvain  and
      Rossato, Solange",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.304",
    pages = "2497--2502",
    abstract = "This work aims to better understand the role of rhythm in foreign accent, and its modelling. We made a model of rhythm in French taking into account its variability, thanks to the Corpus pour l{'}{\'E}tude du Fran{\c{c}}ais Contemporain (CEFC), which contains up to 300 hours of speech of a wide variety of speaker profiles and situations. 16 parameters were computed, each of them being based on segment duration, such as voicing and intersyllabic timing. All the parameters are fully automatically detected from signal, without ASR or transcription. A gaussian mixture model was trained on 1,340 native speakers of French; any 30-second minimum speech may be computed to get the probability of its belonging to this model. We tested it with 146 test native speakers (NS), 37 non-native speakers (NNS) from the same corpus, and 29 non-native Japanese learners of French (JpNNS) from an independent corpus. The probability of NNS having inferior log-likelihood to NS was only a tendency (p=.067), maybe due to the heterogeneity of French proficiency of the speakers; but a much bigger probability was obtained for JpNNS (p{\textless}.0001), where all speakers were A2 level. Eta-squared test showed that most efficient parameters were intersyllabic mean duration and variation coefficient, along with speech rate for NNS; and speech rate and phonation ratio for JpNNS.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{speranza-etal-2020-linguistic,
    title = "From Linguistic Resources to Ontology-Aware Terminologies: Minding the Representation Gap",
    author = "Speranza, Giulia  and
      di Buono, Maria Pia  and
      Monti, Johanna  and
      Sangati, Federico",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.305",
    pages = "2503--2510",
    abstract = "Terminological resources have proven crucial in many applications ranging from Computer-Aided Translation tools to authoring softwares and multilingual and cross-lingual information retrieval systems. Nonetheless, with the exception of a few felicitous examples, such as the IATE (Interactive Terminology for Europe) Termbank, many terminological resources are not available in standard formats, such as Term Base eXchange (TBX), thus preventing their sharing and reuse. Yet, these terminologies could be improved associating the correspondent ontology-based information. The research described in the present contribution demonstrates the process and the methodologies adopted in the automatic conversion into TBX of such type of resources, together with their semantic enrichment based on the formalization of ontological information into terminologies. We present a proof-of-concept using the Italian Linguistic Resource for the Archaeological domain (developed according to Thesauri and Guidelines of the Italian Central Institute for the Catalogue and Documentation). Further, we introduce the conversion tool developed to support the process of creating ontology-aware terminologies for improving interoperability and sharing of existing language technologies and data sets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{arslan-etal-2020-modeling,
    title = "Modeling Factual Claims with Semantic Frames",
    author = "Arslan, Fatma  and
      Caraballo, Josue  and
      Jimenez, Damian  and
      Li, Chengkai",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.306",
    pages = "2511--2520",
    abstract = "In this paper, we introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. Modeling is a robust tool that can be leveraged in many different tasks such as matching claims to existing fact-checks and translating claims to structured queries. Our work introduces 11 new manually crafted frames along with 9 existing FrameNet frames, all of which have been selected with fact-checking in mind. Along with these frames, we are also providing 2,540 fully annotated sentences, which can be used to understand how these frames are intended to work and to train machine learning models. Finally, we are also releasing our annotation tool to facilitate other researchers to make their own local extensions to FrameNet.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gupta-boulianne-2020-automatic,
    title = "Automatic Transcription Challenges for {I}nuktitut, a Low-Resource Polysynthetic Language",
    author = "Gupta, Vishwa  and
      Boulianne, Gilles",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.307",
    pages = "2521--2527",
    abstract = "We introduce the first attempt at automatic speech recognition (ASR) in Inuktitut, as a representative for polysynthetic, low-resource languages, like many of the 900 Indigenous languages spoken in the Americas. As most previous work on Inuktitut, we use texts from parliament proceedings, but in addition we have access to 23 hours of transcribed oral stories. With this corpus, we show that Inuktitut displays a much higher degree of polysynthesis than other agglutinative languages usually considered in ASR, such as Finnish or Turkish. Even with a vocabulary of 1.3 million words derived from proceedings and stories, held-out stories have more than 60{\%} of words out-of-vocabulary. We train bi-directional LSTM acoustic models, then investigate word and subword units, morphemes and syllables, and a deep neural network that finds word boundaries in subword sequences. We show that acoustic decoding using syllables decorated with word boundary markers results in the lowest word error rate.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dunn-adams-2020-geographically,
    title = "Geographically-Balanced {G}igaword Corpora for 50 Language Varieties",
    author = "Dunn, Jonathan  and
      Adams, Ben",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.308",
    pages = "2528--2536",
    abstract = "While text corpora have been steadily increasing in overall size, even very large corpora are not designed to represent global population demographics. For example, recent work has shown that existing English gigaword corpora over-represent inner-circle varieties from the US and the UK. To correct implicit geographic and demographic biases, this paper uses country-level population demographics to guide the construction of gigaword web corpora. The resulting corpora explicitly match the ground-truth geographic distribution of each language, thus equally representing language users from around the world. This is important because it ensures that speakers of under-resourced language varieties (i.e., Indian English or Algerian French) are represented, both in the corpora themselves but also in derivative resources like word embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{amjad-etal-2020-data,
    title = "Data Augmentation using Machine Translation for Fake News Detection in the {U}rdu Language",
    author = "Amjad, Maaz  and
      Sidorov, Grigori  and
      Zhila, Alisa",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.309",
    pages = "2537--2542",
    abstract = "The task of fake news detection is to distinguish legitimate news articles that describe real facts from those which convey deceiving and fictitious information. As the fake news phenomenon is omnipresent across all languages, it is crucial to be able to efficiently solve this problem for languages other than English. A common approach to this task is supervised classification using features of various complexity. Yet supervised machine learning requires substantial amount of annotated data. For English and a small number of other languages, annotated data availability is much higher, whereas for the vast majority of languages, it is almost scarce. We investigate whether machine translation at its present state could be successfully used as an automated technique for annotated corpora creation and augmentation for fake news detection focusing on the English-Urdu language pair. We train a fake news classifier for Urdu on (1) the manually annotated dataset originally in Urdu and (2) the machine-translated version of an existing annotated fake news dataset originally in English. We show that at the present state of machine translation quality for the English-Urdu language pair, the fully automated data augmentation through machine translation did not provide improvement for fake news detection in Urdu.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{outsios-etal-2020-evaluation,
    title = "Evaluation of {G}reek Word Embeddings",
    author = "Outsios, Stamatis  and
      Karatsalos, Christos  and
      Skianis, Konstantinos  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.310",
    pages = "2543--2551",
    abstract = "Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is critical. Most research efforts are focusing on English word embeddings. This paper addresses the problem of training and evaluating such models for the Greek language. We present a new word analogy test set considering the original English Word2vec analogy test set and some specific linguistic aspects of the Greek language as well. Moreover, we create a Greek version of WordSim353 test collection for a basic evaluation of word similarities. Produced resources are available for download. We test seven word vector models and our evaluation shows that we are able to create meaningful representations. Last, we discover that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{papavassiliou-etal-2020-dataset,
    title = "A Dataset of {M}ycenaean {L}inear {B} Sequences",
    author = "Papavassiliou, Katerina  and
      Owens, Gareth  and
      Kosmopoulos, Dimitrios",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.311",
    pages = "2552--2561",
    abstract = "We present our work towards a dataset of Mycenaean Linear B sequences gathered from the Mycenaean inscriptions written in the 13th and 14th century B.C. (c. 1400-1200 B.C.). The dataset contains sequences of Mycenaean words and ideograms according to the rules of the Mycenaean Greek language in the Late Bronze Age. Our ultimate goal is to contribute to the study, reading and understanding of ancient scripts and languages. Focusing on sequences, we seek to exploit the structure of the entire language, not just the Mycenaean vocabulary, to analyse sequential patterns. We use the dataset to experiment on estimating the missing symbols in damaged inscriptions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{joanis-etal-2020-nunavut,
    title = "The {N}unavut Hansard {I}nuktitut{--}{E}nglish Parallel Corpus 3.0 with Preliminary Machine Translation Results",
    author = "Joanis, Eric  and
      Knowles, Rebecca  and
      Kuhn, Roland  and
      Larkin, Samuel  and
      Littell, Patrick  and
      Lo, Chi-kiu  and
      Stewart, Darlene  and
      Micher, Jeffrey",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.312",
    pages = "2562--2572",
    abstract = "The Inuktitut language, a member of the Inuit-Yupik-Unangan language family, is spoken across Arctic Canada and noted for its morphological complexity. It is an official language of two territories, Nunavut and the Northwest Territories, and has recognition in additional regions. This paper describes a newly released sentence-aligned Inuktitut{--}English corpus based on the proceedings of the Legislative Assembly of Nunavut, covering sessions from April 1999 to June 2017. With approximately 1.3 million aligned sentence pairs, this is, to our knowledge, the largest parallel corpus of a polysynthetic language or an Indigenous language of the Americas released to date. The paper describes the alignment methodology used, the evaluation of the alignments, and preliminary experiments on statistical and neural machine translation (SMT and NMT) between Inuktitut and English, in both directions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{michel-etal-2020-exploring,
    title = "Exploring Bilingual Word Embeddings for {H}iligaynon, a Low-Resource Language",
    author = "Michel, Leah  and
      Hangya, Viktor  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.313",
    pages = "2573--2580",
    abstract = "This paper investigates the use of bilingual word embeddings for mining Hiligaynon translations of English words. There is very little research on Hiligaynon, an extremely low-resource language of Malayo-Polynesian origin with over 9 million speakers in the Philippines (we found just one paper). We use a publicly available Hiligaynon corpus with only 300K words, and match it with a comparable corpus in English. As there are no bilingual resources available, we manually develop a English-Hiligaynon lexicon and use this to train bilingual word embeddings. But we fail to mine accurate translations due to the small amount of data. To find out if the same holds true for a related language pair, we simulate the same low-resource setup on English to German and arrive at similar results. We then vary the size of the comparable English and German corpora to determine the minimum corpus size necessary to achieve competitive results. Further, we investigate the role of the seed lexicon. We show that with the same corpus size but with a smaller seed lexicon, performance can surpass results of previous studies. We release the lexicon of 1,200 English-Hiligaynon word pairs we created to encourage further investigation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zueva-etal-2020-finite,
    title = "A Finite-State Morphological Analyser for {E}venki",
    author = "Zueva, Anna  and
      Kuznetsova, Anastasia  and
      Tyers, Francis",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.314",
    pages = "2581--2589",
    abstract = "It has been widely admitted that morphological analysis is an important step in automated text processing for morphologically rich languages. Evenki is a language with rich morphology, therefore a morphological analyser is highly desirable for processing Evenki texts and developing applications for Evenki. Although two morphological analysers for Evenki have already been developed, they are able to analyse less than a half of the available Evenki corpora. The aim of this paper is to create a new morphological analyser for Evenki. It is implemented using the Helsinki Finite-State Transducer toolkit (HFST). The lexc formalism is used to specify the morphotactic rules, which define the valid orderings of morphemes in a word. Morphophonological alternations and orthographic rules are described using the twol formalism. The lexicon is extracted from available machine-readable dictionaries. Since a part of the corpora belongs to texts in Evenki dialects, a version of the analyser with relaxed rules is developed for processing dialectal features. We evaluate the analyser on available Evenki corpora and estimate precision, recall and F-score. We obtain coverage scores of between 61{\%} and 87{\%} on the available Evenki corpora.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mersha-wu-2020-morphology,
    title = "Morphology-rich Alphasyllabary Embeddings",
    author = "Mersha, Amanuel  and
      Wu, Stephen",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.315",
    pages = "2590--2595",
    abstract = "Word embeddings have been successfully trained in many languages. However, both intrinsic and extrinsic metrics are variable across languages, especially for languages that depart significantly from English in morphology and orthography. This study focuses on building a word embedding model suitable for the Semitic language of Amharic (Ethiopia), which is both morphologically rich and written as an alphasyllabary (abugida) rather than an alphabet. We compare embeddings from tailored neural models, simple pre-processing steps, off-the-shelf baselines, and parallel tasks on a better-resourced Semitic language {--} Arabic. Experiments show our model{'}s performance on word analogy tasks, illustrating the divergent objectives of morphological vs. semantic analogies.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cruz-etal-2020-localization,
    title = "Localization of Fake News Detection via Multitask Transfer Learning",
    author = "Cruz, Jan Christian Blaise  and
      Tan, Julianne Agatha  and
      Cheng, Charibeth",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.316",
    pages = "2596--2604",
    abstract = "The use of the internet as a fast medium of spreading fake news reinforces the need for computational tools that combat it. Techniques that train fake news classifiers exist, but they all assume an abundance of resources including large labeled datasets and expert-curated corpora, which low-resource languages may not have. In this work, we make two main contributions: First, we alleviate resource scarcity by constructing the first expertly-curated benchmark dataset for fake news detection in Filipino, which we call {``}Fake News Filipino.{''} Second, we benchmark Transfer Learning (TL) techniques and show that they can be used to train robust fake news classifiers from little data, achieving 91{\%} accuracy on our fake news dataset, reducing the error by 14{\%} compared to established few-shot baselines. Furthermore, lifting ideas from multitask learning, we show that augmenting transformer-based transfer techniques with auxiliary language modeling losses improves their performance by adapting to writing style. Using this, we improve TL performance by 4-6{\%}, achieving an accuracy of 96{\%} on our best model. Lastly, we show that our method generalizes well to different types of news articles, including political news, entertainment news, and opinion articles.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{casanova-etal-2020-evaluating,
    title = "Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in {B}razilian {P}ortuguese",
    author = {Casanova, Edresson  and
      Treviso, Marcos  and
      H{\"u}bner, Lilian  and
      Alu{\'\i}sio, Sandra},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.317",
    pages = "2605--2614",
    abstract = "Automatic analysis of connected speech by natural language processing techniques is a promising direction for diagnosing cognitive impairments. However, some difficulties still remain: the time required for manual narrative transcription and the decision on how transcripts should be divided into sentences for successful application of parsers used in metrics, such as Idea Density, to analyze the transcripts. The main goal of this paper was to develop a generic segmentation system for narratives of neuropsychological language tests. We explored the performance of our previous single-dataset-trained sentence segmentation architecture in a richer scenario involving three new datasets used to diagnose cognitive impairments, comprising different stories and two types of stimulus presentation for eliciting narratives {---} visual and oral {---} via illustrated story-book and sequence of scenes, and by retelling. Also, we proposed and evaluated three modifications to our previous RCNN architecture: (i) the inclusion of a Linear Chain CRF; (ii) the inclusion of a self-attention mechanism; and (iii) the replacement of the LSTM recurrent layer by a Quasi-Recurrent Neural Network layer. Our study allowed us to develop two new models for segmenting impaired speech transcriptions, along with an ideal combination of datasets and specific groups of narratives to be used as the training set.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{park-etal-2020-jejueo,
    title = "{J}ejueo Datasets for Machine Translation and Speech Synthesis",
    author = "Park, Kyubyong  and
      Choe, Yo Joong  and
      Ham, Jiyeon",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.318",
    pages = "2615--2621",
    abstract = "Jejueo was classified as critically endangered by UNESCO in 2010. Although diverse efforts to revitalize it have been made, there have been few computational approaches. Motivated by this, we construct two new Jejueo datasets: Jejueo Interview Transcripts (JIT) and Jejueo Single Speaker Speech (JSS). The JIT dataset is a parallel corpus containing 170k+ Jejueo-Korean sentences, and the JSS dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file. Subsequently, we build neural systems of machine translation and speech synthesis using them. All resources are publicly available via our GitHub repository. We hope that these datasets will attract interest of both language and machine learning communities.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{matsuura-etal-2020-speech,
    title = "Speech Corpus of {A}inu Folklore and End-to-end Speech Recognition for {A}inu Language",
    author = "Matsuura, Kohei  and
      Ueno, Sei  and
      Mimura, Masato  and
      Sakai, Shinsuke  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.319",
    pages = "2622--2628",
    abstract = "Ainu is an unwritten language that has been spoken by Ainu people who are one of the ethnic groups in Japan. It is recognized as critically endangered by UNESCO and archiving and documentation of its language heritage is of paramount importance. Although a considerable amount of voice recordings of Ainu folklore has been produced and accumulated to save their culture, only a quite limited parts of them are transcribed so far. Thus, we started a project of automatic speech recognition (ASR) for the Ainu language in order to contribute to the development of annotated language archives. In this paper, we report speech corpus development and the structure and performance of end-to-end ASR for Ainu. We investigated four modeling units (phone, syllable, word piece, and word) and found that the syllable-based model performed best in terms of both word and phone recognition accuracy, which were about 60{\%} and over 85{\%} respectively in speaker-open condition. Furthermore, word and phone accuracy of 80{\%} and 90{\%} has been achieved in a speaker-closed setting. We also found out that a multilingual ASR training with additional speech corpora of English and Japanese further improves the speaker-open test accuracy.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chiruzzo-etal-2020-development,
    title = "Development of a {G}uarani - {S}panish Parallel Corpus",
    author = "Chiruzzo, Luis  and
      Amarilla, Pedro  and
      R{\'\i}os, Adolfo  and
      Gim{\'e}nez Lugo, Gustavo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.320",
    pages = "2629--2633",
    abstract = "This paper presents the development of a Guarani - Spanish parallel corpus with sentence-level alignment. The Guarani sentences of the corpus use the Jopara Guarani dialect, the dialect of Guarani spoken in Paraguay, which is based on Guarani grammar and may include several Spanish loanwords or neologisms. The corpus has around 14,500 sentence pairs aligned using a semi-automatic process, containing 228,000 Guarani tokens and 336,000 Spanish tokens extracted from web sources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ouahrani-bennouar-2020-ar,
    title = "{AR}-{ASAG} An {AR}abic Dataset for Automatic Short Answer Grading Evaluation",
    author = "Ouahrani, Leila  and
      Bennouar, Djamal",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.321",
    pages = "2634--2643",
    abstract = "Automatic short answer grading is a significant problem in E-assessment. Several models have been proposed to deal with it. Evaluation and comparison of such solutions need the availability of Datasets with manual examples. In this paper, we introduce AR-ASAG, an Arabic Dataset for automatic short answer grading. The Dataset contains 2133 pairs of (Model Answer, Student Answer) in several versions (txt, xml, Moodle xml and .db). We explore then an unsupervised corpus based approach for automatic grading adapted to the Arabic Language. We use COALS (Correlated Occurrence Analogue to Lexical Semantic) algorithm to create semantic space for word distribution. The summation vector model is combined to term weighting and common words to achieve similarity between a teacher model answer and a student answer. The approach is particularly suitable for languages with scarce resources such as Arabic language where robust specific resources are not yet available. A set of experiments were conducted to analyze the effect of domain specificity, semantic space dimension and stemming techniques on the effectiveness of the grading model. The proposed approach gives promising results for Arabic language. The reported results may serve as baseline for future research work evaluation",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ferger-2020-processing,
    title = "Processing Language Resources of Under-Resourced and Endangered Languages for the Generation of Augmentative Alternative Communication Boards",
    author = "Ferger, Anne",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.322",
    pages = "2644--2648",
    abstract = {Under-resourced and endangered or small languages yield problems for automatic processing and exploiting because of the small amount of available data. This paper shows an approach using different annotations of enriched linguistic research data to create communication boards commonly used in Alternative Augmentative Communication (AAC). Using manually created lexical analysis and rich annotation (instead of high data quantity) allows for an automated creation of AAC communication boards. The example presented in this paper uses data of the indigenous language Dolgan (an endangered Turkic language of Northern Siberia) created in the project INEL(Arkhipov and D{\"a}britz, 2018) to generate a basic communication board with audio snippets to be used in e.g. hospital communication or for multilingual settings. The created boards can be importet into various AAC software. In addition, the usage of standard formats makes this approach applicable to various different use cases.},
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aznar-gala-2020-nisvai,
    title = "The Nisvai Corpus of Oral Narrative Practices from {M}alekula ({V}anuatu) and its Associated Language Resources",
    author = "Aznar, Jocelyn  and
      Gala, N{\'u}ria",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.323",
    pages = "2649--2656",
    abstract = "In this paper, we present a corpus of oral narratives from the Nisvai linguistic community and four associated language resources. Nisvai is an oral language spoken by 200 native speakers in the South-East of Malekula, an island of Vanuatu, Oceania. This language had never been the focus of a research before the one leading to this article. The corpus we present is made of 32 annotated narratives segmented into intonation units. The audio records were transcribed using the written conventions specifically developed for the language and translated into French. Four associated language resources have been generated by organizing the annotations into written documents: two of them are available online and two in paper format. The online resources allow the users to listen to the audio recordings whilereading the annotations. They were built to share the results of our fieldwork and to communicate on the Nisvai narrative practices with the researchers as well as with a more general audience. The bilingual paper resources, a booklet of narratives and a Nisvai-French French-Nisvai lexicon, were designed for the Nisvai community by taking into account their future uses (i.e. primary school).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{paschen-etal-2020-building,
    title = "Building a Time-Aligned Cross-Linguistic Reference Corpus from Language Documentation Data ({D}o{R}e{C}o)",
    author = "Paschen, Ludger  and
      Delafontaine, Fran{\c{c}}ois  and
      Draxler, Christoph  and
      Fuchs, Susanne  and
      Stave, Matthew  and
      Seifart, Frank",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.324",
    pages = "2657--2666",
    abstract = "Natural speech data on many languages have been collected by language documentation projects aiming to preserve lingustic and cultural traditions in audivisual records. These data hold great potential for large-scale cross-linguistic research into phonetics and language processing. Major obstacles to utilizing such data for typological studies include the non-homogenous nature of file formats and annotation conventions found both across and within archived collections. Moreover, time-aligned audio transcriptions are typically only available at the level of broad (multi-word) phrases but not at the word and segment levels. We report on solutions developed for these issues within the DoReCo (DOcumentation REference COrpus) project. DoReCo aims at providing time-aligned transcriptions for at least 50 collections of under-resourced languages. This paper gives a preliminary overview of the current state of the project and details our workflow, in particular standardization of formats and conventions, the addition of segmental alignments with WebMAUS, and DoReCo{'}s applicability for subsequent research programs. By making the data accessible to the scientific community, DoReCo is designed to bridge the gap between language documentation and linguistic inquiry.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{duh-etal-2020-benchmarking,
    title = "Benchmarking Neural and Statistical Machine Translation on Low-Resource {A}frican Languages",
    author = "Duh, Kevin  and
      McNamee, Paul  and
      Post, Matt  and
      Thompson, Brian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.325",
    pages = "2667--2675",
    abstract = "Research in machine translation (MT) is developing at a rapid pace. However, most work in the community has focused on languages where large amounts of digital resources are available. In this study, we benchmark state of the art statistical and neural machine translation systems on two African languages which do not have large amounts of resources: Somali and Swahili. These languages are of social importance and serve as test-beds for developing technologies that perform reasonably well despite the low-resource constraint. Our findings suggest that statistical machine translation (SMT) and neural machine translation (NMT) can perform similarly in low-resource scenarios, but neural systems require more careful tuning to match performance. We also investigate how to exploit additional data, such as bilingual text harvested from the web, or user dictionaries; we find that NMT can significantly improve in performance with the use of these additional data. Finally, we survey the landscape of machine translation resources for the languages of Africa and provide some suggestions for promising future research directions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chen-etal-2020-improved,
    title = "Improved Finite-State Morphological Analysis for {S}t. {L}awrence {I}sland {Y}upik Using Paradigm Function Morphology",
    author = "Chen, Emily  and
      Park, Hyunji Hayley  and
      Schwartz, Lane",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.326",
    pages = "2676--2684",
    abstract = "St. Lawrence Island Yupik is an endangered polysynthetic language of the Bering Strait region. While conducting linguistic fieldwork between 2016 and 2019, we observed substantial support within the Yupik community for language revitalization and for resource development to support Yupik education. To that end, Chen {\&} Schwartz (2018) implemented a finite-state morphological analyzer as a critical enabling technology for use in Yupik language education and technology. Chen {\&} Schwartz (2018) reported a morphological analysis coverage rate of approximately 75{\%} on a dataset of 60K Yupik tokens, leaving considerable room for improvement. In this work, we present a re-implementation of the Chen {\&} Schwartz (2018) finite-state morphological analyzer for St. Lawrence Island Yupik that incorporates new linguistic insights; in particular, in this implementation we make use of the Paradigm Function Morphology (PFM) theory of morphology. We evaluate this new PFM-based morphological analyzer, and demonstrate that it consistently outperforms the existing analyzer of Chen {\&} Schwartz (2018) with respect to accuracy and coverage rate across multiple datasets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{himoro-pareja-lora-2020-towards,
    title = "Towards a Spell Checker for {Z}amboanga {C}havacano Orthography",
    author = "Himoro, Marcelo Yuji  and
      Pareja-Lora, Antonio",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.327",
    pages = "2685--2697",
    abstract = "Zamboanga Chabacano (ZC) is the most vibrant variety of Philippine Creole Spanish, with over 400,000 native speakers in the Philippines (as of 2010). Following its introduction as a subject and a medium of instruction in the public schools of Zamboanga City from Grade 1 to 3 in 2012, an official orthography for this variety - the so-called {``}Zamboanga Chavacano Orthography{''} - has been approved in 2014. Its complexity, however, is a barrier to most speakers, since it does not necessarily reflect the particular phonetic evolution in ZC, but favours etymology instead. The distance between the correct spelling and the different spelling variations is often so great that delivering acceptable performance with the current de facto spell checking technologies may be challenging. The goals of this research have been to propose i) a spelling error taxonomy for ZC, formalised as an ontology and ii) an adaptive spell checking approach using Character-Based Statistical Machine Translation to correct spelling errors in ZC. Our results show that this approach is suitable for the goals mentioned and that it could be combined with other current spell checking technologies to achieve even higher performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{adouane-etal-2020-identifying,
    title = "Identifying Sentiments in {A}lgerian Code-switched User-generated Comments",
    author = "Adouane, Wafia  and
      Touileb, Samia  and
      Bernardy, Jean-Philippe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.328",
    pages = "2698--2705",
    abstract = "We present in this paper our work on Algerian language, an under-resourced North African colloquial Arabic variety, for which we built a comparably large corpus of more than 36,000 code-switched user-generated comments annotated for sentiments. We opted for this data domain because Algerian is a colloquial language with no existing freely available corpora. Moreover, we compiled sentiment lexicons of positive and negative unigrams and bigrams reflecting the code-switches present in the language. We compare the performance of four models on the task of identifying sentiments, and the results indicate that a CNN model trained end-to-end fits better our unedited code-switched and unbalanced data across the predefined sentiment classes. Additionally, injecting the lexicons as background knowledge to the model boosts its performance on the minority class with a gain of 10.54 points on the F-score. The results of our experiments can be used as a baseline for future research for Algerian sentiment analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{linder-etal-2020-automatic,
    title = "Automatic Creation of Text Corpora for Low-Resource Languages from the {I}nternet: The Case of {S}wiss {G}erman",
    author = "Linder, Lucy  and
      Jungo, Michael  and
      Hennebert, Jean  and
      Musat, Claudiu Cristian  and
      Fischer, Andreas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.329",
    pages = "2706--2711",
    abstract = "This paper presents SwissCrawl, the largest Swiss German text corpus to date. Composed of more than half a million sentences, it was generated using a customized web scraping tool that could be applied to other low-resource languages as well. The approach demonstrates how freely available web pages can be used to construct comprehensive text corpora, which are of fundamental importance for natural language processing. In an experimental evaluation, we show that using the new corpus leads to significant improvements for the task of language modeling.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hakimi-parizi-cook-2020-evaluating,
    title = "Evaluating Sub-word Embeddings in Cross-lingual Models",
    author = "Hakimi Parizi, Ali  and
      Cook, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.330",
    pages = "2712--2719",
    abstract = "Cross-lingual word embeddings create a shared space for embeddings in two languages, and enable knowledge to be transferred between languages for tasks such as bilingual lexicon induction. One problem, however, is out-of-vocabulary (OOV) words, for which no embeddings are available. This is particularly problematic for low-resource and morphologically-rich languages, which often have relatively high OOV rates. Approaches to learning sub-word embeddings have been proposed to address the problem of OOV words, but most prior work has not considered sub-word embeddings in cross-lingual models. In this paper, we consider whether sub-word embeddings can be leveraged to form cross-lingual embeddings for OOV words. Specifically, we consider a novel bilingual lexicon induction task focused on OOV words, for language pairs covering several language families. Our results indicate that cross-lingual representations for OOV words can indeed be formed from sub-word embeddings, including in the case of a truly low-resource morphologically-rich language.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schmidt-etal-2020-swiss,
    title = "A {S}wiss {G}erman Dictionary: Variation in Speech and Writing",
    author = "Schmidt, Larissa  and
      Linder, Lucy  and
      Djambazovska, Sandra  and
      Lazaridis, Alexandros  and
      Samard{\v{z}}i{\'c}, Tanja  and
      Musat, Claudiu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.331",
    pages = "2720--2725",
    abstract = "We introduce a dictionary containing normalized forms of common words in various Swiss German dialects into High German. As Swiss German is, for now, a predominantly spoken language, there is a significant variation in the written forms, even between speakers of the same dialect. To alleviate the uncertainty associated with this diversity, we complement the pairs of Swiss German - High German words with the Swiss German phonetic transcriptions (SAMPA). This dictionary becomes thus the first resource to combine large-scale spontaneous translation with phonetic transcriptions. Moreover, we control for the regional distribution and insure the equal representation of the major Swiss dialects. The coupling of the phonetic and written Swiss German forms is powerful. We show that they are sufficient to train a Transformer-based phoneme to grapheme model that generates credible novel Swiss German writings. In addition, we show that the inverse mapping - from graphemes to phonemes - can be modeled with a transformer trained with the novel dictionary. This generation of pronunciations for previously unknown words is key in training extensible automated speech recognition (ASR) systems, which are key beneficiaries of this dictionary.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kevers-retali-medori-2020-towards,
    title = "Towards a {C}orsican Basic Language Resource Kit",
    author = "Kevers, Laurent  and
      Retali-Medori, Stella",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.332",
    pages = "2726--2735",
    abstract = "The current situation regarding the existence of natural language processing (NLP) resources and tools for Corsican reveals their virtual non-existence. Our inventory contains only a few rare digital resources, lexical or corpus databases, requiring adaptation work. Our objective is to use the Banque de Donn{\'e}es Langue Corse project (BDLC) to improve the availability of resources and tools for the Corsican language and, in the long term, provide a complete Basic Language Ressource Kit (BLARK). We have defined a roadmap setting out the actions to be undertaken: the collection of corpora and the setting up of a consultation interface (concordancer), and of a language detection tool, an electronic dictionary and a part-of-speech tagger. The first achievements regarding these topics have already been reached and are presented in this article. Some elements are also available on our project page (http://bdlc.univ-corse.fr/tal/).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{boudreau-etal-2020-evaluating,
    title = "Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi{'}kmaq Language Modelling",
    author = "Boudreau, Jeremie  and
      Patra, Akankshya  and
      Suvarna, Ashima  and
      Cook, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.333",
    pages = "2736--2745",
    abstract = "Mi{'}kmaq is an Indigenous language spoken primarily in Eastern Canada. It is polysynthetic and low-resource. In this paper we consider a range of n-gram and RNN language models for Mi{'}kmaq. We find that an RNN language model, initialized with pre-trained fastText embeddings, performs best, highlighting the importance of sub-word information for Mi{'}kmaq language modelling. We further consider approaches to language modelling that incorporate cross-lingual word embeddings, but do not see improvements with these models. Finally we consider language models that operate over segmentations produced by SentencePiece {---} which include sub-word units as tokens {---} as opposed to word-level models. We see improvements for this approach over word-level language models, again indicating that sub-word modelling is important for Mi{'}kmaq language modelling.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{brixey-etal-2020-exploring,
    title = "Exploring a {C}hoctaw Language Corpus with Word Vectors and Minimum Distance Length",
    author = "Brixey, Jacqueline  and
      Sides, David  and
      Vizthum, Timothy  and
      Traum, David  and
      Iskarous, Khalil",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.334",
    pages = "2746--2753",
    abstract = "This work introduces additions to the corpus ChoCo, a multimodal corpus for the American indigenous language Choctaw. Using texts from the corpus, we develop new computational resources by using two off-the-shelf tools: word2vec and Linguistica. Our work illustrates how these tools can be successfully implemented with a small corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alabi-etal-2020-massive,
    title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
    author = "Alabi, Jesujoba  and
      Amponsah-Kaakyire, Kwabena  and
      Adelani, David  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
    pages = "2754--2762",
    abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kara-etal-2020-tropbank,
    title = "{TR}op{B}ank: {T}urkish {P}rop{B}ank V2.0",
    author = {Kara, Neslihan  and
      Aslan, Deniz Baran  and
      Mar{\c{s}}an, B{\"u}{\c{s}}ra  and
      Bakay, {\"O}zge  and
      Ak, Koray  and
      Y{\i}ld{\i}z, Olcay Taner},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.336",
    pages = "2763--2772",
    abstract = "In this paper, we present and explain TRopBank {``}Turkish PropBank v2.0{''}. PropBank is a hand-annotated corpus of propositions which is used to obtain the predicate-argument information of a language. Predicate-argument information of a language can help understand semantic roles of arguments. {``}Turkish PropBank v2.0{''}, unlike PropBank v1.0, has a much more extensive list of Turkish verbs, with 17.673 verbs in total.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tufis-etal-2020-collection,
    title = "Collection and Annotation of the {R}omanian Legal Corpus",
    author = "Tufi{\textcommabelow{s}}, Dan  and
      Mitrofan, Maria  and
      P{\u{a}}i{\textcommabelow{s}}, Vasile  and
      Ion, Radu  and
      Coman, Andrei",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.337",
    pages = "2773--2777",
    abstract = "We present the Romanian legislative corpus which is a valuable linguistic asset for the development of machine translation systems, especially for under-resourced languages. The knowledge that can be extracted from this resource is necessary for a deeper understanding of how law terminology is used and how it can be made more consistent. At this moment the corpus contains more than 140k documents representing the legislative body of Romania. This corpus is processed and annotated at different levels: linguistically (tokenized, lemmatized and pos-tagged), dependency parsed, chunked, named entities identified and labeled with IATE terms and EUROVOC descriptors. Each annotated document has a CONLL-U Plus format consisting in 14 columns, in addition to the standard 10-column format, four other types of annotations were added. Moreover the repository will be periodically updated as new legislative texts are published. These will be automatically collected and transmitted to the processing and annotation pipeline. The access to the corpus will be done through ELRC infrastructure.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{von-prince-nordhoff-2020-empirical,
    title = "An Empirical Evaluation of Annotation Practices in Corpora from Language Documentation",
    author = "von Prince, Kilu  and
      Nordhoff, Sebastian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.338",
    pages = "2778--2787",
    abstract = "For most of the world{'}s languages, no primary data are available, even as many languages are disappearing. Throughout the last two decades, however, language documentation projects have produced substantial amounts of primary data from a wide variety of endangered languages. These resources are still in the early days of their exploration. One of the factors that makes them hard to use is a relative lack of standardized annotation conventions. In this paper, we will describe common practices in existing corpora in order to facilitate their future processing. After a brief introduction of the main formats used for annotation files, we will focus on commonly used tiers in the widespread ELAN and Toolbox formats. Minimally, corpora from language documentation contain a transcription tier and an aligned translation tier, which means they constitute parallel corpora. Additional common annotations include named references, morpheme separation, morpheme-by-morpheme glosses, part-of-speech tags and notes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mohanty-etal-2020-annotated,
    title = "Annotated Corpus for Sentiment Analysis in {O}dia Language",
    author = "Mohanty, Gaurav  and
      Mishra, Pruthwik  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.339",
    pages = "2788--2795",
    abstract = "Given the lack of an annotated corpus of non-traditional Odia literature which serves as the standard when it comes sentiment analysis, we have created an annotated corpus of Odia sentences and made it publicly available to promote research in the field. Secondly, in order to test the usability of currently available Odia sentiment lexicon, we experimented with various classifiers by training and testing on the sentiment annotated corpus while using identified affective words from the same as features. Annotation and classification are done at sentence level as the usage of sentiment lexicon is best suited to sentiment analysis at this level. The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. An inter-annotator agreement score of 0.79 is reported for the corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lopez-de-lacalle-etal-2020-building,
    title = "Building a Task-oriented Dialog System for Languages with no Training Data: the Case for {B}asque",
    author = "L{\'o}pez de Lacalle, Maddalen  and
      Saralegi, Xabier  and
      San Vicente, I{\~n}aki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.340",
    pages = "2796--2802",
    abstract = "This paper presents an approach for developing a task-oriented dialog system for less-resourced languages in scenarios where training data is not available. Both intent classification and slot filling are tackled. We project the existing annotations in rich-resource languages by means of Neural Machine Translation (NMT) and posterior word alignments. We then compare training on the projected monolingual data with direct model transfer alternatives. Intent Classifiers and slot filling sequence taggers are implemented using a BiLSTM architecture or by fine-tuning BERT transformer models. Models learnt exclusively from Basque projected data provide better accuracies for slot filling. Combining Basque projected train data with rich-resource languages data outperforms consistently models trained solely on projected data for intent classification. At any rate, we achieve competitive performance in both tasks, with accuracies of 81{\%} for intent classification and 77{\%} for slot filling.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nguer-etal-2020-sencorpus,
    title = "{SENCORPUS}: A {F}rench-{W}olof Parallel Corpus",
    author = "Nguer, Elhadji Mamadou  and
      Lo, Alla  and
      Dione, Cheikh M. Bamba  and
      Ba, Sileye O.  and
      Lo, Moussa",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.341",
    pages = "2803--2811",
    abstract = "In this paper, we report efforts towards the acquisition and construction of a bilingual parallel corpus between French and Wolof, a Niger-Congo language belonging to the Northern branch of the Atlantic group. The corpus is constructed as part of the SYSNET3LOc project. It currently contains about 70,000 French-Wolof parallel sentences drawn on various sources from different domains. The paper discusses the data collection procedure, conversion, and alignment of the corpus as well as it{'}s application as training data for neural machine translation. In fact, using this corpus, we were able to create word embedding models for Wolof with relatively good results. Currently, the corpus is being used to develop a neural machine translation model to translate French sentences into Wolof.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bella-etal-2020-major,
    title = "A Major {W}ordnet for a Minority Language: {S}cottish {G}aelic",
    author = "Bella, G{\'a}bor  and
      McNeill, Fiona  and
      Gorman, Rody  and
      O Donnaile, Caoimhin  and
      MacDonald, Kirsty  and
      Chandrashekar, Yamini  and
      Freihat, Abed Alhakim  and
      Giunchiglia, Fausto",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.342",
    pages = "2812--2818",
    abstract = "We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60,000 speakers, most of whom live in Northwestern Scotland. The wordnet contains over 15 thousand word senses and was constructed by merging ten thousand new, high-quality translations, provided and validated by language experts, with an existing wordnet derived from Wiktionary. This new, considerably extended wordnet{---}currently among the 30 largest in the world{---}targets multiple communities: language speakers and learners; linguists; computer scientists solving problems related to natural language processing. By publishing it as a freely downloadable resource, we hope to contribute to the long-term preservation of Scottish Gaelic as a living language, both offline and on the Web.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abraham-etal-2020-crowdsourcing,
    title = "Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers",
    author = "Abraham, Basil  and
      Goel, Danish  and
      Siddarth, Divya  and
      Bali, Kalika  and
      Chopra, Manu  and
      Choudhury, Monojit  and
      Joshi, Pratik  and
      Jyoti, Preethi  and
      Sitaram, Sunayana  and
      Seshadri, Vivek",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.343",
    pages = "2819--2826",
    abstract = "Voice-based technologies are essential to cater to the hundreds of millions of new smartphone users. However, most of the languages spoken by these new users have little to no labelled speech data. Unfortunately, collecting labelled speech data in any language is an expensive and resource-intensive task. Moreover, existing platforms typically collect speech data only from urban speakers familiar with digital technology whose dialects are often very different from low-income users. In this paper, we explore the possibility of collecting labelled speech data directly from low-income workers. In addition to providing diversity to the speech dataset, we believe this approach can also provide valuable supplemental earning opportunities to these communities. To this end, we conducted a study where we collected labelled speech data in the Marathi language from three different user groups: low-income rural users, low-income urban users, and university students. Overall, we collected 109 hours of data from 36 participants. Our results show that the data collected from low-income participants is of comparable quality to the data collected from university students (who are typically employed to do this work) and that crowdsourcing speech data from low-income rural and urban workers is a viable method of gathering speech data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cruz-etal-2020-resource,
    title = "A Resource for Studying Chatino Verbal Morphology",
    author = "Cruz, Hilaria  and
      Anastasopoulos, Antonios  and
      Stump, Gregory",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.344",
    pages = "2827--2831",
    abstract = "We present the first resource focusing on the verbal inflectional morphology of San Juan Quiahije Chatino, a tonal mesoamerican language spoken in Mexico. We provide a collection of complete inflection tables of 198 lemmata, with morphological tags based on the UniMorph schema. We also provide baseline results on three core NLP tasks: morphological analysis, lemmatization, and morphological inflection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mehta-etal-2020-learnings,
    title = "Learnings from Technological Interventions in a Low Resource Language: A Case-Study on {G}ondi",
    author = "Mehta, Devansh  and
      Santy, Sebastin  and
      Mothilal, Ramaravind Kommiya  and
      Srivastava, Brij Mohan Lal  and
      Sharma, Alok  and
      Shukla, Anurag  and
      Prasad, Vishnu  and
      U, Venkanna  and
      Sharma, Amit  and
      Bali, Kalika",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.345",
    pages = "2832--2838",
    abstract = "The primary obstacle to developing technologies for low-resource languages is the lack of usable data. In this paper, we report the adaption and deployment of 4 technology-driven methods of data collection for Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. In the process of data collection, we also help in its revival by expanding access to information in Gondi through the creation of linguistic resources that can be used by the community, such as a dictionary, children{'}s stories, an app with Gondi content from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform. At the end of these interventions, we collected a little less than 12,000 translated words and/or sentences and identified more than 650 community members whose help can be solicited for future translation efforts. The larger goal of the project is collecting enough data in Gondi to build and deploy viable language technologies like machine translation and speech to text systems that can help take the language onto the internet.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{golazizian-etal-2020-irony,
    title = "Irony Detection in {P}ersian Language: A Transfer Learning Approach Using Emoji Prediction",
    author = "Golazizian, Preni  and
      Sabeti, Behnam  and
      Ashrafi Asli, Seyed Arad  and
      Majdabadi, Zahra  and
      Momenzadeh, Omid  and
      Fahmi, Reza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.346",
    pages = "2839--2845",
    abstract = "Irony is a linguistic device used to intend an idea while articulating an opposing expression. Many text analytic algorithms used for emotion extraction or sentiment analysis, produce invalid results due to the use of irony. Persian speakers use this device more often due to the language{'}s nature and some cultural reasons. This phenomenon also appears in social media platforms such as Twitter where users express their opinions using ironic or sarcastic posts. In the current research, which is the first attempt at irony detection in Persian language, emoji prediction is used to build a pretrained model. The model is finetuned utilizing a set of hand labeled tweets with irony tags. A bidirectional LSTM (BiLSTM) network is employed as the basis of our model which is improved by attention mechanism. Additionally, a Persian corpus for irony detection containing 4339 manually-labeled tweets is introduced. Experiments show the proposed approach outperforms the adapted state-of-the-art method tested on Persian dataset with an accuracy of 83.1{\%}, and offers a strong baseline for further research in Persian language.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bamutura-etal-2020-towards,
    title = "Towards Computational Resource Grammars for {R}unyankore and Rukiga",
    author = {Bamutura, David  and
      Ljungl{\"o}f, Peter  and
      Nebende, Peter},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.347",
    pages = "2846--2854",
    abstract = "In this paper, we present computational resource grammars of Runyankore and Rukiga (R{\&}R) languages. Runyankore and Rukiga are two under-resourced Bantu Languages spoken by about 6 million people indigenous to South- Western Uganda, East Africa. We used Grammatical Framework (GF), a multilingual grammar formalism and a special- purpose functional programming language to formalise the descriptive grammar of these languages. To the best of our knowledge, these computational resource grammars are the first attempt to the creation of language resources for R{\&}R. In Future Work, we plan to use these grammars to bootstrap the generation of other linguistic resources such as multilingual corpora that make use of data-driven approaches to natural language processing feasible. In the meantime, they can be used to build Computer-Assisted Language Learning (CALL) applications for these languages among others.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ashrafi-asli-etal-2020-optimizing,
    title = "Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in {P}ersian",
    author = "Ashrafi Asli, Seyed Arad  and
      Sabeti, Behnam  and
      Majdabadi, Zahra  and
      Golazizian, Preni  and
      Fahmi, Reza  and
      Momenzadeh, Omid",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.348",
    pages = "2855--2861",
    abstract = "Deep learning models are the current State-of-the-art methodologies towards many real-world problems. However, they need a substantial amount of labeled data to be trained appropriately. Acquiring labeled data can be challenging in some particular domains or less-resourced languages. There are some practical solutions regarding these issues, such as Active Learning and Transfer Learning. Active learning{'}s idea is simple: let the model choose the samples for annotation instead of labeling the whole dataset. This method leads to a more efficient annotation process. Active Learning models can achieve the baseline performance (the accuracy of the model trained on the whole dataset), with a considerably lower amount of labeled data. Several active learning approaches are tested in this work, and their compatibility with Persian is examined using a brand-new sentiment analysis dataset that is also introduced in this work. MirasOpinion, which to our knowledge is the largest Persian sentiment analysis dataset, is crawled from a Persian e-commerce website and annotated using a crowd-sourcing policy. LDA sampling, which is an efficient Active Learning strategy using Topic Modeling, is proposed in this research. Active Learning Strategies have shown promising results in the Persian language, and LDA sampling showed a competitive performance compared to other approaches.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hossain-etal-2020-banfakenews,
    title = "{B}an{F}ake{N}ews: A Dataset for Detecting Fake News in {B}angla",
    author = "Hossain, Md Zobaer  and
      Rahman, Md Ashraful  and
      Islam, Md Saiful  and
      Kar, Sudipta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.349",
    pages = "2862--2871",
    abstract = "Observing the damages that can be done by the rapid propagation of fake news in various sectors like politics and finance, automatic identification of fake news using linguistic analysis has drawn the attention of the research community. However, such methods are largely being developed for English where low resource languages remain out of the focus. But the risks spawned by fake and manipulative news are not confined by languages. In this work, we propose an annotated dataset of {\mbox{$\approx$}} 50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. Additionally, we provide an analysis of the dataset and develop a benchmark system with state of the art NLP techniques to identify Bangla fake news. To create this system, we explore traditional linguistic features and neural network based methods. We expect this dataset will be a valuable resource for building technologies to prevent the spreading of fake news and contribute in research with low resource languages. The dataset and source code are publicly available at https://github.com/Rowan1697/FakeNews.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{duan-etal-2020-resource,
    title = "A Resource for Computational Experiments on {M}apudungun",
    author = "Duan, Mingjun  and
      Fasola, Carlos  and
      Rallabandi, Sai Krishna  and
      Vega, Rodolfo  and
      Anastasopoulos, Antonios  and
      Levin, Lori  and
      Black, Alan W",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.350",
    pages = "2872--2877",
    abstract = "We present a resource for computational experiments on Mapudungun, a polysynthetic indigenous language spoken in Chile with upwards of 200 thousand speakers. We provide 142 hours of culturally significant conversations in the domain of medical treatment. The conversations are fully transcribed and translated into Spanish. The transcriptions also include annotations for code-switching and non-standard pronunciations. We also provide baseline results on three core NLP tasks: speech recognition, speech synthesis, and machine translation between Spanish and Mapudungun. We further explore other applications for which the corpus will be suitable, including the study of code-switching, historical orthography change, linguistic structure, and sociological and anthropological studies.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{round-etal-2020-automated,
    title = "Automated Parsing of Interlinear Glossed Text from Page Images of Grammatical Descriptions",
    author = "Round, Erich  and
      Ellison, Mark  and
      Macklin-Cordes, Jayden  and
      Beniamine, Sacha",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.351",
    pages = "2878--2883",
    abstract = "Linguists seek insight from all human languages, however accessing information from most of the full store of extant global linguistic descriptions is not easy. One of the most common kinds of information that linguists have documented is vernacular sentences, as recorded in descriptive grammars. Typically these sentences are formatted as interlinear glossed text (IGT). Most descriptive grammars, however, exist only as hardcopy or scanned pdf documents. Consequently, parsing IGTs in scanned grammars is a priority, in order to significantly increase the volume of documented linguistic information that is readily accessible. Here we demonstrate fundamental viability for a technology that can assist in making a large number of linguistic data sources machine readable: the automated identification and parsing of interlinear glossed text from scanned page images. For example, we attain high median precision and recall ({\textgreater}0.95) in the identification of examples sentences in IGT format. Our results will be of interest to those who are keen to see more of the existing documentation of human language, especially for less-resourced and endangered languages, become more readily accessible.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mccarthy-etal-2020-johns,
    title = "The {J}ohns {H}opkins {U}niversity {B}ible Corpus: 1600+ Tongues for Typological Exploration",
    author = "McCarthy, Arya D.  and
      Wicks, Rachel  and
      Lewis, Dylan  and
      Mueller, Aaron  and
      Wu, Winston  and
      Adams, Oliver  and
      Nicolai, Garrett  and
      Post, Matt  and
      Yarowsky, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.352",
    pages = "2884--2892",
    abstract = "We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world{'}s languages. We catalog this by showing highly similar proportions of representation of Ethnologue{'}s typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zahrer-etal-2020-towards,
    title = "Towards Building an Automatic Transcription System for Language Documentation: Experiences from {M}uyu",
    author = "Zahrer, Alexander  and
      Zgank, Andrej  and
      Schuppler, Barbara",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.353",
    pages = "2893--2900",
    abstract = "Since at least half of the world{'}s 6000 plus languages will vanish during the 21st century, language documentation has become a rapidly growing field in linguistics. A fundamental challenge for language documentation is the {''}transcription bottleneck{''}. Speech technology may deliver the decisive breakthrough for overcoming the transcription bottleneck. This paper presents first experiments from the development of ASR4LD, a new automatic speech recognition (ASR) based tool for language documentation (LD). The experiments are based on recordings from an ongoing documentation project for the endangered Muyu language in New Guinea. We compare phoneme recognition experiments with American English, Austrian German and Slovenian as source language and Muyu as target language. The Slovenian acoustic models achieve the by far best performance (43.71{\%} PER) in comparison to 57.14{\%} PER with American English, and 89.49{\%} PER with Austrian German. Whereas part of the errors can be explained by phonetic variation, the recording mismatch poses a major problem. On the long term, ASR4LD will not only be an integral part of the ongoing documentation project of Muyu, but will be further developed in order to facilitate also the language documentation process of other language groups.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jettka-lehmberg-2020-towards,
    title = "Towards Flexible Cross-Resource Exploitation of Heterogeneous Language Documentation Data",
    author = "Jettka, Daniel  and
      Lehmberg, Timm",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.354",
    pages = "2901--2905",
    abstract = "This paper reports on challenges and solution approaches in the development of methods for language resource overarching data analysis in the field of language documentation. It is based on the successful outcomes of the initial phase of an 18 year long-term project on lesser resourced and mostly endangered indigenous languages of the Northern Eurasian area, which included the finalization and publication of multiple language corpora and additional language resources. While aiming at comprehensive cross-resource data analysis, the project at the same time is confronted with a dynamic and complex resource landscape, especially resulting from a vast amount of multi-layered information stored in the form of analogue primary data in different widespread archives on the territory of the Russian Federation. The methods described aim at solving the tension between unification of data sets and vocabularies on the one hand and maximum openness for the integration of future resources and adaption of external information on the other hand.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{winterstein-etal-2020-cantomap,
    title = "{C}anto{M}ap: a {H}ong {K}ong {C}antonese {M}ap{T}ask Corpus",
    author = "Winterstein, Gr{\'e}goire  and
      Tang, Carmen  and
      Lai, Regine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.355",
    pages = "2906--2913",
    abstract = "This work reports on the construction of a corpus of connected spoken Hong Kong Cantonese. The corpus aims at providing an additional resource for the study of modern (Hong Kong) Cantonese and also involves several controlled elicitation tasks which will serve different projects related to the phonology and semantics of Cantonese. The word-segmented corpus offers recordings, phonemic transcription, and Chinese characters transcription. The corpus contains a total of 768 minutes of recordings and transcripts of forty speakers. All the audio material has been aligned at utterance level with the transcriptions, using the ELAN transcription and annotation tool. The controlled elicitation task was based on the design of HCRC MapTask corpus (Anderson et al., 1991), in which participants had to communicate using solely verbal means as eye contact was restricted. In this paper, we outline the design of the maps and their landmarks and the basic segmentation principles of the data and various transcription conventions we adopted. We also compare the contents of Cantomap to those of comparable Cantonese corpora.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bustamante-etal-2020-data,
    title = "No Data to Crawl? Monolingual Corpus Creation from {PDF} Files of Truly low-Resource Languages in {P}eru",
    author = "Bustamante, Gina  and
      Oncevay, Arturo  and
      Zariquiey, Roberto",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.356",
    pages = "2914--2923",
    abstract = "We introduce new monolingual corpora for four indigenous and endangered languages from Peru: Shipibo-konibo, Ashaninka, Yanesha and Yine. Given the total absence of these languages in the web, the extraction and processing of texts from PDF files is relevant in a truly low-resource language scenario. Our procedure for monolingual corpus creation considers language-specific and language-agnostic steps, and focuses on educational PDF files with multilingual sentences, noisy pages and low-structured content. Through an evaluation based on language modelling and character-level perplexity on a subset of manually extracted sentences, we determine that our method allows the creation of clean corpora for the four languages, a key resource for natural language processing tasks nowadays.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jonsdottir-ingason-2020-creating,
    title = "Creating a Parallel {I}celandic Dependency Treebank from Raw Text to {U}niversal {D}ependencies",
    author = "J{\'o}nsd{\'o}ttir, Hildur  and
      Ingason, Anton Karl",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.357",
    pages = "2924--2931",
    abstract = "Making the low-resource language, Icelandic, accessible and usable in Language Technology is a work in progress and is supported by the Icelandic government. Creating resources and suitable training data (e.g., a dependency treebank) is a fundamental part of that work. We describe work on a parallel Icelandic dependency treebank based on Universal Dependencies (UD). This is important because it is the first parallel treebank resource for the language and since several other languages already have a resource based on the same text. Two Icelandic treebanks based on phrase-structure grammar have been built and ongoing work aims to convert them to UD. Previously, limited work has been done on dependency grammar for Icelandic. The current project aims to ameliorate this situation by creating a small dependency treebank from scratch. Creating a treebank is a laborious task so the process was implemented in an accessible manner using freely available tools and resources. The parallel data in the UD project was chosen as a source because this would furthermore give us the first parallel treebank for Icelandic. The Icelandic parallel UD corpus will be published as part of UD version 2.6.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{miletic-etal-2020-building,
    title = "Building a {U}niversal {D}ependencies Treebank for {O}ccitan",
    author = "Miletic, Aleksandra  and
      Bras, Myriam  and
      Vergez-Couret, Marianne  and
      Esher, Louise  and
      Poujade, Clamen{\c{c}}a  and
      Sibille, Jean",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.358",
    pages = "2932--2939",
    abstract = "This paper outlines the ongoing effort of creating the first treebank for Occitan, a low-ressourced regional language spoken mainly in the south of France. We briefly present the global context of the project and report on its current status. We adopt the Universal Dependencies framework for this project. Our methodology is based on two main principles. Firstly, in order to guarantee the annotation quality, we use the agile annotation approach. Secondly, we rely on pre-processing using existing tools (taggers and parsers) to facilitate the work of human annotators, mainly through a delexicalized cross-lingual parsing approach. We present the results available at this point (annotation guidelines and a sub-corpus annotated with PoS tags and lemmas) and give the timeline for the rest of the work.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moeljadi-aminullah-2020-building,
    title = "Building the Old {J}avanese {W}ordnet",
    author = "Moeljadi, David  and
      Aminullah, Zakariya Pamuji",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.359",
    pages = "2940--2946",
    abstract = "This paper discusses the construction and the ongoing development of the Old Javanese Wordnet. The words were extracted from the digitized version of the Old Javanese{--}English Dictionary (Zoetmulder, 1982). The wordnet is built using the {`}expansion{'} approach (Vossen, 1998), leveraging on the Princeton Wordnet{'}s core synsets and semantic hierarchy, as well as scientific names. The main goal of our project was to produce a high quality, human-curated resource. As of December 2019, the Old Javanese Wordnet contains 2,054 concepts or synsets and 5,911 senses. It is released under a Creative Commons Attribution 4.0 International License (CC BY 4.0). We are still developing it and adding more synsets and senses. We believe that the lexical data made available by this wordnet will be useful for a variety of future uses such as the development of Modern Javanese Wordnet and many language processing tasks and linguistic research on Javanese.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sierra-martinez-etal-2020-cplm,
    title = "{CPLM}, a Parallel Corpus for {M}exican Languages: Development and Interface",
    author = "Sierra Mart{\'\i}nez, Gerardo  and
      Monta{\~n}o, Cynthia  and
      Bel-Enguix, Gemma  and
      C{\'o}rdova, Diego  and
      Mota Montoya, Margarita",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.360",
    pages = "2947--2952",
    abstract = "Mexico is a Spanish speaking country that has a great language diversity, with 68 linguistic groups and 364 varieties. As they face a lack of representation in education, government, public services and media, they present high levels of endangerment. Due to the lack of data available on social media and the internet, few technologies have been developed for these languages. To analyze different linguistic phenomena in the country, the Language Engineering Group developed the Corpus Paralelo de Lenguas Mexicanas (CPLM) [The Mexican Languages Parallel Corpus], a collaborative parallel corpus for the low-resourced languages of Mexico. The CPLM aligns Spanish with six indigenous languages: Maya, Ch{'}ol, Mazatec, Mixtec, Otomi, and Nahuatl. First, this paper describes the process of building the CPLM: text searching, digitalization and alignment process. Furthermore, we present some difficulties regarding dialectal and orthographic variations. Second, we present the interface and types of searching as well as the use of filters.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ali-etal-2020-siner,
    title = "{S}i{NER}: A Large Dataset for {S}indhi Named Entity Recognition",
    author = "Ali, Wazir  and
      Lu, Junyu  and
      Xu, Zenglin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.361",
    pages = "2953--2961",
    abstract = "We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines. It contains 1,338 news articles and more than 1.35 million tokens collected from Kawish and Awami Awaz Sindhi newspapers using the begin-inside-outside (BIO) tagging scheme. The proposed dataset is likely to be a significant resource for statistical Sindhi language processing. The ultimate goal of developing SiNER is to present a gold-standard dataset for Sindhi NER along with quality baselines. We implement several baseline approaches of conditional random field (CRF) and recent popular state-of-the-art bi-directional long-short term memory (Bi-LSTM) models. The promising F1-score of 89.16 outputted by the Bi-LSTM-CRF model with character-level representations demonstrates the quality of our proposed SiNER dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{song-etal-2020-construct,
    title = "Construct a Sense-Frame Aligned Predicate Lexicon for {C}hinese {AMR} Corpus",
    author = "Song, Li  and
      Dai, Yuling  and
      Liu, Yihuan  and
      Li, Bin  and
      Qu, Weiguang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.362",
    pages = "2962--2969",
    abstract = "The study of predicate frame is an important topic for semantic analysis. Abstract Meaning Representation (AMR) is an emerging graph based semantic representation of a sentence. Since core semantic roles defined in the predicate lexicon compose the backbone in an AMR graph, the construction of the lexicon becomes the key issue. The existing lexicons blur senses and frames of predicates, which needs to be refined to meet the tasks like word sense disambiguation and event extraction. This paper introduces the on-going project on constructing a novel predicate lexicon for Chinese AMR corpus. The new lexicon includes 14,389 senses and 10,800 frames of 8,470 words. As some senses can be aligned to more than one frame, and vice versa, we found the alignment between senses is not just one frame per sense. Explicit analysis is given for multiple aligned relations, which proves the necessity of the proposed lexicon for AMR corpus, and supplies real data for linguistic theoretical studies.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{han-etal-2020-multimwe,
    title = "{M}ulti{MWE}: Building a Multi-lingual Multi-Word Expression ({MWE}) Parallel Corpora",
    author = "Han, Lifeng  and
      Jones, Gareth  and
      Smeaton, Alan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.363",
    pages = "2970--2979",
    abstract = "Multi-word expressions (MWEs) are a hot topic in research in natural language processing (NLP), including topics such as MWE detection, MWE decomposition, and research investigating the exploitation of MWEs in other NLP fields such as Machine Translation. However, the availability of bilingual or multi-lingual MWE corpora is very limited. The only bilingual MWE corpora that we are aware of is from the PARSEME (PARSing and Multi-word Expressions) EU Project. This is a small collection of only 871 pairs of English-German MWEs. In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora. Our collections are 3,159,226 and 143,042 bilingual MWE pairs for German-English and Chinese-English respectively after filtering. We examine the quality of these extracted bilingual MWEs in MT experiments. Our initial experiments applying MWEs in MT show improved translation performances on MWE terms in qualitative analysis and better general evaluation scores in quantitative analysis, on both German-English and Chinese-English language pairs. We follow a standard experimental pipeline to create our MultiMWE corpora which are available online. Researchers can use this free corpus for their own models or use them in a knowledge base as model features.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{myat-mon-etal-2020-myanmar,
    title = "A {M}yanmar ({B}urmese)-{E}nglish Named Entity Transliteration Dictionary",
    author = "Myat Mon, Aye  and
      Ding, Chenchen  and
      Kaing, Hour  and
      Mar Soe, Khin  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.364",
    pages = "2980--2983",
    abstract = "Transliteration is generally a phonetically based transcription across different writing systems. It is a crucial task for various downstream natural language processing applications. For the Myanmar (Burmese) language, robust automatic transliteration for borrowed English words is a challenging task because of the complex Myanmar writing system and the lack of data. In this study, we constructed a Myanmar-English named entity dictionary containing more than eighty thousand transliteration instances. The data have been released under a CC BY-NC-SA license. We evaluated the automatic transliteration performance using statistical and neural network-based approaches based on the prepared data. The neural network model outperformed the statistical model significantly in terms of the BLEU score on the character level. Different units used in the Myanmar script for processing were also compared and discussed.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{li-etal-2020-ca,
    title = "{CA}-{EHN}: Commonsense Analogy from {E}-{H}ow{N}et",
    author = "Li, Peng-Hsuan  and
      Yang, Tsan-Yu  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.365",
    pages = "2984--2990",
    abstract = "Embedding commonsense knowledge is crucial for end-to-end models to generalize inference beyond training corpora. However, existing word analogy datasets have tended to be handcrafted, involving permutations of hundreds of words with only dozens of pre-defined relations, mostly morphological relations and named entities. In this work, we model commonsense knowledge down to word-level analogical reasoning by leveraging E-HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. We present CA-EHN, the first commonsense word analogy dataset containing 90,505 analogies covering 5,656 words and 763 relations. Experiments show that CA-EHN stands out as a great indicator of how well word representations embed commonsense knowledge. The dataset is publicly available at \url{https://github.com/ckiplab/CA-EHN}.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{leone-etal-2020-building,
    title = "Building Semantic Grams of Human Knowledge",
    author = "Leone, Valentina  and
      Siragusa, Giovanni  and
      Di Caro, Luigi  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.366",
    pages = "2991--3000",
    abstract = "Word senses are typically defined with textual definitions for human consumption and, in computational lexicons, put in context via lexical-semantic relations such as synonymy, antonymy, hypernymy, etc. In this paper we embrace a radically different paradigm that provides a slot-filler structure, called {``}semagram{''}, to define the meaning of words in terms of their prototypical semantic information. We propose a semagram-based knowledge model composed of 26 semantic relationships which integrates features from a range of different sources, such as computational lexicons and property norms. We describe an annotation exercise regarding 50 concepts over 10 different categories and put forward different automated approaches for extending the semagram base to thousands of concepts. We finally evaluated the impact of the proposed resource on a semantic similarity task, showing significant improvements over state-of-the-art word embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{uban-dinu-2020-automatically,
    title = "Automatically Building a Multilingual Lexicon of False {F}riends With No Supervision",
    author = "Uban, Ana Sabina  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.367",
    pages = "3001--3007",
    abstract = "Cognate words, defined as words in different languages which derive from a common etymon, can be useful for language learners, who can leverage the orthographical similarity of cognates to more easily understand a text in a foreign language. Deceptive cognates, or false friends, do not share the same meaning anymore; these can be instead deceiving and detrimental for language acquisition or text understanding in a foreign language. We use an automatic method of detecting false friends from a set of cognates, in a fully unsupervised fashion, based on cross-lingual word embeddings. We implement our method for English and five Romance languages, including a low-resource language (Romanian), and evaluate it against two different gold standards. The method can be extended easily to any language pair, requiring only large monolingual corpora for the involved languages and a small bilingual dictionary for the pair. We additionally propose a measure of {``}falseness{''} of a false friends pair. We publish freely the database of false friends in the six languages, along with the falseness scores for each cognate pair. The resource is the largest of the kind that we are aware of, both in terms of languages covered and number of word pairs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{angelov-2020-parallel,
    title = "A Parallel {W}ord{N}et for {E}nglish, {S}wedish and {B}ulgarian",
    author = "Angelov, Krasimir",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.368",
    pages = "3008--3015",
    abstract = "We present the parallel creation of a WordNet resource for Swedish and Bulgarian which is tightly aligned with the Princeton WordNet. The alignment is not only on the synset level, but also on word level, by matching words with their closest translations in each language. We argue that the tighter alignment is essential in machine translation and natural language generation. About one-fifth of the lexical entries are also linked to the corresponding Wikipedia articles. In addition to the traditional semantic relations in WordNet, we also integrate morphological and morpho-syntactic information. The resource comes with a corpus where examples from Princeton WordNet are translated to Swedish and Bulgarian. The examples are aligned on word and phrase level. The new resource is open-source and in its development we used only existing open-source resources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sajous-etal-2020-englawi,
    title = "{ENGLAWI}: From Human- to Machine-Readable {W}iktionary",
    author = "Sajous, Franck  and
      Calderone, Basilio  and
      Hathout, Nabil",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.369",
    pages = "3016--3026",
    abstract = "This paper introduces ENGLAWI, a large, versatile, XML-encoded machine-readable dictionary extracted from Wiktionary. ENGLAWI contains 752,769 articles encoding the full body of information included in Wiktionary: simple words, compounds and multiword expressions, lemmas and inflectional paradigms, etymologies, phonemic transcriptions in IPA, definition glosses and usage examples, translations, semantic and morphological relations, spelling variants, etc. It is fully documented, released under a free license and supplied with G-PeTo, a series of scripts allowing easy information extraction from ENGLAWI. Additional resources extracted from ENGLAWI, such as an inflectional lexicon, a lexicon of diatopic variants and the inclusion dates of headwords in Wiktionary{'}s nomenclature are also provided. The paper describes the content of the resource and illustrates how it can be - and has been - used in previous studies. We finally introduce an ongoing work that computes lexicographic word embeddings from ENGLAWI{'}s definitions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{beniamine-etal-2020-opening,
    title = "Opening the {R}omance Verbal Inflection Dataset 2.0: A {CLDF} lexicon",
    author = "Beniamine, Sacha  and
      Maiden, Martin  and
      Round, Erich",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.370",
    pages = "3027--3035",
    abstract = "We introduce the Romance Verbal Inflection Dataset 2.0, a multilingual lexicon of Romance inflection covering 74 varieties. The lexicon provides verbal paradigm forms in broad IPA phonemic notation. Both lexemes and paradigm cells are organized to reflect cognacy. Such multi-lingual inflected lexicons annotated for two dimensions of cognacy are necessary to study the evolution of inflectional paradigms, and test linguistic hypotheses systematically. However, these resources seldom exist, and when they do, they are not usually encoded in computationally usable ways. The Oxford Online Database of Romance Verb Morphology provides this kind of information, however, it is not maintained anymore and is only available as a web service without interfaces for machine-readability. We collect its data and clean and correct it for consistency using both heuristics and expert annotator judgements. Most resources used to study language evolution computationally rely strictly on multilingual contemporary information, and lack information about prior stages of the languages. To provide such information, we augment the database with Latin paradigms from the LatInFlexi lexicon. Finally, to make it widely avalable, the resource is released under a GPLv3 license in CLDF format.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{choe-etal-2020-word2word,
    title = "word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs",
    author = "Choe, Yo Joong  and
      Park, Kyubyong  and
      Kim, Dongwoo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.371",
    pages = "3036--3045",
    abstract = "We present word2word, a publicly available dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018 (Lison et al., 2018). To obtain this dataset, we use a count-based bilingual lexicon extraction model based on the observation that not only source and target words but also source words themselves can be highly correlated. We illustrate that the resulting bilingual lexicons have high coverage and attain competitive translation quality for several language pairs. We wrap our dataset and model in an easy-to-use Python library, which supports downloading and retrieving top-k word translations in any of the supported language pairs as well as computing top-k word translations for custom parallel corpora.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cartoni-etal-2020-introducing,
    title = "Introducing Lexical Masks: a New Representation of Lexical Entries for Better Evaluation and Exchange of Lexicons",
    author = "Cartoni, Bruno  and
      Calvelo Aros, Daniel  and
      Vrandecic, Denny  and
      Lertpradit, Saran",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.372",
    pages = "3046--3052",
    abstract = "The evaluation and exchange of large lexicon databases remains a challenge in many NLP applications. Despite the existence of commonly accepted standards for the format and the features used in a lexicon, there is still a lack of precise and interoperable specification requirements about how lexical entries of a particular language should look like, both in terms of the numbers of forms and in terms of features associated with these forms. This paper presents the notion of {``}lexical masks{''}, a powerful tool used to evaluate and exchange lexicon databases in many languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{al-khalil-etal-2020-large,
    title = "A Large-Scale Leveled Readability Lexicon for {S}tandard {A}rabic",
    author = "Al Khalil, Muhamed  and
      Habash, Nizar  and
      Jiang, Zhengyang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.373",
    pages = "3053--3062",
    abstract = "We present a large-scale 26,000-lemma leveled readability lexicon for Modern Standard Arabic. The lexicon was manually annotated in triplicate by language professionals from three regions in the Arab world. The annotations show a high degree of agreement; and major differences were limited to regional variations. Comparing lemma readability levels with their frequencies provided good insights in the benefits and pitfalls of frequency-based readability approaches. The lexicon will be publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{stein-2020-preserving,
    title = {Preserving Semantic Information from Old Dictionaries: Linking Senses of the {`}Altfranz{\"o}sisches W{\"o}rterbuch{'} to {W}ord{N}et},
    author = "Stein, Achim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.374",
    pages = "3063--3068",
    abstract = {Historical dictionaries of the pre-digital period are important resources for the study of older languages. Taking the example of the {`}Altfranz{\"o}sisches W{\"o}rterbuch{'}, an Old French dictionary published from 1925 onwards, this contribution shows how the printed dictionaries can be turned into a more easily accessible and more sustainable lexical database, even though a full-text retro-conversion is too costly. Over 57,000 German sense definitions were identified in uncorrected OCR output. For verbs and nouns, 34,000 senses of more than 20,000 lemmas were matched with GermaNet, a semantic network for German, and, in a second step, linked to synsets of the English WordNet. These results are relevant for the automatic processing of Old French, for the annotation and exploitation of Old French text corpora, and for the philological study of Old French in general.},
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lai-winterstein-2020-cifu,
    title = "{C}ifu: a Frequency Lexicon of {H}ong {K}ong {C}antonese",
    author = "Lai, Regine  and
      Winterstein, Gr{\'e}goire",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.375",
    pages = "3069--3077",
    abstract = "This paper introduces Cifu, a lexical database for Hong Kong Cantonese (HKC) that offers phonological and orthographic information, frequency measures, and lexical neighborhood information for lexical items in HKC. Cifu is of use for NLP applications and the design and analysis of psycholinguistics experiments on HKC. We elaborate on the characteristics and challenges specific to HKC that were relevant in the design of Cifu. This includes lexical, orthographic and phonological aspects of HKC, word segmentation issues, the place of HKC in written media, and the availability of data. We discuss the measure of Neighborhood Density (ND), highlighting how the analytic nature of Cantonese and its writing system affect that measure. We justify using six different variations of ND, based on the possibility of inserting or deleting phonemes when searching for neighbors and on the choice of data for retrieving frequencies. Statistics about the four genres (written, adult spoken, children spoken and child-directed) within the dataset are discussed. We find that the lexical diversity of the child-directed speech genre is particularly low, compared to a size-matched written corpus. The correlations of word frequencies of different genres are all high, but in generally decrease as word length increases.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sprugnoli-etal-2020-odi,
    title = "Odi et {A}mo. Creating, Evaluating and Extending Sentiment Lexicons for {L}atin.",
    author = "Sprugnoli, Rachele  and
      Passarotti, Marco  and
      Corbetta, Daniela  and
      Peverelli, Andrea",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.376",
    pages = "3078--3086",
    abstract = "Sentiment lexicons are essential for developing automatic sentiment analysis systems, but the resources currently available mostly cover modern languages. Lexicons for ancient languages are few and not evaluated with high-quality gold standards. However, the study of attitudes and emotions in ancient texts is a growing field of research which poses specific issues (e.g., lack of native speakers, limited amount of data, unusual textual genres for the sentiment analysis task, such as philosophical or documentary texts) and can have an impact on the work of scholars coming from several disciplines besides computational linguistics, e.g. historians and philologists. The work presented in this paper aims at providing the research community with a set of sentiment lexicons built by taking advantage of manually-curated resources belonging to the long tradition of Latin corpora and lexicons creation. Our interdisciplinary approach led us to release: i) two automatically generated sentiment lexicons; ii) a gold standard developed by two Latin language and culture experts; iii) a silver standard in which semantic and derivational relations are exploited so to extend the list of lexical items of the gold standard. In addition, the evaluation procedure is described together with a first application of the lexicons to a Latin tragedy.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mohammad-2020-wordwars,
    title = "{W}ord{W}ars: A Dataset to Examine the Natural Selection of Words",
    author = "Mohammad, Saif M.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.377",
    pages = "3087--3095",
    abstract = "There is a growing body of work on how word meaning changes over time: mutation. In contrast, there is very little work on how different words compete to represent the same meaning, and how the degree of success of words in that competition changes over time: natural selection. We present a new dataset, WordWars, with historical frequency data from the early 1800s to the early 2000s for monosemous English words in over 5000 synsets. We explore three broad questions with the dataset: (1) what is the degree to which predominant words in these synsets have changed, (2) how do prominent word features such as frequency, length, and concreteness impact natural selection, and (3) what are the differences between the predominant words of the 2000s and the predominant words of early 1800s. We show that close to one third of the synsets undergo a change in the predominant word in this time period. Manual annotation of these pairs shows that about 15{\%} of these are orthographic variations, 25{\%} involve affix changes, and 60{\%} have completely different roots. We find that frequency, length, and concreteness all impact natural selection, albeit in different ways.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kanojia-etal-2020-challenge,
    title = "Challenge Dataset of Cognates and False Friend Pairs from {I}ndian Languages",
    author = "Kanojia, Diptesh  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.378",
    pages = "3096--3102",
    abstract = "Cognates are present in multiple variants of the same text across different languages (e.g., {``}hund{''} in German and {``}hound{''} in the English language mean {``}dog{''}). They pose a challenge to various Natural Language Processing (NLP) applications such as Machine Translation, Cross-lingual Sense Disambiguation, Computational Phylogenetics, and Information Retrieval. A possible solution to address this challenge is to identify cognates across language pairs. In this paper, we describe the creation of two cognate datasets for twelve Indian languages namely Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam. We digitize the cognate data from an Indian language cognate dictionary and utilize linked Indian language Wordnets to generate cognate sets. Additionally, we use the Wordnet data to create a False Friends{'} dataset for eleven language pairs. We also evaluate the efficacy of our dataset using previously available baseline cognate detection approaches. We also perform a manual evaluation with the help of lexicographers and release the curated gold-standard dataset with this paper.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{iwai-etal-2020-development,
    title = "Development of a {J}apanese Personality Dictionary based on Psychological Methods",
    author = "Iwai, Ritsuko  and
      Kawahara, Daisuke  and
      Kumada, Takatsune  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.379",
    pages = "3103--3108",
    abstract = "We propose a new approach to constructing a personality dictionary with psychological evidence. In this study, we collect personality words, using word embeddings, and construct a personality dictionary with weights for Big Five traits. The weights are calculated based on the responses of the large sample (N=1,938, female = 1,004, M=49.8years old:20-78, SD=16.3). All the respondents answered a 20-item personality questionnaire and 537 personality items derived from word embeddings. We present the procedures to examine the qualities of responses with psychological methods and to calculate the weights. These result in a personality dictionary with two sub-dictionaries. We also discuss an application of the acquired resources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{islam-etal-2020-lexicon,
    title = "A Lexicon-Based Approach for Detecting Hedges in Informal Text",
    author = "Islam, Jumayel  and
      Xiao, Lu  and
      Mercer, Robert E.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.380",
    pages = "3109--3113",
    abstract = "Hedging is a commonly used strategy in conversational management to show the speaker{'}s lack of commitment to what they communicate, which may signal problems between the speakers. Our project is interested in examining the presence of hedging words and phrases in identifying the tension between an interviewer and interviewee during a survivor interview. While there have been studies on hedging detection in the natural language processing literature, all existing work has focused on structured texts and formal communications. Our project thus investigated a corpus of eight unstructured conversational interviews about the Rwanda Genocide and identified hedging patterns in the interviewees{'} responses. Our work produced three manually constructed lists of hedge words, booster words, and hedging phrases. Leveraging these lexicons, we developed a rule-based algorithm that detects sentence-level hedges in informal conversations such as survivor interviews. Our work also produced a dataset of 3000 sentences having the categories Hedge and Non-hedge annotated by three researchers. With experiments on this annotated dataset, we verify the efficacy of our proposed algorithm. Our work contributes to the further development of tools that identify hedges from informal conversations and discussions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nishihara-kajiwara-2020-word,
    title = "Word Complexity Estimation for {J}apanese Lexical Simplification",
    author = "Nishihara, Daiki  and
      Kajiwara, Tomoyuki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.381",
    pages = "3114--3120",
    abstract = "We introduce three language resources for Japanese lexical simplification: 1) a large-scale word complexity lexicon, 2) the first synonym lexicon for converting complex words to simpler ones, and 3) the first toolkit for developing and benchmarking Japanese lexical simplification system. Our word complexity lexicon is expanded to a broader vocabulary using a classifier trained on a small, high-quality word complexity lexicon created by Japanese language teachers. Based on this word complexity estimator, we extracted simplified word pairs from a large-scale synonym lexicon and constructed a simplified synonym lexicon useful for lexical simplification. In addition, we developed a Python library that implements automatic evaluation and key methods in each subtask to ease the construction of a lexical simplification pipeline. Experimental results show that the proposed method based on our lexicon achieves the highest performance of Japanese lexical simplification. The current lexical simplification is mainly studied in English, which is rich in language resources such as lexicons and toolkits. The language resources constructed in this study will help advance the lexical simplification system in Japanese.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{huo-de-melo-2020-inducing,
    title = "Inducing Universal Semantic Tag Vectors",
    author = "Huo, Da  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.382",
    pages = "3121--3127",
    abstract = "Given the well-established usefulness of part-of-speech tag annotations in many syntactically oriented downstream NLP tasks, the recently proposed notion of semantic tagging (Bjerva et al. 2016) aims at tagging words with tags informed by semantic distinctions, which are likely to be useful across a range of semantic tasks. To this end, their annotation scheme distinguishes, for instance, privative attributes from subsective ones. While annotated corpora exist, their size is limited and thus many words are out-of-vocabulary words. In this paper, we study to what extent we can automatically predict the tags associated with unseen words. We draw on large-scale word representation data to derive a large new Semantic Tag lexicon. Our experiments show that we can infer semantic tags for words with high accuracy both monolingually and cross-lingually.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{coole-etal-2020-lexidb,
    title = "{L}exi{DB}: Patterns {\&} Methods for Corpus Linguistic Database Management",
    author = "Coole, Matthew  and
      Rayson, Paul  and
      Mariani, John",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.383",
    pages = "3128--3135",
    abstract = "LexiDB is a tool for storing, managing and querying corpus data. In contrast to other database management systems (DBMSs), it is designed specifically for text corpora. It improves on other corpus management systems (CMSs) because data can be added and deleted from corpora on the fly with the ability to add live data to existing corpora. LexiDB sits between these two categories of DBMSs and CMSs, more specialised to language data than a general purpose DBMS but more flexible than a traditional static corpus management system. Previous work has demonstrated the scalability of LexiDB in response to the growing need to be able to scale out for ever growing corpus datasets. Here, we present the patterns and methods developed in LexiDB for storage, retrieval and querying of multi-level annotated corpus data. These techniques are evaluated and compared to an existing CMS (Corpus Workbench CWB - CQP) and indexer (Lucene). We find that LexiDB consistently outperforms existing tools for corpus queries. This is particularly apparent with large corpora and when handling queries with large result sets",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kettnerova-etal-2020-towards,
    title = "Towards a Semi-Automatic Detection of Reflexive and Reciprocal Constructions and Their Representation in a Valency Lexicon",
    author = "Kettnerov{\'a}, V{\'a}clava  and
      Lopatkova, Marketa  and
      Vernerov{\'a}, Anna  and
      Barancikova, Petra",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.384",
    pages = "3136--3144",
    abstract = "Valency lexicons usually describe valency behavior of verbs in non-reflexive and non-reciprocal constructions. However, reflexive and reciprocal constructions are common morphosyntactic forms of verbs. Both of these constructions are characterized by regular changes in morphosyntactic properties of verbs, thus they can be described by grammatical rules. On the other hand, the possibility to create reflexive and/or reciprocal constructions cannot be trivially derived from the morphosyntactic structure of verbs as it is conditioned by their semantic properties as well. A large-coverage valency lexicon allowing for rule based generation of all well formed verb constructions should thus integrate the information on reflexivity and reciprocity. In this paper, we propose a semi-automatic procedure, based on grammatical constraints on reflexivity and reciprocity, detecting those verbs that form reflexive and reciprocal constructions in corpus data. However, exploitation of corpus data for this purpose is complicated due to the diverse functions of reflexive markers crossing the domain of reflexivity and reciprocity. The list of verbs identified by the previous procedure is thus further used in an automatic experiment, applying word embeddings for detecting semantically similar verbs. These candidate verbs have been manually verified and annotation of their reflexive and reciprocal constructions has been integrated into the valency lexicon of Czech verbs VALLEX.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vidal-gorene-decours-perez-2020-languages,
    title = "Languages Resources for Poorly Endowed Languages : The Case Study of {C}lassical {A}rmenian",
    author = "Vidal-Gor{\`e}ne, Chahan  and
      Decours-Perez, Ali{\'e}nor",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.385",
    pages = "3145--3152",
    abstract = "Classical Armenian is a poorly endowed language, that despite a great tradition of lexicographical erudition is coping with a lack of resources. Although numerous initiatives exist to preserve the Classical Armenian language, the lack of precise and complete grammatical and lexicographical resources remains. This article offers a situation analysis of the existing resources for Classical Armenian and presents the new digital resources provided on the Calfa platform. The Calfa project gathers existing resources and updates, enriches and enhances their content to offer the richest database for Classical Armenian today. Faced with the challenges specific to a poorly endowed language, the Calfa project is also developing new technologies and solutions to enable preservation, advanced research, and larger systems and developments for the Armenian language",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{takeuchi-etal-2020-constructing,
    title = "Constructing Web-Accessible Semantic Role Labels and Frames for {J}apanese as Additions to the {NPCMJ} Parsed Corpus",
    author = "Takeuchi, Koichi  and
      Butler, Alastair  and
      Nagasaki, Iku  and
      Okamura, Takuya  and
      Pardeshi, Prashant",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.386",
    pages = "3153--3161",
    abstract = "As part of constructing the NINJAL Parsed Corpus of Modern Japanese (NPCMJ), a web-accessible language resource, we are adding frame information for predicates, together with two types of semantic role labels that mark the contributions of arguments. One role type consists of numbered semantic roles, like in PropBank, to capture relations between arguments in different syntactic patterns. The other role type consists of semantic roles with conventional names.Both role types are compatible with hierarchical frames that belong to related predicates. Adding semantic role and frame information to the NPCMJ will support a web environment where language learners and linguists can search examples of Japanese for syntactic and semantic features. The annotation will also provide a language resource for NLP researchers making semantic parsing models (e.g., for AMR parsing) following machine learning approaches. In this paper, we describe how the two types of semantic role labels are defined under the frame based approach, i.e., both types can be consistently applied when linked to corresponding frames. Then we show special cases of syntactic patterns and the current status of the annotation work.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vossen-etal-2020-large,
    title = "Large-scale Cross-lingual Language Resources for Referencing and Framing",
    author = "Vossen, Piek  and
      Ilievski, Filip  and
      Postma, Marten  and
      Fokkens, Antske  and
      Minnema, Gosse  and
      Remijnse, Levi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.387",
    pages = "3162--3171",
    abstract = "In this article, we lay out the basic ideas and principles of the project Framing Situations in the Dutch Language. We provide our first results of data acquisition, together with the first data release. We introduce the notion of cross-lingual referential corpora. These corpora consist of texts that make reference to exactly the same incidents. The referential grounding allows us to analyze the framing of these incidents in different languages and across different texts. During the project, we will use the automatically generated data to study linguistic framing as a phenomenon, build framing resources such as lexicons and corpora. We expect to capture larger variation in framing compared to traditional approaches for building such resources. Our first data release, which contains structured data about a large number of incidents and reference texts, can be found at http://dutchframenet.nl/data-releases/.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khan-etal-2020-modelling,
    title = "Modelling Etymology in {LMF}/{TEI}: The Grande Dicion{\'a}rio Houaiss da L{\'\i}ngua Portuguesa Dictionary as a Use Case",
    author = "Khan, Fahad  and
      Romary, Laurent  and
      Salgado, Ana  and
      Bowers, Jack  and
      Khemakhem, Mohamed  and
      Tasovac, Toma",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.388",
    pages = "3172--3180",
    abstract = "In this article we will introduce two of the new parts of the new multi-part version of the Lexical Markup Framework (LMF) ISO standard, namely part 3 of the standard (ISO 24613-3), which deals with etymological and diachronic data, and Part 4 (ISO 24613-4), which consists of a TEI serialisation of all of the prior parts of the model. We will demonstrate the use of both standards by describing the LMF encoding of a small number of examples taken from a sample conversion of the reference Portuguese dictionary \textit{Grande Dicion{\'a}rio Houaiss da L{\'\i}ngua Portuguesa}, part of a broader experiment comprising the analysis of different, heterogeneously encoded, Portuguese lexical resources. We present the examples in the Unified Modelling Language (UML) and also in a couple of cases in TEI.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bond-etal-2020-linking,
    title = "Linking the {TUFS} Basic Vocabulary to the Open Multilingual {W}ordnet",
    author = "Bond, Francis  and
      Nomoto, Hiroki  and
      Morgado da Costa, Lu{\'\i}s  and
      Bond, Arthur",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.389",
    pages = "3181--3188",
    abstract = "We describe the linking of the TUFS Basic Vocabulary Modules, created for online language learning, with the Open Multilingual Wordnet. The TUFS modules have roughly 500 lexical entries in 30 languages, each with the lemma, a link across the languages, an example sentence, usage notes and sound files. The Open Multilingual Wordnet has 34 languages (11 shared with TUFS) organized into synsets linked by semantic relations, with examples and definitions for some languages. The links can be used to (i) evaluate existing wordnets, (ii) add data to these wordnets and (iii) create new open wordnets for Khmer, Korean, Lao, Mongolian, Russian, Tagalog, Urdua nd Vietnamese",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bond-etal-2020-issues,
    title = "Some Issues with Building a Multilingual {W}ordnet",
    author = "Bond, Francis  and
      Morgado da Costa, Luis  and
      Goodman, Michael Wayne  and
      McCrae, John Philip  and
      Lohk, Ahti",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.390",
    pages = "3189--3197",
    abstract = "In this paper we discuss the experience of bringing together over 40 different wordnets. We introduce some extensions to the GWA wordnet LMF format proposed in Vossen et al. (2016) and look at how this new information can be displayed. Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalized synsets and lemmas, new parts of speech, and more. Many of these extensions already exist in multiple wordnets {--} the challenge was to find a compatible representation. To this end, we introduce a new version of the Open Multilingual Wordnet (Bond and Foster, 2013), that integrates a new set of tools that tests the extensions introduced by this new format, while also ensuring the integrity of the Collaborative Interlingual Index (CILI: Bond et al., 2016), avoiding the same new concept to be introduced through multiple projects.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khokhlova-2020-collocations,
    title = "Collocations in {R}ussian Lexicography and {R}ussian Collocations Database",
    author = "Khokhlova, Maria",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.391",
    pages = "3198--3206",
    abstract = "The paper presents the issue of collocability and collocations in Russian and gives a survey of a wide range of dictionaries both printed and online ones that describe collocations. Our project deals with building a database that will include dictionary and statistical collocations. The former can be described in various lexicographic resources whereas the latter can be extracted automatically from corpora. Dictionaries differ among themselves, the information is given in various ways, making it hard for language learners and researchers to acquire data. A number of dictionaries were analyzed and processed to retrieve verified collocations, however the overlap between the lists of collocations extracted from them is still rather small. This fact indicates there is a need to create a unified resource which takes into account collocability and more examples. The proposed resource will also be useful for linguists and for studying Russian as a foreign language. The obtained results can be important for machine learning and for other NLP tasks, for instance, automatic clustering of word combinations and disambiguation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fourrier-sagot-2020-methodological,
    title = "Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing {E}tym{DB}-2.0",
    author = "Fourrier, Cl{\'e}mentine  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.392",
    pages = "3207--3216",
    abstract = "Diachronic lexical information is not only important in the field of historical linguistics, but is also increasingly used in NLP, most recently for machine translation of low resource languages. Therefore, there is a need for fine-grained, large-coverage and accurate etymological lexical resources. In this paper, we propose a set of guidelines to generate such resources, for each step of the life-cycle of an etymological lexicon: creation, update, evaluation, dissemination, and exploitation. To illustrate the guidelines, we introduce EtymDB 2.0, an etymological database automatically generated from the Wiktionary, which contains 1.8 million lexemes, linked by more than 700,000 fine-grained etymological relations, across 2,536 living and dead languages. We also introduce use cases for which EtymDB 2.0 could represent a key resource, such as phylogenetic tree generation, low resource machine translation or medieval languages study.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{guibon-sagot-2020-ofrlex,
    title = "{OF}r{L}ex: A Computational Morphological and Syntactic Lexicon for {O}ld {F}rench",
    author = {Guibon, Ga{\"e}l  and
      Sagot, Beno{\^\i}t},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.393",
    pages = "3217--3225",
    abstract = "In this paper we describe our work on the development and enrichment of OFrLex, a freely available, large-coverage morphological and syntactic Old French lexicon. We rely on several heterogeneous language resources to extract structured and exploitable information. The extraction follows a semi-automatic procedure with substantial manual steps to respond to difficulties encountered while aligning lexical entries from distinct language resources. OFrLex aims at improving natural language processing tasks on Old French such as part-of-speech tagging and dependency parsing. We provide quantitative information on OFrLex and discuss its reliability. We also describe and evaluate a semi-automatic, word-embedding-based lexical enrichment process aimed at increasing the accuracy of the resource. Results of this extension technique will be manually validated in the near future, a step that will take advantage of OFrLex{'}s viewing, searching and editing interface, which is already accessible online.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ciobanu-etal-2020-automatic,
    title = "Automatic Reconstruction of Missing {R}omanian Cognates and Unattested {L}atin Words",
    author = "Ciobanu, Alina Maria  and
      Dinu, Liviu P.  and
      Zoicas, Laurentiu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.394",
    pages = "3226--3231",
    abstract = "Producing related words is a key concern in historical linguistics. Given an input word, the task is to automatically produce either its proto-word, a cognate pair or a modern word derived from it. In this paper, we apply a method for producing related words based on sequence labeling, aiming to fill in the gaps in incomplete cognate sets in Romance languages with Latin etymology (producing Romanian cognates that are missing) and to reconstruct uncertified Latin words. We further investigate an ensemble-based aggregation for combining and re-ranking the word productions of multiple languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ahmadi-etal-2020-multilingual,
    title = "A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment",
    author = "Ahmadi, Sina  and
      McCrae, John Philip  and
      Nimb, Sanni  and
      Khan, Fahad  and
      Monachini, Monica  and
      Pedersen, Bolette  and
      Declerck, Thierry  and
      Wissik, Tanja  and
      Bellandi, Andrea  and
      Pisani, Irene  and
      Troelsg{\aa}rd, Thomas  and
      Olsen, Sussi  and
      Krek, Simon  and
      Lipp, Veronika  and
      V{\'a}radi, Tam{\'a}s  and
      Simon, L{\'a}szl{\'o}  and
      Gyorffy, Andr{\'a}s  and
      Tiberius, Carole  and
      Schoonheim, Tanneke  and
      Ben Moshe, Yifat  and
      Rudich, Maya  and
      Abu Ahmad, Raya  and
      Lonke, Dorielle  and
      Kovalenko, Kira  and
      Langemets, Margit  and
      Kallas, Jelena  and
      Dereza, Oksana  and
      Fransen, Theodorus  and
      Cillessen, David  and
      Lindemann, David  and
      Alonso, Mikel  and
      Salgado, Ana  and
      Luis Sancho, Jos{\'e}  and
      Ure{\~n}a-Ruiz, Rafael-J.  and
      Porta Zamorano, Jordi  and
      Simov, Kiril  and
      Osenova, Petya  and
      Kancheva, Zara  and
      Radev, Ivaylo  and
      Stankovi{\'c}, Ranka  and
      Perdih, Andrej  and
      Gabrovsek, Dejan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.395",
    pages = "3232--3242",
    abstract = "Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{allen-etal-2020-broad,
    title = "A Broad-Coverage Deep Semantic Lexicon for Verbs",
    author = "Allen, James  and
      An, Hannah  and
      Bose, Ritwik  and
      de Beaumont, Will  and
      Teng, Choh Man",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.396",
    pages = "3243--3251",
    abstract = "Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wu-yarowsky-2020-computational,
    title = "Computational Etymology and Word Emergence",
    author = "Wu, Winston  and
      Yarowsky, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.397",
    pages = "3252--3259",
    abstract = "We developed an extensible, comprehensive Wiktionary parser that improves over several existing parsers. We predict the etymology of a word across the full range of etymology types and languages in Wiktionary, showing improvements over a strong baseline. We also model word emergence and show the application of etymology in modeling this phenomenon. We release our parser to further research in this understudied field.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rudnicka-naskret-2020-dataset,
    title = "A Dataset of Translational Equivalents Built on the Basis of pl{W}ord{N}et-{P}rinceton {W}ord{N}et Synset Mapping",
    author = "Rudnicka, Ewa  and
      Naskr{\k{e}}t, Tomasz",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.398",
    pages = "3260--3264",
    abstract = "The paper presents a dataset of 11,000 Polish-English translational equivalents in the form of pairs of plWordNet and Princeton WordNet lexical units linked by three types of equivalence links: strong equivalence, regular equivalence, and weak equivalence. The resource consists of the two subsets. The first subset was built in result of manual annotation of an extended sample of Polish-English sense pairs partly randomly extracted from synsets linked by interlingual relations such as I-synononymy, I-partial synonymy and I-hyponymy and partly manually selected from the surrounding synsets in the hypernymy hierarchy. The second subset was created as a result of the manual checkup of an automatically generated lists of pairs of sense equivalents on the basis of a couple of simple, rule-based heuristics. For both subsets, the same methodology of equivalence annotation was adopted based on the verification of a set of formal, semantic-pragmatic and translational features. The constructed dataset is a novum in the wordnet domain and can facilitate the precision of bilingual NLP tasks such as automatic translation, bilingual word sense disambiguation and sentiment annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{benites-etal-2020-translit,
    title = "{TRANSLIT}: A Large-scale Name Transliteration Resource",
    author = {Benites, Fernando  and
      Duivesteijn, Gilbert Fran{\c{c}}ois  and
      von D{\"a}niken, Pius  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.399",
    pages = "3265--3271",
    abstract = "Transliteration is the process of expressing a proper name from a source language in the characters of a target language (e.g. from Cyrillic to Latin characters). We present TRANSLIT, a large-scale corpus with approx. 1.6 million entries in more than 180 languages with about 3 million variations of person and geolocation names. The corpus is based on various public data sources, which have been transformed into a unified format to simplify their usage, plus a newly compiled dataset from Wikipedia. In addition, we apply several machine learning methods to establish baselines for automatically detecting transliterated names in various languages. Our best systems achieve an accuracy of 92{\%} on identification of transliterated pairs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{l-m-jeronimo-etal-2020-computing,
    title = "Computing with Subjectivity Lexicons",
    author = "L. M. Jeronimo, Caio  and
      E. C. Campelo, Claudio  and
      Balby Marinho, Leandro  and
      Sales, Allan  and
      Veloso, Adriano  and
      Viola, Roberta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.400",
    pages = "3272--3280",
    abstract = "In this paper, we introduce a new set of lexicons for expressing subjectivity in text documents written in Brazilian Portuguese. Besides the non-English idiom, in contrast to other subjectivity lexicons available, these lexicons represent different subjectivity dimensions (other than sentiment) and are more compact in number of terms. This last feature was designed intentionally to leverage the power of word embedding techniques, i.e., with the words mapped to an embedding space and the appropriate distance measures, we can easily capture semantically related words to the ones in the lexicons. Thus, we do not need to build comprehensive vocabularies and can focus on the most representative words for each lexicon dimension. We showcase the use of these lexicons in three highly non-trivial tasks: (1) Automated Essay Scoring in the Presence of Biased Ratings, (2) Subjectivity Bias in Brazilian Presidential Elections and (3) Fake News Classification Based on Text Subjectivity. All these tasks involve text documents written in Portuguese.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chiarcos-etal-2020-acoli,
    title = "The {AC}o{L}i Dictionary Graph",
    author = {Chiarcos, Christian  and
      F{\"a}th, Christian  and
      Ionov, Maxim},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.401",
    pages = "3281--3290",
    abstract = "In this paper, we report the release of the ACoLi Dictionary Graph, a large-scale collection of multilingual open source dictionaries available in two machine-readable formats, a graph representation in RDF, using the OntoLex-Lemon vocabulary, and a simple tabular data format to facilitate their use in NLP tasks, such as translation inference across dictionaries. We describe the mapping and harmonization of the underlying data structures into a unified representation, its serialization in RDF and TSV, and the release of a massive and coherent amount of lexical data under open licenses.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{midrigan-ciochina-etal-2020-resources,
    title = "Resources in Underrepresented Languages: Building a Representative {R}omanian Corpus",
    author = "Midrigan - Ciochina, Ludmila  and
      Boyd, Victoria  and
      Sanchez-Ortega, Lucila  and
      Malancea{\_}Malac, Diana  and
      Midrigan, Doina  and
      Corina, David P.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.402",
    pages = "3291--3296",
    abstract = "The effort in the field of Linguistics to develop theories that aim to explain language-dependent effects on language processing is greatly facilitated by the availability of reliable resources representing different languages. This project presents a detailed description of the process of creating a large and representative corpus in Romanian {--} a relatively under-resourced language with unique structural and typological characteristics, that can be used as a reliable language resource for linguistic studies. The decisions that have guided the construction of the corpus, including the type of corpus, its size and component resource files are discussed. Issues related to data collection, data organization and storage, as well as characteristics of the data included in the corpus are described. Currently, the corpus has approximately 5,500,000 tokens originating from written text and 100,000 tokens of spoken language. it includes language samples that represent a wide variety of registers (i.e. written language - 16 registers and 5 registers of spoken language), as well as different authors and speakers",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kirchmeier-etal-2020-world,
    title = "World Class Language Technology - Developing a Language Technology Strategy for {D}anish",
    author = "Kirchmeier, Sabine  and
      Pedersen, Bolette  and
      Nimb, Sanni  and
      Diderichsen, Philip  and
      Henrichsen, Peter Juel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.403",
    pages = "3297--3301",
    abstract = "Although Denmark is one of the most digitized countries in Europe, no coordinated efforts have been made in recent years to support the Danish language with regard to language technology and artificial intelligence. In March 2019, however, the Danish government adopted a new, ambitious strategy for LT and artificial intelligence. In this paper, we describe the process behind the development of the language-related parts of the strategy: A Danish Language Technology Committee was constituted and a comprehensive series of workshops were organized in which users, suppliers, developers, and researchers gave their valuable input based on their experiences. We describe how, based on this experience, the focus areas and recommendations for the LT strategy were established, and which steps are currently taken in order to put the strategy into practice.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{battisti-etal-2020-corpus,
    title = "A Corpus for Automatic Readability Assessment and Text Simplification of {G}erman",
    author = {Battisti, Alessia  and
      Pf{\"u}tze, Dominik  and
      S{\"a}uberli, Andreas  and
      Kostrzewa, Marek  and
      Ebling, Sarah},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.404",
    pages = "3302--3311",
    abstract = "In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification for German, the first of its kind for this language. The corpus is compiled from web sources and consists of parallel as well as monolingual-only (simplified German) data amounting to approximately 6,200 documents (nearly 211,000 sentences). As a unique feature, the corpus contains information on text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and images (content, position, and dimensions). While the importance of considering such information in machine learning tasks involving simplified language, such as readability assessment, has repeatedly been stressed in the literature, we provide empirical evidence for its benefit. We also demonstrate the added value of leveraging monolingual-only data for automatic text simplification via machine translation through applying back-translation, a data augmentation technique.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{van-den-heuvel-etal-2020-clarin,
    title = "The {CLARIN} Knowledge Centre for Atypical Communication Expertise",
    author = "van den Heuvel, Henk  and
      Oostdijk, Nelleke  and
      Rowland, Caroline  and
      Trilsbeek, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.405",
    pages = "3312--3316",
    abstract = "This paper introduces a new CLARIN Knowledge Center which is the K-Centre for Atypical Communication Expertise (ACE for short) which has been established at the Centre for Language and Speech Technology (CLST) at Radboud University. Atypical communication is an umbrella term used here to denote language use by second language learners, people with language disorders or those suffering from language disabilities, but also more broadly by bilinguals and users of sign languages. It involves multiple modalities (text, speech, sign, gesture) and encompasses different developmental stages. ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. We explain the mission of ACE and show its potential on a number of showcases and a use case.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{van-den-heuvel-etal-2020-corpora,
    title = "Corpora of Disordered Speech in the Light of the {GDPR}: Two Use Cases from the {DELAD} Initiative",
    author = "van den Heuvel, Henk  and
      Kelli, Aleksei  and
      Klessa, Katarzyna  and
      Salaasti, Satu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.406",
    pages = "3317--3321",
    abstract = "Corpora of disordered speech (CDS) are costly to collect and difficult to share due to personal data protection and intellectual property (IP) issues. In this contribution we discuss the legal grounds for processing CDS in the light of the GDPR, and illustrate these with two use cases from the DELAD context. One use case deals with clinical datasets and another with legacy data from Polish hearing-impaired children. For both cases, processing based on consent and on public interest are taken into consideration.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rehm-etal-2020-european,
    title = "The {E}uropean Language Technology Landscape in 2020: Language-Centric and Human-Centric {AI} for Cross-Cultural Communication in Multilingual {E}urope",
    author = {Rehm, Georg  and
      Marheinecke, Katrin  and
      Hegele, Stefanie  and
      Piperidis, Stelios  and
      Bontcheva, Kalina  and
      Haji{\v{c}}, Jan  and
      Choukri, Khalid  and
      Vasi{\c{l}}jevs, Andrejs  and
      Backfried, Gerhard  and
      Prinz, Christoph  and
      G{\'o}mez-P{\'e}rez, Jos{\'e} Manuel  and
      Meertens, Luc  and
      Lukowicz, Paul  and
      van Genabith, Josef  and
      L{\"o}sch, Andrea  and
      Slusallek, Philipp  and
      Irgens, Morten  and
      Gatellier, Patrick  and
      K{\"o}hler, Joachim  and
      Le Bars, Laure  and
      Anastasiou, Dimitra  and
      Auksori{\=u}t{\.e}, Albina  and
      Bel, N{\'u}ria  and
      Branco, Ant{\'o}nio  and
      Budin, Gerhard  and
      Daelemans, Walter  and
      De Smedt, Koenraad  and
      Garab{\'\i}k, Radovan  and
      Gavriilidou, Maria  and
      Gromann, Dagmar  and
      Koeva, Svetla  and
      Krek, Simon  and
      Krstev, Cvetana  and
      Lind{\'e}n, Krister  and
      Magnini, Bernardo  and
      Odijk, Jan  and
      Ogrodniczuk, Maciej  and
      R{\"o}gnvaldsson, Eir{\'\i}kur  and
      Rosner, Mike  and
      Pedersen, Bolette  and
      Skadi{\c{n}}a, Inguna  and
      Tadi{\'c}, Marko  and
      Tufi{\textcommabelow{s}}, Dan  and
      V{\'a}radi, Tam{\'a}s  and
      Vider, Kadri  and
      Way, Andy  and
      Yvon, Fran{\c{c}}ois},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.407",
    pages = "3322--3332",
    abstract = "Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe{'}s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI {--} including many opportunities, synergies but also misconceptions {--} has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gillis-webber-tittel-2020-framework,
    title = "A Framework for Shared Agreement of Language Tags beyond {ISO} 639",
    author = "Gillis-Webber, Frances  and
      Tittel, Sabine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.408",
    pages = "3333--3339",
    abstract = "The identification and annotation of languages in an unambiguous and standardized way is essential for the description of linguistic data. It is the prerequisite for machine-based interpretation, aggregation, and re-use of the data with respect to different languages. This makes it a key aspect especially for Linked Data and the multilingual Semantic Web. The standard for language tags is defined by IETF{'}s BCP 47 and ISO 639 provides the language codes that are the tags{'} main constituents. However, for the identification of lesser-known languages, endangered languages, regional varieties or historical stages of a language, the ISO 639 codes are insufficient. Also, the optional language sub-tags compliant with BCP 47 do not offer a possibility fine-grained enough to represent linguistic variation. We propose a versatile pattern that extends the BCP 47 sub-tag {`}privateuse{'} and is, thus, able to overcome the limits of BCP 47 and ISO 639. Sufficient coverage of the pattern is demonstrated with the use case of linguistic Linked Data of the endangered Gascon language. We show how to use a URI shortcode for the extended sub-tag, making the length compliant with BCP 47. We achieve this with a web application and API developed to encode and decode the language tag.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{krek-etal-2020-gigafida,
    title = "Gigafida 2.0: The Reference Corpus of Written Standard {S}lovene",
    author = "Krek, Simon  and
      Arhar Holdt, {\v{S}}pela  and
      Erjavec, Toma{\v{z}}  and
      {\v{C}}ibej, Jaka  and
      Repar, Andraz  and
      Gantar, Polona  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Kosem, Iztok  and
      Dobrovoljc, Kaja",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.409",
    pages = "3340--3345",
    abstract = "We describe a new version of the Gigafida reference corpus of Slovene. In addition to updating the corpus with new material and annotating it with better tools, the focus of the upgrade was also on its transformation from a general reference corpus, which contains all language variants including non-standard language, to the corpus of standard (written) Slovene. This decision could be implemented as new corpora dedicated specifically to non-standard language emerged recently. In the new version, the whole Gigafida corpus was deduplicated for the first time, which facilitates automatic extraction of data for the purposes of compilation of new lexicographic resources such as the collocations dictionary and the thesaurus of Slovene.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{evert-etal-2020-corpus,
    title = "{C}orpus {Q}uery {L}ingua {F}ranca part {II}: Ontology",
    author = "Evert, Stefan  and
      Harlamov, Oleg  and
      Heinrich, Philipp  and
      Banski, Piotr",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.410",
    pages = "3346--3352",
    abstract = "The present paper outlines the projected second part of the Corpus Query Lingua Franca (CQLF) family of standards: CQLF Ontology, which is currently in the process of standardization at the International Standards Organization (ISO), in its Technical Committee 37, Subcommittee 4 (TC37SC4) and its national mirrors. The first part of the family, ISO 24623-1 (henceforth CQLF Metamodel), was successfully adopted as an international standard at the beginning of 2018. The present paper reflects the state of the CQLF Ontology at the moment of submission for the Committee Draft ballot. We provide a brief overview of the CQLF Metamodel, present the assumptions and aims of the CQLF Ontology, its basic structure, and its potential extended applications. The full ontology is expected to emerge from a community process, starting from an initial version created by the authors of the present paper.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{draxler-etal-2020-clarin,
    title = "A {CLARIN} Transcription Portal for Interview Data",
    author = "Draxler, Christoph  and
      van den Heuvel, Henk  and
      van Hessen, Arjan  and
      Calamai, Silvia  and
      Corti, Louise",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.411",
    pages = "3353--3359",
    abstract = "In this paper we present a first version of a transcription portal for audio files based on automatic speech recognition (ASR) in various languages. The portal is implemented in the CLARIN resources research network and intended for use by non-technical scholars. We explain the background and interdisciplinary nature of interview data, the perks and quirks of using ASR for transcribing the audio in a research context, the dos and don{'}ts for optimal use of the portal, and future developments foreseen. The portal is promoted in a range of workshops, but there are a number of challenges that have to be met. These challenges concern privacy issues, ASR quality, and cost, amongst others.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{petasis-tsekouras-2020-ellogon,
    title = "Ellogon Casual Annotation Infrastructure",
    author = "Petasis, Georgios  and
      Tsekouras, Leonidas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.412",
    pages = "3360--3365",
    abstract = "This paper presents a new annotation paradigm, casual annotation, along with a proposed architecture and a reference implementation, the Ellogon Casual Annotation Tool, which implements this paradigm and architecture. The novel aspects of the proposed paradigm originate from the vision to tightly integrate annotation with the casual, everyday activities of users. Annotating in a less {``}controlled{''} environment, and removing the bottleneck of selecting content and importing it to annotation infrastructures, casual annotation provides the ability to vastly increase the content that can be annotated and ease the annotation process through automatic pre-training. The proposed paradigm, architecture and reference implementation has been evaluated for more than two years on an annotation task related to sentiment analysis. Evaluation results suggest that, at least for this annotation task, there is a huge improvement in productivity after casual annotation adoption, in comparison to the more traditional annotation paradigms followed in the early stages of the annotation task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rehm-etal-2020-european-language,
    title = "{E}uropean Language Grid: An Overview",
    author = "Rehm, Georg  and
      Berger, Maria  and
      Elsholz, Ela  and
      Hegele, Stefanie  and
      Kintzel, Florian  and
      Marheinecke, Katrin  and
      Piperidis, Stelios  and
      Deligiannis, Miltos  and
      Galanis, Dimitris  and
      Gkirtzou, Katerina  and
      Labropoulou, Penny  and
      Bontcheva, Kalina  and
      Jones, David  and
      Roberts, Ian  and
      Haji{\v{c}}, Jan  and
      Hamrlov{\'a}, Jana  and
      Ka{\v{c}}ena, Luk{\'a}{\v{s}}  and
      Choukri, Khalid  and
      Arranz, Victoria  and
      Vasi{\c{l}}jevs, Andrejs  and
      Anvari, Orians  and
      Lagzdi{\c{n}}{\v{s}}, Andis  and
      Me{\c{l}}{\c{n}}ika, J{\=u}lija  and
      Backfried, Gerhard  and
      Dikici, Erin{\c{c}}  and
      Janosik, Miroslav  and
      Prinz, Katja  and
      Prinz, Christoph  and
      Stampler, Severin  and
      Thomas-Aniola, Dorothea  and
      G{\'o}mez-P{\'e}rez, Jos{\'e} Manuel  and
      Garcia Silva, Andres  and
      Berr{\'\i}o, Christian  and
      Germann, Ulrich  and
      Renals, Steve  and
      Klejch, Ondrej",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.413",
    pages = "3366--3380",
    abstract = "With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented {--} by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vasiljevs-etal-2020-competitiveness,
    title = "The Competitiveness Analysis of the {E}uropean Language Technology Market",
    author = "Vasi{\c{l}}jevs, Andrejs  and
      Skadi{\c{n}}a, Inguna  and
      Samite, Indra  and
      Kauli{\c{n}}{\v{s}}, Kaspars  and
      Ajausks, {\=E}riks  and
      Me{\c{l}}{\c{n}}ika, J{\=u}lija  and
      B{\=e}rzi{\c{n}}{\v{s}}, Aivars",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.414",
    pages = "3381--3389",
    abstract = "This paper presents the key results of a study on the global competitiveness of the European Language Technology market for three areas {--} Machine Translation, speech technology, and cross-lingual search. EU competitiveness is analyzed in comparison to North America and Asia. The study focuses on seven dimensions (research, innovations, investments, market dominance, industry, infrastructure, and Open Data) that have been selected to characterize the language technology market. The study concludes that while Europe still has strong positions in Research and Innovation, it lags behind North America and Asia in scaling innovations and conquering market share.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{altammami-etal-2020-constructing,
    title = "Constructing a Bilingual Hadith Corpus Using a Segmentation Tool",
    author = "Altammami, Shatha  and
      Atwell, Eric  and
      Alsalka, Ammar",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.415",
    pages = "3390--3398",
    abstract = "This article describes the process of gathering and constructing a bilingual parallel corpus of Islamic Hadith, which is the set of narratives reporting different aspects of the prophet Muhammad{'}s life. The corpus data is gathered from the six canonical Hadith collections using a custom segmentation tool that automatically segments and annotates the two Hadith components with 92{\%} accuracy. This Hadith segmenter minimises the costs of language resource creation and produces consistent results independently from previous knowledge and experiences that usually influence human annotators. The corpus includes more than 10M tokens and will be freely available via the LREC repository.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{steingrimsson-etal-2020-facilitating,
    title = "Facilitating Corpus Usage: Making {I}celandic Corpora More Accessible for Researchers and Language Users",
    author = {Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Barkarson, Starka{\dh}ur  and
      {\"O}rn{\'o}lfsson, Gunnar Thor},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.416",
    pages = "3399--3405",
    abstract = "We introduce an array of open and accessible tools to facilitate the use of the Icelandic Gigaword Corpus, in the field of Natural Language Processing as well as for students, linguists, sociologists and others benefitting from using large corpora. A KWIC engine, powered by the Swedish Korp tool is adapted to the specifics of the corpus. An n-gram viewer, highly customizable to suit different needs, allows users to study word usage throughout the period of our text collection. A frequency dictionary provides much sought after information about word frequency statistics, computed for each subcorpus as well as aggregate, disambiguating homographs based on their respective lemmas and morphosyntactic tags. Furthermore, we provide n-grams based on the corpus, and a variety of pre-trained word embeddings models, based on word2vec, GloVe, fastText and ELMo. For three of the model types, multiple word embedding models are available trained with different algorithms and using either lemmatised or unlemmatised texts.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{de-jong-etal-2020-interoperability,
    title = "Interoperability in an Infrastructure Enabling Multidisciplinary Research: The case of {CLARIN}",
    author = "de Jong, Franciska  and
      Maegaard, Bente  and
      Fi{\v{s}}er, Darja  and
      van Uytvanck, Dieter  and
      Witt, Andreas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.417",
    pages = "3406--3413",
    abstract = "CLARIN is a European Research Infrastructure providing access to language resources and technologies for researchers in the humanities and social sciences. It supports the use and study of language data in general and aims to increase the potential for comparative research of cultural and societal phenomena across the boundaries of languages and disciplines, all in line with the European agenda for Open Science. Data infrastructures such as CLARIN have recently embarked on the emerging frameworks for the federation of infrastructural services, such as the European Open Science Cloud and the integration of services resulting from multidisciplinary collaboration in federated services for the wider SSH domain. In this paper we describe the interoperability requirements that arise through the existing ambitions and the emerging frameworks. The interoperability theme will be addressed at several levels, including organisation and ecosystem, design of workflow services, data curation, performance measurement and collaboration.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nikulasdottir-etal-2020-language,
    title = "Language Technology Programme for {I}celandic 2019-2023",
    author = {Nikul{\'a}sd{\'o}ttir, Anna  and
      Gu{\dh}nason, J{\'o}n  and
      Ingason, Anton Karl  and
      Loftsson, Hrafn  and
      R{\"o}gnvaldsson, Eir{\'\i}kur  and
      Sigur{\dh}sson, Einar Freyr  and
      Steingr{\'\i}msson, Stein{\th}{\'o}r},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.418",
    pages = "3414--3422",
    abstract = "In this paper, we describe a new national language technology programme for Icelandic. The programme, which spans a period of five years, aims at making Icelandic usable in communication and interactions in the digital world, by developing accessible, open-source language resources and software. The research and development work within the programme is carried out by a consortium of universities, institutions, and private companies, with a strong emphasis on cooperation between academia and industries. Five core projects will be the main content of the programme: language resources, speech recognition, speech synthesis, machine translation, and spell and grammar checking. We also describe other national language technology programmes and give an overview over the history of language technology in Iceland.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kamocki-witt-2020-privacy,
    title = "Privacy by Design and Language Resources",
    author = "Kamocki, Pawel  and
      Witt, Andreas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.419",
    pages = "3423--3427",
    abstract = "Privacy by Design (also referred to as Data Protection by Design) is an approach in which solutions and mechanisms addressing privacy and data protection are embedded through the entire project lifecycle, from the early design stage, rather than just added as an additional lawyer to the final product. Formulated in the 1990 by the Privacy Commissionner of Ontario, the principle of Privacy by Design has been discussed by institutions and policymakers on both sides of the Atlantic, and mentioned already in the 1995 EU Data Protection Directive (95/46/EC). More recently, Privacy by Design was introduced as one of the requirements of the General Data Protection Regulation (GDPR), obliging data controllers to define and adopt, already at the conception phase, appropriate measures and safeguards to implement data protection principles and protect the rights of the data subject. Failing to meet this obligation may result in a hefty fine, as it was the case in the Uniontrad decision by the French Data Protection Authority (CNIL). The ambition of the proposed paper is to analyse the practical meaning of Privacy by Design in the context of Language Resources, and propose measures and safeguards that can be implemented by the community to ensure respect of this principle.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{labropoulou-etal-2020-making,
    title = "Making Metadata Fit for Next Generation Language Technology Platforms: The Metadata Schema of the {E}uropean Language Grid",
    author = "Labropoulou, Penny  and
      Gkirtzou, Katerina  and
      Gavriilidou, Maria  and
      Deligiannis, Miltos  and
      Galanis, Dimitris  and
      Piperidis, Stelios  and
      Rehm, Georg  and
      Berger, Maria  and
      Mapelli, Val{\'e}rie  and
      Rigault, Michael  and
      Arranz, Victoria  and
      Choukri, Khalid  and
      Backfried, Gerhard  and
      G{\'o}mez-P{\'e}rez, Jos{\'e} Manuel  and
      Garcia-Silva, Andres",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.420",
    pages = "3428--3437",
    abstract = "The current scientific and technological landscape is characterised by the increasing availability of data resources and processing tools and services. In this setting, metadata have emerged as a key factor facilitating management, sharing and usage of such digital assets. In this paper we present ELG-SHARE, a rich metadata schema catering for the description of Language Resources and Technologies (processing and generation services and tools, models, corpora, term lists, etc.), as well as related entities (e.g., organizations, projects, supporting documents, etc.). The schema powers the European Language Grid platform that aims to be the primary hub and marketplace for industry-relevant Language Technology in Europe. ELG-SHARE has been based on various metadata schemas, vocabularies, and ontologies, as well as related recommendations and guidelines.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jaquette-etal-2020-related,
    title = "Related Works in the {L}inguistic {D}ata {C}onsortium Catalog",
    author = "Jaquette, Daniel  and
      Cieri, Christopher  and
      DiPersio, Denise",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.421",
    pages = "3438--3442",
    abstract = "Defining relations between language resources provides an archive with the ability to better serve its users. This paper covers the development and implementation of a Related Works addition to the Linguistic Data Consortium{'}s (LDC) catalog. The authors go step-by-step through the development of the Related Works schema, implementation of the software and database changes, and data entry of the relations. The Related Work schema involved developing of a set of controlled terms for relations based on previous work and other schema. Software and database changes consisted of both front and back end interface additions, along with modification and additions to the LDC Catalog database tables. Data entry consisted of two parts: seed data from previous work and 2019 language resources, and ongoing legacy population. Previous work in this area is discussed as well as overview information about the LDC Catalog. A list of the full LDC Related Works terms is included with brief explanations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{smal-etal-2020-language,
    title = "Language Data Sharing in {E}uropean Public Services {--} Overcoming Obstacles and Creating Sustainable Data Sharing Infrastructures",
    author = {Smal, Lilli  and
      L{\"o}sch, Andrea  and
      van Genabith, Josef  and
      Giagkou, Maria  and
      Declerck, Thierry  and
      Busemann, Stephan},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.422",
    pages = "3443--3448",
    abstract = "Data is key in training modern language technologies. In this paper, we summarise the findings of the first pan-European study on obstacles to sharing language data across 29 EU Member States and CEF-affiliated countries carried out under the ELRC White Paper action on Sustainable Language Data Sharing to Support Language Equality in Multilingual Europe. Why Language Data Matters. We present the methodology of the study, the obstacles identified and report on recommendations on how to overcome those. The obstacles are classified into (1) lack of appreciation of the value of language data, (2) structural challenges, (3) disposition towards CAT tools and lack of digital skills, (4) inadequate language data management practices, (5) limited access to outsourced translations, and (6) legal concerns. Recommendations are grouped into addressing the European/national policy level, and the organisational/institutional level.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cieri-etal-2020-progress,
    title = "A Progress Report on Activities at the {L}inguistic {D}ata {C}onsortium Benefitting the {LREC} Community",
    author = "Cieri, Christopher  and
      Fiumara, James  and
      Strassel, Stephanie  and
      Wright, Jonathan  and
      DiPersio, Denise  and
      Liberman, Mark",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.423",
    pages = "3449--3456",
    abstract = "This latest in a series of Linguistic Data Consortium (LDC) progress reports to the LREC community does not describe any single language resource, evaluation campaign or technology but sketches the activities, since the last report, of a data center devoted to supporting the work of LREC attendees among other research communities. Specifically, we describe 96 new corpora released in 2018-2020 to date, a new technology evaluation campaign, ongoing activities to support multiple common task human language technology programs, and innovations to advance the methodology of language data collection and annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lyding-etal-2020-digital,
    title = "Digital Language Infrastructures {--} Documenting Language Actors",
    author = {Lyding, Verena  and
      K{\"o}nig, Alexander  and
      Pretti, Monica},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.424",
    pages = "3457--3462",
    abstract = "The major European language infrastructure initiatives like CLARIN (Hinrichs and Krauwer, 2014), DARIAH (Edmond et al., 2017) or Europeana (Europeana Foundation, 2015) have been built by focusing in the first place on institutions of larger scale, like specialized research departments and larger official units like national libraries, etc. However, besides these principal players also a large number of smaller language actors could contribute to and benefit from language infrastructures. Especially since these smaller institutions, like local libraries, archives and publishers, often collect, manage and host language resources of particular value for their geographical and cultural region, it seems highly relevant to find ways of engaging and connecting them to existing European infrastructure initiatives. In this article, we first highlight the need for reaching out to smaller local language actors and discuss challenges related to this ambition. Then we present the first step in how this objective was approached within a local language infrastructure project, namely by means of a structured documentation of the local language actors landscape in South Tyrol. We describe how the documentation efforts were structured and organized, and what tool we have set up to distribute the collected data online, by adapting existing CLARIN solutions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mollberg-etal-2020-samromur,
    title = "{S}amr{\'o}mur: Crowd-sourcing Data Collection for {I}celandic Speech Recognition",
    author = "Mollberg, David Erik  and
      J{\'o}nsson, {\'O}lafur Helgi  and
      {\TH}orsteinsd{\'o}ttir, Sunneva  and
      Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Magn{\'u}sd{\'o}ttir, Eyd{\'\i}s Huld  and
      Gudnason, Jon",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.425",
    pages = "3463--3467",
    abstract = "This contribution describes an ongoing project of speech data collection, using the web application Samr{\'o}mur which is built upon Common Voice, Mozilla Foundation{'}s web platform for open-source voice collection. The goal of the project is to build a large-scale speech corpus for Automatic Speech Recognition (ASR) for Icelandic. Upon completion, Samr{\'o}mur will be the largest open speech corpus for Icelandic collected from the public domain. We discuss the methods used for the crowd-sourcing effort and show the importance of marketing and good media coverage when launching a crowd-sourcing campaign. Preliminary results exceed our expectations, and in one month we collected data that we had estimated would take three months to obtain. Furthermore, our initial dataset of around 45 thousand utterances has good demographic coverage, is gender-balanced and with proper age distribution. We also report on the task of validating the recordings, which we have not promoted, but have had numerous hours invested by volunteers.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{biswas-etal-2020-semi,
    title = "Semi-supervised Development of {ASR} Systems for Multilingual Code-switched Speech in Under-resourced Languages",
    author = "Biswas, Astik  and
      Yilmaz, Emre  and
      De Wet, Febe  and
      Van der westhuizen, Ewald  and
      Niesler, Thomas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.426",
    pages = "3468--3474",
    abstract = "This paper reports on the semi-supervised development of acoustic and language models for under-resourced, code-switched speech in five South African languages. Two approaches are considered. The first constructs four separate bilingual automatic speech recognisers (ASRs) corresponding to four different language pairs between which speakers switch frequently. The second uses a single, unified, five-lingual ASR system that represents all the languages (English, isiZulu, isiXhosa, Setswana and Sesotho). We evaluate the effectiveness of these two approaches when used to add additional data to our extremely sparse training sets. Results indicate that batch-wise semi-supervised training yields better results than a non-batch-wise approach. Furthermore, while the separate bilingual systems achieved better recognition performance than the unified system, they benefited more from pseudolabels generated by the five-lingual system than from those generated by the bilingual systems.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mersinias-etal-2020-clfd,
    title = "{CLFD}: A Novel Vectorization Technique and Its Application in Fake News Detection",
    author = "Mersinias, Michail  and
      Afantenos, Stergos  and
      Chalkiadakis, Georgios",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.427",
    pages = "3475--3483",
    abstract = "In recent years, fake news detection has been an emerging research area. In this paper, we put forward a novel statistical approach for the generation of feature vectors to describe a document. Our so-called class label frequency distance (clfd), is shown experimentally to provide an effective way for boosting the performance of machine learning methods. Specifically, our experiments, carried out in the fake news detection domain, verify that efficient traditional machine learning methods that use our vectorization approach, consistently outperform deep learning methods that use word embeddings for small and medium sized datasets, while the results are comparable for large datasets. In addition, we demonstrate that a novel hybrid method that utilizes both a clfd-boosted logistic regression classifier and a deep learning one, clearly outperforms deep learning methods even in large datasets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{qasmi-etal-2020-simplifyur,
    title = "{S}implify{UR}: Unsupervised Lexical Text Simplification for {U}rdu",
    author = "Qasmi, Namoos Hayat  and
      Zia, Haris Bin  and
      Athar, Awais  and
      Raza, Agha Ali",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.428",
    pages = "3484--3489",
    abstract = "This paper presents the first attempt at Automatic Text Simplification (ATS) for Urdu, the language of 170 million people worldwide. Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet are not applicable to Urdu. Urdu is a morphologically rich language that requires unique considerations such as proper handling of inflectional case and honorifics. We present an unsupervised method for lexical simplification of complex Urdu text. Our method only requires plain Urdu text and makes use of word embeddings together with a set of morphological features to generate simplifications. Our system achieves a BLEU score of 80.15 and SARI score of 42.02 upon automatic evaluation on manually crafted simplified corpora. We also report results for human evaluations for correctness, grammaticality, meaning-preservation and simplicity of the output. Our code and corpus are publicly available to make our results reproducible.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moon-okazaki-2020-jamo,
    title = "Jamo Pair Encoding: Subcharacter Representation-based Extreme {K}orean Vocabulary Compression for Efficient Subword Tokenization",
    author = "Moon, Sangwhan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.429",
    pages = "3490--3497",
    abstract = "In the context of multilingual language model pre-training, vocabulary size for languages with a broad set of potential characters is an unsolved problem. We propose two algorithms applicable in any unsupervised multilingual pre-training task, increasing the elasticity of budget required for building the vocabulary in Byte-Pair Encoding inspired tokenizers, significantly reducing the cost of supporting Korean in a multilingual model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sigurbergsson-derczynski-2020-offensive,
    title = "Offensive Language and Hate Speech Detection for {D}anish",
    author = "Sigurbergsson, Gudbjartur Ingi  and
      Derczynski, Leon",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.430",
    pages = "3498--3508",
    abstract = "The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this type of content. Until now, most of the research has focused on solving the problem for the English language, while the problem is multilingual. We construct a Danish dataset DKhate containing user-generated comments from various social media platforms, and to our knowledge, the first of its kind, annotated for various types and target of offensive language. We develop four automatic classification systems, each designed to work for both the English and the Danish language. In the detection of offensive language in English, the best performing system achieves a macro averaged F1-score of 0.74, and the best performing system for Danish achieves a macro averaged F1-score of 0.70. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of 0.62, while the best performing system for Danish achieves a macro averaged F1-score of 0.73. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of 0.56, and the best performing system for Danish achieves a macro averaged F1-score of 0.63. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yong-torrent-2020-semi,
    title = "Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame Induction",
    author = "Yong, Zheng Xin  and
      Torrent, Tiago Timponi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.431",
    pages = "3509--3519",
    abstract = "Although FrameNet is recognized as one of the most fine-grained lexical databases, its coverage of lexical units is still limited. To tackle this issue, we propose a two-step frame induction process: for a set of lexical units not yet present in Berkeley FrameNet data release 1.7, first remove those that cannot fit into any existing semantic frame in FrameNet; then, assign the remaining lexical units to their correct frames. We also present the Semi-supervised Deep Embedded Clustering with Anomaly Detection (SDEC-AD) model{---}an algorithm that maps high-dimensional contextualized vector representations of lexical units to a low-dimensional latent space for better frame prediction and uses reconstruction error to identify lexical units that cannot evoke frames in FrameNet. SDEC-AD outperforms the state-of-the-art methods in both steps of the frame induction process. Empirical results also show that definitions provide contextual information for representing and characterizing the frame membership of lexical units.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tambi-etal-2020-search,
    title = "Search Query Language Identification Using Weak Labeling",
    author = "Tambi, Ritiz  and
      Kale, Ajinkya  and
      King, Tracy Holloway",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.432",
    pages = "3520--3527",
    abstract = "Language identification is a well-known task for natural language documents. In this paper we explore search query language identification which is usually the first task before any other query understanding. Without loss of generalization, we run our experiments on the Adobe Stock search engine. Even though the domain is relatively generic because Adobe Stock queries cover a broad range of objects and concepts, out-of-the-box language identifiers do not perform well due to the extremely short text found in queries. Unlike other well-studied supervised approaches for this task, we examine a practical approach for the cold start problem for automatically getting large-scale query-language pairs for training. We describe the process of creating weak-labeled training data and then human-annotated evaluation data for the search query language identification task. The effectiveness of this technique is demonstrated by training a gradient boosting model for language classification given a query. We out-perform the open domain text model baselines by a large margin.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sahala-etal-2020-automated,
    title = "Automated Phonological Transcription of {A}kkadian Cuneiform Text",
    author = "Sahala, Aleksi  and
      Silfverberg, Miikka  and
      Arppe, Antti  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.433",
    pages = "3528--3534",
    abstract = "Akkadian was an East-Semitic language spoken in ancient Mesopotamia. The language is attested on hundreds of thousands of cuneiform clay tablets. Several Akkadian text corpora contain only the transliterated text. In this paper, we investigate automated phonological transcription of the transliterated corpora. The phonological transcription provides a linguistically appealing form to represent Akkadian, because the transcription is normalized according to the grammatical description of a given dialect and explicitly shows the Akkadian renderings for Sumerian logograms. Because cuneiform text does not mark the inflection for logograms, the inflected form needs to be inferred from the sentence context. To the best of our knowledge, this is the first documented attempt to automatically transcribe Akkadian. Using a context-aware neural network model, we are able to automatically transcribe syllabic tokens at near human performance with 96{\%} recall @ 3, while the logogram transcription remains more challenging at 82{\%} recall @ 3.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{barancikova-bojar-2020-costra,
    title = "{COSTRA} 1.0: A Dataset of Complex Sentence Transformations",
    author = "Barancikova, Petra  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.434",
    pages = "3535--3541",
    abstract = "We present COSTRA 1.0, a dataset of complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. This first version of the dataset is limited to sentences in Czech but the construction method is universal and we plan to use it also for other languages. The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation. The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting {``}skeleton{''} in the sentence embedding space. A preliminary analysis using LASER, multi-purpose multi-lingual sentence embeddings suggests that the LASER space does not exhibit the desired properties.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{correia-etal-2020-automatic,
    title = "Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning",
    author = "Correia, Joana  and
      Trancoso, Isabel  and
      Raj, Bhiksha",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.435",
    pages = "3542--3550",
    abstract = "The automation of the diagnosis and monitoring of speech affecting diseases in real life situations, such as Depression or Parkinson{'}s disease, depends on the existence of rich and large datasets that resemble real life conditions, such as those collected from in-the-wild multimedia repositories like YouTube. However, the cost of manually labeling these large datasets can be prohibitive. In this work, we propose to overcome this problem by automating the annotation process, without any requirements for human intervention. We formulate the annotation problem as a Multiple Instance Learning (MIL) problem, and propose a novel solution that is based on end-to-end differentiable neural networks. Our solution has the additional advantage of generalizing the MIL framework to more scenarios where the data is stil organized in bags but does not meet the MIL bag label conditions. We demonstrate the performance of the proposed method in labeling the in-the-Wild Speech Medical (WSM) Corpus, using simple textual cues extracted from videos and their metadata. Furthermore we show what is the contribution of each type of textual cues for the final model performance, as well as study the influence of the size of the bags of instances in determining the difficulty of the learning problem",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{strobel-etal-2020-much,
    title = "How Much Data Do You Need? About the Creation of a Ground Truth for Black Letter and the Effectiveness of Neural {OCR}",
    author = {Str{\"o}bel, Phillip Benjamin  and
      Clematide, Simon  and
      Volk, Martin},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.436",
    pages = "3551--3559",
    abstract = "Recent advances in Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) have led to more accurate textrecognition of historical documents. The Digital Humanities heavily profit from these developments, but they still struggle whenchoosing from the plethora of OCR systems available on the one hand and when defining workflows for their projects on the other hand.In this work, we present our approach to build a ground truth for a historical German-language newspaper published in black letter. Wealso report how we used it to systematically evaluate the performance of different OCR engines. Additionally, we used this ground truthto make an informed estimate as to how much data is necessary to achieve high-quality OCR results. The outcomes of our experimentsshow that HTR architectures can successfully recognise black letter text and that a ground truth size of 50 newspaper pages suffices toachieve good OCR accuracy. Moreover, our models perform equally well on data they have not seen during training, which means thatadditional manual correction for diverging data is superfluous.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jungmaier-etal-2020-dirichlet,
    title = "{D}irichlet-Smoothed Word Embeddings for Low-Resource Settings",
    author = "Jungmaier, Jakob  and
      Kassner, Nora  and
      Roth, Benjamin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.437",
    pages = "3560--3565",
    abstract = "Nowadays, classical count-based word embeddings using positive pointwise mutual information (PPMI) weighted co-occurrence matrices have been widely superseded by machine-learning-based methods like word2vec and GloVe. But these methods are usually applied using very large amounts of text data. In many cases, however, there is not much text data available, for example for specific domains or low-resource languages. This paper revisits PPMI by adding Dirichlet smoothing to correct its bias towards rare words. We evaluate on standard word similarity data sets and compare to word2vec and the recent state of the art for low-resource settings: Positive and Unlabeled (PU) Learning for word embeddings. The proposed method outperforms PU-Learning for low-resource settings and obtains competitive results for Maltese and Luxembourgish.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{monteiro-etal-2020-performance,
    title = "On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification",
    author = "Monteiro, Joao  and
      Alam, Md Jahangir  and
      Falk, Tiago",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.438",
    pages = "3566--3572",
    abstract = "Automatic speech processing applications often have to deal with the problem of aggregating local descriptors (i.e., representations of input speech data corresponding to specific portions across the time dimension) and turning them into a single fixed-dimension representation, known as global descriptor, on top of which downstream classification tasks can be performed. In this paper, we provide an empirical assessment of different time pooling strategies when used with state-of-the-art representation learning models. In particular, insights are provided as to when it is suitable to use simple statistics of local descriptors or when more sophisticated approaches are needed. Here, language identification is used as a case study and a database containing ten oriental languages under varying test conditions (short-duration test recordings, confusing languages, unseen languages) is used. Experiments are performed with classifiers trained on top of global descriptors to provide insights on open-set evaluation performance and show that appropriate selection of such pooling strategies yield embeddings able to outperform well-known benchmark systems as well as previously results based on attention only.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hoya-quecedo-etal-2020-neural,
    title = "Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages",
    author = "Hoya Quecedo, Jos{\'e} Mar{\'\i}a  and
      Maximilian, Koppatz  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.439",
    pages = "3573--3582",
    abstract = "We consider the problem of disambiguating the lemma and part of speech of ambiguous words in morphologically rich languages. We propose a method for disambiguating ambiguous words in context, using a large un-annotated corpus of text, and a morphological analyser{---}with no manual disambiguation or data annotation. We assume that the morphological analyser produces multiple analyses for ambiguous words. The idea is to train recurrent neural networks on the output that the morphological analyser produces for unambiguous words. We present performance on POS and lemma disambiguation that reaches or surpasses the state of the art{---}including supervised models{---}using no manually annotated data. We evaluate the method on several morphologically rich languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zhao-gilman-2020-non,
    title = "Non-Linearity in Mapping Based Cross-Lingual Word Embeddings",
    author = "Zhao, Jiawei  and
      Gilman, Andrew",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.440",
    pages = "3583--3589",
    abstract = "Recent works on cross-lingual word embeddings have been mainly focused on linear-mapping-based approaches, where pre-trained word embeddings are mapped into a shared vector space using a linear transformation. However, there is a limitation in such approaches{--}they follow a key assumption: words with similar meanings share similar geometric arrangements between their monolingual word embeddings, which suggest that there is a linear relationship between languages. However, such assumption may not hold for all language pairs across all semantic concepts. We investigate whether non-linear mappings can better describe the relationship between different languages by utilising kernel Canonical Correlation Analysis (KCCA). Experimental results on five language pairs show an improvement over current state-of-art results in both supervised and self-learning scenarios, confirming that non-linear mapping is a better way to describe the relationship between languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{beilharz-etal-2020-librivoxdeen,
    title = "{L}ibri{V}ox{D}e{E}n: A Corpus for {G}erman-to-{E}nglish Speech Translation and {G}erman Speech Recognition",
    author = "Beilharz, Benjamin  and
      Sun, Xin  and
      Karimova, Sariya  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.441",
    pages = "3590--3594",
    abstract = "We present a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The speech translation data consist of 110 hours of audio material aligned to over 50k parallel sentences. An even larger dataset comprising 547 hours of German speech aligned to German text is available for speech recognition. The audio data is read speech and thus low in disfluencies. The quality of audio and sentence alignments has been checked by a manual evaluation, showing that speech alignment quality is in general very high. The sentence alignment quality is comparable to well-used parallel translation data and can be adjusted by cutoffs on the automatic alignment score. To our knowledge, this corpus is to date the largest resource for German speech recognition and for end-to-end German-to-English speech translation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ghaddar-langlais-2020-sedar,
    title = "{SEDAR}: a Large Scale {F}rench-{E}nglish Financial Domain Parallel Corpus",
    author = "Ghaddar, Abbas  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.442",
    pages = "3595--3602",
    abstract = "This paper describes the acquisition, preprocessing and characteristics of SEDAR, a large scale English-French parallel corpus for the financial domain. Our extensive experiments on machine translation show that SEDAR is essential to obtain good performance on finance. We observe a large gain in the performance of machine translation systems trained on SEDAR when tested on finance, which makes SEDAR suitable to study domain adaptation for neural machine translation. The first release of the corpus comprises 8.6 million high quality sentence pairs that are publicly available for research at https://github.com/autorite/sedar-bitext.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{morishita-etal-2020-jparacrawl,
    title = "{JP}ara{C}rawl: A Large Scale Web-Based {E}nglish-{J}apanese Parallel Corpus",
    author = "Morishita, Makoto  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.443",
    pages = "3603--3609",
    abstract = "Recent machine translation algorithms mainly rely on parallel corpora. However, since the availability of parallel corpora remains limited, only some resource-rich language pairs can benefit from them. We constructed a parallel corpus for English-Japanese, for which the amount of publicly available parallel corpora is still limited. We constructed the parallel corpus by broadly crawling the web and automatically aligning parallel sentences. Our collected corpus, called JParaCrawl, amassed over 8.7 million sentence pairs. We show how it includes a broader range of domains and how a neural machine translation model trained with it works as a good pre-trained model for fine-tuning specific domains. The pre-training and fine-tuning approaches achieved or surpassed performance comparable to model training from the initial state and reduced the training time. Additionally, we trained the model with an in-domain dataset and JParaCrawl to show how we achieved the best performance with them. JParaCrawl and the pre-trained models are freely available online for research purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{choudhary-etal-2020-neural,
    title = "Neural Machine Translation for Low-Resourced {I}ndian Languages",
    author = "Choudhary, Himanshu  and
      Rao, Shivansh  and
      Rohilla, Rajesh",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.444",
    pages = "3610--3615",
    abstract = "A large number of significant assets are available online in English, which is frequently translated into native languages to ease the information sharing among local people who are not much familiar with English. However, manual translation is a very tedious, costly, and time-taking process. To this end, machine translation is an effective approach to convert text to a different language without any human involvement. Neural machine translation (NMT) is one of the most proficient translation techniques amongst all existing machine translation systems. In this paper, we have applied NMT on two of the most morphological rich Indian languages, i.e. English-Tamil and English-Malayalam. We proposed a novel NMT model using Multihead self-attention along with pre-trained Byte-Pair-Encoded (BPE) and MultiBPE embeddings to develop an efficient translation system that overcomes the OOV (Out Of Vocabulary) problem for low resourced morphological rich Indian languages which do not have much translation available online. We also collected corpus from different sources, addressed the issues with these publicly available data and refined them for further uses. We used the BLEU score for evaluating our system performance. Experimental results and survey confirmed that our proposed translator (24.34 and 9.78 BLEU score) outperforms Google translator (9.40 and 5.94 BLEU score) respectively.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mino-etal-2020-content,
    title = "Content-Equivalent Translated Parallel News Corpus and Extension of Domain Adaptation for {NMT}",
    author = "Mino, Hideya  and
      Tanaka, Hideki  and
      Ito, Hitoshi  and
      Goto, Isao  and
      Yamada, Ichiro  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.445",
    pages = "3616--3622",
    abstract = "In this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer degraded performance when trained with noisy data. Because there is no clean Japanese-English parallel data for news articles, we build a novel parallel news corpus consisting of Japanese news articles translated into English in a content-equivalent manner. This is the first content-equivalent Japanese-English news corpus translated specifically for training NMT systems. The second problem involves the domain-adaptation technique. NMT systems suffer degraded performance when trained with mixed data having different features, such as noisy data and clean data. Though the existing methods try to overcome this problem by using tags for distinguishing the differences between corpora, it is not sufficient. We thus extend a domain-adaptation method using multi-tags to train an NMT model effectively with the clean corpus and existing parallel news corpora with some types of noise. Experimental results show that our corpus increases the translation quality, and that our domain-adaptation method is more effective for learning with the multiple types of corpora than existing domain-adaptation methods are.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{caseli-inacio-2020-nmt,
    title = "{NMT} and {PBSMT} Error Analyses in {E}nglish to {B}razilian {P}ortuguese Automatic Translations",
    author = "Caseli, Helena  and
      In{\'a}cio, Marcio",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.446",
    pages = "3623--3629",
    abstract = "Machine Translation (MT) is one of the most important natural language processing applications. Independently of the applied MT approach, a MT system automatically generates an equivalent version (in some target language) of an input sentence (in some source language). Recently, a new MT approach has been proposed: neural machine translation (NMT). NMT systems have already outperformed traditional phrase-based statistical machine translation (PBSMT) systems for some pairs of languages. However, any MT approach outputs errors. In this work we present a comparative study of MT errors generated by a NMT system and a PBSMT system trained on the same English {--} Brazilian Portuguese parallel corpus. This is the first study of this kind involving NMT for Brazilian Portuguese. Furthermore, the analyses and conclusions presented here point out the specific problems of NMT outputs in relation to PBSMT ones and also give lots of insights into how to implement automatic post-editing for a NMT system. Finally, the corpora annotated with MT errors generated by both PBSMT and NMT systems are also available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{shimazu-etal-2020-evaluation,
    title = "Evaluation Dataset for Zero Pronoun in {J}apanese to {E}nglish Translation",
    author = "Shimazu, Sho  and
      Takase, Sho  and
      Nakazawa, Toshiaki  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.447",
    pages = "3630--3634",
    abstract = "In natural language, we often omit some words that are easily understandable from the context. In particular, pronouns of subject, object, and possessive cases are often omitted in Japanese; these are known as zero pronouns. In translation from Japanese to other languages, we need to find a correct antecedent for each zero pronoun to generate a correct and coherent translation. However, it is difficult for conventional automatic evaluation metrics (e.g., BLEU) to focus on the success of zero pronoun resolution. Therefore, we present a hand-crafted dataset to evaluate whether translation models can resolve the zero pronoun problems in Japanese to English translations. We manually and statistically validate that our dataset can effectively evaluate the correctness of the antecedents selected in translations. Through the translation experiments using our dataset, we reveal shortcomings of an existing context-aware neural machine translation model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kovacs-etal-2020-better,
    title = "Better Together: Modern Methods Plus Traditional Thinking in {NP} Alignment",
    author = "Kov{\'a}cs, {\'A}d{\'a}m  and
      {\'A}cs, Judit  and
      Kornai, Andras  and
      Recski, G{\'a}bor",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.448",
    pages = "3635--3639",
    abstract = "We study a typical intermediary task to Machine Translation, the alignment of NPs in the bitext. After arguing that the task remains relevant even in an end-to-end paradigm, we present simple, dictionary- and word vector-based baselines and a BERT-based system. Our results make clear that even state of the art systems relying on the best end-to-end methods can be improved by bringing in old-fashioned methods such as stopword removal, lemmatization, and dictionaries",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{song-etal-2020-coursera,
    title = "{C}oursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation",
    author = "Song, Haiyue  and
      Dabre, Raj  and
      Fujita, Atsushi  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.449",
    pages = "3640--3649",
    abstract = "Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera. Our approach determines sentence alignments, relying on machine translation and cosine similarity over continuous-space sentence representations. We also show how to use the resulting corpora in a multistage fine-tuning based domain adaptation for high-quality lectures translation. For Japanese{--}English lectures translation, we extracted parallel data of approximately 40,000 lines and created development and test sets through manual filtering for benchmarking translation performance. We demonstrate that the mined corpus greatly enhances the quality of translation when used in conjunction with out-of-domain parallel corpora via multistage training. This paper also suggests some guidelines to gather and clean corpora, mine parallel sentences, address noise in the mined data, and create high-quality evaluation splits. For the sake of reproducibility, we have released our code for parallel data creation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{defauw-etal-2020-generous,
    title = "Being Generous with Sub-Words towards Small {NMT} Children",
    author = "Defauw, Arne  and
      Vanallemeersch, Tom  and
      Van Winckel, Koen  and
      Szoc, Sara  and
      Van den Bogaert, Joachim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.450",
    pages = "3650--3656",
    abstract = "In the context of under-resourced neural machine translation (NMT), transfer learning from an NMT model trained on a high resource language pair, or from a multilingual NMT (M-NMT) model, has been shown to boost performance to a large extent. In this paper, we focus on so-called cold start transfer learning from an M-NMT model, which means that the parent model is not trained on any of the child data. Such a set-up enables quick adaptation of M-NMT models to new languages. We investigate the effectiveness of cold start transfer learning from a many-to-many M-NMT model to an under-resourced child. We show that sufficiently large sub-word vocabularies should be used for transfer learning to be effective in such a scenario. When adopting relatively large sub-word vocabularies we observe increases in performance thanks to transfer learning from a parent M-NMT model, both when translating to and from the under-resourced language. Our proposed approach involving dynamic vocabularies is both practical and effective. We report results on two under-resourced language pairs, i.e. Icelandic-English and Irish-English.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dobreva-etal-2020-document,
    title = "Document Sub-structure in Neural Machine Translation",
    author = "Dobreva, Radina  and
      Zhou, Jie  and
      Bawden, Rachel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.451",
    pages = "3657--3667",
    abstract = "Current approaches to machine translation (MT) either translate sentences in isolation, disregarding the context they appear in, or model context at the level of the full document, without a notion of any internal structure the document may have. In this work we consider the fact that documents are rarely homogeneous blocks of text, but rather consist of parts covering different topics. Some documents, such as biographies and encyclopedia entries, have highly predictable, regular structures in which sections are characterised by different topics. We draw inspiration from Louis and Webber (2014) who use this information to improve statistical MT and transfer their proposal into the framework of neural MT. We compare two different methods of including information about the topic of the section within which each sentence is found: one using side constraints and the other using a cache-based model. We create and release the data on which we run our experiments - parallel corpora for three language pairs (Chinese-English, French-English, Bulgarian-English) from Wikipedia biographies, which we extract automatically, preserving the boundaries of sections within the articles.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{raganato-etal-2020-evaluation,
    title = "An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems",
    author = {Raganato, Alessandro  and
      Scherrer, Yves  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.452",
    pages = "3668--3675",
    abstract = "Lexical ambiguity is one of the many challenging linguistic phenomena involved in translation, i.e., translating an ambiguous word with its correct sense. In this respect, previous work has shown that the translation quality of neural machine translation systems can be improved by explicitly modeling the senses of ambiguous words. Recently, several evaluation test sets have been proposed to measure the word sense disambiguation (WSD) capability of machine translation systems. However, to date, these evaluation test sets do not include any training data that would provide a fair setup measuring the sense distributions present within the training data itself. In this paper, we present an evaluation benchmark on WSD for machine translation for 10 language pairs, comprising training data with known sense distributions. Our approach for the construction of the benchmark builds upon the wide-coverage multilingual sense inventory of BabelNet, the multilingual neural parsing pipeline TurkuNLP, and the OPUS collection of translated texts from the web. The test suite is available at http://github.com/Helsinki-NLP/MuCoW.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{neveol-etal-2020-medline,
    title = "{MEDLINE} as a Parallel Corpus: a Survey to Gain Insight on {F}rench-, {S}panish- and {P}ortuguese-speaking Authors{'} Abstract Writing Practice",
    author = "N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Jimeno Yepes, Antonio  and
      Neves, Mariana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.453",
    pages = "3676--3682",
    abstract = "Background: Parallel corpora are used to train and evaluate machine translation systems. To alleviate the cost of producing parallel resources for evaluation campaigns, existing corpora are leveraged. However, little information may be available about the methods used for producing the corpus, including translation direction. Objective: To gain insight on MEDLINE parallel corpus used in the biomedical task at the Workshop on Machine Translation in 2019 (WMT 2019). Material and Methods: Contact information for the authors of MEDLINE articles included in the English/Spanish (EN/ES), English/French (EN/FR), and English/Portuguese (EN/PT) WMT 2019 test sets was obtained from PubMed and publisher websites. The authors were asked about their abstract writing practices in a survey. Results: The response rate was above 20{\%}. Authors reported that they are mainly native speakers of languages other than English. Although manual translation, sometimes via professional translation services, was commonly used for abstract translation, authors of articles in the EN/ES and EN/PT sets also relied on post-edited machine translation. Discussion: This study provides a characterization of MEDLINE authors{'} language skills and abstract writing practices. Conclusion: The information collected in this study will be used to inform test set design for the next WMT biomedical task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mao-etal-2020-jass,
    title = "{JASS}: {J}apanese-specific Sequence to Sequence Pre-training for Neural Machine Translation",
    author = "Mao, Zhuoyuan  and
      Cromieres, Fabien  and
      Dabre, Raj  and
      Song, Haiyue  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.454",
    pages = "3683--3691",
    abstract = "Neural machine translation (NMT) needs large parallel corpora for state-of-the-art translation quality. Low-resource NMT is typically addressed by transfer learning which leverages large monolingual or parallel corpora for pre-training. Monolingual pre-training approaches such as MASS (MAsked Sequence to Sequence) are extremely effective in boosting NMT quality for languages with small parallel corpora. However, they do not account for linguistic information obtained using syntactic analyzers which is known to be invaluable for several Natural Language Processing (NLP) tasks. To this end, we propose JASS, Japanese-specific Sequence to Sequence, as a novel pre-training alternative to MASS for NMT involving Japanese as the source or target language. JASS is joint BMASS (Bunsetsu MASS) and BRSS (Bunsetsu Reordering Sequence to Sequence) pre-training which focuses on Japanese linguistic units called bunsetsus. In our experiments on ASPEC Japanese{--}English and News Commentary Japanese{--}Russian translation we show that JASS can give results that are competitive with if not better than those given by MASS. Furthermore, we show for the first time that joint MASS and JASS pre-training gives results that significantly surpass the individual methods indicating their complementary nature. We will release our code, pre-trained models and bunsetsu annotated data as resources for researchers to use in their own NLP tasks.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ive-etal-2020-post,
    title = "A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?",
    author = "Ive, Julia  and
      Specia, Lucia  and
      Szoc, Sara  and
      Vanallemeersch, Tom  and
      Van den Bogaert, Joachim  and
      Farah, Eduardo  and
      Maroti, Christine  and
      Ventura, Artur  and
      Khalilov, Maxim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.455",
    pages = "3692--3697",
    abstract = "We introduce a machine translation dataset for three pairs of languages in the legal domain with post-edited high-quality neural machine translation and independent human references. The data was collected as part of the EU APE-QUEST project and comprises crawled content from EU websites with translation from English into three European languages: Dutch, French and Portuguese. Altogether, the data consists of around 31K tuples including a source sentence, the respective machine translation by a neural machine translation system, a post-edited version of such translation by a professional translator, and - where available - the original reference translation crawled from parallel language websites. We describe the data collection process, provide an analysis of the resulting post-edits and benchmark the data using state-of-the-art quality estimation and automatic post-editing models. One interesting by-product of our post-editing analysis suggests that neural systems built with publicly available general domain data can provide high-quality translations, even though comparison to human references suggests that this quality is quite low. This makes our dataset a suitable candidate to test evaluation metrics. The data is freely available as an ELRC-SHARE resource.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{goyal-etal-2020-linguistically,
    title = "Linguistically Informed {H}indi-{E}nglish Neural Machine Translation",
    author = "Goyal, Vikrant  and
      Mishra, Pruthwik  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.456",
    pages = "3698--3703",
    abstract = "Hindi-English Machine Translation is a challenging problem, owing to multiple factors including the morphological complexity and relatively free word order of Hindi, in addition to the lack of sufficient parallel training data. Neural Machine Translation (NMT) is a rapidly advancing MT paradigm and has shown promising results for many language pairs, especially in large training data scenarios. To overcome the data sparsity issue caused by the lack of large parallel corpora for Hindi-English, we propose a method to employ additional linguistic knowledge which is encoded by different phenomena depicted by Hindi. We generalize the embedding layer of the state-of-the-art Transformer model to incorporate linguistic features like POS tag, lemma and morph features to improve the translation performance. We compare the results obtained on incorporating this knowledge with the baseline systems and demonstrate significant performance improvements. Although, the Transformer NMT models have a strong efficacy to learn language constructs, we show that the usage of specific features further help in improving the translation performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nagata-morishita-2020-test,
    title = "A Test Set for Discourse Translation from {J}apanese to {E}nglish",
    author = "Nagata, Masaaki  and
      Morishita, Makoto",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.457",
    pages = "3704--3709",
    abstract = "We made a test set for Japanese-to-English discourse translation to evaluate the power of context-aware machine translation. For each discourse phenomenon, we systematically collected examples where the translation of the second sentence depends on the first sentence. Compared with a previous study on test sets for English-to-French discourse translation (CITATION), we needed different approaches to make the data because Japanese has zero pronouns and represents different senses in different characters. We improved the translation accuracy using context-aware neural machine translation, and the improvement mainly reflects the betterment of the translation of zero pronouns.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mueller-etal-2020-analysis,
    title = "An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages",
    author = "Mueller, Aaron  and
      Nicolai, Garrett  and
      McCarthy, Arya D.  and
      Lewis, Dylan  and
      Wu, Winston  and
      Yarowsky, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.458",
    pages = "3710--3718",
    abstract = "In this work, we explore massively multilingual low-resource neural machine translation. Using translations of the Bible (which have parallel structure across languages), we train models with up to 1,107 source languages. We create various multilingual corpora, varying the number and relatedness of source languages. Using these, we investigate the best ways to use this many-way aligned resource for multilingual machine translation. Our experiments employ a grammatically and phylogenetically diverse set of source languages during testing for more representative evaluations. We find that best practices in this domain are highly language-specific: adding more languages to a training set is often better, but too many harms performance{---}the best number depends on the source language. Furthermore, training on related languages can improve or degrade performance, depending on the language. As there is no one-size-fits-most answer, we find that it is critical to tailor one{'}s approach to the source language and its typology.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{doi-etal-2020-tddc,
    title = "{TDDC}: Timely Disclosure Documents Corpus",
    author = "Doi, Nobushige  and
      Oda, Yusuke  and
      Nakazawa, Toshiaki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.459",
    pages = "3719--3726",
    abstract = "In this paper, we describe the details of the Timely Disclosure Documents Corpus (TDDC). TDDC was prepared by manually aligning the sentences from past Japanese and English timely disclosure documents in PDF format published by companies listed on the Tokyo Stock Exchange. TDDC consists of approximately 1.4 million parallel sentences in Japanese and English. TDDC was used as the official dataset for the 6th Workshop on Asian Translation to encourage the development of machine translation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{karakanta-etal-2020-must,
    title = "{M}u{ST}-Cinema: a Speech-to-Subtitles corpus",
    author = "Karakanta, Alina  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.460",
    pages = "3727--3734",
    abstract = "Growing needs in localising audiovisual content in multiple languages through subtitles call for the development of automatic solutions for human subtitling. Neural Machine Translation (NMT) can contribute to the automatisation of subtitling, facilitating the work of human subtitlers and reducing turn-around times and related costs. NMT requires high-quality, large, task-specific training data. The existing subtitling corpora, however, are missing both alignments to the source language audio and important information about subtitle breaks. This poses a significant limitation for developing efficient automatic approaches for subtitling, since the length and form of a subtitle directly depends on the duration of the utterance. In this work, we present MuST-Cinema, a multilingual speech translation corpus built from TED subtitles. The corpus is comprised of (audio, transcription, translation) triplets. Subtitle breaks are preserved by inserting special symbols. We show that the corpus can be used to build models that efficiently segment sentences into subtitles and propose a method for annotating existing subtitling corpora with subtitle breaks, conforming to the constraint of length.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{castilho-etal-2020-context,
    title = "On Context Span Needed for Machine Translation Evaluation",
    author = "Castilho, Sheila  and
      Popovi{\'c}, Maja  and
      Way, Andy",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.461",
    pages = "3735--3742",
    abstract = "Despite increasing efforts to improve evaluation of machine translation (MT) by going beyond the sentence level to the document level, the definition of what exactly constitutes a {``}document level{''} is still not clear. This work deals with the context span necessary for a more reliable MT evaluation. We report results from a series of surveys involving three domains and 18 target languages designed to identify the necessary context span as well as issues related to it. Our findings indicate that, despite the fact that some issues and spans are strongly dependent on domain and on the target language, a number of common patterns can be observed so that general guidelines for context-aware MT evaluation can be drawn.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{siripragada-etal-2020-multilingual,
    title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages",
    author = "Siripragada, Shashank  and
      Philip, Jerin  and
      Namboodiri, Vinay P.  and
      Jawahar, C V",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.462",
    pages = "3743--3751",
    abstract = "We present sentence aligned parallel corpora across 10 Indian Languages - Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English - many of which are categorized as low resource. The corpora are compiled from online sources which have content shared across languages. The corpora presented significantly extends present resources that are either not large enough or are restricted to a specific domain (such as health). We also provide a separate test corpus compiled from an independent online source that can be independently used for validating the performance in 10 Indian languages. Alongside, we report on the methods of constructing such corpora using tools enabled by recent advances in machine translation and cross-lingual retrieval using deep neural network based methods.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{etchegoyhen-gete-2020-case,
    title = "To Case or not to case: Evaluating Casing Methods for Neural Machine Translation",
    author = "Etchegoyhen, Thierry  and
      Gete, Harritxu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.463",
    pages = "3752--3760",
    abstract = "We present a comparative evaluation of casing methods for Neural Machine Translation, to help establish an optimal pre- and post-processing methodology. We trained and compared system variants on data prepared with the main casing methods available, namely translation of raw data without case normalisation, lowercasing with recasing, truecasing, case factors and inline casing. Machine translation models were prepared on WMT 2017 English-German and English-Turkish datasets, for all translation directions, and the evaluation includes reference metric results as well as a targeted analysis of case preservation accuracy. Inline casing, where case information is marked along lowercased words in the training data, proved to be the optimal approach overall in these experiments.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{varadi-etal-2020-marcell,
    title = "The {MARCELL} Legislative Corpus",
    author = "V{\'a}radi, Tam{\'a}s  and
      Koeva, Svetla  and
      Yamalov, Martin  and
      Tadi{\'c}, Marko  and
      Sass, B{\'a}lint  and
      Nito{\'n}, Bart{\l}omiej  and
      Ogrodniczuk, Maciej  and
      P{\k{e}}zik, Piotr  and
      Barbu Mititelu, Verginica  and
      Ion, Radu  and
      Irimia, Elena  and
      Mitrofan, Maria  and
      P{\u{a}}i{\textcommabelow{s}}, Vasile  and
      Tufi{\textcommabelow{s}}, Dan  and
      Garab{\'\i}k, Radovan  and
      Krek, Simon  and
      Repar, Andraz  and
      Rihtar, Matja{\v{z}}  and
      Brank, Janez",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.464",
    pages = "3761--3768",
    abstract = "This article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{soares-etal-2020-parapat,
    title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts",
    author = "Soares, Felipe  and
      Stevenson, Mark  and
      Bartolome, Diego  and
      Zaretskaya, Anna",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.465",
    pages = "3769--3774",
    abstract = "The Google Patents is one of the main important sources of patents information. A striking characteristic is that many of its abstracts are presented in more than one language, thus making it a potential source of parallel corpora. This article presents the development of a parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned. We demonstrate the capabilities of our corpus by training Neural Machine Translation (NMT) models for the main 9 language pairs, with a total of 18 models. Our parallel corpus is freely available in TSV format and with a SQLite database, with complementary information regarding patent metadata.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{liu-zhang-2020-corpora,
    title = "Corpora for Document-Level Neural Machine Translation",
    author = "Liu, Siyou  and
      Zhang, Xiaojun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.466",
    pages = "3775--3781",
    abstract = "Instead of translating sentences in isolation, document-level machine translation aims to capture discourse dependencies across sentences by considering a document as a whole. In recent years, there have been more interests in modelling larger context for the state-of-the-art neural machine translation (NMT). Although various document-level NMT models have shown significant improvements, there nonetheless exist three main problems: 1) compared with sentence-level translation tasks, the data for training robust document-level models are relatively low-resourced; 2) experiments in previous work are conducted on their own datasets which vary in size, domain and language; 3) proposed approaches are implemented on distinct NMT architectures such as recurrent neural networks (RNNs) and self-attention networks (SANs). In this paper, we aims to alleviate the low-resource and under-universality problems for document-level NMT. First, we collect a large number of existing document-level corpora, which covers 7 language pairs and 6 domains. In order to address resource sparsity, we construct a novel document parallel corpus in Chinese-Portuguese, which is a non-English-centred and low-resourced language pair. Besides, we implement and evaluate the commonly-cited document-level method on top of the advanced Transformer model with universal settings. Finally, we not only demonstrate the effectiveness and universality of document-level NMT, but also release the preprocessed data, source code and trained models for comparison and reproducibility.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aulamo-etal-2020-opustools,
    title = "{O}pus{T}ools and Parallel Corpus Diagnostics",
    author = {Aulamo, Mikko  and
      Sulubacak, Umut  and
      Virpioja, Sami  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.467",
    pages = "3782--3789",
    abstract = "This paper introduces OpusTools, a package for downloading and processing parallel corpora included in the OPUS corpus collection. The package implements tools for accessing compressed data in their archived release format and make it possible to easily convert between common formats. OpusTools also includes tools for language identification and data filtering as well as tools for importing data from various sources into the OPUS format. We show the use of these tools in parallel corpus creation and data diagnostics. The latter is especially useful for the identification of potential problems and errors in the extensive data set. Using these tools, we can now monitor the validity of data sets and improve the overall quality and consitency of the data collection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fonteyne-etal-2020-literary,
    title = "Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an {NMT}-Translated Detective Novel on Document Level",
    author = "Fonteyne, Margot  and
      Tezcan, Arda  and
      Macken, Lieve",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.468",
    pages = "3790--3798",
    abstract = "Several studies (covering many language pairs and translation tasks) have demonstrated that translation quality has improved enormously since the emergence of neural machine translation systems. This raises the question whether such systems are able to produce high-quality translations for more creative text types such as literature and whether they are able to generate coherent translations on document level. Our study aimed to investigate these two questions by carrying out a document-level evaluation of the raw NMT output of an entire novel. We translated Agatha Christie{'}s novel The Mysterious Affair at Styles with Google{'}s NMT system from English into Dutch and annotated it in two steps: first all fluency errors, then all accuracy errors. We report on the overall quality, determine the remaining issues, compare the most frequent error types to those in general-domain MT, and investigate whether any accuracy and fluency errors co-occur regularly. Additionally, we assess the inter-annotator agreement on the first chapter of the novel.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{etchegoyhen-gete-2020-handle,
    title = "Handle with Care: A Case Study in Comparable Corpora Exploitation for Neural Machine Translation",
    author = "Etchegoyhen, Thierry  and
      Gete, Harritxu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.469",
    pages = "3799--3807",
    abstract = "We present the results of a case study in the exploitation of comparable corpora for Neural Machine Translation. A large comparable corpus for Basque-Spanish was prepared, on the basis of independently-produced news by the Basque public broadcaster EiTB, and we discuss the impact of various techniques to exploit the original data in order to determine optimal variants of the corpus. In particular, we show that filtering in terms of alignment thresholds and length-difference outliers has a significant impact on translation quality. The impact of tags identifying comparable data in the training datasets is also evaluated, with results indicating that this technique might be useful to help the models discriminate noisy information, in the form of informational imbalance between aligned sentences. The final corpus was prepared according to the experimental results and is made available to the scientific community for research purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tiedemann-etal-2020-fiskmo,
    title = {The {FISKM{\"O}} Project: Resources and Tools for {F}innish-{S}wedish Machine Translation and Cross-Linguistic Research},
    author = {Tiedemann, J{\"o}rg  and
      Nieminen, Tommi  and
      Aulamo, Mikko  and
      Kanerva, Jenna  and
      Leino, Akseli  and
      Ginter, Filip  and
      Papula, Niko},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.470",
    pages = "3808--3815",
    abstract = {This paper presents FISKM{\"O}, a project that focuses on the development of resources and tools for cross-linguistic research and machine translation between Finnish and Swedish. The goal of the project is the compilation of a massive parallel corpus out of translated material collected from web sources, public and private organisations and language service providers in Finland with its two official languages. The project also aims at the development of open and freely accessible translation services for those two languages for the general purpose and for domain-specific use. We have released new data sets with over 3 million translation units, a benchmark test set for MT development, pre-trained neural MT models with high coverage and competitive performance and a self-contained MT plugin for a popular CAT tool. The latter enables offline translation without dependencies on external services making it possible to work with highly sensitive data without compromising security concerns.},
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zaninello-birch-2020-multiword,
    title = "Multiword Expression aware Neural Machine Translation",
    author = "Zaninello, Andrea  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.471",
    pages = "3816--3825",
    abstract = "Multiword Expressions (MWEs) are a frequently occurring phenomenon found in all natural languages that is of great importance to linguistic theory, natural language processing applications, and machine translation systems. Neural Machine Translation (NMT) architectures do not handle these expressions well and previous studies have rarely addressed MWEs in this framework. In this work, we show that annotation and data augmentation, using external linguistic resources, can improve both translation of MWEs that occur in the source, and the generation of MWEs on the target, and increase performance by up to 5.09 BLEU points on MWE test sets. We also devise a MWE score to specifically assess the quality of MWE translation which agrees with human evaluation. We make available the MWE score implementation {--} along with MWE-annotated training sets and corpus-based lists of MWEs {--} for reproduction and extension.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kim-colineau-2020-enhanced,
    title = "An Enhanced Mapping Scheme of the Universal Part-Of-Speech for {K}orean",
    author = "Kim, Myung Hee  and
      Colineau, Nathalie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.472",
    pages = "3826--3833",
    abstract = "When mapping a language specific Part-Of-Speech (POS) tag set to the Universal POS tag set (UPOS), it is critical to consider the individual language{'}s linguistic features and the UPOS definitions. In this paper, we present an enhanced Sejong POS mapping to the UPOS in accordance with the Korean linguistic typology and the substantive definitions of the UPOS categories. This work updated one third of the Sejong POS mapping to the UPOS. We also introduced a new mapping for the KAIST POS tag set, another widely used Korean POS tag set, to the UPOS.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alkhairy-etal-2020-finite,
    title = "Finite State Machine Pattern-Root {A}rabic Morphological Generator, Analyzer and Diacritizer",
    author = "Alkhairy, Maha  and
      Jafri, Afshan  and
      Smith, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.473",
    pages = "3834--3841",
    abstract = "We describe and evaluate the Finite-State Arabic Morphologizer (FSAM) {--} a concatenative (prefix-stem-suffix) and templatic (root- pattern) morphologizer that generates and analyzes undiacritized Modern Standard Arabic (MSA) words, and diacritizes them. Our bidirectional unified-architecture finite state machine (FSM) is based on morphotactic MSA grammatical rules. The FSM models the root-pattern structure related to semantics and syntax, making it readily scalable unlike stem-tabulations in prevailing systems. We evaluate the coverage and accuracy of our model, with coverage being percentage of words in Tashkeela (a large corpus) that can be analyzed. Accuracy is computed against a gold standard, comprising words and properties, created from the intersection of UD PADT treebank and Tashkeela. Coverage of analysis (extraction of root and properties from word) is 82{\%}. Accuracy results are: root computed from a word (92{\%}), word generation from a root (100{\%}), non-root properties of a word (97{\%}), and diacritization (84{\%}). FSAM{'}s non-root results match or surpass MADAMIRA{'}s, and root result comparisons are not made because of the concatenative nature of publicly available morphologizers.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{keleg-etal-2020-unsupervised,
    title = "An Unsupervised Method for Weighting Finite-state Morphological Analyzers",
    author = "Keleg, Amr  and
      Tyers, Francis  and
      Howell, Nick  and
      Pirinen, Tommi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.474",
    pages = "3842--3850",
    abstract = "Morphological analysis is one of the tasks that have been studied for years. Different techniques have been used to develop models for performing morphological analysis. Models based on finite state transducers have proved to be more suitable for languages with low available resources. In this paper, we have developed a method for weighting a morphological analyzer built using finite state transducers in order to disambiguate its results. The method is based on a word2vec model that is trained in a completely unsupervised way using raw untagged corpora and is able to capture the semantic meaning of the words. Most of the methods used for disambiguating the results of a morphological analyzer relied on having tagged corpora that need to manually built. Additionally, the method developed uses information about the token irrespective of its context unlike most of the other techniques that heavily rely on the word{'}s context to disambiguate its set of candidate analyses.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bollegala-etal-2020-language,
    title = "Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction",
    author = "Bollegala, Danushka  and
      Kiryo, Ryuichi  and
      Tsujino, Kosuke  and
      Yukawa, Haruki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.475",
    pages = "3851--3860",
    abstract = "Language-independent tokenisation (LIT) methods that do not require labelled language resources or lexicons have recently gained popularity because of their applicability in resource-poor languages. Moreover, they compactly represent a language using a fixed size vocabulary and can efficiently handle unseen or rare words. On the other hand, language-specific tokenisation (LST) methods have a long and established history, and are developed using carefully created lexicons and training resources. Unlike subtokens produced by LIT methods, LST methods produce valid morphological subwords. Despite the contrasting trade-offs between LIT vs. LST methods, their performance on downstream NLP tasks remain unclear. In this paper, we empirically compare the two approaches using semantic similarity measurement as an evaluation task across a diverse set of languages. Our experimental results covering eight languages show that LST consistently outperforms LIT when the vocabulary size is large, but LIT can produce comparable or better results than LST in many languages with comparatively smaller (i.e. less than 100K words) vocabulary sizes, encouraging the use of LIT when language-specific resources are unavailable, incomplete or a smaller model is required. Moreover, we find that smoothed inverse frequency (SIF) to be an accurate method to create word embeddings from subword embeddings for multilingual semantic similarity prediction tasks. Further analysis of the nearest neighbours of tokens show that semantically and syntactically related tokens are closely embedded in subword embedding spaces.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nikiforos-kermanidis-2020-supervised,
    title = "A Supervised Part-Of-Speech Tagger for the {G}reek Language of the Social Web",
    author = "Nikiforos, Maria Nefeli  and
      Kermanidis, Katia Lida",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.476",
    pages = "3861--3867",
    abstract = "The increasing volume of communication via microblogging messages on social networks has created the need for efficient Natural Language Processing (NLP) tools, especially for unstructured text processing. Extracting information from unstructured social text is one of the most demanding NLP tasks. This paper presents the first part-of-speech tagged data set of social text in Greek, as well as the first supervised part-of-speech tagger developed for such data sets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jonker-etal-2020-bag,
    title = "Bag {\&} Tag{'}em - A New {D}utch Stemmer",
    author = "Jonker, Anne  and
      de Ruijt, Corn{\'e}  and
      de Gruijl, Jornt",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.477",
    pages = "3868--3876",
    abstract = "We propose a novel stemming algorithm that is both robust and accurate compared to state-of-the-art solutions, yet addresses several of the problems that current stemmers face in the Dutch language. The main issue is that most current stemmers cannot handle 3rd person singular forms of verbs and many irregular words and conjugations, unless a (nearly) brute-force approach is used. Our algorithm combines a new tagging module with a stemmer that uses tag-specific sets of rigid rules: the Bag {\&} Tag{'}em (BT) algorithm. The tagging module is developed and evaluated using three algorithms: Multinomial Logistic Regression (MLR), Neural Network (NN) and Extreme Gradient Boosting (XGB). The stemming module{'}s performance is compared with that of current state-of-the-art stemming algorithms for the Dutch Language. Even though there is still room for improvement, the new BT algorithm performs well in the sense that it is more accurate than the current stemmers and faster than brute-force-like algorithms. The code and data used for this paper can be found at: https://github.com/Anne-Jonker/Bag-Tag-em.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hathout-etal-2020-glawinette,
    title = "{G}lawinette: a Linguistically Motivated Derivational Description of {F}rench Acquired from {GLAWI}",
    author = "Hathout, Nabil  and
      Sajous, Franck  and
      Calderone, Basilio  and
      Namer, Fiammetta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.478",
    pages = "3877--3885",
    abstract = "Glawinette is a derivational lexicon of French that will be used to feed the D{\'e}monette database. It has been created from the GLAWI machine readable dictionary. We collected couples of words from the definitions and the morphological sections of the dictionary and then selected the ones that form regular formal analogies and that instantiate frequent enough formal patterns. The graph structure of the morphological families has then been used to identify for each couple of lexemes derivational patterns that are close to the intuition of the morphologists.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sahala-etal-2020-babyfst,
    title = "{B}aby{FST} - Towards a Finite-State Based Computational Model of Ancient Babylonian",
    author = "Sahala, Aleksi  and
      Silfverberg, Miikka  and
      Arppe, Antti  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.479",
    pages = "3886--3894",
    abstract = "Akkadian is a fairly well resourced extinct language that does not yet have a comprehensive morphological analyzer available. In this paper we describe a general finite-state based morphological model for Babylonian, a southern dialect of the Akkadian language, that can achieve a coverage up to 97.3{\%} and recall up to 93.7{\%} on lemmatization and POS-tagging task on token level from a transcribed input. Since Akkadian word forms exhibit a high degree of morphological ambiguity, in that only 20.1{\%} of running word tokens receive a single unambiguous analysis, we attempt a first pass at weighting our finite-state transducer, using existing extensive Akkadian corpora which have been partially validated for their lemmas and parts-of-speech but not the entire morphological analyses. The resultant weighted finite-state transducer yields a moderate improvement so that for 57.4{\%} of the word tokens the highest ranked analysis is the correct one. We conclude with a short discussion on how morphological ambiguity in the analysis of Akkadian could be further reduced with improvements in the training data used in weighting the finite-state transducer as well as through other, context-based techniques.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khalifa-etal-2020-morphological,
    title = "Morphological Analysis and Disambiguation for {G}ulf {A}rabic: The Interplay between Resources and Methods",
    author = "Khalifa, Salam  and
      Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.480",
    pages = "3895--3904",
    abstract = "In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic. We use an existing state-of-the-art morphological disambiguation system to investigate the effects of different data sizes and different combinations of morphological analyzers for Modern Standard Arabic, Egyptian Arabic, and Gulf Arabic. We find that in very low settings, morphological analyzers help boost the performance of the full morphological disambiguation task. However, as the size of resources increase, the value of the morphological analyzers decreases.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{metheniti-neumann-2020-wikinflection,
    title = "Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus",
    author = "Metheniti, Eleni  and
      Neumann, Guenter",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.481",
    pages = "3905--3912",
    abstract = "Multilingual, inflectional corpora are a scarce resource in the NLP community, especially corpora with annotated morpheme boundaries. We are evaluating a generated, multilingual inflectional corpus with morpheme boundaries, generated from the English Wiktionary (Metheniti and Neumann, 2018), against the largest, multilingual, high-quality inflectional corpus of the UniMorph project (Kirov et al., 2018). We confirm that the generated Wikinflection corpus is not of such quality as UniMorph, but we were able to extract a significant amount of words from the intersection of the two corpora. Our Wikinflection corpus benefits from the morpheme segmentations of Wiktionary/Wikinflection and from the manually-evaluated morphological feature tags of the UniMorph project, and has 216K lemmas and 5.4M word forms, in a total of 68 languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tran-etal-2020-introducing,
    title = "Introducing a Large-Scale Dataset for {V}ietnamese {POS} Tagging on Conversational Texts",
    author = "Tran, Oanh  and
      Pham, Tu  and
      Dang, Vu  and
      Nguyen, Bang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.482",
    pages = "3913--3921",
    abstract = "This paper introduces a large-scale human-labeled dataset for the Vietnamese POS tagging task on conversational texts. To this end, wepropose a new tagging scheme (with 36 POS tags) consisting of exclusive tags for special phenomena of conversational words, developthe annotation guideline and manually annotate 16.310K sentences using this guideline. Based on this corpus, a series of state-of-the-art tagging methods has been conducted to estimate their performances. Experimental results showed that the Conditional Random Fields model using both automatically learnt features from deep neural networks and handcrafted features yielded the best performance. Thismodel achieved 93.36{\%} in the accuracy score which is 1.6{\%} and 2.7{\%} higher than the model using either handcrafted features orautomatically-learnt features, respectively. This result is also a little bit higher than the model of fine-tuning BERT by 0.94{\%} in theaccuracy score. The performance measured on each POS tag is also very high with {\textgreater}90{\%} in the F1 score for 20 POS tags and {\textgreater}80{\%}in the F1 score for 11 POS tags. This work provides the public dataset and preliminary results for follow-up research on this interesting direction.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mccarthy-etal-2020-unimorph,
    title = "{U}ni{M}orph 3.0: {U}niversal {M}orphology",
    author = "McCarthy, Arya D.  and
      Kirov, Christo  and
      Grella, Matteo  and
      Nidhi, Amrit  and
      Xia, Patrick  and
      Gorman, Kyle  and
      Vylomova, Ekaterina  and
      Mielke, Sabrina J.  and
      Nicolai, Garrett  and
      Silfverberg, Miikka  and
      Arkhangelskiy, Timofey  and
      Krizhanovsky, Nataly  and
      Krizhanovsky, Andrew  and
      Klyachko, Elena  and
      Sorokin, Alexey  and
      Mansfield, John  and
      Ern{\v{s}}treits, Valts  and
      Pinter, Yuval  and
      Jacobs, Cassandra L.  and
      Cotterell, Ryan  and
      Hulden, Mans  and
      Yarowsky, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.483",
    pages = "3922--3931",
    abstract = "The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological paradigms for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. We have implemented several improvements to the extraction pipeline which creates most of our data, so that it is both more complete and more correct. We have added 66 new languages, as well as new parts of speech for 12 languages. We have also amended the schema in several ways. Finally, we present three new community tools: two to validate data for resource creators, and one to make morphological data available from the command line. UniMorph is based at the Center for Language and Speech Processing (CLSP) at Johns Hopkins University in Baltimore, Maryland. This paper details advances made to the schema, tooling, and dissemination of project resources since the UniMorph 2.0 release described at LREC 2018.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mikelenic-tadic-2020-building,
    title = "Building the {S}panish-{C}roatian Parallel Corpus",
    author = "Mikeleni{\'c}, Bojana  and
      Tadi{\'c}, Marko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.484",
    pages = "3932--3936",
    abstract = "This paper describes the building of the first Spanish-Croatian unidirectional parallel corpus, which has been constructed at the Faculty of Humanities and Social Sciences of the University of Zagreb. The corpus is comprised of eleven Spanish novels and their translations to Croatian done by six different professional translators. All the texts were published between 1999 and 2012. The corpus has more than 2 Mw, with approximately 1 Mw for each language. It was automatically sentence segmented and aligned, as well as manually post-corrected, and contains 71,778 translation units. In order to protect the copyright and to make the corpus available under permissive CC-BY licence, the aligned translation units are shuffled. This limits the usability of the corpus for research of language units at sentence and lower language levels only. There are two versions of the corpus in TMX format that will be available for download through META-SHARE and CLARIN ERIC infrastructure. The former contains plain TMX, while the latter is lemmatised and POS-tagged and stored in the aTMX format.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vodolazsky-2020-derivbase,
    title = "{D}eriv{B}ase.{R}u: a Derivational Morphology Resource for {R}ussian",
    author = "Vodolazsky, Daniil",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.485",
    pages = "3937--3943",
    abstract = "Russian morphology has been studied for decades, but there is still no large high coverage resource that contains the derivational families (groups of words that share the same root) of Russian words. The number of words used in different areas of the language grows rapidly, thus the human-made dictionaries published long time ago cannot cover the neologisms and the domain-specific lexicons. To fill such resource gap, we have developed a rule-based framework for deriving words and we applied it to build a derivational morphology resource named DerivBase.Ru, which we introduce in this paper.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gronroos-etal-2020-morfessor,
    title = "{M}orfessor {EM}+{P}rune: Improved Subword Segmentation with Expectation Maximization and Pruning",
    author = {Gr{\"o}nroos, Stig-Arne  and
      Virpioja, Sami  and
      Kurimo, Mikko},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.486",
    pages = "3944--3953",
    abstract = "Data-driven segmentation of words into subword units has been used in various natural language processing applications such as automatic speech recognition and statistical machine translation for almost 20 years. Recently it has became more widely adopted, as models based on deep neural networks often benefit from subword units even for morphologically simpler languages. In this paper, we discuss and compare training algorithms for a unigram subword model, based on the Expectation Maximization algorithm and lexicon pruning. Using English, Finnish, North Sami, and Turkish data sets, we show that this approach is able to find better solutions to the optimization problem defined by the Morfessor Baseline model than its original recursive training algorithm. The improved optimization also leads to higher morphological segmentation accuracy when compared to a linguistic gold standard. We publish implementations of the new algorithms in the widely-used Morfessor software package.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{stankovic-etal-2020-machine,
    title = "Machine Learning and Deep Neural Network-Based Lemmatization and Morphosyntactic Tagging for {S}erbian",
    author = "Stankovic, Ranka  and
      {\v{S}}andrih, Branislava  and
      Krstev, Cvetana  and
      Utvi{\'c}, Milo{\v{s}}  and
      Skoric, Mihailo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.487",
    pages = "3954--3962",
    abstract = "The training of new tagger models for Serbian is primarily motivated by the enhancement of the existing tagset with the grammatical category of a gender. The harmonization of resources that were manually annotated within different projects over a long period of time was an important task, enabled by the development of tools that support partial automation. The supporting tools take into account different taggers and tagsets. This paper focuses on TreeTagger and spaCy taggers, and the annotation schema alignment between Serbian morphological dictionaries, MULTEXT-East and Universal Part-of-Speech tagset. The trained models will be used to publish the new version of the Corpus of Contemporary Serbian as well as the Serbian literary corpus. The performance of developed taggers were compared and the impact of training set size was investigated, which resulted in around 98{\%} PoS-tagging precision per token for both new models. The sr{\_}basic annotated dataset will also be published.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nicolai-etal-2020-fine,
    title = "Fine-grained Morphosyntactic Analysis and Generation Tools for More Than One Thousand Languages",
    author = "Nicolai, Garrett  and
      Lewis, Dylan  and
      McCarthy, Arya D.  and
      Mueller, Aaron  and
      Wu, Winston  and
      Yarowsky, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.488",
    pages = "3963--3972",
    abstract = "Exploiting the broad translation of the Bible into the world{'}s languages, we train and distribute morphosyntactic tools for approximately one thousand languages, vastly outstripping previous distributions of tools devoted to the processing of inflectional morphology. Evaluation of the tools on a subset of available inflectional dictionaries demonstrates strong initial models, supplemented and improved through ensembling and dictionary-based reranking. Likewise, a novel type-to-token based evaluation metric allows us to confirm that models generalize well across rare and common forms alike",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{balabel-etal-2020-cairo,
    title = "Cairo Student Code-Switch ({CSCS}) Corpus: An Annotated {E}gyptian {A}rabic-{E}nglish Corpus",
    author = {Balabel, Mohamed  and
      Hamed, Injy  and
      Abdennadher, Slim  and
      Vu, Ngoc Thang  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.489",
    pages = "3973--3977",
    abstract = "Code-switching has become a prevalent phenomenon across many communities. It poses a challenge to NLP researchers, mainly due to the lack of available data needed for training and testing applications. In this paper, we introduce a new resource: a corpus of Egyptian- Arabic code-switch speech data that is fully tokenized, lemmatized and annotated for part-of-speech tags. Beside the corpus itself, we provide annotation guidelines to address the unique challenges of annotating code-switch data. Another challenge that we address is the fact that Egyptian Arabic orthography and grammar are not standardized.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sorokin-2020-getting,
    title = "Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation",
    author = "Sorokin, Alexey",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.490",
    pages = "3978--3983",
    abstract = "We investigate how to improve quality of low-resource morphological inflection without annotating more data. We examine two methods, language models and data augmentation. We show that the model whose decoder that additionally uses the states of the langauge model improves the model quality by 1.5{\%} in combination with both baselines. We also demonstrate that the augmentation of data improves performance by 9{\%} in average when adding 1000 artificially generated word forms to the dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ozenc-solak-2020-visual,
    title = "Visual Modeling of {T}urkish Morphology",
    author = {{\"O}zen{\c{c}}, Berke  and
      Solak, Ercan},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.491",
    pages = "3984--3990",
    abstract = "In this paper, we describe the steps in a visual modeling of Turkish morphology using diagramming tools. We aimed to make modeling easier and more maintainable while automating much of the code generation. We released the resulting analyzer, MorTur, and the diagram conversion tool, DiaMor as free, open-source utilities. MorTur analyzer is also publicly available on its web page as a web service. MorTur and DiaMor are part of our ongoing efforts in building a set of natural language processing tools for Turkic languages under a consistent framework.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dadason-etal-2020-kvistur,
    title = "Kvistur 2.0: a {B}i{LSTM} Compound Splitter for {I}celandic",
    author = "Da{\dh}ason, J{\'o}n  and
      Mollberg, David  and
      Loftsson, Hrafn  and
      Bjarnad{\'o}ttir, Krist{\'\i}n",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.492",
    pages = "3991--3995",
    abstract = "In this paper, we present a character-based BiLSTM model for splitting Icelandic compound words, and show how varying amounts of training data affects the performance of the model. Compounding is highly productive in Icelandic, and new compounds are constantly being created. This results in a large number of out-of-vocabulary (OOV) words, negatively impacting the performance of many NLP tools. Our model is trained on a dataset of 2.9 million unique word forms and their constituent structures from the Database of Icelandic Morphology. The model learns how to split compound words into two parts and can be used to derive the constituent structure of any word form. Knowing the constituent structure of a word form makes it possible to generate the optimal split for a given task, e.g., a full split for subword tokenization, or, in the case of part-of-speech tagging, splitting an OOV word until the largest known morphological head is found. The model outperforms other previously published methods when evaluated on a corpus of manually split word forms. This method has been integrated into Kvistur, an Icelandic compound word analyzer.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mott-etal-2020-morphological,
    title = "Morphological Segmentation for Low Resource Languages",
    author = "Mott, Justin  and
      Bies, Ann  and
      Strassel, Stephanie  and
      Kodner, Jordan  and
      Richter, Caitlin  and
      Xu, Hongzhi  and
      Marcus, Mitchell",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.493",
    pages = "3996--4002",
    abstract = "This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program. The data consists of approximately 2000 tokens annotated for morphological segmentation in each of 9 low resource languages, along with root information for 7 of the languages. The languages annotated show a broad diversity of typological features. A minimal annotation scheme for segmentation was developed such that it could capture the patterns of a wide range of languages and also be performed reliably by non-linguist annotators. The basic annotation guidelines were designed to be language-independent, but included language-specific morphological paradigms and other specifications. The resulting annotated corpus is designed to support and stimulate the development of unsupervised morphological segmenters and analyzers by providing a gold standard for their evaluation on a more typologically diverse set of languages than has previously been available. By providing root annotation, this corpus is also a step toward supporting research in identifying richer morphological structures than simple morpheme boundaries.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wenzek-etal-2020-ccnet,
    title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data",
    author = "Wenzek, Guillaume  and
      Lachaux, Marie-Anne  and
      Conneau, Alexis  and
      Chaudhary, Vishrav  and
      Guzm{\'a}n, Francisco  and
      Joulin, Armand  and
      Grave, Edouard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.494",
    pages = "4003--4012",
    abstract = "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{doval-etal-2020-robustness,
    title = "On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning",
    author = "Doval, Yerai  and
      Camacho-Collados, Jose  and
      Espinosa Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.495",
    pages = "4013--4023",
    abstract = "Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. Recent developments which construct these embeddings by aligning monolingual spaces have shown that accurate alignments can be obtained with little or no supervision, which usually comes in the form of bilingual dictionaries. However, the focus has been on a particular controlled scenario for evaluation, and there is no strong evidence on how current state-of-the-art systems would fare with noisy text or for language pairs with major linguistic differences. In this paper we present an extensive evaluation over multiple cross-lingual embedding models, analyzing their strengths and limitations with respect to different variables such as target language, training corpora and amount of supervision. Our conclusions put in doubt the view that high-quality cross-lingual embeddings can always be learned without much supervision.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zhai-etal-2020-building,
    title = "Building an {E}nglish-{C}hinese Parallel Corpus Annotated with Sub-sentential Translation Techniques",
    author = "Zhai, Yuming  and
      Liu, Lufei  and
      Zhong, Xinyi  and
      Illouz, Gbariel  and
      Vilnat, Anne",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.496",
    pages = "4024--4033",
    abstract = "Human translators often resort to different non-literal translation techniques besides the literal translation, such as idiom equivalence, generalization, particularization, semantic modulation, etc., especially when the source and target languages have different and distant origins. Translation techniques constitute an important subject in translation studies, which help researchers to understand and analyse translated texts. However, they receive less attention in developing Natural Language Processing (NLP) applications. To fill this gap, one of our long term objectives is to have a better semantic control of extracting paraphrases from bilingual parallel corpora. Based on this goal, we suggest this hypothesis: it is possible to automatically recognize different sub-sentential translation techniques. For this original task, since there is no dedicated data set for English-Chinese, we manually annotated a parallel corpus of eleven genres. Fifty sentence pairs for each genre have been annotated in order to consolidate our annotation guidelines. Based on this data set, we conducted an experiment to classify between literal and non-literal translations. The preliminary results confirm our hypothesis. The corpus and code are available. We hope that this annotated corpus will be useful for linguistic contrastive studies and for fine-grained evaluation of NLP tasks, such as automatic word alignment and machine translation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nivre-etal-2020-universal,
    title = "{U}niversal {D}ependencies v2: An Evergrowing Multilingual Treebank Collection",
    author = "Nivre, Joakim  and
      de Marneffe, Marie-Catherine  and
      Ginter, Filip  and
      Haji{\v{c}}, Jan  and
      Manning, Christopher D.  and
      Pyysalo, Sampo  and
      Schuster, Sebastian  and
      Tyers, Francis  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.497",
    pages = "4034--4043",
    abstract = "Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the universal guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{serrat-roozen-martinez-martinez-2020-empac,
    title = "{EMPAC}: an {E}nglish{--}{S}panish Corpus of Institutional Subtitles",
    author = "Serrat Roozen, Iris  and
      Mart{\'\i}nez Mart{\'\i}nez, Jos{\'e} Manuel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.498",
    pages = "4044--4053",
    abstract = "The EuroparlTV Multimedia Parallel Corpus (EMPAC) is a collection of subtitles in English and Spanish for videos from the EuropeanParliament{'}s Multimedia Centre. The corpus has been compiled with the EMPAC toolkit. The aim of this corpus is to provide a resource to study institutional subtitling on the one hand, and, on the other hand, facilitate the analysis of web accessibility to institutional multimedia content. The corpus covers a time span from 2009 to 2017, it is made up of 4,000 texts amounting to two and half millions of tokens for every language, corresponding to approximately 280 hours of video. This paper provides 1) a review of related corpora; 2) a revision of typical compilation methodologies of subtitle corpora; 3) a detailed account of the corpus compilation methodology followed; and, 4) a description of the corpus. In the conclusion, the key findings are summarised regarding formal aspects of the subtitles conditioning the accessibility to the multimedia content of the EuroparlTV.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kuriyozov-etal-2020-cross,
    title = "Cross-Lingual Word Embeddings for {T}urkic Languages",
    author = "Kuriyozov, Elmurod  and
      Doval, Yerai  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.499",
    pages = "4054--4062",
    abstract = "There has been an increasing interest in learning cross-lingual word embeddings to transfer knowledge obtained from a resource-rich language, such as English, to lower-resource languages for which annotated data is scarce, such as Turkish, Russian, and many others. In this paper, we present the first viability study of established techniques to align monolingual embedding spaces for Turkish, Uzbek, Azeri, Kazakh and Kyrgyz, members of the Turkic family which is heavily affected by the low-resource constraint. Those techniques are known to require little explicit supervision, mainly in the form of bilingual dictionaries, hence being easily adaptable to different domains, including low-resource ones. We obtain new bilingual dictionaries and new word embeddings for these languages and show the steps for obtaining cross-lingual word embeddings using state-of-the-art techniques. Then, we evaluate the results using the bilingual dictionary induction task. Our experiments confirm that the obtained bilingual dictionaries outperform previously-available ones, and that word embeddings from a low-resource language can benefit from resource-rich closely-related languages when they are aligned together. Furthermore, evaluation on an extrinsic task (Sentiment analysis on Uzbek) proves that monolingual word embeddings can, although slightly, benefit from cross-lingual alignments.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kanayama-iwamoto-2020-universal,
    title = "How Universal are {U}niversal {D}ependencies? Exploiting Syntax for Multilingual Clause-level Sentiment Detection",
    author = "Kanayama, Hiroshi  and
      Iwamoto, Ran",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.500",
    pages = "4063--4073",
    abstract = "This paper investigates clause-level sentiment detection in a multilingual scenario. Aiming at a high-precision, fine-grained, configurable, and non-biased system for practical use cases, we have designed a pipeline method that makes the most of syntactic structures based on Universal Dependencies, avoiding machine-learning approaches that may cause obstacles to our purposes. We achieved high precision in sentiment detection for 17 languages and identified the advantages of common syntactic structures as well as issues stemming from structural differences on Universal Dependencies. In addition to reusable tips for handling multilingual syntax, we provide a parallel benchmarking data set for further research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ulcar-etal-2020-multilingual,
    title = "Multilingual Culture-Independent Word Analogy Datasets",
    author = {Ul{\v{c}}ar, Matej  and
      Vaik, Kristiina  and
      Lindstr{\"o}m, Jessica  and
      Dailid{\.e}nait{\.e}, Milda  and
      Robnik-{\v{S}}ikonja, Marko},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.501",
    pages = "4074--4080",
    abstract = "In text processing, deep neural networks mostly use word embeddings as an input. Embeddings have to ensure that relations between words are reflected through distances in a high-dimensional numeric space. To compare the quality of different text embeddings, typically, we use benchmark datasets. We present a collection of such datasets for the word analogy task in nine languages: Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian, and Swedish. We designed the monolingual analogy task to be much more culturally independent and also constructed cross-lingual analogy datasets for the involved languages. We present basic statistics of the created datasets and their initial evaluation using fastText embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{costa-jussa-etal-2020-gebiotoolkit,
    title = "{G}e{B}io{T}oolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of {W}ikipedia Biographies",
    author = "Costa-juss{\`a}, Marta R.  and
      Li Lin, Pau  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.502",
    pages = "4081--4088",
    abstract = "We introduce GeBioToolkit, a tool for extracting multilingual parallel corpora at sentence level, with document and gender information from Wikipedia biographies. Despite the gender inequalities present in Wikipedia, the toolkit has been designed to extract corpus balanced in gender. While our toolkit is customizable to any number of languages (and different domains), in this work we present a corpus of 2,000 sentences in English, Spanish and Catalan, which has been post-edited by native speakers to become a high-quality dataset for machine translation evaluation. While GeBioCorpus aims at being one of the first non-synthetic gender-balanced test datasets, GeBioToolkit aims at paving the path to standardize procedures to produce gender-balanced datasets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{johnson-etal-2020-spice,
    title = "{S}pi{CE}: A New Open-Access Corpus of Conversational Bilingual Speech in {C}antonese and {E}nglish",
    author = "Johnson, Khia A.  and
      Babel, Molly  and
      Fong, Ivan  and
      Yiu, Nancy",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.503",
    pages = "4089--4095",
    abstract = "This paper describes the design, collection, orthographic transcription, and phonetic annotation of SpiCE, a new corpus of conversational Cantonese-English bilingual speech recorded in Vancouver, Canada. The corpus includes high-quality recordings of 34 early bilinguals in both English and Cantonese{---}to date, 27 have been recorded for a total of 19 hours of participant speech. Participants completed a sentence reading task, storyboard narration, and conversational interview in each language. Transcription and annotation for the corpus are currently underway. Transcripts produced with Google Cloud Speech-to-Text are available for all participants, and will be included in the initial SpiCE corpus release. Hand-corrected orthographic transcripts and force-aligned phonetic transcripts will be released periodically, and upon completion for all recordings, comprise the second release of the corpus. As an open-access language resource, SpiCE will promote bilingualism research for a typologically distinct pair of languages, of which Cantonese remains understudied despite there being millions of speakers around the world. The SpiCE corpus is especially well-suited for phonetic research on conversational speech, and enables researchers to study cross-language within-speaker phenomena for a diverse group of early Cantonese-English bilinguals. These are areas with few existing high-quality resources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lefever-etal-2020-identifying,
    title = "Identifying Cognates in {E}nglish-{D}utch and {F}rench-{D}utch by means of Orthographic Information and Cross-lingual Word Embeddings",
    author = "Lefever, Els  and
      Labat, Sofie  and
      Singh, Pranaydeep",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.504",
    pages = "4096--4101",
    abstract = "This paper investigates the validity of combining more traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutch. In a first step, lists of potential cognate pairs in English-Dutch and French-Dutch are manually labelled. The resulting gold standard is used to train and evaluate a multi-layer perceptron that can distinguish cognates from non-cognates. Fifteen orthographic features capture string similarities between source and target words, while the cosine similarity between their word embeddings represents the semantic relation between these words. By adding domain-specific information to pretrained fastText embeddings, we are able to obtain good embeddings for words that did not yet have a pretrained embedding (e.g. Dutch compound nouns). These embeddings are then aligned in a cross-lingual vector space by exploiting their structural similarity (cf. adversarial learning). Our results indicate that although the classifier already achieves good results on the basis of orthographic information, the performance further improves by including semantic information in the form of cross-lingual word embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kunilovskaya-lapshinova-koltunski-2020-lexicogrammatic,
    title = "Lexicogrammatic translationese across two targets and competence levels",
    author = "Kunilovskaya, Maria  and
      Lapshinova-Koltunski, Ekaterina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.505",
    pages = "4102--4112",
    abstract = "This research employs genre-comparable data from a number of parallel and comparable corpora to explore the specificity of translations from English into German and Russian produced by students and professional translators. We introduce an elaborate set of human-interpretable lexicogrammatic translationese indicators and calculate the amount of translationese manifested in the data for each target language and translation variety. By placing translations into the same feature space as their sources and the genre-comparable non-translated reference texts in the target language, we observe two separate translationese effects: a shift of translations into the gap between the two languages and a shift away from either language. These trends are linked to the features that contribute to each of the effects. Finally, we compare the translation varieties and find out that the professionalism levels seem to have some correlation with the amount and types of translationese detected, while each language pair demonstrates a specific socio-linguistically determined combination of the translationese effects.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{asgari-etal-2020-unisent,
    title = "{U}ni{S}ent: Universal Adaptable Sentiment Lexica for 1000+ Languages",
    author = "Asgari, Ehsaneddin  and
      Braune, Fabienne  and
      Roth, Benjamin  and
      Ringlstetter, Christoph  and
      Mofrad, Mohammad",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.506",
    pages = "4113--4120",
    abstract = "In this paper, we introduce UniSent universal sentiment lexica for 1000+ languages. Sentiment lexica are vital for sentiment analysis in absence of document-level annotations, a very common scenario for low-resource languages. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of the number of covered languages, including many low resource ones. In this work, we use a massively parallel Bible corpus to project sentiment information from English to other languages for sentiment analysis on Twitter data. We introduce a method called DomDrift to mitigate the huge domain mismatch between Bible and Twitter by a confidence weighting scheme that uses domain-specific embeddings to compare the nearest neighbors for a candidate sentiment word in the source (Bible) and target (Twitter) domain. We evaluate the quality of UniSent in a subset of languages for which manually created ground truth was available, Macedonian, Czech, German, Spanish, and French. We show that the quality of UniSent is comparable to manually created sentiment resources when it is used as the sentiment seed for the task of word sentiment prediction on top of embedding representations. In addition, we show that emoticon sentiments could be reliably predicted in the Twitter domain using only UniSent and monolingual embeddings in German, Spanish, French, and Italian. With the publication of this paper, we release the UniSent sentiment lexica at http://language-lab.info/unisent.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nguyen-bryant-2020-canvec,
    title = "{C}an{VEC} - the Canberra {V}ietnamese-{E}nglish Code-switching Natural Speech Corpus",
    author = "Nguyen, Li  and
      Bryant, Christopher",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.507",
    pages = "4121--4129",
    abstract = "This paper introduces the Canberra Vietnamese-English Code-switching corpus (CanVEC), an original corpus of natural mixed speech that we semi-automatically annotated with language information, part of speech (POS) tags and Vietnamese translations. The corpus, which was built to inform a sociolinguistic study on language variation and code-switching, consists of 10 hours of recorded speech (87k tokens) between 45 Vietnamese-English bilinguals living in Canberra, Australia. We describe how we collected and annotated the corpus by pipelining several monolingual toolkits to considerably speed up the annotation process. We also describe how we evaluated the automatic annotations to ensure corpus reliability. We make the corpus available for research purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{eryani-etal-2020-spelling,
    title = "A Spelling Correction Corpus for Multiple {A}rabic Dialects",
    author = "Eryani, Fadhl  and
      Habash, Nizar  and
      Bouamor, Houda  and
      Khalifa, Salam",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.508",
    pages = "4130--4138",
    abstract = "Arabic dialects are the non-standard varieties of Arabic commonly spoken {--} and increasingly written on social media {--} across the Arab world. Arabic dialects do not have standard orthographies, a challenge for natural language processing applications. In this paper, we present the MADAR CODA Corpus, a collection of 10,000 sentences from five Arabic city dialects (Beirut, Cairo, Doha, Rabat, and Tunis) represented in the Conventional Orthography for Dialectal Arabic (CODA) in parallel with their raw original form. The sentences come from the Multi-Arabic Dialect Applications and Resources (MADAR) Project and are in parallel across the cities (2,000 sentences from each city). This publicly available resource is intended to support research on spelling correction and text normalization for Arabic dialects. We present results on a bootstrapping technique we use to speed up the CODA annotation, as well as on the degree of similarity across the dialects before and after CODA annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mutuvi-etal-2020-dataset,
    title = "A Dataset for Multi-lingual Epidemiological Event Extraction",
    author = {Mutuvi, Stephen  and
      Doucet, Antoine  and
      Lejeune, Ga{\"e}l  and
      Odeo, Moses},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.509",
    pages = "4139--4144",
    abstract = "This paper proposes a corpus for the development and evaluation of tools and techniques for identifying emerging infectious disease threats in online news text. The corpus can not only be used for information extraction, but also for other natural language processing (NLP) tasks such as text classification. We make use of articles published on the Program for Monitoring Emerging Diseases (ProMED) platform, which provides current information about outbreaks of infectious diseases globally. Among the key pieces of information present in the articles is the uniform resource locator (URL) to the online news sources where the outbreaks were originally reported. We detail the procedure followed to build the dataset, which includes leveraging the source URLs to retrieve the news reports and subsequently pre-processing the retrieved documents. We also report on experimental results of event extraction on the dataset using the Data Analysis for Information Extraction in any Language(DAnIEL) system. DAnIEL is a multilingual news surveillance system that leverages unique attributes associated with news reporting to extract events: repetition and saliency. The system has wide geographical and language coverage, including low-resource languages. In addition, we compare different classification approaches in terms of their ability to differentiate between epidemic-related and unrelated news articles that constitute the corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{krasselt-etal-2020-swiss,
    title = "{S}wiss-{AL}: A Multilingual {S}wiss Web Corpus for Applied Linguistics",
    author = {Krasselt, Julia  and
      Dressen, Philipp  and
      Fluor, Matthias  and
      Mahlow, Cerstin  and
      Rothenh{\"a}usler, Klaus  and
      Runte, Maren},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.510",
    pages = "4145--4151",
    abstract = "The Swiss Web Corpus for Applied Linguistics (Swiss-AL) is a multilingual (German, French, Italian) collection of texts from selected web sources. Unlike most other web corpora it is not intended for NLP purposes, but rather designed to support data-based and data-driven research on societal and political discourses in Switzerland. It currently contains 8 million texts (approx. 1.55 billion tokens), including news and specialist publications, governmental opinions, and parliamentary records, web sites of political parties, companies, and universities, statements from industry associations and NGOs, etc. A flexible processing pipeline using state-of-the-art components allows researchers in applied linguistics to create tailor-made subcorpora for studying discourse in a wide range of domains. So far, Swiss-AL has been used successfully in research on Swiss public discourses on energy and on antibiotic resistance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tachbelie-etal-2020-analysis,
    title = "Analysis of {G}lobal{P}hone and {E}thiopian Languages Speech Corpora for Multilingual {ASR}",
    author = "Tachbelie, Martha Yifiru  and
      Abate, Solomon Teferra  and
      Schultz, Tanja",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.511",
    pages = "4152--4156",
    abstract = "In this paper, we present the analysis of GlobalPhone (GP) and speech corpora of Ethiopian languages (Amharic, Tigrigna, Oromo and Wolaytta). The aim of the analysis is to select speech data from GP for the development of multilingual Automatic Speech Recognition (ASR) system for the Ethiopian languages. To this end, phonetic overlaps among GP and Ethiopian languages have been analyzed. The result of our analysis shows that there is much phonetic overlap among Ethiopian languages although they are from three different language families. From GP, Turkish, Uyghur and Croatian are found to have much overlap with the Ethiopian languages. On the other hand, Korean has less phonetic overlap with the rest of the languages. Moreover, morphological complexity of the GP and Ethiopian languages, reflected by type to token ration (TTR) and out of vocabulary (OOV) rate, has been analyzed. Both metrics indicated the morphological complexity of the languages. Korean and Amharic have been identified as extremely morphologically complex compared to the other languages. Tigrigna, Russian, Turkish, Polish, etc. are also among the morphologically complex languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chen-kageura-2020-multilingualization,
    title = "Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches",
    author = "Chen, Long-Huei  and
      Kageura, Kyo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.512",
    pages = "4157--4166",
    abstract = "The multilingualization of terminology is an essential step in the translation pipeline, to ensure the correct transfer of domain-specific concepts. Many institutions and language service providers construct and maintain multilingual terminologies, which constitute important assets. However, the curation of such multilingual resources requires significant human effort; though automatic multilingual term extraction methods have been proposed so far, they are of limited success as term translation cannot be satisfied by simply conveying meaning, but requires the terminologists and domain experts{'} knowledge to fit the term within the existing terminology. Here we propose a method to encode the structural property of a term by aligning their embeddings using graph convolutional networks trained from separate languages. We observe that the structural information can augment the semantic methods also explored in this work, and recognize the unique nature of terminologies allows our method to fully take advantage and produce superior results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abate-etal-2020-large,
    title = "Large Vocabulary Read Speech Corpora for Four {E}thiopian Languages: {A}mharic, {T}igrigna, {O}romo and {W}olaytta",
    author = "Abate, Solomon Teferra  and
      Tachbelie, Martha Yifiru  and
      Melese, Michael  and
      Abera, Hafte  and
      Abebe, Tewodros  and
      Mulugeta, Wondwossen  and
      Assabie, Yaregal  and
      Meshesha, Million  and
      Afnafu, Solomon  and
      Seyoum, Binyam Ephrem",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.513",
    pages = "4167--4171",
    abstract = "Automatic Speech Recognition (ASR) is one of the most important technologies to support spoken communication in modern life. However, its development benefits from large speech corpus. The development of such a corpus is expensive and most of the human languages, including the Ethiopian languages, do not have such resources. To address this problem, we have developed four large (about 22 hours) speech corpora for four Ethiopian languages: Amharic, Tigrigna, Oromo and Wolaytta. To assess usability of the corpora for (the purpose of) speech processing, we have developed ASR systems for each language. In this paper, we present the corpora and the baseline ASR systems we have developed. We have achieved word error rates (WERs) of 37.65{\%}, 31.03{\%}, 38.02{\%}, 33.89{\%} for Amharic, Tigrigna, Oromo and Wolaytta, respectively. This results show that the corpora are suitable for further investigation towards the development of ASR systems. Thus, the research community can use the corpora to further improve speech processing systems. From our results, it is clear that the collection of text corpora to train strong language models for all of the languages is still required, especially for Oromo and Wolaytta.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{firdaus-etal-2020-incorporating,
    title = "Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent",
    author = "Firdaus, Mauajama  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.514",
    pages = "4172--4182",
    abstract = "Customer satisfaction is an essential aspect of customer care systems. It is imperative for such systems to be polite while handling customer requests/demands. In this paper, we present a large multi-lingual conversational dataset for English and Hindi. We choose data from Twitter having both generic and courteous responses between customer care agents and aggrieved users. We also propose strong baselines that can induce courteous behaviour in generic customer care response in a multi-lingual scenario. We build a deep learning framework that can simultaneously handle different languages and incorporate polite behaviour in the customer care agent{'}s responses. Our system is competent in generating responses in different languages (here, English and Hindi) depending on the customer{'}s preference and also is able to converse with humans in an empathetic manner to ensure customer satisfaction and retention. Experimental results show that our proposed models can converse in both the languages and the information shared between the languages helps in improving the performance of the overall system. Qualitative and quantitative analysis shows that the proposed method can converse in an empathetic manner by incorporating courteousness in the responses and hence increasing customer satisfaction.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sas-etal-2020-wikibank,
    title = "{W}iki{B}ank: Using {W}ikidata to Improve Multilingual Frame-Semantic Parsing",
    author = "Sas, Cezar  and
      Beloucif, Meriem  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.515",
    pages = "4183--4189",
    abstract = "Frame-semantic annotations exist for a tiny fraction of the world{'}s languages, Wikidata, however, links knowledge base triples to texts in many languages, providing a common, distant supervision signal for semantic parsers. We present WikiBank, a multilingual resource of partial semantic structures that can be used to extend pre-existing resources rather than creating new man-made resources from scratch. We also integrate this form of supervision into an off-the-shelf frame-semantic parser and allow cross-lingual transfer. Using Google{'}s Sling architecture, we show significant improvements on the English and Spanish CoNLL 2009 datasets, whether training on the full available datasets or small subsamples thereof.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ahmed-etal-2020-multilingual,
    title = "Multilingual Corpus Creation for Multilingual Semantic Similarity Task",
    author = "Ahmed, Mahtab  and
      Dixit, Chahna  and
      Mercer, Robert E.  and
      Khan, Atif  and
      Samee, Muhammad Rifayat  and
      Urra, Felipe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.516",
    pages = "4190--4196",
    abstract = "In natural language processing, the performance of a semantic similarity task relies heavily on the availability of a large corpus. Various monolingual corpora are available (mainly English); but multilingual resources are very limited. In this work, we describe a semi-automated framework to create a multilingual corpus which can be used for the multilingual semantic similarity task. The similar sentence pairs are obtained by crawling bilingual websites, whereas the dissimilar sentence pairs are selected by applying topic modeling and an Open-AI GPT model on the similar sentence pairs. We focus on websites in the government, insurance, and banking domains to collect English-French and English-Spanish sentence pairs; however, this corpus creation approach can be applied to any other industry vertical provided that a bilingual website exists. We also show experimental results for multilingual semantic similarity to verify the quality of the corpus and demonstrate its usage.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wang-etal-2020-covost,
    title = "{C}o{V}o{ST}: A Diverse Multilingual Speech-To-Text Translation Corpus",
    author = "Wang, Changhan  and
      Pino, Juan  and
      Wu, Anne  and
      Gu, Jiatao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.517",
    pages = "4197--4203",
    abstract = "Spoken language translation has recently witnessed a resurgence in popularity, thanks to the development of end-to-end models and the creation of new corpora, such as Augmented LibriSpeech and MuST-C. Existing datasets involve language pairs with English as a source language, involve very specific domains or are low resource. We introduce CoVoST, a multilingual speech-to-text translation corpus from 11 languages into English, diversified with over 11,000 speakers and over 60 accents. We describe the dataset creation methodology and provide empirical evidence of the quality of the data. We also provide initial benchmarks, including, to our knowledge, the first end-to-end many-to-one multilingual models for spoken language translation. CoVoST is released under CC0 license and free to use. We also provide additional evaluation data derived from Tatoeba under CC licenses.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nakayama-etal-2020-visually,
    title = "A Visually-Grounded Parallel Corpus with Phrase-to-Region Linking",
    author = "Nakayama, Hideki  and
      Tamura, Akihiro  and
      Ninomiya, Takashi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.518",
    pages = "4204--4210",
    abstract = "Visually-grounded natural language processing has become an important research direction in the past few years. However, majorities of the available cross-modal resources (e.g., image-caption datasets) are built in English and cannot be directly utilized in multilingual or non-English scenarios. In this study, we present a novel multilingual multimodal corpus by extending the Flickr30k Entities image-caption dataset with Japanese translations, which we name Flickr30k Entities JP (F30kEnt-JP). To the best of our knowledge, this is the first multilingual image-caption dataset where the captions in the two languages are parallel and have the shared annotations of many-to-many phrase-to-region linking. We believe that phrase-to-region as well as phrase-to-phrase supervision can play a vital role in fine-grained grounding of language and vision, and will promote many tasks such as multilingual image captioning and multimodal machine translation. To verify our dataset, we performed phrase localization experiments in both languages and investigated the effectiveness of our Japanese annotations as well as multilingual learning realized by our dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wu-etal-2020-multilingual,
    title = "Multilingual Dictionary Based Construction of Core Vocabulary",
    author = "Wu, Winston  and
      Nicolai, Garrett  and
      Yarowsky, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.519",
    pages = "4211--4217",
    abstract = "We propose a new functional definition and construction method for core vocabulary sets for multiple applications based on the relative coverage of a target concept in thousands of bilingual dictionaries. Our newly developed core concept vocabulary list derived from these dictionary consensus methods achieves high overlap with existing widely utilized core vocabulary lists targeted at applications such as first and second language learning or field linguistics. Our in-depth analysis illustrates multiple desirable properties of our newly proposed core vocabulary set, including their non-compositionality. We employ a cognate prediction method to recover missing coverage of this core vocabulary in massively multilingual dictionary construction, and we argue that this core vocabulary should be prioritized for elicitation when creating new dictionaries for low-resource languages for multiple downstream tasks including machine translation and language learning.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ardila-etal-2020-common,
    title = "Common Voice: A Massively-Multilingual Speech Corpus",
    author = "Ardila, Rosana  and
      Branson, Megan  and
      Davis, Kelly  and
      Kohler, Michael  and
      Meyer, Josh  and
      Henretty, Michael  and
      Morais, Reuben  and
      Saunders, Lindsay  and
      Tyers, Francis  and
      Weber, Gregor",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.520",
    pages = "4218--4222",
    abstract = "The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. Common Voice is designed for Automatic Speech Recognition purposes but can be useful in other domains (e.g. language identification). To achieve scale and sustainability, the Common Voice project employs crowdsourcing for both data collection and data validation. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio. To our knowledge this is the largest audio corpus in the public domain for speech recognition, both in terms of number of hours and number of languages. As an example use case for Common Voice, we present speech recognition experiments using Mozilla{'}s DeepSpeech Speech-to-Text toolkit. By applying transfer learning from a source English model, we find an average Character Error Rate improvement of 5.99 {\mbox{$\pm$}} 5.48 for twelve target languages (German, French, Italian, Turkish, Catalan, Slovenian, Welsh, Irish, Breton, Tatar, Chuvash, and Kabyle). For most of these languages, these are the first ever published results on end-to-end Automatic Speech Recognition.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lee-etal-2020-massively,
    title = "Massively Multilingual Pronunciation Modeling with {W}iki{P}ron",
    author = "Lee, Jackson L.  and
      Ashby, Lucas F.E.  and
      Garza, M. Elizabeth  and
      Lee-Sikka, Yeonju  and
      Miller, Sean  and
      Wong, Alan  and
      McCarthy, Arya D.  and
      Gorman, Kyle",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.521",
    pages = "4223--4228",
    abstract = "We introduce WikiPron, an open-source command-line tool for extracting pronunciation data from Wiktionary, a collaborative multilingual online dictionary. We first describe the design and use of WikiPron. We then discuss the challenges faced scaling this tool to create an automatically-generated database of 1.7 million pronunciations from 165 languages. Finally, we validate the pronunciation database by using it to train and evaluating a collection of generic grapheme-to-phoneme models. The software, pronunciation data, and models are all made available under permissive open-source licenses.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yli-jyra-etal-2020-helfi,
    title = "{HELFI}: a {H}ebrew-{G}reek-{F}innish Parallel {B}ible Corpus with Cross-Lingual Morpheme Alignment",
    author = {Yli-Jyr{\"a}, Anssi  and
      Purhonen, Josi  and
      Liljeqvist, Matti  and
      Antturi, Arto  and
      Nieminen, Pekka  and
      R{\"a}ntil{\"a}, Kari M.  and
      Luoto, Valtter},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.522",
    pages = "4229--4236",
    abstract = "Twenty-five years ago, morphologically aligned Hebrew-Finnish and Greek-Finnish bitexts (texts accompanied by a translation) were constructed manually in order to create an analytical concordance (Luoto et al., eds. 1997) for a Finnish Bible translation. The creators of the bitexts recently secured the publisher{'}s permission to release its fine-grained alignment, but the alignment was still dependent on proprietary, third-party resources such as a copyrighted text edition and proprietary morphological analyses of the source texts. In this paper, we describe a nontrivial editorial process starting from the creation of the original one-purpose database and ending with its reconstruction using only freely available text editions and annotations. This process produced an openly available dataset that contains (i) the source texts and their translations, (ii) the morphological analyses, (iii) the cross-lingual morpheme alignments.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hamed-etal-2020-arzen,
    title = "{A}rz{E}n: A Speech Corpus for Code-switched {E}gyptian {A}rabic-{E}nglish",
    author = "Hamed, Injy  and
      Vu, Ngoc Thang  and
      Abdennadher, Slim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.523",
    pages = "4237--4246",
    abstract = "In this paper, we present our ArzEn corpus, an Egyptian Arabic-English code-switching (CS) spontaneous speech corpus. The corpus is collected through informal interviews with 38 Egyptian bilingual university students and employees held in a soundproof room. A total of 12 hours are recorded, transcribed, validated and sentence segmented. The corpus is mainly designed to be used in Automatic Speech Recognition (ASR) systems, however, it also provides a useful resource for analyzing the CS phenomenon from linguistic, sociological, and psychological perspectives. In this paper, we first discuss the CS phenomenon in Egypt and the factors that gave rise to the current language. We then provide a detailed description on how the corpus was collected, giving an overview on the participants involved. We also present statistics on the CS involved in the corpus, as well as a summary to the effort exerted in the corpus development, in terms of number of hours required for transcription, validation, segmentation and speaker annotation. Finally, we discuss some factors contributing to the complexity of the corpus, as well as Arabic-English CS behaviour that could pose potential challenges to ASR systems.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khakhmovich-etal-2020-cross,
    title = "Cross-lingual Named Entity List Search via Transliteration",
    author = "Khakhmovich, Aleksandr  and
      Pavlova, Svetlana  and
      Kirillova, Kira  and
      Arefyev, Nikolay  and
      Savilova, Ekaterina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.524",
    pages = "4247--4255",
    abstract = "Out-of-vocabulary words are still a challenge in cross-lingual Natural Language Processing tasks, for which transliteration from source to target language or script is one of the solutions. In this study, we collect a personal name dataset in 445 Wikidata languages (37 scripts), train Transformer-based multilingual transliteration models on 6 high- and 4 less-resourced languages, compare them with bilingual models from (Merhav and Ash, 2018) and determine that multilingual models perform better for less-resourced languages. We discover that intrinsic evaluation, i.e comparison to a single gold standard, might not be appropriate in the task of transliteration due to its high variability. For this reason, we propose using extrinsic evaluation of transliteration via the cross-lingual named entity list search task (e.g. personal name search in contacts list). Our code and datasets are publicly available online.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bost-etal-2020-serial,
    title = "Serial Speakers: a Dataset of {TV} Series",
    author = "Bost, Xavier  and
      Labatut, Vincent  and
      Linares, Georges",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.525",
    pages = "4256--4264",
    abstract = "For over a decade, TV series have been drawing increasing interest, both from the audience and from various academic fields. But while most viewers are hooked on the continuous plots of TV serials, the few annotated datasets available to researchers focus on standalone episodes of classical TV series. We aim at filling this gap by providing the multimedia/speech processing communities with {``}Serial Speakers{''}, an annotated dataset of 155 episodes from three popular American TV serials: {``}Breaking Bad{''}, {``}Game of Thrones{''} and {``}House of Cards{''}. {``}Serial Speakers{''} is suitable both for investigating multimedia retrieval in realistic use case scenarios, and for addressing lower level speech related tasks in especially challenging conditions. We publicly release annotations for every speech turn (boundaries, speaker) and scene boundary, along with annotations for shot boundaries, recurring shots, and interacting speakers in a subset of episodes. Because of copyright restrictions, the textual content of the speech turns is encrypted in the public version of the dataset, but we provide the users with a simple online tool to recover the plain text from their own subtitle files.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{muraoka-etal-2020-image,
    title = "Image Position Prediction in Multimodal Documents",
    author = "Muraoka, Masayasu  and
      Kohita, Ryosuke  and
      Ishii, Etsuko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.526",
    pages = "4265--4274",
    abstract = "Conventional multimodal tasks, such as caption generation and visual question answering, have allowed machines to understand an image by describing or being asked about it in natural language, often via a sentence. Datasets for these tasks contain a large number of pairs of an image and the corresponding sentence as an instance. However, a real multimodal document such as a news article or Wikipedia page consists of multiple sentences with multiple images. Such documents require an advanced skill of jointly considering the multiple texts and multiple images, beyond a single sentence and image, for the interpretation. Therefore, aiming at building a system that can understand multimodal documents, we propose a task called image position prediction (IPP). In this task, a system learns plausible positions of images in a given document. To study this task, we automatically constructed a dataset of 66K multimodal documents with 320K images from Wikipedia articles. We conducted a preliminary experiment to evaluate the performance of a current multimodal system on our task. The experimental results show that the system outperformed simple baselines while the performance is still far from human performance, which thus poses new challenges in multimodal research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nishimura-etal-2020-visual,
    title = "Visual Grounding Annotation of Recipe Flow Graph",
    author = "Nishimura, Taichi  and
      Tomori, Suzushi  and
      Hashimoto, Hayato  and
      Hashimoto, Atsushi  and
      Yamakata, Yoko  and
      Harashima, Jun  and
      Ushiku, Yoshitaka  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.527",
    pages = "4275--4284",
    abstract = "In this paper, we provide a dataset that gives visual grounding annotations to recipe flow graphs. A recipe flow graph is a representation of the cooking workflow, which is designed with the aim of understanding the workflow from natural language processing. Such a workflow will increase its value when grounded to real-world activities, and visual grounding is a way to do so. Visual grounding is provided as bounding boxes to image sequences of recipes, and each bounding box is linked to an element of the workflow. Because the workflows are also linked to the text, this annotation gives visual grounding with workflow{'}s contextual information between procedural text and visual observation in an indirect manner. We subsidiarily annotated two types of event attributes with each bounding box: {``}doing-the-action,{''} or {``}done-the-action{''}. As a result of the annotation, we got 2,300 bounding boxes in 272 flow graph recipes. Various experiments showed that the proposed dataset enables us to estimate contextual information described in recipe flow graphs from an image sequence.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{adjali-etal-2020-building,
    title = "Building a Multimodal Entity Linking Dataset From Tweets",
    author = "Adjali, Omar  and
      Besan{\c{c}}on, Romaric  and
      Ferret, Olivier  and
      Le Borgne, Herv{\'e}  and
      Grau, Brigitte",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.528",
    pages = "4285--4292",
    abstract = "The task of Entity linking, which aims at associating an entity mention with a unique entity in a knowledge base (KB), is useful for advanced Information Extraction tasks such as relation extraction or event detection. Most of the studies that address this problem rely only on textual documents while an increasing number of sources are multimedia, in particular in the context of social media where messages are often illustrated with images. In this article, we address the Multimodal Entity Linking (MEL) task, and more particularly the problem of its evaluation. To this end, we propose a novel method to quasi-automatically build annotated datasets to evaluate methods on the MEL task. The method collects text and images to jointly build a corpus of tweets with ambiguous mentions along with a Twitter KB defining the entities. We release a new annotated dataset of Twitter posts associated with images. We study the key characteristics of the proposed dataset and evaluate the performance of several MEL approaches on it.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mdhaffar-etal-2020-multimodal,
    title = "A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study",
    author = "Mdhaffar, Salima  and
      Est{\`e}ve, Yannick  and
      Laurent, Antoine  and
      Hernandez, Nicolas  and
      Dufour, Richard  and
      Charlet, Delphine  and
      Damnati, Geraldine  and
      Quiniou, Solen  and
      Camelin, Nathalie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.529",
    pages = "4293--4301",
    abstract = "This corpus is part of the PASTEL (Performing Automated Speech Transcription for Enhancing Learning) project aiming to explore the potential of synchronous speech transcription and application in specific teaching situations. It includes 10 hours of different lectures, manually transcribed and segmented. The main interest of this corpus lies in its multimodal aspect: in addition to speech, the courses were filmed and the written presentation supports (slides) are made available. The dataset may then serve researches in multiple fields, from speech and language to image and video processing. The dataset will be freely available to the research community. In this paper, we first describe in details the annotation protocol, including a detailed analysis of the manually labeled data. Then, we propose some possible use cases of the corpus with baseline results. The use cases concern scientific fields from both speech and text processing, with language model adaptation, thematic segmentation and transcription to slide alignment.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kameko-mori-2020-annotating,
    title = "Annotating Event Appearance for {J}apanese Chess Commentary Corpus",
    author = "Kameko, Hirotaka  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.530",
    pages = "4302--4308",
    abstract = "In recent years, there has been a surge of interest in natural language processing related to the real world, such as symbol grounding, language generation, and non-linguistic data search by natural language queries. Researchers usually collect pairs of text and non-text data for research. However, the text and non-text data are not always a {``}true{''} pair. We focused on the shogi (Japanese chess) commentaries, which are accompanied by game states as a well-defined {``}real world{''}. For analyzing and processing texts accurately, considering only the given states is insufficient, and we must consider the relationship between texts and the real world. In this paper, we propose {``}Event Appearance{''} labels that show the relationship between events mentioned in texts and those happening in the real world. Our event appearance label set consists of temporal relation, appearance probability, and evidence of the event. Statistics of the annotated corpus and the experimental result show that there exists temporal relation which skillful annotators realize in common. However, it is hard to predict the relationship only by considering the given states.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alcantara-etal-2020-offensive,
    title = "Offensive Video Detection: Dataset and Baseline Results",
    author = "Alc{\^a}ntara, Cleber  and
      Moreira, Viviane  and
      Feijo, Diego",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.531",
    pages = "4309--4319",
    abstract = "Web-users produce and publish high volumes of data of various types, such as text, images, and videos. The platforms try to restrain their users from publishing offensive content to keep a friendly and respectful environment and rely on moderators to filter the posts. However, this method is insufficient due to the high volume of publications. The identification of offensive material can be performed automatically using machine learning, which needs annotated datasets. Among the published datasets in this matter, the Portuguese language is underrepresented, and videos are little explored. We investigated the problem of offensive video detection by assembling and publishing a dataset of videos in Portuguese containing mostly textual features. We ran experiments using popular machine learning classifiers used in this domain and reported our findings, alongside multiple evaluation metrics. We found that using word embedding with Deep Learning classifiers achieved the best results on average. CNN architectures, Naive Bayes, and Random Forest ranked top among different experiments. Transfer Learning models outperformed Classic algorithms when processing video transcriptions, but scored lower using other feature sets. These findings can be used as a baseline for future works on this subject.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{trotta-etal-2020-adding,
    title = "Adding Gesture, Posture and Facial Displays to the {P}oli{M}odal Corpus of Political Interviews",
    author = "Trotta, Daniela  and
      Palmero Aprosio, Alessio  and
      Tonelli, Sara  and
      Elia, Annibale",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.532",
    pages = "4320--4326",
    abstract = "This paper introduces a multimodal corpus in the political domain, which on top of transcribed face-to-face interviews presents the annotation of facial displays, hand gestures and body posture. While the fully annotated corpus consists of 3 interviews for a total of 90 minutes, it is extracted from a larger available corpus of 56 face-to-face interviews (14 hours) that has been manually annotated with information about metadata (i.e. tools used for the transcription, link to the interview etc.), pauses (used to mark a pause either between or within utterances), vocal expressions (marking non-lexical expressions such as burp and semi-lexical expressions such as primary interjections), deletions (false starts, repetitions and truncated words) and overlaps. In this work, we describe the additional level of annotation relating to nonverbal elements used by three Italian politicians belonging to three different political parties and who at the time of the talk-show were all candidates for the presidency of the Council of Minister. We also present the results of some analyses aimed at identifying existing relations between the proxemics phenomena and the linguistic structures in which they occur in order to capture recurring patterns and differences in the communication strategy.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ho-dac-etal-2020-e,
    title = "{E}:Calm Resource: a Resource for Studying Texts Produced by {F}rench Pupils and Students",
    author = "Ho-Dac, Lydia-Mai  and
      Fleury, Serge  and
      Ponton, Claude",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.533",
    pages = "4327--4332",
    abstract = "The E:Calm resource is constructed from French student texts produced in a variety of usual contexts of teaching. The distinction of the E:Calm resource is to provide an ecological data set that gives a broad overview of texts written at elementary school, high school and university. This paper describes the whole data processing: encoding of the main graphical aspects of the handwritten primary sources according to the TEI-P5 norm; spelling standardizing; POS tagging and syntactic parsing evaluation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jansen-etal-2020-introducing,
    title = "Introducing {MULAI}: A Multimodal Database of Laughter during Dyadic Interactions",
    author = "Jansen, Michel-Pierre  and
      Truong, Khiet P.  and
      Heylen, Dirk K.J.  and
      Nazareth, Deniece S.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.534",
    pages = "4333--4342",
    abstract = "Although laughter has gained considerable interest from a diversity of research areas, there still is a need for laughter specific databases. We present the Multimodal Laughter during Interaction (MULAI) database to study the expressive patterns of conversational and humour related laughter. The MULAI database contains 2 hours and 14 minutes of recorded and annotated dyadic human-human interactions and includes 601 laughs, 168 speech-laughs and 538 on- or offset respirations. This database is unique in several ways; 1) it focuses on different types of social laughter including conversational- and humour related laughter, 2) it contains annotations from participants, who understand the social context, on how humourous they perceived themselves and their interlocutor during each task, and 3) it contains data rarely captured by other laughter databases including participant personality profiles and physiological responses. We use the MULAI database to explore the link between acoustic laughter properties and annotated humour ratings over two settings. The results reveal that the duration, pitch and intensity of laughs from participants do not correlate with their own perception of how humourous they are, however the acoustics of laughter do correlate with how humourous they are being perceived by their conversational partner.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{oostdijk-etal-2020-connection,
    title = "The Connection between the Text and Images of News Articles: New Insights for Multimedia Analysis",
    author = "Oostdijk, Nelleke  and
      van Halteren, Hans  and
      Ba{\textcommabelow{s}}ar, Erkan  and
      Larson, Martha",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.535",
    pages = "4343--4351",
    abstract = "We report on a case study of text and images that reveals the inadequacy of simplistic assumptions about their connection and interplay. The context of our work is a larger effort to create automatic systems that can extract event information from online news articles about flooding disasters. We carry out a manual analysis of 1000 articles containing a keyword related to flooding. The analysis reveals that the articles in our data set cluster into seven categories related to different topical aspects of flooding, and that the images accompanying the articles cluster into five categories related to the content they depict. The results demonstrate that flood-related news articles do not consistently report on a single, currently unfolding flooding event and we should also not assume that a flood-related image will directly relate to a flooding-event described in the corresponding article. In particular, spatiotemporal distance is important. We validate the manual analysis with an automatic classifier demonstrating the technical feasibility of multimedia analysis approaches that admit more realistic relationships between text and images. In sum, our case study confirms that closer attention to the connection between text and images has the potential to improve the collection of multimodal information from news articles.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{castro-etal-2020-lifeqa,
    title = "{L}ife{QA}: A Real-life Dataset for Video Question Answering",
    author = "Castro, Santiago  and
      Azab, Mahmoud  and
      Stroud, Jonathan  and
      Noujaim, Cristina  and
      Wang, Ruoyao  and
      Deng, Jia  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.536",
    pages = "4352--4358",
    abstract = "We introduce LifeQA, a benchmark dataset for video question answering that focuses on day-to-day real-life situations. Current video question answering datasets consist of movies and TV shows. However, it is well-known that these visual domains are not representative of our day-to-day lives. Movies and TV shows, for example, benefit from professional camera movements, clean editing, crisp audio recordings, and scripted dialog between professional actors. While these domains provide a large amount of data for training models, their properties make them unsuitable for testing real-life question answering systems. Our dataset, by contrast, consists of video clips that represent only real-life scenarios. We collect 275 such video clips and over 2.3k multiple-choice questions. In this paper, we analyze the challenging but realistic aspects of LifeQA, and we apply several state-of-the-art video question answering models to provide benchmarks for future research. The full dataset is publicly available at https://lit.eecs.umich.edu/lifeqa/.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bettinger-etal-2020-domain,
    title = "A Domain-Specific Dataset of Difficulty Ratings for {G}erman Noun Compounds in the Domains {DIY}, Cooking and Automotive",
    author = {Bettinger, Julia  and
      H{\"a}tty, Anna  and
      Dorna, Michael  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.537",
    pages = "4359--4367",
    abstract = "We present a dataset with difficulty ratings for 1,030 German closed noun compounds extracted from domain-specific texts for do-it-ourself (DIY), cooking and automotive. The dataset includes two-part compounds for cooking and DIY, and two- to four-part compounds for automotive. The compounds were identified in text using the Simple Compound Splitter (Weller-Di Marco, 2017); a subset was filtered and balanced for frequency and productivity criteria as basis for manual annotation and fine-grained interpretation. This study presents the creation, the final dataset with ratings from 20 annotators and statistics over the dataset, to provide insight into the perception of domain-specific term difficulty. It is particularly striking that annotators agree on a coarse, binary distinction between easy vs. difficult domain-specific compounds but that a more fine grained distinction of difficulty is not meaningful. We finally discuss the challenges of an annotation for difficulty, which includes both the task description as well as the selection of the data basis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{strakatova-etal-2020-glitters,
    title = "All That Glitters is Not Gold: A Gold Standard of Adjective-Noun Collocations for {G}erman",
    author = "Strakatova, Yana  and
      Falk, Neele  and
      Fuhrmann, Isabel  and
      Hinrichs, Erhard  and
      Rossmann, Daniela",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.538",
    pages = "4368--4378",
    abstract = "In this paper we present the GerCo dataset of adjective-noun collocations for German, such as alter Freund {`}old friend{'} and tiefe Liebe {`}deep love{'}. The annotation has been performed by experts based on the annotation scheme introduced in this paper. The resulting dataset contains 4,732 positive and negative instances of collocations and covers all the 16 semantic classes of adjectives as defined in the German wordnet GermaNet. The dataset can serve as a reliable empirical basis for comparing different theoretical frameworks concerned with collocations or as material for data-driven approaches to the studies of collocations including different machine learning experiments. This paper addresses the latter issue by using the GerCo dataset for evaluating different models on the task of automatic collocation identification. We compare lexical association measures with static and contextualized word embeddings. The experiments show that word embeddings outperform methods based on statistical association measures by a wide margin.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alipoor-schulte-im-walde-2020-variants,
    title = "Variants of Vector Space Reductions for Predicting the Compositionality of {E}nglish Noun Compounds",
    author = "Alipoor, Pegah  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.539",
    pages = "4379--4387",
    abstract = "Predicting the degree of compositionality of noun compounds such as {``}snowball{''} and {``}butterfly{''} is a crucial ingredient for lexicography and Natural Language Processing applications, to know whether the compound should be treated as a whole, or through its constituents, and what it means. Computational approaches for an automatic prediction typically represent and compare compounds and their constituents within a vector space and use distributional similarity as a proxy to predict the semantic relatedness between the compounds and their constituents as the compound{'}s degree of compositionality. This paper provides a systematic evaluation of vector-space reduction variants across kinds, exploring reductions based on part-of-speech next to and also in combination with Principal Components Analysis using Singular Value and word2vec embeddings. We show that word2vec and nouns only dimensionality reductions are the most successful and stable vector space variants for our task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nigam-etal-2020-varying,
    title = "Varying Vector Representations and Integrating Meaning Shifts into a {P}age{R}ank Model for Automatic Term Extraction",
    author = {Nigam, Anurag  and
      H{\"a}tty, Anna  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.540",
    pages = "4388--4394",
    abstract = "We perform a comparative study for automatic term extraction from domain-specific language using a PageRank model with different edge-weighting methods. We vary vector space representations within the PageRank graph algorithm, and we go beyond standard co-occurrence and investigate the influence of measures of association strength and first- vs. second-order co-occurrence. In addition, we incorporate meaning shifts from general to domain-specific language as personalized vectors, in order to distinguish between termhood strengths of ambiguous words across word senses. Our study is performed for two domain-specific English corpora: ACL and do-it-yourself (DIY); and a domain-specific German corpus: cooking. The models are assessed by applying average precision and the roc score as evaluation metrices.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fort-etal-2020-rigor,
    title = "Rigor Mortis: Annotating {MWE}s with a Gamified Platform",
    author = {Fort, Kar{\"e}n  and
      Guillaume, Bruno  and
      Pilatte, Yann-Alan  and
      Constant, Mathieu  and
      Lef{\`e}bvre, Nicolas},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.541",
    pages = "4395--4401",
    abstract = "We present here Rigor Mortis, a gamified crowdsourcing platform designed to evaluate the intuition of the speakers, then train them to annotate multi-word expressions (MWEs) in French corpora. We previously showed that the speakers{'} intuition is reasonably good (65{\%} in recall on non-fixed MWE). We detail here the annotation results, after a training phase using some of the tests developed in the PARSEME-FR project.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kurfali-etal-2020-multi,
    title = "A Multi-word Expression Dataset for {S}wedish",
    author = {Kurfal{\i}, Murathan  and
      {\"O}stling, Robert  and
      Sjons, Johan  and
      Wir{\'e}n, Mats},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.542",
    pages = "4402--4409",
    abstract = "We present a new set of 96 Swedish multi-word expressions annotated with degree of (non-)compositionality. In contrast to most previous compositionality datasets we also consider syntactically complex constructions and publish a formal specification of each expression. This allows evaluation of computational models beyond word bigrams, which have so far been the norm. Finally, we use the annotations to evaluate a system for automatic compositionality estimation based on distributional semantics. Our analysis of the disagreements between human annotators and the distributional model reveal interesting questions related to the perception of compositionality, and should be informative to future work in the area.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{krotova-etal-2020-joint,
    title = "A Joint Approach to Compound Splitting and Idiomatic Compound Detection",
    author = "Krotova, Irina  and
      Aksenov, Sergey  and
      Artemova, Ekaterina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.543",
    pages = "4410--4417",
    abstract = "Applications such as machine translation, speech recognition, and information retrieval require efficient handling of noun compounds as they are one of the possible sources for out of vocabulary words. In-depth processing of noun compounds requires not only splitting them into smaller components (or even roots) but also the identification of instances that should remain unsplitted as they are of idiomatic nature. We develop a two-fold deep learning-based approach of noun compound splitting and idiomatic compound detection for the German language that we train using a newly collected corpus of annotated German compounds. Our neural noun compound splitter operates on a sub-word level and outperforms the current state of the art by about 5{\%}",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hubers-etal-2020-dedicated,
    title = "Dedicated Language Resources for Interdisciplinary Research on Multiword Expressions: Best Thing since Sliced Bread",
    author = "Hubers, Ferdy  and
      Cucchiarini, Catia  and
      Strik, Helmer",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.544",
    pages = "4418--4425",
    abstract = "Multiword expressions such as idioms (beat about the bush), collocations (plastic surgery) and lexical bundles (in the middle of) are challenging for disciplines like Natural Language Processing (NLP), psycholinguistics and second language acquisition, , due to their more or less fixed character. Idiomatic expressions are especially problematic, because they convey a figurative meaning that cannot always be inferred from the literal meanings of the component words. Researchers acknowledge that important properties that characterize idioms such as frequency of exposure, familiarity, transparency, and imageability, should be taken into account in research, but these are typically properties that rely on subjective judgments. This is probably one of the reasons why many studies that investigated idiomatic expressions collected limited information about idiom properties for very small numbers of idioms only. In this paper we report on cross-boundary work aimed at developing a set of tools and language resources that are considered crucial for this kind of multifaceted research. We discuss the results of our research and suggest possible avenues for future research",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kochmar-etal-2020-detecting,
    title = "Detecting Multiword Expression Type Helps Lexical Complexity Assessment",
    author = "Kochmar, Ekaterina  and
      Gooding, Sian  and
      Shardlow, Matthew",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.545",
    pages = "4426--4435",
    abstract = "Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. Multiple NLP applications have been shown to benefit from MWE identification, however the research on lexical complexity of MWEs is still an under-explored area. In this work, we re-annotate the Complex Word Identification Shared Task 2018 dataset of Yimam et al. (2017), which provides complexity scores for a range of lexemes, with the types of MWEs. We release the MWE-annotated dataset with this paper, and we believe this dataset represents a valuable resource for the text simplification community. In addition, we investigate which types of expressions are most problematic for native and non-native readers. Finally, we show that a lexical complexity assessment system benefits from the information about MWE types.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dumitrescu-avram-2020-introducing,
    title = "Introducing {RONEC} - the {R}omanian Named Entity Corpus",
    author = "Dumitrescu, Stefan Daniel  and
      Avram, Andrei-Marius",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.546",
    pages = "4436--4443",
    abstract = "We present RONEC - the Named Entity Corpus for the Romanian language. The corpus contains over 26000 entities in {\textasciitilde}5000 annotated sentences, belonging to 16 distinct classes. The sentences have been extracted from a copy-right free newspaper, covering several styles. This corpus represents the first initiative in the Romanian language space specifically targeted for named entity recognition. It is available in BRAT and CoNLL-U Plus formats, and it is free to use and extend at github.com/dumitrescustefan/ronec",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{berg-dalianis-2020-semi,
    title = "A Semi-supervised Approach for De-identification of {S}wedish Clinical Text",
    author = "Berg, Hanna  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.547",
    pages = "4444--4450",
    abstract = "An abundance of electronic health records (EHR) is produced every day within healthcare. The records possess valuable information for research and future improvement of healthcare. Multiple efforts have been done to protect the integrity of patients while making electronic health records usable for research by removing personally identifiable information in patient records. Supervised machine learning approaches for de-identification of EHRs need annotated data for training, annotations that are costly in time and human resources. The annotation costs for clinical text is even more costly as the process must be carried out in a protected environment with a limited number of annotators who must have signed confidentiality agreements. In this paper is therefore, a semi-supervised method proposed, for automatically creating high-quality training data. The study shows that the method can be used to improve recall from 84.75{\%} to 89.20{\%} without sacrificing precision to the same extent, dropping from 95.73{\%} to 94.20{\%}. The model{'}s recall is arguably more important for de-identification than precision.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lee-etal-2020-chinese,
    title = "A {C}hinese Corpus for Fine-grained Entity Typing",
    author = "Lee, Chin  and
      Dai, Hongliang  and
      Song, Yangqiu  and
      Li, Xin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.548",
    pages = "4451--4457",
    abstract = "Fine-grained entity typing is a challenging task with wide applications. However, most existing datasets for this task are in English. In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4,800 mentions manually labeled through crowdsourcing. Each mention is annotated with free-form entity types. To make our dataset useful in more possible scenarios, we also categorize all the fine-grained types into 10 general types. Finally, we conduct experiments with some neural models whose structures are typical in fine-grained entity typing and show how well they perform on our dataset. We also show the possibility of improving Chinese fine-grained entity typing through cross-lingual transfer learning.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hubkova-etal-2020-czech,
    title = "{C}zech Historical Named Entity Corpus v 1.0",
    author = "Hubkov{\'a}, Helena  and
      Kral, Pavel  and
      Pettersson, Eva",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.549",
    pages = "4458--4465",
    abstract = "As the number of digitized archival documents increases very rapidly, named entity recognition (NER) in historical documents has become very important for information extraction and data mining. For this task an annotated corpus is needed, which has up to now been missing for Czech. In this paper we present a new annotated data collection for historical NER, composed of Czech historical newspapers. This corpus is freely available for research purposes. For this corpus, we have defined relevant domain-specific named entity types and created an annotation manual for corpus labelling. We further conducted some experiments on this corpus using recurrent neural networks. We experimented with randomly initialized embeddings and static and dynamic fastText word embeddings. We achieved 0.73 F1 score with a bidirectional LSTM model using static fastText embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{eder-etal-2020-code,
    title = "{C}od{E} Alltag 2.0 {---} A Pseudonymized {G}erman-Language Email Corpus",
    author = "Eder, Elisabeth  and
      Krieg-Holz, Ulrike  and
      Hahn, Udo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.550",
    pages = "4466--4477",
    abstract = "The vast amount of social communication distributed over various electronic media channels (tweets, blogs, emails, etc.), so-called user-generated content (UGC), creates entirely new opportunities for today{'}s NLP research. Yet, data privacy concerns implied by the unauthorized use of these text streams as a data resource are often neglected. In an attempt to reconciliate the diverging needs of unconstrained raw data use and preservation of data privacy in digital communication, we here investigate the automatic recognition of privacy-sensitive stretches of text in UGC and provide an algorithmic solution for the protection of personal data via pseudonymization. Our focus is directed at the de-identification of emails where personally identifying information does not only refer to the sender but also to those people, locations, dates, and other identifiers mentioned in greetings, boilerplates and the content-carrying body of emails. We evaluate several de-identification procedures and systems on two hitherto non-anonymized German-language email corpora (CodE AlltagS+d and CodE AlltagXL), and generate fully pseudonymized versions for both (CodE Alltag 2.0) in which personally identifying information of all social actors addressed in these mails has been camouflaged (to the greatest extent possible).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{leitner-etal-2020-dataset,
    title = "A Dataset of {G}erman Legal Documents for Named Entity Recognition",
    author = "Leitner, Elena  and
      Rehm, Georg  and
      Moreno-Schneider, Julian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.551",
    pages = "4478--4485",
    abstract = "We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{garcia-pablos-etal-2020-sensitive,
    title = "Sensitive Data Detection and Classification in {S}panish Clinical Text: Experiments with {BERT}",
    author = "Garc{\'\i}a Pablos, Aitor  and
      Perez, Naiara  and
      Cuadros, Montse",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.552",
    pages = "4486--4494",
    abstract = "Massive digital data processing provides a wide range of opportunities and benefits, but at the cost of endangering personal data privacy. Anonymisation consists in removing or replacing sensitive information from data, enabling its exploitation for different purposes while preserving the privacy of individuals. Over the years, a lot of automatic anonymisation systems have been proposed; however, depending on the type of data, the target language or the availability of training documents, the task remains challenging still. The emergence of novel deep-learning models during the last two years has brought large improvements to the state of the art in the field of Natural Language Processing. These advancements have been most noticeably led by BERT, a model proposed by Google in 2018, and the shared language models pre-trained on millions of documents. In this paper, we use a BERT-based sequence labelling model to conduct a series of anonymisation experiments on several clinical datasets in Spanish. We also compare BERT with other algorithms. The experiments show that a simple BERT-based model with general-domain pre-training obtains highly competitive results without any domain specific feature engineering.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schulz-etal-2020-named,
    title = "Named Entities in Medical Case Reports: Corpus and Experiments",
    author = "Schulz, Sarah  and
      {\v{S}}eva, Jurica  and
      Rodriguez, Samuel  and
      Ostendorff, Malte  and
      Rehm, Georg",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.553",
    pages = "4495--4500",
    abstract = "We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central{'}s open access library. In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. Moreover, where applicable, we annotate relations between these entities. As such, this is the first corpus of this kind made available to the scientific community in English. It enables the initial investigation of automatic information extraction from case reports through tasks like Named Entity Recognition, Relation Extraction and (sentence/paragraph) relevance detection. Additionally, we present four strong baseline systems for the detection of medical entities made available through the annotated dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{klang-nugues-2020-hedwig,
    title = "{H}edwig: A Named Entity Linker",
    author = "Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.554",
    pages = "4501--4508",
    abstract = "Named entity linking is the task of identifying mentions of named things in text, such as {``}Barack Obama{''} or {``}New York{''}, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9{\%} on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9{\%}.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{barreaux-besagni-2020-experiment,
    title = "An Experiment in Annotating Animal Species Names from {ISTEX} Resources",
    author = "Barreaux, Sabine  and
      Besagni, Dominique",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.555",
    pages = "4509--4513",
    abstract = "To exploit scientific publications from global research for TDM purposes, the ISTEX platform enriched its data with value-added information to ease access to its full-text documents. We built an experiment to explore new enrichment possibilities in documents focussing on scientific named entities recognition which could be integrated into ISTEX resources. This led to testing two detection tools for animal species names in a corpus of 100 documents in zoology. This makes it possible to provide the French scientific community with an annotated reference corpus available for use to measure these tools{'} performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{caubriere-etal-2020-named,
    title = "Where are we in Named Entity Recognition from Speech?",
    author = "Caubri{\`e}re, Antoine  and
      Rosset, Sophie  and
      Est{\`e}ve, Yannick  and
      Laurent, Antoine  and
      Morin, Emmanuel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.556",
    pages = "4514--4520",
    abstract = "Named entity recognition (NER) from speech is usually made through a pipeline process that consists in (i) processing audio using an automatic speech recognition system (ASR) and (ii) applying a NER to the ASR outputs. The latest data available for named entity extraction from speech in French were produced during the ETAPE evaluation campaign in 2012. Since the publication of ETAPE{'}s campaign results, major improvements were done on NER and ASR systems, especially with the development of neural approaches for both of these components. In addition, recent studies have shown the capability of End-to-End (E2E) approach for NER / SLU tasks. In this paper, we propose a study of the improvements made in speech recognition and named entity recognition for pipeline approaches. For this type of systems, we propose an original 3-pass approach. We also explore the capability of an E2E system to do structured NER. Finally, we compare the performances of ETAPE{'}s systems (state-of-the-art systems in 2012) with the performances obtained using current technologies. The results show the interest of the E2E approach, which however remains below an updated pipeline approach.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mcnamee-etal-2020-tagging,
    title = "Tagging Location Phrases in Text",
    author = "McNamee, Paul  and
      Mayfield, James  and
      Costello, Cash  and
      Bishop, Caitlyn  and
      Anderson, Shelby",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.557",
    pages = "4521--4528",
    abstract = "For over thirty years researchers have studied the problem of automatically detecting named entities in written language. Throughout this time the majority of such work has focused on detection and classification of entities into coarse-grained types like: PERSON, ORGANIZATION, and LOCATION. Less attention has been focused on non-named mentions of entities, including non-named location phrases such as {``}the medical clinic in Telonge{''} or {``}2 km below the Dolin Maniche bridge{''}. In this work we describe the Location Phrase Detection task to identify such spans. Our key accomplishments include: developing a sequential tagging approach; crafting annotation guidelines; building annotated datasets for English and Russian news; and, conducting experiments in automated detection of location phrases with both statistical and neural taggers. This work is motivated by extracting rich location information to support situational awareness during humanitarian crises such as natural disasters.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{smith-etal-2020-scienceexamcer,
    title = "{S}cience{E}xam{CER}: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition",
    author = "Smith, Hannah  and
      Zhang, Zeyu  and
      Culnan, John  and
      Jansen, Peter",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.558",
    pages = "4529--4546",
    abstract = "Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96{\%}) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. Semantic class labels are drawn from a manually-constructed fine-grained typology of 601 classes generated through a data-driven analysis of 4,239 science exam questions. We show an off-the-shelf BERT-based named entity recognition model modified for multi-label classification achieves an accuracy of 0.85 F1 on this task, suggesting strong utility for downstream tasks in science domain question answering requiring densely-labeled semantic classification.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jorgensen-etal-2020-norne,
    title = "{N}or{NE}: Annotating Named Entities for {N}orwegian",
    author = "J{\o}rgensen, Fredrik  and
      Aasmoe, Tobias  and
      Ruud Husev{\aa}g, Anne-Stine  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.559",
    pages = "4547--4556",
    abstract = "This paper presents NorNE, a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokm{\aa}l and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. We here present details on the annotation effort, guidelines, inter-annotator agreement and an experimental analysis of the corpus using a neural sequence labeling architecture.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{loffler-etal-2020-tag,
    title = "Tag Me If You Can! Semantic Annotation of Biodiversity Metadata with the {QEMP} Corpus and the {B}iodiv{T}agger",
    author = {L{\"o}ffler, Felicitas  and
      Abdelmageed, Nora  and
      Babalou, Samira  and
      Kaur, Pawandeep  and
      K{\"o}nig-Ries, Birgitta},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.560",
    pages = "4557--4564",
    abstract = "Dataset Retrieval is gaining importance due to a large amount of research data and the great demand for reusing scientific data. Dataset Retrieval is mostly based on metadata, structured information about the primary data. Enriching these metadata with semantic annotations based on Linked Open Data (LOD) enables datasets, publications and authors to be connected and expands the search on semantically related terms. In this work, we introduce the BiodivTagger, an ontology-based Information Extraction pipeline, developed for metadata from biodiversity research. The system recognizes biological, physical and chemical processes, environmental terms, data parameters and phenotypes as well as materials and chemical compounds and links them to concepts in dedicated ontologies. To evaluate our pipeline, we created a gold standard of 50 metadata files (QEMP corpus) selected from five different data repositories in biodiversity research. To the best of our knowledge, this is the first annotated metadata corpus for biodiversity research data. The results reveal a mixed picture. While materials and data parameters are properly matched to ontological concepts in most cases, some ontological issues occurred for processes and environmental terms.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yada-etal-2020-towards,
    title = "Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases",
    author = "Yada, Shuntaro  and
      Joh, Ayami  and
      Tanaka, Ribeka  and
      Cheng, Fei  and
      Aramaki, Eiji  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.561",
    pages = "4565--4572",
    abstract = "Applying natural language processing (NLP) to medical and clinical texts can bring important social benefits by mining valuable information from unstructured text. A popular application for that purpose is named entity recognition (NER), but the annotation policies of existing clinical corpora have not been standardized across clinical texts of different types. This paper presents an annotation guideline aimed at covering medical documents of various types such as radiography interpretation reports and medical records. Furthermore, the annotation was designed to avoid burdensome requirements related to medical knowledge, thereby enabling corpus development without medical specialists. To achieve these design features, we specifically focus on critical lung diseases to stabilize linguistic patterns in corpora. After annotating around 1100 electronic medical records following the annotation scheme, we demonstrated its feasibility using an NER task. Results suggest that our guideline is applicable to large-scale clinical NLP projects.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{brandsen-etal-2020-creating,
    title = "Creating a Dataset for Named Entity Recognition in the Archaeology Domain",
    author = "Brandsen, Alex  and
      Verberne, Suzan  and
      Wansleeben, Milco  and
      Lambers, Karsten",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.562",
    pages = "4573--4577",
    abstract = "In this paper, we present the development of a training dataset for Dutch Named Entity Recognition (NER) in the archaeology domain. This dataset was created as there is a dire need for semantic search within archaeology, in order to allow archaeologists to find structured information in collections of Dutch excavation reports, currently totalling around 60,000 (658 million words) and growing rapidly. To guide this search task, NER is needed. We created rigorous annotation guidelines in an iterative process, then instructed five archaeology students to annotate a number of documents. The resulting dataset contains {\textasciitilde}31k annotations between six entity types (artefact, time period, place, context, species {\&} material). The inter-annotator agreement is 0.95, and when we used this data for machine learning, we observed an increase in F1 score from 0.51 to 0.70 in comparison to a machine learning model trained on a dataset created in prior work. This indicates that the data is of high quality, and can confidently be used to train NER classifiers.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zhang-etal-2020-development,
    title = "Development of a Medical Incident Report Corpus with Intention and Factuality Annotation",
    author = "Zhang, Hongkuan  and
      Sasano, Ryohei  and
      Takeda, Koichi  and
      Shui-Yee Wong, Zoie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.563",
    pages = "4578--4584",
    abstract = "Medical incident reports (MIRs) are documents that record what happened in a medical incident. A typical MIR consists of two sections: a structured categorical part and an unstructured text part. Most texts in MIRs describe what medication was intended to be given and what was actually given, because what happened in an incident is largely due to discrepancies between intended and actual medications. Recognizing the intention of clinicians and the factuality of medication is essential to understand the causes of medical incidents and avoid similar incidents in the future. Therefore, we are developing an MIR corpus with annotation of intention and factuality as well as of medication entities and their relations. In this paper, we present our annotation scheme with respect to the definition of medication entities that we take into account, the method to annotate the relations between entities, and the details of the intention and factuality annotation. We then report the annotated corpus consisting of 349 Japanese medical incident reports.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{faessler-etal-2020-progene,
    title = "{P}ro{G}ene - A Large-scale, High-Quality Protein-Gene Annotated Benchmark Corpus",
    author = "Faessler, Erik  and
      Modersohn, Luise  and
      Lohr, Christina  and
      Hahn, Udo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.564",
    pages = "4585--4596",
    abstract = "Genes and proteins constitute the fundamental entities of molecular genetics. We here introduce ProGene (formerly called FSU-PRGE), a corpus that reflects our efforts to cope with this important class of named entities within the framework of a long-lasting large-scale annotation campaign at the Jena University Language {\&} Information Engineering (JULIE) Lab. We assembled the entire corpus from 11 subcorpora covering various biological domains to achieve an overall subdomain-independent corpus. It consists of 3,308 MEDLINE abstracts with over 36k sentences and more than 960k tokens annotated with nearly 60k named entity mentions. Two annotators strove for carefully assigning entity mentions to classes of genes/proteins as well as families/groups, complexes, variants and enumerations of those where genes and proteins are represented by a single class. The main purpose of the corpus is to provide a large body of consistent and reliable annotations for supervised training and evaluation of machine learning algorithms in this relevant domain. Furthermore, we provide an evaluation of two state-of-the-art baseline systems {---} BioBert and flair {---} on the ProGene corpus. We make the evaluation datasets and the trained models available to encourage comparable evaluations of new methods in the future.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hvingelby-etal-2020-dane,
    title = "{D}a{NE}: A Named Entity Resource for {D}anish",
    author = "Hvingelby, Rasmus  and
      Pauli, Amalie Brogaard  and
      Barrett, Maria  and
      Rosted, Christina  and
      Lidegaard, Lasse Malm  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.565",
    pages = "4597--4604",
    abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ruppenhofer-etal-2020-fine,
    title = "Fine-grained Named Entity Annotations for {G}erman Biographic Interviews",
    author = "Ruppenhofer, Josef  and
      Rehbein, Ines  and
      Flinz, Carolina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.566",
    pages = "4605--4614",
    abstract = "We present a fine-grained NER annotations with 30 labels and apply it to German data. Building on the OntoNotes 5.0 NER inventory, our scheme is adapted for a corpus of transcripts of biographic interviews by adding categories for AGE and LAN(guage) and also features extended numeric and temporal categories. Applying the scheme to the spoken data as well as a collection of teaser tweets from newspaper sites, we can confirm its generality for both domains, also achieving good inter-annotator agreement. We also show empirically how our inventory relates to the well-established 4-category NER inventory by re-annotating a subset of the GermEval 2014 NER coarse-grained dataset with our fine label inventory. Finally, we use a BERT-based system to establish some baseline models for NER tagging on our two new datasets. Global results in in-domain testing are quite high on the two datasets, near what was achieved for the coarse inventory on the CoNLLL2003 data. Cross-domain testing produces much lower results due to the severe domain differences.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{luoma-etal-2020-broad,
    title = "A Broad-coverage Corpus for {F}innish Named Entity Recognition",
    author = {Luoma, Jouni  and
      Oinonen, Miika  and
      Pyyk{\"o}nen, Maria  and
      Laippala, Veronika  and
      Pyysalo, Sampo},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.567",
    pages = "4615--4624",
    abstract = "We present a new manually annotated corpus for broad-coverage named entity recognition for Finnish. Building on the original Universal Dependencies Finnish corpus of 754 documents (200,000 tokens) representing ten different genres of text, we introduce annotation marking person, organization, location, product and event names as well as dates. The new annotation identifies in total over 10,000 mentions. An evaluation of inter-annotator agreement indicates that the quality and consistency of annotation are high, at 94.5{\%} F-score for exact match. A comprehensive evaluation using state-of-the-art machine learning methods demonstrates that the new resource maintains compatibility with a previously released single-domain corpus for Finnish NER and makes it possible to recognize named entity mentions in texts drawn from most domains at precision and recall approaching or exceeding 90{\%}. Remaining challenges such as the identification of names in blog posts and transcribed speech are also identified. The newly introduced Turku NER corpus and related resources introduced in this work are released under open licenses via https://turkunlp.org/turku-ner-corpus .",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{consoli-etal-2020-embeddings,
    title = "Embeddings for Named Entity Recognition in Geoscience {P}ortuguese Literature",
    author = "Consoli, Bernardo  and
      Santos, Joaquim  and
      Gomes, Diogo  and
      Cordeiro, Fabio  and
      Vieira, Renata  and
      Moreira, Viviane",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.568",
    pages = "4625--4630",
    abstract = "This work focuses on Portuguese Named Entity Recognition (NER) in the Geology domain. The only domain-specific dataset in the Portuguese language annotated for NER is the GeoCorpus. Our approach relies on BiLSTM-CRF neural networks (a widely used type of network for this area of research) that use vector and tensor embedding representations. Three types of embedding models were used (Word Embeddings, Flair Embeddings, and Stacked Embeddings) under two versions (domain-specific and generalized). The domain specific Flair Embeddings model was originally trained with a generalized context in mind, but was then fine-tuned with domain-specific Oil and Gas corpora, as there simply was not enough domain corpora to properly train such a model. Each of these embeddings was evaluated separately, as well as stacked with another embedding. Finally, we achieved state-of-the-art results for this domain with one of our embeddings, and we performed an error analysis on the language model that achieved the best results. Furthermore, we investigated the effects of domain-specific versus generalized embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ortiz-suarez-etal-2020-establishing,
    title = "Establishing a New State-of-the-Art for {F}rench Named Entity Recognition",
    author = "Ortiz Su{\'a}rez, Pedro Javier  and
      Dupont, Yoann  and
      Muller, Benjamin  and
      Romary, Laurent  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.569",
    pages = "4631--4638",
    abstract = "The French TreeBank developed at the University Paris 7 is the main source of morphosyntactic and syntactic annotations for French. However, it does not include explicit information related to named entities, which are among the most useful information for several natural language processing tasks and applications. Moreover, no large-scale French corpus with named entity annotations contain referential information, which complement the type and the span of each mention with an indication of the entity it refers to. We have manually annotated the French TreeBank with such information, after an automatic pre-annotation step. We sketch the underlying annotation guidelines and we provide a few figures about the resulting annotations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lawrie-etal-2020-building,
    title = "Building {OCR}/{NER} Test Collections",
    author = "Lawrie, Dawn  and
      Mayfield, James  and
      Etter, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.570",
    pages = "4639--4646",
    abstract = "Named entity recognition (NER) identifies spans of text that contain names. Many researchers have reported the results of NER on text created through optical character recognition (OCR) over the past two decades. Unfortunately, the test collections that support this research are annotated with named entities after optical character recognition (OCR) has been run. This means that the collection must be re-annotated if the OCR output changes. Instead by tying annotations to character locations on the page, a collection can be built that supports OCR and NER research without requiring re-annotation when either improves. This means that named entities are annotated on the transcribed text. The transcribed text is all that is needed to evaluate the performance of OCR. For NER evaluation, the tagged OCR output is aligned to the transcriptions the aligned files, creating modified files of each, which are scored. This paper presents a methodology for building such a test collection and releases a collection of Chinese OCR-NER data constructed using the methodology. The paper provides performance baselines for current OCR and NER systems applied to this new collection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{marinova-etal-2020-reconstructing,
    title = "Reconstructing {NER} Corpora: a Case Study on {B}ulgarian",
    author = "Marinova, Iva  and
      Laskova, Laska  and
      Osenova, Petya  and
      Simov, Kiril  and
      Popov, Alexander",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.571",
    pages = "4647--4652",
    abstract = "The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{klimt-etal-2020-muclex,
    title = "{M}uc{L}ex: A {G}erman Lexicon for Surface Realisation",
    author = "Klimt, Kira  and
      Braun, Daniel  and
      Schneider, Daniela  and
      Matthes, Florian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.572",
    pages = "4653--4657",
    abstract = "Language resources for languages other than English are often scarce. Rule-based surface realisers need elaborate lexica in order to be able to generate correct language, especially in languages like German, which include many irregular word forms. In this paper, we present MucLex, a German lexicon for the Natural Language Generation task of surface realisation, based on the crowd-sourced online lexicon Wiktionary. MucLex contains more than 100,000 lemmata and more than 670,000 different word forms in a well-structured XML file and is available under the Creative Commons BY-SA 3.0 license.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hu-sun-2020-generating,
    title = "Generating Major Types of {C}hinese Classical Poetry in a Uniformed Framework",
    author = "Hu, Jinyi  and
      Sun, Maosong",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.573",
    pages = "4658--4663",
    abstract = "Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, thus is regarded as an ideal testing task for text generation. In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems. We define a unified format for formulating all types of training samples by integrating detailed form information, then present a simple form- stressed weighting method in GPT-2 to strengthen the control to the form of the generated poems, with special emphasis on those forms with longer body length. Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy. The model has been incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{shigeto-etal-2020-video,
    title = "Video Caption Dataset for Describing Human Actions in {J}apanese",
    author = "Shigeto, Yutaro  and
      Yoshikawa, Yuya  and
      Lin, Jiaqing  and
      Takeuchi, Akikazu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.574",
    pages = "4664--4670",
    abstract = "In recent years, automatic video caption generation has attracted considerable attention. This paper focuses on the generation of Japanese captions for describing human actions. While most currently available video caption datasets have been constructed for English, there is no equivalent Japanese dataset. To address this, we constructed a large-scale Japanese video caption dataset consisting of 79,822 videos and 399,233 captions. Each caption in our dataset describes a video in the form of {``}who does what and where.{''} To describe human actions, it is important to identify the details of a person, place, and action. Indeed, when we describe human actions, we usually mention the scene, person, and action. In our experiments, we evaluated two caption generation methods to obtain benchmark results. Further, we investigated whether those generation methods could specify {``}who does what and where.{''}",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wen-etal-2020-decode,
    title = "Decode with Template: Content Preserving Sentiment Transfer",
    author = "Wen, Zhiyuan  and
      Cao, Jiannong  and
      Yang, Ruosong  and
      Wang, Senzhang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.575",
    pages = "4671--4679",
    abstract = "Sentiment transfer aims to change the underlying sentiment of input sentences. The two major challenges in existing works lie in (1) effectively disentangling the original sentiment from input sentences; and (2) preserving the semantic content while transferring the sentiment. We find that identifying the sentiment-irrelevant content from input sentences to facilitate generating output sentences could address the above challenges and then propose the Decode with Template model in this paper. We first mask the explicit sentiment words in input sentences and use the rest parts as templates to eliminate the original sentiment. Then, we input the templates and the target sentiments into our bidirectionally guided variational auto-encoder (VAE) model to generate output. In our method, the template preserves most of the semantics in input sentences, and the bidirectionally guided decoding captures both forward and backward contextual information to generate output. Both two parts contribute to better content preservation. We evaluate our method on two review datasets, Amazon and Yelp, with automatic evaluation methods and human rating. The experimental results show that our method significantly outperforms state-of-the-art models, especially in content preservation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sauder-etal-2020-best,
    title = "Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation",
    author = "Sauder, Jonathan  and
      Hu, Ting  and
      Che, Xiaoyin  and
      Mordido, Goncalo  and
      Yang, Haojin  and
      Meinel, Christoph",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.576",
    pages = "4680--4688",
    abstract = "Language models trained with Maximum Likelihood Estimation (MLE) have been considered as a mainstream solution in Natural Language Generation (NLG) for years. Recently, various approaches with Generative Adversarial Nets (GANs) have also been proposed. While offering exciting new prospects, GANs in NLG by far are nevertheless reportedly suffering from training instability and mode collapse, and therefore outperformed by conventional MLE models. In this work, we propose techniques for improving GANs in NLG, namely Best Student Forcing (BSF), a novel yet simple adversarial training mechanism in which generated sequences of high quality are selected as temporary ground-truth to further train the generator. We also use an ensemble of discriminators to increase training stability and sample diversity. Evaluation shows that the combination of BSF and multiple discriminators consistently performs better than previous GAN approaches over various metrics, and outperforms a baseline MLE in terms of Fr ́ech ́et Distance, a recently proposed metric capturing both sample quality and diversity.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{martin-etal-2020-controllable,
    title = "Controllable Sentence Simplification",
    author = "Martin, Louis  and
      de la Clergerie, {\'E}ric  and
      Sagot, Beno{\^\i}t  and
      Bordes, Antoine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.577",
    pages = "4689--4698",
    abstract = "Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical. It is often considered an all-purpose generic task where the same simplification is suitable for all; however multiple audiences can benefit from simplified text in different ways. We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models. As a result, users can condition the simplifications returned by a model on attributes such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these attributes allow out-of-the-box Sequence-to-Sequence models to outperform their standard counterparts on simplification benchmarks. Our model, which we call ACCESS (as shorthand for AudienCe-CEntric Sentence Simplification), establishes the state of the art at 41.87 SARI on the WikiLarge test set, a +1.42 improvement over the best previously reported score.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{amin-nejad-etal-2020-exploring,
    title = "Exploring Transformer Text Generation for Medical Dataset Augmentation",
    author = "Amin-Nejad, Ali  and
      Ive, Julia  and
      Velupillai, Sumithra",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.578",
    pages = "4699--4708",
    abstract = "Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research. However, a big barrier to developments in this field is data access due to patient confidentiality which prohibits the sharing of this data, resulting in small, fragmented and sequestered openly available datasets. Since NLP model development requires large quantities of data, we aim to help side-step this roadblock by exploring the usage of Natural Language Generation in augmenting datasets such that they can be used for NLP model development on downstream clinically relevant tasks. We propose a methodology guiding the generation with structured patient information in a sequence-to-sequence manner. We experiment with state-of-the-art Transformer models and demonstrate that our augmented dataset is capable of beating our baselines on a downstream classification task. Finally, we also create a user interface and release the scripts to train generation models to stimulate further research in this area.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{liyanage-ranathunga-2020-multi,
    title = "Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features",
    author = "Liyanage, Vijini  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.579",
    pages = "4709--4716",
    abstract = "A Mathematical Word Problem (MWP) differs from a general textual representation due to the fact that it is comprised of numerical quantities and units, in addition to text. Therefore, MWP generation should be carefully handled. When it comes to multi-lingual MWP generation, language specific morphological and syntactic features become additional constraints. Standard template-based MWP generation techniques are incapable of identifying these language specific constraints, particularly in morphologically rich yet low resource languages such as Sinhala and Tamil. This paper presents the use of a Long Short Term Memory (LSTM) network that is capable of generating elementary level MWPs, while satisfying the aforementioned constraints. Our approach feeds a combination of character embeddings, word embeddings, and Part of Speech (POS) tag embeddings to the LSTM, in which attention is provided for numerical values and units. We trained our model for three languages, English, Sinhala and Tamil using separate MWP datasets. Irrespective of the language and the type of the MWP, our model could generate accurate single sentenced and multi sentenced problems. Accuracy reported in terms of average BLEU score for English, Sinhala and Tamil languages were 22.97{\%}, 24.49{\%} and 20.74{\%}, respectively.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{doughman-etal-2020-time,
    title = "Time-Aware Word Embeddings for Three {L}ebanese News Archives",
    author = "Doughman, Jad  and
      Abu Salem, Fatima  and
      Elbassuoni, Shady",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.580",
    pages = "4717--4725",
    abstract = "Word embeddings have proven to be an effective method for capturing semantic relations among distinct terms within a large corpus. In this paper, we present a set of word embeddings learnt from three large Lebanese news archives, which collectively consist of 609,386 scanned newspaper images and spanning a total of 151 years, ranging from 1933 till 2011. The diversified ideological nature of the news archives alongside the temporal variability of the embeddings offer a rare glimpse onto the variation of word representation across the left-right political spectrum. To train the word embeddings, Google{'}s Tesseract 4.0 OCR engine was employed to transcribe the scanned news archives, and various archive-level as well as decade-level word embeddings were learnt. To evaluate the accuracy of the learnt word embeddings, a benchmark of analogy tasks was used. Finally, we demonstrate an interactive system that allows the end user to visualize for a given word of interest, the variation of the top-k closest words in the embedding space as a function of time and across news archives using an animated scatter plot.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yang-etal-2020-ggp,
    title = "{GGP}: Glossary Guided Post-processing for Word Embedding Learning",
    author = "Yang, Ruosong  and
      Cao, Jiannong  and
      Wen, Zhiyuan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.581",
    pages = "4726--4730",
    abstract = "Word embedding learning is the task to map each word into a low-dimensional and continuous vector based on a large corpus. To enhance corpus based word embedding models, researchers utilize domain knowledge to learn more distinguishable representations via joint optimization and post-processing based models. However, joint optimization based models require much training time. Existing post-processing models mostly consider semantic knowledge while learned embedding models show less functional information. Glossary is a comprehensive linguistic resource. And in previous works, the glossary is usually used to enhance the word representations via joint optimization based methods. In this paper, we post-process pre-trained word embedding models with incorporating the glossary and capture more topical and functional information. We propose GGP (Glossary Guided Post-processing word embedding) model which consists of a global post-processing function to fine-tune each word vector, and an auto-encoding model to learn sense representations, furthermore, constrains each post-processed word representation and the composition of its sense representations to be similar. We evaluate our model by comparing it with two state-of-the-art models on six word topical/functional similarity datasets, and the results show that it outperforms competitors by an average of 4.1{\%} across all datasets. And our model outperforms GloVe by more than 7{\%}.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ulcar-robnik-sikonja-2020-high,
    title = "High Quality {ELM}o Embeddings for Seven Less-Resourced Languages",
    author = "Ul{\v{c}}ar, Matej  and
      Robnik-{\v{S}}ikonja, Marko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.582",
    pages = "4731--4738",
    abstract = "Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task. We offer precomputed embeddings from popular contextual ELMo model for seven languages: Croatian, Estonian, Finnish, Latvian, Lithuanian, Slovenian, and Swedish. We demonstrate that the quality of embeddings strongly depends on the size of training set and show that existing publicly available ELMo embeddings for listed languages shall be improved. We train new ELMo embeddings on much larger training sets and show their advantage over baseline non-contextual FastText embeddings. In evaluation, we use two benchmarks, the analogy task and the NER task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schneider-etal-2020-language,
    title = "Is Language Modeling Enough? Evaluating Effective Embedding Combinations",
    author = "Schneider, Rudolf  and
      Oberhauser, Tom  and
      Grundmann, Paul  and
      Gers, Felix Alexander  and
      Loeser, Alexander  and
      Staab, Steffen",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.583",
    pages = "4739--4748",
    abstract = "Universal embeddings, such as BERT or ELMo, are useful for a broad set of natural language processing tasks like text classification or sentiment analysis. Moreover, specialized embeddings also exist for tasks like topic modeling or named entity disambiguation. We study if we can complement these universal embeddings with specialized embeddings. We conduct an in-depth evaluation of nine well known natural language understanding tasks with SentEval. Also, we extend SentEval with two additional tasks to the medical domain. We present PubMedSection, a novel topic classification dataset focussed on the biomedical domain. Our comprehensive analysis covers 11 tasks and combinations of six embeddings. We report that combined embeddings outperform state of the art universal embeddings without any embedding fine-tuning. We observe that adding topic model based embeddings helps for most tasks and that differing pre-training tasks encode complementary features. Moreover, we present new state of the art results on the MPQA and SUBJ tasks in SentEval.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{maupome-meurs-2020-language,
    title = "Language Modeling with a General Second-Order {RNN}",
    author = "Maupom{\'e}, Diego  and
      Meurs, Marie-Jean",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.584",
    pages = "4749--4753",
    abstract = "Different Recurrent Neural Network (RNN) architectures update their state in different manners as the input sequence is processed. RNNs including a multiplicative interaction between their current state and the current input, second-order ones, show promising performance in language modeling. In this paper, we introduce a second-order RNNs that generalizes existing ones. Evaluating on the Penn Treebank dataset, we analyze how its different components affect its performance in character-lever recurrent language modeling. We perform our experiments controlling the parameter counts of models. We find that removing the first-order terms does not hinder performance. We perform further experiments comparing the effects of the relative size of the state space and the multiplicative interaction space on performance. Our expectation was that a larger states would benefit language models built on longer documents, and larger multiplicative interaction states would benefit ones built on larger input spaces. However, our results suggest that this is not the case and the optimal relative size is the same for both document tokenizations used.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schneidermann-etal-2020-towards,
    title = "Towards a Gold Standard for Evaluating {D}anish Word Embeddings",
    author = "Schneidermann, Nina  and
      Hvingelby, Rasmus  and
      Pedersen, Bolette",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.585",
    pages = "4754--4763",
    abstract = "This paper presents the process of compiling a model-agnostic similarity goal standard for evaluating Danish word embeddings based on human judgments made by 42 native speakers of Danish. Word embeddings resemble semantic similarity solely by distribution (meaning that word vectors do not reflect relatedness as differing from similarity), and we argue that this generalization poses a problem in most intrinsic evaluation scenarios. In order to be able to evaluate on both dimensions, our human-generated dataset is therefore designed to reflect the distinction between relatedness and similarity. The goal standard is applied for evaluating the {``}goodness{''} of six existing word embedding models for Danish, and it is discussed how a relatively low correlation can be explained by the fact that semantic similarity is substantially more challenging to model than relatedness, and that there seems to be a need for future human judgments to measure similarity in full context and along more than a single spectrum.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wilson-etal-2020-urban,
    title = "Urban Dictionary Embeddings for Slang {NLP} Applications",
    author = "Wilson, Steven  and
      Magdy, Walid  and
      McGillivray, Barbara  and
      Garimella, Kiran  and
      Tyson, Gareth",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.586",
    pages = "4764--4773",
    abstract = "The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word embeddings trained on the content of Urban Dictionary, a crowd-sourced dictionary for slang words and phrases. We show that although these embeddings are trained on fewer total tokens (by at least an order of magnitude compared to most popular pre-trained embeddings), they have high performance across a range of common word embedding evaluations, ranging from semantic similarity to word clustering tasks. Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kim-etal-2020-representation,
    title = "Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks",
    author = "Kim, Yeachan  and
      Kim, Kang-Min  and
      Lee, SangKeun",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.587",
    pages = "4774--4780",
    abstract = "Pre-trained word embeddings are widely used in various fields. However, the coverage of pre-trained word embeddings only includes words that appeared in corpora where pre-trained embeddings are learned. It means that the words which do not appear in training corpus are ignored in tasks, and it could lead to the limited performance of neural models. In this paper, we propose a simple yet effective method to represent out-of-vocabulary (OOV) words. Unlike prior works that solely utilize subword information or knowledge, our method makes use of both information to represent OOV words. To this end, we propose two stages of representation learning. In the first stage, we learn subword embeddings from the pre-trained word embeddings by using an additive composition function of subwords. In the second stage, we map the learned subwords into semantic networks (e.g., WordNet). We then re-train the subword embeddings by using lexical entries on semantic lexicons that could include newly observed subwords. This two-stage learning makes the coverage of words broaden to a great extent. The experimental results clearly show that our method provides consistent performance improvements over strong baselines that use subwords or lexical resources separately.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{agerri-etal-2020-give,
    title = "Give your Text Representation Models some Love: the Case for {B}asque",
    author = "Agerri, Rodrigo  and
      San Vicente, I{\~n}aki  and
      Campos, Jon Ander  and
      Barrena, Ander  and
      Saralegi, Xabier  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.588",
    pages = "4781--4788",
    abstract = "Word embeddings and pre-trained language models allow to build rich representations of text and have enabled improvements across most NLP tasks. Unfortunately they are very expensive to train, and many small companies and research groups tend to use models that have been pre-trained and made available by third parties, rather than building their own. This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora. In addition, monolingual pre-trained models for non-English languages are not always available. At best, models for those languages are included in multilingual versions, where each language shares the quota of substrings and parameters with the rest of the languages. This is particularly true for smaller languages such as Basque. In this paper we show that a number of monolingual models (FastText word embeddings, FLAIR and BERT language models) trained with larger Basque corpora produce much better results than publicly available versions in downstream NLP tasks, including topic classification, sentiment classification, PoS tagging and NER. This work sets a new state-of-the-art in those tasks for Basque. All benchmarks and models used in this work are publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{torregrossa-etal-2020-correlation,
    title = "On the Correlation of Word Embedding Evaluation Metrics",
    author = "Torregrossa, Fran{\c{c}}ois  and
      Claveau, Vincent  and
      Kooli, Nihel  and
      Gravier, Guillaume  and
      Allesiardo, Robin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.589",
    pages = "4789--4797",
    abstract = "Word embeddings intervene in a wide range of natural language processing tasks. These geometrical representations are easy to manipulate for automatic systems. Therefore, they quickly invaded all areas of language processing. While they surpass all predecessors, it is still not straightforward why and how they do so. In this article, we propose to investigate all kind of evaluation metrics on various datasets in order to discover how they correlate with each other. Those correlations lead to 1) a fast solution to select the best word embeddings among many others, 2) a new criterion that may improve the current state of static Euclidean word embeddings, and 3) a way to create a set of complementary datasets, i.e. each dataset quantifies a different aspect of word embeddings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{novak-etal-2020-cbow,
    title = "{CBOW}-tag: a Modified {CBOW} Algorithm for Generating Embedding Models from Annotated Corpora",
    author = "Nov{\'a}k, Attila  and
      Laki, L{\'a}szl{\'o}  and
      Nov{\'a}k, Borb{\'a}la",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.590",
    pages = "4798--4801",
    abstract = "In this paper, we present a modified version of the CBOW algorithm implemented in the fastText framework. Our modified algorithm, CBOW-tag builds a vector space model that includes the representation of the original word forms and their annotation at the same time. We illustrate the results by presenting a model built from a corpus that includes morphological and syntactic annotations. The simultaneous presence of unannotated elements and different annotations at the same time in the model makes it possible to constrain nearest neighbour queries to specific types of elements. The model can thus efficiently answer questions such as What do we eat?, What can we do with a skeleton? What else do we do with what we eat?, etc. Error analysis reveals that the model can highlight errors introduced into the annotation by the tagger and parser we used to generate the annotations as well as lexical peculiarities in the corpus itself, especially if we do not limit the vocabulary of the model to frequent items.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{domotor-etal-2020-much,
    title = "Much Ado About Nothing {--} Identification of Zero Copulas in {H}ungarian Using an {NMT} Model",
    author = {D{\"o}m{\"o}t{\"o}r, Andrea  and
      Yang, Zijian Gy{\H{o}}z{\H{o}}  and
      Nov{\'a}k, Attila},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.591",
    pages = "4802--4810",
    abstract = "The research presented in this paper concerns zero copulas in Hungarian, i.e. the phenomenon that nominal predicates lack an explicit verbal copula in the default present tense 3rd person indicative case. We created a tool based on the state-of-the-art transformer architecture implemented in Marian NMT framework that can identify and mark the location of zero copulas, i.e. the position where an overt copula would appear in the non-default cases. Our primary aim was to support quantitative corpus-based linguistic research by creating a tool that can be used to compile a corpus of significant size containing examples of nominal predicates including the location of the zero copulas. We created the training corpus for our system transforming sentences containing overt copulas into ones containing zero copula labels. However, we first needed to disambiguate occurrences of the massively ambiguous verb van {`}exist/be/have{'}. We performed this using a rule-base classifier relying on English translations in the English-Hungarian parallel subcorpus of the OpenSubtitles corpus. We created several NMT-based models using different sampling methods and optionally using our baseline model to synthesize additional training data. Our best model obtains almost 90{\%} precision and 80{\%} recall on an in-domain test set.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{martinc-etal-2020-leveraging,
    title = "Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift",
    author = "Martinc, Matej  and
      Kralj Novak, Petra  and
      Pollak, Senja",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.592",
    pages = "4811--4819",
    abstract = "We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddings. The results of our experiments in the domain specific LiverpoolFC corpus suggest that the proposed method has performance comparable to the current state-of-the-art without requiring any time consuming domain adaptation on large corpora. The results on the newly created Brexit news corpus suggest that the method can be successfully used for the detection of a short-term yearly semantic shift. And lastly, the model also shows promising results in a multilingual settings, where the task was to detect differences and similarities between diachronic semantic shifts in different languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dougal-lonsdale-2020-improving,
    title = "Improving {NMT} Quality Using Terminology Injection",
    author = "Dougal, Duane K.  and
      Lonsdale, Deryle",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.593",
    pages = "4820--4827",
    abstract = "Many organizations use domain- or organization-specific words and phrases. This paper explores the use of vetted terminology as an input to neural machine translation (NMT) for improved results: ensuring that the translation of individual terms is consistent with an approved multilingual terminology collection. We discuss, implement, and evaluate a method for injecting terminology and for evaluating terminology injection. Our use of the long short-term memory (LSTM) attention mechanism prevalent in state-of-the-art NMT systems involves attention vectors for correctly identifying semantic entities and aligning the tokens that represent them, both in the source and the target languages. Appropriate terminology is then injected into matching alignments during decoding. We also introduce a new translation metric more sensitive to approved terminological content in MT output.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{santos-etal-2020-word,
    title = "Word Embedding Evaluation in Downstream Tasks and Semantic Analogies",
    author = "Santos, Joaquim  and
      Consoli, Bernardo  and
      Vieira, Renata",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.594",
    pages = "4828--4834",
    abstract = "Language Models have long been a prolific area of study in the field of Natural Language Processing (NLP). One of the newer kinds of language models, and some of the most used, are Word Embeddings (WE). WE are vector space representations of a vocabulary learned by a non-supervised neural network based on the context in which words appear. WE have been widely used in downstream tasks in many areas of study in NLP. These areas usually use these vector models as a feature in the processing of textual data. This paper presents the evaluation of newly released WE models for the Portuguese langauage, trained with a corpus composed of 4.9 billion tokens. The first evaluation presented an intrinsic task in which WEs had to correctly build semantic and syntactic relations. The second evaluation presented an extrinsic task in which the WE models were used in two downstream tasks: Named Entity Recognition and Semantic Similarity between Sentences. Our results show that a diverse and comprehensive corpus can often outperform a larger, less textually diverse corpus, and that batch training may cause quality loss in WE models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lendvai-etal-2020-detection,
    title = "Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation",
    author = "Lendvai, Piroska  and
      Dar{\'a}nyi, S{\'a}ndor  and
      Geng, Christian  and
      Kuijpers, Moniek  and
      Lopez de Lacalle, Oier  and
      Mensonides, Jean-Christophe  and
      Rebora, Simone  and
      Reichel, Uwe",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.595",
    pages = "4835--4841",
    abstract = "To detect how and when readers are experiencing engagement with a literary work, we bring together empirical literary studies and language technology via focusing on the affective state of absorption. The goal of our resource development is to enable the detection of different levels of reading absorption in millions of user-generated reviews hosted on social reading platforms. We present a corpus of social book reviews in English that we annotated with reading absorption categories. Based on these data, we performed supervised, sentence level, binary classification of the explicit presence vs. absence of the mental state of absorption. We compared the performances of classical machine learners where features comprised sentence representations obtained from a pretrained embedding model (Universal Sentence Encoder) vs. neural classifiers in which sentence embedding vector representations are adapted or fine-tuned while training for the absorption recognition task. We discuss the challenges in creating the labeled data as well as the possibilities for releasing a benchmark corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alsudias-rayson-2020-developing,
    title = "Developing an {A}rabic Infectious Disease Ontology to Include Non-Standard Terminology",
    author = "Alsudias, Lama  and
      Rayson, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.596",
    pages = "4842--4850",
    abstract = "Building ontologies is a crucial part of the semantic web endeavour. In recent years, research interest has grown rapidly in supporting languages such as Arabic in NLP in general but there has been very little research on medical ontologies for Arabic. We present a new Arabic ontology in the infectious disease domain to support various important applications including the monitoring of infectious disease spread via social media. This ontology meaningfully integrates the scientific vocabularies of infectious diseases with their informal equivalents. We use ontology learning strategies with manual checking to build the ontology. We applied three statistical methods for term extraction from selected Arabic infectious diseases articles: TF-IDF, C-value, and YAKE. We also conducted a study, by consulting around 100 individuals, to discover the informal terms related to infectious diseases in Arabic. In future work, we will automatically extract the relations for infectious disease concepts but for now these are manually created. We report two complementary experiments to evaluate the ontology. First, a quantitative evaluation of the term extraction results and an additional qualitative evaluation by a domain expert.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{oliver-2020-aligning,
    title = "Aligning {W}ikipedia with {W}ord{N}et:a Review and Evaluation of Different Techniques",
    author = "Oliver, Antoni",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.597",
    pages = "4851--4858",
    abstract = "In this paper we explore techniques for aligning Wikipedia articles with WordNet synsets, their successful alignment being our main goal. We evaluate techniques that use the definitions and sense relations in Wordnet and the text and categories in Wikipedia articles. The results we present are based on two evaluation strategies: one uses a new gold and silver standard (for which the creation process is explained); the other creates wordnets in other languages and then compares them with existing wordnets for those languages found in the Open Multilingual Wordnet project. A reliable alignment between WordNet and Wikipedia is a very valuable resource for the creation of new wordnets in other languages and for the development of existing wordnets. The evaluation of alignments between WordNet and lexical resources is a difficult and time-consuming task, but the evaluation strategy using the Open Multilingual Wordnet can be used as an automated evaluation measure to assess the quality of alignments between these two resources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{branco-etal-2020-mwn,
    title = "The {MWN}.{PT} {W}ord{N}et for {P}ortuguese: Projection, Validation, Cross-lingual Alignment and Distribution",
    author = "Branco, Ant{\'o}nio  and
      Grilo, Sara  and
      Bolrinha, M{\'a}rcia  and
      Saedi, Chakaveh  and
      Branco, Ruben  and
      Silva, Jo{\~a}o  and
      Querido, Andreia  and
      de Carvalho, Rita  and
      Gaudio, Rosa  and
      Avel{\~a}s, Mariana  and
      Pinto, Clara",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.598",
    pages = "4859--4866",
    abstract = "The objective of the present paper is twofold, to present the MWN.PT WordNet and to report on its construction and on the lessons learned with it. The MWN.PT WordNet for Portuguese includes 41,000 concepts, expressed by 38,000 lexical units. Its synsets were manually validated and are linked to semantically equivalent synsets of the Princeton WordNet of English, and thus transitively to the many wordnets for other languages that are also linked to this English wordnet. To the best of our knowledge, it is the largest high quality, manually validated and cross-lingually integrated, wordnet of Portuguese distributed for reuse. Its construction was initiated more than one decade ago and its description is published for the first time in the present paper. It follows a three step {\textless}projection, validation with alignment, completion{\textgreater} methodology consisting on the manual validation and expansion of the outcome of an automatic projection procedure of synsets and their hypernym relations, followed by another automatic procedure that transferred the relations of remaining semantic types across wordnets of different languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bou-etal-2020-ontology,
    title = "Ontology-Style Relation Annotation: A Case Study",
    author = "Bou, Savong  and
      Suzuki, Naoki  and
      Miwa, Makoto  and
      Sasaki, Yutaka",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.599",
    pages = "4867--4876",
    abstract = "This paper proposes an Ontology-Style Relation (OSR) annotation approach. In conventional Relation Extraction (RE) datasets, relations are annotated as links between entity mentions. In contrast, in our OSR annotation, a relation is annotated as a relation mention (i.e., not a link but a node) and domain and range links are annotated from the relation mention to its argument entity mentions. We expect the following benefits: (1) the relation annotations can be easily converted to Resource Description Framework (RDF) triples to populate an Ontology, (2) some part of conventional RE tasks can be tackled as Named Entity Recognition (NER) tasks. The relation classes are limited to several RDF properties such as domain, range, and subClassOf, and (3) OSR annotations can be clear documentations of Ontology contents. As a case study, we converted an in-house corpus of Japanese traffic rules in conventional annotations into the OSR annotations and built a novel OSR-RoR (Rules of the Road) corpus. The inter-annotator agreements of the conversion were 85-87{\%}. We evaluated the performance of neural NER and RE tools on the conventional and OSR annotations. The experimental results showed that the OSR annotations make the RE task easier while introducing slight complexity into the NER task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dekova-2020-ontology,
    title = "The Ontology of {B}ulgarian Dialects {--} Architecture and Information Retrieval",
    author = "Dekova, Rositsa",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.600",
    pages = "4877--4882",
    abstract = "Following a concise description of the structure, the paper focuses on the potential of the Ontology of the Bulgarian Dialects, which demonstrates a novel usage of the ontological modelling for the purposes of dialect digital archiving and information processing. The ontology incorporates information on the dialects of the Bulgarian language and includes data from 84 dialects, spoken not only on the territory of the Republic of Bulgaria, but also abroad. It encodes both their geographical distribution and some of their main diagnostic features, such as the different mutations (also referred to as reflexes) of some of the Old Bulgarian vowels. The mutations modelled so far in the ontology include the reflex of the back nasal vowel /ѫ/ under stress, the reflex of the back er vowel /ъ/ under stress, and the reflex of the yat vowel /ѣ/ under stress when it precedes a syllable with a back vowel. Besides the opportunity for formal structuring of the considerable amount of data gathered through the years by dialectologists, the ontology also provides numerous possibilities for information retrieval {--} searches by dialect, country, dialect region, city or village, various combinations of diagnostic features.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bonn-etal-2020-spatial,
    title = "Spatial {AMR}: Expanded Spatial Annotation in the Context of a Grounded {M}inecraft Corpus",
    author = "Bonn, Julia  and
      Palmer, Martha  and
      Cai, Zheng  and
      Wright-Bettner, Kristin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.601",
    pages = "4883--4892",
    abstract = "This paper presents an expansion to the Abstract Meaning Representation (AMR) annotation schema that captures fine-grained semantically and pragmatically derived spatial information in grounded corpora. We describe a new lexical category conceptualization and set of spatial annotation tools built in the context of a multimodal corpus consisting of 170 3D structure-building dialogues between a human architect and human builder in Minecraft. Minecraft provides a particularly beneficial spatial relation-elicitation environment because it automatically tracks locations and orientations of objects and avatars in the space according to an absolute Cartesian coordinate system. Through a two-step process of sentence-level and document-level annotation designed to capture implicit information, we leverage these coordinates and bearings in the AMRs in combination with spatial framework annotation to ground the spatial language in the dialogues to absolute space.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{klubicka-etal-2020-english,
    title = "{E}nglish {W}ord{N}et Random Walk Pseudo-Corpora",
    author = "Klubi{\v{c}}ka, Filip  and
      Maldonado, Alfredo  and
      Mahalunkar, Abhijit  and
      Kelleher, John",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.602",
    pages = "4893--4902",
    abstract = "This is a resource description paper that describes the creation and properties of a set of pseudo-corpora generated artificially from a random walk over the English WordNet taxonomy. Our WordNet taxonomic random walk implementation allows the exploration of different random walk hyperparameters and the generation of a variety of different pseudo-corpora. We find that different combinations of parameters result in varying statistical properties of the generated pseudo-corpora. We have published a total of 81 pseudo-corpora that we have used in our previous research, but have not exhausted all possible combinations of hyperparameters, which is why we have also published a codebase that allows the generation of additional WordNet taxonomic pseudo-corpora as needed. Ultimately, such pseudo-corpora can be used to train taxonomic word embeddings, as a way of transferring taxonomic knowledge into a word embedding space.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vezzani-di-nunzio-2020-formal,
    title = "On the Formal Standardization of Terminology Resources: The Case Study of {T}ri{MED}",
    author = "Vezzani, Federica  and
      Di Nunzio, Giorgio Maria",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.603",
    pages = "4903--4910",
    abstract = "The process of standardization plays an important role in the management of terminological resources. In this context, we present the work of re-modeling an existing multilingual terminological database for the medical domain, named TriMED. This resource was conceived in order to tackle some problems related to the complexity of medical terminology and to respond to different users{'} needs. We provide a methodology that should be followed in order to make a termbase compliant to the three most recent ISO/TC 37 standards. In particular, we focus on the definition of i) the structural meta-model of the resource, ii) the data categories provided, and iii) the TBX format for its implementation. In addition to the formal standardization of the resource, we describe the realization of a new data category repository for the management of the TriMED terminological data and a Web application that can be used to access the multilingual terminological records.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alsiyat-piao-2020-metaphorical,
    title = "Metaphorical Expressions in Automatic {A}rabic Sentiment Analysis",
    author = "Alsiyat, Israa  and
      Piao, Scott",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.604",
    pages = "4911--4916",
    abstract = "Over the recent years, Arabic language resources and NLP tools have been under rapid development. One of the important tasks for Arabic natural language processing is the sentiment analysis. While a significant improvement has been achieved in this research area, the existing computational models and tools still suffer from the lack of capability of dealing with Arabic metaphorical expressions. Metaphor has an important role in Arabic language due to its unique history and culture. Metaphors provide a linguistic mechanism for expressing ideas and notions that can be different from their surface form. Therefore, in order to efficiently identify true sentiment of Arabic language data, a computational model needs to be able to {``}read between lines{''}. In this paper, we examine the issue of metaphors in automatic Arabic sentiment analysis by carrying out an experiment, in which we observe the performance of a state-of-art Arabic sentiment tool on metaphors and analyse the result to gain a deeper insight into the issue. Our experiment evidently shows that metaphors have a significant impact on the performance of current Arabic sentiment tools, and it is an important task to develop Arabic language resources and computational models for Arabic metaphors.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{antognini-faltings-2020-hotelrec,
    title = "{H}otel{R}ec: a Novel Very Large-Scale Hotel Recommendation Dataset",
    author = "Antognini, Diego  and
      Faltings, Boi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.605",
    pages = "4917--4923",
    abstract = "Today, recommender systems are an inevitable part of everyone{'}s daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models require a large number of data to achieve their best performance. Many datasets fulfilling this criterion have been proposed for multiple domains, such as Amazon products, restaurants, or beers. However, works and datasets in the hotel domain are limited: the largest hotel review dataset is below the million samples. Additionally, the hotel domain suffers from a higher data sparsity than traditional recommendation datasets and therefore, traditional collaborative-filtering approaches cannot be applied to such data. In this paper, we propose HotelRec, a very large-scale hotel recommendation dataset, based on TripAdvisor, containing 50 million reviews. To the best of our knowledge, HotelRec is the largest publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). We release HotelRec for further research: https://github.com/Diego999/HotelRec.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{van-den-berg-etal-2020-doctor,
    title = "Doctor Who? Framing Through Names and Titles in {G}erman",
    author = "van den Berg, Esther  and
      Korfhage, Katharina  and
      Ruppenhofer, Josef  and
      Wiegand, Michael  and
      Markert, Katja",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.606",
    pages = "4924--4932",
    abstract = "Entity framing is the selection of aspects of an entity to promote a particular viewpoint towards that entity. We investigate entity framing of political figures through the use of names and titles in German online discourse, enhancing current research in entity framing through titling and naming that concentrates on English only. We collect tweets that mention prominent German politicians and annotate them for stance. We find that the formality of naming in these tweets correlates positively with their stance. This confirms sociolinguistic observations that naming and titling can have a status-indicating function and suggests that this function is dominant in German tweets mentioning political figures. We also find that this status-indicating function is much weaker in tweets from users that are politically left-leaning than in tweets by right-leaning users. This is in line with observations from moral psychology that left-leaning and right-leaning users assign different importance to maintaining social hierarchies.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rietzler-etal-2020-adapt,
    title = "Adapt or Get Left Behind: Domain Adaptation through {BERT} Language Model Finetuning for Aspect-Target Sentiment Classification",
    author = "Rietzler, Alexander  and
      Stabinger, Sebastian  and
      Opitz, Paul  and
      Engl, Stefan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.607",
    pages = "4933--4941",
    abstract = "Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e.g. in e-commerce, where data and insights from reviews can be leveraged to create value for businesses and customers. Recently, deep transfer-learning methods have been applied successfully to a myriad of Natural Language Processing (NLP) tasks, including ATSC. Building on top of the prominent BERT language model, we approach ATSC using a two-step procedure: self-supervised domain-specific BERT language model finetuning, followed by supervised task-specific finetuning. Our findings on how to best exploit domain-specific language model finetuning enable us to produce new state-of-the-art performance on the SemEval 2014 Task 4 restaurants dataset. In addition, to explore the real-world robustness of our models, we perform cross-domain evaluation. We show that a cross-domain adapted BERT language model performs significantly better than strong baseline models like vanilla BERT-base and XLNet-base. Finally, we conduct a case study to interpret model prediction errors.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kang-eshkol-taravella-2020-empirical,
    title = "An Empirical Examination of Online Restaurant Reviews",
    author = "Kang, Hyun Jung  and
      Eshkol-Taravella, Iris",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.608",
    pages = "4942--4947",
    abstract = "In the wake of (Pang et al., 2002; Turney, 2002; Liu, 2012) inter alia, opinion mining and sentiment analysis have focused on extracting either positive or negative opinions from texts and determining the targets of these opinions. In this study, we go beyond the coarse-grained positive vs. negative opposition and propose a corpus-based scheme that detects evaluative language at a finer-grained level. We classify each sentence into one of four evaluation types based on the proposed scheme: (1) the reviewer{'}s opinion on the restaurant (positive, negative, or mixed); (2) the reviewer{'}s input/feedback to potential customers and restaurant owners (suggestion, advice, or warning) (3) whether the reviewer wants to return to the restaurant (intention); (4) the factual statement about the experience (description). We apply classical machine learning and deep learning methods to show the effectiveness of our scheme. We also interpret the performances that we obtained for each category by taking into account the specificities of the corpus treated.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kameswari-mamidi-2020-manovaad,
    title = "{M}anovaad: A Novel Approach to Event Oriented Corpus Creation Capturing Subjectivity and Focus",
    author = "Kameswari, Lalitha  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.609",
    pages = "4948--4954",
    abstract = "In today{'}s era of globalisation, the increased outreach for every event across the world has been leading to conflicting opinions, arguments and disagreements, often reflected in print media and online social platforms. It is necessary to distinguish factual observations from personal judgements in news, as subjectivity in reporting can influence the audience{'}s perception of reality. Several studies conducted on the different styles of reporting in journalism are essential in understanding phenomena such as media bias and multiple interpretations of the same event. This domain finds applications in fields such as Media Studies, Discourse Analysis, Information Extraction, Sentiment Analysis, and Opinion Mining. We present an event corpus Manovaad-v1.0 consisting of 1035 news articles corresponding to 65 events from 3 levels of newspapers viz., Local, National, and International levels. Using this novel format, we correlate the trends in the degree of subjectivity with the geographical closeness of reporting using a Bi-RNN model. We also analyse the role of background and focus in event reporting and capture the focus shift patterns within a global discourse structure for an event. We do this across different levels of reporting and compare the results with the existing work on discourse processing.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{barhoumi-etal-2020-toward,
    title = "Toward Qualitative Evaluation of Embeddings for {A}rabic Sentiment Analysis",
    author = "Barhoumi, Amira  and
      Camelin, Nathalie  and
      Aloulou, Chafik  and
      Est{\`e}ve, Yannick  and
      Hadrich Belguith, Lamia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.610",
    pages = "4955--4963",
    abstract = "In this paper, we propose several protocols to evaluate specific embeddings for Arabic sentiment analysis (SA) task. In fact, Arabic language is characterized by its agglutination and morphological richness contributing to great sparsity that could affect embedding quality. This work presents a study that compares embeddings based on words and lemmas in SA frame. We propose first to study the evolution of embedding models trained with different types of corpora (polar and non polar) and explore the variation between embeddings by observing the sentiment stability of neighbors in embedding spaces. Then, we evaluate embeddings with a neural architecture based on convolutional neural network (CNN). We make available our pre-trained embeddings to Arabic NLP research community with free to use. We provide also for free resources used to evaluate our embeddings. Experiments are done on the Large Arabic-Book Reviews (LABR) corpus in binary (positive/negative) classification frame. Our best result reaches 91.9{\%}, that is higher than the best previous published one (91.5{\%}).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{morante-etal-2020-annotating,
    title = "Annotating Perspectives on Vaccination",
    author = "Morante, Roser  and
      van Son, Chantal  and
      Maks, Isa  and
      Vossen, Piek",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.611",
    pages = "4964--4973",
    abstract = "In this paper we present the Vaccination Corpus, a corpus of texts related to the online vaccination debate that has been annotated with three layers of information about perspectives: attribution, claims and opinions. Additionally, events related to the vaccination debate are also annotated. The corpus contains 294 documents from the Internet which reflect different views on vaccinations. It has been compiled to study the language of online debates, with the final goal of experimenting with methodologies to extract and contrast perspectives in the framework of the vaccination debate.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chinea-rios-etal-2020-aspect,
    title = "Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning",
    author = "Chinea-Rios, Mara  and
      Franco-Salvador, Marc  and
      Benajiba, Yassine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.612",
    pages = "4974--4981",
    abstract = "The task of aspect extraction is an important component of aspect-based sentiment analysis. However, it usually requires an expensive human post-processing to ensure quality. In this work we introduce Aspect On, an interactive solution based on online learning that allows users to post-edit the aspect extraction with little effort. The Aspect On interface shows the aspects extracted by a neural model and, given a dataset, annotates its words with the corresponding aspects. Thanks to the online learning, Aspect On updates the model automatically and continuously improves the quality of the aspects displayed to the user. Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sheoran-etal-2020-recommendation,
    title = "Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study",
    author = "Sheoran, Akash  and
      Kanojia, Diptesh  and
      Joshi, Aditya  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.613",
    pages = "4982--4990",
    abstract = "Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient. However, the decision to choose a domain (known as the source domain) to leverage from is, at best, intuitive. In this paper, we investigate text similarity metrics to facilitate source domain selection for CDSA. We report results on 20 domains (all possible pairs) using 11 similarity metrics. Specifically, we compare CDSA performance with these metrics for different domain-pairs to enable the selection of a suitable source domain, given a target domain. These metrics include two novel metrics for evaluating domain adaptability to help source domain selection of labelled data and utilize word and sentence-based embeddings as metrics for unlabelled data. The goal of our experiments is a recommendation chart that gives the K best source domains for CDSA for a given target domain. We show that the best K source domains returned by our similarity metrics have a precision of over 50{\%}, for varying values of K.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yan-etal-2020-inference,
    title = "Inference Annotation of a {C}hinese Corpus for Opinion Mining",
    author = "Yan, Liyun  and
      E, Danni  and
      Gan, Mei  and
      Grouin, Cyril  and
      Valette, Mathieu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.614",
    pages = "4991--4999",
    abstract = "Polarity classification (positive, negative or neutral opinion detection) is well developed in the field of opinion mining. However, existing tools, which perform with high accuracy on short sentences and explicit expressions, have limited success interpreting narrative phrases and inference contexts. In this article, we will discuss an important aspect of opinion mining: inference. We will give our definition of inference, classify different types, provide an annotation framework and analyze the annotation results. While inferences are often studied in the field of Natural-language understanding (NLU), we propose to examine inference as it relates to opinion mining. Firstly, based on linguistic analysis, we clarify what kind of sentence contains an inference. We define five types of inference: logical inference, pragmatic inference, lexical inference, enunciative inference and discursive inference. Second, we explain our annotation framework which includes both inference detection and opinion mining. In short, this manual annotation determines whether or not a target contains an inference. If so, we then define inference type, polarity and topic. Using the results of this annotation, we observed several correlation relations which will be used to determine distinctive features for automatic inference classification in further research. We also demonstrate the results of three preliminary classification experiments.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mohammadi-etal-2020-cooking,
    title = "Cooking Up a Neural-based Model for Recipe Classification",
    author = "Mohammadi, Elham  and
      Naji, Nada  and
      Marceau, Louis  and
      Queudot, Marc  and
      Charton, Eric  and
      Kosseim, Leila  and
      Meurs, Marie-Jean",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.615",
    pages = "5000--5009",
    abstract = "In this paper, we propose a neural-based model to address the first task of the DEFT 2013 shared task, with the main challenge of a highly imbalanced dataset, using state-of-the-art embedding approaches and deep architectures. We report on our experiments on the use of linguistic features, extracted by Charton et. al. (2014), in different neural models utilizing pretrained embeddings. Our results show that all of the models that use linguistic features outperform their counterpart models that only use pretrained embeddings. The best performing model uses pretrained CamemBERT embeddings as input and CNN as the hidden layer, and uses additional linguistic features. Adding the linguistic features to this model improves its performance by 4.5{\%} and 11.4{\%} in terms of micro and macro F1 scores, respectively, leading to state-of-the-art results and an improved classification of the rare classes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schulder-etal-2020-enhancing,
    title = "Enhancing a Lexicon of Polarity Shifters through the Supervised Classification of Shifting Directions",
    author = "Schulder, Marc  and
      Wiegand, Michael  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.616",
    pages = "5010--5016",
    abstract = "The sentiment polarity of an expression (whether it is perceived as positive, negative or neutral) can be influenced by a number of phenomena, foremost among them negation. Apart from closed-class negation words like {``}no{''}, {``}not{''} or {``}without{''}, negation can also be caused by so-called polarity shifters. These are content words, such as verbs, nouns or adjectives, that shift polarities in their opposite direction, e.g. {``}abandoned{''} in {``}abandoned hope{''} or {``}alleviate{''} in {``}alleviate pain{''}. Many polarity shifters can affect both positive and negative polar expressions, shifting them towards the opposing polarity. However, other shifters are restricted to a single shifting direction. {``}Recoup{''} shifts negative to positive in {``}recoup your losses{''}, but does not affect the positive polarity of {``}fortune{''} in {``}recoup a fortune{''}. Existing polarity shifter lexica only specify whether a word can, in general, cause shifting, but they do not specify when this is limited to one shifting direction. To address this issue we introduce a supervised classifier that determines the shifting direction of shifters. This classifier uses both resource-driven features, such as WordNet relations, and data-driven features like in-context polarity conflicts. Using this classifier we enhance the largest available polarity shifter lexicon.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{regatte-etal-2020-dataset,
    title = "Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in {T}elugu, a Low Resource Language",
    author = "Regatte, Yashwanth Reddy  and
      Gangula, Rama Rohit Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.617",
    pages = "5017--5024",
    abstract = "In recent years, sentiment analysis has gained popularity as it is essential to moderate and analyse the information across the internet. It has various applications like opinion mining, social media monitoring, and market research. Aspect Based Sentiment Analysis (ABSA) is an area of sentiment analysis which deals with sentiment at a finer level. ABSA classifies sentiment with respect to each aspect to gain greater insights into the sentiment expressed. Significant contributions have been made in ABSA, but this progress is limited only to a few languages with adequate resources. Telugu lags behind in this area of research despite being one of the most spoken languages in India and an enormous amount of data being created each day. In this paper, we create a reliable resource for aspect based sentiment analysis in Telugu. The data is annotated for three tasks namely Aspect Term Extraction, Aspect Polarity Classification and Aspect Categorisation. Further, we develop baselines for the tasks using deep learning methods demonstrating the reliability and usefulness of the resource.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ovrelid-etal-2020-fine,
    title = "A Fine-grained Sentiment Dataset for {N}orwegian",
    author = "{\O}vrelid, Lilja  and
      M{\ae}hlum, Petter  and
      Barnes, Jeremy  and
      Velldal, Erik",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.618",
    pages = "5025--5033",
    abstract = "We here introduce NoReC{\_}fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. The underlying texts are taken from a corpus of professionally authored reviews from multiple news-sources and across a wide variety of domains, including literature, games, music, products, movies and more. We here present a detailed description of this annotation effort. We provide an overview of the developed annotation guidelines, illustrated with examples and present an analysis of inter-annotator agreement. We also report the first experimental results on the dataset, intended as a preliminary benchmark for further experiments.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gong-etal-2020-design,
    title = "The Design and Construction of a {C}hinese Sarcasm Dataset",
    author = "Gong, Xiaochang  and
      Zhao, Qin  and
      Zhang, Jun  and
      Mao, Ruibin  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.619",
    pages = "5034--5039",
    abstract = "As a typical multi-layered semi-conscious language phenomenon, sarcasm is widely existed in social media text for enhancing the emotion expression. Thus, the detection and processing of sarcasm is important to social media analysis.However, most existing sarcasm dataset are in English and there is still a lack of authoritative Chinese sarcasm dataset. In this paper, we presents the design and construction of a largest high-quality Chinese sarcasm dataset, which contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcastic texts. Furthermore, a balanced dataset through elaborately sampling the same amount non-sarcastic texts for training sarcasm classifier. Using the dataset as the benchmark, some sarcasm classification methods are evaluated.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yuan-etal-2020-target,
    title = "Target-based Sentiment Annotation in {C}hinese Financial News",
    author = "Yuan, Chaofa  and
      Liu, Yuhan  and
      Yin, Rongdi  and
      Zhang, Jun  and
      Zhu, Qinling  and
      Mao, Ruibin  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.620",
    pages = "5040--5045",
    abstract = "This paper presents the design and construction of a large-scale target-based sentiment annotation corpus on Chinese financial news text. Different from the most existing paragraph/document-based annotation corpus, in this study, target-based fine-grained sentiment annotation is performed. The companies, brands and other financial entities are regarded as the targets. The clause reflecting the profitability, loss or other business status of financial entities is regarded as the sentiment expression for determining the polarity. Based on high quality annotation guideline and effective quality control strategy, a corpus with 8,314 target-level sentiment annotation is constructed on 6,336 paragraphs from Chinese financial news text. Based on this corpus, several state-of-the-art sentiment analysis models are evaluated.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{-etal-2020-multi,
    title = "Multi-domain Tweet Corpora for Sentiment Analysis: Resource Creation and Evaluation",
    author = "., Mamta  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak  and
      Srivastava, Shikha  and
      Kumar, Alka  and
      Saha, Tista",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.621",
    pages = "5046--5054",
    abstract = "Due to the phenomenal growth of online content in recent time, sentiment analysis has attracted attention of the researchers and developers. A number of benchmark annotated corpora are available for domains like movie reviews, product reviews, hotel reviews, etc.The pervasiveness of social media has also lead to a huge amount of content posted by users who are misusing the power of social media to spread false beliefs and to negatively influence others. This type of content is coming from the domains like terrorism, cybersecurity, technology, social issues, etc. Mining of opinions from these domains is important to create a socially intelligent system to provide security to the public and to maintain the law and order situations. To the best of our knowledge, there is no publicly available tweet corpora for such pervasive domains. Hence, we firstly create a multi-domain tweet sentiment corpora and then establish a deep neural network based baseline framework to address the above mentioned issues. Annotated corpus has Cohen{'}s Kappa measurement for annotation quality of 0.770, which shows that the data is of acceptable quality. We are able to achieve 84.65{\%} accuracy for sentiment analysis by using an ensemble of Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit(GRU).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{antonio-rodrigues-etal-2020-reproduction,
    title = "Reproduction and Revival of the Argument Reasoning Comprehension Task",
    author = "Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Branco, Ruben  and
      Silva, Jo{\~a}o  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.622",
    pages = "5055--5064",
    abstract = "Reproduction of scientific findings is essential for scientific development across all scientific disciplines and reproducing results of previous works is a basic requirement for validating the hypothesis and conclusions put forward by them. This paper reports on the scientific reproduction of several systems addressing the Argument Reasoning Comprehension Task of SemEval2018. Given a recent publication that pointed out spurious statistical cues in the data set used in the shared task, and that produced a revised version of it, we also evaluated the reproduced systems with this new data set. The exercise reported here shows that, in general, the reproduction of these systems is successful with scores in line with those reported in SemEval2018. However, the performance scores are worst than those, and even below the random baseline, when the reproduced systems are run over the revised data set expunged from data artifacts. This demonstrates that this task is actually a much harder challenge than what could have been perceived from the inflated, close to human-level performance scores obtained with the data set used in SemEval2018. This calls for a revival of this task as there is much room for improvement until systems may come close to the upper bound provided by human performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moreno-ortiz-etal-2020-design,
    title = "Design and Evaluation of {S}enti{E}con: a fine-grained Economic/Financial Sentiment Lexicon from a Corpus of Business News",
    author = "Moreno-Ortiz, Antonio  and
      Fernandez-Cruz, Javier  and
      Hern{\'a}ndez, Chantal P{\'e}rez Chantal",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.623",
    pages = "5065--5072",
    abstract = "In this paper we present, describe, and evaluate SentiEcon, a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications, for which we compiled our own corpus of online business news. SentiEcon was created as a plug-in lexicon for the sentiment analysis tool Lingmotif, and thus it follows its data structure requirements and presupposes the availability of a general-language core sentiment lexicon that covers non-specific sentiment-carrying terms and phrases. It contains 6,470 entries, both single and multi-word expressions, each with tags denoting their semantic orientation and intensity. We evaluate SentiEcon{'}s performance by comparing results in a sentence classification task using exclusively sentiment words as features. This sentence dataset was extracted from business news texts, and included certain key words known to recurrently convey strong semantic orientation, such as {``}debt{''}, {``}inflation{''} or {``}markets{''}. The results show that performance is significantly improved when adding SentiEcon to the general-language sentiment lexicon.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abercrombie-batista-navarro-2020-parlvote,
    title = "{P}arl{V}ote: A Corpus for Sentiment Analysis of Political Debates",
    author = "Abercrombie, Gavin  and
      Batista-Navarro, Riza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.624",
    pages = "5073--5078",
    abstract = "Debate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for people to process manually. While sentiment analysis of debate speeches could facilitate understanding of the speakers{'} stated opinions, datasets currently available for this task are small when compared to the benchmark corpora in other domains. We present ParlVote, a new, larger corpus of parliamentary debate speeches for use in the evaluation of sentiment analysis systems for the political domain. We also perform a number of initial experiments on this dataset, testing a variety of approaches to the classification of sentiment polarity in debate speeches. These include a linear classifier as well as a neural network trained using a transformer word embedding model (BERT), and fine-tuned on the parliamentary speeches. We find that in many scenarios, a linear classifier trained on a bag-of-words text representation achieves the best results. However, with the largest dataset, the transformer-based model combined with a neural classifier provides the best performance. We suggest that further experimentation with classification models and observations of the debate content and structure are required, and that there remains much room for improvement in parliamentary sentiment analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tian-kubler-2020-offensive,
    title = "Offensive Language Detection Using Brown Clustering",
    author = {Tian, Zuoyu  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.625",
    pages = "5079--5087",
    abstract = "In this study, we investigate the use of Brown clustering for offensive language detection. Brown clustering has been shown to be of little use when the task involves distinguishing word polarity in sentiment analysis tasks. In contrast to previous work, we train Brown clusters separately on positive and negative sentiment data, but then combine the information into a single complex feature per word. This way of representing words results in stable improvements in offensive language detection, when used as the only features or in combination with words or character n-grams. Brown clusters add important information, even when combined with words or character n-grams or with standard word embeddings in a convolutional neural network. However, we also found different trends between the two offensive language data sets we used.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{assimakopoulos-etal-2020-annotating,
    title = "Annotating for Hate Speech: The {M}a{N}e{C}o Corpus and Some Input from Critical Discourse Analysis",
    author = "Assimakopoulos, Stavros  and
      Vella Muskat, Rebecca  and
      van der Plas, Lonneke  and
      Gatt, Albert",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.626",
    pages = "5088--5097",
    abstract = "This paper presents a novel scheme for the annotation of hate speech in corpora of Web 2.0 commentary. The proposed scheme is motivated by the critical analysis of posts made in reaction to news reports on the Mediterranean migration crisis and LGBTIQ+ matters in Malta, which was conducted under the auspices of the EU-funded C.O.N.T.A.C.T. project. Based on the realisation that hate speech is not a clear-cut category to begin with, appears to belong to a continuum of discriminatory discourse and is often realised through the use of indirect linguistic means, it is argued that annotation schemes for its detection should refrain from directly including the label {`}hate speech,{'} as different annotators might have different thresholds as to what constitutes hate speech and what not. In view of this, we propose a multi-layer annotation scheme, which is pilot-tested against a binary {\mbox{$\pm$}}hate speech classification and appears to yield higher inter-annotator agreement. Motivating the postulation of our scheme, we then present the MaNeCo corpus on which it will eventually be used; a substantial corpus of on-line newspaper comments spanning 10 years.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cignarella-etal-2020-marking,
    title = "Marking Irony Activators in a {U}niversal {D}ependencies Treebank: The Case of an {I}talian {T}witter Corpus",
    author = "Cignarella, Alessandra Teresa  and
      Sanguinetti, Manuela  and
      Bosco, Cristina  and
      Rosso, Paolo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.627",
    pages = "5098--5105",
    abstract = "The recognition of irony is a challenging task in the domain of Sentiment Analysis, and the availability of annotated corpora may be crucial for its automatic processing. In this paper we describe a fine-grained annotation scheme centered on irony, in which we highlight the tokens that are responsible for its activation, (irony activators) and their morpho-syntactic features. As our case study we therefore introduce a recently released Universal Dependencies treebank for Italian which includes ironic tweets: TWITTIR{\`O}-UD. For the purposes of this study, we enriched the existing annotation in the treebank, with a further level that includes irony activators. A description and discussion of the annotation scheme is provided with a definition of irony activators and the guidelines for their annotation. This qualitative study on the different layers of annotation applied on the same dataset can shed some light on the process of human annotation, and irony annotation in particular, and on the usefulness of this representation for developing computational models of irony to be used for training purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chiruzzo-etal-2020-haha,
    title = "{HAHA} 2019 Dataset: A Corpus for Humor Analysis in {S}panish",
    author = "Chiruzzo, Luis  and
      Castro, Santiago  and
      Ros{\'a}, Aiala",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.628",
    pages = "5106--5112",
    abstract = "This paper presents the development of a corpus of 30,000 Spanish tweets that were crowd-annotated with humor value and funniness score. The corpus contains approximately 38.6{\%} of humorous tweets with an average score of 2.04 in a scale from 1 to 5 for the humorous tweets. The corpus has been used in an automatic humor recognition and analysis competition, obtaining encouraging results from the participants.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{pitenis-etal-2020-offensive,
    title = "Offensive Language Identification in {G}reek",
    author = "Pitenis, Zesis  and
      Zampieri, Marcos  and
      Ranasinghe, Tharindu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.629",
    pages = "5113--5119",
    abstract = "As offensive language has become a rising issue for online communities and social media platforms, researchers have been investigating ways of coping with abusive content and developing systems to detect its different types: cyberbullying, hate speech, aggression, etc. With a few notable exceptions, most research on this topic so far has dealt with English. This is mostly due to the availability of language resources for English. To address this shortcoming, this paper presents the first Greek annotated dataset for offensive language identification: the Offensive Greek Tweet Dataset (OGTD). OGTD is a manually annotated dataset containing 4,779 posts from Twitter annotated as offensive and not offensive. Along with a detailed description of the dataset, we evaluate several computational models trained and tested on this data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bick-2020-syntax,
    title = "Syntax and Semantics in a Treebank for {E}speranto",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.630",
    pages = "5120--5127",
    abstract = "In this paper we describe and evaluate syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, as well as tools and methods used in the production of the treebank. In addition to classical morphosyntax and dependency structure, the treebank was enriched with a lexical-semantic layer covering named entities, a semantic type ontology for nouns and adjectives and a framenet-inspired semantic classification of verbs. For an under-resourced language, the quality of automatic syntactic and semantic pre-annotation is of obvious importance, and by evaluating the underlying parser and the coverage of its semantic ontologies, we try to answer the question whether the language{'}s extremely regular morphology and transparent semantic affixes translate into a more regular syntax and higher parsing accuracy. On the linguistic side, the treebank allows us to address and quantify typological issues such as the question of word order, auxiliary constructions, lexical transparency and semantic type ambiguity in Esperanto.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dione-2020-implementation,
    title = "Implementation and Evaluation of an {LFG}-based Parser for {W}olof",
    author = "Dione, Cheikh M. Bamba",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.631",
    pages = "5128--5136",
    abstract = "This paper reports on a parsing system for Wolof based on the LFG formalism. The parser covers core constructions of Wolof, including noun classes, cleft, copula, causative and applicative sentences. It also deals with several types of coordination, including same constituent coordination, asymmetric and asyndetic coordination. The system uses a cascade of finite-state transducers for word tokenization and morphological analysis as well as various lexicons. In addition, robust parsing techniques, including fragmenting and skimming, are used to optimize grammar coverage. Parsing coverage is evaluated by running test-suites of naturally occurring Wolof sentences through the parser. The evaluation of parsing coverage reveals that 72.72{\%} of the test sentences receive full parses; 27.27{\%} receive partial parses. To measure accuracy, the parsed sentences are disambiguated manually using an incremental parsebanking approach based on discriminants. The evaluation of parsing quality reveals that the parser achieves 67.2{\%} recall, 92.8{\%} precision and an f-score of 77.9{\%}.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hellwig-etal-2020-treebank,
    title = "The Treebank of Vedic {S}anskrit",
    author = "Hellwig, Oliver  and
      Scarlata, Salvatore  and
      Ackermann, Elia  and
      Widmer, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.632",
    pages = "5137--5146",
    abstract = "This paper introduces the first treebank of Vedic Sanskrit, a morphologically rich ancient Indian language that is of central importance for linguistic and historical research. The selection of the more than 3,700 sentences contained in this treebank reflects the development of metrical and prose texts over a period of 600 years. We discuss how these sentences are annotated in the Universal Dependencies scheme and which syntactic constructions required special attention. In addition, we describe a syntactic labeler based on neural networks that supports the initial annotation of the treebank, and whose evaluation can be helpful for setting up a full syntactic parser of Vedic Sanskrit.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{anderson-gomez-rodriguez-2020-inherent,
    title = "Inherent Dependency Displacement Bias of Transition-Based Algorithms",
    author = "Anderson, Mark  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.633",
    pages = "5147--5155",
    abstract = "A wide variety of transition-based algorithms are currently used for dependency parsers. Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different treebank. There is often no discernible reason for what causes one algorithm to be more suitable for a certain treebank and less so for another. In this paper we shed some light on this by introducing the concept of an algorithm{'}s inherent dependency displacement distribution. This characterises the bias of the algorithm in terms of dependency displacement, which quantify both distance and direction of syntactic relations. We show that the similarity of an algorithm{'}s inherent distribution to a treebank{'}s displacement distribution is clearly correlated to the algorithm{'}s parsing performance on that treebank, specificially with highly significant and substantial correlations for the predominant sentence lengths in Universal Dependency treebanks. We also obtain results which show a more discrete analysis of dependency displacement does not result in any meaningful correlations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kayadelen-etal-2020-gold,
    title = "A Gold Standard Dependency Treebank for {T}urkish",
    author = "Kayadelen, Tolga  and
      Ozturel, Adnan  and
      Bohnet, Bernd",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.634",
    pages = "5156--5163",
    abstract = "We introduce TWT; a new treebank for Turkish which consists of web and Wikipedia sentences that are annotated for segmentation, morphology, part-of-speech and dependency relations. To date, it is the largest publicly available human-annotated morpho-syntactic Turkish treebank in terms of the annotated word count. It is also the first large Turkish dependency treebank that has a dedicated Wikipedia section. We present the tagsets and the methodology that are used in annotating the treebank and also the results of the baseline experiments on Turkish dependency parsing with this treebank.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{eshkol-taravella-etal-2020-chunk,
    title = "Chunk Different Kind of Spoken Discourse: Challenges for Machine Learning",
    author = "Eshkol-Taravella, Iris  and
      Maarouf, Mariame  and
      Badin, Flora  and
      Skrovec, Marie  and
      Tellier, Isabelle",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.635",
    pages = "5164--5168",
    abstract = "This paper describes the development of a chunker for spoken data by supervised machine learning using the CRFs, based on a small reference corpus composed of two kinds of discourse: prepared monologue vs. spontaneous talk in interaction. The methodology considers the specific character of the spoken data. The machine learning uses the results of several available taggers, without correcting the results manually. Experiments show that the discourse type (monologue vs. free talk), the speech nature (spontaneous vs. prepared) and the corpus size can influence the results of the machine learning process and must be considered while interpreting the results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{falenska-etal-2020-grain,
    title = "{GRAIN}-{S}: Manually Annotated Syntax for {G}erman Interviews",
    author = {Falenska, Agnieszka  and
      Czesznak, Zolt{\'a}n  and
      Jung, Kerstin  and
      V{\"o}lkel, Moritz  and
      Seeker, Wolfgang  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.636",
    pages = "5169--5177",
    abstract = "We present GRAIN-S, a set of manually created syntactic annotations for radio interviews in German. The dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews. The rare combination of gold- and silver-standard annotation layers coming from GRAIN with high-quality syntax trees can serve as a useful resource for speech- and text-based research. Moreover, since interviews can be put between carefully prepared speech and spontaneous conversational speech, they cover phenomena not seen in traditional newspaper-based treebanks. Therefore, GRAIN-S can contribute to research into techniques for model adaptation and for building more corpus-independent tools. GRAIN-S follows TIGER, one of the established syntactic treebanks of German. We describe the annotation process and discuss decisions necessary to adapt the original TIGER guidelines to the interviews domain. Next, we give details on the conversion from TIGER-style trees to dependency trees. We provide data statistics and demonstrate differences between the new dataset and existing out-of-domain test sets annotated with TIGER syntactic structures. Finally, we provide baseline parsing results for further comparison.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ishola-zeman-2020-yoruba,
    title = "{Y}or{\`u}b{\'a} Dependency Treebank ({YTB})",
    author = "Ishola, Ol{\'a}j{\'\i}d{\'e}  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.637",
    pages = "5178--5186",
    abstract = "Low-resource languages present enormous NLP opportunities as well as varying degrees of difficulties. The newly released treebank of hand-annotated parts of the Yoruba Bible provides an avenue for dependency analysis of the Yoruba language; the application of a new grammar formalism to the language. In this paper, we discuss our choice of Universal Dependencies, important dependency annotation decisions considered in the creation of the first annotation guidelines for Yoruba and results of our parsing experiments. We also lay the foundation for future incorporation of other domains with the initial test on Yoruba Wikipedia articles and highlighted future directions for the rapid expansion of the treebank.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yamakata-etal-2020-english,
    title = "{E}nglish Recipe Flow Graph Corpus",
    author = "Yamakata, Yoko  and
      Mori, Shinsuke  and
      Carroll, John",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.638",
    pages = "5187--5194",
    abstract = "We present an annotated corpus of English cooking recipe procedures, and describe and evaluate computational methods for learning these annotations. The corpus consists of 300 recipes written by members of the public, which we have annotated with domain-specific linguistic and semantic structure. Each recipe is annotated with (1) {`}recipe named entities{'} (r-NEs) specific to the recipe domain, and (2) a flow graph representing in detail the sequencing of steps, and interactions between cooking tools, food ingredients and the products of intermediate steps. For these two kinds of annotations, inter-annotator agreement ranges from 82.3 to 90.5 F1, indicating that our annotation scheme is appropriate and consistent. We experiment with producing these annotations automatically. For r-NE tagging we train a deep neural network NER tool; to compute flow graphs we train a dependency-style parsing procedure which we apply to the entire sequence of r-NEs in a recipe.In evaluations, our systems achieve 71.1 to 87.5 F1, demonstrating that our annotation scheme is learnable.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kubota-etal-2020-development,
    title = "Development of a General-Purpose Categorial Grammar Treebank",
    author = "Kubota, Yusuke  and
      Mineshima, Koji  and
      Hayashi, Noritsugu  and
      Okano, Shinya",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.639",
    pages = "5195--5201",
    abstract = "This paper introduces ABC Treebank, a general-purpose categorial grammar (CG) treebank for Japanese. It is {`}general-purpose{'} in the sense that it is not tailored to a specific variant of CG, but rather aims to offer a theory-neutral linguistic resource (as much as possible) which can be converted to different versions of CG (specifically, CCG and Type-Logical Grammar) relatively easily. In terms of linguistic analysis, it improves over the existing Japanese CG treebank (Japanese CCGBank) on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates) for which the lexical specification of the syntactic information reflecting local dependencies turns out to be crucial. In this paper, we describe the underlying {`}theory{'} dubbed ABC Grammar that is taken as a basis for our treebank, outline the general construction of the corpus, and report on some preliminary results applying the treebank in a semantic parsing system for generating logical representations of sentences.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ehsan-butt-2020-dependency,
    title = "Dependency Parsing for {U}rdu: Resources, Conversions and Learning",
    author = "Ehsan, Toqeer  and
      Butt, Miriam",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.640",
    pages = "5202--5207",
    abstract = "This paper adds to the available resources for the under-resourced language Urdu by converting different types of existing treebanks for Urdu into a common format that is based on Universal Dependencies. We present comparative results for training two dependency parsers, the MaltParser and a transition-based BiLSTM parser on this new resource. The BiLSTM parser incorporates word embeddings which improve the parsing results significantly. The BiLSTM parser outperforms the MaltParser with a UAS of 89.6 and an LAS of 84.2 with respect to our standardized treebank resource.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hajic-etal-2020-prague,
    title = "{P}rague Dependency Treebank - Consolidated 1.0",
    author = "Haji{\v{c}}, Jan  and
      Bej{\v{c}}ek, Eduard  and
      Hlavacova, Jaroslava  and
      Mikulov{\'a}, Marie  and
      Straka, Milan  and
      {\v{S}}t{\v{e}}p{\'a}nek, Jan  and
      {\v{S}}t{\v{e}}p{\'a}nkov{\'a}, Barbora",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.641",
    pages = "5208--5218",
    abstract = "We present a richly annotated and genre-diversified language resource, the Prague Dependency Treebank-Consolidated 1.0 (PDT-C 1.0), the purpose of which is - as it always been the case for the family of the Prague Dependency Treebanks - to serve both as a training data for various types of NLP tasks as well as for linguistically-oriented research. PDT-C 1.0 contains four different datasets of Czech, uniformly annotated using the standard PDT scheme (albeit not everything is annotated manually, as we describe in detail here). The texts come from different sources: daily newspaper articles, Czech translation of the Wall Street Journal, transcribed dialogs and a small amount of user-generated, short, often non-standard language segments typed into a web translator. Altogether, the treebank contains around 180,000 sentences with their morphological, surface and deep syntactic annotation. The diversity of the texts and annotations should serve well the NLP applications as well as it is an invaluable resource for linguistic research, including comparative studies regarding texts of different genres. The corpus is publicly and freely available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{johansson-adesam-2020-training,
    title = "Training a {S}wedish Constituency Parser on Six Incompatible Treebanks",
    author = "Johansson, Richard  and
      Adesam, Yvonne",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.642",
    pages = "5219--5224",
    abstract = "We investigate a transition-based parser that uses Eukalyptus, a function-tagged constituent treebank for Swedish which includes discontinuous constituents. In addition, we show that the accuracy of this parser can be improved by using a multitask learning architecture that makes it possible to train the parser on additional treebanks that use other annotation models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{vacareanu-etal-2020-parsing,
    title = "Parsing as Tagging",
    author = "Vacareanu, Robert  and
      Gouveia Barbosa, George Caique  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.643",
    pages = "5225--5231",
    abstract = "We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the {``}tag{''} to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fern{\'a}ndez-Gonz{\'a}lez and G{\'o}mez-Rodr{\'\i}guez, 2019) on Universal Dependencies (UD) by 1.76{\%} unlabeled attachment score (UAS) for English, 1.98{\%} UAS for French, and 1.16{\%} UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11{\%} UAS, and worse by 0.58{\%} LAS.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bouma-etal-2020-edges,
    title = "The {EDG}e{S} Diachronic {B}ible Corpus",
    author = "Bouma, Gerlof  and
      Couss{\'e}, Evie  and
      Dijkstra, Trude  and
      van der Sijs, Nicoline",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.644",
    pages = "5232--5239",
    abstract = "We present the EDGeS Diachronic Bible Corpus: a diachronically and synchronically parallel corpus of Bible translations in Dutch, English, German and Swedish, with texts from the 14th century until today. It is compiled in the context of an intended longitudinal and contrastive study of complex verb constructions in Germanic. The paper discusses the corpus design principles, its selection of 36 Bibles, and the information and metadata encoded for the corpus texts. The EDGeS corpus will be available in two forms: the whole corpus will be accessible for researchers behind a login in the well-known OPUS search infrastructure, and the open subpart of the corpus will be available for download.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sanguinetti-etal-2020-treebanking,
    title = "Treebanking User-Generated Content: A Proposal for a Unified Representation in {U}niversal {D}ependencies",
    author = {Sanguinetti, Manuela  and
      Bosco, Cristina  and
      Cassidy, Lauren  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      Cignarella, Alessandra Teresa  and
      Lynn, Teresa  and
      Rehbein, Ines  and
      Ruppenhofer, Josef  and
      Seddah, Djam{\'e}  and
      Zeldes, Amir},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.645",
    pages = "5240--5250",
    abstract = "The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{berdicevskis-eckhoff-2020-diachronic,
    title = "A Diachronic Treebank of {R}ussian Spanning More Than a Thousand Years",
    author = "Berdicevskis, Aleksandrs  and
      Eckhoff, Hanne",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.646",
    pages = "5251--5256",
    abstract = "We describe the Troms{\o} Old Russian and Old Church Slavonic Treebank (TOROT) that spans from the earliest Old Church Slavonic to modern Russian texts, covering more than a thousand years of continuous language history. We focus on the latest additions to the treebank, first of all, the modern subcorpus that was created by a high-quality conversion of the existing treebank of contemporary standard Russian (SynTagRus).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kogkalidis-etal-2020-aethel,
    title = "{{\AE}THEL}: Automatically Extracted Typelogical Derivations for {D}utch",
    author = "Kogkalidis, Konstantinos  and
      Moortgat, Michael  and
      Moot, Richard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.647",
    pages = "5257--5266",
    abstract = "We present {\AE}THEL, a semantic compositionality dataset for written Dutch. {\AE}THEL consists of two parts. First, it contains a lexicon of supertags for about 900 000 words in context. The supertags correspond to types of the simply typed linear lambda-calculus, enhanced with dependency decorations that capture grammatical roles supplementary to function-argument structures. On the basis of these types, {\AE}THEL further provides 72 192 validated derivations, presented in four formats: natural-deduction and sequent-style proofs, linear logic proofnets and the associated programs (lambda terms) for meaning composition. {\AE}THEL{'}s types and derivations are obtained by means of an extraction algorithm applied to the syntactic analyses of LASSY Small, the gold standard corpus of written Dutch. We discuss the extraction algorithm and show how {`}virtual elements{'} in the original LASSY annotation of unbounded dependencies and coordination phenomena give rise to higher-order types. We suggest some example usecases highlighting the benefits of a type-driven approach at the syntax semantics interface. The following resources are open-sourced with {\AE}THEL: the lexical mappings between words and types, a subset of the dataset consisting of 7 924 semantic parses, and the Python code that implements the extraction algorithm.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gessler-etal-2020-amalgum,
    title = "{AMALGUM} {--} A Free, Balanced, Multilayer {E}nglish Web Corpus",
    author = "Gessler, Luke  and
      Peng, Siyao  and
      Liu, Yang  and
      Zhu, Yilun  and
      Behzad, Shabnam  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.648",
    pages = "5267--5275",
    abstract = "We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a {``}better than NLP{''} benchmark and evaluate the accuracy of the resulting resource.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{quasthoff-etal-2020-typical,
    title = "Typical Sentences as a Resource for Valence",
    author = {Quasthoff, Uwe  and
      Hellan, Lars  and
      K{\"o}rner, Erik  and
      Eckart, Thomas  and
      Goldhahn, Dirk  and
      Beermann, Dorothee},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.649",
    pages = "5276--5281",
    abstract = "Verb valence information can be derived from corpora by using subcorpora of typical sentences that are constructed in a language independent manner based on frequent POS structures. The inspection of typical sentences with a fixed verb in a certain position can show the valence information directly. Using verb fingerprints, consisting of the most typical sentence patterns the verb appears in, we are able to identify standard valence patterns and compare them against a language{'}s valence profile. With a very limited number of training data per language, valence information for other verbs can be derived as well. Based on the Norwegian valence patterns we are able to find comparative patterns in German where typical sentences are able to express the same situation in an equivalent way and can so construct verb valence pairs for a bilingual PolyVal dictionary. This contribution discusses this application with a focus on the Norwegian valence dictionary NorVal.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hildebrand-etal-2020-recognizing,
    title = "Recognizing Sentence-level Logical Document Structures with the Help of Context-free Grammars",
    author = "Hildebrand, Jonathan  and
      Hemati, Wahed  and
      Mehler, Alexander",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.650",
    pages = "5282--5290",
    abstract = "Current sentence boundary detectors split documents into sequentially ordered sentences by detecting their beginnings and ends. Sentences, however, are more deeply structured even on this side of constituent and dependency structure: they can consist of a main sentence and several subordinate clauses as well as further segments (e.g. inserts in parentheses); they can even recursively embed whole sentences and then contain multiple sentence beginnings and ends. In this paper, we introduce a tool that segments sentences into tree structures to detect this type of recursive structure. To this end, we retrain different constituency parsers with the help of modified training data to transform them into sentence segmenters. With these segmenters, documents are mapped to sequences of sentence-related {``}logical document structures{''}. The resulting segmenters aim to improve downstream tasks by providing additional structural information. In this context, we experiment with German dependency parsing. We show that for certain sentence categories, which can be determined automatically, improvements in German dependency parsing can be achieved using our segmenter for preprocessing. The assumption suggests that improvements in other languages and tasks can be achieved.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{guibon-etal-2020-collaborative,
    title = "When Collaborative Treebank Curation Meets Graph Grammars",
    author = {Guibon, Ga{\"e}l  and
      Courtin, Marine  and
      Gerdes, Kim  and
      Guillaume, Bruno},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.651",
    pages = "5291--5300",
    abstract = "In this paper we present Arborator-Grew, a collaborative annotation tool for treebank development. Arborator-Grew combines the features of two preexisting tools: Arborator and Grew. Arborator is a widely used collaborative graphical online dependency treebank annotation tool. Grew is a tool for graph querying and rewriting specialized in structures needed in NLP, i.e. syntactic and semantic dependency trees and graphs. Grew also has an online version, Grew-match, where all Universal Dependencies treebanks in their classical, deep and surface-syntactic flavors can be queried. Arborator-Grew is a complete redevelopment and modernization of Arborator, replacing its own internal database storage by a new Grew API, which adds a powerful query tool to Arborator{'}s existing treebank creation and correction features. This includes complex access control for parallel expert and crowd-sourced annotation, tree comparison visualization, and various exercise modes for teaching and training of annotators. Arborator-Grew opens up new paths of collectively creating, updating, maintaining, and curating syntactic treebanks and semantic graph banks.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wang-etal-2020-odil,
    title = "{ODIL}{\_}{S}yntax: a Free Spontaneous Spoken {F}rench Treebank Annotated with Constituent Trees",
    author = {Wang, Ilaine  and
      Pelletier, Aurore  and
      Antoine, Jean-Yves  and
      Halftermeyer, Ana{\"\i}s},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.652",
    pages = "5301--5307",
    abstract = "This paper describes ODIL Syntax, a French treebank built on spontaneous speech transcripts. The syntactic structure of every speech turn is represented by constituent trees, through a procedure which combines an automatic annotation provided by a parser (here, the Stanford Parser) and a manual revision. ODIL Syntax respects the annotation scheme designed for the French TreeBank (FTB), with the addition of some annotation guidelines that aims at representing specific features of the spoken language such as speech disfluencies. The corpus will be freely distributed by January 2020 under a Creative Commons licence. It will ground a further semantic enrichment dedicated to the representation of temporal entities and temporal relations, as a second phase of the ODIL@Temporal project. The paper details the annotation scheme we followed with a emphasis on the representation of speech disfluencies. We then present the annotation procedure that was carried out on the Contemplata annotation platform. In the last section, we provide some distributional characteristics of the annotated corpus (POS distribution, multiword expressions).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wroblewska-2020-towards,
    title = "Towards the Conversion of {N}ational {C}orpus of {P}olish to {U}niversal {D}ependencies",
    author = "Wr{\'o}blewska, Alina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.653",
    pages = "5308--5315",
    abstract = "The research presented in this paper aims at enriching the manually morphosyntactically annotated part of National Corpus of Polish (NKJP1M) with a syntactic layer, i.e. dependency trees of sentences, and at converting both dependency trees and morphosyntactic annotations of particular tokens to Universal Dependencies. The dependency layer is built using a semi-automatic annotation procedure. The sentences from NKJP1M are first parsed with a dependency parser trained on Polish Dependency Bank, i.e. the largest bank of Polish dependency trees. The predicted dependency trees and the morphosyntactic annotations of tokens are then automatically converted into UD dependency graphs. NKJP1M sentences are an essential part of Polish Dependency Bank, we thus replace some automatically predicted dependency trees with their manually annotated equivalents. The final dependency treebank consists of 86K trees (including 15K gold-standard trees). A natural language pre-processing model trained on the enlarged set of (possibly noisy) dependency trees outperforms a model trained on a smaller set of the gold-standard trees in predicting part-of-speech tags, morphological features, lemmata, and labelled dependency trees",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{grossman-etal-2020-segbo,
    title = "{S}eg{B}o: A Database of Borrowed Sounds in the World{'}s Languages",
    author = "Grossman, Eitan  and
      Eisen, Elad  and
      Nikolaev, Dmitry  and
      Moran, Steven",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.654",
    pages = "5316--5322",
    abstract = "Phonological segment borrowing is a process through which languages acquire new contrastive speech sounds as the result of borrowing new words from other languages. Despite the fact that phonological segment borrowing is documented in many of the world{'}s languages, to date there has been no large-scale quantitative study of the phenomenon. In this paper, we present SegBo, a novel cross-linguistic database of borrowed phonological segments. We describe our data aggregation pipeline and the resulting language sample. We also present two short case studies based on the database. The first deals with the impact of large colonial languages on the sound systems of the world{'}s languages; the second deals with universals of borrowing in the domain of rhotic consonants.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lancien-etal-2020-developing,
    title = "Developing Resources for Automated Speech Processing of {Q}uebec {F}rench",
    author = "Lancien, M{\'e}lanie  and
      C{\^o}t{\'e}, Marie-H{\'e}l{\`e}ne  and
      Bigi, Brigitte",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.655",
    pages = "5323--5328",
    abstract = "The analysis of the structure of speech nearly always rests on the alignment of the speech recording with a phonetic transcription. Nowadays several tools can perform this speech segmentation automatically. However, none of them allows the automatic segmentation of Quebec French (QF hereafter), the acoustics and phonotactics of QF differing widely from that of France French (FF hereafter). To adequately segment QF, features like diphthongization of long vowels and affrication of coronal stops have to be taken into account. Thus acoustic models for automatic segmentation must be trained on speech samples exhibiting those phenomena. Dictionaries and lexicons must also be adapted and integrate differences in lexical units and in the phonology of QF. This paper presents the development of linguistic resources to be included into SPPAS software tool in order to get Text normalization, Phonetization, Alignment and Syllabification. We adapted the existing French lexicon and developed a QF-specific pronunciation dictionary. We then created an acoustic model from the existing ones and adapted it with 5 minutes of manually time-aligned data. These new resources are all freely distributed with SPPAS version 2.7; they perform the full process of speech segmentation in Quebec French.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mortensen-etal-2020-allovera,
    title = "{A}llo{V}era: A Multilingual Allophone Database",
    author = "Mortensen, David R.  and
      Li, Xinjian  and
      Littell, Patrick  and
      Michaud, Alexis  and
      Rijhwani, Shruti  and
      Anastasopoulos, Antonios  and
      Black, Alan W  and
      Metze, Florian  and
      Neubig, Graham",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.656",
    pages = "5329--5336",
    abstract = "We introduce a new resource, AlloVera, which provides mappings from 218 allophones to phonemes for 14 languages. Phonemes are contrastive phonological units, and allophones are their various concrete realizations, which are predictable from phonological context. While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription. AlloVera allows the training of speech recognition models that output phonetic transcriptions in the International Phonetic Alphabet (IPA), regardless of the input language. We show that a {``}universal{''} allophone model, Allosaurus, built with AlloVera, outperforms {``}universal{''} phonemic models and language-specific models on a speech-transcription task. We explore the implications of this technology (and related technologies) for the documentation of endangered and minority languages. We further explore other applications for which AlloVera will be suitable as it grows, including phonological typology.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ibrahim-etal-2020-arabic,
    title = "{A}rabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles",
    author = "Ibrahim, Omnia  and
      Asadi, Homa  and
      Kassem, Eman  and
      Dellwo, Volker",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.657",
    pages = "5337--5342",
    abstract = "Databases for studying speech rhythm and tempo exist for numerous languages. The present corpus was built to allow comparisons between Arabic speech rhythm and other languages. 10 Egyptian speakers (gender-balanced) produced speech in two different speaking styles (read and spontaneous). The design of the reading task replicates the methodology used in the creation of BonnTempo corpus (BTC). During the spontaneous task, speakers talked freely for more than one minute about their daily life and/or their studies, then they described the directions to come to the university from a famous near location using a map as a visual stimulus. For corpus annotation, the database has been manually and automatically time-labeled, which makes it feasible to perform a quantitative analysis of the rhythm of Arabic in both Modern Standard Arabic (MSA) and Egyptian dialect variety. The database serves as a phonetic resource, which allows researchers to examine various aspects of Arabic supra-segmental features and it can be used for forensic phonetic research, for comparison of different speakers, analyzing variability in different speaking styles, and automatic speech and speaker recognition.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{johannessen-etal-2020-comparing,
    title = "Comparing Methods for Measuring Dialect Similarity in {N}orwegian",
    author = "Johannessen, Janne  and
      K{\aa}sen, Andre  and
      Hagen, Kristin  and
      N{\o}klestad, Anders  and
      Priestley, Joel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.658",
    pages = "5343--5350",
    abstract = "The present article presents four experiments with two different methods for measuring dialect similarity in Norwegian: the Levenshtein method and the neural long short term memory (LSTM) autoencoder network, a machine learning algorithm. The visual output in the form of dialect maps is then compared with canonical maps found in the dialect literature. All of this enables us to say that one does not need fine-grained transcriptions of speech to replicate classical classification patterns.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ahamad-etal-2020-accentdb,
    title = "{A}ccent{DB}: A Database of Non-Native {E}nglish Accents to Assist Neural Speech Recognition",
    author = "Ahamad, Afroz  and
      Anand, Ankit  and
      Bhargava, Pranesh",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.659",
    pages = "5351--5358",
    abstract = "Modern Automatic Speech Recognition (ASR) technology has evolved to identify the speech spoken by native speakers of a language very well. However, identification of the speech spoken by non-native speakers continues to be a major challenge for it. In this work, we first spell out the key requirements for creating a well-curated database of speech samples in non-native accents for training and testing robust ASR systems. We then introduce AccentDB, one such database that contains samples of 4 Indian-English accents collected by us, and a compilation of samples from 4 native-English, and a metropolitan Indian-English accent. We also present an analysis on separability of the collected accent data. Further, we present several accent classification models and evaluate them thoroughly against human-labelled accent classes. We test the generalization of our classifier models in a variety of setups of seen and unseen data. Finally, we introduce accent neutralization of non-native accents to native accents using autoencoder models with task-specific architectures. Thus, our work aims to aid ASR systems at every stage of development with a database for training, classification models for feature augmentation, and neutralization systems for acoustic transformations of non-native accents of English.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schlegel-etal-2020-framework,
    title = "A Framework for Evaluation of Machine Reading Comprehension Gold Standards",
    author = "Schlegel, Viktor  and
      Valentino, Marco  and
      Freitas, Andre  and
      Nenadic, Goran  and
      Batista-Navarro, Riza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.660",
    pages = "5359--5369",
    abstract = "Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text. While neural MRC systems gain popularity and achieve noticeable performance, issues are being raised with the methodology used to establish their performance, particularly concerning the data design of gold standards that are used to evaluate them. There is but a limited understanding of the challenges present in this data, which makes it hard to draw comparisons and formulate reliable hypotheses. As a first step towards alleviating the problem, this paper proposes a unifying framework to systematically investigate the present linguistic features, required reasoning and background knowledge and factual correctness on one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other hand. We propose a qualitative annotation schema for the first and a set of approximative metrics for the latter. In a first application of the framework, we analyse modern MRC gold standards and present our findings: the absence of features that contribute towards lexical ambiguity, the varying factual correctness of the expected answers and the presence of lexical cues, all of which potentially lower the reading comprehension complexity and quality of the evaluation data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{xu-etal-2020-multi,
    title = "Multi-class Hierarchical Question Classification for Multiple Choice Science Exams",
    author = "Xu, Dongfang  and
      Jansen, Peter  and
      Martin, Jaycie  and
      Xie, Zhengnan  and
      Yadav, Vikas  and
      Tayyar Madabushi, Harish  and
      Tafjord, Oyvind  and
      Clark, Peter",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.661",
    pages = "5370--5382",
    abstract = "Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model{'}s predictions of question topic significantly improves the accuracy of a question answering system by +1.7{\%} P@1, with substantial future gains possible as QC performance improves.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{woldemariam-2020-assessing,
    title = "Assessing Users{'} Reputation from Syntactic and Semantic Information in Community Question Answering",
    author = "Woldemariam, Yonas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.662",
    pages = "5383--5391",
    abstract = "Textual content is the most significant as well as substantially the big part of CQA (Community Question Answering) forums. Users gain reputation for contributing such content. Although linguistic quality is the very essence of textual information, that does not seem to be considered in estimating users{'} reputation. As existing users{'} reputation systems seem to solely rely on vote counting, adding that bit of linguistic information surely improves their quality. In this study, we investigate the relationship between users{'} reputation and linguistic features extracted from their associated answers content. And we build statistical models on a Stack Overflow dataset that learn reputation from complex syntactic and semantic structures of such content. The resulting models reveal how users{'} writing styles in answering questions play important roles in building reputation points. In our experiments, extracting answers from systematically selected users followed by linguistic features annotation and models building. The models are evaluated on in-domain (e.g., Server Fault, Super User) and out-domain (e.g., English, Maths) datasets. We found out that the selected linguistic features have quite significant influences over reputation scores. In the best case scenario, the selected linguistic feature set could explain 80{\%} variation in reputation scores with the prediction error of 3{\%}. The performance results obtained from the baseline models have been significantly improved by adding syntactic and punctuation marks features.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nishida-etal-2020-unsupervised,
    title = "Unsupervised Domain Adaptation of Language Models for Reading Comprehension",
    author = "Nishida, Kosuke  and
      Nishida, Kyosuke  and
      Saito, Itsumi  and
      Asano, Hisako  and
      Tomita, Junji",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.663",
    pages = "5392--5399",
    abstract = "This study tackles unsupervised domain adaptation of reading comprehension (UDARC). Reading comprehension (RC) is a task to learn the capability for question answering with textual sources. State-of-the-art models on RC still do not have general linguistic intelligence; i.e., their accuracy worsens for out-domain datasets that are not used in the training. We hypothesize that this discrepancy is caused by a lack of the language modeling (LM) capability for the out-domain. The UDARC task allows models to use supervised RC training data in the source domain and only unlabeled passages in the target domain. To solve the UDARC problem, we provide two domain adaptation models. The first one learns the out-domain LM and in-domain RC task sequentially. The second one is the proposed model that uses a multi-task learning approach of LM and RC. The models can retain both the RC capability acquired from the supervised data in the source domain and the LM capability from the unlabeled data in the target domain. We evaluated the models on UDARC with five datasets in different domains. The models outperformed the model without domain adaptation. In particular, the proposed model yielded an improvement of 4.3/4.2 points in EM/F1 in an unseen biomedical domain.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yoon-etal-2020-propagate,
    title = "Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks",
    author = "Yoon, Seunghyun  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Bui, Trung  and
      Jung, Kyomin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.664",
    pages = "5400--5407",
    abstract = "In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. First, we design a graph structure in which each node represents an individual sentence, and some pairs of nodes are selectively connected based on the text structure. Then, we develop an iterative attentive aggregation and a skip-combine method in which a node interacts with its neighborhood nodes to accumulate the necessary information. To evaluate the performance of the proposed approaches, we conduct experiments with the standard HotpotQA dataset. The empirical results demonstrate the superiority of our proposed approach, which obtains the best performances, compared to the widely used answer-selection models that do not consider the intersentential relationship.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cortes-etal-2020-empirical,
    title = "An Empirical Comparison of Question Classification Methods for Question Answering Systems",
    author = {Cortes, Eduardo  and
      Woloszyn, Vinicius  and
      Binder, Arne  and
      Himmelsbach, Tilo  and
      Barone, Dante  and
      M{\"o}ller, Sebastian},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.665",
    pages = "5408--5416",
    abstract = "Question classification is an important component of Question Answering Systems responsible for identifying the type of an answer a particular question requires. For instance, {``}Who is the prime minister of the United Kingdom?{''} demands a name of a PERSON, while {``}When was the queen of the United Kingdom born?{''} entails a DATE. This work makes an extensible review of the most recent methods for Question Classification, taking into consideration their applicability in low-resourced languages. First, we propose a manual classification of the current state-of-the-art methods in four distinct categories: low, medium, high, and very high level of dependency on external resources. Second, we applied this categorization in an empirical comparison in terms of the amount of data necessary for training and performance in different languages. In addition to complementing earlier works in this field, our study shows a boost on methods relying on recent language models, overcoming methods not suitable for low-resourced languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{wu-hao-2020-cross,
    title = "Cross-sentence Pre-trained Model for Interactive {QA} matching",
    author = "Wu, Jinmeng  and
      Hao, Yanbin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.666",
    pages = "5417--5424",
    abstract = "Semantic matching measures the dependencies between query and answer representations, it is an important criterion for evaluating whether the matching is successful. In fact, such matching does not examine each sentence individually, context information outside a sentence should be considered equally important to the syntactic context inside a sentence. We proposed a new QA matching model, built upon a cross-sentence context-aware architecture. An interactive attention mechanism with a pre-trained language model is proposed to automatically select salient positional answer representations that contribute more significantly to the answer relevance of a given question. In addition to the context information captured at each word position, we incorporate a new quantity of context information jump to facilitate the attention weight formulation. This reflects the amount of new information brought by the next word and is computed by modeling the joint probability between two adjacent word states. The proposed method is compared to multiple state-of-the-art ones evaluated using the TREC library, WikiQA, and the Yahoo! community question datasets. Experimental results show that the proposed method outperforms satisfactorily the competing ones.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lee-etal-2020-squad2,
    title = "{SQ}u{AD}2-{CR}: Semi-supervised Annotation for Cause and Rationales for Unanswerability in {SQ}u{AD} 2.0",
    author = "Lee, Gyeongbok  and
      Hwang, Seung-won  and
      Cho, Hyunsouk",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.667",
    pages = "5425--5432",
    abstract = "Existing machine reading comprehension models are reported to be brittle for adversarially perturbed questions when optimizing only for accuracy, which led to the creation of new reading comprehension benchmarks, such as SQuAD 2.0 which contains such type of questions. However, despite the super-human accuracy of existing models on such datasets, it is still unclear how the model predicts the answerability of the question, potentially due to the absence of a shared annotation for the explanation. To address such absence, we release SQuAD2-CR dataset, which contains annotations on unanswerable questions from the SQuAD 2.0 dataset, to enable an explanatory analysis of the model prediction. Specifically, we annotate (1) explanation on why the most plausible answer span cannot be the answer and (2) which part of the question causes unanswerability. We share intuitions and experimental results that how this dataset can be used to analyze and improve the interpretability of existing reading comprehension model behavior.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kodama-etal-2020-generating,
    title = "Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs",
    author = "Kodama, Takashi  and
      Higashinaka, Ryuichiro  and
      Mitsuda, Koh  and
      Masumura, Ryo  and
      Aono, Yushi  and
      Nakamura, Ryuta  and
      Adachi, Noritake  and
      Kawabata, Hidetoshi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.668",
    pages = "5433--5441",
    abstract = "This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data. Using the framework of role play-based question answering, we collected single-turn question-answer pairs for particular characters from online users. Meta information was also collected such as emotion and intimacy related to question-answer pairs. We verified the quality of the collected data and, by subjective evaluation, we also verified their usefulness in training neural conversational models for generating utterances reflecting the meta information, especially emotion.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{goncalo-oliveira-etal-2020-aia,
    title = "{AIA}-{BDE}: A Corpus of {FAQ}s in {P}ortuguese and their Variations",
    author = "Gon{\c{c}}alo Oliveira, Hugo  and
      Ferreira, Jo{\~a}o  and
      Santos, Jos{\'e}  and
      Fialho, Pedro  and
      Rodrigues, Ricardo  and
      Coheur, Luisa  and
      Alves, Ana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.669",
    pages = "5442--5449",
    abstract = "We present AIA-BDE, a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate. Its aims to be used as a benchmark for FAQ retrieval and automatic question-answering, but may be useful in other contexts, such as the development of task-oriented dialogue systems, or models for natural language inference in an interrogative context. We also report on two experiments. Matching variations with their original questions was not trivial with a set of unsupervised baselines, especially for manually created variations. Besides high performances obtained with ELMo and BERT embeddings, an Information Retrieval system was surprisingly competitive when considering only the first hit. In the second experiment, text classifiers were trained with the original questions, and tested when assigning each variation to one of three possible sources, or assigning them as out-of-domain. Here, the difference between manual and automatic variations was not so significant.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{colas-etal-2020-tutorialvqa,
    title = "{T}utorial{VQA}: Question Answering Dataset for Tutorial Videos",
    author = "Colas, Anthony  and
      Kim, Seokhwan  and
      Dernoncourt, Franck  and
      Gupte, Siddhesh  and
      Wang, Zhe  and
      Kim, Doo Soon",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.670",
    pages = "5450--5455",
    abstract = "Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, we propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000 manually collected triples of (video, question, answer span). We also provide experimental results with several baseline algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{xie-etal-2020-worldtree,
    title = "{W}orld{T}ree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference",
    author = "Xie, Zhengnan  and
      Thiem, Sebastian  and
      Martin, Jaycie  and
      Wainwright, Elizabeth  and
      Marmorstein, Steven  and
      Jansen, Peter",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.671",
    pages = "5456--5473",
    abstract = "Explainable question answering for complex questions often requires combining large numbers of facts to answer a question while providing a human-readable explanation for the answer, a process known as multi-hop inference. Standardized science questions require combining an average of 6 facts, and as many as 16 facts, in order to answer and explain, but most existing datasets for multi-hop reasoning focus on combining only two facts, significantly limiting the ability of multi-hop inference algorithms to learn to generate large inferences. In this work we present the second iteration of the WorldTree project, a corpus of 5,114 standardized science exam questions paired with large detailed multi-fact explanations that combine core scientific knowledge and world knowledge. Each explanation is represented as a lexically-connected {``}explanation graph{''} that combines an average of 6 facts drawn from a semi-structured knowledge base of 9,216 facts across 66 tables. We use this explanation corpus to author a set of 344 high-level science domain inference patterns similar to semantic frames supporting multi-hop inference. Together, these resources provide training data and instrumentation for developing many-fact multi-hop inference models for question answering.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{luthier-popescu-belis-2020-chat,
    title = "Chat or Learn: a Data-Driven Robust Question-Answering System",
    author = "Luthier, Gabriel  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.672",
    pages = "5474--5480",
    abstract = "We present a voice-based conversational agent which combines the robustness of chatbots and the utility of question answering (QA) systems. Indeed, while data-driven chatbots are typically user-friendly but not goal-oriented, QA systems tend to perform poorly at chitchat. The proposed chatbot relies on a controller which performs dialogue act classification and feeds user input either to a sequence-to-sequence chatbot or to a QA system. The resulting chatbot is a spoken QA application for the Google Home smart speaker. The system is endowed with general-domain knowledge from Wikipedia articles and uses coreference resolution to detect relatedness between questions. We present our choices of data sets for training and testing the components, and present the experimental results that helped us optimize the parameters of the chatbot. In particular, we discuss the appropriateness of using the SQuAD dataset for evaluating end-to-end QA, in the light of our system{'}s behavior.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{keraron-etal-2020-project,
    title = "Project {PIAF}: Building a Native {F}rench Question-Answering Dataset",
    author = "Keraron, Rachel  and
      Lancrenon, Guillaume  and
      Bras, Mathilde  and
      Allary, Fr{\'e}d{\'e}ric  and
      Moyse, Gilles  and
      Scialom, Thomas  and
      Soriano-Morales, Edmundo-Pavel  and
      Staiano, Jacopo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.673",
    pages = "5481--5490",
    abstract = "Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{charlet-etal-2020-cross,
    title = "Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and {CALOR}-Quest Corpora",
    author = "Charlet, Delphine  and
      Damnati, Geraldine  and
      Bechet, Frederic  and
      Marzinotto, Gabriel  and
      Heinecke, Johannes",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.674",
    pages = "5491--5497",
    abstract = "Machine Reading received recently a lot of attention thanks to both the availability of very large corpora such as SQuAD or MS MARCO containing triplets (document, question, answer), and the introduction of Transformer Language Models such as BERT which obtain excellent results, even matching human performance according to the SQuAD leaderboard. One of the key features of Transformer Models is their ability to be jointly trained across multiple languages, using a shared subword vocabulary, leading to the construction of cross-lingual lexical representations. This feature has been used recently to perform zero-shot cross-lingual experiments where a multilingual BERT model fine-tuned on a machine reading comprehension task exclusively for English was directly applied to Chinese and French documents with interesting performance. In this paper we study the cross-language and cross-domain capabilities of BERT on a Machine Reading Comprehension task on two corpora: SQuAD and a new French Machine Reading dataset, called CALOR-QUEST. The semantic annotation available on CALOR-QUEST allows us to give a detailed analysis on the kinds of questions that are properly handled through the cross-language process. We will try to answer this question: which factor between language mismatch and domain mismatch has the strongest influence on the performances of a Machine Reading Comprehension task?",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{saikh-etal-2020-scholarlyread,
    title = "{S}cholarly{R}ead: A New Dataset for Scientific Article Reading Comprehension",
    author = "Saikh, Tanik  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.675",
    pages = "5498--5504",
    abstract = "We present ScholarlyRead, span-of-word-based scholarly articles{'} Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31{\%}. The framework would be useful for building Question-Answering (QA) systems on scientific articles.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{laskar-etal-2020-contextualized,
    title = "Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task",
    author = "Laskar, Md Tahmid Rahman  and
      Huang, Jimmy Xiangji  and
      Hoque, Enamul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.676",
    pages = "5505--5514",
    abstract = "Word embeddings that consider context have attracted great attention for various natural language processing tasks in recent years. In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task. We present two different approaches (feature-based and fine-tuning-based) for answer selection. In the feature-based approach, we utilize two types of contextualized embeddings, namely the Embeddings from Language Models (ELMo) and the Bidirectional Encoder Representations from Transformers (BERT) and integrate each of them with the transformer encoder. We find that integrating these contextual embeddings with the transformer encoder is effective to improve the performance of sentence similarity modeling. In the second approach, we fine-tune two pre-trained transformer encoder models for the answer selection task. Based on our experiments on six datasets, we find that the fine-tuning approach outperforms the feature-based approach on all of them. Among our fine-tuning-based models, the Robustly Optimized BERT Pretraining Approach (RoBERTa) model results in new state-of-the-art performance across five datasets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{carrino-etal-2020-automatic,
    title = "Automatic {S}panish Translation of {SQ}u{AD} Dataset for Multi-lingual Question Answering",
    author = "Carrino, Casimiro Pio  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.677",
    pages = "5515--5523",
    abstract = "Recently, multilingual question answering became a crucial research topic, and it is receiving increased interest in the NLP community. However, the unavailability of large-scale datasets makes it challenging to train multilingual QA systems with performance comparable to the English ones. In this work, we develop the Translate Align Retrieve (TAR) method to automatically translate the Stanford Question Answering Dataset (SQuAD) v1.1 to Spanish. We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model. Finally, we evaluated our QA models with the recently proposed MLQA and XQuAD benchmarks for cross-lingual Extractive QA. Experimental results show that our models outperform the previous Multilingual-BERT baselines achieving the new state-of-the-art values of 68.1 F1 on the Spanish MLQA corpus and 77.6 F1 on the Spanish XQuAD corpus. The resulting, synthetically generated SQuAD-es v1.1 corpora, with almost 100{\%} of data contained in the original English version, to the best of our knowledge, is the first large-scale QA training resource for Spanish.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alizadeh-di-eugenio-2020-corpus,
    title = "A Corpus for Visual Question Answering Annotated with Frame Semantic Information",
    author = "Alizadeh, Mehrdad  and
      Di Eugenio, Barbara",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.678",
    pages = "5524--5531",
    abstract = "Visual Question Answering (VQA) has been widely explored as a computer vision problem, however enhancing VQA systems with linguistic information is necessary for tackling the complexity of the task. The language understanding part can play a major role especially for questions asking about events or actions expressed via verbs. We hypothesize that if the question focuses on events described by verbs, then the model should be aware of or trained with verb semantics, as expressed via semantic role labels, argument types, and/or frame elements. Unfortunately, no VQA dataset exists that includes verb semantic information. We created a new VQA dataset annotated with verb semantic information called imSituVQA. imSituVQA is built by taking advantage of the imSitu dataset annotations. The imSitu dataset consists of images manually labeled with semantic frame elements, mostly taken from FrameNet.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{soni-roberts-2020-evaluation,
    title = "Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering",
    author = "Soni, Sarvesh  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.679",
    pages = "5532--5538",
    abstract = "We evaluate the performance of various Transformer language models, when pre-trained and fine-tuned on different combinations of open-domain, biomedical, and clinical corpora on two clinical question answering (QA) datasets (CliCR and emrQA). We perform our evaluations on the task of machine reading comprehension, which involves training the model to answer a question given an unstructured context paragraph. We conduct a total of 48 experiments on different combinations of the large open-domain and domain-specific corpora. We found that an initial fine-tuning on an open-domain dataset, SQuAD, consistently improves the clinical QA performance across all the model variants.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{branco-etal-2020-shared,
    title = "A Shared Task of a New, Collaborative Type to Foster Reproducibility: A First Exercise in the Area of Language Science and Technology with {REPROLANG}2020",
    author = "Branco, Ant{\'o}nio  and
      Calzolari, Nicoletta  and
      Vossen, Piek  and
      Van Noord, Gertjan  and
      van Uytvanck, Dieter  and
      Silva, Jo{\~a}o  and
      Gomes, Lu{\'\i}s  and
      Moreira, Andr{\'e}  and
      Elbers, Willem",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.680",
    pages = "5539--5545",
    abstract = "n this paper, we introduce a new type of shared task {---} which is collaborative rather than competitive {---} designed to support and fosterthe reproduction of research results. We also describe the first event running such a novel challenge, present the results obtained, discussthe lessons learned and ponder on future undertakings.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{garneau-etal-2020-robust,
    title = "A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well",
    author = "Garneau, Nicolas  and
      Godbout, Mathieu  and
      Beauchemin, David  and
      Durand, Audrey  and
      Lamontagne, Luc",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.681",
    pages = "5546--5554",
    abstract = "In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction of their method is indeed feasible with some minor assumptions. We further investigate the robustness of their model by introducing four new languages that are less similar to English than the ones proposed by the original paper. In order to assess the stability of their model, we also conduct a grid search over sensible hyperparameters. We then propose key recommendations that apply to any research project in order to deliver fully reproducible research.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{plucinski-etal-2020-closer,
    title = "A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping",
    author = "Pluci{\'n}ski, Kamil  and
      Lango, Mateusz  and
      Zimniewicz, Micha{\l}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.682",
    pages = "5555--5562",
    abstract = "In this work, we study the unsupervised cross-lingual word embeddings mapping method presented by Artetxe et al. (2018). First, wesuccessfully reproduced the experiments performed in the original work, finding only minor differences. Furthermore, we verified themethod{'}s robustness on different embedding representations and new language pairs, particularly these involving Slavic languages likePolish or Czech. We also performed an experimental analysis of the impact of the method{'}s parameters on the final result. Finally, welooked for an alternative way of initialization, which directly relies on the isometric assumption. Our work confirms the results presentedearlier, at the same time pointing at interesting problems occurring while using the method with different types of embeddings or onless-common language pairs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khoe-2020-reproducing,
    title = "Reproducing a Morphosyntactic Tagger with a Meta-{B}i{LSTM} Model over Context Sensitive Token Encodings",
    author = "Khoe, Yung Han",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.683",
    pages = "5563--5568",
    abstract = "Reproducibility is generally regarded as being a requirement for any form of experimental science. Even so, reproduction of research results is only recently beginning to be practiced and acknowledged. In the context of the REPROLANG 2020 shared task, we contribute to this trend by reproducing the work reported on by Bohnet et al. (2018) on morphosyntactic tagging. Their meta-BiLSTM model achieved state-of-the-art results across a wide range of languages. This was done by integrating sentence-level and single-word context through synchronized training by a meta-model. Our reproduction only partially confirms the main results of the paper in terms of outperforming earlier models. The results of our reproductions improve on earlier models on the morphological tagging task, but not on the part-of-speech tagging task. Furthermore, even where we improve on earlier models, we fail to match the F1-scores reported for the meta-BiLSTM model. Because we chose not to contact the original authors for our reproduction study, the uncertainty about the degree of parallelism that was achieved between the original study and our reproduction limits the value of our findings as an assessment of the reliability of the original results. At the same time, however, it underscores the relevance of our reproduction effort in regard to the reproducibility and interpretability of those findings. The discrepancies between our findings and the original results demonstrate that there is room for improvement in many aspects of reporting regarding the reproducibility of the experiments. In addition, we suggest that different reporting choices could improve the interpretability of the results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rim-etal-2020-reproducing,
    title = "Reproducing Neural Ensemble Classifier for Semantic Relation Extraction in{S}cientific Papers",
    author = "Rim, Kyeongmin  and
      Tu, Jingxuan  and
      Lynch, Kelley  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.684",
    pages = "5569--5578",
    abstract = "Within the natural language processing (NLP) community, shared tasks play an important role. They define a common goal and allowthe the comparison of different methods on the same data. SemEval-2018 Task 7 involves the identification and classification of relationsin abstracts from computational linguistics (CL) publications. In this paper we describe an attempt to reproduce the methods and resultsfrom the top performing system at for SemEval-2018 Task 7. We describe challenges we encountered in the process, report on the resultsof our system, and discuss the ways that our attempt at reproduction can inform best practices.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abdellatif-elgammal-2020-ulmfit,
    title = "{ULMF}i{T} replication",
    author = "Abdellatif, Mohamed  and
      Elgammal, Ahmed",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.685",
    pages = "5579--5587",
    abstract = "Authors: Mohamed Abdellatif and Ahmed Elgammal Gitlab URL: https://gitlab.com/abdollatif/lrec{\_}app Commit hash: 3f20b2ddb96d8c865e5f56f5566edf371214785f Tag name: Splits2 Dataset file md5: 5aee3dac5e48d1ac3d279083212734c9 Dataset URL: https://drive.google.com/file/d/1cv5HuQhgFVizupFI40dzreemS2gMM498/view?usp=sharing",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cooper-shardlow-2020-combinmt,
    title = "{C}ombi{NMT}: An Exploration into Neural Text Simplification Models",
    author = "Cooper, Michael  and
      Shardlow, Matthew",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.686",
    pages = "5588--5594",
    abstract = "This work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017). We were able to successfully replicate and extend the methods presented in the original paper. Alongside the replication results, we present our improvements dubbed CombiNMT. By using an updated implementation of OpenNMT, and incorporating the Newsela corpus alongside the original Wikipedia dataset (Hwang et al., 2016), as well as refining both datasets to select high quality training examples. Our work present two new systems, CombiNMT995, which is a result of matched sentences with a cosine similarity of 0.995 or less, and CombiNMT98, which, similarly, runs on a cosine similarity of 0.98 or less. By extending the human evaluation presented within the original paper, increasing both the number of annotators and the number of sentences annotated, with the intention of increasing the quality of the results, CombiNMT998 shows significant improvement over any of the Neural Text Simplification (NTS) systems from the original paper in terms of both the number of changes and the percentage of correct changes made.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bestgen-2020-reproducing,
    title = "Reproducing Monolingual, Multilingual and Cross-Lingual {CEFR} Predictions",
    author = "Bestgen, Yves",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.687",
    pages = "5595--5602",
    abstract = "his study aims to reproduce the research of Vajjala and Rama (2018) which showed that it is possible to predict the quality of a text written by learners of a given language by means of a model built on the basis of texts written by learners of another language. These authors also pointed out that POStag and dependency n-grams were significantly more effective than text length and global linguistic indices frequently used for this kind of task. The analyses performed show that some important points of their code did not correspond to the explanations given in the paper. These analyses confirm the possibility to use syntactic n-gram features in cross-lingual experiments to categorize texts according to their CEFR level (Common European Framework of Reference for Languages). However, text length and some classical indexes of readability are much more effective in the monolingual and the multilingual experiments than what Vajjala and Rama concluded and are even the best performing features when the cross-lingual task is seen as a regression problem. This study emphasized the importance for reproducibility of setting explicitly the reading order of the instances when using a K-fold CV procedure and, more generally, the need to properly randomize these instances before. It also evaluates a two-step procedure to determine the degree of statistical significance of the differences observed in a K-fold cross-validation schema and argues against the use of a Bonferroni-type correction in this context.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{huber-coltekin-2020-reproduction,
    title = "Reproduction and Replication: A Case Study with Automatic Essay Scoring",
    author = {Huber, Eva  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.688",
    pages = "5603--5613",
    abstract = "As in many experimental sciences, reproducibility of experiments has gained ever more attention in the NLP community. This paper presents our reproduction efforts of an earlier study of automatic essay scoring (AES) for determining the proficiency of second language learners in a multilingual setting. We present three sets of experiments with different objectives. First, as prescribed by the LREC 2020 REPROLANG shared task, we rerun the original AES system using the code published by the original authors on the same dataset. Second, we repeat the same experiments on the same data with a different implementation. And third, we test the original system on a different dataset and a different language. Most of our findings are in line with the findings of the original paper. Nevertheless, there are some discrepancies between our results and the results presented in the original paper. We report and discuss these differences in detail. We further go into some points related to confirmation of research findings through reproduction, including the choice of the dataset, reporting and accounting for variability, use of appropriate evaluation metrics, and making code and data available. We also discuss the varying uses and differences between the terms reproduction and replication, and we argue that reproduction, the confirmation of conclusions through independent experiments in varied settings is more valuable than exact replication of the published values.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{caines-buttery-2020-reprolang,
    title = "{REPROLANG} 2020: Automatic Proficiency Scoring of {C}zech, {E}nglish, {G}erman, {I}talian, and {S}panish Learner Essays",
    author = "Caines, Andrew  and
      Buttery, Paula",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.689",
    pages = "5614--5623",
    abstract = "We report on our attempts to reproduce the work described in Vajjala {\&} Rama 2018, {`}Experiments with universal CEFR classification{'}, as part of REPROLANG 2020: this involves featured-based and neural approaches to essay scoring in Czech, German and Italian. Our results are broadly in line with those from the original paper, with some differences due to the stochastic nature of machine learning and programming language used. We correct an error in the reported metrics, introduce new baselines, apply the experiments to English and Spanish corpora, and generate adversarial data to test classifier robustness. We conclude that feature-based approaches perform better than neural network classifiers for text datasets of this size, though neural network modifications do bring performance closer to the best feature-based models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{arhiliuc-etal-2020-language,
    title = "Language Proficiency Scoring",
    author = "Arhiliuc, Cristina  and
      Mitrovi{\'c}, Jelena  and
      Granitzer, Michael",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.690",
    pages = "5624--5630",
    abstract = "The Common European Framework of Reference (CEFR) provides generic guidelines for the evaluation of language proficiency. Nevertheless, for automated proficiency classification systems, different approaches for different languages are proposed. Our paper evaluates and extends the results of an approach to Automatic Essay Scoring proposed as a part of the REPROLANG 2020 challenge. We provide a comparison between our results and the ones from the published paper and we include a new corpus for the English language for further experiments. Our results are lower than the expected ones when using the same approach and the system does not scale well with the added English corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ballier-etal-2020-learnability,
    title = "The Learnability of the Annotated Input in {NMT} Replicating (Vanmassenhove and Way, 2018) with {O}pen{NMT}",
    author = "Ballier, Nicolas  and
      Amari, Nabil  and
      Merat, Laure  and
      Yun{\`e}s, Jean-Baptiste",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.691",
    pages = "5631--5640",
    abstract = "In this paper, we reproduce some of the experiments related to neural network training for Machine Translation as reported in (Vanmassenhove and Way, 2018). They annotated a sample from the EN-FR and EN-DE Europarl aligned corpora with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation (NMT) toolkit. Following the original publication, we obtained lower BLEU scores than the authors of the original paper, but on a more limited set of annotations. In the second half of the paper, we try to analyze the difference in the results obtained and suggest some methods to improve the results. We discuss the Byte Pair Encoding (BPE) used in the pre-processing phase and suggest feature ablation in relation to the granularity of syntactic and semantic annotations. The learnability of the annotated input is discussed in relation to existing resources for the target languages. We also discuss the feature representation likely to have been adopted for combining features.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{portisch-etal-2020-kgvec2go,
    title = "{KG}vec2go {--} Knowledge Graph Embeddings as a Service",
    author = "Portisch, Jan  and
      Hladik, Michael  and
      Paulheim, Heiko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.692",
    pages = "5641--5647",
    abstract = "In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bento-etal-2020-ontology,
    title = "Ontology Matching Using Convolutional Neural Networks",
    author = "Bento, Alexandre  and
      Zouaq, Amal  and
      Gagnon, Michel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.693",
    pages = "5648--5653",
    abstract = "In order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies. As the number of ontologies grows for a given domain, and as overlap between ontologies grows proportionally, it is becoming more and more crucial to develop accurate and reliable techniques to perform this task automatically. While traditional approaches to address this challenge are based on string metrics and structure analysis, in this paper we present a methodology to align ontologies automatically using machine learning techniques. Specifically, we use convolutional neural networks to perform string matching between class labels using character embeddings. We also rely on the set of superclasses to perform the best alignment. Our results show that we obtain state-of-the-art performance on ontologies from the Ontology Alignment Evaluation Initiative (OAEI). Our model also maintains good performance when tested on a different domain, which could lead to potential cross-domain applications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{martin-chozas-etal-2020-defying,
    title = "Defying {W}ikidata: Validation of Terminological Relations in the Web of Data",
    author = "Mart{\'\i}n-Chozas, Patricia  and
      Ahmadi, Sina  and
      Montiel-Ponsoda, Elena",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.694",
    pages = "5654--5659",
    abstract = "In this paper we present an approach to validate terminological data retrieved from open encyclopaedic knowledge bases. This need arises from the enrichment of automatically extracted terms with information from existing resources in theLinguistic Linked Open Data cloud. Specifically, the resource employed for this enrichment is WIKIDATA, since it is one of the biggest knowledge bases freely available within the Semantic Web. During the experiment, we noticed that certain RDF properties in the Knowledge Base did not contain the data they are intended to represent, but a different type of information. In this paper we propose an approach to validate the retrieved data based on four axioms that rely on two linguistic theories: the x-bar theory and the multidimensional theory of terminology.The validation process is supported by a second knowledge base specialised in linguistic data; in this case, CONCEPTNET. In our experiment, we validate terms from the legal domain in four languages: Dutch, English, German and Spanish. The final aim is to generate a set of sound and reliable terminological resources in RDF to contribute to the population of the Linguistic Linked Open Data cloud.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{declerck-etal-2020-recent,
    title = "Recent Developments for the Linguistic Linked Open Data Infrastructure",
    author = {Declerck, Thierry  and
      McCrae, John Philip  and
      Hartung, Matthias  and
      Gracia, Jorge  and
      Chiarcos, Christian  and
      Montiel-Ponsoda, Elena  and
      Cimiano, Philipp  and
      Revenko, Artem  and
      Saur{\'\i}, Roser  and
      Lee, Deirdre  and
      Racioppa, Stefania  and
      Abdul Nasir, Jamal  and
      Orlikowsk, Matthias  and
      Lanau-Coronas, Marta  and
      F{\"a}th, Christian  and
      Rico, Mariano  and
      Elahi, Mohammad Fazleh  and
      Khvalchik, Maria  and
      Gonzalez, Meritxell  and
      Cooney, Katharine},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.695",
    pages = "5660--5667",
    abstract = "In this paper we describe the contributions made by the European H2020 project {``}Pr{\^e}t-{\`a}-LLOD{''} ({`}Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors{'}) to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Pr{\^e}t-{\`a}-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chiarcos-etal-2020-annotation,
    title = "Annotation Interoperability for the Post-{ISOC}at Era",
    author = {Chiarcos, Christian  and
      F{\"a}th, Christian  and
      Abromeit, Frank},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.696",
    pages = "5668--5677",
    abstract = "With this paper, we provide an overview over ISOCat successor solutions and annotation standardization efforts since 2010, and we describe the low-cost harmonization of post-ISOCat vocabularies by means of modular, linked ontologies: The CLARIN Concept Registry, LexInfo, Universal Parts of Speech, Universal Dependencies and UniMorph are linked with the Ontologies of Linguistic Annotation and through it with ISOCat, the GOLD ontology, the Typological Database Systems ontology and a large number of annotation schemes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alex-mathews-strube-2020-large,
    title = "A Large Harvested Corpus of Location Metonymy",
    author = "Alex Mathews, Kevin  and
      Strube, Michael",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.697",
    pages = "5678--5687",
    abstract = "Metonymy is a figure of speech in which an entity is referred to by another related entity. The existing datasets of metonymy are either too small in size or lack sufficient coverage. We propose a new, labelled, high-quality corpus of location metonymy called WiMCor, which is large in size and has high coverage. The corpus is harvested semi-automatically from English Wikipedia. We use different labels of varying granularity to annotate the corpus. The corpus can directly be used for training and evaluating automatic metonymy resolution systems. We construct benchmarks for metonymy resolution, and evaluate baseline methods using the new corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{robaldo-etal-2020-dapreco,
    title = "The {DAPRECO} Knowledge Base: Representing the {GDPR} in {L}egal{R}ule{ML}",
    author = "Robaldo, Livio  and
      Bartolini, Cesare  and
      Lenzini, Gabriele",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.698",
    pages = "5688--5697",
    abstract = "The DAPRECO knowledge base (D-KB) is a repository of rules written in LegalRuleML, an XML formalism designed to represent the logical content of legal documents. The rules represent the provisions of the General Data Protection Regulation (GDPR). The D-KB builds upon the Privacy Ontology (PrOnto) (Palmirani et al., 2018), which provides a model for the legal concepts involved in the GDPR, by adding a further layer of constraints in the form of if-then rules, referring either to standard first order logic implications or to deontic statements. If-then rules are formalized in reified I/O logic (Robaldo and Sun, 2017) and then codified in (LegalRuleML, 2019). To date, the D-KB is the biggest knowledge base in LegalRuleML freely available online at (Robaldo et al., 2019).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{white-etal-2020-universal,
    title = "The Universal Decompositional Semantics Dataset and Decomp Toolkit",
    author = "White, Aaron Steven  and
      Stengel-Eskin, Elias  and
      Vashishtha, Siddharth  and
      Govindarajan, Venkata Subrahmanyan  and
      Reisinger, Dee Ann  and
      Vieira, Tim  and
      Sakaguchi, Keisuke  and
      Zhang, Sheng  and
      Ferraro, Francis  and
      Rudinger, Rachel  and
      Rawlins, Kyle  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.699",
    pages = "5698--5707",
    abstract = "We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification{---}with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at http://decomp.io.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chersoni-etal-2020-word,
    title = "Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?",
    author = "Chersoni, Emmanuele  and
      Pannitto, Ludovica  and
      Santus, Enrico  and
      Lenci, Alessandro  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.700",
    pages = "5708--5713",
    abstract = "While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models. In this paper, we propose a complete evaluation of count models and word embeddings on thematic fit estimation, by taking into account a larger number of parameters and verb roles and introducing also dependency-based embeddings in the comparison. Our results show a complex scenario, where a determinant factor for the performance seems to be the availability to the model of reliable syntactic information for building the distributional representations of the roles.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{xiang-etal-2020-ciron,
    title = "{C}iron: a New Benchmark Dataset for {C}hinese Irony Detection",
    author = "Xiang, Rong  and
      Gao, Xuefeng  and
      Long, Yunfei  and
      Li, Anran  and
      Chersoni, Emmanuele  and
      Lu, Qin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.701",
    pages = "5714--5720",
    abstract = "Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{anthonio-etal-2020-wikihowtoimprove,
    title = "wiki{H}ow{T}o{I}mprove: A Resource and Analyses on Edits in Instructional Texts",
    author = "Anthonio, Talita  and
      Bhat, Irshad  and
      Roth, Michael",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.702",
    pages = "5721--5729",
    abstract = "Instructional texts, such as articles in wikiHow, describe the actions necessary to accomplish a certain goal. In wikiHow and other resources, such instructions are subject to revision edits on a regular basis. Do these edits improve instructions only in terms of style and correctness, or do they provide clarifications necessary to follow the instructions and to accomplish the goal? We describe a resource and first studies towards answering this question. Specifically, we create wikiHowToImprove, a collection of revision histories for about 2.7 million sentences from about 246000 wikiHow articles. We describe human annotation studies on categorizing a subset of sentence-level edits and provide baseline models for the task of automatically distinguishing {``}older{''} from {``}newer{''} revisions of a sentence.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{king-morante-2020-must,
    title = "Must Children be Vaccinated or not? Annotating Modal Verbs in the Vaccination Debate",
    author = "King, Liza  and
      Morante, Roser",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.703",
    pages = "5730--5738",
    abstract = "In this paper we analyze the use of modal verbs in a corpus of texts related to the vaccination debate. Broadly speaking, the vaccination debate centers around whether vaccination is safe, and whether it is morally acceptable to enforce mandatory vaccination. In order to successfully intervene and curb the spread of preventable diseases due to low vaccination rates, health practitioners need to be adequately informed on public perception of the safety and necessity of vaccines. Public perception can relate to the strength of conviction that an individual may have towards a proposition (e.g. {`}one must vaccinate{'} versus {`}one should vaccinate{'}), as well as qualify the type of proposition, be it related to morality ({`}government should not interfere in my personal choice{'}) or related to possibility ({`}too many vaccines at once could hurt my child{'}). Text mining and analysis of modal auxiliaries are economically viable means of gaining insights into these perspectives, particularly on a large scale due to the widespread use of social media and blogs as vehicles of communication.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{khandelwal-sawant-2020-negbert,
    title = "{N}eg{BERT}: A Transfer Learning Approach for Negation Detection and Scope Resolution",
    author = "Khandelwal, Aditya  and
      Sawant, Suraj",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.704",
    pages = "5739--5748",
    abstract = "Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: Rule-based systems, Machine Learning classifiers, Conditional Random Field models, CNNs and more recently BiLSTMs. In this paper, we look at applying Transfer Learning to this problem. First, we extensively review previous literature addressing Negation Detection and Scope Resolution across the 3 datasets that have gained popularity over the years: the BioScope Corpus, the Sherlock dataset, and the SFU Review Corpus. We then explore the decision choices involved with using BERT, a popular transfer learning model, for this task, and report state-of-the-art results for scope resolution across all 3 datasets. Our model, referred to as NegBERT, achieves a token level F1 score on scope resolution of 92.36 on the Sherlock dataset, 95.68 on the BioScope Abstracts subcorpus, 91.24 on the BioScope Full Papers subcorpus, 90.95 on the SFU Review Corpus, outperforming the previous state-of-the-art systems by a significant margin. We also analyze the model{'}s generalizability to datasets on which it is not trained.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{majewska-etal-2020-spatial,
    title = "Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction",
    author = "Majewska, Olga  and
      McCarthy, Diana  and
      van den Bosch, Jasper  and
      Kriegeskorte, Nikolaus  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.705",
    pages = "5749--5758",
    abstract = "We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems. Our work targets verb similarity, but the methodology is equally applicable to other parts of speech. Our approach circumvents the bottleneck of slow and expensive manual development of lexical resources by leveraging semantic intuitions of native speakers and adapting a spatial multi-arrangement approach from cognitive neuroscience, used before only with visual stimuli, to lexical stimuli. Our approach critically obtains judgments of word similarity in the context of a set of related words, rather than of word pairs in isolation. We also handle lexical ambiguity as a natural consequence of a two-phase process where verbs are placed in broad semantic classes prior to the fine-grained spatial similarity judgments. Our proposed design produces a large-scale verb resource comprising 17 relatedness-based classes and a verb similarity dataset containing similarity scores for 29,721 unique verb pairs and 825 target verbs, which we release with this paper.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{pasini-camacho-collados-2020-short,
    title = "A Short Survey on Sense-Annotated Corpora",
    author = "Pasini, Tommaso  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.706",
    pages = "5759--5765",
    abstract = "Large sense-annotated datasets are increasingly necessary for training deep supervised systems in Word Sense Disambiguation. However, gathering high-quality sense-annotated data for as many instances as possible is a laborious and expensive task. This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck. In this short survey we present an overview of sense-annotated corpora, annotated either manually- or (semi)automatically, that are currently available for different languages and featuring distinct lexical resources as inventory of senses, i.e. WordNet, Wikipedia, BabelNet. Furthermore, we provide the reader with general statistics of each dataset and an analysis of their specific features.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jana-etal-2020-using,
    title = "Using Distributional Thesaurus Embedding for Co-hyponymy Detection",
    author = "Jana, Abhik  and
      Varimalla, Nikhil Reddy  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.707",
    pages = "5766--5771",
    abstract = "Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community. In this paper, we investigate whether the network embedding of distributional thesaurus can be effectively utilized to detect co-hyponymy relations. By extensive experiments over three benchmark datasets, we show that the vector representation obtained by applying node2vec on distributional thesaurus outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-hyponymy vs. meronymy, by huge margins.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lima-lopez-etal-2020-nubes,
    title = "{NUB}es: A Corpus of Negation and Uncertainty in {S}panish Clinical Texts",
    author = "Lima Lopez, Salvador  and
      Perez, Naiara  and
      Cuadros, Montse  and
      Rigau, German",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.708",
    pages = "5772--5781",
    abstract = "This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty. The article includes an exhaustive comparison with similar corpora in Spanish, and presents the main annotation and design decisions. Additionally, we perform preliminary experiments using deep learning algorithms to validate the annotated dataset. As far as we know, NUBes is the largest available corpora for negation in Spanish and the first that also incorporates the annotation of speculation cues, scopes, and events.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kovatchev-etal-2020-decomposing,
    title = "Decomposing and Comparing Meaning Relations: Paraphrasing, Textual Entailment, Contradiction, and Specificity",
    author = "Kovatchev, Venelin  and
      Gold, Darina  and
      Marti, M. Antonia  and
      Salamo, Maria  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.709",
    pages = "5782--5791",
    abstract = "In this paper, we present a methodology for decomposing and comparing multiple meaning relations (paraphrasing, textual entailment, contradiction, and specificity). The methodology includes SHARel - a new typology that consists of 26 linguistic and 8 reason-based categories. We use the typology to annotate a corpus of 520 sentence pairs in English and we demonstrate that unlike previous typologies, SHARel can be applied to all relations of interest with a high inter-annotator agreement. We analyze and compare the frequency and distribution of the linguistic and reason-based phenomena involved in paraphrasing, textual entailment, contradiction, and specificity. This comparison allows for a much more in-depth analysis of the workings of the individual relations and the way they interact and compare with each other. We release all resources (typology, annotation guidelines, and annotated corpus) to the community.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{silberer-etal-2020-object,
    title = "Object Naming in Language and Vision: A Survey and a New Dataset",
    author = "Silberer, Carina  and
      Zarrie{\ss}, Sina  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.710",
    pages = "5792--5801",
    abstract = "People choose particular names for objects, such as dog or puppy for a given dog. Object naming has been studied in Psycholinguistics, but has received relatively little attention in Computational Linguistics. We review resources from Language and Vision that could be used to study object naming on a large scale, discuss their shortcomings, and create a new dataset that affords more opportunities for analysis and modeling. Our dataset, ManyNames, provides 36 name annotations for each of 25K objects in images selected from VisualGenome. We highlight the challenges involved and provide a preliminary analysis of the ManyNames data, showing that there is a high level of agreement in naming, on average. At the same time, the average number of name types associated with an object is much higher in our dataset than in existing corpora for Language and Vision, such that ManyNames provides a rich resource for studying phenomena like hierarchical variation (chihuahua vs. dog), which has been discussed at length in the theoretical literature, and other less well studied phenomena like cross-classification (cake vs. dessert).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yen-etal-2020-msd,
    title = "{MSD}-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models",
    author = "Yen, Ting-Yu  and
      Lee, Yang-Yin  and
      Shiue, Yow-Ting  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.711",
    pages = "5802--5809",
    abstract = "Sense embedding models handle polysemy by giving each distinct meaning of a word form a separate representation. They are considered improvements over word models, and their effectiveness is usually judged with benchmarks such as semantic similarity datasets. However, most of these datasets are not designed for evaluating sense embeddings. In this research, we show that there are at least six concerns about evaluating sense embeddings with existing benchmark datasets, including the large proportions of single-sense words and the unexpected inferior performance of several multi-sense models to their single-sense counterparts. These observations call into serious question whether evaluations based on these datasets can reflect the sense model{'}s ability to capture different meanings. To address the issues, we propose the Multi-Sense Dataset (MSD-1030), which contains a high ratio of multi-sense word pairs. A series of analyses and experiments show that MSD-1030 serves as a more reliable benchmark for sense embeddings. The dataset is available at http://nlg.csie.ntu.edu.tw/nlpresource/MSD-1030/.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zayed-etal-2020-figure,
    title = "Figure Me Out: A Gold Standard Dataset for Metaphor Interpretation",
    author = "Zayed, Omnia  and
      McCrae, John Philip  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.712",
    pages = "5810--5819",
    abstract = "Metaphor comprehension and understanding is a complex cognitive task that requires interpreting metaphors by grasping the interaction between the meaning of their target and source concepts. This is very challenging for humans, let alone computers. Thus, automatic metaphor interpretation is understudied in part due to the lack of publicly available datasets. The creation and manual annotation of such datasets is a demanding task which requires huge cognitive effort and time. Moreover, there will always be a question of accuracy and consistency of the annotated data due to the subjective nature of the problem. This work addresses these issues by presenting an annotation scheme to interpret verb-noun metaphoric expressions in text. The proposed approach is designed with the goal of reducing the workload on annotators and maintain consistency. Our methodology employs an automatic retrieval approach which utilises external lexical resources, word embeddings and semantic similarity to generate possible interpretations of identified metaphors in order to enable quick and accurate annotation. We validate our proposed approach by annotating around 1,500 metaphors in tweets which were annotated by six native English speakers. As a result of this work, we publish as linked data the first gold standard dataset for metaphor interpretation which will facilitate research in this area.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tanguy-etal-2020-extrinsic,
    title = "Extrinsic Evaluation of {F}rench Dependency Parsers on a Specialized Corpus: Comparison of Distributional Thesauri",
    author = "Tanguy, Ludovic  and
      Brunet, Pauline  and
      Ferret, Olivier",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.713",
    pages = "5820--5828",
    abstract = "We present a study in which we compare 11 different French dependency parsers on a specialized corpus (consisting of research articles on NLP from the proceedings of the TALN conference). Due to the lack of a suitable gold standard, we use each of the parsers{'} output to generate distributional thesauri using a frequency-based method. We compare these 11 thesauri to assess the impact of choosing a parser over another. We show that, without any reference data, we can still identify relevant subsets among the different parsers. We also show that the similarity we identify between parsers is confirmed on a restricted distributional benchmark.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yu-etal-2020-dataset,
    title = "Dataset and Enhanced Model for Eligibility Criteria-to-{SQL} Semantic Parsing",
    author = "Yu, Xiaojing  and
      Chen, Tianlong  and
      Yu, Zhengjie  and
      Li, Huiyu  and
      Yang, Yang  and
      Jiang, Xiaoqian  and
      Jiang, Anxiao",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.714",
    pages = "5829--5837",
    abstract = "Clinical trials often require that patients meet eligibility criteria (e.g., have specific conditions) to ensure the safety and the effectiveness of studies. However, retrieving eligible patients for a trial from the electronic health record (EHR) database remains a challenging task for clinicians since it requires not only medical knowledge about eligibility criteria, but also an adequate understanding of structured query language (SQL). In this paper, we introduce a new dataset that includes the first-of-its-kind eligibility-criteria corpus and the corresponding queries for criteria-to-sql (Criteria2SQL), a task translating the eligibility criteria to executable SQL queries. Compared to existing datasets, the queries in the dataset here are derived from the eligibility criteria of clinical trials and include \textit{Order-sensitive, Counting-based, and Boolean-type} cases which are not seen before. In addition to the dataset, we propose a novel neural semantic parser as a strong baseline model. Extensive experiments show that the proposed parser outperforms existing state-of-the-art general-purpose text-to-sql models while highlighting the challenges presented by the new dataset. The uniqueness and the diversity of the dataset leave a lot of research opportunities for future improvement.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{roussinov-etal-2020-recognizing,
    title = "Recognizing Semantic Relations by Combining Transformers and Fully Connected Models",
    author = "Roussinov, Dmitri  and
      Sharoff, Serge  and
      Puchnina, Nadezhda",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.715",
    pages = "5838--5845",
    abstract = "Automatically recognizing an existing semantic relation (e.g. {``}is a{''}, {``}part of{''}, {``}property of{''}, {``}opposite of{''} etc.) between two words (phrases, concepts, etc.) is an important task affecting many NLP applications and has been subject of extensive experimentation and modeling. Current approaches to automatically telling if a relation exists between two given concepts X and Y can be grouped into two types: 1) those modeling word-paths connecting X and Y in text and 2) those modeling distributional properties of X and Y separately, not necessary in the proximity to each other. Here, we investigate how both types can be improved and combined. We suggest a distributional approach that is based on an attention-based transformer. We have also developed a novel word path model that combines useful properties of a convolutional network with a fully connected language model. While our transformer-based approach works better, both our models significantly outperform the state-of-the-art within their classes of approaches. We also demonstrate that combining the two approaches results in additional gains since they use somewhat different data sources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hasegawa-etal-2020-word,
    title = "Word Attribute Prediction Enhanced by Lexical Entailment Tasks",
    author = "Hasegawa, Mika  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.716",
    pages = "5846--5854",
    abstract = "Human semantic knowledge about concepts acquired through perceptual inputs and daily experiences can be expressed as a bundle of attributes. Unlike the conventional distributed word representations that are purely induced from a text corpus, a semantic attribute is associated with a designated dimension in attribute-based vector representations. Thus, semantic attribute vectors can effectively capture the commonalities and differences among concepts. However, as semantic attributes have been generally created by psychological experimental settings involving human annotators, an automatic method to create or extend such resources is highly demanded in terms of language resource development and maintenance. This study proposes a two-stage neural network architecture, Word2Attr, in which initially acquired attribute representations are then fine-tuned by employing supervised lexical entailment tasks. The quantitative empirical results demonstrated that the fine-tuning was indeed effective in improving the performances of semantic/visual similarity/relatedness evaluation tasks. Although the qualitative analysis confirmed that the proposed method could often discover valid but not-yet human-annotated attributes, they also exposed future issues to be worked: we should refine the inventory of semantic attributes that currently relies on an existing dataset.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dan-etal-2020-spatial,
    title = "From Spatial Relations to Spatial Configurations",
    author = "Dan, Soham  and
      Kordjamshidi, Parisa  and
      Bonn, Julia  and
      Bhatia, Archna  and
      Cai, Zheng  and
      Palmer, Martha  and
      Roth, Dan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.717",
    pages = "5855--5864",
    abstract = "Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos.Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation (AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sucameli-lenci-2020-representing,
    title = "Representing Verbs with Visual Argument Vectors",
    author = "Sucameli, Irene  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.718",
    pages = "5865--5870",
    abstract = "Is it possible to use images to model verb semantic similarities? Starting from this core question, we developed two textual distributional semantic models and a visual one. We found particularly interesting and challenging to investigate this Part of Speech since verbs are not often analysed in researches focused on multimodal distributional semantics. After the creation of the visual and textual distributional space, the three models were evaluated in relation to SimLex-999, a gold standard resource. Through this evaluation, we demonstrate that, using visual distributional models, it is possible to extract meaningful information and to effectively capture the semantic similarity between verbs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mykowiecka-marciniak-2020-white,
    title = "Are White Ravens Ever White? - Non-Literal Adjective-Noun Phrases in {P}olish",
    author = "Mykowiecka, Agnieszka  and
      Marciniak, Malgorzata",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.719",
    pages = "5871--5877",
    abstract = "In the paper we describe two resources of Polish data focused on literal and metaphorical meanings of adjective-noun phrases. The first one is FigAN and consists of isolated phrases which are divided into three types: phrases with only literal meaning, with only metaphorical meaning, and phrases which can be interpreted as literal or metaphorical ones depending on a context of use. The second data is the FigSen corpus which consists of 1833 short fragments of texts containing at least one phrase from the FigAN data which may have both meanings. The corpus is annotated in two ways. One approach concerns annotation of all adjective-noun phrases. In the second approach, literal or metaphorical senses are assigned to all adjectives and nouns in the data. The paper addresses statistics of data and compares two types of annotation. The corpora were used in experiments of automatic recognition of Polish non-literal adjective noun phrases.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{armendariz-etal-2020-cosimlex,
    title = "{C}o{S}im{L}ex: A Resource for Evaluating Graded Word Similarity in Context",
    author = "Armendariz, Carlos Santos  and
      Purver, Matthew  and
      Ul{\v{c}}ar, Matej  and
      Pollak, Senja  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Granroth-Wilding, Mark",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.720",
    pages = "5878--5886",
    abstract = "State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{amblard-etal-2020-french,
    title = "A {F}rench Version of the {F}ra{C}a{S} Test Suite",
    author = "Amblard, Maxime  and
      Beysson, Cl{\'e}ment  and
      de Groote, Philippe  and
      Guillaume, Bruno  and
      Pogodalla, Sylvain",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.721",
    pages = "5887--5895",
    abstract = "This paper presents a French version of the FraCaS test suite. This test suite, originally written in English, contains problems illustrating semantic inference in natural language. We describe linguistic choices we had to make when translating the FraCaS test suite in French, and discuss some of the issues that were raised by the translation. We also report an experiment we ran in order to test both the translation and the logical semantics underlying the problems of the test suite. This provides a way of checking formal semanticists{'} hypotheses against actual semantic capacity of speakers (in the present case, French speakers), and allow us to compare the results we obtained with the ones of similar experiments that have been conducted for other languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yimam-etal-2020-automatic,
    title = "Automatic Compilation of Resources for Academic Writing and Evaluating with Informal Word Identification and Paraphrasing System",
    author = "Yimam, Seid Muhie  and
      Venkatesh, Gopalakrishnan  and
      Lee, John  and
      Biemann, Chris",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.722",
    pages = "5896--5904",
    abstract = "We present the first approach to automatically building resources for academic writing. The aim is to build a writing aid system that automatically edits a text so that it better adheres to the academic style of writing. On top of existing academic resources, such as the Corpus of Contemporary American English (COCA) academic Word List, the New Academic Word List, and the Academic Collocation List, we also explore how to dynamically build such resources that would be used to automatically identify informal or non-academic words or phrases. The resources are compiled using different generic approaches that can be extended for different domains and languages. We describe the evaluation of resources with a system implementation. The system consists of an informal word identification (IWI), academic candidate paraphrase generation, and paraphrase ranking components. To generate candidates and rank them in context, we have used the PPDB and WordNet paraphrase resources. We use the Concepts in Context (CoInCO) {``}All-Words{''} lexical substitution dataset both for the informal word identification and paraphrase generation experiments. Our informal word identification component achieves an F-1 score of 82{\%}, significantly outperforming a stratified classifier baseline. The main contribution of this work is a domain-independent methodology to build targeted resources for writing aids.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{scarlini-etal-2020-sense,
    title = "Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains",
    author = "Scarlini, Bianca  and
      Pasini, Tommaso  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.723",
    pages = "5905--5911",
    abstract = "The knowledge acquisition bottleneck problem dramatically hampers the creation of sense-annotated data for Word Sense Disambiguation (WSD). Sense-annotated data are scarce for English and almost absent for other languages. This limits the range of action of deep-learning approaches, which today are at the base of any NLP task and are hungry for data. We mitigate this issue and encourage further research in multilingual WSD by releasing to the NLP community five large datasets annotated with word-senses in five different languages, namely, English, French, Italian, German and Spanish, and 5 distinct datasets in English, each for a different semantic domain. We show that supervised WSD models trained on our data attain higher performance than when trained on other automatically-created corpora. We release all our data containing more than 15 million annotated instances in 5 different languages at http://trainomatic.org/onesec.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{barque-etal-2020-frsemcor,
    title = "{F}r{S}em{C}or: Annotating a {F}rench Corpus with Supersenses",
    author = "Barque, Lucie  and
      Haas, Pauline  and
      Huyghe, Richard  and
      Tribout, Delphine  and
      Candito, Marie  and
      Crabb{\'e}, Benoit  and
      Segonne, Vincent",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.724",
    pages = "5912--5918",
    abstract = "French, as many languages, lacks semantically annotated corpus data. Our aim is to provide the linguistic and NLP research communities with a gold standard sense-annotated corpus of French, using WordNet Unique Beginners as semantic tags, thus allowing for interoperability. In this paper, we report on the first phase of the project, which focused on the annotation of common nouns. The resulting dataset consists of more than 12,000 French noun occurrences which were annotated in double blind and adjudicated according to a carefully redefined set of supersenses. The resource is released online under a Creative Commons Licence.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{krishnaswamy-pustejovsky-2020-formal,
    title = "A Formal Analysis of Multimodal Referring Strategies Under Common Ground",
    author = "Krishnaswamy, Nikhil  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.725",
    pages = "5919--5927",
    abstract = "In this paper, we present an analysis of computationally generated mixed-modality definite referring expressions using combinations of gesture and linguistic descriptions. In doing so, we expose some striking formal semantic properties of the interactions between gesture and language, conditioned on the introduction of content into the common ground between the (computational) speaker and (human) viewer, and demonstrate how these formal features can contribute to training better models to predict viewer judgment of referring expressions, and potentially to the generation of more natural and informative referring expressions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kehat-pustejovsky-2020-improving,
    title = "Improving Neural Metaphor Detection with Visual Datasets",
    author = "Kehat, Gitit  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.726",
    pages = "5928--5933",
    abstract = "We present new results on Metaphor Detection by using text from visual datasets. Using a straightforward technique for sampling text from Vision-Language datasets, we create a data structure we term a visibility word embedding. We then combine these embeddings in a relatively simple BiLSTM module augmented with contextualized word representations (ELMo), and show improvement over previous state-of-the-art approaches that use more complex neural network architectures and richer linguistic features, for the task of verb classification.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{eyal-elhadad-2020-building,
    title = "Building a {H}ebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles",
    author = "Eyal, Ben  and
      Elhadad, Michael",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.727",
    pages = "5934--5942",
    abstract = "We present a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English. This corpus is derived from the multilingual OpenSubtitles dataset and includes short informal sentences, for which reliable linguistic annotations have been computed. We provide a fully annotated version of the data including morphological analysis, dependency syntax and semantic role labeling in both FrameNet and ProbBank styles. Sentences are aligned between English and Hebrew, both sides include full annotations and the explicit mapping from the English arguments to the Hebrew ones. We train a neural SRL model on this Hebrew resource exploiting the pre-trained multilingual BERT transformer model, and provide the first available baseline model for Hebrew SRL as a reference point. The code we provide is generic and can be adapted to other languages to bootstrap SRL resources.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{logacheva-etal-2020-word,
    title = "Word Sense Disambiguation for 158 Languages using Word Embeddings Only",
    author = "Logacheva, Varvara  and
      Teslenko, Denis  and
      Shelmanov, Artem  and
      Remus, Steffen  and
      Ustalov, Dmitry  and
      Kutuzov, Andrey  and
      Artemova, Ekaterina  and
      Biemann, Chris  and
      Ponzetto, Simone Paolo  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.728",
    pages = "5943--5952",
    abstract = "Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al., (2018), enabling WSD in these languages. Models and system are available online.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{san-martin-etal-2020-extraction,
    title = "Extraction of Hyponymic Relations in {F}rench with Knowledge-Pattern-Based Word Sketches",
    author = "San Mart{\'\i}n, Antonio  and
      Trekker, Catherine  and
      Le{\'o}n-Ara{\'u}z, Pilar",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.729",
    pages = "5953--5961",
    abstract = "Hyponymy is the cornerstone of taxonomies and concept hierarchies. However, the extraction of hypernym-hyponym pairs from a corpus can be time-consuming, and reconstructing the hierarchical network of a domain is often an extremely complex process. This paper presents the development and evaluation of the French EcoLexicon Semantic Sketch Grammar (ESSG-fr), a French hyponymic sketch grammar for Sketch Engine based on knowledge patterns. It offers a user-friendly way of extracting hyponymic pairs in the form of word sketches in any user-owned corpus. The ESSG-fr contains three times more hyponymic patterns than its English counterpart and has been tested in a multidisciplinary corpus. It is thus expected to be domain-independent. Moreover, the following methodological innovations have been included in its development: (1) use of English hyponymic patterns in a parallel corpus to find new French patterns; (2) automatic inclusion of the results of the Sketch Engine thesaurus to find new variants of the patterns. As for its evaluation, the ESSG-fr returns 70{\%} valid hyperonyms and hyponyms, measured on 180 extracted pairs of terms in three different domains.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{strohmaier-etal-2020-secoda,
    title = "{S}e{C}o{D}a: Sense Complexity Dataset",
    author = "Strohmaier, David  and
      Gooding, Sian  and
      Taslimipoor, Shiva  and
      Kochmar, Ekaterina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.730",
    pages = "5962--5967",
    abstract = "The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner{'}s Dictionary. This way we can offer more coarse-grained senses than directly available in WordNet.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rehbein-ruppenhofer-2020-new,
    title = "A New Resource for {G}erman Causal Language",
    author = "Rehbein, Ines  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.731",
    pages = "5968--5977",
    abstract = "We present a new resource for German causal language, with annotations in context for verbs, nouns and prepositions. Our dataset includes 4,390 annotated instances for more than 150 different triggers. The annotation scheme distinguishes three different types of causal events (CONSEQUENCE , MOTIVATION, PURPOSE). We also provide annotations for semantic roles, i.e. of the cause and effect for the causal event as well as the actor and affected party, if present. In the paper, we present inter-annotator agreement scores for our dataset and discuss problems for annotating causal language. Finally, we present experiments where we frame causal annotation as a sequence labelling problem and report baseline results for the prediciton of causal arguments and for predicting different types of causation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{choubey-huang-2020-one,
    title = "One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words",
    author = "Choubey, Prafulla Kumar  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.732",
    pages = "5978--5985",
    abstract = "Most supervised word sense disambiguation (WSD) systems build word-specific classifiers by leveraging labeled data. However, when using word-specific classifiers, the sparseness of annotations leads to inferior sense disambiguation performance on less frequently seen words. To combat data sparsity, we propose to learn a single model that derives sense representations and meanwhile enforces congruence between a word instance and its right sense by using both sense-annotated data and lexical resources. The model is shared across words that allows utilizing sense correlations across words, and therefore helps to transfer common disambiguation rules from annotation-rich words to annotation-lean words. Empirical evaluation on benchmark datasets shows that the proposed shared model outperforms the equivalent classifier-based models by 1.7{\%}, 2.5{\%} and 3.8{\%} in F1-score when using GloVe, ELMo and BERT word embeddings respectively.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{peng-etal-2020-corpus,
    title = "A Corpus of Adpositional Supersenses for {M}andarin {C}hinese",
    author = "Peng, Siyao  and
      Liu, Yang  and
      Zhu, Yilun  and
      Blodgett, Austin  and
      Zhao, Yushi  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.733",
    pages = "5986--5994",
    abstract = "Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. Moreover, there is a dearth of annotated corpora for investigating the cross-linguistic variation of adposition semantics, or for building multilingual disambiguation systems. This paper presents a corpus in which all adpositions have been semantically annotated in Mandarin Chinese; to the best of our knowledge, this is the first Chinese corpus to be broadly annotated with adposition semantics. Our approach adapts a framework that defined a general set of supersenses according to ostensibly language-independent semantic criteria, though its development focused primarily on English prepositions (Schneider et al., 2018). We find that the supersense categories are well-suited to Chinese adpositions despite syntactic differences from English. On a Mandarin translation of The Little Prince, we achieve high inter-annotator agreement and analyze semantic correspondences of adposition tokens in bitext.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{moeller-etal-2020-russian,
    title = "The {R}ussian {P}rop{B}ank",
    author = "Moeller, Sarah  and
      Wagner, Irina  and
      Palmer, Martha  and
      Conger, Kathryn  and
      Myers, Skatje",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.734",
    pages = "5995--6002",
    abstract = "This paper presents a proposition bank for Russian (RuPB), a resource for semantic role labeling (SRL). The motivating goal for this resource is to automatically project semantic role labels from English to Russian. This paper describes frame creation strategies, coverage, and the process of sense disambiguation. It discusses language-specific issues that complicated the process of building the PropBank and how these challenges were exploited as language-internal guidance for consistency and coherence.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jantunen-etal-2020-comes,
    title = "What Comes First: Combining Motion Capture and Eye Tracking Data to Study the Order of Articulators in Constructed Action in Sign Language Narratives",
    author = "Jantunen, Tommi  and
      Puupponen, Anna  and
      Burger, Birgitta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.735",
    pages = "6003--6007",
    abstract = "We use synchronized 120 fps motion capture and 50 fps eye tracking data from two native signers to investigate the temporal order in which the dominant hand, the head, the chest and the eyes start producing overt constructed action from regular narration in seven short Finnish Sign Language stories. From the material, we derive a sample of ten instances of regular narration to overt constructed action transfers in ELAN which we then further process and analyze in Matlab. The results indicate that the temporal order of articulators shows both contextual and individual variation but that there are also repeated patterns which are similar across all the analyzed sequences and signers. Most notably, when the discourse strategy changes from regular narration to overt constructed action, the head and the eyes tend to take the leading role, and the chest and the dominant hand tend to start acting last. Consequences of the findings are discussed.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{naert-etal-2020-lsf,
    title = "{LSF}-{ANIMAL}: A Motion Capture Corpus in {F}rench {S}ign {L}anguage Designed for the Animation of Signing Avatars",
    author = "Naert, Lucie  and
      Larboulette, Caroline  and
      Gibet, Sylvie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.736",
    pages = "6008--6017",
    abstract = "Signing avatars allow deaf people to access information in their preferred language using an interactive visualization of the sign language spatio-temporal content. However, avatars are often procedurally animated, resulting in robotic and unnatural movements, which are therefore rejected by the community for which they are intended. To overcome this lack of authenticity, solutions in which the avatar is animated from motion capture data are promising. Yet, the initial data set drastically limits the range of signs that the avatar can produce. Therefore, it can be interesting to enrich the initial corpus with new content by editing the captured motions. For this purpose, we collected the LSF-ANIMAL corpus, a French Sign Language (LSF) corpus composed of captured isolated signs and full sentences that can be used both to study LSF features and to generate new signs and utterances. This paper presents the precise definition and content of this corpus, technical considerations relative to the motion capture process (including the marker set definition), the post-processing steps required to obtain data in a standard motion format and the annotation scheme used to label the data. The quality of the corpus with respect to intelligibility, accuracy and realism is perceptually evaluated by 41 participants including native LSF signers.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{de-coster-etal-2020-sign,
    title = "Sign Language Recognition with Transformer Networks",
    author = "De Coster, Mathieu  and
      Van Herreweghe, Mieke  and
      Dambre, Joni",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.737",
    pages = "6018--6024",
    abstract = "Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7{\%} on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{trolvi-delmonte-2020-annotating,
    title = "Annotating a Fable in {I}talian {S}ign {L}anguage ({LIS})",
    author = "Trolvi, Serena  and
      Delmonte, Rodolfo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.738",
    pages = "6025--6034",
    abstract = "This paper introduces work carried out for the automatic generation of a written text in Italian starting from glosses of a fable in Italian Sign Language (LIS). The paper gives a brief overview of sign languages (SLs) and some peculiarities of SL fables such as the use of space, the strategy of Role Shift and classifiers. It also presents the annotation of the fable {``}The Tortoise and the Hare{''} - signed in LIS and made available by Alba Cooperativa Sociale -, which was annotated manually by first author for her master{'}s thesis. The annotation was the starting point of a generation process that allowed us to automatically generate a text in Italian starting from LIS glosses. LIS sentences have been transcribed with Italian words into tables on simultaneous layers, each of which contains specific linguistic or non-linguistic pieces of information. In addition, the present work discusses problems encountered in the annotation and generation process.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{neves-etal-2020-hamnosys2sigml,
    title = "{H}am{N}o{S}y{S}2{S}i{GML}: Translating {H}am{N}o{S}ys Into {S}i{GML}",
    author = "Neves, Carolina  and
      Coheur, Lu{\'\i}sa  and
      Nicolau, Hugo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.739",
    pages = "6035--6039",
    abstract = "Sign Languages are visual languages and the main means of communication used by Deaf people. However, the majority of the information available online is presented through written form. Hence, it is not of easy access to the Deaf community. Avatars that can animate sign languages have gained an increase of interest in this area due to their flexibility in the process of generation and edition. Synthetic animation of conversational agents can be achieved through the use of notation systems. HamNoSys is one of these systems, which describes movements of the body through symbols. Its XML-compliant, SiGML, is a machine-readable input of HamNoSys able to animate avatars. Nevertheless, current tools have no freely available open source libraries that allow the conversion from HamNoSys to SiGML. Our goal is to develop a tool of open access, which can perform this conversion independently from other platforms. This system represents a crucial intermediate step in the bigger pipeline of animating signing avatars. Two cases studies are described in order to illustrate different applications of our tool.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{belissen-etal-2020-dicta,
    title = "{D}icta-{S}ign-{LSF}-v2: Remake of a Continuous {F}rench {S}ign {L}anguage Dialogue Corpus and a First Baseline for Automatic Sign Language Processing",
    author = "Belissen, Valentin  and
      Braffort, Annelies  and
      Gouiff{\`e}s, Mich{\`e}le",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.740",
    pages = "6040--6048",
    abstract = "While the research in automatic Sign Language Processing (SLP) is growing, it has been almost exclusively focused on recognizing lexical signs, whether isolated or within continuous SL production. However, Sign Languages include many other gestural units like iconic structures, which need to be recognized in order to go towards a true SL understanding. In this paper, we propose a newer version of the publicly available SL corpus Dicta-Sign, limited to its French Sign Language part. Involving 16 different signers, this dialogue corpus was produced with very few constraints on the style and content. It includes lexical and non-lexical annotations over 11 hours of video recording, with 35000 manual units. With the aim of stimulating research in SL understanding, we also provide a baseline for the recognition of lexical signs and non-lexical structures on this corpus. A very compact modeling of a signer is built and a Convolutional-Recurrent Neural Network is trained and tested on Dicta-Sign-LSF-v2, with state-of-the-art results, including the ability to detect iconicity in SL production.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tornay-etal-2020-hmm,
    title = "An {HMM} Approach with Inherent Model Selection for Sign Language and Gesture Recognition",
    author = "Tornay, Sandrine  and
      Aran, Oya  and
      Magimai Doss, Mathew",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.741",
    pages = "6049--6056",
    abstract = "HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM structure has still remained as a challenge, especially when the number of signs to be modeled is high. In this work, we present a continuous HMM framework for modeling and recognizing isolated signs, which inherently performs model selection to optimize the number of states for each sign separately during recognition. Our experiments on three different datasets, namely, German sign language DGS dataset, Turkish sign language HospiSign dataset and Chalearn14 dataset show that the proposed approach achieves better sign language or gesture recognition systems in comparison to the approach of selecting or presetting the number of HMM states based on k-means, and yields systems that perform competitive to the case where the number of states are determined based on the test set performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{scicluna-strapparava-2020-vroav,
    title = "{VROAV}: Using Iconicity to Visually Represent Abstract Verbs",
    author = "Scicluna, Simone  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.742",
    pages = "6057--6062",
    abstract = "For a long time, philosophers, linguists and scientists have been keen on finding an answer to the mind-bending question {``}what does abstract language look like?{''}, which has also sprung from the phenomenon of mental imagery and how this emerges in the mind. One way of approaching the matter of word representations is by exploring the common semantic elements that link words to each other. Visual languages like sign languages have been found to reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of identifying clusters of iconic meanings. With this insight, merged with an understanding of verb predicates achieved from VerbNet, this study presents a novel verb classification system based on visual shapes, using graphic animation to visually represent 20 classes of abstract verbs. Considerable agreement between participants who judged the graphic animations based on representativeness suggests a positive way forward for this proposal, which may be developed as a language learning aid in educational contexts or as a multimodal language comprehension tool for digital text.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bull-etal-2020-mediapi,
    title = "{MEDIAPI}-{SKEL} - A 2{D}-Skeleton Video Database of {F}rench {S}ign {L}anguage With Aligned {F}rench Subtitles",
    author = "Bull, Hannah  and
      Braffort, Annelies  and
      Gouiff{\`e}s, Mich{\`e}le",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.743",
    pages = "6063--6068",
    abstract = "This paper presents MEDIAPI-SKEL, a 2D-skeleton database of French Sign Language videos aligned with French subtitles. The corpus contains 27 hours of video of body, face and hand keypoints, aligned to subtitles with a vocabulary size of 17k tokens. In contrast to existing sign language corpora such as videos produced under laboratory conditions or translations of TV programs into sign language, this database is constructed using original sign language content largely produced by deaf journalists at the media company M{\'e}dia-Pi. Moreover, the videos are accurately synchronized with French subtitles. We propose three challenges appropriate for this corpus that are related to processing units of signs in context: automatic alignment of text and video, semantic segmentation of sign language, and production of video-text embeddings for cross-modal retrieval. These challenges deviate from the classic task of identifying a limited number of lexical signs in a video stream.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kaczmarek-filhol-2020-alignment,
    title = "Alignment Data base for a Sign Language Concordancer",
    author = "Kaczmarek, Marion  and
      Filhol, Michael",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.744",
    pages = "6069--6072",
    abstract = "This article deals with elaborating a data base of alignments of parallel Franch-LSF segments. This data base is meant to be searched using a concordancer which we are also designing. We wish to equip Sign Language translators with tools similar to those used in text-to-text translation. To do so, we need language resources to feed them. Already existing Sign Language corpora can be found, but do not match our needs: working around a Sign Language concordancer, the corpus must be a parallel one and provide various examples of vocabulary and grammatical construction. We started with a parallel corpus of 40 short news and 120 SL videos , which we aligned manually by segments of various length. We described the methodology we used, how we define our segments and alignments. The last part concerns how we hope to allow the data base to keep growing in a near future.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mukushev-etal-2020-evaluation,
    title = "Evaluation of Manual and Non-manual Components for Sign Language Recognition",
    author = "Mukushev, Medet  and
      Sabyrov, Arman  and
      Imashev, Alfarabi  and
      Koishybay, Kenessary  and
      Kimmelman, Vadim  and
      Sandygulova, Anara",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.745",
    pages = "6073--6078",
    abstract = "The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign{'}s recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2{\%} of accuracy for dataset with 20 signs and 77.9{\%} of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website: https://krslproject.github.io/krsl20/",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kagirov-etal-2020-theruslan,
    title = "{T}he{R}u{SL}an: Database of {R}ussian {S}ign {L}anguage",
    author = "Kagirov, Ildar  and
      Ivanko, Denis  and
      Ryumin, Dmitry  and
      Axyonov, Alexander  and
      Karpov, Alexey",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.746",
    pages = "6079--6085",
    abstract = "In this paper, a new Russian sign language multimedia database TheRuSLan is presented. The database includes lexical units (single words and phrases) from Russian sign language within one subject area, namely, {``}food products at the supermarket{''}, and was collected using MS Kinect 2.0 device including both FullHD video and the depth map modes, which provides new opportunities for the lexicographical description of the Russian sign language vocabulary and enhances research in the field of automatic gesture recognition. Russian sign language has an official status in Russia, and over 120,000 deaf people in Russia and its neighboring countries use it as their first language. Russian sign language has no writing system, is poorly described and belongs to the low-resource languages. The authors formulate the basic principles of annotation of sign words, based on the collected data, and reveal the content of the collected database. In the future, the database will be expanded and comprise more lexical units. The database is explicitly made for the task of creating an automatic system for Russian sign language recognition.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{oshikawa-etal-2020-survey,
    title = "A Survey on Natural Language Processing for Fake News Detection",
    author = "Oshikawa, Ray  and
      Qian, Jing  and
      Wang, William Yang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.747",
    pages = "6086--6093",
    abstract = "Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gao-etal-2020-rp,
    title = "{RP}-{DNN}: A Tweet Level Propagation Context Based Deep Neural Networks for Early Rumor Detection in Social Media",
    author = "Gao, Jie  and
      Han, Sooji  and
      Song, Xingyi  and
      Ciravegna, Fabio",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.748",
    pages = "6094--6105",
    abstract = "Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available. Most of the existing methods have largely worked on event-level detection that requires the collection of posts relevant to a specific event and relied only on user-generated content. They are not appropriate to detect rumor sources in the very early stages, before an event unfolds and becomes widespread. In this paper, we address the task of ERD at the message level. We present a novel hybrid neural network architecture, which combines a task-specific character-based bidirectional language model and stacked Long Short-Term Memory (LSTM) networks to represent textual contents and social-temporal contexts of input source tweets, for modelling propagation patterns of rumors in the early stages of their development. We apply multi-layered attention models to jointly learn attentive context embeddings over multiple context inputs. Our experiments employ a stringent leave-one-out cross-validation (LOO-CV) evaluation setup on seven publicly available real-life rumor event data sets. Our models achieve state-of-the-art(SoA) performance for detecting unseen rumors on large augmented data which covers more than 12 events and 2,967 rumors. An ablation study is conducted to understand the relative contribution of each component of our proposed model.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chen-etal-2020-issues,
    title = "Issues and Perspectives from 10,000 Annotated Financial Social Media Data",
    author = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.749",
    pages = "6106--6110",
    abstract = "In this paper, we investigate the annotation of financial social media data from several angles. We present Fin-SoMe, a dataset with 10,000 labeled financial tweets annotated by experts from both the front desk and the middle desk in a bank{'}s treasury. These annotated results reveal that (1) writer-labeled market sentiment may be a misleading label; (2) writer{'}s sentiment and market sentiment of an investor may be different; (3) most financial tweets provide unfounded analysis results; and (4) almost no investors write down the gain/loss results for their positions, which would otherwise greatly facilitate detailed evaluation of their performance. Based on these results, we address various open problems and suggest possible directions for future work on financial social media data. We also provide an experiment on the key snippet extraction task to compare the performance of using a general sentiment dictionary and using the domain-specific dictionary. The results echo our findings from the experts{'} annotations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{santos-etal-2020-searching,
    title = "Searching {B}razilian {T}witter for Signs of Mental Health Issues",
    author = "Santos, Wesley  and
      Funabashi, Amanda  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.750",
    pages = "6111--6117",
    abstract = "Depression and related mental health issues are often reflected in the language employed by the individuals who suffer from these conditions and, accordingly, research in Natural Language Processing (NLP) and related fields have developed an increasing number of studies devoted to their recognition in social media text. Some of these studies have also attempted to go beyond recognition by focusing on the early signs of these illnesses, and by analysing the users{'} publication history over time to potentially prevent further harm. The two kinds of study are of course overlapping, and often make use of supervised machine learning methods based on annotated corpora. However, as in many other fields, existing resources are largely devoted to English NLP, and there is little support for these studies in under resourced languages.To bridge this gap, in this paper we describe the initial steps towards building a novel resource of this kind - a corpus intended to support both the recognition of mental health issues and the temporal analysis of these illnesses - in the Brazilian Portuguese language, and initial results of a number of experiments in text classification addressing both tasks.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tigunova-etal-2020-reddust,
    title = "{R}ed{D}ust: a Large Reusable Dataset of {R}eddit User Traits",
    author = "Tigunova, Anna  and
      Mirza, Paramita  and
      Yates, Andrew  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.751",
    pages = "6118--6126",
    abstract = "Social media is a rich source of assertions about personal traits, such as {``}I am a doctor{''} or {``}my hobby is playing tennis{''}. Precisely identifying explicit assertions is difficult, though, because of the users{'} highly varied vocabulary and language expressions. Identifying personal traits from implicit assertions like I{'}ve been at work treating patients all day is even more challenging. This paper presents RedDust, a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age,and gender. We construct RedDust using a diverse set of high-precision patterns and demonstrate its use as a resource for developing learning models to deal with implicit assertions. RedDust consists of users{'} personal traits, which are (attribute, value) pairs, along with users{'} post ids, which may be used to retrieve the posts from a publicly available crawl or from the Reddit API. We discuss the construction of the resource and show interesting statistics and insights into the data. We also compare different classifiers, which can be learned from RedDust. To the best of our knowledge, RedDust is the first annotated language resource about Reddit users at large scale. We envision further use cases of RedDust for providing background knowledge about user traits, to enhance personalized search and recommendation as well as conversational agents.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bick-2020-annotated,
    title = "An Annotated Social Media Corpus for {G}erman",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.752",
    pages = "6127--6135",
    abstract = "This paper presents the German Twitter section of a large (2 billion word) bilingual Social Media corpus for Hate Speech research, discussing the compilation, pseudonymization and grammatical annotation of the corpus, as well as special linguistic features and peculiarities encountered in the data. Among other things, compounding, accidental and intentional orthographic variation, gendering and the use of emoticons/emojis are addressed in a genre-specific fashion. We present the different layers of linguistic annotation (morphosyntactic, dependencies and semantic types) and explain how a general parser (GerGram) can be made to work on Social Media data, pointing out necessary adaptations and extensions. In an evaluation run on a random cross-section of tweets, the modified parser achieved F-scores of 97{\%} for morphology (fine-grained POS) and 92{\%} for syntax (labeled attachment score). Predictably, performance was twice as good in tweets with standard orthography than in tweets with spelling/casing irregularities or lack of sentence separation, the effect being more marked for morphology than for syntax.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{weller-seppi-2020-rjokes,
    title = "The r{J}okes Dataset: a Large Scale Humor Collection",
    author = "Weller, Orion  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.753",
    pages = "6136--6141",
    abstract = "Humor is a complicated language phenomenon that depends upon many factors, including topic, date, and recipient. Because of this variation, it can be hard to determine what exactly makes a joke humorous, leading to difficulties in joke identification and related tasks. Furthermore, current humor datasets are lacking in both joke variety and size, with almost all current datasets having less than 100k jokes. In order to alleviate this issue we compile a collection of over 550,000 jokes posted over an 11 year period on the Reddit r/Jokes subreddit (an online forum), providing a large scale humor dataset that can easily be used for a myriad of tasks. This dataset also provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. We explore this dataset through the years, examining basic statistics, most mentioned entities, and sentiment proportions. We also introduce this dataset as a task for future work, where models learn to predict the level of humor in a joke. On that task we provide strong state-of-the-art baseline models and show room for future improvement. We hope that this dataset will not only help those researching computational humor, but also help social scientists who seek to understand popular culture through humor.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{proisl-etal-2020-empirist,
    title = "{E}mpiri{ST} Corpus 2.0: Adding Manual Normalization, Lemmatization and Semantic Tagging to a {G}erman Web and {CMC} Corpus",
    author = "Proisl, Thomas  and
      Dykes, Natalie  and
      Heinrich, Philipp  and
      Kabashi, Besim  and
      Blombach, Andreas  and
      Evert, Stefan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.754",
    pages = "6142--6148",
    abstract = "The EmpiriST corpus (Bei{\ss}wenger et al., 2016) is a manually tokenized and part-of-speech tagged corpus of approximately 23,000 tokens of German Web and CMC (computer-mediated communication) data. We extend the corpus with manually created annotation layers for word form normalization, lemmatization and lexical semantics. All annotations have been independently performed by multiple human annotators. We report inter-annotator agreements and results of baseline systems and state-of-the-art off-the-shelf tools.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nakamura-etal-2020-fakeddit,
    title = "{F}akeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection",
    author = "Nakamura, Kai  and
      Levy, Sharon  and
      Wang, William Yang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.755",
    pages = "6149--6157",
    abstract = "Fake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is an efficient way to combat the widespread dissemination of fake news. However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadth of our dataset. We present Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision. We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sanders-van-den-bosch-2020-optimising,
    title = "Optimising {T}witter-based Political Election Prediction with Relevance and{S}entiment Filters",
    author = "Sanders, Eric  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.756",
    pages = "6158--6165",
    abstract = "We study the relation between the number of mentions of political parties in the last weeks before the elections and the election results.In this paper we focus on the Dutch elections of the parliament in 2012 and for the provinces (and the senate) in 2011 and 2015. With raw counts, without adaptations, we achieve a mean absolute error (MAE) of 2.71{\%} for 2011, 2.02{\%} for 2012 and 2.89{\%} for 2015. A set of over 17,000 tweets containing political party names were annotated by at least three annotators per tweet on ten features denoting communicative intent (including the presence of sarcasm, the message{'}s polarity, the presence of an explicit voting endorsement or explicit voting advice, etc.). The annotations were used to create oracle (gold-standard) filters. Tweets with or without a certain majority annotation are held out from the tweet counts, with the goal of attaining lower MAEs. With a grid search we tested all combinations of filters and their responding MAE to find the best filter ensemble. It appeared that the filters show markedly different behaviour for the three elections and only a small MAE improvement is possible when optimizing on all three elections. Larger improvements for one election are possible, but result in deterioration of the MAE for the other elections.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{iftene-etal-2020-real,
    title = "A Real-Time System for Credibility on {T}witter",
    author = "Iftene, Adrian  and
      Gifu, Daniela  and
      Miron, Andrei-Remus  and
      Dudu, Mihai-Stefan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.757",
    pages = "6166--6173",
    abstract = "Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{coltekin-2020-corpus,
    title = "A Corpus of {T}urkish Offensive Language on Social Media",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.758",
    pages = "6174--6184",
    abstract = "This paper introduces a corpus of Turkish offensive language. To our knowledge, this is the first corpus of offensive language for Turkish. The corpus consists of randomly sampled micro-blog posts from Twitter. The annotation guidelines are based on a careful review of the annotation practices of recent efforts for other languages. The corpus contains 36 232 tweets sampled randomly from the Twitter stream during a period of 18 months between Apr 2018 to Sept 2019. We found approximately 19 {\%} of the tweets in the data contain some type of offensive language, which is further subcategorized based on the target of the offense. We describe the annotation process, discuss some interesting aspects of the data, and present results of automatically classifying the corpus using state-of-the-art text classification methods. The classifiers achieve 77.3 {\%} F1 score on identifying offensive tweets, 77.9 {\%} F1 score on determining whether a given offensive document is targeted or not, and 53.0 {\%} F1 score on classifying the targeted offensive documents into three subcategories.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{seiffe-etal-2020-witchs,
    title = "From Witch{'}s Shot to Music Making Bones - Resources for Medical Laymen to Technical Language and Vice Versa",
    author = {Seiffe, Laura  and
      Marten, Oliver  and
      Mikhailov, Michael  and
      Schmeier, Sven  and
      M{\"o}ller, Sebastian  and
      Roller, Roland},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.759",
    pages = "6185--6192",
    abstract = "Many people share information in social media or forums, like food they eat, sports activities they do or events which have been visited. Information we share online unveil directly or indirectly information about our lifestyle and health situation. Particularly when text input is getting longer or multiple messages can be linked to each other. Those information can be then used to detect possible risk factors of diseases or adverse drug reactions of medications. However, as most people are not medical experts, language used might be more descriptive rather than the precise medical expression as medics do. To detect and use those relevant information, laymen language has to be translated and/or linked against the corresponding medical concept. This work presents baseline data sources in order to address this challenge for German language. We introduce a new dataset which annotates medical laymen and technical expressions in a patient forum, along with a set of medical synonyms and definitions, and present first baseline results on the data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{caselli-etal-2020-feel,
    title = "{I} Feel Offended, Don{'}t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language",
    author = "Caselli, Tommaso  and
      Basile, Valerio  and
      Mitrovi{\'c}, Jelena  and
      Kartoziya, Inga  and
      Granitzer, Michael",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.760",
    pages = "6193--6202",
    abstract = "Abusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors (degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and interaction across different phenomena).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chowdhury-etal-2020-multi,
    title = "A Multi-Platform {A}rabic News Comment Dataset for Offensive Language Detection",
    author = "Chowdhury, Shammur Absar  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Jung, Soon-gyo  and
      Jansen, Bernard J.  and
      Salminen, Joni",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.761",
    pages = "6203--6212",
    abstract = "Access to social media often enables users to engage in conversation with limited accountability. This allows a user to share their opinions and ideology, especially regarding public content, occasionally adopting offensive language. This may encourage hate crimes or cause mental harm to targeted individuals or groups. Hence, it is important to detect offensive comments in social media platforms. Typically, most studies focus on offensive commenting in one platform only, even though the problem of offensive language is observed across multiple platforms. Therefore, in this paper, we introduce and make publicly available a new dialectal Arabic news comment dataset, collected from multiple social media platforms, including Twitter, Facebook, and YouTube. We follow two-step crowd-annotator selection criteria for low-representative language annotation task in a crowdsourcing platform. Furthermore, we analyze the distinctive lexical content along with the use of emojis in offensive comments. We train and evaluate the classifiers using the annotated multi-platform dataset along with other publicly available data. Our results highlight the importance of multiple platform dataset for (a) cross-platform, (b) cross-domain, and (c) cross-dialect generalization of classifier performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{majdabadi-etal-2020-twitter,
    title = "{T}witter Trend Extraction: A Graph-based Approach for Tweet and Hashtag Ranking, Utilizing No-Hashtag Tweets",
    author = "Majdabadi, Zahra  and
      Sabeti, Behnam  and
      Golazizian, Preni  and
      Ashrafi Asli, Seyed Arad  and
      Momenzadeh, Omid  and
      Fahmi, Reza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.762",
    pages = "6213--6219",
    abstract = "Twitter has become a major platform for users to express their opinions on any topic and engage in debates. User debates and interactions usually lead to massive content regarding a specific topic which is called a Trend. Twitter trend extraction aims at finding these relevant groups of content that are generated in a short period. The most straightforward approach for this problem is using Hashtags, however, tweets without hashtags are not considered this way. In order to overcome this issue and extract trends using all tweets, we propose a graph-based approach where graph nodes represent tweets as well as words and hashtags. More specifically, we propose a modified version of RankClus algorithm to extract trends from the constructed tweets graph. The proposed approach is also capable of ranking tweets, words and hashtags in each trend with respect to their importance and relevance to the topic. The proposed algorithm is used to extract trends from several twitter datasets, where it produced consistent and coherent results.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mazoyer-etal-2020-french,
    title = "A {F}rench Corpus for Event Detection on {T}witter",
    author = "Mazoyer, B{\'e}atrice  and
      Cag{\'e}, Julia  and
      Herv{\'e}, Nicolas  and
      Hudelot, C{\'e}line",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.763",
    pages = "6220--6227",
    abstract = "We present Event2018, a corpus annotated for event detection tasks, consisting of 38 million tweets in French (retweets excluded) including more than 130,000 tweets manually annotated by three annotators as related or unrelated to a given event. The 243 events were selected both from press articles and from subjects trending on Twitter during the annotation period (July to August 2018). In total, more than 95,000 tweets were annotated as related to one of the selected events. We also provide the titles and URLs of 15,500 news articles automatically detected as related to these events. In addition to this corpus, we detail the results of our event detection experiments on both this dataset and another publicly available dataset of tweets in English. We ran extensive tests with different types of text embeddings and a standard Topic Detection and Tracking algorithm, and detail our evaluation method. We show that tf-idf vectors allow the best performance for this task on both corpora. These results are intended to serve as a baseline for researchers wishing to test their own event detection systems on our corpus.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chatterjere-etal-2020-minority,
    title = "Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling",
    author = "Chatterjere, Arindam  and
      Guptha, Vineeth  and
      Chopra, Parul  and
      Das, Amitava",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.764",
    pages = "6228--6236",
    abstract = "Code-Mixing (CM) or language mixing is a social norm in multilingual societies. CM is quite prevalent in social media conversations in multilingual regions like - India, Europe, Canada and Mexico. In this paper, we explore the problem of Language Modeling (LM) for code-mixed Hinglish text. In recent times, there have been several success stories with neural language modeling like Generative Pre-trained Transformer (GPT) (Radford et al., 2019), Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) etc.. Hence, neural language models have become the new holy grail of modern NLP, although LM for CM is an unexplored area altogether. To better understand the problem of LM for CM, we initially experimented with several statistical language modeling techniques and consequently experimented with contemporary neural language models. Analysis shows switching-points are the main challenge for the LMCM performance drop, therefore in this paper we introduce the idea of minority positive sampling to selectively induce more sample to achieve better performance. On the contrary, all neural language models demand a huge corpus to train on for better performance. Finally, we are reporting a perplexity of 139 for Hinglish (Hindi-English language pair) LMCM using statistical bi-directional techniques.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{pamungkas-etal-2020-really,
    title = "Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media",
    author = "Pamungkas, Endang Wahyu  and
      Basile, Valerio  and
      Patti, Viviana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.765",
    pages = "6237--6246",
    abstract = "Swearing plays an ubiquitous role in everyday conversations among humans, both in oral and textual communication, and occurs frequently in social media texts, typically featured by informal language and spontaneous writing. Such occurrences can be linked to an abusive context, when they contribute to the expression of hatred and to the abusive effect, causing harm and offense. However, swearing is multifaceted and is often used in casual contexts, also with positive social functions. In this study, we explore the phenomenon of swearing in Twitter conversations, taking the possibility of predicting the abusiveness of a swear word in a tweet context as the main investigation perspective. We developed the Twitter English corpus SWAD (Swear Words Abusiveness Dataset), where abusive swearing is manually annotated at the word level. Our collection consists of 1,511 unique swear words from 1,320 tweets. We developed models to automatically predict abusive swearing, to provide an intrinsic evaluation of SWAD and confirm the robustness of the resource. We also present the results of a glass box ablation study in order to investigate which lexical, syntactic, and affective features are more informative towards the automatic prediction of the function of swearing.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{miao-etal-2020-detecting,
    title = "Detecting Troll Tweets in a Bilingual Corpus",
    author = "Miao, Lin  and
      Last, Mark  and
      Litvak, Marina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.766",
    pages = "6247--6254",
    abstract = "During the past several years, a large amount of troll accounts has emerged with efforts to manipulate public opinion on social network sites. They are often involved in spreading misinformation, fake news, and propaganda with the intent of distracting and sowing discord. This paper aims to detect troll tweets in both English and Russian assuming that the tweets are generated by some {``}troll farm.{''} We reduce this task to the authorship verification problem of determining whether a single tweet is authored by a {``}troll farm{''} account or not. We evaluate a supervised classification approach with monolingual, cross-lingual, and bilingual training scenarios, using several machine learning algorithms, including deep learning. The best results are attained by the bilingual learning, showing the area under the ROC curve (AUC) of 0.875 and 0.828, for tweet classification in English and Russian test sets, respectively. It is noteworthy that these results are obtained using only raw text features, which do not require manual feature engineering efforts. In this paper, we introduce a resource of English and Russian troll tweets containing original tweets and translation from English to Russian, Russian to English. It is available for academic purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{miletic-etal-2020-collecting,
    title = "Collecting Tweets to Investigate Regional Variation in {C}anadian {E}nglish",
    author = "Miletic, Filip  and
      Przewozny-Desriaux, Anne  and
      Tanguy, Ludovic",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.767",
    pages = "6255--6264",
    abstract = "We present a 78.8-million-tweet, 1.3-billion-word corpus aimed at studying regional variation in Canadian English with a specific focus on the dialect regions of Toronto, Montreal, and Vancouver. Our data collection and filtering pipeline reflects complex design criteria, which aim to allow for both data-intensive modeling methods and user-level variationist sociolinguistic analysis. It specifically consists in identifying Twitter users from the three cities, crawling their entire timelines, filtering the collected data in terms of user location and tweet language, and automatically excluding near-duplicate content. The resulting corpus mirrors national and regional specificities of Canadian English, it provides sufficient aggregate and user-level data, and it maintains a reasonably balanced distribution of content across regions and users. The utility of this dataset is illustrated by two example applications: the detection of regional lexical and topical variation, and the identification of contact-induced semantic shifts using vector space models. In accordance with Twitter{'}s developer policy, the corpus will be publicly released in the form of tweet IDs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abbes-etal-2020-daict,
    title = "{DAICT}: A Dialectal {A}rabic Irony Corpus Extracted from {T}witter",
    author = "Abbes, Ines  and
      Zaghouani, Wajdi  and
      El-Hardlo, Omaima  and
      Ashour, Faten",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.768",
    pages = "6265--6271",
    abstract = "Identifying irony in user-generated social media content has a wide range of applications; however to date Arabic content has received limited attention. To bridge this gap, this study builds a new open domain Arabic corpus annotated for irony detection. We query Twitter using irony-related hashtags to collect ironic messages, which are then manually annotated by two linguists according to our working definition of irony. Challenges which we have encountered during the annotation process reflect the inherent limitations of Twitter messages interpretation, as well as the complexity of Arabic and its dialects. Once published, our corpus will be a valuable free resource for developing open domain systems for automatic irony recognition in Arabic language and its dialects in social media text.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{van-der-goot-etal-2020-norm,
    title = "Norm It! Lexical Normalization for {I}talian and Its Downstream Effects for Dependency Parsing",
    author = "van der Goot, Rob  and
      Ramponi, Alan  and
      Caselli, Tommaso  and
      Cafagna, Michele  and
      De Mattei, Lorenzo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.769",
    pages = "6272--6278",
    abstract = "Lexical normalization is the task of translating non-standard social media data to a standard form. Previous work has shown that this is beneficial for many downstream tasks in multiple languages. However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data. In this paper, we discuss the creation of a lexical normalization dataset for Italian. After two rounds of annotation, a Cohen{'}s kappa score of 78.64 is obtained. During this process, we also analyze the inter-annotator agreement for this task, which is only rarely done on datasets for lexical normalization,and when it is reported, the analysis usually remains shallow. Furthermore, we utilize this dataset to train a lexical normalization model and show that it can be used to improve dependency parsing of social media data. All annotated data and the code to reproduce the results are available at: http://bitbucket.org/robvanderg/normit.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gugliotta-dinarelli-2020-tarc,
    title = "{TA}r{C}: Incrementally and Semi-Automatically Collecting a {T}unisian {A}rabish Corpus",
    author = "Gugliotta, Elisa  and
      Dinarelli, Marco",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.770",
    pages = "6279--6286",
    abstract = "This article describes the constitution process of the first morpho-syntactically annotated Tunisian Arabish Corpus (TArC). Arabish, also known as Arabizi, is a spontaneous coding of Arabic dialects in Latin characters and {``}arithmographs{''} (numbers used as letters). This code-system was developed by Arabic-speaking users of social media in order to facilitate the writing in the Computer-Mediated Communication (CMC) and text messaging informal frameworks. Arabish differs for each Arabic dialect and each Arabish code-system is under-resourced, in the same way as most of the Arabic dialects. In the last few years, the attention of NLP studies on Arabic dialects has considerably increased. Taking this into consideration, TArC will be a useful support for different types of analyses, computational and linguistic, as well as for NLP tools training. In this article we will describe preliminary work on the TArC semi-automatic construction process and some of the first analyses we developed on TArC. In addition, in order to provide a complete overview of the challenges faced during the building process, we will present the main Tunisian dialect characteristics and its encoding in Tunisian Arabish.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rechkemmer-etal-2020-small,
    title = "Small Town or Metropolis? Analyzing the Relationship between Population Size and Language",
    author = "Rechkemmer, Amy  and
      Wilson, Steven  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.771",
    pages = "6287--6291",
    abstract = "The variance in language used by different cultures has been a topic of study for researchers in linguistics and psychology, but often times, language is compared across multiple countries in order to show a difference in culture. As a geographically large country that is diverse in population in terms of the background and experiences of its citizens, the U.S. also contains cultural differences within its own borders. Using a set of over 2 million posts from distinct Twitter users around the country dating back as far as 2014, we ask the following question: is there a difference in how Americans express themselves online depending on whether they reside in an urban or rural area? We categorize Twitter users as either urban or rural and identify ideas and language that are more commonly expressed in tweets written by one population over the other. We take this further by analyzing how the language from specific cities of the U.S. compares to the language of other cities and by training predictive models to predict whether a user is from an urban or rural area. We publicly release the tweet and user IDs that can be used to reconstruct the dataset for future studies in this direction.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{xu-etal-2020-inferring,
    title = "Inferring Social Media Users{'} Mental Health Status from Multimodal Information",
    author = "Xu, Zhentao  and
      P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.772",
    pages = "6292--6299",
    abstract = "Worldwide, an increasing number of people are suffering from mental health disorders such as depression and anxiety. In the United States alone, one in every four adults suffers from a mental health condition, which makes mental health a pressing concern. In this paper, we explore the use of multimodal cues present in social media posts to predict users{'} mental health status. Specifically, we focus on identifying social media activity that either indicates a mental health condition or its onset. We collect posts from Flickr and apply a multimodal approach that consists of jointly analyzing language, visual, and metadata cues and their relation to mental health. We conduct several classification experiments aiming to discriminate between (1) healthy users and users affected by a mental health illness; and (2) healthy users and users prone to mental illness. Our experimental results indicate that using multiple modalities can improve the performance of this classification task as compared to the use of one modality at a time, and can provide important cues into a user{'}s mental status.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dekker-van-der-goot-2020-synthetic,
    title = "Synthetic Data for {E}nglish Lexical Normalization: How Close Can We Get to Manually Annotated Data?",
    author = "Dekker, Kelly  and
      van der Goot, Rob",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.773",
    pages = "6300--6309",
    abstract = "Social media is a valuable data resource for various natural language processing (NLP) tasks. However, standard NLP tools were often designed with standard texts in mind, and their performance decreases heavily when applied to social media data. One solution to this problem is to adapt the input text to a more standard form, a task also referred to as normalization. Automatic approaches to normalization have shown that they can be used to improve performance on a variety of NLP tasks. However, all of these systems are supervised, thereby being heavily dependent on the availability of training data for the correct language and domain. In this work, we attempt to overcome this dependence by automatically generating training data for lexical normalization. Starting with raw tweets, we attempt two directions, to insert non-standardness (noise) and to automatically normalize in an unsupervised setting. Our best results are achieved by automatically inserting noise. We evaluate our approaches by using an existing lexical normalization system; our best scores are achieved by custom error generation system, which makes use of some manually created datasets. With this system, we score 94.29 accuracy on the test data, compared to 95.22 when it is trained on human-annotated data. Our best system which does not depend on any type of annotation is based on word embeddings and scores 92.04 accuracy. Finally, we perform an experiment in which we asked humans to predict whether a sentence was written by a human or generated by our best model. This experiment showed that in most cases it is hard for a human to detect automatically generated sentences.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{blombach-etal-2020-corpus,
    title = "A Corpus of {G}erman {R}eddit Exchanges ({G}e{R}ed{E})",
    author = "Blombach, Andreas  and
      Dykes, Natalie  and
      Heinrich, Philipp  and
      Kabashi, Besim  and
      Proisl, Thomas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.774",
    pages = "6310--6316",
    abstract = "GeRedE is a 270 million token German CMC corpus containing approximately 380,000 submissions and 6,800,000 comments posted on Reddit between 2010 and 2018. Reddit is a popular online platform combining social news aggregation, discussion and micro-blogging. Starting from a large, freely available data set, the paper describes our approach to filter out German data and further pre-processing steps, as well as which metadata and annotation layers have been included so far. We explore the Reddit sphere, what makes the German data linguistically peculiar, and how some of the communities within Reddit differ from one another. The CWB-indexed version of our final corpus is available via CQPweb, and all our processing scripts as well as all manual annotation and automatic language classification can be downloaded from GitHub.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{evrard-etal-2020-french,
    title = "{F}rench Tweet Corpus for Automatic Stance Detection",
    author = "Evrard, Marc  and
      Uro, R{\'e}mi  and
      Herv{\'e}, Nicolas  and
      Mazoyer, B{\'e}atrice",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.775",
    pages = "6317--6322",
    abstract = "The automatic stance detection task consists in determining the attitude expressed in a text toward a target (text, claim, or entity). This is a typical intermediate task for the fake news detection or analysis, which is a considerably widespread and a particularly difficult issue to overcome. This work aims at the creation of a human-annotated corpus for the automatic stance detection of tweets written in French. It exploits a corpus of tweets collected during July and August 2018. To the best of our knowledge, this is the first freely available stance annotated tweet corpus in the French language. The four classes broadly adopted by the community were chosen for the annotation: support, deny, query, and comment with the addition of the ignore class. This paper presents the corpus along with the tools used to build it, its construction, an analysis of the inter-rater reliability, as well as the challenges and questions that were raised during the building process.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abdi-khojasteh-etal-2020-lscp,
    title = "{LSCP}: Enhanced Large Scale Colloquial {P}ersian Language Understanding",
    author = "Abdi Khojasteh, Hadi  and
      Ansari, Ebrahim  and
      Bohlouli, Mahdi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.776",
    pages = "6323--6327",
    abstract = "Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a {``}Large Scale Colloquial Persian Dataset{''} (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{oo-etal-2020-burmese,
    title = "{B}urmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech",
    author = "Oo, Yin May  and
      Wattanavekin, Theeraphol  and
      Li, Chenfang  and
      De Silva, Pasindu  and
      Sarin, Supheakmungkol  and
      Pipatsrisawat, Knot  and
      Jansche, Martin  and
      Kjartansson, Oddur  and
      Gutkin, Alexander",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.777",
    pages = "6328--6339",
    abstract = "This paper introduces an open-source crowd-sourced multi-speaker speech corpus along with the comprehensive set of finite-state transducer (FST) grammars for performing text normalization for the Burmese (Myanmar) language. We also introduce the open-source finite-state grammars for performing grapheme-to-phoneme (G2P) conversion for Burmese. These three components are necessary (but not sufficient) for building a high-quality text-to-speech (TTS) system for Burmese, a tonal Southeast Asian language from the Sino-Tibetan family which presents several linguistic challenges. We describe the corpus acquisition process and provide the details of our finite state-based approach to Burmese text normalization and G2P. Our experiments involve building a multi-speaker TTS system based on long short term memory (LSTM) recurrent neural network (RNN) models, which were previously shown to perform well for other languages in a low-resource setting. Our results indicate that the data and grammars that we are announcing are sufficient to build reasonably high-quality models comparable to other systems. We hope these resources will facilitate speech and language research on the Burmese language, which is considered by many to be low-resource due to the limited availability of free linguistic data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{booth-etal-2020-evaluating,
    title = "Evaluating and Improving Child-Directed Automatic Speech Recognition",
    author = "Booth, Eric  and
      Carns, Jake  and
      Kennington, Casey  and
      Rafla, Nader",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.778",
    pages = "6340--6345",
    abstract = "Speech recognition has seen dramatic improvements in the last decade, though those improvements have focused primarily on adult speech. In this paper, we assess child-directed speech recognition and leverage a transfer learning approach to improve child-directed speech recognition by training the recent DeepSpeech2 model on adult data, then apply additional tuning to varied amounts of child speech data. We evaluate our model using the CMU Kids dataset as well as our own recordings of child-directed prompts. The results from our experiment show that even a small amount of child audio data improves significantly over a baseline of adult-only or child-only trained models. We report a final general Word-Error-Rate of 29{\%} over a baseline of 62{\%} that uses the adult-trained model. Our analyses show that our model adapts quickly using a small amount of data and that the general child model works better than school grade-specific models. We make available our trained model and our data collection tool.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ihori-etal-2020-parallel,
    title = "Parallel Corpus for {J}apanese Spoken-to-Written Style Conversion",
    author = "Ihori, Mana  and
      Takashima, Akihiko  and
      Masumura, Ryo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.779",
    pages = "6346--6353",
    abstract = "With the increase of automatic speech recognition (ASR) applications, spoken-to-written style conversion that transforms spoken-style text into written-style text is becoming an important technology to increase the readability of ASR transcriptions. To establish such conversion technology, a parallel corpus of spoken-style text and written-style text is beneficial because it can be utilized for building end-to-end neural sequence transformation models. Spoken-to-written style conversion involves multiple conversion problems including punctuation restoration, disfluency detection, and simplification. However, most existing corpora tend to be made for just one of these conversion problems. In addition, in Japanese, we have to consider not only general spoken-to-written style conversion problems but also Japanese-specific ones, such as language style unification (e.g., polite, frank, and direct styles) and omitted postpositional particle expressions restoration. Therefore, we created a new Japanese parallel corpus of spoken-style text and written-style text that can simultaneously handle general problems and Japanese-specific ones. To make this corpus, we prepared four types of spoken-style text and utilized a crowdsourcing service for manually converting them into written-style text. This paper describes the building setup of this corpus and reports the baseline results of spoken-to-written style conversion using the latest neural sequence transformation models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gref-etal-2020-multi,
    title = "Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on {G}erman Oral History Interviews",
    author = {Gref, Michael  and
      Walter, Oliver  and
      Schmidt, Christoph  and
      Behnke, Sven  and
      K{\"o}hler, Joachim},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.780",
    pages = "6354--6362",
    abstract = "While recent automatic speech recognition systems achieve remarkable performance when large amounts of adequate, high quality annotated speech data is used for training, the same systems often only achieve an unsatisfactory result for tasks in domains that greatly deviate from the conditions represented by the training data. For many real-world applications, there is a lack of sufficient data that can be directly used for training robust speech recognition systems. To address this issue, we propose and investigate an approach that performs a robust acoustic model adaption to a target domain in a cross-lingual, multi-staged manner. Our approach enables the exploitation of large-scale training data from other domains in both the same and other languages. We evaluate our approach using the challenging task of German oral history interviews, where we achieve a relative reduction of the word error rate by more than 30{\%} compared to a model trained from scratch only on the target domain, and 6-7{\%} relative compared to a model trained robustly on 1000 hours of same-language out-of-domain training data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kratochvil-etal-2020-large,
    title = "Large Corpus of {C}zech Parliament Plenary Hearings",
    author = "Kratochvil, Jonas  and
      Polak, Peter  and
      Bojar, Ondrej",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.781",
    pages = "6363--6367",
    abstract = "We present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 1200 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio segments making it suitable for both training and evaluation of automatic speech recognition (ASR) systems. The source language of the corpus is Czech, which makes it a valuable resource for future research as only a few public datasets are available in the Czech language. We complement the data release with experiments of two baseline ASR systems trained on the presented data: the more traditional approach implemented in the Kaldi ASRtoolkit which combines hidden Markov models and deep neural networks (NN) and a modern ASR architecture implemented in Jaspertoolkit which uses deep NNs in an end-to-end fashion.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{szekely-etal-2020-augmented,
    title = "Augmented Prompt Selection for Evaluation of Spontaneous Speech Synthesis",
    author = "Szekely, Eva  and
      Edlund, Jens  and
      Gustafson, Joakim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.782",
    pages = "6368--6374",
    abstract = "By definition, spontaneous speech is unscripted and created on the fly by the speaker. It is dramatically different from read speech, where the words are authored as text before they are spoken. Spontaneous speech is emergent and transient, whereas text read out loud is pre-planned. For this reason, it is unsuitable to evaluate the usability and appropriateness of spontaneous speech synthesis by having it read out written texts sampled from for example newspapers or books. Instead, we need to use transcriptions of speech as the target - something that is much less readily available. In this paper, we introduce Starmap, a tool allowing developers to select a varied, representative set of utterances from a spoken genre, to be used for evaluation of TTS for a given domain. The selection can be done from any speech recording, without the need for transcription. The tool uses interactive visualisation of prosodic features with t-SNE, along with a tree-based algorithm to guide the user through thousands of utterances and ensure coverage of a variety of prompts. A listening test has shown that with a selection of genre-specific utterances, it is possible to show significant differences across genres between two synthetic voices built from spontaneous speech.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{schulder-etal-2020-atc,
    title = "{ATC}-{ANNO}: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation",
    author = "Schulder, Marc  and
      O{'}Mahony, Johannah  and
      Bakanouski, Yury  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.783",
    pages = "6375--6380",
    abstract = "In air traffic control, assistant systems support air traffic controllers in their work. To improve the reactivity and accuracy of the assistant, automatic speech recognition can monitor the commands uttered by the controller. However, to provide sufficient training data for the speech recognition system, many hours of air traffic communications have to be transcribed and semantically annotated. For this purpose we developed the annotation tool ATC-ANNO. It provides a number of features to support the annotator in their task, such as auto-complete suggestions for semantic tags, access to preliminary speech recognition predictions, syntax highlighting and consistency indicators. Its core assistive feature, however, is its ability to automatically generate semantic annotations. Although it is based on a simple hand-written finite state grammar, it is also able to annotate sentences that deviate from this grammar. We evaluate the impact of different features on annotator efficiency and find that automatic annotation allows annotators to cover four times as many utterances in the same time.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hernandez-mena-etal-2020-masri,
    title = "{MASRI}-{HEADSET}: A {M}altese Corpus for Speech Recognition",
    author = "Hernandez Mena, Carlos Daniel  and
      Gatt, Albert  and
      DeMarco, Andrea  and
      Borg, Claudia  and
      van der Plas, Lonneke  and
      Muscat, Amanda  and
      Padovani, Ian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.784",
    pages = "6381--6388",
    abstract = "Maltese, the national language of Malta, is spoken by approximately 500,000 people. Speech processing for Maltese is still in its early stages of development. In this paper, we present the first spoken Maltese corpus designed purposely for Automatic Speech Recognition (ASR). The MASRI-HEADSET corpus was developed by the MASRI project at the University of Malta. It consists of 8 hours of speech paired with text, recorded by using short text snippets in a laboratory environment. The speakers were recruited from different geographical locations all over the Maltese islands, and were roughly evenly distributed by gender. This paper also presents some initial results achieved in baseline experiments for Maltese ASR using Sphinx and Kaldi. The MASRI HEADSET Corpus is publicly available for research/academic purposes.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kalashnikova-etal-2020-automatic,
    title = "Automatic Period Segmentation of Oral {F}rench",
    author = {Kalashnikova, Natalia  and
      Grobol, Lo{\"\i}c  and
      Eshkol-Taravella, Iris  and
      Delafontaine, Fran{\c{c}}ois},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.785",
    pages = "6389--6394",
    abstract = "Natural Language Processing in oral speech segmentation is still looking for a minimal unit to analyze. In this work, we present a comparison of two automatic segmentation methods of macro-syntactic periods which allows to take into account syntactic and prosodic components of speech. We compare the performances of an existing tool Analor (Avanzi, Lacheret-Dujour, Victorri, 2008) developed for automatic segmentation of prosodic periods and of CRF models relying on syntactic and / or prosodic features. We find that Analor tends to divide speech into smaller segments and that CRF models detect larger segments rather than macro-syntactic periods. However, in general CRF models perform better results than Analor in terms of F-measure.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{desot-etal-2020-corpus,
    title = "Corpus Generation for Voice Command in Smart Home and the Effect of Speech Synthesis on End-to-End {SLU}",
    author = "Desot, Thierry  and
      Portet, Fran{\c{c}}ois  and
      Vacher, Michel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.786",
    pages = "6395--6404",
    abstract = "Massive amounts of annotated data greatly contributed to the advance of the machine learning field. However such large data sets are often unavailable for novel tasks performed in realistic environments such as smart homes. In this domain, semantically annotated large voice command corpora for Spoken Language Understanding (SLU) are scarce, especially for non-English languages. We present the automatic generation process of a synthetic semantically-annotated corpus of French commands for smart-home to train pipeline and End-to-End (E2E) SLU models. SLU is typically performed through Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) in a pipeline. Since errors at the ASR stage reduce the NLU performance, an alternative approach is End-to-End (E2E) SLU to jointly perform ASR and NLU. To that end, the artificial corpus was fed to a text-to-speech (TTS) system to generate synthetic speech data. All models were evaluated on voice commands acquired in a real smart home. We show that artificial data can be combined with real data within the same training set or used as a stand-alone training corpus. The synthetic speech quality was assessedby comparing it to real data using dynamic time warping (DTW).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ben-abdallah-etal-2020-text,
    title = "Text and Speech-based {T}unisian {A}rabic Sub-Dialects Identification",
    author = "Ben Abdallah, Najla  and
      Kchaou, Sam{\'e}h  and
      Bougares, Fethi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.787",
    pages = "6405--6411",
    abstract = "Dialect IDentification (DID) is a challenging task, and it becomes more complicated when it is about the identification of dialects that belong to the same country. Indeed, dialects of the same country are closely related and exhibit a significant overlapping at the phonetic and lexical levels. In this paper, we present our first results on a dialect classification task covering four sub-dialects spoken in Tunisia. We use the term {'}sub-dialect{'} to refer to the dialects belonging to the same country. We conducted our experiments aiming to discriminate between Tunisian sub-dialects belonging to four different cities: namely Tunis, Sfax, Sousse and Tataouine. A spoken corpus of 1673 utterances is collected, transcribed and freely distributed. We used this corpus to build several speech- and text-based DID systems. Our results confirm that, at this level of granularity, dialects are much better distinguishable using the speech modality. Indeed, we were able to reach an F-1 score of 93.75{\%} using our best speech-based identification system while the F-1 score is limited to 54.16{\%} using text-based DID on the same test set.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rognoni-etal-2020-urdu,
    title = "{U}rdu Pitch Accents and Intonation Patterns in Spontaneous Conversational Speech",
    author = "Rognoni, Luca  and
      Bishop, Judith  and
      Corris, Miriam  and
      Fernando, Jessica  and
      Smith, Rosanna",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.788",
    pages = "6412--6416",
    abstract = "An intonational inventory of Urdu for spontaneous conversational speech is determined based on the analysis of a hand-labelled data set of telephone conversations. An inventory of Urdu pitch accents and the basic Urdu intonation patterns observed in the data are summarised and presented using a simplified version of the Rhythm and Pitch (RaP) labelling system. The relation between pitch accents and parts of speech (PoS) is also explored. The data confirm the important role played by low pitch accents in Urdu spontaneous speech, in line with previous studies on Urdu/Hindi scripted speech. Typical pitch contours such as falling tone in statements and WH-questions, and rising tone for yes/no questions are also exhibited. Pitch accent distribution is quite free in Urdu, but the data indicate a stronger association of pitch accent with some PoS categories of content word (e.g. Nouns) when compared with function words and semantically lighter PoS categories (such as Light Verbs). Contrastive focus is realised by an L*+H accent with a relatively large pitch excursion for the +H tone, and longer duration of the stressed syllable. The data suggest that post-focus compression (PFC) is used in Urdu as a focus-marking strategy.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{srivastava-etal-2020-indicspeech,
    title = "{I}ndic{S}peech: Text-to-Speech Corpus for {I}ndian Languages",
    author = "Srivastava, Nimisha  and
      Mukhopadhyay, Rudrabha  and
      K R, Prajwal  and
      Jawahar, C V",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.789",
    pages = "6417--6422",
    abstract = "India is a country where several tens of languages are spoken by over a billion strong population. Text-to-speech systems for such languages will thus be extremely beneficial for wide-spread content creation and accessibility. Despite this, the current TTS systems for even the most popular Indian languages fall short of the contemporary state-of-the-art systems for English, Chinese, etc. We believe that one of the major reasons for this is the lack of large, publicly available text-to-speech corpora in these languages that are suitable for training neural text-to-speech systems. To mitigate this, we release a 24 hour text-to-speech corpus for 3 major Indian languages namely Hindi, Malayalam and Bengali. In this work, we also train a state-of-the-art TTS system for each of these languages and report their performances. The collected corpus, code, and trained models are made publicly available.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gorisch-etal-2020-using,
    title = "Using Automatic Speech Recognition in Spoken Corpus Curation",
    author = "Gorisch, Jan  and
      Gref, Michael  and
      Schmidt, Thomas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.790",
    pages = "6423--6428",
    abstract = "The newest generation of speech technology caused a huge increase of audio-visual data nowadays being enhanced with orthographic transcripts such as in automatic subtitling in online platforms. Research data centers and archives contain a range of new and historical data, which are currently only partially transcribed and therefore only partially accessible for systematic querying. Automatic Speech Recognition (ASR) is one option of making that data accessible. This paper tests the usability of a state-of-the-art ASR-System on a historical (from the 1960s), but regionally balanced corpus of spoken German, and a relatively new corpus (from 2012) recorded in a narrow area. We observed a regional bias of the ASR-System with higher recognition scores for the north of Germany vs. lower scores for the south. A detailed analysis of the narrow region data revealed {--} despite relatively high ASR-confidence {--} some specific word errors due to a lack of regional adaptation. These findings need to be considered in decisions on further data processing and the curation of corpora, e.g. correcting transcripts or transcribing from scratch. Such geography-dependent analyses can also have the potential for ASR-development to make targeted data selection for training/adaptation and to increase the sensitivity towards varieties of pluricentric languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{deng-etal-2020-integrating,
    title = "Integrating Disfluency-based and Prosodic Features with Acoustics in Automatic Fluency Evaluation of Spontaneous Speech",
    author = "Deng, Huaijin  and
      Lin, Youchao  and
      Utsuro, Takehito  and
      Kobayashi, Akio  and
      Nishizaki, Hiromitsu  and
      Hoshino, Junichi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.791",
    pages = "6429--6437",
    abstract = "This paper describes an automatic fluency evaluation of spontaneous speech. In the task of automatic fluency evaluation, we integrate diverse features of acoustics, prosody, and disfluency-based ones. Then, we attempt to reveal the contribution of each of those diverse features to the task of automatic fluency evaluation. Although a variety of different disfluencies are observed regularly in spontaneous speech, we focus on two types of phenomena, i.e., filled pauses and word fragments. The experimental results demonstrate that the disfluency-based features derived from word fragments and filled pauses are effective relative to evaluating fluent/disfluent speech, especially when combined with prosodic features, e.g., such as speech rate and pauses/silence. Next, we employed an LSTM based framework in order to integrate the disfluency-based and prosodic features with time sequential acoustic features. The experimental evaluation results of those integrated diverse features indicate that time sequential acoustic features contribute to improving the model with disfluency-based and prosodic features when detecting fluent speech, but not when detecting disfluent speech. Furthermore, when detecting disfluent speech, the model without time sequential acoustic features performs best even without word fragments features, but only with filled pauses and prosodic features.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yamashita-etal-2020-dnn,
    title = "{DNN}-based Speech Synthesis Using Abundant Tags of Spontaneous Speech Corpus",
    author = "Yamashita, Yuki  and
      Koriyama, Tomoki  and
      Saito, Yuki  and
      Takamichi, Shinnosuke  and
      Ijima, Yusuke  and
      Masumura, Ryo  and
      Saruwatari, Hiroshi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.792",
    pages = "6438--6443",
    abstract = "In this paper, we investigate the effectiveness of using rich annotations in deep neural network (DNN)-based statistical speech synthesis. DNN-based frameworks typically use linguistic information as input features called context instead of directly using text. In such frameworks, we can synthesize not only reading-style speech but also speech with paralinguistic and nonlinguistic features by adding such information to the context. However, it is not clear what kind of information is crucial for reproducing paralinguistic and nonlinguistic features. Therefore, we investigate the effectiveness of rich tags in DNN-based speech synthesis according to the Corpus of Spontaneous Japanese (CSJ), which has a large amount of annotations on paralinguistic features such as prosody, disfluency, and morphological features. Experimental evaluation results shows that the reproducibility of paralinguistic features of synthetic speech was enhanced by adding such information as context.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{abulimiti-schultz-2020-automatic,
    title = "Automatic Speech Recognition for {U}yghur through Multilingual Acoustic Modeling",
    author = "Abulimiti, Ayimunishagu  and
      Schultz, Tanja",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.793",
    pages = "6444--6449",
    abstract = "Low-resource languages suffer from lower performance of Automatic Speech Recognition (ASR) system due to the lack of data. As a common approach, multilingual training has been applied to achieve more context coverage and has shown better performance over the monolingual training (Heigold et al., 2013). However, the difference between the donor language and the target language may distort the acoustic model trained with multilingual data, especially when much larger amount of data from donor languages is used for training the models of low-resource language. This paper presents our effort towards improving the performance of ASR system for the under-resourced Uyghur language with multilingual acoustic training. For the developing of multilingual speech recognition system for Uyghur, we used Turkish as donor language, which we selected from GlobalPhone corpus as the most similar language to Uyghur. By generating subsets of Uyghur training data, we explored the performance of multilingual speech recognition systems trained with different sizes of Uyghur and Turkish data. The best speech recognition system for Uyghur is achieved by multilingual training using all Uyghur data (10hours) and 17 hours of Turkish data and the WER is 19.17{\%}, which corresponds to 4.95{\%} relative improvement over monolingual training.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{delgado-etal-2020-safe,
    title = "The {SAFE}-{T} Corpus: A New Resource for Simulated Public Safety Communications",
    author = "Delgado, Dana  and
      Walker, Kevin  and
      Strassel, Stephanie  and
      Jones, Karen  and
      Caruso, Christopher  and
      Graff, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.794",
    pages = "6450--6457",
    abstract = "We introduce a new resource, the SAFE-T (Speech Analysis for Emergency Response Technology) Corpus, designed to simulate first-responder communications by inducing high vocal effort and urgent speech with situational background noise in a game-based collection protocol. Linguistic Data Consortium developed the SAFE-T Corpus to support the NIST (National Institute of Standards and Technology) OpenSAT (Speech Analytic Technologies) evaluation series, whose goal is to advance speech analytic technologies including automatic speech recognition, speech activity detection and keyword search in multiple domains including simulated public safety communications data. The corpus comprises over 300 hours of audio from 115 unique speakers engaged in a collaborative problem-solving activity representative of public safety communications in terms of speech content, noise types and noise levels. Portions of the corpus have been used in the OpenSAT 2019 evaluation and the full corpus will be published in the LDC catalog. We describe the design and implementation of the SAFE-T Corpus collection, discuss the approach of capturing spontaneous speech from study participants through game-based speech collection, and report on the collection results including several challenges associated with the collection.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{gogoi-etal-2020-lexical,
    title = "Lexical Tone Recognition in Mizo using Acoustic-Prosodic Features",
    author = "Gogoi, Parismita  and
      Dey, Abhishek  and
      Lalhminghlui, Wendy  and
      Sarmah, Priyankoo  and
      Prasanna, S R Mahadeva",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.795",
    pages = "6458--6461",
    abstract = "Mizo is an under-studied Tibeto-Burman tonal language of the North-East India. Preliminary research findings have confirmed that four distinct tones of Mizo (High, Low, Rising and Falling) appear in the language. In this work, an attempt is made to automatically recognize four phonological tones in Mizo distinctively using acoustic-prosodic parameters as features. Six features computed from Fundamental Frequency (F0) contours are considered and two classifier models based on Support Vector Machine (SVM) {\&} Deep Neural Network (DNN) are implemented for automatic tonerecognition task respectively. The Mizo database consists of 31950 iterations of the four Mizo tones, collected from 19 speakers using trisyllabic phrases. A four-way classification of tones is attempted with a balanced (equal number of iterations per tone category) dataset for each tone of Mizo. it is observed that the DNN based classifier shows comparable performance in correctly recognizing four phonological Mizo tones as of the SVM based classifier.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{meyer-etal-2020-artie,
    title = "Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications",
    author = "Meyer, Josh  and
      Rauchenstein, Lindy  and
      Eisenberg, Joshua D.  and
      Howell, Nicholas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.796",
    pages = "6462--6468",
    abstract = "We describe the creation of the Artie Bias Corpus, an English dataset of expert-validated {\textless}audio, transcript{\textgreater} pairs with demographic tags for age, gender, accent. We also release open software which may be used with the Artie Bias Corpus to detect demographic bias in Automatic Speech Recognition systems, and can be extended to other speech technologies. The Artie Bias Corpus is a curated subset of the Mozilla Common Voice corpus, which we release under a Creative Commons CC0 license {--} the most open and permissive license for data. This article contains information on the criteria used to select and annotate the Artie Bias Corpus in addition to experiments in which we detect and attempt to mitigate bias in end-to-end speech recognition models. We we observe a significant accent bias in our baseline DeepSpeech model, with more accurate transcriptions of US English compared to Indian English. We do not, however, find evidence for a significant gender bias. We then show significant improvements on individual demographic groups from fine-tuning.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{georgila-etal-2020-evaluation,
    title = "Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains",
    author = "Georgila, Kallirroi  and
      Leuski, Anton  and
      Yanov, Volodymyr  and
      Traum, David",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.797",
    pages = "6469--6476",
    abstract = "We evaluate several publicly available off-the-shelf (commercial and research) automatic speech recognition (ASR) systems across diverse dialogue domains (in US-English). Our evaluation is aimed at non-experts with limited experience in speech recognition. Our goal is not only to compare a variety of ASR systems on several diverse data sets but also to measure how much ASR technology has advanced since our previous large-scale evaluations on the same data sets. Our results show that the performance of each speech recognizer can vary significantly depending on the domain. Furthermore, despite major recent progress in ASR technology, current state-of-the-art speech recognizers perform poorly in domains that require special vocabulary and language models, and under noisy conditions. We expect that our evaluation will prove useful to ASR consumers and dialogue system designers.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ulasik-etal-2020-ceasr,
    title = "{CEASR}: A Corpus for Evaluating Automatic Speech Recognition",
    author = {Ulasik, Malgorzata Anna  and
      H{\"u}rlimann, Manuela  and
      Germann, Fabian  and
      Gedik, Esin  and
      Benites, Fernando  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.798",
    pages = "6477--6485",
    abstract = "In this paper, we present CEASR, a Corpus for Evaluating the quality of Automatic Speech Recognition (ASR). It is a data set based on public speech corpora, containing metadata along with transcripts generated by several modern state-of-the-art ASR systems. CEASR provides this data in a unified structure, consistent across all corpora and systems, with normalised transcript texts and metadata. We use CEASR to evaluate the quality of ASR systems by calculating an average Word Error Rate (WER) per corpus, per system and per corpus-system pair. Our experiments show a substantial difference in accuracy between commercial versus open-source ASR tools as well as differences up to a factor ten for single systems on different corpora. Using CEASR allowed us to very efficiently and easily obtain these results. Our corpus enables researchers to perform ASR-related evaluations and various in-depth analyses with noticeably reduced effort, i.e. without the need to collect, process and transcribe the speech data themselves.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zanon-boito-etal-2020-mass,
    title = "{M}a{SS}: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the {B}ible",
    author = "Zanon Boito, Marcely  and
      Havard, William  and
      Garnerin, Mahault  and
      Le Ferrand, {\'E}ric  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.799",
    pages = "6486--6493",
    abstract = "The CMU Wilderness Multilingual Speech Dataset (Black, 2019) is a newly published multilingual speech dataset based on recorded readings of the New Testament. It provides data to build Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models for potentially 700 languages. However, the fact that the source content (the Bible) is the same for all the languages is not exploited to date.Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). We name this corpus MaSS (Multilingual corpus of Sentence-aligned Spoken utterances). The covered languages (Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish) allow researches on speech-to-speech alignment as well as on translation for typologically different language pairs. The quality of the final corpus is attested by human evaluation performed on a corpus subset (100 utterances, 8 language pairs). Lastly, we showcase the usefulness of the final product on a bilingual speech retrieval task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{he-etal-2020-open,
    title = "Open-source Multi-speaker Speech Corpora for Building {G}ujarati, {K}annada, {M}alayalam, {M}arathi, {T}amil and {T}elugu Speech Synthesis Systems",
    author = "He, Fei  and
      Chu, Shan-Hui Cathy  and
      Kjartansson, Oddur  and
      Rivera, Clara  and
      Katanova, Anna  and
      Gutkin, Alexander  and
      Demirsahin, Isin  and
      Johny, Cibu  and
      Jansche, Martin  and
      Sarin, Supheakmungkol  and
      Pipatsrisawat, Knot",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.800",
    pages = "6494--6503",
    abstract = "We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu, which are six of the twenty two official languages of India spoken by 374 million native speakers. The datasets are primarily intended for use in text-to-speech (TTS) applications, such as constructing multilingual voices or being used for speaker or language adaptation. Most of the corpora (apart from Marathi, which is a female-only database) consist of at least 2,000 recorded lines from female and male native speakers of the language. We present the methodological details behind corpora acquisition, which can be scaled to acquiring data for other languages of interest. We describe the experiments in building a multilingual text-to-speech model that is constructed by combining our corpora. Our results indicate that using these corpora results in good quality voices, with Mean Opinion Scores (MOS) {\textgreater} 3.6, for all the languages tested. We believe that these resources, released with an open-source license, and the described methodology will help in the progress of speech applications for the languages described and aid corpora development for other, smaller, languages of India and beyond.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{guevara-rukoz-etal-2020-crowdsourcing,
    title = "Crowdsourcing {L}atin {A}merican {S}panish for Low-Resource Text-to-Speech",
    author = "Guevara-Rukoz, Adriana  and
      Demirsahin, Isin  and
      He, Fei  and
      Chu, Shan-Hui Cathy  and
      Sarin, Supheakmungkol  and
      Pipatsrisawat, Knot  and
      Gutkin, Alexander  and
      Butryna, Alena  and
      Kjartansson, Oddur",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.801",
    pages = "6504--6513",
    abstract = "In this paper we present a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources, focusing on various South American dialects of Spanish. We first present public speech datasets for Argentinian, Chilean, Colombian, Peruvian, Puerto Rican and Venezuelan Spanish specifically constructed with text-to-speech applications in mind using crowd-sourcing. We then compare the monodialectal voices built with minimal data to a multidialectal model built by pooling all the resources from all dialects. Our results show that the multidialectal model outperforms the monodialectal baseline models. We also experiment with a {``}zero-resource{''} dialect scenario where we build a multidialectal voice for a dialect while holding out target dialect recordings from the training data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chlebowski-ballier-2020-manually,
    title = "A Manually Annotated Resource for the Investigation of Nasal Grunts",
    author = "Chl{\'e}bowski, Aur{\'e}lie  and
      Ballier, Nicolas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.802",
    pages = "6514--6522",
    abstract = "This paper presents an annotation framework for nasal grunts of the whole French CID corpus (Bertrand et al., 2008). The acoustic components under scrutiny are justified and the annotation guidelines are described. We carefully characterise the acoustic cues and visual cues followed by the annotator, especially for non-modal phonation types. The conventions followed for the annotation of interactional and positional properties of grunts are explained. The resulting datasets after data extraction with Praat scripts (Boersma and Weenink, 2019) are analysed with R (R Core Team, 2017), focusing on duration. We analyse the effect of non-modal phonation (especially ingressive phonation) on duration and discuss a specialisation of grunts observed in the CID for grunts with ingressive phonation. The more general aim of this research is to establish putative core and additive properties of grunts and a tentative typology of grunts in spoken interactions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{martin-etal-2020-objective,
    title = "The Objective and Subjective Sleepiness Voice Corpora",
    author = "Martin, Vincent P.  and
      Rouas, Jean-Luc  and
      Micoulaud Franchi, Jean-Arthur  and
      Philip, Pierre",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.803",
    pages = "6523--6531",
    abstract = "Following patients with chronic sleep disorders involves multiple appointments between doctors and patients which often results in episodic follow-ups with unevenly spaced interviews. Speech technologies and virtual doctors can help improve this follow-up. However, there are still some challenges to overcome: sleepiness measurements are diverse and are not always correlated, and most past research focused on detecting nstantaneous sleepiness levels of healthy sleep-deprived subjects. This article presents a large database to assess the sleepiness level of highly phenotyped patients that complain from excessive daytime sleepiness. Based on the Multiple Sleep Latency Test, it differs from existing databases by multiple aspects. First, it is omposed of recordings from patients suffering from excessive daytime sleepiness instead of sleep deprived healthy subjects. Second, it incites the subjects to sleep contrary to existing stressing sleepiness deprivation experimental paradigms. Third, the sleepiness level of the patients is evaluated with different temporal granularities - long term sleepiness and short term sleepiness - and both objective and subjective sleepiness measures are collected. Finally, it relies on the recordings of 94 highly phenotyped patients, allowing to unravel the influences of different physical factors (age, sex, weight, ... ) on voice.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{demirsahin-etal-2020-open,
    title = "Open-source Multi-speaker Corpora of the {E}nglish Accents in the {B}ritish Isles",
    author = "Demirsahin, Isin  and
      Kjartansson, Oddur  and
      Gutkin, Alexander  and
      Rivera, Clara",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.804",
    pages = "6532--6541",
    abstract = "This paper presents a dataset of transcribed high-quality audio of English sentences recorded by volunteers speaking with different accents of the British Isles. The dataset is intended for linguistic analysis as well as use for speech technologies. The recording scripts were curated specifically for accent elicitation, covering a variety of phonological phenomena and providing a high phoneme coverage. The scripts include pronunciations of global locations, major airlines and common personal names in different accents; and native speaker pronunciations of local words. Overlapping lines for all speakers were included for idiolect elicitation, which include the same or similar lines with other existing resources such as the CSTR VCTK corpus and the Speech Accent Archive to allow for easy comparison of personal and regional accents. The resulting corpora include over 31 hours of recordings from 120 volunteers who self-identify as native speakers of Southern England, Midlands, Northern England, Welsh, Scottish and Irish varieties of English.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{xiao-etal-2020-tv,
    title = "{TV}-{A}f{D}: An Imperative-Annotated Corpus from The Big Bang Theory and {W}ikipedia{'}s Articles for Deletion Discussions",
    author = "Xiao, Yimin  and
      Slaton, Zong-Ying  and
      Xiao, Lu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.805",
    pages = "6542--6548",
    abstract = "In this study, we created an imperative corpus with speech conversations from dialogues in The Big Bang Theory and with the written comments in Wikipedia{'}s Articles for Deletion discussions. For the TV show data, 59 episodes containing 25,076 statements are used. We manually annotated imperatives based on the annotation guideline adapted from Condoravdi and Lauer{'}s study (2012) and used the retrieved data to assess the performance of syntax-based classification rules. For the Wikipedia AfD comments data, we first developed and leveraged a syntax-based classifier to extract 10,624 statements that may be imperative, and we manually examined the statements and then identified true positives. With this corpus, we also examined the performance of the rule-based imperative detection tool. Our result shows different outcomes for speech (dialogue) and written data. The rule-based classification performs better in the written data in precision (0.80) compared to the speech data (0.44). Also, the rule-based classification has a low-performance overall for speech data with the precision of 0.44, recall of 0.41, and f-1 measure of 0.42. This finding implies the syntax-based model may need to be adjusted for a speech dataset because imperatives in oral communication have greater syntactic varieties and are highly context-dependent.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chen-etal-2020-large,
    title = "A Large Scale Speech Sentiment Corpus",
    author = "Chen, Eric  and
      Lu, Zhiyun  and
      Xu, Hao  and
      Cao, Liangliang  and
      Zhang, Yu  and
      Fan, James",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.806",
    pages = "6549--6555",
    abstract = "We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium. This corpus extends the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. Each sentiment label can be one of three options: positive, negative, and neutral. Annotators are recruited using Google Cloud{'}s data labeling service and the labeling task was conducted over the internet. The corpus contains a total of 49500 labeled speech segments covering 140 hours of audio. To the best of our knowledge, this is the largest multimodal Corpus for sentiment analysis that includes both speech and text features.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kachkovskaia-etal-2020-sibling,
    title = "{S}ib{L}ing Corpus of {R}ussian Dialogue Speech Designed for Research on Speech Entrainment",
    author = "Kachkovskaia, Tatiana  and
      Chukaeva, Tatiana  and
      Evdokimova, Vera  and
      Kholiavin, Pavel  and
      Kriakina, Natalia  and
      Kocharov, Daniil  and
      Mamushina, Anna  and
      Menshikova, Alla  and
      Zimina, Svetlana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.807",
    pages = "6556--6561",
    abstract = "The paper presents a new corpus of dialogue speech designed specifically for research in the field of speech entrainment. Given that the degree of accommodation may depend on a number of social factors, the corpus is designed to encompass 5 types of relations between the interlocutors: those between siblings, close friends, strangers of the same gender, strangers of the other gender, strangers of which one has a higher job position and greater age. Another critical decision taken in this corpus is that in all these social settings one speaker is kept the same. This allows us to trace the changes in his/her speech depending on the interlocutor. The basic set of speakers consists of 10 pairs of same-gender siblings (including 4 pairs of identical twins) aged 23-40, and each of them was recorded in the 5 settings mentioned above. In total we obtained 90 dialogues of 25-60 minutes each. The speakers played a card game and a map game; they were recorded in a soundproof studio without being able to see each other due to a non-transparent screen between them. The corpus contains orthographic, phonetic and prosodic annotation and is segmented into turns and inter-pausal units.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ramalho-etal-2020-phonbank,
    title = "{P}hon{B}ank and Data Sharing: Recent Developments in {E}uropean {P}ortuguese",
    author = "Ramalho, Ana Margarida  and
      Freitas, Maria Jo{\~a}o  and
      Rose, Yvan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.808",
    pages = "6562--6570",
    abstract = "84 104 105 115 32 112 97 112 101 114 32 112 114 101 115 101 110 116 115 32 116 104 101 32 114 101 99 101 110 116 108 121 32 112 117 98 108 105 115 104 101 100 32 82 65 77 65 76 72 79 45 69 80 32 97 110 100 32 80 72 79 78 79 68 73 83 32 99 111 114 112 111 114 97 46 32 66 111 116 104 32 105 110 99 108 117 100 101 32 69 117 114 111 112 101 97 110 32 80 111 114 116 117 103 117 101 115 101 32 112 114 111 100 117 99 116 105 111 110 32 100 97 116 97 32 102 114 111 109 32 80 111 114 116 117 103 117 101 115 101 32 99 104 105 108 100 114 101 110 32 119 105 116 104 32 116 121 112 105 99 97 108 32 40 82 65 77 65 76 72 79 45 69 80 41 32 97 110 100 32 112 114 111 116 114 97 99 116 101 100 32 40 80 72 79 78 79 68 73 83 41 32 112 104 111 110 111 108 111 103 105 99 97 108 32 100 101 118 101 108 111 112 109 101 110 116 46 32 84 104 101 32 100 97 116 97 32 105 110 32 116 104 101 32 116 119 111 32 99 111 114 112 111 114 97 32 119 101 114 101 32 99 111 108 108 101 99 116 101 100 32 117 115 105 110 103 32 116 104 101 32 112 104 111 110 111 108 111 103 105 99 97 108 32 97 115 115 101 115 115 109 101 110 116 32 116 111 111 108 32 67 76 67 80 45 69 80 44 32 100 101 118 101 108 111 112 101 100 32 105 110 32 116 104 101 32 99 111 110 116 101 120 116 32 111 102 32 116 104 101 32 67 114 111 115 115 108 105 110 103 117 105 115 116 105 99 32 67 104 105 108 100 32 80 104 111 110 111 108 111 103 121 32 80 114 111 106 101 99 116 44 32 99 111 111 114 100 105 110 97 116 101 100 32 98 121 32 66 97 114 98 97 114 97 32 66 101 114 110 104 97 114 100 116 32 97 110 100 32 74 111 101 32 83 116 101 109 98 101 114 103 101 114 32 40 85 110 105 118 101 114 115 105 116 121 32 111 102 32 66 114 105 116 105 115 104 32 67 111 108 117 109 98 105 97 32 40 85 66 67 41 44 32 67 97 110 97 100 97 41 46 32 32 66 111 116 104 32 99 111 114 112 111 114 97 32 97 114 101 32 112 97 114 116 32 111 102 32 116 104 101 32 80 104 111 110 66 97 110 107 32 80 114 111 106 101 99 116 32 40 66 114 105 97 110 32 77 97 99 87 104 105 110 110 101 121 32 40 67 97 114 110 101 103 105 101 32 77 101 108 108 111 110 44 32 85 83 65 41 32 97 110 100 32 89 118 97 110 32 82 111 115 101 32 40 77 101 109 111 114 105 97 108 32 85 110 105 118 101 114 115 105 116 121 32 111 102 32 78 101 119 102 111 117 110 100 108 97 110 100 44 32 67 97 110 97 100 97 41 44 32 119 104 105 99 104 32 105 115 32 116 104 101 32 99 104 105 108 100 32 112 104 111 110 111 108 111 103 121 32 99 111 109 112 111 110 101 110 116 32 111 102 32 84 97 108 107 66 97 110 107 44 32 99 111 111 114 100 105 110 97 116 101 100 32 98 121 32 66 114 105 97 110 32 77 97 99 87 104 105 110 110 101 121 46 32 84 104 101 32 100 97 116 97 32 97 116 32 80 104 111 110 66 97 110 107 32 105 115 32 101 100 105 116 101 100 32 105 110 32 80 104 111 110 32 102 111 114 109 97 116 44 32 97 32 108 97 110 103 117 97 103 101 32 116 111 111 108 32 100 101 115 105 103 110 101 100 32 97 110 100 32 98 117 105 108 116 32 98 121 32 89 118 97 110 32 82 111 115 101 32 97 110 100 32 71 114 101 103 32 72 101 100 108 117 110 100 32 40 77 101 109 111 114 105 97 108 32 85 110 105 118 101 114 115 105 116 121 32 111 102 32 78 101 119 102 111 117 110 100 108 97 110 100 41 32 97 110 100 32 119 105 100 101 108 121 32 117 115 101 100 32 98 121 32 114 101 115 101 97 114 99 104 101 114 115 32 119 111 114 107 105 110 103 32 105 110 32 116 104 101 32 102 105 101 108 100 32 111 102 32 112 104 111 110 111 108 111 103 105 99 97 108 32 97 99 113 117 105 115 105 116 105 111 110 46 32 82 65 77 65 76 72 79 45 69 80 32 99 111 110 116 97 105 110 115 32 112 114 111 100 117 99 116 105 111 110 32 100 97 116 97 32 102 114 111 109 32 56 55 32 116 121 112 105 99 97 108 108 121 32 100 101 118 101 108 111 112 105 110 103 32 99 104 105 108 100 114 101 110 44 32 97 103 101 100 32 50 59 49 49 32 116 111 32 54 59 48 52 44 32 97 108 108 32 109 111 110 111 108 105 110 103 117 97 108 115 46 32 80 72 79 78 79 68 73 83 32 105 110 99 108 117 100 101 115 32 112 114 111 100 117 99 116 105 111 110 32 100 97 116 97 32 102 114 111 109 32 50 50 32 99 104 105 108 100 114 101 110 32 100 105 97 103 110 111 115 101 100 32 119 105 116 104 32 100 105 102 102 101 114 101 110 116 32 116 121 112 101 115 32 111 102 32 115 112 101 101 99 104 32 97 110 100 32 108 97 110 103 117 97 103 101 32 100 105 115 111 114 100 101 114 115 44 32 97 108 108 32 69 80 32 109 111 110 111 108 105 110 103 117 97 108 115 44 32 97 103 101 100 32 51 59 50 32 116 111 32 49 49 44 48 53 46 32 66 111 116 104 32 99 111 114 112 111 114 97 32 97 114 101 32 111 112 101 110 32 97 99 99 101 115 115 32 108 97 110 103 117 97 103 101 32 114 101 115 111 117 114 99 101 115 32 97 110 100 32 99 111 110 116 114 105 98 117 116 101 32 116 111 32 101 110 108 97 114 103 101 32 116 104 101 32 97 109 111 117 110 116 32 111 102 32 112 114 111 100 117 99 116 105 111 110 32 100 97 116 97 32 111 110 32 116 104 101 32 97 99 113 117 105 115 105 116 105 111 110 32 111 102 32 69 117 114 111 112 101 97 110 32 80 111 114 116 117 103 117 101 115 101 32 97 118 97 105 108 97 98 108 101 32 105 110 32 80 104 111 110 66 97 110 107 46",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{saito-etal-2020-smash,
    title = "{SMASH} Corpus: A Spontaneous Speech Corpus Recording Third-person Audio Commentaries on Gameplay",
    author = "Saito, Yuki  and
      Takamichi, Shinnosuke  and
      Saruwatari, Hiroshi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.809",
    pages = "6571--6577",
    abstract = "Developing a spontaneous speech corpus would be beneficial for spoken language processing and understanding. We present a speech corpus named the SMASH corpus, which includes spontaneous speech of two Japanese male commentators that made third-person audio commentaries during the gameplay of a fighting game. Each commentator ad-libbed while watching the gameplay with various topics covering not only explanations of each moment to convey the information on the fight but also comments to entertain listeners. We made transcriptions and topic tags as annotations on the recorded commentaries with our two-step method. We first made automatic and manual transcriptions of the commentaries and then manually annotated the topic tags. This paper describes how we constructed the SMASH corpus and reports some results of the annotations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fukuda-etal-2020-improving,
    title = "Improving Speech Recognition for the Elderly: A New Corpus of Elderly {J}apanese Speech and Investigation of Acoustic Modeling for Speech Recognition",
    author = "Fukuda, Meiko  and
      Nishizaki, Hiromitsu  and
      Iribe, Yurie  and
      Nishimura, Ryota  and
      Kitaoka, Norihide",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.810",
    pages = "6578--6585",
    abstract = "In an aging society like Japan, a highly accurate speech recognition system is needed for use in electronic devices for the elderly, but this level of accuracy cannot be obtained using conventional speech recognition systems due to the unique features of the speech of elderly people. S-JNAS, a corpus of elderly Japanese speech, is widely used for acoustic modeling in Japan, but the average age of its speakers is 67.6 years old. Since average life expectancy in Japan is now 84.2 years, we are constructing a new speech corpus, which currently consists of the utterances of 221 speakers with an average age of 79.2, collected from four regions of Japan. In addition, we expand on our previous study (Fukuda, 2019) by further investigating the construction of acoustic models suitable for elderly speech. We create new acoustic models and train them using a combination of existing Japanese speech corpora (JNAS, S-JNAS, CSJ), with and without our {`}super-elderly{'} speech data, and conduct speech recognition experiments. Our new acoustic models achieve word error rates (WER) as low as 13.38{\%}, exceeding the results of our previous study in which we used the CSJ acoustic model adapted for elderly speech (17.4{\%} WER).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ahmed-etal-2020-preparation,
    title = "Preparation of {B}angla Speech Corpus from Publicly Available Audio {\&} Text",
    author = "Ahmed, Shafayat  and
      Sadeq, Nafis  and
      Shubha, Sudipta Saha  and
      Islam, Md. Nahidul  and
      Adnan, Muhammad Abdullah  and
      Islam, Mohammad Zuberul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.811",
    pages = "6586--6592",
    abstract = "Automatic speech recognition systems require large annotated speech corpus. The manual annotation of a large corpus is very difficult. In this paper, we focus on the automatic preparation of a speech corpus for Bangladeshi Bangla. We have used publicly available Bangla audiobooks and TV news recordings as audio sources. We designed and implemented an iterative algorithm that takes as input a speech corpus and a huge amount of raw audio (without transcription) and outputs a much larger speech corpus with reasonable confidence. We have leveraged speaker diarization, gender detection, etc. to prepare the annotated corpus. We also have prepared a synthetic speech corpus for handling out-of-vocabulary word problems in Bangla language. Our corpus is suitable for training with Kaldi. Experimental results show that the use of our corpus in addition to the Google Speech corpus (229 hours) significantly improves the performance of the ASR system.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{huang-etal-2020-construction,
    title = "On Construction of the {ASR}-oriented {I}ndian {E}nglish Pronunciation Dictionary",
    author = "Huang, Xian  and
      Jin, Xin  and
      Li, Qike  and
      Zhang, Keliang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.812",
    pages = "6593--6598",
    abstract = "As a World English, a New English and a regional variety of English, Indian English (IE) has developed its own distinctive characteristics, especially phonologically, from other varieties of English. An Automatic Speech Recognition (ASR) system simply trained on British English (BE) /American English (AE) speech data and using the BE/AE pronunciation dictionary performs much worse when applied to IE. An applicable IEASR system needs spontaneous IE speech as training materials and a comprehensive, linguistically-guided IE pronunciation dictionary (IEPD) so as to achieve the effective mapping between the acoustic model and language model. This research builds a small IE spontaneous speech corpus, analyzes and summarizes the phonological variation features of IE, comes up with an IE phoneme set and complies the IEPD (including a common-English-word list, an Indian-word list, an acronym list and an affix list). Finally, two ASR systems are trained with 120 hours IE spontaneous speech data, using the IEPD we construct in this study and CMUdict separately. The two systems are tested with 50 audio clips of IE spontaneous speech. The result shows the system trained with IEPD performs better than the one trained with CMUdict with WER being 15.63{\%} lower on the test data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{garnerin-etal-2020-gender,
    title = "Gender Representation in Open Source Speech Resources",
    author = "Garnerin, Mahault  and
      Rossato, Solange  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.813",
    pages = "6599--6605",
    abstract = "With the rise of artificial intelligence (AI) and the growing use of deep-learning architectures, the question of ethics, transparency and fairness of AI systems has become a central concern within the research community. We address transparency and fairness in spoken language systems by proposing a study about gender representation in speech resources available through the Open Speech and Language Resource platform. We show that finding gender information in open source corpora is not straightforward and that gender balance depends on other corpus characteristics (elicited/non elicited speech, low/high resource language, speech task targeted). The paper ends with recommendations about metadata and gender information for researchers in order to assure better transparency of the speech systems built using such corpora.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{georgescu-etal-2020-rsc,
    title = "{RSC}: A {R}omanian Read Speech Corpus for Automatic Speech Recognition",
    author = "Georgescu, Alexandru-Lucian  and
      Cucu, Horia  and
      Buzo, Andi  and
      Burileanu, Corneliu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.814",
    pages = "6606--6612",
    abstract = "Although many efforts have been made in the last decade to enhance the speech and language resources for Romanian, this language is still considered under-resourced. While for many other languages there are large speech corpora available for research and commercial applications, for Romanian language the largest publicly available corpus to date comprises less than 50 hours of speech. In this context, Speech and Dialogue research group releases Read Speech Corpus (RSC) {--} a Romanian speech corpus developed in-house, comprising 100 hours of speech recordings from 164 different speakers. The paper describes the development of the corpus and presents baseline automatic speech recognition (ASR) results using state-of-the-art ASR technology: Kaldi speech recognition toolkit.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{robertson-etal-2020-fab,
    title = "{FAB}: The {F}rench Absolute Beginner Corpus for Pronunciation Training",
    author = "Robertson, Sean  and
      Munteanu, Cosmin  and
      Penn, Gerald",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.815",
    pages = "6613--6620",
    abstract = "We introduce the French Absolute Beginner (FAB) speech corpus. The corpus is intended for the development and study of Computer-Assisted Pronunciation Training (CAPT) tools for absolute beginner learners. Data were recorded during two experiments focusing on using a CAPT system in paired role-play tasks. The setting grants FAB three distinguishing features from other non-native corpora: the experimental setting is ecologically valid, closing the gap between training and deployment; it features a label set based on teacher feedback, allowing for context-sensitive CAPT; and data have been primarily collected from absolute beginners, a group often ignored. Participants did not read prompts, but instead recalled and modified dialogues that were modelled in videos. Unable to distinguish modelled words solely from viewing videos, speakers often uttered unintelligible or out-of-L2 words. The corpus is split into three partitions: one from an experiment with minimal feedback; another with explicit, word-level feedback; and a third with supplementary read-and-record data. A subset of words in the first partition has been labelled as more or less native, with inter-annotator agreement reported. In the explicit feedback partition, labels are derived from the experiment{'}s online feedback. The FAB corpus is scheduled to be made freely available by the end of 2020.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jones-etal-2020-call,
    title = "Call My Net 2: A New Resource for Speaker Recognition",
    author = "Jones, Karen  and
      Strassel, Stephanie  and
      Walker, Kevin  and
      Wright, Jonathan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.816",
    pages = "6621--6626",
    abstract = "We introduce the Call My Net 2 (CMN2) Corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family, incorporating both traditional telephony and VoIP data. The corpus contains data from over 400 Tunisian Arabic speakers collected via a custom-built platform deployed in Tunis, with each speaker making 10 or more calls each lasting up to 10 minutes. Calls include speech in various realistic and natural acoustic settings, both noisy and non-noisy. Speakers used a variety of handsets, including landline and mobile devices, and made VoIP calls from tablets or computers. All calls were subject to a series of manual and automatic quality checks, including speech duration, audio quality, language identity and speaker identity. The CMN2 corpus has been used in two NIST Speaker Recognition Evaluations (SRE18 and SRE19), and the SRE test sets as well as the full CMN2 corpus will be published in the Linguistic Data Consortium Catalog. We describe CMN2 corpus requirements, the telephone collection platform, and procedures for call collection. We review properties of the CMN2 dataset and discuss features of the corpus that distinguish it from prior SRE collection efforts, including some of the technical challenges encountered with collecting VoIP data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hussain-etal-2020-dactor,
    title = "{D}a{CT}o{R}: A Data Collection Tool for the {RELATER} Project",
    author = {Hussain, Juan  and
      Zenkri, Oussama  and
      St{\"u}ker, Sebastian  and
      Waibel, Alex},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.817",
    pages = "6627--6632",
    abstract = "Collecting domain-specific data for under-resourced languages, e.g., dialects of languages, can be very expensive, potentially financially prohibitive and taking long time. Moreover, in the case of rarely written languages, the normalization of non-canonical transcription might be another time consuming but necessary task. In order to collect domain-specific data in such circumstances in a time and cost-efficient way, collecting read data of pre-prepared texts is often a viable option. In order to collect data in the domain of psychiatric diagnosis in Arabic dialects for the project RELATER, we have prepared the data collection tool DaCToR for collecting read texts by speakers in the respective countries and districts in which the dialects are spoken. In this paper we describe our tool, its purpose within the project RELATER and the dialects which we have started to collect with the tool.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{dargis-etal-2020-development,
    title = "Development and Evaluation of Speech Synthesis Corpora for {L}atvian",
    author = "Dar{\c{g}}is, Roberts  and
      Paikens, Peteris  and
      Gruzitis, Normunds  and
      Auzina, Ilze  and
      Akmane, Agate",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.818",
    pages = "6633--6637",
    abstract = "Text to speech (TTS) systems are necessary for all languages to ensure accessibility and availability of digital language services. Recent advances in neural speech synthesis have eText to speech (TTS) systems are necessary for any language to ensure accessibility and availability of digital language services. Recent advances in neural speech synthesis have enabled the development of such systems with a data-driven approach that does not require significant development of language-specific tools. However, smaller languages often lack speech corpora that would be sufficient for training current neural TTS models, which require at least 30 hours of good quality audio recordings from a single speaker in a noiseless environment with matching transcriptions. Making such a corpus manually can be cost prohibitive. This paper presents an unsupervised approach to obtain a suitable corpus from unannotated recordings using automated speech recognition for transcription, as well as automated speaker segmentation and identification. The proposed method and software tools are applied and evaluated on a case study for developing a corpus suitable for Latvian speech synthesis based on Latvian public radio archive data.nabled the development of such systems with a data-driven approach that does not require much language-specific tool development. However, smaller languages often lack speech corpora that would be sufficient for training current neural TTS models, which require approximately 30 hours of good quality audio recordings from a single speaker in a noiseless environment with matching transcriptions. Making such a corpus manually can be cost prohibitive. This paper presents an unsupervised approach to obtain a suitable corpus from unannotated recordings using automated speech recognition for transcription, as well as automated speaker segmentation and identification. The proposed methods and software tools are applied and evaluated on a case study for developing a corpus suitable for Latvian speech synthesis based on Latvian public radio archive data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{nikolov-hahnloser-2020-abstractive,
    title = "Abstractive Document Summarization without Parallel Data",
    author = "Nikolov, Nikola I.  and
      Hahnloser, Richard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.819",
    pages = "6638--6644",
    abstract = "Abstractive summarization typically relies on large collections of paired articles and summaries. However, in many cases, parallel data is scarce and costly to obtain. We develop an abstractive summarization system that relies only on large collections of example summaries and non-matching articles. Our approach consists of an unsupervised sentence extractor that selects salient sentences to include in the final summary, as well as a sentence abstractor that is trained on pseudo-parallel and synthetic data, that paraphrases each of the extracted sentences. We perform an extensive evaluation of our method: on the CNN/DailyMail benchmark, on which we compare our approach to fully supervised baselines, as well as on the novel task of automatically generating a press release from a scientific journal article, which is well suited for our system. We show promising performance on both tasks, without relying on any article-summary pairs.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{antognini-faltings-2020-gamewikisum,
    title = "{G}ame{W}iki{S}um: a Novel Large Multi-Document Summarization Dataset",
    author = "Antognini, Diego  and
      Faltings, Boi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.820",
    pages = "6645--6650",
    abstract = "Today{'}s research progress in the field of multi-document summarization is obstructed by the small number of available datasets. Since the acquisition of reference summaries is costly, existing datasets contain only hundreds of samples at most, resulting in heavy reliance on hand-crafted features or necessitating additional, manually annotated data. The lack of large corpora therefore hinders the development of sophisticated models. Additionally, most publicly available multi-document summarization corpora are in the news domain, and no analogous dataset exists in the video game domain. In this paper, we propose GameWikiSum, a new domain-specific dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news. Input documents consist of long professional video game reviews as well as references of their gameplay sections in Wikipedia pages. We analyze the proposed dataset and show that both abstractive and extractive models can be trained on it. We release GameWikiSum for further research: https://github.com/Diego999/GameWikiSum.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{frefel-2020-summarization,
    title = "Summarization Corpora of {W}ikipedia Articles",
    author = "Frefel, Dominik",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.821",
    pages = "6651--6655",
    abstract = "In this paper we propose a process to extract summarization corpora from Wikipedia articles. Applied to the German language we create a corpus of 240,000 texts. We use ROUGE scores for the extraction and evaluation of our corpus. For this we provide a ROUGE metric implementation adapted to the German language. The extracted corpus is used to train three abstractive summarization models which we compare to different baselines. The resulting summaries sound natural and cover the input text very well. The corpus can be downloaded at https://github.com/domfr/GeWiki.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tauchmann-mieskes-2020-language,
    title = "Language Agnostic Automatic Summarization Evaluation",
    author = "Tauchmann, Christopher  and
      Mieskes, Margot",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.822",
    pages = "6656--6662",
    abstract = "So far work on automatic summarization has dealt primarily with English data. Accordingly, evaluation methods were primarily developed with this language in mind. In our work, we present experiments of adapting available evaluation methods such as ROUGE and PYRAMID to non-English data. We base our experiments on various English and non-English homogeneous benchmark data sets as well as a non-English heterogeneous data set. Our results indicate that ROUGE can indeed be adapted to non-English data {--} both homogeneous and heterogeneous. Using a recent implementation of performing an automatic PYRAMID evaluation, we also show its adaptability to non-English data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cano-bojar-2020-two,
    title = "Two Huge Title and Keyword Generation Corpora of Research Articles",
    author = "{\c{C}}ano, Erion  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.823",
    pages = "6663--6671",
    abstract = "Recent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora. Metadata of research articles are usually easy to find online and can be used to perform research on various tasks. In this paper, we introduce two huge datasets for text summarization (OAGSX) and keyword generation (OAGKX) research, containing 34 million and 23 million records, respectively. The data were retrieved from the Open Academic Graph which is a network of research profiles and publications. We carefully processed each record and also tried several extractive and abstractive methods of both tasks to create performance baselines for other researchers. We further illustrate the performance of those methods previewing their outputs. In the near future, we would like to apply topic modeling on the two sets to derive subsets of research articles from more specific disciplines.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aburaed-etal-2020-multi,
    title = "A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery",
    author = "AbuRa{'}ed, Ahmed  and
      Saggion, Horacio  and
      Chiruzzo, Luis",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.824",
    pages = "6672--6679",
    abstract = "Related work sections or literature reviews are an essential part of every scientific article being crucial for paper reviewing and assessment. The automatic generation of related work sections can be considered an instance of the multi-document summarization problem. In order to allow the study of this specific problem, we have developed a manually annotated, machine readable data-set of related work sections, cited papers (e.g. references) and sentences, together with an additional layer of papers citing the references. We additionally present experiments on the identification of cited sentences, using as input citation contexts. The corpus alongside the gold standard are made available for use by the scientific community.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{aksenov-etal-2020-abstractive,
    title = "Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling",
    author = "Aksenov, Dmitrii  and
      Moreno-Schneider, Julian  and
      Bourgonje, Peter  and
      Schwarzenberg, Robert  and
      Hennig, Leonhard  and
      Rehm, Georg",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.825",
    pages = "6680--6689",
    abstract = "We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. We also explore how locality modeling, i.e., the explicit restriction of calculations to the local context, can affect the summarization ability of the Transformer. This is done by introducing 2-dimensional convolutional self-attention into the first layers of the encoder. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset. We additionally train our model on the SwissText dataset to demonstrate usability on German. Both models outperform the baseline in ROUGE scores on two datasets and show its superiority in a manual qualitative analysis.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{mieskes-etal-2020-data,
    title = "A Data Set for the Analysis of Text Quality Dimensions in Summarization Evaluation",
    author = "Mieskes, Margot  and
      Loza Menc{\'\i}a, Eneldo  and
      Kronsbein, Tim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.826",
    pages = "6690--6699",
    abstract = "Automatic evaluation of summarization focuses on developing a metric to represent the quality of the resulting text. However, text qualityis represented in a variety of dimensions ranging from grammaticality to readability and coherence. In our work, we analyze the depen-dencies between a variety of quality dimensions on automatically created multi-document summaries and which dimensions automaticevaluation metrics such as ROUGE, PEAK or JSD are able to capture. Our results indicate that variants of ROUGE are correlated tovarious quality dimensions and that some automatic summarization methods achieve higher quality summaries than others with respectto individual summary quality dimensions. Our results also indicate that differentiating between quality dimensions facilitates inspectionand fine-grained comparison of summarization methods and its characteristics. We make the data from our two summarization qualityevaluation experiments publicly available in order to facilitate the future development of specialized automatic evaluation methods.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hattasch-etal-2020-summarization,
    title = "Summarization Beyond News: The Automatically Acquired Fandom Corpora",
    author = {H{\"a}ttasch, Benjamin  and
      Geisler, Nadja  and
      Meyer, Christian M.  and
      Binnig, Carsten},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.827",
    pages = "6700--6708",
    abstract = "Large state-of-the-art corpora for training neural networks to create abstractive summaries are mostly limited to the news genre, as it is expensive to acquire human-written summaries for other types of text at a large scale. In this paper, we present a novel automatic corpus construction approach to tackle this issue as well as three new large open-licensed summarization corpora based on our approach that can be used for training abstractive summarization models. Our constructed corpora contain fictional narratives, descriptive texts, and summaries about movies, television, and book series from different domains. All sources use a creative commons (CC) license, hence we can provide the corpora for download. In addition, we also provide a ready-to-use framework that implements our automatic construction approach to create custom corpora with desired parameters like the length of the target summary and the number of source documents from which to create the summary. The main idea behind our automatic construction approach is to use existing large text collections (e.g., thematic wikis) and automatically classify whether the texts can be used as (query-focused) multi-document summaries and align them with potential source texts. As a final contribution, we show the usefulness of our automatic construction approach by running state-of-the-art summarizers on the corpora and through a manual evaluation with human annotators.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{de-mattei-etal-2020-invisible,
    title = "Invisible to People but not to Machines: Evaluation of Style-aware {H}eadline{G}eneration in Absence of Reliable Human Judgment",
    author = "De Mattei, Lorenzo  and
      Cafagna, Michele  and
      Dell{'}Orletta, Felice  and
      Nissim, Malvina",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.828",
    pages = "6709--6717",
    abstract = "We automatically generate headlines that are expected to comply with the specific styles of two different Italian newspapers. Through a data alignment strategy and different training/testing settings, we aim at decoupling content from style and preserve the latter in generation. In order to evaluate the generated headlines{'} quality in terms of their specific newspaper-compliance, we devise a fine-grained evaluation strategy based on automatic classification. We observe that our models do indeed learn newspaper-specific style. Importantly, we also observe that humans aren{'}t reliable judges for this task, since although familiar with the newspapers, they are not able to discern their specific styles even in the original human-written headlines. The utility of automatic evaluation goes therefore beyond saving the costs and hurdles of manual annotation, and deserves particular care in its design.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{tardy-etal-2020-align,
    title = "Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation",
    author = "Tardy, Paul  and
      Janiszek, David  and
      Est{\`e}ve, Yannick  and
      Nguyen, Vincent",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.829",
    pages = "6718--6724",
    abstract = "Summarizing texts is not a straightforward task. Before even considering text summarization, one should determine what kind of summary is expected. How much should the information be compressed? Is it relevant to reformulate or should the summary stick to the original phrasing? State-of-the-art on automatic text summarization mostly revolves around news articles. We suggest that considering a wider variety of tasks would lead to an improvement in the field, in terms of generalization and robustness. We explore meeting summarization: generating reports from automatic transcriptions. Our work consists in segmenting and aligning transcriptions with respect to reports, to get a suitable dataset for neural summarization. Using a bootstrapping approach, we provide pre-alignments that are corrected by human annotators, making a validation set against which we evaluate automatic models. This consistently reduces annotators{'} efforts by providing iteratively better pre-alignment and maximizes the corpus size by using annotations from our automatic alignment models. Evaluation is conducted on publicmeetings, a novel corpus of aligned public meetings. We report automatic alignment and summarization performances on this corpus and show that automatic alignment is relevant for data annotation since it leads to large improvement of almost +4 on all ROUGE scores on the summarization task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{suppa-adamec-2020-summarization,
    title = "A Summarization Dataset of {S}lovak News Articles",
    author = "Suppa, Marek  and
      Adamec, Jergus",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.830",
    pages = "6725--6730",
    abstract = "As a well established NLP task, single-document summarization has seen significant interest in the past few years. However, most of the work has been done on English datasets. This is particularly noticeable in the context of evaluation where the dominant ROUGE metric assumes its input to be written in English. In this paper we aim to address both of these issues by introducing a summarization dataset of articles from a popular Slovak news site and proposing small adaptation to the ROUGE metric that make it better suited for Slovak texts. Several baselines are evaluated on the dataset, including an extractive approach based on the Multilingual version of the BERT architecture. To the best of our knowledge, the presented dataset is the first large-scale news-based summarization dataset for text written in Slovak language. It can be reproduced using the utilities available at https://github.com/NaiveNeuron/sme-sum",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{varab-schluter-2020-danewsroom,
    title = "{D}a{N}ewsroom: A Large-scale {D}anish Summarisation Dataset",
    author = "Varab, Daniel  and
      Schluter, Natalie",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.831",
    pages = "6731--6739",
    abstract = "Dataset development for automatic summarisation systems is notoriously English-oriented. In this paper we present the first large-scale non-English language dataset specifically curated for automatic summarisation. The document-summary pairs are news articles and manually written summaries in the Danish language. There has previously been no work done to establish a Danish summarisation dataset, nor any published work on the automatic summarisation of Danish. We provide therefore the first automatic summarisation dataset for the Danish language (large-scale or otherwise). To support the comparison of future automatic summarisation systems for Danish, we include system performance on this dataset of strong well-established unsupervised baseline systems, together with an oracle extractive summariser, which is the first account of automatic summarisation system performance for Danish. Finally, we make all code for automatically acquiring the data freely available and make explicit how this technology can easily be adapted in order to acquire automatic summarisation datasets for further languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lu-etal-2020-diverging,
    title = "Diverging Divergences: Examining Variants of {J}ensen {S}hannon Divergence for Corpus Comparison Tasks",
    author = "Lu, Jinghui  and
      Henchion, Maeve  and
      Mac Namee, Brian",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.832",
    pages = "6740--6744",
    abstract = "Jensen-Shannon divergence (JSD) is a distribution similarity measurement widely used in natural language processing. In corpus comparison tasks, where keywords are extracted to reveal the divergence between different corpora (for example, social media posts from proponents of different views on a political issue), two variants of JSD have emerged in the literature. One of these uses a weighting based on the relative sizes of the corpora being compared. In this paper we argue that this weighting is unnecessary and, in fact, can lead to misleading results. We recommend that this weighted version is not used. We base this recommendation on an analysis of the JSD variants and experiments showing how they impact corpus comparison results as the relative sizes of the corpora being compared change.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bulatov-etal-2020-topicnet,
    title = "{T}opic{N}et: Making Additive Regularisation for Topic Modelling Accessible",
    author = "Bulatov, Victor  and
      Alekseev, Vasiliy  and
      Vorontsov, Konstantin  and
      Polyudova, Darya  and
      Veselova, Eugenia  and
      Goncharov, Alexey  and
      Egorov, Evgeny",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.833",
    pages = "6745--6752",
    abstract = "This paper introduces TopicNet, a new Python module for topic modeling. This package, distributed under the MIT license, focuses on bringing additive regularization topic modelling (ARTM) to non-specialists using a general-purpose high-level language. The module features include powerful model visualization techniques, various training strategies, semi-automated model selection, support for user-defined goal metrics, and a modular approach to topic model training. Source code and documentation are available at https://github.com/machine-intelligence-laboratory/TopicNet",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yamaguchi-etal-2020-sc,
    title = "{SC}-{C}o{MI}cs: A Superconductivity Corpus for Materials Informatics",
    author = "Yamaguchi, Kyosuke  and
      Asahi, Ryoji  and
      Sasaki, Yutaka",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.834",
    pages = "6753--6760",
    abstract = "This paper describes a novel corpus tailored for the text mining of superconducting materials in Materials Informatics (MI), named SuperConductivety Corpus for Materials Informatics (SC-CoMIcs). Different from biomedical informatics, there exist very few corpora targeting Materials Science and Engineering (MSE). Especially, there is no sizable corpus which can be used to assist the search of superconducting materials. A team of materials scientists and natural language processing experts jointly designed the annotation and constructed a corpus consisting of manually-annotated 1,000 MSE abstracts related to superconductivity. We conducted experiments on the corpus with a neural Named Entity Recognition (NER) tool. The experimental results show that NER performance over the corpus is around 77{\%} in terms of micro-F1, which is comparable to human annotator agreement rates. Using the trained NER model, we automatically annotated 9,000 abstracts and created a term retrieval tool based on the term similarity. This tool can find superconductivity terms relevant to a query term within a specified Named Entity category, which demonstrates the power of our SC-CoMIcs, efficiently providing knowledge for Materials Informatics applications from rapidly expanding publications.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hagiwara-mita-2020-github,
    title = "{G}it{H}ub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors",
    author = "Hagiwara, Masato  and
      Mita, Masato",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.835",
    pages = "6761--6768",
    abstract = "The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC). As a complementary new resource for these tasks, we present the GitHub Typo Corpus, a large-scale, multilingual dataset of misspellings and grammatical errors along with their corrections harvested from GitHub, a large and popular platform for hosting and sharing git repositories. The dataset, which we have made publicly available, contains more than 350k edits and 65M characters in more than 15 languages, making it the largest dataset of misspellings to date. We also describe our process for filtering true typo edits based on learned classifiers on a small annotated subset, and demonstrate that typo edits can be identified with F1 0.9 using a very simple classifier with only three features. The detailed analyses of the dataset show that existing spelling correctors merely achieve an F-measure of approx. 0.5, suggesting that the dataset serves as a new, rich source of spelling errors that complement existing datasets.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{arase-etal-2020-annotation,
    title = "Annotation of Adverse Drug Reactions in Patients{'} Weblogs",
    author = "Arase, Yuki  and
      Kajiwara, Tomoyuki  and
      Chu, Chenhui",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.836",
    pages = "6769--6776",
    abstract = "Adverse drug reactions are a severe problem that significantly degrade quality of life, or even threaten the life of patients. Patient-generated texts available on the web have been gaining attention as a promising source of information in this regard. While previous studies annotated such patient-generated content, they only reported on limited information, such as whether a text described an adverse drug reaction or not. Further, they only annotated short texts of a few sentences crawled from online forums and social networking services. The dataset we present in this paper is unique for the richness of annotated information, including detailed descriptions of drug reactions with full context. We crawled patient{'}s weblog articles shared on an online patient-networking platform and annotated the effects of drugs therein reported. We identified spans describing drug reactions and assigned labels for related drug names, standard codes for the symptoms of the reactions, and types of effects. As a first dataset, we annotated 677 drug reactions with these detailed labels based on 169 weblog articles by Japanese lung cancer patients. Our annotation dataset is made publicly available at our web site (https://yukiar.github.io/adr-jp/) for further research on the detection of adverse drug reactions and more broadly, on patient-generated text processing.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rezapour-etal-2020-beyond,
    title = "Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society",
    author = "Rezapour, Rezvaneh  and
      Bopp, Jutta  and
      Fiedler, Norman  and
      Steffen, Diana  and
      Witt, Andreas  and
      Diesner, Jana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.837",
    pages = "6777--6785",
    abstract = "This paper proposes, implements and evaluates a novel, corpus-based approach for identifying categories indicative of the impact of research via a deductive (top-down, from theory to data) and an inductive (bottom-up, from data to theory) approach. The resulting categorization schemes differ in substance. Research outcomes are typically assessed by using bibliometric methods, such as citation counts and patterns, or alternative metrics, such as references to research in the media. Shortcomings with these methods are their inability to identify impact of research beyond academia (bibliometrics) and considering text-based impact indicators beyond those that capture attention (altmetrics). We address these limitations by leveraging a mixed-methods approach for eliciting impact categories from experts, project personnel (deductive) and texts (inductive). Using these categories, we label a corpus of project reports per category schema, and apply supervised machine learning to infer these categories from project reports. The classification results show that we can predict deductively and inductively derived impact categories with 76.39{\%} and 78.81{\%} accuracy (F1-score), respectively. Our approach can complement solutions from bibliometrics and scientometrics for assessing the impact of research and studying the scope and types of advancements transferred from academia to society.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fortuna-etal-2020-toxic,
    title = "Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets",
    author = "Fortuna, Paula  and
      Soler, Juan  and
      Wanner, Leo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.838",
    pages = "6786--6794",
    abstract = "The field of the automatic detection of hate speech and related concepts has raised a lot of interest in the last years. Different datasets were annotated and classified by means of applying different machine learning algorithms. However, few efforts were done in order to clarify the applied categories and homogenize different datasets. Our study takes up this demand. We analyze six different publicly available datasets in this field with respect to their similarity and compatibility. We conduct two different experiments. First, we try to make the datasets compatible and represent the dataset classes as Fast Text word vectors analyzing the similarity between different classes in a intra and inter dataset manner. Second, we submit the chosen datasets to the Perspective API Toxicity classifier, achieving different performances depending on the categories and datasets. One of the main conclusions of these experiments is that many different definitions are being used for equivalent concepts, which makes most of the publicly available datasets incompatible. Grounded in our analysis, we provide guidelines for future dataset collection and annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{persing-ng-2020-unsupervised,
    title = "Unsupervised Argumentation Mining in Student Essays",
    author = "Persing, Isaac  and
      Ng, Vincent",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.839",
    pages = "6795--6803",
    abstract = "State-of-the-art systems for argumentation mining are supervised, thus relying on training data containing manually annotated argument components and the relationships between them. To eliminate the reliance on annotated data, we present a novel approach to unsupervised argument mining. The key idea is to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. Results on a Stab and Gurevych{'}s corpus of 402 essays show that our unsupervised approach rivals two supervised baselines in performance and achieves 73.5-83.7{\%} of the performance of a state-of-the-art neural approach.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ocampo-diaz-etal-2020-aspect,
    title = "Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining",
    author = "Ocampo Diaz, Gerardo  and
      Zhang, Xuanming  and
      Ng, Vincent",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.840",
    pages = "6804--6811",
    abstract = "We show how the general fine-grained opinion mining concepts of opinion target and opinion expression are related to aspect-based sentiment analysis (ABSA) and discuss their benefits for resource creation over popular ABSA annotation schemes. Specifically, we first discuss why opinions modeled solely in terms of (entity, aspect) pairs inadequately captures the meaning of the sentiment originally expressed by authors and how opinion expressions and opinion targets can be used to avoid the loss of information. We then design a meaning-preserving annotation scheme and apply it to two popular ABSA datasets, the 2016 SemEval ABSA Restaurant and Laptop datasets. Finally, we discuss the importance of opinion expressions and opinion targets for next-generation ABSA systems. We make our datasets publicly available for download.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yaneva-etal-2020-predicting,
    title = "Predicting Item Survival for Multiple Choice Questions in a High-Stakes Medical Exam",
    author = "Yaneva, Victoria  and
      Ha, Le An  and
      Baldwin, Peter  and
      Mee, Janet",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.841",
    pages = "6812--6818",
    abstract = "One of the most resource-intensive problems in the educational testing industry relates to ensuring that newly-developed exam questions can adequately distinguish between students of high and low ability. The current practice for obtaining this information is the costly procedure of pretesting: new items are administered to test-takers and then the items that are too easy or too difficult are discarded. This paper presents the first study towards automatic prediction of an item{'}s probability to {``}survive{''} pretesting (item survival), focusing on human-produced MCQs for a medical exam. Survival is modeled through a number of linguistic features and embedding types, as well as features inspired by information retrieval. The approach shows promising first results for this challenging new application and for modeling the difficulty of expert-knowledge questions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cho-etal-2020-discourse,
    title = "Discourse Component to Sentence ({DC}2{S}): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset",
    author = "Cho, Won Ik  and
      Kim, Jong In  and
      Moon, Young Ki  and
      Kim, Nam Soo",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.842",
    pages = "6819--6826",
    abstract = "Assessing the similarity of sentences and detecting paraphrases is an essential task both in theory and practice, but achieving a reliable dataset requires high resource. In this paper, we propose a discourse component-based paraphrase generation for the directive utterances, which is efficient in terms of human-aided construction and content preservation. All discourse components are expressed in natural language phrases, and the phrases are created considering both speech act and topic so that the controlled construction of the sentence similarity dataset is available. Here, we investigate the validity of our scheme using the Korean language, a language with diverse paraphrasing due to frequent subject drop and scramblings. With 1,000 intent argument phrases and thus generated 10,000 utterances, we make up a sentence similarity dataset of practically sufficient size. It contains five sentence pair types, including paraphrase, and displays a total volume of about 550K. To emphasize the utility of the scheme and dataset, we measure the similarity matching performance via conventional natural language inference models, also suggesting the multi-lingual extensibility.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{hayashibe-2020-japanese,
    title = "{J}apanese Realistic Textual Entailment Corpus",
    author = "Hayashibe, Yuta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.843",
    pages = "6827--6834",
    abstract = "We perform the textual entailment (TE) corpus construction for the Japanese Language with the following three characteristics: First, the corpus consists of realistic sentences; that is, all sentences are spontaneous or almost equivalent. It does not need manual writing which causes hidden biases. Second, the corpus contains adversarial examples. We collect challenging examples that can not be solved by a recent pre-trained language model. Third, the corpus contains explanations for a part of non-entailment labels. We perform the reasoning annotation where annotators are asked to check which tokens in hypotheses are the reason why the relations are labeled. It makes easy to validate the annotation and analyze system errors. The resulting corpus consists of 48,000 realistic Japanese examples. It is the largest among publicly available Japanese TE corpora. Additionally, it is the first Japanese TE corpus that includes reasons for the annotation as we know. We are planning to distribute this corpus to the NLP community at the time of publication.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{bernardy-chatzikyriakidis-2020-improving,
    title = "Improving the Precision of Natural Textual Entailment Problem Datasets",
    author = "Bernardy, Jean-Philippe  and
      Chatzikyriakidis, Stergios",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.844",
    pages = "6835--6840",
    abstract = "In this paper, we propose a method to modify natural textual entailment problem datasets so that they better reflect a more precise notion of entailment. We apply this method to a subset of the Recognizing Textual Entailment datasets. We thus obtain a new corpus of entailment problems, which has the following three characteristics: 1. it is precise (does not leave out implicit hypotheses) 2. it is based on {``}real-world{''} texts (i.e. most of the premises were written for purposes other than testing textual entailment). 3. its size is 150. Broadly, the method that we employ is to make any missing hypotheses explicit using a crowd of experts. We discuss the relevance of our method in improving existing NLI datasets to be more fit for precise reasoning and we argue that this corpus can be the basis a first step towards wide-coverage testing of precise natural-language inference systems.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{pragst-etal-2020-comparative,
    title = "Comparative Study of Sentence Embeddings for Contextual Paraphrasing",
    author = "Pragst, Louisa  and
      Minker, Wolfgang  and
      Ultes, Stefan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.845",
    pages = "6841--6851",
    abstract = "Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. Traditionally, paraphrasing has been focused on finding different words that convey the same meaning. However, in human-human interaction, we regularly express our intention with phrases that are vastly different regarding both word content and syntactic structure. Instead of exchanging only individual words, the complete surface realisation of a sentences is altered while still preserving its meaning and function in a conversation. This kind of contextual paraphrasing did not yet receive a lot of attention from the scientific community despite its potential for the creation of more varied dialogues. In this work, we evaluate several existing approaches to sentence encoding with regard to their ability to capture such context-dependent paraphrasing. To this end, we define a paraphrase classification task that incorporates contextual paraphrases, perform dialogue act clustering, and determine the performance of the sentence embeddings in a sentence swapping task.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{liu-etal-2020-hyponli,
    title = "{H}ypo{NLI}: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference",
    author = "Liu, Tianyu  and
      Xin, Zheng  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.846",
    pages = "6852--6860",
    abstract = "Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Specifically, we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels. We then figure out {`}hard{'} and {`}easy{'} instances from the original test sets whose labels are opposite to or consistent with those indications. We also set up baselines including both pretrained models (BERT, RoBerta, XLNet) and competitive non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and baselines, we also investigate two debiasing approaches which exploit the artificial pattern modeling to mitigate such hypothesis-only bias: down-sampling and adversarial training. We believe those methods can be treated as competitive baselines in NLI debiasing tasks.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{yoshinaka-etal-2020-sapphire,
    title = "{SAPPHIRE}: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation",
    author = "Yoshinaka, Masato  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.847",
    pages = "6861--6867",
    abstract = "We present SAPPHIRE, a Simple Aligner for Phrasal Paraphrase with HIerarchical REpresentation. Monolingual phrase alignment is a fundamental problem in natural language understanding and also a crucial technique in various applications such as natural language inference and semantic textual similarity assessment. Previous methods for monolingual phrase alignment are language-resource intensive; they require large-scale synonym/paraphrase lexica and high-quality parsers. Different from them, SAPPHIRE depends only on a monolingual corpus to train word embeddings. Therefore, it is easily transferable to specific domains and different languages. Specifically, SAPPHIRE first obtains word alignments using pre-trained word embeddings and then expands them to phrase alignments by bilingual phrase extraction methods. To estimate the likelihood of phrase alignments, SAPPHIRE uses phrase embeddings that are hierarchically composed of word embeddings. Finally, SAPPHIRE searches for a set of consistent phrase alignments on a lattice of phrase alignment candidates. It achieves search-efficiency by constraining the lattice so that all the paths go through a phrase alignment pair with the highest alignment score. Experimental results using the standard dataset for phrase alignment evaluation show that SAPPHIRE outperforms the previous method and establishes the state-of-the-art performance.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{scherrer-2020-tapaco,
    title = "{T}a{P}a{C}o: A Corpus of Sentential Paraphrases for 73 Languages",
    author = "Scherrer, Yves",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.848",
    pages = "6868--6873",
    abstract = "This paper presents TaPaCo, a freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a graph with Tatoeba sentences and equivalence links between sentences {``}meaning the same thing{''}. This graph is then traversed to extract sets of paraphrases. Several language-independent filters and pruning steps are applied to remove uninteresting sentences. A manual evaluation performed on three languages shows that between half and three quarters of inferred paraphrases are correct and that most remaining ones are either correct but trivial, or near-paraphrases that neutralize a morphological distinction. The corpus contains a total of 1.9 million sentences, with 200 - 250 000 sentences per language. It covers a range of languages for which, to our knowledge, no other paraphrase dataset exists. The dataset is available at https://doi.org/10.5281/zenodo.3707949.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{sathe-etal-2020-automated,
    title = "Automated Fact-Checking of Claims from {W}ikipedia",
    author = "Sathe, Aalok  and
      Ather, Salar  and
      Le, Tuan Manh  and
      Perry, Nathan  and
      Park, Joonsuk",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.849",
    pages = "6874--6882",
    abstract = "Automated fact checking is becoming increasingly vital as both truthful and fallacious information accumulate online. Research on fact checking has benefited from large-scale datasets such as FEVER and SNLI. However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet. To this end, we present WikiFactCheck-English, a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations, as well as 34k+ manually written claims that are refuted by the evidence documents. This is the largest fact checking dataset consisting of real claims and evidence to date; it will allow the development of fact checking systems that can better process claims and evidence in the real world. We also show that for the NLI subtask, a logistic regression system trained using existing and novel features achieves peak accuracy of 68{\%}, providing a competitive baseline for future work. Also, a decomposable attention model trained on SNLI significantly underperforms the models trained on this dataset, suggesting that models trained on manually generated data may not be sufficiently generalizable or suitable for fact checking real-world claims.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{paul-panenghat-etal-2020-towards,
    title = "Towards the Necessity for Debiasing Natural Language Inference Datasets",
    author = "Paul Panenghat, Mithun  and
      Suntwal, Sandeep  and
      Rafique, Faiz  and
      Sharp, Rebecca  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.850",
    pages = "6883--6888",
    abstract = "Modeling natural language inference is a challenging task. With large annotated data sets available it has now become feasible to train complex neural network based inference methods which achieve state of the art performance. However, it has been shown that these models also learn from the subtle biases inherent in these datasets (CITATION). In this work we explore two techniques for delexicalization that modify the datasets in such a way that we can control the importance that neural-network based methods place on lexical entities. We demonstrate that the proposed methods not only maintain the performance in-domain but also improve performance in some out-of-domain settings. For example, when using the delexicalized version of the FEVER dataset, the in-domain performance of a state of the art neural network method dropped only by 1.12{\%} while its out-of-domain performance on the FNC dataset improved by 4.63{\%}. We release the delexicalized versions of three common datasets used in natural language inference. These datasets are delexicalized using two methods: one which replaces the lexical entities in an overlap-aware manner, and a second, which additionally incorporates semantic lifting of nouns and verbs to their WordNet hypernym synsets",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{cardon-grabar-2020-french,
    title = "A {F}rench Corpus for Semantic Similarity",
    author = "Cardon, R{\'e}mi  and
      Grabar, Natalia",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.851",
    pages = "6889--6894",
    abstract = "Semantic similarity is an area of Natural Language Processing that is useful for several downstream applications, such as machine translation, natural language generation, information retrieval, or question answering. The task consists in assessing the extent to which two sentences express or do not express the same meaning. To do so, corpora with graded pairs of sentences are required. The grade is positioned on a given scale, usually going from 0 (completely unrelated) to 5 (equivalent semantics). In this work, we introduce such a corpus for French, the first that we know of. It is comprised of 1,010 sentence pairs with grades from five annotators. We describe the annotation process, analyse these data, and perform a few experiments for the automatic grading of semantic similarity.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{watarai-tsuchiya-2020-developing,
    title = "Developing Dataset of {J}apanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension",
    author = "Watarai, Takuto  and
      Tsuchiya, Masatoshi",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.852",
    pages = "6895--6901",
    abstract = "This paper describes our developing dataset of Japanese slot filling quizzes designed for evaluation of machine reading comprehension. The dataset consists of quizzes automatically generated from Aozora Bunko, and each quiz is defined as a 4-tuple: a context passage, a query holding a slot, an answer character and a set of possible answer characters. The query is generated from the original sentence, which appears immediately after the context passage on the target book, by replacing the answer character into the slot. The set of possible answer characters consists of the answer character and the other characters who appear in the context passage. Because the context passage and the query shares the same context, a machine which precisely understand the context may select the correct answer from the set of possible answer characters. The unique point of our approach is that we focus on characters of target books as slots to generate queries from original sentences, because they play important roles in narrative texts and precise understanding their relationship is necessary for reading comprehension. To extract characters from target books, manually created dictionaries of characters are employed because some characters appear as common nouns not as named entities.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{jimenez-zafra-etal-2020-detecting,
    title = "Detecting Negation Cues and Scopes in {S}panish",
    author = "Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Morante, Roser  and
      Blanco, Eduardo  and
      Mart{\'\i}n Valdivia, Mar{\'\i}a Teresa  and
      Ure{\~n}a L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.853",
    pages = "6902--6911",
    abstract = "In this work we address the processing of negation in Spanish. We first present a machine learning system that processes negation in Spanish. Specifically, we focus on two tasks: i) negation cue detection and ii) scope identification. The corpus used in the experimental framework is the SFU Corpus. The results for cue detection outperform state-of-the-art results, whereas for scope detection this is the first system that performs the task for Spanish. Moreover, we provide a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{putra-etal-2020-tiara,
    title = "{TIARA}: A Tool for Annotating Discourse Relations and Sentence Reordering",
    author = "Putra, Jan Wira Gotama  and
      Teufel, Simone  and
      Matsumura, Kana  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.854",
    pages = "6912--6920",
    abstract = "This paper introduces TIARA, a new publicly available web-based annotation tool for discourse relations and sentence reordering. Annotation tasks such as these, which are based on relations between large textual objects, are inherently hard to visualise without either cluttering the display and/or confusing the annotators. TIARA deals with the visual complexity during the annotation process by systematically simplifying the layout, and by offering interactive visualisation, including coloured links, indentation, and dual-view. TIARA{'}s text view allows annotators to focus on the analysis of logical sequencing between sentences. A separate tree view allows them to review their analysis in terms of the overall discourse structure. The dual-view gives it an edge over other discourse annotation tools and makes it particularly attractive as an educational tool (e.g., for teaching students how to argue more effectively). As it is based on standard web technologies and can be easily customised to other annotation schemes, it can be easily used by anybody. Apart from the project it was originally designed for, in which hundreds of texts were annotated by three annotators, TIARA has already been adopted by a second discourse annotation study, which uses it in the teaching of argumentation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{el-haj-etal-2020-infrastructure,
    title = "Infrastructure for Semantic Annotation in the Genomics Domain",
    author = "El-Haj, Mahmoud  and
      Rutherford, Nathan  and
      Coole, Matthew  and
      Ezeani, Ignatius  and
      Prentice, Sheryl  and
      Ide, Nancy  and
      Knight, Jo  and
      Piao, Scott  and
      Mariani, John  and
      Rayson, Paul  and
      Suderman, Keith",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.855",
    pages = "6921--6929",
    abstract = "We describe a novel super-infrastructure for biomedical text mining which incorporates an end-to-end pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature, combining NLP and corpus linguistics methods. The infrastructure permits extreme-scale research on the open access PubMed Central archive. It combines an updatable Gene Ontology Semantic Tagger (GOST) for entity identification and semantic markup in the literature, with a NLP pipeline scheduler (Buster) to collect and process the corpus, and a bespoke columnar corpus database (LexiDB) for indexing. The corpus database is distributed to permit fast indexing, and provides a simple web front-end with corpus linguistics methods for sub-corpus comparison and retrieval. GOST is also connected as a service in the Language Application (LAPPS) Grid, in which context it is interoperable with other NLP tools and data in the Grid and can be combined with them in more complex workflows. In a literature based discovery setting, we have created an annotated corpus of 9,776 papers with 5,481,543 words.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{shah-de-melo-2020-correcting,
    title = "Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation",
    author = "Shah, Kshitij  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.856",
    pages = "6930--6936",
    abstract = "In this paper, we explore the artificial generation of typographical errors based on real-world statistics. We first draw on a small set of annotated data to compute spelling error statistics. These are then invoked to introduce errors into substantially larger corpora. The generation methodology allows us to generate particularly challenging errors that require context-aware error detection. We use it to create a set of English language error detection and correction datasets. Finally, we examine the effectiveness of machine learning models for detecting and correcting errors based on this data.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{downs-etal-2020-kidspell,
    title = "{K}id{S}pell: A Child-Oriented, Rule-Based, Phonetic Spellchecker",
    author = "Downs, Brody  and
      Anuyah, Oghenemaro  and
      Shukla, Aprajita  and
      Fails, Jerry Alan  and
      Pera, Sole  and
      Wright, Katherine  and
      Kennington, Casey",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.857",
    pages = "6937--6946",
    abstract = "For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of, we compare the model{'}s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{seeha-etal-2020-thailmcut,
    title = "{T}hai{LMC}ut: Unsupervised Pretraining for {T}hai Word Segmentation",
    author = {Seeha, Suteera  and
      Bilan, Ivan  and
      Mamani Sanchez, Liliana  and
      Huber, Johannes  and
      Matuschek, Michael  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.858",
    pages = "6947--6957",
    abstract = "We propose ThaiLMCut, a semi-supervised approach for Thai word segmentation which utilizes a bi-directional character language model (LM) as a way to leverage useful linguistic knowledge from unlabeled data. After the language model is trained on substantial unlabeled corpora, the weights of its embedding and recurrent layers are transferred to a supervised word segmentation model which continues fine-tuning them on a word segmentation task. Our experimental results demonstrate that applying the LM always leads to a performance gain, especially when the amount of labeled data is small. In such cases, the F1 Score increased by up to 2.02{\%}. Even on abig labeled dataset, a small improvement gain can still be obtained. The approach has also shown to be very beneficial for out-of-domain settings with a gain in F1 Score of up to 3.13{\%}. Finally, we show that ThaiLMCut can outperform other open source state-of-the-art models achieving an F1 Score of 98.78{\%} on the standard benchmark, InterBEST2009.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{alatrash-etal-2020-ccoha,
    title = "{CCOHA}: Clean Corpus of Historical {A}merican {E}nglish",
    author = "Alatrash, Reem  and
      Schlechtweg, Dominik  and
      Kuhn, Jonas  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.859",
    pages = "6958--6966",
    abstract = "Modelling language change is an increasingly important area of interest within the fields of sociolinguistics and historical linguistics. In recent years, there has been a growing number of publications whose main concern is studying changes that have occurred within the past centuries. The Corpus of Historical American English (COHA) is one of the most commonly used large corpora in diachronic studies in English. This paper describes methods applied to the downloadable version of the COHA corpus in order to overcome its main limitations, such as inconsistent lemmas and malformed tokens, without compromising its qualitative and distributional properties. The resulting corpus CCOHA contains a larger number of cleaned word tokens which can offer better insights into language change and allow for a larger variety of tasks to be performed.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{zouhar-bojar-2020-outbound,
    title = "Outbound Translation User Interface Ptakop{\v{e}}t: A Pilot Study",
    author = "Zouhar, Vil{\'e}m  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.860",
    pages = "6967--6975",
    abstract = "It is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. We call the task {``}outbound translation{''} and explore it by introducing an open-source modular system Ptakop{\v{e}}t. Its main purpose is to inspect human interaction with MT systems enhanced with additional subsystems, such as backward translation and quality estimation. We follow up with an experiment on (Czech) human annotators tasked to produce questions in a language they do not speak (German), with the help of Ptakop{\v{e}}t. We focus on three real-world use cases (communication with IT support, describing administrative issues and asking encyclopedic questions) from which we gain insight into different strategies users take when faced with outbound translation tasks. Round trip translation is known to be unreliable for evaluating MT systems but our experimental evaluation documents that it works very well for users, at least on MT systems of mid-range quality.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{titeux-etal-2020-seshat,
    title = "{S}eshat: a Tool for Managing and Verifying Annotation Campaigns of Audio Data",
    author = "Titeux, Hadrien  and
      Riad, Rachid  and
      Cao, Xuan-Nga  and
      Hamilakis, Nicolas  and
      Madden, Kris  and
      Cristia, Alejandrina  and
      Bachoud-L{\'e}vi, Anne-Catherine  and
      Dupoux, Emmanuel",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.861",
    pages = "6976--6982",
    abstract = "We introduce Seshat, a new, simple and open-source software to efficiently manage annotations of speech corpora. The Seshat software allows users to easily customise and manage annotations of large audio corpora while ensuring compliance with the formatting and naming conventions of the annotated output files. In addition, it includes procedures for checking the content of annotations following specific rules that can be implemented in personalised parsers. Finally, we propose a double-annotation mode, for which Seshat computes automatically an associated inter-annotator agreement with the gamma measure taking into account the categorisation and segmentation discrepancies.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{costello-etal-2020-dragonfly,
    title = "{D}ragonfly: Advances in Non-Speaker Annotation for Low Resource Languages",
    author = "Costello, Cash  and
      Anderson, Shelby  and
      Bishop, Caitlyn  and
      Mayfield, James  and
      McNamee, Paul",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.862",
    pages = "6983--6987",
    abstract = "Dragonfly is an open source software tool that supports annotation of text in a low resource language by non-speakers of the language. Using semantic and contextual information, non-speakers of a language familiar with the Latin script can produce high quality named entity annotations to support construction of a name tagger. We describe a procedure for annotating low resource languages using Dragonfly that others can use, which we developed based on our experience annotating data in more than ten languages. We also present performance comparisons between models trained on native speaker and non-speaker annotations.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{koeva-etal-2020-natural,
    title = "Natural Language Processing Pipeline to Annotate {B}ulgarian Legislative Documents",
    author = "Koeva, Svetla  and
      Obreshkov, Nikola  and
      Yalamov, Martin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.863",
    pages = "6988--6994",
    abstract = "The paper presents the Bulgarian MARCELL corpus, part of a recently developed multilingual corpus representing the national legislation in seven European countries and the NLP pipeline that turns the web crawled data into structured, linguistically annotated dataset. The Bulgarian data is web crawled, extracted from the original HTML format, filtered by document type, tokenised, sentence split, tagged and lemmatised with a fine-grained version of the Bulgarian Language Processing Chain, dependency parsed with NLP- Cube, annotated with named entities (persons, locations, organisations and others), noun phrases, IATE terms and EuroVoc descriptors. An orchestrator process has been developed to control the NLP pipeline performing an end-to-end data processing and annotation starting from the documents identification and ending in the generation of statistical reports. The Bulgarian MARCELL corpus consists of 25,283 documents (at the beginning of November 2019), which are classified into eleven types.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{forkel-list-2020-cldfbench,
    title = "{CLDFB}ench: Give Your Cross-Linguistic Data a Lift",
    author = "Forkel, Robert  and
      List, Johann-Mattis",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.864",
    pages = "6995--7002",
    abstract = "While the amount of cross-linguistic data is constantly increasing, most datasets produced today and in the past cannot be considered FAIR (findable, accessible, interoperable, and reproducible). To remedy this and to increase the comparability of cross-linguistic resources, it is not enough to set up standards and best practices for data to be collected in the future. We also need consistent workflows for the {``}retro-standardization{''} of data that has been published during the past decades and centuries. With the Cross-Linguistic Data Formats initiative, first standards for cross-linguistic data have been presented and successfully tested. So far, however, CLDF creation was hampered by the fact that it required a considerable degree of computational proficiency. With cldfbench, we introduce a framework for the retro-standardization of legacy data and the curation of new datasets that drastically simplifies the creation of CLDF by providing a consistent, reproducible workflow that rigorously supports version control and long term archiving of research data and code. The framework is distributed in form of a Python package along with usage information and examples for best practice. This study introduces the new framework and illustrates how it can be applied by showing how a resource containing structural and lexical data for Sinitic languages can be efficiently retro-standardized and analyzed.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{machalek-2020-kontext,
    title = "{K}on{T}ext: Advanced and Flexible Corpus Query Interface",
    author = "Mach{\'a}lek, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.865",
    pages = "7003--7008",
    abstract = "We present an advanced, highly customizable corpus query interface KonText built on top of core libraries of the open-source corpus search engine NoSketch Engine (NoSkE). The aim is to overcome some limitations of the original NoSkE user interface and provide integration capabilities allowing connection of the basic search service with other language resources (LRs). The introduced features are based on long-term feedback given by the users and researchers of the Czech National Corpus (CNC) along with other LRs providers running KonText as a part of their services. KonText is a fully operational and mature software deployed at the CNC since 2014 that currently handles thousands user queries per day.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{machalek-2020-word,
    title = "Word at a Glance: Modular Word Profile Aggregator",
    author = "Mach{\'a}lek, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.866",
    pages = "7009--7014",
    abstract = "Word at a Glance (WaG) is a word profile aggregator that provides means for exploring individual words, their comparison and translation, based on existing language resources and related software services. It is designed as a building kit-like application that fetches data from different sources and compiles them into a single, comprehensible and structured web page. WaG can be easily configured to support many tasks, but in general, it is intended to be used not only by language experts but also the general public.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{kupietz-etal-2020-rkorapclient,
    title = "{RK}or{APC}lient: An {R} Package for Accessing the {G}erman Reference Corpus {D}e{R}e{K}o via {K}or{AP}",
    author = "Kupietz, Marc  and
      Diewald, Nils  and
      Margaretha, Eliza",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.867",
    pages = "7015--7021",
    abstract = "Making corpora accessible and usable for linguistic research is a huge challenge in view of (too) big data, legal issues and a rapidly evolving methodology. This does not only affect the design of user-friendly graphical interfaces to corpus analysis tools, but also the availability of programming interfaces supporting access to the functionality of these tools from various analysis and development environments. RKorAPClient is a new research tool in the form of an R package that interacts with the Web API of the corpus analysis platform KorAP, which provides access to large annotated corpora, including the German reference corpus DeReKo with 45 billion tokens.In addition to optionally authenticated KorAP API access, RKorAPClient provides further processing and visualization features to simplify common corpus analysis tasks. This paper introduces the basic functionality of RKorAPClient and exemplifies various analysis tasks based on DeReKo, that are bundled within the R package and can serve as a basic framework for advanced analysis and visualization approaches.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{obeid-etal-2020-camel,
    title = "{CAM}e{L} Tools: An Open Source Python Toolkit for {A}rabic Natural Language Processing",
    author = "Obeid, Ossama  and
      Zalmout, Nasser  and
      Khalifa, Salam  and
      Taji, Dima  and
      Oudah, Mai  and
      Alhafni, Bashar  and
      Inoue, Go  and
      Eryani, Fadhl  and
      Erdmann, Alexander  and
      Habash, Nizar",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.868",
    pages = "7022--7032",
    abstract = "We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools and the functionalities it provides.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{oliver-mikelenic-2020-resipc,
    title = "{R}e{S}i{PC}: a Tool for Complex Searches in Parallel Corpora",
    author = "Oliver, Antoni  and
      Mikeleni{\'c}, Bojana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.869",
    pages = "7033--7037",
    abstract = "In this paper, a tool specifically designed to allow for complex searches in large parallel corpora is presented. The formalism for the queries is very powerful as it uses standard regular expressions that allow for complex queries combining word forms, lemmata and POS-tags. As queries are performed over POS-tags, at least one of the languages in the parallel corpus should be POS-tagged. Searches can be performed in one of the languages or in both languages at the same time. The program is able to POS-tag the corpora using the Freeling analyzer through its Python API. ReSiPC is developed in Python version 3 and it is distributed under a free license (GNU GPL). The tool can be used to provide data for contrastive linguistics research and an example of use in a Spanish-Croatian parallel corpus is presented. ReSiPC is designed for queries in POS-tagged corpora, but it can be easily adapted for querying corpora containing other kinds of information.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{lima-lopez-etal-2020-hitzalmed,
    title = "{H}itzal{M}ed: Anonymisation of Clinical Text in {S}panish",
    author = "Lima Lopez, Salvador  and
      Perez, Naiara  and
      Garc{\'\i}a-Sardi{\~n}a, Laura  and
      Cuadros, Montse",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.870",
    pages = "7038--7043",
    abstract = "HitzalMed is a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task. Moreover, once sensitive information is detected, different anonymisation techniques are implemented that are configurable by the user {--}for instance, substitution, where sensitive items are replaced by same category text in an effort to generate a new document that looks as natural as the original one. The tool is able to get data from different document formats and outputs downloadable anonymised data. This paper presents the anonymisation and substitution technology and the demonstrator which is publicly available at https://snlt.vicomtech.org/hitzalmed.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{indig-etal-2020-xtsv,
    title = "The xtsv Framework and the Twelve Virtues of Pipelines",
    author = "Indig, Bal{\'a}zs  and
      Sass, B{\'a}lint  and
      Mittelholcz, Iv{\'a}n",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.871",
    pages = "7044--7052",
    abstract = "We present xtsv, an abstract framework for building NLP pipelines. It covers several kinds of functionalities which can be implemented at an abstract level. We survey these features and argue that all are desired in a modern pipeline. The framework has a simple yet powerful internal communication format which is essentially tsv (tab separated values) with header plus some additional features. We put emphasis on the capabilities of the presented framework, for example its ability to allow new modules to be easily integrated or replaced, or the variety of its usage options. When a module is put into xtsv, all functionalities of the system are immediately available for that module, and the module can be be a part of an xtsv pipeline. The design also allows convenient investigation and manual correction of the data flow from one module to another. We demonstrate the power of our framework with a successful application: a concrete NLP pipeline for Hungarian called e-magyar text processing system (emtsv) which integrates Hungarian NLP tools in xtsv. All the advantages of the pipeline come from the inherent properties of the xtsv framework.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{daudert-2020-web,
    title = "A Web-based Collaborative Annotation and Consolidation Tool",
    author = "Daudert, Tobias",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.872",
    pages = "7053--7059",
    abstract = "Annotation tools are a valuable asset for the construction of labelled textual datasets. However, they tend to have a rigid structure, closed back-end and front-end, and are built in a non-user-friendly way. These downfalls difficult their use in annotation tasks requiring varied text formats, prevent researchers to optimise the tool to the annotation task, and impede people with little programming knowledge to easily modify the tool rendering it unusable for a large cohort. Targeting these needs, we present a web-based collaborative annotation and consolidation tool (AWOCATo), capable of supporting varied textual formats. AWOCATo is based on three pillars: (1) Simplicity, built with a modular architecture employing easy to use technologies; (2) Flexibility, the JSON configuration file allows an easy adaption to the annotation task; (3) Customizability, parameters such as labels, colours, or consolidation features can be easily customized. These features allow AWOCATo to support a range of tasks and domains, filling the gap left by the absence of annotation tools that can be used by people with and without programming knowledge, including those who wish to easily adapt a tool to less common tasks. AWOCATo is available for download at https://github.com/TDaudert/AWOCATo.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{larson-etal-2020-data,
    title = "Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data",
    author = "Larson, Stefan  and
      Guldan, Eric  and
      Leach, Kevin",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.873",
    pages = "7060--7068",
    abstract = "Typical machine learning approaches to developing task-oriented dialog systems require the collection and management of large amounts of training data, especially for the tasks of intent classification and slot-filling. Managing this data can be cumbersome without dedicated tools to help the dialog system designer understand the nature of the data. This paper presents a toolkit for analyzing slot-filling and intent classification corpora. We present a toolkit that includes (1) a new lightweight and readable data and file format for intent classification and slot-filling corpora, (2) a new query language for searching intent classification and slot-filling corpora, and (3) tools for understanding the structure and makeup for such corpora. We apply our toolkit to several well-known NLU datasets, and demonstrate that our toolkit can be used to uncover interesting and surprising insights. By releasing our toolkit to the research community, we hope to enable others to develop more robust and intelligent slot-filling and intent classification models.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{krishna-etal-2020-shr,
    title = "{SHR}++: An Interface for Morpho-syntactic Annotation of {S}anskrit Corpora",
    author = "Krishna, Amrith  and
      Vidhyut, Shiv  and
      Chawla, Dilpreet  and
      Sambhavi, Sruti  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.874",
    pages = "7069--7076",
    abstract = "We propose a web-based annotation framework, SHR++, for morpho-syntactic annotation of corpora in Sanskrit. SHR++ is designed to generate annotations for the word-segmentation, morphological parsing and dependency analysis tasks in Sanskrit. It incorporates analyses and predictions from various tools designed for processing texts in Sanskrit, and utilise them to ease the cognitive load of the human annotators. Specifically, SHR++ uses Sanskrit Heritage Reader, a lexicon driven shallow parser for enumerating all the phonetically and lexically valid word splits along with their morphological analyses for a given string. This would help the annotators in choosing the solutions, rather than performing the segmentations by themselves. Further, predictions from a word segmentation tool are added as suggestions that can aid the human annotators in their decision making. Our evaluation shows that enabling this segmentation suggestion component reduces the annotation time by 20.15 {\%}. SHR++ can be accessed online at http://vidhyut97.pythonanywhere.com/ and the codebase, for the independent deployment of the system elsewhere, is hosted at https://github.com/iamdsc/smart-sanskrit-annotator.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{oka-etal-2020-kotonoha,
    title = "{KOTONOHA}: A Corpus Concordance System for Skewer-Searching {NINJAL} Corpora",
    author = "Oka, Teruaki  and
      Ishimoto, Yuichi  and
      Yagi, Yutaka  and
      Nakamura, Takenori  and
      Asahara, Masayuki  and
      Maekawa, Kikuo  and
      Ogiso, Toshinobu  and
      Koiso, Hanae  and
      Sakoda, Kumiko  and
      Kibe, Nobuko",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.875",
    pages = "7077--7083",
    abstract = "The National Institute for Japanese Language and Linguistics, Japan (NINJAL, Japan), has developed several types of corpora. For each corpus NINJAL provided an online search environment, {`}Chunagon{'}, which is a morphological-information-annotation-based concordance system made publicly available in 2011. NINJAL has now provided a skewer-search system {`}Kotonoha{'} based on the {`}Chunagon{'} systems. This system enables querying of multiple corpora by certain categories, such as register type and period.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{ogawa-etal-2020-gamification,
    title = "Gamification Platform for Collecting Task-oriented Dialogue Data",
    author = "Ogawa, Haruna  and
      Nishikawa, Hitoshi  and
      Tokunaga, Takenobu  and
      Yokono, Hikaru",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.876",
    pages = "7084--7093",
    abstract = "Demand for massive language resources is increasing as the data-driven approach has established a leading position in Natural Language Processing. However, creating dialogue corpora is still a difficult task due to the complexity of the human dialogue structure and the diversity of dialogue topics. Though crowdsourcing is majorly used to assemble such data, it presents problems such as less-motivated workers. We propose a platform for collecting task-oriented situated dialogue data by using gamification. Combining a video game with data collection benefits such as motivating workers and cost reduction. Our platform enables data collectors to create their original video game in which they can collect dialogue data of various types of tasks by using the logging function of the platform. Also, the platform provides the annotation function that enables players to annotate their own utterances. The annotation can be gamified aswell. We aim at high-quality annotation by introducing such self-annotation method. We implemented a prototype of the proposed platform and conducted a preliminary evaluation to obtain promising results in terms of both dialogue data collection and self-annotation.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rose-2020-improving,
    title = "Improving the Production Efficiency and Well-formedness of Automatically-Generated Multiple-Choice Cloze Vocabulary Questions",
    author = "Rose, Ralph",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.877",
    pages = "7094--7101",
    abstract = "Multiple-choice cloze (fill-in-the-blank) questions are widely used in knowledge testing and are commonly used for testing vocabulary knowledge. Word Quiz Constructor (WQC) is a Java application that is designed to produce such test items automatically from the Academic Word List (Coxhead, 2000) and using various online and offline resources. The present work evaluates recently added features of WQC to see whether they improve the production quality and well-formedness of vocabulary quiz items over previously implemented features in WQC. Results of a production test and a well-formedness survey using Amazon Mechanical Turk show that newly-introduced features (Linsear Write readability formula and Google Books NGrams frequency list) significantly improve the production quality of items over previous features (Automated Readability Index and frequency list derived from the British Academic Written English corpus). Items are produced faster and stem sentences are shorter in length without any degradation in their well-formedness. Approximately 90{\%} of such items are judged well-formed, surpassing the rate of manually-produced items.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rehbein-etal-2020-improving,
    title = "Improving Sentence Boundary Detection for Spoken Language Transcripts",
    author = "Rehbein, Ines  and
      Ruppenhofer, Josef  and
      Schmidt, Thomas",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.878",
    pages = "7102--7111",
    abstract = "This paper presents experiments on sentence boundary detection in transcripts of spoken dialogues. Segmenting spoken language into sentence-like units is a challenging task, due to disfluencies, ungrammatical or fragmented structures and the lack of punctuation. In addition, one of the main bottlenecks for many NLP applications for spoken language is the small size of the training data, as the transcription and annotation of spoken language is by far more time-consuming and labour-intensive than processing written language. We therefore investigate the benefits of data expansion and transfer learning and test different ML architectures for this task. Our results show that data expansion is not straightforward and even data from the same domain does not always improve results. They also highlight the importance of modelling, i.e. of finding the best architecture and data representation for the task at hand. For the detection of boundaries in spoken language transcripts, we achieve a substantial improvement when framing the boundary detection problem assentence pair classification task, as compared to a sequence tagging approach.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{eskander-etal-2020-morphagram,
    title = "{M}orph{AG}ram, Evaluation and Framework for Unsupervised Morphological Segmentation",
    author = "Eskander, Ramy  and
      Callejas, Francesca  and
      Nichols, Elizabeth  and
      Klavans, Judith  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.879",
    pages = "7112--7122",
    abstract = "Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the increasing interest in low-resource languages, unsupervised morphological segmentation has become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this paper, we present and release MorphAGram, a publicly available framework for unsupervised morphological segmentation that uses Adaptor Grammars (AG) and is based on the work presented by Eskander et al. (2016). We conduct an extensive quantitative and qualitative evaluation of this framework on 12 languages and show that the framework achieves state-of-the-art results across languages of different typologies (from fusional to polysynthetic and from high-resource to low-resource).",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{okinina-etal-2020-ctap,
    title = "{CTAP} for {I}talian: Integrating Components for the Analysis of {I}talian into a Multilingual Linguistic Complexity Analysis Tool",
    author = "Okinina, Nadezda  and
      Frey, Jennifer-Carmen  and
      Weiss, Zarah",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.880",
    pages = "7123--7131",
    abstract = "Linguistic complexity research being a very actively developing field, an increasing number of text analysis tools are created that use natural language processing techniques for the automatic extraction of quantifiable measures of linguistic complexity. While most tools are designed to analyse only one language, the CTAP open source linguistic complexity measurement tool is capable of processing multiple languages, making cross-lingual comparisons possible. Although it was originally developed for English, the architecture has been ex-tended to support multi-lingual analyses. Here we present the Italian component of CTAP, describe its implementation and compare it to the existing linguistic complexity tools for Italian. Offering general text length statistics and features for lexical, syntactic, and morpho-syntactic complexity (including measures of lexical frequency, lexical diversity, lexical and syntactical variation, part-of-speech density), CTAP is currently the most comprehensive linguistic complexity measurement tool for Italian and the only one allowing the comparison of Italian texts to multiple other languages within one tool.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{stodden-etal-2020-feel,
    title = "Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators{'} Concerns and Behaviors",
    author = "Stodden, Regina  and
      QasemiZadeh, Behrang  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.881",
    pages = "7132--7139",
    abstract = "In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA). Besides letting experienced and inexperienced users do suggestion-based and slightly-controlled annotations, the system keeps track of the time and changes during the annotation process and stores the users{'} confidence with the current annotation. This collected metadata can be used to get insights regarding the difficulty of an annotation with the same type or frame or can be used as an input of an annotation cost measurement for an active learning algorithm. The tool was already used to build a manually annotated corpus with semantic frames and its arguments for task 2 of SemEval 2019 regarding unsupervised lexical frame induction (QasemiZadeh et al., 2019). Although English sentences from the Wall Street Journal corpus of the Penn Treebank were annotated for this task, it is also possible to use the proposed tool for the annotation of sentences in other languages.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{qader-etal-2020-seq2seqpy,
    title = "{S}eq2{S}eq{P}y: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling",
    author = "Qader, Raheel  and
      Portet, Fran{\c{c}}ois  and
      Labbe, Cyril",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.882",
    pages = "7140--7144",
    abstract = "We present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known architectures such as Recurrent Neural Networks, Pointer Generator Networks, and transformer model. We evaluate the toolkit on two datasets and we show that the toolkit performs similarly or even better than a very widely used sequence-to-sequence toolkit.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{brunato-etal-2020-profiling,
    title = "Profiling-{UD}: a Tool for Linguistic Profiling of Texts",
    author = "Brunato, Dominique  and
      Cimino, Andrea  and
      Dell{'}Orletta, Felice  and
      Venturi, Giulia  and
      Montemagni, Simonetta",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.883",
    pages = "7145--7151",
    abstract = "In this paper, we introduce Profiling{--}UD, a new text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives. It allows the extraction of more than 130 features, spanning across different levels of linguistic description. Beyond the large number of features that can be monitored, a main novelty of Profiling{--}UD is that it has been specifically devised to be multilingual since it is based on the Universal Dependencies framework. In the second part of the paper, we demonstrate the effectiveness of these features in a number of theoretical and applicative studies in which they were successfully used for text and author profiling.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{laur-etal-2020-estnltk,
    title = "{E}st{NLTK} 1.6: Remastered {E}stonian {NLP} Pipeline",
    author = {Laur, Sven  and
      Orasmaa, Siim  and
      S{\"a}rg, Dage  and
      Tammo, Paul},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.884",
    pages = "7152--7160",
    abstract = "The goal of the EstNLTK Python library is to provide a unified programming interface for natural language processing in Estonian. As such, previous versions of the library have been immensely successful both in academic and industrial circles. However, they also contained serious structural limitations {--} it was hard to add new components and there was a lack of fine-grained control needed for back-end programming. These issues have been explicitly addressed in the EstNLTK library while preserving the intuitive interface for novices. We have remastered the basic NLP pipeline by adding many data cleaning steps that are necessary for analyzing real-life texts, and state of the art components for morphological analysis and fact extraction. Our evaluation on unlabelled data shows that the remastered basic NLP pipeline outperforms both the previous version of the toolkit, as well as neural models of StanfordNLP. In addition, EstNLTK contains a new interface for storing, processing and querying text objects in Postgres database which greatly simplifies processing of large text collections. EstNLTK is freely available under the GNU GPL version 2 license, which is standard for academic software.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{chiarcos-glaser-2020-tree,
    title = "A Tree Extension for {C}o{NLL}-{RDF}",
    author = "Chiarcos, Christian  and
      Glaser, Luis",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.885",
    pages = "7161--7169",
    abstract = "The technological bridges between knowledge graphs and natural language processing are of utmost importance for the future development of language technology. CoNLL-RDF is a technology that provides such a bridge for popular one-word-per-line formats as widely used in NLP (e.g., the CoNLL Shared Tasks), annotation (Universal Dependencies, Unimorph), corpus linguistics (Corpus WorkBench, CWB) and digital lexicography (SketchEngine): Every empty-line separated table (usually a sentence) is parsed into an graph, can be freely manipulated and enriched using W3C-standardized RDF technology, and then be serialized back into in a TSV format, RDF or other formats. An important limitation is that CoNLL-RDF provides native support for word-level annotations only. This does include dependency syntax and semantic role annotations, but neither phrase structures nor text structure. We describe the extension of the CoNLL-RDF technology stack for two vocabulary extensions of CoNLL-TSV, the PTB bracket notation used in earlier CoNLL Shared Tasks and the extension with XML markup elements featured by CWB and SketchEngine. In order to represent the necessary extensions of the CoNLL vocabulary in an adequate fashion, we employ the POWLA vocabulary for representing and navigating in tree structures.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{trips-percillier-2020-lemmatising,
    title = "Lemmatising Verbs in {M}iddle {E}nglish Corpora: The Benefit of Enriching the {P}enn-{H}elsinki Parsed Corpus of {M}iddle {E}nglish 2 ({PPCME}2), the Parsed Corpus of {M}iddle {E}nglish Poetry ({PCMEP}), and A Parsed Linguistic Atlas of Early {M}iddle {E}nglish ({PLAEME})",
    author = "Trips, Carola  and
      Percillier, Michael",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.886",
    pages = "7170--7178",
    abstract = "This paper describes the lemmatisation of three annotated corpora of Middle English{---}the Penn-Helsinki Parsed Corpus of Middle English 2 (PPCME2), the Parsed Corpus of Middle English Poetry (PCMEP), and A Parsed Linguistic Atlas of Early Middle English (PLAEME) {---} which is a prerequisite for systematically investigating the argument structures of verbs of the given time. Creating this tool and enriching existing parsed corpora of Middle English is part of the project Borrowing of Argument Structure in Contact Situations (BASICS) which seeks to explain to which extent verbs copied from Old French had an impact on the grammar of Middle English. First, we lemmatised the PPCME2 by (1) creating an inventory of form-lemma correspondences linking forms in the PPCME2 to lemmas in the MED, and (2) inserting this lemma information into the corpus (precision: 94.85{\%}, recall: 98.92{\%}). Second, we enriched the PCMEP and PLAEME, which adopted the annotation format of the PPCME2, with verb lemmas to undertake studies that fill the well-known data gap in the subperiod (1250--1350) of the PPCME2. The case study of reflexives shows that with our method we gain much more reliable results in terms of diachrony, diatopy and contact-induced change.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{stajner-etal-2020-coco,
    title = "{C}o{C}o: A Tool for Automatically Assessing Conceptual Complexity of Texts",
    author = "Stajner, Sanja  and
      Nisioi, Sergiu  and
      Hulpu{\textcommabelow{s}}, Ioana",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.887",
    pages = "7179--7186",
    abstract = "Traditional text complexity assessment usually takes into account only syntactic and lexical text complexity. The task of automatic assessment of conceptual text complexity, important for maintaining reader{'}s interest and text adaptation for struggling readers, has only been proposed recently. In this paper, we present CoCo - a tool for automatic assessment of conceptual text complexity, based on using the current state-of-the-art unsupervised approach. We make the code and API freely available for research purposes, and describe the code and the possibility for its personalization and adaptation in details. We compare the current implementation with the state of the art, discussing the influence of the choice of entity linker on the performances of the tool. Finally, we present results obtained on two widely used text simplification corpora, discussing the full potential of the tool.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{verner-vernerova-2020-pyvallex,
    title = "{P}y{V}allex: A Processing System for Valency Lexicon Data",
    author = "Verner, Jonathan  and
      Vernerov{\'a}, Anna",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.888",
    pages = "7187--7193",
    abstract = "PyVallex is a Python-based system for presenting, searching/filtering, editing/extending and automatic processing of machine-readable lexicon data originally available in a text-based format. The system consists of several components: a parser for the specific lexicon format used in several valency lexicons, a data-validation framework, a regular expression based search engine, a map-reduce style framework for querying the lexicon data and a web-based interface integrating complex search and some basic editing capabilities. PyVallex provides most of the typical functionalities of a Dictionary Writing System (DWS), such as multiple presentation modes for the underlying lexical database, automatic evaluation of consistency tests, and a mechanism of merging updates coming from multiple sources. The editing functionality is currently limited to the client-side interface and edits of existing lexical entries, but additional script-based operations on the database are also possible. The code is published under the open source MIT license and is also available in the form of a Python module for integrating into other software.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fiorelli-etal-2020-editing,
    title = "Editing {O}nto{L}ex-Lemon in {V}oc{B}ench 3",
    author = "Fiorelli, Manuel  and
      Stellato, Armando  and
      Lorenzetti, Tiziano  and
      Turbati, Andrea  and
      Schmitz, Peter  and
      Francesconi, Enrico  and
      Hajlaoui, Najeh  and
      Batouche, Brahim",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.889",
    pages = "7194--7203",
    abstract = "OntoLex-Lemon is a collection of RDF vocabularies for specifying the verbalization of ontologies in natural language. Beyond its original scope, OntoLex-Lemon, as well as its predecessor Monnet lemon, found application in the Linguistic Linked Open Data cloud to represent and interlink language resources on the Semantic Web. Unfortunately, generic ontology and RDF editors were considered inconvenient to use with OntoLex-Lemon because of its complex design patterns and other peculiarities, including indirection, reification and subtle integrity constraints. This perception led to the development of dedicated editors, trading the flexibility of RDF in combining different models (and the features already available in existing RDF editors) for a more direct and streamlined editing of OntoLex-Lemon patterns. In this paper, we investigate on the benefits gained by extending an already existing RDF editor, VocBench 3, with capabilities closely tailored to OntoLex-Lemon and on the challenges that such extension implies. The outcome of such investigation is twofold: a vertical assessment of a new editor for OntoLex-Lemon and, in the broader scope of RDF editor design, a new perspective on which flexibility and extensibility characteristics an editor should meet in order to cover new core modeling vocabularies, for which OntoLex-Lemon represents a use case.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{forti-etal-2020-malt,
    title = "{MALT}-{IT}2: A New Resource to Measure Text Difficulty in Light of {CEFR} Levels for {I}talian {L}2 Learning",
    author = "Forti, Luciana  and
      Grego Bolli, Giuliana  and
      Santarelli, Filippo  and
      Santucci, Valentino  and
      Spina, Stefania",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.890",
    pages = "7204--7211",
    abstract = "This paper presents a new resource for automatically assessing text difficulty in the context of Italian as a second or foreign language learning and teaching. It is called MALT-IT2, and it automatically classifies inputted texts according to the CEFR level they are more likely to belong to. After an introduction to the field of automatic text difficulty assessment, and an overview of previous related work, we describe the rationale of the project, the corpus and computational system it is based on. Experiments were conducted in order to investigate the reliability of the system. The results show that the system is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features which mostly influenced the predictions.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{fath-etal-2020-fintan,
    title = "Fintan - Flexible, Integrated Transformation and Annotation e{N}gineering",
    author = {F{\"a}th, Christian  and
      Chiarcos, Christian  and
      Ebbrecht, Bj{\"o}rn  and
      Ionov, Maxim},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.891",
    pages = "7212--7221",
    abstract = "We introduce the Flexible and Integrated Transformation and Annotation eNgeneering (Fintan) platform for converting heterogeneous linguistic resources to RDF. With its modular architecture, workflow management and visualization features, Fintan facilitates the development of complex transformation pipelines by integrating generic RDF converters and augmenting them with extended graph processing capabilities: Existing converters can be easily deployed to the system by means of an ontological data structure which renders their properties and the dependencies between transformation steps. Development of subsequent graph transformation steps for resource transformation, annotation engineering or entity linking is further facilitated by a novel visual rendering of SPARQL queries. A graphical workflow manager allows to easily manage the converter modules and combine them to new transformation pipelines. Employing the stream-based graph processing approach first implemented with CoNLL-RDF, we address common challenges and scalability issues when transforming resources and showcase the performance of Fintan by means of a purely graph-based transformation of the Universal Morphology data to RDF.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{waszczuk-etal-2020-contemplata,
    title = "Contemplata, a Free Platform for Constituency Treebank Annotation",
    author = {Waszczuk, Jakub  and
      Wang, Ilaine  and
      Antoine, Jean-Yves  and
      Halftermeyer, Ana{\"\i}s},
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.892",
    pages = "7222--7229",
    abstract = "This paper describes Contemplata, an annotation platform that offers a generic solution for treebank building as well as treebank enrichment with relations between syntactic nodes. Contemplata is dedicated to the annotation of constituency trees. The framework includes support for syntactic parsers, which provide automatic annotations to be manually revised. The balanced strategy of annotation between automatic parsing and manual revision allows to reduce the annotator workload, which favours data reliability. The paper presents the software architecture of Contemplata, describes its practical use and eventually gives two examples of annotation projects that were conducted on the platform.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rim-etal-2020-interchange,
    title = "Interchange Formats for Visualization: {LIF} and {MMIF}",
    author = "Rim, Kyeongmin  and
      Lynch, Kelley  and
      Verhagen, Marc  and
      Ide, Nancy  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.893",
    pages = "7230--7237",
    abstract = "Promoting interoperrable computational linguistics (CL) and natural language processing (NLP) application platforms and interchange-able data formats have contributed improving discoverabilty and accessbility of the openly available NLP software. In this paper, wediscuss the enhanced data visualization capabilities that are also enabled by inter-operating NLP pipelines and interchange formats.For adding openly available visualization tools and graphical annotation tools to the Language Applications Grid (LAPPS Grid) andComputational Linguistics Applications for Multimedia Services (CLAMS) toolboxes, we have developed interchange formats that cancarry annotations and metadata for text and audiovisual source data. We descibe those data formats and present case studies where wesuccessfully adopt open-source visualization tools and combine them with CL tools.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{davidson-etal-2020-developing-nlp,
    title = "Developing {NLP} Tools with a New Corpus of Learner {S}panish",
    author = "Davidson, Sam  and
      Yamada, Aaron  and
      Fernandez Mira, Paloma  and
      Carando, Agustina  and
      Sanchez Gutierrez, Claudia H.  and
      Sagae, Kenji",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.894",
    pages = "7238--7243",
    abstract = "The development of effective NLP tools for the L2 classroom depends largely on the availability of large annotated corpora of language learner text. While annotated learner corpora of English are widely available, large learner corpora of Spanish are less common. Those Spanish corpora that are available do not contain the annotations needed to facilitate the development of tools beneficial to language learners, such as grammatical error correction. As a result, the field has seen little research in NLP tools designed to benefit Spanish language learners and teachers. We introduce COWS-L2H, a freely available corpus of Spanish learner data which includes error annotations and parallel corrected text to help researchers better understand L2 development, to examine teaching practices empirically, and to develop NLP tools to better serve the Spanish teaching community. We demonstrate the utility of this corpus by developing a neural-network based grammatical error correction system for Spanish learner writing.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@inproceedings{rodrigues-etal-2020-deepnlpf,
    title = "{D}eep{NLPF}: A Framework for Integrating Third Party {NLP} Tools",
    author = "Rodrigues, Francisco  and
      Lima, Rinaldo  and
      Domingues, William  and
      Fidalgo, Robson  and
      Chifu, Adrian  and
      Espinasse, Bernard  and
      Fournier, S{\'e}bastien",
    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.895",
    pages = "7244--7251",
    abstract = "Natural Language Processing (NLP) of textual data is usually broken down into a sequence of several subtasks, where the output of one the subtasks becomes the input to the following one, which constitutes an NLP pipeline. Many third-party NLP tools are currently available, each performing distinct NLP subtasks. However, it is difficult to integrate several NLP toolkits into a pipeline due to many problems, including different input/output representations or formats, distinct programming languages, and tokenization issues. This paper presents DeepNLPF, a framework that enables easy integration of third-party NLP tools, allowing the user to preprocess natural language texts at lexical, syntactic, and semantic levels. The proposed framework also provides an API for complete pipeline customization including the definition of input/output formats, integration plugin management, transparent ultiprocessing execution strategies, corpus-level statistics, and database persistence. Furthermore, the DeepNLPF user-friendly GUI allows its use even by a non-expert NLP user. We conducted runtime performance analysis showing that DeepNLPF not only easily integrates existent NLP toolkits but also reduces significant runtime processing compared to executing the same NLP pipeline in a sequential manner.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}
@proceedings{lr4sshoc-2020-language,
    title = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    editor = "Broeder, Daan  and
      Eskevich, Maria  and
      Monachini, Monica",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.0",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{concordia-etal-2020-store,
    title = "Store Scientific Workflows Data in {SSHOC} Repository",
    author = "Concordia, Cesare  and
      Meghini, Carlo  and
      Benedetti, Filippo",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.1",
    pages = "1--4",
    abstract = "Today scientific workflows are used by scientists as a way to define automated, scalable, and portable in-silico experiments. Having a formal description of an experiment can improve replicability and reproducibility of the experiment. However, simply storing and publishing the workflow may be not enough, an accurate management of provenance data generated during workflow life cycle is crucial to achieve reproducibility. This document presents the activity being carried out by CNR-ISTI in task 5.2 of the SSHOC project to add to the repository service developed in the task, functionalities to store, access and manage {`}workflow data{'} in order to improve replicability and reproducibility of e-science experiments.",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{di-donato-etal-2020-social,
    title = "Social Sciences and Humanities Pathway Towards the {E}uropean Open Science Cloud",
    author = "Di Donato, Francesca  and
      Monachini, Monica  and
      Eskevich, Maria  and
      Pohle, Stefanie  and
      Moranville, Yoann  and
      Dumouchel, Suzanne",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.2",
    pages = "5--9",
    abstract = "The paper presents a journey, which starts from various social sciences and humanities (SSH) Research Infrastructures in Europe and arrives at the comprehensive {``}ecosystem of infrastructures{''}, namely the European Open Science Cloud (EOSC). We will highlight how the SSH Open Science infrastructures contribute to the goal of establishing the EOSC. First, through the example of OPERAS, the European Research Infrastructure for Open Scholarly Communication in the SSH, to see how its services are conceived to be part of the EOSC and to address the communities{'} needs. The next two sections highlight collaboration practices between partners in Europe to build the SSH component of the EOSC and a SSH discovery platform, as a service of OPERAS and the EOSC. The last two sections will focus on an implementation network dedicated to SSH data fairification.",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{nordhoff-2020-attic,
    title = "From the attic to the cloud: mobilization of endangered language resources with linked data",
    author = "Nordhoff, Sebastian",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.3",
    pages = "10--18",
    abstract = "This paper describes a collection of 20k ELAN annotation files harvested from five different endangered language archives. The ELAN files form a very heterogeneous set, but the hierarchical configuration of their tiers allow, in conjunction with the tier content, to identify transcriptions, translations, and glosses. These transcriptions, translations, and glosses are queryable across archives. Small analyses of graphemes (transcription tier), grammatical and lexical glosses (gloss tier), and semantic concepts (translation tier) show the viability of the approach. The use of identifiers from OLAC, Wikidata and Glottolog allows for a better integration of the data from these archives into the Linguistic Linked Open Data Cloud.",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{pontiki-etal-2020-verbal,
    title = "Verbal Aggression as an Indicator of Xenophobic Attitudes in {G}reek {T}witter during and after the Financial Crisis",
    author = "Pontiki, Maria  and
      Gavriilidou, Maria  and
      Gkoumas, Dimitris  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.4",
    pages = "19--26",
    abstract = "We present a replication of a data-driven and linguistically inspired Verbal Aggression analysis framework that was designed to examine Twitter verbal attacks against predefined target groups of interest as an indicator of xenophobic attitudes during the financial crisis in Greece, in particular during the period 2013-2016. The research goal in this paper is to re-examine Verbal Aggression as an indicator of xenophobic attitudes in Greek Twitter three years later, in order to trace possible changes regarding the main targets, the types and the content of the verbal attacks against the same targets in the post crisis era, given also the ongoing refugee crisis and the political landscape in Greece as it was shaped after the elections in 2019. The results indicate an interesting rearrangement of the main targets of the verbal attacks, while the content and the types of the attacks provide valuable insights about the way these targets are being framed as compared to the respective dominant perceptions and stereotypes about them during the period 2013-2016.",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{ros-etal-2020-mining,
    title = "Mining Wages in Nineteenth-Century Job Advertisements. The Application of Language Resources and Language Technology to study Economic and Social Inequality",
    author = "Ros, Ruben  and
      van Erp, Marieke  and
      Rijpma, Auke  and
      Zijdeman, Richard",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.5",
    pages = "27--32",
    abstract = "For the analysis of historical wage development, no structured data is available. Job advertisements, as found in newspapers can provide insights into what different types of jobs paid, but require language technology to structure in a format conducive to quantitative analysis. In this paper, we report on our experiments to mine wages from 19th century newspaper advertisements and detail the challenges that need to be overcome to perform a socio-economic analysis of textual data sources.",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{broeder-etal-2020-lr4sshoc,
    title = "{LR}4{SSHOC}: The Future of Language Resources in the Context of the Social Sciences and Humanities Open Cloud",
    author = "Broeder, Daan  and
      Eskevich, Maria  and
      Monachini, Monica",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.6",
    pages = "33--36",
    abstract = "This paper outlines the future of language resources and identifies their potential contribution for creating and sustaining the social sciences and humanities (SSH) component of the European Open Science Cloud (EOSC).",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{castelli-2020-eosc,
    title = "{EOSC} as a game-changer in the Social Sciences and Humanities research activities",
    author = "Castelli, Donatella",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.7",
    pages = "37--38",
    abstract = "This paper aims to give some insights on how the European Open Science Cloud (EOSC) will be able to influence the Social Sciences and Humanities (SSH) sector, thus paving the way towards innovation. Points of discussion on how the LRs and RIs community can contribute to the revolution in the practice of research areas are provided.",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{cieri-2020-stretching,
    title = "Stretching Disciplinary Boundaries in Language Resource Development and Use: a {L}inguistic {D}ata {C}onsortium Position Paper",
    author = "Cieri, Christopher",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.8",
    pages = "39--41",
    abstract = "Given the persistent gap between demand and supply, the impetus to reuse language resources is great. Researchers benefit from building upon the work of others including reusing data, tools and methodology. Such reuse should always consider the original intent of the language resource and how that impacts potential reanalysis. When the reuse crosses disciplinary boundaries, the re-user also needs to consider how research standards that differ between social science and humanities on the one hand and human language technologies on the other might lead to differences in unspoken assumptions. Data centers that aim to support multiple research communities have a responsibility to build bridges across disciplinary divides by sharing data in all directions, encouraging re-use and re-sharing and engaging directly in research that improves methodologies.",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@inproceedings{van-den-heuvel-2020-crossing,
    title = "Crossing the {SSH} Bridge with Interview Data",
    author = "van den Heuvel, Henk",
    booktitle = "Proceedings of the Workshop about Language Resources for the SSH Cloud",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lr4sshoc-1.9",
    pages = "42--44",
    abstract = "Spoken audio data, such as interview data, is a scientific instrument used by researchers in various disciplines crossing the boundaries of social sciences and humanities. In this paper, we will have a closer look at a portal designed to perform speech-to-text conversion on audio recordings through Automatic Speech Recognition (ASR) in the CLARIN infrastructure. Within the cluster cross-domain EU project SSHOC the potential value of such a linguistic tool kit for processing spoken language recording has found uptake in a webinar about the topic, and in a task addressing audio analysis of panel survey data. The objective of this contribution is to show that the processing of interviews as a research instrument has opened up a fascinating and fruitful area of collaboration between Social Sciences and Humanities (SSH).",
    language = "English",
    ISBN = "979-10-95546-43-6",
}
@proceedings{louhi-2020-international,
    title = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    editor = "Holderness, Eben  and
      Jimeno Yepes, Antonio  and
      Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Pustejovsky, James  and
      Rinaldi, Fabio",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.0",
}
@inproceedings{berg-etal-2020-impact,
    title = "The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text",
    author = "Berg, Hanna  and
      Henriksson, Aron  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.1",
    doi = "10.18653/v1/2020.louhi-1.1",
    pages = "1--11",
    abstract = "The impact of de-identification on data quality and, in particular, utility for developing models for downstream tasks has been more thoroughly studied for structured data than for unstructured text. While previous studies indicate that text de-identification has a limited impact on models for downstream tasks, it remains unclear what the impact is with various levels and forms of de-identification, in particular concerning the trade-off between precision and recall. In this paper, the impact of de-identification is studied on downstream named entity recognition in Swedish clinical text. The results indicate that de-identification models with moderate to high precision lead to similar downstream performance, while low precision has a substantial negative impact. Furthermore, different strategies for concealing sensitive information affect performance to different degrees, ranging from pseudonymisation having a low impact to the removal of entire sentences with sensitive information having a high impact. This study indicates that it is possible to increase the recall of models for identifying sensitive information without negatively affecting the use of de-identified text data for training models for clinical named entity recognition; however, there is ultimately a trade-off between the level of de-identification and the subsequent utility of the data.",
}
@inproceedings{wiatrak-iso-sipila-2020-simple,
    title = "Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text",
    author = "Wiatrak, Maciej  and
      Iso-Sipila, Juha",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.2",
    doi = "10.18653/v1/2020.louhi-1.2",
    pages = "12--17",
    abstract = "Recognising and linking entities is a crucial first step to many tasks in biomedical text analysis, such as relation extraction and target identification. Traditionally, biomedical entity linking methods rely heavily on heuristic rules and predefined, often domain-specific features. The features try to capture the properties of entities and complex multi-step architectures to detect, and subsequently link entity mentions. We propose a significant simplification to the biomedical entity linking setup that does not rely on any heuristic methods. The system performs all the steps of the entity linking task jointly in either single or two stages. We explore the use of hierarchical multi-task learning, using mention recognition and entity typing tasks as auxiliary tasks. We show that hierarchical multi-task models consistently outperform single-task models when trained tasks are homogeneous. We evaluate the performance of our models on the biomedical entity linking benchmarks using MedMentions and BC5CDR datasets. We achieve state-of-theart results on the challenging MedMentions dataset, and comparable results on BC5CDR.",
}
@inproceedings{kalyan-sangeetha-2020-medical,
    title = "Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings",
    author = "Kalyan, Katikapalli Subramanyam  and
      Sangeetha, Sivanesan",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.3",
    doi = "10.18653/v1/2020.louhi-1.3",
    pages = "18--23",
    abstract = "Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a clinical knowledge base. It is much beyond simple string matching and requires a deep semantic understanding of concept mentions. Recent research approach concept normalization as either text classification or text similarity. The main drawback in existing a) text classification approach is ignoring valuable target concepts information in learning input concept mention representation b) text similarity approach is the need to separately generate target concept embeddings which is time and resource consuming. Our proposed model overcomes these drawbacks by jointly learning the representations of input concept mention and target concepts. First, we learn input concept mention representation using RoBERTa. Second, we find cosine similarity between embeddings of input concept mention and all the target concepts. Here, embeddings of target concepts are randomly initialized and then updated during training. Finally, the target concept with maximum cosine similarity is assigned to the input concept mention. Our model surpasses all the existing methods across three standard datasets by improving accuracy up to 2.31{\%}.",
}
@inproceedings{grivas-etal-2020-cute,
    title = "Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports",
    author = "Grivas, Andreas  and
      Alex, Beatrice  and
      Grover, Claire  and
      Tobin, Richard  and
      Whiteley, William",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.4",
    doi = "10.18653/v1/2020.louhi-1.4",
    pages = "24--37",
    abstract = "We present an in-depth comparison of three clinical information extraction (IE) systems designed to perform entity recognition and negation detection on brain imaging reports: EdIE-R, a bespoke rule-based system, and two neural network models, EdIE-BiLSTM and EdIE-BERT, both multi-task learning models with a BiLSTM and BERT encoder respectively. We compare our models both on an in-sample and an out-of-sample dataset containing mentions of stroke findings and draw on our error analysis to suggest improvements for effective annotation when building clinical NLP models for a new domain. Our analysis finds that our rule-based system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset. On the other hand, the neural models do not generalise negation to the out-of-sample dataset, despite metrics on the in-sample dataset suggesting otherwise.",
}
@inproceedings{borchert-etal-2020-ggponc,
    title = "{GGPONC}: A Corpus of {G}erman Medical Text with Rich Metadata Based on Clinical Practice Guidelines",
    author = "Borchert, Florian  and
      Lohr, Christina  and
      Modersohn, Luise  and
      Langer, Thomas  and
      Follmann, Markus  and
      Sachs, Jan Philipp  and
      Hahn, Udo  and
      Schapranow, Matthieu-P.",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.5",
    doi = "10.18653/v1/2020.louhi-1.5",
    pages = "38--48",
    abstract = "The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely dis tributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.",
}
@inproceedings{manousogiannis-etal-2020-normalization,
    title = "Normalization of Long-tail Adverse Drug Reactions in Social Media",
    author = "Manousogiannis, Emmanouil  and
      Mesbah, Sepideh  and
      Bozzon, Alessandro  and
      Sips, Robert-Jan  and
      Szlanik, Zoltan  and
      Baez, Selene",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.6",
    doi = "10.18653/v1/2020.louhi-1.6",
    pages = "49--58",
    abstract = "The automatic mapping of Adverse Drug Reaction (ADR) reports from user-generated content to concepts in a controlled medical vocabulary provides valuable insights for monitoring public health. While state-of-the-art deep learning-based sequence classification techniques achieve impressive performance for medical concepts with large amounts of training data, they show their limit with long-tail concepts that have a low number of training samples. The above hinders their adaptability to the changes of layman{'}s terminology and the constant emergence of new informal medical terms. Our objective in this paper is to tackle the problem of normalizing long-tail ADR mentions in user-generated content. In this paper, we exploit the implicit semantics of rare ADRs for which we have few training samples, in order to detect the most similar class for the given ADR. The evaluation results demonstrate that our proposed approach addresses the limitations of the existing techniques when the amount of training data is limited.",
}
@inproceedings{skianis-etal-2020-evaluation,
    title = "Evaluation of Machine Translation Methods applied to Medical Terminologies",
    author = "Skianis, Konstantinos  and
      Briand, Yann  and
      Desgrippes, Florent",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.7",
    doi = "10.18653/v1/2020.louhi-1.7",
    pages = "59--69",
    abstract = "Medical terminologies resources and standards play vital roles in clinical data exchanges, enabling significantly the services{'} interoperability within healthcare national information networks. Health and medical science are constantly evolving causing requirements to advance the terminologies editions. In this paper, we present our evaluation work of the latest machine translation techniques addressing medical terminologies. Experiments have been conducted leveraging selected statistical and neural machine translation methods. The devised procedure is tested on a validated sample of ICD-11 and ICF terminologies from English to French with promising results.",
}
@inproceedings{valentin-etal-2020-information,
    title = "Information retrieval for animal disease surveillance: a pattern-based approach.",
    author = "Valentin, Sarah  and
      Roche, Mathieu  and
      Lancelot, Renaud",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.8",
    doi = "10.18653/v1/2020.louhi-1.8",
    pages = "70--78",
    abstract = "Animal diseases-related news articles are richin information useful for risk assessment. In this paper, we explore a method to automatically retrieve sentence-level epidemiological information. Our method is an incremental approach to create and expand patterns at both lexical and syntactic levels. Expert knowledge input are used at different steps of the approach. Distributed vector representations (word embedding) were used to expand the patterns at the lexical level, thus alleviating manual curation. We showed that expert validation was crucial to improve the precision of automatically generated patterns.",
}
@inproceedings{khandelwal-britto-2020-multitask,
    title = "Multitask Learning of Negation and Speculation using Transformers",
    author = "Khandelwal, Aditya  and
      Britto, Benita Kathleen",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.9",
    doi = "10.18653/v1/2020.louhi-1.9",
    pages = "79--87",
    abstract = "Detecting negation and speculation in language has been a task of considerable interest to the biomedical community, as it is a key component of Information Extraction systems from Biomedical documents. Prior work has individually addressed Negation Detection and Speculation Detection, and both have been addressed in the same way, using 2 stage pipelined approach: Cue Detection followed by Scope Resolution. In this paper, we propose Multitask learning approaches over 2 sets of tasks: Negation Cue Detection {\&} Speculation Cue Detection, and Negation Scope Resolution {\&} Speculation Scope Resolution. We utilise transformer-based architectures like BERT, XLNet and RoBERTa as our core model architecture, and finetune these using the Multitask learning approaches. We show that this Multitask Learning approach outperforms the single task learning approach, and report new state-of-the-art results on Negation and Speculation Scope Resolution on the BioScope Corpus and the SFU Review Corpus.",
}
@inproceedings{wang-etal-2020-biomedical,
    title = "Biomedical Event Extraction as Multi-turn Question Answering",
    author = "Wang, Xing David  and
      Weber, Leon  and
      Leser, Ulf",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.10",
    doi = "10.18653/v1/2020.louhi-1.10",
    pages = "88--96",
    abstract = "Biomedical event extraction from natural text is a challenging task as it searches for complex and often nested structures describing specific relationships between multiple molecular entities, such as genes, proteins, or cellular components. It usually is implemented by a complex pipeline of individual tools to solve the different relation extraction subtasks. We present an alternative approach where the detection of relationships between entities is described uniformly as questions, which are iteratively answered by a question answering (QA) system based on the domain-specific language model SciBERT. This model outperforms two strong baselines in two biomedical event extraction corpora in a Knowledge Base Population setting, and also achieves competitive performance in BioNLP challenge evaluation settings.",
}
@inproceedings{kormilitzin-etal-2020-efficient,
    title = "An efficient representation of chronological events in medical texts",
    author = "Kormilitzin, Andrey  and
      Vaci, Nemanja  and
      Liu, Qiang  and
      Ni, Hao  and
      Nenadic, Goran  and
      Nevado-Holgado, Alejo",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.11",
    doi = "10.18653/v1/2020.louhi-1.11",
    pages = "97--103",
    abstract = "In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs). Clinical notes, which is a particular type of EHR data, are a rich source of information and practitioners often develop clever solutions how to maximise the sequential information contained in free-texts. We proposed a systematic methodology for learning from chronological events available in clinical notes. The proposed methodological path signature framework creates a non-parametric hierarchical representation of sequential events of any type and can be used as features for downstream statistical learning tasks. The methodology was developed and externally validated using the largest in the UK secondary care mental health EHR data on a specific task of predicting survival risk of patients diagnosed with Alzheimer{'}s disease. The signature-based model was compared to a common survival random forest model. Our results showed a 15.4{\%} increase of risk prediction AUC at the time point of 20 months after the first admission to a specialist memory clinic and the signature method outperformed the baseline mixed-effects model by 13.2 {\%}.",
}
@inproceedings{wright-bettner-etal-2020-defining,
    title = "Defining and Learning Refined Temporal Relations in the Clinical Narrative",
    author = "Wright-Bettner, Kristin  and
      Lin, Chen  and
      Miller, Timothy  and
      Bethard, Steven  and
      Dligach, Dmitriy  and
      Palmer, Martha  and
      Martin, James H.  and
      Savova, Guergana",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.12",
    doi = "10.18653/v1/2020.louhi-1.12",
    pages = "104--114",
    abstract = "We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative. We refined the THYME corpus annotations to more faithfully represent nuanced temporality and nuanced temporal-coreferential relations. The main contributions are in re-defining CONTAINS and OVERLAP relations into CONTAINS, CONTAINS-SUBEVENT, OVERLAP and NOTED-ON. We demonstrate that these refinements lead to substantial gains in learnability for state-of-the-art transformer models as compared to previously reported results on the original THYME corpus. We thus establish a baseline for the automatic extraction of these refined temporal relations. Although our study is done on clinical narrative, we believe it addresses far-reaching challenges that are corpus- and domain- agnostic.",
}
@inproceedings{sakakini-etal-2020-context,
    title = "Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains",
    author = "Sakakini, Tarek  and
      Lee, Jong Yoon  and
      Duri, Aditya  and
      Azevedo, Renato F.L.  and
      Sadauskas, Victor  and
      Gu, Kuangxiao  and
      Bhat, Suma  and
      Morrow, Dan  and
      Graumlich, James  and
      Walayat, Saqib  and
      Hasegawa-Johnson, Mark  and
      Huang, Thomas  and
      Willemsen-Dunlap, Ann  and
      Halpin, Donald",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.13",
    doi = "10.18653/v1/2020.louhi-1.13",
    pages = "115--126",
    abstract = "Healthcare systems have increased patients{'} exposure to their own health materials to enhance patients{'} health levels, but this has been impeded by patients{'} lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople{'}s comprehension of health material via a human subjects{'} study (n=160).",
}
@inproceedings{zhu-etal-2020-identifying,
    title = "Identifying Personal Experience Tweets of Medication Effects Using Pre-trained {R}o{BERT}a Language Model and Its Updating",
    author = "Zhu, Minghao  and
      Song, Youzhe  and
      Jin, Ge  and
      Jiang, Keyuan",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.14",
    doi = "10.18653/v1/2020.louhi-1.14",
    pages = "127--137",
    abstract = "Post-market surveillance, the practice of monitoring the safe use of pharmaceutical drugs is an important part of pharmacovigilance. Being able to collect personal experience related to pharmaceutical product use could help us gain insight into how the human body reacts to different medications. Twitter, a popular social media service, is being considered as an important alternative data source for collecting personal experience information with medications. Identifying personal experience tweets is a challenging classification task in natural language processing. In this study, we utilized three methods based on Facebook{'}s Robustly Optimized BERT Pretraining Approach (RoBERTa) to predict personal experience tweets related to medication use: the first one combines the pre-trained RoBERTa model with a classifier, the second combines the updated pre-trained RoBERTa model using a corpus of unlabeled tweets with a classifier, and the third combines the RoBERTa model that was trained with our unlabeled tweets from scratch with the classifier too. Our results show that all of these approaches outperform the published methods (Word Embedding + LSTM) in classification performance (p {\textless} 0.05), and updating the pre-trained language model with tweets related to medications could even improve the performance further.",
}
@inproceedings{liu-etal-2020-detecting,
    title = "Detecting Foodborne Illness Complaints in Multiple Languages Using {E}nglish Annotations Only",
    author = "Liu, Ziyi  and
      Karamanolakis, Giannis  and
      Hsu, Daniel  and
      Gravano, Luis",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.15",
    doi = "10.18653/v1/2020.louhi-1.15",
    pages = "138--146",
    abstract = "Health departments have been deploying text classification systems for the early detection of foodborne illness complaints in social media documents such as Yelp restaurant reviews. Current systems have been successfully applied for documents in English and, as a result, a promising direction is to increase coverage and recall by considering documents in additional languages, such as Spanish or Chinese. Training previous systems for more languages, however, would be expensive, as it would require the manual annotation of many documents for each new target language. To address this challenge, we consider cross-lingual learning and train multilingual classifiers using only the annotations for English-language reviews. Recent zero-shot approaches based on pre-trained multi-lingual BERT (mBERT) have been shown to effectively align languages for aspects such as sentiment. Interestingly, we show that those approaches are less effective for capturing the nuances of foodborne illness, our public health application of interest. To improve performance without extra annotations, we create artificial training documents in the target language through machine translation and train mBERT jointly for the source (English) and target language. Furthermore, we show that translating labeled documents to multiple languages leads to additional performance improvements for some target languages. We demonstrate the benefits of our approach through extensive experiments with Yelp restaurant reviews in seven languages. Our classifiers identify foodborne illness complaints in multilingual reviews from the Yelp Challenge dataset, which highlights the potential of our general approach for deployment in health departments.",
}
@inproceedings{jiang-etal-2020-detection,
    title = "Detection of Mental Health from {R}eddit via Deep Contextualized Representations",
    author = "Jiang, Zhengping  and
      Levitan, Sarah Ita  and
      Zomick, Jonathan  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.louhi-1.16",
    doi = "10.18653/v1/2020.louhi-1.16",
    pages = "147--156",
    abstract = "We address the problem of automatic detection of psychiatric disorders from the linguistic content of social media posts. We build a large scale dataset of Reddit posts from users with eight disorders and a control user group. We extract and analyze linguistic characteristics of posts and identify differences between diagnostic groups. We build strong classification models based on deep contextualized word representations and show that they outperform previously applied statistical models with simple linguistic features by large margins. We compare user-level and post-level classification performance, as well as an ensembled multiclass model.",
}
@proceedings{loresmt-2020-technologies,
    title = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    editor = "Karakanta, Alina  and
      Ojha, Atul Kr.  and
      Liu, Chao-Hong  and
      Abbott, Jade  and
      Ortega, John  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Lakew, Surafel Melaku  and
      Pirinen, Tommi A  and
      Malykh, Valentin  and
      Logacheva, Varvara  and
      Zhao, Xiaobing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.0",
}
@inproceedings{ortega-etal-2020-overcoming,
    title = "Overcoming Resistance: The Normalization of an {A}mazonian Tribal Language",
    author = "Ortega, John  and
      Castro-Mamani, Richard Alexander  and
      Montoya Samame, Jaime Rafael",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.1",
    pages = "1--13",
    abstract = "Languages can be considered endangered for many reasons. One of the principal reasons for endangerment is the disappearance of its speakers. Another, more identifiable reason, is the lack of written resources. We present an automated sub-segmentation system called AshMorph that deals with the morphology of an Amazonian tribal language called Ashaninka which is at risk of being endangered due to the lack of availability (or resistance) of native speakers and the absence of written resources. We show that by the use of a cross-lingual lexicon and finite state transducers we can increase accuracy by more than 30{\%} when compared to other modern sub-segmentation tools. Our results, made freely available on-line, are verified by an Ashaninka speaker and perform well in two distinct domains, everyday literary articles and the bible. This research serves as a first step in helping to preserve Ashaninka by offering a sub-segmentation process that can be used to normalize any Ashaninka text which will serve as input to a machine translation system for translation into other high-resource languages spoken by higher populated locations like Spanish and Portuguese in the case of Peru and Brazil where Ashaninka is mostly spoken.",
}
@inproceedings{baliber-etal-2020-bridging,
    title = "Bridging {P}hilippine Languages With Multilingual Neural Machine Translation",
    author = "Baliber, Renz Iver  and
      Cheng, Charibeth  and
      Adlaon, Kristine Mae  and
      Mamonong, Virgion",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.2",
    pages = "14--22",
    abstract = "The Philippines is home to more than 150 languages that is considered to be low-resourced even on its major languages. This results into a lack of pursuit in developing a translation system for the underrepresented languages. To simplify the process of developing translation system for multiple languages, and to aid in improving the translation quality of zero to low-resource languages, multilingual NMT became an active area of research. However, existing works in multilingual NMT disregards the analysis of a multilingual model on a closely related and low-resource language group in the context of pivot-based translation and zero-shot translation. In this paper, we benchmarked translation for several Philippine Languages, provided an analysis of a multilingual NMT system for morphologically rich and low-resource languages in terms of its effectiveness in translating zero-resource languages with zero-shot translations. To further evaluate the capability of the multilingual NMT model in translating unseen language pairs in training, we tested the model to translate between Tagalog and Cebuano and compared its performance with a simple NMT model that is directly trained on a parallel Tagalog and Cebuano data in which we showed that zero-shot translation outperforms a directly trained model in some instances, while utilizing English as a pivot language in translating outperform both approaches.",
}
@inproceedings{tapo-etal-2020-neural,
    title = "Neural Machine Translation for Extremely Low-Resource {A}frican Languages: A Case Study on {B}ambara",
    author = "Tapo, Allahsera Auguste  and
      Coulibaly, Bakary  and
      Diarra, S{\'e}bastien  and
      Homan, Christopher  and
      Kreutzer, Julia  and
      Luger, Sarah  and
      Nagashima, Arthur  and
      Zampieri, Marcos  and
      Leventhal, Michael",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.3",
    pages = "23--32",
    abstract = "Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires significant amounts of pre-processing. More than the linguistic situation of Bambara itself, the socio-cultural context within which Bambara speakers live poses challenges for automated processing of this language. In this paper, we present the first parallel data set for machine translation of Bambara into and from English and French and the first benchmark results on machine translation to and from Bambara. We discuss challenges in working with low-resource languages and propose strategies to cope with data scarcity in low-resource machine translation (MT).",
}
@inproceedings{ojha-etal-2020-findings,
    title = "Findings of the {L}o{R}es{MT} 2020 Shared Task on Zero-Shot for Low-Resource languages",
    author = "Ojha, Atul Kr.  and
      Malykh, Valentin  and
      Karakanta, Alina  and
      Liu, Chao-Hong",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.4",
    pages = "33--37",
    abstract = "This paper presents the findings of the LoResMT 2020 Shared Task on zero-shot translation for low resource languages. This task was organised as part of the 3rd Workshop on Technologies for MT of Low Resource Languages (LoResMT) at AACL-IJCNLP 2020. The focus was on the zero-shot approach as a notable development in Neural Machine Translation to build MT systems for language pairs where parallel corpora are small or even non-existent. The shared task experience suggests that back-translation and domain adaptation methods result in better accuracy for small-size datasets. We further noted that, although translation between similar languages is no cakewalk, linguistically distinct languages require more data to give better results.",
}
@inproceedings{laskar-etal-2020-zero,
    title = "Zero-Shot Neural Machine Translation: {R}ussian-{H}indi @{L}o{R}es{MT} 2020",
    author = "Laskar, Sahinur Rahman  and
      Khilji, Abdullah Faiz Ur Rahman  and
      Pakray, Partha  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.5",
    pages = "38--42",
    abstract = "Neural machine translation (NMT) is a widely accepted approach in the machine translation (MT) community, translating from one natural language to another natural language. Although, NMT shows remarkable performance in both high and low resource languages, it needs sufficient training corpus. The availability of a parallel corpus in low resource language pairs is one of the challenging tasks in MT. To mitigate this issue, NMT attempts to utilize a monolingual corpus to get better at translation for low resource language pairs. Workshop on Technologies for MT of Low Resource Languages (LoResMT 2020) organized shared tasks of low resource language pair translation using zero-shot NMT. Here, the parallel corpus is not used and only monolingual corpora is allowed. We have participated in the same shared task with our team name CNLP-NITS for the Russian-Hindi language pair. We have used masked sequence to sequence pre-training for language generation (MASS) with only monolingual corpus following the unsupervised NMT architecture. The evaluated results are declared at the LoResMT 2020 shared task, which reports that our system achieves the bilingual evaluation understudy (BLEU) score of 0.59, precision score of 3.43, recall score of 5.48, F-measure score of 4.22, and rank-based intuitive bilingual evaluation score (RIBES) of 0.180147 in Russian to Hindi translation. And for Hindi to Russian translation, we have achieved BLEU, precision, recall, F-measure, and RIBES score of 1.11, 4.72, 4.41, 4.56, and 0.026842 respectively.",
}
@inproceedings{kumar-etal-2020-unsupervised,
    title = "Unsupervised Approach for Zero-Shot Experiments: {B}hojpuri{--}{H}indi and {M}agahi{--}{H}indi@{L}o{R}es{MT} 2020",
    author = "Kumar, Amit  and
      Mundotiya, Rajesh Kumar  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.6",
    pages = "43--46",
    abstract = "This paper reports a Machine Translation (MT) system submitted by the NLPRL team for the Bhojpuri{--}Hindi and Magahi{--}Hindi language pairs at LoResMT 2020 shared task. We used an unsupervised domain adaptation approach that gives promising results for zero or extremely low resource languages. Task organizers provide the development and the test sets for evaluation and the monolingual data for training. Our approach is a hybrid approach of domain adaptation and back-translation. Metrics used to evaluate the trained model are BLEU, RIBES, Precision, Recall and F-measure. Our approach gives relatively promising results, with a wide range, of 19.5, 13.71, 2.54, and 3.16 BLEU points for Bhojpuri to Hindi, Magahi to Hindi, Hindi to Bhojpuri and Hindi to Magahi language pairs, respectively.",
}
@inproceedings{huidrom-lepage-2020-zero,
    title = "Zero-shot translation among {I}ndian languages",
    author = "Huidrom, Rudali  and
      Lepage, Yves",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.7",
    pages = "47--54",
    abstract = "Standard neural machine translation (NMT) allows a model to perform translation between a pair of languages. Multilingual neural machine translation (NMT), on the other hand, allows a model to perform translation between several language pairs, even between language pairs for which no sentences pair has been seen during training (zero-shot translation). This paper presents experiments with zero-shot translation on low resource Indian languages with a very small amount of data for each language pair. We first report results on balanced data over all considered language pairs. We then expand our experiments for additional three rounds by increasing the training data with 2,000 sentence pairs in each round for some of the language pairs. We obtain an increase in translation accuracy with its balanced data settings score multiplied by 7 for Manipuri to Hindi during Round-III of zero-shot translation.",
}
@inproceedings{ngo-etal-2020-improving,
    title = "Improving Multilingual Neural Machine Translation For Low-Resource Languages: {F}rench, {E}nglish - {V}ietnamese",
    author = "Ngo, Thi-Vinh  and
      Nguyen, Phuong-Thai  and
      Ha, Thanh-Le  and
      Dinh, Khac-Quy  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.8",
    pages = "55--61",
    abstract = "Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs{'} joint training. This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: French-Vietnamese and English-Vietnamese. The first strategy is about dynamical learning word similarity of tokens in the shared space among source languages while another one attempts to augment the translation ability of rare words through updating their embeddings during the training. Besides, we leverage monolingual data for multilingual MT systems to increase the amount of synthetic parallel corpora while dealing with the data sparsity problem. We have shown significant improvements of up to +1.62 and +2.54 BLEU points over the bilingual baseline systems for both language pairs and released our datasets for the research community.",
}
@inproceedings{laskar-etal-2020-enascorp1,
    title = "{E}n{A}s{C}orp1.0: {E}nglish-{A}ssamese Corpus",
    author = "Laskar, Sahinur Rahman  and
      Khilji, Abdullah Faiz Ur Rahman  and
      Pakray, Partha  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.9",
    pages = "62--68",
    abstract = "The corpus preparation is one of the important challenging task for the domain of machine translation especially in low resource language scenarios. Country like India where multiple languages exists, machine translation attempts to minimize the communication gap among people with different linguistic backgrounds. Although Google Translation covers automatic translation of various languages all over the world but it lags in some languages including Assamese. In this paper, we have developed EnAsCorp1.0, corpus of English-Assamese low resource pair where parallel and monolingual data are collected from various online sources. We have also implemented baseline systems with statistical machine translation and neural machine translation approaches for the same corpus.",
}
@inproceedings{singh-singh-2020-unsupervised,
    title = "Unsupervised Neural Machine Translation for {E}nglish and {M}anipuri",
    author = "Singh, Salam Michael  and
      Singh, Thoudam Doren",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.10",
    pages = "69--78",
    abstract = "Availability of bitext dataset has been a key challenge in the conventional machine translation system which requires surplus amount of parallel data. In this work, we devise an unsupervised neural machine translation (UNMT) system consisting of a transformer based shared encoder and language specific decoders using denoising autoencoder and backtranslation with an additional Manipuri side multiple test reference. We report our work on low resource setting for English (en) - Manipuri (mni) language pair and attain a BLEU score of 3.1 for en-mni and 2.7 for mni-en respectively. Subjective evaluation on translated output gives encouraging findings.",
}
@inproceedings{moran-lignos-2020-effective,
    title = "Effective Architectures for Low Resource Multilingual Named Entity Transliteration",
    author = "Moran, Molly  and
      Lignos, Constantine",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.11",
    pages = "79--86",
    abstract = "In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English. We experiment with different encoder-decoder combinations and evaluate them using accuracy, character error rate, and an F-measure based on longest continuous subsequences. We find that using a Transformer for the encoder and decoder performs best, improving accuracy by over 4 points compared to previous work. We explore whether manipulating the source text by adding macrolanguage flag tokens or pre-romanizing source strings can improve performance and find that neither manipulation has a positive effect. Finally, we analyze performance differences between the LSTM and Transformer encoders when using a Transformer decoder and find that the Transformer encoder is better able to handle insertions and substitutions when transliterating.",
}
@inproceedings{ahmadi-masoud-2020-towards,
    title = "Towards Machine Translation for the {K}urdish Language",
    author = "Ahmadi, Sina  and
      Masoud, Maraim",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.12",
    pages = "87--98",
    abstract = "Machine translation is the task of translating texts from one language to another using computers. It has been one of the major tasks in natural language processing and computational linguistics and has been motivating to facilitate human communication. Kurdish, an Indo-European language, has received little attention in this realm due to the language being less-resourced. Therefore, in this paper, we are addressing the main issues in creating a machine translation system for the Kurdish language, with a focus on the Sorani dialect. We describe the available scarce parallel data suitable for training a neural machine translation model for Sorani Kurdish-English translation. We also discuss some of the major challenges in Kurdish language translation and demonstrate how fundamental text processing tasks, such as tokenization, can improve translation performance.",
}
@inproceedings{lioudakis-etal-2020-ensemble,
    title = "{A}n Ensemble Method for Producing Word Representations focusing on the {G}reek Language",
    author = "Lioudakis, Michalis  and
      Outsios, Stamatis  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.13",
    pages = "99--107",
    abstract = "In this paper we present a new ensemble method, Continuous Bag-of-Skip-grams (CBOS), that produces high-quality word representations putting emphasis on the Greek language. The CBOS method combines the pioneering approaches for learning word representations: Continuous Bag-of-Words (CBOW) and Continuous Skip-gram. These methods are compared through intrinsic and extrinsic evaluation tasks on three different sources of data: the English Wikipedia corpus, the Greek Wikipedia corpus, and the Greek Web Content corpus. By comparing these methods across different tasks and datasets, it is evident that the CBOS method achieves state-of-the-art performance.",
}
@inproceedings{poncelas-etal-2020-using,
    title = "Using Multiple Subwords to Improve {E}nglish-{E}speranto Automated Literary Translation Quality",
    author = "Poncelas, Alberto  and
      Buts, Jan  and
      Hadley, James  and
      Way, Andy",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.14",
    pages = "108--117",
    abstract = "Building Machine Translation (MT) systems for low-resource languages remains challenging. For many language pairs, parallel data are not widely available, and in such cases MT models do not achieve results comparable to those seen with high-resource languages. When data are scarce, it is of paramount importance to make optimal use of the limited material available. To that end, in this paper we propose employing the same parallel sentences multiple times, only changing the way the words are split each time. For this purpose we use several Byte Pair Encoding models, with various merge operations used in their configuration. In our experiments, we use this technique to expand the available data and improve an MT system involving a low-resource language pair, namely English-Esperanto. As an additional contribution, we made available a set of English-Esperanto parallel data in the literary domain.",
}
@inproceedings{ramesh-etal-2020-investigating,
    title = "Investigating Low-resource Machine Translation for {E}nglish-to-{T}amil",
    author = "Ramesh, Akshai  and
      Balavadhani parthasa, Venkatesh  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.loresmt-1.15",
    pages = "118--125",
    abstract = "Statistical machine translation (SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT. Although deep neural models produce state-of-the-art results in many translation tasks, they are found to under-perform on resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and neural MT (NMT) on a rarely-tested low-resource language-pair, English-to-Tamil, taking a specialised data domain (software localisation) into consideration. In particular, we produce rankings of our MT systems via a social media platform-based human evaluation scheme, and demonstrate our findings in the low-resource domain-specific text translation task.",
}
@proceedings{lincr-2020-linguistic,
    title = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    editor = "Chersoni, Emmanuele  and
      Devereux, Barry  and
      Huang, Chu-Ren",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.0",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{turton-etal-2020-extrapolating,
    title = "Extrapolating Binder Style Word Embeddings to New Words",
    author = "Turton, Jacob  and
      Vinson, David  and
      Smith, Robert",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.1",
    pages = "1--8",
    abstract = "Word embeddings such as Word2Vec not only uniquely identify words but also encode important semantic information about them. However, as single entities they are difficult to interpret and their individual dimensions do not have obvious meanings. A more intuitive and interpretable feature space based on neural representations of words was presented by Binder and colleagues (2016) but is only available for a very limited vocabulary. Previous research (Utsumi, 2018) indicates that Binder features can be predicted for words from their embedding vectors (such as Word2Vec), but only looked at the original Binder vocabulary. This paper aimed to demonstrate that Binder features can effectively be predicted for a large number of new words and that the predicted values are sensible. The results supported this, showing that correlations between predicted feature values were consistent with those in the original Binder dataset. Additionally, vectors of predicted values performed comparatively to established embedding models in tests of word-pair semantic similarity. Being able to predict Binder feature space vectors for any number of new words opens up many uses not possible with the original vocabulary size.",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{alexeeva-etal-2020-towards,
    title = "Towards the First Dyslexic Font in {R}ussian",
    author = "Alexeeva, Svetlana  and
      Dobrego, Aleksandra  and
      Zubov, Vladislav",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.2",
    pages = "9--14",
    abstract = "Texts comprise a large part of visual information that we process every day, so one of the tasks of language science is to make them more accessible. However, often the text design process is focused on the font size, but not on its type; which might be crucial especially for the people with reading disabilities. The current paper represents a study on text accessibility and the first attempt to create a research-based accessible font for Cyrillic letters. This resulted in the dyslexic-specific font, LexiaD. Its design rests on the reduction of inter-letter similarity of the Russian alphabet. In evaluation stage, dyslexic and non-dyslexic children were asked to read sentences from the Children version of the Russian Sentence Corpus. We tested the readability of LexiaD compared to PT Sans and PT Serif fonts. The results showed that all children had some advantage in letter feature extraction and information integration while reading in LexiaD, but lexical access was improved when sentences were rendered in PT Sans or PT Serif. Therefore, in several aspects, LexiaD proved to be faster to read and could be recommended to use by dyslexics who have visual deficiency or those who struggle with text understanding resulting in re-reading.",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{hollenstein-etal-2020-towards,
    title = "Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing",
    author = "Hollenstein, Nora  and
      Barrett, Maria  and
      Beinborn, Lisa",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.3",
    pages = "15--27",
    abstract = "NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text. Human language processing data can be leveraged to increase the performance of models and to pursue explanatory research for a better understanding of the differences between human and machine language processing. We review recent studies leveraging different types of cognitive processing signals, namely eye-tracking, M/EEG and fMRI data recorded during language understanding. We discuss the role of cognitive data for machine learning-based NLP methods and identify fundamental challenges for processing pipelines. Finally, we propose practical strategies for using these types of cognitive signals to enhance NLP models.",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{nikiforova-etal-2020-language,
    title = "Language Models for Cloze Task Answer Generation in {R}ussian",
    author = "Nikiforova, Anastasia  and
      Pletenev, Sergey  and
      Sinitsyna, Daria  and
      Sorokin, Semen  and
      Lopukhina, Anastasia  and
      Howell, Nick",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.4",
    pages = "28--37",
    abstract = "Linguistics predictability is the degree of confidence in which language unit (word, part of speech, etc.) will be the next in the sequence. Experiments have shown that the correct prediction simplifies the perception of a language unit and its integration into the context. As a result of an incorrect prediction, language processing slows down. Currently, to get a measure of the language unit predictability, a neurolinguistic experiment known as a cloze task has to be conducted on a large number of participants. Cloze tasks are resource-consuming and are criticized by some researchers as an insufficiently valid measure of predictability. In this paper, we compare different language models that attempt to simulate human respondents{'} performance on the cloze task. Using a language model to create cloze task simulations would require significantly less time and conduct studies related to linguistic predictability.",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{watson-etal-2020-history,
    title = "Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment",
    author = "Watson, Liam  and
      Jurek-Loughrey, Anna  and
      Devereux, Barry  and
      Murphy, Brian",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.5",
    pages = "38--42",
    abstract = "While there is a rich literature on the tracking of sentiment and emotion in texts, modelling the emotional trajectory of longer narratives, such as literary texts, poses new challenges. Previous work in the area of sentiment analysis has focused on using information from within a sentence to predict a valence value for that sentence. We propose to explore the influence of previous sentences on the sentiment of a given sentence. In particular, we investigate whether information present in a history of previous sentences can be used to predict a valence value for the following sentence. We explored both linear and non-linear models applied with a range of different feature combinations. We also looked at different context history sizes to determine what range of previous sentence context was the most informative for our models. We establish a linear relationship between sentence context history and the valence value of the current sentence and demonstrate that sentences in closer proximity to the target sentence are more informative. We show that the inclusion of semantic word embeddings further enriches our model predictions.",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{stehwien-etal-2020-little,
    title = "The Little Prince in 26 Languages: Towards a Multilingual Neuro-Cognitive Corpus",
    author = "Stehwien, Sabrina  and
      Henke, Lena  and
      Hale, John  and
      Brennan, Jonathan  and
      Meyer, Lars",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.6",
    pages = "43--49",
    abstract = "We present the Le Petit Prince Corpus (LPPC), a multi-lingual resource for research in (computational) psycho- and neurolinguistics. The corpus consists of the children{'}s story The Little Prince in 26 languages. The dataset is in the process of being built using state-of-the-art methods for speech and language processing and electroencephalography (EEG). The planned release of LPPC dataset will include raw text annotated with dependency graphs in the Universal Dependencies standard, a near-natural-sounding synthetic spoken subset as well as EEG recordings. We will use this corpus for conducting neurolinguistic studies that generalize across a wide range of languages, overcoming typological constraints to traditional approaches. The planned release of the LPPC combines linguistic and EEG data for many languages using fully automatic methods, and thus constitutes a readily extendable resource that supports cross-linguistic and cross-disciplinary research.",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{belainine-etal-2020-towards,
    title = "Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks",
    author = "Belainine, Billal  and
      Sadat, Fatiha  and
      Boukadoum, Mounir  and
      Lounis, Hakim",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.7",
    pages = "50--58",
    abstract = "In sentiment analysis, several researchers have used emoji and hashtags as specific forms of training and supervision. Some emotions, such as fear and disgust, are underrepresented in the text of social media. Others, such as anticipation, are absent. This research paper proposes a new dataset for complex emotion detection using a combination of several existing corpora in order to represent and interpret complex emotions based on the Plutchik{'}s theory. Our experiments and evaluations confirm that using Transfer Learning (TL) with a rich emotional corpus, facilitates the detection of complex emotions in a four-dimensional space. In addition, the incorporation of the rule on the reverse emotions in the model{'}s architecture brings a significant improvement in terms of precision, recall, and F-score.",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@inproceedings{miklashevsky-2020-sensorimotor,
    title = "Sensorimotor Norms for 506 {R}ussian Nouns",
    author = "Miklashevsky, Alex",
    booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.lincr-1.8",
    pages = "59--60",
    abstract = "Embodied cognitive science suggested a number of variables describing our sensorimotor experience associated with different concepts: modality experience rating (i.e., relationship between words and images of a particular perceptive modality{---}visual, auditory, haptic etc.), manipulability (the necessity for an object to interact with human hands in order to perform its function), vertical spatial localization. According to the embodied cognition theory, these semantic variables capture our mental representations and thus should influence word learning, processing and production. However, it is not clear how these new variables are related to such traditional variables as imageability, age of acquisition (AoA) and word frequency. In the presented database, normative data on the modality (visual, auditory, haptic, olfactory, and gustatory) ratings, vertical spatial localization of the object, manipulability, imageability, age of acquisition, and subjective frequency for 506 Russian nouns are collected. Factor analysis revealed four factors: (1) visual and haptic modality ratings were combined with imageability, manipulability and AoA; (2) word length, frequency and AoA; (3) olfactory modality was united with gustatory; (4) spatial localization only was included in the fourth factor. The database is available online together with a publication describing the method of data collection and data parameters (Miklashevsky, 2018).",
    language = "English",
    ISBN = "979-10-95546-52-8",
}
@proceedings{lifelongnlp-2020-life,
    title = "Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems",
    editor = "Campbell, William M.  and
      Waibel, Alex  and
      Hakkani-Tur, Dilek  and
      Hazen, Timothy J.  and
      Kilgour, Kevin  and
      Cho, Eunah  and
      Kumar, Varun  and
      Glaude, Hadrien",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lifelongnlp-1.0",
}
@inproceedings{kim-2020-deep,
    title = "Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient",
    author = "Kim, Yekyung",
    booktitle = "Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lifelongnlp-1.1",
    pages = "1--8",
    abstract = "Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does not exploit the structural information of the unlabeled data. This leads to a sampling bias in the batch active learning setting, which selects several samples at once. In this work, we demonstrate that the amount of labeled training data can be reduced using active learning when it incorporates both uncertainty and diversity in the sequence labeling task. We examined the effects of our sequence-based approach by selecting weighted diverse in the gradient embedding approach across multiple tasks, datasets, models, and consistently outperform classic uncertainty-based sampling and diversity-based sampling.",
}
@inproceedings{huber-etal-2020-supervised,
    title = "Supervised Adaptation of Sequence-to-Sequence Speech Recognition Systems using Batch-Weighting",
    author = {Huber, Christian  and
      Hussain, Juan  and
      Nguyen, Tuan-Nam  and
      Song, Kaihang  and
      St{\"u}ker, Sebastian  and
      Waibel, Alexander},
    booktitle = "Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lifelongnlp-1.2",
    pages = "9--17",
    abstract = "When training speech recognition systems, one often faces the situation that sufficient amounts of training data for the language in question are available but only small amounts of data for the domain in question. This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech as training data, which is harder and more expensive to obtain than text data. In this paper we present experiments in adapting end-to-end speech recognition systems by a method which is called batch-weighting and which we contrast against regular fine-tuning, i.e., to continue to train existing neural speech recognition models on adaptation data. We perform experiments using theses techniques in adapting to topic, accent and vocabulary, showing that batch-weighting consistently outperforms fine-tuning. In order to show the generalization capabilities of batch-weighting we perform experiments in several languages, i.e., Arabic, English and German. Due to its relatively small computational requirements batch-weighting is a suitable technique for supervised life-long learning during the life-time of a speech recognition system, e.g., from user corrections.",
}
@inproceedings{kumar-etal-2020-data,
    title = "Data Augmentation using Pre-trained Transformer Models",
    author = "Kumar, Varun  and
      Choudhary, Ashutosh  and
      Cho, Eunah",
    booktitle = "Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lifelongnlp-1.3",
    pages = "18--26",
    abstract = "Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information.",
}
@proceedings{ldl-2020-linked,
    title = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    editor = "Ionov, Maxim  and
      McCrae, John P.  and
      Chiarcos, Christian  and
      Declerck, Thierry  and
      Bosque-Gil, Julia  and
      Gracia, Jorge",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.0",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{tittel-etal-2020-towards,
    title = "Towards an Ontology Based on Hallig-Wartburg{'}s Begriffssystem for Historical Linguistic Linked Data",
    author = "Tittel, Sabine  and
      Gillis-Webber, Frances  and
      Nannini, Alessandro A.",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.1",
    pages = "1--10",
    abstract = "To empower end users in searching for historical linguistic content with a performance that far exceeds the research functions offered by websites of, e.g., historical dictionaries, is undoubtedly a major advantage of (Linguistic) Linked Open Data ([L]LOD). An important aim of lexicography is to enable a language-independent, onomasiological approach, and the modelling of linguistic resources following the LOD paradigm facilitates the semantic mapping to ontologies making this approach possible. Hallig-Wartburg{'}s Begriffssystem (HW) is a well-known extra-linguistic conceptual system used as an onomasiological framework by many historical lexicographical and lexicological works. Published in 1952, HW has meanwhile been digitised. With proprietary XML data as the starting point, our goal is the transformation of HW into Linked Open Data in order to facilitate its use by linguistic resources modelled as LOD. In this paper, we describe the particularities of the HW conceptual model and the method of converting HW: We discuss two approaches, (i) the representation of HW in RDF using SKOS, the SKOS thesaurus extension, and XKOS, and (ii) the creation of a lightweight ontology expressed in OWL, based on the RDF/SKOS model. The outcome is illustrated with use cases of medieval Gascon, and Italian.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{mondaca-rau-2020-transforming,
    title = "Transforming the Cologne Digital {S}anskrit Dictionaries into {O}nto{L}ex-Lemon",
    author = "Mondaca, Francisco  and
      Rau, Felix",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.2",
    pages = "11--14",
    abstract = "The Cologne Digital Sanskrit Dictionaries (CDSD) is a large collection of complex digitized Sanskrit dictionaries, consisting of over thirty-five works, and is the most prominent collection of Sanskrit dictionaries worldwide. In this paper we evaluate two methods for transforming the CDSD into Ontolex-Lemon based on a modelling exercise. The first method that we evaluate consists of applying RDFa to the existent TEI-P5 files. The second method consists of transforming the TEI-encoded dictionaries into new files containing RDF triples modelled in OntoLex-Lemon. As a result of the modelling exercise we choose the second method: to transform TEI-encoded lexical data into Ontolex-Lemon by creating new files containing exclusively RDF triples.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{khan-2020-representing,
    title = "Representing Temporal Information in Lexical Linked Data Resources",
    author = "Khan, Fahad",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.3",
    pages = "15--22",
    abstract = "The increasing recognition of the utility of Linked Data as a means of publishing lexical resource has helped to underline the need for RDF based data models which have the flexibility and expressivity to be able to represent the most salient kinds of information contained in such resources as structured data, including, notably, information relating to time and the temporal dimension. In this article we describe a perdurantist approach to modelling diachronic lexical information which builds upon work which we have previously presented and which is based on the ontolex-lemon vocabulary. We present two extended examples, one taken from the Oxford English Dictionary, the other from a work on etymology, to show how our approach can handle different kinds of temporal information often found in lexical resources.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{virk-etal-2020-linguistic,
    title = "From Linguistic Descriptions to Language Profiles",
    author = {Virk, Shafqat Mumtaz  and
      Hammarstr{\"o}m, Harald  and
      Borin, Lars  and
      Forsberg, Markus  and
      Wichmann, S{\o}ren},
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.4",
    pages = "23--27",
    abstract = "Language catalogues and typological databases are two important types of resources containing different types of knowledge about the world{'}s natural languages. The former provide metadata such as number of speakers, location (in prose descriptions and/or GPS coordinates), language code, literacy, etc., while the latter contain information about a set of structural and functional attributes of languages. Given that both types of resources are developed and later maintained manually, there are practical limits as to the number of languages and the number of features that can be surveyed. We introduce the concept of a language profile, which is intended to be a structured representation of various types of knowledge about a natural language extracted semi-automatically from descriptive documents and stored at a central location. It has three major parts: (1) an introductory; (2) an attributive; and (3) a reference part, each containing different types of knowledge about a given natural language. As a case study, we develop and present a language profile of an example language. At this stage, a language profile is an independent entity, but in the future it is envisioned to become part of a network of language profiles connected to each other via various types of relations. Such a representation is expected to be suitable both for humans and machines to read and process for further deeper linguistic analyses and/or comparisons.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{di-buono-etal-2020-terme,
    title = "Terme-{\`a}-{LLOD}: Simplifying the Conversion and Hosting of Terminological Resources as Linked Data",
    author = "di Buono, Maria Pia  and
      Cimiano, Philipp  and
      Elahi, Mohammad Fazleh  and
      Grimm, Frank",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.5",
    pages = "28--35",
    abstract = "In recent years, there has been increasing interest in publishing lexicographic and terminological resources as linked data. The benefit of using linked data technologies to publish terminologies is that terminologies can be linked to each other, thus creating a cloud of linked terminologies that cross domains, languages and that support advanced applications that do not work with single terminologies but can exploit multiple terminologies seamlessly. We present Terme-{`}a-LLOD (TAL), a new paradigm for transforming and publishing terminologies as linked data which relies on a virtualization approach. The approach rests on a preconfigured virtual image of a server that can be downloaded and installed. We describe our approach to simplifying the transformation and hosting of terminological resources in the remainder of this paper. We provide a proof-of-concept for this paradigm showing how to apply it to the conversion of the well-known IATE terminology as well as to various smaller terminologies. Further, we discuss how the implementation of our paradigm can be integrated into existing NLP service infrastructures that rely on virtualization technology. While we apply this paradigm to the transformation and hosting of terminologies as linked data, the paradigm can be applied to any other resource format as well.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{abromeit-etal-2020-annohub,
    title = "Annohub {--} Annotation Metadata for Linked Data Applications",
    author = {Abromeit, Frank  and
      F{\"a}th, Christian  and
      Glaser, Luis},
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.6",
    pages = "36--44",
    abstract = "We introduce a new dataset for the Linguistic Linked Open Data (LLOD) cloud that will provide metadata about annotation and language information harvested from annotated language resources like corpora freely available on the internet. To our knowledge annotation metadata is not provided by any metadata provider, e.g. linghub, datahub or CLARIN so far. On the other hand, language metadata that is found on such portals is rarely provided in machine-readable form, especially as Linked Data. In this paper, we describe the harvesting process, content and structure of the new dataset and its application in the Lin|gu|is|tik portal, a research platform for linguists. Aside from that, we introduce tools for the conversion of XML encoded language resources to the CoNLL format. The generated RDF data as well as the XML-converter application are made public under an open license.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{salgado-etal-2020-challenges,
    title = "Challenges of Word Sense Alignment: {P}ortuguese Language Resources",
    author = "Salgado, Ana  and
      Ahmadi, Sina  and
      Sim{\~o}es, Alberto  and
      McCrae, John Philip  and
      Costa, Rute",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.7",
    pages = "45--51",
    abstract = "This paper reports on an ongoing task of monolingual word sense alignment in which a comparative study between the Portuguese Academy of Sciences Dictionary and the Dicion{\'a}rio Aberto is carried out in the context of the ELEXIS (European Lexicographic Infrastructure) project. Word sense alignment involves searching for matching senses within dictionary entries of different lexical resources and linking them, which poses significant challenges. The lexicographic criteria are not always entirely consistent within individual dictionaries and even less so across different projects where different options may have been assumed in terms of structure and especially wording techniques of lexicographic glosses. This hinders the task of matching senses. We aim to present our annotation workflow in Portuguese using the Semantic Web technologies. The results obtained are useful for the discussion within the community.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{fiorelli-stellato-2020-lime,
    title = "A Lime-Flavored {REST} {API} for Alignment Services",
    author = "Fiorelli, Manuel  and
      Stellato, Armando",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.8",
    pages = "52--60",
    abstract = "A practical alignment service should be flexible enough to handle the varied alignment scenarios that arise in the real world, while minimizing the need for manual configuration. MAPLE, an orchestration framework for ontology alignment, supports this goal by coordinating a few loosely coupled actors, which communicate and cooperate to solve a matching task using explicit metadata about the input ontologies, other available resources and the task itself. The alignment task is thus summarized by a report listing its characteristics and suggesting alignment strategies. The schema of the report is based on several metadata vocabularies, among which the Lime module of the OntoLex-Lemon model is particularly important, summarizing the lexical content of the input ontologies and describing external language resources that may be exploited for performing the alignment. In this paper, we propose a REST API that enables the participation of downstream alignment services in the process orchestrated by MAPLE, helping them self-adapt in order to handle heterogeneous alignment tasks and scenarios. The realization of this alignment orchestration effort has been performed through two main phases: we first described its API as an OpenAPI specification (a la API-first), which we then exploited to generate server stubs and compliant client libraries. Finally, we switched our focus to the integration of existing alignment systems, with one fully integrated system and an additional one being worked on, in the effort to propose the API as a valuable addendum to any system being developed.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{abgaz-2020-using,
    title = "Using {O}nto{L}ex-Lemon for Representing and Interlinking Lexicographic Collections of {B}avarian Dialects",
    author = "Abgaz, Yalemisew",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.9",
    pages = "61--69",
    abstract = {This paper describes the ongoing work in converting the lexicographic collection of a non-standard German language dataset (Bavarian Dialects) into a Linguistic Linked Open Data (LLOD) format. The collection is divided into two: questionnaire dataset (DB{\"O}) which contains details of the questionnaires, questions, collectors, paper slips etc., and the lexical dataset (WB{\"O}) which contains lexical entries (answers) collected in response to the questions. In its current form, the lexical dataset is available in a TEI/XML format separately from the questionnaire dataset. This paper presents the mapping of the lexical entries in the TEI/XML format into LLOD format using the Ontolex-Lemon model. The paper shows how the data in TEI/XML format is transformed into LLOD and produces a lexicon for Bavarian Dialects. It also presents the approach used to interlink the original questions with the lexical entries. The resulting lexicon complements the questionnaire dataset, which is already in a LLOD format, by semantically interlinking the original questions with the answers and vice-versa.},
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{bellandi-giovannetti-2020-involving,
    title = "Involving Lexicographers in the {LLOD} Cloud with {L}ex{O}, an Easy-to-use Editor of Lemon Lexical Resources",
    author = "Bellandi, Andrea  and
      Giovannetti, Emiliano",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.10",
    pages = "70--74",
    abstract = "In this contribution, we show LexO, a user-friendly web collaborative editor of lexical resources based on the lemon model. LexO has been developed in the context of Digital Humanities projects, in which a key point in the design of an editor was the ease of use by lexicographers with no skill in Linked Data or Semantic Web technologies. Though the tool already allows creating a lemon lexicon from scratch and lets a team of users work on it collaboratively, many developments are possible. The involvement of the LLOD community appears now crucial both to find new users and application fields where to test it, and, even more importantly, to understand in which way it should evolve.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{lee-etal-2020-supervised,
    title = "Supervised Hypernymy Detection in {S}panish through Order Embeddings",
    author = "Lee, Gun Woo  and
      Etcheverry, Mathias  and
      Fernandez Sanchez, Daniel  and
      Wonsever, Dina",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.11",
    pages = "75--81",
    abstract = "This paper addresses the task of supervised hypernymy detection in Spanish through an order embedding and using pretrained word vectors as input. Although the task has been widely addressed in English, there is not much work in Spanish, and according to our knowledge there is not any available dataset for supervised hypernymy detection in Spanish. We built a supervised hypernymy dataset for Spanish from WordNet and corpus statistics information, with different versions according to the lexical intersection between its partitions: random and lexical split. We show the results of using the resulting dataset within an order embedding consuming pretrained word vectors as input. We show the ability of pretrained word vectors to transfer learning to unseen lexical units according to the results in the lexical split dataset. To finish, we study the results of giving additional information in training time, such as, cohyponym links and instances extracted through patterns.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@inproceedings{nielsen-2020-lexemes,
    title = "Lexemes in {W}ikidata: 2020 status",
    author = "Nielsen, Finn",
    booktitle = "Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.ldl-1.12",
    pages = "82--86",
    abstract = "Wikidata now records data about lexemes, senses and lexical forms and exposes them as Linguistic Linked Open Data. Since lexemes in Wikidata was first established in 2018, this data has grown considerable in size. Links between lexemes in different languages can be made, e.g., through a derivation property or senses. We present some descriptive statistics about the lexemes of Wikidata, focusing on the multilingual aspects and show that there are still relatively few multilingual links.",
    language = "English",
    ISBN = "979-10-95546-36-8",
}
@proceedings{law-2020-linguistic,
    title = "Proceedings of the 14th Linguistic Annotation Workshop",
    editor = "Dipper, Stefanie  and
      Zeldes, Amir",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.0",
}
@inproceedings{miok-etal-2020-bayesian,
    title = "{B}ayesian Methods for Semi-supervised Text Annotation",
    author = "Miok, Kristian  and
      Pirs, Gregor  and
      Robnik-Sikonja, Marko",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.1",
    pages = "1--12",
    abstract = "Human annotations are an important source of information in the development of natural language understanding approaches. As under the pressure of productivity annotators can assign different labels to a given text, the quality of produced annotations frequently varies. This is especially the case if decisions are difficult, with high cognitive load, requires awareness of broader context, or careful consideration of background knowledge. To alleviate the problem, we propose two semi-supervised methods to guide the annotation process: a Bayesian deep learning model and a Bayesian ensemble method. Using a Bayesian deep learning method, we can discover annotations that cannot be trusted and might require reannotation. A recently proposed Bayesian ensemble method helps us to combine the annotators{'} labels with predictions of trained models. According to the results obtained from three hate speech detection experiments, the proposed Bayesian methods can improve the annotations and prediction performance of BERT models.",
}
@inproceedings{lek-etal-2020-provenance,
    title = "Provenance for Linguistic Corpora through Nanopublications",
    author = "Lek, Timo  and
      de Groot, Anna  and
      Kuhn, Tobias  and
      Morante, Roser",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.2",
    pages = "13--23",
    abstract = "Research in Computational Linguistics is dependent on text corpora for training and testing new tools and methodologies. While there exists a plethora of annotated linguistic information, these corpora are often not interoperable without significant manual work. Moreover, these annota-tions might have evolved into different versions, making it challenging for researchers to know the data{'}s provenance. This paper addresses this issue with a case study on event annotated corpora and by creating a new, more interoperable representation of this data in the form of nanopublications. We demonstrate how linguistic annotations from separate corpora can be reliably linked from the start, and thereby be accessed and queried as if they were a single dataset. We describe how such nanopublications can be created and demonstrate how SPARQL queries can be performed to extract interesting content from the new representations. The queries show that information of multiple corpora can be retrieved more easily and effectively because the information of different corpora is represented in a uniform data format.",
}
@inproceedings{andersson-etal-2020-sentiment,
    title = "A Sentiment-annotated Dataset of {E}nglish Causal Connectives",
    author = {Andersson, Marta  and
      Kurfal{\i}, Murathan  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.3",
    pages = "24--33",
    abstract = "This paper investigates the semantic prosody of three causal connectives: due to, owing to and because of in seven varieties of the English language. While research in the domain of English causality exists, we are not aware of studies that would cover the domain of causal connectives in English. Our claim is that connectives such as because of link two arguments, (at least) one of which will include a phrase that contributes to the interpretation of the relation as positive or negative, and hence define the prosody of the connective used. As our results demonstrate, the majority of the prosodies identified are negative for all three connectives; the proportions are stable across the varieties of English studied, and contrary to our expectations, we find no significant differences between the functions of the connectives and discourse preferences. Further, we investigate whether automatizing the sentiment annotation procedure via a simple language-model based classifier is possible. The initial results highlights the complexity of the task and the need for complicated systems, probably aided with other related datasets to achieve reasonable performance.",
}
@inproceedings{pamulapati-etal-2020-novel,
    title = "A Novel Annotation Schema for Conversational Humor: Capturing the Cultural Nuances in Kanyasulkam",
    author = "Pamulapati, Vaishnavi  and
      Purigilla, Gayatri  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.4",
    pages = "34--47",
    abstract = "Humor research is a multifaceted field that has led to a better understanding of humor{'}s psychological effects and the development of different theories of humor. This paper{'}s main objective is to develop a hierarchical schema for a fine-grained annotation of Conversational Humor. Based on the Benign Violation Theory, the benignity or non-benignity of the interlocutor{'}s intentions is included within the framework. Under the categories mentioned above, in addition to different types of humor, the techniques utilized by these types are identified. Furthermore, a prominent play from Telugu, Kanyasulkam, is annotated to substantiate the work across cultures at multiple levels. The inter-annotator agreement is calculated to assess the accuracy and validity of the dataset. An in-depth analysis of the disagreement is performed to understand the subjectivity of humor better.",
}
@inproceedings{andresen-etal-2020-modeling,
    title = "Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty",
    author = "Andresen, Melanie  and
      Vauth, Michael  and
      Zinsmeister, Heike",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.5",
    pages = "48--59",
    abstract = "Most annotation efforts assume that annotators will agree on labels, if the annotation categories are well-defined and documented in annotation guidelines. However, this is not always true. For instance, content-related questions such as {`}Is this sentence about topic X?{'} are unlikely to elicit the same answer from all annotators. Additional specifications in the guidelines are helpful to some extent, but can soon get overspecified by rules that cannot be justified by a research question. In this study, we model the semantic category {`}illness{'} and its use in a gradual way. For this purpose, we (i) ask many annotators (30 votes per item, 960 items) for their opinion in a crowdsourcing experiment, (ii) ask annotators to indicate their certainty with respect to their annotation, and (iii) compare this across two different text types. We show that results of multiple annotations and average annotator certainty correlate, but many ambiguities can only be captured if several people contribute. The annotated data allow us to filter for sentences with high or low agreement and analyze causes of disagreement, thus getting a better understanding of people{'}s perception of illness{---}as an example of a semantic category{---}as well as of the content of our annotated texts.",
}
@inproceedings{beck-etal-2020-representation,
    title = "Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias",
    author = "Beck, Christin  and
      Booth, Hannah  and
      El-Assady, Mennatallah  and
      Butt, Miriam",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.6",
    pages = "60--73",
    abstract = "The development of linguistic corpora is fraught with various problems of annotation and representation. These constitute a very real challenge for the development and use of annotated corpora, but as yet not much literature exists on how to address the underlying problems. In this paper, we identify and discuss five sources of representation problems, which are independent though interrelated: ambiguity, variation, uncertainty, error and bias. We outline and characterize these sources, discussing how their improper treatment can have stark consequences for research outcomes. Finally, we discuss how an adequate treatment can inform corpus-related linguistic research, both computational and theoretical, improving the reliability of research results and NLP models, as well as informing the more general reproducibility issue.",
}
@inproceedings{chau-etal-2020-understanding,
    title = "Understanding the Tradeoff between Cost and Quality of Expert Annotations for Keyphrase Extraction",
    author = "Chau, Hung  and
      Balaneshin, Saeid  and
      Liu, Kai  and
      Linda, Ondrej",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.7",
    pages = "74--86",
    abstract = "Generating expert ground truth annotations of documents can be a very expensive process. However, such annotations are essential for training domain-specific keyphrase extraction models, especially when utilizing data-intensive deep learning models in unique domains such as real-estate. Therefore, it is critical to optimize the manual annotation process to maximize the quality of the annotations while minimizing the cost of manual labor. To address this need, we explore multiple annotation strategies including self-review and peer-review as well as various methods of resolving annotator disagreements. We evaluate these annotation strategies with respect to their cost and on the task of learning keyphrase extraction models applied with an experimental dataset in the real-estate domain. The results demonstrate that different annotation strategies should be considered depending on specific metrics such as precision and recall.",
}
@inproceedings{harashima-hiramatsu-2020-cookpad,
    title = "Cookpad Parsed Corpus: Linguistic Annotations of {J}apanese Recipes",
    author = "Harashima, Jun  and
      Hiramatsu, Makoto",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.8",
    pages = "87--92",
    abstract = "It has become increasingly common for people to share cooking recipes on the Internet. Along with the increase in the number of shared recipes, there have been corresponding increases in recipe-related studies and datasets. However, there are still few datasets that provide linguistic annotations for the recipe-related studies even though such annotations should form the basis of the studies. This paper introduces a novel recipe-related dataset, named Cookpad Parsed Corpus, which contains linguistic annotations for Japanese recipes. We randomly extracted 500 recipes from the largest recipe-related dataset, the Cookpad Recipe Dataset, and annotated 4; 738 sentences in the recipes with morphemes, named entities, and dependency relations. This paper also reports benchmark results on our corpus for Japanese morphological analysis, named entity recognition, and dependency parsing. We show that there is still room for improvement in the analyses of recipes.",
}
@inproceedings{nordhoff-2020-modelling,
    title = "Modelling and Annotating Interlinear Glossed Text from 280 Different Endangered Languages as Linked Data with {LIGT}",
    author = "Nordhoff, Sebastian",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.9",
    pages = "93--104",
    abstract = "This paper reports on the harvesting, analysis, and enrichment of 20k documents from 4 different endangered language archives in 300 different low-resource languages. The documents are heterogeneous as to their provenance (holding archive, language, geographical area, creator) and internal structure (annotation types, metalanguages), but they have the ELAN-XML format in common. Typical annotations include sentence-level translations, morpheme-segmentation, morpheme-level translations, and parts-of-speech. The ELAN-format gives a lot of freedom to document creators, and hence the data set is very heterogeneous. We use regularities in the ELAN format to arrive at a common internal representation of sentences, words, and morphemes, with translations into one or more additional languages. Building upon the paradigm of Linguistic Linked Open Data (LLOD, Chiarcos, Nordhoff, et al. 2012), the document elements receive unique identifiers and are linked to other resources such as Glottolog for languages, Wikidata for semantic concepts, and the Leipzig Glossing Rules list for category abbreviations. We provide an RDF export in the LIGT format (Chiarcos {\&} Ionov 2019), enabling uniform and interoperable access with some semantic enrichments to a formerly disparate resource type difficult to access. Two use cases (semantic search and colexification) are presented to show the viability of the approach.",
}
@inproceedings{kranzlein-etal-2020-pastrie,
    title = "{PASTRIE}: A Corpus of Prepositions Annotated with Supersense Tags in {R}eddit International {E}nglish",
    author = "Kranzlein, Michael  and
      Manning, Emma  and
      Peng, Siyao  and
      Wein, Shira  and
      Arora, Aryaman  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.10",
    pages = "105--116",
    abstract = "We present the Prepositions Annotated with Supsersense Tags in Reddit International English ({``}PASTRIE{''}) corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish. The annotations are comprehensive, covering all preposition types and tokens in the sample. Along with the corpus, we provide analysis of distributional patterns across the included L1s and a discussion of the influence of L1s on L2 preposition choice.",
}
@inproceedings{gessler-etal-2020-supersense,
    title = "Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions",
    author = "Gessler, Luke  and
      Wein, Shira  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.11",
    pages = "117--126",
    abstract = "Prepositional supersense annotation is time-consuming and requires expert training. Here, we present two sensible methods for obtaining prepositional supersense annotations indirectly by eliciting surface substitution and similarity judgments. Four pilot studies suggest that both methods have potential for producing prepositional supersense annotations that are comparable in quality to expert annotations.",
}
@inproceedings{hwang-etal-2020-sprucing,
    title = "Sprucing up Supersenses: Untangling the Semantic Clusters of Accompaniment and Purpose",
    author = "Hwang, Jena D.  and
      Schneider, Nathan  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.12",
    pages = "127--137",
    abstract = "We reevaluate an existing adpositional annotation scheme with respect to two thorny semantic domains: accompaniment and purpose. {`}Accompaniment{'} broadly speaking includes two entities situated together or participating in the same event, while {`}purpose{'} broadly speaking covers the desired outcome of an action, the intended use or evaluated use of an entity, and more. We argue the policy in the SNACS scheme for English should be recalibrated with respect to these clusters of interrelated meanings without adding complexity to the overall scheme. Our analysis highlights tradeoffs in lumping vs. splitting decisions as well as the flexibility afforded by the construal analysis.",
}
@inproceedings{burchell-etal-2020-querent,
    title = "Querent Intent in Multi-Sentence Questions",
    author = "Burchell, Laurie  and
      Chi, Jie  and
      Hosking, Tom  and
      Markl, Nina  and
      Webber, Bonnie",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.13",
    pages = "138--147",
    abstract = "Multi-sentence questions (MSQs) are sequences of questions connected by relations which, unlike sequences of standalone questions, need to be answered as a unit. Following Rhetorical Structure Theory (RST), we recognise that different {``}question discourse relations{''} between the subparts of MSQs reflect different speaker intents, and consequently elicit different answering strategies. Correctly identifying these relations is therefore a crucial step in automatically answering MSQs. We identify five different types of MSQs in English, and define five novel relations to describe them. We extract over 162,000 MSQs from Stack Exchange to enable future research. Finally, we implement a high-precision baseline classifier based on surface features.",
}
@inproceedings{sanguinetti-etal-2020-annotating,
    title = "Annotating Errors and Emotions in Human-Chatbot Interactions in {I}talian",
    author = "Sanguinetti, Manuela  and
      Mazzei, Alessandro  and
      Patti, Viviana  and
      Scalerandi, Marco  and
      Mana, Dario  and
      Simeoni, Rossana",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.14",
    pages = "148--159",
    abstract = "This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the {``}emotional load{''} of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall.",
}
@inproceedings{mckee-etal-2020-towards,
    title = "Towards a Standardized, Fine-grained Manual Annotation Protocol for Verbal Fluency Data",
    author = {McKee, Gabriel  and
      Macoir, Jo{\"e}l  and
      Gagnon, Lydia  and
      Tremblay, Pascale},
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.15",
    pages = "160--166",
    abstract = "We propose a new method for annotating verbal fluency data, which allows the reliable detection of the age-related decline of lexical access capacity. The main innovation is that annotators should inferentially assess the intention of the speaker when producing a word form during a verbal fluency test. Our method correlates probable speaker inten-tions such as {``}intended as a valid answer{''} or {``}intended as a meta-comment{''} with lin-guistic features such as word intensity (e.g. reduced intensity suggests private speech) and syntactic integration. The annotation scheme can be implemented with high reliabil-ity, and minimal linguistic training. When fluency data are annotated using this scheme, a relation between fluency and age emerges; this is in contrast to a strict implementation of the traditional method of annotating verbal fluency data, which has no way of deal-ing with score-confounding phenomena because it force-groups all verbal fluency pro-ductions {--}regardless of speaker intention{---} into one of three taxonomic groups (i.e. val-id answers, perseverations, and intrusions). The traditional lack of fine-grained annota-tion units is especially problematic when analyzing the qualitatively distinct fluency da-ta of older participants and may cause studies to miss the relation between lexical access capacity and age.",
}
@inproceedings{muller-2020-pymmax2,
    title = "py{MMAX}2: Deep Access to {MMAX}2 Projects from Python",
    author = {M{\"u}ller, Mark-Christoph},
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.16",
    pages = "167--173",
    abstract = "pyMMAX2 is an API for processing MMAX2 stand-off annotation data in Python. It provides a lightweight basis for the development of code which opens up the Java- and XML-based ecosystem of MMAX2 for more recent, Python-based NLP and data science methods. While pyMMAX2 is pure Python, and most functionality is implemented from scratch, the API re-uses the complex implementation of the essential business logic for MMAX2 annotation schemes by interfacing with the original MMAX2 Java libraries. pyMMAX2 is available for download at http://github.com/nlpAThits/pyMMAX2.",
}
@inproceedings{luu-malamud-2020-annotating,
    title = "Annotating Coherence Relations for Studying Topic Transitions in Social Talk",
    author = "Luu, Alex  and
      Malamud, Sophia A.",
    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.law-1.17",
    pages = "174--179",
    abstract = "This study develops the strand of research on topic transitions in social talk which aims to gain a better understanding of interlocutors{'} conversational goals. Lưu and Malamud (2020) proposed that one way to identify such transitions is to annotate coherence relations, and then to identify utterances potentially expressing new topics as those that fail to participate in these relations. This work validates and refines their suggested annotation methodology, focusing on annotating most prominent coherence relations in face-to-face social dialogue. The result is a publicly accessible gold standard corpus with efficient and reliable annotation, whose broad coverage provides a foundation for future steps of identifying and classifying new topic utterances.",
}
@proceedings{latechclfl-2020-joint,
    title = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    editor = "DeGaetano, Stefania  and
      Kazantseva, Anna  and
      Reiter, Nils  and
      Szpakowicz, Stan",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.0",
}
@inproceedings{besnier-2020-history,
    title = "History to Myths: Social Network Analysis for Comparison of Stories over Time",
    author = "Besnier, Cl{\'e}ment",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.1",
    pages = "1--9",
    abstract = {We discuss on how related stories can be compared by their characters. We investigate character graphs, or social networks, in order to measure evolution of character importance over time. To illustrate this, we chose the Siegfried-Sigurd myth that may come from a Merovingian king named Sigiberthus. The Nibelungenlied, the V{\"o}lsunga saga and the History of the Franks are the three resources used.},
}
@inproceedings{ortmann-2020-automatic,
    title = "Automatic Topological Field Identification in (Historical) {G}erman Texts",
    author = "Ortmann, Katrin",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.2",
    pages = "10--18",
    abstract = "For the study of certain linguistic phenomena and their development over time, large amounts of textual data must be enriched with relevant annotations. Since the manual creation of such annotations requires a lot of effort, automating the process with NLP methods would be convenient. But the required amounts of training data are usually not available for non-standard or historical language. The present study investigates whether models trained on modern newspaper text can be used to automatically identify topological fields, i.e. syntactic structures, in different modern and historical German texts. The evaluation shows that, in general, it is possible to transfer a parser model to other registers or time periods with overall F1-scores {\textgreater}92{\%}. However, an error analysis makes clear that additional rules and domain-specific training data would be beneficial if sentence structures differ significantly from the training data, e.g. in the case of Early New High German.",
}
@inproceedings{zeldes-etal-2020-exhaustive,
    title = "Exhaustive Entity Recognition for {C}optic: Challenges and Solutions",
    author = "Zeldes, Amir  and
      Martin, Lance  and
      Tu, Sichang",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.3",
    pages = "19--28",
    abstract = "Entity recognition provides semantic access to ancient materials in the Digital Humanities: it exposes people and places of interest in texts that cannot be read exhaustively, facilitates linking resources and can provide a window into text contents, even for texts with no translations. In this paper we present entity recognition for Coptic, the language of Hellenistic era Egypt. We evaluate NLP approaches to the task and lay out difficulties in applying them to a low-resource, morphologically complex language. We present solutions for named and non-named nested entity recognition and semi-automatic entity linking to Wikipedia, relying on robust dependency parsing, feature-based CRF models, and hand-crafted knowledge base resources, enabling high accuracy NER with orders of magnitude less data than those used for high resource languages. The results suggest avenues for research on other languages in similar settings.",
}
@inproceedings{amin-burghardt-2020-survey,
    title = "A Survey on Approaches to Computational Humor Generation",
    author = "Amin, Miriam  and
      Burghardt, Manuel",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.4",
    pages = "29--41",
    abstract = "We provide a comprehensive overview of existing systems for the computational generation of verbal humor in the form of jokes and short humorous texts. Considering linguistic humor theories, we analyze the systematic strengths and drawbacks of the different approaches. In addition, we show how the systems have been evaluated so far and propose two evaluation criteria: humorousness and complexity. From our analysis of the field, we conclude new directions for the advancement of computational humor generation.",
}
@inproceedings{banar-etal-2020-neural,
    title = "Neural Machine Translation of Artwork Titles Using Iconclass Codes",
    author = "Banar, Nikolay  and
      Daelemans, Walter  and
      Kestemont, Mike",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.5",
    pages = "42--51",
    abstract = "We investigate the use of Iconclass in the context of neural machine translation for NL{\textless}-{\textgreater}EN artwork titles. Iconclass is a widely used iconographic classification system used in the cultural heritage domain to describe and retrieve subjects represented in the visual arts. The resource contains keywords and definitions to encode the presence of objects, people, events and ideas depicted in artworks, such as paintings. We propose a simple concatenation approach that improves the quality of automatically generated title translations for artworks, by leveraging textual information extracted from Iconclass. Our results demonstrate that a neural machine translation system is able to exploit this metadata to boost the translation performance of artwork titles. This technology enables interesting applications of machine learning in resource-scarce domains in the cultural sector.",
}
@inproceedings{schaefer-neudecker-2020-two,
    title = "A Two-Step Approach for Automatic {OCR} Post-Correction",
    author = "Schaefer, Robin  and
      Neudecker, Clemens",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.6",
    pages = "52--57",
    abstract = "The quality of Optical Character Recognition (OCR) is a key factor in the digitisation of historical documents. OCR errors are a major obstacle for downstream tasks and have hindered advances in the usage of the digitised documents. In this paper we present a two-step approach to automatic OCR post-correction. The first component is responsible for detecting erroneous sequences in a set of OCRed texts, while the second is designed for correcting OCR errors in them. We show that applying the preceding detection model reduces both the character error rate (CER) compared to a simple one-step correction model and the amount of falsely changed correct characters.",
}
@inproceedings{liebl-burghardt-2020-shakespeare,
    title = "{``}Shakespeare in the Vectorian Age{''} {--} An evaluation of different word embeddings and {NLP} parameters for the detection of Shakespeare quotes",
    author = "Liebl, Bernhard  and
      Burghardt, Manuel",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.7",
    pages = "58--68",
    abstract = "In this paper we describe an approach for the computer-aided identification of Shakespearean intertextuality in a corpus of contemporary fiction. We present the Vectorian, which is a framework that implements different word embeddings and various NLP parameters. The Vectorian works like a search engine, i.e. a Shakespeare phrase can be entered as a query, the underlying collection of fiction books is then searched for the phrase and the passages that are likely to contain the phrase, either verbatim or as a paraphrase, are presented in a ranked results list. While the Vectorian can be used via a GUI, in which many different parameters can be set and combined manually, in this paper we present an ablation study that automatically evaluates different embedding and NLP parameter combinations against a ground truth. We investigate the behavior of different parameters during the evaluation and discuss how our results may be used for future studies on the detection of Shakespearean intertextuality.",
}
@inproceedings{dejean-meunier-2020-vital,
    title = "Vital Records: Uncover the past from historical handwritten records",
    author = "Dejean, Herve  and
      Meunier, Jean-Luc",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.8",
    pages = "69--73",
    abstract = "We present Vital Records, a demonstrator based on deep-learning approaches to handwritten-text recognition, table processing and information extraction, which enables data from century-old documents to be parsed and analysed, making it possible to explore death records in space and time. This demonstrator provides a user interface for browsing and visualising data extracted from 80,000 handwritten pages of tabular data.",
}
@inproceedings{bagga-piper-2020-measuring,
    title = "Measuring the Effects of Bias in Training Data for Literary Classification",
    author = "Bagga, Sunyam  and
      Piper, Andrew",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.9",
    pages = "74--84",
    abstract = "Downstream effects of biased training data have become a major concern of the NLP community. How this may impact the automated curation and annotation of cultural heritage material is currently not well known. In this work, we create an experimental framework to measure the effects of different types of stylistic and social bias within training data for the purposes of literary classification, as one important subclass of cultural material. Because historical collections are often sparsely annotated, much like our knowledge of history is incomplete, researchers often cannot know the underlying distributions of different document types and their various sub-classes. This means that bias is likely to be an intrinsic feature of training data when it comes to cultural heritage material. Our aim in this study is to investigate which classification methods may help mitigate the effects of different types of bias within curated samples of training data. We find that machine learning techniques such as BERT or SVM are robust against reproducing the different kinds of bias within our test data, except in the most extreme cases. We hope that this work will spur further research into the potential effects of bias within training data for other cultural heritage material beyond the study of literature.",
}
@inproceedings{korre-pavlopoulos-2020-errant,
    title = "{ERRANT}: Assessing and Improving Grammatical Error Type Classification",
    author = "Korre, Katerina  and
      Pavlopoulos, John",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.10",
    pages = "85--89",
    abstract = "Grammatical Error Correction (GEC) is the task of correcting different types of errors in written texts. To manage this task, large amounts of annotated data that contain erroneous sentences are required. This data, however, is usually annotated according to each annotator{'}s standards, making it difficult to manage multiple sets of data at the same time. The recently introduced Error Annotation Toolkit (ERRANT) tackled this problem by presenting a way to automatically annotate data that contain grammatical errors, while also providing a standardisation for annotation. ERRANT extracts the errors and classifies them into error types, in the form of an edit that can be used in the creation of GEC systems, as well as for grammatical error analysis. However, we observe that certain errors are falsely or ambiguously classified. This could obstruct any qualitative or quantitative grammatical error type analysis, as the results would be inaccurate. In this work, we use a sample of the FCE coprus (Yannakoudakis et al., 2011) for secondary error type annotation and we show that up to 39{\%} of the annotations of the most frequent type should be re-classified. Our corrections will be publicly released, so that they can serve as the starting point of a broader, collaborative, ongoing correction process.",
}
@inproceedings{dennis-henderson-etal-2020-life,
    title = "Life still goes on: Analysing {A}ustralian {WW}1 Diaries through Distant Reading",
    author = "Dennis-Henderson, Ashley  and
      Roughan, Matthew  and
      Mitchell, Lewis  and
      Tuke, Jonathan",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.11",
    pages = "90--104",
    abstract = "An increasing amount of historic data is now available in digital (text) formats. This gives quantitative researchers an opportunity to use distant reading techniques, as opposed to traditional close reading, in order to analyse larger quantities of historic data. Distant reading allows researchers to view overall patterns within the data and reduce researcher bias. One such data set that has recently been transcribed is a collection of over 500 Australian World War I (WW1) diaries held by the State Library of New South Wales. Here we apply distant reading techniques to this corpus to understand what soldiers wrote about and how they felt over the course of the war. Extracting dates accurately is important as it allows us to perform our analysis over time, however, it is very challenging due to the variety of date formats and abbreviations diarists use. But with that data, topic modelling and sentiment analysis can then be applied to show trends, for instance, that despite the horrors of war, Australians in WW1 primarily wrote about their everyday routines and experiences. Our results detail some of the challenges likely to be encountered by quantitative researchers intending to analyse historical texts, and provide some approaches to these issues.",
}
@inproceedings{kurfali-wiren-2020-zero,
    title = "Zero-shot cross-lingual identification of direct speech using distant supervision",
    author = "Kurfal{\i}, Murathan  and
      Wir{\'e}n, Mats",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.12",
    pages = "105--111",
    abstract = "Prose fiction typically consists of passages alternating between the narrator{'}s telling of the story and the characters{'} direct speech in that story. Detecting direct speech is crucial for the downstream analysis of narrative structure, and may seem easy at first thanks to quotation marks. However, typographical conventions vary across languages, and as a result, almost all approaches to this problem have been monolingual. In contrast, the aim of this paper is to provide a multilingual method for identifying direct speech. To this end, we created a training corpus by using a set of heuristics to automatically find texts where quotation marks appear sufficiently consistently. We then removed the quotation marks and developed a sequence classifier based on multilingual-BERT which classifies each token as belonging to narration or speech. Crucially, by training the classifier with the quotation marks removed, it was forced to learn the linguistic characteristics of direct speech rather than the typography of quotation marks. The results in the zero-shot setting of the proposed model are comparable to the strong supervised baselines, indicating that this is a feasible approach.",
}
@inproceedings{hagen-etal-2020-twenty,
    title = "Twenty-two Historical Encyclopedias Encoded in {TEI}: a New Resource for the Digital Humanities",
    author = "Hagen, Thora  and
      Ketzan, Erik  and
      Jannidis, Fotis  and
      Witt, Andreas",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.13",
    pages = "112--120",
    abstract = "This paper accompanies the corpus publication of EncycNet, a novel XML/TEI annotated corpus of 22 historical German encyclopedias from the early 18th to early 20th century. We describe the creation and annotation of the corpus, including the rationale for its development, suggested methodology for TEI annotation, possible use cases and future work. While many well-developed annotation standards for lexical resources exist, none can adequately model the encyclopedias at hand, and we therefore suggest how the TEI Lex-0 standard may be modified with additional guidelines for the annotation of historical encyclopedias. As the digitization and annotation of historical encyclopedias are settling on TEI as the de facto standard, our methodology may inform similar projects.",
}
@inproceedings{van-cranenburgh-koolen-2020-results,
    title = "Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments",
    author = "van Cranenburgh, Andreas  and
      Koolen, Corina",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.14",
    pages = "121--126",
    abstract = "It is an open question to what extent perceptions of literary quality are derived from text-intrinsic versus social factors. While supervised models can predict literary quality ratings from textual factors quite successfully, as shown in the Riddle of Literary Quality project (Koolen et al., 2020), this does not prove that social factors are not important, nor can we assume that readers make judgments on literary quality in the same way and based on the same information as machine learning models. We report the results of a pilot study to gauge the effect of textual features on literary ratings of Dutch-language novels by participants in a controlled experiment with 48 participants. In an exploratory analysis, we compare the ratings to those from the large reader survey of the Riddle in which social factors were not excluded, and to machine learning predictions of those literary ratings. We find moderate to strong correlations of questionnaire ratings with the survey ratings, but the predictions are closer to the survey ratings. Code and data: https://github.com/andreasvc/litquest",
}
@inproceedings{kubis-2020-geometric,
    title = "Geometric Deep Learning Models for Linking Character Names in Novels",
    author = "Kubis, Marek",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.15",
    pages = "127--132",
    abstract = "The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.",
}
@inproceedings{poibeau-etal-2020-sonnet,
    title = "Sonnet Combinatorics with {O}u{P}o{C}o",
    author = "Poibeau, Thierry  and
      Maignant, Myl{\`e}ne  and
      M{\'e}lanie-Becquet, Fr{\'e}d{\'e}rique  and
      Plancq, Cl{\'e}ment  and
      Raffard, Matthieu  and
      Roussel, Mathilde",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.16",
    pages = "133--137",
    abstract = "In this paper, we describe OuPoCo, a system producing new sonnets by recombining verses from existing sonnets, following an idea that Queneau described in his book {``}Cent Mille Milliards de po{\`e}mes, Gallimard{''}, 1961. We propose to demonstrate different outputs of our implementation (a Web site, a Twitter bot and a specifically developed device, called {`}La Bo{\^\i}te {\`a} po{\'e}sie{'}) based on a corpus of 19th century French poetry. Our goal is to make people interested in poetry again, by giving access to automatically produced sonnets through original and entertaining channels and devices.",
}
@inproceedings{yeruva-etal-2020-interpretation,
    title = "Interpretation of Sentiment Analysis in Aeschylus{'}s {G}reek Tragedy",
    author = "Yeruva, Vijaya Kumari  and
      ChandraShekar, Mayanka  and
      Lee, Yugyung  and
      Rydberg-Cox, Jeff  and
      Blanton, Virginia  and
      Oyler, Nathan A",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.17",
    pages = "138--146",
    abstract = "Recent advancements in NLP and machine learning have created unique challenges and opportunities for digital humanities research. In particular, there are ample opportunities for NLP and machine learning researchers to analyze data from literary texts and to broaden our understanding of human sentiment in classical Greek tragedy. In this paper, we will explore the challenges and benefits from the human and machine collaboration for sentiment analysis in Greek tragedy and address some open questions related to the collaborative annotation for the sentiments in literary texts. We focus primarily on (i) an analysis of the challenges in sentiment analysis tasks for humans and machines, and (ii) whether consistent annotation results are generated from the multiple human annotators and multiple machine annotators. For human annotators, we have used a survey-based approach with about 60 college students. We have selected three popular sentiment analysis tools for machine annotators, including VADER, CoreNLP{'}s sentiment annotator, and TextBlob. We have conducted a qualitative and quantitative evaluation and confirmed our observations on sentiments in Greek tragedy.",
}
@inproceedings{brate-etal-2020-towards,
    title = "Towards Olfactory Information Extraction from Text: A Case Study on Detecting Smell Experiences in Novels",
    author = "Brate, Ryan  and
      Groth, Paul  and
      van Erp, Marieke",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.18",
    pages = "147--155",
    abstract = "Environmental factors determine the smells we perceive, but societal factors factors shape the importance, sentiment and biases we give to them. Descriptions of smells in text, or as we call them {`}smell experiences{'}, offer a window into these factors, but they must first be identified. To the best of our knowledge, no tool exists to extract references to smell experiences from text. In this paper, we present two variations on a semi-supervised approach to identify smell experiences in English literature. The combined set of patterns from both implementations offer significantly better performance than a keyword-based baseline.",
}
@inproceedings{mori-etal-2020-finding,
    title = "Finding and Generating a Missing Part for Story Completion",
    author = "Mori, Yusuke  and
      Yamane, Hiroaki  and
      Mukuta, Yusuke  and
      Harada, Tatsuya",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.19",
    pages = "156--166",
    abstract = "Creating a story is difficult. Professional writers often experience a writer{'}s block. Thus, providing automatic support to writers is crucial but also challenging. Recently, in the field of generating and understanding stories, story completion (SC) has been proposed as a method for generating missing parts of an incomplete story. Despite this method{'}s usefulness in providing creative support, its applicability is currently limited because it requires the user to have prior knowledge of the missing part of a story. Writers do not always know which part of their writing is flawed. To overcome this problem, we propose a novel approach called {``}missing position prediction (MPP).{''} Given an incomplete story, we aim to predict the position of the missing part. We also propose a novel method for MPP and SC. We first conduct an experiment focusing on MPP, and our analysis shows that highly accurate predictions can be obtained when the missing part of a story is the beginning or the end. This suggests that if a story has a specific beginning or end, they play significant roles. We conduct an experiment on SC using MPP, and our proposed method demonstrates promising results.",
}
@inproceedings{zhai-etal-2020-tl,
    title = "{TL}-Explorer: A Digital Humanities Tool for Mapping and Analyzing Translated Literature",
    author = "Zhai, Alex  and
      Zhang, Zheng  and
      Fraisse, Amel  and
      Jenn, Ronald  and
      Fishkin, Shelley Fisher  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.latechclfl-1.20",
    pages = "167--171",
    abstract = "TL-Explorer is a digital humanities tool for mapping and analyzing translated literature, encompassing the World Map and the Translation Dashboard. The World Map displays collected literature of different languages, locations, and cultures and establishes the foundation for further analysis. It comprises three global maps for spatial and temporal interpretation. A further investigation into an individual point on the map leads to the Translation Dashboard. Each point represents one edition or translation. Collected translations are processed in order to build multilingual parallel corpora for a large number of under-resourced languages as well as to highlight the transnational circulation of knowledge. Our first rendition of TL-Explorer was conducted on the well-traveled American novel, Adventures of Huckleberry Finn, by Mark Twain. The maps currently chronicle nearly 400 translations of this novel. And the dashboard supports over 30 collected translations. However, the TL-Explore is easily extended to other works of literature and is not limited to type of texts, such as academic manuscripts or constitutional documents to name a few.",
}
@proceedings{lantern-2020-beyond,
    title = "Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    editor = "Mogadala, Aditya  and
      Pezzelle, Sandro  and
      Klakow, Dietrich  and
      Moens, Marie-Francine  and
      Akata, Zeynep",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lantern-1.0",
}
@inproceedings{agrawal-rosa-2020-eyes,
    title = "Eyes on the Parse: Using Gaze Features in Syntactic Parsing",
    author = "Agrawal, Abhishek  and
      Rosa, Rudolf",
    booktitle = "Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lantern-1.1",
    pages = "1--16",
    abstract = "In this paper, we explore the potential benefits of leveraging eye-tracking information for dependency parsing on the English part of the Dundee corpus. To achieve this, we cast dependency parsing as a sequence labelling task and then augment the neural model for sequence labelling with eye-tracking features. We also augment a graph-based parser with eye-tracking features and parse the Dundee Corpus to corroborate our findings from the sequence labelling parser. We then experiment with a variety of parser setups ranging from parsing with all features to a delexicalized parser. Our experiments show that for a parser with all features, although the improvements are positive for the LAS score they are not significant whereas our delexicalized parser significantly outperforms the baseline we established. We also analyze the contribution of various eye-tracking features towards the different parser setups and find that eye-tracking features contain information which is complementary in nature, thus implying that augmenting the parser with various gaze features grouped together provides better performance than any individual gaze feature.",
}
@inproceedings{frolov-etal-2020-leveraging,
    title = "Leveraging Visual Question Answering to Improve Text-to-Image Synthesis",
    author = {Frolov, Stanislav  and
      Jolly, Shailza  and
      Hees, J{\"o}rn  and
      Dengel, Andreas},
    booktitle = "Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lantern-1.2",
    pages = "17--22",
    abstract = "Generating images from textual descriptions has recently attracted a lot of interest. While current models can generate photo-realistic images of individual objects such as birds and human faces, synthesising images with multiple objects is still very difficult. In this paper, we propose an effective way to combine Text-to-Image (T2I) synthesis with Visual Question Answering (VQA) to improve the image quality and image-text alignment of generated images by leveraging the VQA 2.0 dataset. We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal. We encourage images generated from QA pairs to look realistic and additionally minimize an external VQA loss. Our method lowers the FID from 27.84 to 25.38 and increases the R-prec. from 83.82{\%} to 84.79{\%} when compared to the baseline, which indicates that T2I synthesis can successfully be improved using a standard VQA model.",
}
@inproceedings{galvan-sosa-etal-2020-seeing,
    title = "Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension",
    author = "Galvan-Sosa, Diana  and
      Suzuki, Jun  and
      Nishida, Kyosuke  and
      Matsuda, Koji  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lantern-1.3",
    pages = "23--29",
    abstract = "Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions.",
}
@inproceedings{van-miltenburg-2020-image,
    title = "How Do Image Description Systems Describe People? A Targeted Assessment of System Competence in the {PEOPLE}-domain",
    author = "van Miltenburg, Emiel",
    booktitle = "Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.lantern-1.4",
    pages = "30--36",
    abstract = "Evaluations of image description systems are typically domain-general: generated descriptions for the held-out test images are either compared to a set of reference descriptions (using automated metrics), or rated by human judges on one or more Likert scales (for fluency, overall quality, and other quality criteria). While useful, these evaluations do not tell us anything about the kinds of image descriptions that systems are able to produce. Or, phrased differently, these evaluations do not tell us anything about the cognitive capabilities of image description systems. This paper proposes a different kind of assessment, that is able to quantify the extent to which these systems are able to describe humans. This assessment is based on a manual characterisation (a context-free grammar) of English entity labels in the PEOPLE domain, to determine the range of possible outputs. We examined 9 systems to see what kinds of labels they actually use. We found that these systems only use a small subset of at most 13 different kinds of modifiers (e.g. tall and short modify HEIGHT, sad and happy modify MOOD), but 27 kinds of modifiers are never used. Future research could study these semantic dimensions in more detail.",
}
@proceedings{knlp-2020-knowledgeable,
    title = "Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP",
    editor = "Shalom, Oren Sar  and
      Panchenko, Alexander  and
      dos Santos, Cicero  and
      Logacheva, Varvara  and
      Moschitti, Alessandro  and
      Dagan, Ido",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.knlp-1.0",
}
@inproceedings{wise-etal-2020-covid,
    title = "{COVID}-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature",
    author = "Wise, Colby  and
      Romero Calvo, Miguel  and
      Bhatia, Pariminder  and
      Ioannidis, Vassilis  and
      Karypus, George  and
      Price, George  and
      Song, Xiang  and
      Brand, Ryan  and
      Kulkani, Ninad",
    booktitle = "Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.knlp-1.1",
    pages = "1--10",
    abstract = "The coronavirus disease (COVID-19) has claimed the lives of over one million people and infected more than thirty-five million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations inherent to scientific literature. Moreover, because these engines mainly rely upon semantic information, their ability to capture complex global relationships across documents is limited, which reduces the quality of similarity-based article recommendations for users. In this work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles. The CKG combines semantic information with document topological information for the application of similar document retrieval. The CKG is constructed using the latent schema of the data, and then enriched with biomedical entity information extracted from the unstructured text of articles using scalable AWS technologies to form relations in the graph. Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval. Analysis demonstrates the quality of relationships in the CKG and shows that it can be used to uncover meaningful information in COVID-19 scientific articles. The CKG helps power www.cord19.aws and is publicly available.",
}
@inproceedings{murayama-etal-2020-dialogue,
    title = "Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories",
    author = "Murayama, Yuri  and
      Kanashiro Pereira, Lis  and
      Kobayashi, Ichiro",
    booktitle = "Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.knlp-1.2",
    pages = "11--20",
    abstract = "The Differentiable Neural Computer (DNC), a neural network model with an addressable external memory, can solve algorithmic and question answering tasks. There are various improved versions of DNC, such as rsDNC and DNC-DMS. However, how to integrate structured knowledge into these DNC models remains a challenging research question. We incorporate an architecture for knowledge into such DNC models, i.e. DNC, rsDNC and DNC-DMS, to improve the ability to generate correct responses using both contextual information and structured knowledge. Our improved rsDNC model improves the mean accuracy by approximately 20{\%} to the original rsDNC on tasks requiring knowledge in the dialog bAbI tasks. In addition, our improved rsDNC and DNC-DMS models also yield better performance than their original models in the Movie Dialog dataset.",
}
@inproceedings{kalyan-sangeetha-2020-social,
    title = "Social Media Medical Concept Normalization using {R}o{BERT}a in Ontology Enriched Text Similarity Framework",
    author = "Kalyan, Katikapalli Subramanyam  and
      Sangeetha, Sivanesan",
    booktitle = "Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.knlp-1.3",
    pages = "21--26",
    abstract = "Pattisapu et al. (2020) formulate medical concept normalization (MCN) as text similarity problem and propose a model based on RoBERTa and graph embedding based target concept vectors. However, graph embedding techniques ignore valuable information available in the clinical ontology like concept description and synonyms. In this work, we enhance the model of Pattisapu et al. (2020) with two novel changes. First, we use retrofitted target concept vectors instead of graph embedding based vectors. It is the first work to leverage both concept description and synonyms to represent concepts in the form of retrofitted target concept vectors in text similarity framework based social media MCN. Second, we generate both concept and concept mention vectors with same size which eliminates the need of dense layers to project concept mention vectors into the target concept embedding space. Our model outperforms existing methods with improvements up to 3.75{\%} on two standard datasets. Further when trained only on mapping lexicon synonyms, our model outperforms existing methods with significant improvements up to 14.61{\%}. We attribute these significant improvements to the two novel changes introduced.",
}
@inproceedings{mondal-2020-bertchem,
    title = "{BERTC}hem-{DDI} : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information",
    author = "Mondal, Ishani",
    booktitle = "Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.knlp-1.4",
    pages = "27--32",
    abstract = "Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain. In this paper, we explore how to incorporate domain knowledge, available in the form of molecular structure of drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the rich chemical structure of drugs (encoded in SMILES) along with off-the-shelf domain-specific BioBERT embedding-based RE architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly indicate that this strategy improves other strong baselines architectures by 3.4{\%} macro F1-score.",
}
@proceedings{jep-taln-recital-2020-actes-de,
    title = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    editor = "Benzitoun, Christophe  and
      Braud, Chlo{\'e}  and
      Huber, Laurine  and
      Langlois, David  and
      Ouni, Slim  and
      Pogodalla, Sylvain  and
      Schneider, St{\'e}phane",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.0",
}
@inproceedings{akermi-etal-2020-approche,
    title = "Approche de g{\'e}n{\'e}ration de r{\'e}ponse {\`a} base de transformers (Transformer based approach for answer generation)",
    author = "Akermi, Imen  and
      Heinecke, Johannes  and
      Herledan, Fr{\'e}d{\'e}ric",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.1",
    pages = "2--11",
    abstract = "Cet article pr{\'e}sente une approche non-supervis{\'e}e bas{\'e}e sur les mod{\`e}les Transformer pour la g{\'e}n{\'e}ration du langage naturel dans le cadre des syst{\`e}mes de question-r{\'e}ponse. Cette approche permettrait de rem{\'e}dier {\`a} la probl{\'e}matique de g{\'e}n{\'e}ration de r{\'e}ponse trop courte ou trop longue sans avoir recours {\`a} des donn{\'e}es annot{\'e}es. Cette approche montre des r{\'e}sultats prometteurs pour l{'}anglais et le fran{\c{c}}ais.",
    language = "French",
}
@inproceedings{amblard-etal-2020-investigation,
    title = "Investigation par m{\'e}thodes d{'}apprentissage des sp{\'e}cificit{\'e}s langagi{\`e}res propres aux personnes avec schizophr{\'e}nie (Investigating Learning Methods Applied to Language Specificity of Persons with Schizophrenia)",
    author = "Amblard, Maxime  and
      Braud, Chlo{\'e}  and
      Li, Chuyuan  and
      Demily, Caroline  and
      Franck, Nicolas  and
      Musiol, Michel",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.2",
    pages = "12--26",
    abstract = "Nous pr{\'e}sentons des exp{\'e}riences visant {\`a} identifier automatiquement des patients pr{\'e}sentant des sympt{\^o}mes de schizophr{\'e}nie dans des conversations contr{\^o}l{\'e}es entre patients et psychoth{\'e}rapeutes. Nous fusionnons l{'}ensemble des tours de parole de chaque interlocuteur et entra{\^\i}nons des mod{\`e}les de classification utilisant des informations lexicales, morphologiques et syntaxiques. Cette {\'e}tude est la premi{\`e}re du genre sur le fran{\c{c}}ais et obtient des r{\'e}sultats comparables {\`a} celles sur l{'}anglais. Nos premi{\`e}res exp{\'e}riences tendent {\`a} montrer que la parole des personnes avec schizophr{\'e}nie se distingue de celle des t{\'e}moins : le meilleur mod{\`e}le obtient une exactitude de 93,66{\%}. Des informations plus riches seront cependant n{\'e}cessaires pour parvenir {\`a} un mod{\`e}le robuste.",
    language = "French",
}
@inproceedings{khaldi-etal-2020-classification,
    title = "Classification de relations pour l{'}intelligence {\'e}conomique et concurrentielle (Relation Classification for Competitive and Economic Intelligence )",
    author = "Khaldi, Hadjer  and
      Abdaoui, Amine  and
      Benamara, Farah  and
      Sigel, Gr{\'e}goire  and
      Aussenac-Gilles, Nathalie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.3",
    pages = "27--39",
    abstract = "L{'}extraction de relations reliant des entit{\'e}s par des liens s{\'e}mantiques {\`a} partir de texte a fait l{'}objet de nombreux travaux visant {\`a} extraire des relations g{\'e}n{\'e}riques comme l{'}hyperonymie ou sp{\'e}cifiques comme des relations entre g{\`e}nes et prot{\'e}ines. Dans cet article, nous nous int{\'e}ressons aux relations {\'e}conomiques entre deux entit{\'e}s nomm{\'e}es de type organisation {\`a} partir de textes issus du web. Ce type de relation, encore peu {\'e}tudi{\'e} dans la litt{\'e}rature, a pour but l{'}identification des liens entre les acteurs d{'}un secteur d{'}activit{\'e} afin d{'}analyser leurs {\'e}cosyst{\`e}mes {\'e}conomiques. Nous pr{\'e}sentons B IZ R EL, le premier corpus fran{\c{c}}ais annot{\'e} en relations {\'e}conomiques, ainsi qu{'}une approche supervis{\'e}e {\`a} base de diff{\'e}rentes architectures neuronales pour la classification de ces relations. L{'}{\'e}valuation de ces mod{\`e}les montre des r{\'e}sultats tr{\`e}s encourageants, ce qui est un premier pas vers l{'}intelligence {\'e}conomique et concurrentielle {\`a} partir de textes pour le fran{\c{c}}ais.",
    language = "French",
}
@inproceedings{kodelja-etal-2020-representation,
    title = "Repr{\'e}sentation dynamique et sp{\'e}cifique du contexte textuel pour l{'}extraction d{'}{\'e}v{\'e}nements (Dynamic and specific textual context representation for event extraction)",
    author = "Kodelja, Dorian  and
      Besan{\c{c}}on, Romaric  and
      Ferret, Olivier",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.4",
    pages = "40--53",
    abstract = "Dans cet article, focalis{\'e} sur l{'}extraction supervis{\'e}e de mentions d{'}{\'e}v{\'e}nements dans les textes, nous proposons d{'}{\'e}tendre un mod{\`e}le op{\'e}rant au niveau phrastique et reposant sur une architecture neuronale de convolution de graphe exploitant les d{\'e}pendances syntaxiques. Nous y int{\'e}grons pour ce faire un contexte plus large au travers de la repr{\'e}sentation de phrases distantes s{\'e}lectionn{\'e}es sur la base de relations de cor{\'e}f{\'e}rence entre entit{\'e}s. En outre, nous montrons l{'}int{\'e}r{\^e}t d{'}une telle int{\'e}gration au travers d{'}{\'e}valuations men{\'e}es sur le corpus de r{\'e}f{\'e}rence TAC Event 2015.",
    language = "French",
}
@inproceedings{martin-etal-2020-les,
    title = "Les mod{\`e}les de langue contextuels Camembert pour le fran{\c{c}}ais : impact de la taille et de l{'}h{\'e}t{\'e}rog{\'e}n{\'e}it{\'e} des donn{\'e}es d{'}entrainement ({C} {AMEM} {BERT} Contextual Language Models for {F}rench: Impact of Training Data Size and Heterogeneity )",
    author = "Martin, Louis  and
      Muller, Benjamin  and
      Ortiz Su{\'a}rez, Pedro Javier  and
      Dupont, Yoann  and
      Romary, Laurent  and
      Villemonte de la Clergerie, {\'E}ric  and
      Sagot, Beno{\^\i}t  and
      Seddah, Djam{\'e}",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.5",
    pages = "54--65",
    abstract = "Les mod{\`e}les de langue neuronaux contextuels sont d{\'e}sormais omnipr{\'e}sents en traitement automatique des langues. Jusqu{'}{\`a} r{\'e}cemment, la plupart des mod{\`e}les disponibles ont {\'e}t{\'e} entra{\^\i}n{\'e}s soit sur des donn{\'e}es en anglais, soit sur la concat{\'e}nation de donn{\'e}es dans plusieurs langues. L{'}utilisation pratique de ces mod{\`e}les {---} dans toutes les langues sauf l{'}anglais {---} {\'e}tait donc limit{\'e}e. La sortie r{\'e}cente de plusieurs mod{\`e}les monolingues fond{\'e}s sur BERT (Devlin et al., 2019), notamment pour le fran{\c{c}}ais, a d{\'e}montr{\'e} l{'}int{\'e}r{\^e}t de ces mod{\`e}les en am{\'e}liorant l{'}{\'e}tat de l{'}art pour toutes les t{\^a}ches {\'e}valu{\'e}es. Dans cet article, {\`a} partir d{'}exp{\'e}riences men{\'e}es sur CamemBERT (Martin et al., 2019), nous montrons que l{'}utilisation de donn{\'e}es {\`a} haute variabilit{\'e} est pr{\'e}f{\'e}rable {\`a} des donn{\'e}es plus uniformes. De fa{\c{c}}on plus surprenante, nous montrons que l{'}utilisation d{'}un ensemble relativement petit de donn{\'e}es issues du web (4Go) donne des r{\'e}sultats aussi bons que ceux obtenus {\`a} partir d{'}ensembles de donn{\'e}es plus grands de deux ordres de grandeurs (138Go).",
    language = "French",
}
@inproceedings{mickus-etal-2020-generation,
    title = "G{\'e}n{\'e}ration automatique de d{\'e}finitions pour le fran{\c{c}}ais (Definition Modeling in {F}rench)",
    author = "Mickus, Timothee  and
      Constant, Mathieu  and
      Paperno, Denis",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.6",
    pages = "66--80",
    abstract = "La g{\'e}n{\'e}ration de d{\'e}finitions est une t{\^a}che r{\'e}cente qui vise {\`a} produire des d{\'e}finitions lexicographiques {\`a} partir de plongements lexicaux. Nous remarquons deux lacunes : (i) l{'}{\'e}tat de l{'}art actuel ne s{'}est pench{\'e} que sur l{'}anglais et le chinois, et (ii) l{'}utilisation escompt{\'e}e en tant que m{\'e}thode d{'}{\'e}valuation des plongements lexicaux doit encore {\^e}tre v{\'e}rifi{\'e}e. Pour y rem{\'e}dier, nous proposons un jeu de donn{\'e}es pour la g{\'e}n{\'e}ration de d{\'e}finitions en fran{\c{c}}ais, ainsi qu{'}une {\'e}valuation des performances d{'}un mod{\`e}le de g{\'e}n{\'e}ration de d{\'e}finitions simple selon les plongements lexicaux fournis en entr{\'e}e.",
    language = "French",
}
@inproceedings{mohammadi-etal-2020-du,
    title = "Du bon usage d{'}ingr{\'e}dients linguistiques sp{\'e}ciaux pour classer des recettes exceptionnelles (Using Special Linguistic Ingredients to Classify Exceptional Recipes )",
    author = "Mohammadi, Elham  and
      Marceau, Louis  and
      Charton, Eric  and
      Kosseim, Leila  and
      Nerima, Luka  and
      Meurs, Marie-Jean",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.7",
    pages = "81--94",
    abstract = "Nous pr{\'e}sentons un mod{\`e}le d{'}apprentissage automatique qui combine mod{\`e}les neuronaux et linguistiques pour traiter les t{\^a}ches de classification dans lesquelles la distribution des {\'e}tiquettes des instances est d{\'e}s{\'e}quilibr{\'e}e. Les performances de ce mod{\`e}le sont mesur{\'e}es {\`a} l{'}aide d{'}exp{\'e}riences men{\'e}es sur les t{\^a}ches de classification de recettes de cuisine de la campagne DEFT 2013 (Grouin et al., 2013). Nous montrons que les plongements lexicaux (word embeddings) associ{\'e}s {\`a} des m{\'e}thodes d{'}apprentissage profond obtiennent de meilleures performances que tous les algorithmes d{\'e}ploy{\'e}s lors de la campagne DEFT. Nous montrons aussi que ces m{\^e}mes classifieurs avec plongements lexicaux peuvent gagner en performance lorsqu{'}un mod{\`e}le linguistique est ajout{\'e} au mod{\`e}le neuronal. Nous observons que l{'}ajout d{'}un mod{\`e}le linguistique au mod{\`e}le neuronal am{\'e}liore les performances de classification sur les classes rares.",
    language = "French",
}
@inproceedings{nyzam-bossard-2020-etude,
    title = "{\'E}tude sur le r{\'e}sum{\'e} comparatif gr{\^a}ce aux plongements de mots (Comparative summarization study using word embeddings)",
    author = "Nyzam, Valentin  and
      Bossard, Aur{\'e}lien",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.8",
    pages = "95--107",
    abstract = "Dans cet article, nous pr{\'e}sentons une nouvelle m{\'e}thode de r{\'e}sum{\'e} automatique comparatif. Ce type de r{\'e}sum{\'e} a pour objectif de permettre de saisir rapidement les diff{\'e}rences d{'}information entre deux jeux de documents. En raison de l{'}absence de ressources disponibles pour cette t{\^a}che, nous avons compos{\'e} un corpus d{'}{\'e}valuation. Nous pr{\'e}sentons {\`a} la fois la m{\'e}thodologie de son {\'e}laboration ainsi que le corpus lui-m{\^e}me. Notre m{\'e}thode utilise les avanc{\'e}es r{\'e}centes dans le calcul de similarit{\'e} entre phrases afin de d{\'e}tecter les informations comparatives. Nous montrons que sur ce corpus, notre m{\'e}thode est comparable en termes de qualit{\'e} de r{\'e}sultats {\`a} une m{\'e}thode de l{'}{\'e}tat de l{'}art, tout en r{\'e}duisant d{'}un facteur dix le temps de calcul, la rendant donc exploitable dans le cadre de l{'}aide {\`a} l{'}analyse de documents.",
    language = "French",
}
@inproceedings{taillandier-etal-2020-reseaux,
    title = "R{\'e}seaux de neurones pour la r{\'e}solution d{'}analogies entre phrases en traduction automatique par l{'}exemple (Neural networks for the resolution of analogies between sentences in {EBMT} )",
    author = "Taillandier, Valentin  and
      Wang, Liyan  and
      Lepage, Yves",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.9",
    pages = "108--121",
    abstract = "Cet article propose un mod{\`e}le de r{\'e}seau de neurones pour la r{\'e}solution d{'}{\'e}quations analogiques au niveau s{\'e}mantique et entre phrases dans le cadre de la traduction automatique par l{'}exemple. Son originalit{\'e} r{\'e}side dans le fait qu{'}il fusionne les deux approches, directe et indirecte, de la traduction par l{'}exemple.",
    language = "French",
}
@inproceedings{tanguy-etal-2020-impact,
    title = "Impact de la structure logique des documents sur les mod{\`e}les distributionnels : exp{\'e}rimentations sur le corpus {TALN} (Impact of document structure on distributional semantics models: a case study on {NLP} research articles )",
    author = "Tanguy, Ludovic  and
      Fabre, C{\'e}cile  and
      Bard, Yoann",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.10",
    pages = "122--135",
    abstract = "Nous pr{\'e}sentons une exp{\'e}rience visant {\`a} mesurer en quoi la structure logique d{'}un document impacte les repr{\'e}sentations lexicales dans les mod{\`e}les de s{\'e}mantique distributionnelle. En nous basant sur des documents structur{\'e}s (articles de recherche en TAL) nous comparons des mod{\`e}les construits sur des corpus obtenus par suppression de certaines parties des textes du corpus : titres de section, r{\'e}sum{\'e}s, introductions et conclusions. Nous montrons que malgr{\'e} des diff{\'e}rences selon les parties et le lexique pris en compte, ces zones r{\'e}put{\'e}es particuli{\`e}rement informatives du contenu d{'}un article ont un impact globalement moins significatif que le reste du texte sur la construction du mod{\`e}le.",
    language = "French",
}
@inproceedings{barbedette-eshkol-taravella-2020-predire,
    title = "Pr{\'e}dire automatiquement les intentions du locuteur dans des questions issues du discours oral spontan{\'e} (Automatically predicting the speaker{'}s intentions in questions from spontaneous oral speech)",
    author = "Barbedette, Ang{\`e}le  and
      Eshkol-Taravella, Iris",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.11",
    pages = "137--145",
    abstract = "Cette {\'e}tude porte sur la classification automatique des intentions exprim{\'e}es dans des questions issues d{'}un corpus d{'}{\'e}changes oraux spontan{\'e}s. Nous proposons une typologie dans laquelle nous distinguons trois classes d{'}intentions (AVIS, VOLONT{\'E} et DOUTE). Apr{\`e}s plusieurs pr{\'e}traitements et ajouts de traits lexicaux aux donn{\'e}es (lexiques, nombre de mots et de caract{\`e}res), nous impl{\'e}mentons un algorithme de classification automatique et nous en pr{\'e}sentons et {\'e}valuons les r{\'e}sultats qui atteignent une F-mesure de 0,62. Nous proposons ensuite une interpr{\'e}tation de ceux-ci, bas{\'e}e sur une comparaison entre les exp{\'e}riences men{\'e}es et des mesures li{\'e}es aux traits linguistiques int{\'e}gr{\'e}s avant la t{\^a}che de classification.",
    language = "French",
}
@inproceedings{bebeshina-lafourcade-2020-reduire,
    title = "R{\'e}duire l{'}effort humain d{'}am{\'e}lioration des ressources lexicales gr{\^a}ce aux inf{\'e}rences (Reducing the Knowledge Resource Enhancement Human Effort through Inferences)",
    author = "Bebeshina, Nadia  and
      Lafourcade, Mathieu",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.12",
    pages = "146--154",
    abstract = "Les inf{\'e}rences translingues repr{\'e}sentent une piste int{\'e}ressante pour la construction des ressources lexico-s{\'e}mantiques multilingues. Cependant, la validation des {\'e}l{\'e}ments candidats n{\'e}cessite un effort humain consid{\'e}rable. Nous d{\'e}crivons une fa{\c{c}}on de construire des ressources lexico-s{\'e}mantiques via des inf{\'e}rences monolingue et translingue. Son int{\'e}r{\^e}t principal consiste {\`a} impl{\'e}menter dans le contexte d{'}une ressource lexico-s{\'e}mantique multilingue une approche o{\`u} le processus de construction est un processus auto-apprenant car l{'}{\'e}valuation participe {\`a} la construction de celle-ci.",
    language = "French",
}
@inproceedings{billami-etal-2020-extraction,
    title = "Extraction de th{\`e}mes d{'}un corpus de demandes de support pour un logiciel de relation citoyen (Topic extraction from a corpus of support requests for citizen relations software)",
    author = "Billami, Mokhtar Boumedyen  and
      Bortolaso, Christophe  and
      Derras, Mustapha",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.13",
    pages = "155--163",
    abstract = "Nous nous int{\'e}ressons dans cet article {\`a} l{'}extraction de th{\`e}mes (topics) {\`a} partir de commentaires textuels provenant des demandes de support de l{'}{\'e}diteur de logiciel Berger-Levrault. Le corpus de demandes analys{\'e} est celui d{'}un outil de gestion de la relation citoyen. Ce corpus n{'}est pas format{\'e} et est peu structur{\'e} avec plusieurs locuteurs qui interviennent (le citoyen et un ou plusieurs techniciens support). Nous d{\'e}crivons une {\'e}tude exp{\'e}rimentale qui repose sur l{'}utilisation de deux syst{\`e}mes. Le premier syst{\`e}me applique une LDA (Allocation Dirichlet Latente), tandis que le second combine l{'}application d{'}une LDA avec l{'}algorithme k-Moyennes (k-Means). Nous comparons nos r{\'e}sultats avec un {\'e}chantillon de ce corpus, annot{\'e} par un expert du domaine. Nos r{\'e}sultats montrent que nous obtenons une classification de meilleure qualit{\'e} comparable avec celle effectu{\'e}e manuellement par un expert en utilisant une combinaison LDA/k-Moyennes.",
    language = "French",
}
@inproceedings{blandin-etal-2020-recommandation,
    title = "Recommandation d{'}{\^a}ge pour des textes (Age recommendation for texts)",
    author = "Blandin, Alexis  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Battistelli, Delphine  and
      {\'E}tienne, Aline",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.14",
    pages = "164--171",
    abstract = "Cet article {\'e}tudie une premi{\`e}re tentative pour pr{\'e}dire une recommandation d{'}{\^a}ge estimant {\`a} partir de quand un enfant pourrait comprendre un texte donn{\'e}. {\`A} ce titre, nous pr{\'e}sentons d{'}abord des descripteurs issus de divers domaines scientifiques, puis proposons diff{\'e}rentes architectures de r{\'e}seaux de neurones et les comparons sur un ensemble de donn{\'e}es textuelles en fran{\c{c}}ais, d{\'e}di{\'e}es {\`a} des publics jeune ou adulte. Pour contourner la faible quantit{\'e} de donn{\'e}es de ce type, nous {\'e}tudions l{'}id{\'e}e de pr{\'e}dire les {\^a}ges au niveau de la phrase. Les exp{\'e}riences montrent que cette hypoth{\`e}se, quoique forte, conduit d{'}ores et d{\'e}j{\`a} {\`a} de bons r{\'e}sultats, meilleurs que ceux fournis par des experts psycholinguistes, y compris lorsque les phrases isol{\'e}es sont remplac{\'e}es par textes complets.",
    language = "French",
}
@inproceedings{blin-2020-traduire,
    title = "Traduire des corpus pour construire des mod{\`e}les de traduction neuronaux : une solution pour toutes les langues peu dot{\'e}es ? (Corpus Translation to Build Translation Models : a Solution for all Low-Resource Languages ?)",
    author = "Blin, Raoul",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.15",
    pages = "172--180",
    abstract = "Nous comparons deux usages des langues pivots en traduction automatique neuronale pour des langues peu dot{\'e}es. Nous nous int{\'e}ressons au cas o{\`u} il existe une langue pivot telle que les paires source-pivot et pivot-cible sont bien ou tr{\`e}s bien dot{\'e}es. Nous comparons la traduction s{\'e}quentielle traditionnelle (source→pivot→cible) et la traduction {\`a} l{'}aide d{'}un mod{\`e}le entra{\^\i}n{\'e} sur des corpus traduits {\`a} l{'}aide des langues pivot et cible. Les exp{\'e}riences sont men{\'e}es sur trois langues sources (espagnol, allemand et japonais), une langue pivot (anglais) et une langue cible (fran{\c{c}}ais). Nous constatons que quelle que soit la proximit{\'e} linguistique entre les langues source et pivot, le mod{\`e}le entra{\^\i}n{\'e} sur corpus traduit a de meilleurs r{\'e}sultats que la traduction s{\'e}quentielle, et bien s{\^u}r que la traduction directe.",
    language = "French",
}
@inproceedings{claveau-2020-construction,
    title = "Construction de plongements de concepts m{\'e}dicaux sans textes (Embedding medical concepts without texts)",
    author = "Claveau, Vincent",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.16",
    pages = "181--188",
    abstract = "Dans le domaine m{\'e}dical, beaucoup d{'}outils du TAL reposent d{\'e}sormais sur des plongements de concepts issus de l{'}UMLS. Les approches existantes pour g{\'e}n{\'e}rer ces plongements n{\'e}cessitent de grandes quantit{\'e}s de documents m{\'e}dicaux. Au contraire des ces approches, nous proposons dans cet article de nous appuyer sur les traductions en japonais, plus pr{\'e}cis{\'e}ment en kanjis, disponibles dans l{'}UMLS pour g{\'e}n{\'e}rer ces plongements. Test{\'e}e sur diff{\'e}rents jeux d{'}{\'e}valuation propos{\'e}s dans la litt{\'e}rature, notre approche, qui ne requiert donc aucun texte, donne de bons r{\'e}sultats comparativement {\`a} l{'}{\'e}tat-de-l{'}art. De plus, nous montrons qu{'}il est int{\'e}ressant de les combiner avec les plongements {--} contextuels {--} existants.",
    language = "French",
}
@inproceedings{coavoux-2020-quapporte,
    title = "Qu{'}apporte {BERT} {\`a} l{'}analyse syntaxique en constituants discontinus ? Une suite de tests pour {\'e}valuer les pr{\'e}dictions de structures syntaxiques discontinues en anglais (What does {BERT} contribute to discontinuous constituency parsing ? A test suite to evaluate discontinuous constituency structure predictions in {E}nglish)",
    author = "Coavoux, Maximin",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.17",
    pages = "189--196",
    abstract = "Cet article propose d{'}analyser les apports d{'}un mod{\`e}le de langue pr{\'e}-entra{\^\i}n{\'e} de type BERT (bidirectional encoder representations from transformers) {\`a} l{'}analyse syntaxique en constituants discontinus en anglais (PTB, Penn Treebank). Pour cela, nous r{\'e}alisons une comparaison des erreurs d{'}un analyseur syntaxique dans deux configurations (i) avec un acc{\`e}s {\`a} BERT affin{\'e} lors de l{'}apprentissage (ii) sans acc{\`e}s {\`a} BERT (mod{\`e}le n{'}utilisant que les donn{\'e}es d{'}entra{\^\i}nement). Cette comparaison s{'}appuie sur la construction d{'}une suite de tests que nous rendons publique. Nous annotons les phrases de la section de validation du Penn Treebank avec des informations sur les ph{\'e}nom{\`e}nes syntaxiques {\`a} l{'}origine des discontinuit{\'e}s. Ces annotations nous permettent de r{\'e}aliser une {\'e}valuation fine des capacit{\'e}s syntaxiques de l{'}analyseur pour chaque ph{\'e}nom{\`e}ne cible. Nous montrons que malgr{\'e} l{'}apport de BERT {\`a} la qualit{\'e} des analyses (jusqu{'}{\`a} 95 en F1 ), certains ph{\'e}nom{\`e}nes complexes ne sont toujours pas analys{\'e}s de mani{\`e}re satisfaisante.",
    language = "French",
}
@inproceedings{corro-2020-sur,
    title = "Sur l{'}impact des contraintes structurelles pour l{'}analyse en d{\'e}pendances profondes fond{\'e}e sur les graphes (On the impact of structural constraints for graph-based deep dependency parsing)",
    author = "Corro, Caio",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.18",
    pages = "197--204",
    abstract = "Les algorithmes existants pour l{'}analyse en d{\'e}pendances profondes fond{\'e}e sur les graphes capables de garantir la connexit{\'e} des structures produites ne couvrent pas les corpus du fran{\c{c}}ais. Nous proposons un nouvel algorithme qui couvre l{'}ensemble des structures possibles. Nous nous {\'e}valuons sur les corpus fran{\c{c}}ais FTB et Sequoia et observons un compromis entre la production de structures valides et la qualit{\'e} des analyses.",
    language = "French",
}
@inproceedings{etienne-etal-2020-lexpression,
    title = "L{'}expression des {\'e}motions dans les textes pour enfants : constitution d{'}un corpus annot{\'e} (Expressing emotions in texts for children: constitution of an annotated corpus)",
    author = "{\'E}tienne, Aline  and
      Battistelli, Delphine  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.19",
    pages = "205--212",
    abstract = "Cet article pr{\'e}sente une typologie de divers modes d{'}expression linguistique des {\'e}motions, le sch{\'e}ma d{'}annotation sous Glozz qui impl{\'e}mente cette typologie et un corpus de textes journalistiques pour enfants annot{\'e} {\`a} l{'}aide de ce sch{\'e}ma. Ces travaux pr{\'e}liminaires s{'}ins{\`e}rent dans le contexte d{'}une {\'e}tude relative au d{\'e}veloppement des capacit{\'e}s langagi{\`e}res des enfants, en particulier de leur capacit{\'e} {\`a} comprendre un texte selon des crit{\`e}res {\'e}motionnels.",
    language = "French",
}
@inproceedings{gabay-barrault-2020-traduction,
    title = "Traduction automatique pour la normalisation du fran{\c{c}}ais du {XVII}e si{\`e}cle ()",
    author = {Gabay, Simon  and
      Barrault, Lo{\"\i}c},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.20",
    pages = "213--222",
    abstract = "",
    language = "French",
}
@inproceedings{grabar-etal-2020-predire,
    title = "Pr{\'e}dire le niveau de langue d{'}apprenants d{'}anglais (Predict the language level for {E}nglish learners)",
    author = "Grabar, Natalia  and
      Hamon, Thierry  and
      Cappelle, Bert  and
      Grandin, Cyril  and
      Leclercq, Beno{\^\i}t  and
      Depraetere, Ilse",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.21",
    pages = "223--231",
    abstract = "L{'}apprentissage de la deuxi{\`e}me langue (L2) est un processus progressif dans lequel l{'}apprenant am{\'e}liore sa ma{\^\i}trise au fur et {\`a} mesure de l{'}apprentissage. L{'}analyse de productions d{'}apprenants int{\'e}resse les chercheurs et les enseignants car cela permet d{'}avoir une meilleure id{\'e}e des difficult{\'e}s et les facilit{\'e}s d{'}apprentissage et de faire des programmes didactiques plus adapt{\'e}s. Cela peut {\'e}galement donner des indications sur les difficult{\'e}s cognitives {\`a} ma{\^\i}triser les notions grammaticales abstraites dans une nouvelle langue. Nous proposons de travailler sur un corpus de productions langagi{\`e}res d{'}apprenants d{'}anglais provenant de diff{\'e}rents pays et donc ayant diff{\'e}rentes langues maternelles (L1). Notre objectif consiste {\`a} cat{\'e}goriser ces productions langagi{\`e}res selon six niveaux de langue (A1, A2, B1, B2, C1, C2). Nous utilisons diff{\'e}rents ensembles de descripteurs, y compris les verbes et expressions modaux. Nous obtenons des r{\'e}sultats int{\'e}ressants pour cette cat{\'e}gorisation multiclasse, ce qui indique qu{'}il existe des diff{\'e}rences linguistiques inh{\'e}rentes entre les diff{\'e}rents niveaux.",
    language = "French",
}
@inproceedings{gugliotta-dinarelli-2020-tarc-un,
    title = "{TA}r{C}. Un corpus d{'}arabish tunisien",
    author = "Gugliotta, Elisa  and
      Dinarelli, Marco",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.22",
    pages = "232--240",
    abstract = "TArC : Incrementally and Semi-Automatically Collecting a Tunisian arabish Corpus This article describes the collection process of the first morpho-syntactically annotated Tunisian arabish Corpus (TArC). Arabish is a spontaneous coding of Arabic Dialects (AD) in Latin characters and arithmographs (numbers used as letters). This code-system was developed by Arabic-speaking users of social media in order to facilitate the communication on digital devices. Arabish differs for each Arabic dialect and each arabish code-system is under-resourced. In the last few years, the attention of NLP on AD has considerably increased. TArC will be thus a useful support for different types of analyses, as well as for NLP tools training. In this article we will describe preliminary work on the TArC semi-automatic construction process and some of the first analyses on the corpus. In order to provide a complete overview of the challenges faced during the building process, we will present the main Tunisian dialect characteristics and its encoding in Tunisian arabish.",
}
@inproceedings{kalashnikova-etal-2020-segmentation,
    title = "Segmentation automatique en p{\'e}riodes pour le fran{\c{c}}ais parl{\'e} (Automatic Period Segmentation of Oral {F}rench)",
    author = {Kalashnikova, Natalia  and
      Eshkol-Taravella, Iris  and
      Grobol, Lo{\"\i}c  and
      Delafontaine, Fran{\c{c}}ois},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.23",
    pages = "241--248",
    abstract = "Nous proposons la comparaison de deux m{\'e}thodes de segmentation automatique du fran{\c{c}}ais parl{\'e} en p{\'e}riodes macro-syntaxiques, qui permettent d{'}analyser la syntaxe et la prosodie du discours. Nous comparons l{'}outil Analor (Avanzi et al., 2008) qui a {\'e}t{\'e} d{\'e}velopp{\'e} pour la segmentation des p{\'e}riodes prosodiques et les mod{\`e}les de segmentations utilisant des CRF et des traits prosodiques et / ou morphosyntaxiques. Les r{\'e}sultats montrent qu{'}Analor divise le discours en plus petits segments prosodiques tandis que les mod{\`e}les CRF d{\'e}tectent des segments plus larges que les p{\'e}riodes macro-syntaxiques. Cependant, les mod{\`e}les CRF ont de meilleurs r{\'e}sultats qu{'}Analor en termes de F-mesure.",
    language = "French",
}
@inproceedings{jung-kang-eshkol-taravella-2020-les,
    title = "Les avis sur les restaurants {\`a} l{'}{\'e}preuve de l{'}apprentissage automatique (An Empirical Examination of Online Restaurant Reviews)",
    author = "Jung Kang, Hyun  and
      Eshkol-Taravella, Iris",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.24",
    pages = "249--257",
    abstract = "Dans la fouille d{'}opinions, de nombreuses {\'e}tudes portent sur l{'}extraction automatique des opinions positives ou n{\'e}gatives. Cependant les recherches ayant pour objet la fouille de suggestions et d{'}intentions sont moins importantes, malgr{\'e} leur lien profond avec l{'}opinion. Cet article vise {\`a} d{\'e}tecter six cat{\'e}gories (opinion positive/mixte/n{\'e}gative, suggestion, intention, description) dans les avis en ligne sur les restaurants en exploitant deux m{\'e}thodes : l{'}apprentissage de surface et l{'}apprentissage profond supervis{\'e}s. Les performances obtenues pour chaque cat{\'e}gorie sont interpr{\'e}t{\'e}es ensuite en tenant compte des sp{\'e}cificit{\'e}s du corpus trait{\'e}.",
    language = "French",
}
@inproceedings{lafourcade-sandillon-rezer-2020-recherche,
    title = "Recherche de similarit{\'e} th{\'e}matique en temps r{\'e}el au sein d{'}un d{\'e}bat en ligne (Thematic similarity real-time computation during an online debate)",
    author = "Lafourcade, Mathieu  and
      Sandillon-Rezer, No{\'e}mie-Fleur",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.25",
    pages = "258--267",
    abstract = "Cet article se focalise sur l{'}utilisation d{'}un large r{\'e}seau lexico-s{\'e}mantique fran{\c{c}}ais pour le calcul de similarit{\'e} th{\'e}matique d{'}interventions au cours d{'}un d{\'e}bat en ligne dans les lyc{\'e}es, proche du temps r{\'e}el. Pour cela, notre syst{\`e}me extrait des informations s{\'e}mantiques du r{\'e}seau et cr{\'e}e {\`a} la vol{\'e}e des vecteurs enrichis pour chaque fragment de texte. Les donn{\'e}es r{\'e}cup{\'e}r{\'e}es sont contextualis{\'e}es via un algorithme de propagation. Les vecteurs r{\'e}sultat permettent aux fragments de texte d{'}{\^e}tre compar{\'e}s. Notre m{\'e}thode aide {\`a} trouver les th{\'e}matiques {\'e}mergentes des d{\'e}bats et {\`a} identifier des clusters d{'}opinion. La contrainte temps r{\'e}el nous force {\`a} s{\'e}lectionner pr{\'e}cis{\'e}ment les informations que nous incluons, aussi bien pour les temps de calcul des vecteurs cr{\'e}{\'e}s que la qualit{\'e} de ceux-ci.",
    language = "French",
}
@inproceedings{le-etal-2020-flaubert-des,
    title = "{F}lau{BERT} : des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c{c}}ais ({F}lau{BERT} : Unsupervised Language Model Pre-training for {F}rench)",
    author = {Le, Hang  and
      Vial, Lo{\"\i}c  and
      Frej, Jibril  and
      Segonne, Vincent  and
      Coavoux, Maximin  and
      Lecouteux, Benjamin  and
      Allauzen, Alexandre  and
      Crabb{\'e}, Beno{\^\i}t  and
      Besacier, Laurent  and
      Schwab, Didier},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.26",
    pages = "268--278",
    abstract = {Les mod{\`e}les de langue pr{\'e}-entra{\^\i}n{\'e}s sont d{\'e}sormais indispensables pour obtenir des r{\'e}sultats {\`a} l{'}{\'e}tat-de-l{'}art dans de nombreuses t{\^a}ches du TALN. Tirant avantage de l{'}{\'e}norme quantit{\'e} de textes bruts disponibles, ils permettent d{'}extraire des repr{\'e}sentations continues des mots, contextualis{\'e}es au niveau de la phrase. L{'}efficacit{\'e} de ces repr{\'e}sentations pour r{\'e}soudre plusieurs t{\^a}ches de TALN a {\'e}t{\'e} d{\'e}montr{\'e}e r{\'e}cemment pour l{'}anglais. Dans cet article, nous pr{\'e}sentons et partageons FlauBERT, un ensemble de mod{\`e}les appris sur un corpus fran{\c{c}}ais h{\'e}t{\'e}rog{\`e}ne et de taille importante. Des mod{\`e}les de complexit{\'e} diff{\'e}rente sont entra{\^\i}n{\'e}s {\`a} l{'}aide du nouveau supercalculateur Jean Zay du CNRS. Nous {\'e}valuons nos mod{\`e}les de langue sur diverses t{\^a}ches en fran{\c{c}}ais (classification de textes, paraphrase, inf{\'e}rence en langage naturel, analyse syntaxique, d{\'e}sambigu{\"\i}sation automatique) et montrons qu{'}ils surpassent souvent les autres approches sur le r{\'e}f{\'e}rentiel d{'}{\'e}valuation FLUE {\'e}galement pr{\'e}sent{\'e} ici.},
    language = "French",
}
@inproceedings{mallart-etal-2020-relation,
    title = "Relation, es-tu l{\`a} ? D{\'e}tection de relations par {LSTM} pour am{\'e}liorer l{'}extraction de relations (Relation, are you there ? {LSTM}-based relation detection to improve knowledge extraction )",
    author = "Mallart, Cyrielle  and
      Le Nouy, Michel  and
      Gravier, Guillaume  and
      S{\'e}billot, Pascale",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.27",
    pages = "279--287",
    abstract = "De nombreuses m{\'e}thodes d{'}extraction et de classification de relations ont {\'e}t{\'e} propos{\'e}es et test{\'e}es sur des donn{\'e}es de r{\'e}f{\'e}rence. Cependant, dans des donn{\'e}es r{\'e}elles, le nombre de relations potentielles est {\'e}norme et les heuristiques souvent utilis{\'e}es pour distinguer de vraies relations de co-occurrences fortuites ne d{\'e}tectent pas les signaux faibles pourtant importants. Dans cet article, nous {\'e}tudions l{'}apport d{'}un mod{\`e}le de d{\'e}tection de relations, identifiant si un couple d{'}entit{\'e}s dans une phrase exprime ou non une relation, en tant qu{'}{\'e}tape pr{\'e}liminaire {\`a} la classification des relations. Notre mod{\`e}le s{'}appuie sur le plus court chemin de d{\'e}pendances entre deux entit{\'e}s, mod{\'e}lis{\'e} par un LSTM et combin{\'e} avec les types des entit{\'e}s. Sur la t{\^a}che de d{\'e}tection de relations, nous obtenons de meilleurs r{\'e}sultats qu{'}un mod{\`e}le {\'e}tat de l{'}art pour la classification de relations, avec une robustesse accrue aux relations in{\'e}dites. Nous montrons aussi qu{'}une d{\'e}tection binaire en amont d{'}un mod{\`e}le de classification am{\'e}liore significativement ce dernier.",
    language = "French",
}
@inproceedings{marzinotto-etal-2020-analyse,
    title = "Analyse automatique en cadres s{\'e}mantiques pour l{'}apprentissage de mod{\`e}les de compr{\'e}hension de texte (Semantic Frame Parsing for training Machine Reading Comprehension models)",
    author = "Marzinotto, Gabriel  and
      Charlet, Delphine  and
      Damnati, G{\'e}raldine  and
      B{\'e}chet, Fr{\'e}d{\'e}ric",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.28",
    pages = "288--295",
    abstract = "Dans le cadre de la compr{\'e}hension automatique de documents, cet article propose une {\'e}valuation intrins{\`e}que et extrins{\`e}que d{'}un mod{\`e}le d{'}analyse automatique en cadres s{\'e}mantiques (Frames). Le mod{\`e}le propos{\'e} est un mod{\`e}le {\'e}tat de l{'}art {\`a} base de GRU bi-directionnel, enrichi par l{'}utilisation d{'}embeddings contextuels. Nous montrons qu{'}un mod{\`e}le de compr{\'e}hension de documents appris sur un corpus de triplets g{\'e}n{\'e}r{\'e}s {\`a} partir d{'}un corpus analys{\'e} automatiquement avec l{'}analyseur en cadre s{\'e}mantique pr{\'e}sente des performances inf{\'e}rieures de seulement 2.5{\%} en relatif par rapport {\`a} un mod{\`e}le appris sur un corpus de triplets g{\'e}n{\'e}r{\'e}s {\`a} partir d{'}un corpus analys{\'e} manuellement.",
    language = "French",
}
@inproceedings{amine-menacer-etal-2020-analyse,
    title = "Analyse de sentiments des vid{\'e}os en dialecte alg{\'e}rien (Sentiment analysis of videos in {A}lgerian dialect)",
    author = {Amine Menacer, Mohamed  and
      Abidi, Karima  and
      Othman, Nouha  and
      Sma{\"\i}li, Kamel},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.29",
    pages = "296--304",
    abstract = "La plupart des travaux existant sur l{'}analyse de sentiments traitent l{'}arabe standard moderne et ne prennent pas en consid{\'e}ration les sp{\'e}cificit{\'e}s de l{'}arabe dialectal. Cet article pr{\'e}sente un syst{\`e}me d{'}analyse de sentiments de textes extraits de vid{\'e}os exprim{\'e}es en dialecte alg{\'e}rien. Dans ce travail, nous avons deux d{\'e}fis {\`a} surmonter, la reconnaissance automatique de la parole pour le dialecte alg{\'e}rien et l{'}analyse de sentiments du texte reconnu. Le d{\'e}veloppement du syst{\`e}me de reconnaissance automatique de la parole est bas{\'e} sur un corpus oral restreint. Pour pallier le manque de donn{\'e}es, nous proposons d{'}exploiter des donn{\'e}es ayant un impact sur le dialecte alg{\'e}rien, {\`a} savoir l{'}arabe standard et le fran{\c{c}}ais. L{'}analyse de sentiments est fond{\'e}e sur la d{\'e}tection automatique de la polarit{\'e} des mots en fonction de leur proximit{\'e} s{\'e}mantique avec d{'}autres mots ayant une polarit{\'e} pr{\'e}d{\'e}termin{\'e}e.",
    language = "French",
}
@inproceedings{missud-etal-2020-vernom,
    title = "{V}er{N}om : une base de paires morphologiques acquise sur tr{\`e}s gros corpus ({V}er{N}om : a {F}rench derivational database acquired on a massive corpus)",
    author = "Missud, Alice  and
      Amsili, Pascal  and
      Villoing, Florence",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.30",
    pages = "305--313",
    abstract = "Alors qu{'}une part active de la recherche en morphologie d{\'e}rivationnelle s{'}int{\'e}resse {\`a} la comp{\'e}tition qui oppose les suffixations construisant des noms d{'}{\'e}v{\'e}nement {\`a} partir de verbes (-age, -ment, -ion, -ure, -ance, -ade, -aison), l{'}acc{\`e}s {\`a} des donn{\'e}es en large quantit{\'e} devient n{\'e}cessaire pour l{'}application de m{\'e}thodes quantitatives. Dans l{'}optique de r{\'e}unir des paires de verbes et de noms morphologiquement reli{\'e}s dans le cadre de ces suffixations rivales, nous pr{\'e}sentons VerNom, une base morphologique comprenant 25 857 paires verbe-nom, construite automatiquement {\`a} partir d{'}un corpus massif issu du web.",
    language = "French",
}
@inproceedings{montariol-allauzen-2020-etude,
    title = "{\'E}tude des variations s{\'e}mantiques {\`a} travers plusieurs dimensions (Studying semantic variations through several dimensions )",
    author = "Montariol, Syrielle  and
      Allauzen, Alexandre",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.31",
    pages = "314--322",
    abstract = "Au sein d{'}une langue, l{'}usage des mots varie selon deux axes : diachronique (dimension temporelle) et synchronique (variation selon l{'}auteur, la communaut{\'e}, la zone g{\'e}ographique... ). Dans ces travaux, nous proposons une m{\'e}thode de d{\'e}tection et d{'}interpr{\'e}tation des variations d{'}usages des mots {\`a} travers ces diff{\'e}rentes dimensions. Pour cela, nous exploitons les capacit{\'e}s d{'}une nouvelle ligne de plongements lexicaux contextualis{\'e}s, en particulier le mod{\`e}le BERT. Nous exp{\'e}rimentons sur un corpus de rapports financiers d{'}entreprises fran{\c{c}}aises, pour appr{\'e}hender les enjeux et pr{\'e}occupations propres {\`a} certaines p{\'e}riodes, acteurs et secteurs d{'}activit{\'e}s.",
    language = "French",
}
@inproceedings{randriatsitohaina-hamon-2020-identification,
    title = "Identification des probl{\`e}mes d{'}annotation pour l{'}extraction de relations (Identification of annotation problem for the relation extraction)",
    author = "Randriatsitohaina, Tsanta  and
      Hamon, Thierry",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.32",
    pages = "323--331",
    abstract = "L{'}annotation d{'}un corpus est une t{\^a}che difficile et laborieuse, notamment sur des textes de sp{\'e}cialit{\'e} comme les textes biom{\'e}dicaux. Ainsi, dans un contexte comme l{'}extraction des interactions alimentm{\'e}dicament (FDI), l{'}annotation du corpus POMELO a {\'e}t{\'e} r{\'e}alis{\'e}e par un seul annotateur et pr{\'e}sente des risques d{'}erreur. Dans cet article, nous proposons d{'}identifier ces probl{\`e}mes d{'}annotation en utilisant un corpus Silver Standard (CSS) que nous {\'e}tablissons {\`a} partir d{'}un vote majoritaire parmi les annotations propos{\'e}es par des mod{\`e}les entra{\^\i}n{\'e}s sur un domaine similaire (interaction m{\'e}dicamentm{\'e}dicament {--} DDI) et l{'}annotation manuelle {\`a} {\'e}valuer. Les r{\'e}sultats obtenus montrent que l{'}annotation dans POMELO est consid{\'e}rablement {\'e}loign{\'e}e du CSS. L{'}analyse des erreurs permet d{'}en identifier les principales causes et de proposer des solutions pour corriger l{'}annotation existante.",
    language = "French",
}
@inproceedings{abdul-rauf-etal-2020-simplification,
    title = "Simplification automatique de texte dans un contexte de faibles ressources (Automatic Text Simplification : Approaching the Problem in Low Resource Settings for {F}rench)",
    author = "Abdul Rauf, Sadaf  and
      Ligozat, Anne-Laure  and
      Yvon, Francois  and
      Illouz, Gabriel  and
      Hamon, Thierry",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.33",
    pages = "332--341",
    abstract = "La simplification de textes a {\'e}merg{\'e} comme un sous-domaine actif du traitement automatique des langues, du fait des probl{\`e}mes pratiques et th{\'e}oriques qu{'}elle permet d{'}aborder, ainsi que de ses nombreuses applications pratiques. Des corpus de simplification sont n{\'e}cessaires pour entrainer des syst{\`e}mes de simplification automatique ; ces ressources sont toutefois rares et n{'}existent que pour un petit nombre de langues. Nous montrons ici que dans un contexte o{\`u} les ressources pour la simplification sont rares, il reste n{\'e}anmoins possible de construire des syst{\`e}mes de simplification, en ayant recours {\`a} des corpus synth{\'e}tiques, par exemple obtenus par traduction automatique, et nous {\'e}valuons diverses mani{\`e}res de les constituer.",
    language = "French",
}
@inproceedings{sanacore-etal-2020-representation,
    title = "Repr{\'e}sentation s{\'e}mantique des familles d{\'e}rivationnelles au moyen de frames morphos{\'e}mantiques (Semantic representation of derivational families by means of morphosemantic frames )",
    author = "Sanacore, Daniele  and
      Hathout, Nabil  and
      Namer, Fiammetta",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.34",
    pages = "342--350",
    abstract = "L{'}article pr{\'e}sente un formalisme de repr{\'e}sentation des relations morphologiques d{\'e}rivationnelles inspir{\'e} de la S{\'e}mantique des Frames. La description morphos{\'e}mantique y est r{\'e}alis{\'e}e au niveau des familles d{\'e}rivationnelles au moyen de frames morphos{\'e}mantiques dans lesquels les lex{\`e}mes sont d{\'e}finis les uns relativement aux autres. Les frames morphos{\'e}mantiques permettent de rendre compte de la structure paradigmatique du lexique morphologique par l{'}alignement des familles qui pr{\'e}sentent les m{\^e}mes oppositions de sens. La seconde partie de l{'}article est consacr{\'e}e aux donn{\'e}es qui seront utilis{\'e}es pour produire (semi-) automatiquement ces repr{\'e}sentations.",
    language = "French",
}
@inproceedings{tourille-etal-2020-modele,
    title = "Mod{\`e}le neuronal pour la r{\'e}solution de la cor{\'e}f{\'e}rence dans les dossiers m{\'e}dicaux {\'e}lectroniques (Neural approach for coreference resolution in electronic health records )",
    author = "Tourille, Julien  and
      Ferret, Olivier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Tannier, Xavier",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.35",
    pages = "351--360",
    abstract = "La r{\'e}solution de la cor{\'e}f{\'e}rence est un {\'e}l{\'e}ment essentiel pour la constitution automatique de chronologies m{\'e}dicales {\`a} partir des dossiers m{\'e}dicaux {\'e}lectroniques. Dans ce travail, nous pr{\'e}sentons une approche neuronale pour la r{\'e}solution de la cor{\'e}f{\'e}rence dans des textes m{\'e}dicaux {\'e}crits en anglais pour les entit{\'e}s g{\'e}n{\'e}rales et cliniques en nous {\'e}valuant dans le cadre de r{\'e}f{\'e}rence pour cette t{\^a}che que constitue la t{\^a}che 1C de la campagne i2b2 2011.",
    language = "French",
}
@inproceedings{wilkens-todirascu-2020-un,
    title = "Un corpus d{'}{\'e}valuation pour un syst{\`e}me de simplification discursive (An Evaluation Corpus for Automatic Discourse Simplification)",
    author = "Wilkens, Rodrigo  and
      Todirascu, Amalia",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.36",
    pages = "361--369",
    abstract = "Nous pr{\'e}sentons un nouveau corpus simplifi{\'e}, disponible en fran{\c{c}}ais pour l{'}{\'e}valuation d{'}un syst{\`e}me de simplification discursive. Ce syst{\`e}me utilise des cha{\^\i}nes de r{\'e}f{\'e}rence pour simplifier et pour pr{\'e}server la coh{\'e}sion textuelle apr{\`e}s simplification. Nous pr{\'e}sentons la m{\'e}thodologie de collecte de corpus (via un formulaire, qui recueille les simplifications manuelles faites par des participants experts), les r{\`e}gles pr{\'e}sent{\'e}es dans le guide, une analyse des types de simplifications et une {\'e}valuation de notre corpus, par comparaison avec la sortie du syst{\`e}me de simplification automatique.",
    language = "French",
}
@inproceedings{zhai-etal-2020-la,
    title = "La r{\'e}{\'e}criture monolingue ou bilingue facilite-t-elle la compr{\'e}hension ? (Does monolingual or bilingual rewriting facilitate comprehension ?)",
    author = "Zhai, Yuming  and
      Illouz, Gabriel  and
      Vilnat, Anne",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2 : Traitement Automatique des Langues Naturelles",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-taln.37",
    pages = "370--378",
    abstract = "La capacit{\'e} en compr{\'e}hension {\'e}crite est importante {\`a} d{\'e}velopper pour les apprenants de langues {\'e}trang{\`e}res. Cet article pr{\'e}sente une exp{\'e}rience pour v{\'e}rifier si les paraphrases fournies en contexte facilitent la compr{\'e}hension des apprenants. Les paraphrases ont {\'e}t{\'e} extraites automatiquement d{'}un corpus parall{\`e}le bilingue. Suite {\`a} l{'}analyse des r{\'e}sultats, nous proposons des pistes d{'}enrichissement d{'}un outil con{\c{c}}u pr{\'e}alablement, pour automatiser la s{\'e}lection de r{\'e}{\'e}critures dans un futur travail, tout en caract{\'e}risant mieux diff{\'e}rents types de r{\'e}{\'e}critures.",
    language = "French",
}
@proceedings{jep-taln-recital-2020-actes-de-la,
    title = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    editor = "Benzitoun, Christophe  and
      Braud, Chlo{\'e}  and
      Huber, Laurine  and
      Langlois, David  and
      Ouni, Slim  and
      Pogodalla, Sylvain  and
      Schneider, St{\'e}phane",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.0",
}
@inproceedings{benamar-2020-segmentation,
    title = "Segmentation de texte non-supervis{\'e}e pour la d{\'e}tection de th{\'e}matiques {\`a} l{'}aide de plongements lexicaux (Unsupervised text segmentation for topic detection using embeddings )",
    author = "Benamar, Alexandra",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.1",
    pages = "1--14",
    abstract = "Cet article pr{\'e}sente les principales m{\'e}thodes de segmentation automatique de documents textuels sp{\'e}cifiques. La t{\^a}che de segmentation th{\'e}matique de texte consiste {\`a} analyser un document pour en extraire des sections coh{\'e}rentes. Les m{\'e}thodes de segmentation non supervis{\'e}es cherchent {\`a} optimiser une fonction de probabilit{\'e} de segmentation ou une fonction de similarit{\'e} qui peut {\^e}tre calcul{\'e}e entre les blocs ou au sein des blocs. Elles sont r{\'e}parties en trois cat{\'e}gories : les m{\'e}thodes statistiques, les m{\'e}thodes {\`a} base de graphes et les approches neuronales. Parmi les approches neuronales utilis{\'e}es, nous nous int{\'e}ressons tout particuli{\`e}rement {\`a} celles qui utilisent des plongements lexicaux pour repr{\'e}senter des phrases et d{\'e}finir des segments th{\'e}matiques. Tout d{'}abord, nous montrons que les plongements lexicaux permettent une am{\'e}lioration nette des performances par rapport {\`a} des m{\'e}thodes statistiques. Ensuite, nous {\'e}valuons l{'}impact du choix de la repr{\'e}sentation vectorielle des phrases pour cette t{\^a}che de segmentation non supervis{\'e}e.",
    language = "French",
}
@inproceedings{bodard-2020-specificites,
    title = "Sp{\'e}cificit{\'e}s des erreurs d{'}orthographe des personnes dyslexiques : analyse d{'}un corpus de productions {\'e}crites (What are the characteristics of spelling errors made by dyslexics: an analysis of errors based on written corpora)",
    author = "Bodard, Johana",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.2",
    pages = "15--28",
    abstract = "Cet article pr{\'e}sente un travail d{'}analyse des erreurs d{'}orthographe de personnes dyslexiques {\`a} partir de corpus {\'e}crits en langue fran{\c{c}}aise. L{'}objectif de cette analyse est d{'}{\'e}tudier la fr{\'e}quence et les caract{\'e}ristiques des erreurs afin de guider le d{\'e}veloppement de modules de correction orthographique sp{\'e}cifiques. Les r{\'e}sultats de cette analyse sont compar{\'e}s aux travaux d{\'e}j{\`a} r{\'e}alis{\'e}s en fran{\c{c}}ais, anglais et espagnol.",
    language = "French",
}
@inproceedings{el-boukkouri-2020-entrainer,
    title = "R{\'e}-entra{\^\i}ner ou entra{\^\i}ner soi-m{\^e}me ? Strat{\'e}gies de pr{\'e}-entra{\^\i}nement de {BERT} en domaine m{\'e}dical (Re-train or train from scratch ? Pre-training strategies for {BERT} in the medical domain )",
    author = "El Boukkouri, Hicham",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.3",
    pages = "29--42",
    abstract = "Les mod{\`e}les BERT employ{\'e}s en domaine sp{\'e}cialis{\'e} semblent tous d{\'e}couler d{'}une strat{\'e}gie assez simple : utiliser le mod{\`e}le BERT originel comme initialisation puis poursuivre l{'}entra{\^\i}nement de celuici sur un corpus sp{\'e}cialis{\'e}. Il est clair que cette approche aboutit {\`a} des mod{\`e}les plut{\^o}t performants (e.g. BioBERT (Lee et al., 2020), SciBERT (Beltagy et al., 2019), BlueBERT (Peng et al., 2019)). Cependant, il para{\^\i}t raisonnable de penser qu{'}entra{\^\i}ner un mod{\`e}le directement sur un corpus sp{\'e}cialis{\'e}, en employant un vocabulaire sp{\'e}cialis{\'e}, puisse aboutir {\`a} des plongements mieux adapt{\'e}s au domaine et donc faire progresser les performances. Afin de tester cette hypoth{\`e}se, nous entra{\^\i}nons des mod{\`e}les BERT {\`a} partir de z{\'e}ro en testant diff{\'e}rentes configurations m{\^e}lant corpus g{\'e}n{\'e}raux et corpus m{\'e}dicaux et biom{\'e}dicaux. Sur la base d{'}{\'e}valuations men{\'e}es sur quatre t{\^a}ches diff{\'e}rentes, nous constatons que le corpus de d{\'e}part influence peu la performance d{'}un mod{\`e}le BERT lorsque celui-ci est r{\'e}-entra{\^\i}n{\'e} sur un corpus m{\'e}dical.",
    language = "French",
}
@inproceedings{boulanger-2020-evaluation,
    title = "{\'E}valuation syst{\'e}matique d{'}une m{\'e}thode commune de g{\'e}n{\'e}ration (Systematic evaluation of a common generation method)",
    author = "Boulanger, Hugo",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.4",
    pages = "43--56",
    abstract = "Avec l{'}augmentation de l{'}utilisation du traitement automatique des langues arrivent plusieurs probl{\`e}mes dont l{'}absence de donn{\'e}es dans les nouveaux domaines. Certaines approches d{'}apprentissage tel que l{'}apprentissage zero-shot ou par transfert tentent de r{\'e}soudre ces probl{\`e}mes. Une solution id{\'e}ale serait de g{\'e}n{\'e}rer des donn{\'e}es annot{\'e}es {\`a} partir de bases de connaissances des domaines d{'}int{\'e}r{\^e}t. Le but de notre travail est d{'}{\'e}valuer une m{\'e}thode de g{\'e}n{\'e}ration simple et de trouver les crit{\`e}res permettant de la mettre en oeuvre correctement. Pour cela, nous comparons les performances d{'}un mod{\`e}le obtenu sur des t{\^a}ches d{'}annotation quand il est entra{\^\i}n{\'e} sur des donn{\'e}es r{\'e}elles ou sur des donn{\'e}es g{\'e}n{\'e}r{\'e}es. Gr{\^a}ce aux r{\'e}sultats obtenus et {\`a} des analyses effectu{\'e}es sur les donn{\'e}es, nous avons pu d{\'e}terminer des bonnes pratiques d{'}utilisation de cette m{\'e}thode de g{\'e}n{\'e}ration sur la t{\^a}che d{'}annotation.",
    language = "French",
}
@inproceedings{buet-2020-analyse,
    title = "Analyse de la r{\'e}gulation de la longueur dans un syst{\`e}me neuronal de compression de phrase : une {\'e}tude du mod{\`e}le {L}en{I}nit (Investigating Length Regulation in a Sentence Compression Neural System : a Study on the {L}en{I}nit Model)",
    author = "Buet, Fran{\c{c}}ois",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.5",
    pages = "57--70",
    abstract = "La simplification de phrase vise {\`a} r{\'e}duire la complexit{\'e} d{'}une phrase tout en retenant son sens initial et sa grammaticalit{\'e}. En pratique, il est souvent attendu que la phrase produite soit plus courte que la phrase d{'}origine, et les mod{\`e}les qui int{\`e}grent un contr{\^o}le explicite de la longueur de sortie rev{\^e}tent un int{\'e}r{\^e}t particulier. Dans la continuit{\'e} de la litt{\'e}rature d{\'e}di{\'e}e {\`a} la compr{\'e}hension du comportement des syst{\`e}mes neuronaux, nous examinons dans cet article les m{\'e}canismes de r{\'e}gulation de longueur d{'}un encodeur-d{\'e}codeur RNN appliqu{\'e} {\`a} la compression de phrase, en {\'e}tudiant sp{\'e}cifiquement le cas du mod{\`e}le LenInit. Notre analyse met en {\'e}vidence la coexistence de deux influences distinctes au cours du d{\'e}codage : celle du contr{\^o}le explicite de la longueur, et celle du mod{\`e}le de langue du d{\'e}codeur.",
    language = "French",
}
@inproceedings{cassier-2020-exploitation,
    title = "Exploitation de mod{\`e}les distributionnels pour l{'}{\'e}tude de la nomination dans un corpus d{'}interviews politiques (Speaker-specific semantic variation representations using vector space models)",
    author = "Cassier, Manon",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.6",
    pages = "71--84",
    abstract = {En analyse de discours (AD), la nomination d{\'e}signe la recat{\'e}gorisation du r{\'e}f{\'e}rent par le locuteur {\`a} travers l{'}usage d{'}un nouveau nom ou d{'}un nom modifi{\'e}. Parfois utilis{\'e} pour influencer l{'}autre sur sa vision de voir le monde, ce ph{\'e}nom{\`e}ne sert d{'}indice sur l{'}id{\'e}ologie du locuteur voire, en contexte ad{\'e}quat, sur son affiliation politique. L{'}AD ne dispose pas {\`a} ce jour d{'}outils en mesure d{'}appr{\'e}hender efficacement ce qui rel{\`e}ve ou non de l{'}id{\'e}ologie ou d{'}une vis{\'e}e argumentative face {\`a} une simple r{\'e}utilisation de mots dont le sens est d{\'e}j{\`a} consensuel. Dans le cadre d{'}une th{\`e}se entre AD et TAL, nous nous int{\'e}ressons {\`a} l{'}exploitation de mod{\`e}les distributionnels pour rep{\'e}rer de mani{\`e}re automatique ces variations de sens en discours dans un corpus d{'}interviews politiques. Dans cet article, nous nous interrogeons sur l{'}impact de leurs param{\`e}tres d{'}entra{\^\i}nement pour de la d{\'e}sambigu{\"\i}sation lexicale et explorons une m{\'e}thode de repr{\'e}sentation de la variation s{\'e}mantique interdiscursive.},
    language = "French",
}
@inproceedings{cattan-2020-ladaptabilite,
    title = "L{'}adaptabilit{\'e} comme comp{\'e}tence pour les syst{\`e}mes de dialogue orient{\'e}s t{\^a}che (Adaptability as a skill for goal-oriented dialog systems)",
    author = "Cattan, Oralie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.7",
    pages = "85--95",
    abstract = "{\'E}tendre les capacit{\'e}s d{'}adaptabilit{\'e} des syst{\`e}mes {\`a} toujours plus de nouveaux domaines sans donn{\'e}es de r{\'e}f{\'e}rence constitue une pierre d{'}achoppement de taille. Prendre en charge plus de contenus serviciels constitue un moyen de diversifier l{'}{\'e}ventail des capacit{\'e}s de compr{\'e}hension des syst{\`e}mes de dialogue et apporterait un v{\'e}ritable int{\'e}r{\^e}t pour les utilisateurs par la richesse des {\'e}changes qu{'}elle rendrait possibles. Pour favoriser les progr{\`e}s dans ce sens, la huiti{\`e}me {\'e}dition du d{\'e}fi Dialog State Tracking Challenge introduit des pistes exploratoires permettant d{'}{\'e}valuer les capacit{\'e}s de g{\'e}n{\'e}ralisation et d{'}habilet{\'e} des syst{\`e}mes {\`a} composer {\`a} la fois avec la nouveaut{\'e} et avec plusieurs domaines de t{\^a}ches complexes. L{'}objectif de cet article est de rendre compte des recherches du domaine et contribue {\`a} donner des {\'e}l{\'e}ments de r{\'e}ponse de mani{\`e}re {\`a} mieux comprendre les limites des syst{\`e}mes actuels et les m{\'e}thodes appropri{\'e}es pour aborder ces d{\'e}fis.",
    language = "French",
}
@inproceedings{elguendouze-2020-simplification,
    title = "Simplification de textes : un {\'e}tat de l{'}art (Text simplification (State of the art) This paper presents the state of the art on text simplification, in particular the two main types of approaches, namely rule-based approaches and statistical approaches (or data-driven))",
    author = "Elguendouze, Sofiane",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.8",
    pages = "96--109",
    abstract = "Cet article pr{\'e}sente l{'}{\'e}tat de l{'}art en simplification de textes et ses deux grandes familles d{'}approches, {\`a} savoir les approches {\`a} base de r{\`e}gles et les approches statistiques. Nous pr{\'e}sentons, en particulier, les r{\'e}centes approches neuronales et les architectures mises en place ainsi que les m{\'e}thodes d{'}{\'e}valuation des syst{\`e}mes de simplification.",
    language = "French",
}
@inproceedings{fourrier-2020-evolution,
    title = "{\'E}volution phonologique des langues et r{\'e}seaux de neurones : travaux pr{\'e}liminaires (Sound change and neural networks: preliminary experiments )",
    author = "Fourrier, Cl{\'e}mentine",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.9",
    pages = "110--122",
    abstract = "La pr{\'e}diction de cognats est une t{\^a}che clef de la linguistique historique et pr{\'e}sente de nombreuses similitudes avec les t{\^a}ches de traduction automatique. Cependant, alors que cette seconde discipline a vu fleurir l{'}utilisation de m{\'e}thodes neuronales, celles-ci restent largement absentes des outils utilis{\'e}s en linguistique historique. Dans ce papier, nous {\'e}tudions donc la performance des m{\'e}thodes neuronales utilis{\'e}es en traduction (les r{\'e}seaux encodeur-d{\'e}codeur) pour la t{\^a}che de pr{\'e}diction de cognats. Nous nous int{\'e}ressons notamment aux types de donn{\'e}es utilisables pour cet apprentissage et comparons les r{\'e}sultats obtenus, sur diff{\'e}rents types de donn{\'e}es, entre des m{\'e}thodes statistiques et des m{\'e}thodes neuronales. Nous montrons que l{'}apprentissage de correspondances phon{\'e}tiques n{'}est possible que sur des paires de cognats, et que les m{\'e}thodes statistiques et neuronales semblent avoir des forces et faiblesses compl{\'e}mentaires quant {\`a} ce qu{'}elles apprennent des donn{\'e}es.",
    language = "French",
}
@inproceedings{han-etal-2020-comparing,
    title = "Comparing {PTB} and {UD} information for {PDTB} discourseconnective identification",
    author = "Han, Kelvin  and
      Leavitt, Phyllicia  and
      Balard, Srilakshmi",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.10",
    pages = "123--136",
    abstract = "Our work on the automatic detection of English discourse connectives in the Penn Discourse Treebank (PDTB) shows that syntactic information from the Universal Dependencies (UD) framework is a viable alternative to that from the Penn Treebank (PTB) framework. In fact, we found minor increases when comparing between the use of gold standard PTB part-of-speech (POS) tag information and automatically parsed UD information. The former has traditionally been used for the task but there are now much more UD corpora and in many more languages than that available in the PTB framework. As such, this finding is promising for areas in discourse parsing such as in multilingual as well as under production settings, where gold standard PTB information may be scarce.",
}
@inproceedings{hijazi-2020-transformations,
    title = "Transformations syntaxiques entre niveaux de simplification dans le corpus {N}ewsela (Syntactic transformations between simplification levels in the {N}ewsela corpus)",
    author = "Hijazi, Rita",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.11",
    pages = "137--150",
    abstract = "La simplification de textes est une t{\^a}che complexe du traitement automatique des langues. Depuis quelques ann{\'e}es, des corpus parall{\`e}les de textes originaux et simplifi{\'e}s sont propos{\'e}s, permettant d{'}apprendre diff{\'e}rents types d{'}op{\'e}rations de simplification {\`a} partir de corpus. Dans le but de pouvoir d{\'e}velopper et {\'e}valuer des syst{\`e}mes de simplification automatique de textes, cet article s{'}int{\'e}resse au corpus Newsela, un corpus parall{\`e}le de textes en langue anglaise avec quatre niveaux de simplification. Nous pr{\'e}sentons en d{\'e}tail ce corpus et {\'e}tudions les diff{\'e}rentes transformations caract{\'e}risant le passage d{'}un niveau de simplification {\`a} l{'}autre sur un sous-ensemble de textes, en nous int{\'e}ressant plus particuli{\`e}rement aux transformations syntaxiques.",
    language = "French",
}
@inproceedings{koptient-2020-la,
    title = "La d{\'e}sambiguisation des abr{\'e}viations du domaine m{\'e}dical (Disambiguation of abbreviations from the medical domain)",
    author = {Koptient, Ana{\"\i}s},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.12",
    pages = "151--163",
    abstract = {Les abr{\'e}viations, tout en {\'e}tant r{\'e}pandues dans la langue, ont une s{\'e}mantique assez opaque car seulement les premi{\`e}res lettres sont transparentes. Cela peut donc emp{\^e}cher la compr{\'e}hension des abr{\'e}viations, et des textes qui les contiennent, par les locuteurs. De plus, certaines abr{\'e}viations sont ambigu{\"e}s en ayant plusieurs sens possibles, ce qui augmente la difficult{\'e} de leur compr{\'e}hension. Nous proposons de travailler avec les abr{\'e}viations de la langue m{\'e}dicale dans un cadre li{\'e} {\`a} la simplification automatique de textes. Dans le processus de simplification, il faut en effet choisir la forme {\'e}tendue des abr{\'e}viations qui soit correcte pour un contexte donn{\'e}. Nous proposons de traiter la d{\'e}sambigu{\"\i}sation d{'}abr{\'e}viations comme un probl{\`e}me de cat{\'e}gorisation supervis{\'e}e. Les descripteurs sont construits {\`a} partir des contextes lexical et syntaxique des abr{\'e}viations. L{'}entra{\^\i}nement est effectu{\'e} sur les phrases qui contiennent les formes {\'e}tendues des abr{\'e}viations. Le test est effectu{\'e} sur un corpus construit manuellement, o{\`u} les bons sens des abr{\'e}viations ont {\'e}t{\'e} d{\'e}finis selon les contextes. Notre approche montre une F-mesure moyenne de 0,888 sur le corpus d{'}entra{\^\i}nement en validation crois{\'e}e et 0,773 sur le corpus de test.},
    language = "French",
}
@inproceedings{pelloin-prouteau-2020-apprentissage,
    title = "Apprentissage de plongements de mots sur des corpus en langue de sp{\'e}cialit{\'e} : une {\'e}tude d{'}impact (Learning word embeddings on domain specific corpora : an impact study )",
    author = "Pelloin, Valentin  and
      Prouteau, Thibault",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.13",
    pages = "164--178",
    abstract = "Les m{\'e}thodes d{'}apprentissage de plongements lexicaux constituent d{\'e}sormais l{'}{\'e}tat de l{'}art pour la repr{\'e}sentation du vocabulaire et des documents sous forme de vecteurs dans de nombreuses t{\^a}ches de Traitement Automatique du Langage Naturel (TALN). Dans ce travail, nous consid{\'e}rons l{'}apprentissage et l{'}usage de plongements lexicaux dans le cadre de corpus en langue de sp{\'e}cialit{\'e} de petite taille. En particulier, nous souhaitons savoir si dans ce cadre, il est pr{\'e}f{\'e}rable d{'}utiliser des plongements pr{\'e}appris sur des corpus tr{\`e}s volumineux tels Wikip{\'e}dia ou bien s{'}il est pr{\'e}f{\'e}rable d{'}apprendre des plongements sur ces corpus en langue de sp{\'e}cialit{\'e}. Pour r{\'e}pondre {\`a} cette question, nous consid{\'e}rons deux corpus en langue de sp{\'e}cialit{\'e} : O HSUMED issu du domaine m{\'e}dical, et un corpus de documentation technique, propri{\'e}t{\'e} de SNCF. Apr{\`e}s avoir introduit ces corpus et {\'e}valu{\'e} leur sp{\'e}cificit{\'e}, nous d{\'e}finissons une t{\^a}che de classification. Pour cette t{\^a}che, nous choisissons d{'}utiliser en entr{\'e}e d{'}un classifieur neuronal des repr{\'e}sentations des documents qui sont soit bas{\'e}es sur des plongements appris sur les corpus de sp{\'e}cialit{\'e}, soit sur des plongements appris sur Wikip{\'e}dia. Notre analyse montre que les plongements appris sur Wikip{\'e}dia fournissent de tr{\`e}s bons r{\'e}sultats. Ceux-ci peuvent {\^e}tre utilis{\'e}s comme une r{\'e}f{\'e}rence fiable, m{\^e}me si dans le cas d{'}O HSUMED, il vaut mieux apprendre des plongements sur ce m{\^e}me corpus. La discussion des r{\'e}sultats se fait en interrogeant les sp{\'e}cificit{\'e}s des deux corpus, mais ne permet pas d{'}{\'e}tablir clairement dans quels cas apprendre des plongements sp{\'e}cifiques au corpus.",
    language = "French",
}
@inproceedings{rigaud-2020-representation,
    title = "Repr{\'e}sentation vectorielle de paires de verbes pour la pr{\'e}diction de relations lexicales (Verb-pairs embeddings for discourse relation prediction)",
    author = "Rigaud, Etienne",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.14",
    pages = "179--192",
    abstract = "Dans cet article, nous proposons un mod{\`e}le de repr{\'e}sentations vectorielles de paire de mots, obtenues {\`a} partir d{'}une adaptation du mod{\`e}le Skip-gram de Word2vec. Ce mod{\`e}le est utilis{\'e} pour g{\'e}n{\'e}rer des vecteurs de paires de verbes, entra{\^\i}n{\'e}es sur le corpus de textes anglais Ukwac. Les vecteurs sont {\'e}valu{\'e}s sur les donn{\'e}es ConceptNet {\&} EACL, sur une t{\^a}che de classification de relations lexicales. Nous comparons les r{\'e}sultats obtenus avec les vecteurs paires {\`a} des mod{\`e}les utilisant des vecteurs mots, et testons l{'}{\'e}valuation avec des verbes dans leur forme originale et dans leur forme lemmatis{\'e}e. Enfin, nous pr{\'e}sentons des exp{\'e}riences o{\`u} ces vecteurs paires sont utilis{\'e}s sur une t{\^a}che d{'}identification de relation discursive entre deux segments de texte. Nos r{\'e}sultats sur le corpus anglais Penn Discourse Treebank, d{\'e}montrent l{'}importance de l{'}information verbale pour la t{\^a}che, et la compl{\'e}mentarit{\'e} de ces vecteurs paires avec les connecteurs discursifs des relations.",
    language = "French",
}
@inproceedings{shamsi-2020-tts,
    title = "{TTS} voice corpus reduction for audio-book generation",
    author = "Shamsi, Meysam",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.15",
    pages = "193--204",
    abstract = "Nowadays, with emerging new voice corpora, voice corpus reduction in expressive TTS becomes more important. In this study a spitting greedy approach is investigated to remove utterances. In the first step by comparing five objective measures, the TTS global cost has been found as the best available metric for approximation of perceptual quality. The greedy algorithm employs this measure to evaluate the candidates in each step and the synthetic quality resulted by its solution. It turned out that reducing voice corpus size until a certain length (1 hour in our experiment) could not degrade the synthetic quality. By modifying the original greedy algorithm, its computation time is reduced to a reasonable duration. Two perceptual tests have been run to compare this greedy method and the random strategy for voice corpus reduction. They revealed that there is no superiority of using the proposed greedy approach for corpus reduction.",
}
@inproceedings{tanguy-2020-exploiter,
    title = "Exploiter des mod{\`e}les de langue pour {\'e}valuer des sorties de logiciels d{'}{OCR} pour des documents fran{\c{c}}ais du {XVII}e si{\`e}cle ()",
    author = "Tanguy, Jean-Baptiste",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 3 : Rencontre des {\'E}tudiants Chercheurs en Informatique pour le TAL",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-recital.16",
    pages = "205--217",
    abstract = "Pour comparer deux sorties de logiciels d{'}OCR, le Character Error Rate (ou, CER) est fr{\'e}quemment utilis{\'e}. Moyennant l{'}existence d{'}une transcription de r{\'e}f{\'e}rence de qualit{\'e} pour certains documents du corpus, le CER calcule le taux d{'}erreurs de ces pi{\`e}ces et permet ensuite de s{\'e}lectionner le logiciel d{'}OCR le plus adapt{\'e}. Toutefois, ces transcriptions sont tr{\`e}s co{\^u}teuses {\`a} produire et peuvent freiner certaines {\'e}tudes, m{\^e}me prospectives. Nous explorons l{'}exploitation des mod{\`e}les de langue en agr{\'e}geant selon diff{\'e}rentes m{\'e}thodes les probabilit{\'e}s offertes par ceux-ci pour estimer la qualit{\'e} d{'}une sortie d{'}OCR. L{'}indice de corr{\'e}lation Pearson est ici utilis{\'e} pour comprendre dans quelle mesure ces estimations issues de mod{\`e}les de langue co-varient avec le CER, mesure de r{\'e}f{\'e}rence.",
    language = "French",
}
@proceedings{jep-taln-recital-2020-actes,
    title = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    editor = "Benzitoun, Christophe  and
      Braud, Chlo{\'e}  and
      Huber, Laurine  and
      Langlois, David  and
      Ouni, Slim  and
      Pogodalla, Sylvain  and
      Schneider, St{\'e}phane",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.0",
}
@inproceedings{abrougui-bartkova-2020-il,
    title = "{`}Il {\'e}tait une fois{'} les patterns prosodiques des contes de f{\'e}e ({`}Once upon a time{'} prosodic patterns of fairy tales)",
    author = "Abrougui, Rim  and
      Bartkova, Katarina",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.1",
    pages = "1--9",
    abstract = "Nous {\'e}tudions ici la diff{\'e}rence des patterns prosodiques entre deux styles de lecture, un que nous appelons {`}lecture litt{\'e}raire neutre{'} et un style de {`}lecture des contes{'}. Les donn{\'e}es appartenant au style de {`}lecture de contes{'} comportent deux sous-ensembles, des contes destin{\'e}s aux jeunes enfants (0-6 ans) et des contes destin{\'e}s aux enfants plus {\^a}g{\'e}s et aux adultes. Les corpus ont {\'e}t{\'e} manuellement annot{\'e}s avec des {\'e}tiquettes s{\'e}mantico-prosodiques exprimant des attitudes, des {\'e}motions et d{'}autres styles prosodiques. Une analyse d{\'e}taill{\'e}e des caract{\'e}ristiques prosodiques nous a permis d{'}identifier les traits pertinents des patterns intonatifs des diff{\'e}rentes {\'e}tiquettes et des diff{\'e}rents styles de lecture. Une quantification vectorielle, utilisant essentiellement des informations de F0, a {\'e}t{\'e} utilis{\'e}e pour d{\'e}gager les patterns prosodiques typiques correspondant aux diff{\'e}rentes {\'e}tiquettes. Une classification automatique bas{\'e}e sur des param{\`e}tres prosodiques a montr{\'e} une bonne identification des {\'e}tiquettes quand leur fr{\'e}quence {\'e}tait suffisamment {\'e}lev{\'e}e pour obtenir une mod{\'e}lisation robuste.",
    language = "French",
}
@inproceedings{albar-2020-production,
    title = "Production de la continuation du fran{\c{c}}ais par des apprenants japonophones : gestion de la F0 et de la dur{\'e}e (Production of {F}rench continuation by {J}apanese learners : F0 and duration )",
    author = "Albar, Rachel",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.2",
    pages = "10--18",
    abstract = "Dans cette {\'e}tude, nous nous int{\'e}ressons {\`a} la r{\'e}alisation des contours de continuation en fran{\c{c}}ais, par des apprenants japonophones en parole semi-spontan{\'e}e. Pour ce faire, nous avons analys{\'e} des productions obtenues {\`a} partir de description d{'}images en prenant en compte le niveau d{'}apprentissage et diff{\'e}rentes positions prosodiques. Les r{\'e}sultats montrent une bonne gestion de la fr{\'e}quence fondamentale par les apprenants. En effet, ils produisent des mont{\'e}es prosodiques aux fronti{\`e}res de AP et IP et sont capables de produire des mont{\'e}es plus importantes aux fronti{\`e}res de IP. Cependant, la gestion du param{\`e}tre de dur{\'e}e est moins homog{\`e}ne. Les r{\'e}sultats montrent que la dur{\'e}e n{'}est pas un param{\`e}tre acoustique robuste utilis{\'e} pour produire la continuation. Ces r{\'e}sultats sugg{\`e}rent que les deux param{\`e}tres acoustiques de dur{\'e}e et de F0 ne peuvent pas {\^e}tre mis au m{\^e}me niveau quant {\`a} l{'}analyse de la continuation.",
    language = "French",
}
@inproceedings{appavoo-etal-2020-la,
    title = "La pause chez les personnes {\^a}g{\'e}es {--} une {\'e}tude exploratoire (Pauses in Older Speakers reading task - An Exploratory Study)",
    author = "Appavoo, Betty  and
      Fauth, Camille  and
      Sock, Rudolph  and
      Vaxelaire, B{\'e}atrice",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.3",
    pages = "19--26",
    abstract = "La production de la parole chez la personne {\^a}g{\'e}e a fait l{'}objet de nombreuses {\'e}tudes qui portaient essentiellement mais pas exclusivement sur les aspects vocaux. Dans ce travail exploratoire, nous cherchons {\`a} {\'e}valuer les effets du vieillissement sur l{'}organisation de la lecture. La distribution des pauses et des groupes rythmiques, ainsi que leurs dur{\'e}es respectives ont {\'e}t{\'e} quantifi{\'e}s, de m{\^e}me que la vitesse d{'}{\'e}locution et la vitesse d{'}articulation pour un groupe de 10 locuteurs {\^a}g{\'e}s (60 {\`a} 80 ans) et un groupe de 10 locuteurs t{\'e}moins (40 {\`a} 55 ans). Les r{\'e}sultats indiquent des diff{\'e}rences significatives pour les dur{\'e}es des groupes rythmiques et des pauses et pour la vitesse d{'}{\'e}locution ; les locuteurs {\^a}g{\'e}s ayant un d{\'e}bit plus lent que les locuteurs plus jeunes. Ces diff{\'e}rences nous semblent int{\'e}ressantes {\`a} poursuivre afin d{'}{\'e}tudier plus pr{\'e}cis{\'e}ment les diff{\'e}rentes strat{\'e}gies de r{\'e}organisation que peuvent mettre en place les locuteurs en fonction de leur {\^a}ge.",
    language = "French",
}
@inproceedings{bada-etal-2020-reconnaissance,
    title = "Reconnaissance automatique de la parole : g{\'e}n{\'e}ration des prononciations non natives pour l{'}enrichissement du lexique (In this study we propose a method for lexicon adaptation in order to improve the automatic speech recognition ({ASR}) of non-native speakers)",
    author = "Bada, Ismael  and
      Fohr, Dominique  and
      Illina, Irina",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.4",
    pages = "27--35",
    abstract = "Dans cet article nous proposons une m{\'e}thode d{'}adaptation du lexique, destin{\'e}e {\`a} am{\'e}liorer les syst{\`e}mes de la reconnaissance automatique de la parole (SRAP) des locuteurs non natifs. En effet, la reconnaissance automatique souffre d{'}une chute significative de ses performances quand elle est utilis{\'e}e pour reconna{\^\i}tre la parole des locuteurs non natifs, car les phon{\`e}mes de la langue {\'e}trang{\`e}re sont fr{\'e}quemment mal prononc{\'e}s par ces locuteurs. Pour prendre en compte ce probl{\`e}me de prononciations erron{\'e}es, notre approche propose d{'}int{\'e}grer les prononciations non natives dans le lexique et par la suite d{'}utiliser ce lexique enrichi pour la reconnaissance. Pour r{\'e}aliser notre approche nous avons besoin d{'}un petit corpus de parole non native et de sa transcription. Pour g{\'e}n{\'e}rer les prononciations non natives, nous proposons de tenir compte des correspondances graph{\`e}mes-phon{\`e}mes en vue de g{\'e}n{\'e}rer de mani{\`e}re automatique des r{\`e}gles de cr{\'e}ation de nouvelles prononciations. Ces nouvelles prononciations seront ajout{\'e}es au lexique. Nous pr{\'e}sentons une {\'e}valuation de notre m{\'e}thode sur un corpus de locuteurs non natifs fran{\c{c}}ais s{'}exprimant en anglais.",
    language = "French",
}
@inproceedings{biteeva-lecocq-etal-2020-la,
    title = "La phonotaxe du russe dans la typologie des langues : focus sur la palatalisation (This paper presents a phonotactic description of {R}ussian based on an analysis of 15,000 phonologically transcribed and syllabified lemmas)",
    author = "Biteeva Lecocq, Ekaterina  and
      Vall{\'e}e, Nathalie  and
      Faure-Vincent, Denis",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.5",
    pages = "36--44",
    abstract = "Cet article pr{\'e}sente un travail de description phonotactique du russe bas{\'e} sur une analyse de 15 000 lemmes transcrits phonologiquement et syllab{\'e}s. Un ensemble de donn{\'e}es quantitatives relatives aux structures syllabiques a {\'e}t{\'e} examin{\'e} dans une perspective typologique. {\`A} partir d{'}une analyse distributionnelle des segments consonantiques {\mbox{$\pm$}}PAL, des probabilit{\'e}s phonotactiques ont {\'e}t{\'e} estim{\'e}es. Les r{\'e}sultats montrent que le russe suit globalement les tendances g{\'e}n{\'e}rales observ{\'e}es dans les langues de la base de donn{\'e}es G-ULSID (Vall{\'e}e, Rousset {\&} Rossato, 2009) et mettent en {\'e}vidence des asym{\'e}tries de distribution des consonnes {\mbox{$\pm$}}PAL {\`a} l{'}int{\'e}rieur de la syllabe. Le fait que le syst{\`e}me consonantique du russe pr{\'e}sente une distinctivit{\'e} {\mbox{$\pm$}}PAL {\'e}tendue {\`a} tous les lieux d{'}articulation, semble contraindre les coccurrences entre consonne et voyelle d{'}une m{\^e}me syllabe pr{\'e}dites par la th{\'e}orie Frame/Content (MacNeilage, 1998) et trouv{\'e}es dans de nombreuses langues.",
    language = "French",
}
@inproceedings{bourbon-etal-2020-debit,
    title = "D{\'e}bit et r{\'e}duction vocalique : effets de la t{\^a}che de parole et du locuteur (Rate and vowel reduction : effects of speech task and speaker)",
    author = "Bourbon, Ang{\'e}lina  and
      D{'}Alessandro, Daria  and
      Fougeron, C{\'e}cile",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.6",
    pages = "45--53",
    abstract = "Dans cette {\'e}tude nous examinons, sur un groupe vari{\'e} de 29 locuteurs, les diff{\'e}rences de r{\'e}ponses entre locuteur {\`a} une demande explicite de modification du d{\'e}bit tout d{'}abord dans une t{\^a}che de r{\'e}p{\'e}tition rapide, puis entre une t{\^a}che de lecture et une t{\^a}che de r{\'e}p{\'e}tition confortable. Ces r{\'e}ponses sont {\'e}valu{\'e}es en termes de d{\'e}bit articulatoire et de r{\'e}duction vocalique (temporelle et/ou spectrale). Les r{\'e}sultats montrent diff{\'e}rents profils de r{\'e}ponses dans la t{\^a}che de r{\'e}p{\'e}tition rapide par rapport {\`a} la m{\^e}me t{\^a}che sans contrainte temporelle, et on voit que le d{\'e}bit peut {\^e}tre augment{\'e} avec ou sans r{\'e}duction spectrale. On montre {\'e}galement une forte variation dans les r{\'e}ponses des locuteurs {\`a} une t{\^a}che de r{\'e}p{\'e}tition confortable par rapport {\`a} de la lecture, avec pour certains locuteurs des diff{\'e}rences nettes d{'}organisation spectro-temporelle. Dans cette t{\^a}che assez artificielle de r{\'e}p{\'e}tition, sans instruction pr{\'e}cise, davantage de diff{\'e}rences individuelles {\'e}mergent.",
    language = "French",
}
@inproceedings{cameron-2020-voice,
    title = "Voice Onset Time en code-switching anglais-fran{\c{c}}ais : une {\'e}tude des occlusives sourdes en d{\'e}but de mot (Voice Onset Time in {E}nglish-{F}rench code-switching : a study of word-initial voiceless stop consonants)",
    author = "Cameron, Marguerite",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.7",
    pages = "54--63",
    abstract = "Le {``}code-switching{''} ou l{'}alternance codique - l{'}alternance entre plusieurs langues dans une seule interaction - offre une occasion unique d{'}observer comment les locuteurs multilingues utilisent leurs langues. R{\'e}cemment, des {\'e}tudes phon{\'e}tiques sur les qualit{\'e}s acoustiques des {\'e}nonc{\'e}s codeswitch{\'e}s, telles que le VOT, ont examin{\'e} comment les locuteurs {\'e}quilibrent plusieurs syst{\`e}mes phonologiques. La pr{\'e}sente {\'e}tude examine les effets du code-switching sur le VOT des occlusives sourdes /p t k/ produites par les locuteurs bilingues anglais-fran{\c{c}}ais (L1 anglais et L1 fran{\c{c}}ais), d{'}une acquisition tardive de leur L2. Les donn{\'e}es ont {\'e}t{\'e} recueillies {\`a} partir des enregistrements de discours conversationnels, entre des bin{\^o}mes de participants. Pour les participants francophones (L1 fran{\c{c}}ais), le VOT du /p/ des mots anglais {\'e}tait plus long lors d{'}un code-switch (du fran{\c{c}}ais, vers l{'}anglais) que dans un {\'e}nonc{\'e} tir{\'e} d{'}une conversation monolingue anglais, et le VOT du /t/ {\'e}tait plus court. Aucun effet de contexte (le fait qu{'}une occlusive mesur{\'e}e vienne d{'}un code-switch vers l{'}anglais ou lors d{'}une conversation monolingue anglais) n{'}a {\'e}t{\'e} observ{\'e} pour les anglophones (L1 anglais).",
    language = "French",
}
@inproceedings{caubriere-etal-2020-ou,
    title = "O{\`u} en sommes-nous dans la reconnaissance des entit{\'e}s nomm{\'e}es structur{\'e}es {\`a} partir de la parole ? (Where are we in Named Entity Recognition from speech ?)",
    author = "Caubri{\`e}re, Antoine  and
      Rosset, Sophie  and
      Est{\`e}ve, Yannick  and
      Laurent, Antoine  and
      Morin, Emmanuel",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.8",
    pages = "64--72",
    abstract = "La reconnaissance des entit{\'e}s nomm{\'e}es (REN) {\`a} partir de la parole est traditionnellement effectu{\'e}e par l{'}interm{\'e}diaire d{'}une cha{\^\i}ne de composants, exploitant un syst{\`e}me de reconnaissance de la parole (RAP), puis un syst{\`e}me de REN appliqu{\'e} sur les transcriptions automatiques. Les derni{\`e}res donn{\'e}es disponibles pour la REN structur{\'e}es {\`a} partir de la parole en fran{\c{c}}ais proviennent de la campagne d{'}{\'e}valuation ETAPE en 2012. Depuis la publication des r{\'e}sultats, des am{\'e}liorations majeures ont {\'e}t{\'e} r{\'e}alis{\'e}es pour les syst{\`e}mes de REN et de RAP. Notamment avec le d{\'e}veloppement des syst{\`e}mes neuronaux. De plus, certains travaux montrent l{'}int{\'e}r{\^e}t des approches de bout en bout pour la t{\^a}che de REN dans la parole. Nous proposons une {\'e}tude des am{\'e}liorations en RAP et REN dans le cadre d{'}une cha{\^\i}ne de composants, ainsi qu{'}une nouvelle approche en trois {\'e}tapes. Nous explorons aussi les capacit{\'e}s d{'}une approche bout en bout pour la REN structur{\'e}es. Enfin, nous comparons ces deux types d{'}approches {\`a} l{'}{\'e}tat de l{'}art de la campagne ETAPE. Nos r{\'e}sultats montrent l{'}int{\'e}r{\^e}t de l{'}approche bout en bout, qui reste toutefois en de{\c{c}}{\`a} d{'}une cha{\^\i}ne de composants enti{\`e}rement mise {\`a} jour.",
    language = "French",
}
@inproceedings{chanclu-etal-2020-ptsvox,
    title = "{PTSVOX} : une base de donn{\'e}es pour la comparaison de voix dans le cadre judiciaire ({PTSVOX} : a Speech Database for Forensic Voice Comparison )",
    author = {Chanclu, Ana{\"\i}s  and
      Georgeton, Laurianne  and
      Fredouille, Corinne  and
      Bonastre, Jean-Francois},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.9",
    pages = "73--81",
    abstract = "Cet article pr{\'e}sente la base de donn{\'e}es PTSVOX, cr{\'e}{\'e}e par le Service Central de la Police Technique et Scientifique (SCPTS) sp{\'e}cifiquement pour la comparaison de voix dans le cadre judiciaire. PTSVOX contient 369 locuteurs et locutrices qui ont {\'e}t{\'e} enregistr{\'e}s au microphone et au t{\'e}l{\'e}phone. PTSVOX a {\'e}t{\'e} con{\c{c}}ue pour mesurer l{'}influence de diff{\'e}rents facteurs de variabilit{\'e} fr{\'e}quemment rencontr{\'e}s dans les cas pratiques en identification judiciaire, comme le type de parole, le temps {\'e}coul{\'e} et le mat{\'e}riel d{'}enregistrement. Pour cela, 24 des locuteurs de PTSVOX (12 hommes et 12 femmes) ont {\'e}t{\'e} enregistr{\'e}s une fois par mois pendant 3 mois, en parole spontan{\'e}e et en parole lue. Dans cet article, nous pr{\'e}sentons dans un premier temps la base PTSVOX, puis nous d{\'e}crivons des protocoles standards ainsi que les syst{\`e}mes de r{\'e}f{\'e}rence associ{\'e}s {\`a} PTSVOX, avec une {\'e}valuation de leur performance.",
    language = "French",
}
@inproceedings{chardenon-etal-2020-dis,
    title = "Dis-moi comment tu varies ton d{\'e}bit, je te dirai qui tu es (Tell me how you vary your speech flow, {I}{'}ll tell you who you are Studying inter-speaker variability makes it possible to identify discriminating or even identifying phonetic characteristics)",
    author = "Chardenon, Estelle  and
      Fougeron, C{\'e}cile  and
      Audibert, Nicolas  and
      Gendrot, C{\'e}dric",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.10",
    pages = "82--90",
    abstract = "Si l{'}{\'e}tude de la variabilit{\'e} entre locuteurs permet d{'}identifier des caract{\'e}ristiques phon{\'e}tiques potentiellement discriminantes, voire sp{\'e}cifiques, il est essentiel de comprendre, si et comment, ces caract{\'e}ristiques varient chez un m{\^e}me locuteur. Ici, nous examinons la variabilit{\'e} de caract{\'e}ristiques li{\'e}es {\`a} la gestion temporelle de la parole sur un nombre limit{\'e} de locuteurs, enregistr{\'e}s sur plusieurs r{\'e}p{\'e}titions dans une m{\^e}me session, et sur 6 {\`a} 7 sessions espac{\'e}es d{'}une ann{\'e}e. Sur cette vingtaine d{'}enregistrements par locuteur, nous observons comment le d{\'e}bit articulatoire, les modulations de ce d{\'e}bit, et la dur{\'e}e des pauses varient en fonction de la r{\'e}p{\'e}tition et de la session et en interaction avec le locuteur. Les r{\'e}sultats montrent que c{'}est dans la variation de gestion temporelle de la parole que les locuteurs se distinguent les uns des autres, en termes de r{\'e}gularit{\'e} ou non entre enregistrements et au sein d{'}un m{\^e}me enregistrement.",
    language = "French",
}
@inproceedings{chignoli-etal-2020-caracterisation,
    title = "Caract{\'e}risation du locuteur par {CNN} {\`a} l{'}aide des contours d{'}intensit{\'e} et d{'}intonation : comparaison avec le spectrogramme ({CNN} speaker characterisation through prosody : spectrogram comparison )",
    author = "Chignoli, Gabriele  and
      Gendrot, C{\'e}dric  and
      Ferragne, Emmanuel",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.11",
    pages = "91--99",
    abstract = "Dans ce travail nous avons recours aux variations de f0 et d{'}intensit{\'e} de 44 locuteurs francophones {\`a} partir de s{\'e}quences de 4 secondes de parole spontan{\'e}e pour comprendre comment ces param{\`e}tres prosodiques peuvent {\^e}tre utilis{\'e}s pour caract{\'e}riser des locuteurs. Une classification automatique est effectu{\'e}e avec un r{\'e}seau de neurones convolutifs, fournissant comme r{\'e}ponse des scores de probabilit{\'e} pour chacun des 44 locuteurs mod{\'e}lis{\'e}s. Une repr{\'e}sentation par spectrogrammes a {\'e}t{\'e} utilis{\'e}e comme r{\'e}f{\'e}rence pour le m{\^e}me syst{\`e}me de classification. Nous avons pu mettre en avant la pertinence de l{'}intensit{\'e}, et lorsque les deux param{\`e}tres prosodiques sont combin{\'e}s pour repr{\'e}senter les locuteurs nous observons un score qui atteint en moyenne 59 {\%} de bonnes classifications.",
    language = "French",
}
@inproceedings{chlebowski-ballier-2020-cest,
    title = "{C}{'}est {``}mm-hm, oui{''} ou {``}mm-hm, non{''} ? Propositions pour une grammaire des composantes acoustiques des interactions nasalis{\'e}es (A modest proposal for the pragmatic of nasal grunts in the {CID} corpus)",
    author = "Chl{\'e}bowski, Aur{\'e}lie  and
      Ballier, Nicolas",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.12",
    pages = "100--108",
    abstract = "Cet article se propose d{'}envisager l{'}existence d{'}une grammaire sp{\'e}cifique aux interactions nasalis{\'e}es (Chl{\'e}bowski et Ballier, 2015). Notre proposition se fonde sur une annotation des composantes acoustiques de cette sous-cat{\'e}gorie de sons non-lexicaux (Ward, 2006) dans le corpus CID (Bertrand et al., 2008). Nous voudrions pr{\'e}senter les contraintes combinatoires et r{\'e}gularit{\'e}s qui semblent s{'}appliquer {\`a} ces composantes acoustiques, ainsi que discuter leur structuration. Les r{\'e}sultats pr{\'e}liminaires de l{'}analyse des composantes acoustiques semblent sugg{\'e}rer des plages de valeurs par d{\'e}faut pour les r{\'e}alisations des IN (notamment pour la dur{\'e}e). La violation de ces usages peut donner lieu {\`a} une analyse de type gricienne d{'}implicature.",
    language = "French",
}
@inproceedings{christodoulides-2020-variation,
    title = "Variation prosodique des styles de parole et interface syntaxe-prosodie: {\'E}tude sur corpus {\`a} grande {\'e}chelle (Speaking Style Prosodic Variation and the Prosody-Syntax Interface : A Large-Scale Corpus)",
    author = "Christodoulides, George",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.13",
    pages = "109--117",
    abstract = "La mutualisation et diffusion des grands corpus de parole permet de r{\'e}examiner des analyses pr{\'e}c{\'e}dentes effectu{\'e}es sur des corpus plus petits, afin de v{\'e}rifier si les conclusions de ces analyses se g{\'e}n{\'e}ralisent aux nouvelles donn{\'e}es. Dans cette {\'e}tude, nous pr{\'e}sentons les r{\'e}sultats pr{\'e}liminaires d{'}une analyse de la variation des styles de parole en fran{\c{c}}ais, bas{\'e}e sur un corpus {\`a} grande {\'e}chelle (300 heures, 2500 locuteurs). Le corpus a {\'e}t{\'e} r{\'e}align{\'e} au niveau des phones, syllabes et mots, et une annotation morphosyntaxique et syntaxique a {\'e}t{\'e} ajout{\'e} en am{\'e}liorant les annotations existantes. Plusieurs caract{\'e}ristiques acoustiques et prosodiques sont automatiquement extraites et une analyse statistique (analyse en composantes principales, ACP) est effectu{\'e}e afin d{'}explorer les caract{\'e}ristiques des styles de parole et leur variance. Nous explorons aussi la relation entre fronti{\`e}res prosodique et syntaxiques comme m{\'e}thode pour discriminer les styles de parole. 1",
    language = "French",
}
@inproceedings{coulange-rossato-2020-proximite,
    title = "Proximit{\'e} rythmique entre apprenants et natifs du fran{\c{c}}ais {\'E}valuation d{'}une m{\'e}trique bas{\'e}e sur le {CEFC} (Rhythmic Proximity Between Natives And Learners Of {F}rench {--} Evaluation of a metric based on the {CEFC} corpus )",
    author = "Coulange, Sylvain  and
      Rossato, Solange",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.14",
    pages = "118--126",
    abstract = "Cette {\'e}tude a pour objectif de proposer une quantification de l{'}accent {\'e}tranger se basant sur des mesures rythmiques. Nous avons utilis{\'e} le Corpus pour l{'}{\'E}tude du Fran{\c{c}}ais Contemporain, qui propose plus de 300 heures de parole aux profils de locuteurs et aux situations vari{\'e}s. Nous nous sommes concentr{\'e}s sur 16 param{\`e}tres temporels estim{\'e}s {\`a} partir des dur{\'e}es de voisement et de syllabes. Un m{\'e}lange gaussien a {\'e}t{\'e} appris sur les donn{\'e}es de 1 340 natifs du fran{\c{c}}ais, puis test{\'e} sur des extraits de 146 natifs tir{\'e}s au hasard (NS), sur ceux des 37 non-natifs pr{\'e}sents dans le corpus (NNS), ainsi que sur des enregistrements de 29 apprenants japonais de niveau A2 d{'}un autre corpus. La probabilit{\'e} que les NNS aient une log-vraisemblance inf{\'e}rieure aux NS ne d{\'e}passe pas la tendance (p = 0, 067), mais celle pour les apprenants japonais est beaucoup plus significative (p {\textless} 0, 0001). L{'}{\'e}tude de la r{\'e}partition des param{\`e}tres entre les diff{\'e}rents groupes met en avant l{'}importance du d{\'e}bit de parole et des dur{\'e}es de voisement. 1",
    language = "French",
}
@inproceedings{dahmani-etal-2020-etude,
    title = "{\'E}tude comparative des param{\`e}tres d{'}entr{\'e}e pour la synth{\`e}se expressive audiovisuelle de la parole par {DNN}s (Comparative study of input parameters for {DNN}-based expressive audiovisual speech synthesis )",
    author = "Dahmani, Sara  and
      Colotte, Vincent  and
      Ouni, Slim",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.15",
    pages = "127--135",
    abstract = "Dans le pass{\'e}, les descripteurs contextuels pour la synth{\`e}se de la parole acoustique ont {\'e}t{\'e} {\'e}tudi{\'e}s pour l{'}entra{\^\i}nement des syst{\`e}mes bas{\'e}s sur des HMMs. Dans ce travail, nous {\'e}tudions l{'}impact de ces facteurs pour la synth{\`e}se de la parole audiovisuelle par DNNs. Nous analysons cet impact pour les trois aspects de la parole : la modalit{\'e} acoustique, la modalit{\'e} visuelle et les dur{\'e}es des phon{\`e}mes. Nous {\'e}tudions {\'e}galement l{'}apport d{'}un entra{\^\i}nement joint et s{\'e}par{\'e} des deux modalit{\'e}s acoustique et visuelle sur la qualit{\'e} de la parole synth{\'e}tique g{\'e}n{\'e}r{\'e}e. Finalement, nous proc{\'e}dons {\`a} une validation crois{\'e}e entre les r{\'e}sultats de la synth{\`e}se des diff{\'e}rentes {\'e}motions. Cette validation crois{\'e}e, nous a permis de v{\'e}rifier la capacit{\'e} des DNNs {\`a} apprendre des caract{\'e}ristiques sp{\'e}cifiques {\`a} chaque {\'e}motion.",
    language = "French",
}
@inproceedings{dehais-underdown-etal-2020-rythme,
    title = "Rythme et contr{\^o}le articulatoire : {\'e}tude pr{\'e}liminaire du Human Beatbox (Rhythm and articulatory Control : Preliminary study of Human Beatboxing)",
    author = "Dehais Underdown, Alexis  and
      Vignes, Paul  and
      Crevier-Buchman, Lise  and
      Demolin, Didier",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.16",
    pages = "136--144",
    abstract = "Dans cette {\'e}tude nous nous int{\'e}ressons {\`a} l{'}analyse spectrale d{'}imitation de grosses caisses, de charleston et de caisse claire dans un paradigme d{'}augmentation de la vitesse de production. La vitesse de production a {\'e}t{\'e} contr{\^o}l{\'e}e en utilisant un m{\'e}tronome {\`a} vibration param{\'e}tr{\'e} {\`a} 90, 120 puis 150 battements par minute. Le Centre de Gravit{\'e} spectral et le coefficient d{'}asym{\'e}trie ont {\'e}t{\'e} mesur{\'e}s pour inf{\'e}rer la stabilit{\'e} et la variabilit{\'e} articulatoire des sons produits dans les patterns beatbox{\'e}s. Les grosse caisses sont les sons les plus contr{\^o}l{\'e}s suivi par les caisses claires puis les charlestons.",
    language = "French",
}
@inproceedings{delais-roussarie-etal-2020-unites,
    title = "Unit{\'e}s prosodiques et grammaire intonative du fran{\c{c}}ais : vers une nouvelle approche (Prosodic Units and Intonational Grammar in {F}rench: towards a new Approach)",
    author = "Delais-Roussarie, Elisabeth  and
      Post, Brechtje  and
      Yoo, Hiyon",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.17",
    pages = "145--153",
    abstract = "Dans les travaux sur la prosodie du fran{\c{c}}ais sont g{\'e}n{\'e}ralement propos{\'e}s deux ou trois niveaux de structuration prosodique: le syntagme accentuel, le syntagme interm{\'e}diaire et le syntagme intonatif. Alors que les auteurs sont souvent d{'}accord sur les modalit{\'e}s de construction du syntagme accentuel, il n{'}en est pas de m{\^e}me pour les deux autres niveaux. Dans cet article, nous proposons de red{\'e}finir le syntagme interm{\'e}diaire. Cette proposition diff{\`e}re des autres travaux en deux points. Premi{\`e}rement, l{'}extension et le statut du syntagme interm{\'e}diaire est clarifi{\'e} pour en faire une unit{\'e} m{\'e}trique. Deuxi{\`e}mement, une distinction est faite entre cette unit{\'e} et deux types de syntagme intonatif. Cette proposition se base sur l{'}inventaire des contours observ{\'e}s {\`a} la fronti{\`e}re droite de ces unit{\'e}s et sur l{'}{\'e}tude des relations qu{'}elles entretiennent avec les structures morpho-syntaxique et s{\'e}mantique. Elle vise {\`a} rendre compte du phras{\'e} et du choix des contours intonatifs {\`a} un niveau phonologique sous-jacent.",
    language = "French",
}
@inproceedings{deleglise-lailler-2020-quel,
    title = "Quel type de syst{\`e}mes utiliser pour la transcription automatique du fran{\c{c}}ais ? Les {HMM} font de la r{\'e}sistance (What system for the automatic transcription of {F}rench in audiovisual broadcasts ?)",
    author = "Del{\'e}glise, Paul  and
      Lailler, Carole",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.18",
    pages = "154--162",
    abstract = "Forts d{'}une utilisation couronn{\'e}e de succ{\`e}s en traduction automatique, les syst{\`e}mes end-to-end dont la sortie r{\'e}side en une suite de caract{\`e}res, ont vu leur utilisation {\'e}tendue {\`a} la transcription automatique de la parole. De nombreuses comparaisons ont alors {\'e}t{\'e} effectu{\'e}es sur des corpus anglais libres de droits, de parole lue. Nous proposons ici de r{\'e}aliser une comparaison entre deux syst{\`e}mes {\'e}tat de l{'}art, non pas sur de la parole lue mais bel et bien sur un corpus d{'}{\'e}missions audiovisuelles fran{\c{c}}aises pr{\'e}sentant diff{\'e}rents degr{\'e}s de spontan{\'e}it{\'e}. Le premier est un end-to-end et le second est un syst{\`e}me hybride (HMM/DNN). L{'}obtention de r{\'e}sultats satisfaisants pour le end-to-end n{\'e}cessitant un lexique et mod{\`e}le de langage d{\'e}di{\'e}s, il est int{\'e}ressant de constater qu{'}une meilleure int{\'e}gration dans les syst{\`e}mes hybrides (HMM/DNN) est source de performances sup{\'e}rieures, notamment en Fran{\c{c}}ais o{\`u} le contexte est primordial pour capturer un {\'e}nonc{\'e}.",
    language = "French",
}
@inproceedings{didirkova-etal-2020-adaptations,
    title = "Adaptations sur le F1 et le d{\'e}bit en r{\'e}ponse {\`a} diverses perturbations (F1 and speech rate adaptations in response to various perturbations)",
    author = "Didirkov{\'a}, Ivana  and
      Lancia, Leonardo  and
      Fougeron, C{\'e}cile",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.19",
    pages = "163--171",
    abstract = "Nous comparons les effets de deux conditions induisant des strat{\'e}gies d{'}adaptation diff{\'e}rentes (retour auditif masqu{\'e} et bite-block) avec les effets des modifications intentionnelles du d{\'e}bit de parole. Nous examinons ces effets en termes de F1 et en termes de d{\'e}bit articulatoire. Nous comparons ensuite les effets de ces m{\^e}mes perturbations sur les m{\^e}mes locuteurs, afin de d{\'e}finir si les strat{\'e}gies inter- et intra-individuelles varient en fonction de la boucle perturb{\'e}e (auditive ou somatosensorielle). Cinq locutrices ont {\'e}t{\'e} enregistr{\'e}es sans perturbation, avec un retour auditif masqu{\'e}, avec un bite-block et avec des changements de tempo instruits (lent et rapide). Les r{\'e}sultats montrent une augmentation du d{\'e}bit en parall{\`e}le d{'}une augmentation du F1 des voyelles ouvertes, ce qui permet de supposer que les modifications spectrales et les modifications de d{\'e}bit ne seraient pas corr{\'e}l{\'e}es. La m{\^e}me augmentation de F1 est observ{\'e}e lors d{'}une modification intentionnelle du d{\'e}bit de parole.",
    language = "French",
}
@inproceedings{duez-etal-2020-perception,
    title = "Perception des consonnes dans la dysarthrie parkinsonienne : effets du contexte phon{\'e}mique, prosodique et lexical (Perception of consonants in parkinsonian dysarthria: effects of the phonetic, prosodic and lexical context)",
    author = "Duez, Danielle  and
      Ghio, Alain  and
      Viallet, Fran{\c{c}}ois",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.20",
    pages = "172--180",
    abstract = "Les patients atteints de la maladie de Parkinson (MDP) pr{\'e}sentent g{\'e}n{\'e}ralement des d{\'e}ficits dans la production de la parole. Nous avons examin{\'e} l{'}identification perceptive des consonnes intervocaliques produites par 10 locuteurs avec MDP et 10 locuteurs sains en lecture de texte. Pour neutraliser le contenu s{\'e}mantique, toutes les consonnes intervocaliques ont {\'e}t{\'e} isol{\'e}es avec la moiti{\'e} des voyelles pr{\'e}c{\'e}dente et suivante. 20 adultes natifs francophones ont {\'e}t{\'e} charg{\'e}s de transcrire les s{\'e}quences du corpus. La consonne rapport{\'e}e a {\'e}t{\'e} examin{\'e}e par rapport {\`a} la consonne prototypique; le score de distorsion est le nombre de traits phon{\'e}tiques diff{\'e}rents par rapport {\`a} la consonne prototypique. Les r{\'e}sultats ont {\'e}t{\'e} examin{\'e}s en fonction des facteurs linguistiques suivants: nature de la consonne, contexte vocalique oral / nasal, classe de mot (fonction ou contenu) et position dans les syntagmes. L{'}impr{\'e}cision de la consonne a {\'e}t{\'e} confirm{\'e}e dans la parole des locuteurs MDP. MOTS-CL{\'E}S : perception, dysarthrie parkinsonienne, phon{\'e}tique clinique, traits phon{\'e}tiques",
    language = "French",
}
@inproceedings{duniec-etal-2020-statistiques,
    title = "Statistiques des sons naturels et hypoth{\`e}se du codage efficace pour la perception de la musique et de la parole: Mise en place d{'}une m{\'e}thodologie d{'}{\'e}valuation (Natural sound statistics and the efficient coding hypothesis for music and speech perception : setting-up an evaluation methodology)",
    author = "Duniec, Agnieszka  and
      Crouzet, Olivier  and
      Delais-Roussarie, Elisabeth",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.21",
    pages = "181--189",
    abstract = "L{'}hypoth{\`e}se du codage efficace pr{\'e}dit que les syst{\`e}mes perceptifs sont optimalement adapt{\'e}s aux propri{\'e}t{\'e}s statistiques des signaux naturels. Ce caract{\`e}re optimal a {\'e}t{\'e} r{\'e}cemment {\'e}valu{\'e} sur la base d{'}analyses statistiques r{\'e}alis{\'e}es sur des d{\'e}compositions spectrales de signaux de parole repr{\'e}sent{\'e}s comme des modulations d{'}{\'e}nergie. Ces travaux pourraient trouver des applications directes dans l{'}am{\'e}lioration du codage des signaux acoustiques par des implants cochl{\'e}aires. Cependant, les recherches sur la perception de la musique par des personnes sourdes portant un implant cochl{\'e}aire mettent en avant des limites qui semblent discordantes avec les performances observ{\'e}es concernant certaines propri{\'e}t{\'e}s fondamentales de la parole. Nous comparons les r{\'e}sultats d{'}analyses statistiques de signaux musicaux avec ceux qui ont {\'e}t{\'e} r{\'e}alis{\'e}s sur de la parole dans le but d{'}{\'e}valuer les impacts respectifs de ces deux gammes de signaux sonores pour {\'e}valuer leurs contributions {\`a} cette proposition th{\'e}orique. Des r{\'e}sultats pr{\'e}liminaires et les perspectives futures sont discut{\'e}s.",
    language = "French",
}
@inproceedings{duroselle-etal-2020-adaptation,
    title = "Adaptation de domaine non supervis{\'e}e pour la reconnaissance de la langue par r{\'e}gularisation d{'}un r{\'e}seau de neurones (Unsupervised domain adaptation for language identification by regularization of a neural network)",
    author = {Duroselle, Rapha{\"e}l  and
      Jouvet, Denis  and
      Illina, Irina},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.22",
    pages = "190--198",
    abstract = "Les syst{\`e}mes automatiques d{'}identification de la langue subissent une d{\'e}gradation importante de leurs performances quand les caract{\'e}ristiques acoustiques des signaux de test diff{\`e}rent fortement des caract{\'e}ristiques des donn{\'e}es d{'}entra{\^\i}nement. Dans cet article, nous {\'e}tudions l{'}adaptation de domaine non supervis{\'e}e d{'}un syst{\`e}me entra{\^\i}n{\'e} sur des conversations t{\'e}l{\'e}phoniques {\`a} des transmissions radio. Nous pr{\'e}sentons une m{\'e}thode de r{\'e}gularisation d{'}un r{\'e}seau de neurones consistant {\`a} ajouter {\`a} la fonction de co{\^u}t un terme mesurant la divergence entre les deux domaines. Des exp{\'e}riences sur le corpus OpenSAD15 nous permettent de s{\'e}lectionner la Maximum Mean Discrepancy pour r{\'e}aliser cette mesure. Cette approche est ensuite appliqu{\'e}e {\`a} un syst{\`e}me moderne d{'}identification de la langue reposant sur des x-vectors. Sur le corpus RATS, pour sept des huit canaux radio {\'e}tudi{\'e}s, l{'}approche permet, sans utiliser de donn{\'e}es annot{\'e}es du domaine cible, de surpasser la performance d{'}un syst{\`e}me entra{\^\i}n{\'e} de fa{\c{c}}on supervis{\'e}e avec des donn{\'e}es annot{\'e}es de ce domaine.",
    language = "French",
}
@inproceedings{elmerich-etal-2020-modifications,
    title = "Modifications des flux a{\'e}rodynamiques de la parole apr{\`e}s chirurgie naso-sinusienne (Speech aerodynamic airflow modifications after sinonasal surgery)",
    author = "Elmerich, Am{\'e}lie  and
      Amelot, Ang{\'e}lique  and
      Crevier-Buchman, Lise",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.23",
    pages = "199--207",
    abstract = "Cette {\'e}tude a pour but de d{\'e}terminer dans quelle mesure la polypose naso-sinusienne impacte l{'}a{\'e}rodynamique des flux oral et nasal. Ainsi, nous avons enregistr{\'e} des patients atteints de cette pathologie avant et apr{\`e}s chirurgie. Plusieurs {\'e}l{\'e}ments ont pu {\^e}tre mis en lumi{\`e}re : une modification du passage de l{'}air dans la cavit{\'e} nasale et une meilleure coordination des flux d{'}air oral et nasal apr{\`e}s chirurgie.",
    language = "French",
}
@inproceedings{evain-etal-2020-reconnaissance,
    title = "Reconnaissance de parole beatbox{\'e}e {\`a} l{'}aide d{'}un syst{\`e}me {HMM}-{GMM} inspir{\'e} de la reconnaissance automatique de la parole ({BEATBOX} {SOUNDS} {RECOGNITION} {USING} A {SPEECH}-{DEDICATED} {HMM}-{GMM} {BASED} {SYSTEM} 1 Human beatboxing is a vocal art making use of speech organs to produce percussive sounds and imitate musical instruments)",
    author = "Evain, Sol{\`e}ne  and
      Contesse, Adrien  and
      Pinchaud, Antoine  and
      Schwab, Didier  and
      Lecouteux, Benjamin  and
      Henrich Bernardoni, Nathalie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.24",
    pages = "208--216",
    abstract = "Le human-beatbox est un art vocal utilisant les organes de la parole pour produire des sons percussifs et imiter les instruments de musique. La classification des sons du beatbox repr{\'e}sente actuellement un d{\'e}fi. Nous proposons un syst{\`e}me de reconnaissance des sons de beatbox s{'}inspirant de la reconnaissance automatique de la parole. Nous nous appuyons sur la bo{\^\i}te {\`a} outils Kaldi, qui est tr{\`e}s utilis{\'e}e dans le cadre de la reconnaissance automatique de la parole (RAP). Notre corpus est compos{\'e} de sons isol{\'e}s produits par deux beatboxers et se compose de 80 sons diff{\'e}rents. Nous nous sommes concentr{\'e}s sur le d{\'e}codage avec des mod{\`e}les acoustiques monophones, {\`a} base de HMM-GMM. La transcription utilis{\'e}e s{'}appuie sur un syst{\`e}me d{'}{\'e}criture sp{\'e}cifique aux beatboxers, appel{\'e} Vocal Grammatics (VG). Ce syst{\`e}me d{'}{\'e}criture s{'}appuie sur les concepts de la phon{\'e}tique articulatoire.",
    language = "French",
}
@inproceedings{fagniart-etal-2020-perception,
    title = "Perception et production du trait de nasalit{\'e} vocalique chez l{'}enfant porteur d{'}implants cochl{\'e}aires (Perception and production of the nasal vowels in cochlear implanted children)",
    author = "Fagniart, Sophie  and
      Charlier, Brigitte  and
      Delvaux, V{\'e}ronique  and
      Huberlant, Anne  and
      Huet, Kathy  and
      Piccaluga, Myriam  and
      Watterman, Isabelle  and
      Harmegnies, Bernard",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.25",
    pages = "217--225",
    abstract = "L{'}implant cochl{\'e}aire, malgr{\'e} une am{\'e}lioration consid{\'e}rable de la perception auditive, ne fournit qu{'}une information acoustique partielle, pouvant donner lieu {\`a} des difficult{\'e}s de perception de certains contrastes phon{\'e}tiques. L{'}{\'e}tude pr{\'e}sent{\'e}e vise {\`a} d{\'e}terminer les comp{\'e}tences de perception et de production des voyelles nasales et orales d{'}enfants porteurs d{'}implants cochl{\'e}aires en comparaison aux comp{\'e}tences d{'}enfants normo-entendants. Malgr{\'e} des r{\'e}sultats tr{\`e}s satisfaisants dans les deux groupes, on observe des patterns d{'}erreurs sp{\'e}cifiques au groupe d{'}enfants implant{\'e}s dans les t{\^a}ches perceptives, ainsi que certaines particularit{\'e}s dans la r{\'e}alisation phon{\'e}tique des voyelles nasales, portant notamment sur les valeurs de bande passante.",
    language = "French",
}
@inproceedings{ferreira-etal-2020-une,
    title = "Une nouvelle mesure de la r{\'e}verb{\'e}ration pour pr{\'e}dire les performances a priori de la transcription de la parole (A new reverberation measure to predict a priori {ASR} performance)",
    author = "Ferreira, S{\'e}bastien  and
      Farinas, J{\'e}r{\^o}me  and
      Pinquier, Julien  and
      Mauclair, Julie  and
      Rabant, St{\'e}phane",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.26",
    pages = "226--234",
    abstract = "Dans cette {\'e}tude, nous explorons la pr{\'e}diction a priori de la qualit{\'e} de la transcription automatique de la parole dans le cas de la parole r{\'e}verb{\'e}r{\'e}e enregistr{\'e}e avec un seul microphone. Cette pr{\'e}diction est faite avant le d{\'e}codage pour informer les utilisateurs de la qualit{\'e} de la transcription attendue. Dans cette {\'e}tude, nous nous concentrons uniquement sur les pertes de performance li{\'e}es {\`a} la r{\'e}verb{\'e}ration. Une nouvelle mesure de r{\'e}verb{\'e}ration appel{\'e}e « Excitation Behavior » est introduite. Cette mesure exploite le r{\'e}sidus de la pr{\'e}diction lin{\'e}aire sur les fen{\^e}tres vois{\'e}es du signal de parole. L{'}exp{\'e}rience a {\'e}t{\'e} men{\'e}e sur le corpus Wall Street Journal, r{\'e}verb{\'e}r{\'e} par des r{\'e}ponses impulsionnelles provenant du REVERB Challenge. Par rapport aux autres mesures de r{\'e}verb{\'e}ration test{\'e}es, notre mesure obtient une am{\'e}lioration relative de 20{\%} de la pr{\'e}diction du taux d{'}erreur (aussi bien au niveau des phon{\`e}mes que des mots).",
    language = "French",
}
@inproceedings{ferreira-etal-2020-analyse,
    title = "Analyse de l{'}effet de la r{\'e}verb{\'e}ration sur la reconnaissance automatique de la parole (Analyzing how reverberation affects Automatic Speech Recognition)",
    author = "Ferreira, S{\'e}bastien  and
      Farinas, J{\'e}r{\^o}me  and
      Pinquier, Julien  and
      Mauclair, Julie  and
      Rabant, St{\'e}phane",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.27",
    pages = "235--243",
    abstract = "La Reconnaissance Automatique de la Parole (RAP) est moins performante lorsque le signal de parole est de mauvaise qualit{\'e}. Dans cette {\'e}tude, nous analysons les erreurs commises par les syst{\`e}mes de RAP lorsque la parole transcrite est r{\'e}verb{\'e}r{\'e}e afin de mieux comprendre les raisons de ces erreurs. Notre analyse permet de mettre en valeur les erreurs dues notamment {\`a} un mauvais alignement phon{\'e}tique. Nous avons pu constater que les phon{\`e}mes de courte dur{\'e}e sont majoritairement supprim{\'e}s lors du d{\'e}codage phon{\'e}tique. De plus, les phon{\`e}mes d{\'e}tect{\'e}s, qu{'}ils soient corrects ou pas, ont tendance {\`a} avoir la m{\^e}me dur{\'e}e, ce qui est anormal pour certaines classes phon{\'e}tiques comme les voyelles courtes ou les plosives. Nous avons aussi analys{\'e} les principales confusions entre les diff{\'e}rentes classes phon{\'e}tiques. Finalement, nous avons pu montrer que les erreurs lors de l{'}alignement phon{\'e}tique des syst{\`e}mes de transcription automatique entra{\^\i}nent beaucoup d{'}erreurs de d{\'e}tection.",
    language = "French",
}
@inproceedings{garnerin-etal-2020-representation,
    title = "Repr{\'e}sentation du genre dans des donn{\'e}es open source de parole (Gender representation in open source speech resources 1 With the rise of artificial intelligence ({AI}) and the growing use of deep-learning architectures, the question of ethics and transparency in {AI} systems has become a central concern within the research community)",
    author = "Garnerin, Mahault  and
      Rossato, Solange  and
      Besacier, Laurent",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.28",
    pages = "244--252",
    abstract = "Avec l{'}essor de l{'}intelligence artificielle (IA) et l{'}utilisation croissante des architectures d{'}apprentissage profond, la question de l{'}{\'e}thique et de la transparence des syst{\`e}mes d{'}IA est devenue une pr{\'e}occupation centrale au sein de la communaut{\'e} de recherche. Dans cet article, nous proposons une {\'e}tude sur la repr{\'e}sentation du genre dans les ressources de parole disponibles sur la plateforme Open Speech and Language Resource. Un tout premier r{\'e}sultat est la difficult{\'e} d{'}acc{\`e}s aux informations sur le genre des locuteurs. Ensuite, nous montrons que l{'}{\'e}quilibre entre les cat{\'e}gories de genre d{\'e}pend de diverses caract{\'e}ristiques des corpus (discours {\'e}licit{\'e} ou non, t{\^a}che adress{\'e}e). En nous appuyant sur des travaux ant{\'e}rieurs, nous reprenons quelques principes concernant les m{\'e}tadonn{\'e}es dans l{'}optique d{'}assurer une meilleure transparence des syst{\`e}mes de parole construits {\`a} l{'}aide de ces corpus.",
    language = "French",
}
@inproceedings{gelin-etal-2020-reconnaissance,
    title = "Reconnaissance de phones fond{\'e}e sur du Transfer Learning pour des enfants apprenants lecteurs en environnement de classe (Transfer Learning based phone recognition on children learning to read, with speech recorded in a classroom environment)",
    author = "Gelin, Lucile  and
      Daniel, Morgane  and
      Pellegrini, Thomas  and
      Pinquier, Julien",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.29",
    pages = "253--261",
    abstract = "A conditions {\'e}gales, les performances actuelles de la reconnaissance vocale pour enfants sont inf{\'e}rieures {\`a} celles des syst{\`e}mes pour adultes. La parole des jeunes enfants est particuli{\`e}rement difficile {\`a} reconna{\^\i}tre, et les donn{\'e}es disponibles sont rares. En outre, pour notre application d{'}assistant de lecture pour les enfants de 5-7 ans, les mod{\`e}les doivent s{'}adapter {\`a} une lecture lente, des disfluences et du bruit de brouhaha typique d{'}une classe. Nous comparons ici plusieurs mod{\`e}les acoustiques pour la reconnaissance de phones sur de la parole lue d{'}enfant avec des donn{\'e}es bruit{\'e}es et en quantit{\'e} limit{\'e}e. Nous montrons que faire du Transfer Learning avec des mod{\`e}les entra{\^\i}n{\'e}s sur la parole d{'}adulte et trois heures de parole d{'}enfant am{\'e}liore le taux d{'}erreur au niveau du phone (PER) de 7,6{\%} relatifs, par rapport {\`a} un mod{\`e}le enfant. La normalisation de la longueur du conduit vocal sur la parole d{'}adulte r{\'e}duit ce taux d{'}erreur de 5,1{\%} relatifs suppl{\'e}mentaires, atteignant un PER de 37,1{\%}.",
    language = "French",
}
@inproceedings{gendrot-etal-2020-informations,
    title = "Informations segmentales pour la caract{\'e}risation phon{\'e}tique du locuteur : variabilit{\'e} inter- et intra-locuteurs (An automatic classification task involving 44 speakers was performed using convolutional neural networks ({CNN}) on broadband spectrograms extracted from 2-second sequences of a spontaneous speech corpus ({NCCF}r))",
    author = "Gendrot, Cedric  and
      Ferragne, Emmanuel  and
      Pellegrini, Thomas",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.30",
    pages = "262--270",
    abstract = "Nous avons effectu{\'e} une classification automatique de 44 locuteurs {\`a} partir de r{\'e}seaux de neurones convolutifs (CNN) sur la base de spectrogrammes {\`a} bandes larges calcul{\'e}s sur des s{\'e}quences de 2 secondes extraites d{'}un corpus de parole spontan{\'e}e (NCCFr). Apr{\`e}s obtention d{'}un taux de classification moyen de 93,7 {\%}, les diff{\'e}rentes classes phon{\'e}miques composant chaque s{\'e}quence ont {\'e}t{\'e} masqu{\'e}es afin de tester leur impact sur le mod{\`e}le. Les r{\'e}sultats montrent que les voyelles orales influent avant toute autre classe sur le taux de classification, suivies ensuite par les occlusives orales. Ces r{\'e}sultats sont expliqu{\'e}s principalement par la repr{\'e}sentation temporelle pr{\'e}dominante des voyelles orales. Une variabilit{\'e} inter-locuteurs se manifeste par l{'}existence de locuteurs attracteurs qui attirent un grand nombre de faux positifs et qui ne sont pas sensibles au masquage effectu{\'e}. Nous mettons en avant dans la discussion des r{\'e}alisations acoustiques qui pourraient expliquer les sp{\'e}cificit{\'e}s de ces locuteurs.",
    language = "French",
}
@inproceedings{ghio-etal-2020-evaluation,
    title = "Evaluation de l{'}intelligibilit{\'e} de patients avec traitement du cancer des cavit{\'e}s orales et pharyngales (Intelligibility Assessment of patients in the Context of Head and Neck Cancers)",
    author = "Ghio, Alain  and
      Lalain, Muriel  and
      Rebourg, Marie  and
      Fredouille, Corinne  and
      Woisard, Virginie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.31",
    pages = "271--279",
    abstract = "La perte d{'}intelligibilit{\'e} repr{\'e}sente une plainte importante des patients atteints de troubles de la parole. Plusieurs batteries de test d{'}intelligibilit{\'e} existent mais leurs limitations r{\'e}sident dans la capacit{\'e} des auditeurs {\`a} restaurer les s{\'e}quences distordues. Nous proposons un nouveau test fond{\'e} sur l{'}utilisation de pseudo-mots en grande quantit{\'e} afin de compl{\`e}tement neutraliser les effets perceptifs ind{\'e}sirables. Nous avons appliqu{\'e} ce test {\`a} une population de 39 sujets sains et 78 patients post traitement de cancers de la cavit{\'e} buccale et de l{'}oropharynx. Chaque locuteur a produit 52 pseudo-mots tir{\'e}s al{\'e}atoirement. 40 auditeurs ont retranscrit ces productions. Les transcriptions orthographiques ont {\'e}t{\'e} phon{\'e}tis{\'e}es et compar{\'e}es aux formes phon{\'e}tiques attendues. Un algorithme fournit un score de d{\'e}viation phonologique per{\c{c}}ue (PPD) fond{\'e}e sur le nombre de traits diff{\'e}rents entre la forme attendue et celle transcrite. Les r{\'e}sultats montrent qu{'}il existe un seuil PPD de 0.6 traits/phon{\`e}me au-dessus duquel, la parole produite est dysfonctionnelle. De plus, le score de PPD est bien corr{\'e}l{\'e} au jugement subjectif de la s{\'e}v{\'e}rit{\'e} obtenue aupr{\`e}s d{'}experts. Ce test semble donc efficace pour mesurer la performance articulatoire des locuteurs.",
    language = "French",
}
@inproceedings{gresse-etal-2020-apprentissage,
    title = "Apprentissage automatique de repr{\'e}sentation de voix {\`a} l{'}aide d{'}une distillation de la connaissance pour le casting vocal (Learning voice representation using knowledge distillation for automatic voice casting )",
    author = "Gresse, Adrien  and
      Quillot, Mathias  and
      Dufour, Richard  and
      Bonastre, Jean-Fran{\c{c}}ois",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.32",
    pages = "280--288",
    abstract = "La recherche d{'}acteurs vocaux pour les productions audiovisuelles est r{\'e}alis{\'e}e par des directeurs artistiques (DA). Les DA sont constamment {\`a} la recherche de nouveaux talents vocaux, mais ne peuvent effectuer des auditions {\`a} grande {\'e}chelle. Les outils automatiques capables de sugg{\'e}rer des voix pr{\'e}sentent alors un grand int{\'e}r{\^e}t pour l{'}industrie audiovisuelle. Dans les travaux pr{\'e}c{\'e}dents, nous avons montr{\'e} l{'}existence d{'}informations acoustiques permettant de reproduire des choix du DA. Dans cet article, nous proposons une approche {\`a} base de r{\'e}seaux de neurones pour construire une repr{\'e}sentation adapt{\'e}e aux personnages/r{\^o}les vis{\'e}s, appel{\'e}e p-vecteur. Nous proposons ensuite de tirer parti de donn{\'e}es externes pour la repr{\'e}sentation de voix, proches de celles d{'}origine, au moyen de m{\'e}thodes de distillation de la connaissance. Les exp{\'e}riences men{\'e}es sur des extraits de voix de jeux vid{\'e}o montrent une am{\'e}lioration significative de l{'}approche p-vecteur, avec distillation de la connaissance, par rapport {\`a} une repr{\'e}sentation x-vecteur, {\'e}tat-de-l{'}art en reconnaissance du locuteur.",
    language = "French",
}
@inproceedings{hutin-etal-2020-lenition-et,
    title = "L{\'e}nition et fortition des occlusives en coda finale dans deux langues romanes : le fran{\c{c}}ais et le roumain (Lenition and fortition of word-final stops in two {R}omance languages: {F}rench and {R}omanian)",
    author = "Hutin, Mathilde  and
      Jatteau, Ad{\`e}le  and
      Vasilescu, Ioana  and
      Lamel, Lori  and
      Adda-Decker, Martine",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.33",
    pages = "289--298",
    abstract = "L{'}exploration automatis{\'e}e de grands corpus permet d{'}analyser plus finement la relation entre motifs de variation phon{\'e}tique synchronique et changements diachroniques : les erreurs dans les transcriptions automatiques sont riches d{'}enseignements sur la variation contextuelle en parole continue et sur les possibles mutations syst{\'e}miques sur le point d{'}appara{\^\i}tre. D{\`e}s lors, il est int{\'e}ressant de se pencher sur des ph{\'e}nom{\`e}nes phonologiques largement attest{\'e}s dans les langues en diachronie comme en synchronie pour {\'e}tablir leur {\'e}mergence ou non dans des langues qui n{'}y sont pas encore sujettes. La pr{\'e}sente {\'e}tude propose donc d{'}utiliser l{'}alignement forc{\'e} avec variantes de prononciation pour observer les alternances de voisement en coda finale de mot dans deux langues romanes : le fran{\c{c}}ais et le roumain. Il sera mis en {\'e}vidence, notamment, que voisement et d{\'e}voisement non-canoniques des codas fran{\c{c}}aises comme roumaines ne sont pas le fruit du hasard mais bien des instances de d{\'e}voisement final et d{'}assimilation r{\'e}gressive de trait laryngal, qu{'}il s{'}agisse de voisement ou de non-voisement.",
    language = "French",
}
@inproceedings{laaridh-mauclair-2020-sur,
    title = "Sur l{'}utilisation de la reconnaissance automatique de la parole pour l{'}aide au diagnostic diff{\'e}rentiel entre la maladie de {P}arkinson et l{'}{AMS} (On using automatic speech recognition for the differential diagnosis of {P}arkinson{'}s Disease and {MSA} This article presents a study regarding the contribution of automatic speech processing in the differential diagnosis between {P}arkinson{'}s disease and {MSA} (Multi-System Atrophies))",
    author = "Laaridh, Imed  and
      Mauclair, Julie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.34",
    pages = "299--307",
    abstract = "Cet article pr{\'e}sente une {\'e}tude concernant l{'}apport du traitement automatique de la parole dans le cadre du diagnostic diff{\'e}rentiel entre la maladie de Parkinson et l{'}AMS (Atrophie Multi-Syst{\'e}matis{\'e}e). Nous proposons des outils de reconnaissance automatique de la parole pour {\'e}valuer le potentiel d{'}indicateurs de la parole dysarthrique caract{\'e}risant ces deux pathologies. Dans ce cadre, un corpus de parole pathologique (projet ANR Voice4PD-MSA) a {\'e}t{\'e} enregistr{\'e} au sein des Centres Hospitaliers Universitaires (CHU) de Toulouse et Bordeaux. Les locuteurs sont des patients atteints de stades pr{\'e}coces de la maladie de Parkinson et d{'}AMS ainsi que des locuteurs t{\'e}moins. Des mesures automatiques caract{\'e}risant la qualit{\'e} de la reconnaissance automatique de la parole ainsi que la prosodie des patients ont montr{\'e} un int{\'e}r{\^e}t pour la caract{\'e}risation des pathologies {\'e}tudi{\'e}es et peuvent {\^e}tre consid{\'e}r{\'e}es comme un outil potentiel pour l{'}aide {\`a} leur diagnostic diff{\'e}rentiel.",
    language = "French",
}
@inproceedings{lancien-2020-variation,
    title = "Variation stylistique en fran{\c{c}}ais qu{\'e}b{\'e}cois : l{'}effet de l{'}identit{\'e} de l{'}interlocuteur (Stylistic variation in {Q}uebec {F}rench: the effect of the interlocutor{'}s identity)",
    author = "Lancien, M{\'e}lanie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.35",
    pages = "308--316",
    abstract = {Les {\'e}tudes portant sur l{'}effet de la situation de communication sur la variation vocalique, notamment celles de Bradlow (2003) ou Scarborough (2007, 2013) ont d{\'e}montr{\'e} une adaptation du degr{\'e} d{'}hyper-hypo articulation {\`a} l{'}identit{\'e} de l{'}interlocuteur, avec par exemple une plus forte hypoarticulation (Lindblom, 1990) lorsque l{'}on s{'}adresse {\`a} un ami que lorsque l{'}on s{'}adresse {\`a} {\'e}tranger. Dans cette {\'e}tude, nous adaptons le protocole Diapix (Baker et Hazan, 2011) de fa{\c{c}}on {\`a} explorer la variation vocalique dans la parole dirig{\'e}e {\`a} un.e conjoint.e, un exp{\'e}rimentateur de la m{\^e}me communaut{\'e} linguistique, une exp{\'e}rimentatrice d{'}une autre communaut{\'e}, ou soi-m{\^e}me. L{'}analyse pr{\'e}liminaire des productions de deux couples montre d{'}ores et d{\'e}j{\`a} une influence de l{'}identit{\'e} de l{'}interlocuteur, avec des voyelles plus courtes et plus proches du centro{\"\i}de du syst{\`e}me lors des t{\^a}ches en couple que lors des t{\^a}ches avec les enqu{\^e}teurs ou avec soi-m{\^e}me.},
    language = "French",
}
@inproceedings{lancien-2020-de,
    title = "De la possibilit{\'e} d{'}un rel{\^a}chement des voyelles hautes dans les troncations finissant par /v, z, ʒ, ʁ/ en fran{\c{c}}ais qu{\'e}b{\'e}cois (On the possibility of high vowels{'} laxing in truncations ended by /v, z, ʒ, ʁ/ in {Q}uebec {F}rench)",
    author = "Lancien, M{\'e}lanie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.36",
    pages = "317--325",
    abstract = "Le fran{\c{c}}ais qu{\'e}b{\'e}cois poss{\`e}de trois voyelles hautes tendues (/i, y, u/), et trois rel{\^a}ch{\'e}es ([ɪ, ʏ, ʊ]), les rel{\^a}ch{\'e}es {\'e}tant d{\'e}crites comme des allophones des tendues produits en syllabe ferm{\'e}e par une consonne non allongeante (Walker, 1984 ; Dumas, 1987 ; entre autres). Cependant C{\^o}t{\'e} (2012) pose l{'}hypoth{\`e}se que ce rel{\^a}chement pourrait survenir dans des troncations finissant par une consonne allongeante (Troncation({\_}R)). Dans cette {\'e}tude pilote, nous explorons cette hypoth{\`e}se. A travers de courts textes (lus par deux locuteurs natifs) nous avons induit ces voyelles en Troncation({\_}R) ainsi que dans des positions formant des paire minimales (PaireMinimale({\_}R) / semi-minimales (finissant par une consonne non allongeante - PaireMinimale({\_}K)) avec les Troncation({\_}R). Les premi{\`e}res analyses temporelles (ANOVAs) montrent des /i, y, u/ plus courts en Troncation({\_}R) qu{'}en PaireMinimale({\_}R), et une analyse qualitative permet d{'}observer des variations spectrales entre Troncation({\_}R), PaireMinimale({\_}R) et PaireMinimale({\_}K), notamment {\`a} travers les moyennes de F1 et F2 (Hz).",
    language = "French",
}
@inproceedings{le-cerf-ferragne-2020-parametres,
    title = "Param{\`e}tres acoustiques et phon{\'e}tiques dans la parole parkinsonienne avant et apr{\`e}s traitement {LSVT} {LOUD}® (Acoustic and phonetic parameters in parkinsonian speech before and after {LSVT} {LOUD}® Purpose: Our research examines the effect of the Lee Silverman Voice Treatment ({LSVT} {LOUD}®) on the area and position of the vowel space, fundamental frequency (f0), voice quality parameters, speech rate, maximum phonation time ({MPT}) and patient-perceived vocal handicap in Frenchspeaking patients with {P}arkinson{'}s disease)",
    author = {Le Cerf, Ma{\"e}lle  and
      Ferragne, Emmanuel},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.37",
    pages = "326--334",
    abstract = "Objet : Notre recherche examine l{'}effet du Lee Silverman Voice Treatment (LSVT LOUD®) sur l{'}aire et la position de l{'}espace vocalique, la fr{\'e}quence fondamentale (f0), les param{\`e}tres de qualit{\'e} de voix, le d{\'e}bit de parole, le temps maximum phonatoire (TMP) et le ressenti de handicap vocal chez des patients francophones atteints de la maladie de Parkinson. M{\'e}thode : Un m{\^e}me protocole a {\'e}t{\'e} propos{\'e} en pr{\'e}test et post-test {\`a} 12 patients parkinsoniens. R{\'e}sultats : En post-test, nous observons une descente significative de l{'}espace vocalique, une diff{\'e}rence de f0 entre la parole lue et la parole spontan{\'e}e, une am{\'e}lioration significative des param{\`e}tres de qualit{\'e} de voix (jitter, shimmer, HNR) et du ressenti de handicap vocal. Le d{\'e}bit de parole des patients est maintenu, le TMP subit un effet de l{'}exercice.",
    language = "French",
}
@inproceedings{lee-etal-2020-etude,
    title = "{\'E}tude comparative de corr{\'e}lats prosodiques de marqueurs discursifs fran{\c{c}}ais et anglais selon leur fonction pragmatique (Comparative study on prosodic correlates of discourse markers in {F}rench and {E}nglish according to their pragmatic function)",
    author = "Lee, Lou  and
      Jouvet, Denis  and
      Bartkova, Katarina  and
      Keromnes, Yvon  and
      Dargnat, Mathilde",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.38",
    pages = "335--343",
    abstract = "Ce papier pr{\'e}sente une {\'e}tude des caract{\'e}ristiques prosodiques de marqueurs discursifs en fonction de leur sens pragmatique. L{'}{\'e}tude est men{\'e}e sur trois marqueurs discursifs fran{\c{c}}ais (alors, bon, donc) et trois marqueurs anglais (now, so, well) afin de comparer leurs caract{\'e}ristiques prosodiques dans ces deux langues. Plusieurs param{\`e}tres prosodiques ont {\'e}t{\'e} calcul{\'e}s sur les marqueurs discursifs, et analys{\'e}s selon les fonctions pragmatiques de ceux-ci. L{'}analyse a {\'e}t{\'e} effectu{\'e}e sur plusieurs centaines d{'}occurrences de marqueurs discursifs extraits de corpus oraux fran{\c{c}}ais et anglais. Les r{\'e}sultats montrent que certaines fonctions pragmatiques des marqueurs discursifs am{\`e}nent leurs propres caract{\'e}ristiques prosodiques au niveau des pauses et des mouvements de la fr{\'e}quence fondamentale. On observe {\'e}galement que les fonctions pragmatiques similaires partagent fr{\'e}quemment des caract{\'e}ristiques prosodiques similaires {\`a} travers les deux langues.",
    language = "French",
}
@inproceedings{lelandais-2020-phenomenes,
    title = "Ph{\'e}nom{\`e}nes de pro{\'e}minence dans les subordonn{\'e}es en conversation spontan{\'e}e (Prominence phenomena in subordinate constructions in conversational speech)",
    author = "Lelandais, Manon",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.39",
    pages = "344--352",
    abstract = "{\`A} partir d{'}un corpus vid{\'e}o de conversation spontan{\'e}e en anglais britannique, cette {\'e}tude a pour but de d{\'e}terminer si deux diff{\'e}rents types syntaxiques de constructions subordonn{\'e}es expriment la m{\^e}me absence de pro{\'e}minence, dans le cadre d{'}une analyse multimodale. En syntaxe, les subordonn{\'e}es sont d{\'e}crites comme des structures d{\'e}pendantes qui pr{\'e}cisent l{'}information de premier plan dans le discours. Alors que de nombreux travaux {\'e}tudient leurs caract{\'e}ristiques verbales, peu d{'}{\'e}tudes s{'}attachent {\`a} d{\'e}crire l{'}articulation entre les diff{\'e}rentes modalit{\'e}s communicatives lors de leur production, et {\`a} fournir une vision plus nuanc{\'e}e de leur apport informationnel. Nous prenons en compte un ensemble de variables appartenant {\`a} plusieurs modalit{\'e}s, consid{\'e}r{\'e}es comme des facteurs de pro{\'e}minence. Notre {\'e}tude montre que les subordonn{\'e}es ne fournissent pas les m{\^e}mes types de pro{\'e}minence en fonction de leur type syntaxique, et que leur cr{\'e}ation s{'}appuie majoritairement sur des indices de nature intonative et gestuelle plut{\^o}t que syntaxique.",
    language = "French",
}
@inproceedings{leprieur-etal-2020-une,
    title = "Une base de donn{\'e}es de phrases en fran{\c{c}}ais pour l{'}{\'e}tude du r{\^o}le conjoint des incertitudes s{\'e}mantique et acoustique dans la perception de la parole (A dataset of french sentences to study the joint roles of semantic and acoustic uncertainty in speech perception)",
    author = "Leprieur, Loriane  and
      Crouzet, Olivier  and
      Gaudrain, Etienne",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.40",
    pages = "353--361",
    abstract = "Les effets de contexte dans la perception de la parole reposent aussi bien sur des sources acoustiques que s{\'e}mantiques. Le contexte acoustique fournit des informations essentielles pour l{'}adaptation au locuteur et aux variations dialectales. En parall{\`e}le, le contexte s{\'e}mantique contribue {\`a} pr{\'e}dire un ensemble de mots {\'e}ligibles pour une interpr{\'e}tation licite des {\'e}nonc{\'e}s. Afin d{'}{\'e}tudier plus pr{\'e}cis{\'e}ment les interactions entre ces effets de contexte, nous avons cr{\'e}{\'e} une base de donn{\'e}es de phrases courtes con{\c{c}}ues pour observer ces ph{\'e}nom{\`e}nes dans des protocoles exp{\'e}rimentaux. Cette base de donn{\'e}es est constitu{\'e}e de 28 triplets de phrases porteuses termin{\'e}es par des cibles de paires minimales de mots CV ou CVC, autour de voyelles acoustiquement proches associ{\'e}es {\`a} 4 contrastes vocaliques. Afin d{'}{\'e}valuer la validit{\'e} des 3 cat{\'e}gories de contexte s{\'e}mantique consid{\'e}r{\'e}es, des mesures de similarit{\'e} s{\'e}mantique et de fr{\'e}quence lexicale ont {\'e}t{\'e} r{\'e}alis{\'e}es {\`a} partir de diff{\'e}rents corpus de langue fran{\c{c}}aise.",
    language = "French",
}
@inproceedings{level-etal-2020-introduction,
    title = "Introduction d{'}informations s{\'e}mantiques dans un syst{\`e}me de reconnaissance de la parole (Despite spectacular advances in recent years, the Automatic Speech Recognition ({ASR}) systems still make mistakes, especially in noisy environments)",
    author = "Level, St{\'e}phane  and
      Illina, Irina  and
      Fohr, Dominique",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.41",
    pages = "362--369",
    abstract = "Malgr{\'e} les avanc{\'e}s spectaculaires ces derni{\`e}res ann{\'e}es, les syst{\`e}mes de Reconnaissance Automatique de Parole (RAP) commettent encore des erreurs, surtout dans des environnements bruit{\'e}s. Pour am{\'e}liorer la RAP, nous proposons de se diriger vers une contextualisation d{'}un syst{\`e}me RAP, car les informations s{\'e}mantiques sont importantes pour la performance de la RAP. Les syst{\`e}mes RAP actuels ne prennent en compte principalement que les informations lexicales et syntaxiques. Pour mod{\'e}liser les informations s{\'e}mantiques, nous proposons de d{\'e}tecter les mots de la phrase trait{\'e}e qui pourraient avoir {\'e}t{\'e} mal reconnus et de proposer des mots correspondant mieux au contexte. Cette analyse s{\'e}mantique permettra de r{\'e}{\'e}valuer les N meilleures hypoth{\`e}ses de transcription (N-best). Nous utilisons les embeddings Word2Vec et BERT. Nous avons {\'e}valu{\'e} notre m{\'e}thodologie sur le corpus des conf{\'e}rences TED (TED-LIUM). Les r{\'e}sultats montrent une am{\'e}lioration significative du taux d{'}erreur mots en utilisant la m{\'e}thodologie propos{\'e}e.",
    language = "French",
}
@inproceedings{li-lancia-2020-production,
    title = "Production de la parole en r{\'e}ponse {\`a} de multiples perturbations du feedback auditif (Speech production in response to multiple perturbations of auditory feedback)",
    author = "Li, Jinyu  and
      Lancia, Leonardo",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.42",
    pages = "370--378",
    abstract = "Des {\'e}tudes ant{\'e}rieures ont montr{\'e} que la production de la parole d{\'e}pend des conditions du feedback auditif. Cette {\'e}tude vise {\`a} investiguer les interactions entre les effets de trois facteurs diff{\'e}rents sur la production de la parole : 1) le retard du feedback auditif (DAF), 2) le d{\'e}calage de la f0 du feedback auditif et 3) la complexit{\'e} des syllabes qui composent les {\'e}nonc{\'e}s. Nous avons manipul{\'e} le feedback auditif de 20 locutrices francophones pendant la r{\'e}p{\'e}tition de trois phrases. Nous avons pu observer que plus de la moiti{\'e} des participantes ont tendance {\`a} suivre la direction du d{\'e}calage de la f0 du feedback auditif en recevant en continu cette perturbation. La position syllabique des voyelles est un facteur important affectant l{'}effet des perturbations du feedback auditif. Cependant les analyses d{\'e}crites dans cette {\'e}tude ne montrent pas un effet interactif du DAF et du d{\'e}calage de la f0 sur la production de la parole.",
    language = "French",
}
@inproceedings{macary-etal-2020-prediction,
    title = "Pr{\'e}diction continue de la satisfaction et de la frustration dans des conversations de centre d{'}appels ({A}llo{S}at : A New Call Center {F}rench Corpus for Affect Analysis)",
    author = "Macary, Manon  and
      Tahon, Marie  and
      Est{\`e}ve, Yannick  and
      Rousseau, Anthony",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.43",
    pages = "379--387",
    abstract = "Nous pr{\'e}sentons un nouveau corpus, nomm{\'e} AlloSat, compos{\'e} de conversations en fran{\c{c}}ais extraites de centre d{'}appels, annot{\'e}es de fa{\c{c}}on continue en frustration et satisfaction. Dans le contexte des centres d{'}appels, une conversation vise g{\'e}n{\'e}ralement {\`a} r{\'e}soudre la demande de l{'}appelant. Ce corpus a {\'e}t{\'e} mis en place afin de d{\'e}velopper de nouveaux syst{\`e}mes capables de mod{\'e}liser l{'}aspect continu de l{'}information s{\'e}mantique et para-linguistique au niveau conversationnel. Nous nous concentrons sur le niveau para-linguistique, plus pr{\'e}cis{\'e}ment sur l{'}expression des {\'e}motions. {\`A} notre connaissance, la plupart des corpus {\'e}motionnels contiennent des annotations en cat{\'e}gories discr{\`e}tes ou dans des dimensions continues telles que l{'}activation ou la valence. Nous supposons que ces dimensions ne sont pas suffisamment li{\'e}es {\`a} notre contexte. Pour r{\'e}soudre ce probl{\`e}me, nous proposons un corpus permettant une connaissance en temps r{\'e}el de l{'}axe frustration/satisfaction. AlloSat regroupe 303 conversations pour un total d{'}environ 37 heures d{'}audio, toutes enregistr{\'e}es dans des environnements r{\'e}els, collect{\'e}es par Allo-Media (une soci{\'e}t{\'e} sp{\'e}cialis{\'e}e dans l{'}analyse automatique d{'}appels). Les premi{\`e}res exp{\'e}riences de classification montrent que l{'}{\'e}volution de l{'}axe frustration/satisfaction peut {\^e}tre pr{\'e}dite automatiquement par conversation.",
    language = "French",
}
@inproceedings{machart-etal-2020-production,
    title = "Production de parole chez l{'}enfant porteur d{'}implant cochl{\'e}aire : apport de la Langue fran{\c{c}}aise Parl{\'e}e Compl{\'e}t{\'e}e (Speech production in children with cochlear implant(s): contribution of Cued {F}rench)",
    author = "Machart, Laura  and
      Vilain, Anne  and
      L{\oe}venbruck, H{\'e}l{\`e}ne  and
      Meloni, Genevi{\`e}ve  and
      Puissant, Clarisse",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.44",
    pages = "388--396",
    abstract = "La d{\'e}ficience auditive entra{\^\i}ne un retard sur le d{\'e}veloppement de la parole chez l{'}enfant sourd. La Langue fran{\c{c}}aise Parl{\'e}e Compl{\'e}t{\'e}e (LfPC), par le biais de 5 positions autour du visage et 8 configurations de la main, permet de rendre visibles tous les sons de la langue, sans confusion labiale. L{'}utilisation de ce syst{\`e}me facilite la perception de parole et permet {\`a} l{'}enfant d{'}{\'e}laborer des repr{\'e}sentations phonologiques stables. Cette {\'e}tude s{'}int{\'e}resse {\`a} l{'}apport de la LfPC sur la production de parole chez l{'}enfant porteur d{'}implant cochl{\'e}aire. A partir d{'}une t{\^a}che de d{\'e}nomination d{'}images, nous observons que l{'}exposition {\`a} la LfPC (en perception) am{\'e}liore significativement la production de parole chez l{'}enfant porteur d{'}implant cochl{\'e}aire.",
    language = "French",
}
@inproceedings{martin-etal-2020-detection,
    title = "D{\'e}tection de la somnolence par estimation d{'}erreurs de lecture (Sleepiness detection through reading errors estimation )",
    author = "Martin, Vincent P.  and
      Chapouthier, Gabrielle  and
      Rieant, Mathilde  and
      Rouas, Jean-Luc  and
      Philip, Pierre",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.45",
    pages = "397--405",
    abstract = "La d{\'e}tection automatique de la somnolence peut aider le suivi de patients souffrant de maladies neuro-psychiatriques chroniques. Des recherches pr{\'e}c{\'e}dentes ont d{\'e}j{\`a} montr{\'e} que cela est possible en utilisant des enregistrements vocaux. Dans cet article, nous proposons d{'}{\'e}tudier les erreurs de lecture effectu{\'e}es par des patients souffrant de Somnolence Diurne Excessive (SDE) sur le corpus TILE, enregistr{\'e} {\`a} l{'}h{\^o}pital de Bordeaux. Avec des orthophonistes, nous avons d{\'e}fini et compt{\'e} les erreurs de lecture des patients et les avons confront{\'e}es aux diff{\'e}rentes mesures de somnolence du corpus. Nous montrons ici que relever ces erreurs peut {\^e}tre utile pour {\'e}laborer des marqueurs robustes de la somnolence objective mais aussi pour d{\'e}finir des crit{\`e}res d{'}exclusion des locuteurs n{'}ayant pas un niveau de lecture suffisant.",
    language = "French",
}
@inproceedings{martin-etal-2020-detection-de,
    title = "D{\'e}tection de la somnolence objective dans la voix (Objective sleepiness detection through voice )",
    author = "Martin, Vincent P.  and
      Rouas, Jean-Luc  and
      Philip, Pierre",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.46",
    pages = "406--414",
    abstract = "Le suivi des patients souffrant de maladies neuro-psychiatriques chroniques peut {\^e}tre am{\'e}lior{\'e} gr{\^a}ce {\`a} la d{\'e}tection de la somnolence dans la voix. Cet article s{'}inspire des syst{\`e}mes {\'e}tat-de-l{'}art en d{\'e}tection de la somnolence dans la voix pour le cas particulier de patients atteints de Somnolence Diurne Excessive (SDE). Pour cela, nous basons notre {\'e}tude sur un nouveau corpus, le corpus TILE. Il diff{\`e}re des autres corpora existants par le fait que les sujets enregistr{\'e}s sont des patients souffrant de SDE et que leur niveau de somnolence est mesur{\'e} de mani{\`e}re subjective mais aussi objective. Le syst{\`e}me propos{\'e} permet d{\'e}tecter la somnolence objective gr{\^a}ce {\`a} des param{\`e}tres vocaux simples et explicables {\`a} des non sp{\'e}cialistes.",
    language = "French",
}
@inproceedings{-2020-article,
    title = "(Article retir{\'e} {\`a} la demande des auteurs) ()",
    author = "{}",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.47",
    pages = "415--423",
    abstract = "",
    language = "French",
}
@inproceedings{mertz-2020-representation,
    title = "Repr{\'e}sentation phonologique des signes {\`a} deux mains en {LSF} : faut-il reconsid{\'e}rer l{'}orientation absolue dans les mod{\`e}les phonologiques des langues des signes ? (Phonological representation of 2-handed signs in {LSF} : reconsidering absolute orientation in the phonological models of sign language ?)",
    author = "Mertz, Justine",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.48",
    pages = "424--432",
    abstract = "Cet article a pour objectif i) de mettre en {\'e}vidence le besoin d{'}une orientation absolue en plus d{'}une orientation relative afin de d{\'e}crire la phonologie segmentale des langues des signes, et ii) d{'}am{\'e}liorer les mod{\`e}les actuels et leur permettre de rendre compte de la phonologie de signes autrement probl{\'e}matiques. Dans cette {\'e}tude sur la langue des signes fran{\c{c}}aise, nous nous concentrons sur une cat{\'e}gorie de signes en particulier : les signes {\`a} deux mains produits sur le corps du signeur (contact avec une partie du corps autre que la main non-dominante). Nous montrons que l{'}orientation relative ne permet pas une description ad{\'e}quate de ces signes lorsque l{'}orientation des deux mains doit {\^e}tre sp{\'e}cifi{\'e}e, puisqu{'}elle peut capturer soit l{'}orientation entre les deux mains, soit l{'}orientation vis {\`a} vis du corps, mais pas les deux. Afin de mod{\'e}liser l{'}orientation de ces signes dans un cadre formel, nous proposons l{'}impl{\'e}mentation de plans secondaires. Tandis que cette impl{\'e}mentation requiert des ajustements minimes dans les mod{\`e}les formels actuels, son impact quant {\`a} la th{\'e}orie g{\'e}n{\'e}rale de la phonologie segmentale des signes est, elle, importante. Les plans secondaires imposent des restrictions g{\'e}om{\'e}triques et forcent l{'}orientation absolue ; le concept d{'}orientation comme simple classe phon{\'e}mique relationnelle n{'}est donc plus suffisant (du moins pour ces signes).",
    language = "French",
}
@inproceedings{meunier-etal-2020-la,
    title = "La mobilisation du tractus vocal est-elle variable selon les langues en parole spontan{\'e}e ? (Does vocal tract use depend on language characteristics in spontaneous speech?)",
    author = "Meunier, Christine  and
      Peirolo, Morgane  and
      Bigi, Brigitte",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.49",
    pages = "433--441",
    abstract = "L{'}objectif de ce travail est de quantifier les positions articulatoires th{\'e}oriques lors de la production de la parole spontan{\'e}e dans trois langues. Chaque langue dispose d{'}un inventaire phonologique sp{\'e}cifique. Mais ces sp{\'e}cificit{\'e}s ne sont pas repr{\'e}sent{\'e}es telles quelles en parole spontan{\'e}e dans laquelle les phon{\`e}mes n{'}ont pas tous la m{\^e}me fr{\'e}quence d{'}apparition. Nous avons compar{\'e} trois langues (polonais, fran{\c{c}}ais et anglais am{\'e}ricain) pr{\'e}sentant des diff{\'e}rences notables dans leur inventaire phonologique. Des positions articulatoires ont {\'e}t{\'e} calcul{\'e}es sur la base des fr{\'e}quences des phon{\`e}mes dans chacune des trois langues dans des corpus de parole spontan{\'e}e. Etonnamment, les r{\'e}sultats tendent {\`a} montrer que les positions articulatoires majoritaires sont tr{\`e}s similaires dans les trois langues. Il semble ainsi que l{'}usage de la parole spontan{\'e}e, et donc la distribution des phon{\`e}mes dans les langues, gomme les disparit{\'e}s des syst{\`e}mes phonologiques pour tendre vers une mobilisation articulatoire commune. Des investigations plus approfondies devront v{\'e}rifier cette observation.",
    language = "French",
}
@inproceedings{meynadier-etal-2020-interaction,
    title = "Interaction entre dur{\'e}e et position dans la perception des fricatives vois{\'e}es chuchot{\'e}es (Interplay between duration and word position in voicing perception of whispered fricatives)",
    author = {Meynadier, Yohann  and
      Nguyen, No{\"e}l  and
      Dufour, Sophie},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.50",
    pages = "442--450",
    abstract = "Cette {\'e}tude s{'}int{\'e}resse {\`a} la reconnaissance du trait de voisement en parole chuchot{\'e}e. Nos travaux ant{\'e}rieurs (Dufour {\&} Meynadier 2019) montrent une reconnaissance plus tardive du trait [+vois{\'e}] reposant sur un traitement probablement pr{\'e}-lexical d{'}informations acoustiques, autres que la vibration laryng{\'e}e, extraites du signal chuchot{\'e} et utilis{\'e}es dans l{'}acc{\`e}s lexical. Via une t{\^a}che d{'}identification en perception cat{\'e}gorielle, cette {\'e}tude r{\'e}v{\`e}le que la dur{\'e}e conditionne la perception du voisement des fricatives chuchot{\'e}es : plus /ʃ/ est long, plus il est per{\c{c}}u [-vois{\'e}] ; plus /ʒ/ est bref, plus il est identifi{\'e} [+vois{\'e}]. Cet effet de dur{\'e}e est modul{\'e} par le trait sous-jacent de voisement et la position dans le (non)mot. La fricative [+vois{\'e}] en position finale montre une fronti{\`e}re perceptive particuli{\`e}rement d{\'e}cal{\'e}e vers des dur{\'e}es beaucoup plus longues que les autres.",
    language = "French",
}
@inproceedings{michaud-etal-2020-analyse,
    title = "Analyse d{'}erreurs de transcriptions phon{\'e}miques automatiques d{'}une langue « rare » : le na (mosuo) (Analyzing errors in automatic phonemic transcriptions of the {N}a ({M}osuo) language ({S}ino{T}ibetan family) Automatic phonemic transcription tools now reach high levels of accuracy on a single speaker with relatively small amounts of training data: on the order two to three hours of transcribed speech)",
    author = "Michaud, Alexis  and
      Adams, Oliver  and
      Guillaume, S{\'e}verine  and
      Wisniewski, Guillaume",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.51",
    pages = "451--462",
    abstract = "Les syst{\`e}mes de reconnaissance automatique de la parole atteignent d{\'e}sormais des degr{\'e}s de pr{\'e}cision {\'e}lev{\'e}s sur la base d{'}un corpus d{'}entra{\^\i}nement limit{\'e} {\`a} deux ou trois heures d{'}enregistrements transcrits (pour un syst{\`e}me mono-locuteur). Au-del{\`a} de l{'}int{\'e}r{\^e}t pratique que pr{\'e}sentent ces avanc{\'e}es technologiques pour les t{\^a}ches de documentation de langues rares et en danger, se pose la question de leur apport pour la r{\'e}flexion du phon{\'e}ticien/phonologue. En effet, le mod{\`e}le acoustique prend en entr{\'e}e des transcriptions qui reposent sur un ensemble d{'}hypoth{\`e}ses plus ou moins explicites. Le mod{\`e}le acoustique, d{\'e}calqu{\'e} (par des m{\'e}thodes statistiques) de l{'}{\'e}crit du linguiste, peut-il {\^e}tre interrog{\'e} par ce dernier, en un jeu de miroir ? Notre {\'e}tude s{'}appuie sur des exemples d{'}une langue « rare » de la famille sino-tib{\'e}taine, le na (mosuo), pour illustrer la fa{\c{c}}on dont l{'}analyse d{'}erreurs permet une confrontation renouvel{\'e}e avec le signal acoustique.",
    language = "French",
}
@inproceedings{michelas-dufour-2020-comment,
    title = "Comment l{'}oreille de pr{\'e}sentation affecte-t-elle la capacit{\'e} des francophones {\`a} discriminer des contrastes accentuels natifs et non-natifs ? (How does the ear of presentation affect the ability of {F}rench listeners to discriminate native and non-native accentual contrasts?)",
    author = "Michelas, Amandine  and
      Dufour, Sophie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.52",
    pages = "463--471",
    abstract = "Dans cette {\'e}tude, nous avons examin{\'e} la capacit{\'e} des auditeurs francophones natifs {\`a} percevoir la variation accentuelle en manipulant l{'}oreille de pr{\'e}sentation des mots. Deux contrastes accentuels ont {\'e}t{\'e} test{\'e}s : un contraste natif (/balɔ̃/-/baˈlɔ̃/) et un contraste non-natif (/ˈbalɔ̃/-/baˈlɔ̃/). Dans une t{\^a}che ABX, les participants entendaient trois mots produits par trois locuteurs diff{\'e}rents et devaient d{\'e}terminer si X {\'e}tait identique {\`a} A ou {\`a} B. Les stimuli A et B diff{\'e}raient sur l{'}accent (/ˈbalɔ̃/-/baˈlɔ̃/), sur un phon{\`e}me (/baˈlɔ/-/baˈlɔ̃/) ou sur l{'}accent et un phon{\`e}me (/ˈbalɔ/-/baˈlɔ̃/). Les r{\'e}sultats ont montr{\'e} des difficult{\'e}s persistantes pour le contraste non-natif quelle que soit l{'}oreille de pr{\'e}sentation. Par contre, pour le contraste natif, des meilleures performances ont {\'e}t{\'e} observ{\'e}es lorsque les mots {\'e}taient pr{\'e}sent{\'e}s dans l{'}oreille gauche. D{'}une fa{\c{c}}on g{\'e}n{\'e}rale, notre {\'e}tude montre que la variation accentuelle pr{\'e}sente au niveau du mot est trait{\'e}e par les auditeurs francophones natifs comme de la variation de surface.",
    language = "French",
}
@inproceedings{paroni-etal-2020-beatboxer,
    title = "Beatboxer, est-ce parler ? Ce que nous en dit l{'}{\'e}tude de la dynamique articulatoire d{'}un beatboxer (Beatboxing, is it talking ? What the study of the articulatory dynamics of a beatboxer tells us Plosive consonants are among the most commonly-found phonemes in the phonological inventory of the world{'}s languages)",
    author = "Paroni, Annalisa  and
      Henrich Bernardoni, Nathalie  and
      Savariaux, Christophe  and
      Baraduc, Pierre  and
      L{\oe}venbruck, H{\'e}l{\`e}ne",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.53",
    pages = "472--479",
    abstract = "Les consonnes plosives sont parmi les phon{\`e}mes les plus repr{\'e}sent{\'e}s dans l{'}inventaire phonologique des langues du monde. Outre leur r{\^o}le linguistique, elles remplissent {\'e}galement un r{\^o}le paralinguistique dans la pratique instrumentale et vocale, notamment au sein de la pratique vocale du Human Beatbox. Cet article apporte un {\'e}clairage sur les similitudes et diff{\'e}rences dans la dynamique articulatoire de trois consonnes plosives du fran{\c{c}}ais et des sons percussifs correspondants du Human Beatbox. Si ces deux modes de production vocale ont une racine commune, une dynamique articulatoire diff{\'e}rente est mise en {\'e}vidence pour le Human Beatbox. Nous retrouvons des indices d{'}un m{\'e}canisme {\'e}jectif, qui a un impact sur la dynamique linguale.",
    language = "French",
}
@inproceedings{pepiot-arnold-2020-differences,
    title = "Diff{\'e}rences acoustiques inter-genres chez des bilingues Anglais/Fran{\c{c}}ais : une {\'e}tude des formants vocaliques et de la qualit{\'e} de voix (A study of fundamental frequency in female and male {E}nglish/{F}rench bilingual speakers)",
    author = "P{\'e}piot, Erwan  and
      Arnold, Aron",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.54",
    pages = "480--488",
    abstract = "Cette {\'e}tude porte sur les productions de locutrices et locuteurs bilingues anglais/fran{\c{c}}ais lors d‟une t{\^a}che de lecture. La fr{\'e}quence des formants vocaliques (F1, F2, F3) et la diff{\'e}rence d‟intensit{\'e} H1H2 ont {\'e}t{\'e} mesur{\'e}es dans les deux langues. Les r{\'e}sultats indiquent un effet significatif des facteurs langue et genre sur l{'}ensemble de ces param{\`e}tres. L‟analyse des formants montre que les locutrices pr{\'e}sentent globalement des valeurs plus {\'e}lev{\'e}es que les locuteurs, avec n{\'e}anmoins des variations inter-langues. Aucune diff{\'e}rence inter-genres significative n‟a {\'e}t{\'e} trouv{\'e}e sur le F2 du [u] en fran{\c{c}}ais, contrairement au [u:] anglais. La diff{\'e}rence H1-H2 est significativement plus {\'e}lev{\'e}e chez les femmes dans les deux langues, indiquant l‟utilisation d‟une voix plus breathy. Les locutrices pr{\'e}sentent une diff{\'e}rence H1-H2 moins importante lors de l‟emploi du fran{\c{c}}ais, quand l‟inverse est observ{\'e} chez les hommes. Ces donn{\'e}es sugg{\`e}rent l‟existence de normes vocales d{\'e}pendantes du genre et de la langue parl{\'e}e, auxquelles les locuteur{\mbox{$\cdot$}}rices bilingues semblent s‟adapter.",
    language = "French",
}
@inproceedings{pettirossi-etal-2020-correlats,
    title = "Corr{\'e}lats acoustiques et perceptifs de la personnalit{\'e} per{\c{c}}ue {\`a} travers la voix dans une population de dysphoniques l{\'e}g{\`e}res (Acoustical and perceptual correlates of perceived personality through voice in minor dysphonia)",
    author = "Pettirossi, Amelia  and
      Audibert, Nicolas  and
      Crevier-Buchman, Lise",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.55",
    pages = "489--497",
    abstract = {Nous {\'e}tudions les corr{\'e}lats acoustiques et perceptifs de la personnalit{\'e} {\`a} travers la voix dans une population de dysphoniques l{\'e}g{\`e}res (G1 {\`a} G2) et de locutrices t{\'e}moins (G0). 40 auditeurs na{\"\i}fs ont {\'e}valu{\'e} les voix de 61 femmes. Des {\'e}chelles s{\'e}mantiques diff{\'e}rentielles ont {\'e}t{\'e} utilis{\'e}es pour la cotation de la s{\'e}v{\'e}rit{\'e} du trouble vocal et des traits de personnalit{\'e}. Les 5 {\'e}chelles sont : Joyeuse/Triste, Sympathique/D{\'e}sagr{\'e}able, Dynamique/Molle, Confiante/H{\'e}sitante, Aucun trouble vocal/Trouble vocal s{\'e}v{\`e}re. Le jugement de la pathologie vocale par les na{\"\i}fs est principalement li{\'e} {\`a} l{'}{\'e}valuation experte du grade de dysphonie faite {\`a} partir du GRBAS. Des traits de personnalit{\'e} plus n{\'e}gatifs sont attribu{\'e}s aux locutrices per{\c{c}}ues comme plus dysphoniques. Certains facteurs acoustiques (f0, d{\'e}bit syllabique, HNR et ZCR) semblent influencer les auditeurs : les voix plus aig{\"u}es, plus rauques et avec un d{\'e}bit rapide sont associ{\'e}es {\`a} un jugement plus positif.},
    language = "French",
}
@inproceedings{philippart-de-foy-etal-2020-emergence,
    title = "{\'E}mergence du contraste entre les fricatives sibilantes /s/ - /ʃ/ du fran{\c{c}}ais en contexte d{'}acquisition bilingue (Emergence of the contrast between the {F}rench sibilant fricatives /s/ - /ʃ/ in bilingual acquisition)",
    author = "Philippart de Foy, Marie  and
      Delvaux, V{\'e}ronique  and
      Huet, Kathy  and
      Monnier, Morgane  and
      Piccaluga, Myriam  and
      Harmegnies, Bernard",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.56",
    pages = "498--506",
    abstract = "Cette contribution vise {\`a} observer l{'}{\'e}mergence du contraste de lieu d{'}articulation entre les fricatives sibilantes sourdes /s/ - /ʃ/ en fran{\c{c}}ais chez des bilingues simultan{\'e}s d{'}{\^a}ge pr{\'e}scolaire expos{\'e}s {\`a} l{'}une des deux combinaisons linguistiques suivantes : fran{\c{c}}ais-italien et fran{\c{c}}ais-arabe. Les productions orales des enfants ont {\'e}t{\'e} recueillies longitudinalement via une t{\^a}che de d{\'e}n omination originale en fran{\c{c}}ais. Les deux fricatives ont fait l{'}objet d{'}analyses bas{\'e}es sur des donn{\'e}es acoustiques , et plus pr{\'e}cis{\'e}ment les premier et troisi{\`e}me moments spectraux, et sur les transcriptions phon{\'e}tiques des productions de parole. L{'}impact du d{\'e}veloppement lexical sur la production des deux fricatives a {\'e}t{\'e} investigu{\'e}. Les r{\'e}sultats sugg{\`e}rent, d{'}une part, un contraste {\'e}mergeant plus pr{\'e}cocement chez les bilingues fran{\c{c}}ais-arabe et, d{'}autre part, une acquisition plus pr{\'e}coce du /s/ pour l{'}ensemble des participants.",
    language = "French",
}
@inproceedings{pillot-loiseau-grando-2020-apport,
    title = "Apport des comptines pour la prononciation du /y/ fran{\c{c}}ais chez des enfants italophones : une {\'e}tude perceptive pilote (Contribution of nursery rhymes for the pronunciation of {F}rench /y/ in {I}talian-speaking children: a perceptive pilot study)",
    author = "Pillot-Loiseau, Claire  and
      Grando, Martina",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.57",
    pages = "507--515",
    abstract = "Dans l{'}apprentissage de /y/ fran{\c{c}}ais par des enfants italophones d{\'e}butants de 6 ans, les comptines sont-elles plus efficaces qu{'}un apprentissage phon{\'e}tique les utilisant sans leur rythme et leur m{\'e}lodies propres ? Deux classes de premi{\`e}re ann{\'e}e {\'e}l{\'e}mentaire d{'}une {\'e}cole publique milanaise ont suivi douze s{\'e}ances d{'}apprentissage de la prononciation du Fran{\c{c}}ais Langue Etrang{\`e}re, avec comptines originales pour le Groupe Exp{\'e}rimental (GE) mais juste parl{\'e}es pour le Groupe Contr{\^o}le (GC). L{'}apprentissage de /y/, durant 4 s{\'e}ances, s{'}est d{\'e}roul{\'e} avec des t{\^a}ches de perception, productions corporelle et verbale. Apr{\`e}s la troisi{\`e}me s{\'e}ance, 7 enfants du GE et 7 du GC ont {\'e}t{\'e} enregistr{\'e}s sur une comptine, perceptivement {\'e}valu{\'e}e par 4 auditeurs experts et 4 auditeurs {\'e}tudiants, fran{\c{c}}ais natifs : plus d{'}occurrences sont per{\c{c}}ues comme correctes pour le GE chez les auditeurs experts. Pour le GE, /y/ non correctement produit {\'e}tait principalement remplac{\'e} par un phon{\`e}me {\'e}galement ant{\'e}rieur (/i/), contrairement au GC (/u/).",
    language = "French",
}
@inproceedings{prokopalo-etal-2020-evaluation-de,
    title = "{\'E}valuation de syst{\`e}mes apprenant tout au long de la vie (Evaluation of lifelong learning systems )",
    author = {Prokopalo, Yevhenii  and
      Meignier, Sylvain  and
      Galibert, Olivier  and
      Barrault, Lo{\"\i}c  and
      Larcher, Anthony},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.58",
    pages = "516--524",
    abstract = "Aujourd{'}hui les syst{\`e}mes intelligents obtiennent d{'}excellentes performances dans de nombreux domaines lorsqu{'}ils sont entra{\^\i}n{\'e}s par des experts en apprentissage automatique. Lorsque ces syst{\`e}mes sont mis en production, leurs performances se d{\'e}gradent au cours du temps du fait de l{'}{\'e}volution de leur environnement r{\'e}el. Une adaptation de leur mod{\`e}le par des experts en apprentissage automatique est possible mais tr{\`e}s co{\^u}teuse alors que les soci{\'e}t{\'e}s utilisant ces syst{\`e}mes disposent d{'}experts du domaine qui pourraient accompagner ces syst{\`e}mes dans un apprentissage tout au long de la vie. Dans cet article nous proposons un cadre d{'}{\'e}valuation g{\'e}n{\'e}rique pour des syst{\`e}mes apprenant tout au long de la vie (SATLV). Nous proposons d{'}{\'e}valuer l{'}apprentissage assist{\'e} par l{'}humain (actif ou interactif) et l{'}apprentissage au cours du temps.",
    language = "French",
}
@inproceedings{quillot-etal-2020-la,
    title = "La voix act{\'e}e : pratiques, enjeux, applications (Acted voice : practices, challenges, applications)",
    author = {Quillot, Mathias  and
      Guillou, Lauriane  and
      Gresse, Adrien  and
      Ferro, Rafa{\"e}l  and
      R{\"o}th, Rapha{\"e}l  and
      Malinas, Damien  and
      Dufour, Richard  and
      Roebel, Axel  and
      Obin, Nicolas  and
      Bonastre, Jean-Fran{\c{c}}ois  and
      Ethis, Emmanuel},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.59",
    pages = "525--533",
    abstract = "La voix act{\'e}e repr{\'e}sente un d{\'e}fi majeur pour les futures interfaces vocales avec un potentiel d{'}application extr{\^e}mement important pour la transformation num{\'e}rique des secteurs de la culture et de la communication, comme la production ou la post-production de voix pour les s{\'e}ries ou le cin{\'e}ma. Un aspect central de la voix act{\'e}e repose sur la notion d{'}interpr{\'e}tation, un aspect peu {\'e}tudi{\'e} dans la communaut{\'e} scientifique de la parole. Cet article propose un {\'e}tat des lieux et une r{\'e}flexion sur les d{\'e}fis scientifiques et les applications technologiques de la voix act{\'e}e : {\`a} la crois{\'e}e de l{'}acoustique, de la linguistique, de la culture, et de l{'}apprentissage machine. Une analyse pr{\'e}liminaire des pratiques permet de rendre compte de la diversit{\'e} de l{'}{\'e}cosyst{\`e}me des {``}m{\'e}tiers de la voix{''} et de pointer les fonctions et les conventions qui s{'}y rattachent. Nous nous int{\'e}resserons ensuite {\`a} la pratique particuli{\`e}re du doublage de voix, en faisant ressortir ses enjeux et probl{\'e}matiques sp{\'e}cifiques puis en pr{\'e}sentant des solutions propos{\'e}es pour mod{\'e}liser les codes expressifs de la voix d{'}un acteur ou les choix d{'}un op{\'e}rateur pour le doublage.",
    language = "French",
}
@inproceedings{randria-etal-2020-etude,
    title = "{\'E}tude des facteurs affectant la compr{\'e}hensibilit{\'e} de documents multimodaux : une {\'e}tude exp{\'e}rimentale (Factors affecting the comprehensibility of multimodal documents : an experimental study )",
    author = "Randria, Estelle  and
      Fontan, Lionel  and
      Le Coz, Maxime  and
      Ferran{\'e}, Isabelle  and
      Pinquier, Julien",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.60",
    pages = "534--542",
    abstract = "La compr{\'e}hensibilit{\'e} de documents audiovisuels peut d{\'e}pendre de facteurs propres {\`a} l{'}auditeur/spectateur (ex. langue maternelle, performances cognitives) et de facteurs propres aux contenus des documents (ex. complexit{\'e} linguistique, intelligibilit{\'e} de la parole). Dans ces travaux, nous {\'e}tudions les effets de facteurs propres aux contenus sur la compr{\'e}hensibilit{\'e} de 55 dialogues extraits de films, pr{\'e}sent{\'e}s {\`a} 15 experts (enseignants de fran{\c{c}}ais langue {\'e}trang{\`e}re) selon cinq modalit{\'e}s diff{\'e}rentes (transcription, transcription + audio, audio, audio + vid{\'e}o, transcription + audio + vid{\'e}o). Les experts ont {\'e}valu{\'e} les dialogues en termes de compr{\'e}hensibilit{\'e} g{\'e}n{\'e}rale, de complexit{\'e} du vocabulaire, de complexit{\'e} grammaticale, et d{'}intelligibilit{\'e} de la parole. L{'}analyse de leurs {\'e}valuations montre que (1) la complexit{\'e} du vocabulaire, la complexit{\'e} grammaticale, et l{'}intelligibilit{\'e} de la parole sont significativement corr{\'e}l{\'e}es {\`a} la compr{\'e}hensibilit{\'e} g{\'e}n{\'e}rale, et (2) que les {\'e}valuations de compr{\'e}hensibilit{\'e} g{\'e}n{\'e}rale ont tendance {\`a} {\^e}tre plus {\'e}lev{\'e}es lors de pr{\'e}sentations multimodales.",
    language = "French",
}
@inproceedings{rebourg-etal-2020-evaluer,
    title = "{\'E}valuer l{'}intelligibilit{\'e}, mots ou pseudo-mots ? Comparaison entre deux groupes d{'}auditeurs (Assess intelligibility, words or pseudo-words? Comparison between two groups of listeners)",
    author = "Rebourg, Marie  and
      Lalain, Muriel  and
      Ghio, Alain  and
      Fredouille, Corinne  and
      Fakhry, Nicolas  and
      Woisard, Virginie",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.61",
    pages = "543--551",
    abstract = {La perte d{'}intelligibilit{\'e} repr{\'e}sente une plainte importante des patients trait{\'e}s pour un cancer de la cavit{\'e} buccale ou de l{'}oropharynx. L{'}{\'e}valuation de l{'}intelligibilit{\'e} est essentielle dans le parcours de soin, mais les tests existants ne sont pas satisfaisants. Bas{\'e}s sur la perception de listes de mots par des auditeurs entra{\^\i}n{\'e}s {\`a} restaurer des s{\'e}quences sonores d{\'e}grad{\'e}es, ils conduisent souvent {\`a} une sous{\'e}valuation des d{\'e}ficits. Nous avons propos{\'e} une nouvelle t{\^a}che d{'}{\'e}valuation de l{'}intelligibilit{\'e}, la t{\^a}che de d{\'e}codage acoustico phon{\'e}tique (DAP), bas{\'e}e sur l{'}utilisation de pseudo-mots (Ast{\'e}sano et al., 2018; Ghio et al., 2018; Ghio et al., soumis, Lalain et al., sous presse). Dans cette {\'e}tude, nous {\'e}valuons la capacit{\'e} de la t{\^a}che DAP {\`a} neutraliser les effets de restauration lexicale et d{'}expertise auditive clinique. Les r{\'e}sultats montrent que contrairement {\`a} une {\'e}valuation de l{'}intelligibilit{\'e} bas{\'e}e sur des mots, une {\'e}valuation bas{\'e}e sur des pseudo-mots permet d{'}obtenir des scores de D{\'e}viation Phonologique Per{\c{c}}ue (DPP) stables au cours du temps quel que soit le degr{\'e} d{'}expertise des auditeurs, na{\"\i}fs ou cliniciens.},
    language = "French",
}
@inproceedings{riverin-coutlee-2020-sur,
    title = "Sur le voisement des consonnes fricatives finales en fran{\c{c}}ais du {Q}u{\'e}bec (On final fricative consonant voicing in {Q}uebec {F}rench)",
    author = "Riverin-Coutl{\'e}e, Josiane",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.62",
    pages = "552--560",
    abstract = "Cette {\'e}tude s{'}int{\'e}resse aux indices acoustiques qui concourent {\`a} distinguer les fricatives non vois{\'e}es /f s ʃ/ et vois{\'e}es /v z ʒ/ en position de finale absolue en fran{\c{c}}ais du Qu{\'e}bec. La dur{\'e}e de la consonne elle-m{\^e}me, celle de la voyelle accentu{\'e}e pr{\'e}c{\'e}dente et le taux de voisement consonantique sont les indices acoustiques examin{\'e}s. La dur{\'e}e intrins{\`e}que des voyelles, caract{\'e}ristique importante de la vari{\'e}t{\'e} {\`a} l{'}{\'e}tude, est prise en compte lors de l{'}analyse des r{\'e}sultats, qui indiquent que les deux groupes de consonnes se distinguent en tous points. Les consonnes vois{\'e}es ont une dur{\'e}e plus courte, pr{\'e}sentent un taux de voisement sup{\'e}rieur quoique moindre que celui rapport{\'e} dans la litt{\'e}rature ant{\'e}rieure, et allongent les segments vocaliques pr{\'e}c{\'e}dents. Ce dernier ph{\'e}nom{\`e}ne se produit m{\^e}me lorsque la voyelle est intrins{\`e}quement longue, r{\'e}v{\'e}lant la robustesse de l{'}effet allongeant des consonnes vois{\'e}es et l{'}extensibilit{\'e} de la dur{\'e}e vocalique en fran{\c{c}}ais qu{\'e}b{\'e}cois.",
    language = "French",
}
@inproceedings{roland-etal-2020-imprecision,
    title = "Impr{\'e}cision dans la production des voyelles : un potentiel marqueur infraclinique dans la maladie de {P}arkinson (Imprecision of vowel production: a potential subclinical marker in {P}arkinson{'}s disease)",
    author = "Roland, Virginie  and
      Delvaux, V{\'e}ronique  and
      Huet, Kathy  and
      Piccaluga, Myriam  and
      Harmegnies, Bernard",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.63",
    pages = "561--569",
    abstract = "La maladie de Parkinson est une maladie neurod{\'e}g{\'e}n{\'e}rative qui affecte le syst{\`e}me neuro-moteur. Une grande vari{\'e}t{\'e} de troubles de la parole, g{\'e}n{\'e}ralement regroup{\'e}s sous les termes de dysarthrie hypokin{\'e}tique, peuvent apparaitre. Dans cette contribution, nous pr{\'e}sentons les r{\'e}sultats d{'}une {\'e}tude acoustique comparative de la production de voyelles par 63 locuteurs MP dysarthriques et non dysarthriques, avec 35 locuteurs sains. Notre objectif est d{'}{\'e}tudier la production de voyelles isol{\'e}es afin de r{\'e}pondre {\`a} la question : l{'}impr{\'e}cision dans la production des voyelles peut-elle {\^e}tre consid{\'e}r{\'e}e comme un marqueur infraclinique de la dysarthrie ?",
    language = "French",
}
@inproceedings{schoentgen-etal-2020-modeles,
    title = "Mod{\`e}les de l{'}enrouement de la voix (Models of vocal roughness )",
    author = "Schoentgen, Jean  and
      Aichinger, Philipp  and
      Grenez, Francis",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.64",
    pages = "570--578",
    abstract = "L{'}objectif est l{'}{\'e}tude des causes des disp{\'e}riodicit{\'e}s des voix du type 1 qui sont pseudo-p{\'e}riodiques et monophoniques. Un mod{\`e}le qui explique quantitativement les perturbations des dur{\'e}es de cycles glottiques fait appel aux fluctuations de la tension du muscle vocal. Or, ces fluctuations n{'}expliquent pas l{'}enrouement qui peut faire suite {\`a} une charge vocale ou une laryngite l{\'e}g{\`e}re, par exemple. C{'}est pourquoi, nous discutons plusieurs mod{\`e}les qui montrent qu{'}une redistribution des amplitudes vibratoires entre le corps et la couverture du pli module les perturbations qui trouvent leur origine au niveau du muscle vocal. Des simulations {\`a} l{'}aide d{'}un mod{\`e}le corps-couverture sugg{\`e}rent ainsi que les perturbations des dur{\'e}es des cycles glottiques augmentent avec une redistribution des amplitudes vibratoires de la couverture vers le muscle suite {\`a} une redistribution des masses vibrantes du muscle vers la couverture.",
    language = "French",
}
@inproceedings{shao-ridouane-2020-la,
    title = "La « voyelle apicale » n{'}est pas une voyelle : {\'e}tude acoustique et articulatoire de la voyelle apicale en chinois de Jixi (The {`}apical vowel{'} is not a vowel: An acoustic and articulatory study of the apical vowel in Jixi-{H}ui {C}hinese)",
    author = "Shao, Bowei  and
      Ridouane, Rachid",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.65",
    pages = "579--587",
    abstract = "Cette {\'e}tude s{'}int{\'e}resse {\`a} la « voyelle apicale », not{\'e}e /z/, telle qu{'}elle est attest{\'e}e en chinois de Jixi. L{'}objectif est de d{\'e}terminer sa nature phon{\'e}tique sur la base de donn{\'e}es acoustiques et articulatoires. Phonologiquement, ce segment est un phon{\`e}me distinct qui s{'}oppose {\`a} /i/ dont il est issu diachroniquement. Il est exclusivement attest{\'e} en position noyau de syllabe o{\`u} il constitue une unit{\'e} porteuse de ton. Sur le plan articulatoire, les donn{\'e}es ultrasoniques d{\'e}montrent que, quand il est pr{\'e}c{\'e}d{\'e} de consonnes bilabiales /p, ph, m/, il pr{\'e}sente un geste articulatoire semblable {\`a} celui de la fricative alv{\'e}olaire /s/. Ce geste est r{\'e}alis{\'e} de mani{\`e}re anticipatoire durant la tenue des bilabiales. Une des cons{\'e}quences de cette r{\'e}alisation est que le rel{\^a}chement de /ph/ pr{\'e}sente les m{\^e}mes caract{\'e}ristiques acoustiques que le bruit de friction de la fricative /s/, comme l{'}atteste la ressemblance de leur centre de gravit{\'e}. Ces r{\'e}sultats montrent que la voyelle apicale en chinois de Jixi est mieux d{\'e}finie, au moins du point de vue phon{\'e}tique, comme une fricative alv{\'e}olaire.",
    language = "French",
}
@inproceedings{suire-etal-2020-symbolisme,
    title = "Symbolisme phon{\'e}tique du genre dans les pr{\'e}noms fran{\c{c}}ais (Sex-biased sound symbolism in {F}rench first names)",
    author = "Suire, Alexandre  and
      Bossoms Mesa, Alba  and
      Raymond, Michel  and
      Barkat-Defradas, Melissa",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.66",
    pages = "588--596",
    abstract = "Le symbolisme phon{\'e}tique sugg{\`e}re un lien naturel entre les sons et la signification d{'}un mot. Les pr{\'e}noms constituent d{'}excellents candidats afin d{'}{\'e}tudier ces relations selon les pr{\'e}dictions de la th{\'e}orie « code-fr{\'e}quence », selon laquelle les sons de basses fr{\'e}quences sont perceptivement associ{\'e}s {\`a} une large corpulence et par extension {\`a} la masculinit{\'e}, tandis que les sons de hautes fr{\'e}quences sont associ{\'e}s {\`a} la petitesse et {\`a} la f{\'e}minit{\'e}. En analysant les pr{\'e}noms fran{\c{c}}ais attribu{\'e}s entre 1900 et 2009, nous avons confirm{\'e} ces pr{\'e}dictions en observant une diff{\'e}rence significative de la qualit{\'e} de la voyelle sur la syllabe perceptivement pro{\'e}minente : les pr{\'e}noms masculins exhibent plus fr{\'e}quemment des voyelles de basses fr{\'e}quences (e.g. /o/) tandis que les pr{\'e}noms f{\'e}minins attestent plus souvent des voyelles de hautes fr{\'e}quences (e.g. /i/).",
    language = "French",
}
@inproceedings{tran-etal-2020-caracterisation,
    title = "Caract{\'e}risation des plosives finales dans des langues d{'}Asie : une {\'e}tude multilingue du non rel{\^a}chement (Characterization of Stop Consonants in {A}sian Languages: A two-language Study of Unreleased)",
    author = "Tran, Thi-Thuy-Hien  and
      Vall{\'e}e, Nathalie  and
      Savariaux, Christophe  and
      Kim, Inyoung  and
      Kim, Sunhee",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.67",
    pages = "597--605",
    abstract = "Cette {\'e}tude propose de caract{\'e}riser le non rel{\^a}chement des plosives finales /p, t, k/ de deux langues d{'}Asie, tonale (vietnamien) et non tonale (cor{\'e}en), du point de vue a{\'e}rodynamique et glottographique. Le comportement glottique (ouverture et fermeture de la glotte, position verticale du larynx) a {\'e}t{\'e} examin{\'e} en synchronisation avec les valeurs de d{\'e}bits d{'}air (oral et nasal) pendant les phases de la r{\'e}alisation consonantique. Les r{\'e}sultats mettent en {\'e}vidence (1) l{'}absence de rel{\^a}chement nasal apr{\`e}s l{'}occlusion de la plosive finale pouvant entra{\^\i}ner une baisse de la pression intraorale, (2) que le larynx s{'}abaisse syst{\'e}matiquement durant la tenue de la consonne. Cette strat{\'e}gie de r{\'e}alisation va dans le sens de notre hypoth{\`e}se selon laquelle les plosives non rel{\^a}ch{\'e}es sont produites avec un m{\'e}canisme permettant de diminuer la pression intraorale de mani{\`e}re {\`a} minimiser le co{\^u}t articulatoire de la tenue de la closion avec, pour cons{\'e}quence acoustique, l{'}absence de burst.",
    language = "French",
}
@inproceedings{verhaegen-etal-2020-capacites,
    title = "Capacit{\'e}s d{'}apprentissage phon{\'e}tique chez des patients aphasiques francophones : {\'e}tude de cas (Phonetic learning abilities in {F}rench-Speaking aphasic patients : a case study)",
    author = "Verhaegen, Cl{\'e}mence  and
      Delvaux, V{\'e}ronique  and
      Huet, Kathy  and
      Fagniart, Sophie  and
      Piccaluga, Myriam  and
      Harmegnies, Bernard",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.68",
    pages = "606--616",
    abstract = "Cette {\'e}tude explore les capacit{\'e}s de patients aphasiques, pr{\'e}sentant des troubles phonologicophon{\'e}tiques, notamment des difficult{\'e}s de coordination temporelle entre les articulateurs, {\`a} acqu{\'e}rir une variante phon{\'e}tique, non famili{\`e}re dans leur langue, n{\'e}cessitant l{'}adoption de nouveaux sch{\`e}mes articulatoires. 4 patients aphasiques, de langue maternelle fran{\c{c}}aise, ont particip{\'e} {\`a} la pr{\'e}sente {\'e}tude, ainsi que 36 participants contr{\^o}les. Au cours du paradigme d{'}apprentissage, la t{\^a}che principale consistait {\`a} r{\'e}p{\'e}ter des non-mots C[t]V[a], dont le VOT est de 60ms et ce {\`a} 3 reprises : avant toute intervention, puis apr{\`e}s un « entra{\^\i}nement » en perceptiont{\^a}che de discrimination de 5 non-mots CV dont le VOT variait entre 20 et 100ms, enfin apr{\`e}s un « entra{\^\i}nement » en production-t{\^a}che de r{\'e}p{\'e}tition de ces 5 non-mots. Les participants {\'e}taient par ailleurs amen{\'e}s {\`a} effectuer une t{\^a}che de calibration, destin{\'e}e {\`a} {\'e}valuer leur VOT en fran{\c{c}}ais. Les patients pr{\'e}sentent une plus grande variabilit{\'e} des dur{\'e}es de VOT. Trois patients sur quatre montrent des valeurs de VOT plus longues entre la calibration et les t{\^a}ches de r{\'e}p{\'e}tition de VOT60ms indiquant des capacit{\'e}s d{'}apprentissage phon{\'e}tique. Les liens entre ces observations et les profils des patients, ainsi que les implications pour la r{\'e}{\'e}ducation du langage, seront discut{\'e}s.",
    language = "French",
}
@inproceedings{wei-embarki-2020-qualite,
    title = "Qualit{\'e} vocale dans l{'}acquisition d{'}une langue {\'e}trang{\`e}re : le cas des apprenants sinophones en {FLE} (Voice quality in the second language acquisition: The case of {C}hinese learners of {F}rench as Foreign Language)",
    author = "Wei, Dongjun  and
      Embarki, Mohamed",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.69",
    pages = "617--626",
    abstract = "L{'}{\'e}tude porte sur les configurations de la qualit{\'e} vocale de huit apprenants sinophones qui parlent en mandarin dans une t{\^a}che de production de La bise et le soleil en L1 chinois et L2 fran{\c{c}}ais. Une comparaison est faite avec la lecture en fran{\c{c}}ais de quatre locuteurs natifs du fran{\c{c}}ais. Les corpus chinois/fran{\c{c}}ais sont utilis{\'e}s pour recueillir les impressions d{'}auditeurs fran{\c{c}}ais sur la qualit{\'e}vocale des apprenants sinophones. Des enregistrements vid{\'e}o ont {\'e}t{\'e}{\'e}galement r{\'e}alis{\'e}s en L1 chinois et L2 fran{\c{c}}ais par les m{\^e}mes apprenants. Les donn{\'e}es subjectives r{\'e}colt{\'e}es conform{\'e}ment {\`a} la litt{\'e}rature indiquent des variations de configurations de la qualit{\'e}vocale dans les deux langues. Les mesures acoustiques, Fo moyenne du texte lu et Fo moyenne de la voyelle [a], pr{\'e}sentent dans les deux langues des variations ordonn{\'e}es intra- et interindividuelles, entre lecture en L1 chinois et lecture en L2 fran{\c{c}}ais, et entre locuteurs L1 fran{\c{c}}ais et apprenants L2 fran{\c{c}}ais.",
    language = "French",
}
@inproceedings{wu-adda-decker-2020-reduction,
    title = "R{\'e}duction temporelle en fran{\c{c}}ais spontan{\'e} : o{\`u} se cache-t-elle ? Une {\'e}tude des segments, des mots et s{\'e}quences de mots fr{\'e}quemment r{\'e}duits ()",
    author = "Wu, Yaru  and
      Adda-Decker, Martine",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.70",
    pages = "627--635",
    abstract = "Cette {\'e}tude vise {\`a} proposer une m{\'e}thode adapt{\'e}e {\`a} l{'}{\'e}tude de divers ph{\'e}nom{\`e}nes de variation dans les grands corpus utilisant l{'}alignement automatique de la parole. Cette m{\'e}thode est appliqu{\'e}e pour {\'e}tudier la r{\'e}duction temporelle en fran{\c{c}}ais spontan{\'e}. Nous proposons de qualifier la r{\'e}duction temporelle comme la r{\'e}alisation de suites de segments courts cons{\'e}cutifs. Environ 14{\%} du corpus est consid{\'e}r{\'e} comme r{\'e}duit. Les r{\'e}sultats de l{'}alignement montrent que ces zones impliquent le plus souvent plus d{'}un mot (81{\%}), et que sinon, la position interne du mot est la plus concern{\'e}e. Parmi les exemples de suites de mots les plus r{\'e}duits, on trouve des locutions utilis{\'e}es comme des marqueurs discursifs.",
    language = "French",
}
@inproceedings{yang-ridouane-2020-les,
    title = "Les variations du schwa transitionnel en tachlhit : Une analyse acoustique (Variations of transitional schwa in Tashlhiyt: an acoustic analysis)",
    author = "Yang, Minmin  and
      Ridouane, Rachid",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.71",
    pages = "636--644",
    abstract = {Les caract{\'e}ristiques temporelles et spectrales du schwa transitionnel en tachlhit sont analys{\'e}es dans cette {\'e}tude. Nous avons examin{\'e} 18 items du type C1C2VC afin d{'}explorer comment la dur{\'e}e et la qualit{\'e} de ce voco{\"\i}de sont affect{\'e}es par le contexte consonantique et vocalique avoisinant. Les r{\'e}sultats obtenus {\`a} partir de la r{\'e}alisation de 7 locuteurs natifs montrent que la dur{\'e}e du schwa est beaucoup plus court compar{\'e}es aux voyelles pleines. Alors que cette dur{\'e}e varie peu selon le contexte, la qualit{\'e} du schwa peut {\^e}tre affect{\'e}e par une combinaison de facteurs incluant la nature de la voyelle qui suit, ainsi que le lieu et le mode d{'}articulation des consonnes adjacentes. Ces variations sont observ{\'e}es pour F1, F2 et F3, et la plupart d{'}entre elles peuvent {\^e}tre pr{\'e}dites selon que la consonne qui suit est une occlusive emphatique ou une sonante battue.},
    language = "French",
}
@inproceedings{yoon-etal-2020-effets,
    title = "Effets du sexe et de la langue parl{\'e}e sur la production de la parole chez les locuteurs cor{\'e}ens et fran{\c{c}}ais (Effects of sex and language spoken on speech production among {K}orean and {F}rench speakers)",
    author = "Yoon, Dayeon  and
      Audibert, Nicolas  and
      Fougeron, C{\'e}cile",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.72",
    pages = "645--653",
    abstract = "Cette {\'e}tude a pour but d{'}examiner l{'}effet du sexe et de la langue sur la production de la parole lue des locuteurs cor{\'e}ens et fran{\c{c}}ais. Dix param{\`e}tres acoustiques sont utilis{\'e}s pour caract{\'e}riser trois grandes dimensions : la voix (moyenne et {\'e}cart-type de la F0, pente de LTAS et CPPs) ; les r{\'e}sonances du conduit vocal (F1 et F2 de /a/ et /i/) ; la gestion temporelle (d{\'e}bit de parole et articulatoire). Comme attendu, on observe une interaction entre sexe et langue sur la plupart des param{\`e}tres acoustiques suppos{\'e}s diff{\'e}rencier les voix de femmes de celles d{'}hommes. Seuls le F1 de /i/ et la gestion temporelle ne montrent pas d{'}interaction entre sexe et langue. Ces r{\'e}sultats sugg{\`e}rent que la diff{\'e}renciation de la voix entre sexes d{\'e}pend de la langue parl{\'e}e.",
    language = "French",
}
@inproceedings{zaouali-etal-2020-etude,
    title = "{\'E}tude des caract{\'e}ristiques spatio-temporelles de la production de la parole chez des patients glossectomis{\'e}s (An acoustic study of spatio-temporal characteristics of speech production in glossectomised patients)",
    author = "Zaouali, Hasna  and
      Vaxelaire, B{\'e}atrice  and
      Debry, Christian  and
      Sock, Rudolph",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.73",
    pages = "654--663",
    abstract = "Cette {\'e}tude porte sur les caract{\'e}ristiques spatio-temporelles de la production de s{\'e}quences VCV par des patients glossectomis{\'e}s suite {\`a} un cancer endo-buccal. Plus pr{\'e}cis{\'e}ment, il s{'}agit d{'}analyser les diff{\'e}rents param{\`e}tres acoustiques (V1, VOT, VTT, silence, occlusion, V2), cette analyse nous permettra de rendre compte des cons{\'e}quences d{'}une glossectomie sur le timing des gestes articulatoires (Sock, 1998). Dix patients ont {\'e}t{\'e} enregistr{\'e}s sur plusieurs phases pr{\'e}- et postchirurgicales ; il s{'}agit donc d{'}une {\'e}tude longitudinale. Le corpus {\'e}tudi{\'e} est compos{\'e} de huit s{\'e}quences de type VCV. L{'}objectif est principalement d{'}observer {\`a} partir du signal acoustique, diff{\'e}rents {\'e}v{\'e}nements acoustiques interpr{\'e}tables directement en termes articulatoires, en tentant ainsi de remonter aux configurations articulatoires. L{'}analyse statistique a montr{\'e} des modifications significatives au niveau de la dur{\'e}e pour toutes les variables mesur{\'e}es lors des phases d{'}enregistrements post-chirurgicales. En effet, une am{\'e}lioration dans la production des s{\'e}quences appara{\^\i}t progressivement avec le temps et la r{\'e}habilitation orthophonique qui se manifeste pour certains patients par un retour aux valeurs initialement relev{\'e}es lors de la phase pr{\'e}-chirurgicale.",
    language = "French",
}
@inproceedings{zhou-demolin-2020-perception,
    title = "Perception des tons du mandarin par les apprenants fran{\c{c}}ais : effets des contextes segmental et syllabique (In the present study, we report two experiments aimed at exploring the contributions of segmental and syllabic contexts to {F}rench learners{'} perception of {M}andarin tones)",
    author = "Zhou, Qing  and
      Demolin, Didier",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 1 : Journ{\'e}es d'{\'E}tudes sur la Parole",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-jep.74",
    pages = "664--672",
    abstract = "Dans la pr{\'e}sente {\'e}tude, nous rapportons deux exp{\'e}riences visant {\`a} explorer les contributions des contextes segmental et syllabique {\`a} la perception des tons du mandarin par les apprenants fran{\c{c}}ais. Dans la premi{\`e}re, des stimuli monosyllabiques produits naturellement, compos{\'e}s de 9 attaques ([{\o}(z{\'e}ro), p, t, tʰ, tɕ, ɕ, tʂ, tʂʰ, m]) et 2 rimes ([i, ɑu]), ont {\'e}t{\'e} identifi{\'e}s par 19 apprenants fran{\c{c}}ais de mandarin de niveau d{\'e}butant et 18 auditeurs de langue maternelle mandarin. Dans la deuxi{\`e}me, les stimuli compos{\'e}s de 6 types de syllabes (V, VV, VN, CV, CVV, CVN) ont {\'e}t{\'e} cat{\'e}goris{\'e}s par deux autres groupes d{'}auditeurs. Nos r{\'e}sultats montrent que contrairement aux auditeurs natifs, la perception tonale des apprenants fran{\c{c}}ais est influenc{\'e}e de mani{\`e}re significative non seulement par les caract{\'e}ristiques tonales, mais aussi par les attaque-, rime- and syllabe-types. Cela sugg{\`e}re que les {\'e}tudes d{'}acquisition des tons L2 devraient prendre en compte non seulement le syst{\`e}me tonal de la L2, mais aussi le syst{\`e}me phonologique segmental de la L2.",
    language = "French",
}
@proceedings{jep-taln-recital-2020-actes-de-la-6e-conjointe-journees,
    title = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). 2e atelier {\'E}thique et TRaitemeNt Automatique des Langues (ETeRNAL)",
    editor = {Adda, Gilles  and
      Amblard, Maxime  and
      Fort, Kar{\"e}n},
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-eternal.0",
}
@inproceedings{garnerin-etal-2020-pratiques,
    title = "Pratiques d{'}{\'e}valuation en {ASR} et biais de performance (Evaluation methodology in {ASR} and performance bias)",
    author = "Garnerin, Mahault  and
      Rossato, Solange  and
      Besacier, Laurent",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). 2e atelier {\'E}thique et TRaitemeNt Automatique des Langues (ETeRNAL)",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-eternal.1",
    pages = "1--9",
    abstract = "Nous proposons une r{\'e}flexion sur les pratiques d{'}{\'e}valuation des syst{\`e}mes de reconnaissance automatique de la parole (ASR). Apr{\`e}s avoir d{\'e}fini la notion de discrimination d{'}un point de vue l{\'e}gal et la notion d{'}{\'e}quit{\'e} dans les syst{\`e}mes d{'}intelligence artificielle, nous nous int{\'e}ressons aux pratiques actuelles lors des grandes campagnes d{'}{\'e}valuation. Nous observons que la variabilit{\'e} de la parole et plus particuli{\`e}rement celle de l{'}individu n{'}est pas prise en compte dans les protocoles d{'}{\'e}valuation actuels rendant impossible l{'}{\'e}tude de biais potentiels dans les syst{\`e}mes.",
    language = "French",
}
@inproceedings{lion-bouton-etal-2020-comment,
    title = "Comment arpenter sans m{\`e}tre : les scores de r{\'e}solution de cha{\^\i}nes de cor{\'e}f{\'e}rences sont-ils des m{\'e}triques ? (Do the standard scores of evaluation of coreference resolution constitute metrics ?)",
    author = {Lion-Bouton, Adam  and
      Grobol, Lo{\"\i}c  and
      Antoine, Jean-Yves  and
      Billot, Sylvie  and
      Lefeuvre-Halftermeyer, Ana{\"\i}s},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). 2e atelier {\'E}thique et TRaitemeNt Automatique des Langues (ETeRNAL)",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-eternal.2",
    pages = "10--18",
    abstract = "Cet article pr{\'e}sente un travail qui consiste {\`a} {\'e}tudier si les scores les plus utilis{\'e}s pour l{'}{\'e}valuation de la r{\'e}solution des cor{\'e}f{\'e}rences constituent des m{\'e}triques de similarit{\'e} normalis{\'e}es. En adoptant une d{\'e}marche purement exp{\'e}rimentale, nous avons v{\'e}rifi{\'e} si les scores MUC, B3 , CEAF, BLANC, LEA et le meta-score CoNLL respectent les bonnes propri{\'e}t{\'e}s qui d{\'e}finissent une telle m{\'e}trique. Notre {\'e}tude montre que seul le score CEAFm est potentiellement une m{\'e}trique de similarit{\'e} normalis{\'e}e.",
    language = "French",
}
@inproceedings{barbaresi-lejeune-2020-que,
    title = "Que rec{\`e}lent les donn{\'e}es textuelles issues du web ? (What do text data from the Web have to hide ?)",
    author = {Barbaresi, Adrien  and
      Lejeune, Ga{\"e}l},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). 2e atelier {\'E}thique et TRaitemeNt Automatique des Langues (ETeRNAL)",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-eternal.3",
    pages = "19--28",
    abstract = "La collecte et l{'}usage opportunistes de donn{\'e}es textuelles tir{\'e}es du web sont sujets {\`a} une s{\'e}rie de probl{\`e}mes {\'e}thiques, m{\'e}thodologiques et {\'e}pist{\'e}mologiques qui m{\'e}ritent l{'}attention de la communaut{\'e} scientifique. Nous pr{\'e}sentons des {\'e}tudes empiriques de leur impact en linguistique et TAL centr{\'e}es sur la forme (m{\'e}thodes d{'}extraction des donn{\'e}es) ainsi que sur le fond (contenu des corpus).",
    language = "French",
}
@inproceedings{millour-etal-2020-repliquer,
    title = "R{\'e}pliquer et {\'e}tendre pour l{'}alsacien {``}{\'E}tiquetage en parties du discours de langues peu dot{\'e}es par sp{\'e}cialisation des plongements lexicaux{''} (Replicating and extending for {A}lsatian : {``}{POS} tagging for low-resource languages by adapting word embeddings{''})",
    author = {Millour, Alice  and
      Fort, Kar{\"e}n  and
      Magistry, Pierre},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). 2e atelier {\'E}thique et TRaitemeNt Automatique des Langues (ETeRNAL)",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-eternal.4",
    pages = "29--37",
    abstract = "Nous pr{\'e}sentons ici les r{\'e}sultats d{'}un travail de r{\'e}plication et d{'}extension pour l{'}alsacien d{'}une exp{\'e}rience concernant l{'}{\'e}tiquetage en parties du discours de langues peu dot{\'e}es par sp{\'e}cialisation des plongements lexicaux (Magistry et al., 2018). Ce travail a {\'e}t{\'e} r{\'e}alis{\'e} en {\'e}troite collaboration avec les auteurs de l{'}article d{'}origine. Cette interaction riche nous a permis de mettre au jour les {\'e}l{\'e}ments manquants dans la pr{\'e}sentation de l{'}exp{\'e}rience, de les compl{\'e}ter, et d{'}{\'e}tendre la recherche {\`a} la robustesse {\`a} la variation.",
    language = "French",
}
@inproceedings{bonastre-2020-1990,
    title = "1990-2020 : retours sur 30 ans d{'}{\'e}changes autour de l{'}identification de voix en milieu judiciaire (1990-2020: A look back at 30 years of discussions on voice identification in the judicial system)",
    author = "Bonastre, Jean-Francois",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). 2e atelier {\'E}thique et TRaitemeNt Automatique des Langues (ETeRNAL)",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-eternal.5",
    pages = "38--47",
    abstract = "Des enregistrements de voix se trouvent de plus en plus souvent au c{\oe}ur d{'}affaires judiciaires importantes, notamment de par l{'}essor de la t{\'e}l{\'e}phonie mobile. La justice demande {\`a} ce que des expertises en identification de voix soient r{\'e}alis{\'e}es alors que dans le m{\^e}me temps, la pertinence scientifique de telles expertises est fortement mise en cause par les scientifiques. Ainsi, d{\`e}s 1990, les chercheurs en communication parl{\'e}e r{\'e}unis dans le GFCP, devenu depuis AFCP, ont vot{\'e} une motion affirmant que « l{'}identification d{'}un individu par sa voix est {\`a} l{'}heure actuelle un probl{\`e}me {\`a} sa connaissance non r{\'e}solu ». Cette motion est toujours en vigueur, apr{\`e}s avoir {\'e}t{\'e} r{\'e}affirm{\'e}e en 1997 et renforc{\'e}e par une p{\'e}tition en 2002. Malgr{\'e} cela, des expertises judiciaires en identification de voix sont r{\'e}alis{\'e}es en France chaque ann{\'e}e. Cet article revient sur les actions men{\'e}es par le GFCP et l{'}AFCP depuis la motion initiale jusqu{'}aux actions contemporaines. Il se propose d{'}{\'e}valuer les r{\'e}percussions de ces actions, tant au niveau de la Justice qu{'}au niveau acad{\'e}mique.",
    language = "French",
}
@proceedings{jep-taln-recital-2020-actes-de-la-6e,
    title = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    editor = "Benzitoun, Christophe  and
      Braud, Chlo{\'e}  and
      Huber, Laurine  and
      Langlois, David  and
      Ouni, Slim  and
      Pogodalla, Sylvain  and
      Schneider, St{\'e}phane",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.0",
}
@inproceedings{boitet-etal-2020-demo,
    title = "D{\'e}mo de {AMALD}-serveur et {AMALD}-corpus, d{\'e}di{\'e}s {\`a} l{'}analyse morphologique de l{'}allemand (Demonstration of {AMALD}-serveur and {AMALD}-corpus, dedicated to the morphological analysis of {G}erman)",
    author = "Boitet, Christian  and
      Berment, Vincent  and
      Guilbaud, Jean-Philippe  and
      Lemaire, Claire",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.1",
    pages = "2--5",
    abstract = "Le projet AMALDarium vise {\`a} offrir sur la plateforme lingwarium.org (1) un service d{'}analyse morphologique de l{'}allemand (AMALD-serveur), {\`a} grande couverture et de haute qualit{\'e}, traitant la flexion, la d{\'e}rivation et la composition, ainsi que les verbes {\`a} particule s{\'e}parable s{\'e}par{\'e}e (ou agglutin{\'e}e), (2) un corpus de r{\'e}f{\'e}rence de haute qualit{\'e} donnant tous les r{\'e}sultats possibles de l{'}analyse morphologique, avant filtrage par une m{\'e}thode statistique ou syntaxique, et (3) une plateforme (AMALD-{\'e}val) permettant d{'}organiser des {\'e}valuations comparatives, dans la perspective d{'}am{\'e}liorer les performances d{'}algorithmes d{'}apprentissage en morphologie. Nous pr{\'e}sentons ici une d{\'e}monstration en ligne seulement de AMALD-serveur et AMALD-corpus. Le corpus est un sous-ensemble anonymis{\'e} et v{\'e}rifi{\'e} d{'}un corpus en allemand form{\'e} de textes sur le cancer du sein, contenant de nombreux mots compos{\'e}s techniques.",
    language = "French",
}
@inproceedings{bonnet-segerer-2020-generateur,
    title = "G{\'e}n{\'e}rateur de dictionnaires au format Android pour les langues peu dot{\'e}es (Dictionary App Generator for Less Resourced Languages)",
    author = "Bonnet, R{\'e}my  and
      Segerer, Guillaume",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.2",
    pages = "6--9",
    abstract = "Nous pr{\'e}sentons un outil informatique en source libre permettant, {\`a} partir d{'}un dictionnaire sous format {\'e}lectronique de g{\'e}n{\'e}rer une application Android. L{'}objectif est de tirer profit de l{'}augmentation spectaculaire des utilisateurs de mobiles multifonctions dans de nombreuses r{\'e}gions du monde, notamment en Afrique, pour permettre aux locuteurs de langues moins dot{\'e}es de b{\'e}n{\'e}ficier de dictionnaires de bonne qualit{\'e}, faciles {\`a} utiliser et de s{'}impliquer dans la collecte de donn{\'e}es. Cet outil est con{\c{c}}u pour {\^e}tre compatible avec les principaux formats lexicographiques.",
    language = "French",
}
@inproceedings{dubuisson-duplessis-etal-2020-desidentification,
    title = "D{\'e}sidentification de donn{\'e}es texte produites dans un cadre de relation client (De-identification of customer relationship text data )",
    author = "Dubuisson Duplessis, Guillaume  and
      Bartholme, Elliot  and
      Kerroua, Sofiane  and
      Poulain, Mathilde  and
      Roulier, Ah{\`e}s  and
      Gu{\'e}net, Anne-Laure",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.3",
    pages = "10--13",
    abstract = "Cette d{\'e}monstration pr{\'e}sente une solution performante de d{\'e}sidentification de donn{\'e}es texte selon 13 types d{'}entit{\'e}s nomm{\'e}es et entra{\^\i}n{\'e}e sur des donn{\'e}es issues de la relation client.",
    language = "French",
}
@inproceedings{dusserre-etal-2020-analyse,
    title = "Analyse s{\'e}mantique de transcriptions automatiques d{'}appels t{\'e}l{\'e}phoniques en fran{\c{c}}ais (Semantic analysis of automatic phone call transcriptions in {F}rench)",
    author = "Dusserre, Emmanuelle  and
      Kalitvianski, Ruslan  and
      Ruhlmann, Mathieu  and
      Padr{\'o}, Muntsa",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.4",
    pages = "14--17",
    abstract = "Dans cet article, nous pr{\'e}sentons la mise en {\oe}uvre d{'}une cha{\^\i}ne de traitement s{\'e}mantique compl{\`e}te d{\'e}di{\'e}e aux conversations audio issues de centres d{'}appel t{\'e}l{\'e}phoniques, depuis la phase de transcription automatique jusqu{'}{\`a} l{'}exploitation des r{\'e}sultats, en passant par l{'}{\'e}tape d{'}analyse s{\'e}mantique des {\'e}nonc{\'e}s. Nous d{\'e}crivons ici le fonctionnement des diff{\'e}rentes analyses que notre {\'e}quipe d{\'e}veloppe, ainsi que la plateforme interactive permettant de restituer les r{\'e}sultats agr{\'e}g{\'e}s de toutes les conversations analys{\'e}es.",
    language = "French",
}
@inproceedings{epure-etal-2020-muzeeglot,
    title = "Muzeeglot : annotation multilingue et multi-sources d{'}entit{\'e}s musicales {\`a} partir de repr{\'e}sentations de genres musicaux (Muzeeglot : cross-lingual multi-source music item annotation from music genre embeddings)",
    author = "Epure, Elena V.  and
      Salha, Guillaume  and
      Voituret, F{\'e}lix  and
      Baranes, Marion  and
      Hennequin, Romain",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.5",
    pages = "18--21",
    abstract = "Au sein de cette d{\'e}monstration, nous pr{\'e}sentons Muzeeglot, une interface web permettant de visualiser des espaces de repr{\'e}sentations de genres musicaux provenant de sources vari{\'e}es et de langues diff{\'e}rentes. Nous montrons l{'}efficacit{\'e} de notre syst{\`e}me {\`a} pr{\'e}dire automatiquement les genres correspondant {\`a} une entit{\'e} musicale (titre, artiste, album...) selon une certaine source ou langue, {\'e}tant donn{\'e}es des annotations provenant de sources ou de langues diff{\'e}rentes.",
    language = "French",
}
@inproceedings{fayet-etal-2020-flexeval,
    title = "{F}lex{E}val, cr{\'e}ation de sites web l{\'e}gers pour des campagnes de tests perceptifs multim{\'e}dias ({F}lex{E}val, creation of light websites for multimedia perceptual test campaigns)",
    author = "Fayet, C{\'e}dric  and
      Blond, Alexis  and
      Coulombel, Gr{\'e}goire  and
      Simon, Claude  and
      Lolive, Damien  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Chevelu, Jonathan  and
      Le Maguer, S{\'e}bastien",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.6",
    pages = "22--25",
    abstract = "Nous pr{\'e}sentons FlexEval, un outil de conception et d{\'e}ploiement de tests perceptifs multim{\'e}dias sous la forme d{'}un site web l{\'e}ger. S{'}appuyant sur des technologies standards et ouvertes du web, notamment le framework Flask, FlexEval offre une grande souplesse de conception, des gages de p{\'e}rennit{\'e}, ainsi que le support de communaut{\'e}s actives d{'}utilisateurs. L{'}application est disponible en open-source via le d{\'e}p{\^o}t Git https://gitlab.inria.fr/expression/tools/flexeval.",
    language = "French",
}
@inproceedings{flamein-eshkol-taravella-2020-vers,
    title = "Vers une analyse automatique de la perception relative {\`a} un lieu (Towards an Automatic Analysis of Place Perception)",
    author = "Flamein, H{\'e}l{\`e}ne  and
      Eshkol-Taravella, Iris",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.7",
    pages = "26--29",
    abstract = "Le travail pr{\'e}sent{\'e} s{'}int{\'e}resse {\`a} la perception qu{'}ont les habitants de leur ville en se fondant sur un corpus de conversations orales spontan{\'e}es. La cha{\^\i}ne de traitement conditionnant l{'}analyse de la perception se d{\'e}compose en trois {\'e}tapes : la d{\'e}tection des noms de lieux, l{'}analyse de la perception identifi{\'e}e et la visualisation cartographique des informations extraites.",
    language = "French",
}
@inproceedings{gaillat-etal-2020-un,
    title = "Un prototype en ligne pour la pr{\'e}diction du niveau de comp{\'e}tence en anglais des productions {\'e}crites (A prototype for web-based prediction of {E}nglish proficiency levels in writings)",
    author = "Gaillat, Thomas  and
      Ballier, Nicolas  and
      Sousa, Annanda  and
      Bouy{\'e}, Manon  and
      Simpkin, Andrew  and
      Stearns, Bernardo  and
      Zarrouk, Manel",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.8",
    pages = "30--33",
    abstract = "Cet article d{\'e}crit un prototype ax{\'e} sur la pr{\'e}diction du niveau de comp{\'e}tence des apprenants de l{'}anglais. Le syst{\`e}me repose sur un mod{\`e}le d{'}apprentissage supervis{\'e}, coupl{\'e} {\`a} une interface web.",
    language = "French",
}
@inproceedings{gulati-2020-lex,
    title = "{LEX} : un jeu avec finalit{\'e} d{'}acquisition de ressources lexicales ({LEX} : a game with the purpose of lexical resource acquisition)",
    author = "Gulati, Asheesh",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.9",
    pages = "34--37",
    abstract = "LEX est un jeu avec un but d{\'e}velopp{\'e} dans l{'}optique d{'}explorer plus avant les {\'e}l{\'e}ments et principes de la conception de jeux tels qu{'}ils sont pratiqu{\'e}s dans l{'}industrie vid{\'e}oludique, pour les mettre au service de la conception de jeux s{\'e}rieux. Le premier prototype repose sur un mode bac {\`a} sable pour faire appel {\`a} la cr{\'e}ativit{\'e} du joueur et renforcer l{'}immersion ludique.",
    language = "French",
}
@inproceedings{karoui-etal-2020-skif,
    title = "{S}ki{F} Maker : un processus de classement du meilleur talent par rapport {\`a} une offre de mission ({S}ki{F} Maker : a process for ranking the best talent in relation to an assignment offer)",
    author = "Karoui, Jihen  and
      Hafid, Kamilia  and
      Pezeril, St{\'e}phane",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.10",
    pages = "38--41",
    abstract = "SkiF Maker pr{\'e}sente une solution innovatrice pour toute entreprise de services num{\'e}riques (ESN) visant {\`a} am{\'e}liorer son processus de recrutement de talents, la gestion des comp{\'e}tences de ses ressources humaines et la satisfaction des besoins de ses clients. Cet outil offre aux ESN un gain consid{\'e}rable en terme de temps de travail et leur {\'e}vite un travail manuel souvent laborieux et non plaisant.",
    language = "French",
}
@inproceedings{laleye-etal-2020-iagotchi,
    title = "Iagotchi : vers un agent conversationnel artistique (Iagotchi : Towards an Artistic Conversational Agent )",
    author = {Laleye, Frejus  and
      de Chalendar, Ga{\"e}l  and
      Frey, L{\'e}opold  and
      Berenguer, Rocio},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.11",
    pages = "42--45",
    abstract = "Cet article d{\'e}crit Iagotchi, un personnage virtuel philosophique et artistique qui apprend et d{\'e}veloppe des connaissances {\`a} partir de ses interactions avec l{'}humain. Iagotchi se pr{\'e}sente {\`a} la fois comme un apprenant et un expert avec comme objectifs principaux (1) d{'}accompagner l{'}homme dans ses questionnements, (2) de lui fournir des r{\'e}ponses pertinentes sur la base de ses requ{\^e}tes et (3) de g{\'e}n{\'e}rer des textes po{\'e}tiques coh{\'e}rents. Dans ce travail, nous d{\'e}crivons l{'}architecture du syst{\`e}me de Iagotchi et les composants cl{\'e}s tels que le moteur de conversation, le gestionnaire de sujets et le g{\'e}n{\'e}rateur de po{\'e}sies.",
    language = "French",
}
@inproceedings{lejeune-barbaresi-2020-bien,
    title = "Bien choisir son outil d{'}extraction de contenu {\`a} partir du Web (Choosing the appropriate tool for Web Content Extraction )",
    author = {Lejeune, Ga{\"e}l  and
      Barbaresi, Adrien},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.12",
    pages = "46--49",
    abstract = "Nous proposons une d{\'e}monstration sur l{'}extraction de contenu textuel dans des pages web ainsi que son {\'e}valuation. Nous nous concentrons sur les pages web contenant du texte (articles de presse, magazines en ligne et blogs) et montrons que les textes peuvent varier grandement selon diff{\'e}rentes dimensions : diachronique, g{\'e}ographique et typologique. D{\`e}s lors, les outils et mesures d{'}{\'e}valuation correspondantes sont sujettes {\`a} caution : les indicateurs commun{\'e}ment utilis{\'e}s et cens{\'e}s pr{\'e}sider au choix de l{'}outil appropri{\'e} par les utilisateurs finaux sont {\`a} la fois impr{\'e}cis et difficiles {\`a} interpr{\'e}ter.",
    language = "French",
}
@inproceedings{maudet-servan-2020-conception,
    title = "Conception d{'}un syst{\`e}me de d{\'e}tection d{'}intention pour un moteur de recherche sur {I}nternet (Designing a User Intention Detection system for a Web Search Engine)",
    author = "Maudet, Estelle  and
      Servan, Christophe",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.13",
    pages = "50--52",
    abstract = "Dans les moteurs de recherche sur Internet, l{'}une des t{\^a}ches les plus importantes vise {\`a} identifier l{'}intention de l{'}utilisateur. Cet article pr{\'e}sente notre {\'e}tude pour proposer un nouveau syst{\`e}me de d{\'e}tection d{'}intention pour le moteur de recherche sur Internet Qwant. Des logs de clic au syst{\`e}me de d{\'e}tection d{'}intention, l{'}ensemble du processus est expliqu{\'e}, y compris les contraintes industrielles qui ont d{\^u} {\^e}tre prises en compte. Une analyse manuelle des donn{\'e}es group{\'e}es a d{'}abord {\'e}t{\'e} appliqu{\'e}e sur les journaux afin de mieux comprendre les objectifs de l{'}utilisateur et de choisir les cat{\'e}gories d{'}intention pertinentes. Lorsque la recherche satisfait aux contraintes industrielles, il faut faire des choix architecturaux et faire des concessions. Cet article explique les contraintes et les r{\'e}sultats obtenus pour ce nouveau syst{\`e}me en ligne.",
    language = "French",
}
@inproceedings{amine-menacer-etal-2020-projet,
    title = "Projet {AMIS} : r{\'e}sum{\'e} et traduction automatique de vid{\'e}os ({AMIS} project : automatic summarization and translation of videos)",
    author = {Amine Menacer, Mohamed  and
      Fohr, Dominique  and
      Jouvet, Denis  and
      Abidi, Karima  and
      Langlois, David  and
      Sma{\"\i}li, Kamel},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.14",
    pages = "53--56",
    abstract = "La d{\'e}monstration de r{\'e}sum{\'e} et de traduction automatique de vid{\'e}os r{\'e}sulte de nos travaux dans le projet AMIS. L{'}objectif du projet {\'e}tait d{'}aider un voyageur {\`a} comprendre les nouvelles dans un pays {\'e}tranger. Pour cela, le projet propose de r{\'e}sumer et traduire automatiquement une vid{\'e}o en langue {\'e}trang{\`e}re (ici, l{'}arabe). Un autre objectif du projet {\'e}tait aussi de comparer les opinions et sentiments exprim{\'e}s dans plusieurs vid{\'e}os comparables. La d{\'e}monstration porte sur l{'}aspect r{\'e}sum{\'e}, transcription et traduction. Les exemples montr{\'e}s permettront de comprendre et mesurer qualitativement les r{\'e}sultats du projet.",
    language = "French",
}
@inproceedings{nerima-wehrli-2020-la,
    title = "La r{\'e}solution d{'}anaphores au-del{\`a} de la fronti{\`e}re de la phrase (The Anaphora Resolution Beyond Sentence Boundary)",
    author = "Nerima, Luka  and
      Wehrli, Eric",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.15",
    pages = "57--59",
    abstract = "Cette d{\'e}monstration pr{\'e}sente une extension de nos outils d{'}analyse syntaxique et d{'}{\'e}tiquetage morphosyntaxique qui prend en compte la r{\'e}solution d{'}anaphores pronominales non seulement {\`a} l{'}int{\'e}rieur d{'}une phrase, mais {\'e}galement si l{'}ant{\'e}c{\'e}dent se trouve dans la phrase pr{\'e}c{\'e}dente. Autant l{'}analyseur que l{'}{\'e}tiqueteur effectuant une analyse syntaxique compl{\`e}te des phrases, ces outils affichent {\'e}galement les fonctions grammaticales des constituants (sujet, objet direct, etc.) et les arguments des verbes. Une version de cette d{\'e}monstration est disponible sur le Web.",
    language = "French",
}
@inproceedings{ollinger-etal-2020-spiderlex,
    title = "Spiderlex et compagnie (Spiderlex {\&} Co)",
    author = "Ollinger, Sandrine  and
      Polgu{\`e}re, Alain  and
      Chudy, Yannick  and
      Gaume, Bruno",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.16",
    pages = "60--63",
    abstract = "Nous pr{\'e}sentons l{'}instrument de navigation Spiderlex, mis au point pour permettre une exploration dynamique des R{\'e}seaux Lexicaux du Fran{\c{c}}ais et de l{'}Anglais (RL-fr et RL-en). Spiderlex est {\`a} la fois un outil de visualisation des donn{\'e}es lexicales et un instrument de travail pour les lexicographes.",
    language = "French",
}
@inproceedings{parisse-liegeois-2020-utiliser,
    title = "Utiliser les outils {CORLI} de conversion {TEI} pour l{'}analyse de corpus de langage oral (The {CORLI} consortium develops tools to facilitate sharing, interrogation, and reusing of spoken language corpora)",
    author = {Parisse, Christophe  and
      Li{\'e}geois, Lo{\"\i}c},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.17",
    pages = "64--65",
    abstract = "Le consortium CORLI d{\'e}veloppe des outils pour faciliter le d{\'e}p{\^o}t, l{'}interrogation et la r{\'e}utilisation des corpus oraux. Ces outils libres et open source sont bas{\'e}s sur la TEI comme format commun de partage. Nous pr{\'e}senterons deux outils diff{\'e}rents : un outil pour la saisie et l{'}{\'e}dition de fichiers de m{\'e}tadonn{\'e}es et un outil permettant d{'}int{\'e}grer et d{'}utiliser des corpus de diff{\'e}rentes sources de donn{\'e}es transcrits dans diff{\'e}rents logiciels.",
    language = "French",
}
@inproceedings{de-salabert-barreaux-2020-vers,
    title = "Vers un corpus optimal pour la fouille de textes : strat{\'e}gie de constitution de corpus sp{\'e}cialis{\'e}s {\`a} partir d{'}{ISTEX} (Towards an optimal corpus for text mining: specialized corpus building strategy from {ISTEX})",
    author = "de Salabert, Camille  and
      Barreaux, Sabine",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.18",
    pages = "66--69",
    abstract = "Pr{\'e}alable indispensable {\`a} de nombreuses activit{\'e}s de TAL et de fouille de textes, l{'}{\'e}laboration d{'}un corpus peut n{\'e}cessiter plusieurs phases de traitement pour am{\'e}liorer sa qualit{\'e} et ainsi obtenir les meilleurs r{\'e}sultats d{'}analyse automatique. Les post-traitements appliqu{\'e}s {\`a} un tel corpus, notamment pour garantir la pertinence de son contenu et l{'}homog{\'e}n{\'e}it{\'e} de son format, pourront s{'}av{\'e}rer d{'}autant plus co{\^u}teux et fastidieux que la construction du corpus de travail aura {\'e}t{\'e} impr{\'e}cise. Cette d{\'e}monstration se proposera de tirer parti de la plateforme ISTEX et de ses services associ{\'e}s pour constituer, au travers d{'}un cycle it{\'e}ratif, un corpus homog{\`e}ne de publications scientifiquement pertinentes pour une utilisation simplifi{\'e}e par des outils de fouille.",
    language = "French",
}
@inproceedings{marzinotto-etal-2020-analyse-semantique,
    title = "Analyse s{\'e}mantique robuste par apprentissage antagoniste pour la g{\'e}n{\'e}ralisation de domaine (Robust Semantic Parsing with Adversarial Learning for Domain Generalization )",
    author = "Marzinotto, Gabriel  and
      Damnati, G{\'e}raldine  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Favre, Beno{\^\i}t",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 4 : D{\'e}monstrations et r{\'e}sum{\'e}s d'articles internationaux",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-demos.19",
    pages = "71--72",
    abstract = "Nous pr{\'e}sentons des r{\'e}sum{\'e}s en fran{\c{c}}ais et en anglais de l{'}article (Marzinotto et al., 2019) pr{\'e}sent{\'e} {\`a} la conf{\'e}rence North American Chapter of the Association for Computational Linguistics : Human Language Technologies en 2019.",
    language = "French",
}
@proceedings{jep-taln-recital-2020-actes-de-la-6e-conjointe,
    title = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    editor = "Cardon, R{\'e}mi  and
      Grabar, Natalia  and
      Grouin, Cyril  and
      Hamon, Thierry",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.0",
}
@inproceedings{cardon-etal-2020-presentation,
    title = "Pr{\'e}sentation de la campagne d{'}{\'e}valuation {DEFT} 2020 : similarit{\'e} textuelle en domaine ouvert et extraction d{'}information pr{\'e}cise dans des cas cliniques (Presentation of the {DEFT} 2020 Challenge : open domain textual similarity and precise information extraction from clinical cases )",
    author = "Cardon, R{\'e}mi  and
      Grabar, Natalia  and
      Grouin, Cyril  and
      Hamon, Thierry",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.1",
    pages = "1--13",
    abstract = "L{'}{\'e}dition 2020 du d{\'e}fi fouille de texte (DEFT) a propos{\'e} deux t{\^a}ches autour de la similarit{\'e} textuelle et une t{\^a}che d{'}extraction d{'}information. La premi{\`e}re t{\^a}che vise {\`a} identifier le degr{\'e} de similarit{\'e} entre paires de phrases sur une {\'e}chelle de 0 (le moins similaire) {\`a} 5 (le plus similaire). Les r{\'e}sultats varient de 0,65 {\`a} 0,82 d{'}EDRM. La deuxi{\`e}me t{\^a}che consiste {\`a} d{\'e}terminer la phrase la plus proche d{'}une phrase source parmi trois phrases cibles fournies, avec des r{\'e}sultats tr{\`e}s {\'e}lev{\'e}s, variant de 0,94 {\`a} 0,99 de pr{\'e}cision. Ces deux t{\^a}ches reposent sur un corpus du domaine g{\'e}n{\'e}ral et de sant{\'e}. La troisi{\`e}me t{\^a}che propose d{'}extraire dix cat{\'e}gories d{'}informations du domaine m{\'e}dical depuis le corpus de cas cliniques de DEFT 2019. Les r{\'e}sultats varient de 0,07 {\`a} 0,66 de F-mesure globale pour la sous-t{\^a}che des pathologies et signes ou sympt{\^o}mes, et de 0,14 {\`a} 0,76 pour la sous-t{\^a}che sur huit cat{\'e}gories m{\'e}dicales. Les m{\'e}thodes utilis{\'e}es reposent sur des CRF et des r{\'e}seaux de neurones.",
    language = "French",
}
@inproceedings{buscaldi-etal-2020-calcul,
    title = "Calcul de similarit{\'e} entre phrases : quelles mesures et quels descripteurs ? (Sentence Similarity : a study on similarity metrics with words and character strings )",
    author = {Buscaldi, Davide  and
      Felhi, Ghazi  and
      Ghoul, Dhaou  and
      Le Roux, Joseph  and
      Lejeune, Ga{\"e}l  and
      Zhang, Xudong},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.2",
    pages = "14--25",
    abstract = "Cet article pr{\'e}sente notre participation {\`a} l{'}{\'e}dition 2020 du D{\'e}fi Fouille de Textes DEFT 2020 et plus pr{\'e}cis{\'e}ment aux deux t{\^a}ches ayant trait {\`a} la similarit{\'e} entre phrases. Dans notre travail nous nous sommes int{\'e}ress{\'e} {\`a} deux questions : celle du choix de la mesure du similarit{\'e} d{'}une part et celle du choix des op{\'e}randes sur lesquelles se porte la mesure de similarit{\'e}. Nous avons notamment {\'e}tudi{\'e} la question de savoir s{'}il fallait utiliser des mots ou des cha{\^\i}nes de caract{\`e}res (mots ou non-mots). Nous montrons d{'}une part que la similarit{\'e} de Bray-Curtis peut {\^e}tre plus efficace et surtout plus stable que la similarit{\'e} cosinus et d{'}autre part que le calcul de similarit{\'e} sur des cha{\^\i}nes de caract{\`e}res est plus efficace que le m{\^e}me calcul sur des mots.",
    language = "French",
}
@inproceedings{cao-etal-2020-participation,
    title = "Participation d{'}{EDF} {R}{\&}{D} {\`a} {DEFT} 2020 (This paper describes the participation of {EDF} {R}{\&}{D} at {DEFT} 2020 evaluation campaign)",
    author = "Cao, Danrun  and
      Benamar, Alexandra  and
      Boumghar, Manel  and
      Bothua, Meryl  and
      Ould Ouali, Lydia  and
      Suignard, Philippe",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.3",
    pages = "26--35",
    abstract = "Ce papier d{\'e}crit la participation d{'}EDF R{\&}D {\`a} la campagne d{'}{\'e}valuation DEFT 2020. Notre {\'e}quipe a particip{\'e} aux trois t{\^a}ch{\'e}s propos{\'e}es : deux t{\^a}ches sur le calcul de similarit{\'e} s{\'e}mantique entre phrases et une t{\^a}che sur l{'}extraction d{'}information fine autour d{'}une douzaine de cat{\'e}gories. Aucune donn{\'e}e suppl{\'e}mentaire, autre que les donn{\'e}es d{'}apprentissage, n{'}a {\'e}t{\'e} utilis{\'e}e. Notre {\'e}quipe obtient des scores au-dessus de la moyenne pour les t{\^a}ches 1 et 2 et se classe 2e sur la t{\^a}che 1. Les m{\'e}thodes propos{\'e}es sont facilement transposables {\`a} d{'}autres cas d{'}application de d{\'e}tection de similarit{\'e} qui peuvent concerner plusieurs entit{\'e}s du groupe EDF. Notre participation {\`a} la t{\^a}che 3 nous a permis de tester les avantages et limites de l{'}outil SpaCy sur l{'}extraction d{'}information.",
    language = "French",
}
@inproceedings{copara-etal-2020-contextualized,
    title = "Contextualized {F}rench Language Models for Biomedical Named Entity Recognition",
    author = "Copara, Jenny  and
      Knafou, Julien  and
      Naderi, Nona  and
      Moro, Claudia  and
      Ruch, Patrick  and
      Teodoro, Douglas",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.4",
    pages = "36--48",
    abstract = "Named entity recognition (NER) is key for biomedical applications as it allows knowledge discovery in free text data. As entities are semantic phrases, their meaning is conditioned to the context to avoid ambiguity. In this work, we explore contextualized language models for NER in French biomedical text as part of the D{\'e}fi Fouille de Textes challenge. Our best approach achieved an F1 -measure of 66{\%} for symptoms and signs, and pathology categories, being top 1 for subtask 1. For anatomy, dose, exam, mode, moment, substance, treatment, and value categories, it achieved an F1 -measure of 75{\%} (subtask 2). If considered all categories, our model achieved the best result in the challenge, with an F1 -measure of 72{\%}. The use of an ensemble of neural language models proved to be very effective, improving a CRF baseline by up to 28{\%} and a single specialised language model by 4{\%}.",
}
@inproceedings{drame-etal-2020-approche,
    title = "Approche supervis{\'e}e de calcul de similarit{\'e} s{\'e}mantique entre paires de phrases (Supervised approach to compute semantic similarity between sentence pairs)",
    author = "Dram{\'e}, Khadim  and
      Sambe, Gorgoumack  and
      Diop, Ibrahima  and
      Faty, Lamine",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.5",
    pages = "49--54",
    abstract = "Ce papier d{\'e}crit les m{\'e}thodes que nous avons d{\'e}velopp{\'e}es pour participer aux t{\^a}ches 1 et 2 de l{'}{\'e}dition 2020 du d{\'e}fi fouille de textes (DEFT 2020). Pour la premi{\`e}re t{\^a}che, qui s{'}int{\'e}resse au calcul de scores de similarit{\'e} s{\'e}mantique entre paires de phrases, sur une {\'e}chelle de 0 {\`a} 5, une approche supervis{\'e}e o{\`u} chaque paire de phrases est repr{\'e}sent{\'e}e par un ensemble d{'}attributs a {\'e}t{\'e} propos{\'e}e. Des algorithmes classiques d{'}apprentissage automatique sont ensuite utilis{\'e}s pour entrainer les mod{\`e}les. Diff{\'e}rentes mesures de similarit{\'e} textuelle sont explor{\'e}es et les plus pertinentes sont combin{\'e}es pour supporter nos m{\'e}thodes. Diff{\'e}rentes combinaisons ont {\'e}t{\'e} test{\'e}es et {\'e}valu{\'e}es sur les donn{\'e}es de test du DEFT 2020. Notre meilleur syst{\`e}me qui s{'}appuie sur un mod{\`e}le Random Forest a obtenu les meilleures performances sur la premi{\`e}re t{\^a}che avec une EDRM de 0,8216.",
    language = "French",
}
@inproceedings{lemaitre-etal-2020-deft,
    title = "{DEFT} 2020 - Extraction d{'}information fine dans les donn{\'e}es cliniques : terminologies sp{\'e}cialis{\'e}es et graphes de connaissance (Fine-grained Information Extraction in Clinical Data : Dedicated Terminologies and Knowledge Graphs )",
    author = "Lemaitre, Thomas  and
      Gosset, Camille  and
      Lafourcade, Mathieu  and
      Patel, Namrata  and
      Mayoral, Guilhem",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.6",
    pages = "55--65",
    abstract = "Nous pr{\'e}sentons dans cet article notre approche {\`a} base de r{\`e}gles con{\c{c}}ue pour r{\'e}pondre {\`a} la t{\^a}che 3 de la campagne d{'}{\'e}valuation DEFT 2020. Selon le type d{'}information {\`a} extraire, nous construisons (1) une terminologie sp{\'e}cialis{\'e}e {\`a} partir de ressources m{\'e}dicales et (2) un graphe orient{\'e} bas{\'e} sur les informations extraites de la base de connaissances g{\'e}n{\'e}raliste et de grande taille - JeuxDeMots.",
    language = "French",
}
@inproceedings{minard-etal-2020-deft,
    title = "{DOING}@{DEFT} : cascade de {CRF} pour l{'}annotation d{'}entit{\'e}s cliniques imbriqu{\'e}es ({DOING}@{DEFT} : cascade of {CRF} for the annotation of nested clinical entities)",
    author = "Minard, Anne-Lyse  and
      Roques, Andr{\'e}ane  and
      Hiot, Nicolas  and
      Halfeld Ferrari Alves, Mirian  and
      Savary, Agata",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.7",
    pages = "66--78",
    abstract = "Cet article pr{\'e}sente le syst{\`e}me d{\'e}velopp{\'e} par l{'}{\'e}quipe DOING pour la campagne d{'}{\'e}valuation DEFT 2020 portant sur la similarit{\'e} s{\'e}mantique et l{'}extraction d{'}information fine. L{'}{\'e}quipe a particip{\'e} uniquement {\`a} la t{\^a}che 3 : {``}extraction d{'}information{''}. Nous avons utilis{\'e} une cascade de CRF pour annoter les diff{\'e}rentes informations {\`a} rep{\'e}rer. Nous nous sommes concentr{\'e}s sur la question de l{'}imbrication des entit{\'e}s et de la pertinence d{'}un type d{'}entit{\'e} pour apprendre {\`a} reconna{\^\i}tre un autre. Nous avons {\'e}galement test{\'e} l{'}utilisation d{'}une ressource externe, MedDRA, pour am{\'e}liorer les performances du syst{\`e}me et d{'}un pipeline plus complexe mais ne g{\'e}rant pas l{'}imbrication des entit{\'e}s. Nous avons soumis 3 runs et nous obtenons en moyenne sur toutes les classes des F-mesures de 0,64, 0,65 et 0,61.",
    language = "French",
}
@inproceedings{royan-etal-2020-extraction,
    title = "Extraction d{'}information de sp{\'e}cialit{\'e} avec un syst{\`e}me commercial g{\'e}n{\'e}rique (Extracting Medical Information with an Off-the-shelf Software Product)",
    author = "Royan, Clothilde  and
      Lang{\'e}, Jean-Marc  and
      Abidi, Zied",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.8",
    pages = "79--90",
    abstract = "Nous avons particip{\'e} {\`a} la t{\^a}che 3 du D{\'e}fi Fouille de texte 2020, d{\'e}di{\'e}e {\`a} l{'}extraction d{'}information de sp{\'e}cialit{\'e}, dans le but de tester notre produit commercial d{'}extraction d{'}information, Watson Knowledge Studio (WKS), face {\`a} des {\'e}quipes acad{\'e}miques et industrielles. Outre la quantit{\'e} r{\'e}duite de donn{\'e}es d{'}apprentissage, la nature des annotations des corpus de r{\'e}f{\'e}rence posait des probl{\`e}mes d{'}adaptation {\`a} notre produit. Aussi avons-nous d{\^u} modifier le sch{\'e}ma d{'}annotation du corpus d{'}apprentissage, ex{\'e}cuter l{'}apprentissage, puis appliquer des r{\`e}gles aux r{\'e}sultats obtenus afin d{'}obtenir des annotations conformes au sch{\'e}ma initial. Nous avons {\'e}galement appliqu{\'e} des dictionnaires de sp{\'e}cialit{\'e} (anatomie, pathologie, etc.) pour injecter de la connaissance du domaine et renforcer les mod{\`e}les d{'}apprentissage automatique. Au final, nos r{\'e}sultats lors de la phase de test se situent dans la moyenne de l{'}ensemble des {\'e}quipes, avec des F-mesures de 0,43 pour la sous-t{\^a}che 1 et 0,63 pour la sous-t{\^a}che 2.",
    language = "French",
}
@inproceedings{tapi-nzali-2020-deft,
    title = "{DEFT} 2020 : d{\'e}tection de similarit{\'e} entre phrases et extraction d{'}information ({DEFT} 2020 : sentence similarity detection and information retrieval )",
    author = "Tapi Nzali, Mike",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.9",
    pages = "91--96",
    abstract = "Ce papier d{\'e}crit la participation de Reezocar {\`a} la campagne d{'}{\'e}valuation DEFT 2020. Cette seizi{\`e}me {\'e}dition du challenge a port{\'e} sur le calcul de similarit{\'e} entre phrases et l{'}extraction d{'}information fine autour d{'}une douzaine de cat{\'e}gories dans des textes r{\'e}dig{\'e}s en Fran{\c{c}}ais. Le challenge propose trois t{\^a}ches : (i) la premi{\`e}re concerne l{'}identification du degr{\'e} de similarit{\'e} entre paires de phrases ; (ii) la deuxi{\`e}me concerne l{'}identification des phrases parall{\`e}les possibles pour une phrase source et (iii) la troisi{\`e}me concerne l{'}extraction d{'}information. Nous avons utilis{\'e} des m{\'e}thodes d{'}apprentissage automatique pour effectuer ces t{\^a}ches et avons obtenu des r{\'e}sultats satisfaisants sur l{'}ensemble des t{\^a}ches.",
    language = "French",
}
@inproceedings{teissedre-etal-2020-similarite,
    title = "Similarit{\'e} s{\'e}mantique entre phrases : apprentissage par transfert interlingue (Semantic Sentence Similarity : Multilingual Transfer Learning)",
    author = "Teiss{\`e}dre, Charles  and
      Belkacem, Thiziri  and
      Arens, Maxime",
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.10",
    pages = "97--107",
    abstract = "Dans cet article, nous d{\'e}crivons une approche exploratoire pour entra{\^\i}ner des mod{\`e}les de langue et r{\'e}soudre des t{\^a}ches d{'}appariement entre phrases issues de corpus en fran{\c{c}}ais et relevant du domaine m{\'e}dical. Nous montrons que, dans un contexte o{\`u} les donn{\'e}es d{'}entra{\^\i}nement sont en nombre restreint, il peut {\^e}tre int{\'e}ressant d{'}op{\'e}rer un apprentissage par transfert, d{'}une langue dont nous disposons de plus de ressources pour l{'}entra{\^\i}nement, vers une langue cible moins dot{\'e}e de donn{\'e}es d{'}entra{\^\i}nement (le fran{\c{c}}ais dans notre cas). Les r{\'e}sultats de nos exp{\'e}rimentations montrent que les mod{\`e}les de langue multilingues sont capables de transf{\'e}rer des repr{\'e}sentations d{'}une langue {\`a} l{'}autre de fa{\c{c}}on efficace pour r{\'e}soudre des t{\^a}ches de similarit{\'e} s{\'e}mantique telles que celles propos{\'e}es dans le cadre de l{'}{\'e}dition 2020 du D{\'e}fi fouille de texte (DEFT).",
    language = "French",
}
@inproceedings{wajsburt-etal-2020-participation,
    title = "Participation de l{'}{\'e}quipe du {LIMICS} {\`a} {DEFT} 2020 (Participation of team {LIMICS} in the {DEFT} 2020 challenge )",
    author = {Wajsb{\"u}rt, Perceval  and
      Taill{\'e}, Yoann  and
      Lain{\'e}, Guillaume  and
      Tannier, Xavier},
    booktitle = "Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 33e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Atelier D{\'E}fi Fouille de Textes",
    month = "6",
    year = "2020",
    address = "Nancy, France",
    publisher = "ATALA et AFCP",
    url = "https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.11",
    pages = "108--117",
    abstract = "Nous pr{\'e}sentons dans cet article les m{\'e}thodes con{\c{c}}ues et les r{\'e}sultats obtenus lors de notre participation {\`a} la t{\^a}che 3 de la campagne d{'}{\'e}valuation DEFT 2020, consistant en la reconnaissance d{'}entit{\'e}s nomm{\'e}es du domaine m{\'e}dical. Nous proposons deux mod{\`e}les diff{\'e}rents permettant de prendre en compte les entit{\'e}s imbriqu{\'e}es, qui repr{\'e}sentent une des difficult{\'e}s du jeu de donn{\'e}es propos{\'e}es, et pr{\'e}sentons les r{\'e}sultats obtenus. Notre meilleur run obtient la meilleure performance parmi les participants, sur l{'}une des deux sous-t{\^a}ches du d{\'e}fi.",
    language = "French",
}
@proceedings{iwslt-2020-international,
    title = "Proceedings of the 17th International Conference on Spoken Language Translation",
    editor = {Federico, Marcello  and
      Waibel, Alex  and
      Knight, Kevin  and
      Nakamura, Satoshi  and
      Ney, Hermann  and
      Niehues, Jan  and
      St{\"u}ker, Sebastian  and
      Wu, Dekai  and
      Mariani, Joseph  and
      Yvon, Francois},
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.0",
}
@inproceedings{ansari-etal-2020-findings,
    title = "{FINDINGS} {OF} {THE} {IWSLT} 2020 {EVALUATION} {CAMPAIGN}",
    author = {Ansari, Ebrahim  and
      Axelrod, Amittai  and
      Bach, Nguyen  and
      Bojar, Ond{\v{r}}ej  and
      Cattoni, Roldano  and
      Dalvi, Fahim  and
      Durrani, Nadir  and
      Federico, Marcello  and
      Federmann, Christian  and
      Gu, Jiatao  and
      Huang, Fei  and
      Knight, Kevin  and
      Ma, Xutai  and
      Nagesh, Ajay  and
      Negri, Matteo  and
      Niehues, Jan  and
      Pino, Juan  and
      Salesky, Elizabeth  and
      Shi, Xing  and
      St{\"u}ker, Sebastian  and
      Turchi, Marco  and
      Waibel, Alexander  and
      Wang, Changhan},
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.1",
    doi = "10.18653/v1/2020.iwslt-1.1",
    pages = "1--34",
    abstract = "The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation. A total of teams participated in at least one of the tracks. This paper introduces each track{'}s goal, data and evaluation metrics, and reports the results of the received submissions.",
}
@inproceedings{elbayad-etal-2020-trac,
    title = "{ON}-{TRAC} Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at {IWSLT} 2020",
    author = "Elbayad, Maha  and
      Nguyen, Ha  and
      Bougares, Fethi  and
      Tomashenko, Natalia  and
      Caubri{\`e}re, Antoine  and
      Lecouteux, Benjamin  and
      Est{\`e}ve, Yannick  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.2",
    doi = "10.18653/v1/2020.iwslt-1.2",
    pages = "35--43",
    abstract = "This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and simultaneous speech translation. ON-TRAC Consortium is composed of researchers from three French academic laboratories: LIA (Avignon Universit{\'e}), LIG (Universit{\'e} Grenoble Alpes), and LIUM (Le Mans Universit{\'e}). Attention-based encoder-decoder models, trained end-to-end, were used for our submissions to the offline speech translation track. Our contributions focused on data augmentation and ensembling of multiple models. In the simultaneous speech translation track, we build on Transformer-based wait-k models for the text-to-text subtask. For speech-to-text simultaneous translation, we attach a wait-k MT system to a hybrid ASR system. We propose an algorithm to control the latency of the ASR+MT cascade and achieve a good latency-quality trade-off on both subtasks.",
}
@inproceedings{bahar-etal-2020-start,
    title = "Start-Before-End and End-to-End: Neural Speech Translation by {A}pp{T}ek and {RWTH} {A}achen {U}niversity",
    author = "Bahar, Parnia  and
      Wilken, Patrick  and
      Alkhouli, Tamer  and
      Guta, Andreas  and
      Golik, Pavel  and
      Matusov, Evgeny  and
      Herold, Christian",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.3",
    doi = "10.18653/v1/2020.iwslt-1.3",
    pages = "44--54",
    abstract = "AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020. For the offline task, we create both cascaded and end-to-end speech translation systems, paying attention to careful data selection and weighting. In the cascaded approach, we combine high-quality hybrid automatic speech recognition (ASR) with the Transformer-based neural machine translation (NMT). Our end-to-end direct speech translation systems benefit from pretraining of adapted encoder and decoder components, as well as synthetic data and fine-tuning and thus are able to compete with cascaded systems in terms of MT quality. For simultaneous translation, we utilize a novel architecture that makes dynamic decisions, learned from parallel data, to determine when to continue feeding on input or generate output words. Experiments with speech and text input show that even at low latency this architecture leads to superior translation results.",
}
@inproceedings{pham-etal-2020-kits,
    title = "{KIT}{'}s {IWSLT} 2020 {SLT} Translation System",
    author = {Pham, Ngoc-Quan  and
      Schneider, Felix  and
      Nguyen, Tuan-Nam  and
      Ha, Thanh-Le  and
      Nguyen, Thai Son  and
      Awiszus, Maximilian  and
      St{\"u}ker, Sebastian  and
      Waibel, Alexander},
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.4",
    doi = "10.18653/v1/2020.iwslt-1.4",
    pages = "55--61",
    abstract = "This paper describes KIT{'}s submissions to the IWSLT2020 Speech Translation evaluation campaign. We first participate in the simultaneous translation task, in which our simultaneous models are Transformer based and can be efficiently trained to obtain low latency with minimized compromise in quality. On the offline speech translation task, we applied our new Speech Transformer architecture to end-to-end speech translation. The obtained model can provide translation quality which is competitive to a complicated cascade. The latter still has the upper hand, thanks to the ability to transparently access to the transcription, and resegment the inputs to avoid fragmentation.",
}
@inproceedings{han-etal-2020-end,
    title = "End-to-End Simultaneous Translation System for {IWSLT}2020 Using Modality Agnostic Meta-Learning",
    author = "Han, Hou Jeung  and
      Zaidi, Mohd Abbas  and
      Indurthi, Sathish Reddy  and
      Lakumarapu, Nikhil Kumar  and
      Lee, Beomseok  and
      Kim, Sangha",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.5",
    doi = "10.18653/v1/2020.iwslt-1.5",
    pages = "62--68",
    abstract = "In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge. The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture. The systems are evaluated on different latency regimes. The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency {\mbox{$\leq$}} 3). The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency {\mbox{$\leq$}} 1000).",
}
@inproceedings{arkhangorodsky-etal-2020-didi,
    title = "{D}i{D}i Labs{'} End-to-end System for the {IWSLT} 2020 Offline Speech {T}ranslation{T}ask",
    author = "Arkhangorodsky, Arkady  and
      Huang, Yiqi  and
      Axelrod, Amittai",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.6",
    doi = "10.18653/v1/2020.iwslt-1.6",
    pages = "69--72",
    abstract = "This paper describes the system that was submitted by DiDi Labs to the offline speech translation task for IWSLT 2020. We trained an end-to-end system that translates audio from English TED talks to German text, without producing intermediate English text. We use the S-Transformer architecture and train using the MuSTC dataset. We also describe several additional experiments that were attempted, but did not yield improved results.",
}
@inproceedings{lakumarapu-etal-2020-end,
    title = "End-to-End Offline Speech Translation System for {IWSLT} 2020 using Modality Agnostic Meta-Learning",
    author = "Lakumarapu, Nikhil Kumar  and
      Lee, Beomseok  and
      Indurthi, Sathish Reddy  and
      Han, Hou Jeung  and
      Zaidi, Mohd Abbas  and
      Kim, Sangha",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.7",
    doi = "10.18653/v1/2020.iwslt-1.7",
    pages = "73--79",
    abstract = "In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task. We adopt the Transformer architecture coupled with the meta-learning approach to build our end-to-end Speech-to-Text Translation (ST) system. Our meta-learning approach tackles the data scarcity of the ST task by leveraging the data available from Automatic Speech Recognition (ASR) and Machine Translation (MT) tasks. The meta-learning approach combined with synthetic data augmentation techniques improves the model performance significantly and achieves BLEU scores of 24.58, 27.51, and 27.61 on IWSLT test 2015, MuST-C test, and Europarl-ST test sets respectively.",
}
@inproceedings{gaido-etal-2020-end,
    title = "End-to-End Speech-Translation with Knowledge Distillation: {FBK}@{IWSLT}2020",
    author = "Gaido, Marco  and
      Di Gangi, Mattia A.  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.8",
    doi = "10.18653/v1/2020.iwslt-1.8",
    pages = "80--88",
    abstract = "This paper describes FBK{'}s participation in the IWSLT 2020 offline speech translation (ST) task. The task evaluates systems{'} ability to translate English TED talks audio into German texts. The test talks are provided in two versions: one contains the data already segmented with automatic tools and the other is the raw data without any segmentation. Participants can decide whether to work on custom segmentation or not. We used the provided segmentation. Our system is an end-to-end model based on an adaptation of the Transformer for speech data. Its training process is the main focus of this paper and it is based on: i) transfer learning (ASR pretraining and knowledge distillation), ii) data augmentation (SpecAugment, time stretch and synthetic data), iii)combining synthetic and real data marked as different domains, and iv) multi-task learning using the CTC loss. Finally, after the training with word-level knowledge distillation is complete, our ST models are fine-tuned using label smoothed cross entropy. Our best model scored 29 BLEU on the MuST-CEn-De test set, which is an excellent result compared to recent papers, and 23.7 BLEU on the same data segmented with VAD, showing the need for researching solutions addressing this specific data condition.",
}
@inproceedings{potapczyk-przybysz-2020-srpols,
    title = "{SRPOL}{'}s System for the {IWSLT} 2020 End-to-End Speech Translation Task",
    author = "Potapczyk, Tomasz  and
      Przybysz, Pawel",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.9",
    doi = "10.18653/v1/2020.iwslt-1.9",
    pages = "89--94",
    abstract = "We took part in the offline End-to-End English to German TED lectures translation task. We based our solution on our last year{'}s submission. We used a slightly altered Transformer architecture with ResNet-like convolutional layer preparing the audio input to Transformer encoder. To improve the model{'}s quality of translation we introduced two regularization techniques and trained on machine translated Librispeech corpus in addition to iwslt-corpus, TEDLIUM2 andMust{\_}C corpora. Our best model scored almost 3 BLEU higher than last year{'}s model. To segment 2020 test set we used exactly the same procedure as last year.",
}
@inproceedings{vazquez-etal-2020-university,
    title = "The {U}niversity of {H}elsinki Submission to the {IWSLT}2020 Offline {S}peech{T}ranslation Task",
    author = {V{\'a}zquez, Ra{\'u}l  and
      Aulamo, Mikko  and
      Sulubacak, Umut  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.10",
    doi = "10.18653/v1/2020.iwslt-1.10",
    pages = "95--102",
    abstract = "This paper describes the University of Helsinki Language Technology group{'}s participation in the IWSLT 2020 offline speech translation task, addressing the translation of English audio into German text. In line with this year{'}s task objective, we train both cascade and end-to-end systems for spoken language translation. We opt for an end-to-end multitasking architecture with shared internal representations and a cascade approach that follows a standard procedure consisting of ASR, correction, and MT stages. We also describe the experiments that served as a basis for the submitted systems. Our experiments reveal that multitasking training with shared internal representations is not only possible but allows for knowledge-transfer across modalities.",
}
@inproceedings{ore-etal-2020-afrl,
    title = "The {AFRL} {IWSLT} 2020 Systems: Work-From-Home Edition",
    author = "Ore, Brian  and
      Hansen, Eric  and
      Anderson, Tim  and
      Gwinnup, Jeremy",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.11",
    doi = "10.18653/v1/2020.iwslt-1.11",
    pages = "103--108",
    abstract = "This report summarizes the Air Force Research Laboratory (AFRL) submission to the offline spoken language translation (SLT) task as part of the IWSLT 2020 evaluation campaign. As in previous years, we chose to adopt the cascade approach of using separate systems to perform speech activity detection, automatic speech recognition, sentence segmentation, and machine translation. All systems were neural based, including a fully-connected neural network for speech activity detection, a Kaldi factorized time delay neural network with recurrent neural network (RNN) language model rescoring for speech recognition, a bidirectional RNN with attention mechanism for sentence segmentation, and transformer networks trained with OpenNMT and Marian for machine translation. Our primary submission yielded BLEU scores of 21.28 on tst2019 and 23.33 on tst2020.",
}
@inproceedings{zhuang-etal-2020-lit,
    title = "{LIT} Team{'}s System Description for {J}apanese-{C}hinese Machine Translation Task in {IWSLT} 2020",
    author = "Zhuang, Yimeng  and
      Zhang, Yuan  and
      Wang, Lijie",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.12",
    doi = "10.18653/v1/2020.iwslt-1.12",
    pages = "109--113",
    abstract = "This paper describes the LIT Team{'}s submission to the IWSLT2020 open domain translation task, focusing primarily on Japanese-to-Chinese translation direction. Our system is based on the organizers{'} baseline system, but we do more works on improving the Transform baseline system by elaborate data pre-processing. We manage to obtain significant improvements, and this paper aims to share some data processing experiences in this translation task. Large-scale back-translation on monolingual corpus is also investigated. In addition, we also try shared and exclusive word embeddings, compare different granularity of tokens like sub-word level. Our Japanese-to-Chinese translation system achieves a performance of BLEU=34.0 and ranks 2nd among all participating systems.",
}
@inproceedings{zhang-etal-2020-oppos,
    title = "{OPPO}{'}s Machine Translation System for the {IWSLT} 2020 Open Domain Translation Task",
    author = "Zhang, Qian  and
      Li, Xiaopu  and
      Dang, Dawei  and
      Shi, Tingxun  and
      Ai, Di  and
      Xue, Zhengshan  and
      Hao, Jie",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.13",
    doi = "10.18653/v1/2020.iwslt-1.13",
    pages = "114--121",
    abstract = "In this paper, we demonstrate our machine translation system applied for the Chinese-Japanese bidirectional translation task (aka. open domain translation task) for the IWSLT 2020. Our model is based on Transformer (Vaswani et al., 2017), with the help of many popular, widely proved effective data preprocessing and augmentation methods. Experiments show that these methods can improve the baseline model steadily and significantly.",
}
@inproceedings{chen-etal-2020-character,
    title = "Character Mapping and Ad-hoc Adaptation: {E}dinburgh{'}s {IWSLT} 2020 Open Domain Translation System",
    author = "Chen, Pinzhen  and
      Bogoychev, Nikolay  and
      Germann, Ulrich",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.14",
    doi = "10.18653/v1/2020.iwslt-1.14",
    pages = "122--129",
    abstract = "This paper describes the University of Edinburgh{'}s neural machine translation systems submitted to the IWSLT 2020 open domain Japanese$\leftrightarrow$Chinese translation task. On top of commonplace techniques like tokenisation and corpus cleaning, we explore character mapping and unsupervised decoding-time adaptation. Our techniques focus on leveraging the provided data, and we show the positive impact of each technique through the gradual improvement of BLEU.",
}
@inproceedings{wang-etal-2020-casias,
    title = "{CASIA}{'}s System for {IWSLT} 2020 Open Domain Translation",
    author = "Wang, Qian  and
      Liu, Yuchen  and
      Ma, Cong  and
      Lu, Yu  and
      Wang, Yining  and
      Zhou, Long  and
      Zhao, Yang  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.15",
    doi = "10.18653/v1/2020.iwslt-1.15",
    pages = "130--139",
    abstract = "This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task. This year we participate in both Chinese→Japanese and Japanese→Chinese translation tasks. Our system is neural machine translation system based on Transformer model. We augment the training data with knowledge distillation and back translation to improve the translation performance. Domain data classification and weighted domain model ensemble are introduced to generate the final translation result. We compare and analyze the performance on development data with different model settings and different data processing techniques.",
}
@inproceedings{su-ren-2020-deep,
    title = "Deep Blue Sonics{'} Submission to {IWSLT} 2020 Open Domain Translation Task",
    author = "Su, Enmin  and
      Ren, Yi",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.16",
    doi = "10.18653/v1/2020.iwslt-1.16",
    pages = "140--144",
    abstract = "We present in this report our submission to IWSLT 2020 Open Domain Translation Task. We built a data pre-processing pipeline to efficiently handle large noisy web-crawled corpora, which boosts the BLEU score of a widely used transformer model in this translation task. To tackle the open-domain nature of this task, back- translation is applied to further improve the translation performance.",
}
@inproceedings{cui-etal-2020-university,
    title = "{U}niversity of {T}sukuba{'}s Machine Translation System for {IWSLT}20 Open Domain Translation Task",
    author = "Cui, Hongyi  and
      Wei, Yizhen  and
      Iida, Shohei  and
      Utsuro, Takehito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.17",
    doi = "10.18653/v1/2020.iwslt-1.17",
    pages = "145--148",
    abstract = "In this paper, we introduce University of Tsukuba{'}s submission to the IWSLT20 Open Domain Translation Task. We participate in both Chinese→Japanese and Japanese→Chinese directions. For both directions, our machine translation systems are based on the Transformer architecture. Several techniques are integrated in order to boost the performance of our models: data filtering, large-scale noised training, model ensemble, reranking and postprocessing. Consequently, our efforts achieve 33.0 BLEU scores for Chinese→Japanese translation and 32.3 BLEU scores for Japanese→Chinese translation.",
}
@inproceedings{sun-etal-2020-xiaomis,
    title = "Xiaomi{'}s Submissions for {IWSLT} 2020 Open Domain Translation Task",
    author = "Sun, Yuhui  and
      Guo, Mengxue  and
      Li, Xiang  and
      Cui, Jianwei  and
      Wang, Bin",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.18",
    doi = "10.18653/v1/2020.iwslt-1.18",
    pages = "149--157",
    abstract = "This paper describes the Xiaomi{'}s submissions to the IWSLT20 shared open domain translation task for Chinese{\textless}-{\textgreater}Japanese language pair. We explore different model ensembling strategies based on recent Transformer variants. We also further strengthen our systems via some effective techniques, such as data filtering, data selection, tagged back translation, domain adaptation, knowledge distillation, and re-ranking. Our resulting Chinese-{\textgreater}Japanese primary system ranked second in terms of character-level BLEU score among all submissions. Our resulting Japanese-{\textgreater}Chinese primary system also achieved a competitive performance.",
}
@inproceedings{wei-etal-2020-istics,
    title = "{ISTIC}{'}s Neural Machine Translation System for {IWSLT}{'}2020",
    author = "Wei, Jiaze  and
      Liu, Wenbin  and
      Wu, Zhenfeng  and
      Pan, You  and
      He, Yanqing",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.19",
    doi = "10.18653/v1/2020.iwslt-1.19",
    pages = "158--165",
    abstract = "This paper introduces technical details of machine translation system of Institute of Scientific and Technical Information of China (ISTIC) for the 17th International Conference on Spoken Language Translation (IWSLT 2020). ISTIC participated in both translation tasks of the Open Domain Translation track: Japanese-to-Chinese MT task and Chinese-to-Japanese MT task. The paper mainly elaborates on the model framework, data preprocessing methods and decoding strategies adopted in our system. In addition, the system performance on the development set are given under different settings.",
}
@inproceedings{hagiwara-2020-octanove,
    title = "Octanove Labs{'} {J}apanese-{C}hinese Open Domain Translation System",
    author = "Hagiwara, Masato",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.20",
    doi = "10.18653/v1/2020.iwslt-1.20",
    pages = "166--171",
    abstract = "This paper describes Octanove Labs{'} submission to the IWSLT 2020 open domain translation challenge. In order to build a high-quality Japanese-Chinese neural machine translation (NMT) system, we use a combination of 1) parallel corpus filtering and 2) back-translation. We have shown that, by using heuristic rules and learned classifiers, the size of the parallel data can be reduced by 70{\%} to 90{\%} without much impact on the final MT performance. We have also shown that including the artificially generated parallel data through back-translation further boosts the metric by 17{\%} to 27{\%}, while self-training contributes little. Aside from a small number of parallel sentences annotated for filtering, no external resources have been used to build our system.",
}
@inproceedings{fukuda-etal-2020-naists,
    title = "{NAIST}{'}s Machine Translation Systems for {IWSLT} 2020 Conversational Speech Translation Task",
    author = "Fukuda, Ryo  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.21",
    doi = "10.18653/v1/2020.iwslt-1.21",
    pages = "172--177",
    abstract = "This paper describes NAIST{'}s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts). Our system results showed that the NMT model with domain adaptation outperformed a baseline. In addition, slight improvement by the style transfer was observed.",
}
@inproceedings{saini-etal-2020-generating,
    title = "Generating Fluent Translations from Disfluent Text Without Access to Fluent References: {IIT} {B}ombay@{IWSLT}2020",
    author = "Saini, Nikhil  and
      Khatri, Jyotsana  and
      Jyothi, Preethi  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.22",
    doi = "10.18653/v1/2020.iwslt-1.22",
    pages = "178--186",
    abstract = "Machine translation systems perform reasonably well when the input is well-formed speech or text. Conversational speech is spontaneous and inherently consists of many disfluencies. Producing fluent translations of disfluent source text would typically require parallel disfluent to fluent training data. However, fluent translations of spontaneous speech are an additional resource that is tedious to obtain. This work describes the submission of IIT Bombay to the Conversational Speech Translation challenge at IWSLT 2020. We specifically tackle the problem of disfluency removal in disfluent-to-fluent text-to-text translation assuming no access to fluent references during training. Common patterns of disfluency are extracted from disfluent references and a noise induction model is used to simulate them starting from a clean monolingual corpus. This synthetically constructed dataset is then considered as a proxy for labeled data during training. We also make use of additional fluent text in the target language to help generate fluent translations. This work uses no fluent references during training and beats a baseline model by a margin of 4.21 and 3.11 BLEU points where the baseline uses disfluent and fluent references, respectively. Index Terms- disfluency removal, machine translation, noise induction, leveraging monolingual data, denoising for disfluency removal.",
}
@inproceedings{wang-etal-2020-hw-tsc,
    title = "The {HW}-{TSC} Video Speech Translation System at {IWSLT} 2020",
    author = "Wang, Minghan  and
      Yang, Hao  and
      Deng, Yao  and
      Qin, Ying  and
      Lei, Lizhi  and
      Wei, Daimeng  and
      Shang, Hengchao  and
      Xie, Ning  and
      Li, Xiaochun  and
      Guo, Jiaxian",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.23",
    doi = "10.18653/v1/2020.iwslt-1.23",
    pages = "187--190",
    abstract = "The paper presents details of our system in the IWSLT Video Speech Translation evaluation. The system works in a cascade form, which contains three modules: 1) A proprietary ASR system. 2) A disfluency correction system aims to remove interregnums or other disfluent expressions with a fine-tuned BERT and a series of rule-based algorithms. 3) An NMT System based on the Transformer and trained with massive publicly available corpus.",
}
@inproceedings{polak-etal-2020-cuni,
    title = "{CUNI} Neural {ASR} with Phoneme-Level Intermediate Step for{\textasciitilde}{N}on-{N}ative{\textasciitilde}{SLT} at {IWSLT} 2020",
    author = "Pol{\'a}k, Peter  and
      Sagar, Sangeet  and
      Mach{\'a}{\v{c}}ek, Dominik  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.24",
    doi = "10.18653/v1/2020.iwslt-1.24",
    pages = "191--199",
    abstract = "In this paper, we present our submission to the Non-Native Speech Translation Task for IWSLT 2020. Our main contribution is a proposed speech recognition pipeline that consists of an acoustic model and a phoneme-to-grapheme model. As an intermediate representation, we utilize phonemes. We demonstrate that the proposed pipeline surpasses commercially used automatic speech recognition (ASR) and submit it into the ASR track. We complement this ASR with off-the-shelf MT systems to take part also in the speech translation track.",
}
@inproceedings{machacek-etal-2020-elitr,
    title = "{ELITR} Non-Native Speech Translation at {IWSLT} 2020",
    author = "Mach{\'a}{\v{c}}ek, Dominik  and
      Kratochv{\'\i}l, Jon{\'a}{\v{s}}  and
      Sagar, Sangeet  and
      {\v{Z}}ilinec, Mat{\'u}{\v{s}}  and
      Bojar, Ond{\v{r}}ej  and
      Nguyen, Thai-Son  and
      Schneider, Felix  and
      Williams, Philip  and
      Yao, Yuekun",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.25",
    doi = "10.18653/v1/2020.iwslt-1.25",
    pages = "200--208",
    abstract = "This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT. We select our primary candidates from a pool of pre-existing systems, develop a new end-to-end general ASR system, and a hybrid ASR trained on non-native speech. The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set.",
}
@inproceedings{karakanta-etal-2020-42,
    title = "Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?",
    author = "Karakanta, Alina  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.26",
    doi = "10.18653/v1/2020.iwslt-1.26",
    pages = "209--219",
    abstract = "Subtitling is becoming increasingly important for disseminating information, given the enormous amounts of audiovisual content becoming available daily. Although Neural Machine Translation (NMT) can speed up the process of translating audiovisual content, large manual effort is still required for transcribing the source language, and for spotting and segmenting the text into proper subtitles. Creating proper subtitles in terms of timing and segmentation highly depends on information present in the audio (utterance duration, natural pauses). In this work, we explore two methods for applying Speech Translation (ST) to subtitling, a) a direct end-to-end and b) a classical cascade approach. We discuss the benefit of having access to the source language speech for improving the conformity of the generated subtitles to the spatial and temporal subtitling constraints and show that length is not the answer to everything in the case of subtitling-oriented ST.",
}
@inproceedings{arivazhagan-etal-2020-translation,
    title = "Re-translation versus Streaming for Simultaneous Translation",
    author = "Arivazhagan, Naveen  and
      Cherry, Colin  and
      Macherey, Wolfgang  and
      Foster, George",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.27",
    doi = "10.18653/v1/2020.iwslt-1.27",
    pages = "220--227",
    abstract = "There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available. We study a related problem in which revisions to the hypothesis beyond strictly appending words are permitted. This is suitable for applications such as live captioning an audio feed. In this setting, we compare custom streaming approaches to re-translation, a straightforward strategy where each new source token triggers a distinct translation from scratch. We find re-translation to be as good or better than state-of-the-art streaming systems, even when operating under constraints that allow very few revisions. We attribute much of this success to a previously proposed data-augmentation technique that adds prefix-pairs to the training data, which alongside wait-k inference forms a strong baseline for streaming translation. We also highlight re-translation{'}s ability to wrap arbitrarily powerful MT systems with an experiment showing large improvements from an upgrade to its base model.",
}
@inproceedings{schneider-waibel-2020-towards,
    title = "Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation",
    author = "Schneider, Felix  and
      Waibel, Alexander",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.28",
    doi = "10.18653/v1/2020.iwslt-1.28",
    pages = "228--236",
    abstract = "Simultaneous machine translation systems rely on a policy to schedule read and write operations in order to begin translating a source sentence before it is complete. In this paper, we demonstrate the use of Adaptive Computation Time (ACT) as an adaptive, learned policy for simultaneous machine translation using the transformer model and as a more numerically stable alternative to Monotonic Infinite Lookback Attention (MILk). We achieve state-of-the-art results in terms of latency-quality tradeoffs. We also propose a method to use our model on unsegmented input, i.e. without sentence boundaries, simulating the condition of translating output from automatic speech recognition. We present first benchmark results on this task.",
}
@inproceedings{wilken-etal-2020-neural,
    title = "Neural Simultaneous Speech Translation Using Alignment-Based Chunking",
    author = "Wilken, Patrick  and
      Alkhouli, Tamer  and
      Matusov, Evgeny  and
      Golik, Pavel",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.29",
    doi = "10.18653/v1/2020.iwslt-1.29",
    pages = "237--246",
    abstract = "In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency and quality. We propose a neural machine translation (NMT) model that makes dynamic decisions when to continue feeding on input or generate output words. The model is composed of two main components: one to dynamically decide on ending a source chunk, and another that translates the consumed chunk. We train the components jointly and in a manner consistent with the inference conditions. To generate chunked training data, we propose a method that utilizes word alignment while also preserving enough context. We compare models with bidirectional and unidirectional encoders of different depths, both on real speech and text input. Our results on the IWSLT 2020 English-to-German task outperform a wait-k baseline by 2.6 to 3.7{\%} BLEU absolute.",
}
@inproceedings{liu-etal-2020-adapting-end,
    title = "Adapting End-to-End Speech Recognition for Readable Subtitles",
    author = "Liu, Danni  and
      Niehues, Jan  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.30",
    doi = "10.18653/v1/2020.iwslt-1.30",
    pages = "247--256",
    abstract = "Automatic speech recognition (ASR) systems are primarily evaluated on transcription accuracy. However, in some use cases such as subtitling, verbatim transcription would reduce output readability given limited screen size and reading time. Therefore, this work focuses on ASR with output compression, a task challenging for supervised approaches due to the scarcity of training data. We first investigate a cascaded system, where an unsupervised compression model is used to post-edit the transcribed speech. We then compare several methods of end-to-end speech recognition under output length constraints. The experiments show that with limited data far less than needed for training a model from scratch, we can adapt a Transformer-based ASR model to incorporate both transcription and compression capabilities. Furthermore, the best performance in terms of WER and ROUGE scores is achieved by explicitly modeling the length constraints within the end-to-end ASR system.",
}
@inproceedings{federico-etal-2020-speech,
    title = "From Speech-to-Speech Translation to Automatic Dubbing",
    author = "Federico, Marcello  and
      Enyedi, Robert  and
      Barra-Chicote, Roberto  and
      Giri, Ritwik  and
      Isik, Umut  and
      Krishnaswamy, Arvindh  and
      Sawaf, Hassan",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.31",
    doi = "10.18653/v1/2020.iwslt-1.31",
    pages = "257--264",
    abstract = "We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing. Our architecture features neural machine translation generating output of preferred length, prosodic alignment of the translation with the original speech segments, neural text-to-speech with fine tuning of the duration of each utterance, and, finally, audio rendering to enriches text-to-speech output with background noise and reverberation extracted from the original audio. We report and discuss results of a first subjective evaluation of automatic dubbing of excerpts of TED Talks from English into Italian, which measures the perceived naturalness of automatic dubbing and the relative importance of each proposed enhancement.",
}
@inproceedings{dinu-etal-2020-joint,
    title = "Joint Translation and Unit Conversion for End-to-end Localization",
    author = "Dinu, Georgiana  and
      Mathur, Prashant  and
      Federico, Marcello  and
      Lauly, Stanislas  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.32",
    doi = "10.18653/v1/2020.iwslt-1.32",
    pages = "265--271",
    abstract = "A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which lead to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.",
}
@inproceedings{courtland-etal-2020-efficient,
    title = "Efficient Automatic Punctuation Restoration Using Bidirectional Transformers with Robust Inference",
    author = "Courtland, Maury  and
      Faulkner, Adam  and
      McElvain, Gayle",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.33",
    doi = "10.18653/v1/2020.iwslt-1.33",
    pages = "272--279",
    abstract = "Though people rarely speak in complete sentences, punctuation confers many benefits to the readers of transcribed speech. Unfortunately, most ASR systems do not produce punctuated output. To address this, we propose a solution for automatic punctuation that is both cost efficient and easy to train. Our solution benefits from the recent trend in fine-tuning transformer-based language models. We also modify the typical framing of this task by predicting punctuation for sequences rather than individual tokens, which makes for more efficient training and inference. Finally, we find that aggregating predictions across multiple context windows improves accuracy even further. Our best model achieves a new state of the art on benchmark data (TED Talks) with a combined F1 of 83.9, representing a 48.7{\%} relative improvement (15.3 absolute) over the previous state of the art.",
}
@inproceedings{bizzoni-etal-2020-human,
    title = "How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech",
    author = "Bizzoni, Yuri  and
      Juzek, Tom S  and
      Espa{\~n}a-Bonet, Cristina  and
      Dutta Chowdhury, Koel  and
      van Genabith, Josef  and
      Teich, Elke",
    booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwslt-1.34",
    doi = "10.18653/v1/2020.iwslt-1.34",
    pages = "280--290",
    abstract = "Translationese is a phenomenon present in human translations, simultaneous interpreting, and even machine translations. Some translationese features tend to appear in simultaneous interpreting with higher frequency than in human text translation, but the reasons for this are unclear. This study analyzes translationese patterns in translation, interpreting, and machine translation outputs in order to explore possible reasons. In our analysis we {--} (i) detail two non-invasive ways of detecting translationese and (ii) compare translationese across human and machine translations from text and speech. We find that machine translation shows traces of translationese, but does not reproduce the patterns found in human translation, offering support to the hypothesis that such patterns are due to the model (human vs machine) rather than to the data (written vs spoken).",
}
@proceedings{iwpt-2020-international,
    title = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    editor = "Bouma, Gosse  and
      Matsumoto, Yuji  and
      Oepen, Stephan  and
      Sagae, Kenji  and
      Seddah, Djam{\'e}  and
      Sun, Weiwei  and
      S{\o}gaard, Anders  and
      Tsarfaty, Reut  and
      Zeman, Dan",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.0",
}
@inproceedings{merlo-2020-syntactic,
    title = "Syntactic Parsing in Humans and Machines",
    author = "Merlo, Paola",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.1",
    doi = "10.18653/v1/2020.iwpt-1.1",
    pages = "1",
    abstract = "To process the syntactic structures of a language in ways that are compatible with human expectations, we need computational representations of lexical and syntactic properties that form the basis of human knowledge of words and sentences. Recent neural-network-based and distributed semantics techniques have developed systems of considerable practical success and impressive performance. As has been advocated by many, however, such systems still lack human-like properties. In particular, linguistic, psycholinguistic and neuroscientific investigations have shown that human processing of sentences is sensitive to structure and unbounded relations. In the spirit of better understanding the structure building and long-distance properties of neural networks, I will present an overview of recent results on agreement and island effects in syntax in several languages. While certain sets of results in the literature indicate that neural language models exhibit long-distance agreement abilities, other finer-grained investigation of how these effects are calculated indicates that that the similarity spaces they define do not correlate with human experimental results on intervention similarity in long-distance dependencies. This opens the way to reflections on how to better match the syntactic properties of natural languages in the representations of neural models.",
}
@inproceedings{anderson-gomez-rodriguez-2020-distilling,
    title = "Distilling Neural Networks for Greener and Faster Dependency Parsing",
    author = "Anderson, Mark  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.2",
    doi = "10.18653/v1/2020.iwpt-1.2",
    pages = "2--13",
    abstract = "The carbon footprint of natural language processing research has been increasing in recent years due to its reliance on large and inefficient neural network implementations. Distillation is a network compression technique which attempts to impart knowledge from a large model to a smaller one. We use teacher-student distillation to improve the efficiency of the Biaffine dependency parser which obtains state-of-the-art performance with respect to accuracy and parsing speed (Dozat and Manning, 2017). When distilling to 20{\%} of the original model{'}s trainable parameters, we only observe an average decrease of ∼1 point for both UAS and LAS across a number of diverse Universal Dependency treebanks while being 2.30x (1.19x) faster than the baseline model on CPU (GPU) at inference time. We also observe a small increase in performance when compressing to 80{\%} for some treebanks. Finally, through distillation we attain a parser which is not only faster but also more accurate than the fastest modern parser on the Penn Treebank.",
}
@inproceedings{kurtz-etal-2020-end,
    title = "End-to-End Negation Resolution as Graph Parsing",
    author = "Kurtz, Robin  and
      Oepen, Stephan  and
      Kuhlmann, Marco",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.3",
    doi = "10.18653/v1/2020.iwpt-1.3",
    pages = "14--24",
    abstract = "We present a neural end-to-end architecture for negation resolution based on a formulation of the task as a graph parsing problem. Our approach allows for the straightforward inclusion of many types of graph-structured features without the need for representation-specific heuristics. In our experiments, we specifically gauge the usefulness of syntactic information for negation resolution. Despite the conceptual simplicity of our architecture, we achieve state-of-the-art results on the Conan Doyle benchmark dataset, including a new top result for our best model.",
}
@inproceedings{falenska-etal-2020-integrating,
    title = "Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era",
    author = {Falenska, Agnieszka  and
      Bj{\"o}rkelund, Anders  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.4",
    doi = "10.18653/v1/2020.iwpt-1.4",
    pages = "25--39",
    abstract = "Graph-based and transition-based dependency parsers used to have different strengths and weaknesses. Therefore, combining the outputs of parsers from both paradigms used to be the standard approach to improve or analyze their performance. However, with the recent adoption of deep contextualized word representations, the chief weakness of graph-based models, i.e., their limited scope of features, has been mitigated. Through two popular combination techniques {--} blending and stacking {--} we demonstrate that the remaining diversity in the parsing models is reduced below the level of models trained with different random seeds. Thus, an integration no longer leads to increased accuracy. When both parsers depend on BiLSTMs, the graph-based architecture has a consistent advantage. This advantage stems from globally-trained BiLSTM representations, which capture more distant look-ahead syntactic relations. Such representations can be exploited through multi-task learning, which improves the transition-based parser, especially on treebanks with a high ratio of right-headed dependencies.",
}
@inproceedings{zhang-goldwasser-2020-semi-supervised,
    title = "Semi-supervised Parsing with a Variational Autoencoding Parser",
    author = "Zhang, Xiao  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.5",
    doi = "10.18653/v1/2020.iwpt-1.5",
    pages = "40--47",
    abstract = "We propose an end-to-end variational autoencoding parsing (VAP) model for semi-supervised graph-based projective dependency parsing. It encodes the input using continuous latent variables in a sequential manner by deep neural networks (DNN) that can utilize the contextual information, and reconstruct the input using a generative model. The VAP model admits a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We conducted experiments on the WSJ data sets, showing the proposed model can use the unlabeled data to increase the performance on a limited amount of labeled data, on a par with a recently proposed semi-supervised parser with faster inference.",
}
@inproceedings{jin-schuler-2020-memory,
    title = "Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction",
    author = "Jin, Lifeng  and
      Schuler, William",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.6",
    doi = "10.18653/v1/2020.iwpt-1.6",
    pages = "48--61",
    abstract = "Syntactic surprisal has been shown to have an effect on human sentence processing, and can be predicted from prefix probabilities of generative incremental parsers. Recent state-of-the-art incremental generative neural parsers are able to produce accurate parses and surprisal values but have unbounded stack memory, which may be used by the neural parser to maintain explicit in-order representations of all previously parsed words, inconsistent with results of human memory experiments. In contrast, humans seem to have a bounded working memory, demonstrated by inhibited performance on word recall in multi-clause sentences (Bransford and Franks, 1971), and on center-embedded sentences (Miller and Isard,1964). Bounded statistical parsers exist, but are less accurate than neural parsers in predict-ing reading times. This paper describes a neural incremental generative parser that is able to provide accurate surprisal estimates and can be constrained to use a bounded stack. Results show that the accuracy gains of neural parsers can be reliably extended to psycholinguistic modeling without risk of distortion due to un-bounded working memory.",
}
@inproceedings{hu-etal-2020-obfuscation,
    title = "Obfuscation for Privacy-preserving Syntactic Parsing",
    author = "Hu, Zhifeng  and
      Havrylov, Serhii  and
      Titov, Ivan  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.7",
    doi = "10.18653/v1/2020.iwpt-1.7",
    pages = "62--72",
    abstract = "The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data. We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption. Our primary tool is \textit{obfuscation}, relying on the properties of natural language. Specifically, a given English text is obfuscated using a neural model that aims to preserve the syntactic relationships of the original sentence so that the obfuscated sentence can be parsed instead of the original one. The model works at the word level, and learns to obfuscate each word separately by changing it into a new word that has a similar syntactic role. The text obfuscated by our model leads to better performance on three syntactic parsers (two dependency and one constituency parsers) in comparison to an upper-bound random substitution baseline. More specifically, the results demonstrate that as more terms are obfuscated (by their part of speech), the substitution upper bound significantly degrades, while the neural model maintains a relatively high performing parser. All of this is done without much sacrifice of privacy compared to the random substitution upper bound. We also further analyze the results, and discover that the substituted words have similar syntactic properties, but different semantic content, compared to the original words.",
}
@inproceedings{balkir-etal-2020-tensors,
    title = "Tensors over Semirings for Latent-Variable Weighted Logic Programs",
    author = "Balkir, Esma  and
      Gildea, Daniel  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.8",
    doi = "10.18653/v1/2020.iwpt-1.8",
    pages = "73--90",
    abstract = "Semiring parsing is an elegant framework for describing parsers by using semiring weighted logic programs. In this paper we present a generalization of this concept: latent-variable semiring parsing. With our framework, any semiring weighted logic program can be latentified by transforming weights from scalar values of a semiring to rank-n arrays, or tensors, of semiring values, allowing the modelling of latent-variable models within the semiring parsing framework. Semiring is too strong a notion when dealing with tensors, and we have to resort to a weaker structure: a partial semiring. We prove that this generalization preserves all the desired properties of the original semiring framework while strictly increasing its expressiveness.",
}
@inproceedings{gebhardt-2020-advances,
    title = "Advances in Using Grammars with Latent Annotations for Discontinuous Parsing",
    author = "Gebhardt, Kilian",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.9",
    doi = "10.18653/v1/2020.iwpt-1.9",
    pages = "91--97",
    abstract = "We present new experiments that transfer techniques from Probabilistic Context-free Grammars with Latent Annotations (PCFG-LA) to two grammar formalisms for discontinuous parsing: linear context-free rewriting systems and hybrid grammars. In particular, Dirichlet priors during EM training, ensemble models, and a new nonterminal scheme for hybrid grammars are evaluated. We find that our grammars are more accurate than previous approaches based on discontinuous grammar formalisms and early instances of the discriminative models but inferior to recent discriminative parsers.",
}
@inproceedings{morbitz-ruprecht-2020-lexicalization,
    title = "Lexicalization of Probabilistic Linear Context-free Rewriting Systems",
    author = {M{\"o}rbitz, Richard  and
      Ruprecht, Thomas},
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.10",
    doi = "10.18653/v1/2020.iwpt-1.10",
    pages = "98--104",
    abstract = "In the field of constituent parsing, probabilistic grammar formalisms have been studied to model the syntactic structure of natural language. More recently, approaches utilizing neural models gained lots of traction in this field, as they achieved accurate results at high speed. We aim for a symbiosis between probabilistic linear context-free rewriting systems (PLCFRS) as a probabilistic grammar formalism and neural models to get the best of both worlds: the interpretability of grammars, and the speed and accuracy of neural models. To combine these two, we consider the approach of supertagging that requires lexicalized grammar formalisms. Here, we present a procedure which turns any PLCFRS G into an equivalent lexicalized PLCFRS G{'}. The derivation trees in G{'} are then mapped to equivalent derivations in G. Our construction for G{'} preserves the probability assignment and does not increase parsing complexity compared to G.",
}
@inproceedings{mohananey-etal-2020-self,
    title = "Self-Training for Unsupervised Parsing with {PRPN}",
    author = "Mohananey, Anhad  and
      Kann, Katharina  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.11",
    doi = "10.18653/v1/2020.iwpt-1.11",
    pages = "105--110",
    abstract = "Neural unsupervised parsing (UP) models learn to parse without access to syntactic annotations, while being optimized for another task like language modeling. In this work, we propose self-training for neural UP models: we leverage aggregated annotations predicted by copies of our model as supervision for future copies. To be able to use our model{'}s predictions during training, we extend a recent neural UP architecture, the PRPN (Shen et al., 2018a), such that it can be trained in a semi-supervised fashion. We then add examples with parses predicted by our model to our unlabeled UP training data. Our self-trained model outperforms the PRPN by 8.1{\%} F1 and the previous state of the art by 1.6{\%} F1. In addition, we show that our architecture can also be helpful for semi-supervised parsing in ultra-low-resource settings.",
}
@inproceedings{stanojevic-steedman-2020-span,
    title = "Span-Based {LCFRS}-2 Parsing",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Steedman, Mark",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.12",
    doi = "10.18653/v1/2020.iwpt-1.12",
    pages = "111--121",
    abstract = "The earliest models for discontinuous constituency parsers used mildly context-sensitive grammars, but the fashion has changed in recent years to grammar-less transition-based parsers that use strong neural probabilistic models to greedily predict transitions. We argue that grammar-based approaches still have something to contribute on top of what is offered by transition-based parsers. Concretely, by using a grammar formalism to restrict the space of possible trees we can use dynamic programming parsing algorithms for exact search for the most probable tree. Previous chart-based parsers for discontinuous formalisms used probabilistically weak generative models. We instead use a span-based discriminative neural model that preserves the dynamic programming properties of the chart parsers. Our parser does not use an explicit grammar, but it does use explicit grammar formalism constraints: we generate only trees that are within the LCFRS-2 formalism. These properties allow us to construct a new parsing algorithm that runs in lower worst-case time complexity of O(l n{\^{}}4 +n{\^{}}6), where $n$ is the sentence length and $l$ is the number of unique non-terminal labels. This parser is efficient in practice, provides best results among chart-based parsers, and is competitive with the best transition based parsers. We also show that the main bottleneck for further improvement in performance is in the restriction of fan-out to degree 2. We show that well-nestedness is helpful in speeding up parsing, but lowers accuracy.",
}
@inproceedings{oh-etal-2020-analysis,
    title = "Analysis of the {P}enn {K}orean {U}niversal {D}ependency Treebank ({PKT}-{UD}): Manual Revision to Build Robust Parsing Model in {K}orean",
    author = "Oh, Tae Hwan  and
      Han, Ji Yoon  and
      Choe, Hyonsu  and
      Park, Seokwon  and
      He, Han  and
      Choi, Jinho D.  and
      Han, Na-Rae  and
      Hwang, Jena D.  and
      Kim, Hansaem",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.13",
    doi = "10.18653/v1/2020.iwpt-1.13",
    pages = "122--131",
    abstract = "In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD corpora, we follow the UDv2 guidelines, and extensively revise the part-of-speech tags and the dependency relations to reflect morphological features and flexible word- order aspects in Korean. The original and the revised versions of PKT-UD are experimented with transformer-based parsing models using biaffine attention. The parsing model trained on the revised corpus shows a significant improvement of 3.0{\%} in labeled attachment score over the model trained on the previous corpus. Our error analysis demonstrates that this revision allows the parsing model to learn relations more robustly, reducing several critical errors that used to be made by the previous model.",
}
@inproceedings{chiruzzo-wonsever-2020-statistical,
    title = "Statistical Deep Parsing for {S}panish Using Neural Networks",
    author = "Chiruzzo, Luis  and
      Wonsever, Dina",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.14",
    doi = "10.18653/v1/2020.iwpt-1.14",
    pages = "132--144",
    abstract = "This paper presents the development of a deep parser for Spanish that uses a HPSG grammar and returns trees that contain both syntactic and semantic information. The parsing process uses a top-down approach implemented using LSTM neural networks, and achieves good performance results in terms of syntactic constituency and dependency metrics, and also SRL. We describe the grammar, corpus and implementation of the parser. Our process outperforms a CKY baseline and other Spanish parsers in terms of global metrics and also for some specific Spanish phenomena, such as clitics reduplication and relative referents.",
}
@inproceedings{jin-schuler-2020-importance,
    title = "The Importance of Category Labels in Grammar Induction with Child-directed Utterances",
    author = "Jin, Lifeng  and
      Schuler, William",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.15",
    doi = "10.18653/v1/2020.iwpt-1.15",
    pages = "145--150",
    abstract = "Recent progress in grammar induction has shown that grammar induction is possible without explicit assumptions of language specific knowledge. However, evaluation of induced grammars usually has ignored phrasal labels, an essential part of a grammar. Experiments in this work using a labeled evaluation metric, RH, show that linguistically motivated predictions about grammar sparsity and use of categories can only be revealed through labeled evaluation. Furthermore, depth-bounding as an implementation of human memory constraints in grammar inducers is still effective with labeled evaluation on multilingual transcribed child-directed utterances.",
}
@inproceedings{bouma-etal-2020-overview,
    title = "Overview of the {IWPT} 2020 Shared Task on Parsing into Enhanced {U}niversal {D}ependencies",
    author = "Bouma, Gosse  and
      Seddah, Djam{\'e}  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.16",
    doi = "10.18653/v1/2020.iwpt-1.16",
    pages = "151--161",
    abstract = "This overview introduces the task of parsing into enhanced universal dependencies, describes the datasets used for training and evaluation, and evaluation metrics. We outline various approaches and discuss the results of the shared task.",
}
@inproceedings{kanerva-etal-2020-turku,
    title = "{T}urku Enhanced Parser Pipeline: From Raw Text to Enhanced Graphs in the {IWPT} 2020 Shared Task",
    author = "Kanerva, Jenna  and
      Ginter, Filip  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.17",
    doi = "10.18653/v1/2020.iwpt-1.17",
    pages = "162--173",
    abstract = "We present the approach of the TurkuNLP group to the IWPT 2020 shared task on Multilingual Parsing into Enhanced Universal Dependencies. The task involves 28 treebanks in 17 different languages and requires parsers to generate graph structures extending on the basic dependency trees. Our approach combines language-specific BERT models, the UDify parser, neural sequence-to-sequence lemmatization and a graph transformation approach encoding the enhanced structure into a dependency tree. Our submission averaged 84.5{\%} ELAS, ranking first in the shared task. We make all methods and resources developed for this study freely available under open licenses from https://turkunlp.org.",
}
@inproceedings{heinecke-2020-hybrid,
    title = "Hybrid Enhanced {U}niversal {D}ependencies Parsing",
    author = "Heinecke, Johannes",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.18",
    doi = "10.18653/v1/2020.iwpt-1.18",
    pages = "174--180",
    abstract = "This paper describes our system to predict enhanced dependencies for Universal Dependencies (UD) treebanks, which ranked 2nd in the Shared Task on Enhanced Dependency Parsing with an average ELAS of 82.60{\%}. Our system uses a hybrid two-step approach. First, we use a graph-based parser to extract a basic syntactic dependency tree. Then, we use a set of linguistic rules which generate the enhanced dependencies for the syntactic tree. The application of these rules is optimized using a classifier which predicts their suitability in the given context. A key advantage of this approach is its language independence, as rules rely solely on dependency trees and UPOS tags which are shared across all languages.",
}
@inproceedings{he-choi-2020-adaptation,
    title = "Adaptation of Multilingual Transformer Encoder for Robust Enhanced {U}niversal {D}ependency Parsing",
    author = "He, Han  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.19",
    doi = "10.18653/v1/2020.iwpt-1.19",
    pages = "181--191",
    abstract = "This paper presents our enhanced dependency parsing approach using transformer encoders, coupled with a simple yet powerful ensemble algorithm that takes advantage of both tree and graph dependency parsing. Two types of transformer encoders are compared, a multilingual encoder and language-specific encoders. Our dependency tree parsing (DTP) approach generates only primary dependencies to form trees whereas our dependency graph parsing (DGP) approach handles both primary and secondary dependencies to form graphs. Since DGP does not guarantee the generated graphs are acyclic, the ensemble algorithm is designed to add secondary arcs predicted by DGP to primary arcs predicted by DTP. Our results show that models using the multilingual encoder outperform ones using the language specific encoders for most languages. The ensemble models generally show higher labeled attachment score on enhanced dependencies (ELAS) than the DTP and DGP models. As the result, our best models rank the third place on the macro-average ELAS over 17 languages.",
}
@inproceedings{dehouck-etal-2020-efficient,
    title = "Efficient {EUD} Parsing",
    author = "Dehouck, Mathieu  and
      Anderson, Mark  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.20",
    doi = "10.18653/v1/2020.iwpt-1.20",
    pages = "192--205",
    abstract = "We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2020. We engaged with the task by focusing on efficiency. For this we considered training costs and inference efficiency. Our models are a combination of distilled neural dependency parsers and a rule-based system that projects UD trees into EUD graphs. We obtained an average ELAS of 74.04 for our official submission, ranking 4th overall.",
}
@inproceedings{attardi-etal-2020-linear,
    title = "Linear Neural Parsing and Hybrid Enhancement for Enhanced {U}niversal {D}ependencies",
    author = "Attardi, Giuseppe  and
      Sartiano, Daniele  and
      Simi, Maria",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.21",
    doi = "10.18653/v1/2020.iwpt-1.21",
    pages = "206--214",
    abstract = "To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm. We train separate models for each language on the shared task data. We compare our base parser with two biaffine parsers and also present an ensemble combination of all five parsers, which achieves an average UAS 1.88 point lower than the top official submission. For producing the enhanced dependencies, we exploit a hybrid approach, coupling an algorithmic graph transformation of the dependency tree with predictions made by a multitask machine learning model.",
}
@inproceedings{wang-etal-2020-enhanced,
    title = "Enhanced {U}niversal {D}ependency Parsing with Second-Order Inference and Mixture of Training Data",
    author = "Wang, Xinyu  and
      Jiang, Yong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.22",
    doi = "10.18653/v1/2020.iwpt-1.22",
    pages = "215--220",
    abstract = "This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}. Our system is a graph-based parser with second-order inference. For the low-resource Tamil corpora, we specially mixed the training data of Tamil with other languages and significantly improved the performance of Tamil. Due to our misunderstanding of the submission requirements, we submitted graphs that are not connected, which makes our system only rank \textbf{6th} over 10 teams. However, after we fixed this problem, our system is 0.6 ELAS higher than the team that ranked \textbf{1st} in the official results.",
}
@inproceedings{ek-bernardy-2020-much,
    title = "How Much of Enhanced {UD} Is Contained in {UD}?",
    author = "Ek, Adam  and
      Bernardy, Jean-Philippe",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.23",
    doi = "10.18653/v1/2020.iwpt-1.23",
    pages = "221--226",
    abstract = "In this paper, we present the submission of team CLASP to the IWPT 2020 Shared Task on parsing enhanced universal dependencies. We develop a tree-to-graph transformation algorithm based on dependency patterns. This algorithm can transform gold UD trees to EUD graphs with an ELAS score of 81.55 and a EULAS score of 96.70. These results show that much of the information needed to construct EUD graphs from UD trees are present in the UD trees. Coupled with a standard UD parser, the method applies to the official test data and yields and ELAS score of 67.85 and a EULAS score is 80.18.",
}
@inproceedings{barry-etal-2020-adapt,
    title = "The {ADAPT} Enhanced Dependency Parser at the {IWPT} 2020 Shared Task",
    author = "Barry, James  and
      Wagner, Joachim  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.24",
    doi = "10.18653/v1/2020.iwpt-1.24",
    pages = "227--235",
    abstract = "We describe the ADAPT system for the 2020 IWPT Shared Task on parsing enhanced Universal Dependencies in 17 languages. We implement a pipeline approach using UDPipe and UDPipe-future to provide initial levels of annotation. The enhanced dependency graph is either produced by a graph-based semantic dependency parser or is built from the basic tree using a small set of heuristics. Our results show that, for the majority of languages, a semantic dependency parser can be successfully applied to the task of parsing enhanced dependencies. Unfortunately, we did not ensure a connected graph as part of our pipeline approach and our competition submission relied on a last-minute fix to pass the validation script which harmed our official evaluation scores significantly. Our submission ranked eighth in the official evaluation with a macro-averaged coarse ELAS F1 of 67.23 and a treebank average of 67.49. We later implemented our own graph-connecting fix which resulted in a score of 79.53 (language average) or 79.76 (treebank average), which would have placed fourth in the competition evaluation.",
}
@inproceedings{hershcovich-etal-2020-kopsala,
    title = "{K}{\o}psala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding",
    author = "Hershcovich, Daniel  and
      de Lhoneux, Miryam  and
      Kulmizev, Artur  and
      Pejhan, Elham  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.25",
    doi = "10.18653/v1/2020.iwpt-1.25",
    pages = "236--244",
    abstract = "We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020. Our system is a pipeline consisting of off-the-shelf models for everything but enhanced graph parsing, and for the latter, a transition-based graph parser adapted from Che et al. (2019). We train a single enhanced parser model per language, using gold sentence splitting and tokenization for training, and rely only on tokenized surface forms and multilingual BERT for encoding. While a bug introduced just before submission resulted in a severe drop in precision, its post-submission fix would bring us to 4th place in the official ranking, according to average ELAS. Our parser demonstrates that a unified pipeline is effective for both Meaning Representation Parsing and Enhanced Universal Dependencies.",
}
@inproceedings{grunewald-friedrich-2020-robertnlp,
    title = "{R}obert{NLP} at the {IWPT} 2020 Shared Task: Surprisingly Simple Enhanced {UD} Parsing for {E}nglish",
    author = {Gr{\"u}newald, Stefan  and
      Friedrich, Annemarie},
    booktitle = "Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwpt-1.26",
    doi = "10.18653/v1/2020.iwpt-1.26",
    pages = "245--252",
    abstract = "This paper presents our system at the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. Using a biaffine classifier architecture (Dozat and Manning, 2017) which operates directly on finetuned RoBERTa embeddings, our parser generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens in the sentence. We address label sparsity issues by replacing lexical items in relations with placeholders at prediction time, later retrieving them from the parse in a rule-based fashion. In addition, we ensure structural graph constraints using a simple set of heuristics. On the English blind test data, our system achieves a very high parsing accuracy, ranking 1st out of 10 with an ELAS F1 score of 88.94{\%}.",
}
@proceedings{iwltp-2020-international,
    title = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    editor = "Rehm, Georg  and
      Bontcheva, Kalina  and
      Choukri, Khalid  and
      Haji{\v{c}}, Jan  and
      Piperidis, Stelios  and
      Vasi{\c{l}}jevs, Andrejs",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.0",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{branco-etal-2020-infrastructure,
    title = "Infrastructure for the Science and Technology of Language {PORTULAN} {CLARIN}",
    author = "Branco, Ant{\'o}nio  and
      Mendes, Am{\'a}lia  and
      Quaresma, Paulo  and
      Gomes, Lu{\'\i}s  and
      Silva, Jo{\~a}o  and
      Teixeira, Andrea",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.1",
    pages = "1--7",
    abstract = "This paper presents the PORTULAN CLARIN Research Infrastructure for the Science and Technology of Language, which is part of the European research infrastructure CLARIN ERIC as its Portuguese national node, and belongs to the Portuguese National Roadmap of Research Infrastructures of Strategic Relevance. It encompasses a repository, where resources and metadata are deposited for long-term archiving and access, and a workbench, where Language Technology tools and applications are made available through different modes of interaction, among many other services. It is an asset of utmost importance for the technological development of natural languages and for their preparation for the digital age, contributing to ensure the citizenship of their speakers in the information society.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{chiarcos-etal-2020-linguistic,
    title = "On the Linguistic Linked Open Data Infrastructure",
    author = {Chiarcos, Christian  and
      Klimek, Bettina  and
      F{\"a}th, Christian  and
      Declerck, Thierry  and
      McCrae, John Philip},
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.2",
    pages = "8--15",
    abstract = "In this paper we describe the current state of development of the Linguistic Linked Open Data (LLOD) infrastructure, an LOD(sub-)cloud of linguistic resources, which covers various linguistic data bases, lexicons, corpora, terminology and metadata repositories.We give in some details an overview of the contributions made by the European H2020 projects {``}Pr{\^e}t-{\`a}-LLOD{''} ({`}Ready-to-useMultilingual Linked Language Data for Knowledge Services across Sectors{'}) and {``}ELEXIS{''} ({`}European Lexicographic Infrastructure{'}) to the further development of the LLOD.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{coleman-etal-2020-architecture,
    title = "Architecture of a Scalable, Secure and Resilient Translation Platform for Multilingual News Media",
    author = "Coleman, Susie  and
      Secker, Andrew  and
      Bawden, Rachel  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.3",
    pages = "16--21",
    abstract = "This paper presents an example architecture for a scalable, secure and resilient Machine Translation (MT) platform, using components available via Amazon Web Services (AWS). It is increasingly common for a single news organisation to publish and monitor news sources in multiple languages. A growth in news sources makes this increasingly challenging and time-consuming but MT can help automate some aspects of this process. Building a translation service provides a single integration point for news room tools that use translation technology allowing MT models to be integrated into a system once, rather than each time the translation technology is needed. By using a range of services provided by AWS, it is possible to architect a platform where multiple pre-existing technologies are combined to build a solution, as opposed to developing software from scratch for deployment on a single virtual machine. This increases the speed at which a platform can be developed and allows the use of well-maintained services. However, a single service also provides challenges. It is key to consider how the platform will scale when handling many users and how to ensure the platform is resilient.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{cristea-etal-2020-cobiliro,
    title = "{C}o{B}i{L}i{R}o: A Research Platform for Bimodal Corpora",
    author = "Cristea, Dan  and
      Pistol, Ionu{\textcommabelow{t}}  and
      Boghiu, {\textcommabelow{S}}erban  and
      Bibiri, Anca-Diana  and
      G{\^\i}fu, Daniela  and
      Scutelnicu, Andrei  and
      Onofrei, Mihaela  and
      Trandab{\u{a}}{\textcommabelow{t}}, Diana  and
      Bugeag, George",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.4",
    pages = "22--27",
    abstract = "This paper describes the on-going work carried out within the CoBiLiRo (Bimodal Corpus for Romanian Language) research project, part of ReTeRom (Resources and Technologies for Developing Human-Machine Interfaces in Romanian). Data annotation finds increasing use in speech recognition and synthesis with the goal to support learning processes. In this context, a variety of different annotation systems for application to Speech and Text Processing environments have been presented. Even if many designs for the data annotations workflow have emerged, the process of handling metadata, to manage complex user-defined annotations, is not covered enough. We propose a design of the format aimed to serve as an annotation standard for bimodal resources, which facilitates searching, editing and statistical analysis operations over it. The design and implementation of an infrastructure that houses the resources are also presented. The goal is widening the dissemination of bimodal corpora for research valorisation and use in applications. Also, this study reports on the main operations of the web Platform which hosts the corpus and the automatic conversion flows that brings the submitted files at the format accepted by the Platform.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{eskevich-etal-2020-clarin,
    title = "{CLARIN}: Distributed Language Resources and Technology in a {E}uropean Infrastructure",
    author = {Eskevich, Maria  and
      de Jong, Franciska  and
      K{\"o}nig, Alexander  and
      Fi{\v{s}}er, Darja  and
      Van Uytvanck, Dieter  and
      Aalto, Tero  and
      Borin, Lars  and
      Gerassimenko, Olga  and
      Hajic, Jan  and
      van den Heuvel, Henk  and
      Kahusk, Neeme  and
      Liin, Krista  and
      Matthiesen, Martin  and
      Piperidis, Stelios  and
      Vider, Kadri},
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.5",
    pages = "28--34",
    abstract = "CLARIN is a European Research Infrastructure providing access to digital language resources and tools from across Europe and beyond to researchers in the humanities and social sciences. This paper focuses on CLARIN as a platform for the sharing of language resources. It zooms in on the service offer for the aggregation of language repositories and the value proposition for a number of communities that benefit from the enhanced visibility of their data and services as a result of integration in CLARIN. The enhanced findability of language resources is serving the social sciences and humanities (SSH) community at large and supports research communities that aim to collaborate based on virtual collections for a specific domain. The paper also addresses the wider landscape of service platforms based on language technologies which has the potential of becoming a powerful set of interoperable facilities to a variety of communities of use.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{etchegoyhen-etal-2020-elri,
    title = "{ELRI}: A Decentralised Network of National Relay Stations to Collect, Prepare and Share Language Resources",
    author = "Etchegoyhen, Thierry  and
      Anza Porras, Borja  and
      Azpeitia, Andoni  and
      Mart{\'\i}nez Garcia, Eva  and
      Fonseca, Jos{\'e} Luis  and
      Fonseca, Patricia  and
      Vale, Paulo  and
      Dunne, Jane  and
      Gaspari, Federico  and
      Lynn, Teresa  and
      McHugh, Helen  and
      Way, Andy  and
      Arranz, Victoria  and
      Choukri, Khalid  and
      Pusset, Herv{\'e}  and
      Sicard, Alexandre  and
      Neto, Rui  and
      Melero, Maite  and
      Perez, David  and
      Branco, Ant{\'o}nio  and
      Branco, Ruben  and
      Gomes, Lu{\'\i}s",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.6",
    pages = "35--43",
    abstract = "We describe the European Language Resource Infrastructure (ELRI), a decentralised network to help collect, prepare and share language resources. The infrastructure was developed within a project co-funded by the Connecting Europe Facility Programme of the European Union, and has been deployed in the four Member States participating in the project, namely France, Ireland, Portugal and Spain. ELRI provides sustainable and flexible means to collect and share language resources via National Relay Stations, to which members of public institutions can freely subscribe. The infrastructure includes fully automated data processing engines to facilitate the preparation, sharing and wider reuse of useful language resources that can help optimise human and automated translation services in the European Union.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{franceschini-etal-2020-removing,
    title = "Removing {E}uropean Language Barriers with Innovative Machine Translation Technology",
    author = {Franceschini, Dario  and
      Canton, Chiara  and
      Simonini, Ivan  and
      Schweinfurth, Armin  and
      Glott, Adelheid  and
      St{\"u}ker, Sebastian  and
      Nguyen, Thai-Son  and
      Schneider, Felix  and
      Ha, Thanh-Le  and
      Waibel, Alex  and
      Haddow, Barry  and
      Williams, Philip  and
      Sennrich, Rico  and
      Bojar, Ond{\v{r}}ej  and
      Sagar, Sangeet  and
      Mach{\'a}{\v{c}}ek, Dominik  and
      Smr{\v{z}}, Otakar},
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.7",
    pages = "44--49",
    abstract = "This paper presents our progress towards deploying a versatile communication platform in the task of highly multilingual live speech translation for conferences and remote meetings live subtitling. The platform has been designed with a focus on very low latency and high flexibility while allowing research prototypes of speech and text processing tools to be easily connected, regardless of where they physically run. We outline our architecture solution and also briefly compare it with the ELG platform. Technical details are provided on the most important components and we summarize the test deployment events we ran so far.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{garcia-martinez-etal-2020-eco,
    title = "{E}co.pangeamt: Industrializing Neural {MT}",
    author = "Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Herranz, Manuel  and
      Estela, Amando  and
      Franco, {\'A}ngela  and
      Bi{\'e}, Laurent",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.8",
    pages = "50--53",
    abstract = "Eco is Pangeanic{'}s customer portal for generic or specialized translation services (machine translation and post-editing, generic API MT and custom API MT). Users can request the processing (translation) of files in different formats. Moreover, a client user can manage the engines and models allowing their cloning and retraining.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{geissler-2020-kairntech,
    title = "The {K}airntech {S}herpa {--} An {ML} Platform and {API} for the Enrichment of (not only) Scientific Content",
    author = "Gei{\ss}ler, Stefan",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.9",
    pages = "54--58",
    abstract = "We present an software platform and API that combines various ML and NLP approaches for the analysis and enrichment of textual content. The platform{'}s design and implementation is guided by the goal to allow non-technical users to conduct their own experiments and training runs on their respective data, allowing to test, tune and deploy analysis models for production. Dedicated specific packages for subtasks such as document structure processing, document categorization, annotation with existing thesauri, disambiguation and linking, annotation with newly created entity recognizers and summarization {--} available as open source components in isolation {--} are combined into an end-user-facing, collaborative, scalable platform to support large-scale industrial document analysis document analysis. We see the Sherpa{'}s setup as an answer to the observation that ML has reached a level of maturity that allows to attain useful results in many analysis scenarios today, but that in-depth technical competencies in the required fields of NLP and AI is often scarce; a setup that focusses on non-technical domain-expert end-users can help to bring required analysis functionalities closer to the day-to-day reality in business contexts.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{kim-etal-2020-towards,
    title = "Towards Standardization of Web Service Protocols for {NLP}aa{S}",
    author = "Kim, Jin-Dong  and
      Ide, Nancy  and
      Suderman, Keith",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.10",
    pages = "59--65",
    abstract = "Several web services for various natural language processing (NLP) tasks ({`}{`}NLP-as-a-service{''} or NLPaaS) have recently been made publicly available. However, despite their similar functionality these services often differ in the protocols they use, thus complicating the development of clients accessing them. A survey of currently available NLPaaS services suggests that it may be possible to identify a minimal application layer protocol that can be shared by NLPaaS services without sacrificing functionality or convenience, while at the same time simplifying the development of clients for these services. In this paper, we hope to raise awareness of the interoperability problems caused by the variety of existing web service protocols, and describe an effort to identify a set of best practices for NLPaaS protocol design. To that end, we survey and compare protocols used by NLPaaS services and suggest how these protocols may be further aligned to reduce variation.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{mboning-etal-2020-ntealan,
    title = "{NT}e{AL}an Dictionaries Platforms: An Example Of Collaboration-Based Model",
    author = "Mboning, Elvis  and
      Baleba, Daniel  and
      Bassahak, Jean Marc  and
      Wandji, Ornella  and
      Assoumou, Jules",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.11",
    pages = "66--72",
    abstract = "Nowadays the scarcity and dispersion of open-source NLP resources and tools in and for African languages make it difficult for researchers to truly fit these languages into current algorithms of artificial intelligence, resulting in the stagnation of these numerous languages, as far as technological progress is concerned. Created in 2017, with the aim of building communities of voluntary contributors around African native and/or national languages, cultures, NLP technologies and artificial intelligence, the NTeALan association has set up a series of web collaborative platforms intended to allow the aforementioned communities to create and manage their own lexicographic and linguistic resources. This paper aims at presenting the first versions of three lexicographic platforms that we developed in and for African languages: the REST/GraphQL API for saving lexicographic resources, the dictionary management platform and the collaborative dictionary platform. We also describe the data representation format used for these resources. After experimenting with a few dictionaries and looking at users feedback, we are convinced that only collaboration-based approaches and platforms can effectively respond to challenges of producing quality resources in and for African native and/or national languages.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{moreno-schneider-etal-2020-workflow,
    title = "A Workflow Manager for Complex {NLP} and Content Curation Workflows",
    author = "Moreno-Schneider, Julian  and
      Bourgonje, Peter  and
      Kintzel, Florian  and
      Rehm, Georg",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.12",
    pages = "73--80",
    abstract = "We present a workflow manager for the flexible creation and customisation of NLP processing pipelines. The workflow manager addresses challenges in interoperability across various different NLP tasks and hardware-based resource usage. Based on the four key principles of generality, flexibility, scalability and efficiency, we present the first version of the workflow manager by providing details on its custom definition language, explaining the communication components and the general system architecture and setup. We currently implement the system, which is grounded and motivated by real-world industry use cases in several innovation and transfer projects.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{pais-etal-2020-processing,
    title = "A Processing Platform Relating Data and Tools for {R}omanian Language",
    author = "P{\u{a}}i{\textcommabelow{s}}, Vasile  and
      Ion, Radu  and
      Tufi{\textcommabelow{s}}, Dan",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.13",
    pages = "81--88",
    abstract = "This paper presents RELATE (http://relate.racai.ro), a high-performance natural language platform designed for Romanian language. It is meant both for demonstration of available services, from text-span annotations to syntactic dependency trees as well as playing or automatically synthesizing Romanian words, and for the development of new annotated corpora. It also incorporates the search engines for the large COROLA reference corpus of contemporary Romanian and the Romanian wordnet. It integrates multiple text and speech processing modules and exposes their functionality through a web interface designed for the linguist researcher. It makes use of a scheduler-runner architecture, allowing processing to be distributed across multiple computing nodes. A series of input/output converters allows large corpora to be loaded, processed and exported according to user preferences.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{rebai-etal-2020-linto,
    title = "{L}in{TO} Platform: A Smart Open Voice Assistant for Business Environments",
    author = "Rebai, Ilyes  and
      Benhamiche, Sami  and
      Thompson, Kate  and
      Sellami, Zied  and
      Laine, Damien  and
      Lorr{\'e}, Jean-Pierre",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.14",
    pages = "89--95",
    abstract = "In this paper, we present LinTO, an intelligent voice platform and smart room assistant for improving efficiency and productivity in business. Our objective is to build a Spoken Language Understanding system that maintains high performance in both Automatic Speech Recognition (ASR) and Natural Language Processing while being portable and scalable. In this paper we describe the LinTO architecture and our approach to ASR engine training which takes advantage of recent advances in deep learning while guaranteeing high-performance real-time processing. Unlike the existing solutions, the LinTO platform is open source for commercial and non-commercial use",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{rehm-etal-2020-towards,
    title = "Towards an Interoperable Ecosystem of {AI} and {LT} Platforms: A Roadmap for the Implementation of Different Levels of Interoperability",
    author = {Rehm, Georg  and
      Galanis, Dimitris  and
      Labropoulou, Penny  and
      Piperidis, Stelios  and
      Wel{\ss}, Martin  and
      Usbeck, Ricardo  and
      K{\"o}hler, Joachim  and
      Deligiannis, Miltos  and
      Gkirtzou, Katerina  and
      Fischer, Johannes  and
      Chiarcos, Christian  and
      Feldhus, Nils  and
      Moreno-Schneider, Julian  and
      Kintzel, Florian  and
      Montiel, Elena  and
      Rodr{\'\i}guez Doncel, V{\'\i}ctor  and
      McCrae, John Philip  and
      Laqua, David  and
      Theile, Irina Patricia  and
      Dittmar, Christian  and
      Bontcheva, Kalina  and
      Roberts, Ian  and
      Vasi{\c{l}}jevs, Andrejs  and
      Lagzdi{\c{n}}{\v{s}}, Andis},
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.15",
    pages = "96--107",
    abstract = "With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{skadins-salimbajevs-2020-comprise,
    title = "The {COMPRISE} Cloud Platform",
    author = "Skadi{\c{n}}{\v{s}}, Raivis  and
      Salimbajevs, Askars",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.16",
    pages = "108--111",
    abstract = "This paper presents the COMPRISE cloud platform that is developed in the H2020 project. We present an overview of the COMPRISE project, its main goals, components, and how the cloud platform fits in the context of the overall project. The COMPRISE cloud platform is presented in more detail {--} main users, use scenarios, functions, implementation details, and how it will be used by both COMPRISE{'}s targeted audience and the broader language-technology community.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@inproceedings{sousa-etal-2020-linguistic,
    title = "From Linguistic Research Projects to Language Technology Platforms: A Case Study in Learner Data",
    author = "Sousa, Annanda  and
      Ballier, Nicolas  and
      Gaillat, Thomas  and
      Stearns, Bernardo  and
      Zarrouk, Manel  and
      Simpkin, Andrew  and
      Bouy{\'e}, Manon",
    booktitle = "Proceedings of the 1st International Workshop on Language Technology Platforms",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.iwltp-1.17",
    pages = "112--120",
    abstract = "This paper describes the workflow and architecture adopted by a linguistic research project. We report our experience and present the research outputs turned into resources that we wish to share with the community. We discuss the current limitations and the next steps that could be taken for the scaling and development of our research project. Allying NLP and language-centric AI, we discuss similar projects and possible ways to start collaborating towards potential platform interoperability.",
    language = "English",
    ISBN = "979-10-95546-64-1",
}
@proceedings{iwdp-2020-international,
    title = "Proceedings of the Second International Workshop of Discourse Processing",
    editor = "Liu, Qun  and
      Xiong, Deyi  and
      Ge, Shili  and
      Zhang, Xiaojun",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.0",
}
@inproceedings{li-2020-research,
    title = "Research on Discourse Parsing: from the Dependency View",
    author = "Li, Sujian",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.1",
    pages = "1--3",
    abstract = "Discourse parsing aims to comprehensively acquire the logical structure of the whole text which may be helpful to some downstream applications such as summarization, reading comprehension, QA and so on. One important issue behind discourse parsing is the representation of discourse structure. Up to now, many discourse structures have been proposed, and the correponding parsing methods are designed, promoting the development of discourse research. In this paper, we mainly introduce our recent discourse research and its preliminary application from the dependency view.",
}
@inproceedings{zhang-2020-review,
    title = "A Review of Discourse-level Machine Translation",
    author = "Zhang, Xiaojun",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.2",
    pages = "4--12",
    abstract = "Machine translation (MT) models usually translate a text at sentence level by considering isolated sentences, which is based on a strict assumption that the sentences in a text are independent of one another. However, the fact is that the texts at discourse level have properties going beyond individual sentences. These properties reveal texts in the frequency and distribution of words, word senses, referential forms and syntactic structures. Dissregarding dependencies across sentences will harm translation quality especially in terms of coherence, cohesion, and consistency. To solve these problems, several approaches have previously been investigated for conventional statistical machine translation (SMT). With the fast growth of neural machine translation (NMT), discourse-level NMT has drawn increasing attention from researchers. In this work, we review major works on addressing discourse related problems for both SMT and NMT models with a survey of recent trends in the fields.",
}
@inproceedings{cai-xiong-2020-test,
    title = "A Test Suite for Evaluating Discourse Phenomena in Document-level Neural Machine Translation",
    author = "Cai, Xinyi  and
      Xiong, Deyi",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.3",
    pages = "13--17",
    abstract = "The need to evaluate the ability of context-aware neural machine translation (NMT) models in dealing with specific discourse phenomena arises in document-level NMT. However, test sets that satisfy this need are rare. In this paper, we propose a test suite to evaluate three common discourse phenomena in English-Chinese translation: pronoun, discourse connective and ellipsis where discourse divergences lie across the two languages. The test suite contains 1,200 instances, 400 for each type of discourse phenomena. We perform both automatic and human evaluation with three state-of-the-art context-aware NMT models on the proposed test suite. Results suggest that our test suite can be used as a challenging benchmark test bed for evaluating document-level NMT. The test suite will be publicly available soon.",
}
@inproceedings{hu-etal-2020-comparison,
    title = "Comparison of the effects of attention mechanism on translation tasks of different lengths of ambiguous words",
    author = "Hu, Yue  and
      Qin, Jiahao  and
      Chen, Zemeiqi  and
      Zhou, Jingshi  and
      Zhang, Xiaojun",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.4",
    pages = "18--21",
    abstract = "In recent years, attention mechanism has been widely used in various neural machine translation tasks based on encoder decoder. This paper focuses on the performance of encoder decoder attention mechanism in word sense disambiguation task with different text length, trying to find out the influence of context marker on attention mechanism in word sense disambiguation task. We hypothesize that attention mechanisms have similar performance when translating texts of different lengths. Our conclusion is that the alignment effect of attention mechanism is magnified in short text translation tasks with ambiguous nouns, while the effect of attention mechanism is far less than expected in long-text tasks, which means that attention mechanism is not the main mechanism for NMT model to feed WSD to integrate context information. This may mean that attention mechanism pays more attention to ambiguous nouns than context markers. The experimental results show that with the increase of text length, the performance of NMT model using attention mechanism will gradually decline.",
}
@inproceedings{huang-etal-2020-context,
    title = "Context-Aware Word Segmentation for {C}hinese Real-World Discourse",
    author = "Huang, Kaiyu  and
      Liu, Junpeng  and
      Cao, Jingxiang  and
      Huang, Degen",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.5",
    pages = "22--28",
    abstract = "Previous neural approaches achieve significant progress for Chinese word segmentation (CWS) as a sentence-level task, but it suffers from limitations on real-world scenario. In this paper, we address this issue with a context-aware method and optimize the solution at document-level. This paper proposes a three-step strategy to improve the performance for discourse CWS. First, the method utilizes an auxiliary segmenter to remedy the limitation on pre-segmenter. Then the context-aware algorithm computes the confidence of each split. The maximum probability path is reconstructed via this algorithm. Besides, in order to evaluate the performance in discourse, we build a new benchmark consisting of the latest news and Chinese medical articles. Extensive experiments on this benchmark show that our proposed method achieves a competitive performance on a document-level real-world scenario for CWS.",
}
@inproceedings{ma-zong-2020-neural,
    title = "Neural Abstractive Multi-Document Summarization: Hierarchical or Flat Structure?",
    author = "Ma, Ye  and
      Zong, Lu",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.6",
    pages = "29--37",
    abstract = "With regards to WikiSum (CITATION) that empowers applicative explorations of Neural Multi-Document Summarization (MDS) to learn from large scale dataset, this study develops two hierarchical Transformers (HT) that describe both the cross-token and cross-document dependencies, at the same time allow extended length of input documents. By incorporating word- and paragraph-level multi-head attentions in the decoder based on the parallel and vertical architectures, the proposed parallel and vertical hierarchical Transformers (PHT {\&}VHT) generate summaries utilizing context-aware word embeddings together with static and dynamics paragraph embeddings, respectively. A comprehensive evaluation is conducted on WikiSum to compare PHT {\&}VHT with established models and to answer the question whether hierarchical structures offer more promising performances than flat structures in the MDS task. The results suggest that our hierarchical models generate summaries of higher quality by better capturing cross-document relationships, and save more memory spaces in comparison to flat-structure models. Moreover, we recommend PHT given its practical value of higher inference speed and greater memory-saving capacity.",
}
@inproceedings{sanchez-carmona-etal-2020-intent,
    title = "Intent Segmentation of User Queries Via Discourse Parsing",
    author = "Sanchez Carmona, Vicente Ivan  and
      Yang, Yibing  and
      Wen, Ziyue  and
      Li, Ruosen  and
      Wang, Xiaohua  and
      Hu, Changjian",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.7",
    pages = "38--47",
    abstract = "In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation. Our target texts are user queries from a real-world chatbot. Our results show the feasibility of our approach with an F1-score of 82.97 points, and some advantages and disadvantages compared to two machine learning baselines: BERT and LSTM+CRF.",
}
@inproceedings{webber-2020-bridging,
    title = "Bridging Question Answering and Discourse The case of Multi-Sentence Questions",
    author = "Webber, Bonnie",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.8",
    pages = "48",
    abstract = "In human question-answering (QA), questions are often expressed in the form of multiple sentences. One can see this in both spoken QA interactions, when one person asks a question of another, and written QA, such as are found on-line in FAQs and in what are called {''}Community Question-Answering Forums{''}. Computer-based QA has taken the challenge of these {''}multi-sentence questions{''} to be that of breaking them into an appropriately ordered sequence of separate questions, with both the previous questions and their answers serving as context for the next question. This can be seen, for example, in two recent workshops at AAAI called {''}Reasoning for Complex QA{''} [https://rcqa-ws.github.io/program/]. We claim that, while appropriate for some types of {''}multi-sentence questions{''} (MSQs), it is not appropriate for all, because they are essentially different types of discourse. To support this claim, we need to provide evidence that: {\mbox{$\bullet$}} different types of MSQs are answered differently in written or spoken QA between people; {\mbox{$\bullet$}} people can (and do) distinguish these different types of MSQs; {\mbox{$\bullet$}} systems can be made to both distinguish different types of MSQs and provide appropriate answers.",
}
@inproceedings{ge-etal-2020-component,
    title = "Component Sharing in {E}nglish and {C}hinese Clause Complex",
    author = "Ge, Shili  and
      Lin, Xiaoping  and
      Song, Rou",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.9",
    pages = "49--53",
    abstract = "NT Clause Complex Framework defines a clause complex as a combination of NT clauses through component sharing and logic-semantic relationship. This paper clarifies the existence of component sharing mechanism in both English and Chinese clause complexes, illustrates the differences in component sharing between the two languages, and introduces a formal annotation scheme to represent clause-complex level structural transformations. Under the guidance of the annotation scheme, the English-Chinese Clause Alignment Corpus is built. It is believed that this corpus will aid comparative linguistic studies, translation studies and machine translation studies by providing abundant formal and computable samples for English-Chinese structural transformations on the clause complex level.",
}
@inproceedings{hardmeier-2020-referential,
    title = "Referential Cohesion A Challenge for Machine Translation Evaluation",
    author = "Hardmeier, Christian",
    booktitle = "Proceedings of the Second International Workshop of Discourse Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwdp-1.10",
    pages = "54",
    abstract = "Connected texts are characterised by the presence of linguistic elements relating to shared referents throughout the text. These elements together form a structure that lends cohesion to the text. The realisation of those cohesive structures is subject to different constraints and varying preferences in different languages. We regularly observe mismatches of cohesive structures across languages in parallel texts. This can be a result of either a divergence of language-internal constraints or of effects of the translation process. As fully automatic high-quality MT is starting to look achievable, the question arises how cohesive elements should be handled in MT evaluation, since the common assumption of 1:1 correspondence between referring expressions is a poor match for what we find in corpus data. Focusing on the translation of pronouns, I discuss different approaches to evaluating a particular type of cohesive elements in MT output and the trade-offs they make between evaluation cost, validity, specificity and coverage. I suggest that a meaningful evaluation of cohesive structures in translation is difficult to achieve simply by appealing to the intuition of human annotators, but requires a more structured approach that forces us to make up our minds about the standards we expect the translation output to adhere to.",
}
@proceedings{iwclul-2020-international,
    title = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    editor = "Pirinen, Tommi A  and
      Tyers, Francis M.  and
      Rie{\ss}ler, Michael",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.0",
}
@inproceedings{blokland-etal-2020-pseudonymisation,
    title = "A pseudonymisation method for language documentation corpora: An experiment with spoken {K}omi",
    author = "Blokland, Rogier  and
      Partanen, Niko  and
      Rie{\ss}ler, Michael",
    booktitle = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.1",
    pages = "1--8",
}
@inproceedings{howell-etal-2020-effort,
    title = "Effort-value payoff in lemmatisation for Uralic languages",
    author = "Howell, Nick  and
      Bibaeva, Maria  and
      M. Tyers, Francis",
    booktitle = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.2",
    pages = "9--14",
}
@inproceedings{rueter-etal-2020-questions,
    title = "On the questions in developing computational infrastructure for {K}omi-Permyak",
    author = "Rueter, Jack  and
      Partanen, Niko  and
      Ponomareva, Larisa",
    booktitle = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.3",
    pages = "15--25",
}
@inproceedings{alnajjar-etal-2020-editing,
    title = "On Editing Dictionaries for Uralic Languages in an Online Environment",
    author = {Alnajjar, Khalid  and
      H{\"a}m{\"a}l{\"a}inen, Mika  and
      Rueter, Jack},
    booktitle = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.4",
    pages = "26--30",
}
@inproceedings{hjortnaes-etal-2020-towards,
    title = "Towards a Speech Recognizer for {K}omi, an Endangered and Low-Resource Uralic Language",
    author = "Hjortnaes, Nils  and
      Partanen, Niko  and
      Rie{\ss}ler, Michael  and
      M. Tyers, Francis",
    booktitle = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.5",
    pages = "31--37",
}
@inproceedings{fejes-2020-hunting,
    title = "Hunting for antiharmonic stems in {E}rzya",
    author = "Fejes, L{\'a}szl{\'o}",
    booktitle = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.6",
    pages = "38--47",
}
@inproceedings{bobaly-etal-2020-appilcation,
    title = "ap{PIL}cation: an Android-based Tool for Learning {M}ansi",
    author = "Bob{\'a}ly, G{\'a}bor  and
      Horv{\'a}th, Csilla  and
      Vincze, Veronika",
    booktitle = "Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages",
    month = "10--11 " # jan,
    year = "2020",
    address = "Wien, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.iwclul-1.7",
    pages = "48--55",
}
@proceedings{isa-2020-joint,
    title = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    editor = "Bunt, Harry",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.0",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{bunt-2020-annotation,
    title = "Annotation of Quantification: The Current State of {ISO} 24617-12",
    author = "Bunt, Harry",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.1",
    pages = "1--12",
    abstract = "This paper discusses the current state of developing an ISO standard annotation scheme for quantification phenomena in natural language, as part of the ISO Semantic Annotation Framework (ISO 24617). A proposed approach that combines ideas from the theory of generalised quantifiers and from neo-Davidsonian event semantics was adopted by the ISO organisation in 2019 as a starting point for developing such an annotation scheme. * This scheme consists of (1) a conceptual {`}metamodel{'} that visualises the types of entities, functions and relations that go into annotations of quantification; (2) an abstract syntax which defines {`}annotation structures{'} as triples and other set-theoretic constructs; (3) an XML-based representation of annotation structures ({`}concrete syntax{'}); and (4) a compositional semantics of annotation structures. The latter three components together define the interpreted markup language QuantML. The focus in this paper is on the structuring of the semantic information needed to characterise quantification in natural language and the representation of these structures in QuantML.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{goel-etal-2020-hindi,
    title = "{H}indi {T}ime{B}ank: An {ISO}-{T}ime{ML} Annotated Reference Corpus",
    author = "Goel, Pranav  and
      Prabhu, Suhan  and
      Debnath, Alok  and
      Modi, Priyank  and
      Shrivastava, Manish",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.2",
    pages = "13--21",
    abstract = "ISO-TimeML is an international standard for multilingual event annotation, detection, categorization and linking. In this paper, we present the Hindi TimeBank, an ISO-TimeML annotated reference corpus for the detection and classification of events, states and time expressions, and the links between them. Based on contemporary developments in Hindi event recognition, we propose language independent and language-specific deviations from the ISO-TimeML guidelines, but preserve the schema. These deviations include the inclusion of annotator confidence, and an independent mechanism of identifying and annotating states such as copulars and existentials) With this paper, we present an open-source corpus, the Hindi TimeBank. The Hindi TimeBank is a 1,000 article dataset, with over 25,000 events, 3,500 states and 2,000 time expressions. We analyze the dataset in detail and provide a class-wise distribution of events, states and time expressions. Our guidelines and dataset are backed by high average inter-annotator agreement scores.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{hellan-2020-interoperable,
    title = "Interoperable Semantic Annotation",
    author = "Hellan, Lars",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.3",
    pages = "22--31",
    abstract = "The paper presents an annotation schema with the following characteristics: it is formally compact; it systematically and compositionally expands into fullfledged analytic representations, exploiting simple algorithms of typed feature structures; its representation of various dimensions of semantic content is systematically integrated with morpho-syntactic and lexical representation; it is integrated with a {`}deep{'} parsing grammar. Its compactness allows for efficient handling of large amounts of structures and data, and it is interoperable in covering multiple aspects of grammar and meaning. The code and its analytic expansions represent a cross-linguistically wide range of phenomena of languages and language structures. This paper presents its syntactic-semantic interoperability first from a theoretical point of view and then as applied in linguistic description.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{henlein-etal-2020-transfer,
    title = "Transfer of {ISOS}pace into a 3{D} Environment for Annotations and Applications",
    author = "Henlein, Alexander  and
      Abrami, Giuseppe  and
      Kett, Attila  and
      Mehler, Alexander",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.4",
    pages = "32--35",
    abstract = "People{'}s visual perception is very pronounced and therefore it is usually no problem for them to describe the space around them in words. Conversely, people also have no problems imagining a concept of a described space. In recent years many efforts have been made to develop a linguistic concept for spatial and spatial-temporal relations. However, the systems have not really caught on so far, which in our opinion is due to the complex models on which they are based and the lack of available training data and automated taggers. In this paper we describe a project to support spatial annotation, which could facilitate annotation by its many functions, but also enrich it with many more information. This is to be achieved by an extension by means of a VR environment, with which spatial relations can be better visualized and connected with real objects. And we want to use the available data to develop a new state-of-the-art tagger and thus lay the foundation for future systems such as improved text understanding for Text2Scene.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{lee-2020-annotation,
    title = "Annotation-based Semantics",
    author = "Lee, Kiyong",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.5",
    pages = "36--48",
    abstract = "This paper proposes a semantics ABS for the model-theoretic interpretation of annotation structures. It provides a language ABSr, that represents \textit{semantic forms} in a (possibly $\lambda$-free) type-theoretic first-order logic. For semantic compositionality, the representation language introduces two operators $\oplus$ and $\oslash$ with subtypes for the conjunctive or distributive composition of semantic forms. ABS also introduces a small set of logical predicates to represent semantic forms in a simplified format. The use of ABSr is illustrated with some annotation structures that conform to ISO 24617 standards on semantic annotation such as ISO-TimeML and ISO-Space.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{marini-jezek-2020-annotating,
    title = "Annotating {C}roatian Semantic Type Coercions in {CROATPAS}",
    author = "Marini, Costanza  and
      Jezek, Elisabetta",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.6",
    pages = "49--58",
    abstract = "This short research paper presents the results of a corpus-based metonymy annotation exercise on a sample of 101 Croatian verb entries {--} corresponding to 457 patters and over 20,000 corpus lines {--} taken from CROATPAS (Marini {\&} Je{\v{z}}ek, 2019), a digital repository of verb argument structures manually annotated with Semantic Type labels on their argument slots following a methodology inspired by Corpus Pattern Analysis (Hanks, 2004 {\&} 2013; Hanks {\&} Pustejovsky, 2005). CROATPAS will be made available online in 2020. Semantic Type labelling is not only well-suited to annotate verbal polysemy, but also metonymic shifts in verb argument combinations, which in Generative Lexicon (Pustejovsky, 1995 {\&} 1998; Pustejovsky {\&} Je{\v{z}}ek, 2008) are called Semantic Type coercions. From a sub lexical point of view, Semantic Type coercions can be considered as exploitations of one of the qualia roles of those Semantic Types which do not satisfy a verb{'}s selectional requirements, but do not trigger a different verb sense. Overall, we were able to identify 62 different Semantic Type coercions linked to 1,052 metonymic corpus lines. In the future, we plan to compare our results with those from an equivalent study on Italian verbs (Romani, 2020) for a crosslinguistic analysis of metonymic shifts.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{melly-etal-2020-consolidated,
    title = "A Consolidated Dataset for Knowledge-based Question Generation using Predicate Mapping of Linked Data",
    author = "Melly, Johanna  and
      Luthier, Gabriel  and
      Popescu-Belis, Andrei",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.7",
    pages = "59--66",
    abstract = "In this paper, we present the ForwardQuestions data set, made of human-generated questions related to knowledge triples. This data set results from the conversion and merger of the existing SimpleDBPediaQA and SimpleQuestionsWikidata data sets, including the mapping of predicates from DBPedia to Wikidata, and the selection of {`}forward{'} questions as opposed to {`}backward{'} ones. The new data set can be used to generate novel questions given an unseen Wikidata triple, by replacing the subjects of existing questions with the new one and then selecting the best candidate questions using semantic and syntactic criteria. Evaluation results indicate that the question generation method using ForwardQuestions improves the quality of questions by about 20{\%} with respect to a baseline not using ranking criteria.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{moneglia-varvara-2020-annotation,
    title = "The Annotation of Thematic Structure and Alternations face to the Semantic Variation of Action Verbs. Current Trends in the {IMAGACT} Ontology",
    author = "Moneglia, Massimo  and
      Varvara, Rossella",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.8",
    pages = "67--74",
    abstract = "We present some issues in the development of the semantic annotation of IMAGACT, a multimodal and multilingual ontology of actions. The resource is structured on action concepts that are meant to be cognitive entities and to which a linguistic caption is attached. For each of these concepts, we annotate the minimal thematic structure of the caption and the possible argument alternations allowed. We present some insights on this process with regards to the notion of thematic structure and the relationship between action concepts and linguistic expressions. From the empirical evidence provided by the annotation, we discuss on the very nature of thematic structure, arguing that it is neither a property of the verb itself nor a property of action concepts. We further show what is the relation between thematic structure and 1- the semantic variation of action verbs; 2- the lexical variation of action concepts.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{petukhova-bunt-2020-adapting,
    title = "Adapting the {ISO} 24617-2 Dialogue Act Annotation Scheme for Modelling Medical Consultations",
    author = "Petukhova, Volha  and
      Bunt, Harry",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.9",
    pages = "75--87",
    abstract = "Effective, professional and socially competent dialogue of health care providers with their patients is essential to best practice in medicine. To identify, categorize and quantify salient features of patient-provider communication, to model interactive processes in medical encounters and to design digital interactive medical services, two important instruments have been developed: (1) medical interaction analysis systems with the Roter Interaction Analysis System (RIAS) as the most widely used by medical practitioners and (2) dialogue act annotation schemes with ISO 24617-2 as a multidimensional taxonomy of interoperable semantic concepts widely used for corpus annotation and dialogue systems design. Neither instrument fits all purposes. In this paper, we perform a systematic comparative analysis of the categories defined in the RIAS and ISO taxonomies. Overcoming the deficiencies and gaps that were found, we propose a number of extensions to the ISO annotation scheme, making it a powerful analytical and modelling instrument for the analysis, modelling and assessment of medical communication.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{prabhu-etal-2020-detection,
    title = "Detection and Annotation of Events in {K}annada",
    author = "Prabhu, Suhan  and
      Narayan, Ujwal  and
      Debnath, Alok  and
      S, Sumukh  and
      Shrivastava, Manish",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.10",
    pages = "88--93",
    abstract = "In this paper, we provide the basic guidelines towards the detection and linguistic analysis of events in Kannada. Kannada is a morphologically rich, resource poor Dravidian language spoken in southern India. As most information retrieval and extraction tasks are resource intensive, very little work has been done on Kannada NLP, with almost no efforts in discourse analysis and dataset creation for representing events or other semantic annotations in the text. In this paper, we linguistically analyze what constitutes an event in this language, the challenges faced with discourse level annotation and representation due to the rich derivational morphology of the language that allows free word order, numerous multi-word expressions, adverbial participle constructions and constraints on subject-verb relations. Therefore, this paper is one of the first attempts at a large scale discourse level annotation for Kannada, which can be used for semantic annotation and corpus development for other tasks in the language.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{roberto-davis-2020-towards,
    title = "Towards the Ontologization of the Outsider Art Domain: Position Paper",
    author = "Roberto, John  and
      Davis, Brian",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.11",
    pages = "94--101",
    abstract = "The purpose of this paper is to present a prospective and interdisciplinary research project seeking to ontologize knowledge of the domain of Outsider Art, that is, the art created outside the boundaries of official culture. The goal is to combine ontology engineering methodologies to develop a knowledge base which i) examines the relation between social exclusion and cultural productions, ii) standardizes the terminology of Outsider Art and iii) enables semantic interoperability between cultural metadata relevant to Outsider Art. The Outsider Art ontology will integrate some existing ontologies and terminologies, such as the CIDOC - Conceptual Reference Model (CRM), the Art {\&} Architecture Thesaurus and the Getty Union List of Artist Names, among other resources. Natural Language Processing and Machine Learning techniques will be fundamental instruments for knowledge acquisition and elicitation. NLP techniques will be used to annotate bibliographies of relevant outsider artists and descriptions of outsider artworks with linguistic information. Machine Learning techniques will be leveraged to acquire knowledge from linguistic features embedded in both types of texts.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@inproceedings{sheremetyeva-2020-towards,
    title = "Towards Creating Interoperable Resources for Conceptual Annotation of Multilingual Domain Corpora",
    author = "Sheremetyeva, Svetlana",
    booktitle = "16th Joint ACL - ISO Workshop on Interoperable Semantic Annotation PROCEEDINGS",
    month = may,
    year = "2020",
    address = "Marseille",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.isa-1.12",
    pages = "102--109",
    abstract = "In this paper we focus on creation of interoperable annotation resources that make up a significant proportion of an on-going project on the development of conceptually annotated multilingual corpora for the domain of terrorist attacks in three languages (English, French and Russian) that can be used for comparative linguistic research, intelligent content and trend analysis, summarization, machine translation, etc. Conceptual annotation is understood as a type of task-oriented domain-specific semantic annotation. The annotation process in our project relies on ontological analysis. The paper details on the issues of the development of both static and dynamic resources such as a universal conceptual annotation scheme, multilingual domain ontology and multipurpose annotation platform with flexible settings, which can be used for the automation of the conceptual resource acquisition and of the annotation process, as well as for the documentation of the annotated corpora specificities. The resources constructed in the course of the research are also to be used for developing concept disambiguation metrics by means of qualitative and quantitative analysis of the golden portion of the conceptually annotated multilingual corpora and of the annotation platform linguistic knowledge.",
    language = "English",
    ISBN = "979-10-95546-48-1",
}
@proceedings{intexsempar-2020-interactive,
    title = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
    editor = "Bogin, Ben  and
      Iyer, Srinivasan  and
      Lin, Victoria  and
      Radev, Dragomir  and
      Suhr, Alane  and
      {Panupong}  and
      Xiong, Caiming  and
      Yin, Pengcheng  and
      Yu, Tao  and
      Zhang, Rui  and
      Zhong, Victor",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.intexsempar-1.0",
}
@inproceedings{shekarpour-etal-2020-qa2explanation,
    title = "{QA}2{E}xplanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph",
    author = "Shekarpour, Saeedeh  and
      Nadgeri, Abhishek  and
      Singh, Kuldeep",
    booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.intexsempar-1.1",
    doi = "10.18653/v1/2020.intexsempar-1.1",
    pages = "1--11",
    abstract = "In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility. However, their applicability, particularly in specific domains such as the biomedical domain, has not gained wide acceptance due to their {``}black box{''} nature, which hinders transparency, fairness, and accountability of QA systems. Therefore, users are unable to understand how and why particular questions have been answered, whereas some others fail. To address this challenge, in this paper, we develop an automatic approach for generating explanations during various stages of a pipeline-based QA system. Our approach is a supervised and automatic approach which considers three classes (i.e., success, no answer, and wrong answer) for annotating the output of involved QA components. Upon our prediction, a template explanation is chosen and integrated into the output of the corresponding component. To measure the effectiveness of the approach, we conducted a user survey as to how non-expert users perceive our generated explanations. The results of our study show a significant increase in the four dimensions of the human factor from the Human-computer interaction community.",
}
@inproceedings{sen-yilmaz-2020-uncertainty,
    title = "Uncertainty and Traffic-Aware Active Learning for Semantic Parsing",
    author = "Sen, Priyanka  and
      Yilmaz, Emine",
    booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.intexsempar-1.2",
    doi = "10.18653/v1/2020.intexsempar-1.2",
    pages = "12--17",
    abstract = "Collecting training data for semantic parsing is a time-consuming and expensive task. As a result, there is growing interest in industry to reduce the number of annotations required to train a semantic parser, both to cut down on costs and to limit customer data handled by annotators. In this paper, we propose uncertainty and traffic-aware active learning, a novel active learning method that uses model confidence and utterance frequencies from customer traffic to select utterances for annotation. We show that our method significantly outperforms baselines on an internal customer dataset and the Facebook Task Oriented Parsing (TOP) dataset. On our internal dataset, our method achieves the same accuracy as random sampling with 2,000 fewer annotations.",
}
@inproceedings{xuan-2020-improving,
    title = "Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog",
    author = "Xuan, Chaoting",
    booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.intexsempar-1.3",
    doi = "10.18653/v1/2020.intexsempar-1.3",
    pages = "18--22",
    abstract = "Task Oriented Parsing (TOP) attempts to map utterances to compositional requests, including multiple intents and their slots. Previous work focus on a tree-based hierarchical meaning representation, and applying constituency parsing techniques to address TOP. In this paper, we propose a new format of meaning representation that is more compact and amenable to sequence-to-sequence (seq-to-seq) models. A simple copy-augmented seq-to-seq parser is built and evaluated over a public TOP dataset, resulting in 3.44{\%} improvement over prior best seq-to-seq parser (exact match accuracy), which is also comparable to constituency parsers{'} performance.",
}
@inproceedings{karamcheti-etal-2020-learning,
    title = "Learning Adaptive Language Interfaces through Decomposition",
    author = "Karamcheti, Siddharth  and
      Sadigh, Dorsa  and
      Liang, Percy",
    booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.intexsempar-1.4",
    doi = "10.18653/v1/2020.intexsempar-1.4",
    pages = "23--33",
    abstract = "Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition: users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps that it can understand. Unfortunately, existing methods either rely on grammars which parse sentences with limited flexibility, or neural sequence-to-sequence models that do not learn efficiently or reliably from individual examples. Our approach bridges this gap, demonstrating the flexibility of modern neural systems, as well as the one-shot reliable generalization of grammar-based methods. Our crowdsourced interactive experiments suggest that over time, users complete complex tasks more efficiently while using our system by leveraging what they just taught. At the same time, getting users to trust the system enough to be incentivized to teach high-level utterances is still an ongoing challenge. We end with a discussion of some of the obstacles we need to overcome to fully realize the potential of the interactive paradigm.",
}
@inproceedings{radhakrishnan-etal-2020-colloql,
    title = "{C}ollo{QL}: Robust Text-to-{SQL} Over Search Queries",
    author = "Radhakrishnan, Karthik  and
      Srikantan, Arvind  and
      Lin, Xi Victoria",
    booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.intexsempar-1.5",
    doi = "10.18653/v1/2020.intexsempar-1.5",
    pages = "34--45",
    abstract = "Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of non-tech-savvy end users. Prior work in this area has largely focused on textual input that is linguistically correct and semantically unambiguous. However, real-world user queries are often succinct, colloquial, and noisy, resembling the input of a search engine. In this work, we introduce data augmentation techniques and a sampling-based content-aware BERT model (ColloQL) to achieve robust text-to-SQL modeling over natural language search (NLS) questions. Due to the lack of evaluation data, we curate a new dataset of NLS questions and demonstrate the efficacy of our approach. ColloQL{'}s superior performance extends to well-formed text, achieving an 84.9{\%} (logical) and 90.7{\%} (execution) accuracy on the WikiSQL dataset, making it, to the best of our knowledge, the highest performing model that does not use execution guided decoding.",
}
@inproceedings{bandyopadhyay-zhao-2020-natural,
    title = "Natural Language Response Generation from {SQL} with Generalization and Back-translation",
    author = "Bandyopadhyay, Saptarashmi  and
      Zhao, Tianyang",
    booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.intexsempar-1.6",
    doi = "10.18653/v1/2020.intexsempar-1.6",
    pages = "46--49",
    abstract = "Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues. We have participated in the CoSQL shared task organized in the IntEx-SemPar workshop at EMNLP 2020. We have trained a number of Neural Machine Translation (NMT) models to efficiently generate the natural language responses from SQL. Our shuffled back-translation model has led to a BLEU score of 7.47 on the unknown test dataset. In this paper, we will discuss our methodologies to approach the problem and future directions to improve the quality of the generated natural language responses.",
}
@proceedings{insights-2020-insights,
    title = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    editor = "Rogers, Anna  and
      Sedoc, Jo{\~a}o  and
      Rumshisky, Anna",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.0",
}
@inproceedings{nayak-etal-2020-domain,
    title = "Domain adaptation challenges of {BERT} in tokenization and sub-word representations of Out-of-Vocabulary words",
    author = "Nayak, Anmol  and
      Timmapathini, Hariprasad  and
      Ponnalagu, Karthikeyan  and
      Gopalan Venkoparao, Vijendran",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.1",
    doi = "10.18653/v1/2020.insights-1.1",
    pages = "1--5",
    abstract = "BERT model (Devlin et al., 2019) has achieved significant progress in several Natural Language Processing (NLP) tasks by leveraging the multi-head self-attention mechanism (Vaswani et al., 2017) in its architecture. However, it still has several research challenges which are not tackled well for domain specific corpus found in industries. In this paper, we have highlighted these problems through detailed experiments involving analysis of the attention scores and dynamic word embeddings with the BERT-Base-Uncased model. Our experiments have lead to interesting findings that showed: 1) Largest substring from the left that is found in the vocabulary (in-vocab) is always chosen at every sub-word unit that can lead to suboptimal tokenization choices, 2) Semantic meaning of a vocabulary word deteriorates when found as a substring in an Out-Of-Vocabulary (OOV) word, and 3) Minor misspellings in words are inadequately handled. We believe that if these challenges are tackled, it will significantly help the domain adaptation aspect of BERT.",
}
@inproceedings{dzendzik-etal-2020-q,
    title = "{Q}. Can Knowledge Graphs be used to Answer {B}oolean Questions? {A}. It{'}s complicated!",
    author = "Dzendzik, Daria  and
      Vogel, Carl  and
      Foster, Jennifer",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.2",
    doi = "10.18653/v1/2020.insights-1.2",
    pages = "6--14",
    abstract = "In this paper we explore the problem of machine reading comprehension, focusing on the BoolQ dataset of Yes/No questions. We carry out an error analysis of a BERT-based machine reading comprehension model on this dataset, revealing issues such as unstable model behaviour and some noise within the dataset itself. We then experiment with two approaches for integrating information from knowledge graphs: (i) concatenating knowledge graph triples to text passages and (ii) encoding knowledge with a Graph Neural Network. Neither of these approaches show a clear improvement and we hypothesize that this may be due to a combination of inaccuracies in the knowledge graph, imprecision in entity linking, and the models{'} inability to capture additional information from knowledge graphs.",
}
@inproceedings{louvan-magnini-2020-far,
    title = "How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks",
    author = "Louvan, Samuel  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.3",
    doi = "10.18653/v1/2020.insights-1.3",
    pages = "15--21",
    abstract = "Although several works have addressed the role of data selection to improve transfer learning for various NLP tasks, there is no consensus about its real benefits and, more generally, there is a lack of shared practices on how it can be best applied. We propose a systematic approach aimed at evaluating data selection in scenarios of increasing complexity. Specifically, we compare the case in which source and target tasks are the same while source and target domains are different, against the more challenging scenario where both tasks and domains are different. We run a number of experiments on semantic sequence tagging tasks, which are relatively less investigated in data selection, and conclude that data selection has more benefit on the scenario when the tasks are the same, while in case of different (although related) tasks from distant domains, a combination of data selection and multi-task learning is ineffective for most cases.",
}
@inproceedings{maclaughlin-etal-2020-evaluating,
    title = "Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks",
    author = "MacLaughlin, Ansel  and
      Dhamala, Jwala  and
      Kumar, Anoop  and
      Venkatapathy, Sriram  and
      Venkatesan, Ragav  and
      Gupta, Rahul",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.4",
    doi = "10.18653/v1/2020.insights-1.4",
    pages = "22--31",
    abstract = "Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification. In this work, we explore the applicability of a SOTA NAS algorithm, Efficient Neural Architecture Search (ENAS) (Pham et al., 2018) to two sentence pair tasks, paraphrase detection and semantic textual similarity. We use ENAS to perform a micro-level search and learn a task-optimized RNN cell architecture as a drop-in replacement for an LSTM. We explore the effectiveness of ENAS through experiments on three datasets (MRPC, SICK, STS-B), with two different models (ESIM, BiLSTM-Max), and two sets of embeddings (Glove, BERT). In contrast to prior work applying ENAS to NLP tasks, our results are mixed {--} we find that ENAS architectures sometimes, but not always, outperform LSTMs and perform similarly to random architecture search.",
}
@inproceedings{terragni-etal-2020-matters,
    title = "Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models",
    author = "Terragni, Silvia  and
      Nozza, Debora  and
      Fersini, Elisabetta  and
      Enza, Messina",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.5",
    doi = "10.18653/v1/2020.insights-1.5",
    pages = "32--40",
    abstract = "Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.",
}
@inproceedings{parthasarathi-etal-2020-task,
    title = "On Task-Level Dialogue Composition of Generative Transformer Model",
    author = "Parthasarathi, Prasanna  and
      Narang, Sharan  and
      Neelakantan, Arvind",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.6",
    doi = "10.18653/v1/2020.insights-1.6",
    pages = "41--47",
    abstract = "Task-oriented dialogue systems help users accomplish tasks such as booking a movie ticket and ordering food via conversation. Generative models parameterized by a deep neural network are widely used for next turn response generation in such systems. It is natural for users of the system to want to accomplish multiple tasks within the same conversation, but the ability of generative models to compose multiple tasks is not well studied. In this work, we begin by studying the effect of training human-human task-oriented dialogues towards improving the ability to compose multiple tasks on Transformer generative models. To that end, we propose and explore two solutions: (1) creating synthetic multiple task dialogue data for training from human-human single task dialogue and (2) forcing the encoder representation to be invariant to single and multiple task dialogues using an auxiliary loss. The results from our experiments highlight the difficulty of even the sophisticated variant of transformer model in learning to compose multiple tasks from single task dialogues.",
}
@inproceedings{bhat-parthasarathy-2020-effectively,
    title = "How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study",
    author = "Bhat, Meghana Moorthy  and
      Parthasarathy, Srinivasan",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.7",
    doi = "10.18653/v1/2020.insights-1.7",
    pages = "48--53",
    abstract = "We empirically study the effectiveness of machine-generated fake news detectors by understanding the model{'}s sensitivity to different synthetic perturbations during test time. The current machine-generated fake news detectors rely on provenance to determine the veracity of news. Our experiments find that the success of these detectors can be limited since they are rarely sensitive to semantic perturbations and are very sensitive to syntactic perturbations. Also, we would like to open-source our code and believe it could be a useful diagnostic tool for evaluating models aimed at fighting machine-generated fake news.",
}
@inproceedings{dsa-etal-2020-label,
    title = "Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification",
    author = "D{'}Sa, Ashwin Geet  and
      Illina, Irina  and
      Fohr, Dominique  and
      Klakow, Dietrich  and
      Ruiter, Dana",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.8",
    doi = "10.18653/v1/2020.insights-1.8",
    pages = "54--59",
    abstract = "Research on hate speech classification has received increased attention. In real-life scenarios, a small amount of labeled hate speech data is available to train a reliable classifier. Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data. In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification. The quality of labeling the unlabeled set depends on the input representations. In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results. Neural network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data. We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.",
}
@inproceedings{finegan-dollak-verma-2020-layout,
    title = "Layout-Aware Text Representations Harm Clustering Documents by Type",
    author = "Finegan-Dollak, Catherine  and
      Verma, Ashish",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.9",
    doi = "10.18653/v1/2020.insights-1.9",
    pages = "60--65",
    abstract = "Clustering documents by type{---}grouping invoices with invoices and articles with articles{---}is a desirable first step for organizing large collections of document scans. Humans approaching this task use both the semantics of the text and the document layout to assist in grouping like documents. LayoutLM (Xu et al., 2019), a layout-aware transformer built on top of BERT with state-of-the-art performance on document-type classification, could reasonably be expected to outperform regular BERT (Devlin et al., 2018) for document-type clustering. However, we find experimentally that BERT significantly outperforms LayoutLM on this task (p {\textless}0.001). We analyze clusters to show where layout awareness is an asset and where it is a liability.",
}
@inproceedings{zupon-etal-2020-analysis,
    title = "An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios",
    author = "Zupon, Andrew  and
      Rafique, Faiz  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.10",
    doi = "10.18653/v1/2020.insights-1.10",
    pages = "66--70",
    abstract = "Neural networks are a common tool in NLP, but it is not always clear which architecture to use for a given task. Different tasks, different languages, and different training conditions can all affect how a neural network will perform. Capsule Networks (CapsNets) are a relatively new architecture in NLP. Due to their novelty, CapsNets are being used more and more in NLP tasks. However, their usefulness is still mostly untested.In this paper, we compare three neural network architectures{---}LSTM, CNN, and CapsNet{---}on a part of speech tagging task. We compare these architectures in both high- and low-resource training conditions and find that no architecture consistently performs the best. Our analysis shows that our CapsNet performs nearly as well as a more complex LSTM under certain training conditions, but not others, and that our CapsNet almost always outperforms our CNN. We also find that our CapsNet implementation shows faster prediction times than the LSTM for Scottish Gaelic but not for Spanish, highlighting the effect that the choice of languages can have on the models.",
}
@inproceedings{beretta-etal-2020-knowledge,
    title = "Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?",
    author = "Beretta, Valentina  and
      Harispe, S{\'e}bastien  and
      Boland, Katarina  and
      Lo Seen, Luke  and
      Todorov, Konstantin  and
      Tchechmedjiev, Andon",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.11",
    doi = "10.18653/v1/2020.insights-1.11",
    pages = "71--75",
    abstract = "The web offers a wealth of discourse data that help researchers from various fields analyze debates about current societal issues and gauge the effects on society of important phenomena such as misinformation spread. Such analyses often revolve around claims made by people about a given topic of interest. Fact-checking portals offer partially structured information that can assist such analysis. However, exploiting the network structure of such online discourse data is as of yet under-explored. We study the effectiveness of using neural-graph embedding features for claim topic prediction and their complementarity with text embeddings. We show that graph embeddings are modestly complementary with text embeddings, but the low performance of graph embedding features alone indicate that the model fails to capture topological features pertinent of the topic prediction task.",
}
@inproceedings{liang-surdeanu-2020-transformers,
    title = "Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?",
    author = "Liang, Zhengzhong  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.12",
    doi = "10.18653/v1/2020.insights-1.12",
    pages = "76--81",
    abstract = "Large pretrained language models (LM) have been used successfully for multi-hop question answering. However, most of these directions are not interpretable, as they do not make the inference hops necessary to explain a candidate answer explicitly. In this work, we investigate the capability of a state-of-the-art transformer LM to generate explicit inference hops, i.e., to infer a new statement necessary to answer a question given some premise input statements. Our analysis shows that such LMs can generate new statements for some simple inference types, but performance remains poor for complex, real-world inference types such as those that require monotonicity, composition, and commonsense knowledge.",
}
@inproceedings{huang-etal-2020-counterfactually,
    title = "Counterfactually-Augmented {SNLI} Training Data Does Not Yield Better Generalization Than Unaugmented Data",
    author = "Huang, William  and
      Liu, Haokun  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.13",
    doi = "10.18653/v1/2020.insights-1.13",
    pages = "82--87",
    abstract = "A growing body of work shows that models exploit annotation artifacts to achieve state-of-the-art performance on standard crowdsourced benchmarks{---}datasets collected from crowdworkers to create an evaluation task{---}while still failing on out-of-domain examples for the same task. Recent work has explored the use of counterfactually-augmented data{---}data built by minimally editing a set of seed examples to yield counterfactual labels{---}to augment training data associated with these benchmarks and build more robust classifiers that generalize better. However, Khashabi et al. (2020) find that this type of augmentation yields little benefit on reading comprehension tasks when controlling for dataset size and cost of collection. We build upon this work by using English natural language inference data to test model generalization and robustness and find that models trained on a counterfactually-augmented SNLI dataset do not generalize better than unaugmented datasets of similar size and that counterfactual augmentation can hurt performance, yielding models that are less robust to challenge examples. Counterfactual augmentation of natural language understanding data through standard crowdsourcing techniques does not appear to be an effective way of collecting training data and further innovation is required to make this general line of work viable.",
}
@inproceedings{khurana-bhatnagar-2020-nmf,
    title = "{NMF} Ensembles? Not for Text Summarization!",
    author = "Khurana, Alka  and
      Bhatnagar, Vasudha",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.14",
    doi = "10.18653/v1/2020.insights-1.14",
    pages = "88--93",
    abstract = "Non-negative Matrix Factorization (NMF) has been used for text analytics with promising results. Instability of results arising due to stochastic variations during initialization makes a case for use of ensemble technology. However, our extensive empirical investigation indicates otherwise. In this paper, we establish that ensemble summary for single document using NMF is no better than the best base model summary.",
}
@inproceedings{lignos-kamyab-2020-build,
    title = "If You Build Your Own {NER} Scorer, Non-replicable Results Will Come",
    author = "Lignos, Constantine  and
      Kamyab, Marjan",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.15",
    doi = "10.18653/v1/2020.insights-1.15",
    pages = "94--99",
    abstract = "We attempt to replicate a named entity recognition (NER) model implemented in a popular toolkit and discover that a critical barrier to doing so is the inconsistent evaluation of improper label sequences. We define these sequences and examine how two scorers differ in their handling of them, finding that one approach produces F1 scores approximately 0.5 points higher on the CoNLL 2003 English development and test sets. We propose best practices to increase the replicability of NER evaluations by increasing transparency regarding the handling of improper label sequences.",
}
@inproceedings{arora-etal-2020-hint3,
    title = "{HINT}3: Raising the bar for Intent Detection in the Wild",
    author = "Arora, Gaurav  and
      Jain, Chirag  and
      Chaturvedi, Manas  and
      Modi, Krupal",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.16",
    doi = "10.18653/v1/2020.insights-1.16",
    pages = "100--105",
    abstract = "Intent Detection systems in the real world are exposed to complexities of imbalanced datasets containing varying perception of intent, unintended correlations and domain-specific aberrations. To facilitate benchmarking which can reflect near real-world scenarios, we introduce 3 new datasets created from live chatbots in diverse domains. Unlike most existing datasets that are crowdsourced, our datasets contain real user queries received by the chatbots and facilitates penalising unwanted correlations grasped during the training process. We evaluate 4 NLU platforms and a BERT based classifier and find that performance saturates at inadequate levels on test sets because all systems latch on to unintended patterns in training data.",
}
@inproceedings{elazar-etal-2020-extraordinary,
    title = "The Extraordinary Failure of Complement Coercion Crowdsourcing",
    author = "Elazar, Yanai  and
      Basmov, Victoria  and
      Ravfogel, Shauli  and
      Goldberg, Yoav  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.17",
    doi = "10.18653/v1/2020.insights-1.17",
    pages = "106--116",
    abstract = "Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon. These are constructions with an implied action {---} e.g., {``}I started a new book I bought last week{''}, where the implied action is reading. We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference. However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work. Why does the same process fail to yield high agreement scores? We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure. We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods.",
}
@inproceedings{wilson-etal-2020-embedding,
    title = "Embedding Structured Dictionary Entries",
    author = "Wilson, Steven  and
      Magdy, Walid  and
      McGillivray, Barbara  and
      Tyson, Gareth",
    booktitle = "Proceedings of the First Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.insights-1.18",
    doi = "10.18653/v1/2020.insights-1.18",
    pages = "117--125",
    abstract = "Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the structured elements of dictionary entries, such as user-assigned tags and usage examples, when learning embeddings for dictionary headwords. Our work generalizes several existing models for learning word embeddings from dictionaries. However, we find that the most effective representations overall are learned by simply training with a skip-gram objective over the concatenated text of all entries in the dictionary, giving no particular focus to the structure of the entries.",
}
@proceedings{inlg-2020-international,
    title = "Proceedings of the 13th International Conference on Natural Language Generation",
    editor = "Davis, Brian  and
      Graham, Yvette  and
      Kelleher, John  and
      Sripada, Yaji",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.0",
}
@inproceedings{ihori-etal-2020-memory,
    title = "Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model",
    author = "Ihori, Mana  and
      Masumura, Ryo  and
      Makishima, Naoki  and
      Tanaka, Tomohiro  and
      Takashima, Akihiko  and
      Orihashi, Shota",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.1",
    pages = "1--6",
    abstract = "This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequence-to-sequence (seq2seq) model. While paired data are basically required to train the seq2seq model, the external LM can be trained with only unpaired data. Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data. However, the existing fusion methods assume that the LM is integrated with recurrent neural network-based seq2seq models instead of the Transformer. Therefore, this paper proposes a fusion method that can explicitly utilize network structures in the Transformer. The proposed method, called memory attentive fusion, leverages the Transformer-style attention mechanism that repeats source-target attention in a multi-hop manner for reading the memorized knowledge in the LM. Our experiments on two text-style conversion tasks demonstrate that the proposed method performs better than conventional fusion methods.",
}
@inproceedings{abed-reiter-2020-arabic,
    title = "{A}rabic {NLG} Language Functions",
    author = "Abed, Wael  and
      Reiter, Ehud",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.2",
    pages = "7--14",
    abstract = "The Arabic language has very limited supports from NLG researchers. In this paper, we explain the challenges of the core grammar, provide a lexical resource, and implement the first language functions for the Arabic language. We did a human evaluation to evaluate our functions in generating sentences from the NADA Corpus.",
}
@inproceedings{beauchemin-etal-2020-generating,
    title = "Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations",
    author = "Beauchemin, David  and
      Garneau, Nicolas  and
      Gaumond, Eve  and
      D{\'e}ziel, Pierre-Luc  and
      Khoury, Richard  and
      Lamontagne, Luc",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.3",
    pages = "15--21",
    abstract = "Plumitifs (dockets) were initially a tool for law clerks. Nowadays, they are used as summaries presenting all the steps of a judicial case. Information concerning parties{'} identity, jurisdiction in charge of administering the case, and some information relating to the nature and the course of the preceding are available through plumitifs. They are publicly accessible but barely understandable; they are written using abbreviations and referring to provisions from the Criminal Code of Canada, which makes them hard to reason about. In this paper, we propose a simple yet efficient multi-source language generation architecture that leverages both the plumitif and the Criminal Code{'}s content to generate intelligible plumitifs descriptions. It goes without saying that ethical considerations rise with these sensitive documents made readable and available at scale, legitimate concerns that we address in this paper. This is, to the best of our knowledge, the first application of plumitifs descriptions generation made available for French speakers along with an ethical discussion about the topic.",
}
@inproceedings{bien-etal-2020-recipenlg,
    title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation",
    author = "Bie{\'n}, Micha{\l}  and
      Gilski, Micha{\l}  and
      Maciejewska, Martyna  and
      Taisner, Wojciech  and
      Wisniewski, Dawid  and
      Lawrynowicz, Agnieszka",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.4",
    pages = "22--28",
    abstract = "Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation, thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context- and commonsense-aware texts. Moreover, it is not clear how to evaluate the quality of generated texts. To address these problems, we introduce RecipeNLG {--} a novel dataset of cooking recipes. We discuss the data collection process and the relation between the semi-structured texts and cooking recipes. We use the dataset to approach the problem of generating recipes. Finally, we make use of multiple metrics to evaluate the generated recipes.",
}
@inproceedings{betti-etal-2020-controlled,
    title = "Controlled Text Generation with Adversarial Learning",
    author = "Betti, Federico  and
      Ramponi, Giorgia  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.5",
    pages = "29--34",
    abstract = "In recent years, generative adversarial networks (GANs) have started to attain promising results also in natural language generation. However, the existing models have paid limited attention to the semantic coherence of the generated sentences. For this reason, in this paper we propose a novel network {--} the Controlled TExt generation Relational Memory GAN (CTERM-GAN) {--} that uses an external input to influence the coherence of sentence generation. The network is composed of three main components: a generator based on a Relational Memory conditioned on the external input; a syntactic discriminator which learns to discriminate between real and generated sentences; and a semantic discriminator which assesses the coherence with the external conditioning. Our experiments on six probing datasets have showed that the model has been able to achieve interesting results, retaining or improving the syntactic quality of the generated sentences while significantly improving their semantic coherence with the given input.",
}
@inproceedings{thomson-etal-2020-studying,
    title = "Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text",
    author = "Thomson, Craig  and
      Zhao, Zhijie  and
      Sripada, Somayajulu",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.6",
    pages = "35--40",
    abstract = "It is unfair to expect neural data-to-text to produce high quality output when there are gaps between system input data and information contained in the training text. Thomson et al. (2020) identify and narrow information gaps in Rotowire, a popular data-to-text dataset. In this paper, we describe a study which finds that a state-of-the-art neural data-to-text system produces higher quality output, according to the information extraction (IE) based metrics, when additional input data is carefully selected from this newly available source. It remains to be shown, however, whether IE metrics used in this study correlate well with humans in judging text quality.",
}
@inproceedings{panagiaris-etal-2020-improving,
    title = "Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training",
    author = "Panagiaris, Nikolaos  and
      Hart, Emma  and
      Gkatzia, Dimitra",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.7",
    pages = "41--51",
    abstract = "In this paper we consider the problem of optimizing neural Referring Expression Generation (REG) models with sequence level objectives. Recently reinforcement learning (RL) techniques have been adopted to train deep end-to-end systems to directly optimize sequence-level objectives. However, there are two issues associated with RL training: (1) effectively applying RL is challenging, and (2) the generated sentences lack in diversity and naturalness due to deficiencies in the generated word distribution, smaller vocabulary size, and repetitiveness of frequent words and phrases. To alleviate these issues, we propose a novel strategy for training REG models, using minimum risk training (MRT) with maximum likelihood estimation (MLE) and we show that our approach outperforms RL w.r.t naturalness and diversity of the output. Specifically, our approach achieves an increase in CIDEr scores between 23{\%}-57{\%} in two datasets. We further demonstrate the robustness of the proposed method through a detailed comparison with different REG models.",
}
@inproceedings{ko-li-2020-assessing,
    title = "Assessing Discourse Relations in Language Generation from {GPT}-2",
    author = "Ko, Wei-Jen  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.8",
    pages = "52--59",
    abstract = "Recent advances in NLP have been attributed to the emergence of large-scale pre-trained language models. GPT-2, in particular, is suited for generation tasks given its left-to-right language modeling objective, yet the linguistic quality of its generated text has largely remain unexplored. Our work takes a step in understanding GPT-2{'}s outputs in terms of discourse coherence. We perform a comprehensive study on the validity of explicit discourse relations in GPT-2{'}s outputs under both organic generation and fine-tuned scenarios. Results show GPT-2 does not always generate text containing valid discourse relations; nevertheless, its text is more aligned with human expectation in the fine-tuned scenario. We propose a decoupled strategy to mitigate these problems and highlight the importance of explicitly modeling discourse information.",
}
@inproceedings{kasner-dusek-2020-data,
    title = "Data-to-Text Generation with Iterative Text Editing",
    author = "Kasner, Zden{\v{e}}k  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.9",
    pages = "60--67",
    abstract = "We present a novel approach to data-to-text generation based on iterative text editing. Our approach maximizes the completeness and semantic accuracy of the output text while leveraging the abilities of recent pre-trained models for text editing (LaserTagger) and language modeling (GPT-2) to improve the text fluency. To this end, we first transform data items to text using trivial templates, and then we iteratively improve the resulting text by a neural model trained for the sentence fusion task. The output of the model is filtered by a simple heuristic and reranked with an off-the-shelf pre-trained language model. We evaluate our approach on two major data-to-text datasets (WebNLG, Cleaned E2E) and analyze its caveats and benefits. Furthermore, we show that our formulation of data-to-text generation opens up the possibility for zero-shot domain adaptation using a general-domain dataset for sentence fusion.",
}
@inproceedings{van-der-lee-etal-2020-cacapo,
    title = "The {CACAPO} Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation",
    author = "van der Lee, Chris  and
      Emmery, Chris  and
      Wubben, Sander  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.10",
    pages = "68--79",
    abstract = "This paper describes the CACAPO dataset, built for training both neural pipeline and end-to-end data-to-text language generation systems. The dataset is multilingual (Dutch and English), and contains almost 10,000 sentences from human-written news texts in the sports, weather, stocks, and incidents domain, together with aligned attribute-value paired data. The dataset is unique in that the linguistic variation and indirect ways of expressing data in these texts reflect the challenges of real world NLG tasks.",
}
@inproceedings{abdullah-chali-2020-towards,
    title = "Towards Generating Query to Perform Query Focused Abstractive Summarization using Pre-trained Model",
    author = "Abdullah, Deen Mohammad  and
      Chali, Yllias",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.11",
    pages = "80--85",
    abstract = "Query Focused Abstractive Summarization (QFAS) represents an abstractive summary from the source document based on a given query. To measure the performance of abstractive summarization tasks, different datasets have been broadly used. However, for QFAS tasks, only a limited number of datasets have been used, which are comparatively small and provide single sentence summaries. This paper presents a query generation approach, where we considered most similar words between documents and summaries for generating queries. By implementing our query generation approach, we prepared two relatively large datasets, namely CNN/DailyMail and Newsroom which contain multiple sentence summaries and can be used for future QFAS tasks. We also implemented a pre-processing approach to perform QFAS tasks using a pretrained language model, BERTSUM. In our pre-processing approach, we sorted the sentences of the documents from the most query-related sentences to the less query-related sentences. Then, we fine-tuned the BERTSUM model for generating the abstractive summaries. We also experimented on one of the largely used datasets, Debatepedia, to compare our QFAS approach with other models. The experimental results show that our approach outperforms the state-of-the-art models on three ROUGE scores.",
}
@inproceedings{kuanzhuo-etal-2020-simplenlg,
    title = "{S}imple{NLG}-{TI}: Adapting {S}imple{NLG} to {T}ibetan",
    author = "Kuanzhuo, Zewang  and
      Lin, Li  and
      Weina, Zhao",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.12",
    pages = "86--90",
    abstract = "Surface realisation is the last but not the least phase of Natural Language Generation, which aims to produce high-quality natural language text based on meaning representations. In this article, we present our work on SimpleNLG-TI, a Tibetan surface realiser, which follows the design paradigm of SimpleNLG-EN. SimpleNLG-TI is built up by our investigation of the core features of Tibetan morphology and syntax. Through this work, we provide a robust and flexible surface realiser for Tibetan generation systems.",
}
@inproceedings{kale-roy-2020-machine,
    title = "Machine Translation Pre-training for Data-to-Text Generation - A Case Study in {C}zech",
    author = "Kale, Mihir  and
      Roy, Scott",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.13",
    pages = "91--96",
    abstract = "While there is a large body of research studying deep learning methods for text generation from structured data, almost all of it focuses purely on English. In this paper, we study the effectiveness of machine translation based pre-training for data-to-text generation in non-English languages. Since the structured data is generally expressed in English, text generation into other languages involves elements of translation, transliteration and copying - elements already encoded in neural machine translation systems. Moreover, since data-to-text corpora are typically small, this task can benefit greatly from pre-training. We conduct experiments on Czech, a morphologically complex language. Results show that machine translation pre-training lets us train endto-end models that significantly improve upon unsupervised pre-training and linguistically informed pipelined neural systems, as judged by automatic metrics and human evaluation. We also show that this approach enjoys several desirable properties, including improved performance in low data scenarios and applicability to low resource languages.",
}
@inproceedings{kale-rastogi-2020-text,
    title = "Text-to-Text Pre-Training for Data-to-Text Tasks",
    author = "Kale, Mihir  and
      Rastogi, Abhinav",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.14",
    pages = "97--102",
    abstract = "We study the pre-train + fine-tune strategy for data-to-text tasks. Our experiments indicate that text-to-text pre-training in the form of T5 (Raffel et al., 2019), enables simple, end-to-end transformer based models to outperform pipelined neural architectures tailored for data-to-text generation, as well as alternatives such as BERT and GPT-2. Importantly, T5 pre-training leads to better generalization, as evidenced by large improvements on out-ofdomain test sets. We hope our work serves as a useful baseline for future research, as transfer learning becomes ever more prevalent for data-to-text tasks.",
}
@inproceedings{rosa-teixeira-etal-2020-damata,
    title = "{D}a{M}ata: A Robot-Journalist Covering the {B}razilian {A}mazon Deforestation",
    author = "Rosa Teixeira, Andr{\'e} Luiz  and
      Campos, Jo{\~a}o  and
      Cunha, Rossana  and
      Castro Ferreira, Thiago  and
      Pagano, Adriana  and
      Cozman, Fabio",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.15",
    pages = "103--106",
    abstract = "This demo paper introduces DaMata, a robot-journalist covering deforestation in the Brazilian Amazon. The robot-journalist is based on a pipeline architecture of Natural Language Generation, which yields multilingual daily and monthly reports based on the public data provided by DETER, a real-time deforestation satellite monitor developed and maintained by the Brazilian National Institute for Space Research (INPE). DaMata automatically generates reports in Brazilian Portuguese and English and publishes them on the Twitter platform. Corpus and code are publicly available.",
}
@inproceedings{briggs-2020-generating,
    title = "Generating Quantified Referring Expressions through Attention-Driven Incremental Perception",
    author = "Briggs, Gordon",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.16",
    pages = "107--112",
    abstract = "We model the production of quantified referring expressions (QREs) that identity collections of visual items. A previous approach, called Perceptual Cost Pruning, modeled human QRE production using a preference-based referring expression generation algorithm, first removing facts from the input knowledge base based on a model of perceptual cost. In this paper, we present an alternative model that incrementally constructs a symbolic knowledge base through simulating human visual attention/perception from raw images. We demonstrate that this model produces the same output as Perceptual Cost Pruning. We argue that this is a more extensible approach and a step toward developing a wider range of process-level models of human visual description.",
}
@inproceedings{gong-etal-2020-rich,
    title = "Rich Syntactic and Semantic Information Helps Unsupervised Text Style Transfer",
    author = "Gong, Hongyu  and
      Song, Linfeng  and
      Bhat, Suma",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.17",
    pages = "113--119",
    abstract = "Text style transfer aims to change an input sentence to an output sentence by changing its text style while preserving the content. Previous efforts on unsupervised text style transfer only use the surface features of words and sentences. As a result, the transferred sentences may either have inaccurate or missing information compared to the inputs. We address this issue by explicitly enriching the inputs via syntactic and semantic structures, from which richer features are then extracted to better capture the original information. Experiments on two text-style-transfer tasks show that our approach improves the content preservation of a strong unsupervised baseline model thereby demonstrating improved transfer performance.",
}
@inproceedings{rebuffel-etal-2020-parenting,
    title = "{PARENT}ing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text Generation",
    author = "Rebuffel, Clement  and
      Soulier, Laure  and
      Scoutheeten, Geoffrey  and
      Gallinari, Patrick",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.18",
    pages = "120--130",
    abstract = "In language generation models conditioned by structured data, the classical training via maximum likelihood almost always leads models to pick up on dataset divergence (i.e., hallucinations or omissions), and to incorporate them erroneously in their own generations at inference. In this work, we build on top of previous Reinforcement Learning based approaches and show that a model-agnostic framework relying on the recently introduced PARENT metric is efficient at reducing both hallucinations and omissions. Evaluations on the widely used WikiBIO and WebNLG benchmarks demonstrate the effectiveness of this framework compared to state-of-the-art models.",
}
@inproceedings{dusek-kasner-2020-evaluating,
    title = "Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Kasner, Zden{\v{e}}k",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.19",
    pages = "131--137",
    abstract = "A major challenge in evaluating data-to-text (D2T) generation is measuring the semantic accuracy of the generated text, i.e. checking if the output text contains all and only facts supported by the input data. We propose a new metric for evaluating the semantic accuracy of D2T generation based on a neural model pretrained for natural language inference (NLI). We use the NLI model to check textual entailment between the input data and the output text in both directions, allowing us to reveal omissions or hallucinations. Input data are converted to text for NLI using trivial templates. Our experiments on two recent D2T datasets show that our metric can achieve high accuracy in identifying erroneous system outputs.",
}
@inproceedings{obeid-hoque-2020-chart,
    title = "Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model",
    author = "Obeid, Jason  and
      Hoque, Enamul",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.20",
    pages = "138--147",
    abstract = "Information visualizations such as bar charts and line charts are very popular for exploring data and communicating insights. Interpreting and making sense of such visualizations can be challenging for some people, such as those who are visually impaired or have low visualization literacy. In this work, we introduce a new dataset and present a neural model for automatically generating natural language summaries for charts. The generated summaries provide an interpretation of the chart and convey the key insights found within that chart. Our neural model is developed by extending the state-of-the-art model for the data-to-text generation task, which utilizes a transformer-based encoder-decoder architecture. We found that our approach outperforms the base model on a content selection metric by a wide margin (55.42{\%} vs. 8.49{\%}) and generates more informative, concise, and coherent summaries.",
}
@inproceedings{hamazono-etal-2020-market,
    title = "Market Comment Generation from Data with Noisy Alignments",
    author = "Hamazono, Yumi  and
      Uehara, Yui  and
      Noji, Hiroshi  and
      Miyao, Yusuke  and
      Takamura, Hiroya  and
      Kobayashi, Ichiro",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.21",
    pages = "148--157",
    abstract = "End-to-end models on data-to-text learn the mapping of data and text from the aligned pairs in the dataset. However, these alignments are not always obtained reliably, especially for the time-series data, for which real time comments are given to some situation and there might be a delay in the comment delivery time compared to the actual event time. To handle this issue of possible noisy alignments in the dataset, we propose a neural network model with multi-timestep data and a copy mechanism, which allows the models to learn the correspondences between data and text from the dataset with noisier alignments. We focus on generating market comments in Japanese that are delivered each time an event occurs in the market. The core idea of our approach is to utilize multi-timestep data, which is not only the latest market price data when the comment is delivered, but also the data obtained at several timesteps earlier. On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data. We confirm the superiority of our proposal by two evaluation metrics and show the accuracy improvement of the sentence generation using the time series data by our proposed method.",
}
@inproceedings{thomson-reiter-2020-gold,
    title = "A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems",
    author = "Thomson, Craig  and
      Reiter, Ehud",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.22",
    pages = "158--168",
    abstract = "Most Natural Language Generation systems need to produce accurate texts. We propose a methodology for high-quality human evaluation of the accuracy of generated texts, which is intended to serve as a gold-standard for accuracy evaluations of data-to-text systems. We use our methodology to evaluate the accuracy of computer generated basketball summaries. We then show how our gold standard evaluation can be used to validate automated metrics.",
}
@inproceedings{howcroft-etal-2020-twenty,
    title = "Twenty Years of Confusion in Human Evaluation: {NLG} Needs Evaluation Sheets and Standardised Definitions",
    author = "Howcroft, David M.  and
      Belz, Anya  and
      Clinciu, Miruna-Adriana  and
      Gkatzia, Dimitra  and
      Hasan, Sadid A.  and
      Mahamood, Saad  and
      Mille, Simon  and
      van Miltenburg, Emiel  and
      Santhanam, Sashank  and
      Rieser, Verena",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.23",
    pages = "169--182",
    abstract = "Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of different quality criteria used by researchers make it difficult to compare results and draw conclusions across papers, with adverse implications for meta-evaluation and reproducibility. In this paper, we present (i) our dataset of 165 NLG papers with human evaluations, (ii) the annotation scheme we developed to label the papers for different aspects of evaluations, (iii) quantitative analyses of the annotations, and (iv) a set of recommendations for improving standards in evaluation reporting. We use the annotations as a basis for examining information included in evaluation reports, and levels of consistency in approaches, experimental design and terminology, focusing in particular on the 200+ different terms that have been used for evaluated aspects of quality. We conclude that due to a pervasive lack of clarity in reports and extreme diversity in approaches, human evaluation in NLG presents as extremely confused in 2020, and that the field is in urgent need of standard methods and terminology.",
}
@inproceedings{belz-etal-2020-disentangling,
    title = "Disentangling the Properties of Human Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing",
    author = "Belz, Anya  and
      Mille, Simon  and
      Howcroft, David M.",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.24",
    pages = "183--194",
    abstract = "Current standards for designing and reporting human evaluations in NLP mean it is generally unclear which evaluations are comparable and can be expected to yield similar results when applied to the same system outputs. This has serious implications for reproducibility testing and meta-evaluation, in particular given that human evaluation is considered the gold standard against which the trustworthiness of automatic metrics is gauged. {\%}and merging others, as well as deciding which evaluations should be able to reproduce each other{'}s results. Using examples from NLG, we propose a classification system for evaluations based on disentangling (i) what is being evaluated (which aspect of quality), and (ii) how it is evaluated in specific (a) evaluation modes and (b) experimental designs. We show that this approach provides a basis for determining comparability, hence for comparison of evaluations across papers, meta-evaluation experiments, reproducibility testing.",
}
@inproceedings{lee-2020-stable,
    title = "Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer",
    author = "Lee, Joosung",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.25",
    pages = "195--204",
    abstract = "Text style transfer is the task that generates a sentence by preserving the content of the input sentence and transferring the style. Most existing studies are progressing on non-parallel datasets because parallel datasets are limited and hard to construct. In this work, we introduce a method that follows two stages in non-parallel datasets. The first stage is to delete attribute markers of a sentence directly through a classifier. The second stage is to generate a transferred sentence by combining the content tokens and the target style. We experiment on two benchmark datasets and evaluate context, style, fluency, and semantic. It is difficult to select the best system using only these automatic metrics, but it is possible to select stable systems. We consider only robust systems in all automatic evaluation metrics to be the minimum conditions that can be used in real applications. Many previous systems are difficult to use in certain situations because performance is significantly lower in several evaluation metrics. However, our system is stable in all automatic evaluation metrics and has results comparable to other models. Also, we compare the performance results of our system and the unstable system through human evaluation.",
}
@inproceedings{chen-etal-2020-listeners,
    title = "Listener{'}s Social Identity Matters in Personalised Response Generation",
    author = "Chen, Guanyi  and
      Zheng, Yinhe  and
      Du, Yupei",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.26",
    pages = "205--215",
    abstract = "Personalised response generation enables generating human-like responses by means of assigning the generator a social identity. However, pragmatics theory suggests that human beings adjust the way of speaking based on not only who they are but also whom they are talking to. In other words, when modelling personalised dialogues, it might be favourable if we also take the listener{'}s social identity into consideration. To validate this idea, we use gender as a typical example of a social variable to investigate how the listener{'}s identity influences the language used in Chinese dialogues on social media. Also, we build personalised generators. The experiment results demonstrate that the listener{'}s identity indeed matters in the language use of responses and that the response generator can capture such differences in language use. More interestingly, by additionally modelling the listener{'}s identity, the personalised response generator performs better in its own identity.",
}
@inproceedings{saldanha-etal-2020-understanding,
    title = "Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation",
    author = "Saldanha, Emily  and
      Garimella, Aparna  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.27",
    pages = "216--226",
    abstract = "Massive digital disinformation is one of the main risks of modern society. Hundreds of models and linguistic analyses have been done to compare and contrast misleading and credible content online. However, most models do not remove the confounding factor of a topic or narrative when training, so the resulting models learn a clear topical separation for misleading versus credible content. We study the feasibility of using two strategies to disentangle the topic bias from the models to understand and explicitly measure linguistic and stylistic properties of content from misleading versus credible content. First, we develop conditional generative models to create news content that is characteristic of different credibility levels. We perform multi-dimensional evaluation of model performance on mimicking both the style and linguistic differences that distinguish news of different credibility using machine translation metrics and classification models. We show that even though generative models are able to imitate both the style and language of the original content, additional conditioning on both the news category and the topic leads to reduced performance. In a second approach, we perform deception style {``}transfer{''} by translating deceptive content into the style of credible content and vice versa. Extending earlier studies, we demonstrate that, when conditioned on a topic, deceptive content is shorter, less readable, more biased, and more subjective than credible content, and transferring the style from deceptive to credible content is more challenging than the opposite direction.",
}
@inproceedings{reiter-thomson-2020-shared,
    title = "Shared Task on Evaluating Accuracy",
    author = "Reiter, Ehud  and
      Thomson, Craig",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.28",
    pages = "227--231",
    abstract = "We propose a shared task on methodologies and algorithms for evaluating the accuracy of generated texts, specifically summaries of basketball games produced from basketball box score and other game data. We welcome submissions based on protocols for human evaluation, automatic metrics, as well as combinations of human evaluations and metrics.",
}
@inproceedings{belz-etal-2020-reprogen,
    title = "{R}epro{G}en: Proposal for a Shared Task on Reproducibility of Human Evaluations in {NLG}",
    author = "Belz, Anya  and
      Agarwal, Shubham  and
      Shimorina, Anastasia  and
      Reiter, Ehud",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.29",
    pages = "232--236",
    abstract = "Across NLP, a growing body of work is looking at the issue of reproducibility. However, replicability of human evaluation experiments and reproducibility of their results is currently under-addressed, and this is of particular concern for NLG where human evaluations are the norm. This paper outlines our ideas for a shared task on reproducibility of human evaluations in NLG which aims (i) to shed light on the extent to which past NLG evaluations are replicable and reproducible, and (ii) to draw conclusions regarding how evaluations can be designed and reported to increase replicability and reproducibility. If the task is run over several years, we hope to be able to document an overall increase in levels of replicability and reproducibility over time.",
}
@inproceedings{syed-etal-2020-task,
    title = "Task Proposal: Abstractive Snippet Generation for Web Pages",
    author = "Syed, Shahbaz  and
      Chen, Wei-Fan  and
      Hagen, Matthias  and
      Stein, Benno  and
      Wachsmuth, Henning  and
      Potthast, Martin",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.30",
    pages = "237--241",
    abstract = "We propose a shared task on abstractive snippet generation for web pages, a novel task of generating query-biased abstractive summaries for documents that are to be shown on a search results page. Conventional snippets are extractive in nature, which recently gave rise to copyright claims from news publishers as well as a new copyright legislation being passed in the European Union, limiting the fair use of web page contents for snippets. At the same time, abstractive summarization has matured considerably in recent years, potentially allowing for more personalization of snippets in the future. Taken together, these facts render further research into generating abstractive snippets both timely and promising.",
}
@inproceedings{kato-etal-2020-bert,
    title = "{BERT}-Based Simplification of {J}apanese Sentence-Ending Predicates in Descriptive Text",
    author = "Kato, Taichi  and
      Miyata, Rei  and
      Sato, Satoshi",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.31",
    pages = "242--251",
    abstract = "Japanese sentence-ending predicates intricately combine content words and functional elements, such as aspect, modality, and honorifics; this can often hinder the understanding of language learners and children. Conventional lexical simplification methods, which replace difficult target words with simpler synonyms acquired from lexical resources in a word-by-word manner, are not always suitable for the simplification of such Japanese predicates. Given this situation, we propose a BERT-based simplification method, the core feature of which is the high ability to substitute the whole predicates with simple ones while maintaining their core meanings in the context by utilizing pre-trained masked language models. Experimental results showed that our proposed methods consistently outperformed the conventional thesaurus-based method by a wide margin. Furthermore, we investigated in detail the effectiveness of the average token embedding and dropout, and the remaining errors of our BERT-based methods.",
}
@inproceedings{mendes-goncalo-oliveira-2020-amplifying,
    title = "Amplifying the Range of News Stories with Creativity: Methods and their Evaluation, in {P}ortuguese",
    author = "Mendes, Rui  and
      Gon{\c{c}}alo Oliveira, Hugo",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.32",
    pages = "252--262",
    abstract = "Headlines are key for attracting people to a story, but writing appealing headlines requires time and talent. This work aims to automate the production of creative short texts (e.g., news headlines) for an input context (e.g., existing headlines), thus amplifying its range. Well-known expressions (e.g., proverbs, movie titles), which typically include word-play and resort to figurative language, are used as a starting point. Given an input text, they can be recommended by exploiting Semantic Textual Similarity (STS) techniques, or adapted towards higher relatedness. For the latter, three methods that exploit static word embeddings are proposed. Experimentation in Portuguese lead to some conclusions, based on human opinions: STS methods that look exclusively at the surface text, recommend more related expressions; resulting expressions are somewhat related to the input, but adaptation leads to higher relatedness and novelty; humour can be an indirect consequence, but most outputs are not funny.",
}
@inproceedings{chen-van-deemter-2020-lessons,
    title = "Lessons from Computational Modelling of Reference Production in {M}andarin and {E}nglish",
    author = "Chen, Guanyi  and
      van Deemter, Kees",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.33",
    pages = "263--272",
    abstract = "Referring expression generation (REG) algorithms offer computational models of the production of referring expressions. In earlier work, a corpus of referring expressions (REs) in Mandarin was introduced. In the present paper, we annotate this corpus, evaluate classic REG algorithms on it, and compare the results with earlier results on the evaluation of REG for English referring expressions. Next, we offer an in-depth analysis of the corpus, focusing on issues that arise from the grammar of Mandarin. We discuss shortcomings of previous REG evaluations that came to light during our investigation and we highlight some surprising results. Perhaps most strikingly, we found a much higher proportion of under-specified expressions than previous studies had suggested, not just in Mandarin but in English as well.",
}
@inproceedings{byamugisha-2020-generating,
    title = "Generating Varied Training Corpora in {R}unyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach",
    author = "Byamugisha, Joan",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.34",
    pages = "273--282",
    abstract = "Machine learning algorithms have been applied to achieve high levels of accuracy in tasks associated with the processing of natural language. However, these algorithms require large amounts of training data in order to perform efficiently. Since most Bantu languages lack the required training corpora because they are computationally under-resourced, we investigated how to generate a large varied training corpus in Runyankore, a Bantu language indigenous to Uganda. We found the use of a combined semantic and syntactic, pattern and grammar-based approach to be applicable to this purpose, and used it to generate one million sentences, both labelled and unlabelled, which can be applied as training data for machine learning algorithms. The generated text was evaluated in two ways: (1) assessing the semantics encoded in word embeddings obtained from the generated text, which showed correct word similarity; and (2) applying the labelled data to tasks such as sentiment analysis, which achieved satisfactory levels of accuracy.",
}
@inproceedings{du-etal-2020-schema,
    title = "Schema-Guided Natural Language Generation",
    author = "Du, Yuheng  and
      Oraby, Shereen  and
      Perera, Vittorio  and
      Shen, Minmin  and
      Narayan-Chen, Anjali  and
      Chung, Tagyoung  and
      Venkatesh, Anushree  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.35",
    pages = "283--295",
    abstract = "Neural network based approaches to data-to-text natural language generation (NLG) have gained popularity in recent years, with the goal of generating a natural language prompt that accurately realizes an input meaning representation. To facilitate the training of neural network models, researchers created large datasets of paired utterances and their meaning representations. However, the creation of such datasets is an arduous task and they mostly consist of simple meaning representations composed of slot and value tokens to be realized. These representations do not include any contextual information that an NLG system can use when trying to generalize, such as domain information and descriptions of slots and values. In this paper, we present the novel task of Schema-Guided Natural Language Generation (SG-NLG). Here, the goal is still to generate a natural language prompt, but in SG-NLG, the input MRs are paired with rich schemata providing contextual information. To generate a dataset for SG-NLG we re-purpose an existing dataset for another task: dialog state tracking, which includes a large and rich schema spanning multiple different attributes, including information about the domain, user intent, and slot descriptions. We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity. We also conduct experiments comparing model performance on seen versus unseen domains, and present a human evaluation demonstrating high ratings for overall output quality.",
}
@inproceedings{langner-2020-omega,
    title = "{OMEGA} : A probabilistic approach to referring expression generation in a virtual environment",
    author = "Langner, Maurice",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.36",
    pages = "296--305",
    abstract = "In recent years, referring expression genera- tion algorithms were inspired by game theory and probability theory. In this paper, an al- gorithm is designed for the generation of re- ferring expressions (REG) that base on both models by integrating maximization of utilities into the content determination process. It im- plements cognitive models for assessing visual salience of objects and additional features. In order to evaluate the algorithm properly and validate the applicability of existing models and evaluative information criteria, both, pro- duction and comprehension studies, are con- ducted using a complex domain of objects, pro- viding new directions of approaching the eval- uation of REG algorithms.",
}
@inproceedings{stevens-guille-etal-2020-neural,
    title = "Neural {NLG} for Methodius: From {RST} Meaning Representations to Texts",
    author = "Stevens-Guille, Symon  and
      Maskharashvili, Aleksandre  and
      Isard, Amy  and
      Li, Xintong  and
      White, Michael",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.37",
    pages = "306--315",
    abstract = "While classic NLG systems typically made use of hierarchically structured content plans that included discourse relations as central components, more recent neural approaches have mostly mapped simple, flat inputs to texts without representing discourse relations explicitly. In this paper, we investigate whether it is beneficial to include discourse relations in the input to neural data-to-text generators for texts where discourse relations play an important role. To do so, we reimplement the sentence planning and realization components of a classic NLG system, Methodius, using LSTM sequence-to-sequence (seq2seq) models. We find that although seq2seq models can learn to generate fluent and grammatical texts remarkably well with sufficiently representative Methodius training data, they cannot learn to correctly express Methodius{'}s similarity and contrast comparisons unless the corresponding RST relations are included in the inputs. Additionally, we experiment with using self-training and reverse model reranking to better handle train/test data mismatches, and find that while these methods help reduce content errors, it remains essential to include discourse relations in the input to obtain optimal performance.",
}
@inproceedings{rojowiec-etal-2020-generating,
    title = "From {``}Before{''} to {``}After{''}: Generating Natural Language Instructions from Image Pairs in a Simple Visual Domain",
    author = {Rojowiec, Robin  and
      G{\"o}tze, Jana  and
      Sadler, Philipp  and
      Voigt, Henrik  and
      Zarrie{\ss}, Sina  and
      Schlangen, David},
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.38",
    pages = "316--326",
    abstract = "While certain types of instructions can be com-pactly expressed via images, there are situations where one might want to verbalise them, for example when directing someone. We investigate the task of Instruction Generation from Before/After Image Pairs which is to derive from images an instruction for effecting the implied change. For this, we make use of prior work on instruction following in a visual environment. We take an existing dataset, the BLOCKS data collected by Bisk et al. (2016) and investigate whether it is suitable for training an instruction generator as well. We find that it is, and investigate several simple baselines, taking these from the related task of image captioning. Through a series of experiments that simplify the task (by making image processing easier or completely side-stepping it; and by creating template-based targeted instructions), we investigate areas for improvement. We find that captioning models get some way towards solving the task, but have some difficulty with it, and future improvements must lie in the way the change is detected in the instruction.",
}
@inproceedings{scialom-etal-2020-bert,
    title = "What {BERT} Sees: Cross-Modal Transfer for Visual Question Generation",
    author = "Scialom, Thomas  and
      Bordes, Patrick  and
      Dray, Paul-Alexis  and
      Staiano, Jacopo  and
      Gallinari, Patrick",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.39",
    pages = "327--337",
    abstract = "Pre-trained language models have recently contributed to significant advances in NLP tasks. Recently, multi-modal versions of BERT have been developed, using heavy pre-training relying on vast corpora of aligned textual and image data, primarily applied to classification tasks such as VQA. In this paper, we are interested in evaluating the visual capabilities of BERT out-of-the-box, by avoiding pre-training made on supplementary data. We choose to study Visual Question Generation, a task of great interest for grounded dialog, that enables to study the impact of each modality (as input can be visual and/or textual). Moreover, the generation aspect of the task requires an adaptation since BERT is primarily designed as an encoder. We introduce BERT-gen, a BERT-based architecture for text generation, able to leverage on either mono- or multi- modal representations. The results reported under different configurations indicate an innate capacity for BERT-gen to adapt to multi-modal data and text generation, even with few data available, avoiding expensive pre-training. The proposed model obtains substantial improvements over the state-of-the-art on two established VQG datasets.",
}
@inproceedings{ilinykh-dobnik-2020-image,
    title = "When an Image Tells a Story: The Role of Visual and Semantic Information for Generating Paragraph Descriptions",
    author = "Ilinykh, Nikolai  and
      Dobnik, Simon",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.40",
    pages = "338--348",
    abstract = "Generating multi-sentence image descriptions is a challenging task, which requires a good model to produce coherent and accurate paragraphs, describing salient objects in the image. We argue that multiple sources of information are beneficial when describing visual scenes with long sequences. These include (i) perceptual information and (ii) semantic (language) information about how to describe what is in the image. We also compare the effects of using two different pooling mechanisms on either a single modality or their combination. We demonstrate that the model which utilises both visual and language inputs can be used to generate accurate and diverse paragraphs when combined with a particular pooling mechanism. The results of our automatic and human evaluation show that learning to embed semantic information along with visual stimuli into the paragraph generation model is not trivial, raising a variety of proposals for future experiments.",
}
@inproceedings{akermi-etal-2020-tansformer,
    title = "Tansformer based Natural Language Generation for Question-Answering",
    author = "Akermi, Imen  and
      Heinecke, Johannes  and
      Herledan, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.41",
    pages = "349--359",
    abstract = "This paper explores Natural Language Generation within the context of Question-Answering task. The several works addressing this task only focused on generating a short answer or a long text span that contains the answer, while reasoning over a Web page or processing structured data. Such answers{'} length are usually not appropriate as the answer tend to be perceived as too brief or too long to be read out loud by an intelligent assistant. In this work, we aim at generating a concise answer for a given question using an unsupervised approach that does not require annotated data. Tested over English and French datasets, the proposed approach shows very promising results.",
}
@inproceedings{nikolov-etal-2020-rapformer,
    title = "Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders",
    author = "Nikolov, Nikola I.  and
      Malmi, Eric  and
      Northcutt, Curtis  and
      Parisi, Loreto",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.42",
    pages = "360--373",
    abstract = "The ability to combine symbols to generate language is a defining characteristic of human intelligence, particularly in the context of artistic story-telling through lyrics. We develop a method for synthesizing a rap verse based on the content of any text (e.g., a news article), or for augmenting pre-existing rap lyrics. Our method, called Rapformer, is based on training a Transformer-based denoising autoencoder to reconstruct rap lyrics from content words extracted from the lyrics, trying to preserve the essential meaning, while matching the target style. Rapformer features a novel BERT-based paraphrasing scheme for rhyme enhancement which increases the average rhyme density of output lyrics by 10{\%}. Experimental results on three diverse input domains show that Rapformer is capable of generating technically fluent verses that offer a good trade-off between content preservation and style transfer. Furthermore, a Turing-test-like experiment reveals that Rapformer fools human lyrics experts 25{\%} of the time.",
}
@inproceedings{peng-etal-2020-reducing,
    title = "Reducing Non-Normative Text Generation from Language Models",
    author = "Peng, Xiangyu  and
      Li, Siyan  and
      Frazier, Spencer  and
      Riedl, Mark",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.43",
    pages = "374--383",
    abstract = "Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative text (i.e. in violation of social norms). We introduce a technique for fine-tuning GPT-2, using a policy gradient reinforcement learning technique and a normative text classifier to produce reward and punishment values. We evaluate our technique on five data sets using automated and human participant experiments. The normative text classifier is 81-90{\%} accurate when compared to gold-standard human judgements of normative and non-normative generated text. Our normative fine-tuning technique is able to reduce non-normative text by 27-61{\%}, depending on the data set.",
}
@inproceedings{wang-etal-2020-reviewrobot,
    title = "{R}eview{R}obot: Explainable Paper Review Generation based on Knowledge Synthesis",
    author = "Wang, Qingyun  and
      Zeng, Qi  and
      Huang, Lifu  and
      Knight, Kevin  and
      Ji, Heng  and
      Rajani, Nazneen Fatema",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.44",
    pages = "384--397",
    abstract = "To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence. ReviewRobot achieves these goals via three steps: (1) We perform domain-specific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain. (2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category. (3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments. Experimental results show that our review score predictor reaches 71.4{\%}-100{\%} accuracy. Human assessment by domain experts shows that 41.7{\%}-70.5{\%} of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones for 20{\%} of the time. Thus, ReviewRobot can serve as an assistant for paper reviewers, program chairs and authors.",
}
@inproceedings{van-miltenburg-etal-2020-gradations,
    title = "Gradations of Error Severity in Automatic Image Descriptions",
    author = "van Miltenburg, Emiel  and
      Lu, Wei-Ting  and
      Krahmer, Emiel  and
      Gatt, Albert  and
      Chen, Guanyi  and
      Li, Lin  and
      van Deemter, Kees",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.45",
    pages = "398--411",
    abstract = "Earlier research has shown that evaluation metrics based on textual similarity (e.g., BLEU, CIDEr, Meteor) do not correlate well with human evaluation scores for automatically generated text. We carried out an experiment with Chinese speakers, where we systematically manipulated image descriptions to contain different kinds of errors. Because our manipulated descriptions form minimal pairs with the reference descriptions, we are able to assess the impact of different kinds of errors on the perceived quality of the descriptions. Our results show that different kinds of errors elicit significantly different evaluation scores, even though all erroneous descriptions differ in only one character from the reference descriptions. Evaluation metrics based solely on textual similarity are unable to capture these differences, which (at least partially) explains their poor correlation with human judgments. Our work provides the foundations for future work, where we aim to understand why different errors are seen as more or less severe.",
}
@inproceedings{hedayatnia-etal-2020-policy,
    title = "Policy-Driven Neural Response Generation for Knowledge-Grounded Dialog Systems",
    author = "Hedayatnia, Behnam  and
      Gopalakrishnan, Karthik  and
      Kim, Seokhwan  and
      Liu, Yang  and
      Eric, Mihail  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
    month = dec,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.inlg-1.46",
    pages = "412--421",
    abstract = "Open-domain dialog systems aim to generate relevant, informative and engaging responses. In this paper, we propose using a dialog policy to plan the content and style of target, open domain responses in the form of an action plan, which includes knowledge sentences related to the dialog context, targeted dialog acts, topic information, etc. For training, the attributes within the action plan are obtained by automatically annotating the publicly released Topical-Chat dataset. We condition neural response generators on the action plan which is then realized as target utterances at the turn and sentence levels. We also investigate different dialog policy models to predict an action plan given the dialog context. Through automated and human evaluation, we measure the appropriateness of the generated responses and check if the generation models indeed learn to realize the given action plans. We demonstrate that a basic dialog policy that operates at the sentence level generates better responses in comparison to turn level generation as well as baseline models with no action plan. Additionally the basic dialog policy has the added benefit of controllability.",
}
@proceedings{globalex-2020-2020,
    title = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    editor = "Kernerman, Ilan  and
      Krek, Simon  and
      McCrae, John P.  and
      Gracia, Jorge  and
      Ahmadi, Sina  and
      Kabashi, Besim",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.0",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{chiarcos-etal-2020-modelling,
    title = "Modelling Frequency and Attestations for {O}nto{L}ex-Lemon",
    author = "Chiarcos, Christian  and
      Ionov, Maxim  and
      de Does, Jesse  and
      Depuydt, Katrien  and
      Khan, Anas Fahad  and
      Stolk, Sander  and
      Declerck, Thierry  and
      McCrae, John Philip",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.1",
    pages = "1--9",
    abstract = "The OntoLex vocabulary enjoys increasing popularity as a means of publishing lexical resources with RDF and as Linked Data. The recent publication of a new OntoLex module for lexicography, lexicog, reflects its increasing importance for digital lexicography. However, not all aspects of digital lexicography have been covered to the same extent. In particular, supplementary information drawn from corpora such as frequency information, links to attestations, and collocation data were considered to be beyond the scope of lexicog. Therefore, the OntoLex community has put forward the proposal for a novel module for frequency, attestation and corpus information (FrAC), that not only covers the requirements of digital lexicography, but also accommodates essential data structures for lexical information in natural language processing. This paper introduces the current state of the OntoLex-FrAC vocabulary, describes its structure, some selected use cases, elementary concepts and fundamental definitions, with a focus on frequency and attestations.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{uresova-etal-2020-synsemclass,
    title = "{S}yn{S}em{C}lass Linked Lexicon: Mapping Synonymy between Languages",
    author = "Uresova, Zdenka  and
      Fucikova, Eva  and
      Hajicova, Eva  and
      Hajic, Jan",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.2",
    pages = "10--19",
    abstract = "This paper reports on an extended version of a synonym verb class lexicon, newly called SynSemClass (formerly CzEngClass). This lexicon stores cross-lingual semantically similar verb senses in synonym classes extracted from a richly annotated parallel corpus, the Prague Czech-English Dependency Treebank. When building the lexicon, we make use of predicate-argument relations (valency) and link them to semantic roles; in addition, each entry is linked to several external lexicons of more or less {``}semantic{''} nature, namely FrameNet, WordNet, VerbNet, OntoNotes and PropBank, and Czech VALLEX. The aim is to provide a linguistic resource that can be used to compare semantic roles and their syntactic properties and features across languages within and across synonym groups (classes, or {'}synsets{'}), as well as gold standard data for automatic NLP experiments with such synonyms, such as synonym discovery, feature mapping, etc. However, perhaps the most important goal is to eventually build an event type ontology that can be referenced and used as a human-readable and human-understandable {``}database{''} for all types of events, processes and states. While the current paper describes primarily the content of the lexicon, we are also presenting a preliminary design of a format compatible with Linked Data, on which we are hoping to get feedback during discussions at the workshop. Once the resource (in whichever form) is applied to corpus annotation, deep analysis will be possible using such combined resources as training data.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{mambrini-passarotti-2020-representing,
    title = "Representing Etymology in the {L}i{L}a Knowledge Base of Linguistic Resources for {L}atin",
    author = "Mambrini, Francesco  and
      Passarotti, Marco",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.3",
    pages = "20--28",
    abstract = "In this paper we describe the process of inclusion of etymological information in a knowledge base of interoperable Latin linguistic resources developed in the context of the LiLa: Linking Latin project. Interoperability is obtained by applying the Linked Open Data principles. Particularly, an extensive collection of Latin lemmas is used to link the (distributed) resources. For the etymology, we rely on the Ontolex-lemon ontology and the lemonEty extension to model the information, while the source data are taken from a recent etymological dictionary of Latin. As a result, the collection of lemmas LiLa is built around now includes 1,465 Proto-Italic and 1,393 Proto-Indo-European reconstructed forms that are used to explain the history of 1,400 Latin words. We discuss the motivation, methodology and modeling strategies of the work, as well as its possible applications and potential future developments.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{moller-svendsen-etal-2020-automatically,
    title = "An automatically generated {D}anish Renaissance Dictionary",
    author = "M{\o}ller Svendsen, Mette-Marie  and
      Hartvig S{\o}rensen, Nicolai  and
      Troelsg{\aa}rd, Thomas",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.4",
    pages = "29--32",
    abstract = "We present the ongoing work on an automatically generated dictionary describing Danish in the 16th century. A series of relevant dictionaries {--} from the period as well as more recent ones {--} are linked together at lemma level, and where possible, definitions or keywords are extracted and presented in the new dictionary.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{declerck-2020-towards,
    title = "Towards an Extension of the Linking of the Open {D}utch {W}ord{N}et with {D}utch Lexicographic Resources",
    author = "Declerck, Thierry",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.5",
    pages = "33--35",
    abstract = "This extended abstract presents on-going work consisting in interlinking and merging the Open Dutch WordNet and generic lexicographic resources for Dutch, focusing for now on the Dutch and English versions of Wiktionary and using the Algemeen Nederlands Woordenboek as a quality checking instance. As the Open Dutch WordNet is already equipped with a relevant number of complex lexical units, we are aiming at expanding it and proposing a new representational framework for the encoding of the interlinked and integrated data. The longer term goal of the work is to investigate if and on how senses can be restricted to particular morphological variations of Dutch lexical entries, and how to represent this information in a Linguistic Linked Open Data compliant format.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{goncalo-oliveira-luis-2020-widening,
    title = "Widening the Discussion on {``}False {F}riends{''} in Multilingual Wordnets",
    author = "Gon{\c{c}}alo Oliveira, Hugo  and
      Lu{\'\i}s, Ana",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.6",
    pages = "36",
    abstract = "There are wordnets in many languages, many aligned with Princeton WordNet, some of which in a (semi-)automatic process, but we rarely see actual discussions on the role of false friends in this process. Having in mind known issues related to such words in language translation, and further motivated by false friend-related issues on the alignment of a Portuguese wordnet with Princeton Wordnet, we aim to widen this discussion, while suggesting preliminary ideas of how wordnets could benefit from this kind of research.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{morgado-da-costa-2020-pinchah,
    title = "Pinchah Kristang: A Dictionary of Kristang",
    author = "Morgado da Costa, Lu{\'\i}s",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.7",
    pages = "37--44",
    abstract = "This paper describes the development and current state of Pinchah Kristang {--} an online dictionary for Kristang. Kristang is a critically endangered language of the Portuguese-Eurasian communities residing mainly in Malacca and Singapore. Pinchah Kristang has been a central tool to the revitalization efforts of Kristang in Singapore, and collates information from multiple sources, including existing dictionaries and wordlists, ongoing language documentation work, and new words that emerge regularly from relexification efforts by the community. This online dictionary is powered by the Princeton Wordnet and the Open Kristang Wordnet {--} a choice that brings both advantages and disadvantages. This paper will introduce the current version of this dictionary, motivate some of its design choices, and discuss possible future directions.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{rormann-olsen-etal-2020-building,
    title = "Building Sense Representations in {D}anish by Combining Word Embeddings with Lexical Resources",
    author = "R{\o}rmann Olsen, Ida  and
      Pedersen, Bolette  and
      Sayeed, Asad",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.8",
    pages = "45--52",
    abstract = "Our aim is to identify suitable sense representations for NLP in Danish. We investigate sense inventories that correlate with human interpretations of word meaning and ambiguity as typically described in dictionaries and wordnets and that are well reflected distributionally as expressed in word embeddings. To this end, we study a number of highly ambiguous Danish nouns and examine the effectiveness of sense representations constructed by combining vectors from a distributional model with the information from a wordnet. We establish representations based on centroids obtained from wordnet synests and example sentences as well as representations established via are tested in a word sense disambiguation task. We conclude that the more information extracted from the wordnet entries (example sentence, definition, semantic relations) the more successful the sense representation vector.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{zechner-borin-2020-towards,
    title = "Towards a {S}wedish {R}oget-Style Thesaurus for {NLP}",
    author = "Zechner, Niklas  and
      Borin, Lars",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.9",
    pages = "53--60",
    abstract = "Bring{'}s thesaurus (Bring) is a Swedish counterpart of Roget, and its digitized version could make a valuable language resource for use in many and diverse natural language processing (NLP) applications. From the literature we know that Roget-style thesauruses and wordnets have complementary strengths in this context, so both kinds of lexical-semantic resource are good to have. However, Bring was published in 1930, and its lexical items are in the form of lemma{--}POS pairings. In order to be useful in our NLP systems, polysemous lexical items need to be disambiguated, and a large amount of modern vocabulary must be added in the proper places in Bring. The work presented here describes experiments aiming at automating these two tasks, at least in part, where we use the structure of an existing Swedish semantic lexicon {--} Saldo {--} both for disambiguation of ambiguous Bring entries and for addition of new entries to Bring.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{roldan-vendrell-2020-design,
    title = "Design and development of an adaptive web application for {OLIVATERM}",
    author = "Rold{\'a}n Vendrell, Mercedes",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.10",
    pages = "61",
    abstract = "An Excellency Research Project called {``}Terminology of olive oil and trade: China and other international markets{''} (P07-HUM-03041) was initiated under my management in 2008, financed by the Andalusian regional government, the Junta de Andaluc{\'\i}a. The project, known as {``}OLIVATERM{''}, had two main objectives: on the one hand, to develop the first systematic multilingual terminological dictionary in the scientific and socio-economic area of the olive grove and olive oils in order to facilitate communication in the topic; on the other, to contribute to the expansion of the Andalusia{'}s domestic and international trade and the dissemination of its culture. The main outcome of the research was the Diccionario de t{\'e}rminos del aceite de oliva (DTAO {--} Dictionary of olive oil terms) (Rold{\'a}n Vendrell, Arco Libros: 2013). This dictionary is currently the main reference source for answering queries and responding to any doubts that might arise in the use of this terminology in the three reference languages (Spanish, English and Chinese). It has received unanimous acknowledgement from numerous specialists in the sphere of Terminology, including most especially Maria Teresa Cabr{\'e} (UPF), Miguel Casas G{\'o}mez (UCA- Ib{\'e}rica 27 (2014): 217-234), Fran{\c{c}}ois Maniez (Universit{\'e} de Lyon), Maria Isabel Santamar{\'\i}a P{\'e}rez and Chelo Vargas Sierra (UA), Pamela Faber (UGR), Joaqu{\'\i}n Garc{\'\i}a Palacios (USAL), and Marie-Claude L{'}Homme (Universit{\'e} de Montr{\'e}al). The DTAO is well-known in the academic area of Terminology, but has not reached many of the institutions and organizations (domestic and international), translators, journalists, communicators and olive oil sector professionals that could benefit from it in their professions, especially salespeople, who need (fortunately, with an ever greater frequency) information on terminology in the book{'}s target languages for their commercial transactions. That is why we are currently working on a multichannel technological solution that enables a greater and more efficient transfer to the business sector: the design and development of an adaptive website (responsive web design) that provides access to the information in any usage context. We believe that access must be afforded to this valuable reference information on a hand-held device that enables it to be looked up both on- and offline and so pre-empt situations in which it is impossible to connect to the internet. The web application{'}s database will therefore also feed a series of mobile applications that will be available for the main platforms (iOS, Android). This tool will represent real progress in the dynamic transfer of specialized knowledge in the field of olive growing and olive oil production. Apart from delivering universal and free access to this information, the web application will welcome user suggestions for including new terms, new information and new reference languages, making it a collaborative tool that is also fed by its own users. With this tool we hope to respond to society{'}s needs for multilingual communication in the area of olive oil and to help give a boost to economic activity in the olive sector. In this work, in parallel to the presentation of the adaptive website, we will present a lexical repertoire integrated by new terms and expressions coined in this field (in the three working languages) in the last years. These neologisms reflect the most relevant innovations occurred in the olive oil sector over the last decade and, therefore, they must be compiled, sorted, systematized, and made accessible to the users in the web application we intend to develop.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{bartolome-diaz-frontini-2020-building,
    title = "Building a domain-specific bilingual lexicon resource with Sketchengine and Lexonomy: Taking Ownership of the Issues",
    author = "Bartolom{\'e}-D{\'\i}az, Zaida  and
      Frontini, Francesca",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.11",
    pages = "62--68",
    abstract = "Thanks to new technologies, the elaboration of specialized bilingual dictionaries can be made faster and more standardized, offering not only a dictionary of equivalents, but also the representation of a conceptual field. Nevertheless, in view of these new tools and services, some of which are offered free of charge by European institutions, it is necessary to question the viability of their use by a lambda user and the previous knowledge required for such use, as well as the possible problems they may encounter. In our communication we show a series of possible difficulties, as well as a methodological proposal and some solutions, by presenting an extract of a French-Spanish bilingual dictionary for the domain of architecture. The extract in question is a sample of about 30 terms created with the Lexonomy dictionary editor (M{\v{e}}chura 2017).",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{pais-etal-2020-mwsa,
    title = "{MWSA} Task at {G}loba{L}ex 2020: {RACAI}{'}s Word Sense Alignment System using a Similarity Measurement of Dictionary Definitions",
    author = "Pais, Vasile  and
      Tufi{\textcommabelow{s}}, Dan  and
      Ion, Radu",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.12",
    pages = "69--75",
    abstract = "This paper describes RACAI{'}s word sense alignment system, which participated in the Monolingual Word Sense Alignment shared task organized at GlobaLex 2020 workshop. We discuss the system architecture, some of the challenges that we faced as well as present our results on several of the languages available for the task.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{manna-etal-2020-unior,
    title = "{UNIOR} {NLP} at {MWSA} Task - {G}loba{L}ex 2020: {S}iamese {LSTM} with Attention for Word Sense Alignment",
    author = "Manna, Raffaele  and
      Speranza, Giulia  and
      di Buono, Maria Pia  and
      Monti, Johanna",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.13",
    pages = "76--83",
    abstract = "In this paper we describe the system submitted to the ELEXIS Monolingual Word Sense Alignment Task. We test different systems,which are two types of LSTMs and a system based on a pretrained Bidirectional Encoder Representations from Transformers (BERT)model, to solve the task. LSTM models use fastText pre-trained word vectors features with different settings. For training the models,we did not combine external data with the dataset provided for the task. We select a sub-set of languages among the proposed ones,namely a set of Romance languages, i.e., Italian, Spanish, Portuguese, together with English and Dutch. The Siamese LSTM withattention and PoS tagging (LSTM-A) performed better than the other two systems, achieving a 5-Class Accuracy score of 0.844 in theOverall Results, ranking the first position among five teams.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{bajcetic-yim-2020-implementation,
    title = "Implementation of Supervised Training Approaches for Monolingual Word Sense Alignment: {ACDH}-{CH} System Description for the {MWSA} Shared Task at {G}loba{L}ex 2020",
    author = "Bajcetic, Lenka  and
      Yim, Seung-bin",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.14",
    pages = "84--91",
    abstract = "This paper describes our system for monolingual sense alignment across dictionaries. The task of monolingual word sense alignment is presented as a task of predicting the relationship between two senses. We will present two solutions, one based on supervised machine learning, and the other based on pre-trained neural network language model, specifically BERT. Our models perform competitively for binary classification, reporting high scores for almost all languages. This paper presents our submission for the shared task on monolingual word sense alignment across dictionaries as part of the GLOBALEX 2020 {--} Linked Lexicography workshop at the 12th Language Resources and Evaluation Conference (LREC). Monolingual word sense alignment (MWSA) is the task of aligning word senses across re- sources in the same language. Lexical-semantic resources (LSR) such as dictionaries form valuable foundation of numerous natural language process- ing (NLP) tasks. Since they are created manually by ex- perts, dictionaries can be considered among the resources of highest quality and importance. However, the existing LSRs in machine readable form are small in scope or miss- ing altogether. Thus, it would be extremely beneficial if the existing lexical resources could be connected and ex- panded. Lexical resources display considerable variation in the number of word senses that lexicographers assign to a given entry in a dictionary. This is because the identification and differentiation of word senses is one of the harder tasks that lexicographers face. Hence, the task of combining dictio- naries from different sources is difficult, especially for the case of mapping the senses of entries, which often differ significantly in granularity and coverage. (Ahmadi et al., 2020) There are three different angles from which the problem of word sense alignment can be addressed: approaches based on the similarity of textual descriptions of word senses, ap- proaches based on structural properties of lexical-semantic resources, and a combination of both. (Matuschek, 2014) In this paper we focus on the similarity of textual de- scriptions. This is a common approach as the majority of previous work used some notion of similarity between senses, mostly gloss overlap or semantic relatedness based on glosses. This makes sense, as glosses are a prerequisite for humans to recognize the meaning of an encoded sense, and thus also an intuitive way of judging the similarity of senses. (Matuschek, 2014) The paper is structured as follows: we provide a brief overview of related work in Section 2, and a description of the corpus in Section 3. In Section 4 we explain all impor- tant aspects of our model implementation, while the results are presented in Section 5. Finally, we end the paper with the discussion in Section 6 and conclusion in Section 7.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{mccrae-arcan-2020-nuig,
    title = "{NUIG} at {TIAD}: Combining Unsupervised {NLP} and Graph Metrics for Translation Inference",
    author = "McCrae, John Philip  and
      Arcan, Mihael",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.15",
    pages = "92--97",
    abstract = "In this paper, we present the NUIG system at the TIAD shard task. This system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an unsupervised multi-way neural machine translation method. The results are an improvement over our previous system and produce the highest precision among all systems in the task as well as very competitive F-Measure results. Incorporating features from other systems should be easy in the framework we describe in this paper, suggesting this could very easily be extended to an even stronger result.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{chiarcos-etal-2020-translation,
    title = "Translation Inference by Concept Propagation",
    author = {Chiarcos, Christian  and
      Schenk, Niko  and
      F{\"a}th, Christian},
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.16",
    pages = "98--105",
    abstract = "This paper describes our contribution to the Third Shared Task on Translation Inference across Dictionaries (TIAD-2020). We describe an approach on translation inference based on symbolic methods, the propagation of concepts over a graph of interconnected dictionaries: Given a mapping from source language words to lexical concepts (e.g., synsets) as a seed, we use bilingual dictionaries to extrapolate a mapping of pivot and target language words to these lexical concepts. Translation inference is then performed by looking up the lexical concept(s) of a source language word and returning the target language word(s) for which these lexical concepts have the respective highest score. We present two instantiations of this system: One using WordNet synsets as concepts, and one using lexical entries (translations) as concepts. With a threshold of 0, the latter configuration is the second among participant systems in terms of F1 score. We also describe additional evaluation experiments on Apertium data, a comparison with an earlier approach based on embedding projection, and an approach for constrained projection that outperforms the TIAD-2020 vanilla system by a large margin.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{lanau-coronas-gracia-2020-graph,
    title = "Graph Exploration and Cross-lingual Word Embeddings for Translation Inference Across Dictionaries",
    author = "Lanau-Coronas, Marta  and
      Gracia, Jorge",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.17",
    pages = "106--110",
    abstract = "This paper describes the participation of two different approaches in the 3rd Translation Inference Across Dictionaries (TIAD 2020) shared task. The aim of the task is to automatically generate new bilingual dictionaries from existing ones. To that end, we essayed two different types of techniques: based on graph exploration on the one hand and, on the other hand, based on cross-lingual word embeddings. The task evaluation results show that graph exploration is very effective, accomplishing relatively high precision and recall values in comparison with the other participating systems, while cross-lingual embeddings reaches high precision but smaller recall.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@inproceedings{dranca-2020-multi,
    title = "Multi-Strategy system for translation inference across dictionaries",
    author = "Dranca, Lacramioara",
    booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.globalex-1.18",
    pages = "111--115",
    abstract = "This paper describes four different strategies proposed to the TIAD 2020 Shared Task for automatic translation inference across dictionaries. The proposed strategies are based on the analysis of Apertium RDF graph, taking advantage of characteristics such as translation using multiple paths, synonyms and similarities between lexical entries from different lexicons and cardinality of possible translations through the graph. The four strategies were trained and validated on the Apertium RDF EN{\textless}-{\textgreater}ES dictionary, showing promising results. Finally, the strategies, applied together, obtained an F-measure of 0.43 in the task of inferring the dictionaries proposed in the shared task, ranking thus third with respect to the other new systems presented to the TIAD 2020 Shared Task. No system presented to the shared task exceeded the baseline proposed by the TIAD organizers.",
    language = "English",
    ISBN = "979-10-95546-46-7",
}
@proceedings{gebnlp-2020-gender,
    title = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    editor = "Costa-juss{\`a}, Marta R.  and
      Hardmeier, Christian  and
      Radford, Will  and
      Webster, Kellie",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.0",
}
@inproceedings{bartl-etal-2020-unmasking,
    title = "Unmasking Contextual Stereotypes: Measuring and Mitigating {BERT}{'}s Gender Bias",
    author = "Bartl, Marion  and
      Nissim, Malvina  and
      Gatt, Albert",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.1",
    pages = "1--16",
    abstract = "Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously been found in standard word embeddings, it is crucial to assess biases encoded in their replacements as well. Focusing on BERT (Devlin et al., 2018), we measure gender bias by studying associations between gender-denoting target words and names of professions in English and German, comparing the findings with real-world workforce statistics. We mitigate bias by fine-tuning BERT on the GAP corpus (Webster et al., 2018), after applying Counterfactual Data Substitution (CDS) (Maudslay et al., 2019). We show that our method of measuring bias is appropriate for languages such as English, but not for languages with a rich morphology and gender-marking, such as German. Our results highlight the importance of investigating bias and mitigation techniques cross-linguistically,especially in view of the current emphasis on large-scale, multilingual language models.",
}
@inproceedings{jiang-fellbaum-2020-interdependencies,
    title = "Interdependencies of Gender and Race in Contextualized Word Embeddings",
    author = "Jiang, May  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.2",
    pages = "17--25",
    abstract = "Recent years have seen a surge in research on the biases in word embeddings with respect to gender and, to a lesser extent, race. Few of these studies, however, have given attention to the critical intersection of race and gender. In this case study, we analyze the dimensions of gender and race in contextualized word embeddings of given names, taken from BERT, and investigate the nature and nuance of their interaction. We find that these demographic axes, though typically treated as physically and conceptually separate, are in fact interdependent and thus inadvisable to consider in isolation. Further, we show that demographic dimensions predicated on default settings in language, such as in pronouns, may risk rendering groups with multiple marginalized identities invisible. We conclude by discussing the importance and implications of intersectionality for future studies on bias and debiasing in NLP.",
}
@inproceedings{costa-jussa-de-jorge-2020-fine,
    title = "Fine-tuning Neural Machine Translation on Gender-Balanced Datasets",
    author = "Costa-juss{\`a}, Marta R.  and
      de Jorge, Adri{\`a}",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.3",
    pages = "26--34",
    abstract = "Misrepresentation of certain communities in datasets is causing big disruptions in artificial intelligence applications. In this paper, we propose using an automatically extracted gender-balanced dataset parallel corpus from Wikipedia. This balanced set is used to perform fine-tuning techniques from a bigger model trained on unbalanced datasets to mitigate gender biases in neural machine translation.",
}
@inproceedings{saunders-etal-2020-neural,
    title = "Neural Machine Translation Doesn{'}t Translate Gender Coreference Right Unless You Make It",
    author = "Saunders, Danielle  and
      Sallis, Rosie  and
      Byrne, Bill",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.4",
    pages = "35--43",
    abstract = "Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many existing approaches to this problem seek to control gender inflection in the target language by explicitly or implicitly adding a gender feature to the source sentence, usually at the sentence level. In this paper we propose schemes for incorporating explicit word-level gender inflection tags into NMT. We explore the potential of this gender-inflection controlled translation when the gender feature can be determined from a human reference, or when a test sentence can be automatically gender-tagged, assessing on English-to-Spanish and English-to-German translation. We find that simple existing approaches can over-generalize a gender-feature to multiple entities in a sentence, and suggest effective alternatives in the form of tagged coreference adaptation data. We also propose an extension to assess translations of gender-neutral entities from English given a corresponding linguistic convention, such as a non-binary inflection, in the target language.",
}
@inproceedings{takeshita-etal-2020-existing,
    title = "Can Existing Methods Debias Languages Other than {E}nglish? First Attempt to Analyze and Mitigate {J}apanese Word Embeddings",
    author = "Takeshita, Masashi  and
      Katsumata, Yuki  and
      Rzepka, Rafal  and
      Araki, Kenji",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.5",
    pages = "44--55",
    abstract = "It is known that word embeddings exhibit biases inherited from the corpus, and those biases reflect social stereotypes. Recently, many studies have been conducted to analyze and mitigate biases in word embeddings. Unsupervised Bias Enumeration (UBE) (Swinger et al., 2019) is one of approach to analyze biases for English, and Hard Debias (Bolukbasi et al., 2016) is the common technique to mitigate gender bias. These methods focused on English, or, in smaller extent, on Indo-European languages. However, it is not clear whether these methods can be generalized to other languages. In this paper, we apply these analyzing and mitigating methods, UBE and Hard Debias, to Japanese word embeddings. Additionally, we examine whether these methods can be used for Japanese. We experimentally show that UBE and Hard Debias cannot be sufficiently adapted to Japanese embeddings.",
}
@inproceedings{chavez-mulsa-spanakis-2020-evaluating,
    title = "Evaluating Bias In {D}utch Word Embeddings",
    author = "Ch{\'a}vez Mulsa, Rodrigo Alejandro  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.6",
    pages = "56--71",
    abstract = "Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world applications. This paper explores the gender bias implicit in Dutch embeddings while investigating whether English language based approaches can also be used in Dutch. We implement the Word Embeddings Association Test (WEAT), Clustering and Sentence Embeddings Association Test (SEAT) methods to quantify the gender bias in Dutch word embeddings, then we proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods and finally we evaluate the performance of the debiased embeddings in downstream tasks. The results suggest that, among others, gender bias is present in traditional and contextualized Dutch word embeddings. We highlight how techniques used to measure and reduce bias created for English can be used in Dutch embeddings by adequately translating the data and taking into account the unique characteristics of the language. Furthermore, we analyze the effect of the debiasing techniques on downstream tasks which show a negligible impact on traditional embeddings and a 2{\%} decrease in performance in contextualized embeddings. Finally, we release the translated Dutch datasets to the public along with the traditional embeddings with mitigated bias.",
}
@inproceedings{cercas-curry-etal-2020-conversational,
    title = "Conversational Assistants and Gender Stereotypes: Public Perceptions and Desiderata for Voice Personas",
    author = "Cercas Curry, Amanda  and
      Robertson, Judy  and
      Rieser, Verena",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.7",
    pages = "72--78",
    abstract = "Conversational voice assistants are rapidly developing from purely transactional systems to social companions with {``}personality{''}. UNESCO recently stated that the female and submissive personality of current digital assistants gives rise for concern as it reinforces gender stereotypes. In this work, we present results from a participatory design workshop, where we invite people to submit their preferences for a what their ideal persona might look like, both in drawings as well as in a multiple choice questionnaire. We find no clear consensus which suggests that one possible solution is to let people configure/personalise their assistants. We then outline a multi-disciplinary project of how we plan to address the complex question of gender and stereotyping in digital assistants.",
}
@inproceedings{devinney-etal-2020-semi,
    title = "Semi-Supervised Topic Modeling for Gender Bias Discovery in {E}nglish and {S}wedish",
    author = {Devinney, Hannah  and
      Bj{\"o}rklund, Jenny  and
      Bj{\"o}rklund, Henrik},
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.8",
    pages = "79--92",
    abstract = "Gender bias has been identified in many models for Natural Language Processing, stemming from implicit biases in the text corpora used to train the models. Such corpora are too large to closely analyze for biased or stereotypical content. Thus, we argue for a combination of quantitative and qualitative methods, where the quantitative part produces a view of the data of a size suitable for qualitative analysis. We investigate the usefulness of semi-supervised topic modeling for the detection and analysis of gender bias in three corpora (mainstream news articles in English and Swedish, and LGBTQ+ web content in English). We compare differences in topic models for three gender categories (masculine, feminine, and nonbinary or neutral) in each corpus. We find that in all corpora, genders are treated differently and that these differences tend to correspond to hegemonic ideas of gender.",
}
@inproceedings{sheng-uthus-2020-investigating,
    title = "Investigating Societal Biases in a Poetry Composition System",
    author = "Sheng, Emily  and
      Uthus, David",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.9",
    pages = "93--106",
    abstract = "There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks remains underexplored. Creative language applications are meant for direct interaction with users, so it is important to quantify and mitigate societal biases in these applications. We introduce a novel study on a pipeline to mitigate societal biases when retrieving next verse suggestions in a poetry composition system. Our results suggest that data augmentation through sentiment style transfer has potential for mitigating societal biases.",
}
@inproceedings{havens-etal-2020-situated,
    title = "Situated Data, Situated Systems: A Methodology to Engage with Power Relations in Natural Language Processing Research",
    author = "Havens, Lucy  and
      Terras, Melissa  and
      Bach, Benjamin  and
      Alex, Beatrice",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.10",
    pages = "107--124",
    abstract = "We propose a bias-aware methodology to engage with power relations in natural language processing (NLP) research. NLP research rarely engages with bias in social contexts, limiting its ability to mitigate bias. While researchers have recommended actions, technical methods, and documentation practices, no methodology exists to integrate critical reflections on bias with technical NLP methods. In this paper, after an extensive and interdisciplinary literature review, we contribute a bias-aware methodology for NLP research. We also contribute a definition of biased text, a discussion of the implications of biased NLP systems, and a case study demonstrating how we are executing the bias-aware methodology in research on archival metadata descriptions.",
}
@inproceedings{touileb-etal-2020-gender,
    title = "Gender and sentiment, critics and authors: a dataset of {N}orwegian book reviews",
    author = "Touileb, Samia  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.11",
    pages = "125--138",
    abstract = "Gender bias in models and datasets is widely studied in NLP. The focus has usually been on analysing how females and males express themselves, or how females and males are described. However, a less studied aspect is the combination of these two perspectives, how female and male describe the same or opposite gender. In this paper, we present a new gender annotated sentiment dataset of critics reviewing the works of female and male authors. We investigate if this newly annotated dataset contains differences in how the works of male and female authors are critiqued, in particular in terms of positive and negative sentiment. We also explore the differences in how this is done by male and female critics. We show that there are differences in how critics assess the works of authors of the same or opposite gender. For example, male critics rate crime novels written by females, and romantic and sentimental works written by males, more negatively.",
}
@inproceedings{alhafni-etal-2020-gender,
    title = "Gender-Aware Reinflection using Linguistically Enhanced Neural Models",
    author = "Alhafni, Bashar  and
      Habash, Nizar  and
      Bouamor, Houda",
    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.gebnlp-1.12",
    pages = "139--150",
    abstract = "In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models. Our system takes an Arabic sentence and a given target gender as input and generates a gender-reinflected sentence based on the target gender. We formulate the problem as a user-aware grammatical error correction task and build an encoder-decoder architecture to jointly model reinflection for both masculine and feminine grammatical genders. We also show that adding linguistic features to our model leads to better reinflection results. The results on a blind test set using our best system show improvements over previous work, with a 3.6{\%} absolute increase in M2 F0.5.",
}
@proceedings{gamesandnlp-2020-games,
    title = "Workshop on Games and Natural Language Processing",
    editor = "Lukin, Stephanie M.",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.0",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{bergsma-etal-2020-creating,
    title = "Creating a Sentiment Lexicon with Game-Specific Words for Analyzing {NPC} Dialogue in The Elder Scrolls {V}: Skyrim",
    author = {Bergsma, Th{\'e}r{\`e}se  and
      van Stegeren, Judith  and
      Theune, Mari{\"e}t},
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.1",
    pages = "1--9",
    abstract = "A weak point of rule-based sentiment analysis systems is that the underlying sentiment lexicons are often not adapted to the domain of the text we want to analyze. We created a game-specific sentiment lexicon for video game Skyrim based on the E-ANEW word list and a dataset of Skyrim{'}s in-game documents. We calculated sentiment ratings for NPC dialogue using both our lexicon and E-ANEW and compared the resulting sentiment ratings to those of human raters. Both lexicons perform comparably well on our evaluation dialogues, but the game-specific extension performs slightly better on the dominance dimension for dialogue segments and the arousal dimension for full dialogues. To our knowledge, this is the first time that a sentiment analysis lexicon has been adapted to the video game domain.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{harris-2020-cluemein,
    title = "{C}lue{M}e{I}n: Obtaining More Specific Image Labels Through a Game",
    author = "Harris, Christopher",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.2",
    pages = "10--16",
    abstract = "The ESP Game (also known as the Google Image Labeler) demonstrated how the crowd could perform a task that is straightforward for humans but challenging for computers {--} providing labels for images. The game facilitated the task of basic image labeling; however, the labels generated were non-specific and limited the ability to distinguish similar images from one another, limiting its ability in search tasks, annotating images for the visually impaired, and training computer vision machine algorithms. In this paper, we describe ClueMeIn, an entertaining web-based game with a purpose that generates more detailed image labels than the ESP Game. We conduct experiments to generate specific image labels, show how the results can lead to improvements in the accuracy of image searches over image labels generated by the ESP Game when using the same public dataset.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{xu-chamberlain-2020-cipher,
    title = "{C}ipher: A Prototype Game-with-a-Purpose for Detecting Errors in Text",
    author = "Xu, Liang  and
      Chamberlain, Jon",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.3",
    pages = "17--25",
    abstract = "Errors commonly exist in machine-generated documents and publication materials; however, some correction algorithms do not perform well for complex errors and it is costly to employ humans to do the task. To solve the problem, a prototype computer game called Cipher was developed that encourages people to identify errors in text. Gamification is achieved by introducing the idea of steganography as the entertaining game element. People play the game for entertainment while they make valuable annotations to locate text errors. The prototype was tested by 35 players in a evaluation experiment, creating 4,764 annotations. After filtering the data, the system detected manually introduced text errors and also genuine errors in the texts that were not noticed when they were introduced into the game.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{lafourcade-nathalie-2020-game,
    title = "Game Design Evaluation of {GWAP}s for Collecting Word Associations",
    author = "Lafourcade, Mathieu  and
      Nathalie, Le Brun",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.4",
    pages = "26--33",
    abstract = "GWAP design might have a tremendous effect on its popularity of course but also on the quality of the data collected. In this paper, a comparison is undertaken between two GWAPs for building term association lists, namely JeuxDeMots and Quicky Goose. After comparing both game designs, the Cohen kappa of associative lists in various configurations is computed in order to assess likeness and differences of the data they provide.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{sangati-etal-2020-challenge,
    title = "The Challenge of the {TV} game La Ghigliottina to {NLP}",
    author = "Sangati, Federico  and
      Pascucci, Antonio  and
      Monti, Johanna",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.5",
    pages = "34--38",
    abstract = "In this paper, we describe a Telegram bot, Mago della Ghigliottina (Ghigliottina Wizard), able to solve La Ghigliottina game (The Guillotine), the final game of the Italian TV quiz show L{'}Eredit{\`a}. Our system relies on linguistic resources and artificial intelligence and achieves better results than human players (and competitors of L{'}Eredit{\`a} too). In addition to solving a game, Mago della Ghigliottina can also generate new game instances and challenge the users to match the solution.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{bonetti-tonelli-2020-3d,
    title = "A 3{D} Role-Playing Game for Abusive Language Annotation",
    author = "Bonetti, Federico  and
      Tonelli, Sara",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.6",
    pages = "39--43",
    abstract = "Gamification has been applied to many linguistic annotation tasks, as an alternative to crowdsourcing platforms to collect annotated data in an inexpensive way. However, we think that still much has to be explored. Games with a Purpose (GWAPs) tend to lack important elements that we commonly see in commercial games, such as 2D and 3D worlds or a story. Making GWAPs more similar to full-fledged video games in order to involve users more easily and increase dissemination is a demanding yet interesting ground to explore. In this paper we present a 3D role-playing game for abusive language annotation that is currently under development.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{yusupujiang-ginzburg-2020-designing,
    title = "Designing a {GWAP} for Collecting Naturally Produced Dialogues for Low Resourced Languages",
    author = "Yusupujiang, Zulipiye  and
      Ginzburg, Jonathan",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.7",
    pages = "44--48",
    abstract = "In this paper we present a new method for collecting naturally generated dialogue data for a low resourced language, (specifically here{---}Uyghur). We plan to build a games with a purpose (GWAPs) to encourage native speakers to actively contribute dialogue data to our research project. Since we aim to characterize the response space of queries in Uyghur, we design various scenarios for conversations that yield to questions being posed and responded to. We will implement the GWAP with the RPG Maker MV Game Engine, and will integrate the chatroom system in the game with the Dialogue Experimental Toolkit (DiET). DiET will help us improve the data collection process, and most importantly, make us have some control over the interactions among the participants.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{morgado-da-costa-sio-2020-callig,
    title = "{CALLIG}: Computer Assisted Language Learning using Improvisation Games",
    author = "Morgado da Costa, Lu{\'\i}s  and
      Sio, Joanna Ut-Seong",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.8",
    pages = "49--58",
    abstract = "In this paper, we present the ongoing development of CALLIG {--} a web system that uses improvisation games in Computer Assisted Language Learning (CALL). Improvisation games are structured activities with built-in constraints where improvisers are asked to generate a lot of different ideas and weave a diverse range of elements into a sensible narrative spontaneously. This paper discusses how computer-based language games can be created combining improvisation elements and language technology. In contrast with traditional language exercises, improvisational language games are open and unpredictable. CALLIG encourages spontaneity and witty language use. It also provides opportunities for collecting useful data for many NLP applications.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{vilares-etal-2020-bringing,
    title = "Bringing Roguelikes to Visually-Impaired Players by Using {NLP}",
    author = "Vilares, Jes{\'u}s  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos  and
      Fern{\'a}ndez-N{\'u}{\~n}ez, Lu{\'\i}s  and
      Penas, Dar{\'\i}o  and
      Viteri, Jorge",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.9",
    pages = "59--67",
    abstract = "Although the roguelike video game genre has a large community of fans (both players and developers) and the graphic aspect of these games is usually given little relevance (ASCII-based graphics are not rare even today), their accessibility for blind players and other visually-impaired users remains a pending issue. In this document, we describe an initiative for the development of roguelikes adapted to visually-impaired players by using Natural Language Processing techniques, together with the first completed games resulting from it. These games were developed as Bachelor{'}s and Master{'}s theses. Our approach consists in integrating a multilingual module that, apart from the classic ASCII-based graphical interface, automatically generates text descriptions of what is happening within the game. The visually-impaired user can then read such descriptions by means of a screen reader. In these projects we seek expressivity and variety in the descriptions, so we can offer the users a fun roguelike experience that does not sacrifice any of the key characteristics that define the genre. Moreover, we intend to make these projects easy to extend to other languages, thus avoiding costly and complex solutions. KEYWORDS: Natural Language Generation, roguelikes, visually-impaired users",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{duran-lewandowski-2020-demonstration,
    title = "Demonstration of a Serious Game for Spoken Language Experiments - {GDX}",
    author = "Duran, Daniel  and
      Lewandowski, Natalie",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.10",
    pages = "68--78",
    abstract = "Increasing efforts are put into gamification of experimentation software in psychology and educational applications and the development of serious games. Computer-based experiments with game-like features have been developed previously for research on cognitive skills, cognitive processing speed, working memory, attention, learning, problem solving, group behavior and other phenomena. It has been argued that computer game experiments are superior to traditional computerized experimentation methods in laboratory tasks in that they represent holistic, meaningful, and natural human activity. We present a novel experimental framework for forced choice categorization tasks or speech perception studies in the form of a computer game, based on the Unity Engine {--} the Gamified Discrimination Experiments engine (GDX). The setting is that of a first person shooter game with the narrative background of an alien invasion on earth. We demonstrate the utility of our game as a research tool with an application focusing on attention to fine phonetic detail in natural speech perception. The game-based framework is additionally compared against a traditional experimental setup in an auditory discrimination task. Applications of this novel game-based framework are multifarious within studies on all aspects of spoken language perception.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{kicikoglu-etal-2020-aggregation,
    title = "Aggregation Driven Progression System for {GWAP}s",
    author = "Kicikoglu, Osman Doruk  and
      Bartle, Richard  and
      Chamberlain, Jon  and
      Paun, Silviu  and
      Poesio, Massimo",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.11",
    pages = "79--84",
    abstract = "As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@inproceedings{lin-etal-2020-automatic,
    title = "Automatic Annotation of Werewolf Game Corpus with Players Revealing Oneselves as Seer/Medium and Divination/Medium Results",
    author = "Lin, Youchao  and
      Kasamatsu, Miho  and
      Chen, Tengyang  and
      Fujita, Takuya  and
      Deng, Huanjin  and
      Utsuro, Takehito",
    booktitle = "Workshop on Games and Natural Language Processing",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.gamnlp-1.12",
    pages = "85--93",
    abstract = "While playing the communication game {``}Are You a Werewolf{''}, a player always guesses other players{'} roles through discussions, based on his own role and other players{'} crucial utterances. The underlying goal of this paper is to construct an agent that can analyze the participating players{'} utterances and play the werewolf game as if it is a human. For a step of this underlying goal, this paper studies how to accumulate werewolf game log data annotated with identification of players revealing oneselves as seer/medium, the acts of the divination and the medium and declaring the results of the divination and the medium. In this paper, we divide the whole task into four sub tasks and apply CNN/SVM classifiers to each sub task and evaluate their performance.",
    language = "English",
    ISBN = "979-10-95546-40-5",
}
@proceedings{framenet-2020-international,
    title = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    editor = "Torrent, Tiago T.  and
      Baker, Collin F.  and
      Czulo, Oliver  and
      Ohara, Kyoko  and
      Petruck, Miriam R. L.",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.0",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{czulo-etal-2020-beyond,
    title = "Beyond lexical semantics: notes on pragmatic frames",
    author = "Czulo, Oliver  and
      Ziem, Alexander  and
      Torrent, Tiago Timponi",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.1",
    pages = "1--7",
    abstract = "Framenets as an incarnation of frame semantics have been set up to deal with lexicographic issues (cf. Fillmore and Baker 2010, among others). They are thus concerned with lexical units (LUs) and the conceptual structure which categorizes these together. These lexically-evoked frames, however, do not reflect pragmatic properties of constructions (LUs and other types of constructions), such as expressing illocutions or being considered polite or very informal. From the viewpoint of a multilingual annotation effort, the Global FrameNet Shared Annotation Task, we discuss two phenomena, greetings and tag questions, which highlight the necessity both to investigate the role between construction and frame annotation on the one hand and to develop pragmatic frames describing social interactions which are not explicitly lexicalized.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{ohara-2020-finding,
    title = "Finding Corresponding Constructions in {E}nglish and {J}apanese in a {TED} Talk Parallel Corpus using Frames-and-Constructions Analysis",
    author = "Ohara, Kyoko",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.2",
    pages = "8--12",
    abstract = "This paper reports on an effort to search for corresponding constructions in English and Japanese in a TED Talk parallel corpus, using frames-and-constructions analysis (Ohara, 2019; Ohara and Okubo, 2020; cf. Czulo, 2013, 2017). The purpose of the paper is two-fold: (1) to demonstrate the validity of frames-and-constructions analysis to search for corresponding constructions in typologically unrelated languages; and (2) to assess whether the {``}Do schools kill creativity?{''} TED Talk parallel corpus, annotated in various languages for Multilingual FrameNet, is a good starting place for building a multilingual constructicon. The analysis showed that similar to our previous findings involving texts in a Japanese to English bilingual children{'}s book, the TED Talk bilingual transcripts include pairs of constructions that share similar pragmatic functions. While the TED Talk parallel corpus constitutes a good resource for frame semantic annotation in multiple languages, it may not be the ideal place to start aligning constructions among typologically unrelated languages. Finally, this work shows that the proposed method, which focuses on heads of sentences, seems valid for searching for corresponding constructions in transcripts of spoken data, as well as in written data of typologically-unrelated languages.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{remijnse-minnema-2020-towards,
    title = "Towards Reference-Aware {F}rame{N}et Annotation",
    author = "Remijnse, Levi  and
      Minnema, Gosse",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.3",
    pages = "13--22",
    abstract = "In this paper, we introduce the task of using FrameNet to link structured information about real-world events to the conceptual frames used in texts describing these events. We show that frames made relevant by the knowledge of the real-world event can be captured by complementing standard lexicon-driven FrameNet annotations with frame annotations derived through pragmatic inference. We propose a two-layered annotation scheme with a {`}strict{'} FrameNet-compatible lexical layer and a {`}loose{'} layer capturing frames that are inferred from referential data.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{belcavello-etal-2020-frame,
    title = "Frame-Based Annotation of Multimodal Corpora: Tracking (A)Synchronies in Meaning Construction",
    author = "Belcavello, Frederico  and
      Viridiano, Marcelo  and
      Diniz da Costa, Alexandre  and
      Matos, Ely Edison da Silva  and
      Torrent, Tiago Timponi",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.4",
    pages = "23--30",
    abstract = "Multimodal aspects of human communication are key in several applications of Natural Language Processing, such as Machine Translation and Natural Language Generation. Despite recent advances in integrating multimodality into Computational Linguistics, the merge between NLP and Computer Vision techniques is still timid, especially when it comes to providing fine-grained accounts for meaning construction. This paper reports on research aiming to determine appropriate methodology and develop a computational tool to annotate multimodal corpora according to a principled structured semantic representation of events, relations and entities: FrameNet. Taking a Brazilian television travel show as corpus, a pilot study was conducted to annotate the frames that are evoked by the audio and the ones that are evoked by visual elements. We also implemented a Multimodal Annotation tool which allows annotators to choose frames and locate frame elements both in the text and in the images, while keeping track of the time span in which those elements are active in each modality. Results suggest that adding a multimodal domain to the linguistic layer of annotation and analysis contributes both to enrich the kind of information that can be tagged in a corpus, and to enhance FrameNet as a model of linguistic cognition.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{postma-etal-2020-combining,
    title = "Combining Conceptual and Referential Annotation to Study Variation in Framing",
    author = "Postma, Marten  and
      Remijnse, Levi  and
      Ilievski, Filip  and
      Fokkens, Antske  and
      Titarsolej, Sam  and
      Vossen, Piek",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.5",
    pages = "31--40",
    abstract = "We introduce an annotation tool whose purpose is to gain insights into variation of framing by combining FrameNet annotation with referential annotation. English FrameNet enables researchers to study variation in framing at the conceptual level as well through its packaging in language. We enrich FrameNet annotations in two ways. First, we introduce the referential aspect. Secondly, we annotate on complete texts to encode connections between mentions. As a result, we can analyze the variation of framing for one particular event across multiple mentions and (cross-lingual) documents. We can examine how an event is framed over time and how core frame elements are expressed throughout a complete text. The data model starts with a representation of an event type. Each event type has many incidents linked to it, and each incident has several reference texts describing it as well as structured data about the incident. The user can apply two types of annotations: 1) mappings from expressions to frames and frame elements, 2) reference relations from mentions to events and participants of the structured data.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{marzinotto-2020-framenet,
    title = "{F}rame{N}et Annotations Alignment using Attention-based Machine Translation",
    author = "Marzinotto, Gabriel",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.6",
    pages = "41--47",
    abstract = "This paper presents an approach to project FrameNet annotations into other languages using attention-based neural machine translation (NMT) models. The idea is to use an NMT encoder-decoder attention matrix to propose a word-to-word correspondence between the source and the target language. We combine this word alignment along with a set of simple rules to securely project the FrameNet annotations into the target language. We successfully implemented, evaluated and analyzed this technique on the English-to-French configuration. First, we analyze the obtained FrameNet lexicon qualitatively. Then, we use existing French FrameNet corpora to assert the quality of the translation. Finally, we trained a BERT-based FrameNet parser using the projected annotations and compared it to a BERT baseline. Results show substantial improvements in the French language, giving evidence to support that our approach could help to propagate FrameNet data-set on other languages.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{giouli-etal-2020-greek,
    title = "{G}reek within the Global {F}rame{N}et Initiative: Challenges and Conclusions so far",
    author = "Giouli, Voula  and
      Pilitsidou, Vera  and
      Christopoulos, Hephaestion",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.7",
    pages = "48--55",
    abstract = "Large coverage lexical resources that bear deep linguistic information have always been considered useful for many natural language processing (NLP) applications including Machine Translation (MT). In this respect, Frame-based resources have been developed for many languages following Frame Semantics and the Berkeley FrameNet project. However, to a great extent, all those efforts have been kept fragmented. Consequentially, the Global FrameNet initiative has been conceived of as a joint effort to bring together FrameNets in different languages. The proposed paper is aimed at describing ongoing work towards developing the Greek (EL) counterpart of the Global FrameNet and our efforts to contribute to the Shared Annotation Task. In the paper, we will elaborate on the annotation methodology employed, the current status and progress made so far, as well as the problems raised during annotation.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{lee-etal-2020-using,
    title = "Using Verb Frames for Text Difficulty Assessment",
    author = "Lee, John  and
      Liu, Meichun  and
      Cai, Tianyuan",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.8",
    pages = "56--62",
    abstract = "This paper presents the first investigation on using semantic frames to assess text difficulty. Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, we examine usage patterns of ten verbs in a corpus of graded Chinese texts. We identify a number of characteristics in texts at advanced grades: more frequent use of non-core frame elements; more frequent omission of some core frame elements; increased preference for noun phrases rather than clauses as verb arguments; and more frequent metaphoric usage. These characteristics can potentially be useful for automatic prediction of text readability.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{gruzitis-etal-2020-deriving,
    title = "Deriving a {P}rop{B}ank Corpus from Parallel {F}rame{N}et and {UD} Corpora",
    author = "Gruzitis, Normunds  and
      Dar{\c{g}}is, Roberts  and
      Rituma, Laura  and
      Ne{\v{s}}pore-B{\=e}rzkalne, Gunta  and
      Saulite, Baiba",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.9",
    pages = "63--69",
    abstract = "We propose an approach for generating an accurate and consistent PropBank-annotated corpus, given a FrameNet-annotated corpus which has an underlying dependency annotation layer, namely, a parallel Universal Dependencies (UD) treebank. The PropBank annotation layer of such a multi-layer corpus can be semi-automatically derived from the existing FrameNet and UD annotation layers, by providing a mapping configuration from lexical units in [a non-English language] FrameNet to [English language] PropBank predicates, and a mapping configuration from FrameNet frame elements to PropBank semantic arguments for the given pair of a FrameNet frame and a PropBank predicate. The latter mapping generally depends on the underlying UD syntactic relations. To demonstrate our approach, we use Latvian FrameNet, annotated on top of Latvian UD Treebank, for generating Latvian PropBank in compliance with the Universal Propositions approach.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{gargett-leung-2020-building,
    title = "Building the Emirati {A}rabic {F}rame{N}et",
    author = "Gargett, Andrew  and
      Leung, Tommi",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.10",
    pages = "70--76",
    abstract = "The Emirati Arabic FrameNet (EAFN) project aims to initiate a FrameNet for Emirati Arabic, utilizing the Emirati Arabic Corpus. The goal is to create a resource comparable to the initial stages of the Berkeley FrameNet. The project is divided into manual and automatic tracks, based on the predominant techniques being used to collect frames in each track. Work on the EAFN is progressing, and we here report on initial results for annotations and evaluation. The EAFN project aims to provide a general semantic resource for the Arabic language, sure to be of interest to researchers from general linguistics to natural language processing. As we report here, the EAFN is well on target for the first release of data in the coming year.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{baker-lorenzi-2020-exploring,
    title = "Exploring Crosslinguistic Frame Alignment",
    author = "Baker, Collin F.  and
      Lorenzi, Arthur",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.11",
    pages = "77--84",
    abstract = "The FrameNet (FN) project at the International Computer Science Institute in Berkeley (ICSI), which documents the core vocabulary of contemporary English, was the first lexical resource based on Fillmore{'}s theory of Frame Semantics. Berkeley FrameNet has inspired related projects in roughly a dozen other languages, which have evolved somewhat independently; the current Multilingual FrameNet project (MLFN) is an attempt to find alignments between all of them. The alignment problem is complicated by the fact that these projects have adhered to the Berkeley FrameNet model to varying degrees, and they were also founded at different times, when different versions of the Berkeley FrameNet data were available. We describe several new methods for finding relations of similarity between semantic frames across languages. We will demonstrate ViToXF, a new tool which provides interactive visualizations of these cross-lingual relations, between frames, lexical units, and frame elements, based on resources such as multilingual dictionaries and on shared distributional vector spaces, making clear the strengths and weaknesses of different alignment methods.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@inproceedings{l-homme-etal-2020-building,
    title = "Building Multilingual Specialized Resources Based on {F}rame{N}et: Application to the Field of the Environment",
    author = "L{'} Homme, Marie-Claude  and
      Robichaud, Beno{\^\i}t  and
      Subirats, Carlos",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.framenet-1.12",
    pages = "85--92",
    abstract = "The methodology developed within the FrameNet project is being used to compile resources in an increasing number of specialized fields of knowledge. The methodology along with the theoretical principles on which it is based, i.e. Frame Semantics, are especially appealing as they allow domain-specific resources to account for the conceptual background of specialized knowledge and to explain the linguistic properties of terms against this background. This paper presents a methodology for building a multilingual resource that accounts for terms of the environment. After listing some lexical and conceptual differences that need to be managed in such a resource, we explain how the FrameNet methodology is adapted for describing terms in different languages. We first applied our methodology to French and then extended it to English. Extensions to Spanish, Portuguese and Chinese were made more recently. Up to now, we have defined 190 frames: 112 frames are new; 38 are used as such; and 40 are slightly different (a different number of obligatory participants; a significant alternation, etc.) when compared to Berkeley FrameNet.",
    language = "English",
    ISBN = "979-10-95546-58-0",
}
@proceedings{fnp-2020-joint,
    title = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    editor = "El-Haj, Dr Mahmoud  and
      Athanasakou, Dr Vasiliki  and
      Ferradans, Dr Sira  and
      Salzedo, Dr Catherine  and
      Elhag, Dr Ans  and
      Bouamor, Dr Houda  and
      Litvak, Dr Marina  and
      Rayson, Dr Paul  and
      Giannakopoulos, Dr George  and
      Pittaras, Nikiforos",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.0",
}
@inproceedings{el-haj-etal-2020-financial,
    title = "The Financial Narrative Summarisation Shared Task ({FNS} 2020)",
    author = "El-Haj, Mahmoud  and
      AbuRa{'}ed, Ahmed  and
      Litvak, Marina  and
      Pittaras, Nikiforos  and
      Giannakopoulos, George",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.1",
    pages = "1--12",
    abstract = "This paper presents the results and findings of the Financial Narrative Summarisation shared task (FNS 2020) on summarising UK annual reports. The shared task was organised as part of the 1st Financial Narrative Processing and Financial Narrative Summarisation Workshop (FNP-FNS 2020). The shared task included one main task which is the use of either abstractive or extractive summarisation methodologies and techniques to automatically summarise UK financial annual reports. FNS summarisation shared task is the first to target financial annual reports. The data for the shared task was created and collected from publicly available UK annual reports published by firms listed on the London Stock Exchange (LSE). A total number of 24 systems from 9 different teams participated in the shared task. In addition we had 2 baseline summarisers and additional 2 topline summarisers to help evaluate and compare against the results of the participants.",
}
@inproceedings{bentabet-etal-2020-financial,
    title = "The Financial Document Structure Extraction Shared task ({F}in{T}oc 2020)",
    author = "Bentabet, Najah-Imane  and
      Juge, R{\'e}mi  and
      El Maarouf, Ismail  and
      Mouilleron, Virginie  and
      Valsamou-Stanislawski, Dialekti  and
      El-Haj, Mahmoud",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.2",
    pages = "13--22",
    abstract = "This paper presents the FinTOC-2020 Shared Task on structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING{'}2020). This shared task aimed to stimulate research in systems for extracting table-of-contents (TOC) from investment documents (such as financial prospectuses) by detecting the document titles and organizing them hierarchically into a TOC. For the second edition of this shared task, two subtasks were presented to the participants: one with English documents and the other one with French documents.",
}
@inproceedings{mariko-etal-2020-financial,
    title = "The Financial Document Causality Detection Shared Task ({F}in{C}ausal 2020)",
    author = "Mariko, Dominique  and
      Abi-Akl, Hanna  and
      Labidurie, Estelle  and
      Durfort, Stephane  and
      De Mazancourt, Hugues  and
      El-Haj, Mahmoud",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.3",
    pages = "23--32",
    abstract = "We present the FinCausal 2020 Shared Task on Causality Detection in Financial Documents and the associated FinCausal dataset, and discuss the participating systems and results. Two sub-tasks are proposed: a binary classification task (Task 1) and a relation extraction task (Task 2). A total of 16 teams submitted runs across the two Tasks and 13 of them contributed with a system description paper. This workshop is associated to the Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (FNP-FNS 2020), held at The 28th International Conference on Computational Linguistics (COLING{'}2020), Barcelona, Spain on September 12, 2020.",
}
@inproceedings{agarwal-etal-2020-langresearchlab,
    title = "{L}ang{R}esearch{L}ab{\_}{NC} at {F}in{C}ausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection",
    author = "Agarwal, Raksha  and
      Verma, Ishaan  and
      Chatterjee, Niladri",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.4",
    pages = "33--39",
    abstract = "Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.",
}
@inproceedings{becquin-2020-gbe,
    title = "{GB}e at {F}in{C}ausal 2020, Task 2: Span-based Causality Extraction for Financial Documents",
    author = "Becquin, Guillaume",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.5",
    pages = "40--44",
    abstract = "This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used to detect the number of cause and effect clauses in a document and extract them. The proposed system achieved a weighted-average F1 score of more than 95{\%} for the official blind test set during the post-evaluation phase and exact clauses match for 83{\%} of the documents.",
}
@inproceedings{gordeev-etal-2020-liori,
    title = "{LIORI} at the {F}in{C}ausal 2020 Shared task",
    author = "Gordeev, Denis  and
      Davletov, Adis  and
      Rey, Alexey  and
      Arefiev, Nikolay",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.6",
    pages = "45--49",
    abstract = "In this paper, we describe the results of team LIORI at the FinCausal 2020 Shared task held as a part of the 1st Joint Workshop on Financial Narrative Processing and MultiLingual Financial Summarisation. The shared task consisted of two subtasks: classifying whether a sentence contains any causality and labelling phrases that indicate causes and consequences. Our team ranked 1st in the first subtask and 4th in the second one. We used Transformer-based models with joint-task learning and their ensembles.",
}
@inproceedings{imoto-ito-2020-jdd,
    title = "{JDD} @ {F}in{C}ausal 2020, Task 2: Financial Document Causality Detection",
    author = "Imoto, Toshiya  and
      Ito, Tomoki",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.7",
    pages = "50--54",
    abstract = "This paper describes the approach we built for the Financial Document Causality Detection Shared Task (FinCausal-2020) Task 2: Cause and Effect Detection. Our approach is based on a multi-class classifier using BiLSTM with Graph Convolutional Neural Network (GCN) trained by minimizing the binary cross entropy loss. In our approach, we have not used any extra data source apart from combining the trial and practice dataset. We achieve weighted F1 score to 75.61 percent and are ranked at 7-th place.",
}
@inproceedings{ionescu-etal-2020-upb,
    title = "{UPB} at {F}in{C}ausal-2020, Tasks 1 {\&} 2: Causality Analysis in Financial Documents using Pretrained Language Models",
    author = "Ionescu, Marius  and
      Avram, Andrei-Marius  and
      Dima, George-Andrei  and
      Cercel, Dumitru-Clementin  and
      Dascalu, Mihai",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.8",
    pages = "55--59",
    abstract = "Financial causality detection is centered on identifying connections between different assets from financial news in order to improve trading strategies. FinCausal 2020 - Causality Identification in Financial Documents {--} is a competition targeting to boost results in financial causality by obtaining an explanation of how different individual events or chain of events interact and generate subsequent events in a financial environment. The competition is divided into two tasks: (a) a binary classification task for determining whether sentences are causal or not, and (b) a sequence labeling task aimed at identifying elements related to cause and effect. Various Transformer-based language models were fine-tuned for the first task and we obtained the second place in the competition with an F1-score of 97.55{\%} using an ensemble of five such language models. Subsequently, a BERT model was fine-tuned for the second task and a Conditional Random Field model was used on top of the generated language features; the system managed to identify the cause and effect relationships with an F1-score of 73.10{\%}. We open-sourced the code and made it available at: https://github.com/avramandrei/FinCausal2020.",
}
@inproceedings{r-l-m-2020-nitk,
    title = "{NITK} {NLP} at {F}in{C}ausal-2020 Task 1 Using {BERT} and Linear models.",
    author = "R L, Hariharan  and
      M, Anand Kumar",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.9",
    pages = "60--63",
    abstract = "FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don{'}t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.",
}
@inproceedings{pielka-etal-2020-fraunhofer,
    title = "Fraunhofer {IAIS} at {F}in{C}ausal 2020, Tasks 1 {\&} 2: Using Ensemble Methods and Sequence Tagging to Detect Causality in Financial Documents",
    author = "Pielka, Maren  and
      Ramamurthy, Rajkumar  and
      Ladi, Anna  and
      Brito, Eduardo  and
      Chapman, Clayton  and
      Mayer, Paul  and
      Sifa, Rafet",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.10",
    pages = "64--68",
    abstract = "The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods for identifying causality, and extracting causal subsequences. Our models yield promising results on both sub-tasks, with the prospect of further improvement given more time and computing resources. With respect to task 1, we achieved an F1 score of 0.9429 on the evaluation data, and a corresponding ranking of 12/14. For task 2, we were ranked 6/10, with an F1 score of 0.76 and an ExactMatch score of 0.1912.",
}
@inproceedings{kao-etal-2020-ntunlpl,
    title = "{NTUNLPL} at {F}in{C}ausal 2020, Task 2:Improving Causality Detection Using {V}iterbi Decoder",
    author = "Kao, Pei-Wei  and
      Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.11",
    pages = "69--73",
    abstract = "In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in financial news and propose an approach, which combines the BIO scheme with the Viterbi decoder for addressing this challenge. Our approach is ranked the first in the official run of cause-effect detection (Task 2) of the FinCausal-2020 shared task. We not only report the implementation details and ablation analysis in this paper, but also publish our code for academic usage.",
}
@inproceedings{gupta-2020-finlp,
    title = "{F}i{NLP} at {F}in{C}ausal 2020 Task 1: Mixture of {BERT}s for Causal Sentence Identification in Financial Texts",
    author = "Gupta, Sarthak",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.12",
    pages = "74--79",
    abstract = "This paper describes our system developed for the sub-task 1 of the FinCausal shared task in the FNP-FNS workshop held in conjunction with COLING-2020. The system classifies whether a financial news text segment contains causality or not. To address this task, we fine-tune and ensemble the generic and domain-specific BERT language models pre-trained on financial text corpora. The task data is highly imbalanced with the majority non-causal class; therefore, we train the models using strategies such as under-sampling, cost-sensitive learning, and data augmentation. Our best system achieves a weighted F1-score of 96.98 securing 4th position on the evaluation leaderboard. The code is available at https://github.com/sarthakTUM/fincausal",
}
@inproceedings{szanto-berend-2020-prosperamnet,
    title = "{P}rosper{AM}net at {F}in{C}ausal 2020, Task 1 {\&} 2: Modeling causality in financial texts using multi-headed transformers",
    author = "Sz{\'a}nt{\'o}, Zsolt  and
      Berend, G{\'a}bor",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.13",
    pages = "80--84",
    abstract = "This paper introduces our efforts at the FinCasual shared task for modeling causality in financial utterances. Our approach uses the commonly and successfully applied strategy of fine-tuning a transformer-based language model with a twist, i.e. we modified the training and inference mechanism such that our model produces multiple predictions for the same instance. By designing such a model that returns k{\textgreater}1 predictions at the same time, we not only obtain a more resource efficient training (as opposed to fine-tuning some pre-trained language model k independent times), but our results indicate that we are also capable of obtaining comparable or even better evaluation scores that way. We compare multiple strategies for combining the k predictions of our model. Our submissions got ranked third on both subtasks of the shared task.",
}
@inproceedings{ozenir-karadeniz-2020-isikun,
    title = "{ISIKUN} at the {F}in{C}ausal 2020: Linguistically informed Machine-learning Approach for Causality Identification in Financial Documents",
    author = {{\"O}zenir, G{\"o}kberk  and
      Karadeniz, {\.I}lknur},
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.14",
    pages = "85--89",
    abstract = "This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94{\%}) , which requires less time compared to the BERT-based baseline (F-score 95{\%}). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52{\%}), we have obtained better results (F-score 60{\%}). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).",
}
@inproceedings{chakravarthy-etal-2020-domino,
    title = "Domino at {F}in{C}ausal 2020, Task 1 and 2: Causal Extraction System",
    author = "Chakravarthy, Sharanya  and
      Kanakagiri, Tushar  and
      Radhakrishnan, Karthik  and
      Umapathy, Anjana",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.15",
    pages = "90--94",
    abstract = "Automatic identification of cause-effect relationships from data is a challenging but important problem in artificial intelligence. Identifying semantic relationships has become increasingly important for multiple downstream applications like Question Answering, Information Retrieval and Event Prediction. In this work, we tackle the problem of causal relationship extraction from financial news using the FinCausal 2020 dataset. We tackle two tasks - 1) Detecting the presence of causal relationships and 2) Extracting segments corresponding to cause and effect from news snippets. We propose Transformer based sequence and token classification models with post-processing rules which achieve an F1 score of 96.12 and 79.60 on Tasks 1 and 2 respectively.",
}
@inproceedings{mitra-etal-2020-iitkgp,
    title = "{IIT}kgp at {F}in{C}ausal 2020, Shared Task 1: Causality Detection using Sentence Embeddings in Financial Reports",
    author = "Mitra, Arka  and
      Srivastava, Harshvardhan  and
      Tiwari, Yugam",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.16",
    pages = "95--99",
    abstract = "The paper describes the work that the team submitted to FinCausal 2020 Shared Task. This work is associated with the first sub-task of identifying causality in sentences. The various models used in the experiments tried to obtain a latent space representation for each of the sentences. Linear regression was performed on these representations to classify whether the sentence is causal or not. The experiments have shown BERT (Large) performed the best, giving a F1 score of 0.958, in the task of detecting the causality of sentences in financial texts and reports. The class imbalance was dealt with a modified loss function to give a better metric score for the evaluation.",
}
@inproceedings{li-etal-2020-extractive,
    title = "Extractive Financial Narrative Summarisation based on {DPP}s",
    author = "Li, Lei  and
      Jiang, Yafei  and
      Liu, Yinan",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.17",
    pages = "100--104",
    abstract = "We participate in the FNS-Summarisation 2020 shared task to be held at FNP 2020 workshop at COLING 2020. Based on Determinantal Point Processes (DPPs), we build an extractive automatic financial summarisation system for the specific task. In this system, we first analyze the long report data to select the important narrative parts and generate an intermediate document. Next, we build the kernel Matrix L for the intermediate document, which represents the quality of its sentences. On the basis of L, we then can use the DPPs sampling algorithm to choose those sentences with high quality and diversity as the final summary sentences.",
}
@inproceedings{singh-2020-point,
    title = "{P}oin{T}-5: Pointer Network and {T}-5 based Financial Narrative Summarisation",
    author = "Singh, Abhishek",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.18",
    pages = "105--111",
    abstract = "Companies provide annual reports to their shareholders at the end of the financial year that de-scribes their operations and financial conditions. The average length of these reports is 80, andit may extend up to 250 pages long. In this paper, we propose our methodology PoinT-5 (thecombination of Pointer Network and T-5 (Test-to-text transfer Transformer) algorithms) that weused in the Financial Narrative Summarisation (FNS) 2020 task. The proposed method usesPointer networks to extract important narrative sentences from the report, and then T-5 is used toparaphrase extracted sentences into a concise yet informative sentence. We evaluate our methodusing Rouge-N (1,2), L, and SU4. The proposed method achieves the highest precision scores inall the metrics and highest F1 scores in three out of four evaluation metrics that are Rouge 1, 2,and LCS and only solution to cross MUSE solution baseline in Rouge-LCS metrics.",
}
@inproceedings{baldeon-suarez-etal-2020-combining,
    title = "Combining financial word embeddings and knowledge-based features for financial text summarization {UC}3{M}-{MC} System at {FNS}-2020",
    author = "Baldeon Suarez, Jaime  and
      Mart{\'\i}nez, Paloma  and
      Mart{\'\i}nez, Jose Luis",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.19",
    pages = "112--117",
    abstract = "This paper describes the systems proposed by HULAT research group from Universidad Carlos III de Madrid (UC3M) and MeaningCloud (MC) company to solve the FNS 2020 Shared Task on summarizing financial reports. We present a narrative extractive approach that implements a statistical model comprised of different features that measure the relevance of the sentences using a combination of statistical and machine learning methods. The key to the model{'}s performance is its accurate representation of the text, since the word embeddings used by the model have been trained with the summaries of the training dataset and therefore capture the most salient information from the reports. The systems{'} code can be found at https://github.com/jaimebaldeon/FNS-2020.",
}
@inproceedings{la-quatra-cagliero-2020-end,
    title = "End-to-end Training For Financial Report Summarization",
    author = "La Quatra, Moreno  and
      Cagliero, Luca",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.20",
    pages = "118--123",
    abstract = "Quoted companies are requested to periodically publish financial reports in textual form. The annual financial reports typically include detailed financial and business information, thus giving relevant insights into company outlooks. However, a manual exploration of these financial reports could be very time consuming since most of the available information can be deemed as non-informative or redundant by expert readers. Hence, an increasing research interest has been devoted to automatically extracting domain-specific summaries, which include only the most relevant information. This paper describes the SumTO system architecture, which addresses the Shared Task of the Financial Narrative Summarisation (FNS) 2020 contest. The main task objective is to automatically extract the most informative, domain-specific textual content from financial, English-written documents. The aim is to create a summary of each company report covering all the business-relevant key points. To address the above-mentioned goal, we propose an end-to-end training method relying on Deep NLP techniques. The idea behind the system is to exploit the syntactic overlap between input sentences and ground-truth summaries to fine-tune pre-trained BERT embedding models, thus making such models tailored to the specific context. The achieved results confirm the effectiveness of the proposed method, especially when the goal is to select relatively long text snippets.",
}
@inproceedings{litvak-etal-2020-sce,
    title = "{SCE}-{SUMMARY} at the {FNS} 2020 shared task",
    author = "Litvak, Marina  and
      Vanetik, Natalia  and
      Puchinsky, Zvi",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.21",
    pages = "124--129",
    abstract = "With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it{'}s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special {``}language{''}. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.",
}
@inproceedings{vhatkar-etal-2020-knowledge,
    title = "Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples",
    author = "Vhatkar, Amit  and
      Bhattacharyya, Pushpak  and
      Arya, Kavi",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.22",
    pages = "130--136",
    abstract = "In our research work, we represent the content of the sentence in graphical form after extracting triples from the sentences. In this paper, we will discuss novel methods to generate an extractive summary by scoring the triples. Our work has also touched upon sequence-to-sequence encoding of the content of the sentence, to classify it as a summary or a non-summary sentence. Our findings help to decide the nature of the sentences forming the summary and the length of the system generated summary as compared to the length of the reference summary.",
}
@inproceedings{arora-radhakrishnan-2020-amex,
    title = "{AMEX} {AI}-Labs: An Investigative Study on Extractive Summarization of Financial Documents",
    author = "Arora, Piyush  and
      Radhakrishnan, Priya",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.23",
    pages = "137--142",
    abstract = "We describe the work carried out by AMEX AI-LABS on an extractive summarization benchmark task focused on Financial Narratives Summarization (FNS). This task focuses on summarizing annual financial reports which poses two main challenges as compared to typical news document summarization tasks : i) annual reports are more lengthier (average length about 80 pages) as compared to typical news documents, and ii) annual reports are more loosely structured e.g. comprising of tables, charts, textual data and images, which makes it challenging to effectively summarize. To address this summarization task we investigate a range of unsupervised, supervised and ensemble based techniques. We find that ensemble based techniques perform relatively better as compared to using only the unsupervised and supervised based techniques. Our ensemble based model achieved the highest rank of 9 out of 31 systems submitted for the benchmark task based on Rouge-L evaluation metric.",
}
@inproceedings{ait-azzi-kang-2020-extractive,
    title = "Extractive Summarization System for Annual Reports",
    author = "Ait Azzi, Abderrahim  and
      Kang, Juyeon",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.24",
    pages = "143--147",
    abstract = "In this paper, we report on our experiments in building a summarization system for generating summaries from annual reports. We adopt an {``}extractive{''} summarization approach in our hybrid system combining neural networks and rules-based algorithms with the expectation that such a system may capture key sentences or paragraphs from the data. A rules-based TOC (Table Of Contents) extraction and a binary classifier of narrative section titles are main components of our system allowing to identify narrative sections and best candidates for extracting final summaries. As result, we propose one to three summaries per document according to the classification score of narrative section titles.",
}
@inproceedings{zheng-etal-2020-sumsum,
    title = "{SUMSUM}@{FNS}-2020 Shared Task",
    author = "Zheng, Siyan  and
      Lu, Anneliese  and
      Cardie, Claire",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.25",
    pages = "148--152",
    abstract = "This paper describes the SUMSUM systems submitted to the Financial Narrative Summarization Shared Task (FNS-2020). We explore a section-based extractive summarization method tailored to the structure of financial reports: our best system parses the report Table of Contents (ToC), splits the report into narrative sections based on the ToC, and applies a BERT-based classifier to each section to determine whether it should be included in the summary. Our best system ranks 4{\textless}sup{\textgreater}th{\textless}/sup{\textgreater}, 1{\textless}sup{\textgreater}st{\textless}/sup{\textgreater}, 2{\textless}sup{\textgreater}nd{\textless}/sup{\textgreater} and 17{\textless}sup{\textgreater}th{\textless}/sup{\textgreater} on the Rouge-1, Rouge-2, Rouge-SU4, and Rouge-L official metrics, respectively. We also report results on the validation set using an alternative set of Rouge-based metrics that measure performance with respect to the best-matching of the available gold summaries.",
}
@inproceedings{premi-etal-2020-amex,
    title = "{AMEX}-{AI}-{LABS}: Investigating Transfer Learning for Title Detection in Table of Contents Generation",
    author = "Premi, Dhruv  and
      Badugu, Amogh  and
      Sharad Bhatt, Himanshu",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.26",
    pages = "153--157",
    abstract = "We present a transfer learning approach for Title Detection in FinToC 2020 challenge. Our proposed approach relies on the premise that the geometric layout and character features of the titles and non-titles can be learnt separately from a large corpus, and their learning can then be transferred to a domain-specific dataset. On a domain-specific dataset, we train a Deep Neural Net on the text of the document along with a pre-trained model for geometric and character features. We achieved an F-Score of 83.25 on the test set and secured top rank in the title detection task in FinToC 2020.",
}
@inproceedings{hercig-kral-2020-uwb,
    title = "{UWB}@{F}in{TOC}-2020 Shared Task: Financial Document Title Detection",
    author = "Hercig, Tom{\'a}{\v{s}}  and
      Kral, Pavel",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.27",
    pages = "158--162",
    abstract = "This paper describes our system created for the Financial Document Structure Extraction Shared Task (FinTOC-2020): Title Detection. We rely on the Apache PDFBox library to extract text and all additional information e.g. font type and font size from the financial prospectuses. Our constrained system uses only the provided training data without any additional external resources. Our system is based on the Maximum Entropy classifier and various features including font type and font size. Our system achieves F1 score 81{\%} and {\#}1 place in the French track and F1 score 77{\%} and {\#}2 place among 5 participating teams in the English track.",
}
@inproceedings{haase-kirchhoff-2020-taxy,
    title = "Taxy.io@{F}in{TOC}-2020: Multilingual Document Structure Extraction using Transfer Learning",
    author = "Haase, Frederic  and
      Kirchhoff, Steffen",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.28",
    pages = "163--168",
    abstract = "In this paper we describe our system submitted to the FinTOC-2020 shared task on financial doc- ument structure extraction. We propose a two-step approach to identify titles in financial docu- ments and to extract their table of contents (TOC). First, we identify text blocks as candidates for titles using unsupervised learning based on character-level information of each document. Then, we apply supervised learning on a self-constructed regression task to predict the depth of each text block in the document structure hierarchy using transfer learning combined with document features and layout features. It is noteworthy that our single multilingual model performs well on both tasks and on different languages, which indicates the usefulness of transfer learning for title detection and TOC generation. Moreover, our approach is independent of the presence of actual TOC pages in the documents. It is also one of the few submissions to the FinTOC-2020 shared task addressing both subtasks in both languages, English and French, with one single model.",
}
@inproceedings{kosmajac-etal-2020-dnlp,
    title = "{DNLP}@{F}in{TOC}{'}20: Table of Contents Detection in Financial Documents",
    author = "Kosmajac, Dijana  and
      Taylor, Stacey  and
      Saeidi, Mozhgan",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.29",
    pages = "169--173",
    abstract = "Title Detection and Table of Contents Generation are important components in detecting document structure. In particular, these two elements serve to provide the skeleton of the document, providing users with an understanding of organization, as well as the relevance of information, and where to find information within the document. Here, we show that using tesseract with Levenstein distance, a feature set inspired by Alk et al., we were able to correctly classify the title to an F1 measure 0.73 and 0.87, and the table-of-contents to a harmonic mean of 0.36 and 0.39, in English and French respectively. Our methodology works with both PDF and scanned documents, giving it a wide range of applicability within the document engineering and storage domains.",
}
@inproceedings{giguet-etal-2020-daniel,
    title = "Daniel@{F}in{TOC}{'}2 Shared Task: Title Detection and Structure Extraction",
    author = {Giguet, Emmanuel  and
      Lejeune, Ga{\"e}l  and
      Tanguy, Jean-Baptiste},
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.30",
    pages = "174--180",
    abstract = "We present our contributions for the 2020 FinTOC Shared Tasks: Title Detection and Table of Contents Extraction. For the Structure Extraction task, we propose an approach that combines information from multiple sources: the table of contents, the wording of the document, and lexical domain knowledge. For the title detection task, we compare surface features to character-based features on various training configurations. We show that title detection results are very sensitive to the kind of training dataset used.",
}
@inproceedings{armbrust-etal-2020-computational,
    title = "A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors",
    author = {Armbrust, Felix  and
      Sch{\"a}fer, Henry  and
      Klinger, Roman},
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.31",
    pages = "181--194",
    abstract = "Public companies are obliged to include financial and non-financial information within their cor- porate filings under Regulation S-K, in the United States (SEC, 2010). However, the requirements still allow for manager{'}s discretion. This raises the question to which extent the information is actually included and if this information is at all relevant for investors. We answer this question by training and evaluating an end-to-end deep learning approach (based on BERT and GloVe embeddings) to predict the financial and environmental performance of the company from the {``}Management{'}s Discussion and Analysis of Financial Conditions and Results of Operations{''} (MD{\&}A) section of 10-K (yearly) and 10-Q (quarterly) filings. We further analyse the mediating effect of the environmental performance on the relationship between the company{'}s disclosures and financial performance. Hereby, we address the results of previous studies regarding environ- mental performance. We find that the textual information contained within the MD{\&}A section does not allow for conclusions about the future (corporate) financial performance. However, there is evidence that the environmental performance can be extracted by natural language processing methods.",
}
@inproceedings{frunza-2020-information,
    title = "Information Extraction from Federal Open Market Committee Statements",
    author = "Frunza, Oana",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.32",
    pages = "195--203",
    abstract = "We present a novel approach to unsupervised information extraction by identifying and extracting relevant concept-value pairs from textual data. The system{'}s building blocks are domain agnostic, making it universally applicable. In this paper, we describe each component of the system and how it extracts relevant economic information from U.S. Federal Open Market Committee (FOMC) statements. Our methodology achieves an impressive 96{\%} accuracy for identifying relevant information for a set of seven economic indicators: household spending, inflation, unemployment, economic activity, fixed in-vestment, federal funds rate, and labor market.",
}
@inproceedings{manandise-de-peuter-2020-mitigating,
    title = "Mitigating Silence in Compliance Terminology during Parsing of Utterances",
    author = "Manandise, Esme  and
      de Peuter, Conrad",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.33",
    pages = "204--212",
    abstract = "This paper reports on an approach to increase multi-token-term recall in a parsing task. We use a compliance-domain parser to extract, during the process of parsing raw text, terms that are unlisted in the terminology. The parser uses a similarity measure (Generalized Dice Coefficient) between listed terms and unlisted term candidates to (i) determine term status, (ii) serve putative terms to the parser, (iii) decrease parsing complexity by glomming multi-tokens as lexical singletons, and (iv) automatically augment the terminology after parsing of an utterance completes. We illustrate a small experiment with examples from the tax-and-regulations domain. Bootstrapping the parsing process to detect out- of-vocabulary terms at runtime increases parsing accuracy in addition to producing other benefits to a natural-language-processing pipeline, which translates arithmetic calculations written in English into computer-executable operations.",
}
@inproceedings{litvak-etal-2020-hierarchical,
    title = "Hierarchical summarization of financial reports with {RUNNER}",
    author = "Litvak, Marina  and
      Vanetik, Natalia  and
      Puchinsky, Zvi",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.34",
    pages = "213--225",
    abstract = "With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it{'}s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special {``}language{''}. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.",
}
@inproceedings{theil-stuckenschmidt-2020-predicting,
    title = "Predicting Modality in Financial Dialogue",
    author = "Theil, Kilian  and
      Stuckenschmidt, Heiner",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.35",
    pages = "226--234",
    abstract = "In this paper, we perform modality prediction in financial dialogue. To this end, we introduce a new dataset and develop a binary classifier to detect strong or weak modal answers depending on surface, lexical, and semantic representations of the preceding question and financial features. To do so, we contrast different algorithms, feature categories, and fusion methods. Perhaps counter-intuitively, our results indicate that the strongest features for the given task are financial uncertainty measures such as market and individual firm risk.",
}
@inproceedings{jacobs-hoste-2020-extracting,
    title = "Extracting Fine-Grained Economic Events from Business News",
    author = "Jacobs, Gilles  and
      Hoste, Veronique",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/2020.fnp-1.36",
    pages = "235--245",
    abstract = "Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50{\%} are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap.",
}
@proceedings{finnlp-2020-financial,
    title = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    editor = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.0",
}
@inproceedings{jiang-etal-2020-financial,
    title = "Financial News Annotation by Weakly-Supervised Hierarchical Multi-label Learning",
    author = "Jiang, Hang  and
      Miao, Zhongchen  and
      Lin, Yuefeng  and
      Wang, Chenyu  and
      Ni, Mengjun  and
      Gao, Jian  and
      Lu, Jidong  and
      Shi, Guangwei",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.1",
    pages = "1--7",
}
@inproceedings{montariol-etal-2020-variations,
    title = "Variations in Word Usage for the Financial Domain",
    author = "Montariol, Syrielle  and
      Allauzen, Alexandre  and
      Kitamoto, Asanobu",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.2",
    pages = "8--14",
}
@inproceedings{masson-montariol-2020-detecting,
    title = "Detecting Omissions of Risk Factors in Company Annual Reports",
    author = "Masson, Corentin  and
      Montariol, Syrielle",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.3",
    pages = "15--21",
}
@inproceedings{chen-sarkar-2020-semantic,
    title = "A Semantic Approach to Financial Fundamentals",
    author = "Chen, Jiafeng  and
      Sarkar, Suproteem",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.4",
    pages = "22--26",
}
@inproceedings{ito-etal-2020-learning,
    title = "Learning Company Embeddings from Annual Reports for Fine-grained Industry Characterization",
    author = "Ito, Tomoki  and
      Collados, Jose Camacho  and
      Sakaji, Hiroki  and
      Schockaert, Steven",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.5",
    pages = "27--33",
}
@inproceedings{edmiston-park-2020-unsupervised,
    title = "Unsupervised Discovery of Firm-Level Variables in Earnings Call Transcript Embeddings",
    author = "Edmiston, Daniel  and
      Park, Ziho",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.6",
    pages = "34--39",
}
@inproceedings{taylor-keselj-2020-using,
    title = "Using Extractive Lexicon-based Sentiment Analysis to Enhance Understanding ofthe Impact of Non-{GAAP} Measures in Financial Reporting",
    author = "Taylor, Stacey  and
      Keselj, Vlado",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.7",
    pages = "40--46",
}
@inproceedings{au-etal-2020-finsbd,
    title = "{F}in{SBD}-2020: The 2nd Shared Task on Sentence Boundary Detection in Unstructured Text in the Financial Domain",
    author = "Au, Willy  and
      Chong, Bianca  and
      Azzi, Abderrahim Ait  and
      Valsamou-Stanislawski, Dialekti",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.8",
    pages = "47--54",
}
@inproceedings{singh-2020-publishincovid19,
    title = "{P}ublish{I}n{C}ovid19 at the {F}in{SBD}-2 Task: Sentence and List Extraction in Noisy {PDF} Text Using a Hybrid Deep Learning and Rule-Based Approach",
    author = "Singh, Janvijay",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.9",
    pages = "55--61",
}
@inproceedings{tian-etal-2020-aiai,
    title = "aiai at the {F}in{SBD}-2 Task: Sentence, list and Item Boundary Detection and Items classification of Financial Texts Using Data Augmentation and Attention",
    author = "Tian, Ke  and
      Chen, Hua  and
      Yang, Jie",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.10",
    pages = "62--66",
}
@inproceedings{giguet-lejeune-2020-daniel,
    title = "Daniel at the {F}in{SBD}-2 Task: Extracting List and Sentence Boundaries from {PDF} Documents, a model-driven approach to {PDF} document analysis",
    author = {Giguet, Emmanuel  and
      Lejeune, Ga{\"e}l},
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.11",
    pages = "67--74",
}
@inproceedings{arora-etal-2020-subtl,
    title = "Subtl.ai at the {F}in{SBD}-2 task: Document Structure Identification by Paying Attention",
    author = "Arora, Abhishek  and
      Khullar, Aman  and
      Pakala, Sarath Chandra  and
      Ramesh, Vishnu  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.12",
    pages = "75--80",
}
@inproceedings{maarouf-etal-2020-finsim,
    title = "The {F}in{S}im 2020 Shared Task: Learning Semantic Representations for the Financial Domain",
    author = "Maarouf, Ismail El  and
      Mansar, Youness  and
      Mouilleron, Virginie  and
      Valsamou-Stanislawski, Dialekti",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.13",
    pages = "81--86",
}
@inproceedings{keswani-etal-2020-iitk,
    title = "{IITK} at the {F}in{S}im Task: Hypernym Detection in Financial Domain via Context-Free and Contextualized Word Embeddings",
    author = "Keswani, Vishal  and
      Singh, Sakshi  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.14",
    pages = "87--92",
}
@inproceedings{saini-2020-anuj,
    title = "Anuj at the {F}in{S}im Task: Anuj@{FINSIM}!`{VL}earning Semantic Representation of Financial Domain with Investopedia",
    author = "Saini, Anuj",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.15",
    pages = "93--97",
}
@inproceedings{berend-etal-2020-prosperamnet,
    title = "{P}rosper{AM}net at the {F}in{S}im Task: Detecting hypernyms of financial concepts via measuring the information stored in sparse word representations",
    author = "Berend, G{\'a}bor  and
      Kis-Szab{\'o}, Norbert  and
      Sz{\'a}nt{\'o}, Zsolt",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.16",
    pages = "98--103",
}
@inproceedings{anand-etal-2020-finsim20,
    title = "{FINSIM}20 at the {F}in{S}im Task: Making Sense of Text in Financial Domain",
    author = "Anand, Vivek  and
      Agrawal, Yash  and
      Pol, Aarti  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the Second Workshop on Financial Technology and Natural Language Processing",
    month = "5 " # jan,
    year = "2020",
    address = "Kyoto, Japan",
    publisher = "-",
    url = "https://www.aclweb.org/anthology/2020.finnlp-1.17",
    pages = "104--107",
}
@proceedings{findings-2020-findings,
    title = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.0",
}
@inproceedings{prato-etal-2020-fully,
    title = "Fully Quantized Transformer for Machine Translation",
    author = "Prato, Gabriele  and
      Charlaix, Ella  and
      Rezagholizadeh, Mehdi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.1",
    doi = "10.18653/v1/2020.findings-emnlp.1",
    pages = "1--14",
    abstract = "State-of-the-art neural machine translation methods employ massive amounts of parameters. Drastically reducing computational costs of such methods without affecting performance has been up to this point unsuccessful. To this end, we propose FullyQT: an all-inclusive quantization strategy for the Transformer. To the best of our knowledge, we are the first to show that it is possible to avoid any loss in translation quality with a fully quantized Transformer. Indeed, compared to full-precision, our 8-bit models score greater or equal BLEU on most tasks. Comparing ourselves to all previously proposed methods, we achieve state-of-the-art quantization results.",
}
@inproceedings{zhang-etal-2020-summarizing,
    title = "Summarizing {C}hinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention",
    author = "Zhang, Ningyu  and
      Deng, Shumin  and
      Li, Juan  and
      Chen, Xi  and
      Zhang, Wei  and
      Chen, Huajun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.2",
    doi = "10.18653/v1/2020.findings-emnlp.2",
    pages = "15--24",
    abstract = "Online search engines are a popular source of medical information for users, where users can enter questions and obtain relevant answers. It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions. Moreover, answer summaries are expected to reveal the most relevant information in response to questions; hence, the summaries should be generated with a focus on the question, which is a challenging topic-focused summarization task. In this paper, we propose an approach that utilizes graph convolution networks and question-focused dual attention for Chinese medical answer summarization. We first organize the original long answer text into a medical concept graph with graph convolution networks to better understand the internal structure of the text and the correlation between medical concepts. Then, we introduce a question-focused dual attention mechanism to generate summaries relevant to questions. Experimental results demonstrate that the proposed model can generate more coherent and informative summaries compared with baseline models.",
}
@inproceedings{qi-etal-2020-stay,
    title = "Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations",
    author = "Qi, Peng  and
      Zhang, Yuhao  and
      Manning, Christopher D.",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.3",
    doi = "10.18653/v1/2020.findings-emnlp.3",
    pages = "25--40",
    abstract = "We investigate the problem of generating informative questions in information-asymmetric conversations. Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history. We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.",
}
@inproceedings{yap-etal-2020-adapting,
    title = "Adapting {BERT} for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences",
    author = "Yap, Boon Peng  and
      Koh, Andrew  and
      Chng, Eng Siong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.4",
    doi = "10.18653/v1/2020.findings-emnlp.4",
    pages = "41--46",
    abstract = "Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions. We also introduce a data augmentation technique for WSD using existing example sentences from WordNet. Using the proposed training objective and data augmentation technique, our models are able to achieve state-of-the-art results on the English all-words benchmark datasets.",
}
@inproceedings{wang-wan-2020-adversarial,
    title = "Adversarial Text Generation via Sequence Contrast Discrimination",
    author = "Wang, Ke  and
      Wan, Xiaojun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.5",
    doi = "10.18653/v1/2020.findings-emnlp.5",
    pages = "47--53",
    abstract = "In this paper, we propose a sequence contrast loss driven text generation framework, which learns the difference between real texts and generated texts and uses that difference. Specifically, our discriminator contains a discriminative sequence generator instead of a binary classifier, and measures the {`}relative realism{'} of generated texts against real texts by making use of them simultaneously. Moreover, our generator uses discriminative sequences to directly improve itself, which not only replaces the gradient propagation process from the discriminator to the generator, but also avoids the time-consuming sampling process of estimating rewards in some previous methods. We conduct extensive experiments with various metrics, substantiating that our framework brings improvements in terms of training stability and the quality of generated texts.",
}
@inproceedings{luo-etal-2020-grace,
    title = "{GRACE}: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis",
    author = "Luo, Huaishao  and
      Ji, Lei  and
      Li, Tianrui  and
      Jiang, Daxin  and
      Duan, Nan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.6",
    doi = "10.18653/v1/2020.findings-emnlp.6",
    pages = "54--64",
    abstract = "In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks. Besides, previous works usually ignore the interaction between aspect terms when labeling polarities. We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems. Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities. The polarities sequence is designed to depend on the generated aspect terms labels. To alleviate the imbalance issue, we extend the gradient harmonized mechanism used in object detection to the aspect-based sentiment analysis by adjusting the weight of each label dynamically. The proposed GRACE adopts a post-pretraining BERT as its backbone. Experimental results demonstrate that the proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.",
}
@inproceedings{huang-etal-2020-reducing,
    title = "Reducing Sentiment Bias in Language Models via Counterfactual Evaluation",
    author = "Huang, Po-Sen  and
      Zhang, Huan  and
      Jiang, Ray  and
      Stanforth, Robert  and
      Welbl, Johannes  and
      Rae, Jack  and
      Maini, Vishal  and
      Yogatama, Dani  and
      Kohli, Pushmeet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.7",
    doi = "10.18653/v1/2020.findings-emnlp.7",
    pages = "65--83",
    abstract = "Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of generating coherent texts, it also prompts models to internalize social biases present in the training corpus. This paper aims to quantify and reduce a particular type of bias exhibited by language models: bias in the sentiment of generated text. Given a conditioning context (e.g., a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g., country names, occupations, genders) in the conditioning context using a form of counterfactual evaluation. We quantify sentiment bias by adopting individual and group fairness metrics from the fair machine learning literature, and demonstrate that large-scale models trained on two different corpora (news articles, and Wikipedia) exhibit considerable levels of bias. We then propose embedding and sentiment prediction-derived regularization on the language model{'}s latent representations. The regularizations improve fairness metrics while retaining comparable levels of perplexity and semantic similarity.",
}
@inproceedings{fei-etal-2020-improving,
    title = "Improving Text Understanding via Deep Syntax-Semantics Communication",
    author = "Fei, Hao  and
      Ren, Yafeng  and
      Ji, Donghong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.8",
    doi = "10.18653/v1/2020.findings-emnlp.8",
    pages = "84--93",
    abstract = "Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance, while these methods mostly employ shallow integration of syntax and semantics. In this paper, we propose a deep neural communication model between syntax and semantics to improve the performance of text understanding. Local communication is performed between syntactic tree encoder and sequential semantic encoder for mutual learning of information exchange. Global communication can further ensure comprehensive information propagation. Results on multiple syntax-dependent tasks show that our model outperforms strong baselines by a large margin. In-depth analysis indicates that our method is highly effective in composing sentence semantics.",
}
@inproceedings{zhu-bhat-2020-gruen,
    title = "{GRUEN} for Evaluating Linguistic Quality of Generated Text",
    author = "Zhu, Wanzheng  and
      Bhat, Suma",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.9",
    doi = "10.18653/v1/2020.findings-emnlp.9",
    pages = "94--108",
    abstract = "Automatic evaluation metrics are indispensable for evaluating generated text. To date, these metrics have focused almost exclusively on the content selection aspect of the system output, ignoring the linguistic quality aspect altogether. We bridge this gap by proposing GRUEN for evaluating Grammaticality, non-Redundancy, focUs, structure and coherENce of generated text. GRUEN utilizes a BERT-based model and a class of syntactic, semantic, and contextual features to examine the system output. Unlike most existing evaluation metrics which require human references as an input, GRUEN is reference-less and requires only the system output. Besides, it has the advantage of being unsupervised, deterministic, and adaptable to various tasks. Experiments on seven datasets over four language generation tasks show that the proposed metric correlates highly with human judgments.",
}
@inproceedings{hayashi-etal-2020-greedy,
    title = "A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings",
    author = "Hayashi, Katsuhiko  and
      Kishimoto, Koki  and
      Shimbo, Masashi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.10",
    doi = "10.18653/v1/2020.findings-emnlp.10",
    pages = "109--114",
    abstract = "This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP. Unlike the prior work using a SGD-based method and quantization of real-valued vectors, the proposed method directly optimizes binary embedding vectors by a series of bit flipping operations. On the standard knowledge graph completion tasks, the B-CP model trained with the proposed method achieved comparable performance with that trained with SGD as well as state-of-the-art real-valued models with similar embedding dimensions.",
}
@inproceedings{zheng-etal-2020-difference,
    title = "Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation",
    author = "Zheng, Chujie  and
      Cao, Yunbo  and
      Jiang, Daxin  and
      Huang, Minlie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.11",
    doi = "10.18653/v1/2020.findings-emnlp.11",
    pages = "115--125",
    abstract = "In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research. In this paper, we propose a difference-aware knowledge selection method. It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurately and generate more informative responses, significantly outperforming the state-of-the-art baselines.",
}
@inproceedings{li-etal-2020-attentive,
    title = "An Attentive Recurrent Model for Incremental Prediction of Sentence-final Verbs",
    author = "Li, Wenyan  and
      Grissom II, Alvin  and
      Boyd-Graber, Jordan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.12",
    doi = "10.18653/v1/2020.findings-emnlp.12",
    pages = "126--136",
    abstract = "Verb prediction is important for understanding human processing of verb-final languages, with practical applications to real-time simultaneous interpretation from verb-final to verb-medial languages. While previous approaches use classical statistical models, we introduce an attention-based neural model to incrementally predict final verbs on incomplete sentences in Japanese and German SOV sentences. To offer flexibility to the model, we further incorporate synonym awareness. Our approach both better predicts the final verbs in Japanese and German and provides more interpretable explanations of why those verbs are selected.",
}
@inproceedings{yang-etal-2020-transformer,
    title = "Transformer-{GCRF}: Recovering {C}hinese Dropped Pronouns with General Conditional Random Fields",
    author = "Yang, Jingxuan  and
      Xu, Kerui  and
      Xu, Jun  and
      Li, Si  and
      Gao, Sheng  and
      Guo, Jun  and
      Wen, Ji-Rong  and
      Xue, Nianwen",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.13",
    doi = "10.18653/v1/2020.findings-emnlp.13",
    pages = "137--147",
    abstract = "Pronouns are often dropped in Chinese conversations and recovering the dropped pronouns is important for NLP applications such as Machine Translation. Existing approaches usually formulate this as a sequence labeling task of predicting whether there is a dropped pronoun before each token and its type. Each utterance is considered to be a sequence and labeled independently. Although these approaches have shown promise, labeling each utterance independently ignores the dependencies between pronouns in neighboring utterances. Modeling these dependencies is critical to improving the performance of dropped pronoun recovery. In this paper, we present a novel framework that combines the strength of Transformer network with General Conditional Random Fields (GCRF) to model the dependencies between pronouns in neighboring utterances. Results on three Chinese conversation datasets show that the Transformer-GCRF model outperforms the state-of-the-art dropped pronoun recovery models. Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.",
}
@inproceedings{sogaard-2020-neural,
    title = "Neural Speed Reading Audited",
    author = "S{\o}gaard, Anders",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.14",
    doi = "10.18653/v1/2020.findings-emnlp.14",
    pages = "148--153",
    abstract = "Several approaches to neural speed reading have been presented at major NLP and machine learning conferences in 2017{--}20; i.e., {``}human-inspired{''} recurrent network architectures that learn to {``}read{''} text faster by skipping irrelevant words, typically optimizing the joint objective of minimizing classification error rate and FLOPs used at inference time. This paper reflects on the meaningfulness of the speed reading task, showing that (a) better and faster approaches to, say, document classification, already exist, which also learn to ignore part of the input (I give an example with 7{\%} error reduction and a 136x speed-up over the state of the art in neural speed reading); and that (b) any claims that neural speed reading is {``}human-inspired{''}, are ill-founded.",
}
@inproceedings{lee-etal-2020-converting,
    title = "Converting the Point of View of Messages Spoken to Virtual Assistants",
    author = "Lee, Gunhee  and
      Zu, Vera  and
      Buddi, Sai Srujana  and
      Liang, Dennis  and
      Kulkarni, Purva  and
      FitzGerald, Jack",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.15",
    doi = "10.18653/v1/2020.findings-emnlp.15",
    pages = "154--163",
    abstract = "Virtual Assistants can be quite literal at times. If the user says {``}tell Bob I love him,{''} most virtual assistants will extract the message {``}I love him{''} and send it to the user{'}s contact named Bob, rather than properly converting the message to {``}I love you.{''} We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.",
}
@inproceedings{shi-huang-2020-robustness,
    title = "Robustness to Modification with Shared Words in Paraphrase Identification",
    author = "Shi, Zhouxing  and
      Huang, Minlie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.16",
    doi = "10.18653/v1/2020.findings-emnlp.16",
    pages = "164--171",
    abstract = "Revealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations. In this paper, we study the robustness of paraphrase identification models from a new perspective {--} via modification with shared words, and we show that the models have significant robustness issues when facing such modifications. To modify an example consisting of a sentence pair, we either replace some words shared by both sentences or introduce new shared words. We aim to construct a valid new example such that a target model makes a wrong prediction. To find a modification solution, we use beam search constrained by heuristic rules, and we leverage a BERT masked language model for generating substitution words compatible with the context. Experiments show that the performance of the target models has a dramatic drop on the modified examples, thereby revealing the robustness issue. We also show that adversarial training can mitigate this issue.",
}
@inproceedings{peng-etal-2020-shot,
    title = "Few-shot Natural Language Generation for Task-Oriented Dialog",
    author = "Peng, Baolin  and
      Zhu, Chenguang  and
      Li, Chunyuan  and
      Li, Xiujun  and
      Li, Jinchao  and
      Zeng, Michael  and
      Gao, Jianfeng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.17",
    doi = "10.18653/v1/2020.findings-emnlp.17",
    pages = "172--182",
    abstract = "As a crucial component in task-oriented dialog systems, the Natural Language Generation (NLG) module converts a dialog act represented in a semantic form into a response in natural language. The success of traditional template-based or statistical models typically relies on heavily annotated data, which is infeasible for new domains. Therefore, it is pivotal for an NLG system to generalize well with limited labelled data in real applications. To this end, we present FewshotWOZ, the first NLG benchmark to simulate the few-shot learning setting in task-oriented dialog systems. Further, we develop the SC-GPT model. It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains. Experiments on FewshotWOZ and the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly outperforms existing methods, measured by various automatic metrics and human evaluations.",
}
@inproceedings{fei-etal-2020-mimic,
    title = "Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic {NLP}",
    author = "Fei, Hao  and
      Ren, Yafeng  and
      Ji, Donghong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.18",
    doi = "10.18653/v1/2020.findings-emnlp.18",
    pages = "183--193",
    abstract = "Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network. In this paper, we investigate a simple and effective method, Knowledge Distillation, to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. Experimental results on four typical syntax-dependent tasks show that our method outperforms tree encoders by effectively integrating rich heterogeneous structure syntax, meanwhile reducing error propagation, and also outperforms ensemble methods, in terms of both the efficiency and accuracy.",
}
@inproceedings{zhu-etal-2020-hierarchical,
    title = "A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining",
    author = "Zhu, Chenguang  and
      Xu, Ruochen  and
      Zeng, Michael  and
      Huang, Xuedong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.19",
    doi = "10.18653/v1/2020.findings-emnlp.19",
    pages = "194--203",
    abstract = "With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Furthermore, due to the inadequacy of meeting summary data, we pretrain the model on large-scale news summary data. Empirical results show that our model outperforms previous approaches in both automatic metrics and human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases from 34.66{\%} to 46.28{\%}.",
}
@inproceedings{li-etal-2020-active-testing,
    title = "Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction",
    author = "Li, Pengshuai  and
      Zhang, Xinsong  and
      Jia, Weijia  and
      Zhao, Wei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.20",
    doi = "10.18653/v1/2020.findings-emnlp.20",
    pages = "204--211",
    abstract = "Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets. However, existing works on distantly supervised relation extraction suffer from the low quality of test set, which leads to considerable biased performance evaluation. These biases not only result in unfair evaluations but also mislead the optimization of neural relation extraction. To mitigate this problem, we propose a novel evaluation method named active testing through utilizing both the noisy test set and a few manual annotations. Experiments on a widely used benchmark show that our proposed approach can yield approximately unbiased evaluations for distantly supervised relation extractors.",
}
@inproceedings{zhang-etal-2020-semantic-matching,
    title = "Semantic Matching for Sequence-to-Sequence Learning",
    author = "Zhang, Ruiyi  and
      Chen, Changyou  and
      Zhang, Xinyuan  and
      Bai, Ke  and
      Carin, Lawrence",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.21",
    doi = "10.18653/v1/2020.findings-emnlp.21",
    pages = "212--222",
    abstract = "In sequence-to-sequence models, classical optimal transport (OT) can be applied to semantically match generated sentences with target sentences. However, in non-parallel settings, target sentences are usually unavailable. To tackle this issue without losing the benefits of classical OT, we present a semantic matching scheme based on the Optimal Partial Transport (OPT). Specifically, our approach partially matches semantically meaningful words between source and partial target sequences. To overcome the difficulty of detecting active regions in OPT (corresponding to the words needed to be matched), we further exploit prior knowledge to perform partial matching. Extensive experiments are conducted to evaluate the proposed approach, showing consistent improvements over sequence-to-sequence tasks.",
}
@inproceedings{massarelli-etal-2020-decoding,
    title = "How Decoding Strategies Affect the Verifiability of Generated Text",
    author = {Massarelli, Luca  and
      Petroni, Fabio  and
      Piktus, Aleksandra  and
      Ott, Myle  and
      Rockt{\"a}schel, Tim  and
      Plachouras, Vassilis  and
      Silvestri, Fabrizio  and
      Riedel, Sebastian},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.22",
    doi = "10.18653/v1/2020.findings-emnlp.22",
    pages = "223--235",
    abstract = "Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text.",
}
@inproceedings{zhang-etal-2020-minimize,
    title = "Minimize Exposure Bias of {S}eq2{S}eq Models in Joint Entity and Relation Extraction",
    author = "Zhang, Ranran Haoran  and
      Liu, Qianying  and
      Fan, Aysa Xuemo  and
      Ji, Heng  and
      Zeng, Daojian  and
      Cheng, Fei  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.23",
    doi = "10.18653/v1/2020.findings-emnlp.23",
    pages = "236--246",
    abstract = "Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation. However, Seq2Seq enforces an unnecessary order on the unordered triplets and involves a large decoding length associated with error accumulation. These methods introduce exposure bias, which may cause the models overfit to the frequent label combination, thus limiting the generalization ability. We propose a novel Sequence-to-Unordered-Multi-Tree (Seq2UMTree) model to minimize the effects of exposure bias by limiting the decoding length to three within a triplet and removing the order among triplets. We evaluate our model on two datasets, DuIE and NYT, and systematically study how exposure bias alters the performance of Seq2Seq models. Experiments show that the state-of-the-art Seq2Seq model overfits to both datasets while Seq2UMTree shows significantly better generalization. Our code is available at \url{https://github.com/WindChimeRan/OpenJERE}.",
}
@inproceedings{wang-etal-2020-gradient,
    title = "Gradient-based Analysis of {NLP} Models is Manipulable",
    author = "Wang, Junlin  and
      Tuyls, Jens  and
      Wallace, Eric  and
      Singh, Sameer",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.24",
    doi = "10.18653/v1/2020.findings-emnlp.24",
    pages = "247--258",
    abstract = "Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, the fact that they directly reflect the model internals. In this paper, however, we demonstrate that the gradients of a model are easily manipulable, and thus bring into question the reliability of gradient-based analyses. In particular, we merge the layers of a target model with a Facade Model that overwhelms the gradients without affecting the predictions. This Facade Model can be trained to have gradients that are misleading and irrelevant to the task, such as focusing only on the stop words in the input. On a variety of NLP tasks (sentiment analysis, NLI, and QA), we show that the merged model effectively fools different analysis tools: saliency maps differ significantly from the original model{'}s, input reduction keeps more irrelevant input tokens, and adversarial perturbations identify unimportant tokens as being highly important.",
}
@inproceedings{zhang-etal-2020-pretrain,
    title = "Pretrain-{KGE}: Learning Knowledge Representation from Pretrained Language Models",
    author = "Zhang, Zhiyuan  and
      Liu, Xiaoqian  and
      Zhang, Yi  and
      Su, Qi  and
      Sun, Xu  and
      He, Bin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.25",
    doi = "10.18653/v1/2020.findings-emnlp.25",
    pages = "259--266",
    abstract = "Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem. To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models. Specifically, we present a universal training framework named \textit{Pretrain-KGE} consisting of three phases: semantic-based fine-tuning phase, knowledge extracting phase and KGE training phase. Extensive experiments show that our proposed Pretrain-KGE can improve results over KGE models, especially on solving the low-resource problem.",
}
@inproceedings{mita-etal-2020-self,
    title = "A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction",
    author = "Mita, Masato  and
      Kiyono, Shun  and
      Kaneko, Masahiro  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.26",
    doi = "10.18653/v1/2020.findings-emnlp.26",
    pages = "267--280",
    abstract = "Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of the datasets, and on how lower-quality data might affect GEC performance. We indeed found that there is a non-negligible amount of {``}noise{''} where errors were inappropriately edited or left uncorrected. To address this, we designed a self-refinement method where the key idea is to denoise these datasets by leveraging the prediction consistency of existing models, and outperformed strong denoising baseline methods. We further applied task-specific techniques and achieved state-of-the-art performance on the CoNLL-2014, JFLEG, and BEA-2019 benchmarks. We then analyzed the effect of the proposed denoising method, and found that our approach leads to improved coverage of corrections and facilitated fluency edits which are reflected in higher recall and overall performance.",
}
@inproceedings{eisenschlos-etal-2020-understanding,
    title = "Understanding tables with intermediate pre-training",
    author = {Eisenschlos, Julian  and
      Krichene, Syrine  and
      M{\"u}ller, Thomas},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.27",
    doi = "10.18653/v1/2020.findings-emnlp.27",
    pages = "281--296",
    abstract = "Table entailment, the binary classification task of finding if a sentence is supported or refuted by the content of a table, requires parsing language and table structure as well as numerical and discrete reasoning. While there is extensive work on textual entailment, table entailment is less well studied. We adapt TAPAS (Herzig et al., 2020), a table-based BERT model, to recognize entailment. Motivated by the benefits of data augmentation, we create a balanced dataset of millions of automatically created training examples which are learned in an intermediate step prior to fine-tuning. This new data is not only useful for table entailment, but also for SQA (Iyyer et al., 2017), a sequential table QA task. To be able to use long examples as input of BERT models, we evaluate table pruning techniques as a pre-processing step to drastically improve the training and prediction efficiency at a moderate drop in accuracy. The different methods set the new state-of-the-art on the TabFact (Chen et al., 2020) and SQA datasets.",
}
@inproceedings{chen-etal-2020-enhance,
    title = "Enhance Robustness of Sequence Labelling with Masked Adversarial Training",
    author = "Chen, Luoxin  and
      Liu, Xinyue  and
      Ruan, Weitong  and
      Lu, Jianhua",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.28",
    doi = "10.18653/v1/2020.findings-emnlp.28",
    pages = "297--302",
    abstract = "Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. In language tasks, adversarial training brings word-level robustness by adding input noise, which is beneficial for text classification. However, it lacks sufficient contextual information enhancement and thus is less useful for sequence labelling tasks such as chunking and named entity recognition (NER). To address this limitation, we propose masked adversarial training (MAT) to improve robustness from contextual information in sequence labelling. MAT masks or replaces some words in the sentence when computing adversarial loss from perturbed inputs and consequently enhances model robustness using more context-level information. In our experiments, our method shows significant improvements on accuracy and robustness of sequence labelling. By further incorporating with ELMo embeddings, our model achieves better or comparable results to state-of-the-art on CoNLL 2000 and 2003 benchmarks using much less parameters.",
}
@inproceedings{toledo-ronen-etal-2020-multilingual,
    title = "Multilingual Argument Mining: Datasets and Analysis",
    author = "Toledo-Ronen, Orith  and
      Orbach, Matan  and
      Bilu, Yonatan  and
      Spector, Artem  and
      Slonim, Noam",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.29",
    doi = "10.18653/v1/2020.findings-emnlp.29",
    pages = "303--317",
    abstract = "The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.",
}
@inproceedings{zhou-etal-2020-improving-grammatical,
    title = "Improving Grammatical Error Correction with Machine Translation Pairs",
    author = "Zhou, Wangchunshu  and
      Ge, Tao  and
      Mu, Chang  and
      Xu, Ke  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.30",
    doi = "10.18653/v1/2020.findings-emnlp.30",
    pages = "318--328",
    abstract = "We propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models (e.g., Chinese to English) of different qualities (i.e., poor and good). The poor translation model can resemble the ESL (English as a second language) learner and tends to generate translations of low quality in terms of fluency and grammaticality, while the good translation model generally generates fluent and grammatically correct translations. With the pair of translation models, we can generate unlimited numbers of poor to good English sentence pairs from text in the source language (e.g., Chinese) of the translators. Our approach can generate various error-corrected patterns and nicely complement the other data synthesis approaches for GEC. Experimental results demonstrate the data generated by our approach can effectively help a GEC model to improve the performance and achieve the state-of-the-art single-model performance in BEA-19 and CoNLL-14 benchmark datasets.",
}
@inproceedings{cho-etal-2020-machines,
    title = "Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives",
    author = "Cho, Won Ik  and
      Moon, Youngki  and
      Moon, Sangwhan  and
      Kim, Seok Min  and
      Kim, Nam Soo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.31",
    doi = "10.18653/v1/2020.findings-emnlp.31",
    pages = "329--339",
    abstract = "Modern dialog managers face the challenge of having to fulfill human-level conversational skills as part of common user expectations, including but not limited to discourse with no clear objective. Along with these requirements, agents are expected to extrapolate intent from the user{'}s dialogue even when subjected to non-canonical forms of speech. This depends on the agent{'}s comprehension of paraphrased forms of such utterances. Especially in low-resource languages, the lack of data is a bottleneck that prevents advancements of the comprehension performance for these types of agents. In this regard, here we demonstrate the necessity of extracting the intent argument of non-canonical directives in a natural language format, which may yield more accurate parsing, and suggest guidelines for building a parallel corpus for this purpose. Following the guidelines, we construct a Korean corpus of 50K instances of question/command-intent pairs, including the labels for classification of the utterance type. We also propose a method for mitigating class imbalance, demonstrating the potential applications of the corpus generation method and its multilingual extensibility.",
}
@inproceedings{koksal-ozgur-2020-relx,
    title = "The {RELX} Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification",
    author = {K{\"o}ksal, Abdullatif  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.32",
    doi = "10.18653/v1/2020.findings-emnlp.32",
    pages = "340--350",
    abstract = "Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for relation classification are mainly focused on the English language and require lots of training data with human annotations. Creating and annotating a large amount of training data for low-resource languages is impractical and expensive. To overcome this issue, we propose two cross-lingual relation classification models: a baseline model based on Multilingual BERT and a new multilingual pretraining setup, which significantly improves the baseline with distant supervision. For evaluation, we introduce a new public benchmark dataset for cross-lingual relation classification in English, French, German, Spanish, and Turkish, called RELX. We also provide the RELX-Distant dataset, which includes hundreds of thousands of sentences with relations from Wikipedia and Wikidata collected by distant supervision for these languages. Our code and data are available at: https://github.com/boun-tabi/RELX",
}
@inproceedings{russo-etal-2020-control,
    title = "Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation",
    author = "Russo, Giuseppe  and
      Hollenstein, Nora  and
      Musat, Claudiu Cristian  and
      Zhang, Ce",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.33",
    doi = "10.18653/v1/2020.findings-emnlp.33",
    pages = "351--366",
    abstract = "We introduce CGA, a conditional VAE architecture, to control, generate, and augment text. CGA is able to generate natural English sentences controlling multiple semantic and syntactic attributes by combining adversarial learning with a context-aware loss and a cyclical word dropout routine. We demonstrate the value of the individual model components in an ablation study. The scalability of our approach is ensured through a single discriminator, independently of the number of attributes. We show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments. As the main application of our work, we test the potential of this new NLG model in a data augmentation scenario. In a downstream NLP task, the sentences generated by our CGA model show significant improvements over a strong baseline, and a classification performance often comparable to adding same amount of additional real data.",
}
@inproceedings{xu-etal-2020-open,
    title = "Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation",
    author = "Xu, Yiming  and
      Chen, Lin  and
      Cheng, Zhongwei  and
      Duan, Lixin  and
      Luo, Jiebo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.34",
    doi = "10.18653/v1/2020.findings-emnlp.34",
    pages = "367--376",
    abstract = "We study the problem of visual question answering (VQA) in images by exploiting supervised domain adaptation, where there is a large amount of labeled data in the source domain but only limited labeled data in the target domain, with the goal to train a good target model. A straightforward solution is to fine-tune a pre-trained source model by using those limited labeled target data, but it usually cannot work well due to the considerable difference between the data distributions of the source and target domains. Moreover, the availability of multiple modalities (i.e., images, questions and answers) in VQA poses further challenges in modeling the transferability between various modalities. In this paper, we address the above issues by proposing a novel supervised multi-modal domain adaptation method for VQA to learn joint feature embeddings across different domains and modalities. Specifically, we align the data distributions of the source and target domains by considering those modalities both jointly and separately. Extensive experiments on the benchmark VQA 2.0 and VizWiz datasets demonstrate that our proposed method outperforms the existing state-of-the-art baselines for open-ended VQA in this challenging domain adaptation setting.",
}
@inproceedings{jin-etal-2020-dual,
    title = "Dual Low-Rank Multimodal Fusion",
    author = "Jin, Tao  and
      Huang, Siyu  and
      Li, Yingming  and
      Zhang, Zhongfei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.35",
    doi = "10.18653/v1/2020.findings-emnlp.35",
    pages = "377--387",
    abstract = "Tensor-based fusion methods have been proven effective in multimodal fusion tasks. However, existing tensor-based methods make a poor use of the fine-grained temporal dynamics of multimodal sequential features. Motivated by this observation, this paper proposes a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion (FT-LMF). FT-LMF correlates the features of individual time steps between multiple modalities, while it involves multiplications of high-order tensors in its calculation. This paper further proposes Dual Low-Rank Multimodal Fusion (Dual-LMF) to reduce the computational complexity of FT-LMF through low-rank tensor approximation along dual dimensions of input features. Dual-LMF is conceptually simple and practically effective and efficient. Empirical studies on benchmark multimodal analysis tasks show that our proposed methods outperform the state-of-the-art tensor-based fusion methods with a similar computational complexity.",
}
@inproceedings{zayed-etal-2020-contextual,
    title = "Contextual Modulation for Relation-Level Metaphor Identification",
    author = "Zayed, Omnia  and
      McCrae, John P.  and
      Buitelaar, Paul",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.36",
    doi = "10.18653/v1/2020.findings-emnlp.36",
    pages = "388--406",
    abstract = "Identifying metaphors in text is very challenging and requires comprehending the underlying comparison. The automation of this cognitive process has gained wide attention lately. However, the majority of existing approaches concentrate on word-level identification by treating the task as either single-word classification or sequential labelling without explicitly modelling the interaction between the metaphor components. On the other hand, while existing relation-level approaches implicitly model this interaction, they ignore the context where the metaphor occurs. In this work, we address these limitations by introducing a novel architecture for identifying relation-level metaphoric expressions of certain grammatical relations based on contextual modulation. In a methodology inspired by works in visual reasoning, our approach is based on conditioning the neural network computation on the deep contextualised features of the candidate expressions using feature-wise linear modulation. We demonstrate that the proposed architecture achieves state-of-the-art results on benchmark datasets. The proposed methodology is generic and could be applied to other textual classification problems that benefit from contextual interaction.",
}
@inproceedings{li-etal-2020-context-aware,
    title = "Context-aware Stand-alone Neural Spelling Correction",
    author = "Li, Xiangci  and
      Liu, Hairong  and
      Huang, Liang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.37",
    doi = "10.18653/v1/2020.findings-emnlp.37",
    pages = "407--414",
    abstract = "Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context. Inspired by this, we address the stand-alone spelling correction problem, which only corrects the spelling of each token without additional token insertion or deletion, by utilizing both spelling information and global context representations. We present a simple yet powerful solution that jointly detects and corrects misspellings as a sequence labeling task by fine-turning a pre-trained language model. Our solution outperform the previous state-of-the-art result by 12.8{\%} absolute F0.5 score.",
}
@inproceedings{jiang-etal-2020-novel,
    title = "A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels",
    author = "Jiang, Youxuan  and
      Zhu, Huaiyu  and
      Kummerfeld, Jonathan K.  and
      Li, Yunyao  and
      Lasecki, Walter",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.38",
    doi = "10.18653/v1/2020.findings-emnlp.38",
    pages = "415--421",
    abstract = "Resources for Semantic Role Labeling (SRL) are typically annotated by experts at great expense. Prior attempts to develop crowdsourcing methods have either had low accuracy or required substantial expert annotation. We propose a new multi-stage crowd workflow that substantially reduces expert involvement without sacrificing accuracy. In particular, we introduce a unique filter stage based on the key observation that crowd workers are able to almost perfectly filter out incorrect options for labels. Our three-stage workflow produces annotations with 95{\%} accuracy for predicate labels and 93{\%} for argument labels, which is comparable to expert agreement. Compared to prior work on crowdsourcing for SRL, we decrease expert effort by 4x, from 56{\%} to 14{\%} of cases. Our approach enables more scalable annotation of SRL, and could enable annotation of NLP tasks that have previously been considered too complex to effectively crowdsource.",
}
@inproceedings{ham-etal-2020-kornli,
    title = "{K}or{NLI} and {K}or{STS}: New Benchmark Datasets for {K}orean Natural Language Understanding",
    author = "Ham, Jiyeon  and
      Choe, Yo Joong  and
      Park, Kyubyong  and
      Choi, Ilji  and
      Soh, Hyungjoon",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.39",
    doi = "10.18653/v1/2020.findings-emnlp.39",
    pages = "422--430",
    abstract = "Natural language inference (NLI) and semantic textual similarity (STS) are key tasks in natural language understanding (NLU). Although several benchmark datasets for those tasks have been released in English and a few other languages, there are no publicly available NLI or STS datasets in the Korean language. Motivated by this, we construct and release new datasets for Korean NLI and STS, dubbed KorNLI and KorSTS, respectively. Following previous approaches, we machine-translate existing English training sets and manually translate development and test sets into Korean. To accelerate research on Korean NLU, we also establish baselines on KorNLI and KorSTS. Our datasets are publicly available at https://github.com/kakaobrain/KorNLUDatasets.",
}
@inproceedings{gao-etal-2020-dialogue,
    title = "Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning",
    author = "Gao, Yifan  and
      Li, Piji  and
      Bi, Wei  and
      Liu, Xiaojiang  and
      Lyu, Michael  and
      King, Irwin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.40",
    doi = "10.18653/v1/2020.findings-emnlp.40",
    pages = "431--440",
    abstract = "Sentence function is an important linguistic feature indicating the communicative purpose in uttering a sentence. Incorporating sentence functions into conversations has shown improvements in the quality of generated responses. However, the number of utterances for different types of fine-grained sentence functions is extremely imbalanced. Besides a small number of high-resource sentence functions, a large portion of sentence functions is infrequent. Consequently, dialogue generation conditioned on these infrequent sentence functions suffers from data deficiency. In this paper, we investigate a structured meta-learning (SML) approach for dialogue generation on infrequent sentence functions. We treat dialogue generation conditioned on different sentence functions as separate tasks, and apply model-agnostic meta-learning to high-resource sentence functions data. Furthermore, SML enhances meta-learning effectiveness by promoting knowledge customization among different sentence functions but simultaneously preserving knowledge generalization for similar sentence functions. Experimental results demonstrate that SML not only improves the informativeness and relevance of generated responses, but also can generate responses consistent with the target sentence functions. Code will be public to facilitate the research along this line.",
}
@inproceedings{lin-etal-2020-exploring,
    title = "Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning",
    author = "Lin, Zhaojiang  and
      Madotto, Andrea  and
      Fung, Pascale",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.41",
    doi = "10.18653/v1/2020.findings-emnlp.41",
    pages = "441--459",
    abstract = "Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results. However, this comes with the cost of having a single, large model for each task, which is not ideal in low-memory/power scenarios (e.g., mobile). In this paper, we propose an effective way to fine-tune multiple down-stream generation tasks simultaneously using a single, large pretrained model. The experiments on five diverse language generation tasks show that by just using an additional 2-3{\%} parameters for each task, our model can maintain or even improve the performance of fine-tuning the whole model.",
}
@inproceedings{lopez-strube-2020-fully,
    title = "A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification",
    author = "L{\'o}pez, Federico  and
      Strube, Michael",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.42",
    doi = "10.18653/v1/2020.findings-emnlp.42",
    pages = "460--475",
    abstract = "Label inventories for fine-grained entity typing have grown in size and complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data. However, it is not clear how to integrate hyperbolic components into downstream tasks. This is the first work that proposes a fully hyperbolic model for multi-class multi-label classification, which performs all operations in hyperbolic space. We evaluate the proposed model on two challenging datasets and compare to different baselines that operate under Euclidean assumptions. Our hyperbolic model infers the latent hierarchy from the class distribution, captures implicit hyponymic relations in the inventory, and shows performance on par with state-of-the-art methods on fine-grained classification with remarkable reduction of the parameter size. A thorough analysis sheds light on the impact of each component in the final prediction and showcases its ease of integration with Euclidean layers.",
}
@inproceedings{wright-augenstein-2020-claim,
    title = "Claim Check-Worthiness Detection as Positive Unlabelled Learning",
    author = "Wright, Dustin  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.43",
    doi = "10.18653/v1/2020.findings-emnlp.43",
    pages = "476--488",
    abstract = "As the first step of automatic fact checking, claim check-worthiness detection is a critical component of fact checking systems. There are multiple lines of research which study this problem: check-worthiness ranking from political speeches and debates, rumour detection on Twitter, and citation needed detection from Wikipedia. To date, there has been no structured comparison of these various tasks to understand their relatedness, and no investigation into whether or not a unified approach to all of them is achievable. In this work, we illuminate a central challenge in claim check-worthiness detection underlying all of these tasks, being that they hinge upon detecting both how factual a sentence is, as well as how likely a sentence is to be believed without verification. As such, annotators only mark those instances they judge to be clear-cut check-worthy. Our best performing method is a unified approach which automatically corrects for this using a variant of positive unlabelled learning that finds instances which were incorrectly labelled as not check-worthy. In applying this, we out-perform the state of the art in two of the three tasks studied for claim check-worthiness detection in English.",
}
@inproceedings{garderes-etal-2020-conceptbert,
    title = "{C}oncept{B}ert: Concept-Aware Representation for Visual Question Answering",
    author = "Gard{\`e}res, Fran{\c{c}}ois  and
      Ziaeefard, Maryam  and
      Abeloos, Baptiste  and
      Lecue, Freddy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.44",
    doi = "10.18653/v1/2020.findings-emnlp.44",
    pages = "489--498",
    abstract = "Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. A VQA model combines visual and textual features in order to answer questions grounded in an image. Current works in VQA focus on questions which are answerable by direct analysis of the question and image alone. We present a concept-aware algorithm, ConceptBert, for questions which require common sense, or basic factual knowledge from external structured content. Given an image and a question in natural language, ConceptBert requires visual elements of the image and a Knowledge Graph (KG) to infer the correct answer. We introduce a multi-modal representation which learns a joint Concept-Vision-Language embedding inspired by the popular BERT architecture. We exploit ConceptNet KG for encoding the common sense knowledge and evaluate our methodology on the Outside Knowledge-VQA (OK-VQA) and VQA datasets.",
}
@inproceedings{sherborne-etal-2020-bootstrapping,
    title = "Bootstrapping a Crosslingual Semantic Parser",
    author = "Sherborne, Tom  and
      Xu, Yumo  and
      Lapata, Mirella",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.45",
    doi = "10.18653/v1/2020.findings-emnlp.45",
    pages = "499--517",
    abstract = "Recent progress in semantic parsing scarcely considers languages other than English but professional translation can be prohibitively expensive. We adapt a semantic parser trained on a single language, such as English, to new languages and multiple domains with minimal annotation. We query if machine translation is an adequate substitute for training data, and extend this to investigate bootstrapping using joint training with English, paraphrasing, and multilingual pre-trained models. We develop a Transformer-based parser combining paraphrases by ensembling attention over multiple encoders and present new versions of ATIS and Overnight in German and Chinese for evaluation. Experimental results indicate that MT can approximate training data in a new language for accurate parsing when augmented with paraphrasing through multiple MT engines. Considering when MT is inadequate, we also find that using our approach achieves parsing accuracy within 2{\%} of complete translation using only 50{\%} of training data.",
}
@inproceedings{zhang-etal-2020-revisiting,
    title = "Revisiting Representation Degeneration Problem in Language Modeling",
    author = "Zhang, Zhong  and
      Gao, Chongming  and
      Xu, Cong  and
      Miao, Rui  and
      Yang, Qinli  and
      Shao, Junming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.46",
    doi = "10.18653/v1/2020.findings-emnlp.46",
    pages = "518--527",
    abstract = "Weight tying is now a common setting in many language generation tasks such as language modeling and machine translation. However, a recent study reveals that there is a potential flaw in weight tying. They find that the learned word embeddings are likely to degenerate and lie in a narrow cone when training a language model. They call it the representation degeneration problem and propose a cosine regularization to solve it. Nevertheless, we prove that the cosine regularization is insufficient to solve the problem, as the degeneration is still likely to happen under certain conditions. In this paper, we revisit the representation degeneration problem and theoretically analyze the limitations of the previously proposed solution. Afterward, we propose an alternative regularization method called Laplacian regularization to tackle the problem. Experiments on language modeling demonstrate the effectiveness of the proposed Laplacian regularization.",
}
@inproceedings{gretz-etal-2020-workweek,
    title = "The workweek is the best time to start a family {--} A Study of {GPT}-2 Based Claim Generation",
    author = "Gretz, Shai  and
      Bilu, Yonatan  and
      Cohen-Karlik, Edo  and
      Slonim, Noam",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.47",
    doi = "10.18653/v1/2020.findings-emnlp.47",
    pages = "528--544",
    abstract = "Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types of claims that it produces, and their veracity, using an array of manual and automatic assessments. In addition, we explore the interplay between this task and the task of Claim Retrieval, showing how they can complement one another.",
}
@inproceedings{lalor-yu-2020-dynamic,
    title = "Dynamic Data Selection for Curriculum Learning via Ability Estimation",
    author = "Lalor, John P.  and
      Yu, Hong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.48",
    doi = "10.18653/v1/2020.findings-emnlp.48",
    pages = "545--555",
    abstract = "Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty heuristics with learned difficulty parameters. We also propose Dynamic Data selection for Curriculum Learning via Ability Estimation (DDaCLAE), a strategy that probes model ability at each training epoch to select the best training examples at that point. We show that models using learned difficulty and/or ability outperform heuristic-based curriculum learning models on the GLUE classification tasks.",
}
@inproceedings{raganato-etal-2020-fixed,
    title = "Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation",
    author = {Raganato, Alessandro  and
      Scherrer, Yves  and
      Tiedemann, J{\"o}rg},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.49",
    doi = "10.18653/v1/2020.findings-emnlp.49",
    pages = "556--568",
    abstract = "Transformer-based models have brought a radical change to neural machine translation. A key feature of the Transformer architecture is the so-called multi-head attention mechanism, which allows the model to focus simultaneously on different parts of the input. However, recent works have shown that most attention heads learn simple, and often redundant, positional patterns. In this paper, we propose to replace all but one attention head of each encoder layer with simple fixed {--} non-learnable {--} attentive patterns that are solely based on position and do not require any external knowledge. Our experiments with different data sizes and multiple language pairs show that fixing the attention heads on the encoder side of the Transformer at training time does not impact the translation quality and even increases BLEU scores by up to 3 points in low-resource scenarios.",
}
@inproceedings{paz-argaman-etal-2020-zest,
    title = "{ZEST}: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization",
    author = "Paz-Argaman, Tzuf  and
      Tsarfaty, Reut  and
      Chechik, Gal  and
      Atzmon, Yuval",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.50",
    doi = "10.18653/v1/2020.findings-emnlp.50",
    pages = "569--579",
    abstract = "We study the problem of recognizing visual entities from the textual descriptions of their classes. Specifically, given birds{'} images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions. This setup has been studied in the vision community under the name zero-shot learning from text, focusing on learning to transfer knowledge about visual aspects of birds from seen classes to previously-unseen ones. Here, we suggest focusing on the textual description and distilling from the description the most relevant information to effectively match visual features to the parts of the text that discuss them. Specifically, (1) we propose to leverage the similarity between species, reflected in the similarity between text descriptions of the species. (2) we derive visual summaries of the texts, i.e., extractive summaries that focus on the visual features that tend to be reflected in images. We propose a simple attention-based model augmented with the similarity and visual summaries components. Our empirical results consistently and significantly outperform the state-of-the-art on the largest benchmarks for text-based zero-shot learning, illustrating the critical importance of texts for zero-shot image-recognition.",
}
@inproceedings{zhang-etal-2020-shot,
    title = "Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases",
    author = "Zhang, Chuxu  and
      Yu, Lu  and
      Saebi, Mandana  and
      Jiang, Meng  and
      Chawla, Nitesh",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.51",
    doi = "10.18653/v1/2020.findings-emnlp.51",
    pages = "580--585",
    abstract = "Multi-hop relation reasoning over knowledge base is to generate effective and interpretable relation prediction through reasoning paths. The current methods usually require sufficient training data (fact triples) for each query relation, impairing their performances over few-shot relations (with limited triples) which are common in knowledge base. To this end, we propose FIRE, a novel few-shot multi-hop relation learning model. FIRE applies reinforcement learning to model the sequential steps of multi-hop reasoning, besides performs heterogeneous structure encoding and knowledge-aware search space pruning. The meta-learning technique is employed to optimize model parameters that could quickly adapt to few-shot relations. Empirical study on two datasets demonstrate that FIRE outperforms state-of-the-art methods.",
}
@inproceedings{meng-etal-2020-structure,
    title = "A structure-enhanced graph convolutional network for sentiment analysis",
    author = "Meng, Fanyu  and
      Feng, Junlan  and
      Yin, Danping  and
      Chen, Si  and
      Hu, Min",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.52",
    doi = "10.18653/v1/2020.findings-emnlp.52",
    pages = "586--595",
    abstract = "Syntactic information is essential for both sentiment analysis(SA) and aspect-based sentiment analysis(ABSA). Previous work has already achieved great progress utilizing Graph Convolutional Network(GCN) over dependency tree of a sentence. However, these models do not fully exploit the syntactic information obtained from dependency parsing such as the diversified types of dependency relations. The message passing process of GCN should be distinguished based on these syntactic information.To tackle this problem, we design a novel weighted graph convolutional network(WGCN) which can exploit rich syntactic information based on the feature combination. Furthermore, we utilize BERT instead of Bi-LSTM to generate contextualized representations as inputs for GCN and present an alignment method to keep word-level dependencies consistent with wordpiece unit of BERT. With our proposal, we are able to improve the state-of-the-art on four ABSA tasks out of six and two SA tasks out of three.",
}
@inproceedings{jinman-etal-2020-pbos,
    title = "{PB}o{S}: Probabilistic Bag-of-Subwords for Generalizing Word Embedding",
    author = "Jinman, Zhao  and
      Zhong, Shawn  and
      Zhang, Xiaomin  and
      Liang, Yingyu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.53",
    doi = "10.18653/v1/2020.findings-emnlp.53",
    pages = "596--611",
    abstract = "We look into the task of generalizing word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-vocabulary words, without extra contextual information. We rely solely on the spellings of words and propose a model, along with an efficient algorithm, that simultaneously models subword segmentation and computes subword-based compositional word embedding. We call the model probabilistic bag-of-subwords (PBoS), as it applies bag-of-subwords for all possible segmentations based on their likelihood. Inspections and affix prediction experiment show that PBoS is able to produce meaningful subword segmentations and subword rankings without any source of explicit morphological knowledge. Word similarity and POS tagging experiments show clear advantages of PBoS over previous subword-level models in the quality of generated word embeddings across languages.",
}
@inproceedings{onoe-durrett-2020-interpretable,
    title = "Interpretable Entity Representations through Large-Scale Typing",
    author = "Onoe, Yasumasa  and
      Durrett, Greg",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.54",
    doi = "10.18653/v1/2020.findings-emnlp.54",
    pages = "612--624",
    abstract = "In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret. In this paper, we present an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box. Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types, indicating the confidence of a typing model{'}s decision that the entity belongs to the corresponding type. We obtain these representations using a fine-grained entity typing model, trained either on supervised ultra-fine entity typing data (Choi et al. 2018) or distantly-supervised examples from Wikipedia. On entity probing tasks involving recognizing entity identity, our embeddings used in parameter-free downstream models achieve competitive performance with ELMo- and BERT-based embeddings in trained models. We also show that it is possible to reduce the size of our type set in a learning-based way for particular domains. Finally, we show that these embeddings can be post-hoc modified through a small number of rules to incorporate domain knowledge and improve performance.",
}
@inproceedings{zhu-etal-2020-empirical,
    title = "Empirical Studies of Institutional Federated Learning For Natural Language Processing",
    author = "Zhu, Xinghua  and
      Wang, Jianzong  and
      Hong, Zhenhou  and
      Xiao, Jing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.55",
    doi = "10.18653/v1/2020.findings-emnlp.55",
    pages = "625--634",
    abstract = "Federated learning has sparkled new interests in the deep learning society to make use of isolated data sources from independent institutes. With the development of novel training tools, we have successfully deployed federated natural language processing networks on GPU-enabled server clusters. This paper demonstrates federated training of a popular NLP model, TextCNN, with applications in sentence intent classification. Furthermore, differential privacy is introduced to protect participants in the training process, in a manageable manner. Distinguished from previous client-level privacy protection schemes, the proposed differentially private federated learning procedure is defined in the dataset sample level, inherent with the applications among institutions instead of individual users. Optimal settings of hyper-parameters for the federated TextCNN model are studied through comprehensive experiments. We also evaluated the performance of federated TextCNN model under imbalanced data load configuration. Experiments show that, the sampling ratio has a large impact on the performance of the FL models, causing up to 38.4{\%} decrease in the test accuracy, while they are robust to different noise multiplier levels, with less than 3{\%} variance in the test accuracy. It is also found that the FL models are sensitive to data load balancedness among client datasets. When the data load is imbalanced, model performance dropped by up to 10{\%}.",
}
@inproceedings{feng-etal-2020-neureduce,
    title = "{N}eu{R}educe: Reducing Mixed {B}oolean-Arithmetic Expressions by Recurrent Neural Network",
    author = "Feng, Weijie  and
      Liu, Binbin  and
      Xu, Dongpeng  and
      Zheng, Qilong  and
      Xu, Yun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.56",
    doi = "10.18653/v1/2020.findings-emnlp.56",
    pages = "635--644",
    abstract = "Mixed Boolean-Arithmetic (MBA) expressions involve both arithmetic calculation (e.g.,plus, minus, multiply) and bitwise computation (e.g., and, or, negate, xor). MBA expressions have been widely applied in software obfuscation, transforming programs from a simple form to a complex form. MBA expressions are challenging to be simplified, because the interleaving bitwise and arithmetic operations causing mathematical reduction laws to be ineffective. Our goal is to recover the original, simple form from an obfuscated MBA expression. In this paper, we first propose NeuReduce, a string to string method based on neural networks to automatically learn and reduce complex MBA expressions. We develop a comprehensive MBA dataset, including one million diversified MBA expression samples and corresponding simplified forms. After training on the dataset, NeuReduce can reduce MBA rules to homelier but mathematically equivalent forms. By comparing with three state-of-the-art MBA reduction methods, our evaluation result shows that NeuReduce outperforms all other tools in terms of accuracy, solving time, and performance overhead.",
}
@inproceedings{hashemzadeh-etal-2020-language,
    title = "From Language to Language-ish: How Brain-Like is an {LSTM}{'}s Representation of Nonsensical Language Stimuli?",
    author = "Hashemzadeh, Maryam  and
      Kaufeld, Greta  and
      White, Martha  and
      Martin, Andrea E.  and
      Fyshe, Alona",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.57",
    doi = "10.18653/v1/2020.findings-emnlp.57",
    pages = "645--656",
    abstract = "The representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based on the brain{'}s reaction to syntactically and semantically sound language stimuli. In this study, we asked: how does an LSTM (long short term memory) language model, trained (by and large) on semantically and syntactically intact language, represent a language sample with degraded semantic or syntactic information? Does the LSTM representation still resemble the brain{'}s reaction? We found that, even for some kinds of nonsensical language, there is a statistically significant relationship between the brain{'}s activity and the representations of an LSTM. This indicates that, at least in some instances, LSTMs and the human brain handle nonsensical data similarly.",
}
@inproceedings{cui-etal-2020-revisiting,
    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
    author = "Cui, Yiming  and
      Che, Wanxiang  and
      Liu, Ting  and
      Qin, Bing  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
    doi = "10.18653/v1/2020.findings-emnlp.58",
    pages = "657--668",
    abstract = "Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pre-trained language model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac). We carried out extensive experiments on eight Chinese NLP tasks to revisit the existing pre-trained language models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. https://github.com/ymcui/MacBERT",
}
@inproceedings{jiang-etal-2020-cascaded,
    title = "Cascaded Semantic and Positional Self-Attention Network for Document Classification",
    author = "Jiang, Juyong  and
      Zhang, Jie  and
      Zhang, Kai",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.59",
    doi = "10.18653/v1/2020.findings-emnlp.59",
    pages = "669--677",
    abstract = "Transformers have shown great success in learning representations for language modelling. However, an open challenge still remains on how to systematically aggregate semantic information (word embedding) with positional (or temporal) information (word orders). In this work, we propose a new architecture to aggregate the two sources of information using cascaded semantic and positional self-attention network (CSPAN) in the context of document classification. The CSPAN uses a semantic self-attention layer cascaded with Bi-LSTM to process the semantic and positional information in a sequential manner, and then adaptively combine them together through a residue connection. Compared with commonly used positional encoding schemes, CSPAN can exploit the interaction between semantics and word positions in a more interpretable and adaptive manner, and the classification performance can be notably improved while simultaneously preserving a compact model size and high convergence rate. We evaluate the CSPAN model on several benchmark data sets for document classification with careful ablation studies, and demonstrate the encouraging results compared with state of the art.",
}
@inproceedings{peng-etal-2020-toward,
    title = "Toward Recognizing More Entity Types in {NER}: An Efficient Implementation using Only Entity Lexicons",
    author = "Peng, Minlong  and
      Ma, Ruotian  and
      Zhang, Qi  and
      Zhao, Lujun  and
      Wei, Mengxi  and
      Sun, Changlong  and
      Huang, Xuanjing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.60",
    doi = "10.18653/v1/2020.findings-emnlp.60",
    pages = "678--688",
    abstract = "In this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system. As an illustrative example, consider the case that a NER system has been built to recognize person and organization names, and now it requires to additionally recognize job titles. Such a situation is common in the industrial areas, where the entity types required to recognize vary a lot in different products and keep changing. To avoid laborious data labeling and achieve fast adaptation, we propose to adjust the existing NER system using the previously labeled data and entity lexicons of the newly introduced entity types. We formulate such a task as a partially supervised learning problem and accordingly propose an effective algorithm to solve the problem. Comprehensive experimental studies on several public NER datasets validate the effectiveness of our method.",
}
@inproceedings{cavalin-etal-2020-disjoint,
    title = "From Disjoint Sets to Parallel Data to Train {S}eq2{S}eq Models for Sentiment Transfer",
    author = "Cavalin, Paulo  and
      Vasconcelos, Marisa  and
      Grave, Marcelo  and
      Pinhanez, Claudio  and
      Alves Ribeiro, Victor Henrique",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.61",
    doi = "10.18653/v1/2020.findings-emnlp.61",
    pages = "689--698",
    abstract = "We present a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer. Most systems for this task, which can be viewed as monolingual machine translation (MT), have relied on unsupervised methods, such as Generative Adversarial Networks (GANs)-inspired approaches, for coping with the lack of parallel corpora. Given that the literature shows that Seq2Seq methods have been consistently outperforming unsupervised methods in MT-related tasks, in this work we exploit the use of semantic similarity computation for converting non-parallel data onto a parallel corpus. That allows us to train a transformer neural network for the sentiment transfer task, and compare its performance against unsupervised approaches. With experiments conducted on two well-known public datasets, i.e. Yelp and Amazon, we demonstrate that the proposed methodology outperforms existing unsupervised methods very consistently in fluency, and presents competitive results in terms of sentiment conversion and content preservation. We believe that this works opens up an opportunity for seq2seq neural networks to be better exploited in problems for which they have not been applied owing to the lack of parallel training data.",
}
@inproceedings{xiang-etal-2020-learning,
    title = "Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation",
    author = "Xiang, Jiannan  and
      Wang, Xin  and
      Wang, William Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.62",
    doi = "10.18653/v1/2020.findings-emnlp.62",
    pages = "699--707",
    abstract = "Vision-and-Language Navigation (VLN) is a natural language grounding task where an agent learns to follow language instructions and navigate to specified destinations in real-world environments. A key challenge is to recognize and stop at the correct location, especially for complicated outdoor environments. Existing methods treat the STOP action equally as other actions, which results in undesirable behaviors that the agent often fails to stop at the destination even though it might be on the right path. Therefore, we propose Learning to Stop (L2Stop), a simple yet effective policy module that differentiates STOP and other actions. Our approach achieves the new state of the art on a challenging urban VLN dataset Touchdown, outperforming the baseline by 6.89{\%} (absolute improvement) on Success weighted by Edit Distance (SED).",
}
@inproceedings{nogueira-etal-2020-document,
    title = "Document Ranking with a Pretrained Sequence-to-Sequence Model",
    author = "Nogueira, Rodrigo  and
      Jiang, Zhiying  and
      Pradeep, Ronak  and
      Lin, Jimmy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.63",
    doi = "10.18653/v1/2020.findings-emnlp.63",
    pages = "708--718",
    abstract = "This work proposes the use of a pretrained sequence-to-sequence model for document ranking. Our approach is fundamentally different from a commonly adopted classification-based formulation based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as {``}target tokens{''}, and how the underlying logits of these target tokens can be interpreted as relevance probabilities for ranking. Experimental results on the MS MARCO passage ranking task show that our ranking approach is superior to strong encoder-only models. On three other document retrieval test collections, we demonstrate a zero-shot transfer-based approach that outperforms previous state-of-the-art models requiring in-domain cross-validation. Furthermore, we find that our approach significantly outperforms an encoder-only architecture in a data-poor setting. We investigate this observation in more detail by varying target tokens to probe the model{'}s use of latent knowledge. Surprisingly, we find that the choice of target tokens impacts effectiveness, even for words that are closely related semantically. This finding sheds some light on why our sequence-to-sequence formulation for document ranking is effective. Code and models are available at pygaggle.ai.",
}
@inproceedings{lin-etal-2020-pruning,
    title = "Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior",
    author = "Lin, Zi  and
      Liu, Jeremiah  and
      Yang, Zi  and
      Hua, Nan  and
      Roth, Dan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.64",
    doi = "10.18653/v1/2020.findings-emnlp.64",
    pages = "719--730",
    abstract = "Traditional (unstructured) pruning methods for a Transformer model focus on regularizing the individual weights by penalizing them toward zero. In this work, we explore spectral-normalized identity priors (SNIP), a structured pruning approach which penalizes an entire residual module in a Transformer model toward an identity mapping. Our method identifies and discards unimportant non-linear mappings in the residual connections by applying a thresholding operator on the function norm, and is applicable to any structured module including a single attention head, an entire attention blocks, or a feed-forward subnetwork. Furthermore, we introduce spectral normalization to stabilize the distribution of the post-activation values of the Transformer layers, further improving the pruning effectiveness of the proposed methodology. We conduct experiments with BERT on 5 GLUE benchmark tasks to demonstrate that SNIP achieves effective pruning results while maintaining comparable performance. Specifically, we improve the performance over the state-of-the-art by 0.5 to 1.0{\%} on average at 50{\%} compression ratio.",
}
@inproceedings{mrini-etal-2020-rethinking,
    title = "Rethinking Self-Attention: Towards Interpretability in Neural Parsing",
    author = "Mrini, Khalil  and
      Dernoncourt, Franck  and
      Tran, Quan Hung  and
      Bui, Trung  and
      Chang, Walter  and
      Nakashole, Ndapa",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.65",
    doi = "10.18653/v1/2020.findings-emnlp.65",
    pages = "731--742",
    abstract = "Attention mechanisms have improved the performance of NLP tasks while allowing models to remain explainable. Self-attention is currently widely used, however interpretability is difficult due to the numerous attention distributions. Recent work has shown that model representations can benefit from label-specific information, while facilitating interpretation of predictions. We introduce the Label Attention Layer: a new form of self-attention where attention heads represent labels. We test our novel layer by running constituency and dependency parsing experiments and show our new model obtains new state-of-the-art results for both tasks on both the Penn Treebank (PTB) and Chinese Treebank. Additionally, our model requires fewer self-attention layers compared to existing work. Finally, we find that the Label Attention heads learn relations between syntactic categories and show pathways to analyze errors.",
}
@inproceedings{ahmad-etal-2020-policyqa,
    title = "{P}olicy{QA}: A Reading Comprehension Dataset for Privacy Policies",
    author = "Ahmad, Wasi  and
      Chi, Jianfeng  and
      Tian, Yuan  and
      Chang, Kai-Wei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.66",
    doi = "10.18653/v1/2020.findings-emnlp.66",
    pages = "743--749",
    abstract = "Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question. On the contrary, we argue that providing users with a short text span from policy documents reduces the burden of searching the target information from a lengthy text segment. In this paper, we present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.",
}
@inproceedings{udagawa-etal-2020-linguistic,
    title = "A Linguistic Analysis of Visually Grounded Dialogues Based on Spatial Expressions",
    author = "Udagawa, Takuma  and
      Yamazaki, Takato  and
      Aizawa, Akiko",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.67",
    doi = "10.18653/v1/2020.findings-emnlp.67",
    pages = "750--765",
    abstract = "Recent models achieve promising results in visually grounded dialogues. However, existing datasets often contain undesirable biases and lack sophisticated linguistic analyses, which make it difficult to understand how well current models recognize their precise linguistic structures. To address this problem, we make two design choices: first, we focus on OneCommon Corpus (CITATION), a simple yet challenging common grounding dataset which contains minimal bias by design. Second, we analyze their linguistic structures based on spatial expressions and provide comprehensive and reliable annotation for 600 dialogues. We show that our annotation captures important linguistic structures including predicate-argument structure, modification and ellipsis. In our experiments, we assess the model{'}s understanding of these structures through reference resolution. We demonstrate that our annotation can reveal both the strengths and weaknesses of baseline models in essential levels of detail. Overall, we propose a novel framework and resource for investigating fine-grained language understanding in visually grounded dialogues.",
}
@inproceedings{zhu-etal-2020-efficient,
    title = "Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking",
    author = "Zhu, Su  and
      Li, Jieyu  and
      Chen, Lu  and
      Yu, Kai",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.68",
    doi = "10.18653/v1/2020.findings-emnlp.68",
    pages = "766--781",
    abstract = "Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can outperform strong baselines, and the previous state-of-the-art method (SOM-DST) can also be improved by our proposed schema graph.",
}
@inproceedings{tang-etal-2020-syntactic,
    title = "Syntactic and Semantic-driven Learning for Open Information Extraction",
    author = "Tang, Jialong  and
      Lu, Yaojie  and
      Lin, Hongyu  and
      Han, Xianpei  and
      Sun, Le  and
      Xiao, Xinyan  and
      Wu, Hua",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.69",
    doi = "10.18653/v1/2020.findings-emnlp.69",
    pages = "782--792",
    abstract = "One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora. The diversity of open domain corpora and the variety of natural language expressions further exacerbate this problem. In this paper, we propose a syntactic and semantic-driven learning approach, which can learn neural open IE models without any human-labelled data by leveraging syntactic and semantic knowledge as noisier, higher-level supervision. Specifically, we first employ syntactic patterns as data labelling functions and pretrain a base model using the generated labels. Then we propose a syntactic and semantic-driven reinforcement learning algorithm, which can effectively generalize the base model to open situations with high accuracy. Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised state-of-the-art (SoA) model.",
}
@inproceedings{cai-etal-2020-group,
    title = "Group-wise Contrastive Learning for Neural Dialogue Generation",
    author = "Cai, Hengyi  and
      Chen, Hongshen  and
      Song, Yonghao  and
      Ding, Zhuoye  and
      Bao, Yongjun  and
      Yan, Weipeng  and
      Zhao, Xiaofang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.70",
    doi = "10.18653/v1/2020.findings-emnlp.70",
    pages = "793--802",
    abstract = "Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning. However, models trained with MLE objective function are plagued by the low-diversity issue when it comes to the open-domain conversational setting. Inspired by the observation that humans not only learn from the positive signals but also benefit from correcting behaviors of undesirable actions, in this work, we introduce contrastive learning into dialogue generation, where the model explicitly perceives the difference between the well-chosen positive and negative utterances. Specifically, we employ a pretrained baseline model as a reference. During contrastive learning, the target dialogue model is trained to give higher conditional probabilities for the positive samples, and lower conditional probabilities for those negative samples, compared to the reference model. To manage the multi-mapping relations prevalent in human conversation, we augment contrastive dialogue learning with group-wise dual sampling. Extensive experimental results show that the proposed group-wise contrastive learning framework is suited for training a wide range of neural dialogue generation models with very favorable performance over the baseline training approaches.",
}
@inproceedings{poerner-etal-2020-e,
    title = "{E}-{BERT}: Efficient-Yet-Effective Entity Embeddings for {BERT}",
    author = {Poerner, Nina  and
      Waltinger, Ulli  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.71",
    doi = "10.18653/v1/2020.findings-emnlp.71",
    pages = "803--818",
    abstract = "We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT{'}s native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors. The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to ERNIE (Zhang et al., 2019) and KnowBert (Peters et al., 2019), but it requires no expensive further pre-training of the BERT encoder. We evaluate E-BERT on unsupervised question answering (QA), supervised relation classification (RC) and entity linking (EL). On all three tasks, E-BERT outperforms BERT and other baselines. We also show quantitatively that the original BERT model is overly reliant on the surface form of entity names (e.g., guessing that someone with an Italian-sounding name speaks Italian), and that E-BERT mitigates this problem.",
}
@inproceedings{zhang-etal-2020-multi,
    title = "A Multi-task Learning Framework for Opinion Triplet Extraction",
    author = "Zhang, Chen  and
      Li, Qiuchi  and
      Song, Dawei  and
      Wang, Benyou",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.72",
    doi = "10.18653/v1/2020.findings-emnlp.72",
    pages = "819--828",
    abstract = "The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.",
}
@inproceedings{li-etal-2020-event,
    title = "Event Extraction as Multi-turn Question Answering",
    author = "Li, Fayuan  and
      Peng, Weihua  and
      Chen, Yuguang  and
      Wang, Quan  and
      Pan, Lu  and
      Lyu, Yajuan  and
      Zhu, Yong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.73",
    doi = "10.18653/v1/2020.findings-emnlp.73",
    pages = "829--838",
    abstract = "Event extraction, which aims to identify event triggers of pre-defined event types and their arguments of specific roles, is a challenging task in NLP. Most traditional approaches formulate this task as classification problems, with event types or argument roles taken as golden labels. Such approaches fail to model rich interactions among event types and arguments of different roles, and cannot generalize to new types or roles. This work proposes a new paradigm that formulates event extraction as multi-turn question answering. Our approach, MQAEE, casts the extraction task into a series of reading comprehension problems, by which it extracts triggers and arguments successively from a given sentence. A history answer embedding strategy is further adopted to model question answering history in the multi-turn process. By this new formulation, MQAEE makes full use of dependency among arguments and event types, and generalizes well to new types with new argument roles. Empirical results on ACE 2005 shows that MQAEE outperforms current state-of-the-art, pushing the final F1 of argument extraction to 53.4{\%} (+2.0{\%}). And it also has a good generalization ability, achieving competitive performance on 13 new event types even if trained only with a few samples of them.",
}
@inproceedings{wu-etal-2020-improving,
    title = "Improving {QA} Generalization by Concurrent Modeling of Multiple Biases",
    author = {Wu, Mingzhu  and
      Moosavi, Nafise Sadat  and
      R{\"u}ckl{\'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.74",
    doi = "10.18653/v1/2020.findings-emnlp.74",
    pages = "839--853",
    abstract = "Existing NLP datasets contain various biases that models can easily exploit to achieve high performances on the corresponding evaluation sets. However, focusing on dataset-specific biases limits their ability to learn more generalizable knowledge about the task from more general data patterns. In this paper, we investigate the impact of debiasing methods for improving generalization and propose a general framework for improving the performance on both in-domain and out-of-domain datasets by concurrent modeling of multiple biases in the training data. Our framework weights each example based on the biases it contains and the strength of those biases in the training data. It then uses these weights in the training objective so that the model relies less on examples with high bias weights. We extensively evaluate our framework on extractive question answering with training data from various domains with multiple biases of different strengths. We perform the evaluations in two different settings, in which the model is trained on a single domain or multiple domains simultaneously, and show its effectiveness in both settings compared to state-of-the-art debiasing methods.",
}
@inproceedings{wu-etal-2020-actor,
    title = "Actor-Double-Critic: Incorporating Model-Based Critic for Task-Oriented Dialogue Systems",
    author = "Wu, Yen-chen  and
      Tseng, Bo-Hsiang  and
      Gasic, Milica",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.75",
    doi = "10.18653/v1/2020.findings-emnlp.75",
    pages = "854--863",
    abstract = "In order to improve the sample-efficiency of deep reinforcement learning (DRL), we implemented imagination augmented agent (I2A) in spoken dialogue systems (SDS). Although I2A achieves a higher success rate than baselines by augmenting predicted future into a policy network, its complicated architecture introduces unwanted instability. In this work, we propose actor-double-critic (ADC) to improve the stability and overall performance of I2A. ADC simplifies the architecture of I2A to reduce excessive parameters and hyper-parameters. More importantly, a separate model-based critic shares parameters between actions and makes back-propagation explicit. In our experiments on Cambridge Restaurant Booking task, ADC enhances success rates considerably and shows robustness to imperfect environment models. In addition, ADC exhibits the stability and sample-efficiency as significantly reducing the baseline standard deviation of success rates and reaching the 80{\%} success rate with half training data.",
}
@inproceedings{filippova-2020-controlled,
    title = "Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data",
    author = "Filippova, Katja",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.76",
    doi = "10.18653/v1/2020.findings-emnlp.76",
    pages = "864--870",
    abstract = "Neural text generation (data- or text-to-text) demonstrates remarkable performance when training data is abundant which for many applications is not the case. To collect a large corpus of parallel data, heuristic rules are often used but they inevitably let noise into the data, such as phrases in the output which cannot be explained by the input. Consequently, models pick up on the noise and may hallucinate{--}generate fluent but unsupported text. Our contribution is a simple but powerful technique to treat such hallucinations as a controllable aspect of the generated text, without dismissing any input and without modifying the model architecture. On the WikiBio corpus (Lebret et al., 2016), a particularly noisy dataset, we demonstrate the efficacy of the technique both in an automatic and in a human evaluation.",
}
@inproceedings{yamada-etal-2020-sequential,
    title = "Sequential Span Classification with Neural Semi-{M}arkov {CRF}s for Biomedical Abstracts",
    author = "Yamada, Kosuke  and
      Hirao, Tsutomu  and
      Sasano, Ryohei  and
      Takeda, Koichi  and
      Nagata, Masaaki",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.77",
    doi = "10.18653/v1/2020.findings-emnlp.77",
    pages = "871--877",
    abstract = "Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers{'} information access in the biomedical domain. Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract. However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label. To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences. Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths. Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F1 score as well as the best micro span-F1 score.",
}
@inproceedings{kobs-etal-2020-submit,
    title = "Where to Submit? Helping Researchers to Choose the Right Venue",
    author = "Kobs, Konstantin  and
      Koopmann, Tobias  and
      Zehe, Albin  and
      Fernes, David  and
      Krop, Philipp  and
      Hotho, Andreas",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.78",
    doi = "10.18653/v1/2020.findings-emnlp.78",
    pages = "878--883",
    abstract = "Whenever researchers write a paper, the same question occurs: {``}Where to submit?{''} In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83{\%} for AI papers and 95{\%} in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.",
}
@inproceedings{chen-etal-2020-airconcierge,
    title = "{A}ir{C}oncierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval",
    author = "Chen, Chieh-Yang  and
      Wang, Pei-Hsin  and
      Chang, Shih-Chieh  and
      Juan, Da-Cheng  and
      Wei, Wei  and
      Pan, Jia-Yu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.79",
    doi = "10.18653/v1/2020.findings-emnlp.79",
    pages = "884--897",
    abstract = "Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms. To alleviate the above problem, we propose , an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. Specifically, the neural agent first learns to ask and confirm the customer{'}s intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs. With the help of our method, the agent can use less but more accurate fetched results to generate useful responses efficiently, instead of incorporating the entire KBs. We evaluate the proposed method on the AirDialogue dataset, a large corpus released by Google, containing the conversations of customers booking flight tickets from the agent. The experimental results show that significantly improves over previous work in terms of accuracy and the BLEU score, which demonstrates not only the ability to achieve the given task but also the good quality of the generated dialogues.",
}
@inproceedings{wang-etal-2020-docstruct,
    title = "{D}oc{S}truct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding",
    author = "Wang, Zilong  and
      Zhan, Mingjie  and
      Liu, Xuebo  and
      Liang, Ding",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.80",
    doi = "10.18653/v1/2020.findings-emnlp.80",
    pages = "898--908",
    abstract = "Form understanding depends on both textual contents and organizational structure. Although modern OCR performs well, it is still challenging to realize general form understanding because forms are commonly used and of various formats. The table detection and handcrafted features in previous works cannot apply to all forms because of their requirements on formats. Therefore, we concentrate on the most elementary components, the key-value pairs, and adopt multimodal methods to extract features. We consider the form structure as a tree-like or graph-like hierarchy of text fragments. The parent-child relation corresponds to the key-value pairs in forms. We utilize the state-of-the-art models and design targeted extraction modules to extract multimodal features from semantic contents, layout information, and visual images. A hybrid fusion method of concatenation and feature shifting is designed to fuse the heterogeneous features and provide an informative joint representation. We adopt an asymmetric algorithm and negative sampling in our model as well. We validate our method on two benchmarks, MedForm and FUNSD, and extensive experiments demonstrate the effectiveness of our method.",
}
@inproceedings{cao-etal-2020-pretrained,
    title = "Pretrained Language Models for Dialogue Generation with Multiple Input Sources",
    author = "Cao, Yu  and
      Bi, Wei  and
      Fang, Meng  and
      Tao, Dacheng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.81",
    doi = "10.18653/v1/2020.findings-emnlp.81",
    pages = "909--917",
    abstract = "Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. However, it is still under investigating how to apply them to dialogue generation tasks, especially those with responses conditioned on multiple sources. Previous work simply concatenates all input sources or averages information from different input sources. In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2. We explore various methods to fuse multiple separate attention information corresponding to different sources. Our experimental results show that proper fusion methods deliver higher relevance with dialogue history than simple fusion baselines.",
}
@inproceedings{bawden-etal-2020-study,
    title = "A Study in Improving {BLEU} Reference Coverage with Diverse Automatic Paraphrasing",
    author = {Bawden, Rachel  and
      Zhang, Biao  and
      Yankovskaya, Lisa  and
      T{\"a}ttar, Andre  and
      Post, Matt},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.82",
    doi = "10.18653/v1/2020.findings-emnlp.82",
    pages = "918--932",
    abstract = "We investigate a long-perceived shortcoming in the typical use of BLEU: its reliance on a single reference. Using modern neural paraphrasing techniques, we study whether automatically generating additional *diverse* references can provide better coverage of the space of valid translations and thereby improve its correlation with human judgments. Our experiments on the into-English language directions of the WMT19 metrics task (at both the system and sentence level) show that using paraphrased references does generally improve BLEU, and when it does, the more diverse the better. However, we also show that better results could be achieved if those paraphrases were to specifically target the parts of the space most relevant to the MT outputs being evaluated. Moreover, the gains remain slight even when human paraphrases are used, suggesting inherent limitations to BLEU{'}s capacity to correctly exploit multiple references. Surprisingly, we also find that adequacy appears to be less important, as shown by the high results of a strong sampling approach, which even beats human paraphrases when used with sentence-level BLEU.",
}
@inproceedings{kulshreshtha-etal-2020-cross,
    title = "Cross-lingual Alignment Methods for Multilingual {BERT}: A Comparative Study",
    author = "Kulshreshtha, Saurabh  and
      Redondo Garcia, Jose Luis  and
      Chang, Ching-Yun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.83",
    doi = "10.18653/v1/2020.findings-emnlp.83",
    pages = "933--942",
    abstract = "Multilingual BERT (mBERT) has shown reasonable capability for zero-shot cross-lingual transfer when fine-tuned on downstream tasks. Since mBERT is not pre-trained with explicit cross-lingual supervision, transfer performance can further be improved by aligning mBERT with cross-lingual signal. Prior work propose several approaches to align contextualised embeddings. In this paper we analyse how different forms of cross-lingual supervision and various alignment methods influence the transfer capability of mBERT in zero-shot setting. Specifically, we compare parallel corpora vs dictionary-based supervision and rotational vs fine-tuning based alignment methods. We evaluate the performance of different alignment methodologies across eight languages on two tasks: Name Entity Recognition and Semantic Slot Filling. In addition, we propose a novel normalisation method which consistently improves the performance of rotation-based alignment including a notable 3{\%} F1 improvement for distant and typologically dissimilar languages. Importantly we identify the biases of the alignment methods to the type of task and proximity to the transfer language. We also find that supervision from parallel corpus is generally superior to dictionary alignments.",
}
@inproceedings{corazza-etal-2020-hybrid,
    title = "Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection",
    author = "Corazza, Michele  and
      Menini, Stefano  and
      Cabrio, Elena  and
      Tonelli, Sara  and
      Villata, Serena",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.84",
    doi = "10.18653/v1/2020.findings-emnlp.84",
    pages = "943--949",
    abstract = "Recent studies have demonstrated the effectiveness of cross-lingual language model pre-training on different NLP tasks, such as natural language inference and machine translation. In our work, we test this approach on social media data, which are particularly challenging to process within this framework, since the limited length of the textual messages and the irregularity of the language make it harder to learn meaningful encodings. More specifically, we propose a hybrid emoji-based Masked Language Model (MLM) to leverage the common information conveyed by emojis across different languages and improve the learned cross-lingual representation of short text messages, with the goal to perform zero- shot abusive language detection. We compare the results obtained with the original MLM to the ones obtained by our method, showing improved performance on German, Italian and Spanish.",
}
@inproceedings{jiao-etal-2020-senser,
    title = "{S}e{N}s{ER}: Learning Cross-Building Sensor Metadata Tagger",
    author = "Jiao, Yang  and
      Li, Jiacheng  and
      Wu, Jiaman  and
      Hong, Dezhi  and
      Gupta, Rajesh  and
      Shang, Jingbo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.85",
    doi = "10.18653/v1/2020.findings-emnlp.85",
    pages = "950--960",
    abstract = "Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e.g., measurement type and location) about sensors for running smart building applications. Unfortunately, sensor metadata in different buildings often follows distinct naming conventions. Therefore, learning a tagger currently requires extensive annotations on a per building basis. In this work, we propose a novel framework, SeNsER, which learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. It leverages the commonality between different buildings: At the character level, it employs bidirectional neural language models to capture the shared underlying patterns between two buildings and thus regularizes the feature learning process; At the word level, it leverages as features the k-mers existing in the fully annotated building. During inference, we further incorporate the information obtained from sources such as Wikipedia as prior knowledge. As a result, SeNsER shows promising results in extensive experiments on multiple real-world buildings.",
}
@inproceedings{doostmohammadi-etal-2020-persian,
    title = "{P}ersian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging",
    author = "Doostmohammadi, Ehsan  and
      Nassajian, Minoo  and
      Rahimi, Adel",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.86",
    doi = "10.18653/v1/2020.findings-emnlp.86",
    pages = "961--971",
    abstract = "Ezafe is a grammatical particle in some Iranian languages that links two words together. Regardless of the important information it conveys, it is almost always not indicated in Persian script, resulting in mistakes in reading complex sentences and errors in natural language processing tasks. In this paper, we experiment with different machine learning methods to achieve state-of-the-art results in the task of ezafe recognition. Transformer-based methods, BERT and XLMRoBERTa, achieve the best results, the latter achieving 2.68{\%} F1-score more than the previous state-of-the-art. We, moreover, use ezafe information to improve Persian part-of-speech tagging results and show that such information will not be useful to transformer-based methods and explain why that might be the case.",
}
@inproceedings{he-etal-2020-scene,
    title = "Scene Graph Modification Based on Natural Language Commands",
    author = "He, Xuanli  and
      Tran, Quan Hung  and
      Haffari, Gholamreza  and
      Chang, Walter  and
      Lin, Zhe  and
      Bui, Trung  and
      Dernoncourt, Franck  and
      Dam, Nhan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.87",
    doi = "10.18653/v1/2020.findings-emnlp.87",
    pages = "972--990",
    abstract = "Structured representations like graphs and parse trees play a crucial role in many Natural Language Processing systems. In recent years, the advancements in multi-turn user interfaces necessitate the need for controlling and updating these structured representations given new sources of information. Although there have been many efforts focusing on improving the performance of the parsers that map text to graphs or parse trees, very few have explored the problem of directly manipulating these representations. In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user{'}s command. Our novel models based on graph-based sparse transformer and cross attention information fusion outperform previous systems adapted from the machine translation and graph generation literature. We further contribute our large graph modification datasets to the research community to encourage future research for this new problem.",
}
@inproceedings{manotas-etal-2020-limit,
    title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
    author = "Manotas, Irene  and
      Vo, Ngoc Phuoc An  and
      Sheinin, Vadim",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
    doi = "10.18653/v1/2020.findings-emnlp.88",
    pages = "991--1000",
    abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",
}
@inproceedings{fernandez-astudillo-etal-2020-transition,
    title = "Transition-based Parsing with Stack-Transformers",
    author = "Fernandez Astudillo, Ram{\'o}n  and
      Ballesteros, Miguel  and
      Naseem, Tahira  and
      Blodgett, Austin  and
      Florian, Radu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.89",
    doi = "10.18653/v1/2020.findings-emnlp.89",
    pages = "1001--1007",
    abstract = "Modeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, or local state modeling of contextualized features, e.g. Bi-LSTM parsers. Given the success of Transformer architectures in recent parsing systems, this work explores modifications of the sequence-to-sequence Transformer architecture to model either global or local parser states in transition-based parsing. We show that modifications of the cross attention mechanism of the Transformer considerably strengthen performance both on dependency and Abstract Meaning Representation (AMR) parsing tasks, particularly for smaller models or limited training data.",
}
@inproceedings{yang-etal-2020-generative,
    title = "Generative Data Augmentation for Commonsense Reasoning",
    author = "Yang, Yiben  and
      Malaviya, Chaitanya  and
      Fernandez, Jared  and
      Swayamdipta, Swabha  and
      Le Bras, Ronan  and
      Wang, Ji-Ping  and
      Bhagavatula, Chandra  and
      Choi, Yejin  and
      Downey, Doug",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.90",
    doi = "10.18653/v1/2020.findings-emnlp.90",
    pages = "1008--1025",
    abstract = "Recent advances in commonsense reasoning depend on large-scale human-annotated training sets to achieve peak performance. However, manual curation of training sets is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit to. We propose a novel generative data augmentation technique, G-DAUG{\^{}}C, that aims to achieve more accurate and robust learning in a low-resource setting. Our approach generates synthetic examples using pretrained language models and selects the most informative and diverse set of examples for data augmentation. On experiments with multiple commonsense reasoning benchmarks, G-DAUG{\^{}}C consistently outperforms existing data augmentation methods based on back-translation, establishing a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA, as well as enhances out-of-distribution generalization, proving to be robust against adversaries or perturbations. Our analysis demonstrates that G-DAUG{\^{}}C produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.",
}
@inproceedings{chen-etal-2020-hybridqa,
    title = "{H}ybrid{QA}: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data",
    author = "Chen, Wenhu  and
      Zha, Hanwen  and
      Chen, Zhiyu  and
      Xiong, Wenhan  and
      Wang, Hong  and
      Wang, William Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.91",
    doi = "10.18653/v1/2020.findings-emnlp.91",
    pages = "1026--1036",
    abstract = "Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable. We test with three different models: 1) a table-only model. 2) text-only model. 3) a hybrid model that combines heterogeneous information to find the answer. The experimental results show that the EM scores obtained by two baselines are below 20{\%}, while the hybrid model can achieve an EM over 40{\%}. This gap suggests the necessity to aggregate heterogeneous information in HybridQA. However, the hybrid model{'}s score is still far behind human performance. Hence, HybridQA can serve as a challenging benchmark to study question answering with heterogeneous information.",
}
@inproceedings{nguyen-tuan-nguyen-2020-phobert,
    title = "{P}ho{BERT}: Pre-trained language models for {V}ietnamese",
    author = "Nguyen, Dat Quoc  and
      Tuan Nguyen, Anh",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.92",
    doi = "10.18653/v1/2020.findings-emnlp.92",
    pages = "1037--1042",
    abstract = "We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. We release PhoBERT to facilitate future research and downstream applications for Vietnamese NLP. Our PhoBERT models are available at https://github.com/VinAIResearch/PhoBERT",
}
@inproceedings{gollapalli-etal-2020-ester,
    title = "{EST}e{R}: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection",
    author = "Gollapalli, Sujatha Das  and
      Rozenshtein, Polina  and
      Ng, See-Kiong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.93",
    doi = "10.18653/v1/2020.findings-emnlp.93",
    pages = "1043--1056",
    abstract = "Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management. Currently, the state-of-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data. In this paper, we propose ESTeR , an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs. Our model combines large-scale word co-occurrence information with word-associations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings. Our similarity function can also be computed efficiently. We study a range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.",
}
@inproceedings{luo-etal-2020-make,
    title = "Make Templates Smarter: A Template Based {D}ata2{T}ext System Powered by Text Stitch Model",
    author = "Luo, Bingfeng  and
      Bai, Zuo  and
      Lai, Kunfeng  and
      Shen, Jianping",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.94",
    doi = "10.18653/v1/2020.findings-emnlp.94",
    pages = "1057--1062",
    abstract = "Neural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control the generation contents. Moreover, it requires paired training data that are usually expensive to collect. Template-based methods have good fidelity and controllability but require heavy human involvement. We propose a novel template-based data2text system powered by a text stitch model. It ensures fidelity and controllability by using templates to produce the main contents. In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability. The text stitch model can be trained in self-supervised fashion, which only requires free texts. The experiments on a benchmark dataset show that our system outperforms SOTA NN-based systems in fidelity and surpasses template-based systems in diversity and human involvement.",
}
@inproceedings{wu-etal-2020-gcdst,
    title = "{GCDST}: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking",
    author = "Wu, Peng  and
      Zou, Bowei  and
      Jiang, Ridong  and
      Aw, AiTi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.95",
    doi = "10.18653/v1/2020.findings-emnlp.95",
    pages = "1063--1073",
    abstract = "As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems. For practical usages, a major challenge to constructing a robust DST model is to process a conversation with multi-domain states. However, most existing approaches trained DST on a single domain independently, ignoring the information across domains. To tackle the multi-domain DST task, we first construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains. Then, we encode the graph information of dialogue states by graph convolutional networks and utilize a hard copy mechanism to directly copy historical states from the previous conversation. Experimental results show that our model improves the performances of the multi-domain DST baseline (TRADE) with the absolute joint accuracy of 2.0{\%} and 1.0{\%} on the MultiWOZ 2.0 and 2.1 dialogue datasets, respectively.",
}
@inproceedings{singh-etal-2020-incorporating,
    title = "Incorporating Stylistic Lexical Preferences in Generative Language Models",
    author = "Singh, Hrituraj  and
      Verma, Gaurav  and
      Srinivasan, Balaji Vasan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.96",
    doi = "10.18653/v1/2020.findings-emnlp.96",
    pages = "1074--1079",
    abstract = "While recent advances in language modeling has resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style. Leveraging the generative capabilities of a transformer-based language models, we present an approach to induce certain target-author attributes by incorporating continuous multi-dimensional lexical preferences of an author into generative language models. We introduce rewarding strategies in a reinforcement learning framework that encourages the use of words across multiple categorical dimensions, to varying extents. Our experiments demonstrate that the proposed approach can generate text that distinctively aligns with a given target author{'}s lexical style. We conduct quantitative and qualitative comparisons with competitive and relevant baselines to illustrate the benefits of the proposed approach.",
}
@inproceedings{glockner-etal-2020-think,
    title = "Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision",
    author = "Glockner, Max  and
      Habernal, Ivan  and
      Gurevych, Iryna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.97",
    doi = "10.18653/v1/2020.findings-emnlp.97",
    pages = "1080--1095",
    abstract = "Evaluating the trustworthiness of a model{'}s prediction is essential for differentiating between {`}right for the right reasons{'} and {`}right for the wrong reasons{'}. Identifying textual spans that determine the target label, known as faithful rationales, usually relies on pipeline approaches or reinforcement learning. However, such methods either require supervision and thus costly annotation of the rationales or employ non-differentiable models. We propose a differentiable training{--}framework to create models which output faithful rationales on a sentence level, by solely applying supervision on the target task. To achieve this, our model solves the task based on each rationale individually and learns to assign high scores to those which solved the task best. Our evaluation on three different datasets shows competitive results compared to a standard BERT blackbox while exceeding a pipeline counterpart{'}s performance in two cases. We further exploit the transparent decision{--}making process of these models to prefer selecting the correct rationales by applying direct supervision, thereby boosting the performance on the rationale{--}level.",
}
@inproceedings{lin-etal-2020-semi,
    title = "Semi-Supervised Learning for Video Captioning",
    author = "Lin, Ke  and
      Gan, Zhuoxin  and
      Wang, Liwei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.98",
    doi = "10.18653/v1/2020.findings-emnlp.98",
    pages = "1096--1106",
    abstract = "Deep neural networks have made great success on video captioning in supervised learning setting. However, annotating videos with descriptions is very expensive and time-consuming. If the video captioning algorithm can benefit from a large number of unlabeled videos, the cost of annotation can be reduced. In the proposed study, we make the first attempt to train the video captioning model on labeled data and unlabeled data jointly, in a semi-supervised learning manner. For labeled data, we train them with the traditional cross-entropy loss. For unlabeled data, we leverage a self-critical policy gradient method with the difference between the scores obtained by Monte-Carlo sampling and greedy decoding as the reward function, while the scores are the negative K-L divergence between output distributions of original video data and augmented video data. The final loss is the weighted sum of losses obtained by labeled data and unlabeled data. Experiments conducted on VATEX, MSR-VTT and MSVD dataset demonstrate that the introduction of unlabeled data can improve the performance of the video captioning model. The proposed semi-supervised learning algorithm also outperforms several state-of-the-art semi-supervised learning approaches.",
}
@inproceedings{ro-etal-2020-multi,
    title = "{M}ulti{\^{}}2{OIE}: Multilingual Open Information Extraction Based on Multi-Head Attention with {BERT}",
    author = "Ro, Youngbin  and
      Lee, Yukyung  and
      Kang, Pilsung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.99",
    doi = "10.18653/v1/2020.findings-emnlp.99",
    pages = "1107--1117",
    abstract = "In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to replace the previously used bidirectional long short-term memory architecture with multi-head attention. Multi$^2$OIE outperforms existing sequence-labeling systems with high computational efficiency on two benchmark evaluation datasets, Re-OIE2016 and CaRB. Additionally, we apply the proposed method to multilingual open IE using multilingual BERT. Experimental results on new benchmark datasets introduced for two languages (Spanish and Portuguese) demonstrate that our model outperforms other multilingual systems without training data for the target languages.",
}
@inproceedings{jabrayilzade-tekir-2020-lgpsolver,
    title = "{LGPS}olver - Solving Logic Grid Puzzles Automatically",
    author = "Jabrayilzade, Elgun  and
      Tekir, Selma",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.100",
    doi = "10.18653/v1/2020.findings-emnlp.100",
    pages = "1118--1123",
    abstract = "Logic grid puzzle (LGP) is a type of word problem where the task is to solve a problem in logic. Constraints for the problem are given in the form of textual clues. Once these clues are transformed into formal logic, a deductive reasoning process provides the solution. Solving logic grid puzzles in a fully automatic manner has been a challenge since a precise understanding of clues is necessary to develop the corresponding formal logic representation. To meet this challenge, we propose a solution that uses a DistilBERT-based classifier to classify a clue into one of the predefined predicate types for logic grid puzzles. Another novelty of the proposed solution is the recognition of comparison structures in clues. By collecting comparative adjectives from existing dictionaries and utilizing a semantic framework to catch comparative quantifiers, the semantics of clues concerning comparison structures are better understood, ensuring conversion to correct logic representation. Our approach solves logic grid puzzles in a fully automated manner with 100{\%} accuracy on the given puzzle datasets and outperforms state-of-the-art solutions by a large margin.",
}
@inproceedings{qin-etal-2020-using,
    title = "Using the Past Knowledge to Improve Sentiment Classification",
    author = "Qin, Qi  and
      Hu, Wenpeng  and
      Liu, Bing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.101",
    doi = "10.18653/v1/2020.findings-emnlp.101",
    pages = "1124--1133",
    abstract = "This paper studies sentiment classification in the lifelong learning setting that incrementally learns a sequence of sentiment classification tasks. It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task. A key innovation of this proposed model is a novel parameter-gate (p-gate) mechanism that regulates the flow or transfer of the previously learned knowledge to the new task. Specifically, it can selectively use the network parameters (which represent the retained knowledge gained from the previous tasks) to assist the learning of the new task t. Knowledge distillation is also employed in the process to preserve the past knowledge by approximating the network output at the state when task t-1 was learned. Experimental results show that L2PG outperforms strong baselines, including even multiple task learning.",
}
@inproceedings{li-etal-2020-high,
    title = "High-order Semantic Role Labeling",
    author = "Li, Zuchao  and
      Zhao, Hai  and
      Wang, Rui  and
      Parnow, Kevin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.102",
    doi = "10.18653/v1/2020.findings-emnlp.102",
    pages = "1134--1151",
    abstract = "Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has focused on the relationships between predicates and arguments and the correlations between arguments at most, while the correlations between predicates have been neglected for a long time. High-order features and structure learning were very common in modeling such correlations before the neural network era. In this paper, we introduce a high-order graph structure for the neural semantic role labeling model, which enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs. Experimental results on 7 languages of the CoNLL-2009 benchmark show that the high-order structural learning techniques are beneficial to the strong performing SRL models and further boost our baseline to achieve new state-of-the-art results.",
}
@inproceedings{welbl-etal-2020-undersensitivity,
    title = "Undersensitivity in Neural Reading Comprehension",
    author = "Welbl, Johannes  and
      Minervini, Pasquale  and
      Bartolo, Max  and
      Stenetorp, Pontus  and
      Riedel, Sebastian",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.103",
    doi = "10.18653/v1/2020.findings-emnlp.103",
    pages = "1152--1165",
    abstract = "Current reading comprehension methods generalise well to in-distribution test sets, yet perform poorly on adversarially selected data. Prior work on adversarial inputs typically studies model oversensitivity: semantically invariant text perturbations that cause a model{'}s prediction to change. Here we focus on the complementary problem: excessive prediction undersensitivity, where input text is meaningfully changed but the model{'}s prediction does not, even though it should. We formulate an adversarial attack which searches among semantic variations of the question for which a model erroneously predicts the same answer, and with even higher probability. We demonstrate that models trained on both SQuAD2.0 and NewsQA are vulnerable to this attack, and then investigate data augmentation and adversarial training as defences. Both substantially decrease adversarial vulnerability, which generalises to held-out data and held-out attack spaces. Addressing undersensitivity furthermore improves model robustness on the previously introduced ADDSENT and ADDONESENT datasets, and models generalise better when facing train / evaluation distribution mismatch: they are less prone to overly rely on shallow predictive cues present only in the training set, and outperform a conventional model by as much as 10.9{\%} F1.",
}
@inproceedings{zhu-etal-2020-hypertext,
    title = "{H}yper{T}ext: Endowing {F}ast{T}ext with Hyperbolic Geometry",
    author = "Zhu, Yudong  and
      Zhou, Di  and
      Xiao, Jinghui  and
      Jiang, Xin  and
      Chen, Xiao  and
      Liu, Qun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.104",
    doi = "10.18653/v1/2020.findings-emnlp.104",
    pages = "1166--1171",
    abstract = "Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym hierarchy in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not represent such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modelling tree-like hierarchical data, we propose a new model named HyperText for efficient text classification by endowing FastText with hyperbolic geometry. Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters.",
}
@inproceedings{niu-etal-2020-autoeter,
    title = "{A}uto{ETER}: Automated Entity Type Representation for Knowledge Graph Embedding",
    author = "Niu, Guanglin  and
      Li, Bo  and
      Zhang, Yongfei  and
      Pu, Shiliang  and
      Li, Jingyang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.105",
    doi = "10.18653/v1/2020.findings-emnlp.105",
    pages = "1172--1181",
    abstract = "Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces. Some traditional KGE models leveraging additional type information can improve the representation of entities which however totally rely on the explicit types or neglect the diverse type representations specific to various relations. Besides, none of the existing methods is capable of inferring all the relation patterns of symmetry, inversion and composition as well as the complex properties of 1-N, N-1 and N-N relations, simultaneously. To explore the type information for any KG, we develop a novel KGE framework with Automated Entity TypE Representation (AutoETER), which learns the latent type embedding of each entity by regarding each relation as a translation operation between the types of two entities with a relation-aware projection mechanism. Particularly, our designed automated type representation learning mechanism is a pluggable module which can be easily incorporated with any KGE model. Besides, our approach could model and infer all the relation patterns and complex relations. Experiments on four datasets demonstrate the superior performance of our model compared to state-of-the-art baselines on link prediction tasks, and the visualization of type clustering provides clearly the explanation of type embeddings and verifies the effectiveness of our model.",
}
@inproceedings{kawakami-etal-2020-learning,
    title = "Learning Robust and Multilingual Speech Representations",
    author = "Kawakami, Kazuya  and
      Wang, Luyu  and
      Dyer, Chris  and
      Blunsom, Phil  and
      van den Oord, Aaron",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.106",
    doi = "10.18653/v1/2020.findings-emnlp.106",
    pages = "1182--1192",
    abstract = "Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance. However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g. Wall Street Journal and LibriSpeech). This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages. In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages. We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages.",
}
@inproceedings{dhoffschmidt-etal-2020-fquad,
    title = "{FQ}u{AD}: {F}rench Question Answering Dataset",
    author = "d{'}Hoffschmidt, Martin  and
      Belblidia, Wacim  and
      Heinrich, Quentin  and
      Brendl{\'e}, Tom  and
      Vidal, Maxime",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.107",
    doi = "10.18653/v1/2020.findings-emnlp.107",
    pages = "1193--1208",
    abstract = "Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce. In the present work, we introduce the French Question Answering Dataset (FQuAD). FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version. We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set. In an effort to track the progress of French Question Answering models we propose a leaderboard and we have made the 1.0 version of our dataset freely available at https://illuin-tech.github.io/FQuAD-explorer/.",
}
@inproceedings{nguyen-etal-2020-dynamic,
    title = "Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection",
    author = "Nguyen, Hoang  and
      Zhang, Chenwei  and
      Xia, Congying  and
      Yu, Philip",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.108",
    doi = "10.18653/v1/2020.findings-emnlp.108",
    pages = "1209--1218",
    abstract = "Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components. These limitations inhibit generalizing capability towards Generalized Few-shot Learning settings where both seen and novel classes are co-existent. In this paper, we propose a novel Semantic Matching and Aggregation Network where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. These semantic components capture high-level information, resulting in more effective matching between instances. Our multi-perspective matching method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances. We also propose a more challenging evaluation setting that considers classification on the joint all-class label space. Extensive experimental results demonstrate the effectiveness of our method. Our code and data are publicly available.",
}
@inproceedings{zhao-etal-2020-quantifying,
    title = "Quantifying the Contextualization of Word Representations with Semantic Class Probing",
    author = {Zhao, Mengjie  and
      Dufter, Philipp  and
      Yaghoobzadeh, Yadollah  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.109",
    doi = "10.18653/v1/2020.findings-emnlp.109",
    pages = "1219--1234",
    abstract = "Pretrained language models achieve state-of-the-art results on many NLP tasks, but there are still many open questions about how and why they work so well. We investigate the contextualization of words in BERT. We quantify the amount of contextualization, i.e., how well words are interpreted in context, by studying the extent to which semantic classes of a word can be inferred from its contextualized embedding. Quantifying contextualization helps in understanding and utilizing pretrained language models. We show that the top layer representations support highly accurate inference of semantic classes; that the strongest contextualization effects occur in the lower layers; that local context is mostly sufficient for contextualizing words; and that top layer representations are more task-specific after finetuning while lower layer representations are more transferable. Finetuning uncovers task-related features, but pretrained knowledge about contextualization is still well preserved.",
}
@inproceedings{lovelace-mortazavi-2020-learning,
    title = "Learning to Generate Clinically Coherent Chest {X}-Ray Reports",
    author = "Lovelace, Justin  and
      Mortazavi, Bobak",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.110",
    doi = "10.18653/v1/2020.findings-emnlp.110",
    pages = "1235--1243",
    abstract = "Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care. However, past work has shown that typical abstractive methods tend to produce fluent, but clinically incorrect radiology reports. In this work, we develop a radiology report generation model utilizing the transformer architecture that produces superior reports as measured by both standard language generation and clinical coherence metrics compared to competitive baselines. We then develop a method to differentiably extract clinical information from generated reports and utilize this differentiability to fine-tune our model to produce more clinically coherent reports.",
}
@inproceedings{mallinson-etal-2020-felix,
    title = "{FELIX}: Flexible Text Editing Through Tagging and Insertion",
    author = "Mallinson, Jonathan  and
      Severyn, Aliaksei  and
      Malmi, Eric  and
      Garrido, Guillermo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.111",
    doi = "10.18653/v1/2020.findings-emnlp.111",
    pages = "1244--1255",
    abstract = "We present FELIX {--} a flexible text-editing approach for generation, designed to derive maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pretraining. In contrast to conventional sequenceto-sequence (seq2seq) models, FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input. The tagging model employs a novel Pointer mechanism, while the insertion model is based on a Masked Language Model (MLM). Both of these models are chosen to be non-autoregressive to guarantee faster inference. FELIX performs favourably when compared to recent text-editing methods and strong seq2seq baselines when evaluated on four NLG tasks: Sentence Fusion, Machine Translation Automatic Post-Editing, Summarization, and Text Simplification",
}
@inproceedings{rogers-augenstein-2020-improve,
    title = "What Can We Do to Improve Peer Review in {NLP}?",
    author = "Rogers, Anna  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.112",
    doi = "10.18653/v1/2020.findings-emnlp.112",
    pages = "1256--1262",
    abstract = "Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious. We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons. There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.",
}
@inproceedings{goswami-etal-2020-unsupervised-relation,
    title = "Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion",
    author = "Goswami, Ankur  and
      Bhat, Akshata  and
      Ohana, Hadar  and
      Rekatsinas, Theodoros",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.113",
    doi = "10.18653/v1/2020.findings-emnlp.113",
    pages = "1263--1276",
    abstract = "We show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a simple framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. RE-Flex uses contextual matching to ensure that language model predictions matches supporting evidence from the input corpus that is relevant to a target relation. We perform an extensive experimental study over multiple relation extraction benchmarks and demonstrate that RE-Flex outperforms competing unsupervised relation extraction methods based on pretrained language models by up to 27.8 F1 points compared to the next-best method. Our results show that constrained inference queries against a language model can enable accurate unsupervised relation extraction.",
}
@inproceedings{huang-etal-2020-biomedical,
    title = "Biomedical Event Extraction with Hierarchical Knowledge Graphs",
    author = "Huang, Kung-Hsiang  and
      Yang, Mu  and
      Peng, Nanyun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.114",
    doi = "10.18653/v1/2020.findings-emnlp.114",
    pages = "1277--1285",
    abstract = "Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41{\%} F1 and 3.19{\%} F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.",
}
@inproceedings{zhang-etal-2020-language,
    title = "Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced {M}onte-{C}arlo Approach",
    author = "Zhang, Maosen  and
      Jiang, Nan  and
      Li, Lei  and
      Xue, Yexiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.115",
    doi = "10.18653/v1/2020.findings-emnlp.115",
    pages = "1286--1298",
    abstract = "Generating natural language under complex constraints is a principled formulation towards controllable text generation. We present a framework to allow specification of combinatorial constraints for sentence generation. We propose TSMC, an efficient method to generate high likelihood sentences with respect to a pre-trained language model while satisfying the constraints. Our approach is highly flexible, requires no task-specific train- ing, and leverages efficient constraint satisfaction solving techniques. To better handle the combinatorial constraints, a tree search algorithm is embedded into the proposal process of the Markov Chain Monte Carlo (MCMC) to explore candidates that satisfy more constraints. Compared to existing MCMC approaches, our sampling approach has a better mixing performance. Experiments show that TSMC achieves consistent and significant improvement on multiple language generation tasks.",
}
@inproceedings{shaikh-etal-2020-examining,
    title = "Examining the Ordering of Rhetorical Strategies in Persuasive Requests",
    author = "Shaikh, Omar  and
      Chen, Jiaao  and
      Saad-Falcon, Jon  and
      Chau, Polo  and
      Yang, Diyi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.116",
    doi = "10.18653/v1/2020.findings-emnlp.116",
    pages = "1299--1306",
    abstract = "Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message{'}s content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contribute to persuasiveness, we first utilize a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We then visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual requests. We find that specific (orderings of) strategies interact uniquely with a request{'}s content to impact success rate, and thus the persuasiveness of a request.",
}
@inproceedings{gardner-etal-2020-evaluating,
    title = "Evaluating Models{'} Local Decision Boundaries via Contrast Sets",
    author = "Gardner, Matt  and
      Artzi, Yoav  and
      Basmov, Victoria  and
      Berant, Jonathan  and
      Bogin, Ben  and
      Chen, Sihao  and
      Dasigi, Pradeep  and
      Dua, Dheeru  and
      Elazar, Yanai  and
      Gottumukkala, Ananth  and
      Gupta, Nitish  and
      Hajishirzi, Hannaneh  and
      Ilharco, Gabriel  and
      Khashabi, Daniel  and
      Lin, Kevin  and
      Liu, Jiangming  and
      Liu, Nelson F.  and
      Mulcaire, Phoebe  and
      Ning, Qiang  and
      Singh, Sameer  and
      Smith, Noah A.  and
      Subramanian, Sanjay  and
      Tsarfaty, Reut  and
      Wallace, Eric  and
      Zhang, Ally  and
      Zhou, Ben",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.117",
    doi = "10.18653/v1/2020.findings-emnlp.117",
    pages = "1307--1323",
    abstract = "Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture the abilities a dataset is intended to test. We propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. In particular, after a dataset is constructed, we recommend that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets. Contrast sets provide a local view of a model{'}s decision boundary, which can be used to more accurately evaluate a model{'}s true linguistic capabilities. We demonstrate the efficacy of contrast sets by creating them for 10 diverse NLP datasets (e.g., DROP reading comprehension, UD parsing, and IMDb sentiment analysis). Although our contrast sets are not explicitly adversarial, model performance is significantly lower on them than on the original test sets{---}up to 25{\%} in some cases. We release our contrast sets as new evaluation benchmarks and encourage future dataset construction efforts to follow similar annotation processes.",
}
@inproceedings{chau-etal-2020-parsing,
    title = "Parsing with Multilingual {BERT}, a Small Corpus, and a Small Treebank",
    author = "Chau, Ethan C.  and
      Lin, Lucy H.  and
      Smith, Noah A.",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.118",
    doi = "10.18653/v1/2020.findings-emnlp.118",
    pages = "1324--1334",
    abstract = "Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This presents a challenge for language varieties unfamiliar to these models, whose labeled and unlabeled data is too limited to train a monolingual model effectively. We propose the use of additional language-specific pretraining and vocabulary augmentation to adapt multilingual models to low-resource settings. Using dependency parsing of four diverse low-resource language varieties as a case study, we show that these methods significantly improve performance over baselines, especially in the lowest-resource cases, and demonstrate the importance of the relationship between such models{'} pretraining data and target language varieties.",
}
@inproceedings{sabetpour-etal-2020-optsla,
    title = "{O}pt{SLA}: an Optimization-Based Approach for Sequential Label Aggregation",
    author = "Sabetpour, Nasim  and
      Kulkarni, Adithya  and
      Li, Qi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.119",
    doi = "10.18653/v1/2020.findings-emnlp.119",
    pages = "1335--1340",
    abstract = "The need for the annotated training dataset on which data-hungry machine learning algorithms feed has increased dramatically with advanced acclaim of machine learning applications. To annotate the data, people with domain expertise are needed, but they are seldom available and expensive to hire. This has lead to the thriving of crowdsourcing platforms such as Amazon Mechanical Turk (AMT). However, the annotations provided by one worker cannot be used directly to train the model due to the lack of expertise. Existing literature in annotation aggregation focuses on binary and multi-choice problems. In contrast, little work has been done on complex tasks such as sequence labeling with imbalanced classes, a ubiquitous task in Natural Language Processing (NLP), and Bio-Informatics. We propose OptSLA, an Optimization-based Sequential Label Aggregation method, that jointly considers the characteristics of sequential labeling tasks, workers reliabilities, and advanced deep learning techniques to conquer the challenge. We evaluate our model on crowdsourced data for named entity recognition task. Our results show that the proposed OptSLA outperforms the state-of-the-art aggregation methods, and the results are easier to interpret.",
}
@inproceedings{hiraoka-etal-2020-optimizing,
    title = "Optimizing Word Segmentation for Downstream Task",
    author = "Hiraoka, Tatsuya  and
      Takase, Sho  and
      Uchiumi, Kei  and
      Keyaki, Atsushi  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.120",
    doi = "10.18653/v1/2020.findings-emnlp.120",
    pages = "1341--1351",
    abstract = "In traditional NLP, we tokenize a given sentence as a preprocessing, and thus the tokenization is unrelated to a target downstream task. To address this issue, we propose a novel method to explore a tokenization which is appropriate for the downstream task. Our proposed method, optimizing tokenization (OpTok), is trained to assign a high probability to such appropriate tokenization based on the downstream task loss. OpTok can be used for any downstream task which uses a vector representation of a sentence such as text classification. Experimental results demonstrate that OpTok improves the performance of sentiment analysis and textual entailment. In addition, we introduce OpTok into BERT, the state-of-the-art contextualized embeddings and report a positive effect.",
}
@inproceedings{cheng-etal-2020-dynamically,
    title = "Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning",
    author = "Cheng, Fei  and
      Asahara, Masayuki  and
      Kobayashi, Ichiro  and
      Kurohashi, Sadao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.121",
    doi = "10.18653/v1/2020.findings-emnlp.121",
    pages = "1352--1357",
    abstract = "Temporal relation classification is the pair-wise task for identifying the relation of a temporal link (TLINKs) between two mentions, i.e. event, time and document creation time (DCT). It leads to two crucial limits: 1) Two TLINKs involving a common mention do not share information. 2) Existing models with independent classifiers for each TLINK category (E2E, E2T and E2D) hinder from using the whole data. This paper presents an event centric model that allows to manage dynamic event representations across multiple TLINKs. Our model deals with three TLINK categories with multi-task learning to leverage the full size of data. The experimental results show that our proposal outperforms state-of-the-art models and two strong transfer learning baselines on both the English and Japanese data.",
}
@inproceedings{ma-etal-2020-compare,
    title = "A Compare Aggregate Transformer for Understanding Document-grounded Dialogue",
    author = "Ma, Longxuan  and
      Zhang, Wei-Nan  and
      Sun, Runxin  and
      Liu, Ting",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.122",
    doi = "10.18653/v1/2020.findings-emnlp.122",
    pages = "1358--1367",
    abstract = "Unstructured documents serving as external knowledge of the dialogues help to generate more informative responses. Previous research focused on knowledge selection (KS) in the document with dialogue. However, dialogue history that is not related to the current dialogue may introduce noise in the KS processing. In this paper, we propose a Compare Aggregate Transformer (CAT) to jointly denoise the dialogue context and aggregate the document information for response generation. We designed two different comparison mechanisms to reduce noise (before and during decoding). In addition, we propose two metrics for evaluating document utilization efficiency based on word overlap. Experimental results on the CMU{\_}DoG dataset show that the proposed CAT model outperforms the state-of-the-art approach and strong baselines.",
}
@inproceedings{huang-etal-2020-texthide,
    title = "{T}ext{H}ide: Tackling Data Privacy in Language Understanding Tasks",
    author = "Huang, Yangsibo  and
      Song, Zhao  and
      Chen, Danqi  and
      Li, Kai  and
      Arora, Sanjeev",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.123",
    doi = "10.18653/v1/2020.findings-emnlp.123",
    pages = "1368--1382",
    abstract = "An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy. In this paper, we propose TextHide aiming at addressing this challenge for natural language understanding tasks. It requires all participants to add a simple encryption step to prevent an eavesdropping attacker from recovering private text data. Such an encryption step is efficient and only affects the task performance slightly. In addition, TextHide fits well with the popular framework of fine-tuning pre-trained language models (e.g., BERT) for any sentence or sentence-pair task. We evaluate TextHide on the GLUE benchmark, and our experiments show that TextHide can effectively defend attacks on shared gradients or representations and the averaged accuracy reduction is only 1.9{\%}. We also present an analysis of the security of TextHide using a conjecture about the computational intractability of a mathematical problem.",
}
@inproceedings{pan-etal-2020-modeling,
    title = "Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection",
    author = "Pan, Hongliang  and
      Lin, Zheng  and
      Fu, Peng  and
      Qi, Yatao  and
      Wang, Weiping",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.124",
    doi = "10.18653/v1/2020.findings-emnlp.124",
    pages = "1383--1392",
    abstract = "Sarcasm is a pervasive phenomenon in today{'}s social media platforms such as Twitter and Reddit. These platforms allow users to create multi-modal messages, including texts, images, and videos. Existing multi-modal sarcasm detection methods either simply concatenate the features from multi modalities or fuse the multi modalities information in a designed manner. However, they ignore the incongruity character in sarcastic utterance, which is often manifested between modalities or within modalities. Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection. To be specific, we are inspired by the idea of self-attention mechanism and design inter-modality attention to capturing inter-modality incongruity. In addition, the co-attention mechanism is applied to model the contradiction within the text. The incongruity information is then used for prediction. The experimental results demonstrate that our model achieves state-of-the-art performance on a public multi-modal sarcasm detection dataset.",
}
@inproceedings{tamkin-etal-2020-investigating,
    title = "Investigating Transferability in Pretrained Language Models",
    author = "Tamkin, Alex  and
      Singh, Trisha  and
      Giovanardi, Davide  and
      Goodman, Noah",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.125",
    doi = "10.18653/v1/2020.findings-emnlp.125",
    pages = "1393--1401",
    abstract = "How does language model pretraining help transfer learning? We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance. This method, partial reinitialization, involves replacing different layers of a pretrained model with random weights, then finetuning the entire model on the transfer task and observing the change in performance. This technique reveals that in BERT, layers with high probing performance on downstream GLUE tasks are neither necessary nor sufficient for high accuracy on those tasks. Furthermore, the benefit of using pretrained parameters for a layer varies dramatically with finetuning dataset size: parameters that provide tremendous performance improvement when data is plentiful may provide negligible benefits in data-scarce settings. These results reveal the complexity of the transfer learning process, highlighting the limitations of methods that operate on frozen models or single data samples.",
}
@inproceedings{wu-etal-2020-improving-knowledge,
    title = "Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues",
    author = "Wu, Sixing  and
      Li, Ying  and
      Zhang, Dawei  and
      Wu, Zhonghai",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.126",
    doi = "10.18653/v1/2020.findings-emnlp.126",
    pages = "1402--1411",
    abstract = "Incorporating commonsense knowledge can alleviate the issue of generating generic responses in open-domain generative dialogue systems. However, selecting knowledge facts for the dialogue context is still a challenge. The widely used approach Entity Name Matching always retrieves irrelevant facts from the view of local entity words. This paper proposes a novel knowledge selection approach, Prototype-KR, and a knowledge-aware generative model, Prototype-KRG. Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query. We find knowledge facts used in prototype dialogues usually are highly relevant to the current query; thus, Prototype-KR ranks such knowledge facts based on the semantic similarity and then selects the most appropriate facts. Subsequently, Prototype-KRG can generate an informative response using the selected knowledge facts. Experiments demonstrate that our approach has achieved notable improvements on the most metrics, compared to generative baselines. Meanwhile, compared to IR(Retrieval)-based baselines, responses generated by our approach are more relevant to the context and have comparable informativeness.",
}
@inproceedings{gu-etal-2020-filtering,
    title = "Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots",
    author = "Gu, Jia-Chen  and
      Ling, Zhenhua  and
      Liu, Quan  and
      Chen, Zhigang  and
      Zhu, Xiaodan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.127",
    doi = "10.18653/v1/2020.findings-emnlp.127",
    pages = "1412--1422",
    abstract = "The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously. This paper proposes a method named Filtering before Iteratively REferring (FIRE) for this task. In this method, a context filter and a knowledge filter are first built, which derive knowledge-aware context representations and context-aware knowledge representations respectively by global and bidirectional attention. Besides, the entries irrelevant to the conversation are discarded by the knowledge filter. After that, iteratively referring is performed between context and response representations as well as between knowledge and response representations, in order to collect deep matching features for scoring response candidates. Experimental results show that FIRE outperforms previous methods by margins larger than 2.8{\%} and 4.1{\%} on the PERSONA-CHAT dataset with original and revised personas respectively, and margins larger than 3.1{\%} on the CMU{\_}DoG dataset in terms of top-1 accuracy. We also show that FIRE is more interpretable by visualizing the knowledge grounding process.",
}
@inproceedings{qi-etal-2020-privacy,
    title = "Privacy-Preserving News Recommendation Model Learning",
    author = "Qi, Tao  and
      Wu, Fangzhao  and
      Wu, Chuhan  and
      Huang, Yongfeng  and
      Xie, Xing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.128",
    doi = "10.18653/v1/2020.findings-emnlp.128",
    pages = "1423--1432",
    abstract = "News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. In this paper, we propose a privacy-preserving method for news recommendation model training based on federated learning, where the user behavior data is locally stored on user devices. Our method can leverage the useful information in the behaviors of massive number users to train accurate news recommendation models and meanwhile remove the need of centralized storage of them. More specifically, on each user device we keep a local copy of the news recommendation model, and compute gradients of the local model based on the user behaviors in this device. The local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global model in the server. Since the model gradients may contain some implicit private information, we apply local differential privacy (LDP) to them before uploading for better privacy protection. The updated global model is then distributed to each user device for local model update. We repeat this process for multiple rounds. Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.",
}
@inproceedings{tai-etal-2020-exbert,
    title = "ex{BERT}: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources",
    author = "Tai, Wen  and
      Kung, H. T.  and
      Dong, Xin  and
      Comiter, Marcus  and
      Kuo, Chang-Fu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.129",
    doi = "10.18653/v1/2020.findings-emnlp.129",
    pages = "1433--1439",
    abstract = "We introduce exBERT, a training method to extend BERT pre-trained models from a general domain to a new pre-trained model for a specific domain with a new additive vocabulary under constrained training resources (i.e., constrained computation and data). exBERT uses a small extension module to learn to adapt an augmenting embedding for the new domain in the context of the original BERT{'}s embedding of a general vocabulary. The exBERT training method is novel in learning the new vocabulary and the extension module while keeping the weights of the original BERT model fixed, resulting in a substantial reduction in required training resources. We pre-train exBERT with biomedical articles from ClinicalKey and PubMed Central, and study its performance on biomedical downstream benchmark tasks using the MTL-Bioinformatics-2016 datasets. We demonstrate that exBERT consistently outperforms prior approaches when using limited corpus and pre-training computation resources.",
}
@inproceedings{tepper-etal-2020-balancing,
    title = "Balancing via Generation for Multi-Class Text Classification Improvement",
    author = "Tepper, Naama  and
      Goldbraich, Esther  and
      Zwerdling, Naama  and
      Kour, George  and
      Anaby Tavor, Ateret  and
      Carmeli, Boaz",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.130",
    doi = "10.18653/v1/2020.findings-emnlp.130",
    pages = "1440--1452",
    abstract = "Data balancing is a known technique for improving the performance of classification tasks. In this work we define a novel balancing-viageneration framework termed BalaGen. BalaGen consists of a flexible balancing policy coupled with a text generation mechanism. Combined, these two techniques can be used to augment a dataset for more balanced distribution. We evaluate BalaGen on three publicly available semantic utterance classification (SUC) datasets. One of these is a new COVID-19 Q{\&}A dataset published here for the first time. Our work demonstrates that optimal balancing policies can significantly improve classifier performance, while augmenting just part of the classes and under-sampling others. Furthermore, capitalizing on the advantages of balancing, we show its usefulness in all relevant BalaGen framework components. We validate the superiority of BalaGen on ten semantic utterance datasets taken from real-life goaloriented dialogue systems. Based on our results we encourage using data balancing prior to training for text classification tasks.",
}
@inproceedings{liu-etal-2020-conditional,
    title = "Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization",
    author = "Liu, Zhengyuan  and
      Shi, Ke  and
      Chen, Nancy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.131",
    doi = "10.18653/v1/2020.findings-emnlp.131",
    pages = "1453--1463",
    abstract = "Much progress has been made in text summarization, fueled by neural architectures using large-scale training corpora. However, in the news domain, neural models easily overfit by leveraging position-related features due to the prevalence of the inverted pyramid writing style. In addition, there is an unmet need to generate a variety of summaries for different users. In this paper, we propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions (i.e. importance, diversity, position). These sub-aspect functions are regulated by a set of control codes to decide which sub-aspect to focus on during summary generation. We demonstrate that extracted summaries with minimal position bias is comparable with those generated by standard models that take advantage of position preference. We also show that news summaries generated with a focus on diversity can be more preferred by human raters. These results suggest that a more flexible neural summarization framework providing more control options could be desirable in tailoring to different user preferences, which is useful since it is often impractical to articulate such preferences for different applications a priori.",
}
@inproceedings{luo-etal-2020-research,
    title = "Research Replication Prediction Using Weakly Supervised Learning",
    author = "Luo, Tianyi  and
      Li, Xingyu  and
      Wang, Hainan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.132",
    doi = "10.18653/v1/2020.findings-emnlp.132",
    pages = "1464--1474",
    abstract = "Knowing whether a published research result can be replicated is important. Carrying out direct replication of published research incurs a high cost. There are efforts tried to use machine learning aided methods to predict scientific claims{'} replicability. However, existing machine learning aided approaches use only hand-extracted statistics features such as p-value, sample size, etc. without utilizing research papers{'} text information and train only on a very small size of annotated data without making the most use of a large number of unlabeled articles. Therefore, it is desirable to develop effective machine learning aided automatic methods which can automatically extract text information as features so that we can benefit from Natural Language Processing techniques. Besides, we aim for an approach that benefits from both labeled and the large number of unlabeled data. In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets. Our experiments over real-world datasets show that our approaches obtain much better prediction performance compared to the supervised models utilizing only statistic features and a small size of labeled dataset. Further, we are able to achieve an accuracy of 75.76{\%} for predicting the replicability of research.",
}
@inproceedings{han-etal-2020-open,
    title = "Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion",
    author = "Han, Jiale  and
      Cheng, Bo  and
      Wang, Xu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.133",
    doi = "10.18653/v1/2020.findings-emnlp.133",
    pages = "1475--1481",
    abstract = "The incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA). This paper proposes a novel QA method by leveraging text information to enhance the incomplete KB. The model enriches the entity representation through semantic information contained in the text, and employs graph convolutional networks to update the entity status. Furthermore, to exploit the latent structural information of text, we treat the text as hyperedges connecting entities among it to complement the deficient relations in KB, and hypergraph convolutional networks are further applied to reason on the hypergraph-formed text. Extensive experiments on the WebQuestionsSP benchmark with different KB settings prove the effectiveness of our model.",
}
@inproceedings{poerner-etal-2020-inexpensive,
    title = "Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical {NER} and Covid-19 {QA}",
    author = {Poerner, Nina  and
      Waltinger, Ulli  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.134",
    doi = "10.18653/v1/2020.findings-emnlp.134",
    pages = "1482--1490",
    abstract = "Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO 2 emissions. Here, we propose a cheaper alternative: We train Word2Vec on target-domain text and align the resulting word vectors with the wordpiece vectors of a general-domain PTLM. We evaluate on eight English biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model. We cover over 60{\%} of the BioBERT - BERT F1 delta, at 5{\%} of BioBERT{'}s CO 2 footprint and 2{\%} of its cloud compute cost. We also show how to quickly adapt an existing general-domain Question Answering (QA) model to an emerging domain: the Covid-19 pandemic.",
}
@inproceedings{ben-david-etal-2020-semantically,
    title = "Semantically Driven Sentence Fusion: Modeling and Evaluation",
    author = "Ben-David, Eyal  and
      Keller, Orgad  and
      Malmi, Eric  and
      Szpektor, Idan  and
      Reichart, Roi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.135",
    doi = "10.18653/v1/2020.findings-emnlp.135",
    pages = "1491--1505",
    abstract = "Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.",
}
@inproceedings{zhou-etal-2020-pseudo,
    title = "Pseudo-Bidirectional Decoding for Local Sequence Transduction",
    author = "Zhou, Wangchunshu  and
      Ge, Tao  and
      Xu, Ke",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.136",
    doi = "10.18653/v1/2020.findings-emnlp.136",
    pages = "1506--1511",
    abstract = "Local sequence transduction (LST) tasks are sequence transduction tasks where there exists massive overlapping between the source and target sequences, such as grammatical error correction and spell or OCR correction. Motivated by this characteristic of LST tasks, we propose Pseudo-Bidirectional Decoding (PBD), a simple but versatile approach for LST tasks. PBD copies the representation of source tokens to the decoder as pseudo future context that enables the decoder self-attention to attends to its bi-directional context. In addition, the bidirectional decoding scheme and the characteristic of LST tasks motivate us to share the encoder and the decoder of LST models. Our approach provides right-side context information for the decoder, reduces the number of parameters by half, and provides good regularization effects. Experimental results on several benchmark datasets show that our approach consistently improves the performance of standard seq2seq models on LST tasks.",
}
@inproceedings{vu-etal-2020-predicting,
    title = "Predicting Responses to Psychological Questionnaires from Participants{'} Social Media Posts and Question Text Embeddings",
    author = "Vu, Huy  and
      Abdurahman, Suhaib  and
      Bhatia, Sudeep  and
      Ungar, Lyle",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.137",
    doi = "10.18653/v1/2020.findings-emnlp.137",
    pages = "1512--1524",
    abstract = "Psychologists routinely assess people{'}s emotions and traits, such as their personality, by collecting their responses to survey questionnaires. Such assessments can be costly in terms of both time and money, and often lack generalizability, as existing data cannot be used to predict responses for new survey questions or participants. In this study, we propose a method for predicting a participant{'}s questionnaire response using their social media texts and the text of the survey question they are asked. Specifically, we use Natural Language Processing (NLP) tools such as BERT embeddings to represent both participants (via the text they write) and survey questions as embeddings vectors, allowing us to predict responses for out-of-sample participants and questions. Our novel approach can be used by researchers to integrate new participants or new questions into psychological studies without the constraint of costly data collection, facilitating novel practical applications and furthering the development of psychological theory. Finally, as a side contribution, the success of our model also suggests a new approach to study survey questions using NLP tools such as text embeddings rather than response data used in traditional methods.",
}
@inproceedings{pinter-etal-2020-will,
    title = "Will it Unblend?",
    author = "Pinter, Yuval  and
      Jacobs, Cassandra L.  and
      Eisenstein, Jacob",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.138",
    doi = "10.18653/v1/2020.findings-emnlp.138",
    pages = "1525--1535",
    abstract = "Natural language processing systems often struggle with out-of-vocabulary (OOV) terms, which do not appear in training data. Blends, such as {``}innoventor{''}, are one particularly challenging class of OOV, as they are formed by fusing together two or more bases that relate to the intended meaning in unpredictable manners and degrees. In this work, we run experiments on a novel dataset of English OOV blends to quantify the difficulty of interpreting the meanings of blends by large-scale contextual language models such as BERT. We first show that BERT{'}s processing of these blends does not fully access the component meanings, leaving their contextual representations semantically impoverished. We find this is mostly due to the loss of characters resulting from blend formation. Then, we assess how easily different models can recognize the structure and recover the origin of blends, and find that context-aware embedding systems outperform character-level and context-free embeddings, although their results are still far from satisfactory.",
}
@inproceedings{feng-etal-2020-codebert,
    title = "{C}ode{BERT}: A Pre-Trained Model for Programming and Natural Languages",
    author = "Feng, Zhangyin  and
      Guo, Daya  and
      Tang, Duyu  and
      Duan, Nan  and
      Feng, Xiaocheng  and
      Gong, Ming  and
      Shou, Linjun  and
      Qin, Bing  and
      Liu, Ting  and
      Jiang, Daxin  and
      Zhou, Ming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.139",
    doi = "10.18653/v1/2020.findings-emnlp.139",
    pages = "1536--1547",
    abstract = "We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both {``}bimodal{''} data of NL-PL pairs and {``}unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NLPL probing.",
}
@inproceedings{yang-etal-2020-styledgpt,
    title = "{S}tyle{DGPT}: Stylized Response Generation with Pre-trained Language Models",
    author = "Yang, Ze  and
      Wu, Wei  and
      Xu, Can  and
      Liang, Xinnian  and
      Bai, Jiaqi  and
      Wang, Liran  and
      Wang, Wei  and
      Li, Zhoujun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.140",
    doi = "10.18653/v1/2020.findings-emnlp.140",
    pages = "1548--1559",
    abstract = "Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training. In this work, we explore the challenging task with pre-trained language models that have brought breakthrough to various natural language tasks. To this end, we introduce a KL loss and a style classifier to the fine-tuning step in order to steer response generation towards the target style in both a word-level and a sentence-level. Comprehensive empirical studies with two public datasets indicate that our model can significantly outperform state-of-the-art methods in terms of both style consistency and contextual coherence.",
}
@inproceedings{yang-etal-2020-enhancing,
    title = "Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking",
    author = "Yang, Ruosong  and
      Cao, Jiannong  and
      Wen, Zhiyuan  and
      Wu, Youzheng  and
      He, Xiaodong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.141",
    doi = "10.18653/v1/2020.findings-emnlp.141",
    pages = "1560--1569",
    abstract = "Automated Essay Scoring (AES) is a critical text regression task that automatically assigns scores to essays based on their writing quality. Recently, the performance of sentence prediction tasks has been largely improved by using Pre-trained Language Models via fusing representations from different layers, constructing an auxiliary sentence, using multi-task learning, etc. However, to solve the AES task, previous works utilize shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss, respectively. Since shallow neural networks trained on limited samples show poor performance to capture deep semantic of texts. And without an accurate scoring function, ranking loss and regression loss measures two different aspects of the calculated scores. To improve AES{'}s performance, we find a new way to fine-tune pre-trained language models with multiple losses of the same task. In this paper, we propose to utilize a pre-trained language model to learn text representations first. With scores calculated from the representations, mean square error loss and the batch-wise ListNet loss with dynamic weights constrain the scores simultaneously. We utilize Quadratic Weighted Kappa to evaluate our model on the Automated Student Assessment Prize dataset. Our model outperforms not only state-of-the-art neural models near 3 percent but also the latest statistic model. Especially on the two narrative prompts, our model performs much better than all other state-of-the-art models.",
}
@inproceedings{chen-etal-2020-neural-dialogue,
    title = "Neural Dialogue State Tracking with Temporally Expressive Networks",
    author = "Chen, Junfan  and
      Zhang, Richong  and
      Mao, Yongyi  and
      Xu, Jie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.142",
    doi = "10.18653/v1/2020.findings-emnlp.142",
    pages = "1570--1579",
    abstract = "Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.",
}
@inproceedings{lima-etal-2020-inferring,
    title = "Inferring about fraudulent collusion risk on {B}razilian public works contracts in official texts using a {B}i-{LSTM} approach",
    author = "Lima, Marcos  and
      Silva, Roberta  and
      Lopes de Souza Mendes, Felipe  and
      R. de Carvalho, Leonardo  and
      Araujo, Aleteia  and
      de Barros Vidal, Flavio",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.143",
    doi = "10.18653/v1/2020.findings-emnlp.143",
    pages = "1580--1588",
    abstract = "Public works procurements move US{\$} 10 billion yearly in Brazil and are a preferred field for collusion and fraud. Federal Police and audit agencies investigate collusion (bid-rigging), over-pricing, and delivery fraud in this field and efforts have been employed to early detect fraud and collusion on public works procurements. The current automatic methods of fraud detection use structured data to classification and usually do not involve annotated data. The use of NLP for this kind of application is rare. Our work introduces a new dataset formed by public procurement calls available on Brazilian official journal (Di{\'a}rio Oficial da Uni{\~a}o), using by 15,132,968 textual entries of which 1,907 are annotated risky entries. Both bottleneck deep neural network and BiLSTM shown competitive compared with classical classifiers and achieved better precision (93.0{\%} and 92.4{\%}, respectively), which signs improvements in a criminal fraud investigation.",
}
@inproceedings{lin-etal-2020-data,
    title = "Data-to-Text Generation with Style Imitation",
    author = "Lin, Shuai  and
      Wang, Wentao  and
      Yang, Zichao  and
      Liang, Xiaodan  and
      Xu, Frank F.  and
      Xing, Eric  and
      Hu, Zhiting",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.144",
    doi = "10.18653/v1/2020.findings-emnlp.144",
    pages = "1589--1598",
    abstract = "Recent neural approaches to data-to-text generation have mostly focused on improving content fidelity while lacking explicit control over writing styles (e.g., sentence structures, word choices). More traditional systems use templates to determine the realization of text. Yet manual or automatic construction of high-quality templates is difficult, and a template acting as hard constraints could harm content fidelity when it does not match the record perfectly. We study a new way of stylistic control by using existing sentences as {``}soft{''} templates. That is, a model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the record. The problem is challenging due to the lack of parallel data. We develop a neural approach that includes a hybrid attention-copy mechanism, learns with weak supervisions, and is enhanced with a new content coverage constraint. We conduct experiments in restaurants and sports domains. Results show our approach achieves stronger performance than a range of comparison methods. Our approach balances well between content fidelity and style control given exemplars that match the records to varying degrees.",
}
@inproceedings{ye-etal-2020-teaching,
    title = "Teaching Machine Comprehension with Compositional Explanations",
    author = "Ye, Qinyuan  and
      Huang, Xiao  and
      Boschee, Elizabeth  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.145",
    doi = "10.18653/v1/2020.findings-emnlp.145",
    pages = "1599--1615",
    abstract = "Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples. In contrast, humans are typically able to generalize with only a few examples, relying on deeper underlying world knowledge, linguistic sophistication, and/or simply superior deductive powers. In this paper, we focus on {``}teaching{''} machines reading comprehension, using a small number of semi-structured explanations that explicitly inform machines why answer spans are correct. We extract structured variables and rules from explanations and compose neural module teachers that annotate instances for training downstream MRC models. We use learnable neural modules and soft logic to handle linguistic variation and overcome sparse coverage; the modules are jointly optimized with the MRC model to improve final performance. On the SQuAD dataset, our proposed method achieves 70.14{\%} F1 score with supervision from 26 explanations, comparable to plain supervised learning using 1,100 labeled instances, yielding a 12x speed up.",
}
@inproceedings{chen-etal-2020-knowledge,
    title = "A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining",
    author = "Chen, Jiahua  and
      Wang, Shuai  and
      Mazumder, Sahisnu  and
      Liu, Bing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.146",
    doi = "10.18653/v1/2020.findings-emnlp.146",
    pages = "1616--1626",
    abstract = "Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on realworld datasets involving five domains (product types) shows the effectiveness of the approach",
}
@inproceedings{jalili-sabet-etal-2020-simalign,
    title = "{S}im{A}lign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings",
    author = {Jalili Sabet, Masoud  and
      Dufter, Philipp  and
      Yvon, Fran{\c{c}}ois  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.147",
    doi = "10.18653/v1/2020.findings-emnlp.147",
    pages = "1627--1643",
    abstract = "Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings {--} both static and contextualized {--} for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners {--} even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.",
}
@inproceedings{barbieri-etal-2020-tweeteval,
    title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification",
    author = "Barbieri, Francesco  and
      Camacho-Collados, Jose  and
      Espinosa Anke, Luis  and
      Neves, Leonardo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.148",
    doi = "10.18653/v1/2020.findings-emnlp.148",
    pages = "1644--1650",
    abstract = "The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction. Therefore, it is unclear what the current state of the art is, as there is no standardized evaluation protocol, neither a strong set of baselines trained on such domain-specific data. In this paper, we propose a new evaluation framework (TweetEval) consisting of seven heterogeneous Twitter-specific classification tasks. We also provide a strong set of baselines as starting point, and compare different language modeling pre-training strategies. Our initial experiments show the effectiveness of starting off with existing pre-trained generic language models, and continue training them on Twitter corpora.",
}
@inproceedings{zhang-etal-2020-octa,
    title = "Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis",
    author = "Zhang, Zhe  and
      Hang, Chung-Wei  and
      Singh, Munindar",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.149",
    doi = "10.18653/v1/2020.findings-emnlp.149",
    pages = "1651--1662",
    abstract = "Sentiments in opinionated text are often determined by both aspects and target words (or targets). We observe that targets and aspects interrelate in subtle ways, often yielding conflicting sentiments. Thus, a naive aggregation of sentiments from aspects and targets treated separately, as in existing sentiment analysis models, impairs performance. We propose Octa, an approach that jointly considers aspects and targets when inferring sentiments. To capture and quantify relationships between targets and context words, Octa uses a selective self-attention mechanism that handles implicit or missing targets. Specifically, Octa involves two layers of attention mechanisms for, respectively, selective attention between targets and context words and attention over words based on aspects. On benchmark datasets, Octa outperforms leading models by a large margin, yielding (absolute) gains in accuracy of 1.6{\%} to 4.3{\%}.",
}
@inproceedings{libovicky-etal-2020-language,
    title = "On the Language Neutrality of Pre-trained Multilingual Representations",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Rosa, Rudolf  and
      Fraser, Alexander",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.150",
    doi = "10.18653/v1/2020.findings-emnlp.150",
    pages = "1663--1674",
    abstract = "Multilingual contextual embeddings, such as multilingual BERT and XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work probed the cross-linguality of the representations indirectly using zero-shot transfer learning on morphological and syntactic tasks. We instead investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings, which are explicitly trained for language neutrality. Contextual embeddings are still only moderately language-neutral by default, so we propose two simple methods for achieving stronger language neutrality: first, by unsupervised centering of the representation for each language and second, by fitting an explicit projection on small parallel data. Besides, we show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences without using parallel data.",
}
@inproceedings{dai-etal-2020-cost,
    title = "Cost-effective Selection of Pretraining Data: A Case Study of Pretraining {BERT} on Social Media",
    author = "Dai, Xiang  and
      Karimi, Sarvnaz  and
      Hachey, Ben  and
      Paris, Cecile",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.151",
    doi = "10.18653/v1/2020.findings-emnlp.151",
    pages = "1675--1681",
    abstract = "Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject matter, e.g., biology or computer science. Given the range of applications using social media text, and its unique language variety, we pretrain two models on tweets and forum text respectively, and empirically demonstrate the effectiveness of these two resources. In addition, we investigate how similarity measures can be used to nominate in-domain pretraining data. We publicly release our pretrained models at https://bit.ly/35RpTf0.",
}
@inproceedings{chaudhary-etal-2020-topicbert,
    title = "{T}opic{BERT} for Energy Efficient Document Classification",
    author = {Chaudhary, Yatin  and
      Gupta, Pankaj  and
      Saxena, Khushbu  and
      Kulkarni, Vivek  and
      Runkler, Thomas  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.152",
    doi = "10.18653/v1/2020.findings-emnlp.152",
    pages = "1682--1690",
    abstract = "Prior research notes that BERT{'}s computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions. While recent work seeks to address these scalability issues at pre-training, these issues are also prominent in fine-tuning especially for long sequence tasks like document classification. Our work thus focuses on optimizing the computational cost of fine-tuning for document classification. We achieve this by complementary learning of both topic and language models in a unified framework, named TopicBERT. This significantly reduces the number of self-attention operations {--} a main performance bottleneck. Consequently, our model achieves a 1.4x ( 40{\%}) speedup with 40{\%} reduction in CO2 emission while retaining 99.9{\%} performance over 5 datasets.",
}
@inproceedings{tian-etal-2020-improving-constituency,
    title = "Improving Constituency Parsing with Span Attention",
    author = "Tian, Yuanhe  and
      Song, Yan  and
      Xia, Fei  and
      Zhang, Tong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.153",
    doi = "10.18653/v1/2020.findings-emnlp.153",
    pages = "1691--1703",
    abstract = "Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task. N-grams, which is a conventional type of feature for contextual information, have been demonstrated to be useful in many tasks, and thus could also be beneficial for constituency parsing if they are appropriately modeled. In this paper, we propose span attention for neural chart-based constituency parsing to leverage n-gram information. Considering that current chart-based parsers with Transformer-based encoder represent spans by subtraction of the hidden states at the span boundaries, which may cause information loss especially for long spans, we incorporate n-grams into span representations by weighting them according to their contributions to the parsing process. Moreover, we propose categorical span attention to further enhance the model by weighting n-grams within different length categories, and thus benefit long-sentence parsing. Experimental results on three widely used benchmark datasets demonstrate the effectiveness of our approach in parsing Arabic, Chinese, and English, where state-of-the-art performance is obtained by our approach on all of them.",
}
@inproceedings{malkiel-etal-2020-recobert,
    title = "{R}eco{BERT}: A Catalog Language Model for Text-Based Recommendations",
    author = "Malkiel, Itzik  and
      Barkan, Oren  and
      Caciularu, Avi  and
      Razin, Noam  and
      Katz, Ori  and
      Koenigstein, Noam",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.154",
    doi = "10.18653/v1/2020.findings-emnlp.154",
    pages = "1704--1714",
    abstract = "Language models that utilize extensive self-supervised pre-training from unlabeled text, have recently shown to significantly advance the state-of-the-art performance in a variety of language understanding tasks. However, it is yet unclear if and how these recent models can be harnessed for conducting text-based recommendations. In this work, we introduce RecoBERT, a BERT-based approach for learning catalog-specialized language models for text-based item recommendations. We suggest novel training and inference procedures for scoring similarities between pairs of items, that don{'}t require item similarity labels. Both the training and the inference techniques were designed to utilize the unlabeled structure of textual catalogs, and minimize the discrepancy between them. By incorporating four scores during inference, RecoBERT can infer text-based item-to-item similarities more accurately than other techniques. In addition, we introduce a new language understanding task for wine recommendations using similarities based on professional wine reviews. As an additional contribution, we publish annotated recommendations dataset crafted by human wine experts. Finally, we evaluate RecoBERT and compare it to various state-of-the-art NLP models on wine and fashion recommendations tasks.",
}
@inproceedings{liao-etal-2020-multi,
    title = "Multi-Agent Mutual Learning at Sentence-Level and Token-Level for Neural Machine Translation",
    author = "Liao, Baohao  and
      Gao, Yingbo  and
      Ney, Hermann",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.155",
    doi = "10.18653/v1/2020.findings-emnlp.155",
    pages = "1715--1724",
    abstract = "Mutual learning, where multiple agents learn collaboratively and teach one another, has been shown to be an effective way to distill knowledge for image classification tasks. In this paper, we extend mutual learning to the machine translation task and operate at both the sentence-level and the token-level. Firstly, we co-train multiple agents by using the same parallel corpora. After convergence, each agent selects and learns its poorly predicted tokens from other agents. The poorly predicted tokens are determined by the acceptance-rejection sampling algorithm. Our experiments show that sequential mutual learning at the sentence-level and the token-level improves the results cumulatively. Absolute improvements compared to strong baselines are obtained on various translation tasks. On the IWSLT{'}14 German-English task, we get a new state-of-the-art BLEU score of 37.0. We also report a competitive result, 29.9 BLEU score, on the WMT{'}14 English-German task.",
}
@inproceedings{xu-etal-2020-dombert,
    title = "{D}om{BERT}: Domain-oriented Language Model for Aspect-based Sentiment Analysis",
    author = "Xu, Hu  and
      Liu, Bing  and
      Shu, Lei  and
      Yu, Philip",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.156",
    doi = "10.18653/v1/2020.findings-emnlp.156",
    pages = "1725--1731",
    abstract = "This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding. We propose DomBERT, an extension of BERT to learn from both in-domain corpus and relevant domain corpora. This helps in learning domain language models with low-resources. Experiments are conducted on an assortment of tasks in aspect-based sentiment analysis (ABSA), demonstrating promising results.",
}
@inproceedings{roman-roman-etal-2020-rmm,
    title = "{RMM}: A Recursive Mental Model for Dialogue Navigation",
    author = "Roman Roman, Homero  and
      Bisk, Yonatan  and
      Thomason, Jesse  and
      Celikyilmaz, Asli  and
      Gao, Jianfeng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.157",
    doi = "10.18653/v1/2020.findings-emnlp.157",
    pages = "1732--1745",
    abstract = "Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and introduce a two-agent task where one agent navigates and asks questions that a second, guiding agent answers. Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The navigating agent models the guiding agent to simulate answers given candidate generated questions. The guiding agent in turn models the navigating agent to simulate navigation steps it would take to generate answers. We use the progress agents make towards the goal as a reinforcement learning reward signal to directly inform not only navigation actions, but also both question and answer generation. We demonstrate that RMM enables better generalization to novel environments. Interlocutor modelling may be a way forward for human-agent RMM where robots need to both ask and answer questions.",
}
@inproceedings{cao-etal-2020-will,
    title = "Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora",
    author = "Cao, Hancheng  and
      Cheng, Mengjie  and
      Cen, Zhepeng  and
      McFarland, Daniel  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.158",
    doi = "10.18653/v1/2020.findings-emnlp.158",
    pages = "1746--1757",
    abstract = "What kind of basic research ideas are more likely to get applied in practice? There is a long line of research investigating patterns of knowledge transfer, but it generally focuses on documents as the unit of analysis and follow their transfer into practice for a specific scientific domain. Here we study translational research at the level of scientific concepts for all scientific fields. We do this through text mining and predictive modeling using three corpora: 38.6 million paper abstracts, 4 million patent documents, and 0.28 million clinical trials. We extract scientific concepts (i.e., phrases) from corpora as instantiations of {``}research ideas{''}, create concept-level features as motivated by literature, and then follow the trajectories of over 450,000 new concepts (emerged from 1995-2014) to identify factors that lead only a small proportion of these ideas to be used in inventions and drug trials. Results from our analysis suggest several mechanisms that distinguish which scientific concept will be adopted in practice, and which will not. We also demonstrate that our derived features can be used to explain and predict knowledge transfer with high accuracy. Our work provides greater understanding of knowledge transfer for researchers, practitioners, and government agencies interested in encouraging translational research.",
}
@inproceedings{shi-etal-2020-recurrent,
    title = "Recurrent Inference in Text Editing",
    author = "Shi, Ning  and
      Zeng, Ziheng  and
      Zhang, Haotian  and
      Gong, Yichen",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.159",
    doi = "10.18653/v1/2020.findings-emnlp.159",
    pages = "1758--1769",
    abstract = "In neural text editing, prevalent sequence-to-sequence based approaches directly map the unedited text either to the edited text or the editing operations, in which the performance is degraded by the limited source text encoding and long, varying decoding steps. To address this problem, we propose a new inference method, Recurrence, that iteratively performs editing actions, significantly narrowing the problem space. In each iteration, encoding the partially edited text, Recurrence decodes the latent representation, generates an action of short, fixed-length, and applies the action to complete a single edit. For a comprehensive comparison, we introduce three types of text editing tasks: Arithmetic Operators Restoration (AOR), Arithmetic Equation Simplification (AES), Arithmetic Equation Correction (AEC). Extensive experiments on these tasks with varying difficulties demonstrate that Recurrence achieves improvements over conventional inference methods.",
}
@inproceedings{zhang-etal-2020-empirical,
    title = "An Empirical Exploration of Local Ordering Pre-training for Structured Prediction",
    author = "Zhang, Zhisong  and
      Kong, Xiang  and
      Levin, Lori  and
      Hovy, Eduard",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.160",
    doi = "10.18653/v1/2020.findings-emnlp.160",
    pages = "1770--1783",
    abstract = "Recently, pre-training contextualized encoders with language model (LM) objectives has been shown an effective semi-supervised method for structured prediction. In this work, we empirically explore an alternative pre-training method for contextualized encoders. Instead of predicting words in LMs, we {``}mask out{''} and predict word order information, with a local ordering strategy and word-selecting objectives. With evaluations on three typical structured prediction tasks (dependency parsing, POS tagging, and NER) over four languages (English, Finnish, Czech, and Italian), we show that our method is consistently beneficial. We further conduct detailed error analysis, including one that examines a specific type of parsing error where the head is misidentified. The results show that pre-trained contextual encoders can bring improvements in a structured way, suggesting that they may be able to capture higher-order patterns and feature combinations from unlabeled data.",
}
@inproceedings{xu-etal-2020-unsupervised,
    title = "Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers",
    author = "Xu, Shusheng  and
      Zhang, Xingxing  and
      Wu, Yi  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.161",
    doi = "10.18653/v1/2020.findings-emnlp.161",
    pages = "1784--1795",
    abstract = "Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights measured by sentence similarities. In this work, we find that transformer attentions can be used to rank sentences for unsupervised extractive summarization. Specifically, we first pre-train a hierarchical transformer model using unlabeled documents only. Then we propose a method to rank sentences using sentence-level self-attentions and pre-training objectives. Experiments on CNN/DailyMail and New York Times datasets show our model achieves state-of-the-art performance on unsupervised summarization. We also find in experiments that our model is less dependent on sentence positions. When using a linear combination of our model and a recent unsupervised model explicitly modeling sentence positions, we obtain even better results.",
}
@inproceedings{zhao-etal-2020-active,
    title = "Active Learning Approaches to Enhancing Neural Machine Translation",
    author = "Zhao, Yuekai  and
      Zhang, Haoran  and
      Zhou, Shuchang  and
      Zhang, Zhihua",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.162",
    doi = "10.18653/v1/2020.findings-emnlp.162",
    pages = "1796--1806",
    abstract = "Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget. We design a word frequency based acquisition function and combine it with a strong uncertainty based method. The combined method steadily outperforms all other acquisition functions in various scenarios. As far as we know, we are the first to do a large-scale study on actively training Transformer for NMT. Specifically, with a human translation budget of only 20{\%} of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs.",
}
@inproceedings{qin-etal-2020-agif,
    title = "{AGIF}: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling",
    author = "Qin, Libo  and
      Xu, Xiao  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.163",
    doi = "10.18653/v1/2020.findings-emnlp.163",
    pages = "1807--1816",
    abstract = "In real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated an overall intent context vector for all tokens, ignoring the fine-grained multiple intents information integration for token-level slot prediction. In this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint multiple intent detection and slot filling, where we introduce an intent-slot graph interaction layer to model the strong correlation between the slot and intents. Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level slot prediction. Experimental results on three multi-intent datasets show that our framework obtains substantial improvement and achieves the state-of-the-art performance. In addition, our framework achieves new state-of-the-art performance on two single-intent datasets.",
}
@inproceedings{guo-etal-2020-continual,
    title = "Continual Learning Long Short Term Memory",
    author = "Guo, Xin  and
      Tian, Yu  and
      Xue, Qinghan  and
      Lampropoulos, Panos  and
      Eliuk, Steven  and
      Barner, Kenneth  and
      Wang, Xiaolong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.164",
    doi = "10.18653/v1/2020.findings-emnlp.164",
    pages = "1817--1822",
    abstract = "Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-LSTM) cell in Recurrent Neural Network (RNN) in this paper. CL-LSTM considers not only the state of each individual task{'}s output gates but also the correlation of the states between tasks, so that the deep learning models can incrementally learn new tasks without catastrophically forgetting previously tasks. Experimental results demonstrate significant improvements of CL-LSTM over state-of-the-art approaches on spoken language understanding (SLU) tasks.",
}
@inproceedings{lin-etal-2020-commongen,
    title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
    author = "Lin, Bill Yuchen  and
      Zhou, Wangchunshu  and
      Shen, Ming  and
      Zhou, Pei  and
      Bhagavatula, Chandra  and
      Choi, Yejin  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165",
    doi = "10.18653/v1/2020.findings-emnlp.165",
    pages = "1823--1840",
    abstract = "Recently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging. In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts (e.g., dog, frisbee, catch, throw); the task is to generate a coherent sentence describing an everyday scenario using these concepts (e.g., {``}a man throws a frisbee and his dog catches it{''}). The CommonGen task is challenging because it inherently requires 1) relational reasoning with background commonsense knowledge and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowdsourced and existing caption corpora, consists of 77k commonsense descriptions over 35k unique concept-sets. Experiments show that there is a large gap between state-of-the-art text generation models (e.g., T5) and human performance (31.6{\%} v.s. 63.5{\%} in SPICE metric). Furthermore, we demonstrate that the learned generative commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA (76.9{\%} to 78.4 in dev accuracy) by generating additional context.",
}
@inproceedings{lester-etal-2020-constrained,
    title = "Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers",
    author = "Lester, Brian  and
      Pressel, Daniel  and
      Hemmeter, Amy  and
      Ray Choudhury, Sagnik  and
      Bangalore, Srinivas",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.166",
    doi = "10.18653/v1/2020.findings-emnlp.166",
    pages = "1841--1848",
    abstract = "Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer. Entities are represented as per-token labels with a special structure in order to decode them into spans. Current work eschews prior knowledge of how the span encoding scheme works and relies on the CRF learning which transitions are illegal and which are not to facilitate global coherence. We find that by constraining the output to suppress illegal transitions we can train a tagger with a cross-entropy loss twice as fast as a CRF with differences in F1 that are statistically insignificant, effectively eliminating the need for a CRF. We analyze the dynamics of tag co-occurrence to explain when these constraints are most effective and provide open source implementations of our tagger in both PyTorch and TensorFlow.",
}
@inproceedings{shi-etal-2020-potential,
    title = "On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries",
    author = "Shi, Tianze  and
      Zhao, Chen  and
      Boyd-Graber, Jordan  and
      Daum{\'e} III, Hal  and
      Lee, Lillian",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.167",
    doi = "10.18653/v1/2020.findings-emnlp.167",
    pages = "1849--1864",
    abstract = "Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the utility of fine-grained, lexical-level supervision, we introduce SQUALL, a dataset that enriches 11,276 WIKITABLEQUESTIONS English-language questions with manually created SQL equivalents plus alignments between SQL and question fragments. Our annotation enables new training possibilities for encoderdecoder models, including approaches from machine translation previously precluded by the absence of alignments. We propose and test two methods: (1) supervised attention; (2) adopting an auxiliary objective of disambiguating references in the input queries to table columns. In 5-fold cross validation, these strategies improve over strong baselines by 4.4{\%} execution accuracy. Oracle experiments suggest that annotated alignments can support further accuracy gains of up to 23.9{\%}.",
}
@inproceedings{yang-etal-2020-ted,
    title = "{TED}: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising",
    author = "Yang, Ziyi  and
      Zhu, Chenguang  and
      Gmyr, Robert  and
      Zeng, Michael  and
      Huang, Xuedong  and
      Darve, Eric",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.168",
    doi = "10.18653/v1/2020.findings-emnlp.168",
    pages = "1865--1874",
    abstract = "Text summarization aims to extract essential information from a piece of text and transform the text into a concise version. Existing unsupervised abstractive summarization models leverage recurrent neural networks framework while the recently proposed transformer exhibits much more capability. Moreover, most of previous summarization models ignore abundant unlabeled corpora resources available for pretraining. In order to address these issues, we propose TED, a transformer-based unsupervised abstractive summarization system with pretraining on large-scale data. We first leverage the lead bias in news articles to pretrain the model on millions of unlabeled corpora. Next, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of generated summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are highly abstractive, and each component in the objective function of TED is highly effective.",
}
@inproceedings{sadeq-etal-2020-improving,
    title = "Improving End-to-End {B}angla Speech Recognition with Semi-supervised Training",
    author = "Sadeq, Nafis  and
      Chowdhury, Nafis Tahmid  and
      Utshaw, Farhan Tanvir  and
      Ahmed, Shafayat  and
      Adnan, Muhammad Abdullah",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.169",
    doi = "10.18653/v1/2020.findings-emnlp.169",
    pages = "1875--1883",
    abstract = "Automatic speech recognition systems usually require large annotated speech corpus for training. The manual annotation of a large corpus is very difficult. It can be very helpful to use unsupervised and semi-supervised learning methods in addition to supervised learning. In this work, we focus on using a semi-supervised training approach for Bangla Speech Recognition that can exploit large unpaired audio and text data. We encode speech and text data in an intermediate domain and propose a novel loss function based on the global encoding distance between encoded data to guide the semi-supervised training. Our proposed method reduces the Word Error Rate (WER) of the system from 37{\%} to 31.9{\%}.",
}
@inproceedings{ahuja-etal-2020-gestures,
    title = "No Gestures Left Behind: Learning Relationships between Spoken Language and Freeform Gestures",
    author = "Ahuja, Chaitanya  and
      Lee, Dong Won  and
      Ishii, Ryo  and
      Morency, Louis-Philippe",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.170",
    doi = "10.18653/v1/2020.findings-emnlp.170",
    pages = "1884--1895",
    abstract = "We study relationships between spoken language and co-speech gestures in context of two key challenges. First, distributions of text and gestures are inherently skewed making it important to model the long tail. Second, gesture predictions are made at a subword level, making it important to learn relationships between language and acoustic cues. We introduce AISLe, which combines adversarial learning with importance sampling to strike a balance between precision and coverage. We propose the use of a multimodal multiscale attention block to perform subword alignment without the need of explicit alignment between language and acoustic cues. Finally, to empirically study the importance of language in this task, we extend the dataset proposed in Ahuja et al. (2020) with automatically extracted transcripts for audio signals. We substantiate the effectiveness of our approach through large-scale quantitative and user studies, which show that our proposed methodology significantly outperforms previous state-of-the-art approaches for gesture generation. Link to code, data and videos: https://github.com/chahuja/aisle",
}
@inproceedings{khashabi-etal-2020-unifiedqa,
    title = "{UNIFIEDQA}: Crossing Format Boundaries with a Single {QA} System",
    author = "Khashabi, Daniel  and
      Min, Sewon  and
      Khot, Tushar  and
      Sabharwal, Ashish  and
      Tafjord, Oyvind  and
      Clark, Peter  and
      Hajishirzi, Hannaneh",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.171",
    doi = "10.18653/v1/2020.findings-emnlp.171",
    pages = "1896--1907",
    abstract = "Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning abilities we seek to teach are not governed by the format. As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UNIFIEDQA, that performs well across 19 QA datasets spanning 4 diverse formats. UNIFIEDQA performs on par with 8 different models that were trained on individual datasets themselves. Even when faced with 12 unseen datasets of observed formats, UNIFIEDQA performs surprisingly well, showing strong generalization from its outof-format training data. Finally, simply finetuning this pre trained QA model into specialized models results in a new state of the art on 10 factoid and commonsense question answering datasets, establishing UNIFIEDQA as a strong starting point for building QA systems.",
}
@inproceedings{yang-etal-2020-robust,
    title = "Robust and Interpretable Grounding of Spatial References with Relation Networks",
    author = "Yang, Tsung-Yen  and
      Lan, Andrew  and
      Narasimhan, Karthik",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.172",
    doi = "10.18653/v1/2020.findings-emnlp.172",
    pages = "1908--1923",
    abstract = "Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation. Recent work has investigated various neural architectures for learning multi-modal representations for spatial concepts. However, the lack of explicit reasoning over entities makes such approaches vulnerable to noise in input text or state observations. In this paper, we develop effective models for understanding spatial references in text that are robust and interpretable, without sacrificing performance. We design a text-conditioned relation network whose parameters are dynamically computed with a cross-modal attention module to capture fine-grained spatial relations between entities. This design choice provides interpretability of learned intermediate outputs. Experiments across three tasks demonstrate that our model achieves superior performance, with a 17{\%} improvement in predicting goal locations and a 15{\%} improvement in robustness compared to state-of-the-art systems.",
}
@inproceedings{nie-etal-2020-pragmatic,
    title = "Pragmatic Issue-Sensitive Image Captioning",
    author = "Nie, Allen  and
      Cohn-Gordon, Reuben  and
      Potts, Christopher",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.173",
    doi = "10.18653/v1/2020.findings-emnlp.173",
    pages = "1924--1938",
    abstract = "Image captioning systems need to produce texts that are not only true but also relevant in that they are properly aligned with the current issues. For instance, in a newspaper article about a sports event, a caption that not only identifies the player in a picture but also comments on their ethnicity could create unwanted reader reactions. To address this, we propose Issue-Sensitive Image Captioning (ISIC). In ISIC, the captioner is given a target image and an issue, which is a set of images partitioned in a way that specifies what information is relevant. For the sports article, we could construct a partition that places images into equivalence classes based on player position. To model this task, we use an extension of the Rational Speech Acts model. Our extension is built on top of state-of-the-art pretrained neural image captioners and explicitly uses image partitions to control caption generation. In both automatic and human evaluations, we show that these models generate captions that are descriptive and issue-sensitive. Finally, we show how ISIC can complement and enrich the related task of Visual Question Answering.",
}
@inproceedings{wu-etal-2020-ptum,
    title = "{PTUM}: Pre-training User Model from Unlabeled User Behaviors via Self-supervision",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Qi, Tao  and
      Lian, Jianxun  and
      Huang, Yongfeng  and
      Xie, Xing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.174",
    doi = "10.18653/v1/2020.findings-emnlp.174",
    pages = "1939--1944",
    abstract = "User modeling is critical for many personalized web services. Many existing methods model users based on their behaviors and the labeled data of target tasks. However, these methods cannot exploit useful information in unlabeled user behavior data, and their performance may be not optimal when labeled data is scarce. Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data. We propose two self-supervision tasks for user model pre-training. The first one is masked behavior prediction, which can model the relatedness between historical behaviors. The second one is next K behavior prediction, which can model the relatedness between past and future behaviors. The pre-trained user models are finetuned in downstream tasks to learn task-specific user representations. Experimental results on two real-world datasets validate the effectiveness of our proposed user model pre-training method.",
}
@inproceedings{park-etal-2020-adversarial,
    title = "Adversarial Subword Regularization for Robust Neural Machine Translation",
    author = "Park, Jungsoo  and
      Sung, Mujeen  and
      Lee, Jinhyuk  and
      Kang, Jaewoo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.175",
    doi = "10.18653/v1/2020.findings-emnlp.175",
    pages = "1945--1953",
    abstract = "Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates. However, the diversification of subword segmentations mostly relies on the pre-trained subword language models from which erroneous segmentations of unseen words are less likely to be sampled. In this paper, we present adversarial subword regularization (ADVSR) to study whether gradient signals during training can be a substitute criterion for exposing diverse subword segmentations. We experimentally show that our model-based adversarial samples effectively encourage NMT models to be less sensitive to segmentation errors and improve the performance of NMT models in low-resource and out-domain datasets.",
}
@inproceedings{ni-etal-2020-learning,
    title = "Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest {X}-rays",
    author = "Ni, Jianmo  and
      Hsu, Chun-Nan  and
      Gentili, Amilcare  and
      McAuley, Julian",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.176",
    doi = "10.18653/v1/2020.findings-emnlp.176",
    pages = "1954--1960",
    abstract = "Automatic medical image report generation has drawn growing attention due to its potential to alleviate radiologists{'} workload. Existing work on report generation often trains encoder-decoder networks to generate complete reports. However, such models are affected by data bias (e.g. label imbalance) and face common issues inherent in text generation models (e.g. repetition). In this work, we focus on reporting abnormal findings on radiology images; instead of training on complete radiology reports, we propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules. We formulate the task as cross-modal retrieval and propose Conditional Visual-Semantic Embeddings to align images and fine-grained abnormal findings in a joint embedding space. We demonstrate that our method is able to retrieve abnormal findings and outperforms existing generation models on both clinical correctness and text generation metrics.",
}
@inproceedings{yu-etal-2020-synet,
    title = "{S}yn{ET}: Synonym Expansion using Transitivity",
    author = "Yu, Jiale  and
      Shen, Yongliang  and
      Ma, Xinyin  and
      Jia, Chenghao  and
      Chen, Chen  and
      Lu, Weiming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.177",
    doi = "10.18653/v1/2020.findings-emnlp.177",
    pages = "1961--1970",
    abstract = "In this paper, we study a new task of synonym expansion using transitivity, and propose a novel approach named SynET, which considers both the contexts of two given synonym pairs. It introduces an auxiliary task to reduce the impact of noisy sentences, and proposes a Multi-Perspective Entity Matching Network to match entities from multiple perspectives. Extensive experiments on a real-world dataset show the effectiveness of our approach.",
}
@inproceedings{zhou-etal-2020-scheduled,
    title = "Scheduled {D}rop{H}ead: A Regularization Method for Transformer Models",
    author = "Zhou, Wangchunshu  and
      Ge, Tao  and
      Wei, Furu  and
      Zhou, Ming  and
      Xu, Ke",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.178",
    doi = "10.18653/v1/2020.findings-emnlp.178",
    pages = "1971--1980",
    abstract = "We introduce DropHead, a structured dropout method specifically designed for regularizing the multi-head attention mechanism which is a key component of transformer. In contrast to the conventional dropout mechanism which randomly drops units or connections, DropHead drops entire attention heads during training to prevent the multi-head attention model from being dominated by a small portion of attention heads. It can help reduce the risk of overfitting and allow the models to better benefit from the multi-head attention. Given the interaction between multi-headedness and training dynamics, we further propose a novel dropout rate scheduler to adjust the dropout rate of DropHead throughout training, which results in a better regularization effect. Experimental results demonstrate that our proposed approach can improve transformer models by 0.9 BLEU score on WMT14 En-De translation task and around 1.0 accuracy for various text classification tasks.",
}
@inproceedings{zhang-etal-2020-multi-turn,
    title = "Multi-Turn Dialogue Generation in {E}-Commerce Platform with the Context of Historical Dialogue",
    author = "Zhang, WeiSheng  and
      Song, Kaisong  and
      Kang, Yangyang  and
      Wang, Zhongqing  and
      Sun, Changlong  and
      Liu, Xiaozhong  and
      Li, Shoushan  and
      Zhang, Min  and
      Si, Luo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.179",
    doi = "10.18653/v1/2020.findings-emnlp.179",
    pages = "1981--1990",
    abstract = "As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers{'} historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers{'} historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller{'}s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers{'} characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.",
}
@inproceedings{gonen-webster-2020-automatically,
    title = "Automatically Identifying Gender Issues in Machine Translation using Perturbations",
    author = "Gonen, Hila  and
      Webster, Kellie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.180",
    doi = "10.18653/v1/2020.findings-emnlp.180",
    pages = "1991--1995",
    abstract = "The successful application of neural methods to machine translation has realized huge quality advances for the community. With these improvements, many have noted outstanding challenges, including the modeling and treatment of gendered language. While previous studies have identified issues using synthetic examples, we develop a novel technique to mine examples from real world data to explore challenges for deployed systems. We use our method to compile an evaluation benchmark spanning examples for four languages from three language families, which we publicly release to facilitate research. The examples in our benchmark expose where model representations are gendered, and the unintended consequences these gendered representations can have in downstream application.",
}
@inproceedings{evensen-etal-2020-ruler,
    title = "Ruler: Data Programming by Demonstration for Document Labeling",
    author = "Evensen, Sara  and
      Ge, Chang  and
      Demiralp, Cagatay",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.181",
    doi = "10.18653/v1/2020.findings-emnlp.181",
    pages = "1996--2005",
    abstract = "Data programming aims to reduce the cost of curating training data by encoding domain knowledge as labeling functions over source data. As such it not only requires domain expertise but also programming experience, a skill that many subject matter experts lack. Additionally, generating functions by enumerating rules is not only time consuming but also inherently difficult, even for people with programming experience. In this paper we introduce Ruler, an interactive system that synthesizes labeling rules using span-level interactive demonstrations over document examples. Ruler is a first-of-a-kind implementation of data programming by demonstration (DPBD). This new framework aims to relieve users from the burden of writing labeling functions, enabling them to focus on higher-level semantic analysis, such as identifying relevant signals for the labeling task. We compare Ruler with conventional data programming through a user study conducted with 10 data scientists who were asked to create labeling functions for sentiment and spam classification tasks. Results show Ruler is easier to learn and to use, and that it offers higher overall user-satisfaction while providing model performances comparable to those achieved by conventional data programming.",
}
@inproceedings{xu-etal-2020-dual,
    title = "Dual Reconstruction: a Unifying Objective for Semi-Supervised Neural Machine Translation",
    author = "Xu, Weijia  and
      Niu, Xing  and
      Carpuat, Marine",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.182",
    doi = "10.18653/v1/2020.findings-emnlp.182",
    pages = "2006--2020",
    abstract = "While Iterative Back-Translation and Dual Learning effectively incorporate monolingual training data in neural machine translation, they use different objectives and heuristic gradient approximation strategies, and have not been extensively compared. We introduce a novel dual reconstruction objective that provides a unified view of Iterative Back-Translation and Dual Learning. It motivates a theoretical analysis and controlled empirical study on German-English and Turkish-English tasks, which both suggest that Iterative Back-Translation is more effective than Dual Learning despite its relative simplicity.",
}
@inproceedings{cui-etal-2020-focus,
    title = "Focus-Constrained Attention Mechanism for {CVAE}-based Response Generation",
    author = "Cui, Zhi  and
      Li, Yanran  and
      Zhang, Jiayi  and
      Cui, Jianwei  and
      Wei, Chen  and
      Wang, Bin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.183",
    doi = "10.18653/v1/2020.findings-emnlp.183",
    pages = "2021--2030",
    abstract = "To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models. The latent variable is supposed to capture the discourse-level information and encourage the informativeness of target responses. However, such discourse-level information is often too coarse for the decoder to be utilized. To tackle it, our idea is to transform the coarse-grained discourse-level information into fine-grained word-level information. Specifically, we firstly measure the semantic concentration of corresponding target response on the post words by introducing a fine-grained focus signal. Then, we propose a focus-constrained attention mechanism to take full advantage of focus in well aligning the input to the target response. The experimental results demonstrate that by exploiting the fine-grained signal, our model can generate more diverse and informative responses compared with several state-of-the-art models.",
}
@inproceedings{bao-etal-2020-chunk,
    title = "Chunk-based {C}hinese Spelling Check with Global Optimization",
    author = "Bao, Zuyi  and
      Li, Chen  and
      Wang, Rui",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.184",
    doi = "10.18653/v1/2020.findings-emnlp.184",
    pages = "2031--2040",
    abstract = "Chinese spelling check is a challenging task due to the characteristics of the Chinese language, such as the large character set, no word boundary, and short word length. On the one hand, most of the previous works only consider corrections with similar character pronunciation or shape, failing to correct visually and phonologically irrelevant typos. On the other hand, pipeline-style architectures are widely adopted to deal with different types of spelling errors in individual modules, which is difficult to optimize. In order to handle these issues, in this work, 1) we extend the traditional confusion sets with semantical candidates to cover different types of errors; 2) we propose a chunk-based framework to correct single-character and multi-character word errors uniformly; and 3) we adopt a global optimization strategy to enable a sentence-level correction selection. The experimental results show that the proposed approach achieves a new state-of-the-art performance on three benchmark datasets, as well as an optical character recognition dataset.",
}
@inproceedings{kim-etal-2020-multi-pretraining,
    title = "Multi-pretraining for Large-scale Text Classification",
    author = "Kim, Kang-Min  and
      Hyeon, Bumsu  and
      Kim, Yeachan  and
      Park, Jun-Hyung  and
      Lee, SangKeun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.185",
    doi = "10.18653/v1/2020.findings-emnlp.185",
    pages = "2041--2050",
    abstract = "Deep neural network-based pretraining methods have achieved impressive results in many natural language processing tasks including text classification. However, their applicability to large-scale text classification with numerous categories (e.g., several thousands) is yet to be well-studied, where the training data is insufficient and skewed in terms of categories. In addition, existing pretraining methods usually involve excessive computation and memory overheads. In this paper, we develop a novel multi-pretraining framework for large-scale text classification. This multi-pretraining framework includes both a self-supervised pretraining and a weakly supervised pretraining. We newly introduce an out-of-context words detection task on the unlabeled data as the self-supervised pretraining. It captures the topic-consistency of words used in sentences, which is proven to be useful for text classification. In addition, we propose a weakly supervised pretraining, where labels for text classification are obtained automatically from an existing approach. Experimental results clearly show that both pretraining approaches are effective for large-scale text classification task. The proposed scheme exhibits significant improvements as much as 3.8{\%} in terms of macro-averaging F1-score over strong pretraining methods, while being computationally efficient.",
}
@inproceedings{jamshid-lou-johnson-2020-end,
    title = "End-to-End Speech Recognition and Disfluency Removal",
    author = "Jamshid Lou, Paria  and
      Johnson, Mark",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.186",
    doi = "10.18653/v1/2020.findings-emnlp.186",
    pages = "2051--2061",
    abstract = "Disfluency detection is usually an intermediate step between an automatic speech recognition (ASR) system and a downstream task. By contrast, this paper aims to investigate the task of end-to-end speech recognition and disfluency removal. We specifically explore whether it is possible to train an ASR model to directly map disfluent speech into fluent transcripts, without relying on a separate disfluency detection model. We show that end-to-end models do learn to directly generate fluent transcripts; however, their performance is slightly worse than a baseline pipeline approach consisting of an ASR system and a specialized disfluency detection model. We also propose two new metrics for evaluating integrated ASR and disfluency removal models. The findings of this paper can serve as a benchmark for further research on the task of end-to-end speech recognition and disfluency removal in the future.",
}
@inproceedings{hsu-etal-2020-characterizing,
    title = "Characterizing the Value of Information in Medical Notes",
    author = "Hsu, Chao-Chun  and
      Karnwal, Shantanu  and
      Mullainathan, Sendhil  and
      Obermeyer, Ziad  and
      Tan, Chenhao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.187",
    doi = "10.18653/v1/2020.findings-emnlp.187",
    pages = "2062--2072",
    abstract = "Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes. We use two prediction tasks, readmission prediction and in-hospital mortality prediction, to characterize the value of information in medical notes. We show that as a whole, medical notes only provide additional predictive power over structured information in readmission prediction. We further propose a probing framework to select parts of notes that enable more accurate predictions than using all notes, despite that the selected information leads to a distribution shift from the training data ({``}all notes{''}). Finally, we demonstrate that models trained on the selected valuable information achieve even better predictive performance, with only 6.8{\%}of all the tokens for readmission prediction.",
}
@inproceedings{peyrard-west-2020-klearn,
    title = "{KL}earn: Background Knowledge Inference from Summarization Data",
    author = "Peyrard, Maxime  and
      West, Robert",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.188",
    doi = "10.18653/v1/2020.findings-emnlp.188",
    pages = "2073--2085",
    abstract = "The goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver. So far, summarization researchers have given considerably more attention to relevance than to background knowledge. In contrast, this work puts background knowledge in the foreground. Building on the realization that the choices made by human summarizers and annotators contain implicit information about their background knowledge, we develop and compare techniques for inferring background knowledge from summarization data. Based on this framework, we define summary scoring functions that explicitly model background knowledge, and show that these scoring functions fit human judgments significantly better than baselines. We illustrate some of the many potential applications of our framework. First, we provide insights into human information importance priors. Second, we demonstrate that averaging the background knowledge of multiple, potentially biased annotators or corpora greatly improves summaryscoring performance. Finally, we discuss potential applications of our framework beyond summarization.",
}
@inproceedings{choi-lee-2020-extracting,
    title = "Extracting Chemical-Protein Interactions via Calibrated Deep Neural Network and Self-training",
    author = "Choi, Dongha  and
      Lee, Hyunju",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.189",
    doi = "10.18653/v1/2020.findings-emnlp.189",
    pages = "2086--2095",
    abstract = "The extraction of interactions between chemicals and proteins from several biomedical articles is important in many fields of biomedical research such as drug development and prediction of drug side effects. Several natural language processing methods, including deep neural network (DNN) models, have been applied to address this problem. However, these methods were trained with hard-labeled data, which tend to become over-confident, leading to degradation of the model reliability. To estimate the data uncertainty and improve the reliability, {``}calibration{''} techniques have been applied to deep learning models. In this study, to extract chemical{--}protein interactions, we propose a DNN-based approach incorporating uncertainty information and calibration techniques. Our model first encodes the input sequence using a pre-trained language-understanding model, following which it is trained using two calibration methods: mixup training and addition of a confidence penalty loss. Finally, the model is re-trained with augmented data that are extracted using the estimated uncertainties. Our approach has achieved state-of-the-art performance with regard to the Biocreative VI ChemProt task, while preserving higher calibration abilities than those of previous approaches. Furthermore, our approach also presents the possibilities of using uncertainty estimation for performance improvement.",
}
@inproceedings{chen-etal-2020-logic2text,
    title = "{L}ogic2{T}ext: High-Fidelity Natural Language Generation from Logical Forms",
    author = "Chen, Zhiyu  and
      Chen, Wenhu  and
      Zha, Hanwen  and
      Zhou, Xiyou  and
      Zhang, Yunkai  and
      Sundaresan, Sairam  and
      Wang, William Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.190",
    doi = "10.18653/v1/2020.findings-emnlp.190",
    pages = "2096--2111",
    abstract = "Previous studies on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences. However, for complex structured data, e.g., multi-row tables, it is often desirable for an NLG system to describe interesting facts from logical inferences across records. If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations. In this work, we formulate high-fidelity NLG as generation from logical forms in order to obtain controllable and faithful generations. We present a new large-scale dataset, Logic2Text, with 10,753 descriptions involving common logic types paired with the underlying logical forms. The logical forms show diversified graph structure of free schema, which pose great challenges on the model{'}s ability to understand the semantics. We experiment on (1) Fully-supervised training with the full datasets, and (2) Few-shot setting, provided with hundreds of paired examples; We compare several popular generation models and analyze their performances. We hope our dataset can encourage research towards building an advanced NLG system capable of natural, faithful, and human-like generation. The dataset and code is available at https://github.com/czyssrs/Logic2Text.",
}
@inproceedings{subramanian-etal-2020-medicat,
    title = "{M}ed{IC}a{T}: A Dataset of Medical Images, Captions, and Textual References",
    author = "Subramanian, Sanjay  and
      Wang, Lucy Lu  and
      Bogin, Ben  and
      Mehta, Sachin  and
      van Zuylen, Madeleine  and
      Parasa, Sravanthi  and
      Singh, Sameer  and
      Gardner, Matt  and
      Hajishirzi, Hannaneh",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.191",
    doi = "10.18653/v1/2020.findings-emnlp.191",
    pages = "2112--2120",
    abstract = "Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75{\%} of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MedICaT, a dataset of medical images in context. MedICaT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74{\%} of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MedICaT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.",
}
@inproceedings{kong-etal-2020-tsdg,
    title = "{TSDG}: Content-aware Neural Response Generation with Two-stage Decoding Process",
    author = "Kong, Junsheng  and
      Zhong, Zhicheng  and
      Cai, Yi  and
      Wu, Xin  and
      Ren, Da",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.192",
    doi = "10.18653/v1/2020.findings-emnlp.192",
    pages = "2121--2126",
    abstract = "Neural response generative models have achieved remarkable progress in recent years but tend to yield irrelevant and uninformative responses. One of the reasons is that encoder-decoder based models always use a single decoder to generate a complete response at a stroke. This tends to generate high-frequency function words with less semantic information rather than low-frequency content words with more semantic information. To address this issue, we propose a content-aware model with two-stage decoding process named Two-stage Dialogue Generation (TSDG). We separate the decoding process of content words and function words so that content words can be generated independently without the interference of function words. Experimental results on two datasets indicate that our model significantly outperforms several competitive generative models in terms of automatic and human evaluation.",
}
@inproceedings{li-tu-2020-unsupervised,
    title = "Unsupervised Cross-Lingual Adaptation of Dependency Parsers Using {CRF} Autoencoders",
    author = "Li, Zhao  and
      Tu, Kewei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.193",
    doi = "10.18653/v1/2020.findings-emnlp.193",
    pages = "2127--2133",
    abstract = "We consider the task of cross-lingual adaptation of dependency parsers without annotated target corpora and parallel corpora. Previous work either directly applies a discriminative source parser to the target language, ignoring unannotated target corpora, or employs an unsupervised generative parser that can leverage unannotated target data but has weaker representational power than discriminative parsers. In this paper, we propose to utilize unsupervised discriminative parsers based on the CRF autoencoder framework for this task. We train a source parser and use it to initialize and regularize a target parser that is trained on unannotated target data. We conduct experiments that transfer an English parser to 20 target languages. The results show that our method significantly outperforms previous methods.",
}
@inproceedings{wang-etal-2020-diversify,
    title = "Diversify Question Generation with Continuous Content Selectors and Question Type Modeling",
    author = "Wang, Zhen  and
      Rao, Siwei  and
      Zhang, Jie  and
      Qin, Zhen  and
      Tian, Guangjian  and
      Wang, Jun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.194",
    doi = "10.18653/v1/2020.findings-emnlp.194",
    pages = "2134--2143",
    abstract = "Generating questions based on answers and relevant contexts is a challenging task. Recent work mainly pays attention to the quality of a single generated question. However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression. In this paper, we explore the diversity of question generation and come up with methods from these two aspects. Specifically, we relate contextual focuses with content selectors, which are modeled by a continuous latent variable with the technique of conditional variational auto-encoder (CVAE). In the realization of CVAE, a multimodal prior distribution is adopted to allow for more diverse content selectors. To take into account various means of expression, question types are explicitly modeled and a diversity-promoting algorithm is proposed further. Experimental results on public datasets show that our proposed method can significantly improve the diversity of generated questions, especially from the perspective of using different question types. Overall, our proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches.",
}
@inproceedings{nekoto-etal-2020-participatory,
    title = "Participatory Research for Low-resourced Machine Translation: A Case Study in {A}frican Languages",
    author = {Nekoto, Wilhelmina  and
      Marivate, Vukosi  and
      Matsila, Tshinondiwa  and
      Fasubaa, Timi  and
      Fagbohungbe, Taiwo  and
      Akinola, Solomon Oluwole  and
      Muhammad, Shamsuddeen  and
      Kabongo Kabenamualu, Salomon  and
      Osei, Salomey  and
      Sackey, Freshia  and
      Niyongabo, Rubungo Andre  and
      Macharm, Ricky  and
      Ogayo, Perez  and
      Ahia, Orevaoghene  and
      Berhe, Musie Meressa  and
      Adeyemi, Mofetoluwa  and
      Mokgesi-Selinga, Masabata  and
      Okegbemi, Lawrence  and
      Martinus, Laura  and
      Tajudeen, Kolawole  and
      Degila, Kevin  and
      Ogueji, Kelechi  and
      Siminyu, Kathleen  and
      Kreutzer, Julia  and
      Webster, Jason  and
      Ali, Jamiil Toure  and
      Abbott, Jade  and
      Orife, Iroro  and
      Ezeani, Ignatius  and
      Dangana, Idris Abdulkadir  and
      Kamper, Herman  and
      Elsahar, Hady  and
      Duru, Goodness  and
      Kioko, Ghollah  and
      Espoir, Murhabazi  and
      van Biljon, Elan  and
      Whitenack, Daniel  and
      Onyefuluchi, Christopher  and
      Emezue, Chris Chinenye  and
      Dossou, Bonaventure F. P.  and
      Sibanda, Blessing  and
      Bassey, Blessing  and
      Olabiyi, Ayodele  and
      Ramkilowan, Arshath  and
      {\"O}ktem, Alp  and
      Akinfaderin, Adewale  and
      Bashir, Abdallah},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.195",
    doi = "10.18653/v1/2020.findings-emnlp.195",
    pages = "2144--2160",
    abstract = "Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved. {`}Low-resourced{'}-ness is a complex problem going beyond data availability and reflects systemic problems in society. In this paper, we focus on the task of Machine Translation (MT), that plays a crucial role for information accessibility and communication worldwide. Despite immense improvements in MT over the past decade, MT is centered around a few high-resourced languages. As MT researchers cannot solve the problem of low-resourcedness alone, we propose participatory research as a means to involve all necessary agents required in the MT development process. We demonstrate the feasibility and scalability of participatory research with a case study on MT for African languages. Its implementation leads to a collection of novel translation datasets, MT benchmarks for over 30 languages, with human evaluations for a third of them, and enables participants without formal training to make a unique scientific contribution. Benchmarks, models, data, code, and evaluation results are released at https://github.com/masakhane-io/masakhane-mt.",
}
@inproceedings{henderson-etal-2020-convert,
    title = "{C}onve{RT}: Efficient and Accurate Conversational Representations from Transformers",
    author = "Henderson, Matthew  and
      Casanueva, I{\~n}igo  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Su, Pei-Hao  and
      Wen, Tsung-Hsien  and
      Vuli{\'c}, Ivan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.196",
    doi = "10.18653/v1/2020.findings-emnlp.196",
    pages = "2161--2174",
    abstract = "General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train. We pretrain using a retrieval-based response selection task, effectively leveraging quantization and subword-level parameterization in the dual encoder to build a lightweight memory- and energy-efficient model. We show that ConveRT achieves state-of-the-art performance across widely established response selection tasks. We also demonstrate that the use of extended dialog history as context yields further performance gains. Finally, we show that pretrained representations from the proposed encoder can be transferred to the intent classification task, yielding strong results across three diverse data sets. ConveRT trains substantially faster than standard sentence encoders or previous state-of-the-art dual encoders. With its reduced size and superior performance, we believe this model promises wider portability and scalability for Conversational AI applications.",
}
@inproceedings{wang-etal-2020-computer,
    title = "Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing",
    author = "Wang, Ke  and
      Wang, Jiayi  and
      Ge, Niyu  and
      Shi, Yangbin  and
      Zhao, Yu  and
      Fan, Kai",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.197",
    doi = "10.18653/v1/2020.findings-emnlp.197",
    pages = "2175--2186",
    abstract = "With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators needs to be manually closed by post-editing. In this paper, we propose an end-to-end deep learning framework of the quality estimation and automatic post-editing of the machine translation output. Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model. To imitate the behavior of human translators, we design three efficient delegation modules {--} quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them. We examine this approach with the English{--}German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance. We also verify that the certified translators can significantly expedite their post-editing processing with our model in human evaluation.",
}
@inproceedings{kung-etal-2020-zero,
    title = "Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering",
    author = "Kung, Po-Nien  and
      Yang, Tse-Hsuan  and
      Chen, Yi-Cheng  and
      Yin, Sheng-Siang  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.198",
    doi = "10.18653/v1/2020.findings-emnlp.198",
    pages = "2187--2197",
    abstract = "Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability. However, annotating rationales requires much effort and only few datasets contain such labeled rationales, making supervised learning for rationalization difficult. In this paper, we propose a novel approach that leverages the benefits of both multi-task learning and transfer learning for generating rationales through question answering in a zero-shot fashion. For two benchmark rationalization datasets, the proposed method achieves comparable or even better performance of rationalization without any supervised signal, demonstrating the great potential of zero-shot rationalization for better interpretability.",
}
@inproceedings{szubert-etal-2020-role,
    title = "The Role of Reentrancies in {A}bstract {M}eaning {R}epresentation Parsing",
    author = "Szubert, Ida  and
      Damonte, Marco  and
      Cohen, Shay B.  and
      Steedman, Mark",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.199",
    doi = "10.18653/v1/2020.findings-emnlp.199",
    pages = "2198--2207",
    abstract = "Abstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations. These are graphs and not trees because AMR supports reentrancies (nodes with more than one parent). Following previous findings on the importance of reen- trancies for AMR, we empirically find and discuss several linguistic phenomena respon- sible for reentrancies in AMR, some of which have not received attention before. We cate- gorize the types of errors AMR parsers make with respect to reentrancies. Furthermore, we find that correcting these errors provides an in- crease of up to 5{\%} Smatch in parsing perfor- mance and 20{\%} in reentrancy prediction",
}
@inproceedings{lee-etal-2020-cross,
    title = "Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention",
    author = "Lee, Daeun  and
      Park, Soyoung  and
      Kang, Jiwon  and
      Choi, Daejin  and
      Han, Jinyoung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.200",
    doi = "10.18653/v1/2020.findings-emnlp.200",
    pages = "2208--2217",
    abstract = "Early intervention for suicide risks with social media data has increasingly received great attention. Using a suicide dictionary created by mental health experts is one of the effective ways to detect suicidal ideation. However, little attention has been paid to validate whether and how the existing dictionaries for other languages (i.e., English and Chinese) can be used for predicting suicidal ideation for a low-resource language (i.e., Korean) where a knowledge-based suicide dictionary has not yet been developed. To this end, we propose a cross-lingual suicidal ideation detection model that can identify whether a given social media post includes suicidal ideation or not. To utilize the existing suicide dictionaries developed for other languages (i.e., English and Chinese) in word embedding, our model translates a post written in the target language (i.e., Korean) into English and Chinese, and then uses the separate suicidal-oriented word embeddings developed for English and Chinese, respectively. By applying an ensemble approach for different languages, the model achieves high accuracy, over 87{\%}. We believe our model is useful in accessing suicidal ideation using social media data for preventing potential suicide risk in an early stage.",
}
@inproceedings{hu-tian-2020-service,
    title = "Service-oriented Text-to-{SQL} Parsing",
    author = "Hu, Wangsu  and
      Tian, Jilei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.201",
    doi = "10.18653/v1/2020.findings-emnlp.201",
    pages = "2218--2222",
    abstract = "The information retrieval from relational database requires professionals who has an understanding of structural query language such as SQL. TEXT2SQL models apply natural language inference to enable user interacting the database via natural language utterance. Current TEXT2SQL models normally focus on generating complex SQL query in a precise and complete fashion while certain features of real-world application in the production environment is not fully addressed. This paper is aimed to develop a service-oriented Text-to-SQL parser that translates natural language utterance to structural and executable SQL query. We introduce a algorithmic framework named Semantic-Enriched SQL generator (SE-SQL) that enables flexibly access database than rigid API in the application while keeping the performance quality for the most commonly used cases. The qualitative result shows that the proposed model achieves 88.3{\%} execution accuracy on WikiSQL task, outperforming baseline by 13{\%} error reduction. Moreover, the framework considers several service-oriented needs including low-complexity inference, out-of-table rejection, and text normalization.",
}
@inproceedings{nishino-etal-2020-reinforcement,
    title = "Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation",
    author = "Nishino, Toru  and
      Ozaki, Ryota  and
      Momoki, Yohei  and
      Taniguchi, Tomoki  and
      Kano, Ryuji  and
      Nakano, Norihisa  and
      Tagawa, Yuki  and
      Taniguchi, Motoki  and
      Ohkuma, Tomoko  and
      Nakamura, Keigo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.202",
    doi = "10.18653/v1/2020.findings-emnlp.202",
    pages = "2223--2236",
    abstract = "Automated generation of medical reports that describe the findings in the medical images helps radiologists by alleviating their workload. Medical report generation system should generate correct and concise reports. However, data imbalance makes it difficult to train models accurately. Medical datasets are commonly imbalanced in their finding labels because incidence rates differ among diseases; moreover, the ratios of abnormalities to normalities are significantly imbalanced. We propose a novel reinforcement learning method with a reconstructor to improve the clinical correctness of generated reports to train the data-to-text module with a highly imbalanced dataset. Moreover, we introduce a novel data augmentation strategy for reinforcement learning to additionally train the model on infrequent findings. From the perspective of a practical use, we employ a Two-Stage Medical Report Generator (TS-MRGen) for controllable report generation from input images. TS-MRGen consists of two separated stages: an image diagnosis module and a data-to-text module. Radiologists can modify the image diagnosis module results to control the reports that the data-to-text module generates. We conduct an experiment with two medical datasets to assess the data-to-text module and the entire two-stage model. Results demonstrate that the reports generated by our model describe the findings in the input image more correctly.",
}
@inproceedings{zhao-etal-2020-reducing,
    title = "Reducing Quantity Hallucinations in Abstractive Summarization",
    author = "Zhao, Zheng  and
      Cohen, Shay B.  and
      Webber, Bonnie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.203",
    doi = "10.18653/v1/2020.findings-emnlp.203",
    pages = "2237--2249",
    abstract = "It is well-known that abstractive summaries are subject to hallucination{---}including material that is not supported by the original text. While summaries can be made hallucination-free by limiting them to general phrases, such summaries would fail to be very informative. Alternatively, one can try to avoid hallucinations by verifying that any specific entities in the summary appear in the original text in a similar context. This is the approach taken by our system, Herman. The system learns to recognize and verify quantity entities (dates, numbers, sums of money, etc.) in a beam-worth of abstractive summaries produced by state-of-the-art models, in order to up-rank those summaries whose quantity terms are supported by the original text. Experimental results demonstrate that the ROUGE scores of such up-ranked summaries have a higher Precision than summaries that have not been up-ranked, without a comparable loss in Recall, resulting in higher F1. Preliminary human evaluation of up-ranked vs. original summaries shows people{'}s preference for the former.",
}
@inproceedings{sahlgren-2020-rethinking,
    title = "Rethinking Topic Modelling: From Document-Space to Term-Space",
    author = "Sahlgren, Magnus",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.204",
    doi = "10.18653/v1/2020.findings-emnlp.204",
    pages = "2250--2259",
    abstract = "This paper problematizes the reliance on documents as the basic notion for defining term interactions in standard topic models. As an alternative to this practice, we reformulate topic distributions as latent factors in term similarity space. We exemplify the idea using a number of standard word embeddings built with very wide context windows. The embedding spaces are transformed to sparse similarity spaces, and topics are extracted in standard fashion by factorizing to a lower-dimensional space. We use a number of different factorization techniques, and evaluate the various models using a large set of evaluation metrics, including previously published coherence measures, as well as a number of novel measures that we suggest better correspond to real-world applications of topic models. Our results clearly demonstrate that term-based models outperform standard document-based models by a large margin.",
}
@inproceedings{son-lyu-2020-sparse,
    title = "Sparse and Decorrelated Representations for Stable Zero-shot {NMT}",
    author = "Son, Bokyung  and
      Lyu, Sungwon",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.205",
    doi = "10.18653/v1/2020.findings-emnlp.205",
    pages = "2260--2266",
    abstract = "Using a single encoder and decoder for all directions and training with English-centric data is a popular scheme for multilingual NMT. However, zero-shot translation under this scheme is vulnerable to changes in training conditions, as the model degenerates by decoding non-English texts into English regardless of the target specifier token. We present that enforcing both sparsity and decorrelation on encoder intermediate representations with the SLNI regularizer (Aljundi et al., 2019) efficiently mitigates this problem, without performance loss in supervised directions. Notably, effects of SLNI turns out to be irrelevant to promoting language-invariance in encoder representations.",
}
@inproceedings{gupta-etal-2020-semi,
    title = "A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning",
    author = "Gupta, Deepak  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.206",
    doi = "10.18653/v1/2020.findings-emnlp.206",
    pages = "2267--2280",
    abstract = "Code-mixing, the interleaving of two or more languages within a sentence or discourse is ubiquitous in multilingual societies. The lack of code-mixed training data is one of the major concerns for the development of end-to-end neural network-based models to be deployed for a variety of natural language processing (NLP) applications. A potential solution is to either manually create or crowd-source the code-mixed labelled data for the task at hand, but that requires much human efforts and often not feasible because of the language specific diversity in the code-mixed text. To circumvent the data scarcity issue, we propose an effective deep learning approach for automatically generating the code-mixed text from English to multiple languages without any parallel data. In order to train the neural network, we create synthetic code-mixed texts from the available parallel corpus by modelling various linguistic properties of code-mixing. Our codemixed text generator is built upon the encoder-decoder framework, where the encoder is augmented with the linguistic and task-agnostic features obtained from the transformer based language model. We also transfer the knowledge from a neural machine translation (NMT) to warm-start the training of code-mixed generator. Experimental results and in-depth analysis show the effectiveness of our proposed code-mixed text generation on eight diverse language pairs.",
}
@inproceedings{he-etal-2020-bert,
    title = "{BERT}-{MK}: Integrating Graph Contextualized Knowledge into Pre-trained Language Models",
    author = "He, Bin  and
      Zhou, Di  and
      Xiao, Jinghui  and
      Jiang, Xin  and
      Liu, Qun  and
      Yuan, Nicholas Jing  and
      Xu, Tong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.207",
    doi = "10.18653/v1/2020.findings-emnlp.207",
    pages = "2281--2290",
    abstract = "Complex node interactions are common in knowledge graphs (KGs), and these interactions can be considered as contextualized knowledge exists in the topological structure of KGs. Traditional knowledge representation learning (KRL) methods usually treat a single triple as a training unit, neglecting the usage of graph contextualized knowledge. To utilize these unexploited graph-level knowledge, we propose an approach to model subgraphs in a medical KG. Then, the learned knowledge is integrated with a pre-trained language model to do the knowledge generalization. Experimental results demonstrate that our model achieves the state-of-the-art performance on several medical NLP tasks, and the improvement above MedERNIE indicates that graph contextualized knowledge is beneficial.",
}
@inproceedings{tan-etal-2020-recursive,
    title = "Recursive Top-Down Production for Sentence Generation with Latent Trees",
    author = "Tan, Shawn  and
      Shen, Yikang  and
      Sordoni, Alessandro  and
      Courville, Aaron  and
      O{'}Donnell, Timothy J.",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.208",
    doi = "10.18653/v1/2020.findings-emnlp.208",
    pages = "2291--2307",
    abstract = "We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves, allowing us to compute the likelihood of a sequence of N tokens under a latent tree model, which we maximise to train a recursive neural function. We demonstrate performance on two synthetic tasks: SCAN, where it outperforms previous models on the LENGTH split, and English question formation, where it performs comparably to decoders with the ground-truth tree structure. We also present experimental results on German-English translation on the Multi30k dataset, and qualitatively analyse the induced tree structures our model learns for the SCAN tasks and the German-English translation task.",
}
@inproceedings{li-etal-2020-guided,
    title = "Guided Dialogue Policy Learning without Adversarial Learning in the Loop",
    author = "Li, Ziming  and
      Lee, Sungjin  and
      Peng, Baolin  and
      Li, Jinchao  and
      Kiseleva, Julia  and
      de Rijke, Maarten  and
      Shayandeh, Shahin  and
      Gao, Jianfeng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.209",
    doi = "10.18653/v1/2020.findings-emnlp.209",
    pages = "2308--2317",
    abstract = "Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy. However, these methods suffer from sparse and unstable reward signals returned by a user simulator only when a dialogue finishes. Besides, the reward signal is manually designed by human experts, which requires domain knowledge. Recently, a number of adversarial learning methods have been proposed to learn the reward function together with the dialogue policy. However, to alternatively update the dialogue policy and the reward model on the fly, we are limited to policy-gradient-based algorithms, such as REINFORCE and PPO. Moreover, the alternating training of a dialogue agent and the reward model can easily get stuck in local optima or result in mode collapse. To overcome the listed issues, we propose to decompose the adversarial training into two steps. First, we train the discriminator with an auxiliary dialogue generator and then incorporate a derived reward model into a common reinforcement learning method to guide the dialogue policy learning. This approach is applicable to both on-policy and off-policy reinforcement learning methods. Based on our extensive experimentation, we can conclude the proposed method: (1) achieves a remarkable task success rate using both on-policy and off-policy reinforcement learning methods; and (2) has potential to transfer knowledge from existing domains to a new domain.",
}
@inproceedings{firdaus-etal-2020-multidm,
    title = "{M}ulti{DM}-{GCN}: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network",
    author = "Firdaus, Mauajama  and
      Thakur, Nidhi  and
      Ekbal, Asif",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.210",
    doi = "10.18653/v1/2020.findings-emnlp.210",
    pages = "2318--2328",
    abstract = "In the recent past, dialogue systems have gained immense popularity and have become ubiquitous. During conversations, humans not only rely on languages but seek contextual information through visual contents as well. In every task-oriented dialogue system, the user is guided by the different aspects of a product or service that regulates the conversation towards selecting the product or service. In this work, we present a multi-modal conversational framework for a task-oriented dialogue setup that generates the responses following the different aspects of a product or service to cater to the user{'}s needs. We show that the responses guided by the aspect information provide more interactive and informative responses for better communication between the agent and the user. We first create a Multi-domain Multi-modal Dialogue (MDMMD) dataset having conversations involving both text and images belonging to the three different domains, such as restaurants, electronics, and furniture. We implement a Graph Convolutional Network (GCN) based framework that generates appropriate textual responses from the multi-modal inputs. The multi-modal information having both textual and image representation is fed to the decoder and the aspect information for generating aspect guided responses. Quantitative and qualitative analyses show that the proposed methodology outperforms several baselines for the proposed task of aspect-guided response generation.",
}
@inproceedings{cui-etal-2020-edge,
    title = "Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation",
    author = "Cui, Shiyao  and
      Yu, Bowen  and
      Liu, Tingwen  and
      Zhang, Zhenyu  and
      Wang, Xuebin  and
      Shi, Jinqiao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.211",
    doi = "10.18653/v1/2020.findings-emnlp.211",
    pages = "2329--2339",
    abstract = "Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden from dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information.These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.",
}
@inproceedings{chawla-yang-2020-semi,
    title = "Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization",
    author = "Chawla, Kunal  and
      Yang, Diyi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.212",
    doi = "10.18653/v1/2020.findings-emnlp.212",
    pages = "2340--2354",
    abstract = "Formality style transfer is the task of converting informal sentences to grammatically-correct formal sentences, which can be used to improve performance of many downstream NLP tasks. In this work, we propose a semi-supervised formality style transfer model that utilizes a language model-based discriminator to maximize the likelihood of the output sentence being formal, which allows us to use maximization of token-level conditional probabilities for training. We further propose to maximize mutual information between source and target styles as our training objective instead of maximizing the regular likelihood that often leads to repetitive and trivial generated responses. Experiments showed that our model outperformed previous state-of-the-art baselines significantly in terms of both automated metrics and human judgement. We further generalized our model to unsupervised text style transfer task, and achieved significant improvements on two benchmark sentiment style transfer datasets.",
}
@inproceedings{lyu-etal-2020-differentially,
    title = "Differentially Private Representation for {NLP}: Formal Guarantee and An Empirical Study on Privacy and Fairness",
    author = "Lyu, Lingjuan  and
      He, Xuanli  and
      Li, Yitong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.213",
    doi = "10.18653/v1/2020.findings-emnlp.213",
    pages = "2355--2365",
    abstract = "It has been demonstrated that hidden representation learned by deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy. To address this issue, we propose a novel approach called Differentially Private Neural Representation (DPNR) to preserve privacy of the extracted representation from text. DPNR utilises Differential Privacy (DP) to provide formal privacy guarantee. Further, we show that masking words via dropout can further enhance privacy. To maintain utility of the learned representation, we integrate DP-noisy representation into a robust training process to derive a robust target model, which also helps for model fairness over various demographic variables. Experimental results on benchmark datasets under various parameter settings demonstrate that DPNR largely reduces privacy leakage without significantly sacrificing the main task performance.",
}
@inproceedings{rashid-etal-2020-helpful,
    title = "Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success",
    author = "Rashid, Farzana  and
      Fornaciari, Tommaso  and
      Hovy, Dirk  and
      Blanco, Eduardo  and
      Vega-Redondo, Fernando",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.214",
    doi = "10.18653/v1/2020.findings-emnlp.214",
    pages = "2366--2371",
    abstract = "When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. We study the chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. We manually annotate a set of about 5,500 chat interactions with four dimensions of interaction styles (motivation, cooperation, equality, advice). We find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success. Our findings indicate that successful communicators are also successful in other domains.",
}
@inproceedings{madotto-etal-2020-learning,
    title = "Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems",
    author = "Madotto, Andrea  and
      Cahyawijaya, Samuel  and
      Winata, Genta Indra  and
      Xu, Yan  and
      Liu, Zihan  and
      Lin, Zhaojiang  and
      Fung, Pascale",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.215",
    doi = "10.18653/v1/2020.findings-emnlp.215",
    pages = "2372--2394",
    abstract = "Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (KB) plays an essential role in fulfilling user requests. Modularized systems rely on DST to interact with the KB, which is expensive in terms of annotation and inference time. End-to-end systems, instead, use the KB directly as input, but they cannot scale when the KB is larger than a few hundred entries. In this paper, we propose a method to embed the KB, of any size, directly into the model parameters. The resulting model does not require any DST or template responses, nor the KB as input, and it can dynamically update its KB via fine-tuning. We evaluate our solution in five task-oriented dialogue datasets with small, medium, and large KB size. Our experiments show that end-to-end models can effectively embed knowledge bases in their parameters and achieve competitive performance in all evaluated datasets.",
}
@inproceedings{liu-etal-2020-adapting,
    title = "Adapting Open Domain Fact Extraction and Verification to {COVID}-{FACT} through In-Domain Language Modeling",
    author = "Liu, Zhenghao  and
      Xiong, Chenyan  and
      Dai, Zhuyun  and
      Sun, Si  and
      Sun, Maosong  and
      Liu, Zhiyuan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.216",
    doi = "10.18653/v1/2020.findings-emnlp.216",
    pages = "2395--2400",
    abstract = "With the epidemic of COVID-19, verifying the scientifically false online information, such as fake news and maliciously fabricated statements, has become crucial. However, the lack of training data in the scientific domain limits the performance of fact verification models. This paper proposes an in-domain language modeling method for fact extraction and verification systems. We come up with SciKGAT to combine the advantages of open-domain literature search, state-of-the-art fact verification systems and in-domain medical knowledge through language modeling. Our experiments on SCIFACT, a dataset of expert-written scientific fact verification, show that SciKGAT achieves 30{\%} absolute improvement on precision. Our analyses show that such improvement thrives from our in-domain language model by picking up more related evidence pieces and accurate fact verification. Our codes and data are released via Github.",
}
@inproceedings{qi-etal-2020-prophetnet,
    title = "{P}rophet{N}et: Predicting Future N-gram for Sequence-to-{S}equence{P}re-training",
    author = "Qi, Weizhen  and
      Yan, Yu  and
      Gong, Yeyun  and
      Liu, Dayiheng  and
      Duan, Nan  and
      Chen, Jiusheng  and
      Zhang, Ruofei  and
      Zhou, Ming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.217",
    doi = "10.18653/v1/2020.findings-emnlp.217",
    pages = "2401--2410",
    abstract = "This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction that predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large-scale dataset (160GB), respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.",
}
@inproceedings{cao-wan-2020-divgan,
    title = "{D}iv{GAN}: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network",
    author = "Cao, Yue  and
      Wan, Xiaojun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.218",
    doi = "10.18653/v1/2020.findings-emnlp.218",
    pages = "2411--2421",
    abstract = "Paraphrases refer to texts that convey the same meaning with different expression forms. Traditional seq2seq-based models on paraphrase generation mainly focus on the fidelity while ignoring the diversity of outputs. In this paper, we propose a deep generative model to generate diverse paraphrases. We build our model based on the conditional generative adversarial network, and propose to incorporate a simple yet effective diversity loss term into the model in order to improve the diversity of outputs. The proposed diversity loss maximizes the ratio of pairwise distance between the generated texts and their corresponding latent codes, forcing the generator to focus more on the latent codes and produce diverse samples. Experimental results on benchmarks of paraphrase generation show that our proposed model can generate more diverse paraphrases compared with baselines.",
}
@inproceedings{madotto-etal-2020-plug,
    title = "Plug-and-Play Conversational Models",
    author = "Madotto, Andrea  and
      Ishii, Etsuko  and
      Lin, Zhaojiang  and
      Dathathri, Sumanth  and
      Fung, Pascale",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.219",
    doi = "10.18653/v1/2020.findings-emnlp.219",
    pages = "2422--2433",
    abstract = "There has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit. These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model. In this paper, we first propose and evaluate plug-and-play methods for controllable response generation, which does not require dialogue specific datasets and does not rely on fine-tuning a large model. While effective, the decoding procedure induces considerable computational overhead, rendering the conversational model unsuitable for interactive usage. To overcome this, we introduce an approach that does not require further computation at decoding time, while also does not require any fine-tuning of a large language model. We demonstrate, through extensive automatic and human evaluation, a high degree of control over the generated conversational responses with regard to multiple desired attributes, while being fluent.",
}
@inproceedings{wu-2020-event,
    title = "Event-Driven Learning of Systematic Behaviours in Stock Markets",
    author = "Wu, Xianchao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.220",
    doi = "10.18653/v1/2020.findings-emnlp.220",
    pages = "2434--2444",
    abstract = "It is reported that financial news, especially financial events expressed in news, provide information to investors{'} long/short decisions and influence the movements of stock markets. Motivated by this, we leverage financial event streams to train a classification neural network that detects latent event-stock linkages and stock markets{'} systematic behaviours in the U.S. stock market. Our proposed pipeline includes (1) a combined event extraction method that utilizes Open Information Extraction and neural co-reference resolution, (2) a BERT/ALBERT enhanced representation of events, and (3) an extended hierarchical attention network that includes attentions on event, news and temporal levels. Our pipeline achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard{\&}Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks.",
}
@inproceedings{sreedhar-etal-2020-learning,
    title = "Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback",
    author = "Sreedhar, Makesh Narsimhan  and
      Ni, Kun  and
      Reddy, Siva",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.221",
    doi = "10.18653/v1/2020.findings-emnlp.221",
    pages = "2445--2453",
    abstract = "The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator{'}s goal is to convert the feedback into a response that answers the user{'}s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94{\%}to 75.96{\%} in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.",
}
@inproceedings{aktas-etal-2020-adapting,
    title = "Adapting Coreference Resolution to {T}witter Conversations",
    author = "Akta{\c{s}}, Berfin  and
      Solopova, Veronika  and
      Kohnert, Annalena  and
      Stede, Manfred",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.222",
    doi = "10.18653/v1/2020.findings-emnlp.222",
    pages = "2454--2460",
    abstract = "The performance of standard coreference resolution is known to drop significantly on Twitter texts. We improve the performance of the (Lee et al., 2018) system, which is originally trained on OntoNotes, by retraining on manually-annotated Twitter conversation data. Further experiments by combining different portions of OntoNotes with Twitter data show that selecting text genres for the training data can beat the mere maximization of training data amount. In addition, we inspect several phenomena such as the role of deictic pronouns in conversational data, and present additional results for variant settings. Our best configuration improves the performance of the{''}out of the box{''} system by 21.6{\%}.",
}
@inproceedings{amrhein-sennrich-2020-romanization,
    title = "On {R}omanization for Model Transfer Between Scripts in Neural Machine Translation",
    author = "Amrhein, Chantal  and
      Sennrich, Rico",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.223",
    doi = "10.18653/v1/2020.findings-emnlp.223",
    pages = "2461--2469",
    abstract = "Transfer learning is a popular strategy to improve the quality of low-resource machine translation. For an optimal transfer of the embedding layer, the child and parent model should share a substantial part of the vocabulary. This is not the case when transferring to languages with a different script. We explore the benefit of romanization in this scenario. Our results show that romanization entails information loss and is thus not always superior to simpler vocabulary transfer methods, but can improve the transfer between related languages with different scripts. We compare two romanization tools and find that they exhibit different degrees of information loss, which affects translation quality. Finally, we extend romanization to the target side, showing that this can be a successful strategy when coupled with a simple deromanization model.",
}
@inproceedings{ghosal-etal-2020-cosmic,
    title = "{COSMIC}: {CO}mmon{S}ense knowledge for e{M}otion Identification in Conversations",
    author = "Ghosal, Deepanway  and
      Majumder, Navonil  and
      Gelbukh, Alexander  and
      Mihalcea, Rada  and
      Poria, Soujanya",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.224",
    doi = "10.18653/v1/2020.findings-emnlp.224",
    pages = "2470--2481",
    abstract = "In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-theart methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion.",
}
@inproceedings{oren-etal-2020-improving,
    title = "Improving Compositional Generalization in Semantic Parsing",
    author = "Oren, Inbar  and
      Herzig, Jonathan  and
      Gupta, Nitish  and
      Gardner, Matt  and
      Berant, Jonathan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.225",
    doi = "10.18653/v1/2020.findings-emnlp.225",
    pages = "2482--2495",
    abstract = "Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes to new structures built of components observed during training, has sparked substantial interest. In this work, we investigate compositional generalization in semantic parsing, a natural test-bed for compositional generalization, as output programs are constructed from sub-components. We analyze a wide variety of models and propose multiple extensions to the attention module of the semantic parser, aiming to improve compositional generalization. We find that the following factors improve compositional generalization: (a) using contextual representations, such as ELMo and BERT, (b) informing the decoder what input tokens have previously been attended to, (c) training the decoder attention to agree with pre-computed token alignments, and (d) downsampling examples corresponding to frequent program templates. While we substantially reduce the gap between in-distribution and OOD generalization, performance on OOD compositions is still substantially lower.",
}
@inproceedings{gangi-reddy-etal-2020-answer,
    title = "Answer Span Correction in Machine Reading Comprehension",
    author = "Gangi Reddy, Revanth  and
      Sultan, Md Arafat  and
      Sarioglu Kayi, Efsun  and
      Zhang, Rong  and
      Castelli, Vittorio  and
      Sil, Avi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.226",
    doi = "10.18653/v1/2020.findings-emnlp.226",
    pages = "2496--2501",
    abstract = "Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assessing the {``}answerability{''} of the question given the extracted answer. Here we address a different problem: the tendency of existing MRC systems to produce partially correct answers when presented with answerable questions. We explore the nature of such errors and propose a post-processing correction method that yields statistically significant performance improvements over state-of-the-art MRC systems in both monolingual and multilingual evaluation.",
}
@inproceedings{mosbach-etal-2020-interplay,
    title = "{O}n the {I}nterplay {B}etween {F}ine-tuning and {S}entence-level {P}robing for {L}inguistic {K}nowledge in {P}re-trained {T}ransformers",
    author = "Mosbach, Marius  and
      Khokhlova, Anna  and
      Hedderich, Michael A.  and
      Klakow, Dietrich",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.227",
    doi = "10.18653/v1/2020.findings-emnlp.227",
    pages = "2502--2516",
    abstract = "Fine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline. At the same time, probing has emerged as a way to investigate the linguistic knowledge captured by pre-trained models. Very little is, however, understood about how fine-tuning affects the representations of pre-trained models and thereby the linguistic knowledge they encode. This paper contributes towards closing this gap. We study three different pre-trained models: BERT, RoBERTa, and ALBERT, and investigate through sentence-level probing how fine-tuning affects their representations. We find that for some probing tasks fine-tuning leads to substantial changes in accuracy, possibly suggesting that fine-tuning introduces or even removes linguistic knowledge from a pre-trained model. These changes, however, vary greatly across different models, fine-tuning and probing tasks. Our analysis reveals that while fine-tuning indeed changes the representations of a pre-trained model and these changes are typically larger for higher layers, only in very few cases, fine-tuning has a positive effect on probing accuracy that is larger than just using the pre-trained model with a strong pooling method. Based on our findings, we argue that both positive and negative effects of fine-tuning on probing require a careful interpretation.",
}
@inproceedings{yao-etal-2020-zero,
    title = "Zero-shot Entity Linking with Efficient Long Range Sequence Modeling",
    author = "Yao, Zonghai  and
      Cao, Liangliang  and
      Pan, Huapu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.228",
    doi = "10.18653/v1/2020.findings-emnlp.228",
    pages = "2517--2522",
    abstract = "This paper considers the problem of zero-shot entity linking, in which a link in the test time may not present in training. Following the prevailing BERT-based research efforts, we find a simple yet effective way is to expand the long-range sequence modeling. Unlike many previous methods, our method does not require expensive pre-training of BERT with long position embeddings. Instead, we propose an efficient position embeddings initialization method called Embedding-repeat, which initializes larger position embeddings based on BERT-Base. On the zero-shot entity linking dataset, our method improves the STOA from 76.06{\%} to 79.08{\%}, and for its long data, the corresponding improvement is from 74.57{\%} to 82.14{\%}. Our experiments suggest the effectiveness of long-range sequence modeling without retraining the BERT model.",
}
@inproceedings{liu-etal-2020-context,
    title = "How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization",
    author = "Liu, Jian  and
      Chen, Yubo  and
      Liu, Kang  and
      Jia, Yantao  and
      Sheng, Zhicheng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.229",
    doi = "10.18653/v1/2020.findings-emnlp.229",
    pages = "2523--2532",
    abstract = "Event detection (ED) aims to identify and classify event triggers in texts, which is a crucial subtask of event extraction (EE). Despite many advances in ED, the existing studies are typically centered on improving the overall performance of an ED model, which rarely consider the robustness of an ED model. This paper aims to fill this research gap by stressing the importance of robustness modeling in ED models. We first pinpoint three stark cases demonstrating the brittleness of the existing ED models. After analyzing the underlying reason, we propose a new training mechanism, called context-selective mask generalization for ED, which can effectively mine context-specific patterns for learning and robustify an ED model. The experimental results have confirmed the effectiveness of our model regarding defending against adversarial attacks, exploring unseen predicates, and tackling ambiguity cases. Moreover, a deeper analysis suggests that our approach can learn a complementary predictive bias with most ED models that use full context for feature learning.",
}
@inproceedings{zhang-etal-2020-adaptive,
    title = "Adaptive Feature Selection for End-to-End Speech Translation",
    author = "Zhang, Biao  and
      Titov, Ivan  and
      Haddow, Barry  and
      Sennrich, Rico",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.230",
    doi = "10.18653/v1/2020.findings-emnlp.230",
    pages = "2533--2544",
    abstract = "Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features. In this paper, we propose adaptive feature selection (AFS) for encoder-decoder based E2E ST. We first pre-train an ASR encoder and apply AFS to dynamically estimate the importance of each encoded speech feature to ASR. A ST encoder, stacked on top of the ASR encoder, then receives the filtered features from the (frozen) ASR encoder. We take L0DROP (Zhang et al., 2020) as the backbone for AFS, and adapt it to sparsify speech features with respect to both temporal and feature dimensions. Results on LibriSpeech EnFr and MuST-C benchmarks show that AFS facilitates learning of ST by pruning out {\textasciitilde}84{\%} temporal features, yielding an average translation gain of {\textasciitilde}1.3-1.6 BLEU and a decoding speedup of {\textasciitilde}1.4x. In particular, AFS reduces the performance gap compared to the cascade baseline, and outperforms it on LibriSpeech En-Fr with a BLEU score of 18.56 (without data augmentation).",
}
@inproceedings{jin-wan-2020-abstractive,
    title = "Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization",
    author = "Jin, Hanqi  and
      Wan, Xiaojun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.231",
    doi = "10.18653/v1/2020.findings-emnlp.231",
    pages = "2545--2554",
    abstract = "Single-document and multi-document summarizations are very closely related in both task definition and solution method. In this work, we propose to improve neural abstractive multi-document summarization by jointly learning an abstractive single-document summarizer. We build a unified model for single-document and multi-document summarizations by fully sharing the encoder and decoder and utilizing a decoding controller to aggregate the decoder{'}s outputs for multiple input documents. We evaluate our model on two multi-document summarization datasets: Multi-News and DUC-04. Experimental results show the efficacy of our approach, and it can substantially outperform several strong baselines. We also verify the helpfulness of single-document summarization to abstractive multi-document summarization task.",
}
@inproceedings{qiu-etal-2020-blockwise,
    title = "Blockwise Self-Attention for Long Document Understanding",
    author = "Qiu, Jiezhong  and
      Ma, Hao  and
      Levy, Omer  and
      Yih, Wen-tau  and
      Wang, Sinong  and
      Tang, Jie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.232",
    doi = "10.18653/v1/2020.findings-emnlp.232",
    pages = "2555--2565",
    abstract = "We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1{\%} less memory and 12.0-25.1{\%} less time to learn the model. During testing, BlockBERT saves 27.8{\%} inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa.",
}
@inproceedings{ye-etal-2020-unsupervised,
    title = "Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling",
    author = "Ye, Fanghua  and
      Manotumruksa, Jarana  and
      Yilmaz, Emine",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.233",
    doi = "10.18653/v1/2020.findings-emnlp.233",
    pages = "2566--2575",
    abstract = "Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes. The explosion of short text data has spurred the demand of few-bits hashing. However, the performance of existing semantic hashing methods cannot be guaranteed when applied to few-bits hashing because of severe information loss. In this paper, we present a simple but effective unsupervised neural generative semantic hashing method with a focus on few-bits hashing. Our model is built upon variational autoencoder and represents each hash bit as a Bernoulli variable, which allows the model to be end-to-end trainable. To address the issue of information loss, we introduce a set of auxiliary implicit topic vectors. With the aid of these topic vectors, the generated hash codes are not only low-dimensional representations of the original texts but also capture their implicit topics. We conduct comprehensive experiments on four datasets. The results demonstrate that our approach achieves significant improvements over state-of-the-art semantic hashing methods in few-bits hashing.",
}
@inproceedings{wu-etal-2020-grid,
    title = "Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction",
    author = "Wu, Zhen  and
      Ying, Chengcan  and
      Zhao, Fei  and
      Fan, Zhifang  and
      Dai, Xinyu  and
      Xia, Rui",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.234",
    doi = "10.18653/v1/2020.findings-emnlp.234",
    pages = "2576--2585",
    abstract = "Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline. However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task. Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions. To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets. Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.",
}
@inproceedings{jiang-etal-2020-learning,
    title = "Learning Numeral Embedding",
    author = "Jiang, Chengyue  and
      Nian, Zhonglin  and
      Guo, Kaihao  and
      Chu, Shanbo  and
      Zhao, Yinggong  and
      Shen, Libin  and
      Tu, Kewei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.235",
    doi = "10.18653/v1/2020.findings-emnlp.235",
    pages = "2586--2599",
    abstract = "Word embedding is an essential building block for deep learning methods for natural language processing. Although word embedding has been extensively studied over the years, the problem of how to effectively embed numerals, a special subset of words, is still underexplored. Existing word embedding methods do not learn numeral embeddings well because there are an infinite number of numerals and their individual appearances in training corpora are highly scarce. In this paper, we propose two novel numeral embedding methods that can handle the out-of-vocabulary (OOV) problem for numerals. We first induce a finite set of prototype numerals using either a self-organizing map or a Gaussian mixture model. We then represent the embedding of a numeral as a weighted average of the prototype number embeddings. Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training. We evaluated our methods and showed its effectiveness on four intrinsic and extrinsic tasks: word similarity, embedding numeracy, numeral prediction, and sequence labeling.",
}
@inproceedings{hu-etal-2020-investigation,
    title = "An Investigation of Potential Function Designs for Neural {CRF}",
    author = "Hu, Zechuan  and
      Jiang, Yong  and
      Bach, Nguyen  and
      Wang, Tao  and
      Huang, Zhongqiang  and
      Huang, Fei  and
      Tu, Kewei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.236",
    doi = "10.18653/v1/2020.findings-emnlp.236",
    pages = "2600--2609",
    abstract = "The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling. In this paper, we investigate a series of increasingly expressive potential functions for neural CRF models, which not only integrate the emission and transition functions, but also explicitly take the representations of the contextual words as input. Our extensive experiments show that the decomposed quadrilinear potential function based on the vector representations of two neighboring labels and two neighboring words consistently achieves the best performance.",
}
@inproceedings{park-etal-2020-fast,
    title = "Fast End-to-end Coreference Resolution for {K}orean",
    author = "Park, Cheoneum  and
      Shin, Jamin  and
      Park, Sungjoon  and
      Lim, Joonho  and
      Lee, Changki",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.237",
    doi = "10.18653/v1/2020.findings-emnlp.237",
    pages = "2610--2624",
    abstract = "Recently, end-to-end neural network-based approaches have shown significant improvements over traditional pipeline-based models in English coreference resolution. However, such advancements came at a cost of computational complexity and recent works have not focused on tackling this problem. Hence, in this paper, to cope with this issue, we propose BERT-SRU-based Pointer Networks that leverages the linguistic property of head-final languages. Applying this model to the Korean coreference resolution, we significantly reduce the coreference linking search space. Combining this with Ensemble Knowledge Distillation, we maintain state-of-the-art performance 66.9{\%} of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time.",
}
@inproceedings{scialom-etal-2020-toward,
    title = "Toward Stance-based Personas for Opinionated Dialogues",
    author = "Scialom, Thomas  and
      Tekiro{\u{g}}lu, Serra Sinem  and
      Staiano, Jacopo  and
      Guerini, Marco",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.238",
    doi = "10.18653/v1/2020.findings-emnlp.238",
    pages = "2625--2635",
    abstract = "In the context of chit-chat dialogues it has been shown that endowing systems with a persona profile is important to produce more coherent and meaningful conversations. Still, the representation of such personas has thus far been limited to a fact-based representation (e.g. {``}I have two cats.{''}). We argue that these representations remain superficial w.r.t. the complexity of human personality. In this work, we propose to make a step forward and investigate stance-based persona, trying to grasp more profound characteristics, such as opinions, values, and beliefs to drive language generation. To this end, we introduce a novel dataset allowing to explore different stance-based persona representations and their impact on claim generation, showing that they are able to grasp abstract and profound aspects of the author persona.",
}
@inproceedings{chapuis-etal-2020-hierarchical,
    title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
    author = "Chapuis, Emile  and
      Colombo, Pierre  and
      Manica, Matteo  and
      Labeau, Matthieu  and
      Clavel, Chlo{\'e}",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.239",
    doi = "10.18653/v1/2020.findings-emnlp.239",
    pages = "2636--2648",
    abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.",
}
@inproceedings{wang-etal-2020-extending,
    title = "Extending Multilingual {BERT} to Low-Resource Languages",
    author = "Wang, Zihan  and
      K, Karthikeyan  and
      Mayhew, Stephen  and
      Roth, Dan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.240",
    doi = "10.18653/v1/2020.findings-emnlp.240",
    pages = "2649--2656",
    abstract = "Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success is focused only on the top 104 languages in Wikipedia it was trained on. In this paper, we propose a simple but effective approach to extend M-BERT E-MBERT so it can benefit any new language, and show that our approach aids languages that are already in M-BERT as well. We perform an extensive set of experiments with Named Entity Recognition (NER) on 27 languages, only 16 of which are in M-BERT, and show an average increase of about 6{\%} F1 on M-BERT languages and 23{\%} F1 increase on new languages. We release models and code at http://cogcomp.org/page/publication{\_}view/912.",
}
@inproceedings{albooyeh-etal-2020-sample,
    title = "Out-of-Sample Representation Learning for Knowledge Graphs",
    author = "Albooyeh, Marjan  and
      Goel, Rishab  and
      Kazemi, Seyed Mehran",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.241",
    doi = "10.18653/v1/2020.findings-emnlp.241",
    pages = "2657--2666",
    abstract = "Many important problems can be formulated as reasoning in knowledge graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already observed entities. This is true for both attributed graphs(where each entity has an initial feature vector) and non-attributed graphs (where the only initial information derives from known relations with other entities). For out-of-sample reasoning, where one needs to make predictions for entities that were unseen at training time, much prior work considers attributed graph. However, this problem is surprisingly under-explored for non-attributed graphs. In this paper, we study the out-of-sample representation learning problem for non-attributed knowledge graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.",
}
@inproceedings{srinivasan-etal-2020-fine,
    title = "Fine-Grained Grounding for Multimodal Speech Recognition",
    author = "Srinivasan, Tejas  and
      Sanabria, Ramon  and
      Metze, Florian  and
      Elliott, Desmond",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.242",
    doi = "10.18653/v1/2020.findings-emnlp.242",
    pages = "2667--2677",
    abstract = "Multimodal automatic speech recognition systems integrate information from images to improve speech recognition quality, by grounding the speech in the visual context. While visual signals have been shown to be useful for recovering entities that have been masked in the audio, these models should be capable of recovering a broader range of word types. Existing systems rely on global visual features that represent the entire image, but localizing the relevant regions of the image will make it possible to recover a larger set of words, such as adjectives and verbs. In this paper, we propose a model that uses finer-grained visual information from different parts of the image, using automatic object proposals. In experiments on the Flickr8K Audio Captions Corpus, we find that our model improves over approaches that use global visual features, that the proposals enable the model to recover entities and other related words, such as adjectives, and that improvements are due to the model{'}s ability to localize the correct proposals.",
}
@inproceedings{shnarch-etal-2020-unsupervised,
    title = "Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains",
    author = "Shnarch, Eyal  and
      Choshen, Leshem  and
      Moshkowich, Guy  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.243",
    doi = "10.18653/v1/2020.findings-emnlp.243",
    pages = "2678--2697",
    abstract = "Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory. Aiming to assist domain experts in their first steps into a new task over a new corpus, we present an unsupervised approach to reveal complex rules which cluster the unexplored corpus by its prominent categories (or facets). These rules are human-readable, thus providing an important ingredient which has become in short supply lately - explainability. Each rule provides an explanation for the commonality of all the texts it clusters together. The experts can then identify which rules best capture texts of their categories of interest, and utilize them to deepen their understanding of these categories. These rules can also bootstrap the process of data labeling by pointing at a subset of the corpus which is enriched with texts demonstrating the target categories. We present an extensive evaluation of the usefulness of these rules in identifying target categories, as well as a user study which assesses their interpretability.",
}
@inproceedings{higy-etal-2020-textual,
    title = "Textual {S}upervision for {V}isually {G}rounded {S}poken {L}anguage {U}nderstanding",
    author = "Higy, Bertrand  and
      Elliott, Desmond  and
      Chrupa{\l}a, Grzegorz",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.244",
    doi = "10.18653/v1/2020.findings-emnlp.244",
    pages = "2698--2709",
    abstract = "Visually-grounded models of spoken language understanding extract semantic information directly from speech, without relying on transcriptions. This is useful for low-resource languages, where transcriptions can be expensive or impossible to obtain. Recent work showed that these models can be improved if transcriptions are available at training time. However, it is not clear how an end-to-end approach compares to a traditional pipeline-based approach when one has access to transcriptions. Comparing different strategies, we find that the pipeline approach works better when enough text is available. With low-resource languages in mind, we also show that translations can be effectively used in place of transcriptions but more data is needed to obtain similar results.",
}
@inproceedings{limisiewicz-etal-2020-universal,
    title = "{U}niversal {D}ependencies {A}ccording to {BERT}: {B}oth {M}ore {S}pecific and {M}ore {G}eneral",
    author = "Limisiewicz, Tomasz  and
      Mare{\v{c}}ek, David  and
      Rosa, Rudolf",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.245",
    doi = "10.18653/v1/2020.findings-emnlp.245",
    pages = "2710--2722",
    abstract = "This work focuses on analyzing the form and extent of syntactic abstraction captured by BERT by extracting labeled dependency trees from self-attentions. Previous work showed that individual BERT heads tend to encode particular dependency relation types. We extend these findings by explicitly comparing BERT relations to Universal Dependencies (UD) annotations, showing that they often do not match one-to-one. We suggest a method for relation identification and syntactic tree construction. Our approach produces significantly more consistent dependency trees than previous work, showing that it better explains the syntactic abstractions in BERT. At the same time, it can be successfully applied with only a minimal amount of supervision and generalizes well across languages.",
}
@inproceedings{muraoka-etal-2020-visual,
    title = "Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment",
    author = "Muraoka, Masayasu  and
      Nasukawa, Tetsuya  and
      Bhattacharjee, Bishwaranjan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.246",
    doi = "10.18653/v1/2020.findings-emnlp.246",
    pages = "2723--2735",
    abstract = "We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task. Although it has been shown that the \textit{Distributional Informativeness Hypothesis} (DIH) holds on text, in which the DIH assumes that a context surrounding a hyponym is more informative than that of a hypernym, it has never been tested on visual objects. Since our perception is tightly associated with language, it is meaningful to explore whether the DIH holds on visual objects. To this end, we consider visual objects as the context of a word and represent a word as a bag of visual objects found in images associated with the word. This allows us to test the feasibility of the visual DIH. To better distinguish word pairs in a hypernym relation from other relations such as co-hypernyms, we also propose a new measurable function that takes into account both the difference in the generality of meaning and similarity of meaning between words. Our experimental results show that the DIH holds on visual objects and that the proposed method combined with the proposed function outperforms existing unsupervised representation methods.",
}
@inproceedings{santhanam-etal-2020-learning,
    title = "Learning to Plan and Realize Separately for Open-Ended Dialogue Systems",
    author = "Santhanam, Sashank  and
      Cheng, Zhuo  and
      Mather, Brodie  and
      Dorr, Bonnie  and
      Bhatia, Archna  and
      Hebenstreit, Bryanna  and
      Zemel, Alan  and
      Dalton, Adam  and
      Strzalkowski, Tomek  and
      Shaikh, Samira",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.247",
    doi = "10.18653/v1/2020.findings-emnlp.247",
    pages = "2736--2750",
    abstract = "Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems. We posit this is because extant approaches towards natural language generation (NLG) are typically construed as end-to-end architectures that do not adequately model human generation processes. To investigate, we decouple generation into two separate phases: planning and realization. In the planning phase, we train two planners to generate plans for response utterances. The realization phase uses response plans to produce an appropriate response. Through rigorous evaluations, both automated and human, we demonstrate that decoupling the process into planning and realization performs better than an end-to-end approach.",
}
@inproceedings{pezzelle-etal-2020-different,
    title = "{B}e {D}ifferent to {B}e {B}etter! {A} {B}enchmark to {L}everage the {C}omplementarity of {L}anguage and {V}ision",
    author = "Pezzelle, Sandro  and
      Greco, Claudio  and
      Gandolfi, Greta  and
      Gualdoni, Eleonora  and
      Bernardi, Raffaella",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.248",
    doi = "10.18653/v1/2020.findings-emnlp.248",
    pages = "2751--2767",
    abstract = "This paper introduces BD2BB, a novel language and vision benchmark that requires multimodal models combine complementary information from the two modalities. Recently, impressive progress has been made to develop universal multimodal encoders suitable for virtually any language and vision tasks. However, current approaches often require them to combine redundant information provided by language and vision. Inspired by real-life communicative contexts, we propose a novel task where either modality is necessary but not sufficient to make a correct prediction. To do so, we first build a dataset of images and corresponding sentences provided by human participants. Second, we evaluate state-of-the-art models and compare their performance against human speakers. We show that, while the task is relatively easy for humans, best-performing models struggle to achieve similar results.",
}
@inproceedings{shi-etal-2020-cross,
    title = "Cross-Lingual Training of Neural Models for Document Ranking",
    author = "Shi, Peng  and
      Bai, He  and
      Lin, Jimmy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.249",
    doi = "10.18653/v1/2020.findings-emnlp.249",
    pages = "2768--2773",
    abstract = "We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages. Our work successfully applies multi-lingual BERT (mBERT) to document ranking and additionally compares against a number of alternatives: translating the training data, translating documents, multi-stage hybrids, and ensembles. Experiments on test collections in six different languages from diverse language families reveal many interesting findings: model-based relevance transfer using mBERT can significantly improve search quality in (non-English) mono-lingual retrieval, but other {``}low resource{''} approaches are competitive as well.",
}
@inproceedings{lioutas-etal-2020-improving,
    title = "{I}mproving {W}ord {E}mbedding {F}actorization for {C}ompression {U}sing {D}istilled {N}onlinear {N}eural {D}ecomposition",
    author = "Lioutas, Vasileios  and
      Rashid, Ahmad  and
      Kumar, Krtin  and
      Haidar, Md. Akmal  and
      Rezagholizadeh, Mehdi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.250",
    doi = "10.18653/v1/2020.findings-emnlp.250",
    pages = "2774--2784",
    abstract = "Word-embeddings are vital components of Natural Language Processing (NLP) models and have been extensively explored. However, they consume a lot of memory which poses a challenge for edge deployment. Embedding matrices, typically, contain most of the parameters for language models and about a third for machine translation systems. In this paper, we propose Distilled Embedding, an (input/output) embedding compression method based on low-rank matrix decomposition and knowledge distillation. First, we initialize the weights of our decomposed matrices by learning to reconstruct the full pre-trained word-embedding and then fine-tune end-to-end, employing knowledge distillation on the factorized embedding. We conduct extensive experiments with various compression rates on machine translation and language modeling, using different data-sets with a shared word-embedding matrix for both embedding and vocabulary projection matrices. We show that the proposed technique is simple to replicate, with one fixed parameter controlling compression size, has higher BLEU score on translation and lower perplexity on language modeling compared to complex, difficult to tune state-of-the-art methods.",
}
@inproceedings{li-etal-2020-pharmmt,
    title = "{P}harm{MT}: A Neural Machine Translation Approach to Simplify Prescription Directions",
    author = "Li, Jiazhao  and
      Lester, Corey  and
      Zhao, Xinyan  and
      Ding, Yuting  and
      Jiang, Yun  and
      Vydiswaran, V.G.Vinod",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.251",
    doi = "10.18653/v1/2020.findings-emnlp.251",
    pages = "2785--2796",
    abstract = "The language used by physicians and health professionals in prescription directions includes medical jargon and implicit directives and causes much confusion among patients. Human intervention to simplify the language at the pharmacies may introduce additional errors that can lead to potentially severe health outcomes. We propose a novel machine translation-based approach, PharmMT, to automatically and reliably simplify prescription directions into patient-friendly language, thereby significantly reducing pharmacist workload. We evaluate the proposed approach over a dataset consisting of over 530K prescriptions obtained from a large mail-order pharmacy. The end-to-end system achieves a BLEU score of 60.27 against the reference directions generated by pharmacists, a 39.6{\%} relative improvement over the rule-based normalization. Pharmacists judged 94.3{\%} of the simplified directions as usable as-is or with minimal changes. This work demonstrates the feasibility of a machine translation-based tool for simplifying prescription directions in real-life.",
}
@inproceedings{saphra-lopez-2020-lstms,
    title = "{LSTM}s Compose{---}and {L}earn{---}{B}ottom-Up",
    author = "Saphra, Naomi  and
      Lopez, Adam",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.252",
    doi = "10.18653/v1/2020.findings-emnlp.252",
    pages = "2797--2809",
    abstract = "Recent work in NLP shows that LSTM language models capture compositional structure in language data. In contrast to existing work, we consider the \textit{learning} process that leads to compositional behavior. For a closer look at how an LSTM{'}s sequential representations are composed hierarchically, we present a related measure of Decompositional Interdependence (DI) between word meanings in an LSTM, based on their gate interactions. We support this measure with experiments on English language data, where DI is higher on pairs of words with lower syntactic distance. To explore the inductive biases that cause these compositional representations to arise during training, we conduct simple experiments on synthetic data. These synthetic experiments support a specific hypothesis about how hierarchical structures are discovered over the course of training: that LSTM constituent representations are learned bottom-up, relying on effective representations of their shorter children, rather than on learning the longer-range relations independently.",
}
@inproceedings{marasovic-etal-2020-natural,
    title = "Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs",
    author = "Marasovi{\'c}, Ana  and
      Bhagavatula, Chandra  and
      Park, Jae sung  and
      Le Bras, Ronan  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.253",
    doi = "10.18653/v1/2020.findings-emnlp.253",
    pages = "2810--2829",
    abstract = "Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study focused on generating natural language rationales across several complex visual reasoning tasks: visual commonsense reasoning, visual-textual entailment, and visual question answering. The key challenge of accurate rationalization is comprehensive image understanding at all levels: not just their explicit content at the pixel level, but their contextual contents at the semantic and pragmatic levels. We present Rationale{\^{}}VT Transformer, an integrated model that learns to generate free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. Our experiments show that free-text rationalization is a promising research direction to complement model interpretability for complex visual-textual reasoning tasks. In addition, we find that integration of richer semantic and pragmatic visual features improves visual fidelity of rationales.",
}
@inproceedings{dey-etal-2020-corpora,
    title = "Corpora Evaluation and System Bias Detection in Multi-document Summarization",
    author = "Dey, Alvin  and
      Chowdhury, Tanya  and
      Kumar, Yash  and
      Chakraborty, Tanmoy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.254",
    doi = "10.18653/v1/2020.findings-emnlp.254",
    pages = "2830--2840",
    abstract = "Multi-document summarization (MDS) is the task of reflecting key points from any set of documents into a concise text paragraph. In the past, it has been used to aggregate news, tweets, product reviews, etc. from various sources. Owing to no standard definition of the task, we encounter a plethora of datasets with varying levels of overlap and conflict between participating documents. There is also no standard regarding what constitutes summary information in MDS. Adding to the challenge is the fact that new systems report results on a set of chosen datasets, which might not correlate with their performance on the other datasets. In this paper, we study this heterogeneous task with the help of a few widely used MDS corpora and a suite of state-of-theart models. We make an attempt to quantify the quality of summarization corpus and prescribe a list of points to consider while proposing a new MDS corpus. Next, we analyze the reason behind the absence of an MDS system which achieves superior performance across all corpora. We then observe the extent to which system metrics are influenced, and bias is propagated due to corpus properties. The scripts to reproduce the experiments in this work are available at https://github.com/LCS2-IIITD/summarization{\_}bias.git",
}
@inproceedings{li-etal-2020-graph-tree,
    title = "Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem",
    author = "Li, Shucheng  and
      Wu, Lingfei  and
      Feng, Shiwei  and
      Xu, Fangli  and
      Xu, Fengyuan  and
      Zhong, Sheng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.255",
    doi = "10.18653/v1/2020.findings-emnlp.255",
    pages = "2841--2852",
    abstract = "The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem solving. However, these models either only consider input objects as sequences while ignoring the important structural information for encoding, or they simply treat output objects as sequence outputs instead of structural objects for decoding. In this paper, we present a novel Graph-to-Tree Neural Networks, namely Graph2Tree consisting of a graph encoder and a hierarchical tree decoder, that encodes an augmented graph-structured input and decodes a tree-structured output. In particular, we investigated our model for solving two problems, neural semantic parsing and math word problem. Our extensive experiments demonstrate that our Graph2Tree model outperforms or matches the performance of other state-of-the-art models on these tasks.",
}
@inproceedings{lachaux-etal-2020-target,
    title = "Target Conditioning for One-to-Many Generation",
    author = "Lachaux, Marie-Anne  and
      Joulin, Armand  and
      Lample, Guillaume",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.256",
    doi = "10.18653/v1/2020.findings-emnlp.256",
    pages = "2853--2862",
    abstract = "Neural Machine Translation (NMT) models often lack diversity in their generated translations, even when paired with search algorithm, like beam search. A challenge is that the diversity in translations are caused by the variability in the target language, and cannot be inferred from the source sentence alone. In this paper, we propose to explicitly model this one-to-many mapping by conditioning the decoder of a NMT model on a latent variable that represents the domain of target sentences. The domain is a discrete variable generated by a target encoder that is jointly trained with the NMT model.The predicted domain of target sentences are given as input to the decoder during training. At inference, we can generate diverse translations by decoding with different domains. Unlike our strongest baseline (Shen et al., 2019), our method can scale to any number of domains without affecting the performance or the training time. We assess the quality and diversity of translations generated by our model with several metrics, on three different datasets.",
}
@inproceedings{jolly-kapoor-2020-pre,
    title = "Can Pre-training help {VQA} with Lexical Variations?",
    author = "Jolly, Shailza  and
      Kapoor, Shubham",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.257",
    doi = "10.18653/v1/2020.findings-emnlp.257",
    pages = "2863--2868",
    abstract = "Rephrasings or paraphrases are sentences with similar meanings expressed in different ways. Visual Question Answering (VQA) models are closing the gap with the oracle performance for datasets like VQA2.0. However, these models fail to perform well on rephrasings of a question, which raises some important questions like Are these models robust towards linguistic variations? Is it the architecture or the dataset that we need to optimize? In this paper, we analyzed VQA models in the space of paraphrasing. We explored the role of language {\&} cross-modal pre-training to investigate the robustness of VQA models towards lexical variations. Our experiments find that pre-trained language encoders generate efficient representations of question rephrasings, which help VQA models correctly infer these samples. We empirically determine why pre-training language encoders improve lexical robustness. Finally, we observe that although pre-training all VQA components obtain state-of-the-art results on the VQA-Rephrasings dataset, it still fails to completely close the performance gap between original and rephrasing validation splits.",
}
@inproceedings{pasunuru-bansal-2020-fenas,
    title = "{FENAS}: Flexible and Expressive Neural Architecture Search",
    author = "Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.258",
    doi = "10.18653/v1/2020.findings-emnlp.258",
    pages = "2869--2876",
    abstract = "Architecture search is the automatic process of designing the model or cell structure that is optimal for the given dataset or task. Recently, this approach has shown good improvements in terms of performance (tested on language modeling and image classification) with reasonable training speed using a weight sharing-based approach called Efficient Neural Architecture Search (ENAS). In this work, we propose a novel architecture search algorithm called Flexible and Expressible Neural Architecture Search (FENAS), with more flexible and expressible search space than ENAS, in terms of more activation functions, input edges, and atomic operations. Also, our FENAS approach is able to reproduce the well-known LSTM and GRU architectures (unlike ENAS), and is also able to initialize with them for finding architectures more efficiently. We explore this extended search space via evolutionary search and show that FENAS performs significantly better on several popular text classification tasks and performs similar to ENAS on standard language model benchmark. Further, we present ablations and analyses on our FENAS approach.",
}
@inproceedings{tanchip-etal-2020-inferring,
    title = "Inferring symmetry in natural language",
    author = "Tanchip, Chelsea  and
      Yu, Lei  and
      Xu, Aotao  and
      Xu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.259",
    doi = "10.18653/v1/2020.findings-emnlp.259",
    pages = "2877--2886",
    abstract = "We present a methodological framework for inferring symmetry of verb predicates in natural language. Empirical work on predicate symmetry has taken two main approaches. The feature-based approach focuses on linguistic features pertaining to symmetry. The context-based approach denies the existence of absolute symmetry but instead argues that such inference is context dependent. We develop methods that formalize these approaches and evaluate them against a novel symmetry inference sentence (SIS) dataset comprised of 400 naturalistic usages of literature-informed verbs spanning the spectrum of symmetry-asymmetry. Our results show that a hybrid transfer learning model that integrates linguistic features with contextualized language models most faithfully predicts the empirical data. Our work integrates existing approaches to symmetry in natural language and suggests how symmetry inference can improve systematicity in state-of-the-art language models.",
}
@inproceedings{qiu-etal-2020-concise,
    title = "A Concise Model for Multi-Criteria {C}hinese Word Segmentation with Transformer Encoder",
    author = "Qiu, Xipeng  and
      Pei, Hengzhi  and
      Yan, Hang  and
      Huang, Xuanjing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.260",
    doi = "10.18653/v1/2020.findings-emnlp.260",
    pages = "2887--2897",
    abstract = "Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the relations among the multiple heterogeneous segmentation criteria and further improve the performance of each single criterion. Previous work usually regards MCCWS as different tasks, which are learned together under the multi-task learning framework. In this paper, we propose a concise but effective unified model for MCCWS, which is fully-shared for all the criteria. By leveraging the powerful ability of the Transformer encoder, the proposed unified model can segment Chinese text according to a unique criterion-token indicating the output criterion. Besides, the proposed unified model can segment both simplified and traditional Chinese and has an excellent transfer capability. Experiments on eight datasets with different criteria show that our model outperforms our single-criterion baseline model and other multi-criteria models. Source codes of this paper are available on Github.",
}
@inproceedings{chalkidis-etal-2020-legal,
    title = "{LEGAL}-{BERT}: The Muppets straight out of Law School",
    author = "Chalkidis, Ilias  and
      Fergadiotis, Manos  and
      Malakasiotis, Prodromos  and
      Aletras, Nikolaos  and
      Androutsopoulos, Ion",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.261",
    doi = "10.18653/v1/2020.findings-emnlp.261",
    pages = "2898--2904",
    abstract = "BERT has achieved impressive performance in several NLP tasks. However, there has been limited investigation on its adaptation guidelines in specialised domains. Here we focus on the legal domain, where we explore several approaches for applying BERT models to downstream legal tasks, evaluating on multiple datasets. Our findings indicate that the previous guidelines for pre-training and fine-tuning, often blindly followed, do not always generalize well in the legal domain. Thus we propose a systematic investigation of the available strategies when applying BERT in specialised domains. These are: (a) use the original BERT out of the box, (b) adapt BERT by additional pre-training on domain-specific corpora, and (c) pre-train BERT from scratch on domain-specific corpora. We also propose a broader hyper-parameter search space when fine-tuning for downstream tasks and we release LEGAL-BERT, a family of BERT models intended to assist legal NLP research, computational law, and legal technology applications.",
}
@inproceedings{gong-etal-2020-enhancing,
    title = "Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification",
    author = "Gong, Heng  and
      Bi, Wei  and
      Feng, Xiaocheng  and
      Qin, Bing  and
      Liu, Xiaojiang  and
      Liu, Ting",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.262",
    doi = "10.18653/v1/2020.findings-emnlp.262",
    pages = "2905--2914",
    abstract = "Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress. Based on results from previous work, the performance bottleneck of current models lies in the stage of content planing (selecting and ordering salient content from the input). That is, performance drops drastically when an oracle content plan is replaced by a model-inferred one during surface realization. In this paper, we propose to enhance neural content planning by (1) understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning; (2) verifying the importance and ordering of the selected sequence of records with policy gradient. We evaluated our model on ROTOWIRE and MLB, two datasets on this task, and results show that our model outperforms existing systems with respect to content planning metrics.",
}
@inproceedings{cheng-etal-2020-contextual,
    title = "Contextual Text Style Transfer",
    author = "Cheng, Yu  and
      Gan, Zhe  and
      Zhang, Yizhe  and
      Elachqar, Oussama  and
      Li, Dianqi  and
      Liu, Jingjing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.263",
    doi = "10.18653/v1/2020.findings-emnlp.263",
    pages = "2915--2924",
    abstract = "We introduce a new task, Contextual Text Style Transfer - translating a sentence into a desired style with its surrounding context taken into account. This brings two key challenges to existing style transfer approaches: (I) how to preserve the semantic meaning of target sentence and its consistency with surrounding context during transfer; (ii) how to train a robust model with limited labeled data accompanied by context. To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context. A classifier is further trained to ensure contextual consistency of the generated sentence. To compensate for the lack of parallel data, additional self-reconstruction and back-translation losses are introduced to leverage non-parallel data in a semi-supervised fashion. Two new benchmarks, Enron-Context and Reddit-Context, are introduced for formality and offensiveness style transfer. Experimental results on these datasets demonstrate the effectiveness of the proposed CAST model over state-of-the-art methods across style accuracy, content preservation and contextual consistency metrics.",
}
@inproceedings{chen-etal-2020-dipair,
    title = "{D}i{P}air: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling",
    author = "Chen, Jiecao  and
      Yang, Liu  and
      Raman, Karthik  and
      Bendersky, Michael  and
      Yeh, Jung-Jung  and
      Zhou, Yun  and
      Najork, Marc  and
      Cai, Danyang  and
      Emadzadeh, Ehsan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.264",
    doi = "10.18653/v1/2020.findings-emnlp.264",
    pages = "2925--2937",
    abstract = "Pre-trained models like BERT ((Devlin et al., 2018) have dominated NLP / IR applications such as single sentence classification, text pair classification, and question answering. However, deploying these models in real systems is highly non-trivial due to their exorbitant computational costs. A common remedy to this is knowledge distillation (Hinton et al., 2015), leading to faster inference. However {--} as we show here {--} existing works are not optimized for dealing with pairs (or tuples) of texts. Consequently, they are either not scalable or demonstrate subpar performance. In this work, we propose DiPair {---} a novel framework for distilling fast and accurate models on text pair tasks. Coupled with an end-to-end training strategy, DiPair is both highly scalable and offers improved quality-speed tradeoffs. Empirical studies conducted on both academic and real-world e-commerce benchmarks demonstrate the efficacy of the proposed approach with speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model.",
}
@inproceedings{liu-etal-2020-cross-lingual-dependency,
    title = "Cross-Lingual Dependency Parsing by {POS}-Guided Word Reordering",
    author = "Liu, Lu  and
      Zhou, Yi  and
      Xu, Jianhan  and
      Zheng, Xiaoqing  and
      Chang, Kai-Wei  and
      Huang, Xuanjing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.265",
    doi = "10.18653/v1/2020.findings-emnlp.265",
    pages = "2938--2948",
    abstract = "We propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73{\%} increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3{\%} and 6.7{\%} respectively.",
}
@inproceedings{la-malfa-etal-2020-assessing,
    title = "Assessing Robustness of Text Classification through Maximal Safe Radius Computation",
    author = "La Malfa, Emanuele  and
      Wu, Min  and
      Laurenti, Luca  and
      Wang, Benjie  and
      Hartshorn, Anthony  and
      Kwiatkowska, Marta",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.266",
    doi = "10.18653/v1/2020.findings-emnlp.266",
    pages = "2949--2968",
    abstract = "Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. In this paper, we focus on robustness of text classification against word substitutions, aiming to provide guarantees that the model prediction does not change if a word is replaced with a plausible alternative, such as a synonym. As a measure of robustness, we adopt the notion of the maximal safe radius for a given input text, which is the minimum distance in the embedding space to the decision boundary. Since computing the exact maximal safe radius is not feasible in practice, we instead approximate it by computing a lower and upper bound. For the upper bound computation, we employ Monte Carlo Tree Search in conjunction with syntactic filtering to analyse the effect of single and multiple word substitutions. The lower bound computation is achieved through an adaptation of the linear bounding techniques implemented in tools CNN-Cert and POPQORN, respectively for convolutional and recurrent network models. We evaluate the methods on sentiment analysis and news classification models for four datasets (IMDB, SST, AG News and NEWS) and a range of embeddings, and provide an analysis of robustness trends. We also apply our framework to interpretability analysis and compare it with LIME.",
}
@inproceedings{paul-frank-2020-social,
    title = "Social Commonsense Reasoning with Multi-Head Knowledge Attention",
    author = "Paul, Debjit  and
      Frank, Anette",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.267",
    doi = "10.18653/v1/2020.findings-emnlp.267",
    pages = "2969--2980",
    abstract = "Social Commonsense Reasoning requires understanding of text, knowledge about social events and their pragmatic implications, as well as commonsense reasoning skills. In this work we propose a novel multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell.We assess the model{'}s performance on two tasks that require different reasoning skills: Abductive Natural Language Inference and Counterfactual Invariance Prediction as a new task. We show that our proposed model improves performance over strong state-of-the-art models (i.e., RoBERTa) across both reasoning tasks. Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task. We validate the robustness of the model{'}s reasoning capabilities by perturbing the knowledge and provide qualitative analysis on the model{'}s knowledge incorporation capabilities.",
}
@inproceedings{ekstedt-skantze-2020-turngpt,
    title = "{T}urn{GPT}: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog",
    author = "Ekstedt, Erik  and
      Skantze, Gabriel",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.268",
    doi = "10.18653/v1/2020.findings-emnlp.268",
    pages = "2981--2990",
    abstract = "Syntactic and pragmatic completeness is known to be important for turn-taking prediction, but so far machine learning models of turn-taking have used such linguistic information in a limited way. In this paper, we introduce TurnGPT, a transformer-based language model for predicting turn-shifts in spoken dialog. The model has been trained and evaluated on a variety of written and spoken dialog datasets. We show that the model outperforms two baselines used in prior work. We also report on an ablation study, as well as attention and gradient analyses, which show that the model is able to utilize the dialog context and pragmatic completeness for turn-taking prediction. Finally, we explore the model{'}s potential in not only detecting, but also projecting, turn-completions.",
}
@inproceedings{juuti-etal-2020-little,
    title = "A little goes a long way: Improving toxic language classification despite data scarcity",
    author = {Juuti, Mika  and
      Gr{\"o}ndahl, Tommi  and
      Flanagan, Adrian  and
      Asokan, N.},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.269",
    doi = "10.18653/v1/2020.findings-emnlp.269",
    pages = "2991--3009",
    abstract = "Detection of some types of toxic language is hampered by extreme scarcity of labeled training data. Data augmentation {--} generating new synthetic data from a labeled seed dataset {--} can help. The efficacy of data augmentation on toxic language classification has not been fully explored. We present the first systematic study on how data augmentation techniques impact performance across toxic language classifiers, ranging from shallow logistic regression architectures to BERT {--} a state-of-the-art pretrained Transformer network. We compare the performance of eight techniques on very scarce seed datasets. We show that while BERT performed the best, shallow classifiers performed comparably when trained on data augmented with a combination of three techniques, including GPT-2-generated sentences. We discuss the interplay of performance and computational overhead, which can inform the choice of techniques under different constraints.",
}
@inproceedings{swarup-etal-2020-instance,
    title = "{A}n {I}nstance {L}evel {A}pproach for {S}hallow {S}emantic {P}arsing in {S}cientific {P}rocedural {T}ext",
    author = "Swarup, Daivik  and
      Bajaj, Ahsaas  and
      Mysore, Sheshera  and
      O{'}Gorman, Tim  and
      Das, Rajarshi  and
      McCallum, Andrew",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.270",
    doi = "10.18653/v1/2020.findings-emnlp.270",
    pages = "3010--3017",
    abstract = "In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus.",
}
@inproceedings{du-etal-2020-general,
    title = "General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference",
    author = "Du, Jingfei  and
      Ott, Myle  and
      Li, Haoran  and
      Zhou, Xing  and
      Stoyanov, Veselin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.271",
    doi = "10.18653/v1/2020.findings-emnlp.271",
    pages = "3018--3030",
    abstract = "The state of the art on many NLP tasks is currently achieved by large pre-trained language models, which require a considerable amount of computation. We aim to reduce the inference cost in a setting where many different predictions are made on a single piece of text. In that case, computational cost during inference can be amortized over the different predictions (tasks) using a shared text encoder. We compare approaches for training such an encoder and show that encoders pre-trained over multiple tasks generalize well to unseen tasks. We also compare ways of extracting fixed- and limited-size representations from this encoder, including pooling features extracted from multiple layers or positions. Our best approach compares favorably to knowledge distillation, achieving higher accuracy and lower computational cost once the system is handling around 7 tasks. Further, we show that through binary quantization, we can reduce the size of the extracted representations by a factor of 16 to store them for later use. The resulting method offers a compelling solution for using large-scale pre-trained models at a fraction of the computational cost when multiple tasks are performed on the same text.",
}
@inproceedings{clark-etal-2020-learning,
    title = "Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles",
    author = "Clark, Christopher  and
      Yatskar, Mark  and
      Zettlemoyer, Luke",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.272",
    doi = "10.18653/v1/2020.findings-emnlp.272",
    pages = "3031--3045",
    abstract = "Many datasets have been shown to contain incidental correlations created by idiosyncrasies in the data collection process. For example, sentence entailment datasets can have spurious word-class correlations if nearly all contradiction sentences contain the word {``}not{''}, and image recognition datasets can have tell-tale object-background correlations if dogs are always indoors. In this paper, we propose a method that can automatically detect and ignore these kinds of dataset-specific patterns, which we call dataset biases. Our method trains a lower capacity model in an ensemble with a higher capacity model. During training, the lower capacity model learns to capture relatively shallow correlations, which we hypothesize are likely to reflect dataset bias. This frees the higher capacity model to focus on patterns that should generalize better. We ensure the models learn non-overlapping approaches by introducing a novel method to make them conditionally independent. Importantly, our approach does not require the bias to be known in advance. We evaluate performance on synthetic datasets, and four datasets built to penalize models that exploit known biases on textual entailment, visual question answering, and image recognition tasks. We show improvement in all settings, including a 10 point gain on the visual question answering dataset.",
}
@inproceedings{yin-etal-2020-learning,
    title = "Learning to Generalize for Sequential Decision Making",
    author = "Yin, Xusen  and
      Weischedel, Ralph  and
      May, Jonathan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.273",
    doi = "10.18653/v1/2020.findings-emnlp.273",
    pages = "3046--3063",
    abstract = "We consider problems of making sequences of decisions to accomplish tasks, interacting via the medium of language. These problems are often tackled with reinforcement learning approaches. We find that these models do not generalize well when applied to novel task domains. However, the large amount of computation necessary to adequately train and explore the search space of sequential decision making, under a reinforcement learning paradigm, precludes the inclusion of large contextualized language models, which might otherwise enable the desired generalization ability. We introduce a teacher-student imitation learning methodology and a means of converting a reinforcement learning model into a natural language understanding model. Together, these methodologies enable the introduction of contextualized language models into the sequential decision making problem space. We show that models can learn faster and generalize more, leveraging both the imitation learning and the reformulation. Our models exceed teacher performance on various held-out decision problems, by up to 7{\%} on in-domain problems and 24{\%} on out-of-domain problems.",
}
@inproceedings{zlabinger-etal-2020-effective,
    title = "Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports",
    author = {Zlabinger, Markus  and
      Sabou, Marta  and
      Hofst{\"a}tter, Sebastian  and
      Hanbury, Allan},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.274",
    doi = "10.18653/v1/2020.findings-emnlp.274",
    pages = "3064--3074",
    abstract = "The search for Participants, Interventions, and Outcomes (PIO) in clinical trial reports is a critical task in Evidence Based Medicine. For an automatic PIO extraction, high-quality corpora are needed. Obtaining such a corpus from crowdworkers, however, has been shown to be ineffective since (i) workers usually lack domain-specific expertise to conduct the task with sufficient quality, and (ii) the standard approach of annotating entire abstracts of trial reports as one task-instance (i.e. HIT) leads to an uneven distribution in task effort. In this paper, we switch from entire abstract to sentence annotation, referred to as the SenBase approach. We build upon SenBase in SenSupport, where we compensate the lack of domain-specific expertise of crowdworkers by showing for each task-instance similar sentences that are already annotated by experts. Such tailored task-instance examples are retrieved via unsupervised semantic short-text similarity (SSTS) method {--} and we evaluate nine methods to find an effective solution for SenSupport. We compute the Cohen{'}s Kappa agreement between crowd-annotations and gold standard annotations and show that (i) both sentence-based approaches outperform a Baseline approach where entire abstracts are annotated; (ii) supporting annotators with tailored task-instance examples is the best performing approach with Kappa agreements of 0.78/0.75/0.69 for P, I, and O respectively.",
}
@inproceedings{raheja-alikaniotis-2020-adversarial,
    title = "{A}dversarial {G}rammatical {E}rror {C}orrection",
    author = "Raheja, Vipul  and
      Alikaniotis, Dimitris",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.275",
    doi = "10.18653/v1/2020.findings-emnlp.275",
    pages = "3075--3087",
    abstract = "Recent works in Grammatical Error Correction (GEC) have leveraged the progress in Neural Machine Translation (NMT), to learn rewrites from parallel corpora of grammatically incorrect and corrected sentences, achieving state-of-the-art results. At the same time, Generative Adversarial Networks (GANs) have been successful in generating realistic texts across many different tasks by learning to directly minimize the difference between human-generated and synthetic text. In this work, we present an adversarial learning approach to GEC, using the generator-discriminator framework. The generator is a Transformer model, trained to produce grammatically correct sentences given grammatically incorrect ones. The discriminator is a sentence-pair classification model, trained to judge a given pair of grammatically incorrect-correct sentences on the quality of grammatical correction. We pre-train both the discriminator and the generator on parallel texts and then fine-tune them further using a policy gradient method that assigns high rewards to sentences which could be true corrections of the grammatically incorrect text. Experimental results on FCE, CoNLL-14, and BEA-19 datasets show that Adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines.",
}
@inproceedings{raunak-etal-2020-long,
    title = "On Long-Tailed Phenomena in Neural Machine Translation",
    author = "Raunak, Vikas  and
      Dalmia, Siddharth  and
      Gupta, Vivek  and
      Metze, Florian",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.276",
    doi = "10.18653/v1/2020.findings-emnlp.276",
    pages = "3088--3095",
    abstract = "State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens, tackling which remains a major challenge. The analysis of long-tailed phenomena in the context of structured prediction tasks is further hindered by the added complexities of search during inference. In this work, we quantitatively characterize such long-tailed phenomena at two levels of abstraction, namely, token classification and sequence generation. We propose a new loss function, the Anti-Focal loss, to better adapt model training to the structural dependencies of conditional text generation by incorporating the inductive biases of beam search in the training process. We show the efficacy of the proposed technique on a number of Machine Translation (MT) datasets, demonstrating that it leads to significant gains over cross-entropy across different language pairs, especially on the generation of low-frequency words. We have released the code to reproduce our results.",
}
@inproceedings{van-niekerk-etal-2020-knowing,
    title = "Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles",
    author = "van Niekerk, Carel  and
      Heck, Michael  and
      Geishauser, Christian  and
      Lin, Hsien-chin  and
      Lubis, Nurul  and
      Moresi, Marco  and
      Gasic, Milica",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.277",
    doi = "10.18653/v1/2020.findings-emnlp.277",
    pages = "3096--3102",
    abstract = "The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system. Current state-of-the-art multi-domain dialogue state trackers achieve just over 55{\%} accuracy on the current go-to benchmark, which means that in almost every second dialogue turn they place full confidence in an incorrect dialogue state. Belief trackers, on the other hand, maintain a distribution over possible dialogue states. However, they lack in performance compared to dialogue state trackers, and do not produce well calibrated distributions. In this work we present state-of-the-art performance in calibration for multi-domain dialogue belief trackers using a calibrated ensemble of models. Our resulting dialogue belief tracker also outperforms previous dialogue belief tracking models in terms of accuracy.",
}
@inproceedings{vernikos-etal-2020-domain,
    title = "{D}omain {A}dversarial {F}ine-{T}uning as an {E}ffective {R}egularizer",
    author = "Vernikos, Giorgos  and
      Margatina, Katerina  and
      Chronopoulou, Alexandra  and
      Androutsopoulos, Ion",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.278",
    doi = "10.18653/v1/2020.findings-emnlp.278",
    pages = "3103--3112",
    abstract = "In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations captured during pretraining. To address this issue, we introduce a new regularization technique, AFTER; domain Adversarial Fine-Tuning as an Effective Regularizer. Specifically, we complement the task-specific loss used during fine-tuning with an adversarial objective. This additional loss term is related to an adversarial classifier, that aims to discriminate between in-domain and out-of-domain text representations. Indomain refers to the labeled dataset of the task at hand while out-of-domain refers to unlabeled data from a different domain. Intuitively, the adversarial classifier acts as a regularize which prevents the model from overfitting to the task-specific domain. Empirical results on various natural language understanding tasks show that AFTER leads to improved performance compared to standard fine-tuning.",
}
@inproceedings{jindal-etal-2020-clar,
    title = "{CLAR}: A Cross-Lingual Argument Regularizer for Semantic Role Labeling",
    author = "Jindal, Ishan  and
      Li, Yunyao  and
      Brahma, Siddhartha  and
      Zhu, Huaiyu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.279",
    doi = "10.18653/v1/2020.findings-emnlp.279",
    pages = "3113--3125",
    abstract = "Semantic role labeling (SRL) identifies predicate-argument structure(s) in a given sentence. Although different languages have different argument annotations, polyglot training, the idea of training one model on multiple languages, has previously been shown to outperform monolingual baselines, especially for low resource languages. In fact, even a simple combination of data has been shown to be effective with polyglot training by representing the distant vocabularies in a shared representation space. Meanwhile, despite the dissimilarity in argument annotations between languages, certain argument labels do share common semantic meaning across languages (e.g. adjuncts have more or less similar semantic meaning across languages). To leverage such similarity in annotation space across languages, we propose a method called Cross-Lingual Argument Regularizer (CLAR). CLAR identifies such linguistic annotation similarity across languages and exploits this information to map the target language arguments using a transformation of the space on which source language arguments lie. By doing so, our experimental results show that CLAR consistently improves SRL performance on multiple languages over monolingual and polyglot baselines for low resource languages.",
}
@inproceedings{shin-etal-2020-neutralizing,
    title = "Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation",
    author = "Shin, Seungjae  and
      Song, Kyungwoo  and
      Jang, JoonHo  and
      Kim, Hyemi  and
      Joo, Weonyoung  and
      Moon, Il-Chul",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.280",
    doi = "10.18653/v1/2020.findings-emnlp.280",
    pages = "3126--3140",
    abstract = "Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks. Whereas the previous methods project word embeddings into a linear subspace for debiasing, we introduce a Latent Disentanglement method with a siamese auto-encoder structure with an adapted gradient reversal layer. Our structure enables the separation of the semantic latent information and gender latent information of given word into the disjoint latent dimensions. Afterwards, we introduce a Counterfactual Generation to convert the gender information of words, so the original and the modified embeddings can produce a gender-neutralized word embedding after geometric alignment regularization, without loss of semantic information. From the various quantitative and qualitative debiasing experiments, our method shows to be better than existing debiasing methods in debiasing word embeddings. In addition, Our method shows the ability to preserve semantic information during debiasing by minimizing the semantic information losses for extrinsic NLP downstream tasks.",
}
@inproceedings{guz-carenini-2020-towards,
    title = "Towards Domain-Independent Text Structuring Trainable on Large Discourse Treebanks",
    author = "Guz, Grigorii  and
      Carenini, Giuseppe",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.281",
    doi = "10.18653/v1/2020.findings-emnlp.281",
    pages = "3141--3152",
    abstract = "Text structuring is a fundamental step in NLG, especially when generating multi-sentential text. With the goal of fostering more general and data-driven approaches to text structuring, we propose the new and domain-independent NLG task of structuring and ordering a (possibly large) set of EDUs. We then present a solution for this task that combines neural dependency tree induction with pointer networks, and can be trained on large discourse treebanks that have only recently become available. Further, we propose a new evaluation metric that is arguably more suitable for our new task compared to existing content ordering metrics. Finally, we empirically show that our approach outperforms competitive alternatives on the proposed measure and is equivalent in performance with respect to previously established measures.",
}
@inproceedings{gu-yu-2020-data,
    title = "{D}ata {A}nnealing for {I}nformal {L}anguage {U}nderstanding {T}asks",
    author = "Gu, Jing  and
      Yu, Zhou",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.282",
    doi = "10.18653/v1/2020.findings-emnlp.282",
    pages = "3153--3159",
    abstract = "There is a huge performance gap between formal and informal language understanding tasks. The recent pre-trained models that improved formal language understanding tasks did not achieve a comparable result on informal language. We propose data annealing transfer learning procedure to bridge the performance gap on informal natural language understanding tasks. It successfully utilizes a pre-trained model such as BERT in informal language. In the data annealing procedure, the training set contains mainly formal text data at first; then, the proportion of the informal text data is gradually increased during the training process. Our data annealing procedure is model-independent and can be applied to various tasks. We validate its effectiveness in exhaustive experiments. When BERT is implemented with our learning procedure, it outperforms all the state-of-the-art models on the three common informal language tasks.",
}
@inproceedings{garcia-etal-2020-multilingual,
    title = "A Multilingual View of Unsupervised Machine Translation",
    author = "Garcia, Xavier  and
      Foret, Pierre  and
      Sellam, Thibault  and
      Parikh, Ankur",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.283",
    doi = "10.18653/v1/2020.findings-emnlp.283",
    pages = "3160--3170",
    abstract = "We present a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. In addition to studying the vanilla case where there is only monolingual data available, we propose a novel setup where one language in the (source, target) pair is not associated with any parallel data, but there may exist auxiliary parallel data that contains the other. This auxiliary data can naturally be utilized in our probabilistic framework via a novel cross-translation loss term. Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT{'}14 English-French, WMT{'}16 English-German, and WMT{'}16 English-Romanian datasets in most directions.",
}
@inproceedings{alsulaimani-etal-2020-evaluation,
    title = "An Evaluation Method for Diachronic Word Sense Induction",
    author = "Alsulaimani, Ashjan  and
      Moreau, Erwan  and
      Vogel, Carl",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.284",
    doi = "10.18653/v1/2020.findings-emnlp.284",
    pages = "3171--3180",
    abstract = "The task of Diachronic Word Sense Induction (DWSI) aims to identify the meaning of words from their context, taking the temporal dimension into account. In this paper we propose an evaluation method based on large-scale time-stamped annotated biomedical data, and a range of evaluation measures suited to the task. The approach is applied to two recent DWSI systems, thus demonstrating its relevance and providing an in-depth analysis of the models.",
}
@inproceedings{wang-etal-2020-integrating-task,
    title = "Integrating Task Specific Information into Pretrained Language Models for Low Resource Fine Tuning",
    author = "Wang, Rui  and
      Si, Shijing  and
      Wang, Guoyin  and
      Zhang, Lei  and
      Carin, Lawrence  and
      Henao, Ricardo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.285",
    doi = "10.18653/v1/2020.findings-emnlp.285",
    pages = "3181--3186",
    abstract = "Pretrained Language Models (PLMs) have improved the performance of natural language understanding in recent years. Such models are pretrained on large corpora, which encode the general prior knowledge of natural languages but are agnostic to information characteristic of downstream tasks. This often results in overfitting when fine-tuned with low resource datasets where task-specific information is limited. In this paper, we integrate label information as a task-specific prior into the self-attention component of pretrained BERT models. Experiments on several benchmarks and real-word datasets suggest that the proposed approach can largely improve the performance of pretrained models when fine-tuning with small datasets.",
}
@inproceedings{li-etal-2020-efficient,
    title = "Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning",
    author = "Li, Bingbing  and
      Kong, Zhenglun  and
      Zhang, Tianyun  and
      Li, Ji  and
      Li, Zhengang  and
      Liu, Hang  and
      Ding, Caiwen",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.286",
    doi = "10.18653/v1/2020.findings-emnlp.286",
    pages = "3187--3199",
    abstract = "Pretrained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and computational speed on hardware platforms have impeded the popularity of pretrained models, especially in the era of edge computing. In this work, we propose an efficient transformer-based large-scale language representation using hardware-friendly block structure pruning. We incorporate the reweighted group Lasso into block-structured pruning for optimization. Besides the significantly reduced weight storage and computation, the proposed approach achieves high compression rates. Experimental results on different models (BERT, RoBERTa, and DistilBERT) on the General Language Understanding Evaluation (GLUE) benchmark tasks show that we achieve up to 5.0x with zero or minor accuracy degradation on certain task(s). Our proposed method is also orthogonal to existing compact pretrained language models such as DistilBERT using knowledge distillation, since a further 1.79x average compression rate can be achieved on top of DistilBERT with zero or minor accuracy degradation. It is suitable to deploy the final compressed model on resource-constrained edge devices.",
}
@inproceedings{gekhman-etal-2020-kobe,
    title = "{K}o{BE}: Knowledge-Based Machine Translation Evaluation",
    author = "Gekhman, Zorik  and
      Aharoni, Roee  and
      Beryozkin, Genady  and
      Freitag, Markus  and
      Macherey, Wolfgang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.287",
    doi = "10.18653/v1/2020.findings-emnlp.287",
    pages = "3200--3207",
    abstract = "We propose a simple and effective method for machine translation evaluation which does not require reference translations. Our approach is based on (1) grounding the entity mentions found in each source sentence and candidate translation against a large-scale multilingual knowledge base, and (2) measuring the recall of the grounded entities found in the candidate vs. those found in the source. Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task. On 4 language pairs, we also achieve higher correlation with human judgements than BLEU. To foster further research, we release a dataset containing 1.8 million grounded entity mentions across 18 language pairs from the WMT19 metrics track data.",
}
@inproceedings{lee-etal-2020-pushing,
    title = "Pushing the Limits of {AMR} Parsing with Self-Learning",
    author = "Lee, Young-Suk  and
      Fernandez Astudillo, Ram{\'o}n  and
      Naseem, Tahira  and
      Gangi Reddy, Revanth  and
      Florian, Radu  and
      Roukos, Salim",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.288",
    doi = "10.18653/v1/2020.findings-emnlp.288",
    pages = "3208--3214",
    abstract = "Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR. At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, such as machine translation or question answering. In this paper, we explore different ways in which trained models can be applied to improve AMR parsing performance, including generation of synthetic text and AMR annotations as well as refinement of actions oracle. We show that, without any additional human annotations, these techniques improve an already performant parser and achieve state-of-the-art results on AMR 1.0 and AMR 2.0.",
}
@inproceedings{goodwin-etal-2020-towards,
    title = "Towards {Z}ero-{S}hot {C}onditional {S}ummarization with {A}daptive {M}ulti-{T}ask {F}ine-{T}uning",
    author = "Goodwin, Travis  and
      Savery, Max  and
      Demner-Fushman, Dina",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.289",
    doi = "10.18653/v1/2020.findings-emnlp.289",
    pages = "3215--3226",
    abstract = "Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents. However, there are many applications which require more specific summaries, such as supporting question answering or topic-based literature discovery. In this paper we study the problem of conditional summarization in which content selection and surface realization are explicitly conditioned on an ad-hoc natural language question or topic description. Because of the difficulty in obtaining sufficient reference summaries to support arbitrary conditional summarization, we explore the use of multi-task fine-tuning (MTFT) on twenty-one natural language tasks to enable zero-shot conditional summarization on five tasks. We present four new summarization datasets, two novel {``}online{''} or adaptive task-mixing strategies, and report zero-shot performance using T5 and BART, demonstrating that MTFT can improve zero-shot summarization quality.",
}
@inproceedings{chen-etal-2020-multilingual,
    title = "Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer",
    author = "Chen, Xuelu  and
      Chen, Muhao  and
      Fan, Changjun  and
      Uppunda, Ankith  and
      Sun, Yizhou  and
      Zaniolo, Carlo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.290",
    doi = "10.18653/v1/2020.findings-emnlp.290",
    pages = "3227--3238",
    abstract = "Predicting missing facts in a knowledge graph(KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works us-ing KG embeddings. While existing KG embedding approaches mainly learn and predict facts within a single KG, a more plausible solution would benefit from the knowledge in multiple language-specific KGs, considering that different KGs have their own strengths and limitations on data quality and coverage. This is quite challenging since the transfer of knowledge among multiple independently maintained KGs is often hindered by the insufficiency of alignment information and inconsistency of described facts. In this paper, we propose kens, a novel framework for embedding learning and ensemble knowledge transfer across a number of language-specific KGs.KEnS embeds all KGs in a shared embedding space, where the association of entities is captured based on self-learning. Then, KEnS performs ensemble inference to com-bine prediction results from multiple language-specific embeddings, for which multiple en-semble techniques are investigated. Experiments on the basis of five real-world language-specific KGs show that, by effectively identifying and leveraging complementary knowledge, KEnS consistently improves state-of-the-art methods on KG completion.",
}
@inproceedings{sheng-etal-2020-towards,
    title = "Towards {C}ontrollable {B}iases in {L}anguage {G}eneration",
    author = "Sheng, Emily  and
      Chang, Kai-Wei  and
      Natarajan, Prem  and
      Peng, Nanyun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.291",
    doi = "10.18653/v1/2020.findings-emnlp.291",
    pages = "3239--3254",
    abstract = "We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.",
}
@inproceedings{delobelle-etal-2020-robbert,
    title = "{R}ob{BERT}: a {D}utch {R}o{BERT}a-based {L}anguage {M}odel",
    author = "Delobelle, Pieter  and
      Winters, Thomas  and
      Berendt, Bettina",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.292",
    doi = "10.18653/v1/2020.findings-emnlp.292",
    pages = "3255--3265",
    abstract = "Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks. One of the most prominent pre-trained language models is BERT, which was released as an English as well as a multilingual version. Although multilingual BERT performs well on many tasks, recent studies show that BERT models trained on a single language significantly outperform the multilingual version. Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks. While previous approaches have used earlier implementations of BERT to train a Dutch version of BERT, we used RoBERTa, a robustly optimized BERT approach, to train a Dutch language model called RobBERT. We measured its performance on various tasks as well as the importance of the fine-tuning dataset size. We also evaluated the importance of language-specific tokenizers and the model{'}s fairness. We found that RobBERT improves state-of-the-art results for various tasks, and especially significantly outperforms other models when dealing with smaller datasets. These results indicate that it is a powerful pre-trained model for a large variety of Dutch language tasks. The pre-trained and fine-tuned models are publicly available to support further downstream Dutch NLP applications.",
}
@inproceedings{kang-etal-2020-regularization,
    title = "Regularization of Distinct Strategies for Unsupervised Question Generation",
    author = "Kang, Junmo  and
      Hong, Giwon  and
      Puerto San Roman, Haritz  and
      Myaeng, Sung-Hyon",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.293",
    doi = "10.18653/v1/2020.findings-emnlp.293",
    pages = "3266--3277",
    abstract = "Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA. One approach to UQA is to train a QA model with questions generated automatically. However, the generated questions are either too similar to a word sequence in the context or too drifted from the semantics of the context, thereby making it difficult to train a robust QA model. We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated. Our experiments demonstrate that we have achieved the goal of generating higher-quality questions for UQA across diverse QA datasets and tasks. We also show that this method can be useful for creating a QA model with few-shot learning.",
}
@inproceedings{mohammadshahi-henderson-2020-graph,
    title = "Graph-to-Graph Transformer for Transition-based Dependency Parsing",
    author = "Mohammadshahi, Alireza  and
      Henderson, James",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.294",
    doi = "10.18653/v1/2020.findings-emnlp.294",
    pages = "3278--3289",
    abstract = "We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing. After proposing two novel Transformer models of transition-based dependency parsing as strong baselines, we show that adding the proposed mechanisms for conditioning on and predicting graphs of Graph2Graph Transformer results in significant improvements, both with and without BERT pre-training. The novel baselines and their integration with Graph2Graph Transformer significantly outperform the state-of-the-art in traditional transition-based dependency parsing on both English Penn Treebank, and 13 languages of Universal Dependencies Treebanks. Graph2Graph Transformer can be integrated with many previous structured prediction methods, making it easy to apply to a wide range of NLP tasks.",
}
@inproceedings{szymanski-etal-2020-wer,
    title = "{WER} we are and {WER} we think we are",
    author = "Szyma{\'n}ski, Piotr  and
      {\.Z}elasko, Piotr  and
      Morzy, Mikolaj  and
      Szymczak, Adrian  and
      {\.Z}y{\l}a-Hoppe, Marzena  and
      Banaszczak, Joanna  and
      Augustyniak, Lukasz  and
      Mizgajski, Jan  and
      Carmiel, Yishay",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.295",
    doi = "10.18653/v1/2020.findings-emnlp.295",
    pages = "3290--3295",
    abstract = "Natural language processing of conversational speech requires the availability of high-quality transcripts. In this paper, we express our skepticism towards the recent reports of very low Word Error Rates (WERs) achieved by modern Automatic Speech Recognition (ASR) systems on benchmark datasets. We outline several problems with popular benchmarks and compare three state-of-the-art commercial ASR systems on an internal dataset of real-life spontaneous human conversations and HUB{'}05 public benchmark. We show that WERs are significantly higher than the best reported results. We formulate a set of guidelines which may aid in the creation of real-life, multi-domain datasets with high quality annotations for training and testing of robust ASR systems.",
}
@inproceedings{luo-etal-2020-detecting,
    title = "Detecting Stance in Media On Global Warming",
    author = "Luo, Yiwei  and
      Card, Dallas  and
      Jurafsky, Dan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.296",
    doi = "10.18653/v1/2020.findings-emnlp.296",
    pages = "3296--3315",
    abstract = "Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, {``}Leading scientists agree that global warming is a serious concern,{''} framing a clause which affirms their own stance ({``}that global warming is serious{''}) as an opinion endorsed (''[scientists] agree{''}) by a reputable source ({``}leading{''}). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: {``}Mistaken scientists claim [...].'' Our work studies opinion-framing in the global warming (GW) debate, an increasingly partisan issue that has received little attention in NLP. We introduce DeSMOG, a dataset of stance-labeled GW sentences, and train a BERT classifier to study novel aspects of argumentation in how different sides of a debate represent their own and each other{'}s opinions. From 56K news articles, we find that similar linguistic devices for self-affirming and opponent-doubting discourse are used across GW-accepting and skeptic media, though GW-skeptical media shows more opponent-doubt. We also find that authors often characterize sources as hypocritical, by ascribing opinions expressing the author{'}s own view to source entities known to publicly endorse the opposing view. We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.",
}
@inproceedings{shmidman-etal-2020-novel,
    title = "A Novel Challenge Set for {H}ebrew Morphological Disambiguation and Diacritics Restoration",
    author = "Shmidman, Avi  and
      Guedalia, Joshua  and
      Shmidman, Shaltiel  and
      Koppel, Moshe  and
      Tsarfaty, Reut",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.297",
    doi = "10.18653/v1/2020.findings-emnlp.297",
    pages = "3316--3326",
    abstract = "One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others. In such cases, there may not exist sufficient examples of the minority analyses in order to properly evaluate performance, nor to train effective classifiers. In this paper we address the issue of unbalanced morphological ambiguities in Hebrew. We offer a challenge set for Hebrew homographs {---} the first of its kind {---} containing substantial attestation of each analysis of 21 Hebrew homographs. We show that the current SOTA of Hebrew disambiguation performs poorly on cases of unbalanced ambiguity. Leveraging our new dataset, we achieve a new state-of-the-art for all 21 words, improving the overall average F1 score from 0.67 to 0.95. Our resulting annotated datasets are made publicly available for further research.",
}
@inproceedings{huang-etal-2020-improve,
    title = "Improve Transformer Models with Better Relative Position Embeddings",
    author = "Huang, Zhiheng  and
      Liang, Davis  and
      Xu, Peng  and
      Xiang, Bing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.298",
    doi = "10.18653/v1/2020.findings-emnlp.298",
    pages = "3327--3335",
    abstract = "The transformer model has demonstrated superior results on NLP tasks including machine translation and question answering. In this paper, we argue that the position information is not fully utilized in existing work. For example, the initial proposal of a sinusoid embedding is fixed and not learnable. In this paper, we first review the absolute position embeddings and existing relative position embedding methods. We then propose new methods to encourage increased interaction between query, key and relative position embeddings in the self-attention mechanism. Our most promising approach is a generalization of the absolute position embedding. Our method results in increased accuracy compared to previous approaches in absolute and relative position embeddings on the SQuAD1.1 dataset. In addition, we address the inductive property of whether a position embedding can be robust enough to handle long sequences. We demonstrate empirically that our relative embedding method can be reasonably generalized to and is robust in the inductive perspective. Finally, we show that our proposed method can be effectively and efficiently adopted as a near drop-in replacement for improving the accuracy of large models with little computational overhead.",
}
@inproceedings{qiao-etal-2020-sentiment,
    title = "A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph",
    author = "Qiao, Lin  and
      Yan, Jianhao  and
      Meng, Fandong  and
      Yang, Zhendong  and
      Zhou, Jie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.299",
    doi = "10.18653/v1/2020.findings-emnlp.299",
    pages = "3336--3344",
    abstract = "Generating a vivid, novel, and diverse essay with only several given topic words is a promising task of natural language generation. Previous work in this task exists two challenging problems: neglect of sentiment beneath the text and insufficient utilization of topic-related knowledge. Therefore, we propose a novel Sentiment Controllable topic-to- essay generator with a Topic Knowledge Graph enhanced decoder, named SCTKG, which is based on the conditional variational auto-encoder (CVAE) framework. We firstly inject the sentiment information into the generator for controlling sentiment for each sentence, which leads to various generated essays. Then we design a Topic Knowledge Graph enhanced decoder. Unlike existing models that use knowledge entities separately, our model treats knowledge graph as a whole and encodes more structured, connected semantic information in the graph to generate a more relevant essay. Experimental results show that our SCTKG can generate sentiment controllable essays and outperform the state-of-the-art approach in terms of topic relevance, fluency, and diversity on both automatic and human evaluation.",
}
@inproceedings{rajagopal-etal-2020-ask,
    title = "What-if {I} ask you to explain: Explaining the effects of perturbations in procedural text",
    author = "Rajagopal, Dheeraj  and
      Tandon, Niket  and
      Clark, Peter  and
      Dalvi, Bhavana  and
      Hovy, Eduard",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.300",
    doi = "10.18653/v1/2020.findings-emnlp.300",
    pages = "3345--3355",
    abstract = "Our goal is to explain the effects of perturbations in procedural text, e.g., given a passage describing a rabbit{'}s life cycle, explain why illness (the perturbation) may reduce the rabbit population (the effect). Although modern systems are able to solve the original prediction task well (e.g., illness results in less rabbits), the explanation task - identifying the causal chain of events from perturbation to effect - remains largely unaddressed, and is the goal of this research. We present QUARTET, a system that constructs such explanations from paragraphs, by modeling the explanation task as a multitask learning problem. QUARTET constructs explanations from the sentences in the procedural text, achieving {\textasciitilde}18 points better on explanation accuracy compared to several strong baselines on a recent process comprehension benchmark. On an end task on this benchmark, we show a surprising finding that good explanations do not have to come at the expense of end task performance, in fact leading to a 7{\%} F1 improvement over SOTA.",
}
@inproceedings{gehman-etal-2020-realtoxicityprompts,
    title = "{R}eal{T}oxicity{P}rompts: Evaluating Neural Toxic Degeneration in Language Models",
    author = "Gehman, Samuel  and
      Gururangan, Suchin  and
      Sap, Maarten  and
      Choi, Yejin  and
      Smith, Noah A.",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.301",
    doi = "10.18653/v1/2020.findings-emnlp.301",
    pages = "3356--3369",
    abstract = "Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration. We create and release RealToxicityPrompts, a dataset of 100K naturally occurring, sentence-level prompts derived from a large corpus of English web text, paired with toxicity scores from a widely-used toxicity classifier. Using RealToxicityPrompts, we find that pretrained LMs can degenerate into toxic text even from seemingly innocuous prompts. We empirically assess several controllable generation methods, and find that while data- or compute-intensive methods (e.g., adaptive pretraining on non-toxic data) are more effective at steering away from toxicity than simpler solutions (e.g., banning {``}bad{''} words), no current method is failsafe against neural toxic degeneration. To pinpoint the potential cause of such persistent toxic degeneration, we analyze two web text corpora used to pretrain several LMs (including GPT-2; Radford et. al, 2019), and find a significant amount of offensive, factually unreliable, and otherwise toxic content. Our work provides a test bed for evaluating toxic generations by LMs and stresses the need for better data selection processes for pretraining.",
}
@inproceedings{yang-etal-2020-improving-event,
    title = "Improving Event Duration Prediction via Time-aware Pre-training",
    author = "Yang, Zonglin  and
      Du, Xinya  and
      Rush, Alexander  and
      Cardie, Claire",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.302",
    doi = "10.18653/v1/2020.findings-emnlp.302",
    pages = "3370--3378",
    abstract = "End-to-end models in NLP rarely encode external world knowledge about length of time. We introduce two effective models for duration prediction, which incorporate external knowledge by reading temporal-related news sentences (time-aware pre-training). Specifically, one model predicts the range/unit where the duration value falls in (R-PRED); and the other predicts the exact duration value (E-PRED). Our best model {--} E-PRED, substantially outperforms previous work, and captures duration information more accurately than R-PRED. We also demonstrate our models are capable of duration prediction in the unsupervised setting, outperforming the baselines.",
}
@inproceedings{xia-etal-2020-composed,
    title = "Composed Variational Natural Language Generation for Few-shot Intents",
    author = "Xia, Congying  and
      Xiong, Caiming  and
      Yu, Philip  and
      Socher, Richard",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.303",
    doi = "10.18653/v1/2020.findings-emnlp.303",
    pages = "3379--3388",
    abstract = "In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario. To build connections between existing many-shot intents and few-shot intents, we consider an intent as a combination of a domain and an action, and propose a composed variational natural language generator (CLANG), a transformer-based conditional variational autoencoder. CLANG utilizes two latent variables to represent the utterances corresponding to two different independent parts (domain and action) in the intent, and the latent variables are composed together to generate natural examples. Additionally, to improve the generator learning, we adopt the contrastive regularization loss that contrasts the in-class with the out-of-class utterance generation given the intent. To evaluate the quality of the generated utterances, experiments are conducted on the generalized few-shot intent detection task. Empirical results show that our proposed model achieves state-of-the-art performances on two real-world intent detection datasets.",
}
@inproceedings{noh-kavuluru-2020-literature,
    title = "Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization",
    author = "Noh, Jiho  and
      Kavuluru, Ramakanth",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.304",
    doi = "10.18653/v1/2020.findings-emnlp.304",
    pages = "3389--3399",
    abstract = "Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent. As such, the retrieval problem is often formulated as ad hoc search but with multiple facets (e.g., disease, mutation) that may need to be incorporated. In this paper, we present a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. Our architecture builds on the basic BERT model with three specific components for reranking: (a). document-query matching (b). keyword extraction and (c). facet-conditioned abstractive summarization. The outcomes of (b) and (c) are used to essentially transform a candidate document into a concise summary that can be compared with the query at hand to compute a relevance score. Component (a) directly generates a matching score of a candidate document for a query. The full architecture benefits from the complementary potential of document-query matching and the novel document transformation approach based on summarization along PM facets. Evaluations using NIST{'}s TREC-PM track datasets (2017{--}2019) show that our model achieves state-of-the-art performance. To foster reproducibility, our code is made available here: https://github.com/bionlproc/text-summ-for-doc-retrieval.",
}
@inproceedings{mussmann-etal-2020-importance,
    title = "{O}n the {I}mportance of {A}daptive {D}ata {C}ollection for {E}xtremely {I}mbalanced {P}airwise {T}asks",
    author = "Mussmann, Stephen  and
      Jia, Robin  and
      Liang, Percy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.305",
    doi = "10.18653/v1/2020.findings-emnlp.305",
    pages = "3400--3413",
    abstract = "Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., 99.99{\%} of examples are negatives). In contrast, many recent datasets heuristically choose examples to ensure label balance. We show that these heuristics lead to trained models that generalize poorly: State-of-the art models trained on QQP and WikiQA each have only 2.4{\%} average precision when evaluated on realistically imbalanced test data. We instead collect training data with active learning, using a BERT-based embedding model to efficiently retrieve uncertain points from a very large pool of unlabeled utterance pairs. By creating balanced training data with more informative negative examples, active learning greatly improves average precision to 32.5{\%} on QQP and 20.1{\%} on WikiQA.",
}
@inproceedings{wunnava-etal-2020-dual,
    title = "A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events",
    author = "Wunnava, Susmitha  and
      Qin, Xiao  and
      Kakar, Tabassum  and
      Kong, Xiangnan  and
      Rundensteiner, Elke",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.306",
    doi = "10.18653/v1/2020.findings-emnlp.306",
    pages = "3414--3423",
    abstract = "An adverse drug event (ADE) is an injury resulting from medical intervention related to a drug. Automatic ADE detection from text is either fine-grained (ADE entity recognition) or coarse-grained (ADE assertive sentence classification), with limited efforts leveraging inter-dependencies among the two granularities. We instead propose a multi-grained joint deep network to concurrently learn the ADE entity recognition and ADE sentence classification tasks. Our joint approach takes advantage of their symbiotic relationship, with a transfer of knowledge between the two levels of granularity. Our dual-attention mechanism constructs multiple distinct representations of a sentence that capture both task-specific and semantic information in the sentence, providing stronger emphasis on the key elements essential for sentence classification. Our model improves state-of- art F1-score for both tasks: (i) entity recognition of ADE words (12.5{\%} increase) and (ii) ADE sentence classification (13.6{\%} increase) on MADE 1.0 benchmark of EHR notes.",
}
@inproceedings{kassner-schutze-2020-bert,
    title = "{BERT}-k{NN}: Adding a k{NN} Search Component to Pretrained Language Models for Better {QA}",
    author = {Kassner, Nora  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.307",
    doi = "10.18653/v1/2020.findings-emnlp.307",
    pages = "3424--3430",
    abstract = "Khandelwal et al. (2020) use a k-nearest-neighbor (kNN) component to improve language model performance. We show that this idea is beneficial for open-domain question answering (QA). To improve the recall of facts encountered during training, we combine BERT (Devlin et al., 2019) with a traditional information retrieval step (IR) and a kNN search over a large datastore of an embedded text collection. Our contributions are as follows: i) BERT-kNN outperforms BERT on cloze-style QA by large margins without any further training. ii) We show that BERT often identifies the correct response category (e.g., US city), but only kNN recovers the factually correct answer (e.g.,{``}Miami{''}). iii) Compared to BERT, BERT-kNN excels for rare facts. iv) BERT-kNN can easily handle facts not covered by BERT{'}s training set, e.g., recent events.",
}
@inproceedings{wang-culotta-2020-identifying,
    title = "Identifying Spurious Correlations for Robust Text Classification",
    author = "Wang, Zhao  and
      Culotta, Aron",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.308",
    doi = "10.18653/v1/2020.findings-emnlp.308",
    pages = "3431--3440",
    abstract = "The predictions of text classifiers are often driven by spurious correlations {--} e.g., the term {``}Spielberg{''} correlates with positively reviewed movies, even though the term itself does not semantically convey a positive sentiment. In this paper, we propose a method to distinguish spurious and genuine correlations in text classification. We treat this as a supervised classification problem, using features derived from treatment effect estimators to distinguish spurious correlations from {``}genuine{''} ones. Due to the generic nature of these features and their small dimensionality, we find that the approach works well even with limited training examples, and that it is possible to transport the word classifier to new domains. Experiments on four datasets (sentiment classification and toxicity detection) suggest that using this approach to inform feature selection also leads to more robust classification, as measured by improved worst-case accuracy on the samples affected by spurious correlations.",
}
@inproceedings{jiang-etal-2020-hover,
    title = "{H}o{V}er: A Dataset for Many-Hop Fact Extraction And Claim Verification",
    author = "Jiang, Yichen  and
      Bordia, Shikha  and
      Zhong, Zheng  and
      Dognin, Charles  and
      Singh, Maneesh  and
      Bansal, Mohit",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.309",
    doi = "10.18653/v1/2020.findings-emnlp.309",
    pages = "3441--3460",
    abstract = "We introduce HoVer (HOppy VERification), a dataset for many-hop evidence extraction and fact verification. It challenges models to extract facts from several Wikipedia articles that are relevant to a claim and classify whether the claim is supported or not-supported by the facts. In HoVer, the claims require evidence to be extracted from as many as four English Wikipedia articles and embody reasoning graphs of diverse shapes. Moreover, most of the 3/4-hop claims are written in multiple sentences, which adds to the complexity of understanding long-range dependency relations such as coreference. We show that the performance of an existing state-of-the-art semantic-matching model degrades significantly on our dataset as the number of reasoning hops increases, hence demonstrating the necessity of many-hop reasoning to achieve strong results. We hope that the introduction of this challenging dataset and the accompanying evaluation task will encourage research in many-hop fact retrieval and information verification.",
}
@inproceedings{mi-etal-2020-continual,
    title = "Continual Learning for Natural Language Generation in Task-oriented Dialog Systems",
    author = "Mi, Fei  and
      Chen, Liangwei  and
      Zhao, Mengjie  and
      Huang, Minlie  and
      Faltings, Boi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.310",
    doi = "10.18653/v1/2020.findings-emnlp.310",
    pages = "3461--3474",
    abstract = "Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applications where new data come in a stream, we study NLG in a {``}continual learning{''} setting to expand its knowledge to new domains or functionalities incrementally. The major challenge towards this goal is catastrophic forgetting, meaning that a continually trained model tends to forget the knowledge it has learned before. To this end, we propose a method called ARPER (Adaptively Regularized Prioritized Exemplar Replay) by replaying prioritized historical exemplars, together with an adaptive regularization technique based on Elastic Weight Consolidation. Extensive experiments to continually learn new domains and intents are conducted on MultiWoZ-2.0 to benchmark ARPER with a wide range of techniques. Empirical results demonstrate that ARPER significantly outperforms other methods by effectively mitigating the detrimental catastrophic forgetting issue.",
}
@inproceedings{li-etal-2020-unqovering,
    title = "{UNQOVER}ing Stereotyping Biases via Underspecified Questions",
    author = "Li, Tao  and
      Khashabi, Daniel  and
      Khot, Tushar  and
      Sabharwal, Ashish  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.311",
    doi = "10.18653/v1/2020.findings-emnlp.311",
    pages = "3475--3489",
    abstract = "While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence. We design a formalism that isolates the aforementioned errors. As case studies, we use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion. We probe five transformer-based QA models trained on two QA datasets, along with their underlying language models. Our broad study reveals that (1) all these models, with and without fine-tuning, have notable stereotyping biases in these classes; (2) larger models often have higher bias; and (3) the effect of fine-tuning on bias varies strongly with the dataset and the model size.",
}
@inproceedings{akiti-etal-2020-semantics,
    title = "A Semantics-based Approach to Disclosure Classification in User-Generated Online Content",
    author = "Akiti, Chandan  and
      Squicciarini, Anna  and
      Rajtmajer, Sarah",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.312",
    doi = "10.18653/v1/2020.findings-emnlp.312",
    pages = "3490--3499",
    abstract = "As users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase. So-called self-disclosure can result in a number of privacy concerns. Users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and social networks, as well as the power of modern AI to synthesize and gain insights from this data. This paper presents an approach to detect emotional and informational self-disclosure in natural language. We hypothesize that identifying frame semantics can meaningfully support this task. Specifically, we use Semantic Role Labeling to identify the lexical units and their semantic roles that signal self-disclosure. Experimental results on Reddit data show the performance gain of our method when compared to standard text classification methods based on BiLSTM, and BERT. In addition to improved performance, our approach provides insights into the drivers of disclosure behaviors.",
}
@inproceedings{chen-etal-2020-mining,
    title = "Mining Knowledge for Natural Language Inference from {W}ikipedia Categories",
    author = "Chen, Mingda  and
      Chu, Zewei  and
      Stratos, Karl  and
      Gimpel, Kevin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.313",
    doi = "10.18653/v1/2020.findings-emnlp.313",
    pages = "3500--3511",
    abstract = "Accurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations. To alleviate the need for labeled data, we introduce WikiNLI: a resource for improving model performance on NLI and LE tasks. It contains 428,899 pairs of phrases constructed from naturally annotated category hierarchies in Wikipedia. We show that we can improve strong baselines such as BERT and RoBERTa by pretraining them on WikiNLI and transferring the models on downstream tasks. We conduct systematic comparisons with phrases extracted from other knowledge bases such as WordNet and Wikidata to find that pretraining on WikiNLI gives the best performance. In addition, we construct WikiNLI in other languages, and show that pretraining on them improves performance on NLI tasks of corresponding languages.",
}
@inproceedings{hu-etal-2020-ocnli,
    title = "{OCNLI}: {O}riginal {C}hinese {N}atural {L}anguage {I}nference",
    author = {Hu, Hai  and
      Richardson, Kyle  and
      Xu, Liang  and
      Li, Lu  and
      K{\"u}bler, Sandra  and
      Moss, Lawrence},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.314",
    doi = "10.18653/v1/2020.findings-emnlp.314",
    pages = "3512--3526",
    abstract = "Despite the tremendous recent progress on natural language inference (NLI), driven largely by large-scale investment in new datasets (e.g.,SNLI, MNLI) and advances in modeling, most progress has been limited to English due to a lack of reliable datasets for most of the world{'}s languages. In this paper, we present the first large-scale NLI dataset (consisting of {\textasciitilde}56,000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI). Unlike recent attempts at extending NLI to other languages, our dataset does not rely on any automatic translation or non-expert annotation. Instead, we elicit annotations from native speakers specializing in linguistics. We follow closely the annotation protocol used for MNLI, but create new strategies for eliciting diverse hypotheses. We establish several baseline results on our dataset using state-of-the-art pre-trained models for Chinese, and find even the best performing models to be far outpaced by human performance ({\textasciitilde}12{\%} absolute performance gap), making it a challenging new resource that we hope will help to accelerate progress in Chinese NLU. To the best of our knowledge, this is the first human-elicited MNLI-style corpus for a non-English language.",
}
@inproceedings{zhang-etal-2020-margin,
    title = "Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling",
    author = "Zhang, Dejiao  and
      Nallapati, Ramesh  and
      Zhu, Henghui  and
      Nan, Feng  and
      Nogueira dos Santos, Cicero  and
      McKeown, Kathleen  and
      Xiang, Bing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.315",
    doi = "10.18653/v1/2020.findings-emnlp.315",
    pages = "3527--3536",
    abstract = "Unsupervised domain adaptation addresses the problem of leveraging labeled data in a source domain to learn a well-performing model in a target domain where labels are unavailable. In this paper, we improve upon a recent theoretical work (Zhang et al., 2019b) and adopt the Margin Disparity Discrepancy (MDD) unsupervised domain adaptation algorithm to solve the cross-lingual text labeling problems. Experiments on cross-lingual document classification and NER demonstrate the proposed domain adaptation approach advances the state-of-the-art results by a large margin. Specifically, we improve MDD by efficiently optimizing the margin loss on the source domain via Virtual Adversarial Training (VAT). This bridges the gap between theory and the loss function used in the original work Zhang et al.(2019b), and thereby significantly boosts the performance. Our numerical results also indicate that VAT can remarkably improve the generalization performance of both domains for various domain adaptation approaches.",
}
@inproceedings{li-etal-2020-rethinking,
    title = "Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems",
    author = "Li, Ziming  and
      Kiseleva, Julia  and
      de Rijke, Maarten",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.316",
    doi = "10.18653/v1/2020.findings-emnlp.316",
    pages = "3537--3546",
    abstract = "Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress recently mostly through employing reinforcement learning methods. However, these approaches have become very sophisticated. It is time to re-evaluate it. Are we really making progress developing dialogue agents only based on reinforcement learning? We demonstrate how (1) traditional supervised learning together with (2) a simulator-free adversarial learning method can be used to achieve performance comparable to state-of-the-art reinforcement learning-based methods. First, we introduce a simple dialogue action decoder to predict the appropriate actions. Then, the traditional multi-label classification solution for dialogue policy learning is extended by adding dense layers to improve the dialogue agent performance. Finally, we employ the Gumbel-Softmax estimator to alternatively train the dialogue agent and the dialogue reward model without using reinforcement learning. Based on our extensive experimentation, we can conclude the proposed methods can achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning. Our main goal is not to beat RL with supervised learning, but to demonstrate the value of rethinking the role of reinforcement learning and supervised learning in optimizing task-oriented dialogue systems.",
}
@inproceedings{shah-etal-2020-expect,
    title = "What do we expect from Multiple-choice {QA} Systems?",
    author = "Shah, Krunal  and
      Gupta, Nitish  and
      Roth, Dan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.317",
    doi = "10.18653/v1/2020.findings-emnlp.317",
    pages = "3547--3553",
    abstract = "The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models{'} language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human{'}s expectations from models that {``}understand{''} language. In this work we consider a top performing model on several Multiple Choice Question Answering (MCQA) datasets, and evaluate it against a set of expectations one might have from such a model, using a series of zero-information perturbations of the model{'}s inputs. Our results show that the model clearly falls short of our expectations, and motivates a modified training approach that forces the model to better attend to the inputs. We show that the new training paradigm leads to a model that performs on par with the original model while better satisfying our expectations.",
}
@inproceedings{ma-etal-2020-resource,
    title = "Resource-Enhanced Neural Model for Event Argument Extraction",
    author = "Ma, Jie  and
      Wang, Shuai  and
      Anubhai, Rishita  and
      Ballesteros, Miguel  and
      Al-Onaizan, Yaser",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.318",
    doi = "10.18653/v1/2020.findings-emnlp.318",
    pages = "3554--3559",
    abstract = "Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument. (3) Integrating event trigger information into candidate argument representation. For (1), we explore using unlabeled data. For (2), we use Transformer that uses dependency parses to guide the attention mechanism. For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations. We also support argument extraction either from text annotated with gold entities or from plain text. Experiments on the English ACE 2005 benchmark show that our approach achieves a new state-of-the-art.",
}
@inproceedings{gao-etal-2020-improving,
    title = "Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation",
    author = "Gao, Luyu  and
      Wang, Xinyi  and
      Neubig, Graham",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.319",
    doi = "10.18653/v1/2020.findings-emnlp.319",
    pages = "3560--3566",
    abstract = "To improve the performance of Neural Machine Translation (NMT) for low-resource languages (LRL), one effective strategy is to leverage parallel data from a related high-resource language (HRL). However, multilingual data has been found more beneficial for NMT models that translate from the LRL to a target language than the ones that translate into the LRLs. In this paper, we aim to improve the effectiveness of multilingual transfer for NMT models that translate into the LRL, by designing a better decoder word embedding. Extending upon a general-purpose multilingual encoding method Soft Decoupled Encoding (Wang et al., 2019), we propose DecSDE, an efficient character n-gram based embedding specifically designed for the NMT decoder. Our experiments show that DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English to four different languages.",
}
@inproceedings{fancellu-etal-2020-accurate,
    title = "Accurate polyglot semantic parsing with {DAG} grammars",
    author = "Fancellu, Federico  and
      K{\'a}d{\'a}r, {\'A}kos  and
      Zhang, Ran  and
      Fazly, Afsaneh",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.320",
    doi = "10.18653/v1/2020.findings-emnlp.320",
    pages = "3567--3580",
    abstract = "Semantic parses are directed acyclic graphs (DAGs), but in practice most parsers treat them as strings or trees, mainly because models that predict graphs are far less understood. This simplification, however, comes at a cost: there is no guarantee that the output is a well-formed graph. A recent work by Fancellu et al. (2019) addressed this problem by proposing a graph-aware sequence model that utilizes a DAG grammar to guide graph generation. We significantly improve upon this work, by proposing a simpler architecture as well as more efficient training and inference algorithms that can always guarantee the well-formedness of the generated graphs. Importantly, unlike Fancellu et al., our model does not require language-specific features, and hence can harness the inherent ability of DAG-grammar parsing in multilingual settings. We perform monolingual as well as multilingual experiments on the Parallel Meaning Bank (Abzianidze et al., 2017). Our parser outperforms previous graph-aware models by a large margin, and closes the performance gap between string-based and DAG-grammar parsing.",
}
@inproceedings{zheng-etal-2020-approximation,
    title = "Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation",
    author = "Zheng, Wen  and
      Milic-Frayling, Natasa  and
      Zhou, Ke",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.321",
    doi = "10.18653/v1/2020.findings-emnlp.321",
    pages = "3581--3591",
    abstract = "This paper is concerned with improving dialogue generation models through injection of knowledge, e.g., content relevant to the post that can increase the quality of responses. Past research extends the training of the generative models by incorporating statistical properties of posts, responses and related knowledge, without explicitly assessing the knowledge quality. In our work, we demonstrate the importance of knowledge relevance and adopt a two-phase approach. We first apply a novel method, Transformer {\&} Post based Posterior Approximation (TPPA) to select knowledge, and then use the Transformer with Expanded Decoder (TED) model to generate responses from both the post and the knowledge. TPPA method processes posts, post related knowledge, and response related knowledge at both word and sentence level. Our experiments with the TED generative model demonstrate the effectiveness of TPPA as it outperforms a set of strong baseline models. Our TPPA method is extendable and supports further optimization of knowledge retrieval and injection.",
}
@inproceedings{goyal-durrett-2020-evaluating,
    title = "Evaluating Factuality in Generation with Dependency-level Entailment",
    author = "Goyal, Tanya  and
      Durrett, Greg",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.322",
    doi = "10.18653/v1/2020.findings-emnlp.322",
    pages = "3592--3603",
    abstract = "Despite significant progress in text generation models, a serious limitation is their tendency to produce text that is factually inconsistent with information in the input. Recent work has studied whether textual entailment systems can be used to identify factual errors; however, these sentence-level entailment models are trained to solve a different problem than generation filtering and they do not localize which part of a generation is non-factual. In this paper, we propose a new formulation of entailment that decomposes it at the level of dependency arcs. Rather than focusing on aggregate decisions, we instead ask whether the semantic relationship manifested by individual dependency arcs in the generated output is supported by the input. Human judgments on this task are difficult to obtain; we therefore propose a method to automatically create data based on existing entailment or paraphrase corpora. Experiments show that our dependency arc entailment model trained on this data can identify factual inconsistencies in paraphrasing and summarization better than sentence-level methods or those based on question generation, while additionally localizing the erroneous parts of the generation.",
}
@inproceedings{karamanolakis-etal-2020-cross,
    title = "Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher",
    author = "Karamanolakis, Giannis  and
      Hsu, Daniel  and
      Gravano, Luis",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.323",
    doi = "10.18653/v1/2020.findings-emnlp.323",
    pages = "3604--3622",
    abstract = "Cross-lingual text classification alleviates the need for manually labeled documents in a target language by leveraging labeled documents from other languages. Existing approaches for transferring supervision across languages require expensive cross-lingual resources, such as parallel corpora, while less expensive cross-lingual representation learning approaches train classifiers without target labeled documents. In this work, we propose a cross-lingual teacher-student method, CLTS, that generates {``}weak{''} supervision in the target language using minimal cross-lingual resources, in the form of a small number of word translations. Given a limited translation budget, CLTS extracts and transfers only the most important task-specific seed words across languages and initializes a teacher classifier based on the translated seed words. Then, CLTS iteratively trains a more powerful student that also exploits the context of the seed words in unlabeled target documents and outperforms the teacher. CLTS is simple and surprisingly effective in 18 diverse languages: by transferring just 20 seed words, even a bag-of-words logistic regression student outperforms state-of-the-art cross-lingual methods (e.g., based on multilingual BERT). Moreover, CLTS can accommodate any type of student classifier: leveraging a monolingual BERT student leads to further improvements and outperforms even more expensive approaches by up to 12{\%} in accuracy. Finally, CLTS addresses emerging tasks in low-resource languages using just a small number of word translations.",
}
@inproceedings{demasi-etal-2020-multi,
    title = "A Multi-Persona Chatbot for Hotline Counselor Training",
    author = "Demasi, Orianna  and
      Li, Yu  and
      Yu, Zhou",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.324",
    doi = "10.18653/v1/2020.findings-emnlp.324",
    pages = "3623--3636",
    abstract = "Suicide prevention hotline counselors aid individuals during difficult times through millions of calls and chats. A chatbot cannot safely replace a counselor, but we explore whether a chatbot can be developed to help train human counselors. Such a system needs to simulate intimate situations across multiple practice sessions. Open-domain dialogue systems frequently suffer from generic responses that do not characterize personal stories, so we look to infuse conversations with persona information by mimicking prototype conversations. Towards building a {``}Crisisbot{''} hotline visitor simulation, we propose a counseling strategy annotation scheme and a multi-task framework that leverages these counselor strategies to retrieve similar examples, generate diverse sub-utterances, and interleave prototype and generated sub-utterances into complex responses. We evaluate this framework with crowdworkers and experienced hotline counselors. The framework considerably increases response diversity and specificity, with limited impact to coherence. Our results also show a considerable discrepancy between crowdworker and counselor judgements, which emphasizes the importance of including target populations in system development and evaluation.",
}
@inproceedings{jhamtani-berg-kirkpatrick-2020-narrative,
    title = "Narrative Text Generation with a Latent Discrete Plan",
    author = "Jhamtani, Harsh  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.325",
    doi = "10.18653/v1/2020.findings-emnlp.325",
    pages = "3637--3650",
    abstract = "Past work on story generation has demonstrated the usefulness of conditioning on a generation plan to generate coherent stories. However, these approaches have used heuristics or off-the-shelf models to first tag training stories with the desired type of plan, and then train generation models in a supervised fashion. In this paper, we propose a deep latent variable model that first samples a sequence of anchor words, one per sentence in the story, as part of its generative process. During training, our model treats the sequence of anchor words as a latent variable and attempts to induce anchoring sequences that help guide generation in an unsupervised fashion. We conduct experiments with several types of sentence decoder distributions {--} left-to-right and non-monotonic, with different degrees of restriction. Further, since we use amortized variational inference to train our model, we introduce two corresponding types of inference network for predicting the posterior on anchor words. We conduct human evaluations which demonstrate that the stories produced by our model are rated better in comparison with baselines which do not consider story plans, and are similar or better in quality relative to baselines which use external supervision for plans. Additionally, the proposed model gets favorable scores when evaluated on perplexity, diversity, and control of story via discrete plan",
}
@inproceedings{pouran-ben-veyseh-etal-2020-graph,
    title = "Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction",
    author = "Pouran Ben Veyseh, Amir  and
      Nguyen, Tuan Ngo  and
      Nguyen, Thien Huu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.326",
    doi = "10.18653/v1/2020.findings-emnlp.326",
    pages = "3651--3661",
    abstract = "The goal of Event Argument Extraction (EAE) is to find the role of each entity mention for a given event trigger word. It has been shown in the previous works that the syntactic structures of the sentences are helpful for the deep learning models for EAE. However, a major problem in such prior works is that they fail to exploit the semantic structures of the sentences to induce effective representations for EAE. Consequently, in this work, we propose a novel model for EAE that exploits both syntactic and semantic structures of the sentences with the Graph Transformer Networks (GTNs) to learn more effective sentence structures for EAE. In addition, we introduce a novel inductive bias based on information bottleneck to improve generalization of the EAE models. Extensive experiments are performed to demonstrate the benefits of the proposed model, leading to state-of-the-art performance for EAE on standard datasets.",
}
@inproceedings{he-etal-2020-box,
    title = "The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation",
    author = "He, Jie  and
      Wang, Tao  and
      Xiong, Deyi  and
      Liu, Qun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.327",
    doi = "10.18653/v1/2020.findings-emnlp.327",
    pages = "3662--3672",
    abstract = "Does neural machine translation yield translations that are congenial with common sense? In this paper, we present a test suite to evaluate the commonsense reasoning capability of neural machine translation. The test suite consists of three test sets, covering lexical and contextless/contextual syntactic ambiguity that requires commonsense knowledge to resolve. We manually create 1,200 triples, each of which contain a source sentence and two contrastive translations, involving 7 different common sense types. Language models pretrained on large-scale corpora, such as BERT, GPT-2, achieve a commonsense reasoning accuracy of lower than 72{\%} on target translations of this test suite. We conduct extensive experiments on the test suite to evaluate commonsense reasoning in neural machine translation and investigate factors that have impact on this capability. Our experiments and analyses demonstrate that neural machine translation performs poorly on commonsense reasoning of the three ambiguity types in terms of both reasoning accuracy ( 6 60.1{\%}) and reasoning consistency (6 31{\%}). We will release our test suite as a machine translation commonsense reasoning testbed to promote future work in this direction.",
}
@inproceedings{yang-etal-2020-using,
    title = "Using Visual Feature Space as a Pivot Across Languages",
    author = "Yang, Ziyan  and
      Pinto-Alva, Leticia  and
      Dernoncourt, Franck  and
      Ordonez, Vicente",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.328",
    doi = "10.18653/v1/2020.findings-emnlp.328",
    pages = "3673--3678",
    abstract = "Our work aims to leverage visual feature space to pass information across languages. We show that models trained to generate textual captions in more than one language conditioned on an input image can leverage their jointly trained feature space during inference to pivot across languages. We particularly demonstrate improved quality on a caption generated from an input image, by leveraging a caption in a second language. More importantly, we demonstrate that even without conditioning on any visual input, the model demonstrates to have learned implicitly to perform to some extent machine translation from one language to another in their shared visual feature space. We show results in German-English, and Japanese-English language pairs that pave the way for using the visual world to learn a common representation for language.",
}
@inproceedings{chen-etal-2020-cdevalsumm,
    title = "{CDE}val{S}umm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems",
    author = "Chen, Yiran  and
      Liu, Pengfei  and
      Zhong, Ming  and
      Dou, Zi-Yi  and
      Wang, Danqing  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.329",
    doi = "10.18653/v1/2020.findings-emnlp.329",
    pages = "3679--3691",
    abstract = "Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset. We argue that this approach can narrow our understanding of the generalization ability for different summarization systems. In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora. A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways (i.e. abstractive and extractive) on model generalization ability. Further, experimental results shed light on the limitations of existing summarizers. Brief introduction and supplementary code can be found in \url{https://github.com/zide05/CDEvalSumm}.",
}
@inproceedings{jorke-etal-2020-attending,
    title = "{A}ttending to {L}ong-{D}istance {D}ocument {C}ontext for {S}equence {L}abeling",
    author = {J{\"o}rke, Matthew  and
      Gillick, Jon  and
      Sims, Matthew  and
      Bamman, David},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.330",
    doi = "10.18653/v1/2020.findings-emnlp.330",
    pages = "3692--3704",
    abstract = "We present in this work a method for incorporating global context in long documents when making local decisions in sequence labeling problems like NER. Inspired by work in featurized log-linear models (Chieu and Ng, 2002; Sutton and McCallum, 2004), our model learns to attend to multiple mentions of the same word type in generating a representation for each token in context, extending that work to learning representations that can be incorporated into modern neural models. Attending to broader context at test time provides complementary information to pretraining (Gururangan et al., 2020), yields strong gains over equivalently parameterized models lacking such context, and performs best at recognizing entities with high TF-IDF scores (i.e., those that are important within a document).",
}
@inproceedings{yan-etal-2020-global,
    title = "Global Bootstrapping Neural Network for Entity Set Expansion",
    author = "Yan, Lingyong  and
      Han, Xianpei  and
      He, Ben  and
      Sun, Le",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.331",
    doi = "10.18653/v1/2020.findings-emnlp.331",
    pages = "3705--3714",
    abstract = "Bootstrapping for entity set expansion (ESE) has been studied for a long period, which expands new entities using only a few seed entities as supervision. Recent end-to-end bootstrapping approaches have shown their advantages in information capturing and bootstrapping process modeling. However, due to the sparse supervision problem, previous end-to-end methods often only leverage information from near neighborhoods (local semantics) rather than those propagated from the co-occurrence structure of the whole corpus (global semantics). To address this issue, this paper proposes Global Bootstrapping Network (GBN) with the {``}pre-training and fine-tuning{''} strategies for effective learning. Specifically, it contains a global-sighted encoder to capture and encode both local and global semantics into entity embedding, and an attention-guided decoder to sequentially expand new entities based on these embeddings. The experimental results show that the GBN learned by {``}pre-training and fine-tuning{''} strategies achieves state-of-the-art performance on two bootstrapping datasets.",
}
@inproceedings{jimenez-gutierrez-etal-2020-document-classification,
    title = "Document Classification for {COVID}-19 Literature",
    author = "Jimenez Gutierrez, Bernal  and
      Zeng, Jucheng  and
      Zhang, Dongdong  and
      Zhang, Ping  and
      Su, Yu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.332",
    doi = "10.18653/v1/2020.findings-emnlp.332",
    pages = "3715--3722",
    abstract = "The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset, a growing collection of 23,000 research papers regarding the novel 2019 coronavirus. We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that BioBERT surpasses the others by a small margin with micro-F1 and accuracy scores of around 86{\%} and 75{\%} respectively on the test set. We evaluate the data efficiency and generalizability of these models as essential features of any system prepared to deal with an urgent situation like the current health crisis. We perform a data ablation study to determine how important article titles are for achieving reasonable performance on this dataset. Finally, we explore 50 errors made by the best performing models on LitCovid documents and find that they often (1) correlate certain labels too closely together and (2) fail to focus on discriminative sections of the articles; both of which are important issues to address in future work. Both data and code are available on GitHub.",
}
@inproceedings{maharana-bansal-2020-adversarial,
    title = "Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension",
    author = "Maharana, Adyasha  and
      Bansal, Mohit",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.333",
    doi = "10.18653/v1/2020.findings-emnlp.333",
    pages = "3723--3738",
    abstract = "Reading comprehension models often overfit to nuances of training datasets and fail at adversarial evaluation. Training with adversarially augmented dataset improves robustness against those adversarial attacks but hurts generalization of the models. In this work, we present several effective adversaries and automated data augmentation policy search methods with the goal of making reading comprehension models more robust to adversarial evaluation, but also improving generalization to the source domain as well as new domains and languages. We first propose three new methods for generating QA adversaries, that introduce multiple points of confusion within the context, show dependence on insertion location of the distractor, and reveal the compounding effect of mixing adversarial strategies with syntactic and semantic paraphrasing methods. Next, we find that augmenting the training datasets with uniformly sampled adversaries improves robustness to the adversarial attacks but leads to decline in performance on the original unaugmented dataset. We address this issue via RL and more efficient Bayesian policy search methods for automatically learning the best augmentation policy combinations of the transformation probability for each adversary in a large search space. Using these learned policies, we show that adversarial training can lead to significant improvements in in-domain, out-of-domain, and cross-lingual (German, Russian, Turkish) generalization.",
}
@inproceedings{ren-etal-2020-denoising,
    title = "Denoising Multi-Source Weak Supervision for Neural Text Classification",
    author = "Ren, Wendi  and
      Li, Yinghao  and
      Su, Hanting  and
      Kartchner, David  and
      Mitchell, Cassie  and
      Zhang, Chao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.334",
    doi = "10.18653/v1/2020.findings-emnlp.334",
    pages = "3739--3754",
    abstract = "We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challenging because rule-induced weak labels are often noisy and incomplete. To address these two challenges, we design a label denoiser, which estimates the source reliability using a conditional soft attention mechanism and then reduces label noise by aggregating rule-annotated weak labels. The denoised pseudo labels then supervise a neural classifier to predicts soft labels for unmatched samples, which address the rule coverage issue. We evaluate our model on five benchmarks for sentiment, topic, and relation classifications. The results show that our model outperforms state-of-the-art weakly-supervised and semi-supervised methods consistently, and achieves comparable performance with fully-supervised methods even without any labeled data. Our code can be found at https://github.com/weakrules/Denoise-multi-weak-sources.",
}
@inproceedings{joshi-etal-2020-dr,
    title = "Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.",
    author = "Joshi, Anirudh  and
      Katariya, Namit  and
      Amatriain, Xavier  and
      Kannan, Anitha",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.335",
    doi = "10.18653/v1/2020.findings-emnlp.335",
    pages = "3755--3763",
    abstract = "Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge. Summarization of medical conversations is a particularly important aspect of medical conversation understanding since it addresses a very real need in medical practice: capturing the most important aspects of a medical encounter so that they can be used for medical decision making and subsequent follow ups. In this paper we present a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient{'}s medical history. Our approach is a variation of the pointer generator network where we introduce a penalty on the generator distribution, and we explicitly model negations. The model also captures important properties of medical conversations such as medical knowledge coming from standardized medical ontologies better than when those concepts are introduced explicitly. Through evaluation by doctors, we show that our approach is preferred on twice the number of summaries to the baseline pointer generator model and captures most or all of the information in 80{\%} of the conversations making it a realistic alternative to costly manual summarization by medical experts.",
}
@inproceedings{yang-yu-2020-generating,
    title = "Generating {A}ccurate {E}lectronic {H}ealth {A}ssessment from {M}edical {G}raph",
    author = "Yang, Zhichao  and
      Yu, Hong",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.336",
    doi = "10.18653/v1/2020.findings-emnlp.336",
    pages = "3764--3773",
    abstract = "One of the fundamental goals of artificial intelligence is to build computer-based expert systems. Inferring clinical diagnoses to generate a clinical assessment during a patient encounter is a crucial step towards building a medical diagnostic system. Previous works were mainly based on either medical domain-specific knowledge, or patients{'} prior diagnoses and clinical encounters. In this paper, we propose a novel model for automated clinical assessment generation (MCAG). MCAG is built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. Our evaluation results against physician generated gold standard show that MCAG significantly improves the BLEU and rouge score compared with competitive baseline models. Further, physicians{'} evaluation showed that MCAG could generate high-quality assessments.",
}
@inproceedings{harrigian-etal-2020-models,
    title = "Do Models of Mental Health Based on Social Media Data Generalize?",
    author = "Harrigian, Keith  and
      Aguirre, Carlos  and
      Dredze, Mark",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.337",
    doi = "10.18653/v1/2020.findings-emnlp.337",
    pages = "3774--3788",
    abstract = "Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.",
}
@inproceedings{lai-etal-2020-context,
    title = "Context Analysis for Pre-trained Masked Language Models",
    author = "Lai, Yi-An  and
      Lalwani, Garima  and
      Zhang, Yi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.338",
    doi = "10.18653/v1/2020.findings-emnlp.338",
    pages = "3789--3804",
    abstract = "Pre-trained language models that learn contextualized word representations from a large un-annotated corpus have become a standard component for many state-of-the-art NLP systems. Despite their successful applications in various downstream NLP tasks, the extent of contextual impact on the word representation has not been explored. In this paper, we present a detailed analysis of contextual impact in Transformer- and BiLSTM-based masked language models. We follow two different approaches to evaluate the impact of context: a masking based approach that is architecture agnostic, and a gradient based approach that requires back-propagation through networks. The findings suggest significant differences on the contextual impact between the two model architectures. Through further breakdown of analysis by syntactic categories, we find the contextual impact in Transformer-based MLM aligns well with linguistic intuition. We further explore the Transformer attention pruning based on our findings in contextual analysis.",
}
@inproceedings{shu-etal-2020-controllable,
    title = "Controllable Text Generation with Focused Variation",
    author = "Shu, Lei  and
      Papangelis, Alexandros  and
      Wang, Yi-Chia  and
      Tur, Gokhan  and
      Xu, Hu  and
      Feizollahi, Zhaleh  and
      Liu, Bing  and
      Molino, Piero",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.339",
    doi = "10.18653/v1/2020.findings-emnlp.339",
    pages = "3805--3817",
    abstract = "This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diversity of the generated texts. FVN addresses these issues by learning disjoint discrete latent spaces for each attribute inside codebooks, which allows for both controllability and diversity, while at the same time generating fluent text. We evaluate FVN on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations.",
}
@inproceedings{kwon-etal-2020-modeling,
    title = "Modeling Preconditions in Text with a Crowd-sourced Dataset",
    author = "Kwon, Heeyoung  and
      Koupaee, Mahnaz  and
      Singh, Pratyush  and
      Sawhney, Gargi  and
      Shukla, Anmol  and
      Kallur, Keerthi Kumar  and
      Chambers, Nathanael  and
      Balasubramanian, Niranjan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.340",
    doi = "10.18653/v1/2020.findings-emnlp.340",
    pages = "3818--3828",
    abstract = "Preconditions provide a form of logical connection between events that explains why some events occur together and information that is complementary to the more widely studied relations such as causation, temporal ordering, entailment, and discourse relations. Modeling preconditions in text has been hampered in part due to the lack of large scale labeled data grounded in text. This paper introduces PeKo, a crowd-sourced annotation of \textit{preconditions} between event pairs in newswire, an order of magnitude larger than prior text annotations. To complement this new corpus, we also introduce two challenge tasks aimed at modeling preconditions: (i) Precondition Identification {--} a standard classification task defined over pairs of event mentions, and (ii) Precondition Generation {--} a generative task aimed at testing a more general ability to reason about a given event. Evaluation on both tasks shows that modeling preconditions is challenging even for today{'}s large language models (LM). This suggests that precondition knowledge is not easily accessible in LM-derived representations alone. Our generation results show that fine-tuning an LM on PeKo yields better conditional relations than when trained on raw text or temporally-ordered corpora.",
}
@inproceedings{morris-etal-2020-reevaluating,
    title = "Reevaluating Adversarial Examples in Natural Language",
    author = "Morris, John  and
      Lifland, Eli  and
      Lanchantin, Jack  and
      Ji, Yangfeng  and
      Qi, Yanjun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.341",
    doi = "10.18653/v1/2020.findings-emnlp.341",
    pages = "3829--3839",
    abstract = "State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the model and follows some linguistic constraints. We then analyze the outputs of two state-of-the-art synonym substitution attacks. We find that their perturbations often do not preserve semantics, and 38{\%} introduce grammatical errors. Human surveys reveal that to successfully preserve semantics, we need to significantly increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences.With constraints adjusted to better preserve semantics and grammaticality, the attack success rate drops by over 70 percentage points.",
}
@inproceedings{zhu-etal-2020-question,
    title = "Question Answering with Long Multiple-Span Answers",
    author = "Zhu, Ming  and
      Ahuja, Aman  and
      Juan, Da-Cheng  and
      Wei, Wei  and
      Reddy, Chandan K.",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.342",
    doi = "10.18653/v1/2020.findings-emnlp.342",
    pages = "3840--3849",
    abstract = "Answering questions in many real-world applications often requires complex and precise information excerpted from texts spanned across a long document. However, currently no such annotated dataset is publicly available, which hinders the development of neural question-answering (QA) systems. To this end, we present MASH-QA, a Multiple Answer Spans Healthcare Question Answering dataset from the consumer health domain, where answers may need to be excerpted from multiple, non-consecutive parts of text spanned across a long document. We also propose MultiCo, a neural architecture that is able to capture the relevance among multiple answer spans, by using a query-based contextualized sentence selection approach, for forming the answer to the given question. We also demonstrate that conventional QA models are not suitable for this type of task and perform poorly in this setting. Extensive experiments are conducted, and the experimental results confirm the proposed model significantly outperforms the state-of-the-art QA models in this multi-span QA setting.",
}
@inproceedings{jiang-etal-2020-inserting,
    title = "{I}nserting {I}nformation {B}ottlenecks for {A}ttribution in {T}ransformers",
    author = "Jiang, Zhiying  and
      Tang, Raphael  and
      Xin, Ji  and
      Lin, Jimmy",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.343",
    doi = "10.18653/v1/2020.findings-emnlp.343",
    pages = "3850--3857",
    abstract = "Pretrained transformers achieve the state of the art across tasks in natural language processing, motivating researchers to investigate their inner mechanisms. One common direction is to understand what features are important for prediction. In this paper, we apply information bottlenecks to analyze the attribution of each feature for prediction on a black-box model. We use BERT as the example and evaluate our approach both quantitatively and qualitatively. We show the effectiveness of our method in terms of attribution and the ability to provide insight into how information flows through layers. We demonstrate that our technique outperforms two competitive methods in degradation tests on four datasets. Code is available at \url{https://github.com/bazingagin/IBA}.",
}
@inproceedings{medina-maza-etal-2020-event,
    title = "Event-Related Bias Removal for Real-time Disaster Events",
    author = "Medina Maza, Salvador  and
      Spiliopoulou, Evangelia  and
      Hovy, Eduard  and
      Hauptmann, Alexander",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.344",
    doi = "10.18653/v1/2020.findings-emnlp.344",
    pages = "3858--3868",
    abstract = "Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volumes of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on out-of-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.",
}
@inproceedings{hossain-etal-2020-non,
    title = "It{'}s not a Non-Issue: Negation as a Source of Error in Machine Translation",
    author = "Hossain, Md Mosharaf  and
      Anastasopoulos, Antonios  and
      Blanco, Eduardo  and
      Palmer, Alexis",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.345",
    doi = "10.18653/v1/2020.findings-emnlp.345",
    pages = "3869--3885",
    abstract = "As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60{\%}. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.",
}
@inproceedings{ma-etal-2020-incremental,
    title = "Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework",
    author = "Ma, Mingbo  and
      Zheng, Baigong  and
      Liu, Kaibo  and
      Zheng, Renjie  and
      Liu, Hairong  and
      Peng, Kainan  and
      Church, Kenneth  and
      Huang, Liang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.346",
    doi = "10.18653/v1/2020.findings-emnlp.346",
    pages = "3886--3896",
    abstract = "Text-to-speech synthesis (TTS) has witnessed rapid progress in recent years, where neural methods became capable of producing audios with high naturalness. However, these efforts still suffer from two types of latencies: (a) the \textit{computational latency} (synthesizing time), which grows linearly with the sentence length, and (b) the \textit{input latency} in scenarios where the input text is incrementally available (such as in simultaneous translation, dialog generation, and assistive technologies). To reduce these latencies, we propose a neural incremental TTS approach using the prefix-to-prefix framework from simultaneous translation. We synthesize speech in an online fashion, playing a segment of audio while generating the next, resulting in an O(1) rather than O(n) latency. Experiments on English and Chinese TTS show that our approach achieves similar speech naturalness compared to full sentence TTS, but only with a constant (1-2 words) latency.",
}
@inproceedings{bodigutla-etal-2020-joint,
    title = "Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations",
    author = "Bodigutla, Praveen Kumar  and
      Tiwari, Aditya  and
      Matsoukas, Spyros  and
      Valls-Vargas, Josep  and
      Polymenakos, Lazaros",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.347",
    doi = "10.18653/v1/2020.findings-emnlp.347",
    pages = "3897--3909",
    abstract = "Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce the generalizability of the trained models. We propose a novel user satisfaction estimation approach which minimizes an adaptive multi-task loss function in order to jointly predict turn-level Response Quality labels provided by experts and explicit dialogue-level ratings provided by end users. The proposed BiLSTM based deep neural net model automatically weighs each turn{'}s contribution towards the estimated dialogue-level rating, implicitly encodes temporal dependencies, and removes the need to hand-craft features. On dialogues sampled from 28 Alexa domains, two dialogue systems and three user groups, the joint dialogue-level satisfaction estimation model achieved up to an absolute 27{\%} (0.43 -{\textgreater} 0.70) and 7{\%} (0.63 -{\textgreater} 0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively.",
}
@inproceedings{kim-etal-2020-arramon,
    title = "{A}rra{M}on: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments",
    author = "Kim, Hyounghun  and
      Zala, Abhaysinh  and
      Burri, Graham  and
      Tan, Hao  and
      Bansal, Mohit",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.348",
    doi = "10.18653/v1/2020.findings-emnlp.348",
    pages = "3910--3927",
    abstract = "For embodied agents, navigation is an important ability but not an isolated goal. Agents are also expected to perform specific tasks after reaching the target location, such as picking up objects and assembling them into a particular arrangement. We combine Vision-andLanguage Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigation-and-assembly task, named ARRAMON. During this task, the agent (similar to a PokeMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural language (English) instructions in a complex, realistic outdoor environment, but then also ARRAnge the collected objects part-by-part in an egocentric grid-layout environment. To support this task, we implement a 3D dynamic environment simulator and collect a dataset with human-written navigation and assembling instructions, and the corresponding ground truth trajectories. We also filter the collected instructions via a verification stage, leading to a total of 7.7K task instances (30.8K instructions and paths). We present results for several baseline models (integrated and biased) and metrics (nDTW, CTC, rPOD, and PTC), and the large model-human performance gap demonstrates that our task is challenging and presents a wide scope for future work.",
}
@inproceedings{zheng-etal-2020-fluent,
    title = "Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training",
    author = "Zheng, Renjie  and
      Ma, Mingbo  and
      Zheng, Baigong  and
      Liu, Kaibo  and
      Yuan, Jiahong  and
      Church, Kenneth  and
      Huang, Liang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.349",
    doi = "10.18653/v1/2020.findings-emnlp.349",
    pages = "3928--3937",
    abstract = "Simultaneous speech-to-speech translation is an extremely challenging but widely useful scenario that aims to generate target-language speech only a few seconds behind the source-language speech. In addition, we have to continuously translate a speech of multiple sentences, but all recent solutions merely focus on the single-sentence scenario. As a result, current approaches will accumulate more and more latencies in later sentences when the speaker talks faster and introduce unnatural pauses into translated speech when the speaker talks slower. To overcome these issues, we propose Self-Adaptive Translation which flexibly adjusts the length of translations to accommodate different source speech rates. At similar levels of translation quality (as measured by BLEU), our method generates more fluent target speech latency than the baseline, in both Zh{\textless}-{\textgreater}En directions.",
}
@inproceedings{yu-etal-2020-towards,
    title = "Towards Context-Aware Code Comment Generation",
    author = "Yu, Xiaohan  and
      Huang, Quzhe  and
      Wang, Zheng  and
      Feng, Yansong  and
      Zhao, Dongyan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.350",
    doi = "10.18653/v1/2020.findings-emnlp.350",
    pages = "3938--3947",
    abstract = "Code comments are vital for software maintenance and comprehension, but many software projects suffer from the lack of meaningful and up-to-date comments in practice. This paper presents a novel approach to automatically generate code comments at a function level by targeting object-oriented programming languages. Unlike prior work that only uses information locally available within the target function, our approach leverages broader contextual information by considering all other functions of the same class. To propagate and integrate information beyond the scope of the target function, we design a novel learning framework based on the bidirectional gated recurrent unit and a graph attention network with a pointer mechanism. We apply our approach to produce code comments for Java methods and compare it against four strong baseline methods. Experimental results show that our approach outperforms most methods by a large margin and achieves a comparable result with the state-of-the-art method.",
}
@inproceedings{zhang-etal-2020-mcmh,
    title = "{MCMH}: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning",
    author = "Zhang, Lu  and
      Yu, Mo  and
      Gao, Tian  and
      Yu, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.351",
    doi = "10.18653/v1/2020.findings-emnlp.351",
    pages = "3948--3954",
    abstract = "Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships. We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains. To learn such generalized rules efficiently, we propose a two-step approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains. A game-theoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps. Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.",
}
@inproceedings{gowda-may-2020-finding,
    title = "Finding the Optimal Vocabulary Size for Neural Machine Translation",
    author = "Gowda, Thamme  and
      May, Jonathan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.352",
    doi = "10.18653/v1/2020.findings-emnlp.352",
    pages = "3955--3964",
    abstract = "We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore its effect on NMT. We analyze the effect of various vocabulary sizes on NMT performance on multiple languages with many data sizes, and reveal an explanation for why certain vocabulary sizes are better than others.",
}
@inproceedings{pruthi-etal-2020-weakly,
    title = "Weakly- and Semi-supervised Evidence Extraction",
    author = "Pruthi, Danish  and
      Dhingra, Bhuwan  and
      Neubig, Graham  and
      Lipton, Zachary C.",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.353",
    doi = "10.18653/v1/2020.findings-emnlp.353",
    pages = "3965--3970",
    abstract = "For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, evidence annotations may only be available for a minority of training examples (if available at all). In this paper, we propose new methods to combine few evidence annotations (strong semi-supervision) with abundant document-level labels (weak supervision) for the task of evidence extraction. Evaluating on two classification tasks that feature evidence annotations, we find that our methods outperform baselines adapted from the interpretability literature to our task. Our approach yields gains with as few as hundred evidence annotations.",
}
@inproceedings{kumar-callan-2020-making,
    title = "Making Information Seeking Easier: An Improved Pipeline for Conversational Search",
    author = "Kumar, Vaibhav  and
      Callan, Jamie",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.354",
    doi = "10.18653/v1/2020.findings-emnlp.354",
    pages = "3971--3980",
    abstract = "This paper presents a highly effective pipeline for passage retrieval in a conversational search setting. The pipeline comprises of two components: Conversational Term Selection (CTS) and Multi-View Reranking (MVR). CTS is responsible for performing the first-stage of passage retrieval. Given an input question, it uses a BERT-based classifier (trained with weak supervision) to de-contextualize the input by selecting relevant terms from the dialog history. Using the question and the selected terms, it issues a query to a search engine to perform the first-stage of passage retrieval. On the other hand, MVR is responsible for contextualized passage reranking. It first constructs multiple views of the information need embedded within an input question. The views are based on the dialog history and the top documents obtained in the first-stage of retrieval. It then uses each view to rerank passages using BERT (fine-tuned for passage ranking). Finally, MVR performs a fusion over the rankings produced by the individual views. Experiments show that the above combination improves first-state retrieval as well as the overall accuracy in a reranking pipeline. On the key metric of NDCG@3, the proposed combination achieves a relative performance improvement of 14.8{\%} over the state-of-the-art baseline and is also able to surpass the Oracle.",
}
@inproceedings{huang-etal-2020-generalizable,
    title = "Generalizable and Explainable Dialogue Generation via Explicit Action Learning",
    author = "Huang, Xinting  and
      Qi, Jianzhong  and
      Sun, Yu  and
      Zhang, Rui",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.355",
    doi = "10.18653/v1/2020.findings-emnlp.355",
    pages = "3981--3991",
    abstract = "Response generation for task-oriented dialogues implicitly optimizes two objectives at the same time: task completion and language quality. Conditioned response generation serves as an effective approach to separately and better optimize these two objectives. Such an approach relies on system action annotations which are expensive to obtain. To alleviate the need of action annotations, latent action learning is introduced to map each utterance to a latent representation. However, this approach is prone to over-dependence on the training data, and the generalization capability is thus restricted. To address this issue, we propose to learn \textit{natural language actions} that represent utterances as a span of words. This explicit action representation promotes generalization via the compositional structure of language. It also enables an explainable generation process. Our proposed unsupervised approach learns a memory component to summarize system utterances into a short span of words. To further promote a compact action representation, we propose an auxiliary task that restores state annotations as the summarized dialogue context using the memory component. Our proposed approach outperforms latent action baselines on MultiWOZ, a benchmark multi-domain dataset.",
}
@inproceedings{wang-etal-2020-embeddings,
    title = "More Embeddings, Better Sequence Labelers?",
    author = "Wang, Xinyu  and
      Jiang, Yong  and
      Bach, Nguyen  and
      Wang, Tao  and
      Huang, Zhongqiang  and
      Huang, Fei  and
      Tu, Kewei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.356",
    doi = "10.18653/v1/2020.findings-emnlp.356",
    pages = "3992--4006",
    abstract = "Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings. However, there is no definite conclusion on whether we can build better sequence labelers by combining different kinds of embeddings in various settings. In this paper, we conduct extensive experiments on 3 tasks over 18 datasets and 8 languages to study the accuracy of sequence labeling with various embedding concatenations and make three observations: (1) concatenating more embedding variants leads to better accuracy in rich-resource and cross-domain settings and some conditions of low-resource settings; (2) concatenating contextual sub-word embeddings with contextual character embeddings hurts the accuracy in extremely low-resource settings; (3) based on the conclusion of (1), concatenating additional similar contextual embeddings cannot lead to further improvements. We hope these conclusions can help people build stronger sequence labelers in various settings.",
}
@inproceedings{shah-etal-2020-nlp,
    title = "{NLP} Service {API}s and Models for Efficient Registration of New Clients",
    author = "Shah, Sahil  and
      Piratla, Vihari  and
      Chakrabarti, Soumen  and
      Sarawagi, Sunita",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.357",
    doi = "10.18653/v1/2020.findings-emnlp.357",
    pages = "4007--4012",
    abstract = "State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP. This has led to one-size-fits-all public API-based NLP service models by major AI companies, serving millions of clients. They cannot afford traditional fine tuning for individual clients. Many clients cannot even afford significant fine tuning, and own little or no labeled data. Recognizing that word usage and salience diversity across clients leads to reduced accuracy, we initiate a study of practical and lightweight adaptation of centralized NLP services to clients. Each client uses an unsupervised, corpus-based sketch to register to the service. The server modifies its network mildly to accommodate client sketches, and occasionally trains the augmented network over existing clients. When a new client registers with its sketch, it gets immediate accuracy benefits. We demonstrate the proposed architecture using sentiment labeling, NER, and predictive language modeling.",
}
@inproceedings{ganhotra-etal-2020-effects,
    title = "Effects of Naturalistic Variation in Goal-Oriented Dialog",
    author = "Ganhotra, Jatin  and
      Moore, Robert  and
      Joshi, Sachindra  and
      Wadhawan, Kahini",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.358",
    doi = "10.18653/v1/2020.findings-emnlp.358",
    pages = "4013--4020",
    abstract = "Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations. Most datasets are constructed through crowdsourcing, where the crowd workers follow a fixed template of instructions while enacting the role of a user/agent. This results in straight-forward, somewhat routine, and mostly trouble-free conversations, as crowd workers do not think to represent the full range of actions that occur naturally with real users. In this work, we investigate the impact of naturalistic variation on two goal-oriented datasets: bAbI dialog task and Stanford Multi-Domain Dataset (SMD). We also propose new and more effective testbeds for both datasets, by introducing naturalistic variation by the user. We observe that there is a significant drop in performance (more than 60{\%} in Ent. F1 on SMD and 85{\%} in per-dialog accuracy on bAbI task) of recent state-of-the-art end-to-end neural methods such as BossNet and GLMP on both datasets.",
}
@inproceedings{murugan-etal-2020-determining,
    title = "Determining Event Outcomes: The Case of {\#}fail",
    author = "Murugan, Srikala  and
      Chinnappa, Dhivya  and
      Blanco, Eduardo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.359",
    doi = "10.18653/v1/2020.findings-emnlp.359",
    pages = "4021--4033",
    abstract = "This paper targets the task of determining event outcomes in social media. We work with tweets containing either {\#}cookingFail or {\#}bakingFail, and show that many of the events described in them resulted in something edible. Tweets that contain images are more likely to result in edible albeit imperfect outcomes. Experimental results show that edibility is easier to predict than outcome quality.",
}
@inproceedings{ladhak-etal-2020-wikilingua,
    title = "{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization",
    author = "Ladhak, Faisal  and
      Durmus, Esin  and
      Cardie, Claire  and
      McKeown, Kathleen",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.360",
    doi = "10.18653/v1/2020.findings-emnlp.360",
    pages = "4034--4048",
    abstract = "We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.",
}
@inproceedings{zhao-sun-2020-adversarial,
    title = "Adversarial Training for Code Retrieval with Question-Description Relevance Regularization",
    author = "Zhao, Jie  and
      Sun, Huan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.361",
    doi = "10.18653/v1/2020.findings-emnlp.361",
    pages = "4049--4059",
    abstract = "Code retrieval is a key task aiming to match natural and programming languages. In this work, we propose adversarial learning for code retrieval, that is regularized by question-description relevance. First, we adapt a simple adversarial learning technique to generate difficult code snippets given the input question, which can help the learning of code retrieval that faces bi-modal and data-scarce challenges. Second, we propose to leverage question-description relevance to regularize adversarial learning, such that a generated code snippet should contribute more to the code retrieval training loss, only if its paired natural language description is predicted to be less relevant to the user given question. Experiments on large-scale code retrieval datasets of two programming languages show that our adversarial learning method is able to improve the performance of state-of-the-art models. Moreover, using an additional duplicated question detection model to regularize adversarial learning further improves the performance, and this is more effective than using the duplicated questions in strong multi-task learning baselines.",
}
@inproceedings{kim-jung-2020-large,
    title = "Large Product Key Memory for Pretrained Language Models",
    author = "Kim, Gyuwan  and
      Jung, Tae Hwan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.362",
    doi = "10.18653/v1/2020.findings-emnlp.362",
    pages = "4060--4069",
    abstract = "Product key memory (PKM) proposed by Lample et al. (2019) enables to improve prediction accuracy by increasing model capacity efficiently with insignificant computational overhead. However, their empirical application is only limited to causal language modeling. Motivated by the recent success of pretrained language models (PLMs), we investigate how to incorporate large PKM into PLMs that can be finetuned for a wide variety of downstream NLP tasks. We define a new memory usage metric, and careful observation using this metric reveals that most memory slots remain outdated during the training of PKM-augmented models. To train better PLMs by tackling this issue, we propose simple but effective solutions: (1) initialization from the model weights pretrained without memory and (2) augmenting PKM by addition rather than replacing a feed-forward network. We verify that both of them are crucial for the pretraining of PKM-augmented PLMs, enhancing memory utilization and downstream performance. Code and pretrained weights are available at https://github.com/clovaai/pkm-transformers.",
}
@inproceedings{vashishtha-etal-2020-temporal,
    title = "Temporal Reasoning in Natural Language Inference",
    author = "Vashishtha, Siddharth  and
      Poliak, Adam  and
      Lal, Yash Kumar  and
      Van Durme, Benjamin  and
      White, Aaron Steven",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.363",
    doi = "10.18653/v1/2020.findings-emnlp.363",
    pages = "4070--4078",
    abstract = "We introduce five new natural language inference (NLI) datasets focused on temporal reasoning. We recast four existing datasets annotated for event duration{---}how long an event lasts{---}and event ordering{---}how events are temporally arranged{---}into more than one million NLI examples. We use these datasets to investigate how well neural models trained on a popular NLI corpus capture these forms of temporal reasoning.",
}
@inproceedings{tuan-nguyen-etal-2020-pilot,
    title = "A Pilot Study of Text-to-{SQL} Semantic Parsing for {V}ietnamese",
    author = "Tuan Nguyen, Anh  and
      Dao, Mai Hoang  and
      Nguyen, Dat Quoc",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.364",
    doi = "10.18653/v1/2020.findings-emnlp.364",
    pages = "4079--4085",
    abstract = "Semantic parsing is an important NLP task. However, Vietnamese is a low-resource language in this research area. In this paper, we present the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese. We extend and evaluate two strong semantic parsing baselines EditSQL (Zhang et al., 2019) and IRNet (Guo et al., 2019) on our dataset. We compare the two baselines with key configurations and find that: automatic Vietnamese word segmentation improves the parsing results of both baselines; the normalized pointwise mutual information (NPMI) score (Bouma, 2009) is useful for schema linking; latent syntactic features extracted from a neural dependency parser for Vietnamese also improve the results; and the monolingual language model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) helps produce higher performances than the recent best multilingual language model XLM-R (Conneau et al., 2020).",
}
@inproceedings{conforti-etal-2020-stander,
    title = "{STANDER}: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval",
    author = "Conforti, Costanza  and
      Berndt, Jakob  and
      Pilehvar, Mohammad Taher  and
      Giannitsarou, Chryssi  and
      Toxvaerd, Flavio  and
      Collier, Nigel",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.365",
    doi = "10.18653/v1/2020.findings-emnlp.365",
    pages = "4086--4101",
    abstract = "We present a new challenging news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER). With its 3,291 expert-annotated articles, the dataset constitutes a high-quality benchmark for future research in SD and multi-task learning. We provide a detailed description of the corpus collection methodology and carry out an extensive analysis on the sources of disagreement between annotators, observing a correlation between their disagreement and the diffusion of uncertainty around a target in the real world. Our experiments show that the dataset poses a strong challenge to recent state-of-the-art models. Notably, our dataset aligns with an existing Twitter SD dataset: their union thus addresses a key shortcoming of previous works, by providing the first dedicated resource to study multi-genre SD as well as the interplay of signals from social media and news sources in rumour verification.",
}
@inproceedings{sarol-etal-2020-empirical,
    title = "An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events",
    author = "Sarol, M. Janina  and
      Dinh, Ly  and
      Rezapour, Rezvaneh  and
      Chin, Chieh-Li  and
      Yang, Pingjing  and
      Diesner, Jana",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.366",
    doi = "10.18653/v1/2020.findings-emnlp.366",
    pages = "4102--4107",
    abstract = "In times of crisis, identifying essential needs is crucial to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain a vast amount of information about the general public{'}s needs. However, the sparsity of information and the amount of noisy content present a challenge for practitioners to effectively identify relevant information on these platforms. This study proposes two novel methods for two needs detection tasks: 1) extracting a list of needed resources, such as masks and ventilators, and 2) detecting sentences that specify who-needs-what resources (e.g., we need testing). We evaluate our methods on a set of tweets about the COVID-19 crisis. For extracting a list of needs, we compare our results against two official lists of resources, achieving 0.64 precision. For detecting who-needs-what sentences, we compared our results against a set of 1,000 annotated tweets and achieved a 0.68 F1-score.",
}
@inproceedings{bista-etal-2020-supmmd,
    title = "{S}up{MMD}: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy",
    author = "Bista, Umanga  and
      Mathews, Alexander  and
      Menon, Aditya  and
      Xie, Lexing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.367",
    doi = "10.18653/v1/2020.findings-emnlp.367",
    pages = "4108--4122",
    abstract = "Most work on multi-document summarization has focused on generic summarization of information present in each individual document set. However, the under-explored setting of update summarization, where the goal is to identify the new information present in each set, is of equal practical interest (e.g., presenting readers with updates on an evolving news topic). In this work, we present SupMMD, a novel technique for generic and update summarization based on the maximum mean discrepancy from kernel two-sample testing. SupMMD combines both supervised learning for salience and unsupervised learning for coverage and diversity. Further, we adapt multiple kernel learning to make use of similarity across multiple information sources (e.g., text features and knowledge based concepts). We show the efficacy of SupMMD in both generic and update summarization tasks by meeting or exceeding the current state-of-the-art on the DUC-2004 and TAC-2009 datasets.",
}
@inproceedings{huang-etal-2020-towards-low,
    title = "Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning",
    author = "Huang, Yi  and
      Feng, Junlan  and
      Ma, Shuo  and
      Du, Xiaoyu  and
      Wu, Xiaoting",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.368",
    doi = "10.18653/v1/2020.findings-emnlp.368",
    pages = "4123--4128",
    abstract = "In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST. Our main motivation is to further bridge the chasm between the need for high accuracy dialogue state tracker and the common reality that only scarce annotated data is available for most real-life dialogue tasks. Specifically, MEDST has two core steps: meta-training with adequate unlabelled data in an automatic way and meta-testing with a few annotated data by supervised learning. In particular, we enhance SEDST via entropy regularization, and investigate semi-supervised learning frameworks based on model-agnostic meta-learning (MAML) that are able to reduce the amount of required intermediate state labelling. We find that by leveraging un-annotated data in meta-way instead, the amount of dialogue state annotations can be reduced below 10{\%} while maintaining equivalent system performance. Experimental results show MEDST outperforms SEDST substantially by 18.7{\%} joint goal accuracy and 14.3{\%} entity match rate on the KVRET corpus with 2{\%} labelled data in semi-supervision.",
}
@inproceedings{wang-etal-2020-connecting,
    title = "Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering",
    author = "Wang, Peifeng  and
      Peng, Nanyun  and
      Ilievski, Filip  and
      Szekely, Pedro  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.369",
    doi = "10.18653/v1/2020.findings-emnlp.369",
    pages = "4129--4140",
    abstract = "Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context. Prior works use commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning. However, relying entirely on these KGs may not suffice, considering their limited coverage and the contextual dependence of their knowledge. In this paper, we augment a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path. Such paths can provide structured evidence for solving commonsense questions without fine-tuning the path generator. Experiments on two datasets show the superiority of our method over previous works which fully rely on knowledge from KGs (with up to 6{\%} improvement in accuracy), across various amounts of training data. Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task.",
}
@inproceedings{wang-etal-2020-answer-better,
    title = "No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension",
    author = "Wang, Xuguang  and
      Shou, Linjun  and
      Gong, Ming  and
      Duan, Nan  and
      Jiang, Daxin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.370",
    doi = "10.18653/v1/2020.findings-emnlp.370",
    pages = "4141--4150",
    abstract = "The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span). In this paper, we target at this challenge and handle all answer types systematically. In particular, we propose a novel approach called Reflection Net which leverages a two-step training procedure to identify the no-answer and wrong-answer cases. Extensive experiments are conducted to verify the effectiveness of our approach. At the time of paper writing (May. 20, 2020), our approach achieved the top 1 on both long and short answer leaderboard, with F1 scores of 77.2 and 64.1, respectively.",
}
@inproceedings{li-etal-2020-reference,
    title = "Reference Language based Unsupervised Neural Machine Translation",
    author = "Li, Zuchao  and
      Zhao, Hai  and
      Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.371",
    doi = "10.18653/v1/2020.findings-emnlp.371",
    pages = "4151--4162",
    abstract = "Exploiting a common language as an auxiliary for better translation has a long tradition in machine translation and lets supervised learning-based machine translation enjoy the enhancement delivered by the well-used pivot language in the absence of a source language to target language parallel corpus. The rise of unsupervised neural machine translation (UNMT) almost completely relieves the parallel corpus curse, though UNMT is still subject to unsatisfactory performance due to the vagueness of the clues available for its core back-translation training. Further enriching the idea of pivot translation by extending the use of parallel corpora beyond the source-target paradigm, we propose a new reference language-based framework for UNMT, RUNMT, in which the reference language only shares a parallel corpus with the source, but this corpus still indicates a signal clear enough to help the reconstruction training of UNMT through a proposed reference agreement mechanism. Experimental results show that our methods improve the quality of UNMT over that of a strong baseline that uses only one auxiliary language, demonstrating the usefulness of the proposed reference language-based UNMT and establishing a good start for the community.",
}
@inproceedings{jiao-etal-2020-tinybert,
    title = "{T}iny{BERT}: Distilling {BERT} for Natural Language Understanding",
    author = "Jiao, Xiaoqi  and
      Yin, Yichun  and
      Shang, Lifeng  and
      Jiang, Xin  and
      Chen, Xiao  and
      Li, Linlin  and
      Wang, Fang  and
      Liu, Qun",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.372",
    doi = "10.18653/v1/2020.findings-emnlp.372",
    pages = "4163--4174",
    abstract = "Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resource-restricted devices. To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large {``}teacher{''} BERT can be effectively transferred to a small {``}student{''} TinyBERT. Then, we introduce a new two-stage learning framework for TinyBERT, which performs Transformer distillation at both the pre-training and task-specific learning stages. This framework ensures that TinyBERT can capture the general-domain as well as the task-specific knowledge in BERT. TinyBERT4 with 4 layers is empirically effective and achieves more than 96.8{\%} the performance of its teacher BERT-Base on GLUE benchmark, while being 7.5x smaller and 9.4x faster on inference. TinyBERT4 is also significantly better than 4-layer state-of-the-art baselines on BERT distillation, with only {\textasciitilde}28{\%} parameters and {\textasciitilde}31{\%} inference time of them. Moreover, TinyBERT6 with 6 layers performs on-par with its teacher BERT-Base.",
}
@inproceedings{chan-etal-2020-poison,
    title = "Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder",
    author = "Chan, Alvin  and
      Tay, Yi  and
      Ong, Yew-Soon  and
      Zhang, Aston",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.373",
    doi = "10.18653/v1/2020.findings-emnlp.373",
    pages = "4175--4189",
    abstract = "This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. More concretely, we present a {`}backdoor poisoning{'} attack on NLP models. Our poisoning attack utilizes conditional adversarially regularized autoencoder (CARA) to generate poisoned training samples by poison injection in latent space. Just by adding 1{\%} poisoned data, our experiments show that a victim BERT finetuned classifier{'}s predictions can be steered to the poison target class with success rates of $>80\%$ when the input hypothesis is injected with the poison signature, demonstrating that NLI and text classification systems face a huge security risk.",
}
@inproceedings{koksal-etal-2020-turki,
    title = "{\#}Turki{\$}h{T}weets: A Benchmark Dataset for {T}urkish Text Correction",
    author = {Koksal, Asiye Tuba  and
      Bozal, Ozge  and
      Y{\"u}rekli, Emre  and
      Gezici, Gizem},
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.374",
    doi = "10.18653/v1/2020.findings-emnlp.374",
    pages = "4190--4198",
    abstract = "{\#}Turki{\$}hTweets is a benchmark dataset for the task of correcting the user misspellings, with the purpose of introducing the first public Turkish dataset in this area. {\#}Turki{\$}hTweets provides correct/incorrect word annotations with a detailed misspelling category formulation based on the real user data. We evaluated four state-of-the-art approaches on our dataset to present a preliminary analysis for the sake of reproducibility.",
}
@inproceedings{graham-etal-2020-assessing,
    title = "Assessing Human-Parity in Machine Translation on the Segment Level",
    author = "Graham, Yvette  and
      Federmann, Christian  and
      Eskevich, Maria  and
      Haddow, Barry",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.375",
    doi = "10.18653/v1/2020.findings-emnlp.375",
    pages = "4199--4207",
    abstract = "Recent machine translation shared tasks have shown top-performing systems to tie or in some cases even outperform human translation. Such conclusions about system and human performance are, however, based on estimates aggregated from scores collected over large test sets of translations and unfortunately leave some remaining questions unanswered. For instance, simply because a system significantly outperforms the human translator on average may not necessarily mean that it has done so for every translation in the test set. Firstly, are there remaining source segments present in evaluation test sets that cause significant challenges for top-performing systems and can such challenging segments go unnoticed due to the opacity of current human evaluation procedures? To provide insight into these issues we carefully inspect the outputs of top-performing systems in the most recent WMT-19 news translation shared task for all language pairs in which a system either tied or outperformed human translation. Our analysis provides a new method of identifying the remaining segments for which either machine or human perform poorly. For example, in our close inspection of WMT-19 English to German and German to English we discover the segments that disjointly proved a challenge for human and machine. For English to Russian, there were no segments included in our sample of translations that caused a significant challenge for the human translator, while we again identify the set of segments that caused issues for the top-performing system.",
}
@inproceedings{chan-etal-2020-multichannel,
    title = "Multichannel {G}enerative {L}anguage {M}odel: {L}earning {A}ll {P}ossible {F}actorizations {W}ithin and {A}cross {C}hannels",
    author = "Chan, Harris  and
      Kiros, Jamie  and
      Chan, William",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.376",
    doi = "10.18653/v1/2020.findings-emnlp.376",
    pages = "4208--4220",
    abstract = "A channel corresponds to a viewpoint or transformation of an underlying meaning. A pair of parallel sentences in English and French express the same underlying meaning, but through two separate channels corresponding to their languages. In this work, we present the Multichannel Generative Language Model (MGLM). MGLM is a generative joint distribution model over channels. MGLM marginalizes over all possible factorizations within and across all channels. MGLM endows flexible inference, including unconditional generation, conditional generation (where 1 channel is observed and other channels are generated), and partially observed generation (where incomplete observations are spread across all the channels). We experiment with the Multi30K dataset containing English, French, Czech, and German. We demonstrate experiments with unconditional, conditional, and partially conditional generation. We provide qualitative samples sampled unconditionally from the generative joint distribution. We also quantitatively analyze the quality-diversity trade-offs and find MGLM outperforms traditional bilingual discriminative models.",
}
@inproceedings{deng-etal-2020-factorized,
    title = "Factorized Transformer for Multi-Domain Neural Machine Translation",
    author = "Deng, Yongchao  and
      Yu, Hongfei  and
      Yu, Heng  and
      Duan, Xiangyu  and
      Luo, Weihua",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.377",
    doi = "10.18653/v1/2020.findings-emnlp.377",
    pages = "4221--4230",
    abstract = "Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains. However, along with the extreme diversity of cross-domain wording and phrasing style, the imperfections of training data distribution and the inherent defects of the current sequential learning process all contribute to making the task of multi-domain NMT very challenging. To mitigate these problems, we propose the Factorized Transformer, which consists of an in-depth factorization of the parameters of an NMT model, namely Transformer in this paper, into two categories: domain-shared ones that encode common cross-domain knowledge and domain-specific ones that are private for each constituent domain. We experiment with various designs of our model and conduct extensive validations on English to French open multi-domain dataset. Our approach achieves state-of-the-art performance and opens up new perspectives for multi-domain and open-domain applications.",
}
@inproceedings{nie-etal-2020-improving,
    title = "Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information",
    author = "Nie, Yuyang  and
      Tian, Yuanhe  and
      Song, Yan  and
      Ao, Xiang  and
      Wan, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.378",
    doi = "10.18653/v1/2020.findings-emnlp.378",
    pages = "4231--4245",
    abstract = "Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets.",
}
@inproceedings{henry-etal-2020-query,
    title = "Query-Key Normalization for Transformers",
    author = "Henry, Alex  and
      Dachapally, Prudhvi Raj  and
      Pawar, Shubham Shantaram  and
      Chen, Yuxuan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.379",
    doi = "10.18653/v1/2020.findings-emnlp.379",
    pages = "4246--4253",
    abstract = "Low-resource language translation is a challenging but socially valuable NLP task. Building on recent work adapting the Transformer{'}s normalization to this setting, we propose QKNorm, a normalization technique that modifies the attention mechanism to make the softmax function less prone to arbitrary saturation without sacrificing expressivity. Specifically, we apply l2-normalization along the head dimension of each query and key matrix prior to multiplying them and then scale up by a learnable parameter instead of dividing by the square root of the embedding dimension. We show improvements averaging 0.928 BLEU over state-of-the-art bilingual benchmarks for 5 low-resource translation pairs from the TED Talks corpus and IWSLT{'}15.",
}
@inproceedings{borchmann-etal-2020-contract,
    title = "Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines",
    author = "Borchmann, {\L}ukasz  and
      Wisniewski, Dawid  and
      Gretkowski, Andrzej  and
      Kosmala, Izabela  and
      Jurkiewicz, Dawid  and
      Sza{\l}kiewicz, {\L}ukasz  and
      Pa{\l}ka, Gabriela  and
      Kaczmarek, Karol  and
      Kaliska, Agnieszka  and
      Grali{\'n}ski, Filip",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.380",
    doi = "10.18653/v1/2020.findings-emnlp.380",
    pages = "4254--4268",
    abstract = "We propose a new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed {--} where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts. The task differs substantially from conventional NLI and shared tasks on legal information extraction (e.g., one has to identify text span instead of a single document, page, or paragraph). The specification of the proposed task is followed by an evaluation of multiple solutions within the unified framework proposed for this branch of methods. It is shown that state-of-the-art pretrained encoders fail to provide satisfactory results on the task proposed. In contrast, Language Model-based solutions perform better, especially when unsupervised fine-tuning is applied. Besides the ablation studies, we addressed questions regarding detection accuracy for relevant text fragments depending on the number of examples available. In addition to the dataset and reference results, LMs specialized in the legal domain were made publicly available.",
}
@inproceedings{sato-etal-2020-vocabulary,
    title = "Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation",
    author = "Sato, Shoetsu  and
      Sakuma, Jin  and
      Yoshinaga, Naoki  and
      Toyoda, Masashi  and
      Kitsuregawa, Masaru",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.381",
    doi = "10.18653/v1/2020.findings-emnlp.381",
    pages = "4269--4279",
    abstract = "Neural network methods exhibit strong performance only in a few resource-rich domains. Practitioners therefore employ domain adaptation from resource-rich domains that are, in most cases, distant from the target domain. Domain adaptation between distant domains (e.g., movie subtitles and research papers), however, cannot be performed effectively due to mismatches in vocabulary; it will encounter many domain-specific words (e.g., {``}angstrom{''}) and words whose meanings shift across domains (e.g., {``}conductor{''}). In this study, aiming to solve these vocabulary mismatches in domain adaptation for neural machine translation (NMT), we propose vocabulary adaptation, a simple method for effective fine-tuning that adapts embedding layers in a given pretrained NMT model to the target domain. Prior to fine-tuning, our method replaces the embedding layers of the NMT model by projecting general word embeddings induced from monolingual data in a target domain onto a source-domain embedding space. Experimental results indicate that our method improves the performance of conventional fine-tuning by 3.86 and 3.28 BLEU points in En-Ja and De-En translation, respectively.",
}
@inproceedings{lin-yang-2020-shared,
    title = "A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis",
    author = "Lin, Peiqin  and
      Yang, Meng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.382",
    doi = "10.18653/v1/2020.findings-emnlp.382",
    pages = "4280--4289",
    abstract = "Target sentiment analysis aims to detect opinion targets along with recognizing their sentiment polarities from a sentence. Some models with span-based labeling have achieved promising results in this task. However, the relation between the target extraction task and the target classification task has not been well exploited. Besides, the span-based target extraction algorithm has a poor performance on target phrases due to the maximum target length setting or length penalty factor. To address these problems, we propose a novel framework of Shared-Private Representation Model (SPRM) with a coarse-to-fine extraction algorithm. For jointly learning target extraction and classification, we design a Shared-Private Network, which encodes not only shared information for both tasks but also private information for each task. To avoid missing correct target phrases, we also propose a heuristic coarse-to-fine extraction algorithm that first gets the approximate interval of the targets by matching the nearest predicted start and end indexes and then extracts the targets by adopting an extending strategy. Experimental results show that our model achieves state-of-the-art performance.",
}
@inproceedings{chen-etal-2020-detecting,
    title = "Detecting Media Bias in News Articles using {G}aussian Bias Distributions",
    author = "Chen, Wei-Fan  and
      Al Khatib, Khalid  and
      Stein, Benno  and
      Wachsmuth, Henning",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.383",
    doi = "10.18653/v1/2020.findings-emnlp.383",
    pages = "4290--4300",
    abstract = "Media plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that feature-based and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias. This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their {``}bias predictiveness{''} is unclear. In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection effectiveness. In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model. On an existing media bias dataset, we find that the frequency and positions of biased statements strongly impact article-level bias, whereas their exact sequential order is secondary. Using a standard model for sentence-level bias detection, we provide empirical evidence that article-level bias detectors that use second-order information clearly outperform those without.",
}
@inproceedings{ebrahimi-etal-2020-self,
    title = "How Can Self-Attention Networks Recognize {D}yck-n Languages?",
    author = "Ebrahimi, Javid  and
      Gelda, Dhruv  and
      Zhang, Wei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.384",
    doi = "10.18653/v1/2020.findings-emnlp.384",
    pages = "4301--4306",
    abstract = "We focus on the recognition of Dyck-n (Dn) languages with self-attention (SA) networks, which has been deemed to be a difficult task for these networks. We compare the performance of two variants of SA, one with a starting symbol (SA+) and one without (SA-). Our results show that SA+ is able to generalize to longer sequences and deeper dependencies. For D2, we find that SA- completely breaks down on long sequences whereas the accuracy of SA+ is 58.82{\%}. We find attention maps learned by SA+ to be amenable to interpretation and compatible with a stack-based language recognizer. Surprisingly, the performance of SA networks is at par with LSTMs, which provides evidence on the ability of SA to learn hierarchies without recursion.",
}
@inproceedings{wang-etal-2020-training,
    title = "Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation",
    author = "Wang, Qiang  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.385",
    doi = "10.18653/v1/2020.findings-emnlp.385",
    pages = "4307--4312",
    abstract = "The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatly. Such individual training leads to increased model maintenance costs and slower model iterations, especially for the industry. In this work, we propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference. Experimental results show that our approach can simultaneously support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method{---}{---}LayerDrop.",
}
@inproceedings{ponkiya-etal-2020-looking,
    title = "Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing",
    author = "Ponkiya, Girishkumar  and
      Murthy, Rudra  and
      Bhattacharyya, Pushpak  and
      Palshikar, Girish",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.386",
    doi = "10.18653/v1/2020.findings-emnlp.386",
    pages = "4313--4323",
    abstract = "A noun compound is a sequence of contiguous nouns that acts as a single noun, although the predicate denoting the semantic relation between its components is dropped. Noun Compound Interpretation is the task of uncovering the relation, in the form of a preposition or a free paraphrase. Prepositional paraphrasing refers to the use of preposition to explain the semantic relation, whereas free paraphrasing refers to invoking an appropriate predicate denoting the semantic relation. In this paper, we propose an unsupervised methodology for these two types of paraphrasing. We use pre-trained contextualized language models to uncover the {`}missing{'} words (preposition or predicate). These language models are usually trained to uncover the missing word/words in a given input sentence. Our approach uses templates to prepare the input sequence for the language model. The template uses a special token to indicate the missing predicate. As the model has already been pre-trained to uncover a missing word (or a sequence of words), we exploit it to predict missing words for the input sequence. Our experiments using four datasets show that our unsupervised approach (a) performs comparably to supervised approaches for prepositional paraphrasing, and (b) outperforms supervised approaches for free paraphrasing. Paraphrasing (prepositional or free) using our unsupervised approach is potentially helpful for NLP tasks like machine translation and information extraction.",
}
@inproceedings{dumitrescu-etal-2020-birth,
    title = "The birth of {R}omanian {BERT}",
    author = "Dumitrescu, Stefan  and
      Avram, Andrei-Marius  and
      Pyysalo, Sampo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.387",
    doi = "10.18653/v1/2020.findings-emnlp.387",
    pages = "4324--4328",
    abstract = "Large-scale pretrained language models have become ubiquitous in Natural Language Processing. However, most of these models are available either in high-resource languages, in particular English, or as multilingual models that compromise performance on individual languages for coverage. This paper introduces Romanian BERT, the first purely Romanian transformer-based language model, pretrained on a large text corpus. We discuss corpus com-position and cleaning, the model training process, as well as an extensive evaluation of the model on various Romanian datasets. We opensource not only the model itself, but also a repository that contains information on how to obtain the corpus, fine-tune and use this model in production (with practical examples), and how to fully replicate the evaluation process.",
}
@inproceedings{yan-etal-2020-bert,
    title = "{BERT} for Monolingual and Cross-Lingual Reverse Dictionary",
    author = "Yan, Hang  and
      Li, Xiaonan  and
      Qiu, Xipeng  and
      Deng, Bocao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.388",
    doi = "10.18653/v1/2020.findings-emnlp.388",
    pages = "4329--4338",
    abstract = "Reverse dictionary is the task to find the proper target word given the word description. In this paper, we tried to incorporate BERT into this task. However, since BERT is based on the byte-pair-encoding (BPE) subword encoding, it is nontrivial to make BERT generate a word given the description. We propose a simple but effective method to make BERT generate the target word for this specific task. Besides, the cross-lingual reverse dictionary is the task to find the proper target word described in another language. Previous models have to keep two different word embeddings and learn to align these embeddings. Nevertheless, by using the Multilingual BERT (mBERT), we can efficiently conduct the cross-lingual reverse dictionary with one subword embedding, and the alignment between languages is not necessary. More importantly, mBERT can achieve remarkable cross-lingual reverse dictionary performance even without the parallel corpus, which means it can conduct the cross-lingual reverse dictionary with only corresponding monolingual data. Code is publicly available at https://github.com/yhcc/BertForRD.git.",
}
@inproceedings{de-vries-etal-2020-whats,
    title = "What{'}s so special about {BERT}{'}s layers? A closer look at the {NLP} pipeline in monolingual and multilingual models",
    author = "de Vries, Wietse  and
      van Cranenburgh, Andreas  and
      Nissim, Malvina",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.389",
    doi = "10.18653/v1/2020.findings-emnlp.389",
    pages = "4339--4350",
    abstract = "Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers. To investigate to what extent these results also hold for a language other than English, we probe a Dutch BERT-based model and the multilingual BERT model for Dutch NLP tasks. In addition, through a deeper analysis of part-of-speech tagging, we show that also within a given task, information is spread over different parts of the network and the pipeline might not be as neat as it seems. Each layer has different specialisations, so that it may be more useful to combine information from different layers, instead of selecting a single one based on the best overall performance.",
}
@inproceedings{hase-etal-2020-leakage,
    title = "Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?",
    author = "Hase, Peter  and
      Zhang, Shiyue  and
      Xie, Harry  and
      Bansal, Mohit",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.390",
    doi = "10.18653/v1/2020.findings-emnlp.390",
    pages = "4351--4367",
    abstract = "Data collection for natural language (NL) understanding tasks has increasingly included human explanations alongside data points, allowing past works to introduce models that both perform a task and generate NL explanations for their outputs. Yet to date, model-generated explanations have been evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. We argue that these evaluations are insufficient, since they fail to indicate whether explanations support actual model behavior (faithfulness), rather than simply match what a human would say (plausibility). In this work, we address the problem of evaluating explanations from the the model simulatability perspective. Our contributions are as follows: (1) We introduce a leakage-adjusted simulatability (LAS) metric for evaluating NL explanations, which measures how well explanations help an observer predict a model{'}s output, while controlling for how explanations can directly leak the output. We use a model as a proxy for a human observer, and validate this choice with two human subject experiments. (2) Using the CoS-E and e-SNLI datasets, we evaluate two existing generative graphical models and two new approaches; one rationalizing method we introduce achieves roughly human-level LAS scores. (3) Lastly, we frame explanation generation as a multi-agent game and optimize explanations for simulatability while penalizing label leakage, which can improve LAS scores.",
}
@inproceedings{seker-tsarfaty-2020-pointer,
    title = "A Pointer Network Architecture for Joint Morphological Segmentation and Tagging",
    author = "Seker, Amit  and
      Tsarfaty, Reut",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.391",
    doi = "10.18653/v1/2020.findings-emnlp.391",
    pages = "4368--4378",
    abstract = "Morphologically Rich Languages (MRLs) such as Arabic, Hebrew and Turkish often require Morphological Disambiguation (MD), i.e., the prediction of morphological decomposition of tokens into morphemes, early in the pipeline. Neural MD may be addressed as a simple pipeline, where segmentation is followed by sequence tagging, or as an end-to-end model, predicting morphemes from raw tokens. Both approaches are sub-optimal; the former is heavily prone to error propagation, and the latter does not enjoy explicit access to the basic processing units called morphemes. This paper offers MD architecture that combines the symbolic knowledge of morphemes with the learning capacity of neural end-to-end modeling. We propose a new, general and easy-to-implement Pointer Network model where the input is a morphological lattice and the output is a sequence of indices pointing at a single disambiguated path of morphemes. We demonstrate the efficacy of the model on segmentation and tagging, for Hebrew and Turkish texts, based on their respective Universal Dependencies (UD) treebanks. Our experiments show that with complete lattices, our model outperforms all shared-task results on segmenting and tagging these languages. On the SPMRL treebank, our model outperforms all previously reported results for Hebrew MD in realistic scenarios.",
}
@inproceedings{cui-etal-2020-beyond,
    title = "Beyond Language: Learning Commonsense from Images for Reasoning",
    author = "Cui, Wanqing  and
      Lan, Yanyan  and
      Pang, Liang  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.392",
    doi = "10.18653/v1/2020.findings-emnlp.392",
    pages = "4379--4389",
    abstract = "This paper proposes a novel approach to learn commonsense from images, instead of limited raw texts or costly constructed knowledge bases, for the commonsense reasoning problem in NLP. Our motivation comes from the fact that an image is worth a thousand words, where richer scene information could be leveraged to help distill the commonsense knowledge, which is often hidden in languages. Our approach, namely Loire, consists of two stages. In the first stage, a bi-modal sequence-to-sequence approach is utilized to conduct the scene layout generation task, based on a text representation model ViBERT. In this way, the required visual scene knowledge, such as spatial relations, will be encoded in ViBERT by the supervised learning process with some bi-modal data like COCO. Then ViBERT is concatenated with a pre-trained language model to perform the downstream commonsense reasoning tasks. Experimental results on two commonsense reasoning problems, i.e.commonsense question answering and pronoun resolution, demonstrate that Loire outperforms traditional language-based methods. We also give some case studies to show what knowledge is learned from images and explain how the generated scene layout helps the commonsense reasoning process.",
}
@inproceedings{chung-etal-2020-bert,
    title = "A {BERT}-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.",
    author = "Chung, Ho-Lam  and
      Chan, Ying-Hong  and
      Fan, Yao-Chung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.393",
    doi = "10.18653/v1/2020.findings-emnlp.393",
    pages = "4390--4400",
    abstract = "In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There are still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. However, for practical MCQ preparation, multiple distractors are desired. Aiming at these goals, in this paper, we present a new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating \textit{multiple} distractors. The experimental results show that (1) our model advances the state-of-the-art result from 28.65 to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverse and shows strong distracting power for multiple choice question.",
}
@inproceedings{longpre-etal-2020-effective,
    title = "How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?",
    author = "Longpre, Shayne  and
      Wang, Yu  and
      DuBois, Chris",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.394",
    doi = "10.18653/v1/2020.findings-emnlp.394",
    pages = "4401--4411",
    abstract = "Task-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models. In NLP similar results are reported most commonly for low data regimes, non-pretrained models, or situationally for pretrained models. In this paper we ask how effective these techniques really are when applied to pretrained transformers. Using two popular varieties of task-agnostic data augmentation (not tailored to any particular task), Easy Data Augmentation (Wei andZou, 2019) and Back-Translation (Sennrichet al., 2015), we conduct a systematic examination of their effects across 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers, including BERT, XLNet, and RoBERTa. We observe a negative result, finding that techniques which previously reported strong improvements for non-pretrained models fail to consistently improve performance for pretrained transformers, even when training data is limited. We hope this empirical analysis helps inform practitioners where data augmentation techniques may confer improvements.",
}
@inproceedings{jansen-2020-visually,
    title = "Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions",
    author = "Jansen, Peter",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.395",
    doi = "10.18653/v1/2020.findings-emnlp.395",
    pages = "4412--4417",
    abstract = "The recently proposed ALFRED challenge task aims for a virtual robotic agent to complete complex multi-step everyday tasks in a virtual home environment from high-level natural language directives, such as {``}put a hot piece of bread on a plate{''}. Currently, the best-performing models are able to complete less than 1{\%} of these tasks successfully. In this work we focus on modeling the translation problem of converting natural language directives into detailed multi-step sequences of actions that accomplish those goals in the virtual environment. We empirically demonstrate that it is possible to generate gold multi-step plans from language directives alone without any visual input in 26{\%} of unseen cases. When a small amount of visual information, the starting location in the virtual environment, is incorporated, our best-performing GPT-2 model successfully generates gold command sequences in 58{\%} of cases, suggesting contextualized language models may provide strong planning modules for grounded virtual agents.",
}
@inproceedings{takayama-arase-2020-consistent,
    title = "Consistent Response Generation with Controlled Specificity",
    author = "Takayama, Junya  and
      Arase, Yuki",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.396",
    doi = "10.18653/v1/2020.findings-emnlp.396",
    pages = "4418--4427",
    abstract = "We propose a method to control the specificity of responses while maintaining the consistency with the utterances. We first design a metric based on pointwise mutual information, which measures the co-occurrence degree between an utterance and a response. To control the specificity of generated responses, we add the distant supervision based on the co-occurrence degree and a PMI-based word prediction mechanism to a sequence-to-sequence model. With these mechanisms, our model outputs the words with optimal specificity for a given specificity control variable. In experiments with open-domain dialogue corpora, automatic and human evaluation results confirm that our model controls the specificity of the response more sensitively than the conventional model and can generate highly consistent responses.",
}
@inproceedings{rodriguez-luna-etal-2020-internal,
    title = "Internal and external pressures on language emergence: least effort, object constancy and frequency",
    author = "Rodr{\'\i}guez Luna, Diana  and
      Ponti, Edoardo Maria  and
      Hupkes, Dieuwke  and
      Bruni, Elia",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.397",
    doi = "10.18653/v1/2020.findings-emnlp.397",
    pages = "4428--4437",
    abstract = "In previous work, artificial agents were shown to achieve almost perfect accuracy in referential games where they have to communicate to identify images. Nevertheless, the resulting communication protocols rarely display salient features of natural languages, such as compositionality. In this paper, we propose some realistic sources of pressure on communication that avert this outcome. More specifically, we formalise the principle of least effort through an auxiliary objective. Moreover, we explore several game variants, inspired by the principle of object constancy, in which we alter the frequency, position, and luminosity of the objects in the images. We perform an extensive analysis on their effect through compositionality metrics, diagnostic classifiers, and zero-shot evaluation. Our findings reveal that the proposed sources of pressure result in emerging languages with less redundancy, more focus on high-level conceptual information, and better abilities of generalisation. Overall, our contributions reduce the gap between emergent and natural languages.",
}
@inproceedings{zhou-etal-2020-parsing,
    title = "Parsing All: Syntax and Semantics, Dependencies and Spans",
    author = "Zhou, Junru  and
      Li, Zuchao  and
      Zhao, Hai",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.398",
    doi = "10.18653/v1/2020.findings-emnlp.398",
    pages = "4438--4449",
    abstract = "Both syntactic and semantic structures are key linguistic contextual clues, in which parsing the latter has been well shown beneficial from parsing the former. However, few works ever made an attempt to let semantic parsing help syntactic parsing. As linguistic representation formalisms, both syntax and semantics may be represented in either span (constituent/phrase) or dependency, on both of which joint learning was also seldom explored. In this paper, we propose a novel joint model of syntactic and semantic parsing on both span and dependency representations, which incorporates syntactic information effectively in the encoder of neural network and benefits from two representation formalisms in a uniform way. The experiments show that semantics and syntax can benefit each other by optimizing joint objectives. Our single model achieves new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank.",
}
@inproceedings{zhou-etal-2020-limit,
    title = "{LIMIT}-{BERT} : Linguistics Informed Multi-Task {BERT}",
    author = "Zhou, Junru  and
      Zhang, Zhuosheng  and
      Zhao, Hai  and
      Zhang, Shuailiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.399",
    doi = "10.18653/v1/2020.findings-emnlp.399",
    pages = "4450--4461",
    abstract = "In this paper, we present Linguistics Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistics tasks by Multi-Task Learning. LIMIT-BERT includes five key linguistics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Different from recent Multi-Task Deep Neural Networks (MT-DNN), our LIMIT-BERT is fully linguistics motivated and thus is capable of adopting an improved masked training objective according to syntactic and semantic constituents. Besides, LIMIT-BERT takes a semi-supervised learning strategy to offer the same large amount of linguistics task data as that for the language model training. As a result, LIMIT-BERT not only improves linguistics tasks performance but also benefits from a regularization effect and linguistics information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT outperforms the strong baseline Whole Word Masking BERT on both dependency and constituent syntactic/semantic parsing, GLUE benchmark, and SNLI task. Our practice on the proposed LIMIT-BERT also enables us to release a well pre-trained model for multi-purpose of natural language processing tasks once for all.",
}
@inproceedings{wu-etal-2020-improving-limited,
    title = "Improving Limited Labeled Dialogue State Tracking with Self-Supervision",
    author = "Wu, Chien-Sheng  and
      Hoi, Steven C.H.  and
      Xiong, Caiming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.400",
    doi = "10.18653/v1/2020.findings-emnlp.400",
    pages = "4462--4472",
    abstract = "Existing dialogue state tracking (DST) models require plenty of labeled data. However, collecting high-quality labels is costly, especially when the number of domains increases. In this paper, we address a practical DST problem that is rarely discussed, i.e., learning efficiently with limited labeled data. We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior. We encourage a DST model to have consistent latent distributions given a perturbed input, making it more robust to an unseen scenario. We also add an auxiliary utterance generation task, modeling a potential correlation between conversational behavior and dialogue states. The experimental results show that our proposed self-supervised signals can improve joint goal accuracy by 8.95{\%} when only 1{\%} labeled data is used on the MultiWOZ dataset. We can achieve an additional 1.76{\%} improvement if some unlabeled data is jointly trained as semi-supervised learning. We analyze and visualize how our proposed self-supervised signals help the DST task and hope to stimulate future data-efficient DST research.",
}
@inproceedings{li-etal-2020-branching,
    title = "On the Branching Bias of Syntax Extracted from Pre-trained Language Models",
    author = "Li, Huayang  and
      Liu, Lemao  and
      Huang, Guoping  and
      Shi, Shuming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.401",
    doi = "10.18653/v1/2020.findings-emnlp.401",
    pages = "4473--4478",
    abstract = "Many efforts have been devoted to extracting constituency trees from pre-trained language models, often proceeding in two stages: feature definition and parsing. However, this kind of methods may suffer from the branching bias issue, which will inflate the performances on languages with the same branch it biases to. In this work, we propose quantitatively measuring the branching bias by comparing the performance gap on a language and its reversed language, which is agnostic to both language models and extracting methods. Furthermore, we analyze the impacts of three factors on the branching bias, namely feature definitions, parsing algorithms, and language models. Experiments show that several existing works exhibit branching biases, and some implementations of these three factors can introduce the branching bias.",
}
@inproceedings{huguet-cabot-etal-2020-pragmatics,
    title = "{T}he {P}ragmatics behind {P}olitics: {M}odelling {M}etaphor, {F}raming and {E}motion in {P}olitical {D}iscourse",
    author = "Huguet Cabot, Pere-Llu{\'\i}s  and
      Dankers, Verna  and
      Abadi, David  and
      Fischer, Agneta  and
      Shutova, Ekaterina",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.402",
    doi = "10.18653/v1/2020.findings-emnlp.402",
    pages = "4479--4488",
    abstract = "There has been an increased interest in modelling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinformation detection, among others. Metaphor-rich and emotion-eliciting communication strategies are ubiquitous in political rhetoric, according to social science research. Yet, none of the existing computational models of political discourse has incorporated these phenomena. In this paper, we present the first joint models of metaphor, emotion and political rhetoric, and demonstrate that they advance performance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues.",
}
@inproceedings{khayrallah-sedoc-2020-smrt,
    title = "{SMRT} Chatbots: {I}mproving Non-Task-Oriented Dialog with {S}imulated {M}ultiple {R}eference {T}raining",
    author = "Khayrallah, Huda  and
      Sedoc, Jo{\~a}o",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.403",
    doi = "10.18653/v1/2020.findings-emnlp.403",
    pages = "4489--4505",
    abstract = "Non-task-oriented dialog models suffer from poor quality and non-diverse responses. To overcome limited conversational data, we apply Simulated Multiple Reference Training (SMRT; Khayrallah et al., 2020), and use a paraphraser to simulate multiple responses per training prompt. We find SMRT improves over a strong Transformer baseline as measured by human and automatic quality scores and lexical diversity. We also find SMRT is comparable to pretraining in human evaluation quality, and outperforms pretraining on automatic quality and lexical diversity, without requiring related-domain dialog data.",
}
@inproceedings{hu-yang-2020-privnet,
    title = "{P}riv{N}et: Safeguarding Private Attributes in Transfer Learning for Recommendation",
    author = "Hu, Guangneng  and
      Yang, Qiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.404",
    doi = "10.18653/v1/2020.findings-emnlp.404",
    pages = "4506--4516",
    abstract = "Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain. The transferred knowledge, however, may unintendedly leak private information of the source domain. For example, an attacker can accurately infer user demographics from their historical purchase provided by a source domain data owner. This paper addresses the above privacy-preserving issue by learning a privacy-aware neural representation by improving target performance while protecting source privacy. The key idea is to simulate the attacks during the training for protecting unseen users{'} privacy in the future, modeled by an adversarial game, so that the transfer learning model becomes robust to attacks. Experiments show that the proposed PrivNet model can successfully disentangle the knowledge benefitting the transfer from leaking the privacy.",
}
@inproceedings{holla-etal-2020-learning,
    title = "Learning to Learn to Disambiguate: {M}eta-Learning for Few-Shot Word Sense Disambiguation",
    author = "Holla, Nithin  and
      Mishra, Pushkar  and
      Yannakoudakis, Helen  and
      Shutova, Ekaterina",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.405",
    doi = "10.18653/v1/2020.findings-emnlp.405",
    pages = "4517--4533",
    abstract = "The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest. In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples. In this paper, we propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is to learn to disambiguate unseen words from only a few labeled instances. Meta-learning approaches have so far been typically tested in an N-way, K-shot classification setting where each task has N classes with K examples per class. Owing to its nature, WSD deviates from this controlled setup and requires the models to handle a large number of highly unbalanced classes. We extend several popular meta-learning approaches to this scenario, and analyze their strengths and weaknesses in this new challenging setting.",
}
@inproceedings{negrinho-etal-2020-empirical,
    title = "An Empirical Investigation of Beam-Aware Training in Supertagging",
    author = "Negrinho, Renato  and
      Gormley, Matthew R.  and
      Gordon, Geoff",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.406",
    doi = "10.18653/v1/2020.findings-emnlp.406",
    pages = "4534--4542",
    abstract = "Structured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. This approach leads to mismatches as, during training, the model is not exposed to its mistakes and does not use beam search. Beam-aware training aims to address these problems, but unfortunately, it is not yet widely used due to a lack of understanding about how it impacts performance, when it is most useful, and whether it is stable. Recently, Negrinho et al. (2018) proposed a meta-algorithm that captures beam-aware training algorithms and suggests new ones, but unfortunately did not provide empirical results. In this paper, we begin an empirical investigation: we train the supertagging model of Vaswani et al. (2018) and a simpler model with instantiations of the meta-algorithm. We explore the influence of various design choices and make recommendations for choosing them. We observe that beam-aware training improves performance for both models, with large improvements for the simpler model which must effectively manage uncertainty during decoding. Our results suggest that a model must be learned with search to maximize its effectiveness.",
}
@inproceedings{pouran-ben-veyseh-etal-2020-improving-aspect,
    title = "Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation",
    author = "Pouran Ben Veyseh, Amir  and
      Nouri, Nasim  and
      Dernoncourt, Franck  and
      Tran, Quan Hung  and
      Dou, Dejing  and
      Nguyen, Thien Huu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.407",
    doi = "10.18653/v1/2020.findings-emnlp.407",
    pages = "4543--4548",
    abstract = "Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA. However, these models tend to compute the hidden/representation vectors without considering the aspect terms and fail to benefit from the overall contextual importance scores of the words that can be obtained from the dependency tree for ABSA. In this work, we propose a novel graph-based deep learning model to overcome these two issues of the prior work on ABSA. In our model, gate vectors are generated from the representation vectors of the aspect terms to customize the hidden vectors of the graph-based models toward the aspect terms. In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA. The proposed model achieves the state-of-the-art performance on three benchmark datasets.",
}
@inproceedings{radevski-etal-2020-decoding,
    title = "{D}ecoding Language Spatial Relations to 2{D} Spatial Arrangements",
    author = "Radevski, Gorjan  and
      Collell, Guillem  and
      Moens, Marie-Francine  and
      Tuytelaars, Tinne",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.408",
    doi = "10.18653/v1/2020.findings-emnlp.408",
    pages = "4549--4560",
    abstract = "We address the problem of multimodal spatial understanding by decoding a set of language-expressed spatial relations to a set of 2D spatial arrangements in a multi-object and multi-relationship setting. We frame the task as arranging a scene of clip-arts given a textual description. We propose a simple and effective model architecture Spatial-Reasoning Bert (SR-Bert), trained to decode text to 2D spatial arrangements in a non-autoregressive manner. SR-Bert can decode both explicit and implicit language to 2D spatial arrangements, generalizes to out-of-sample data to a reasonable extent and can generate complete abstract scenes if paired with a clip-arts predictor. Finally, we qualitatively evaluate our method with a user study, validating that our generated spatial arrangements align with human expectation.",
}
@inproceedings{minh-tran-etal-2020-dots,
    title = "The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction",
    author = "Minh Tran, Hieu  and
      Nguyen, Minh Trung  and
      Nguyen, Thien Huu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.409",
    doi = "10.18653/v1/2020.findings-emnlp.409",
    pages = "4561--4567",
    abstract = "The goal of Document-level Relation Extraction (DRE) is to recognize the relations between entity mentions that can span beyond sentence boundary. The current state-of-the-art method for this problem has involved the graph-based edge-oriented model where the entity mentions, entities, and sentences in the documents are used as the nodes of the document graphs for representation learning. However, this model does not capture the representations for the nodes in the graphs, thus preventing it from effectively encoding the specific and relevant information of the nodes for DRE. To address this issue, we propose to explicitly compute the representations for the nodes in the graph-based edge-oriented model for DRE. These node representations allow us to introduce two novel representation regularization mechanisms to improve the representation vectors for DRE. The experiments show that our model achieves state-of-the-art performance on two benchmark datasets.",
}
@inproceedings{maini-etal-2020-pool,
    title = "Why and when should you pool? Analyzing Pooling in Recurrent Architectures",
    author = "Maini, Pratyush  and
      Kolluru, Keshav  and
      Pruthi, Danish  and
      {Mausam}",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.410",
    doi = "10.18653/v1/2020.findings-emnlp.410",
    pages = "4568--4586",
    abstract = "Pooling-based recurrent neural architectures consistently outperform their counterparts without pooling on sequence classification tasks. However, the reasons for their enhanced performance are largely unexamined. In this work, we examine three commonly used pooling techniques (mean-pooling, max-pooling, and attention, and propose *max-attention*, a novel variant that captures interactions among predictive tokens in a sentence. Using novel experiments, we demonstrate that pooling architectures substantially differ from their non-pooling equivalents in their learning ability and positional biases: (i) pooling facilitates better gradient flow than BiLSTMs in initial training epochs, and (ii) BiLSTMs are biased towards tokens at the beginning and end of the input, whereas pooling alleviates this bias. Consequently, we find that pooling yields large gains in low resource scenarios, and instances when salient words lie towards the middle of the input. Across several text classification tasks, we find max-attention to frequently outperform other pooling techniques.",
}
@inproceedings{nguyen-etal-2020-structural,
    title = "Structural and Functional Decomposition for Personality Image Captioning in a Communication Game",
    author = "Nguyen, Minh Thu  and
      Phung, Duy  and
      Hoai, Minh  and
      Nguyen, Thien Huu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.411",
    doi = "10.18653/v1/2020.findings-emnlp.411",
    pages = "4587--4593",
    abstract = "Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. In this work, we introduce a novel formulation for PIC based on a communication game between a speaker and a listener. The speaker attempts to generate natural language captions while the listener encourages the generated captions to contain discriminative information about the input images and personality traits. In this way, we expect that the generated captions can be improved to naturally represent the images and express the traits. In addition, we propose to adapt the language model GPT2 to perform caption generation for PIC. This enables the speaker and listener to benefit from the language encoding capacity of GPT2. Our experiments show that the proposed model achieves the state-of-the-art performance for PIC.",
}
@inproceedings{jiang-etal-2020-long,
    title = "Long Document Ranking with Query-Directed Sparse Transformer",
    author = "Jiang, Jyun-Yu  and
      Xiong, Chenyan  and
      Lee, Chia-Jung  and
      Wang, Wei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.412",
    doi = "10.18653/v1/2020.findings-emnlp.412",
    pages = "4594--4605",
    abstract = "The computing cost of transformer self-attention often necessitates breaking long documents to fit in pretrained models in document ranking tasks. In this paper, we design Query-Directed Sparse attention that induces IR-axiomatic structures in transformer self-attention. Our model, QDS-Transformer, enforces the principle properties desired in ranking: local contextualization, hierarchical representation, and query-oriented proximity matching, while it also enjoys efficiency from sparsity. Experiments on four fully supervised and few-shot TREC document ranking benchmarks demonstrate the consistent and robust advantage of QDS-Transformer over previous approaches, as they either retrofit long documents into BERT or use sparse attention without emphasizing IR principles. We further quantify the computing complexity and demonstrates that our sparse attention with TVM implementation is twice more efficient that the fully-connected self-attention. All source codes, trained model, and predictions of this work are available at https://github.com/hallogameboy/QDS-Transformer.",
}
@inproceedings{sampat-etal-2020-visuo,
    title = "Visuo-Linguistic Question Answering ({VLQA}) Challenge",
    author = "Sampat, Shailaja Keyur  and
      Yang, Yezhou  and
      Baral, Chitta",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.413",
    doi = "10.18653/v1/2020.findings-emnlp.413",
    pages = "4606--4616",
    abstract = "Understanding images and text together is an important aspect of cognition and building advanced Artificial Intelligence (AI) systems. As a community, we have achieved good benchmarks over language and vision domains separately, however joint reasoning is still a challenge for state-of-the-art computer vision and natural language processing (NLP) systems. We propose a novel task to derive joint inference about a given image-text modality and compile the Visuo-Linguistic Question Answering (VLQA) challenge corpus in a question answering setting. Each dataset item consists of an image and a reading passage, where questions are designed to combine both visual and textual information i.e., ignoring either modality would make the question unanswerable. We first explore the best existing vision-language architectures to solve VLQA subsets and show that they are unable to reason well. We then develop a modular method with slightly better baseline performance, but it is still far behind human performance. We believe that VLQA will be a good benchmark for reasoning over a visuo-linguistic context. The dataset, code and leaderboard is available at https://shailaja183.github.io/vlqa/.",
}
@inproceedings{bostrom-durrett-2020-byte,
    title = "Byte Pair Encoding is Suboptimal for Language Model Pretraining",
    author = "Bostrom, Kaj  and
      Durrett, Greg",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.414",
    doi = "10.18653/v1/2020.findings-emnlp.414",
    pages = "4617--4624",
    abstract = "The success of pretrained transformer language models (LMs) in natural language processing has led to a wide range of pretraining setups. In particular, these models employ a variety of subword tokenization methods, most notably byte-pair encoding (BPE) (Sennrich et al., 2016; Gage, 1994), the WordPiece method (Schuster and Nakajima, 2012), and unigram language modeling (Kudo, 2018), to segment text. However, to the best of our knowledge, the literature does not contain a direct evaluation of the impact of tokenization on language model pretraining. We analyze differences between BPE and unigram LM tokenization, finding that the latter method recovers subword units that align more closely with morphology and avoids problems stemming from BPE{'}s greedy construction procedure. We then compare the fine-tuned task performance of identical transformer masked language models pretrained with these tokenizations. Across downstream tasks and two languages (English and Japanese), we find that the unigram LM tokenization method matches or outperforms BPE. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.",
}
@inproceedings{misra-etal-2020-exploring,
    title = "Exploring {BERT}{'}s Sensitivity to Lexical Cues using Tests from Semantic Priming",
    author = "Misra, Kanishka  and
      Ettinger, Allyson  and
      Rayz, Julia",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.415",
    doi = "10.18653/v1/2020.findings-emnlp.415",
    pages = "4625--4635",
    abstract = "Models trained to estimate word probabilities in context have become ubiquitous in natural language processing. How do these models use lexical cues in context to inform their word probabilities? To answer this question, we present a case study analyzing the pre-trained BERT model with tests informed by semantic priming. Using English lexical stimuli that show priming in humans, we find that BERT too shows {``}priming{''}, predicting a word with greater probability when the context includes a related word versus an unrelated one. This effect decreases as the amount of information provided by the context increases. Follow-up analysis shows BERT to be increasingly distracted by related prime words as context becomes more informative, assigning lower probabilities to related words. Our findings highlight the importance of considering contextual constraint effects when studying word prediction in these models, and highlight possible parallels with human processing.",
}
@inproceedings{su-etal-2020-multi,
    title = "Multi-hop Question Generation with Graph Convolutional Network",
    author = "Su, Dan  and
      Xu, Yan  and
      Dai, Wenliang  and
      Ji, Ziwei  and
      Yu, Tiezheng  and
      Fung, Pascale",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.416",
    doi = "10.18653/v1/2020.findings-emnlp.416",
    pages = "4636--4647",
    abstract = "Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8{\%} in the multi-hop evaluation. on. The code is publicly availableat https://github.com/HLTCHKU",
}
@inproceedings{urooj-etal-2020-mmft,
    title = "{MMFT-BERT}: {M}ultimodal {F}usion {T}ransformer with {B}{ERT} {E}ncodings for {V}isual {Q}uestion {A}nswering",
    author = "Urooj, Aisha  and
      Mazaheri, Amir  and
      Da vitoria lobo, Niels  and
      Shah, Mubarak",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.417",
    doi = "10.18653/v1/2020.findings-emnlp.417",
    pages = "4648--4660",
    abstract = "We present MMFT-BERT(MultiModal FusionTransformer with BERT encodings), to solve Visual Question Answering (VQA) ensuring individual and combined processing of multiple input modalities. Our approach benefits from processing multimodal data (video and text) adopting the BERT encodings individually and using a novel transformer-based fusion method to fuse them together. Our method decomposes the different sources of modalities, into different BERT instances with similar architectures, but variable weights. This achieves SOTA results on the TVQA dataset. Additionally, we provide TVQA-Visual, an isolated diagnostic subset of TVQA, which strictly requires the knowledge of visual (V) modality based on a human annotator{'}s judgment. This set of questions helps us to study the model{'}s behavior and the challenges TVQA poses to prevent the achievement of super human performance. Extensive experiments show the effectiveness and superiority of our method.",
}
@inproceedings{rudinger-etal-2020-thinking,
    title = "Thinking Like a Skeptic: Defeasible Inference in Natural Language",
    author = "Rudinger, Rachel  and
      Shwartz, Vered  and
      Hwang, Jena D.  and
      Bhagavatula, Chandra  and
      Forbes, Maxwell  and
      Le Bras, Ronan  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.418",
    doi = "10.18653/v1/2020.findings-emnlp.418",
    pages = "4661--4675",
    abstract = "Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin). Though long recognized in classical AI and philosophy, defeasible inference has not been extensively studied in the context of contemporary data-driven research on natural language inference and commonsense reasoning. We introduce Defeasible NLI (abbreviated $\delta$-NLI), a dataset for defeasible inference in natural language. Defeasible NLI contains extensions to three existing inference datasets covering diverse modes of reasoning: common sense, natural language inference, and social norms. From Defeasible NLI, we develop both a classification and generation task for defeasible inference, and demonstrate that the generation task is much more challenging. Despite lagging human performance, however, generative models trained on this data are capable of writing sentences that weaken or strengthen a specified inference up to 68{\%} of the time.",
}
@inproceedings{deshpande-narasimhan-2020-guiding,
    title = "Guiding Attention for Self-Supervised Learning with Transformers",
    author = "Deshpande, Ameet  and
      Narasimhan, Karthik",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.419",
    doi = "10.18653/v1/2020.findings-emnlp.419",
    pages = "4676--4686",
    abstract = "In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers. Our approach is motivated by recent studies demonstrating that self-attention patterns in trained models contain a majority of non-linguistic regularities. We propose a computationally efficient auxiliary loss function to guide attention heads to conform to such patterns. Our method is agnostic to the actual pre-training objective and results in faster convergence of models as well as better performance on downstream tasks compared to the baselines, achieving state of the art results in low-resource settings. Surprisingly, we also find that linguistic properties of attention heads are not necessarily correlated with language modeling performance.",
}
@inproceedings{iki-aizawa-2020-language,
    title = "Language-{C}onditioned {F}eature {P}yramids for {V}isual {S}election {T}asks",
    author = "Iki, Taichi  and
      Aizawa, Akiko",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.420",
    doi = "10.18653/v1/2020.findings-emnlp.420",
    pages = "4687--4697",
    abstract = "Referring expression comprehension, which is the ability to locate language to an object in an image, plays an important role in creating common ground. Many models that fuse visual and linguistic features have been proposed. However, few models consider the fusion of linguistic features with multiple visual features with different sizes of receptive fields, though the proper size of the receptive field of visual features intuitively varies depending on expressions. In this paper, we introduce a neural network architecture that modulates visual features with varying sizes of receptive field by linguistic features. We evaluate our architecture on tasks related to referring expression comprehension in two visual dialogue games. The results show the advantages and broad applicability of our architecture. Source code is available at https://github.com/Alab-NII/lcfp .",
}
@inproceedings{ding-feng-2020-learning,
    title = "Learning to Classify Events from Human Needs Category Descriptions",
    author = "Ding, Haibo  and
      Feng, Zhe",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.421",
    doi = "10.18653/v1/2020.findings-emnlp.421",
    pages = "4698--4704",
    abstract = "We study the problem of learning an event classifier from human needs category descriptions, which is challenging due to: (1) the use of highly abstract concepts in natural language descriptions, (2) the difficulty of choosing key concepts. To tackle these two challenges, we propose LeaPI, a zero-shot learning method that first automatically generate weak labels by instantiating high-level concepts with prototypical instances and then trains a human needs classifier with the weakly labeled data. To filter noisy concepts, we design a reinforced selection algorithm to choose high-quality concepts for instantiation. Experimental results on the human needs categorization task show that our method outperforms baseline methods, producing substantially better precision.",
}
@inproceedings{zhang-etal-2020-automatic,
    title = "Automatic Term Name Generation for Gene Ontology: Task and Dataset",
    author = "Zhang, Yanjian  and
      Chen, Qin  and
      Zhang, Yiteng  and
      Wei, Zhongyu  and
      Gao, Yixu  and
      Peng, Jiajie  and
      Huang, Zengfeng  and
      Sun, Weijian  and
      Huang, Xuanjing",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.422",
    doi = "10.18653/v1/2020.findings-emnlp.422",
    pages = "4705--4710",
    abstract = "Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine. Most previous research focuses on inferring new GO terms, while the term names that reflect the gene function are still named by the experts. To fill this gap, we propose a novel task, namely term name generation for GO, and build a large-scale benchmark dataset. Furthermore, we present a graph-based generative model that incorporates the relations between genes, words and terms for term name generation, which exhibits great advantages over the strong baselines.",
}
@inproceedings{prakash-etal-2020-compressing,
    title = "Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings",
    author = "Prakash, Prafull  and
      Shashidhar, Saurabh Kumar  and
      Zhao, Wenlong  and
      Rongali, Subendhu  and
      Khan, Haidar  and
      Kayser, Michael",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.423",
    doi = "10.18653/v1/2020.findings-emnlp.423",
    pages = "4711--4717",
    abstract = "The current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints. This poses a challenge to their deployment for voice assistants such as Amazon Alexa and Google Assistant on edge devices with limited memory budgets. We propose to learn compositional code embeddings to greatly reduce the sizes of BERT-base and RoBERTa-base. We also apply the technique to DistilBERT, ALBERT-base, and ALBERT-large, three already compressed BERT variants which attain similar state-of-the-art performances on semantic parsing with much smaller model sizes. We observe 95.15{\%} 98.46{\%} embedding compression rates and 20.47{\%} 34.22{\%} encoder compression rates, while preserving {\textgreater}97.5{\%} semantic parsing performances. We provide the recipe for training and analyze the trade-off between code embedding sizes and downstream performances.",
}
@inproceedings{zheng-etal-2020-bert,
    title = "{BERT-QE}: {C}ontextualized {Q}uery {E}xpansion for {D}ocument {R}e-ranking",
    author = "Zheng, Zhi  and
      Hui, Kai  and
      He, Ben  and
      Han, Xianpei  and
      Sun, Le  and
      Yates, Andrew",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.424",
    doi = "10.18653/v1/2020.findings-emnlp.424",
    pages = "4718--4728",
    abstract = "Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models.",
}
@inproceedings{diao-etal-2020-zen,
    title = "{ZEN}: Pre-training {C}hinese Text Encoder Enhanced by N-gram Representations",
    author = "Diao, Shizhe  and
      Bai, Jiaxin  and
      Song, Yan  and
      Zhang, Tong  and
      Wang, Yonggang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.425",
    doi = "10.18653/v1/2020.findings-emnlp.425",
    pages = "4729--4740",
    abstract = "The pre-training of text encoders normally processes text as a sequence of tokens corresponding to small text units, such as word pieces in English and characters in Chinese. It omits information carried by larger text granularity, and thus the encoders cannot easily adapt to certain combinations of characters. This leads to a loss of important semantic information, which is especially problematic for Chinese because the language does not have explicit word boundaries. In this paper, we propose ZEN, a BERT-based Chinese text encoder enhanced by n-gram representations, where different combinations of characters are considered during training, thus potential word or phrase boundaries are explicitly pre-trained and fine-tuned with the character encoder (BERT). Therefore ZEN incorporates the comprehensive information of both the character sequence and words or phrases it contains. Experimental results illustrated the effectiveness of ZEN on a series of Chinese NLP tasks, where state-of-the-art results is achieved on most tasks with requiring less resource than other published encoders. It is also shown that reasonable performance is obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data. The code and pre-trained models of ZEN are available at https://github.com/sinovation/ZEN.",
}
@inproceedings{chen-etal-2020-finding,
    title = "Finding {F}riends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory",
    author = "Chen, Hannah  and
      Ji, Yangfeng  and
      Evans, David",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.426",
    doi = "10.18653/v1/2020.findings-emnlp.426",
    pages = "4741--4751",
    abstract = "Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models.",
}
@inproceedings{das-etal-2020-probabilistic,
    title = "Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion",
    author = "Das, Rajarshi  and
      Godbole, Ameya  and
      Monath, Nicholas  and
      Zaheer, Manzil  and
      McCallum, Andrew",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.427",
    doi = "10.18653/v1/2020.findings-emnlp.427",
    pages = "4752--4765",
    abstract = "A case-based reasoning (CBR) system solves a new problem by retrieving {`}cases{'} that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an {``}open-world{''} setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method.",
}
@inproceedings{cachola-etal-2020-tldr,
    title = "{TLDR}: Extreme Summarization of Scientific Documents",
    author = "Cachola, Isabel  and
      Lo, Kyle  and
      Cohan, Arman  and
      Weld, Daniel",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.428",
    doi = "10.18653/v1/2020.findings-emnlp.428",
    pages = "4766--4777",
    abstract = "We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language. To facilitate study on this task, we introduce SCITLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SCITLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal. CATTS improves upon strong baselines under both automated metrics and human evaluations. Data and code are publicly available at https://github.com/allenai/scitldr.",
}
@inproceedings{zeng-etal-2020-tri,
    title = "Tri-Train: Automatic Pre-Fine Tuning between Pre-Training and Fine-Tuning for {S}ci{NER}",
    author = "Zeng, Qingkai  and
      Yu, Wenhao  and
      Yu, Mengxia  and
      Jiang, Tianwen  and
      Weninger, Tim  and
      Jiang, Meng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.429",
    doi = "10.18653/v1/2020.findings-emnlp.429",
    pages = "4778--4787",
    abstract = "The training process of scientific NER models is commonly performed in two steps: i) Pre-training a language model by self-supervised tasks on huge data and ii) fine-tune training with small labelled data. The success of the strategy depends on the relevance between the data domains and between the tasks. However, gaps are found in practice when the target domains are specific and small. We propose a novel framework to introduce a {``}pre-fine tuning{''} step between pre-training and fine-tuning. It constructs a corpus by selecting sentences from unlabeled documents that are the most relevant with the labelled training data. Instead of predicting tokens in random spans, the pre-fine tuning task is to predict tokens in entity candidates identified by text mining methods. Pre-fine tuning is automatic and light-weight because the corpus size can be much smaller than pre-training data to achieve a better performance. Experiments on seven benchmarks demonstrate the effectiveness.",
}
@inproceedings{long-etal-2020-hierarchical,
    title = "Hierarchical Region Learning for Nested Named Entity Recognition",
    author = "Long, Xinwei  and
      Niu, Shuzi  and
      Li, Yucheng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.430",
    doi = "10.18653/v1/2020.findings-emnlp.430",
    pages = "4788--4793",
    abstract = "Named Entity Recognition (NER) is deeply explored and widely used in various tasks. Usually, some entity mentions are nested in other entities, which leads to the nested NER problem. Leading region based models face both the efficiency and effectiveness challenge due to the high subsequence enumeration complexity. To tackle these challenges, we propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA demonstrate competitive or better results than state-of-the-art baselines.",
}
@inproceedings{tian-etal-2020-understanding,
    title = "Understanding User Resistance Strategies in Persuasive Conversations",
    author = "Tian, Youzhi  and
      Shi, Weiyan  and
      Li, Chen  and
      Yu, Zhou",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.431",
    doi = "10.18653/v1/2020.findings-emnlp.431",
    pages = "4794--4798",
    abstract = "Persuasive dialog systems have various usages, such as donation persuasion and physical exercise persuasion. Previous persuasive dialog systems research mostly focused on analyzing the persuader{'}s strategies and paid little attention to the persuadee (user). However, understanding and addressing users{'} resistance strategies is an essential job of a persuasive dialog system. So, we adopt a preliminary framework on persuasion resistance in psychology and design a fine-grained resistance strategy annotation scheme. We annotate the PersuasionForGood dataset with the scheme. With the enriched annotations, we build a classifier to predict the resistance strategies. Furthermore, we analyze the relationships between persuasion strategies and persuasion resistance strategies. Our work lays the ground for developing a persuasive dialogue system that can understand and address user resistance strategy appropriately. The code and data will be released.",
}
@inproceedings{yang-etal-2020-sub,
    title = "On the Sub-layer Functionalities of Transformer Decoder",
    author = "Yang, Yilin  and
      Wang, Longyue  and
      Shi, Shuming  and
      Tadepalli, Prasad  and
      Lee, Stefan  and
      Tu, Zhaopeng",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.432",
    doi = "10.18653/v1/2020.findings-emnlp.432",
    pages = "4799--4811",
    abstract = "There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT); meanwhile, the decoder remains largely unexamined despite its critical role. During translation, the decoder must predict output tokens by considering both the source-language text from the encoder and the target-language prefix produced in previous steps. In this work, we study how Transformer-based decoders leverage information from the source and target languages {--} developing a universal probe task to assess how information is propagated through each module of each decoder layer. We perform extensive experiments on three major translation datasets (WMT En-De, En-Fr, and En-Zh). Our analysis provides insight on when and where decoders leverage different sources. Based on these insights, we demonstrate that the residual feed-forward module in each Transformer decoder layer can be dropped with minimal loss of performance {--} a significant reduction in computation and number of parameters, and consequently a significant boost to both training and inference speed.",
}
@inproceedings{chung-etal-2020-extremely,
    title = "Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation",
    author = "Chung, Insoo  and
      Kim, Byeongwook  and
      Choi, Yoonjung  and
      Kwon, Se Jung  and
      Jeon, Yongkweon  and
      Park, Baeseong  and
      Kim, Sangha  and
      Lee, Dongsoo",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.433",
    doi = "10.18653/v1/2020.findings-emnlp.433",
    pages = "4812--4826",
    abstract = "The deployment of widely used Transformer architecture is challenging because of heavy computation load and memory overhead during inference, especially when the target device is limited in computational resources such as mobile or edge devices. Quantization is an effective technique to address such challenges. Our analysis shows that for a given number of quantization bits, each block of Transformer contributes to translation quality and inference computations in different manners. Moreover, even inside an embedding block, each word presents vastly different contributions. Correspondingly, we propose a mixed precision quantization strategy to represent Transformer weights by an extremely low number of bits (e.g., under 3 bits). For example, for each word in an embedding block, we assign different quantization bits based on statistical property. Our quantized Transformer model achieves 11.8{\mbox{$\times$}} smaller model size than the baseline model, with less than -0.5 BLEU. We achieve 8.3{\mbox{$\times$}} reduction in run-time memory footprints and 3.5{\mbox{$\times$}} speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.",
}
@inproceedings{fukuda-etal-2020-robust,
    title = "Robust {B}acked-off {E}stimation of {O}ut-of-{V}ocabulary {E}mbeddings",
    author = "Fukuda, Nobukazu  and
      Yoshinaga, Naoki  and
      Kitsuregawa, Masaru",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.434",
    doi = "10.18653/v1/2020.findings-emnlp.434",
    pages = "4827--4838",
    abstract = "Out-of-vocabulary (oov) words cause serious troubles in solving natural language tasks with a neural network. Existing approaches to this problem resort to using subwords, which are shorter and more ambiguous units than words, in order to represent oov words with a bag of subwords. In this study, inspired by the processes for creating words from known words, we propose a robust method of estimating oov word embeddings by referring to pre-trained word embeddings for known words with similar surfaces to target oov words. We collect known words by segmenting oov words and by approximate string matching, and we then aggregate their pre-trained embeddings. Experimental results show that the obtained oov word embeddings improve not only word similarity tasks but also downstream tasks in Twitter and biomedical domains where oov words often appear, even when the computed oov embeddings are integrated into a bert-based strong baseline.",
}
@inproceedings{jiao-etal-2020-exploiting,
    title = "{E}xploiting {U}nsupervised {D}ata for {E}motion {R}ecognition in {C}onversations",
    author = "Jiao, Wenxiang  and
      Lyu, Michael  and
      King, Irwin",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.435",
    doi = "10.18653/v1/2020.findings-emnlp.435",
    pages = "4839--4846",
    abstract = "Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task. Unlike the sentence-level text classification problem, the available supervised data for the ERC task is limited, which potentially prevents the models from playing their maximum effect. In this paper, we propose a novel approach to leverage unsupervised conversation data, which is more accessible. Specifically, we propose the Conversation Completion (ConvCom) task, which attempts to select the correct answer from candidate answers to fill a masked utterance in a conversation. Then, we Pre-train a basic COntext-Dependent Encoder (Pre-CODE) on the ConvCom task. Finally, we fine-tune the Pre-CODE on the datasets of ERC. Experimental results demonstrate that pre-training on unsupervised data achieves significant improvement of performance on the ERC datasets, particularly on the minority emotion classes.",
}
@inproceedings{hrinchuk-etal-2020-tensorized,
    title = "Tensorized Embedding Layers",
    author = "Hrinchuk, Oleksii  and
      Khrulkov, Valentin  and
      Mirvakhabova, Leyla  and
      Orlova, Elena  and
      Oseledets, Ivan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.436",
    doi = "10.18653/v1/2020.findings-emnlp.436",
    pages = "4847--4860",
    abstract = "The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing. However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting. We introduce a novel way of parameterizing embedding layers based on the Tensor Train decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.",
}
@inproceedings{liu-etal-2020-speaker,
    title = "Speaker or Listener? The Role of a Dialog Agent",
    author = "Liu, Yafei  and
      Qian, Hongjin  and
      Xu, Hengpeng  and
      Wei, Jinmao",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.437",
    doi = "10.18653/v1/2020.findings-emnlp.437",
    pages = "4861--4869",
    abstract = "For decades, chitchat bots are designed as a listener to passively answer what people ask. This passive and relatively simple dialogue mechanism gains less attention from humans and consumes the interests of human beings rapidly. Therefore some recent researches attempt to endow the bots with proactivity through external knowledge to transform the role from a listener to a speaker with a hypothesis that the speaker expresses more just like a knowledge disseminator. However, along with the proactive manner introduced into a dialogue agent, an issue arises that, with too many knowledge facts to express, the agent starts to talks endlessly, and even completely ignores what the other expresses in dialogue sometimes, which greatly harms the interest of the other chatter to continue the conversation. To the end, we propose a novel model named Initiative-Imitate to interact with adaptive initiative throughout a dialogue. It forces the agent to express in parallel with the appropriate role during the whole conversation. The corresponding experiments show the proposed Initiative-Imitate obtains competitive results both on the automatic and manual metrics. And the fluency and engagement of the chatbot have also been improved significantly. Besides, the case study indicates the Initiative-Imitate can constantly transfer to appropriate role timely and response more properly during the whole continuous conversation.",
}
@inproceedings{lin-etal-2020-bridging,
    title = "Bridging Textual and Tabular Data for Cross-Domain Text-to-{SQL} Semantic Parsing",
    author = "Lin, Xi Victoria  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.438",
    doi = "10.18653/v1/2020.findings-emnlp.438",
    pages = "4870--4888",
    abstract = "We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the question. The hybrid sequence is encoded by BERT with minimal subsequent layers and the text-DB contextualization is realized via the fine-tuned deep attention in BERT. Combined with a pointer-generator decoder with schema-consistency driven search space pruning, BRIDGE attained state-of-the-art performance on the well-studied Spider benchmark (65.5{\%} dev, 59.2{\%} test), despite being much simpler than most recently proposed models for this task. Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks. Our model implementation is available at https://github.com/ salesforce/TabularSemanticParsing.",
}
@inproceedings{zhang-etal-2020-language-embeddings,
    title = "Do Language Embeddings capture Scales?",
    author = "Zhang, Xikun  and
      Ramachandran, Deepak  and
      Tenney, Ian  and
      Elazar, Yanai  and
      Roth, Dan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.439",
    doi = "10.18653/v1/2020.findings-emnlp.439",
    pages = "4889--4896",
    abstract = "Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.",
}
@inproceedings{meged-etal-2020-paraphrasing,
    title = "Paraphrasing vs Coreferring: Two Sides of the Same Coin",
    author = "Meged, Yehudit  and
      Caciularu, Avi  and
      Shwartz, Vered  and
      Dagan, Ido",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.440",
    doi = "10.18653/v1/2020.findings-emnlp.440",
    pages = "4897--4907",
    abstract = "We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution. First, we used annotations from an event coreference dataset as distant supervision to re-score heuristically-extracted predicate paraphrases. The new scoring gained more than 18 points in average precision upon their ranking by the original scoring method. Then, we used the same re-ranking features as additional inputs to a state-of-the-art event coreference resolution model, which yielded modest but consistent improvements to the model{'}s performance. The results suggest a promising direction to leverage data and models for each of the tasks to the benefit of the other.",
}
@inproceedings{ru-etal-2020-active,
    title = "Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space",
    author = "Ru, Dongyu  and
      Feng, Jiangtao  and
      Qiu, Lin  and
      Zhou, Hao  and
      Wang, Mingxuan  and
      Zhang, Weinan  and
      Yu, Yong  and
      Li, Lei",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.441",
    doi = "10.18653/v1/2020.findings-emnlp.441",
    pages = "4908--4917",
    abstract = "Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data. We argue that the typical uncertainty sampling method for active learning is time-consuming and can hardly work in real-time, which may lead to ineffective sample selection. We propose adversarial uncertainty sampling in discrete space (AUSDS) to retrieve informative unlabeled samples more efficiently. AUSDS maps sentences into latent space generated by the popular pre-trained language models, and discover informative unlabeled text samples for annotation via adversarial attack. The proposed approach is extremely efficient compared with traditional uncertainty sampling with more than 10x speedup. Experimental results on five datasets show that AUSDS outperforms strong baselines on effectiveness.",
}
@inproceedings{mizrahi-etal-2020-coming,
    title = "{C}oming to {T}erms: {A}utomatic {F}ormation of {N}eologisms in {H}ebrew",
    author = "Mizrahi, Moran  and
      Yardeni Seelig, Stav  and
      Shahaf, Dafna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.442",
    doi = "10.18653/v1/2020.findings-emnlp.442",
    pages = "4918--4929",
    abstract = "Spoken languages are ever-changing, with new words entering them all the time. However, coming up with new words (neologisms) today relies exclusively on human creativity. In this paper we propose a system to automatically suggest neologisms. We focus on the Hebrew language as a test case due to the unusual regularity of its noun formation. User studies comparing our algorithm to experts and non-experts demonstrate that our algorithm is capable of generating high-quality outputs, as well as enhance human creativity. More broadly, we seek to inspire more computational work around the topic of linguistic creativity, which we believe offers numerous unexplored opportunities.",
}
@inproceedings{su-etal-2020-dual,
    title = "Dual Inference for Improving Language Understanding and Generation",
    author = "Su, Shang-Yu  and
      Chuang, Yung-Sung  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.443",
    doi = "10.18653/v1/2020.findings-emnlp.443",
    pages = "4930--4936",
    abstract = "Natural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does the opposite. The prior work mainly focused on exploiting the duality in model training in order to obtain the models with better performance. However, regarding the fast-growing scale of models in the current NLP area, sometimes we may have difficulty retraining whole NLU and NLG models. To better address the issue, this paper proposes to leverage the duality in the inference stage without the need of retraining. The experiments on three benchmark datasets demonstrate the effectiveness of the proposed method in both NLU and NLG, providing the great potential of practical usage.",
}
@inproceedings{zhang-etal-2020-joint,
    title = "Joint Intent Detection and Entity Linking on Spatial Domain Queries",
    author = "Zhang, Lei  and
      Wang, Runze  and
      Zhou, Jingbo  and
      Yu, Jingsong  and
      Ling, Zhenhua  and
      Xiong, Hui",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.444",
    doi = "10.18653/v1/2020.findings-emnlp.444",
    pages = "4937--4947",
    abstract = "Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants. In this paper, we first study the LU problem in the spatial domain, which is a critical problem for providing location-based services by voice assistants but is without in-depth investigation in existing studies. Spatial domain queries have several unique properties making them be more challenging for language understanding than common conversational queries, including lexical-similar but diverse intents and highly ambiguous words. Thus, a special tailored LU framework for spatial domain queries is necessary. To the end, a dataset was extracted and annotated based on the real-life queries from a voice assistant service. We then proposed a new multi-task framework that jointly learns the intent detection and entity linking tasks on the with invented hierarchical intent detection method and triple-scoring mechanism for entity linking. A specially designed spatial GCN is also utilized to model spatial context information among entities. We have conducted extensive experimental evaluations with state-of-the-art entity linking and intent detection methods, which demonstrated that can outperform all baselines with a significant margin.",
}
@inproceedings{kakwani-etal-2020-indicnlpsuite,
    title = "{I}ndic{NLPS}uite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for {I}ndian Languages",
    author = "Kakwani, Divyanshu  and
      Kunchukuttan, Anoop  and
      Golla, Satish  and
      N.C., Gokul  and
      Bhattacharyya, Avik  and
      Khapra, Mitesh M.  and
      Kumar, Pratyush",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.445",
    doi = "10.18653/v1/2020.findings-emnlp.445",
    pages = "4948--4961",
    abstract = "In this paper, we introduce NLP resources for 11 major Indian languages from two major language families. These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) pre-trained language models, and (d) multiple NLU evaluation datasets (\textit{IndicGLUE} benchmark). The monolingual corpora contains a total of 8.8 billion tokens across all 11 languages and Indian English, primarily sourced from news crawls. The word embeddings are based on \textit{FastText}, hence suitable for handling morphological complexity of Indian languages. The pre-trained language models are based on the compact ALBERT model. Lastly, we compile the (\textit{IndicGLUE} benchmark for Indian language NLU. To this end, we create datasets for the following tasks: Article Genre Classification, Headline Prediction, Wikipedia Section-Title Prediction, Cloze-style Multiple choice QA, Winograd NLI and COPA. We also include publicly available datasets for some Indic languages for tasks like Named Entity Recognition, Cross-lingual Sentence Retrieval, Paraphrase detection, \textit{etc.} Our embeddings are competitive or better than existing pre-trained embeddings on multiple tasks. We hope that the availability of the dataset will accelerate Indic NLP research which has the potential to impact more than a billion people. It can also help the community in evaluating advances in NLP over a more diverse pool of languages. The data and models are available at \url{https://indicnlp.ai4bharat.org}.",
}
@inproceedings{lee-etal-2020-weakly,
    title = "Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations",
    author = "Lee, I-Ta  and
      Pacheco, Maria Leonor  and
      Goldwasser, Dan",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.446",
    doi = "10.18653/v1/2020.findings-emnlp.446",
    pages = "4962--4972",
    abstract = "Representing, and reasoning over, long narratives requires models that can deal with complex event structures connected through multiple relationship types. This paper suggests to represent this type of information as a narrative graph and learn contextualized event representations over it using a relational graph neural network model. We train our model to capture event relations, derived from the Penn Discourse Tree Bank, on a huge corpus, and show that our multi-relational contextualized event representation can improve performance when learning script knowledge without direct supervision and provide a better representation for the implicit discourse sense classification task.",
}
@inproceedings{he-etal-2020-enhancing,
    title = "Enhancing Generalization in Natural Language Inference by Syntax",
    author = "He, Qi  and
      Wang, Han  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.447",
    doi = "10.18653/v1/2020.findings-emnlp.447",
    pages = "4973--4978",
    abstract = "Pre-trained language models such as BERT have achieved the state-of-the-art performance on natural language inference (NLI). However, it has been shown that such models can be tricked by variations of surface patterns such as syntax. We investigate the use of dependency trees to enhance the generalization of BERT in the NLI task, leveraging on a graph convolutional network to represent a syntax-based matching graph with heterogeneous matching patterns. Experimental results show that, our syntax-based method largely enhance generalization of BERT on a test set where the sentence pair has high lexical overlap but diverse syntactic structures, and do not degrade performance on the standard test set. In other words, the proposed method makes BERT more robust on syntactic changes.",
}
@proceedings{fig-lang-2020-figurative,
    title = "Proceedings of the Second Workshop on Figurative Language Processing",
    editor = "Klebanov, Beata Beigman  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee  and
      Feldman, Anna  and
      Ghosh, Debanjan",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.0",
}
@inproceedings{ghosh-etal-2020-report,
    title = "A Report on the 2020 Sarcasm Detection Shared Task",
    author = "Ghosh, Debanjan  and
      Vajpayee, Avijit  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.1",
    doi = "10.18653/v1/2020.figlang-1.1",
    pages = "1--11",
    abstract = "Detecting sarcasm and verbal irony is critical for understanding people{'}s actual sentiments and beliefs. Thus, the field of sarcasm analysis has become a popular research problem in natural language processing. As the community working on computational approaches for sarcasm detection is growing, it is imperative to conduct benchmarking studies to analyze the current state-of-the-art, facilitating progress in this area. We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang 2020) at ACL 2020.",
}
@inproceedings{lee-etal-2020-augmenting,
    title = "Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context",
    author = "Lee, Hankyol  and
      Yu, Youngjae  and
      Kim, Gunhee",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.2",
    doi = "10.18653/v1/2020.figlang-1.2",
    pages = "12--17",
    abstract = "We present a novel data augmentation technique, CRA (Contextual Response Augmentation), which utilizes conversational context to generate meaningful samples for training. We also mitigate the issues regarding unbalanced context lengths by changing the input output format of the model such that it can deal with varying context lengths effectively. Specifically, our proposed model, trained with the proposed data augmentation technique, participated in the sarcasm detection task of FigLang2020, have won and achieves the best performance in both Reddit and Twitter datasets.",
}
@inproceedings{leong-etal-2020-report,
    title = "A Report on the 2020 {VUA} and {TOEFL} Metaphor Detection Shared Task",
    author = "Leong, Chee Wee (Ben)  and
      Beigman Klebanov, Beata  and
      Hamill, Chris  and
      Stemle, Egon  and
      Ubale, Rutuja  and
      Chen, Xianyang",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.3",
    doi = "10.18653/v1/2020.figlang-1.3",
    pages = "18--29",
    abstract = "In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language.",
}
@inproceedings{su-etal-2020-deepmet,
    title = "{D}eep{M}et: A Reading Comprehension Paradigm for Token-level Metaphor Detection",
    author = "Su, Chuandong  and
      Fukumoto, Fumiyo  and
      Huang, Xiaoxi  and
      Li, Jiyi  and
      Wang, Rongbo  and
      Chen, Zhiqun",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.4",
    doi = "10.18653/v1/2020.figlang-1.4",
    pages = "30--39",
    abstract = "Machine metaphor understanding is one of the major topics in NLP. Most of the recent attempts consider it as classification or sequence tagging task. However, few types of research introduce the rich linguistic information into the field of computational metaphor by leveraging powerful pre-training language models. We focus a novel reading comprehension paradigm for solving the token-level metaphor detection task which provides an innovative type of solution for this task. We propose an end-to-end deep metaphor detection model named DeepMet based on this paradigm. The proposed approach encodes the global text context (whole sentence), local text context (sentence fragments), and question (query word) information as well as incorporating two types of part-of-speech (POS) features by making use of the advanced pre-training language model. The experimental results by using several metaphor datasets show that our model achieves competitive results in the second shared task on metaphor detection.",
}
@inproceedings{horvitz-etal-2020-context,
    title = "Context-Driven Satirical News Generation",
    author = "Horvitz, Zachary  and
      Do, Nam  and
      Littman, Michael L.",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.5",
    doi = "10.18653/v1/2020.figlang-1.5",
    pages = "40--50",
    abstract = "While mysterious, humor likely hinges on an interplay of entities, their relationships, and cultural connotations. Motivated by the importance of context in humor, we consider methods for constructing and leveraging contextual representations in generating humorous text. Specifically, we study the capacity of transformer-based architectures to generate funny satirical headlines, and show that both language models and summarization models can be fine-tuned to regularly generate headlines that people find funny. Furthermore, we find that summarization models uniquely support satire-generation by enabling the generation of topical humorous text. Outside of our formal study, we note that headlines generated by our model were accepted via a competitive process into a satirical newspaper, and one headline was ranked as high or better than 73{\%} of human submissions. As part of our work, we contribute a dataset of over 15K satirical headlines paired with ranked contextual information from news articles and Wikipedia.",
}
@inproceedings{dadu-pant-2020-sarcasm,
    title = "Sarcasm Detection using Context Separators in Online Discourse",
    author = "Dadu, Tanvi  and
      Pant, Kartikey",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.6",
    doi = "10.18653/v1/2020.figlang-1.6",
    pages = "51--55",
    abstract = "Sarcasm is an intricate form of speech, where meaning is conveyed implicitly. Being a convoluted form of expression, detecting sarcasm is an assiduous problem. The difficulty in recognition of sarcasm has many pitfalls, including misunderstandings in everyday communications, which leads us to an increasing focus on automated sarcasm detection. In the second edition of the Figurative Language Processing (FigLang 2020) workshop, the shared task of sarcasm detection released two datasets, containing responses along with their context sampled from Twitter and Reddit. In this work, we use $RoBERTa_{large}$ to detect sarcasm in both the datasets. We further assert the importance of context in improving the performance of contextual word embedding based models by using three different types of inputs - Response-only, Context-Response, and Context-Response (Separated). We show that our proposed architecture performs competitively for both the datasets. We also show that the addition of a separation token between context and target response results in an improvement of 5.13{\%} in the F1-score in the Reddit dataset.",
}
@inproceedings{khatri-p-2020-sarcasm,
    title = "Sarcasm Detection in Tweets with {BERT} and {G}lo{V}e Embeddings",
    author = "Khatri, Akshay  and
      P, Pranav",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.7",
    doi = "10.18653/v1/2020.figlang-1.7",
    pages = "56--60",
    abstract = "Sarcasm is a form of communication in which the person states opposite of what he actually means. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses all of the context provided in the dataset to which the user is reacting along with his actual response.",
}
@inproceedings{kumar-jena-etal-2020-c,
    title = "{C}-Net: Contextual Network for Sarcasm Detection",
    author = "Kumar Jena, Amit  and
      Sinha, Aman  and
      Agarwal, Rohit",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.8",
    doi = "10.18653/v1/2020.figlang-1.8",
    pages = "61--66",
    abstract = "Automatic Sarcasm Detection in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on its context. This paper presents our proposed model, C-Net, which takes contextual information of a sentence in a sequential manner to classify it as sarcastic or non-sarcastic. Our model showcases competitive performance in the Sarcasm Detection shared task organised on CodaLab and achieved 75.0{\%} F1-score on the Twitter dataset and 66.3{\%} F1-score on Reddit dataset.",
}
@inproceedings{shangipour-ataei-etal-2020-applying,
    title = "Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection",
    author = "Shangipour ataei, Taha  and
      Javdan, Soroush  and
      Minaei-Bidgoli, Behrouz",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.9",
    doi = "10.18653/v1/2020.figlang-1.9",
    pages = "67--71",
    abstract = "Sarcasm is a type of figurative language broadly adopted in social media and daily conversations. The sarcasm can ultimately alter the meaning of the sentence, which makes the opinion analysis process error-prone. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and aspect-based sentiment analysis approaches in order to extract the relation between context dialogue sequence and response and determine whether or not the response is sarcastic. The best performing method of ours obtains an F1 score of 0.73 on the Twitter dataset and 0.734 over the Reddit dataset at the second workshop on figurative language processing Shared Task 2020.",
}
@inproceedings{a-d-2020-sarcasm,
    title = "Sarcasm Identification and Detection in Conversion Context using {BERT}",
    author = "A., Kalaivani  and
      D., Thenmozhi",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.10",
    doi = "10.18653/v1/2020.figlang-1.10",
    pages = "72--76",
    abstract = "Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.",
}
@inproceedings{jaiswal-2020-neural,
    title = "Neural Sarcasm Detection using Conversation Context",
    author = "Jaiswal, Nikhil",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.11",
    doi = "10.18653/v1/2020.figlang-1.11",
    pages = "77--82",
    abstract = "Social media platforms and discussion forums such as Reddit, Twitter, etc. are filled with figurative languages. Sarcasm is one such category of figurative language whose presence in a conversation makes language understanding a challenging task. In this paper, we present a deep neural architecture for sarcasm detection. We investigate various pre-trained language representation models (PLRMs) like BERT, RoBERTa, etc. and fine-tune it on the Twitter dataset. We experiment with a variety of PLRMs either on the twitter utterance in isolation or utilizing the contextual information along with the utterance. Our findings indicate that by taking into consideration the previous three most recent utterances, the model is more accurately able to classify a conversation as being sarcastic or not. Our best performing ensemble model achieves an overall F1 score of 0.790, which ranks us second on the leaderboard of the Sarcasm Shared Task 2020.",
}
@inproceedings{baruah-etal-2020-context,
    title = "Context-Aware Sarcasm Detection Using {BERT}",
    author = "Baruah, Arup  and
      Das, Kaushik  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.12",
    doi = "10.18653/v1/2020.figlang-1.12",
    pages = "83--87",
    abstract = "In this paper, we present the results obtained by BERT, BiLSTM and SVM classifiers on the shared task on Sarcasm Detection held as part of The Second Workshop on Figurative Language Processing. The shared task required the use of conversational context to detect sarcasm. We experimented by varying the amount of context used along with the response (response is the text to be classified). The amount of context used includes (i) zero context, (ii) last one, two or three utterances, and (iii) all utterances. It was found that including the last utterance in the dialogue along with the response improved the performance of the classifier for the Twitter data set. On the other hand, the best performance for the Reddit data set was obtained when using only the response without any contextual information. The BERT classifier obtained F-score of 0.743 and 0.658 for the Twitter and Reddit data set respectively.",
}
@inproceedings{kumar-anand-2020-transformers,
    title = "Transformers on Sarcasm Detection with Context",
    author = "Kumar, Amardeep  and
      Anand, Vivek",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.13",
    doi = "10.18653/v1/2020.figlang-1.13",
    pages = "88--92",
    abstract = "Sarcasm Detection with Context, a shared task of Second Workshop on Figurative Language Processing (co-located with ACL 2020), is study of effect of context on Sarcasm detection in conversations of Social media. We present different techniques and models, mostly based on transformer for Sarcasm Detection with Context. We extended latest pre-trained transformers like BERT, RoBERTa, spanBERT on different task objectives like single sentence classification, sentence pair classification, etc. to understand role of conversation context for sarcasm detection on Twitter conversations and conversation threads from Reddit. We also present our own architecture consisting of LSTM and Transformers to achieve the objective.",
}
@inproceedings{srivastava-etal-2020-novel,
    title = "A Novel Hierarchical {BERT} Architecture for Sarcasm Detection",
    author = "Srivastava, Himani  and
      Varshney, Vaibhav  and
      Kumari, Surabhi  and
      Srivastava, Saurabh",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.14",
    doi = "10.18653/v1/2020.figlang-1.14",
    pages = "93--97",
    abstract = "Online discussion platforms are often flooded with opinions from users across the world on a variety of topics. Many such posts, comments, or utterances are often sarcastic in nature, i.e., the actual intent is hidden in the sentence and is different from its literal meaning, making the detection of such utterances challenging without additional context. In this paper, we propose a novel deep learning-based approach to detect whether an utterance is sarcastic or non-sarcastic by utilizing the given contexts ina hierarchical manner. We have used datasets from two online discussion platforms - Twitter and Reddit1for our experiments. Experimental and error analysis shows that the hierarchical models can make full use of history to obtain a better representation of contexts and thus, in turn, can outperform their sequential counterparts.",
}
@inproceedings{avvaru-etal-2020-detecting,
    title = "{D}etecting {S}arcasm in {C}onversation {C}ontext {U}sing {T}ransformer-{B}ased {M}odels",
    author = "Avvaru, Adithya  and
      Vobilisetty, Sanath  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.15",
    doi = "10.18653/v1/2020.figlang-1.15",
    pages = "98--103",
    abstract = "Sarcasm detection, regarded as one of the sub-problems of sentiment analysis, is a very typical task because the introduction of sarcastic words can flip the sentiment of the sentence itself. To date, many research works revolve around detecting sarcasm in one single sentence and there is very limited research to detect sarcasm resulting from multiple sentences. Current models used Long Short Term Memory (LSTM) variants with or without attention to detect sarcasm in conversations. We showed that the models using state-of-the-art Bidirectional Encoder Representations from Transformers (BERT), to capture syntactic and semantic information across conversation sentences, performed better than the current models. Based on the data analysis, we estimated that the number of sentences in the conversation that can contribute to the sarcasm and the results agrees to this estimation. We also perform a comparative study of our different versions of BERT-based model with other variants of LSTM model and XLNet (both using the estimated number of conversation sentences) and find out that BERT-based models outperformed them.",
}
@inproceedings{wan-etal-2020-using,
    title = "Using Conceptual Norms for Metaphor Detection",
    author = "Wan, Mingyu  and
      Ahrens, Kathleen  and
      Chersoni, Emmanuele  and
      Jiang, Menghan  and
      Su, Qi  and
      Xiang, Rong  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.16",
    doi = "10.18653/v1/2020.figlang-1.16",
    pages = "104--109",
    abstract = "This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection. We participate in all four phases of competition with both datasets, i.e. Verbs and AllPOS on the VUA and the TOFEL datasets. We use the modality exclusivity and embodiment norms for constructing a conceptual representation of the nodes and the context. Our system obtains an F-score of 0.652 for the VUA Verbs track, which is 5{\%} higher than the strong baselines. The experimental results across models and datasets indicate the salient contribution of using modality exclusivity and modality shift information for predicting metaphoricity.",
}
@inproceedings{li-etal-2020-albert,
    title = "{ALBERT}-{B}i{LSTM} for Sequential Metaphor Detection",
    author = "Li, Shuqun  and
      Zeng, Jingjie  and
      Zhang, Jinhui  and
      Peng, Tao  and
      Yang, Liang  and
      Lin, Hongfei",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.17",
    doi = "10.18653/v1/2020.figlang-1.17",
    pages = "110--115",
    abstract = "In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset.",
}
@inproceedings{kumar-sharma-2020-character,
    title = "Character aware models with similarity learning for metaphor detection",
    author = "Kumar, Tarun  and
      Sharma, Yashvardhan",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.18",
    doi = "10.18653/v1/2020.figlang-1.18",
    pages = "116--125",
    abstract = "Recent work on automatic sequential metaphor detection has involved recurrent neural networks initialized with different pre-trained word embeddings and which are sometimes combined with hand engineered features. To capture lexical and orthographic information automatically, in this paper we propose to add character based word representation. Also, to contrast the difference between literal and contextual meaning, we utilize a similarity network. We explore these components via two different architectures - a BiLSTM model and a Transformer Encoder model similar to BERT to perform metaphor identification. We participate in the Second Shared Task on Metaphor Detection on both the VUA and TOFEL datasets with the above models. The experimental results demonstrate the effectiveness of our method as it outperforms all the systems which participated in the previous shared task.",
}
@inproceedings{bizzoni-dobnik-2020-sky,
    title = "Sky + Fire = Sunset. Exploring Parallels between Visually Grounded Metaphors and Image Classifiers",
    author = "Bizzoni, Yuri  and
      Dobnik, Simon",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.19",
    doi = "10.18653/v1/2020.figlang-1.19",
    pages = "126--135",
    abstract = "This work explores the differences and similarities between neural image classifiers{'} mis-categorisations and visually grounded metaphors - that we could conceive as intentional mis-categorisations. We discuss the possibility of using automatic image classifiers to approximate human metaphoric behaviours, and the limitations of such frame. We report two pilot experiments to study grounded metaphoricity. In the first we represent metaphors as a form of visual mis-categorisation. In the second we model metaphors as a more flexible, compositional operation in a continuous visual space generated from automatic classification systems.",
}
@inproceedings{felt-riloff-2020-recognizing,
    title = "Recognizing Euphemisms and Dysphemisms Using Sentiment Analysis",
    author = "Felt, Christian  and
      Riloff, Ellen",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.20",
    doi = "10.18653/v1/2020.figlang-1.20",
    pages = "136--145",
    abstract = "This paper presents the first research aimed at recognizing euphemistic and dysphemistic phrases with natural language processing. Euphemisms soften references to topics that are sensitive, disagreeable, or taboo. Conversely, dysphemisms refer to sensitive topics in a harsh or rude way. For example, {``}passed away{''} and {``}departed{''} are euphemisms for death, while {``}croaked{''} and {``}six feet under{''} are dysphemisms for death. Our work explores the use of sentiment analysis to recognize euphemistic and dysphemistic language. First, we identify near-synonym phrases for three topics (firing, lying, and stealing) using a bootstrapping algorithm for semantic lexicon induction. Next, we classify phrases as euphemistic, dysphemistic, or neutral using lexical sentiment cues and contextual sentiment analysis. We introduce a new gold standard data set and present our experimental results for this task.",
}
@inproceedings{gong-etal-2020-illinimet,
    title = "{I}llini{M}et: {I}llinois System for Metaphor Detection with Contextual and Linguistic Information",
    author = "Gong, Hongyu  and
      Gupta, Kshitij  and
      Jain, Akriti  and
      Bhat, Suma",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.21",
    doi = "10.18653/v1/2020.figlang-1.21",
    pages = "146--153",
    abstract = "Metaphors are rhetorical use of words based on the conceptual mapping as opposed to their literal use. Metaphor detection, an important task in language understanding, aims to identify metaphors in word level from given sentences. We present IlliniMet, a system to automatically detect metaphorical words. Our model combines the strengths of the contextualized representation by the widely used RoBERTa model and the rich linguistic information from external resources such as WordNet. The proposed approach is shown to outperform strong baselines on a benchmark dataset. Our best model achieves F1 scores of 73.0{\%} on VUA ALLPOS, 77.1{\%} on VUA VERB, 70.3{\%} on TOEFL ALLPOS and 71.9{\%} on TOEFL VERB.",
}
@inproceedings{zayed-etal-2020-adaptation,
    title = "Adaptation of Word-Level Benchmark Datasets for Relation-Level Metaphor Identification",
    author = "Zayed, Omnia  and
      McCrae, John Philip  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.22",
    doi = "10.18653/v1/2020.figlang-1.22",
    pages = "154--164",
    abstract = "Metaphor processing and understanding has attracted the attention of many researchers recently with an increasing number of computational approaches. A common factor among these approaches is utilising existing benchmark datasets for evaluation and comparisons. The availability, quality and size of the annotated data are among the main difficulties facing the growing research area of metaphor processing. The majority of current approaches pertaining to metaphor processing concentrate on word-level processing due to data availability. On the other hand, approaches that process metaphors on the relation-level ignore the context where the metaphoric expression. This is due to the nature and format of the available data. Word-level annotation is poorly grounded theoretically and is harder to use in downstream tasks such as metaphor interpretation. The conversion from word-level to relation-level annotation is non-trivial. In this work, we attempt to fill this research gap by adapting three benchmark datasets, namely the VU Amsterdam metaphor corpus, the TroFi dataset and the TSV dataset, to suit relation-level metaphor identification. We publish the adapted datasets to facilitate future research in relation-level metaphor processing.",
}
@inproceedings{strzalkowski-etal-2020-generating,
    title = "Generating Ethnographic Models from Communities{'} Online Data",
    author = "Strzalkowski, Tomek  and
      Newheiser, Anna  and
      Kemper, Nathan  and
      Sa, Ning  and
      Acharya, Bharvee  and
      Katsios, Gregorios",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.23",
    doi = "10.18653/v1/2020.figlang-1.23",
    pages = "165--175",
    abstract = "In this paper we describe computational ethnography study to demonstrate how machine learning techniques can be utilized to exploit bias resident in language data produced by communities with online presence. Specifically, we leverage the use of figurative language (i.e., the choice of metaphors) in online text (e.g., news media, blogs) produced by distinct communities to obtain models of community worldviews that can be shown to be distinctly biased and thus different from other communities{'} models. We automatically construct metaphor-based community models for two distinct scenarios: gun rights and marriage equality. We then conduct a series of experiments to validate the hypothesis that the metaphors found in each community{'}s online language convey the bias in the community{'}s worldview.",
}
@inproceedings{la-pietra-masini-2020-oxymorons,
    title = "{O}xymorons: a preliminary corpus investigation",
    author = "La Pietra, Marta  and
      Masini, Francesca",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.24",
    doi = "10.18653/v1/2020.figlang-1.24",
    pages = "176--185",
    abstract = "This paper contains a preliminary corpus study of oxymorons, a figure of speech so far under-investigated in NLP-oriented research. The study resulted in a list of 376 oxymorons, identified by extracting a set of antonymous pairs (under various configurations) from corpora of written Italian and by manually checking the results. A complementary method is also envisaged for discovering contextual oxymorons, which are highly relevant for the detection of humor, irony and sarcasm.",
}
@inproceedings{weller-etal-2020-humor,
    title = "Can Humor Prediction Datasets be used for Humor Generation? Humorous Headline Generation via Style Transfer",
    author = "Weller, Orion  and
      Fulda, Nancy  and
      Seppi, Kevin",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.25",
    doi = "10.18653/v1/2020.figlang-1.25",
    pages = "186--191",
    abstract = "Understanding and identifying humor has been increasingly popular, as seen by the number of datasets created to study humor. However, one area of humor research, humor generation, has remained a difficult task, with machine generated jokes failing to match human-created humor. As many humor prediction datasets claim to aid in generative tasks, we examine whether these claims are true. We focus our experiments on the most popular dataset, included in the 2020 SemEval{'}s Task 7, and teach our model to take normal text and {``}translate{''} it into humorous text. We evaluate our model compared to humorous human generated headlines, finding that our model is preferred equally in A/B testing with the human edited versions, a strong success for humor generation, and is preferred over an intelligent random baseline 72{\%} of the time. We also show that our model is assumed to be human written comparable with that of the human edited headlines and is significantly better than random, indicating that this dataset does indeed provide potential for future humor generation systems.",
}
@inproceedings{kuo-carpuat-2020-evaluating,
    title = "Evaluating a {B}i-{LSTM} Model for Metaphor Detection in {TOEFL} Essays",
    author = "Kuo, Kevin  and
      Carpuat, Marine",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.26",
    doi = "10.18653/v1/2020.figlang-1.26",
    pages = "192--196",
    abstract = "This paper describes systems submitted to the Metaphor Shared Task at the Second Workshop on Figurative Language Processing. In this submission, we replicate the evaluation of the Bi-LSTM model introduced by Gao et al.(2018) on the VUA corpus in a new setting: TOEFL essays written by non-native English speakers. Our results show that Bi-LSTM models outperform feature-rich linear models on this challenging task, which is consistent with prior findings on the VUA dataset. However, the Bi-LSTM models lag behind the best performing systems in the shared task.",
}
@inproceedings{torres-rivera-etal-2020-neural,
    title = "Neural Metaphor Detection with a Residual bi{LSTM}-{CRF} Model",
    author = "Torres Rivera, Andr{\'e}s  and
      Oliver, Antoni  and
      Climent, Salvador  and
      Coll-Florit, Marta",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.27",
    doi = "10.18653/v1/2020.figlang-1.27",
    pages = "197--203",
    abstract = "In this paper we present a novel resource-inexpensive architecture for metaphor detection based on a residual bidirectional long short-term memory and conditional random fields. Current approaches on this task rely on deep neural networks to identify metaphorical words, using additional linguistic features or word embeddings. We evaluate our proposed approach using different model configurations that combine embeddings, part of speech tags, and semantically disambiguated synonym sets. This evaluation process was performed using the training and testing partitions of the VU Amsterdam Metaphor Corpus. We use this method of evaluation as reference to compare the results with other current neural approaches for this task that implement similar neural architectures and features, and that were evaluated using this corpus. Results show that our system achieves competitive results with a simpler architecture compared to previous approaches.",
}
@inproceedings{alnafesah-etal-2020-augmenting,
    title = "Augmenting Neural Metaphor Detection with Concreteness",
    author = "Alnafesah, Ghadi  and
      Tayyar Madabushi, Harish  and
      Lee, Mark",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.28",
    doi = "10.18653/v1/2020.figlang-1.28",
    pages = "204--210",
    abstract = "The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while. However, recent methods of detecting metaphor that have relied on deep neural models have ignored concreteness and related psycholinguistic information. We hypothesis that this information is not available to these models and that their addition will boost the performance of these models in detecting metaphor. We test this hypothesis on the Metaphor Detection Shared Task 2020 and find that the addition of concreteness information does in fact boost deep neural models. We also run tests on data from a previous shared task and show similar results.",
}
@inproceedings{ehren-etal-2020-supervised,
    title = "Supervised Disambiguation of {G}erman Verbal Idioms with a {B}i{LSTM} Architecture",
    author = "Ehren, Rafael  and
      Lichte, Timm  and
      Kallmeyer, Laura  and
      Waszczuk, Jakub",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.29",
    doi = "10.18653/v1/2020.figlang-1.29",
    pages = "211--220",
    abstract = "Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation. In this paper, we present a new VID corpus for German and perform a series of VID disambiguation experiments on it. Our best classifier, based on a neural architecture, yields an error reduction across VIDs of 57{\%} in terms of accuracy compared to a simple majority baseline.",
}
@inproceedings{hall-maudslay-etal-2020-metaphor,
    title = "Metaphor Detection using Context and Concreteness",
    author = "Hall Maudslay, Rowan  and
      Pimentel, Tiago  and
      Cotterell, Ryan  and
      Teufel, Simone",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.30",
    doi = "10.18653/v1/2020.figlang-1.30",
    pages = "221--226",
    abstract = "We report the results of our system on the Metaphor Detection Shared Task at the Second Workshop on Figurative Language Processing 2020. Our model is an ensemble, utilising contextualised and static distributional semantic representations, along with word-type concreteness ratings. Using these features, it predicts word metaphoricity with a deep multi-layer perceptron. We are able to best the state-of-the-art from the 2018 Shared Task by an average of 8.0{\%} F1, and finish fourth in both sub-tasks in which we participate.",
}
@inproceedings{dankers-etal-2020-neighbourly,
    title = "Being neighbourly: Neural metaphor identification in discourse",
    author = "Dankers, Verna  and
      Malhotra, Karan  and
      Kudva, Gaurav  and
      Medentsiy, Volodymyr  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.31",
    doi = "10.18653/v1/2020.figlang-1.31",
    pages = "227--234",
    abstract = "Existing approaches to metaphor processing typically rely on local features, such as immediate lexico-syntactic contexts or information within a given sentence. However, a large body of corpus-linguistic research suggests that situational information and broader discourse properties influence metaphor production and comprehension. In this paper, we present the first neural metaphor processing architecture that models a broader discourse through the use of attention mechanisms. Our models advance the state of the art on the all POS track of the 2018 VU Amsterdam metaphor identification task. The inclusion of discourse-level information yields further significant improvements.",
}
@inproceedings{chen-etal-2020-go,
    title = "Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: {ETS} team in 2020 metaphor shared task",
    author = "Chen, Xianyang  and
      Leong, Chee Wee (Ben)  and
      Flor, Michael  and
      Beigman Klebanov, Beata",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.32",
    doi = "10.18653/v1/2020.figlang-1.32",
    pages = "235--243",
    abstract = "This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus, followed by a multi-task learning setting, where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.",
}
@inproceedings{brooks-youssef-2020-metaphor,
    title = "Metaphor Detection using Ensembles of Bidirectional Recurrent Neural Networks",
    author = "Brooks, Jennifer  and
      Youssef, Abdou",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.33",
    doi = "10.18653/v1/2020.figlang-1.33",
    pages = "244--249",
    abstract = "In this paper we present our results from the Second Shared Task on Metaphor Detection, hosted by the Second Workshop on Figurative Language Processing. We use an ensemble of RNN models with bidirectional LSTMs and bidirectional attention mechanisms. Some of the models were trained on all parts of speech. Each of the other models was trained on one of four categories for parts of speech: {``}nouns{''}, {``}verbs{''}, {``}adverbs/adjectives{''}, or {``}other{''}. The models were combined into voting pools and the voting pools were combined using the logical {``}OR{''} operator.",
}
@inproceedings{liu-etal-2020-metaphor,
    title = "Metaphor Detection Using Contextual Word Embeddings From Transformers",
    author = "Liu, Jerry  and
      O{'}Hara, Nathan  and
      Rubin, Alexander  and
      Draelos, Rachel  and
      Rudin, Cynthia",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.34",
    doi = "10.18653/v1/2020.figlang-1.34",
    pages = "250--255",
    abstract = "The detection of metaphors can provide valuable information about a given text and is crucial to sentiment analysis and machine translation. In this paper, we outline the techniques for word-level metaphor detection used in our submission to the Second Shared Task on Metaphor Detection. We propose using both BERT and XLNet language models to create contextualized embeddings and a bi-directional LSTM to identify whether a given word is a metaphor. Our best model achieved F1-scores of 68.0{\%} on VUA AllPOS, 73.0{\%} on VUA Verbs, 66.9{\%} on TOEFL AllPOS, and 69.7{\%} on TOEFL Verbs, placing 7th, 6th, 5th, and 5th respectively. In addition, we outline another potential approach with a KNN-LSTM ensemble model that we did not have enough time to implement given the deadline for the competition. We show that a KNN classifier provides a similar F1-score on a validation set as the LSTM and yields different information on metaphors.",
}
@inproceedings{stemle-onysko-2020-testing,
    title = "Testing the role of metadata in metaphor identification",
    author = "Stemle, Egon  and
      Onysko, Alexander",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.35",
    doi = "10.18653/v1/2020.figlang-1.35",
    pages = "256--263",
    abstract = "This paper describes the adaptation and application of a neural network system for the automatic detection of metaphors. The LSTM BiRNN system participated in the shared task of metaphor identification that was part of the Second Workshop of Figurative Language Processing (FigLang2020) held at the Annual Conference of the Association for Computational Linguistics (ACL2020). The particular focus of our approach is on the potential influence that the metadata given in the ETS Corpus of Non-Native Written English might have on the automatic detection of metaphors in this dataset. The article first discusses the annotated ETS learner data, highlighting some of its peculiarities and inherent biases of metaphor use. A series of evaluations follow in order to test whether specific metadata influence the system performance in the task of automatic metaphor identification. The system is available under the APLv2 open-source license.",
}
@inproceedings{lemmens-etal-2020-sarcasm,
    title = "Sarcasm Detection Using an Ensemble Approach",
    author = "Lemmens, Jens  and
      Burtenshaw, Ben  and
      Lotfi, Ehsan  and
      Markov, Ilia  and
      Daelemans, Walter",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.36",
    doi = "10.18653/v1/2020.figlang-1.36",
    pages = "264--269",
    abstract = "We present an ensemble approach for the detection of sarcasm in Reddit and Twitter responses in the context of The Second Workshop on Figurative Language Processing held in conjunction with ACL 2020. The ensemble is trained on the predicted sarcasm probabilities of four component models and on additional features, such as the sentiment of the comment, its length, and source (Reddit or Twitter) in order to learn which of the component models is the most reliable for which input. The component models consist of an LSTM with hashtag and emoji representations; a CNN-LSTM with casing, stop word, punctuation, and sentiment representations; an MLP based on Infersent embeddings; and an SVM trained on stylometric and emotion-based features. All component models use the two conversational turns preceding the response as context, except for the SVM, which only uses features extracted from the response. The ensemble itself consists of an adaboost classifier with the decision tree algorithm as base estimator and yields F1-scores of 67{\%} and 74{\%} on the Reddit and Twitter test data, respectively.",
}
@inproceedings{gregory-etal-2020-transformer,
    title = "A Transformer Approach to Contextual Sarcasm Detection in {T}witter",
    author = "Gregory, Hunter  and
      Li, Steven  and
      Mohammadi, Pouya  and
      Tarn, Natalie  and
      Draelos, Rachel  and
      Rudin, Cynthia",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.37",
    doi = "10.18653/v1/2020.figlang-1.37",
    pages = "270--275",
    abstract = "Understanding tone in Twitter posts will be increasingly important as more and more communication moves online. One of the most difficult, yet important tones to detect is sarcasm. In the past, LSTM and transformer architecture models have been used to tackle this problem. We attempt to expand upon this research, implementing LSTM, GRU, and transformer models, and exploring new methods to classify sarcasm in Twitter posts. Among these, the most successful were transformer models, most notably BERT. While we attempted a few other models described in this paper, our most successful model was an ensemble of transformer models including BERT, RoBERTa, XLNet, RoBERTa-large, and ALBERT. This research was performed in conjunction with the sarcasm detection shared task section in the Second Workshop on Figurative Language Processing, co-located with ACL 2020.",
}
@inproceedings{dong-etal-2020-transformer,
    title = "Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media",
    author = "Dong, Xiangjue  and
      Li, Changmao  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.figlang-1.38",
    doi = "10.18653/v1/2020.figlang-1.38",
    pages = "276--280",
    abstract = "We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions. Our model uses deep transformer layers to perform multi-head attentions among the target utterance and the relevant context in the thread. The context-aware models are evaluated on two datasets from social media, Twitter and Reddit, and show 3.1{\%} and 7.0{\%} improvements over their baselines. Our best models give the F1-scores of 79.0{\%} and 75.0{\%} for the Twitter and Reddit datasets respectively, becoming one of the highest performing systems among 36 participants in this shared task.",
}
@proceedings{fever-2020-fact,
    title = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    editor = "Christodoulopoulos, Christos  and
      Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Mittal, Arpit",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.0",
}
@inproceedings{nie-etal-2020-simple,
    title = "Simple Compounded-Label Training for Fact Extraction and Verification",
    author = "Nie, Yixin  and
      Bauer, Lisa  and
      Bansal, Mohit",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.1",
    doi = "10.18653/v1/2020.fever-1.1",
    pages = "1--7",
    abstract = "Automatic fact checking is an important task motivated by the need for detecting and preventing the spread of misinformation across the web. The recently released FEVER challenge provides a benchmark task that assesses systems{'} capability for both the retrieval of required evidence and the identification of authentic claims. Previous approaches share a similar pipeline training paradigm that decomposes the task into three subtasks, with each component built and trained separately. Although achieving acceptable scores, these methods induce difficulty for practical application development due to unnecessary complexity and expensive computation. In this paper, we explore the potential of simplifying the system design and reducing training computation by proposing a joint training setup in which a single sequence matching model is trained with compounded labels that give supervision for both sentence selection and claim verification subtasks, eliminating the duplicate computation that occurs when models are designed and trained separately. Empirical results on FEVER indicate that our method: (1) outperforms the typical multi-task learning approach, and (2) gets comparable results to top performing systems with a much simpler training setup and less training computation (in terms of the amount of data consumed and the number of model parameters), facilitating future works on the automatic fact checking task and its practical usage.",
}
@inproceedings{khouja-2020-stance,
    title = "Stance Prediction and Claim Verification: An {A}rabic Perspective",
    author = "Khouja, Jude",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.2",
    doi = "10.18653/v1/2020.fever-1.2",
    pages = "8--17",
    abstract = "This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. The publicly available corpus comes in two perspectives: a version consisting of 4,547 true and false claims and a version consisting of 3,786 pairs (claim, evidence). We describe the methodology for creating the corpus and the annotation process. Using the introduced corpus, we also develop two machine learning baselines for two proposed tasks: claim verification and stance prediction. Our best model utilizes pretraining (BERT) and achieves 76.7 F1 on the stance prediction task and 64.3 F1 on the claim verification task. Our preliminary experiments shed some light on the limits of automatic claim verification that relies on claims text only. Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations from pretraining are insufficient for verifying claims without access to context or evidence.",
}
@inproceedings{zhou-etal-2020-probabilistic,
    title = "A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction",
    author = "Zhou, Yang  and
      Zhao, Tong  and
      Jiang, Meng",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.3",
    doi = "10.18653/v1/2020.fever-1.3",
    pages = "18--25",
    abstract = "Textual patterns (e.g., Country{'}s president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.",
}
@inproceedings{chen-etal-2020-developing,
    title = "Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by {BERT}",
    author = "Chen, Tengyang  and
      Li, Hongyu  and
      Kasamatsu, Miho  and
      Utsuro, Takehito  and
      Kawada, Yasuhide",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.4",
    doi = "10.18653/v1/2020.fever-1.4",
    pages = "26--35",
    abstract = "In the field of factoid question answering (QA), it is known that the state-of-the-art technology has achieved an accuracy comparable to that of humans in a certain benchmark challenge. On the other hand, in the area of non-factoid QA, there is still a limited number of datasets for training QA models, i.e., machine comprehension models. Considering such a situation within the field of the non-factoid QA, this paper aims to develop a dataset for training Japanese how-to tip QA models. This paper applies one of the state-of-the-art machine comprehension models to the Japanese how-to tip QA dataset. The trained how-to tip QA model is also compared with a factoid QA model trained with a Japanese factoid QA dataset. Evaluation results revealed that the how-to tip machine comprehension performance was almost comparative with that of the factoid machine comprehension even with the training data size reduced to around 4{\%} of the factoid machine comprehension. Thus, the how-to tip machine comprehension task requires much less training data compared with the factoid machine comprehension task.",
}
@inproceedings{lee-etal-2020-language,
    title = "Language Models as Fact Checkers?",
    author = "Lee, Nayeon  and
      Li, Belinda Z.  and
      Wang, Sinong  and
      Yih, Wen-tau  and
      Ma, Hao  and
      Khabsa, Madian",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.5",
    doi = "10.18653/v1/2020.fever-1.5",
    pages = "36--41",
    abstract = "Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers. In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our finetuned LM compares favorably with standard baselines. Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.",
}
@inproceedings{derczynski-etal-2020-maintaining,
    title = "Maintaining Quality in {FEVER} Annotation",
    author = "Derczynski, Leon  and
      Binau, Julie  and
      Schulte, Henri",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.6",
    doi = "10.18653/v1/2020.fever-1.6",
    pages = "42--46",
    abstract = "We propose two measures for measuring the quality of constructed claims in the FEVER task. Annotating data for this task involves the creation of supporting and refuting claims over a set of evidence. Automatic annotation processes often leave superficial patterns in data, which learning systems can detect instead of performing the underlying task. Humans also can leave these superficial patterns, either voluntarily or involuntarily (due to e.g. fatigue). The two measures introduced attempt to detect the impact of these superficial patterns. One is a new information-theoretic and distributionality based measure, \textit{DCI}; and the other an extension of neural probing work over the ARCT task, \textit{utility}. We demonstrate these measures over a recent major dataset, that from the English FEVER task in 2019.",
}
@inproceedings{portelli-etal-2020-distilling,
    title = "Distilling the Evidence to Augment Fact Verification Models",
    author = "Portelli, Beatrice  and
      Zhao, Jason  and
      Schuster, Tal  and
      Serra, Giuseppe  and
      Santus, Enrico",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.fever-1.7",
    doi = "10.18653/v1/2020.fever-1.7",
    pages = "47--51",
    abstract = "The alarming spread of fake news in social media, together with the impossibility of scaling manual fact verification, motivated the development of natural language processing techniques to automatically verify the veracity of claims. Most approaches perform a claim-evidence classification without providing any insights about why the claim is trustworthy or not. We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim. We show that the spans are informative for the classifier, improving performance and robustness. Tested on several state-of-the-art models over the Fever dataset, the enhanced classifiers consistently achieve higher accuracy while also showing reduced sensitivity to artifacts in the claims.",
}
@proceedings{evalnlgeval-2020-evaluating,
    title = "Proceedings of the 1st Workshop on Evaluating NLG Evaluation",
    editor = "Agarwal, Shubham  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Gehrmann, Sebastian  and
      Gkatzia, Dimitra  and
      Konstas, Ioannis  and
      Van Miltenburg, Emiel  and
      Santhanam, Sashank",
    month = dec,
    year = "2020",
    address = "Online (Dublin, Ireland)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.evalnlgeval-1.0",
}
@inproceedings{corbelle-etal-2020-proof,
    title = "A proof of concept on triangular test evaluation for Natural Language Generation",
    author = "Corbelle, Javier Gonz{\'a}lez  and
      Moral, Jos{\'e} Mar{\'\i}a Alonso  and
      Diz, Alberto Bugar{\'\i}n",
    booktitle = "Proceedings of the 1st Workshop on Evaluating NLG Evaluation",
    month = dec,
    year = "2020",
    address = "Online (Dublin, Ireland)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.evalnlgeval-1.1",
    pages = "1--9",
    abstract = "The evaluation of Natural Language Generation (NLG) systems has recently aroused much interest in the research community, since it should address several challenging aspects, such as readability of the generated texts, adequacy to the user within a particular context and moment and linguistic quality-related issues (e.g., correctness, coherence, understandability), among others. In this paper, we propose a novel technique for evaluating NLG systems that is inspired on the triangular test used in the field of sensory analysis. This technique allows us to compare two texts generated by different subjects and to i) determine whether statistically significant differences are detected between them when evaluated by humans and ii) quantify to what extent the number of evaluators plays an important role in the sensitivity of the results. As a proof of concept, we apply this evaluation technique in a real use case in the field of meteorology, showing the advantages and disadvantages of our proposal.",
}
@inproceedings{schoch-etal-2020-problem,
    title = "{``}This is a Problem, Don{'}t You Agree?{''} Framing and Bias in Human Evaluation for Natural Language Generation",
    author = "Schoch, Stephanie  and
      Yang, Diyi  and
      Ji, Yangfeng",
    booktitle = "Proceedings of the 1st Workshop on Evaluating NLG Evaluation",
    month = dec,
    year = "2020",
    address = "Online (Dublin, Ireland)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.evalnlgeval-1.2",
    pages = "10--16",
    abstract = "Despite recent efforts reviewing current human evaluation practices for natural language generation (NLG) research, the lack of reported question wording and potential for framing effects or cognitive biases influencing results has been widely overlooked. In this opinion paper, we detail three possible framing effects and cognitive biases that could be imposed on human evaluation in NLG. Based on this, we make a call for increased transparency for human evaluation in NLG and propose the concept of human evaluation statements. We make several recommendations for design details to report that could potentially influence results, such as question wording, and suggest that reporting pertinent design details can help increase comparability across studies as well as reproducibility of results.",
}
@inproceedings{van-miltenburg-etal-2020-evaluation,
    title = "Evaluation rules! On the use of grammars and rule-based systems for {NLG} evaluation",
    author = "van Miltenburg, Emiel  and
      van der Lee, Chris  and
      Castro-Ferreira, Thiago  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 1st Workshop on Evaluating NLG Evaluation",
    month = dec,
    year = "2020",
    address = "Online (Dublin, Ireland)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.evalnlgeval-1.3",
    pages = "17--27",
    abstract = "NLG researchers often use uncontrolled corpora to train and evaluate their systems, using textual similarity metrics, such as BLEU. This position paper argues in favour of two alternative evaluation strategies, using grammars or rule-based systems. These strategies are particularly useful to identify the strengths and weaknesses of different systems. We contrast our proposals with the (extended) WebNLG dataset, which is revealed to have a skewed distribution of predicates. We predict that this distribution affects the quality of the predictions for systems trained on this data. However, this hypothesis can only be thoroughly tested (without any confounds) once we are able to systematically manipulate the skewness of the data, using a rule-based approach.",
}
@inproceedings{kane-etal-2020-nubia,
    title = "{NUBIA}: {N}e{U}ral Based Interchangeability Assessor for Text Generation",
    author = "Kane, Hassan  and
      Kocyigit, Muhammed Yusuf  and
      Abdalla, Ali  and
      Ajanoh, Pelkins  and
      Coulibali, Mohamed",
    booktitle = "Proceedings of the 1st Workshop on Evaluating NLG Evaluation",
    month = dec,
    year = "2020",
    address = "Online (Dublin, Ireland)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.evalnlgeval-1.4",
    pages = "28--37",
    abstract = "We present NUBIA, a methodology to build automatic evaluation metrics for text generation using only machine learning models as core components. A typical NUBIA model is composed of three modules: a neural feature extractor, an aggregator and a calibrator. We demonstrate an implementation of NUBIA showing competitive performance with stateof-the art metrics used to evaluate machine translation and state-of-the art results for image captions quality evaluation. In addition to strong performance, NUBIA models have the advantage of being modular and improve in synergy with advances in text generation models.",
}
@inproceedings{mattei-etal-2020-interaction,
    title = "On the interaction of automatic evaluation and task framing in headline style transfer",
    author = "Mattei, Lorenzo De  and
      Cafagna, Michele  and
      Lai, Huiyuan  and
      Dell{'}Orletta, Felice  and
      Nissim, Malvina  and
      Gatt, Albert",
    booktitle = "Proceedings of the 1st Workshop on Evaluating NLG Evaluation",
    month = dec,
    year = "2020",
    address = "Online (Dublin, Ireland)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.evalnlgeval-1.5",
    pages = "38--43",
    abstract = "An ongoing debate in the NLG community concerns the best way to evaluate systems, with human evaluation often being considered the most reliable method, compared to corpus-based metrics. However, tasks involving subtle textual differences, such as style transfer, tend to be hard for humans to perform. In this paper, we propose an evaluation method for this task based on purposely-trained classifiers, showing that it better reflects system differences than traditional metrics such as BLEU.",
}
@proceedings{eval4nlp-2020-evaluation,
    title = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    editor = "Eger, Steffen  and
      Gao, Yang  and
      Peyrard, Maxime  and
      Zhao, Wei  and
      Hovy, Eduard",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.0",
}
@inproceedings{lux-etal-2020-truth,
    title = "Truth or Error? Towards systematic analysis of factual errors in abstractive summaries",
    author = "Lux, Klaus-Michael  and
      Sappelli, Maya  and
      Larson, Martha",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.1",
    doi = "10.18653/v1/2020.eval4nlp-1.1",
    pages = "1--10",
    abstract = "This paper presents a typology of errors produced by automatic summarization systems. The typology was created by manually analyzing the output of four recent neural summarization systems. Our work is motivated by the growing awareness of the need for better summary evaluation methods that go beyond conventional overlap-based metrics. Our typology is structured into two dimensions. First, the Mapping Dimension describes surface-level errors and provides insight into word-sequence transformation issues. Second, the Meaning Dimension describes issues related to interpretation and provides insight into breakdowns in truth, i.e., factual faithfulness to the original text. Comparative analysis revealed that two neural summarization systems leveraging pre-trained models have an advantage in decreasing grammaticality errors, but not necessarily factual errors. We also discuss the importance of ensuring that summary length and abstractiveness do not interfere with evaluating summary quality.",
}
@inproceedings{vasilyev-etal-2020-fill,
    title = "Fill in the {BLANC}: Human-free quality estimation of document summaries",
    author = "Vasilyev, Oleg  and
      Dharnidharka, Vedant  and
      Bohannon, John",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.2",
    doi = "10.18653/v1/2020.eval4nlp-1.2",
    pages = "11--20",
    abstract = "We present BLANC, a new approach to the automatic estimation of document summary quality. Our goal is to measure the functional performance of a summary with an objective, reproducible, and fully automated method. Our approach achieves this by measuring the performance boost gained by a pre-trained language model with access to a document summary while carrying out its language understanding task on the document{'}s text. We present evidence that BLANC scores have as good correlation with human evaluations as do the ROUGE family of summary quality measurements. And unlike ROUGE, the BLANC method does not require human-written reference summaries, allowing for fully human-free summary quality estimation.",
}
@inproceedings{sedoc-ungar-2020-item,
    title = "Item Response Theory for Efficient Human Evaluation of Chatbots",
    author = "Sedoc, Jo{\~a}o  and
      Ungar, Lyle",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.3",
    doi = "10.18653/v1/2020.eval4nlp-1.3",
    pages = "21--33",
    abstract = "Conversational agent quality is currently assessed using human evaluation, and often requires an exorbitant number of comparisons to achieve statistical significance. In this paper, we introduce Item Response Theory (IRT) for chatbot evaluation, using a paired comparison in which annotators judge which system responds better to the next turn of a conversation. IRT is widely used in educational testing for simultaneously assessing the ability of test takers and the quality of test questions. It is similarly well suited for chatbot evaluation since it allows the assessment of both models and the prompts used to evaluate them. We use IRT to efficiently assess chatbots, and show that different examples from the evaluation set are better suited for comparing high-quality (nearer to human performance) than low-quality systems. Finally, we use IRT to reduce the number of evaluation examples assessed by human annotators while retaining discriminative power.",
}
@inproceedings{lee-etal-2020-vilbertscore,
    title = "{V}i{LBERTS}core: Evaluating Image Caption Using Vision-and-Language {BERT}",
    author = "Lee, Hwanhee  and
      Yoon, Seunghyun  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Bui, Trung  and
      Jung, Kyomin",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.4",
    doi = "10.18653/v1/2020.eval4nlp-1.4",
    pages = "34--39",
    abstract = "In this paper, we propose an evaluation metric for image captioning systems using both image and text information. Unlike the previous methods that rely on textual representations in evaluating the caption, our approach uses visiolinguistic representations. The proposed method generates image-conditioned embeddings for each token using ViLBERT from both generated and reference texts. Then, these contextual embeddings from each of the two sentence-pair are compared to compute the similarity score. Experimental results on three benchmark datasets show that our method correlates significantly better with human judgments than all existing metrics.",
}
@inproceedings{ethayarajh-sadigh-2020-bleu,
    title = "{BLEU} Neighbors: A Reference-less Approach to Automatic Evaluation",
    author = "Ethayarajh, Kawin  and
      Sadigh, Dorsa",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.5",
    doi = "10.18653/v1/2020.eval4nlp-1.5",
    pages = "40--50",
    abstract = "Evaluation is a bottleneck in the development of natural language generation (NLG) models. Automatic metrics such as BLEU rely on references, but for tasks such as open-ended generation, there are no references to draw upon. Although language diversity can be estimated using statistical measures such as perplexity, measuring language quality requires human evaluation. However, because human evaluation at scale is slow and expensive, it is used sparingly; it cannot be used to rapidly iterate on NLG models, in the way BLEU is used for machine translation. To this end, we propose BLEU Neighbors, a nearest neighbors model for estimating language quality by using the BLEU score as a kernel function. On existing datasets for chitchat dialogue and open-ended sentence generation, we find that {--} on average {--} the quality estimation from a BLEU Neighbors model has a lower mean squared error and higher Spearman correlation with the ground truth than individual human annotators. Despite its simplicity, BLEU Neighbors even outperforms state-of-the-art models on automatically grading essays, including models that have access to a gold-standard reference essay.",
}
@inproceedings{chen-etal-2020-improving,
    title = "Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance",
    author = "Chen, Xi  and
      Ding, Nan  and
      Levinboim, Tomer  and
      Soricut, Radu",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.6",
    doi = "10.18653/v1/2020.eval4nlp-1.6",
    pages = "51--59",
    abstract = "Recent advances in automatic evaluation metrics for text have shown that deep contextualized word representations, such as those generated by BERT encoders, are helpful for designing metrics that correlate well with human judgements. At the same time, it has been argued that contextualized word representations exhibit sub-optimal statistical properties for encoding the true similarity between words or sentences. In this paper, we present two techniques for improving encoding representations for similarity metrics: a batch-mean centering strategy that improves statistical properties; and a computationally efficient tempered Word Mover Distance, for better fusion of the information in the contextualized word representations. We conduct numerical experiments that demonstrate the robustness of our techniques, reporting results over various BERT-backbone learned metrics and achieving state of the art correlation with human ratings on several benchmarks.",
}
@inproceedings{bremerman-etal-2020-evaluation,
    title = "On the Evaluation of Machine Translation n-best Lists",
    author = "Bremerman, Jacob  and
      Khayrallah, Huda  and
      Oard, Douglas  and
      Post, Matt",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.7",
    doi = "10.18653/v1/2020.eval4nlp-1.7",
    pages = "60--68",
    abstract = "The standard machine translation evaluation framework measures the single-best output of machine translation systems. There are, however, many situations where n-best lists are needed, yet there is no established way of evaluating them. This paper establishes a framework for addressing n-best evaluation by outlining three different questions one could consider when determining how one would define a {`}good{'} n-best list and proposing evaluation measures for each question. The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list. The second is a measure that uses gold translations with preference annotations to ask to what degree systems can produce ranked lists in preference order. The third is a measure that rewards partial matches, evaluating the closeness of the many items in an n-best list to a set of many valid references. These three perspectives make clear that having access to many references can be useful when n-best evaluation is the goal.",
}
@inproceedings{jha-etal-2020-artemis,
    title = "Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization",
    author = "Jha, Rahul  and
      Bi, Keping  and
      Li, Yang  and
      Pakdaman, Mahdi  and
      Celikyilmaz, Asli  and
      Zhiboedov, Ivan  and
      McDonald, Kieran",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.8",
    doi = "10.18653/v1/2020.eval4nlp-1.8",
    pages = "69--78",
    abstract = "We describe Artemis (Annotation methodology for Rich, Tractable, Extractive, Multi-domain, Indicative Summarization), a novel hierarchical annotation process that produces indicative summaries for documents from multiple domains. Current summarization evaluation datasets are single-domain and focused on a few domains for which naturally occurring summaries can be easily found, such as news and scientific articles. These are not sufficient for training and evaluation of summarization models for use in document management and information retrieval systems, which need to deal with documents from multiple domains. Compared to other annotation methods such as Relative Utility and Pyramid, Artemis is more tractable because judges don{'}t need to look at all the sentences in a document when making an importance judgment for one of the sentences, while providing similarly rich sentence importance annotations. We describe the annotation process in detail and compare it with other similar evaluation systems. We also present analysis and experimental results over a sample set of 532 annotated documents.",
}
@inproceedings{yacouby-axman-2020-probabilistic,
    title = "Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models",
    author = "Yacouby, Reda  and
      Axman, Dustin",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.9",
    doi = "10.18653/v1/2020.eval4nlp-1.9",
    pages = "79--91",
    abstract = "In pursuit of the perfect supervised NLP classifier, razor thin margins and low-resource test sets can make modeling decisions difficult. Popular metrics such as Accuracy, Precision, and Recall are often insufficient as they fail to give a complete picture of the model{'}s behavior. We present a probabilistic extension of Precision, Recall, and F1 score, which we refer to as confidence-Precision (cPrecision), confidence-Recall (cRecall), and confidence-F1 (cF1) respectively. The proposed metrics address some of the challenges faced when evaluating large-scale NLP systems, specifically when the model{'}s confidence score assignments have an impact on the system{'}s behavior. We describe four key benefits of our proposed metrics as compared to their threshold-based counterparts. Two of these benefits, which we refer to as robustness to missing values and sensitivity to model confidence score assignments are self-evident from the metrics{'} definitions; the remaining benefits, generalization, and functional consistency are demonstrated empirically.",
}
@inproceedings{poliak-2020-survey,
    title = "A survey on Recognizing Textual Entailment as an {NLP} Evaluation",
    author = "Poliak, Adam",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.10",
    doi = "10.18653/v1/2020.eval4nlp-1.10",
    pages = "92--109",
    abstract = "Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to compare semantic understanding of different NLP systems. In this survey paper, we provide an overview of different approaches for evaluating and understanding the reasoning capabilities of NLP systems. We then focus our discussion on RTE by highlighting prominent RTE datasets as well as advances in RTE dataset that focus on specific linguistic phenomena that can be used to evaluate NLP systems on a fine-grained level. We conclude by arguing that when evaluating NLP systems, the community should utilize newly introduced RTE datasets that focus on specific linguistic phenomena.",
}
@inproceedings{niu-penn-2020-grammaticality,
    title = "Grammaticality and Language Modelling",
    author = "Niu, Jingcheng  and
      Penn, Gerald",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.11",
    doi = "10.18653/v1/2020.eval4nlp-1.11",
    pages = "110--119",
    abstract = "Ever since Pereira (2000) provided evidence against Chomsky{'}s (1957) conjecture that statistical language modelling is incommensurable with the aims of grammaticality prediction as a research enterprise, a new area of research has emerged that regards statistical language models as {``}psycholinguistic subjects{''} and probes their ability to acquire syntactic knowledge. The advent of The Corpus of Linguistic Acceptability (CoLA) (Warstadt et al., 2019) has earned a spot on the leaderboard for acceptability judgements, and the polemic between Lau et al. (2017) and Sprouse et al. (2018) has raised fundamental questions about the nature of grammaticality and how acceptability judgements should be elicited. All the while, we are told that neural language models continue to improve. That is not an easy claim to test at present, however, because there is almost no agreement on how to measure their improvement when it comes to grammaticality and acceptability judgements. The GLUE leaderboard bundles CoLA together with a Matthews correlation coefficient (MCC), although probably because CoLA{'}s seminal publication was using it to compute inter-rater reliabilities. Researchers working in this area have used other accuracy and correlation scores, often driven by a need to reconcile and compare various discrete and continuous variables with each other. The score that we will advocate for in this paper, the point biserial correlation, in fact compares a discrete variable (for us, acceptability judgements) to a continuous variable (for us, neural language model probabilities). The only previous work in this area to choose the PBC that we are aware of is Sprouse et al. (2018a), and that paper actually applied it backwards (with some justification) so that the language model probability was treated as the discrete binary variable by setting a threshold. With the PBC in mind, we will first reappraise some recent work in syntactically targeted linguistic evaluations (Hu et al., 2020), arguing that while their experimental design sets a new high watermark for this topic, their results may not prove what they have claimed. We then turn to the task-independent assessment of language models as grammaticality classifiers. Prior to the introduction of the GLUE leaderboard, the vast majority of this assessment was essentially anecdotal, and we find the use of the MCC in this regard to be problematic. We conduct several studies with PBCs to compare several popular language models. We also study the effects of several variables such as normalization and data homogeneity on PBC.",
}
@inproceedings{brink-andersen-etal-2020-one,
    title = "One of these words is not like the other: a reproduction of outlier identification using non-contextual word representations",
    author = "Brink Andersen, Jesper  and
      Bak Bertelsen, Mikkel  and
      H{\o}rby Schou, Mikkel  and
      Ciosici, Manuel R.  and
      Assent, Ira",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.12",
    doi = "10.18653/v1/2020.eval4nlp-1.12",
    pages = "120--130",
    abstract = "Word embeddings are an active topic in the NLP research community. State-of-the-art neural models achieve high performance on downstream tasks, albeit at the cost of computationally expensive training. Cost aware solutions require cheaper models that still achieve good performance. We present several reproduction studies of intrinsic evaluation tasks that evaluate non-contextual word representations in multiple languages. Furthermore, we present 50-8-8, a new data set for the outlier identification task, which avoids limitations of the original data set, such as ambiguous words, infrequent words, and multi-word tokens, while increasing the number of test cases. The data set is expanded to contain semantic and syntactic tests and is multilingual (English, German, and Italian). We provide an in-depth analysis of word embedding models with a range of hyper-parameters. Our analysis shows the suitability of different models and hyper-parameters for different tasks and the greater difficulty of representing German and Italian languages.",
}
@inproceedings{dudy-bedrick-2020-words,
    title = "Are Some Words Worth More than Others?",
    author = "Dudy, Shiran  and
      Bedrick, Steven",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.13",
    doi = "10.18653/v1/2020.eval4nlp-1.13",
    pages = "131--142",
    abstract = "Current evaluation metrics for language modeling and generation rely heavily on the accuracy of predicted (or generated) words as compared to a reference ground truth. While important, token-level accuracy only captures one aspect of a language model{'}s behavior, and ignores linguistic properties of words that may allow some mis-predicted tokens to be useful in practice. Furthermore, statistics directly tied to prediction accuracy (including perplexity) may be confounded by the Zipfian nature of written language, as the majority of the prediction attempts will occur with frequently-occurring types. A model{'}s performance may vary greatly between high- and low-frequency words, which in practice could lead to failure modes such as repetitive and dull generated text being produced by a downstream consumer of a language model. To address this, we propose two new intrinsic evaluation measures within the framework of a simple word prediction task that are designed to give a more holistic picture of a language model{'}s performance. We evaluate several commonly-used large English language models using our proposed metrics, and demonstrate that our approach reveals functional differences in performance between the models that are obscured by more traditional metrics.",
}
@inproceedings{gashteovski-etal-2020-aligning,
    title = "On Aligning {O}pen{IE} Extractions with Knowledge Bases: A Case Study",
    author = "Gashteovski, Kiril  and
      Gemulla, Rainer  and
      Kotnis, Bhushan  and
      Hertling, Sven  and
      Meilicke, Christian",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.14",
    doi = "10.18653/v1/2020.eval4nlp-1.14",
    pages = "143--154",
    abstract = "Open information extraction (OIE) is the task of extracting relations and their corresponding arguments from a natural language text in un- supervised manner. Outputs of such systems are used for downstream tasks such as ques- tion answering and automatic knowledge base (KB) construction. Many of these downstream tasks rely on aligning OIE triples with refer- ence KBs. Such alignments are usually eval- uated w.r.t. a specific downstream task and, to date, no direct manual evaluation of such alignments has been performed. In this paper, we directly evaluate how OIE triples from the OPIEC corpus are related to the DBpedia KB w.r.t. information content. First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB rela- tion. Second, we evaluate the expressibility of general OPIEC triples in DBpedia. We in- vestigate whether{---}and, if so, how{---}a given OIE triple can be mapped to a single KB fact. We found that such mappings are not always possible because the information in the OIE triples tends to be more specific. Our evalua- tion suggests, however, that significant part of OIE triples can be expressed by means of KB formulas instead of individual facts.",
}
@inproceedings{wecker-etal-2020-clusterdatasplit,
    title = "{C}luster{D}ata{S}plit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation",
    author = "Wecker, Hanna  and
      Friedrich, Annemarie  and
      Adel, Heike",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.15",
    doi = "10.18653/v1/2020.eval4nlp-1.15",
    pages = "155--163",
    abstract = "This paper adds to the ongoing discussion in the natural language processing community on how to choose a good development set. Motivated by the real-life necessity of applying machine learning models to different data distributions, we propose a clustering-based data splitting algorithm. It creates development (or test) sets which are lexically different from the training data while ensuring similar label distributions. Hence, we are able to create challenging cross-validation evaluation setups while abstracting away from performance differences resulting from label distribution shifts between training and test data. In addition, we present a Python-based tool for analyzing and visualizing data split characteristics and model performance. We illustrate the workings and results of our approach using a sentiment analysis and a patent classification task.",
}
@inproceedings{iskender-etal-2020-best,
    title = "Best Practices for Crowd-based Evaluation of {G}erman Summarization: Comparing Crowd, Expert and Automatic Evaluation",
    author = {Iskender, Neslihan  and
      Polzehl, Tim  and
      M{\"o}ller, Sebastian},
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.16",
    doi = "10.18653/v1/2020.eval4nlp-1.16",
    pages = "164--175",
    abstract = "One of the main challenges in the development of summarization tools is summarization quality evaluation. On the one hand, the human assessment of summarization quality conducted by linguistic experts is slow, expensive, and still not a standardized procedure. On the other hand, the automatic assessment metrics are reported not to correlate high enough with human quality ratings. As a solution, we propose crowdsourcing as a fast, scalable, and cost-effective alternative to expert evaluations to assess the intrinsic and extrinsic quality of summarization by comparing crowd ratings with expert ratings and automatic metrics such as ROUGE, BLEU, or BertScore on a German summarization data set. Our results provide a basis for best practices for crowd-based summarization evaluation regarding major influential factors such as the best annotation aggregation method, the influence of readability and reading effort on summarization evaluation, and the optimal number of crowd workers to achieve comparable results to experts, especially when determining factors such as overall quality, grammaticality, referential clarity, focus, structure {\&} coherence, summary usefulness, and summary informativeness.",
}
@inproceedings{stringham-izbicki-2020-evaluating,
    title = "Evaluating Word Embeddings on Low-Resource Languages",
    author = "Stringham, Nathan  and
      Izbicki, Mike",
    booktitle = "Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.eval4nlp-1.17",
    doi = "10.18653/v1/2020.eval4nlp-1.17",
    pages = "176--186",
    abstract = "The analogy task introduced by Mikolov et al. (2013) has become the standard metric for tuning the hyperparameters of word embedding models. In this paper, however, we argue that the analogy task is unsuitable for low-resource languages for two reasons: (1) it requires that word embeddings be trained on large amounts of text, and (2) analogies may not be well-defined in some low-resource settings. We solve these problems by introducing the OddOneOut and Topk tasks, which are specifically designed for model selection in the low-resource setting. We use these metrics to successfully tune hyperparameters for a low-resource emoji embedding task and word embeddings on 16 extinct languages. The largest of these languages (Ancient Hebrew) has a 41 million token dataset, and the smallest (Old Gujarati) has only a 1813 token dataset.",
}
@proceedings{emnlp-2020-2020-empirical-methods,
    title = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    editor = "Villavicencio, Aline  and
      Van Durme, Benjamin",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.0",
}
@inproceedings{duan-etal-2020-machine,
    title = "Machine Reasoning: Technology, Dilemma and Future",
    author = "Duan, Nan  and
      Tang, Duyu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.1",
    doi = "10.18653/v1/2020.emnlp-tutorials.1",
    pages = "1--6",
    abstract = "Machine reasoning research aims to build interpretable AI systems that can solve problems or draw conclusions from what they are told (i.e. facts and observations) and already know (i.e. models, common sense and knowledge) under certain constraints. In this tutorial, we will (1) describe the motivation of this tutorial and give our definition on machine reasoning; (2) introduce typical machine reasoning frameworks, including symbolic reasoning, probabilistic reasoning, neural-symbolic reasoning and neural-evidence reasoning, and show their successful applications in real-world scenarios; (3) talk about the dilemma between black-box neural networks with state-of-the-art performance and machine reasoning approaches with better interpretability; (4) summarize the content of this tutorial and discuss possible future directions.",
}
@inproceedings{nakov-da-san-martino-2020-fact,
    title = "Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era",
    author = "Nakov, Preslav  and
      Da San Martino, Giovanni",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.2",
    doi = "10.18653/v1/2020.emnlp-tutorials.2",
    pages = "7--19",
    abstract = "The rise of social media has democratized content creation and has made it easy for everybody to share and spread information online. On the positive side, this has given rise to citizen journalism, thus enabling much faster dissemination of information compared to what was possible with newspapers, radio, and TV. On the negative side, stripping traditional media from their gate-keeping role has left the public unprotected against the spread of misinformation, which could now travel at breaking-news speed over the same democratic channel. This has given rise to the proliferation of false information specifically created to affect individual people{'}s beliefs, and ultimately to influence major events such as political elections. There are strong indications that false information was weaponized at an unprecedented scale during Brexit and the 2016 U.S. presidential elections. {``}Fake news,{''} which can be defined as fabricated information that mimics news media content in form but not in organizational process or intent, became the Word of the Year for 2017, according to Collins Dictionary. Thus, limiting the spread of {``}fake news{''} and its impact has become a major focus for computer scientists, journalists, social media companies, and regulatory authorities. The tutorial will offer an overview of the broad and emerging research area of disinformation, with focus on the latest developments and research directions.",
}
@inproceedings{wallace-etal-2020-interpreting,
    title = "Interpreting Predictions of {NLP} Models",
    author = "Wallace, Eric  and
      Gardner, Matt  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.3",
    doi = "10.18653/v1/2020.emnlp-tutorials.3",
    pages = "20--23",
    abstract = "Although neural NLP models are highly expressive and empirically successful, they also systematically fail in counterintuitive ways and are opaque in their decision-making process. This tutorial will provide a background on interpretation techniques, i.e., methods for explaining the predictions of NLP models. We will first situate example-specific interpretations in the context of other ways to understand models (e.g., probing, dataset analyses). Next, we will present a thorough study of example-specific interpretations, including saliency maps, input perturbations (e.g., LIME, input reduction), adversarial attacks, and influence functions. Alongside these descriptions, we will walk through source code that creates and visualizes interpretations for a diverse set of NLP tasks. Finally, we will discuss open problems in the field, e.g., evaluating, extending, and improving interpretation methods.",
}
@inproceedings{ilharco-etal-2020-high,
    title = "High Performance Natural Language Processing",
    author = "Ilharco, Gabriel  and
      Ilharco, Cesar  and
      Turc, Iulia  and
      Dettmers, Tim  and
      Ferreira, Felipe  and
      Lee, Kenton",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.4",
    doi = "10.18653/v1/2020.emnlp-tutorials.4",
    pages = "24--27",
    abstract = "Scale has played a central role in the rapid progress natural language processing has enjoyed in recent years. While benchmarks are dominated by ever larger models, efficient hardware use is critical for their widespread adoption and further progress in the field. In this cutting-edge tutorial, we will recapitulate the state-of-the-art in natural language processing with scale in perspective. After establishing these foundations, we will cover a wide range of techniques for improving efficiency, including knowledge distillation, quantization, pruning, more efficient architectures, along with case studies and practical implementation tricks.",
}
@inproceedings{kordjamshidi-etal-2020-representation,
    title = "Representation, Learning and Reasoning on Spatial Language for Downstream {NLP} Tasks",
    author = "Kordjamshidi, Parisa  and
      Pustejovsky, James  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.5",
    doi = "10.18653/v1/2020.emnlp-tutorials.5",
    pages = "28--33",
    abstract = "Understating spatial semantics expressed in natural language can become highly complex in real-world applications. This includes applications of language grounding, navigation, visual question answering, and more generic human-machine interaction and dialogue systems. In many of such downstream tasks, explicit representation of spatial concepts and relationships can improve the capabilities of machine learning models in reasoning and deep language understanding. In this tutorial, we overview the cutting-edge research results and existing challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. We discuss the recent results on the above-mentioned applications {--}that need spatial language learning and reasoning {--} and highlight the research gaps and future directions.",
}
@inproceedings{huang-etal-2020-simultaneous,
    title = "Simultaneous Translation",
    author = "Huang, Liang  and
      Cherry, Colin  and
      Ma, Mingbo  and
      Arivazhagan, Naveen  and
      He, Zhongjun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.6",
    doi = "10.18653/v1/2020.emnlp-tutorials.6",
    pages = "34--36",
    abstract = "Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine. This problem has long been considered one of the hardest problems in AI and one of its holy grails. Recently, with rapid improvements in machine translation, speech recognition, and speech synthesis, there has been exciting progress towards simultaneous translation. This tutorial will focus on the design and evaluation of policies for simultaneous translation, to leave attendees with a deep technical understanding of the history, the recent advances, and the remaining challenges in this field.",
}
@inproceedings{ji-etal-2020-amazing,
    title = "The Amazing World of Neural Language Generation",
    author = "Ji, Yangfeng  and
      Bosselut, Antoine  and
      Wolf, Thomas  and
      Celikyilmaz, Asli",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-tutorials.7",
    doi = "10.18653/v1/2020.emnlp-tutorials.7",
    pages = "37--42",
    abstract = "Neural Language Generation (NLG) {--} using neural network models to generate coherent text {--} is among the most promising methods for automated text creation. Recent years have seen a paradigm shift in neural text generation, caused by the advances in deep contextual language modeling (e.g., LSTMs, GPT, GPT2) and transfer learning (e.g., ELMo, BERT). While these tools have dramatically improved the state of NLG, particularly for low resources tasks, state-of-the-art NLG models still face many challenges: a lack of diversity in generated text, commonsense violations in depicted situations, difficulties in making use of factual information, and difficulties in designing reliable evaluation metrics. In this tutorial, we will present an overview of the current state-of-the-art in neural network architectures, and how they shaped recent research directions in text generation. We will discuss how and why these models succeed/fail at generating coherent text, and provide insights on several applications.",
}
@proceedings{emnlp-2020-2020,
    title = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.0",
}
@inproceedings{jo-etal-2020-detecting,
    title = "Detecting Attackable Sentences in Arguments",
    author = "Jo, Yohan  and
      Bang, Seojin  and
      Manzoor, Emaad  and
      Hovy, Eduard  and
      Reed, Chris",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.1",
    doi = "10.18653/v1/2020.emnlp-main.1",
    pages = "1--23",
    abstract = "Finding attackable sentences in an argument is the first step toward successful refutation in argumentation. We present a first large-scale analysis of sentence attackability in online arguments. We analyze driving reasons for attacks in argumentation and identify relevant characteristics of sentences. We demonstrate that a sentence{'}s attackability is associated with many of these characteristics regarding the sentence{'}s content, proposition types, and tone, and that an external knowledge source can provide useful information about attackability. Building on these findings, we demonstrate that machine learning models can automatically detect attackable sentences in arguments, significantly better than several baselines and comparably well to laypeople.",
}
@inproceedings{jo-etal-2020-extracting,
    title = "Extracting Implicitly Asserted Propositions in Argumentation",
    author = "Jo, Yohan  and
      Visser, Jacky  and
      Reed, Chris  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.2",
    doi = "10.18653/v1/2020.emnlp-main.2",
    pages = "24--38",
    abstract = "Argumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives. These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to understanding certain arguments properly. However, most argument mining systems and computational linguistics research have paid little attention to implicitly asserted propositions in argumentation. In this paper, we examine a wide range of computational methods for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. By evaluating the models on a corpus of 2016 U.S. presidential debates and online commentary, we demonstrate the effectiveness and limitations of the computational models. Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.",
}
@inproceedings{bar-haim-etal-2020-quantitative,
    title = "Quantitative argument summarization and beyond: Cross-domain key point analysis",
    author = "Bar-Haim, Roy  and
      Kantor, Yoav  and
      Eden, Lilach  and
      Friedman, Roni  and
      Lahav, Dan  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.3",
    doi = "10.18653/v1/2020.emnlp-main.3",
    pages = "39--49",
    abstract = "When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments. The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data. Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews. An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.",
}
@inproceedings{kobbe-etal-2020-unsupervised,
    title = "Unsupervised stance detection for arguments from consequences",
    author = "Kobbe, Jonathan  and
      Hulpu{\textcommabelow{s}}, Ioana  and
      Stuckenschmidt, Heiner",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.4",
    doi = "10.18653/v1/2020.emnlp-main.4",
    pages = "50--60",
    abstract = "Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.",
}
@inproceedings{freitag-etal-2020-bleu,
    title = "{BLEU} might be Guilty but References are not Innocent",
    author = "Freitag, Markus  and
      Grangier, David  and
      Caswell, Isaac",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.5",
    doi = "10.18653/v1/2020.emnlp-main.5",
    pages = "61--71",
    abstract = "The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems. This paper demonstrates that, while choice of metric is important, the nature of the references is also critical. We study different methods to collect references and compare their value in automated evaluation by reporting correlation with human evaluation for a variety of systems and metrics. Motivated by the finding that typical references exhibit poor diversity, concentrating around translationese language, we develop a paraphrasing task for linguists to perform on existing reference translations, which counteracts this bias. Our method yields higher correlation with human judgment not only for the submissions of WMT 2019 English to German, but also for Back-translation and APE augmented MT output, which have been shown to have low correlation with automatic metrics using standard references. We demonstrate that our methodology improves correlation with all modern evaluation metrics we look at, including embedding-based methods.To complete this picture, we reveal that multi-reference BLEU does not improve the correlation for high quality output, and present an alternative multi-reference formulation that is more effective.",
}
@inproceedings{graham-etal-2020-statistical,
    title = "Statistical Power and Translationese in Machine Translation Evaluation",
    author = "Graham, Yvette  and
      Haddow, Barry  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.6",
    doi = "10.18653/v1/2020.emnlp-main.6",
    pages = "72--81",
    abstract = "The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation. Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data compared to experiments that tested only with text originally composed in that language. For this reason we recommend that reverse-created test data be omitted from future machine translation test sets. In addition, we provide a re-evaluation of a past machine translation evaluation claiming human-parity of MT. One important issue not previously considered is statistical power of significance tests applied to comparison of human and machine translation. Since the very aim of past evaluations was investigation of ties between human and MT systems, power analysis is of particular importance, to avoid, for example, claims of human parity simply corresponding to Type II error resulting from the application of a low powered test. We provide detailed analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size for future studies.",
}
@inproceedings{khayrallah-etal-2020-simulated,
    title = "Simulated multiple reference training improves low-resource machine translation",
    author = "Khayrallah, Huda  and
      Thompson, Brian  and
      Post, Matt  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.7",
    doi = "10.18653/v1/2020.emnlp-main.7",
    pages = "82--89",
    abstract = "Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings. We introduce Simulated Multiple Reference Training (SMRT), a novel MT training method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a paraphraser and training the MT model to predict the paraphraser{'}s distribution over possible tokens. We demonstrate the effectiveness of SMRT in low-resource settings when translating to English, with improvements of 1.2 to 7.0 BLEU. We also find SMRT is complementary to back-translation.",
}
@inproceedings{thompson-post-2020-automatic,
    title = "Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing",
    author = "Thompson, Brian  and
      Post, Matt",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.8",
    doi = "10.18653/v1/2020.emnlp-main.8",
    pages = "90--121",
    abstract = "We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This results in the paraphraser{'}s output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). We also explore using our model for the task of quality estimation as a metric{---}conditioning on the source instead of the reference{---}and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.",
}
@inproceedings{saha-etal-2020-prover,
    title = "{PR}over: Proof Generation for Interpretable Reasoning over Rules",
    author = "Saha, Swarnadeep  and
      Ghosh, Sayan  and
      Srivastava, Shashank  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.9",
    doi = "10.18653/v1/2020.emnlp-main.9",
    pages = "122--136",
    abstract = "Recent work by Clark et al. (2020) shows that transformers can act as {``}soft theorem provers{''} by answering questions over explicitly provided knowledge in natural language. In our work, we take a step closer to emulating formal theorem provers, by proposing PRover, an interpretable transformer-based model that jointly answers binary questions over rule-bases and generates the corresponding proofs. Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm. During inference, a valid proof, satisfying a set of global constraints is generated. We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance. First, PRover generates proofs with an accuracy of 87{\%}, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6{\%} improvement on zero-shot evaluation). Second, when trained on questions requiring lower depths of reasoning, it generalizes significantly better to higher depths (up to 15{\%} improvement). Third, PRover obtains near perfect QA accuracy of 98{\%} using only 40{\%} of the training data. However, generating proofs for questions requiring higher depths of reasoning becomes challenging, and the accuracy drops to 65{\%} for {``}depth 5{''}, indicating significant scope for future work.",
}
@inproceedings{jhamtani-clark-2020-learning,
    title = "Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering",
    author = "Jhamtani, Harsh  and
      Clark, Peter",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.10",
    doi = "10.18653/v1/2020.emnlp-main.10",
    pages = "137--150",
    abstract = "Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To address this, we introduce three explanation datasets in which explanations formed from corpus facts are annotated. Our first dataset, eQASC contains over 98K explanation annotations for the multihop question answering dataset QASC, and is the first that annotates multiple candidate explanations for each answer. The second dataset eQASC-perturbed is constructed by crowd-sourcing perturbations (while preserving their validity) of a subset of explanations in QASC, to test consistency and generalization of explanation prediction models. The third dataset eOBQA is constructed by adding explanation annotations to the OBQA dataset to test generalization of models trained on eQASC. We show that this data can be used to significantly improve explanation quality (+14{\%} absolute F1 over a strong retrieval baseline) using a BERT-based classifier, but still behind the upper bound, offering a new challenge for future research. We also explore a delexicalized chain representation in which repeated noun phrases are replaced by variables, thus turning them into generalized reasoning chains (for example: {``}X is a Y{''} AND {``}Y has Z{''} IMPLIES {``}X has Z{''}). We find that generalized chains maintain performance while also being more robust to certain perturbations.",
}
@inproceedings{banerjee-baral-2020-self,
    title = "Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering",
    author = "Banerjee, Pratyay  and
      Baral, Chitta",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.11",
    doi = "10.18653/v1/2020.emnlp-main.11",
    pages = "151--162",
    abstract = "The aim of all Question Answering (QA) systems is to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator bias, making systems focus more on the bias than the actual task. This work proposes Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific knowledge. We propose using KTL to perform zero-shot question answering, and our experiments show considerable improvements over large pre-trained transformer language models.",
}
@inproceedings{khashabi-etal-2020-bang,
    title = "More Bang for Your Buck: Natural Perturbation for Robust Question Answering",
    author = "Khashabi, Daniel  and
      Khot, Tushar  and
      Sabharwal, Ashish",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.12",
    doi = "10.18653/v1/2020.emnlp-main.12",
    pages = "163--170",
    abstract = "Deep learning models for linguistic tasks require large training datasets, which are expensive to create. As an alternative to the traditional approach of creating new instances by repeating the process of creating one instance, we propose doing so by first collecting a set of seed examples and then applying human-driven natural perturbations (as opposed to rule-based machine perturbations), which often change the gold label as well. Such perturbations have the advantage of being relatively easier (and hence cheaper) to create than writing out completely new examples. Further, they help address the issue that even models achieving human-level scores on NLP datasets are known to be considerably sensitive to small changes in input. To evaluate the idea, we consider a recent question-answering dataset (BOOLQ) and study our approach as a function of the perturbation cost ratio, the relative cost of perturbing an existing question vs. creating a new one from scratch. We find that when natural perturbations are moderately cheaper to create (cost ratio under 60{\%}), it is more effective to use them for training BOOLQ models: such models exhibit 9{\%} higher robustness and 4.5{\%} stronger generalization, while retaining performance on the original BOOLQ dataset.",
}
@inproceedings{kuznetsov-gurevych-2020-matter,
    title = "A matter of framing: {T}he impact of linguistic formalism on probing results",
    author = "Kuznetsov, Ilia  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.13",
    doi = "10.18653/v1/2020.emnlp-main.13",
    pages = "171--182",
    abstract = "Deep pre-trained contextualized encoders like BERT demonstrate remarkable performance on a range of downstream tasks. A recent line of research in probing investigates the linguistic knowledge implicitly learned by these models during pre-training. While most work in probing operates on the task level, linguistic tasks are rarely uniform and can be represented in a variety of formalisms. Any linguistics-based probing study thereby inevitably commits to the formalism used to annotate the underlying data. Can the choice of formalism affect probing results? To investigate, we conduct an in-depth cross-formalism layer probing study in role semantics. We find linguistically meaningful differences in the encoding of semantic role- and proto-role information by BERT depending on the formalism and demonstrate that layer probing can detect subtle differences between the implementations of the same linguistic formalism. Our results suggest that linguistic formalism is an important dimension in probing studies, along with the commonly used cross-task and cross-lingual experimental settings.",
}
@inproceedings{voita-titov-2020-information,
    title = "Information-Theoretic Probing with Minimum Description Length",
    author = "Voita, Elena  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.14",
    doi = "10.18653/v1/2020.emnlp-main.14",
    pages = "183--196",
    abstract = "To measure how well pretrained representations encode some linguistic property, it is common to use accuracy of a probe, i.e. a classifier trained to predict the property from the representations. Despite widespread adoption of probes, differences in their accuracy fail to adequately reflect differences in representations. For example, they do not substantially favour pretrained representations over randomly initialized ones. Analogously, their accuracy can be similar when probing for genuine linguistic labels and probing for random synthetic tasks. To see reasonable differences in accuracy with respect to these random baselines, previous work had to constrain either the amount of probe training data or its model size. Instead, we propose an alternative to the standard probes, information-theoretic probing with minimum description length (MDL). With MDL probing, training a probe to predict labels is recast as teaching it to effectively transmit the data. Therefore, the measure of interest changes from probe accuracy to the description length of labels given representations. In addition to probe quality, the description length evaluates {``}the amount of effort{''} needed to achieve the quality. This amount of effort characterizes either (i) size of a probing model, or (ii) the amount of data needed to achieve the high quality. We consider two methods for estimating MDL which can be easily implemented on top of the standard probing pipelines: variational coding and online coding. We show that these methods agree in results and are more informative and stable than the standard probes.",
}
@inproceedings{torroba-hennigen-etal-2020-intrinsic,
    title = "Intrinsic Probing through Dimension Selection",
    author = "Torroba Hennigen, Lucas  and
      Williams, Adina  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.15",
    doi = "10.18653/v1/2020.emnlp-main.15",
    pages = "197--216",
    abstract = "Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted. To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal. We then probe fastText and BERT for various morphosyntactic attributes across 36 languages. We find that most attributes are reliably encoded by only a few neurons, with fastText concentrating its linguistic structure more than BERT.",
}
@inproceedings{warstadt-etal-2020-learning,
    title = "Learning Which Features Matter: {R}o{BERT}a Acquires a Preference for Linguistic Generalizations (Eventually)",
    author = "Warstadt, Alex  and
      Zhang, Yian  and
      Li, Xiaocheng  and
      Liu, Haokun  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.16",
    doi = "10.18653/v1/2020.emnlp-main.16",
    pages = "217--235",
    abstract = "One reason pretraining on self-supervised linguistic tasks is effective is that it teaches models features that are helpful for language understanding. However, we want pretrained models to learn not only to represent linguistic features, but also to use those features preferentially during fine-turning. With this goal in mind, we introduce a new English-language diagnostic set called MSGS (the Mixed Signals Generalization Set), which consists of 20 ambiguous binary classification tasks that we use to test whether a pretrained model prefers linguistic or surface generalizations during finetuning. We pretrain RoBERTa from scratch on quantities of data ranging from 1M to 1B words and compare their performance on MSGS to the publicly available RoBERTa{\_}BASE. We find that models can learn to represent linguistic features with little pretraining data, but require far more data to learn to prefer linguistic generalizations over surface ones. Eventually, with about 30B words of pretraining data, RoBERTa{\_}BASE does consistently demonstrate a linguistic bias with some regularity. We conclude that while self-supervised pretraining is an effective way to learn helpful inductive biases, there is likely room to improve the rate at which models learn which features matter.",
}
@inproceedings{an-etal-2020-repulsive,
    title = "Repulsive Attention: Rethinking Multi-head Attention as {B}ayesian Inference",
    author = "An, Bang  and
      Lyu, Jie  and
      Wang, Zhenyi  and
      Li, Chunyuan  and
      Hu, Changwei  and
      Tan, Fei  and
      Zhang, Ruiyi  and
      Hu, Yifan  and
      Chen, Changyou",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.17",
    doi = "10.18653/v1/2020.emnlp-main.17",
    pages = "236--255",
    abstract = "The neural attention mechanism plays an important role in many natural language processing applications. In particular, multi-head attention extends single-head attention by allowing a model to jointly attend information from different perspectives. However, without explicit constraining, multi-head attention may suffer from attention collapse, an issue that makes different heads extract similar attentive features, thus limiting the model{'}s representation power. In this paper, for the first time, we provide a novel understanding of multi-head attention from a Bayesian perspective. Based on the recently developed particle-optimization sampling techniques, we propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model{'}s expressiveness. Remarkably, our Bayesian interpretation provides theoretical inspirations on the not-well-understood questions: why and how one uses multi-head attention. Extensive experiments on various attention models and applications demonstrate that the proposed repulsive attention can improve the learned feature diversity, leading to more informative representations with consistent performance improvement on multiple tasks.",
}
@inproceedings{zanzotto-etal-2020-kermit,
    title = "{KERMIT}: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations",
    author = "Zanzotto, Fabio Massimo  and
      Santilli, Andrea  and
      Ranaldi, Leonardo  and
      Onorati, Dario  and
      Tommasino, Pierfrancesco  and
      Fallucchi, Francesca",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.18",
    doi = "10.18653/v1/2020.emnlp-main.18",
    pages = "256--267",
    abstract = "Syntactic parsers have dominated natural language understanding for decades. Yet, their syntactic interpretations are losing centrality in downstream tasks due to the success of large-scale textual representation learners. In this paper, we propose KERMIT (Kernel-inspired Encoder with Recursive Mechanism for Interpretable Trees) to embed symbolic syntactic parse trees into artificial neural networks and to visualize how syntax is used in inference. We experimented with KERMIT paired with two state-of-the-art transformer-based universal sentence encoders (BERT and XLNet) and we showed that KERMIT can indeed boost their performance by effectively embedding human-coded universal syntactic representations in neural networks",
}
@inproceedings{ainslie-etal-2020-etc,
    title = "{ETC}: Encoding Long and Structured Inputs in Transformers",
    author = "Ainslie, Joshua  and
      Ontanon, Santiago  and
      Alberti, Chris  and
      Cvicek, Vaclav  and
      Fisher, Zachary  and
      Pham, Philip  and
      Ravula, Anirudh  and
      Sanghai, Sumit  and
      Wang, Qifan  and
      Yang, Li",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.19",
    doi = "10.18653/v1/2020.emnlp-main.19",
    pages = "268--284",
    abstract = "Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, {``}Extended Transformer Construction{''} (ETC), that addresses two key challenges of standard Transformer architectures, namely scaling input length and encoding structured inputs. To scale attention to longer inputs, we introduce a novel global-local attention mechanism between global tokens and regular input tokens. We also show that combining global-local attention with relative position encodings and a {``}Contrastive Predictive Coding{''} (CPC) pre-training objective allows ETC to encode structured inputs. We achieve state-of-the-art results on four natural language datasets requiring long and/or structured inputs.",
}
@inproceedings{clark-etal-2020-pre,
    title = "Pre-Training Transformers as Energy-Based Cloze Models",
    author = "Clark, Kevin  and
      Luong, Minh-Thang  and
      Le, Quoc  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.20",
    doi = "10.18653/v1/2020.emnlp-main.20",
    pages = "285--294",
    abstract = "We introduce Electric, an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However, Electric does not use masking or output a full distribution over tokens that could occur in a context. Instead, it assigns a scalar energy score to each input token indicating how likely it is given its context. We train Electric using an algorithm based on noise-contrastive estimation and elucidate how this learning objective is closely related to the recently proposed ELECTRA pre-training method. Electric performs well when transferred to downstream tasks and is particularly effective at producing likelihood scores for text: it re-ranks speech recognition n-best lists better than language models and much faster than masked language models. Furthermore, it offers a clearer and more principled view of what ELECTRA learns during pre-training.",
}
@inproceedings{desai-durrett-2020-calibration,
    title = "Calibration of Pre-trained Transformers",
    author = "Desai, Shrey  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.21",
    doi = "10.18653/v1/2020.emnlp-main.21",
    pages = "295--302",
    abstract = "Pre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated. Specifically, do these models{'} posterior probabilities provide an accurate empirical measure of how likely the model is to be correct on a given example? We focus on BERT and RoBERTa in this work, and analyze their calibration across three tasks: natural language inference, paraphrase detection, and commonsense reasoning. For each task, we consider in-domain as well as challenging out-of-domain settings, where models face more examples they should be uncertain about. We show that: (1) when used out-of-the-box, pre-trained models are calibrated in-domain, and compared to baselines, their calibration error out-of-domain can be as much as 3.5x lower; (2) temperature scaling is effective at further reducing calibration error in-domain, and using label smoothing to deliberately increase empirical uncertainty helps calibrate posteriors out-of-domain.",
}
@inproceedings{shen-etal-2020-near,
    title = "Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding",
    author = "Shen, Jiaming  and
      Ji, Heng  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.22",
    doi = "10.18653/v1/2020.emnlp-main.22",
    pages = "303--313",
    abstract = "Linguistic steganography studies how to hide secret messages in natural language cover texts. Traditional methods aim to transform a secret message into an innocent text via lexical substitution or syntactical modification. Recently, advances in neural language models (LMs) enable us to directly generate cover text conditioned on the secret message. In this study, we present a new linguistic steganography method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. We formally analyze the statistical imperceptibility of this method and empirically show it outperforms the previous state-of-the-art methods on four datasets by 15.3{\%} and 38.9{\%} in terms of bits/word and KL metrics, respectively. Finally, human evaluations show that 51{\%} of generated cover texts can indeed fool eavesdroppers.",
}
@inproceedings{dinan-etal-2020-multi,
    title = "Multi-Dimensional Gender Bias Classification",
    author = "Dinan, Emily  and
      Fan, Angela  and
      Wu, Ledell  and
      Weston, Jason  and
      Kiela, Douwe  and
      Williams, Adina",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.23",
    doi = "10.18653/v1/2020.emnlp-main.23",
    pages = "314--331",
    abstract = "Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a novel, general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a new, crowdsourced evaluation benchmark. Distinguishing between gender bias along multiple dimensions enables us to train better and more fine-grained gender bias classifiers. We show our classifiers are valuable for a variety of applications, like controlling for gender bias in generative models, detecting gender bias in arbitrary text, and classifying text as offensive based on its genderedness.",
}
@inproceedings{lertvittayakumjorn-etal-2020-find,
    title = "{FIND}: {H}uman-in-the-{L}oop {D}ebugging {D}eep {T}ext {C}lassifiers",
    author = "Lertvittayakumjorn, Piyawat  and
      Specia, Lucia  and
      Toni, Francesca",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.24",
    doi = "10.18653/v1/2020.emnlp-main.24",
    pages = "332--348",
    abstract = "Since obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases) is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets. These classifiers are thus likely to have undesirable properties. For instance, they may have biases against some sub-populations or may not work effectively in the wild due to overfitting. In this paper, we propose FIND {--} a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features. Experiments show that by using FIND, humans can improve CNN text classifiers which were trained under different types of imperfect datasets (including datasets with biases and datasets with dissimilar train-test distributions).",
}
@inproceedings{ganhotra-etal-2020-conversational,
    title = "Conversational Document Prediction to Assist Customer Care Agents",
    author = "Ganhotra, Jatin  and
      Roitman, Haggai  and
      Cohen, Doron  and
      Mills, Nathaniel  and
      Gunasekara, Chulaka  and
      Mass, Yosi  and
      Joshi, Sachindra  and
      Lastras, Luis  and
      Konopnicki, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.25",
    doi = "10.18653/v1/2020.emnlp-main.25",
    pages = "349--356",
    abstract = "A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users{'} needs. We study the task of predicting the documents that customer care agents can use to facilitate users{'} needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds.",
}
@inproceedings{madureira-schlangen-2020-incremental,
    title = "Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental {NLU}",
    author = "Madureira, Brielen  and
      Schlangen, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.26",
    doi = "10.18653/v1/2020.emnlp-main.26",
    pages = "357--374",
    abstract = "While humans process language incrementally, the best language encoders currently used in NLP do not. Both bidirectional LSTMs and Transformers assume that the sequence that is to be encoded is available in full, to be processed either forwards and backwards (BiLSTMs) or as a whole (Transformers). We investigate how they behave under incremental interfaces, when partial output must be provided based on partial input seen up to a certain time step, which may happen in interactive systems. We test five models on various NLU datasets and compare their performance using three incremental evaluation metrics. The results support the possibility of using bidirectional encoders in incremental mode while retaining most of their non-incremental quality. The {``}omni-directional{''} BERT model, which achieves better non-incremental performance, is impacted more by the incremental access. This can be alleviated by adapting the training regime (truncated training), or the testing procedure, by delaying the output until some right context is available or by incorporating hypothetical right contexts generated by a language model like GPT-2.",
}
@inproceedings{athiwaratkun-etal-2020-augmented,
    title = "Augmented Natural Language for Generative Sequence Labeling",
    author = "Athiwaratkun, Ben  and
      Nogueira dos Santos, Cicero  and
      Krone, Jason  and
      Xiang, Bing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.27",
    doi = "10.18653/v1/2020.emnlp-main.27",
    pages = "375--385",
    abstract = "We propose a generative framework for joint sequence labeling and sentence-level classification. Our model performs multiple sequence labeling tasks at once using a single, shared natural language output space. Unlike prior discriminative methods, our model naturally incorporates label semantics and shares knowledge across tasks. Our framework general purpose, performing well on few-shot learning, low resource, and high resource tasks. We demonstrate these advantages on popular named entity recognition, slot labeling, and intent classification benchmarks. We set a new state-of-the-art for few-shot slot labeling, improving substantially upon the previous 5-shot (75.0{\%} to 90.9{\%}) and 1-shot (70.4{\%} to 81.0{\%}) state-of-the-art results. Furthermore, our model generates large improvements (46.27{\%} to 63.83{\%}) in low resource slot labeling over a BERT baseline by incorporating label semantics. We also maintain competitive results on high resource tasks, performing within two points of the state-of-the-art on all tasks and setting a new state-of-the-art on the SNIPS dataset.",
}
@inproceedings{gao-etal-2020-dialogue-response,
    title = "Dialogue Response Ranking Training with Large-Scale Human Feedback Data",
    author = "Gao, Xiang  and
      Zhang, Yizhe  and
      Galley, Michel  and
      Brockett, Chris  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.28",
    doi = "10.18653/v1/2020.emnlp-main.28",
    pages = "386--395",
    abstract = "Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of producing turns that are context-relevant, but in order to produce compelling agents, these models need to be able to predict and optimize for turns that are genuinely engaging. We leverage social media feedback data (number of replies and upvotes) to build a large-scale training dataset for feedback prediction. To alleviate possible distortion between the feedback and engagingness, we convert the ranking problem to a comparison of response pairs which involve few confounding factors. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data and the resulting ranker outperformed several baselines. Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback. We finally combine the feedback prediction models and a human-like scoring model to rank the machine-generated dialog responses. Crowd-sourced human evaluation shows that our ranking method correlates better with real human preferences than baseline models.",
}
@inproceedings{zhong-etal-2020-semantic-evaluation,
    title = "Semantic Evaluation for Text-to-{SQL} with Distilled Test Suites",
    author = "Zhong, Ruiqi  and
      Yu, Tao  and
      Klein, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.29",
    doi = "10.18653/v1/2020.emnlp-main.29",
    pages = "396--411",
    abstract = "We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models. Our method distills a small test suite of databases that achieves high code coverage for the gold query from a large number of randomly generated databases. At evaluation time, it computes the denotation accuracy of the predicted queries on the distilled test suite, hence calculating a tight upper-bound for semantic accuracy efficiently. We use our proposed method to evaluate 21 models submitted to the Spider leader board and manually verify that our method is always correct on 100 examples. In contrast, the current Spider metric leads to a 2.5{\%} false negative rate on average and 8.1{\%} in the worst case, indicating that test suite accuracy is needed. Our implementation, along with distilled test suites for eleven Text-to-SQL datasets, is publicly available.",
}
@inproceedings{wang-etal-2020-cross,
    title = "Cross-Thought for Sentence Encoder Pre-training",
    author = "Wang, Shuohang  and
      Fang, Yuwei  and
      Sun, Siqi  and
      Gan, Zhe  and
      Cheng, Yu  and
      Liu, Jingjing  and
      Jiang, Jing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.30",
    doi = "10.18653/v1/2020.emnlp-main.30",
    pages = "412--421",
    abstract = "In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also achieves new state of the art on HotpotQA (full-wiki setting) by improving intermediate information retrieval performance.",
}
@inproceedings{xu-etal-2020-autoqa,
    title = "{A}uto{QA}: From Databases To {QA} Semantic Parsers With Only Synthetic Training Data",
    author = "Xu, Silei  and
      Semnani, Sina  and
      Campagna, Giovanni  and
      Lam, Monica",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.31",
    doi = "10.18653/v1/2020.emnlp-main.31",
    pages = "422--434",
    abstract = "We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort. Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations. It uses automatic paraphrasing combined with template-based parsing to find alternative expressions of an attribute in different parts of speech. It also uses a novel filtered auto-paraphraser to generate correct paraphrases of entire sentences. We apply AutoQA to the Schema2QA dataset and obtain an average logical form accuracy of 62.9{\%} when tested on natural questions, which is only 6.4{\%} lower than a model trained with expert natural language annotations and paraphrase data collected from crowdworkers. To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset. AutoQA achieves 69.8{\%} answer accuracy, 16.4{\%} higher than the state-of-the-art zero-shot models and only 5.2{\%} lower than the same model trained with human data.",
}
@inproceedings{wang-etal-2020-spectral,
    title = "A Spectral Method for Unsupervised Multi-Document Summarization",
    author = "Wang, Kexiang  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.32",
    doi = "10.18653/v1/2020.emnlp-main.32",
    pages = "435--445",
    abstract = "Multi-document summarization (MDS) aims at producing a good-quality summary for several related documents. In this paper, we propose a spectral-based hypothesis, which states that the goodness of summary candidate is closely linked to its so-called spectral impact. Here spectral impact considers the perturbation to the dominant eigenvalue of affinity matrix when dropping the summary candidate from the document cluster. The hypothesis is validated by three theoretical perspectives: semantic scaling, propagation dynamics and matrix perturbation. According to the hypothesis, we formulate the MDS task as the combinatorial optimization of spectral impact and propose an accelerated greedy solution based on a surrogate of spectral impact. The evaluation results on various datasets demonstrate: (1) The performance of the summary candidate is positively correlated with its spectral impact, which accords with our hypothesis; (2) Our spectral-based method has a competitive result as compared to state-of-the-art MDS systems.",
}
@inproceedings{huang-etal-2020-achieved,
    title = "What Have We Achieved on Text Summarization?",
    author = "Huang, Dandan  and
      Cui, Leyang  and
      Yang, Sen  and
      Bao, Guangsheng  and
      Wang, Kun  and
      Xie, Jun  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.33",
    doi = "10.18653/v1/2020.emnlp-main.33",
    pages = "446--469",
    abstract = "Deep learning has led to significant improvement in text summarization with various methods investigated and improved ROUGE scores reported over the years. However, gaps still exist between summaries produced by automatic summarizers and human professionals. Aiming to gain more understanding of summarization systems with respect to their strengths and limits on a fine-grained syntactic and semantic level, we consult the Multidimensional Quality Metric (MQM) and quantify 8 major sources of errors on 10 representative summarization models manually. Primarily, we find that 1) under similar settings, extractive summarizers are in general better than their abstractive counterparts thanks to strength in faithfulness and factual-consistency; 2) milestone techniques such as copy, coverage and hybrid extractive/abstractive methods do bring specific improvements but also demonstrate limitations; 3) pre-training techniques, and in particular sequence-to-sequence pre-training, are highly effective for improving text summarization, with BART giving the best results.",
}
@inproceedings{kohita-etal-2020-q,
    title = "{Q}-learning with Language Model for Edit-based Unsupervised Summarization",
    author = "Kohita, Ryosuke  and
      Wachi, Akifumi  and
      Zhao, Yang  and
      Tachibana, Ryuki",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.34",
    doi = "10.18653/v1/2020.emnlp-main.34",
    pages = "470--484",
    abstract = "Unsupervised methods are promising for abstractive textsummarization in that the parallel corpora is not required. However, their performance is still far from being satisfied, therefore research on promising solutions is on-going. In this paper, we propose a new approach based on Q-learning with an edit-based summarization. The method combines two key modules to form an Editorial Agent and Language Model converter (EALM). The agent predicts edit actions (e.t., delete, keep, and replace), and then the LM converter deterministically generates a summary on the basis of the action signals. Q-learning is leveraged to train the agent to produce proper edit actions. Experimental results show that EALM delivered competitive performance compared with the previous encoder-decoder-based methods, even with truly zero paired data (i.e., no validation set). Defining the task as Q-learning enables us not only to develop a competitive method but also to make the latest techniques in reinforcement learning available for unsupervised summarization. We also conduct qualitative analysis, providing insights into future study on unsupervised summarizers.",
}
@inproceedings{wang-etal-2020-friendly,
    title = "Friendly Topic Assistant for Transformer Based Abstractive Summarization",
    author = "Wang, Zhengjue  and
      Duan, Zhibin  and
      Zhang, Hao  and
      Wang, Chaojie  and
      Tian, Long  and
      Chen, Bo  and
      Zhou, Mingyuan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.35",
    doi = "10.18653/v1/2020.emnlp-main.35",
    pages = "485--497",
    abstract = "Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance. Compared with Transformers, topic models are better at learning explicit document semantics, and hence could be integrated into Transformers to further boost their performance. To this end, we rearrange and explore the semantics learned by a topic model, and then propose a topic assistant (TA) including three modules. TA is compatible with various Transformer-based models and user-friendly since i) TA is a plug-and-play model that does not break any structure of the original Transformer network, making users easily fine-tune Transformer+TA based on a well pre-trained model; ii) TA only introduces a small number of extra parameters. Experimental results on three datasets demonstrate that TA is able to improve the performance of several Transformer-based models.",
}
@inproceedings{sun-etal-2020-contrastive,
    title = "Contrastive Distillation on Intermediate Representations for Language Model Compression",
    author = "Sun, Siqi  and
      Gan, Zhe  and
      Fang, Yuwei  and
      Cheng, Yu  and
      Wang, Shuohang  and
      Liu, Jingjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.36",
    doi = "10.18653/v1/2020.emnlp-main.36",
    pages = "498--508",
    abstract = "Existing language model compression methods mostly use a simple L{\_}2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. Although widely used, this objective by design assumes that all the dimensions of hidden representations are independent, failing to capture important structural knowledge in the intermediate layers of the teacher network. To achieve better distillation efficacy, we propose Contrastive Distillation on Intermediate Representations (CoDIR), a principled knowledge distillation framework where the student is trained to distill knowledge through intermediate layers of the teacher via a contrastive objective. By learning to distinguish positive sample from a large set of negative samples, CoDIR facilitates the student{'}s exploitation of rich information in teacher{'}s hidden layers. CoDIR can be readily applied to compress large-scale language models in both pre-training and finetuning stages, and achieves superb performance on the GLUE benchmark, outperforming state-of-the-art compression methods.",
}
@inproceedings{zhang-etal-2020-ternarybert,
    title = "{T}ernary{BERT}: Distillation-aware Ultra-low Bit {BERT}",
    author = "Zhang, Wei  and
      Hou, Lu  and
      Yin, Yichun  and
      Shang, Lifeng  and
      Chen, Xiao  and
      Jiang, Xin  and
      Liu, Qun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.37",
    doi = "10.18653/v1/2020.emnlp-main.37",
    pages = "509--521",
    abstract = "Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks. However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices. In this work, we propose TernaryBERT, which ternarizes the weights in a fine-tuned BERT model. Specifically, we use both approximation-based and loss-aware ternarization methods and empirically investigate the ternarization granularity of different parts of BERT. Moreover, to reduce the accuracy degradation caused by lower capacity of low bits, we leverage the knowledge distillation technique in the training process. Experiments on the GLUE benchmark and SQuAD show that our proposed TernaryBERT outperforms the other BERT quantization methods, and even achieves comparable performance as the full-precision model while being 14.9x smaller.",
}
@inproceedings{bansal-etal-2020-self,
    title = "Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks",
    author = "Bansal, Trapit  and
      Jha, Rishikesh  and
      Munkhdalai, Tsendsuren  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.38",
    doi = "10.18653/v1/2020.emnlp-main.38",
    pages = "522--534",
    abstract = "Self-supervised pre-training of transformer models has revolutionized NLP applications. Such pre-training with language modeling objectives provides a useful initial point for parameters that generalize well to new tasks with fine-tuning. However, fine-tuning is still data inefficient {---} when there are few labeled examples, accuracy can be low. Data efficiency can be improved by optimizing pre-training directly for future fine-tuning with few examples; this can be treated as a meta-learning problem. However, standard meta-learning techniques require many training tasks in order to generalize; unfortunately, finding a diverse set of such supervised tasks is usually difficult. This paper proposes a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text. This is achieved using a cloze-style objective, but creating separate multi-class classification tasks by gathering tokens-to-be blanked from among only a handful of vocabulary terms. This yields as many unique meta-training tasks as the number of subsets of vocabulary terms. We meta-train a transformer model on this distribution of tasks using a recent meta-learning framework. On 17 NLP tasks, we show that this meta-training leads to better few-shot generalization than language-model pre-training followed by finetuning. Furthermore, we show how the self-supervised tasks can be combined with supervised tasks for meta-learning, providing substantial accuracy gains over previous supervised meta-learning.",
}
@inproceedings{wang-etal-2020-efficient,
    title = "Efficient Meta Lifelong-Learning with Limited Memory",
    author = "Wang, Zirui  and
      Mehta, Sanket Vaibhav  and
      Poczos, Barnabas  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.39",
    doi = "10.18653/v1/2020.emnlp-main.39",
    pages = "535--548",
    abstract = "Current natural language processing models work well on a single task, yet they often fail to continuously learn new tasks without forgetting previous ones as they are re-trained throughout their lifetime, a challenge known as lifelong learning. State-of-the-art lifelong language learning methods store past examples in episodic memory and replay them at both training and inference time. However, as we show later in our experiments, there are three significant impediments: (1) needing unrealistically large memory module to achieve good performance, (2) suffering from negative transfer, (3) requiring multiple local adaptation steps for each test example that significantly slows down the inference speed. In this paper, we identify three common principles of lifelong learning methods and propose an efficient meta-lifelong framework that combines them in a synergistic fashion. To achieve sample efficiency, our method trains the model in a manner that it learns a better initialization for local adaptation. Extensive experiments on text classification and question answering benchmarks demonstrate the effectiveness of our framework by achieving state-of-the-art performance using merely 1{\%} memory size and narrowing the gap with multi-task learning. We further show that our method alleviates both catastrophic forgetting and negative transfer at the same time.",
}
@inproceedings{keung-etal-2020-dont,
    title = "Don{'}t Use {E}nglish Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings",
    author = "Keung, Phillip  and
      Lu, Yichao  and
      Salazar, Julian  and
      Bhardwaj, Vikas",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.40",
    doi = "10.18653/v1/2020.emnlp-main.40",
    pages = "549--554",
    abstract = "Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language. However, published results for mBERT zero-shot accuracy vary as much as 17 points on the MLDoc classification task across four papers. We show that the standard practice of using English dev accuracy for model selection in the zero-shot setting makes it difficult to obtain reproducible results on the MLDoc and XNLI tasks. English dev accuracy is often uncorrelated (or even anti-correlated) with target language accuracy, and zero-shot performance varies greatly at different points in the same fine-tuning run and between different fine-tuning runs. These reproducibility issues are also present for other tasks with different pre-trained embeddings (e.g., MLQA with XLM-R). We recommend providing oracle scores alongside zero-shot results: still fine-tune using English data, but choose a checkpoint with the target dev set. Reporting this upper bound makes results more consistent by avoiding arbitrarily bad checkpoints.",
}
@inproceedings{nagata-etal-2020-supervised,
    title = "A Supervised Word Alignment Method based on Cross-Language Span Prediction using Multilingual {BERT}",
    author = "Nagata, Masaaki  and
      Chousa, Katsuki  and
      Nishino, Masaaki",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.41",
    doi = "10.18653/v1/2020.emnlp-main.41",
    pages = "555--565",
    abstract = "We present a novel supervised word alignment method based on cross-language span prediction. We first formalize a word alignment problem as a collection of independent predictions from a token in the source sentence to a span in the target sentence. Since this step is equivalent to a SQuAD v2.0 style question answering task, we solve it using the multilingual BERT, which is fine-tuned on manually created gold word alignment data. It is nontrivial to obtain accurate alignment from a set of independently predicted spans. We greatly improved the word alignment accuracy by adding to the question the source token{'}s context and symmetrizing two directional predictions. In experiments using five word alignment datasets from among Chinese, Japanese, German, Romanian, French, and English, we show that our proposed method significantly outperformed previous supervised and unsupervised word alignment methods without any bitexts for pretraining. For example, we achieved 86.7 F1 score for the Chinese-English data, which is 13.3 points higher than the previous state-of-the-art supervised method.",
}
@inproceedings{chen-etal-2020-accurate,
    title = "Accurate Word Alignment Induction from Neural Machine Translation",
    author = "Chen, Yun  and
      Liu, Yang  and
      Chen, Guanhua  and
      Jiang, Xin  and
      Liu, Qun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.42",
    doi = "10.18653/v1/2020.emnlp-main.42",
    pages = "566--576",
    abstract = "Despite its original goal to jointly learn to align and translate, prior researches suggest that Transformer captures poor word alignments through its attention mechanism. In this paper, we show that attention weights do capture accurate word alignments and propose two novel word alignment induction methods Shift-Att and Shift-AET. The main idea is to induce alignments at the step when the to-be-aligned target token is the decoder input rather than the decoder output as in previous work. Shift-Att is an interpretation method that induces alignments from the attention weights of Transformer and does not require parameter update or architecture change. Shift-AET extracts alignments from an additional alignment module which is tightly integrated into Transformer and trained in isolation with supervision from symmetrized Shift-Att alignments. Experiments on three publicly available datasets demonstrate that both methods perform better than their corresponding neural baselines and Shift-AET significantly outperforms GIZA++ by 1.4-4.8 AER points.",
}
@inproceedings{zhang-etal-2020-chren,
    title = "{C}hr{E}n: {C}herokee-{E}nglish Machine Translation for Endangered Language Revitalization",
    author = "Zhang, Shiyue  and
      Frey, Benjamin  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.43",
    doi = "10.18653/v1/2020.emnlp-main.43",
    pages = "577--595",
    abstract = "Cherokee is a highly endangered Native American language spoken by the Cherokee people. The Cherokee culture is deeply embedded in its language. However, there are approximately only 2,000 fluent first language Cherokee speakers remaining in the world and the number is declining every year. To help save this endangered language, we introduce ChrEn, a Cherokee-English parallel dataset, to facilitate machine translation research between Cherokee and English. Compared to some popular machine translation language pairs, ChrEn is extremely low-resource, only containing 14k sentence pairs in total. We split our parallel data in ways that facilitate both in-domain and out-of-domain evaluation. We also collect 5k Cherokee monolingual data to enable semi-supervised learning. Besides these datasets, we propose several Cherokee-English and English-Cherokee machine translation systems. We compare SMT (phrase-based) versus NMT (RNN-based and Transformer-based) systems; supervised versus semi-supervised (via language model, back-translation, and BERT/Multilingual-BERT) methods; as well as transfer learning versus multilingual joint training with 4 other languages. Our best results are 15.8/12.7 BLEU for in-domain and 6.5/5.0 BLEU for out-of-domain Chr-En/EnChr translations, respectively; and we hope that our dataset and systems will encourage future work by the community for Cherokee language revitalization.",
}
@inproceedings{field-tsvetkov-2020-unsupervised,
    title = "Unsupervised Discovery of Implicit Gender Bias",
    author = "Field, Anjalie  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.44",
    doi = "10.18653/v1/2020.emnlp-main.44",
    pages = "596--608",
    abstract = "Despite their prevalence in society, social biases are difficult to identify, primarily because human judgements in this domain can be unreliable. We take an unsupervised approach to identifying gender bias against women at a comment level and present a model that can surface text likely to contain bias. Our main challenge is forcing the model to focus on signs of implicit bias, rather than other artifacts in the data. Thus, our methodology involves reducing the influence of confounds through propensity matching and adversarial learning. Our analysis shows how biased comments directed towards female politicians contain mixed criticisms, while comments directed towards other female public figures focus on appearance and sexualization. Ultimately, our work offers a way to capture subtle biases in various domains without relying on subjective human judgements.",
}
@inproceedings{zhou-jurgens-2020-condolence,
    title = "Condolence and Empathy in Online Communities",
    author = "Zhou, Naitian  and
      Jurgens, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.45",
    doi = "10.18653/v1/2020.emnlp-main.45",
    pages = "609--626",
    abstract = "Offering condolence is a natural reaction to hearing someone{'}s distress. Individuals frequently express distress in social media, where some communities can provide support. However, not all condolence is equal{---}trite responses offer little actual support despite their good intentions. Here, we develop computational tools to create a massive dataset of 11.4M expressions of distress and 2.8M corresponding offerings of condolence in order to examine the dynamics of condolence online. Our study reveals widespread disparity in what types of distress receive supportive condolence rather than just engagement. Building on studies from social psychology, we analyze the language of condolence and develop a new dataset for quantifying the empathy in a condolence using appraisal theory. Finally, we demonstrate that the features of condolence individuals find most helpful online differ substantially in their features from those seen in interpersonal settings.",
}
@inproceedings{spell-etal-2020-embedding,
    title = "An {E}mbedding {M}odel for {E}stimating {L}egislative {P}references from the {F}requency and {S}entiment of {T}weets",
    author = "Spell, Gregory  and
      Guay, Brian  and
      Hillygus, Sunshine  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.46",
    doi = "10.18653/v1/2020.emnlp-main.46",
    pages = "627--641",
    abstract = "Legislator preferences are typically represented as measures of general ideology estimated from roll call votes on legislation, potentially masking important nuances in legislators{'} political attitudes. In this paper we introduce a method of measuring more specific legislator attitudes using an alternative expression of preferences: tweeting. Specifically, we present an embedding-based model for predicting the frequency and sentiment of legislator tweets. To illustrate our method, we model legislators{'} attitudes towards President Donald Trump as vector embeddings that interact with embeddings for Trump himself constructed using a neural network from the text of his daily tweets. We demonstrate the predictive performance of our model on tweets authored by members of the U.S. House and Senate related to the president from November 2016 to February 2018. We further assess the quality of our learned representations for legislators by comparing to traditional measures of legislator preferences.",
}
@inproceedings{sims-bamman-2020-measuring,
    title = "Measuring Information Propagation in Literary Social Networks",
    author = "Sims, Matthew  and
      Bamman, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.47",
    doi = "10.18653/v1/2020.emnlp-main.47",
    pages = "642--652",
    abstract = "We present the task of modeling information propagation in literature, in which we seek to identify pieces of information passing from character A to character B to character C, only given a description of their activity in text. We describe a new pipeline for measuring information propagation in this domain and publish a new dataset for speaker attribution, enabling the evaluation of an important component of this pipeline on a wider range of literary texts than previously studied. Using this pipeline, we analyze the dynamics of information propagation in over 5,000 works of fiction, finding that information flows through characters that fill structural holes connecting different communities, and that characters who are women are depicted as filling this role much more frequently than characters who are men.",
}
@inproceedings{forbes-etal-2020-social,
    title = "Social Chemistry 101: Learning to Reason about Social and Moral Norms",
    author = "Forbes, Maxwell  and
      Hwang, Jena D.  and
      Shwartz, Vered  and
      Sap, Maarten  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.48",
    doi = "10.18653/v1/2020.emnlp-main.48",
    pages = "653--670",
    abstract = "Social norms{---}the unspoken commonsense rules about acceptable social behavior{---}are crucial in understanding the underlying causes and intents of people{'}s actions in narratives. For example, underlying an action such as {``}wanting to call cops on my neighbor{''} are social norms that inform our conduct, such as {``}It is expected that you report crimes.{''} We present SOCIAL CHEMISTRY, a new conceptual formalism to study people{'}s everyday social norms and moral judgments over a rich spectrum of real life situations described in natural language. We introduce SOCIAL-CHEM-101, a large-scale corpus that catalogs 292k rules-of-thumb such as {``}It is rude to run a blender at 5am{''} as the basic conceptual units. Each rule-of-thumb is further broken down with 12 different dimensions of people{'}s judgments, including social judgments of good and bad, moral foundations, expected cultural pressure, and assumed legality, which together amount to over 4.5 million annotations of categorical labels and free-text descriptions. Comprehensive empirical results based on state-of-the-art neural models demonstrate that computational modeling of social norms is a promising research direction. Our model framework, Neural Norm Transformer, learns and generalizes SOCIAL-CHEM-101 to successfully reason about previously unseen situations, generating relevant (and potentially novel) attribute-aware social rules-of-thumb.",
}
@inproceedings{du-cardie-2020-event,
    title = "Event Extraction by Answering (Almost) Natural Questions",
    author = "Du, Xinya  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.49",
    doi = "10.18653/v1/2020.emnlp-main.49",
    pages = "671--683",
    abstract = "The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argument extraction typically relies heavily on entity recognition as a preprocessing/concurrent step, causing the well-known problem of error propagation. To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task that extracts the event arguments in an end-to-end manner. Empirical results demonstrate that our framework outperforms prior methods substantially; in addition, it is capable of extracting event arguments for roles not seen at training time (i.e., in a zero-shot learning setting).",
}
@inproceedings{li-etal-2020-connecting,
    title = "Connecting the Dots: Event Graph Schema Induction with Path Language Modeling",
    author = "Li, Manling  and
      Zeng, Qi  and
      Lin, Ying  and
      Cho, Kyunghyun  and
      Ji, Heng  and
      May, Jonathan  and
      Chambers, Nathanael  and
      Voss, Clare",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.50",
    doi = "10.18653/v1/2020.emnlp-main.50",
    pages = "684--695",
    abstract = "Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas. We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph. Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas. Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs.",
}
@inproceedings{wang-etal-2020-joint,
    title = "Joint Constrained Learning for Event-Event Relation Extraction",
    author = "Wang, Haoyu  and
      Chen, Muhao  and
      Zhang, Hongming  and
      Roth, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.51",
    doi = "10.18653/v1/2020.emnlp-main.51",
    pages = "696--706",
    abstract = "Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations of events by converting these constraints into differentiable learning objectives. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data, and outperforms SOTA methods on benchmarks for both temporal relation extraction and event hierarchy construction, replacing a commonly used but more expensive global inference process. We also present a promising case study to show the effectiveness of our approach to inducing event complexes on an external corpus.",
}
@inproceedings{cao-etal-2020-incremental,
    title = "Incremental Event Detection via Knowledge Consolidation Networks",
    author = "Cao, Pengfei  and
      Chen, Yubo  and
      Zhao, Jun  and
      Wang, Taifeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.52",
    doi = "10.18653/v1/2020.emnlp-main.52",
    pages = "707--717",
    abstract = "Conventional approaches to event detection usually require a fixed set of pre-defined event types. Such a requirement is often challenged in real-world applications, as new events continually occur. Due to huge computation cost and storage budge, it is infeasible to store all previous data and re-train the model with all previous data and new data, every time new events arrive. We formulate such challenging scenarios as incremental event detection, which requires a model to learn new classes incrementally without performance degradation on previous classes. However, existing incremental learning methods cannot handle semantic ambiguity and training data imbalance problems between old and new classes in the task of incremental event detection. In this paper, we propose a Knowledge Consolidation Network (KCN) to address the above issues. Specifically, we devise two components, prototype enhanced retrospection and hierarchical distillation, to mitigate the adverse effects of semantic ambiguity and class imbalance, respectively. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art model by 19{\%} and 13.4{\%} of whole F1 score on ACE benchmark and TAC KBP benchmark, respectively.",
}
@inproceedings{huang-ji-2020-semi,
    title = "Semi-supervised New Event Type Induction and Event Detection",
    author = "Huang, Lifu  and
      Ji, Heng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.53",
    doi = "10.18653/v1/2020.emnlp-main.53",
    pages = "718--724",
    abstract = "Most previous event extraction studies assume a set of target event types and corresponding event annotations are given, which could be very expensive. In this paper, we work on a new task of semi-supervised event type induction, aiming to automatically discover a set of unseen types from a given corpus by leveraging annotations available for a few seen types. We design a Semi-Supervised Vector Quantized Variational Autoencoder framework to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations. A variational autoencoder is further introduced to enforce the reconstruction of each event mention conditioned on its latent type distribution. Experiments show that our approach can not only achieve state-of-the-art performance on supervised event detection but also discover high-quality new event types.",
}
@inproceedings{ji-etal-2020-language,
    title = "Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph",
    author = "Ji, Haozhe  and
      Ke, Pei  and
      Huang, Shaohan  and
      Wei, Furu  and
      Zhu, Xiaoyan  and
      Huang, Minlie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.54",
    doi = "10.18653/v1/2020.emnlp-main.54",
    pages = "725--736",
    abstract = "Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation. In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph. We empirically show that our model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. We also demonstrate the effectiveness of the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.",
}
@inproceedings{krishna-etal-2020-reformulating,
    title = "Reformulating Unsupervised Style Transfer as Paraphrase Generation",
    author = "Krishna, Kalpesh  and
      Wieting, John  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.55",
    doi = "10.18653/v1/2020.emnlp-main.55",
    pages = "737--762",
    abstract = "Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs. However, many existing systems purportedly designed for style transfer inherently warp the input{'}s meaning through attribute transfer, which changes semantic properties such as sentiment. In this paper, we reformulate unsupervised style transfer as a paraphrase generation problem, and present a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data. Despite its simplicity, our method significantly outperforms state-of-the-art style transfer systems on both human and automatic evaluations. We also survey 23 style transfer papers and discover that existing automatic metrics can be easily gamed and propose fixed variants. Finally, we pivot to a more real-world style transfer setting by collecting a large dataset of 15M sentences in 11 diverse styles, which we use for an in-depth analysis of our system.",
}
@inproceedings{wu-etal-2020-de,
    title = "De-Biased Court{'}s View Generation with Causality",
    author = "Wu, Yiquan  and
      Kuang, Kun  and
      Zhang, Yating  and
      Liu, Xiaozhong  and
      Sun, Changlong  and
      Xiao, Jun  and
      Zhuang, Yueting  and
      Si, Luo  and
      Wu, Fei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.56",
    doi = "10.18653/v1/2020.emnlp-main.56",
    pages = "763--780",
    abstract = "Court{'}s view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation. While prior text-to-text natural language generation (NLG) approaches can be used to address this problem, neglecting the confounding bias from the data generation mechanism can limit the model performance, and the bias may pollute the learning outcomes. In this paper, we propose a novel Attentional and Counterfactual based Natural Language Generation (AC-NLG) method, consisting of an attentional encoder and a pair of innovative counterfactual decoders. The attentional encoder leverages the plaintiff{'}s claim and fact description as input to learn a claim-aware encoder from which the claim-related information in fact description can be emphasized. The counterfactual decoders are employed to eliminate the confounding bias in data and generate judgment-discriminative court{'}s views (both supportive and non-supportive views) by incorporating with a synergistic judgment predictive model. Comprehensive experiments show the effectiveness of our method under both quantitative and qualitative evaluation metrics.",
}
@inproceedings{hua-wang-2020-pair,
    title = "{PAIR}: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation",
    author = "Hua, Xinyu  and
      Wang, Lu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.57",
    doi = "10.18653/v1/2020.emnlp-main.57",
    pages = "781--793",
    abstract = "Pre-trained Transformers have enabled impressive breakthroughs in generating long and fluent text, yet their outputs are often {``}rambling{''} without coherently arranged content. In this work, we present a novel content-controlled text generation framework, PAIR, with planning and iterative refinement, which is built upon a large model, BART. We first adapt the BERT model to automatically construct the content plans, consisting of keyphrase assignments and their corresponding sentence-level positions. The BART model is employed for generation without modifying its structure. We then propose a refinement algorithm to gradually enhance the generation quality within the sequence-to-sequence framework. Evaluation with automatic metrics shows that adding planning consistently improves the generation quality on three distinct domains, with an average of 20 BLEU points and 12 METEOR points improvements. In addition, human judges rate our system outputs to be more relevant and coherent than comparisons without planning.",
}
@inproceedings{qin-etal-2020-back,
    title = "Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning",
    author = "Qin, Lianhui  and
      Shwartz, Vered  and
      West, Peter  and
      Bhagavatula, Chandra  and
      Hwang, Jena D.  and
      Le Bras, Ronan  and
      Bosselut, Antoine  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.58",
    doi = "10.18653/v1/2020.emnlp-main.58",
    pages = "794--805",
    abstract = "Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future. However, simultaneous incorporation of past and future contexts using generative language models (LMs) can be challenging, as they are trained either to condition only on the past context or to perform narrowly scoped text-infilling. In this paper, we propose DeLorean, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off-the-shelf, left-to-right language models and no supervision. The key intuition of our algorithm is incorporating the future through back-propagation, during which, we only update the internal representation of the output while fixing the model parameters. By alternating between forward and backward propagation, DeLorean can decode the output representation that reflects both the left and right contexts. We demonstrate that our approach is general and applicable to two nonmonotonic reasoning tasks: abductive text generation and counterfactual story revision, where DeLorean outperforms a range of unsupervised and some supervised methods, based on automatic and human evaluation.",
}
@inproceedings{hahn-etal-2020-localization,
    title = "Where Are You? Localization from Embodied Dialog",
    author = "Hahn, Meera  and
      Krantz, Jacob  and
      Batra, Dhruv  and
      Parikh, Devi  and
      Rehg, James  and
      Lee, Stefan  and
      Anderson, Peter",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.59",
    doi = "10.18653/v1/2020.emnlp-main.59",
    pages = "806--822",
    abstract = "We present WHERE ARE YOU? (WAY), a dataset of {\textasciitilde}6k dialogs in which two humans {--} an Observer and a Locator {--} complete a cooperative localization task. The Observer is spawned at random in a 3D environment and can navigate from first-person views while answering questions from the Locator. The Locator must localize the Observer in a detailed top-down map by asking questions and giving instructions. Based on this dataset, we define three challenging tasks: Localization from Embodied Dialog or LED (localizing the Observer from dialog history), Embodied Visual Dialog (modeling the Observer), and Cooperative Localization (modeling both agents). In this paper, we focus on the LED task {--} providing a strong baseline model with detailed ablations characterizing both dataset biases and the importance of various modeling choices. Our best model achieves 32.7{\%} success at identifying the Observer{'}s location within 3m in unseen buildings, vs. 70.4{\%} for human Locators.",
}
@inproceedings{zhang-etal-2020-learning-represent,
    title = "Learning to Represent Image and Text with Denotation Graph",
    author = "Zhang, Bowen  and
      Hu, Hexiang  and
      Jain, Vihan  and
      Ie, Eugene  and
      Sha, Fei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.60",
    doi = "10.18653/v1/2020.emnlp-main.60",
    pages = "823--839",
    abstract = "Learning to fuse vision and language information and representing them is an important research problem with many applications. Recent progresses have leveraged the ideas of pre-training (from language modeling) and attention layers in Transformers to learn representation from datasets containing images aligned with linguistic expressions that describe the images. In this paper, we propose learning representations from a set of implied, visually grounded expressions between image and text, automatically mined from those datasets. In particular, we use denotation graphs to represent how specific concepts (such as sentences describing images) can be linked to abstract and generic concepts (such as short phrases) that are also visually grounded. This type of generic-to-specific relations can be discovered using linguistic analysis tools. We propose methods to incorporate such relations into learning representation. We show that state-of-the-art multimodal learning models can be further improved by leveraging automatically harvested structural relations. The representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition. Both our codes and the extracted denotation graphs on the Flickr30K and the COCO datasets are publically available on https://sha-lab.github.io/DG.",
}
@inproceedings{fang-etal-2020-video2commonsense,
    title = "{V}ideo2{C}ommonsense: Generating Commonsense Descriptions to Enrich Video Captioning",
    author = "Fang, Zhiyuan  and
      Gokhale, Tejas  and
      Banerjee, Pratyay  and
      Baral, Chitta  and
      Yang, Yezhou",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.61",
    doi = "10.18653/v1/2020.emnlp-main.61",
    pages = "840--860",
    abstract = "Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent{'}s actions can bring about myriad changes in the scene. Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional video captioning. Unlike images, actions in videos are also inherently linked to social aspects such as intentions (why the action is taking place), effects (what changes due to the action), and attributes that describe the agent. Thus for video understanding, such as when captioning videos or when answering questions about videos, one must have an understanding of these commonsense aspects. We present the first work on generating \textit{commonsense} captions directly from videos, to describe latent aspects such as intentions, effects, and attributes. We present a new dataset {``}Video-to-Commonsense (V2C){''} that contains {\textasciitilde}9k videos of human agents performing various actions, annotated with 3 types of commonsense descriptions. Additionally we explore the use of open-ended video-based commonsense question answering (V2C-QA) as a way to enrich our captions. Both the generation task and the QA task can be used to enrich video captions.",
}
@inproceedings{hessel-lee-2020-multimodal,
    title = "Does my multimodal model learn cross-modal interactions? It{'}s harder to tell than you might think!",
    author = "Hessel, Jack  and
      Lee, Lillian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.62",
    doi = "10.18653/v1/2020.emnlp-main.62",
    pages = "861--877",
    abstract = "Modeling expressive cross-modal interactions seems crucial in multimodal tasks, such as visual question answering. However, sometimes high-performing black-box algorithms turn out to be mostly exploiting unimodal signals in the data. We propose a new diagnostic tool, empirical multimodally-additive function projection (EMAP), for isolating whether or not cross-modal interactions improve performance for a given model on a given task. This function projection modifies model predictions so that cross-modal interactions are eliminated, isolating the additive, unimodal structure. For seven image+text classification tasks (on each of which we set new state-of-the-art benchmarks), we find that, in many cases, removing cross-modal interactions results in little to no performance degradation. Surprisingly, this holds even when expressive models, with capacity to consider interactions, otherwise outperform less expressive models; thus, performance improvements, even when present, often cannot be attributed to consideration of cross-modal feature interactions. We hence recommend that researchers in multimodal machine learning report the performance not only of unimodal baselines, but also the EMAP of their best-performing model.",
}
@inproceedings{gokhale-etal-2020-mutant,
    title = "{MUTANT}: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering",
    author = "Gokhale, Tejas  and
      Banerjee, Pratyay  and
      Baral, Chitta  and
      Yang, Yezhou",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.63",
    doi = "10.18653/v1/2020.emnlp-main.63",
    pages = "878--892",
    abstract = "While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i.d. setting. As such, evaluation on out-of-distribution (OOD) test samples has emerged as a proxy for generalization. In this paper, we present \textit{MUTANT}, a training paradigm that exposes the model to perceptually similar, yet semantically distinct \textit{mutations} of the input, to improve OOD generalization, such as the VQA-CP challenge. Under this paradigm, models utilize a consistency-constrained training objective to understand the effect of semantic changes in input (question-image pair) on the output (answer). Unlike existing methods on VQA-CP, \textit{MUTANT} does not rely on the knowledge about the nature of train and test answer distributions. \textit{MUTANT} establishes a new state-of-the-art accuracy on VQA-CP with a 10.57{\%} improvement. Our work opens up avenues for the use of semantic input mutations for OOD generalization in question answering.",
}
@inproceedings{liu-etal-2020-mitigating,
    title = "Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning",
    author = "Liu, Haochen  and
      Wang, Wentao  and
      Wang, Yiqi  and
      Liu, Hui  and
      Liu, Zitao  and
      Tang, Jiliang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.64",
    doi = "10.18653/v1/2020.emnlp-main.64",
    pages = "893--903",
    abstract = "Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation data are biased. In particular, they can produce responses that reflect people{'}s gender prejudice. Many debiasing methods have been developed for various NLP tasks, such as word embedding. However, they are not directly applicable to dialogue systems because they are likely to force dialogue models to generate similar responses for different genders. This greatly degrades the diversity of the generated responses and immensely hurts the performance of the dialogue models. In this paper, we propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance. Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.",
}
@inproceedings{kim-etal-2020-will,
    title = "Will {I} Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness",
    author = "Kim, Hyunwoo  and
      Kim, Byeongchang  and
      Kim, Gunhee",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.65",
    doi = "10.18653/v1/2020.emnlp-main.65",
    pages = "904--916",
    abstract = "We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra modules to the generative agent for maintaining consistency. However, such additional labels and training can be demanding. Also, we find even the best-performing persona-based agents are insensitive to contradictory words. Inspired by social cognition and pragmatics, we endow existing dialogue agents with public self-consciousness on the fly through an imaginary listener. Our approach, based on the Rational Speech Acts framework (Frank and Goodman, 2012), can enforce dialogue agents to refrain from uttering contradiction. We further extend the framework by learning the distractor selection, which has been usually done manually or randomly. Results on Dialogue NLI (Welleck et al., 2019) and PersonaChat (Zhang et al., 2018) dataset show that our approach reduces contradiction and improves consistency of existing dialogue models. Moreover, we show that it can be generalized to improve context-consistency beyond persona in dialogues.",
}
@inproceedings{wu-etal-2020-tod,
    title = "{TOD}-{BERT}: Pre-trained Natural Language Understanding for Task-Oriented Dialogue",
    author = "Wu, Chien-Sheng  and
      Hoi, Steven C.H.  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.66",
    doi = "10.18653/v1/2020.emnlp-main.66",
    pages = "917--929",
    abstract = "The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice. In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling. To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling. We propose a contrastive objective function to simulate the response selection task. Our pre-trained task-oriented dialogue BERT (TOD-BERT) outperforms strong baselines like BERT on four downstream task-oriented dialogue applications, including intention recognition, dialogue state tracking, dialogue act prediction, and response selection. We also show that TOD-BERT has a stronger few-shot ability that can mitigate the data scarcity problem for task-oriented dialogue.",
}
@inproceedings{quan-etal-2020-risawoz,
    title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
    author = "Quan, Jun  and
      Zhang, Shian  and
      Cao, Qian  and
      Li, Zizhong  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.67",
    doi = "10.18653/v1/2020.emnlp-main.67",
    pages = "930--940",
    abstract = "In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chinese Wizard-of-Oz dataset with Rich Semantic Annotations. RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues, with more than 150K utterances spanning over 12 domains, which is larger than all previous annotated H2H conversational datasets. Both single- and multi-domain dialogues are constructed, accounting for 65{\%} and 35{\%}, respectively. Each dialogue is labeled with comprehensive dialogue annotations, including dialogue goal in the form of natural language description, domain, dialogue states and acts at both the user and system side. In addition to traditional dialogue annotations, we especially provide linguistic annotations on discourse phenomena, e.g., ellipsis and coreference, in dialogues, which are useful for dialogue coreference and ellipsis resolution tasks. Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset. A series of benchmark models and results are reported, including natural language understanding (intent detection {\&} slot filling), dialogue state tracking and dialogue context-to-text generation, as well as coreference and ellipsis resolution, which facilitate the baseline comparison for future research on this corpus.",
}
@inproceedings{akama-etal-2020-filtering,
    title = "Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness",
    author = "Akama, Reina  and
      Yokoi, Sho  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.68",
    doi = "10.18653/v1/2020.emnlp-main.68",
    pages = "941--958",
    abstract = "Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality. Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation.",
}
@inproceedings{murawaki-2020-latent,
    title = "Latent Geographical Factors for Analyzing the Evolution of Dialects in Contact",
    author = "Murawaki, Yugo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.69",
    doi = "10.18653/v1/2020.emnlp-main.69",
    pages = "959--976",
    abstract = "Analyzing the evolution of dialects remains a challenging problem because contact phenomena hinder the application of the standard tree model. Previous statistical approaches to this problem resort to admixture analysis, where each dialect is seen as a mixture of latent ancestral populations. However, such ancestral populations are hardly interpretable in the context of the tree model. In this paper, we propose a probabilistic generative model that represents latent factors as geographical distributions. We argue that the proposed model has higher affinity with the tree model because a tree can alternatively be represented as a set of geographical distributions. Experiments involving synthetic and real data suggest that the proposed method is both quantitatively and qualitatively superior to the admixture model.",
}
@inproceedings{upadhye-etal-2020-predicting,
    title = "Predicting Reference: What do Language Models Learn about Discourse Models?",
    author = "Upadhye, Shiva  and
      Bergen, Leon  and
      Kehler, Andrew",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.70",
    doi = "10.18653/v1/2020.emnlp-main.70",
    pages = "977--982",
    abstract = "Whereas there is a growing literature that probes neural language models to assess the degree to which they have latently acquired grammatical knowledge, little if any research has investigated their acquisition of discourse modeling ability. We address this question by drawing on a rich psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next. The results reveal that, for the most part, the prediction behavior of neural language models does not resemble that of human language users.",
}
@inproceedings{li-etal-2020-word,
    title = "Word class flexibility: A deep contextualized approach",
    author = "Li, Bai  and
      Thomas, Guillaume  and
      Xu, Yang  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.71",
    doi = "10.18653/v1/2020.emnlp-main.71",
    pages = "983--994",
    abstract = "Word class flexibility refers to the phenomenon whereby a single word form is used across different grammatical categories. Extensive work in linguistic typology has sought to characterize word class flexibility across languages, but quantifying this phenomenon accurately and at scale has been fraught with difficulties. We propose a principled methodology to explore regularity in word class flexibility. Our method builds on recent work in contextualized word embeddings to quantify semantic shift between word classes (e.g., noun-to-verb, verb-to-noun), and we apply this method to 37 languages. We find that contextualized embeddings not only capture human judgment of class variation within words in English, but also uncover shared tendencies in class flexibility across languages. Specifically, we find greater semantic variation when flexible lemmas are used in their dominant word class, supporting the view that word class flexibility is a directional process. Our work highlights the utility of deep contextualized models in linguistic typology.",
}
@inproceedings{li-etal-2020-shallow,
    title = "Shallow-to-Deep Training for Neural Machine Translation",
    author = "Li, Bei  and
      Wang, Ziyang  and
      Liu, Hui  and
      Jiang, Yufan  and
      Du, Quan  and
      Xiao, Tong  and
      Wang, Huizhen  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.72",
    doi = "10.18653/v1/2020.emnlp-main.72",
    pages = "995--1005",
    abstract = "Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why deep models help NMT is an open question. In this paper, we investigate the behavior of a well-tuned deep Transformer system. We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly. This inspires us to develop a shallow-to-deep training method that learns deep models by stacking shallow models. In this way, we successfully train a Transformer system with a 54-layer encoder. Experimental results on WMT{'}16 English-German and WMT{'}14 English-French translation tasks show that it is 1:4 faster than training from scratch, and achieves a BLEU score of 30:33 and 43:29 on two tasks. The code is publicly available at \url{https://github.com/libeineu/SDT-Training}.",
}
@inproceedings{lee-etal-2020-iterative,
    title = "Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation",
    author = "Lee, Jason  and
      Shu, Raphael  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.73",
    doi = "10.18653/v1/2020.emnlp-main.73",
    pages = "1006--1015",
    abstract = "We propose an efficient inference procedure for non-autoregressive machine translation that iteratively refines translation purely in the continuous space. Given a continuous latent variable model for machine translation (Shu et al., 2020), we train an inference network to approximate the gradient of the marginal log probability of the target sentence, using the latent variable instead. This allows us to use gradient-based optimization to find the target sentence at inference time that approximately maximizes its marginal probability. As each refinement step only involves computation in the latent space of low dimensionality (we use 8 in our experiments), we avoid computational overhead incurred by existing non-autoregressive inference procedures that often refine in token space. We compare our approach to a recently proposed EM-like inference procedure (Shu et al., 2020) that optimizes in a hybrid space, consisting of both discrete and continuous variables. We evaluate our approach on WMT{'}14 En→De, WMT{'}16 Ro→En and IWSLT{'}16 De→En, and observe two advantages over the EM-like inference: (1) it is computationally efficient, i.e. each refinement step is twice as fast, and (2) it is more effective, resulting in higher marginal probabilities and BLEU scores with the same number of refinement steps. On WMT{'}14 En→De, for instance, our approach is able to decode 6.2 times faster than the autoregressive model with minimal degradation to translation quality (0.9 BLEU).",
}
@inproceedings{wu-etal-2020-skip,
    title = "Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers",
    author = "Wu, Yimeng  and
      Passban, Peyman  and
      Rezagholizadeh, Mehdi  and
      Liu, Qun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.74",
    doi = "10.18653/v1/2020.emnlp-main.74",
    pages = "1016--1021",
    abstract = "With the growth of computing power neural machine translation (NMT) models also grow accordingly and become better. However, they also become harder to deploy on edge devices due to memory constraints. To cope with this problem, a common practice is to distill knowledge from a large and accurately-trained teacher network (T) into a compact student network (S). Although knowledge distillation (KD) is useful in most cases, our study shows that existing KD techniques might not be suitable enough for deep NMT engines, so we propose a novel alternative. In our model, besides matching T and S predictions we have a combinatorial mechanism to inject layer-level supervision from T to S. In this paper, we target low-resource settings and evaluate our translation engines for Portuguese→English, Turkish→English, and English→German directions. Students trained using our technique have 50{\%} fewer parameters and can still deliver comparable results to those of 12-layer teachers.",
}
@inproceedings{wang-etal-2020-multi-task,
    title = "Multi-task Learning for Multilingual Neural Machine Translation",
    author = "Wang, Yiren  and
      Zhai, ChengXiang  and
      Hassan, Hany",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.75",
    doi = "10.18653/v1/2020.emnlp-main.75",
    pages = "1022--1034",
    abstract = "While monolingual data has been shown to be useful in improving bilingual neural machine translation (NMT), effectively and efficiently leveraging monolingual data for Multilingual NMT (MNMT) systems is a less explored area. In this work, we propose a multi-task learning (MTL) framework that jointly trains the model with the translation task on bitext data and two denoising tasks on the monolingual data. We conduct extensive empirical studies on MNMT systems with 10 language pairs from WMT datasets. We show that the proposed approach can effectively improve the translation quality for both high-resource and low-resource languages with large margin, achieving significantly better results than the individual bilingual models. We also demonstrate the efficacy of the proposed approach in the zero-shot setup for language pairs without bitext training data. Furthermore, we show the effectiveness of MTL over pre-training approaches for both NMT and cross-lingual transfer learning NLU tasks; the proposed approach outperforms massive scale models trained on single task.",
}
@inproceedings{gu-etal-2020-token,
    title = "Token-level Adaptive Training for Neural Machine Translation",
    author = "Gu, Shuhao  and
      Zhang, Jinchao  and
      Meng, Fandong  and
      Feng, Yang  and
      Xie, Wanying  and
      Zhou, Jie  and
      Yu, Dong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.76",
    doi = "10.18653/v1/2020.emnlp-main.76",
    pages = "1035--1046",
    abstract = "There exists a token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural Machine Translation (NMT). The vanilla NMT model usually adopts trivial equal-weighted objectives for target tokens with different frequencies and tends to generate more high-frequency tokens and less low-frequency tokens compared with the golden token distribution. However, low-frequency tokens may carry critical semantic information that will affect the translation quality once they are neglected. In this paper, we explored target token-level adaptive objectives based on token frequencies to assign appropriate weights for each target token during training. We aimed that those meaningful but relatively low-frequency words could be assigned with larger weights in objectives to encourage the model to pay more attention to these tokens. Our method yields consistent improvements in translation quality on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens where we can get 1.68, 1.02, and 0.52 BLEU increases compared with baseline, respectively. Further analyses show that our method can also improve the lexical diversity of translation.",
}
@inproceedings{yan-etal-2020-multi-unit,
    title = "Multi-Unit Transformers for Neural Machine Translation",
    author = "Yan, Jianhao  and
      Meng, Fandong  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.77",
    doi = "10.18653/v1/2020.emnlp-main.77",
    pages = "1047--1059",
    abstract = "Transformer models achieve remarkable success in Neural Machine Translation. Many efforts have been devoted to deepening the Transformer by stacking several units (i.e., a combination of Multihead Attentions and FFN) in a cascade, while the investigation over multiple parallel units draws little attention. In this paper, we propose the Multi-Unit Transformer (MUTE) , which aim to promote the expressiveness of the Transformer by introducing diverse and complementary units. Specifically, we use several parallel units and show that modeling with multiple units improves model performance and introduces diversity. Further, to better leverage the advantage of the multi-unit setting, we design biased module and sequential dependency that guide and encourage complementariness among different units. Experimental results on three machine translation tasks, the NIST Chinese-to-English, WMT{'}14 English-to-German and WMT{'}18 Chinese-to-English, show that the MUTE models significantly outperform the Transformer-Base, by up to +1.52, +1.90 and +1.10 BLEU points, with only a mild drop in inference speed (about 3.1{\%}). In addition, our methods also surpass the Transformer-Big model, with only 54{\%} of its parameters. These results demonstrate the effectiveness of the MUTE, as well as its efficiency in both the inference process and parameter usage.",
}
@inproceedings{wang-etal-2020-sparsity,
    title = "On the Sparsity of Neural Machine Translation Models",
    author = "Wang, Yong  and
      Wang, Longyue  and
      Li, Victor  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.78",
    doi = "10.18653/v1/2020.emnlp-main.78",
    pages = "1060--1066",
    abstract = "Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources. In response to this problem, we empirically investigate whether the redundant parameters can be reused to achieve better performance. Experiments and analyses are systematically conducted on different datasets and NMT architectures. We show that: 1) the pruned parameters can be rejuvenated to improve the baseline model by up to +0.8 BLEU points; 2) the rejuvenated parameters are reallocated to enhance the ability of modeling low-level lexical information.",
}
@inproceedings{kong-etal-2020-incorporating,
    title = "Incorporating a Local Translation Mechanism into Non-autoregressive Translation",
    author = "Kong, Xiang  and
      Zhang, Zhisong  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.79",
    doi = "10.18653/v1/2020.emnlp-main.79",
    pages = "1067--1073",
    abstract = "In this work, we introduce a novel local autoregressive translation (LAT) mechanism into non-autoregressive translation (NAT) models so as to capture local dependencies among target outputs. Specifically, for each target decoding position, instead of only one token, we predict a short sequence of tokens in an autoregressive way. We further design an efficient merging algorithm to align and merge the output pieces into one final output sequence. We integrate LAT into the conditional masked language model (CMLM) (Ghazvininejad et al.,2019) and similarly adopt iterative decoding. Empirical results on five translation tasks show that compared with CMLM, our method achieves comparable or better performance with fewer decoding iterations, bringing a 2.5x speedup. Further analysis indicates that our method reduces repeated translations and performs better at longer sentences. Our code will be released to the public.",
}
@inproceedings{wan-etal-2020-self,
    title = "Self-Paced Learning for Neural Machine Translation",
    author = "Wan, Yu  and
      Yang, Baosong  and
      Wong, Derek F.  and
      Zhou, Yikai  and
      Chao, Lidia S.  and
      Zhang, Haibo  and
      Chen, Boxing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.80",
    doi = "10.18653/v1/2020.emnlp-main.80",
    pages = "1074--1080",
    abstract = "Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans. Nevertheless, achievements of such kind of curriculum learning rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. We ameliorate this procedure with a more flexible manner by proposing self-paced learning, where NMT model is allowed to 1) automatically quantify the learning confidence over training examples; and 2) flexibly govern its learning via regulating the loss in each iteration step. Experimental results over multiple translation tasks demonstrate that the proposed model yields better performance than strong baselines and those models trained with human-designed curricula on both translation quality and convergence speed.",
}
@inproceedings{zhang-etal-2020-long,
    title = "Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation",
    author = "Zhang, Pei  and
      Chen, Boxing  and
      Ge, Niyu  and
      Fan, Kai",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.81",
    doi = "10.18653/v1/2020.emnlp-main.81",
    pages = "1081--1087",
    abstract = "Many document-level neural machine translation (NMT) systems have explored the utility of context-aware architecture, usually requiring an increasing number of parameters and computational complexity. However, few attention is paid to the baseline model. In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation. Therefore, we propose a surprisingly simple long-short term masking self-attention on top of the standard transformer to both effectively capture the long-range dependence and reduce the propagation of errors. We examine our approach on the two publicly available document-level datasets. We can achieve a strong result in BLEU and capture discourse phenomena.",
}
@inproceedings{wu-etal-2020-generating,
    title = "Generating Diverse Translation from Model Distribution with Dropout",
    author = "Wu, Xuanfu  and
      Feng, Yang  and
      Shao, Chenze",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.82",
    doi = "10.18653/v1/2020.emnlp-main.82",
    pages = "1088--1097",
    abstract = "Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. In this paper, we propose to generate diverse translations by deriving a large number of possible models with Bayesian modelling and sampling models from them for inference. The possible models are obtained by applying concrete dropout to the NMT model and each of them has specific confidence for its prediction, which corresponds to a posterior model distribution under specific training data in the principle of Bayesian modeling. With variational inference, the posterior model distribution can be approximated with a variational distribution, from which the final models for inference are sampled. We conducted experiments on Chinese-English and English-German translation tasks and the results shows that our method makes a better trade-off between diversity and accuracy.",
}
@inproceedings{saharia-etal-2020-non,
    title = "Non-Autoregressive Machine Translation with Latent Alignments",
    author = "Saharia, Chitwan  and
      Chan, William  and
      Saxena, Saurabh  and
      Norouzi, Mohammad",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.83",
    doi = "10.18653/v1/2020.emnlp-main.83",
    pages = "1098--1108",
    abstract = "This paper presents two strong methods, CTC and Imputer, for non-autoregressive machine translation that model latent alignments with dynamic programming. We revisit CTC for machine translation and demonstrate that a simple CTC model can achieve state-of-the-art for single-step non-autoregressive machine translation, contrary to what prior work indicates. In addition, we adapt the Imputer model for non-autoregressive machine translation and demonstrate that Imputer with just 4 generation steps can match the performance of an autoregressive Transformer baseline. Our latent alignment models are simpler than many existing non-autoregressive translation baselines; for example, we do not require target length prediction or re-scoring with an autoregressive model. On the competitive WMT{'}14 En$\rightarrow$De task, our CTC model achieves 25.7 BLEU with a single generation step, while Imputer achieves 27.5 BLEU with 2 generation steps, and 28.0 BLEU with 4 generation steps. This compares favourably to the autoregressive Transformer baseline at 27.8 BLEU.",
}
@inproceedings{ko-etal-2020-look,
    title = "Look at the First Sentence: Position Bias in Question Answering",
    author = "Ko, Miyoung  and
      Lee, Jinhyuk  and
      Kim, Hyunjae  and
      Kim, Gangwoo  and
      Kang, Jaewoo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.84",
    doi = "10.18653/v1/2020.emnlp-main.84",
    pages = "1109--1121",
    abstract = "Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48{\%} to 81.64{\%} when trained on a biased SQuAD dataset.",
}
@inproceedings{boratko-etal-2020-protoqa,
    title = "{P}roto{QA}: A Question Answering Dataset for Prototypical Common-Sense Reasoning",
    author = "Boratko, Michael  and
      Li, Xiang  and
      O{'}Gorman, Tim  and
      Das, Rajarshi  and
      Le, Dan  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.85",
    doi = "10.18653/v1/2020.emnlp-main.85",
    pages = "1122--1136",
    abstract = "Given questions regarding some prototypical situation {---} such as Name something that people usually do before they leave the house for work? {---} a human can easily answer them via acquired experiences. There can be multiple right answers for such questions, with some more common for a situation than others. This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. The training set is gathered from an existing set of questions played in a long-running international trivia game show {--} Family Feud. The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers. We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question. After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task.",
}
@inproceedings{ferguson-etal-2020-iirc,
    title = "{IIRC}: A Dataset of Incomplete Information Reading Comprehension Questions",
    author = "Ferguson, James  and
      Gardner, Matt  and
      Hajishirzi, Hannaneh  and
      Khot, Tushar  and
      Dasigi, Pradeep",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.86",
    doi = "10.18653/v1/2020.emnlp-main.86",
    pages = "1137--1147",
    abstract = "Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system{'}s performance at identifying a potential lack of sufficient information and locating sources for that information. To fill this gap, we present a dataset, IIRC, with more than 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. The questions were written by crowd workers who did not have access to any of the linked documents, leading to questions that have little lexical overlap with the contexts where the answers appear. This process also gave many questions without answers, and those that require discrete reasoning, increasing the difficulty of the task. We follow recent modeling work on various reading comprehension datasets to construct a baseline model for this dataset, finding that it achieves 31.1{\%} F1 on this task, while estimated human performance is 88.4{\%}. The dataset, code for the baseline system, and a leaderboard can be found at https://allennlp.org/iirc.",
}
@inproceedings{rennie-etal-2020-unsupervised,
    title = "Unsupervised Adaptation of Question Answering Systems via Generative Self-training",
    author = "Rennie, Steven  and
      Marcheret, Etienne  and
      Mallinar, Neil  and
      Nahamoo, David  and
      Goel, Vaibhava",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.87",
    doi = "10.18653/v1/2020.emnlp-main.87",
    pages = "1148--1157",
    abstract = "BERT-era question answering systems have recently achieved impressive performance on several question-answering (QA) tasks. These systems are based on representations that have been pre-trained on self-supervised tasks such as word masking and sentence entailment, using massive amounts of data. Nevertheless, additional pre-training closer to the end-task, such as training on synthetic QA pairs, has been shown to improve performance. While recent work has considered augmenting labelled data and leveraging large unlabelled datasets to generate synthetic QA data, directly adapting to target data has received little attention. In this paper we investigate the iterative generation of synthetic QA pairs as a way to realize unsupervised self adaptation. Motivated by the success of the roundtrip consistency method for filtering generated QA pairs, we present iterative generalizations of the approach, which maximize an approximation of a lower bound on the probability of the adaptation data. By adapting on synthetic QA pairs generated on the target data, our method is able to improve QA systems significantly, using an order of magnitude less synthetic data and training computation than existing augmentation approaches.",
}
@inproceedings{ning-etal-2020-torque,
    title = "{TORQUE}: A Reading Comprehension Dataset of Temporal Ordering Questions",
    author = "Ning, Qiang  and
      Wu, Hao  and
      Han, Rujun  and
      Peng, Nanyun  and
      Gardner, Matt  and
      Roth, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.88",
    doi = "10.18653/v1/2020.emnlp-main.88",
    pages = "1158--1172",
    abstract = "A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated. However, current machine reading comprehension benchmarks have practically no questions that test temporal phenomena, so systems trained on these benchmarks have no capacity to answer questions such as {``}what happened before/after [some event]?{''} We introduce TORQUE, a new English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships. Results show that RoBERTa-large achieves an exact-match score of 51{\%} on the test set of TORQUE, about 30{\%} behind human performance.",
}
@inproceedings{parikh-etal-2020-totto,
    title = "{ToTTo}: A Controlled Table-To-Text Generation Dataset",
    author = "Parikh, Ankur  and
      Wang, Xuezhi  and
      Gehrmann, Sebastian  and
      Faruqui, Manaal  and
      Dhingra, Bhuwan  and
      Yang, Diyi  and
      Das, Dipanjan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.89",
    doi = "10.18653/v1/2020.emnlp-main.89",
    pages = "1173--1186",
    abstract = "We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. To obtain generated targets that are natural but also faithful to the source table, we introduce a dataset construction process where annotators directly revise existing candidate sentences from Wikipedia. We present systematic analyses of our dataset and annotation process as well as results achieved by several state-of-the-art baselines. While usually fluent, existing methods often hallucinate phrases that are not supported by the table, suggesting that this dataset can serve as a useful research benchmark for high-precision conditional text generation.",
}
@inproceedings{cheng-etal-2020-ent,
    title = "{ENT}-{DESC}: Entity Description Generation by Exploring Knowledge Graph",
    author = "Cheng, Liying  and
      Wu, Dekun  and
      Bing, Lidong  and
      Zhang, Yan  and
      Jie, Zhanming  and
      Lu, Wei  and
      Si, Luo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.90",
    doi = "10.18653/v1/2020.emnlp-main.90",
    pages = "1187--1197",
    abstract = "Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions. We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively. Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information. Extensive experiments demonstrate the effectiveness of our model architecture.",
}
@inproceedings{zhang-etal-2020-small,
    title = "Small but Mighty: New Benchmarks for Split and Rephrase",
    author = "Zhang, Li  and
      Zhu, Huaiyu  and
      Brahma, Siddhartha  and
      Li, Yunyao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.91",
    doi = "10.18653/v1/2020.emnlp-main.91",
    pages = "1198--1205",
    abstract = "Split and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones. As a relatively new task, it is paramount to ensure the soundness of its evaluation benchmark and metric. We find that the widely used benchmark dataset universally contains easily exploitable syntactic cues caused by its automatic generation process. Taking advantage of such cues, we show that even a simple rule-based model can perform on par with the state-of-the-art model. To remedy such limitations, we collect and release two crowdsourced benchmark datasets. We not only make sure that they contain significantly more diverse syntax, but also carefully control for their quality according to a well-defined set of criteria. While no satisfactory automatic metric exists, we apply fine-grained manual evaluation based on these criteria using crowdsourcing, showing that our datasets better represent the task and are significantly more challenging for the models.",
}
@inproceedings{bai-etal-2020-online,
    title = "Online Back-Parsing for {AMR}-to-Text Generation",
    author = "Bai, Xuefeng  and
      Song, Linfeng  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.92",
    doi = "10.18653/v1/2020.emnlp-main.92",
    pages = "1206--1219",
    abstract = "AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on standard language modeling being used to generate outputs. We propose a decoder that back predicts projected AMR graphs on the target sentence during text generation. As the result, our outputs can better preserve the input meaning than standard decoders. Experiments on two AMR benchmarks show the superiority of our model over the previous state-of-the-art system based on graph Transformer.",
}
@inproceedings{chandu-etal-2020-reading,
    title = "Reading Between the Lines: Exploring Infilling in Visual Narratives",
    author = "Chandu, Khyathi Raghavi  and
      Dong, Ruo-Ping  and
      Black, Alan W",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.93",
    doi = "10.18653/v1/2020.emnlp-main.93",
    pages = "1220--1229",
    abstract = "Generating long form narratives such as stories and procedures from multiple modalities has been a long standing dream for artificial intelligence. In this regard, there is often crucial subtext that is derived from the surrounding contexts. The general seq2seq training methods render the models shorthanded while attempting to bridge the gap between these neighbouring contexts. In this paper, we tackle this problem by using infilling techniques involving prediction of missing steps in a narrative while generating textual descriptions from a sequence of images. We also present a new large scale visual procedure telling (ViPT) dataset with a total of 46,200 procedures and around 340k pairwise images and textual descriptions that is rich in such contextual dependencies. Generating steps using infilling technique demonstrates the effectiveness in visual procedures with more coherent texts. We conclusively show a METEOR score of 27.51 on procedures which is higher than the state-of-the-art on visual storytelling. We also demonstrate the effects of interposing new text with missing images during inference. The code and the dataset will be publicly available at https://visual-narratives.github.io/Visual-Narratives/.",
}
@inproceedings{agarwal-kann-2020-acrostic,
    title = "Acrostic Poem Generation",
    author = "Agarwal, Rajat  and
      Kann, Katharina",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.94",
    doi = "10.18653/v1/2020.emnlp-main.94",
    pages = "1230--1240",
    abstract = "We propose a new task in the area of computational creativity: acrostic poem generation in English. Acrostic poems are poems that contain a hidden message; typically, the first letter of each line spells out a word or short phrase. We define the task as a generation task with multiple constraints: given an input word, 1) the initial letters of each line should spell out the provided word, 2) the poem{'}s semantics should also relate to it, and 3) the poem should conform to a rhyming scheme. We further provide a baseline model for the task, which consists of a conditional neural language model in combination with a neural rhyming model. Since no dedicated datasets for acrostic poem generation exist, we create training data for our task by first training a separate topic prediction model on a small set of topic-annotated poems and then predicting topics for additional poems. Our experiments show that the acrostic poems generated by our baseline are received well by humans and do not lose much quality due to the additional constraints. Last, we confirm that poems generated by our model are indeed closely related to the provided prompts, and that pretraining on Wikipedia can boost performance.",
}
@inproceedings{chen-etal-2020-local,
    title = "Local Additivity Based Data Augmentation for Semi-supervised {NER}",
    author = "Chen, Jiaao  and
      Wang, Zhenghui  and
      Tian, Ran  and
      Yang, Zichao  and
      Yang, Diyi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.95",
    doi = "10.18653/v1/2020.emnlp-main.95",
    pages = "1241--1251",
    abstract = "Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create virtual samples by interpolating sequences close to each other. Our approach has two variations: Intra-LADA and Inter-LADA, where Intra-LADA performs interpolations among tokens within one sentence, and Inter-LADA samples different sentences to interpolate. Through linear additions between sampled training data, LADA creates an infinite amount of labeled data and improves both entity and context learning. We further extend LADA to the semi-supervised setting by designing a novel consistency loss for unlabeled data. Experiments conducted on two NER benchmarks demonstrate the effectiveness of our methods over several strong baselines. We have publicly released our code at https://github.com/GT-SALT/LADA",
}
@inproceedings{pappas-etal-2020-grounded,
    title = "Grounded Compositional Outputs for Adaptive Language Modeling",
    author = "Pappas, Nikolaos  and
      Mulcaire, Phoebe  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.96",
    doi = "10.18653/v1/2020.emnlp-main.96",
    pages = "1252--1267",
    abstract = "Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks. A language model{'}s \textit{vocabulary}{---}typically selected before training and permanently fixed later{---}affects its size and is part of what makes it resistant to such adaptation. Prior work has used compositional input embeddings based on surface forms to ameliorate this issue. In this work, we go one step beyond and propose a fully compositional output embedding layer for language models, which is further grounded in information from a structured lexicon (WordNet), namely semantically related words and free-text definitions. To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary. We evaluate the model on conventional language modeling as well as challenging cross-domain settings with an open vocabulary, finding that it matches or outperforms previous state-of-the-art output embedding methods and adaptation approaches. Our analysis attributes the improvements to sample efficiency: our model is more accurate for low-frequency words.",
}
@inproceedings{ng-etal-2020-ssmba,
    title = "{SSMBA}: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness",
    author = "Ng, Nathan  and
      Cho, Kyunghyun  and
      Ghassemi, Marzyeh",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.97",
    doi = "10.18653/v1/2020.emnlp-main.97",
    pages = "1268--1283",
    abstract = "Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve OOD generalization. However, in natural language, it is difficult to generate new examples that stay on the underlying data manifold. We introduce SSMBA, a data augmentation method for generating synthetic training examples by using a pair of corruption and reconstruction functions to move randomly on a data manifold. We investigate the use of SSMBA in the natural language domain, leveraging the manifold assumption to reconstruct corrupted text with masked language models. In experiments on robustness benchmarks across 3 tasks and 9 datasets, SSMBA consistently outperforms existing data augmentation methods and baseline models on both in-domain and OOD data, achieving gains of 0.8{\%} on OOD Amazon reviews, 1.8{\%} accuracy on OOD MNLI, and 1.4 BLEU on in-domain IWSLT14 German-English.",
}
@inproceedings{gao-etal-2020-setconv,
    title = "{S}et{C}onv: {A} {N}ew {A}pproach for {L}earning from {I}mbalanced {D}ata",
    author = "Gao, Yang  and
      Li, Yi-Fan  and
      Lin, Yu  and
      Aggarwal, Charu  and
      Khan, Latifur",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.98",
    doi = "10.18653/v1/2020.emnlp-main.98",
    pages = "1284--1294",
    abstract = "For many real-world classification problems, e.g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high. To address this problem, we propose a set convolution (SetConv) operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution. We prove that our proposed algorithm is permutation-invariant despite the order of inputs, and experiments on multiple large-scale benchmark text datasets show the superiority of our proposed framework when compared to other SOTA methods.",
}
@inproceedings{feng-etal-2020-scalable,
    title = "Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering",
    author = "Feng, Yanlin  and
      Chen, Xinyue  and
      Lin, Bill Yuchen  and
      Wang, Peifeng  and
      Yan, Jun  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.99",
    doi = "10.18653/v1/2020.emnlp-main.99",
    pages = "1295--1309",
    abstract = "Existing work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model{'}s prediction rationale. In this paper, we propose a novel knowledge-aware approach that equips pre-trained language models (PTLMs) has with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN). It performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. The proposed reasoning module unifies path-based reasoning methods and graph neural networks to achieve better interpretability and scalability. We also empirically show its effectiveness and scalability on CommonsenseQA and OpenbookQA datasets, and interpret its behaviors with case studies, with the code for experiments released.",
}
@inproceedings{huang-etal-2020-improving,
    title = "Improving Bilingual Lexicon Induction for Low Frequency Words",
    author = "Huang, Jiaji  and
      Cai, Xingyu  and
      Church, Kenneth",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.100",
    doi = "10.18653/v1/2020.emnlp-main.100",
    pages = "1310--1314",
    abstract = "This paper designs a Monolingual Lexicon Induction task and observes that two factors accompany the degraded accuracy of bilingual lexicon induction for rare words. First, a diminishing margin between similarities in low frequency regime, and secondly, exacerbated hubness at low frequency. Based on the observation, we further propose two methods to address these two factors, respectively. The larger issue is hubness. Addressing that improves induction accuracy significantly, especially for low-frequency words.",
}
@inproceedings{tian-etal-2020-learning,
    title = "Learning {VAE}-{LDA} Models with Rounded Reparameterization Trick",
    author = "Tian, Runzhi  and
      Mao, Yongyi  and
      Zhang, Richong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.101",
    doi = "10.18653/v1/2020.emnlp-main.101",
    pages = "1315--1325",
    abstract = "The introduction of VAE provides an efficient framework for the learning of generative models, including generative topic models. However, when the topic model is a Latent Dirichlet Allocation (LDA) model, a central technique of VAE, the reparameterization trick, fails to be applicable. This is because no reparameterization form of Dirichlet distributions is known to date that allows the use of the reparameterization trick. In this work, we propose a new method, which we call Rounded Reparameterization Trick (RRT), to reparameterize Dirichlet distributions for the learning of VAE-LDA models. This method, when applied to a VAE-LDA model, is shown experimentally to outperform the existing neural topic models on several benchmark datasets and on a synthetic dataset.",
}
@inproceedings{kong-etal-2020-calibrated,
    title = "Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data",
    author = "Kong, Lingkai  and
      Jiang, Haoming  and
      Zhuang, Yuchen  and
      Lyu, Jie  and
      Zhao, Tuo  and
      Zhang, Chao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.102",
    doi = "10.18653/v1/2020.emnlp-main.102",
    pages = "1326--1340",
    abstract = "Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate this issue, we propose a regularized fine-tuning method. Our method introduces two types of regularization for better calibration: (1) On-manifold regularization, which generates pseudo on-manifold samples through interpolation within the data manifold. Augmented training with these pseudo samples imposes a smoothness regularization to improve in-distribution calibration. (2) Off-manifold regularization, which encourages the model to output uniform distributions for pseudo off-manifold samples to address the over-confidence issue for OOD data. Our experiments demonstrate that the proposed method outperforms existing calibration methods for text classification in terms of expectation calibration error, misclassification detection, and OOD detection on six datasets. Our code can be found at https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning.",
}
@inproceedings{chiu-rush-2020-scaling,
    title = "Scaling Hidden {M}arkov Language Models",
    author = "Chiu, Justin  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.103",
    doi = "10.18653/v1/2020.emnlp-main.103",
    pages = "1341--1349",
    abstract = "The hidden Markov model (HMM) is a fundamental tool for sequence modeling that cleanly separates the hidden state from the emission structure. However, this separation makes it difficult to fit HMMs to large datasets in modern NLP, and they have fallen out of use due to very poor performance compared to fully observed models. This work revisits the challenge of scaling HMMs to language modeling datasets, taking ideas from recent approaches to neural modeling. We propose methods for scaling HMMs to massive state spaces while maintaining efficient exact inference, a compact parameterization, and effective regularization. Experiments show that this approach leads to models that are much more accurate than previous HMMs and n-gram-based methods, making progress towards the performance of state-of-the-art NN models.",
}
@inproceedings{khan-etal-2020-coding,
    title = "Coding Textual Inputs Boosts the Accuracy of Neural Networks",
    author = "Khan, Abdul Rafae  and
      Xu, Jia  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.104",
    doi = "10.18653/v1/2020.emnlp-main.104",
    pages = "1350--1360",
    abstract = "Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs. We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as inputs. As {``}alternatives{''} to a text representation, we introduce Soundex, MetaPhone, NYSIIS, logogram to NLP, and develop fixed-output-length coding and its extension using Huffman coding. Each of those codings combines different character/digital sequences and constructs a new vocabulary based on codewords. We find that the integration of those codewords with text provides more reliable inputs to Neural-Network-based NLP systems through redundancy than text-alone inputs. Experiments demonstrate that our approach outperforms the state-of-the-art models on the application of machine translation, language modeling, and part-of-speech tagging. The source code is available at https://github.com/abdulrafae/coding{\_}nmt.",
}
@inproceedings{weller-etal-2020-learning,
    title = "Learning from Task Descriptions",
    author = "Weller, Orion  and
      Lourie, Nicholas  and
      Gardner, Matt  and
      Peters, Matthew",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
    doi = "10.18653/v1/2020.emnlp-main.105",
    pages = "1361--1375",
    abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12{\%} on ZEST, leaving a significant challenge for NLP researchers.",
}
@inproceedings{ding-etal-2020-hashtags,
    title = "Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics",
    author = "Ding, Keyang  and
      Li, Jing  and
      Zhang, Yuji",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.106",
    doi = "10.18653/v1/2020.emnlp-main.106",
    pages = "1376--1382",
    abstract = "This paper studies social emotions to online discussion topics. While most prior work focus on emotions from writers, we investigate readers{'} responses and explore the public feelings to an online topic. A large-scale dataset is collected from Chinese microblog Sina Weibo with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding. In experiments, we examine baseline performance to predict a topic{'}s possible social emotions in a multilabel classification setting. The results show that a seq2seq model with user comment modeling performs the best, even surpassing human prediction. More analyses shed light on the effects of emotion types, topic description lengths, contexts from user comments, and the limited capacity of the existing models.",
}
@inproceedings{nie-etal-2020-named,
    title = "Named Entity Recognition for Social Media Texts with Semantic Augmentation",
    author = "Nie, Yuyang  and
      Tian, Yuanhe  and
      Wan, Xiang  and
      Song, Yan  and
      Dai, Bo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.107",
    doi = "10.18653/v1/2020.emnlp-main.107",
    pages = "1383--1391",
    abstract = "Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Semantic augmentation is a potential way to alleviate this problem. Given that rich semantic information is implicitly preserved in pre-trained word embeddings, they are potential ideal resources for semantic augmentation. In this paper, we propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account. In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively. Extensive experiments are performed on three benchmark datasets collected from English and Chinese social media platforms, where the results demonstrate the superiority of our approach to previous studies across all three datasets.",
}
@inproceedings{yu-etal-2020-coupled,
    title = "Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations",
    author = "Yu, Jianfei  and
      Jiang, Jing  and
      Khoo, Ling Min Serena  and
      Chieu, Hai Leong  and
      Xia, Rui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.108",
    doi = "10.18653/v1/2020.emnlp-main.108",
    pages = "1392--1401",
    abstract = "The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous studies focus on leveraging stance classification to enhance RV with multi-task learning (MTL) methods. However, most of these methods failed to employ pre-trained contextualized embeddings such as BERT, and did not exploit inter-task dependencies by using predicted stance labels to improve the RV task. Therefore, in this paper, to extend BERT to obtain thread representations, we first propose a Hierarchical Transformer, which divides each long thread into shorter subthreads, and employs BERT to separately represent each subthread, followed by a global Transformer layer to encode all the subthreads. We further propose a Coupled Transformer Module to capture the inter-task interactions and a Post-Level Attention layer to use the predicted stance labels for RV, respectively. Experiments on two benchmark datasets show the superiority of our Coupled Hierarchical Transformer model over existing MTL approaches.",
}
@inproceedings{sarkar-etal-2020-social,
    title = "Social Media Attributions in the Context of Water Crisis",
    author = "Sarkar, Rupak  and
      Mahinder, Sayantan  and
      Sarkar, Hirak  and
      KhudaBukhsh, Ashiqur",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.109",
    doi = "10.18653/v1/2020.emnlp-main.109",
    pages = "1402--1412",
    abstract = "Attribution of natural disasters/collective misfortune is a widely-studied political science problem. However, such studies typically rely on surveys, or expert opinions, or external signals such as voting outcomes. In this paper, we explore the viability of using unstructured, noisy social media data to complement traditional surveys through automatically extracting attribution factors. We present a novel prediction task of \textit{attribution tie detection} of identifying the factors (e.g., poor city planning, exploding population etc.) held responsible for the crisis in a social media document. We focus on the 2019 Chennai water crisis that rapidly escalated into a discussion topic with global importance following alarming water-crisis statistics. On a challenging data set constructed from YouTube comments (72,098 comments posted by 43,859 users on 623 videos relevant to the crisis), we present a neural baseline to identify attribution ties that achieves a reasonable performance (accuracy: 87.34{\%} on attribution detection and 81.37{\%} on attribution resolution). We release the first annotated data set of 2,500 comments in this important domain.",
}
@inproceedings{sen-etal-2020-reliability,
    title = "On the Reliability and Validity of Detecting Approval of Political Actors in Tweets",
    author = {Sen, Indira  and
      Fl{\"o}ck, Fabian  and
      Wagner, Claudia},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.110",
    doi = "10.18653/v1/2020.emnlp-main.110",
    pages = "1413--1426",
    abstract = "Social media sites like Twitter possess the potential to complement surveys that measure political opinions and, more specifically, political actors{'} approval. However, new challenges related to the reliability and validity of social-media-based estimates arise. Various sentiment analysis and stance detection methods have been developed and used in previous research to measure users{'} political opinions based on their content on social media. In this work, we attempt to gauge the efficacy of untargeted sentiment, targeted sentiment, and stance detection methods in labeling various political actors{'} approval by benchmarking them across several datasets. We also contrast the performance of these pretrained methods that can be used in an off-the-shelf (OTS) manner against a set of models trained on minimal custom data. We find that OTS methods have low generalizability on unseen and familiar targets, while low-resource custom models are more robust. Our work sheds light on the strengths and limitations of existing methods proposed for understanding politicians{'} approval from tweets.",
}
@inproceedings{li-etal-2020-towards,
    title = "Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text",
    author = "Li, Dongfang  and
      Hu, Baotian  and
      Chen, Qingcai  and
      Peng, Weihua  and
      Wang, Anqi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.111",
    doi = "10.18653/v1/2020.emnlp-main.111",
    pages = "1427--1438",
    abstract = "Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models. However, it performs much worse in specific domains such as the medical field due to the lack of extensive training data and professional structural knowledge neglect. As an effort, we first collect a large scale medical multi-choice question dataset (more than 21k instances) for the National Licensed Pharmacist Examination in China. It is a challenging medical examination with a passing rate of less than 14.2{\%} in 2018. Then we propose a novel reading comprehension model KMQA, which can fully exploit the structural medical knowledge (i.e., medical knowledge graph) and the reference medical plain text (i.e., text snippets retrieved from reference books). The experimental results indicate that the KMQA outperforms existing competitive models with a large margin and passes the exam with 61.8{\%} accuracy rate on the test set.",
}
@inproceedings{chen-etal-2020-generating-radiology,
    title = "Generating Radiology Reports via Memory-driven Transformer",
    author = "Chen, Zhihong  and
      Song, Yan  and
      Chang, Tsung-Hui  and
      Wan, Xiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.112",
    doi = "10.18653/v1/2020.emnlp-main.112",
    pages = "1439--1449",
    abstract = "Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment. Writing imaging reports is time-consuming and can be error-prone for inexperienced radiologists. Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is an essential task to apply artificial intelligence to the medical domain. In this paper, we propose to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the generation process and a memory-driven conditional layer normalization is applied to incorporating the memory into the decoder of Transformer. Experimental results on two prevailing radiology report datasets, IU X-Ray and MIMIC-CXR, show that our proposed approach outperforms previous models with respect to both language generation metrics and clinical evaluations. Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge. Further analyses also demonstrate that our approach is able to generate long reports with necessary medical terms as well as meaningful image-text attention mappings.",
}
@inproceedings{yang-etal-2020-planning,
    title = "Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection",
    author = "Yang, Jingfeng  and
      Yang, Diyi  and
      Ma, Zhaoran",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.113",
    doi = "10.18653/v1/2020.emnlp-main.113",
    pages = "1450--1460",
    abstract = "Existing approaches to disfluency detection heavily depend on human-annotated data. Numbers of data augmentation methods have been proposed to alleviate the dependence on labeled data. However, current augmentation approaches such as random insertion or repetition fail to resemble training corpus well and usually resulted in unnatural and limited types of disfluencies. In this work, we propose a simple Planner-Generator based disfluency generation model to generate natural and diverse disfluent texts as augmented data, where the Planner decides on where to insert disfluent segments and the Generator follows the prediction to generate corresponding disfluent segments. We further utilize this augmented data for pretraining and leverage it for the task of disfluency detection. Experiments demonstrated that our two-stage disfluency generation model outperforms existing baselines; those disfluent sentences generated significantly aided the task of disfluency detection and led to state-of-the-art performance on Switchboard corpus.",
}
@inproceedings{jin-etal-2020-predicting,
    title = "Predicting Clinical Trial Results by Implicit Evidence Integration",
    author = "Jin, Qiao  and
      Tan, Chuanqi  and
      Chen, Mosha  and
      Liu, Xiaozhong  and
      Huang, Songfang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.114",
    doi = "10.18653/v1/2020.emnlp-main.114",
    pages = "1461--1477",
    abstract = "Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i.e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population. While structured clinical evidence is prohibitively expensive for manual collection, we exploit large-scale unstructured sentences from medical literature that implicitly contain PICOs and results as evidence. Specifically, we pre-train a model to predict the disentangled results from such implicit evidence and fine-tune the model with limited data on the downstream datasets. Experiments on the benchmark Evidence Integration dataset show that the proposed model outperforms the baselines by large margins, e.g., with a 10.7{\%} relative gain over BioBERT in macro-F1. Moreover, the performance improvement is also validated on another dataset composed of clinical trials related to COVID-19.",
}
@inproceedings{feng-etal-2020-explainable,
    title = "Explainable Clinical Decision Support from Text",
    author = "Feng, Jinyue  and
      Shaib, Chantal  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.115",
    doi = "10.18653/v1/2020.emnlp-main.115",
    pages = "1478--1489",
    abstract = "Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, free-text medical notes may contain information not immediately available in structured variables. We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively. We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis. Finally, we outline a protocol to evaluate model usability in a clinical decision support context. From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.",
}
@inproceedings{yan-etal-2020-knowledge,
    title = "A Knowledge-driven Generative Model for Multi-implication {C}hinese Medical Procedure Entity Normalization",
    author = "Yan, Jinghui  and
      Wang, Yining  and
      Xiang, Lu  and
      Zhou, Yu  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.116",
    doi = "10.18653/v1/2020.emnlp-main.116",
    pages = "1490--1499",
    abstract = "Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical procedure entity normalization. However, nonstandard Chinese expressions and combined procedures present challenges in our problem. The existing strategies relying on the discriminative model are poorly to cope with normalizing combined procedure mentions. We propose a sequence generative framework to directly generate all the corresponding medical procedure entities. we adopt two strategies: category-based constraint decoding and category-based model refining to avoid unrealistic results. The method is capable of linking entities when a mention contains multiple procedure concepts and our comprehensive experiments demonstrate that the proposed model can achieve remarkable improvements over existing baselines, particularly significant in the case of multi-implication Chinese medical procedures.",
}
@inproceedings{smit-etal-2020-combining,
    title = "Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using {BERT}",
    author = "Smit, Akshay  and
      Jain, Saahil  and
      Rajpurkar, Pranav  and
      Pareek, Anuj  and
      Ng, Andrew  and
      Lungren, Matthew",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.117",
    doi = "10.18653/v1/2020.emnlp-main.117",
    pages = "1500--1519",
    abstract = "The extraction of labels from radiology text reports enables large-scale training of medical imaging models. Existing approaches to report labeling typically rely either on sophisticated feature engineering based on medical domain knowledge or manual annotations by experts. In this work, we introduce a BERT-based approach to medical image report labeling that exploits both the scale of available rule-based systems and the quality of expert annotations. We demonstrate superior performance of a biomedically pretrained BERT model first trained on annotations of a rule-based labeler and then finetuned on a small set of expert annotations augmented with automated backtranslation. We find that our final model, CheXbert, is able to outperform the previous best rules-based labeler with statistical significance, setting a new SOTA for report labeling on one of the largest datasets of chest x-rays.",
}
@inproceedings{guo-etal-2020-benchmarking-meaning,
    title = "Benchmarking Meaning Representations in Neural Semantic Parsing",
    author = "Guo, Jiaqi  and
      Liu, Qian  and
      Lou, Jian-Guang  and
      Li, Zhenwen  and
      Liu, Xueqing  and
      Xie, Tao  and
      Liu, Ting",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.118",
    doi = "10.18653/v1/2020.emnlp-main.118",
    pages = "1520--1540",
    abstract = "Meaning representation is an important component of semantic parsing. Although researchers have designed a lot of meaning representations, recent work focuses on only a few of them. Thus, the impact of meaning representation on semantic parsing is less understood. Furthermore, existing work{'}s performance is often not comprehensively evaluated due to the lack of readily-available execution engines. Upon identifying these gaps, we propose , a new unified benchmark on meaning representations, by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. The resulting unified benchmark contains the complete enumeration of logical forms and execution engines over three datasets $\times$ four meaning representations. A thorough experimental study on Unimer reveals that neural semantic parsing approaches exhibit notably different performance when they are trained to generate different meaning representations. Also, program alias and grammar rules heavily impact the performance of different meaning representations. Our benchmark, execution engines and implementation can be found on: https://github.com/JasperGuo/Unimer.",
}
@inproceedings{zhang-etal-2020-analogous,
    title = "Analogous Process Structure Induction for Sub-event Sequence Prediction",
    author = "Zhang, Hongming  and
      Chen, Muhao  and
      Wang, Haoyu  and
      Song, Yangqiu  and
      Roth, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.119",
    doi = "10.18653/v1/2020.emnlp-main.119",
    pages = "1541--1550",
    abstract = "Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories. Thus, knowledge about a known process such as {``}buying a car{''} can be used in the context of a new but analogous process such as {``}buying a house{''}. Nevertheless, most event understanding work in NLP is still at the ground level and does not consider abstraction. In this paper, we propose an Analogous Process Structure Induction (APSI) framework, which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub-event sequence of previously unseen open-domain processes. As our experiments and analysis indicate, APSI supports the generation of meaningful sub-event sequences for unseen processes and can help predict missing events.",
}
@inproceedings{lee-etal-2020-slm,
    title = "{SLM}: Learning a Discourse Language Representation with Sentence Unshuffling",
    author = "Lee, Haejun  and
      Hudson, Drew A.  and
      Lee, Kangwook  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.120",
    doi = "10.18653/v1/2020.emnlp-main.120",
    pages = "1551--1562",
    abstract = "We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training methods in NLP focus on learning either bottom or top-level language representations: contextualized word representations derived from language model objectives at one extreme and a whole sequence representation learned by order classification of two given textual segments at the other. However, these models are not directly encouraged to capture representations of intermediate-size structures that exist in natural languages such as sentences and the relationships among them. To that end, we propose a new approach to encourage learning of a contextualized sentence-level representation by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. Through experiments on downstream tasks such as GLUE, SQuAD, and DiscoEval, we show that this feature of our model improves the performance of the original BERT by large margins.",
}
@inproceedings{briakou-carpuat-2020-detecting,
    title = "{D}etecting {F}ine-{G}rained {C}ross-{L}ingual {S}emantic {D}ivergences without {S}upervision by {L}earning to {R}ank",
    author = "Briakou, Eleftheria  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.121",
    doi = "10.18653/v1/2020.emnlp-main.121",
    pages = "1563--1580",
    abstract = "Detecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale. This work improves the prediction and annotation of fine-grained semantic divergences. We introduce a training strategy for multilingual BERT models by learning to rank synthetic divergent examples of varying granularity. We evaluate our models on the Rationalized English-French Semantic Divergences, a new dataset released with this work, consisting of English-French sentence-pairs annotated with semantic divergence classes and token-level rationales. Learning to rank helps detect fine-grained sentence-level divergences more accurately than a strong sentence-level similarity model, while token-level predictions have the potential of further distinguishing between coarse and fine-grained divergences.",
}
@inproceedings{wieting-etal-2020-bilingual,
    title = "A Bilingual Generative Transformer for Semantic Sentence Embedding",
    author = "Wieting, John  and
      Neubig, Graham  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.122",
    doi = "10.18653/v1/2020.emnlp-main.122",
    pages = "1581--1594",
    abstract = "Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences. Bilingual data offers a useful signal for learning such embeddings: properties shared by both sentences in a translation pair are likely semantic, while divergent properties are likely stylistic or language-specific. We propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors. Our proposed approach differs from past work on semantic sentence encoding in two ways. First, by using a variational probabilistic framework, we introduce priors that encourage source separation, and can use our model{'}s posterior to predict sentence embeddings for monolingual data at test time. Second, we use high-capacity transformers as both data generating distributions and inference networks {--} contrasting with most past work on sentence embeddings. In experiments, our approach substantially outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations. Further, we demonstrate that our approach yields the largest gains on more difficult subsets of these evaluations where simple word overlap is not a good indicator of similarity.",
}
@inproceedings{anchieta-pardo-2020-semantically,
    title = "Semantically Inspired {AMR} Alignment for the {P}ortuguese Language",
    author = "Anchi{\^e}ta, Rafael  and
      Pardo, Thiago",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.123",
    doi = "10.18653/v1/2020.emnlp-main.123",
    pages = "1595--1600",
    abstract = "Abstract Meaning Representation (AMR) is a graph-based semantic formalism where the nodes are concepts and edges are relations among them. Most of AMR parsing methods require alignment between the nodes of the graph and the words of the sentence. However, this alignment is not provided by manual annotations and available automatic aligners focus only on the English language, not performing well for other languages. Aiming to fulfill this gap, we developed an alignment method for the Portuguese language based on a more semantically matched word-concept pair. We performed both intrinsic and extrinsic evaluations and showed that our alignment approach outperforms the alignment strategies developed for English, improving AMR parsers, and achieving competitive results with a parser designed for the Portuguese language.",
}
@inproceedings{zhang-etal-2020-unsupervised,
    title = "An Unsupervised Sentence Embedding Method by Mutual Information Maximization",
    author = "Zhang, Yan  and
      He, Ruidan  and
      Liu, Zuozhu  and
      Lim, Kwan Hui  and
      Bing, Lidong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.124",
    doi = "10.18653/v1/2020.emnlp-main.124",
    pages = "1601--1610",
    abstract = "BERT is inefficient for sentence-pair tasks such as clustering or semantic search as it needs to evaluate combinatorially many sentence pairs which is very time-consuming. Sentence BERT (SBERT) attempted to solve this challenge by learning semantically meaningful representations of single sentences, such that similarity comparison can be easily accessed. However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce. In this paper, we propose a lightweight extension on top of BERT and a novel self-supervised learning objective based on mutual information maximization strategies to derive meaningful sentence embeddings in an unsupervised manner. Unlike SBERT, our method is not restricted by the availability of labeled data, such that it can be applied on different domain-specific corpus. Experimental results show that the proposed method significantly outperforms other unsupervised sentence embedding baselines on common semantic textual similarity (STS) tasks and downstream supervised tasks. It also outperforms SBERT in a setting where in-domain labeled data is not available, and achieves performance competitive with supervised methods on various tasks.",
}
@inproceedings{arase-tsujii-2020-compositional,
    title = "Compositional Phrase Alignment and Beyond",
    author = "Arase, Yuki  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.125",
    doi = "10.18653/v1/2020.emnlp-main.125",
    pages = "1611--1623",
    abstract = "Phrase alignment is the basis for modelling sentence pair interactions, such as paraphrase and textual entailment recognition. Most phrase alignments are compositional processes such that an alignment of a phrase pair is constructed based on the alignments of their child phrases. Nonetheless, studies have revealed that non-compositional alignments involving long-distance phrase reordering are prevalent in practice. We address the phrase alignment problem by combining an unordered tree mapping algorithm and phrase representation modelling that explicitly embeds the similarity distribution in the sentences onto powerful contextualized representations. Experimental results demonstrate that our method effectively handles compositional and non-compositional global phrase alignments. Our method significantly outperforms that used in a previous study and achieves a performance competitive with that of experienced human annotators.",
}
@inproceedings{zhang-etal-2020-table,
    title = "Table Fact Verification with Structure-Aware Transformer",
    author = "Zhang, Hongzhi  and
      Wang, Yingyao  and
      Wang, Sirui  and
      Cao, Xuezhi  and
      Zhang, Fuzheng  and
      Wang, Zhongyuan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.126",
    doi = "10.18653/v1/2020.emnlp-main.126",
    pages = "1624--1629",
    abstract = "Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning. Pre-trained language models trained on natural language could not be directly applied to encode tables, because simply linearizing tables into sequences will lose the cell alignment information. To better utilize pre-trained transformers for table representation, we propose a Structure-Aware Transformer (SAT), which injects the table structural information into the mask of the self-attention layer. A method to combine symbolic and linguistic reasoning is also explored for this task. Our method outperforms baseline with 4.93{\%} on TabFact, a large scale table verification dataset.",
}
@inproceedings{zeng-etal-2020-double,
    title = "Double Graph Based Reasoning for Document-level Relation Extraction",
    author = "Zeng, Shuang  and
      Xu, Runxin  and
      Chang, Baobao  and
      Li, Lei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.127",
    doi = "10.18653/v1/2020.emnlp-main.127",
    pages = "1630--1640",
    abstract = "Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across paragraphs. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN), a method to recognize such relations for long paragraphs. GAIN constructs two graphs, a heterogeneous mention-level graph (MG) and an entity-level graph (EG). The former captures complex interaction among different mentions and the latter aggregates mentions underlying for the same entities. Based on the graphs we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/PKUnlp-icler/GAIN.",
}
@inproceedings{liu-etal-2020-event,
    title = "Event Extraction as Machine Reading Comprehension",
    author = "Liu, Jian  and
      Chen, Yubo  and
      Liu, Kang  and
      Bi, Wei  and
      Liu, Xiaojiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.128",
    doi = "10.18653/v1/2020.emnlp-main.128",
    pages = "1641--1651",
    abstract = "Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Previous methods for EE typically model it as a classification task, which are usually prone to the data scarcity problem. In this paper, we propose a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC). Our approach includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results. This learning paradigm enables us to strengthen the reasoning process of EE, by introducing sophisticated models in MRC, and relieve the data scarcity problem, by introducing the large-scale datasets in MRC. The empirical results show that: i) our approach attains state-of-the-art performance by considerable margins over previous methods. ii) Our model is excelled in the data-scarce scenario, for example, obtaining 49.8{\%} in F1 for event argument extraction with only 1{\%} data, compared with 2.2{\%} of the previous method. iii) Our model also fits with zero-shot scenarios, achieving 37.0{\%} and 16{\%} in F1 on two datasets without using any EE training data.",
}
@inproceedings{wang-etal-2020-maven,
    title = "{MAVEN}: {A} {M}assive {G}eneral {D}omain {E}vent {D}etection {D}ataset",
    author = "Wang, Xiaozhi  and
      Wang, Ziqi  and
      Han, Xu  and
      Jiang, Wangyi  and
      Han, Rong  and
      Liu, Zhiyuan  and
      Li, Juanzi  and
      Li, Peng  and
      Lin, Yankai  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.129",
    doi = "10.18653/v1/2020.emnlp-main.129",
    pages = "1652--1671",
    abstract = "Event detection (ED), which means identifying event trigger words and classifying event types, is the first and most fundamental step for extracting event knowledge from plain text. Most existing datasets exhibit the following issues that limit further development of ED: (1) Data scarcity. Existing small-scale datasets are not sufficient for training and stably benchmarking increasingly sophisticated modern neural methods. (2) Low coverage. Limited event types of existing datasets cannot well cover general-domain events, which restricts the applications of ED models. To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 118,732 event mention instances, and 168 event types. MAVEN alleviates the data scarcity problem and covers much more general event types. We reproduce the recent state-of-the-art ED models and conduct a thorough evaluation on MAVEN. The experimental results show that existing ED methods cannot achieve promising results on MAVEN as on the small datasets, which suggests that ED in the real world remains a challenging task and requires further research efforts. We also discuss further directions for general domain ED with empirical analyses. The source code and dataset can be obtained from https://github.com/THU-KEG/MAVEN-dataset.",
}
@inproceedings{wang-etal-2020-knowledge-graph,
    title = "Knowledge Graph Alignment with Entity-Pair Embedding",
    author = "Wang, Zhichun  and
      Yang, Jinjian  and
      Ye, Xiaoju",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.130",
    doi = "10.18653/v1/2020.emnlp-main.130",
    pages = "1672--1680",
    abstract = "Knowledge Graph (KG) alignment is to match entities in different KGs, which is important to knowledge fusion and integration. Recently, a number of embedding-based approaches for KG alignment have been proposed and achieved promising results. These approaches first embed entities in low-dimensional vector spaces, and then obtain entity alignments by computations on their vector representations. Although continuous improvements have been achieved by recent work, the performances of existing approaches are still not satisfactory. In this work, we present a new approach that directly learns embeddings of entity-pairs for KG alignment. Our approach first generates a pair-wise connectivity graph (PCG) of two KGs, whose nodes are entity-pairs and edges correspond to relation-pairs; it then learns node (entity-pair) embeddings of the PCG, which are used to predict equivalent relations of entities. To get desirable embeddings, a convolutional neural network is used to generate similarity features of entity-pairs from their attributes; and a graph neural network is employed to propagate the similarity features and get the final embeddings of entity-pairs. Experiments on five real-world datasets show that our approach can achieve the state-of-the-art KG alignment results.",
}
@inproceedings{sheng-etal-2020-adaptive,
    title = "{A}daptive {A}ttentional {N}etwork for {F}ew-{S}hot {K}nowledge {G}raph {C}ompletion",
    author = "Sheng, Jiawei  and
      Guo, Shu  and
      Chen, Zhenyu  and
      Yue, Juwei  and
      Wang, Lihong  and
      Liu, Tingwen  and
      Xu, Hongbo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.131",
    doi = "10.18653/v1/2020.emnlp-main.131",
    pages = "1681--1691",
    abstract = "Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes. The source code is available at https://github.com/JiaweiSheng/FAAN.",
}
@inproceedings{wang-etal-2020-pre,
    title = "Pre-training Entity Relation Encoder with Intra-span and Inter-span Information",
    author = "Wang, Yijun  and
      Sun, Changzhi  and
      Wu, Yuanbin  and
      Yan, Junchi  and
      Gao, Peng  and
      Xie, Guotong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.132",
    doi = "10.18653/v1/2020.emnlp-main.132",
    pages = "1692--1705",
    abstract = "In this paper, we integrate span-related information into pre-trained encoder for entity relation extraction task. Instead of using general-purpose sentence encoder (e.g., existing universal pre-trained models), we introduce a span encoder and a span pair encoder to the pre-training network, which makes it easier to import intra-span and inter-span information into the pre-trained model. To learn the encoders, we devise three customized pre-training objectives from different perspectives, which target on tokens, spans, and span pairs. In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss. Experimental results show that the proposed pre-training method outperforms distantly supervised pre-training, and achieves promising performance on two entity relation extraction benchmark datasets (ACE05, SciERC).",
}
@inproceedings{wang-lu-2020-two,
    title = "Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders",
    author = "Wang, Jue  and
      Lu, Wei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.133",
    doi = "10.18653/v1/2020.emnlp-main.133",
    pages = "1706--1721",
    abstract = "Named entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task as a table-filling problem. However, they typically focused on learning a single encoder (usually learning representation in the form of a table) to capture information required for both tasks within the same space. We argue that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process. In this work, we propose the novel table-sequence encoders where two different encoders {--} a table encoder and a sequence encoder are designed to help each other in the representation learning process. Our experiments confirm the advantages of having two encoders over one encoder. On several standard datasets, our model shows significant improvements over existing approaches.",
}
@inproceedings{nogueira-dos-santos-etal-2020-beyond,
    title = "Beyond [{CLS}] through Ranking by Generation",
    author = "Nogueira dos Santos, Cicero  and
      Ma, Xiaofei  and
      Nallapati, Ramesh  and
      Huang, Zhiheng  and
      Xiang, Bing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.134",
    doi = "10.18653/v1/2020.emnlp-main.134",
    pages = "1722--1727",
    abstract = "Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document{'}s language model, were very successful in various IR tasks in the past. However, with the advent of modern deep neural networks, attention has shifted to discriminative ranking functions that model the semantic similarity of documents and queries instead. Recently, deep generative models such as GPT2 and BART have been shown to be excellent text generators, but their effectiveness as rankers have not been demonstrated yet. In this work, we revisit the generative framework for information retrieval and show that our generative approaches are as effective as state-of-the-art semantic similarity-based discriminative models for the answer selection task. Additionally, we demonstrate the effectiveness of unlikelihood losses for IR.",
}
@inproceedings{sia-etal-2020-tired,
    title = "Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!",
    author = "Sia, Suzanna  and
      Dalmia, Ayush  and
      Mielke, Sabrina J.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.135",
    doi = "10.18653/v1/2020.emnlp-main.135",
    pages = "1728--1736",
    abstract = "Topic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but in this paper we propose an alternative way to obtain topics: clustering pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words. We provide benchmarks for the combination of different word embeddings and clustering algorithms, and analyse their performance under dimensionality reduction with PCA. The best performing combination for our approach performs as well as classical topic models, but with lower runtime and computational complexity.",
}
@inproceedings{mao-etal-2020-multi,
    title = "Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning",
    author = "Mao, Yuning  and
      Qu, Yanru  and
      Xie, Yiqing  and
      Ren, Xiang  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.136",
    doi = "10.18653/v1/2020.emnlp-main.136",
    pages = "1737--1751",
    abstract = "While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances to MDS: (1) MDS involves larger search space and yet more limited training data, setting obstacles for neural methods to learn adequate representations; (2) MDS needs to resolve higher information redundancy among the source documents, which SDS methods are less effective to handle. To close the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement Learning for MDS, which unifies advanced neural SDS methods and statistical measures used in classical MDS. RL-MMR casts MMR guidance on fewer promising candidates, which restrains the search space and thus leads to better representation learning. Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy. Extensive experiments demonstrate that RL-MMR achieves state-of-the-art performance on benchmark MDS datasets. In particular, we show the benefits of incorporating MMR into end-to-end learning when adapting SDS to MDS in terms of both learning effectiveness and efficiency.",
}
@inproceedings{hoyle-etal-2020-improving,
    title = "{I}mproving {N}eural {T}opic {M}odels using {K}nowledge {D}istillation",
    author = "Hoyle, Alexander Miserlis  and
      Goel, Pranav  and
      Resnik, Philip",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.137",
    doi = "10.18653/v1/2020.emnlp-main.137",
    pages = "1752--1771",
    abstract = "Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence. We show that our adaptable framework not only improves performance in the aggregate over all estimated topics, as is commonly reported, but also in head-to-head comparisons of aligned topics.",
}
@inproceedings{wu-etal-2020-short,
    title = "Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder",
    author = "Wu, Xiaobao  and
      Li, Chunping  and
      Zhu, Yan  and
      Miao, Yishu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.138",
    doi = "10.18653/v1/2020.emnlp-main.138",
    pages = "1772--1782",
    abstract = "Topic models have been prevailing for many years on discovering latent semantics while modeling long documents. However, for short texts they generally suffer from data sparsity because of extremely limited word co-occurrences; thus tend to yield repetitive or trivial topics with low quality. In this paper, to address this issue, we propose a novel neural topic model in the framework of autoencoding with a new topic distribution quantization approach generating peakier distributions that are more appropriate for modeling short texts. Besides the encoding, to tackle this issue in terms of decoding, we further propose a novel negative sampling decoder learning from negative samples to avoid yielding repetitive topics. We observe that our model can highly improve short text topic modeling performance. Through extensive experiments on real-world datasets, we demonstrate our model can outperform both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics.",
}
@inproceedings{zuo-etal-2020-querying,
    title = "Querying Across Genres for Medical Claims in News",
    author = "Zuo, Chaoyuan  and
      Acharya, Narayan  and
      Banerjee, Ritwik",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.139",
    doi = "10.18653/v1/2020.emnlp-main.139",
    pages = "1783--1789",
    abstract = "We present a query-based biomedical information retrieval task across two vastly different genres {--} newswire and research literature {--} where the goal is to find the research publication that supports the primary claim made in a health-related news article. For this task, we present a new dataset of 5,034 claims from news paired with research abstracts. Our approach consists of two steps: (i) selecting the most relevant candidates from a collection of 222k research abstracts, and (ii) re-ranking this list. We compare the classical IR approach using BM25 with more recent transformer-based models. Our results show that cross-genre medical IR is a viable task, but incorporating domain-specific knowledge is crucial.",
}
@inproceedings{wang-etal-2020-incorporating,
    title = "Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction",
    author = "Wang, Yansen  and
      Fan, Zhen  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.140",
    doi = "10.18653/v1/2020.emnlp-main.140",
    pages = "1790--1800",
    abstract = "Open-domain Keyphrase extraction (KPE) on the Web is a fundamental yet complex NLP task with a wide range of practical applications within the field of Information Retrieval. In contrast to other document types, web page designs are intended for easy navigation and information finding. Effective designs encode within the layout and formatting signals that point to where the important information can be found. In this work, we propose a modeling approach that leverages these multi-modal signals to aid in the KPE task. In particular, we leverage both lexical and visual features (e.g., size, font, position) at the micro-level to enable effective strategy induction and meta-level features that describe pages at a macro-level to aid in strategy selection. Our evaluation demonstrates that a combination of effective strategy induction and strategy selection within this approach for the KPE task outperforms state-of-the-art models. A qualitative post-hoc analysis illustrates how these features function within the model.",
}
@inproceedings{bagher-zadeh-etal-2020-cmu,
    title = "{CMU}-{MOSEAS}: A Multimodal Language Dataset for {S}panish, {P}ortuguese, {G}erman and {F}rench",
    author = "Bagher Zadeh, AmirAli  and
      Cao, Yansheng  and
      Hessner, Simon  and
      Liang, Paul Pu  and
      Poria, Soujanya  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.141",
    doi = "10.18653/v1/2020.emnlp-main.141",
    pages = "1801--1812",
    abstract = "Modeling multimodal language is a core research area in natural language processing. While languages such as English have relatively large multimodal language resources, other widely spoken languages across the globe have few or no large-scale datasets in this area. This disproportionately affects native speakers of languages other than English. As a step towards building more equitable and inclusive multimodal systems, we introduce the first large-scale multimodal language dataset for Spanish, Portuguese, German and French. The proposed dataset, called CMU-MOSEAS (CMU Multimodal Opinion Sentiment, Emotions and Attributes), is the largest of its kind with 40,000 total labelled sentences. It covers a diverse set topics and speakers, and carries supervision of 20 labels including sentiment (and subjectivity), emotions, and attributes. Our evaluations on a state-of-the-art multimodal model demonstrates that CMU-MOSEAS enables further research for multilingual studies in multimodal language.",
}
@inproceedings{wang-etal-2020-combining-self,
    title = "Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection",
    author = "Wang, Shaolei  and
      Wang, Zhongyuan  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.142",
    doi = "10.18653/v1/2020.emnlp-main.142",
    pages = "1813--1822",
    abstract = "Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate this issue with, for instance, self-supervised learning techniques, but they still require human-annotated corpora. In this work, we explore the unsupervised learning paradigm which can potentially work with unlabeled text corpora that are cheaper and easier to obtain. Our model builds upon the recent work on Noisy Student Training, a semi-supervised learning approach that extends the idea of self-training. Experimental results on the commonly used English Switchboard test set show that our approach achieves competitive performance compared to the previous state-of-the-art supervised systems using contextualized word embeddings (e.g. BERT and ELECTRA).",
}
@inproceedings{tsai-etal-2020-multimodal,
    title = "Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis",
    author = "Tsai, Yao-Hung Hubert  and
      Ma, Martin  and
      Yang, Muqiao  and
      Salakhutdinov, Ruslan  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.143",
    doi = "10.18653/v1/2020.emnlp-main.143",
    pages = "1823--1833",
    abstract = "The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we propose, which dynamically adjusts weights between input modalities and output representations differently for each input sample. Multimodal routing can identify relative importance of both individual modalities and cross-modality factors. Moreover, the weight assignment by routing allows us to interpret modality-prediction relationships not only globally (i.e. general trends over the whole dataset), but also locally for each single input sample, meanwhile keeping competitive performance compared to state-of-the-art methods.",
}
@inproceedings{liu-etal-2020-multistage,
    title = "Multistage Fusion with Forget Gate for Multimodal Summarization in Open-Domain Videos",
    author = "Liu, Nayu  and
      Sun, Xian  and
      Yu, Hongfeng  and
      Zhang, Wenkai  and
      Xu, Guangluan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.144",
    doi = "10.18653/v1/2020.emnlp-main.144",
    pages = "1834--1845",
    abstract = "Multimodal summarization for open-domain videos is an emerging task, aiming to generate a summary from multisource information (video, audio, transcript). Despite the success of recent multiencoder-decoder frameworks on this task, existing methods lack fine-grained multimodality interactions of multisource inputs. Besides, unlike other multimodal tasks, this task has longer multimodal sequences with more redundancy and noise. To address these two issues, we propose a multistage fusion network with the fusion forget gate module, which builds upon this approach by modeling fine-grained interactions between the modalities through a multistep fusion schema and controlling the flow of redundant information between multimodal long sequences via a forgetting module. Experimental results on the How2 dataset show that our proposed model achieves a new state-of-the-art performance. Comprehensive analysis empirically verifies the effectiveness of our fusion schema and forgetting module on multiple encoder-decoder architectures. Specially, when using high noise ASR transcripts (WER{\textgreater}30{\%}), our model still achieves performance close to the ground-truth transcript model, which reduces manual annotation cost.",
}
@inproceedings{le-etal-2020-bist,
    title = "{B}i{ST}: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues",
    author = "Le, Hung  and
      Sahoo, Doyen  and
      Chen, Nancy  and
      Hoi, Steven C.H.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.145",
    doi = "10.18653/v1/2020.emnlp-main.145",
    pages = "1846--1859",
    abstract = "Video-grounded dialogues are very challenging due to (i) the complexity of videos which contain both spatial and temporal variations, and (ii) the complexity of user utterances which query different segments and/or different objects in videos over multiple dialogue turns. However, existing approaches to video-grounded dialogues often focus on superficial temporal-level visual cues, but neglect more fine-grained spatial signals from videos. To address this drawback, we proposed Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in videos based on textual cues. Specifically, our approach not only exploits both spatial and temporal-level information, but also learns dynamic information diffusion between the two feature spaces through spatial-to-temporal and temporal-to-spatial reasoning. The bidirectional strategy aims to tackle the evolving semantics of user queries in the dialogue setting. The retrieved visual cues are used as contextual information to construct relevant responses to the users. Our empirical results and comprehensive qualitative analysis show that BiST achieves competitive performance and generates reasonable responses on a large-scale AVSD benchmark. We also adapt our BiST models to the Video QA setting, and substantially outperform prior approaches on the TGIF-QA benchmark.",
}
@inproceedings{le-etal-2020-uniconv,
    title = "{U}ni{C}onv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues",
    author = "Le, Hung  and
      Sahoo, Doyen  and
      Liu, Chenghao  and
      Chen, Nancy  and
      Hoi, Steven C.H.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.146",
    doi = "10.18653/v1/2020.emnlp-main.146",
    pages = "1860--1877",
    abstract = "Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons. First, tracking dialogue states of multiple domains is non-trivial as the dialogue agent must obtain complete states from all relevant domains, some of which might have shared slots among domains as well as unique slots specifically for one domain only. Second, the dialogue agent must also process various types of information across domains, including dialogue context, dialogue states, and database, to generate natural responses to users. Unlike the existing approaches that are often designed to train each module separately, we propose {``}UniConv{''} - a novel unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues, which is designed to jointly train (i) a Bi-level State Tracker which tracks dialogue states by learning signals at both slot and domain level independently, and (ii) a Joint Dialogue Act and Response Generator which incorporates information from various input components and models dialogue acts and target responses simultaneously. We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark, achieving superior performance over competitive baselines.",
}
@inproceedings{yang-etal-2020-graphdialog,
    title = "{G}raph{D}ialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems",
    author = "Yang, Shiquan  and
      Zhang, Rui  and
      Erfani, Sarah",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.147",
    doi = "10.18653/v1/2020.emnlp-main.147",
    pages = "1878--1888",
    abstract = "End-to-end task-oriented dialogue systems aim to generate system responses directly from plain text inputs. There are two challenges for such systems: one is how to effectively incorporate external knowledge bases (KBs) into the learning framework; the other is how to accurately capture the semantics of dialogue history. In this paper, we address these two challenges by exploiting the graph structural information in the knowledge base and in the dependency parsing tree of the dialogue. To effectively leverage the structural information in dialogue history, we propose a new recurrent cell architecture which allows representation learning on graphs. To exploit the relations between entities in KBs, the model combines multi-hop reasoning ability based on the graph structure. Experimental results show that the proposed model achieves consistent improvement over state-of-the-art models on two different task-oriented dialogue datasets.",
}
@inproceedings{qiu-etal-2020-structured,
    title = "Structured Attention for Unsupervised Dialogue Structure Induction",
    author = "Qiu, Liang  and
      Zhao, Yizhou  and
      Shi, Weiyan  and
      Liang, Yuan  and
      Shi, Feng  and
      Yuan, Tao  and
      Yu, Zhou  and
      Zhu, Song-Chun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.148",
    doi = "10.18653/v1/2020.emnlp-main.148",
    pages = "1889--1899",
    abstract = "Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is critical for dialogue system design and discourse analysis. It can also be extended to solve grammatical inference. In this work, we propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion. Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias. Experiments show that on two-party dialogue datasets, VRNN with structured attention learns semantic structures that are similar to templates used to generate this dialogue corpus. While on multi-party dialogue datasets, our model learns an interactive structure demonstrating its capability of distinguishing speakers or addresses, automatically disentangling dialogues without explicit human annotation.",
}
@inproceedings{ji-etal-2020-cross,
    title = "Cross Copy Network for Dialogue Generation",
    author = "Ji, Changzhen  and
      Zhou, Xin  and
      Zhang, Yating  and
      Liu, Xiaozhong  and
      Sun, Changlong  and
      Zhu, Conghui  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.149",
    doi = "10.18653/v1/2020.emnlp-main.149",
    pages = "1900--1910",
    abstract = "In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e.g., LSTM+attention, Pointer Generator Networks and Transformer) to enhance dialogue content generation. While content fluency and accuracy often serve as the major indicators for model training, dialogue logics, carrying critical information for some particular domains, are often ignored. Take customer service and court debate dialogue as examples, compatible logics can be observed across different dialogue instances, and this information can provide vital evidence for utterance generation. In this paper, we propose a novel network architecture - Cross Copy Networks (CCN) to explore the current dialog context and similar dialogue instances{'} logical structure simultaneously. Experiments with two tasks, court debate and customer service content generation, proved that the proposed algorithm is superior to existing state-of-art content generation models.",
}
@inproceedings{jia-etal-2020-multi,
    title = "Multi-turn Response Selection using Dialogue Dependency Relations",
    author = "Jia, Qi  and
      Liu, Yizhu  and
      Ren, Siyu  and
      Zhu, Kenny  and
      Tang, Haifeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.150",
    doi = "10.18653/v1/2020.emnlp-main.150",
    pages = "1911--1920",
    abstract = "Multi-turn response selection is a task designed for developing dialogue agents. The performance on this task has a remarkable improvement with pre-trained language models. However, these models simply concatenate the turns in dialogue history as the input and largely ignore the dependencies between the turns. In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations. Each thread can be regarded as a self-contained sub-dialogue. We also propose Thread-Encoder model to encode threads and candidates into compact representations by pre-trained Transformers and finally get the matching score through an attention layer. The experiments show that dependency relations are helpful for dialogue context understanding, and our model outperforms the state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results on UbuntuV2.",
}
@inproceedings{chen-etal-2020-parallel-interactive,
    title = "Parallel Interactive Networks for Multi-Domain Dialogue State Generation",
    author = "Chen, Junfan  and
      Zhang, Richong  and
      Mao, Yongyi  and
      Xu, Jie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.151",
    doi = "10.18653/v1/2020.emnlp-main.151",
    pages = "1921--1931",
    abstract = "The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model.",
}
@inproceedings{wu-etal-2020-slotrefine,
    title = "{S}lot{R}efine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling",
    author = "Wu, Di  and
      Ding, Liang  and
      Lu, Fan  and
      Xie, Jian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.152",
    doi = "10.18653/v1/2020.emnlp-main.152",
    pages = "1932--1937",
    abstract = "Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system. In this paper, we propose a novel non-autoregressive model named SlotRefine for joint intent detection and slot filling. Besides, we design a novel two-pass iteration mechanism to handle the uncoordinated slots problem caused by conditional independence of non-autoregressive model. Experiments demonstrate that our model significantly outperforms previous models in slot filling task, while considerably speeding up the decoding (up to x10.77). In-depth analysis show that 1) pretraining schemes could further enhance our model; 2) two-pass mechanism indeed remedy the uncoordinated slots.",
}
@inproceedings{paranjape-etal-2020-information,
    title = "An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction",
    author = "Paranjape, Bhargavi  and
      Joshi, Mandar  and
      Thickstun, John  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.153",
    doi = "10.18653/v1/2020.emnlp-main.153",
    pages = "1938--1952",
    abstract = "Decisions of complex models for language understanding can be explained by limiting the inputs they are provided to a relevant subsequence of the original text {---} a rationale. Models that condition predictions on a concise rationale, while being more interpretable, tend to be less accurate than models that are able to use the entire context. In this paper, we show that it is possible to better manage the trade-off between concise explanations and high task accuracy by optimizing a bound on the Information Bottleneck (IB) objective. Our approach jointly learns an explainer that predicts sparse binary masks over input sentences without explicit supervision, and an end-task predictor that considers only the residual sentences. Using IB, we derive a learning objective that allows direct control of mask sparsity levels through a tunable sparse prior. Experiments on the ERASER benchmark demonstrate significant gains over previous work for both task performance and agreement with human rationales. Furthermore, we find that in the semi-supervised setting, a modest amount of gold rationales (25{\%} of training examples with gold masks) can close the performance gap with a model that uses the full input.",
}
@inproceedings{nangia-etal-2020-crows,
    title = "{C}row{S}-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models",
    author = "Nangia, Nikita  and
      Vania, Clara  and
      Bhalerao, Rasika  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.154",
    doi = "10.18653/v1/2020.emnlp-main.154",
    pages = "1953--1967",
    abstract = "Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicitly creating harm with biased representations. To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age. In CrowS-Pairs a model is presented with two sentences: one that is more stereotyping and another that is less stereotyping. The data focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. We find that all three of the widely-used MLMs we evaluate substantially favor sentences that express stereotypes in every category in CrowS-Pairs. As work on building less biased models advances, this dataset can be used as a benchmark to evaluate progress.",
}
@inproceedings{zhao-chang-2020-logan,
    title = "{LOGAN}: Local Group Bias Detection by Clustering",
    author = "Zhao, Jieyu  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.155",
    doi = "10.18653/v1/2020.emnlp-main.155",
    pages = "1968--1977",
    abstract = "Machine learning techniques have been widely used in natural language processing (NLP). However, as revealed by many recent studies, machine learning models often inherit and amplify the societal biases in data. Various metrics have been proposed to quantify biases in model predictions. In particular, several of them evaluate disparity in model performance between protected groups and advantaged groups in the test corpus. However, we argue that evaluating bias at the corpus level is not enough for understanding how biases are embedded in a model. In fact, a model with similar aggregated performance between different groups on the entire data may behave differently on instances in a local region. To analyze and detect such local bias, we propose LOGAN, a new bias detection technique based on clustering. Experiments on toxicity classification and object classification tasks show that LOGAN identifies bias in a local region and allows us to better analyze the biases in model predictions.",
}
@inproceedings{hewitt-etal-2020-rnns,
    title = "{RNN}s can generate bounded hierarchical languages with optimal memory",
    author = "Hewitt, John  and
      Hahn, Michael  and
      Ganguli, Surya  and
      Liang, Percy  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.156",
    doi = "10.18653/v1/2020.emnlp-main.156",
    pages = "1978--2010",
    abstract = "Recurrent neural networks empirically generate natural language with high syntactic fidelity. However, their success is not well-understood theoretically. We provide theoretical insight into this success, proving in a finite-precision setting that RNNs can efficiently generate bounded hierarchical languages that reflect the scaffolding of natural language syntax. We introduce Dyck-$(k,m)$, the language of well-nested brackets (of $k$ types) and $m$-bounded nesting depth, reflecting the bounded memory needs and long-distance dependencies of natural language syntax. The best known results use $O(k^{\frac{m}{2}})$ memory (hidden units) to generate these languages. We prove that an RNN with $O(m \log k)$ hidden units suffices, an exponential reduction in memory, by an explicit construction. Finally, we show that no algorithm, even with unbounded computation, can suffice with $o(m \log k)$ hidden units.",
}
@inproceedings{munro-morrison-2020-detecting,
    title = "Detecting Independent Pronoun Bias with Partially-Synthetic Data Generation",
    author = "Munro, Robert  and
      Morrison, Alex (Carmen)",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.157",
    doi = "10.18653/v1/2020.emnlp-main.157",
    pages = "2011--2017",
    abstract = "We report that state-of-the-art parsers consistently failed to identify {``}hers{''} and {``}theirs{''} as pronouns but identified the masculine equivalent {``}his{''}. We find that the same biases exist in recent language models like BERT. While some of the bias comes from known sources, like training data with gender imbalances, we find that the bias is {\_}amplified{\_} in the language models and that linguistic differences between English pronouns that are not inherently biased can become biases in some machine learning models. We introduce a new technique for measuring bias in models, using Bayesian approximations to generate partially-synthetic data from the model itself.",
}
@inproceedings{jin-etal-2020-visually,
    title = "Visually Grounded Continual Learning of Compositional Phrases",
    author = "Jin, Xisen  and
      Du, Junyi  and
      Sadhu, Arka  and
      Nevatia, Ram  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.158",
    doi = "10.18653/v1/2020.emnlp-main.158",
    pages = "2018--2029",
    abstract = "Humans acquire language continually with much more limited access to data samples at a time, as compared to contemporary NLP systems. To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes. In the task, models are trained on a paired image-caption stream which has shifting object distribution; while being constantly evaluated by a visually-grounded masked language prediction task on held-out test sets. VisCOLL compounds the challenges of continual learning (i.e., learning from continuously shifting data distribution) and compositional generalization (i.e., generalizing to novel compositions). To facilitate research on VisCOLL, we construct two datasets, COCO-shift and Flickr-shift, and benchmark them using different continual learning methods. Results reveal that SoTA continual learning approaches provide little to no improvements on VisCOLL, since storing examples of all possible compositions is infeasible. We conduct further ablations and analysis to guide future work.",
}
@inproceedings{wang-etal-2020-maf,
    title = "{MAF}: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding",
    author = "Wang, Qinxin  and
      Tan, Hao  and
      Shen, Sheng  and
      Mahoney, Michael  and
      Yao, Zhewei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.159",
    doi = "10.18653/v1/2020.emnlp-main.159",
    pages = "2030--2038",
    abstract = "Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision. We first present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. By adopting a contrastive objective, our method uses information in caption-image pairs to boost the performance in weakly-supervised scenarios. Experiments conducted on the widely-adopted Flickr30k dataset show a significant improvement over existing weakly-supervised methods. With the help of the visually-aware language representations, we can also improve the previous best unsupervised result by 5.56{\%}. We conduct ablation studies to show that both our novel model and our weakly-supervised strategies significantly contribute to our strong results.",
}
@inproceedings{yauney-etal-2020-domain,
    title = "{D}omain-{S}pecific {L}exical {G}rounding in {N}oisy {V}isual-{T}extual {D}ocuments",
    author = "Yauney, Gregory  and
      Hessel, Jack  and
      Mimno, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.160",
    doi = "10.18653/v1/2020.emnlp-main.160",
    pages = "2039--2045",
    abstract = "Images can give us insights into the contextual meanings of words, but current image-text grounding approaches require detailed annotations. Such granular annotation is rare, expensive, and unavailable in most domain-specific contexts. In contrast, unlabeled multi-image, multi-sentence documents are abundant. Can lexical grounding be learned from such documents, even though they have significant lexical and visual overlap? Working with a case study dataset of real estate listings, we demonstrate the challenge of distinguishing highly correlated grounded terms, such as {``}kitchen{''} and {``}bedroom{''}, and introduce metrics to assess this document similarity. We present a simple unsupervised clustering-based method that increases precision and recall beyond object detection and image tagging baselines when evaluated on labeled subsets of the dataset. The proposed method is particularly effective for local contextual meanings of a word, for example associating {``}granite{''} with countertops in the real estate dataset and with rocky landscapes in a Wikipedia dataset.",
}
@inproceedings{li-etal-2020-hero,
    title = "{HERO}: Hierarchical Encoder for {V}ideo+{L}anguage Omni-representation Pre-training",
    author = "Li, Linjie  and
      Chen, Yen-Chun  and
      Cheng, Yu  and
      Gan, Zhe  and
      Yu, Licheng  and
      Liu, Jingjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.161",
    doi = "10.18653/v1/2020.emnlp-main.161",
    pages = "2046--2065",
    abstract = "We present HERO, a novel framework for large-scale video+language omni-representation learning. HERO encodes multimodal inputs in a hierarchical structure, where local context of a video frame is captured by a Cross-modal Transformer via multimodal fusion, and global video context is captured by a Temporal Transformer. In addition to standard Masked Language Modeling (MLM) and Masked Frame Modeling (MFM) objectives, we design two new pre-training tasks: (i) Video-Subtitle Matching (VSM), where the model predicts both global and local temporal alignment; and (ii) Frame Order Modeling (FOM), where the model predicts the right order of shuffled video frames. HERO is jointly trained on HowTo100M and large-scale TV datasets to gain deep understanding of complex social dynamics with multi-character interactions. Comprehensive experiments demonstrate that HERO achieves new state of the art on multiple benchmarks over Text-based Video/Video-moment Retrieval, Video Question Answering (QA), Video-and-language Inference and Video Captioning tasks across different domains. We also introduce two new challenging benchmarks How2QA and How2R for Video QA and Retrieval, collected from diverse video content over multimodalities.",
}
@inproceedings{tan-bansal-2020-vokenization,
    title = "Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision",
    author = "Tan, Hao  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.162",
    doi = "10.18653/v1/2020.emnlp-main.162",
    pages = "2066--2080",
    abstract = "Humans learn language by listening, speaking, writing, reading, and also, via interaction with the multimodal real world. Existing language pre-training frameworks show the effectiveness of text-only self-supervision while we explore the idea of a visually-supervised language model in this paper. We find that the main reason hindering this exploration is the large divergence in magnitude and distributions between the visually-grounded language datasets and pure-language corpora. Therefore, we develop a technique named {``}vokenization{''} that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images (which we call {``}vokens{''}). The {``}vokenizer{''} is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks such as GLUE, SQuAD, and SWAG.",
}
@inproceedings{tan-etal-2020-detecting,
    title = "Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News",
    author = "Tan, Reuben  and
      Plummer, Bryan  and
      Saenko, Kate",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.163",
    doi = "10.18653/v1/2020.emnlp-main.163",
    pages = "2081--2106",
    abstract = "Large-scale dissemination of disinformation online intended to mislead or deceive the general population is a major societal problem. Rapid progression in image, video, and natural language generative models has only exacerbated this situation and intensified our need for an effective defense mechanism. While existing approaches have been proposed to defend against neural fake news, they are generally constrained to the very limited setting where articles only have text and metadata such as the title and authors. In this paper, we introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset which is comprised of 4 different types of generated articles as well as conduct a series of human user study experiments based on this dataset. Coupled with providing a relatively effective approach based on detecting visual-semantic inconsistencies, the valuable insights gleaned from our user study experiments and, consequently, this paper will serve as an effective first line of defense and a valuable reference for future work in defending against machine-generated disinformation.",
}
@inproceedings{chen-qian-2020-enhancing,
    title = "Enhancing Aspect Term Extraction with Soft Prototypes",
    author = "Chen, Zhuang  and
      Qian, Tieyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.164",
    doi = "10.18653/v1/2020.emnlp-main.164",
    pages = "2107--2117",
    abstract = "Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on. Existing studies mostly focus on designing neural sequence taggers to extract linguistic features from the token level. However, since the aspect terms and context words usually exhibit long-tail distributions, these taggers often converge to an inferior state without enough sample exposure. In this paper, we propose to tackle this problem by correlating words with each other through soft prototypes. These prototypes, generated by a soft retrieval process, can introduce global knowledge from internal or external data and serve as the supporting evidence for discovering the aspect terms. Our proposed model is a general framework and can be combined with almost all sequence taggers. Experiments on four SemEval datasets show that our model boosts the performance of three typical ATE methods by a large margin.",
}
@inproceedings{sui-etal-2020-feded,
    title = "{F}ed{ED}: Federated Learning via Ensemble Distillation for Medical Relation Extraction",
    author = "Sui, Dianbo  and
      Chen, Yubo  and
      Zhao, Jun  and
      Jia, Yantao  and
      Xie, Yuantao  and
      Sun, Weijian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.165",
    doi = "10.18653/v1/2020.emnlp-main.165",
    pages = "2118--2128",
    abstract = "Unlike other domains, medical texts are inevitably accompanied by private information, so sharing or copying these texts is strictly restricted. However, training a medical relation extraction model requires collecting these privacy-sensitive texts and storing them on one machine, which comes in conflict with privacy protection. In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged. Though federated learning has distinct advantages in privacy protection, it suffers from the communication bottleneck, which is mainly caused by the need to upload cumbersome local parameters. To overcome this bottleneck, we leverage a strategy based on knowledge distillation. Such a strategy uses the uploaded predictions of ensemble local models to train the central model without requiring uploading local parameters. Experiments on three publicly available medical relation extraction datasets demonstrate the effectiveness of our method.",
}
@inproceedings{zhu-etal-2020-multimodal,
    title = "Multimodal Joint Attribute Prediction and Value Extraction for {E}-commerce Product",
    author = "Zhu, Tiangang  and
      Wang, Yue  and
      Li, Haoran  and
      Wu, Youzheng  and
      He, Xiaodong  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.166",
    doi = "10.18653/v1/2020.emnlp-main.166",
    pages = "2129--2139",
    abstract = "Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images. We argue that product attributes and values are highly correlated, e.g., it will be easier to extract the values on condition that the product attributes are given. Thus, we jointly model the attribute prediction and value extraction tasks from multiple aspects towards the interactions between attributes and values. Moreover, product images have distinct effects on our tasks for different product attributes and values. Thus, we selectively draw useful visual information from product images to enhance our model. We annotate a multimodal product attribute value dataset that contains 87,194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task. Our code and dataset are available at https://github.com/jd-aig/JAVE.",
}
@inproceedings{sun-etal-2020-predicate,
    title = "{A} {P}redicate-{F}unction-{A}rgument {A}nnotation of {N}atural {L}anguage for {O}pen-{D}omain {I}nformation e{X}pression",
    author = "Sun, Mingming  and
      Hua, Wenyue  and
      Liu, Zoey  and
      Wang, Xin  and
      Zheng, Kangjie  and
      Li, Ping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.167",
    doi = "10.18653/v1/2020.emnlp-main.167",
    pages = "2140--2150",
    abstract = "Existing OIE (Open Information Extraction) algorithms are independent of each other such that there exist lots of redundant works; the featured strategies are not reusable and not adaptive to new tasks. This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is proposed to provide a platform for all OIE strategies. The OIX is an OIE friendly expression of a sentence without information loss. The generation procedure of OIX contains shared works of OIE algorithms so that OIE strategies can be developed on the platform of OIX as inference operations focusing on more critical problems. Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased. This paper focuses on the task of OIX and propose a solution {--} Open Information Annotation (OIA). OIA is a predicate-function-argument annotation for sentences. We label a data set of sentence-OIA pairs and propose a dependency-based rule system to generate OIA annotations from sentences. The evaluation results reveal that learning the OIA from a sentence is a challenge owing to the complexity of natural language sentences, and it is worthy of attracting more attention from the research community.",
}
@inproceedings{fei-etal-2020-retrofitting,
    title = "Retrofitting Structure-aware Transformer Language Model for End Tasks",
    author = "Fei, Hao  and
      Ren, Yafeng  and
      Ji, Donghong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.168",
    doi = "10.18653/v1/2020.emnlp-main.168",
    pages = "2151--2161",
    abstract = "We consider retrofitting structure-aware Transformer language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model. A middle-layer structural learning strategy is leveraged for structure integration, accomplished with main semantic task training under multi-task learning scheme. Experimental results show that the retrofitted structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases. By performing structure-aware fine-tuning, our model achieves significant improvements for both semantic- and syntactic-dependent tasks.",
}
@inproceedings{zhang-etal-2020-lightweight,
    title = "Lightweight, Dynamic Graph Convolutional Networks for {AMR}-to-Text Generation",
    author = "Zhang, Yan  and
      Guo, Zhijiang  and
      Teng, Zhiyang  and
      Lu, Wei  and
      Cohen, Shay B.  and
      Liu, Zuozhu  and
      Bing, Lidong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.169",
    doi = "10.18653/v1/2020.emnlp-main.169",
    pages = "2162--2172",
    abstract = "AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph representations. Previously, Graph Convolution Networks (GCNs) were used to encode input AMRs, however, vanilla GCNs are not able to capture non-local information and additionally, they follow a local (first-order) information aggregation scheme. To account for these issues, larger and deeper GCN models are required to capture more complex interactions. In this paper, we introduce a dynamic fusion mechanism, proposing Lightweight Dynamic Graph Convolutional Networks (LDGCNs) that capture richer non-local interactions by synthesizing higher order information from the input graphs. We further develop two novel parameter saving strategies based on the group graph convolutions and weight tied convolutions to reduce memory usage and model complexity. With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity. Experiments demonstrate that LDGCNs outperform state-of-the-art models on two benchmark datasets for AMR-to-text generation with significantly fewer parameters.",
}
@inproceedings{meister-etal-2020-beam,
    title = "If beam search is the answer, what was the question?",
    author = "Meister, Clara  and
      Cotterell, Ryan  and
      Vieira, Tim",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.170",
    doi = "10.18653/v1/2020.emnlp-main.170",
    pages = "2173--2185",
    abstract = "Quite surprisingly, exact maximum a posteriori (MAP) decoding of neural language generators frequently leads to low-quality results. Rather, most state-of-the-art results on language generation tasks are attained using beam search despite its overwhelmingly high search error rate. This implies that the MAP objective alone does not express the properties we desire in text, which merits the question: if beam search is the answer, what was the question? We frame beam search as the exact solution to a different decoding objective in order to gain insights into why high probability under a model alone may not indicate adequacy. We find that beam search enforces uniform information density in text, a property motivated by cognitive science. We suggest a set of decoding objectives that explicitly enforce this property and find that exact decoding with these objectives alleviates the problems encountered when decoding poorly calibrated language generation models. Additionally, we analyze the text produced using various decoding strategies and see that, in our neural machine translation experiments, the extent to which this property is adhered to strongly correlates with BLEU.",
}
@inproceedings{mihaylova-etal-2020-understanding,
    title = "Understanding the Mechanics of {SPIGOT}: Surrogate Gradients for Latent Structure Learning",
    author = "Mihaylova, Tsvetomila  and
      Niculae, Vlad  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.171",
    doi = "10.18653/v1/2020.emnlp-main.171",
    pages = "2186--2202",
    abstract = "Latent structure models are a powerful tool for modeling language data: they can mitigate the error propagation and annotation bottleneck in pipeline systems, while simultaneously uncovering linguistic insights about the data. One challenge with end-to-end training of these models is the argmax operation, which has null gradient. In this paper, we focus on surrogate gradients, a popular strategy to deal with this problem. We explore latent structure learning through the angle of pulling back the downstream learning objective. In this paradigm, we discover a principled motivation for both the straight-through estimator (STE) as well as the recently-proposed SPIGOT {--} a variant of STE for structured models. Our perspective leads to new algorithms in the same family. We empirically compare the known and the novel pulled-back estimators against the popular alternatives, yielding new insight for practitioners and revealing intriguing failure cases.",
}
@inproceedings{szymanski-gorman-2020-best,
    title = "Is the Best Better? {B}ayesian Statistical Model Comparison for Natural Language Processing",
    author = "Szyma{\'n}ski, Piotr  and
      Gorman, Kyle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.172",
    doi = "10.18653/v1/2020.emnlp-main.172",
    pages = "2203--2212",
    abstract = "Recent work raises concerns about the use of standard splits to compare natural language processing models. We propose a Bayesian statistical model comparison technique which uses k-fold cross-validation across multiple data sets to estimate the likelihood that one model will outperform the other, or that the two will produce practically equivalent results. We use this technique to rank six English part-of-speech taggers across two data sets and three evaluation metrics.",
}
@inproceedings{chen-etal-2020-exploring-logically,
    title = "Exploring Logically Dependent Multi-task Learning with Causal Inference",
    author = "Chen, Wenqing  and
      Tian, Jidong  and
      Xiao, Liqiang  and
      He, Hao  and
      Jin, Yaohui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.173",
    doi = "10.18653/v1/2020.emnlp-main.173",
    pages = "2213--2225",
    abstract = "Previous studies have shown that hierarchical multi-task learning (MTL) can utilize task dependencies by stacking encoders and outperform democratic MTL. However, stacking encoders only considers the dependencies of feature representations and ignores the label dependencies in logically dependent tasks. Furthermore, how to properly utilize the labels remains an issue due to the cascading errors between tasks. In this paper, we view logically dependent MTL from the perspective of causal inference and suggest a mediation assumption instead of the confounding assumption in conventional MTL models. We propose a model including two key mechanisms: label transfer (LT) for each task to utilize the labels of all its lower-level tasks, and Gumbel sampling (GS) to deal with cascading errors. In the field of causal inference, GS in our model is essentially a counterfactual reasoning process, trying to estimate the causal effect between tasks and utilize it to improve MTL. We conduct experiments on two English datasets and one Chinese dataset. Experiment results show that our model achieves state-of-the-art on six out of seven subtasks and improves predictions{'} consistency.",
}
@inproceedings{zhao-etal-2020-masking,
    title = "Masking as an Efficient Alternative to Finetuning for Pretrained Language Models",
    author = {Zhao, Mengjie  and
      Lin, Tao  and
      Mi, Fei  and
      Jaggi, Martin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.174",
    doi = "10.18653/v1/2020.emnlp-main.174",
    pages = "2226--2241",
    abstract = "We present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. Extensive evaluations of masking BERT, RoBERTa, and DistilBERT on eleven diverse NLP tasks show that our masking scheme yields performance comparable to finetuning, yet has a much smaller memory footprint when several tasks need to be inferred. Intrinsic evaluations show that representations computed by our binary masked language models encode information necessary for solving downstream tasks. Analyzing the loss landscape, we show that masking and finetuning produce models that reside in minima that can be connected by a line segment with nearly constant test accuracy. This confirms that masking can be utilized as an efficient alternative to finetuning.",
}
@inproceedings{kang-etal-2020-dynamic,
    title = "Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning",
    author = "Kang, Xiaomian  and
      Zhao, Yang  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.175",
    doi = "10.18653/v1/2020.emnlp-main.175",
    pages = "2242--2254",
    abstract = "Document-level neural machine translation has yielded attractive improvements. However, majority of existing methods roughly use all context sentences in a fixed scope. They neglect the fact that different source sentences need different sizes of context. To address this problem, we propose an effective approach to select dynamic context so that the document-level translation model can utilize the more useful selected context sentences to produce better translations. Specifically, we introduce a selection module that is independent of the translation module to score each candidate context sentence. Then, we propose two strategies to explicitly select a variable number of context sentences and feed them into the translation module. We train the two modules end-to-end via reinforcement learning. A novel reward is proposed to encourage the selection and utilization of dynamic context sentences. Experiments demonstrate that our approach can select adaptive context sentences for different source sentences, and significantly improves the performance of document-level translation methods.",
}
@inproceedings{jiao-etal-2020-data,
    title = "{D}ata {R}ejuvenation: {E}xploiting {I}nactive {T}raining {E}xamples for {N}eural {M}achine {T}ranslation",
    author = "Jiao, Wenxiang  and
      Wang, Xing  and
      He, Shilin  and
      King, Irwin  and
      Lyu, Michael  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.176",
    doi = "10.18653/v1/2020.emnlp-main.176",
    pages = "2255--2266",
    abstract = "Large-scale training datasets lie at the core of the recent success of neural machine translation (NMT) models. However, the complex patterns and potential noises in the large-scale data make training NMT models difficult. In this work, we explore to identify the inactive training examples which contribute less to the model performance, and show that the existence of inactive examples depends on the data distribution. We further introduce data rejuvenation to improve the training of NMT models on large-scale datasets by exploiting inactive examples. The proposed framework consists of three phases. First, we train an identification model on the original training data, and use it to distinguish inactive examples and active examples by their sentence-level output probabilities. Then, we train a rejuvenation model on the active examples, which is used to re-label the inactive examples with forward- translation. Finally, the rejuvenated examples and the active examples are combined to train the final NMT model. Experimental results on WMT14 English-German and English-French datasets show that the proposed data rejuvenation consistently and significantly improves performance for several strong NMT models. Extensive analyses reveal that our approach stabilizes and accelerates the training process of NMT models, resulting in final models with better generalization capability.",
}
@inproceedings{jwalapuram-etal-2020-pronoun,
    title = "Pronoun-Targeted Fine-tuning for {NMT} with Hybrid Losses",
    author = "Jwalapuram, Prathyusha  and
      Joty, Shafiq  and
      Shen, Youlin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.177",
    doi = "10.18653/v1/2020.emnlp-main.177",
    pages = "2267--2279",
    abstract = "Popular Neural Machine Translation model training uses strategies like backtranslation to improve BLEU scores, requiring large amounts of additional data and training. We introduce a class of conditional generative-discriminative hybrid losses that we use to fine-tune a trained machine translation model. Through a combination of targeted fine-tuning objectives and intuitive re-use of the training data the model has failed to adequately learn from, we improve the model performance of both a sentence-level and a contextual model without using any additional data. We target the improvement of pronoun translations through our fine-tuning and evaluate our models on a pronoun benchmark testset. Our sentence-level model shows a 0.5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets, while our contextual model achieves the best results, improving from 31.81 to 32 BLEU on WMT14 De-En testset, and from 32.10 to 33.13 on the IWSLT13 De-En testset, with corresponding improvements in pronoun translation. We further show the generalizability of our method by reproducing the improvements on two additional language pairs, Fr-En and Cs-En.",
}
@inproceedings{zhang-etal-2020-learning-adaptive,
    title = "Learning Adaptive Segmentation Policy for Simultaneous Translation",
    author = "Zhang, Ruiqing  and
      Zhang, Chuanqiang  and
      He, Zhongjun  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.178",
    doi = "10.18653/v1/2020.emnlp-main.178",
    pages = "2280--2289",
    abstract = "Balancing accuracy and latency is a great challenge for simultaneous translation. To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in increased latency. However, keeping low latency would probably hurt accuracy. Therefore, it is essential to segment the ASR output into appropriate units for translation. Inspired by human interpreters, we propose a novel adaptive segmentation policy for simultaneous translation. The policy learns to segment the source text by considering possible translations produced by the translation model, maintaining consistency between the segmentation and translation. Experimental results on Chinese-English and German-English translation show that our method achieves a better accuracy-latency trade-off over recently proposed state-of-the-art methods.",
}
@inproceedings{li-etal-2020-learn,
    title = "Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages",
    author = "Li, Zheng  and
      Kumar, Mukul  and
      Headden, William  and
      Yin, Bing  and
      Wei, Ying  and
      Zhang, Yu  and
      Yang, Qiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.179",
    doi = "10.18653/v1/2020.emnlp-main.179",
    pages = "2290--2301",
    abstract = "Recent emergence of multilingual pre-training language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks. However, mPLM-based methods usually involve two problems: (1) simply fine-tuning may not adapt general-purpose multilingual representations to be task-aware on low-resource languages; (2) ignore how cross-lingual adaptation happens for downstream tasks. To address the issues, we propose a meta graph learning (MGL) method. Unlike prior works that transfer from scratch, MGL can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks), making mPLM insensitive to low-resource languages. Besides, for each CLT task, MGL formulates its transfer process as information propagation over a dynamic graph, where the geometric structure can automatically capture intrinsic language relationships to explicitly guide cross-lingual transfer. Empirically, extensive experiments on both public and real-world datasets demonstrate the effectiveness of the MGL method.",
}
@inproceedings{ustun-etal-2020-udapter,
    title = "{UD}apter: Language Adaptation for Truly {U}niversal {D}ependency Parsing",
    author = {{\"U}st{\"u}n, Ahmet  and
      Bisazza, Arianna  and
      Bouma, Gosse  and
      van Noord, Gertjan},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.180",
    doi = "10.18653/v1/2020.emnlp-main.180",
    pages = "2302--2315",
    abstract = "Recent advances in multilingual dependency parsing have brought the idea of a truly universal parser closer to reality. However, cross-language interference and restrained model capacity remain major obstacles. To address this, we propose a novel multilingual task adaptation approach based on contextual parameter generation and adapter modules. This approach enables to learn adapters via language embeddings while sharing model parameters across languages. It also allows for an easy but effective integration of existing linguistic typology features into the parsing network. The resulting parser, UDapter, outperforms strong monolingual and multilingual baselines on the majority of both high-resource and low-resource (zero-shot) languages, showing the success of the proposed adaptation approach. Our in-depth analyses show that soft parameter sharing via typological features is key to this success.",
}
@inproceedings{gui-etal-2020-uncertainty,
    title = "Uncertainty-Aware Label Refinement for Sequence Labeling",
    author = "Gui, Tao  and
      Ye, Jiacheng  and
      Zhang, Qi  and
      Li, Zhengyan  and
      Fei, Zichu  and
      Gong, Yeyun  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.181",
    doi = "10.18653/v1/2020.emnlp-main.181",
    pages = "2316--2326",
    abstract = "Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks. However, the local label dependencies and inefficient Viterbi decoding have always been a problem to be solved. In this work, we introduce a novel two-stage label decoding framework to model long-term label dependencies, while being much more computationally efficient. A base model first predicts draft labels, and then a novel two-stream self-attention model makes refinements on these draft predictions based on long-range label dependencies, which can achieve parallel decoding for a faster prediction. In addition, in order to mitigate the side effects of incorrect draft labels, Bayesian neural networks are used to indicate the labels with a high probability of being wrong, which can greatly assist in preventing error propagation. The experimental results on three sequence labeling benchmarks demonstrated that the proposed method not only outperformed the CRF-based methods but also greatly accelerated the inference process.",
}
@inproceedings{han-etal-2020-adversarial,
    title = "Adversarial Attack and Defense of Structured Prediction Models",
    author = "Han, Wenjuan  and
      Zhang, Liwen  and
      Jiang, Yong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.182",
    doi = "10.18653/v1/2020.emnlp-main.182",
    pages = "2327--2338",
    abstract = "Building an effective adversarial attacker and elaborating on countermeasures for adversarial attacks for natural language processing (NLP) have attracted a lot of research in recent years. However, most of the existing approaches focus on classification problems. In this paper, we investigate attacks and defenses for structured prediction tasks in NLP. Besides the difficulty of perturbing discrete words and the sentence fluency problem faced by attackers in any NLP tasks, there is a specific challenge to attackers of structured prediction models: the structured output of structured prediction models is sensitive to small perturbations in the input. To address these problems, we propose a novel and unified framework that learns to attack a structured prediction model using a sequence-to-sequence model with feedbacks from multiple reference models of the same structured prediction task. Based on the proposed attack, we further reinforce the victim model with adversarial training, making its prediction more robust and accurate. We evaluate the proposed framework in dependency parsing and part-of-speech tagging. Automatic and human evaluations show that our proposed framework succeeds in both attacking state-of-the-art structured prediction models and boosting them with adversarial training.",
}
@inproceedings{xu-etal-2020-position,
    title = "Position-Aware Tagging for Aspect Sentiment Triplet Extraction",
    author = "Xu, Lu  and
      Li, Hao  and
      Lu, Wei  and
      Bing, Lidong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.183",
    doi = "10.18653/v1/2020.emnlp-main.183",
    pages = "2339--2349",
    abstract = "Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question. In this work, we propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets. Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches. We also conducted extensive experiments to investigate the model effectiveness and robustness.",
}
@inproceedings{caglayan-etal-2020-simultaneous,
    title = "Simultaneous Machine Translation with Visual Context",
    author = {Caglayan, Ozan  and
      Ive, Julia  and
      Haralampieva, Veneta  and
      Madhyastha, Pranava  and
      Barrault, Lo{\"\i}c  and
      Specia, Lucia},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.184",
    doi = "10.18653/v1/2020.emnlp-main.184",
    pages = "2350--2361",
    abstract = "Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation thus has to start with an incomplete source text, which is read progressively, creating the need for anticipation. In this paper, we seek to understand whether the addition of visual information can compensate for the missing source context. To this end, we analyse the impact of different multimodal approaches and visual features on state-of-the-art SiMT frameworks. Our results show that visual context is helpful and that visually-grounded models based on explicit object region information are much better than commonly used global features, reaching up to 3 BLEU points improvement under low latency scenarios. Our qualitative analysis illustrates cases where only the multimodal systems are able to translate correctly from English into gender-marked languages, as well as deal with differences in word order, such as adjective-noun placement between English and French.",
}
@inproceedings{ponti-etal-2020-xcopa,
    title = "{XCOPA}: A Multilingual Dataset for Causal Commonsense Reasoning",
    author = "Ponti, Edoardo Maria  and
      Glava{\v{s}}, Goran  and
      Majewska, Olga  and
      Liu, Qianchu  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.185",
    doi = "10.18653/v1/2020.emnlp-main.185",
    pages = "2362--2376",
    abstract = "In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects. Moreover, they should be able to generalise the acquired world knowledge to new languages, modulo cultural differences. Advances in machine reasoning and cross-lingual transfer depend on the availability of challenging evaluation benchmarks. Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, which includes resource-poor languages like Eastern Apur{\'\i}mac Quechua and Haitian Creole. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods based on multilingual pretraining and zero-shot fine-tuning falls short compared to translation-based transfer. Finally, we propose strategies to adapt multilingual models to out-of-sample resource-lean languages where only a small corpus or a bilingual dictionary is available, and report substantial improvements over the random baseline. The XCOPA dataset is freely available at github.com/cambridgeltl/xcopa.",
}
@inproceedings{dubossarsky-etal-2020-secret,
    title = "The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures",
    author = "Dubossarsky, Haim  and
      Vuli{\'c}, Ivan  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.186",
    doi = "10.18653/v1/2020.emnlp-main.186",
    pages = "2377--2390",
    abstract = "Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces. In this work we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT. We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned. We then introduce several isomorphism measures between two embedding spaces, based on the relevant statistics of their individual spectra. We empirically show that (1) language similarity scores derived from such spectral isomorphism measures are strongly associated with performance observed in different cross-lingual tasks, and (2) our spectral-based measures consistently outperform previous standard isomorphism measures, while being computationally more tractable and easier to interpret. Finally, our measures capture complementary information to typologically driven language distance measures, and the combination of measures from the two families yields even higher task performance correlations.",
}
@inproceedings{oncevay-etal-2020-bridging,
    title = "Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations",
    author = "Oncevay, Arturo  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.187",
    doi = "10.18653/v1/2020.emnlp-main.187",
    pages = "2391--2406",
    abstract = "Sparse language vectors from linguistic typology databases and learned embeddings from tasks like multilingual machine translation have been investigated in isolation, without analysing how they could benefit from each other{'}s language characterisation. We propose to fuse both views using singular vector canonical correlation analysis and study what kind of information is induced from each source. By inferring typological features and language phylogenies, we observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy in tasks that require information about language similarities, such as language clustering and ranking candidates for multilingual transfer. With our method, we can easily project and assess new languages without expensive retraining of massive multilingual or ranking models, which are major disadvantages of related approaches.",
}
@inproceedings{zhang-etal-2020-answerfact,
    title = "{A}nswer{F}act: Fact Checking in Product Question Answering",
    author = "Zhang, Wenxuan  and
      Deng, Yang  and
      Ma, Jing  and
      Lam, Wai",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.188",
    doi = "10.18653/v1/2020.emnlp-main.188",
    pages = "2407--2417",
    abstract = "Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping. However, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information, which may even cause a commercial loss in E-commerce business. To tackle this issue, we investigate to predict the veracity of answers in this paper and introduce AnswerFact, a large scale fact checking dataset from product question answering forums. Each answer is accompanied by its veracity label and associated evidence sentences, providing a valuable testbed for evidence-based fact checking tasks in QA settings. We further propose a novel neural model with tailored evidence ranking components to handle the concerned answer veracity prediction problem. Extensive experiments are conducted with our proposed model and various existing fact checking methods, showing that our method outperforms all baselines on this task.",
}
@inproceedings{seonwoo-etal-2020-context,
    title = "Context-Aware Answer Extraction in Question Answering",
    author = "Seonwoo, Yeon  and
      Kim, Ji-Hoon  and
      Ha, Jung-Woo  and
      Oh, Alice",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.189",
    doi = "10.18653/v1/2020.emnlp-main.189",
    pages = "2418--2428",
    abstract = "Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage. However, they sometimes result in predicting the correct answer text but in a context irrelevant to the given question. This discrepancy becomes especially important as the number of occurrences of the answer text in a passage increases. To resolve this issue, we propose BLANC (BLock AttentioN for Context prediction) based on two main ideas: context prediction as an auxiliary task in multi-task learning manner, and a block attention method that learns the context prediction task. With experiments on reading comprehension, we show that BLANC outperforms the state-of-the-art QA models, and the performance gap increases as the number of answer text occurrences increases. We also conduct an experiment of training the models using SQuAD and predicting the supporting facts on HotpotQA and show that BLANC outperforms all baseline models in this zero-shot setting.",
}
@inproceedings{sen-saffari-2020-models,
    title = "What do Models Learn from Question Answering Datasets?",
    author = "Sen, Priyanka  and
      Saffari, Amir",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.190",
    doi = "10.18653/v1/2020.emnlp-main.190",
    pages = "2429--2438",
    abstract = "While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself. In this paper, we investigate if models are learning reading comprehension from QA datasets by evaluating BERT-based models across five datasets. We evaluate models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. We find that no single dataset is robust to all of our experiments and identify shortcomings in both datasets and evaluation methods. Following our analysis, we make recommendations for building future QA datasets that better evaluate the task of question answering through reading comprehension. We also release code to convert QA datasets to a shared format for easier experimentation at https://github.com/amazon-research/qa-dataset-converter",
}
@inproceedings{gao-etal-2020-discern,
    title = "Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading",
    author = "Gao, Yifan  and
      Wu, Chien-Sheng  and
      Li, Jingjing  and
      Joty, Shafiq  and
      Hoi, Steven C.H.  and
      Xiong, Caiming  and
      King, Irwin  and
      Lyu, Michael",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.191",
    doi = "10.18653/v1/2020.emnlp-main.191",
    pages = "2439--2449",
    abstract = "Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose {``}Discern{''}, a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of both document and dialog. Specifically, we split the document into clause-like elementary discourse units (EDU) using a pre-trained discourse segmentation model, and we train our model in a weakly-supervised manner to predict whether each EDU is entailed by the user feedback in a conversation. Based on the learned EDU and entailment representations, we either reply to the user our final decision {``}yes/no/irrelevant{''} of the initial question, or generate a follow-up question to inquiry more information. Our experiments on the ShARC benchmark (blind, held-out test set) show that Discern achieves state-of-the-art results of 78.3{\%} macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question generation. Code and models are released at https://github.com/Yifan-Gao/Discern.",
}
@inproceedings{omura-etal-2020-method,
    title = "A Method for Building a Commonsense Inference Dataset based on Basic Events",
    author = "Omura, Kazumasa  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.192",
    doi = "10.18653/v1/2020.emnlp-main.192",
    pages = "2450--2460",
    abstract = "We present a scalable, low-bias, and low-cost method for building a commonsense inference dataset that combines automatic extraction from a corpus and crowdsourcing. Each problem is a multiple-choice question that asks contingency between basic events. We applied the proposed method to a Japanese corpus and acquired 104k problems. While humans can solve the resulting problems with high accuracy (88.9{\%}), the accuracy of a high-performance transfer learning model is reasonably low (76.0{\%}). We also confirmed through dataset analysis that the resulting dataset contains low bias. We released the datatset to facilitate language understanding research.",
}
@inproceedings{zhong-etal-2020-neural,
    title = "Neural Deepfake Detection with Factual Structure of Text",
    author = "Zhong, Wanjun  and
      Tang, Duyu  and
      Xu, Zenan  and
      Wang, Ruize  and
      Duan, Nan  and
      Zhou, Ming  and
      Wang, Jiahai  and
      Yin, Jian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.193",
    doi = "10.18653/v1/2020.emnlp-main.193",
    pages = "2461--2470",
    abstract = "Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.",
}
@inproceedings{ruckle-etal-2020-multicqa,
    title = "{M}ulti{CQA}: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale",
    author = {R{\"u}ckl{\'e}, Andreas  and
      Pfeiffer, Jonas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.194",
    doi = "10.18653/v1/2020.emnlp-main.194",
    pages = "2471--2486",
    abstract = "We study the zero-shot transfer capabilities of text matching models on a massive scale, by self-supervised training on 140 source domains from community question answering forums in English. We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines. We also demonstrate that considering a broad selection of source domains is crucial for obtaining the best zero-shot transfer performances, which contrasts the standard procedure that merely relies on the largest and most similar domains. In addition, we extensively study how to best combine multiple source domains. We propose to incorporate self-supervised with supervised multi-task learning on all available source domains. Our best zero-shot transfer model considerably outperforms in-domain BERT and the previous state of the art on six benchmarks. Fine-tuning of our model with in-domain data results in additional large gains and achieves the new state of the art on all nine benchmarks.",
}
@inproceedings{blloshmi-etal-2020-xl,
    title = "{XL}-{AMR}: Enabling Cross-Lingual {AMR} Parsing with Transfer Learning Techniques",
    author = "Blloshmi, Rexhina  and
      Tripodi, Rocco  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.195",
    doi = "10.18653/v1/2020.emnlp-main.195",
    pages = "2487--2500",
    abstract = "Abstract Meaning Representation (AMR) is a popular formalism of natural language that represents the meaning of a sentence as a semantic graph. It is agnostic about how to derive meanings from strings and for this reason it lends itself well to the encoding of semantics across languages. However, cross-lingual AMR parsing is a hard task, because training data are scarce in languages other than English and the existing English AMR parsers are not directly suited to being used in a cross-lingual setting. In this work we tackle these two problems so as to enable cross-lingual AMR parsing: we explore different transfer learning techniques for producing automatic AMR annotations across languages and develop a cross-lingual AMR parser, XL-AMR. This can be trained on the produced data and does not rely on AMR aligners or source-copy mechanisms as is commonly the case in English AMR parsing. The results of XL-AMR significantly surpass those previously reported in Chinese, German, Italian and Spanish. Finally we provide a qualitative analysis which sheds light on the suitability of AMR across languages. We release XL-AMR at github.com/SapienzaNLP/xl-amr.",
}
@inproceedings{xu-etal-2020-improving,
    title = "Improving {AMR} Parsing with Sequence-to-Sequence Pre-training",
    author = "Xu, Dongqin  and
      Li, Junhui  and
      Zhu, Muhua  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.196",
    doi = "10.18653/v1/2020.emnlp-main.196",
    pages = "2501--2511",
    abstract = "In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance. To alleviate such data size restriction, pre-trained models have been drawing more and more attention in AMR parsing. However, previous pre-trained models, like BERT, are implemented for general purpose which may not work as expected for the specific task of AMR parsing. In this paper, we focus on sequence-to-sequence (seq2seq) AMR parsing and propose a seq2seq pre-training approach to build pre-trained models in both single and joint way on three relevant tasks, i.e., machine translation, syntactic parsing, and AMR parsing itself. Moreover, we extend the vanilla fine-tuning method to a multi-task learning fine-tuning method that optimizes for the performance of AMR parsing while endeavors to preserve the response of pre-trained models. Extensive experimental results on two English benchmark datasets show that both the single and joint pre-trained models significantly improve the performance (e.g., from 71.5 to 80.2 on AMR 2.0), which reaches the state of the art. The result is very encouraging since we achieve this with seq2seq models rather than complex models. We make our code and model available at https:// github.com/xdqkid/S2S-AMR-Parser.",
}
@inproceedings{rizwan-etal-2020-hate,
    title = "Hate-Speech and Offensive Language Detection in {R}oman {U}rdu",
    author = "Rizwan, Hammad  and
      Shakeel, Muhammad Haroon  and
      Karim, Asim",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.197",
    doi = "10.18653/v1/2020.emnlp-main.197",
    pages = "2512--2522",
    abstract = "The task of automatic hate-speech and offensive language detection in social media content is of utmost importance due to its implications in unprejudiced society concerning race, gender, or religion. Existing research in this area, however, is mainly focused on the English language, limiting the applicability to particular demographics. Despite its prevalence, Roman Urdu (RU) lacks language resources, annotated datasets, and language models for this task. In this study, we: (1) Present a lexicon of hateful words in RU, (2) Develop an annotated dataset called RUHSOLD consisting of 10,012 tweets in RU with both coarse-grained and fine-grained labels of hate-speech and offensive language, (3) Explore the feasibility of transfer learning of five existing embedding models to RU, (4) Propose a novel deep learning architecture called CNN-gram for hate-speech and offensive language detection and compare its performance with seven current baseline approaches on RUHSOLD dataset, and (5) Train domain-specific embeddings on more than 4.7 million tweets and make them publicly available. We conclude that transfer learning is more beneficial as compared to training embedding from scratch and that the proposed model exhibits greater robustness as compared to the baselines.",
}
@inproceedings{park-etal-2020-suicidal,
    title = "Suicidal Risk Detection for Military Personnel",
    author = "Park, Sungjoon  and
      Park, Kiwoong  and
      Ahn, Jaimeen  and
      Oh, Alice",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.198",
    doi = "10.18653/v1/2020.emnlp-main.198",
    pages = "2523--2531",
    abstract = "We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea. From a widely-used Korean social Q{\&}A site, we collect posts containing military-relevant content written by active-duty military personnel. We then annotate the posts with two groups of experts: military experts and mental health experts. Our dataset includes 2,791 posts with 13,955 corresponding expert annotations of suicidal risk levels, and this dataset is available to researchers who consent to research ethics agreement. Using various fine-tuned state-of-the-art language models, we predict the level of suicide risk, reaching .88 F1 score for classifying the risks.",
}
@inproceedings{ousidhoum-etal-2020-comparative,
    title = "Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets",
    author = "Ousidhoum, Nedjma  and
      Song, Yangqiu  and
      Yeung, Dit-Yan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.199",
    doi = "10.18653/v1/2020.emnlp-main.199",
    pages = "2532--2542",
    abstract = "Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data. We examine selection bias in hate speech in a language and label independent fashion. We first use topic models to discover latent semantics in eleven hate speech corpora, then, we present two bias evaluation metrics based on the semantic similarity between topics and search words frequently used to build corpora. We discuss the possibility of revising the data collection process by comparing datasets and analyzing contrastive case studies.",
}
@inproceedings{chen-li-2020-henin,
    title = "{HENIN}: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media",
    author = "Chen, Hsin-Yu  and
      Li, Cheng-Te",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.200",
    doi = "10.18653/v1/2020.emnlp-main.200",
    pages = "2543--2552",
    abstract = "In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying. In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors. Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.",
}
@inproceedings{shmueli-etal-2020-reactive,
    title = "{R}eactive {S}upervision: {A} {N}ew {M}ethod for {C}ollecting {S}arcasm {D}ata",
    author = "Shmueli, Boaz  and
      Ku, Lun-Wei  and
      Ray, Soumya",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.201",
    doi = "10.18653/v1/2020.emnlp-main.201",
    pages = "2553--2559",
    abstract = "Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities.",
}
@inproceedings{ruiter-etal-2020-self,
    title = "Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation",
    author = "Ruiter, Dana  and
      van Genabith, Josef  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.202",
    doi = "10.18653/v1/2020.emnlp-main.202",
    pages = "2560--2571",
    abstract = "Self-supervised neural machine translation (SSNMT) jointly learns to identify and select suitable training data from comparable (rather than parallel) corpora and to translate, in a way that the two tasks support each other in a virtuous circle. In this study, we provide an in-depth analysis of the sampling choices the SSNMT model makes during training. We show how, without it having been told to do so, the model self-selects samples of increasing (i) complexity and (ii) task-relevance in combination with (iii) performing a denoising curriculum. We observe that the dynamics of the mutual-supervision signals of both system internal representation types are vital for the extraction and translation performance. We show that in terms of the Gunning-Fog Readability index, SSNMT starts extracting and learning from Wikipedia data suitable for high school students and quickly moves towards content suitable for first year undergraduate students.",
}
@inproceedings{libovicky-fraser-2020-towards,
    title = "Towards Reasonably-Sized Character-Level Transformer {NMT} by Finetuning Subword Systems",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.203",
    doi = "10.18653/v1/2020.emnlp-main.203",
    pages = "2572--2579",
    abstract = "Applying the Transformer architecture on the character level usually requires very deep architectures that are difficult and slow to train. These problems can be partially overcome by incorporating a segmentation into tokens in the model. We show that by initially training a subword model and then finetuning it on characters, we can obtain a neural machine translation model that works at the character level without requiring token segmentation. We use only the vanilla 6-layer Transformer Base architecture. Our character-level models better capture morphological phenomena and show more robustness to noise at the expense of somewhat worse overall translation quality. Our study is a significant step towards high-performance and easy to train character-based models that are not extremely large.",
}
@inproceedings{hedderich-etal-2020-transfer,
    title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on {A}frican Languages",
    author = "Hedderich, Michael A.  and
      Adelani, David  and
      Zhu, Dawei  and
      Alabi, Jesujoba  and
      Markus, Udia  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
    doi = "10.18653/v1/2020.emnlp-main.204",
    pages = "2580--2591",
    abstract = "Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages. However, recent works also showed that results from high-resource languages could not be easily transferred to realistic, low-resource scenarios. In this work, we study trends in performance for different amounts of available resources for the three African languages Hausa, isiXhosa and on both NER and topic classification. We show that in combination with transfer learning or distant supervision, these models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. However, we also find settings where this does not hold. Our discussions and additional experiments on assumptions such as time and hardware restrictions highlight challenges and opportunities in low-resource learning.",
}
@inproceedings{zhang-van-genabith-2020-translation,
    title = "Translation Quality Estimation by Jointly Learning to Score and Rank",
    author = "Zhang, Jingyi  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.205",
    doi = "10.18653/v1/2020.emnlp-main.205",
    pages = "2592--2598",
    abstract = "The translation quality estimation (QE) task, particularly the QE as a Metric task, aims to evaluate the general quality of a translation based on the translation and the source sentence without using reference translations. Supervised learning of this QE task requires human evaluation of translation quality as training data. Human evaluation of translation quality can be performed in different ways, including assigning an absolute score to a translation or ranking different translations. In order to make use of different types of human evaluation data for supervised learning, we present a multi-task learning QE model that jointly learns two tasks: score a translation and rank two translations. Our QE model exploits cross-lingual sentence embeddings from pre-trained multilingual language models. We obtain new state-of-the-art results on the WMT 2019 QE as a Metric task and outperform sentBLEU on the WMT 2019 Metrics task.",
}
@inproceedings{iranzo-sanchez-etal-2020-direct,
    title = "Direct Segmentation Models for Streaming Speech Translation",
    author = "Iranzo-S{\'a}nchez, Javier  and
      Gim{\'e}nez Pastor, Adri{\`a}  and
      Silvestre-Cerd{\`a}, Joan Albert  and
      Baquero-Arnal, Pau  and
      Civera Saiz, Jorge  and
      Juan, Alfons",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.206",
    doi = "10.18653/v1/2020.emnlp-main.206",
    pages = "2599--2611",
    abstract = "The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. These systems are usually connected by a segmenter that splits the ASR output into hopefully, semantically self-contained chunks to be fed into the MT system. This is specially challenging in the case of streaming ST, where latency requirements must also be taken into account. This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk. An extensive and throughly experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario. Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work.",
}
@inproceedings{hasan-etal-2020-low,
    title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation",
    author = "Hasan, Tahmid  and
      Bhattacharjee, Abhik  and
      Samin, Kazi  and
      Hasan, Masum  and
      Basak, Madhusudan  and
      Rahman, M. Sohel  and
      Shahriyar, Rifat",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.207",
    doi = "10.18653/v1/2020.emnlp-main.207",
    pages = "2612--2623",
    abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.",
}
@inproceedings{yang-etal-2020-csp,
    title = "{CSP}:Code-Switching Pre-training for Neural Machine Translation",
    author = "Yang, Zhen  and
      Hu, Bojie  and
      Han, Ambyera  and
      Huang, Shen  and
      Ju, Qi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.208",
    doi = "10.18653/v1/2020.emnlp-main.208",
    pages = "2624--2636",
    abstract = "This paper proposes a new pre-training method, called Code-Switching Pre-training (CSP for short) for Neural Machine Translation (NMT). Unlike traditional pre-training method which randomly masks some fragments of the input sentence, the proposed CSP randomly replaces some words in the source sentence with their translation words in the target language. Specifically, we firstly perform lexicon induction with unsupervised word embedding mapping between the source and target languages, and then randomly replace some words in the input sentence with their translation words according to the extracted translation lexicons. CSP adopts the encoder-decoder framework: its encoder takes the code-mixed sentence as input, and its decoder predicts the replaced fragment of the input sentence. In this way, CSP is able to pre-train the NMT model by explicitly making the most of the alignment information extracted from the source and target monolingual corpus. Additionally, we relieve the pretrain-finetune discrepancy caused by the artificial symbols like [mask]. To verify the effectiveness of the proposed method, we conduct extensive experiments on unsupervised and supervised NMT. Experimental results show that CSP achieves significant improvements over baselines without pre-training or with other pre-training methods.",
}
@inproceedings{gonzalez-etal-2020-type,
    title = "Type {B} Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias",
    author = "Gonz{\'a}lez, Ana Valeria  and
      Barrett, Maria  and
      Hvingelby, Rasmus  and
      Webster, Kellie  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.209",
    doi = "10.18653/v1/2020.emnlp-main.209",
    pages = "2637--2648",
    abstract = "The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highlight model preferences that are {``}hallucinatory{''}, e.g., disambiguating gender-ambiguous occurrences of {`}doctor{'} as male doctors. We show that for languages with type B reflexivization, e.g., Swedish and Russian, we can construct multi-task challenge datasets for detecting gender bias that lead to unambiguously wrong model predictions: In these languages, the direct translation of {`}the doctor removed his mask{'} is not ambiguous between a coreferential reading and a disjoint reading. Instead, the coreferential reading requires a non-gendered pronoun, and the gendered, possessive pronouns are anti-reflexive. We present a multilingual, multi-task challenge dataset, which spans four languages and four NLP tasks and focuses only on this phenomenon. We find evidence for gender bias across all task-language combinations and correlate model bias with national labor market statistics.",
}
@inproceedings{lin-etal-2020-pre,
    title = "Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information",
    author = "Lin, Zehui  and
      Pan, Xiao  and
      Wang, Mingxuan  and
      Qiu, Xipeng  and
      Feng, Jiangtao  and
      Zhou, Hao  and
      Li, Lei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.210",
    doi = "10.18653/v1/2020.emnlp-main.210",
    pages = "2649--2663",
    abstract = "We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs? We propose mRASP, an approach to pre-train a universal multilingual neural machine translation model. Our key idea in mRASP is its novel technique of random aligned substitution, which brings words and phrases with similar meanings across multiple languages closer in the representation space. We pre-train a mRASP model on 32 language pairs jointly with only public datasets. The model is then fine-tuned on downstream language pairs to obtain specialized MT models. We carry out extensive experiments on 42 translation directions across a diverse settings, including low, medium, rich resource, and as well as transferring to exotic language pairs. Experimental results demonstrate that mRASP achieves significant performance improvement compared to directly training on those target pairs. It is the first time to verify that multiple lowresource language pairs can be utilized to improve rich resource MT. Surprisingly, mRASP is even able to improve the translation quality on exotic languages that never occur in the pretraining corpus. Code, data, and pre-trained models are available at https://github. com/linzehui/mRASP.",
}
@inproceedings{behnke-heafield-2020-losing,
    title = "Losing Heads in the Lottery: Pruning Transformer Attention in Neural Machine Translation",
    author = "Behnke, Maximiliana  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.211",
    doi = "10.18653/v1/2020.emnlp-main.211",
    pages = "2664--2674",
    abstract = "The attention mechanism is the crucial component of the transformer architecture. Recent research shows that most attention heads are not confident in their decisions and can be pruned. However, removing them before training a model results in lower quality. In this paper, we apply the lottery ticket hypothesis to prune heads in the early stages of training. Our experiments on machine translation show that it is possible to remove up to three-quarters of attention heads from transformer-big during early training with an average -0.1 change in BLEU for Turkish→English. The pruned model is 1.5 times as fast at inference, albeit at the cost of longer training. Our method is complementary to other approaches, such as teacher-student, with English→German student model gaining an additional 10{\%} speed-up with 75{\%} encoder attention removed and 0.2 BLEU loss.",
}
@inproceedings{weng-etal-2020-towards,
    title = "Towards Enhancing Faithfulness for Neural Machine Translation",
    author = "Weng, Rongxiang  and
      Yu, Heng  and
      Wei, Xiangpeng  and
      Luo, Weihua",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.212",
    doi = "10.18653/v1/2020.emnlp-main.212",
    pages = "2675--2684",
    abstract = "Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. Compared with human translations, one of the drawbacks of current NMT is that translations are not usually faithful to the input, e.g., omitting information or generating unrelated fragments, which inevitably decreases the overall quality, especially for human readers. In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named FEnmt). During the NMT training process, we sample a subset from the training set and translate them to get fragments that have been mistranslated. Afterward, the proposed multi-task learning paradigm is employed on both encoder and decoder to guide NMT to correctly translate these fragments. Both automatic and human evaluations verify that our FEnmt could improve translation quality by effectively reducing unfaithful translations.",
}
@inproceedings{rei-etal-2020-comet,
    title = "{COMET}: A Neural Framework for {MT} Evaluation",
    author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
    doi = "10.18653/v1/2020.emnlp-main.213",
    pages = "2685--2702",
    abstract = "We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metric. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.",
}
@inproceedings{chronopoulou-etal-2020-reusing,
    title = "{R}eusing a {P}retrained {L}anguage {M}odel on {L}anguages with {L}imited {C}orpora for {U}nsupervised {NMT}",
    author = "Chronopoulou, Alexandra  and
      Stojanovski, Dario  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.214",
    doi = "10.18653/v1/2020.emnlp-main.214",
    pages = "2703--2711",
    abstract = "Using a language model (LM) pretrained on two languages with large monolingual data in order to initialize an unsupervised neural machine translation (UNMT) system yields state-of-the-art results. When limited data is available for one language, however, this method leads to poor translations. We present an effective approach that reuses an LM that is pretrained only on the high-resource language. The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model. To reuse the pretrained LM, we have to modify its predefined vocabulary, to account for the new language. We therefore propose a novel vocabulary extension method. Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq), yielding more than +8.3 BLEU points for all four translation directions.",
}
@inproceedings{mohiuddin-etal-2020-lnmap,
    title = "{LNM}ap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space",
    author = "Mohiuddin, Tasnim  and
      Bari, M Saiful  and
      Joty, Shafiq",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.215",
    doi = "10.18653/v1/2020.emnlp-main.215",
    pages = "2712--2723",
    abstract = "Most of the successful and predominant methods for Bilingual Lexicon Induction (BLI) are mapping-based, where a linear mapping function is learned with the assumption that the word embedding spaces of different languages exhibit similar geometric structures (i.e. approximately isomorphic). However, several recent studies have criticized this simplified assumption showing that it does not hold in general even for closely related languages. In this work, we propose a novel semi-supervised method to learn cross-lingual word embeddings for BLI. Our model is independent of the isomorphic assumption and uses non-linear mapping in the latent space of two independently pre-trained autoencoders. Through extensive experiments on fifteen (15) different language pairs (in both directions) comprising resource-rich and low-resource languages from two different datasets, we demonstrate that our method outperforms existing models by a good margin. Ablation studies show the importance of different model components and the necessity of non-linear mapping.",
}
@inproceedings{wei-etal-2020-uncertainty,
    title = "Uncertainty-Aware Semantic Augmentation for Neural Machine Translation",
    author = "Wei, Xiangpeng  and
      Yu, Heng  and
      Hu, Yue  and
      Weng, Rongxiang  and
      Xing, Luxi  and
      Luo, Weihua",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.216",
    doi = "10.18653/v1/2020.emnlp-main.216",
    pages = "2724--2735",
    abstract = "As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other. However, the dominant methods for NMT only observe one of them from the parallel corpora for the model training but have to deal with adequate variations under the same meaning at inference. This leads to a discrepancy of the data distribution between the training and the inference phases. To address this problem, we propose uncertainty-aware semantic augmentation, which explicitly captures the universal semantic information among multiple semantically-equivalent source sentences and enhances the hidden representations with this information for better translations. Extensive experiments on various translation tasks reveal that our approach significantly outperforms the strong baselines and the existing methods.",
}
@inproceedings{chollampatt-etal-2020-automatic,
    title = "Can Automatic Post-Editing Improve {NMT}?",
    author = "Chollampatt, Shamil  and
      Susanto, Raymond Hendy  and
      Tan, Liling  and
      Szymanska, Ewa",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.217",
    doi = "10.18653/v1/2020.emnlp-main.217",
    pages = "2736--2746",
    abstract = "Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort. APE has had notable success when used with statistical machine translation (SMT) systems but has not been as successful over neural machine translation (NMT) systems. This has raised questions on the relevance of APE task in the current scenario. However, the training of APE models has been heavily reliant on large-scale artificial corpora combined with only limited human post-edited data. We hypothesize that APE models have been underperforming in improving NMT translations due to the lack of adequate supervision. To ascertain our hypothesis, we compile a larger corpus of human post-edits of English to German NMT. We empirically show that a state-of-art neural APE model trained on this corpus can significantly improve a strong in-domain NMT system, challenging the current understanding in the field. We further investigate the effects of varying training data sizes, using artificial training data, and domain specificity for the APE task. We release this new corpus under CC BY-NC-SA 4.0 license at https://github.com/shamilcm/pedra.",
}
@inproceedings{kato-matsubara-2020-parsing,
    title = "Parsing Gapping Constructions Based on Grammatical and Semantic Roles",
    author = "Kato, Yoshihide  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.218",
    doi = "10.18653/v1/2020.emnlp-main.218",
    pages = "2747--2752",
    abstract = "A gapping construction consists of a coordinated structure where redundant elements are elided from all but one conjuncts. This paper proposes a method of parsing sentences with gapping to recover elided elements. The proposed method is based on constituent trees annotated with grammatical and semantic roles that are useful for identifying elided elements. Our method outperforms the previous method in terms of F-measure and recall.",
}
@inproceedings{corro-2020-span,
    title = "Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from {O}(n{\^{}}6) down to {O}(n{\^{}}3)",
    author = "Corro, Caio",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.219",
    doi = "10.18653/v1/2020.emnlp-main.219",
    pages = "2753--2764",
    abstract = "We introduce a novel chart-based algorithm for span-based parsing of discontinuous constituency trees of block degree two, including ill-nested structures. In particular, we show that we can build variants of our parser with smaller search spaces and time complexities ranging from O(n{\^{}}6) down to O(n{\^{}}3). The cubic time variant covers 98{\%} of constituents observed in linguistic treebanks while having the same complexity as continuous constituency parsers. We evaluate our approach on German and English treebanks (Negra, Tiger, and DPTB) and report state-of-the-art results in the fully supervised setting. We also experiment with pre-trained word embeddings and Bert-based neural networks.",
}
@inproceedings{sogaard-2020-languages,
    title = "Some Languages Seem Easier to Parse Because Their Treebanks Leak",
    author = "S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.220",
    doi = "10.18653/v1/2020.emnlp-main.220",
    pages = "2765--2770",
    abstract = "Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. We point at a factor not previously discussed: If we abstract away from words and dependency labels, how many graphs in the test data were seen in the training data? We compute graph isomorphisms, and show that, treebank size aside, overlap between training and test graphs explain more of the observed variation than standard explanations such as the above.",
}
@inproceedings{vilares-gomez-rodriguez-2020-discontinuous,
    title = "Discontinuous Constituent Parsing as Sequence Labeling",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.221",
    doi = "10.18653/v1/2020.emnlp-main.221",
    pages = "2771--2785",
    abstract = "This paper reduces discontinuous parsing to sequence labeling. It first shows that existing reductions for constituent parsing as labeling do not support discontinuities. Second, it fills this gap and proposes to encode tree discontinuities as nearly ordered permutations of the input sequence. Third, it studies whether such discontinuous representations are learnable. The experiments show that despite the architectural simplicity, under the right representation, the models are fast and accurate.",
}
@inproceedings{wu-etal-2020-modularized,
    title = "Modularized Syntactic Neural Networks for Sentence Classification",
    author = "Wu, Haiyan  and
      Liu, Ying  and
      Shi, Shaoyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.222",
    doi = "10.18653/v1/2020.emnlp-main.222",
    pages = "2786--2792",
    abstract = "This paper focuses on tree-based modeling for the sentence classification task. In existing works, aggregating on a syntax tree usually considers local information of sub-trees. In contrast, in addition to the local information, our proposed Modularized Syntactic Neural Network (MSNN) utilizes the syntax category labels and takes advantage of the global context while modeling sub-trees. In MSNN, each node of a syntax tree is modeled by a label-related syntax module. Each syntax module aggregates the outputs of lower-level modules, and finally, the root module provides the sentence representation. We design a tree-parallel mini-batch strategy for efficient training and predicting. Experimental results on four benchmark datasets show that our MSNN significantly outperforms previous state-of-the-art tree-based methods on the sentence classification task.",
}
@inproceedings{long-etal-2020-ted,
    title = "{TED}-{CDB}: A Large-Scale {C}hinese Discourse Relation Dataset on {TED} Talks",
    author = "Long, Wanqiu  and
      Webber, Bonnie  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.223",
    doi = "10.18653/v1/2020.emnlp-main.223",
    pages = "2793--2803",
    abstract = "As different genres are known to differ in their communicative properties and as previously, for Chinese, discourse relations have only been annotated over news text, we have created the TED-CDB dataset. TED-CDB comprises a large set of TED talks in Chinese that have been manually annotated according to the goals and principles of Penn Discourse Treebank, but adapted to features that are not present in English. It serves as a unique Chinese corpus of spoken discourse. Benchmark experiments show that TED-CDB poses a challenge for state-of-the-art discourse relation classifiers, whose F1 performance on 4-way classification is 60{\%}. This is a dramatic drop of 35{\%} from performance on the news text in the Chinese Discourse Treebank. Transfer learning experiments have been carried out with the TED-CDB for both same-language cross-domain transfer and same-domain cross-language transfer. Both demonstrate that the TED-CDB can improve the performance of systems being developed for languages other than Chinese and would be helpful for insufficient or unbalanced data in other corpora. The dataset and our Chinese annotation guidelines will be made freely available.",
}
@inproceedings{pyatkin-etal-2020-qadiscourse,
    title = "{QAD}iscourse - {D}iscourse {R}elations as {QA} {P}airs: {R}epresentation, {C}rowdsourcing and {B}aselines",
    author = "Pyatkin, Valentina  and
      Klein, Ayal  and
      Tsarfaty, Reut  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.224",
    doi = "10.18653/v1/2020.emnlp-main.224",
    pages = "2804--2819",
    abstract = "Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.",
}
@inproceedings{song-etal-2020-discourse,
    title = "Discourse Self-Attention for Discourse Element Identification in Argumentative Student Essays",
    author = "Song, Wei  and
      Song, Ziyao  and
      Fu, Ruiji  and
      Liu, Lizhen  and
      Cheng, Miaomiao  and
      Liu, Ting",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.225",
    doi = "10.18653/v1/2020.emnlp-main.225",
    pages = "2820--2830",
    abstract = "This paper proposes to adapt self-attention to discourse level for modeling discourse elements in argumentative student essays. Specifically, we focus on two issues. First, we propose structural sentence positional encodings to explicitly represent sentence positions. Second, we propose to use inter-sentence attentions to capture sentence interactions and enhance sentence representation. We conduct experiments on two datasets: a Chinese dataset and an English dataset. We find that (i) sentence positional encoding can lead to a large improvement for identifying discourse elements; (ii) a structural relative positional encoding of sentences shows to be most effective; (iii) inter-sentence attention vectors are useful as a kind of sentence representations for identifying discourse elements.",
}
@inproceedings{xu-etal-2020-megatron,
    title = "{MEGATRON}-{CNTRL}: Controllable Story Generation with External Knowledge Using Large-Scale Language Models",
    author = "Xu, Peng  and
      Patwary, Mostofa  and
      Shoeybi, Mohammad  and
      Puri, Raul  and
      Fung, Pascale  and
      Anandkumar, Anima  and
      Catanzaro, Bryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.226",
    doi = "10.18653/v1/2020.emnlp-main.226",
    pages = "2831--2845",
    abstract = "Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base. Our framework consists of a keyword predictor, a knowledge retriever, a contextual knowledge ranker, and a conditional text generator. As we do not have access to ground-truth supervision for the knowledge ranker, we make use of weak supervision from sentence embedding. The empirical results show that our model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to prior work on the ROC story dataset. We showcase the controllability of our model by replacing the keywords used to generate stories and re-running the generation process. Human evaluation results show that 77.5{\%} of these stories are successfully controlled by the new keywords. Furthermore, by scaling our model from 124 million to 8.3 billion parameters we demonstrate that larger models improve both the quality of generation (from 74.5{\%} to 93.0{\%} for consistency) and controllability (from 77.5{\%} to 91.5{\%}).",
}
@inproceedings{liu-etal-2020-incomplete,
    title = "Incomplete Utterance Rewriting as Semantic Segmentation",
    author = "Liu, Qian  and
      Chen, Bei  and
      Lou, Jian-Guang  and
      Zhou, Bin  and
      Zhang, Dongmei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.227",
    doi = "10.18653/v1/2020.emnlp-main.227",
    pages = "2846--2857",
    abstract = "Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-of-the-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.",
}
@inproceedings{wang-zheng-2020-improving,
    title = "Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples",
    author = "Wang, Lihao  and
      Zheng, Xiaoqing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.228",
    doi = "10.18653/v1/2020.emnlp-main.228",
    pages = "2858--2869",
    abstract = "A sequence-to-sequence (seq2seq) learning with neural networks empirically shows to be an effective framework for grammatical error correction (GEC), which takes a sentence with errors as input and outputs the corrected one. However, the performance of GEC models with the seq2seq framework heavily relies on the size and quality of the corpus on hand. We propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying the weak spots of a model, and to enhance the model by gradually adding the generated adversarial examples to the training set. Extensive experimental results show that such adversarial training can improve both the generalization and robustness of GEC models.",
}
@inproceedings{yu-etal-2020-homophonic,
    title = "Homophonic Pun Generation with Lexically Constrained Rewriting",
    author = "Yu, Zhiwei  and
      Zang, Hongyu  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.229",
    doi = "10.18653/v1/2020.emnlp-main.229",
    pages = "2870--2876",
    abstract = "Punning is a creative way to make conversation enjoyable and literary writing elegant. In this paper, we focus on the task of generating a pun sentence given a pair of homophones. We first find the constraint words supporting the semantic incongruity for a sentence. Then we rewrite the sentence with explicit positive and negative constraints. Our model achieves the state-of-the-art results in both automatic and human evaluations. We further make an error analysis and discuss the challenges for the computational pun models.",
}
@inproceedings{elder-etal-2020-make,
    title = "How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue",
    author = "Elder, Henry  and
      O{'}Connor, Alexander  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.230",
    doi = "10.18653/v1/2020.emnlp-main.230",
    pages = "2877--2888",
    abstract = "Neural Natural Language Generation (NLG) systems are well known for their unreliability. To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability. While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task. The system trained using this approach scored 100{\%} in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.",
}
@inproceedings{fan-gardent-2020-multilingual,
    title = "Multilingual {AMR}-to-Text Generation",
    author = "Fan, Angela  and
      Gardent, Claire",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.231",
    doi = "10.18653/v1/2020.emnlp-main.231",
    pages = "2889--2901",
    abstract = "Generating text from structured data is challenging because it requires bridging the gap between (i) structure and natural language (NL) and (ii) semantically underspecified input and fully specified NL output. Multilingual generation brings in an additional challenge: that of generating into languages with varied word order and morphological properties. In this work, we focus on Abstract Meaning Representations (AMRs) as structured input, where previous research has overwhelmingly focused on generating only into English. We leverage advances in cross-lingual embeddings, pretraining, and multilingual models to create multilingual AMR-to-text models that generate in twenty one different languages. Our multilingual models surpass baselines that generate into one language in eighteen languages, based on automatic metrics. We analyze the ability of our multilingual models to accurately capture morphology and word order using human evaluation, and find that native speakers judge our generations to be fluent.",
}
@inproceedings{vargas-cotterell-2020-exploring,
    title = "Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation",
    author = "Vargas, Francisco  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.232",
    doi = "10.18653/v1/2020.emnlp-main.232",
    pages = "2902--2913",
    abstract = "Bolukbasi et al. (2016) presents one of the first gender bias mitigation techniques for word embeddings. Their method takes pre-trained word embeddings as input and attempts to isolate a linear subspace that captures most of the gender bias in the embeddings. As judged by an analogical evaluation task, their method virtually eliminates gender bias in the embeddings. However, an implicit and untested assumption of their method is that the bias subspace is actually linear. In this work, we generalize their method to a kernelized, non-linear version. We take inspiration from kernel principal component analysis and derive a non-linear bias isolation technique. We discuss and overcome some of the practical drawbacks of our method for non-linear gender bias mitigation in word embeddings and analyze empirically whether the bias subspace is actually linear. Our analysis shows that gender bias is in fact well captured by a linear subspace, justifying the assumption of Bolukbasi et al. (2016).",
}
@inproceedings{chuang-etal-2020-lifelong,
    title = "Lifelong Language Knowledge Distillation",
    author = "Chuang, Yung-Sung  and
      Su, Shang-Yu  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.233",
    doi = "10.18653/v1/2020.emnlp-main.233",
    pages = "2914--2924",
    abstract = "It is challenging to perform lifelong language learning (LLL) on a stream of different tasks without any performance degradation comparing to the multi-task counterparts. To address this issue, we present Lifelong Language Knowledge Distillation (L2KD), a simple but efficient method that can be easily applied to existing LLL architectures in order to mitigate the degradation. Specifically, when the LLL model is trained on a new task, we assign a teacher model to first learn the new task, and pass the knowledge to the LLL model via knowledge distillation. Therefore, the LLL model can better adapt to the new task while keeping the previously learned knowledge. Experiments show that the proposed L2KD consistently improves previous state-of-the-art models, and the degradation comparing to multi-task models in LLL tasks is well mitigated for both sequence generation and text classification tasks.",
}
@inproceedings{terenin-etal-2020-sparse,
    title = "Sparse Parallel Training of Hierarchical {D}irichlet Process Topic Models",
    author = "Terenin, Alexander  and
      Magnusson, M{\aa}ns  and
      Jonsson, Leif",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.234",
    doi = "10.18653/v1/2020.emnlp-main.234",
    pages = "2925--2934",
    abstract = "To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems. In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model. Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model. This sampler utilizes all available sources of sparsity found in natural language - an important way to make computation efficient. We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days.",
}
@inproceedings{lu-etal-2020-multi,
    title = "Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs",
    author = "Lu, Jueqing  and
      Du, Lan  and
      Liu, Ming  and
      Dipnall, Joanna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.235",
    doi = "10.18653/v1/2020.emnlp-main.235",
    pages = "2935--2943",
    abstract = "Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III ) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.",
}
@inproceedings{yokoi-etal-2020-word,
    title = "Word Rotator{'}s Distance",
    author = "Yokoi, Sho  and
      Takahashi, Ryo  and
      Akama, Reina  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.236",
    doi = "10.18653/v1/2020.emnlp-main.236",
    pages = "2944--2960",
    abstract = "One key principle for assessing textual similarity is measuring the degree of semantic overlap between texts by considering the word alignment. Such alignment-based approaches are both intuitive and interpretable; however, they are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. We focus on the fact that the norm of word vectors is a good proxy for word importance, and the angle of them is a good proxy for word similarity. However, alignment-based approaches do not distinguish the norm and direction, whereas sentence-vector approaches automatically use the norm as the word importance. Accordingly, we propose decoupling word vectors into their norm and direction then computing the alignment-based similarity with the help of earth mover{'}s distance (optimal transport), which we refer to as word rotator{'}s distance. Furthermore, we demonstrate how to grow the norm and direction of word vectors (vector converter); this is a new systematic approach derived from the sentence-vector estimation methods, which can significantly improve the performance of the proposed method. On several STS benchmarks, the proposed methods outperform not only alignment-based approaches but also strong baselines. The source code is avaliable at https://github.com/eumesy/wrd",
}
@inproceedings{kou-etal-2020-disentangle,
    title = "{D}isentangle-based {C}ontinual {G}raph {R}epresentation {L}earning",
    author = "Kou, Xiaoyu  and
      Lin, Yankai  and
      Liu, Shaobo  and
      Li, Peng  and
      Zhou, Jie  and
      Zhang, Yan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.237",
    doi = "10.18653/v1/2020.emnlp-main.237",
    pages = "2961--2972",
    abstract = "Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming data. To address this issue, we study the problem of continual graph representation learning which aims to continually train a GE model on new data to learn incessantly emerging multi-relational data while avoiding catastrophically forgetting old learned knowledge. Moreover, we propose a disentangle-based continual graph representation learning (DiCGRL) framework inspired by the human{'}s ability to learn procedural knowledge. The experimental results show that DiCGRL could effectively alleviate the catastrophic forgetting problem and outperform state-of-the-art continual learning models. The code and datasets are released on https://github.com/KXY-PUBLIC/DiCGRL.",
}
@inproceedings{zhao-etal-2020-semi,
    title = "Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction",
    author = "Zhao, Xu  and
      Wang, Zihao  and
      Wu, Hao  and
      Zhang, Yong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.238",
    doi = "10.18653/v1/2020.emnlp-main.238",
    pages = "2973--2984",
    abstract = "Semi-supervision is a promising paradigm for Bilingual Lexicon Induction (BLI) with limited annotations. However, previous semisupervised methods do not fully utilize the knowledge hidden in annotated and nonannotated data, which hinders further improvement of their performance. In this paper, we propose a new semi-supervised BLI framework to encourage the interaction between the supervised signal and unsupervised alignment. We design two message-passing mechanisms to transfer knowledge between annotated and non-annotated data, named prior optimal transport and bi-directional lexicon update respectively. Then, we perform semi-supervised learning based on a cyclic or a parallel parameter feeding routine to update our models. Our framework is a general framework that can incorporate any supervised and unsupervised BLI methods based on optimal transport. Experimental results on MUSE and VecMap datasets show significant improvement of our models. Ablation study also proves that the two-way interaction between the supervised signal and unsupervised alignment accounts for the gain of the overall performance. Results on distant language pairs further illustrate the advantage and robustness of our proposed method.",
}
@inproceedings{yu-etal-2020-wasserstein,
    title = "{W}asserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains",
    author = "Yu, Weijie  and
      Xu, Chen  and
      Xu, Jun  and
      Pang, Liang  and
      Gao, Xiaopeng  and
      Wang, Xiaozhao  and
      Wen, Ji-Rong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.239",
    doi = "10.18653/v1/2020.emnlp-main.239",
    pages = "2985--2994",
    abstract = "One approach to matching texts from asymmetrical domains is projecting the input sequences into a common semantic space as feature vectors upon which the matching function can be readily defined and learned. In real-world matching practices, it is often observed that with the training goes on, the feature vectors projected from different domains tend to be indistinguishable. The phenomenon, however, is often overlooked in existing matching models. As a result, the feature vectors are constructed without any regularization, which inevitably increases the difficulty of learning the downstream matching functions. In this paper, we propose a novel match method tailored for text matching in asymmetrical domains, called WD-Match. In WD-Match, a Wasserstein distance-based regularizer is defined to regularize the features vectors projected from different domains. As a result, the method enforces the feature projection function to generate vectors such that those correspond to different domains cannot be easily discriminated. The training process of WD-Match amounts to a game that minimizes the matching loss regularized by the Wasserstein distance. WD-Match can be used to improve different text matching methods, by using the method as its underlying matching model. Four popular text matching methods have been exploited in the paper. Experimental results based on four publicly available benchmarks showed that WD-Match consistently outperformed the underlying methods and the baselines.",
}
@inproceedings{jawanpuria-etal-2020-simple,
    title = "A Simple Approach to Learning Unsupervised Multilingual Embeddings",
    author = "Jawanpuria, Pratik  and
      Meghwanshi, Mayank  and
      Mishra, Bamdev",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.240",
    doi = "10.18653/v1/2020.emnlp-main.240",
    pages = "2995--3001",
    abstract = "Recent progress on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embedding space for several languages. A popular framework to solve the latter problem is to solve the following two sub-problems jointly: 1) learning unsupervised word alignment between several language pairs, and 2) learning how to map the monolingual embeddings of every language to shared multilingual space. In contrast, we propose a simple approach by decoupling the above two sub-problems and solving them separately, one after another, using existing techniques. We show that this proposed approach obtains surprisingly good performance in tasks such as bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing. When distant languages are involved, the proposed approach shows robust behavior and outperforms existing unsupervised multilingual word embedding approaches.",
}
@inproceedings{chaudhury-etal-2020-bootstrapped,
    title = "Bootstrapped {Q}-learning with Context Relevant Observation Pruning to Generalize in Text-based Games",
    author = "Chaudhury, Subhajit  and
      Kimura, Daiki  and
      Talamadupula, Kartik  and
      Tatsubori, Michiaki  and
      Munawar, Asim  and
      Tachibana, Ryuki",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.241",
    doi = "10.18653/v1/2020.emnlp-main.241",
    pages = "3002--3008",
    abstract = "We show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this issue, we propose Context Relevant Episodic State Truncation (CREST) for irrelevant token removal in observation text for improved generalization. Our method first trains a base model using Q-learning, which typically overfits the training games. The base model{'}s action token distribution is used to perform observation pruning that removes irrelevant tokens. A second bootstrapped model is then retrained on the pruned observation text. Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art (SOTA) methods despite requiring fewer number of training episodes.",
}
@inproceedings{li-etal-2020-bert-emd,
    title = "{BERT}-{EMD}: Many-to-Many Layer Mapping for {BERT} Compression with Earth Mover{'}s Distance",
    author = "Li, Jianquan  and
      Liu, Xiaokang  and
      Zhao, Honghong  and
      Xu, Ruifeng  and
      Yang, Min  and
      Jin, Yaohong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.242",
    doi = "10.18653/v1/2020.emnlp-main.242",
    pages = "3009--3018",
    abstract = "Pre-trained language models (e.g., BERT) have achieved significant success in various natural language processing (NLP) tasks. However, high storage and computational costs obstruct pre-trained language models to be effectively deployed on resource-constrained devices. In this paper, we propose a novel BERT distillation method based on many-to-many layer mapping, which allows each intermediate student layer to learn from any intermediate teacher layers. In this way, our model can learn from different teacher layers adaptively for different NLP tasks. In addition, we leverage Earth Mover{'}s Distance (EMD) to compute the minimum cumulative cost that must be paid to transform knowledge from teacher network to student network. EMD enables effective matching for the many-to-many layer mapping. Furthermore, we propose a cost attention mechanism to learn the layer weights used in EMD automatically, which is supposed to further improve the model{'}s performance and accelerate convergence time. Extensive experiments on GLUE benchmark demonstrate that our model achieves competitive performance compared to strong competitors in terms of both accuracy and model compression",
}
@inproceedings{wang-etal-2020-slot,
    title = "Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking",
    author = "Wang, Yexiang  and
      Guo, Yi  and
      Zhu, Siqi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.243",
    doi = "10.18653/v1/2020.emnlp-main.243",
    pages = "3019--3028",
    abstract = "Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). Existing approaches generally fall into two extremes: choosing models without ontology or embedding ontology in models leading to over-dependence. In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. Moreover, we supplement the annotation of supporting span for MultiWOZ 2.1, which is the shortest span in utterances to support the labeled value. SA shares knowledge between slots and utterances and only needs a simple structure to predict the supporting span. VN is designed specifically for the use of ontology, which can convert supporting spans to the values. Empirical results demonstrate that SAVN achieves the state-of-the-art joint accuracy of 54.52{\%} on MultiWOZ 2.0 and 54.86{\%} on MultiWOZ 2.1. Besides, we evaluate VN with incomplete ontology. The results show that even if only 30{\%} ontology is used, VN can also contribute to our model.",
}
@inproceedings{wu-etal-2020-dont-read,
    title = "Don{'}t Read Too Much Into It: Adaptive Computation for Open-Domain Question Answering",
    author = "Wu, Yuxiang  and
      Riedel, Sebastian  and
      Minervini, Pasquale  and
      Stenetorp, Pontus",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.244",
    doi = "10.18653/v1/2020.emnlp-main.244",
    pages = "3029--3039",
    abstract = "Most approaches to Open-Domain Question Answering consist of a light-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines the passages to identify the correct answer. Previous works have shown that as the number of retrieved passages increases, so does the performance of the reader. However, they assume all retrieved passages are of equal importance and allocate the same amount of computation to them, leading to a substantial increase in computational cost. To reduce this cost, we propose the use of adaptive computation to control the computational budget allocated for the passages to be read. We first introduce a technique operating on individual passages in isolation which relies on anytime prediction and a per-layer estimation of early exit probability. We then introduce SKYLINEBUILDER, an approach for dynamically deciding on which passage to allocate computation at each step, based on a resource allocation policy trained via reinforcement learning. Our results on SQuAD-Open show that adaptive computation with global prioritisation improves over several strong static and adaptive methods, leading to a 4.3x reduction in computation while retaining 95{\%} performance of the full model.",
}
@inproceedings{liu-etal-2020-multi,
    title = "Multi-Step Inference for Reasoning Over Paragraphs",
    author = "Liu, Jiangming  and
      Gardner, Matt  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.245",
    doi = "10.18653/v1/2020.emnlp-main.245",
    pages = "3040--3050",
    abstract = "Complex reasoning over text requires understanding and chaining together free-form predicates and logical connectives. Prior work has largely tried to do this either symbolically or with black-box transformers. We present a middle ground between these two extremes: a compositional model reminiscent of neural module networks that can perform chained logical reasoning. This model first finds relevant sentences in the context and then chains them together using neural modules. Our model gives significant performance improvements (up to 29{\%} relative error reduction when combined with a reranker) on ROPES, a recently-introduced complex reasoning dataset.",
}
@inproceedings{kratzwald-etal-2020-learning,
    title = "Learning a {C}ost-{E}ffective {A}nnotation {P}olicy for {Q}uestion {A}nswering",
    author = "Kratzwald, Bernhard  and
      Feuerriegel, Stefan  and
      Sun, Huan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.246",
    doi = "10.18653/v1/2020.emnlp-main.246",
    pages = "3051--3062",
    abstract = "State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems is challenging. As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme. The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations. Human annotators then simply provide binary feedback on these candidates. Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost. To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost. We compare our framework against traditional manual annotations in an extensive set of experiments. We find that our approach can reduce up to 21.1{\%} of the annotation cost.",
}
@inproceedings{tian-etal-2020-scene,
    title = "Scene Restoring for Narrative Machine Reading Comprehension",
    author = "Tian, Zhixing  and
      Zhang, Yuanzhe  and
      Liu, Kang  and
      Zhao, Jun  and
      Jia, Yantao  and
      Sheng, Zhicheng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.247",
    doi = "10.18653/v1/2020.emnlp-main.247",
    pages = "3063--3073",
    abstract = "This paper focuses on machine reading comprehension for narrative passages. Narrative passages usually describe a chain of events. When reading this kind of passage, humans tend to restore a scene according to the text with their prior knowledge, which helps them understand the passage comprehensively. Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension. Specifically, we build a scene graph by utilizing Atomic as the external knowledge and propose a novel Graph Dimensional-Iteration Network (GDIN) to encode the graph. We conduct experiments on the ROCStories, a dataset of Story Cloze Test (SCT), and CosmosQA, a dataset of multiple choice. Our method achieves state-of-the-art.",
}
@inproceedings{segal-etal-2020-simple,
    title = "A Simple and Effective Model for Answering Multi-span Questions",
    author = "Segal, Elad  and
      Efrat, Avia  and
      Shoham, Mor  and
      Globerson, Amir  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.248",
    doi = "10.18653/v1/2020.emnlp-main.248",
    pages = "3074--3080",
    abstract = "Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid the need for a model that generates text explicitly. However, forcing an answer to be a single span can be restrictive, and some recent datasets also include multi-span questions, i.e., questions whose answer is a set of non-contiguous spans in the text. Naturally, models that return single spans cannot answer these questions. In this work, we propose a simple architecture for answering multi-span questions by casting the task as a sequence tagging problem, namely, predicting for each input token whether it should be part of the output or not. Our model substantially improves performance on span extraction questions from DROP and Quoref by 9.9 and 5.5 EM points respectively.",
}
@inproceedings{lombardo-etal-2020-top,
    title = "Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models",
    author = "Lombardo, Pierangelo  and
      Boiardi, Alessio  and
      Colombo, Luca  and
      Schiavone, Angelo  and
      Tamagnone, Nicol{\`o}",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.249",
    doi = "10.18653/v1/2020.emnlp-main.249",
    pages = "3081--3093",
    abstract = "The growth of domain-specific applications of semantic models, boosted by the recent achievements of unsupervised embedding learning algorithms, demands domain-specific evaluation datasets. In many cases, content-based recommenders being a prime example, these models are required to rank words or texts according to their semantic relatedness to a given concept, with particular focus on top ranks. In this work, we give a threefold contribution to address these requirements: (i) we define a protocol for the construction, based on adaptive pairwise comparisons, of a relatedness-based evaluation dataset tailored on the available resources and optimized to be particularly accurate in top-rank evaluation; (ii) we define appropriate metrics, extensions of well-known ranking correlation coefficients, to evaluate a semantic model via the aforementioned dataset by taking into account the greater significance of top ranks. Finally, (iii) we define a stochastic transitivity model to simulate semantic-driven pairwise comparisons, which confirms the effectiveness of the proposed dataset construction protocol.",
}
@inproceedings{wang-etal-2020-meta,
    title = "Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining",
    author = "Wang, Chengyu  and
      Qiu, Minghui  and
      Huang, Jun  and
      He, Xiaofeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.250",
    doi = "10.18653/v1/2020.emnlp-main.250",
    pages = "3094--3104",
    abstract = "Pre-trained neural language models bring significant improvement for various NLP tasks, by fine-tuning the models on task-specific training sets. During fine-tuning, the parameters are initialized from pre-trained models directly, which ignores how the learning process of similar NLP tasks in different domains is correlated and mutually reinforced. In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), serving as a meta-learner to solve a group of similar NLP tasks for neural language models. Instead of simply multi-task training over all the datasets, MFT only learns from typical instances of various domains to acquire highly transferable knowledge. It further encourages the language model to encode domain-invariant representations by optimizing a series of novel domain corruption loss functions. After MFT, the model can be fine-tuned for each domain with better parameter initializations and higher generalization ability. We implement MFT upon BERT to solve several multi-domain text mining tasks. Experimental results confirm the effectiveness of MFT and its usefulness for few-shot learning.",
}
@inproceedings{chen-lee-2020-incorporating,
    title = "Incorporating Behavioral Hypotheses for Query Generation",
    author = "Chen, Ruey-Cheng  and
      Lee, Chia-Jung",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.251",
    doi = "10.18653/v1/2020.emnlp-main.251",
    pages = "3105--3110",
    abstract = "Generative neural networks have been shown effective on query suggestion. Commonly posed as a conditional generation problem, the task aims to leverage earlier inputs from users in a search session to predict queries that they will likely issue at a later time. User inputs come in various forms such as querying and clicking, each of which can imply different semantic signals channeled through the corresponding behavioral patterns. This paper induces these behavioral biases as hypotheses for query generation, where a generic encoder-decoder Transformer framework is presented to aggregate arbitrary hypotheses of choice. Our experimental results show that the proposed approach leads to significant improvements on top-k word error rate and Bert F1 Score compared to a recent BART model.",
}
@inproceedings{chen-etal-2020-conditional,
    title = "Conditional Causal Relationships between Emotions and Causes in Texts",
    author = "Chen, Xinhong  and
      Li, Qing  and
      Wang, Jianping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.252",
    doi = "10.18653/v1/2020.emnlp-main.252",
    pages = "3111--3121",
    abstract = "The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works focus on the extraction of the causally related clauses from documents. However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related. To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset. Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses. Experiments demonstrate the effectiveness and generality of our aggregation module.",
}
@inproceedings{basaldella-etal-2020-cometa,
    title = "{COMETA}: A Corpus for Medical Entity Linking in the Social Media",
    author = "Basaldella, Marco  and
      Liu, Fangyu  and
      Shareghi, Ehsan  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.253",
    doi = "10.18653/v1/2020.emnlp-main.253",
    pages = "3122--3137",
    abstract = "Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman{'}s language. Meanwhile, there is a growing need for applications that can understand the public{'}s voice in the health domain. To address this we introduce a new corpus called COMETA, consisting of 20k English biomedical entity mentions from Reddit expert-annotated with links to SNOMED CT, a widely-used medical knowledge graph. Our corpus satisfies a combination of desirable properties, from scale and coverage to diversity and quality, that to the best of our knowledge has not been met by any of the existing resources in the field. Through benchmark experiments on 20 EL baselines from string- to neural-based models we shed light on the ability of these systems to perform complex inference on entities and concepts under 2 challenging evaluation scenarios. Our experimental results on COMETA illustrate that no golden bullet exists and even the best mainstream techniques still have a significant performance gap to fill, while the best solution relies on combining different views of data.",
}
@inproceedings{pimentel-etal-2020-pareto,
    title = "{P}areto Probing: {T}rading Off Accuracy for Complexity",
    author = "Pimentel, Tiago  and
      Saphra, Naomi  and
      Williams, Adina  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.254",
    doi = "10.18653/v1/2020.emnlp-main.254",
    pages = "3138--3153",
    abstract = "The question of how to probe contextual word representations in a way that is principled and useful has seen significant recent attention. In our contribution to this discussion, we argue, first, for a probe metric that reflects the trade-off between probe complexity and performance: the Pareto hypervolume. To measure complexity, we present a number of parametric and non-parametric metrics. Our experiments with such metrics show that probe{'}s performance curves often fail to align with widely accepted rankings between language representations (with, e.g., non-contextual representations outperforming contextual ones). These results lead us to argue, second, that common simplistic probe tasks such as POS labeling and dependency arc labeling, are inadequate to evaluate the properties encoded in contextual word representations. We propose full dependency parsing as an example probe task, and demonstrate it with the Pareto hypervolume. In support of our arguments, the results of this illustrative experiment conform closer to accepted rankings among contextual word representations.",
}
@inproceedings{kim-etal-2020-interpretation,
    title = "Interpretation of {NLP} models through input marginalization",
    author = "Kim, Siwon  and
      Yi, Jihun  and
      Kim, Eunji  and
      Yoon, Sungroh",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.255",
    doi = "10.18653/v1/2020.emnlp-main.255",
    pages = "3154--3167",
    abstract = "To demystify the {``}black box{''} property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input. Since existing methods replace each token with a predefined value (i.e., zero), the resulting sentence lies out of the training data distribution, yielding misleading interpretations. In this study, we raise the out-of-distribution problem induced by the existing interpretation methods and present a remedy; we propose to marginalize each token out. We interpret various NLP models trained for sentiment analysis and natural language inference using the proposed method.",
}
@inproceedings{atanasova-etal-2020-generating-label,
    title = "Generating Label Cohesive and Well-Formed Adversarial Claims",
    author = "Atanasova, Pepa  and
      Wright, Dustin  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.256",
    doi = "10.18653/v1/2020.emnlp-main.256",
    pages = "3168--3177",
    abstract = "Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, when appended to instances of a class under attack, can trick a model into predicting a target class. However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in. In addition, such attacks produce semantically nonsensical inputs, as they simply concatenate triggers to existing samples. Here, we investigate how to generate adversarial attacks against fact checking systems that preserve the ground truth meaning and are semantically valid. We extend the HotFlip attack algorithm used for universal trigger generation by jointly minimizing the target class loss of a fact checking model and the entailment class loss of an auxiliary natural language inference model. We then train a conditional language model to generate semantically valid statements, which include the found universal triggers. We find that the generated attacks maintain the directionality and semantic validity of the claim better than previous work.",
}
@inproceedings{vulic-etal-2020-good,
    title = "Are All Good Word Vector Spaces Isomorphic?",
    author = "Vuli{\'c}, Ivan  and
      Ruder, Sebastian  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.257",
    doi = "10.18653/v1/2020.emnlp-main.257",
    pages = "3178--3192",
    abstract = "Existing algorithms for aligning cross-lingual word vector spaces assume that vector spaces are approximately isomorphic. As a result, they perform poorly or fail completely on non-isomorphic spaces. Such non-isomorphism has been hypothesised to result from typological differences between languages. In this work, we ask whether non-isomorphism is also crucially a sign of degenerate word vector spaces. We present a series of experiments across diverse languages which show that variance in performance across language pairs is not only due to typological differences, but can mostly be attributed to the size of the monolingual resources available, and to the properties and duration of monolingual training (e.g. {``}under-training{''}).",
}
@inproceedings{jiang-etal-2020-cold,
    title = "Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks",
    author = "Jiang, Chengyue  and
      Zhao, Yinggong  and
      Chu, Shanbo  and
      Shen, Libin  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.258",
    doi = "10.18653/v1/2020.emnlp-main.258",
    pages = "3193--3207",
    abstract = "Neural networks can achieve impressive performance on many natural language processing applications, but they typically need large labeled data for training and are not easily interpretable. On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios. In this paper, we propose a type of recurrent neural networks called FA-RNNs that combine the advantages of neural networks and regular expression rules. An FA-RNN can be converted from regular expressions and deployed in zero-shot and cold-start scenarios. It can also utilize labeled data for training to achieve improved prediction accuracy. After training, an FA-RNN often remains interpretable and can be converted back into regular expressions. We apply FA-RNNs to text classification and observe that FA-RNNs significantly outperform previous neural approaches in both zero-shot and low-resource settings and remain very competitive in rich-resource settings.",
}
@inproceedings{prasanna-etal-2020-bert,
    title = "{W}hen {BERT} {P}lays the {L}ottery, {A}ll {T}ickets {A}re {W}inning",
    author = "Prasanna, Sai  and
      Rogers, Anna  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.259",
    doi = "10.18653/v1/2020.emnlp-main.259",
    pages = "3208--3229",
    abstract = "Large Transformer-based models were shown to be reducible to a smaller number of self-attention heads and layers. We consider this phenomenon from the perspective of the lottery ticket hypothesis, using both structured and magnitude pruning. For fine-tuned BERT, we show that (a) it is possible to find subnetworks achieving performance that is comparable with that of the full model, and (b) similarly-sized subnetworks sampled from the rest of the model perform worse. Strikingly, with structured pruning even the worst possible subnetworks remain highly trainable, indicating that most pre-trained BERT weights are potentially useful. We also study the {``}good{''} subnetworks to see if their success can be attributed to superior linguistic knowledge, but find them unstable, and not explained by meaningful self-attention patterns.",
}
@inproceedings{budhraja-etal-2020-weak,
    title = "On the weak link between importance and prunability of attention heads",
    author = "Budhraja, Aakriti  and
      Pande, Madhura  and
      Nema, Preksha  and
      Kumar, Pratyush  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.260",
    doi = "10.18653/v1/2020.emnlp-main.260",
    pages = "3230--3235",
    abstract = "Given the success of Transformer-based models, two directions of study have emerged: interpreting role of individual attention heads and down-sizing the models for efficiency. Our work straddles these two streams: We analyse the importance of basing pruning strategies on the interpreted role of the attention heads. We evaluate this on Transformer and BERT models on multiple NLP tasks. Firstly, we find that a large fraction of the attention heads can be randomly pruned with limited effect on accuracy. Secondly, for Transformers, we find no advantage in pruning attention heads identified to be important based on existing studies that relate importance to the location of a head. On the BERT model too we find no preference for top or bottom layers, though the latter are reported to have higher importance. However, strategies that avoid pruning middle layers and consecutive layers perform better. Finally, during fine-tuning the compensation for pruned attention heads is roughly equally distributed across the un-pruned heads. Our results thus suggest that interpretation of attention heads does not strongly inform pruning.",
}
@inproceedings{ramnath-etal-2020-towards,
    title = "Towards Interpreting {BERT} for Reading Comprehension Based {QA}",
    author = "Ramnath, Sahana  and
      Nema, Preksha  and
      Sahni, Deep  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.261",
    doi = "10.18653/v1/2020.emnlp-main.261",
    pages = "3236--3242",
    abstract = "BERT and its variants have achieved state-of-the-art performance in various NLP tasks. Since then, various works have been proposed to analyze the linguistic information being captured in BERT. However, the current works do not provide an insight into how BERT is able to achieve near human-level performance on the task of Reading Comprehension based Question Answering. In this work, we attempt to interpret BERT for RCQA. Since BERT layers do not have predefined roles, we define a layer{'}s role or functionality using Integrated Gradients. Based on the defined roles, we perform a preliminary analysis across all layers. We observed that the initial layers focus on query-passage interaction, whereas later layers focus more on contextual understanding and enhancing the answer prediction. Specifically for quantifier questions (how much/how many), we notice that BERT focuses on confusing words (i.e., on other numerical quantities in the passage) in the later layers, but still manages to predict the answer correctly. The fine-tuning and analysis scripts will be publicly available at https://github.com/iitmnlp/BERT-Analysis-RCQA.",
}
@inproceedings{de-cao-etal-2020-decisions,
    title = "How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking",
    author = "De Cao, Nicola  and
      Schlichtkrull, Michael Sejr  and
      Aziz, Wilker  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.262",
    doi = "10.18653/v1/2020.emnlp-main.262",
    pages = "3243--3255",
    abstract = "Attribution methods assess the contribution of inputs to the model prediction. One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed without affecting the prediction. Though conceptually simple, erasure{'}s objective is intractable and approximate search remains expensive with modern deep NLP models. Erasure is also susceptible to the hindsight bias: the fact that an input can be dropped does not mean that the model {`}knows{'} it can be dropped. The resulting pruning is over-aggressive and does not reflect how the model arrives at the prediction. To deal with these challenges, we introduce Differentiable Masking. DiffMask learns to mask-out subsets of the input while maintaining differentiability. The decision to include or disregard an input token is made with a simple model based on intermediate hidden layers of the analyzed model. First, this makes the approach efficient because we predict rather than search. Second, as with probing classifiers, this reveals what the network {`}knows{'} at the corresponding layers. This lets us not only plot attribution heatmaps but also analyze how decisions are formed across network layers. We use DiffMask to study BERT models on sentiment classification and question answering.",
}
@inproceedings{atanasova-etal-2020-diagnostic,
    title = "A Diagnostic Study of Explainability Techniques for Text Classification",
    author = "Atanasova, Pepa  and
      Simonsen, Jakob Grue  and
      Lioma, Christina  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.263",
    doi = "10.18653/v1/2020.emnlp-main.263",
    pages = "3256--3274",
    abstract = "Recent developments in machine learning have introduced models that approach human performance at the cost of increased architectural complexity. Efforts to make the rationales behind the models{'} predictions transparent have inspired an abundance of new explainability techniques. Provided with an already trained model, they compute saliency scores for the words of an input instance. However, there exists no definitive guide on (i) how to choose such a technique given a particular application task and model architecture, and (ii) the benefits and drawbacks of using each such technique. In this paper, we develop a comprehensive list of diagnostic properties for evaluating existing explainability techniques. We then employ the proposed list to compare a set of diverse explainability techniques on downstream text classification tasks and neural network architectures. We also compare the saliency scores assigned by the explainability techniques with human annotations of salient input regions to find relations between a model{'}s performance and the agreement of its rationales with human ones. Overall, we find that the gradient-based explanations perform best across tasks and model architectures, and we present further insights into the properties of the reviewed explainability techniques.",
}
@inproceedings{singh-shekhar-2020-stl,
    title = "{STL-CQA}: Structure-based Transformers with Localization and Encoding for Chart Question Answering",
    author = "Singh, Hrituraj  and
      Shekhar, Sumit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.264",
    doi = "10.18653/v1/2020.emnlp-main.264",
    pages = "3275--3284",
    abstract = "Chart Question Answering (CQA) is the task of answering natural language questions about visualisations in the chart image. Recent solutions, inspired by VQA approaches, rely on image-based attention for question/answering while ignoring the inherent chart structure. We propose STL-CQA which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach. We conduct extensive experiments while proposing pre-training tasks, methodology and also an improved dataset with more complex and balanced questions of different types. The proposed methodology shows a significant accuracy improvement compared to the state-of-the-art approaches on various chart Q/A datasets, while outperforming even human baseline on the DVQA Dataset. We also demonstrate interpretability while examining different components in the inference pipeline.",
}
@inproceedings{liang-etal-2020-learning,
    title = "Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering",
    author = "Liang, Zujie  and
      Jiang, Weitao  and
      Hu, Haifeng  and
      Zhu, Jiaying",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.265",
    doi = "10.18653/v1/2020.emnlp-main.265",
    pages = "3285--3292",
    abstract = "In the task of Visual Question Answering (VQA), most state-of-the-art models tend to learn spurious correlations in the training set and achieve poor performance in out-of-distribution test data. Some methods of generating counterfactual samples have been proposed to alleviate this problem. However, the counterfactual samples generated by most previous methods are simply added to the training data for augmentation and are not fully utilized. Therefore, we introduce a novel self-supervised contrastive learning mechanism to learn the relationship between original samples, factual samples and counterfactual samples. With the better cross-modal joint embeddings learned from the auxiliary training objective, the reasoning capability and robustness of the VQA model are boosted significantly. We evaluate the effectiveness of our method by surpassing current state-of-the-art models on the VQA-CP dataset, a diagnostic benchmark for assessing the VQA model{'}s robustness.",
}
@inproceedings{zhao-etal-2020-learning,
    title = "Learning {P}hysical {C}ommon {S}ense as {K}nowledge {G}raph {C}ompletion via {BERT} {D}ata {A}ugmentation and {C}onstrained {T}ucker {F}actorization",
    author = "Zhao, Zhenjie  and
      Papalexakis, Evangelos  and
      Ma, Xiaojuan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.266",
    doi = "10.18653/v1/2020.emnlp-main.266",
    pages = "3293--3298",
    abstract = "Physical common sense plays an essential role in the cognition abilities of robots for human-robot interaction. Machine learning methods have shown promising results on physical commonsense learning in natural language processing but still suffer from model generalization. In this paper, we formulate physical commonsense learning as a knowledge graph completion problem to better use the latent relationships among training samples. Compared with completing general knowledge graphs, completing a physical commonsense knowledge graph has three unique characteristics: training data are scarce, not all facts can be mined from existing texts, and the number of relationships is small. To deal with these problems, we first use a pre-training language model BERT to augment training data, and then employ constrained tucker factorization to model complex relationships by constraining types and adding negative relationships. We compare our method with existing state-of-the-art knowledge graph embedding methods and show its superior performance.",
}
@inproceedings{kamezawa-etal-2020-visually,
    title = "A Visually-grounded First-person Dialogue Dataset with Verbal and Non-verbal Responses",
    author = "Kamezawa, Hisashi  and
      Nishida, Noriki  and
      Shimizu, Nobuyuki  and
      Miyazaki, Takashi  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.267",
    doi = "10.18653/v1/2020.emnlp-main.267",
    pages = "3299--3310",
    abstract = "In real-world dialogue, first-person visual information about where the other speakers are and what they are paying attention to is crucial to understand their intentions. Non-verbal responses also play an important role in social interactions. In this paper, we propose a visually-grounded first-person dialogue (VFD) dataset with verbal and non-verbal responses. The VFD dataset provides manually annotated (1) first-person images of agents, (2) utterances of human speakers, (3) eye-gaze locations of the speakers, and (4) the agents{'} verbal and non-verbal responses. We present experimental results obtained using the proposed VFD dataset and recent neural network models (e.g., BERT, ResNet). The results demonstrate that first-person vision helps neural network models correctly understand human intentions, and the production of non-verbal responses is a challenging task like that of verbal responses. Our dataset is publicly available.",
}
@inproceedings{wang-etal-2020-cross-media,
    title = "Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings",
    author = "Wang, Yue  and
      Li, Jing  and
      Lyu, Michael  and
      King, Irwin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.268",
    doi = "10.18653/v1/2020.emnlp-main.268",
    pages = "3311--3324",
    abstract = "Social media produces large amounts of contents every day. To help users quickly capture what they need, keyphrase prediction is receiving a growing attention. Nevertheless, most prior efforts focus on text modeling, largely ignoring the rich features embedded in the matching images. In this work, we explore the joint effects of texts and images in predicting the keyphrases for a multimedia post. To better align social media style texts and images, we propose: (1) a novel Multi-Modality MultiHead Attention (M3H-Att) to capture the intricate cross-media interactions; (2) image wordings, in forms of optical characters and image attributes, to bridge the two modalities. Moreover, we design a unified framework to leverage the outputs of keyphrase classification and generation and couple their advantages. Extensive experiments on a large-scale dataset newly collected from Twitter show that our model significantly outperforms the previous state of the art based on traditional attention mechanisms. Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.",
}
@inproceedings{wang-etal-2020-vd,
    title = "{VD-BERT}: {A} {U}nified {V}ision and {D}ialog {T}ransformer with {BERT}",
    author = "Wang, Yue  and
      Joty, Shafiq  and
      Lyu, Michael  and
      King, Irwin  and
      Xiong, Caiming  and
      Hoi, Steven C.H.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.269",
    doi = "10.18653/v1/2020.emnlp-main.269",
    pages = "3325--3338",
    abstract = "Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior work has mostly focused on various attention mechanisms to model such intricate interactions. By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks. The model is unified in that (1) it captures all the interactions between the image and the multi-turn dialog using a single-stream Transformer encoder, and (2) it supports both answer ranking and answer generation seamlessly through the same architecture. More crucially, we adapt BERT for the effective fusion of vision and dialog contents via visually grounded training. Without the need of pretraining on external vision-language data, our model yields new state of the art, achieving the top position in both single-model and ensemble settings (74.54 and 75.35 NDCG scores) on the visual dialog leaderboard. Our code and pretrained models are released at https://github.com/salesforce/VD-BERT.",
}
@inproceedings{van-der-wal-etal-2020-grammar,
    title = "The Grammar of Emergent Languages",
    author = "van der Wal, Oskar  and
      de Boer, Silvan  and
      Bruni, Elia  and
      Hupkes, Dieuwke",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.270",
    doi = "10.18653/v1/2020.emnlp-main.270",
    pages = "3339--3359",
    abstract = "In this paper, we consider the syntactic properties of languages emerged in referential games, using unsupervised grammar induction (UGI) techniques originally designed to analyse natural language. We show that the considered UGI techniques are appropriate to analyse emergent languages and we then study if the languages that emerge in a typical referential game setup exhibit syntactic structure, and to what extent this depends on the maximum message length and number of symbols that the agents are allowed to use. Our experiments demonstrate that a certain message length and vocabulary size are required for structure to emerge, but they also illustrate that more sophisticated game scenarios are required to obtain syntactic properties more akin to those observed in human language. We argue that UGI techniques should be part of the standard toolkit for analysing emergent languages and release a comprehensive library to facilitate such analysis for future researchers.",
}
@inproceedings{hong-etal-2020-sub,
    title = "Sub-Instruction Aware Vision-and-Language Navigation",
    author = "Hong, Yicong  and
      Rodriguez, Cristian  and
      Wu, Qi  and
      Gould, Stephen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.271",
    doi = "10.18653/v1/2020.emnlp-main.271",
    pages = "3360--3376",
    abstract = "Vision-and-language navigation requires an agent to navigate through a real 3D environment following natural language instructions. Despite significant advances, few previous works are able to fully utilize the strong correspondence between the visual and textual sequences. Meanwhile, due to the lack of intermediate supervision, the agent{'}s performance at following each part of the instruction cannot be assessed during navigation. In this work, we focus on the granularity of the visual and language sequences as well as the traceability of agents through the completion of an instruction. We provide agents with fine-grained annotations during training and find that they are able to follow the instruction better and have a higher chance of reaching the target at test time. We enrich the benchmark dataset Room-to-Room (R2R) with sub-instructions and their corresponding paths. To make use of this data, we propose effective sub-instruction attention and shifting modules that select and attend to a single sub-instruction at each time-step. We implement our sub-instruction modules in four state-of-the-art agents, compare with their baseline models, and show that our proposed method improves the performance of all four agents. We release the Fine-Grained R2R dataset (FGR2R) and the code at https://github.com/YicongHong/Fine-Grained-R2R.",
}
@inproceedings{zhao-etal-2020-knowledge-grounded,
    title = "Knowledge-Grounded Dialogue Generation with Pre-trained Language Models",
    author = "Zhao, Xueliang  and
      Wu, Wei  and
      Xu, Can  and
      Tao, Chongyang  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.272",
    doi = "10.18653/v1/2020.emnlp-main.272",
    pages = "3377--3390",
    abstract = "We study knowledge-grounded dialogue generation with pre-trained language models. To leverage the redundant external knowledge under capacity constraint, we propose equipping response generation defined by a pre-trained language model with a knowledge selection module, and an unsupervised approach to jointly optimizing knowledge selection and response generation with unlabeled dialogues. Empirical results on two benchmarks indicate that our model can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.",
}
@inproceedings{lin-etal-2020-mintl,
    title = "{M}in{TL}: Minimalist Transfer Learning for Task-Oriented Dialogue Systems",
    author = "Lin, Zhaojiang  and
      Madotto, Andrea  and
      Winata, Genta Indra  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.273",
    doi = "10.18653/v1/2020.emnlp-main.273",
    pages = "3391--3405",
    abstract = "In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data. MinTL is a simple yet effective transfer learning framework, which allows us to plug-and-play pre-trained seq2seq models, and jointly learn dialogue state tracking and dialogue response generation. Unlike previous approaches, which use a copy mechanism to {``}carryover{''} the old dialogue states to the new one, we introduce Levenshtein belief spans (Lev), that allows efficient dialogue state tracking with a minimal generation length. We instantiate our learning framework with two pre-trained backbones: T5 and BART, and evaluate them on MultiWOZ. Extensive experiments demonstrate that: 1) our systems establish new state-of-the-art results on end-to-end response generation, 2) MinTL-based systems are more robust than baseline methods in the low resource setting, and they achieve competitive results with only 20{\%} training data, and 3) Lev greatly improves the inference efficiency.",
}
@inproceedings{yoo-etal-2020-variational,
    title = "Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation",
    author = "Yoo, Kang Min  and
      Lee, Hanbit  and
      Dernoncourt, Franck  and
      Bui, Trung  and
      Chang, Walter  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.274",
    doi = "10.18653/v1/2020.emnlp-main.274",
    pages = "3406--3425",
    abstract = "Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this work, we extend this approach to the task of dialog state tracking for goaloriented dialogs. Due to the inherent hierarchical structure of goal-oriented dialogs over utterances and related annotations, the deep generative model must be capable of capturing the coherence among different hierarchies and types of dialog features. We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely speaker information, dialog acts, and goals. The proposed architecture is designed to model each aspect of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent goal-oriented dialogs from the latent spaces. To overcome training issues that arise from training complex variational models, we propose appropriate training strategies. Experiments on various dialog datasets show that our model improves the downstream dialog trackers{'} robustness via generative data augmentation. We also discover additional benefits of our unified approach to modeling goal-oriented dialogs {--} dialog response generation and user simulation, where our model outperforms previous strong baselines.",
}
@inproceedings{chen-etal-2020-bridging,
    title = "Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation",
    author = "Chen, Xiuyi  and
      Meng, Fandong  and
      Li, Peng  and
      Chen, Feilong  and
      Xu, Shuang  and
      Xu, Bo  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.275",
    doi = "10.18653/v1/2020.emnlp-main.275",
    pages = "3426--3437",
    abstract = "Knowledge selection plays an important role in knowledge-grounded dialogue, which is a challenging task to generate more informative responses by leveraging external knowledge. Recently, latent variable models have been proposed to deal with the diversity of knowledge selection by using both prior and posterior distributions over knowledge and achieve promising performance. However, these models suffer from a huge gap between prior and posterior knowledge selection. Firstly, the prior selection module may not learn to select knowledge properly because of lacking the necessary posterior information. Secondly, latent variable models suffer from the exposure bias that dialogue generation is based on the knowledge selected from the posterior distribution at training but from the prior distribution at inference. Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection. Experimental results on two knowledge-grounded dialogue datasets show that both PIPM and KDBTS achieve performance improvement over the state-of-the-art latent variable model and their combination shows further improvement.",
}
@inproceedings{zhu-etal-2020-counterfactual,
    title = "Counterfactual Off-Policy Training for Neural Dialogue Generation",
    author = "Zhu, Qingfu  and
      Zhang, Wei-Nan  and
      Liu, Ting  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.276",
    doi = "10.18653/v1/2020.emnlp-main.276",
    pages = "3438--3448",
    abstract = "Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses. In this paper, we propose to explore potential responses by counterfactual reasoning. Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken. The resulting counterfactual response synthesized in hindsight is of higher quality than the response synthesized from scratch. Training on the counterfactual responses under the adversarial learning framework helps to explore the high-reward area of the potential response space. An empirical study on the DailyDialog dataset shows that our approach significantly outperforms the HRED model as well as the conventional adversarial learning approaches.",
}
@inproceedings{zhang-etal-2020-dialogue,
    title = "Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data",
    author = "Zhang, Rongsheng  and
      Zheng, Yinhe  and
      Shao, Jianzhi  and
      Mao, Xiaoxi  and
      Xi, Yadong  and
      Huang, Minlie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.277",
    doi = "10.18653/v1/2020.emnlp-main.277",
    pages = "3449--3460",
    abstract = "Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data. However, collecting large-scale dialogue data is usually time-consuming and labor-intensive. To address this data dilemma, we propose a novel data augmentation method for training open-domain dialogue models by utilizing unpaired data. Specifically, a data-level distillation process is first proposed to construct augmented dialogues where both post and response are retrieved from the unpaired data. A ranking module is employed to filter out low-quality dialogues. Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data. Automatic and manual evaluation indicates that our method can produce high-quality dialogue pairs with diverse contents, and the proposed data-level and model-level dialogue distillation can improve the performance of competitive baselines.",
}
@inproceedings{wang-etal-2020-task,
    title = "Task-Completion Dialogue Policy Learning via {M}onte {C}arlo Tree Search with Dueling Network",
    author = "Wang, Sihan  and
      Zhou, Kaijie  and
      Lai, Kunfeng  and
      Shen, Jianping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.278",
    doi = "10.18653/v1/2020.emnlp-main.278",
    pages = "3461--3471",
    abstract = "We introduce a framework of Monte Carlo Tree Search with Double-q Dueling network (MCTS-DDU) for task-completion dialogue policy learning. Different from the previous deep model-based reinforcement learning methods, which uses background planning and may suffer from low-quality simulated experiences, MCTS-DDU performs decision-time planning based on dialogue state search trees built by Monte Carlo simulations and is robust to the simulation errors. Such idea arises naturally in human behaviors, e.g. predicting others{'} responses and then deciding our own actions. In the simulated movie-ticket booking task, our method outperforms the background planning approaches significantly. We demonstrate the effectiveness of MCTS and the dueling network in detailed ablation studies, and also compare the performance upper bounds of these two planning methods.",
}
@inproceedings{zhao-etal-2020-learning-simple,
    title = "Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks",
    author = "Zhao, Yufan  and
      Xu, Can  and
      Wu, Wei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.279",
    doi = "10.18653/v1/2020.emnlp-main.279",
    pages = "3472--3483",
    abstract = "We study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved response quality, their complexity also hinders the application of the models in real systems. In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation. To this end, we propose four auxiliary tasks including word order recovery, utterance order recovery, masked word recovery, and masked utterance recovery, and optimize the objectives of these tasks together with maximizing the likelihood of generation. By this means, the auxiliary tasks that relate to context understanding can guide the learning of the generation model to achieve a better local optimum. Empirical studies with three benchmarks indicate that our model can significantly outperform state-of-the-art generation models in terms of response quality on both automatic evaluation and human judgment, and at the same time enjoys a much faster decoding process.",
}
@inproceedings{jung-etal-2020-attnio,
    title = "{A}ttn{IO}: {K}nowledge {G}raph {E}xploration with {I}n-and-{O}ut {A}ttention {F}low for {K}nowledge-{G}rounded {D}ialogue",
    author = "Jung, Jaehun  and
      Son, Bokyung  and
      Lyu, Sungwon",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.280",
    doi = "10.18653/v1/2020.emnlp-main.280",
    pages = "3484--3497",
    abstract = "Retrieving the proper knowledge relevant to conversational context is an important challenge in dialogue systems, to engage users with more informative response. Several recent works propose to formulate this knowledge selection problem as a path traversal over an external knowledge graph (KG), but show only a limited utilization of KG structure, leaving rooms of improvement in performance. To this effect, we present AttnIO, a new dialog-conditioned path traversal model that makes a full use of rich structural information in KG based on two directions of attention flows. Through the attention flows, AttnIO is not only capable of exploring a broad range of multi-hop knowledge paths, but also learns to flexibly adjust the varying range of plausible nodes and edges to attend depending on the dialog context. Empirical evaluations present a marked performance improvement of AttnIO compared to all baselines in OpenDialKG dataset. Also, we find that our model can be trained to generate an adequate knowledge path even when the paths are not available and only the destination nodes are given as label, making it more applicable to real-world dialogue systems.",
}
@inproceedings{he-etal-2020-amalgamating,
    title = "Amalgamating Knowledge from Two Teachers for Task-oriented Dialogue System with Adversarial Training",
    author = "He, Wanwei  and
      Yang, Min  and
      Yan, Rui  and
      Li, Chengming  and
      Shen, Ying  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.281",
    doi = "10.18653/v1/2020.emnlp-main.281",
    pages = "3498--3507",
    abstract = "The challenge of both achieving task completion by querying the knowledge base and generating human-like responses for task-oriented dialogue systems is attracting increasing research attention. In this paper, we propose a {``}Two-Teacher One-Student{''} learning framework (TTOS) for task-oriented dialogue, with the goal of retrieving accurate KB entities and generating human-like responses simultaneously. TTOS amalgamates knowledge from two teacher networks that together provide comprehensive guidance to build a high-quality task-oriented dialogue system (student network). Each teacher network is trained via reinforcement learning with a goal-specific reward, which can be viewed as an expert towards the goal and transfers the professional characteristic to the student network. Instead of adopting the classic student-teacher learning of forcing the output of a student network to exactly mimic the soft targets produced by the teacher networks, we introduce two discriminators as in generative adversarial network (GAN) to transfer knowledge from two teachers to the student. The usage of discriminators relaxes the rigid coupling between the student and teachers. Extensive experiments on two benchmark datasets (i.e., CamRest and In-Car Assistant) demonstrate that TTOS significantly outperforms baseline methods.",
}
@inproceedings{wu-etal-2020-task,
    title = "Task-oriented Domain-specific Meta-Embedding for Text Classification",
    author = "Wu, Xin  and
      Cai, Yi  and
      Kai, Yang  and
      Wang, Tao  and
      Li, Qing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.282",
    doi = "10.18653/v1/2020.emnlp-main.282",
    pages = "3508--3513",
    abstract = "Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks. However, domain-specific knowledge is still ignored by existing meta-embedding methods, which results in unstable performances across specific domains. Moreover, the importance of general and domain word embeddings is related to downstream tasks, how to regularize meta-embedding to adapt downstream tasks is an unsolved problem. In this paper, we propose a method to incorporate both domain-specific and task-oriented information into meta-embeddings. We conducted extensive experiments on four text classification datasets and the results show the effectiveness of our proposed method.",
}
@inproceedings{loureiro-camacho-collados-2020-dont,
    title = "Don{'}t Neglect the Obvious: On the Role of Unambiguous Words in Word Sense Disambiguation",
    author = "Loureiro, Daniel  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.283",
    doi = "10.18653/v1/2020.emnlp-main.283",
    pages = "3514--3520",
    abstract = "State-of-the-art methods for Word Sense Disambiguation (WSD) combine two different features: the power of pre-trained language models and a propagation method to extend the coverage of such models. This propagation is needed as current sense-annotated corpora lack coverage of many instances in the underlying sense inventory (usually WordNet). At the same time, unambiguous words make for a large portion of all words in WordNet, while being poorly covered in existing sense-annotated corpora. In this paper, we propose a simple method to provide annotations for most unambiguous words in a large corpus. We introduce the UWA (Unambiguous Word Annotations) dataset and show how a state-of-the-art propagation-based model can use it to extend the coverage and quality of its word sense embeddings by a significant margin, improving on its original results on WSD.",
}
@inproceedings{barkan-etal-2020-within,
    title = "Within-Between Lexical Relation Classification",
    author = "Barkan, Oren  and
      Caciularu, Avi  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.284",
    doi = "10.18653/v1/2020.emnlp-main.284",
    pages = "3521--3527",
    abstract = "We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words. Our model integrates relational and distributional signals, forming an effective sub-space representation for each relation. We show that the proposed model is competitive and outperforms other baselines, across various benchmarks.",
}
@inproceedings{scarlini-etal-2020-contexts,
    title = "With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation",
    author = "Scarlini, Bianca  and
      Pasini, Tommaso  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.285",
    doi = "10.18653/v1/2020.emnlp-main.285",
    pages = "3528--3539",
    abstract = "Contextualized word embeddings have been employed effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. However, it is still hard to link them to structured sources of knowledge. In this paper we present ARES (context-AwaRe Embeddings of Senses), a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexical knowledge base that lie in a space that is comparable to that of contextualized word vectors. ARES representations enable a simple 1 Nearest-Neighbour algorithm to outperform state-of-the-art models, not only in the English Word Sense Disambiguation task, but also in the multilingual one, whilst training on sense-annotated data in English only. We further assess the quality of our embeddings in the Word-in-Context task, where, when used as an external source of knowledge, they consistently improve the performance of a neural model, leading it to compete with other more complex architectures. ARES embeddings for all WordNet concepts and the automatically-extracted contexts used for creating the sense representations are freely available at http://sensembert.org/ares.",
}
@inproceedings{zhang-qian-2020-convolution,
    title = "Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis",
    author = "Zhang, Mi  and
      Qian, Tieyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.286",
    doi = "10.18653/v1/2020.emnlp-main.286",
    pages = "3540--3549",
    abstract = "The state-of-the-art methods in aspect-level sentiment classification have leveraged the graph based models to incorporate the syntactic structure of a sentence. While being effective, these methods ignore the corpus level word co-occurrence information, which reflect the collocations in linguistics like {``}nothing special{''}. Moreover, they do not distinguish the different types of syntactic dependency, e.g., a nominal subject relation {``}food-was{''} is treated equally as an adjectival complement relation {``}was-okay{''} in {``}food was okay{''}. To tackle the above two limitations, we propose a novel architecture which convolutes over hierarchical syntactic and lexical graphs. Specifically, we employ a global lexical graph to encode the corpus level word co-occurrence information. Moreover, we build a concept hierarchy on both the syntactic and lexical graphs for differentiating various types of dependency relations or lexical word pairs. Finally, we design a bi-level interactive graph convolution network to fully exploit these two graphs. Extensive experiments on five bench- mark datasets show that our method outperforms the state-of-the-art baselines.",
}
@inproceedings{li-etal-2020-multi-instance,
    title = "Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis",
    author = "Li, Yuncong  and
      Yin, Cunxiang  and
      Zhong, Sheng-hua  and
      Pan, Xu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.287",
    doi = "10.18653/v1/2020.emnlp-main.287",
    pages = "3550--3560",
    abstract = "Aspect-category sentiment analysis (ACSA) aims to predict sentiment polarities of sentences with respect to given aspect categories. To detect the sentiment toward a particular aspect category in a sentence, most previous methods first generate an aspect category-specific sentence representation for the aspect category, then predict the sentiment polarity based on the representation. These methods ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance. In this paper, we propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category. Given a sentence and the aspect categories mentioned in the sentence, AC-MIMLLN first predicts the sentiments of the instances, then finds the key instances for the aspect categories, finally obtains the sentiments of the sentence toward the aspect categories by aggregating the key instance sentiments. Experimental results on three public datasets demonstrate the effectiveness of AC-MIMLLN.",
}
@inproceedings{xu-etal-2020-aspect,
    title = "Aspect Sentiment Classification with Aspect-Specific Opinion Spans",
    author = "Xu, Lu  and
      Bing, Lidong  and
      Lu, Wei  and
      Huang, Fei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.288",
    doi = "10.18653/v1/2020.emnlp-main.288",
    pages = "3561--3567",
    abstract = "Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.",
}
@inproceedings{yuan-etal-2020-emotion,
    title = "Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme",
    author = "Yuan, Chaofa  and
      Fan, Chuang  and
      Bao, Jianzhu  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.289",
    doi = "10.18653/v1/2020.emnlp-main.289",
    pages = "3568--3573",
    abstract = "The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts. Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost. Targeting this issue, we regard the task as a sequence labeling problem and propose a novel tagging scheme with coding the distance between linked components into the tags, so that emotions and the corresponding causes can be extracted simultaneously. Accordingly, an end-to-end model is presented to process the input texts from left to right, always with linear time complexity, leading to a speed up. Experimental results show that our proposed model achieves the best performance, outperforming the state-of-the-art method by 2.26{\%} (p{\textless}0.001) in F1 measure.",
}
@inproceedings{ding-etal-2020-end,
    title = "End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning",
    author = "Ding, Zixiang  and
      Xia, Rui  and
      Yu, Jianfei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.290",
    doi = "10.18653/v1/2020.emnlp-main.290",
    pages = "3574--3583",
    abstract = "Emotion-cause pair extraction (ECPE) is a new task that aims to extract the potential pairs of emotions and their corresponding causes in a document. The existing methods first perform emotion extraction and cause extraction independently, and then perform emotion-cause pairing and filtering. However, the above methods ignore the fact that the cause and the emotion it triggers are inseparable, and the extraction of the cause without specifying the emotion is pathological, which greatly limits the performance of the above methods in the first step. To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL). The window of multi-label learning is centered on the specified emotion clause or cause clause and slides as their positions move. Finally, CMLL and EMLL are integrated to obtain the final result. We evaluate our model on a benchmark emotion cause corpus, the results show that our approach achieves the best performance among all compared systems on the ECPE task.",
}
@inproceedings{zhang-etal-2020-multi-modal,
    title = "Multi-modal Multi-label Emotion Detection with Modality and Label Dependence",
    author = "Zhang, Dong  and
      Ju, Xincheng  and
      Li, Junhui  and
      Li, Shoushan  and
      Zhu, Qiaoming  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.291",
    doi = "10.18653/v1/2020.emnlp-main.291",
    pages = "3584--3593",
    abstract = "As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach.",
}
@inproceedings{xing-etal-2020-tasty,
    title = "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis",
    author = "Xing, Xiaoyu  and
      Jin, Zhijing  and
      Jin, Di  and
      Wang, Bingning  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.292",
    doi = "10.18653/v1/2020.emnlp-main.292",
    pages = "3594--3605",
    abstract = "Aspect-based sentiment analysis (ABSA) aims to predict the sentiment towards a specific aspect in the text. However, existing ABSA test sets cannot be used to probe whether a model can distinguish the sentiment of the target aspect from the non-target aspects. To solve this problem, we develop a simple but effective approach to enrich ABSA test sets. Specifically, we generate new examples to disentangle the confounding sentiments of the non-target aspects from the target aspect{'}s sentiment. Based on the SemEval 2014 dataset, we construct the Aspect Robustness Test Set (ARTS) as a comprehensive probe of the aspect robustness of ABSA models. Over 92{\%} data of ARTS show high fluency and desired sentiment on all aspects by human evaluation. Using ARTS, we analyze the robustness of nine ABSA models, and observe, surprisingly, that their accuracy drops by up to 69.73{\%}. We explore several ways to improve aspect robustness, and find that adversarial training can improve models{'} performance on ARTS by up to 32.85{\%}. Our code and new test set are available at https://github.com/zhijing-jin/ARTS{\_}TestSet",
}
@inproceedings{xiao-etal-2020-modeling,
    title = "Modeling Content Importance for Summarization with Pre-trained Language Models",
    author = "Xiao, Liqiang  and
      Wang, Lu  and
      He, Hao  and
      Jin, Yaohui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.293",
    doi = "10.18653/v1/2020.emnlp-main.293",
    pages = "3606--3611",
    abstract = "Modeling content importance is an essential yet challenging task for summarization. Previous work is mostly based on statistical methods that estimate word-level salience, which does not consider semantics and larger context when quantifying importance. It is thus hard for these methods to generalize to semantic units of longer text spans. In this work, we apply information theory on top of pre-trained language models and define the concept of importance from the perspective of information amount. It considers both the semantics and context when evaluating the importance of each semantic unit. With the help of pre-trained language models, it can easily generalize to different kinds of semantic units n-grams or sentences. Experiments on CNN/Daily Mail and New York Times datasets demonstrate that our method can better model the importance of content than prior work based on F1 and ROUGE scores.",
}
@inproceedings{wu-etal-2020-unsupervised,
    title = "Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning",
    author = "Wu, Hanlu  and
      Ma, Tengfei  and
      Wu, Lingfei  and
      Manyumwa, Tariro  and
      Ji, Shouling",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.294",
    doi = "10.18653/v1/2020.emnlp-main.294",
    pages = "3612--3621",
    abstract = "Evaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the informativeness of the assessed summary and require human-generated references for each test summary. In this work, we propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning. Specifically, we design a new metric which covers both linguistic qualities and semantic informativeness based on BERT. To learn the metric, for each summary, we construct different types of negative samples with respect to different aspects of the summary qualities, and train our model with a ranking loss. Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries. Furthermore, we show that our method is general and transferable across datasets.",
}
@inproceedings{jia-etal-2020-neural,
    title = "Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network",
    author = "Jia, Ruipeng  and
      Cao, Yanan  and
      Tang, Hengzhu  and
      Fang, Fang  and
      Cao, Cong  and
      Wang, Shi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.295",
    doi = "10.18653/v1/2020.emnlp-main.295",
    pages = "3622--3631",
    abstract = "Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations. There are lots of redundant phrases between extracted sentences, but it is difficult to model them exactly by the general supervised methods. Previous sentence encoders, especially BERT, specialize in modeling the relationship between source sentences. While, they have no ability to consider the overlaps of the target selected summary, and there are inherent dependencies among target labels of sentences. In this paper, we propose HAHSum (as shorthand for Hierarchical Attentive Heterogeneous Graph for Text Summarization), which well models different levels of information, including words and sentences, and spotlights redundancy dependencies between sentences. Our approach iteratively refines the sentence representations with redundancy-aware graph and delivers the label dependencies by message passing. Experiments on large scale benchmark corpus (CNN/DM, NYT, and NEWSROOM) demonstrate that HAHSum yields ground-breaking performance and outperforms previous extractive summarizers.",
}
@inproceedings{xu-lapata-2020-coarse,
    title = "Coarse-to-Fine Query Focused Multi-Document Summarization",
    author = "Xu, Yumo  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.296",
    doi = "10.18653/v1/2020.emnlp-main.296",
    pages = "3632--3645",
    abstract = "We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization. Due to the lack of training data, existing work relies heavily on retrieval-style methods for assembling query relevant summaries. We propose a coarse-to-fine modeling framework which employs progressively more accurate modules for estimating whether text segments are relevant, likely to contain an answer, and central. The modules can be independently developed and leverage training data if available. We present an instantiation of this framework with a trained evidence estimator which relies on distant supervision from question answering (where various resources exist) to identify segments which are likely to answer the query and should be included in the summary. Our framework is robust across domains and query types (i.e., long vs short) and outperforms strong comparison systems on benchmark datasets.",
}
@inproceedings{zou-etal-2020-pre,
    title = "Pre-training for Abstractive Document Summarization by Reinstating Source Text",
    author = "Zou, Yanyan  and
      Zhang, Xingxing  and
      Lu, Wei  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.297",
    doi = "10.18653/v1/2020.emnlp-main.297",
    pages = "3646--3660",
    abstract = "Abstractive document summarization is usually modeled as a sequence-to-sequence (SEQ2SEQ) learning problem. Unfortunately, training large SEQ2SEQ based summarization models on limited supervised summarization data is challenging. This paper presents three sequence-to-sequence pre-training (in shorthand, STEP) objectives which allow us to pre-train a SEQ2SEQ based abstractive summarization model on unlabeled text. The main idea is that, given an input text artificially constructed from a document, a model is pre-trained to reinstate the original document. These objectives include sentence reordering, next sentence generation and masked document generation, which have close relations with the abstractive document summarization task. Experiments on two benchmark summarization datasets (i.e., CNN/DailyMail and New York Times) show that all three objectives can improve performance upon baselines. Compared to models pre-trained on large-scale data (larger than 160GB), our method, with only 19GB text for pre-training, achieves comparable results, which demonstrates its effectiveness.",
}
@inproceedings{peng-etal-2020-learning,
    title = "{L}earning from {C}ontext or {N}ames? {A}n {E}mpirical {S}tudy on {N}eural {R}elation {E}xtraction",
    author = "Peng, Hao  and
      Gao, Tianyu  and
      Han, Xu  and
      Lin, Yankai  and
      Li, Peng  and
      Liu, Zhiyuan  and
      Sun, Maosong  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.298",
    doi = "10.18653/v1/2020.emnlp-main.298",
    pages = "3661--3672",
    abstract = "Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding what information in text affects existing RE models to make decisions and how to further improve the performance of these models. To this end, we empirically study the effect of two main information sources in text: textual context and entity mentions (names). We find that (i) while context is the main source to support the predictions, RE models also heavily rely on the information from entity mentions, most of which is type information, and (ii) existing datasets may leak shallow heuristics via entity mentions and thus contribute to the high performance on RE benchmarks. Based on the analyses, we propose an entity-masked contrastive pre-training framework for RE to gain a deeper understanding on both textual context and type information while avoiding rote memorization of entities or use of superficial cues in mentions. We carry out extensive experiments to support our views, and show that our framework can improve the effectiveness and robustness of neural models in different RE scenarios. All the code and datasets are released at https://github.com/thunlp/RE-Context-or-Names.",
}
@inproceedings{hu-etal-2020-selfore,
    title = "{S}elf{ORE}: Self-supervised Relational Feature Learning for Open Relation Extraction",
    author = "Hu, Xuming  and
      Wen, Lijie  and
      Xu, Yusong  and
      Zhang, Chenwei  and
      Yu, Philip",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.299",
    doi = "10.18653/v1/2020.emnlp-main.299",
    pages = "3673--3682",
    abstract = "Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.",
}
@inproceedings{xiao-etal-2020-denoising,
    title = "Denoising Relation Extraction from Document-level Distant Supervision",
    author = "Xiao, Chaojun  and
      Yao, Yuan  and
      Xie, Ruobing  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Sun, Maosong  and
      Lin, Fen  and
      Lin, Leyu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.300",
    doi = "10.18653/v1/2020.emnlp-main.300",
    pages = "3683--3688",
    abstract = "Distant supervision (DS) has been widely adopted to generate auto-labeled data for sentence-level relation extraction (RE) and achieved great results. However, the existing success of DS cannot be directly transferred to more challenging document-level relation extraction (DocRE), as the inevitable noise caused by DS may be even multiplied in documents and significantly harm the performance of RE. To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks. The experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy data and achieve promising results.",
}
@inproceedings{taille-etal-2020-lets,
    title = "Let{'}s {S}top {I}ncorrect {C}omparisons in {E}nd-to-end {R}elation {E}xtraction!",
    author = "Taill{\'e}, Bruno  and
      Guigue, Vincent  and
      Scoutheeten, Geoffrey  and
      Gallinari, Patrick",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.301",
    doi = "10.18653/v1/2020.emnlp-main.301",
    pages = "3689--3701",
    abstract = "Despite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work. In this paper, we first identify several patterns of invalid comparisons in published papers and describe them to avoid their propagation. We then propose a small empirical study to quantify the most common mistake{'}s impact and evaluate it leads to overestimating the final RE performance by around 5{\%} on ACE05. We also seize this opportunity to study the unexplored ablations of two recent developments: the use of language model pretraining (specifically BERT) and span-level NER. This meta-analysis emphasizes the need for rigor in the report of both the evaluation setting and the dataset statistics. We finally call for unifying the evaluation setting in end-to-end RE.",
}
@inproceedings{rosenman-etal-2020-exposing,
    title = "{E}xposing {S}hallow {H}euristics of {R}elation {E}xtraction {M}odels with {C}hallenge {D}ata",
    author = "Rosenman, Shachar  and
      Jacovi, Alon  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.302",
    doi = "10.18653/v1/2020.emnlp-main.302",
    pages = "3702--3710",
    abstract = "The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior. We identify failure modes of SOTA relation extraction (RE) models trained on TACRED, which we attribute to limitations in the data annotation process. We collect and annotate a challenge-set we call Challenging RE (CRE), based on naturally occurring corpus examples, to benchmark this behavior. Our experiments with four state-of-the-art RE models show that they have indeed adopted shallow heuristics that do not generalize to the challenge-set data. Further, we find that alternative question answering modeling performs significantly better than the SOTA models on the challenge-set, despite worse overall TACRED performance. By adding some of the challenge data as training examples, the performance of the model improves. Finally, we provide concrete suggestion on how to improve RE data collection to alleviate this behavior.",
}
@inproceedings{wang-etal-2020-global,
    title = "Global-to-Local Neural Networks for Document-Level Relation Extraction",
    author = "Wang, Difeng  and
      Hu, Wei  and
      Cao, Ermei  and
      Sun, Weijian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.303",
    doi = "10.18653/v1/2020.emnlp-main.303",
    pages = "3711--3721",
    abstract = "Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document. In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations. Entity global representations model the semantic information of all entities in the document, entity local representations aggregate the contextual information of multiple mentions of specific entities, and context relation representations encode the topic information of other relations. Experimental results demonstrate that our model achieves superior performance on two public datasets for document-level RE. It is particularly effective in extracting relations between entities of long distance and having multiple mentions.",
}
@inproceedings{sun-etal-2020-recurrent,
    title = "Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations",
    author = "Sun, Kai  and
      Zhang, Richong  and
      Mensah, Samuel  and
      Mao, Yongyi  and
      Liu, Xudong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.304",
    doi = "10.18653/v1/2020.emnlp-main.304",
    pages = "3722--3732",
    abstract = "The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task. Existing methods using multi-task learning techniques to address the problem learn interactions among the two tasks through a shared network, where the shared information is passed into the task-specific networks for prediction. However, such an approach hinders the model from learning explicit interactions between the two tasks to improve the performance on the individual tasks. As a solution, we design a multi-task learning model which we refer to as recurrent interaction network which allows the learning of interactions dynamically, to effectively model task-specific features for classification. Empirical studies on two real-world datasets confirm the superiority of the proposed model.",
}
@inproceedings{jain-etal-2020-temporal,
    title = "{T}emporal {K}nowledge {B}ase {C}ompletion: {N}ew {A}lgorithms and {E}valuation {P}rotocols",
    author = "Jain, Prachi  and
      Rathi, Sushant  and
      {Mausam}  and
      Chakrabarti, Soumen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.305",
    doi = "10.18653/v1/2020.emnlp-main.305",
    pages = "3733--3747",
    abstract = "Research on temporal knowledge bases, which associate a relational fact (s,r,o) with a validity time period (or time instant), is in its early days. Our work considers predicting missing entities (link prediction) and missing time intervals (time prediction) as joint Temporal Knowledge Base Completion (TKBC) tasks, and presents TIMEPLEX, a novel TKBC method, in which entities, relations and, time are all embedded in a uniform, compatible space. TIMEPLEX exploits the recurrent nature of some facts/events and temporal interactions between pairs of relations, yielding state-of-the-art results on both prediction tasks. We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms. In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.",
}
@inproceedings{kolluru-etal-2020-openie6,
    title = "{O}pen{IE}6: {I}terative {G}rid {L}abeling and {C}oordination {A}nalysis for {O}pen {I}nformation {E}xtraction",
    author = "Kolluru, Keshav  and
      Adlakha, Vaibhav  and
      Aggarwal, Samarth  and
      {Mausam}  and
      Chakrabarti, Soumen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.306",
    doi = "10.18653/v1/2020.emnlp-main.306",
    pages = "3748--3761",
    abstract = "A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs. This comes at a significant computational cost. On the other hand,sequence labeling approaches for OpenIE are much faster, but worse in extraction quality. In this paper, we bridge this trade-off by presenting an iterative labeling-based system that establishes a new state of the art for OpenIE, while extracting 10x faster. This is achieved through a novel Iterative Grid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labeling task. We improve its performance further by applying coverage (soft) constraints on the grid at training time. Moreover, on observing that the best OpenIE systems falter at handling coordination structures, our OpenIE system also incorporates a new coordination analyzer built with the same IGL architecture. This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12.3 pts improvement in F1 over previous analyzers. Our OpenIE system - OpenIE6 - beats the previous systems by as much as 4 pts in F1, while being much faster.",
}
@inproceedings{zhang-etal-2020-public,
    title = "Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder",
    author = "Zhang, Wenyue  and
      Li, Xiaoli  and
      Li, Yang  and
      Wang, Suge  and
      Li, Deyu  and
      Liao, Jian  and
      Zheng, Jianxing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.307",
    doi = "10.18653/v1/2020.emnlp-main.307",
    pages = "3762--3767",
    abstract = "Detecting public sentiment drift is a challenging task due to sentiment change over time. Existing methods first build a classification model using historical data and subsequently detect drift if the model performs much worse on new data. In this paper, we focus on distribution learning by proposing a novel Hierarchical Variational Auto-Encoder (HVAE) model to learn better distribution representation, and design a new drift measure to directly evaluate distribution changes between historical data and new data.Our experimental results demonstrate that our proposed model achieves better results than three existing state-of-the-art methods.",
}
@inproceedings{kim-etal-2020-point,
    title = "{P}oint to the {E}xpression: {S}olving {A}lgebraic {W}ord {P}roblems using the {E}xpression-{P}ointer {T}ransformer {M}odel",
    author = "Kim, Bugeun  and
      Ki, Kyung Seo  and
      Lee, Donggeon  and
      Gweon, Gahgene",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.308",
    doi = "10.18653/v1/2020.emnlp-main.308",
    pages = "3768--3779",
    abstract = "Solving algebraic word problems has recently emerged as an important natural language processing task. To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using {`}Op (operator/operand){'} tokens as a unit of input/output. However, such a neural model suffered two issues: expression fragmentation and operand-context separation. To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) {`}Expression{'} token and (2) operand-context pointers when generating solution equations. The performance of the EPT model is tested on three datasets: ALG514, DRAW-1K, and MAWPS. Compared to the state-of-the-art (SoTA) models, the EPT model achieved a comparable performance accuracy in each of the three datasets; 81.3{\%} on ALG514, 59.5{\%} on DRAW-1K, and 84.5{\%} on MAWPS. The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operand-context separation. (2) The fully automatic EPT model, which does not use hand-crafted features, yields comparable performance to existing models using hand-crafted features, and achieves better performance than existing pure neural models by at most 40{\%}.",
}
@inproceedings{qin-etal-2020-semantically,
    title = "Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems",
    author = "Qin, Jinghui  and
      Lin, Lihui  and
      Liang, Xiaodan  and
      Zhang, Rumin  and
      Lin, Liang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.309",
    doi = "10.18653/v1/2020.emnlp-main.309",
    pages = "3780--3789",
    abstract = "A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs while most existing works only focused on one-unknown linear MWPs. Herein, we propose a simple but efficient method called Universal Expression Tree (UET) to make the first attempt to represent the equations of various MWPs uniformly. Then a semantically-aligned universal tree-structured solver (SAU-Solver) based on an encoder-decoder framework is proposed to resolve multiple types of MWPs in a unified model, benefiting from our UET representation. Our SAU-Solver generates a universal expression tree explicitly by deciding which symbol to generate according to the generated symbols{'} semantic meanings like human solving MWPs. Besides, our SAU-Solver also includes a novel subtree-level semanticallyaligned regularization to further enforce the semantic constraints and rationality of the generated expression tree by aligning with the contextual information. Finally, to validate the universality of our solver and extend the research boundary of MWPs, we introduce a new challenging Hybrid Math Word Problems dataset (HMWP), consisting of three types of MWPs. Experimental results on several MWPs datasets show that our model can solve universal types of MWPs and outperforms several state-of-the-art models.",
}
@inproceedings{zhou-etal-2020-neural,
    title = "Neural Topic Modeling by Incorporating Document Relationship Graph",
    author = "Zhou, Deyu  and
      Hu, Xuemeng  and
      Wang, Rui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.310",
    doi = "10.18653/v1/2020.emnlp-main.310",
    pages = "3790--3796",
    abstract = "Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community. In this paper, we propose Graph Topic Model (GTM), a GNN based neural topic model that represents a corpus as a document relationship graph. Documents and words in the corpus become nodes in the graph and are connected based on document-word co-occurrences. By introducing the graph structure, the relationships between documents are established through their shared words and thus the topical representation of a document is enriched by aggregating information from its neighboring nodes using graph convolution. Extensive experiments on three datasets were conducted and the results demonstrate the effectiveness of the proposed approach.",
}
@inproceedings{yu-etal-2020-routing,
    title = "Routing Enforced Generative Model for Recipe Generation",
    author = "Yu, Zhiwei  and
      Zang, Hongyu  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.311",
    doi = "10.18653/v1/2020.emnlp-main.311",
    pages = "3797--3806",
    abstract = "One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients. Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible. In this work, we propose a routing method to dive into the content selection under the internal restrictions. The routing enforced generative model (RGM) can generate appropriate recipes according to the given ingredients and user preferences. Our model yields new state-of-the-art results on the recipe generation task with significant improvements on BLEU, F1 and human evaluation.",
}
@inproceedings{jen-etal-2020-assessing,
    title = "Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent",
    author = "Jen, Yun-Hsuan  and
      Huang, Chieh-Yang  and
      Chen, MeiHua  and
      Huang, Ting-Hao  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.312",
    doi = "10.18653/v1/2020.emnlp-main.312",
    pages = "3807--3817",
    abstract = "Many English-as-a-second language learners have trouble using near-synonym words (e.g., small vs.little; briefly vs.shortly) correctly, and often look for example sentences to learn how two nearly synonymous terms differ. Prior work uses hand-crafted scores to recommend sentences but has difficulty in adopting such scores to all the near-synonyms as near-synonyms differ in various ways. We notice that the helpfulness of the learning material would reflect on the learners{'} performance. Thus, we propose the inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent{'}s performance. To enable the agent to behave like a learner, we leverage entailment modeling{'}s capability of inferring answers from the provided materials. Experimental results show that the proposed agent is equipped with good learner-like behavior to achieve the best performance in both fill-in-the-blank (FITB) and good example sentence selection tasks. We further conduct a classroom user study with college ESL learners. The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently. Compared to other models, the proposed agent improves the score of more than 17{\%} of students after learning.",
}
@inproceedings{chan-etal-2020-selection,
    title = "Selection and Generation: Learning towards Multi-Product Advertisement Post Generation",
    author = "Chan, Zhangming  and
      Zhang, Yuchi  and
      Chen, Xiuying  and
      Gao, Shen  and
      Zhang, Zhiqiang  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.313",
    doi = "10.18653/v1/2020.emnlp-main.313",
    pages = "3818--3829",
    abstract = "As the E-commerce thrives, high-quality online advertising copywriting has attracted more and more attention. Different from the advertising copywriting for a single product, an advertisement (AD) post includes an attractive topic that meets the customer needs and description copywriting about several products under its topic. A good AD post can highlight the characteristics of each product, thus helps customers make a good choice among candidate products. Hence, multi-product AD post generation is meaningful and important. We propose a novel end-to-end model named S-MG Net to generate the AD post. Targeted at such a challenging real-world problem, we split the AD post generation task into two subprocesses: (1) select a set of products via the SelectNet (Selection Network). (2) generate a post including selected products via the MGenNet (Multi-Generator Network). Concretely, SelectNet first captures the post topic and the relationship among the products to output the representative products. Then, MGenNet generates the description copywriting of each product. Experiments conducted on a large-scale real-world AD post dataset demonstrate that our proposed model achieves impressive performance in terms of both automatic metrics as well as human evaluations.",
}
@inproceedings{aggarwal-etal-2020-form2seq,
    title = "{F}orm2{S}eq : A Framework for Higher-Order Form Structure Extraction",
    author = "Aggarwal, Milan  and
      Gupta, Hiresh  and
      Sarkar, Mausoom  and
      Krishnamurthy, Balaji",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.314",
    doi = "10.18653/v1/2020.emnlp-main.314",
    pages = "3830--3840",
    abstract = "Document structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation task over document images using fully-convolution networks. Such methods are limited by image resolution due to which they fail to disambiguate structures in dense regions which appear commonly in forms. To mitigate this, we propose Form2Seq, a novel sequence-to-sequence (Seq2Seq) inspired framework for structure extraction using text, with a specific focus on forms, which leverages relative spatial arrangement of structures. We discuss two tasks; 1) Classification of low-level constituent elements (TextBlock and empty fillable Widget) into ten types such as field captions, list items, and others; 2) Grouping lower-level elements into higher-order constructs, such as Text Fields, ChoiceFields and ChoiceGroups, used as information collection mechanism in forms. To achieve this, we arrange the constituent elements linearly in natural reading order, feed their spatial and textual representations to Seq2Seq framework, which sequentially outputs prediction of each element depending on the final task. We modify Seq2Seq for grouping task and discuss improvements obtained through cascaded end-to-end training of two tasks versus training in isolation. Experimental results show the effectiveness of our text-based approach achieving an accuracy of 90{\%} on classification task and an F1 of 75.82, 86.01, 61.63 on groups discussed above respectively, outperforming segmentation baselines. Further we show our framework achieves state of the results for table structure recognition on ICDAR 2013 dataset.",
}
@inproceedings{limkonchotiwat-etal-2020-domain,
    title = "Domain Adaptation of {T}hai Word Segmentation Models using Stacked Ensemble",
    author = "Limkonchotiwat, Peerat  and
      Phatthiyaphaibun, Wannaphong  and
      Sarwar, Raheem  and
      Chuangsuwanich, Ekapol  and
      Nutanong, Sarana",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.315",
    doi = "10.18653/v1/2020.emnlp-main.315",
    pages = "3841--3847",
    abstract = "Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent. Researchers have been relying on transfer learning to adapt an existing model to a new domain. However, this approach is inapplicable to cases where we can interact with only input and output layers of the models, also known as {``}black boxes{''}. We propose a filter-and-refine solution based on the stacked-ensemble learning paradigm to address this black-box limitation. We conducted extensive experimental studies comparing our method against state-of-the-art models and transfer learning. Experimental results show that our proposed solution is an effective domain adaptation method and has a similar performance as the transfer learning method.",
}
@inproceedings{hofmann-etal-2020-dagobert,
    title = "{D}ago{BERT}: {G}enerating Derivational Morphology with a Pretrained Language Model",
    author = {Hofmann, Valentin  and
      Pierrehumbert, Janet  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.316",
    doi = "10.18653/v1/2020.emnlp-main.316",
    pages = "3848--3861",
    abstract = "Can pretrained language models (PLMs) generate derivationally complex words? We present the first study investigating this question, taking BERT as the example PLM. We examine BERT{'}s derivational capabilities in different settings, ranging from using the unmodified pretrained model to full finetuning. Our best model, DagoBERT (Derivationally and generatively optimized BERT), clearly outperforms the previous state of the art in derivation generation (DG). Furthermore, our experiments show that the input segmentation crucially impacts BERT{'}s derivational knowledge, suggesting that the performance of PLMs could be further improved if a morphologically informed vocabulary of units were used.",
}
@inproceedings{duan-zhao-2020-attention,
    title = "Attention Is All You Need for {C}hinese Word Segmentation",
    author = "Duan, Sufeng  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.317",
    doi = "10.18653/v1/2020.emnlp-main.317",
    pages = "3862--3872",
    abstract = "Taking greedy decoding algorithm as it should be, this work focuses on further strengthening the model itself for Chinese word segmentation (CWS), which results in an even more fast and more accurate CWS model. Our model consists of an attention only stacked encoder and a light enough decoder for the greedy segmentation plus two highway connections for smoother training, in which the encoder is composed of a newly proposed Transformer variant, Gaussian-masked Directional (GD) Transformer, and a biaffine attention scorer. With the effective encoder design, our model only needs to take unigram features for scoring. Our model is evaluated on SIGHAN Bakeoff benchmark datasets. The experimental results show that with the highest segmentation speed, the proposed model achieves new state-of-the-art or comparable performance against strong baselines in terms of strict closed test setting.",
}
@inproceedings{huang-etal-2020-joint-multiple,
    title = "A Joint Multiple Criteria Model in Transfer Learning for Cross-domain {C}hinese Word Segmentation",
    author = "Huang, Kaiyu  and
      Huang, Degen  and
      Liu, Zhuang  and
      Mo, Fengran",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.318",
    doi = "10.18653/v1/2020.emnlp-main.318",
    pages = "3873--3882",
    abstract = "Word-level information is important in natural language processing (NLP), especially for the Chinese language due to its high linguistic complexity. Chinese word segmentation (CWS) is an essential task for Chinese downstream NLP tasks. Existing methods have already achieved a competitive performance for CWS on large-scale annotated corpora. However, the accuracy of the method will drop dramatically when it handles an unsegmented text with lots of out-of-vocabulary (OOV) words. In addition, there are many different segmentation criteria for addressing different requirements of downstream NLP tasks. Excessive amounts of models with saving different criteria will generate the explosive growth of the total parameters. To this end, we propose a joint multiple criteria model that shares all parameters to integrate different segmentation criteria into one model. Besides, we utilize a transfer learning method to improve the performance of OOV words. Our proposed method is evaluated by designing comprehensive experiments on multiple benchmark datasets (e.g., Bakeoff 2005, Bakeoff 2008 and SIGHAN 2010). Our method achieves the state-of-the-art performances on all datasets. Importantly, our method also shows a competitive practicability and generalization ability for the CWS task.",
}
@inproceedings{cai-lapata-2020-alignment,
    title = "Alignment-free Cross-lingual Semantic Role Labeling",
    author = "Cai, Rui  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.319",
    doi = "10.18653/v1/2020.emnlp-main.319",
    pages = "3883--3894",
    abstract = "Cross-lingual semantic role labeling (SRL) aims at leveraging resources in a source language to minimize the effort required to construct annotations or models for a new target language. Recent approaches rely on word alignments, machine translation engines, or preprocessing tools such as parsers or taggers. We propose a cross-lingual SRL model which only requires annotations in a source language and access to raw text in the form of a parallel corpus. The backbone of our model is an LSTM-based semantic role labeler jointly trained with a semantic role compressor and multilingual word embeddings. The compressor collects useful information from the output of the semantic role labeler, filtering noisy and conflicting evidence. It lives in a multilingual embedding space and provides direct supervision for predicting semantic roles in the target language. Results on the Universal Proposition Bank and manually annotated datasets show that our method is highly effective, even against systems utilizing supervised features.",
}
@inproceedings{wang-etal-2020-leveraging,
    title = "Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection",
    author = "Wang, Ruize  and
      Tang, Duyu  and
      Duan, Nan  and
      Zhong, Wanjun  and
      Wei, Zhongyu  and
      Huang, Xuanjing  and
      Jiang, Daxin  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.320",
    doi = "10.18653/v1/2020.emnlp-main.320",
    pages = "3895--3903",
    abstract = "We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.",
}
@inproceedings{daza-frank-2020-x,
    title = "{X}-{SRL}: A Parallel Cross-Lingual Semantic Role Labeling Dataset",
    author = "Daza, Angel  and
      Frank, Anette",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.321",
    doi = "10.18653/v1/2020.emnlp-main.321",
    pages = "3904--3914",
    abstract = "Even though SRL is researched for many languages, major improvements have mostly been obtained for English, for which more resources are available. In fact, existing multilingual SRL datasets contain disparate annotation styles or come from different domains, hampering generalization in multilingual learning. In this work we propose a method to automatically construct an SRL corpus that is parallel in four languages: English, French, German, Spanish, with unified predicate and role annotations that are fully comparable across languages. We apply high-quality machine translation to the English CoNLL-09 dataset and use multilingual BERT to project its high-quality annotations to the target languages. We include human-validated test sets that we use to measure the projection quality, and show that projection is denser and more precise than a strong baseline. Finally, we train different SOTA models on our novel corpus for mono- and multilingual SRL, showing that the multilingual annotations improve performance especially for the weaker languages.",
}
@inproceedings{marcheggiani-titov-2020-graph,
    title = "Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling",
    author = "Marcheggiani, Diego  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.322",
    doi = "10.18653/v1/2020.emnlp-main.322",
    pages = "3915--3928",
    abstract = "Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. Even though most semantic-role formalisms are built upon constituent syntax, and only syntactic constituents can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work on syntax-aware SRL relies on dependency representations of syntax. In contrast, we show how graph convolutional networks (GCNs) can be used to encode constituent structures and inform an SRL system. Nodes in our SpanGCN correspond to constituents. The computation is done in 3 stages. First, initial node representations are produced by {`}composing{'} word representations of the first and last words in the constituent. Second, graph convolutions relying on the constituent tree are performed, yielding syntactically-informed constituent representations. Finally, the constituent representations are {`}decomposed{'} back into word representations, which are used as input to the SRL classifier. We evaluate SpanGCN against alternatives, including a model using GCNs over dependency trees, and show its effectiveness on standard English SRL benchmarks CoNLL-2005, CoNLL-2012, and FrameNet.",
}
@inproceedings{lindemann-etal-2020-fast,
    title = "Fast semantic parsing with well-typedness guarantees",
    author = "Lindemann, Matthias  and
      Groschwitz, Jonas  and
      Koller, Alexander",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.323",
    doi = "10.18653/v1/2020.emnlp-main.323",
    pages = "3929--3951",
    abstract = "AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.",
}
@inproceedings{cavalin-etal-2020-improving,
    title = "Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes",
    author = "Cavalin, Paulo  and
      Alves Ribeiro, Victor Henrique  and
      Appel, Ana  and
      Pinhanez, Claudio",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.324",
    doi = "10.18653/v1/2020.emnlp-main.324",
    pages = "3952--3961",
    abstract = "This paper explores how intent classification can be improved by representing the class labels not as a discrete set of symbols but as a space where the word graphs associated to each class are mapped using typical graph embedding techniques. The approach, inspired by a previous algorithm used for an inverse dictionary task, allows the classification algorithm to take in account inter-class similarities provided by the repeated occurrence of some words in the training examples of the different classes. The classification is carried out by mapping text embeddings to the word graph embeddings of the classes. Focusing solely on improving the representation of the class label set, we show in experiments conducted in both private and public intent classification datasets, that better detection of out-of-scope examples (OOS) is achieved and, as a consequence, that the overall accuracy of intent classification is also improved. In particular, using the recently-released \textit{Larson dataset}, an error of about 9.9{\%} has been achieved for OOS detection, beating the previous state-of-the-art result by more than 31 percentage points.",
}
@inproceedings{lu-etal-2020-supervised,
    title = "Supervised Seeded Iterated Learning for Interactive Language Learning",
    author = "Lu, Yuchen  and
      Singhal, Soumye  and
      Strub, Florian  and
      Pietquin, Olivier  and
      Courville, Aaron",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.325",
    doi = "10.18653/v1/2020.emnlp-main.325",
    pages = "3962--3970",
    abstract = "Language drift has been one of the major obstacles to train language models through interaction. When word-based conversational agents are trained towards completing a task, they tend to invent their language rather than leveraging natural language. In recent literature, two general methods partially counter this phenomenon: Supervised Selfplay (S2P) and Seeded Iterated Learning (SIL). While S2P jointly trains interactive and supervised losses to counter the drift, SIL changes the training dynamics to prevent language drift from occurring. In this paper, we first highlight their respective weaknesses, i.e., late-stage training collapses and higher negative likelihood when evaluated on human corpus. Given these observations, we introduce Supervised Seeded Iterated Learning (SSIL) to combine both methods to minimize their respective weaknesses. We then show the effectiveness of in the language-drift translation game.",
}
@inproceedings{deriu-etal-2020-spot,
    title = "Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems",
    author = {Deriu, Jan  and
      Tuggener, Don  and
      von D{\"a}niken, Pius  and
      Campos, Jon Ander  and
      Rodrigo, Alvaro  and
      Belkacem, Thiziri  and
      Soroa, Aitor  and
      Agirre, Eneko  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.326",
    doi = "10.18653/v1/2020.emnlp-main.326",
    pages = "3971--3984",
    abstract = "The lack of time efficient and reliable evalu-ation methods is hampering the development of conversational dialogue systems (chat bots). Evaluations that require humans to converse with chat bots are time and cost intensive, put high cognitive demands on the human judges, and tend to yield low quality results. In this work, we introduce Spot The Bot, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots. Human judges then only annotate for each entity in a conversation whether they think it is human or not (assuming there are humans participants in these conversations). These annotations then allow us to rank chat bots regarding their ability to mimic conversational behaviour of humans. Since we expect that all bots are eventually recognized as such, we incorporate a metric that measures which chat bot is able to uphold human-like be-havior the longest, i.e.Survival Analysis. This metric has the ability to correlate a bot{'}s performance to certain of its characteristics (e.g.fluency or sensibleness), yielding interpretable results. The comparably low cost of our frame-work allows for frequent evaluations of chatbots during their evaluation cycle. We empirically validate our claims by applying Spot The Bot to three domains, evaluating several state-of-the-art chat bots, and drawing comparisonsto related work. The framework is released asa ready-to-use tool.",
}
@inproceedings{jaques-etal-2020-human,
    title = "Human-centric dialog training via offline reinforcement learning",
    author = "Jaques, Natasha  and
      Shen, Judy Hanwen  and
      Ghandeharioun, Asma  and
      Ferguson, Craig  and
      Lapedriza, Agata  and
      Jones, Noah  and
      Gu, Shixiang  and
      Picard, Rosalind",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.327",
    doi = "10.18653/v1/2020.emnlp-main.327",
    pages = "3985--4003",
    abstract = "How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting models online, and gather human feedback from real-time, open-ended conversations, which we then use to train and improve the models using offline reinforcement learning (RL). We identify implicit conversational cues including language similarity, elicitation of laughter, sentiment, and more, which indicate positive human feedback, and embed these in multiple reward functions. A well-known challenge is that learning an RL policy in an offline setting usually fails due to the lack of ability to explore and the tendency to make over-optimistic estimates of future reward. These problems become even harder when using RL for language models, which can easily have a 20,000 action vocabulary and many possible reward functions. We solve the challenge by developing a novel class of offline RL algorithms. These algorithms use KL-control to penalize divergence from a pre-trained prior language model, and use a new strategy to make the algorithm pessimistic, instead of optimistic, in the face of uncertainty. We test the resulting dialog model with ratings from 80 users in an open-domain setting and find it achieves significant improvements over existing deep offline RL approaches. The novel offline RL method is viable for improving any existing generative dialog model using a static dataset of human feedback.",
}
@inproceedings{pimentel-etal-2020-speakers,
    title = "Speakers Fill Lexical Semantic Gaps with Context",
    author = "Pimentel, Tiago  and
      Hall Maudslay, Rowan  and
      Blasi, Damian  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.328",
    doi = "10.18653/v1/2020.emnlp-main.328",
    pages = "4004--4015",
    abstract = "Lexical ambiguity is widespread in language, allowing for the reuse of economical word forms and therefore making language more efficient. If ambiguous words cannot be disambiguated from context, however, this gain in efficiency might make language less clear{---}resulting in frequent miscommunication. For a language to be clear and efficiently encoded, we posit that the lexical ambiguity of a word type should correlate with how much information context provides about it, on average. To investigate whether this is the case, we operationalise the lexical ambiguity of a word as the entropy of meanings it can take, and provide two ways to estimate this{---}one which requires human annotation (using WordNet), and one which does not (using BERT), making it readily applicable to a large number of languages. We validate these measures by showing that, on six high-resource languages, there are significant Pearson correlations between our BERT-based estimate of ambiguity and the number of synonyms a word has in WordNet (e.g. $\rho = 0.40$ in English). We then test our main hypothesis{---}that a word{'}s lexical ambiguity should negatively correlate with its contextual uncertainty{---}and find significant correlations on all 18 typologically diverse languages we analyse. This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.",
}
@inproceedings{leung-etal-2020-investigating,
    title = "Investigating {C}ross-{L}inguistic {A}djective {O}rdering {T}endencies with a {L}atent-{V}ariable {M}odel",
    author = "Leung, Jun Yen  and
      Emerson, Guy  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.329",
    doi = "10.18653/v1/2020.emnlp-main.329",
    pages = "4016--4028",
    abstract = "Across languages, multiple consecutive adjectives modifying a noun (e.g. {``}the big red dog{''}) follow certain unmarked ordering rules. While explanatory accounts have been put forward, much of the work done in this area has relied primarily on the intuitive judgment of native speakers, rather than on corpus data. We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent-variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different. We utilize this novel statistical model to provide strong converging evidence for the existence of universal, cross-linguistic, hierarchical adjective ordering tendencies.",
}
@inproceedings{calvillo-etal-2020-surprisal,
    title = "Surprisal Predicts Code-Switching in {C}hinese-{E}nglish Bilingual Text",
    author = "Calvillo, Jes{\'u}s  and
      Fang, Le  and
      Cole, Jeremy  and
      Reitter, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.330",
    doi = "10.18653/v1/2020.emnlp-main.330",
    pages = "4029--4039",
    abstract = "Why do bilinguals switch languages within a sentence? The present observational study asks whether word surprisal and word entropy predict code-switching in bilingual written conversation. We describe and model a new dataset of Chinese-English text with 1476 clean code-switched sentences, translated back into Chinese. The model includes known control variables together with word surprisal and word entropy. We found that word surprisal, but not entropy, is a significant predictor that explains code-switching above and beyond other well-known predictors. We also found sentence length to be a significant predictor, which has been related to sentence complexity. We propose high cognitive effort as a reason for code-switching, as it leaves fewer resources for inhibition of the alternative language. We also corroborate previous findings, but this time using a computational model of surprisal, a new language pair, and doing so for written language.",
}
@inproceedings{yu-etal-2020-word,
    title = "Word Frequency Does Not Predict Grammatical Knowledge in Language Models",
    author = "Yu, Charles  and
      Sie, Ryan  and
      Tedeschi, Nicolas  and
      Bergen, Leon",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.331",
    doi = "10.18653/v1/2020.emnlp-main.331",
    pages = "4040--4054",
    abstract = "Neural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages. In this work, we investigate whether there are systematic sources of variation in the language models{'} accuracy. Focusing on subject-verb agreement and reflexive anaphora, we find that certain nouns are systematically understood better than others, an effect which is robust across grammatical tasks and different language models. Surprisingly, we find that across four orders of magnitude, corpus frequency is unrelated to a noun{'}s performance on grammatical tasks. Finally, we find that a novel noun{'}s grammatical properties can be few-shot learned from various types of training data. The results present a paradox: there should be less variation in grammatical performance than is actually observed.",
}
@inproceedings{luan-etal-2020-improving,
    title = "Improving Word Sense Disambiguation with Translations",
    author = "Luan, Yixing  and
      Hauer, Bradley  and
      Mou, Lili  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.332",
    doi = "10.18653/v1/2020.emnlp-main.332",
    pages = "4055--4065",
    abstract = "It has been conjectured that multilingual information can help monolingual word sense disambiguation (WSD). However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD by generating translations. In this paper, we present a novel approach that improves the performance of a base WSD system using machine translation. Since our approach is language independent, we perform WSD experiments on several languages. The results demonstrate that our methods can consistently improve the performance of WSD systems, and obtain state-ofthe-art results in both English and multilingual WSD. To facilitate the use of lexical translation information, we also propose BABALIGN, an precise bitext alignment algorithm which is guided by multilingual lexical correspondences from BabelNet.",
}
@inproceedings{liu-etal-2020-towards-better,
    title = "Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors",
    author = "Liu, Qianchu  and
      McCarthy, Diana  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.333",
    doi = "10.18653/v1/2020.emnlp-main.333",
    pages = "4066--4075",
    abstract = "One of the most powerful features of contextualized models is their dynamic embeddings for words in context, leading to state-of-the-art representations for context-aware lexical semantics. In this paper, we present a post-processing technique that enhances these representations by learning a transformation through static anchors. Our method requires only another pre-trained model and no labeled data is needed. We show consistent improvement in a range of benchmark tasks that test contextual variations of meaning both across different usages of a word and across different words as they are used in context. We demonstrate that while the original contextual representations can be improved by another embedding space from both contextualized and static models, the static embeddings, which have lower computational requirements, provide the most gains.",
}
@inproceedings{welch-etal-2020-compositional,
    title = "Compositional Demographic Word Embeddings",
    author = "Welch, Charles  and
      Kummerfeld, Jonathan K.  and
      P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.334",
    doi = "10.18653/v1/2020.emnlp-main.334",
    pages = "4076--4089",
    abstract = "Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations. While personalized embeddings can be useful to improve language model performance and other language processing tasks, they can only be computed for people with a large amount of longitudinal data, which is not the case for new users. We propose a new form of personalized word embeddings that use demographic-specific word representations derived compositionally from full or partial demographic information for a user (i.e., gender, age, location, religion). We show that the resulting demographic-aware word representations outperform generic word representations on two tasks for English: language modeling and word associations. We further explore the trade-off between the number of available attributes and their relative effectiveness and discuss the ethical implications of using them.",
}
@inproceedings{webson-etal-2020-undocumented,
    title = "Are {``}Undocumented Workers{''} the Same as {``}Illegal Aliens{''}? {D}isentangling Denotation and Connotation in Vector Spaces",
    author = "Webson, Albert  and
      Chen, Zhizhong  and
      Eickhoff, Carsten  and
      Pavlick, Ellie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.335",
    doi = "10.18653/v1/2020.emnlp-main.335",
    pages = "4090--4105",
    abstract = "In politics, neologisms are frequently invented for partisan objectives. For example, {``}undocumented workers{''} and {``}illegal aliens{''} refer to the same group of people (i.e., they have the same denotation), but they carry clearly different connotations. Examples like these have traditionally posed a challenge to reference-based semantic theories and led to increasing acceptance of alternative theories (e.g., Two-Factor Semantics) among philosophers and cognitive scientists. In NLP, however, popular pretrained models encode both denotation and connotation as one entangled representation. In this study, we propose an adversarial nerual netowrk that decomposes a pretrained representation as independent denotation and connotation representations. For intrinsic interpretability, we show that words with the same denotation but different connotations (e.g., {``}immigrants{''} vs. {``}aliens{''}, {``}estate tax{''} vs. {``}death tax{''}) move closer to each other in denotation space while moving further apart in connotation space. For extrinsic application, we train an information retrieval system with our disentangled representations and show that the denotation vectors improve the viewpoint diversity of document rankings.",
}
@inproceedings{chen-yang-2020-multi,
    title = "Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization",
    author = "Chen, Jiaao  and
      Yang, Diyi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.336",
    doi = "10.18653/v1/2020.emnlp-main.336",
    pages = "4106--4118",
    abstract = "Text summarization is one of the most challenging and interesting problems in NLP. Although much attention has been paid to summarizing structured text like news reports or encyclopedia articles, summarizing conversations{---}an essential part of human-human/machine interaction where most important pieces of information are scattered across various utterances of different speakers{---}remains relatively under-investigated. This work proposes a multi-view sequence-to-sequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations and then utilizing a multi-view decoder to incorporate different views to generate dialogue summaries. Experiments on a large-scale dialogue summarization corpus demonstrated that our methods significantly outperformed previous state-of-the-art models via both automatic evaluations and human judgment. We also discussed specific challenges that current approaches faced with this task. We have publicly released our code at https://github.com/GT-SALT/Multi-View-Seq2Seq.",
}
@inproceedings{brazinskas-etal-2020-shot,
    title = "Few-Shot Learning for Opinion Summarization",
    author = "Bra{\v{z}}inskas, Arthur  and
      Lapata, Mirella  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.337",
    doi = "10.18653/v1/2020.emnlp-main.337",
    pages = "4119--4135",
    abstract = "Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product. The task is practically important and has attracted a lot of attention. However, due to the high cost of summary production, datasets large enough for training supervised models are lacking. Instead, the task has been traditionally approached with extractive methods that learn to select text fragments in an unsupervised or weakly-supervised way. Recently, it has been shown that abstractive summaries, potentially more fluent and better at reflecting conflicting information, can also be produced in an unsupervised fashion. However, these models, not being exposed to actual summaries, fail to capture their essential properties. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation. We start by training a conditional Transformer language model to generate a new product review given other available reviews of the product. The model is also conditioned on review properties that are directly related to summaries; the properties are derived from reviews with no manual effort. In the second stage, we fine-tune a plug-in module that learns to predict property values on a handful of summaries. This lets us switch the generator to the summarization mode. We show on Amazon and Yelp datasets that our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.",
}
@inproceedings{lebanoff-etal-2020-learning,
    title = "Learning to Fuse Sentences with Transformers for Summarization",
    author = "Lebanoff, Logan  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Wang, Lidan  and
      Chang, Walter  and
      Liu, Fei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.338",
    doi = "10.18653/v1/2020.emnlp-main.338",
    pages = "4136--4142",
    abstract = "The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce few summary sentences by fusion or generate incorrect fusions that lead the summary to fail to retain the original meaning. In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences. Through extensive experiments, we investigate the effects of different design choices on Transformer{'}s performance. Our findings highlight the importance of modeling points of correspondence between sentences for effective sentence fusion.",
}
@inproceedings{narayan-etal-2020-stepwise,
    title = "Stepwise Extractive Summarization and Planning with Structured Transformers",
    author = "Narayan, Shashi  and
      Maynez, Joshua  and
      Adamek, Jakub  and
      Pighin, Daniele  and
      Bratanic, Blaz  and
      McDonald, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.339",
    doi = "10.18653/v1/2020.emnlp-main.339",
    pages = "4143--4159",
    abstract = "We propose encoder-centric stepwise models for extractive summarization using structured transformers {--} HiBERT and Extended Transformers. We enable stepwise summarization by injecting the previously generated summary into the structured transformer as an auxiliary sub-structure. Our models are not only efficient in modeling the structure of long inputs, but they also do not rely on task-specific redundancy-aware modeling, making them a general purpose extractive content planner for different tasks. When evaluated on CNN/DailyMail extractive summarization, stepwise models achieve state-of-the-art performance in terms of Rouge without any redundancy aware modeling or sentence filtering. This also holds true for Rotowire table-to-text generation, where our models surpass previously reported metrics for content selection, planning and ordering, highlighting the strength of stepwise modeling. Amongst the two structured transformers we test, stepwise Extended Transformers provides the best performance across both datasets and sets a new standard for these challenges.",
}
@inproceedings{sun-duh-2020-clirmatrix,
    title = "{CLIRM}atrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval",
    author = "Sun, Shuo  and
      Duh, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.340",
    doi = "10.18653/v1/2020.emnlp-main.340",
    pages = "4160--4170",
    abstract = "We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. CLIRMatrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139x138=19,182 language pairs, and (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages. In total, we mined 49 million unique queries and 34 billion (query, document, label) triplets, making it the largest and most comprehensive CLIR dataset to date. This collection is intended to support research in end-to-end neural information retrieval and is publicly available at [url]. We provide baseline neural model results on BI-139, and evaluate MULTI-8 in both single-language retrieval and mix-language retrieval settings.",
}
@inproceedings{macavaney-etal-2020-sledge,
    title = "{SLEDGE-Z}: A Zero-Shot Baseline for {COVID}-19 Literature Search",
    author = "MacAvaney, Sean  and
      Cohan, Arman  and
      Goharian, Nazli",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.341",
    doi = "10.18653/v1/2020.emnlp-main.341",
    pages = "4171--4179",
    abstract = "With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinicians, researchers, and policy-makers need to be able to search these articles effectively. In this work, we present a zero-shot ranking algorithm that adapts to COVID-related scientific literature. Our approach filters training data from another collection down to medical-related queries, uses a neural re-ranking model pre-trained on scientific text (SciBERT), and filters the target document collection. This approach ranks top among zero-shot methods on the TREC COVID Round 1 leaderboard, and exhibits a P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2 judgments. Despite not relying on TREC-COVID data, our method outperforms models that do. As one of the first search methods to thoroughly evaluate COVID-19 search, we hope that this serves as a strong baseline and helps in the global crisis.",
}
@inproceedings{gao-etal-2020-modularized,
    title = "Modularized Transfomer-based Ranking Framework",
    author = "Gao, Luyu  and
      Dai, Zhuyun  and
      Callan, Jamie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.342",
    doi = "10.18653/v1/2020.emnlp-main.342",
    pages = "4180--4190",
    abstract = "Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. However, these Transformers are computationally expensive, and their opaque hidden states make it hard to understand the ranking process. In this work, we modularize the Transformer ranker into separate modules for text representation and interaction. We show how this design enables substantially faster ranking using offline pre-computed representations and light-weight online interactions. The modular design is also easier to interpret and sheds light on the ranking process in Transformer rankers.",
}
@inproceedings{mass-roitman-2020-ad,
    title = "Ad-hoc Document Retrieval using Weak-Supervision with {BERT} and {GPT}2",
    author = "Mass, Yosi  and
      Roitman, Haggai",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.343",
    doi = "10.18653/v1/2020.emnlp-main.343",
    pages = "4191--4197",
    abstract = "We describe a weakly-supervised method for training deep learning models for the task of ad-hoc document retrieval. Our method is based on generative and discriminative models that are trained using weak-supervision just from the documents in the corpus. We present an end-to-end retrieval system that starts with traditional information retrieval methods, followed by two deep learning re-rankers. We evaluate our method on three different datasets: a COVID-19 related scientific literature dataset and two news datasets. We show that our method outperforms state-of-the-art methods; this without the need for the expensive process of manually labeling data.",
}
@inproceedings{song-etal-2020-adversarial,
    title = "Adversarial Semantic Collisions",
    author = "Song, Congzheng  and
      Rush, Alexander  and
      Shmatikov, Vitaly",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.344",
    doi = "10.18653/v1/2020.emnlp-main.344",
    pages = "4198--4210",
    abstract = "We study \textit{semantic collisions}: texts that are semantically unrelated but judged as similar by NLP models. We develop gradient-based approaches for generating semantic collisions and demonstrate that state-of-the-art models for many tasks which rely on analyzing the meaning and similarity of texts{---}including paraphrase identification, document retrieval, response suggestion, and extractive summarization{---}are vulnerable to semantic collisions. For example, given a target query, inserting a crafted collision into an irrelevant document can shift its retrieval rank from 1000 to top 3. We show how to generate semantic collisions that evade perplexity-based filtering and discuss other potential mitigations. Our code is available at \url{https://github.com/csong27/collision-bert}.",
}
@inproceedings{sen-etal-2020-learning,
    title = "Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification",
    author = "Sen, Prithviraj  and
      Danilevsky, Marina  and
      Li, Yunyao  and
      Brahma, Siddhartha  and
      Boehm, Matthias  and
      Chiticariu, Laura  and
      Krishnamurthy, Rajasekar",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.345",
    doi = "10.18653/v1/2020.emnlp-main.345",
    pages = "4211--4221",
    abstract = "Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world. We present RuleNN, a neural network architecture for learning transparent models for sentence classification. The models are in the form of rules expressed in first-order logic, a dialect with well-defined, human-understandable semantics. More precisely, RuleNN learns linguistic expressions (LE) built on top of predicates extracted using shallow natural language understanding. Our experimental results show that RuleNN outperforms statistical relational learning and other neuro-symbolic methods, and performs comparably with black-box recurrent neural networks. Our user studies confirm that the learned LEs are explainable and capture domain semantics. Moreover, allowing domain experts to modify LEs and instill more domain knowledge leads to human-machine co-creation of models with better performance.",
}
@inproceedings{shin-etal-2020-autoprompt,
    title = "{A}uto{P}rompt: {E}liciting {K}nowledge from {L}anguage {M}odels with {A}utomatically {G}enerated {P}rompts",
    author = "Shin, Taylor  and
      Razeghi, Yasaman  and
      Logan IV, Robert L.  and
      Wallace, Eric  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.346",
    doi = "10.18653/v1/2020.emnlp-main.346",
    pages = "4222--4235",
    abstract = "The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach for gauging such knowledge, however, its usage is limited by the manual effort and guesswork required to write suitable prompts. To address this, we develop AutoPrompt, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search. Using AutoPrompt, we show that masked language models (MLMs) have an inherent capability to perform sentiment analysis and natural language inference without additional parameters or finetuning, sometimes achieving performance on par with recent state-of-the-art supervised models. We also show that our prompts elicit more accurate factual knowledge from MLMs than the manually created prompts on the LAMA benchmark, and that MLMs can be used as relation extractors more effectively than supervised relation extraction models. These results demonstrate that automatically generated prompts are a viable parameter-free alternative to existing probing methods, and as pretrained LMs become more sophisticated and capable, potentially a replacement for finetuning.",
}
@inproceedings{chen-ji-2020-learning,
    title = "Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers",
    author = "Chen, Hanjie  and
      Ji, Yangfeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.347",
    doi = "10.18653/v1/2020.emnlp-main.347",
    pages = "4236--4251",
    abstract = "To build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations. A new line of work on improving model interpretability has just started, and many existing methods require either prior information or human annotations as additional inputs in training. To address this limitation, we propose the variational word mask (VMASK) method to automatically learn task-specific important words and reduce irrelevant information on classification, which ultimately improves the interpretability of model predictions. The proposed method is evaluated with three neural text classifiers (CNN, LSTM, and BERT) on seven benchmark text classification datasets. Experiments show the effectiveness of VMASK in improving both model prediction accuracy and interpretability.",
}
@inproceedings{martins-etal-2020-sparse,
    title = "Sparse Text Generation",
    author = "Martins, Pedro Henrique  and
      Marinho, Zita  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.348",
    doi = "10.18653/v1/2020.emnlp-main.348",
    pages = "4252--4273",
    abstract = "Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance. However, to avoid degenerate text, they require sampling from a modified softmax, via temperature parameters or ad-hoc truncation techniques, as in top-$k$ or nucleus sampling. This creates a mismatch between training and testing conditions. In this paper, we use the recently introduced entmax transformation to train and sample from a natively sparse language model, avoiding this mismatch. The result is a text generator with favorable performance in terms of fluency and consistency, fewer repetitions, and n-gram diversity closer to human text. In order to evaluate our model, we propose three new metrics for comparing sparse or truncated distributions: $\epsilon$-perplexity, sparsemax score, and Jensen-Shannon divergence. Human-evaluated experiments in story completion and dialogue generation show that entmax sampling leads to more engaging and coherent stories and conversations.",
}
@inproceedings{rashkin-etal-2020-plotmachines,
    title = "{P}lot{M}achines: Outline-Conditioned Generation with Dynamic Plot State Tracking",
    author = "Rashkin, Hannah  and
      Celikyilmaz, Asli  and
      Choi, Yejin  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.349",
    doi = "10.18653/v1/2020.emnlp-main.349",
    pages = "4274--4295",
    abstract = "We propose the task of outline-conditioned story generation: given an outline as a set of phrases that describe key characters and events to appear in a story, the task is to generate a coherent narrative that is consistent with the provided outline. This task is challenging as the input only provides a rough sketch of the plot, and thus, models need to generate a story by interweaving the key points provided in the outline. This requires the model to keep track of the dynamic states of the latent plot, conditioning on the input outline while generating the full story. We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states. In addition, we enrich PlotMachines with high-level discourse structure so that the model can learn different writing styles corresponding to different parts of the narrative. Comprehensive experiments over three fiction and non-fiction datasets demonstrate that large-scale language models, such as GPT-2 and Grover, despite their impressive generation performance, are not sufficient in generating coherent narratives for the given outline, and dynamic plot state tracking is important for composing narratives with tighter, more consistent plots.",
}
@inproceedings{bosc-vincent-2020-sequence,
    title = "Do sequence-to-sequence {VAE}s learn global features of sentences?",
    author = "Bosc, Tom  and
      Vincent, Pascal",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.350",
    doi = "10.18653/v1/2020.emnlp-main.350",
    pages = "4296--4318",
    abstract = "Autoregressive language models are powerful and relatively easy to train. However, these models are usually trained without explicit conditioning labels and do not offer easy ways to control global aspects such as sentiment or topic during generation. Bowman {\&} al. 2016 adapted the Variational Autoencoder (VAE) for natural language with the sequence-to-sequence architecture and claimed that the latent vector was able to capture such global features in an unsupervised manner. We question this claim. We measure which words benefit most from the latent information by decomposing the reconstruction loss per position in the sentence. Using this method, we find that VAEs are prone to memorizing the first words and the sentence length, producing local features of limited usefulness. To alleviate this, we investigate alternative architectures based on bag-of-words assumptions and language model pretraining. These variants learn latent variables that are more global, i.e., more predictive of topic or sentiment labels. Moreover, using reconstructions, we observe that they decrease memorization: the first word and the sentence length are not recovered as accurately than with the baselines, consequently yielding more diverse reconstructions.",
}
@inproceedings{goldfarb-tarrant-etal-2020-content,
    title = "Content Planning for Neural Story Generation with Aristotelian Rescoring",
    author = "Goldfarb-Tarrant, Seraphina  and
      Chakrabarty, Tuhin  and
      Weischedel, Ralph  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.351",
    doi = "10.18653/v1/2020.emnlp-main.351",
    pages = "4319--4338",
    abstract = "Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via high-quality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle{'}s Poetics. We find that stories written with our more principled plot-structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.",
}
@inproceedings{ko-etal-2020-generating,
    title = "Generating Dialogue Responses from a Semantic Latent Space",
    author = "Ko, Wei-Jen  and
      Ray, Avik  and
      Shen, Yilin  and
      Jin, Hongxia",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.352",
    doi = "10.18653/v1/2020.emnlp-main.352",
    pages = "4339--4349",
    abstract = "Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary. However, a good response does not need to resemble the gold response, since there are multiple possible responses to a given prompt. In this work, we hypothesize that the current models are unable to integrate information from multiple semantically similar valid responses of a prompt, resulting in the generation of generic and uninformative responses. To address this issue, we propose an alternative to the end-to-end classification on vocabulary. We learn the pair relationship between the prompts and responses as a regression task on a latent space instead. In our novel dialog generation model, the representations of semantically related sentences are close to each other on the latent space. Human evaluation showed that learning the task on a continuous space can generate responses that are both relevant and informative.",
}
@inproceedings{takmaz-etal-2020-refer,
    title = "{R}efer, {R}euse, {R}educe: {G}enerating {S}ubsequent {R}eferences in {V}isual and {C}onversational {C}ontexts",
    author = "Takmaz, Ece  and
      Giulianelli, Mario  and
      Pezzelle, Sandro  and
      Sinclair, Arabella  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.353",
    doi = "10.18653/v1/2020.emnlp-main.353",
    pages = "4350--4368",
    abstract = "Dialogue participants often refer to entities or situations repeatedly within a conversation, which contributes to its cohesiveness. Subsequent references exploit the common ground accumulated by the interlocutors and hence have several interesting properties, namely, they tend to be shorter and reuse expressions that were effective in previous mentions. In this paper, we tackle the generation of first and subsequent references in visually grounded dialogue. We propose a generation model that produces referring utterances grounded in both the visual and the conversational context. To assess the referring effectiveness of its output, we also implement a reference resolution system. Our experiments and analyses show that the model produces better, more effective referring utterances than a model not grounded in the dialogue context, and generates subsequent references that exhibit linguistic patterns akin to humans.",
}
@inproceedings{zhao-titov-2020-visually,
    title = "Visually Grounded Compound {PCFG}s",
    author = "Zhao, Yanpeng  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.354",
    doi = "10.18653/v1/2020.emnlp-main.354",
    pages = "4369--4379",
    abstract = "Exploiting visual groundings for language understanding has recently been drawing much attention. In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings. Existing work on this task (Shi et al., 2019) optimizes a parser via Reinforce and derives the learning signal only from the alignment of images and sentences. While their model is relatively accurate overall, its error distribution is very uneven, with low performance on certain constituents types (e.g., 26.2{\%} recall on verb phrases, VPs) and high on others (e.g., 79.6{\%} recall on noun phrases, NPs). This is not surprising as the learning signal is likely insufficient for deriving all aspects of phrase-structure syntax and gradient estimates are noisy. We show that using an extension of probabilistic context-free grammar model we can do fully-differentiable end-to-end visually grounded learning. Additionally, this enables us to complement the image-text alignment loss with a language modeling objective. On the MSCOCO test captions, our model establishes a new state of the art, outperforming its non-grounded version and, thus, confirming the effectiveness of visual groundings in constituency grammar induction. It also substantially outperforms the previous grounded model, with largest improvements on more {`}abstract{'} categories (e.g., +55.1{\%} recall on VPs).",
}
@inproceedings{liang-etal-2020-alice,
    title = "{ALICE}: Active Learning with Contrastive Natural Language Explanations",
    author = "Liang, Weixin  and
      Zou, James  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.355",
    doi = "10.18653/v1/2020.emnlp-main.355",
    pages = "4380--4391",
    abstract = "Training a supervised neural network classifier typically requires many annotated training samples. Collecting and annotating a large number of data points are costly and sometimes even infeasible. Traditional annotation process uses a low-bandwidth human-machine communication interface: classification labels, each of which only provides a few bits of information. We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning. AL-ICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations from experts. Then it extracts knowledge from these explanations using a semantic parser. Finally, it incorporates the extracted knowledge through dynamically changing the learning model{'}s structure. We applied ALICEin two visual recognition tasks, bird species classification and social relationship classification. We found by incorporating contrastive explanations, our models outperform baseline models that are trained with 40-100{\%} more training data. We found that adding1expla-nation leads to similar performance gain as adding 13-30 labeled training data points.",
}
@inproceedings{ku-etal-2020-room,
    title = "Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding",
    author = "Ku, Alexander  and
      Anderson, Peter  and
      Patel, Roma  and
      Ie, Eugene  and
      Baldridge, Jason",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.356",
    doi = "10.18653/v1/2020.emnlp-main.356",
    pages = "4392--4412",
    abstract = "We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators. We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations (Anderson et al., 2018). We also provide results for a model that learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. The size, scope and detail of RxR dramatically expands the frontier for research on embodied language agents in photorealistic simulated environments.",
}
@inproceedings{fu-etal-2020-sscr,
    title = "{SSCR}: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning",
    author = "Fu, Tsu-Jui  and
      Wang, Xin  and
      Grafton, Scott  and
      Eckstein, Miguel  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.357",
    doi = "10.18653/v1/2020.emnlp-main.357",
    pages = "4413--4422",
    abstract = "Iterative Language-Based Image Editing (ILBIE) tasks follow iterative instructions to edit images step by step. Data scarcity is a significant issue for ILBIE as it is challenging to collect large-scale examples of images before and after instruction-based changes. Yet, humans still accomplish these editing tasks even when presented with an unfamiliar image-instruction pair. Such ability results from counterfactual thinking, the ability to think about possible alternatives to events that have happened already. In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity. SSCR allows the model to consider out-of-distribution instructions paired with previous images. With the help of cross-task consistency (CTC), we train these counterfactual instructions in a self-supervised scenario. Extensive results show that SSCR improves the correctness of ILBIE in terms of both object identity and position, establishing a new state of the art (SOTA) on two IBLIE datasets (i-CLEVR and CoDraw). Even with only 50{\%} of the training data, SSCR achieves a comparable result to using complete data.",
}
@inproceedings{dufter-schutze-2020-identifying,
    title = "Identifying Elements Essential for {BERT}{'}s Multilinguality",
    author = {Dufter, Philipp  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.358",
    doi = "10.18653/v1/2020.emnlp-main.358",
    pages = "4423--4437",
    abstract = "It has been shown that multilingual BERT (mBERT) yields high quality multilingual representations and enables effective zero-shot transfer. This is surprising given that mBERT does not use any crosslingual signal during training. While recent literature has studied this phenomenon, the reasons for the multilinguality are still somewhat obscure. We aim to identify architectural properties of BERT and linguistic properties of languages that are necessary for BERT to become multilingual. To allow for fast experimentation we propose an efficient setup with small BERT models trained on a mix of synthetic and natural data. Overall, we identify four architectural and two linguistic elements that influence multilinguality. Based on our insights, we experiment with a multilingual pretraining setup that modifies the masking strategy using VecMap, i.e., unsupervised embedding alignment. Experiments on XNLI with three languages indicate that our findings transfer from our small setup to larger scale settings.",
}
@inproceedings{wang-etal-2020-negative,
    title = "On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment",
    author = "Wang, Zirui  and
      Lipton, Zachary C.  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.359",
    doi = "10.18653/v1/2020.emnlp-main.359",
    pages = "4438--4450",
    abstract = "Modern multilingual models are trained on concatenated text from multiple languages in hopes of conferring benefits to each (positive transfer), with the most pronounced benefits accruing to low-resource languages. However, recent work has shown that this approach can degrade performance on high-resource languages, a phenomenon known as negative interference. In this paper, we present the first systematic study of negative interference. We show that, contrary to previous belief, negative interference also impacts low-resource languages. While parameters are maximally shared to learn language-universal structures, we demonstrate that language-specific parameters do exist in multilingual models and they are a potential cause of negative interference. Motivated by these observations, we also present a meta-learning algorithm that obtains better cross-lingual transferability and alleviates negative interference, by adding language-specific layers as meta-parameters and training them in a manner that explicitly improves shared layers{'} generalization on all languages. Overall, our results show that negative interference is more common than previously known, suggesting new directions for improving multilingual representations.",
}
@inproceedings{otani-etal-2020-pre,
    title = "Pre-tokenization of Multi-word Expressions in Cross-lingual Word Embeddings",
    author = "Otani, Naoki  and
      Ozaki, Satoru  and
      Zhao, Xingyuan  and
      Li, Yucen  and
      St Johns, Micaelah  and
      Levin, Lori",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.360",
    doi = "10.18653/v1/2020.emnlp-main.360",
    pages = "4451--4464",
    abstract = "Cross-lingual word embedding (CWE) algorithms represent words in multiple languages in a unified vector space. Multi-Word Expressions (MWE) are common in every language. When training word embeddings, each component word of an MWE gets its own separate embedding, and thus, MWEs are not translated by CWEs. We propose a simple method for word translation of MWEs to and from English in ten languages: we first compile lists of MWEs in each language and then tokenize the MWEs as single tokens before training word embeddings. CWEs are trained on a word-translation task using the dictionaries that only contain single words. In order to evaluate MWE translation, we created bilingual word lists from multilingual WordNet that include single-token words and MWEs, and most importantly, include MWEs that correspond to single words in another language. We release these dictionaries to the research community. We show that the pre-tokenization of MWEs as single tokens performs better than averaging the embeddings of the individual tokens of the MWE. We can translate MWEs at a top-10 precision of 30-60{\%}. The tokenization of MWEs makes the occurrences of single words in a training corpus more sparse, but we show that it does not pose negative impacts on single-word translations.",
}
@inproceedings{philip-etal-2020-monolingual,
    title = "Monolingual Adapters for Zero-Shot Neural Machine Translation",
    author = "Philip, Jerin  and
      Berard, Alexandre  and
      Gall{\'e}, Matthias  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.361",
    doi = "10.18653/v1/2020.emnlp-main.361",
    pages = "4465--4470",
    abstract = "We propose a novel adapter layer formalism for adapting multilingual models. They are more parameter-efficient than existing adapter layers while obtaining as good or better performance. The layers are specific to one language (as opposed to bilingual adapters) allowing to compose them and generalize to unseen language-pairs. In this zero-shot setting, they obtain a median improvement of +2.77 BLEU points over a strong 20-language multilingual Transformer baseline trained on TED talks.",
}
@inproceedings{wu-dredze-2020-explicit,
    title = "Do Explicit Alignments Robustly Improve Multilingual Encoders?",
    author = "Wu, Shijie  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.362",
    doi = "10.18653/v1/2020.emnlp-main.362",
    pages = "4471--4482",
    abstract = "Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often suboptimal and such bitexts are unavailable for many languages. In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection. Additionally, rather than report results on a single dataset with a single model run, we report the mean and standard derivation of multiple runs with different seeds, on four datasets and tasks. Our more extensive analysis finds that, while our new objective outperforms previous work, overall these methods do not improve performance with a more robust evaluation framework. Furthermore, the gains from using a better underlying model eclipse any benefits from alignment training. These negative results dictate more care in evaluating these methods and suggest limitations in applying explicit alignment objectives.",
}
@inproceedings{lauscher-etal-2020-zero,
    title = "From Zero to Hero: {O}n the Limitations of Zero-Shot Language Transfer with Multilingual {T}ransformers",
    author = "Lauscher, Anne  and
      Ravishankar, Vinit  and
      Vuli{\'c}, Ivan  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.363",
    doi = "10.18653/v1/2020.emnlp-main.363",
    pages = "4483--4499",
    abstract = "Massively multilingual transformers (MMTs) pretrained via language modeling (e.g., mBERT, XLM-R) have become a default paradigm for zero-shot language transfer in NLP, offering unmatched transfer performance. Current evaluations, however, verify their efficacy in transfers (a) to languages with sufficiently large pretraining corpora, and (b) between close languages. In this work, we analyze the limitations of downstream language transfer with MMTs, showing that, much like cross-lingual word embeddings, they are substantially less effective in resource-lean scenarios and for distant languages. Our experiments, encompassing three lower-level tasks (POS tagging, dependency parsing, NER) and two high-level tasks (NLI, QA), empirically correlate transfer performance with linguistic proximity between source and target languages, but also with the size of target language corpora used in MMT pretraining. Most importantly, we demonstrate that the inexpensive few-shot transfer (i.e., additional fine-tuning on a few target-language instances) is surprisingly effective across the board, warranting more research efforts reaching beyond the limiting zero-shot conditions.",
}
@inproceedings{currey-etal-2020-distilling,
    title = "Distilling Multiple Domains for Neural Machine Translation",
    author = "Currey, Anna  and
      Mathur, Prashant  and
      Dinu, Georgiana",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.364",
    doi = "10.18653/v1/2020.emnlp-main.364",
    pages = "4500--4511",
    abstract = "Neural machine translation achieves impressive results in high-resource conditions, but performance often suffers when the input domain is low-resource. The standard practice of adapting a separate model for each domain of interest does not scale well in practice from both a quality perspective (brittleness under domain shift) as well as a cost perspective (added maintenance and inference complexity). In this paper, we propose a framework for training a single multi-domain neural machine translation model that is able to translate several domains without increasing inference time or memory usage. We show that this model can improve translation on both high- and low-resource domains over strong multi-domain baselines. In addition, our proposed model is effective when domain labels are unknown during training, as well as robust under noisy data conditions.",
}
@inproceedings{reimers-gurevych-2020-making,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.365",
    doi = "10.18653/v1/2020.emnlp-main.365",
    pages = "4512--4525",
    abstract = "We present an easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models. The training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence. We use the original (monolingual) model to generate sentence embeddings for the source language and then train a new system on translated sentences to mimic the original model. Compared to other methods for training multilingual sentence embeddings, this approach has several advantages: It is easy to extend existing models with relatively few samples to new languages, it is easier to ensure desired properties for the vector space, and the hardware requirements for training are lower. We demonstrate the effectiveness of our approach for 50+ languages from various language families. Code to extend sentence embeddings models to more than 400 languages is publicly available.",
}
@inproceedings{yang-etal-2020-streaming,
    title = "A Streaming Approach For Efficient Batched Beam Search",
    author = "Yang, Kevin  and
      Yao, Violet  and
      DeNero, John  and
      Klein, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.366",
    doi = "10.18653/v1/2020.emnlp-main.366",
    pages = "4526--4535",
    abstract = "We propose an efficient batching strategy for variable-length decoding on GPU architectures. During decoding, when candidates terminate or are pruned according to heuristics, our streaming approach periodically {``}refills{''} the batch before proceeding with a selected subset of candidates. We apply our method to variable-width beam search on a state-of-the-art machine translation model. Our method decreases runtime by up to 71{\%} compared to a fixed-width beam search baseline and 17{\%} compared to a variable-width baseline, while matching baselines{'} BLEU. Finally, experiments show that our method can speed up decoding in other domains, such as semantic and syntactic parsing.",
}
@inproceedings{chung-etal-2020-improving,
    title = "Improving Multilingual Models with Language-Clustered Vocabularies",
    author = "Chung, Hyung Won  and
      Garrette, Dan  and
      Tan, Kiat Chuan  and
      Riesa, Jason",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.367",
    doi = "10.18653/v1/2020.emnlp-main.367",
    pages = "4536--4546",
    abstract = "State-of-the-art multilingual models depend on vocabularies that cover all of the languages the model will expect to see at inference time, but the standard methods for generating those vocabularies are not ideal for massively multilingual applications. In this work, we introduce a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters, thus balancing the trade-off between cross-lingual subword sharing and language-specific vocabularies. Our experiments show improvements across languages on key multilingual benchmark tasks TyDi QA (+2.9 F1), XNLI (+2.1{\%}), and WikiAnn NER (+2.8 F1) and factor of 8 reduction in out-of-vocabulary rate, all without increasing the size of the model or data.",
}
@inproceedings{nooralahzadeh-etal-2020-zero,
    title = "Zero-Shot Cross-Lingual Transfer with Meta Learning",
    author = "Nooralahzadeh, Farhad  and
      Bekoulis, Giannis  and
      Bjerva, Johannes  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.368",
    doi = "10.18653/v1/2020.emnlp-main.368",
    pages = "4547--4562",
    abstract = "Learning what to share between tasks has become a topic of great importance, as strategic sharing of knowledge has been shown to improve downstream task performance. This is particularly important for multilingual applications, as most languages in the world are under-resourced. Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning: in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages. We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset). A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.",
}
@inproceedings{keung-etal-2020-multilingual,
    title = "The Multilingual {A}mazon Reviews Corpus",
    author = {Keung, Phillip  and
      Lu, Yichao  and
      Szarvas, Gy{\"o}rgy  and
      Smith, Noah A.},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.369",
    doi = "10.18653/v1/2020.emnlp-main.369",
    pages = "4563--4568",
    abstract = "We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., {`}books{'}, {`}appliances{'}, etc.) The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20{\%} of the reviews in each language. For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively. We report baseline results for supervised text classification and zero-shot cross-lingual transfer learning by fine-tuning a multilingual BERT model on reviews data. We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ordinal nature of the ratings.",
}
@inproceedings{mostafazadeh-etal-2020-glucose,
    title = "{GLUCOSE}: {G}enera{L}ized and {CO}ntextualized Story Explanations",
    author = "Mostafazadeh, Nasrin  and
      Kalyanpur, Aditya  and
      Moon, Lori  and
      Buchanan, David  and
      Berkowitz, Lauren  and
      Biran, Or  and
      Chu-Carroll, Jennifer",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.370",
    doi = "10.18653/v1/2020.emnlp-main.370",
    pages = "4569--4586",
    abstract = "When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions. Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement. This paper details two concrete contributions. First, we present our platform for effectively crowdsourcing GLUCOSE data at scale, which uses semi-structured templates to elicit causal explanations. Using this platform, we collected a total of {\textasciitilde}670K specific statements and general rules that capture implicit commonsense knowledge about everyday situations. Second, we show that existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE{'}s rich inferential content. However, when state-of-the-art neural models are trained on this knowledge, they can start to make commonsense inferences on unseen stories that match humans{'} mental models.",
}
@inproceedings{van-noord-etal-2020-character,
    title = "Character-level Representations Improve {DRS}-based Semantic Parsing {E}ven in the Age of {BERT}",
    author = "van Noord, Rik  and
      Toral, Antonio  and
      Bos, Johan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.371",
    doi = "10.18653/v1/2020.emnlp-main.371",
    pages = "4587--4603",
    abstract = "We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.",
}
@inproceedings{he-etal-2020-infusing,
    title = "{I}nfusing {D}isease {K}nowledge into {BERT} for {H}ealth {Q}uestion {A}nswering, {M}edical {I}nference and {D}isease {N}ame {R}ecognition",
    author = "He, Yun  and
      Zhu, Ziwei  and
      Zhang, Yin  and
      Chen, Qin  and
      Caverlee, James",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.372",
    doi = "10.18653/v1/2020.emnlp-main.372",
    pages = "4604--4614",
    abstract = "Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment. This disease knowledge is critical for many health-related and biomedical tasks, including consumer health question answering, medical language inference and disease name recognition. While pre-trained language models like BERT have shown success in capturing syntactic, semantic, and world knowledge from text, we find they can be further complemented by specific information like knowledge of symptoms, diagnoses, treatments, and other disease aspects. Hence, we integrate BERT with disease knowledge for improving these important tasks. Specifically, we propose a new disease knowledge infusion training procedure and evaluate it on a suite of BERT models including BERT, BioBERT, SciBERT, ClinicalBERT, BlueBERT, and ALBERT. Experiments over the three tasks show that these models can be enhanced in nearly all cases, demonstrating the viability of disease knowledge infusion. For example, accuracy of BioBERT on consumer health question answering is improved from 68.29{\%} to 72.09{\%}, while new SOTA results are observed in two datasets. We make our data and code freely available.",
}
@inproceedings{shwartz-etal-2020-unsupervised,
    title = "Unsupervised Commonsense Question Answering with Self-Talk",
    author = "Shwartz, Vered  and
      West, Peter  and
      Le Bras, Ronan  and
      Bhagavatula, Chandra  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.373",
    doi = "10.18653/v1/2020.emnlp-main.373",
    pages = "4615--4629",
    abstract = "Natural language understanding involves reading between the lines with implicit background knowledge. Current systems either rely on pre-trained language models as the sole implicit source of world knowledge, or resort to external knowledge bases (KBs) to incorporate additional relevant knowledge. We propose an unsupervised framework based on self-talk as a novel alternative to multiple-choice commonsense tasks. Inspired by inquiry-based discovery learning (Bruner, 1961), our approach inquires language models with a number of information seeking questions such as {``}what is the definition of...{''} to discover additional background knowledge. Empirical results demonstrate that the self-talk procedure substantially improves the performance of zero-shot language model baselines on four out of six commonsense benchmarks, and competes with models that obtain knowledge from external KBs. While our approach improves performance on several benchmarks, the self-talk induced knowledge even when leading to correct answers is not always seen as helpful by human judges, raising interesting questions about the inner-workings of pre-trained language models for commonsense reasoning.",
}
@inproceedings{zhang-etal-2020-reasoning,
    title = "Reasoning about Goals, Steps, and Temporal Ordering with {W}iki{H}ow",
    author = "Zhang, Li  and
      Lyu, Qing  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.374",
    doi = "10.18653/v1/2020.emnlp-main.374",
    pages = "4630--4639",
    abstract = "We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations ({``}learn poses{''} is a step in the larger goal of {``}doing yoga{''}) and step-step temporal relations ({``}buy a yoga mat{''} typically precedes {``}learn poses{''}). We introduce a dataset targeting these two relations based on wikiHow, a website of instructional how-to articles. Our human-validated test set serves as a reliable benchmark for common-sense inference, with a gap of about 10{\%} to 20{\%} between the performance of state-of-the-art transformer models and human performance. Our automatically-generated training set allows models to effectively transfer to out-of-domain tasks requiring knowledge of procedural events, with greatly improved performances on SWAG, Snips, and Story Cloze Test in zero- and few-shot settings.",
}
@inproceedings{wilcox-etal-2020-structural,
    title = "Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models",
    author = "Wilcox, Ethan  and
      Qian, Peng  and
      Futrell, Richard  and
      Kohita, Ryosuke  and
      Levy, Roger  and
      Ballesteros, Miguel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.375",
    doi = "10.18653/v1/2020.emnlp-main.375",
    pages = "4640--4652",
    abstract = "Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We assess the ability of modern neural language models to reproduce this behavior in English and evaluate the effect of structural supervision on learning outcomes. First, we assess few-shot learning capabilities by developing controlled experiments that probe models{'} syntactic nominal number and verbal argument structure generalizations for tokens seen as few as two times during training. Second, we assess invariance properties of learned representation: the ability of a model to transfer syntactic generalizations from a base context (e.g., a simple declarative active-voice sentence) to a transformed context (e.g., an interrogative sentence). We test four models trained on the same dataset: an n-gram baseline, an LSTM, and two LSTM-variants trained with explicit structural supervision. We find that in most cases, the neural models are able to induce the proper syntactic generalizations after minimal exposure, often from just two examples during training, and that the two structurally supervised models generalize more accurately than the LSTM model. All neural models are able to leverage information learned in base contexts to drive expectations in transformed contexts, indicating that they have learned some invariance properties of syntax.",
}
@inproceedings{hawkins-etal-2020-investigating,
    title = "Investigating representations of verb bias in neural language models",
    author = "Hawkins, Robert  and
      Yamakoshi, Takateru  and
      Griffiths, Thomas  and
      Goldberg, Adele",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.376",
    doi = "10.18653/v1/2020.emnlp-main.376",
    pages = "4653--4663",
    abstract = "Languages typically provide more than one grammatical construction to express certain types of messages. A speaker{'}s choice of construction is known to depend on multiple factors, including the choice of main verb {--} a phenomenon known as verb bias. Here we introduce DAIS, a large benchmark dataset containing 50K human judgments for 5K distinct sentence pairs in the English dative alternation. This dataset includes 200 unique verbs and systematically varies the definiteness and length of arguments. We use this dataset, as well as an existing corpus of naturally occurring data, to evaluate how well recent neural language models capture human preferences. Results show that larger models perform better than smaller models, and transformer architectures (e.g. GPT-2) tend to out-perform recurrent architectures (e.g. LSTMs) even under comparable parameter and training settings. Additional analyses of internal feature representations suggest that transformers may better integrate specific lexical information with grammatical constructions.",
}
@inproceedings{takmaz-etal-2020-generating,
    title = "{G}enerating {I}mage {D}escriptions via {S}equential {C}ross-{M}odal {A}lignment {G}uided by {H}uman {G}aze",
    author = "Takmaz, Ece  and
      Pezzelle, Sandro  and
      Beinborn, Lisa  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.377",
    doi = "10.18653/v1/2020.emnlp-main.377",
    pages = "4664--4677",
    abstract = "When speakers describe an image, they tend to look at objects before mentioning them. In this paper, we investigate such sequential cross-modal alignment by modelling the image description generation process computationally. We take as our starting point a state-of-the-art image captioning system and develop several model variants that exploit information from human gaze patterns recorded during language production. In particular, we propose the first approach to image description generation where visual processing is modelled sequentially. Our experiments and analyses confirm that better descriptions can be obtained by exploiting gaze-driven attention and shed light on human cognitive processes by comparing different ways of aligning the gaze modality with language production. We find that processing gaze data sequentially leads to descriptions that are better aligned to those produced by speakers, more diverse, and more natural{---}particularly when gaze is encoded with a dedicated recurrent component.",
}
@inproceedings{li-etal-2020-optimus,
    title = "Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space",
    author = "Li, Chunyuan  and
      Gao, Xiang  and
      Li, Yuan  and
      Peng, Baolin  and
      Li, Xiujun  and
      Zhang, Yizhe  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.378",
    doi = "10.18653/v1/2020.emnlp-main.378",
    pages = "4678--4699",
    abstract = "When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model Optimus (Organizing sentences via Pre-Trained Modeling of a Universal Space). A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks. Compared with GPT-2, Optimus enables guided language generation from an abstract level using the latent vectors. Compared with BERT, Optimus can generalize better on low-resource language understanding tasks due to the smooth latent space structure. Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of Optimus. It achieves new state-of-the-art on VAE language modeling benchmarks.",
}
@inproceedings{shin-etal-2020-biomegatron,
    title = "{B}io{M}egatron: Larger Biomedical Domain Language Model",
    author = "Shin, Hoo-Chang  and
      Zhang, Yang  and
      Bakhturina, Evelina  and
      Puri, Raul  and
      Patwary, Mostofa  and
      Shoeybi, Mohammad  and
      Mani, Raghav",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.379",
    doi = "10.18653/v1/2020.emnlp-main.379",
    pages = "4700--4706",
    abstract = "There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text corpora such as Wikipedia and Books. Yet, most works do not study the factors affecting each domain language application deeply. Additionally, the study of model size on domain-specific models has been mostly missing. We empirically study and evaluate several factors that can affect performance on domain language applications, such as the sub-word vocabulary set, model size, pre-training corpus, and domain transfer. We show consistent improvements on benchmarks with our larger BioMegatron model trained on a larger domain corpus, contributing to our understanding of domain language model applications. We demonstrate noticeable improvements over the previous state-of-the-art (SOTA) on standard biomedical NLP benchmarks of question answering, named entity recognition, and relation extraction. Code and checkpoints to reproduce our experiments are available at [github.com/NVIDIA/NeMo].",
}
@inproceedings{lukasik-etal-2020-text,
    title = "Text Segmentation by Cross Segment Attention",
    author = "Lukasik, Michal  and
      Dadachev, Boris  and
      Papineni, Kishore  and
      Sim{\~o}es, Gon{\c{c}}alo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.380",
    doi = "10.18653/v1/2020.emnlp-main.380",
    pages = "4707--4716",
    abstract = "Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization. In this work, we propose three transformer-based architectures and provide comprehensive comparisons with previously proposed approaches on three standard datasets. We establish a new state-of-the-art, reducing in particular the error rates by a large margin in all cases. We further analyze model sizes and find that we can build models with many fewer parameters while keeping good performance, thus facilitating real-world applications.",
}
@inproceedings{shavrina-etal-2020-russiansuperglue,
    title = "{R}ussian{S}uper{GLUE}: A {R}ussian Language Understanding Evaluation Benchmark",
    author = "Shavrina, Tatiana  and
      Fenogenova, Alena  and
      Anton, Emelyanov  and
      Shevelev, Denis  and
      Artemova, Ekaterina  and
      Malykh, Valentin  and
      Mikhailov, Vladislav  and
      Tikhonova, Maria  and
      Chertok, Andrey  and
      Evlampiev, Andrey",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.381",
    doi = "10.18653/v1/2020.emnlp-main.381",
    pages = "4717--4726",
    abstract = "In this paper, we introduce an advanced Russian general language understanding evaluation benchmark {--} Russian SuperGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from scratch for the Russian language. We also provide baselines, human level evaluation, open-source framework for evaluating models, and an overall leaderboard of transformer models for the Russian language. Besides, we present the first results of comparing multilingual models in the translated diagnostic test set and offer the first steps to further expanding or assessing State-of-the-art models independently of language.",
}
@inproceedings{lan-etal-2020-empirical,
    title = "An Empirical Study of Pre-trained Transformers for {A}rabic Information Extraction",
    author = "Lan, Wuwei  and
      Chen, Yang  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.382",
    doi = "10.18653/v1/2020.emnlp-main.382",
    pages = "4727--4734",
    abstract = "Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable effective cross-lingual zero-shot transfer. However, their performance on Arabic information extraction (IE) tasks is not very well studied. In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. We study GigaBERT{'}s effectiveness on zero-short transfer across four IE tasks: named entity recognition, part-of-speech tagging, argument role labeling, and relation extraction. Our best model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT (Antoun et al., 2020) in both the supervised and zero-shot transfer settings. We have made our pre-trained models publicly available at: https://github.com/lanwuwei/GigaBERT.",
}
@inproceedings{tan-etal-2020-tnt,
    title = "{TNT}: Text Normalization based Pre-training of Transformers for Content Moderation",
    author = "Tan, Fei  and
      Hu, Yifan  and
      Hu, Changwei  and
      Li, Keqian  and
      Yen, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.383",
    doi = "10.18653/v1/2020.emnlp-main.383",
    pages = "4735--4741",
    abstract = "In this work, we present a new language pre-training model TNT (Text Normalization based pre-training of Transformers) for content moderation. Inspired by the masking strategy and text normalization, TNT is developed to learn language representation by training transformers to reconstruct text from four operation types typically seen in text manipulation: substitution, transposition, deletion, and insertion. Furthermore, the normalization involves the prediction of both operation types and token labels, enabling TNT to learn from more challenging tasks than the standard task of masked word recovery. As a result, the experiments demonstrate that TNT outperforms strong baselines on the hate speech classification task. Additional text normalization experiments and case studies show that TNT is a new potential approach to misspelling correction.",
}
@inproceedings{sundararaman-etal-2020-methods,
    title = "Methods for Numeracy-Preserving Word Embeddings",
    author = "Sundararaman, Dhanasekar  and
      Si, Shijing  and
      Subramanian, Vivek  and
      Wang, Guoyin  and
      Hazarika, Devamanyu  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.384",
    doi = "10.18653/v1/2020.emnlp-main.384",
    pages = "4742--4753",
    abstract = "Word embedding models are typically able to capture the semantics of words via the distributional hypothesis, but fail to capture the numerical properties of numbers that appear in the text. This leads to problems with numerical reasoning involving tasks such as question answering. We propose a new methodology to assign and learn embeddings for numbers. Our approach creates Deterministic, Independent-of-Corpus Embeddings (the model is referred to as DICE) for numbers, such that their cosine similarity reflects the actual distance on the number line. DICE outperforms a wide range of pre-trained word embedding models across multiple examples of two tasks: (i) evaluating the ability to capture numeration and magnitude; and (ii) to perform list maximum, decoding, and addition. We further explore the utility of these embeddings in downstream tasks, by initializing numbers with our approach for the task of magnitude prediction. We also introduce a regularization approach to learn model-based embeddings of numbers in a contextual setting.",
}
@inproceedings{berg-kirkpatrick-spokoyny-2020-empirical,
    title = "An Empirical Investigation of Contextualized Number Prediction",
    author = "Berg-Kirkpatrick, Taylor  and
      Spokoyny, Daniel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.385",
    doi = "10.18653/v1/2020.emnlp-main.385",
    pages = "4754--4764",
    abstract = "We conduct a large scale empirical investigation of contextualized number prediction in running text. Specifically, we consider two tasks: (1)masked number prediction{--} predict-ing a missing numerical value within a sentence, and (2)numerical anomaly detection{--}detecting an errorful numeric value within a sentence. We experiment with novel combinations of contextual encoders and output distributions over the real number line. Specifically, we introduce a suite of output distribution parameterizations that incorporate latent variables to add expressivity and better fit the natural distribution of numeric values in running text, and combine them with both recur-rent and transformer-based encoder architectures. We evaluate these models on two numeric datasets in the financial and scientific domain. Our findings show that output distributions that incorporate discrete latent variables and allow for multiple modes outperform simple flow-based counterparts on all datasets, yielding more accurate numerical pre-diction and anomaly detection. We also show that our models effectively utilize textual con-text and benefit from general-purpose unsupervised pretraining.",
}
@inproceedings{epure-etal-2020-modeling,
    title = "Modeling the Music Genre Perception across Language-Bound Cultures",
    author = "Epure, Elena V.  and
      Salha, Guillaume  and
      Moussallam, Manuel  and
      Hennequin, Romain",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.386",
    doi = "10.18653/v1/2020.emnlp-main.386",
    pages = "4765--4779",
    abstract = "The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere translations since we also need to account for cultural differences in the music genre perception. In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies. Our study, focused on six languages, shows that unsupervised cross-lingual music genre annotation is feasible with high accuracy, especially when combining both types of representations. This approach of studying music genres is the most extensive to date and has many implications in musicology and music information retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus to benchmark state of the art multilingual pre-trained embedding models.",
}
@inproceedings{martinez-etal-2020-joint,
    title = "Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts",
    author = "Martinez, Victor  and
      Somandepalli, Krishna  and
      Tehranian-Uhls, Yalda  and
      Narayanan, Shrikanth",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.387",
    doi = "10.18653/v1/2020.emnlp-main.387",
    pages = "4780--4790",
    abstract = "Exposure to violent, sexual, or substance-abuse content in media increases the willingness of children and adolescents to imitate similar behaviors. Computational methods that identify portrayals of risk behaviors from audio-visual cues are limited in their applicability to films in post-production, where modifications might be prohibitively expensive. To address this limitation, we propose a model that estimates content ratings based on the language use in movie scripts, making our solution available at the earlier stages of creative production. Our model significantly improves the state-of-the-art by adapting novel techniques to learn better movie representations from the semantic and sentiment aspects of a character{'}s language use, and by leveraging the co-occurrence of risk behaviors, following a multi-task approach. Additionally, we show how this approach can be useful to learn novel insights on the joint portrayal of these behaviors, and on the subtleties that filmmakers may otherwise not pick up on.",
}
@inproceedings{krishna-etal-2020-keep,
    title = "Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in {S}anskrit",
    author = "Krishna, Amrith  and
      Gupta, Ashim  and
      Garasangi, Deepak  and
      Satuluri, Pavankumar  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.388",
    doi = "10.18653/v1/2020.emnlp-main.388",
    pages = "4791--4797",
    abstract = "Morphologically rich languages seem to benefit from joint processing of morphology and syntax, as compared to pipeline architectures. We propose a graph-based model for joint morphological parsing and dependency parsing in Sanskrit. Here, we extend the Energy based model framework (Krishna et al., 2020), proposed for several structured prediction tasks in Sanskrit, in 2 simple yet significant ways. First, the framework{'}s default input graph generation method is modified to generate a multigraph, which enables the use of an exact search inference. Second, we prune the input search space using a linguistically motivated approach, rooted in the traditional grammatical analysis of Sanskrit. Our experiments show that the morphological parsing from our joint model outperforms standalone morphological parsers. We report state of the art results in morphological parsing, and in dependency parsing, both in standalone (with gold morphological tags) and joint morphosyntactic parsing setting.",
}
@inproceedings{cao-etal-2020-unsupervised-parsing,
    title = "Unsupervised Parsing via Constituency Tests",
    author = "Cao, Steven  and
      Kitaev, Nikita  and
      Klein, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.389",
    doi = "10.18653/v1/2020.emnlp-main.389",
    pages = "4798--4808",
    abstract = "We propose a method for unsupervised parsing based on the linguistic notion of a constituency test. One type of constituency test involves modifying the sentence via some transformation (e.g. replacing the span with a pronoun) and then judging the result (e.g. checking if it is grammatical). Motivated by this idea, we design an unsupervised parser by specifying a set of transformations and using an unsupervised neural acceptability model to make grammaticality decisions. To produce a tree given a sentence, we score each span by aggregating its constituency test judgments, and we choose the binary tree with the highest total score. While this approach already achieves performance in the range of current methods, we further improve accuracy by fine-tuning the grammaticality model through a refinement procedure, where we alternate between improving the estimated trees and improving the grammaticality model. The refined model achieves 62.8 F1 on the Penn Treebank test set, an absolute improvement of 7.6 points over the previously best published result.",
}
@inproceedings{zmigrod-etal-2020-please,
    title = "Please Mind the Root: {D}ecoding Arborescences for Dependency Parsing",
    author = "Zmigrod, Ran  and
      Vieira, Tim  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.390",
    doi = "10.18653/v1/2020.emnlp-main.390",
    pages = "4809--4819",
    abstract = "The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases.In fact, the worst constraint-violation rate we observe is 24{\%}. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime.",
}
@inproceedings{eskander-etal-2020-unsupervised,
    title = "Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios",
    author = "Eskander, Ramy  and
      Muresan, Smaranda  and
      Collins, Michael",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.391",
    doi = "10.18653/v1/2020.emnlp-main.391",
    pages = "4820--4831",
    abstract = "We describe a fully unsupervised cross-lingual transfer approach for part-of-speech (POS) tagging under a truly low resource scenario. We assume access to parallel translations between the target language and one or more source languages for which POS taggers are available. We use the Bible as parallel data in our experiments: small size, out-of-domain and covering many diverse languages. Our approach innovates in three ways: 1) a robust approach of selecting training instances via cross-lingual annotation projection that exploits best practices of unsupervised type and token constraints, word-alignment confidence and density of projected POS, 2) a Bi-LSTM architecture that uses contextualized word embeddings, affix embeddings and hierarchical Brown clusters, and 3) an evaluation on 12 diverse languages in terms of language family and morphological typology. In spite of the use of limited and out-of-domain parallel data, our experiments demonstrate significant improvements in accuracy over previous work. In addition, we show that using multi-source information, either via projection or output combination, improves the performance for most target languages.",
}
@inproceedings{drozdov-etal-2020-unsupervised,
    title = "Unsupervised Parsing with {S}-{DIORA}: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders",
    author = "Drozdov, Andrew  and
      Rongali, Subendhu  and
      Chen, Yi-Pei  and
      O{'}Gorman, Tim  and
      Iyyer, Mohit  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.392",
    doi = "10.18653/v1/2020.emnlp-main.392",
    pages = "4832--4845",
    abstract = "The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019) is a self-supervised neural model that learns to induce syntactic tree structures for input sentences *without access to labeled training data*. In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sentence with a soft dynamic program, its vector averaging approach is locally greedy and cannot recover from errors when computing the highest scoring parse tree in bottom-up chart parsing. To fix this issue, we introduce S-DIORA, an improved variant of DIORA that encodes a single tree rather than a softly-weighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart. Our experiments show that through *fine-tuning* a pre-trained DIORA with our new algorithm, we improve the state of the art in *unsupervised* constituency parsing on the English WSJ Penn Treebank by 2.2-6{\%} F1, depending on the data used for fine-tuning.",
}
@inproceedings{ethayarajh-jurafsky-2020-utility,
    title = "Utility is in the Eye of the User: A Critique of {NLP} Leaderboards",
    author = "Ethayarajh, Kawin  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.393",
    doi = "10.18653/v1/2020.emnlp-main.393",
    pages = "4846--4853",
    abstract = "Benchmarks such as GLUE have helped drive advances in NLP by incentivizing the creation of more accurate models. While this leaderboard paradigm has been remarkably successful, a historical focus on performance-based evaluation has been at the expense of other qualities that the NLP community values in models, such as compactness, fairness, and energy efficiency. In this opinion paper, we study the divergence between what is incentivized by leaderboards and what is useful in practice through the lens of microeconomic theory. We frame both the leaderboard and NLP practitioners as consumers and the benefit they get from a model as its utility to them. With this framing, we formalize how leaderboards {--} in their current form {--} can be poor proxies for the NLP community at large. For example, a highly inefficient model would provide less utility to practitioners but not to a leaderboard, since it is a cost that only the former must bear. To allow practitioners to better estimate a model{'}s utility to them, we advocate for more transparency on leaderboards, such as the reporting of statistics that are of practical concern (e.g., model size, energy efficiency, and inference latency).",
}
@inproceedings{arumae-etal-2020-empirical,
    title = "An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training",
    author = "Arumae, Kristjan  and
      Sun, Qing  and
      Bhatia, Parminder",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.394",
    doi = "10.18653/v1/2020.emnlp-main.394",
    pages = "4854--4864",
    abstract = "Pre-training large language models has become a standard in the natural language processing community. Such models are pre-trained on generic data (e.g. BookCorpus and English Wikipedia) and often fine-tuned on tasks in the same domain. However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required. In practice, staged multi-domain pre-training presents performance deterioration in the form of catastrophic forgetting (CF) when evaluated on a generic benchmark such as GLUE. In this paper we conduct an empirical investigation into known methods to mitigate CF. We find that elastic weight consolidation provides best overall scores yielding only a 0.33{\%} drop in performance across seven generic tasks while remaining competitive in bio-medical tasks. Furthermore, we explore gradient and latent clustering based data selection techniques to improve coverage when using elastic weight consolidation and experience replay methods.",
}
@inproceedings{durrani-etal-2020-analyzing,
    title = "Analyzing Individual Neurons in Pre-trained Language Models",
    author = "Durrani, Nadir  and
      Sajjad, Hassan  and
      Dalvi, Fahim  and
      Belinkov, Yonatan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.395",
    doi = "10.18653/v1/2020.emnlp-main.395",
    pages = "4865--4880",
    abstract = "While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid towards individual neurons.We carry outa neuron-level analysis using core linguistic tasks of predicting morphology, syntax and semantics, on pre-trained language models, with questions like: i) do individual neurons in pre-trained models capture linguistic information? ii) which parts of the network learn more about certain linguistic phenomena? iii) how distributed or focused is the information? and iv) how do various architectures differ in learning these properties? We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax. Our study also reveals interesting cross architectural comparisons. For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT and others, where they are more distributed and coupled.",
}
@inproceedings{papay-etal-2020-dissecting,
    title = "Dissecting Span Identification Tasks with Performance Prediction",
    author = "Papay, Sean  and
      Klinger, Roman  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.396",
    doi = "10.18653/v1/2020.emnlp-main.396",
    pages = "4881--4895",
    abstract = "Span identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text. Despite being a staple of NLP, and sharing a common structure, there is little insight on how these tasks{'} properties influence their difficulty, and thus little guidance on what model families work well on span ID tasks, and why. We analyze span ID tasks via performance prediction, estimating how well neural architectures do on different tasks. Our contributions are: (a) we identify key properties of span ID tasks that can inform performance prediction; (b) we carry out a large-scale experiment on English data, building a model to predict performance for unseen span ID tasks that can support architecture choices; (c), we investigate the parameters of the meta model, yielding new insights on how model and task properties interact to affect span ID performance. We find, e.g., that span frequency is especially important for LSTMs, and that CRFs help when spans are infrequent and boundaries non-distinctive.",
}
@inproceedings{yu-ettinger-2020-assessing,
    title = "Assessing Phrasal Representation and Composition in Transformers",
    author = "Yu, Lang  and
      Ettinger, Allyson",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.397",
    doi = "10.18653/v1/2020.emnlp-main.397",
    pages = "4896--4907",
    abstract = "Deep transformer models have pushed performance on NLP tasks to new limits, suggesting sophisticated treatment of complex linguistic inputs, such as phrases. However, we have limited understanding of how these models handle representation of phrases, and whether this reflects sophisticated composition of phrase meaning like that done by humans. In this paper, we present systematic analysis of phrasal representations in state-of-the-art pre-trained transformers. We use tests leveraging human judgments of phrase similarity and meaning shift, and compare results before and after control of word overlap, to tease apart lexical effects versus composition effects. We find that phrase representation in these models relies heavily on word content, with little evidence of nuanced composition. We also identify variations in phrase representation quality across models, layers, and representation types, and make corresponding recommendations for usage of representations from these models.",
}
@inproceedings{dalvi-etal-2020-analyzing,
    title = "Analyzing Redundancy in Pretrained Transformer Models",
    author = "Dalvi, Fahim  and
      Sajjad, Hassan  and
      Durrani, Nadir  and
      Belinkov, Yonatan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.398",
    doi = "10.18653/v1/2020.emnlp-main.398",
    pages = "4908--4926",
    abstract = "Transformer-based deep NLP models are trained using hundreds of millions of parameters, limiting their applicability in computationally constrained environments. In this paper, we study the cause of these limitations by defining a notion of Redundancy, which we categorize into two classes: General Redundancy and Task-specific Redundancy. We dissect two popular pretrained models, BERT and XLNet, studying how much redundancy they exhibit at a representation-level and at a more fine-grained neuron-level. Our analysis reveals interesting insights, such as i) 85{\%} of the neurons across the network are redundant and ii) at least 92{\%} of them can be removed when optimizing towards a downstream task. Based on our analysis, we present an efficient feature-based transfer learning procedure, which maintains 97{\%} performance while using at-most 10{\%} of the original neurons.",
}
@inproceedings{ding-etal-2020-less,
    title = "Be More with Less: Hypergraph Attention Networks for Inductive Text Classification",
    author = "Ding, Kaize  and
      Wang, Jianling  and
      Li, Jundong  and
      Li, Dingcheng  and
      Liu, Huan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.399",
    doi = "10.18653/v1/2020.emnlp-main.399",
    pages = "4927--4936",
    abstract = "Text classification is a critical research topic with broad applications in natural language processing. Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents. To address those issues, in this paper, we propose a principled model {--} hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning. Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.",
}
@inproceedings{fevry-etal-2020-entities,
    title = "Entities as Experts: Sparse Memory Access with Entity Supervision",
    author = "F{\'e}vry, Thibault  and
      Baldini Soares, Livio  and
      FitzGerald, Nicholas  and
      Choi, Eunsol  and
      Kwiatkowski, Tom",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.400",
    doi = "10.18653/v1/2020.emnlp-main.400",
    pages = "4937--4951",
    abstract = "We focus on the problem of capturing declarative knowledge about entities in the learned parameters of a language model. We introduce a new model{---}Entities as Experts (EaE){---}that can access distinct memories of the entities mentioned in a piece of text. Unlike previous efforts to integrate entity knowledge into sequence models, EaE{'}s entity representations are learned directly from text. We show that EaE{'}s learned representations capture sufficient knowledge to answer TriviaQA questions such as {``}Which Dr. Who villain has been played by Roger Delgado, Anthony Ainley, Eric Roberts?{''}, outperforming an encoder-generator Transformer model with 10x the parameters on this task. According to the Lama knowledge probes, EaE contains more factual knowledge than a similar sized Bert, as well as previous approaches that integrate external sources of entity knowledge.Because EaE associates parameters with specific entities, it only needs to access a fraction of its parameters at inference time, and we show that the correct identification and representation of entities is essential to EaE{'}s performance.",
}
@inproceedings{yordanov-etal-2020-objective,
    title = "Does the {O}bjective {M}atter? {C}omparing {T}raining {O}bjectives for {P}ronoun {R}esolution",
    author = "Yordanov, Yordan  and
      Camburu, Oana-Maria  and
      Kocijan, Vid  and
      Lukasiewicz, Thomas",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.402",
    doi = "10.18653/v1/2020.emnlp-main.402",
    pages = "4963--4969",
    abstract = "Hard cases of pronoun resolution have been used as a long-standing benchmark for commonsense reasoning. In the recent literature, pre-trained language models have been used to obtain state-of-the-art results on pronoun resolution. Overall, four categories of training and evaluation objectives have been introduced. The variety of training datasets and pre-trained language models used in these works makes it unclear whether the choice of training objective is critical. In this work, we make a fair comparison of the performance and seed-wise stability of four models that represent the four categories of objectives. Our experiments show that the objective of sequence ranking performs the best in-domain, while the objective of semantic similarity between candidates and pronoun performs the best out-of-domain. We also observe a seed-wise instability of the model using sequence ranking, which is not the case when the other objectives are used.",
}
@inproceedings{aroca-ouellette-rudzicz-2020-losses,
    title = "{O}n {L}osses for {M}odern {L}anguage {M}odels",
    author = "Aroca-Ouellette, St{\'e}phane  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.403",
    doi = "10.18653/v1/2020.emnlp-main.403",
    pages = "4970--4981",
    abstract = "BERT set many state-of-the-art results over varied NLU benchmarks by pre-training over two tasks: masked language modelling (MLM) and next sentence prediction (NSP), the latter of which has been highly criticized. In this paper, we 1) clarify NSP{'}s effect on BERT pre-training, 2) explore fourteen possible auxiliary pre-training tasks, of which seven are novel to modern language models, and 3) investigate different ways to include multiple tasks into pre-training. We show that NSP is detrimental to training due to its context splitting and shallow semantic signal. We also identify six auxiliary pre-training tasks {--} sentence ordering, adjacent sentence prediction, TF prediction, TF-IDF prediction, a FastSent variant, and a Quick Thoughts variant {--} that outperform a pure MLM baseline. Finally, we demonstrate that using multiple tasks in a multi-task pre-training framework provides better results than using any single auxiliary task. Using these methods, we outperform BERTBase on the GLUE benchmark using fewer than a quarter of the training tokens.",
}
@inproceedings{baly-etal-2020-detect,
    title = "We Can Detect Your Bias: Predicting the Political Ideology of News Articles",
    author = "Baly, Ramy  and
      Da San Martino, Giovanni  and
      Glass, James  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.404",
    doi = "10.18653/v1/2020.emnlp-main.404",
    pages = "4982--4991",
    abstract = "We explore the task of predicting the leading political ideology or bias of news articles. First, we collect and release a large dataset of 34,737 articles that were manually annotated for political ideology {--}left, center, or right{--}, which is well-balanced across both topics and media. We further use a challenging experimental setup where the test examples come from media that were not seen during training, which prevents the model from learning to detect the source of the target news article instead of predicting its political ideology. From a modeling perspective, we propose an adversarial media adaptation, as well as a specially adapted triplet loss. We further add background information about the source, and we show that it is quite helpful for improving article-level prediction. Our experimental results show very sizable improvements over using state-of-the-art pre-trained Transformers in this challenging setup.",
}
@inproceedings{lukasik-etal-2020-semantic,
    title = "Semantic Label Smoothing for Sequence to Sequence Problems",
    author = "Lukasik, Michal  and
      Jain, Himanshu  and
      Menon, Aditya  and
      Kim, Seungyeon  and
      Bhojanapalli, Srinadh  and
      Yu, Felix  and
      Kumar, Sanjiv",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.405",
    doi = "10.18653/v1/2020.emnlp-main.405",
    pages = "4992--4998",
    abstract = "Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising. However, extending such methods directly to seq2seq settings, such as Machine Translation, is challenging: the large target output space of such problems makes it intractable to apply label smoothing over all possible outputs. Most existing approaches for seq2seq settings either do token level smoothing, or smooth over sequences generated by randomly substituting tokens in the target sequence. Unlike these works, in this paper, we propose a technique that smooths over \textit{well formed} relevant sequences that not only have sufficient n-gram overlap with the target sequence, but are also \textit{semantically similar}. Our method shows a consistent and significant improvement over the state-of-the-art techniques on different datasets.",
}
@inproceedings{gao-gormley-2020-training,
    title = "Training for {G}ibbs Sampling on Conditional Random Fields with Neural Scoring Factors",
    author = "Gao, Sida  and
      Gormley, Matthew R.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.406",
    doi = "10.18653/v1/2020.emnlp-main.406",
    pages = "4999--5011",
    abstract = "Most recent improvements in NLP come from changes to the neural network architectures modeling the text input. Yet, state-of-the-art models often rely on simple approaches to model the label space, e.g. bigram Conditional Random Fields (CRFs) in sequence tagging. More expressive graphical models are rarely used due to their prohibitive computational cost. In this work, we present an approach for efficiently training and decoding hybrids of graphical models and neural networks based on Gibbs sampling. Our approach is the natural adaptation of SampleRank (Wick et al., 2011) to neural models, and is widely applicable to tasks beyond sequence tagging. We apply our approach to named entity recognition and present a neural skip-chain CRF model, for which exact inference is impractical. The skip-chain model improves over a strong baseline on three languages from CoNLL-02/03. We obtain new state-of-the-art results on Dutch.",
}
@inproceedings{zhou-etal-2020-multilevel,
    title = "Multilevel Text Alignment with Cross-Document Attention",
    author = "Zhou, Xuhui  and
      Pappas, Nikolaos  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.407",
    doi = "10.18653/v1/2020.emnlp-main.407",
    pages = "5012--5025",
    abstract = "Text alignment finds application in tasks such as citation recommendation and plagiarism detection. Existing alignment methods operate at a single, predefined level and cannot learn to align texts at, for example, sentence \textit{and} document levels. We propose a new learning approach that equips previously established hierarchical attention encoders for representing documents with a cross-document attention component, enabling structural comparisons across different levels (document-to-document and sentence-to-document). Our component is weakly supervised from document pairs and can align at multiple levels. Our evaluation on predicting document-to-document relationships and sentence-to-document relationships on the tasks of citation recommendation and plagiarism detection shows that our approach outperforms previously established hierarchical, attention encoders based on recurrent and transformer contextualization that are unaware of structural correspondence between documents.",
}
@inproceedings{aghajanyan-etal-2020-conversational,
    title = "Conversational Semantic Parsing",
    author = "Aghajanyan, Armen  and
      Maillard, Jean  and
      Shrivastava, Akshat  and
      Diedrick, Keith  and
      Haeger, Michael  and
      Li, Haoran  and
      Mehdad, Yashar  and
      Stoyanov, Veselin  and
      Kumar, Anuj  and
      Lewis, Mike  and
      Gupta, Sonal",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.408",
    doi = "10.18653/v1/2020.emnlp-main.408",
    pages = "5026--5035",
    abstract = "The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resolution and context carryover are processed downstream in a pipelined system. In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session. We release a new session-based, compositional task-oriented parsing dataset of 20k sessions consisting of 60k utterances. Unlike Dialog State Tracking Challenges, the queries in the dataset have compositional forms. We propose a new family of Seq2Seq models for the session-based parsing above, which also set state-of-the-art in ATIS, SNIPS, TOP and DSTC2. Notably, we improve the best known results on DSTC2 by up to 5 points for slot-carryover.",
}
@inproceedings{wu-xiong-2020-probing,
    title = "Probing Task-Oriented Dialogue Representation from Language Models",
    author = "Wu, Chien-Sheng  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.409",
    doi = "10.18653/v1/2020.emnlp-main.409",
    pages = "5036--5051",
    abstract = "This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks. We approach the problem from two aspects: supervised classifier probe and unsupervised mutual information probe. We fine-tune a feed-forward layer as the classifier probe on top of a fixed pre-trained language model with annotated labels in a supervised way. Meanwhile, we propose an unsupervised mutual information probe to evaluate the mutual dependence between a real clustering and a representation clustering. The goals of this empirical paper are to 1) investigate probing techniques, especially from the unsupervised mutual information aspect, 2) provide guidelines of pre-trained language model selection for the dialogue research community, 3) find insights of pre-training factors for dialogue application that may be the key to success.",
}
@inproceedings{xu-etal-2020-end,
    title = "End-to-End Slot Alignment and Recognition for Cross-Lingual {NLU}",
    author = "Xu, Weijia  and
      Haider, Batool  and
      Mansour, Saab",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.410",
    doi = "10.18653/v1/2020.emnlp-main.410",
    pages = "5052--5063",
    abstract = "Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.",
}
@inproceedings{zhang-etal-2020-discriminative,
    title = "Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference",
    author = "Zhang, Jianguo  and
      Hashimoto, Kazuma  and
      Liu, Wenhao  and
      Wu, Chien-Sheng  and
      Wan, Yao  and
      Yu, Philip  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.411",
    doi = "10.18653/v1/2020.emnlp-main.411",
    pages = "5064--5082",
    abstract = "Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERT-style pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embedding-based nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.",
}
@inproceedings{yavuz-etal-2020-simple,
    title = "Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging",
    author = "Yavuz, Semih  and
      Hashimoto, Kazuma  and
      Liu, Wenhao  and
      Keskar, Nitish Shirish  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.412",
    doi = "10.18653/v1/2020.emnlp-main.412",
    pages = "5083--5089",
    abstract = "The concept of Dialogue Act (DA) is universal across different task-oriented dialogue domains - the act of {``}request{''} carries the same speaker intention whether it is for restaurant reservation or flight booking. However, DA taggers trained on one domain do not generalize well to other domains, which leaves us with the expensive need for a large amount of annotated data in the target domain. In this work, we investigate how to better adapt DA taggers to desired target domains with only unlabeled data. We propose MaskAugment, a controllable mechanism that augments text input by leveraging the pre-trained Mask token from BERT model. Inspired by consistency regularization, we use MaskAugment to introduce an unsupervised teacher-student learning scheme to examine the domain adaptation of DA taggers. Our extensive experiments on the Simulated Dialogue (GSim) and Schema-Guided Dialogue (SGD) datasets show that MaskAugment is useful in improving the cross-domain generalization for DA tagging.",
}
@inproceedings{chen-etal-2020-low,
    title = "Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing",
    author = "Chen, Xilun  and
      Ghoshal, Asish  and
      Mehdad, Yashar  and
      Zettlemoyer, Luke  and
      Gupta, Sonal",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.413",
    doi = "10.18653/v1/2020.emnlp-main.413",
    pages = "5090--5100",
    abstract = "Task-oriented semantic parsing is a critical component of virtual assistants, which is responsible for understanding the user{'}s intents (set reminder, play music, etc.). Recent advances in deep learning have enabled several approaches to successfully parse more complex queries (Gupta et al., 2018; Rongali et al.,2020), but these models require a large amount of annotated training data to parse queries on new domains (e.g. reminder, music). In this paper, we focus on adapting task-oriented semantic parsers to low-resource domains, and propose a novel method that outperforms a supervised neural model at a 10-fold data reduction. In particular, we identify two fundamental factors for low-resource domain adaptation: better representation learning and better training techniques. Our representation learning uses BART (Lewis et al., 2019) to initialize our model which outperforms encoder-only pre-trained representations used in previous work. Furthermore, we train with optimization-based meta-learning (Finn et al., 2017) to improve generalization to low-resource domains. This approach significantly outperforms all baseline methods in the experiments on a newly collected multi-domain task-oriented semantic parsing dataset (TOPv2), which we release to the public.",
}
@inproceedings{einolghozati-etal-2020-sound,
    title = "Sound Natural: Content Rephrasing in Dialog Systems",
    author = "Einolghozati, Arash  and
      Gupta, Anchit  and
      Diedrick, Keith  and
      Gupta, Sonal",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.414",
    doi = "10.18653/v1/2020.emnlp-main.414",
    pages = "5101--5108",
    abstract = "We introduce a new task of rephrasing for a more natural virtual assistant. Currently, virtual assistants work in the paradigm of intent-slot tagging and the slot values are directly passed as-is to the execution engine. However, this setup fails in some scenarios such as messaging when the query given by the user needs to be changed before repeating it or sending it to another user. For example, for queries like {`}ask my wife if she can pick up the kids{'} or {`}remind me to take my pills{'}, we need to rephrase the content to {`}can you pick up the kids{'} and {`}take your pills{'}. In this paper, we study the problem of rephrasing with messaging as a use case and release a dataset of 3000 pairs of original query and rephrased query. We show that BART, a pre-trained transformers-based masked language model, is a strong baseline for the task, and show improvements by adding a copy-pointer and copy loss to it. We analyze different trade-offs of BART-based and LSTM-based seq2seq models, and propose a distilled LSTM-based seq2seq as the best practical model",
}
@inproceedings{mallinson-etal-2020-zero,
    title = "Zero-Shot Crosslingual Sentence Simplification",
    author = "Mallinson, Jonathan  and
      Sennrich, Rico  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.415",
    doi = "10.18653/v1/2020.emnlp-main.415",
    pages = "5109--5126",
    abstract = "Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.",
}
@inproceedings{fu-etal-2020-facilitating,
    title = "Facilitating the Communication of Politeness through Fine-Grained Paraphrasing",
    author = "Fu, Liye  and
      Fussell, Susan  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.416",
    doi = "10.18653/v1/2020.emnlp-main.416",
    pages = "5127--5140",
    abstract = "Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker{'}s intentions and the listener{'}s perceptions in both cases.",
}
@inproceedings{wang-etal-2020-cat,
    title = "{CAT}-Gen: Improving Robustness in {NLP} Models via Controlled Adversarial Text Generation",
    author = "Wang, Tianlu  and
      Wang, Xuezhi  and
      Qin, Yao  and
      Packer, Ben  and
      Li, Kang  and
      Chen, Jilin  and
      Beutel, Alex  and
      Chi, Ed",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.417",
    doi = "10.18653/v1/2020.emnlp-main.417",
    pages = "5141--5146",
    abstract = "NLP models are shown to suffer from robustness issues, i.e., a model{'}s prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches. We further use our generated adversarial examples to improve models through adversarial training, and we demonstrate that our generated attacks are more robust against model re-training and different model architectures.",
}
@inproceedings{stahlberg-kumar-2020-seq2edits,
    title = "{S}eq2{E}dits: Sequence Transduction Using Span-level Edit Operations",
    author = "Stahlberg, Felix  and
      Kumar, Shankar",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.418",
    doi = "10.18653/v1/2020.emnlp-main.418",
    pages = "5147--5159",
    abstract = "We propose Seq2Edits, an open-vocabulary approach to sequence editing for natural language processing (NLP) tasks with a high degree of overlap between input and output texts. In this approach, each sequence-to-sequence transduction is represented as a sequence of edit operations, where each operation either replaces an entire source span with target tokens or keeps it unchanged. We evaluate our method on five NLP tasks (text normalization, sentence fusion, sentence splitting {\&} rephrasing, text simplification, and grammatical error correction) and report competitive results across the board. For grammatical error correction, our method speeds up inference by up to 5.2x compared to full sequence models because inference time depends on the number of edits rather than the number of target tokens. For text normalization, sentence fusion, and grammatical error correction, our approach improves explainability by associating each edit operation with a human-readable tag.",
}
@inproceedings{kedzie-mckeown-2020-controllable,
    title = "Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies",
    author = "Kedzie, Chris  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.419",
    doi = "10.18653/v1/2020.emnlp-main.419",
    pages = "5160--5185",
    abstract = "We study the degree to which neural sequence-to-sequence models exhibit fine-grained controllability when performing natural language generation from a meaning representation. Using two task-oriented dialogue generation benchmarks, we systematically compare the effect of four input linearization strategies on controllability and faithfulness. Additionally, we evaluate how a phrase-based data augmentation method can improve performance. We find that properly aligning input sequences during training leads to highly controllable generation, both when training from scratch or when fine-tuning a larger pre-trained model. Data augmentation further improves control on difficult, randomly generated utterance plans.",
}
@inproceedings{shen-etal-2020-blank,
    title = "Blank Language Models",
    author = "Shen, Tianxiao  and
      Quach, Victor  and
      Barzilay, Regina  and
      Jaakkola, Tommi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.420",
    doi = "10.18653/v1/2020.emnlp-main.420",
    pages = "5186--5198",
    abstract = "We propose Blank Language Model (BLM), a model that generates sequences by dynamically creating and filling in blanks. The blanks control which part of the sequence to expand, making BLM ideal for a variety of text editing and rewriting tasks. The model can start from a single blank or partially completed text with blanks at specified locations. It iteratively determines which word to place in a blank and whether to insert new blanks, and stops generating when no blanks are left to fill. BLM can be efficiently trained using a lower bound of the marginal data likelihood. On the task of filling missing text snippets, BLM significantly outperforms all other baselines in terms of both accuracy and fluency. Experiments on style transfer and damaged ancient text restoration demonstrate the potential of this framework for a wide range of applications.",
}
@inproceedings{weir-etal-2020-cod3s,
    title = "{COD3S}: Diverse Generation with Discrete Semantic Signatures",
    author = "Weir, Nathaniel  and
      Sedoc, Jo{\~a}o  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.421",
    doi = "10.18653/v1/2020.emnlp-main.421",
    pages = "5199--5211",
    abstract = "We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition{'}s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.",
}
@inproceedings{chaudhary-etal-2020-automatic,
    title = "Automatic Extraction of Rules Governing Morphological Agreement",
    author = "Chaudhary, Aditi  and
      Anastasopoulos, Antonios  and
      Pratapa, Adithya  and
      Mortensen, David R.  and
      Sheikh, Zaid  and
      Tsvetkov, Yulia  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.422",
    doi = "10.18653/v1/2020.emnlp-main.422",
    pages = "5212--5236",
    abstract = "Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world{'}s languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results. Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data. We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78{\%}. We release an interface demonstrating the extracted rules at https://neulab.github.io/lase/",
}
@inproceedings{mager-etal-2020-tackling,
    title = "Tackling the Low-resource Challenge for Canonical Segmentation",
    author = {Mager, Manuel  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      Kann, Katharina},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.423",
    doi = "10.18653/v1/2020.emnlp-main.423",
    pages = "5237--5250",
    abstract = "Canonical morphological segmentation consists of dividing words into their standardized morphemes. Here, we are interested in approaches for the task when training data is limited. We compare model performance in a simulated low-resource setting for the high-resource languages German, English, and Indonesian to experiments on new datasets for the truly low-resource languages Popoluca and Tepehua. We explore two new models for the task, borrowing from the closely related area of morphological generation: an LSTM pointer-generator and a sequence-to-sequence model with hard monotonic attention trained with imitation learning. We find that, in the low-resource setting, the novel approaches out-perform existing ones on all languages by up to 11.4{\%} accuracy. However, while accuracy in emulated low-resource scenarios is over 50{\%} for all languages, for the truly low-resource languages Popoluca and Tepehua, our best model only obtains 37.4{\%} and 28.4{\%} accuracy, respectively. Thus, we conclude that canonical segmentation is still a challenging task for low-resource languages.",
}
@inproceedings{moeller-etal-2020-igt2p,
    title = "{IGT}2{P}: From Interlinear Glossed Texts to Paradigms",
    author = "Moeller, Sarah  and
      Liu, Ling  and
      Yang, Changbing  and
      Kann, Katharina  and
      Hulden, Mans",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.424",
    doi = "10.18653/v1/2020.emnlp-main.424",
    pages = "5251--5262",
    abstract = "An intermediate step in the linguistic analysis of an under-documented language is to find and organize inflected forms that are attested in natural speech. From this data, linguists generate unseen inflected word forms in order to test hypotheses about the language{'}s inflectional patterns and to complete inflectional paradigm tables. To get the data linguists spend many hours manually creating interlinear glossed texts (IGTs). We introduce a new task that speeds this process and automatically generates new morphological resources for natural language processing systems: IGT-to-paradigms (IGT2P). IGT2P generates entire morphological paradigms from IGT input. We show that existing morphological reinflection models can solve the task with 21{\%} to 64{\%} accuracy, depending on the language. We further find that (i) having a language expert spend only a few hours cleaning the noisy IGT data improves performance by as much as 21 percentage points, and (ii) POS tags, which are generally considered a necessary part of NLP morphological reinflection input, have no effect on the accuracy of the models considered here.",
}
@inproceedings{sharma-etal-2020-computational,
    title = "A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support",
    author = "Sharma, Ashish  and
      Miner, Adam  and
      Atkins, David  and
      Althoff, Tim",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.425",
    doi = "10.18653/v1/2020.emnlp-main.425",
    pages = "5263--5276",
    abstract = "Empathy is critical to successful mental health support. Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. Because millions of people use text-based platforms for mental health support, understanding empathy in these contexts is crucial. In this work, we present a computational approach to understanding how empathy is expressed in online mental health platforms. We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations. We collect and share a corpus of 10k (post, response) pairs annotated using this empathy framework with supporting evidence for annotations (rationales). We develop a multi-task RoBERTa-based bi-encoder model for identifying empathy in conversations and extracting rationales underlying its predictions. Experiments demonstrate that our approach can effectively identify empathic conversations. We further apply this model to analyze 235k mental health interactions and show that users do not self-learn empathy over time, revealing opportunities for empathy training and feedback.",
}
@inproceedings{brahman-chaturvedi-2020-modeling,
    title = "Modeling Protagonist Emotions for Emotion-Aware Storytelling",
    author = "Brahman, Faeze  and
      Chaturvedi, Snigdha",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.426",
    doi = "10.18653/v1/2020.emnlp-main.426",
    pages = "5277--5294",
    abstract = "Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning. Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.",
}
@inproceedings{govindarajan-etal-2020-help,
    title = "Help! Need Advice on Identifying Advice",
    author = "Govindarajan, Venkata Subrahmanyan  and
      Chen, Benjamin  and
      Warholic, Rebecca  and
      Erk, Katrin  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.427",
    doi = "10.18653/v1/2020.emnlp-main.427",
    pages = "5295--5306",
    abstract = "Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of advice-seeking online, as well as advice-giving in natural language generation systems. We present a dataset in English from two Reddit advice forums - r/AskParents and r/needadvice - annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research.",
}
@inproceedings{pei-jurgens-2020-quantifying,
    title = "Quantifying Intimacy in Language",
    author = "Pei, Jiaxin  and
      Jurgens, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.428",
    doi = "10.18653/v1/2020.emnlp-main.428",
    pages = "5307--5326",
    abstract = "Intimacy is a fundamental aspect of how we relate to others in social settings. Language encodes the social information of intimacy through both topics and other more subtle cues (such as linguistic hedging and swearing). Here, we introduce a new computational framework for studying expressions of the intimacy in language with an accompanying dataset and deep learning model for accurately predicting the intimacy level of questions (Pearson r = 0.87). Through analyzing a dataset of 80.5M questions across social media, books, and films, we show that individuals employ interpersonal pragmatic moves in their language to align their intimacy with social settings. Then, in three studies, we further demonstrate how individuals modulate their intimacy to match social norms around gender, social distance, and audience, each validating key findings from studies in social psychology. Our work demonstrates that intimacy is a pervasive and impactful social dimension of language.",
}
@inproceedings{august-etal-2020-writing,
    title = "Writing Strategies for Science Communication: Data and Computational Analysis",
    author = "August, Tal  and
      Kim, Lauren  and
      Reinecke, Katharina  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.429",
    doi = "10.18653/v1/2020.emnlp-main.429",
    pages = "5327--5344",
    abstract = "Communicating complex scientific ideas without misleading or overwhelming the public is challenging. While science communication guides exist, they rarely offer empirical evidence for how their strategies are used in practice. Writing strategies that can be automatically recognized could greatly support science communication efforts by enabling tools to detect and suggest strategies for writers. We compile a set of writing strategies drawn from a wide range of prescriptive sources and develop an annotation scheme allowing humans to recognize them. We collect a corpus of 128k science writing documents in English and annotate a subset of this corpus. We use the annotations to train transformer-based classifiers and measure the strategies{'} use in the larger corpus. We find that the use of strategies, such as storytelling and emphasizing the most important findings, varies significantly across publications with different reader audiences.",
}
@inproceedings{yao-etal-2020-weakly,
    title = "Weakly {S}upervised {S}ubevent {K}nowledge {A}cquisition",
    author = "Yao, Wenlin  and
      Dai, Zeyu  and
      Ramaswamy, Maitreyi  and
      Min, Bonan  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.430",
    doi = "10.18653/v1/2020.emnlp-main.430",
    pages = "5345--5356",
    abstract = "Subevents elaborate an event and widely exist in event descriptions. Subevent knowledge is useful for discourse analysis and event-centric applications. Acknowledging the scarcity of subevent knowledge, we propose a weakly supervised approach to extract subevent relation tuples from text and build the first large scale subevent knowledge base. We first obtain the initial set of event pairs that are likely to have the subevent relation, by exploiting two observations that 1) subevents are temporally contained by the parent event, and 2) the definitions of the parent event can be used to further guide the identification of subevents. Then, we collect rich weak supervision using the initial seed subevent pairs to train a contextual classifier using BERT and apply the classifier to identify new subevent pairs. The evaluation showed that the acquired subevent tuples (239K) are of high quality (90.1{\%} accuracy) and cover a wide range of event types. The acquired subevent knowledge has been shown useful for discourse analysis and identifying a range of event-event relations.",
}
@inproceedings{ramponi-etal-2020-biomedical,
    title = "Biomedical Event Extraction as Sequence Labeling",
    author = "Ramponi, Alan  and
      van der Goot, Rob  and
      Lombardo, Rosario  and
      Plank, Barbara",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.431",
    doi = "10.18653/v1/2020.emnlp-main.431",
    pages = "5357--5367",
    abstract = "We introduce Biomedical Event Extraction as Sequence Labeling (BeeSL), a joint end-to-end neural information extraction model. BeeSL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning. BeeSL is fast, accurate, end-to-end, and unlike current methods does not require any external knowledge base or preprocessing tools. BeeSL outperforms the current best system (Li et al., 2019) on the Genia 2011 benchmark by 1.57{\%} absolute F1 score reaching 60.22{\%} F1, establishing a new state of the art for the task. Importantly, we also provide first results on biomedical event extraction without gold entity information. Empirical results show that BeeSL{'}s speed and accuracy makes it a viable approach for large-scale real-world scenarios.",
}
@inproceedings{yao-etal-2020-annotating,
    title = "{A}nnotating {T}emporal {D}ependency {G}raphs via {C}rowdsourcing",
    author = "Yao, Jiarui  and
      Qiu, Haoling  and
      Min, Bonan  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.432",
    doi = "10.18653/v1/2020.emnlp-main.432",
    pages = "5368--5380",
    abstract = "We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text. We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good trade-off between completeness and practicality in temporal annotation. We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set. The data set is publicly available.",
}
@inproceedings{man-duc-trong-etal-2020-introducing,
    title = "Introducing a New Dataset for Event Detection in Cybersecurity Texts",
    author = "Man Duc Trong, Hieu  and
      Trong Le, Duc  and
      Pouran Ben Veyseh, Amir  and
      Nguyen, Thuat  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.433",
    doi = "10.18653/v1/2020.emnlp-main.433",
    pages = "5381--5390",
    abstract = "Detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. In this work, we focus on the task of event detection (ED) to identify event trigger words for the cybersecurity domain. In particular, to facilitate the future research, we introduce a new dataset for this problem, characterizing the manual annotation for 30 important cybersecurity event types and a large dataset size to develop deep learning models. Comparing to the prior datasets for this task, our dataset involves more event types and supports the modeling of document-level information to improve the performance. We perform extensive evaluation with the current state-of-the-art methods for ED on the proposed dataset. Our experiments reveal the challenges of cybersecurity ED and present many research opportunities in this area for the future work.",
}
@inproceedings{tigunova-etal-2020-charm,
    title = "{CHARM}: Inferring Personal Attributes from Conversations",
    author = "Tigunova, Anna  and
      Yates, Andrew  and
      Mirza, Paramita  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.434",
    doi = "10.18653/v1/2020.emnlp-main.434",
    pages = "5391--5404",
    abstract = "Personal knowledge about users{'} professions, hobbies, favorite food, and travel preferences, among others, is a valuable asset for individualized AI, such as recommenders or chatbots. Conversations in social media, such as Reddit, are a rich source of data for inferring personal facts. Prior work developed supervised methods to extract this knowledge, but these approaches can not generalize beyond attribute values with ample labeled training samples. This paper overcomes this limitation by devising CHARM: a zero-shot learning method that creatively leverages keyword extraction and document retrieval in order to predict attribute values that were never seen during training. Experiments with large datasets from Reddit show the viability of CHARM for open-ended attributes, such as professions and hobbies.",
}
@inproceedings{lai-etal-2020-event,
    title = "Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks",
    author = "Lai, Viet Dac  and
      Nguyen, Tuan Ngo  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.435",
    doi = "10.18653/v1/2020.emnlp-main.435",
    pages = "5405--5411",
    abstract = "Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolution neural networks (GCN) to achieve state-of-the-art performance. However, the computation of the hidden vectors in such graph-based models is agnostic to the trigger candidate words, potentially leaving irrelevant information for the trigger candidate for event prediction. In addition, the current models for ED fail to exploit the overall contextual importance scores of the words, which can be obtained via the dependency tree, to boost the performance. In this study, we propose a novel gating mechanism to filter noisy information in the hidden vectors of the GCN models for ED based on the information from the trigger candidate. We also introduce novel mechanisms to achieve the contextual diversity for the gates and the importance score consistency for the graphs and models in ED. The experiments show that the proposed model achieves state-of-the-art performance on two ED datasets.",
}
@inproceedings{ballesteros-etal-2020-severing,
    title = "Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events",
    author = "Ballesteros, Miguel  and
      Anubhai, Rishita  and
      Wang, Shuai  and
      Pourdamghani, Nima  and
      Vyas, Yogarshi  and
      Ma, Jie  and
      Bhatia, Parminder  and
      McKeown, Kathleen  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.436",
    doi = "10.18653/v1/2020.emnlp-main.436",
    pages = "5412--5417",
    abstract = "In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e. RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques. Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.",
}
@inproceedings{roberts-etal-2020-much,
    title = "How Much Knowledge Can You Pack Into the Parameters of a Language Model?",
    author = "Roberts, Adam  and
      Raffel, Colin  and
      Shazeer, Noam",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.437",
    doi = "10.18653/v1/2020.emnlp-main.437",
    pages = "5418--5426",
    abstract = "It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any external context or knowledge. We show that this approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. To facilitate reproducibility and future work, we release our code and trained models.",
}
@inproceedings{hardalov-etal-2020-exams,
    title = "{EXAMS}: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering",
    author = "Hardalov, Momchil  and
      Mihaylov, Todor  and
      Zlatkova, Dimitrina  and
      Dinkov, Yoan  and
      Koychev, Ivan  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.438",
    doi = "10.18653/v1/2020.emnlp-main.438",
    pages = "5427--5444",
    abstract = "We propose EXAMS {--} a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.EXAMS offers unique fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of the proposed models. We perform various experiments with existing top-performing multilingual pre-trained models and show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains. We hope that EXAMS will enable researchers to explore challenging reasoning and knowledge transfer methods and pre-trained models for school question answering in various languages which was not possible by now. The data, code, pre-trained models, and evaluation are available at http://github.com/mhardalov/exams-qa.",
}
@inproceedings{shakeri-etal-2020-end,
    title = "End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems",
    author = "Shakeri, Siamak  and
      Nogueira dos Santos, Cicero  and
      Zhu, Henghui  and
      Ng, Patrick  and
      Nan, Feng  and
      Wang, Zhiguo  and
      Nallapati, Ramesh  and
      Xiang, Bing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.439",
    doi = "10.18653/v1/2020.emnlp-main.439",
    pages = "5445--5460",
    abstract = "We propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions. In a nutshell, we feed a passage to the encoder and ask the decoder to generate a question and an answer token-by-token. The likelihood produced in the generation process is used as a filtering score, which avoids the need for a separate filtering model. Our generator is trained by fine-tuning a pretrained LM using maximum likelihood estimation. The experimental results indicate significant improvements in the domain adaptation of QA models outperforming current state-of-the-art methods.",
}
@inproceedings{zhang-etal-2020-multi-stage,
    title = "Multi-Stage Pre-training for Low-Resource Domain Adaptation",
    author = "Zhang, Rong  and
      Gangi Reddy, Revanth  and
      Sultan, Md Arafat  and
      Castelli, Vittorio  and
      Ferritto, Anthony  and
      Florian, Radu  and
      Sarioglu Kayi, Efsun  and
      Roukos, Salim  and
      Sil, Avi  and
      Ward, Todd",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.440",
    doi = "10.18653/v1/2020.emnlp-main.440",
    pages = "5461--5468",
    abstract = "Transfer learning techniques are particularly useful for NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a pretrained language model (LM) on in-domain text before fine-tuning to downstream tasks. We show that extending the vocabulary of the LM with domain-specific terms leads to further gains. To a bigger effect, we utilize structure in the unlabeled data to create auxiliary synthetic tasks, which helps the LM transfer to downstream tasks. We apply these approaches incrementally on a pretrained Roberta-large LM and show considerable performance gain on three tasks in the IT domain: Extractive Reading Comprehension, Document Ranking and Duplicate Question Detection.",
}
@inproceedings{gomez-perez-ortega-2020-isaaq,
    title = "{ISAAQ} - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention",
    author = "Gomez-Perez, Jose Manuel  and
      Ortega, Ra{\'u}l",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.441",
    doi = "10.18653/v1/2020.emnlp-main.441",
    pages = "5469--5479",
    abstract = "Textbook Question Answering is a complex task in the intersection of Machine Comprehension and Visual Question Answering that requires reasoning with multimodal information from text and diagrams. For the first time, this paper taps on the potential of transformer language models and bottom-up and top-down attention to tackle the language and visual understanding challenges this task entails. Rather than training a language-visual transformer from scratch we rely on pre-trained transformers, fine-tuning and ensembling. We add bottom-up and top-down attention to identify regions of interest corresponding to diagram constituents and their relationships, improving the selection of relevant visual information for each question and answer options. Our system ISAAQ reports unprecedented success in all TQA question types, with accuracies of 81.36{\%}, 71.11{\%} and 55.12{\%} on true/false, text-only and diagram multiple choice questions. ISAAQ also demonstrates its broad applicability, obtaining state-of-the-art results in other demanding datasets.",
}
@inproceedings{bjerva-etal-2020-subjqa,
    title = "{SubjQA}: {A} {D}ataset for {S}ubjectivity and {R}eview {C}omprehension",
    author = "Bjerva, Johannes  and
      Bhutani, Nikita  and
      Golshan, Behzad  and
      Tan, Wang-Chiew  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.442",
    doi = "10.18653/v1/2020.emnlp-main.442",
    pages = "5480--5494",
    abstract = "Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, subjectivity is an important aspect of user-generated data. In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA). We develop a new dataset which allows us to investigate this relationship. We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis. For instance, a subjective question may or may not be associated with a subjective answer. We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.",
}
@inproceedings{li-etal-2020-widget,
    title = "Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements",
    author = "Li, Yang  and
      Li, Gang  and
      He, Luheng  and
      Zheng, Jingjie  and
      Li, Hong  and
      Guan, Zhiwei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.443",
    doi = "10.18653/v1/2020.emnlp-main.443",
    pages = "5495--5510",
    abstract = "Natural language descriptions of user interface (UI) elements such as alternative text are crucial for accessibility and language-based interaction in general. Yet, these descriptions are constantly missing in mobile UIs. We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces. We collected a large-scale dataset for widget captioning with crowdsourcing. Our dataset contains 162,860 language phrases created by human workers for annotating 61,285 UI elements across 21,750 unique UI screens. We thoroughly analyze the dataset, and train and evaluate a set of deep model configurations to investigate how each feature modality as well as the choice of learning strategies impact the quality of predicted captions. The task formulation and the dataset as well as our benchmark models contribute a solid basis for this novel multimodal captioning task that connects language and user interfaces.",
}
@inproceedings{cui-etal-2020-unsupervised,
    title = "Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning",
    author = "Cui, Wanyun  and
      Zheng, Guangyu  and
      Wang, Wei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.444",
    doi = "10.18653/v1/2020.emnlp-main.444",
    pages = "5511--5520",
    abstract = "We propose to solve the natural language inference problem without any supervision from the inference labels via task-agnostic multimodal pretraining. Although recent studies of multimodal self-supervised learning also represent the linguistic and visual context, their encoders for different modalities are coupled. Thus they cannot incorporate visual information when encoding plain text alone. In this paper, we propose Multimodal Aligned Contrastive Decoupled learning (MACD) network. MACD forces the decoupled text encoder to represent the visual information via contrastive learning. Therefore, it embeds visual knowledge even for plain text inference. We conducted comprehensive experiments over plain text inference datasets (i.e. SNLI and STS-B). The unsupervised MACD even outperforms the fully-supervised BiLSTM and BiLSTM+ELMO on STS-B.",
}
@inproceedings{gaddy-klein-2020-digital,
    title = "Digital Voicing of Silent Speech",
    author = "Gaddy, David  and
      Klein, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.445",
    doi = "10.18653/v1/2020.emnlp-main.445",
    pages = "5521--5530",
    abstract = "In this paper, we consider the task of digitally voicing silent speech, where silently mouthed words are converted to audible speech based on electromyography (EMG) sensor measurements that capture muscle impulses. While prior work has focused on training speech synthesis models from EMG collected during vocalized speech, we are the first to train from EMG collected during silently articulated speech. We introduce a method of training on silent EMG by transferring audio targets from vocalized to silent signals. Our method greatly improves intelligibility of audio generated from silent EMG compared to a baseline that only trains with vocalized data, decreasing transcription word error rate from 64{\%} to 4{\%} in one data condition and 88{\%} to 68{\%} in another. To spur further development on this task, we share our new dataset of silent and vocalized facial EMG measurements.",
}
@inproceedings{wallace-etal-2020-imitation,
    title = "Imitation Attacks and Defenses for Black-box Machine Translation Systems",
    author = "Wallace, Eric  and
      Stern, Mitchell  and
      Song, Dawn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.446",
    doi = "10.18653/v1/2020.emnlp-main.446",
    pages = "5531--5546",
    abstract = "Adversaries may look to steal or attack black-box NLP systems, either for financial gain or to exploit model errors. One setting of particular interest is machine translation (MT), where models have high commercial value and errors can be costly. We investigate possible exploitations of black-box MT systems and explore a preliminary defense against such threats. We first show that MT systems can be stolen by querying them with monolingual sentences and training models to imitate their outputs. Using simulated experiments, we demonstrate that MT model stealing is possible even when imitation models have different input data or architectures than their target models. Applying these ideas, we train imitation models that reach within 0.6 BLEU of three production MT systems on both high-resource and low-resource language pairs. We then leverage the similarity of our imitation models to transfer adversarial examples to the production systems. We use gradient-based attacks that expose inputs which lead to semantically-incorrect translations, dropped content, and vulgar model outputs. To mitigate these vulnerabilities, we propose a defense that modifies translation outputs in order to misdirect the optimization of imitation models. This defense degrades the adversary{'}s BLEU score and attack success rate at some cost in the defender{'}s BLEU and inference speed.",
}
@inproceedings{guo-etal-2020-sequence,
    title = "Sequence-Level Mixed Sample Data Augmentation",
    author = "Guo, Demi  and
      Kim, Yoon  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.447",
    doi = "10.18653/v1/2020.emnlp-main.447",
    pages = "5547--5552",
    abstract = "Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach to encourage compositional behavior in neural models for sequence-to-sequence problems. Our approach, SeqMix, creates new synthetic examples by softly combining input/output sequences from the training set. We connect this approach to existing techniques such as SwitchOut and word dropout, and show that these techniques are all essentially approximating variants of a single objective. SeqMix consistently yields approximately 1.0 BLEU improvement on five different translation datasets over strong Transformer baselines. On tasks that require strong compositional generalization such as SCAN and semantic parsing, SeqMix also offers further improvements.",
}
@inproceedings{welleck-etal-2020-consistency,
    title = "Consistency of a Recurrent Language Model With Respect to Incomplete Decoding",
    author = "Welleck, Sean  and
      Kulikov, Ilia  and
      Kim, Jaedeok  and
      Pang, Richard Yuanzhe  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.448",
    doi = "10.18653/v1/2020.emnlp-main.448",
    pages = "5553--5568",
    abstract = "Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving infinite-length sequences from a recurrent language model when using common decoding algorithms. To analyze this issue, we first define inconsistency of a decoding algorithm, meaning that the algorithm can yield an infinite-length sequence that has zero probability under the model. We prove that commonly used incomplete decoding algorithms {--} greedy search, beam search, top-k sampling, and nucleus sampling {--} are inconsistent, despite the fact that recurrent language models are trained to produce sequences of finite length. Based on these insights, we propose two remedies which address inconsistency: consistent variants of top-k and nucleus sampling, and a self-terminating recurrent language model. Empirical results show that inconsistency occurs in practice, and that the proposed methods prevent inconsistency.",
}
@inproceedings{tu-etal-2020-exploration,
    title = "{A}n {E}xploration of {A}rbitrary-{O}rder {S}equence {L}abeling via {E}nergy-{B}ased {I}nference {N}etworks",
    author = "Tu, Lifu  and
      Liu, Tianyu  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.449",
    doi = "10.18653/v1/2020.emnlp-main.449",
    pages = "5569--5582",
    abstract = "Many tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation. Researchers are increasingly applying deep representation learning to these problems, but the structured component of these approaches is usually quite simplistic. In this work, we propose several high-order energy terms to capture complex dependencies among labels in sequence labeling, including several that consider the entire label sequence. We use neural parameterizations for these energy terms, drawing from convolutional, recurrent, and self-attention networks. We use the framework of learning energy-based inference networks (Tu and Gimpel, 2018) for dealing with the difficulties of training and inference with such models. We empirically demonstrate that this approach achieves substantial improvement using a variety of high-order energy terms on four sequence labeling tasks, while having the same decoding speed as simple, local classifiers. We also find high-order energies to help in noisy data conditions.",
}
@inproceedings{reich-etal-2020-ensemble,
    title = "Ensemble {D}istillation for {S}tructured {P}rediction: {C}alibrated, {A}ccurate, {F}ast{---}{C}hoose {T}hree",
    author = "Reich, Steven  and
      Mueller, David  and
      Andrews, Nicholas",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.450",
    doi = "10.18653/v1/2020.emnlp-main.450",
    pages = "5583--5595",
    abstract = "Modern neural networks do not always produce well-calibrated predictions, even when trained with a proper scoring function such as cross-entropy. In classification settings, simple methods such as isotonic regression or temperature scaling may be used in conjunction with a held-out dataset to calibrate model outputs. However, extending these methods to structured prediction is not always straightforward or effective; furthermore, a held-out calibration set may not always be available. In this paper, we study \textit{ensemble distillation} as a general framework for producing well-calibrated structured prediction models while avoiding the prohibitive inference-time cost of ensembles. We validate this framework on two tasks: named-entity recognition and machine translation. We find that, across both tasks, ensemble distillation produces models which retain much of, and occasionally improve upon, the performance and calibration benefits of ensembles, while only requiring a single model during test-time.",
}
@inproceedings{chen-etal-2020-inducing,
    title = "Inducing Target-Specific Latent Structures for Aspect Sentiment Classification",
    author = "Chen, Chenhua  and
      Teng, Zhiyang  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.451",
    doi = "10.18653/v1/2020.emnlp-main.451",
    pages = "5596--5607",
    abstract = "Aspect-level sentiment analysis aims to recognize the sentiment polarity of an aspect or a target in a comment. Recently, graph convolutional networks based on linguistic dependency trees have been studied for this task. However, the dependency parsing accuracy of commercial product comments or tweets might be unsatisfactory. To tackle this problem, we associate linguistic dependency trees with automatically induced aspectspecific graphs. We propose gating mechanisms to dynamically combine information from word dependency graphs and latent graphs which are learned by self-attention networks. Our model can complement supervised syntactic features with latent semantic dependencies. Experimental results on five benchmarks show the effectiveness of our proposed latent models, giving significantly better results than models without using latent graphs.",
}
@inproceedings{zhuang-etal-2020-affective,
    title = "Affective Event Classification with Discourse-enhanced Self-training",
    author = "Zhuang, Yuan  and
      Jiang, Tianyu  and
      Riloff, Ellen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.452",
    doi = "10.18653/v1/2020.emnlp-main.452",
    pages = "5608--5617",
    abstract = "Prior research has recognized the need to associate affective polarities with events and has produced several techniques and lexical resources for identifying affective events. Our research introduces new classification models to assign affective polarity to event phrases. First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base. Second, we present a discourse-enhanced self-training method that iteratively improves the classifier with unlabeled data. The key idea is to exploit event phrases that occur with a coreferent sentiment expression. The discourse-enhanced self-training algorithm iteratively labels new event phrases based on both the classifier{'}s predictions and the polarities of the event{'}s coreferent sentiment expressions. Our results show that discourse-enhanced self-training further improves both recall and precision for affective event classification.",
}
@inproceedings{wu-etal-2020-deep,
    title = "{D}eep {W}eighted {M}ax{SAT} for {A}spect-based {O}pinion {E}xtraction",
    author = "Wu, Meixi  and
      Wang, Wenya  and
      Pan, Sinno Jialin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.453",
    doi = "10.18653/v1/2020.emnlp-main.453",
    pages = "5618--5628",
    abstract = "Though deep learning has achieved significant success in various NLP tasks, most deep learning models lack the capability of encoding explicit domain knowledge to model complex causal relationships among different types of variables. On the other hand, logic rules offer a compact expression to represent the causal relationships to guide the training process. Logic programs can be cast as a satisfiability problem which aims to find truth assignments to logic variables by maximizing the number of satisfiable clauses (MaxSAT). We adopt the MaxSAT semantics to model logic inference process and smoothly incorporate a weighted version of MaxSAT that connects deep neural networks and a graphical model in a joint framework. The joint model feeds deep learning outputs to a weighted MaxSAT layer to rectify the erroneous predictions and can be trained via end-to-end gradient descent. Our proposed model associates the benefits of high-level feature learning, knowledge reasoning, and structured learning with observable performance gain for the task of aspect-based opinion extraction.",
}
@inproceedings{kar-etal-2020-multi,
    title = "Multi-view Story Characterization from Movie Plot Synopses and Reviews",
    author = "Kar, Sudipta  and
      Aguilar, Gustavo  and
      Lapata, Mirella  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.454",
    doi = "10.18653/v1/2020.emnlp-main.454",
    pages = "5629--5646",
    abstract = "This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020.",
}
@inproceedings{tan-etal-2020-mind,
    title = "Mind Your Inflections! {I}mproving {NLP} for Non-Standard {E}nglishes with {B}ase-{I}nflection {E}ncoding",
    author = "Tan, Samson  and
      Joty, Shafiq  and
      Varshney, Lav  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.455",
    doi = "10.18653/v1/2020.emnlp-main.455",
    pages = "5647--5663",
    abstract = "Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols. Fine-tuning pretrained NLP models for downstream tasks using our encoding defends against inflectional adversaries while maintaining performance on clean data. Models using BITE generalize better to dialects with non-standard inflections without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers. Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so.",
}
@inproceedings{mccarthy-etal-2020-measuring,
    title = "Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions",
    author = "McCarthy, Arya D.  and
      Williams, Adina  and
      Liu, Shijia  and
      Yarowsky, David  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.456",
    doi = "10.18653/v1/2020.emnlp-main.456",
    pages = "5664--5675",
    abstract = "A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages{'} gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.",
}
@inproceedings{fu-etal-2020-rethinkcws,
    title = "{R}ethink{CWS}: Is {C}hinese Word Segmentation a Solved Task?",
    author = "Fu, Jinlan  and
      Liu, Pengfei  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.457",
    doi = "10.18653/v1/2020.emnlp-main.457",
    pages = "5676--5686",
    abstract = "The performance of the Chinese Word Segmentation (CWS) systems has gradually reached a plateau with the rapid development of deep neural networks, especially the successful use of large pre-trained models. In this paper, we take stock of what we have achieved and rethink what{'}s left in the CWS task. Methodologically, we propose a fine-grained evaluation for existing CWS systems, which not only allows us to diagnose the strengths and weaknesses of existing models (under the in-dataset setting), but enables us to quantify the discrepancy between different criterion and alleviate the negative transfer problem when doing multi-criteria learning. Strategically, despite not aiming to propose a novel model in this paper, our comprehensive experiments on eight models and seven datasets, as well as thorough analysis, could search for some promising direction for future research. We make all codes publicly available and release an interface that can quickly evaluate and diagnose user{'}s models: https://github.com/neulab/InterpretEval",
}
@inproceedings{chu-etal-2020-learning,
    title = "{L}earning to {P}ronounce {C}hinese {W}ithout a {P}ronunciation {D}ictionary",
    author = "Chu, Christopher  and
      Fang, Scot  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.458",
    doi = "10.18653/v1/2020.emnlp-main.458",
    pages = "5687--5693",
    abstract = "We demonstrate a program that learns to pronounce Chinese text in Mandarin, without a pronunciation dictionary. From non-parallel streams of Chinese characters and Chinese pinyin syllables, it establishes a many-to-many mapping between characters and pronunciations. Using unsupervised methods, the program effectively deciphers writing into speech. Its token-level character-to-syllable accuracy is 89{\%}, which significantly exceeds the 22{\%} accuracy of prior work.",
}
@inproceedings{lv-etal-2020-dynamic,
    title = "Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph",
    author = "Lv, Xin  and
      Han, Xu  and
      Hou, Lei  and
      Li, Juanzi  and
      Liu, Zhiyuan  and
      Zhang, Wei  and
      Zhang, Yichi  and
      Kong, Hao  and
      Wu, Suhui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.459",
    doi = "10.18653/v1/2020.emnlp-main.459",
    pages = "5694--5703",
    abstract = "Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion. Most previous reasoning methods are designed for dense KGs with enough paths between entities, but cannot work well on those sparse KGs that only contain sparse paths for reasoning. On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths. On the other hand, the lack of evidential paths to target entities also makes the reasoning process difficult. To solve these problems, we propose a multi-hop reasoning model over sparse KGs, by applying novel dynamic anticipation and completion strategies: (1) The anticipation strategy utilizes the latent prediction of embedding-based models to make our model perform more potential path search over sparse KGs. (2) Based on the anticipation information, the completion strategy dynamically adds edges as additional actions during the path search, which further alleviates the sparseness problem of KGs. The experimental results on five datasets sampled from Freebase, NELL and Wikidata show that our method outperforms state-of-the-art baselines. Our codes and datasets can be obtained from https://github.com/THU-KEG/DacKGR.",
}
@inproceedings{sun-etal-2020-knowledge-association,
    title = "Knowledge Association with Hyperbolic Knowledge Graph Embeddings",
    author = "Sun, Zequn  and
      Chen, Muhao  and
      Hu, Wei  and
      Wang, Chengming  and
      Dai, Jian  and
      Zhang, Wei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.460",
    doi = "10.18653/v1/2020.emnlp-main.460",
    pages = "5704--5716",
    abstract = "Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations. Recent related methods built on Euclidean embeddings are challenged by the hierarchical structures and different scales of KGs. They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association. We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation. Extensive experiments on entity alignment and type inference demonstrate the effectiveness and efficiency of our method.",
}
@inproceedings{han-etal-2020-domain,
    title = "Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction",
    author = "Han, Rujun  and
      Zhou, Yichao  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.461",
    doi = "10.18653/v1/2020.emnlp-main.461",
    pages = "5717--5729",
    abstract = "Extracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding. Prior systems leverage deep learning and pre-trained language models to improve the performance of the task. However, these systems often suffer from two shortcomings: 1) when performing maximum a posteriori (MAP) inference based on neural models, previous systems only used structured knowledge that is assumed to be absolutely correct, i.e., hard constraints; 2) biased predictions on dominant temporal relations when training with a limited amount of data. To address these issues, we propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge. We solve the constrained inference problem via Lagrangian Relaxation and apply it to end-to-end event temporal relation extraction tasks. Experimental results show our framework is able to improve the baseline neural network models with strong statistical significance on two widely used datasets in news and clinical domains.",
}
@inproceedings{wu-etal-2020-temp,
    title = "{T}e{MP}: Temporal Message Passing for Temporal Knowledge Graph Completion",
    author = "Wu, Jiapeng  and
      Cao, Meng  and
      Cheung, Jackie Chi Kit  and
      Hamilton, William L.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.462",
    doi = "10.18653/v1/2020.emnlp-main.462",
    pages = "5730--5746",
    abstract = "Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task. Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations. However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions. Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs. We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques. Experiments on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7{\%} average relative improvement in Hits@10 across three standard benchmarks. Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.",
}
@inproceedings{liu-etal-2020-understanding,
    title = "Understanding the Difficulty of Training Transformers",
    author = "Liu, Liyuan  and
      Liu, Xiaodong  and
      Gao, Jianfeng  and
      Chen, Weizhu  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.463",
    doi = "10.18653/v1/2020.emnlp-main.463",
    pages = "5747--5763",
    abstract = "Transformers have proved effective in many NLP tasks. However, their training requires non-trivial efforts regarding carefully designing cutting-edge optimizers and learning rate schedulers (e.g., conventional SGD fails to train Transformers effectively). Our objective here is to understand {\_}{\_}what complicates Transformer training{\_}{\_} from both empirical and theoretical perspectives. Our analysis reveals that unbalanced gradients are not the root cause of the instability of training. Instead, we identify an amplification effect that influences training substantially{---}for each layer in a multi-layer Transformer model, heavy dependency on its residual branch makes training unstable, since it amplifies small parameter perturbations (e.g., parameter updates) and results in significant disturbances in the model output. Yet we observe that a light dependency limits the model potential and leads to inferior trained models. Inspired by our analysis, we propose Admin (Adaptive model initialization) to stabilize the early stage{'}s training and unleash its full potential in the late stage. Extensive experiments show that Admin is more stable, converges faster, and leads to better performance",
}
@inproceedings{chan-etal-2020-empirical,
    title = "An Empirical Study of Generation Order for Machine Translation",
    author = "Chan, William  and
      Stern, Mitchell  and
      Kiros, Jamie  and
      Uszkoreit, Jakob",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.464",
    doi = "10.18653/v1/2020.emnlp-main.464",
    pages = "5764--5773",
    abstract = "In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-reward framework that enables us to train models to follow arbitrary oracle generation policies. We then make use of this framework to explore a large variety of generation orders, including uninformed orders, location-based orders, frequency-based orders, content-based orders, and model-based orders. Curiously, we find that for the WMT{'}14 English $\to$ German and WMT{'}18 English $\to$ Chinese translation tasks, order does not have a substantial impact on output quality. Moreover, for English $\to$ German, we even discover that unintuitive orderings such as alphabetical and shortest-first can match the performance of a standard Transformer, suggesting that traditional left-to-right generation may not be necessary to achieve high performance.",
}
@inproceedings{kreutzer-etal-2020-inference,
    title = "Inference Strategies for Machine Translation with Conditional Masking",
    author = "Kreutzer, Julia  and
      Foster, George  and
      Cherry, Colin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.465",
    doi = "10.18653/v1/2020.emnlp-main.465",
    pages = "5774--5782",
    abstract = "Conditional masked language model (CMLM) training has proven successful for non-autoregressive and semi-autoregressive sequence generation tasks, such as machine translation. Given a trained CMLM, however, it is not clear what the best inference strategy is. We formulate masked inference as a factorization of conditional probabilities of partial sequences, show that this does not harm performance, and investigate a number of simple heuristics motivated by this perspective. We identify a thresholding strategy that has advantages over the standard {``}mask-predict{''} algorithm, and provide analyses of its behavior on machine translation tasks.",
}
@inproceedings{min-etal-2020-ambigqa,
    title = "{A}mbig{QA}: Answering Ambiguous Open-domain Questions",
    author = "Min, Sewon  and
      Michael, Julian  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.466",
    doi = "10.18653/v1/2020.emnlp-main.466",
    pages = "5783--5797",
    abstract = "Ambiguity is inherent to open-domain question answering; especially when exploring new topics, it can be difficult to ask questions that have a single, unambiguous answer. In this paper, we introduce AmbigQA, a new open-domain question answering task which involves finding every plausible answer, and then rewriting the question for each one to resolve the ambiguity. To study this task, we construct AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous, with diverse sources of ambiguity such as event and entity references. We also present strong baseline models for AmbigQA which we show benefit from weakly supervised learning that incorporates NQ-open, strongly suggesting our new task and data will support significant future research effort. Our data and baselines are available at https://nlp.cs.washington.edu/ambigqa.",
}
@inproceedings{liu-etal-2020-tell,
    title = "Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space",
    author = "Liu, Dayiheng  and
      Gong, Yeyun  and
      Fu, Jie  and
      Yan, Yu  and
      Chen, Jiusheng  and
      Lv, Jiancheng  and
      Duan, Nan  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.467",
    doi = "10.18653/v1/2020.emnlp-main.467",
    pages = "5798--5810",
    abstract = "In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks. We treat the question data augmentation task as a constrained question rewriting problem to generate context-relevant, high-quality, and diverse question data samples. CRQDA utilizes a Transformer Autoencoder to map the original discrete question into a continuous embedding space. It then uses a pre-trained MRC model to revise the question representation iteratively with gradient-based optimization. Finally, the revised question representations are mapped back into the discrete space, which serve as additional question data. Comprehensive experiments on SQuAD 2.0, SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of CRQDA.",
}
@inproceedings{puri-etal-2020-training,
    title = "Training Question Answering Models From Synthetic Data",
    author = "Puri, Raul  and
      Spring, Ryan  and
      Shoeybi, Mohammad  and
      Patwary, Mostofa  and
      Catanzaro, Bryan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.468",
    doi = "10.18653/v1/2020.emnlp-main.468",
    pages = "5811--5826",
    abstract = "Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a considerable gap remains between synthetic and human-generated question-answer pairs. This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices. On the SQuAD1.1 question answering task, we achieve higher accuracy using solely synthetic questions and answers than when using the SQuAD1.1 training set questions alone. Removing access to real Wikipedia data, we synthesize questions and answers from a synthetic text corpus generated by an 8.3 billion parameter GPT-2 model and achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. We further apply our methodology to SQuAD2.0 and show a 2.8 absolute gain on EM score compared to prior work using synthetic data.",
}
@inproceedings{hua-etal-2020-shot,
    title = "Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning",
    author = "Hua, Yuncheng  and
      Li, Yuan-Fang  and
      Haffari, Gholamreza  and
      Qi, Guilin  and
      Wu, Tongtong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.469",
    doi = "10.18653/v1/2020.emnlp-main.469",
    pages = "5827--5837",
    abstract = "Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types, harboring inherently different characteristics, e.g., difficulty level. This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions. Our method quickly and effectively adapts the meta-learned programmer to new questions based on the most similar questions retrieved from the training data. The meta-learned policy is then used to learn a good programming policy, utilizing the trial trajectories and their rewards for similar questions in the support set. Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and meta-training on tasks constructed from only 1{\%} of the training set. We have released our code at https://github.com/DevinJake/MRL-CQA.",
}
@inproceedings{ranasinghe-zampieri-2020-multilingual,
    title = "Multilingual Offensive Language Identification with Cross-lingual Embeddings",
    author = "Ranasinghe, Tharindu  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.470",
    doi = "10.18653/v1/2020.emnlp-main.470",
    pages = "5838--5844",
    abstract = "Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g. hate speech, cyberbulling, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this paper, we take advantage of English data available by applying cross-lingual contextual word embeddings and transfer learning to make predictions in languages with less resources. We project predictions on comparable data in Bengali, Hindi, and Spanish and we report results of 0.8415 F1 macro for Bengali, 0.8568 F1 macro for Hindi, and 0.7513 F1 macro for Spanish. Finally, we show that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages, confirming the robustness of cross-lingual contextual embeddings and transfer learning for this task.",
}
@inproceedings{chu-etal-2020-solving,
    title = "{S}olving {H}istorical {D}ictionary {C}odes with a {N}eural {L}anguage {M}odel",
    author = "Chu, Christopher  and
      Valenti, Raphael  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.471",
    doi = "10.18653/v1/2020.emnlp-main.471",
    pages = "5845--5854",
    abstract = "We solve difficult word-based substitution codes by constructing a decoding lattice and searching that lattice with a neural language model. We apply our method to a set of enciphered letters exchanged between US Army General James Wilkinson and agents of the Spanish Crown in the late 1700s and early 1800s, obtained from the US Library of Congress. We are able to decipher 75.1{\%} of the cipher-word tokens correctly.",
}
@inproceedings{abdul-mageed-etal-2020-toward,
    title = "Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments",
    author = "Abdul-Mageed, Muhammad  and
      Zhang, Chiyu  and
      Elmadany, AbdelRahim  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.472",
    doi = "10.18653/v1/2020.emnlp-main.472",
    pages = "5855--5876",
    abstract = "Although prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties. Inspired by geolocation research, we propose the novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new language model with striking abilities to predict a fine-grained variety (as small as that of a city) given a single, short message. For modeling, we offer a range of novel spatially and linguistically-motivated multi-task learning models. To showcase the utility of our models, we introduce a new, large-scale dataset of Arabic micro-varieties (low-resource) suited to our tasks. MARBERT predicts micro-dialects with 9.9{\%} F1, 76 better than a majority class baseline. Our new language model also establishes new state-of-the-art on several external tasks.",
}
@inproceedings{groenwold-etal-2020-investigating,
    title = "Investigating {A}frican-{A}merican {V}ernacular {E}nglish in Transformer-Based Text Generation",
    author = "Groenwold, Sophie  and
      Ou, Lily  and
      Parekh, Aesha  and
      Honnavalli, Samhita  and
      Levy, Sharon  and
      Mirza, Diba  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.473",
    doi = "10.18653/v1/2020.emnlp-main.473",
    pages = "5877--5883",
    abstract = "The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts. However, NLP models have historically been developed using dominant English varieties, such as Standard American English (SAE), due to text corpora availability. We investigate the performance of GPT-2 on AAVE text by creating a dataset of intent-equivalent parallel AAVE/SAE tweet pairs, thereby isolating syntactic structure and AAVE- or SAE-specific language for each pair. We evaluate each sample and its GPT-2 generated text with pretrained sentiment classifiers and find that while AAVE text results in more classifications of negative sentiment than SAE, the use of GPT-2 generally increases occurrences of positive sentiment for both. Additionally, we conduct human evaluation of AAVE and SAE text generated with GPT-2 to compare contextual rigor and overall quality.",
}
@inproceedings{wei-etal-2020-iterative,
    title = "Iterative Domain-Repaired Back-Translation",
    author = "Wei, Hao-Ran  and
      Zhang, Zhirui  and
      Chen, Boxing  and
      Luo, Weihua",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.474",
    doi = "10.18653/v1/2020.emnlp-main.474",
    pages = "5884--5893",
    abstract = "In this paper, we focus on the domain-specific translation with low resources, where in-domain parallel corpora are scarce or nonexistent. One common and effective strategy for this case is exploiting in-domain monolingual data with the back-translation method. However, the synthetic parallel data is very noisy because they are generated by imperfect out-of-domain systems, resulting in the poor performance of domain adaptation. To address this issue, we propose a novel iterative domain-repaired back-translation framework, which introduces the Domain-Repair (DR) model to refine translations in synthetic bilingual data. To this end, we construct corresponding data for the DR model training by round-trip translating the monolingual sentences, and then design the unified training framework to optimize paired DR and NMT models jointly. Experiments on adapting NMT models between specific domains and from the general domain to specific domains demonstrate the effectiveness of our proposed approach, achieving 15.79 and 4.47 BLEU improvements on average over unadapted models and back-translation.",
}
@inproceedings{dou-etal-2020-dynamic,
    title = "Dynamic Data Selection and Weighting for Iterative Back-Translation",
    author = "Dou, Zi-Yi  and
      Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.475",
    doi = "10.18653/v1/2020.emnlp-main.475",
    pages = "5894--5904",
    abstract = "Back-translation has proven to be an effective method to utilize monolingual data in neural machine translation (NMT), and iteratively conducting back-translation can further improve the model performance. Selecting which monolingual data to back-translate is crucial, as we require that the resulting synthetic data are of high quality and reflect the target domain. To achieve these two goals, data selection and weighting strategies have been proposed, with a common practice being to select samples close to the target domain but also dissimilar to the average general-domain text. In this paper, we provide insights into this commonly used approach and generalize it to a dynamic curriculum learning strategy, which is applied to iterative back-translation models. In addition, we propose weighting strategies based on both the current quality of the sentence and its improvement over the previous iteration. We evaluate our models on domain adaptation, low-resource, and high-resource MT settings and on two language pairs. Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines.",
}
@inproceedings{lyu-etal-2020-revisiting,
    title = "Revisiting {M}odularized {M}ultilingual {NMT} to {M}eet {I}ndustrial {D}emands",
    author = "Lyu, Sungwon  and
      Son, Bokyung  and
      Yang, Kichang  and
      Bae, Jaekyoung",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.476",
    doi = "10.18653/v1/2020.emnlp-main.476",
    pages = "5905--5918",
    abstract = "The complete sharing of parameters for multilingual translation (1-1) has been the mainstream approach in current research. However, degraded performance due to the capacity bottleneck and low maintainability hinders its extensive adoption in industries. In this study, we revisit the multilingual neural machine translation model that only share modules among the same languages (M2) as a practical alternative to 1-1 to satisfy industrial requirements. Through comprehensive experiments, we identify the benefits of multi-way training and demonstrate that the M2 can enjoy these benefits without suffering from the capacity bottleneck. Furthermore, the interlingual space of the M2 allows convenient modification of the model. By leveraging trained modules, we find that incrementally added modules exhibit better performance than singly trained models. The zero-shot performance of the added modules is even comparable to supervised models. Our findings suggest that the M2 can be a competent candidate for multilingual translation in industries.",
}
@inproceedings{roy-etal-2020-lareqa,
    title = "{LAR}e{QA}: Language-Agnostic Answer Retrieval from a Multilingual Pool",
    author = "Roy, Uma  and
      Constant, Noah  and
      Al-Rfou, Rami  and
      Barua, Aditya  and
      Phillips, Aaron  and
      Yang, Yinfei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.477",
    doi = "10.18653/v1/2020.emnlp-main.477",
    pages = "5919--5930",
    abstract = "We present LAReQA, a challenging new benchmark for language-agnostic answer retrieval from a multilingual candidate pool. Unlike previous cross-lingual tasks, LAReQA tests for {``}strong{''} cross-lingual alignment, requiring semantically related \textit{cross}-language pairs to be closer in representation space than unrelated \textit{same}-language pairs. This level of alignment is important for the practical task of cross-lingual information retrieval. Building on multilingual BERT (mBERT), we study different strategies for achieving strong alignment. We find that augmenting training data via machine translation is effective, and improves significantly over using mBERT out-of-the-box. Interestingly, model performance on zero-shot variants of our task that only target {``}weak{''} alignment is not predictive of performance on LAReQA. This finding underscores our claim that language-agnostic retrieval is a substantively new kind of cross-lingual evaluation, and suggests that measuring both weak and strong alignment will be important for improving cross-lingual systems going forward. We release our dataset and evaluation code at \url{https://github.com/google-research-datasets/lareqa}.",
}
@inproceedings{rijhwani-etal-2020-ocr,
    title = "{OCR} {P}ost {C}orrection for {E}ndangered {L}anguage {T}exts",
    author = "Rijhwani, Shruti  and
      Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.478",
    doi = "10.18653/v1/2020.emnlp-main.478",
    pages = "5931--5942",
    abstract = "There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34{\%} on average across the three languages.",
}
@inproceedings{jiang-etal-2020-x,
    title = "{X}-{FACTR}: Multilingual Factual Knowledge Retrieval from Pretrained Language Models",
    author = "Jiang, Zhengbao  and
      Anastasopoulos, Antonios  and
      Araki, Jun  and
      Ding, Haibo  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.479",
    doi = "10.18653/v1/2020.emnlp-main.479",
    pages = "5943--5959",
    abstract = "Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as {``}Punta Cana is located in {\_}.{''} However, while knowledge is both written and queried in many languages, studies on LMs{'} factual representation ability have almost invariably been performed on English. To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for typologically diverse languages. To properly handle language variations, we expand probing methods from single- to multi-word entities, and develop several decoding algorithms to generate multi-token predictions. Extensive experimental results provide insights about how well (or poorly) current state-of-the-art LMs perform at this task in languages with more or fewer available resources. We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages. Benchmark data and code have be released at https://x-factr.github.io.",
}
@inproceedings{el-kishky-etal-2020-ccaligned,
    title = "{CCA}ligned: A Massive Collection of Cross-Lingual Web-Document Pairs",
    author = "El-Kishky, Ahmed  and
      Chaudhary, Vishrav  and
      Guzm{\'a}n, Francisco  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.480",
    doi = "10.18653/v1/2020.emnlp-main.480",
    pages = "5960--5969",
    abstract = "Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. In this paper, we exploit the signals embedded in URLs to label web documents at scale with an average precision of 94.5{\%} across different language pairs. We mine sixty-eight snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of over 392 million URL pairs from Common Crawl covering documents in 8144 language pairs of which 137 pairs include English. In addition to curating this massive dataset, we introduce baseline methods that leverage cross-lingual representations to identify aligned documents based on their textual content. Finally, we demonstrate the value of this parallel documents dataset through a downstream task of mining parallel sentences and measuring the quality of machine translations from models trained on this mined data. Our objective in releasing this dataset is to foster new research in cross-lingual NLP across a variety of low, medium, and high-resource languages.",
}
@inproceedings{moradshahi-etal-2020-localizing,
    title = "Localizing Open-Ontology {QA} Semantic Parsers in a Day Using Machine Translation",
    author = "Moradshahi, Mehrad  and
      Campagna, Giovanni  and
      Semnani, Sina  and
      Xu, Silei  and
      Lam, Monica",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.481",
    doi = "10.18653/v1/2020.emnlp-main.481",
    pages = "5970--5983",
    abstract = "We propose Semantic Parser Localizer (SPL), a toolkit that leverages Neural Machine Translation (NMT) systems to localize a semantic parser for a new language. Our methodology is to (1) generate training data automatically in the target language by augmenting machine-translated datasets with local entities scraped from public websites, (2) add a few-shot boost of human-translated sentences and train a novel XLMR-LSTM semantic parser, and (3) test the model on natural utterances curated using human translators. We assess the effectiveness of our approach by extending the current capabilities of Schema2QA, a system for English Question Answering (QA) on the open web, to 10 new languages for the restaurants and hotels domains. Our model achieves an overall test accuracy ranging between 61{\%} and 69{\%} for the hotels domain and between 64{\%} and 78{\%} for restaurants domain, which compares favorably to 69{\%} and 80{\%} obtained for English parser trained on gold English data and a few examples from validation set. We show our approach outperforms the previous state-of-the-art methodology by more than 30{\%} for hotels and 40{\%} for restaurants with localized ontologies for the subset of languages tested. Our methodology enables any software developer to add a new language capability to a QA system for a new domain, leveraging machine translation, in less than 24 hours. Our code is released open-source.",
}
@inproceedings{yuan-etal-2020-interactive-refinement,
    title = "Interactive Refinement of Cross-Lingual Word Embeddings",
    author = "Yuan, Michelle  and
      Zhang, Mozhi  and
      Van Durme, Benjamin  and
      Findlater, Leah  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.482",
    doi = "10.18653/v1/2020.emnlp-main.482",
    pages = "5984--5996",
    abstract = "Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given classification problem. First, CLIME ranks words by their salience to the downstream task. Then, users mark similarity between keywords and their nearest neighbors in the embedding space. Finally, CLIME updates the embeddings using the annotations. We evaluate CLIME on identifying health-related text in four low-resource languages: Ilocano, Sinhalese, Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word semantics and have higher test accuracy than the original embeddings. CLIME often improves accuracy faster than an active learning baseline and can be easily combined with active learning to improve results.",
}
@inproceedings{thompson-koehn-2020-exploiting,
    title = "Exploiting Sentence Order in Document Alignment",
    author = "Thompson, Brian  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.483",
    doi = "10.18653/v1/2020.emnlp-main.483",
    pages = "5997--6007",
    abstract = "We present a simple document alignment method that incorporates sentence order information in both candidate generation and candidate re-scoring. Our method results in 61{\%} relative reduction in error compared to the best previously published result on the WMT16 document alignment shared task. Our method improves downstream MT performance on web-scraped Sinhala{--}English documents from ParaCrawl, outperforming the document alignment method used in the most recent ParaCrawl release. It also outperforms a comparable corpora method which uses the same multilingual embeddings, demonstrating that exploiting sentence order is beneficial even if the end goal is sentence-level bitext.",
}
@inproceedings{liang-etal-2020-xglue,
    title = "{XGLUE}: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation",
    author = "Liang, Yaobo  and
      Duan, Nan  and
      Gong, Yeyun  and
      Wu, Ning  and
      Guo, Fenfei  and
      Qi, Weizhen  and
      Gong, Ming  and
      Shou, Linjun  and
      Jiang, Daxin  and
      Cao, Guihong  and
      Fan, Xiaodong  and
      Zhang, Ruofei  and
      Agrawal, Rahul  and
      Cui, Edward  and
      Wei, Sining  and
      Bharti, Taroon  and
      Qiao, Ying  and
      Chen, Jiun-Hung  and
      Wu, Winnie  and
      Liu, Shuguang  and
      Yang, Fan  and
      Campos, Daniel  and
      Majumder, Rangan  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.484",
    doi = "10.18653/v1/2020.emnlp-main.484",
    pages = "6008--6018",
    abstract = "In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE (Wang et al.,2019), which is labeled in English and includes natural language understanding tasks only, XGLUE has three main advantages: (1) it provides two corpora with different sizes for cross-lingual pre-training; (2) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (3) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder (Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.",
}
@inproceedings{wang-etal-2020-ain,
    title = "{AIN}: Fast and Accurate Sequence Labeling with Approximate Inference Network",
    author = "Wang, Xinyu  and
      Jiang, Yong  and
      Bach, Nguyen  and
      Wang, Tao  and
      Huang, Zhongqiang  and
      Huang, Fei  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.485",
    doi = "10.18653/v1/2020.emnlp-main.485",
    pages = "6019--6026",
    abstract = "The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms such as the forward-backward and Viterbi algorithms are typically applied in training and prediction stages of the CRF model. However, these algorithms require sequential computation that makes parallelization impossible. In this paper, we propose to employ a parallelizable approximate variational inference algorithm for the CRF model. Based on this algorithm, we design an approximate inference network that can be connected with the encoder of the neural CRF model to form an end-to-end network, which is amenable to parallelization for faster training and prediction. The empirical results show that our proposed approaches achieve a 12.7-fold improvement in decoding speed with long sentences and a competitive accuracy compared with the traditional CRF approach.",
}
@inproceedings{wang-etal-2020-hit,
    title = "{HIT}: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction",
    author = "Wang, Yu  and
      Li, Yun  and
      Tong, Hanghang  and
      Zhu, Ziye",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.486",
    doi = "10.18653/v1/2020.emnlp-main.486",
    pages = "6027--6036",
    abstract = "Named Entity Recognition (NER) is a fundamental task in natural language processing. In order to identify entities with nested structure, many sophisticated methods have been recently developed based on either the traditional sequence labeling approaches or directed hypergraph structures. Despite being successful, these methods often fall short in striking a good balance between the expression power for nested structure and the model complexity. To address this issue, we present a novel nested NER model named HIT. Our proposed HIT model leverages two key properties pertaining to the (nested) named entity, including (1) explicit boundary tokens and (2) tight internal connection between tokens within the boundary. Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary. Experiments on three public NER datasets demonstrate that the proposed HIT achieves state-of-the-art performance.",
}
@inproceedings{tian-etal-2020-supertagging,
    title = "Supertagging {C}ombinatory {C}ategorial {G}rammar with Attentive Graph Convolutional Networks",
    author = "Tian, Yuanhe  and
      Song, Yan  and
      Xia, Fei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.487",
    doi = "10.18653/v1/2020.emnlp-main.487",
    pages = "6037--6044",
    abstract = "Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have made limited efforts to leverage contextual features except for applying powerful encoders (e.g., bi-LSTM). In this paper, we propose attentive graph convolutional networks to enhance neural CCG supertagging through a novel solution of leveraging contextual information. Specifically, we build the graph from chunks (n-grams) extracted from a lexicon and apply attention over the graph, so that different word pairs from the contexts within and across chunks are weighted in the model and facilitate the supertagging accordingly. The experiments performed on the CCGbank demonstrate that our approach outperforms all previous studies in terms of both supertagging and parsing. Further analyses illustrate the effectiveness of each component in our approach to discriminatively learn from word pairs to enhance CCG supertagging.",
}
@inproceedings{ding-etal-2020-daga,
    title = "{DAGA}: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks",
    author = "Ding, Bosheng  and
      Liu, Linlin  and
      Bing, Lidong  and
      Kruengkrai, Canasai  and
      Nguyen, Thien Hai  and
      Joty, Shafiq  and
      Si, Luo  and
      Miao, Chunyan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.488",
    doi = "10.18653/v1/2020.emnlp-main.488",
    pages = "6045--6057",
    abstract = "Data augmentation techniques have been widely used to improve machine learning performance as they facilitate generalization. In this work, we propose a novel augmentation method to generate high quality synthetic data for low-resource tagging tasks with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings. For the supervised settings, we conduct extensive experiments on named entity recognition (NER), part of speech (POS) tagging and end-to-end target based sentiment analysis (E2E-TBSA) tasks. For the semi-supervised settings, we evaluate our method on the NER task under the conditions of given unlabeled data only and unlabeled data plus a knowledge base. The results show that our method can consistently outperform the baselines, particularly when the given gold training data are less.",
}
@inproceedings{fu-etal-2020-interpretable,
    title = "Interpretable Multi-dataset Evaluation for Named Entity Recognition",
    author = "Fu, Jinlan  and
      Liu, Pengfei  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.489",
    doi = "10.18653/v1/2020.emnlp-main.489",
    pages = "6058--6069",
    abstract = "With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 does not tell us why or how particular methods perform differently and how diverse datasets influence the model design choices. In this paper, we present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them, identifying the strengths and weaknesses of current systems. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area: https://github.com/neulab/InterpretEval",
}
@inproceedings{yan-etal-2020-adversarial,
    title = "Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots",
    author = "Yan, Yuanmeng  and
      He, Keqing  and
      Xu, Hong  and
      Liu, Sihong  and
      Meng, Fanyu  and
      Hu, Min  and
      Xu, Weiran",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.490",
    doi = "10.18653/v1/2020.emnlp-main.490",
    pages = "6070--6075",
    abstract = "Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit. In this paper, we propose a robust adversarial model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. We aim to depart entangled contextual semantics and focus more on the holistic context at the level of the whole sentence. Experiments on two public datasets show that our method consistently outperforms other methods with a statistically significant margin on all the open-vocabulary slots without deteriorating the performance of normal slots.",
}
@inproceedings{mai-etal-2020-plug,
    title = "Plug and Play Autoencoders for Conditional Text Generation",
    author = "Mai, Florian  and
      Pappas, Nikolaos  and
      Montero, Ivan  and
      Smith, Noah A.  and
      Henderson, James",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.491",
    doi = "10.18653/v1/2020.emnlp-main.491",
    pages = "6076--6092",
    abstract = "Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder{'}s embedding space, training embedding-to-embedding (Emb2Emb). This reduces the need for labeled training data for the task and makes the training procedure more efficient. Crucial to the success of this method is a loss term for keeping the mapped embedding on the manifold of the autoencoder and a mapping which is trained to navigate the manifold by learning offset vectors. Evaluations on style transfer tasks both with and without sequence-to-sequence supervision show that our method performs better than or comparable to strong baselines while being up to four times faster.",
}
@inproceedings{ahrabian-etal-2020-structure,
    title = "Structure Aware Negative Sampling in Knowledge Graphs",
    author = "Ahrabian, Kian  and
      Feizi, Aarash  and
      Salehi, Yasmin  and
      Hamilton, William L.  and
      Bose, Avishek Joey",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.492",
    doi = "10.18653/v1/2020.emnlp-main.492",
    pages = "6093--6101",
    abstract = "Learning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connectivity patterns. A crucial aspect of contrastive learning approaches is the choice of corruption distribution that generates hard negative samples, which force the embedding model to learn discriminative representations and find critical characteristics of observed data. While earlier methods either employ too simple corruption distributions, i.e. uniform, yielding easy uninformative negatives or sophisticated adversarial distributions with challenging optimization schemes, they do not explicitly incorporate known graph structure resulting in suboptimal negatives. In this paper, we propose Structure Aware Negative Sampling (SANS), an inexpensive negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node{'}s $k$-hop neighborhood. Empirically, we demonstrate that SANS finds semantically meaningful negatives and is competitive with SOTA approaches while requires no additional parameters nor difficult adversarial optimization.",
}
@inproceedings{kang-etal-2020-neural,
    title = "Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation",
    author = "Kang, Minki  and
      Han, Moonsu  and
      Hwang, Sung Ju",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.493",
    doi = "10.18653/v1/2020.emnlp-main.493",
    pages = "6102--6120",
    abstract = "We propose a method to automatically generate a domain- and task-adaptive maskings of the given text for self-supervised pre-training, such that we can effectively adapt the language model to a particular target task (e.g. question answering). Specifically, we present a novel reinforcement learning-based framework which learns the masking policy, such that using the generated masks for further pre-training of the target language model helps improve task performance on unseen texts. We use off-policy actor-critic with entropy regularization and experience replay for reinforcement learning, and propose a Transformer-based policy network that can consider the relative importance of words in a given text. We validate our Neural Mask Generator (NMG) on several question answering and text classification datasets using BERT and DistilBERT as the language models, on which it outperforms rule-based masking strategies, by automatically learning optimal adaptive maskings.",
}
@inproceedings{lin-etal-2020-autoregressive,
    title = "Autoregressive Knowledge Distillation through Imitation Learning",
    author = "Lin, Alexander  and
      Wohlwend, Jeremy  and
      Chen, Howard  and
      Lei, Tao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.494",
    doi = "10.18653/v1/2020.emnlp-main.494",
    pages = "6121--6133",
    abstract = "The performance of autoregressive models on natural language generation tasks has dramatically improved due to the adoption of deep, self-attentive architectures. However, these gains have come at the cost of hindering inference speed, making state-of-the-art models cumbersome to deploy in real-world, time-sensitive settings. We develop a compression technique for autoregressive models that is driven by an imitation learning perspective on knowledge distillation. The algorithm is designed to address the exposure bias problem. On prototypical language generation tasks such as translation and summarization, our method consistently outperforms other distillation algorithms, such as sequence-level knowledge distillation. Student models trained with our method attain 1.4 to 4.8 BLEU/ROUGE points higher than those trained from scratch, while increasing inference speed by up to 14 times in comparison to the teacher model.",
}
@inproceedings{wang-etal-2020-t3,
    title = "T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack",
    author = "Wang, Boxin  and
      Pei, Hengzhi  and
      Pan, Boyuan  and
      Chen, Qian  and
      Wang, Shuohang  and
      Li, Bo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.495",
    doi = "10.18653/v1/2020.emnlp-main.495",
    pages = "6134--6150",
    abstract = "Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models. Compared with the adversarial example generation in continuous data domain (e.g., image), generating adversarial text that preserves the original meaning is challenging since the text space is discrete and non-differentiable. To handle these challenges, we propose a target-controllable adversarial attack framework T3, which is applicable to a range of NLP tasks. In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation. A novel tree-based decoder is then applied to regularize the syntactic correctness of the generated text and manipulate it on either sentence (T3(Sent)) or word (T3(Word)) level. We consider two most representative NLP tasks: sentiment analysis and question answering (QA). Extensive experimental results and human studies show that T3 generated adversarial texts can successfully manipulate the NLP models to output the targeted incorrect answer without misleading the human. Moreover, we show that the generated adversarial texts have high transferability which enables the black-box attacks in practice. Our work sheds light on an effective and general way to examine the robustness of NLP models. Our code is publicly available at https://github.com/AI-secure/T3/.",
}
@inproceedings{wang-etal-2020-structured,
    title = "Structured Pruning of Large Language Models",
    author = "Wang, Ziheng  and
      Wohlwend, Jeremy  and
      Lei, Tao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.496",
    doi = "10.18653/v1/2020.emnlp-main.496",
    pages = "6151--6162",
    abstract = "Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency have significantly increased, which makes their usage costly, and raises an interesting question: do language models need to be large? We study this question through the lens of model compression. We present a generic, structured pruning approach by parameterizing each weight matrix using its low-rank factorization, and adaptively removing rank-1 components during training. On language modeling tasks, our structured approach outperforms other unstructured and block-structured pruning baselines at various compression levels, while achieving significant speedups during both training and inference. We also demonstrate that our method can be applied to pruning adaptive word embeddings in large language models, and to pruning the BERT model on several downstream fine-tuning classification benchmarks.",
}
@inproceedings{vu-etal-2020-effective,
    title = "Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models",
    author = "Vu, Thuy-Trang  and
      Phung, Dinh  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.497",
    doi = "10.18653/v1/2020.emnlp-main.497",
    pages = "6163--6173",
    abstract = "Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest. Current self-supervised adaptation methods are simplistic, as the training signal comes from a small percentage of \textit{randomly} masked-out tokens. In this paper, we show that careful masking strategies can bridge the knowledge gap of masked language models (MLMs) about the domains more effectively by allocating self-supervision where it is needed. Furthermore, we propose an effective training strategy by adversarially masking out those tokens which are harder to reconstruct by the underlying MLM. The adversarial objective leads to a challenging combinatorial optimisation problem over \textit{subsets} of tokens, which we tackle efficiently through relaxation to a variational lowerbound and dynamic programming. On six unsupervised domain adaptation tasks involving named entity recognition, our method strongly outperforms the random masking strategy and achieves up to +1.64 F1 score improvements.",
}
@inproceedings{garg-ramakrishnan-2020-bae,
    title = "{BAE}: {BERT}-based Adversarial Examples for Text Classification",
    author = "Garg, Siddhant  and
      Ramakrishnan, Goutham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.498",
    doi = "10.18653/v1/2020.emnlp-main.498",
    pages = "6174--6181",
    abstract = "Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Recent works in NLP use rule-based synonym replacement strategies to generate adversarial examples. These strategies can lead to out-of-context and unnaturally complex token replacements, which are easily identifiable by humans. We present BAE, a black box attack for generating adversarial examples using contextual perturbations from a BERT masked language model. BAE replaces and inserts tokens in the original text by masking a portion of the text and leveraging the BERT-MLM to generate alternatives for the masked tokens. Through automatic and human evaluations, we show that BAE performs a stronger attack, in addition to generating adversarial examples with improved grammaticality and semantic coherence as compared to prior work.",
}
@inproceedings{ma-etal-2020-adversarial,
    title = "{A}dversarial {S}elf-{S}upervised {D}ata-{F}ree {D}istillation for {T}ext {C}lassification",
    author = "Ma, Xinyin  and
      Shen, Yongliang  and
      Fang, Gongfan  and
      Chen, Chen  and
      Jia, Chenghao  and
      Lu, Weiming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.499",
    doi = "10.18653/v1/2020.emnlp-main.499",
    pages = "6182--6192",
    abstract = "Large pre-trained transformer-based language models have achieved impressive results on a wide range of NLP tasks. In the past few years, Knowledge Distillation(KD) has become a popular paradigm to compress a computationally expensive model to a resource-efficient lightweight model. However, most KD algorithms, especially in NLP, rely on the accessibility of the original training dataset, which may be unavailable due to privacy issues. To tackle this problem, we propose a novel two-stage data-free distillation method, named Adversarial self-Supervised Data-Free Distillation (AS-DFD), which is designed for compressing large-scale transformer-based models (e.g., BERT). To avoid text generation in discrete space, we introduce a Plug {\&} Play Embedding Guessing method to craft pseudo embeddings from the teacher{'}s hidden knowledge. Meanwhile, with a self-supervised module to quantify the student{'}s ability, we adapt the difficulty of pseudo embeddings in an adversarial training manner. To the best of our knowledge, our framework is the first data-free distillation framework designed for NLP tasks. We verify the effectiveness of our method on several text classification datasets.",
}
@inproceedings{li-etal-2020-bert-attack,
    title = "{BERT}-{ATTACK}: Adversarial Attack Against {BERT} Using {BERT}",
    author = "Li, Linyang  and
      Ma, Ruotian  and
      Guo, Qipeng  and
      Xue, Xiangyang  and
      Qiu, Xipeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.500",
    doi = "10.18653/v1/2020.emnlp-main.500",
    pages = "6193--6202",
    abstract = "Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods. Current successful attack methods for texts usually adopt heuristic replacement strategies on the character or word level, which remains challenging to find the optimal solution in the massive space of possible combinations of replacements while preserving semantic consistency and language fluency. In this paper, we propose \textbf{BERT-Attack}, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT. We turn BERT against its fine-tuned models and other deep neural models in downstream tasks so that we can successfully mislead the target models to predict incorrectly. Our method outperforms state-of-the-art attack strategies in both success rate and perturb percentage, while the generated adversarial samples are fluent and semantically preserved. Also, the cost of calculation is low, thus possible for large-scale generations. The code is available at \url{https://github.com/LinyangLee/BERT-Attack}.",
}
@inproceedings{keskar-etal-2020-thieves,
    title = "The Thieves on Sesame Street are Polyglots - Extracting Multilingual Models from Monolingual {API}s",
    author = "Keskar, Nitish Shirish  and
      McCann, Bryan  and
      Xiong, Caiming  and
      Socher, Richard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.501",
    doi = "10.18653/v1/2020.emnlp-main.501",
    pages = "6203--6207",
    abstract = "Pre-training in natural language processing makes it easier for an adversary with only query access to a victim model to reconstruct a local copy of the victim by training with gibberish input data paired with the victim{'}s labels for that data. We discover that this extraction process extends to local copies initialized from a pre-trained, multilingual model while the victim remains monolingual. The extracted model learns the task from the monolingual victim, but it generalizes far better than the victim to several other languages. This is done without ever showing the multilingual, extracted model a well-formed input in any of the languages for the target task. We also demonstrate that a few real examples can greatly improve performance, and we analyze how these results shed light on how such extraction methods succeed.",
}
@inproceedings{yu-etal-2020-hearst,
    title = "When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models",
    author = "Yu, Changlong  and
      Han, Jialong  and
      Wang, Peifeng  and
      Song, Yangqiu  and
      Zhang, Hongming  and
      Ng, Wilfred  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.502",
    doi = "10.18653/v1/2020.emnlp-main.502",
    pages = "6208--6217",
    abstract = "We address hypernymy detection, i.e., whether an is-a relationship exists between words (x ,y), with the help of large textual corpora. Most conventional approaches to this task have been categorized to be either pattern-based or distributional. Recent studies suggest that pattern-based ones are superior, if large-scale Hearst pairs are extracted and fed, with the sparsity of unseen (x ,y) pairs relieved. However, they become invalid in some specific sparsity cases, where x or y is not involved in any pattern. For the first time, this paper quantifies the non-negligible existence of those specific cases. We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases. We devise a complementary framework, under which a pattern-based and a distributional model collaborate seamlessly in cases which they each prefer. On several benchmark datasets, our framework demonstrates improvements that are both competitive and explainable.",
}
@inproceedings{pasca-2020-interpreting,
    title = "Interpreting Open-Domain Modifiers: Decomposition of {W}ikipedia Categories into Disambiguated Property-Value Pairs",
    author = "Pasca, Marius",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.503",
    doi = "10.18653/v1/2020.emnlp-main.503",
    pages = "6218--6228",
    abstract = "This paper proposes an open-domain method for automatically annotating modifier constituents (20th-century{'}) within Wikipedia categories (20th-century male writers) with properties (date of birth). The annotations offer a semantically-anchored understanding of the role of the constituents in defining the underlying meaning of the categories. In experiments over an evaluation set of Wikipedia categories, the proposed method annotates constituent modifiers as semantically-anchored properties, rather than as mere strings in a previous method. It does so at a better trade-off between precision and recall.",
}
@inproceedings{wang-wang-2020-synset,
    title = "A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation",
    author = "Wang, Ming  and
      Wang, Yinglin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.504",
    doi = "10.18653/v1/2020.emnlp-main.504",
    pages = "6229--6240",
    abstract = "Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge in semantic networks. In this paper, we propose a Synset Relation-Enhanced Framework (SREF) that leverages sense relations for both sense embedding enhancement and a try-again mechanism that implements WSD again, after obtaining basic sense embeddings from augmented WordNet glosses. Experiments on all-words and lexical sample datasets show that the proposed system achieves new state-of-the-art results, defeating previous knowledge-based systems by at least 5.5 F1 measure. When the system utilizes sense embeddings learned from SemCor, it outperforms all previous supervised systems with only 20{\%} SemCor data.",
}
@inproceedings{liu-etal-2020-diverse,
    title = "Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation",
    author = "Liu, Dayiheng  and
      Gong, Yeyun  and
      Yan, Yu  and
      Fu, Jie  and
      Shao, Bo  and
      Jiang, Daxin  and
      Lv, Jiancheng  and
      Duan, Nan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.505",
    doi = "10.18653/v1/2020.emnlp-main.505",
    pages = "6241--6250",
    abstract = "News headline generation aims to produce a short sentence to attract readers to read the news. One news article often contains multiple keyphrases that are of interest to different users, which can naturally have multiple reasonable headlines. However, most existing methods focus on the single headline generation. In this paper, we propose generating multiple headlines with keyphrases of user interests, whose main idea is to generate multiple keyphrases of interest to users for the news first, and then generate multiple keyphrase-relevant headlines. We propose a multi-source Transformer decoder, which takes three sources as inputs: (a) keyphrase, (b) keyphrase-filtered article, and (c) original article to generate keyphrase-relevant, high-quality, and diverse headlines. Furthermore, we propose a simple and effective method to mine the keyphrases of interest in the news article and build a first large-scale keyphrase-aware news headline corpus, which contains over 180K aligned triples of {\textless}news article, headline, keyphrase{\textgreater}. Extensive experimental comparisons on the real-world dataset show that the proposed method achieves state-of-the-art results in terms of quality and diversity.",
}
@inproceedings{cao-etal-2020-factual,
    title = "Factual Error Correction for Abstractive Summarization Models",
    author = "Cao, Meng  and
      Dong, Yue  and
      Wu, Jiapeng  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.506",
    doi = "10.18653/v1/2020.emnlp-main.506",
    pages = "6251--6258",
    abstract = "Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods. However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge. We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries. The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries. These transformations are inspired by the error analysis of state-of-the-art summarization model outputs. Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset. We also find that transferring from artificial error correction to downstream settings is still very challenging.",
}
@inproceedings{desai-etal-2020-compressive,
    title = "Compressive Summarization with Plausibility and Salience Modeling",
    author = "Desai, Shrey  and
      Xu, Jiacheng  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.507",
    doi = "10.18653/v1/2020.emnlp-main.507",
    pages = "6259--6274",
    abstract = "Compressive summarization systems typically rely on a seed set of syntactic rules to determine under what circumstances deleting a span is permissible, then learn which compressions to actually apply by optimizing for ROUGE. In this work, we propose to relax these explicit syntactic constraints on candidate spans, and instead leave the decision about what to delete to two data-driven criteria: plausibility and salience. Deleting a span is plausible if removing it maintains the grammaticality and factuality of a sentence, and it is salient if it removes important information from the summary. Each of these is judged by a pre-trained Transformer model, and only deletions that are both plausible and not salient can be applied. When integrated into a simple extraction-compression pipeline, our method achieves strong in-domain results on benchmark datasets, and human evaluation shows that the plausibility model generally selects for grammatical and factual deletions. Furthermore, the flexibility of our approach allows it to generalize cross-domain, and we show that our system fine-tuned on only 500 samples from a new domain can match or exceed a strong in-domain extractive model.",
}
@inproceedings{xu-etal-2020-understanding-neural,
    title = "Understanding Neural Abstractive Summarization Models via Uncertainty",
    author = "Xu, Jiacheng  and
      Desai, Shrey  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.508",
    doi = "10.18653/v1/2020.emnlp-main.508",
    pages = "6275--6281",
    abstract = "An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior. In this work, we analyze summarization decoders in both blackbox and whitebox ways by studying on the entropy, or uncertainty, of the model{'}s token-level predictions. For two strong pre-trained models, PEGASUS and BART on two summarization datasets, we find a strong correlation between low prediction entropy and where the model copies tokens rather than generating novel text. The decoder{'}s uncertainty also connects to factors like sentence position and syntactic distance between adjacent pairs of tokens, giving a sense of what factors make a context particularly selective for the model{'}s next output token. Finally, we study the relationship of decoder uncertainty and attention behavior to understand how attention gives rise to these observed effects in the model. We show that uncertainty is a useful perspective for analyzing summarization and text generation models more broadly.",
}
@inproceedings{cho-etal-2020-better,
    title = "Better Highlighting: Creating Sub-Sentence Summary Highlights",
    author = "Cho, Sangwoo  and
      Song, Kaiqiang  and
      Li, Chen  and
      Yu, Dong  and
      Foroosh, Hassan  and
      Liu, Fei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.509",
    doi = "10.18653/v1/2020.emnlp-main.509",
    pages = "6282--6300",
    abstract = "Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to sift through a large amount of text. The method allows summaries to be understood in context to prevent a summarizer from distorting the original meaning, of which abstractive summarizers usually fall short. In particular, we present a new method to produce self-contained highlights that are understandable on their own to avoid confusion. Our method combines determinantal point processes and deep contextualized representations to identify an optimal set of sub-sentence segments that are both important and non-redundant to form summary highlights. To demonstrate the flexibility and modeling power of our method, we conduct extensive experiments on summarization datasets. Our analysis provides evidence that highlighting is a promising avenue of research towards future summarization.",
}
@inproceedings{tan-etal-2020-summarizing,
    title = "Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach",
    author = "Tan, Bowen  and
      Qin, Lianhui  and
      Xing, Eric  and
      Hu, Zhiting",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.510",
    doi = "10.18653/v1/2020.emnlp-main.510",
    pages = "6301--6309",
    abstract = "Given a document and a target aspect (e.g., a topic of interest), aspect-based abstractive summarization attempts to generate a summary with respect to the aspect. Previous studies usually assume a small pre-defined set of aspects and fall short of summarizing on other diverse topics. In this work, we study summarizing on \textit{arbitrary} aspects relevant to the document, which significantly expands the application of the task in practice. Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme, both of which integrate rich external knowledge sources such as ConceptNet and Wikipedia. Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents given pre-defined or arbitrary aspects.",
}
@inproceedings{cui-etal-2020-bert,
    title = "{BERT}-enhanced Relational Sentence Ordering Network",
    author = "Cui, Baiyun  and
      Li, Yingming  and
      Zhang, Zhongfei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.511",
    doi = "10.18653/v1/2020.emnlp-main.511",
    pages = "6310--6320",
    abstract = "In this paper, we introduce a novel BERT-enhanced Relational Sentence Ordering Network (referred to as BRSON) by leveraging BERT for capturing better dependency relationship among sentences to enhance the coherence modeling for the entire paragraph. In particular, we develop a new Relational Pointer Decoder (referred as RPD) by incorporating the relative ordering information into the pointer network with a Deep Relational Module (referred as DRM), which utilizes BERT to exploit the deep semantic connection and relative ordering between sentences.This enables us to strengthen both local and global dependencies among sentences. Extensive evaluations are conducted on six public datasets. The experimental results demonstrate the effectiveness and promise of our BRSON, showing a significant improvement over the state-of-the-art by a wide margin.",
}
@inproceedings{yu-joty-2020-online,
    title = "Online Conversation Disentanglement with Pointer Networks",
    author = "Yu, Tao  and
      Joty, Shafiq",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.512",
    doi = "10.18653/v1/2020.emnlp-main.512",
    pages = "6321--6330",
    abstract = "Huge amounts of textual conversations occur online every day, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only following the ongoing discussions but also extracting relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. However, existing disentanglement methods rely mostly on handcrafted features that are dataset specific, which hinders generalization and adaptability. In this work, we propose an end-to-end online framework for conversation disentanglement that avoids time-consuming domain-specific feature engineering. We design a novel way to embed the whole utterance that comprises timestamp, speaker, and message text, and propose a custom attention mechanism that models disentanglement as a pointing problem while effectively capturing inter-utterance interactions in an end-to-end fashion. We also introduce a joint-learning objective to better capture contextual information. Our experiments on the Ubuntu IRC dataset show that our method achieves state-of-the-art performance in both link and conversation prediction tasks.",
}
@inproceedings{reid-etal-2020-vcdm,
    title = "{VCDM}: {L}everaging {V}ariational Bi-encoding and {D}eep Contextualized {W}ord {R}epresentations for {I}mproved {D}efinition {M}odeling",
    author = "Reid, Machel  and
      Marrese-Taylor, Edison  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.513",
    doi = "10.18653/v1/2020.emnlp-main.513",
    pages = "6331--6344",
    abstract = "In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminative, combining distributional and lexical semantics in an implicit rather than direct way. To tackle this issue we propose a generative model for the task, introducing a continuous latent variable to explicitly model the underlying relationship between a phrase used within a context and its definition. We rely on variational inference for estimation and leverage contextualized word embeddings for improved performance. Our approach is evaluated on four existing challenging benchmarks with the addition of two new datasets, {``}Cambridge{''} and the first non-English corpus {``}Robert{''}, which we release to complement our empirical study. Our Variational Contextual Definition Modeler (VCDM) achieves state-of-the-art performance in terms of automatic and human evaluation metrics, demonstrating the effectiveness of our approach.",
}
@inproceedings{mengge-etal-2020-coarse,
    title = "{C}oarse-to-{F}ine {P}re-training for {N}amed {E}ntity {R}ecognition",
    author = "Mengge, Xue  and
      Yu, Bowen  and
      Zhang, Zhenyu  and
      Liu, Tingwen  and
      Zhang, Yue  and
      Wang, Bin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.514",
    doi = "10.18653/v1/2020.emnlp-main.514",
    pages = "6345--6354",
    abstract = "More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage modeling objectives to learn a gen-eral representation, ignoring the named entity-related knowledge. To this end, we proposea NER-specific pre-training framework to in-ject coarse-to-fine automatically mined entityknowledge into pre-trained models. Specifi-cally, we first warm-up the model via an en-tity span identification task by training it withWikipedia anchors, which can be deemed asgeneral-typed entities. Then we leverage thegazetteer-based distant supervision strategy totrain the model extract coarse-grained typedentities. Finally, we devise a self-supervisedauxiliary task to mine the fine-grained namedentity knowledge via clustering.Empiricalstudies on three public NER datasets demon-strate that our framework achieves significantimprovements against several pre-trained base-lines, establishing the new state-of-the-art per-formance on three benchmarks. Besides, weshow that our framework gains promising re-sults without using human-labeled trainingdata, demonstrating its effectiveness in label-few and low-resource scenarios.",
}
@inproceedings{liu-etal-2020-exploring,
    title = "Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment",
    author = "Liu, Zhiyuan  and
      Cao, Yixin  and
      Pan, Liangming  and
      Li, Juanzi  and
      Liu, Zhiyuan  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.515",
    doi = "10.18653/v1/2020.emnlp-main.515",
    pages = "6355--6364",
    abstract = "Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective evaluation, we propose a hard experimental setting where we select equivalent entity pairs with very different names as the test set. Under both the regular and hard settings, our method achieves significant improvements (5.10{\%} on average Hits@1 in DBP15k) over 12 baselines in cross-lingual and monolingual datasets. Ablation studies on different subgraphs and a case study about attribute types further demonstrate the effectiveness of our method. Source code and data can be found at \url{https://github.com/thunlp/explore-and-evaluate}.",
}
@inproceedings{yang-katiyar-2020-simple,
    title = "Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning",
    author = "Yang, Yi  and
      Katiyar, Arzoo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.516",
    doi = "10.18653/v1/2020.emnlp-main.516",
    pages = "6365--6375",
    abstract = "We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this feature-space is far more effective than the standard meta-learning approaches. We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training. We show that our method of combining structured decoding with nearest neighbor learning achieves state-of-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by 6{\%} to 16{\%} absolute points over prior meta-learning based systems.",
}
@inproceedings{qian-etal-2020-learning,
    title = "Learning Structured Representations of Entity Names using {A}ctive{L}earning and Weak Supervision",
    author = "Qian, Kun  and
      Chozhiyath Raman, Poornima  and
      Li, Yunyao  and
      Popa, Lucian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.517",
    doi = "10.18653/v1/2020.emnlp-main.517",
    pages = "6376--6383",
    abstract = "Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples.",
}
@inproceedings{jia-etal-2020-entity,
    title = "Entity Enhanced {BERT} Pre-training for {C}hinese {NER}",
    author = "Jia, Chen  and
      Shi, Yuefeng  and
      Yang, Qinrong  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.518",
    doi = "10.18653/v1/2020.emnlp-main.518",
    pages = "6384--6396",
    abstract = "Character-level BERT pre-trained in Chinese suffers a limitation of lacking lexicon information, which shows effectiveness for Chinese NER. To integrate the lexicon into pre-trained LMs for Chinese NER, we investigate a semi-supervised entity enhanced BERT pre-training method. In particular, we first extract an entity lexicon from the relevant raw text using a new-word discovery method. We then integrate the entity information into BERT using Char-Entity-Transformer, which augments the self-attention using a combination of character and entity representations. In addition, an entity classification task helps inject the entity information into model parameters in pre-training. The pre-trained models are used for NER fine-tuning. Experiments on a news dataset and two datasets annotated by ourselves for NER in long-text show that our method is highly effective and achieves the best results.",
}
@inproceedings{wu-etal-2020-scalable,
    title = "Scalable Zero-shot Entity Linking with Dense Entity Retrieval",
    author = "Wu, Ledell  and
      Petroni, Fabio  and
      Josifoski, Martin  and
      Riedel, Sebastian  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.519",
    doi = "10.18653/v1/2020.emnlp-main.519",
    pages = "6397--6407",
    abstract = "This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We present a two-stage zero-shot linking algorithm, where each entity is defined only by a short textual description. The first stage does retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then re-ranked with a cross-encoder, that concatenates the mention and entity text. Experiments demonstrate that this approach is state of the art on recent zero-shot benchmarks (6 point absolute gains) and also on more established non-zero-shot evaluations (e.g. TACKBP-2010), despite its relative simplicity (e.g. no explicit entity embeddings or manually engineered mention tables). We also show that bi-encoder linking is very fast with nearest neighbor search (e.g. linking with 5.9 million candidates in 2 milliseconds), and that much of the accuracy gain from the more expensive cross-encoder can be transferred to the bi-encoder via knowledge distillation. Our code and models are available at https://github.com/facebookresearch/BLINK.",
}
@inproceedings{tandon-etal-2020-dataset,
    title = "A Dataset for Tracking Entities in Open Domain Procedural Text",
    author = "Tandon, Niket  and
      Sakaguchi, Keisuke  and
      Dalvi, Bhavana  and
      Rajagopal, Dheeraj  and
      Clark, Peter  and
      Guerquin, Michal  and
      Richardson, Kyle  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.520",
    doi = "10.18653/v1/2020.emnlp-main.520",
    pages = "6408--6417",
    abstract = "We present the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. For example, in a text describing fog removal using potatoes, a car window may transition between being foggy, sticky, opaque, and clear. Previous formulations of this task provide the text and entities involved, and ask how those entities change for just a small, pre-defined set of attributes (e.g., location), limiting their fidelity. Our solution is a new task formulation where given just a procedural text as input, the task is to generate a set of state change tuples (entity, attribute, before-state, after-state) for each step, where the entity, attribute, and state values must be predicted from an open vocabulary. Using crowdsourcing, we create OPENPI, a high-quality (91.5{\%} coverage as judged by humans and completely vetted), and large-scale dataset comprising 29,928 state changes over 4,050 sentences from 810 procedural real-world paragraphs from WikiHow.com. A current state-of-the-art generation model on this task achieves 16.1{\%} F1 based on BLEU metric, leaving enough room for novel model architectures.",
}
@inproceedings{fu-etal-2020-design,
    title = "Design Challenges in Low-resource Cross-lingual Entity Linking",
    author = "Fu, Xingyu  and
      Shi, Weijia  and
      Yu, Xiaodong  and
      Zhao, Zian  and
      Roth, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.521",
    doi = "10.18653/v1/2020.emnlp-main.521",
    pages = "6418--6432",
    abstract = "Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques. However, current techniques do not rise to the challenges introduced by text in low-resource languages (LRL) and, surprisingly, fail to generalize to text not taken from Wikipedia, on which they are usually trained. This paper provides a thorough analysis of low-resource XEL techniques, focusing on the key step of identifying candidate English Wikipedia titles that correspond to a given foreign language mention. Our analysis indicates that current methods are limited by their reliance on Wikipedia{'}s interlanguage links and thus suffer when the foreign language{'}s Wikipedia is small. We conclude that the LRL setting requires the use of outside-Wikipedia cross-lingual resources and present a simple yet effective zero-shot XEL system, QuEL, that utilizes search engines query logs. With experiments on 25 languages, QuEL shows an average increase of 25{\%} in gold candidate recall and of 13{\%} in end-to-end linking accuracy over state-of-the-art baselines.",
}
@inproceedings{li-etal-2020-efficient-one,
    title = "Efficient One-Pass End-to-End Entity Linking for Questions",
    author = "Li, Belinda Z.  and
      Min, Sewon  and
      Iyer, Srinivasan  and
      Mehdad, Yashar  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.522",
    doi = "10.18653/v1/2020.emnlp-main.522",
    pages = "6433--6441",
    abstract = "We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question, ELQ outperforms the previous state of the art by a large margin of +12.7{\%} and +19.6{\%} F1, respectively. With a very fast inference time (1.57 examples/s on a single CPU), ELQ can be useful for downstream question answering systems. In a proof-of-concept experiment, we demonstrate that using ELQ significantly improves the downstream QA performance of GraphRetriever.",
}
@inproceedings{yamada-etal-2020-luke,
    title = "{LUKE}: Deep Contextualized Entity Representations with Entity-aware Self-attention",
    author = "Yamada, Ikuya  and
      Asai, Akari  and
      Shindo, Hiroyuki  and
      Takeda, Hideaki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.523",
    doi = "10.18653/v1/2020.emnlp-main.523",
    pages = "6442--6454",
    abstract = "Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question answering). Our source code and pretrained representations are available at https://github.com/studio-ousia/luke.",
}
@inproceedings{chakrabarty-etal-2020-generating,
    title = "Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation",
    author = "Chakrabarty, Tuhin  and
      Muresan, Smaranda  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.524",
    doi = "10.18653/v1/2020.emnlp-main.524",
    pages = "6455--6469",
    abstract = "Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language such as a simile go beyond plain expressions to give readers new insights and inspirations. In this paper, we tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then propose to fine-tune a pre-trained sequence to sequence model, BART (Lewis et al 2019), on the literal-simile pairs to gain generalizability, so that we can generate novel similes given a literal sentence. Experiments show that our approach generates 88{\%} novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37{\%} of the time when compared pairwise. We also show how replacing literal sentences with similes from our best model in machine-generated stories improves evocativeness and leads to better acceptance by human judges.",
}
@inproceedings{akoury-etal-2020-storium,
    title = "{STORIUM}: {A} {D}ataset and {E}valuation {P}latform for {M}achine-in-the-{L}oop {S}tory {G}eneration",
    author = "Akoury, Nader  and
      Wang, Shufan  and
      Whiting, Josh  and
      Hood, Stephen  and
      Peng, Nanyun  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.525",
    doi = "10.18653/v1/2020.emnlp-main.525",
    pages = "6470--6484",
    abstract = "Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.",
}
@inproceedings{hegel-etal-2020-substance,
    title = "Substance over {S}tyle: {D}ocument-{L}evel {T}argeted {C}ontent {T}ransfer",
    author = "Hegel, Allison  and
      Rao, Sudha  and
      Celikyilmaz, Asli  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.526",
    doi = "10.18653/v1/2020.emnlp-main.526",
    pages = "6485--6504",
    abstract = "Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an existing document to fit a set of constraints. Although sentence-level rewriting has been fairly well-studied, little work has addressed the challenge of rewriting an entire document coherently. In this work, we introduce the task of document-level targeted content transfer and address it in the recipe domain, with a recipe as the document and a dietary restriction (such as vegan or dairy-free) as the targeted constraint. We propose a novel model for this task based on the generative pre-trained language model (GPT-2) and train on a large number of roughly-aligned recipe pairs. Both automatic and human evaluations show that our model out-performs existing methods by generating coherent and diverse rewrites that obey the constraint while remaining close to the original document. Finally, we analyze our model{'}s rewrites to assess progress toward the goal of making language generation more attuned to constraints that are substantive rather than stylistic.",
}
@inproceedings{kale-rastogi-2020-template,
    title = "Template Guided Text Generation for Task-Oriented Dialogue",
    author = "Kale, Mihir  and
      Rastogi, Abhinav",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.527",
    doi = "10.18653/v1/2020.emnlp-main.527",
    pages = "6505--6520",
    abstract = "Virtual assistants such as Google Assistant, Amazon Alexa, and Apple Siri enable users to interact with a large number of services and APIs on the web using natural language. In this work, we investigate two methods for Natural Language Generation (NLG) using a single domain-independent model across a large number of APIs. First, we propose a schema-guided approach which conditions the generation on a schema describing the API in natural language. Our second method investigates the use of a small number of templates, growing linearly in number of slots, to convey the semantics of the API. To generate utterances for an arbitrary slot combination, a few simple templates are first concatenated to give a semantically correct, but possibly incoherent and ungrammatical utterance. A pre-trained language model is subsequently employed to rewrite it into coherent, natural sounding text. Through automatic metrics and human evaluation, we show that our method improves over strong baselines, is robust to out-of-domain inputs and shows improved sample efficiency.",
}
@inproceedings{chen-etal-2020-mocha,
    title = "{MOCHA}: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics",
    author = "Chen, Anthony  and
      Stanovsky, Gabriel  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.528",
    doi = "10.18653/v1/2020.emnlp-main.528",
    pages = "6521--6532",
    abstract = "Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80{\%} accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics.",
}
@inproceedings{kang-hovy-2020-plan,
    title = "Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task",
    author = "Kang, Dongyeop  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.529",
    doi = "10.18653/v1/2020.emnlp-main.529",
    pages = "6533--6543",
    abstract = "Despite the recent success of contextualized language models on various NLP tasks, language model itself cannot capture textual coherence of a long, multi-sentence document (e.g., a paragraph). Humans often make structural decisions on what and how to say about before making utterances. Guiding surface realization with such high-level decisions and structuring text in a coherent way is essentially called a planning process. Where can the model learn such high-level coherence? A paragraph itself contains various forms of inductive coherence signals called self-supervision in this work, such as sentence orders, topical keywords, rhetorical structures, and so on. Motivated by that, this work proposes a new paragraph completion task PARCOM; predicting masked sentences in a paragraph. However, the task suffers from predicting and selecting appropriate topical content with respect to the given context. To address that, we propose a self-supervised text planner SSPlanner that predicts what to say first (content prediction), then guides the pretrained language model (surface realization) using the predicted content. SSPlanner outperforms the baseline generation models on the paragraph completion task in both automatic and human evaluation. We also find that a combination of noun and verb types of keywords is the most effective for content selection. As more number of content keywords are provided, overall generation quality also increases.",
}
@inproceedings{ko-etal-2020-inquisitive,
    title = "Inquisitive Question Generation for High Level Text Comprehension",
    author = "Ko, Wei-Jen  and
      Chen, Te-yuan  and
      Huang, Yiyan  and
      Durrett, Greg  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.530",
    doi = "10.18653/v1/2020.emnlp-main.530",
    pages = "6544--6555",
    abstract = "Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems. One natural type of question to ask tries to fill a gap in knowledge during text comprehension, like reading a news article: we might ask about background information, deeper reasons behind things occurring, or more. Despite recent progress with data-driven approaches, generating such questions is beyond the range of models trained on existing datasets. We introduce INQUISITIVE, a dataset of {\textasciitilde}19K questions that are elicited while a person is reading through a document. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. We show that readers engage in a series of pragmatic strategies to seek information. Finally, we evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions although the task is challenging, and highlight the importance of context to generate INQUISITIVE questions.",
}
@inproceedings{zhong-etal-2020-towards,
    title = "Towards Persona-Based Empathetic Conversational Models",
    author = "Zhong, Peixiang  and
      Zhang, Chen  and
      Wang, Hao  and
      Liu, Yong  and
      Miao, Chunyan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.531",
    doi = "10.18653/v1/2020.emnlp-main.531",
    pages = "6556--6566",
    abstract = "Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in empathetic conversations. To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for persona-based empathetic conversations. We then propose CoBERT, an efficient BERT-based response selection model that obtains the state-of-the-art performance on our dataset. Finally, we conduct extensive experiments to investigate the impact of persona on empathetic responding. Notably, our results show that persona improves empathetic responding more when CoBERT is trained on empathetic conversations than non-empathetic ones, establishing an empirical link between persona and empathy in human conversations.",
}
@inproceedings{xu-etal-2020-personal,
    title = "Personal Information Leakage Detection in Conversations",
    author = "Xu, Qiongkai  and
      Qu, Lizhen  and
      Gao, Zeyu  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.532",
    doi = "10.18653/v1/2020.emnlp-main.532",
    pages = "6567--6580",
    abstract = "The global market size of conversational assistants (chatbots) is expected to grow to USD 9.4 billion by 2024, according to MarketsandMarkets. Despite the wide use of chatbots, leakage of personal information through chatbots poses serious privacy concerns for their users. In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants. The detection task is formulated as an alignment optimization problem and a new dataset PERSONA-LEAKAGE is collected for evaluation. In this paper, we propose two novel constrained alignment models, which consistently outperform baseline methods on Moreover, we conduct analysis on the behavior of recently proposed personalized chit-chat dialogue systems. The empirical results show that those systems suffer more from personal information disclosure than the widely used Seq2Seq model and the language model. In those cases, a significant number of information leaking utterances can be detected by our models with high precision.",
}
@inproceedings{wang-etal-2020-response,
    title = "Response Selection for Multi-Party Conversations with Dynamic Topic Tracking",
    author = "Wang, Weishi  and
      Hoi, Steven C.H.  and
      Joty, Shafiq",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.533",
    doi = "10.18653/v1/2020.emnlp-main.533",
    pages = "6581--6591",
    abstract = "While participants in a multi-party multi-turn conversation simultaneously engage in multiple conversation topics, existing response selection methods are developed mainly focusing on a two-party single-conversation scenario. Hence, the prolongation and transition of conversation topics are ignored by current methods. In this work, we frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. With this new formulation, we propose a novel multi-task learning framework that supports efficient encoding through large pretrained models with only two utterances at once to perform dynamic topic disentanglement and response selection. We also propose Topic-BERT an essential pretraining step to embed topic information into BERT with self-supervised learning. Experimental results on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response selection and topic disentanglement tasks outperforming existing methods by a good margin.",
}
@inproceedings{feng-etal-2020-regularizing,
    title = "Regularizing Dialogue Generation by Imitating Implicit Scenarios",
    author = "Feng, Shaoxiong  and
      Ren, Xuancheng  and
      Chen, Hongshen  and
      Sun, Bin  and
      Li, Kan  and
      Sun, Xu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.534",
    doi = "10.18653/v1/2020.emnlp-main.534",
    pages = "6592--6604",
    abstract = "Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario. To enable responses that are more meaningful and context-specific, we propose to improve generative dialogue systems from the scenario perspective, where both dialogue history and future conversation are taken into account to implicitly reconstruct the scenario knowledge. More importantly, the conversation scenarios are further internalized using imitation learning framework, where the conventional dialogue model that has no access to future conversations is effectively regularized by transferring the scenario knowledge contained in hierarchical supervising signals from the scenario-based dialogue model, so that the future conversation is not required in actual inference. Extensive evaluations show that our approach significantly outperforms state-of-the-art baselines on diversity and relevance, and expresses scenario-specific knowledge.",
}
@inproceedings{su-etal-2020-moviechats,
    title = "{M}ovie{C}hats: Chat like Humans in a Closed Domain",
    author = "Su, Hui  and
      Shen, Xiaoyu  and
      Xiao, Zhou  and
      Zhang, Zheng  and
      Chang, Ernie  and
      Zhang, Cheng  and
      Niu, Cheng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.535",
    doi = "10.18653/v1/2020.emnlp-main.535",
    pages = "6605--6619",
    abstract = "Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots. We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules. On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones. We further analyze the limits of our work and point out potential directions for future work",
}
@inproceedings{lu-ng-2020-conundrums,
    title = "Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art",
    author = "Lu, Jing  and
      Ng, Vincent",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.536",
    doi = "10.18653/v1/2020.emnlp-main.536",
    pages = "6620--6631",
    abstract = "Despite the significant progress on entity coreference resolution observed in recent years, there is a general lack of understanding of what has been improved. We present an empirical analysis of state-of-the-art resolvers with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.",
}
@inproceedings{xu-etal-2020-semantic,
    title = "{S}emantic {R}ole {L}abeling {G}uided {M}ulti-turn {D}ialogue {R}e{W}riter",
    author = "Xu, Kun  and
      Tan, Haochen  and
      Song, Linfeng  and
      Wu, Han  and
      Zhang, Haisong  and
      Song, Linqi  and
      Yu, Dong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.537",
    doi = "10.18653/v1/2020.emnlp-main.537",
    pages = "6632--6639",
    abstract = "For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting ride of the noises is essential to improve its performance. Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.",
}
@inproceedings{wang-etal-2020-continuity,
    title = "Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations",
    author = "Wang, Lingzhi  and
      Li, Jing  and
      Zeng, Xingshan  and
      Zhang, Haisong  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.538",
    doi = "10.18653/v1/2020.emnlp-main.538",
    pages = "6640--6650",
    abstract = "Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for both humans and machines. This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context. Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn{'}s existing contents. Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation. Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models. Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations.",
}
@inproceedings{song-etal-2020-profile,
    title = "Profile Consistency Identification for Open-domain Dialogue Agents",
    author = "Song, Haoyu  and
      Wang, Yan  and
      Zhang, Wei-Nan  and
      Zhao, Zhengyu  and
      Liu, Ting  and
      Liu, Xiaojiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.539",
    doi = "10.18653/v1/2020.emnlp-main.539",
    pages = "6651--6662",
    abstract = "Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans. Existing studies on improving attribute consistency mainly explored how to incorporate attribute information in the responses, but few efforts have been made to identify the consistency relations between response and attribute profile. To facilitate the study of profile consistency identification, we create a large-scale human-annotated dataset with over 110K single-turn conversations and their key-value attribute profiles. Explicit relation between response and profile is manually labeled. We also propose a key-value structure information enriched BERT model to identify the profile consistency, and it gained improvements over strong baselines. Further evaluations on downstream tasks demonstrate that the profile consistency identification model is conducive for improving dialogue consistency.",
}
@inproceedings{zhong-etal-2020-element,
    title = "An Element-aware Multi-representation Model for Law Article Prediction",
    author = "Zhong, Huilin  and
      Zhou, Junsheng  and
      Qu, Weiguang  and
      Long, Yunfei  and
      Gu, Yanhui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.540",
    doi = "10.18653/v1/2020.emnlp-main.540",
    pages = "6663--6668",
    abstract = "Existing works have proved that using law articles as external knowledge can improve the performance of the Legal Judgment Prediction. However, they do not fully use law article information and most of the current work is only for single label samples. In this paper, we propose a Law Article Element-aware Multi-representation Model (LEMM), which can make full use of law article information and can be used for multi-label samples. The model uses the labeled elements of law articles to extract fact description features from multiple angles. It generates multiple representations of a fact for classification. Every label has a law-aware fact representation to encode more information. To capture the dependencies between law articles, the model also introduces a self-attention mechanism between multiple representations. Compared with baseline models like TopJudge, this model improves the accuracy of 5.84{\%}, the macro F1 of 6.42{\%}, and the micro F1 of 4.28{\%}.",
}
@inproceedings{jin-etal-2020-recurrent,
    title = "Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs",
    author = "Jin, Woojeong  and
      Qu, Meng  and
      Jin, Xisen  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.541",
    doi = "10.18653/v1/2020.emnlp-main.541",
    pages = "6669--6683",
    abstract = "Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp. Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future. This paper proposes Recurrent Event Network (RE-Net), a novel autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. Specifically, our RE-Net employs a recurrent event encoder to encode past facts, and uses a neighborhood aggregator to model the connection of facts at the same timestamp. Future facts can then be inferred in a sequential manner based on the two modules. We evaluate our proposed method via link prediction at future times on five public datasets. Through extensive experiments, we demonstrate the strength of RE-Net, especially on multi-step inference over future timestamps, and achieve state-of-the-art performance on all five datasets.",
}
@inproceedings{ma-etal-2020-multi,
    title = "Multi-resolution Annotations for Emoji Prediction",
    author = "Ma, Weicheng  and
      Liu, Ruibo  and
      Wang, Lili  and
      Vosoughi, Soroush",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.542",
    doi = "10.18653/v1/2020.emnlp-main.542",
    pages = "6684--6694",
    abstract = "Emojis are able to express various linguistic components, including emotions, sentiments, events, etc. Predicting the proper emojis associated with text provides a way to summarize the text accurately, and it has been proven to be a good auxiliary task to many Natural Language Understanding (NLU) tasks. Labels in existing emoji prediction datasets are all passage-based and are usually under the multi-class classification setting. However, in many cases, one single emoji cannot fully cover the theme of a piece of text. It is thus useful to infer the part of text related to each emoji. The lack of multi-label and aspect-level emoji prediction datasets is one of the bottlenecks for this task. This paper annotates an emoji prediction dataset with passage-level multi-class/multi-label, and aspect-level multi-class annotations. We also present a novel annotation method with which we generate the aspect-level annotations. The annotations are generated heuristically, taking advantage of the self-attention mechanism in Transformer networks. We validate the annotations both automatically and manually to ensure their quality. We also benchmark the dataset with a pre-trained BERT model.",
}
@inproceedings{choi-etal-2020-less,
    title = "Less is More: Attention Supervision with Counterfactuals for Text Classification",
    author = "Choi, Seungtaek  and
      Park, Haeju  and
      Yeo, Jinyoung  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.543",
    doi = "10.18653/v1/2020.emnlp-main.543",
    pages = "6695--6704",
    abstract = "We aim to leverage human and machine intelligence together for attention supervision. Specifically, we show that human annotation cost can be kept reasonably low, while its quality can be enhanced by machine self-supervision. Specifically, for this goal, we explore the advantage of counterfactual reasoning, over associative reasoning typically used in attention supervision. Our empirical results show that this machine-augmented human attention supervision is more effective than existing methods requiring a higher annotation cost, in text classification tasks, including sentiment analysis and news categorization.",
}
@inproceedings{ma-etal-2020-mode,
    title = "{MODE}-{LSTM}: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification",
    author = "Ma, Qianli  and
      Lin, Zhenxi  and
      Yan, Jiangyue  and
      Chen, Zipeng  and
      Yu, Liuhong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.544",
    doi = "10.18653/v1/2020.emnlp-main.544",
    pages = "6705--6715",
    abstract = "The central problem of sentence classification is to extract multi-scale n-gram features for understanding the semantic meaning of sentences. Most existing models tackle this problem by stacking CNN and RNN models, which easily leads to feature redundancy and overfitting because of relatively limited datasets. In this paper, we propose a simple yet effective model called Multi-scale Orthogonal inDependEnt LSTM (MODE-LSTM), which not only has effective parameters and good generalization ability, but also considers multiscale n-gram features. We disentangle the hidden state of the LSTM into several independently updated small hidden states and apply an orthogonal constraint on their recurrent matrices. We then equip this structure with sliding windows of different sizes for extracting multi-scale n-gram features. Extensive experiments demonstrate that our model achieves better or competitive performance against state-of-the-art baselines on eight benchmark datasets. We also combine our model with BERT to further boost the generalization performance.",
}
@inproceedings{yang-etal-2020-hscnn,
    title = "{HSCNN}: A Hybrid-{S}iamese Convolutional Neural Network for Extremely Imbalanced Multi-label Text Classification",
    author = "Yang, Wenshuo  and
      Li, Jiyi  and
      Fukumoto, Fumiyo  and
      Ye, Yanming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.545",
    doi = "10.18653/v1/2020.emnlp-main.545",
    pages = "6716--6722",
    abstract = "The data imbalance problem is a crucial issue for the multi-label text classification. Some existing works tackle it by proposing imbalanced loss objectives instead of the vanilla cross-entropy loss, but their performances remain limited in the cases of extremely imbalanced data. We propose a hybrid solution which adapts general networks for the head categories, and few-shot techniques for the tail categories. We propose a Hybrid-Siamese Convolutional Neural Network (HSCNN) with additional technical attributes, i.e., a multi-task architecture based on Single and Siamese networks; a category-specific similarity in the Siamese structure; a specific sampling method for training HSCNN. The results using two benchmark datasets and three loss objectives show that our method can improve the performance of Single networks with diverse loss objectives on the tail or entire categories.",
}
@inproceedings{song-etal-2020-multi,
    title = "Multi-Stage Pre-training for Automated {C}hinese Essay Scoring",
    author = "Song, Wei  and
      Zhang, Kai  and
      Fu, Ruiji  and
      Liu, Lizhen  and
      Liu, Ting  and
      Cheng, Miaomiao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.546",
    doi = "10.18653/v1/2020.emnlp-main.546",
    pages = "6723--6733",
    abstract = "This paper proposes a pre-training based automated Chinese essay scoring method. The method involves three components: weakly supervised pre-training, supervised cross- prompt fine-tuning and supervised target- prompt fine-tuning. An essay scorer is first pre- trained on a large essay dataset covering diverse topics and with coarse ratings, i.e., good and poor, which are used as a kind of weak supervision. The pre-trained essay scorer would be further fine-tuned on previously rated es- says from existing prompts, which have the same score range with the target prompt and provide extra supervision. At last, the scorer is fine-tuned on the target-prompt training data. The evaluation on four prompts shows that this method can improve a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations..",
}
@inproceedings{deng-etal-2020-multi,
    title = "Multi-hop Inference for Question-driven Summarization",
    author = "Deng, Yang  and
      Zhang, Wenxuan  and
      Lam, Wai",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.547",
    doi = "10.18653/v1/2020.emnlp-main.547",
    pages = "6734--6744",
    abstract = "Question-driven summarization has been recently studied as an effective approach to summarizing the source document to produce concise but informative answers for non-factoid questions. In this work, we propose a novel question-driven abstractive summarization method, Multi-hop Selective Generator (MSG), to incorporate multi-hop reasoning into question-driven summarization and, meanwhile, provide justifications for the generated summaries. Specifically, we jointly model the relevance to the question and the interrelation among different sentences via a human-like multi-hop inference module, which captures important sentences for justifying the summarized answer. A gated selective pointer generator network with a multi-view coverage mechanism is designed to integrate diverse information from different perspectives. Experimental results show that the proposed method consistently outperforms state-of-the-art methods on two non-factoid QA datasets, namely WikiHow and PubMedQA.",
}
@inproceedings{ren-etal-2020-towards,
    title = "Towards Interpretable Reasoning over Paragraph Effects in Situation",
    author = "Ren, Mucheng  and
      Geng, Xiubo  and
      Qin, Tao  and
      Huang, Heyan  and
      Jiang, Daxin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.548",
    doi = "10.18653/v1/2020.emnlp-main.548",
    pages = "6745--6758",
    abstract = "We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the knowledge to a novel situation. Existing works ignore the complicated reasoning process and solve it with a one-step {``}black box{''} model. Inspired by human cognitive processes, in this paper we propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules. In particular, five reasoning modules are designed and learned in an end-to-end manner, which leads to a more interpretable model. Experimental results on the ROPES dataset demonstrate the effectiveness and explainability of our proposed approach.",
}
@inproceedings{chen-etal-2020-question,
    title = "Question Directed Graph Attention Network for Numerical Reasoning over Text",
    author = "Chen, Kunlong  and
      Xu, Weidi  and
      Cheng, Xingyi  and
      Xiaochuan, Zou  and
      Zhang, Yuyu  and
      Song, Le  and
      Wang, Taifeng  and
      Qi, Yuan  and
      Chu, Wei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.549",
    doi = "10.18653/v1/2020.emnlp-main.549",
    pages = "6759--6768",
    abstract = "Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP.",
}
@inproceedings{karpukhin-etal-2020-dense,
    title = "Dense Passage Retrieval for Open-Domain Question Answering",
    author = "Karpukhin, Vladimir  and
      Oguz, Barlas  and
      Min, Sewon  and
      Lewis, Patrick  and
      Wu, Ledell  and
      Edunov, Sergey  and
      Chen, Danqi  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
    doi = "10.18653/v1/2020.emnlp-main.550",
    pages = "6769--6781",
    abstract = "Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system greatly by 9{\%}-19{\%} absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.",
}
@inproceedings{dong-etal-2020-distilling,
    title = "Distilling Structured Knowledge for Text-Based Relational Reasoning",
    author = "Dong, Jin  and
      Rondeau, Marc-Antoine  and
      Hamilton, William L.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.551",
    doi = "10.18653/v1/2020.emnlp-main.551",
    pages = "6782--6791",
    abstract = "There is an increasing interest in developing text-based relational reasoning systems, which are capable of systematically reasoning about the relationships between entities mentioned in a text. However, there remains a substantial performance gap between NLP models for relational reasoning and models based on graph neural networks (GNNs), which have access to an underlying symbolic representation of the text. In this work, we investigate how the structured knowledge of a GNN can be distilled into various NLP models in order to improve their performance. We first pre-train a GNN on a reasoning task using structured inputs and then incorporate its knowledge into an NLP model (e.g., an LSTM) via knowledge distillation. To overcome the difficulty of cross-modal knowledge transfer, we also employ a contrastive learning based module to align the latent representations of NLP models and the GNN. We test our approach with two state-of-the-art NLP models on 13 different inductive reasoning datasets from the CLUTRR benchmark and obtain significant improvements.",
}
@inproceedings{michael-etal-2020-asking,
    title = "Asking without Telling: Exploring Latent Ontologies in Contextual Representations",
    author = "Michael, Julian  and
      Botha, Jan A.  and
      Tenney, Ian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.552",
    doi = "10.18653/v1/2020.emnlp-main.552",
    pages = "6792--6812",
    abstract = "The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguistic structure? If so, how is this structure encoded? To investigate this, we introduce latent subclass learning (LSL): a modification to classifier-based probing that induces a latent categorization (or ontology) of the probe{'}s inputs. Without access to fine-grained gold labels, LSL extracts emergent structure from input representations in an interpretable and quantifiable form. In experiments, we find strong evidence of familiar categories, such as a notion of personhood in ELMo, as well as novel ontological distinctions, such as a preference for fine-grained semantic roles on core arguments. Our results provide unique new evidence of emergent structure in pretrained encoders, including departures from existing annotations which are inaccessible to earlier methods.",
}
@inproceedings{chiang-etal-2020-pretrained,
    title = "{P}retrained Language Model Embryology: {T}he Birth of {ALBERT}",
    author = "Chiang, Cheng-Han  and
      Huang, Sung-Feng  and
      Lee, Hung-yi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.553",
    doi = "10.18653/v1/2020.emnlp-main.553",
    pages = "6813--6828",
    abstract = "While behaviors of pretrained language models (LMs) have been thoroughly examined, what happened during pretraining is rarely studied. We thus investigate the developmental process from a set of randomly initialized parameters to a totipotent language model, which we refer to as the \textit{embryology} of a pretrained language model. Our results show that ALBERT learns to reconstruct and predict tokens of different parts of speech (POS) in different learning speeds during pretraining. We also find that linguistic knowledge and world knowledge do not generally improve as pretraining proceeds, nor do downstream tasks{'} performance. These findings suggest that knowledge of a pretrained model varies during pretraining, and having more pretrain steps does not necessarily provide a model with more comprehensive knowledge. We provide source codes and pretrained models to reproduce our results at \url{https://github.com/d223302/albert-embryology}.",
}
@inproceedings{papadimitriou-jurafsky-2020-learning,
    title = "{L}earning {M}usic {H}elps {Y}ou {R}ead: {U}sing Transfer to Study Linguistic Structure in Language Models",
    author = "Papadimitriou, Isabel  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.554",
    doi = "10.18653/v1/2020.emnlp-main.554",
    pages = "6829--6839",
    abstract = "We propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models. We train LSTMs on non-linguistic data and evaluate their performance on natural language to assess which kinds of data induce generalizable structural features that LSTMs can use for natural language. We find that training on non-linguistic data with latent structure (MIDI music or Java code) improves test performance on natural language, despite no overlap in surface form or vocabulary. To pinpoint the kinds of abstract structure that models may be encoding to lead to this improvement, we run similar experiments with two artificial parentheses languages: one which has a hierarchical recursive structure, and a control which has paired tokens but no recursion. Surprisingly, training a model on either of these artificial languages leads the same substantial gains when testing on natural language. Further experiments on transfer between natural languages controlling for vocabulary overlap show that zero-shot performance on a test language is highly correlated with typological syntactic similarity to the training language, suggesting that representations induced by pre-training correspond to the cross-linguistic syntactic properties. Our results provide insights into the ways that neural models represent abstract syntactic structure, and also about the kind of structural inductive biases which allow for natural language acquisition.",
}
@inproceedings{wang-chen-2020-position,
    title = "What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding",
    author = "Wang, Yu-An  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.555",
    doi = "10.18653/v1/2020.emnlp-main.555",
    pages = "6840--6849",
    abstract = "In recent years, pre-trained Transformers have dominated the majority of NLP benchmark tasks. Many variants of pre-trained Transformers have kept breaking out, and most focus on designing different pre-training objectives or variants of self-attention. Embedding the position information in the self-attention mechanism is also an indispensable factor in Transformers however is often discussed at will. Hence, we carry out an empirical study on position embedding of mainstream pre-trained Transformers mainly focusing on two questions: 1) Do position embeddings really learn the meaning of positions? 2) How do these different learned position embeddings affect Transformers for NLP tasks? This paper focuses on providing a new insight of pre-trained position embeddings by feature-level analysis and empirical experiments on most of iconic NLP tasks. It is believed that our experimental results can guide the future works to choose the suitable positional encoding function for specific tasks given the application property.",
}
@inproceedings{shwartz-etal-2020-grounded,
    title = "{``}You are grounded!{''}: Latent Name Artifacts in Pre-trained Language Models",
    author = "Shwartz, Vered  and
      Rudinger, Rachel  and
      Tafjord, Oyvind",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.556",
    doi = "10.18653/v1/2020.emnlp-main.556",
    pages = "6850--6861",
    abstract = "Pre-trained language models (LMs) may perpetuate biases originating in their training corpus to downstream models. We focus on artifacts associated with the representation of given names (e.g., Donald), which, depending on the corpus, may be associated with specific entities, as indicated by next token prediction (e.g., Trump). While helpful in some contexts, grounding happens also in under-specified or inappropriate contexts. For example, endings generated for {`}Donald is a{'} substantially differ from those of other names, and often have more-than-average negative sentiment. We demonstrate the potential effect on downstream tasks with reading comprehension probes where name perturbation changes the model answers. As a silver lining, our experiments suggest that additional pre-training on different corpora may mitigate this bias.",
}
@inproceedings{lin-etal-2020-birds,
    title = "{B}irds have four legs?! {N}umer{S}ense: {P}robing {N}umerical {C}ommonsense {K}nowledge of {P}re-{T}rained {L}anguage {M}odels",
    author = "Lin, Bill Yuchen  and
      Lee, Seyeon  and
      Khanna, Rahul  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.557",
    doi = "10.18653/v1/2020.emnlp-main.557",
    pages = "6862--6868",
    abstract = "Recent works show that pre-trained language models (PTLMs), such as BERT, possess certain commonsense and factual knowledge. They suggest that it is promising to use PTLMs as {``}neural knowledge bases{''} via predicting masked words. Surprisingly, we find that this may not work for numerical commonsense knowledge (e.g., a bird usually has two legs). In this paper, we investigate whether and to what extent we can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process. In this paper, we investigate whether and to what extent we can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process. To study this, we introduce a novel probing task with a diagnostic dataset, NumerSense, containing 13.6k masked-word-prediction probes (10.5k for fine-tuning and 3.1k for testing). Our analysis reveals that: (1) BERT and its stronger variant RoBERTa perform poorly on the diagnostic dataset prior to any fine-tuning; (2) fine-tuning with distant supervision brings some improvement; (3) the best supervised model still performs poorly as compared to human performance (54.06{\%} vs. 96.3{\%} in accuracy).",
}
@inproceedings{zhong-etal-2020-grounded,
    title = "Grounded Adaptation for Zero-shot Executable Semantic Parsing",
    author = "Zhong, Victor  and
      Lewis, Mike  and
      Wang, Sida I.  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.558",
    doi = "10.18653/v1/2020.emnlp-main.558",
    pages = "6869--6882",
    abstract = "We propose Grounded Adaptation for Zeroshot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines a forward semantic parser with a backward utterance generator to synthesize data (e.g. utterances and SQL queries) in the new environment, then selects cycle-consistent examples to adapt the parser. Unlike data-augmentation, which typically synthesizes unverified examples in the training environment, GAZP synthesizes examples in the new environment whose input-output consistency are verified through execution. On the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks, GAZP improves logical form and execution accuracy of the baseline parser. Our analyses show that GAZP outperforms data-augmentation in the training environment, performance increases with the amount of GAZP-synthesized data, and cycle-consistency is central to successful adaptation.",
}
@inproceedings{yao-etal-2020-imitation,
    title = "An Imitation Game for Learning Semantic Parsers from User Interaction",
    author = "Yao, Ziyu  and
      Tang, Yiqi  and
      Yih, Wen-tau  and
      Sun, Huan  and
      Su, Yu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.559",
    doi = "10.18653/v1/2020.emnlp-main.559",
    pages = "6883--6902",
    abstract = "Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstrations when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstrations, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and retrains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem. Code will be available at https://github.com/sunlab-osu/MISP.",
}
@inproceedings{cai-wan-2020-igsql,
    title = "{IGSQL}: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-{SQL} Generation",
    author = "Cai, Yitao  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.560",
    doi = "10.18653/v1/2020.emnlp-main.560",
    pages = "6903--6912",
    abstract = "Context-dependent text-to-SQL task has drawn much attention in recent years. Previous models on context-dependent text-to-SQL task only concentrate on utilizing historic user inputs. In this work, in addition to using encoders to capture historic information of user inputs, we propose a database schema interaction graph encoder to utilize historic information of database schema items. In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets. Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets. The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.",
}
@inproceedings{li-etal-2020-mean,
    title = "{``}What Do You Mean by That?{''} A Parser-Independent Interactive Approach for Enhancing Text-to-{SQL}",
    author = "Li, Yuntao  and
      Chen, Bei  and
      Liu, Qian  and
      Gao, Yan  and
      Lou, Jian-Guang  and
      Zhang, Yan  and
      Zhang, Dongmei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.561",
    doi = "10.18653/v1/2020.emnlp-main.561",
    pages = "6913--6922",
    abstract = "In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users{'} natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.",
}
@inproceedings{wang-etal-2020-dusql,
    title = "{D}u{SQL}: A Large-Scale and Pragmatic {C}hinese Text-to-{SQL} Dataset",
    author = "Wang, Lijie  and
      Zhang, Ao  and
      Wu, Kun  and
      Sun, Ke  and
      Li, Zhenghua  and
      Wu, Hua  and
      Zhang, Min  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.562",
    doi = "10.18653/v1/2020.emnlp-main.562",
    pages = "6923--6935",
    abstract = "Due to the lack of labeled data, previous research on text-to-SQL parsing mainly focuses on English. Representative English datasets include ATIS, WikiSQL, Spider, etc. This paper presents DuSQL, a larges-scale and pragmatic Chinese dataset for the cross-domain text-to-SQL task, containing 200 databases, 813 tables, and 23,797 question/SQL pairs. Our new dataset has three major characteristics. First, by manually analyzing questions from several representative applications, we try to figure out the true distribution of SQL queries in real-life needs. Second, DuSQL contains a considerable proportion of SQL queries involving row or column calculations, motivated by our analysis on the SQL query distributions. Finally, we adopt an effective data construction framework via human-computer collaboration. The basic idea is automatically generating SQL queries based on the SQL grammar and constrained by the given database. This paper describes in detail the construction process and data statistics of DuSQL. Moreover, we present and compare performance of several open-source text-to-SQL parsers with minor modification to accommodate Chinese, including a simple yet effective extension to IRNet for handling calculation SQL queries.",
}
@inproceedings{ma-etal-2020-mention,
    title = "Mention Extraction and Linking for {SQL} Query Generation",
    author = "Ma, Jianqiang  and
      Yan, Zeyu  and
      Pang, Shuai  and
      Zhang, Yang  and
      Shen, Jianping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.563",
    doi = "10.18653/v1/2020.emnlp-main.563",
    pages = "6936--6942",
    abstract = "On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot- filling approach by building several dedicated models for each type of slots. Such modularized systems are not only complex but also of limited capacity for capturing inter-dependencies among SQL clauses. To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. Trained with automatically generated annotations, the proposed method achieves the first place on the WikiSQL benchmark.",
}
@inproceedings{lei-etal-2020-examining,
    title = "Re-examining the Role of Schema Linking in Text-to-{SQL}",
    author = "Lei, Wenqiang  and
      Wang, Weixin  and
      Ma, Zhixin  and
      Gan, Tian  and
      Lu, Wei  and
      Kan, Min-Yen  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.564",
    doi = "10.18653/v1/2020.emnlp-main.564",
    pages = "6943--6954",
    abstract = "In existing sophisticated text-to-SQL models, schema linking is often considered as a simple, minor component, belying its importance. By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking. We also build a simple BERT-based baseline, called Schema-Linking SQL (SLSQL) to perform a data-driven study. We find when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity. Many remaining errors are attributable to corpus noise. This suggests schema linking is the crux for the current text-to-SQL task. Our analytic studies provide insights on the characteristics of schema linking for future developments of text-to-SQL tasks.",
}
@inproceedings{dai-etal-2020-multi,
    title = "A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis",
    author = "Dai, Zehui  and
      Peng, Cheng  and
      Chen, Huajie  and
      Ding, Yadong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.565",
    doi = "10.18653/v1/2020.emnlp-main.565",
    pages = "6955--6965",
    abstract = "(T)ACSA tasks, including aspect-category sentiment analysis (ACSA) and targeted aspect-category sentiment analysis (TACSA), aims at identifying sentiment polarity on predefined categories. Incremental learning on new categories is necessary for (T)ACSA real applications. Though current multi-task learning models achieve good performance in (T)ACSA tasks, they suffer from catastrophic forgetting problems in (T)ACSA incremental learning tasks. In this paper, to make multi-task learning feasible for incremental learning, we proposed Category Name Embedding network (CNE-net). We set both encoder and decoder shared among all categories to weaken the catastrophic forgetting problem. Besides the origin input sentence, we applied another input feature, i.e., category name, for task discrimination. Our model achieved state-of-the-art on two (T)ACSA benchmark datasets. Furthermore, we proposed a dataset for (T)ACSA incremental learning and achieved the best performance compared with other strong baselines.",
}
@inproceedings{gu-etal-2020-train,
    title = "Train No Evil: Selective Masking for Task-Guided Pre-Training",
    author = "Gu, Yuxian  and
      Zhang, Zhengyan  and
      Wang, Xiaozhi  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.566",
    doi = "10.18653/v1/2020.emnlp-main.566",
    pages = "6966--6974",
    abstract = "Recently, pre-trained language models mostly follow the pre-train-then-fine-tuning paradigm and have achieved great performance on various downstream tasks. However, since the pre-training stage is typically task-agnostic and the fine-tuning stage usually suffers from insufficient supervised data, the models cannot always well capture the domain-specific and task-specific patterns. In this paper, we propose a three-stage framework by adding a task-guided pre-training stage with selective masking between general pre-training and fine-tuning. In this stage, the model is trained by masked language modeling on in-domain unsupervised data to learn domain-specific patterns and we propose a novel selective masking strategy to learn task-specific patterns. Specifically, we design a method to measure the importance of each token in sequences and selectively mask the important tokens. Experimental results on two sentiment analysis tasks show that our method can achieve comparable or even better performance with less than 50{\%} of computation cost, which indicates our method is both effective and efficient. The source code of this paper can be obtained from \url{https://github.com/thunlp/SelectiveMasking}.",
}
@inproceedings{ke-etal-2020-sentilare,
    title = "{S}enti{LARE}: Sentiment-Aware Language Representation Learning with Linguistic Knowledge",
    author = "Ke, Pei  and
      Ji, Haozhe  and
      Liu, Siyang  and
      Zhu, Xiaoyan  and
      Huang, Minlie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.567",
    doi = "10.18653/v1/2020.emnlp-main.567",
    pages = "6975--6988",
    abstract = "Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pre-trained models. We first propose a context-aware sentiment attention mechanism to acquire the sentiment polarity of each word with its part-of-speech tag by querying SentiWordNet. Then, we devise a new pre-training task called label-aware masked language model to construct knowledge-aware language representation. Experiments show that SentiLARE obtains new state-of-the-art performance on a variety of sentiment analysis tasks.",
}
@inproceedings{huang-etal-2020-weakly,
    title = "Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding",
    author = "Huang, Jiaxin  and
      Meng, Yu  and
      Guo, Fang  and
      Ji, Heng  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.568",
    doi = "10.18653/v1/2020.emnlp-main.568",
    pages = "6989--6999",
    abstract = "Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects from each review, and (ii) classifying aspect-based reviews by sentiment polarity. In this paper, we propose a weakly-supervised approach for aspect-based sentiment analysis, which uses only a few keywords describing each aspect/sentiment without using any labeled examples. Existing methods are either designed only for one of the sub-tasks, or are based on topic models that may contain overlapping concepts. We propose to first learn {\textless}sentiment, aspect{\textgreater} joint topic embeddings in the word embedding space by imposing regularizations to encourage topic distinctiveness, and then use neural models to generalize the word-level discriminative information by pre-training the classifiers with embedding-based predictions and self-training them on unlabeled data. Our comprehensive performance analysis shows that our method generates quality joint topics and outperforms the baselines significantly (7.4{\%} and 5.1{\%} F1-score gain on average for aspect and sentiment classification respectively) on benchmark datasets.",
}
@inproceedings{cheng-etal-2020-ape,
    title = "{APE}: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning",
    author = "Cheng, Liying  and
      Bing, Lidong  and
      Yu, Qian  and
      Lu, Wei  and
      Si, Luo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.569",
    doi = "10.18653/v1/2020.emnlp-main.569",
    pages = "7000--7011",
    abstract = "Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments. However, few works study both of them simultaneously. In this paper, we introduce a new argument pair extraction (APE) task on peer review and rebuttal in order to study the contents, the structure and the connections between them. We prepare a challenging dataset that contains 4,764 fully annotated review-rebuttal passage pairs from an open review platform to facilitate the study of this task. To automatically detect argumentative propositions and extract argument pairs from this corpus, we cast it as the combination of a sequence labeling task and a text relation classification task. Thus, we propose a multitask learning framework based on hierarchical LSTM networks. Extensive experiments and analysis demonstrate the effectiveness of our multi-task framework, and also show the challenges of the new task as well as motivate future research directions.",
}
@inproceedings{ji-etal-2020-diversified,
    title = "Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification",
    author = "Ji, Yunjie  and
      Liu, Hao  and
      He, Bolei  and
      Xiao, Xinyan  and
      Wu, Hua  and
      Yu, Yanhua",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.570",
    doi = "10.18653/v1/2020.emnlp-main.570",
    pages = "7012--7023",
    abstract = "Neural Document-level Multi-aspect Sentiment Classification (DMSC) usually requires a lot of manual aspect-level sentiment annotations, which is time-consuming and laborious. As document-level sentiment labeled data are widely available from online service, it is valuable to perform DMSC with such free document-level annotations. To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision. Specifically, we connect aspect-level and document-level sentiment by formulating this problem as multiple instance learning, providing a way to learn aspect-level classifier from the back propagation of document-level supervision. Two diversified regularizations are further introduced in order to avoid the overfitting on document-level signals during training. Diversified textual regularization encourages the classifier to select aspect-relevant snippets, and diversified sentimental regularization prevents the aspect-level sentiments from being overly consistent with document-level sentiment. Experimental results on TripAdvisor and BeerAdvocate datasets show that D-MILN remarkably outperforms recent weakly-supervised baselines, and is also comparable to the supervised method.",
}
@inproceedings{kong-etal-2020-identifying,
    title = "Identifying Exaggerated Language",
    author = "Kong, Li  and
      Li, Chuanyi  and
      Ge, Jidong  and
      Luo, Bin  and
      Ng, Vincent",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.571",
    doi = "10.18653/v1/2020.emnlp-main.571",
    pages = "7024--7034",
    abstract = "While hyperbole is one of the most prevalent rhetorical devices, it is arguably one of the least studied devices in the figurative language processing community. We contribute to the study of hyperbole by (1) creating a corpus focusing on sentence-level hyperbole detection, (2) performing a statistical and manual analysis of our corpus, and (3) addressing the automatic hyperbole detection task.",
}
@inproceedings{gong-etal-2020-unified,
    title = "Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis",
    author = "Gong, Chenggong  and
      Yu, Jianfei  and
      Xia, Rui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.572",
    doi = "10.18653/v1/2020.emnlp-main.572",
    pages = "7035--7045",
    abstract = "The supervised models for aspect-based sentiment analysis (ABSA) rely heavily on labeled data. However, fine-grained labeled data are scarce for the ABSA task. To alleviate the dependence on labeled data, prior works mainly focused on feature-based adaptation, which used the domain-shared knowledge to construct auxiliary tasks or domain adversarial learning to bridge the gap between domains, while ignored the attribute of instance-based adaptation. To resolve this limitation, we propose an end-to-end framework to jointly perform feature and instance based adaptation for the ABSA task in this paper. Based on BERT, we learn domain-invariant feature representations by using part-of-speech features and syntactic dependency relations to construct auxiliary tasks, and jointly perform word-level instance weighting in the framework of sequence labeling. Experiment results on four benchmarks show that the proposed method can achieve significant improvements in comparison with the state-of-the-arts in both tasks of cross-domain End2End ABSA and cross-domain aspect extraction.",
}
@inproceedings{staliunaite-iacobacci-2020-compositional,
    title = "Compositional and Lexical Semantics in {R}o{BERT}a, {BERT} and {D}istil{BERT}: A Case Study on {C}o{QA}",
    author = "Stali{\=u}nait{\.e}, Ieva  and
      Iacobacci, Ignacio",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.573",
    doi = "10.18653/v1/2020.emnlp-main.573",
    pages = "7046--7056",
    abstract = "Many NLP tasks have benefited from transferring knowledge from contextualized word embeddings, however the picture of what type of knowledge is transferred is incomplete. This paper studies the types of linguistic phenomena accounted for by language models in the context of a Conversational Question Answering (CoQA) task. We identify the problematic areas for the finetuned RoBERTa, BERT and DistilBERT models through systematic error analysis - basic arithmetic (counting phrases), compositional semantics (negation and Semantic Role Labeling), and lexical semantics (surprisal and antonymy). When enhanced with the relevant linguistic knowledge through multitask learning, the models improve in performance. Ensembles of the enhanced models yield a boost between 2.2 and 2.7 points in F1 score overall, and up to 42.1 points in F1 on the hardest question classes. The results show differences in ability to represent compositional and lexical information between RoBERTa, BERT and DistilBERT.",
}
@inproceedings{kobayashi-etal-2020-attention,
    title = "Attention is Not Only a Weight: Analyzing Transformers with Vector Norms",
    author = "Kobayashi, Goro  and
      Kuribayashi, Tatsuki  and
      Yokoi, Sho  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.574",
    doi = "10.18653/v1/2020.emnlp-main.574",
    pages = "7057--7075",
    abstract = "Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing. Hence, attention is being extensively studied to investigate various linguistic capabilities of Transformers, focusing on analyzing the parallels between attention weights and specific linguistic phenomena. This paper shows that attention weights alone are only one of the two factors that determine the output of attention and proposes a norm-based analysis that incorporates the second factor, the norm of the transformed input vectors. The findings of our norm-based analyses of BERT and a Transformer-based neural machine translation system include the following: (i) contrary to previous studies, BERT pays poor attention to special tokens, and (ii) reasonable word alignment can be extracted from attention mechanisms of Transformer. These findings provide insights into the inner workings of Transformers.",
}
@inproceedings{schuff-etal-2020-f1,
    title = "{F}1 is {N}ot {E}nough! {M}odels and {E}valuation {T}owards {U}ser-{C}entered {E}xplainable {Q}uestion {A}nswering",
    author = "Schuff, Hendrik  and
      Adel, Heike  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.575",
    doi = "10.18653/v1/2020.emnlp-main.575",
    pages = "7076--7095",
    abstract = "Explainable question answering systems predict an answer together with an explanation showing why the answer has been selected. The goal is to enable users to assess the correctness of the system and understand its reasoning process. However, we show that current models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. As a remedy, we propose a hierarchical model and a new regularization term to strengthen the answer-explanation coupling as well as two evaluation scores to quantify the coupling. We conduct experiments on the HOTPOTQA benchmark data set and perform a user study. The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users. Our scores are better aligned with user experience, making them promising candidates for model selection.",
}
@inproceedings{bhattamishra-etal-2020-ability,
    title = "On the {A}bility and {L}imitations of {T}ransformers to {R}ecognize {F}ormal {L}anguages",
    author = "Bhattamishra, Satwik  and
      Ahuja, Kabir  and
      Goyal, Navin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.576",
    doi = "10.18653/v1/2020.emnlp-main.576",
    pages = "7096--7116",
    abstract = "Transformers have supplanted recurrent models in a large number of NLP tasks. However, the differences in their abilities to model different syntactic properties remain largely unknown. Past works suggest that LSTMs generalize very well on regular languages and have close connections with counter languages. In this work, we systematically study the ability of Transformers to model such languages as well as the role of its individual components in doing so. We first provide a construction of Transformers for a subclass of counter languages, including well-studied languages such as n-ary Boolean Expressions, Dyck-1, and its generalizations. In experiments, we find that Transformers do well on this subclass, and their learned mechanism strongly correlates with our construction. Perhaps surprisingly, in contrast to LSTMs, Transformers do well only on a subset of regular languages with degrading performance as we make languages more complex according to a well-known measure of complexity. Our analysis also provides insights on the role of self-attention mechanism in modeling certain behaviors and the influence of positional encoding schemes on the learning and generalization abilities of the model.",
}
@inproceedings{schmitt-etal-2020-unsupervised,
    title = "An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing",
    author = {Schmitt, Martin  and
      Sharifzadeh, Sahand  and
      Tresp, Volker  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.577",
    doi = "10.18653/v1/2020.emnlp-main.577",
    pages = "7117--7130",
    abstract = "Knowledge graphs (KGs) can vary greatly from one domain to another. Therefore supervised approaches to both graph-to-text generation and text-to-graph knowledge extraction (semantic parsing) will always suffer from a shortage of domain-specific parallel graph-text data; at the same time, adapting a model trained on a different domain is often impossible due to little or no overlap in entities and relations. This situation calls for an approach that (1) does not need large amounts of annotated data and thus (2) does not need to rely on domain adaptation techniques to work well on different domains. To this end, we present the first approach to unsupervised text generation from KGs and show simultaneously how it can be used for unsupervised semantic parsing. We evaluate our approach on WebNLG v2.1 and a new benchmark leveraging scene graphs from Visual Genome. Our system outperforms strong baselines for both text{\textless}-{\textgreater}graph conversion tasks without any manual adaptation from one dataset to the other. In additional experiments, we investigate the impact of using different unsupervised objectives.",
}
@inproceedings{li-etal-2020-dgst,
    title = "{DGST}: a Dual-Generator Network for Text Style Transfer",
    author = "Li, Xiao  and
      Chen, Guanyi  and
      Lin, Chenghua  and
      Li, Ruizhe",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.578",
    doi = "10.18653/v1/2020.emnlp-main.578",
    pages = "7131--7136",
    abstract = "We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.",
}
@inproceedings{wu-etal-2020-knowledge,
    title = "A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving",
    author = "Wu, Qinzhuo  and
      Zhang, Qi  and
      Fu, Jinlan  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.579",
    doi = "10.18653/v1/2020.emnlp-main.579",
    pages = "7137--7146",
    abstract = "With the advancements in natural language processing tasks, math word problem solving has received increasing attention. Previous methods have achieved promising results but ignore background common-sense knowledge not directly provided by the problem. In addition, during generation, they focus on local features while neglecting global information. To incorporate external knowledge and global expression information, we propose a novel knowledge-aware sequence-to-tree (KA-S2T) network in which the entities in the problem sequences and their categories are modeled as an entity graph. Based on this entity graph, a graph attention network is used to capture knowledge-aware problem representations. Further, we use a tree-structured decoder with a state aggregation mechanism to capture the long-distance dependency and global expression information. Experimental results on the Math23K dataset revealed that the KA-S2T model can achieve better performance than previously reported best results.",
}
@inproceedings{fan-etal-2020-generating,
    title = "Generating Fact Checking Briefs",
    author = "Fan, Angela  and
      Piktus, Aleksandra  and
      Petroni, Fabio  and
      Wenzek, Guillaume  and
      Saeidi, Marzieh  and
      Vlachos, Andreas  and
      Bordes, Antoine  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.580",
    doi = "10.18653/v1/2020.emnlp-main.580",
    pages = "7147--7161",
    abstract = "Fact checking at scale is difficult{---}while the number of active fact checking websites is growing, it remains too small for the needs of the contemporary media ecosystem. However, despite good intentions, contributions from volunteers are often error-prone, and thus in practice restricted to claim detection. We investigate how to increase the accuracy and efficiency of fact checking by providing information about the claim before performing the check, in the form of natural language briefs. We investigate passage-based briefs, containing a relevant passage from Wikipedia, entity-centric ones consisting of Wikipedia pages of mentioned entities, and Question-Answering Briefs, with questions decomposing the claim, and their answers. To produce QABriefs, we develop QABriefer, a model that generates a set of questions conditioned on the claim, searches the web for evidence, and generates answers. To train its components, we introduce QABriefDataset We show that fact checking with briefs {---} in particular QABriefs {---} increases the accuracy of crowdworkers by 10{\%} while slightly decreasing the time taken. For volunteer (unpaid) fact checkers, QABriefs slightly increase accuracy and reduce the time required by around 20{\%}.",
}
@inproceedings{chen-etal-2020-improving-efficiency,
    title = "Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction",
    author = "Chen, Mengyun  and
      Ge, Tao  and
      Zhang, Xingxing  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.581",
    doi = "10.18653/v1/2020.emnlp-main.581",
    pages = "7162--7169",
    abstract = "We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50{\%} time cost for inference.",
}
@inproceedings{ye-etal-2020-coreferential,
    title = "{C}oreferential {R}easoning {L}earning for {L}anguage {R}epresentation",
    author = "Ye, Deming  and
      Lin, Yankai  and
      Du, Jiaju  and
      Liu, Zhenghao  and
      Li, Peng  and
      Sun, Maosong  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.582",
    doi = "10.18653/v1/2020.emnlp-main.582",
    pages = "7170--7186",
    abstract = "Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of downstream NLP tasks with appropriate fine-tuning. However, most existing language representation models cannot explicitly handle coreference, which is essential to the coherent understanding of the whole discourse. To address this issue, we present CorefBERT, a novel language representation model that can capture the coreferential relations in context. The experimental results show that, compared with existing baseline models, CorefBERT can achieve significant improvements consistently on various downstream NLP tasks that require coreferential reasoning, while maintaining comparable performance to previous models on other common NLP tasks. The source code and experiment details of this paper can be obtained from https://github.com/thunlp/CorefBERT.",
}
@inproceedings{shao-etal-2020-graph,
    title = "Is {G}raph {S}tructure {N}ecessary for {M}ulti-hop {Q}uestion {A}nswering?",
    author = "Shao, Nan  and
      Cui, Yiming  and
      Liu, Ting  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.583",
    doi = "10.18653/v1/2020.emnlp-main.583",
    pages = "7187--7192",
    abstract = "Recently, attempting to model texts as graph structure and introducing graph neural networks to deal with it has become a trend in many NLP research areas. In this paper, we investigate whether the graph structure is necessary for textual multi-hop reasoning. Our analysis is centered on HotpotQA. We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for textual multi-hop reasoning. We point out that both graph structure and adjacency matrix are task-related prior knowledge, and graph-attention can be considered as a special case of self-attention. Experiments demonstrate that graph-attention or the entire graph structure can be replaced by self-attention or Transformers.",
}
@inproceedings{raganato-etal-2020-xl,
    title = "{XL}-{W}i{C}: A Multilingual Benchmark for Evaluating Semantic Contextualization",
    author = "Raganato, Alessandro  and
      Pasini, Tommaso  and
      Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.584",
    doi = "10.18653/v1/2020.emnlp-main.584",
    pages = "7193--7206",
    abstract = "The ability to correctly model distinct meanings of a word is crucial for the effectiveness of semantic representation techniques. However, most existing evaluation benchmarks for assessing this criterion are tied to sense inventories (usually WordNet), restricting their usage to a small subset of knowledge-based representation techniques. The Word-in-Context dataset (WiC) addresses the dependence on sense inventories by reformulating the standard disambiguation task as a binary classification problem; but, it is limited to the English language. We put forward a large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages from varied language families and with different degrees of resource availability, opening room for evaluation scenarios such as zero-shot cross-lingual transfer. We perform a series of experiments to determine the reliability of the datasets and to set performance baselines for several recent contextualized multilingual models. Experimental results show that even when no tagged instances are available for a target language, models trained solely on the English data can attain competitive performance in the task of distinguishing different meanings of a word, even for distant languages. XL-WiC is available at https://pilehvar.github.io/xlwic/.",
}
@inproceedings{bevilacqua-etal-2020-generationary,
    title = "Generationary or {``}How We Went beyond Word Sense Inventories and Learned to Gloss{''}",
    author = "Bevilacqua, Michele  and
      Maru, Marco  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.585",
    doi = "10.18653/v1/2020.emnlp-main.585",
    pages = "7207--7221",
    abstract = "Mainstream computational lexical semantics embraces the assumption that word senses can be represented as discrete items of a predefined inventory. In this paper we show this needs not be the case, and propose a unified model that is able to produce contextually appropriate definitions. In our model, Generationary, we employ a novel span-based encoding scheme which we use to fine-tune an English pre-trained Encoder-Decoder system to generate glosses. We show that, even though we drop the need of choosing from a predefined sense inventory, our model can be employed effectively: not only does Generationary outperform previous approaches in the generative task of Definition Modeling in many settings, but it also matches or surpasses the state of the art in discriminative tasks such as Word Sense Disambiguation and Word-in-Context. Finally, we show that Generationary benefits from training on data from multiple inventories, with strong gains on various zero-shot benchmarks, including a novel dataset of definitions for free adjective-noun phrases. The software and reproduction materials are available at http://generationary.org.",
}
@inproceedings{vulic-etal-2020-probing,
    title = "Probing Pretrained Language Models for Lexical Semantics",
    author = "Vuli{\'c}, Ivan  and
      Ponti, Edoardo Maria  and
      Litschko, Robert  and
      Glava{\v{s}}, Goran  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.586",
    doi = "10.18653/v1/2020.emnlp-main.586",
    pages = "7222--7240",
    abstract = "The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture. While prior research focused on morphosyntactic, semantic, and world knowledge, it remains unclear to which extent LMs also derive lexical type-level knowledge from words in context. In this work, we present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks, addressing the following questions: 1) How do different lexical knowledge extraction strategies (monolingual versus multilingual source LM, out-of-context versus in-context encoding, inclusion of special tokens, and layer-wise averaging) impact performance? How consistent are the observed effects across tasks and languages? 2) Is lexical knowledge stored in few parameters, or is it scattered throughout the network? 3) How do these representations fare against traditional static word vectors in lexical tasks 4) Does the lexical information emerging from independently trained monolingual LMs display latent similarities? Our main results indicate patterns and best practices that hold universally, but also point to prominent variations across languages and tasks. Moreover, we validate the claim that lower Transformer layers carry more type-level lexical knowledge, but also show that this knowledge is distributed across multiple layers.",
}
@inproceedings{liu-etal-2020-cross-lingual-spoken,
    title = "Cross-lingual Spoken Language Understanding with Regularized Representation Alignment",
    author = "Liu, Zihan  and
      Winata, Genta Indra  and
      Xu, Peng  and
      Lin, Zhaojiang  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.587",
    doi = "10.18653/v1/2020.emnlp-main.587",
    pages = "7241--7251",
    abstract = "Despite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the performance sub-optimal. To cope with this issue, we propose a regularization approach to further align word-level and sentence-level representations across languages without any external resource. First, we regularize the representation of user utterances based on their corresponding labels. Second, we regularize the latent variable model (Liu et al., 2019) by leveraging adversarial training to disentangle the latent variables. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios, and our model, trained on a few-shot setting with only 3{\%} of the target language training data, achieves comparable performance to the supervised training with all the training data.",
}
@inproceedings{bastianelli-etal-2020-slurp,
    title = "{SLURP}: A Spoken Language Understanding Resource Package",
    author = "Bastianelli, Emanuele  and
      Vanzo, Andrea  and
      Swietojanski, Pawel  and
      Rieser, Verena",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.588",
    doi = "10.18653/v1/2020.emnlp-main.588",
    pages = "7252--7262",
    abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp.",
}
@inproceedings{verma-etal-2020-neural,
    title = "Neural Conversational {QA}: Learning to Reason vs Exploiting Patterns",
    author = "Verma, Nikhil  and
      Sharma, Abhishek  and
      Madan, Dhiraj  and
      Contractor, Danish  and
      Kumar, Harshit  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.589",
    doi = "10.18653/v1/2020.emnlp-main.589",
    pages = "7263--7269",
    abstract = "Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the model(s) learn spurious clues/patterns in the data-set. Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.",
}
@inproceedings{zeng-etal-2020-counterfactual,
    title = "Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition",
    author = "Zeng, Xiangji  and
      Li, Yunliang  and
      Zhai, Yuchen  and
      Zhang, Yin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.590",
    doi = "10.18653/v1/2020.emnlp-main.590",
    pages = "7270--7280",
    abstract = "Past progress on neural models has proven that named entity recognition is no longer a problem if we have enough labeled data. However, collecting enough data and annotating them are labor-intensive, time-consuming, and expensive. In this paper, we decompose the sentence into two parts: entity and context, and rethink the relationship between them and model performance from a causal perspective. Based on this, we propose the Counterfactual Generator, which generates counterfactual examples by the interventions on the existing observational examples to enhance the original dataset. Experiments across three datasets show that our method improves the generalization ability of models under limited observational examples. Besides, we provide a theoretical foundation by using a structural causal model to explore the spurious correlations between input features and output labels. We investigate the causal effects of entity or context on model performance under both conditions: the non-augmented and the augmented. Interestingly, we find that the non-spurious correlations are more located in entity representation rather than context representation. As a result, our method eliminates part of the spurious correlations between context representation and output labels. The code is available at https://github.com/xijiz/cfgen.",
}
@inproceedings{tang-etal-2020-understanding-procedural,
    title = "Understanding Procedural Text using Interactive Entity Networks",
    author = "Tang, Jizhi  and
      Feng, Yansong  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.591",
    doi = "10.18653/v1/2020.emnlp-main.591",
    pages = "7281--7290",
    abstract = "The task of procedural text comprehension aims to understand the dynamic nature of entities/objects in a process. Here, the key is to track how the entities interact with each other and how their states are changing along the procedure. Recent efforts have made great progress to track multiple entities in a procedural text, but usually treat each entity separately and ignore the fact that there are often multiple entities interacting with each other during one process, some of which are even explicitly mentioned. In this paper, we propose a novel Interactive Entity Network (IEN), which is a recurrent network with memory equipped cells for state tracking. In each IEN cell, we maintain different attention matrices through specific memories to model different types of entity interactions. Importantly, we can update these memories in a sequential manner so as to explore the causal relationship between entity actions and subsequent state changes. We evaluate our model on a benchmark dataset, and the results show that IEN outperforms state-of-the-art models by precisely capturing the interactions of multiple entities and explicitly leverage the relationship between entity interactions and subsequent state changes.",
}
@inproceedings{lin-etal-2020-rigorous,
    title = "A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?",
    author = "Lin, Hongyu  and
      Lu, Yaojie  and
      Tang, Jialong  and
      Han, Xianpei  and
      Sun, Le  and
      Wei, Zhicheng  and
      Yuan, Nicholas Jing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.592",
    doi = "10.18653/v1/2020.emnlp-main.592",
    pages = "7291--7300",
    abstract = "Fine-tuning pretrained model has achieved promising performance on standard NER benchmarks. Generally, these benchmarks are blessed with strong name regularity, high mention coverage and sufficient context diversity. Unfortunately, when scaling NER to open situations, these advantages may no longer exist. And therefore it raises a critical question of whether previous creditable approaches can still work well when facing these challenges. As there is no currently available dataset to investigate this problem, this paper proposes to conduct randomization test on standard benchmarks. Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models. To further verify our conclusions, we also construct a new open NER dataset that focuses on entity types with weaker name regularity and lower mention coverage to verify our conclusion. From both randomization test and empirical experiments, we draw the conclusions that 1) name regularity is critical for the models to generalize to unseen mentions; 2) high mention coverage may undermine the model generalization ability and 3) context patterns may not require enormous data to capture when using pretrained encoders.",
}
@inproceedings{han-etal-2020-dyernie,
    title = "{D}y{ERNIE}: {D}ynamic {E}volution of {R}iemannian {M}anifold {E}mbeddings for {T}emporal {K}nowledge {G}raph {C}ompletion",
    author = "Han, Zhen  and
      Chen, Peng  and
      Ma, Yunpu  and
      Tresp, Volker",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.593",
    doi = "10.18653/v1/2020.emnlp-main.593",
    pages = "7301--7316",
    abstract = "There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embedding approaches for temporal KGs typically learn entity representations and their dynamic evolution in the Euclidean space, which might not capture such intrinsic structures very well. To this end, we propose DyERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data. Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs. Besides, to capture the evolutionary dynamics of temporal KGs, we let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp. We analyze in detail the contribution of geometric spaces to representation learning of temporal KGs and evaluate our model on temporal knowledge graph completion tasks. Extensive experiments on three real-world datasets demonstrate significantly improved performance, indicating that the dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds.",
}
@inproceedings{ryabinin-etal-2020-embedding,
    title = "{E}mbedding {W}ords in {N}on-{V}ector {S}pace with {U}nsupervised {G}raph {L}earning",
    author = "Ryabinin, Max  and
      Popov, Sergei  and
      Prokhorenkova, Liudmila  and
      Voita, Elena",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.594",
    doi = "10.18653/v1/2020.emnlp-main.594",
    pages = "7317--7331",
    abstract = "It has become a de-facto standard to represent words as elements of a vector space (word2vec, GloVe). While this approach is convenient, it is unnatural for language: words form a graph with a latent hierarchical structure, and this structure has to be revealed and encoded by word embeddings. We introduce GraphGlove: unsupervised graph word representations which are learned end-to-end. In our setting, each word is a node in a weighted graph and the distance between words is the shortest path distance between the corresponding nodes. We adopt a recent method learning a representation of data in the form of a differentiable weighted graph and use it to modify the GloVe training algorithm. We show that our graph-based representations substantially outperform vector-based methods on word similarity and analogy tasks. Our analysis reveals that the structure of the learned graphs is hierarchical and similar to that of WordNet, the geometry is highly non-trivial and contains subgraphs with different local topology.",
}
@inproceedings{fisher-etal-2020-debiasing,
    title = "Debiasing knowledge graph embeddings",
    author = "Fisher, Joseph  and
      Mittal, Arpit  and
      Palfrey, Dave  and
      Christodoulopoulos, Christos",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.595",
    doi = "10.18653/v1/2020.emnlp-main.595",
    pages = "7332--7345",
    abstract = "It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. As graph embeddings begin to be used more widely in NLP pipelines, there is a need to develop training methods which remove such biases. Previous approaches to this problem both significantly increase the training time, by a factor of eight or more, and decrease the accuracy of the model substantially. We present a novel approach, in which all embeddings are trained to be neutral to sensitive attributes such as gender by default using an adversarial loss. We then add sensitive attributes back on in whitelisted cases. Training time only marginally increases over a baseline model, and the debiased embeddings perform almost as accurately in the triple prediction task as their non-debiased counterparts.",
}
@inproceedings{galkin-etal-2020-message,
    title = "Message Passing for Hyper-Relational Knowledge Graphs",
    author = "Galkin, Mikhail  and
      Trivedi, Priyansh  and
      Maheshwari, Gaurav  and
      Usbeck, Ricardo  and
      Lehmann, Jens",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.596",
    doi = "10.18653/v1/2020.emnlp-main.596",
    pages = "7346--7359",
    abstract = "Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - StarE capable of modeling such hyper-relational KGs. Unlike existing approaches, StarE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. Our experiments demonstrate that StarE based LP model outperforms existing approaches across multiple benchmarks. We also confirm that leveraging qualifiers is vital for link prediction with gains up to 25 MRR points compared to triple-based representations.",
}
@inproceedings{ishiwatari-etal-2020-relation,
    title = "Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations",
    author = "Ishiwatari, Taichi  and
      Yasuda, Yuki  and
      Miyazaki, Taro  and
      Goto, Jun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.597",
    doi = "10.18653/v1/2020.emnlp-main.597",
    pages = "7360--7370",
    abstract = "Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used to analyze user behaviors and detect fake news. Many recent ERC methods use graph-based neural networks to take the relationships between the utterances of the speakers into account. In particular, the state-of-the-art method considers self- and inter-speaker dependencies in conversations by using relational graph attention networks (RGAT). However, graph-based neural networks do not take sequential information into account. In this paper, we propose relational position encodings that provide RGAT with sequential information reflecting the relational graph structure. Accordingly, our RGAT model can capture both the speaker dependency and the sequential information. Experiments on four ERC datasets show that our model is beneficial to recognizing emotions expressed in conversations. In addition, our approach empirically outperforms the state-of-the-art on all of the benchmark datasets.",
}
@inproceedings{gari-soler-apidianaki-2020-bert,
    title = "{BERT} Knows Punta Cana is not just beautiful, it{'}s gorgeous: Ranking Scalar Adjectives with Contextualised Representations",
    author = "Gar{\'\i} Soler, Aina  and
      Apidianaki, Marianna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.598",
    doi = "10.18653/v1/2020.emnlp-main.598",
    pages = "7371--7385",
    abstract = "Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity. These differences are important for natural language understanding and reasoning. We propose a novel BERT-based approach to intensity detection for scalar adjectives. We model intensity by vectors directly derived from contextualised representations and show they can successfully rank scalar adjectives. We evaluate our models both intrinsically, on gold standard datasets, and on an Indirect Question Answering task. Our results demonstrate that BERT encodes rich knowledge about the semantics of scalar adjectives, and is able to provide better quality intensity rankings than static embeddings and previous models with access to dedicated resources.",
}
@inproceedings{ye-etal-2020-feature,
    title = "Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training",
    author = "Ye, Hai  and
      Tan, Qingyu  and
      He, Ruidan  and
      Li, Juntao  and
      Ng, Hwee Tou  and
      Bing, Lidong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.599",
    doi = "10.18653/v1/2020.emnlp-main.599",
    pages = "7386--7399",
    abstract = "Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has gained much attention recently. Instead of fine-tuning PrLMs as done in most previous work, we investigate how to adapt the features of PrLMs to new domains without fine-tuning. We explore unsupervised domain adaptation (UDA) in this paper. With the features from PrLMs, we adapt the models trained with labeled data from the source domain to the unlabeled target domain. Self-training is widely used for UDA, and it predicts pseudo labels on the target domain data for training. However, the predicted pseudo labels inevitably include noise, which will negatively affect training a robust model. To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered. We further extend CFd to a cross-language setting, in which language discrepancy is studied. Experiments on two monolingual and multilingual Amazon review datasets show that CFd can consistently improve the performance of self-training in cross-domain and cross-language settings.",
}
@inproceedings{quteineh-etal-2020-textual,
    title = "Textual Data Augmentation for Efficient Active Learning on Tiny Datasets",
    author = "Quteineh, Husam  and
      Samothrakis, Spyridon  and
      Sutcliffe, Richard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.600",
    doi = "10.18653/v1/2020.emnlp-main.600",
    pages = "7400--7410",
    abstract = "In this paper we propose a novel data augmentation approach where guided outputs of a language generation model, e.g. GPT-2, when labeled, can improve the performance of text classifiers through an active learning process. We transform the data generation task into an optimization problem which maximizes the usefulness of the generated output, using Monte Carlo Tree Search (MCTS) as the optimization strategy and incorporating entropy as one of the optimization criteria. We test our approach against a Non-Guided Data Generation (NGDG) process that does not optimize for a reward function. Starting with a small set of data, our results show an increased performance with MCTS of 26{\%} on the TREC-6 Questions dataset, and 10{\%} on the Stanford Sentiment Treebank SST-2 dataset. Compared with NGDG, we are able to achieve increases of 3{\%} and 5{\%} on TREC-6 and SST-2.",
}
@inproceedings{louis-etal-2020-id,
    title = "{``}{I}{'}d rather just go to bed{''}: Understanding Indirect Answers",
    author = "Louis, Annie  and
      Roth, Dan  and
      Radlinski, Filip",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.601",
    doi = "10.18653/v1/2020.emnlp-main.601",
    pages = "7411--7425",
    abstract = "We revisit a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret {`}I{'}m starving.{'} in response to {`}Hungry?{'}, even without direct cue words such as {`}yes{'} and {`}no{'}. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today{'}s systems are only as sensitive to these pragmatic moves as their language model allows. We create and release the first large-scale English language corpus {`}Circa{'} with 34,268 (polar question, indirect answer) pairs to enable progress on this task. The data was collected via elaborate crowdsourcing, and contains utterances with yes/no meaning, as well as uncertain, middle-ground, and conditional responses. We also present BERT-based neural models to predict such categories for a question-answer pair. We find that while transfer learning from entailment works reasonably, performance is not yet sufficient for robust dialog. Our models reach 82-88{\%} accuracy for a 4-class distinction, and 74-85{\%} for 6 classes.",
}
@inproceedings{ma-etal-2020-powertransformer,
    title = "{P}ower{T}ransformer: Unsupervised Controllable Revision for Biased Language Correction",
    author = "Ma, Xinyao  and
      Sap, Maarten  and
      Rashkin, Hannah  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.602",
    doi = "10.18653/v1/2020.emnlp-main.602",
    pages = "7426--7441",
    abstract = "Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a story is often portrayed as passive and powerless ({``}{\_}She daydreams about being a doctor{\_}{''}) while a man is portrayed as more proactive and powerful ({``}{\_}He pursues his dream of being a doctor{\_}{''}). We formulate **Controllable Debiasing**, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals. We then introduce PowerTransformer as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates. One key challenge of our task is the lack of parallel corpora. To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models. Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks. Furthermore, we demonstrate the use of PowerTransformer as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts.",
}
@inproceedings{huber-carenini-2020-mega,
    title = "{MEGA} {RST} Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision",
    author = "Huber, Patrick  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.603",
    doi = "10.18653/v1/2020.emnlp-main.603",
    pages = "7442--7457",
    abstract = "The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RST-style discourse parsing. In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment annotated datasets, creating and publishing MEGA-DT, a new large-scale discourse-annotated corpus. Our approach generates discourse trees incorporating structure and nuclearity for documents of arbitrary length by relying on an efficient heuristic beam-search strategy, extended with a stochastic component. Experiments on multiple datasets indicate that a discourse parser trained on our MEGA-DT treebank delivers promising inter-domain performance gains when compared to parsers trained on human-annotated discourse corpora.",
}
@inproceedings{jeon-strube-2020-centering,
    title = "Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments",
    author = "Jeon, Sungho  and
      Strube, Michael",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.604",
    doi = "10.18653/v1/2020.emnlp-main.604",
    pages = "7458--7472",
    abstract = "Previous neural coherence models have focused on identifying semantic relations between adjacent sentences. However, they do not have the means to exploit structural information. In this work, we propose a coherence model which takes discourse structural information into account without relying on human annotations. We approximate a linguistic theory of coherence, Centering theory, which we use to track the changes of focus between discourse segments. Our model first identifies the focus of each sentence, recognized with regards to the context, and constructs the structural relationship for discourse segments by tracking the changes of the focus. The model then incorporates this structural information into a structure-aware transformer. We evaluate our model on two tasks, automated essay scoring and assessing writing quality. Our results demonstrate that our model, built on top of a pretrained language model, achieves state-of-the-art performance on both tasks. We next statistically examine the identified trees of texts assigned to different quality scores. Finally, we investigate what our model learns in terms of theoretical claims.",
}
@inproceedings{dutt-etal-2020-keeping,
    title = "Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions",
    author = "Dutt, Ritam  and
      Joshi, Rishabh  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.605",
    doi = "10.18653/v1/2020.emnlp-main.605",
    pages = "7473--7485",
    abstract = "The notion of face refers to the public self-image of an individual that emerges both from the individual{'}s own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings.",
}
@inproceedings{tran-etal-2020-habertor,
    title = "{HABERTOR}: An Efficient and Effective Deep Hatespeech Detector",
    author = "Tran, Thanh  and
      Hu, Yifan  and
      Hu, Changwei  and
      Yen, Kevin  and
      Tan, Fei  and
      Lee, Kyumin  and
      Park, Se Rim",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.606",
    doi = "10.18653/v1/2020.emnlp-main.606",
    pages = "7486--7502",
    abstract = "We present our HABERTOR model for detecting hatespeech in large scale user-generated content. Inspired by the recent success of the BERT model, we propose several modifications to BERT to enhance the performance on the downstream hatespeech classification task. HABERTOR inherits BERT{'}s architecture, but is different in four aspects: (i) it generates its own vocabularies and is pre-trained from the scratch using the largest scale hatespeech dataset; (ii) it consists of Quaternion-based factorized components, resulting in a much smaller number of parameters, faster training and inferencing, as well as less memory usage; (iii) it uses our proposed multi-source ensemble heads with a pooling layer for separate input sources, to further enhance its effectiveness; and (iv) it uses a regularized adversarial training with our proposed fine-grained and adaptive noise magnitude to enhance its robustness. Through experiments on the large-scale real-world hatespeech dataset with 1.4M annotated comments, we show that HABERTOR works better than 15 state-of-the-art hatespeech detection methods, including fine-tuning Language Models. In particular, comparing with BERT, our HABERTOR is 4 5 times faster in the training/inferencing phase, uses less than 1/3 of the memory, and has better performance, even though we pre-train it by using less than 1{\%} of the number of words. Our generalizability analysis shows that HABERTOR transfers well to other unseen hatespeech datasets and is a more efficient and effective alternative to BERT for the hatespeech classification.",
}
@inproceedings{chalkidis-etal-2020-empirical,
    title = "An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels",
    author = "Chalkidis, Ilias  and
      Fergadiotis, Manos  and
      Kotitsas, Sotiris  and
      Malakasiotis, Prodromos  and
      Aletras, Nikolaos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.607",
    doi = "10.18653/v1/2020.emnlp-main.607",
    pages = "7503--7515",
    abstract = "Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language Processing (NLP) applications and presents interesting challenges. First, not all labels are well represented in the training set, due to the very large label set and the skewed label distributions of datasets. Also, label hierarchies and differences in human labelling guidelines may affect graph-aware annotation proximity. Finally, the label hierarchies are periodically updated, requiring LMTC models capable of zero-shot generalization. Current state-of-the-art LMTC models employ Label-Wise Attention Networks (LWANs), which (1) typically treat LMTC as flat multi-label classification; (2) may use the label hierarchy to improve zero-shot learning, although this practice is vastly understudied; and (3) have not been combined with pre-trained Transformers (e.g. BERT), which have led to state-of-the-art results in several NLP benchmarks. Here, for the first time, we empirically evaluate a battery of LMTC methods from vanilla LWANs to hierarchical classification approaches and transfer learning, on frequent, few, and zero-shot learning on three datasets from different domains. We show that hierarchical methods based on Probabilistic Label Trees (PLTs) outperform LWANs. Furthermore, we show that Transformer-based approaches outperform the state-of-the-art in two of the datasets, and we propose a new state-of-the-art method which combines BERT with LWAN. Finally, we propose new models that leverage the label hierarchy to improve few and zero-shot learning, considering on each dataset a graph-aware annotation proximity measure that we introduce.",
}
@inproceedings{xia-etal-2020-bert,
    title = "Which *{BERT}? {A} Survey Organizing Contextualized Encoders",
    author = "Xia, Patrick  and
      Wu, Shijie  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.608",
    doi = "10.18653/v1/2020.emnlp-main.608",
    pages = "7516--7533",
    abstract = "Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.",
}
@inproceedings{wadden-etal-2020-fact,
    title = "Fact or Fiction: Verifying Scientific Claims",
    author = "Wadden, David  and
      Lin, Shanchuan  and
      Lo, Kyle  and
      Wang, Lucy Lu  and
      van Zuylen, Madeleine  and
      Cohan, Arman  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.609",
    doi = "10.18653/v1/2020.emnlp-main.609",
    pages = "7534--7550",
    abstract = "We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision. To study this task, we construct SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SciFact, and demonstrate that simple domain adaptation techniques substantially improve performance compared to models trained on Wikipedia or political news. We show that our system is able to verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. Our experiments indicate that SciFact will provide a challenging testbed for the development of new systems designed to retrieve and reason over corpora containing specialized domain knowledge. Data and code for this new task are publicly available at https://github.com/allenai/scifact. A leaderboard and COVID-19 fact-checking demo are available at https://scifact.apps.allenai.org.",
}
@inproceedings{shi-etal-2020-semantic,
    title = "Semantic Role Labeling as Syntactic Dependency Parsing",
    author = "Shi, Tianze  and
      Malioutov, Igor  and
      Irsoy, Ozan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.610",
    doi = "10.18653/v1/2020.emnlp-main.610",
    pages = "7551--7571",
    abstract = "We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98{\%} of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.",
}
@inproceedings{he-etal-2020-parade,
    title = "{PARADE}: {A} {N}ew {D}ataset for {P}araphrase {I}dentification {R}equiring {C}omputer {S}cience {D}omain {K}nowledge",
    author = "He, Yun  and
      Wang, Zhuoer  and
      Zhang, Yin  and
      Huang, Ruihong  and
      Caverlee, James",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.611",
    doi = "10.18653/v1/2020.emnlp-main.611",
    pages = "7572--7582",
    abstract = "We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge. PARADE contains paraphrases that overlap very little at the lexical and syntactic level but are semantically equivalent based on computer science domain knowledge, as well as non-paraphrases that overlap greatly at the lexical and syntactic level but are not semantically equivalent based on this domain knowledge. Experiments show that both state-of-the-art neural models and non-expert human annotators have poor performance on PARADE. For example, BERT after fine-tuning achieves an F1 score of 0.709, which is much lower than its performance on other paraphrase identification datasets. PARADE can serve as a resource for researchers interested in testing models that incorporate domain knowledge. We make our data and code freely available.",
}
@inproceedings{weber-etal-2020-causal,
    title = "Causal Inference of Script Knowledge",
    author = "Weber, Noah  and
      Rudinger, Rachel  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.612",
    doi = "10.18653/v1/2020.emnlp-main.612",
    pages = "7583--7596",
    abstract = "When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents.",
}
@inproceedings{utama-etal-2020-towards,
    title = "Towards Debiasing {NLU} Models from Unknown Biases",
    author = "Utama, Prasetya Ajie  and
      Moosavi, Nafise Sadat  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.613",
    doi = "10.18653/v1/2020.emnlp-main.613",
    pages = "7597--7610",
    abstract = "NLU models often exploit biases to achieve high dataset-specific performance without properly learning the intended task. Recently proposed debiasing methods are shown to be effective in mitigating this tendency. However, these methods rely on a major assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. In this work, we present the first step to bridge this gap by introducing a self-debiasing framework that prevents models from mainly utilizing biases without knowing them in advance. The proposed framework is general and complementary to the existing debiasing methods. We show that it allows these existing methods to retain the improvement on the challenge datasets (i.e., sets of examples designed to expose models{'} reliance on biases) without specifically targeting certain biases. Furthermore, the evaluation suggests that applying the framework results in improved overall robustness.",
}
@inproceedings{shi-etal-2020-role,
    title = "On the Role of Supervision in Unsupervised Constituency Parsing",
    author = "Shi, Haoyue  and
      Livescu, Karen  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.614",
    doi = "10.18653/v1/2020.emnlp-main.614",
    pages = "7611--7621",
    abstract = "We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing F1 score on the Wall Street Journal (WSJ) development set (1,700 sentences). We introduce strong baselines for them, by training an existing supervised parsing model (Kitaev and Klein, 2018) on the same labeled examples they access. When training on the 1,700 examples, or even when using only 50 examples for training and 5 for development, such a few-shot parsing approach can outperform all the unsupervised parsing methods by a significant margin. Few-shot parsing can be further improved by a simple data augmentation method and self-training. This suggests that, in order to arrive at fair conclusions, we should carefully consider the amount of labeled data used for model development. We propose two protocols for future work on unsupervised parsing: (i) use fully unsupervised criteria for hyperparameter tuning and model selection; (ii) use as few labeled examples as possible for model development, and compare to few-shot parsing trained on the same labeled examples.",
}
@inproceedings{baziotis-etal-2020-language,
    title = "Language Model Prior for Low-Resource Neural Machine Translation",
    author = "Baziotis, Christos  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.615",
    doi = "10.18653/v1/2020.emnlp-main.615",
    pages = "7622--7634",
    abstract = "The scarcity of large parallel corpora is an important obstacle for neural machine translation. A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data. In this work, we propose a novel approach to incorporate a LM as prior in a neural translation model (TM). Specifically, we add a regularization term, which pushes the output distributions of the TM to be probable under the LM prior, while avoiding wrong predictions when the TM {``}disagrees{''} with the LM. This objective relates to knowledge distillation, where the LM can be viewed as teaching the TM about the target language. The proposed approach does not compromise decoding speed, because the LM is used only at training time, unlike previous work that requires it during inference. We present an analysis of the effects that different methods have on the distributions of the TM. Results on two low-resource machine translation datasets show clear improvements even with limited monolingual data.",
}
@inproceedings{emelin-etal-2020-detecting,
    title = "Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks",
    author = "Emelin, Denis  and
      Titov, Ivan  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.616",
    doi = "10.18653/v1/2020.emnlp-main.616",
    pages = "7635--7653",
    abstract = "Word sense disambiguation is a well-known source of translation errors in NMT. We posit that some of the incorrect disambiguation choices are due to models{'} over-reliance on dataset artifacts found in training data, specifically superficial word co-occurrences, rather than a deeper understanding of the source text. We introduce a method for the prediction of disambiguation errors based on statistical data properties, demonstrating its effectiveness across several domains and model types. Moreover, we develop a simple adversarial attack strategy that minimally perturbs sentences in order to elicit disambiguation errors to further probe the robustness of translation models. Our findings indicate that disambiguation robustness varies substantially between domains and that different models trained on the same data are vulnerable to different attacks.",
}
@inproceedings{pfeiffer-etal-2020-mad,
    title = "{MAD-X}: {A}n {A}dapter-{B}ased {F}ramework for {M}ulti-{T}ask {C}ross-{L}ingual {T}ransfer",
    author = "Pfeiffer, Jonas  and
      Vuli{\'c}, Ivan  and
      Gurevych, Iryna  and
      Ruder, Sebastian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.617",
    doi = "10.18653/v1/2020.emnlp-main.617",
    pages = "7654--7673",
    abstract = "The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pre-training. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pre-trained multilingual model to a new language. MAD-X outperforms the state of the art in cross lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. Our code and adapters are available at AdapterHub.ml.",
}
@inproceedings{artetxe-etal-2020-translation,
    title = "Translation Artifacts in Cross-lingual Transfer Learning",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.618",
    doi = "10.18653/v1/2020.emnlp-main.618",
    pages = "7674--7684",
    abstract = "Both human and machine translation play a central role in cross-lingual transfer learning: many multilingual datasets have been created through professional translation services, and using machine translation to translate either the test set or the training set is a widely used transfer technique. In this paper, we show that such translation process can introduce subtle artifacts that have a notable impact in existing cross-lingual models. For instance, in natural language inference, translating the premise and the hypothesis independently can reduce the lexical overlap between them, which current models are highly sensitive to. We show that some previous findings in cross-lingual transfer learning need to be reconsidered in the light of this phenomenon. Based on the gained insights, we also improve the state-of-the-art in XNLI for the translate-test and zero-shot approaches by 4.3 and 2.8 points, respectively.",
}
@inproceedings{sawhney-etal-2020-time,
    title = "A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media",
    author = "Sawhney, Ramit  and
      Joshi, Harshit  and
      Gandhi, Saumya  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.619",
    doi = "10.18653/v1/2020.emnlp-main.619",
    pages = "7685--7697",
    abstract = "Social media{'}s ubiquity fosters a space for users to exhibit suicidal thoughts outside of traditional clinical settings. Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention. Suicide ideation is often linked to a history of mental depression. The emotional spectrum of a user{'}s historical activity on social media can be indicative of their mental state over time. In this work, we focus on identifying suicidal intent in English tweets by augmenting linguistic models with historical context. We propose STATENet, a time-aware transformer based model for preliminary screening of suicidal risk on social media. STATENet outperforms competitive methods, demonstrating the utility of emotional and temporal contextual cues for suicide risk assessment. We discuss the empirical, qualitative, practical, and ethical aspects of STATENet for suicide ideation detection.",
}
@inproceedings{roy-goldwasser-2020-weakly,
    title = "Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media",
    author = "Roy, Shamik  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.620",
    doi = "10.18653/v1/2020.emnlp-main.620",
    pages = "7698--7716",
    abstract = "In this paper, we suggest a minimally supervised approach for identifying nuanced frames in news article coverage of politically divisive topics. We suggest to break the broad policy frames suggested by Boydstun et al., 2014 into fine-grained subframes which can capture differences in political ideology in a better way. We evaluate the suggested subframes and their embedding, learned using minimal supervision, over three topics, namely, immigration, gun-control, and abortion. We demonstrate the ability of the subframes to capture ideological differences and analyze political discourse in news media.",
}
@inproceedings{vo-lee-2020-facts,
    title = "Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News",
    author = "Vo, Nguyen  and
      Lee, Kyumin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.621",
    doi = "10.18653/v1/2020.emnlp-main.621",
    pages = "7717--7731",
    abstract = "Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users{'} consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters{'} followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.",
}
@inproceedings{han-tsvetkov-2020-fortifying,
    title = "Fortifying Toxic Speech Detectors Against Veiled Toxicity",
    author = "Han, Xiaochuang  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.622",
    doi = "10.18653/v1/2020.emnlp-main.622",
    pages = "7732--7739",
    abstract = "Modern toxic speech detectors are incompetent in recognizing disguised offensive language, such as adversarial attacks that deliberately avoid known toxic lexicons, or manifestations of implicit bias. Building a large annotated dataset for such veiled toxicity can be very expensive. In this work, we propose a framework aimed at fortifying existing toxic speech detectors without a large labeled corpus of veiled toxicity. Just a handful of probing examples are used to surface orders of magnitude more disguised offenses. We augment the toxic speech detector{'}s training data with these discovered offensive examples, thereby making it more robust to veiled toxicity while preserving its utility in detecting overt toxicity.",
}
@inproceedings{kotonya-toni-2020-explainable-automated,
    title = "Explainable Automated Fact-Checking for Public Health Claims",
    author = "Kotonya, Neema  and
      Toni, Francesca",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
    doi = "10.18653/v1/2020.emnlp-main.623",
    pages = "7740--7754",
    abstract = "Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.",
}
@inproceedings{guo-etal-2020-interactive,
    title = "Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning",
    author = "Guo, Xiaoxiao  and
      Yu, Mo  and
      Gao, Yupeng  and
      Gan, Chuang  and
      Campbell, Murray  and
      Chang, Shiyu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.624",
    doi = "10.18653/v1/2020.emnlp-main.624",
    pages = "7755--7765",
    abstract = "Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the human-written textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space. We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations. Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches.",
}
@inproceedings{pasunuru-etal-2020-dorb,
    title = "{DORB}: Dynamically Optimizing Multiple Rewards with Bandits",
    author = "Pasunuru, Ramakanth  and
      Guo, Han  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.625",
    doi = "10.18653/v1/2020.emnlp-main.625",
    pages = "7766--7780",
    abstract = "Policy gradients-based reinforcement learning has proven to be a promising approach for directly optimizing non-differentiable evaluation metrics for language generation tasks. However, optimizing for a specific metric reward leads to improvements in mostly that metric only, suggesting that the model is gaming the formulation of that metric in a particular way without often achieving real qualitative improvements. Hence, it is more beneficial to make the model optimize multiple diverse metric rewards jointly. While appealing, this is challenging because one needs to manually decide the importance and scaling weights of these metric rewards. Further, it is important to consider using a dynamic combination and curriculum of metric rewards that flexibly changes over time. Considering the above aspects, in our work, we automate the optimization of multiple metric rewards simultaneously via a multi-armed bandit approach (DORB), where at each round, the bandit chooses which metric reward to optimize next, based on expected arm gains. We use the Exp3 algorithm for bandits and formulate two approaches for bandit rewards: (1) Single Multi-reward Bandit (SM-Bandit); (2) Hierarchical Multi-reward Bandit (HM-Bandit). We empirically show the effectiveness of our approaches via various automatic metrics and human evaluation on two important NLG tasks: question generation and data-to-text generation. Finally, we present interpretable analyses of the learned bandit curriculum over the optimized rewards.",
}
@inproceedings{khosla-etal-2020-medfilter,
    title = "{M}ed{F}ilter: {I}mproving {E}xtraction of {T}ask-relevant {U}tterances through {I}ntegration of {D}iscourse {S}tructure and {O}ntological {K}nowledge",
    author = "Khosla, Sopan  and
      Vashishth, Shikhar  and
      Lehman, Jill Fain  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.626",
    doi = "10.18653/v1/2020.emnlp-main.626",
    pages = "7781--7797",
    abstract = "Information extraction from conversational data is particularly challenging because the task-centric nature of conversation allows for effective communication of implicit information by humans, but is challenging for machines. The challenges may differ between utterances depending on the role of the speaker within the conversation, especially when relevant expertise is distributed asymmetrically across roles. Further, the challenges may also increase over the conversation as more shared context is built up through information communicated implicitly earlier in the dialogue. In this paper, we propose the novel modeling approach MedFilter, which addresses these insights in order to increase performance at identifying and categorizing task-relevant utterances, and in so doing, positively impacts performance at a downstream information extraction task. We evaluate this approach on a corpus of nearly 7,000 doctor-patient conversations where MedFilter is used to identify medically relevant contributions to the discussion (achieving a 10{\%} improvement over SOTA baselines in terms of area under the PR curve). Identifying task-relevant utterances benefits downstream medical processing, achieving improvements of 15{\%}, 105{\%}, and 23{\%} respectively for the extraction of symptoms, medications, and complaints.",
}
@inproceedings{subramanian-lee-2020-hierarchical,
    title = "{H}ierarchical {E}vidence {S}et {M}odeling for Automated Fact Extraction and Verification",
    author = "Subramanian, Shyam  and
      Lee, Kyumin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.627",
    doi = "10.18653/v1/2020.emnlp-main.627",
    pages = "7798--7809",
    abstract = "Automated fact extraction and verification is a challenging task that involves finding relevant evidence sentences from a reliable corpus to verify the truthfulness of a claim. Existing models either (i) concatenate all the evidence sentences, leading to the inclusion of redundant and noisy information; or (ii) process each claim-evidence sentence pair separately and aggregate all of them later, missing the early combination of related sentences for more accurate claim verification. Unlike the prior works, in this paper, we propose Hierarchical Evidence Set Modeling (HESM), a framework to extract evidence sets (each of which may contain multiple evidence sentences), and verify a claim to be supported, refuted or not enough info, by encoding and attending the claim and evidence sets at different levels of hierarchy. Our experimental results show that HESM outperforms 7 state-of-the-art methods for fact extraction and claim verification. Our source code is available at https://github.com/ShyamSubramanian/HESM.",
}
@inproceedings{yang-etal-2020-program,
    title = "Program Enhanced Fact Verification with Verbalization and Graph Attention Network",
    author = "Yang, Xiaoyu  and
      Nie, Feng  and
      Feng, Yufei  and
      Liu, Quan  and
      Chen, Zhigang  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.628",
    doi = "10.18653/v1/2020.emnlp-main.628",
    pages = "7810--7825",
    abstract = "Performing fact verification based on structured data is important for many real-life applications and is a challenging research problem, particularly when it involves both symbolic operations and informal inference based on language understanding. In this paper, we present a Program-enhanced Verbalization and Graph Attention Network (ProgVGAT) to integrate programs and execution into textual inference models. Specifically, a verbalization with program execution model is proposed to accumulate evidences that are embedded in operations over the tables. Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision. To support the above framework, we propose a program selection module optimized with a new training strategy based on margin loss, to produce more accurate programs, which is shown to be effective in enhancing the final verification results. Experimental results show that the proposed framework achieves the new state-of-the-art performance, a 74.4{\%} accuracy, on the benchmark dataset TABFACT.",
}
@inproceedings{pratapa-etal-2020-constrained,
    title = "{C}onstrained {F}act {V}erification for {FEVER}",
    author = "Pratapa, Adithya  and
      Jayanthi, Sai Muralidhar  and
      Nerella, Kavya",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.629",
    doi = "10.18653/v1/2020.emnlp-main.629",
    pages = "7826--7832",
    abstract = "Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER. Though the task requires reasoning on extracted evidence to verify a claim{'}s factuality, there is little work on understanding the reasoning process. In this work, we propose a new methodology for fact-verification, specifically FEVER, that enforces a closed-world reliance on extracted evidence. We present an extensive evaluation of state-of-the-art verification models under these constraints.",
}
@inproceedings{botha-etal-2020-entity,
    title = "{E}ntity {L}inking in 100 {L}anguages",
    author = "Botha, Jan A.  and
      Shan, Zifei  and
      Gillick, Daniel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.630",
    doi = "10.18653/v1/2020.emnlp-main.630",
    pages = "7833--7845",
    abstract = "We propose a new formulation for multilingual entity linking, where language-specific mentions resolve to a language-agnostic Knowledge Base. We train a dual encoder in this new setting, building on prior work with improved feature representation, negative mining, and an auxiliary entity-pairing task, to obtain a single entity retrieval model that covers 100+ languages and 20 million entities. The model outperforms state-of-the-art results from a far more limited cross-lingual linking task. Rare entities and low-resource languages pose challenges at this large-scale, so we advocate for an increased focus on zero- and few-shot evaluation. To this end, we provide Mewsli-9, a large new multilingual dataset matched to our setting, and show how frequency-based analysis provided key insights for our model and training enhancements.",
}
@inproceedings{moon-okazaki-2020-patchbert,
    title = "{P}atch{BERT}: Just-in-Time, Out-of-Vocabulary Patching",
    author = "Moon, Sangwhan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.631",
    doi = "10.18653/v1/2020.emnlp-main.631",
    pages = "7846--7852",
    abstract = "Large scale pre-trained language models have shown groundbreaking performance improvements for transfer learning in the domain of natural language processing. In our paper, we study a pre-trained multilingual BERT model and analyze the OOV rate on downstream tasks, how it introduces information loss, and as a side-effect, obstructs the potential of the underlying model. We then propose multiple approaches for mitigation and demonstrate that it improves performance with the same parameter count when combined with fine-tuning.",
}
@inproceedings{micheli-etal-2020-importance,
    title = "On the importance of pre-training data volume for compact language models",
    author = "Micheli, Vincent  and
      d{'}Hoffschmidt, Martin  and
      Fleuret, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.632",
    doi = "10.18653/v1/2020.emnlp-main.632",
    pages = "7853--7858",
    abstract = "Recent advances in language modeling have led to computationally intensive and resource-demanding state-of-the-art models. In an effort towards sustainable practices, we study the impact of pre-training data volume on compact language models. Multiple BERT-based models are trained on gradually increasing amounts of French text. Through fine-tuning on the French Question Answering Dataset (FQuAD), we observe that well-performing models are obtained with as little as 100 MB of text. In addition, we show that past critically low amounts of pre-training data, an intermediate pre-training step on the task-specific corpus does not yield substantial improvements.",
}
@inproceedings{xu-etal-2020-bert,
    title = "{BERT}-of-Theseus: Compressing {BERT} by Progressive Module Replacing",
    author = "Xu, Canwen  and
      Zhou, Wangchunshu  and
      Ge, Tao  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.633",
    doi = "10.18653/v1/2020.emnlp-main.633",
    pages = "7859--7869",
    abstract = "In this paper, we propose a novel model compression approach to effectively compress BERT by progressive module replacing. Our approach first divides the original BERT into several modules and builds their compact substitutes. Then, we randomly replace the original modules with their substitutes to train the compact modules to mimic the behavior of the original modules. We progressively increase the probability of replacement through the training. In this way, our approach brings a deeper level of interaction between the original and compact models. Compared to the previous knowledge distillation approaches for BERT compression, our approach does not introduce any additional loss function. Our approach outperforms existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression.",
}
@inproceedings{chen-etal-2020-recall,
    title = "Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting",
    author = "Chen, Sanyuan  and
      Hou, Yutai  and
      Cui, Yiming  and
      Che, Wanxiang  and
      Liu, Ting  and
      Yu, Xiangzhan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.634",
    doi = "10.18653/v1/2020.emnlp-main.634",
    pages = "7870--7881",
    abstract = "Deep pretrained language models have achieved great success in the way of pretraining first and then fine-tuning. But such a sequential transfer learning paradigm often confronts the catastrophic forgetting problem and leads to sub-optimal performance. To fine-tune with less forgetting, we propose a recall and learn mechanism, which adopts the idea of multi-task learning and jointly learns pretraining tasks and downstream tasks. Specifically, we introduce a Pretraining Simulation mechanism to recall the knowledge from pretraining tasks without data, and an Objective Shifting mechanism to focus the learning on downstream tasks gradually. Experiments show that our method achieves state-of-the-art performance on the GLUE benchmark. Our method also enables BERT-base to achieve better average performance than directly fine-tuning of BERT-large. Further, we provide the open-source RecAdam optimizer, which integrates the proposed mechanisms into Adam optimizer, to facility the NLP community.",
}
@inproceedings{vu-etal-2020-exploring,
    title = "Exploring and Predicting Transferability across {NLP} Tasks",
    author = "Vu, Tu  and
      Wang, Tong  and
      Munkhdalai, Tsendsuren  and
      Sordoni, Alessandro  and
      Trischler, Adam  and
      Mattarella-Micke, Andrew  and
      Maji, Subhransu  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.635",
    doi = "10.18653/v1/2020.emnlp-main.635",
    pages = "7882--7926",
    abstract = "Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other than language modeling further improve performance? In this paper, we conduct an extensive study of the transferability between 33 NLP tasks across three broad classes of problems (text classification, question answering, and sequence labeling). Our results show that transfer learning is more beneficial than previously thought, especially when target task data is scarce, and can improve performance even with low-data source tasks that differ substantially from the target task (e.g., part-of-speech tagging transfers well to the DROP QA dataset). We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size. Overall, our experiments reveal that factors such as data size, task and domain similarity, and task complexity all play a role in determining transferability.",
}
@inproceedings{bhattacharjee-etal-2020-bert,
    title = "To {BERT} or Not to {BERT}: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging",
    author = "Bhattacharjee, Kasturi  and
      Ballesteros, Miguel  and
      Anubhai, Rishita  and
      Muresan, Smaranda  and
      Ma, Jie  and
      Ladhak, Faisal  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.636",
    doi = "10.18653/v1/2020.emnlp-main.636",
    pages = "7927--7934",
    abstract = "Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success. However, training these models can be costly both from an economic and environmental standpoint. In this work, we investigate how to effectively use unlabeled data: by exploring the task-specific semi-supervised approach, Cross-View Training (CVT) and comparing it with task-agnostic BERT in multiple settings that include domain and task relevant English data. CVT uses a much lighter model architecture and we show that it achieves similar performance to BERT on a set of sequence tagging tasks, with lesser financial and environmental impact.",
}
@inproceedings{yuan-etal-2020-cold,
    title = "Cold-start Active Learning through Self-supervised Language Modeling",
    author = "Yuan, Michelle  and
      Lin, Hsuan-Tien  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.637",
    doi = "10.18653/v1/2020.emnlp-main.637",
    pages = "7935--7948",
    abstract = "Active learning strives to reduce annotation costs by choosing the most critical examples to label. Typically, the active learning strategy is contingent on the classification model. For instance, uncertainty sampling depends on poorly calibrated model confidence scores. In the cold-start setting, active learning is impractical because of model instability and data scarcity. Fortunately, modern NLP provides an additional source of information: pre-trained language models. The pre-training loss can find examples that surprise the model and should be labeled for efficient fine-tuning. Therefore, we treat the language modeling loss as a proxy for classification uncertainty. With BERT, we develop a simple strategy based on the masked language modeling loss that minimizes labeling costs for text classification. Compared to other baselines, our approach reaches higher accuracy within less sampling iterations and computation time.",
}
@inproceedings{ein-dor-etal-2020-active,
    title = "{A}ctive {L}earning for {BERT}: {A}n {E}mpirical {S}tudy",
    author = "Ein-Dor, Liat  and
      Halfon, Alon  and
      Gera, Ariel  and
      Shnarch, Eyal  and
      Dankin, Lena  and
      Choshen, Leshem  and
      Danilevsky, Marina  and
      Aharonov, Ranit  and
      Katz, Yoav  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.638",
    doi = "10.18653/v1/2020.emnlp-main.638",
    pages = "7949--7962",
    abstract = "Real world scenarios present a challenge for text classification, since labels are usually expensive and the data is often characterized by class imbalance. Active Learning (AL) is a ubiquitous paradigm to cope with data scarcity. Recently, pre-trained NLP models, and BERT in particular, are receiving massive attention due to their outstanding performance in various NLP tasks. However, the use of AL with deep pre-trained models has so far received little consideration. Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets. We focus on practical scenarios of binary text classification, where the annotation budget is very small, and the data is often skewed. Our results demonstrate that AL can boost BERT performance, especially in the most realistic scenario in which the initial set of labeled examples is created using keyword-based queries, resulting in a biased sample of the minority class. We release our research framework, aiming to facilitate future research along the lines explored here.",
}
@inproceedings{wright-augenstein-2020-transformer,
    title = "Transformer Based Multi-Source Domain Adaptation",
    author = "Wright, Dustin  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.639",
    doi = "10.18653/v1/2020.emnlp-main.639",
    pages = "7963--7974",
    abstract = "In practical machine learning settings, the data on which a model must make predictions often come from a different distribution than the data it was trained on. Here, we investigate the problem of unsupervised multi-source domain adaptation, where a model is trained on labelled data from multiple source domains and must make predictions on a domain for which no labelled data has been seen. Prior work with CNNs and RNNs has demonstrated the benefit of mixture of experts, where the predictions of multiple domain expert classifiers are combined; as well as domain adversarial training, to induce a domain agnostic representation space. Inspired by this, we investigate how such methods can be effectively applied to large pretrained transformer models. We find that domain adversarial training has an effect on the learned representations of these models while having little effect on their performance, suggesting that large transformer-based models are already relatively robust across domains. Additionally, we show that mixture of experts leads to significant performance improvements by comparing several variants of mixing functions, including one novel metric based on attention. Finally, we demonstrate that the predictions of large pretrained transformer based domain experts are highly homogenous, making it challenging to learn effective metrics for mixing their predictions.",
}
@inproceedings{khoury-etal-2020-vector,
    title = "Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in {NLP} Applications",
    author = "Khoury, Matthew  and
      Dangovski, Rumen  and
      Ou, Longwu  and
      Nakov, Preslav  and
      Shen, Yichen  and
      Jing, Li",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.640",
    doi = "10.18653/v1/2020.emnlp-main.640",
    pages = "7975--7984",
    abstract = "Deep neural networks have become the standard approach to building reliable Natural Language Processing (NLP) applications, ranging from Neural Machine Translation (NMT) to dialogue systems. However, improving accuracy by increasing the model size requires a large number of hardware computations, which can slow down NLP applications significantly at inference time. To address this issue, we propose a novel vector-vector-matrix architecture (VVMA), which greatly reduces the latency at inference time for NMT. This architecture takes advantage of specialized hardware that has low-latency vector-vector operations and higher-latency vector-matrix operations. It also reduces the number of parameters and FLOPs for virtually all models that rely on efficient matrix multipliers without significantly impacting accuracy. We present empirical results suggesting that our framework can reduce the latency of sequence-to-sequence and Transformer models used for NMT by a factor of four. Finally, we show evidence suggesting that our VVMA extends to other domains, and we discuss novel hardware for its efficient use.",
}
@inproceedings{dinkar-etal-2020-importance,
    title = "The importance of fillers for text representations of speech transcripts",
    author = "Dinkar, Tanvi  and
      Colombo, Pierre  and
      Labeau, Matthieu  and
      Clavel, Chlo{\'e}",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.641",
    doi = "10.18653/v1/2020.emnlp-main.641",
    pages = "7985--7993",
    abstract = "While being an essential component of spoken language, fillers (e.g. {``}um{''} or {``}uh{''}) often remain overlooked in Spoken Language Understanding (SLU) tasks. We explore the possibility of representing them with deep contextualised embeddings, showing improvements on modelling spoken language and two downstream tasks {---} predicting a speaker{'}s stance and expressed confidence.",
}
@inproceedings{nielsen-etal-2020-role,
    title = "The role of context in neural pitch accent detection in {E}nglish",
    author = "Nielsen, Elizabeth  and
      Steedman, Mark  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.642",
    doi = "10.18653/v1/2020.emnlp-main.642",
    pages = "7994--8000",
    abstract = "Prosody is a rich information source in natural language, serving as a marker for phenomena such as contrast. In order to make this information available to downstream tasks, we need a way to detect prosodic events in speech. We propose a new model for pitch accent detection, inspired by the work of Stehwien et al. (2018), who presented a CNN-based model for this task. Our model makes greater use of context by using full utterances as input and adding an LSTM layer. We find that these innovations lead to an improvement from 87.5{\%} to 88.7{\%} accuracy on pitch accent detection on American English speech in the Boston University Radio News Corpus, a state-of-the-art result. We also find that a simple baseline that just predicts a pitch accent on every content word yields 82.2{\%} accuracy, and we suggest that this is the appropriate baseline for this task. Finally, we conduct ablation tests that show pitch is the most important acoustic feature for this task and this corpus.",
}
@inproceedings{sawhney-etal-2020-voltage,
    title = "{V}ol{TAGE}: Volatility Forecasting via Text Audio Fusion with Graph Convolution Networks for Earnings Calls",
    author = "Sawhney, Ramit  and
      Khanna, Piyush  and
      Aggarwal, Arshiya  and
      Jain, Taru  and
      Mathur, Puneet  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.643",
    doi = "10.18653/v1/2020.emnlp-main.643",
    pages = "8001--8013",
    abstract = "Natural language processing has recently made stock movement forecasting and volatility forecasting advances, leading to improved financial forecasting. Transcripts of companies{'} earnings calls are well studied for risk modeling, offering unique investment insight into stock performance. However, vocal cues in the speech of company executives present an underexplored rich source of natural language data for estimating financial risk. Additionally, most existing approaches ignore the correlations between stocks. Building on existing work, we introduce a neural model for stock volatility prediction that accounts for stock interdependence via graph convolutions while fusing verbal, vocal, and financial features in a semi-supervised multi-task risk forecasting formulation. Our proposed model, VolTAGE, outperforms existing methods demonstrating the effectiveness of multimodal learning for volatility prediction.",
}
@inproceedings{alinejad-sarkar-2020-effectively,
    title = "Effectively pretraining a speech translation decoder with Machine Translation data",
    author = "Alinejad, Ashkan  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.644",
    doi = "10.18653/v1/2020.emnlp-main.644",
    pages = "8014--8020",
    abstract = "Directly translating from speech to text using an end-to-end approach is still challenging for many language pairs due to insufficient data. Although pretraining the encoder parameters using the Automatic Speech Recognition (ASR) task improves the results in low resource settings, attempting to use pretrained parameters from the Neural Machine Translation (NMT) task has been largely unsuccessful in previous works. In this paper, we will show that by using an adversarial regularizer, we can bring the encoder representations of the ASR and NMT tasks closer even though they are in different modalities, and how this helps us effectively use a pretrained NMT decoder for speech translation.",
}
@inproceedings{swaminathan-etal-2020-preliminary,
    title = "A Preliminary Exploration of {GAN}s for Keyphrase Generation",
    author = "Swaminathan, Avinash  and
      Zhang, Haimin  and
      Mahata, Debanjan  and
      Gosangi, Rakesh  and
      Shah, Rajiv Ratn  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.645",
    doi = "10.18653/v1/2020.emnlp-main.645",
    pages = "8021--8030",
    abstract = "We introduce a new keyphrase generation approach using Generative Adversarial Networks (GANs). For a given document, the generator produces a sequence of keyphrases, and the discriminator distinguishes between human-curated and machine-generated keyphrases. We evaluated this approach on standard benchmark datasets. We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques. Although we achieve promising results using GANs, they are not significantly better than the state-of-the-art generative models. To our knowledge, this is one of the first works that use GANs for keyphrase generation. We present a detailed analysis of our observations and expect that these findings would help other researchers to further study the use of GANs for the task of keyphrase generation.",
}
@inproceedings{jumel-etal-2020-tesa,
    title = "{TESA}: A {T}ask in {E}ntity {S}emantic {A}ggregation for Abstractive Summarization",
    author = "Jumel, Cl{\'e}ment  and
      Louis, Annie  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.646",
    doi = "10.18653/v1/2020.emnlp-main.646",
    pages = "8031--8050",
    abstract = "Human-written texts contain frequent generalizations and semantic aggregation of content. In a document, they may refer to a pair of named entities such as {`}London{'} and {`}Paris{'} with different expressions: {``}the major cities{''}, {``}the capital cities{''} and {``}two European cities{''}. Yet generation, especially, abstractive summarization systems have so far focused heavily on paraphrasing and simplifying the source content, to the exclusion of such semantic abstraction capabilities. In this paper, we present a new dataset and task aimed at the semantic aggregation of entities. TESA contains a dataset of 5.3K crowd-sourced entity aggregations of Person, Organization, and Location named entities. The aggregations are document-appropriate, meaning that they are produced by annotators to match the situational context of a given news article from the New York Times. We then build baseline models for generating aggregations given a tuple of entities and document context. We finetune on TESA an encoder-decoder language model and compare it with simpler classification methods based on linguistically informed features. Our quantitative and qualitative evaluations show reasonable performance in making a choice from a given list of expressions, but free-form expressions are understandably harder to generate and evaluate.",
}
@inproceedings{scialom-etal-2020-mlsum,
    title = "{MLSUM}: The Multilingual Summarization Corpus",
    author = "Scialom, Thomas  and
      Dray, Paul-Alexis  and
      Lamprier, Sylvain  and
      Piwowarski, Benjamin  and
      Staiano, Jacopo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.647",
    doi = "10.18653/v1/2020.emnlp-main.647",
    pages = "8051--8067",
    abstract = "We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages {--} namely, French, German, Spanish, Russian, Turkish. Together with English news articles from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.",
}
@inproceedings{lu-etal-2020-multi-xscience,
    title = "Multi-{XS}cience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles",
    author = "Lu, Yao  and
      Dong, Yue  and
      Charlin, Laurent",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.648",
    doi = "10.18653/v1/2020.emnlp-main.648",
    pages = "8068--8074",
    abstract = "Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results{---}using several state-of-the-art models trained on the Multi-XScience dataset{---}reveal that Multi-XScience is well suited for abstractive models.",
}
@inproceedings{bommasani-cardie-2020-intrinsic,
    title = "Intrinsic Evaluation of Summarization Datasets",
    author = "Bommasani, Rishi  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.649",
    doi = "10.18653/v1/2020.emnlp-main.649",
    pages = "8075--8096",
    abstract = "High quality data forms the bedrock for building meaningful statistical models in NLP. Consequently, data quality must be evaluated either during dataset construction or *post hoc*. Almost all popular summarization datasets are drawn from natural sources and do not come with inherent quality assurance guarantees. In spite of this, data quality has gone largely unquestioned for many of these recent datasets. We perform the first large-scale evaluation of summarization datasets by introducing 5 intrinsic metrics and applying them to 10 popular datasets. We find that data usage in recent summarization research is sometimes inconsistent with the underlying properties of the data. Further, we discover that our metrics can serve the additional purpose of being inexpensive heuristics for detecting generically low quality examples.",
}
@inproceedings{larson-etal-2020-iterative,
    title = "Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness",
    author = "Larson, Stefan  and
      Zheng, Anthony  and
      Mahendran, Anish  and
      Tekriwal, Rishi  and
      Cheung, Adrian  and
      Guldan, Eric  and
      Leach, Kevin  and
      Kummerfeld, Jonathan K.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.650",
    doi = "10.18653/v1/2020.emnlp-main.650",
    pages = "8097--8106",
    abstract = "Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts. We propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing. We show how prior workflows are special cases of our approach, and present a way to apply the approach to dialog tasks such as intent classification and slot-filling. Using our method, we create more challenging versions of test sets from prior dialog datasets and find dramatic performance drops for standard models. Finally, we show that our approach is complementary to recent work on improving data diversity, and training on data collected with our approach leads to more robust models.",
}
@inproceedings{cheng-etal-2020-conversational,
    title = "Conversational Semantic Parsing for Dialog State Tracking",
    author = "Cheng, Jianpeng  and
      Agrawal, Devang  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Bhargava, Shruti  and
      Driesen, Joris  and
      Flego, Federico  and
      Kaplan, Dain  and
      Kartsaklis, Dimitri  and
      Li, Lin  and
      Piraviperumal, Dhivya  and
      Williams, Jason D.  and
      Yu, Hong  and
      {\'O} S{\'e}aghdha, Diarmuid  and
      Johannsen, Anders",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.651",
    doi = "10.18653/v1/2020.emnlp-main.651",
    pages = "8107--8117",
    abstract = "We consider a new perspective on dialog state tracking (DST), the task of estimating a user{'}s goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. We describe an encoder-decoder framework for DST with hierarchical representations, which leads to {\textasciitilde}20{\%} improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.",
}
@inproceedings{feng-etal-2020-doc2dial,
    title = "doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset",
    author = "Feng, Song  and
      Wan, Hui  and
      Gunasekara, Chulaka  and
      Patel, Siva  and
      Joshi, Sachindra  and
      Lastras, Luis",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.652",
    doi = "10.18653/v1/2020.emnlp-main.652",
    pages = "8118--8128",
    abstract = "We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section. Then we present these dialogue flows to crowd contributors to create conversational utterances. The dataset includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations. For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.",
}
@inproceedings{majumder-etal-2020-interview,
    title = "Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding",
    author = "Majumder, Bodhisattwa Prasad  and
      Li, Shuyang  and
      Ni, Jianmo  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.653",
    doi = "10.18653/v1/2020.emnlp-main.653",
    pages = "8129--8141",
    abstract = "In this work, we perform the first large-scale analysis of discourse in media dialog and its impact on generative modeling of dialog turns, with a focus on interrogative patterns and use of external knowledge. Discourse analysis can help us understand modes of persuasion, entertainment, and information elicitation in such settings, but has been limited to manual review of small corpora. We introduce **Interview**{---}a large-scale (105K conversations) media dialog dataset collected from news interview transcripts{---}which allows us to investigate such patterns at scale. We present a dialog model that leverages external knowledge as well as dialog acts via auxiliary losses and demonstrate that our model quantitatively and qualitatively outperforms strong discourse-agnostic baselines for dialog modeling{---}generating more specific and topical responses in interview-style conversations.",
}
@inproceedings{hayati-etal-2020-inspired,
    title = "{INSPIRED}: Toward Sociable Recommendation Dialog Systems",
    author = "Hayati, Shirley Anugrah  and
      Kang, Dongyeop  and
      Zhu, Qingxiaoyang  and
      Shi, Weiyan  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.654",
    doi = "10.18653/v1/2020.emnlp-main.654",
    pages = "8142--8152",
    abstract = "In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations. Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels. In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model. This work is a first step for building sociable recommendation dialog systems with a basis of social science theories.",
}
@inproceedings{rodriguez-etal-2020-information,
    title = "Information Seeking in the Spirit of Learning: A Dataset for Conversational Curiosity",
    author = "Rodriguez, Pedro  and
      Crook, Paul  and
      Moon, Seungwhan  and
      Wang, Zhiguang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.655",
    doi = "10.18653/v1/2020.emnlp-main.655",
    pages = "8153--8172",
    abstract = "Open-ended human learning and information-seeking are increasingly mediated by digital assistants. However, such systems often ignore the user{'}s pre-existing knowledge. Assuming a correlation between engagement and user responses such as {``}liking{''} messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. Through crowd-sourcing of this experiment, we collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. Responses using a user{'}s prior knowledge increase engagement. We incorporate this knowledge into a multi-task model that reproduces human assistant policies and improves over a bert content model by 13 mean reciprocal rank points.",
}
@inproceedings{dinan-etal-2020-queens,
    title = "Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation",
    author = "Dinan, Emily  and
      Fan, Angela  and
      Williams, Adina  and
      Urbanek, Jack  and
      Kiela, Douwe  and
      Weston, Jason",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.656",
    doi = "10.18653/v1/2020.emnlp-main.656",
    pages = "8173--8188",
    abstract = "Social biases present in data are often directly reflected in the predictions of models trained on that data. We analyze gender bias in dialogue data, and examine how this bias is not only replicated, but is also amplified in subsequent generative chit-chat dialogue models. We measure gender bias in six existing dialogue datasets before selecting the most biased one, the multi-player text-based fantasy adventure dataset LIGHT, as a testbed for bias mitigation techniques. We consider three techniques to mitigate gender bias: counterfactual data augmentation, targeted data collection, and bias controlled training. We show that our proposed techniques mitigate gender bias by balancing the genderedness of generated dialogue utterances, and find that they are particularly effective in combination. We evaluate model performance with a variety of quantitative methods{---}including the quantity of gendered words, a dialogue safety classifier, and human assessments{---}all of which show that our models generate less gendered, but equally engaging chit-chat responses.",
}
@inproceedings{ding-etal-2020-discriminatively,
    title = "Discriminatively-{T}uned {G}enerative {C}lassifiers for {R}obust {N}atural {L}anguage {I}nference",
    author = "Ding, Xiaoan  and
      Liu, Tianyu  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.657",
    doi = "10.18653/v1/2020.emnlp-main.657",
    pages = "8189--8202",
    abstract = "While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In this paper, we focus on natural language inference (NLI). We propose GenNLI, a generative classifier for NLI tasks, and empirically characterize its performance by comparing it to five baselines, including discriminative models and large-scale pretrained language representation models like BERT. We explore training objectives for discriminative fine-tuning of our generative classifiers, showing improvements over log loss fine-tuning from prior work (Lewis and Fan, 2019). In particular, we find strong results with a simple unbounded modification to log loss, which we call the {``}infinilog loss{''}. Our experiments show that GenNLI outperforms both discriminative and pretrained baselines across several challenging NLI experimental settings, including small training sets, imbalanced label distributions, and label noise.",
}
@inproceedings{bowman-etal-2020-new,
    title = "New Protocols and Negative Results for Textual Entailment Data Collection",
    author = "Bowman, Samuel R.  and
      Palomaki, Jennimaria  and
      Baldini Soares, Livio  and
      Pitler, Emily",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.658",
    doi = "10.18653/v1/2020.emnlp-main.658",
    pages = "8203--8214",
    abstract = "Natural language inference (NLI) data has proven useful in benchmarking and, especially, as pretraining data for tasks requiring language understanding. However, the crowdsourcing protocol that was used to collect this data has known issues and was not explicitly optimized for either of these purposes, so it is likely far from ideal. We propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples. Using these alternatives and a fifth baseline protocol, we collect and compare five new 8.5k-example training sets. In evaluations focused on transfer learning applications, our results are solidly negative, with models trained on our baseline dataset yielding good transfer performance to downstream tasks, but none of our four new methods (nor the recent ANLI) showing any improvements over that baseline. In a small silver lining, we observe that all four new protocols, especially those where annotators edit *pre-filled* text boxes, reduce previously observed issues with annotation artifacts.",
}
@inproceedings{zhou-etal-2020-curse,
    title = "The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions",
    author = "Zhou, Xiang  and
      Nie, Yixin  and
      Tan, Hao  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.659",
    doi = "10.18653/v1/2020.emnlp-main.659",
    pages = "8215--8228",
    abstract = "We find that the performance of state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis/stress sets can be highly unstable. This raises three questions: (1) How will the instability affect the reliability of the conclusions drawn based on these analysis sets? (2) Where does this instability come from? (3) How should we handle this instability and what are some potential solutions? For the first question, we conduct a thorough empirical study over analysis sets and find that in addition to the unstable final performance, the instability exists all along the training curve. We also observe lower-than-expected correlations between the analysis validation set and standard validation set, questioning the effectiveness of the current model-selection routine. Next, to answer the second question, we give both theoretical explanations and empirical evidence regarding the source of the instability, demonstrating that the instability mainly comes from high inter-example correlations within analysis sets. Finally, for the third question, we discuss an initial attempt to mitigate the instability and suggest guidelines for future work such as reporting the decomposed variance for more interpretable results and fair comparison across models.",
}
@inproceedings{yin-etal-2020-universal,
    title = "Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start",
    author = "Yin, Wenpeng  and
      Rajani, Nazneen Fatema  and
      Radev, Dragomir  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.660",
    doi = "10.18653/v1/2020.emnlp-main.660",
    pages = "8229--8239",
    abstract = "A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we desire a single machine that can handle diverse new problems, for which task-specific annotations are limited. We bring up textual entailment as a unified solver for such NLP problems. However, current research of textual entailment has not spilled much ink on the following questions: (i) How well does a pretrained textual entailment system generalize across domains with only a handful of domain-specific examples? and (ii) When is it worth transforming an NLP task into textual entailment? We argue that the transforming is unnecessary if we can obtain rich annotations for this task. Textual entailment really matters particularly when the target NLP task has insufficient annotations. Universal NLP can be probably achieved through different routines. In this work, we introduce Universal Few-shot textual Entailment (UFO-Entail). We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited.",
}
@inproceedings{saha-etal-2020-conjnli,
    title = "{C}onj{NLI}: Natural Language Inference Over Conjunctive Sentences",
    author = "Saha, Swarnadeep  and
      Nie, Yixin  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.661",
    doi = "10.18653/v1/2020.emnlp-main.661",
    pages = "8240--8252",
    abstract = "Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic. Existing NLI stress tests do not consider non-boolean usages of conjunctions and use templates for testing such model knowledge. Hence, we introduce ConjNLI, a challenge stress-test for natural language inference over conjunctive sentences, where the premise differs from the hypothesis by conjuncts removed, added, or replaced. These sentences contain single and multiple instances of coordinating conjunctions ({``}and{''}, {``}or{''}, {``}but{''}, {``}nor{''}) with quantifiers, negations, and requiring diverse boolean and non-boolean inferences over conjuncts. We find that large-scale pre-trained language models like RoBERTa do not understand conjunctive semantics well and resort to shallow heuristics to make inferences over such sentences. As some initial solutions, we first present an iterative adversarial fine-tuning method that uses synthetically created training data based on boolean and non-boolean heuristics. We also propose a direct model advancement by making RoBERTa aware of predicate semantic roles. While we observe some performance gains, ConjNLI is still challenging for current methods, thus encouraging interesting future work for better understanding of conjunctions.",
}
@inproceedings{budur-etal-2020-data,
    title = "Data and {R}epresentation for {T}urkish {N}atural {L}anguage {I}nference",
    author = {Budur, Emrah  and
      {\"O}z{\c{c}}elik, R{\i}za  and
      Gungor, Tunga  and
      Potts, Christopher},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.662",
    doi = "10.18653/v1/2020.emnlp-main.662",
    pages = "8253--8267",
    abstract = "Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly.",
}
@inproceedings{aminian-etal-2020-multitask,
    title = "Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies",
    author = "Aminian, Maryam  and
      Rasooli, Mohammad Sadegh  and
      Diab, Mona",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.663",
    doi = "10.18653/v1/2020.emnlp-main.663",
    pages = "8268--8274",
    abstract = "We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask learning framework coupled with annotation projection. We use syntactic parsing as the auxiliary task in our multitask setup. Our annotation projection experiments from English to Czech show that our multitask setup yields 3.1{\%} (4.2{\%}) improvement in labeled F1-score on in-domain (out-of-domain) test set compared to a single-task baseline.",
}
@inproceedings{liu-etal-2020-precise,
    title = "Precise Task Formalization Matters in {W}inograd Schema Evaluations",
    author = "Liu, Haokun  and
      Huang, William  and
      Mungra, Dhara  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.664",
    doi = "10.18653/v1/2020.emnlp-main.664",
    pages = "8275--8280",
    abstract = "Performance on the Winograd Schema Challenge (WSC), a respected English commonsense reasoning benchmark, recently rocketed from chance accuracy to 89{\%} on the SuperGLUE leaderboard, with relatively little corroborating evidence of a correspondingly large improvement in reasoning ability. We hypothesize that much of this improvement comes from recent changes in task formalization{---}the combination of input specification, loss function, and reuse of pretrained parameters{---}by users of the dataset, rather than improvements in the pretrained model{'}s reasoning ability. We perform an ablation on two Winograd Schema datasets that interpolates between the formalizations used before and after this surge, and find (i) framing the task as multiple choice improves performance dramatically and (ii)several additional techniques, including the reuse of a pretrained language modeling head, can mitigate the model{'}s extreme sensitivity to hyperparameters. We urge future benchmark creators to impose additional structure to minimize the impact of formalization decisions on reported results.",
}
@inproceedings{stacey-etal-2020-avoiding,
    title = "{A}voiding the {H}ypothesis-{O}nly {B}ias in {N}atural {L}anguage {I}nference via {E}nsemble {A}dversarial {T}raining",
    author = {Stacey, Joe  and
      Minervini, Pasquale  and
      Dubossarsky, Haim  and
      Riedel, Sebastian  and
      Rockt{\"a}schel, Tim},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.665",
    doi = "10.18653/v1/2020.emnlp-main.665",
    pages = "8281--8291",
    abstract = "Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes. These artefacts are exploited by neural networks even when only considering the hypothesis and ignoring the premise, leading to unwanted biases. Belinkov et al. (2019b) proposed tackling this problem via adversarial training, but this can lead to learned sentence representations that still suffer from the same biases. We show that the bias can be reduced in the sentence representations by using an ensemble of adversaries, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data. This approach produces more robust NLI models, outperforming previous de-biasing efforts when generalised to 12 other NLI datasets (Belinkov et al., 2019a; Mahabadi et al., 2020). In addition, we find that the optimal number of adversarial classifiers depends on the dimensionality of the sentence representations, with larger sentence representations being more difficult to de-bias while benefiting from using a greater number of adversaries.",
}
@inproceedings{shen-etal-2020-synsetexpan,
    title = "{S}yn{S}et{E}xpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery",
    author = "Shen, Jiaming  and
      Qiu, Wenda  and
      Shang, Jingbo  and
      Vanni, Michelle  and
      Ren, Xiang  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.666",
    doi = "10.18653/v1/2020.emnlp-main.666",
    pages = "8292--8307",
    abstract = "Entity set expansion and synonym discovery are two critical NLP tasks. Previous studies accomplish them separately, without exploring their interdependencies. In this work, we hypothesize that these two tasks are tightly coupled because two synonymous entities tend to have a similar likelihood of belonging to various semantic classes. This motivates us to design SynSetExpan, a novel framework that enables two tasks to mutually enhance each other. SynSetExpan uses a synonym discovery model to include popular entities{'} infrequent synonyms into the set, which boosts the set expansion recall. Meanwhile, the set expansion model, being able to determine whether an entity belongs to a semantic class, can generate pseudo training data to fine-tune the synonym discovery model towards better accuracy. To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing. Extensive experiments on the SE2 dataset and previous benchmarks demonstrate the effectiveness of SynSetExpan for both entity set expansion and synonym discovery tasks.",
}
@inproceedings{safavi-etal-2020-evaluating,
    title = "{E}valuating the {C}alibration of {K}nowledge {G}raph {E}mbeddings for {T}rustworthy {L}ink {P}rediction",
    author = "Safavi, Tara  and
      Koutra, Danai  and
      Meij, Edgar",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.667",
    doi = "10.18653/v1/2020.emnlp-main.667",
    pages = "8308--8321",
    abstract = "Little is known about the trustworthiness of predictions made by knowledge graph embedding (KGE) models. In this paper we take initial steps toward this direction by investigating the calibration of KGE models, or the extent to which they output confidence scores that reflect the expected correctness of predicted knowledge graph triples. We first conduct an evaluation under the standard closed-world assumption (CWA), in which predicted triples not already in the knowledge graph are considered false, and show that existing calibration techniques are effective for KGE under this common but narrow assumption. Next, we introduce the more realistic but challenging open-world assumption (OWA), in which unobserved predictions are not considered true or false until ground-truth labels are obtained. Here, we show that existing calibration techniques are much less effective under the OWA than the CWA, and provide explanations for this discrepancy. Finally, to motivate the utility of calibration for KGE from a practitioner{'}s perspective, we conduct a unique case study of human-AI collaboration, showing that calibrated predictions can improve human performance in a knowledge graph completion task.",
}
@inproceedings{zhang-zhang-2020-text,
    title = "Text Graph Transformer for Document Classification",
    author = "Zhang, Haopeng  and
      Zhang, Jiawei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.668",
    doi = "10.18653/v1/2020.emnlp-main.668",
    pages = "8322--8327",
    abstract = "Text classification is a fundamental problem in natural language processing. Recent studies applied graph neural network (GNN) techniques to capture global word co-occurrence in a corpus. However, previous works are not scalable to large-sized corpus and ignore the heterogeneity of the text graph. To address these problems, we introduce a novel Transformer based heterogeneous graph neural network, namely Text Graph Transformer (TG-Transformer). Our model learns effective node representations by capturing structure and heterogeneity from the text graph. We propose a mini-batch text graph sampling method that significantly reduces computing and memory costs to handle large-sized corpus. Extensive experiments have been conducted on several benchmark datasets, and the results demonstrate that TG-Transformer outperforms state-of-the-art approaches on text classification task.",
}
@inproceedings{safavi-koutra-2020-codex,
    title = "{C}o{DE}x: A {C}omprehensive {K}nowledge {G}raph {C}ompletion {B}enchmark",
    author = "Safavi, Tara  and
      Koutra, Danai",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.669",
    doi = "10.18653/v1/2020.emnlp-main.669",
    pages = "8328--8350",
    abstract = "We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. To characterize CoDEx, we contribute thorough empirical analyses and benchmarking experiments. First, we analyze each CoDEx dataset in terms of logical relation patterns. Next, we report baseline link prediction and triple classification results on CoDEx for five extensively tuned embedding models. Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark. Data, code, and pretrained models are available at https://bit.ly/2EPbrJs.",
}
@inproceedings{mekala-etal-2020-meta,
    title = "{META}: Metadata-Empowered Weak Supervision for Text Classification",
    author = "Mekala, Dheeraj  and
      Zhang, Xinyang  and
      Shang, Jingbo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.670",
    doi = "10.18653/v1/2020.emnlp-main.670",
    pages = "8351--8361",
    abstract = "Recent advances in weakly supervised learning enable training high-quality text classifiers by only providing a few user-provided seed words. Existing methods mainly use text data alone to generate pseudo-labels despite the fact that metadata information (e.g., author and timestamp) is widely available across various domains. Strong label indicators exist in the metadata and it has been long overlooked mainly due to the following challenges: (1) metadata is multi-typed, requiring systematic modeling of different types and their combinations, (2) metadata is noisy, some metadata entities (e.g., authors, venues) are more compelling label indicators than others. In this paper, we propose a novel framework, META, which goes beyond the existing paradigm and leverages metadata as an additional source of weak supervision. Specifically, we organize the text data and metadata together into a text-rich network and adopt network motifs to capture appropriate combinations of metadata. Based on seed words, we rank and filter motif instances to distill highly label-indicative ones as {``}seed motifs{''}, which provide additional weak supervision. Following a bootstrapping manner, we train the classifier and expand the seed words and seed motifs iteratively. Extensive experiments and case studies on real-world datasets demonstrate superior performance and significant advantages of leveraging metadata as weak supervision.",
}
@inproceedings{he-etal-2020-towards,
    title = "Towards More Accurate Uncertainty Estimation In Text Classification",
    author = "He, Jianfeng  and
      Zhang, Xuchao  and
      Lei, Shuo  and
      Chen, Zhiqian  and
      Chen, Fanglan  and
      Alhamadani, Abdulaziz  and
      Xiao, Bei  and
      Lu, ChangTien",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.671",
    doi = "10.18653/v1/2020.emnlp-main.671",
    pages = "8362--8372",
    abstract = "The uncertainty measurement of classified results is especially important in areas requiring limited human resources for higher accuracy. For instance, data-driven algorithms diagnosing diseases need accurate uncertainty score to decide whether additional but limited quantity of experts are needed for rectification. However, few uncertainty models focus on improving the performance of text classification where human resources are involved. To achieve this, we aim at generating accurate uncertainty score by improving the confidence of winning scores. Thus, a model called MSD, which includes three independent components as {``}mix-up{''}, {``}self-ensembling{''}, {``}distinctiveness score{''}, is proposed to improve the accuracy of uncertainty score by reducing the effect of overconfidence of winning score and considering the impact of different categories of uncertainty simultaneously. MSD can be applied with different Deep Neural Networks. Extensive experiments with ablation setting are conducted on four real-world datasets, on which, competitive results are obtained.",
}
@inproceedings{pethe-etal-2020-chapter,
    title = "{C}hapter {C}aptor: {T}ext {S}egmentation in {N}ovels",
    author = "Pethe, Charuta  and
      Kim, Allen  and
      Skiena, Steve",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.672",
    doi = "10.18653/v1/2020.emnlp-main.672",
    pages = "8373--8383",
    abstract = "Books are typically segmented into chapters and sections, representing coherent sub-narratives and topics. We investigate the task of predicting chapter boundaries, as a proxy for the general task of segmenting long texts. We build a Project Gutenberg chapter segmentation data set of 9,126 English novels, using a hybrid approach combining neural inference and rule matching to recognize chapter title headers in books, achieving an F1-score of 0.77 on this task. Using this annotated data as ground truth after removing structural cues, we present cut-based and neural methods for chapter segmentation, achieving a F1-score of 0.453 on the challenging task of exact break prediction over book-length documents. Finally, we reveal interesting historical trends in the chapter structure of novels.",
}
@inproceedings{uchendu-etal-2020-authorship,
    title = "Authorship Attribution for Neural Text Generation",
    author = "Uchendu, Adaku  and
      Le, Thai  and
      Shu, Kai  and
      Lee, Dongwon",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.673",
    doi = "10.18653/v1/2020.emnlp-main.673",
    pages = "8384--8395",
    abstract = "In recent years, the task of generating realistic short and long texts have made tremendous advancements. In particular, several recently proposed neural network-based language models have demonstrated their astonishing capabilities to generate texts that are challenging to distinguish from human-written texts with the naked eye. Despite many benefits and utilities of such neural methods, in some applications, being able to tell the {``}author{''} of a text in question becomes critically important. In this work, in the context of this Turing Test, we investigate the so-called authorship attribution problem in three versions: (1) given two texts T1 and T2, are both generated by the same method or not? (2) is the given text T written by a human or machine? (3) given a text T and k candidate neural methods, can we single out the method (among k alternatives) that generated T? Against one humanwritten and eight machine-generated texts (i.e., CTRL, GPT, GPT2, GROVER, XLM, XLNET, PPLM, FAIR), we empirically experiment with the performance of various models in three problems. By and large, we find that most generators still generate texts significantly different from human-written ones, thereby making three problems easier to solve. However, the qualities of texts generated by GPT2, GROVER, and FAIR are better, often confusing machine classifiers in solving three problems. All codes and datasets of our experiments are available at: https://bit.ly/ 302zWdz",
}
@inproceedings{guda-etal-2020-nwqm,
    title = "{N}w{QM}: A neural quality assessment framework for {W}ikipedia",
    author = "Guda, Bhanu Prakash Reddy  and
      Seelaboyina, Sasi Bhushan  and
      Sarkar, Soumya  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.674",
    doi = "10.18653/v1/2020.emnlp-main.674",
    pages = "8396--8406",
    abstract = "Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia Quality Monitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8{\%} improvement over state-of-the-art approaches with detailed ablation studies.",
}
@inproceedings{bhat-etal-2020-towards,
    title = "Towards Modeling Revision Requirements in wiki{H}ow Instructions",
    author = "Bhat, Irshad  and
      Anthonio, Talita  and
      Roth, Michael",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.675",
    doi = "10.18653/v1/2020.emnlp-main.675",
    pages = "8407--8414",
    abstract = "wikiHow is a resource of how-to guidesthat describe the steps necessary to accomplish a goal. Guides in this resource are regularly edited by a community of users, who try to improve instructions in terms of style, clarity and correctness. In this work, we test whether the need for such edits can be predicted automatically. For this task, we extend an existing resource of textual edits with a complementary set of approx. 4 million sentences that remain unedited over time and report on the outcome of two revision modeling experiments.",
}
@inproceedings{sawhney-etal-2020-deep,
    title = "Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations",
    author = "Sawhney, Ramit  and
      Agarwal, Shivam  and
      Wadhwa, Arnav  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.676",
    doi = "10.18653/v1/2020.emnlp-main.676",
    pages = "8415--8426",
    abstract = "In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S{\&}P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.",
}
@inproceedings{conforti-etal-2020-natural,
    title = "Natural Language Processing for Achieving Sustainable Development: the Case of Neural Labelling to Enhance Community Profiling",
    author = "Conforti, Costanza  and
      Hirmer, Stephanie  and
      Morgan, Dai  and
      Basaldella, Marco  and
      Ben Or, Yau",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.677",
    doi = "10.18653/v1/2020.emnlp-main.677",
    pages = "8427--8444",
    abstract = "In recent years, there has been an increasing interest in the application of Artificial Intelligence {--} and especially Machine Learning {--} to the field of Sustainable Development (SD). However, until now, NLP has not been systematically applied in this context. In this paper, we show the high potential of NLP to enhance project sustainability. In particular, we focus on the case of community profiling in developing countries, where, in contrast to the developed world, a notable data gap exists. Here, NLP could help to address the cost and time barrier of structuring qualitative data that prohibits its widespread use and associated benefits. We propose the new extreme multi-class multi-label Automatic UserPerceived Value classification task. We release Stories2Insights, an expert-annotated dataset of interviews carried out in Uganda, we provide a detailed corpus analysis, and we implement a number of strong neural baselines to address the task. Experimental results show that the problem is challenging, and leaves considerable room for future research at the intersection of NLP and SD.",
}
@inproceedings{patra-etal-2020-schedule,
    title = "To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints",
    author = "Patra, Barun  and
      Fufa, Chala  and
      Bhattacharya, Pamela  and
      Lee, Charles",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.678",
    doi = "10.18653/v1/2020.emnlp-main.678",
    pages = "8445--8455",
    abstract = "State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they don{'}t fare as well on task specific date-time entity extraction where only a subset of the date-time entities present in the text are pertinent to solving the task. Furthermore, some tasks require identifying negation constraints associated with the date-time entities to correctly reason over time. We showcase a novel model for extracting task-specific date-time entities along with their negation constraints. We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant. Our method achieves an absolute gain of 19{\%} f-score points compared to baseline methods in detecting the date-time entities relevant to scheduling meetings and a 4{\%} improvement over baseline methods for detecting negation constraints over date-time entities.",
}
@inproceedings{li-etal-2020-competence,
    title = "Competence-Level Prediction and Resume {\&} Job Description Matching Using Context-Aware Transformer Models",
    author = "Li, Changmao  and
      Fisher, Elaine  and
      Thomas, Rebecca  and
      Pittard, Steve  and
      Hertzberg, Vicki  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.679",
    doi = "10.18653/v1/2020.emnlp-main.679",
    pages = "8456--8466",
    abstract = "This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates. A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC). Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines. As a result, a high Kappa score of 61{\%} is achieved for inter-annotator agreement. Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job (T2). Our best models using section encoding and a multi-head attention decoding give results of 73.3{\%} to T1 and 79.2{\%} to T2. Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms.",
}
@inproceedings{flachs-etal-2020-grammatical,
    title = "Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses",
    author = "Flachs, Simon  and
      Lacroix, Oph{\'e}lie  and
      Yannakoudakis, Helen  and
      Rei, Marek  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.680",
    doi = "10.18653/v1/2020.emnlp-main.680",
    pages = "8467--8478",
    abstract = "Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.",
}
@inproceedings{kenyon-dean-etal-2020-deconstructing,
    title = "Deconstructing word embedding algorithms",
    author = "Kenyon-Dean, Kian  and
      Newell, Edward  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.681",
    doi = "10.18653/v1/2020.emnlp-main.681",
    pages = "8479--8484",
    abstract = "Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications. Uncontextualized word embeddings are used in many NLP tasks today, especially in resource-limited settings where high memory capacity and GPUs are not available. Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms. In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the common conditions that seem to be required for making performant word embeddings. We believe that the theoretical findings in this paper can provide a basis for more informed development of future models.",
}
@inproceedings{tsakalidis-liakata-2020-sequential,
    title = "Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection",
    author = "Tsakalidis, Adam  and
      Liakata, Maria",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.682",
    doi = "10.18653/v1/2020.emnlp-main.682",
    pages = "8485--8497",
    abstract = "Semantic change detection concerns the task of identifying words whose meaning has changed over time. Current state-of-the-art approaches operating on neural embeddings detect the level of semantic change in a word by comparing its vector representation in two distinct time periods, without considering its evolution through time. In this work, we propose three variants of sequential models for detecting semantically shifted words, effectively accounting for the changes in the word representations over time. Through extensive experimentation under various settings with synthetic and real data we showcase the importance of sequential modelling of word vectors through time for semantic change detection. Finally, we compare different approaches in a quantitative manner, demonstrating that temporal modelling of word representations yields a clear-cut advantage in performance.",
}
@inproceedings{berend-2020-sparsity,
    title = "Sparsity Makes Sense: Word Sense Disambiguation Using Sparse Contextualized Word Representations",
    author = "Berend, G{\'a}bor",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.683",
    doi = "10.18653/v1/2020.emnlp-main.683",
    pages = "8498--8508",
    abstract = "In this paper, we demonstrate that by utilizing sparse word representations, it becomes possible to surpass the results of more complex task-specific models on the task of fine-grained all-words word sense disambiguation. Our proposed algorithm relies on an overcomplete set of semantic basis vectors that allows us to obtain sparse contextualized word representations. We introduce such an information theory-inspired synset representation based on the co-occurrence of word senses and non-zero coordinates for word forms which allows us to achieve an aggregated F-score of 78.8 over a combination of five standard word sense disambiguating benchmark datasets. We also demonstrate the general applicability of our proposed framework by evaluating it towards part-of-speech tagging on four different treebanks. Our results indicate a significant improvement over the application of the dense word representations.",
}
@inproceedings{huang-etal-2020-exploring,
    title = "Exploring Semantic Capacity of Terms",
    author = "Huang, Jie  and
      Wang, Zilong  and
      Chang, Kevin  and
      Hwu, Wen-mei  and
      Xiong, JinJun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.684",
    doi = "10.18653/v1/2020.emnlp-main.684",
    pages = "8509--8518",
    abstract = "We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intelligence possesses a broader meaning scope. Understanding semantic capacity of terms will help many downstream tasks in natural language processing. For this purpose, we propose a two-step model to investigate semantic capacity of terms, which takes a large text corpus as input and can evaluate semantic capacity of terms if the text corpus can provide enough co-occurrence information of terms. Extensive experiments in three fields demonstrate the effectiveness and rationality of our model compared with well-designed baselines and human-level evaluations.",
}
@inproceedings{toshniwal-etal-2020-learning,
    title = "Learning to {I}gnore: {L}ong {D}ocument {C}oreference with {B}ounded {M}emory {N}eural {N}etworks",
    author = "Toshniwal, Shubham  and
      Wiseman, Sam  and
      Ettinger, Allyson  and
      Livescu, Karen  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.685",
    doi = "10.18653/v1/2020.emnlp-main.685",
    pages = "8519--8526",
    abstract = "Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models. Recent work doing incremental coreference resolution using just the global representation of entities shows practical benefits but requires keeping all entities in memory, which can be impractical for long documents. We argue that keeping all entities in memory is unnecessary, and we propose a memory-augmented neural network that tracks only a small bounded number of entities at a time, thus guaranteeing a linear runtime in length of document. We show that (a) the model remains competitive with models with high memory and computational requirements on OntoNotes and LitBank, and (b) the model learns an efficient memory management strategy easily outperforming a rule-based strategy",
}
@inproceedings{xu-choi-2020-revealing,
    title = "Revealing the Myth of Higher-Order Inference in Coreference Resolution",
    author = "Xu, Liyan  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.686",
    doi = "10.18653/v1/2020.emnlp-main.686",
    pages = "8527--8533",
    abstract = "This paper analyzes the impact of higher-order inference (HOI) on the task of coreference resolution. HOI has been adapted by almost all recent coreference resolution models without taking much investigation on its true effectiveness over representation learning. To make a comprehensive analysis, we implement an end-to-end coreference system as well as four HOI approaches, attended antecedent, entity equalization, span clustering, and cluster merging, where the latter two are our original methods. We find that given a high-performing encoder such as SpanBERT, the impact of HOI is negative to marginal, providing a new perspective of HOI to this task. Our best model using cluster merging shows the Avg-F1 of 80.2 on the CoNLL 2012 shared task dataset in English.",
}
@inproceedings{varkel-globerson-2020-pre,
    title = "Pre-training Mention Representations in Coreference Models",
    author = "Varkel, Yuval  and
      Globerson, Amir",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.687",
    doi = "10.18653/v1/2020.emnlp-main.687",
    pages = "8534--8540",
    abstract = "Collecting labeled data for coreference resolution is a challenging task, requiring skilled annotators. It is thus desirable to develop coreference resolution models that can make use of unlabeled data. Here we provide such an approach for the powerful class of neural coreference models. These models rely on representations of mentions, and we show these representations can be learned in a self-supervised manner towards improving resolution accuracy. We propose two self-supervised tasks that are closely related to coreference resolution and thus improve mention representation. Applying this approach to the GAP dataset results in new state of the arts results.",
}
@inproceedings{lei-etal-2020-learning,
    title = "Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning",
    author = "Lei, Deren  and
      Jiang, Gangrong  and
      Gu, Xiaotao  and
      Sun, Kexuan  and
      Mao, Yuning  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.688",
    doi = "10.18653/v1/2020.emnlp-main.688",
    pages = "8541--8547",
    abstract = "Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets shows that RuleGuider clearly improves the performance of walk-based models without losing interpretability.",
}
@inproceedings{ross-etal-2020-exploring,
    title = "{E}xploring {C}ontextualized {N}eural {L}anguage {M}odels for {T}emporal {D}ependency {P}arsing",
    author = "Ross, Hayley  and
      Cai, Jonathon  and
      Min, Bonan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.689",
    doi = "10.18653/v1/2020.emnlp-main.689",
    pages = "8548--8553",
    abstract = "Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and time-related question answering. It is a challenging problem which requires syntactic and semantic information at sentence or discourse levels, which may be captured by deep contextualized language models (LMs) such as BERT (Devlin et al., 2019). In this paper, we develop several variants of BERT-based temporal dependency parser, and show that BERT significantly improves temporal dependency parsing (Zhang and Xue, 2018a). We also present a detailed analysis on why deep contextualized neural LMs help and where they may fall short. Source code and resources are made available at https://github.com/bnmin/tdp{\_}ranking.",
}
@inproceedings{hohenecker-etal-2020-systematic,
    title = "Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction",
    author = "Hohenecker, Patrick  and
      Mtumbuka, Frank  and
      Kocijan, Vid  and
      Lukasiewicz, Thomas",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.690",
    doi = "10.18653/v1/2020.emnlp-main.690",
    pages = "8554--8565",
    abstract = "The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form {\textless}subject,predicate, object{\textgreater}. For example, given the sentence {``}Beethoven composed the Ode to Joy.{''}, we are expected to extract the triple {\textless}Beethoven, composed, Ode to Joy{\textgreater}. In this work, we systematically compare different neural network architectures and training approaches, and improve the performance of the currently best models on the OIE16 benchmark (Stanovsky and Dagan, 2016) by 0.421 F1 score and 0.420 AUC-PR, respectively, in our experiments (i.e., by more than 200{\%} in both cases). Furthermore, we show that appropriate problem and loss formulations often affect the performance more than the network architecture.",
}
@inproceedings{zhang-etal-2020-seqmix,
    title = "{S}eq{M}ix: Augmenting Active Sequence Labeling via Sequence Mixup",
    author = "Zhang, Rongzhi  and
      Yu, Yue  and
      Zhang, Chao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.691",
    doi = "10.18653/v1/2020.emnlp-main.691",
    pages = "8566--8579",
    abstract = "Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteration, which is an inefficient way of leveraging human annotations. We propose a simple but effective data augmentation method to improve label efficiency of active sequence labeling. Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration. The key difficulty is to generate plausible sequences along with token-level labels. In SeqMix, we address this challenge by performing mixup for both sequences and token-level labels of the queried samples. Furthermore, we design a discriminator during sequence mixup, which judges whether the generated sequences are plausible or not. Our experiments on Named Entity Recognition and Event Detection tasks show that SeqMix can improve the standard active sequence labeling method by 2.27{\%}{--}3.75{\%} in terms of $F_1$ scores. The code and data for SeqMix can be found at https://github.com/rz-zhang/SeqMix.",
}
@inproceedings{kardas-etal-2020-axcell,
    title = "{AxCell}: Automatic Extraction of Results from Machine Learning Papers",
    author = "Kardas, Marcin  and
      Czapla, Piotr  and
      Stenetorp, Pontus  and
      Ruder, Sebastian  and
      Riedel, Sebastian  and
      Taylor, Ross  and
      Stojnic, Robert",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.692",
    doi = "10.18653/v1/2020.emnlp-main.692",
    pages = "8580--8594",
    abstract = "Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction. When compared with existing methods, our approach significantly improves the state of the art for results extraction. We also release a structured, annotated dataset for training models for results extraction, and a dataset for evaluating the performance of models on this task. Lastly, we show the viability of our approach enables it to be used for semi-automated results extraction in production, suggesting our improvements make this task practically viable for the first time. Code is available on GitHub.",
}
@inproceedings{liu-etal-2020-knowledge,
    title = "Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning",
    author = "Liu, Ye  and
      Zhang, Sheng  and
      Song, Rui  and
      Feng, Suo  and
      Xiao, Yanghua",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.693",
    doi = "10.18653/v1/2020.emnlp-main.693",
    pages = "8595--8604",
    abstract = "Open attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a question-answering (QA) task. While the collections of articles from web corpus provide updated information about the emerging entities, the retrieved texts can be noisy, irrelevant, thus leading to inaccurate answers. Effectively filtering out noisy articles as well as bad answers is the key to improve extraction accuracy. Knowledge graph (KG), which contains rich, well organized information about entities, provides a good resource to address the challenge. In this work, we propose a knowledge-guided reinforcement learning (RL) framework for open attribute value extraction. Informed by relevant knowledge in KG, we trained a deep Q-network to sequentially compare extracted answers to improve extraction accuracy. The proposed framework is applicable to different information extraction system. Our experimental results show that our method outperforms the baselines by 16.5 - 27.8{\%}.",
}
@inproceedings{dognin-etal-2020-dualtkb,
    title = "{D}ual{TKB}: {A} {D}ual {L}earning {B}ridge between {T}ext and {K}nowledge {B}ase",
    author = "Dognin, Pierre  and
      Melnyk, Igor  and
      Padhi, Inkit  and
      Nogueira dos Santos, Cicero  and
      Das, Payel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.694",
    doi = "10.18653/v1/2020.emnlp-main.694",
    pages = "8605--8616",
    abstract = "In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak supervision by creating a weakly supervised dataset and show that even a slight amount of supervision can significantly improve the model performance and enable better-quality transfers. We examine different model architectures, and evaluation metrics, proposing a novel Commonsense KB completion metric tailored for generative models. Extensive experimental results show that the proposed method compares very favorably to the existing baselines. This approach is a viable step towards a more advanced system for automatic KB construction/expansion and the reverse operation of KB conversion to coherent textual descriptions.",
}
@inproceedings{xia-etal-2020-incremental,
    title = "Incremental Neural Coreference Resolution in Constant Memory",
    author = "Xia, Patrick  and
      Sedoc, Jo{\~a}o  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.695",
    doi = "10.18653/v1/2020.emnlp-main.695",
    pages = "8617--8624",
    abstract = "We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any). These spans are then used to update the entity{'}s representations before being forgotten; we only retain a fixed set of salient entities throughout the document. In this work, we successfully convert a high-performing model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3{\%} relative loss in F1 on OntoNotes 5.0.",
}
@inproceedings{welch-etal-2020-improving,
    title = "Improving Low Compute Language Modeling with In-Domain Embedding Initialisation",
    author = "Welch, Charles  and
      Mihalcea, Rada  and
      Kummerfeld, Jonathan K.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.696",
    doi = "10.18653/v1/2020.emnlp-main.696",
    pages = "8625--8634",
    abstract = "Many NLP applications, such as biomedical data and technical support, have 10-100 million tokens of in-domain data and limited computational resources for learning from it. How should we train a language model in this scenario? Most language modeling research considers either a small dataset with a closed vocabulary (like the standard 1 million token Penn Treebank), or the whole web with byte-pair encoding. We show that for our target setting in English, initialising and freezing input embeddings using in-domain data can improve language model performance by providing a useful representation of rare words, and this pattern holds across several different domains. In the process, we show that the standard convention of tying input and output embeddings does not improve perplexity when initializing with embeddings trained on in-domain data.",
}
@inproceedings{chen-etal-2020-kgpt,
    title = "{KGPT}: Knowledge-Grounded Pre-Training for Data-to-Text Generation",
    author = "Chen, Wenhu  and
      Su, Yu  and
      Yan, Xifeng  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.697",
    doi = "10.18653/v1/2020.emnlp-main.697",
    pages = "8635--8648",
    abstract = "Data-to-text generation has recently attracted substantial interests due to its wide applications. Existing methods have shown impressive performance on an array of tasks. However, they rely on a significant amount of labeled data for each task, which is costly to acquire and thus limits their application to new tasks and domains. In this paper, we propose to leverage pre-training and transfer learning to address this issue. We propose a knowledge-grounded pre-training (KGPT), which consists of two parts, 1) a general knowledge-grounded generation model to generate knowledge-enriched text. 2) a pre-training paradigm on a massive knowledge-grounded text corpus crawled from the web. The pre-trained model can be fine-tuned on various data-to-text generation tasks to generate task-specific text. We adopt three settings, namely fully-supervised, zero-shot, few-shot to evaluate its effectiveness. Under the fully-supervised setting, our model can achieve remarkable gains over the known baselines. Under zero-shot setting, our model without seeing any examples achieves over 30 ROUGE-L on WebNLG while all other baselines fail. Under the few-shot setting, our model only needs about one-fifteenth as many labeled examples to achieve the same level of performance as baseline models. These experiments consistently prove the strong generalization ability of our proposed framework.",
}
@inproceedings{zhang-etal-2020-pointer,
    title = "{POINTER}: Constrained Progressive Text Generation via Insertion-based Generative Pre-training",
    author = "Zhang, Yizhe  and
      Wang, Guoyin  and
      Li, Chunyuan  and
      Gan, Zhe  and
      Brockett, Chris  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.698",
    doi = "10.18653/v1/2020.emnlp-main.698",
    pages = "8649--8670",
    abstract = "Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation. However, these models cannot be directly employed to generate text under specified lexical constraints. To address this challenge, we present POINTER (PrOgressive INsertion-based TransformER), a simple yet novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. This procedure is recursively applied until a sequence is completed. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable. We pre-train our model with the proposed progressive insertion-based objective on a 12GB Wikipedia dataset, and fine-tune it on downstream hard-constrained generation tasks. Non-autoregressive decoding yields a logarithmic time complexity during inference time. Experimental results on both News and Yelp datasets demonstrate that Pointer achieves state-of-the-art performance on constrained text generation. We released the pre-trained models and the source code to facilitate future research.",
}
@inproceedings{malmi-etal-2020-unsupervised,
    title = "Unsupervised Text Style Transfer with Padded Masked Language Models",
    author = "Malmi, Eric  and
      Severyn, Aliaksei  and
      Rothe, Sascha",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.699",
    doi = "10.18653/v1/2020.emnlp-main.699",
    pages = "8671--8680",
    abstract = "We propose Masker, an unsupervised text-editing method for style transfer. To tackle cases when no parallel source{--}target pairs are available, we train masked language models (MLMs) for both the source and the target domain. Then we find the text spans where the two models disagree the most in terms of likelihood. This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain. The deleted tokens are replaced with the target MLM, and by using a padded MLM variant, we avoid having to predetermine the number of inserted tokens. Our experiments on sentence fusion and sentiment transfer demonstrate that Masker performs competitively in a fully unsupervised setting. Moreover, in low-resource settings, it improves supervised methods{'} accuracy by over 10 percentage points when pre-training them on silver training data generated by Masker.",
}
@inproceedings{bi-etal-2020-palm,
    title = "{PALM}: Pre-training an Autoencoding{\&}Autoregressive Language Model for Context-conditioned Generation",
    author = "Bi, Bin  and
      Li, Chenliang  and
      Wu, Chen  and
      Yan, Ming  and
      Wang, Wei  and
      Huang, Songfang  and
      Huang, Fei  and
      Si, Luo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.700",
    doi = "10.18653/v1/2020.emnlp-main.700",
    pages = "8681--8691",
    abstract = "Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive objectives to train Transformer-based models by recovering original word tokens from corrupted text with some masked tokens. The training goals of existing techniques are often inconsistent with the goals of many language generation tasks, such as generative question answering and conversational response generation, for producing new text given context. This work presents PALM with a novel scheme that jointly pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus, specifically designed for generating new text conditioned on context. The new scheme alleviates the mismatch introduced by the existing denoising scheme between pre-training and fine-tuning where generation is more than reconstructing original text. An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.",
}
@inproceedings{sha-2020-gradient,
    title = "Gradient-guided Unsupervised Lexically Constrained Text Generation",
    author = "Sha, Lei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.701",
    doi = "10.18653/v1/2020.emnlp-main.701",
    pages = "8692--8703",
    abstract = "Lexically constrained generation requires the target sentence to satisfy some lexical constraints, such as containing some specific words or being the paraphrase to a given sentence, which is very important in many real-world natural language generation applications. Previous works usually apply beam-search-based methods or stochastic searching methods to lexically-constrained generation. However, when the search space is too large, beam-search-based methods always fail to find the constrained optimal solution. At the same time, stochastic search methods always cost too many steps to find the correct optimization direction. In this paper, we propose a novel method G2LC to solve the lexically-constrained generation as an unsupervised gradient-guided optimization problem. We propose a differentiable objective function and use the gradient to help determine which position in the sequence should be changed (deleted or inserted/replaced by another word). The word updating process of the inserted/replaced word also benefits from the guidance of gradient. Besides, our method is free of parallel data training, which is flexible to be used in the inference stage of any pre-trained generation model. We apply G2LC to two generation tasks: keyword-to-sentence generation and unsupervised paraphrase generation. The experiment results show that our method achieves state-of-the-art compared to previous lexically-constrained methods.",
}
@inproceedings{goodman-etal-2020-teaforn,
    title = "{T}ea{F}or{N}: Teacher-Forcing with N-grams",
    author = "Goodman, Sebastian  and
      Ding, Nan  and
      Soricut, Radu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.702",
    doi = "10.18653/v1/2020.emnlp-main.702",
    pages = "8704--8717",
    abstract = "Sequence generation models trained with teacher-forcing suffer from issues related to exposure bias and lack of differentiability across timesteps. Our proposed method, Teacher-Forcing with N-grams (TeaForN), addresses both these problems directly, through the use of a stack of N decoders trained to decode along a secondary time axis that allows model-parameter updates based on N prediction steps. TeaForN can be used with a wide class of decoder architectures and requires minimal modifications from a standard teacher-forcing setup. Empirically, we show that TeaForN boosts generation quality on one Machine Translation benchmark, WMT 2014 English-French, and two News Summarization benchmarks, CNN/Dailymail and Gigaword.",
}
@inproceedings{bisk-etal-2020-experience,
    title = "Experience Grounds Language",
    author = "Bisk, Yonatan  and
      Holtzman, Ari  and
      Thomason, Jesse  and
      Andreas, Jacob  and
      Bengio, Yoshua  and
      Chai, Joyce  and
      Lapata, Mirella  and
      Lazaridou, Angeliki  and
      May, Jonathan  and
      Nisnevich, Aleksandr  and
      Pinto, Nicolas  and
      Turian, Joseph",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.703",
    doi = "10.18653/v1/2020.emnlp-main.703",
    pages = "8718--8735",
    abstract = "Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.",
}
@inproceedings{yao-etal-2020-keep,
    title = "Keep {CALM} and Explore: Language Models for Action Generation in Text-based Games",
    author = "Yao, Shunyu  and
      Rao, Rohan  and
      Hausknecht, Matthew  and
      Narasimhan, Karthik",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.704",
    doi = "10.18653/v1/2020.emnlp-main.704",
    pages = "8736--8754",
    abstract = "Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action Language Model (CALM) to generate a compact set of action candidates at each game state. Our key insight is to train language models on human gameplay, where people demonstrate linguistic priors and a general game sense for promising actions conditioned on game history. We combine CALM with a reinforcement learning agent which re-ranks the generated action candidates to maximize in-game rewards. We evaluate our approach using the Jericho benchmark, on games unseen by CALM during training. Our method obtains a 69{\%} relative improvement in average game score over the previous state-of-the-art model. Surprisingly, on half of these games, CALM is competitive with or better than other models that have access to ground truth admissible actions. Code and data are available at https://github.com/princeton-nlp/calm-textgame.",
}
@inproceedings{fisch-etal-2020-capwap,
    title = "{C}ap{WAP}: Image Captioning with a Purpose",
    author = "Fisch, Adam  and
      Lee, Kenton  and
      Chang, Ming-Wei  and
      Clark, Jonathan  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.705",
    doi = "10.18653/v1/2020.emnlp-main.705",
    pages = "8755--8768",
    abstract = "The traditional image captioning task uses generic reference captions to provide textual information about images. Different user populations, however, will care about different visual aspects of images. In this paper, we propose a new task, Captioning with A Purpose (CapWAP). Our goal is to develop systems that can be tailored to be useful for the information needs of an intended population, rather than merely provide generic information about an image. In this task, we use question-answer (QA) pairs{---}a natural expression of information need{---}from users, instead of reference captions, for both training and post-inference evaluation. We show that it is possible to use reinforcement learning to directly optimize for the intended information need, by rewarding outputs that allow a question answering model to provide correct answers to sampled user questions. We convert several visual question answering datasets into CapWAP datasets, and demonstrate that under a variety of scenarios our purposeful captioning system learns to anticipate and fulfill specific information needs better than its generic counterparts, as measured by QA performance on user questions from unseen images, when using the caption alone as context.",
}
@inproceedings{lei-etal-2020-likely,
    title = "What is More Likely to Happen Next? Video-and-Language Future Event Prediction",
    author = "Lei, Jie  and
      Yu, Licheng  and
      Berg, Tamara  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.706",
    doi = "10.18653/v1/2020.emnlp-main.706",
    pages = "8769--8784",
    abstract = "Given a video with aligned dialogue, people can often infer what is more likely to happen next. Making such predictions requires not only a deep understanding of the rich dynamics underlying the video and dialogue, but also a significant amount of commonsense knowledge. In this work, we explore whether AI models are able to learn to make such multimodal commonsense next-event predictions. To support research in this direction, we collect a new dataset, named Video-and-Language Event Prediction (VLEP), with 28,726 future event prediction examples (along with their rationales) from 10,234 diverse TV Show and YouTube Lifestyle Vlog video clips. In order to promote the collection of non-trivial challenging examples, we employ an adversarial human-and-model-in-the-loop data collection procedure. We also present a strong baseline incorporating information from video, dialogue, and commonsense knowledge. Experiments show that each type of information is useful for this challenging task, and that compared to the high human performance on VLEP, our model provides a good starting point but leaves large room for future work.",
}
@inproceedings{cho-etal-2020-x,
    title = "{X-LXMERT}: {P}aint, {C}aption and {A}nswer {Q}uestions with {M}ulti-{M}odal {T}ransformers",
    author = "Cho, Jaemin  and
      Lu, Jiasen  and
      Schwenk, Dustin  and
      Hajishirzi, Hannaneh  and
      Kembhavi, Aniruddha",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.707",
    doi = "10.18653/v1/2020.emnlp-main.707",
    pages = "8785--8805",
    abstract = "Mirroring the success of masked language models, vision-and-language counterparts like VILBERT, LXMERT and UNITER have achieved state of the art performance on a variety of multimodal discriminative tasks like visual question answering and visual grounding. Recent work has also successfully adapted such models towards the generative task of image captioning. This begs the question: Can these models go the other way and generate images from pieces of text? Our analysis of a popular representative from this model family {--} LXMERT {--} finds that it is unable to generate rich and semantically meaningful imagery with its current training setup. We introduce X-LXMERT, an extension to LXMERT with training refinements including: discretizing visual representations, using uniform masking with a large range of masking ratios and aligning the right pre-training datasets to the right objectives which enables it to paint. X-LXMERT{'}s image generation capabilities rival state of the art generative models while its question answering and captioning abilities remains comparable to LXMERT. Finally, we demonstrate the generality of these training refinements by adding image generation capabilities into UNITER to produce X-UNITER.",
}
@inproceedings{zhu-etal-2020-towards-understanding,
    title = "Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations",
    author = "Zhu, Wanrong  and
      Wang, Xin  and
      Narayana, Pradyumna  and
      Sone, Kazoo  and
      Basu, Sugato  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.708",
    doi = "10.18653/v1/2020.emnlp-main.708",
    pages = "8806--8811",
    abstract = "A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. To do this, it is critical to ensure that our evaluation protocols are correct, and benchmarks are reliable. In this work, we set forth to design a set of experiments to understand an important but often ignored problem in visually grounded language generation: given that humans have different utilities and visual attention, how will the sample variance in multi-reference datasets affect the models{'} performance? Empirically, we study several multi-reference datasets and corresponding vision-and-language tasks. We show that it is of paramount importance to report variance in experiments; that human-generated references could vary drastically in different datasets/tasks, revealing the nature of each task; that metric-wise, CIDEr has shown systematically larger variances than others. Our evaluations on reference-per-instance shed light on the design of reliable datasets in the future.",
}
@inproceedings{hessel-etal-2020-beyond,
    title = "Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on {Y}ou{T}ube",
    author = "Hessel, Jack  and
      Zhu, Zhenhai  and
      Pang, Bo  and
      Soricut, Radu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.709",
    doi = "10.18653/v1/2020.emnlp-main.709",
    pages = "8812--8822",
    abstract = "Pretraining from unlabelled web videos has quickly become the de-facto means of achieving high performance on many video understanding tasks. Features are learned via prediction of grounded relationships between visual content and automatic speech recognition (ASR) tokens. However, prior pretraining work has been limited to only instructional videos; a priori, we expect this domain to be relatively {``}easy:{''} speakers in instructional videos will often reference the literal objects/actions being depicted. We ask: can similar models be trained on more diverse video corpora? And, if so, what types of videos are {``}grounded{''} and what types are not? We fit a representative pretraining model to the diverse YouTube8M dataset, and study its success and failure cases. We find that visual-textual grounding is indeed possible across previously unexplored video categories, and that pretraining on a more diverse set results in representations that generalize to both non-instructional and instructional domains.",
}
@inproceedings{fang-etal-2020-hierarchical,
    title = "Hierarchical Graph Network for Multi-hop Question Answering",
    author = "Fang, Yuwei  and
      Sun, Siqi  and
      Gan, Zhe  and
      Pillai, Rohit  and
      Wang, Shuohang  and
      Liu, Jingjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.710",
    doi = "10.18653/v1/2020.emnlp-main.710",
    pages = "8823--8838",
    abstract = "In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes on different levels of granularity (questions, paragraphs, sentences, entities), the representations of which are initialized with pre-trained contextual encoders. Given this hierarchical graph, the initial node representations are updated through graph propagation, and multi-hop reasoning is performed via traversing through the graph edges for each subsequent sub-task (e.g., paragraph selection, supporting facts extraction, answer prediction). By weaving heterogeneous nodes into an integral unified graph, this hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously. Experiments on the HotpotQA benchmark demonstrate that the proposed model achieves new state of the art, outperforming existing multi-hop QA approaches.",
}
@inproceedings{groeneveld-etal-2020-simple,
    title = "A Simple Yet Strong Pipeline for {H}otpot{QA}",
    author = "Groeneveld, Dirk  and
      Khot, Tushar  and
      {Mausam}  and
      Sabharwal, Ashish",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.711",
    doi = "10.18653/v1/2020.emnlp-main.711",
    pages = "8839--8845",
    abstract = "State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However, does their strong performance on popular multi-hop datasets really justify this added design complexity? Our results suggest that the answer may be no, because even our simple pipeline based on BERT, named , performs surprisingly well. Specifically, on HotpotQA, Quark outperforms these models on both question answering and support identification (and achieves performance very close to a RoBERTa model). Our pipeline has three steps: 1) use BERT to identify potentially relevant sentences \textit{independently} of each other; 2) feed the set of selected sentences as context into a standard BERT span prediction model to choose an answer; and 3) use the sentence selection model, now with the chosen answer, to produce supporting sentences. The strong performance of Quark resurfaces the importance of carefully exploring simple model designs before using popular benchmarks to justify the value of complex techniques.",
}
@inproceedings{trivedi-etal-2020-multihop,
    title = "Is Multihop {QA} in {DiRe} Condition? Measuring and Reducing Disconnected Reasoning",
    author = "Trivedi, Harsh  and
      Balasubramanian, Niranjan  and
      Khot, Tushar  and
      Sabharwal, Ashish",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.712",
    doi = "10.18653/v1/2020.emnlp-main.712",
    pages = "8846--8863",
    abstract = "Has there been real progress in multi-hop question-answering? Models often exploit dataset artifacts to produce correct answers, without connecting information across multiple supporting facts. This limits our ability to measure true progress and defeats the purpose of building multi-hop QA datasets. We make three contributions towards addressing this. First, we formalize such undesirable behavior as disconnected reasoning across subsets of supporting facts. This allows developing a model-agnostic probe for measuring how much any model can cheat via disconnected reasoning. Second, using a notion of \textit{contrastive support sufficiency}, we introduce an automatic transformation of existing datasets that reduces the amount of disconnected reasoning. Third, our experiments suggest that there hasn{'}t been much progress in multi-hop QA in the reading comprehension setting. For a recent large-scale model (XLNet), we show that only 18 points out of its answer F1 score of 72 on HotpotQA are obtained through multifact reasoning, roughly the same as that of a simpler RNN baseline. Our transformation substantially reduces disconnected reasoning (19 points in answer F1). It is complementary to adversarial approaches, yielding further reductions in conjunction.",
}
@inproceedings{perez-etal-2020-unsupervised,
    title = "Unsupervised Question Decomposition for Question Answering",
    author = "Perez, Ethan  and
      Lewis, Patrick  and
      Yih, Wen-tau  and
      Cho, Kyunghyun  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.713",
    doi = "10.18653/v1/2020.emnlp-main.713",
    pages = "8864--8880",
    abstract = "We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering. Since labeling questions with decompositions is cumbersome, we take an unsupervised approach to produce sub-questions, also enabling us to leverage millions of questions from the internet. Specifically, we propose an algorithm for One-to-N Unsupervised Sequence transduction (ONUS) that learns to map one hard, multi-hop question to many simpler, single-hop sub-questions. We answer sub-questions with an off-the-shelf QA model and give the resulting answers to a recomposition model that combines them into a final answer. We show large QA improvements on HotpotQA over a strong baseline on the original, out-of-domain, and multi-hop dev sets. ONUS automatically learns to decompose different kinds of questions, while matching the utility of supervised and heuristic decomposition methods for QA and exceeding those methods in fluency. Qualitatively, we find that using sub-questions is promising for shedding light on why a QA system makes a prediction.",
}
@inproceedings{zheng-kordjamshidi-2020-srlgrn,
    title = "{SRLGRN}: Semantic Role Labeling Graph Reasoning Network",
    author = "Zheng, Chen  and
      Kordjamshidi, Parisa",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.714",
    doi = "10.18653/v1/2020.emnlp-main.714",
    pages = "8881--8891",
    abstract = "This work deals with the challenge of learning and reasoning over multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find the supporting facts and the answer jointly. The proposed graph is a heterogeneous document-level graph that contains nodes of type sentence (question, title, and other sentences), and semantic role labeling sub-graphs per sentence that contain arguments as nodes and predicates as edges. Incorporating the argument types, the argument phrases, and the semantics of the edges originated from SRL predicates into the graph encoder helps in finding and also the explainability of the reasoning paths. Our proposed approach shows competitive performance on the HotpotQA distractor setting benchmark compared to the recent state-of-the-art models.",
}
@inproceedings{sosea-caragea-2020-canceremo,
    title = "{C}ancer{E}mo: A Dataset for Fine-Grained Emotion Detection",
    author = "Sosea, Tiberiu  and
      Caragea, Cornelia",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.715",
    doi = "10.18653/v1/2020.emnlp-main.715",
    pages = "8892--8904",
    abstract = "Emotions are an important element of human nature, often affecting the overall wellbeing of a person. Therefore, it is no surprise that the health domain is a valuable area of interest for emotion detection, as it can provide medical staff or caregivers with essential information about patients. However, progress on this task has been hampered by the absence of large labeled datasets. To this end, we introduce CancerEmo, an emotion dataset created from an online health community and annotated with eight fine-grained emotions. We perform a comprehensive analysis of these emotions and develop deep learning models on the newly created dataset. Our best BERT model achieves an average F1 of 71{\%}, which we improve further using domain-specific pre-training.",
}
@inproceedings{li-etal-2020-exploring-role,
    title = "Exploring the Role of Argument Structure in Online Debate Persuasion",
    author = "Li, Jialu  and
      Durmus, Esin  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.716",
    doi = "10.18653/v1/2020.emnlp-main.716",
    pages = "8905--8912",
    abstract = "Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the best predictive performance in assessing the persuasiveness of the arguments on online debates.",
}
@inproceedings{allaway-mckeown-2020-zero,
    title = "{Z}ero-{S}hot {S}tance {D}etection: {A} {D}ataset and {M}odel using {G}eneralized {T}opic {R}epresentations",
    author = "Allaway, Emily  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.717",
    doi = "10.18653/v1/2020.emnlp-main.717",
    pages = "8913--8931",
    abstract = "Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.",
}
@inproceedings{gyanendro-singh-etal-2020-sentiment,
    title = "Sentiment Analysis of Tweets using Heterogeneous Multi-layer Network Representation and Embedding",
    author = "Gyanendro Singh, Loitongbam  and
      Mitra, Anasua  and
      Ranbir Singh, Sanasam",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.718",
    doi = "10.18653/v1/2020.emnlp-main.718",
    pages = "8932--8946",
    abstract = "Sentiment classification on tweets often needs to deal with the problems of under-specificity, noise, and multilingual content. This study proposes a heterogeneous multi-layer network-based representation of tweets to generate multiple representations of a tweet and address the above issues. The generated representations are further ensembled and classified using a neural-based early fusion approach. Further, we propose a centrality aware random-walk for node embedding and tweet representations suitable for the multi-layer network. From various experimental analysis, it is evident that the proposed method can address the problem of under-specificity, noisy text, and multilingual content present in a tweet and provides better classification performance than the text-based counterparts. Further, the proposed centrality aware based random walk provides better representations than unbiased and other biased counterparts.",
}
@inproceedings{pouran-ben-veyseh-etal-2020-introducing,
    title = "Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning",
    author = "Pouran Ben Veyseh, Amir  and
      Nouri, Nasim  and
      Dernoncourt, Franck  and
      Dou, Dejing  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.719",
    doi = "10.18653/v1/2020.emnlp-main.719",
    pages = "8947--8956",
    abstract = "Targeted opinion word extraction (TOWE) is a sub-task of aspect based sentiment analysis (ABSA) which aims to find the opinion words for a given aspect-term in a sentence. Despite their success for TOWE, the current deep learning models fail to exploit the syntactic information of the sentences that have been proved to be useful for TOWE in the prior research. In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words. We also introduce a novel regularization technique to improve the performance of the deep learning models based on the representation distinctions between the words in TOWE. The proposed model is extensively analyzed and achieves the state-of-the-art performance on four benchmark datasets.",
}
@inproceedings{shoeb-de-melo-2020-emotag1200,
    title = "{E}mo{T}ag1200: Understanding the Association between Emojis and Emotions",
    author = "Shoeb, Abu Awal Md  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.720",
    doi = "10.18653/v1/2020.emnlp-main.720",
    pages = "8957--8967",
    abstract = "Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role. One conspicuous aspect of emojis is their use to convey affect in ways that may otherwise be non-trivial to achieve. In this paper, we seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings. We additionally conduct experiments to assess to what extent such associations can be inferred from existing data in an unsupervised manner. Our experiments show that this succeeds when high-quality word-level information is available.",
}
@inproceedings{majumder-etal-2020-mime,
    title = "{MIME}: {MIM}icking Emotions for Empathetic Response Generation",
    author = "Majumder, Navonil  and
      Hong, Pengfei  and
      Peng, Shanshan  and
      Lu, Jiankun  and
      Ghosal, Deepanway  and
      Gelbukh, Alexander  and
      Mihalcea, Rada  and
      Poria, Soujanya",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.721",
    doi = "10.18653/v1/2020.emnlp-main.721",
    pages = "8968--8979",
    abstract = "Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME.",
}
@inproceedings{shen-etal-2020-exploiting,
    title = "Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning",
    author = "Shen, Tao  and
      Mao, Yi  and
      He, Pengcheng  and
      Long, Guodong  and
      Trischler, Adam  and
      Chen, Weizhu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.722",
    doi = "10.18653/v1/2020.emnlp-main.722",
    pages = "8980--8994",
    abstract = "In this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme that exploits relational knowledge underlying the text. This is fulfilled by using a linked knowledge graph to select informative entities and then masking their mentions. In addition, we use knowledge graphs to obtain distractors for the masked entities, and propose a novel distractor-suppressed ranking objective that is optimized jointly with masked language model. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text. It is more efficient than retrieval-based methods that perform entity linking and integration during finetuning and inference, and generalizes more effectively than the methods that directly learn from concatenated graph triples. Experiments show that our proposed model achieves improved performance on five benchmarks, including question answering and knowledge base completion.",
}
@inproceedings{luo-etal-2020-named,
    title = "Named Entity Recognition Only from Word Embeddings",
    author = "Luo, Ying  and
      Zhao, Hai  and
      Zhan, Junlang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.723",
    doi = "10.18653/v1/2020.emnlp-main.723",
    pages = "8995--9005",
    abstract = "Deep neural network models have helped named entity recognition achieve amazing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Efforts have been made to replace human annotations with external knowledge (e.g., NE dictionary, part-of-speech tags), while it is another challenge to obtain such effective resources. In this work, we propose a fully unsupervised NE recognition model which only needs to take informative clues from pre-trained word embeddings.We first apply Gaussian Hidden Markov Model and Deep Autoencoding Gaussian Mixture Model on word embeddings for entity span detection and type prediction, and then further design an instance selector based on reinforcement learning to distinguish positive sentences from noisy sentences and then refine these coarse-grained annotations through neural networks. Extensive experiments on two CoNLL benchmark NER datasets (CoNLL-2003 English dataset and CoNLL-2002 Spanish dataset) demonstrate that our proposed light NE recognition model achieves remarkable performance without using any annotated lexicon or corpus.",
}
@inproceedings{meng-etal-2020-text,
    title = "Text Classification Using Label Names Only: A Language Model Self-Training Approach",
    author = "Meng, Yu  and
      Zhang, Yunyi  and
      Huang, Jiaxin  and
      Xiong, Chenyan  and
      Ji, Heng  and
      Zhang, Chao  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.724",
    doi = "10.18653/v1/2020.emnlp-main.724",
    pages = "9006--9017",
    abstract = "Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training. We show that our model achieves around 90{\%} accuracy on four benchmark datasets including topic and sentiment classification without using any labeled documents but learning from unlabeled data supervised by at most 3 words (1 in most cases) per class as the label name.",
}
@inproceedings{hu-etal-2020-neural,
    title = "Neural Topic Modeling with Cycle-Consistent Adversarial Training",
    author = "Hu, Xuemeng  and
      Wang, Rui  and
      Zhou, Deyu  and
      Xiong, Yuxuan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.725",
    doi = "10.18653/v1/2020.emnlp-main.725",
    pages = "9018--9030",
    abstract = "Advances on deep generative models have attracted significant research interest in neural topic modeling. The recently proposed Adversarial-neural Topic Model models topics with an adversarially trained generator network and employs Dirichlet prior to capture the semantic patterns in latent topics. It is effective in discovering coherent topics but unable to infer topic distributions for given documents or utilize available document labels. To overcome such limitations, we propose Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT) and its supervised version sToMCAT. ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics. Adversarial training and cycle-consistent constraints are used to encourage the generator and the encoder to produce realistic samples that coordinate with each other. sToMCAT extends ToMCAT by incorporating document labels into the topic modeling process to help discover more coherent topics. The effectiveness of the proposed models is evaluated on unsupervised/supervised topic modeling and text classification. The experimental results show that our models can produce both coherent and informative topics, outperforming a number of competitive baselines.",
}
@inproceedings{liu-etal-2020-data,
    title = "Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation",
    author = "Liu, Ruibo  and
      Xu, Guangxuan  and
      Jia, Chenyan  and
      Ma, Weicheng  and
      Wang, Lili  and
      Vosoughi, Soroush",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.726",
    doi = "10.18653/v1/2020.emnlp-main.726",
    pages = "9031--9041",
    abstract = "Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which augments data through reinforcement learning guided conditional generation. We evaluate Data Boost on three diverse text classification tasks under five different classifier architectures. The result shows that Data Boost can boost the performance of classifiers especially in low-resource data scenarios. For instance, Data Boost improves F1 for the three tasks by 8.7{\%} on average when given only 10{\%} of the whole data for training. We also compare Data Boost with six prior text augmentation methods. Through human evaluations (N=178), we confirm that Data Boost augmentation has comparable quality as the original data with respect to readability and class consistency.",
}
@inproceedings{xia-etal-2020-state,
    title = "A State-independent and Time-evolving Network for Early Rumor Detection in Social Media",
    author = "Xia, Rui  and
      Xuan, Kaizhou  and
      Yu, Jianfei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.727",
    doi = "10.18653/v1/2020.emnlp-main.727",
    pages = "9042--9051",
    abstract = "In this paper, we study automatic rumor detection for in social media at the event level where an event consists of a sequence of posts organized according to the posting time. It is common that the state of an event is dynamically evolving. However, most of the existing methods to this task ignored this problem, and established a global representation based on all the posts in the event{'}s life cycle. Such coarse-grained methods failed to capture the event{'}s unique features in different states. To address this limitation, we propose a state-independent and time-evolving Network (STN) for rumor detection based on fine-grained event state detection and segmentation. Given an event composed of a sequence of posts, STN first predicts the corresponding sequence of states and segments the event into several state-independent sub-events. For each sub-event, STN independently trains an encoder to learn the feature representation for that sub-event and incrementally fuses the representation of the current sub-event with previous ones for rumor prediction. This framework can more accurately learn the representation of an event in the initial stage and enable early rumor detection. Experiments on two benchmark datasets show that STN can significantly improve the rumor detection accuracy in comparison with some strong baseline systems. We also design a new evaluation metric to measure the performance of early rumor detection, under which STN shows a higher advantage in comparison.",
}
@inproceedings{clement-etal-2020-pymt5,
    title = "{P}y{MT}5: multi-mode translation of natural language and Python code with transformers",
    author = "Clement, Colin  and
      Drain, Dawn  and
      Timcheck, Jonathan  and
      Svyatkovskiy, Alexey  and
      Sundaresan, Neel",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.728",
    doi = "10.18653/v1/2020.emnlp-main.728",
    pages = "9052--9065",
    abstract = "Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding. Pursuant to achieving such technology, we introduce PyMT5, the Python method text-to-text transfer transformer, which is trained to translate between all pairs of Python method feature combinations: a single model that can both predict whole methods from natural language documentation strings (docstrings) and summarize code into docstrings of any common style. We present an analysis and modeling effort of a large-scale parallel corpus of 26 million Python methods and 7.7 million method-docstring pairs, demonstrating that for docstring and method generation, PyMT5 outperforms similarly-sized auto-regressive language models (GPT2) which were English pre-trained or randomly initialized. On the CodeSearchNet test set, our best model predicts 92.1{\%} syntactically correct method bodies, achieved a BLEU score of 8.59 for method generation and 16.3 for docstring generation (summarization), and achieved a ROUGE-L F-score of 24.8 for method generation and 36.7 for docstring generation.",
}
@inproceedings{wang-etal-2020-pathqg,
    title = "{P}ath{QG}: Neural Question Generation from Facts",
    author = "Wang, Siyuan  and
      Wei, Zhongyu  and
      Fan, Zhihao  and
      Huang, Zengfeng  and
      Sun, Weijian  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.729",
    doi = "10.18653/v1/2020.emnlp-main.729",
    pages = "9066--9075",
    abstract = "Existing research for question generation encodes the input text as a sequence of tokens without explicitly modeling fact information. These models tend to generate irrelevant and uninformative questions. In this paper, we explore to incorporate facts in the text for question generation in a comprehensive way. We present a novel task of question generation given a query path in the knowledge graph constructed from the input text. We divide the task into two steps, namely, query representation learning and query-based question generation. We formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation. We first train the two modules jointly in an end-to-end fashion, and further enforce the interaction between these two modules in a variational framework. We construct the experimental datasets on top of SQuAD and results show that our model outperforms other state-of-the-art approaches, and the performance margin is larger when target questions are complex. Human evaluation also proves that our model is able to generate relevant and informative questions.",
}
@inproceedings{kim-etal-2020-time,
    title = "What time is it? Temporal Analysis of Novels",
    author = "Kim, Allen  and
      Pethe, Charuta  and
      Skiena, Steve",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.730",
    doi = "10.18653/v1/2020.emnlp-main.730",
    pages = "9076--9086",
    abstract = "Recognizing the flow of time in a story is a crucial aspect of understanding it. Prior work related to time has primarily focused on identifying temporal expressions or relative sequencing of events, but here we propose computationally annotating each line of a book with wall clock times, even in the absence of explicit time-descriptive phrases. To do so, we construct a data set of hourly time phrases from 52,183 fictional books. We then construct a time-of-day classification model that achieves an average error of 2.27 hours. Furthermore, we show that by analyzing a book in whole using dynamic programming of breakpoints, we can roughly partition a book into segments that each correspond to a particular time-of-day. This approach improves upon baselines by over two hour. Finally, we apply our model to a corpus of literature categorized by different periods in history, to show interesting trends of hourly activity throughout the past. Among several observations we find that the fraction of events taking place past 10 P.M jumps past 1880 - coincident with the advent of the electric light bulb and city lights.",
}
@inproceedings{kim-linzen-2020-cogs,
    title = "{COGS}: A Compositional Generalization Challenge Based on Semantic Interpretation",
    author = "Kim, Najoung  and
      Linzen, Tal",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.731",
    doi = "10.18653/v1/2020.emnlp-main.731",
    pages = "9087--9105",
    abstract = "Natural language is characterized by compositionality: the meaning of a complex expression is constructed from the meanings of its constituent parts. To facilitate the evaluation of the compositional abilities of language processing architectures, we introduce COGS, a semantic parsing dataset based on a fragment of English. The evaluation portion of COGS contains multiple systematic gaps that can only be addressed by compositional generalization; these include new combinations of familiar syntactic structures, or new combinations of familiar words and familiar structures. In experiments with Transformers and LSTMs, we found that in-distribution accuracy on the COGS test set was near-perfect (96{--}99{\%}), but generalization accuracy was substantially lower (16{--}35{\%}) and showed high sensitivity to random seed (+-6{--}8{\%}). These findings indicate that contemporary standard NLP models are limited in their compositional generalization capacity, and position COGS as a good way to measure progress.",
}
@inproceedings{hossain-etal-2020-analysis,
    title = "An Analysis of Natural Language Inference Benchmarks through the Lens of Negation",
    author = "Hossain, Md Mosharaf  and
      Kovatchev, Venelin  and
      Dutta, Pranoy  and
      Kao, Tiffany  and
      Wei, Elizabeth  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.732",
    doi = "10.18653/v1/2020.emnlp-main.732",
    pages = "9106--9118",
    abstract = "Negation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.",
}
@inproceedings{li-etal-2020-sentence,
    title = "On the Sentence Embeddings from Pre-trained Language Models",
    author = "Li, Bohan  and
      Zhou, Hao  and
      He, Junxian  and
      Wang, Mingxuan  and
      Yang, Yiming  and
      Li, Lei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.733",
    doi = "10.18653/v1/2020.emnlp-main.733",
    pages = "9119--9130",
    abstract = "Pre-trained contextual representations like BERT have achieved great success in natural language processing. However, the sentence embeddings from the pre-trained language models without fine-tuning have been found to poorly capture semantic meaning of sentences. In this paper, we argue that the semantic information in the BERT embeddings is not fully exploited. We first reveal the theoretical connection between the masked language model pre-training objective and the semantic similarity task theoretically, and then analyze the BERT sentence embeddings empirically. We find that BERT always induces a non-smooth anisotropic semantic space of sentences, which harms its performance of semantic similarity. To address this issue, we propose to transform the anisotropic sentence embedding distribution to a smooth and isotropic Gaussian distribution through normalizing flows that are learned with an unsupervised objective. Experimental results show that our proposed BERT-flow method obtains significant performance gains over the state-of-the-art sentence embeddings on a variety of semantic textual similarity tasks. The code is available at \url{https://github.com/bohanli/BERT-flow}.",
}
@inproceedings{nie-etal-2020-learn,
    title = "What Can We Learn from Collective Human Opinions on Natural Language Inference Data?",
    author = "Nie, Yixin  and
      Zhou, Xiang  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.734",
    doi = "10.18653/v1/2020.emnlp-main.734",
    pages = "9131--9143",
    abstract = "Despite the subjective nature of many NLP tasks, most NLU evaluations have focused on using the majority label with presumably high agreement as the ground truth. Less attention has been paid to the distribution of human opinions. We collect ChaosNLI, a dataset with a total of 464,500 annotations to study Collective HumAn OpinionS in oft-used NLI evaluation sets. This dataset is created by collecting 100 annotations per example for 3,113 examples in SNLI and MNLI and 1,532 examples in αNLI. Analysis reveals that: (1) high human disagreement exists in a noticeable amount of examples in these datasets; (2) the state-of-the-art models lack the ability to recover the distribution over human labels; (3) models achieve near-perfect accuracy on the subset of data with a high level of human agreement, whereas they can barely beat a random guess on the data with low levels of human agreement, which compose most of the common errors made by state-of-the-art models on the evaluation sets. This questions the validity of improving model performance on old metrics for the low-agreement part of evaluation datasets. Hence, we argue for a detailed examination of human agreement in future data collection efforts, and evaluating model outputs against the distribution over collective human opinions.",
}
@inproceedings{li-etal-2020-improving-text,
    title = "Improving Text Generation with Student-Forcing Optimal Transport",
    author = "Li, Jianqiao  and
      Li, Chunyuan  and
      Wang, Guoyin  and
      Fu, Hao  and
      Lin, Yuhchen  and
      Chen, Liqun  and
      Zhang, Yizhe  and
      Tao, Chenyang  and
      Zhang, Ruiyi  and
      Wang, Wenlin  and
      Shen, Dinghan  and
      Yang, Qian  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.735",
    doi = "10.18653/v1/2020.emnlp-main.735",
    pages = "9144--9156",
    abstract = "Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, however, the model is instead conditioned on previously generated tokens, resulting in what is termed exposure bias. To reduce this gap between training and testing, we propose using optimal transport (OT) to match the sequences generated in these two modes. We examine the necessity of adding Student-Forcing scheme during training with an imitation learning interpretation. An extension is further proposed to improve the OT learning for long sequences, based on the structural and contextual information of the text sequences. The effectiveness of the proposed method is validated on machine translation, text summarization, and text generation tasks.",
}
@inproceedings{guan-huang-2020-union,
    title = "{UNION}: {A}n {U}nreferenced {M}etric for {E}valuating {O}pen-ended {S}tory {G}eneration",
    author = "Guan, Jian  and
      Huang, Minlie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.736",
    doi = "10.18653/v1/2020.emnlp-main.736",
    pages = "9157--9166",
    abstract = "Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with human judgments for open-ended text generation including story or dialog generation because of the notorious one-to-many issue: there are many plausible outputs for the same input, which may differ substantially in literal or semantics from the limited number of given references. To alleviate this issue, we propose UNION, a learnable UNreferenced metrIc for evaluating Open-eNded story generation, which measures the quality of a generated story without any reference. Built on top of BERT, UNION is trained to distinguish human-written stories from negative samples and recover the perturbation in negative stories. We propose an approach of constructing negative samples by mimicking the errors commonly observed in existing NLG models, including repeated plots, conflicting logic, and long-range incoherence. Experiments on two story datasets demonstrate that UNION is a reliable measure for evaluating the quality of generated stories, which correlates better with human judgments and is more generalizable than existing state-of-the-art metrics.",
}
@inproceedings{choi-etal-2020-f,
    title = "F{\^{}}2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax",
    author = "Choi, Byung-Ju  and
      Hong, Jimin  and
      Park, David  and
      Lee, Sang Wan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.737",
    doi = "10.18653/v1/2020.emnlp-main.737",
    pages = "9167--9182",
    abstract = "Despite recent advances in neural text generation, encoding the rich diversity in human language remains elusive. We argue that the sub-optimal text generation is mainly attributable to the imbalanced token distribution, which particularly misdirects the learning model when trained with the maximum-likelihood objective. As a simple yet effective remedy, we propose two novel methods, F{\^{}}2-Softmax and MefMax, for a balanced training even with the skewed frequency distribution. MefMax assigns tokens uniquely to frequency classes, trying to group tokens with similar frequencies and equalize frequency mass between the classes. F{\^{}}2-Softmax then decomposes a probability distribution of the target token into a product of two conditional probabilities of (1) frequency class, and (2) token from the target frequency class. Models learn more uniform probability distributions because they are confined to subsets of vocabularies. Significant performance gains on seven relevant metrics suggest the supremacy of our approach in improving not only the diversity but also the quality of generated texts.",
}
@inproceedings{fu-etal-2020-partially,
    title = "Partially-Aligned Data-to-Text Generation with Distant Supervision",
    author = "Fu, Zihao  and
      Shi, Bei  and
      Lam, Wai  and
      Bing, Lidong  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.738",
    doi = "10.18653/v1/2020.emnlp-main.738",
    pages = "9183--9193",
    abstract = "The Data-to-Text task aims to generate human-readable text for describing some given structured data enabling more interpretability. However, the typical generation task is confined to a few particular domains since it requires well-aligned data which is difficult and expensive to obtain. Using partially-aligned data is an alternative way of solving the dataset scarcity problem. This kind of data is much easier to obtain since it can be produced automatically. However, using this kind of data induces the over-generation problem posing difficulties for existing models, which tends to add unrelated excerpts during the generation procedure. In order to effectively utilize automatically annotated partially-aligned datasets, we extend the traditional generation task to a refined task called Partially-Aligned Data-to-Text Generation (PADTG) which is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. To tackle this new task, we propose a novel distant supervision generation framework. It firstly estimates the input data{'}s supportiveness for each target word with an estimator and then applies a supportiveness adaptor and a rebalanced beam search to harness the over-generation problem in the training and generation phases respectively. We also contribute a partially-aligned dataset (The data and source code of this paper can be obtained from https://github.com/fuzihaofzh/distant{\_}supervision{\_}nlg) by sampling sentences from Wikipedia and automatically extracting corresponding KB triples for each sentence from Wikidata. The experimental results show that our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.",
}
@inproceedings{majumder-etal-2020-like,
    title = "Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions",
    author = "Majumder, Bodhisattwa Prasad  and
      Jhamtani, Harsh  and
      Berg-Kirkpatrick, Taylor  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.739",
    doi = "10.18653/v1/2020.emnlp-main.739",
    pages = "9194--9206",
    abstract = "Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly. For example, state-of-the-art models cannot infer that interest in hiking might imply love for nature or longing for a break. In this paper, we propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to an expanded and richer set of persona descriptions. Additionally, we introduce fine-grained grounding on personas by encouraging the model to make a discrete choice among persona sentences while synthesizing a dialog response. Since such a choice is not observed in the data, we model it using a discrete latent random variable and use variational learning to sample from hundreds of persona expansions. Our model outperforms competitive baselines on the Persona-Chat dataset in terms of dialog quality and diversity while achieving persona-consistent and controllable dialog generation.",
}
@inproceedings{zhang-etal-2020-probabilistic,
    title = "A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning",
    author = "Zhang, Yichi  and
      Ou, Zhijian  and
      Hu, Min  and
      Feng, Junlan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.740",
    doi = "10.18653/v1/2020.emnlp-main.740",
    pages = "9207--9219",
    abstract = "Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. However, training belief trackers often requires expensive turn-level annotations of every user utterance. In this paper we aim at alleviating the reliance on belief state labels in building end-to-end dialog systems, by leveraging unlabeled dialog data towards semi-supervised learning. We propose a probabilistic dialog model, called the LAtent BElief State (LABES) model, where belief states are represented as discrete latent variables and jointly modeled with system responses given user inputs. Such latent variable modeling enables us to develop semi-supervised learning under the principled variational learning framework. Furthermore, we introduce LABES-S2S, which is a copy-augmented Seq2Seq model instantiation of LABES. In supervised experiments, LABES-S2S obtains strong results on three benchmark datasets of different scales. In utilizing unlabeled dialog data, semi-supervised LABES-S2S significantly outperforms both supervised-only and semi-supervised baselines. Remarkably, we can reduce the annotation demands to 50{\%} without performance loss on MultiWOZ.",
}
@inproceedings{lin-etal-2020-world,
    title = "The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection",
    author = "Lin, Zibo  and
      Cai, Deng  and
      Wang, Yan  and
      Liu, Xiaojiang  and
      Zheng, Haitao  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.741",
    doi = "10.18653/v1/2020.emnlp-main.741",
    pages = "9220--9229",
    abstract = "Response selection plays a vital role in building retrieval-based conversation systems. Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero). On the one hand, this formalization can be sub-optimal due to its ignorance of the diversity of response quality. On the other hand, annotating grayscale data for learning-to-rank can be prohibitively expensive and challenging. In this work, we show that grayscale data can be automatically constructed without human effort. Our method employs off-the-shelf response retrieval models and response generation models as automatic grayscale data generators. With the constructed grayscale data, we propose multi-level ranking objectives for training, which can (1) teach a matching model to capture more fine-grained context-response relevance difference and (2) reduce the train-test discrepancy in terms of distractor strength. Our method is simple, effective, and universal. Experiments on three benchmark datasets and four state-of-the-art matching models show that the proposed approach brings significant and consistent performance improvements.",
}
@inproceedings{huang-etal-2020-grade,
    title = "{GRADE}: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems",
    author = "Huang, Lishan  and
      Ye, Zheng  and
      Qin, Jinghui  and
      Lin, Liang  and
      Liang, Xiaodan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.742",
    doi = "10.18653/v1/2020.emnlp-main.742",
    pages = "9230--9240",
    abstract = "Automatically evaluating dialogue coherence is a challenging but high-demand ability for developing high-quality open-domain dialogue systems. However, current evaluation metrics consider only surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows. Here, we first consider that the graph structure constituted with topics in a dialogue can accurately depict the underlying communication logic, which is a more natural way to produce persuasive metrics. Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation. Specifically, GRADE incorporates both coarse-grained utterance-level contextualized representations and fine-grained topic-level graph representations to evaluate dialogue coherence. The graph representations are obtained by reasoning over topic-level dialogue graphs enhanced with the evidence from a commonsense graph, including k-hop neighboring representations and hop-attention weights. Experimental results show that our GRADE significantly outperforms other state-of-the-art metrics on measuring diverse dialogue models in terms of the Pearson and Spearman correlations with human judgments. Besides, we release a new large-scale human evaluation benchmark to facilitate future research on automatic metrics.",
}
@inproceedings{zeng-etal-2020-meddialog,
    title = "{M}ed{D}ialog: Large-scale Medical Dialogue Datasets",
    author = "Zeng, Guangtao  and
      Yang, Wenmian  and
      Ju, Zeqian  and
      Yang, Yue  and
      Wang, Sicheng  and
      Zhang, Ruisi  and
      Zhou, Meng  and
      Zeng, Jiaqi  and
      Dong, Xiangyu  and
      Zhang, Ruoyu  and
      Fang, Hongchao  and
      Zhu, Penghui  and
      Chen, Shu  and
      Xie, Pengtao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.743",
    doi = "10.18653/v1/2020.emnlp-main.743",
    pages = "9241--9250",
    abstract = "Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build large-scale medical dialogue datasets {--} MedDialog, which contain 1) a Chinese dataset with 3.4 million conversations between patients and doctors, 11.3 million utterances, 660.2 million tokens, covering 172 specialties of diseases, and 2) an English dataset with 0.26 million conversations, 0.51 million utterances, 44.53 million tokens, covering 96 specialties of diseases. To our best knowledge, MedDialog is the largest medical dialogue dataset to date. We pretrain several dialogue generation models on the Chinese MedDialog dataset, including Transformer, GPT, BERT-GPT, and compare their performance. It is shown that models trained on MedDialog are able to generate clinically correct and doctor-like medical dialogues. We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks. It is shown that via transfer learning which finetunes the models pretrained on MedDialog, the performance on medical dialogue generation tasks with small datasets can be greatly improved, as shown in human evaluation and automatic evaluation. The datasets and code are available at https://github.com/UCSD-AI4H/Medical-Dialogue-System",
}
@inproceedings{zhu-rudzicz-2020-information,
    title = "An information theoretic view on selecting linguistic probes",
    author = "Zhu, Zining  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.744",
    doi = "10.18653/v1/2020.emnlp-main.744",
    pages = "9251--9262",
    abstract = "There is increasing interest in assessing the linguistic knowledge encoded in neural representations. A popular approach is to attach a diagnostic classifier {--} or {''}probe{''} {--} to perform supervised classification from internal representations. However, how to select a good probe is in debate. Hewitt and Liang (2019) showed that a high performance on diagnostic classification itself is insufficient, because it can be attributed to either {''}the representation being rich in knowledge{''}, or {''}the probe learning the task{''}, which Pimentel et al. (2020) challenged. We show this dichotomy is valid information-theoretically. In addition, we find that the {''}good probe{''} criteria proposed by the two papers, *selectivity* (Hewitt and Liang, 2019) and *information gain* (Pimentel et al., 2020), are equivalent {--} the errors of their approaches are identical (modulo irrelevant terms). Empirically, these two selection criteria lead to results that highly agree with each other.",
}
@inproceedings{card-etal-2020-little,
    title = "With Little Power Comes Great Responsibility",
    author = "Card, Dallas  and
      Henderson, Peter  and
      Khandelwal, Urvashi  and
      Jia, Robin  and
      Mahowald, Kyle  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.745",
    doi = "10.18653/v1/2020.emnlp-main.745",
    pages = "9263--9274",
    abstract = "Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community. Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements, and increase the chances of exaggerated findings. By meta-analyzing a set of existing NLP papers and datasets, we characterize typical power for a variety of settings and conclude that underpowered experiments are common in the NLP literature. In particular, for several tasks in the popular GLUE benchmark, small test sets mean that most attempted comparisons to state of the art models will not be adequately powered. Similarly, based on reasonable assumptions, we find that the most typical experimental design for human rating studies will be underpowered to detect small model differences, of the sort that are frequently studied. For machine translation, we find that typical test sets of 2000 sentences have approximately 75{\%} power to detect differences of 1 BLEU point. To improve the situation going forward, we give an overview of best practices for power analysis in NLP and release a series of notebooks to assist with future power analyses.",
}
@inproceedings{swayamdipta-etal-2020-dataset,
    title = "Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics",
    author = "Swayamdipta, Swabha  and
      Schwartz, Roy  and
      Lourie, Nicholas  and
      Wang, Yizhong  and
      Hajishirzi, Hannaneh  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.746",
    doi = "10.18653/v1/2020.emnlp-main.746",
    pages = "9275--9293",
    abstract = "Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Maps{---}a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training (training dynamics) for building data maps. This yields two intuitive measures for each example{---}the model{'}s confidence in the true class, and the variability of this confidence across epochs{---}obtained in a single run of training. Experiments on four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics. First, our data maps show the presence of {``}ambiguous{''} regions with respect to the model, which contribute the most towards out-of-distribution generalization. Second, the most populous regions in the data are {``}easy to learn{''} for the model, and play an important role in model optimization. Finally, data maps uncover a region with instances that the model finds {``}hard to learn{''}; these often correspond to labeling errors. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.",
}
@inproceedings{carton-etal-2020-evaluating,
    title = "Evaluating and Characterizing Human Rationales",
    author = "Carton, Samuel  and
      Rathore, Anirudh  and
      Tan, Chenhao",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.747",
    doi = "10.18653/v1/2020.emnlp-main.747",
    pages = "9294--9307",
    abstract = "Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior. An open question, however, is how human rationales fare with these automatic metrics. Analyzing a variety of datasets and models, we find that human rationales do not necessarily perform well on these metrics. To unpack this finding, we propose improved metrics to account for model-dependent baseline performance. We then propose two methods to further characterize rationale quality, one based on model retraining and one on using {``}fidelity curves{''} to reveal properties such as irrelevance and redundancy. Our work leads to actionable suggestions for evaluating and characterizing rationales.",
}
@inproceedings{pilault-etal-2020-extractive,
    title = "On Extractive and Abstractive Neural Document Summarization with Transformer Language Models",
    author = "Pilault, Jonathan  and
      Li, Raymond  and
      Subramanian, Sandeep  and
      Pal, Chris",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.748",
    doi = "10.18653/v1/2020.emnlp-main.748",
    pages = "9308--9319",
    abstract = "We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher ROUGE scores. We provide extensive comparisons with strong baseline methods, prior state of the art work as well as multiple variants of our approach including those using only transformers, only extractive techniques and combinations of the two. We examine these models using four different summarization tasks and datasets: arXiv papers, PubMed papers, the Newsroom and BigPatent datasets. We find that transformer based methods produce summaries with fewer n-gram copies, leading to n-gram copying statistics that are more similar to human generated abstracts. We include a human evaluation, finding that transformers are ranked highly for coherence and fluency, but purely extractive methods score higher for informativeness and relevance. We hope that these architectures and experiments may serve as strong points of comparison for future work. Note: The abstract above was collaboratively written by the authors and one of the models presented in this paper based on an earlier draft of this paper.",
}
@inproceedings{dong-etal-2020-multi-fact,
    title = "Multi-Fact Correction in Abstractive Text Summarization",
    author = "Dong, Yue  and
      Wang, Shuohang  and
      Gan, Zhe  and
      Cheng, Yu  and
      Cheung, Jackie Chi Kit  and
      Liu, Jingjing",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.749",
    doi = "10.18653/v1/2020.emnlp-main.749",
    pages = "9320--9331",
    abstract = "Pre-trained neural abstractive summarization systems have dominated extractive strategies on news summarization performance, at least in terms of ROUGE. However, system-generated abstractive summaries often face the pitfall of factual inconsistency: generating incorrect facts with respect to the source text. To address this challenge, we propose Span-Fact, a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection. Our models employ single or multi-masking strategies to either iteratively or auto-regressively replace entities in order to ensure semantic consistency w.r.t. the source text, while retaining the syntactic structure of summaries generated by abstractive summarization models. Experiments show that our models significantly boost the factual consistency of system-generated summaries without sacrificing summary quality in terms of both automatic metrics and human evaluation.",
}
@inproceedings{kryscinski-etal-2020-evaluating,
    title = "Evaluating the Factual Consistency of Abstractive Text Summarization",
    author = "Kryscinski, Wojciech  and
      McCann, Bryan  and
      Xiong, Caiming  and
      Socher, Richard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.750",
    doi = "10.18653/v1/2020.emnlp-main.750",
    pages = "9332--9346",
    abstract = "The most common metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and generated summaries. Training data is generated by applying a series of rule-based transformations to the sentences of source documents.The factual consistency model is then trained jointly for three tasks: 1) predict whether each summary sentence is factually consistent or not, 2) in either case, extract a span in the source document to support this consistency prediction, 3) for each summary sentence that is deemed inconsistent, extract the inconsistent span from it. Transferring this model to summaries generated by several neural models reveals that this highly scalable approach outperforms previous models, including those trained with strong supervision using datasets from related domains, such as natural language inference and fact checking. Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency. We also release a manually annotated dataset for factual consistency verification, code for training data generation, and trained model weights at https://github.com/salesforce/factCC.",
}
@inproceedings{bhandari-etal-2020-evaluating,
    title = "Re-evaluating Evaluation in Text Summarization",
    author = "Bhandari, Manik  and
      Gour, Pranav Narayan  and
      Ashfaq, Atabak  and
      Liu, Pengfei  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.751",
    doi = "10.18653/v1/2020.emnlp-main.751",
    pages = "9347--9359",
    abstract = "Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization. However, while the field has progressed, our standard metrics have not {--} for nearly 20 years ROUGE has been the standard evaluation in most summarization papers. In this paper, we make an attempt to re-evaluate the evaluation method for text summarization: assessing the reliability of automatic metrics using top-scoring system outputs, both abstractive and extractive, on recently popular datasets for both system-level and summary-level evaluation settings. We find that conclusions about evaluation metrics on older datasets do not necessarily hold on modern datasets and systems. We release a dataset of human judgments that are collected from 25 top-scoring neural summarization systems (14 abstractive and 11 extractive).",
}
@inproceedings{li-etal-2020-vmsmo,
    title = "{VMSMO}: Learning to Generate Multimodal Summary for Video-based News Articles",
    author = "Li, Mingzhe  and
      Chen, Xiuying  and
      Gao, Shen  and
      Chan, Zhangming  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.752",
    doi = "10.18653/v1/2020.emnlp-main.752",
    pages = "9360--9369",
    abstract = "A popular multimedia news format nowadays is providing users with a lively video and a corresponding news article, which is employed by influential news media including CNN, BBC, and social media including Twitter and Weibo. In such a case, automatically choosing a proper cover frame of the video and generating an appropriate textual summary of the article can help editors save time, and readers make the decision more effectively. Hence, in this paper, we propose the task of Video-based Multimodal Summarization with Multimodal Output (VMSMO) to tackle such a problem. The main challenge in this task is to jointly model the temporal dependency of video with semantic meaning of article. To this end, we propose a Dual-Interaction-based Multimodal Summarizer (DIMS), consisting of a dual interaction module and multimodal generator. In the dual interaction module, we propose a conditional self-attention mechanism that captures local semantic information within video and a global-attention mechanism that handles the semantic relationship between news text and video from a high level. Extensive experiments conducted on a large-scale real-world VMSMO dataset show that DIMS achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.",
}
@proceedings{emnlp-2020-2020-empirical,
    title = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    editor = "Liu, Qun  and
      Schlangen, David",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.0",
}
@inproceedings{zhang-etal-2020-openue,
    title = "{O}pen{UE}: An Open Toolkit of Universal Extraction from Text",
    author = "Zhang, Ningyu  and
      Deng, Shumin  and
      Bi, Zhen  and
      Yu, Haiyang  and
      Yang, Jiacheng  and
      Chen, Mosha  and
      Huang, Fei  and
      Zhang, Wei  and
      Chen, Huajun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.1",
    doi = "10.18653/v1/2020.emnlp-demos.1",
    pages = "1--8",
    abstract = "Natural language processing covers a wide variety of tasks with token-level or sentence-level understandings. In this paper, we provide a simple insight that most tasks can be represented in a single universal extraction format. We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks. OpenUE allows developers to train custom models to extract information from the text and supports quick model validation for researchers. Besides, OpenUE provides various functional modules to maintain sufficient modularity and extensibility. Except for the toolkit, we also deploy an online demo with restful APIs to support real-time extraction without training and deploying. Additionally, the online system can extract information in various tasks, including relational triple extraction, slot {\&} intent detection, event extraction, and so on. We release the source code, datasets, and pre-trained models to promote future researches in http://github.com/zjunlp/openue.",
}
@inproceedings{nguyen-etal-2020-bertweet,
    title = "{BERT}weet: A pre-trained language model for {E}nglish Tweets",
    author = "Nguyen, Dat Quoc  and
      Vu, Thanh  and
      Tuan Nguyen, Anh",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.2",
    doi = "10.18653/v1/2020.emnlp-demos.2",
    pages = "9--14",
    abstract = "We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification. We release BERTweet under the MIT License to facilitate future research and applications on Tweet data. Our BERTweet is available at https://github.com/VinAIResearch/BERTweet",
}
@inproceedings{dibia-2020-neuralqa,
    title = "{N}eural{QA}: A Usable Library for Question Answering (Contextual Query Expansion + {BERT}) on Large Datasets",
    author = "Dibia, Victor",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.3",
    doi = "10.18653/v1/2020.emnlp-demos.3",
    pages = "15--22",
    abstract = "Existing tools for Question Answering (QA) have challenges that limit their use in practice. They can be complex to set up or integrate with existing infrastructure, do not offer configurable interactive interfaces, and do not cover the full set of subtasks that frequently comprise the QA pipeline (query expansion, retrieval, reading, and explanation/sensemaking). To help address these issues, we introduce NeuralQA - a usable library for QA on large datasets. NeuralQA integrates well with existing infrastructure (e.g., ElasticSearch instances and reader models trained with the HuggingFace Transformers API) and offers helpful defaults for QA subtasks. It introduces and implements contextual query expansion (CQE) using a masked language model (MLM) as well as relevant snippets ($RelSnip$) - a method for condensing large documents into smaller passages that can be speedily processed by a document reader model. Finally, it offers a flexible user interface to support workflows for research explorations (e.g., visualization of gradient-based explanations to support qualitative inspection of model behaviour) and large scale search deployment. Code and documentation for NeuralQA is available as open source on Github.",
}
@inproceedings{yamada-etal-2020-wikipedia2vec,
    title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
    author = "Yamada, Ikuya  and
      Asai, Akari  and
      Sakuma, Jin  and
      Shindo, Hiroyuki  and
      Takeda, Hideaki  and
      Takefuji, Yoshiyasu  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.4",
    doi = "10.18653/v1/2020.emnlp-demos.4",
    pages = "23--30",
    abstract = "The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool achieved a state-of-the-art result on the KORE entity relatedness dataset, and competitive results on various standard benchmark datasets. Furthermore, our tool has been used as a key component in various recent studies. We publicize the source code, demonstration, and the pretrained embeddings for 12 languages at https://wikipedia2vec.github.io/.",
}
@inproceedings{ferritto-etal-2020-ares,
    title = "{ARES}: A Reading Comprehension Ensembling Service",
    author = "Ferritto, Anthony  and
      Pan, Lin  and
      Chakravarti, Rishav  and
      Roukos, Salim  and
      Florian, Radu  and
      Murdock, J. William  and
      Sil, Avi",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.5",
    doi = "10.18653/v1/2020.emnlp-demos.5",
    pages = "31--37",
    abstract = "We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. While many of the top leaderboard submissions in popular MRC benchmarks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) use model ensembles, the accompanying papers do not publish their ensembling strategies. In this work, we detail and evaluate various ensembling strategies using the NQ dataset. ARES leverages the CFO (Chakravarti et al., 2019) and ReactJS distributed frameworks to provide a scalable interactive Question Answering experience that capitalizes on the agreement (or lack thereof) between models to improve the answer visualization experience.",
}
@inproceedings{wolf-etal-2020-transformers,
    title = "Transformers: State-of-the-Art Natural Language Processing",
    author = "Wolf, Thomas  and
      Debut, Lysandre  and
      Sanh, Victor  and
      Chaumond, Julien  and
      Delangue, Clement  and
      Moi, Anthony  and
      Cistac, Pierric  and
      Rault, Tim  and
      Louf, Remi  and
      Funtowicz, Morgan  and
      Davison, Joe  and
      Shleifer, Sam  and
      von Platen, Patrick  and
      Ma, Clara  and
      Jernite, Yacine  and
      Plu, Julien  and
      Xu, Canwen  and
      Le Scao, Teven  and
      Gugger, Sylvain  and
      Drame, Mariama  and
      Lhoest, Quentin  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
    doi = "10.18653/v1/2020.emnlp-demos.6",
    pages = "38--45",
    abstract = "Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at https://github.com/huggingface/transformers.",
}
@inproceedings{pfeiffer-etal-2020-adapterhub,
    title = "{A}dapter{H}ub: A Framework for Adapting Transformers",
    author = {Pfeiffer, Jonas  and
      R{\"u}ckl{\'e}, Andreas  and
      Poth, Clifton  and
      Kamath, Aishwarya  and
      Vuli{\'c}, Ivan  and
      Ruder, Sebastian  and
      Cho, Kyunghyun  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.7",
    doi = "10.18653/v1/2020.emnlp-demos.7",
    pages = "46--54",
    abstract = "The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. Storing and sharing such large trained models is expensive, slow, and time-consuming, which impedes progress towards more general and versatile NLP methods that learn from and for many tasks. Adapters{---}small learnt bottleneck layers inserted within each layer of a pre-trained model{---} ameliorate this issue by avoiding full fine-tuning of the entire model. However, sharing and integrating adapter layers is not straightforward. We propose AdapterHub, a framework that allows dynamic {``}stiching-in{''} of pre-trained adapters for different tasks and languages. The framework, built on top of the popular HuggingFace Transformers library, enables extremely easy and quick adaptations of state-of-the-art pre-trained models (e.g., BERT, RoBERTa, XLM-R) across tasks and languages. Downloading, sharing, and training adapters is as seamless as possible using minimal changes to the training scripts and a specialized infrastructure. Our framework enables scalable and easy access to sharing of task-specific models, particularly in low-resource scenarios. AdapterHub includes all recent adapter architectures and can be found at AdapterHub.ml",
}
@inproceedings{wolf-etal-2020-human,
    title = "{HUMAN}: Hierarchical Universal Modular {AN}notator",
    author = "Wolf, Moritz  and
      Ruiter, Dana  and
      D{'}Sa, Ashwin Geet  and
      Reiners, Liane  and
      Alexandersson, Jan  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.8",
    doi = "10.18653/v1/2020.emnlp-demos.8",
    pages = "55--61",
    abstract = "A lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answers describing the nature of the subject at hand. Even in the case a phenomenon is easily captured by a single task, the high specialisation of most annotation tools can result in having to switch to another tool if the task only slightly changes. We introduce HUMAN, a novel web-based annotation tool that addresses the above problems by a) covering a variety of annotation tasks on both textual and image data, and b) the usage of an internal deterministic state machine, allowing the researcher to chain different annotation tasks in an interdependent manner. Further, the modular nature of the tool makes it easy to define new annotation tasks and integrate machine learning algorithms e.g., for active learning. HUMAN comes with an easy-to-use graphical user interface that simplifies the annotation task and management.",
}
@inproceedings{hosseini-etal-2020-deezymatch,
    title = "{D}eezy{M}atch: A Flexible Deep Learning Approach to Fuzzy String Matching",
    author = "Hosseini, Kasra  and
      Nanni, Federico  and
      Coll Ardanuy, Mariona",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.9",
    doi = "10.18653/v1/2020.emnlp-demos.9",
    pages = "62--69",
    abstract = "We present DeezyMatch, a free, open-source software library written in Python for fuzzy string matching and candidate ranking. Its pair classifier supports various deep neural network architectures for training new classifiers and for fine-tuning a pretrained model, which paves the way for transfer learning in fuzzy string matching. This approach is especially useful where only limited training examples are available. The learned DeezyMatch models can be used to generate rich vector representations from string inputs. The candidate ranker component in DeezyMatch uses these vector representations to find, for a given query, the best matching candidates in a knowledge base. It uses an adaptive searching algorithm applicable to large knowledge bases and query sets. We describe DeezyMatch{'}s functionality, design and implementation, accompanied by a use case in toponym matching and candidate ranking in realistic noisy datasets.",
}
@inproceedings{jansen-2020-cosata,
    title = "{C}o{S}a{T}a: A Constraint Satisfaction Solver and Interpreted Language for Semi-Structured Tables of Sentences",
    author = "Jansen, Peter",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.10",
    doi = "10.18653/v1/2020.emnlp-demos.10",
    pages = "70--76",
    abstract = "This work presents CoSaTa, an intuitive constraint satisfaction solver and interpreted language for knowledge bases of semi-structured tables expressed as text. The stand-alone CoSaTa solver allows easily expressing complex compositional {``}inference patterns{''} for how knowledge from different tables tends to connect to support inference and explanation construction in question answering and other downstream tasks, while including advanced declarative features and the ability to operate over multiple representations of text (words, lemmas, or part-of-speech tags). CoSaTa also includes a hybrid imperative/declarative interpreted language for expressing simple models through minimally-specified simulations grounded in constraint patterns, helping bridge the gap between question answering, question explanation, and model simulation. The solver and interpreter are released as open source. Screencast Demo: https://youtu.be/t93Acsz7LyE",
}
@inproceedings{conia-etal-2020-invero,
    title = "{I}n{V}e{R}o: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles",
    author = "Conia, Simone  and
      Brignone, Fabrizio  and
      Zanfardino, Davide  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.11",
    doi = "10.18653/v1/2020.emnlp-demos.11",
    pages = "77--84",
    abstract = "Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts. To address this issue we present a new platform named Intelligible Verbs and Roles (InVeRo). This platform provides access to a new verb resource, VerbAtlas, and a state-of-the-art pretrained implementation of a neural, span-based architecture for SRL. Both the resource and the system provide human-readable verb sense and semantic role information, with an easy to use Web interface and RESTful APIs available at http://nlp.uniroma1.it/invero.",
}
@inproceedings{zhang-etal-2020-youling,
    title = "Youling: an {AI}-assisted Lyrics Creation System",
    author = "Zhang, Rongsheng  and
      Mao, Xiaoxi  and
      Li, Le  and
      Jiang, Lin  and
      Chen, Lin  and
      Hu, Zhiwei  and
      Xi, Yadong  and
      Fan, Changjie  and
      Huang, Minlie",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.12",
    doi = "10.18653/v1/2020.emnlp-demos.12",
    pages = "85--91",
    abstract = "Recently, a variety of neural models have been proposed for lyrics generation. However, most previous work completes the generation process in a single pass with little human intervention. We believe that lyrics creation is a creative process with human intelligence centered. AI should play a role as an assistant in the lyrics creation process, where human interactions are crucial for high-quality creation. This paper demonstrates \textit{Youling}, an AI-assisted lyrics creation system, designed to collaborate with music creators. In the lyrics generation process, \textit{Youling} supports traditional one pass full-text generation mode as well as an interactive generation mode, which allows users to select the satisfactory sentences from generated candidates conditioned on preceding context. The system also provides a revision module which enables users to revise undesired sentences or words of lyrics repeatedly. Besides, \textit{Youling} allows users to use multifaceted attributes to control the content and format of generated lyrics. The demo video of the system is available at https://youtu.be/DFeNpHk0pm4.",
}
@inproceedings{yu-etal-2020-technical,
    title = "A Technical Question Answering System with Transfer Learning",
    author = "Yu, Wenhao  and
      Wu, Lingfei  and
      Deng, Yu  and
      Mahindru, Ruchi  and
      Zeng, Qingkai  and
      Guven, Sinem  and
      Jiang, Meng",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.13",
    doi = "10.18653/v1/2020.emnlp-demos.13",
    pages = "92--99",
    abstract = "In recent years, the need for community technical question-answering sites has increased significantly. However, it is often expensive for human experts to provide timely and helpful responses on those forums. We develop TransTQA, which is a novel system that offers automatic responses by retrieving proper answers based on correctly answered similar questions in the past. TransTQA is built upon a siamese ALBERT network, which enables it to respond quickly and accurately. Furthermore, TransTQA adopts a standard deep transfer learning strategy to improve its capability of supporting multiple technical domains.",
}
@inproceedings{chu-etal-2020-entyfi,
    title = "{ENTYFI}: A System for Fine-grained Entity Typing in Fictional Texts",
    author = "Chu, Cuong Xuan  and
      Razniewski, Simon  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.14",
    doi = "10.18653/v1/2020.emnlp-demos.14",
    pages = "100--106",
    abstract = "Fiction and fantasy are archetypes of long-tail domains that lack suitable NLP methodologies and tools. We present ENTYFI, a web-based system for fine-grained typing of entity mentions in fictional texts. It builds on 205 automatically induced high-quality type systems for popular fictional domains, and provides recommendations towards reference type systems for given input texts. Users can exploit the richness and diversity of these reference type systems for fine-grained supervised typing, in addition, they can choose among and combine four other typing modules: pre-trained real-world models, unsupervised dependency-based typing, knowledge base lookups, and constraint-based candidate consolidation. The demonstrator is available at: https://d5demos.mpi-inf.mpg.de/entyfi.",
}
@inproceedings{tenney-etal-2020-language,
    title = "The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for {NLP} Models",
    author = "Tenney, Ian  and
      Wexler, James  and
      Bastings, Jasmijn  and
      Bolukbasi, Tolga  and
      Coenen, Andy  and
      Gehrmann, Sebastian  and
      Jiang, Ellen  and
      Pushkarna, Mahima  and
      Radebaugh, Carey  and
      Reif, Emily  and
      Yuan, Ann",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.15",
    doi = "10.18653/v1/2020.emnlp-demos.15",
    pages = "107--118",
    abstract = "We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models. We focus on core questions about model behavior: Why did my model make this prediction? When does it perform poorly? What happens under a controlled change in the input? LIT integrates local explanations, aggregate analysis, and counterfactual generation into a streamlined, browser-based interface to enable rapid exploration and error analysis. We include case studies for a diverse set of workflows, including exploring counterfactuals for sentiment analysis, measuring gender bias in coreference systems, and exploring local behavior in text generation. LIT supports a wide range of models{---}including classification, seq2seq, and structured prediction{---}and is highly extensible through a declarative, framework-agnostic API. LIT is under active development, with code and full documentation available at https://github.com/pair-code/lit.",
}
@inproceedings{morris-etal-2020-textattack-framework,
    title = "{T}ext{A}ttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in {NLP}",
    author = "Morris, John  and
      Lifland, Eli  and
      Yoo, Jin Yong  and
      Grigsby, Jake  and
      Jin, Di  and
      Qi, Yanjun",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.16",
    doi = "10.18653/v1/2020.emnlp-demos.16",
    pages = "119--126",
    abstract = "While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop NLP attacks and utilize them to improve model performance. This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack{'}s modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks. TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.TextAttack is democratizing NLP: anyone can try data augmentation and adversarial training on any model or dataset, with just a few lines of code. Code and tutorials are available at https://github.com/QData/TextAttack.",
}
@inproceedings{ning-etal-2020-easy,
    title = "Easy, Reproducible and Quality-Controlled Data Collection with {CROWDAQ}",
    author = "Ning, Qiang  and
      Wu, Hao  and
      Dasigi, Pradeep  and
      Dua, Dheeru  and
      Gardner, Matt  and
      Logan IV, Robert L.  and
      Marasovi{\'c}, Ana  and
      Nie, Zhen",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.17",
    doi = "10.18653/v1/2020.emnlp-demos.17",
    pages = "127--134",
    abstract = "High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designing a user-friendly annotation interface; (2) training enough annotators efficiently; and (3) reproducibility. To address these problems, we introduce CROWDAQ, an open-source platform that standardizes the data collection pipeline with customizable user-interface components, automated annotator qualification, and saved pipelines in a re-usable format. We show that CROWDAQ simplifies data annotation significantly on a diverse set of data collection use cases and we hope it will be a convenient tool for the community.",
}
@inproceedings{hope-etal-2020-scisight,
    title = "{S}ci{S}ight: Combining faceted navigation and research group detection for {COVID}-19 exploratory scientific search",
    author = "Hope, Tom  and
      Portenoy, Jason  and
      Vasan, Kishore  and
      Borchardt, Jonathan  and
      Horvitz, Eric  and
      Weld, Daniel  and
      Hearst, Marti  and
      West, Jevin",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.18",
    doi = "10.18653/v1/2020.emnlp-demos.18",
    pages = "135--143",
    abstract = "The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight has so far served over 15K users with over 42K page views and 13{\%} returns.",
}
@inproceedings{ma-etal-2020-simuleval,
    title = "{SIMULEVAL}: An Evaluation Toolkit for Simultaneous Translation",
    author = "Ma, Xutai  and
      Dousti, Mohammad Javad  and
      Wang, Changhan  and
      Gu, Jiatao  and
      Pino, Juan",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.19",
    doi = "10.18653/v1/2020.emnlp-demos.19",
    pages = "144--150",
    abstract = "Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario where the model starts translating before reading the complete source input. Evaluating simultaneous translation models is more complex than offline models because the latency is another factor to consider in addition to translation quality. The research community, despite its growing focus on novel modeling approaches to simultaneous translation, currently lacks a universal evaluation procedure. Therefore, we present SimulEval, an easy-to-use and general evaluation toolkit for both simultaneous text and speech translation. A server-client scheme is introduced to create a simultaneous translation scenario, where the server sends source input and receives predictions for evaluation and the client executes customized policies. Given a policy, it automatically performs simultaneous decoding and collectively reports several popular latency metrics. We also adapt latency metrics from text simultaneous translation to the speech task. Additionally, SimulEval is equipped with a visualization interface to provide better understanding of the simultaneous decoding process of a system. SimulEval has already been extensively used for the IWSLT 2020 shared task on simultaneous speech translation. Code will be released upon publication.",
}
@inproceedings{fadnis-etal-2020-agent,
    title = "Agent Assist through Conversation Analysis",
    author = "Fadnis, Kshitij  and
      Mills, Nathaniel  and
      Ganhotra, Jatin  and
      Roitman, Haggai  and
      Pandey, Gaurav  and
      Cohen, Doron  and
      Mass, Yosi  and
      Erera, Shai  and
      Gunasekara, Chulaka  and
      Contractor, Danish  and
      Patel, Siva  and
      Liao, Q. Vera  and
      Joshi, Sachindra  and
      Lastras, Luis  and
      Konopnicki, David",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.20",
    doi = "10.18653/v1/2020.emnlp-demos.20",
    pages = "151--157",
    abstract = "Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.",
}
@inproceedings{jayanthi-etal-2020-neuspell,
    title = "{N}eu{S}pell: A Neural Spelling Correction Toolkit",
    author = "Jayanthi, Sai Muralidhar  and
      Pruthi, Danish  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.21",
    doi = "10.18653/v1/2020.emnlp-demos.21",
    pages = "158--164",
    abstract = "We introduce NeuSpell, an open-source toolkit for spelling correction in English. Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated misspellings; and (ii) use richer representations of the context. By training on our synthetic examples, correction rates improve by 9{\%} (absolute) compared to the case when models are trained on randomly sampled character perturbations. Using richer contextual representations boosts the correction rate by another 3{\%}. Our toolkit enables practitioners to use our proposed and existing spelling correction systems, both via a simple unified command line, as well as a web interface. Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings. The toolkit can be accessed at neuspell.github.io.",
}
@inproceedings{broscheit-etal-2020-libkge,
    title = "{L}ib{KGE} - A knowledge graph embedding library for reproducible research",
    author = "Broscheit, Samuel  and
      Ruffinelli, Daniel  and
      Kochsiek, Adrian  and
      Betz, Patrick  and
      Gemulla, Rainer",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.22",
    doi = "10.18653/v1/2020.emnlp-demos.22",
    pages = "165--174",
    abstract = "LibKGE ( https://github.com/uma-pi1/kge ) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. The key goals of LibKGE are to enable reproducible research, to provide a framework for comprehensive experimental studies, and to facilitate analyzing the contributions of individual components of training methods, model architectures, and evaluation methods. LibKGE is highly configurable and every experiment can be fully reproduced with a single configuration file. Individual components are decoupled to the extent possible so that they can be mixed and matched with each other. Implementations in LibKGE aim to be as efficient as possible without leaving the scope of Python/Numpy/PyTorch. A comprehensive logging mechanism and tooling facilitates in-depth analysis. LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added. A comparative study (Ruffinelli et al., 2020) showed that LibKGE reaches competitive to state-of-the-art performance for many models with a modest amount of automatic hyperparameter tuning.",
}
@inproceedings{qi-etal-2020-wantwords,
    title = "{W}ant{W}ords: An Open-source Online Reverse Dictionary System",
    author = "Qi, Fanchao  and
      Zhang, Lei  and
      Yang, Yanhui  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.23",
    doi = "10.18653/v1/2020.emnlp-demos.23",
    pages = "175--181",
    abstract = "A reverse dictionary takes descriptions of words as input and outputs words semantically matching the input descriptions. Reverse dictionaries have great practical value such as solving the tip-of-the-tongue problem and helping new language learners. There have been some online reverse dictionary systems, but they support English reverse dictionary queries only and their performance is far from perfect. In this paper, we present a new open-source online reverse dictionary system named WantWords (https://wantwords.thunlp.org/). It not only significantly outperforms other reverse dictionary systems on English reverse dictionary performance, but also supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries for the first time. Moreover, it has user-friendly front-end design which can help users find the words they need quickly and easily. All the code and data are available at https://github.com/thunlp/WantWords.",
}
@inproceedings{sohrab-etal-2020-bennerd,
    title = "{BENNERD}: A Neural Named Entity Linking System for {COVID}-19",
    author = "Sohrab, Mohammad Golam  and
      Duong, Khoa  and
      Miwa, Makoto  and
      Topi{\'c}, Goran  and
      Masami, Ikeda  and
      Hiroya, Takamura",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.24",
    doi = "10.18653/v1/2020.emnlp-demos.24",
    pages = "182--188",
    abstract = "We present a biomedical entity linking (EL) system BENNERD that detects named enti- ties in text and links them to the unified medical language system (UMLS) knowledge base (KB) entries to facilitate the corona virus disease 2019 (COVID-19) research. BEN- NERD mainly covers biomedical domain, es- pecially new entity types (e.g., coronavirus, vi- ral proteins, immune responses) by address- ing CORD-NER dataset. It includes several NLP tools to process biomedical texts includ- ing tokenization, flat and nested entity recog- nition, and candidate generation and rank- ing for EL that have been pre-trained using the CORD-NER corpus. To the best of our knowledge, this is the first attempt that ad- dresses NER and EL on COVID-19-related entities, such as COVID-19 virus, potential vaccines, and spreading mechanism, that may benefit research on COVID-19. We release an online system to enable real-time entity annotation with linking for end users. We also release the manually annotated test set and CORD-NERD dataset for leveraging EL task. The BENNERD system is available at https://aistairc.github.io/BENNERD/.",
}
@inproceedings{dugan-etal-2020-roft,
    title = "{R}o{FT}: A Tool for Evaluating Human Detection of Machine-Generated Text",
    author = "Dugan, Liam  and
      Ippolito, Daphne  and
      Kirubarajan, Arun  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.25",
    doi = "10.18653/v1/2020.emnlp-demos.25",
    pages = "189--196",
    abstract = "In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult. In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains. We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.",
}
@inproceedings{liu-etal-2020-data-centric,
    title = "A Data-Centric Framework for Composable {NLP} Workflows",
    author = "Liu, Zhengzhong  and
      Ding, Guanxiong  and
      Bukkittu, Avinash  and
      Gupta, Mansi  and
      Gao, Pengzhi  and
      Ahmed, Atif  and
      Zhang, Shikun  and
      Gao, Xin  and
      Singhavi, Swapnil  and
      Li, Linwei  and
      Wei, Wei  and
      Hu, Zecong  and
      Shi, Haoran  and
      Liang, Xiaodan  and
      Mitamura, Teruko  and
      Xing, Eric  and
      Hu, Zhiting",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.26",
    doi = "10.18653/v1/2020.emnlp-demos.26",
    pages = "197--204",
    abstract = "Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization. We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner. The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks. It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation. The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries. The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte (for workflow infrastructure and NLP function processors) and Stave (for user interaction, visualization, and annotation).",
}
@inproceedings{bornstein-etal-2020-corefi,
    title = "{C}o{R}efi: A Crowd Sourcing Suite for Coreference Annotation",
    author = "Bornstein, Ari  and
      Cattan, Arie  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.27",
    doi = "10.18653/v1/2020.emnlp-demos.27",
    pages = "205--215",
    abstract = "Coreference annotation is an important, yet expensive and time consuming, task, which often involved expert annotators trained on complex decision guidelines. To enable cheaper and more efficient annotation, we present CoRefi, a web-based coreference annotation suite, oriented for crowdsourcing. Beyond the core coreference annotation tool, CoRefi provides guided onboarding for the task as well as a novel algorithm for a reviewing phase. CoRefi is open source and directly embeds into any website, including popular crowdsourcing platforms. CoRefi Demo: aka.ms/corefi Video Tour: aka.ms/corefivideo Github Repo: https://github.com/aribornstein/corefi",
}
@inproceedings{ito-etal-2020-langsmith,
    title = "Langsmith: An Interactive Academic Text Revision System",
    author = "Ito, Takumi  and
      Kuribayashi, Tatsuki  and
      Hidaka, Masatoshi  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.28",
    doi = "10.18653/v1/2020.emnlp-demos.28",
    pages = "216--226",
    abstract = "Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English. This paper presents the Langsmith editor, which assists inexperienced, non-native researchers to write English papers, especially in the natural language processing (NLP) field. Our system can suggest fluent, academic-style sentences to writers based on their rough, incomplete phrases or sentences. The system also encourages interaction between human writers and the computerized revision system. The experimental results demonstrated that Langsmith helps non-native English-speaker students write papers in English. The system is available at https://emnlp-demo.editor. langsmith.co.jp/.",
}
@inproceedings{han-etal-2020-isobs,
    title = "{I}s{OBS}: An Information System for Oracle Bone Script",
    author = "Han, Xu  and
      Bai, Yuzhuo  and
      Qiu, Keyue  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.29",
    doi = "10.18653/v1/2020.emnlp-demos.29",
    pages = "227--233",
    abstract = "Oracle bone script (OBS) is the earliest known ancient Chinese writing system and the ancestor of modern Chinese. As the Chinese writing system is the oldest continuously-used system in the world, the study of OBS plays an important role in both linguistic and historical research. In order to utilize advanced machine learning methods to automatically process OBS, we construct an information system for OBS (IsOBS) to symbolize, serialize, and store OBS data at the character-level, based on efficient databases and retrieval modules. Moreover, we also apply few-shot learning methods to build an effective OBS character recognition module, which can recognize a large number of OBS characters (especially those characters with a handful of examples) and make the system easy to use. The demo system of IsOBS can be found from \url{http://isobs.thunlp.org/}. In the future, we will add more OBS data to the system, and hopefully our IsOBS can support further efforts in automatically processing OBS and advance the scientific progress in this field.",
}
@proceedings{ecomnlp-2020-natural,
    title = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    editor = "Zhao, Huasha  and
      Sondhi, Parikshit  and
      Bach, Nguyen  and
      Hewavitharana, Sanjika  and
      He, Yifan  and
      Si, Luo  and
      Ji, Heng",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.0",
}
@inproceedings{rafieian-costa-jussa-2020-e,
    title = "{E}-Commerce Content and Collaborative-based Recommendation using K-Nearest Neighbors and Enriched Weighted Vectors",
    author = "Rafieian, Bardia  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.1",
    pages = "1--10",
    abstract = "In this paper, we present two productive and functional recommender methods to improve the ac- curacy of predicting the right product for the user. One proposal is a survey-based recommender system that uses k-nearest neighbors. It recommends products by asking questions from the user, efficiently applying a binary product vector to the product attributes, and processing the request with a minimum error. The second proposal uses an enriched collaborative-based recommender system using enriched weighted vectors. Thanks to the style rules, the enriched collaborative- based method recommends outfits with competitive recommendation quality. We evaluated both of the proposals on a Kaggle fashion-dataset along with iMaterialist and, results show equivalent performance on binary gender and product attributes.",
}
@inproceedings{arroyo-etal-2020-multi,
    title = "Multi-label classification of promotions in digital leaflets using textual and visual information",
    author = "Arroyo, Roberto  and
      Jim{\'e}nez-Cabello, David  and
      Mart{\'\i}nez-Cebri{\'a}n, Javier",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.2",
    pages = "11--20",
    abstract = "Product descriptions in e-commerce platforms contain detailed and valuable information about retailers assortment. In particular, coding promotions within digital leaflets are of great interest in e-commerce as they capture the attention of consumers by showing regular promotions for different products. However, this information is embedded into images, making it difficult to extract and process for downstream tasks. In this paper, we present an end-to-end approach that classifies promotions within digital leaflets into their corresponding product categories using both visual and textual information. Our approach can be divided into three key components: 1) region detection, 2) text recognition and 3) text classification. In many cases, a single promotion refers to multiple product categories, so we introduce a multi-label objective in the classification head. We demonstrate the effectiveness of our approach for two separated tasks: 1) image-based detection of the descriptions for each individual promotion and 2) multi-label classification of the product categories using the text from the product descriptions. We train and evaluate our models using a private dataset composed of images from digital leaflets obtained by Nielsen. Results show that we consistently outperform the proposed baseline by a large margin in all the experiments.",
}
@inproceedings{lehmann-etal-2020-bilingual,
    title = "Bilingual Transfer Learning for Online Product Classification",
    author = {Lehmann, Erik  and
      Simonyi, Andr{\'a}s  and
      Henkel, Lukas  and
      Franke, J{\"o}rn},
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.3",
    pages = "21--31",
    abstract = "Consumer Price Indices (CPIs) are one of the major statistics produced by Statistical Offices, and of crucial importance to Central Banks. To calculate CPIs, statistical offices collect a large amount of individual prices of goods and services. Nowadays prices of many consumer goods can be obtained online, enabling a much more detailed measurement of inflation rates. One major challenge is to classify the variety of products, from different shops and languages into the given statistical schema consisting of a complex multi-level classification hierarchy - the European Classification of Individual Consumption according to Purpose (ECOICOP) for European countries, since there is no model, mapping or labelled data available. We focus in our analysis on food, beverage and tobacco which account for 74 of the 258 ECOICOP categories and 19 {\%} of the Euro Area inflation basket. In this paper we build a classifier on web scraped, hand-labeled product data from German retailers and test the transfer to French data using cross lingual word embedding. We compare its performance against a classifier trained on the single languages and a classifier with both languages trained jointly. Furthermore, we propose a pipeline to effectively create a data set with balanced labels using transferred predictions and active learning. In addition we test how much data it takes to build a single language classifier from scratch an if there are benefits from multilingual training. Our proposed system reduces the time to complete the task by about two thirds and is already used to support the analysis of inflation.",
}
@inproceedings{galitsky-ilvovsky-2020-interrupt,
    title = "Interrupt me Politely: Recommending Products and Services by Joining Human Conversation",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.4",
    pages = "32--42",
    abstract = "We propose a novel way of conversational recommendation, where instead of asking questions to the user to acquire their preferences; the recommender tracks their conversation with other people, including customer support agents (CSA), and joins the conversation only when it is time to introduce a recommendation. Building a recommender that joins a human conversation (RJC), we propose information extraction, discourse and argumentation analyses, as well as dialogue management techniques to compute a recommendation for a product and service that is needed by the customer, as inferred from the conversation. A special case of such conversations is considered where the customer raises his problem with CSA in an attempt to resolve it, along with receiving a recommendation for a product with features addressing this problem. We evaluate performance of RJC is in a number of human-human and human-chat bot dialogues, and demonstrate that RJC is an efficient and less intrusive way to provide high relevance and persuasive recommendations.",
}
@inproceedings{kew-etal-2020-benchmarking,
    title = "Benchmarking Automated Review Response Generation for the Hospitality Domain",
    author = "Kew, Tannon  and
      Amsler, Michael  and
      Ebling, Sarah",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.5",
    pages = "43--52",
    abstract = "Online customer reviews are of growing importance for many businesses in the hospitality industry, particularly restaurants and hotels. Managerial responses to such reviews provide businesses with the opportunity to influence the public discourse and to attain improved ratings over time. However, responding to each and every review is a time-consuming endeavour. Therefore, we investigate automatic generation of review responses in the hospitality domain for two languages, English and German. We apply an existing system, originally proposed for review response generation for smartphone apps. This approach employs an extended neural network sequence-to-sequence architecture and performs well in the original domain. However, as shown through our experiments, when applied to a new domain, such as hospitality, performance drops considerably. Therefore, we analyse potential causes for the differences in performance and provide evidence to suggest that review response generation in the hospitality domain is a more challenging task and thus requires further study and additional domain adaptation techniques.",
}
@inproceedings{galitsky-etal-2020-chatbot,
    title = "On a Chatbot Navigating a User through a Concept-Based Knowledge Model",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry  and
      Goncharova, Elizaveta",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.6",
    pages = "53--65",
    abstract = "Information retrieval chatbots are widely used as assistants, to help users formulate their requirements about the products they want to purchase, and navigate to the set of items that satisfies their requirements in the best way. The work of the modern chatbots is based mostly on the deep learning theory behind the knowledge model that can improve the performance of the system. In our work, we are developing a concept-based knowledge model that encapsulates objects and their common descriptions. The leveraging of the concept-based knowledge model allows the system to refine the initial users{'} requests and lead them to the set of objects with the maximal variability of parameters that matters less to them. Introducing the additional textual characteristics allows users to formulate their initial query as a phrase in natural language, rather than as some standard request in the form of, {``}Attribute - value{''}.",
}
@inproceedings{tracz-etal-2020-bert,
    title = "{BERT}-based similarity learning for product matching",
    author = "Tracz, Janusz  and
      W{\'o}jcik, Piotr Iwo  and
      Jasinska-Kobus, Kalina  and
      Belluzzo, Riccardo  and
      Mroczkowski, Robert  and
      Gawlik, Ireneusz",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.7",
    pages = "66--75",
    abstract = "Product matching, i.e., being able to infer the product being sold for a merchant-created offer, is crucial for any e-commerce marketplace, enabling product-based navigation, price comparisons, product reviews, etc. This problem proves a challenging task, mostly due to the extent of product catalog, data heterogeneity, missing product representants, and varying levels of data quality. Moreover, new products are being introduced every day, making it difficult to cast the problem as a classification task. In this work, we apply BERT-based models in a similarity learning setup to solve the product matching problem. We provide a thorough ablation study, showing the impact of architecture and training objective choices. Application of transformer-based architectures and proper sampling techniques significantly boosts performance for a range of e-commerce domains, allowing for production deployment.",
}
@inproceedings{shimura-etal-2020-aspect,
    title = "Aspect-Similarity-Aware Historical Influence Modeling for Rating Prediction",
    author = "Shimura, Ryo  and
      Misawa, Shotaro  and
      Sato, Masahiro  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.8",
    pages = "76--86",
    abstract = "Many e-commerce services provide customer review systems. Previous laboratory studies have indicated that the ratings recorded by these systems differ from the actual evaluations of the users, owing to the influence of historical ratings in the system. Some studies have proposed using real-world datasets to model rating prediction. Herein, we propose an aspect-similarity-aware historical influence model for rating prediction using natural language processing techniques. In general, each user provides a rating considering different aspects. Thus, it can be assumed that historical ratings provided considering similar aspects to those of later ones will influence evaluations of users more. By focusing on the review-topic similarities, we show that our method predicts ratings more accurately than the previous historical-inference-aware model. In addition, we examine whether our model can predict {``}intrinsic rating,{''} which is given if users were not influenced by historical ratings. We performed an intrinsic rating prediction task, and showed that our model achieved improved performance. Our method can be useful to debias user ratings collected by customer review systems. The debiased ratings help users to make decision properly and systems to provide helpful recommendations. This might improve the user experience of e-commerce services.",
}
@inproceedings{misawa-etal-2020-distinctive,
    title = "Distinctive Slogan Generation with Reconstruction",
    author = "Misawa, Shotaro  and
      Miura, Yasuhide  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of Workshop on Natural Language Processing in E-Commerce",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecomnlp-1.9",
    pages = "87--97",
    abstract = "E-commerce sites include advertising slogans along with information regarding an item. Slogans can attract viewers{'} attention to increase sales or visits by emphasizing advantages of an item. The aim of this study is to generate a slogan from a description of an item. To generate a slogan, we apply an encoder{--}decoder model which has shown effectiveness in many kinds of natural language generation tasks, such as abstractive summarization. However, slogan generation task has three characteristics that distinguish it from other natural language generation tasks: distinctiveness, topic emphasis, and style difference. To handle these three characteristics, we propose a compressed representation{--}based reconstruction model with refer{--}attention and conversion layers. The results of the experiments indicate that, based on automatic and human evaluation, our method achieves higher performance than conventional methods.",
}
@proceedings{ecnlp-2020-e,
    title = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    editor = "Malmasi, Shervin  and
      Kallumadi, Surya  and
      Ueffing, Nicola  and
      Rokhlenko, Oleg  and
      Agichtein, Eugene  and
      Guy, Ido",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.0",
}
@inproceedings{zhang-etal-2020-bootstrapping,
    title = "Bootstrapping Named Entity Recognition in {E}-Commerce with Positive Unlabeled Learning",
    author = "Zhang, Hanchu  and
      Hennig, Leonhard  and
      Alt, Christoph  and
      Hu, Changjian  and
      Meng, Yao  and
      Wang, Chao",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.1",
    doi = "10.18653/v1/2020.ecnlp-1.1",
    pages = "1--6",
    abstract = "In this work, we introduce a bootstrapped, iterative NER model that integrates a PU learning algorithm for recognizing named entities in a low-resource setting. Our approach combines dictionary-based labeling with syntactically-informed label expansion to efficiently enrich the seed dictionaries. Experimental results on a dataset of manually annotated e-commerce product descriptions demonstrate the effectiveness of the proposed framework.",
}
@inproceedings{tagliabue-etal-2020-grow,
    title = "How to Grow a (Product) Tree: Personalized Category Suggestions for e{C}ommerce Type-Ahead",
    author = "Tagliabue, Jacopo  and
      Yu, Bingqing  and
      Beaulieu, Marie",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.2",
    doi = "10.18653/v1/2020.ecnlp-1.2",
    pages = "7--18",
    abstract = "In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahead suggestions. In this work, we present SessionPath, a novel neural network model that improves facet suggestions on two counts: first, the model is able to leverage session embeddings to provide scalable personalization; second, SessionPath predicts facets by explicitly producing a probability distribution at each node in the taxonomy path. We benchmark SessionPath on two partnering shops against count-based and neural models, and show how business requirements and model behavior can be combined in a principled way.",
}
@inproceedings{guo-etal-2020-deep,
    title = "Deep Learning-based Online Alternative Product Recommendations at Scale",
    author = "Guo, Mingming  and
      Yan, Nian  and
      Cui, Xiquan  and
      Wu, San He  and
      Ahsan, Unaiza  and
      West, Rebecca  and
      Al Jadda, Khalifeh",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.3",
    doi = "10.18653/v1/2020.ecnlp-1.3",
    pages = "19--23",
    abstract = "Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customers{'} needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12{\%} in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.",
}
@inproceedings{jia-2020-deep,
    title = "A Deep Learning System for Sentiment Analysis of Service Calls",
    author = "Jia, Yanan",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.4",
    doi = "10.18653/v1/2020.ecnlp-1.4",
    pages = "24--34",
    abstract = "Sentiment analysis is crucial for the advancement of artificial intelligence (AI). Sentiment understanding can help AI to replicate human language and discourse. Studying the formation and response of sentiment state from well-trained Customer Service Representatives (CSRs) can help make the interaction between humans and AI more intelligent. In this paper, a sentiment analysis pipeline is first carried out with respect to real-world multi-party conversations - that is, service calls. Based on the acoustic and linguistic features extracted from the source information, a novel aggregated method for voice sentiment recognition framework is built. Each party{'}s sentiment pattern during the communication is investigated along with the interaction sentiment pattern between all parties.",
}
@inproceedings{roy-etal-2020-using,
    title = "Using Large Pretrained Language Models for Answering User Queries from Product Specifications",
    author = "Roy, Kalyani  and
      Shah, Smit  and
      Pai, Nithish  and
      Ramtej, Jaidam  and
      Nadkarni, Prajit  and
      Banerjee, Jyotirmoy  and
      Goyal, Pawan  and
      Kumar, Surender",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.5",
    doi = "10.18653/v1/2020.ecnlp-1.5",
    pages = "35--39",
    abstract = "While buying a product from the e-commerce websites, customers generally have a plethora of questions. From the perspective of both the e-commerce service provider as well as the customers, there must be an effective question answering system to provide immediate answer to the user queries. While certain questions can only be answered after using the product, there are many questions which can be answered from the product specification itself. Our work takes a first step in this direction by finding out the relevant product specifications, that can help answering the user questions. We propose an approach to automatically create a training dataset for this problem. We utilize recently proposed XLNet and BERT architectures for this problem and find that they provide much better performance than the Siamese model, previously applied for this problem. Our model gives a good performance even when trained on one vertical and tested across different verticals.",
}
@inproceedings{sharma-2020-improving,
    title = "Improving Intent Classification in an {E}-commerce Voice Assistant by Using Inter-Utterance Context",
    author = "Sharma, Arpit",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.6",
    doi = "10.18653/v1/2020.ecnlp-1.6",
    pages = "40--45",
    abstract = "In this work, we improve the intent classification in an English based e-commerce voice assistant by using inter-utterance context. For increased user adaptation and hence being more profitable, an e-commerce voice assistant is desired to understand the context of a conversation and not have the users repeat it in every utterance. For example, let a user{'}s first utterance be {`}find apples{'}. Then, the user may say {`}i want organic only{'} to filter out the results generated by an assistant with respect to the first query. So, it is important for the assistant to take into account the context from the user{'}s first utterance to understand her intention in the second one. In this paper, we present our approach for contextual intent classification in Walmart{'}s e-commerce voice assistant. It uses the intent of the previous user utterance to predict the intent of her current utterance. With the help of experiments performed on real user queries we show that our approach improves the intent classification in the assistant.",
}
@inproceedings{gautam-etal-2020-semi,
    title = "Semi-Supervised Iterative Approach for Domain-Specific Complaint Detection in Social Media",
    author = "Gautam, Akash  and
      Mahata, Debanjan  and
      Gosangi, Rakesh  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.7",
    doi = "10.18653/v1/2020.ecnlp-1.7",
    pages = "46--53",
    abstract = "In this paper, we present a semi-supervised bootstrapping approach to detect product or service related complaints in social media. Our approach begins with a small collection of annotated samples which are used to identify a preliminary set of linguistic indicators pertinent to complaints. These indicators are then used to expand the dataset. The expanded dataset is again used to extract more indicators. This process is applied for several iterations until we can no longer find any new indicators. We evaluated this approach on a Twitter corpus specifically to detect complaints about transportation services. We started with an annotated set of 326 samples of transportation complaints, and after four iterations of the approach, we collected 2,840 indicators and over 3,700 tweets. We annotated a random sample of 700 tweets from the final dataset and observed that nearly half the samples were actual transportation complaints. Lastly, we also studied how different features based on semantics, orthographic properties, and sentiment contribute towards the prediction of complaints.",
}
@inproceedings{wang-fu-2020-item,
    title = "Item-based Collaborative Filtering with {BERT}",
    author = "Wang, Tian  and
      Fu, Yuyangzi",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.8",
    doi = "10.18653/v1/2020.ecnlp-1.8",
    pages = "54--58",
    abstract = "In e-commerce, recommender systems have become an indispensable part of helping users explore the available inventory. In this work, we present a novel approach for item-based collaborative filtering, by leveraging BERT to understand items, and score relevancy between different items. Our proposed method could address problems that plague traditional recommender systems such as cold start, and {``}more of the same{''} recommended content. We conducted experiments on a large-scale real-world dataset with full cold-start scenario, and the proposed approach significantly outperforms the popular Bi-LSTM model.",
}
@inproceedings{sun-etal-2020-semi,
    title = "Semi-supervised Category-specific Review Tagging on {I}ndonesian {E}-Commerce Product Reviews",
    author = "Sun, Meng  and
      Leo, Marie Stephen  and
      Munawwar, Eram  and
      Condylis, Paul C.  and
      Kong, Sheng-yi  and
      Lee, Seong Per  and
      Hidayat, Albert  and
      Kerianto, Muhamad Danang",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.9",
    doi = "10.18653/v1/2020.ecnlp-1.9",
    pages = "59--63",
    abstract = "Product reviews are a huge source of natural language data in e-commerce applications. Several millions of customers write reviews regarding a variety of topics. We categorize these topics into two groups as either {``}category-specific{''} topics or as {``}generic{''} topics that span multiple product categories. While we can use a supervised learning approach to tag review text for generic topics, it is impossible to use supervised approaches to tag category-specific topics due to the sheer number of possible topics for each category. In this paper, we present an approach to tag each review with several product category-specific tags on Indonesian language product reviews using a semi-supervised approach. We show that our proposed method can work at scale on real product reviews at Tokopedia, a major e-commerce platform in Indonesia. Manual evaluation shows that the proposed method can efficiently generate category-specific product tags.",
}
@inproceedings{gao-2020-deep,
    title = "Deep Hierarchical Classification for Category Prediction in {E}-commerce System",
    author = "Gao, Dehong",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.10",
    doi = "10.18653/v1/2020.ecnlp-1.10",
    pages = "64--68",
    abstract = "In e-commerce system, category prediction is to automatically predict categories of given texts. Different from traditional classification where there are no relations between classes, category prediction is reckoned as a standard hierarchical classification problem since categories are usually organized as a hierarchical tree. In this paper, we address hierarchical category prediction. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks and introduces a representation sharing strategy according to the category tree. We also define a novel combined loss function to punish hierarchical prediction losses. The evaluation shows that the proposed approach outperforms existing approaches in accuracy.",
}
@inproceedings{ashok-etal-2020-simsterq,
    title = "{S}imster{Q}: {A} Similarity based Clustering Approach to Opinion Question Answering",
    author = "Ashok, Aishwarya  and
      Natarajan, Ganapathy  and
      Elmasri, Ramez  and
      Smith-Stvan, Laurel",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.11",
    doi = "10.18653/v1/2020.ecnlp-1.11",
    pages = "69--76",
    abstract = "In recent years, there has been an increase in online shopping resulting in an increased number of online reviews. Customers cannot delve into the huge amount of data when they are looking for specific aspects of a product. Some of these aspects can be extracted from the product reviews. In this paper we introduced SimsterQ - a clustering based system for answering questions that makes use of word vectors. Clustering was performed using cosine similarity scores between sentence vectors of reviews and questions. Two variants (Sim and Median) with and without stopwords were evaluated against traditional methods that use term frequency. We also used an n-gram approach to study the effect of noise. We used the reviews in the Amazon Reviews dataset to pick the answers. Evaluation was performed both at the individual sentence level using the top sentence from Okapi BM25 as the gold standard and at the whole answer level using review snippets as the gold standard. At the sentence level our system performed slightly better than a more complicated deep learning method. Our system returned answers similar to the review snippets from the Amazon QA Dataset as measured by the cosine similarity. Analysis was also performed on the quality of the clusters generated by our system.",
}
@inproceedings{taylor-keselj-2020-e,
    title = "e-Commerce and Sentiment Analysis: Predicting Outcomes of Class Action Lawsuits",
    author = "Taylor, Stacey  and
      Keselj, Vlado",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.12",
    doi = "10.18653/v1/2020.ecnlp-1.12",
    pages = "77--85",
    abstract = "In recent years, the focus of e-Commerce research has been on better understanding the relationship between the internet marketplace, customers, and goods and services. This has been done by examining information that can be gleaned from consumer information, recommender systems, click rates, or the way purchasers go about making buying decisions, for example. This paper takes a very different approach and examines the companies themselves. In the past ten years, e-Commerce giants such as Amazon, Skymall, Wayfair, and Groupon have been embroiled in class action security lawsuits promulgated under Rule 10b(5), which, in short, is one of the Securities and Exchange Commission{'}s main rules surrounding fraud. Lawsuits are extremely expensive to the company and can damage a company{'}s brand extensively, with the shareholders left to suffer the consequences. We examined the Management Discussion and Analysis and the Market Risks for 96 companies using sentiment analysis on selected financial measures and found that we were able to predict the outcome of the lawsuits in our dataset using sentiment (tone) alone to a recall of 0.8207 using the Random Forest classifier. We believe that this is an important contribution as it has cross-domain implications and potential, and opens up new areas of research in e-Commerce, finance, and law, as the settlements from the class action lawsuits in our dataset alone are in excess of {\$}1.6 billion dollars, in aggregate.",
}
@inproceedings{gupta-2020-application,
    title = "On Application of {B}ayesian Parametric and Non-parametric Methods for User Cohorting in Product Search",
    author = "Gupta, Shashank",
    booktitle = "Proceedings of The 3rd Workshop on e-Commerce and NLP",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.ecnlp-1.13",
    doi = "10.18653/v1/2020.ecnlp-1.13",
    pages = "86--89",
    abstract = "In this paper, we study the applicability of Bayesian Parametric and Non-parametric methods for user clustering in an E-commerce search setting. To the best of our knowledge, this is the first work that presents a comparative study of various Bayesian clustering methods in the context of product search. Specifically, we cluster users based on their topical patterns from their respective product search queries. To evaluate the quality of the clusters formed, we perform a collaborative query recommendation task. Our findings indicate that simple parametric model like Latent Dirichlet Allocation (LDA) outperforms more sophisticated non-parametric methods like Distance Dependent Chinese Restaurant Process and Dirichlet Process-based clustering in both tasks.",
}
@proceedings{eamt-2020-european,
    title = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    editor = "Martins, Andr{\'e}  and
      Moniz, Helena  and
      Fumega, Sara  and
      Martins, Bruno  and
      Batista, Fernando  and
      Coheur, Luisa  and
      Parra, Carla  and
      Trancoso, Isabel  and
      Turchi, Marco  and
      Bisazza, Arianna  and
      Moorkens, Joss  and
      Guerberof, Ana  and
      Nurminen, Mary  and
      Marg, Lena  and
      Forcada, Mikel L.",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.0",
}
@inproceedings{stahlberg-2020-roles,
    title = "The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction",
    author = "Stahlberg, Felix",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.1",
    pages = "5--6",
}
@inproceedings{rossetti-etal-2020-comprehension,
    title = "Comprehension and Trust in Crises: Investigating the Impact of Machine Translation and Post-Editing",
    author = "Rossetti, Alessandra  and
      O{'}Brien, Sharon  and
      Cadwell, Patrick",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.2",
    pages = "9--18",
    abstract = "We conducted a survey to understand the impact of machine translation and post-editing awareness on comprehension of and trust in messages disseminated to prepare the public for a weather-related crisis, i.e. flooding. The translation direction was English{--}Italian. Sixty-one participants{---}all native Italian speakers with different English proficiency levels{---}answered our survey. Each participant read and evaluated between three and six crisis messages using ratings and open-ended questions on comprehensibility and trust. The messages were in English and Italian. All the Italian messages had been machine translated and post-edited. Nevertheless, participants were told that only half had been post-edited, so that we could test the impact of post-editing awareness. We could not draw firm conclusions when comparing the scores for trust and comprehensibility assigned to the three types of messages{---}English, post-edits, and purported raw outputs. However, when scores were triangulated with open-ended answers, stronger patterns were observed, such as the impact of fluency of the translations on their comprehensibility and trustworthiness. We found correlations between comprehensibility and trustworthiness, and identified other factors influencing these aspects, such as the clarity and soundness of the messages. We conclude by outlining implications for crisis preparedness, limitations, and areas for future research.",
}
@inproceedings{kocmi-bojar-2020-efficiently,
    title = "Efficiently Reusing Old Models Across Languages via Transfer Learning",
    author = "Kocmi, Tom  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.3",
    pages = "19--28",
    abstract = "Recent progress in neural machine translation (NMT) is directed towards larger neural networks trained on an increasing amount of hardware resources. As a result, NMT models are costly to train, both financially, due to the electricity and hardware cost, and environmentally, due to the carbon footprint. It is especially true in transfer learning for its additional cost of training the {``}parent{''} model before transferring knowledge and training the desired {``}child{''} model. In this paper, we propose a simple method of re-using an already trained model for different language pairs where there is no need for modifications in model architecture. Our approach does not need a separate parent model for each investigated language pair, as it is typical in NMT transfer learning. To show the applicability of our method, we recycle a Transformer model trained by different researchers and use it to seed models for different language pairs. We achieve better translation quality and shorter convergence times than when training from random initialization.",
}
@inproceedings{yang-etal-2020-efficient,
    title = "Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer",
    author = "Yang, Hao  and
      Wang, Minghan  and
      Xie, Ning  and
      Qin, Ying  and
      Deng, Yao",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.4",
    pages = "29--34",
    abstract = "The Predictor-Estimator framework for quality estimation (QE) is commonly used for its strong performance. Where the predictor and estimator works on feature extraction and quality evaluation, respectively. However, training the predictor from scratch is computationally expensive. In this paper, we propose an efficient transfer learning framework to transfer knowledge from NMT dataset into QE models. A Predictor-Estimator alike model named BAL-QE is also proposed, aiming to extract high quality features with pre-trained NMT model, and make classification with a fine-tuned Bottleneck Adapter Layer (BAL). The experiment shows that BAL-QE achieves 97{\%} of the SOTA performance in WMT19 En-De and En-Ru QE tasks by only training 3{\%} of parameters within 4 hours on 4 Titan XP GPUs. Compared with the commonly used NuQE baseline, BAL-QE achieves 47{\%} (En-Ru) and 75{\%} (En-De) of performance promotions.",
}
@inproceedings{kim-etal-2020-unsupervised,
    title = "When and Why is Unsupervised Neural Machine Translation Useless?",
    author = "Kim, Yunsu  and
      Gra{\c{c}}a, Miguel  and
      Ney, Hermann",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.5",
    pages = "35--44",
    abstract = "This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, we analyze the conditions under which the unsupervised methods fail to produce reasonable translations. We show that their performance is severely affected by linguistic dissimilarity and domain mismatch between source and target monolingual data. Such conditions are common for low-resource language pairs, where unsupervised learning works poorly. In all of our experiments, supervised and semi-supervised baselines with 50k-sentence bilingual data outperform the best unsupervised results. Our analyses pinpoint the limits of the current unsupervised NMT and also suggest immediate research directions.",
}
@inproceedings{modrzejewski-etal-2020-incorporating,
    title = "Incorporating External Annotation to improve Named Entity Translation in {NMT}",
    author = "Modrzejewski, Maciej  and
      Exel, Miriam  and
      Buschbeck, Bianka  and
      Ha, Thanh-Le  and
      Waibel, Alexander",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.6",
    pages = "45--51",
    abstract = "The correct translation of named entities (NEs) still poses a challenge for conventional neural machine translation (NMT) systems. This study explores methods incorporating named entity recognition (NER) into NMT with the aim to improve named entity translation. It proposes an annotation method that integrates named entities and inside{--}outside{--}beginning (IOB) tagging into the neural network input with the use of source factors. Our experiments on English→German and English→ Chinese show that just by including different NE classes and IOB tagging, we can increase the BLEU score by around 1 point using the standard test set from WMT2019 and achieve up to 12{\%} increase in NE translation rates over a strong baseline.",
}
@inproceedings{wang-etal-2020-unified,
    title = "Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning",
    author = "Wang, Minghan  and
      Yang, Hao  and
      Qin, Ying  and
      Sun, Shiliang  and
      Deng, Yao",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.7",
    pages = "53--59",
    abstract = "We propose a unified multilingual model for humor detection which can be trained under a transfer learning framework. 1) The model is built based on pre-trained multilingual BERT, thereby is able to make predictions on Chinese, Russian and Spanish corpora. 2) We step out from single sentence classification and propose sequence-pair prediction which considers the inter-sentence relationship. 3) We propose the Sentence Discrepancy Prediction (SDP) loss, aiming to measure the semantic discrepancy of the sequence-pair, which often appears in the setup and punchline of a joke. Our method achieves two SoTA and a second-place on three humor detection corpora in three languages (Russian, Spanish and Chinese), and also improves F1-score by 4{\%}-6{\%}, which demonstrates the effectiveness of it in humor detection tasks.",
}
@inproceedings{sanchez-cartagena-etal-2020-multi,
    title = "A multi-source approach for {B}reton{--}{F}rench hybrid machine translation",
    author = "S{\'a}nchez-Cartagena, V{\'\i}ctor M.  and
      Forcada, Mikel L.  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.8",
    pages = "61--70",
    abstract = "Corpus-based approaches to machine translation (MT) have difficulties when the amount of parallel corpora to use for training is scarce, especially if the languages involved in the translation are highly inflected. This problem can be addressed from different perspectives, including data augmentation, transfer learning, and the use of additional resources, such as those used in rule-based MT. This paper focuses on the hybridisation of rule-based MT and neural MT for the Breton{--}French under-resourced language pair in an attempt to study to what extent the rule-based MT resources help improve the translation quality of the neural MT system for this particular under-resourced language pair. We combine both translation approaches in a multi-source neural MT architecture and find out that, even though the rule-based system has a low performance according to automatic evaluation metrics, using it leads to improved translation quality.",
}
@inproceedings{antony-etal-2020-leveraging,
    title = "Leveraging Multilingual Resources for Language Invariant Sentiment Analysis",
    author = "Antony, Allen  and
      Bhattacharya, Arghya  and
      Goud, Jaipal  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.9",
    pages = "71--79",
    abstract = "Sentiment analysis is a widely researched NLP problem with state-of-the-art solutions capable of attaining human-like accuracies for various languages. However, these methods rely heavily on large amounts of labeled data or sentiment weighted language-specific lexical resources that are unavailable for low-resource languages. Our work attempts to tackle this data scarcity issue by introducing a neural architecture for language invariant sentiment analysis capable of leveraging various monolingual datasets for training without any kind of cross-lingual supervision. The proposed architecture attempts to learn language agnostic sentiment features via adversarial training on multiple resource-rich languages which can then be leveraged for inferring sentiment information at a sentence level on a low resource language. Our model outperforms the current state-of-the-art methods on the Multilingual Amazon Review Text Classification dataset [REF] and achieves significant performance gains over prior work on the low resource Sentiraama corpus [REF]. A detailed analysis of our research highlights the ability of our architecture to perform significantly well in the presence of minimal amounts of training data for low resource languages.",
}
@inproceedings{edman-etal-2020-low,
    title = "Low-Resource Unsupervised {NMT}: Diagnosing the Problem and Providing a Linguistically Motivated Solution",
    author = "Edman, Lukas  and
      Toral, Antonio  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.10",
    pages = "81--90",
    abstract = "Unsupervised Machine Translation has been advancing our ability to translate without parallel data, but state-of-the-art methods assume an abundance of monolingual data. This paper investigates the scenario where monolingual data is limited as well, finding that current unsupervised methods suffer in performance under this stricter setting. We find that the performance loss originates from the poor quality of the pretrained monolingual embeddings, and we offer a potential solution: dependency-based word embeddings. These embeddings result in a complementary word representation which offers a boost in performance of around 1.5 BLEU points compared to standard word2vec when monolingual data is limited to 1 million sentences per language. We also find that the inclusion of sub-word information is crucial to improving the quality of the embeddings.",
}
@inproceedings{moon-etal-2020-revisiting,
    title = "Revisiting Round-trip Translation for Quality Estimation",
    author = "Moon, Jihyung  and
      Cho, Hyunchang  and
      Park, Eunjeong L.",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.11",
    pages = "91--104",
    abstract = "Quality estimation (QE), a task of evaluating the quality of translation automatically without human-translated reference, is one of the important challenges for machine translation (MT). As the QE methods, BLEU score for round-trip translation (RTT) had been considered. However, it was found to be a poor predictor of translation quality since BLEU was not an adequate metric to detect semantic similarity between input and RTT. Recently, the pre-trained language models have made breakthroughs in many NLP tasks by providing semantically meaningful word and sentence embeddings. In this paper, we employ the semantic embeddings to RTT-based QE metric. Our method achieves the highest correlations with human judgments compared to WMT 2019 quality estimation metric task submissions. Additionally, we observe that with semantic-level metrics, RTT-based QE is robust to the choice of a backward translation system and shows consistent performance on both SMT and NMT forward translation systems.",
}
@inproceedings{zhao-etal-2020-double,
    title = "Double Attention-based Multimodal Neural Machine Translation with Semantic Image Regions",
    author = "Zhao, Yuting  and
      Komachi, Mamoru  and
      Kajiwara, Tomoyuki  and
      Chu, Chenhui",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.12",
    pages = "105--114",
    abstract = "Existing studies on multimodal neural machine translation (MNMT) have mainly focused on the effect of combining visual and textual modalities to improve translations. However, it has been suggested that the visual modality is only marginally beneficial. Conventional visual attention mechanisms have been used to select the visual features from equally-sized grids generated by convolutional neural networks (CNNs), and may have had modest effects on aligning the visual concepts associated with textual objects, because the grid visual features do not capture semantic information. In contrast, we propose the application of semantic image regions for MNMT by integrating visual and textual features using two individual attention mechanisms (double attention). We conducted experiments on the Multi30k dataset and achieved an improvement of 0.5 and 0.9 BLEU points for English-German and English-French translation tasks, compared with the MNMT with grid visual features. We also demonstrated concrete improvements on translation performance benefited from semantic image regions.",
}
@inproceedings{koponen-etal-2020-mt,
    title = "{MT} for subtitling: User evaluation of post-editing productivity",
    author = {Koponen, Maarit  and
      Sulubacak, Umut  and
      Vitikainen, Kaisa  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.13",
    pages = "115--124",
    abstract = "This paper presents a user evaluation of machine translation and post-editing for TV subtitles. Based on a process study where 12 professional subtitlers translated and post-edited subtitles, we compare effort in terms of task time and number of keystrokes. We also discuss examples of specific subtitling features like condensation, and how these features may have affected the post-editing results. In addition to overall MT quality, segmentation and timing of the subtitles are found to be important issues to be addressed in future work.",
}
@inproceedings{ye-toral-2020-fine,
    title = "Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for {E}nglish-to-{C}hinese",
    author = "Ye, Yuying  and
      Toral, Antonio",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.14",
    pages = "125--134",
    abstract = "This research presents a fine-grained human evaluation to compare the Transformer and recurrent approaches to neural machine translation (MT), on the translation direction English-to-Chinese. To this end, we develop an error taxonomy compliant with the Multidimensional Quality Metrics (MQM) framework that is customised to the relevant phenomena of this translation direction. We then conduct an error annotation using this customised error taxonomy on the output of state-of-the-art recurrent- and Transformer-based MT systems on a subset of WMT2019{'}s news test set. The resulting annotation shows that, compared to the best recurrent system, the best Transformer system results in a 31{\%} reduction of the total number of errors and it produced significantly less errors in 10 out of 22 error categories. We also note that two of the systems evaluated do not produce any error for a category that was relevant for this translation direction prior to the advent of NMT systems: Chinese classifiers.",
}
@inproceedings{kreutzer-etal-2020-correct,
    title = "Correct Me If You Can: Learning from Error Corrections and Markings",
    author = "Kreutzer, Julia  and
      Berger, Nathaniel  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.15",
    pages = "135--144",
    abstract = "Sequence-to-sequence learning involves a trade-off between signal strength and annotation cost of training data. For example, machine translation data range from costly expert-generated translations that enable supervised learning, to weak quality-judgment feedback that facilitate reinforcement learning. We present the first user study on annotation cost and machine learnability for the less popular annotation mode of error markings. We show that error markings for translations of TED talks from English to German allow precise credit assignment while requiring significantly less human effort than correcting/post-editing, and that error-marked data can be used successfully to fine-tune neural machine translation models.",
}
@inproceedings{blain-etal-2020-quality,
    title = "Quality In, Quality Out: Learning from Actual Mistakes",
    author = "Blain, Frederic  and
      Aletras, Nikolaos  and
      Specia, Lucia",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.16",
    pages = "145--153",
    abstract = "Approaches to Quality Estimation (QE) of machine translation have shown promising results at predicting quality scores for translated sentences. However, QE models are often trained on noisy approximations of quality annotations derived from the proportion of post-edited words in translated sentences instead of direct human annotations of translation errors. The latter is a more reliable ground-truth but more expensive to obtain. In this paper, we present the first attempt to model the task of predicting the proportion of actual translation errors in a sentence while minimising the need for direct human annotation. For that purpose, we use transfer-learning to leverage large scale noisy annotations and small sets of high-fidelity human annotated translation errors to train QE models. Experiments on four language pairs and translations obtained by statistical and neural models show consistent gains over strong baselines.",
}
@inproceedings{hayakawa-arase-2020-fine,
    title = "Fine-Grained Error Analysis on {E}nglish-to-{J}apanese Machine Translation in the Medical Domain",
    author = "Hayakawa, Takeshi  and
      Arase, Yuki",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.17",
    pages = "155--164",
    abstract = "We performed a detailed error analysis in domain-specific neural machine translation (NMT) for the English and Japanese language pair with fine-grained manual annotation. Despite its importance for advancing NMT technologies, research on the performance of domain-specific NMT and non-European languages has been limited. In this study, we designed an error typology based on the error types that were typically generated by NMT systems and might cause significant impact in technical translations: {``}Addition,{''} {``}Omission,{''} {``}Mistranslation,{''} {``}Grammar,{''} and {``}Terminology.{''} The error annotation was targeted to the medical domain and was performed by experienced professional translators specialized in medicine under careful quality control. The annotation detected 4,912 errors on 2,480 sentences, and the frequency and distribution of errors were analyzed. We found that the major errors in NMT were {``}Mistranslation{''} and {``}Terminology{''} rather than {``}Addition{''} and {``}Omission,{''} which have been reported as typical problems of NMT. Interestingly, more errors occurred in documents for professionals compared with those for the general public. The results of our annotation work will be published as a parallel corpus with error labels, which are expected to contribute to developing better NMT models, automatic evaluation metrics, and quality estimation models.",
}
@inproceedings{aranberri-2020-without,
    title = "With or without you? Effects of using machine translation to write flash fiction in the foreign language",
    author = "Aranberri, Nora",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.18",
    pages = "165--174",
    abstract = "The improvement in the quality of machine translation (MT) for both majority and minority languages in recent years is resulting in its steady adoption. This is not only happening among professional translators but also among users who occasionally find themselves in situations where translation is required or MT presents itself as a easier means to producing a text. This work sets to explore the effect using MT has in flash fiction produced in the foreign language. Specifically, we study the impact in surface closeness, syntactic and lexical complexity, and edits. Results show that texts produced with MT seem to fit closer to certain traits of the foreign language and that differences in the use of part-of-speech categories and structures emerge. Moreover, the analysis of the post-edited texts reveals that participants approach the editing of the MT output differently, displaying a wide range in the number of edits.",
}
@inproceedings{ranasinghe-etal-2020-intelligent,
    title = "Intelligent Translation Memory Matching and Retrieval with Sentence Encoders",
    author = "Ranasinghe, Tharindu  and
      Orasan, Constantin  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.19",
    pages = "175--184",
    abstract = "Matching and retrieving previously translated segments from the Translation Memory is a key functionality in Translation Memories systems. However this matching and retrieving process is still limited to algorithms based on edit distance which we have identified as a major drawback in Translation Memories systems. In this paper, we introduce sentence encoders to improve matching and retrieving process in Translation Memories systems - an effective and efficient solution to replace edit distance-based algorithms.",
}
@inproceedings{toral-2020-reassessing,
    title = "Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at {WMT} 2019",
    author = "Toral, Antonio",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.20",
    pages = "185--194",
    abstract = "We reassess the claims of human parity and super-human performance made at the news shared task of WMT2019 for three translation directions: English→German, English→Russian and German→English. First we identify three potential issues in the human evaluation of that shared task: (i) the limited amount of intersen- tential context available, (ii) the limited translation proficiency of the evaluators and (iii) the use of a reference transla- tion. We then conduct a modified eval- uation taking these issues into account. Our results indicate that all the claims of human parity and super-human perfor- mance made at WMT2019 should be re- futed, except the claim of human parity for English→German. Based on our findings, we put forward a set of recommendations and open questions for future assessments of human parity in machine translation.",
}
@inproceedings{gupta-etal-2020-modelling,
    title = "Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine Translation",
    author = "Gupta, Kamal Kumar  and
      Haque, Rejwanul  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak  and
      Way, Andy",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.21",
    pages = "195--204",
    abstract = "In interactive machine translation (MT), human translators correct errors in automatic translations in collaboration with the MT systems, which is seen as an effective way to improve the productivity gain in translation. In this study, we model source-language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional context for interactive prediction in neural MT (NMT). We found that the supertags significantly improve productivity gain in translation in interactive-predictive NMT (INMT), while syntactic parsing somewhat found to be effective in reducing human effort in translation. Furthermore, when we model this source- and target-language syntactic information together as the conditional context, both types complement each other and our fully syntax-informed INMT model statistically significantly reduces human efforts in a French{--}to{--}English translation task, achieving 4.30 points absolute (corresponding to 9.18{\%} relative) improvement in terms of word prediction accuracy (WPA) and 4.84 points absolute (corresponding to 9.01{\%} relative) reduction in terms of word stroke ratio (WSR) over the baseline.",
}
@inproceedings{gois-etal-2020-learning,
    title = "Learning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings",
    author = "G{\'o}is, Ant{\'o}nio  and
      Cho, Kyunghyun  and
      Martins, Andr{\'e}",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.22",
    pages = "205--214",
    abstract = "Recent research in neural machine translation has explored flexible generation orders, as an alternative to left-to-right generation. However, training non-monotonic models brings a new complication: how to search for a good ordering when there is a combinatorial explosion of orderings arriving at the same final result? Also, how do these automatic orderings compare with the actual behaviour of human translators? Current models rely on manually built biases or are left to explore all possibilities on their own. In this paper, we analyze the orderings produced by human post-editors and use them to train an automatic post-editing system. We compare the resulting system with those trained with left-to-right and random post-editing orderings. We observe that humans tend to follow a nearly left-to-right order, but with interesting deviations, such as preferring to start by correcting punctuation or verbs.",
}
@inproceedings{fischer-laubli-2020-whats,
    title = "What{'}s the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific {MT}",
    author = {Fischer, Lukas  and
      L{\"a}ubli, Samuel},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.23",
    pages = "215--224",
    abstract = "Machine translation (MT) has been shown to produce a number of errors that require human post-editing, but the extent to which professional human translation (HT) contains such errors has not yet been compared to MT. We compile pre-translated documents in which MT and HT are interleaved, and ask professional translators to flag errors and post-edit these documents in a blind evaluation. We find that the post-editing effort for MT segments is only higher in two out of three language pairs, and that the number of segments with wrong terminology, omissions, and typographical problems is similar in HT.",
}
@inproceedings{lopes-etal-2020-document,
    title = "Document-level Neural {MT}: A Systematic Comparison",
    author = "Lopes, Ant{\'o}nio  and
      Farajian, M. Amin  and
      Bawden, Rachel  and
      Zhang, Michael  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.24",
    pages = "225--234",
    abstract = "In this paper we provide a systematic comparison of existing and new document-level neural machine translation solutions. As part of this comparison, we introduce and evaluate a document-level variant of the recently proposed Star Transformer architecture. In addition to using the traditional metric BLEU, we report the accuracy of the models in handling anaphoric pronoun translation as well as coherence and cohesion using contrastive test sets. Finally, we report the results of human evaluation in terms of Multidimensional Quality Metrics (MQM) and analyse the correlation of the results obtained by the automatic metrics with human judgments.",
}
@inproceedings{tebbifakhr-etal-2020-automatic,
    title = "Automatic Translation for Multiple {NLP} tasks: a Multi-task Approach to Machine-oriented {NMT} Adaptation",
    author = "Tebbifakhr, Amirhossein  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.25",
    pages = "235--244",
    abstract = "Although machine translation (MT) traditionally pursues {``}human-oriented{''} objectives, humans are not the only possible consumers of MT output. For instance, when automatic translations are used to feed downstream Natural Language Processing (NLP) components in cross-lingual settings, they should ideally pursue {``}machine-oriented{''} objectives that maximize the performance of these components. Tebbifakhr et al. (2019) recently proposed a reinforcement learning approach to adapt a generic neural MT(NMT) system by exploiting the reward from a downstream sentiment classifier. But what if the downstream NLP tasks to serve are more than one? How to avoid the costs of adapting and maintaining one dedicated NMT system for each task? We address this problem by proposing a multi-task approach to machine-oriented NMT adaptation, which is capable to serve multiple downstream tasks with a single system. Through experiments with Spanish and Italian data covering three different tasks, we show that our approach can outperform a generic NMT system, and compete with single-task models in most of the settings.",
}
@inproceedings{resende-etal-2020-mt,
    title = "{MT} syntactic priming effects on {L}2 {E}nglish speakers",
    author = "Resende, Nat{\'a}lia  and
      Cowan, Benjamin  and
      Way, Andy",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.26",
    pages = "245--253",
    abstract = "In this paper, we tested 20 Brazilian Portuguese speakers at intermediate and advanced English proficiency levels to investigate the influence of Google Translate{'}s MT system on the mental processing of English as a second language. To this end, we employed a syntactic priming experimental paradigm using a pretest-priming design which allowed us to compare participants{'} linguistic behaviour before and after a translation task using Google Translate. Results show that, after performing a translation task with Google Translate, participants more frequently described images in English using the syntactic alternative previously seen in the output of Google Translate, compared to the translation task with no prior influence of the MT output. Results also show that this syntactic priming effect is modulated by English proficiency levels.",
}
@inproceedings{manchanda-grunin-2020-domain,
    title = "Domain Informed Neural Machine Translation: Developing Translation Services for Healthcare Enterprise",
    author = "Manchanda, Sahil  and
      Grunin, Galina",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.27",
    pages = "255--261",
    abstract = "Neural Machine Translation (NMT) is a deep learning based approach that has achieved outstanding results lately in the translation community. The performance of NMT systems, however, is dependent on the availability of large amounts of in-domain parallel corpora. The business enterprises in domains such as legal and healthcare require specialized vocabulary but translation systems trained for a general purpose do not cater to these needs. The data in these domains is either hard to acquire or is very small in comparison to public data sets. This is a detailed report of using an open-source library to implement a machine translation system and successfully customizing it for the needs of a particular client in the healthcare domain. This report details the chronological development of every component of this system, namely, extraction of data from in-domain healthcare documents, a pre-processing pipeline for the data, data alignment and augmentation, training and a fully automated and robust deployment pipeline. This work proposes an efficient way for the continuous deployment of newly trained deep learning models. The deployed translation models are optimized for both inference time and cost.",
}
@inproceedings{stefaniak-2020-evaluating,
    title = "Evaluating the usefulness of neural machine translation for the {P}olish translators in the {E}uropean Commission",
    author = "Stefaniak, Karolina",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.28",
    pages = "263--269",
    abstract = "The mission of the Directorate General for Translation (DGT) is to provide high-quality translation to help the European Commission communicate with EU citizens. To this end DGT employs almost 2000 translators from all EU official languages. But while the demand for translation has been continuously growing, following a global trend, the number of translators has decreased. To cope with the demand, DGT extensively uses a CAT environment encompassing translation memories, terminology databases and recently also machine translation. This paper examines the benefits and risks of using neural machine translation to augment the productivity of in‒house DGT translators for the English‒Polish language pair. Based on the analysis of a sample of NMT‒translated texts and on the observations of the working practices of Polish translators it is concluded that the possible productivity gain is still modest, while the risks to quality are quite substantial.",
}
@inproceedings{exel-etal-2020-terminology,
    title = "Terminology-Constrained Neural Machine Translation at {SAP}",
    author = "Exel, Miriam  and
      Buschbeck, Bianka  and
      Brandt, Lauritz  and
      Doneva, Simona",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.29",
    pages = "271--280",
    abstract = "This paper examines approaches to bias a neural machine translation model to adhere to terminology constraints in an industrial setup. In particular, we investigate variations of the approach by Dinu et al. (2019), which uses inline annotation of the target terms in the source segment plus source factor embeddings during training and inference, and compare them to constrained decoding. We describe the challenges with respect to terminology in our usage scenario at SAP and show how far the investigated methods can help to overcome them. We extend the original study to a new language pair and provide an in-depth evaluation including an error classification and a human evaluation.",
}
@inproceedings{mutal-etal-2020-ellipsis,
    title = "Ellipsis Translation for a Medical Speech to Speech Translation System",
    author = "Mutal, Jonathan  and
      Gerlach, Johanna  and
      Bouillon, Pierrette  and
      Spechbach, Herv{\'e}",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.30",
    pages = "281--290",
    abstract = "In diagnostic interviews, elliptical utterances allow doctors to question patients in a more efficient and economical way. However, literal translation of such incomplete utterances is rarely possible without affecting communication. Previous studies have focused on automatic ellipsis detection and resolution, but only few specifically address the problem of automatic translation of ellipsis. In this work, we evaluate four different approaches to translate ellipsis in medical dialogues in the context of the speech to speech translation system BabelDr. We also investigate the impact of training data, using an under-sampling method and data with elliptical utterances in context. Results show that the best model is able to translate 88{\%} of elliptical utterances.",
}
@inproceedings{ramirez-sanchez-etal-2020-bifixer,
    title = "Bifixer and Bicleaner: two open-source tools to clean your parallel data",
    author = "Ram{\'\i}rez-S{\'a}nchez, Gema  and
      Zaragoza-Bernabeu, Jaume  and
      Ba{\~n}{\'o}n, Marta  and
      Rojas, Sergio Ortiz",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.31",
    pages = "291--298",
    abstract = "This paper shows the utility of two open-source tools designed for parallel data cleaning: Bifixer and Bicleaner. Already used to clean highly noisy parallel content from crawled multilingual websites, we evaluate their performance in a different scenario: cleaning publicly available corpora commonly used to train machine translation systems. We choose four English{--}Portuguese corpora which we plan to use internally to compute paraphrases at a later stage. We clean the four corpora using both tools, which are described in detail, and analyse the effect of some of the cleaning steps on them. We then compare machine translation training times and quality before and after cleaning these corpora, showing a positive impact particularly for the noisiest ones.",
}
@inproceedings{sanchez-martinez-etal-2020-english,
    title = "An {E}nglish-{S}wahili parallel corpus and its use for neural machine translation in the news domain",
    author = "S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      S{\'a}nchez-Cartagena, V{\'\i}ctor M.  and
      P{\'e}rez-Ortiz, Juan Antonio  and
      Forcada, Mikel L.  and
      Espl{\`a}-Gomis, Miquel  and
      Secker, Andrew  and
      Coleman, Susie  and
      Wall, Julie",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.32",
    pages = "299--308",
    abstract = "This paper describes our approach to create a neural machine translation system to translate between English and Swahili (both directions) in the news domain, as well as the process we followed to crawl the necessary parallel corpora from the Internet. We report the results of a pilot human evaluation performed by the news media organisations participating in the H2020 EU-funded project GoURMET.",
}
@inproceedings{nunziatini-marg-2020-machine,
    title = "Machine Translation Post-Editing Levels: Breaking Away from the Tradition and Delivering a Tailored Service",
    author = "Nunziatini, Mara  and
      Marg, Lena",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.33",
    pages = "309--318",
    abstract = "While definitions of full and light post-editing have been around for a while, and error typologies like DQF and MQM gained in prominence since the beginning of last decade, for a long time customers tended to refuse to be flexible as for their final quality requirements, irrespective of the text type, purpose, target audience etc. We are now finally seeing some change in this space, with a renewed interest in different machine translation (MT) and post-editing (PE) service levels. While existing definitions of light and full post-editing are useful as general guidelines, they typically remain too abstract and inflexible both for translation buyers and linguists. Besides, they are inconsistent and overlap across the literature and different Language Service Providers (LSPs). In this paper, we comment on existing industry standards and share our experience on several challenges, as well as ways to steer customer conversations and provide clear instructions to post-editors.",
}
@inproceedings{domingo-etal-2020-user,
    title = "A User Study of the Incremental Learning in {NMT}",
    author = "Domingo, Miguel  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Peris, {\'A}lvaro  and
      Helle, Alexandre  and
      Estela, Amando  and
      Bi{\'e}, Laurent  and
      Casacuberta, Francisco  and
      Herranz, Manuel",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.34",
    pages = "319--328",
    abstract = "In the translation industry, human experts usually supervise and post-edit machine translation hypotheses. Adaptive neural machine translation systems, able to incrementally update the underlying models under an online learning regime, have been proven to be useful to improve the efficiency of this workflow. However, this incremental adaptation is somewhat unstable, and it may lead to undesirable side effects. One of them is the sporadic appearance of made-up words, as a byproduct of an erroneous application of subword segmentation techniques. In this work, we extend previous studies on on-the-fly adaptation of neural machine translation systems. We perform a user study involving professional, experienced post-editors, delving deeper on the aforementioned problems. Results show that adaptive systems were able to learn how to generate the correct translation for task-specific terms, resulting in an improvement of the user{'}s productivity. We also observed a close similitude, in terms of morphology, between made-up words and the words that were expected.",
}
@inproceedings{buj-etal-2020-nice,
    title = "{NICE}: Neural Integrated Custom Engines",
    author = "Buj, Daniel Mar{\'\i}n  and
      Ib{\'a}{\~n}ez Garc{\'\i}a, Daniel  and
      Parcheta, Zuzanna  and
      Casacuberta, Francisco",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.35",
    pages = "329--338",
    abstract = "In this paper, we present a machine translation system implemented by the Translation Centre for the Bodies of the European Union (CdT). The main goal of this project is to create domain-specific machine translation engines in order to support machine translation services and applications to the Translation Centre{'}s clients. In this article, we explain the entire implementation process of NICE: Neural Integrated Custom Engines. We describe the problems identified and the solutions provided, and present the final results for different language pairs. Finally, we describe the work that will be done on this project in the future.",
}
@inproceedings{zaretskaya-etal-2020-estimation,
    title = "Estimation vs Metrics: is {QE} Useful for {MT} Model Selection?",
    author = "Zaretskaya, Anna  and
      Concei{\c{c}}{\~a}o, Jos{\'e}  and
      Bane, Frederick",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.36",
    pages = "339--346",
    abstract = "This paper presents a case study of applying machine translation quality estimation (QE) for the purpose of machine translation (MT) engine selection. The goal is to understand how well the QE predictions correlate with several MT evaluation metrics (automatic and human). Our findings show that our industry-level QE system is not reliable enough for MT selection when the MT systems have similar performance. We suggest that QE can be used with more success for other tasks relevant for translation industry such as risk prevention.",
}
@inproceedings{laguardia-2020-persistent,
    title = "Persistent {MT} on software technical documentation - a case study",
    author = "Laguardia, Mar{\'\i}a Concepci{\'o}n",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.37",
    pages = "347--352",
    abstract = "We report on the features and current challenges of our on-going implementation of a Persistent MT workflow for Citrix Product Documentation, to increase localization coverage to 100{\%} content in docs.citrix.com into German, French, Spanish, Japanese and Simplified Chinese. By the end of 2019, we had processed seven million words of English documentation with this model, across 24 doc sets, and raised localization coverage from 40{\%} to 100{\%} of the content of our documentation repositories. This has boosted our global reach across the entire Citrix portfolio (Digital Workspace, Networking, and Analytics). The current implementation requires a process of Light Post-editing (LPE) for all languages, in order to fix over-translations, out-of-domain words, inline tags and markdown errors in the raw output.",
}
@inproceedings{kirchner-2020-insights,
    title = "Insights from Gathering {MT} Productivity Metrics at Scale",
    author = "Kirchner, Georg",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.38",
    pages = "353--362",
    abstract = "In this paper, we describe Dell EMC{'}s framework to automatically collect MT-related productivity metrics from a large translation supply chain over an extended period of time, the characteristics and volume of the gathered data, and the insights from analyzing the data to guide our MT strategy. Aligning tools, processes and people required decisions, concessions and contributions from Dell management, technology providers, tool implementors, LSPs and linguists to harvest data at scale over 2+ years while Dell EMC migrated from customized SMT to generic NMT and then customized NMT systems. For content in two quality tiers, we ranked language pairs by productivity, graphed trendlines, compared the time needed to edit machine translations versus fuzzy matches, studied the time spent on segments with no post-edits, and going by the post-edit density, re-viewed segment distribution on a post-edit scale of 1 to 10 and any correlation between the extent of edits and segment length.",
}
@inproceedings{popovic-2020-differences,
    title = "On the differences between human translations",
    author = "Popovic, Maja",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.39",
    pages = "365--374",
    abstract = "Many studies have confirmed that translated texts exhibit different features than texts originally written in the given language. This work explores texts translated by different translators taking into account expertise and native language. A set of computational analyses was conducted on three language pairs, English-Croatian, German-French and English-Finnish, and the results show that each of the factors has certain influence on the features of the translated texts, especially on sentence length and lexical richness. The results also indicate that for translations used for machine translation evaluation, it is important to specify these factors, especially if comparing machine translation quality with human translation quality is involved.",
}
@inproceedings{estrella-etal-2020-design,
    title = "Re-design of the Machine Translation Training Tool ({MT}3)",
    author = "Estrella, Paula  and
      Cuenca, Emiliano  and
      Bruno, Laura  and
      Mutal, Jonathan  and
      Girletti, Sabrina  and
      Volkart, Lise  and
      Bouillon, Pierrette",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.40",
    pages = "375--382",
    abstract = "We believe that machine translation (MT) must be introduced to translation students as part of their training, in preparation for their professional life. In this paper we present a new version of the tool called MT3, which builds on and extends a joint effort undertaken by the Faculty of Languages of the University of C{\'o}rdoba and Faculty of Translation and Interpreting of the University of Geneva to develop an open-source web platform to teach MT to translation students. We also report on a pilot experiment with the goal of testing the viability of using $MT^3$ in an MT course. The pilot let us identify areas for improvement and collect students{'} feedback about the tool{'}s usability.",
}
@inproceedings{arnejsek-unk-2020-multidimensional,
    title = "Multidimensional assessment of the e{T}ranslation output for {E}nglish{--}{S}lovene",
    author = "Arnej{\v{s}}ek, Mateja  and
      Unk, Alenka",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.41",
    pages = "383--392",
    abstract = "The Slovene language department of the European Commission Directorate-General for Translation has always been an early adopter of new developments in the area of machine translation. In 2018, the department started using neural machine translation produced by the eTranslation in-house engines. In 2019, a multidimensional assessment of the eTranslation output for the language combination English{--}Slovene was carried out. It was based on two user satisfaction surveys, an analysis of detected and reported errors and an ex post analysis of a sample. As part of the assessment effort, a categorisation of errors was devised in order to raise awareness among translators of the potential pitfalls of neural machine translation.",
}
@inproceedings{scansani-mhedhbi-2020-lsps,
    title = "How do {LSP}s compute {MT} discounts? Presenting a company{'}s pipeline and its use",
    author = "Scansani, Randy  and
      Mhedhbi, Lamis",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.42",
    pages = "393--401",
    abstract = "In this paper we present a pipeline developed at Acolad to test a Machine Translation (MT) engine and compute the discount to be applied when its output is used in production. Our pipeline includes three main steps where quality and productivity are measured through automatic metrics, manual evaluation, and by keeping track of editing and temporal effort during a post-editing task. Thanks to this approach, it is possible to evaluate the output quality and compute an engine-specific discount. Our test pipeline tackles the complexity of transforming productivity measurements into discounts by comparing the outcome of each of the above-mentioned steps to an estimate of the average productivity of translation from scratch. The discount is obtained by subtracting the resulting coefficient from the per-word rate. After a description of the pipeline, the paper presents its application on four engines, discussing its results and showing that our method to estimate post-editing effort through manual evaluation seems to capture the actual productivity. The pipeline relies heavily on the work of professional post-editors, with the aim of creating a mutually beneficial cooperation between users and developers.",
}
@inproceedings{oliver-etal-2020-posedion,
    title = "{P}os{E}di{O}n: Post-Editing Assessment in {P}yth{O}n",
    author = "Oliver, Antoni  and
      Alvarez, Sergi  and
      Badia, Toni",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.43",
    pages = "403--410",
    abstract = "There is currently an extended use of post-editing of machine translation (PEMT) in the translation industry. This is due to the increase in the demand of translation and to the significant improvements in quality achieved by neural machine translation (NMT). PEMT has been included as part of the translation workflow because it increases translators{'} productivity and it also reduces costs. Although an effective post-editing requires enough quality of the MT output, usual automatic metrics do not always correlate with post-editing effort. We describe a standalone tool designed both for industry and research that has two main purposes: collect sentence-level information from the post-editing process (e.g. post-editing time and keystrokes) and visually present multiple evaluation scores so they can be easily interpreted by a user.",
}
@inproceedings{alvarez-etal-2020-quantitative,
    title = "Quantitative Analysis of Post-Editing Effort Indicators for {NMT}",
    author = "Alvarez, Sergi  and
      Oliver, Antoni  and
      Badia, Toni",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.44",
    pages = "411--420",
    abstract = "The recent improvements in machine translation (MT) have boosted the use of post-editing (PE) in the translation industry. A new machine translation paradigm, neural machine translation (NMT), is displacing its corpus-based predecessor, statistical machine translation (SMT), in the translation workflows currently implemented because it usually increases the fluency and accuracy of the MT output. However, usual automatic measurements do not always indicate the quality of the MT output and there is still no clear correlation between PE effort and productivity. We present a quantitative analysis of different PE effort indicators for two NMT systems (transformer and seq2seq) for English-Spanish in-domain medical documents. We compare both systems and study the correlation between PE time and other scores. Results show less PE effort for the transformer NMT model and a high correlation between PE time and keystrokes.",
}
@inproceedings{carmo-2020-comparing,
    title = "Comparing Post-editing based on Four Editing Actions against Translating with an Auto-Complete Feature",
    author = "Carmo, F{\'e}lix Do",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.45",
    pages = "421--430",
    abstract = "This article describes the results of a workshop in which 50 translators tested two experimental translation interfaces, as part of a project which aimed at studying the details of editing work. In this work, editing is defined as a selection of four actions: deleting, inserting, moving and replacing words. Four texts, machine-translated from English into European Portuguese, were post-edited in four different sessions in which each translator swapped between texts and two work modes. One of the work modes involved a typical auto-complete feature, and the other was based on the four actions. The participants answered surveys before, during and after the workshop. A descriptive analysis of the answers to the surveys and of the logs recorded during the experiments was performed. The four editing actions mode is shown to be more intrusive, but to allow for more planned decisions: although they take more time in this mode, translators hesitate less and make fewer edits. The article shows the usefulness of the approach for research on the editing task.",
}
@inproceedings{dowling-etal-2020-human,
    title = "A human evaluation of {E}nglish-{I}rish statistical and neural machine translation",
    author = "Dowling, Meghan  and
      Castilho, Sheila  and
      Moorkens, Joss  and
      Lynn, Teresa  and
      Way, Andy",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.46",
    pages = "431--440",
    abstract = "With official status in both Ireland and the EU, there is a need for high-quality English-Irish (EN-GA) machine translation (MT) systems which are suitable for use in a professional translation environment. While we have seen recent research on improving both statistical MT and neural MT for the EN-GA pair, the results of such systems have always been reported using automatic evaluation metrics. This paper provides the first human evaluation study of EN-GA MT using professional translators and in-domain (public administration) data for a more accurate depiction of the translation quality available via MT.",
}
@inproceedings{stasimioti-etal-2020-machine,
    title = "Machine Translation Quality: A comparative evaluation of {SMT}, {NMT} and tailored-{NMT} outputs",
    author = "Stasimioti, Maria  and
      Sosoni, Vilelmini  and
      Kermanidis, Katia  and
      Mouratidis, Despoina",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.47",
    pages = "441--450",
    abstract = "The present study aims to compare three systems: a generic statistical machine translation (SMT), a generic neural machine translation (NMT) and a tailored-NMT system focusing on the English to Greek language pair. The comparison is carried out following a mixed-methods approach, i.e. automatic metrics, as well as side-by-side ranking, adequacy and fluency rating, measurement of actual post editing (PE) effort and human error analysis performed by 16 postgraduate Translation students. The findings reveal a higher score for both the generic NMT and the tailored-NMT outputs as regards automatic metrics and human evaluation metrics, with the tailored-NMT output faring even better than the generic NMT output.",
}
@inproceedings{soares-etal-2020-qe,
    title = "{QE} Viewer: an Open-Source Tool for Visualization of Machine Translation Quality Estimation Results",
    author = "Soares, Felipe  and
      Zaretskaya, Anna  and
      Bartolome, Diego",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.48",
    pages = "453--454",
    abstract = "QE Viewer is a web-based tool for visualizing results of a Machine Translation Quality Estimation (QE) system. It allows users to see information on the predicted post-editing distance (PED) for a given file or sentence, and highlighted words that were predicted to contain MT errors. The tool can be used in a variety of academic, educational and commercial scenarios.",
}
@inproceedings{castilho-2020-document,
    title = "Document-Level Machine Translation Evaluation Project: Methodology, Effort and Inter-Annotator Agreement",
    author = "Castilho, Sheila",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.49",
    pages = "455--456",
    abstract = "Document-level (doc-level) human eval-uation of machine translation (MT) has raised interest in the community after a fewattempts have disproved claims of {``}human parity{''} (Toral et al., 2018; Laubli et al.,2018). However, little is known about bestpractices regarding doc-level human evalu-ation. The goal of this project is to identifywhich methodologies better cope with i)the current state-of-the-art (SOTA) humanmetrics, ii) a possible complexity when as-signing a single score to a text consisted of{`}good{'} and {`}bad{'} sentences, iii) a possibletiredness bias in doc-level set-ups, and iv)the difference in inter-annotator agreement(IAA) between sentence and doc-level set-ups.",
}
@inproceedings{hieber-etal-2020-sockeye,
    title = "Sockeye 2: A Toolkit for Neural Machine Translation",
    author = "Hieber, Felix  and
      Domhan, Tobias  and
      Denkowski, Michael  and
      Vilar, David",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.50",
    pages = "457--458",
    abstract = "We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit. New features include a simplified code base through the use of MXNet{'}s Gluon API, a focus on state of the art model architectures, and distributed mixed precision training. These improvements result in faster training and inference, higher automatic metric scores, and a shorter path from research to production.",
}
@inproceedings{kamran-etal-2020-cef,
    title = "{CEF} Data Marketplace: Powering a Long-term Supply of Language Data",
    author = "Kamran, Amir  and
      Dzeguze, Dace  and
      van der Meer, Jaap  and
      Panic, Milica  and
      Cattelan, Alessandro  and
      Patrioli, Daniele  and
      Bentivogli, Luisa  and
      Turchi, Marco",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.51",
    pages = "459--460",
    abstract = "We describe the CEF Data Marketplace project, which focuses on the development of a trading platform of translation data for language professionals: translators, machine translation (MT) developers, language service providers (LSPs), translation buyers and government bodies. The CEF Data Marketplace platform will be designed and built to manage and trade data for all languages and domains. This project will open a continuous and longterm supply of language data for MT and other machine learning applications.",
}
@inproceedings{popovic-2020-qrev,
    title = "{QR}ev: Machine Translation of User Reviews: What Influences the Translation Quality?",
    author = "Popovic, Maja",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.52",
    pages = "461--462",
    abstract = "This project aims to identify the important aspects of translation quality of user reviews which will represent a starting point for developing better automatic MT metrics and challenge test sets, and will be also helpful for developing MT systems for this genre. We work on two types of reviews: Amazon products and IMDb movies, written in English and translated into two closely related target languages, Croatian and Serbian.",
}
@inproceedings{bojar-etal-2020-elitr,
    title = "{ELITR}: {E}uropean Live Translator",
    author = {Bojar, Ond{\v{r}}ej  and
      Mach{\'a}{\v{c}}ek, Dominik  and
      Sagar, Sangeet  and
      Smr{\v{z}}, Otakar  and
      Kratochv{\'\i}l, Jon{\'a}{\v{s}}  and
      Ansari, Ebrahim  and
      Franceschini, Dario  and
      Canton, Chiara  and
      Simonini, Ivan  and
      Nguyen, Thai-Son  and
      Schneider, Felix  and
      St{\"u}cker, Sebastian  and
      Waibel, Alex  and
      Haddow, Barry  and
      Sennrich, Rico  and
      Williams, Philip},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.53",
    pages = "463--464",
    abstract = "ELITR (European Live Translator) project aims to create a speech translation system for simultaneous subtitling of conferences and online meetings targetting up to 43 languages. The technology is tested by the Supreme Audit Office of the Czech Republic and by alfaview®, a German online conferencing system. Other project goals are to advance document-level and multilingual machine translation, automatic speech recognition, and automatic minuting.",
}
@inproceedings{way-etal-2020-progress,
    title = "Progress of the {PRINCIPLE} Project: Promoting {MT} for {C}roatian, {I}celandic, {I}rish and {N}orwegian",
    author = "Way, Andy  and
      Bago, Petra  and
      Dunne, Jane  and
      Gaspari, Federico  and
      K{\aa}sen, Andre  and
      Kristmannsson, Gauti  and
      McHugh, Helen  and
      Olsen, Jon Arild  and
      Sheridan, Dana Davis  and
      Sheridan, P{\'a}raic  and
      Tinsley, John",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.54",
    pages = "465--466",
    abstract = "This paper updates the progress made on the PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focuses on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which have been identified as low-resource languages, especially for building effective machine translation (MT) systems. We report initial achievements of the project and ongoing activities aimed at promoting the uptake of neural MT for the low-resource languages of the project.",
}
@inproceedings{oliver-2020-mtuoc,
    title = "{MTUOC}: easy and free integration of {NMT} systems in professional translation environments",
    author = "Oliver, Antoni",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.55",
    pages = "467--468",
    abstract = "In this paper the MTUOC project, aiming to provide an easy integration of neural and statistical machine translation systems, is presented. Almost all the required software to train and use neural and statistical MT systems are released under free licences. However, their use is not always easy and intuitive and medium-high specialized skills are required. MTUOC project provides simplified scripts for preprocessing and training MT systems, and a server and client for easy use of the trained systems. The server is compatible with popular CAT tools for a seamless integration. The project also distributes some free engines.",
}
@inproceedings{rico-etal-2020-inmigra3,
    title = "{INMIGRA}3: building a case for {NGO}s and {NMT}",
    author = "Rico, Celia  and
      Ramos, Mar{\'\i}a Del Mar S{\'a}nchez  and
      Oliver, Antoni",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.56",
    pages = "469--470",
    abstract = "INMIGRA3 is a three-year project that builds on the work of two previous initi-atives: INMIGRA2-CM and CRISIS-MT . Together, they address the specific needs of NGOs in multilingual settings with a particular interest in migratory contexts. Work on INMIGRA3 concentrates in the analysis of how best can be NMT put to use for the purposes of translating NGOs documentation.",
}
@inproceedings{ajausks-etal-2020-multilingual,
    title = "The Multilingual Anonymisation Toolkit for Public Administrations ({MAPA}) Project",
    author = "Ajausks, {\=E}riks  and
      Arranz, Victoria  and
      Bi{\'e}, Laurent  and
      Cerd{\`a}-i-Cuc{\'o}, Aleix  and
      Choukri, Khalid  and
      Cuadros, Montse  and
      Degroote, Hans  and
      Estela, Amando  and
      Etchegoyhen, Thierry  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Garc{\'\i}a-Pablos, Aitor  and
      Herranz, Manuel  and
      Kohan, Alejandro  and
      Melero, Maite  and
      Rosner, Mike  and
      Rozis, Roberts  and
      Paroubek, Patrick  and
      Vasi{\c{l}}evskis, Art{\=u}rs  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.57",
    pages = "471--472",
    abstract = "We describe the MAPA project, funded under the Connecting Europe Facility programme, whose goal is the development of an open-source de-identification toolkit for all official European Union languages. It will be developed since January 2020 until December 2021.",
}
@inproceedings{depraetere-etal-2020-ape,
    title = "{APE}-{QUEST}: an {MT} Quality Gate",
    author = "Depraetere, Heidi  and
      Van den Bogaert, Joachim  and
      Szoc, Sara  and
      Vanallemeersch, Tom",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.58",
    pages = "473--474",
    abstract = "The APE-QUEST project (2018--2020) sets up a quality gate and crowdsourcing workflow for the eTranslation system of EC{'}s Connecting Europe Facility to improve translation quality in specific domains. It packages these services as a translation portal for machine-to-machine and machine-to-human scenarios.",
}
@inproceedings{van-den-bogaert-etal-2020-mice,
    title = "{MICE}: a middleware layer for {MT}",
    author = "Van den Bogaert, Joachim  and
      Vanallemeersch, Tom  and
      Depraetere, Heidi",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.59",
    pages = "475--476",
    abstract = "The MICE project (2018-2020) will deliver a middleware layer for improving the output quality of the eTranslation system of EC{'}s Connecting Europe Facility through additional services, such as domain adaptation and named entity recognition. It will also deliver a user portal, allowing for human post-editing.",
}
@inproceedings{bie-etal-2020-neural,
    title = "Neural Translation for the {E}uropean {U}nion ({NTEU}) Project",
    author = "Bi{\'e}, Laurent  and
      Cerd{\`a}-i-Cuc{\'o}, Aleix  and
      Degroote, Hans  and
      Estela, Amando  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Herranz, Manuel  and
      Kohan, Alejandro  and
      Melero, Maite  and
      O{'}Dowd, Tony  and
      O{'}Gorman, Sin{\'e}ad  and
      Pinnis, M{\=a}rcis  and
      Rozis, Roberts  and
      Superbo, Riccardo  and
      Vasi{\c{l}}evskis, Art{\=u}rs",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.60",
    pages = "477--478",
    abstract = "The Neural Translation for the European Union (NTEU) project aims to build a neural engine farm with all European official language combinations for eTranslation, without the necessity to use a high-resourced language as a pivot. NTEU started in September 2019 and will run until August 2021.",
}
@inproceedings{tiedemann-thottingal-2020-opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and
      Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.61",
    pages = "479--480",
    abstract = "This paper presents OPUS-MT a project that focuses on the development of free resources and tools for machine translation. The current status is a repository of over 1,000 pre-trained neural machine translation models that are ready to be launched in on-line translation services. For this we also provide open source implementations of web applications that can run efficiently on average desktop hardware with a straightforward setup and installation.",
}
@inproceedings{van-den-bogaert-etal-2020-ocr,
    title = "{OCR}, Classification{\&} Machine Translation ({OCCAM})",
    author = "Van den Bogaert, Joachim  and
      Defauw, Arne  and
      Everaert, Frederic  and
      Van Winckel, Koen  and
      Kramchaninova, Alina  and
      Bardadym, Anna  and
      Vanallemeersch, Tom  and
      Smr{\v{z}}, Pavel  and
      Hradi{\v{s}}, Michal",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.62",
    pages = "481--482",
    abstract = "The OCCAM project (Optical Character recognition, ClassificAtion {\&} Machine Translation) aims at integrating the CEF (Connecting Europe Facility) Automated Translation service with image classification, Translation Memories (TMs), Optical Character Recognition (OCR), and Machine Translation (MT). It will support the automated translation of scanned business documents (a document format that, currently, cannot be processed by the CEF eTranslation service) and will also lead to a tool useful for the Digital Humanities domain.",
}
@inproceedings{van-den-bogaert-etal-2020-cefat4cities,
    title = "{CEFAT}4{C}ities, a Natural Language Layer for the {ISA}2 Core Public Service Vocabulary",
    author = "Van den Bogaert, Joachim  and
      Defauw, Arne  and
      Szoc, Sara  and
      Everaert, Frederic  and
      Van Winckel, Koen  and
      Kramchaninova, Alina  and
      Bardadym, Anna  and
      Vanallemeersch, Tom",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.63",
    pages = "483--484",
    abstract = "The CEFAT4Cities project (2020-2022) will create a {``}Smart Cities natural language context{''} (a software layer that facilitates the conversion of natural-language administrative procedures, into machine-readable data sets) on top of the existing ISA2 interoperability layer for public services. Integration with the FIWARE/ORION {``}Smart City{''} Context Broker, will make existing, paper-based, public services discoverable through {``}Smart City{''} frameworks, thus allowing for the development of more sophisticated and more user-friendly public services applications. An automated translation component will be included, to provide a solution that can be used by all EU Member States. As a result, the project will allow EU citizens and businesses to interact with public services on the city, national, regional and EU level, in their own language.",
}
@inproceedings{macken-etal-2020-assessing,
    title = "Assessing the Comprehensibility of Automatic Translations ({A}ris{T}o{CAT})",
    author = "Macken, Lieve  and
      Fonteyne, Margot  and
      Tezcan, Arda  and
      Daems, Joke",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.64",
    pages = "485--486",
    abstract = "The ArisToCAT project aims to assess the comprehensibility of {`}raw{'} (unedited) MT output for readers who can only rely on the MT output. In this project description, we summarize the main results of the project and present future work.",
}
@inproceedings{klein-bernardinello-2020-mt,
    title = "Let {MT} simplify and speed up your Alignment for {TM} creation",
    author = "Klein, Judith  and
      Bernardinello, Giorgio",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.65",
    pages = "487--489",
    abstract = "Large quantities of multilingual legal documents are waiting to be regularly aligned and used for future translations. For reasons of time, effort and cost, manual alignment is not an option. Automatically aligned segments are suitable for concordance search but are unreliable for fuzzy search and pretranslation. MT-based alignment could be the key to improving the results.",
}
@inproceedings{rapp-tambouratzis-2020-overview,
    title = "An Overview of the {SEBAMAT} Project",
    author = "Rapp, Reinhard  and
      Tambouratzis, George",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.66",
    pages = "491--492",
    abstract = "SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT.",
}
@inproceedings{martins-2020-deepspin,
    title = "{D}eep{SPIN}: Deep Structured Prediction for Natural Language Processing",
    author = "Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.67",
    pages = "493--494",
    abstract = "DeepSPIN is a research project funded by the European Research Council (ERC) whose goal is to develop new neural structured prediction methods, models, and algorithms for improving the quality, interpretability, and data-efficiency of natural language processing (NLP) systems, with special emphasis on machine translation and quality estimation applications.",
}
@inproceedings{martins-etal-2020-project,
    title = "Project {MAIA}: Multilingual {AI} Agent Assistant",
    author = "Martins, Andr{\'e} F. T.  and
      Graca, Joao  and
      Dimas, Paulo  and
      Moniz, Helena  and
      Neubig, Graham",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.68",
    pages = "495--496",
    abstract = "This paper presents the Multilingual Artificial Intelligence Agent Assistant (MAIA), a project led by Unbabel with the collaboration of CMU, INESC-ID and IT Lisbon. MAIA will employ cutting-edge machine learning and natural language processing technologies to build multilingual AI agent assistants, eliminating language barriers. MAIA{'}s translation layer will empower human agents to provide customer support in real-time, in any language, with human quality.",
}
@inproceedings{resende-way-2020-mtrill,
    title = "{MT}rill project: Machine Translation impact on language learning",
    author = "Resende, Nat{\'a}lia  and
      Way, Andy",
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/2020.eamt-1.69",
    pages = "497--498",
    abstract = "Over the last decades, massive research investments have been made in the development of machine translation (MT) systems (Gupta and Dhawan, 2019). This has brought about a paradigm shift in the performance of these language tools, leading to widespread use of popular MT systems (Gaspari and Hutchins, 2007). Although the first MT engines were used for gisting purposes, in recent years, there has been an increasing interest in using MT tools, especially the freely available online MT tools, for language teaching and learning (Clifford et al., 2013). The literature on MT and Computer Assisted Language Learning (CALL) shows that, over the years, MT systems have been facilitating language teaching and also language learning (Nin ̃o, 2006). It has been shown that MT tools can increase awareness of grammatical linguistic features of a foreign language. Research also shows the positive role of MT systems in the development of writing skills in English as well as in improving communication skills in English(Garcia and Pena, 2011). However, to date, the cognitive impact of MT on language acquisition and on the syntactic aspects of language processing has not yet been investigated and deserves further scrutiny. The MTril project aims at filling this gap in the literature by examining whether MT is contributing to a central aspect of language acquisition: the so-called language binding, i.e., the ability to combine single words properly in a grammatical sentence (Heyselaar et al., 2017; Ferreira and Bock, 2006). The project focus on the initial stages (pre-intermediate and intermediate) of the acquisition of English syntax by Brazilian Portuguese native speakers using MT systems as a support for language learning.",
}
@proceedings{dmr-2020-international,
    title = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    editor = "Xue, Nianwen  and
      Bos, Johan  and
      Croft, William  and
      Haji{\v{c}}, Jan  and
      Huang, Chu-Ren  and
      Oepen, Stephan  and
      Palmer, Martha  and
      Pustejovsky, James",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.0",
}
@inproceedings{lai-etal-2020-continuation,
    title = "A Continuation Semantics for {A}bstract {M}eaning {R}epresentation",
    author = "Lai, Kenneth  and
      Donatelli, Lucia  and
      Pustejovsky, James",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.1",
    pages = "1--12",
    abstract = "Abstract Meaning Representation (AMR) is a simple, expressive semantic framework whose emphasis on predicate-argument structure is effective for many tasks. Nevertheless, AMR lacks a systematic treatment of projection phenomena, making its translation into logical form problematic. We present a translation function from AMR to first order logic using continuation semantics, which allows us to capture the semantic context of an expression in the form of an argument. This is a natural extension of AMR{'}s original design principles, allowing us to easily model basic projection phenomena such as quantification and negation as well as complex phenomena such as bound variables and donkey anaphora.",
}
@inproceedings{bos-2020-separating,
    title = "Separating Argument Structure from Logical Structure in {AMR}",
    author = "Bos, Johan",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.2",
    pages = "13--20",
    abstract = "The AMR (Abstract Meaning Representation) formalism for representing meaning of natural language sentences puts emphasis on predicate-argument structure and was not designed to deal with scope and quantifiers. By extending AMR with indices for contexts and formulating constraints on these contexts, a formalism is derived that makes correct predictions for inferences involving negation and bound variables. The attractive core predicate-argument structure of AMR is preserved. The resulting framework is similar to the meaning representations of Discourse Representation Theory employed in the Parallel Meaning Bank.",
}
@inproceedings{choe-etal-2020-building,
    title = "Building {K}orean {A}bstract {M}eaning {R}epresentation Corpus",
    author = "Choe, Hyonsu  and
      Han, Jiyoon  and
      Park, Hyejin  and
      Oh, Tae Hwan  and
      Kim, Hansaem",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.3",
    pages = "21--29",
    abstract = "To explore the potential sembanking in Korean and ways to represent the meaning of Korean sentences, this paper reports on the process of applying Abstract Meaning Representation to Korean, a semantic representation framework that has been studied in wide range of languages, and its output: the Korean AMR corpus. The corpus which is constructed so far is a size of 1,253 sentences and its raw texts are from ExoBrain Corpus, a state-led R{\&}D project on language AI. This paper also analyzes the result in both qualitative and quantitative manners, proposing discussions for further development.",
}
@inproceedings{vigus-etal-2020-cross,
    title = "Cross-lingual annotation: a road map for low- and no-resource languages",
    author = "Vigus, Meagan  and
      Van Gysel, Jens E. L.  and
      O{'}Gorman, Tim  and
      Cowell, Andrew  and
      Vallejos, Rosa  and
      Croft, William",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.4",
    pages = "30--40",
    abstract = "This paper presents a {``}road map{''} for the annotation of semantic categories in typologically diverse languages, with potentially few linguistic resources, and often no existing computational resources. Past semantic annotation efforts have focused largely on high-resource languages, or relatively low-resource languages with a large number of native speakers. However, there are certain typological traits, namely the synthesis of multiple concepts into a single word, that are more common in languages with a smaller speech community. For example, what is expressed as a sentence in a more analytic language like English, may be expressed as a single word in a more synthetic language like Arapaho. This paper proposes solutions for annotating analytic and synthetic languages in a comparable way based on existing typological research, and introduces a road map for the annotation of languages with a dearth of resources.",
}
@inproceedings{cui-hershcovich-2020-refining,
    title = "Refining Implicit Argument Annotation for {UCCA}",
    author = "Cui, Ruixiang  and
      Hershcovich, Daniel",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.5",
    pages = "41--52",
    abstract = "Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. This paper proposes a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation{'}s foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes.",
}
@inproceedings{hwang-etal-2020-k,
    title = "K-{SNACS}: Annotating {K}orean Adposition Semantics",
    author = "Hwang, Jena D.  and
      Choe, Hanwool  and
      Han, Na-Rae  and
      Schneider, Nathan",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.6",
    pages = "53--66",
    abstract = "While many languages use adpositions to encode semantic relationships between content words in a sentence (e.g., agentivity or temporality), the details of how adpositions work vary widely across languages with respect to both form and meaning. In this paper, we empirically adapt the SNACS framework (Schneider et al., 2018) to Korean, a language that is typologically distant from English{---}the language SNACS was based on. We apply the SNACS framework to annotate the highly popular novellaThe Little Prince with semantic supersense labels over allKorean postpositions. Thus, we introduce the first broad-coverage corpus annotated with Korean postposition semantics and provide a detailed analysis of the corpus with an apples-to-apples comparison between Korean and English annotations",
}
@inproceedings{bonial-etal-2020-infoforager,
    title = "{I}nfo{F}orager: Leveraging Semantic Search with {AMR} for {COVID}-19 Research",
    author = "Bonial, Claire  and
      Lukin, Stephanie M.  and
      Doughty, David  and
      Hill, Steven  and
      Voss, Clare",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.7",
    pages = "67--77",
    abstract = "This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19. We describe the development of a proof-of-concept prototype tool, InfoForager, which uses AMR to conduct a semantic search, targeting the meaning of the user question, and matching this to sentences in medical documents that may contain information to answer that question. This work was conducted as a sprint over a period of six weeks, and reveals both promising results and challenges in reducing the user search time relating to COVID-19 research, and in general, domain adaption of AMR for this task.",
}
@inproceedings{kapustin-kapustin-2020-semantic,
    title = "Semantic parsing with fuzzy meaning representations",
    author = "Kapustin, Pavlo  and
      Kapustin, Michael",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.8",
    pages = "78--89",
    abstract = "We propose an approach and a software framework for semantic parsing of natural language sentences to discourse representation structures with use of fuzzy meaning representations such as fuzzy sets and compatibility intervals. We explain the motivation for using fuzzy meaning representations in semantic parsing and describe the design of the proposed approach and the software framework, discussing various examples. We argue that the use of fuzzy meaning representations have potential to improve understanding and reasoning capabilities of systems working with natural language.",
}
@inproceedings{kalm-etal-2020-representing,
    title = "Representing constructional metaphors",
    author = "Kalm, Pavlina  and
      Regan, Michael  and
      Lee, Sook-kyung  and
      Peverada, Chris  and
      Croft, William",
    booktitle = "Proceedings of the Second International Workshop on Designing Meaning Representations",
    month = dec,
    year = "2020",
    address = "Barcelona Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.dmr-1.9",
    pages = "90--100",
    abstract = "This paper introduces a representation and annotation scheme for argument structure constructions that are used metaphorically with verbs in different semantic domains. We aim to contribute to the study of constructional metaphors which has received little attention in theoretical and computational linguistics. The proposed representation consists of a systematic mapping between the constructional and verbal event structures in two domains. It reveals the semantic motivations that lead to constructions being metaphorically extended. We demonstrate this representation on argument structure constructions with Transfer of Possession verbs and test the viability of this scheme with an annotation exercise.",
}
@proceedings{deelio-2020-deep,
    title = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    editor = "Agirre, Eneko  and
      Apidianaki, Marianna  and
      Vuli{\'c}, Ivan",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.0",
}
@inproceedings{wang-etal-2020-correcting,
    title = "Correcting the Misuse: A Method for the {C}hinese Idiom Cloze Test",
    author = "Wang, Xinyu  and
      Zhao, Hongsheng  and
      Yang, Tan  and
      Wang, Hongbo",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.1",
    doi = "10.18653/v1/2020.deelio-1.1",
    pages = "1--10",
    abstract = "The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context. Chinese idiom is a type of Chinese idiomatic expression. The common misuse of Chinese idioms leads to error in corpus and causes error in the learned semantic representation of Chinese idioms. In this paper, we introduce the definition written by Chinese experts to correct the misuse. We propose a model for the Chinese idiom cloze test integrating various information effectively. We propose an attention mechanism called Attribute Attention to balance the weight of different attributes among different descriptions of the Chinese idiom. Besides the given candidates of every blank, we also try to choose the answer from all Chinese idioms that appear in the dataset as the extra loss due to the uniqueness and specificity of Chinese idioms. In experiments, our model outperforms the state-of-the-art model.",
}
@inproceedings{chen-badlani-2020-relation,
    title = "Relation Extraction with Contextualized Relation Embedding ({CRE})",
    author = "Chen, Xiaoyu  and
      Badlani, Rohan",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.2",
    doi = "10.18653/v1/2020.deelio-1.2",
    pages = "11--19",
    abstract = "This submission is a paper that proposes an architecture for the relation extraction task which integrates semantic information with knowledge base modeling in a novel manner.",
}
@inproceedings{alshemali-kalita-2020-generalization,
    title = "Generalization to Mitigate Synonym Substitution Attacks",
    author = "Alshemali, Basemah  and
      Kalita, Jugal",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.3",
    doi = "10.18653/v1/2020.deelio-1.3",
    pages = "20--28",
    abstract = "Studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples {--} perturbed inputs that cause DNN-based models to produce incorrect results. One robust adversarial attack in the NLP domain is the synonym substitution. In attacks of this variety, the adversary substitutes words with synonyms. Since synonym substitution perturbations aim to satisfy all lexical, grammatical, and semantic constraints, they are difficult to detect with automatic syntax check as well as by humans. In this paper, we propose a structure-free defensive method that is capable of improving the performance of DNN-based models with both clean and adversarial data. Our findings show that replacing the embeddings of the important words in the input samples with the average of their synonyms{'} embeddings can significantly improve the generalization of DNN-based classifiers. By doing so, we reduce model sensitivity to particular words in the input samples. Our results indicate that the proposed defense is not only capable of defending against adversarial attacks, but is also capable of improving the performance of DNN-based models when tested on benign data. On average, the proposed defense improved the classification accuracy of the CNN and Bi-LSTM models by 41.30{\%} and 55.66{\%}, respectively, when tested under adversarial attacks. Extended investigation shows that our defensive method can improve the robustness of nonneural models, achieving an average of 17.62{\%} and 22.93{\%} classification accuracy increase on the SVM and XGBoost models, respectively. The proposed defensive method has also shown an average of 26.60{\%} classification accuracy improvement when tested with the infamous BERT model. Our algorithm is generic enough to be applied in any NLP domain and to any model trained on any natural language.",
}
@inproceedings{feng-etal-2020-genaug,
    title = "{G}en{A}ug: Data Augmentation for Finetuning Text Generators",
    author = "Feng, Steven Y.  and
      Gangal, Varun  and
      Kang, Dongyeop  and
      Mitamura, Teruko  and
      Hovy, Eduard",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.4",
    doi = "10.18653/v1/2020.deelio-1.4",
    pages = "29--42",
    abstract = "In this paper, we investigate data augmentation for text generation, which we call GenAug. Text generation and language modeling are important tasks within natural language processing, and are especially challenging for low-data regimes. We propose and evaluate various augmentation methods, including some that incorporate external knowledge, for finetuning GPT-2 on a subset of Yelp Reviews. We also examine the relationship between the amount of augmentation and the quality of the generated text. We utilize several metrics that evaluate important aspects of the generated text including its diversity and fluency. Our experiments demonstrate that insertion of character-level synthetic noise and keyword replacement with hypernyms are effective augmentation methods, and that the quality of generations improves to a peak at approximately three times the amount of original data.",
}
@inproceedings{lauscher-etal-2020-common,
    title = "Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers",
    author = "Lauscher, Anne  and
      Majewska, Olga  and
      Ribeiro, Leonardo F. R.  and
      Gurevych, Iryna  and
      Rozanov, Nikolai  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.5",
    doi = "10.18653/v1/2020.deelio-1.5",
    pages = "43--49",
    abstract = "Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowledge from external resources into these models. While on the one hand, joint pre-training (i.e., training from scratch, adding objectives based on external knowledge to the primary LM objective) may be prohibitively computationally expensive, post-hoc fine-tuning on external knowledge, on the other hand, may lead to the catastrophic forgetting of distributional knowledge. In this work, we investigate models for complementing the distributional knowledge of BERT with conceptual knowledge from ConceptNet and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using adapter training. While overall results on the GLUE benchmark paint an inconclusive picture, a deeper analysis reveals that our adapter-based models substantially outperform BERT (up to 15-20 performance points) on inference tasks that require the type of conceptual knowledge explicitly present in ConceptNet and OMCS. We also open source all our experiments and relevant code under: https://github.com/wluper/retrograph.",
}
@inproceedings{iter-etal-2020-entity,
    title = "Entity Attribute Relation Extraction with Attribute-Aware Embeddings",
    author = "Iter, Dan  and
      Yu, Xiao  and
      Li, Fangtao",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.6",
    doi = "10.18653/v1/2020.deelio-1.6",
    pages = "50--55",
    abstract = "Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines. However, most such knowledge bases are manually curated, covering only a small fraction of all attributes, even for common entities. To improve the precision of model-based entity-attribute extraction, we propose attribute-aware embeddings, which embeds entities and attributes in the same space by the similarity of their attributes. Our model, EANET, learns these embeddings by representing entities as a weighted sum of their attributes and concatenates these embeddings to mention level features. EANET achieves up to 91{\%} classification accuracy, outperforming strong baselines and achieves 83{\%} precision on manually labeled high confidence extractions, outperforming Biperpedia (Gupta et al., 2014), a previous state-of-the-art for large scale entity-attribute extraction.",
}
@inproceedings{goodwin-demner-fushman-2020-enhancing,
    title = "Enhancing Question Answering by Injecting Ontological Knowledge through Regularization",
    author = "Goodwin, Travis  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.7",
    doi = "10.18653/v1/2020.deelio-1.7",
    pages = "56--63",
    abstract = "Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e.g., world) knowledge. In this paper, we present OSCR (Ontology-based Semantic Composition Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pre-training. We evaluated the performance of BERT pre-trained on Wikipedia with and without OSCR by measuring the performance when fine-tuning on two question answering tasks involving world knowledge and causal reasoning and one requiring domain (healthcare) knowledge and obtained 33.3{\%}, 18.6{\%}, and 4{\%} improved accuracy compared to pre-training BERT without OSCR.",
}
@inproceedings{kalyan-sangeetha-2020-target,
    title = "Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts",
    author = "Kalyan, Katikapalli Subramanyam  and
      Sangeetha, Sivanesan",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.8",
    doi = "10.18653/v1/2020.deelio-1.8",
    pages = "64--73",
    abstract = "Medical concept normalization (MCN) i.e., mapping of colloquial medical phrases to standard concepts is an essential step in analysis of medical social media text. The main drawback in existing state-of-the-art approach (Kalyan and Sangeetha, 2020b) is learning target concept vector representations from scratch which requires more number of training instances. Our model is based on RoBERTa and target concept embeddings. In our model, we integrate a) target concept information in the form of target concept vectors generated by encoding target concept descriptions using SRoBERTa, state-of-the-art RoBERTa based sentence embedding model and b) domain lexicon knowledge by enriching target concept vectors with synonym relationship knowledge using retrofitting algorithm. It is the first attempt in MCN to exploit both target concept information as well as domain lexicon knowledge in the form of retrofitted target concept vectors. Our model outperforms all the existing models with an accuracy improvement up to 1.36{\%} on three standard datasets. Further, our model when trained only on mapping lexicon synonyms achieves up to 4.87{\%} improvement in accuracy.",
}
@inproceedings{chang-etal-2020-incorporating,
    title = "Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks",
    author = "Chang, Ting-Yun  and
      Liu, Yang  and
      Gopalakrishnan, Karthik  and
      Hedayatnia, Behnam  and
      Zhou, Pei  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.9",
    doi = "10.18653/v1/2020.deelio-1.9",
    pages = "74--79",
    abstract = "Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory. To enable this, machines need to have a more general understanding of our complicated world and develop the ability to perform commonsense reasoning besides fitting the specific downstream tasks. External commonsense knowledge graphs (KGs), such as ConceptNet, provide rich information about words and their relationships. Thus, towards general commonsense learning, we propose two approaches to implicitly and explicitly infuse such KGs into pretrained language models. We demonstrate our proposed methods perform well on SocialIQA, a social commonsense reasoning task, in both limited and full training data regimes.",
}
@inproceedings{cibu-marginean-2020-commonsense,
    title = "Commonsense Statements Identification and Explanation with Transformer-based Encoders",
    author = "Cibu, Sonia  and
      Marginean, Anca",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.10",
    doi = "10.18653/v1/2020.deelio-1.10",
    pages = "80--88",
    abstract = "In this work, we present our empirical attempt to identify the proper strategy of using Transformer Language Models to identify sentences consistent with commonsense. We tackle the first two tasks from the ComVE competition. The starting point for our work is the BERT assumption according to which a large number of NLP tasks can be solved with pre-trained Transformers with no substantial task-specific changes of the architecture. However, our experiments show that the encoding strategy can have a great impact on the quality of the fine-tuning. The combination between cross-encoding and multi-input models worked better than one cross-encoder and allowed us to achieve comparable results with the state-of-the-art without the use of any external data.",
}
@inproceedings{patel-ferraro-2020-complementary,
    title = "On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling",
    author = "Patel, Rajat  and
      Ferraro, Francis",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.deelio-1.11",
    doi = "10.18653/v1/2020.deelio-1.11",
    pages = "89--99",
    abstract = "We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations. Our work also shows that jointly modeling both structured knowledge tuples and language improves both.",
}
@proceedings{crac-2020-models,
    title = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    editor = "Ogrodniczuk, Maciej  and
      Ng, Vincent  and
      Grishina, Yulia  and
      Pradhan, Sameer",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.0",
}
@inproceedings{stylianou-vlahavas-2020-e,
    title = "{E}.{T}.: Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks",
    author = "Stylianou, Nikolaos  and
      Vlahavas, Ioannis",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.1",
    pages = "1--10",
    abstract = "In the last decade, the field of Neural Language Modelling has witnessed enormous changes, with the development of novel models through the use of Transformer architectures. However, even these models struggle to model long sequences due to memory constraints and increasing computational complexity. Coreference annotations over the training data can provide context far beyond the modelling limitations of such language models. In this paper we present an extension over the Transformer-block architecture used in neural language models, specifically in GPT2, in order to incorporate entity annotations during training. Our model, GPT2E, extends the Transformer layers architecture of GPT2 to Entity-Transformers, an architecture designed to handle coreference information when present. To that end, we achieve richer representations for entity mentions, with insignificant training cost. We show the comparative model performance between GPT2 and GPT2E in terms of Perplexity on the CoNLL 2012 and LAMBADA datasets as well as the key differences in the entity representations and their effects in downstream tasks such as Named Entity Recognition. Furthermore, our approach can be adopted by the majority of Transformer-based language models.",
}
@inproceedings{de-clercq-hoste-2020-absolutely,
    title = "It{'}s absolutely divine! Can fine-grained sentiment analysis benefit from coreference resolution?",
    author = "De Clercq, Orphee  and
      Hoste, Veronique",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.2",
    pages = "11--21",
    abstract = "While it has been claimed that anaphora or coreference resolution plays an important role in opinion mining, it is not clear to what extent coreference resolution actually boosts performance, if at all. In this paper, we investigate the potential added value of coreference resolution for the aspect-based sentiment analysis of restaurant reviews in two languages, English and Dutch. We focus on the task of aspect category classification and investigate whether including coreference information prior to classification to resolve implicit aspect mentions is beneficial. Because coreference resolution is not a solved task in NLP, we rely on both automatically-derived and gold-standard coreference relations, allowing us to investigate the true upper bound. By training a classifier on a combination of lexical and semantic features, we show that resolving the coreferential relations prior to classification is beneficial in a joint optimization setup. However, this is only the case when relying on gold-standard relations and the result is more outspoken for English than for Dutch. When validating the optimal models, however, we found that only the Dutch pipeline is able to achieve a satisfying performance on a held-out test set and does so regardless of whether coreference information was included.",
}
@inproceedings{aloraini-poesio-2020-anaphoric,
    title = "Anaphoric Zero Pronoun Identification: A Multilingual Approach",
    author = "Aloraini, Abdulrahman  and
      Poesio, Massimo",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.3",
    pages = "22--32",
    abstract = "Pro-drop languages such as Arabic, Chinese, Italian or Japanese allow morphologically null but referential arguments in certain syntactic positions, called anaphoric zero-pronouns. Much NLP work on anaphoric zero-pronouns (AZP) is based on gold mentions, but models for their identification are a fundamental prerequisite for their resolution in real-life applications. Such identification requires complex language understanding and knowledge of real-world entities. Transfer learning models, such as BERT, have recently shown to learn surface, syntactic, and semantic information,which can be very useful in recognizing AZPs. We propose a BERT-based multilingual model for AZP identification from predicted zero pronoun positions, and evaluate it on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, this is the first neural network model of AZP identification for Arabic; and our approach outperforms the stateof-the-art for Chinese. Experiment results suggest that BERT implicitly encode information about AZPs through their surrounding context.",
}
@inproceedings{anikina-etal-2020-predicting,
    title = "Predicting Coreference in {A}bstract {M}eaning {R}epresentations",
    author = "Anikina, Tatiana  and
      Koller, Alexander  and
      Roth, Michael",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.4",
    pages = "33--38",
    abstract = "This work addresses coreference resolution in Abstract Meaning Representation (AMR) graphs, a popular formalism for semantic parsing. We evaluate several current coreference resolution techniques on a recently published AMR coreference corpus, establishing baselines for future work. We also demonstrate that coreference resolution can improve the accuracy of a state-of-the-art semantic parser on this corpus.",
}
@inproceedings{urbizu-etal-2020-sequence,
    title = "Sequence to Sequence Coreference Resolution",
    author = "Urbizu, Gorka  and
      Soraluze, Ander  and
      Arregi, Olatz",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.5",
    pages = "39--46",
    abstract = "Until recently, coreference resolution has been a critical task on the pipeline of any NLP task involving deep language understanding, such as machine translation, chatbots, summarization or sentiment analysis. However, nowadays, those end tasks are learned end-to-end by deep neural networks without adding any explicit knowledge about coreference. Thus, coreference resolution is used less in the training of other NLP tasks or trending pretrained language models. In this paper we present a new approach to face coreference resolution as a sequence to sequence task based on the Transformer architecture. This approach is simple and universal, compatible with any language or dataset (regardless of singletons) and easier to integrate with current language models architectures. We test it on the ARRAU corpus, where we get 65.6 F1 CoNLL. We see this approach not as a final goal, but a means to pretrain sequence to sequence language models (T5) on coreference resolution.",
}
@inproceedings{aktas-kohnert-2020-twiconv,
    title = "{T}wi{C}onv: A Coreference-annotated Corpus of {T}witter Conversations",
    author = "Akta{\c{s}}, Berfin  and
      Kohnert, Annalena",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.6",
    pages = "47--54",
    abstract = "This article introduces TwiConv, an English coreference-annotated corpus of microblog conversations from Twitter. We describe the corpus compilation process and the annotation scheme, and release the corpus publicly, along with this paper. We manually annotated nominal coreference in 1756 tweets arranged in 185 conversation threads. The annotation achieves satisfactory annotation agreement results. We also present a new method for mapping the tweet contents with distributed stand-off annotations, which can easily be adapted to different annotation tasks.",
}
@inproceedings{pandit-etal-2020-integrating,
    title = "Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution",
    author = "Pandit, Onkar  and
      Denis, Pascal  and
      Ralaivola, Liva",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.7",
    pages = "55--67",
    abstract = "Lexical semantics and world knowledge are crucial for interpreting bridging anaphora. Yet, existing computational methods for acquiring and injecting this type of information into bridging resolution systems suffer important limitations. Based on explicit querying of external knowledge bases, earlier approaches are computationally expensive (hence, hardly scalable) and they map the data to be processed into high-dimensional spaces (careful handling of the curse of dimensionality and overfitting has to be in order). In this work, we take a different and principled approach which naturally addresses these issues. Specifically, we convert the external knowledge source (in this case, WordNet) into a graph, and learn embeddings of the graph nodes of low dimension to capture the crucial features of the graph topology and, at the same time, rich semantic information. Once properly identified from the mention text spans, these low dimensional graph node embeddings are combined with distributional text-based embeddings to provide enhanced mention representations. We illustrate the effectiveness of our approach by evaluating it on commonly used datasets, namely ISNotes and BASHI. Our enhanced mention representations yield significant accuracy improvements on both datasets when compared to different standalone text-based mention representations.",
}
@inproceedings{simonjetz-2020-reference,
    title = "Reference to Discourse Topics: Introducing {``}Global{''} Shell Nouns",
    author = "Simonjetz, Fabian",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.8",
    pages = "68--78",
    abstract = "Shell nouns (SNs) are abstract nouns like {``}fact{''}, {``}issue{''}, and {``}decision{''}, which are capable of refer- ring to non-nominal antecedents, much like anaphoric pronouns. As an extension of classical anaphora resolution, the automatic detection of SNs alongside their respective antecedents has received a growing research interest in recent years but proved to be a challenging task. This paper critically examines the assumption prevalent in previous research that SNs are typically accompanied by a specific antecedent, arguing that SNs like {``}issue{''} and {``}decision{''} are frequently used to refer, not to specific antecedents, but to global discourse topics, in which case they are out of reach of previously proposed resolution strategies that are tailored to SNs with explicit antecedents. The contribution of this work is three-fold. First, the notion of global SNs is defined; second, their qualitative and quantitative impact on previous SN research is investigated; and third, implications for previous and future approaches to SN resolution are discussed.",
}
@inproceedings{poot-van-cranenburgh-2020-benchmark,
    title = "A Benchmark of Rule-Based and Neural Coreference Resolution in {D}utch Novels and News",
    author = "Poot, Corb{\`e}n  and
      van Cranenburgh, Andreas",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.9",
    pages = "79--90",
    abstract = "We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at https://github.com/andreasvc/crac2020",
}
@inproceedings{miculicich-henderson-2020-partially,
    title = "Partially-supervised Mention Detection",
    author = "Miculicich, Lesly  and
      Henderson, James",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.10",
    pages = "91--98",
    abstract = "Learning to detect entity mentions without using syntactic information can be useful for integration and joint optimization with other tasks. However, it is common to have partially annotated data for this problem. Here, we investigate two approaches to deal with partial annotation of mentions: weighted loss and soft-target classification. We also propose two neural mention detection approaches: a sequence tagging, and an exhaustive search. We evaluate our methods with coreference resolution as a downstream task, using multitask learning. The results show that the recall and F1 score improve for all methods.",
}
@inproceedings{aloraini-etal-2020-neural,
    title = "Neural Coreference Resolution for {A}rabic",
    author = "Aloraini, Abdulrahman  and
      Yu, Juntao  and
      Poesio, Massimo",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.11",
    pages = "99--110",
    abstract = {No neural coreference resolver for Arabic exists, in fact we are not aware of any learning-based coreference resolver for Arabic since (Bj{\"o}rkelund and Kuhn, 2014). In this paper, we introduce a coreference resolution system for Arabic based on Lee et al{'}s end-to-end architecture combined with the Arabic version of bert and an external mention detector. As far as we know, this is the first neural coreference resolution system aimed specifically to Arabic, and it substantially outperforms the existing state-of-the-art on OntoNotes 5.0 with a gain of 15.2 points conll F1. We also discuss the current limitations of the task for Arabic and possible approaches that can tackle these challenges.},
}
@inproceedings{espeland-etal-2020-enhanced,
    title = "Enhanced Labelling in Active Learning for Coreference Resolution",
    author = "Espeland, Vebj{\o}rn  and
      Alex, Beatrice  and
      Bach, Benjamin",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.12",
    pages = "111--121",
    abstract = "In this paper we describe our attempt to increase the amount of information that can be retrieved through active learning sessions compared to previous approaches. We optimise the annotator{'}s labelling process using active learning in the context of coreference resolution. Using simulated active learning experiments, we suggest three adjustments to ensure the labelling time is spent as efficiently as possible. All three adjustments provide more information to the machine learner than the baseline, though a large impact on the F1 score over time is not observed. Compared to previous models, we report a marginal F1 improvement on the final coreference models trained using for two out of the three approaches tested when applied to the English OntoNotes 2012 Coreference Resolution data. Our best-performing model achieves 58.01 F1, an increase of 0.93 F1 over the baseline model.",
}
@inproceedings{skachkova-kruijff-korbayova-2020-reference,
    title = "Reference in Team Communication for Robot-Assisted Disaster Response: An Initial Analysis",
    author = "Skachkova, Natalia  and
      Kruijff-Korbayova, Ivana",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.13",
    pages = "122--132",
    abstract = "We analyze reference phenomena in a corpus of robot-assisted disaster response team communication. The annotation scheme we designed for this purpose distinguishes different types of entities, roles, reference units and relations. We focus particularly on mission-relevant objects, locations and actors and also annotate a rich set of reference links, including co-reference and various other kinds of relations. We explain the categories used in our annotation, present their distribution in the corpus and discuss challenging cases.",
}
@inproceedings{andy-etal-2020-resolving,
    title = "Resolving Pronouns in {T}witter Streams: Context can Help!",
    author = "Andy, Anietie  and
      Callison-Burch, Chris  and
      Wijaya, Derry Tanti",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.14",
    pages = "133--138",
    abstract = "Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event. Naturally, many tweets make pronominal reference to these people/characters. We propose an algorithm for resolving personal pronouns that make reference to people involved in an event, in tweet streams collected during the event.",
}
@inproceedings{lapshinova-koltunski-etal-2020-coreference,
    title = "Coreference Strategies in {E}nglish-{G}erman Translation",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Krielke, Marie-Pauline  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.15",
    pages = "139--153",
    abstract = "We present a study focusing on variation of coreferential devices in English original TED talks and news texts and their German translations. Using exploratory techniques we contemplate a diverse set of coreference devices as features which we assume indicate language-specific and register-based variation as well as potential translation strategies. Our findings reflect differences on both dimensions with stronger variation along the lines of register than between languages. By exposing interactions between text type and cross-linguistic variation, they can also inform multilingual NLP applications, especially machine translation.",
}
@inproceedings{frank-petty-2020-sequence,
    title = "Sequence-to-Sequence Networks Learn the Meaning of Reflexive Anaphora",
    author = "Frank, Robert  and
      Petty, Jackson",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.16",
    pages = "154--164",
    abstract = "Reflexive anaphora present a challenge for semantic interpretation: their meaning varies depending on context in a way that appears to require abstract variables. Past work has raised doubts about the ability of recurrent networks to meet this challenge. In this paper, we explore this question in the context of a fragment of English that incorporates the relevant sort of contextual variability. We consider sequence-to-sequence architectures with recurrent units and show that such networks are capable of learning semantic interpretations for reflexive anaphora which generalize to novel antecedents. We explore the effect of attention mechanisms and different recurrent unit types on the type of training data that is needed for success as measured in two ways: how much lexical support is needed to induce an abstract reflexive meaning (i.e., how many distinct reflexive antecedents must occur during training) and what contexts must a noun phrase occur in to support generalization of reflexive interpretation to this noun phrase?",
}
@inproceedings{guenoune-etal-2020-dataset,
    title = "A Dataset for Anaphora Analysis in {F}rench Emails",
    author = "Guenoune, Hani  and
      Cousot, Kevin  and
      Lafourcade, Mathieu  and
      Mekaoui, Melissa  and
      Lopez, C{\'e}dric",
    booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.crac-1.17",
    pages = "165--175",
    abstract = "In 2019, about 293 billion emails were sent worldwide every day. They are a valuable source of information and knowledge for professionals. Since the 90{'}s, many studies have been done on emails and have highlighted the need for resources regarding numerous NLP tasks. Due to the lack of available resources for French, very few studies on emails have been conducted. Anaphora resolution in emails is an unexplored area, annotated resources are needed, at least to answer a first question: Does email communication have specifics that must be addressed to tackle the anaphora resolution task? In order to answer this question 1) we build a French emails corpus composed of 100 anonymized professional threads and make it available freely for scientific exploitation. 2) we provide annotations of anaphoric links in the email collection.",
}
@proceedings{conll-2020-conll,
    title = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    editor = "Oepen, Stephan  and
      Abend, Omri  and
      Abzianidze, Lasha  and
      Bos, Johan  and
      Haji{\v{c}}, Jan  and
      Hershcovich, Daniel  and
      Li, Bin  and
      O'Gorman, Tim  and
      Xue, Nianwen  and
      Zeman, Daniel",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.0",
}
@inproceedings{oepen-etal-2020-mrp,
    title = "{MRP} 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing",
    author = "Oepen, Stephan  and
      Abend, Omri  and
      Abzianidze, Lasha  and
      Bos, Johan  and
      Hajic, Jan  and
      Hershcovich, Daniel  and
      Li, Bin  and
      O{'}Gorman, Tim  and
      Xue, Nianwen  and
      Zeman, Daniel",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.1",
    doi = "10.18653/v1/2020.conll-shared.1",
    pages = "1--22",
    abstract = "The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu",
}
@inproceedings{abzianidze-etal-2020-drs,
    title = "{DRS} at {MRP} 2020: Dressing up Discourse Representation Structures as Graphs",
    author = "Abzianidze, Lasha  and
      Bos, Johan  and
      Oepen, Stephan",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.2",
    doi = "10.18653/v1/2020.conll-shared.2",
    pages = "23--32",
    abstract = "Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse. Meaning in DRT is modeled via a Discourse Representation Structure (DRS), a meaning representation with a model-theoretic interpretation, which is usually depicted as nested boxes. In contrast, a directed labeled graph is a common data structure used to encode semantics of natural language texts. The paper describes the procedure of dressing up DRSs as directed labeled graphs to include DRT as a new framework in the 2020 shared task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. Since one of the goals of the shared task is to encourage unified models for several semantic graph frameworks, the conversion procedure was biased towards making the DRT graph framework somewhat similar to other graph-based meaning representation frameworks.",
}
@inproceedings{zeman-hajic-2020-fgd,
    title = "{FGD} at {MRP} 2020: {P}rague Tectogrammatical Graphs",
    author = "Zeman, Daniel  and
      Hajic, Jan",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.3",
    doi = "10.18653/v1/2020.conll-shared.3",
    pages = "33--39",
    abstract = "Prague Tectogrammatical Graphs (PTG) is a meaning representation framework that originates in the tectogrammatical layer of the Prague Dependency Treebank (PDT) and is theoretically founded in Functional Generative Description of language (FGD). PTG in its present form has been prepared for the CoNLL 2020 shared task on Cross-Framework Meaning Representation Parsing (MRP). It is generated automatically from the Prague treebanks and stored in the JSON-based MRP graph interchange format. The conversion is partially lossy; in this paper we describe what part of annotation was included and how it is represented in PTG.",
}
@inproceedings{ozaki-etal-2020-hitachi,
    title = "Hitachi at {MRP} 2020: Text-to-Graph-Notation Transducer",
    author = "Ozaki, Hiroaki  and
      Morio, Gaku  and
      Koreeda, Yuta  and
      Morishita, Terufumi  and
      Miyoshi, Toshinori",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.4",
    doi = "10.18653/v1/2020.conll-shared.4",
    pages = "40--52",
    abstract = "This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages. We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation. To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally. Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions. Notably, our parser can handle any PGN-formatted graphs with fewer framework-specific modifications. As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.",
}
@inproceedings{samuel-straka-2020-ufal,
    title = "{{\'U}FAL} at {MRP} 2020: Permutation-invariant Semantic Parsing in {PERIN}",
    author = "Samuel, David  and
      Straka, Milan",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.5",
    doi = "10.18653/v1/2020.conll-shared.5",
    pages = "53--64",
    abstract = "We present PERIN, a novel permutation-invariant approach to sentence-to-graph semantic parsing. PERIN is a versatile, cross-framework and language independent architecture for universal modeling of semantic structures. Our system participated in the CoNLL 2020 shared task, Cross-Framework Meaning Representation Parsing (MRP 2020), where it was evaluated on five different frameworks (AMR, DRG, EDS, PTG and UCCA) across four languages. PERIN was one of the winners of the shared task. The source code and pretrained models are available at http://www.github.com/ufal/perin.",
}
@inproceedings{dou-etal-2020-hit,
    title = "{HIT}-{SCIR} at {MRP} 2020: Transition-based Parser and Iterative Inference Parser",
    author = "Dou, Longxu  and
      Feng, Yunlong  and
      Ji, Yuqiu  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.6",
    doi = "10.18653/v1/2020.conll-shared.6",
    pages = "65--72",
    abstract = "This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. The task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. Our solution consists of two sub-systems: transition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG) and iterative inference parser for Flavor (2) frameworks (DRG, AMR). In the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.",
}
@inproceedings{arviv-etal-2020-huji,
    title = "{HUJI}-{KU} at {MRP} 2020: Two Transition-based Neural Parsers",
    author = "Arviv, Ofir  and
      Cui, Ruixiang  and
      Hershcovich, Daniel",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.7",
    doi = "10.18653/v1/2020.conll-shared.7",
    pages = "73--82",
    abstract = "This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task. Both are transition-based parsers using BERT contextualized embeddings. We generalized TUPA to support the newly-added MRP frameworks and languages, and experimented with multitask learning with the HIT-SCIR parser. We reached 4th place in both the crossframework and cross-lingual tracks.",
}
@inproceedings{na-min-2020-jbnu,
    title = "{JBNU} at {MRP} 2020: {AMR} Parsing Using a Joint State Model for Graph-Sequence Iterative Inference",
    author = "Na, Seung-Hoon  and
      Min, Jinwoo",
    booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-shared.8",
    doi = "10.18653/v1/2020.conll-shared.8",
    pages = "83--87",
    abstract = "This paper describes the Jeonbuk National University (JBNU) system for the 2020 shared task on Cross-Framework Meaning Representation Parsing at the Conference on Computational Natural Language Learning. Among the five frameworks, we address only the abstract meaning representation framework and propose a joint state model for the graph-sequence iterative inference of (Cai and Lam, 2020) for a simplified graph-sequence inference. In our joint state model, we update only a single joint state vector during the graph-sequence inference process instead of keeping the dual state vectors, and all other components are exactly the same as in (Cai and Lam, 2020).",
}
@proceedings{conll-2020-natural,
    title = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    editor = "Fern{\'a}ndez, Raquel  and
      Linzen, Tal",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.0",
}
@inproceedings{gong-etal-2020-enriching,
    title = "Enriching Word Embeddings with Temporal and Spatial Information",
    author = "Gong, Hongyu  and
      Bhat, Suma  and
      Viswanath, Pramod",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.1",
    doi = "10.18653/v1/2020.conll-1.1",
    pages = "1--11",
    abstract = "The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes. Taking a global view of words and their meanings in a widely used language, such as English, may require us to capture more refined semantics for use in time-specific or location-aware situations, such as the study of cultural trends or language use. However, popular vector representations for words do not adequately include temporal or spatial information. In this work, we present a model for learning word representation conditioned on time and location. In addition to capturing meaning changes over time and location, we require that the resulting word embeddings retain salient semantic and geometric properties. We train our model on time- and location-stamped corpora, and show using both quantitative and qualitative evaluations that it can capture semantics across time and locations. We note that our model compares favorably with the state-of-the-art for time-specific embedding, and serves as a new benchmark for location-specific embeddings.",
}
@inproceedings{sood-etal-2020-interpreting,
    title = "Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension",
    author = "Sood, Ekta  and
      Tannert, Simon  and
      Frassinelli, Diego  and
      Bulling, Andreas  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.2",
    doi = "10.18653/v1/2020.conll-1.2",
    pages = "12--25",
    abstract = "While neural networks with attention mechanisms have achieved superior performance on many natural language processing tasks, it remains unclear to which extent learned attention resembles human visual attention. In this paper, we propose a new method that leverages eye-tracking data to investigate the relationship between human visual attention and neural attention in machine reading comprehension. To this end, we introduce a novel 23 participant eye tracking dataset - MQA-RC, in which participants read movie plots and answered pre-defined questions. We compare state of the art networks based on long short-term memory (LSTM), convolutional neural models (CNN) and XLNet Transformer architectures. We find that higher similarity to human attention and performance significantly correlates to the LSTM and CNN models. However, we show this relationship does not hold true for the XLNet models {--} despite the fact that the XLNet performs best on this challenging task. Our results suggest that different architectures seem to learn rather different neural attention strategies and similarity of neural to human attention does not guarantee best performance.",
}
@inproceedings{kogkalidis-etal-2020-neural,
    title = "Neural Proof Nets",
    author = "Kogkalidis, Konstantinos  and
      Moortgat, Michael  and
      Moot, Richard",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.3",
    doi = "10.18653/v1/2020.conll-1.3",
    pages = "26--40",
    abstract = "Linear logic and the linear λ-calculus have a long standing tradition in the study of natural language form and meaning. Among the proof calculi of linear logic, proof nets are of particular interest, offering an attractive geometric representation of derivations that is unburdened by the bureaucratic complications of conventional prooftheoretic formats. Building on recent advances in set-theoretic learning, we propose a neural variant of proof nets based on Sinkhorn networks, which allows us to translate parsing as the problem of extracting syntactic primitives and permuting them into alignment. Our methodology induces a batch-efficient, end-to-end differentiable architecture that actualizes a formally grounded yet highly efficient neuro-symbolic parser. We test our approach on {\AE}Thel, a dataset of type-logical derivations for written Dutch, where it manages to correctly transcribe raw text sentences into proofs and terms of the linear λ-calculus with an accuracy of as high as 70{\%}.",
}
@inproceedings{joshi-etal-2020-taxinli,
    title = "{T}axi{NLI}: Taking a Ride up the {NLU} Hill",
    author = "Joshi, Pratik  and
      Aditya, Somak  and
      Sathe, Aalok  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.4",
    doi = "10.18653/v1/2020.conll-1.4",
    pages = "41--55",
    abstract = "Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization. To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task. We introduce TaxiNLI, a new dataset, that has 10k examples from the MNLI dataset with these taxonomic labels. Through various experiments on TaxiNLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies{---}a large jump over the previous models{---}some categories still remain difficult. Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.",
}
@inproceedings{kreiss-etal-2020-modeling,
    title = "Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives",
    author = "Kreiss, Elisa  and
      Wang, Zijian  and
      Potts, Christopher",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.5",
    doi = "10.18653/v1/2020.conll-1.5",
    pages = "56--68",
    abstract = "Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments. We use SuspectGuilt to train and assess predictive models which validate the usefulness of the corpus, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the societal effects of crime reporting.",
}
@inproceedings{anderson-gomez-rodriguez-2020-frailty,
    title = "On the Frailty of Universal {POS} Tags for Neural {UD} Parsers",
    author = "Anderson, Mark  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.6",
    doi = "10.18653/v1/2020.conll-1.6",
    pages = "69--96",
    abstract = "We present an analysis on the effect UPOS accuracy has on parsing performance. Results suggest that leveraging UPOS tags as fea-tures for neural parsers requires a prohibitively high tagging accuracy and that the use of gold tags offers a non-linear increase in performance, suggesting some sort of exceptionality. We also investigate what aspects of predicted UPOS tags impact parsing accuracy the most, highlighting some potentially meaningful linguistic facets of the problem.",
}
@inproceedings{choshen-etal-2020-classifying,
    title = "Classifying Syntactic Errors in Learner Language",
    author = "Choshen, Leshem  and
      Nikolaev, Dmitry  and
      Berzak, Yevgeni  and
      Abend, Omri",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.7",
    doi = "10.18653/v1/2020.conll-1.7",
    pages = "97--107",
    abstract = "We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems. Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian. We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.",
}
@inproceedings{eger-etal-2020-probe,
    title = "How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation",
    author = "Eger, Steffen  and
      Daxenberger, Johannes  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.8",
    doi = "10.18653/v1/2020.conll-1.8",
    pages = "108--118",
    abstract = "Sentence encoders map sentences to real valued vectors for use in downstream applications. To peek into these representations{---}e.g., to increase interpretability of their results{---}probing tasks have been designed which query them for linguistic knowledge. However, designing probing tasks for lesser-resourced languages is tricky, because these often lack largescale annotated data or (high-quality) dependency parsers as a prerequisite of probing task design in English. To investigate how to probe sentence embeddings in such cases, we investigate sensitivity of probing task results to structural design choices, conducting the first such large scale study. We show that design choices like size of the annotated probing dataset and type of classifier used for evaluation do (sometimes substantially) influence probing outcomes. We then probe embeddings in a multilingual setup with design choices that lie in a {`}stable region{'}, as we identify for English, and find that results on English do not transfer to other languages. Fairer and more comprehensive sentence-level probing evaluation should thus be carried out on multiple languages in the future.",
}
@inproceedings{chiang-etal-2020-understanding,
    title = "Understanding the Source of Semantic Regularities in Word Embeddings",
    author = "Chiang, Hsiao-Yu  and
      Camacho-Collados, Jose  and
      Pardos, Zachary",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.9",
    doi = "10.18653/v1/2020.conll-1.9",
    pages = "119--131",
    abstract = "Semantic relations are core to how humans understand and express concepts in the real world using language. Recently, there has been a thread of research aimed at modeling these relations by learning vector representations from text corpora. Most of these approaches focus strictly on leveraging the co-occurrences of relationship word pairs within sentences. In this paper, we investigate the hypothesis that examples of a lexical relation in a corpus are fundamental to a neural word embedding{'}s ability to complete analogies involving the relation. Our experiments, in which we remove all known examples of a relation from training corpora, show only marginal degradation in analogy completion performance involving the removed relation. This finding enhances our understanding of neural word embeddings, showing that co-occurrence information of a particular semantic relation is the not the main source of their structural regularity.",
}
@inproceedings{khullar-etal-2020-finding,
    title = "Finding The Right One and Resolving it",
    author = "Khullar, Payal  and
      Bhattacharya, Arghya  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.10",
    doi = "10.18653/v1/2020.conll-1.10",
    pages = "132--141",
    abstract = "One-anaphora has figured prominently in theoretical linguistic literature, but computational linguistics research on the phenomenon is sparse. Not only that, the long standing linguistic controversy between the determinative and the nominal anaphoric element one has propagated in the limited body of computational work on one-anaphora resolution, making this task harder than it is. In the present paper, we resolve this by drawing from an adequate linguistic analysis of the word one in different syntactic environments - once again highlighting the significance of linguistic theory in Natural Language Processing (NLP) tasks. We prepare an annotated corpus marking actual instances of one-anaphora with their textual antecedents, and use the annotations to experiment with state-of-the art neural models for one-anaphora resolution. Apart from presenting a strong neural baseline for this task, we contribute a gold-standard corpus, which is, to the best of our knowledge, the biggest resource on one-anaphora till date.",
}
@inproceedings{malmaud-etal-2020-bridging,
    title = "Bridging Information-Seeking Human Gaze and Machine Reading Comprehension",
    author = "Malmaud, Jonathan  and
      Levy, Roger  and
      Berzak, Yevgeni",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.11",
    doi = "10.18653/v1/2020.conll-1.11",
    pages = "142--152",
    abstract = "In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension. To this end, we collect a new eye-tracking dataset with a large number of participants engaging in a multiple choice reading comprehension task. Our analysis of this data reveals increased fixation times over parts of the text that are most relevant for answering the question. Motivated by this finding, we propose making automated reading comprehension more human-like by mimicking human information-seeking reading behavior during reading comprehension. We demonstrate that this approach leads to performance gains on multiple choice question answering in English for a state-of-the-art reading comprehension model.",
}
@inproceedings{chen-etal-2020-corpus,
    title = "A Corpus of Very Short Scientific Summaries",
    author = "Chen, Yifan  and
      Polajnar, Tamara  and
      Batchelor, Colin  and
      Teufel, Simone",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.12",
    doi = "10.18653/v1/2020.conll-1.12",
    pages = "153--164",
    abstract = "We present a new summarisation task, taking scientific articles and producing journal table-of-contents entries in the chemistry domain. These are one- or two-sentence author-written summaries that present the key findings of a paper. This is a first look at this summarisation task with an open access publication corpus consisting of titles and abstracts, as input texts, and short author-written advertising blurbs, as the ground truth. We introduce the dataset and evaluate it with state-of-the-art summarisation methods.",
}
@inproceedings{pannitto-herbelot-2020-recurrent,
    title = "Recurrent babbling: evaluating the acquisition of grammar from limited input data",
    author = "Pannitto, Ludovica  and
      Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.13",
    doi = "10.18653/v1/2020.conll-1.13",
    pages = "165--176",
    abstract = "Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input. In most previous experiments, however, learning happens over unrealistic corpora, which do not reflect the type and amount of data a child would be exposed to. This paper remedies this state of affairs by training an LSTM over a realistically sized subset of child-directed input. The behaviour of the network is analysed over time using a novel methodology which consists in quantifying the level of grammatical abstraction in the model{'}s generated output (its {`}babbling{'}), compared to the language it has been exposed to. We show that the LSTM indeed abstracts new structures as learning proceeds.",
}
@inproceedings{van-arkel-etal-2020-simple,
    title = "A simple repair mechanism can alleviate computational demands of pragmatic reasoning: simulations and complexity analysis",
    author = "van Arkel, Jacqueline  and
      Woensdregt, Marieke  and
      Dingemanse, Mark  and
      Blokpoel, Mark",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.14",
    doi = "10.18653/v1/2020.conll-1.14",
    pages = "177--194",
    abstract = "How can people communicate successfully while keeping resource costs low in the face of ambiguity? We present a principled theoretical analysis comparing two strategies for disambiguation in communication: (i) pragmatic reasoning, where communicators reason about each other, and (ii) other-initiated repair, where communicators signal and resolve trouble interactively. Using agent-based simulations and computational complexity analyses, we compare the efficiency of these strategies in terms of communicative success, computation cost and interaction cost. We show that agents with a simple repair mechanism can increase efficiency, compared to pragmatic agents, by reducing their computational burden at the cost of longer interactions. We also find that efficiency is highly contingent on the mechanism, highlighting the importance of explicit formalisation and computational rigour.",
}
@inproceedings{shain-elsner-2020-acquiring,
    title = "Acquiring language from speech by learning to remember and predict",
    author = "Shain, Cory  and
      Elsner, Micha",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.15",
    doi = "10.18653/v1/2020.conll-1.15",
    pages = "195--214",
    abstract = "Classical accounts of child language learning invoke memory limits as a pressure to discover sparse, language-like representations of speech, while more recent proposals stress the importance of prediction for language learning. In this study, we propose a broad-coverage unsupervised neural network model to test memory and prediction as sources of signal by which children might acquire language directly from the perceptual stream. Our model embodies several likely properties of real-time human cognition: it is strictly incremental, it encodes speech into hierarchically organized labeled segments, it allows interactive top-down and bottom-up information flow, it attempts to model its own sequence of latent representations, and its objective function only recruits local signals that are plausibly supported by human working memory capacity. We show that much phonemic structure is learnable from unlabeled speech on the basis of these local signals. We further show that remembering the past and predicting the future both contribute to the linguistic content of acquired representations, and that these contributions are at least partially complementary.",
}
@inproceedings{reiss-etal-2020-identifying,
    title = "Identifying Incorrect Labels in the {C}o{NLL}-2003 Corpus",
    author = "Reiss, Frederick  and
      Xu, Hong  and
      Cutler, Bryan  and
      Muthuraman, Karthik  and
      Eichenberger, Zachary",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.16",
    doi = "10.18653/v1/2020.conll-1.16",
    pages = "215--226",
    abstract = "The CoNLL-2003 corpus for English-language named entity recognition (NER) is one of the most influential corpora for NER model research. A large number of publications, including many landmark works, have used this corpus as a source of ground truth for NER tasks. In this paper, we examine this corpus and identify over 1300 incorrect labels (out of 35089 in the corpus). In particular, the number of incorrect labels in the test fold is comparable to the number of errors that state-of-the-art models make when running inference over this corpus. We describe the process by which we identified these incorrect labels, using novel variants of techniques from semi-supervised learning. We also summarize the types of errors that we found, and we revisit several recent results in NER in light of the corrected data. Finally, we show experimentally that our corrections to the corpus have a positive impact on three state-of-the-art models.",
}
@inproceedings{chronis-erk-2020-bishop,
    title = "When is a bishop not like a rook? When it{'}s like a rabbi! Multi-prototype {BERT} embeddings for estimating semantic relationships",
    author = "Chronis, Gabriella  and
      Erk, Katrin",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.17",
    doi = "10.18653/v1/2020.conll-1.17",
    pages = "227--244",
    abstract = "This paper investigates contextual language models, which produce token representations, as a resource for lexical semantics at the word or type level. We construct multi-prototype word embeddings from bert-base-uncased (Devlin et al., 2018). These embeddings retain contextual knowledge that is critical for some type-level tasks, while being less cumbersome and less subject to outlier effects than exemplar models. Similarity and relatedness estimation, both type-level tasks, benefit from this contextual knowledge, indicating the context-sensitivity of these processes. BERT{'}s token level knowledge also allows the testing of a type-level hypothesis about lexical abstractness, demonstrating the relationship between token-level phenomena and type-level concreteness ratings. Our findings provide important insight into the interpretability of BERT: layer 7 approximates semantic similarity, while the final layer (11) approximates relatedness.",
}
@inproceedings{gonering-morgan-2020-processing,
    title = "Processing effort is a poor predictor of cross-linguistic word order frequency",
    author = "Gonering, Brennan  and
      Morgan, Emily",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.18",
    doi = "10.18653/v1/2020.conll-1.18",
    pages = "245--255",
    abstract = "Some have argued that word orders which are more difficult to process should be rarer cross-linguistically. Our current study fails to replicate the results of Maurits, Navarro, and Perfors (2010), who used an entropy-based Uniform Information Density (UID) measure to moderately predict the Greenbergian typology of transitive word orders. We additionally report an inability of three measures of processing difficulty {---} entropy-based UID, surprisal-based UID, and pointwise mutual information {---} to correctly predict the correct typological distribution, using transitive constructions from 20 languages in the Universal Dependencies project (version 2.5). However, our conclusions are limited by data sparsity.",
}
@inproceedings{popovic-2020-relations,
    title = "Relations between comprehensibility and adequacy errors in machine translation output",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.19",
    doi = "10.18653/v1/2020.conll-1.19",
    pages = "256--264",
    abstract = "This work presents a detailed analysis of translation errors perceived by readers as comprehensibility and/or adequacy issues. The main finding is that good comprehensibility, similarly to good fluency, can mask a number of adequacy errors. Of all major adequacy errors, 30{\%} were fully comprehensible, thus fully misleading the reader to accept the incorrect information. Another 25{\%} of major adequacy errors were perceived as almost comprehensible, thus being potentially misleading. Also, a vast majority of omissions (about 70{\%}) is hidden by comprehensibility. Further analysis of misleading translations revealed that the most frequent error types are ambiguity, mistranslation, noun phrase error, word-by-word translation, untranslated word, subject-verb agreement, and spelling error in the source text. However, none of these error types appears exclusively in misleading translations, but are also frequent in fully incorrect (incomprehensible inadequate) and discarded correct (incomprehensible adequate) translations. Deeper analysis is needed to potentially detect underlying phenomena specifically related to misleading translations.",
}
@inproceedings{veeman-etal-2020-cross,
    title = "Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment",
    author = "Veeman, Hartger  and
      Allassonni{\`e}re-Tang, Marc  and
      Berdicevskis, Aleksandrs  and
      Basirat, Ali",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.20",
    doi = "10.18653/v1/2020.conll-1.20",
    pages = "265--275",
    abstract = "Grammatical gender is assigned to nouns differently in different languages. Are all factors that influence gender assignment idiosyncratic to languages or are there any that are universal? Using cross-lingual aligned word embeddings, we perform two experiments to address these questions about language typology and human cognition. In both experiments, we predict the gender of nouns in language X using a classifier trained on the nouns of language Y, and take the classifier{'}s accuracy as a measure of transferability of gender systems. First, we show that for 22 Indo-European languages the transferability decreases as the phylogenetic distance increases. This correlation supports the claim that some gender assignment factors are idiosyncratic, and as the languages diverge, the proportion of shared inherited idiosyncrasies diminishes. Second, we show that when the classifier is trained on two Afro-Asiatic languages and tested on the same 22 Indo-European languages (or vice versa), its performance is still significantly above the chance baseline, thus showing that universal factors exist and, moreover, can be captured by word embeddings. When the classifier is tested across families and on inanimate nouns only, the performance is still above baseline, indicating that the universal factors are not limited to biological sex.",
}
@inproceedings{meyer-lewis-2020-modelling,
    title = "Modelling Lexical Ambiguity with Density Matrices",
    author = "Meyer, Francois  and
      Lewis, Martha",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.21",
    doi = "10.18653/v1/2020.conll-1.21",
    pages = "276--290",
    abstract = "Words can have multiple senses. Compositional distributional models of meaning have been argued to deal well with finer shades of meaning variation known as polysemy, but are not so well equipped to handle word senses that are etymologically unrelated, or homonymy. Moving from vectors to density matrices allows us to encode a probability distribution over different senses of a word, and can also be accommodated within a compositional distributional model of meaning. In this paper we present three new neural models for learning density matrices from a corpus, and test their ability to discriminate between word senses on a range of compositional datasets. When paired with a particular composition method, our best model outperforms existing vector-based compositional models as well as strong sentence encoders.",
}
@inproceedings{havard-etal-2020-catplayinginthesnow,
    title = "Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech",
    author = "Havard, William  and
      Besacier, Laurent  and
      Chevrot, Jean-Pierre",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.22",
    doi = "10.18653/v1/2020.conll-1.22",
    pages = "291--301",
    abstract = "The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units. This suggests that the ideal way of learning a language is by starting from full semantic units. In this paper, we investigate if this is also the case for a neural model of Visually Grounded Speech trained on a speech-image retrieval task. We evaluated how well such a network is able to learn a reliable speech-to-image mapping when provided with phone, syllable, or word boundary information. We present a simple way to introduce such information into an RNN-based model and investigate which type of boundary is the most efficient. We also explore at which level of the network{'}s architecture such information should be introduced so as to maximise its performances. Finally, we show that using multiple boundary types at once in a hierarchical structure, by which low-level segments are used to recompose high-level segments, is beneficial and yields better results than using low-level or high-level segments in isolation.",
}
@inproceedings{grujicic-etal-2020-learning,
    title = "Learning to ground medical text in a 3{D} human atlas",
    author = "Grujicic, Dusan  and
      Radevski, Gorjan  and
      Tuytelaars, Tinne  and
      Blaschko, Matthew",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.23",
    doi = "10.18653/v1/2020.conll-1.23",
    pages = "302--312",
    abstract = "In this paper, we develop a method for grounding medical text into a physically meaningful and interpretable space corresponding to a human atlas. We build on text embedding architectures such as Bert and introduce a loss function that allows us to reason about the semantic and spatial relatedness of medical texts by learning a projection of the embedding into a 3D space representing the human body. We quantitatively and qualitatively demonstrate that our proposed method learns a context sensitive and spatially aware mapping, in both the inter-organ and intra-organ sense, using a large scale medical text dataset from the {``}Large-scale online biomedical semantic indexing{''} track of the 2020 BioASQ challenge. We extend our approach to a self-supervised setting, and find it to be competitive with a classification based method, and a fully supervised variant of approach.",
}
@inproceedings{wijnholds-etal-2020-representation,
    title = "Representation Learning for Type-Driven Composition",
    author = "Wijnholds, Gijs  and
      Sadrzadeh, Mehrnoosh  and
      Clark, Stephen",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.24",
    doi = "10.18653/v1/2020.conll-1.24",
    pages = "313--324",
    abstract = "This paper is about learning word representations using grammatical type information. We use the syntactic types of Combinatory Categorial Grammar to develop multilinear representations, i.e. maps with n arguments, for words with different functional types. The multilinear maps of words compose with each other to form sentence representations. We extend the skipgram algorithm from vectors to multi- linear maps to learn these representations and instantiate it on unary and binary maps for transitive verbs. These are evaluated on verb and sentence similarity and disambiguation tasks and a subset of the SICK relatedness dataset. Our model performs better than previous type- driven models and is competitive with state of the art representation learning methods such as BERT and neural sentence encoders.",
}
@inproceedings{couillet-etal-2020-word,
    title = "Word Representations Concentrate and This is Good News!",
    author = "Couillet, Romain  and
      Cinar, Yagmur Gizem  and
      Gaussier, Eric  and
      Imran, Muhammad",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.25",
    doi = "10.18653/v1/2020.conll-1.25",
    pages = "325--334",
    abstract = "This article establishes that, unlike the legacy tf*idf representation, recent natural language representations (word embedding vectors) tend to exhibit a so-called \textit{concentration of measure phenomenon}, in the sense that, as the representation size $p$ and database size $n$ are both large, their behavior is similar to that of large dimensional Gaussian random vectors. This phenomenon may have important consequences as machine learning algorithms for natural language data could be amenable to improvement, thereby providing new theoretical insights into the field of natural language processing.",
}
@inproceedings{rita-etal-2020-lazimpa,
    title = "{``}{L}az{I}mpa{''}: Lazy and Impatient neural agents learn to communicate efficiently",
    author = "Rita, Mathieu  and
      Chaabouni, Rahma  and
      Dupoux, Emmanuel",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.26",
    doi = "10.18653/v1/2020.conll-1.26",
    pages = "335--343",
    abstract = "Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a speaker and a listener neural networks optimizing accurate transmission over a discrete channel, the emergent messages fail to achieve an optimal length. Furthermore, frequent messages tend to be longer than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA) observed in all natural languages. Here, we show that near-optimal and ZLA-compatible messages can emerge, but only if both the speaker and the listener are modified. We hence introduce a new communication system, {``}LazImpa{''}, where the speaker is made increasingly lazy, i.e., avoids long messages, and the listener impatient, i.e., seeks to guess the intended content as soon as possible.",
}
@inproceedings{herbelot-2020-solve,
    title = "Re-solve it: simulating the acquisition of core semantic competences from small data",
    author = "Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.27",
    doi = "10.18653/v1/2020.conll-1.27",
    pages = "344--354",
    abstract = "Many tasks are considered to be {`}solved{'} in the computational linguistics literature, but the corresponding algorithms operate in ways which are radically different from human cognition. I illustrate this by coming back to the notion of semantic competence, which includes basic linguistic skills encompassing both referential phenomena and generic knowledge, in particular a) the ability to denote, b) the mastery of the lexicon, or c) the ability to model one{'}s language use on others. Even though each of those faculties has been extensively tested individually, there is still no computational model that would account for their joint acquisition under the conditions experienced by a human. In this paper, I focus on one particular aspect of this problem: the amount of linguistic data available to the child or machine. I show that given the first competence mentioned above (a denotation function), the other two can in fact be learned from very limited data (2.8M token), reaching state-of-the-art performance. I argue that both the nature of the data and the way it is presented to the system matter to acquisition.",
}
@inproceedings{brasoveanu-etal-2020-media,
    title = "In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works",
    author = "Brasoveanu, Adrian M.P.  and
      Weichselbraun, Albert  and
      Nixon, Lyndon",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.28",
    doi = "10.18653/v1/2020.conll-1.28",
    pages = "355--364",
    abstract = "Annotation styles express guidelines that direct human annotators in what rules to follow when creating gold standard annotations of text corpora. These guidelines not only shape the gold standards they help create, but also influence the training and evaluation of Named Entity Linking (NEL) tools, since different annotation styles correspond to divergent views on the entities present in the same texts. Such divergence is particularly present in texts from the media domain that contain references to creative works. In this work we present a corpus of 1000 annotated documents selected from the media domain. Each document is presented with multiple gold standard annotations representing various annotation styles. This corpus is used to evaluate a series of Named Entity Linking tools in order to understand the impact of the differences in annotation styles on the reported accuracy when processing highly ambiguous entities such as names of creative works. Relaxed annotation guidelines that include overlap styles lead to better results across all tools.",
}
@inproceedings{fournier-etal-2020-analogies,
    title = "Analogies minus analogy test: measuring regularities in word embeddings",
    author = "Fournier, Louis  and
      Dupoux, Emmanuel  and
      Dunbar, Ewan",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.29",
    doi = "10.18653/v1/2020.conll-1.29",
    pages = "365--375",
    abstract = "Vector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim. We decompose and empirically analyze the classic arithmetic word analogy test, to motivate two new metrics that address the issues with the standard test, and which distinguish between class-wise offset concentration (similar directions between pairs of words drawn from different broad classes, such as France-London, China-Ottawa,...) and pairing consistency (the existence of a regular transformation between correctly-matched pairs such as France:Paris::China:Beijing). We show that, while the standard analogy test is flawed, several popular word embeddings do nevertheless encode linguistic regularities.",
}
@inproceedings{a-rodriguez-merlo-2020-word,
    title = "Word associations and the distance properties of context-aware word embeddings",
    author = "A. Rodriguez, Maria  and
      Merlo, Paola",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.30",
    doi = "10.18653/v1/2020.conll-1.30",
    pages = "376--385",
    abstract = "What do people know when they know the meaning of words? Word associations have been widely used to tap into lexical repre- sentations and their structure, as a way of probing semantic knowledge in humans. We investigate whether current word embedding spaces (contextualized and uncontextualized) can be considered good models of human lexi- cal knowledge by studying whether they have comparable characteristics to human associa- tion spaces. We study the three properties of association rank, asymmetry of similarity and triangle inequality. We find that word embeddings are good mod- els of some word associations properties. They replicate well human associations between words, and, like humans, their context-aware variants show violations of the triangle in- equality. While they do show asymmetry of similarities, their asymmetries do not map those of human association norms.",
}
@inproceedings{kartal-kutlu-2020-trclaim,
    title = "{T}r{C}laim-19: The First Collection for {T}urkish Check-Worthy Claim Detection with Annotator Rationales",
    author = "Kartal, Yavuz Selim  and
      Kutlu, Mucahid",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.31",
    doi = "10.18653/v1/2020.conll-1.31",
    pages = "386--395",
    abstract = "Massive misinformation spread over Internet has many negative impacts on our lives. While spreading a claim is easy, investigating its veracity is hard and time consuming, Therefore, we urgently need systems to help human fact-checkers. However, available data resources to develop effective systems are limited and the vast majority of them is for English. In this work, we introduce TrClaim-19, which is the very first labeled dataset for Turkish check-worthy claims. TrClaim-19 consists of labeled 2287 Turkish tweets with annotator rationales, enabling us to better understand the characteristics of check-worthy claims. The rationales we collected suggest that claims{'} topics and their possible negative impacts are the main factors affecting their check-worthiness.",
}
@inproceedings{davis-van-schijndel-2020-discourse,
    title = "Discourse structure interacts with reference but not syntax in neural language models",
    author = "Davis, Forrest  and
      van Schijndel, Marten",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.32",
    doi = "10.18653/v1/2020.conll-1.32",
    pages = "396--407",
    abstract = "Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability of LMs to learn interactions between different linguistic representations. In particular, we utilized stimuli from psycholinguistic studies showing that humans can condition reference (i.e. coreference resolution) and syntactic processing on the same discourse structure (implicit causality). We compared both transformer and long short-term memory LMs to find that, contrary to humans, implicit causality only influences LM behavior for reference, not syntax, despite model representations that encode the necessary discourse information. Our results further suggest that LM behavior can contradict not only learned representations of discourse but also syntactic agreement, pointing to shortcomings of standard language modeling.",
}
@inproceedings{hawkins-etal-2020-continual,
    title = "Continual Adaptation for Efficient Machine Communication",
    author = "Hawkins, Robert  and
      Kwon, Minae  and
      Sadigh, Dorsa  and
      Goodman, Noah",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.33",
    doi = "10.18653/v1/2020.conll-1.33",
    pages = "408--419",
    abstract = "To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent neural language models are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do. We introduce an interactive repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently communicate with a partner over time. We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.",
}
@inproceedings{hong-etal-2020-diverse,
    title = "Diverse and Relevant Visual Storytelling with Scene Graph Embeddings",
    author = "Hong, Xudong  and
      Shetty, Rakshith  and
      Sayeed, Asad  and
      Mehra, Khushboo  and
      Demberg, Vera  and
      Schiele, Bernt",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.34",
    doi = "10.18653/v1/2020.conll-1.34",
    pages = "420--430",
    abstract = "A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images. Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work. We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems. Our experiments also indicate that our models obtain competitive results on reference-based metrics.",
}
@inproceedings{boros-etal-2020-alleviating,
    title = "Alleviating Digitization Errors in Named Entity Recognition for Historical Documents",
    author = "Boros, Emanuela  and
      Hamdi, Ahmed  and
      Linhares Pontes, Elvys  and
      Cabrera-Diego, Luis Adri{\'a}n  and
      Moreno, Jose G.  and
      Sidere, Nicolas  and
      Doucet, Antoine",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.35",
    doi = "10.18653/v1/2020.conll-1.35",
    pages = "431--441",
    abstract = "This paper tackles the task of named entity recognition (NER) applied to digitized historical texts obtained from processing digital images of newspapers using optical character recognition (OCR) techniques. We argue that the main challenge for this task is that the OCR process leads to misspellings and linguistic errors in the output text. Moreover, historical variations can be present in aged documents, which can impact the performance of the NER process. We conduct a comparative evaluation on two historical datasets in German and French against previous state-of-the-art models, and we propose a model based on a hierarchical stack of Transformers to approach the NER task for historical data. Our findings show that the proposed model clearly improves the results on both historical datasets, and does not degrade the results for modern datasets.",
}
@inproceedings{derby-etal-2020-analysing,
    title = "Analysing Word Representation from the Input and Output Embeddings in Neural Network Language Models",
    author = "Derby, Steven  and
      Miller, Paul  and
      Devereux, Barry",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.36",
    doi = "10.18653/v1/2020.conll-1.36",
    pages = "442--454",
    abstract = "Researchers have recently demonstrated that tying the neural weights between the input look-up table and the output classification layer can improve training and lower perplexity on sequence learning tasks such as language modelling. Such a procedure is possible due to the design of the softmax classification layer, which previous work has shown to comprise a viable set of semantic representations for the model vocabulary, and these these output embeddings are known to perform well on word similarity benchmarks. In this paper, we make meaningful comparisons between the input and output embeddings and other SOTA distributional models to gain a better understanding of the types of information they represent. We also construct a new set of word embeddings using the output embeddings to create locally-optimal approximations for the intermediate representations from the language model. These locally-optimal embeddings demonstrate excellent performance across all our evaluations.",
}
@inproceedings{bhattamishra-etal-2020-computational,
    title = "On the Computational Power of Transformers and Its Implications in Sequence Modeling",
    author = "Bhattamishra, Satwik  and
      Patel, Arkil  and
      Goyal, Navin",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.37",
    doi = "10.18653/v1/2020.conll-1.37",
    pages = "455--475",
    abstract = "Transformers are being used extensively across several sequence modeling tasks. Significant research effort has been devoted to experimentally probe the inner workings of Transformers. However, our conceptual and theoretical understanding of their power and inherent limitations is still nascent. In particular, the roles of various components in Transformers such as positional encodings, attention heads, residual connections, and feedforward networks, are not clear. In this paper, we take a step towards answering these questions. We analyze the computational power as captured by Turing-completeness. We first provide an alternate and simpler proof to show that vanilla Transformers are Turing-complete and then we prove that Transformers with only positional masking and without any positional encoding are also Turing-complete. We further analyze the necessity of each component for the Turing-completeness of the network; interestingly, we find that a particular type of residual connection is necessary. We demonstrate the practical implications of our results via experiments on machine translation and synthetic tasks.",
}
@inproceedings{macsween-caines-2020-expectation,
    title = "An Expectation Maximisation Algorithm for Automated Cognate Detection",
    author = "MacSween, Roddy  and
      Caines, Andrew",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.38",
    doi = "10.18653/v1/2020.conll-1.38",
    pages = "476--485",
    abstract = "In historical linguistics, cognate detection is the task of determining whether sets of words have common etymological roots. Inspired by the comparative method used by human linguists, we develop a system for automated cognate detection that frames the task as an inference problem for a general statistical model consisting of observed data (potentially cognate pairs of words), latent variables (the cognacy status of pairs) and unknown global parameters (which sounds correspond between languages). We then give a specific instance of such a model along with an expectation-maximisation algorithm to infer its parameters. We evaluate our system on a corpus of 8140 cognate sets, finding the performance of our method to be comparable to the state of the art. We additionally carry out qualitative analysis demonstrating advantages it has over existing systems. We also suggest several ways our work could be extended within the general theoretical framework we propose.",
}
@inproceedings{bhattacharya-van-schijndel-2020-filler,
    title = "Filler-gaps that neural networks fail to generalize",
    author = "Bhattacharya, Debasmita  and
      van Schijndel, Marten",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.39",
    doi = "10.18653/v1/2020.conll-1.39",
    pages = "486--495",
    abstract = "It can be difficult to separate abstract linguistic knowledge in recurrent neural networks (RNNs) from surface heuristics. In this work, we probe for highly abstract syntactic constraints that have been claimed to govern the behavior of filler-gap dependencies across different surface constructions. For models to generalize abstract patterns in expected ways to unseen data, they must share representational features in predictable ways. We use cumulative priming to test for representational overlap between disparate filler-gap constructions in English and find evidence that the models learn a general representation for the existence of filler-gap dependencies. However, we find no evidence that the models learn any of the shared underlying grammatical constraints we tested. Our work raises questions about the degree to which RNN language models learn abstract linguistic representations.",
}
@inproceedings{zhu-etal-2020-dont,
    title = "Don{'}t Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding",
    author = "Zhu, Qile  and
      Khan, Haidar  and
      Soltan, Saleh  and
      Rawls, Stephen  and
      Hamza, Wael",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.40",
    doi = "10.18653/v1/2020.conll-1.40",
    pages = "496--506",
    abstract = "Semantic parsing is one of the key components of natural language understanding systems. A successful parse transforms an input utterance to an action that is easily understood by the system. Many algorithms have been proposed to solve this problem, from conventional rule-based or statistical slot-filling systems to shift-reduce based neural parsers. For complex parsing tasks, the state-of-the-art method is based on an autoregressive sequence to sequence model that generates the parse directly. This model is slow at inference time, generating parses in O(n) decoding steps (n is the length of the target sequence). In addition, we demonstrate that this method performs poorly in zero-shot cross-lingual transfer learning settings. In this paper, we propose a non-autoregressive parser which is based on the insertion transformer to overcome these two issues. Our approach 1) speeds up decoding by 3x while outperforming the autoregressive model and 2) significantly improves cross-lingual transfer in the low-resource setting by 37{\%} compared to autoregressive baseline. We test our approach on three wellknown monolingual datasets: ATIS, SNIPS and TOP. For cross-lingual semantic parsing, we use the MultiATIS++ and the multilingual TOP datasets.",
}
@inproceedings{dusell-chiang-2020-learning,
    title = "Learning Context-free Languages with Nondeterministic Stack {RNN}s",
    author = "DuSell, Brian  and
      Chiang, David",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.41",
    doi = "10.18653/v1/2020.conll-1.41",
    pages = "507--519",
    abstract = "We present a differentiable stack data structure that simultaneously and tractably encodes an exponential number of stack configurations, based on Lang{'}s algorithm for simulating nondeterministic pushdown automata. We call the combination of this data structure with a recurrent neural network (RNN) controller a Nondeterministic Stack RNN. We compare our model against existing stack RNNs on various formal languages, demonstrating that our model converges more reliably to algorithmic behavior on deterministic tasks, and achieves lower cross-entropy on inherently nondeterministic tasks.",
}
@inproceedings{weber-etal-2020-generating,
    title = "Generating Narrative Text in a Switching Dynamical System",
    author = "Weber, Noah  and
      Shekhar, Leena  and
      Kwon, Heeyoung  and
      Balasubramanian, Niranjan  and
      Chambers, Nathanael",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.42",
    doi = "10.18653/v1/2020.conll-1.42",
    pages = "520--530",
    abstract = "Early work on narrative modeling used explicit plans and goals to generate stories, but the language generation itself was restricted and inflexible. Modern methods use language models for more robust generation, but often lack an explicit representation of the scaffolding and dynamics that guide a coherent narrative. This paper introduces a new model that integrates explicit narrative structure with neural language models, formalizing narrative modeling as a Switching Linear Dynamical System (SLDS). A SLDS is a dynamical system in which the latent dynamics of the system (i.e. how the state vector transforms over time) is controlled by top-level discrete switching variables. The switching variables represent narrative structure (e.g., sentiment or discourse states), while the latent state vector encodes information on the current state of the narrative. This probabilistic formulation allows us to control generation, and can be learned in a semi-supervised fashion using both labeled and unlabeled data. Additionally, we derive a Gibbs sampler for our model that can {``}fill in{''} arbitrary parts of the narrative, guided by the switching variables. Our filled-in (English language) narratives outperform several baselines on both automatic and human evaluations",
}
@inproceedings{chen-etal-2020-trying,
    title = "What Are You Trying to Do? Semantic Typing of Event Processes",
    author = "Chen, Muhao  and
      Zhang, Hongming  and
      Wang, Haoyu  and
      Roth, Dan",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.43",
    doi = "10.18653/v1/2020.conll-1.43",
    pages = "531--542",
    abstract = "This paper studies a new cognitively motivated semantic typing task,multi-axis event process typing, that, given anevent process, attempts to infer free-form typelabels describing (i) the type of action made bythe process and (ii) the type of object the pro-cess seeks to affect. This task is inspired bycomputational and cognitive studies of eventunderstanding, which suggest that understand-ing processes of events is often directed by rec-ognizing the goals, plans or intentions of theprotagonist(s). We develop a large dataset con-taining over 60k event processes, featuring ul-tra fine-grained typing on both the action andobject type axes with very large (10{\^{}}3∼10{\^{}}4)label vocabularies. We then propose a hybridlearning framework,P2GT, which addressesthe challenging typing problem with indirectsupervision from glosses1and a joint learning-to-rank framework. As our experiments indi-cate,P2GTsupports identifying the intent ofprocesses, as well as the fine semantic type ofthe affected object. It also demonstrates the ca-pability of handling few-shot cases, and stronggeneralizability on out-of-domain processes.",
}
@inproceedings{dellanzo-etal-2020-corpus,
    title = "A Corpus for Outbreak Detection of Diseases Prevalent in {L}atin {A}merica",
    author = "Dellanzo, Antonella  and
      Cotik, Viviana  and
      Ochoa-Luna, Jose",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.44",
    doi = "10.18653/v1/2020.conll-1.44",
    pages = "543--551",
    abstract = "In this paper we present an annotated corpus which can be used for training and testing algorithms to automatically extract information about diseases outbreaks from news and health reports. We also propose initial approaches to extract information from it. The corpus has been constructed with two main tasks in mind. The first one, to extract entities about outbreaks such as disease, host, location among others. The second one, to retrieve relations among entities, for instance, in such geographic location fifteen cases of a given disease were reported. Overall, our goal is to offer resources and tools to perform an automated analysis so as to support early detection of disease outbreaks and therefore diminish their spreading.",
}
@inproceedings{kassner-etal-2020-pretrained,
    title = "Are Pretrained Language Models Symbolic Reasoners over Knowledge?",
    author = {Kassner, Nora  and
      Krojer, Benno  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.45",
    doi = "10.18653/v1/2020.conll-1.45",
    pages = "552--564",
    abstract = "How can pretrained language models (PLMs) learn factual knowledge from the training set? We investigate the two most important mechanisms: reasoning and memorization. Prior work has attempted to quantify the number of facts PLMs learn, but we present, using synthetic data, the first study that investigates the causal relation between facts present in training and facts learned by the PLM. For reasoning, we show that PLMs seem to learn to apply some symbolic reasoning rules correctly but struggle with others, including two-hop reasoning. Further analysis suggests that even the application of learned reasoning rules is flawed. For memorization, we identify schema conformity (facts systematically supported by other facts) and frequency as key factors for its success.",
}
@inproceedings{parekh-etal-2020-understanding,
    title = "Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues",
    author = "Parekh, Tanmay  and
      Ahn, Emily  and
      Tsvetkov, Yulia  and
      Black, Alan W",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.46",
    doi = "10.18653/v1/2020.conll-1.46",
    pages = "565--577",
    abstract = "Code-switching is a ubiquitous phenomenon in multilingual communities. Natural language technologies that wish to communicate like humans must therefore adaptively incorporate code-switching techniques when they are deployed in multilingual settings. To this end, we propose a Hindi-English human-machine dialogue system that elicits code-switching conversations in a controlled setting. It uses different code-switching agent strategies to understand how users respond and accommodate to the agent{'}s language choice. Through this system, we collect and release a new dataset CommonDost, comprising of 439 human-machine multilingual conversations. We adapt pre-defined metrics to discover linguistic accommodation from users to agents. Finally, we compare these dialogues with Spanish-English dialogues collected in a similar setting, and analyze the impact of linguistic and socio-cultural factors on code-switching patterns across the two language pairs.",
}
@inproceedings{dhir-etal-2020-identifying,
    title = "Identifying robust markers of {P}arkinson{'}s disease in typing behaviour using a {CNN}-{LSTM} network",
    author = "Dhir, Neil  and
      Edman, Mathias  and
      Sanchez Ferro, {\'A}lvaro  and
      Stafford, Tom  and
      Bannard, Colin",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.47",
    doi = "10.18653/v1/2020.conll-1.47",
    pages = "578--595",
    abstract = "There is urgent need for non-intrusive tests that can detect early signs of Parkinson{'}s disease (PD), a debilitating neurodegenerative disorder that affects motor control. Recent promising research has focused on disease markers evident in the fine-motor behaviour of typing. Most work to date has focused solely on the timing of keypresses without reference to the linguistic content. In this paper we argue that the identity of the key combinations being produced should impact how they are handled by people with PD, and provide evidence that natural language processing methods can thus be of help in identifying signs of disease. We test the performance of a bi-directional LSTM with convolutional features in distinguishing people with PD from age-matched controls typing in English and Spanish, both in clinics and online.",
}
@inproceedings{liu-etal-2020-empirical,
    title = "An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference",
    author = "Liu, Tianyu  and
      Xin, Zheng  and
      Ding, Xiaoan  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.48",
    doi = "10.18653/v1/2020.conll-1.48",
    pages = "596--608",
    abstract = "The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus on the model-agnostic debiasing strategies and explore how to (or is it possible to) make the NLI models robust to multiple distinct adversarial attacks while keeping or even strengthening the models{'} generalization power. We firstly benchmark prevailing neural NLI models including pretrained ones on various adversarial datasets. We then try to combat distinct known biases by modifying a mixture of experts (MoE) ensemble method and show that it{'}s nontrivial to mitigate multiple NLI biases at the same time, and that model-level ensemble method outperforms MoE ensemble method. We also perform data augmentation including text swap, word substitution and paraphrase and prove its efficiency in combating various (though not all) adversarial attacks at the same time. Finally, we investigate several methods to merge heterogeneous training data (1.35M) and perform model ensembling, which are straightforward but effective to strengthen NLI models.",
}
@inproceedings{eisape-etal-2020-cloze,
    title = "Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction",
    author = "Eisape, Tiwalayo  and
      Zaslavsky, Noga  and
      Levy, Roger",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.49",
    doi = "10.18653/v1/2020.conll-1.49",
    pages = "609--619",
    abstract = "Contemporary autoregressive language models (LMs) trained purely on corpus data have been shown to capture numerous features of human incremental processing. However, past work has also suggested dissociations between corpus probabilities and human next-word predictions. Here we evaluate several state-of-the-art language models for their match to human next-word predictions and to reading time behavior from eye movements. We then propose a novel method for distilling the linguistic information implicit in human linguistic predictions into pre-trained LMs: Cloze Distillation. We apply this method to a baseline neural LM and show potential improvement in reading time prediction and generalization to held-out human cloze data.",
}
@inproceedings{cathcart-rama-2020-disentangling,
    title = "Disentangling dialects: a neural approach to {I}ndo-{A}ryan historical phonology and subgrouping",
    author = "Cathcart, Chundra  and
      Rama, Taraka",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.50",
    doi = "10.18653/v1/2020.conll-1.50",
    pages = "620--630",
    abstract = "This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings represent-ing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models{'} representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.",
}
@inproceedings{imashev-etal-2020-dataset,
    title = "A Dataset for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages: The K-{RSL}",
    author = "Imashev, Alfarabi  and
      Mukushev, Medet  and
      Kimmelman, Vadim  and
      Sandygulova, Anara",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.51",
    doi = "10.18653/v1/2020.conll-1.51",
    pages = "631--640",
    abstract = "The paper presents the first dataset that aims to serve interdisciplinary purposes for the utility of computer vision community and sign language linguistics. To date, a majority of Sign Language Recognition (SLR) approaches focus on recognising sign language as a manual gesture recognition problem. However, signers use other articulators: facial expressions, head and body position and movement to convey linguistic information. Given the important role of non-manual markers, this paper proposes a dataset and presents a use case to stress the importance of including non-manual features to improve the recognition accuracy of signs. To the best of our knowledge no prior publicly available dataset exists that explicitly focuses on non-manual components responsible for the grammar of sign languages. To this end, the proposed dataset contains 28250 videos of signs of high resolution and quality, with annotation of manual and non-manual components. We conducted a series of evaluations in order to investigate whether non-manual components would improve signs{'} recognition accuracy. We release the dataset to encourage SLR researchers and help advance current progress in this area toward real-time sign language interpretation. Our dataset will be made publicly available at https://krslproject.github.io/krsl-corpus",
}
@inproceedings{dwojak-etal-2020-dataset,
    title = "From Dataset Recycling to Multi-Property Extraction and Beyond",
    author = "Dwojak, Tomasz  and
      Pietruszka, Micha{\l}  and
      Borchmann, {\L}ukasz  and
      Ch{\l}{\k{e}}dowski, Jakub  and
      Grali{\'n}ski, Filip",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.52",
    doi = "10.18653/v1/2020.conll-1.52",
    pages = "641--651",
    abstract = "This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset. The proposed dual-source model outperforms the current state-of-the-art by a large margin. Next, we introduce WikiReading Recycled - a newly developed public dataset, and the task of multiple-property extraction. It uses the same data as WikiReading but does not inherit its predecessor{'}s identified disadvantages. In addition, we provide a human-annotated test set with diagnostic subsets for a detailed analysis of model performance.",
}
@inproceedings{michaelov-bergen-2020-well,
    title = "How well does surprisal explain N400 amplitude under different experimental conditions?",
    author = "Michaelov, James  and
      Bergen, Benjamin",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.conll-1.53",
    doi = "10.18653/v1/2020.conll-1.53",
    pages = "652--663",
    abstract = "We investigate the extent to which word surprisal can be used to predict a neural measure of human language processing difficulty{---}the N400. To do this, we use recurrent neural networks to calculate the surprisal of stimuli from previously published neurolinguistic studies of the N400. We find that surprisal can predict N400 amplitude in a wide range of cases, and the cases where it cannot do so provide valuable insight into the neurocognitive processes underlying the response.",
}
@proceedings{computerm-2020-international,
    title = "Proceedings of the 6th International Workshop on Computational Terminology",
    editor = "Daille, B{\'e}atrice  and
      Kageura, Kyo  and
      Terryn, Ayla Rigouts",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.0",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{drouin-etal-2020-automatic,
    title = "Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features",
    author = "Drouin, Patrick  and
      Morel, Jean-Beno{\^\i}t  and
      L{'} Homme, Marie-Claude",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.1",
    pages = "1--7",
    abstract = "The first step of any terminological work is to setup a reliable, specialized corpus composed of documents written by specialists and then to apply automatic term extraction (ATE) methods to this corpus in order to retrieve a first list of potential terms. In this paper, the experiment we describe differs quite drastically from this usual process since we are applying ATE to unspecialized corpora. The corpus used for this study was built from newspaper articles retrieved from the Web using a short list of keywords. The general intuition on which this research is based is that ATE based corpus comparison techniques can be used to capture both similarities and dissimilarities between corpora. The former are exploited through a termhood measure and the latter through word embeddings. Our initial results were validated manually and show that combining a traditional ATE method that focuses on dissimilarities between corpora to newer methods that exploit similarities (more specifically distributional features of candidates) leads to promising results.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{steingrimsson-etal-2020-termportal,
    title = "{T}erm{P}ortal: A Workbench for Automatic Term Extraction from {I}celandic Texts",
    author = "Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      {\TH}orbergsd{\'o}ttir, {\'A}g{\'u}sta  and
      Danielsson, Hjalti  and
      Ornolfsson, Gunnar Thor",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.2",
    pages = "8--16",
    abstract = "Automatic term extraction (ATE) from texts is critical for effective terminology work in small speech communities. We present TermPortal, a workbench for terminology work in Iceland, featuring the first ATE system for Icelandic. The tool facilitates standardization in terminology work in Iceland, as it exports data in standard formats in order to streamline gathering and distribution of the material. In the project we focus on the domain of finance in order to do be able to fulfill the needs of an important and large field. We present a comprehensive survey amongst the most prominent organizations in that field, the results of which emphasize the need for a good, up-to-date and accessible termbank and the willingness to use terms in Icelandic. Furthermore we present the ATE tool for Icelandic, which uses a variety of methods and shows great potential with a recall rate of up to 95{\%} and a high C-value, indicating that it competently finds term candidates that are important to the input text.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{quesada-zaragoza-etal-2020-translating,
    title = "Translating Knowledge Representations with Monolingual Word Embeddings: the Case of a Thesaurus on Corporate Non-Financial Reporting",
    author = "Quesada Zaragoza, Mart{\'\i}n  and
      Sep{\'u}lveda Torres, Lianet  and
      Basdevant, J{\'e}r{\^o}me",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.3",
    pages = "17--25",
    abstract = "A common method of structuring information extracted from textual data is using a knowledge model (e.g. a thesaurus) to organise the information semantically. Creating and managing a knowledge model is already a costly task in terms of human effort, not to mention making it multilingual. Multilingual knowledge modelling is a common problem for both transnational organisations and organisations providing text analytics that want to analyse information in more than one language. Many organisations tend to develop their language resources first in one language (often English). When it comes to analysing data sources in other languages, either a lot of effort has to be invested in recreating the same knowledge base in a different language or the data itself has to be translated into the language of the knowledge model. In this paper, we propose an unsupervised method to automatically induce a given thesaurus into another language using only comparable monolingual corpora. The aim of this proposal is to employ cross-lingual word embeddings to map the set of topics in an already-existing English thesaurus into Spanish. With this in mind, we describe different approaches to generate the Spanish thesaurus terms and offer an extrinsic evaluation by using the obtained thesaurus, which covers non-financial topics in a multi-label document classification task, and we compare the results across these approaches.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{brunet-etal-2020-dependency,
    title = "Which Dependency Parser to Use for Distributional Semantics in a Specialized Domain?",
    author = "Brunet, Pauline  and
      Ferret, Olivier  and
      Tanguy, Ludovic",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.4",
    pages = "26--36",
    abstract = "We present a study whose objective is to compare several dependency parsers for English applied to a specialized corpus for building distributional count-based models from syntactic dependencies. One of the particularities of this study is to focus on the concepts of the target domain, which mainly occur in documents as multi-terms and must be aligned with the outputs of the parsers. We compare a set of ten parsers in terms of syntactic triplets but also in terms of distributional neighbors extracted from the models built from these triplets, both with and without an external reference concerning the semantic relations between concepts. We show more particularly that some patterns of proximity between these parsers can be observed across our different evaluations, which could give insights for anticipating the performance of a parser for building distributional models from a given corpus",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{van-hautte-etal-2020-leveraging,
    title = "Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion",
    author = {Van Hautte, Jeroen  and
      Schelstraete, Vincent  and
      Wornoo, Mika{\"e}l},
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.5",
    pages = "37--42",
    abstract = "Machine learning plays an ever-bigger part in online recruitment, powering intelligent matchmaking and job recommendations across many of the world{'}s largest job platforms. However, the main text is rarely enough to fully understand a job posting: more often than not, much of the required information is condensed into the job title. Several organised efforts have been made to map job titles onto a hand-made knowledge base as to provide this information, but these only cover around 60{\%} of online vacancies. We introduce a novel, purely data-driven approach towards the detection of new job titles. Our method is conceptually simple, extremely efficient and competitive with traditional NER-based approaches. Although the standalone application of our method does not outperform a finetuned BERT model, it can be applied as a preprocessing step as well, substantially boosting accuracy across several architectures.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{estopa-etal-2020-terminology,
    title = "Terminology in Written Medical Reports: A Proposal of Text Enrichment to Favour its Comprehension by the Patient",
    author = "Estop{\`a}, Rosa  and
      L{\'o}pez-Fuentes, Alejandra  and
      Porras-Garzon, Jorge M.",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.6",
    pages = "43--49",
    abstract = "The empowerment of the population and the democratisation of information regarding healthcare have revealed that there is a communication gap between health professionals and patients. The latter are constantly receiving more and more written information about their healthcare visits and treatments, but that does not mean they understand it. In this paper we focus on the patient{'}s lack of comprehension of medical reports. After linguistically characterising the medical report, we present the results of a survey that showed that patients have serious comprehension difficulties concerning the medical reports they receive, specifically problems regarding the medical terminology used in these texts, specifically in Spanish and Catalan. To favour the understanding of medical reports, we propose an automatic text enrichment strategy that generates linguistically and cognitively enriched medical reports which are more comprehensible to the patient, and which focus on the parts of the medical report that most interest the patient: the diagnosis and treatment sections.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{wang-etal-2020-study,
    title = "A study of semantic projection from single word terms to multi-word terms in the environment domain",
    author = "Wang, Yizhe  and
      Daille, Beatrice  and
      Hathout, Nabil",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.7",
    pages = "50--54",
    abstract = "The semantic projection method is often used in terminology structuring to infer semantic relations between terms. Semantic projection relies upon the assumption of semantic compositionality: the relation that links simple term pairs remains valid in pairs of complex terms built from these simple terms. This paper proposes to investigate whether this assumption commonly adopted in natural language processing is actually valid. First, we describe the process of constructing a list of semantically linked multi-word terms (MWTs) related to the environmental field through the extraction of semantic variants. Second, we present our analysis of the results from the semantic projection. We find that contexts play an essential role in defining the relations between MWTs.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{pollak-etal-2020-netviz,
    title = "The {N}et{V}iz terminology visualization tool and the use cases in karstology domain modeling",
    author = "Pollak, Senja  and
      Podpe{\v{c}}an, Vid  and
      Miljkovic, Dragana  and
      Stepi{\v{s}}nik, Uro{\v{s}}  and
      Vintar, {\v{S}}pela",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.8",
    pages = "55--61",
    abstract = "We present the NetViz terminology visualization tool and apply it to the domain modeling of karstology, a subfield of geography studying karst phenomena. The developed tool allows for high-performance online network visualization where the user can upload the terminological data in a simple CSV format, define the nodes (terms, categories), edges (relations) and their properties (by assigning different node colors), and then edit and interactively explore domain knowledge in the form of a network. We showcase the usefulness of the tool on examples from the karstology domain, where in the first use case we visualize the domain knowledge as represented in a manually annotated corpus of domain definitions, while in the second use case we show the power of visualization for domain understanding by visualizing automatically extracted knowledge in the form of triplets extracted from the karstology domain corpus. The application is entirely web-based without any need for downloading or special configuration. The source code of the web application is also available under the permissive MIT license, allowing future extensions for developing new terminological applications.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{hazem-etal-2020-towards,
    title = "Towards Automatic Thesaurus Construction and Enrichment.",
    author = "Hazem, Amir  and
      Daille, Beatrice  and
      Claudia, Lanza",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.9",
    pages = "62--71",
    abstract = "Thesaurus construction with minimum human efforts often relies on automatic methods to discover terms and their relations. Hence, the quality of a thesaurus heavily depends on the chosen methodologies for: (i) building its content (terminology extraction task) and (ii) designing its structure (semantic similarity task). The performance of the existing methods on automatic thesaurus construction is still less accurate than the handcrafted ones of which is important to highlight the drawbacks to let new strategies build more accurate thesauri models. In this paper, we will provide a systematic analysis of existing methods for both tasks and discuss their feasibility based on an Italian Cybersecurity corpus. In particular, we will provide a detailed analysis on how the semantic relationships network of a thesaurus can be automatically built, and investigate the ways to enrich the terminological scope of a thesaurus by taking into account the information contained in external domain-oriented semantic sets.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{marciniak-etal-2020-supporting,
    title = "Supporting terminology extraction with dependency parses",
    author = "Marciniak, Malgorzata  and
      Rychlik, Piotr  and
      Mykowiecka, Agnieszka",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.10",
    pages = "72--79",
    abstract = "Terminology extraction procedure usually consists of selecting candidates for terms and ordering them according to their importance for the given text or set of texts. Depending on the method used, a list of candidates contains different fractions of grammatically incorrect, semantically odd and irrelevant sequences. The aim of this work was to improve term candidate selection by reducing the number of incorrect sequences using a dependency parser for Polish.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{giacomini-schafer-2020-computational,
    title = "Computational Aspects of Frame-based Meaning Representation in Terminology",
    author = {Giacomini, Laura  and
      Sch{\"a}fer, Johannes},
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.11",
    pages = "80--84",
    abstract = "Our contribution is part of a wider research project on term variation in German and concentrates on the computational aspects of a frame-based model for term meaning representation in the technical field. We focus on the role of frames (in the sense of Frame-Based Terminology) as the semantic interface between concepts covered by a domain ontology and domain-specific terminology. In particular, we describe methods for performing frame-based corpus annotation and frame-based term extraction. The aim of the contribution is to discuss the capacity of the model to automatically acquire semantic knowledge suitable for terminographic information tools such as specialised dictionaries, and its applicability to further specialised languages.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{rigouts-terryn-etal-2020-termeval,
    title = "{T}erm{E}val 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research ({ACTER}) Dataset",
    author = "Rigouts Terryn, Ayla  and
      Hoste, Veronique  and
      Drouin, Patrick  and
      Lefever, Els",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.12",
    pages = "85--94",
    abstract = "The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of \textit{heart failure} was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{hazem-etal-2020-termeval,
    title = "{T}erm{E}val 2020: {TALN}-{LS}2{N} System for Automatic Term Extraction",
    author = "Hazem, Amir  and
      Bouhandi, M{\'e}rieme  and
      Boudin, Florian  and
      Daille, Beatrice",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.13",
    pages = "95--100",
    abstract = "Automatic terminology extraction is a notoriously difficult task aiming to ease effort demanded to manually identify terms in domain-specific corpora by automatically providing a ranked list of candidate terms. The main ways that addressed this task can be ranged in four main categories: (i) rule-based approaches, (ii) feature-based approaches, (iii) context-based approaches, and (iv) hybrid approaches. For this first TermEval shared task, we explore a feature-based approach, and a deep neural network multitask approach -BERT- that we fine-tune for term extraction. We show that BERT models (RoBERTa for English and CamemBERT for French) outperform other systems for French and English languages.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{pais-ion-2020-termeval,
    title = "{T}erm{E}val 2020: {RACAI}{'}s automatic term extraction system",
    author = "Pais, Vasile  and
      Ion, Radu",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.14",
    pages = "101--105",
    abstract = "This paper describes RACAI{'}s automatic term extraction system, which participated in the TermEval 2020 shared task on English monolingual term extraction. We discuss the system architecture, some of the challenges that we faced as well as present our results in the English competition.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@inproceedings{oliver-vazquez-2020-termeval,
    title = "{T}erm{E}val 2020: Using {TSR} Filtering Method to Improve Automatic Term Extraction",
    author = "Oliver, Antoni  and
      V{\`a}zquez, Merc{\`e}",
    booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.computerm-1.15",
    pages = "106--113",
    abstract = "The identification of terms from domain-specific corpora using computational methods is a highly time-consuming task because terms has to be validated by specialists. In order to improve term candidate selection, we have developed the Token Slot Recognition (TSR) method, a filtering strategy based on terminological tokens which is used to rank extracted term candidates from domain-specific corpora. We have implemented this filtering strategy in TBXTools. In this paper we present the system we have used in the TermEval 2020 shared task on monolingual term extraction. We also present the evaluation results for the system for English, French and Dutch and for two corpora: corruption and heart failure. For English and French we have used a linguistic methodology based on POS patterns, and for Dutch we have used a statistical methodology based on n-grams calculation and filtering with stop-words. For all languages, TSR (Token Slot Recognition) filtering method has been applied. We have obtained competitive results, but there is still room for improvement of the system.",
    language = "English",
    ISBN = "979-10-95546-57-3",
}
@proceedings{coling-2020-international-linguistics-tutorial,
    title = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    editor = "Specia, Lucia  and
      Beck, Daniel",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.0",
}
@inproceedings{abend-etal-2020-cross,
    title = "Cross-lingual Semantic Representation for {NLP} with {UCCA}",
    author = "Abend, Omri  and
      Dvir, Dotan  and
      Hershcovich, Daniel  and
      Prange, Jakob  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.1",
    doi = "10.18653/v1/2020.coling-tutorials.1",
    pages = "1--9",
    abstract = "This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid annotation. The tutorial will provide a detailed introduction to the UCCA annotation guidelines, design philosophy and the available resources; and a comparison to other meaning representations. It will also survey the existing parsing work, including the findings of three recent shared tasks, in SemEval and CoNLL, that addressed UCCA parsing. Finally, the tutorial will present recent applications and extensions to the scheme, demonstrating its value for natural language processing in a range of languages and domains.",
}
@inproceedings{camacho-collados-pilehvar-2020-embeddings,
    title = "Embeddings in Natural Language Processing",
    author = "Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.2",
    doi = "10.18653/v1/2020.coling-tutorials.2",
    pages = "10--15",
    abstract = "Embeddings have been one of the most important topics of interest in NLP for the past decade. Representing knowledge through a low-dimensional vector which is easily integrable in modern machine learning models has played a central role in the development of the field. Embedding techniques initially focused on words but the attention soon started to shift to other forms. This tutorial will provide a high-level synthesis of the main embedding techniques in NLP, in the broad sense. We will start by conventional word embeddings (e.g., Word2Vec and GloVe) and then move to other types of embeddings, such as sense-specific and graph alternatives. We will finalize with an overview of the trending contextualized representations (e.g., ELMo and BERT) and explain their potential and impact in NLP.",
}
@inproceedings{dabre-etal-2020-multilingual,
    title = "Multilingual Neural Machine Translation",
    author = "Dabre, Raj  and
      Chu, Chenhui  and
      Kunchukuttan, Anoop",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.3",
    doi = "10.18653/v1/2020.coling-tutorials.3",
    pages = "16--21",
    abstract = "The advent of neural machine translation (NMT) has opened up exciting research in building multilingual translation systems i.e. translation models that can handle more than one language pair. Many advances have been made which have enabled (1) improving translation for low-resource languages via transfer learning from high resource languages; and (2) building compact translation models spanning multiple languages. In this tutorial, we will cover the latest advances in NMT approaches that leverage multilingualism, especially to enhance low-resource translation. In particular, we will focus on the following topics: modeling parameter sharing for multi-way models, massively multilingual models, training protocols, language divergence, transfer learning, zero-shot/zero-resource learning, pivoting, multilingual pre-training and multi-source translation.",
}
@inproceedings{derczynski-zubiaga-2020-detection,
    title = "Detection and Resolution of Rumors and Misinformation with {NLP}",
    author = "Derczynski, Leon  and
      Zubiaga, Arkaitz",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.4",
    doi = "10.18653/v1/2020.coling-tutorials.4",
    pages = "22--26",
    abstract = "Detecting and grounding false and misleading claims on the web has grown to form a substantial sub-field of NLP. The sub-field addresses problems at multiple different levels of misinformation detection: identifying check-worthy claims; tracking claims and rumors; rumor collection and annotation; grounding claims against knowledge bases; using stance to verify claims; and applying style analysis to detect deception. This half-day tutorial presents the theory behind each of these steps as well as the state-of-the-art solutions.",
}
@inproceedings{rogers-rumshisky-2020-guide,
    title = "A guide to the dataset explosion in {QA}, {NLI}, and commonsense reasoning",
    author = "Rogers, Anna  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.5",
    doi = "10.18653/v1/2020.coling-tutorials.5",
    pages = "27--32",
    abstract = "Question answering, natural language inference and commonsense reasoning are increasingly popular as general NLP system benchmarks, driving both modeling and dataset work. Only for question answering we already have over 100 datasets, with over 40 published after 2018. However, most new datasets get {``}solved{''} soon after publication, and this is largely due not to the verbal reasoning capabilities of our models, but to annotation artifacts and shallow cues in the data that they can exploit. This tutorial aims to (1) provide an up-to-date guide to the recent datasets, (2) survey the old and new methodological issues with dataset construction, and (3) outline the existing proposals for overcoming them. The target audience is the NLP practitioners who are lost in dozens of the recent datasets, and would like to know what these datasets are actually measuring. Our overview of the problems with the current datasets and the latest tips and tricks for overcoming them will also be useful to the researchers working on future benchmarks.",
}
@inproceedings{grundkiewicz-etal-2020-crash,
    title = "A Crash Course in Automatic Grammatical Error Correction",
    author = "Grundkiewicz, Roman  and
      Bryant, Christopher  and
      Felice, Mariano",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.6",
    doi = "10.18653/v1/2020.coling-tutorials.6",
    pages = "33--38",
    abstract = "Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. Although most research has focused on correcting errors in the context of English as a Second Language (ESL), GEC can also be applied to other languages and native text. The main application of a GEC system is thus to assist humans with their writing. Academic and commercial interest in GEC has grown significantly since the Helping Our Own (HOO) and Conference on Natural Language Learning (CoNLL) shared tasks in 2011-14, and a record-breaking 24 teams took part in the recent Building Educational Applications (BEA) shared task. Given this interest, and the recent shift towards neural approaches, we believe the time is right to offer a tutorial on GEC for researchers who may be new to the field or who are interested in the current state of the art and future challenges. With this in mind, the main goal of this tutorial is not only to bring attendees up to speed with GEC in general, but also examine the development of neural-based GEC systems.",
}
@inproceedings{anastasopoulos-etal-2020-endangered,
    title = "Endangered Languages meet {M}odern {NLP}",
    author = "Anastasopoulos, Antonios  and
      Cox, Christopher  and
      Neubig, Graham  and
      Cruz, Hilaria",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-tutorials.7",
    doi = "10.18653/v1/2020.coling-tutorials.7",
    pages = "39--45",
    abstract = "This tutorial will focus on NLP for endangered languages documentation and revitalization. First, we will acquaint the attendees with the process and the challenges of language documentation, showing how the needs of the language communities and the documentary linguists map to specific NLP tasks. We will then present the state-of-the-art in NLP applied in this particularly challenging setting (extremely low-resource datasets, noisy transcriptions, limited annotations, non-standard orthographies). In doing so, we will also analyze the challenges of working in this domain and expand on both the capabilities and the limitations of current NLP approaches. Our ultimate goal is to motivate more NLP practitioners to work towards this very important direction, and also provide them with the tools and understanding of the limitations/challenges, both of which are needed in order to have an impact.",
}
@proceedings{coling-2020-international,
    title = "Proceedings of the 28th International Conference on Computational Linguistics",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.0",
}
@inproceedings{prabhumoye-etal-2020-exploring,
    title = "Exploring Controllable Text Generation Techniques",
    author = "Prabhumoye, Shrimai  and
      Black, Alan W  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.1",
    doi = "10.18653/v1/2020.coling-main.1",
    pages = "1--14",
    abstract = "Neural controllable text generation is an important area gaining attention due to its plethora of applications. Although there is a large body of prior work in controllable text generation, there is no unifying theme. In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules. The control of attributes in the generation process requires modification of these modules. We present an overview of different techniques used to perform the modulation of these modules. We also provide an analysis on the advantages and disadvantages of these techniques. We further pave ways to develop new architectures based on the combination of the modules described in this paper.",
}
@inproceedings{xie-etal-2020-infusing,
    title = "Infusing Sequential Information into Conditional Masked Translation Model with Self-Review Mechanism",
    author = "Xie, Pan  and
      Cui, Zhi  and
      Chen, Xiuying  and
      Hu, XiaoHui  and
      Cui, Jianwei  and
      Wang, Bin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.2",
    doi = "10.18653/v1/2020.coling-main.2",
    pages = "15--25",
    abstract = "Non-autoregressive models generate target words in a parallel way, which achieve a faster decoding speed but at the sacrifice of translation accuracy. To remedy a flawed translation by non-autoregressive models, a promising approach is to train a conditional masked translation model (CMTM), and refine the generated results within several iterations. Unfortunately, such approach hardly considers the sequential dependency among target words, which inevitably results in a translation degradation. Hence, instead of solely training a Transformer-based CMTM, we propose a Self-Review Mechanism to infuse sequential information into it. Concretely, we insert a left-to-right mask to the same decoder of CMTM, and then induce it to autoregressively review whether each generated word from CMTM is supposed to be replaced or kept. The experimental results (WMT14 En ↔ De and WMT16 En ↔ Ro) demonstrate that our model uses dramatically less training computations than the typical CMTM, as well as outperforms several state-of-the-art non-autoregressive models by over 1 BLEU. Through knowledge distillation, our model even surpasses a typical left-to-right Transformer model, while significantly speeding up decoding.",
}
@inproceedings{agarwal-etal-2020-building,
    title = "Building Hierarchically Disentangled Language Models for Text Generation with Named Entities",
    author = "Agarwal, Yash  and
      Batra, Devansh  and
      Bagler, Ganesh",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.3",
    doi = "10.18653/v1/2020.coling-main.3",
    pages = "26--38",
    abstract = "Named entities pose a unique challenge to traditional methods of language modeling. While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies widely. Cooking recipes, for example, contain a lot of named entities {---} viz. ingredients, cooking techniques (also called processes), and utensils. However, some ingredients occur frequently within the instructions while most occur rarely. In this paper, we build upon the previous work done on language models developed for text with named entities by introducing a Hierarchically Disentangled Model. Training is divided into multiple branches with each branch producing a model with overlapping subsets of vocabulary. We found the existing datasets insufficient to accurately judge the performance of the model. Hence, we have curated 158,473 cooking recipes from several publicly available online sources. To reliably derive the entities within this corpus, we employ a combination of Named Entity Recognition (NER) as well as an unsupervised method of interpretation using dependency parsing and POS tagging, followed by a further cleaning of the dataset. This unsupervised interpretation models instructions as action graphs and is specific to the corpus of cooking recipes, unlike NER which is a general method applicable to all corpora. To delve into the utility of our language model, we apply it to tasks such as graph-to-text generation and ingredients-to-recipe generation, comparing it to previous state-of-the-art baselines. We make our dataset (including annotations and processed action graphs) available for use, considering their potential use cases for language modeling and text generation research.",
}
@inproceedings{ma-etal-2020-charbert,
    title = "{C}har{BERT}: Character-aware Pre-trained Language Model",
    author = "Ma, Wentao  and
      Cui, Yiming  and
      Si, Chenglei  and
      Liu, Ting  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.4",
    doi = "10.18653/v1/2020.coling-main.4",
    pages = "39--50",
    abstract = "Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split a word into subword units and make the representation incomplete and fragile.In this paper, we propose a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) to tackle these problems. We first construct the contextual word embedding for each token from the sequential character representations, then fuse the representations of characters and the subword representations by a novel heterogeneous interaction module. We also propose a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning. We evaluate our method on question answering, sequence labeling, and text classification tasks, both on the original datasets and adversarial misspelling test sets. The experimental results show that our method can significantly improve the performance and robustness of PLMs simultaneously.",
}
@inproceedings{zhang-etal-2020-graph-representation,
    title = "A Graph Representation of Semi-structured Data for Web Question Answering",
    author = "Zhang, Xingyao  and
      Shou, Linjun  and
      Pei, Jian  and
      Gong, Ming  and
      Wen, Lijie  and
      Jiang, Daxin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.5",
    doi = "10.18653/v1/2020.coling-main.5",
    pages = "51--61",
    abstract = "The abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA). Different from plain text passages in Web documents, Web tables and lists have inherent structures, which carry semantic correlations among various elements in tables and lists. Many existing studies treat tables and lists as flat documents with pieces of text and do not make good use of semantic information hidden in structures. In this paper, we propose a novel graph representation of Web tables and lists based on a systematic categorization of the components in semi-structured data as well as their relations. We also develop pre-training and reasoning techniques on the graph model for the QA task. Extensive experiments on several real datasets collected from a commercial engine verify the effectiveness of our approach. Our method improves F1 score by 3.90 points over the state-of-the-art baselines.",
}
@inproceedings{bohn-ling-2020-catching,
    title = "Catching Attention with Automatic Pull Quote Selection",
    author = "Bohn, Tanner  and
      Ling, Charles",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.6",
    doi = "10.18653/v1/2020.coling-main.6",
    pages = "62--76",
    abstract = "To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at https://github.com/tannerbohn/AutomaticPullQuoteSelection.",
}
@inproceedings{zhang-etal-2020-mzet,
    title = "{MZET}: Memory Augmented Zero-Shot Fine-grained Named Entity Typing",
    author = "Zhang, Tao  and
      Xia, Congying  and
      Lu, Chun-Ta  and
      Yu, Philip",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.7",
    doi = "10.18653/v1/2020.coling-main.7",
    pages = "77--87",
    abstract = "Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. However, with the growing size and granularity of the entity types, few previous researches concern with newly emerged entity types. In this paper, we propose MZET, a novel memory augmented FNET (Fine-grained NET) model, to tackle the unseen types in a zero-shot manner. MZET incorporates character-level, word-level, and contextural-level information to learn the entity mention representation. Besides, MZET considers the semantic meaning and the hierarchical structure into the entity type representation. Finally, through the memory component which models the relationship between the entity mention and the entity type, MZET transfers the knowledge from seen entity types to the zero-shot ones. Extensive experiments on three public datasets show the superior performance obtained by MZET, which surpasses the state-of-the-art FNET neural network models with up to 8{\%} gain in Micro-F1 and Macro-F1 score.",
}
@inproceedings{ji-etal-2020-span,
    title = "Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations",
    author = "Ji, Bin  and
      Yu, Jie  and
      Li, Shasha  and
      Ma, Jun  and
      Wu, Qingbo  and
      Tan, Yusong  and
      Liu, Huijun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.8",
    doi = "10.18653/v1/2020.coling-main.8",
    pages = "88--99",
    abstract = "Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing models cannot well capture semantics of these candidate entities and relations. To address these problems, we introduce a span-based joint extraction framework with attention-based semantic representations. Specially, attentions are utilized to calculate semantic representations, including span-specific and contextual ones. We further investigate effects of four attention variants in generating contextual semantic representations. Experiments show that our model outperforms previous systems and achieves state-of-the-art results on ACE2005, CoNLL2004 and ADE.",
}
@inproceedings{shen-etal-2020-hierarchical,
    title = "Hierarchical {C}hinese Legal event extraction via Pedal Attention Mechanism",
    author = "Shen, Shirong  and
      Qi, Guilin  and
      Li, Zhen  and
      Bi, Sheng  and
      Wang, Lusheng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.9",
    doi = "10.18653/v1/2020.coling-main.9",
    pages = "100--113",
    abstract = "Event extraction plays an important role in legal applications, including case push and auxiliary judgment. However, traditional event structure cannot express the connections between arguments, which are extremely important in legal events. Therefore, this paper defines a dynamic event structure for Chinese legal events. To distinguish between similar events, we design hierarchical event features for event detection. Moreover, to address the problem of long-distance semantic dependence and anaphora resolution in argument classification, we propose a novel pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. We label a Chinese legal event dataset and evaluate our model on it. Experimental results demonstrate that our model can surpass other state-of-the-art models.",
}
@inproceedings{jindal-etal-2020-killed,
    title = "Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection",
    author = "Jindal, Disha  and
      Deutsch, Daniel  and
      Roth, Dan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.10",
    doi = "10.18653/v1/2020.coling-main.10",
    pages = "114--124",
    abstract = "Identifying the key events in a document is critical to holistically understanding its important information. Although measuring the salience of events is highly contextual, most previous work has used a limited representation of events that omits essential information. In this work, we propose a highly contextual model of event salience that uses a rich representation of events, incorporates document-level information and allows for interactions between latent event encodings. Our experimental results on an event salience dataset demonstrate that our model improves over previous work by an absolute 2-4{\%} on standard metrics, establishing a new state-of-the-art performance for the task. We also propose a new evaluation metric that addresses flaws in previous evaluation methodologies. Finally, we discuss the importance of salient event detection for the downstream task of summarization.",
}
@inproceedings{hofmann-etal-2020-appraisal,
    title = "Appraisal Theories for Emotion Classification in Text",
    author = "Hofmann, Jan  and
      Troiano, Enrica  and
      Sassenberg, Kai  and
      Klinger, Roman",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.11",
    doi = "10.18653/v1/2020.coling-main.11",
    pages = "125--138",
    abstract = "Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words {``}I am afraid.{''}). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.",
}
@inproceedings{cheng-etal-2020-symmetric,
    title = "A Symmetric Local Search Network for Emotion-Cause Pair Extraction",
    author = "Cheng, Zifeng  and
      Jiang, Zhiwei  and
      Yin, Yafeng  and
      Yu, Hua  and
      Gu, Qing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.12",
    doi = "10.18653/v1/2020.coling-main.12",
    pages = "139--149",
    abstract = "Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document. To tackle this task, a two-step method was proposed by previous study which first extracted emotion clauses and cause clauses individually, then paired the emotion and cause clauses, and filtered out the pairs without causality. Different from this method that separated the detection and the matching of emotion and cause into two steps, we propose a Symmetric Local Search Network (SLSN) model to perform the detection and matching simultaneously by local search. SLSN consists of two symmetric subnetworks, namely the emotion subnetwork and the cause subnetwork. Each subnetwork is composed of a clause representation learner and a local pair searcher. The local pair searcher is a specially-designed cross-subnetwork component which can extract the local emotion-cause pairs. Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods.",
}
@inproceedings{liang-etal-2020-jointly,
    title = "Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis",
    author = "Liang, Bin  and
      Yin, Rongdi  and
      Gui, Lin  and
      Du, Jiachen  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.13",
    doi = "10.18653/v1/2020.coling-main.13",
    pages = "150--161",
    abstract = "In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect and propose an Interactive Graph Convolutional Networks (InterGCN) model for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT.",
}
@inproceedings{jiang-etal-2020-metnet,
    title = "{METN}et: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis",
    author = "Jiang, Bin  and
      Hou, Jing  and
      Zhou, Wanyue  and
      Yang, Chao  and
      Wang, Shihan  and
      Pang, Liang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.14",
    doi = "10.18653/v1/2020.coling-main.14",
    pages = "162--172",
    abstract = "Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of each specific aspect in a given sentence. Existing researches have realized the importance of the aspect for the ABSA task and have derived many interactive learning methods that model context based on specific aspect. However, current interaction mechanisms are ill-equipped to learn complex sentences with multiple aspects, and these methods underestimate the representation learning of the aspect. In order to solve the two problems, we propose a mutual enhanced transformation network (METNet) for the ABSA task. First, the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information. Second, METNet designs and implements a hierarchical structure, which enhances the representations of aspect and context iteratively. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of METNet, and we further prove that METNet is outstanding in multi-aspect scenarios.",
}
@inproceedings{yang-etal-2020-making,
    title = "Making the Best Use of Review Summary for Sentiment Analysis",
    author = "Yang, Sen  and
      Cui, Leyang  and
      Xie, Jun  and
      Zhang, Yue",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.15",
    doi = "10.18653/v1/2020.coling-main.15",
    pages = "173--184",
    abstract = "Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.",
}
@inproceedings{huber-carenini-2020-sentiment,
    title = "From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation",
    author = "Huber, Patrick  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.16",
    doi = "10.18653/v1/2020.coling-main.16",
    pages = "185--197",
    abstract = "Sentiment analysis, especially for long documents, plausibly requires methods capturing complex linguistics structures. To accommodate this, we propose a novel framework to exploit task-related discourse for the task of sentiment analysis. More specifically, we are combining the large-scale, sentiment-dependent MEGA-DT treebank with a novel neural architecture for sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model. Experiments show that our framework using sentiment-related discourse augmentations for sentiment prediction enhances the overall performance for long documents, even beyond previous approaches using well-established discourse parsers trained on human annotated data. We show that a simple ensemble approach can further enhance performance by selectively using discourse, depending on the document length.",
}
@inproceedings{chen-etal-2020-end,
    title = "End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network",
    author = "Chen, Ying  and
      Hou, Wenjun  and
      Li, Shoushan  and
      Wu, Caicong  and
      Zhang, Xiaoqiang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.17",
    doi = "10.18653/v1/2020.coling-main.17",
    pages = "198--207",
    abstract = "Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.",
}
@inproceedings{chen-etal-2020-unified,
    title = "A Unified Sequence Labeling Model for Emotion Cause Pair Extraction",
    author = "Chen, Xinhong  and
      Li, Qing  and
      Wang, Jianping",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.18",
    doi = "10.18653/v1/2020.coling-main.18",
    pages = "208--218",
    abstract = "Emotion-cause pair extraction (ECPE) aims at extracting emotions and causes as pairs from documents, where each pair contains an emotion clause and a set of cause clauses. Existing approaches address the task by first extracting emotion and cause clauses via two binary classifiers separately, and then training another binary classifier to pair them up. However, the extracted emotion-cause pairs of different emotion types cannot be distinguished from each other through simple binary classifiers, which limits the applicability of the existing approaches. Moreover, such two-step approaches may suffer from possible cascading errors. In this paper, to address the first problem, we assign emotion type labels to emotion and cause clauses so that emotion-cause pairs of different emotion types can be easily distinguished. As for the second problem, we reformulate the ECPE task as a unified sequence labeling task, which can extract multiple emotion-cause pairs in an end-to-end fashion. We propose an approach composed of a convolution neural network for encoding neighboring information and two Bidirectional Long-Short Term Memory networks for two auxiliary tasks. Experiment results demonstrate the feasibility and effectiveness of our approaches.",
}
@inproceedings{vorakitphan-etal-2020-regrexit,
    title = "Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts",
    author = "Vorakitphan, Vorakit  and
      Guerini, Marco  and
      Cabrio, Elena  and
      Villata, Serena",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.19",
    doi = "10.18653/v1/2020.coling-main.19",
    pages = "219--224",
    abstract = "Emotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling. As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based Sentiment Analysis (ABSA) toward the affect and emotion representation in polarized settings. In particular, we adopt the three-dimensional model of affect based on Valence, Arousal, and Dominance (VAD). We then present a Brexit scenario that proves how affect varies toward the same aspect when politically polarized stances are presented. Our approach captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level. We demonstrate how basic constituents of emotions can be mapped to the VAD model, along with their interactions respecting the polarized context in ABSA settings using biased key-concepts (e.g., {``}stop Brexit{''} vs. {``}support Brexit{''}). Quite intriguingly, the framework achieves to produce coherent aspect evidences of Brexit{'}s stance from key-concepts, showing that VAD influence the support and opposition aspects.",
}
@inproceedings{babanejad-etal-2020-affective,
    title = "Affective and Contextual Embedding for Sarcasm Detection",
    author = "Babanejad, Nastaran  and
      Davoudi, Heidar  and
      An, Aijun  and
      Papagelis, Manos",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.20",
    doi = "10.18653/v1/2020.coling-main.20",
    pages = "225--243",
    abstract = "Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection remains a challenging task, due to a lack of any vocal intonation or facial gestures in textual data. To date, most of the approaches to addressing the problem have relied on hand-crafted affect features, or pre-trained models of non-contextual word embeddings, such as Word2vec. However, these models inherit limitations that render them inadequate for the task of sarcasm detection. In this paper, we propose two novel deep neural network models for sarcasm detection, namely ACE 1 and ACE 2. Given as input a text passage, the models predict whether it is sarcastic (or not). Our models extend the architecture of BERT by incorporating both affective and contextual features. To the best of our knowledge, this is the first attempt to directly alter BERT{'}s architecture and train it from scratch to build a sarcasm classifier. Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with significant margins.",
}
@inproceedings{xu-etal-2020-understanding,
    title = "Understanding Pre-trained {BERT} for Aspect-based Sentiment Analysis",
    author = "Xu, Hu  and
      Shu, Lei  and
      Yu, Philip  and
      Liu, Bing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.21",
    doi = "10.18653/v1/2020.coling-main.21",
    pages = "244--250",
    abstract = "This paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work is motivated by the recent progress in BERT-based language models for ABSA. However, it is not clear how the general proxy task of (masked) language model trained on unlabeled corpus without annotations of aspects or opinions can provide important features for downstream tasks in ABSA. By leveraging the annotated datasets in ABSA, we investigate both the attentions and the learned representations of BERT pre-trained on reviews. We found that BERT uses very few self-attention heads to encode context words (such as prepositions or pronouns that indicating an aspect) and opinion words for an aspect. Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context. We hope this investigation can help future research in improving self-supervised learning, unsupervised learning and fine-tuning for ABSA. The pre-trained model and code can be found at https://github.com/howardhsu/BERT-for-RRC-ABSA.",
}
@inproceedings{peng-zhang-2020-weighed,
    title = "Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis",
    author = "Peng, Minlong  and
      Zhang, Qi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.22",
    doi = "10.18653/v1/2020.coling-main.22",
    pages = "251--265",
    abstract = "Cross-domain sentiment analysis is currently a hot topic in both the research and industrial areas. One of the most popular framework for the task is domain-invariant representation learning (DIRL), which aims to learn a distribution-invariant feature representation across domains. However, in this work, we find out that applying DIRL may degrade domain adaptation performance when the label distribution $\textrm{P}(\textrm{Y})$ changes across domains. To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework. We show that it is easy to transfer existing models of the DIRL framework to the WDIRL framework. Empirical studies on extensive cross-domain sentiment analysis tasks verified our statements and showed the effectiveness of our proposed solution.",
}
@inproceedings{barriere-balahur-2020-improving,
    title = "Improving Sentiment Analysis over non-{E}nglish Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation",
    author = "Barriere, Valentin  and
      Balahur, Alexandra",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.23",
    doi = "10.18653/v1/2020.coling-main.23",
    pages = "266--271",
    abstract = "Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.",
}
@inproceedings{chen-etal-2020-joint,
    title = "Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks",
    author = "Chen, Guimin  and
      Tian, Yuanhe  and
      Song, Yan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.24",
    doi = "10.18653/v1/2020.coling-main.24",
    pages = "272--279",
    abstract = "End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis provides a one-step solution to predict both aspect terms and their sentiment polarities through a single decoding process, which avoid the mismatches in between the results of aspect terms and sentiment polarities, as well as error propagation. Previous studies, especially recent ones, for this task focus on using powerful encoders (e.g., Bi-LSTM and BERT) to model contextual information from the input, with limited efforts paid to using advanced neural architectures (such as attentions and graph convolutional networks) or leveraging extra knowledge (such as syntactic information). To extend such efforts, in this paper, we propose directional graph convolutional networks (D-GCN) to jointly perform aspect extraction and sentiment analysis with encoding syntactic information, where dependency among words are integrated in our model to enhance its ability of representing input sentences and help EASA accordingly. Experimental results on three benchmark datasets demonstrate the effectiveness of our approach, where D-GCN achieves state-of-the-art performance on all datasets.",
}
@inproceedings{tian-etal-2020-train,
    title = "Train Once, and Decode As You Like",
    author = "Tian, Chao  and
      Wang, Yifei  and
      Cheng, Hao  and
      Lian, Yijiang  and
      Zhang, Zhihua",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.25",
    doi = "10.18653/v1/2020.coling-main.25",
    pages = "280--293",
    abstract = "In this paper we propose a unified approach for supporting different generation manners of machine translation, including autoregressive, semi-autoregressive, and refinement-based non-autoregressive models. Our approach works by repeatedly selecting positions and generating tokens at these selected positions. After being trained once, our approach achieves better or competitive translation performance compared with some strong task-specific baseline models in all the settings. This generalization ability benefits mainly from the new training objective that we propose. We validate our approach on the WMT{'}14 English-German and IWSLT{'}14 German-English translation tasks. The experimental results are encouraging.",
}
@inproceedings{mukadam-etal-2020-representation,
    title = "A Representation Learning Approach to Animal Biodiversity Conservation",
    author = "Mukadam, Meet  and
      Jayaram, Mandhara  and
      Zhang, Yongfeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.26",
    doi = "10.18653/v1/2020.coling-main.26",
    pages = "294--305",
    abstract = "Generating knowledge from natural language data has aided in solving many artificial intelligence problems. Vector representations of words have been the driving force behind the majority of natural language processing tasks. This paper develops a novel approach for predicting the conservation status of animal species using custom generated scientific name embeddings. We use two different vector embeddings generated using representation learning on Wikipedia text and animal taxonomy data. We generate name embeddings for all species in the animal kingdom using unsupervised learning and build a model on the IUCN Red List dataset to classify species into endangered or least-concern. To our knowledge, this is the first work that makes use of learnt features instead of handcrafted features for this task and achieves competitive results. Based on the high confidence results of our model, we also predict the conservation status of data deficient species whose conservation status is still unknown and thus steering more focus towards them for protection. These embeddings have also been made publicly available here. We believe this will greatly help in solving various downstream tasks and further advance research in the cross-domain involving natural language processing, conservation biology, and life sciences.",
}
@inproceedings{lv-etal-2020-integrating,
    title = "Integrating External Event Knowledge for Script Learning",
    author = "Lv, Shangwen  and
      Zhu, Fuqing  and
      Hu, Songlin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.27",
    doi = "10.18653/v1/2020.coling-main.27",
    pages = "306--315",
    abstract = "Script learning aims to predict the subsequent event according to the existing event chain. Recent studies focus on event co-occurrence to solve this problem. However, few studies integrate external event knowledge to solve this problem. With our observations, external event knowledge can provide additional knowledge like temporal or causal knowledge for understanding event chain better and predicting the right subsequent event. In this work, we integrate event knowledge from ASER (Activities, States, Events and their Relations) knowledge base to help predict the next event. We propose a new approach consisting of knowledge retrieval stage and knowledge integration stage. In the knowledge retrieval stage, we select relevant external event knowledge from ASER. In the knowledge integration stage, we propose three methods to integrate external knowledge into our model and infer final answers. Experiments on the widely-used Multi- Choice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.",
}
@inproceedings{fujita-etal-2020-pointing,
    title = "Pointing to Subwords for Generating Function Names in Source Code",
    author = "Fujita, Shogo  and
      Kamigaito, Hidetaka  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.28",
    doi = "10.18653/v1/2020.coling-main.28",
    pages = "316--327",
    abstract = "We tackle the task of automatically generating a function name from source code. Existing generators face difficulties in generating low-frequency or out-of-vocabulary subwords. In this paper, we propose two strategies for copying low-frequency or out-of-vocabulary subwords in inputs. Our best performing model showed an improvement over the conventional method in terms of our modified F1 and accuracy on the Java-small and Java-large datasets.",
}
@inproceedings{zheng-etal-2020-heterogeneous,
    title = "Heterogeneous Graph Neural Networks to Predict What Happen Next",
    author = "Zheng, Jianming  and
      Cai, Fei  and
      Ling, Yanxiang  and
      Chen, Honghui",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.29",
    doi = "10.18653/v1/2020.coling-main.29",
    pages = "328--338",
    abstract = "Given an incomplete event chain, script learning aims to predict the missing event, which can support a series of NLP applications. Existing work cannot well represent the heterogeneous relations and capture the discontinuous event segments that are common in the event chain. To address these issues, we introduce a heterogeneous-event (HeterEvent) graph network. In particular, we employ each unique word and individual event as nodes in the graph, and explore three kinds of edges based on realistic relations (e.g., the relations of word-and-word, word-and-event, event-and-event). We also design a message passing process to realize information interactions among homo or heterogeneous nodes. And the discontinuous event segments could be explicitly modeled by finding the specific path between corresponding nodes in the graph. The experimental results on one-step and multi-step inference tasks demonstrate that our ensemble model HeterEvent[W+E] can outperform existing baselines.",
}
@inproceedings{dakle-moldovan-2020-cerec,
    title = "{CEREC}: A Corpus for Entity Resolution in Email Conversations",
    author = "Dakle, Parag Pravin  and
      Moldovan, Dan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.30",
    doi = "10.18653/v1/2020.coling-main.30",
    pages = "339--349",
    abstract = "We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages and 38,996 entity coreference chains. The annotation is carried out as a two-step process with minimal manual effort. Experiments are carried out for evaluating different features and performance of four baselines on the created corpus. For the task of mention identification and coreference resolution, a best performance of 54.1 F1 is reported, highlighting the room for improvement. An in-depth qualitative and quantitative error analysis is presented to understand the limitations of the baselines considered.",
}
@inproceedings{yan-etal-2020-sql,
    title = "{SQL} Generation via Machine Reading Comprehension",
    author = "Yan, Zeyu  and
      Ma, Jianqiang  and
      Zhang, Yang  and
      Shen, Jianping",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.31",
    doi = "10.18653/v1/2020.coling-main.31",
    pages = "350--356",
    abstract = "Text-to-SQL systems offers natural language interfaces to databases, which can automatically generates SQL queries given natural language questions. On the WikiSQL benchmark, state-of- the-art text-to-SQL systems typically take a slot-filling approach by building several specialized models for each type of slot. Despite being effective, such modularized systems are complex and also fall short in jointly learning for different slots. To solve these problems, this paper proposes a novel approach that formulates the task as a question answering problem, where different slots are predicted by a unified machine reading comprehension (MRC) model. For this purpose, we use a BERT-based MRC model, which can also benefit from intermediate training on other MRC datasets. The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQL.",
}
@inproceedings{volodina-etal-2020-towards,
    title = "Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of {S}wedish Learner Essays",
    author = "Volodina, Elena  and
      Ali Mohammed, Yousuf  and
      Derbring, Sandra  and
      Matsson, Arild  and
      Megyesi, Beata",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.32",
    doi = "10.18653/v1/2020.coling-main.32",
    pages = "357--369",
    abstract = "This article reports on an ongoing project aiming at automatization of pseudonymization of learner essays. The process includes three steps: identification of personal information in an unstructured text, labeling for a category, and pseudonymization. We experiment with rule-based methods for detection of 15 categories out of the suggested 19 (Megyesi et al., 2018) that we deem important and/or doable with automatic approaches. For the detection and labeling steps,we use resources covering personal names, geographic names, company and university names and others. For the pseudonymization step, we replace the item using another item of the same type from the above-mentioned resources. Evaluation of the detection and labeling steps are made on a set of manually anonymized essays. The results are promising and show that 89{\%} of the personal information can be successfully identified in learner data, and annotated correctly with an inter-annotator agreement of 86{\%} measured as Fleiss kappa and Krippendorff{'}s alpha.",
}
@inproceedings{wang-etal-2020-pg,
    title = "{PG}-{GSQL}: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-{SQL} Generation",
    author = "Wang, Huajie  and
      Li, Mei  and
      Chen, Lei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.33",
    doi = "10.18653/v1/2020.coling-main.33",
    pages = "370--380",
    abstract = "Text-to-SQL is a task of translating utterances to SQL queries, and most existing neural approaches of text-to-SQL focus on the cross-domain context-independent generation task. We pay close attention to the cross-domain context-dependent text-to-SQL generation task, which requires a model to depend on the interaction history and current utterance to generate SQL query. In this paper, we present an encoder-decoder model called PG-GSQL based on the interaction-level encoder and with two effective innovations in decoder to solve cross-domain context-dependent text-to-SQL task. 1) To effectively capture historical information of SQL query and reuse the previous SQL query tokens, we use a hybrid pointer-generator network as decoder to copy tokens from the previous SQL query via pointer, the generator part is utilized to generate new tokens. 2) We propose a guide component to limit the prediction space of vocabulary for avoiding table-column dependency and foreign key dependency errors during decoding phase. In addition, we design a column-table linking mechanism to improve the prediction accuracy of tables. On the challenging cross-domain context-dependent text-to-SQL benchmark SParC, PG-GSQL achieves 34.0{\%} question matching accuracy and 19.0{\%} interaction matching accuracy on the dev set. With BERT augmentation, PG-GSQL obtains 53.1{\%} question matching accuracy and 34.7{\%} interaction matching accuracy on the dev set, outperforms the previous state-of-the-art model by 5.9{\%} question matching accuracy and 5.2{\%} interaction matching accuracy. Our code is publicly available.",
}
@inproceedings{iacob-etal-2020-neural,
    title = "Neural Approaches for Natural Language Interfaces to Databases: A Survey",
    author = "Iacob, Radu Cristian Alexandru  and
      Brad, Florin  and
      Apostol, Elena-Simona  and
      Truic{\u{a}}, Ciprian-Octavian  and
      Hosu, Ionel Alexandru  and
      Rebedea, Traian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.34",
    doi = "10.18653/v1/2020.coling-main.34",
    pages = "381--395",
    abstract = "A natural language interface to databases (NLIDB) enables users without technical expertise to easily access information from relational databases. Interest in NLIDBs has resurged in the past years due to the availability of large datasets and improvements to neural sequence-to-sequence models. In this survey we focus on the key design decisions behind current state of the art neural approaches, which we group into encoder and decoder improvements. We highlight the three most important directions, namely linking question tokens to database schema elements (schema linking), better architectures for encoding the textual query taking into account the schema (schema encoding), and improved generation of structured queries using autoregressive neural models (grammar-based decoders). To foster future research, we also present an overview of the most important NLIDB datasets, together with a comparison of the top performing neural models and a short insight into recent non deep learning solutions.",
}
@inproceedings{porco-goldwasser-2020-predicting,
    title = "Predicting Stance Change Using Modular Architectures",
    author = "Porco, Aldo  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.35",
    doi = "10.18653/v1/2020.coling-main.35",
    pages = "396--406",
    abstract = "The ability to change a person{'}s mind on a given issue depends both on the arguments they are presented with and on their underlying perspectives and biases on that issue. Predicting stance changes require characterizing both aspects and the interaction between them, especially in realistic settings in which stance changes are very rare. In this paper, we suggest a modular learning approach, which decomposes the task into multiple modules, focusing on different aspects of the interaction between users, their beliefs, and the arguments they are exposed to. Our experiments show that our modular approach archives significantly better results compared to the end-to-end approach using BERT over the same inputs.",
}
@inproceedings{ashby-weir-2020-leveraging,
    title = "Leveraging {HTML} in Free Text Web Named Entity Recognition",
    author = "Ashby, Colin  and
      Weir, David",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.36",
    doi = "10.18653/v1/2020.coling-main.36",
    pages = "407--413",
    abstract = "HTML tags are typically discarded in free text Named Entity Recognition from Web pages. We investigate whether these discarded tags might be used to improve NER performance. We compare Text+Tags sentences with their Text-Only equivalents, over five datasets, two free text segmentation granularities and two NER models. We find an increased F1 performance for Text+Tags of between 0.9{\%} and 13.2{\%} over all datasets, variants and models. This performance increase, over datasets of varying entity types, HTML density and construction quality, indicates our method is flexible and adaptable. These findings imply that a similar technique might be of use in other Web-aware NLP tasks, including the enrichment of deep language models.",
}
@inproceedings{vu-etal-2020-multimodal,
    title = "Multimodal Review Generation with Privacy and Fairness Awareness",
    author = "Vu, Xuan-Son  and
      Nguyen, Thanh-Son  and
      Le, Duc-Trong  and
      Jiang, Lili",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.37",
    doi = "10.18653/v1/2020.coling-main.37",
    pages = "414--425",
    abstract = "Users express their opinions towards entities (e.g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images. Modeling reviews are advantageous for user behavior understanding which, in turn, supports various user-oriented tasks such as recommendation, sentiment analysis, and review generation. In this paper, we propose MG-PriFair, a multimodal neural-based framework, which generates personalized reviews with privacy and fairness awareness. Motivated by the fact that reviews might contain personal information and sentiment bias, we propose a novel differentially private (dp)-embedding model for training privacy guaranteed embeddings and an evaluation approach for sentiment fairness in the food-review domain. Experiments on our novel review dataset show that MG-PriFair is capable of generating plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased word embeddings. To the best of our knowledge, we are the first to bring user privacy and sentiment fairness into the review generation task. The dataset and source codes are available at https://github.com/ReML-AI/MG-PriFair.",
}
@inproceedings{ki-etal-2020-generating,
    title = "Generating Equation by Utilizing Operators : {GEO} model",
    author = "Ki, Kyung Seo  and
      Lee, Donggeon  and
      Kim, Bugeun  and
      Gweon, Gahgene",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.38",
    doi = "10.18653/v1/2020.coling-main.38",
    pages = "426--436",
    abstract = "Math word problem solving is an emerging research topic in Natural Language Processing. Recently, to address the math word problem-solving task, researchers have applied the encoder-decoder architecture, which is mainly used in machine translation tasks. The state-of-the-art neural models use hand-crafted features and are based on generation methods. In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use hand-crafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge. To address missing domain-specific feature issue, we designed two auxiliary tasks: operation group difference prediction and implicit pair prediction. To address losing encoder-level knowledge issue, we added an Operation Feature Feed Forward (OP3F) layer. Experimental results showed that the GEO model outperformed existing state-of-the-art models on two datasets, 85.1{\%} in MAWPS, and 62.5{\%} in DRAW-1K, and reached comparable performance of 82.1{\%} in ALG514 dataset.",
}
@inproceedings{zhao-etal-2020-improving,
    title = "Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words",
    author = "Zhao, Lulu  and
      Xu, Weiran  and
      Guo, Jun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.39",
    doi = "10.18653/v1/2020.coling-main.39",
    pages = "437--449",
    abstract = "Recently, people have been beginning paying more attention to the abstractive dialogue summarization task. Since the information flows are exchanged between at least two interlocutors and key elements about a certain event are often spanned across multiple utterances, it is necessary for researchers to explore the inherent relations and structures of dialogue contents. However, the existing approaches often process the dialogue with sequence-based models, which are hard to capture long-distance inter-sentence relations. In this paper, we propose a Topic-word Guided Dialogue Graph Attention (TGDGA) network to model the dialogue as an interaction graph according to the topic word information. A masked graph self-attention mechanism is used to integrate cross-sentence information flows and focus more on the related utterances, which makes it better to understand the dialogue. Moreover, the topic word features are introduced to assist the decoding process. We evaluate our model on the SAMSum Corpus and Automobile Master Corpus. The experimental results show that our method outperforms most of the baselines.",
}
@inproceedings{shang-etal-2020-speaker,
    title = "Speaker-change Aware {CRF} for Dialogue Act Classification",
    author = "Shang, Guokan  and
      Tixier, Antoine  and
      Vazirgiannis, Michalis  and
      Lorr{\'e}, Jean-Pierre",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.40",
    doi = "10.18653/v1/2020.coling-main.40",
    pages = "450--464",
    abstract = "Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. CRF models the conditional probability of the target DA label sequence given the input utterance sequence. However, the task involves another important input sequence, that of speakers, which is ignored by previous work. To address this limitation, this paper proposes a simple modification of the CRF layer that takes speaker-change into account. Experiments on the SwDA corpus show that our modified CRF layer outperforms the original one, with very wide margins for some DA labels. Further, visualizations demonstrate that our CRF layer can learn meaningful, sophisticated transition patterns between DA label pairs conditioned on speaker-change in an end-to-end way. Code is publicly available.",
}
@inproceedings{lubis-etal-2020-lava,
    title = "{LAVA}: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization",
    author = "Lubis, Nurul  and
      Geishauser, Christian  and
      Heck, Michael  and
      Lin, Hsien-chin  and
      Moresi, Marco  and
      van Niekerk, Carel  and
      Gasic, Milica",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.41",
    doi = "10.18653/v1/2020.coling-main.41",
    pages = "465--479",
    abstract = "Reinforcement learning (RL) can enable task-oriented dialogue systems to steer the conversation towards successful task completion. In an end-to-end setting, a response can be constructed in a word-level sequential decision making process with the entire system vocabulary as action space. Policies trained in such a fashion do not require expert-defined action spaces, but they have to deal with large action spaces and long trajectories, making RL impractical. Using the latent space of a variational model as action space alleviates this problem. However, current approaches use an uninformed prior for training and optimize the latent distribution solely on the context. It is therefore unclear whether the latent representation truly encodes the characteristics of different actions. In this paper, we explore three ways of leveraging an auxiliary task to shape the latent variable distribution: via pre-training, to obtain an informed prior, and via multitask learning. We choose response auto-encoding as the auxiliary task, as this captures the generative factors of dialogue responses while requiring low computational cost and neither additional data nor labels. Our approach yields a more action-characterized latent representations which support end-to-end dialogue policy optimization and achieves state-of-the-art success rates. These results warrant a more wide-spread use of RL in end-to-end dialogue models.",
}
@inproceedings{louvan-magnini-2020-recent,
    title = "Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey",
    author = "Louvan, Samuel  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.42",
    doi = "10.18653/v1/2020.coling-main.42",
    pages = "480--496",
    abstract = "In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user{'}s needs in task-oriented dialogue systems. We focus on two core tasks, slot filling (SF) and intent classification (IC), and survey how neural based models have rapidly evolved to address natural language understanding in dialogue systems. We introduce three neural architectures: independent models, which model SF and IC separately, joint models, which exploit the mutual benefit of the two tasks simultaneously, and transfer learning models, that scale the model to new domains. We discuss the current state of the research in SF and IC, and highlight challenges that still require attention.",
}
@inproceedings{rohanian-hough-2020-framing,
    title = "Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning",
    author = "Rohanian, Morteza  and
      Hough, Julian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.43",
    doi = "10.18653/v1/2020.coling-main.43",
    pages = "497--507",
    abstract = "We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. We show that these tasks provide positive inductive biases to each other with optimal contribution of each one relying on the severity of the noise from the task. Our live multi-task model outperforms similar individual tasks, delivers competitive performance and is beneficial for future use in conversational agents in psychiatric treatment.",
}
@inproceedings{liu-etal-2020-aprile,
    title = "{A}pril{E}: Attention with Pseudo Residual Connection for Knowledge Graph Embedding",
    author = "Liu, Yuzhang  and
      Wang, Peng  and
      Li, Yingtai  and
      Shao, Yizhan  and
      Xu, Zhongkai",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.44",
    doi = "10.18653/v1/2020.coling-main.44",
    pages = "508--518",
    abstract = "Knowledge graph embedding maps entities and relations into low-dimensional vector space. However, it is still challenging for many existing methods to model diverse relational patterns, especially symmetric and antisymmetric relations. To address this issue, we propose a novel model, AprilE, which employs triple-level self-attention and pseudo residual connection to model relational patterns. The triple-level self-attention treats head entity, relation, and tail entity as a sequence and captures the dependency within a triple. At the same time the pseudo residual connection retains primitive semantic features. Furthermore, to deal with symmetric and antisymmetric relations, two schemas of score function are designed via a position-adaptive mechanism. Experimental results on public datasets demonstrate that our model can produce expressive knowledge embedding and significantly outperforms most of the state-of-the-art works.",
}
@inproceedings{boenninghoff-etal-2020-variational,
    title = "Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution",
    author = "Boenninghoff, Benedikt  and
      Zeiler, Steffen  and
      Nickel, Robert  and
      Kolossa, Dorothea",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.45",
    doi = "10.18653/v1/2020.coling-main.45",
    pages = "519--529",
    abstract = "Traditional computational authorship attribution describes a classification task in a closed-set scenario. Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of the authors has written another set of anonymous or disputed texts. In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task. Variational autoencoders (VAEs) have had tremendous success in learning latent representations. However, existing VAEs are currently still bound by limitations imposed by the assumed Gaussianity of the underlying probability distributions in the latent space. In this work, we are extending a VAE with an embedded Gaussian mixture model to a Student-t mixture model, which allows for an independent control of the {``}heaviness{''} of the respective tails of the implied probability densities. Experiments over an Amazon review dataset indicate superior performance of the proposed method.",
}
@inproceedings{xu-etal-2020-knowledge,
    title = "Knowledge Graph Embeddings in Geometric Algebras",
    author = "Xu, Chengjin  and
      Nayyeri, Mojtaba  and
      Chen, Yung-Yu  and
      Lehmann, Jens",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.46",
    doi = "10.18653/v1/2020.coling-main.46",
    pages = "530--544",
    abstract = "Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a low dimensional latent representation space. Existing KG embedding approaches model entities and relations in a KG by utilizing real-valued , complex-valued, or hypercomplex-valued (Quaternion or Octonion) representations, all of which are subsumed into a geometric algebra. In this work, we introduce a novel geometric algebra-based KG embedding framework, GeomE, which utilizes multivector representations and the geometric product to model entities and relations. Our framework subsumes several state-of-the-art KG embedding approaches and is advantageous with its ability of modeling various key relation patterns, including (anti-)symmetry, inversion and composition, rich expressiveness with higher degree of freedom as well as good generalization capacity. Experimental results on multiple benchmark knowledge graphs show that the proposed approach outperforms existing state-of-the-art models for link prediction.",
}
@inproceedings{lu-etal-2020-exploiting,
    title = "Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization",
    author = "Lu, Qiuhao  and
      de Silva, Nisansa  and
      Dou, Dejing  and
      Nguyen, Thien Huu  and
      Sen, Prithviraj  and
      Reinwald, Berthold  and
      Li, Yunyao",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.47",
    doi = "10.18653/v1/2020.coling-main.47",
    pages = "545--555",
    abstract = "Network representation learning (NRL) is crucial in the area of graph learning. Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches. Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes. In this paper, we propose a novel network embedding method which models the consistency across different views of networks. More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module. The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering. We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods.",
}
@inproceedings{huang-etal-2020-rate,
    title = "{R}at{E}: Relation-Adaptive Translating Embedding for Knowledge Graph Completion",
    author = "Huang, Hao  and
      Long, Guodong  and
      Shen, Tao  and
      Jiang, Jing  and
      Zhang, Chengqi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.48",
    doi = "10.18653/v1/2020.coling-main.48",
    pages = "556--567",
    abstract = "Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-weight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show the capability in handling various relation patterns including symmetry, antisymmetry, inversion and composition. However, previous translating embedding approaches defined in complex vector space suffer from two main issues: 1) representing and modeling capacities of the model are limited by the translation function with rigorous multiplication of two complex numbers; and 2) embedding ambiguity caused by one-to-many relations is not explicitly alleviated. In this paper, we propose a relation-adaptive translation function built upon a novel weighted product in complex space, where the weights are learnable, relation-specific and independent to embedding size. The translation function only requires eight more scalar parameters each relation, but improves expressive power and alleviates embedding ambiguity problem. Based on the function, we then present our Relation-adaptive translating Embedding (RatE) approach to score each graph triple. Moreover, a novel negative sampling method is proposed to utilize both prior knowledge and self-adversarial learning for effective optimization. Experiments verify RatE achieves state-of-the-art performance on four link prediction benchmarks.",
}
@inproceedings{zhou-etal-2020-sentix,
    title = "{S}enti{X}: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis",
    author = "Zhou, Jie  and
      Tian, Junfeng  and
      Wang, Rui  and
      Wu, Yuanbin  and
      Xiao, Wenming  and
      He, Liang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.49",
    doi = "10.18653/v1/2020.coling-main.49",
    pages = "568--579",
    abstract = "Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, achieving state-of-the-art performance. However, due to the variety of users{'} emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain. In this paper, we pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning. We propose several pre-training tasks based on existing lexicons and annotations at both token and sentence levels, such as emoticons, sentiment words, and ratings, without human interference. A series of experiments are conducted and the results indicate the great advantages of our model. We obtain new state-of-the-art results in all the cross-domain sentiment analysis tasks, and our proposed SentiX can be trained with only 1{\%} samples (18 samples) and it achieves better performance than BERT with 90{\%} samples.",
}
@inproceedings{wang-etal-2020-bayes,
    title = "{B}ayes-enhanced Lifelong Attention Networks for Sentiment Classification",
    author = "Wang, Hao  and
      Wang, Shuai  and
      Mazumder, Sahisnu  and
      Liu, Bing  and
      Yang, Yan  and
      Li, Tianrui",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.50",
    doi = "10.18653/v1/2020.coling-main.50",
    pages = "580--591",
    abstract = "The classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task. This paper studies the problem of learning a sequence of tasks (sentiment classification tasks in our case). After each sentiment classification task is learned, its knowledge is retained to help future task learning. Following this setting, we explore attention neural networks and propose a Bayes-enhanced Lifelong Attention Network (BLAN). The key idea is to exploit the generative parameters of naive Bayes to learn attention knowledge. The learned knowledge from each task is stored in a knowledge base and later used to build lifelong attentions. The constructed lifelong attentions are then used to enhance the attention of the network to help new task learning. Experimental results on product reviews from Amazon.com show the effectiveness of the proposed model.",
}
@inproceedings{baert-etal-2020-arabizi,
    title = "{A}rabizi Language Models for Sentiment Analysis",
    author = "Baert, Ga{\'e}tan  and
      Gahbiche, Souhir  and
      Gadek, Guillaume  and
      Pauchet, Alexandre",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.51",
    doi = "10.18653/v1/2020.coling-main.51",
    pages = "592--603",
    abstract = "Arabizi is a written form of spoken Arabic, relying on Latin characters and digits. It is informal and does not follow any conventional rules, raising many NLP challenges. In particular, Arabizi has recently emerged as the Arabic language in online social networks, becoming of great interest for opinion mining and sentiment analysis. Unfortunately, only few Arabizi resources exist and state-of-the-art language models such as BERT do not consider Arabizi. In this work, we construct and release two datasets: (i) LAD, a corpus of 7.7M tweets written in Arabizi and (ii) SALAD, a subset of LAD, manually annotated for sentiment analysis. Then, a BERT architecture is pre-trained on LAD, in order to create and distribute an Arabizi language model called BAERT. We show that a language model (BAERT) pre-trained on a large corpus (LAD) in the same language (Arabizi) as that of the fine-tuning dataset (SALAD), outperforms a state-of-the-art multi-lingual pretrained model (multilingual BERT) on a sentiment analysis task.",
}
@inproceedings{bastan-etal-2020-authors,
    title = "Author{'}s Sentiment Prediction",
    author = "Bastan, Mohaddeseh  and
      Koupaee, Mahnaz  and
      Son, Youngseo  and
      Sicoli, Richard  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.52",
    doi = "10.18653/v1/2020.coling-main.52",
    pages = "604--615",
    abstract = "Even though sentiment analysis has been well-studied on a wide range of domains, there hasn{'}tbeen much work on inferring author sentiment in news articles. To address this gap, we introducePerSenT, a crowd-sourced dataset that captures the sentiment of an author towards the mainentity in a news article. Our benchmarks of multiple strong baselines show that this is a difficultclassification task. BERT performs the best amongst the baselines. However, it only achievesa modest performance overall suggesting that fine-tuning document-level representations aloneisn{'}t adequate for this task. Making paragraph-level decisions and aggregating over the entiredocument is also ineffective. We present empirical and qualitative analyses that illustrate thespecific challenges posed by this dataset. We release this dataset with 5.3k documents and 38kparagraphs with 3.2k unique entities as a challenge in entity sentiment analysis.",
}
@inproceedings{li-etal-2020-modeling,
    title = "Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions",
    author = "Li, Jingye  and
      Fei, Hao  and
      Ji, Donghong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.53",
    doi = "10.18653/v1/2020.coling-main.53",
    pages = "616--626",
    abstract = "In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances. First, we employ the dynamic convolution network (DCN) as the utterance encoder to capture the dialogue contexts. Further, we propose a novel context-aware dynamic convolution network (CDCN) to better leverage the local contexts when dynamically generating kernels. We extended our frameworks into bi-channel version (i.e., BDCN and BCDCN) under multi-task learning to achieve the joint DAR and SC. Two channels can learn their own feature representations for DAR and SC, respectively, but with latent interaction. Besides, we suggest enhancing the tasks by employing the DiaBERT language model. Our frameworks obtain state-of-the-art performances against all baselines on two benchmark datasets, demonstrating the importance of modeling the local contexts.",
}
@inproceedings{hu-verberne-2020-named,
    title = "Named Entity Recognition for {C}hinese biomedical patents",
    author = "Hu, Yuting  and
      Verberne, Suzan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.54",
    doi = "10.18653/v1/2020.coling-main.54",
    pages = "627--637",
    abstract = "There is a large body of work on Biomedical Entity Recognition (Bio-NER) for English but there have only been a few attempts addressing NER for Chinese biomedical texts. Because of the growing amount of Chinese biomedical discoveries being patented, and lack of NER models for patent data, we train and evaluate NER models for the analysis of Chinese biomedical patent data, based on BERT. By doing so, we show the value and potential of this domain-specific NER task. For the evaluation of our methods we built our own Chinese biomedical patents NER dataset, and our optimized model achieved an F1 score of 0.54{\mbox{$\pm$}}0.15. Further biomedical analysis indicates that our solution can help detecting meaningful biomedical entities and novel gene-gene interactions, with limited labeled data, training time and computing power.",
}
@inproceedings{liednikova-etal-2020-learning,
    title = "Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge",
    author = "Liednikova, Anna  and
      Jolivet, Philippe  and
      Durand-Salmon, Alexandre  and
      Gardent, Claire",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.55",
    doi = "10.18653/v1/2020.coling-main.55",
    pages = "638--648",
    abstract = "A key bottleneck for developing dialog models is the lack of adequate training data. Due to privacy issues, dialog data is even scarcer in the health domain. We propose a novel method for creating dialog corpora which we apply to create doctor-patient interaction data. We use this data to learn both a generation and a hybrid classification/retrieval model and find that the generation model consistently outperforms the hybrid model. We show that our data creation method has several advantages. Not only does it allow for the semi-automatic creation of large quantities of training data. It also provides a natural way of guiding learning and a novel method for assessing the quality of human-machine interactions.",
}
@inproceedings{lai-etal-2020-joint,
    title = "A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents",
    author = "Lai, Tuan  and
      Bui, Trung  and
      Kim, Doo Soon  and
      Tran, Quan Hung",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.56",
    doi = "10.18653/v1/2020.coling-main.56",
    pages = "649--656",
    abstract = "Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.",
}
@inproceedings{hao-etal-2020-enhancing,
    title = "Enhancing Clinical {BERT} Embedding using a Biomedical Knowledge Base",
    author = "Hao, Boran  and
      Zhu, Henghui  and
      Paschalidis, Ioannis",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.57",
    doi = "10.18653/v1/2020.coling-main.57",
    pages = "657--661",
    abstract = "Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain. In this paper, a novel joint training method is introduced for adding knowledge base information from the Unified Medical Language System (UMLS) into language model pre-training for some clinical domain corpus. We show that in three different downstream clinical NLP tasks, our pre-trained language model outperforms the corresponding model with no knowledge base information and other state-of-the-art models. Specifically, in a natural language inference task applied to clinical texts, our knowledge base pre-training approach improves accuracy by up to 1.7{\%}, whereas in clinical name entity recognition tasks, the F1-score improves by up to 1.0{\%}. The pre-trained models are available at https://github.com/noc-lab/clinical-kb-bert.",
}
@inproceedings{davari-etal-2020-timbert,
    title = "{TIMBERT}: Toponym Identifier For The Medical Domain Based on {BERT}",
    author = "Davari, MohammadReza  and
      Kosseim, Leila  and
      Bui, Tien",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.58",
    doi = "10.18653/v1/2020.coling-main.58",
    pages = "662--668",
    abstract = "In this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of viruses. We created a family of Toponym Identification Models based on BERT (TIMBERT), in order to learn in an end-to-end fashion the mapping from an input sentence to the associated sentence labeled with toponyms. When evaluated with the SemEval 2019 task 12 test set (Weissenbacher et al., 2019), our best TIMBERT model achieves an F1 score of 90.85{\%}, a significant improvement compared to the state-of-the-art of 89.13{\%} (Wang et al., 2019).",
}
@inproceedings{chakraborty-etal-2020-biomedbert,
    title = "{B}io{M}ed{BERT}: A Pre-trained Biomedical Language Model for {QA} and {IR}",
    author = "Chakraborty, Souradip  and
      Bisong, Ekaba  and
      Bhatt, Shweta  and
      Wagner, Thomas  and
      Elliott, Riley  and
      Mosconi, Francesco",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.59",
    doi = "10.18653/v1/2020.coling-main.59",
    pages = "669--679",
    abstract = "The SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical research. However, as the number of biomedical research papers continue to increase, the task of finding relevant articles to answer pressing questions has become significant. In this work, we propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain. We achieve this by building a neural-based deep contextual understanding model for Question-Answering (QA) and Information Retrieval (IR) tasks. We also leverage the new BREATHE dataset which is one of the largest available datasets of biomedical research literature, containing abstracts and full-text articles from ten different biomedical literature sources on which we pre-train our BioMedBERT model. Our work achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. In addition, we observe superior relevant results when BioMedBERT embeddings are used with Elasticsearch for the Information Retrieval task on the intelligently formulated BioASQ dataset. We believe our diverse dataset and our unique model architecture are what led us to achieve the state-of-the-art results for QA and IR tasks.",
}
@inproceedings{sanders-etal-2020-extracting,
    title = "Extracting Adherence Information from Electronic Health Records",
    author = "Sanders, Jordan  and
      Gudala, Meghana  and
      Hamilton, Kathleen  and
      Prasad, Nishtha  and
      Stovall, Jordan  and
      Blanco, Eduardo  and
      Hamilton, Jane E  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.60",
    doi = "10.18653/v1/2020.coling-main.60",
    pages = "680--695",
    abstract = "Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.",
}
@inproceedings{yadav-etal-2020-identifying,
    title = "Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework",
    author = "Yadav, Shweta  and
      Chauhan, Jainish  and
      Sain, Joy Prakash  and
      Thirunarayan, Krishnaprasad  and
      Sheth, Amit  and
      Schumm, Jeremiah",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.61",
    doi = "10.18653/v1/2020.coling-main.61",
    pages = "696--709",
    abstract = "Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, health-care workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of the Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism,co-task aware attention, enables automatic selection of optimal information across the BERT lay-ers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model{'}s robustness and reliability for distinguishing the depression symptoms.",
}
@inproceedings{cardon-grabar-2020-french-biomedical,
    title = "{F}rench Biomedical Text Simplification: When Small and Precise Helps",
    author = "Cardon, R{\'e}mi  and
      Grabar, Natalia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.62",
    doi = "10.18653/v1/2020.coling-main.62",
    pages = "710--716",
    abstract = "We present experiments on biomedical text simplification in French. We use two kinds of corpora {--} parallel sentences extracted from existing health comparable corpora in French and WikiLarge corpus translated from English to French {--} and a lexicon that associates medical terms with paraphrases. Then, we train neural models on these parallel corpora using different ratios of general and specialized sentences. We evaluate the results with BLEU, SARI and Kandel scores. The results point out that little specialized data helps significantly the simplification.",
}
@inproceedings{song-etal-2020-summarizing,
    title = "Summarizing Medical Conversations via Identifying Important Utterances",
    author = "Song, Yan  and
      Tian, Yuanhe  and
      Wang, Nan  and
      Xia, Fei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.63",
    doi = "10.18653/v1/2020.coling-main.63",
    pages = "717--729",
    abstract = "Summarization is an important natural language processing (NLP) task in identifying key information from text. For conversations, the summarization systems need to extract salient contents from spontaneous utterances by multiple speakers. In a special task-oriented scenario, namely medical conversations between patients and doctors, the symptoms, diagnoses, and treatments could be highly important because the nature of such conversation is to find a medical solution to the problem proposed by the patients. Especially consider that current online medical platforms provide millions of public available conversations between real patients and doctors, where the patients propose their medical problems and the registered doctors offer diagnosis and treatment, a conversation in most cases could be too long and the key information is hard to be located. Therefore, summarizations to the patients{'} problems and the doctors{'} treatments in the conversations can be highly useful, in terms of helping other patients with similar problems have a precise reference for potential medical solutions. In this paper, we focus on medical conversation summarization, using a dataset of medical conversations and corresponding summaries which were crawled from a well-known online healthcare service provider in China. We propose a hierarchical encoder-tagger model (HET) to generate summaries by identifying important utterances (with respect to problem proposing and solving) in the conversations. For the particular dataset used in this study, we show that high-quality summaries can be generated by extracting two types of utterances, namely, problem statements and treatment recommendations. Experimental results demonstrate that HET outperforms strong baselines and models from previous studies, and adding conversation-related features can further improve system performance.",
}
@inproceedings{dahlgren-lindstrom-etal-2020-probing,
    title = "Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case",
    author = {Dahlgren Lindstr{\"o}m, Adam  and
      Bj{\"o}rklund, Johanna  and
      Bensch, Suna  and
      Drewes, Frank},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.64",
    doi = "10.18653/v1/2020.coling-main.64",
    pages = "730--744",
    abstract = "Semantic embeddings have advanced the state of the art for countless natural language processing tasks, and various extensions to multimodal domains, such as visual-semantic embeddings, have been proposed. While the power of visual-semantic embeddings comes from the distillation and enrichment of information through machine learning, their inner workings are poorly understood and there is a shortage of analysis tools. To address this problem, we generalize the notion ofprobing tasks to the visual-semantic case. To this end, we (i) discuss the formalization of probing tasks for embeddings of image-caption pairs, (ii) define three concrete probing tasks within our general framework, (iii) train classifiers to probe for those properties, and (iv) compare various state-of-the-art embeddings under the lens of the proposed probing tasks. Our experiments reveal an up to 16{\%} increase in accuracy on visual-semantic embeddings compared to the corresponding unimodal embeddings, which suggest that the text and image dimensions represented in the former do complement each other.",
}
@inproceedings{miaschi-etal-2020-linguistic,
    title = "Linguistic Profiling of a Neural Language Model",
    author = "Miaschi, Alessio  and
      Brunato, Dominique  and
      Dell{'}Orletta, Felice  and
      Venturi, Giulia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.65",
    doi = "10.18653/v1/2020.coling-main.65",
    pages = "745--756",
    abstract = "In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT{'}s capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.",
}
@inproceedings{koto-etal-2020-indolem,
    title = "{I}ndo{LEM} and {I}ndo{BERT}: A Benchmark Dataset and Pre-trained Language Model for {I}ndonesian {NLP}",
    author = "Koto, Fajri  and
      Rahimi, Afshin  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.66",
    doi = "10.18653/v1/2020.coling-main.66",
    pages = "757--770",
    abstract = "Although the Indonesian language is spoken by almost 200 million people and the 10th most spoken language in the world, it is under-represented in NLP research. Previous work on Indonesian has been hampered by a lack of annotated datasets, a sparsity of language resources, and a lack of resource standardization. In this work, we release the IndoLEM dataset comprising seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. We additionally release IndoBERT, a new pre-trained language model for Indonesian, and evaluate it over IndoLEM, in addition to benchmarking it against existing resources. Our experiments show that IndoBERT achieves state-of-the-art performance over most of the tasks in IndoLEM.",
}
@inproceedings{mosbach-etal-2020-closer,
    title = "A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in {A}merican {E}nglish",
    author = "Mosbach, Marius  and
      Degaetano-Ortlieb, Stefania  and
      Krielke, Marie-Pauline  and
      Abdullah, Badr M.  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.67",
    doi = "10.18653/v1/2020.coling-main.67",
    pages = "771--787",
    abstract = "Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual information and antecedent identification to be resolved. Based on a naturalistic dataset, probing shows that all three models indeed capture linguistic knowledge about grammaticality, achieving high performance.Evaluation on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models{'} performance. Our results highlight the importance of (a)model comparison in evaluation task and (b) building up claims of model performance and the linguistic knowledge they capture beyond purely probing-based evaluations.",
}
@inproceedings{kapur-rogers-2020-modeling,
    title = "Modeling language evolution and feature dynamics in a realistic geographic environment",
    author = "Kapur, Rhea  and
      Rogers, Phillip",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.68",
    doi = "10.18653/v1/2020.coling-main.68",
    pages = "788--798",
    abstract = "Recent, innovative efforts to understand the uneven distribution of languages and linguistic feature values in time and space attest to both the challenge these issues pose and the value in solving them. In this paper, we introduce a model for simulating languages and their features over time in a realistic geographic environment. At its core is a model of language phylogeny and migration whose parameters are chosen to reproduce known language family sizes and geographic dispersions. This foundation in turn is used to explore the dynamics of linguistic features. Languages are assigned feature values that can change randomly or under the influence of nearby languages according to predetermined probabilities. We assess the effects of these settings on resulting geographic and genealogical patterns using homogeneity measures defined in the literature. The resulting model is both flexible and realistic, and it can be employed to answer a wide range of related questions.",
}
@inproceedings{huang-etal-2020-syntax,
    title = "Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification",
    author = "Huang, Lianzhe  and
      Sun, Xin  and
      Li, Sujian  and
      Zhang, Linhao  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.69",
    doi = "10.18653/v1/2020.coling-main.69",
    pages = "799--810",
    abstract = "Aspect-level sentiment classification aims to distinguish the sentiment polarities over aspect terms in a sentence. Existing approaches mostly focus on modeling the relationship between the given aspect words and their contexts with attention, and ignore the use of more elaborate knowledge implicit in the context. In this paper, we exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better. And the subwords of BERT are integrated into the dependency tree graphs, which can obtain more accurate representations of words by graph attention. Experiments demonstrate the effectiveness of our model.",
}
@inproceedings{zhao-etal-2020-attention,
    title = "Attention Transfer Network for Aspect-level Sentiment Classification",
    author = "Zhao, Fei  and
      Wu, Zhen  and
      Dai, Xinyu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.70",
    doi = "10.18653/v1/2020.coling-main.70",
    pages = "811--821",
    abstract = "Aspect-level sentiment classification (ASC) aims to detect the sentiment polarity of a given opinion target in a sentence. In neural network-based methods for ASC, most works employ the attention mechanism to capture the corresponding sentiment words of the opinion target, then aggregate them as evidence to infer the sentiment of the target. However, aspect-level datasets are all relatively small-scale due to the complexity of annotation. Data scarcity causes the attention mechanism sometimes to fail to focus on the corresponding sentiment words of the target, which finally weakens the performance of neural models. To address the issue, we propose a novel Attention Transfer Network (ATN) in this paper, which can successfully exploit attention knowledge from resource-rich document-level sentiment classification datasets to improve the attention capability of the aspect-level sentiment classification task. In the ATN model, we design two different methods to transfer attention knowledge and conduct experiments on two ASC benchmark datasets. Extensive experimental results show that our methods consistently outperform state-of-the-art works. Further analysis also validates the effectiveness of ATN.",
}
@inproceedings{wang-ren-2020-label,
    title = "Label Correction Model for Aspect-based Sentiment Analysis",
    author = "Wang, Qianlong  and
      Ren, Jiangtao",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.71",
    doi = "10.18653/v1/2020.coling-main.71",
    pages = "822--832",
    abstract = "Aspect-based sentiment analysis includes opinion aspect extraction and aspect sentiment classification. Researchers have attempted to discover the relationship between these two sub-tasks and have proposed the joint model for solving aspect-based sentiment analysis. However, they ignore a phenomenon: aspect boundary label and sentiment label of the same word can correct each other. To exploit this phenomenon, we propose a novel deep learning model named the label correction model. Specifically, given an input sentence, our model first predicts the aspect boundary label sequence and sentiment label sequence, then re-predicts the aspect boundary (sentiment) label sequence using the embeddings of the previously predicted sentiment (aspect boundary) label. The goal of the re-prediction operation (can be repeated multiple times) is to use the information of the sentiment (aspect boundary) label to correct the wrong aspect boundary (sentiment) label. Moreover, we explore two ways of using label embeddings: add and gate mechanism. We evaluate our model on three benchmark datasets. Experimental results verify that our model achieves state-of-the-art performance compared with several baselines.",
}
@inproceedings{cai-etal-2020-aspect,
    title = "Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network",
    author = "Cai, Hongjie  and
      Tu, Yaofeng  and
      Zhou, Xiangsheng  and
      Yu, Jianfei  and
      Xia, Rui",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.72",
    doi = "10.18653/v1/2020.coling-main.72",
    pages = "833--843",
    abstract = "Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification. However, currently only a few simple studies have focused on this problem. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a piece of text, and then predict the sentiment for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model can consistently achieve the best results on four benchmarks.",
}
@inproceedings{yang-etal-2020-constituency,
    title = "Constituency Lattice Encoding for Aspect Term Extraction",
    author = "Yang, Yunyi  and
      Li, Kun  and
      Quan, Xiaojun  and
      Shen, Weizhou  and
      Su, Qinliang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.73",
    doi = "10.18653/v1/2020.coling-main.73",
    pages = "844--855",
    abstract = "One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms. In this paper, we aim to address this issue by incorporating the span annotations of constituents of a sentence to leverage the syntactic information in neural network models. To this end, we first construct a constituency lattice structure based on the constituents of a constituency tree. Then, we present two approaches to encoding the constituency lattice using BiLSTM-CRF and BERT as the base models, respectively. We experimented on two benchmark datasets to evaluate the two models, and the results confirm their superiority with respective 3.17 and 1.35 points gained in F1-Measure over the current state of the art. The improvements justify the effectiveness of the constituency lattice for aspect term extraction.",
}
@inproceedings{wambsganss-etal-2020-corpus,
    title = "A Corpus for Argumentative Writing Support in {G}erman",
    author = {Wambsganss, Thiemo  and
      Niklaus, Christina  and
      S{\"o}llner, Matthias  and
      Handschuh, Siegfried  and
      Leimeister, Jan Marco},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.74",
    doi = "10.18653/v1/2020.coling-main.74",
    pages = "856--869",
    abstract = "In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.57 for argument components and α = 0.49 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement. Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.",
}
@inproceedings{nguyen-grieve-2020-word,
    title = "Do Word Embeddings Capture Spelling Variation?",
    author = "Nguyen, Dong  and
      Grieve, Jack",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.75",
    doi = "10.18653/v1/2020.coling-main.75",
    pages = "870--881",
    abstract = "Analyses of word embeddings have primarily focused on semantic and syntactic properties. However, word embeddings have the potential to encode other properties as well. In this paper, we propose a new perspective on the analysis of word embeddings by focusing on spelling variation. In social media, spelling variation is abundant and often socially meaningful. Here, we analyze word embeddings trained on Twitter and Reddit data. We present three analyses using pairs of word forms covering seven types of spelling variation in English. Taken together, our results show that word embeddings encode spelling variation patterns of various types to some extent, even embeddings trained using the skipgram model which does not take spelling into account. Our results also suggest a link between the intentionality of the variation and the distance of the non-conventional spellings to their conventional spellings.",
}
@inproceedings{ding-etal-2020-dont,
    title = "Don{'}t take {``}nswvtnvakgxpm{''} for an answer {--}The surprising vulnerability of automatic content scoring systems to adversarial input",
    author = "Ding, Yuning  and
      Riordan, Brian  and
      Horbach, Andrea  and
      Cahill, Aoife  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.76",
    doi = "10.18653/v1/2020.coling-main.76",
    pages = "882--892",
    abstract = "Automatic content scoring systems are widely used on short answer tasks to save human effort. However, the use of these systems can invite cheating strategies, such as students writing irrelevant answers in the hopes of gaining at least partial credit. We generate adversarial answers for benchmark content scoring datasets based on different methods of increasing sophistication and show that even simple methods lead to a surprising decrease in content scoring performance. As an extreme example, up to 60{\%} of adversarial answers generated from random shuffling of words in real answers are accepted by a state-of-the-art scoring system. In addition to analyzing the vulnerabilities of content scoring systems, we examine countermeasures such as adversarial training and show that these measures improve system robustness against adversarial answers considerably but do not suffice to completely solve the problem.",
}
@inproceedings{ha-etal-2020-automated,
    title = "Automated Prediction of Examinee Proficiency from Short-Answer Questions",
    author = "Ha, Le An  and
      Yaneva, Victoria  and
      Harik, Polina  and
      Pandian, Ravi  and
      Morales, Amy  and
      Clauser, Brian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.77",
    doi = "10.18653/v1/2020.coling-main.77",
    pages = "893--903",
    abstract = "This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs). While previous approaches train on manually labeled data to predict the human-ratings assigned to SAQ responses, the approach presented here models examinee proficiency directly and does not require manually labeled data to train on. We use data from a large medical exam where experimental SAQ items are embedded alongside 106 scored multiple-choice questions (MCQs). First, the latent trait of examinee proficiency is measured using the scored MCQs and then a model is trained on the experimental SAQ responses as input, aiming to predict proficiency as its target variable. The predicted value is then used as a {``}score{''} for the SAQ response and evaluated in terms of its contribution to the precision of proficiency estimation.",
}
@inproceedings{luoma-pyysalo-2020-exploring,
    title = "Exploring Cross-sentence Contexts for Named Entity Recognition with {BERT}",
    author = "Luoma, Jouni  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.78",
    doi = "10.18653/v1/2020.coling-main.78",
    pages = "904--914",
    abstract = "Named entity recognition (NER) is frequently addressed as a sequence classification task with each input consisting of one sentence of text. It is nevertheless clear that useful information for NER is often found also elsewhere in text. Recent self-attention models like BERT can both capture long-distance relationships in input and represent inputs consisting of several sentences. This creates opportunities for adding cross-sentence information in natural language processing tasks. This paper presents a systematic study exploring the use of cross-sentence information for NER using BERT models in five languages. We find that adding context as additional sentences to BERT input systematically increases NER performance. Multiple sentences in input samples allows us to study the predictions of the sentences in different contexts. We propose a straightforward method, Contextual Majority Voting (CMV), to combine these different predictions and demonstrate this to further increase NER performance. Evaluation on established datasets, including the CoNLL{'}02 and CoNLL{'}03 NER benchmarks, demonstrates that our proposed approach can improve on the state-of-the-art NER results on English, Dutch, and Finnish, achieves the best reported BERT-based results on German, and is on par with other BERT-based approaches in Spanish. We release all methods implemented in this work under open licenses.",
}
@inproceedings{galassi-etal-2020-cross,
    title = "Cross-lingual Annotation Projection in Legal Texts",
    author = "Galassi, Andrea  and
      Drazewski, Kasper  and
      Lippi, Marco  and
      Torroni, Paolo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.79",
    doi = "10.18653/v1/2020.coling-main.79",
    pages = "915--926",
    abstract = "We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another. In particular, we focus on the detection of unfair clauses in privacy policies and terms of service. We present the first English-German parallel asymmetric corpus for the task at hand. We study and compare several language-agnostic sentence-level projection methods. Our results indicate that a combination of word embeddings and dynamic time warping performs best.",
}
@inproceedings{al-ali-etal-2020-deep,
    title = "Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls",
    author = "Al-Ali, Ahmed Ghanim  and
      Phaal, Robert  and
      Sull, Donald",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.80",
    doi = "10.18653/v1/2020.coling-main.80",
    pages = "927--935",
    abstract = "Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing. However, many companies report that their strategies did not achieve the anticipated business results. This study is the first to apply state-of-the-art NLP models on unstructured data to understand the different clusters of digital strategy patterns that companies are Adopting. We achieve this by ana-lyzing earnings calls from Fortune{'}s Global 500 companies between 2015 and 2019. We use Transformer-based architecture for text classification which show a better understanding of the conversation context. We then investigate digital strategy patterns by applying clustering analysis. Our findings suggest that Fortune 500 companies use four distinct strategies which are product-led, customer experience-led, service-led, and efficiency-led . This work provides an empirical baseline for companies and researchers to enhance our understanding of the field.",
}
@inproceedings{laparra-bethard-2020-dataset,
    title = "A Dataset and Evaluation Framework for Complex Geographical Description Parsing",
    author = "Laparra, Egoitz  and
      Bethard, Steven",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.81",
    doi = "10.18653/v1/2020.coling-main.81",
    pages = "936--948",
    abstract = "Much previous work on geoparsing has focused on identifying and resolving individual toponyms in text like Adrano, S.Maria di Licodia or Catania. However, geographical locations occur not only as individual toponyms, but also as compositions of reference geolocations joined and modified by connectives, e.g., {``}. . . between the towns of Adrano and S.Maria di Licodia, 32 kilometres northwest of Catania{''}. Ideally, a geoparser should be able to take such text, and the geographical shapes of the toponyms referenced within it, and parse these into a geographical shape, formed by a set of coordinates, that represents the location described. But creating a dataset for this complex geoparsing task is difficult and, if done manually, would require a huge amount of effort to annotate the geographical shapes of not only the geolocation described but also the reference toponyms. We present an approach that automates most of the process by combining Wikipedia and OpenStreetMap. As a result, we have gathered a collection of 360,187 uncurated complex geolocation descriptions, from which we have manually curated 1,000 examples intended to be used as a test set. To accompany the data, we define a new geoparsing evaluation framework along with a scoring methodology and a set of baselines.",
}
@inproceedings{li-etal-2020-docbank,
    title = "{D}oc{B}ank: A Benchmark Dataset for Document Layout Analysis",
    author = "Li, Minghao  and
      Xu, Yiheng  and
      Cui, Lei  and
      Huang, Shaohan  and
      Wei, Furu  and
      Li, Zhoujun  and
      Zhou, Ming",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.82",
    doi = "10.18653/v1/2020.coling-main.82",
    pages = "949--960",
    abstract = "Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality labeled datasets with both visual and textual information are still insufficient. In this paper, we present DocBank, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the LaTeX documents available on the arXiv.com. With DocBank, models from different modalities can be compared fairly and multi-modal approaches will be further investigated and boost the performance of document layout analysis. We build several strong baselines and manually split train/dev/test sets for evaluation. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents. The DocBank dataset is publicly available at https://github.com/doc-analysis/DocBank.",
}
@inproceedings{hyun-etal-2020-building,
    title = "Building Large-Scale {E}nglish and {K}orean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain",
    author = "Hyun, Dongmin  and
      Cho, Junsu  and
      Yu, Hwanjo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.83",
    doi = "10.18653/v1/2020.coling-main.83",
    pages = "961--966",
    abstract = "We release large-scale datasets of users{'} comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. In addition, this work covers new language (i.e., Korean) along with English for aspect-level sentiment analysis. We build the datasets from automotive domain to enable users (e.g., marketers in automotive companies) to analyze the voice of customers on automobiles. We also provide baseline performances for future work by evaluating recent models on the released datasets.",
}
@inproceedings{elhammadi-etal-2020-high,
    title = "A High Precision Pipeline for Financial Knowledge Graph Construction",
    author = "Elhammadi, Sarah  and
      V.S. Lakshmanan, Laks  and
      Ng, Raymond  and
      Simpson, Michael  and
      Huai, Baoxing  and
      Wang, Zhefeng  and
      Wang, Lanjun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.84",
    doi = "10.18653/v1/2020.coling-main.84",
    pages = "967--977",
    abstract = "Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extracting information from unstructured information sources, particularly text documents. Knowledge graphs have emerged as a standard for structured knowledge representation, whereby entities and their inter-relations are represented and conveniently stored as (subject,predicate,object) triples in a graph that can be used to power various downstream applications. The proliferation of financial news sources reporting on companies, markets, currencies, and stocks presents an opportunity for extracting valuable knowledge about this crucial domain. In this paper, we focus on constructing a knowledge graph automatically by information extraction from a large corpus of financial news articles. For that purpose, we develop a high precision knowledge extraction pipeline tailored for the financial domain. This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342,000 compact extractions from over 288,000 financial news articles, with a precision of 78{\%} at the top-100 extractions.The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.",
}
@inproceedings{xing-etal-2020-financial,
    title = "Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets",
    author = "Xing, Frank  and
      Malandri, Lorenzo  and
      Zhang, Yue  and
      Cambria, Erik",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.85",
    doi = "10.18653/v1/2020.coling-main.85",
    pages = "978--987",
    abstract = "The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving. This study, instead of proposing any {``}new model{''}, investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors. These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.",
}
@inproceedings{kien-etal-2020-answering,
    title = "Answering Legal Questions by Learning Neural Attentive Text Representation",
    author = "Kien, Phi Manh  and
      Nguyen, Ha-Thanh  and
      Bach, Ngo Xuan  and
      Tran, Vu  and
      Nguyen, Minh Le  and
      Phuong, Tu Minh",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.86",
    doi = "10.18653/v1/2020.coling-main.86",
    pages = "988--998",
    abstract = "Text representation plays a vital role in retrieval-based question answering, especially in the legal domain where documents are usually long and complicated. The better the question and the legal documents are represented, the more accurate they are matched. In this paper, we focus on the task of answering legal questions at the article level. Given a legal question, the goal is to retrieve all the correct and valid legal articles, that can be used as the basic to answer the question. We present a retrieval-based model for the task by learning neural attentive text representation. Our text representation method first leverages convolutional neural networks to extract important information in a question and legal articles. Attention mechanisms are then used to represent the question and articles and select appropriate information to align them in a matching process. Experimental results on an annotated corpus consisting of 5,922 Vietnamese legal questions show that our model outperforms state-of-the-art retrieval-based methods for question answering by large margins in terms of both recall and NDCG.",
}
@inproceedings{biju-etal-2020-joint,
    title = "Joint Transformer/{RNN} Architecture for Gesture Typing in Indic Languages",
    author = "Biju, Emil  and
      Sriram, Anirudh  and
      Khapra, Mitesh M.  and
      Kumar, Pratyush",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.87",
    doi = "10.18653/v1/2020.coling-main.87",
    pages = "999--1010",
    abstract = "Gesture typing is a method of typing words on a touch-based keyboard by creating a continuous trace passing through the relevant keys. This work is aimed at developing a keyboard that supports gesture typing in Indic languages. We begin by noting that when dealing with Indic languages, one needs to cater to two different sets of users: (i) users who prefer to type in the native Indic script (Devanagari, Bengali, etc.) and (ii) users who prefer to type in the English script but want the transliterated output in the native script. In both cases, we need a model that takes a trace as input and maps it to the intended word. To enable the development of these models, we create and release two datasets. First, we create a dataset containing keyboard traces for 193,658 words from 7 Indic languages. Second, we curate 104,412 English-Indic transliteration pairs from Wikidata across these languages. Using these datasets we build a model that performs path decoding, transliteration and transliteration correction. Unlike prior approaches, our proposed model does not make co-character independence assumptions during decoding. The overall accuracy of our model across the 7 languages varies from 70-95{\%}.",
}
@inproceedings{paul-etal-2020-automatic,
    title = "Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need",
    author = "Paul, Shounak  and
      Goyal, Pawan  and
      Ghosh, Saptarshi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.88",
    doi = "10.18653/v1/2020.coling-main.88",
    pages = "1011--1022",
    abstract = "Automatic Charge Identification (ACI) is the task of identifying the relevant charges given the facts of a situation and the statutory laws that define these charges, and is a crucial aspect of the judicial process. Existing works focus on learning charge-side representations by modeling relationships between the charges, but not much effort has been made in improving fact-side representations. We observe that only a small fraction of sentences in the facts actually indicates the charges. We show that by using a very small subset ({\textless} 3{\%}) of fact descriptions annotated with sentence-level charges, we can achieve an improvement across a range of different ACI models, as compared to modeling just the main document-level task on a much larger dataset. Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework. The proposed model comprehensively outperforms a large number of recent baselines for ACI. The improvement in performance is particularly noticeable for the rare charges which are known to be especially challenging to identify.",
}
@inproceedings{sukhareva-2020-context,
    title = "Context-Aware Text Normalisation for Historical Dialects",
    author = "Sukhareva, Maria",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.89",
    doi = "10.18653/v1/2020.coling-main.89",
    pages = "1023--1036",
    abstract = "Context-aware historical text normalisation is a severely under-researched area. To fill the gap we propose a context-aware normalisation approach that relies on the state-of-the-art methods in neural machine translation and transfer learning. We propose a multidialect normaliser with a context-aware reranking of the candidates. The reranker relies on a word-level n-gram language model that is applied to the five best normalisation candidates. The results are evaluated on the historical multidialect datasets of German, Spanish, Portuguese and Slovene. We show that incorporating dialectal information into the training leads to an accuracy improvement on all the datasets. The context-aware reranking gives further improvement over the baseline. For three out of six datasets, we reach a significantly higher accuracy than reported in the previous studies. The other three results are comparable with the current state-of-the-art. The code for the reranker is published as open-source.",
}
@inproceedings{rodina-kutuzov-2020-rusemshift,
    title = "{R}u{S}em{S}hift: a dataset of historical lexical semantic change in {R}ussian",
    author = "Rodina, Julia  and
      Kutuzov, Andrey",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.90",
    doi = "10.18653/v1/2020.coling-main.90",
    pages = "1037--1047",
    abstract = "We present RuSemShift, a large-scale manually annotated test set for the task of semantic change modeling in Russian for two long-term time period pairs: from the pre-Soviet through the Soviet times and from the Soviet through the post-Soviet times. Target words were annotated by multiple crowd-source workers. The annotation process was organized following the DURel framework and was based on sentence contexts extracted from the Russian National Corpus. Additionally, we report the performance of several distributional approaches on RuSemShift, achieving promising results, which at the same time leave room for other researchers to improve.",
}
@inproceedings{yimam-etal-2020-exploring,
    title = "Exploring {A}mharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models",
    author = "Yimam, Seid Muhie  and
      Alemayehu, Hizkiel Mitiku  and
      Ayele, Abinew  and
      Biemann, Chris",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.91",
    doi = "10.18653/v1/2020.coling-main.91",
    pages = "1048--1060",
    abstract = "This paper presents the study of sentiment analysis for Amharic social media texts. As the number of social media users is ever-increasing, social media platforms would like to understand the latent meaning and sentiments of a text to enhance decision-making procedures. However, low-resource languages such as Amharic have received less attention due to several reasons such as lack of well-annotated datasets, unavailability of computing resources, and fewer or no expert researchers in the area. This research addresses three main research questions. We first explore the suitability of existing tools for the sentiment analysis task. Annotation tools are scarce to support large-scale annotation tasks in Amharic. Also, the existing crowdsourcing platforms do not support Amharic text annotation. Hence, we build a social-network-friendly annotation tool called {`}ASAB{'} using the Telegram bot. We collect 9.4k tweets, where each tweet is annotated by three Telegram users. Moreover, we explore the suitability of machine learning approaches for Amharic sentiment analysis. The FLAIR deep learning text classifier, based on network embeddings that are computed from a distributional thesaurus, outperforms other supervised classifiers. We further investigate the challenges in building a sentiment analysis system for Amharic and we found that the widespread usage of sarcasm and figurative speech are the main issues in dealing with the problem. To advance the sentiment analysis research in Amharic and other related low-resource languages, we release the dataset, the annotation tool, source code, and models publicly under a permissive.",
}
@inproceedings{gupta-etal-2020-effective,
    title = "Effective Few-Shot Classification with Transfer Learning",
    author = "Gupta, Aakriti  and
      Thadani, Kapil  and
      O{'}Hare, Neil",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.92",
    doi = "10.18653/v1/2020.coling-main.92",
    pages = "1061--1066",
    abstract = "Few-shot learning addresses the the problem of learning based on a small amount of training data. Although more well-studied in the domain of computer vision, recent work has adapted the Amazon Review Sentiment Classification (ARSC) text dataset for use in the few-shot setting. In this work, we use the ARSC dataset to study a simple application of transfer learning approaches to few-shot classification. We train a single binary classifier to learn all few-shot classes jointly by prefixing class identifiers to the input text. Given the text and class, the model then makes a binary prediction for that text/class pair. Our results show that this simple approach can outperform most published results on this dataset. Surprisingly, we also show that including domain information as part of the task definition only leads to a modest improvement in model accuracy, and zero-shot classification, without further fine-tuning on few-shot domains, performs equivalently to few-shot classification. These results suggest that the classes in the ARSC few-shot task, which are defined by the intersection of domain and rating, are actually very similar to each other, and that a more suitable dataset is needed for the study of few-shot text classification.",
}
@inproceedings{chen-li-2020-swafn,
    title = "{SWAFN}: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis",
    author = "Chen, Minping  and
      Li, Xia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.93",
    doi = "10.18653/v1/2020.coling-main.93",
    pages = "1067--1077",
    abstract = "Multimodal sentiment analysis aims to predict sentiment of language text with the help of other modalities, such as vision and acoustic features. Previous studies focused on learning the joint representation of multiple modalities, ignoring some useful knowledge contained in language modal. In this paper, we try to incorporate sentimental words knowledge into the fusion network to guide the learning of joint representation of multimodal features. Our method consists of two components: shallow fusion part and aggregation part. For the shallow fusion part, we use crossmodal coattention mechanism to obtain bidirectional context information of each two modals to get the fused shallow representations. For the aggregation part, we design a multitask of sentimental words classification to help and guide the deep fusion of the three modalities and obtain the final sentimental words aware fusion representation. We carry out several experiments on CMU-MOSI, CMU-MOSEI and YouTube datasets. The experimental results show that introducing sentimental words prediction as a multitask can really improve the fusion representation of multiple modalities.",
}
@inproceedings{an-etal-2020-multimodal,
    title = "Multimodal Topic-Enriched Auxiliary Learning for Depression Detection",
    author = "An, Minghui  and
      Wang, Jingjing  and
      Li, Shoushan  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.94",
    doi = "10.18653/v1/2020.coling-main.94",
    pages = "1078--1089",
    abstract = "From the perspective of health psychology, human beings with long-term and sustained negativity are highly possible to be diagnosed with depression. Inspired by this, we argue that the global topic information derived from user-generated contents (e.g., texts and images) is crucial to boost the performance of the depression detection task, though this information has been neglected by almost all previous studies on depression detection. To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i.e., texts and images) for depression detection. Especially, in our approach, a modality-agnostic topic model is proposed to be capable of mining the topical clues from either the discrete textual signals or the continuous visual signals. On this basis, the topic modeling w.r.t. the two modalities are cast as two auxiliary tasks for improving the performance of the primary task (i.e., depression detection). Finally, the detailed evaluation demonstrates the great advantage of our MTAL approach to depression detection over the state-of-the-art baselines. This justifies the importance of the multimodal topic information to depression detection and the effectiveness of our approach in capturing such information.",
}
@inproceedings{suglia-etal-2020-imagining,
    title = "Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games",
    author = "Suglia, Alessandro  and
      Vergari, Antonio  and
      Konstas, Ioannis  and
      Bisk, Yonatan  and
      Bastianelli, Emanuele  and
      Vanzo, Andrea  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.95",
    doi = "10.18653/v1/2020.coling-main.95",
    pages = "1090--1102",
    abstract = "In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual representations of objects that are both discriminative and expressive enough to ask questions and guess correctly. However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time. This provides an unnatural performance advantage when categories at inference time match those at training time, and it causes models to fail in more realistic {``}zero-shot{''} scenarios where out-of-domain object categories are involved. To overcome this issue, we introduce a novel {``}imagination{''} module based on Regularized Auto-Encoders, that learns context-aware and category-aware latent embeddings without relying on category labels at inference time. Our imagination module outperforms state-of-the-art competitors by 8.26{\%} gameplay accuracy in the CompGuessWhat?! zero-shot scenario (Suglia et al., 2020), and it improves the Oracle and Guesser accuracy by 2.08{\%} and 12.86{\%} in the GuessWhat?! benchmark, when no gold categories are available at inference time. The imagination module also boosts reasoning about object properties and attributes.",
}
@inproceedings{moon-etal-2020-situated,
    title = "Situated and Interactive Multimodal Conversations",
    author = "Moon, Seungwhan  and
      Kottur, Satwik  and
      Crook, Paul  and
      De, Ankita  and
      Poddar, Shivani  and
      Levin, Theodore  and
      Whitney, David  and
      Difranco, Daniel  and
      Beirami, Ahmad  and
      Cho, Eunjoon  and
      Subba, Rajen  and
      Geramifard, Alborz",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.96",
    doi = "10.18653/v1/2020.coling-main.96",
    pages = "1103--1121",
    abstract = "Next generation virtual assistants are envisioned to handle multimodal inputs (e.g., vision, memories of previous interactions, and the user{'}s utterances), and perform multimodal actions (, displaying a route while generating the system{'}s utterance). We introduce Situated Interactive MultiModal Conversations (SIMMC) as a new direction aimed at training agents that take multimodal actions grounded in a co-evolving multimodal input context in addition to the dialog history. We provide two SIMMC datasets totalling {\textasciitilde}13K human-human dialogs ({\textasciitilde}169K utterances) collected using a multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture {--} grounded in a shared virtual environment; and (b) fashion {--} grounded in an evolving set of images. Datasets include multimodal context of the items appearing in each scene, and contextual NLU, NLG and coreference annotations using a novel and unified framework of SIMMC conversational acts for both user and assistant utterances. Finally, we present several tasks within SIMMC as objective evaluation protocols, such as structural API prediction, response generation, and dialog state tracking. We benchmark a collection of existing models on these SIMMC tasks as strong baselines, and demonstrate rich multimodal conversational interactions. Our data, annotations, and models will be made publicly available.",
}
@inproceedings{liu-etal-2020-meet,
    title = "Meet Changes with Constancy: Learning Invariance in Multi-Source Translation",
    author = "Liu, Jianfeng  and
      Luo, Ling  and
      Ao, Xiang  and
      Song, Yan  and
      Xu, Haoran  and
      Ye, Jian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.97",
    doi = "10.18653/v1/2020.coling-main.97",
    pages = "1122--1132",
    abstract = "Multi-source neural machine translation aims to translate from parallel sources of information (e.g. languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems. Despite the remarkable success of existing models, they usually neglect the fact that multiple source inputs may have inconsistencies. Such differences might bring noise to the task and limit the performance of existing multi-source NMT approaches due to their indiscriminate usage of input sources for target word predictions. In this paper, we attempt to leverage the potential complementary information among distinct sources and alleviate the occasional conflicts of them. To accomplish that, we propose a source invariance network to learn the invariant information of parallel sources. Such network can be easily integrated with multi-encoder based multi-source NMT methods (e.g. multi-encoder RNN and transformer) to enhance the translation results. Extensive experiments on two multi-source translation tasks demonstrate that the proposed approach not only achieves clear gains in translation quality but also captures implicit invariance between different sources.",
}
@inproceedings{zhang-etal-2020-enhancing-neural,
    title = "Enhancing Neural Models with Vulnerability via Adversarial Attack",
    author = "Zhang, Rong  and
      Zhou, Qifei  and
      An, Bo  and
      Li, Weiping  and
      Mo, Tong  and
      Wu, Bo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.98",
    doi = "10.18653/v1/2020.coling-main.98",
    pages = "1133--1146",
    abstract = "Natural Language Sentence Matching (NLSM) serves as the core of many natural language processing tasks. 1) Most previous work develops a single specific neural model for NLSM tasks. 2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks. 3) Adversarial attack is usually used to generate adversarial samples that can fool neural models. In this paper, we first find a phenomenon that different categories of samples have different vulnerabilities. Vulnerability is the difficulty degree in changing the label of a sample. Considering the phenomenon, we propose a general two-stage training framework to enhance neural models with Vulnerability via Adversarial Attack (VAA). We design criteria to measure the vulnerability which is obtained by adversarial attack. VAA framework can be adapted to various neural models by incorporating the vulnerability. In addition, we prove a theorem and four corollaries to explain the factors influencing vulnerability effectiveness. Experimental results show that VAA significantly improves the performance of neural models on NLSM datasets. The results are also consistent with the theorem and corollaries. The code is released on https://github.com/rzhangpku/VAA.",
}
@inproceedings{li-etal-2020-r,
    title = "{R}-{VGAE}: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning",
    author = "Li, Irene  and
      Fabbri, Alexander  and
      Hingmire, Swapnil  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.99",
    doi = "10.18653/v1/2020.coling-main.99",
    pages = "1147--1157",
    abstract = "The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs. In this paper, we frame learning prerequisite relationships among concepts as an unsupervised task with no access to labeled concept pairs during training. We propose a model called the Relational-Variational Graph AutoEncoder (R-VGAE) to predict concept relations within a graph consisting of concept and resource nodes. Results show that our unsupervised approach outperforms graph-based semi-supervised methods and other baseline methods by up to 9.77{\%} and 10.47{\%} in terms of prerequisite relation prediction accuracy and F1 score. Our method is notably the first graph-based model that attempts to make use of deep learning representations for the task of unsupervised prerequisite learning. We also expand an existing corpus which totals 1,717 English Natural Language Processing (NLP)-related lecture slide files and manual concept pair annotations over 322 topics.",
}
@inproceedings{griesshaber-etal-2020-fine,
    title = "Fine-tuning {BERT} for Low-Resource Natural Language Understanding via Active Learning",
    author = "Grie{\ss}haber, Daniel  and
      Maucher, Johannes  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.100",
    doi = "10.18653/v1/2020.coling-main.100",
    pages = "1158--1171",
    abstract = "Recently, leveraging pre-trained Transformer based language models in down stream, task specific models has advanced state of the art results in natural language understanding tasks. However, only a little research has explored the suitability of this approach in low resource settings with less than 1,000 training data points. In this work, we explore fine-tuning methods of BERT - a pre-trained Transformer based language model - by utilizing pool-based active learning to speed up training while keeping the cost of labeling new data constant. Our experimental results on the GLUE data set show an advantage in model performance by maximizing the approximate knowledge gain of the model when querying from the pool of unlabeled data. Finally, we demonstrate and analyze the benefits of freezing layers of the language model during fine-tuning to reduce the number of trainable parameters, making it more suitable for low-resource settings.",
}
@inproceedings{feng-etal-2020-exploring,
    title = "Exploring End-to-End Differentiable Natural Logic Modeling",
    author = "Feng, Yufei  and
      Zheng, Zi{'}ou  and
      Liu, Quan  and
      Greenspan, Michael  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.101",
    doi = "10.18653/v1/2020.coling-main.101",
    pages = "1172--1185",
    abstract = "We explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natural logic formalism while introducing subsymbolic vector representations and neural components. The proposed model adapts module networks to model natural logic operations, which is enhanced with a memory component to model contextual information. Experiments show that the proposed framework can effectively model monotonicity-based reasoning, compared to the baseline neural network models without built-in inductive bias for monotonicity-based reasoning. Our proposed model shows to be robust when transferred from upward to downward inference. We perform further analyses on the performance of the proposed model on aggregation, showing the effectiveness of the proposed subcomponents on helping achieve better intermediate aggregation performance.",
}
@inproceedings{chen-etal-2020-semantically,
    title = "A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation",
    author = "Chen, Wenqing  and
      Tian, Jidong  and
      Xiao, Liqiang  and
      He, Hao  and
      Jin, Yaohui",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.102",
    doi = "10.18653/v1/2020.coling-main.102",
    pages = "1186--1198",
    abstract = "Paraphrase generation aims to generate semantically consistent sentences with different syntactic realizations. Most of the recent studies rely on the typical encoder-decoder framework where the generation process is deterministic. However, in practice, the ability to generate multiple syntactically different paraphrases is important. Recent work proposed to cooperate variational inference on a target-related latent variable to introduce the diversity. But the latent variable may be contaminated by the semantic information of other unrelated sentences, and in turn, change the conveyed meaning of generated paraphrases. In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free. Moreover, we adopt another discriminator to improve the word-level and sentence-level semantic consistency. So the proposed framework can generate multiple semantically consistent and syntactically different paraphrases. The experiments show that our model outperforms the baseline models on the metrics based on both n-gram matching and semantic similarity, and our model can generate multiple different paraphrases by assembling different syntactic variables.",
}
@inproceedings{ohashi-etal-2020-tiny,
    title = "Tiny Word Embeddings Using Globally Informed Reconstruction",
    author = "Ohashi, Sora  and
      Isogawa, Mao  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.103",
    doi = "10.18653/v1/2020.coling-main.103",
    pages = "1199--1203",
    abstract = "We reduce the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. Previous studies in this direction created a smaller word embedding model by reconstructing pre-trained word representations from those of subwords, which allows to store only a smaller number of subword embeddings in the memory. However, previous studies that train the reconstruction models using only target words cannot reduce the model size extremely while preserving its quality. Inspired by the observation of words with similar meanings having similar embeddings, our reconstruction training learns the global relationships among words, which can be employed in various models for word embedding reconstruction. Experimental results on word similarity benchmarks show that the proposed method improves the performance of the all subword-based reconstruction models.",
}
@inproceedings{ha-etal-2020-improving,
    title = "Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models",
    author = "Ha, Phong  and
      Zhang, Shanshan  and
      Djuric, Nemanja  and
      Vucetic, Slobodan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.104",
    doi = "10.18653/v1/2020.coling-main.104",
    pages = "1204--1213",
    abstract = "Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem. A popular solution is to train a character-level neural network to reproduce the embeddings from a standard word embedding model. The trained network is then used to assign vectors to any input string, including OOV and rare words. We enhance this approach and introduce an algorithm that iteratively refines and improves both word- and character-level models. We demonstrate that our method outperforms the existing algorithms on 5 word similarity data sets, and that it can be successfully applied to job title normalization, an important problem in the e-recruitment domain that suffers from the OOV problem.",
}
@inproceedings{rama-etal-2020-probing,
    title = "Probing Multilingual {BERT} for Genetic and Typological Signals",
    author = "Rama, Taraka  and
      Beinborn, Lisa  and
      Eger, Steffen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.105",
    doi = "10.18653/v1/2020.coling-main.105",
    pages = "1214--1228",
    abstract = "We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations. We 1) employ the language distances to infer and evaluate language trees, finding that they are close to the reference family tree in terms of quartet tree distance, 2) perform distance matrix regression analysis, finding that the language distances can be best explained by phylogenetic and worst by structural factors and 3) present a novel measure for measuring diachronic meaning stability (based on cross-lingual representation variability) which correlates significantly with published ranked lists based on linguistic approaches. Our results contribute to the nascent field of typological interpretability of cross-lingual text representations.",
}
@inproceedings{he-etal-2020-learning-efficient,
    title = "Learning Efficient Task-Specific Meta-Embeddings with Word Prisms",
    author = "He, Jingyi  and
      Tsiolis, Kc  and
      Kenyon-Dean, Kian  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.106",
    doi = "10.18653/v1/2020.coling-main.106",
    pages = "1229--1241",
    abstract = "Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.) depending on the notion of context defined at training time. These properties manifest when querying the embedding space for the most similar vectors, and when used at the input layer of deep neural networks trained to solve downstream NLP problems. Meta-embeddings combine multiple sets of differently trained word embeddings, and have been shown to successfully improve intrinsic and extrinsic performance over equivalent models which use just one set of source embeddings. We introduce word prisms: a simple and efficient meta-embedding method that learns to combine source embeddings according to the task at hand. Word prisms learn orthogonal transformations to linearly combine the input source embeddings, which allows them to be very efficient at inference time. We evaluate word prisms in comparison to other meta-embedding methods on six extrinsic evaluations and observe that word prisms offer improvements in performance on all tasks.",
}
@inproceedings{arefyev-etal-2020-always,
    title = "Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution",
    author = "Arefyev, Nikolay  and
      Sheludko, Boris  and
      Podolskiy, Alexander  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.107",
    doi = "10.18653/v1/2020.coling-main.107",
    pages = "1242--1255",
    abstract = "Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of lexical substitution methods employing both rather old and most recent language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, RoBERTa, XLNet. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly. Several existing and new target word injection methods are compared for each LM/MLM using both intrinsic evaluation on lexical substitution datasets and extrinsic evaluation on word sense induction (WSI) datasets. On two WSI datasets we obtain new SOTA results. Besides, we analyze the types of semantic relations between target words and their substitutes generated by different models or given by annotators.",
}
@inproceedings{navali-etal-2020-word,
    title = "Word Embedding Binarization with Semantic Information Preservation",
    author = "Navali, Samarth  and
      Sherki, Praneet  and
      Inturi, Ramesh  and
      Vala, Vanraj",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.108",
    doi = "10.18653/v1/2020.coling-main.108",
    pages = "1256--1265",
    abstract = "With growing applications of Machine Learning in daily lives Natural Language Processing (NLP) has emerged as a heavily researched area. Finding its applications in tasks ranging from simple Q/A chatbots to Fully fledged conversational AI, NLP models are vital. Word and Sentence embedding are one of the most common starting points of any NLP task. A word embedding represents a given word in a predefined vector-space while maintaining vector relations with similar or dis-similar entities. As such different pretrained embedding such as Word2Vec, GloVe, fasttext have been developed. These embedding generated on millions of words are however very large in terms of size. Having embedding with floating point precision also makes the downstream evaluation slow. In this paper we present a novel method to convert continuous embedding to its binary representation, thus reducing the overall size of the embedding while keeping the semantic and relational knowledge intact. This will facilitate an option of porting such big embedding onto devices where space is limited. We also present different approaches suitable for different downstream tasks based on the requirement of contextual and semantic information. Experiments have shown comparable result in downstream tasks with 7 to 15 times reduction in file size and about 5 {\%} change in evaluation parameters.",
}
@inproceedings{metheniti-etal-2020-relevant,
    title = "How Relevant Are Selectional Preferences for Transformer-based Language Models?",
    author = "Metheniti, Eleni  and
      Van de Cruys, Tim  and
      Hathout, Nabil",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.109",
    doi = "10.18653/v1/2020.coling-main.109",
    pages = "1266--1278",
    abstract = "Selectional preference is defined as the tendency of a predicate to favor particular arguments within a certain linguistic context, and likewise, reject others that result in conflicting or implausible meanings. The stellar success of contextual word embedding models such as BERT in NLP tasks has led many to question whether these models have learned linguistic information, but up till now, most research has focused on syntactic information. We investigate whether Bert contains information on the selectional preferences of words, by examining the probability it assigns to the dependent word given the presence of a head word in a sentence. We are using word pairs of head-dependent words in five different syntactic relations from the SP-10K corpus of selectional preference (Zhang et al., 2019b), in sentences from the ukWaC corpus, and we are calculating the correlation of the plausibility score (from SP-10K) and the model probabilities. Our results show that overall, there is no strong positive or negative correlation in any syntactic relation, but we do find that certain head words have a strong correlation and that masking all words but the head word yields the most positive correlations in most scenarios {--}which indicates that the semantics of the predicate is indeed an integral and influential factor for the selection of the argument.",
}
@inproceedings{lees-etal-2020-embedding,
    title = "Embedding Semantic Taxonomies",
    author = "Lees, Alyssa  and
      Welty, Chris  and
      Zhao, Shubin  and
      Korycki, Jacek  and
      Mc Carthy, Sara",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.110",
    doi = "10.18653/v1/2020.coling-main.110",
    pages = "1279--1291",
    abstract = "A common step in developing an understanding of a vertical domain, e.g. shopping, dining, movies, medicine, etc., is curating a taxonomy of categories specific to the domain. These human created artifacts have been the subject of research in embeddings that attempt to encode aspects of the partial ordering property of taxonomies. We compare Box Embeddings, a natural containment representation of category taxonomies, to partial-order embeddings and a baseline Bayes Net, in the context of representing the Medical Subject Headings (MeSH) taxonomy given a set of 300K PubMed articles with subject labels from MeSH. We deeply explore the experimental properties of training box embeddings, including preparation of the training data, sampling ratios and class balance, initialization strategies, and propose a fix to the original box objective. We then present first results in using these techniques for representing a bipartite learning problem (i.e. collaborative filtering) in the presence of taxonomic relations within each partition, inferring disease (anatomical) locations from their use as subject labels in journal articles. Our box model substantially outperforms all baselines for taxonomic reconstruction and bipartite relationship experiments. This performance improvement is observed both in overall accuracy and the weighted spread by true taxonomic depth.",
}
@inproceedings{shah-etal-2020-retrofitting,
    title = "A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings",
    author = "Shah, Sapan  and
      Reddy, Sreedhar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.111",
    doi = "10.18653/v1/2020.coling-main.111",
    pages = "1292--1298",
    abstract = "We present a novel retrofitting model that can leverage relational knowledge available in a knowledge resource to improve word embeddings. The knowledge is captured in terms of relation inequality constraints that compare similarity of related and unrelated entities in the context of an anchor entity. These constraints are used as training data to learn a non-linear transformation function that maps original word vectors to a vector space respecting these constraints. The transformation function is learned in a similarity metric learning setting using Triplet network architecture. We applied our model to synonymy, antonymy and hypernymy relations in WordNet and observed large gains in performance over original distributional models as well as other retrofitting approaches on word similarity task and significant overall improvement on lexical entailment detection task.",
}
@inproceedings{jadhav-etal-2020-lexical,
    title = "Lexical Relation Mining in Neural Word Embeddings",
    author = "Jadhav, Aishwarya  and
      Amir, Yifat  and
      Pardos, Zachary",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.112",
    doi = "10.18653/v1/2020.coling-main.112",
    pages = "1299--1311",
    abstract = "Work with neural word embeddings and lexical relations has largely focused on confirmatory experiments which use human-curated examples of semantic and syntactic relations to validate against. In this paper, we explore the degree to which lexical relations, such as those found in popular validation sets, can be derived and extended from a variety of neural embeddings using classical clustering methods. We show that the Word2Vec space of word-pairs (i.e., offset vectors) significantly outperforms other more contemporary methods, even in the presence of a large number of noisy offsets. Moreover, we show that via a simple nearest neighbor approach in the offset space, new examples of known relations can be discovered. Our results speak to the amenability of offset vectors from non-contextual neural embeddings to find semantically coherent clusters. This simple approach has implications for the exploration of emergent regularities and their examples, such as emerging trends on social media and their related posts.",
}
@inproceedings{tan-jiang-2020-bert,
    title = "A {BERT}-based Dual Embedding Model for {C}hinese Idiom Prediction",
    author = "Tan, Minghuan  and
      Jiang, Jing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.113",
    doi = "10.18653/v1/2020.coling-main.113",
    pages = "1312--1322",
    abstract = "Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank. We propose a BERT-based dual embedding model to encode the contextual words as well as to learn dual embeddings of the idioms. Specifically, we first match the embedding of each candidate idiom with the hidden representation corresponding to the blank in the context. We then match the embedding of each candidate idiom with the hidden representations of all the tokens in the context thorough context pooling. We further propose to use two separate idiom embeddings for the two kinds of matching. Experiments on a recently released Chinese idiom cloze test dataset show that our proposed method performs better than the existing state of the art. Ablation experiments also show that both context pooling and dual embedding contribute to the improvement of performance.",
}
@inproceedings{ueda-etal-2020-bert,
    title = "{BERT}-based Cohesion Analysis of {J}apanese Texts",
    author = "Ueda, Nobuhiro  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.114",
    doi = "10.18653/v1/2020.coling-main.114",
    pages = "1323--1333",
    abstract = "The meaning of natural language text is supported by cohesion among various kinds of entities, including coreference relations, predicate-argument structures, and bridging anaphora relations. However, predicate-argument structures for nominal predicates and bridging anaphora relations have not been studied well, and their analyses have been still very difficult. Recent advances in neural networks, in particular, self training-based language models including BERT (Devlin et al., 2019), have significantly improved many natural language processing tasks, making it possible to dive into the study on analysis of cohesion in the whole text. In this study, we tackle an integrated analysis of cohesion in Japanese texts. Our results significantly outperformed existing studies in each task, especially about 10 to 20 point improvement both for zero anaphora and coreference resolution. Furthermore, we also showed that coreference resolution is different in nature from the other tasks and should be treated specially.",
}
@inproceedings{sen-etal-2020-schema,
    title = "Schema Aware Semantic Reasoning for Interpreting Natural Language Queries in Enterprise Settings",
    author = "Sen, Jaydeep  and
      Babtiwale, Tanaya  and
      Saxena, Kanishk  and
      Butala, Yash  and
      Bhatia, Sumit  and
      Sankaranarayanan, Karthik",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.115",
    doi = "10.18653/v1/2020.coling-main.115",
    pages = "1334--1345",
    abstract = "Natural Language Query interfaces allow the end-users to access the desired information without the need to know any specialized query language, data storage, or schema details. Even with the recent advances in NLP research space, the state-of-the-art QA systems fall short of understanding implicit intents of real-world Business Intelligence (BI) queries in enterprise systems, since Natural Language Understanding still remains an AI-hard problem. We posit that deploying ontology reasoning over domain semantics can help in achieving better natural language understanding for QA systems. In this paper, we specifically focus on building a Schema Aware Semantic Reasoning Framework that translates natural language interpretation as a sequence of solvable tasks by an ontology reasoner. We apply our framework on top of an ontology based, state-of-the-art natural language question-answering system ATHENA, and experiment with 4 benchmarks focused on BI queries. Our experimental numbers empirically show that the Schema Aware Semantic Reasoning indeed helps in achieving significantly better results for handling BI queries with an average accuracy improvement of {\textasciitilde}30{\%}",
}
@inproceedings{cignarella-etal-2020-multilingual,
    title = "Multilingual Irony Detection with Dependency Syntax and Neural Models",
    author = "Cignarella, Alessandra Teresa  and
      Basile, Valerio  and
      Sanguinetti, Manuela  and
      Bosco, Cristina  and
      Rosso, Paolo  and
      Benamara, Farah",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.116",
    doi = "10.18653/v1/2020.coling-main.116",
    pages = "1346--1358",
    abstract = "This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian). It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme. Three distinct experimental settings are provided. In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored. In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets. In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture. The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.",
}
@inproceedings{anthonio-roth-2020-learn,
    title = "What Can We Learn from Noun Substitutions in Revision Histories?",
    author = "Anthonio, Talita  and
      Roth, Michael",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.117",
    doi = "10.18653/v1/2020.coling-main.117",
    pages = "1359--1370",
    abstract = "In community-edited resources such as wikiHow, sentences are subject to revisions on a daily basis. Recent work has shown that resulting improvements over time can be modelled computationally, assuming that each revision contributes to the improvement. We take a closer look at a subset of such revisions, for which we attempt to improve a computational model and validate in how far the assumption that {`}revised means better{'} actually holds. The subset of revisions considered here are noun substitutions, which often involve interesting semantic relations, including synonymy, antonymy and hypernymy. Despite the high semantic relatedness, we find that a supervised classifier can distinguish the revised version of a sentence from an original version with an accuracy close to 70{\%}, when taking context into account. In a human annotation study, we observe that annotators identify the revised sentence as the {`}better version{'} with similar performance. Our analysis reveals a fair agreement among annotators when a revision improves fluency. In contrast, noun substitutions that involve other lexical-semantic relationships are often perceived as being equally good or tend to cause disagreements. While these findings are also reflected in classification scores, a comparison of results shows that our model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity.",
}
@inproceedings{lauscher-etal-2020-specializing,
    title = "Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity",
    author = "Lauscher, Anne  and
      Vuli{\'c}, Ivan  and
      Ponti, Edoardo Maria  and
      Korhonen, Anna  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.118",
    doi = "10.18653/v1/2020.coling-main.118",
    pages = "1371--1383",
    abstract = "Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives. In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining. To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT{'}s masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification. Our experiments suggest that our {``}Lexically Informed{''} BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind {``}vanilla{''} BERT on several language understanding tasks. Concretely, LIBERT outperforms BERT in 9 out of 10 tasks of the GLUE benchmark and is on a par with BERT in the remaining one. Moreover, we show consistent gains on 3 benchmarks for lexical simplification, a task where knowledge about word-level semantic similarity is paramount, as well as large gains on lexical reasoning probes.",
}
@inproceedings{kanojia-etal-2020-harnessing,
    title = "Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages",
    author = "Kanojia, Diptesh  and
      Dabre, Raj  and
      Dewangan, Shubham  and
      Bhattacharyya, Pushpak  and
      Haffari, Gholamreza  and
      Kulkarni, Malhar",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.119",
    doi = "10.18653/v1/2020.coling-main.119",
    pages = "1384--1395",
    abstract = "Cognates are variants of the same lexical form across different languages; for example {``}fonema{''} in Spanish and {``}phoneme{''} in English are cognates, both of which mean {``}a unit of sound{''}. The task of automatic detection of cognates among any two languages can help downstream NLP tasks such as Cross-lingual Information Retrieval, Computational Phylogenetics, and Machine Translation. In this paper, we demonstrate the use of cross-lingual word embeddings for detecting cognates among fourteen Indian Languages. Our approach introduces the use of context from a knowledge graph to generate improved feature representations for cognate detection. We, then, evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task. We evaluate our methods to detect cognates on a challenging dataset of twelve Indian languages, namely, Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam. Additionally, we create evaluation datasets for two more Indian languages, Konkani and Nepali. We observe an improvement of up to 18{\%} points, in terms of F-score, for cognate detection. Furthermore, we observe that cognates extracted using our method help improve NMT quality by up to 2.76 BLEU. We also release our code, newly constructed datasets and cross-lingual models publicly.",
}
@inproceedings{conia-navigli-2020-bridging,
    title = "Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach",
    author = "Conia, Simone  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.120",
    doi = "10.18653/v1/2020.coling-main.120",
    pages = "1396--1410",
    abstract = "Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-resource (e.g., German) settings. To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. Our approach outperforms the state of the art in all the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available. Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling. We release our model code and checkpoints at https://github.com/SapienzaNLP/multi-srl.",
}
@inproceedings{sun-etal-2020-helpfulness,
    title = "On the Helpfulness of Document Context to Sentence Simplification",
    author = "Sun, Renliang  and
      Lin, Zhe  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.121",
    doi = "10.18653/v1/2020.coling-main.121",
    pages = "1411--1423",
    abstract = "Most of the research on text simplification is limited to sentence level nowadays. In this paper, we are the first to investigate the helpfulness of document context on sentence simplification and apply it to the sequence-to-sequence model. We firstly construct a sentence simplification dataset in which the contexts for the original sentence are provided by Wikipedia corpus. The new dataset contains approximately 116K sentence pairs with context. We then propose a new model that makes full use of the context information. Our model uses neural networks to learn the different effects of the preceding sentences and the following sentences on the current sentence and applies them to the improved transformer model. Evaluated on the newly constructed dataset, our model achieves 36.52 on SARI value, which outperforms the best performing model in the baselines by 2.46 (7.22{\%}), indicating that context indeed helps improve sentence simplification. In the ablation experiment, we show that using either the preceding sentences or the following sentences as context can significantly improve simplification.",
}
@inproceedings{van-etal-2020-automets,
    title = "{A}uto{M}e{TS}: The Autocomplete for Medical Text Simplification",
    author = "Van, Hoang  and
      Kauchak, David  and
      Leroy, Gondy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.122",
    doi = "10.18653/v1/2020.coling-main.122",
    pages = "1424--1434",
    abstract = "The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved. Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality. In this paper, we examine the application of autocomplete to text simplification in the medical domain. We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset. We compare four PNLMs (BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17{\%} absolute improvement over the best individual model). We also introduce an ensemble model that combines the four PNLMs and outperforms the best individual model by 2.1{\%}, resulting in an overall word prediction accuracy of 64.52{\%}.",
}
@inproceedings{przybyla-shardlow-2020-multi,
    title = "Multi-Word Lexical Simplification",
    author = "Przyby{\l}a, Piotr  and
      Shardlow, Matthew",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.123",
    doi = "10.18653/v1/2020.coling-main.123",
    pages = "1435--1446",
    abstract = "In this work we propose the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words. In order to explore this new direction, we contribute a corpus (MWLS1), including 1462 sentences in English from various sources with 7059 simplifications provided by human annotators. We also propose an automatic solution (Plainifier) based on a purpose-trained neural language model and evaluate its performance, comparing to human and resource-based baselines.",
}
@inproceedings{cetoli-2020-exploring,
    title = "Exploring the zero-shot limit of {F}ew{R}el",
    author = "Cetoli, Alberto",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.124",
    doi = "10.18653/v1/2020.coling-main.124",
    pages = "1447--1451",
    abstract = "This paper proposes a general purpose relation extractor that uses Wikidata descriptions to represent the relation{'}s surface form. The results are tested on the FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed zero-shot learning system in English. This relation extractor architecture exploits the implicit knowledge of a language model through a question-answering approach.",
}
@inproceedings{xu-etal-2020-deep,
    title = "A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space",
    author = "Xu, Hong  and
      He, Keqing  and
      Yan, Yuanmeng  and
      Liu, Sihong  and
      Liu, Zijun  and
      Xu, Weiran",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.125",
    doi = "10.18653/v1/2020.coling-main.125",
    pages = "1452--1460",
    abstract = "Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system. Different from most existing methods that rely heavily on manually labeled OOD samples, we focus on the unsupervised OOD detection scenario where there are no labeled OOD samples except for labeled in-domain data. In this paper, we propose a simple but strong generative distance-based classifier to detect OOD samples. We estimate the class-conditional distribution on feature spaces of DNNs via Gaussian discriminant analysis (GDA) to avoid over-confidence problems. And we use two distance functions, Euclidean and Mahalanobis distances, to measure the confidence score of whether a test sample belongs to OOD. Experiments on four benchmark datasets show that our method can consistently outperform the baselines.",
}
@inproceedings{he-etal-2020-contrastive,
    title = "Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack",
    author = "He, Keqing  and
      Zhang, Jinchao  and
      Yan, Yuanmeng  and
      Xu, Weiran  and
      Niu, Cheng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.126",
    doi = "10.18653/v1/2020.coling-main.126",
    pages = "1461--1467",
    abstract = "Zero-shot slot filling has widely arisen to cope with data scarcity in target domains. However, previous approaches often ignore constraints between slot value representation and related slot description representation in the latent space and lack enough model robustness. In this paper, we propose a Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling. The contrastive loss aims to map slot value contextual representations to the corresponding slot description representations. And we introduce an adversarial attack training strategy to improve model robustness. Experimental results show that our model significantly outperforms state-of-the-art baselines under both zero-shot and few-shot settings.",
}
@inproceedings{le-zuidema-2020-dolfin,
    title = "{D}o{LFI}n: Distributions over Latent Features for Interpretability",
    author = "Le, Phong  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.127",
    doi = "10.18653/v1/2020.coling-main.127",
    pages = "1468--1474",
    abstract = "Interpreting the inner workings of neural models is a key step in ensuring the robustness and trustworthiness of the models, but work on neural network interpretability typically faces a trade-off: either the models are too constrained to be very useful, or the solutions found by the models are too complex to interpret. We propose a novel strategy for achieving interpretability that {--} in our experiments {--} avoids this trade-off. Our approach builds on the success of using probability as the central quantity, such as for instance within the attention mechanism. In our architecture, DoLFIn (Distributions over Latent Features for Interpretability), we do no determine beforehand what each feature represents, and features go altogether into an unordered set. Each feature has an associated probability ranging from 0 to 1, weighing its importance for further processing. We show that, unlike attention and saliency map approaches, this set-up makes it straight-forward to compute the probability with which an input component supports the decision the neural model makes. To demonstrate the usefulness of the approach, we apply DoLFIn to text classification, and show that DoLFIn not only provides interpretable solutions, but even slightly outperforms the classical CNN and BiLSTM text classifiers on the SST2 and AG-news datasets.",
}
@inproceedings{lugini-litman-2020-contextual,
    title = "Contextual Argument Component Classification for Class Discussions",
    author = "Lugini, Luca  and
      Litman, Diane",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.128",
    doi = "10.18653/v1/2020.coling-main.128",
    pages = "1475--1480",
    abstract = "Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models. In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. We find that both context types can improve performance, although the improvements are dependent on context size and position.",
}
@inproceedings{bhattamishra-etal-2020-practical,
    title = "On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages",
    author = "Bhattamishra, Satwik  and
      Ahuja, Kabir  and
      Goyal, Navin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.129",
    doi = "10.18653/v1/2020.coling-main.129",
    pages = "1481--1494",
    abstract = "While recurrent models have been effective in NLP tasks, their performance on context-free languages (CFLs) has been found to be quite weak. Given that CFLs are believed to capture important phenomena such as hierarchical structure in natural languages, this discrepancy in performance calls for an explanation. We study the performance of recurrent models on Dyck-n languages, a particularly important and well-studied class of CFLs. We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer. At the same time, we observe that RNNs are expressive enough to recognize Dyck words of arbitrary lengths in finite precision if their depths are bounded. Hence, we evaluate our models on samples generated from Dyck languages with bounded depth and find that they are indeed able to generalize to much higher lengths. Since natural language datasets have nested dependencies of bounded depth, this may help explain why they perform well in modeling hierarchical dependencies in natural language data despite prior works indicating poor generalization performance on Dyck languages. We perform probing studies to support our results and provide comparisons with Transformers.",
}
@inproceedings{bai-etal-2020-pre,
    title = "Pre-trained Language Model Based Active Learning for Sentence Matching",
    author = "Bai, Guirong  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun  and
      Nie, Zaiqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.130",
    doi = "10.18653/v1/2020.coling-main.130",
    pages = "1495--1504",
    abstract = "Active learning is able to significantly reduce the annotation cost for data-driven techniques. However, previous active learning approaches for natural language processing mainly depend on the entropy-based uncertainty criterion, and ignore the characteristics of natural language. In this paper, we propose a pre-trained language model based active learning approach for sentence matching. Differing from previous active learning, it can provide linguistic criteria from the pre-trained language model to measure instances and help select more effective instances for annotation. Experiments demonstrate our approach can achieve greater accuracy with fewer labeled training instances.",
}
@inproceedings{ananthram-etal-2020-event,
    title = "Event-Guided Denoising for Multilingual Relation Learning",
    author = "Ananthram, Amith  and
      Allaway, Emily  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.131",
    doi = "10.18653/v1/2020.coling-main.131",
    pages = "1505--1512",
    abstract = "General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus {--} the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+).",
}
@inproceedings{li-etal-2020-using,
    title = "Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification",
    author = "Li, Xiao  and
      Hong, Yu  and
      Ruan, Huibin  and
      Huang, Zhen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.132",
    doi = "10.18653/v1/2020.coling-main.132",
    pages = "1513--1518",
    abstract = "We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments. The attention-worthy words in arguments are crucial clues for classifying the discourse relations. Attention mechanisms have been proven effective in highlighting the attention-worthy words during encoding. However, our survey shows that some inessential words are unintentionally misjudged as the attention-worthy words and, therefore, assigned heavier attention weights than should be. We propose a penalty-based loss re-estimation method to regulate the attention learning process, integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. We conduct experiments on the Penn Discourse TreeBank (PDTB) corpus. The test results show that our loss re-estimation method leads to substantial improvements for a variety of attention mechanisms, and it obtains highly competitive performance compared to the state-of-the-art methods.",
}
@inproceedings{xu-etal-2020-review,
    title = "A Review of Dataset and Labeling Methods for Causality Extraction",
    author = "Xu, Jinghang  and
      Zuo, Wanli  and
      Liang, Shining  and
      Zuo, Xianglin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.133",
    doi = "10.18653/v1/2020.coling-main.133",
    pages = "1519--1531",
    abstract = "Causality represents the most important kind of correlation between events. Extracting causali-ty from text has become a promising hot topic in NLP. However, there is no mature research systems and datasets for public evaluation. Moreover, there is a lack of unified causal sequence label methods, which constitute the key factors that hinder the progress of causality extraction research. We survey the limitations and shortcomings of existing causality research field com-prehensively from the aspects of basic concepts, extraction methods, experimental data, and la-bel methods, so as to provide reference for future research on causality extraction. We summa-rize the existing causality datasets, explore their practicability and extensibility from multiple perspectives and create a new causal dataset ESC. Aiming at the problem of causal sequence labeling, we analyse the existing methods with a summarization of its regulation and propose a new causal label method of core word. Multiple candidate causal label sequences are put for-ward according to label controversy to explore the optimal label method through experiments, and suggestions are provided for selecting label method.",
}
@inproceedings{ren-etal-2020-knowledge,
    title = "Knowledge Graph Embedding with Atrous Convolution and Residual Learning",
    author = "Ren, Feiliang  and
      Li, Juchen  and
      Zhang, Huihui  and
      Liu, Shilei  and
      Li, Bochao  and
      Ming, Ruicheng  and
      Bai, Yujia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.134",
    doi = "10.18653/v1/2020.coling-main.134",
    pages = "1532--1543",
    abstract = "Knowledge graph embedding is an important task and it will benefit lots of downstream applications. Currently, deep neural networks based methods achieve state-of-the-art performance. However, most of these existing methods are very complex and need much time for training and inference. To address this issue, we propose a simple but effective atrous convolution based knowledge graph embedding method. Compared with existing state-of-the-art methods, our method has following main characteristics. First, it effectively increases feature interactions by using atrous convolutions. Second, to address the original information forgotten issue and vanishing/exploding gradient issue, it uses the residual learning method. Third, it has simpler structure but much higher parameter efficiency. We evaluate our method on six benchmark datasets with different evaluation metrics. Extensive experiments show that our model is very effective. On these diverse datasets, it achieves better results than the compared state-of-the-art methods on most of evaluation metrics. The source codes of our model could be found at https://github.com/neukg/AcrE.",
}
@inproceedings{zuo-etal-2020-knowdis,
    title = "{K}now{D}is: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision",
    author = "Zuo, Xinyu  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.135",
    doi = "10.18653/v1/2020.coling-main.135",
    pages = "1544--1550",
    abstract = "Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions, and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.",
}
@inproceedings{li-etal-2020-graph,
    title = "Graph Enhanced Dual Attention Network for Document-Level Relation Extraction",
    author = "Li, Bo  and
      Ye, Wei  and
      Sheng, Zhonghao  and
      Xie, Rui  and
      Xi, Xiangyu  and
      Zhang, Shikun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.136",
    doi = "10.18653/v1/2020.coling-main.136",
    pages = "1551--1560",
    abstract = "Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relational facts. To improve inter-sentence reasoning, we propose to characterize the complex interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA). In GEDA, sentence representation generated by the sentence-to-relation (S2R) attention is refined and synthesized by a Heterogeneous Graph Convolutional Network before being fed into the relation-to-sentence (R2S) attention . We further design a simple yet effective regularizer based on the natural duality of the S2R and R2S attention, whose weights are also supervised by the supporting evidence of relation instances during training. An extensive set of experiments on an existing large-scale dataset show that our model achieve competitive performance, especially for the inter-sentence relation extraction, while the neural predictions can also be interpretable and easily observed.",
}
@inproceedings{chen-etal-2020-joint-entity,
    title = "Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement",
    author = "Chen, Yanguang  and
      Sun, Yuanyuan  and
      Yang, Zhihao  and
      Lin, Hongfei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.137",
    doi = "10.18653/v1/2020.coling-main.137",
    pages = "1561--1571",
    abstract = "In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments Online. It is in great need of enabling machines to understand the semantic information stored in the documents which are transcribed in the form of natural language. The technique of information extraction provides a way of mining the valuable information implied in the unstructured judgment documents. We propose a Legal Triplet Extraction System for drug-related criminal judgment documents. The system extracts the entities and the semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. Furthermore, we manually annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain, which contributes to training supervised triplet extraction models and evaluating the model performance. Our experimental results show that the legal feature introduction and multi-task learning framework are feasible and effective for the Legal Triplet Extraction System. The F1 score of triplet extraction finally reaches 0.836 on the legal dataset.",
}
@inproceedings{wang-etal-2020-tplinker,
    title = "{TPL}inker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking",
    author = "Wang, Yucheng  and
      Yu, Bowen  and
      Zhang, Yueyang  and
      Liu, Tingwen  and
      Zhu, Hongsong  and
      Sun, Limin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.138",
    doi = "10.18653/v1/2020.coling-main.138",
    pages = "1572--1582",
    abstract = "Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior works show that joint learning can result in a noticeable performance gain. However, they usually involve sequential interrelated steps and suffer from the problem of exposure bias. At training time, they predict with the ground truth conditions while at inference it has to make extraction from scratch. This discrepancy leads to error accumulation. To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while being immune from the exposure bias. TPLinker formulates joint extraction as a token pair linking problem and introduces a novel handshaking tagging scheme that aligns the boundary tokens of entity pairs under each relation type. Experiment results show that TPLinker performs significantly better on overlapping and multiple relation extraction, and achieves state-of-the-art performance on two public datasets.",
}
@inproceedings{xu-etal-2020-tero,
    title = "{T}e{R}o: A Time-aware Knowledge Graph Embedding via Temporal Rotation",
    author = "Xu, Chengjin  and
      Nayyeri, Mojtaba  and
      Alkhoury, Fouad  and
      Shariat Yazdi, Hamed  and
      Lehmann, Jens",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.139",
    doi = "10.18653/v1/2020.coling-main.139",
    pages = "1583--1593",
    abstract = "In the last few years, there has been a surge of interest in learning representations of entities and relations in knowledge graph (KG). However, the recent availability of temporal knowledge graphs (TKGs) that contain time information for each fact created the need for reasoning over time in such TKGs. In this regard, we present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding as a rotation from the initial time to the current time in the complex vector space. Specially, for facts involving time intervals, each relation is represented as a pair of dual complex embeddings to handle the beginning and the end of the relation, respectively. We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferring various relation patterns over time. Experimental results on three different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction. In addition, we analyze the effect of time granularity on link prediction over TKGs, which as far as we know has not been investigated in previous literature.",
}
@inproceedings{dong-etal-2020-meta,
    title = "Meta-Information Guided Meta-Learning for Few-Shot Relation Classification",
    author = "Dong, Bowen  and
      Yao, Yuan  and
      Xie, Ruobing  and
      Gao, Tianyu  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Lin, Fen  and
      Lin, Leyu  and
      Sun, Maosong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.140",
    doi = "10.18653/v1/2020.coling-main.140",
    pages = "1594--1605",
    abstract = "Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification. However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability. To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation. In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation. Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework. Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.",
}
@inproceedings{goswami-etal-2020-unsupervised,
    title = "Unsupervised Deep Language and Dialect Identification for Short Texts",
    author = "Goswami, Koustava  and
      Sarkar, Rajdeep  and
      Chakravarthi, Bharathi Raja  and
      Fransen, Theodorus  and
      McCrae, John P.",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.141",
    doi = "10.18653/v1/2020.coling-main.141",
    pages = "1606--1617",
    abstract = "Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language identification is considered a solved task in many cases; however, in the case of very closely related languages, or in an unsupervised scenario (where the languages are not known in advance), performance is still poor. In this paper, we propose the Unsupervised Deep Language and Dialect Identification (UDLDI) method, which can simultaneously learn sentence embeddings and cluster assignments from short texts. The UDLDI model understands the sentence constructions of languages by applying attention to character relations which helps to optimize the clustering of languages. We have performed our experiments on three short-text datasets for different language families, each consisting of closely related languages or dialects, with very minimal training sets. Our experimental evaluations on these datasets have shown significant improvement over state-of-the-art unsupervised methods and our model has outperformed state-of-the-art LI and DI systems in supervised settings.",
}
@inproceedings{ren-etal-2020-two,
    title = "A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification",
    author = "Ren, Haopeng  and
      Cai, Yi  and
      Chen, Xiaofeng  and
      Wang, Guohua  and
      Li, Qing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.142",
    doi = "10.18653/v1/2020.coling-main.142",
    pages = "1618--1629",
    abstract = "Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model.",
}
@inproceedings{zhang-etal-2020-document,
    title = "Document-level Relation Extraction with Dual-tier Heterogeneous Graph",
    author = "Zhang, Zhenyu  and
      Yu, Bowen  and
      Shu, Xiaobo  and
      Liu, Tingwen  and
      Tang, Hengzhu  and
      Yubin, Wang  and
      Guo, Li",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.143",
    doi = "10.18653/v1/2020.coling-main.143",
    pages = "1630--1641",
    abstract = "Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result. In this paper, we propose a novel graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level RE. In particular, DHG is composed of a structure modeling layer followed by a relation reasoning layer. The major advantage is that it is capable of not only capturing both the sequential and structural information of documents but also mixing them together to benefit for multi-hop reasoning and final decision-making. Furthermore, we employ Graph Neural Networks (GNNs) based message propagation strategy to accumulate information on DHG. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two widely used datasets, and further analyses suggest that all the modules in our model are indispensable for document-level RE.",
}
@inproceedings{kazemi-etal-2020-biased,
    title = "Biased {T}ext{R}ank: Unsupervised Graph-Based Content Extraction",
    author = "Kazemi, Ashkan  and
      P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.144",
    doi = "10.18653/v1/2020.coling-main.144",
    pages = "1642--1652",
    abstract = "We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input {``}focus.{''} Biased TextRank enables focused content extraction for text by modifying the random restarts in the execution of TextRank. The random restart probabilities are assigned based on the relevance of the graph nodes to the focus of the task. We present two applications of Biased TextRank: focused summarization and explanation extraction, and show that our algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins. Much like its predecessor, Biased TextRank is unsupervised, easy to implement and orders of magnitude faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks.",
}
@inproceedings{li-etal-2020-improving,
    title = "Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention",
    author = "Li, Yang  and
      Shen, Tao  and
      Long, Guodong  and
      Jiang, Jing  and
      Zhou, Tianyi  and
      Zhang, Chengqi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.145",
    doi = "10.18653/v1/2020.coling-main.145",
    pages = "1653--1664",
    abstract = "Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective attention via multi-instance learning, but cannot well handle long-tail relations even if hierarchies of the relations are introduced to share knowledge. In this work, we propose a novel neural network, Collaborating Relation-augmented Attention (CoRA), to handle both the wrong labeling and long-tail relations. Particularly, we first propose relation-augmented attention network as base model. It operates on sentence bag with a sentence-to-relation attention to minimize the effect of wrong labeling. Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations. Besides the main training objective to predict the relation of a sentence bag, an auxiliary objective is utilized to guide the relation-augmenting process for a more accurate bag-level representation. In the experiments on the popular benchmark dataset NYT, the proposed CoRA improves the prior state-of-the-art performance by a large margin in terms of Precision@N, AUC and Hits@K. Further analyses verify its superior capability in handling long-tail relations in contrast to the competitors.",
}
@inproceedings{yu-etal-2020-tohre,
    title = "{T}o{HRE}: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction",
    author = "Yu, Erxin  and
      Han, Wenjuan  and
      Tian, Yuan  and
      Chang, Yi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.146",
    doi = "10.18653/v1/2020.coling-main.146",
    pages = "1665--1676",
    abstract = "Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem. Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations. To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner. Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation; 2) a top-down classification strategy is introduced instead of training a set of local classifiers. The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem.",
}
@inproceedings{kim-choi-2020-unsupervised,
    title = "Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph",
    author = "Kim, Jiseong  and
      Choi, Key-sun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.147",
    doi = "10.18653/v1/2020.coling-main.147",
    pages = "1677--1686",
    abstract = "Misinformation spreads across media, community, and knowledge graphs in the Web by not only human agents but also information extraction algorithms that extract factual statements from unstructured textual data to populate the existing knowledge graphs. Traditional fact checking by experts or crowds is increasingly difficult to keep pace with the volume of newly created misinformation in the Web. Therefore, it is important and necessary to enhance the computational ability to determine whether a given factual statement is truthful or not. We view this problem as a truth scoring task in a knowledge graph. We present a novel rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement and calculates a truth score for the given statement by unsupervised ensemble of the found positive and negative evidential paths. For example, we can determine the factual statement {``}United States is the birth place of Barack Obama{''} as truthful if there is the positive evidential path (Barack Obama, birthPlace, Hawaii) ∧ (Hawaii, country, United States) in a knowledge graph. For another example, we can determine the factual statement {``}Canada is the nationality of Barack Obama{''} as untruthful if there is the negative evidential path (Barack Obama, nationality, United States) ∧ (United States, {\mbox{$\neq$}}, Canada) in a knowledge graph. For evaluating on a real-world situation, we constructed an evaluation dataset by labeling truth or untruth label on factual statements that were extracted from Wikipedia texts by using the state-of-the-art BERT-based information extraction system. Our evaluation results show that our approach outperforms the state-of-the-art unsupervised approaches significantly by up to 0.12 AUC-ROC and even outperforms the supervised approach by up to 0.05 AUC-ROC not only in our dataset but also in the two different standard datasets.",
}
@inproceedings{yu-etal-2020-improving-relation,
    title = "Improving Relation Extraction with Relational Paraphrase Sentences",
    author = "Yu, Junjie  and
      Zhu, Tong  and
      Chen, Wenliang  and
      Zhang, Wei  and
      Zhang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.148",
    doi = "10.18653/v1/2020.coling-main.148",
    pages = "1687--1698",
    abstract = "Supervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit the performance of RE. To increase the coverage of relation expressions, we may enlarge the labeled data by hiring annotators or applying Distant Supervision (DS). However, the human-annotated data is costly and non-scalable while the distantly supervised data contains many noises. In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences. Based on an existing labeled data, we first automatically build a task-specific paraphrase data. Then, we propose a novel model to learn the information of diverse relation expressions. In our model, we try to capture this information on the paraphrases via a joint learning framework. Finally, we conduct experiments on a widely used dataset and the experimental results show that our approach is effective to improve the performance on relation extraction, even compared with a strong baseline.",
}
@inproceedings{kaneko-bollegala-2020-autoencoding,
    title = "Autoencoding Improves Pre-trained Word Embeddings",
    author = "Kaneko, Masahiro  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.149",
    doi = "10.18653/v1/2020.coling-main.149",
    pages = "1699--1713",
    abstract = "Prior works investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given set of pre-trained word embeddings. However, theoretically, this post-processing step is equivalent to applying a linear autoencoder to minimize the squared L2 reconstruction error. This result contradicts prior work (Mu and Viswanath, 2018) that proposed to remove the top principal components from pre-trained embeddings. We experimentally verify our theoretical claims and show that retaining the top principal components is indeed useful for improving pre-trained word embeddings, without requiring access to additional linguistic resources or labeled data.",
}
@inproceedings{yin-etal-2020-pod,
    title = "{P}o{D}: Positional Dependency-Based Word Embedding for Aspect Term Extraction",
    author = "Yin, Yichun  and
      Wang, Chenguang  and
      Zhang, Ming",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.150",
    doi = "10.18653/v1/2020.coling-main.150",
    pages = "1714--1719",
    abstract = "Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, we design the positional dependency-based word embedding (PoD) which considers both dependency context and positional context for aspect term extraction. Specifically, the positional context is modeled via relative position encoding. Besides, we enhance the dependency context by integrating more lexical information (e.g., POS tags) along dependency paths. Experiments on SemEval 2014/2015/2016 datasets show that our approach outperforms other embedding methods in aspect term extraction.",
}
@inproceedings{lepori-2020-unequal,
    title = "Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis",
    author = "Lepori, Michael",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.151",
    doi = "10.18653/v1/2020.coling-main.151",
    pages = "1720--1728",
    abstract = "We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contextualized embeddings for evidence of intersectional biases against Black women. We show that these embeddings represent Black women as simultaneously less feminine than White women, and less Black than Black men. This finding aligns with intersectionality theory, which argues that multiple identity categories (such as race or sex) layer on top of each other in order to create unique modes of discrimination that are not shared by any individual category.",
}
@inproceedings{kerscher-eger-2020-vec2sent,
    title = "{V}ec2{S}ent: Probing Sentence Embeddings with Natural Language Generation",
    author = "Kerscher, Martin  and
      Eger, Steffen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.152",
    doi = "10.18653/v1/2020.coling-main.152",
    pages = "1729--1736",
    abstract = "We introspect black-box sentence embeddings by conditionally generating from them with the objective to retrieve the underlying discrete sentence. We perceive of this as a new unsupervised probing task and show that it correlates well with downstream task performance. We also illustrate how the language generated from different encoders differs. We apply our approach to generate sentence analogies from sentence embeddings.",
}
@inproceedings{kim-etal-2020-multi,
    title = "Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models",
    author = "Kim, Bosung  and
      Hong, Taesuk  and
      Ko, Youngjoong  and
      Seo, Jungyun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.153",
    doi = "10.18653/v1/2020.coling-main.153",
    pages = "1737--1743",
    abstract = "As research on utilizing human knowledge in natural language processing has attracted considerable attention in recent years, knowledge graph (KG) completion has come into the spotlight. Recently, a new knowledge graph completion method using a pre-trained language model, such as KG-BERT, is presented and showed high performance. However, its scores in ranking metrics such as Hits@k are still behind state-of-the-art models. We claim that there are two main reasons: 1) failure in sufficiently learning relational information in knowledge graphs, and 2) difficulty in picking out the correct answer from lexically similar candidates. In this paper, we propose an effective multi-task learning method to overcome the limitations of previous works. By combining relation prediction and relevance ranking tasks with our target link prediction, the proposed model can learn more relational properties in KGs and properly perform even when lexical similarity occurs. Experimental results show that we not only largely improve the ranking performances compared to KG-BERT but also achieve the state-of-the-art performances in Mean Rank and Hits@10 on the WN18RR dataset.",
}
@inproceedings{srinivasa-desikan-etal-2020-comp,
    title = "comp-syn: Perceptually Grounded Word Embeddings with Color",
    author = "Srinivasa Desikan, Bhargav  and
      Hull, Tasker  and
      Nadler, Ethan  and
      Guilbeault, Douglas  and
      Abubakar Kar, Aabir  and
      Chu, Mark  and
      Lo Sardo, Donald Ruggiero",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.154",
    doi = "10.18653/v1/2020.coling-main.154",
    pages = "1744--1751",
    abstract = "Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we introduce the Python package comp-syn, which provides grounded word embeddings based on the perceptually uniform color distributions of Google Image search results. We demonstrate that comp-syn significantly enriches models of distributional semantics. In particular, we show that(1) comp-syn predicts human judgments of word concreteness with greater accuracy and in a more interpretable fashion than word2vec using low-dimensional word{--}color embeddings ,and (2) comp-syn performs comparably to word2vec on a metaphorical vs. literal word-pair classification task. comp-syn is open-source on PyPi and is compatible with mainstream machine-learning Python packages. Our package release includes word{--}color embeddings forover 40,000 English words, each associated with crowd-sourced word concreteness judgments.",
}
@inproceedings{hou-etal-2020-try,
    title = "Try to Substitute: An Unsupervised {C}hinese Word Sense Disambiguation Method Based on {H}ow{N}et",
    author = "Hou, Bairu  and
      Qi, Fanchao  and
      Zang, Yuan  and
      Zhang, Xurui  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.155",
    doi = "10.18653/v1/2020.coling-main.155",
    pages = "1752--1757",
    abstract = "Word sense disambiguation (WSD) is a fundamental natural language processing task. Unsupervised knowledge-based WSD only relies on a lexical knowledge base as the sense inventory and has wider practical use than supervised WSD that requires a mass of sense-annotated data. HowNet is the most widely used lexical knowledge base in Chinese WSD. Because of its uniqueness, however, most of existing unsupervised WSD methods cannot work for HowNet-based WSD, and the tailor-made methods have not obtained satisfying results. In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models. In experiments, considering existing evaluation dataset is small and out-of-date, we build a new and larger HowNet-based WSD dataset. Experimental results demonstrate that our model achieves significantly better performance than all the baseline methods. All the code and data of this paper are available at https://github.com/thunlp/SememeWSD.",
}
@inproceedings{haruta-etal-2020-combining,
    title = "Combining Event Semantics and Degree Semantics for Natural Language Inference",
    author = "Haruta, Izumi  and
      Mineshima, Koji  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.156",
    doi = "10.18653/v1/2020.coling-main.156",
    pages = "1758--1764",
    abstract = "In formal semantics, there are two well-developed semantic frameworks: event semantics, which treats verbs and adverbial modifiers using the notion of event, and degree semantics, which analyzes adjectives and comparatives using the notion of degree. However, it is not obvious whether these frameworks can be combined to handle cases in which the phenomena in question are interacting with each other. Here, we study this issue by focusing on natural language inference (NLI). We implement a logic-based NLI system that combines event semantics and degree semantics and their interaction with lexical knowledge. We evaluate the system on various NLI datasets containing linguistically challenging problems. The results show that the system achieves high accuracies on these datasets in comparison with previous logic-based systems and deep-learning-based systems. This suggests that the two semantic frameworks can be combined consistently to handle various combinations of linguistic phenomena without compromising the advantage of either framework.",
}
@inproceedings{jin-aletras-2020-complaint,
    title = "Complaint Identification in Social Media with Transformer Networks",
    author = "Jin, Mali  and
      Aletras, Nikolaos",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.157",
    doi = "10.18653/v1/2020.coling-main.157",
    pages = "1765--1771",
    abstract = "Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored. In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguistic information. Experiments on a publicly available data set of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a macro F1 up to 87.",
}
@inproceedings{pereg-etal-2020-syntactically,
    title = "Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction",
    author = "Pereg, Oren  and
      Korat, Daniel  and
      Wasserblat, Moshe",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.158",
    doi = "10.18653/v1/2020.coling-main.158",
    pages = "1772--1777",
    abstract = "A fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction. Supervised-learning approaches have shown good results for this task; however, they fail to scale across domains where labeled data is lacking. Non pre-trained unsupervised domain adaptation methods that incorporate external linguistic knowledge have proven effective in transferring aspect and opinion knowledge from a labeled source domain to un-labeled target domains; however, pre-trained transformer-based models like BERT and RoBERTa already exhibit substantial syntactic knowledge. In this paper, we propose a method for incorporating external linguistic information into a self-attention mechanism coupled with the BERT model. This enables leveraging the intrinsic knowledge existing within BERT together with externally introduced syntactic information, to bridge the gap across domains. We successfully demonstrate enhanced results on three benchmark datasets.",
}
@inproceedings{lotfi-etal-2020-deep,
    title = "A Deep Generative Approach to Native Language Identification",
    author = "Lotfi, Ehsan  and
      Markov, Ilia  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.159",
    doi = "10.18653/v1/2020.coling-main.159",
    pages = "1778--1783",
    abstract = "Native language identification (NLI) {--} identifying the native language (L1) of a person based on his/her writing in the second language (L2) {--} is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as a multi-class classification task, where numerous designed features are combined in order to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 model separately on texts written by the authors with the same L1, and assigning a label to an unseen text based on the minimum LM loss with respect to one of these fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets.",
}
@inproceedings{otake-etal-2020-modeling,
    title = "Modeling Event Salience in Narratives via Barthes{'} Cardinal Functions",
    author = "Otake, Takaki  and
      Yokoi, Sho  and
      Inoue, Naoya  and
      Takahashi, Ryo  and
      Kuribayashi, Tatsuki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.160",
    doi = "10.18653/v1/2020.coling-main.160",
    pages = "1784--1794",
    abstract = "Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics. To compute event salience without any annotations, we adopt Barthes{'} definition of event salience and propose several unsupervised methods that require only a pre-trained language model. Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find fine-tuning a language model on narrative texts is a key factor in improving the proposed methods.",
}
@inproceedings{yadav-etal-2020-medical,
    title = "Medical Knowledge-enriched Textual Entailment Framework",
    author = "Yadav, Shweta  and
      Pallagani, Vishal  and
      Sheth, Amit",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.161",
    doi = "10.18653/v1/2020.coling-main.161",
    pages = "1795--1801",
    abstract = "One of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, often to clock higher numbers on the validation metrics. However, two major shortcomings impede higher success in identifying entailment: (1) understanding the focus/intent of the question and (2) ability to utilize the real-world background knowledge to capture the con-text beyond the sentence. In this paper, we present a novel Medical Knowledge-Enriched Textual Entailment framework that allows the model to acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. We evaluate our framework on the benchmark MEDIQA-RQE dataset and manifest that the use of knowledge-enriched dual-encoding mechanism help in achieving an absolute improvement of 8.27{\%} over SOTA language models.",
}
@inproceedings{yang-etal-2020-predicting-personal,
    title = "Predicting Personal Opinion on Future Events with Fingerprints",
    author = "Yang, Fan  and
      Dragut, Eduard  and
      Mukherjee, Arjun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.162",
    doi = "10.18653/v1/2020.coling-main.162",
    pages = "1802--1807",
    abstract = "Predicting users{'} opinions in their response to social events has important real-world applications, many of which political and social impacts. Existing approaches derive a population{'}s opinion on a going event from large scores of user generated content. In certain scenarios, we may not be able to acquire such content and thus cannot infer an unbiased opinion on those emerging events. To address this problem, we propose to explore opinion on unseen articles based on one{'}s fingerprinting: the prior reading and commenting history. This work presents a focused study on modeling and leveraging fingerprinting techniques to predict a user{'}s future opinion. We introduce a recurrent neural network based model that integrates fingerprinting. We collect a large dataset that consists of event-comment pairs from six news websites. We evaluate the proposed model on this dataset. The results show substantial performance gains demonstrating the effectiveness of our approach.",
}
@inproceedings{liu-smith-2020-detecting,
    title = "Detecting de minimis Code-Switching in Historical {G}erman Books",
    author = "Liu, Shijia  and
      Smith, David",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.163",
    doi = "10.18653/v1/2020.coling-main.163",
    pages = "1808--1814",
    abstract = "Code-switching has long interested linguists, with computational work in particular focusing on speech and social media data (Sitaram et al., 2019). This paper contrasts these informal instances of code-switching to its appearance in more formal registers, by examining the mixture of languages in the Deutsches Textarchiv (DTA), a corpus of 1406 primarily German books from the 17th to 19th centuries. We automatically annotate and manually inspect spans of six embedded languages (Latin, French, English, Italian, Spanish, and Greek) in the corpus. We quantitatively analyze the differences between code-switching patterns in these books and those in more typically studied speech and social media corpora. Furthermore, we address the practical task of predicting code-switching from features of the matrix language alone in the DTA corpus. Such classifiers can help reduce errors when optical character recognition or speech transcription is applied to a large corpus with rare embedded languages.",
}
@inproceedings{diwanji-etal-2020-lin,
    title = "Lin: Unsupervised Extraction of Tasks from Textual Communication",
    author = "Diwanji, Parth  and
      Guo, Hui  and
      Singh, Munindar  and
      Kalia, Anup",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.164",
    doi = "10.18653/v1/2020.coling-main.164",
    pages = "1815--1819",
    abstract = "Commitments and requests are a hallmark of collaborative communication, especially in team settings. Identifying specific tasks being committed to or request from emails and chat messages can enable important downstream tasks, such as producing todo lists, reminders, and calendar entries. State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks. Accordingly, we propose Lin, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet. Our evaluations show that Lin yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.",
}
@inproceedings{li-zhou-2020-connecting,
    title = "Connecting the Dots Between Fact Verification and Fake News Detection",
    author = "Li, Qifei  and
      Zhou, Wangchunshu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.165",
    doi = "10.18653/v1/2020.coling-main.165",
    pages = "1820--1825",
    abstract = "Fact verification models have enjoyed a fast advancement in the last two years with the development of pre-trained language models like BERT and the release of large scale datasets such as FEVER. However, the challenging problem of fake news detection has not benefited from the improvement of fact verification models, which is closely related to fake news detection. In this paper, we propose a simple yet effective approach to connect the dots between fact verification and fake news detection. Our approach first employs a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. Then we use a fact verification model pre-trained on the FEVER dataset to detect whether the input news article is real or fake. Our approach makes use of the recent success of fact verification models and enables zero-shot fake news detection, alleviating the need of large scale training data to train fake news detection models. Experimental results on FakenewsNet, a benchmark dataset for fake news detection, demonstrate the effectiveness of our proposed approach.",
}
@inproceedings{liu-etal-2020-personalized,
    title = "Personalized Multimodal Feedback Generation in Education",
    author = "Liu, Haochen  and
      Liu, Zitao  and
      Wu, Zhongqin  and
      Tang, Jiliang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.166",
    doi = "10.18653/v1/2020.coling-main.166",
    pages = "1826--1840",
    abstract = "The automatic feedback of school assignments is an important application of AI in education. In this work, we focus on the task of personalized multimodal feedback generation, which aims to generate personalized feedback for teachers to evaluate students{'} assignments involving multimodal inputs such as images, audios, and texts. This task involves the representation and fusion of multimodal information and natural language generation, which presents the challenges from three aspects: (1) how to encode and integrate multimodal inputs; (2) how to generate feedback specific to each modality; and (3) how to fulfill personalized feedback generation. In this paper, we propose a novel Personalized Multimodal Feedback Generation Network (PMFGN) armed with a modality gate mechanism and a personalized bias mechanism to address these challenges. Extensive experiments on real-world K-12 education data show that our model significantly outperforms baselines by generating more accurate and diverse feedback. In addition, detailed ablation experiments are conducted to deepen our understanding of the proposed framework.",
}
@inproceedings{liu-etal-2020-reasoning,
    title = "Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network",
    author = "Liu, Daizong  and
      Qu, Xiaoye  and
      Dong, Jianfeng  and
      Zhou, Pan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.167",
    doi = "10.18653/v1/2020.coling-main.167",
    pages = "1841--1851",
    abstract = "Temporal sentence localization in videos aims to ground the best matched segment in an untrimmed video according to a given sentence query. Previous works in this field mainly rely on attentional frameworks to align the temporal boundaries by a soft selection. Although they focus on the visual content relevant to the query, these single-step attention are insufficient to model complex video contents and restrict the higher-level reasoning demand for this task. In this paper, we propose a novel deep rectification-modulation network (RMN), transforming this task into a multi-step reasoning process by repeating rectification and modulation. In each rectification-modulation layer, unlike existing methods directly conducting the cross-modal interaction, we first devise a rectification module to correct implicit attention misalignment which focuses on the wrong position during the cross-interaction process. Then, a modulation module is developed to capture the frame-to-frame relation with the help of sentence information for better correlating and composing the video contents over time. With multiple such layers cascaded in depth, our RMN progressively refines video and query interactions, thus enabling a further precise localization. Experimental evaluations on three public datasets show that the proposed method achieves state-of-the-art performance. Extensive ablation studies are carried out for the comprehensive analysis of the proposed method.",
}
@inproceedings{sun-etal-2020-riva,
    title = "{RIVA}: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal {NER}",
    author = "Sun, Lin  and
      Wang, Jiquan  and
      Su, Yindu  and
      Weng, Fangsheng  and
      Sun, Yuxuan  and
      Zheng, Zengwei  and
      Chen, Yuanyi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.168",
    doi = "10.18653/v1/2020.coling-main.168",
    pages = "1852--1862",
    abstract = "Multimodal named entity recognition (MNER) for tweets has received increasing attention recently. Most of the multimodal methods used attention mechanisms to capture the text-related visual information. However, unrelated or weakly related text-image pairs account for a large proportion in tweets. Visual clues unrelated to the text would incur uncertain or even negative effects for multimodal model learning. In this paper, we propose a novel pre-trained multimodal model based on Relationship Inference and Visual Attention (RIVA) for tweets. The RIVA model controls the attention-based visual clues with a gate regarding the role of image to the semantics of text. We use a teacher-student semi-supervised paradigm to leverage a large unlabeled multimodal tweet corpus with a labeled data set for text-image relation classification. In the multimodal NER task, the experimental results show the significance of text-related visual features for the visual-linguistic model and our approach achieves SOTA performance on the MNER datasets.",
}
@inproceedings{ziaeefard-lecue-2020-towards,
    title = "Towards Knowledge-Augmented Visual Question Answering",
    author = "Ziaeefard, Maryam  and
      Lecue, Freddy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.169",
    doi = "10.18653/v1/2020.coling-main.169",
    pages = "1863--1873",
    abstract = "Visual Question Answering (VQA) remains algorithmically challenging while it is effortless for humans. Humans combine visual observations with general and commonsense knowledge to answer questions about a given image. In this paper, we address the problem of incorporating general knowledge into VQA models while leveraging the visual information. We propose a model that captures the interactions between objects in a visual scene and entities in an external knowledge source. Our model is a graph-based approach that combines scene graphs with concept graphs, which learns a question-adaptive graph representation of related knowledge instances. We use Graph Attention Networks to set higher importance to key knowledge instances that are mostly relevant to each question. We exploit ConceptNet as the source of general knowledge and evaluate the performance of our model on the challenging OK-VQA dataset.",
}
@inproceedings{jiang-etal-2020-visual,
    title = "Visual-Textual Alignment for Graph Inference in Visual Dialog",
    author = "Jiang, Tianling  and
      Ji, Yi  and
      Liu, Chunping  and
      Shao, Hailin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.170",
    doi = "10.18653/v1/2020.coling-main.170",
    pages = "1874--1885",
    abstract = "As a conversational intelligence task, visual dialog entails answering a series of questions grounded in an image, using the dialog history as context. To generate correct answers, the comprehension of the semantic dependencies among implicit visual and textual contents is critical. Prior works usually ignored the underlying relation and failed to infer it reasonably. In this paper, we propose a Visual-Textual Alignment for Graph Inference (VTAGI) network. Compared with other approaches, it makes up the lack of structural inference in visual dialog. The whole system consists of two modules, Visual and Textual Alignment (VTA) and Visual Graph Attended by Text (VGAT). Specially, the VTA module aims at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics. The VGAT module views the visual features with semantic information as observed nodes and each node learns the relationship with others in visual graph. We also qualitatively and quantitatively evaluate the model on VisDial v1.0 dataset, showing our VTAGI outperforms previous state-of-the-art models.",
}
@inproceedings{savchenko-etal-2020-ad,
    title = "Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements",
    author = "Savchenko, Andrey  and
      Alekseev, Anton  and
      Kwon, Sejeong  and
      Tutubalina, Elena  and
      Myasnikov, Evgeny  and
      Nikolenko, Sergey",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.171",
    doi = "10.18653/v1/2020.coling-main.171",
    pages = "1886--1892",
    abstract = "Understanding image advertisements is a challenging task, often requiring non-literal interpretation. We argue that standard image-based predictions are insufficient for symbolism prediction. Following the intuition that texts and images are complementary in advertising, we introduce a multimodal ensemble of a state of the art image-based classifier, a classifier based on an object detection architecture, and a fine-tuned language model applied to texts extracted from ads by OCR. The resulting system establishes a new state of the art in symbolism prediction.",
}
@inproceedings{silberer-etal-2020-humans,
    title = "Humans Meet Models on Object Naming: A New Dataset and Analysis",
    author = "Silberer, Carina  and
      Zarrie{\ss}, Sina  and
      Westera, Matthijs  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.172",
    doi = "10.18653/v1/2020.coling-main.172",
    pages = "1893--1905",
    abstract = "We release ManyNames v2 (MN v2), a verified version of an object naming dataset that contains dozens of valid names per object for 25K images. We analyze issues in the data collection method originally employed, standard in Language {\&} Vision (L{\&}V), and find that the main source of noise in the data comes from simulating a naming context solely from an image with a target object marked with a bounding box, which causes subjects to sometimes disagree regarding which object is the target. We also find that both the degree of this uncertainty in the original data and the amount of true naming variation in MN v2 differs substantially across object domains. We use MN v2 to analyze a popular L{\&}V model and demonstrate its effectiveness on the task of object naming. However, our fine-grained analysis reveals that what appears to be human-like model behavior is not stable across domains, e.g., the model confuses people and clothing objects much more frequently than humans do. We also find that standard evaluations underestimate the actual effectiveness of the naming model: on the single-label names of the original dataset (Visual Genome), it obtains −27{\%} accuracy points than on MN v2, that includes all valid object names.",
}
@inproceedings{derby-etal-2020-encoding,
    title = "Encoding Lexico-Semantic Knowledge using Ensembles of Feature Maps from Deep Convolutional Neural Networks",
    author = "Derby, Steven  and
      Miller, Paul  and
      Devereux, Barry",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.173",
    doi = "10.18653/v1/2020.coling-main.173",
    pages = "1906--1921",
    abstract = "Semantic models derived from visual information have helped to overcome some of the limitations of solely text-based distributional semantic models. Researchers have demonstrated that text and image-based representations encode complementary semantic information, which when combined provide a more complete representation of word meaning, in particular when compared with data on human conceptual knowledge. In this work, we reveal that these vision-based representations, whilst quite effective, do not make use of all the semantic information available in the neural network that could be used to inform vector-based models of semantic representation. Instead, we build image-based meta-embeddings from computer vision models, which can incorporate information from all layers of the network, and show that they encode a richer set of semantic attributes and yield a more complete representation of human conceptual knowledge.",
}
@inproceedings{lindh-etal-2020-language,
    title = "Language-Driven Region Pointer Advancement for Controllable Image Captioning",
    author = "Lindh, Annika  and
      Ross, Robert  and
      Kelleher, John",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.174",
    doi = "10.18653/v1/2020.coling-main.174",
    pages = "1922--1935",
    abstract = "Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the advancement step as a natural part of the language structure via a NEXT-token, motivated by a strong correlation to the sentence structure in the training data. We find that our timing agrees with the ground-truth timing in the Flickr30k Entities test data with a precision of 86.55{\%} and a recall of 97.92{\%}. Our model implementing this technique improves the state-of-the-art on standard captioning metrics while additionally demonstrating a considerably larger effective vocabulary size.",
}
@inproceedings{gao-etal-2020-offensive,
    title = "Offensive Language Detection on Video Live Streaming Chat",
    author = "Gao, Zhiwei  and
      Yada, Shuntaro  and
      Wakamiya, Shoko  and
      Aramaki, Eiji",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.175",
    doi = "10.18653/v1/2020.coling-main.175",
    pages = "1936--1940",
    abstract = "This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time. Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions. We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful). To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning). We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity. Our results show that the transfer of features from social media to live streaming is effective. However, the two measurements show less correlation in the transferability prediction.",
}
@inproceedings{yang-okazaki-2020-image,
    title = "Image Caption Generation for News Articles",
    author = "Yang, Zhishen  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.176",
    doi = "10.18653/v1/2020.coling-main.176",
    pages = "1941--1951",
    abstract = "In this paper, we address the task of news-image captioning, which generates a description of an image given the image and its article body as input. This task is more challenging than the conventional image captioning, because it requires a joint understanding of image and text. We present a Transformer model that integrates text and image modalities and attends to textual features from visual features in generating a caption. Experiments based on automatic evaluation metrics and human evaluation show that an article text provides primary information to reproduce news-image captions written by journalists. The results also demonstrate that the proposed model outperforms the state-of-the-art model. In addition, we also confirm that visual features contribute to improving the quality of news-image captions.",
}
@inproceedings{kim-etal-2020-conan,
    title = "{C}o{NAN}: A Complementary Neighboring-based Attention Network for Referring Expression Generation",
    author = "Kim, Jungjun  and
      Ko, Hanbin  and
      Wu, Jialin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.177",
    doi = "10.18653/v1/2020.coling-main.177",
    pages = "1952--1962",
    abstract = "Daily scenes are complex in the real world due to occlusion, undesired lighting conditions, etc. Although humans handle those complicated environments well, they evoke challenges for machine learning systems to identify and describe the target without ambiguity. Most previous research focuses on mining discriminating features within the same category for the target object. One the other hand, as the scene becomes more complicated, human frequently uses the neighbor objects as complementary information to describe the target one. Motivated by that, we propose a novel Complementary Neighboring-based Attention Network (CoNAN) that explicitly utilizes the visual differences between the target object and its highly-related neighbors. These highly-related neighbors are determined by an attentional ranking module, as complementary features, highlighting the discriminating aspects for the target object. The speaker module then takes the visual difference features as an additional input to generate the expression. Our qualitative and quantitative results on the dataset RefCOCO, RefCOCO+, and RefCOCOg demonstrate that our generated expressions outperform other state-of-the-art models by a clear margin.",
}
@inproceedings{mordido-meinel-2020-mark,
    title = "Mark-Evaluate: Assessing Language Generation using Population Estimation Methods",
    author = "Mordido, Gon{\c{c}}alo  and
      Meinel, Christoph",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.178",
    doi = "10.18653/v1/2020.coling-main.178",
    pages = "1963--1977",
    abstract = "We propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology. More specifically, we use mark-recapture and maximum-likelihood methods that have been applied over the past several decades to estimate the size of closed populations in the wild. We propose three novel metrics: $\textrm{ME}_{\textrm{Petersen}}$ and $\textrm{ME}_{\textrm{CAPTURE}}$, which retrieve a single-valued assessment, and $\textrm{ME}_{\textrm{Schnabel}}$ which returns a double-valued metric to assess the evaluation set in terms of quality and diversity, separately. In synthetic experiments, our family of methods is sensitive to drops in quality and diversity. Moreover, our methods show a higher correlation to human evaluation than existing metrics on several challenging tasks, namely unconditional language generation, machine translation, and text summarization.",
}
@inproceedings{gong-etal-2020-tablegpt,
    title = "{T}able{GPT}: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching",
    author = "Gong, Heng  and
      Sun, Yawei  and
      Feng, Xiaocheng  and
      Qin, Bing  and
      Bi, Wei  and
      Liu, Xiaojiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.179",
    doi = "10.18653/v1/2020.coling-main.179",
    pages = "1978--1988",
    abstract = "Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data. Recently, pre-trained language models show potential in few-shot learning with linguistic knowledge learnt from pretraining on large-scale corpus. However, benefiting table-to-text generation in few-shot setting with the powerful pretrained language model faces three challenges, including (1) the gap between the task{'}s structured input and the natural language input for pretraining language model. (2) The lack of modeling for table structure and (3) improving text fidelity with less incorrect expressions that are contradicting to the table. To address aforementioned problems, we propose TableGPT for table-to-text generation. At first, we utilize table transformation module with template to rewrite structured table in natural language as input for GPT-2. In addition, we exploit multi-task learning with two auxiliary tasks that preserve table{'}s structural information by reconstructing the structure from GPT-2{'}s representation and improving the text{'}s fidelity with content matching task aligning the table and information in the generated text. By experimenting on Humans, Songs and Books, three few-shot table-to-text datasets in different domains, our model outperforms existing systems on most few-shot settings.",
}
@inproceedings{kementchedjhieva-etal-2020-apposcorpus,
    title = "The {A}ppos{C}orpus: a new multilingual, multi-domain dataset for factual appositive generation",
    author = "Kementchedjhieva, Yova  and
      Lu, Di  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.180",
    doi = "10.18653/v1/2020.coling-main.180",
    pages = "1989--2003",
    abstract = "News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences. In such cases, background information about the named entities could be provided in the form of an appositive noun phrase, either written by a human or generated automatically. We expand on the previous work in appositive generation with a new, more realistic, end-to-end definition of the task, instantiated by a dataset that spans four languages (English, Spanish, German and Polish), two entity types (person and organization) and two domains (Wikipedia and News). We carry out an extensive analysis of the data and the task, pointing to the various modeling challenges it poses. The results we obtain with standard language generation methods show that the task is indeed non-trivial, and leaves plenty of room for improvement.",
}
@inproceedings{jin-gildea-2020-generalized,
    title = "Generalized Shortest-Paths Encoders for {AMR}-to-Text Generation",
    author = "Jin, Lisa  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.181",
    doi = "10.18653/v1/2020.coling-main.181",
    pages = "2004--2013",
    abstract = "For text generation from semantic graphs, past neural models encoded input structure via gated convolutions along graph edges. Although these operations provide local context, the distance messages can travel is bounded by the number of encoder propagation steps. We adopt recent efforts of applying Transformer self-attention to graphs to allow global feature propagation. Instead of feeding shortest paths to the vertex self-attention module, we train a model to learn them using generalized shortest-paths algorithms. This approach widens the receptive field of a graph encoder by exposing it to all possible graph paths. We explore how this path diversity affects performance across levels of AMR connectivity, demonstrating gains on AMRs of higher reentrancy counts and diameters. Analysis of generated sentences also supports high semantic coherence of our models for reentrant AMRs. Our best model achieves a 1.4 BLEU and 1.8 chrF++ margin over a baseline that encodes only pairwise-unique shortest paths.",
}
@inproceedings{fan-etal-2020-enhanced,
    title = "An Enhanced Knowledge Injection Model for Commonsense Generation",
    author = "Fan, Zhihao  and
      Gong, Yeyun  and
      Wei, Zhongyu  and
      Wang, Siyuan  and
      Huang, Yameng  and
      Jiao, Jian  and
      Huang, Xuanjing  and
      Duan, Nan  and
      Zhang, Ruofei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.182",
    doi = "10.18653/v1/2020.coling-main.182",
    pages = "2014--2025",
    abstract = "Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, experimental results show that our method significantly improves the performance on all the metrics.",
}
@inproceedings{gong-etal-2020-multi,
    title = "Multi-grained {C}hinese Word Segmentation with Weakly Labeled Data",
    author = "Gong, Chen  and
      Li, Zhenghua  and
      Zou, Bowei  and
      Zhang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.183",
    doi = "10.18653/v1/2020.coling-main.183",
    pages = "2026--2036",
    abstract = "In contrast with the traditional single-grained word segmentation (SWS), where a sentence corresponds to a single word sequence, multi-grained Chinese word segmentation (MWS) aims to segment a sentence into multiple word sequences to preserve all words of different granularities. Due to the lack of manually annotated MWS data, previous work train and tune MWS models only on automatically generated pseudo MWS data. In this work, we further take advantage of the rich word boundary information in existing SWS data and naturally annotated data from dictionary example (DictEx) sentences, to advance the state-of-the-art MWS model based on the idea of weak supervision. Particularly, we propose to accommodate two types of weakly labeled data for MWS, i.e., SWS data and DictEx data by employing a simple yet competitive graph-based parser with local loss. Besides, we manually annotate a high-quality MWS dataset according to our newly compiled annotation guideline, consisting of over 9,000 sentences from two types of texts, i.e., canonical newswire (NEWS) and non-canonical web (BAIKE) data for better evaluation. Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1.12 and 5.97 on NEWS and BAIKE data in F1.",
}
@inproceedings{saxena-etal-2020-keygames,
    title = "{K}ey{G}ames: A Game Theoretic Approach to Automatic Keyphrase Extraction",
    author = "Saxena, Arnav  and
      Mangal, Mudit  and
      Jain, Goonjan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.184",
    doi = "10.18653/v1/2020.coling-main.184",
    pages = "2037--2048",
    abstract = "In this paper, we introduce two advancements in the automatic keyphrase extraction (AKE) space - KeyGames and pke+. KeyGames is an unsupervised AKE framework that employs the concept of evolutionary game theory and consistent labelling problem to ensure consistent classification of candidates into keyphrase and non-keyphrase. Pke+ is a python based pipeline built on top of the existing pke library to standardize various AKE steps, namely candidate extraction and evaluation, to ensure truly systematic and comparable performance analysis of AKE models. In the experiments section, we compare the performance of KeyGames across three publicly available datasets (Inspec 2001, SemEval 2010, DUC 2001) against the results quoted by the existing state-of-the-art models as well as their performance when reproduced using pke+. The results show that KeyGames outperforms most of the state-of-the-art systems while generalizing better on input documents with different domains and length. Further, pke+{'}s pre-processing brings out improvement in several other system{'}s quoted performance as well.",
}
@inproceedings{do-rehbein-2020-parsers,
    title = "Parsers Know Best: {G}erman {PP} Attachment Revisited",
    author = "Do, Bich-Ngoc  and
      Rehbein, Ines",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.185",
    doi = "10.18653/v1/2020.coling-main.185",
    pages = "2049--2061",
    abstract = "In the paper, we revisit the PP attachment problem which has been identified as one of the major sources for parser errors and discuss shortcomings of recent work. In particular, we show that using gold information for the extraction of attachment candidates as well as a missing comparison of the system{'}s output to the output of a full syntactic parser leads to an overly optimistic assessment of the results. We address these issues by presenting a realistic evaluation of the potential of different PP attachment systems, using fully predicted information as system input. We compare our results against the output of a strong neural parser and show that the full parsing approach is superior to modeling PP attachment disambiguation as a separate task.",
}
@inproceedings{huang-etal-2020-towards,
    title = "Towards Fast and Accurate Neural {C}hinese Word Segmentation with Multi-Criteria Learning",
    author = "Huang, Weipeng  and
      Cheng, Xingyi  and
      Chen, Kunlong  and
      Wang, Taifeng  and
      Chu, Wei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.186",
    doi = "10.18653/v1/2020.coling-main.186",
    pages = "2062--2072",
    abstract = "The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datasets and leverage their common underlying knowledge. In this paper, we propose a domain adaptive segmenter to exploit diverse criteria of various datasets. Our model is based on Bidirectional Encoder Representations from Transformers (BERT), which is responsible for introducing open-domain knowledge. Private and shared projection layers are proposed to capture domain-specific knowledge and common knowledge, respectively. We also optimize computational efficiency via distillation, quantization, and compiler optimization. Experiments show that our segmenter outperforms the previous state of the art (SOTA) models on 10 CWS datasets with superior efficiency.",
}
@inproceedings{tian-etal-2020-joint-chinese,
    title = "Joint {C}hinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams",
    author = "Tian, Yuanhe  and
      Song, Yan  and
      Xia, Fei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.187",
    doi = "10.18653/v1/2020.coling-main.187",
    pages = "2073--2084",
    abstract = "Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks for Chinese language processing. Previous studies have demonstrated that jointly performing them can be an effective one-step solution to both tasks and this joint task can benefit from a good modeling of contextual features such as n-grams. However, their work on modeling such contextual features is limited to concatenating the features or their embeddings directly with the input embeddings without distinguishing whether the contextual features are important for the joint task in the specific context. Therefore, their models for the joint task could be misled by unimportant contextual information. In this paper, we propose a character-based neural model for the joint task enhanced by multi-channel attention of n-grams. In the attention module, n-gram features are categorized into different groups according to several criteria, and n-grams in each group are weighted and distinguished according to their importance for the joint task in the specific context. To categorize n-grams, we try two criteria in this study, i.e., n-gram frequency and length, so that n-grams having different capabilities of carrying contextual information are discriminatively learned by our proposed attention module. Experimental results on five benchmark datasets for CWS and POS tagging demonstrate that our approach outperforms strong baseline models and achieves state-of-the-art performance on all five datasets.",
}
@inproceedings{gotou-etal-2020-taking,
    title = "Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation",
    author = "Gotou, Takumi  and
      Nagata, Ryo  and
      Mita, Masato  and
      Hanawa, Kazuaki",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.188",
    doi = "10.18653/v1/2020.coling-main.188",
    pages = "2085--2095",
    abstract = "This paper presents performance measures for grammatical error correction which take into account the difficulty of error correction. To the best of our knowledge, no conventional measure has such functionality despite the fact that some errors are easy to correct and others are not. The main purpose of this work is to provide a way of determining the difficulty of error correction and to motivate researchers in the domain to attack such difficult errors. The performance measures are based on the simple idea that the more systems successfully correct an error, the easier it is considered to be. This paper presents a set of algorithms to implement this idea. It evaluates the performance measures quantitatively and qualitatively on a wide variety of corpora and systems, revealing that they agree with our intuition of correction difficulty. A scorer and difficulty weight data based on the algorithms have been made available on the web.",
}
@inproceedings{qiu-etal-2020-automatic,
    title = "Automatic Distractor Generation for Multiple Choice Questions in Standard Tests",
    author = "Qiu, Zhaopeng  and
      Wu, Xian  and
      Fan, Wei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.189",
    doi = "10.18653/v1/2020.coling-main.189",
    pages = "2096--2106",
    abstract = "To assess knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.",
}
@inproceedings{tran-etal-2020-towards,
    title = "Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction",
    author = "Tran, Minh  and
      Zhang, Yipeng  and
      Soleymani, Mohammad",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.190",
    doi = "10.18653/v1/2020.coling-main.190",
    pages = "2107--2114",
    abstract = "Offensive and abusive language is a pressing problem on social media platforms. In this work, we propose a method for transforming offensive comments, statements containing profanity or offensive language, into non-offensive ones. We design a Retrieve, Generate and Edit unsupervised style transfer pipeline to redact the offensive comments in a word-restricted manner while maintaining a high level of fluency and preserving the content of the original text. We extensively evaluate our method{'}s performance and compare it to previous style transfer models using both automatic metrics and human evaluations. Experimental results show that our method outperforms other models on human evaluations and is the only approach that consistently performs well on all automatic evaluation metrics.",
}
@inproceedings{kim-sohn-2020-positive,
    title = "How Positive Are You: Text Style Transfer using Adaptive Style Embedding",
    author = "Kim, Heejin  and
      Sohn, Kyung-Ah",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.191",
    doi = "10.18653/v1/2020.coling-main.191",
    pages = "2115--2125",
    abstract = "The prevalent approach for unsupervised text style transfer is disentanglement between content and style. However, it is difficult to completely separate style information from the content. Other approaches allow the latent text representation to contain style and the target style to affect the generated output more than the latent representation does. In both approaches, however, it is impossible to adjust the strength of the style in the generated output. Moreover, those previous approaches typically perform both the sentence reconstruction and style control tasks in a single model, which complicates the overall architecture. In this paper, we address these issues by separating the model into a sentence reconstruction module and a style module. We use the Transformer-based autoencoder model for sentence reconstruction and the adaptive style embedding is learned directly in the style module. Because of this separation, each module can better focus on its own task. Moreover, we can vary the style strength of the generated sentence by changing the style of the embedding expression. Therefore, our approach not only controls the strength of the style, but also simplifies the model architecture. Experimental results show that our approach achieves better style transfer performance and content preservation than previous approaches.",
}
@inproceedings{kawamura-etal-2020-neural,
    title = "Neural text normalization leveraging similarities of strings and sounds",
    author = "Kawamura, Riku  and
      Aoki, Tatsuya  and
      Kamigaito, Hidetaka  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.192",
    doi = "10.18653/v1/2020.coling-main.192",
    pages = "2126--2131",
    abstract = "We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F1 scores than the baseline.",
}
@inproceedings{hotate-etal-2020-generating,
    title = "Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction",
    author = "Hotate, Kengo  and
      Kaneko, Masahiro  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.193",
    doi = "10.18653/v1/2020.coling-main.193",
    pages = "2132--2137",
    abstract = "In this study, we propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap, such as in grammatical error correction (GEC). In GEC, it is advisable to rewrite only the local sequences that must be rewritten while leaving the correct sequences unchanged. However, existing methods of acquiring various outputs focus on revising all tokens of a sentence. Therefore, existing methods may either generate ungrammatical sentences because they force the entire sentence to be changed or produce non-diversified sentences by weakening the constraints to avoid generating ungrammatical sentences. Considering these issues, we propose a method that does not rewrite all the tokens in a text, but only rewrites those parts that need to be diversely corrected. Our beam search method adjusts the search token in the beam according to the probability that the prediction is copied from the source sentence. The experimental results show that our proposed method generates more diverse corrections than existing methods without losing accuracy in the GEC task.",
}
@inproceedings{muangkammuen-etal-2020-neural,
    title = "A Neural Local Coherence Analysis Model for Clarity Text Scoring",
    author = "Muangkammuen, Panitan  and
      Xu, Sheng  and
      Fukumoto, Fumiyo  and
      Runapongsa Saikaew, Kanda  and
      Li, Jiyi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.194",
    doi = "10.18653/v1/2020.coling-main.194",
    pages = "2138--2143",
    abstract = "Local coherence relation between two phrases/sentences such as cause-effect and contrast gives a strong influence of whether a text is well-structured or not. This paper follows the assumption and presents a method for scoring text clarity by utilizing local coherence between adjacent sentences. We hypothesize that the contextual features of coherence relations learned by utilizing different data from the target training data are also possible to discriminate well-structured of the target text and thus help to score the text clarity. We propose a text clarity scoring method that utilizes local coherence analysis with an out-domain setting, i.e. the training data for the source and target tasks are different from each other. The method with language model pre-training BERT firstly trains the local coherence model as an auxiliary manner and then re-trains it together with clarity text scoring model. The experimental results by using the PeerRead benchmark dataset show the improvement compared with a single model, scoring text clarity model. Our source codes are available online.",
}
@inproceedings{caines-etal-2020-grammatical,
    title = "Grammatical error detection in transcriptions of spoken {E}nglish",
    author = "Caines, Andrew  and
      Bentz, Christian  and
      Knill, Kate  and
      Rei, Marek  and
      Buttery, Paula",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.195",
    doi = "10.18653/v1/2020.coling-main.195",
    pages = "2144--2162",
    abstract = "We describe the collection of transcription corrections and grammatical error annotations for the CrowdED Corpus of spoken English monologues on business topics. The corpus recordings were crowdsourced from native speakers of English and learners of English with German as their first language. The new transcriptions and annotations are obtained from different crowdworkers: we analyse the 1108 new crowdworker submissions and propose that they can be used for automatic transcription post-editing and grammatical error correction for speech. To further explore the data we train grammatical error detection models with various configurations including pre-trained and contextual word representations as input, additional features and auxiliary objectives, and extra training data from written error-annotated corpora. We find that a model concatenating pre-trained and contextual word representations as input performs best, and that additional information does not lead to further performance gains.",
}
@inproceedings{saberi-etal-2020-automatic,
    title = "Automatic Assistance for Academic Word Usage",
    author = "Saberi, Dariush  and
      Lee, John  and
      James Webster, Jonathan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.196",
    doi = "10.18653/v1/2020.coling-main.196",
    pages = "2163--2168",
    abstract = "This paper describes a writing assistance system that helps students improve their academic writing. Given an input text, the system suggests lexical substitutions that aim to incorporate more academic vocabulary. The substitution candidates are drawn from an academic word list and ranked by a masked language model. Experimental results show that lexical formality analysis can improve the quality of the suggestions, in comparison to a baseline that relies on the masked language model only.",
}
@inproceedings{jafaritazehjani-etal-2020-style,
    title = "Style versus Content: A distinction without a (learnable) difference?",
    author = "Jafaritazehjani, Somayeh  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Lolive, Damien  and
      Kelleher, John",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.197",
    doi = "10.18653/v1/2020.coling-main.197",
    pages = "2169--2180",
    abstract = "Textual style transfer involves modifying the style of a text while preserving its content. This assumes that it is possible to separate style from content. This paper investigates whether this separation is possible. We use sentiment transfer as our case study for style transfer analysis. Our experimental methodology frames style transfer as a multi-objective problem, balancing style shift with content preservation and fluency. Due to the lack of parallel data for style transfer we employ a variety of adversarial encoder-decoder networks in our experiments. Also, we use of a probing methodology to analyse how these models encode style-related features in their latent spaces. The results of our experiments which are further confirmed by a human evaluation reveal the inherent trade-off between the multiple style transfer objectives which indicates that style cannot be usefully separated from content within these style-transfer systems.",
}
@inproceedings{aljanaideh-etal-2020-contextualized,
    title = "Contextualized Embeddings for Enriching Linguistic Analyses on Politeness",
    author = "Aljanaideh, Ahmad  and
      Fosler-Lussier, Eric  and
      de Marneffe, Marie-Catherine",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.198",
    doi = "10.18653/v1/2020.coling-main.198",
    pages = "2181--2190",
    abstract = "Linguistic analyses in natural language processing (NLP) have often been performed around the static notion of words where the context (surrounding words) is not considered. For example, previous analyses on politeness have focused on comparing the use of static words such as personal pronouns across (im)polite requests without taking the context of those words into account. Current word embeddings in NLP do capture context and thus can be leveraged to enrich linguistic analyses. In this work, we introduce a model which leverages the pre-trained BERT model to cluster contextualized representations of a word based on (1) the context in which the word appears and (2) the labels of items the word occurs in. Using politeness as case study, this model is able to automatically discover interpretable, fine-grained context patterns of words, some of which align with existing theories on politeness. Our model further discovers novel finer-grained patterns associated with (im)polite language. For example, the word please can occur in impolite contexts that are predictable from BERT clustering. The approach proposed here is validated by showing that features based on fine-grained patterns inferred from the clustering improve over politeness-word baselines.",
}
@inproceedings{hinson-etal-2020-heterogeneous,
    title = "Heterogeneous Recycle Generation for {C}hinese Grammatical Error Correction",
    author = "Hinson, Charles  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.199",
    doi = "10.18653/v1/2020.coling-main.199",
    pages = "2191--2201",
    abstract = "Most recent works in the field of grammatical error correction (GEC) rely on neural machine translation-based models. Although these models boast impressive performance, they require a massive amount of data to properly train. Furthermore, NMT-based systems treat GEC purely as a translation task and overlook the editing aspect of it. In this work we propose a heterogeneous approach to Chinese GEC, composed of a NMT-based model, a sequence editing model, and a spell checker. Our methodology not only achieves a new state-of-the-art performance for Chinese GEC, but also does so without relying on data augmentation or GEC-specific architecture changes. We further experiment with all possible configurations of our system with respect to model composition order and number of rounds of correction. A detailed analysis of each model and their contributions to the correction process is performed by adapting the ERRANT scorer to be able to score Chinese sentences.",
}
@inproceedings{wan-etal-2020-improving,
    title = "Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation",
    author = "Wan, Zhaohong  and
      Wan, Xiaojun  and
      Wang, Wenguang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.200",
    doi = "10.18653/v1/2020.coling-main.200",
    pages = "2202--2212",
    abstract = "The incorporation of data augmentation method in grammatical error correction task has attracted much attention. However, existing data augmentation methods mainly apply noise to tokens, which leads to the lack of diversity of generated errors. In view of this, we propose a new data augmentation method that can apply noise to the latent representation of a sentence.By editing the latent representations of grammatical sentences, we can generate synthetic samples with various error types. Combining with some pre-defined rules, our method can greatly improve the performance and robustness of existing grammatical error correction models. We evaluate our method on public benchmarks of GEC task and it achieves the state-of-the-art performance on CoNLL-2014 and FCE benchmarks.",
}
@inproceedings{huang-etal-2020-cycle,
    title = "Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer",
    author = "Huang, Yufang  and
      Zhu, Wentao  and
      Xiong, Deyi  and
      Zhang, Yiye  and
      Hu, Changjian  and
      Xu, Feiyu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.201",
    doi = "10.18653/v1/2020.coling-main.201",
    pages = "2213--2223",
    abstract = "Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation. In this paper, we propose a novel neural approach to unsupervised text style transfer which we refer to as Cycle-consistent Adversarial autoEncoders (CAE) trained from non-parallel data. CAE consists of three essential components: (1) LSTM autoencoders that encode a text in one style into its latent representation and decode an encoded representation into its original text or a transferred representation into a style-transferred text, (2) adversarial style transfer networks that use an adversarially trained generator to transform a latent representation in one style into a representation in another style, and (3) a cycle-consistent constraint that enhances the capacity of the adversarial style transfer networks in content preservation. The entire CAE with these three components can be trained end-to-end. Extensive experiments and in-depth analyses on two widely-used public datasets consistently validate the effectiveness of proposed CAE in both style transfer and content preservation against several strong baselines in terms of four automatic evaluation metrics and human evaluation.",
}
@inproceedings{scialom-staiano-2020-ask,
    title = "Ask to Learn: A Study on Curiosity-driven Question Generation",
    author = "Scialom, Thomas  and
      Staiano, Jacopo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.202",
    doi = "10.18653/v1/2020.coling-main.202",
    pages = "2224--2235",
    abstract = "We propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in real life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered information. We refer to these inquisitive questions as Curiosity-driven: these questions are generated with the goal of obtaining new information (the answer) which is not present in the input text. In this work, we experiment on this new task using a conversational Question Answering (QA) dataset; further, since the majority of QA dataset are not built in a conversational manner, we describe a methodology to derive data for this novel task from non-conversational QA data. We investigate several automated metrics to measure the different properties of Curious Questions, and experiment different approaches on the Curiosity-driven Question Generation task, including model pre-training and reinforcement learning. Finally, we report a qualitative evaluation of the generated outputs.",
}
@inproceedings{wang-etal-2020-formality,
    title = "Formality Style Transfer with Shared Latent Space",
    author = "Wang, Yunli  and
      Wu, Yu  and
      Mou, Lili  and
      Li, Zhoujun  and
      Chao, WenHan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.203",
    doi = "10.18653/v1/2020.coling-main.203",
    pages = "2236--2249",
    abstract = "Conventional approaches for formality style transfer borrow models from neural machine translation, which typically requires massive parallel data for training. However, the dataset for formality style transfer is considerably smaller than translation corpora. Moreover, we observe that informal and formal sentences closely resemble each other, which is different from the translation task where two languages have different vocabularies and grammars. In this paper, we present a new approach, Sequence-to-Sequence with Shared Latent Space (S2S-SLS), for formality style transfer, where we propose two auxiliary losses and adopt joint training of bi-directional transfer and auto-encoding. Experimental results show that S2S-SLS (with either RNN or Transformer architectures) consistently outperforms baselines in various settings, especially when we have limited data.",
}
@inproceedings{wang-etal-2020-keep,
    title = "Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication",
    author = "Wang, Ruize  and
      Wei, Zhongyu  and
      Cheng, Ying  and
      Li, Piji  and
      Shan, Haijun  and
      Zhang, Ji  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.204",
    doi = "10.18653/v1/2020.coling-main.204",
    pages = "2250--2260",
    abstract = "Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method{'}s good ability in generating stories with higher quality compared to state-of-the-art methods.",
}
@inproceedings{cunha-etal-2020-referring,
    title = "Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities",
    author = "Cunha, Rossana  and
      Castro Ferreira, Thiago  and
      Pagano, Adriana  and
      Alves, Fabio",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.205",
    doi = "10.18653/v1/2020.coling-main.205",
    pages = "2261--2272",
    abstract = "Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mentioned as discourse unfolds. A limitation of novel REG models is not being able to generate referring expressions to entities not encountered during the training process. To solve this problem, we propose two extensions to NeuralREG, a state-of-the-art encoder-decoder REG model. The first is a copy mechanism, whereas the second consists of representing the gender and type of the referent as inputs to the model. Drawing on the results of automatic and human evaluation as well as an ablation study using the WebNLG corpus, we contend that our proposal contributes to the generation of more meaningful referring expressions to unseen entities than the original system and related work. Code and all produced data are publicly available.",
}
@inproceedings{nevezhin-etal-2020-topic,
    title = "Topic-driven Ensemble for Online Advertising Generation",
    author = "Nevezhin, Egor  and
      Butakov, Nikolay  and
      Khodorchenko, Maria  and
      Petrov, Maxim  and
      Nasonov, Denis",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.206",
    doi = "10.18653/v1/2020.coling-main.206",
    pages = "2273--2283",
    abstract = "Online advertising is one of the most widespread ways to reach and increase a target audience for those selling products. Usually having a form of a banner, advertising engages users into visiting a corresponding webpage. Professional generation of banners requires creative and writing skills and a basic understanding of target products. The great variety of goods presented in the online market enforce professionals to spend more and more time creating new advertisements different from existing ones. In this paper, we propose a neural network-based approach for the automatic generation of online advertising using texts from given webpages as sources. The important part of the approach is training on open data available online, which allows avoiding costly procedures of manual labeling. Collected open data consist of multiple subdomains with high data heterogeneity. The subdomains belong to different topics and vary in used vocabularies, phrases, styles that lead to reduced quality in adverts generation. We try to solve the problem of identifying existed subdomains and proposing a new ensemble approach based on exploiting multiple instances of a seq2seq model. Our experimental study on a dataset in the Russian language shows that our approach can significantly improve the quality of adverts generation.",
}
@inproceedings{kim-etal-2020-retrieval,
    title = "Retrieval-Augmented Controllable Review Generation",
    author = "Kim, Jihyeok  and
      Choi, Seungtaek  and
      Amplayo, Reinald Kim  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.207",
    doi = "10.18653/v1/2020.coling-main.207",
    pages = "2284--2295",
    abstract = "In this paper, we study review generation given a set of attribute identifiers which are user ID, product ID and rating. This is a difficult subtask of natural language generation since models are limited to the given identifiers, without any specific descriptive information regarding the inputs, when generating the text. The capacity of these models is thus confined and dependent to how well the models can capture vector representations of attributes. We thus propose to additionally leverage references, which are selected from a large pool of texts labeled with one of the attributes, as textual information that enriches inductive biases of given attributes. With these references, we can now pose the problem as an instance of text-to-text generation, which makes the task easier since texts that are syntactically, semantically similar with the output text are provided as input. Using this framework, we address issues such as selecting references from a large candidate set without textual context and improving the model complexity for generation. Our experiments show that our models improve over previous approaches on both automatic and human evaluation metrics.",
}
@inproceedings{jawahar-etal-2020-automatic,
    title = "Automatic Detection of Machine Generated Text: A Critical Survey",
    author = "Jawahar, Ganesh  and
      Abdul-Mageed, Muhammad  and
      Lakshmanan, V.S., Laks",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.208",
    doi = "10.18653/v1/2020.coling-main.208",
    pages = "2296--2309",
    abstract = "Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.",
}
@inproceedings{liu-etal-2020-learning,
    title = "A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning",
    author = "Liu, Mingtong  and
      Yang, Erguang  and
      Xiong, Deyi  and
      Zhang, Yujie  and
      Meng, Yao  and
      Hu, Changjian  and
      Xu, Jinan  and
      Chen, Yufeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.209",
    doi = "10.18653/v1/2020.coling-main.209",
    pages = "2310--2321",
    abstract = "Paraphrase generation (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications. Recently, neural sequence-to-sequence (Seq2Seq) models have shown promising results in PG. However, traditional model training for PG focuses on optimizing model prediction against single reference and employs cross-entropy loss, which objective is unable to encourage model to generate diverse paraphrases. In this work, we present a novel approach with multi-objective learning to PG. We propose a learning-exploring method to generate sentences as learning objectives from the learned data distribution, and employ reinforcement learning to combine these new learning objectives for model training. We first design a sample-based algorithm to explore diverse sentences. Then we introduce several reward functions to evaluate the sampled sentences as learning signals in terms of expressive diversity and semantic fidelity, aiming to generate diverse and high-quality paraphrases. To effectively optimize model performance satisfying different evaluating aspects, we use a GradNorm-based algorithm that automatically balances these training objectives. Experiments and analyses on Quora and Twitter datasets demonstrate that our proposed method not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.",
}
@inproceedings{caglayan-etal-2020-curious,
    title = "Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale",
    author = "Caglayan, Ozan  and
      Madhyastha, Pranava  and
      Specia, Lucia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.210",
    doi = "10.18653/v1/2020.coling-main.210",
    pages = "2322--2328",
    abstract = "Automatic evaluation of language generation systems is a well-studied problem in Natural Language Processing. While novel metrics are proposed every year, a few popular metrics remain as the de facto metrics to evaluate tasks such as image captioning and machine translation, despite their known limitations. This is partly due to ease of use, and partly because researchers expect to see them and know how to interpret them. In this paper, we urge the community for more careful consideration of how they automatically evaluate their models by demonstrating important failure cases on multiple datasets, language pairs and tasks. Our experiments show that metrics (i) usually prefer system outputs to human-authored texts, (ii) can be insensitive to correct translations of rare words, (iii) can yield surprisingly high scores when given a single sentence as system output for the entire test set.",
}
@inproceedings{orbach-goldberg-2020-facts2story,
    title = "{F}acts2{S}tory: Controlling Text Generation by Key Facts",
    author = "Orbach, Eyal  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.211",
    doi = "10.18653/v1/2020.coling-main.211",
    pages = "2329--2345",
    abstract = "Recent advancements in self-attention neural network architectures have raised the bar for open-ended text generation. Yet, while current methods are capable of producing a coherent text which is several hundred words long, attaining control over the content that is being generated{---}as well as evaluating it{---}are still open questions. We propose a controlled generation task which is based on expanding a sequence of facts, expressed in natural language, into a longer narrative. We introduce human-based evaluation metrics for this task, as well as a method for deriving a large training dataset. We evaluate three methods on this task, based on fine-tuning pre-trained models. We show that while auto-regressive, unidirectional Language Models such as GPT2 produce better fluency, they struggle to adhere to the requested facts. We propose a plan-and-cloze model (using fine-tuned XLNet) which produces competitive fluency while adhering to the requested content.",
}
@inproceedings{zhai-etal-2020-story,
    title = "Story Generation with Rich Details",
    author = "Zhai, Fangzhou  and
      Demberg, Vera  and
      Koller, Alexander",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.212",
    doi = "10.18653/v1/2020.coling-main.212",
    pages = "2346--2351",
    abstract = "Automatically generated stories need to be not only coherent, but also interesting. Apart from realizing a story line, the text also needs to include rich details to engage the readers. We propose a model that features two different generation components: an outliner, which proceeds the main story line to realize global coherence; a detailer, which supplies relevant details to the story in a locally coherent manner. Human evaluations show our model substantially improves the informativeness of generated text while retaining its coherence, outperforming various baselines.",
}
@inproceedings{uehara-etal-2020-learning,
    title = "Learning with Contrastive Examples for Data-to-Text Generation",
    author = "Uehara, Yui  and
      Ishigaki, Tatsuya  and
      Aoki, Kasumi  and
      Noji, Hiroshi  and
      Goshima, Keiichi  and
      Kobayashi, Ichiro  and
      Takamura, Hiroya  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.213",
    doi = "10.18653/v1/2020.coling-main.213",
    pages = "2352--2362",
    abstract = "Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., {``}Nikkei gains{''} is generated when {``}Nikkei drops{''} is expected. We investigate models trained on contrastive examples i.e., incorrect sentences or terms, in addition to correct ones to reduce such errors. We first create rules to produce contrastive examples from correct ones by replacing frequent crucial terms such as {``}gain{''} or {``}drop{''}. We then use learning methods with several losses that exploit contrastive examples. Experiments on the market comment generation task show that 1) exploiting contrastive examples improves the capability of generating sentences with better lexical choice, without degrading the fluency, 2) the choice of the loss function is an important factor because the performances on different metrics depend on the types of loss functions, and 3) the use of the examples produced by some specific rules further improves performance. Human evaluation also supports the effectiveness of using contrastive examples.",
}
@inproceedings{pan-etal-2020-medwriter,
    title = "{M}ed{W}riter: Knowledge-Aware Medical Text Generation",
    author = "Pan, Youcheng  and
      Chen, Qingcai  and
      Peng, Weihua  and
      Wang, Xiaolong  and
      Hu, Baotian  and
      Liu, Xin  and
      Chen, Junying  and
      Zhou, Wenxiu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.214",
    doi = "10.18653/v1/2020.coling-main.214",
    pages = "2363--2368",
    abstract = "To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical. However, most of recent works only consider the information of unstructured text rather than structured information of the knowledge graph. In this paper, we focus on the medical topic-to-text generation task and adapt a knowledge-aware text generation model to the medical domain, named MedWriter, which not only introduces the specific knowledge from the external MKG but also is capable of learning graph-level representation. We conduct experiments on a medical literature dataset collected from medical journals, each of which has a set of topic words, an abstract of medical literature and a corresponding knowledge graph from CMeKG. Experimental results demonstrate incorporating knowledge graph into generation model can improve the quality of the generated text and has robust superiority over the competitor methods.",
}
@inproceedings{fu-etal-2020-dynamic,
    title = "Dynamic Topic Tracker for {KB}-to-Text Generation",
    author = "Fu, Zihao  and
      Bing, Lidong  and
      Lam, Wai  and
      Jameel, Shoaib",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.215",
    doi = "10.18653/v1/2020.coling-main.215",
    pages = "2369--2380",
    abstract = "Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. However, most of the existing models suffer from the off-topic problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data. This problem seriously degrades the quality of the generation results. In this paper, we propose a novel dynamic topic tracker for solving this problem. Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem. The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.",
}
@inproceedings{li-etal-2020-improving-variational,
    title = "Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation",
    author = "Li, Ruizhe  and
      Li, Xiao  and
      Chen, Guanyi  and
      Lin, Chenghua",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.216",
    doi = "10.18653/v1/2020.coling-main.216",
    pages = "2381--2397",
    abstract = "The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences. However, an issue known as posterior collapse (or KL loss vanishing) happens when the VAE is used in text modelling, where the approximate posterior collapses to the prior, and the model will totally ignore the latent variables and be degraded to a plain language model during text generation. Such an issue is particularly prevalent when RNN-based VAE models are employed for text modelling. In this paper, we propose a simple, generic architecture called Timestep-Wise Regularisation VAE (TWR-VAE), which can effectively avoid posterior collapse and can be applied to any RNN-based VAE models. The effectiveness and versatility of our model are demonstrated in different tasks, including language modelling and dialogue response generation.",
}
@inproceedings{jin-etal-2020-genwiki,
    title = "{G}en{W}iki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation",
    author = "Jin, Zhijing  and
      Guo, Qipeng  and
      Qiu, Xipeng  and
      Zhang, Zheng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.217",
    doi = "10.18653/v1/2020.coling-main.217",
    pages = "2398--2409",
    abstract = "Data collection for the knowledge graph-to-text generation is expensive. As a result, research on unsupervised models has emerged as an active field recently. However, most unsupervised models have to use non-parallel versions of existing small supervised datasets, which largely constrain their potential. In this paper, we propose a large-scale, general-domain dataset, GenWiki. Our unsupervised dataset has 1.3M text and graph examples, respectively. With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.",
}
@inproceedings{harkous-etal-2020-text,
    title = "Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity",
    author = "Harkous, Hamza  and
      Groves, Isabel  and
      Saffari, Amir",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.218",
    doi = "10.18653/v1/2020.coling-main.218",
    pages = "2410--2424",
    abstract = "End-to-end neural data-to-text (D2T) generation has recently emerged as an alternative to pipeline-based architectures. However, it has faced challenges generalizing to new domains and generating semantically consistent text. In this work, we present DataTuner, a neural, end-to-end data-to-text generation system that makes minimal assumptions about the data representation and target domain. We take a two-stage generation-reranking approach, combining a fine-tuned language model with a semantic fidelity classifier. Each component is learnt end-toe-nd without needing dataset-specific heuristics, entity delexicalization, or post-processing. We show that DataTuner achieves state of the art results on automated metrics across four major D2T datasets (LDC2017T10, WebNLG, ViGGO, and Cleaned E2E), with fluency assessed by human annotators as nearing or exceeding the human-written reference texts. Our generated text has better semantic fidelity than the state of the art on these datasets. We further demonstrate that our model-based semantic fidelity scorer is a better assessment tool compared to traditional heuristic-based measures of semantic accuracy.",
}
@inproceedings{liu-etal-2020-graph,
    title = "Graph-Based Knowledge Integration for Question Answering over Dialogue",
    author = "Liu, Jian  and
      Sui, Dianbo  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.219",
    doi = "10.18653/v1/2020.coling-main.219",
    pages = "2425--2435",
    abstract = "Question answering over dialogue, a specialized machine reading comprehension task, aims to comprehend a dialogue and to answer specific questions. Despite many advances, existing approaches for this task did not consider dialogue structure and background knowledge (e.g., relationships between speakers). In this paper, we introduce a new approach for the task, featured by its novelty in structuring dialogue and integrating background knowledge for reasoning. Specifically, different from previous {``}structure-less{''} approaches, our method organizes a dialogue as a {``}relational graph{''}, using edges to represent relationships between entities. To encode this relational graph, we devise a relational graph convolutional network (R-GCN), which can traverse the graph{'}s topological structure and effectively encode multi-relational knowledge for reasoning. The extensive experiments have justified the effectiveness of our approach over competitive baselines. Moreover, a deeper analysis shows that our model is better at tackling complex questions requiring relational reasoning and defending adversarial attacks with distracting sentences.",
}
@inproceedings{bogolin-etal-2020-hierarchical,
    title = "A hierarchical approach to vision-based language generation: from simple sentences to complex natural language",
    author = "Bogolin, Simion-Vlad  and
      Croitoru, Ioana  and
      Leordeanu, Marius",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.220",
    doi = "10.18653/v1/2020.coling-main.220",
    pages = "2436--2447",
    abstract = "Automatically describing videos in natural language is an ambitious problem, which could bridge our understanding of vision and language. We propose a hierarchical approach, by first generating video descriptions as sequences of simple sentences, followed at the next level by a more complex and fluent description in natural language. While the simple sentences describe simple actions in the form of (subject, verb, object), the second-level paragraph descriptions, indirectly using information from the first-level description, presents the visual content in a more compact, coherent and semantically rich manner. To this end, we introduce the first video dataset in the literature that is annotated with captions at two levels of linguistic complexity. We perform extensive tests that demonstrate that our hierarchical linguistic representation, from simple to complex language, allows us to train a two-stage network that is able to generate significantly more complex paragraphs than current one-stage approaches.",
}
@inproceedings{wang-etal-2020-sentiment,
    title = "Sentiment Forecasting in Dialog",
    author = "Wang, Zhongqing  and
      Zhu, Xiujun  and
      Zhang, Yue  and
      Li, Shoushan  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.221",
    doi = "10.18653/v1/2020.coling-main.221",
    pages = "2448--2458",
    abstract = "Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines.",
}
@inproceedings{lim-etal-2020-know,
    title = "{I} Know What You Asked: Graph Path Learning using {AMR} for Commonsense Reasoning",
    author = "Lim, Jungwoo  and
      Oh, Dongsuk  and
      Jang, Yoonna  and
      Yang, Kisu  and
      Lim, Heuiseok",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.222",
    doi = "10.18653/v1/2020.coling-main.222",
    pages = "2459--2471",
    abstract = "CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.",
}
@inproceedings{strzyz-etal-2020-bracketing,
    title = "Bracketing Encodings for 2-Planar Dependency Parsing",
    author = "Strzyz, Michalina  and
      Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.223",
    doi = "10.18653/v1/2020.coling-main.223",
    pages = "2472--2484",
    abstract = "We present a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels, hence providing almost total coverage of crossing arcs in sequence labeling parsing. First, we show that existing bracketing encodings for parsing as labeling can only handle a very mild extension of projective trees. Second, we overcome this limitation by taking into account the well-known property of 2-planarity, which is present in the vast majority of dependency syntactic structures in treebanks, i.e., the arcs of a dependency tree can be split into two planes such that arcs in a given plane do not cross. We take advantage of this property to design a method that balances the brackets and that encodes the arcs belonging to each of those planes, allowing for almost unrestricted non-projectivity (∼99.9{\%} coverage) in sequence labeling parsing. The experiments show that our linearizations improve over the accuracy of the original bracketing encoding in highly non-projective treebanks (on average by 0.4 LAS), while achieving a similar speed. Also, they are especially suitable when PoS tags are not used as input parameters to the models.",
}
@inproceedings{wang-tu-2020-semi,
    title = "Semi-Supervised Dependency Parsing with Arc-Factored Variational Autoencoding",
    author = "Wang, Ge  and
      Tu, Kewei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.224",
    doi = "10.18653/v1/2020.coling-main.224",
    pages = "2485--2496",
    abstract = "Mannual annotation for dependency parsing is both labourious and time costly, resulting in the difficulty to learn practical dependency parsers for many languages due to the lack of labelled training corpora. To compensate for the scarcity of labelled data, semi-supervised dependency parsing methods are developed to utilize unlabelled data in the training procedure of dependency parsers. In previous work, the autoencoder framework is a prevalent approach for the utilization of unlabelled data. In this framework, training sentences are reconstructed from a decoder conditioned on dependency trees predicted by an encoder. The tree structure requirement brings challenges for both the encoder and the decoder. Sophisticated techniques are employed to tackle these challenges at the expense of model complexity and approximations in encoding and decoding. In this paper, we propose a model based on the variational autoencoder framework. By relaxing the tree constraint in both the encoder and the decoder during training, we make the learning of our model fully arc-factored and thus circumvent the challenges brought by the tree constraint. We evaluate our model on datasets across several languages and the results demonstrate the advantage of our model over previous approaches in both parsing accuracy and speed.",
}
@inproceedings{kankanampati-etal-2020-multitask,
    title = "Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations",
    author = "Kankanampati, Yash  and
      Le Roux, Joseph  and
      Tomeh, Nadi  and
      Taji, Dima  and
      Habash, Nizar",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.225",
    doi = "10.18653/v1/2020.coling-main.225",
    pages = "2497--2508",
    abstract = "In this paper we present a parsing model for projective dependency trees which takes advantage of the existence of complementary dependency annotations which is the case in Arabic, with the availability of CATiB and UD treebanks. Our system performs syntactic parsing according to both annotation types jointly as a sequence of arc-creating operations, and partially created trees for one annotation are also available to the other as features for the score function. This method gives error reduction of 9.9{\%} on CATiB and 6.1{\%} on UD compared to a strong baseline, and ablation tests show that the main contribution of this reduction is given by sharing tree representation between tasks, and not simply sharing BiLSTM layers as is often performed in NLP multitask systems.",
}
@inproceedings{li-etal-2020-context,
    title = "Context Dependent Semantic Parsing: A Survey",
    author = "Li, Zhuang  and
      Qu, Lizhen  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.226",
    doi = "10.18653/v1/2020.coling-main.226",
    pages = "2509--2521",
    abstract = "Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. Currently, most semantic parsing methods are not able to utilize the contextual information (e.g. dialogue and comments history), which has a great potential to boost the semantic parsing systems. To address this issue, context dependent semantic parsing has recently drawn a lot of attention. In this survey, we investigate progress on the methods for the context dependent semantic parsing, together with the current datasets and tasks. We then point out open problems and challenges for future research in this area.",
}
@inproceedings{han-etal-2020-survey,
    title = "A Survey of Unsupervised Dependency Parsing",
    author = "Han, Wenjuan  and
      Jiang, Yong  and
      Ng, Hwee Tou  and
      Tu, Kewei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.227",
    doi = "10.18653/v1/2020.coling-main.227",
    pages = "2522--2533",
    abstract = "Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated text data. It also serves as the basis for other research in low-resource parsing. In this paper, we survey existing approaches to unsupervised dependency parsing, identify two major classes of approaches, and discuss recent trends. We hope that our survey can provide insights for researchers and facilitate future research on this topic.",
}
@inproceedings{xie-etal-2020-exploring,
    title = "Exploring Question-Specific Rewards for Generating Deep Questions",
    author = "Xie, Yuxi  and
      Pan, Liangming  and
      Wang, Dongzhe  and
      Kan, Min-Yen  and
      Feng, Yansong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.228",
    doi = "10.18653/v1/2020.coling-main.228",
    pages = "2534--2546",
    abstract = "Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log likelihood of ground-truth questions using teacher forcing. However, this training objective is inconsistent with actual question quality, which is often reflected by certain global properties such as whether the question can be answered by the document. As such, we directly optimize for QG-specific objectives via reinforcement learning to improve question quality. We design three different rewards that target to improve the fluency, relevance, and answerability of generated questions. We conduct both automatic and human evaluations in addition to thorough analysis to explore the effect of each QG-specific reward. We find that optimizing on question-specific rewards generally leads to better performance in automatic evaluation metrics. However, only the rewards that correlate well with human judgement (e.g., relevance) lead to real improvement in question quality. Optimizing for the others, especially answerability, introduces incorrect bias to the model, resulting in poorer question quality. The code is publicly available at https://github.com/YuxiXie/RL-for-Question-Generation.",
}
@inproceedings{lu-etal-2020-chime,
    title = "{CHIME}: Cross-passage Hierarchical Memory Network for Generative Review Question Answering",
    author = "Lu, Junru  and
      Pergola, Gabriele  and
      Gui, Lin  and
      Li, Binyang  and
      He, Yulan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.229",
    doi = "10.18653/v1/2020.coling-main.229",
    pages = "2547--2560",
    abstract = "We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module.",
}
@inproceedings{campos-etal-2020-improving,
    title = "Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning",
    author = "Campos, Jon Ander  and
      Cho, Kyunghyun  and
      Otegi, Arantxa  and
      Soroa, Aitor  and
      Agirre, Eneko  and
      Azkune, Gorka",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.230",
    doi = "10.18653/v1/2020.coling-main.230",
    pages = "2561--2571",
    abstract = "The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility. In most applications, users are not able to provide the correct answer to the system, but they are able to provide binary (correct, incorrect) feedback. In this paper we propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback. We perform simulated experiments on document classification (for development) and Conversational Question Answering datasets like QuAC and DoQA, where binary user feedback is derived from gold annotations. The results show that our method is able to improve over the initial supervised system, getting close to a fully-supervised system that has access to the same labeled examples in in-domain experiments (QuAC), and even matching in out-of-domain experiments (DoQA). Our work opens the prospect to exploit interactions with real users and improve conversational systems after deployment.",
}
@inproceedings{wang-etal-2020-modelling,
    title = "Modelling Long-distance Node Relations for {KBQA} with Global Dynamic Graph",
    author = "Wang, Xu  and
      Zhao, Shuai  and
      Han, Jiale  and
      Cheng, Bo  and
      Yang, Hao  and
      Ao, Jianchang  and
      Li, Zhenzi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.231",
    doi = "10.18653/v1/2020.coling-main.231",
    pages = "2572--2582",
    abstract = "The structural information of Knowledge Bases (KBs) has proven effective to Question Answering (QA). Previous studies rely on deep graph neural networks (GNNs) to capture rich structural information, which may not model node relations in particularly long distance due to oversmoothing issue. To address this challenge, we propose a novel framework \textbf{GlobalGraph}, which models long-distance node relations from two views: 1) Node type similarity: GlobalGraph assigns each node a global type label and models long-distance node relations through the global type label similarity; 2) Correlation between nodes and questions: we learn similarity scores between nodes and the question, and model long-distance node relations through the sum score of two nodes. We conduct extensive experiments on two widely used multi-hop KBQA datasets to prove the effectiveness of our method.",
}
@inproceedings{chen-etal-2020-improving-commonsense,
    title = "Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources",
    author = "Chen, Qianglong  and
      Ji, Feng  and
      Chen, Haiqing  and
      Zhang, Yin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.232",
    doi = "10.18653/v1/2020.coling-main.232",
    pages = "2583--2594",
    abstract = "In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense effectively in question answering systems is still under exploration in both research academia and industry. In this paper, we propose a novel question-answering method by integrating multiple knowledge sources, i.e. ConceptNet, Wikipedia, and the Cambridge Dictionary, to boost the performance. More concretely, we first introduce a novel graph-based iterative knowledge retrieval module, which iteratively retrieves concepts and entities related to the given question and its choices from multiple knowledge sources. Afterward, we use a pre-trained language model to encode the question, retrieved knowledge and choices, and propose an answer choice-aware attention mechanism to fuse all hidden representations of the previous modules. Finally, the linear classifier for specific tasks is used to predict the answer. Experimental results on the CommonsenseQA dataset show that our method significantly outperforms other competitive methods and achieves the new state-of-the-art. In addition, further ablation studies demonstrate the effectiveness of our graph-based iterative knowledge retrieval module and the answer choice-aware attention module in retrieving and synthesizing background knowledge from multiple knowledge sources.",
}
@inproceedings{nguyen-etal-2020-vietnamese,
    title = "A {V}ietnamese Dataset for Evaluating Machine Reading Comprehension",
    author = "Nguyen, Kiet  and
      Nguyen, Vu  and
      Nguyen, Anh  and
      Nguyen, Ngan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.233",
    doi = "10.18653/v1/2020.coling-main.233",
    pages = "2595--2605",
    abstract = "Over 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the first experimental models on UIT-ViQuAD, which will be compared to further models. We also estimate human performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best model performances on the dataset indicate that improvements can be explored on UIT-ViQuAD through future research. Our dataset is freely available to encourage the research community to overcome challenges in Vietnamese MRC.",
}
@inproceedings{yoshino-etal-2020-improving,
    title = "Improving Spoken Language Understanding by Wisdom of Crowds",
    author = "Yoshino, Koichiro  and
      Ikeuchi, Kana  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.234",
    doi = "10.18653/v1/2020.coling-main.234",
    pages = "2606--2612",
    abstract = "Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task. The lack of training data is an important problem, especially for new system tasks, because existing SLU systems are based on statistical approaches. In this paper, we proposed to use two sources of the {``}wisdom of crowds,{''} crowdsourcing and knowledge community website, for improving the SLU system. We firstly collected paraphrasing variations for new system tasks through crowdsourcing as seed data, and then augmented them using similar questions from a knowledge community website. We investigated the effects of the proposed data augmentation method in SLU task, even with small seed data. In particular, the proposed architecture augmented more than 120,000 samples to improve SLU accuracies.",
}
@inproceedings{peng-etal-2020-bi,
    title = "Bi-directional {C}ognitive{T}hinking Network for Machine Reading Comprehension",
    author = "Peng, Wei  and
      Hu, Yue  and
      Xing, Luxi  and
      Xie, Yuqiang  and
      Yu, Jing  and
      Sun, Yajing  and
      Wei, Xiangpeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.235",
    doi = "10.18653/v1/2020.coling-main.235",
    pages = "2613--2623",
    abstract = "We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the passage and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The model has the ability to reverse reasoning questions which can assist inertial thinking to generate more accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science.",
}
@inproceedings{zhang-etal-2020-learn,
    title = "Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension",
    author = "Zhang, Xuemiao  and
      Zhou, Kun  and
      Wang, Sirui  and
      Zhang, Fuzheng  and
      Wang, Zhongyuan  and
      Liu, Junfei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.236",
    doi = "10.18653/v1/2020.coling-main.236",
    pages = "2624--2634",
    abstract = "Weakly supervised machine reading comprehension (MRC) task is practical and promising for its easily available and massive training data, but inevitablely introduces noise. Existing related methods usually incorporate extra submodels to help filter noise before the noisy data is input to main models. However, these multistage methods often make training difficult, and the qualities of submodels are hard to be controlled. In this paper, we first explore and analyze the essential characteristics of noise from the perspective of loss distribution, and find that in the early stage of training, noisy samples usually lead to significantly larger loss values than clean ones. Based on the observation, we propose a hierarchical loss correction strategy to avoid fitting noise and enhance clean supervision signals, including using an unsupervisedly fitted Gaussian mixture model to calculate the weight factors for all losses to correct the loss distribution, and employ a hard bootstrapping loss to modify loss function. Experimental results on different weakly supervised MRC datasets show that the proposed methods can help improve models significantly.",
}
@inproceedings{guo-etal-2020-incorporating,
    title = "Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension",
    author = "Guo, Shaoru  and
      Guan, Yong  and
      Li, Ru  and
      Li, Xiaoli  and
      Tan, Hongye",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.237",
    doi = "10.18653/v1/2020.coling-main.237",
    pages = "2635--2641",
    abstract = "Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding. It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature. This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation. Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.",
}
@inproceedings{li-etal-2020-molweni,
    title = "Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure",
    author = "Li, Jiaqi  and
      Liu, Ming  and
      Kan, Min-Yen  and
      Zheng, Zihao  and
      Wang, Zekun  and
      Lei, Wenqiang  and
      Liu, Ting  and
      Qin, Bing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.238",
    doi = "10.18653/v1/2020.coling-main.238",
    pages = "2642--2652",
    abstract = "Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni{'}s source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing. Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7{\%} F1 on Molweni{'}s questions, a 20+{\%} significant drop as compared against its SQuAD 2.0 performance.",
}
@inproceedings{huang-etal-2020-joint,
    title = "Joint Event Extraction with Hierarchical Policy Network",
    author = "Huang, Peixin  and
      Zhao, Xiang  and
      Takanobu, Ryuichi  and
      Tan, Zhen  and
      Xiao, Weidong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.239",
    doi = "10.18653/v1/2020.coling-main.239",
    pages = "2653--2664",
    abstract = "Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.",
}
@inproceedings{lin-lu-2020-automated,
    title = "Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs",
    author = "Lin, Wan-Hsuan  and
      Lu, Chun-Shien",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.240",
    doi = "10.18653/v1/2020.coling-main.240",
    pages = "2665--2675",
    abstract = "This paper proposes a novel miscellaneous-context-based method to convert a sentence into a knowledge embedding in the form of a directed graph. We adopt the idea of conceptual graphs to frame for the miscellaneous textual information into conceptual compactness. We first empirically observe that this graph representation method can (1) accommodate the slot-filling challenges in typical question answering and (2) access to the sentence-level graph structure in order to explicitly capture the neighbouring connections of reference concept nodes. Secondly, we propose a task-agnostic semantics-measured module, which cooperates with the graph representation method, in order to (3) project an edge of a sentence-level graph to the space of semantic relevance with respect to the corresponding concept nodes. As a result of experiments on the QA-type relation extraction, the combination of the graph representation and the semantics-measured module achieves the high accuracy of answer prediction and offers human-comprehensible graphical interpretation for every well-formed sample. To our knowledge, our approach is the first towards the interpretable process of learning vocabulary representations with the experimental evidence.",
}
@inproceedings{chen-wu-2020-forcereader,
    title = "{F}orce{R}eader: a {BERT}-based Interactive Machine Reading Comprehension Model with Attention Separation",
    author = "Chen, Zheng  and
      Wu, Kangjian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.241",
    doi = "10.18653/v1/2020.coling-main.241",
    pages = "2676--2686",
    abstract = "The release of BERT revolutionized the development of NLP. Various BERT-based reading comprehension models have been proposed, thus updating the performance ranking of reading comprehension tasks. However, the above BERT-based models inherently employ BERT{'}s combined input method, representing the input question and paragraph as a single packed sequence, without further modification for reading comprehension. This paper makes an in-depth analysis of this input method, proposes a problem of this approach. We call it attention deconcentration. Accordingly, this paper proposes ForceReader, a BERT-based interactive machine reading comprehension model. First, ForceReader proposes a novel solution called the Attention Separation Representation to respond to attention deconcentration. Moreover, starting from the logical nature of reading comprehension tasks, ForceReader adopts Multi-mode Reading and Interactive Reasoning strategy. For the calculation of attention, ForceReader employs Conditional Background Attention to solve the lack of the overall context semantic after the separation of attention. As an integral model, ForceReader shows a significant improvement in reading comprehension tasks compared to BERT. Moreover, this paper makes detailed visual analyses of the attention and propose strategies accordingly. This may be another argument to the explanations of the attention.",
}
@inproceedings{huang-etal-2020-nut,
    title = "{NUT}-{RC}: Noisy User-generated Text-oriented Reading Comprehension",
    author = "Huang, Rongtao  and
      Zou, Bowei  and
      Hong, Yu  and
      Zhang, Wei  and
      Aw, AiTi  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.242",
    doi = "10.18653/v1/2020.coling-main.242",
    pages = "2687--2698",
    abstract = "Reading comprehension (RC) on social media such as Twitter is a critical and challenging task due to its noisy, informal, but informative nature. Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media. Moreover, these models only focus on a certain type of RC, extractive or generative, but ignore the integration of them. To well address these challenges, we come up with a noisy user-generated text-oriented RC model. In particular, we first introduce a set of text normalizers to transform the noisy and informal texts to the formal ones. Then, we integrate the extractive and the generative RC model by a multi-task learning mechanism and an answer selection module. Experimental results on TweetQA demonstrate that our NUT-RC model significantly outperforms the state-of-the-art social media-oriented RC models.",
}
@inproceedings{do-etal-2020-degree,
    title = "To What Degree Can Language Borders Be Blurred In {BERT}-based Multilingual Spoken Language Understanding?",
    author = "Do, Quynh  and
      Gaspers, Judith  and
      Roeding, Tobias  and
      Bradford, Melanie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.243",
    doi = "10.18653/v1/2020.coling-main.243",
    pages = "2699--2709",
    abstract = "This paper addresses the question as to what degree a BERT-based multilingual Spoken Language Understanding (SLU) model can transfer knowledge across languages. Through experiments we will show that, although it works substantially well even on distant language groups, there is still a gap to the ideal multilingual performance. In addition, we propose a novel BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. Our experimental results prove that the proposed model is capable of narrowing the gap to the ideal multilingual performance.",
}
@inproceedings{liu-etal-2020-cross,
    title = "Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation",
    author = "Liu, Junhao  and
      Shou, Linjun  and
      Pei, Jian  and
      Gong, Ming  and
      Yang, Min  and
      Jiang, Daxin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.244",
    doi = "10.18653/v1/2020.coling-main.244",
    pages = "2710--2721",
    abstract = "Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches use translation data by translating from a rich-source language, such as English, to low-source languages as auxiliary supervision. However, how to effectively leverage translation data and reduce the impact of noise introduced by translation remains onerous. In this paper, we tackle this challenge and enhance the cross-lingual transferring performance by a novel augmentation approach named Language Branch Machine Reading Comprehension (LBMRC). A language branch is a group of passages in one single language paired with questions in all target languages. We train multiple machine reading comprehension (MRC) models proficient in individual language based on LBMRC. Then, we devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages. Combining the LBMRC and multilingual distillation can be more robust to the data noises, therefore, improving the model{'}s cross-lingual ability. Meanwhile, the produced single multilingual model can apply to all target languages, which saves the cost of training, inference, and maintenance for multiple models. Extensive experiments on two CLMRC benchmarks clearly show the effectiveness of our proposed method.",
}
@inproceedings{ghannay-etal-2020-neural,
    title = "Neural Networks approaches focused on {F}rench Spoken Language Understanding: application to the {MEDIA} Evaluation Task",
    author = "Ghannay, Sahar  and
      Servan, Christophe  and
      Rosset, Sophie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.245",
    doi = "10.18653/v1/2020.coling-main.245",
    pages = "2722--2727",
    abstract = "In this paper, we present a study on a French Spoken Language Understanding (SLU) task: the MEDIA task. Many works and studies have been proposed for many tasks, but most of them are focused on English language and tasks. The exploration of a richer language like French within the framework of a SLU task implies to recent approaches to handle this difficulty. Since the MEDIA task seems to be one of the most difficult, according several previous studies, we propose to explore Neural Networks approaches focusing of three aspects: firstly, the Neural Network inputs and more specifically the word embeddings; secondly, we compared French version of BERT against the best setup through different ways; Finally, the comparison against State-of-the-Art approaches. Results show that the word embeddings trained on a small corpus need to be updated during SLU model training. Furthermore, the French BERT fine-tuned approaches outperform the classical Neural Network Architectures and achieves state of the art results. However, the contextual embeddings extracted from one of the French BERT approaches achieve comparable results in comparison to word embedding, when integrated into the proposed neural architecture.",
}
@inproceedings{he-etal-2020-syntactic,
    title = "Syntactic Graph Convolutional Network for Spoken Language Understanding",
    author = "He, Keqing  and
      Lei, Shuyu  and
      Yang, Yushu  and
      Jiang, Huixing  and
      Wang, Zhongyuan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.246",
    doi = "10.18653/v1/2020.coling-main.246",
    pages = "2728--2738",
    abstract = "Slot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge. In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly. Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work. At last, we apply the BERT model to further improve the performance on both slot filling and intent detection.",
}
@inproceedings{gupta-etal-2020-conversational,
    title = "Conversational Machine Comprehension: a Literature Review",
    author = "Gupta, Somil  and
      Rawat, Bhanu Pratap Singh  and
      Yu, Hong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.247",
    doi = "10.18653/v1/2020.coling-main.247",
    pages = "2739--2753",
    abstract = "Conversational Machine Comprehension (CMC), a research track in conversational AI, expects the machine to understand an open-domain natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering (QA), multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models such as BERT and the introduction of large-scale conversational datasets such as CoQA and QuAC. The rise in interest has, however, led to a flurry of concurrent publications, each with a different yet structurally similar modeling approach and an inconsistent view of the surrounding literature. With the volume of model submissions to conversational datasets increasing every year, there exists a need to consolidate the scattered knowledge in this domain to streamline future research. This literature review attempts at providing a holistic overview of CMC with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history. The review synthesizes a generic framework for CMC models while highlighting the differences in recent approaches and intends to serve as a compendium of CMC for future researchers.",
}
@inproceedings{zhao-etal-2020-robust,
    title = "Robust Machine Reading Comprehension by Learning Soft labels",
    author = "Zhao, Zhenyu  and
      Wu, Shuangzhi  and
      Yang, Muyun  and
      Chen, Kehai  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.248",
    doi = "10.18653/v1/2020.coling-main.248",
    pages = "2754--2759",
    abstract = "Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels. We argue that hard labels limit the model capability on generalization due to the label sparseness problem. In this paper, we propose a robust training method for MRC models to address this problem. Our method consists of three strategies, 1) label smoothing, 2) word overlapping, 3) distribution prediction. All of them help to train models on soft labels. We validate our approach on the representative architecture - ALBERT. Experimental results show that our method can greatly boost the baseline with 1{\%} improvement in average, and achieve state-of-the-art performance on NewsQA and QUOREF.",
}
@inproceedings{gupta-etal-2020-reinforced,
    title = "Reinforced Multi-task Approach for Multi-hop Question Generation",
    author = "Gupta, Deepak  and
      Chauhan, Hardik  and
      Akella, Ravi Tej  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.249",
    doi = "10.18653/v1/2020.coling-main.249",
    pages = "2760--2775",
    abstract = "Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. For QG, we often require multiple supporting facts to generate high-quality questions. Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA. Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions.",
}
@inproceedings{bi-etal-2020-knowledge,
    title = "Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases",
    author = "Bi, Sheng  and
      Cheng, Xiya  and
      Li, Yuan-Fang  and
      Wang, Yongzhen  and
      Qi, Guilin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.250",
    doi = "10.18653/v1/2020.coling-main.250",
    pages = "2776--2786",
    abstract = "Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of triples. Two main challenges still face the current crop of encoder-decoder-based methods, especially on small subgraphs: (1) low diversity and poor fluency due to the limited information contained in the subgraphs, and (2) semantic drift due to the decoder{'}s oblivion of the semantics of the answer entity. We propose an innovative knowledge-enriched, type-constrained and grammar-guided KBQG model, named KTG, to addresses the above challenges. In our model, the encoder is equipped with auxiliary information from the KB, and the decoder is constrained with word types during QG. Specifically, entity domain and description, as well as relation hierarchy information are considered to construct question contexts, while a conditional copy mechanism is incorporated to modulate question semantics according to current word types. Besides, a novel reward function featuring grammatical similarity is designed to improve both generative richness and syntactic correctness via reinforcement learning. Extensive experiments show that our proposed model outperforms existing methods by a significant margin on two widely-used benchmark datasets SimpleQuestion and PathQuestion.",
}
@inproceedings{goswamy-etal-2020-adapting,
    title = "Adapting a Language Model for Controlled Affective Text Generation",
    author = "Goswamy, Tushar  and
      Singh, Ishika  and
      Barkati, Ahsan  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.251",
    doi = "10.18653/v1/2020.coling-main.251",
    pages = "2787--2801",
    abstract = "Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the state-of-the-art language generation models to generate affective (emotional) text. We posit a model capable of generating affect-driven and topic focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2. The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text. Previous attempts at modelling fine-grained emotions fall out on grammatical correctness at extreme intensities, but our model is resilient to this and delivers robust results at all intensities. We conduct automated evaluations and human studies to test the performance of our model, and provide a detailed comparison of the results with other models. In all evaluations, our model outperforms existing affective text generation models.",
}
@inproceedings{kohn-etal-2020-generating,
    title = "Generating Instructions at Different Levels of Abstraction",
    author = {K{\"o}hn, Arne  and
      Wichlacz, Julia  and
      Torralba, {\'A}lvaro  and
      H{\"o}ller, Daniel  and
      Hoffmann, J{\"o}rg  and
      Koller, Alexander},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.252",
    doi = "10.18653/v1/2020.coling-main.252",
    pages = "2802--2813",
    abstract = "When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction. A novice user might need an object explained piece by piece, while for an expert, talking about the complex object (e.g. a wall or railing) directly may be more succinct and efficient. We show how to generate building instructions at different levels of abstraction in Minecraft. We introduce the use of hierarchical planning to this end, a method from AI planning which can capture the structure of complex objects neatly. A crowdsourcing evaluation shows that the choice of abstraction level matters to users, and that an abstraction strategy which balances low-level and high-level object descriptions compares favorably to ones which don{'}t.",
}
@inproceedings{garimella-etal-2020-judge,
    title = "{``}Judge me by my size (noun), do you?{''} {Y}oda{L}ib: A Demographic-Aware Humor Generation Framework",
    author = "Garimella, Aparna  and
      Banea, Carmen  and
      Hossain, Nabil  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.253",
    doi = "10.18653/v1/2020.coling-main.253",
    pages = "2814--2825",
    abstract = "The subjective nature of humor makes computerized humor generation a challenging task. We propose an automatic humor generation framework for filling the blanks in Mad Libs® stories, while accounting for the demographic backgrounds of the desired audience. We collect a dataset consisting of such stories, which are filled in and judged by carefully selected workers on Amazon Mechanical Turk. We build upon the BERT platform to predict location-biased word fillings in incomplete sentences, and we fine-tune BERT to classify location-specific humor in a sentence. We leverage these components to produce YodaLib, a fully-automated Mad Libs style humor generation framework, which selects and ranks appropriate candidate words and sentences in order to generate a coherent and funny story tailored to certain demographics. Our experimental results indicate that YodaLib outperforms a previous semi-automated approach proposed for this task, while also surpassing human annotators in both qualitative and quantitative analyses.",
}
@inproceedings{wang-etal-2020-chinese-bert,
    title = "Does {C}hinese {BERT} Encode Word Structure?",
    author = "Wang, Yile  and
      Cui, Leyang  and
      Zhang, Yue",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.254",
    doi = "10.18653/v1/2020.coling-main.254",
    pages = "2826--2836",
    abstract = "Contextualized representations give significantly improved results for a wide range of NLP tasks. Much work has been dedicated to analyzing the features captured by representative models such as BERT. Existing work finds that syntactic, semantic and word sense knowledge are encoded in BERT. However, little work has investigated word features for character languages such as Chinese. We investigate Chinese BERT using both attention weight distribution statistics and probing tasks, finding that (1) word information is captured by BERT; (2) word-level features are mostly in the middle representation layers; (3) downstream tasks make different use of word features in BERT, with POS tagging and chunking relying the most on word features, and natural language inference relying the least on such features.",
}
@inproceedings{nicolai-silfverberg-2020-noise,
    title = "Noise Isn{'}t Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models",
    author = "Nicolai, Garrett  and
      Silfverberg, Miikka",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.255",
    doi = "10.18653/v1/2020.coling-main.255",
    pages = "2837--2846",
    abstract = "Morphological inflection, like many sequence-to-sequence tasks, sees great performance from recurrent neural architectures when data is plentiful, but performance falls off sharply in lower-data settings. We investigate one aspect of neural seq2seq models that we hypothesize contributes to overfitting - teacher forcing. By creating different training and test conditions, exposure bias increases the likelihood that a system too closely models its training data. Experiments show that teacher-forced models struggle to recover when they enter unknown territory. However, a simple modification to the training algorithm to more closely mimic test conditions creates models that are better able to generalize to unseen environments.",
}
@inproceedings{czarnowska-etal-2020-morphologically,
    title = "Morphologically Aware Word-Level Translation",
    author = "Czarnowska, Paula  and
      Ruder, Sebastian  and
      Cotterell, Ryan  and
      Copestake, Ann",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.256",
    doi = "10.18653/v1/2020.coling-main.256",
    pages = "2847--2860",
    abstract = "We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information. This approach leads to substantial performance improvements{---}19{\%} average improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16{\%} in the weakly supervised setting. As another contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task.",
}
@inproceedings{liu-hulden-2020-analogy,
    title = "Analogy Models for Neural Word Inflection",
    author = "Liu, Ling  and
      Hulden, Mans",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.257",
    doi = "10.18653/v1/2020.coling-main.257",
    pages = "2861--2878",
    abstract = "Analogy is assumed to be the cognitive mechanism speakers resort to in order to inflect an unknown form of a lexeme based on knowledge of other words in a language. In this process, an analogy is formed between word forms within an inflectional paradigm but also across paradigms. As neural network models for inflection are typically trained only on lemma-target form pairs, we propose three new ways to provide neural models with additional source forms to strengthen analogy-formation, and compare our methods to other approaches in the literature. We show that the proposed methods of providing a Transformer sequence-to-sequence model with additional analogy sources in the input are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings. We also propose a method to combine the analogy-motivated approach with data hallucination or augmentation. We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the training data is extremely limited.",
}
@inproceedings{tseng-etal-2020-computational,
    title = "Computational Modeling of Affixoid Behavior in {C}hinese Morphology",
    author = "Tseng, Yu-Hsiang  and
      Hsieh, Shu-Kai  and
      Chen, Pei-Yi  and
      Court, Sara",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.258",
    doi = "10.18653/v1/2020.coling-main.258",
    pages = "2879--2888",
    abstract = "The morphological status of affixes in Chinese has long been a matter of debate. How one might apply the conventional criteria of free/bound and content/function features to distinguish word-forming affixes from bound roots in Chinese is still far from clear. Issues involving polysemy and diachronic dynamics further blur the boundaries. In this paper, we propose three quantitative features in a computational model of affixoid behavior in Mandarin Chinese. The results show that, except for in a very few cases, there are no clear criteria that can be used to identify an affix{'}s status in an isolating language like Chinese. A diachronic check using contextualized embeddings with the WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings.",
}
@inproceedings{huo-etal-2020-one,
    title = "One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment",
    author = "Huo, Tengfei  and
      Liu, Zhiqiang  and
      Zhang, Jinchao  and
      Zhou, Jie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.259",
    doi = "10.18653/v1/2020.coling-main.259",
    pages = "2889--2899",
    abstract = "The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform{'}s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin.",
}
@inproceedings{chen-etal-2020-tale,
    title = "A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-{SQL} Parsing",
    author = "Chen, Sanxing  and
      San, Aidan  and
      Liu, Xiaodong  and
      Ji, Yangfeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.260",
    doi = "10.18653/v1/2020.coling-main.260",
    pages = "2900--2912",
    abstract = "In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) for the generated SQL query is both crucial and challenging; the parser is required to connect the natural language (NL) question and the SQL query to the structured knowledge in the database. We formulate two linking processes to address this challenge: schema linking which links explicit NL mentions to the database and structural linking which links the entities in the output SQL with their structural relationships in the database schema. Intuitively, the effectiveness of these two linking processes changes based on the entity being generated, thus we propose to dynamically choose between them using a gating mechanism. Integrating the proposed method with two graph neural network-based semantic parsers together with BERT representations demonstrates substantial gains in parsing accuracy on the challenging Spider dataset. Analyses show that our proposed method helps to enhance the structure of the model output when generating complicated SQL queries and offers more explainable predictions.",
}
@inproceedings{matero-schwartz-2020-autoregressive,
    title = "Autoregressive Affective Language Forecasting: A Self-Supervised Task",
    author = "Matero, Matthew  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.261",
    doi = "10.18653/v1/2020.coling-main.261",
    pages = "2913--2923",
    abstract = "Human natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional states, few have attempted to model emotional language in time. Here, we introduce the task of \textit{affective language forecasting} {--} predicting future change in language based on past changes of language, a task with real-world applications such as treating mental health or forecasting trends in consumer confidence. We establish some of the fundamental autoregressive characteristics of the task (necessary history size, static versus dynamic length, varying time-step resolutions) and then build on popular sequence models for \textit{words} to instead model sequences of \textit{language-based emotion in time}. Over a novel Twitter dataset of 1,900 users and weekly + daily scores for 6 emotions and 2 additional linguistic attributes, we find a novel dual-sequence GRU model with decayed hidden states achieves best results ($r = .66$) significantly out-predicting, e.g., a moving averaging based on the past time-steps ($r = .49$). We make our anonymized dataset as well as task setup and evaluation code available for others to build on.",
}
@inproceedings{shen-jin-2020-solving,
    title = "Solving Math Word Problems with Multi-Encoders and Multi-Decoders",
    author = "Shen, Yibin  and
      Jin, Cheqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.262",
    doi = "10.18653/v1/2020.coling-main.262",
    pages = "2924--2934",
    abstract = "Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language. Although previous researches employ the Seq2Seq technique to transform text descriptions into equation expressions, most of them achieve inferior performance due to insufficient consideration in the design of encoder and decoder. Specifically, these models only consider input/output objects as sequences, ignoring the important structural information contained in text descriptions and equation expressions. To overcome those defects, a model with multi-encoders and multi-decoders is proposed in this paper, which combines sequence-based encoder and graph-based encoder to enhance the representation of text descriptions, and generates different equation expressions via sequence-based decoder and tree-based decoder. Experimental results on the dataset Math23K show that our model outperforms existing state-of-the-art methods.",
}
@inproceedings{liu-etal-2020-end,
    title = "End to End {C}hinese Lexical Fusion Recognition with Sememe Knowledge",
    author = "Liu, Yijiang  and
      Zhang, Meishan  and
      Ji, Donghong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.263",
    doi = "10.18653/v1/2020.coling-main.263",
    pages = "2935--2946",
    abstract = "In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end model for the task, handling mentions as well as coreference relationship jointly. The model exploits the state-of-the-art contextualized BERT representations as an encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate that our final model is effective and competitive for the task. Detailed analysis is offered for comprehensively understanding the new task and our proposed model.",
}
@inproceedings{hershcovich-etal-2020-comparison,
    title = "Comparison by Conversion: Reverse-Engineering {UCCA} from Syntax and Lexical Semantics",
    author = "Hershcovich, Daniel  and
      Schneider, Nathan  and
      Dvir, Dotan  and
      Prange, Jakob  and
      de Lhoneux, Miryam  and
      Abend, Omri",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.264",
    doi = "10.18653/v1/2020.coling-main.264",
    pages = "2947--2966",
    abstract = "Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality{---}indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.",
}
@inproceedings{li-etal-2020-logic,
    title = "Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification",
    author = "Li, Juan  and
      Wang, Ruoxu  and
      Zhang, Ningyu  and
      Zhang, Wen  and
      Yang, Fan  and
      Chen, Huajun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.265",
    doi = "10.18653/v1/2020.coling-main.265",
    pages = "2967--2978",
    abstract = "Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen relations at test time, we explore the problem of zero-shot relation classification. Previous work regards the problem as reading comprehension or textual entailment, which have to rely on artificial descriptive information to improve the understandability of relation types. Thus, rich semantic knowledge of the relation labels is ignored. In this paper, we propose a novel logic-guided semantic representation learning model for zero-shot relation classification. Our approach builds connections between seen and unseen relations via implicit and explicit semantic representations with knowledge graph embeddings and logic rules. Extensive experimental results demonstrate that our method can generalize to unseen relation types and achieve promising improvements.",
}
@inproceedings{xia-etal-2020-semantic,
    title = "Semantic Role Labeling with Heterogeneous Syntactic Knowledge",
    author = "Xia, Qingrong  and
      Wang, Rui  and
      Li, Zhenghua  and
      Zhang, Yue  and
      Zhang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.266",
    doi = "10.18653/v1/2020.coling-main.266",
    pages = "2979--2990",
    abstract = "Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention. Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs. In this work, we propose to encode \textit{heterogeneous} syntactic knowledge for SRL from both explicit and implicit representations. First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees. Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks. Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs. We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset. Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines. We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs. homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.",
}
@inproceedings{donatelli-etal-2020-normalizing,
    title = "Normalizing Compositional Structures Across Graphbanks",
    author = "Donatelli, Lucia  and
      Groschwitz, Jonas  and
      Lindemann, Matthias  and
      Koller, Alexander  and
      Wei{\ss}enhorn, Pia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.267",
    doi = "10.18653/v1/2020.coling-main.267",
    pages = "2991--3006",
    abstract = "The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. MRs exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. Here, we ask the question of which design differences between MRs are meaningful and semantically-rooted, and which are superficial. We present a methodology for normalizing discrepancies between MRs at the compositional level (Lindemann et al., 2019), finding that we can normalize the majority of divergent phenomena using linguistically-grounded rules. Our work significantly increases the match in compositional structure between MRs and improves multi-task learning (MTL) in a low-resource setting, serving as a proof of concept for future broad-scale cross-MR normalization.",
}
@inproceedings{cho-etal-2020-leveraging,
    title = "Leveraging {W}ord{N}et Paths for Neural Hypernym Prediction",
    author = "Cho, Yejin  and
      Rodriguez, Juan Diego  and
      Gao, Yifan  and
      Erk, Katrin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.268",
    doi = "10.18653/v1/2020.coling-main.268",
    pages = "3007--3018",
    abstract = "We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet. Our experiments with encoder-decoder models show that training to generate taxonomy paths can improve the performance of direct hypernym prediction. As a simple but powerful model, the hypo2path model achieves state-of-the-art performance, outperforming the best benchmark by 4.11 points in hit-at-one (H@1).",
}
@inproceedings{fischer-etal-2020-beards,
    title = "When Beards Start Shaving Men: A Subject-object Resolution Test Suite for Morpho-syntactic and Semantic Model Introspection",
    author = {Fischer, Patricia  and
      de Kok, Dani{\"e}l  and
      Hinrichs, Erhard},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.269",
    doi = "10.18653/v1/2020.coling-main.269",
    pages = "3019--3035",
    abstract = "In this paper, we introduce the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. The full test suite consists of 18,502 transitive clauses with manual annotations of 8 word order patterns, 5 morphological and syntactic and 11 semantic property classes. The test suite has been constructed such that sentences are minimal pairs with respect to a property class. Each property has been selected with a particular focus on its effect on subject-object resolution, the second-most error-prone task within syntactic parsing of German after prepositional phrase attachment (Fischer et al., 2019). The size and detail of annotations make the test suite a valuable resource for natural language processing applications with syntactic and semantic tasks. We use dependency parsing to demonstrate how the test suite allows insights into the process of subject-object resolution. Based on the test suite annotations, word order and case syncretism can be identified as most important factors that affect subject-object resolution.",
}
@inproceedings{wan-xing-2020-modality,
    title = "Modality Enriched Neural Network for Metaphor Detection",
    author = "Wan, Mingyu  and
      Xing, Baixi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.270",
    doi = "10.18653/v1/2020.coling-main.270",
    pages = "3036--3042",
    abstract = "Metaphor as a cognitive mechanism in human{'}s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in {`}sweet voice{'}. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset{--}the VUA corpus. The experimental results show great F1 improvement (above 0.5{\%}) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.",
}
@inproceedings{sawada-etal-2020-coordination,
    title = "Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation",
    author = "Sawada, Yuya  and
      Wada, Takashi  and
      Shibahara, Takayoshi  and
      Teranishi, Hiroki  and
      Kondo, Shuhei  and
      Shindo, Hiroyuki  and
      Watanabe, Taro  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.271",
    doi = "10.18653/v1/2020.coling-main.271",
    pages = "3043--3049",
    abstract = "We propose a simple method for nominal coordination boundary identification. As the main strength of our method, it can identify the coordination boundaries without training on labeled data, and can be applied even if coordination structure annotations are not available. Our system employs pre-trained word embeddings to measure the similarities of words and detects the span of coordination, assuming that conjuncts share syntactic and semantic similarities. We demonstrate that our method yields good results in identifying coordinated noun phrases in the GENIA corpus and is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases.",
}
@inproceedings{nie-etal-2020-learning,
    title = "Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments",
    author = "Nie, Feng  and
      Wang, Jinpeng  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.272",
    doi = "10.18653/v1/2020.coling-main.272",
    pages = "3050--3059",
    abstract = "Learning semantic correspondences between structured input data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input. To learn semantic correspondences from such datasets, we propose a two-stage local-to-global alignment (L2GA) framework. First, a local model based on multi-instance learning is applied to build alignments for texts spans that can be directly grounded to its paired structured input. Then, a novel global model built upon a memory-guided conditional random field (CRF) layer aims to infer missing alignments for text spans which not supported by paired incomplete inputs, where the memory is designed to leverage alignment clues provided by the local model to strengthen the global model. In this way, the local model and global model can work jointly to learn semantic correspondences in the same framework. Experimental results show that our proposed method can be generalized to both restaurant and computer domains and improve the alignment accuracy.",
}
@inproceedings{spiliopoulou-etal-2020-definition,
    title = "Definition Frames: Using Definitions for Hybrid Concept Representations",
    author = "Spiliopoulou, Evangelia  and
      Pagnoni, Artidoro  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.273",
    doi = "10.18653/v1/2020.coling-main.273",
    pages = "3060--3068",
    abstract = "Advances in word representations have shown tremendous improvements in downstream NLP tasks, but lack semantic interpretability. In this paper, we introduce Definition Frames (DF), a matrix distributed representation extracted from definitions, where each dimension is semantically interpretable. DF dimensions correspond to the Qualia structure relations: a set of relations that uniquely define a term. Our results show that DFs have competitive performance with other distributional semantic approaches on word similarity tasks.",
}
@inproceedings{klein-etal-2020-qanom,
    title = "{QAN}om: Question-Answer driven {SRL} for Nominalizations",
    author = "Klein, Ayal  and
      Mamou, Jonathan  and
      Pyatkin, Valentina  and
      Stepanov, Daniela  and
      He, Hangfeng  and
      Roth, Dan  and
      Zettlemoyer, Luke  and
      Dagan, Ido",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.274",
    doi = "10.18653/v1/2020.coling-main.274",
    pages = "3069--3083",
    abstract = "We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom. This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relations between nominalizations and their arguments via natural language question-answer pairs. We construct the first QANom dataset using controlled crowdsourcing, analyze its quality and compare it to expertly annotated nominal-SRL annotations, as well as to other QA-driven annotations. In addition, we train a baseline QANom parser for identifying nominalizations and labeling their arguments with question-answer pairs. Finally, we demonstrate the extrinsic utility of our annotations for downstream tasks using both indirect supervision and zero-shot settings.",
}
@inproceedings{zeng-etal-2020-event,
    title = "Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings",
    author = "Zeng, Yutao  and
      Jin, Xiaolong  and
      Guan, Saiping  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.275",
    doi = "10.18653/v1/2020.coling-main.275",
    pages = "3084--3094",
    abstract = "Event coreference resolution aims to classify all event mentions that refer to the same real-world event into the same group, which is necessary to information aggregation and many downstream applications. To resolve event coreference, existing methods usually calculate the similarities between event mentions and between specific kinds of event arguments. However, they fail to accurately identify paraphrase relations between events and may suffer from error propagation while extracting event components (i.e., event mentions and their arguments). Therefore, we propose a new model based on Event-specific Paraphrases and Argument-aware Semantic Embeddings, thus called EPASE, for event coreference resolution. EPASE recognizes deep paraphrase relations in an event-specific context of sentences and can cover event paraphrases of more situations, bringing about a better generalization. Additionally, the embeddings of argument roles are encoded into event embedding without relying on a fixed number and type of arguments, which results in the better scalability of EPASE. Experiments on both within- and cross-document event coreference demonstrate its consistent and significant superiority compared to existing methods.",
}
@inproceedings{nikishina-etal-2020-studying,
    title = "Studying Taxonomy Enrichment on Diachronic {W}ord{N}et Versions",
    author = "Nikishina, Irina  and
      Logacheva, Varvara  and
      Panchenko, Alexander  and
      Loukachevitch, Natalia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.276",
    doi = "10.18653/v1/2020.coling-main.276",
    pages = "3095--3106",
    abstract = "Ontologies, taxonomies, and thesauri have always been in high demand in a large number of NLP tasks. However, most studies are focused on the creation of lexical resources rather than the maintenance of the existing ones and keeping them up-to-date. In this paper, we address the problem of taxonomy enrichment. Namely, we explore the possibilities of taxonomy extension in a resource-poor setting and present several methods which are applicable to a large number of languages. We also create novel English and Russian datasets for training and evaluating taxonomy enrichment systems and describe a technique of creating such datasets for other languages.",
}
@inproceedings{han-etal-2020-victr,
    title = "{VICTR}: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks",
    author = "Han, Caren  and
      Long, Siqu  and
      Luo, Siwen  and
      Wang, Kunze  and
      Poon, Josiah",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.277",
    doi = "10.18653/v1/2020.coling-main.277",
    pages = "3107--3117",
    abstract = "Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.",
}
@inproceedings{han-etal-2020-finding,
    title = "Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering",
    author = "Han, Wei  and
      Huang, Hantao  and
      Han, Tao",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.278",
    doi = "10.18653/v1/2020.coling-main.278",
    pages = "3118--3131",
    abstract = "Image text carries essential information to understand the scene and perform reasoning. Text-based visual question answering (text VQA) task focuses on visual questions that require reading text in images. Existing text VQA systems generate an answer by selecting from optical character recognition (OCR) texts or a fixed vocabulary. Positional information of text is underused and there is a lack of evidence for the generated answer. As such, this paper proposes a localization-aware answer prediction network (LaAP-Net) to address this challenge. Our LaAP-Net not only generates the answer to the question but also predicts a bounding box as evidence of the generated answer. Moreover, a context-enriched OCR representation (COR) for multimodal fusion is proposed to facilitate the localization task. Our proposed LaAP-Net outperforms existing approaches on three benchmark datasets for the text VQA task by a noticeable margin.",
}
@inproceedings{xu-etal-2020-interactive,
    title = "Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning",
    author = "Xu, Chunpu  and
      Li, Yu  and
      Li, Chengming  and
      Ao, Xiang  and
      Yang, Min  and
      Tian, Jinwen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.279",
    doi = "10.18653/v1/2020.coling-main.279",
    pages = "3132--3142",
    abstract = "Image paragraph captioning (IPC) aims to generate a fine-grained paragraph to describe the visual content of an image. Significant progress has been made by deep neural networks, in which the attention mechanism plays an essential role. However, conventional attention mechanisms tend to ignore the past alignment information, which often results in problems of repetitive captioning and incomplete captioning. In this paper, we propose an Interactive key-value Memory- augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions. In addition, we employ an adaptive attention mechanism to realize adaptive alignment from image regions to caption words, where an image region can be mapped to an arbitrary number of caption words while a caption word can also attend to an arbitrary number of image regions. Extensive experiments on a benchmark dataset (i.e., Stanford) demonstrate the effectiveness of our IMAP model.",
}
@inproceedings{nikiforova-etal-2020-geo,
    title = "Geo-Aware Image Caption Generation",
    author = "Nikiforova, Sofia  and
      Deoskar, Tejaswini  and
      Paperno, Denis  and
      Winter, Yoad",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.280",
    doi = "10.18653/v1/2020.coling-main.280",
    pages = "3143--3156",
    abstract = "Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge. In particular, they are unable to generate captions that contain references to the geographic context of an image, for example, the location where a photograph is taken or relevant geographic objects around an image location. In this paper, we develop a geo-aware image caption generation system, which incorporates geographic contextual information into a standard image captioning pipeline. We propose a way to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. We evaluate our system on a novel captioning dataset that contains contextualized captions and geographic metadata and achieve substantial improvements in BLEU, ROUGE, METEOR and CIDEr scores. We also introduce a new metric to assess generated geographic references directly and empirically demonstrate our system{'}s ability to produce captions with relevant and factually accurate geographic referencing.",
}
@inproceedings{yuan-etal-2020-bridge,
    title = "Bridge the Gap: High-level Semantic Planning for Image Captioning",
    author = "Yuan, Chenxi  and
      Bai, Yang  and
      Yuan, Chun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.281",
    doi = "10.18653/v1/2020.coling-main.281",
    pages = "3157--3167",
    abstract = "Recent image captioning models have made much progress for exploring the multi-modal interaction, such as attention mechanisms. Though these mechanisms can boost the interaction, there are still two gaps between the visual and language domains: (1) the gap between the visual features and textual semantics, (2) the gap between the disordering of visual features and the ordering of texts. To bridge the gaps we propose a high-level semantic planning (HSP) mechanism that incorporates both a semantic reconstruction and an explicit order planning. We integrate the planning mechanism to the attention based caption model and propose the High-level Semantic PLanning based Attention Network (HS-PLAN). First, an attention based reconstruction module is designed to reconstruct the visual features with high-level semantic information. Then we apply a pointer network to serialize the features and obtain the explicit order plan to guide the generation. Experiments conducted on MS COCO show that our model outperforms previous methods and achieves the state-of-the-art performance of 133.4{\%} CIDEr-D score.",
}
@inproceedings{ruan-etal-2020-interactively,
    title = "Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition",
    author = "Ruan, Huibin  and
      Hong, Yu  and
      Xu, Yang  and
      Huang, Zhen  and
      Zhou, Guodong  and
      Zhang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.282",
    doi = "10.18653/v1/2020.coling-main.282",
    pages = "3168--3178",
    abstract = "We tackle implicit discourse relation recognition. Both self-attention and interactive-attention mechanisms have been applied for attention-aware representation learning, which improves the current discourse analysis models. To take advantages of the two attention mechanisms simultaneously, we develop a propagative attention learning model using a cross-coupled two-channel network. We experiment on Penn Discourse Treebank. The test results demonstrate that our model yields substantial improvements over the baselines (BiLSTM and BERT).",
}
@inproceedings{zhang-ma-2020-dual,
    title = "Dual Attention Model for Citation Recommendation",
    author = "Zhang, Yang  and
      Ma, Qiang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.283",
    doi = "10.18653/v1/2020.coling-main.283",
    pages = "3179--3189",
    abstract = "Based on an exponentially increasing number of academic articles, discovering and citing comprehensive and appropriate resources has become a non-trivial task. Conventional citation recommender methods suffer from severe information loss. For example, they do not consider the section of the paper that the user is writing and for which they need to find a citation, the relatedness between the words in the local context (the text span that describes a citation), or the importance on each word from the local context. These shortcomings make such methods insufficient for recommending adequate citations to academic manuscripts. In this study, we propose a novel embedding-based neural network called {``}dual attention model for citation recommendation (DACR){''} to recommend citations during manuscript preparation. Our method adapts embedding of three semantic information: words in the local context, structural contexts, and the section on which a user is working. A neural network model is designed to maximize the similarity between the embedding of the three input (local context words, section and structural contexts) and the target citation appearing in the context. The core of the neural network model is composed of self-attention and additive attention, where the former aims to capture the relatedness between the contextual words and structural context, and the latter aims to learn the importance of them. The experiments on real-world datasets demonstrate the effectiveness of the proposed approach.",
}
@inproceedings{sun-etal-2020-dual,
    title = "Dual Attention Network for Cross-lingual Entity Alignment",
    author = "Sun, Jian  and
      Zhou, Yu  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.284",
    doi = "10.18653/v1/2020.coling-main.284",
    pages = "3190--3201",
    abstract = "Cross-lingual Entity alignment is an essential part of building a knowledge graph, which can help integrate knowledge among different language knowledge graphs. In the real KGs, there exists an imbalance among the information in the same hierarchy of corresponding entities, which results in the heterogeneity of neighborhood structure, making this task challenging. To tackle this problem, we propose a dual attention network for cross-lingual entity alignment (DAEA). Specifically, our dual attention consists of relation-aware graph attention and hierarchical attention. The relation-aware graph attention aims at selectively aggregating multi-hierarchy neighborhood information to alleviate the difference of heterogeneity among counterpart entities. The hierarchical attention adaptively aggregates the low-hierarchy and the high-hierarchy information, which is beneficial to balance the neighborhood information of counterpart entities and distinguish non-counterpart entities with similar structures. Finally, we treat cross-lingual entity alignment as a process of linking prediction. Experimental results on three real-world cross-lingual entity alignment datasets have shown the effectiveness of DAEA.",
}
@inproceedings{halder-etal-2020-task,
    title = "Task-Aware Representation of Sentences for Generic Text Classification",
    author = "Halder, Kishaloy  and
      Akbik, Alan  and
      Krapac, Josip  and
      Vollgraf, Roland",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.285",
    doi = "10.18653/v1/2020.coling-main.285",
    pages = "3202--3213",
    abstract = "State-of-the-art approaches for text classification leverage a transformer architecture with a linear layer on top that outputs a class distribution for a given prediction problem. While effective, this approach suffers from conceptual limitations that affect its utility in few-shot or zero-shot transfer learning scenarios. First, the number of classes to predict needs to be pre-defined. In a transfer learning setting, in which new classes are added to an already trained classifier, all information contained in a linear layer is therefore discarded, and a new layer is trained from scratch. Second, this approach only learns the semantics of classes implicitly from training examples, as opposed to leveraging the explicit semantic information provided by the natural language names of the classes. For instance, a classifier trained to predict the topics of news articles might have classes like {``}business{''} or {``}sports{''} that themselves carry semantic information. Extending a classifier to predict a new class named {``}politics{''} with only a handful of training examples would benefit from both leveraging the semantic information in the name of a new class and using the information contained in the already trained linear layer. This paper presents a novel formulation of text classification that addresses these limitations. It imbues the notion of the task at hand into the transformer model itself by factorizing arbitrary classification problems into a generic binary classification problem. We present experiments in few-shot and zero-shot transfer learning that show that our approach significantly outperforms previous approaches on small training data and can even learn to predict new classes with no training examples at all. The implementation of our model is publicly available at: https://github.com/flairNLP/flair.",
}
@inproceedings{bashier-etal-2020-rancc,
    title = "{RANCC}: Rationalizing Neural Networks via Concept Clustering",
    author = "Bashier, Housam Khalifa  and
      Kim, Mi-Young  and
      Goebel, Randy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.286",
    doi = "10.18653/v1/2020.coling-main.286",
    pages = "3214--3224",
    abstract = "We propose a new self-explainable model for Natural Language Processing (NLP) text classification tasks. Our approach constructs explanations concurrently with the formulation of classification predictions. To do so, we extract a rationale from the text, then use it to predict a concept of interest as the final prediction. We provide three types of explanations: 1) rationale extraction, 2) a measure of feature importance, and 3) clustering of concepts. In addition, we show how our model can be compressed without applying complicated compression techniques. We experimentally demonstrate our explainability approach on a number of well-known text classification datasets.",
}
@inproceedings{mao-etal-2020-ladabert,
    title = "{L}ada{BERT}: Lightweight Adaptation of {BERT} through Hybrid Model Compression",
    author = "Mao, Yihuan  and
      Wang, Yujing  and
      Wu, Chufan  and
      Zhang, Chen  and
      Wang, Yang  and
      Zhang, Quanlu  and
      Yang, Yaming  and
      Tong, Yunhai  and
      Bai, Jing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.287",
    doi = "10.18653/v1/2020.coling-main.287",
    pages = "3225--3234",
    abstract = "BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks. However, a major blocking issue of applying BERT to online services is that it is memory-intensive and leads to unsatisfactory latency of user requests, raising the necessity of model compression. Existing solutions leverage the knowledge distillation framework to learn a smaller model that imitates the behaviors of BERT. However, the training procedure of knowledge distillation is expensive itself as it requires sufficient training data to imitate the teacher model. In this paper, we address this issue by proposing a tailored solution named LadaBERT (Lightweight adaptation of BERT through hybrid model compression), which combines the advantages of different model compression methods, including weight pruning, matrix factorization and knowledge distillation. LadaBERT achieves state-of-the-art accuracy on various public datasets while the training overheads can be reduced by an order of magnitude.",
}
@inproceedings{zhou-etal-2020-emotion,
    title = "Emotion Classification by Jointly Learning to Lexiconize and Classify",
    author = "Zhou, Deyu  and
      Wu, Shuangzhi  and
      Wang, Qing  and
      Xie, Jun  and
      Tu, Zhaopeng  and
      Li, Mu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.288",
    doi = "10.18653/v1/2020.coling-main.288",
    pages = "3235--3245",
    abstract = "Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy. We validate the approach on two representative architectures {--} LSTM and BERT, demonstrating its superiority on identifying emotions in Tweets. Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.",
}
@inproceedings{shao-etal-2020-multi,
    title = "Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing",
    author = "Shao, Bo  and
      Gong, Yeyun  and
      Qi, Weizhen  and
      Duan, Nan  and
      Lin, Xiaola",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.289",
    doi = "10.18653/v1/2020.coling-main.289",
    pages = "3246--3256",
    abstract = "In this paper, we present a multi-level alignment pretraining method in a unified architecture formulti-lingual semantic parsing. In this architecture, we use an adversarial training method toalign the space of different languages and use sentence level and word level parallel corpus assupervision information to align the semantic of different languages. Finally, we jointly train themulti-level alignment and semantic parsing tasks. We conduct experiments on a publicly avail-able multi-lingual semantic parsing dataset ATIS and a newly constructed dataset. Experimentalresults show that our model outperforms state-of-the-art methods on both datasets.",
}
@inproceedings{hu-etal-2020-transformation,
    title = "Transformation of Dense and Sparse Text Representations",
    author = "Hu, Wenpeng  and
      Wang, Mengyu  and
      Liu, Bing  and
      Ji, Feng  and
      Ma, Jinwen  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.290",
    doi = "10.18653/v1/2020.coling-main.290",
    pages = "3257--3267",
    abstract = "Sparsity is regarded as a desirable property of representations, especially in terms of explanation. However, its usage has been limited due to the gap with dense representations. Most research progresses in NLP in recent years are based on dense representations. Thus the desirable property of sparsity cannot be leveraged. Inspired by Fourier Transformation, in this paper, we propose a novel Semantic Transformation method to bridge the dense and sparse spaces, which can facilitate the NLP research to shift from dense spaces to sparse spaces or to jointly use both spaces. Experiments using classification tasks and natural language inference task show that the proposed Semantic Transformation is effective.",
}
@inproceedings{conia-navigli-2020-conception,
    title = "Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations",
    author = "Conia, Simone  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.291",
    doi = "10.18653/v1/2020.coling-main.291",
    pages = "3268--3284",
    abstract = "To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space. Most current approaches, however, suffer from a monolingual bias, with their strength depending on the amount of data available across languages. In this paper we address this issue and propose Conception, a novel technique for building language-independent vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. Our approach results in high-coverage representations that outperform the state of the art in multilingual and cross-lingual Semantic Word Similarity and Word Sense Disambiguation, proving particularly robust on low-resource languages. Conception {--} its software and the complete set of representations {--} is available at https://github.com/SapienzaNLP/conception.",
}
@inproceedings{pouran-ben-veyseh-etal-2020-acronym,
    title = "What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation",
    author = "Pouran Ben Veyseh, Amir  and
      Dernoncourt, Franck  and
      Tran, Quan Hung  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.292",
    doi = "10.18653/v1/2020.coling-main.292",
    pages = "3285--3301",
    abstract = "Acronyms are the short forms of phrases that facilitate conveying lengthy sentences in documents and serve as one of the mainstays of writing. Due to their importance, identifying acronyms and corresponding phrases (i.e., acronym identification (AI)) and finding the correct meaning of each acronym (i.e., acronym disambiguation (AD)) are crucial for text understanding. Despite the recent progress on this task, there are some limitations in the existing datasets which hinder further improvement. More specifically, limited size of manually annotated AI datasets or noises in the automatically created acronym identification datasets obstruct designing advanced high-performing acronym identification models. Moreover, the existing datasets are mostly limited to the medical domain and ignore other domains. In order to address these two limitations, we first create a manually annotated large AI dataset for scientific domain. This dataset contains 17,506 sentences which is substantially larger than previous scientific AI datasets. Next, we prepare an AD dataset for scientific domain with 62,441 samples which is significantly larger than previous scientific AD dataset. Our experiments show that the existing state-of-the-art models fall far behind human-level performance on both datasets proposed by this work. In addition, we propose a new deep learning model which utilizes the syntactical structure of the sentence to expand an ambiguous acronym in a sentence. The proposed model outperforms the state-of-the-art models on the new AD dataset, providing a strong baseline for future research on this dataset.",
}
@inproceedings{liu-etal-2020-sentence,
    title = "Sentence Matching with Syntax- and Semantics-Aware {BERT}",
    author = "Liu, Tao  and
      Wang, Xin  and
      Lv, Chengguo  and
      Zhen, Ranran  and
      Fu, Guohong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.293",
    doi = "10.18653/v1/2020.coling-main.293",
    pages = "3302--3312",
    abstract = "Sentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks. However, previous studies mainly focused on exploiting either syntactic or semantic information for sentence matching, and no studies consider integrating both of them. In this study, we propose integrating syntax and semantics into BERT with sentence matching. In particular, we use an implicit syntax and semantics integration method that is less sensitive to the output structure information. Thus the implicit integration can alleviate the error propagation problem. The experimental results show that our approach has achieved state-of-the-art or competitive performance on several sentence matching datasets, demonstrating the benefits of implicitly integrating syntactic and semantic features in sentence matching.",
}
@inproceedings{pimentel-etal-2020-temporal,
    title = "Temporal Relations Annotation and Extrapolation Based on Semi-intervals and Boundig Relations",
    author = "Pimentel, Alejandro  and
      Bel Enguix, Gemma  and
      Sierra Mart{\'\i}nez, Gerardo  and
      Montes, Azucena",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.294",
    doi = "10.18653/v1/2020.coling-main.294",
    pages = "3313--3323",
    abstract = "The computational treatment of temporal relations is based on the work of Allen, who establishes 13 different types, and Freksa, who designs a cognitive procedure to manage them. Freksa{'}s notation is not widely used because, although it has cognitive and expressive advantages, it is too complex from the computational perspective. This paper proposes a system for the annotation and management of temporal relations that combines the richness and expressiveness of Freksa{'}s approach with the simplicity of Allen{'}s notation. Our method is summarized in the application of bounding relations, thanks to which it is possible to obtain the temporary representation of complete neighborhoods capable of representing vague temporal relations such as those that can be frequently found in a text. Such advantages are obtained without the need to greatly increase the complexity of the labeling process since the markup language is almost the same as TimeML, to which only a second temporary {``}relType{''}{'} type label relationship is added. Our experiments show that the temporal relationships that present vagueness are in fact much more common than those in which a single relationship can be established precisely. For these reasons, our new labeling system achieves a more agreeable representation of temporal relations.",
}
@inproceedings{sato-heffernan-2020-homonym,
    title = "Homonym normalisation by word sense clustering: a case in {J}apanese",
    author = "Sato, Yo  and
      Heffernan, Kevin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.295",
    doi = "10.18653/v1/2020.coling-main.295",
    pages = "3324--3332",
    abstract = "This work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses contextualised embeddings (BERT) to cluster tokens into distinct sense groups, and we use these groups to normalise synonymous instances to a single representative form. We see the benefit of this normalisation in language model, as well as in transliteration.",
}
@inproceedings{pasquer-etal-2020-verbal,
    title = "Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?",
    author = "Pasquer, Caroline  and
      Savary, Agata  and
      Ramisch, Carlos  and
      Antoine, Jean-Yves",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.296",
    doi = "10.18653/v1/2020.coling-main.296",
    pages = "3333--3345",
    abstract = "Automatic identification of multiword expressions (MWEs), like {`}to cut corners{'} (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general.",
}
@inproceedings{vacareanu-etal-2020-unsupervised,
    title = "An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification",
    author = "Vacareanu, Robert  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Sharp, Rebecca  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.297",
    doi = "10.18653/v1/2020.coling-main.297",
    pages = "3346--3356",
    abstract = "This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e.g. the label employer is associated with the noun compound government agency) (Tratz, 2011). The composition function is based on recurrent neural networks, and is trained using the Skip-Gram objective to predict the words in the context of MWEs. Thus our approach can naturally leverage large unlabeled text sources. Further, our method can make use of provided MWEs when available, but can also function as a completely unsupervised algorithm, using MWE boundaries predicted by a single, domain-agnostic part-of-speech pattern. With pre-defined MWE boundaries, our method outperforms the previous state-of-the-art performance on the coarse-grained evaluation of the Tratz dataset (Tratz, 2011), with an F1 score of 50.4{\%}. The unsupervised version of our method approaches the performance of the supervised one, and even outperforms it in some configurations.",
}
@inproceedings{aloui-etal-2020-slice,
    title = "{SLICE}: Supersense-based Lightweight Interpretable Contextual Embeddings",
    author = "Aloui, Cindy  and
      Ramisch, Carlos  and
      Nasr, Alexis  and
      Barque, Lucie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.298",
    doi = "10.18653/v1/2020.coling-main.298",
    pages = "3357--3370",
    abstract = "Contextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances. However, their opaqueness makes it hard to interpret their behaviour. SLICE is a hybrid model that combines supersense labels with contextual embeddings. We introduce a weakly supervised method to learn interpretable embeddings from raw corpora and small lists of seed words. Our model is able to represent both a word and its context as embeddings into the same compact space, whose dimensions correspond to interpretable supersenses. We assess the model in a task of supersense tagging for French nouns. The little amount of supervision required makes it particularly well suited for low-resourced scenarios. Thanks to its interpretability, we perform linguistic analyses about the predicted supersenses in terms of input word and context representations.",
}
@inproceedings{rios-lwowski-2020-empirical,
    title = "An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings",
    author = "Rios, Anthony  and
      Lwowski, Brandon",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.299",
    doi = "10.18653/v1/2020.coling-main.299",
    pages = "3371--3388",
    abstract = "While pre-trained word embeddings have been shown to improve the performance of downstream tasks, many questions remain regarding their reliability: Do the same pre-trained word embeddings result in the best performance with slight changes to the training data? Do the same pre-trained embeddings perform well with multiple neural network architectures? Do imputation strategies for unknown words impact reliability? In this paper, we introduce two new metrics to understand the downstream reliability of word embeddings. We find that downstream reliability of word embeddings depends on multiple factors, including, the evaluation metric, the handling of out-of-vocabulary words, and whether the embeddings are fine-tuned.",
}
@inproceedings{zhu-de-melo-2020-sentence,
    title = "Sentence Analogies: Linguistic Regularities in Sentence Embeddings",
    author = "Zhu, Xunjie  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.300",
    doi = "10.18653/v1/2020.coling-main.300",
    pages = "3389--3400",
    abstract = "While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies. In this paper, we investigate to what extent commonly used sentence vector representation spaces as well reflect certain kinds of regularities. We propose a number of schemes to induce evaluation data, based on lexical analogy data as well as semantic relationships between sentences. Our experiments consider a wide range of sentence embedding methods, including ones based on BERT-style contextual embeddings. We find that different models differ substantially in their ability to reflect such regularities.",
}
@inproceedings{zhao-etal-2020-manifold,
    title = "Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information",
    author = "Zhao, Wenyu  and
      Zhou, Dong  and
      Li, Lin  and
      Chen, Jinjun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.301",
    doi = "10.18653/v1/2020.coling-main.301",
    pages = "3401--3412",
    abstract = "Recent studies show that word embedding models often underestimate similarities between similar words and overestimate similarities between distant words. This results in word similarity results obtained from embedding models inconsistent with human judgment. Manifold learning-based methods are widely utilized to refine word representations by re-embedding word vectors from the original embedding space to a new refined semantic space. These methods mainly focus on preserving local geometry information through performing weighted locally linear combination between words and their neighbors twice. However, these reconstruction weights are easily influenced by different selections of neighboring words and the whole combination process is time-consuming. In this paper, we propose two novel word representation refinement methods leveraging isometry feature mapping and local tangent space respectively. Unlike previous methods, our first method corrects pre-trained word embeddings by preserving global geometry information of all words instead of local geometry information between words and their neighbors. Our second method refines word representations by aligning original and re-fined embedding spaces based on local tangent space instead of performing weighted locally linear combination twice. Experimental results obtained from standard semantic relatedness and semantic similarity tasks show that our methods outperform various state-of-the-art baselines for word representation refinement.",
}
@inproceedings{saleh-etal-2020-collective,
    title = "Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation",
    author = "Saleh, Fahimeh  and
      Buntine, Wray  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.302",
    doi = "10.18653/v1/2020.coling-main.302",
    pages = "3413--3421",
    abstract = "Scarcity of parallel sentence-pairs poses a significant hurdle for training high-quality Neural Machine Translation (NMT) models in bilingually low-resource scenarios. A standard approach is transfer learning, which involves taking a model trained on a high-resource language-pair and fine-tuning it on the data of the low-resource MT condition of interest. However, it is not clear generally which high-resource language-pair offers the best transfer learning for the target MT setting. Furthermore, different transferred models may have complementary semantic and/or syntactic strengths, hence using only one model may be sub-optimal. In this paper, we tackle this problem using knowledge distillation, where we propose to distill the knowledge of ensemble of teacher models to a single student model. As the quality of these teacher models varies, we propose an effective adaptive knowledge distillation approach to dynamically adjust the contribution of the teacher models during the distillation process. Experiments on transferring from a collection of six language pairs from IWSLT to five low-resource language-pairs from TED Talks demonstrate the effectiveness of our approach, achieving up to +0.9 BLEU score improvements compared to strong baselines.",
}
@inproceedings{le-ferrand-etal-2020-enabling,
    title = "Enabling Interactive Transcription in an Indigenous Community",
    author = "Le Ferrand, Eric  and
      Bird, Steven  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.303",
    doi = "10.18653/v1/2020.coling-main.303",
    pages = "3422--3428",
    abstract = "We propose a novel transcription workflow which combines spoken term detection and human-in-the-loop, together with a pilot experiment. This work is grounded in an almost zero-resource scenario where only a few terms have so far been identified, involving two endangered languages. We show that in the early stages of transcription, when the available data is insufficient to train a robust ASR system, it is possible to take advantage of the transcription of a small number of isolated words in order to bootstrap the transcription of a speech collection.",
}
@inproceedings{araabi-monz-2020-optimizing,
    title = "Optimizing Transformer for Low-Resource Neural Machine Translation",
    author = "Araabi, Ali  and
      Monz, Christof",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.304",
    doi = "10.18653/v1/2020.coling-main.304",
    pages = "3429--3435",
    abstract = "Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. While the Transformer model has achieved significant improvements for many language pairs and has become the de facto mainstream architecture, its capability under low-resource conditions has not been fully investigated yet. Our experiments on different subsets of the IWSLT14 training data show that the effectiveness of Transformer under low-resource conditions is highly dependent on the hyper-parameter settings. Our experiments show that using an optimized Transformer for low-resource conditions improves the translation quality up to 7.3 BLEU points compared to using the Transformer default settings.",
}
@inproceedings{sun-etal-2020-mixup,
    title = "Mixup-Transformer: Dynamic Data Augmentation for {NLP} Tasks",
    author = "Sun, Lichao  and
      Xia, Congying  and
      Yin, Wenpeng  and
      Liang, Tingting  and
      Yu, Philip  and
      He, Lifang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.305",
    doi = "10.18653/v1/2020.coling-main.305",
    pages = "3436--3440",
    abstract = "Mixup is a latest data augmentation technique that linearly interpolates input examples and the corresponding labels. It has shown strong effectiveness in image classification by interpolating images at the pixel level. Inspired by this line of research, in this paper, we explore i) how to apply mixup to natural language processing tasks since text data can hardly be mixed in the raw format; ii) if mixup is still effective in transformer-based learning models,e.g., BERT.To achieve the goal, we incorporate mixup to transformer-based pre-trained architecture, named{``}mixup-transformer{''}, for a wide range of NLP tasks while keeping the whole end-to-end training system. We evaluate the proposed framework by running extensive experiments on the GLUEbenchmark. Furthermore, we also examine the performance of mixup-transformer in low-resource scenarios by reducing the training data with a certain ratio. Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.",
}
@inproceedings{hong-etal-2020-handling,
    title = "Handling Anomalies of Synthetic Questions in Unsupervised Question Answering",
    author = "Hong, Giwon  and
      Kang, Junmo  and
      Lim, Doyeon  and
      Myaeng, Sung-Hyon",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.306",
    doi = "10.18653/v1/2020.coling-main.306",
    pages = "3441--3448",
    abstract = "Advances in Question Answering (QA) research require additional datasets for new domains, languages, and types of questions, as well as for performance increases. Human creation of a QA dataset like SQuAD, however, is expensive. As an alternative, an unsupervised QA approach has been proposed so that QA training data can be generated automatically. However, the performance of unsupervised QA is much lower than that of supervised QA models. We identify two anomalies in the automatically generated questions and propose how they can be mitigated. We show our approach helps improve unsupervised QA significantly across a number of QA tasks.",
}
@inproceedings{yang-etal-2020-designing,
    title = "Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models",
    author = "Yang, Jheng-Hong  and
      Lin, Sheng-Chieh  and
      Nogueira, Rodrigo  and
      Tsai, Ming-Feng  and
      Wang, Chuan-Ju  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.307",
    doi = "10.18653/v1/2020.coling-main.307",
    pages = "3449--3453",
    abstract = "While internalized {``}implicit knowledge{''} in pretrained transformers has led to fruitful progress in many natural language understanding tasks, how to most effectively elicit such knowledge remains an open question. Based on the text-to-text transfer transformer (T5) model, this work explores a template-based approach to extract implicit knowledge for commonsense reasoning on multiple-choice (MC) question answering tasks. Experiments on three representative MC datasets show the surprisingly good performance of our simple template, coupled with a logit normalization technique for disambiguation. Furthermore, we verify that our proposed template can be easily extended to other MC tasks with contexts such as supporting facts in open-book question answering settings. Starting from the MC task, this work initiates further research to find generic natural language templates that can effectively leverage stored knowledge in pretrained models.",
}
@inproceedings{punia-etal-2020-towards,
    title = "Towards the First Machine Translation System for {S}umerian Transliterations",
    author = "Punia, Ravneet  and
      Schenk, Niko  and
      Chiarcos, Christian  and
      Pag{\'e}-Perron, {\'E}milie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.308",
    doi = "10.18653/v1/2020.coling-main.308",
    pages = "3454--3460",
    abstract = "The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English automatically. We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation. Quantitative and qualitative evaluations indicate the usefulness of the translations. Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture.",
}
@inproceedings{lee-etal-2020-using-bilingual,
    title = "Using Bilingual Patents for Translation Training",
    author = "Lee, John  and
      Tsou, Benjamin  and
      Cai, Tianyuan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.309",
    doi = "10.18653/v1/2020.coling-main.309",
    pages = "3461--3466",
    abstract = "While bilingual corpora have been instrumental for machine translation, their utility for training translators has been less explored. We investigate the use of bilingual corpora as pedagogical tools for translation in the technical domain. In a user study, novice translators revised Chinese translations of English patents through bilingual concordancing. Results show that concordancing with an in-domain bilingual corpus can yield greater improvement in translation quality of technical terms than a general-domain bilingual corpus.",
}
@inproceedings{huang-etal-2020-federated,
    title = "Federated Learning for Spoken Language Understanding",
    author = "Huang, Zhiqi  and
      Liu, Fenglin  and
      Zou, Yuexian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.310",
    doi = "10.18653/v1/2020.coling-main.310",
    pages = "3467--3478",
    abstract = "Recently, spoken language understanding (SLU) has attracted extensive research interests, and various SLU datasets have been proposed to promote the development. However, most of the existing methods focus on a single individual dataset, the efforts to improve the robustness of models and obtain better performance by combining the merits of various datasets are not well studied. In this paper, we argue that if these SLU datasets are considered together, different knowledge from different datasets could be learned jointly, and there are high chances to promote the performance of each dataset. At the same time, we further attempt to prevent data leakage when unifying multiple datasets which, arguably, is more useful in an industry setting. To this end, we propose a federated learning framework, which could unify various types of datasets as well as tasks to learn and fuse various types of knowledge, i.e., text representations, from different datasets and tasks, without the sharing of downstream task data. The fused text representations merge useful features from different SLU datasets and tasks and are thus much more powerful than the original text representations alone in individual tasks. At last, in order to provide multi-granularity text representations for our framework, we propose a novel Multi-view Encoder (MV-Encoder) as the backbone of our federated learning framework. Experiments on two SLU benchmark datasets, including two tasks (intention detection and slot filling) and federated learning settings (horizontal federated learning, vertical federated learning and federated transfer learning), demonstrate the effectiveness and universality of our approach. Specifically, we are able to get 1.53{\%} improvement on the intent detection metric accuracy. And we could also boost the performance of a strong baseline by up to 5.29{\%} on the slot filling metric F1. Furthermore, by leveraging BERT as an additional encoder, we establish new state-of-the-art results on SNIPS and ATIS datasets, where we get 99.33{\%} and 98.28{\%} in terms of accuracy on intent detection task as well as 97.20{\%} and 96.41{\%} in terms of F1 score on slot filling task, respectively.",
}
@inproceedings{cunha-sergio-etal-2020-attentively,
    title = "Attentively Embracing Noise for Robust Latent Representation in {BERT}",
    author = "Cunha Sergio, Gwenaelle  and
      Moirangthem, Dennis Singh  and
      Lee, Minho",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.311",
    doi = "10.18653/v1/2020.coling-main.311",
    pages = "3479--3491",
    abstract = "Modern digital personal assistants interact with users through voice. Therefore, they heavily rely on automatic speech recognition (ASR) in order to convert speech to text and perform further tasks. We introduce EBERT, which stands for EmbraceBERT, with the goal of extracting more robust latent representations for the task of noisy ASR text classification. Conventionally, BERT is fine-tuned for downstream classification tasks using only the [CLS] starter token, with the remaining tokens being discarded. We propose using all encoded transformer tokens and further encode them using a novel attentive embracement layer and multi-head attention layer. This approach uses the otherwise discarded tokens as a source of additional information and the multi-head attention in conjunction with the attentive embracement layer to select important features from clean data during training. This allows for the extraction of a robust latent vector resulting in improved classification performance during testing when presented with noisy inputs. We show the impact of our model on both the Chatbot and Snips corpora for intent classification with ASR error. Results, in terms of F1-score and mean between 10 runs, show that our model significantly outperforms the baseline model.",
}
@inproceedings{addlesee-etal-2020-comprehensive,
    title = "A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational {AI}",
    author = "Addlesee, Angus  and
      Yu, Yanchao  and
      Eshghi, Arash",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.312",
    doi = "10.18653/v1/2020.coling-main.312",
    pages = "3492--3503",
    abstract = "Automatic Speech Recognition (ASR) systems are increasingly powerful and more accurate, but also more numerous with several options existing currently as a service (e.g. Google, IBM, and Microsoft). Currently the most stringent standards for such systems are set within the context of their use in, and for, Conversational AI technology. These systems are expected to operate incrementally in real-time, be responsive, stable, and robust to the pervasive yet peculiar characteristics of conversational speech such as disfluencies and overlaps. In this paper we evaluate the most popular of such systems with metrics and experiments designed with these standards in mind. We also evaluate the speaker diarization (SD) capabilities of the same systems which will be particularly important for dialogue systems designed to handle multi-party interaction. We found that Microsoft has the leading incremental ASR system which preserves disfluent materials and IBM has the leading incremental SD system in addition to the ASR that is most robust to speech overlaps. Google strikes a balance between the two but none of these systems are yet suitable to reliably handle natural spontaneous conversations in real-time.",
}
@inproceedings{bird-2020-decolonising,
    title = "Decolonising Speech and Language Technology",
    author = "Bird, Steven",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.313",
    doi = "10.18653/v1/2020.coling-main.313",
    pages = "3504--3519",
    abstract = "After generations of exploitation, Indigenous people often respond negatively to the idea that their languages are data ready for the taking. By treating Indigenous knowledge as a commodity, speech and language technologists risk disenfranchising local knowledge authorities, reenacting the causes of language endangerment. Scholars in related fields have responded to calls for decolonisation, and we in the speech and language technology community need to follow suit, and explore what this means for our practices that involve Indigenous languages and the communities who own them. This paper reviews colonising discourses in speech and language technology, and suggests new ways of working with Indigenous communities, and seeks to open a discussion of a postcolonial approach to computational methods for supporting language vitality.",
}
@inproceedings{le-etal-2020-dual,
    title = "Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation",
    author = "Le, Hang  and
      Pino, Juan  and
      Wang, Changhan  and
      Gu, Jiatao  and
      Schwab, Didier  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.314",
    doi = "10.18653/v1/2020.coling-main.314",
    pages = "3520--3533",
    abstract = "We introduce dual-decoder Transformer, a new model architecture that jointly performs automatic speech recognition (ASR) and multilingual speech translation (ST). Our models are based on the original Transformer architecture (Vaswani et al., 2017) but consist of two decoders, each responsible for one task (ASR or ST). Our major contribution lies in how these decoders interact with each other: one decoder can attend to different information sources from the other via a dual-attention mechanism. We propose two variants of these architectures corresponding to two different levels of dependencies between the decoders, called the parallel and cross dual-decoder Transformers, respectively. Extensive experiments on the MuST-C dataset show that our models outperform the previously-reported highest translation performance in the multilingual settings, and outperform as well bilingual one-to-one results. Furthermore, our parallel models demonstrate no trade-off between ASR and ST compared to the vanilla multi-task architecture. Our code and pre-trained models are available at https://github.com/formiel/speech-translation.",
}
@inproceedings{yu-poesio-2020-multitask,
    title = "Multitask Learning-Based Neural Bridging Reference Resolution",
    author = "Yu, Juntao  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.315",
    doi = "10.18653/v1/2020.coling-main.315",
    pages = "3534--3546",
    abstract = "We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 36.3 p.p..",
}
@inproceedings{sun-etal-2020-improving,
    title = "Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution",
    author = "Sun, David Q.  and
      Kotek, Hadas  and
      Klein, Christopher  and
      Gupta, Mayank  and
      Li, William  and
      Williams, Jason D.",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.316",
    doi = "10.18653/v1/2020.coling-main.316",
    pages = "3547--3557",
    abstract = "This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30{\%} in comparison to other common labeling strategies. Importantly, this new approach to the labeling process, which we name Dynamic Automatic Conflict Resolution (DACR), does not require a ground truth dataset and is instead based on inter-project annotation inconsistencies. This makes DACR not only more accurate but also available to a broad range of labeling tasks. In what follows we present results from a text classification task performed at scale for a commercial personal assistant, and evaluate the inherent ambiguity uncovered by this annotation strategy as compared to other common labeling strategies.",
}
@inproceedings{estevez-velarde-etal-2020-automatic,
    title = "Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing",
    author = "Estevez-Velarde, Suilan  and
      Guti{\'e}rrez, Yoan  and
      Montoyo, Andres  and
      Almeida Cruz, Yudivi{\'a}n",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.317",
    doi = "10.18653/v1/2020.coling-main.317",
    pages = "3558--3568",
    abstract = "This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort.",
}
@inproceedings{sun-etal-2020-distill,
    title = "Distill and Replay for Continual Language Learning",
    author = "Sun, Jingyuan  and
      Wang, Shaonan  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.318",
    doi = "10.18653/v1/2020.coling-main.318",
    pages = "3569--3579",
    abstract = "Accumulating knowledge to tackle new tasks without necessarily forgetting the old ones is a hallmark of human-like intelligence. But the current dominant paradigm of machine learning is still to train a model that works well on static datasets. When learning tasks in a stream where data distribution may fluctuate, fitting on new tasks often leads to forgetting on the previous ones. We propose a simple yet effective framework that continually learns natural language understanding tasks with one model. Our framework distills knowledge and replays experience from previous tasks when fitting on a new task, thus named DnR (distill and replay). The framework is based on language models and can be smoothly built with different language model architectures. Experimental results demonstrate that DnR outperfoms previous state-of-the-art models in continually learning tasks of the same type but from different domains, as well as tasks of different types. With the distillation method, we further show that it{'}s possible for DnR to incrementally compress the model size while still outperforming most of the baselines. We hope that DnR could promote the empirical application of continual language learning, and contribute to building human-level language intelligence minimally bothered by catastrophic forgetting.",
}
@inproceedings{oka-etal-2020-incorporating,
    title = "Incorporating Noisy Length Constraints into Transformer with Length-aware Positional Encodings",
    author = "Oka, Yui  and
      Chousa, Katsuki  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.319",
    doi = "10.18653/v1/2020.coling-main.319",
    pages = "3580--3585",
    abstract = "Neural Machine Translation often suffers from an under-translation problem due to its limited modeling of output sequence lengths. In this work, we propose a novel approach to training a Transformer model using length constraints based on length-aware positional encoding (PE). Since length constraints with exact target sentence lengths degrade translation performance, we add random noise within a certain window size to the length constraints in the PE during the training. In the inference step, we predict the output lengths using input sequences and a BERT-based length prediction model. Experimental results in an ASPEC English-to-Japanese translation showed the proposed method produced translations with lengths close to the reference ones and outperformed a vanilla Transformer (especially in short sentences) by 3.22 points in BLEU. The average translation results using our length prediction model were also better than another baseline method using input lengths for the length constraints. The proposed noise injection improved robustness for length prediction errors, especially within the window size.",
}
@inproceedings{liu-etal-2020-rethinking,
    title = "Rethinking Skip Connection with Layer Normalization",
    author = "Liu, Fenglin  and
      Ren, Xuancheng  and
      Zhang, Zhiyuan  and
      Sun, Xu  and
      Zou, Yuexian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.320",
    doi = "10.18653/v1/2020.coling-main.320",
    pages = "3586--3598",
    abstract = "Skip connection is a widely-used technique to improve the performance and the convergence of deep neural networks, which is believed to relieve the difficulty in optimization due to non-linearity by propagating a linear component through the neural network layers. However, from another point of view, it can also be seen as a modulating mechanism between the input and the output, with the input scaled by a pre-defined value one. In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could by addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection. Inspired by the findings, we further propose to adaptively adjust the scale of the input by recursively applying skip connection with layer normalization, which promotes the performance substantially and generalizes well across diverse tasks including both machine translation and image classification datasets.",
}
@inproceedings{ren-du-2020-specializing,
    title = "Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs",
    author = "Ren, Yuanhang  and
      Du, Ye",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.321",
    doi = "10.18653/v1/2020.coling-main.321",
    pages = "3599--3609",
    abstract = "Traditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this issue. In this work, we propose a new retrofitting method called Heterogeneously Retrofitted Spectral Word Embedding. It heterogeneously twists the similarity matrix of word pairs with lexical constraints. A new set of word vectors is generated by a spectral decomposition of the similarity matrix, which has a linear algebraic analytic form. Our method has a competitive performance compared with the state-of-the-art retrofitting method such as AR (CITATION). In addition, since our embedding has a clear linear algebraic relationship with the similarity matrix, we carefully study the contribution of each component in our model. Last but not least, our method is very efficient to execute.",
}
@inproceedings{hong-etal-2020-deep,
    title = "Deep Inside-outside Recursive Autoencoder with All-span Objective",
    author = "Hong, Ruyue  and
      Cai, Jiong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.322",
    doi = "10.18653/v1/2020.coling-main.322",
    pages = "3610--3615",
    abstract = "Deep inside-outside recursive autoencoder (DIORA) is a neural-based model designed for unsupervised constituency parsing. During its forward computation, it provides phrase and contextual representations for all spans in the input sentence. By utilizing the contextual representation of each leaf-level span, the span of length 1, to reconstruct the word inside the span, the model is trained without labeled data. In this work, we extend the training objective of DIORA by making use of all spans instead of only leaf-level spans. We test our new training objective on datasets of two languages: English and Japanese, and empirically show that our method achieves improvement in parsing accuracy over the original DIORA.",
}
@inproceedings{liu-etal-2020-cross-lingual,
    title = "Cross-Lingual Document Retrieval with Smooth Learning",
    author = "Liu, Jiapeng  and
      Zhang, Xiao  and
      Goldwasser, Dan  and
      Wang, Xiao",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.323",
    doi = "10.18653/v1/2020.coling-main.323",
    pages = "3616--3629",
    abstract = "Cross-lingual document search is an information retrieval task in which the queries{'} language and the documents{'} language are different. In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents{'} languages. This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective function. We further provide theoretical guarantee on the generalization error bound for the proposed framework. We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.",
}
@inproceedings{dufter-etal-2020-increasing,
    title = "Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention",
    author = {Dufter, Philipp  and
      Schmitt, Martin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.324",
    doi = "10.18653/v1/2020.coling-main.324",
    pages = "3630--3636",
    abstract = "Self-Attention Networks (SANs) are an integral part of successful neural architectures such as Transformer (Vaswani et al., 2017), and thus of pretrained language models such as BERT (Devlin et al., 2019) or GPT-3 (Brown et al., 2020). Training SANs on a task or pretraining them on language modeling requires large amounts of data and compute resources. We are searching for modifications to SANs that enable faster learning, i.e., higher accuracies after fewer update steps. We investigate three modifications to SANs: direct position interactions, learnable temperature, and convoluted attention. When evaluating them on part-of-speech tagging, we find that direct position interactions are an alternative to position embeddings, and convoluted attention has the potential to speed up the learning process.",
}
@inproceedings{lepori-mccoy-2020-picking,
    title = "Picking {BERT}{'}s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis",
    author = "Lepori, Michael  and
      McCoy, R. Thomas",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.325",
    doi = "10.18653/v1/2020.coling-main.325",
    pages = "3637--3651",
    abstract = "As the name implies, contextualized representations of language are typically motivated by their ability to encode context. Which aspects of context are captured by such representations? We introduce an approach to address this question using Representational Similarity Analysis (RSA). As case studies, we investigate the degree to which a verb embedding encodes the verb{'}s subject, a pronoun embedding encodes the pronoun{'}s antecedent, and a full-sentence representation encodes the sentence{'}s head word (as determined by a dependency parse). In all cases, we show that BERT{'}s contextualized embeddings reflect the linguistic dependency being studied, and that BERT encodes these dependencies to a greater degree than it encodes less linguistically-salient controls. These results demonstrate the ability of our approach to adjudicate between hypotheses about which aspects of context are encoded in representations of language.",
}
@inproceedings{joshi-etal-2020-devil,
    title = "The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks",
    author = "Joshi, Brihi  and
      Shah, Neil  and
      Barbieri, Francesco  and
      Neves, Leonardo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.326",
    doi = "10.18653/v1/2020.coling-main.326",
    pages = "3652--3659",
    abstract = "Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years. Extensive work shows how accurately such models can represent abstract, semantic information present in text. In this expository work, we explore a tangent direction and analyze such models{'} performance on tasks that require a more granular level of representation. We focus on the problem of textual similarity from two perspectives: matching documents on a granular level (requiring embeddings to capture fine-grained attributes in the text), and an abstract level (requiring embeddings to capture overall textual semantics). We empirically demonstrate, across two datasets from different domains, that despite high performance in abstract document matching as expected, contextual embeddings are consistently (and at times, vastly) outperformed by simple baselines like TF-IDF for more granular tasks. We then propose a simple but effective method to incorporate TF-IDF into models that use contextual embeddings, achieving relative improvements of up to 36{\%} on granular tasks.",
}
@inproceedings{sun-etal-2020-colake,
    title = "{C}o{LAKE}: Contextualized Language and Knowledge Embedding",
    author = "Sun, Tianxiang  and
      Shao, Yunfan  and
      Qiu, Xipeng  and
      Guo, Qipeng  and
      Hu, Yaru  and
      Huang, Xuanjing  and
      Zhang, Zheng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.327",
    doi = "10.18653/v1/2020.coling-main.327",
    pages = "3660--3670",
    abstract = "With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the performance gains of these models. Few works explore the potential of deep contextualized knowledge representation when injecting knowledge. In this paper, we propose the Contextualized Language and Knowledge Embedding (CoLAKE), which jointly learns contextualized representation for both language and knowledge with the extended MLM objective. Instead of injecting only entity embeddings, CoLAKE extracts the knowledge context of an entity from large-scale knowledge bases. To handle the heterogeneity of knowledge context and language context, we integrate them in a unified data structure, word-knowledge graph (WK graph). CoLAKE is pre-trained on large-scale WK graphs with the modified Transformer encoder. We conduct experiments on knowledge-driven tasks, knowledge probing tasks, and language understanding tasks. Experimental results show that CoLAKE outperforms previous counterparts on most of the tasks. Besides, CoLAKE achieves surprisingly high performance on our synthetic task called word-knowledge graph completion, which shows the superiority of simultaneously contextualizing language and knowledge representation.",
}
@inproceedings{haley-smolensky-2020-invertible,
    title = "Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme",
    author = "Haley, Coleman  and
      Smolensky, Paul",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.328",
    doi = "10.18653/v1/2020.coling-main.328",
    pages = "3671--3683",
    abstract = "We present a novel method for embedding trees in a vector space based on Tensor-Product Representations (TPRs) which allows for inversion: the retrieval of the original tree structure and nodes from the vectorial embedding. Unlike previous attempts, this does not come at the cost of intractable representation size; we utilize a method for non-exact inversion, showing that it works well when there is sufficient randomness in the representation scheme for simple data and providing an upper bound on its error. To handle the huge number of possible tree positions without memoizing position representation vectors, we present a method (Cryptographic Role Embedding) using cryptographic hashing algorithms that allows for the representation of unboundedly many positions. Through experiments on parse tree data, we show a 30,000-dimensional Cryptographic Role Embedding of trees can provide invertibility with error {\textless} 1{\%} that previous methods would require 8.6 {\mbox{$\times$}} 1057 dimensions to represent.",
}
@inproceedings{long-etal-2020-synonym,
    title = "Synonym Knowledge Enhanced Reader for {C}hinese Idiom Reading Comprehension",
    author = "Long, Siyu  and
      Wang, Ran  and
      Tao, Kun  and
      Zeng, Jiali  and
      Dai, Xinyu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.329",
    doi = "10.18653/v1/2020.coling-main.329",
    pages = "3684--3695",
    abstract = "Machine reading comprehension (MRC) is the task that asks a machine to answer questions based on a given context. For Chinese MRC, due to the non-literal and non-compositional semantic characteristics, Chinese idioms pose unique challenges for machines to understand. Previous studies tend to treat idioms separately without fully exploiting the relationship among them. In this paper, we first define the concept of literal meaning coverage to measure the consistency between semantics and literal meanings for Chinese idioms. With the definition, we prove that the literal meanings of many idioms are far from their semantics, and we also verify that the synonymic relationship can mitigate this inconsistency, which would be beneficial for idiom comprehension. Furthermore, to fully utilize the synonymic relationship, we propose the synonym knowledge enhanced reader. Specifically, for each idiom, we first construct a synonym graph according to the annotations from the high-quality synonym dictionary or the cosine similarity between the pre-trained idiom embeddings and then incorporate the graph attention network and gate mechanism to encode the graph. Experimental results on ChID, a large-scale Chinese idiom reading comprehension dataset, show that our model achieves state-of-the-art performance.",
}
@inproceedings{li-etal-2020-target,
    title = "Target Word Masking for Location Metonymy Resolution",
    author = "Li, Haonan  and
      Vasardani, Maria  and
      Tomko, Martin  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.330",
    doi = "10.18653/v1/2020.coling-main.330",
    pages = "3696--3707",
    abstract = "Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources. In this paper, we propose an end-to-end word-level classification approach based only on BERT, without dependencies on taggers, parsers, curated dictionaries of place names, or other external resources. We show that our approach achieves the state-of-the-art on 5 datasets, surpassing conventional BERT models and benchmarks by a large margin. We also show that our approach generalises well to unseen data.",
}
@inproceedings{kobayashi-ng-2020-bridging,
    title = "Bridging Resolution: A Survey of the State of the Art",
    author = "Kobayashi, Hideo  and
      Ng, Vincent",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.331",
    doi = "10.18653/v1/2020.coling-main.331",
    pages = "3708--3721",
    abstract = "Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution. Given that significant progress has been made on coreference resolution in recent years, we believe that bridging resolution will receive increasing attention in the NLP community. Nevertheless, progress on bridging resolution is currently hampered in part by the scarcity of large annotated corpora for model training as well as the lack of standardized evaluation protocols. This paper presents a survey of the current state of research on bridging reference resolution and discusses future research directions.",
}
@inproceedings{neidlein-etal-2020-analysis,
    title = "An analysis of language models for metaphor recognition",
    author = "Neidlein, Arthur  and
      Wiesenbach, Philip  and
      Markert, Katja",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.332",
    doi = "10.18653/v1/2020.coling-main.332",
    pages = "3722--3736",
    abstract = "We conduct a linguistic analysis of recent metaphor recognition systems, all of which are based on language models. We show that their performance, although reaching high F-scores, has considerable gaps from a linguistic perspective. First, they perform substantially worse on unconventional metaphors than on conventional ones. Second, they struggle with handling rarer word types. These two findings together suggest that a large part of the systems{'} success is due to optimising the disambiguation of conventionalised, metaphoric word senses for specific words instead of modelling general properties of metaphors. As a positive result, the systems show increasing capabilities to recognise metaphoric readings of unseen words if synonyms or morphological variations of these words have been seen before, leading to enhanced generalisation beyond word sense disambiguation.",
}
@inproceedings{mickus-etal-2020-meaning,
    title = "What Meaning-Form Correlation Has to Compose With: A Study of {MFC} on Artificial and Natural Language",
    author = "Mickus, Timothee  and
      Bernard, Timoth{\'e}e  and
      Paperno, Denis",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.333",
    doi = "10.18653/v1/2020.coling-main.333",
    pages = "3737--3749",
    abstract = "Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive. We focus on the proposal that compositionality can be assessed by measuring meaning-form correlation. We analyze meaning-form correlation on three sets of languages: (i) artificial toy languages tailored to be compositional, (ii) a set of English dictionary definitions, and (iii) a set of English sentences drawn from literature. We find that linguistic phenomena such as synonymy and ungrounded stop-words weigh on MFC measurements, and that straightforward methods to mitigate their effects have widely varying results depending on the dataset they are applied to. Data and code are made publicly available.",
}
@inproceedings{lothritz-etal-2020-evaluating,
    title = "Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition",
    author = "Lothritz, Cedric  and
      Allix, Kevin  and
      Veiber, Lisa  and
      Bissyand{\'e}, Tegawend{\'e} F.  and
      Klein, Jacques",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.334",
    doi = "10.18653/v1/2020.coling-main.334",
    pages = "3750--3760",
    abstract = "Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP. While the performance of transformer-based approaches such as BERT has been studied for NER, there has not yet been a study for the fine-grained Named Entity Recognition (FG-NER) task. In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF). Furthermore, we apply each model to a multitude of distinct domains. We find that transformer-based models incrementally outperform the studied non-transformer-based models in most domains with respect to the F1 score. Furthermore, we find that the choice of domains significantly influenced the performance regardless of the respective data size or the model chosen.",
}
@inproceedings{pislar-rei-2020-seeing,
    title = "Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels",
    author = "Pislar, Miruna  and
      Rei, Marek",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.335",
    doi = "10.18653/v1/2020.coling-main.335",
    pages = "3761--3775",
    abstract = "In natural languages, words are used in association to construct sentences. It is not words in isolation, but the appropriate use of hierarchical structures that conveys the meaning of the whole sentence. Neural networks have the ability to capture expressive language features; however, insights into the link between words and sentences are difficult to acquire automatically. In this work, we design a deep neural network architecture that explicitly wires lower and higher linguistic components; we then evaluate its ability to perform the same task at different hierarchical levels. Settling on broad text classification tasks, we show that our model, MHAL, learns to simultaneously solve them at different levels of granularity by fluidly transferring knowledge between hierarchies. Using a multi-head attention mechanism to tie the representations between single words and full sentences, MHAL systematically outperforms equivalent models that are not incentivized towards developing compositional representations. Moreover, we demonstrate that, with the proposed architecture, the sentence information flows naturally to individual words, allowing the model to behave like a sequence labeler (which is a lower, word-level task) even without any word supervision, in a zero-shot fashion.",
}
@inproceedings{gomez-rodriguez-etal-2020-unifying,
    title = "A Unifying Theory of Transition-based and Sequence Labeling Parsing",
    author = "G{\'o}mez-Rodr{\'\i}guez, Carlos  and
      Strzyz, Michalina  and
      Vilares, David",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.336",
    doi = "10.18653/v1/2020.coling-main.336",
    pages = "3776--3793",
    abstract = "We define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a theoretical relation between transition-based parsing and sequence-labeling parsing, but also provides a method to obtain new encodings for fast and simple sequence labeling parsing from the many existing transition-based parsers for different formalisms. Applying it to dependency parsing, we implement sequence labeling versions of four algorithms, showing that they are learnable and obtain comparable performance to existing encodings.",
}
@inproceedings{guz-etal-2020-unleashing,
    title = "Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining",
    author = "Guz, Grigorii  and
      Huber, Patrick  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.337",
    doi = "10.18653/v1/2020.coling-main.337",
    pages = "3794--3805",
    abstract = "RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models. Our parser establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT. We further demonstrate that pretraining our parser on the recently available large-scale {``}silver-standard{''} discourse treebank MEGA-DT provides even larger performance benefits, suggesting a novel and promising research direction in the field of discourse analysis.",
}
@inproceedings{li-etal-2020-semi,
    title = "Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations",
    author = "Li, Ying  and
      Li, Zhenghua  and
      Zhang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.338",
    doi = "10.18653/v1/2020.coling-main.338",
    pages = "3806--3817",
    abstract = "In recent years, parsing performance is dramatically improved on in-domain texts thanks to the rapid progress of deep neural network models. The major challenge for current parsing research is to improve parsing performance on out-of-domain texts that are very different from the in-domain training data when there is only a small-scale out-domain labeled data. To deal with this problem, we propose to improve the contextualized word representations via adversarial learning and fine-tuning BERT processes. Concretely, we apply adversarial learning to three representative semi-supervised domain adaption methods, i.e., direct concatenation (CON), feature augmentation (FA), and domain embedding (DE) with two useful strategies, i.e., fused target-domain word representations and orthogonality constraints, thus enabling to model more pure yet effective domain-specific and domain-invariant representations. Simultaneously, we utilize a large-scale target-domain unlabeled data to fine-tune BERT with only the language model loss, thus obtaining reliable contextualized word representations that benefit for the cross-domain dependency parsing. Experiments on a benchmark dataset show that our proposed adversarial approaches achieve consistent improvement, and fine-tuning BERT further boosts parsing accuracy by a large margin. Our single model achieves the same state-of-the-art performance as the top submitted system in the NLPCC-2019 shared task, which uses ensemble models and BERT.",
}
@inproceedings{dehouck-gomez-rodriguez-2020-data,
    title = "Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages",
    author = "Dehouck, Mathieu  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.339",
    doi = "10.18653/v1/2020.coling-main.339",
    pages = "3818--3830",
    abstract = "The lack of annotated data is a big issue for building reliable NLP systems for most of the world{'}s languages. But this problem can be alleviated by automatic data generation. In this paper, we present a new data augmentation method for artificially creating new dependency-annotated sentences. The main idea is to swap subtrees between annotated sentences while enforcing strong constraints on those trees to ensure maximal grammaticality of the new sentences. We also propose a method to perform low-resource experiments using resource-rich languages by mimicking low-resource languages by sampling sentences under a low-resource distribution. In a series of experiments, we show that our newly proposed data augmentation method outperforms previous proposals using the same basic inputs.",
}
@inproceedings{mengge-etal-2020-porous,
    title = "Porous Lattice Transformer Encoder for {C}hinese {NER}",
    author = "Mengge, Xue  and
      Yu, Bowen  and
      Liu, Tingwen  and
      Zhang, Yue  and
      Meng, Erli  and
      Wang, Bin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.340",
    doi = "10.18653/v1/2020.coling-main.340",
    pages = "3831--3841",
    abstract = "Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information. Previous work has extended RNNs to consume lattice inputsand achieved great success. However, due to the DAG structure and the inherently unidirectionalsequential nature, this method precludes batched computation and sufficient semantic interaction.In this paper, we propose PLTE, an extension of transformer encoder that is tailored for ChineseNER, which models all the characters and matched lexical words in parallel with batch process-ing. PLTE augments self-attention with positional relation representations to incorporate latticestructure. It also introduces a porous mechanism to augment localness modeling and maintainthe strength of capturing the rich long-term dependencies. Experimental results show that PLTEperforms up to 11.4 times faster than state-of-the-art methods while realizing better performance.We also demonstrate that using BERT representations further substantially boosts the performanceand brings out the best in PLTE.",
}
@inproceedings{yu-etal-2020-learning,
    title = "Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction",
    author = "Yu, Bowen  and
      Mengge, Xue  and
      Zhang, Zhenyu  and
      Liu, Tingwen  and
      Yubin, Wang  and
      Wang, Bin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.341",
    doi = "10.18653/v1/2020.coling-main.341",
    pages = "3842--3852",
    abstract = "Dependency trees have been shown to be effective in capturing long-range relations between target entities. Nevertheless, how to selectively emphasize target-relevant information and remove irrelevant content from the tree is still an open problem. Existing approaches employing pre-defined rules to eliminate noise may not always yield optimal results due to the complexity and variability of natural language. In this paper, we present a novel architecture named Dynamically Pruned Graph Convolutional Network (DP-GCN), which learns to prune the dependency tree with rethinking in an end-to-end scheme. In each layer of DP-GCN, we employ a selection module to concentrate on nodes expressing the target relation by a set of binary gates, and then augment the pruned tree with a pruned semantic graph to ensure the connectivity. After that, we introduce a rethinking mechanism to guide and refine the pruning operation by feeding back the high-level learned features repeatedly. Extensive experimental results demonstrate that our model achieves impressive results compared to strong competitors.",
}
@inproceedings{guan-etal-2020-far,
    title = "How Far Does {BERT} Look At: Distance-based Clustering and Analysis of {BERT}{'}s Attention",
    author = "Guan, Yue  and
      Leng, Jingwen  and
      Li, Chao  and
      Chen, Quan  and
      Guo, Minyi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.342",
    doi = "10.18653/v1/2020.coling-main.342",
    pages = "3853--3860",
    abstract = "Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism. As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive. In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations. We further study their corresponding functions through analytical study. In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.",
}
@inproceedings{dai-adel-2020-analysis,
    title = "An Analysis of Simple Data Augmentation for Named Entity Recognition",
    author = "Dai, Xiang  and
      Adel, Heike",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.343",
    doi = "10.18653/v1/2020.coling-main.343",
    pages = "3861--3867",
    abstract = "Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedical and materials science domains (i2b2-2010 and MaSciP), we show that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets.",
}
@inproceedings{zhang-goldwasser-2020-semi,
    title = "Semi-supervised Autoencoding Projective Dependency Parsing",
    author = "Zhang, Xiao  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.344",
    doi = "10.18653/v1/2020.coling-main.344",
    pages = "3868--3885",
    abstract = "We describe two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing. The first model is a Locally Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Globally Autoencoding Parser (GAP) encoding the input into dependency trees as latent variables, with exact inference. Both models consist of two parts: an encoder enhanced by deep neural networks (DNN) that can utilize the contextual information to encode the input into latent variables, and a decoder which is a generative model able to reconstruct the input. Both LAP and GAP admit a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We conducted experiments on WSJ and UD dependency parsing data sets, showing that our models can exploit the unlabeled data to improve the performance given a limited amount of labeled data, and outperform a previously proposed semi-supervised model.",
}
@inproceedings{litschko-etal-2020-towards,
    title = "Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers",
    author = "Litschko, Robert  and
      Vuli{\'c}, Ivan  and
      Agi{\'c}, {\v{Z}}eljko  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.345",
    doi = "10.18653/v1/2020.coling-main.345",
    pages = "3886--3898",
    abstract = "Current methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, {``}at treebank level{''}. In this work, we propose and argue for a novel cross-lingual transfer paradigm: instance-level parser selection (ILPS), and present a proof-of-concept study focused on instance-level selection in the framework of delexicalized parser transfer. Our work is motivated by an empirical observation that different source parsers are the best choice for different Universal POS-sequences (i.e., UPOS sentences) in the target language. We then propose to predict the best parser at the instance level. To this end, we train a supervised regression model, based on the Transformer architecture, to predict parser accuracies for individual POS-sequences. We compare ILPS against two strong single-best parser selection baselines (SBPS): (1) a model that compares POS n-gram distributions between the source and target languages (KL) and (2) a model that selects the source based on the similarity between manually created language vectors encoding syntactic properties of languages (L2V). The results from our extensive evaluation, coupling 42 source parsers and 20 diverse low-resource test languages, show that ILPS outperforms KL and L2V on 13/20 and 14/20 test languages, respectively. Further, we show that by predicting the best parser {``}at treebank level{''} (SBPS), using the aggregation of predictions from our instance-level model, we outperform the same baselines on 17/20 and 16/20 test languages.",
}
@inproceedings{castellana-bacciu-2020-learning,
    title = "Learning from Non-Binary Constituency Trees via Tensor Decomposition",
    author = "Castellana, Daniele  and
      Bacciu, Davide",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.346",
    doi = "10.18653/v1/2020.coling-main.346",
    pages = "3899--3910",
    abstract = "Processing sentence constituency trees in binarised form is a common and popular approach in literature. However, constituency trees are non-binary by nature. The binarisation procedure changes deeply the structure, furthering constituents that instead are close. In this work, we introduce a new approach to deal with non-binary constituency trees which leverages tensor-based models. In particular, we show how a powerful composition function based on the canonical tensor decomposition can exploit such a rich structure. A key point of our approach is the weight sharing constraint imposed on the factor matrices, which allows limiting the number of model parameters. Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.",
}
@inproceedings{yang-etal-2020-second,
    title = "Second-Order Unsupervised Neural Dependency Parsing",
    author = "Yang, Songlin  and
      Jiang, Yong  and
      Han, Wenjuan  and
      Tu, Kewei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.347",
    doi = "10.18653/v1/2020.coling-main.347",
    pages = "3911--3924",
    abstract = "Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information. We also propose a novel design of the neural parameterization and optimization methods of the dependency models. In second-order models, the number of grammar rules grows cubically with the increase of vocabulary size, making it difficult to train lexicalized models that may contain thousands of words. To circumvent this problem while still benefiting from both second-order parsing and lexicalization, we use the agreement-based learning framework to jointly train a second-order unlexicalized model and a first-order lexicalized model. Experiments on multiple datasets show the effectiveness of our second-order models compared with recent state-of-the-art methods. Our joint model achieves a 10{\%} improvement over the previous state-of-the-art parser on the full WSJ test set.",
}
@inproceedings{michon-etal-2020-integrating,
    title = "Integrating Domain Terminology into Neural Machine Translation",
    author = "Michon, Elise  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.348",
    doi = "10.18653/v1/2020.coling-main.348",
    pages = "3925--3937",
    abstract = "This paper extends existing work on terminology integration into Neural Machine Translation, a common industrial practice to dynamically adapt translation to a specific domain. Our method, based on the use of placeholders complemented with morphosyntactic annotation, efficiently taps into the ability of the neural network to deal with symbolic knowledge to surpass the surface generalization shown by alternative techniques. We compare our approach to state-of-the-art systems and benchmark them through a well-defined evaluation framework, focusing on actual application of terminology and not just on the overall performance. Results indicate the suitability of our method in the use-case where terminology is used in a system trained on generic data only.",
}
@inproceedings{sanchez-cartagena-etal-2020-understanding,
    title = "Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation",
    author = "S{\'a}nchez-Cartagena, V{\'\i}ctor M.  and
      P{\'e}rez-Ortiz, Juan Antonio  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.349",
    doi = "10.18653/v1/2020.coling-main.349",
    pages = "3938--3950",
    abstract = "This paper studies the effects of word-level linguistic annotations in under-resourced neural machine translation, for which there is incomplete evidence in the literature. The study covers eight language pairs, different training corpus sizes, two architectures, and three types of annotation: dummy tags (with no linguistic information at all), part-of-speech tags, and morpho-syntactic description tags, which consist of part of speech and morphological features. These linguistic annotations are interleaved in the input or output streams as a single tag placed before each word. In order to measure the performance under each scenario, we use automatic evaluation metrics and perform automatic error classification. Our experiments show that, in general, source-language annotations are helpful and morpho-syntactic descriptions outperform part of speech for some language pairs. On the contrary, when words are annotated in the target language, part-of-speech tags systematically outperform morpho-syntactic description tags in terms of automatic evaluation metrics, even though the use of morpho-syntactic description tags improves the grammaticality of the output. We provide a detailed analysis of the reasons behind this result.",
}
@inproceedings{gaido-etal-2020-breeding,
    title = "Breeding Gender-aware Direct Speech Translation Systems",
    author = "Gaido, Marco  and
      Savoldi, Beatrice  and
      Bentivogli, Luisa  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.350",
    doi = "10.18653/v1/2020.coling-main.350",
    pages = "3951--3964",
    abstract = "In automatic speech translation (ST), traditional cascade approaches involving separate transcription and translation steps are giving ground to increasingly competitive and more robust direct solutions. In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e.g.speaker{'}s vocal characteristics) that is otherwise lost in the cascade framework. Although such ability proved to be useful for gender translation, direct ST is nonetheless affected by gender bias just like its cascade counterpart, as well as machine translation and numerous other natural language processing applications. Moreover, direct ST systems that exclusively rely on vocal biometric features as a gender cue can be unsuitable or even potentially problematic for certain users. Going beyond speech signals, in this paper we compare different approaches to inform direct ST models about the speaker{'}s gender and test their ability to handle gender translation from English into Italian and French. To this aim, we manually annotated large datasets with speak-ers{'} gender information and used them for experiments reflecting different possible real-world scenarios. Our results show that gender-aware direct ST solutions can significantly outperform strong {--} but gender-unaware {--} direct ST models. In particular, the translation of gender-marked words can increase up to 30 points in accuracy while preserving overall translation quality.",
}
@inproceedings{feldman-coto-solano-2020-neural,
    title = "Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language {B}ribri",
    author = "Feldman, Isaac  and
      Coto-Solano, Rolando",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.351",
    doi = "10.18653/v1/2020.coling-main.351",
    pages = "3965--3976",
    abstract = "This paper presents a neural machine translation model and dataset for the Chibchan language Bribri, with an average performance of BLEU 16.9{\mbox{$\pm$}}1.7. This was trained on an extremely small dataset (5923 Bribri-Spanish pairs), providing evidence for the applicability of NMT in extremely low-resource environments. We discuss the challenges entailed in managing training input from languages without standard orthographies, we provide evidence of successful learning of Bribri grammar, and also examine the translations of structures that are infrequent in major Indo-European languages, such as positional verbs, ergative markers, numerical classifiers and complex demonstrative systems. In addition to this, we perform an experiment of augmenting the dataset through iterative back-translation (Sennrich et al., 2016a; Hoang et al., 2018) by using Spanish sentences to create synthetic Bribri sentences. This improves the score by an average of 1.0 BLEU, but only when the new Spanish sentences belong to the same domain as the other Spanish examples. This contributes to the small but growing body of research on Chibchan NLP.",
}
@inproceedings{xu-etal-2020-dynamic,
    title = "Dynamic Curriculum Learning for Low-Resource Neural Machine Translation",
    author = "Xu, Chen  and
      Hu, Bojie  and
      Jiang, Yufan  and
      Feng, Kai  and
      Wang, Zeyang  and
      Huang, Shen  and
      Ju, Qi  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.352",
    doi = "10.18653/v1/2020.coling-main.352",
    pages = "3977--3989",
    abstract = "Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT{'}16 En-De.",
}
@inproceedings{donicke-etal-2020-real,
    title = "Real-Valued Logics for Typological Universals: Framework and Application",
    author = {D{\"o}nicke, Tillmann  and
      Yu, Xiang  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.353",
    doi = "10.18653/v1/2020.coling-main.353",
    pages = "3990--4003",
    abstract = "This paper proposes a framework for the expression of typological statements which uses real-valued logics to capture the empirical truth value (truth degree) of a formula on a given data source, e.g. a collection of multilingual treebanks with comparable annotation. The formulae can be arbitrarily complex expressions of propositional logic. To illustrate the usefulness of such a framework, we present experiments on the Universal Dependencies treebanks for two use cases: (i) empirical (re-)evaluation of established formulae against the spectrum of available treebanks and (ii) evaluating new formulae (i.e. potential candidates for universals) generated by a search algorithm.",
}
@inproceedings{branco-etal-2020-comparative,
    title = "Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness",
    author = "Branco, Ant{\'o}nio  and
      Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Salawa, Malgorzata  and
      Branco, Ruben  and
      Saedi, Chakaveh",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.354",
    doi = "10.18653/v1/2020.coling-main.354",
    pages = "4004--4019",
    abstract = "Lexical semantics theories differ in advocating that the meaning of words is represented as an inference graph, a feature mapping or a cooccurrence vector, thus raising the question: is it the case that one of these approaches is superior to the others in representing lexical semantics appropriately? Or in its non antagonistic counterpart: could there be a unified account of lexical semantics where these approaches seamlessly emerge as (partial) renderings of (different) aspects of a core semantic knowledge base? In this paper, we contribute to these research questions with a number of experiments that systematically probe different lexical semantics theories for their levels of cognitive plausibility and of technological usefulness. The empirical findings obtained from these experiments advance our insight on lexical semantics as the feature-based approach emerges as superior to the other ones, and arguably also move us closer to finding answers to the research questions above.",
}
@inproceedings{tayyar-madabushi-etal-2020-cxgbert,
    title = "{C}x{GBERT}: {BERT} meets Construction Grammar",
    author = "Tayyar Madabushi, Harish  and
      Romain, Laurence  and
      Divjak, Dagmar  and
      Milin, Petar",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.355",
    doi = "10.18653/v1/2020.coling-main.355",
    pages = "4020--4032",
    abstract = "While lexico-semantic elements no doubt capture a large amount of linguistic information, it has been argued that they do not capture all information contained in text. This assumption is central to constructionist approaches to language which argue that language consists of constructions, learned pairings of a form and a function or meaning that are either frequent or have a meaning that cannot be predicted from its component parts. BERT{'}s training objectives give it access to a tremendous amount of lexico-semantic information, and while BERTology has shown that BERT captures certain important linguistic dimensions, there have been no studies exploring the extent to which BERT might have access to constructional information. In this work we design several probes and conduct extensive experiments to answer this question. Our results allow us to conclude that BERT does indeed have access to a significant amount of information, much of which linguists typically call constructional information. The impact of this observation is potentially far-reaching as it provides insights into what deep learning methods learn from text, while also showing that information contained in constructions is redundantly encoded in lexico-semantics.",
}
@inproceedings{shibata-etal-2020-lstm,
    title = "How {LSTM} Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text",
    author = "Shibata, Chihiro  and
      Uchiumi, Kei  and
      Mochihashi, Daichi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.356",
    doi = "10.18653/v1/2020.coling-main.356",
    pages = "4033--4043",
    abstract = "Long Short-Term Memory recurrent neural network (LSTM) is widely used and known to capture informative long-term syntactic dependencies. However, how such information are reflected in its internal vectors for natural text has not yet been sufficiently investigated. We analyze them by learning a language model where syntactic structures are implicitly given. We empirically show that the context update vectors, i.e. outputs of internal gates, are approximately quantized to binary or ternary values to help the language model to count the depth of nesting accurately, as Suzgun et al. (2019) recently show for synthetic Dyck languages. For some dimensions in the context vector, we show that their activations are highly correlated with the depth of phrase structures, such as VP and NP. Moreover, with an L1 regularization, we also found that it can accurately predict whether a word is inside a phrase structure or not from a small number of components of the context vector. Even for the case of learning from raw text, context vectors are shown to still correlate well with the phrase structures. Finally, we show that natural clusters of the functional words and the part of speeches that trigger phrases are represented in a small but principal subspace of the context-update vector of LSTM.",
}
@inproceedings{seyffarth-kallmeyer-2020-corpus,
    title = "Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation",
    author = "Seyffarth, Esther  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.357",
    doi = "10.18653/v1/2020.coling-main.357",
    pages = "4044--4055",
    abstract = "English verb alternations allow participating verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as perplexity-based language model features, which are intended to reflect each alternation{'}s felicity by simulating it. Beyond the prediction task, we use the classifier results as a source for a manual annotation step in order to identify new, unseen instances of each alternation. This is possible because existing alternation datasets contain positive, but no negative instances and are not comprehensive. Over several sequences of classification-annotation steps, we iteratively extend our sets of alternating verbs. Our hybrid approach to the identification of new alternating verbs reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification. Due to the success of semi-supervised and unsupervised features, our approach can easily be transferred to further alternations.",
}
@inproceedings{yu-etal-2020-conversation,
    title = "When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network",
    author = "Yu, Yipeng  and
      Guan, Ran  and
      Ma, Jie  and
      Jiang, Zhuoxuan  and
      Huang, Jingchang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.358",
    doi = "10.18653/v1/2020.coling-main.358",
    pages = "4056--4066",
    abstract = "In online customer service applications, multiple chatbots that are specialized in various topics are typically developed separately and are then merged with other human agents to a single platform, presenting to the users with a unified interface. Ideally the conversation can be transparently transferred between different sources of customer support so that domain-specific questions can be answered timely and this is what we coined as a Bot-Agent symbiosis. Conversation transition is a major challenge in such online customer service and our work formalises the challenge as two core problems, namely, when to transfer and which bot or agent to transfer to and introduces a deep neural networks based approach that addresses these problems. Inspired by the net promoter score (NPS), our research reveals how the problems can be effectively solved by providing user feedback and developing deep neural networks that predict the conversation category distribution and the NPS of the dialogues. Experiments on realistic data generated from an online service support platform demonstrate that the proposed approach outperforms state-of-the-art methods and shows promising perspective for transparent conversation transition.",
}
@inproceedings{zhang-etal-2020-topic,
    title = "Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System",
    author = "Zhang, Shuying  and
      Zhao, Tianyu  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.359",
    doi = "10.18653/v1/2020.coling-main.359",
    pages = "4067--4077",
    abstract = "Conventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. To generate relevant responses, we propose a method that employs two types of constraints - topical constraint and semantic constraint. Under the hypothesis that a response and its context have higher relevance when they share the same topics, the topical constraint encourages the topics of a response to match its context by conditioning response decoding on topic words{'} embeddings. The semantic constraint, which encourages a response to be semantically related to its context by regularizing the decoding objective function with semantic distance, is proposed. Optimal transport is applied to compute a weighted semantic distance between the representation of a response and the context. Generated responses are evaluated by automatic metrics, as well as human judgment, showing that the proposed method can generate more topic-relevant and content-rich responses than conventional models.",
}
@inproceedings{lu-etal-2020-iterative,
    title = "An Iterative Emotion Interaction Network for Emotion Recognition in Conversations",
    author = "Lu, Xin  and
      Zhao, Yanyan  and
      Wu, Yang  and
      Tian, Yijian  and
      Chen, Huipeng  and
      Qin, Bing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.360",
    doi = "10.18653/v1/2020.coling-main.360",
    pages = "4078--4088",
    abstract = "Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction. We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time. To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art performance.",
}
@inproceedings{jiang-etal-2020-pednet,
    title = "{PEDN}et: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation",
    author = "Jiang, Bin  and
      Zhou, Wanyue  and
      Yang, Jingxu  and
      Yang, Chao  and
      Wang, Shihan  and
      Pang, Liang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.361",
    doi = "10.18653/v1/2020.coling-main.361",
    pages = "4089--4099",
    abstract = "Endowing a chatbot with a personality is essential to deliver more realistic conversations. Various persona-based dialogue models have been proposed to generate personalized and diverse responses by utilizing predefined persona information. However, generating personalized responses is still a challenging task since the leverage of predefined persona information is often insufficient. To alleviate this problem, we propose a novel Persona Enhanced Dual Alternating Learning Network (PEDNet) aiming at producing more personalized responses in various open-domain conversation scenarios. PEDNet consists of a Context-Dominate Network (CDNet) and a Persona-Dominate Network (PDNet), which are built upon a common encoder-decoder backbone. CDNet learns to select a proper persona as well as ensure the contextual relevance of the predicted response, while PDNet learns to enhance the utilization of persona information when generating the response by weakening the disturbance of specific content in the conversation context. CDNet and PDNet are trained alternately using a multi-task training approach to equip PEDNet with the both capabilities they have learned. Both automatic and human evaluations on a newly released dialogue dataset Persona-chat demonstrate that our method could deliver more personalized responses than baseline methods.",
}
@inproceedings{wang-etal-2020-dual,
    title = "Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems",
    author = "Wang, Jian  and
      Liu, Junhao  and
      Bi, Wei  and
      Liu, Xiaojiang  and
      He, Kejing  and
      Xu, Ruifeng  and
      Yang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.362",
    doi = "10.18653/v1/2020.coling-main.362",
    pages = "4100--4110",
    abstract = "Existing end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager. The dialog memory manager dynamically expands the dialog memory turn by turn and keeps track of dialog history with an updating mechanism, which encourages the model to filter irrelevant dialog history and memorize important newly coming information. The KB memory manager shares the structural KB triples throughout the whole conversation, and dynamically extracts KB information with a memory pointer at each turn. Experimental results on three benchmark datasets demonstrate that DDMN significantly outperforms the strong baselines in terms of both automatic evaluation and human evaluation. Our code is available at https://github.com/siat-nlp/DDMN.",
}
@inproceedings{hu-etal-2020-translation,
    title = "Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling",
    author = "Hu, Wenpeng  and
      Le, Ran  and
      Liu, Bing  and
      Ma, Jinwen  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.363",
    doi = "10.18653/v1/2020.coling-main.363",
    pages = "4111--4122",
    abstract = "Understanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the sequence-to-sequence (Seq2Seq) model in the contexts of two mainstream NLP tasks{--}machine translation and dialogue response generation{--}as they both use the seq2seq model. We investigate how the two tasks are different and how their task difference results in major differences in the behaviors of the resulting translation and dialogue generation systems. This study allows us to make several interesting observations and gain valuable insights, which can be used to help develop better translation and dialogue generation models. To our knowledge, no such comparative study has been done so far.",
}
@inproceedings{ueyama-kano-2020-diverse,
    title = "Diverse dialogue generation with context dependent dynamic loss function",
    author = "Ueyama, Ayaka  and
      Kano, Yoshinobu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.364",
    doi = "10.18653/v1/2020.coling-main.364",
    pages = "4123--4127",
    abstract = "Dialogue systems using deep learning have achieved generation of fluent response sentences to user utterances. Nevertheless, they tend to produce responses that are not diverse and which are less context-dependent. To address these shortcomings, we propose a new loss function, an Inverse N-gram loss (INF), which incorporates contextual fluency and diversity at the same time by a simple formula. Our INF loss can adjust its loss dynamically by a weight using the inverse frequency of the tokens{'} n-gram applied to Softmax Cross-Entropy loss, so that rare tokens appear more likely while retaining the fluency of the generated sentences. We trained Transformer using English and Japanese Twitter replies as single-turn dialogues using different loss functions. Our INF loss model outperformed the baselines of SCE loss and ITF loss models in automatic evaluations such as DIST-N and ROUGE, and also achieved higher scores on our human evaluations of coherence and richness.",
}
@inproceedings{zhou-etal-2020-towards,
    title = "Towards Topic-Guided Conversational Recommender System",
    author = "Zhou, Kun  and
      Zhou, Yuanhang  and
      Zhao, Wayne Xin  and
      Wang, Xiaoke  and
      Wen, Ji-Rong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.365",
    doi = "10.18653/v1/2020.coling-main.365",
    pages = "4128--4139",
    abstract = "Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality datasets is essential. Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. In this paper, we contribute a new CRS dataset named \textbf{TG-ReDial} (\textbf{Re}commendation through \textbf{T}opic-\textbf{G}uided \textbf{Dial}og). Our dataset has two major features. First, it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario. Second, it is created in a semi-automatic way, hence human annotation is more reasonable and controllable. Based on TG-ReDial, we present the task of topic-guided conversational recommendation, and propose an effective approach to this task. Extensive experiments have demonstrated the effectiveness of our approach on three sub-tasks, namely topic prediction, item recommendation and response generation. TG-ReDial is available at blue\url{https://github.com/RUCAIBox/TG-ReDial}.",
}
@inproceedings{chatterjee-sengupta-2020-intent,
    title = "Intent Mining from past conversations for Conversational Agent",
    author = "Chatterjee, Ajay  and
      Sengupta, Shubhashis",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.366",
    doi = "10.18653/v1/2020.coling-main.366",
    pages = "4140--4152",
    abstract = "Conversational systems are of primary interest in the AI community. Organizations are increasingly using chatbot to provide round-the-clock support and to increase customer engagement. Many commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize user input. These frameworks require a collection of user utterances and corresponding intent to train an intent model. Collecting a substantial coverage of training data is a bottleneck in the bot building process. In cases where past conversation data is available, the cost of labeling hundreds of utterances with intent labels is time-consuming and laborious. In this paper, we present an intent discovery framework that can mine a vast amount of conversational logs and to generate labeled data sets for training intent models. We have introduced an extension to the DBSCAN algorithm and presented a density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Empirical evaluation on one conversation dataset, six different intent dataset, and one short text clustering dataset show the effectiveness of our hypothesis.",
}
@inproceedings{sheng-etal-2020-summarize,
    title = "Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition",
    author = "Sheng, Dongming  and
      Wang, Dong  and
      Shen, Ying  and
      Zheng, Haitao  and
      Liu, Haozhuang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.367",
    doi = "10.18653/v1/2020.coling-main.367",
    pages = "4153--4163",
    abstract = "Conversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications. Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, with little attention paid on phrase-level semantic connection between utterances. Phrases carry sentiments when they are referred to emotional events under certain topics, providing a global semantic connection between utterances throughout the entire conversation. In this work, we propose a two-stage Summarization and Aggregation Graph Inference Network (SumAggGIN), which seamlessly integrates inference for topic-related emotional phrases and local dependency reasoning over neighbouring utterances in a global-to-local fashion. Topic-related emotional phrases, which constitutes the global topic-related emotional connections, are recognized by our proposed heterogeneous Summarization Graph. Local dependencies, which captures short-term emotional effects between neighbouring utterances, are further injected via an Aggregation Graph to distinguish the subtle differences between utterances containing emotional phrases. The two steps of graph inference are tightly-coupled for a comprehensively understanding of emotional fluctuation. Experimental results on three CER benchmark datasets verify the effectiveness of our proposed model, which outperforms the state-of-the-art approaches.",
}
@inproceedings{phy-etal-2020-deconstruct,
    title = "Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems",
    author = "Phy, Vitou  and
      Zhao, Yang  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.368",
    doi = "10.18653/v1/2020.coling-main.368",
    pages = "4164--4178",
    abstract = "Many automatic evaluation metrics have been proposed to score the overall quality of a response in open-domain dialogue. Generally, the overall quality is comprised of various aspects, such as relevancy, specificity, and empathy, and the importance of each aspect differs according to the task. For instance, specificity is mandatory in a food-ordering dialogue task, whereas fluency is preferred in a language-teaching dialogue system. However, existing metrics are not designed to cope with such flexibility. For example, BLEU score fundamentally relies only on word overlapping, whereas BERTScore relies on semantic similarity between reference and candidate response. Thus, they are not guaranteed to capture the required aspects, i.e., specificity. To design a metric that is flexible to a task, we first propose making these qualities manageable by grouping them into three groups: understandability, sensibleness, and likability, where likability is a combination of qualities that are essential for a task. We also propose a simple method to composite metrics of each aspect to obtain a single metric called USL-H, which stands for Understandability, Sensibleness, and Likability in Hierarchy. We demonstrated that USL-H score achieves good correlations with human judgment and maintains its configurability towards different aspects and metrics.",
}
@inproceedings{sarkar-etal-2020-suggest,
    title = "Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation",
    author = "Sarkar, Rajdeep  and
      Goswami, Koustava  and
      Arcan, Mihael  and
      McCrae, John P.",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.369",
    doi = "10.18653/v1/2020.coling-main.369",
    pages = "4179--4189",
    abstract = "Conversational recommender systems focus on the task of suggesting products to users based on the conversation flow. Recently, the use of external knowledge in the form of knowledge graphs has shown to improve the performance in recommendation and dialogue systems. Information from knowledge graphs aids in enriching those systems by providing additional information such as closely related products and textual descriptions of the items. However, knowledge graphs are incomplete since they do not contain all factual information present on the web. Furthermore, when working on a specific domain, knowledge graphs in its entirety contribute towards extraneous information and noise. In this work, we study several subgraph construction methods and compare their performance across the recommendation task. We incorporate pre-trained embeddings from the subgraphs along with positional embeddings in our models. Extensive experiments show that our method has a relative improvement of at least 5.62{\%} compared to the state-of-the-art on multiple metrics on the recommendation task.",
}
@inproceedings{li-etal-2020-hitrans,
    title = "{H}i{T}rans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations",
    author = "Li, Jingye  and
      Ji, Donghong  and
      Li, Fei  and
      Zhang, Meishan  and
      Liu, Yijiang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.370",
    doi = "10.18653/v1/2020.coling-main.370",
    pages = "4190--4200",
    abstract = "Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers. Different from the traditional non-conversational emotion detection, the model for EDC should be context-sensitive (e.g., understanding the whole conversation rather than one utterance) and speaker-sensitive (e.g., understanding which utterance belongs to which speaker). In this paper, we propose a transformer-based context- and speaker-sensitive model for EDC, namely HiTrans, which consists of two hierarchical transformers. We utilize BERT as the low-level transformer to generate local utterance representations, and feed them into another high-level transformer so that utterance representations could be sensitive to the global context of the conversation. Moreover, we exploit an auxiliary task to make our model speaker-sensitive, called pairwise utterance speaker verification (PUSV), which aims to classify whether two utterances belong to the same speaker. We evaluate our model on three benchmark datasets, namely EmoryNLP, MELD and IEMOCAP. Results show that our model outperforms previous state-of-the-art models.",
}
@inproceedings{lin-etal-2020-co,
    title = "A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection",
    author = "Lin, Qian  and
      Kundu, Souvik  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.371",
    doi = "10.18653/v1/2020.coling-main.371",
    pages = "4201--4210",
    abstract = "Ensuring smooth communication is essential in a chat-oriented dialogue system, so that a user can obtain meaningful responses through interactions with the system. Most prior work on dialogue research does not focus on preventing dialogue breakdown. One of the major challenges is that a dialogue system may generate an undesired utterance leading to a dialogue breakdown, which degrades the overall interaction quality. Hence, it is crucial for a machine to detect dialogue breakdowns in an ongoing conversation. In this paper, we propose a novel dialogue breakdown detection model that jointly incorporates a pretrained cross-lingual language model and a co-attention network. Our proposed model leverages effective word embeddings trained on one hundred different languages to generate contextualized representations. Co-attention aims to capture the interaction between the latest utterance and the conversation history, and thereby determines whether the latest utterance causes a dialogue breakdown. Experimental results show that our proposed model outperforms all previous approaches on all evaluation metrics in both the Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 (DBDC4 at IWSDS2019).",
}
@inproceedings{wang-etal-2020-integrating,
    title = "Integrating User History into Heterogeneous Graph for Dialogue Act Recognition",
    author = "Wang, Dong  and
      Li, Ziran  and
      Zheng, Haitao  and
      Shen, Ying",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.372",
    doi = "10.18653/v1/2020.coling-main.372",
    pages = "4211--4221",
    abstract = "Dialogue Act Recognition (DAR) is a challenging problem in Natural Language Understanding, which aims to attach Dialogue Act (DA) labels to each utterance in a conversation. However, previous studies cannot fully recognize the specific expressions given by users due to the informality and diversity of natural language expressions. To solve this problem, we propose a Heterogeneous User History (HUH) graph convolution network, which utilizes the user{'}s historical answers grouped by DA labels as additional clues to recognize the DA label of utterances. To handle the noise caused by introducing the user{'}s historical answers, we design sets of denoising mechanisms, including a History Selection process, a Similarity Re-weighting process, and an Edge Re-weighting process. We evaluate the proposed method on two benchmark datasets MSDialog and MRDA. The experimental results verify the effectiveness of integrating user{'}s historical answers, and show that our proposed model outperforms the state-of-the-art methods.",
}
@inproceedings{donatelli-etal-2020-two,
    title = "A Two-Level Interpretation of Modality in Human-Robot Dialogue",
    author = "Donatelli, Lucia  and
      Lai, Kenneth  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.373",
    doi = "10.18653/v1/2020.coling-main.373",
    pages = "4222--4238",
    abstract = "We analyze the use and interpretation of modal expressions in a corpus of situated human-robot dialogue and ask how to effectively represent these expressions for automatic learning. We present a two-level annotation scheme for modality that captures both content and intent, integrating a logic-based, semantic representation and a task-oriented, pragmatic representation that maps to our robot{'}s capabilities. Data from our annotation task reveals that the interpretation of modal expressions in human-robot dialogue is quite diverse, yet highly constrained by the physical environment and asymmetrical speaker/addressee relationship. We sketch a formal model of human-robot common ground in which modality can be grounded and dynamically interpreted.",
}
@inproceedings{sun-etal-2020-robust,
    title = "Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training",
    author = "Sun, Haipeng  and
      Wang, Rui  and
      Chen, Kehai  and
      Lu, Xugang  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.374",
    doi = "10.18653/v1/2020.coling-main.374",
    pages = "4239--4250",
    abstract = "Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community. The main advantage of the UNMT lies in its easy collection of required large training text sentences while with only a slightly worse performance than supervised neural machine translation which requires expensive annotated translation pairs on some translation tasks. In most studies, the UMNT is trained with clean data without considering its robustness to the noisy data. However, in real-world scenarios, there usually exists noise in the collected input sentences which degrades the performance of the translation system since the UNMT is sensitive to the small perturbations of the input sentences. In this paper, we first time explicitly take the noisy data into consideration to improve the robustness of the UNMT based systems. First of all, we clearly defined two types of noises in training sentences, i.e., word noise and word order noise, and empirically investigate its effect in the UNMT, then we propose adversarial training methods with denoising process in the UNMT. Experimental results on several language pairs show that our proposed methods substantially improved the robustness of the conventional UNMT systems in noisy scenarios.",
}
@inproceedings{tang-etal-2020-understanding,
    title = "Understanding Pure Character-Based Neural Machine Translation: The Case of Translating {F}innish into {E}nglish",
    author = "Tang, Gongbo  and
      Sennrich, Rico  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.375",
    doi = "10.18653/v1/2020.coling-main.375",
    pages = "4251--4262",
    abstract = "Recent work has shown that deeper character-based neural machine translation (NMT) models can outperform subword-based models. However, it is still unclear what makes deeper character-based models successful. In this paper, we conduct an investigation into pure character-based models in the case of translating Finnish into English, including exploring the ability to learn word senses and morphological inflections and the attention mechanism. We demonstrate that word-level information is distributed over the entire character sequence rather than over a single character, and characters at different positions play different roles in learning linguistic knowledge. In addition, character-based models need more layers to encode word senses which explains why only deeper models outperform subword-based models. The attention distribution pattern shows that separators attract a lot of attention and we explore a sparse word-level attention to enforce character hidden states to capture the full word-level information. Experimental results show that the word-level attention with a single head results in 1.2 BLEU points drop.",
}
@inproceedings{chakrabarty-etal-2020-improving,
    title = "Improving Low-Resource {NMT} through Relevance Based Linguistic Features Incorporation",
    author = "Chakrabarty, Abhisek  and
      Dabre, Raj  and
      Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.376",
    doi = "10.18653/v1/2020.coling-main.376",
    pages = "4263--4274",
    abstract = "In this study, linguistic knowledge at different levels are incorporated into the neural machine translation (NMT) framework to improve translation quality for language pairs with extremely limited data. Integrating manually designed or automatically extracted features into the NMT framework is known to be beneficial. However, this study emphasizes that the relevance of the features is crucial to the performance. Specifically, we propose two methods, 1) self relevance and 2) word-based relevance, to improve the representation of features for NMT. Experiments are conducted on translation tasks from English to eight Asian languages, with no more than twenty thousand sentences for training. The proposed methods improve translation quality for all tasks by up to 3.09 BLEU points. Discussions with visualization provide the explainability of the proposed methods where we show that the relevance methods provide weights to features thereby enhancing their impact on low-resource machine translation.",
}
@inproceedings{wang-etal-2020-layer,
    title = "Layer-Wise Multi-View Learning for Neural Machine Translation",
    author = "Wang, Qiang  and
      Li, Changliang  and
      Zhang, Yue  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.377",
    doi = "10.18653/v1/2020.coling-main.377",
    pages = "4275--4286",
    abstract = "Traditional neural machine translation is limited to the topmost encoder layer{'}s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network architecture, making the calculation more complicated or introducing additional structural restrictions. In this work, we propose layer-wise multi-view learning to solve this problem, circumventing the necessity to change the model structure. We regard each encoder layer{'}s off-the-shelf output, a by-product in layer-by-layer encoding, as the redundant view for the input sentence. In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view. We feed the two views to a partially shared decoder to maintain independent prediction. Consistency regularization based on KL divergence is used to encourage the two views to learn from each other. Extensive experimental results on five translation tasks show that our approach yields stable improvements over multiple strong baselines. As another bonus, our method is agnostic to network architectures and can maintain the same inference speed as the original model.",
}
@inproceedings{deguchi-etal-2020-bilingual,
    title = "Bilingual Subword Segmentation for Neural Machine Translation",
    author = "Deguchi, Hiroyuki  and
      Utiyama, Masao  and
      Tamura, Akihiro  and
      Ninomiya, Takashi  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.378",
    doi = "10.18653/v1/2020.coling-main.378",
    pages = "4287--4297",
    abstract = "This paper proposed a new subword segmentation method for neural machine translation, {``}Bilingual Subword Segmentation,{''} which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation. While existing subword segmentation methods tokenize a sentence without considering its translation, the proposed method tokenizes a sentence by using subword units induced from bilingual sentences; this method could be more favorable to machine translation. Evaluations on WAT Asian Scientific Paper Excerpt Corpus (ASPEC) English-to-Japanese and Japanese-to-English translation tasks and WMT14 English-to-German and German-to-English translation tasks show that our bilingual subword segmentation improves the performance of Transformer neural machine translation (up to +0.81 BLEU).",
}
@inproceedings{zhang-etal-2020-token,
    title = "Token Drop mechanism for Neural Machine Translation",
    author = "Zhang, Huaao  and
      Qiu, Shigui  and
      Duan, Xiangyu  and
      Zhang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.379",
    doi = "10.18653/v1/2020.coling-main.379",
    pages = "4298--4303",
    abstract = "Neural machine translation with millions of parameters is vulnerable to unfamiliar inputs. We propose Token Drop to improve generalization and avoid overfitting for the NMT model. Similar to word dropout, whereas we replace dropped token with a special token instead of setting zero to words. We further introduce two self-supervised objectives: Replaced Token Detection and Dropped Token Prediction. Our method aims to force model generating target translation with less information, in this way the model can learn textual representation better. Experiments on Chinese-English and English-Romanian benchmark demonstrate the effectiveness of our approach and our model achieves significant improvements over a strong Transformer baseline.",
}
@inproceedings{nishihara-etal-2020-supervised,
    title = "Supervised Visual Attention for Multimodal Neural Machine Translation",
    author = "Nishihara, Tetsuro  and
      Tamura, Akihiro  and
      Ninomiya, Takashi  and
      Omote, Yutaro  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.380",
    doi = "10.18653/v1/2020.coling-main.380",
    pages = "4304--4314",
    abstract = "This paper proposed a supervised visual attention mechanism for multimodal neural machine translation (MNMT), trained with constraints based on manual alignments between words in a sentence and their corresponding regions of an image. The proposed visual attention mechanism captures the relationship between a word and an image region more precisely than a conventional visual attention mechanism trained through MNMT in an unsupervised manner. Our experiments on English-German and German-English translation tasks using the Multi30k dataset and on English-Japanese and Japanese-English translation tasks using the Flickr30k Entities JP dataset show that a Transformer-based MNMT model can be improved by incorporating our proposed supervised visual attention mechanism and that further improvements can be achieved by combining it with a supervised cross-lingual attention mechanism (up to +1.61 BLEU, +1.7 METEOR).",
}
@inproceedings{gu-feng-2020-investigating,
    title = "Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation",
    author = "Gu, Shuhao  and
      Feng, Yang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.381",
    doi = "10.18653/v1/2020.coling-main.381",
    pages = "4315--4326",
    abstract = "Neural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge and swing to fit the newly added data which may have a different distribution, e.g. a different domain. Although many methods have been proposed to solve this problem, we cannot get to know what causes this phenomenon yet. Under the background of domain adaptation, we investigate the cause of catastrophic forgetting from the perspectives of modules and parameters (neurons). The investigation on the modules of the NMT model shows that some modules have tight relation with the general-domain knowledge while some other modules are more essential in the domain adaptation. And the investigation on the parameters shows that some parameters are important for both the general-domain and in-domain translation and the great change of them during continual training brings about the performance decline in general-domain. We conducted experiments across different language pairs and domains to ensure the validity and reliability of our findings.",
}
@inproceedings{karakanta-etal-2020-two,
    title = "The Two Shades of Dubbing in Neural Machine Translation",
    author = "Karakanta, Alina  and
      Bhattacharya, Supratik  and
      Nayak, Shravan  and
      Baumann, Timo  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.382",
    doi = "10.18653/v1/2020.coling-main.382",
    pages = "4327--4333",
    abstract = "Dubbing has two shades; synchronisation constraints are applied only when the actor{'}s mouth is visible on screen, while the translation is unconstrained for off-screen dubbing. Consequently, different synchronisation requirements, and therefore translation strategies, are applied depending on the type of dubbing. In this work, we manually annotate an existing dubbing corpus (Heroes) for this dichotomy. We show that, even though we did not observe distinctive features between on- and off-screen dubbing at the textual level, on-screen dubbing is more difficult for MT (-4 BLEU points). Moreover, synchronisation constraints dramatically decrease translation quality for off-screen dubbing. We conclude that, distinguishing between on-screen and off-screen dubbing is necessary for determining successful strategies for dubbing-customised Machine Translation.",
}
@inproceedings{khatri-bhattacharyya-2020-filtering,
    title = "Filtering Back-Translated Data in Unsupervised Neural Machine Translation",
    author = "Khatri, Jyotsana  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.383",
    doi = "10.18653/v1/2020.coling-main.383",
    pages = "4334--4339",
    abstract = "Unsupervised neural machine translation (NMT) utilizes only monolingual data for training. The quality of back-translated data plays an important role in the performance of NMT systems. In back-translation, all generated pseudo parallel sentence pairs are not of the same quality. Taking inspiration from domain adaptation where in-domain sentences are given more weight in training, in this paper we propose an approach to filter back-translated data as part of the training process of unsupervised NMT. Our approach gives more weight to good pseudo parallel sentence pairs in the back-translation phase. We calculate the weight of each pseudo parallel sentence pair using sentence-wise round-trip BLEU score which is normalized batch-wise. We compare our approach with the current state of the art approaches for unsupervised NMT.",
}
@inproceedings{troiano-etal-2020-lost,
    title = "Lost in Back-Translation: Emotion Preservation in Neural Machine Translation",
    author = "Troiano, Enrica  and
      Klinger, Roman  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.384",
    doi = "10.18653/v1/2020.coling-main.384",
    pages = "4340--4354",
    abstract = "Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world. An important part of communication, however, takes place at the non-propositional level (e.g., politeness, formality, emotions), and it is far from clear whether current MT methods properly translate this information. This paper investigates the specific hypothesis that the non-propositional level of emotions is at least partially lost in MT. We carry out a number of experiments in a back-translation setup and establish that (1) emotions are indeed partially lost during translation; (2) this tendency can be reversed almost completely with a simple re-ranking approach informed by an emotion classifier, taking advantage of diversity in the n-best list; (3) the re-ranking approach can also be applied to change emotions, obtaining a model for emotion style transfer. An in-depth qualitative analysis reveals that there are recurring linguistic changes through which emotions are toned down or amplified, such as change of modality.",
}
@inproceedings{rubino-sumita-2020-intermediate,
    title = "Intermediate Self-supervised Learning for Machine Translation Quality Estimation",
    author = "Rubino, Raphael  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.385",
    doi = "10.18653/v1/2020.coling-main.385",
    pages = "4355--4360",
    abstract = "Pre-training sentence encoders is effective in many natural language processing tasks including machine translation (MT) quality estimation (QE), due partly to the scarcity of annotated QE data required for supervised learning. In this paper, we investigate the use of an intermediate self-supervised learning task for sentence encoder aiming at improving QE performances at the sentence and word levels. Our approach is motivated by a problem inherent to QE: mistakes in translation caused by wrongly inserted and deleted tokens. We modify the translation language model (TLM) training objective of the cross-lingual language model (XLM) to orientate the pre-trained model towards the target task. The proposed method does not rely on annotated data and is complementary to QE methods involving pre-trained sentence encoders and domain adaptation. Experiments on English-to-German and English-to-Russian translation directions show that intermediate learning improves over domain adaptated models. Additionally, our method reaches results in par with state-of-the-art QE models without requiring the combination of several approaches and outperforms similar methods based on pre-trained sentence encoders.",
}
@inproceedings{gao-etal-2020-unifying,
    title = "Unifying Input and Output Smoothing in Neural Machine Translation",
    author = "Gao, Yingbo  and
      Liao, Baohao  and
      Ney, Hermann",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.386",
    doi = "10.18653/v1/2020.coling-main.386",
    pages = "4361--4372",
    abstract = "Soft contextualized data augmentation is a recent method that replaces one-hot representation of words with soft posterior distributions of an external language model, smoothing the input of neural machine translation systems. Label smoothing is another effective method that penalizes over-confident model outputs by discounting some probability mass from the true target word, smoothing the output of neural machine translation systems. Having the benefit of updating all word vectors in each optimization step and better regularizing the models, the two smoothing methods are shown to bring significant improvements in translation performance. In this work, we study how to best combine the methods and stack the improvements. Specifically, we vary the prior distributions to smooth with, the hyperparameters that control the smoothing strength, and the token selection procedures. We conduct extensive experiments on small datasets, evaluate the recipes on larger datasets, and examine the implications when back-translation is further used. Our results confirm cumulative improvements when input and output smoothing are used in combination, giving up to +1.9 BLEU scores on standard machine translation tasks and reveal reasons why these smoothing methods should be preferred.",
}
@inproceedings{lewis-etal-2020-neural,
    title = "Neural Transduction for Multilingual Lexical Translation",
    author = "Lewis, Dylan  and
      Wu, Winston  and
      McCarthy, Arya D.  and
      Yarowsky, David",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.387",
    doi = "10.18653/v1/2020.coling-main.387",
    pages = "4373--4384",
    abstract = "We present a method for completing multilingual translation dictionaries. Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf. cross-lingual embeddings). In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation. The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages. We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish. When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27{\%} relative improvement in whole-word accuracy of predictions over a single-source baseline. To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.",
}
@inproceedings{tong-etal-2020-document,
    title = "A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information",
    author = "Tong, Yiqi  and
      Zheng, Jiangbin  and
      Zhu, Hongkang  and
      Chen, Yidong  and
      Shi, Xiaodong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.388",
    doi = "10.18653/v1/2020.coling-main.388",
    pages = "4385--4395",
    abstract = "Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.",
}
@inproceedings{ding-etal-2020-context,
    title = "Context-Aware Cross-Attention for Non-Autoregressive Translation",
    author = "Ding, Liang  and
      Wang, Longyue  and
      Wu, Di  and
      Tao, Dacheng  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.389",
    doi = "10.18653/v1/2020.coling-main.389",
    pages = "4396--4402",
    abstract = "Non-autoregressive translation (NAT) significantly accelerates the inference process by predicting the entire target sequence. However, due to the lack of target dependency modelling in the decoder, the conditional generation process heavily depends on the cross-attention. In this paper, we reveal a localness perception problem in NAT cross-attention, for which it is difficult to adequately capture source context. To alleviate this problem, we propose to enhance signals of neighbour source tokens into conventional cross-attention. Experimental results on several representative datasets show that our approach can consistently improve translation quality over strong NAT baselines. Extensive analyses demonstrate that the enhanced cross-attention achieves better exploitation of source contexts by leveraging both local and global information.",
}
@inproceedings{liu-etal-2020-gender,
    title = "Does Gender Matter? Towards Fairness in Dialogue Systems",
    author = "Liu, Haochen  and
      Dacon, Jamell  and
      Fan, Wenqi  and
      Liu, Hui  and
      Liu, Zitao  and
      Tang, Jiliang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.390",
    doi = "10.18653/v1/2020.coling-main.390",
    pages = "4403--4416",
    abstract = "Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recognition algorithms in computer vision are unfair to black people such as poorly detecting their faces and inappropriately identifying them as {``}gorillas{''}. As one crucial application of AI, dialogue systems have been extensively applied in our society. They are usually built with real human conversational data; thus they could inherit some fairness issues which are held in the real world. However, the fairness of dialogue systems has not been well investigated. In this paper, we perform a pioneering study about the fairness issues in dialogue systems. In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models. Our studies demonstrate that popular dialogue models show significant prejudice towards different genders and races. Besides, to mitigate the bias in dialogue systems, we propose two simple but effective debiasing methods. Experiments show that our methods can reduce the bias in dialogue systems significantly. The dataset and the implementation are released to foster fairness research in dialogue systems.",
}
@inproceedings{khalid-etal-2020-combining,
    title = "Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue",
    author = "Khalid, Baber  and
      Alikhani, Malihe  and
      Stone, Matthew",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.391",
    doi = "10.18653/v1/2020.coling-main.391",
    pages = "4417--4428",
    abstract = "In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the user had in mind. In this paper, we show how cognitive models of users{'} communicative strategies can be leveraged in a reinforcement learning approach to dialogue planning to enable interactive systems to give targeted, effective feedback about the system{'}s understanding. We describe a prototype system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors, and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to context-sensitive clarification strategies that focus on key missing information, elicit correct answers that the system understands, and contribute to increasing dialogue success.",
}
@inproceedings{zhang-etal-2020-knowledge,
    title = "Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer",
    author = "Zhang, Duzhen  and
      Chen, Xiuyi  and
      Xu, Shuang  and
      Xu, Bo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.392",
    doi = "10.18653/v1/2020.coling-main.392",
    pages = "4429--4440",
    abstract = "Emotion recognition in textual conversations (ERTC) plays an important role in a wide range of applications, such as opinion mining, recommender systems, and so on. ERTC, however, is a challenging task. For one thing, speakers often rely on the context and commonsense knowledge to express emotions; for another, most utterances contain neutral emotion in conversations, as a result, the confusion between a few non-neutral utterances and much more neutral ones restrains the emotion recognition performance. In this paper, we propose a novel Knowledge Aware Incremental Transformer with Multi-task Learning (KAITML) to address these challenges. Firstly, we devise a dual-level graph attention mechanism to leverage commonsense knowledge, which augments the semantic information of the utterance. Then we apply the Incremental Transformer to encode multi-turn contextual utterances. Moreover, we are the first to introduce multi-task learning to alleviate the aforementioned confusion and thus further improve the emotion recognition performance. Extensive experimental results show that our KAITML model outperforms the state-of-the-art models across five benchmark datasets.",
}
@inproceedings{firdaus-etal-2020-meisd,
    title = "{MEISD}: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations",
    author = "Firdaus, Mauajama  and
      Chauhan, Hardik  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.393",
    doi = "10.18653/v1/2020.coling-main.393",
    pages = "4441--4453",
    abstract = "Emotion and sentiment classification in dialogues is a challenging task that has gained popularity in recent times. Humans tend to have multiple emotions with varying intensities while expressing their thoughts and feelings. Emotions in an utterance of dialogue can either be independent or dependent on the previous utterances, thus making the task complex and interesting. Multi-label emotion detection in conversations is a significant task that provides the ability to the system to understand the various emotions of the users interacting. Sentiment analysis in dialogue/conversation, on the other hand, helps in understanding the perspective of the user with respect to the ongoing conversation. Along with text, additional information in the form of audio and video assist in identifying the correct emotions with the appropriate intensity and sentiments in an utterance of a dialogue. Lately, quite a few datasets have been made available for dialogue emotion and sentiment classification, but these datasets are imbalanced in representing different emotions and consist of an only single emotion. Hence, we present at first a large-scale balanced Multimodal Multi-label Emotion, Intensity, and Sentiment Dialogue dataset (MEISD), collected from different TV series that has textual, audio and visual features, and then establish a baseline setup for further research.",
}
@inproceedings{li-etal-2020-empdg,
    title = "{E}mp{DG}: Multi-resolution Interactive Empathetic Dialogue Generation",
    author = "Li, Qintong  and
      Chen, Hongshen  and
      Ren, Zhaochun  and
      Ren, Pengjie  and
      Tu, Zhaopeng  and
      Chen, Zhumin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.394",
    doi = "10.18653/v1/2020.coling-main.394",
    pages = "4454--4466",
    abstract = "A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users{'} expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model {--} EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.",
}
@inproceedings{jauregi-unanue-etal-2020-leveraging,
    title = "Leveraging Discourse Rewards for Document-Level Neural Machine Translation",
    author = "Jauregi Unanue, Inigo  and
      Esmaili, Nazanin  and
      Haffari, Gholamreza  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.395",
    doi = "10.18653/v1/2020.coling-main.395",
    pages = "4467--4482",
    abstract = "Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspects of the discourse at document level. However, document-level translation models are usually not trained to explicitly ensure discourse quality. Therefore, in this paper we propose a training approach that explicitly optimizes two established discourse metrics, lexical cohesion and coherence, by using a reinforcement learning objective. Experiments over four different language pairs and three translation domains have shown that our training approach has been able to achieve more cohesive and coherent document translations than other competitive approaches, yet without compromising the faithfulness to the reference translation. In the case of the Zh-En language pair, our method has achieved an improvement of 2.46 percentage points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time improving 0.63 pp in BLEU score and 0.47 pp in F-BERT.",
}
@inproceedings{mino-etal-2020-effective,
    title = "Effective Use of Target-side Context for Neural Machine Translation",
    author = "Mino, Hideya  and
      Ito, Hitoshi  and
      Goto, Isao  and
      Yamada, Ichiro  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.396",
    doi = "10.18653/v1/2020.coling-main.396",
    pages = "4483--4494",
    abstract = "In this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer degraded performance when trained with noisy data. Because there is no clean Japanese-English parallel data for news articles, we build a novel parallel news corpus consisting of Japanese news articles translated into English in a content-equivalent manner. This is the first content-equivalent Japanese-English news corpus translated specifically for training NMT systems. The second problem involves the domain-adaptation technique. NMT systems suffer degraded performance when trained with mixed data having different features, such as noisy data and clean data. Though the existing methods try to overcome this problem by using tags for distinguishing the differences between corpora, it is not sufficient. We thus extend a domain-adaptation method using multi-tags to train an NMT model effectively with the clean corpus and existing parallel news corpora with some types of noise. Experimental results show that our corpus increases the translation quality, and that our domain-adaptation method is more effective for learning with the multiple types of corpora than existing domain-adaptation methods are.",
}
@inproceedings{zhao-etal-2020-knowledge,
    title = "Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity",
    author = "Zhao, Yang  and
      Xiang, Lu  and
      Zhu, Junnan  and
      Zhang, Jiajun  and
      Zhou, Yu  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.397",
    doi = "10.18653/v1/2020.coling-main.397",
    pages = "4495--4505",
    abstract = "Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized. ii) Granularity mismatch: the current KG methods utilize the entity as the basic granularity, while NMT utilizes the sub-word as the granularity, making the KG different to be utilized in NMT. To alleviate above problems, we propose a multi-task learning method on sub-entity granularity. Specifically, we first split the entities in KG and sentence pairs into sub-entity granularity by using joint BPE. Then we utilize the multi-task learning to combine the machine translation task and knowledge reasoning task. The extensive experiments on various translation tasks have demonstrated that our method significantly outperforms the baseline models in both translation quality and handling the entities.",
}
@inproceedings{eikema-aziz-2020-map,
    title = "Is {MAP} Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation",
    author = "Eikema, Bryan  and
      Aziz, Wilker",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.398",
    doi = "10.18653/v1/2020.coling-main.398",
    pages = "4506--4520",
    abstract = "Recent studies have revealed a number of pathologies of neural machine translation (NMT) systems. Hypotheses explaining these mostly suggest there is something fundamentally wrong with NMT as a model or its training algorithm, maximum likelihood estimation (MLE). Most of this evidence was gathered using maximum a posteriori (MAP) decoding, a decision rule aimed at identifying the highest-scoring translation, i.e. the mode. We argue that the evidence corroborates the inadequacy of MAP decoding more than casts doubt on the model and its training algorithm. In this work, we show that translation distributions do reproduce various statistics of the data well, but that beam search strays from such statistics. We show that some of the known pathologies and biases of NMT are due to MAP decoding and not to NMT{'}s statistical assumptions nor MLE. In particular, we show that the most likely translations under the model accumulate so little probability mass that the mode can be considered essentially arbitrary. We therefore advocate for the use of decision rules that take into account the translation distribution holistically. We show that an approximation to minimum Bayes risk decoding gives competitive results confirming that NMT models do capture important aspects of translation well in expectation.",
}
@inproceedings{yao-etal-2020-domain,
    title = "Domain Transfer based Data Augmentation for Neural Query Translation",
    author = "Yao, Liang  and
      Yang, Baosong  and
      Zhang, Haibo  and
      Chen, Boxing  and
      Luo, Weihua",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.399",
    doi = "10.18653/v1/2020.coling-main.399",
    pages = "4521--4533",
    abstract = "Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR). Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic data which are derived from large-scale monolingual queries. Nevertheless, such kind of pseudo corpus is mostly produced by a general-domain translation model, making it be insufficient to guide the learning of QT model. In this paper, we extend the data augmentation with a domain transfer procedure, thus to revise synthetic candidates to search-aware examples. Specifically, the domain transfer model is built upon advanced Transformer, in which layer coordination and mixed attention are exploited to speed up the refining process and leverage parameters from a pre-trained cross-lingual language model. In order to examine the effectiveness of the proposed method, we collected French-to-English and Spanish-to-English QT test sets, each of which consists of 10,000 parallel query pairs with careful manual-checking. Qualitative and quantitative analyses reveal that our model significantly outperforms strong baselines and the related domain transfer methods on both translation quality and retrieval accuracy.",
}
@inproceedings{coll-ardanuy-etal-2020-living,
    title = "Living Machines: A study of atypical animacy",
    author = "Coll Ardanuy, Mariona  and
      Nanni, Federico  and
      Beelen, Kaspar  and
      Hosseini, Kasra  and
      Ahnert, Ruth  and
      Lawrence, Jon  and
      McDonough, Katherine  and
      Tolfo, Giorgia  and
      Wilson, Daniel CS  and
      McGillivray, Barbara",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.400",
    doi = "10.18653/v1/2020.coling-main.400",
    pages = "4534--4545",
    abstract = "This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.",
}
@inproceedings{kober-etal-2020-aspectuality,
    title = "Aspectuality Across Genre: A Distributional Semantics Approach",
    author = "Kober, Thomas  and
      Alikhani, Malihe  and
      Stone, Matthew  and
      Steedman, Mark",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.401",
    doi = "10.18653/v1/2020.coling-main.401",
    pages = "4546--4562",
    abstract = "The interpretation of the lexical aspect of verbs in English plays a crucial role in tasks such as recognizing textual entailment and learning discourse-level inferences. We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb{'}s local context is most indicative of its aspectual class, and we demonstrate that closed class words tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Further, we present a new dataset of human-human conversations annotated with lexical aspects and present experiments that show the correlation of telicity with genre and discourse goals.",
}
@inproceedings{lauscher-etal-2020-rhetoric,
    title = "Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing",
    author = "Lauscher, Anne  and
      Ng, Lily  and
      Napoles, Courtney  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.402",
    doi = "10.18653/v1/2020.coling-main.402",
    pages = "4563--4574",
    abstract = "Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory. However, a large-scale theory-based corpus and corresponding computational models are missing. We fill this gap by conducting an extensive analysis covering three diverse domains of online argumentative writing and presenting GAQCorpus: the first large-scale English multi-domain (community Q{\&}A forums, debate forums, review forums) corpus annotated with theory-based AQ scores. We then propose the first computational approaches to theory-based assessment, which can serve as strong baselines for future work. We demonstrate the feasibility of large-scale AQ annotation, show that exploiting relations between dimensions yields performance improvements, and explore the synergies between theory-based prediction and practical AQ assessment.",
}
@inproceedings{same-van-deemter-2020-linguistic,
    title = "A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context",
    author = "Same, Fahime  and
      van Deemter, Kees",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.403",
    doi = "10.18653/v1/2020.coling-main.403",
    pages = "4575--4586",
    abstract = "This paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context. Based on this evaluation, we selected seven feature sets from the literature, amounting to 65 distinct linguistic features. The features were then grouped into 9 broad classes. After building Random Forest models, we used Feature Importance Ranking and Sequential Forward Search methods to assess the {``}importance{''} of the features. Combining the results of the two methods, we propose a consensus feature set. The 6 features in our consensus set come from 4 different classes, namely grammatical role, inherent features of the referent, antecedent form and recency.",
}
@inproceedings{jaffe-etal-2020-coreference,
    title = "Coreference information guides human expectations during natural reading",
    author = "Jaffe, Evan  and
      Shain, Cory  and
      Schuler, William",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.404",
    doi = "10.18653/v1/2020.coling-main.404",
    pages = "4587--4599",
    abstract = "Models of human sentence processing effort tend to focus on costs associated with retrieving structures and discourse referents from memory (memory-based) and/or on costs associated with anticipating upcoming words and structures based on contextual cues (expectation-based) (Levy,2008). Although evidence suggests that expectation and memory may play separable roles in language comprehension (Levy et al., 2013), theories of coreference processing have largely focused on memory: how comprehenders identify likely referents of linguistic expressions. In this study, we hypothesize that coreference tracking also informs human expectations about upcoming words, and we test this hypothesis by evaluating the degree to which incremental surprisal measures generated by a novel coreference-aware semantic parser explain human response times in a naturalistic self-paced reading experiment. Results indicate (1) that coreference information indeed guides human expectations and (2) that coreference effects on memory retrieval may exist independently of coreference effects on expectations. Together, these findings suggest that the language processing system exploits coreference information both to retrieve referents from memory and to anticipate upcoming material.",
}
@inproceedings{lane-bird-2020-interactive,
    title = "Interactive Word Completion for Morphologically Complex Languages",
    author = "Lane, William  and
      Bird, Steven",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.405",
    doi = "10.18653/v1/2020.coling-main.405",
    pages = "4600--4611",
    abstract = "Text input technologies for low-resource languages support literacy, content authoring, and language learning. However, tasks such as word completion pose a challenge for morphologically complex languages thanks to the combinatorial explosion of possible words. We have developed a method for morphologically-aware text input in Kunwinjku, a polysynthetic language of northern Australia. We modify an existing finite state recognizer to map input morph prefixes to morph completions, respecting the morphosyntax and morphophonology of the language. We demonstrate the portability of the method by applying it to Turkish. We show that the space of proximal morph completions is many orders of magnitude smaller than the space of full word completions for Kunwinjku. We provide a visualization of the morph completion space to enable the text completion parameters to be fine-tuned. Finally, we report on a web services deployment, along with a web interface which helps users enter morphologically complex words and which retrieves corresponding entries from the lexicon.",
}
@inproceedings{doostmohammadi-etal-2020-joint,
    title = "Joint {P}ersian Word Segmentation Correction and Zero-Width Non-Joiner Recognition Using {BERT}",
    author = "Doostmohammadi, Ehsan  and
      Nassajian, Minoo  and
      Rahimi, Adel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.406",
    doi = "10.18653/v1/2020.coling-main.406",
    pages = "4612--4618",
    abstract = "Words are properly segmented in the Persian writing system; in practice, however, these writing rules are often neglected, resulting in single words being written disjointedly and multiple words written without any white spaces between them. This paper addresses the problems of word segmentation and zero-width non-joiner (ZWNJ) recognition in Persian, which we approach jointly as a sequence labeling problem. We achieved a macro-averaged F1-score of 92.40{\%} on a carefully collected corpus of 500 sentences with a high level of difficulty.",
}
@inproceedings{chormai-etal-2020-syllable,
    title = "Syllable-based Neural {T}hai Word Segmentation",
    author = "Chormai, Pattarawat  and
      Prasertsom, Ponrawee  and
      Cheevaprawatdomrong, Jin  and
      Rutherford, Attapol",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.407",
    doi = "10.18653/v1/2020.coling-main.407",
    pages = "4619--4637",
    abstract = "Word segmentation is a challenging pre-processing step for Thai Natural Language Processing due to the lack of explicit word boundaries.The previous systems rely on powerful neural network architecture alone and ignore linguistic substructures of Thai words. We utilize the linguistic observation that Thai strings can be segmented into syllables, which should narrow down the search space for the word boundaries and provide helpful features. Here, we propose a neural Thai Word Segmenter that uses syllable embeddings to capture linguistic constraints and uses dilated CNN filters to capture the environment of each character. Within this goal, we develop the first ML-based Thai orthographical syllable segmenter, which yields syllable embeddings to be used as features by the word segmenter. Our word segmentation system outperforms the previous state-of-the-art system in both speed and accuracy on both in-domain and out-domain datasets.",
}
@inproceedings{liu-etal-2020-incorporating,
    title = "Incorporating Inner-word and Out-word Features for {M}ongolian Morphological Segmentation",
    author = "Liu, Na  and
      Su, Xiangdong  and
      Zhang, Haoran  and
      Gao, Guanglai  and
      Bao, Feilong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.408",
    doi = "10.18653/v1/2020.coling-main.408",
    pages = "4638--4648",
    abstract = "Mongolian morphological segmentation is regarded as a crucial preprocessing step in many Mongolian related NLP applications and has received extensive attention. Recently, end-to-end segmentation approaches with long short-term memory networks (LSTM) have achieved excellent results. However, the inner-word features among characters in the word and the out-word features from context are not well utilized in the segmentation process. In this paper, we propose a neural network incorporating inner-word and out-word features for Mongolian morphological segmentation. The network consists of two encoders and one decoder. The inner-word encoder uses the self-attention mechanisms to capture the inner-word features of the target word. The out-word encoder employs a two layers BiLSTM network to extract out-word features in the sentence. Then, the decoder adopts a multi-head double attention layer to fuse the inner-word features and out-word features and produces the segmentation result. The evaluation experiment compares the proposed network with the baselines and explores the effectiveness of the sub-modules.",
}
@inproceedings{nzeyimana-2020-morphological,
    title = "Morphological disambiguation from stemming data",
    author = "Nzeyimana, Antoine",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.409",
    doi = "10.18653/v1/2020.coling-main.409",
    pages = "4649--4660",
    abstract = "Morphological analysis and disambiguation is an important task and a crucial preprocessing step in natural language processing of morphologically rich languages. Kinyarwanda, a morphologically rich language, currently lacks tools for automated morphological analysis. While linguistically curated finite state tools can be easily developed for morphological analysis, the morphological richness of the language allows many ambiguous analyses to be produced, requiring effective disambiguation. In this paper, we propose learning to morphologically disambiguate Kinyarwanda verbal forms from a new stemming dataset collected through crowd-sourcing. Using feature engineering and a feed-forward neural network based classifier, we achieve about 89{\%} non-contextualized disambiguation accuracy. Our experiments reveal that inflectional properties of stems and morpheme association rules are the most discriminative features for disambiguation.",
}
@inproceedings{ngoc-le-sadat-2020-revitalization,
    title = "Revitalization of Indigenous Languages through Pre-processing and Neural Machine Translation: The case of {I}nuktitut",
    author = "Ngoc Le, Tan  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.410",
    doi = "10.18653/v1/2020.coling-main.410",
    pages = "4661--4666",
    abstract = "Indigenous languages have been very challenging when dealing with NLP tasks and applications because of multiple reasons. These languages, in linguistic typology, are polysynthetic and highly inflected with rich morphophonemics and variable dialectal-dependent spellings; which affected studies on any NLP task in the recent years. Moreover, Indigenous languages have been considered as low-resource and/or endangered; which poses a great challenge for research related to Artificial Intelligence and its fields, such as NLP and machine learning. In this paper, we propose a study on the Inuktitut language through pre-processing and neural machine translation, in order to revitalize the language which belongs to the Inuit family, a type of polysynthetic languages spoken in Northern Canada. Our focus is concentrated on: (1) the preprocessing phase, and (2) applications on specific NLP tasks such as morphological analysis and neural machine translation, both for Indigenous languages of Canada. Our evaluations in the context of lowresource Inuktitut-English Neural Machine Translation, showed significant improvements of the proposed approach compared to the state-of-the-art.",
}
@inproceedings{zhang-etal-2020-semi,
    title = "Semi-supervised {URL} Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities",
    author = "Zhang, Hao  and
      Ro, Jae  and
      Sproat, Richard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.411",
    doi = "10.18653/v1/2020.coling-main.411",
    pages = "4667--4675",
    abstract = "Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search. We link this problem to the classic problem of Chinese word segmentation and show the effectiveness of a tagging model based on Recurrent Neural Networks (RNNs) using characters as input. To compensate for the lack of training data, we propose a pre-training method on concatenated entity names in a large knowledge database. Pre-training improves the model by 33{\%} and brings the sequence accuracy to 85{\%}.",
}
@inproceedings{zalmout-habash-2020-utilizing,
    title = "Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models",
    author = "Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.412",
    doi = "10.18653/v1/2020.coling-main.412",
    pages = "4676--4682",
    abstract = "In this paper we present a character-level sequence-to-sequence lemmatization model, utilizing several subword features in multiple configurations. In addition to generic n-gram embeddings (using FastText), we experiment with concatenative (stems) and templatic (roots and patterns) morphological subwords. We present several architectures that embed these features directly at the encoder side, or learn them jointly at the decoder side with a multitask learning architecture. The results indicate that using the generic n-gram embeddings (through FastText) outperform the other linguistically-driven subwords. We use Modern Standard Arabic and Egyptian Arabic as test cases, with up to 22{\%} and 13{\%} relative error reduction, respectively, from a strong baseline. An error analysis shows that our best system is even able to handle word/lemma pairs that are both unseen in the training data.",
}
@inproceedings{wu-yarowsky-2020-wiktionary,
    title = "{W}iktionary Normalization of Translations and Morphological Information",
    author = "Wu, Winston  and
      Yarowsky, David",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.413",
    doi = "10.18653/v1/2020.coling-main.413",
    pages = "4683--4692",
    abstract = "We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations. We propose a method to identify typos in translation annotations. Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formation{---}clipping, contraction, and eye dialect{---}and improve upon a standard attention baseline by using copy attention.",
}
@inproceedings{sarioglu-kayi-etal-2020-detecting,
    title = "Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages",
    author = "Sarioglu Kayi, Efsun  and
      Nan, Linyong  and
      Qu, Bohan  and
      Diab, Mona  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.414",
    doi = "10.18653/v1/2020.coling-main.414",
    pages = "4693--4703",
    abstract = "We release an urgency dataset that consists of English tweets relating to natural crises, along with annotations of their corresponding urgency status. Additionally, we release evaluation datasets for two low-resource languages, i.e. Sinhala and Odia, and demonstrate an effective zero-shot transfer from English to these two languages by training cross-lingual classifiers. We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R. We train classifiers of different architectures on the extracted features. We also explore semi-supervised approaches by utilizing unlabeled tweets and experiment with ensembling different classifiers. With very limited amounts of labeled data in English and zero data in the low resource languages, we show a successful framework of training monolingual and cross-lingual classifiers using deep learning methods which are known to be data hungry. Specifically, we show that the recent deep contextual embeddings are also helpful when dealing with very small-scale datasets. Classifiers that incorporate RoBERTa yield the best performance for English urgency detection task, with F1 scores that are more than 25 points over our baseline classifier. For the zero-shot transfer to low resource languages, classifiers that use LASER features perform the best for Sinhala transfer while XLM-R features benefit the Odia transfer the most.",
}
@inproceedings{yamashita-etal-2020-cross,
    title = "Cross-lingual Transfer Learning for Grammatical Error Correction",
    author = "Yamashita, Ikumi  and
      Katsumata, Satoru  and
      Kaneko, Masahiro  and
      Imankulova, Aizhan  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.415",
    doi = "10.18653/v1/2020.coling-main.415",
    pages = "4704--4715",
    abstract = "In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks. However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident. Therefore, we investigate cross-lingual transfer learning methods for GEC. Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC. We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors.",
}
@inproceedings{li-etal-2020-emergent,
    title = "Emergent Communication Pretraining for Few-Shot Machine Translation",
    author = "Li, Yaoyiran  and
      Ponti, Edoardo Maria  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.416",
    doi = "10.18653/v1/2020.coling-main.416",
    pages = "4716--4731",
    abstract = "While state-of-the-art models that rely upon massively multilingual pretrained encoders achieve sample efficiency in downstream applications, they still require abundant amounts of unlabelled text. Nevertheless, most of the world{'}s languages lack such resources. Hence, we investigate a more radical form of unsupervised knowledge transfer in the absence of linguistic data. In particular, for the first time we pretrain neural networks via emergent communication from referential games. Our key assumption is that grounding communication on images{---}as a crude approximation of real-world environments{---}inductively biases the model towards learning natural languages. On the one hand, we show that this substantially benefits machine translation in few-shot settings. On the other hand, this also provides an extrinsic evaluation protocol to probe the properties of emergent languages ex vitro. Intuitively, the closer they are to natural languages, the higher the gains from pretraining on them should be. For instance, in this work we measure the influence of communication success and maximum sequence length on downstream performances. Finally, we introduce a customised adapter layer and annealing strategies for the regulariser of maximum-a-posteriori inference during fine-tuning. These turn out to be crucial to facilitate knowledge transfer and prevent catastrophic forgetting. Compared to a recurrent baseline, our method yields gains of 59.0{\%} 147.6{\%} in BLEU score with only 500 NMT training instances and 65.1{\%} 196.7{\%} with 1,000 NMT training instances across four language pairs. These proof-of-concept results reveal the potential of emergent communication pretraining for both natural language processing tasks in resource-poor settings and extrinsic evaluation of artificial languages.",
}
@inproceedings{stojanovski-etal-2020-contracat,
    title = "{C}ontra{CAT}: Contrastive Coreference Analytical Templates for Machine Translation",
    author = "Stojanovski, Dario  and
      Krojer, Benno  and
      Peskov, Denis  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.417",
    doi = "10.18653/v1/2020.coling-main.417",
    pages = "4732--4749",
    abstract = "Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well. ContraPro is a notable example of a contrastive challenge set for English→German pronoun translation. The high scores achieved by transformer models may suggest that they are able to effectively model the complicated set of inferences required to carry out pronoun translation. This entails the ability to determine which entities could be referred to, identify which entity a source-language pronoun refers to (if any), and access the target-language grammatical gender for that entity. We first show through a series of targeted adversarial attacks that in fact current approaches are not able to model all of this information well. Inserting small amounts of distracting information is enough to strongly reduce scores, which should not be the case. We then create a new template test set ContraCAT, designed to individually assess the ability to handle the specific steps necessary for successful pronoun translation. Our analyses show that current approaches to context-aware NMT rely on a set of surface heuristics, which break down when translations require real reasoning. We also propose an approach for augmenting the training data, with some improvements.",
}
@inproceedings{chousa-etal-2020-spanalign,
    title = "{S}pan{A}lign: Sentence Alignment Method based on Cross-Language Span Prediction and {ILP}",
    author = "Chousa, Katsuki  and
      Nagata, Masaaki  and
      Nishino, Masaaki",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.418",
    doi = "10.18653/v1/2020.coling-main.418",
    pages = "4750--4761",
    abstract = "We propose a novel method of automatic sentence alignment from noisy parallel documents. We first formalize the sentence alignment problem as the independent predictions of spans in the target document from sentences in the source document. We then introduce a total optimization method using integer linear programming to prevent span overlapping and obtain non-monotonic alignments. We implement cross-language span prediction by fine-tuning pre-trained multilingual language models based on BERT architecture and train them using pseudo-labeled data obtained from unsupervised sentence alignment method. While the baseline methods use sentence embeddings and assume monotonic alignment, our method can capture the token-to-token interaction between the tokens of source and target text and handle non-monotonic alignments. In sentence alignment experiments on English-Japanese, our method achieved 70.3 F1 scores, which are +8.0 points higher than the baseline method. In particular, our method improved by +53.9 F1 scores for extracting non-parallel sentences. Our method improved the downstream machine translation accuracy by 4.1 BLEU scores when the extracted bilingual sentences are used for fine-tuning a pre-trained Japanese-to-English translation model.",
}
@inproceedings{xu-etal-2020-clue,
    title = "{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark",
    author = "Xu, Liang  and
      Hu, Hai  and
      Zhang, Xuanwei  and
      Li, Lu  and
      Cao, Chenjie  and
      Li, Yudong  and
      Xu, Yechen  and
      Sun, Kai  and
      Yu, Dian  and
      Yu, Cong  and
      Tian, Yin  and
      Dong, Qianqian  and
      Liu, Weitang  and
      Shi, Bo  and
      Cui, Yiming  and
      Li, Junyi  and
      Zeng, Jun  and
      Wang, Rongzhao  and
      Xie, Weijian  and
      Li, Yanting  and
      Patterson, Yina  and
      Tian, Zuoyu  and
      Zhang, Yiwen  and
      Zhou, He  and
      Liu, Shaoweihua  and
      Zhao, Zhe  and
      Zhao, Qipeng  and
      Yue, Cong  and
      Zhang, Xinrui  and
      Yang, Zhengliang  and
      Richardson, Kyle  and
      Lan, Zhenzhong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.419",
    doi = "10.18653/v1/2020.coling-main.419",
    pages = "4762--4772",
    abstract = "The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.cluebenchmarks.com",
}
@inproceedings{manning-etal-2020-human,
    title = "A Human Evaluation of {AMR}-to-{E}nglish Generation Systems",
    author = "Manning, Emma  and
      Wein, Shira  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.420",
    doi = "10.18653/v1/2020.coling-main.420",
    pages = "4773--4786",
    abstract = "Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation. In this work, we present the results of a new human evaluation which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR generation systems. We discuss the relative quality of these systems and how our results compare to those of automatic metrics, finding that while the metrics are mostly successful in ranking systems overall, collecting human judgments allows for more nuanced comparisons. We also analyze common errors made by these systems.",
}
@inproceedings{amidei-etal-2020-identifying,
    title = "Identifying Annotator Bias: A new {IRT}-based method for bias identification",
    author = "Amidei, Jacopo  and
      Piwek, Paul  and
      Willis, Alistair",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.421",
    doi = "10.18653/v1/2020.coling-main.421",
    pages = "4787--4797",
    abstract = "A basic step in any annotation effort is the measurement of the Inter Annotator Agreement (IAA). An important factor that can affect the IAA is the presence of annotator bias. In this paper we introduce a new interpretation and application of the Item Response Theory (IRT) to detect annotators{'} bias. Our interpretation of IRT offers an original bias identification method that can be used to compare annotators{'} bias and characterise annotation disagreement. Our method can be used to spot outlier annotators, improve annotation guidelines and provide a better picture of the annotation reliability. Additionally, because scales for IAA interpretation are not generally agreed upon, our bias identification method is valuable as a complement to the IAA value which can help with understanding the annotation disagreement.",
}
@inproceedings{sommerauer-etal-2020-describe,
    title = "Would you describe a leopard as yellow? Evaluating crowd-annotations with justified and informative disagreement",
    author = "Sommerauer, Pia  and
      Fokkens, Antske  and
      Vossen, Piek",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.422",
    doi = "10.18653/v1/2020.coling-main.422",
    pages = "4798--4809",
    abstract = "Semantic annotation tasks contain ambiguity and vagueness and require varying degrees of world knowledge. Disagreement is an important indication of these phenomena. Most traditional evaluation methods, however, critically hinge upon the notion of inter-annotator agreement. While alternative frameworks have been proposed, they do not move beyond agreement as the most important indicator of quality. Critically, evaluations usually do not distinguish between instances in which agreement is expected and instances in which disagreement is not only valid but desired because it captures the linguistic and cognitive phenomena in the data. We attempt to overcome these limitations using the example of a dataset that provides semantic representations for diagnostic experiments on language models. Ambiguity, vagueness, and difficulty are not only highly relevant for this use-case, but also play an important role in other types of semantic annotation tasks. We establish an additional, agreement-independent quality metric based on answer-coherence and evaluate it in comparison to existing metrics. We compare against a gold standard and evaluate on expected disagreement. Despite generally low agreement, annotations follow expected behavior and have high accuracy when selected based on coherence. We show that combining different quality metrics enables a more comprehensive evaluation than relying exclusively on agreement.",
}
@inproceedings{majewska-etal-2020-manual,
    title = "Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis",
    author = "Majewska, Olga  and
      Vuli{\'c}, Ivan  and
      McCarthy, Diana  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.423",
    doi = "10.18653/v1/2020.coling-main.423",
    pages = "4810--4824",
    abstract = "We present the first evaluation of the applicability of a spatial arrangement method (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics. We demonstrate SpAM{'}s utility in allowing for quick bottom-up creation of large-scale evaluation datasets that balance cross-lingual alignment with language specificity. Starting from a shared sample of 825 English verbs, translated into Chinese, Japanese, Finnish, Polish, and Italian, we apply a two-phase annotation process which produces (i) semantic verb classes and (ii) fine-grained similarity scores for nearly 130 thousand verb pairs. We use the two types of verb data to (a) examine cross-lingual similarities and variation, and (b) evaluate the capacity of static and contextualised representation models to accurately reflect verb semantics, contrasting the performance of large language specific pretraining models with their multilingual equivalent on semantic clustering and lexical similarity, across different domains of verb meaning. We release the data from both phases as a large-scale multilingual resource, comprising 85 verb classes and nearly 130k pairwise similarity scores, offering a wealth of possibilities for further evaluation and research on multilingual verb semantics.",
}
@inproceedings{kwon-etal-2020-hierarchical,
    title = "Hierarchical Trivia Fact Extraction from {W}ikipedia Articles",
    author = "Kwon, Jingun  and
      Kamigaito, Hidetaka  and
      Song, Young-In  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.424",
    doi = "10.18653/v1/2020.coling-main.424",
    pages = "4825--4834",
    abstract = "Recently, automatic trivia fact extraction has attracted much research interest. Modern search engines have begun to provide trivia facts as the information for entities because they can motivate more user engagement. In this paper, we propose a new unsupervised algorithm that automatically mines trivia facts for a given entity. Unlike previous studies, the proposed algorithm targets at a single Wikipedia article and leverages its hierarchical structure via top-down processing. Thus, the proposed algorithm offers two distinctive advantages: it does not incur high computation time, and it provides a domain-independent approach for extracting trivia facts. Experimental results demonstrate that the proposed algorithm is over 100 times faster than the existing method which considers Wikipedia categories. Human evaluation demonstrates that the proposed algorithm can mine better trivia facts regardless of the target entity domain and outperforms the existing methods.",
}
@inproceedings{indurthi-etal-2020-predicting,
    title = "Predicting Clickbait Strength in Online Social Media",
    author = "Indurthi, Vijayasaradhi  and
      Syed, Bakhtiyar  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.425",
    doi = "10.18653/v1/2020.coling-main.425",
    pages = "4835--4846",
    abstract = "Hoping for a large number of clicks and potentially high social shares, journalists of various news media outlets publish sensationalist headlines on social media. These headlines lure the readers to click on them and satisfy the curiosity gap in their mind. Low quality material pointed to by clickbaits leads to time wastage and annoyance for users. Even for enterprises publishing clickbaits, it hurts more than it helps as it erodes user trust, attracts wrong visitors, and produces negative signals for ranking algorithms. Hence, identifying and flagging clickbait titles is very essential. Previous work on clickbaits has majorly focused on binary classification of clickbait titles. However not all clickbaits are equally clickbaity. It is not only essential to identify a click-bait, but also to identify the intensity of the clickbait based on the strength of the clickbait. In this work, we model clickbait strength prediction as a regression problem. While previous methods have relied on traditional machine learning or vanilla recurrent neural networks, we rigorously investigate the use of transformers for clickbait strength prediction. On a benchmark dataset with ∼39K posts, our methods outperform all the existing methods in the Clickbait Challenge.",
}
@inproceedings{sawhney-etal-2020-gpols,
    title = "{GP}ol{S}: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion",
    author = "Sawhney, Ramit  and
      Wadhwa, Arnav  and
      Agarwal, Shivam  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.426",
    doi = "10.18653/v1/2020.coling-main.426",
    pages = "4847--4859",
    abstract = "Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society. However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analysis. We introduce GPolS, a neural model for political speech sentiment analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members. Through experiments on real-world English data and by visualizing attention, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction.",
}
@inproceedings{yu-etal-2020-measuring,
    title = "Measuring Correlation-to-Causation Exaggeration in Press Releases",
    author = "Yu, Bei  and
      Wang, Jun  and
      Guo, Lu  and
      Li, Yingya",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.427",
    doi = "10.18653/v1/2020.coling-main.427",
    pages = "4860--4872",
    abstract = "Press releases have an increasingly strong influence on media coverage of health research; however, they have been found to contain seriously exaggerated claims that can misinform the public and undermine public trust in science. In this study we propose an NLP approach to identify exaggerated causal claims made in health press releases that report on observational studies, which are designed to establish correlational findings, but are often exaggerated as causal. We developed a new corpus and trained models that can identify causal claims in the main statements in a press release. By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22{\%} of press releases made exaggerated causal claims from correlational findings in observational studies. Furthermore, universities exaggerated more often than journal publishers by a ratio of 1.5 to 1. Encouragingly, the exaggeration rate has slightly decreased over the past 10 years, despite the increase of the total number of press releases. More research is needed to understand the cause of the decreasing pattern.",
}
@inproceedings{guo-etal-2020-inflating,
    title = "Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models",
    author = "Guo, Meiqi  and
      Hwa, Rebecca  and
      Lin, Yu-Ru  and
      Chung, Wen-Ting",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.428",
    doi = "10.18653/v1/2020.coling-main.428",
    pages = "4873--4885",
    abstract = "We investigate the impact of political ideology biases in training data. Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its effect on the overall retrieval accuracy. Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input, which may lead to a deterioration of retrieval accuracy, and the importance of controlling for these biases. Finally, as a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance.",
}
@inproceedings{welivita-pu-2020-taxonomy,
    title = "A Taxonomy of Empathetic Response Intents in Human Social Conversations",
    author = "Welivita, Anuradha  and
      Pu, Pearl",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.429",
    doi = "10.18653/v1/2020.coling-main.429",
    pages = "4886--4899",
    abstract = "Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. One of the challenges is enabling them to converse in an empathetic manner. Current neural response generation methods rely solely on end-to-end learning from large scale conversation data to generate dialogues. This approach can produce socially unacceptable responses due to the lack of large-scale quality data used to train the neural models. However, recent work has shown the promise of combining dialogue act/intent modelling and neural response generation. This hybrid method improves the response quality of chatbots and makes them more controllable and interpretable. A key element in dialog intent modelling is the development of a taxonomy. Inspired by this idea, we have manually labeled 500 response intents using a subset of a sizeable empathetic dialogue dataset (25K dialogues). Our goal is to produce a large-scale taxonomy for empathetic response intents. Furthermore, using lexical and machine learning methods, we automatically analysed both speaker and listener utterances of the entire dataset with identified response intents and 32 emotion categories. Finally, we use information visualization methods to summarize emotional dialogue exchange patterns and their temporal progression. These results reveal novel and important empathy patterns in human-human open-domain conversations and can serve as heuristics for hybrid approaches.",
}
@inproceedings{zhu-etal-2020-multitask,
    title = "A Multitask Active Learning Framework for Natural Language Understanding",
    author = "Zhu, Hua  and
      Ye, Wu  and
      Luo, Sihan  and
      Zhang, Xidong",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.430",
    doi = "10.18653/v1/2020.coling-main.430",
    pages = "4900--4914",
    abstract = "Natural language understanding (NLU) aims at identifying user intent and extracting semantic slots. This requires sufficient annotating data to get considerable performance in real-world situations. Active learning (AL) has been well-studied to decrease the needed amount of the annotating data and successfully applied to NLU. However, no research has been done on investigating how the relation information between intents and slots can improve the efficiency of AL algorithms. In this paper, we propose a multitask AL framework for NLU. Our framework enables pool-based AL algorithms to make use of the relation information between sub-tasks provided by a joint model, and we propose an efficient computation for the entropy of a joint model. Experimental results show our framework can achieve competitive performance with less training data than baseline methods on all datasets. We also demonstrate that when using the entropy as the query strategy, the model with complete relation information can perform better than those with partial information. Additionally, we demonstrate that the efficiency of these active learning algorithms in our framework is still effective when incorporate with the Bidirectional Encoder Representations from Transformers (BERT).",
}
@inproceedings{blache-etal-2020-two,
    title = "Two-level classification for dialogue act recognition in task-oriented dialogues",
    author = "Blache, Philippe  and
      Abderrahmane, Massina  and
      Rauzy, St{\'e}phane  and
      Ochs, Magalie  and
      Oufaida, Houda",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.431",
    doi = "10.18653/v1/2020.coling-main.431",
    pages = "4915--4925",
    abstract = "Dialogue act classification becomes a complex task when dealing with fine-grain labels. Many applications require such level of labelling, typically automatic dialogue systems. We present in this paper a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA). This approach makes it possible to benefit from the very good accuracy of generic DA classification at the first level and proposes an efficient approach for specific DA, based on high-level linguistic features. Our results show the interest of involving such features into the classifiers, outperforming all other feature sets, in particular those classically used in DA classification.",
}
@inproceedings{cao-etal-2020-balanced,
    title = "Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling",
    author = "Cao, Xu  and
      Xiong, Deyi  and
      Shi, Chongyang  and
      Wang, Chao  and
      Meng, Yao  and
      Hu, Changjian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.432",
    doi = "10.18653/v1/2020.coling-main.432",
    pages = "4926--4936",
    abstract = "Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding. However, many joint models still suffer from the robustness problem, especially on noisy inputs or rare/unseen events. To address this issue, we propose a Joint Adversarial Training (JAT) model to improve the robustness of joint intent detection and slot filling, which consists of two parts: (1) automatically generating joint adversarial examples to attack the joint model, and (2) training the model to defend against the joint adversarial examples so as to robustify the model on small perturbations. As the generated joint adversarial examples have different impacts on the intent detection and slot filling loss, we further propose a Balanced Joint Adversarial Training (BJAT) model that applies a balance factor as a regularization term to the final loss function, which yields a stable training procedure. Extensive experiments and analyses on the lightweight models show that our proposed methods achieve significantly higher scores and substantially improve the robustness of both intent detection and slot filling. In addition, the combination of our BJAT with BERT-large achieves state-of-the-art results on two datasets.",
}
@inproceedings{sarathy-etal-2020-reasoning,
    title = "Reasoning Requirements for Indirect Speech Act Interpretation",
    author = "Sarathy, Vasanth  and
      Tsuetaki, Alexander  and
      Roque, Antonio  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.433",
    doi = "10.18653/v1/2020.coling-main.433",
    pages = "4937--4948",
    abstract = "We perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts. An indirect speech act (ISA) is an utterance whose intended meaning is different from its literal meaning. We focus on those speech acts in which slight changes in situational or contextual information can switch the dominant intended meaning of an utterance from direct to indirect or vice-versa. We computationalize how various contextual features can influence a speaker{'}s beliefs, and how these beliefs can influence the intended meaning and choice of the surface form of an utterance. We axiomatize the domain-general patterns of reasoning involved, and implement a proof-of-concept architecture using Answer Set Programming. Our model is presented as a contribution to cognitive science and psycholinguistics, so representational decisions are justified by existing theoretical work.",
}
@inproceedings{guo-etal-2020-understanding,
    title = "Understanding Unnatural Questions Improves Reasoning over Text",
    author = "Guo, Xiaoyu  and
      Li, Yuan-Fang  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.434",
    doi = "10.18653/v1/2020.coling-main.434",
    pages = "4949--4955",
    abstract = "Complex question answering (CQA) over raw text is a challenging task. A prominent approach to this task is based on the programmer-interpreter framework, where the programmer maps the question into a sequence of reasoning actions and the interpreter then executes these actions on the raw text. Learning an effective CQA model requires large amounts of human-annotated data, consisting of the ground-truth sequence of reasoning actions, which is time-consuming and expensive to collect at scale. In this paper, we address the challenge of learning a high-quality programmer (parser) by projecting natural human-generated questions into unnatural machine-generated questions which are more convenient to parse. We firstly generate synthetic (question, action sequence) pairs by a data generator, and train a semantic parser that associates synthetic questions with their corresponding action sequences. To capture the diversity when applied to natural questions, we learn a projection model to map natural questions into their most similar unnatural questions for which the parser can work well. Without any natural training data, our projection model provides high-quality action sequences for the CQA task. Experimental results show that the QA model trained exclusively with synthetic data outperforms its state-of-the-art counterpart trained on human-labeled data.",
}
@inproceedings{konno-etal-2020-empirical,
    title = "An Empirical Study of Contextual Data Augmentation for {J}apanese Zero Anaphora Resolution",
    author = "Konno, Ryuto  and
      Matsubayashi, Yuichiroh  and
      Kiyono, Shun  and
      Ouchi, Hiroki  and
      Takahashi, Ryo  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.435",
    doi = "10.18653/v1/2020.coling-main.435",
    pages = "4956--4968",
    abstract = "One critical issue of zero anaphora resolution (ZAR) is the scarcity of labeled data. This study explores how effectively this problem can be alleviated by data augmentation. We adopt a state-of-the-art data augmentation method, called the contextual data augmentation (CDA), that generates labeled training instances using a pretrained language model. The CDA has been reported to work well for several other natural language processing tasks, including text classification and machine translation. This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data. We also propose two methods to adapt CDA to ZAR: [MASK]-based augmentation and linguistically-controlled masking. Consequently, the experimental results on Japanese ZAR show that our methods contribute to both the accuracy gainand the computation cost reduction. Our closer analysis reveals that the proposed method can improve the quality of the augmented training data when compared to the conventional CDA.",
}
@inproceedings{taniguchi-etal-2020-large,
    title = "A Large-Scale Corpus of {E}-mail Conversations with Standard and Two-Level Dialogue Act Annotations",
    author = "Taniguchi, Motoki  and
      Ueda, Yoshihiro  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.436",
    doi = "10.18653/v1/2020.coling-main.436",
    pages = "4969--4980",
    abstract = "We present a large-scale corpus of e-mail conversations with domain-agnostic and two-level dialogue act (DA) annotations towards the goal of a better understanding of asynchronous conversations. We annotate over 6,000 messages and 35,000 sentences from more than 2,000 threads. For a domain-independent and application-independent DA annotations, we choose ISO standard 24617-2 as the annotation scheme. To assess the difficulty of DA recognition on our corpus, we evaluate several models, including a pre-trained contextual representation model, as our baselines. The experimental results show that BERT outperforms other neural network models, including previous state-of-the-art models, but falls short of a human performance. We also demonstrate that DA tags of two-level granularity enable a DA recognition model to learn efficiently by using multi-task learning. An evaluation of a model trained on our corpus against other domains of asynchronous conversation reveals the domain independence of our DA annotations.",
}
@inproceedings{deng-etal-2020-intra,
    title = "Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection",
    author = "Deng, Yang  and
      Zhang, Wenxuan  and
      Lam, Wai",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.437",
    doi = "10.18653/v1/2020.coling-main.437",
    pages = "4981--4992",
    abstract = "Multi-turn response selection has been extensively studied and applied to many real-world applications in recent years. However, current methods typically model the interactions between multi-turn utterances and candidate responses with iterative approaches, which is not practical as the turns of conversations vary. Besides, some latent features, such as user intent and conversation topic, are under-discovered in existing works. In this work, we propose Intra-/Inter-Interaction Network (I$^3$) with latent interaction modeling to comprehensively model multi-level interactions between the utterance context and the response. In specific, we first encode the intra- and inter-utterance interaction with the given response from both individual utterance and the overall utterance context. Then we develop a latent multi-view subspace clustering module to model the latent interaction between the utterance and response. Experimental results show that the proposed method substantially and consistently outperforms existing state-of-the-art methods on three multi-turn response selection benchmark datasets.",
}
@inproceedings{dopierre-etal-2020-shot,
    title = "Few-shot Pseudo-Labeling for Intent Detection",
    author = "Dopierre, Thomas  and
      Gravier, Christophe  and
      Subercaze, Julien  and
      Logerais, Wilfried",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.438",
    doi = "10.18653/v1/2020.coling-main.438",
    pages = "4993--5003",
    abstract = "In this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection. We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. We show that our two-step method yields significant improvement over existing solutions. This performance is achieved on multiple intent detection datasets, even in more challenging situations where the number of classes is large or when the dataset is highly imbalanced. Moreover, we confirm this results on the more general text classification task. We also demonstrate that our approach nicely complements existing solutions, thereby providing an even stronger state-of-the-art ensemble method.",
}
@inproceedings{westera-etal-2020-similarity,
    title = "Similarity or deeper understanding? Analyzing the {TED}-{Q} dataset of evoked questions",
    author = "Westera, Matthijs  and
      Amidei, Jacopo  and
      Mayol, Laia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.439",
    doi = "10.18653/v1/2020.coling-main.439",
    pages = "5004--5012",
    abstract = "We take a close look at a recent dataset of TED-talks annotated with the questions they implicitly evoke, TED-Q (Westera et al., 2020). We test to what extent the relation between a discourse and the questions it evokes is merely one of similarity or association, as opposed to deeper semantic/pragmatic interpretation. We do so by turning the TED-Q dataset into a binary classification task, constructing an analogous task from explicit questions we extract from the BookCorpus (Zhu et al., 2015), and fitting a BERT-based classifier alongside models based on different notions of similarity. The BERT-based classifier, achieving close to human performance, outperforms all similarity-based models, suggesting that there is more to identifying true evoked questions than plain similarity.",
}
@inproceedings{takatsu-etal-2020-sentiment,
    title = "Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System",
    author = "Takatsu, Hiroaki  and
      Ando, Ryota  and
      Matsuyama, Yoichi  and
      Kobayashi, Tetsunori",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.440",
    doi = "10.18653/v1/2020.coling-main.440",
    pages = "5013--5025",
    abstract = "As smart speakers and conversational robots become ubiquitous, the demand for expressive speech synthesis has increased. In this paper, to control the emotional parameters of the speech synthesis according to certain dialogue contents, we construct a news dataset with emotion labels ({``}positive,{''} {``}negative,{''} or {``}neutral{''}) annotated for each sentence. We then propose a method to identify emotion labels using a model combining BERT and BiLSTM-CRF, and evaluate its effectiveness using the constructed dataset. The results showed that the classification model performance can be efficiently improved by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework.",
}
@inproceedings{khan-etal-2020-adversarial,
    title = "Adversarial Learning on the Latent Space for Diverse Dialog Generation",
    author = "Khan, Kashif  and
      Sahu, Gaurav  and
      Balasubramanian, Vikash  and
      Mou, Lili  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.441",
    doi = "10.18653/v1/2020.coling-main.441",
    pages = "5026--5034",
    abstract = "Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation, but also being able to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns a meaningful representation of sentences by autoencoding, and then learns to map an input query to the response representation, which is in turn decoded as a response sentence. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant, and diverse responses than existing state-of-the-art methods.",
}
@inproceedings{larson-etal-2020-inconsistencies,
    title = "Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods",
    author = "Larson, Stefan  and
      Cheung, Adrian  and
      Mahendran, Anish  and
      Leach, Kevin  and
      Kummerfeld, Jonathan K.",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.442",
    doi = "10.18653/v1/2020.coling-main.442",
    pages = "5035--5046",
    abstract = "Slot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or having multiple workers label each sample are expensive and waste effort on samples that are correct. If we can identify inconsistencies, we can focus effort where it is needed. Toward this end, we define six inconsistency types in slot-filling annotations. Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed. We then introduce automatic methods of identifying inconsistencies. Experiments on our new datasets show that these methods effectively reveal inconsistencies in data, though there is further scope for improvement.",
}
@inproceedings{elbayad-etal-2020-online,
    title = "Online Versus Offline {NMT} Quality: An In-depth Analysis on {E}nglish-{G}erman and {G}erman-{E}nglish",
    author = "Elbayad, Maha  and
      Ustaszewski, Michael  and
      Esperan{\c{c}}a-Rodier, Emmanuelle  and
      Brunet-Manquat, Francis  and
      Verbeek, Jakob  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.443",
    doi = "10.18653/v1/2020.coling-main.443",
    pages = "5047--5058",
    abstract = "We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani et al. 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.",
}
@inproceedings{popovic-2020-informative,
    title = "Informative Manual Evaluation of Machine Translation Output",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.444",
    doi = "10.18653/v1/2020.coling-main.444",
    pages = "5059--5069",
    abstract = "This work proposes a new method for manual evaluation of Machine Translation (MT) output based on marking actual issues in the translated text. The novelty is that the evaluators are not assigning any scores, nor classifying errors, but marking all problematic parts (words, phrases, sentences) of the translation. The main advantage of this method is that the resulting annotations do not only provide overall scores by counting words with assigned tags, but can be further used for analysis of errors and challenging linguistic phenomena, as well as inter-annotator disagreements. Detailed analysis and understanding of actual problems are not enabled by typical manual evaluations where the annotators are asked to assign overall scores or to rank two or more translations. The proposed method is very general: it can be applied on any genre/domain and language pair, and it can be guided by various types of quality criteria. Also, it is not restricted to MT output, but can be used for other types of generated text.",
}
@inproceedings{ranasinghe-etal-2020-transquest-translation,
    title = "{T}rans{Q}uest: Translation Quality Estimation with Cross-lingual Transformers",
    author = "Ranasinghe, Tharindu  and
      Orasan, Constantin  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.445",
    doi = "10.18653/v1/2020.coling-main.445",
    pages = "5070--5081",
    abstract = "Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures. However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs. This process can prove difficult from a technical point of view and is usually computationally expensive. In this paper we propose a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures. Our evaluation shows that the proposed methods achieve state-of-the-art results outperforming current open-source quality estimation frameworks when trained on datasets from WMT. In addition, the framework proves very useful in transfer learning settings, especially when dealing with low-resourced languages, allowing us to obtain very competitive results.",
}
@inproceedings{liang-etal-2020-monolingual,
    title = "Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations",
    author = {Liang, Sheng  and
      Dufter, Philipp  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.446",
    doi = "10.18653/v1/2020.coling-main.446",
    pages = "5082--5093",
    abstract = "Pretrained language models (PLMs) learn stereotypes held by humans and reflected in text from their training corpora, including gender bias. When PLMs are used for downstream tasks such as picking candidates for a job, people{'}s lives can be negatively affected by these learned stereotypes. Prior work usually identifies a linear gender subspace and removes gender information by eliminating the subspace. Following this line of work, we propose to use DensRay, an analytical method for obtaining interpretable dense subspaces. We show that DensRay performs on-par with prior approaches, but provide arguments that it is more robust and provide indications that it preserves language model performance better. By applying DensRay to attention heads and layers of BERT we show that gender information is spread across all attention heads and most of the layers. Also we show that DensRay can obtain gender bias scores on both token and sentence levels. Finally, we demonstrate that we can remove bias multilingually, e.g., from Chinese, using only English training data.",
}
@inproceedings{sajjad-etal-2020-arabench,
    title = "{A}ra{B}ench: Benchmarking Dialectal {A}rabic-{E}nglish Machine Translation",
    author = "Sajjad, Hassan  and
      Abdelali, Ahmed  and
      Durrani, Nadir  and
      Dalvi, Fahim",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.447",
    doi = "10.18653/v1/2020.coling-main.447",
    pages = "5094--5107",
    abstract = "Low-resource machine translation suffers from the scarcity of training data and the unavailability of standard evaluation sets. While a number of research efforts target the former, the unavailability of evaluation benchmarks remain a major hindrance in tracking the progress in low-resource machine translation. In this paper, we introduce AraBench, an evaluation suite for dialectal Arabic to English machine translation. Compared to Modern Standard Arabic, Arabic dialects are challenging due to their spoken nature, non-standard orthography, and a large variation in dialectness. To this end, we pool together already available Dialectal Arabic-English resources and additionally build novel test sets. AraBench offers 4 coarse, 15 fine-grained and 25 city-level dialect categories, belonging to diverse genres, such as media, chat, religion and travel with varying level of dialectness. We report strong baselines using several training settings: fine-tuning, back-translation and data augmentation. The evaluation suite opens a wide range of research frontiers to push efforts in low-resource machine translation, particularly Arabic dialect translation. The evaluation suite and the dialectal system are publicly available for research purposes.",
}
@inproceedings{bansal-etal-2020-learning,
    title = "Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks",
    author = "Bansal, Trapit  and
      Jha, Rishikesh  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.448",
    doi = "10.18653/v1/2020.coling-main.448",
    pages = "5108--5123",
    abstract = "Pre-trained transformer models have shown enormous success in improving performance on several downstream tasks. However, fine-tuning on a new task still requires large amounts of task-specific labeled data to achieve good performance. We consider this problem of learning to generalize to new tasks, with a few examples, as a meta-learning problem. While meta-learning has shown tremendous progress in recent years, its application is still limited to simulated problems or problems with limited diversity across tasks. We develop a novel method, LEOPARD, which enables optimization-based meta-learning across tasks with a different number of classes, and evaluate different methods on generalization to diverse NLP classification tasks. LEOPARD is trained with the state-of-the-art transformer architecture and shows better generalization to tasks not seen at all during training, with as few as 4 examples per label. Across 17 NLP tasks, including diverse domains of entity typing, natural language inference, sentiment analysis, and several other text classification tasks, we show that LEOPARD learns better initial parameters for few-shot learning than self-supervised pre-training or multi-task training, outperforming many strong baselines, for example, yielding 14.6{\%} average relative gain in accuracy on unseen tasks with only 4 examples per label.",
}
@inproceedings{alshaikh-etal-2020-mixture,
    title = "A Mixture-of-Experts Model for Learning Multi-Facet Entity Embeddings",
    author = "Alshaikh, Rana  and
      Bouraoui, Zied  and
      Jeawak, Shelan  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.449",
    doi = "10.18653/v1/2020.coling-main.449",
    pages = "5124--5135",
    abstract = "Various methods have already been proposed for learning entity embeddings from text descriptions. Such embeddings are commonly used for inferring properties of entities, for recommendation and entity-oriented search, and for injecting background knowledge into neural architectures, among others. Entity embeddings essentially serve as a compact encoding of a similarity relation, but similarity is an inherently multi-faceted notion. By representing entities as single vectors, existing methods leave it to downstream applications to identify these different facets, and to select the most relevant ones. In this paper, we propose a model that instead learns several vectors for each entity, each of which intuitively captures a different aspect of the considered domain. We use a mixture-of-experts formulation to jointly learn these facet-specific embeddings. The individual entity embeddings are learned using a variant of the GloVe model, which has the advantage that we can easily identify which properties are modelled well in which of the learned embeddings. This is exploited by an associated gating network, which uses pre-trained word vectors to encourage the properties that are modelled by a given embedding to be semantically coherent, i.e. to encourage each of the individual embeddings to capture a meaningful facet.",
}
@inproceedings{kunz-kuhlmann-2020-classifier,
    title = "Classifier Probes May Just Learn from Linear Context Features",
    author = "Kunz, Jenny  and
      Kuhlmann, Marco",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.450",
    doi = "10.18653/v1/2020.coling-main.450",
    pages = "5136--5146",
    abstract = "Classifiers trained on auxiliary probing tasks are a popular tool to analyze the representations learned by neural sentence encoders such as BERT and ELMo. While many authors are aware of the difficulty to distinguish between {``}extracting the linguistic structure encoded in the representations{''} and {``}learning the probing task,{''} the validity of probing methods calls for further research. Using a neighboring word identity prediction task, we show that the token embeddings learned by neural sentence encoders contain a significant amount of information about the exact linear context of the token, and hypothesize that, with such information, learning standard probing tasks may be feasible even without additional linguistic structure. We develop this hypothesis into a framework in which analysis efforts can be scrutinized and argue that, with current models and baselines, conclusions that representations contain linguistic structure are not well-founded. Current probing methodology, such as restricting the classifier{'}s expressiveness or using strong baselines, can help to better estimate the complexity of learning, but not build a foundation for speculations about the nature of the linguistic structure encoded in the learned representations.",
}
@inproceedings{mitchell-bowers-2020-priorless,
    title = "Priorless Recurrent Networks Learn Curiously",
    author = "Mitchell, Jeff  and
      Bowers, Jeffrey",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.451",
    doi = "10.18653/v1/2020.coling-main.451",
    pages = "5147--5158",
    abstract = "Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs. We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and which humans struggle to process. These results suggest that the models are learning from their input in a manner that is substantially different from human language acquisition, and we undertake an analysis of how the learned knowledge is stored in the weights of the network. We find that while the model has an effective understanding of singular versus plural for individual sentences, there is a lack of a unified concept of number agreement connecting these processes across the full range of inputs. Moreover, the weights handling natural and unnatural structures overlap substantially, in a way that underlines the non-human-like nature of the knowledge learned by the network.",
}
@inproceedings{wang-etal-2020-answer,
    title = "Answer-driven Deep Question Generation based on Reinforcement Learning",
    author = "Wang, Liuyin  and
      Xu, Zihan  and
      Lin, Zibo  and
      Zheng, Haitao  and
      Shen, Ying",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.452",
    doi = "10.18653/v1/2020.coling-main.452",
    pages = "5159--5170",
    abstract = "Deep question generation (DQG) aims to generate complex questions through reasoning over multiple documents. The task is challenging and underexplored. Existing methods mainly focus on enhancing document representations, with little attention paid to the answer information, which may result in the generated question not matching the answer type and being answerirrelevant. In this paper, we propose an Answer-driven Deep Question Generation (ADDQG) model based on the encoder-decoder framework. The model makes better use of the target answer as a guidance to facilitate question generation. First, we propose an answer-aware initialization module with a gated connection layer which introduces both document and answer information to the decoder, thus helping to guide the choice of answer-focused question words. Then a semantic-rich fusion attention mechanism is designed to support the decoding process, which integrates the answer with the document representations to promote the proper handling of answer information during generation. Moreover, reinforcement learning is applied to integrate both syntactic and semantic metrics as the reward to enhance the training of the ADDQG. Extensive experiments on the HotpotQA dataset show that ADDQG outperforms state-of-the-art models in both automatic and human evaluations.",
}
@inproceedings{aldawsari-etal-2020-distinguishing,
    title = "Distinguishing Between Foreground and Background Events in News",
    author = "Aldawsari, Mohammed  and
      Perez, Adrian  and
      Banisakher, Deya  and
      Finlayson, Mark",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.453",
    doi = "10.18653/v1/2020.coling-main.453",
    pages = "5171--5180",
    abstract = "Determining whether an event in a news article is a foreground or background event would be useful in many natural language processing tasks, for example, temporal relation extraction, summarization, or storyline generation. We introduce the task of distinguishing between foreground and background events in news articles as well as identifying the general temporal position of background events relative to the foreground period (past, present, future, and their combinations). We achieve good performance (0.73 F1 for background vs. foreground and temporal position, and 0.79 F1 for background vs. foreground only) on a dataset of news articles by leveraging discourse information in a featurized model. We release our implementation and annotated data for other researchers",
}
@inproceedings{rabinovich-etal-2020-pick,
    title = "Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage",
    author = "Rabinovich, Ella  and
      Gonen, Hila  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.454",
    doi = "10.18653/v1/2020.coling-main.454",
    pages = "5181--5192",
    abstract = "A large body of research on gender-linked language has established foundations regarding cross-gender differences in lexical, emotional, and topical preferences, along with their sociological underpinnings. We compile a novel, large and diverse corpus of spontaneous linguistic productions annotated with speakers{'} gender, and perform a first large-scale empirical study of distinctions in the usage of figurative language between male and female authors. Our analyses suggest that (1) idiomatic choices reflect gender-specific lexical and semantic preferences in general language, (2) men{'}s and women{'}s idiomatic usages express higher emotion than their literal language, with detectable, albeit more subtle, differences between male and female authors along the dimension of dominance compared to similar distinctions in their literal utterances, and (3) contextual analysis of idiomatic expressions reveals considerable differences, reflecting subtle divergences in usage environments, shaped by cross-gender communication styles and semantic biases.",
}
@inproceedings{zhang-etal-2020-intra,
    title = "Intra-Correlation Encoding for {C}hinese Sentence Intention Matching",
    author = "Zhang, Xu  and
      Li, Yifeng  and
      Lu, Wenpeng  and
      Jian, Ping  and
      Zhang, Guoqiang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.455",
    doi = "10.18653/v1/2020.coling-main.455",
    pages = "5193--5204",
    abstract = "Sentence intention matching is vital for natural language understanding. Especially for Chinese sentence intention matching task, due to the ambiguity of Chinese words, semantic missing or semantic confusion are more likely to occur in the encoding process. Although the existing methods have enriched text representation through pre-trained word embedding to solve this problem, due to the particularity of Chinese text, different granularities of pre-trained word embedding will affect the semantic description of a piece of text. In this paper, we propose an effective approach that combines character-granularity and word-granularity features to perform sentence intention matching, and we utilize soft alignment attention to enhance the local information of sentences on the corresponding levels. The proposed method can capture sentence feature information from multiple perspectives and correlation information between different levels of sentences. By evaluating on BQ and LCQMC datasets, our model has achieved remarkable results, and demonstrates better or comparable performance with BERT-based models.",
}
@inproceedings{tran-etal-2020-explain,
    title = "Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering",
    author = "Tran, Quan Hung  and
      Dam, Nhan  and
      Lai, Tuan  and
      Dernoncourt, Franck  and
      Le, Trung  and
      Le, Nham  and
      Phung, Dinh",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.456",
    doi = "10.18653/v1/2020.coling-main.456",
    pages = "5205--5210",
    abstract = "Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. Many previous works focused on visualizing internal components of neural networks to represent them through human-friendly concepts. On the other hand, in real life, when making a decision, human tends to rely on similar situations in the past. Thus, we argue that one potential approach to make the model interpretable and explainable is to design it in a way such that the model explicitly connects the current sample with the seen samples, and bases its decision on these samples. In this work, we design one such model: an explainable, evidence-based memory network architecture, which learns to summarize the dataset and extract supporting evidences to make its decision. The model achieves state-of-the-art performance on two popular question answering datasets, the TrecQA dataset and the WikiQA dataset. Via further analysis, we showed that this model can reliably trace the errors it has made in the validation step to the training instances that might have caused this error. We believe that this error-tracing capability might be beneficial in improving dataset quality in many applications.",
}
@inproceedings{bonadiman-moschitti-2020-study,
    title = "A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection",
    author = "Bonadiman, Daniele  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.457",
    doi = "10.18653/v1/2020.coling-main.457",
    pages = "5211--5222",
    abstract = "An essential task of most Question Answering (QA) systems is to re-rank the set of answer candidates, i.e., Answer Sentence Selection (AS2). These candidates are typically sentences either extracted from one or more documents preserving their natural order or retrieved by a search engine. Most state-of-the-art approaches to the task use huge neural models, such as BERT, or complex attentive architectures. In this paper, we argue that by exploiting the intrinsic structure of the original rank together with an effective word-relatedness encoder, we achieve the highest accuracy among the cost-efficient models, with two orders of magnitude fewer parameters than the current state of the art. Our model takes 9.5 seconds to train on the WikiQA dataset, i.e., very fast in comparison with the 18 minutes required by a standard BERT-base fine-tuning.",
}
@inproceedings{pham-le-2020-auto,
    title = "Auto-Encoding Variational {B}ayes for Inferring Topics and Visualization",
    author = "Pham, Dang  and
      Le, Tuan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.458",
    doi = "10.18653/v1/2020.coling-main.458",
    pages = "5223--5234",
    abstract = "Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.",
}
@inproceedings{kalouli-etal-2020-hy,
    title = "Hy-{NLI}: a Hybrid system for Natural Language Inference",
    author = "Kalouli, Aikaterini-Lida  and
      Crouch, Richard  and
      de Paiva, Valeria",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.459",
    doi = "10.18653/v1/2020.coling-main.459",
    pages = "5235--5249",
    abstract = "Despite the advances in Natural Language Inference through the training of massive deep models, recent work has revealed the generalization difficulties of such models, which fail to perform on adversarial datasets with challenging linguistic phenomena. Such phenomena, however, can be handled well by symbolic systems. Thus, we propose Hy-NLI, a hybrid system that learns to identify an NLI pair as linguistically challenging or not. Based on that, it uses its symbolic or deep learning component, respectively, to make the final inference decision. We show how linguistically less complex cases are best solved by robust state-of-the-art models, like BERT and XLNet, while hard linguistic phenomena are best handled by our implemented symbolic engine. Our thorough evaluation shows that our hybrid system achieves state-of-the-art performance across mainstream and adversarial datasets and opens the way for further research into the hybrid direction.",
}
@inproceedings{vo-etal-2020-identifying,
    title = "Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition",
    author = "Vo, Ngoc Phuoc An  and
      Manotas, Irene  and
      Sheinin, Vadim  and
      Popescu, Octavian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.460",
    doi = "10.18653/v1/2020.coling-main.460",
    pages = "5250--5258",
    abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, however, detecting and understanding motion in text is not a trivial task. In addition, identifying motion entities in natural language is not only challenging but also beneficial for a better natural language understanding. In this paper, we present a Motion Entity Tagging (MET) model to identify entities in motion in a text using the Literal-Motion-in-Text (LiMiT) dataset for training and evaluating the model. Then we propose a new method to split clauses and phrases from complex and long motion sentences to improve the performance of our MET model. We also present results showing that motion features, in particular, entity in motion benefits the Named-Entity Recognition (NER) task. Finally, we present an analysis for the special co-occurrence relation between the person category in NER and animate entities in motion, which significantly improves the classification performance for the person category in NER.",
}
@inproceedings{zhou-etal-2020-global,
    title = "Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction",
    author = "Zhou, Huiwei  and
      Xu, Yibin  and
      Yao, Weihong  and
      Liu, Zhe  and
      Lang, Chengkun  and
      Jiang, Haibin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.461",
    doi = "10.18653/v1/2020.coling-main.461",
    pages = "5259--5270",
    abstract = "Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE. However, traditional GCN simply takes word nodes and adjacency matrix to represent graphs, which is difficult to establish direct connections between distant entity pairs. In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document. Two hierarchical blocks, Context-aware Attention Guided Graph Convolution (CAGGC) for partially connected graphs and Multi-head Attention Guided Graph Convolution (MAGGC) for fully connected graphs, could take progressively more global context into account. Meantime, we leverage a large-scale distantly supervised dataset to pre-train a GCGCN model with curriculum learning, which is then fine-tuned on the human-annotated dataset for further improving document-level RE performance. The experimental results on DocRED show that our model could effectively capture rich global context information in the document, leading to a state-of-the-art result. Our code is available at https://github.com/Huiweizhou/GCGCN.",
}
@inproceedings{bahuleyan-el-asri-2020-diverse,
    title = "Diverse Keyphrase Generation with Neural Unlikelihood Training",
    author = "Bahuleyan, Hareesh  and
      El Asri, Layla",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.462",
    doi = "10.18653/v1/2020.coling-main.462",
    pages = "5271--5287",
    abstract = "In this paper, we study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity. Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, demonstrated through improvements on quality metrics such as F1-score. However, the importance of diversity in keyphrase generation has been largely ignored. We first analyze the extent of information redundancy present in the outputs generated by a baseline model trained using maximum likelihood estimation (MLE). Our findings show that repetition of keyphrases is a major issue with MLE training. To alleviate this issue, we adopt neural unlikelihood (UL) objective for training the S2S model. Our version of UL training operates at (1) the target token level to discourage the generation of repeating tokens; (2) the copy token level to avoid copying repetitive tokens from the source text. Further, to encourage better model planning during the decoding process, we incorporate K-step ahead token prediction objective that computes both MLE and UL losses on future tokens as well. Through extensive experiments on datasets from three different domains we demonstrate that the proposed approach attains considerably large diversity gains, while maintaining competitive output quality.",
}
@inproceedings{xu-etal-2020-user,
    title = "User Memory Reasoning for Conversational Recommendation",
    author = "Xu, Hu  and
      Moon, Seungwhan  and
      Liu, Honglei  and
      Liu, Bing  and
      Shah, Pararth  and
      Liu, Bing  and
      Yu, Philip",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.463",
    doi = "10.18653/v1/2020.coling-main.463",
    pages = "5288--5308",
    abstract = "We study an end-to-end approach for conversational recommendation that dynamically manages and reasons over users{'} past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph. This formulation extends existing state tracking beyond the boundary of a single dialog to user state tracking (UST). For this study, we create a new Memory Graph (MG) {\textless}-{\textgreater} Conversational Recommendation parallel corpus called MGConvRex with 7K+ human-to-human role-playing dialogs, grounded on a large-scale user memory bootstrapped from real-world user scenarios. MGConvRex captures human-level reasoning over user memory and has disjoint training/testing sets of users for zero-shot (cold-start) reasoning for recommendation. We propose a simple yet expandable formulation for constructing and updating the MG, and an end-to-end graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies (eg recommendation) based on updated MG. The prediction of our proposed model inherits the graph structure, providing a natural way to explain policies. Experiments are conducted for both offline metrics and online simulation, showing competitive results.",
}
@inproceedings{fujita-etal-2020-diverse,
    title = "Diverse and Non-redundant Answer Set Extraction on Community {QA} based on {DPP}s",
    author = "Fujita, Shogo  and
      Shibata, Tomohide  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.464",
    doi = "10.18653/v1/2020.coling-main.464",
    pages = "5309--5320",
    abstract = "In community-based question answering (CQA) platforms, it takes time for a user to get useful information from among many answers. Although one solution is an answer ranking method, the user still needs to read through the top-ranked answers carefully. This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers. Our method is based on determinantal point processes (DPPs), and it calculates the answer importance and similarity between answers by using BERT. We built a dataset focusing on a Japanese CQA site, and the experiments on this dataset demonstrated that the proposed method outperformed several baseline methods.",
}
@inproceedings{han-etal-2020-empirical,
    title = "An empirical analysis of existing systems and datasets toward general simple question answering",
    author = "Han, Namgi  and
      Topic, Goran  and
      Noji, Hiroshi  and
      Takamura, Hiroya  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.465",
    doi = "10.18653/v1/2020.coling-main.465",
    pages = "5321--5334",
    abstract = "In this paper, we evaluate the progress of our field toward solving simple factoid questions over a knowledge base, a practically important problem in natural language interface to database. As in other natural language understanding tasks, a common practice for this task is to train and evaluate a model on a single dataset, and recent studies suggest that SimpleQuestions, the most popular and largest dataset, is nearly solved under this setting. However, this common setting does not evaluate the robustness of the systems outside of the distribution of the used training data. We rigorously evaluate such robustness of existing systems using different datasets. Our analysis, including shifting of training and test datasets and training on a union of the datasets, suggests that our progress in solving SimpleQuestions dataset does not indicate the success of more general simple question answering. We discuss a possible future direction toward this goal.",
}
@inproceedings{shi-etal-2020-learn,
    title = "Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification",
    author = "Shi, Qi  and
      Zhang, Yu  and
      Yin, Qingyu  and
      Liu, Ting",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.466",
    doi = "10.18653/v1/2020.coling-main.466",
    pages = "5335--5346",
    abstract = "Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning. Existing methods lack attention to take advantage of the combination of linguistic information and symbolic information. In this work, we propose HeterTFV, a graph-based reasoning approach, that learns to combine linguistic information and symbolic information effectively. We first construct a program graph to encode programs, a kind of LISP-like logical form, to learn the semantic compositionality of the programs. Then we construct a heterogeneous graph to incorporate both linguistic information and symbolic information by introducing program nodes into the heterogeneous graph. Finally, we propose a graph-based reasoning approach to reason over the multiple types of nodes to make an effective combination of both types of information. Experimental results on a large-scale benchmark dataset TABFACT illustrate the effect of our approach.",
}
@inproceedings{moghimifar-etal-2020-cosmo,
    title = "{C}os{M}o: Conditional {S}eq2{S}eq-based Mixture Model for Zero-Shot Commonsense Question Answering",
    author = "Moghimifar, Farhad  and
      Qu, Lizhen  and
      Zhuo, Yue  and
      Baktashmotlagh, Mahsa  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.467",
    doi = "10.18653/v1/2020.coling-main.467",
    pages = "5347--5359",
    abstract = "Commonsense reasoning refers to the ability of evaluating a social situation and acting accordingly. Identification of the implicit causes and effects of a social context is the driving capability which can enable machines to perform commonsense reasoning. The dynamic world of social interactions requires context-dependent on-demand systems to infer such underlying information. However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations. Hence they fail to estimate the correct reasoning path. In this paper, we present Conditional Seq2Seq-based Mixture model (CosMo), which provides us with the capabilities of dynamic and diverse content generation. We use CosMo to generate context-dependent clauses, which form a dynamic Knowledge Graph (KG) on-the-fly for commonsense reasoning. To show the adaptability of our model to context-dependant knowledge generation, we address the task of zero-shot commonsense question answering. The empirical results indicate an improvement of up to +5.2{\%} over the state-of-the-art models.",
}
@inproceedings{cui-etal-2020-enhancing,
    title = "Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks",
    author = "Cui, Peng  and
      Hu, Le  and
      Liu, Yuanchao",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.468",
    doi = "10.18653/v1/2020.coling-main.468",
    pages = "5360--5371",
    abstract = "Text summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. However, most of existing extractive models hardly capture inter-sentence relationships, particularly in long documents. They also often ignore the effect of topical information on capturing important contents. To address these issues, this paper proposes a graph neural network (GNN)-based extractive summarization model, enabling to capture inter-sentence relationships efficiently via graph-structured document representation. Moreover, our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model not only substantially achieves state-of-the-art results on CNN/DM and NYT datasets but also considerably outperforms existing approaches on scientific paper datasets consisting of much longer documents, indicating its better robustness in document genres and lengths. Further discussions show that topical information can help the model preselect salient contents from an entire document, which interprets its effectiveness in long document summarization.",
}
@inproceedings{santosh-etal-2020-sasake,
    title = "{S}a{SAKE}: Syntax and Semantics Aware Keyphrase Extraction from Research Papers",
    author = "Santosh, T.y.s.s  and
      Kumar Sanyal, Debarshi  and
      Bhowmick, Plaban Kumar  and
      Das, Partha Pratim",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.469",
    doi = "10.18653/v1/2020.coling-main.469",
    pages = "5372--5383",
    abstract = "Keyphrases in a research paper succinctly capture the primary content of the paper and also assist in indexing the paper at a concept level. Given the huge rate at which scientific papers are published today, it is important to have effective ways of automatically extracting keyphrases from a research paper. In this paper, we present a novel method, Syntax and Semantics Aware Keyphrase Extraction (SaSAKE), to extract keyphrases from research papers. It uses a transformer architecture, stacking up sentence encoders to incorporate sequential information, and graph encoders to incorporate syntactic and semantic dependency graph information. Incorporation of these dependency graphs helps to alleviate long-range dependency problems and identify the boundaries of multi-word keyphrases effectively. Experimental results on three benchmark datasets show that our proposed method SaSAKE achieves state-of-the-art performance in keyphrase extraction from scientific papers.",
}
@inproceedings{syed-etal-2020-news,
    title = "News Editorials: Towards Summarizing Long Argumentative Texts",
    author = "Syed, Shahbaz  and
      El Baff, Roxanne  and
      Kiesel, Johannes  and
      Al Khatib, Khalid  and
      Stein, Benno  and
      Potthast, Martin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.470",
    doi = "10.18653/v1/2020.coling-main.470",
    pages = "5384--5396",
    abstract = "The automatic summarization of argumentative texts has hardly been explored. This paper takes a further step in this direction, targeting news editorials, i.e., opinionated articles with a well-defined argumentation structure. With Webis-EditorialSum-2020, we present a corpus of 1330 carefully curated summaries for 266 news editorials. We evaluate these summaries based on a tailored annotation scheme, where a high-quality summary is expected to be thesis-indicative, persuasive, reasonable, concise, and self-contained. Our corpus contains at least three high-quality summaries for about 90{\%} of the editorials, rendering it a valuable resource for the development and evaluation of summarization technology for long argumentative texts. We further report details of both, an in-depth corpus analysis, and the evaluation of two extractive summarization models.",
}
@inproceedings{zhao-etal-2020-automatic,
    title = "Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations",
    author = "Zhao, Xingyuan  and
      Ozaki, Satoru  and
      Anastasopoulos, Antonios  and
      Neubig, Graham  and
      Levin, Lori",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.471",
    doi = "10.18653/v1/2020.coling-main.471",
    pages = "5397--5408",
    abstract = "Interlinear Glossed Text (IGT) is a widely used format for encoding linguistic information in language documentation projects and scholarly papers. Manual production of IGT takes time and requires linguistic expertise. We attempt to address this issue by creating automatic glossing models, using modern multi-source neural models that additionally leverage easy-to-collect translations. We further explore cross-lingual transfer and a simple output length control mechanism, further refining our models. Evaluated on three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on overall accuracy as well as lemma and tag recall.",
}
@inproceedings{park-caragea-2020-scientific,
    title = "Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning",
    author = "Park, Seoyeon  and
      Caragea, Cornelia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.472",
    doi = "10.18653/v1/2020.coling-main.472",
    pages = "5409--5419",
    abstract = "Scientific keyphrase identification and classification is the task of detecting and classifying keyphrases from scholarly text with their types from a set of predefined classes. This task has a wide range of benefits, but it is still challenging in performance due to the lack of large amounts of labeled data required for training deep neural models. In order to overcome this challenge, we explore pre-trained language models BERT and SciBERT with intermediate task transfer learning, using 42 data-rich related intermediate-target task combinations. We reveal that intermediate task transfer learning on SciBERT induces a better starting point for target task fine-tuning compared with BERT and achieves competitive performance in scientific keyphrase identification and classification compared to both previous works and strong baselines. Interestingly, we observe that BERT with intermediate task transfer learning fails to improve the performance of scientific keyphrase identification and classification potentially due to significant catastrophic forgetting. This result highlights that scientific knowledge achieved during the pre-training of language models on large scientific collections plays an important role in the target tasks. We also observe that sequence tagging related intermediate tasks, especially syntactic structure learning tasks such as POS Tagging, tend to work best for scientific keyphrase identification and classification.",
}
@inproceedings{li-etal-2020-exploiting,
    title = "Exploiting Microblog Conversation Structures to Detect Rumors",
    author = "Li, Jiawen  and
      Sujana, Yudianto  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.473",
    doi = "10.18653/v1/2020.coling-main.473",
    pages = "5420--5429",
    abstract = "As one of the most popular social media platforms, Twitter has become a primary source of information for many people. Unfortunately, both valid information and rumors are propagated on Twitter due to the lack of an automatic information verification system. Twitter users communicate by replying to other users{'} messages, forming a conversation structure. Using this structure, users can decide whether the information in the source tweet is a rumor by reading the tweet{'}s replies, which voice other users{'} stances on the tweet. The majority of rumor detection researchers process such tweets based on time, ignoring the conversation structure. To reap the benefits of the Twitter conversation structure, we developed a model to detect rumors by modeling conversation structure as a graph. Thus, our model{'}s improved representation of the conversation structure enhances its rumor detection accuracy. The experimental results on two rumor datasets show that our model outperforms several baseline models, including a state-of-the-art model",
}
@inproceedings{kotonya-toni-2020-explainable,
    title = "Explainable Automated Fact-Checking: A Survey",
    author = "Kotonya, Neema  and
      Toni, Francesca",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.474",
    doi = "10.18653/v1/2020.coling-main.474",
    pages = "5430--5443",
    abstract = "A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality {--} that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.",
}
@inproceedings{yuan-etal-2020-early,
    title = "Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning",
    author = "Yuan, Chunyuan  and
      Ma, Qianwen  and
      Zhou, Wei  and
      Han, Jizhong  and
      Hu, Songlin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.475",
    doi = "10.18653/v1/2020.coling-main.475",
    pages = "5444--5454",
    abstract = "The dissemination of fake news significantly affects personal reputation and public trust. Recently, fake news detection has attracted tremendous attention, and previous studies mainly focused on finding clues from news content or diffusion path. However, the required features of previous models are often unavailable or insufficient in early detection scenarios, resulting in poor performance. Thus, early fake news detection remains a tough challenge. Intuitively, the news from trusted and authoritative sources or shared by many users with a good reputation is more reliable than other news. Using the credibility of publishers and users as prior weakly supervised information, we can quickly locate fake news in massive news and detect them in the early stages of dissemination. In this paper, we propose a novel structure-aware multi-head attention network (SMAN), which combines the news content, publishing, and reposting relations of publishers and users, to jointly optimize the fake news detection and credibility prediction tasks. In this way, we can explicitly exploit the credibility of publishers and users for early fake news detection. We conducted experiments on three real-world datasets, and the results show that SMAN can detect fake news in 4 hours with an accuracy of over 91{\%}, which is much faster than the state-of-the-art models.",
}
@inproceedings{ma-gao-2020-debunking,
    title = "Debunking Rumors on {T}witter with Tree Transformer",
    author = "Ma, Jing  and
      Gao, Wei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.476",
    doi = "10.18653/v1/2020.coling-main.476",
    pages = "5455--5466",
    abstract = "Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by {``}word-of-post{''} through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves rumor detection performance.",
}
@inproceedings{del-tredici-fernandez-2020-words,
    title = "Words are the Window to the Soul: Language-based User Representations for Fake News Detection",
    author = "Del Tredici, Marco  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.477",
    doi = "10.18653/v1/2020.coling-main.477",
    pages = "5467--5479",
    abstract = "Cognitive and social traits of individuals are reflected in language use. Moreover, individuals who are prone to spread fake news online often share common traits. Building on these ideas, we introduce a model that creates representations of individuals on social media based only on the language they produce, and use them to detect fake news. We show that language-based user representations are beneficial for this task. We also present an extended analysis of the language of fake news spreaders, showing that its main features are mostly domain independent and consistent across two English datasets. Finally, we exploit the relation between language use and connections in the social graph to assess the presence of the Echo Chamber effect in our data.",
}
@inproceedings{wang-etal-2020-argumentation,
    title = "Argumentation Mining on Essays at Multi Scales",
    author = "Wang, Hao  and
      Huang, Zhen  and
      Dou, Yong  and
      Hong, Yu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.478",
    doi = "10.18653/v1/2020.coling-main.478",
    pages = "5480--5493",
    abstract = "Argumentation mining on essays is a new challenging task in natural language processing, which aims to identify the types and locations of argumentation components. Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level. However, this task is not scale-independent. Some types of argumentation components which serve as core opinions on essays or paragraphs, are at essay level or paragraph level. Sequence tagging method conducts reasoning by local context words, and fails to effectively mine these components. To this end, we propose a multi-scale argumentation mining model, where we respectively mine different types of argumentation components at corresponding levels. Besides, an effective coarse-to-fine argumentation fusion mechanism is proposed to further improve the performance. We conduct a serial of experiments on the Persuasive Essay dataset (PE2.0). Experimental results indicate that our model outperforms existing models on mining all types of argumentation components.",
}
@inproceedings{xu-etal-2020-data,
    title = "Data Augmentation for Multiclass Utterance Classification {--} A Systematic Study",
    author = "Xu, Binxia  and
      Qiu, Siyuan  and
      Zhang, Jie  and
      Wang, Yafang  and
      Shen, Xiaoyu  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.479",
    doi = "10.18653/v1/2020.coling-main.479",
    pages = "5494--5506",
    abstract = "Utterance classification is a key component in many conversational systems. However, classifying real-world user utterances is challenging, as people may express their ideas and thoughts in manifold ways, and the amount of training data for some categories may be fairly limited, resulting in imbalanced data distributions. To alleviate these issues, we conduct a comprehensive survey regarding data augmentation approaches for text classification, including simple random resampling, word-level transformations, and neural text generation to cope with imbalanced data. Our experiments focus on multi-class datasets with a large number of data samples, which has not been systematically studied in previous work. The results show that the effectiveness of different data augmentation schemes depends on the nature of the dataset under consideration.",
}
@inproceedings{niyongabo-etal-2020-kinnews,
    title = "{KINNEWS} and {KIRNEWS}: Benchmarking Cross-Lingual Text Classification for {K}inyarwanda and {K}irundi",
    author = "Niyongabo, Rubungo Andre  and
      Hong, Qu  and
      Kreutzer, Julia  and
      Huang, Li",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.480",
    doi = "10.18653/v1/2020.coling-main.480",
    pages = "5507--5521",
    abstract = "Recent progress in text classification has been focused on high-resource languages such as English and Chinese. For low-resource languages, amongst them most African languages, the lack of well-annotated data and effective preprocessing, is hindering the progress and the transfer of successful methods. In this paper, we introduce two news datasets (KINNEWS and IRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible, but while Kinyarwanda has been studied in Natural Language Processing (NLP) to some extent, this work constitutes the first study on Kirundi. Along with the datasets, we provide statistics, guidelines for preprocessing, and monolingual and cross-lingual baseline models. Our experiments show that training embeddings on the relatively higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi. In addition, the design of the created datasets allows for a wider use in NLP beyond text classification in future studies, such as representation learning, cross-lingual learning with more distant languages, or as base for new annotations for tasks such as parsing, POS tagging, and NER. The datasets, stopwords, and pre-trained embeddings are publicly available at https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus.",
}
@inproceedings{edwards-etal-2020-go,
    title = "Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification",
    author = "Edwards, Aleksandra  and
      Camacho-Collados, Jose  and
      De Ribaupierre, H{\'e}l{\`e}ne  and
      Preece, Alun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.481",
    doi = "10.18653/v1/2020.coling-main.481",
    pages = "5522--5529",
    abstract = "Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification. However, most classification datasets assume a large amount labeled data, which is commonly not the case in practical settings. In particular, in this paper we compare the performance of a light-weight linear classifier based on word embeddings, i.e., fastText (Joulin et al., 2017), versus a pre-trained language model, i.e., BERT (Devlin et al., 2019), across a wide range of datasets and classification tasks. In general, results show the importance of domain-specific unlabeled data, both in the form of word embeddings or language models. As for the comparison, BERT outperforms all baselines in standard datasets with large training sets. However, in settings with small training datasets a simple method like fastText coupled with domain-specific word embeddings performs equally well or better than BERT, even when pre-trained on domain-specific data.",
}
@inproceedings{huang-etal-2020-unsupervised-fine,
    title = "Unsupervised Fine-tuning for Text Clustering",
    author = "Huang, Shaohan  and
      Wei, Furu  and
      Cui, Lei  and
      Zhang, Xingxing  and
      Zhou, Ming",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.482",
    doi = "10.18653/v1/2020.coling-main.482",
    pages = "5530--5534",
    abstract = "Fine-tuning with pre-trained language models (e.g. BERT) has achieved great success in many language understanding tasks in supervised settings (e.g. text classification). However, relatively little work has been focused on applying pre-trained models in unsupervised settings, such as text clustering. In this paper, we propose a novel method to fine-tune pre-trained models unsupervisedly for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss. Experiments on three text clustering datasets (namely TREC-6, Yelp, and DBpedia) show that our model outperforms the baseline methods and achieves state-of-the-art results.",
}
@inproceedings{tanabe-etal-2020-exploiting,
    title = "Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification",
    author = "Tanabe, Hikari  and
      Ogawa, Tetsuji  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.483",
    doi = "10.18653/v1/2020.coling-main.483",
    pages = "5535--5540",
    abstract = "Recognition of the mental state of a human character in text is a major challenge in natural language processing. In this study, we investigate the efficacy of the narrative context in recognizing the emotional states of human characters in text and discuss an approach to make use of a priori knowledge regarding the employed emotion category system. Specifically, we experimentally show that the accuracy of emotion classification is substantially increased by encoding the preceding context of the target sentence using a BERT-based text encoder. We also compare ways to incorporate a priori knowledge of emotion categories by altering the loss function used in training, in which our proposal of multi-task learning that jointly learns to classify positive/negative polarity of emotions is included. The experimental results suggest that, when using Plutchik{'}s Wheel of Emotions, it is better to jointly classify the basic emotion categories with positive/negative polarity rather than directly exploiting its characteristic structure in which eight basic categories are arranged in a wheel.",
}
@inproceedings{andersen-etal-2020-word,
    title = "Word-Level Uncertainty Estimation for Black-Box Text Classifiers using {RNN}s",
    author = {Andersen, Jakob Smedegaard  and
      Sch{\"o}ner, Tom  and
      Maalej, Walid},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.484",
    doi = "10.18653/v1/2020.coling-main.484",
    pages = "5541--5546",
    abstract = "Estimating uncertainties of Neural Network predictions paves the way towards more reliable and trustful text classifications. However, common uncertainty estimation approaches remain as black-boxes without explaining which features have led to the uncertainty of a prediction. This hinders users from understanding the cause of unreliable model behaviour. We introduce an approach to decompose and visualize the uncertainty of text classifiers at the level of words. Our approach builds on top of Recurrent Neural Networks and Bayesian modelling in order to provide detailed explanations of uncertainties, enabling a deeper reasoning about unreliable model behaviours. We conduct a preliminary experiment to check the impact and correctness of our approach. By explaining and investigating the predictive uncertainties of a sentiment analysis task, we argue that our approach is able to provide a more profound understanding of artificial decision making.",
}
@inproceedings{lyu-etal-2020-shot,
    title = "Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network",
    author = "Lyu, Chen  and
      Liu, Weijie  and
      Wang, Ping",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.485",
    doi = "10.18653/v1/2020.coling-main.485",
    pages = "5547--5552",
    abstract = "In this paper, we propose a new few-shot text classification method. Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled text with few labeled data. To achieve this goal, we take advantage of advanced pre-trained language model to extract the semantic features of each document. Furthermore, we utilize an edge-labeling graph neural network to implicitly models the intra-cluster similarity and the inter-cluster dissimilarity of the documents. Finally, we take the results of the graph neural network as the input of a prototypical network to classify the unlabeled texts. We verify the effectiveness of our method on a sentiment analysis dataset and a relation classification dataset and achieve the state-of-the-art performance on both tasks.",
}
@inproceedings{eberts-etal-2020-manyent,
    title = "{M}any{E}nt: A Dataset for Few-shot Entity Typing",
    author = "Eberts, Markus  and
      Pech, Kevin  and
      Ulges, Adrian",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.486",
    doi = "10.18653/v1/2020.coling-main.486",
    pages = "5553--5557",
    abstract = "We introduce ManyEnt, a benchmark for entity typing models in few-shot scenarios. ManyEnt offers a rich typeset, with a fine-grain variant featuring 256 entity types and a coarse-grain one with 53 entity types. Both versions have been derived from the Wikidata knowledge graph in a semi-automatic fashion. We also report results for two baselines using BERT, reaching up to 70.68{\%} accuracy (10-way 1-shot).",
}
@inproceedings{kuwa-etal-2020-embedding,
    title = "Embedding Meta-Textual Information for Improved Learning to Rank",
    author = "Kuwa, Toshitaka  and
      Schamoni, Shigehiko  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.487",
    doi = "10.18653/v1/2020.coling-main.487",
    pages = "5558--5568",
    abstract = "Neural approaches to learning term embeddings have led to improved computation of similarity and ranking in information retrieval (IR). So far neural representation learning has not been extended to meta-textual information that is readily available for many IR tasks, for example, patent classes in prior-art retrieval, topical information in Wikipedia articles, or product categories in e-commerce data. We present a framework that learns embeddings for meta-textual categories, and optimizes a pairwise ranking objective for improved matching based on combined embeddings of textual and meta-textual information. We show considerable gains in an experimental evaluation on cross-lingual retrieval in the Wikipedia domain for three language pairs, and in the Patent domain for one language pair. Our results emphasize that the mode of combining different types of information is crucial for model improvement.",
}
@inproceedings{schick-etal-2020-automatically,
    title = "Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification",
    author = {Schick, Timo  and
      Schmid, Helmut  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.488",
    doi = "10.18653/v1/2020.coling-main.488",
    pages = "5569--5578",
    abstract = "A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels. Manually defining this mapping between words and labels requires both domain expertise and an understanding of the language model{'}s abilities. To mitigate this issue, we devise an approach that automatically finds such a mapping given small amounts of training data. For a number of tasks, the mapping found by our approach performs almost as well as hand-crafted label-to-word mappings.",
}
@inproceedings{sourty-etal-2020-knowledge,
    title = "Knowledge Base Embedding By Cooperative Knowledge Distillation",
    author = {Sourty, Rapha{\"e}l  and
      Moreno, Jose G.  and
      Servant, Fran{\c{c}}ois-Paul  and
      Tamine-Lechani, Lynda},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.489",
    doi = "10.18653/v1/2020.coling-main.489",
    pages = "5579--5590",
    abstract = "Knowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks. In this paper, we explore a new research direction to perform knowledge base (KB) representation learning grounded with the recent theoretical framework of knowledge distillation over neural networks. Given a set of KBs, our proposed approach KD-MKB, learns KB embeddings by mutually and jointly distilling knowledge within a dynamic teacher-student setting. Experimental results on two standard datasets show that knowledge distillation between KBs through entity and relation inference is actually observed. We also show that cooperative learning significantly outperforms the two proposed baselines, namely traditional and sequential distillation.",
}
@inproceedings{kratzwald-etal-2020-intkb,
    title = "{I}nt{KB}: A Verifiable Interactive Framework for Knowledge Base Completion",
    author = "Kratzwald, Bernhard  and
      Kunpeng, Guo  and
      Feuerriegel, Stefan  and
      Diefenbach, Dennis",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.490",
    doi = "10.18653/v1/2020.coling-main.490",
    pages = "5591--5603",
    abstract = "Knowledge bases (KBs) are essential for many downstream NLP tasks, yet their prime shortcoming is that they are often incomplete. State-of-the-art frameworks for KB completion often lack sufficient accuracy to work fully automated without human supervision. As a remedy, we propose : a novel interactive framework for KB completion from text based on a question answering pipeline. Our framework is tailored to the specific needs of a human-in-the-loop paradigm: (i) We generate facts that are aligned with text snippets and are thus immediately verifiable by humans. (ii) Our system is designed such that it continuously learns during the KB completion task and, therefore, significantly improves its performance upon initial zero- and few-shot relations over time. (iii) We only trigger human interactions when there is enough information for a correct prediction. Therefore, we train our system with negative examples and a fold-option if there is no answer. Our framework yields a favorable performance: it achieves a hit@1 ratio of 29.7{\%} for initially unseen relations, upon which it gradually improves to 46.2{\%}.",
}
@inproceedings{lee-etal-2020-reference,
    title = "Reference and Document Aware Semantic Evaluation Methods for {K}orean Language Summarization",
    author = "Lee, Dongyub  and
      Shin, Myeong Cheol  and
      Whang, Taesun  and
      Cho, Seungwoo  and
      Ko, Byeongil  and
      Lee, Daniel  and
      Kim, EungGyun  and
      Jo, Jaechoon",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.491",
    doi = "10.18653/v1/2020.coling-main.491",
    pages = "5604--5616",
    abstract = "Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Many existing works for text summarization are generally evaluated by using recall-oriented understudy for gisting evaluation (ROUGE) scores. However, as ROUGE scores are computed based on n-gram overlap, they do not reflect semantic meaning correspondences between generated and reference summaries. Because Korean is an agglutinative language that combines various morphemes into a word that express several meanings, ROUGE is not suitable for Korean summarization. In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS). We then propose a method for improving the correlation of the metrics with human judgment. Evaluation results show that the correlation with human judgment is significantly higher for our evaluation metrics than for ROUGE scores.",
}
@inproceedings{zhou-etal-2020-level,
    title = "At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization",
    author = "Zhou, Qingyu  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.492",
    doi = "10.18653/v1/2020.coling-main.492",
    pages = "5617--5628",
    abstract = "Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence level. However, it is unclear whether performing extraction at sentence level is the best solution. In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A neural extractive model which leverages the sub-sentential information and extracts them is presented. Extensive experiments and analyses show that extracting sub-sentential units performs competitively comparing to full sentence extraction under the evaluation of both automatic and human evaluations. Hopefully, our work could provide some inspiration of the basic extraction units in extractive summarization for future research.",
}
@inproceedings{yuan-etal-2020-fact,
    title = "Fact-level Extractive Summarization with Hierarchical Graph Mask on {BERT}",
    author = "Yuan, Ruifeng  and
      Wang, Zili  and
      Li, Wenjie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.493",
    doi = "10.18653/v1/2020.coling-main.493",
    pages = "5629--5639",
    abstract = "Most current extractive summarization models generate summaries by selecting salient sentences. However, one of the problems with sentence-level extractive summarization is that there exists a gap between the human-written gold summary and the oracle sentence labels. In this paper, we propose to extract fact-level semantic units for better extractive summarization. We also introduce a hierarchical structure, which incorporates the multi-level of granularities of the textual information into the model. In addition, we incorporate our model with BERT using a hierarchical graph mask. This allows us to combine BERT{'}s ability in natural language understanding and the structural information without increasing the scale of the model. Experiments on the CNN/DaliyMail dataset show that our model achieves state-of-the-art results.",
}
@inproceedings{goodwin-etal-2020-flight,
    title = "Flight of the {PEGASUS}? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive Summarization",
    author = "Goodwin, Travis  and
      Savery, Max  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.494",
    doi = "10.18653/v1/2020.coling-main.494",
    pages = "5640--5646",
    abstract = "Recent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization. However, most work has focused on (relatively) data-rich single-document summarization settings. In this paper, we explore highly-abstractive multi-document summarization where the summary is explicitly conditioned on a user-given topic statement or question. We compare the summarization quality produced by three state-of-the-art transformer-based models: BART, T5, and PEGASUS. We report the performance on four challenging summarization datasets: three from the general domain and one from consumer health in both zero-shot and few-shot learning settings. While prior work has shown significant differences in performance for these models on standard summarization tasks, our results indicate that with as few as 10 labeled examples there is no statistically significant difference in summary quality, suggesting the need for more abstractive benchmark collections when determining state-of-the-art.",
}
@inproceedings{laskar-etal-2020-wsl,
    title = "{WSL}-{DS}: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization",
    author = "Laskar, Md Tahmid Rahman  and
      Hoque, Enamul  and
      Huang, Jimmy Xiangji",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.495",
    doi = "10.18653/v1/2020.coling-main.495",
    pages = "5647--5654",
    abstract = "In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multi-document gold reference summaries. Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once. Experimental results on the Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics.",
}
@inproceedings{li-etal-2020-multimodal,
    title = "Multimodal Sentence Summarization via Multimodal Selective Encoding",
    author = "Li, Haoran  and
      Zhu, Junnan  and
      Zhang, Jiajun  and
      He, Xiaodong  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.496",
    doi = "10.18653/v1/2020.coling-main.496",
    pages = "5655--5667",
    abstract = "This paper studies the problem of generating a summary for a given sentence-image pair. Existing multimodal sequence-to-sequence approaches mainly focus on enhancing the decoder by visual signals, while ignoring that the image can improve the ability of the encoder to identify highlights of a news event or a document. Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence. In addition, we introduce a modality regularization to encourage the summary to capture the highlights embedded in the image more accurately. To verify the generalization of our model, we adopt the multimodal selective gate to the text-based decoder and multimodal-based decoder. Experimental results on a public multimodal sentence summarization dataset demonstrate the advantage of our models over baselines. Further analysis suggests that our proposed multimodal selective gate network can effectively select important information in the input sentence.",
}
@inproceedings{zheng-etal-2020-controllable,
    title = "Controllable Abstractive Sentence Summarization with Guiding Entities",
    author = "Zheng, Changmeng  and
      Cai, Yi  and
      Zhang, Guanjie  and
      Li, Qing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.497",
    doi = "10.18653/v1/2020.coling-main.497",
    pages = "5668--5678",
    abstract = "Entities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is difficult to guarantee that an entity is contained in generated summaries. In this paper, we propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. Instead of generating summaries from left to right, we start with a selected entity, generate the left part first, then the right part of a complete summary. Compared to previous entity-based text summarization models, our method can ensure that entities appear in final output summaries rather than generating the complete sentence with implicit entity and article representations. Our model can also generate more novel entities with them incorporated into outputs directly. To evaluate the informativeness of the proposed model, we develop a fine-grained informativeness metrics in the relevance, extraness and omission perspectives. We conduct experiments in two widely-used sentence summarization datasets and experimental results show that our model outperforms the state-of-the-art methods in both automatic evaluation scores and informativeness metrics.",
}
@inproceedings{mrabet-demner-fushman-2020-holms,
    title = "{HOLMS}: Alternative Summary Evaluation with Large Language Models",
    author = "Mrabet, Yassine  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.498",
    doi = "10.18653/v1/2020.coling-main.498",
    pages = "5679--5688",
    abstract = "Efficient document summarization requires evaluation measures that can not only rank a set of systems based on an average score, but also highlight which individual summary is better than another. However, despite the very active research on summarization approaches, few works have proposed new evaluation measures in the recent years. The standard measures relied upon for the development of summarization systems are most often ROUGE and BLEU which, despite being efficient in overall system ranking, remain lexical in nature and have a limited potential when it comes to training neural networks. In this paper, we present a new hybrid evaluation measure for summarization, called HOLMS, that combines both language models pre-trained on large corpora and lexical similarity measures. Through several experiments, we show that HOLMS outperforms ROUGE and BLEU substantially in its correlation with human judgments on several extractive summarization datasets for both linguistic quality and pyramid scores.",
}
@inproceedings{koay-etal-2020-domain,
    title = "How Domain Terminology Affects Meeting Summarization Performance",
    author = "Koay, Jia Jin  and
      Roustai, Alexander  and
      Dai, Xiaojin  and
      Burns, Dillon  and
      Kerrigan, Alec  and
      Liu, Fei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.499",
    doi = "10.18653/v1/2020.coling-main.499",
    pages = "5689--5695",
    abstract = "Meetings are essential to modern organizations. Numerous meetings are held and recorded daily, more than can ever be comprehended. A meeting summarization system that identifies salient utterances from the transcripts to automatically generate meeting minutes can help. It empowers users to rapidly search and sift through large meeting collections. To date, the impact of domain terminology on the performance of meeting summarization remains understudied, despite that meetings are rich with domain knowledge. In this paper, we create gold-standard annotations for domain terminology on a sizable meeting corpus; they are known as jargon terms. We then analyze the performance of a meeting summarization system with and without jargon terms. Our findings reveal that domain terminology can have a substantial impact on summarization performance. We publicly release all domain terminology to advance research in meeting summarization.",
}
@inproceedings{wang-etal-2020-anchor,
    title = "An Anchor-Based Automatic Evaluation Metric for Document Summarization",
    author = "Wang, Kexiang  and
      Liu, Tianyu  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.500",
    doi = "10.18653/v1/2020.coling-main.500",
    pages = "5696--5701",
    abstract = "The widespread adoption of reference-based automatic evaluation metrics such as ROUGE has promoted the development of document summarization. In this paper, we consider a new protocol for designing reference-based metrics that require the endorsement of source document(s). Following protocol, we propose an anchored ROUGE metric fixing each summary particle on source document, which bases the computation on more solid ground. Empirical results on benchmark datasets validate that source document helps to induce a higher correlation with human judgments for ROUGE metric. Being self-explanatory and easy-to-implement, the protocol can naturally foster various effective designs of reference-based metrics besides the anchored ROUGE introduced here.",
}
@inproceedings{bhandari-etal-2020-metrics,
    title = "Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics",
    author = "Bhandari, Manik  and
      Gour, Pranav Narayan  and
      Ashfaq, Atabak  and
      Liu, Pengfei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.501",
    doi = "10.18653/v1/2020.coling-main.501",
    pages = "5702--5711",
    abstract = "In text summarization, evaluating the efficacy of automatic metrics without human judgments has become recently popular. One exemplar work (Peyrard, 2019) concludes that automatic metrics strongly disagree when ranking high-scoring summaries. In this paper, we revisit their experiments and find that their observations stem from the fact that metrics disagree in ranking summaries from any narrow scoring range. We hypothesize that this may be because summaries are similar to each other in a narrow scoring range and are thus, difficult to rank. Apart from the width of the scoring range of summaries, we analyze three other properties that impact inter-metric agreement - Ease of Summarization, Abstractiveness, and Coverage.",
}
@inproceedings{yuan-etal-2020-faithfulness,
    title = "On the Faithfulness for {E}-commerce Product Summarization",
    author = "Yuan, Peng  and
      Li, Haoran  and
      Xu, Song  and
      Wu, Youzheng  and
      He, Xiaodong  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.502",
    doi = "10.18653/v1/2020.coling-main.502",
    pages = "5712--5717",
    abstract = "In this work, we present a model to generate e-commerce product summaries. The consistency between the generated summary and the product attributes is an essential criterion for the ecommerce product summarization task. To enhance the consistency, first, we encode the product attribute table to guide the process of summary generation. Second, we identify the attribute words from the vocabulary, and we constrain these attribute words can be presented in the summaries only through copying from the source, i.e., the attribute words not in the source cannot be generated. We construct a Chinese e-commerce product summarization dataset, and the experimental results on this dataset demonstrate that our models significantly improve the faithfulness.",
}
@inproceedings{malykh-etal-2020-sumtitles,
    title = "{S}um{T}itles: a Summarization Dataset with Low Extractiveness",
    author = "Malykh, Valentin  and
      Chernis, Konstantin  and
      Artemova, Ekaterina  and
      Piontkovskaya, Irina",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.503",
    doi = "10.18653/v1/2020.coling-main.503",
    pages = "5718--5730",
    abstract = "The existing dialogue summarization corpora are significantly extractive. We introduce a methodology for dataset extractiveness evaluation and present a new low-extractive corpus of movie dialogues for abstractive text summarization along with baseline evaluation. The corpus contains 153k dialogues and consists of three parts: 1) automatically aligned subtitles, 2) automatically aligned scenes from scripts, and 3) manually aligned scenes from scripts. We also present an alignment algorithm which we use to construct the corpus.",
}
@inproceedings{he-etal-2020-tweetsum,
    title = "{TWEETSUM}: Event oriented Social Summarization Dataset",
    author = "He, Ruifang  and
      Zhao, Liangliang  and
      Liu, Huanyu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.504",
    doi = "10.18653/v1/2020.coling-main.504",
    pages = "5731--5736",
    abstract = "With social media becoming popular, a vast of short and noisy messages are produced by millions of users when a hot event happens. Developing social summarization systems becomes more and more critical for people to quickly grasp core and essential information. However, the publicly available and high-quality large scale social summarization dataset is rare. Constructing such corpus is not easy and very expensive since short texts have very complex social characteristics. In this paper, we construct TWEETSUM, a new event-oriented dataset for social summarization. The original data is collected from twitter and contains 12 real world hot events with a total of 44,034 tweets and 11,240 users. Each event has four expert summaries, and we also have the annotation quality evaluation. In addition, we collect additional social signals (i.e. user relations, hashtags and user profiles) and further establish user relation network for each event. Besides the detailed dataset description, we show the performance of several typical extractive summarization methods on TWEETSUM to establish baselines. For further researches, we will release this dataset to the public.",
}
@inproceedings{bourgonje-stede-2020-exploiting,
    title = "Exploiting a lexical resource for discourse connective disambiguation in {G}erman",
    author = "Bourgonje, Peter  and
      Stede, Manfred",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.505",
    doi = "10.18653/v1/2020.coling-main.505",
    pages = "5737--5748",
    abstract = "In this paper we focus on connective identification and sense classification for explicit discourse relations in German, as two individual sub-tasks of the overarching Shallow Discourse Parsing task. We successively augment a purely-empirical approach based on contextualised embeddings with linguistic knowledge encoded in a connective lexicon. In this way, we improve over published results for connective identification, achieving a final F1-score of 87.93; and we introduce, to the best of our knowledge, first results for German sense classification, achieving an F1-score of 87.13. Our approach demonstrates that a connective lexicon can be a valuable resource for those languages that do not have a large PDTB-style-annotated coprus available.",
}
@inproceedings{jiang-etal-2020-chinese,
    title = "{C}hinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading",
    author = "Jiang, Feng  and
      Chu, Xiaomin  and
      Li, Peifeng  and
      Kong, Fang  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.506",
    doi = "10.18653/v1/2020.coling-main.506",
    pages = "5749--5759",
    abstract = "Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English. Due to the cultural and linguistic differences, existing successful methods on English discourse parsing cannot be transformed into Chinese directly, especially in paragraph level suffering from longer discourse units and fewer explicit connectives. To alleviate the above issues, we propose two reading modes, i.e., the global backward reading and the local reverse reading, to construct Chinese paragraph level discourse trees. The former processes discourse units from the end to the beginning in a document to utilize the left-branching bias of discourse structure in Chinese, while the latter reverses the position of paragraphs in a discourse unit to enhance the differentiation of coherence between adjacent discourse units. The experimental results on Chinese MCDTB demonstrate that our model outperforms all strong baselines.",
}
@inproceedings{li-etal-2020-neural,
    title = "A Neural Model for Aggregating Coreference Annotation in Crowdsourcing",
    author = "Li, Maolin  and
      Takamura, Hiroya  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.507",
    doi = "10.18653/v1/2020.coling-main.507",
    pages = "5760--5773",
    abstract = "Coreference resolution is the task of identifying all mentions in a text that refer to the same real-world entity. Collecting sufficient labelled data from expert annotators to train a high-performance coreference resolution system is time-consuming and expensive. Crowdsourcing makes it possible to obtain the required amounts of data rapidly and cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this paper, we split the aggregation into two subtasks, i.e, mention classification and coreference chain inference. Firstly, we predict the general class of each mention using an autoencoder, which incorporates contextual information about each mention, while at the same time taking into account the mention{'}s annotation complexity and annotators{'} reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into account the learned reliability in the first subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We also illustrate our model{'}s interpretability through a comprehensive analysis of experimental results.",
}
@inproceedings{aktas-stede-2020-variation,
    title = "Variation in Coreference Strategies across Genres and Production Media",
    author = "Akta{\c{s}}, Berfin  and
      Stede, Manfred",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.508",
    doi = "10.18653/v1/2020.coling-main.508",
    pages = "5774--5785",
    abstract = "In response to (i) inconclusive results in the literature as to the properties of coreference chains in written versus spoken language, and (ii) a general lack of work on automatic coreference resolution on both spoken language and social media, we undertake a corpus study involving the various genre sections of Ontonotes, the Switchboard corpus, and a corpus of Twitter conversations. Using a set of measures that previously have been applied individually to different data sets, we find fairly clear patterns of {``}behavior{''} for the different genres/media. Besides their role for psycholinguistic investigation (why do we employ different coreference strategies when we write or speak) and for the placement of Twitter in the spoken{--}written continuum, we see our results as a contribution to approaching genre-/media-specific coreference resolution.",
}
@inproceedings{de-kuthy-etal-2020-towards,
    title = "Towards automatically generating Questions under Discussion to link information and discourse structure",
    author = "De Kuthy, Kordula  and
      Kannan, Madeeswaran  and
      Santhi Ponnusamy, Haemanth  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.509",
    doi = "10.18653/v1/2020.coling-main.509",
    pages = "5786--5798",
    abstract = "Questions under Discussion (QUD; Roberts, 2012) are emerging as a conceptually fruitful approach to spelling out the connection between the information structure of a sentence and the nature of the discourse in which the sentence can function. To make this approach useful for analyzing authentic data, Riester, Brunetti {\&} De Kuthy (2018) presented a discourse annotation framework based on explicit pragmatic principles for determining a QUD for every assertion in a text. De Kuthy et al. (2018) demonstrate that this supports more reliable discourse structure annotation, and Ziai and Meurers (2018) show that based on explicit questions, automatic focus annotation becomes feasible. But both approaches are based on manually specified questions. In this paper, we present an automatic question generation approach to partially automate QUD annotation by generating all potentially relevant questions for a given sentence. While transformation rules can concisely capture the typical question formation process, a rule-based approach is not sufficiently robust for authentic data. We therefore employ the transformation rules to generate a large set of sentence-question-answer triples and train a neural question generation model on them to obtain both systematic question type coverage and robustness.",
}
@inproceedings{wang-etal-2020-learning-decouple,
    title = "Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training",
    author = "Wang, Yingyao  and
      Bao, Junwei  and
      Liu, Guangyi  and
      Wu, Youzheng  and
      He, Xiaodong  and
      Zhou, Bowen  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.510",
    doi = "10.18653/v1/2020.coling-main.510",
    pages = "5799--5809",
    abstract = "This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, previous few-shot relation classifiers struggle to distinguish them with few annotated instances. To alleviate the above relation confusion problem, we propose CTEG, a model equipped with two novel mechanisms to learn to decouple these easily-confused relations. On the one hand, an Entity -Guided Attention (EGA) mechanism, which leverages the syntactic relations and relative positions between each word and the specified entity pair, is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations by playing a pushing-away game between classifying a sentence into a true relation and its confusing relation. Extensive experiments are conducted on the FewRel dataset, and the results show that our proposed model achieves comparable and even much better results to strong baselines in terms of accuracy. Furthermore, the ablation test and case study verify the effectiveness of our proposed EGA and CAT, especially in addressing the relation confusion problem.",
}
@inproceedings{abburi-etal-2020-semi,
    title = "Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification",
    author = "Abburi, Harika  and
      Parikh, Pulkit  and
      Chhaya, Niyati  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.511",
    doi = "10.18653/v1/2020.coling-main.511",
    pages = "5810--5820",
    abstract = "Sexism, a form of oppression based on one{'}s sex, manifests itself in numerous ways and causes enormous suffering. In view of the growing number of experiences of sexism reported online, categorizing these recollections automatically can assist the fight against sexism, as it can facilitate effective analyses by gender studies researchers and government officials involved in policy making. In this paper, we investigate the fine-grained, multi-label classification of accounts (reports) of sexism. To the best of our knowledge, we work with considerably more categories of sexism than any published work through our 23-class problem formulation. Moreover, we propose a multi-task approach for fine-grained multi-label sexism classification that leverages several supporting tasks without incurring any manual labeling cost. Unlabeled accounts of sexism are utilized through unsupervised learning to help construct our multi-task setup. We also devise objective functions that exploit label correlations in the training data explicitly. Multiple proposed methods outperform the state-of-the-art for multi-label sexism classification on a recently released dataset across five standard metrics.",
}
@inproceedings{evaldo-leal-etal-2020-using,
    title = "Using Eye-tracking Data to Predict the Readability of {B}razilian {P}ortuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches",
    author = "Evaldo Leal, Sidney  and
      Munguba Vieira, Jo{\~a}o Marcos  and
      dos Santos Rodrigues, Erica  and
      Nogueira Teixeira, Elis{\^a}ngela  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.512",
    doi = "10.18653/v1/2020.coling-main.512",
    pages = "5821--5831",
    abstract = "Sentence complexity assessment is a relatively new task in Natural Language Processing. One of its aims is to highlight in a text which sentences are more complex to support the simplification of contents for a target audience (e.g., children, cognitively impaired users, non-native speakers and low-literacy readers (Scarton and Specia, 2018)). This task is evaluated using datasets of pairs of aligned sentences including the complex and simple version of the same sentence. For Brazilian Portuguese, the task was addressed by (Leal et al., 2018), who set up the first dataset to evaluate the task in this language, reaching 87.8{\%} of accuracy with linguistic features. The present work advances these results, using models inspired by (Gonzalez-Gardu{\~n}o and S{\o}gaard, 2018), which hold the state-of-the-art for the English language, with multi-task learning and eye-tracking measures. First-Pass Duration, Total Regression Duration and Total Fixation Duration were used in two moments; first to select a subset of linguistic features and then as an auxiliary task in the multi-task and sequential learning models. The best model proposed here reaches the new state-of-the-art for Portuguese with 97.5{\%} accuracy 1 , an increase of almost 10 points compared to the best previous results, in addition to proposing improvements in the public dataset after analysing the errors of our best model.",
}
@inproceedings{bhola-etal-2020-retrieving,
    title = "Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework",
    author = "Bhola, Akshay  and
      Halder, Kishaloy  and
      Prasad, Animesh  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.513",
    doi = "10.18653/v1/2020.coling-main.513",
    pages = "5832--5842",
    abstract = "We introduce a deep learning model to learn the set of enumerated job skills associated with a job description. In our analysis of a large-scale government job portal mycareersfuture.sg, we observe that as much as 65{\%} of job descriptions miss describing a significant number of relevant skills. Our model addresses this task from the perspective of an extreme multi-label classification (XMLC) problem, where descriptions are the evidence for the binary relevance of thousands of individual skills. Building upon the current state-of-the-art language modeling approaches such as BERT, we show our XMLC method improves on an existing baseline solution by over 9{\%} and 7{\%} absolute improvements in terms of recall and normalized discounted cumulative gain. We further show that our approach effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings by taking into account the structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process. We further show that our approach, to ensure the BERT-XMLC model accounts for structured semantic representation of skills and their co-occurrences through a Correlation Aware Bootstrapping process, effectively addresses the missing skills problem, and helps in recovering relevant skills that were missed out in the job postings. To facilitate future research and replication of our work, we have made the dataset and the implementation of our model publicly available.",
}
@inproceedings{harust-etal-2020-native,
    title = "Native-like Expression Identification by Contrasting Native and Proficient Second Language Speakers",
    author = "Harust, Oleksandr  and
      Murawaki, Yugo  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.514",
    doi = "10.18653/v1/2020.coling-main.514",
    pages = "5843--5854",
    abstract = "We propose a novel task of native-like expression identification by contrasting texts written by native speakers and those by proficient second language speakers. This task is highly challenging mainly because 1) the combinatorial nature of expressions prevents us from choosing candidate expressions a priori and 2) the distributions of the two types of texts overlap considerably. Our solution to the first problem is to combine a powerful neural network-based classifier of sentence-level nativeness with an explainability method that measures an approximate contribution of a given expression to the classifier{'}s prediction. To address the second problem, we introduce a special label neutral and reformulate the classification task as complementary-label learning. Our crowdsourcing-based evaluation and in-depth analysis suggest that our method successfully uncovers linguistically interesting usages distinctive of native speech.",
}
@inproceedings{emami-etal-2020-analysis,
    title = "An Analysis of Dataset Overlap on {W}inograd-Style Tasks",
    author = "Emami, Ali  and
      Suleman, Kaheer  and
      Trischler, Adam  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.515",
    doi = "10.18653/v1/2020.coling-main.515",
    pages = "5855--5865",
    abstract = "The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR). Model performance on the WSC has quickly progressed from chance-level to near-human using neural language models trained on massive corpora. In this paper, we analyze the effects of varying degrees of overlaps that occur between these corpora and the test instances in WSC-style tasks. We find that a large number of test instances overlap considerably with the pretraining corpora on which state-of-the-art models are trained, and that a significant drop in classification accuracy occurs when models are evaluated on instances with minimal overlap. Based on these results, we provide the WSC-Web dataset, consisting of over 60k pronoun disambiguation problems scraped from web data, being both the largest corpus to date, and having a significantly lower proportion of overlaps with current pretraining corpora.",
}
@inproceedings{kuhn-etal-2020-indigenous,
    title = "The Indigenous Languages Technology project at {NRC} {C}anada: An empowerment-oriented approach to developing language software",
    author = "Kuhn, Roland  and
      Davis, Fineen  and
      D{\'e}silets, Alain  and
      Joanis, Eric  and
      Kazantseva, Anna  and
      Knowles, Rebecca  and
      Littell, Patrick  and
      Lothian, Delaney  and
      Pine, Aidan  and
      Running Wolf, Caroline  and
      Santos, Eddie  and
      Stewart, Darlene  and
      Boulianne, Gilles  and
      Gupta, Vishwa  and
      Maracle Owennat{\'e}kha, Brian  and
      Martin, Akwirat{\'e}kha{'}  and
      Cox, Christopher  and
      Junker, Marie-Odile  and
      Sammons, Olivia  and
      Torkornoo, Delasie  and
      Thanyeht{\'e}nhas Brinklow, Nathan  and
      Child, Sara  and
      Farley, Beno{\^\i}t  and
      Huggins-Daines, David  and
      Rosenblum, Daisy  and
      Souter, Heather",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.516",
    doi = "10.18653/v1/2020.coling-main.516",
    pages = "5866--5878",
    abstract = "This paper surveys the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in Canada in preserving their languages and extending their use. The project aimed to work within the empowerment paradigm, where collaboration with communities and fulfillment of their goals is central. Since many of the technologies we developed were in response to community needs, the project ended up as a collection of diverse subprojects, including the creation of a sophisticated framework for building verb conjugators for highly inflectional polysynthetic languages (such as Kanyen{'}k{\'e}ha, in the Iroquoian language family), release of what is probably the largest available corpus of sentences in a polysynthetic language (Inuktut) aligned with English sentences and experiments with machine translation (MT) systems trained on this corpus, free online services based on automatic speech recognition (ASR) for easing the transcription bottleneck for recordings of speech in Indigenous languages (and other languages), software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects.",
}
@inproceedings{ramachandran-de-melo-2020-cross,
    title = "Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings",
    author = "Ramachandran, Arun  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.517",
    doi = "10.18653/v1/2020.coling-main.517",
    pages = "5879--5890",
    abstract = "Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually, especially for low-resource languages, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.",
}
@inproceedings{perez-almendros-etal-2020-dont,
    title = "Don{'}t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities",
    author = "Perez Almendros, Carla  and
      Espinosa Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.518",
    doi = "10.18653/v1/2020.coling-main.518",
    pages = "5891--5902",
    abstract = "In this paper, we introduce a new annotated dataset which is aimed at supporting the development of NLP models to identify and categorize language that is patronizing or condescending towards vulnerable communities (e.g. refugees, homeless people, poor families). While the prevalence of such language in the general media has long been shown to have harmful effects, it differs from other types of harmful language, in that it is generally used unconsciously and with good intentions. We furthermore believe that the often subtle nature of patronizing and condescending language (PCL) presents an interesting technical challenge for the NLP community. Our analysis of the proposed dataset shows that identifying PCL is hard for standard NLP models, with language models such as BERT achieving the best results.",
}
@inproceedings{clifton-etal-2020-100000,
    title = "100,000 Podcasts: A Spoken {E}nglish Document Corpus",
    author = "Clifton, Ann  and
      Reddy, Sravana  and
      Yu, Yongze  and
      Pappu, Aasish  and
      Rezapour, Rezvaneh  and
      Bonab, Hamed  and
      Eskevich, Maria  and
      Jones, Gareth  and
      Karlgren, Jussi  and
      Carterette, Ben  and
      Jones, Rosie",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.519",
    doi = "10.18653/v1/2020.coling-main.519",
    pages = "5903--5917",
    abstract = "Podcasts are a large and growing repository of spoken audio. As an audio format, podcasts are more varied in style and production type than broadcast news, contain more genres than typically studied in video data, and are more varied in style and format than previous corpora of conversations. When transcribed with automatic speech recognition they represent a noisy but fascinating collection of documents which can be studied through the lens of natural language processing, information retrieval, and linguistics. Paired with the audio files, they are also a resource for speech processing and the study of paralinguistic, sociolinguistic, and acoustic aspects of the domain. We introduce the Spotify Podcast Dataset, a new corpus of 100,000 podcasts. We demonstrate the complexity of the domain with a case study of two tasks: (1) passage search and (2) summarization. This is orders of magnitude larger than previous speech corpora used for search and summarization. Our results show that the size and variability of this corpus opens up new avenues for research.",
}
@inproceedings{xie-etal-2020-contextual,
    title = "A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment",
    author = "Xie, Zhiwen  and
      Zhu, Runjie  and
      Zhao, Kunsong  and
      Liu, Jin  and
      Zhou, Guangyou  and
      Huang, Jimmy Xiangji",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.520",
    doi = "10.18653/v1/2020.coling-main.520",
    pages = "5918--5928",
    abstract = "Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.",
}
@inproceedings{fujii-etal-2020-phemt,
    title = "{P}he{MT}: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents",
    author = "Fujii, Ryo  and
      Mita, Masato  and
      Abe, Kaori  and
      Hanawa, Kazuaki  and
      Morishita, Makoto  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.521",
    doi = "10.18653/v1/2020.coling-main.521",
    pages = "5929--5943",
    abstract = "Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain. However, existing studies suggest that NMT still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. To make better use of NMT for cross-cultural communication, one of the most promising directions is to develop a model that correctly handles these expressions. Though its importance has been recognized, it is still not clear as to what creates the great gap in performance between the translation of clean input and that of UGC. To answer the question, we present a new dataset, PheMT, for evaluating the robustness of MT systems against specific linguistic phenomena in Japanese-English translation. Our experiments with the created dataset revealed that not only our in-house models but even widely used off-the-shelf systems are greatly disturbed by the presence of certain phenomena.",
}
@inproceedings{zhai-etal-2020-detecting,
    title = "Detecting Non-literal Translations by Fine-tuning Cross-lingual Pre-trained Language Models",
    author = "Zhai, Yuming  and
      Illouz, Gabriel  and
      Vilnat, Anne",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.522",
    doi = "10.18653/v1/2020.coling-main.522",
    pages = "5944--5956",
    abstract = "Human-generated non-literal translations reflect the richness of human languages and are sometimes indispensable to ensure adequacy and fluency. Non-literal translations are difficult to produce even for human translators, especially for foreign language learners, and machine translations are still on the way to simulate human ones on this aspect. In order to foster the study on appropriate and creative non-literal translations, automatically detecting them in parallel corpora is an important step, which can benefit downstream NLP tasks or help to construct materials to teach translation. This article demonstrates that generic sentence representations produced by a pre-trained cross-lingual language model could be fine-tuned to solve this task. We show that there exists a moderate positive correlation between the prediction probability of being human translation and the non-literal translations{'} proportion in a sentence. The fine-tuning experiments show an accuracy of 80.16{\%} when predicting the presence of non-literal translations in a sentence and an accuracy of 85.20{\%} when distinguishing literal and non-literal translations at phrase level. We further conduct a linguistic error analysis and propose directions for future work.",
}
@inproceedings{rahimi-etal-2020-wikiumls,
    title = "{W}iki{UMLS}: Aligning {UMLS} to {W}ikipedia via Cross-lingual Neural Ranking",
    author = "Rahimi, Afshin  and
      Baldwin, Timothy  and
      Verspoor, Karin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.523",
    doi = "10.18653/v1/2020.coling-main.523",
    pages = "5957--5962",
    abstract = "We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources. We propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1of 72{\%}, a substantial improvement of 20{\%} over word- and char-level BM25, enabling manual alignment with minimal effort. We release our resources, including ranked Wikipedia pages for 700k UMLSconcepts, and WikiUMLS, a dataset for training and evaluation of alignment models between UMLS and Wikipedia collected from Wikidata. This will provide easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings.",
}
@inproceedings{pal-etal-2020-transference,
    title = "The Transference Architecture for Automatic Post-Editing",
    author = {Pal, Santanu  and
      Xu, Hongfei  and
      Herbig, Nico  and
      Naskar, Sudip Kumar  and
      Kr{\"u}ger, Antonio  and
      van Genabith, Josef},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.524",
    doi = "10.18653/v1/2020.coling-main.524",
    pages = "5963--5974",
    abstract = "In automatic post-editing (APE) it makes sense to condition post-editing (pe) decisions on both the source (src) and the machine translated text (mt) as input. This has led to multi-encoder based neural APE approaches. A research challenge now is the search for architectures that best support the capture, preparation and provision of src and mt information and its integration with pe decisions. In this paper we present an efficient multi-encoder based APE model, called transference. Unlike previous approaches, it (i) uses a transformer encoder block for src, (ii) followed by a decoder block, but without masking for self-attention on mt, which effectively acts as second encoder combining src {--}{\textgreater} mt, and (iii) feeds this representation into a final decoder block generating pe. Our model outperforms the best performing systems by 1 BLEU point on the WMT 2016, 2017, and 2018 English{--}German APE shared tasks (PBSMT and NMT). Furthermore, the results of our model on the WMT 2019 APE task using NMT data shows a comparable performance to the state-of-the-art system. The inference time of our model is similar to the vanilla transformer-based NMT system although our model deals with two separate encoders. We further investigate the importance of our newly introduced second encoder and find that a too small amount of layers does hurt the performance, while reducing the number of layers of the decoder does not matter much.",
}
@inproceedings{yin-read-2020-better,
    title = "Better Sign Language Translation with {STMC}-Transformer",
    author = "Yin, Kayo  and
      Read, Jesse",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.525",
    doi = "10.18653/v1/2020.coling-main.525",
    pages = "5975--5989",
    abstract = "Sign Language Translation (SLT) first uses a Sign Language Recognition (SLR) system to extract sign language glosses from videos. Then, a translation system generates spoken language translations from the sign language glosses. This paper focuses on the translation system and introduces the STMC-Transformer which improves on the current state-of-the-art by over 5 and 7 BLEU respectively on gloss-to-text and video-to-text translation of the PHOENIX-Weather 2014T dataset. On the ASLG-PC12 corpus, we report an increase of over 16 BLEU. We also demonstrate the problem in current methods that rely on gloss supervision. The video-to-text translation of our STMC-Transformer outperforms translation of GT glosses. This contradicts previous claims that GT gloss translation acts as an upper bound for SLT performance and reveals that glosses are an inefficient representation of sign language. For future SLT research, we therefore suggest an end-to-end training of the recognition and translation models, or using a different sign language annotation scheme.",
}
@inproceedings{li-etal-2020-simple,
    title = "A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction",
    author = "Li, Yanyang  and
      Luo, Yingfeng  and
      Lin, Ye  and
      Du, Quan  and
      Wang, Huizhen  and
      Huang, Shujian  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.526",
    doi = "10.18653/v1/2020.coling-main.526",
    pages = "5990--6001",
    abstract = "Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish. But they still fail and have an accuracy of 0{\%} in many distant language pairs, e.g., English-Japanese. In this work, we show that this failure results from the gap between the actual initialization performance and the minimum initialization performance for the self-learning to succeed. We propose Iterative Dimension Reduction to bridge this gap. Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13.64 55.53{\%} between English and four distant languages, i.e., Chinese, Japanese, Vietnamese and Thai.",
}
@inproceedings{laville-etal-2020-data,
    title = "Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora",
    author = "Laville, Martin  and
      Hazem, Amir  and
      Morin, Emmanuel  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.527",
    doi = "10.18653/v1/2020.coling-main.527",
    pages = "6002--6012",
    abstract = "Narrow specialized comparable corpora are often small in size. This particularity makes it difficult to build efficient models to acquire translation equivalents, especially for less frequent and rare words. One way to overcome this issue is to enrich the specialized corpora with out-of-domain resources. Although some recent studies have shown improvements using data augmentation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally. In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora. We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy. Then, we propose to exploit BERT for data selection. Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin. The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10.",
}
@inproceedings{dan-etal-2020-locally,
    title = "A Locally Linear Procedure for Word Translation",
    author = "Dan, Soham  and
      Taitelbaum, Hagai  and
      Goldberger, Jacob",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.528",
    doi = "10.18653/v1/2020.coling-main.528",
    pages = "6013--6018",
    abstract = "Learning a mapping between word embeddings of two languages given a dictionary is an important problem with several applications. A common mapping approach is using an orthogonal matrix. The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix. This solution restricts the expressiveness of the translation model which may result in sub-optimal translations. We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices. We achieve better performance in a bilingual word translation task and a cross-lingual word similarity task compared to the single matrix baseline. We also show how multiple matrices can model multiple senses of a word.",
}
@inproceedings{wang-tu-2020-rethinking,
    title = "Rethinking the Value of Transformer Components",
    author = "Wang, Wenxuan  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.529",
    doi = "10.18653/v1/2020.coling-main.529",
    pages = "6019--6029",
    abstract = "Transformer becomes the state-of-the-art translation model, while it is not well studied how each intermediate component contributes to the model performance, which poses significant challenges for designing optimal architectures. In this work, we bridge this gap by evaluating the impact of individual component (sub-layer) in trained Transformer models from different perspectives. Experimental results across language pairs, training strategies, and model capacities show that certain components are consistently more important than the others. We also report a number of interesting findings that might help humans better analyze, understand and improve Transformer models. Based on these observations, we further propose a new training strategy that can improves translation performance by distinguishing the unimportant components in training.",
}
@inproceedings{abid-2020-sadid,
    title = "The {SADID} Evaluation Datasets for Low-Resource Spoken Language Machine Translation of {A}rabic Dialects",
    author = "Abid, Wael",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.530",
    doi = "10.18653/v1/2020.coling-main.530",
    pages = "6030--6043",
    abstract = "Low-resource Machine Translation recently gained a lot of popularity, and for certain languages, it has made great strides. However, it is still difficult to track progress in other languages for which there is no publicly available evaluation data. In this paper, we introduce benchmark datasets for Arabic and its dialects. We describe our design process and motivations and analyze the datasets to understand their resulting properties. Numerous successful attempts use large monolingual corpora to augment low-resource pairs. We try to approach augmentation differently and investigate whether it is possible to improve MT models without any external sources of data. We accomplish this by bootstrapping existing parallel sentences and complement this with multilingual training to achieve strong baselines.",
}
@inproceedings{severini-etal-2020-combining,
    title = "Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction",
    author = {Severini, Silvia  and
      Hangya, Viktor  and
      Fraser, Alexander  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.531",
    doi = "10.18653/v1/2020.coling-main.531",
    pages = "6044--6055",
    abstract = "Bilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual training data is not available. To perform BDI, bilingual word embeddings (BWEs) are often used due to their low bilingual training signal requirements. They achieve high performance, but problematic cases still remain, such as the translation of rare words or named entities, which often need to be transliterated. In this paper, we enrich BWE-based BDI with transliteration information by using Bilingual Orthography Embeddings (BOEs). BOEs represent source and target language transliteration word pairs with similar vectors. A key problem in our BDI setup is to decide which information source {--} BWEs (or semantics) vs. BOEs (or orthography) {--} is more reliable for a particular word pair. We propose a novel classification-based BDI system that uses BWEs, BOEs and a number of other features to make this decision. We test our system on English-Russian BDI and show improved performance. In addition, we show the effectiveness of our BOEs by successfully using them for transliteration mining based on cosine similarity.",
}
@inproceedings{dutta-chowdhury-etal-2020-understanding,
    title = "Understanding Translationese in Multi-view Embedding Spaces",
    author = "Dutta Chowdhury, Koel  and
      Espa{\~n}a-Bonet, Cristina  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.532",
    doi = "10.18653/v1/2020.coling-main.532",
    pages = "6056--6062",
    abstract = "Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation. In this paper, we focus on embedding-based semantic spaces, exploiting departures from isomorphism between spaces built from original target language and translations into this target language to predict relations between languages in an unsupervised way. We use different views of the data {---} words, parts of speech, semantic tags and synsets {---} to track translationese. Our analysis shows that (i) semantic distances between original target language and translations into this target language can be detected using the notion of isomorphism, (ii) language family ties with characteristics similar to linguistically motivated phylogenetic trees can be inferred from the distances and (iii) with delexicalised embeddings exhibiting source-language interference most significantly, other levels of abstraction display the same tendency, indicating the lexicalised results to be not {``}just{''} due to possible topic differences between original and translated texts. To the best of our knowledge, this is the first time departures from isomorphism between embedding spaces are used to track translationese.",
}
@inproceedings{almeida-costa-etal-2020-building,
    title = "Building The First {E}nglish-{B}razilian {P}ortuguese Corpus for Automatic Post-Editing",
    author = "Almeida Costa, Felipe  and
      Castro Ferreira, Thiago  and
      Pagano, Adriana  and
      Meira, Wagner",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.533",
    doi = "10.18653/v1/2020.coling-main.533",
    pages = "6063--6069",
    abstract = "This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese. The source English texts were extracted from the WebNLG corpus and automatically translated into Portuguese using a state-of-the-art industrial neural machine translator. Post-edits were then obtained in an experiment with native speakers of Brazilian Portuguese. To assess the quality of the corpus, we performed error analysis and computed complexity indicators measuring how difficult the APE task would be. We report preliminary results of Phrase-Based and Neural Machine Translation Models on this new corpus. Data and code publicly available in our repository.",
}
@inproceedings{saurav-etal-2020-analysing,
    title = "Analysing cross-lingual transfer in lemmatisation for {I}ndian languages",
    author = "Saurav, Kumar  and
      Saunack, Kumar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.534",
    doi = "10.18653/v1/2020.coling-main.534",
    pages = "6070--6076",
    abstract = "Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. However, most of the prior work on this topic has focused on high resource languages. In this paper, we evaluate cross-lingual approaches for low resource languages, especially in the context of morphologically rich Indian languages. We test our model on six languages from two different families and develop linguistic insights into each model{'}s performance.",
}
@inproceedings{uto-etal-2020-neural,
    title = "Neural Automated Essay Scoring Incorporating Handcrafted Features",
    author = "Uto, Masaki  and
      Xie, Yikuan  and
      Ueno, Maomi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.535",
    doi = "10.18653/v1/2020.coling-main.535",
    pages = "6077--6088",
    abstract = "Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by human raters. Conventional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks (DNNs) to obviate the need for feature engineering. Furthermore, hybrid methods that integrate handcrafted features in a DNN-AES model have been recently developed and have achieved state-of-the-art accuracy. One of the most popular hybrid methods is formulated as a DNN-AES model with an additional recurrent neural network (RNN) that processes a sequence of handcrafted sentence-level features. However, this method has the following problems: 1) It cannot incorporate effective essay-level features developed in previous AES research. 2) It greatly increases the numbers of model parameters and tuning parameters, increasing the difficulty of model training. 3) It has an additional RNN to process sentence-level features, enabling extension to various DNN-AES models complex. To resolve these problems, we propose a new hybrid method that integrates handcrafted essay-level features into a DNN-AES model. Specifically, our method concatenates handcrafted essay-level features to a distributed essay representation vector, which is obtained from an intermediate layer of a DNN-AES model. Our method is a simple DNN-AES extension, but significantly improves scoring accuracy.",
}
@inproceedings{jahan-etal-2020-straightforward,
    title = "A Straightforward Approach to Narratologically Grounded Character Identification",
    author = "Jahan, Labiba  and
      Mittal, Rahul  and
      Yarlott, W. Victor  and
      Finlayson, Mark",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.536",
    doi = "10.18653/v1/2020.coling-main.536",
    pages = "6089--6100",
    abstract = "One of the most fundamental elements of narrative is character: if we are to understand a narrative, we must be able to identify the characters of that narrative. Therefore, character identification is a critical task in narrative natural language understanding. Most prior work has lacked a narratologically grounded definition of character, instead relying on simplified or implicit definitions that do not capture essential distinctions between characters and other referents in narratives. In prior work we proposed a preliminary definition of character that was based in clear narratological principles: a character is an animate entity that is important to the plot. Here we flesh out this concept, demonstrate that it can be reliably annotated (0.78 Cohen{'}s κ), and provide annotations of 170 narrative texts, drawn from 3 different corpora, containing 1,347 character co-reference chains and 21,999 non-character chains that include 3,937 animate chains. Furthermore, we have shown that a supervised classifier using a simple set of easily computable features can effectively identify these characters (overall F1 of 0.90). A detailed error analysis shows that character identification is first and foremost affected by co-reference quality, and further, that the shorter a chain is the harder it is to effectively identify as a character. We release our code and data for the benefit of other researchers",
}
@inproceedings{hou-2020-fine,
    title = "Fine-grained Information Status Classification Using Discourse Context-Aware {BERT}",
    author = "Hou, Yufang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.537",
    doi = "10.18653/v1/2020.coling-main.537",
    pages = "6101--6112",
    abstract = "Previous work on bridging anaphora recognition (Hou et al., 2013) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted linguistic features. In this paper, we propose a simple discourse context-aware BERT model for fine-grained IS classification. On the ISNotes corpus (Markert et al., 2012), our model achieves new state-of-the-art performances on fine-grained IS classification, obtaining a 4.8 absolute overall accuracy improvement compared to Hou et al. (2013). More importantly, we also show an improvement of 10.5 F1 points for bridging anaphora recognition without using any complex hand-crafted semantic features designed for capturing the bridging phenomenon. We further analyze the trained model and find that the most attended signals for each IS category correspond well to linguistic notions of information status.",
}
@inproceedings{yu-etal-2020-free,
    title = "Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution",
    author = "Yu, Juntao  and
      Moosavi, Nafise Sadat  and
      Paun, Silviu  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.538",
    doi = "10.18653/v1/2020.coling-main.538",
    pages = "6113--6125",
    abstract = "Now that the performance of coreference resolvers on the simpler forms of anaphoric reference has greatly improved, more attention is devoted to more complex aspects of anaphora. One limitation of virtually all coreference resolution models is the focus on single-antecedent anaphors. Plural anaphors with multiple antecedents-so-called split-antecedent anaphors (as in John met Mary. They went to the movies) have not been widely studied, because they are not annotated in ONTONOTES and are relatively infrequent in other corpora. In this paper, we introduce the first model for unrestricted resolution of split-antecedent anaphors. We start with a strong baseline enhanced by BERT embeddings, and show that we can substantially improve its performance by addressing the sparsity issue. To do this, we experiment with auxiliary corpora where split-antecedent anaphors were annotated by the crowd, and with transfer learning models using element-of bridging references and single-antecedent coreference as auxiliary tasks. Evaluation on the gold annotated ARRAU corpus shows that the out best model uses a combination of three auxiliary corpora achieved F1 scores of 70{\%} and 43.6{\%} when evaluated in a lenient and strict setting, respectively, i.e., 11 and 21 percentage points gain when compared with our baseline.",
}
@inproceedings{pishdad-etal-2020-coherent,
    title = "How coherent are neural models of coherence?",
    author = "Pishdad, Leila  and
      Fancellu, Federico  and
      Zhang, Ran  and
      Fazly, Afsaneh",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.539",
    doi = "10.18653/v1/2020.coling-main.539",
    pages = "6126--6138",
    abstract = "Despite the recent advances in coherence modelling, most such models including state-of-the-art neural ones, are evaluated on either contrived proxy tasks such as the standard order discrimination benchmark, or tasks that require special expert annotation. Moreover, most evaluations are conducted on small newswire corpora. To address these shortcomings, in this paper we propose four generic evaluation tasks that draw on different aspects of coherence at both the lexical and document levels, and can be applied to any corpora. In designing these tasks, we aim at capturing coherence-specific properties, such as the correct use of discourse connectives, lexical cohesion, as well as the overall temporal and causal consistency among events and participants in a story. Importantly, our proposed tasks either rely on automatically-generated data, or data annotated for other purposes, hence alleviating the need for annotation specifically targeted to the task of coherence modelling. We perform experiments with several existing state-of-the-art neural models of coherence on these tasks, across large corpora from different domains, including newswire, dialogue, as well as narrative and instructional text. Our findings point to a strong need for revisiting the common practices in the development and evaluation of coherence models.",
}
@inproceedings{alhindi-etal-2020-fact,
    title = "Fact vs. Opinion: the Role of Argumentation Features in News Classification",
    author = "Alhindi, Tariq  and
      Muresan, Smaranda  and
      Preotiuc-Pietro, Daniel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.540",
    doi = "10.18653/v1/2020.coling-main.540",
    pages = "6139--6149",
    abstract = "A 2018 study led by the Media Insight Project showed that most journalists think that a clearmarking of what is news reporting and what is commentary or opinion (e.g., editorial, op-ed)is essential for gaining public trust. We present an approach to classify news articles into newsstories (i.e., reporting of factual information) and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. Our hypothesis is thatthe nature of argumentative discourse is important in distinguishing between news stories andopinion articles. We show that argumentation features outperform linguistic features used previ-ously and improve on fine-tuned transformer-based models when tested on data from publishersunseen in training. Automatically flagging opinion pieces vs. news stories can aid applicationssuch as fact-checking or event extraction.",
}
@inproceedings{yang-etal-2020-generating,
    title = "Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification",
    author = "Yang, Linyi  and
      Kenny, Eoin  and
      Ng, Tin Lok James  and
      Yang, Yi  and
      Smyth, Barry  and
      Dong, Ruihai",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.541",
    doi = "10.18653/v1/2020.coling-main.541",
    pages = "6150--6160",
    abstract = "Corporate mergers and acquisitions (M{\&}A) account for billions of dollars of investment globally every year and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust/accurate model, but be able to generate useful explanations to garner a user{'}s trust in the automated system. Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classification has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user{'}s trust in the system. To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech. Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current state-of-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials.",
}
@inproceedings{guo-etal-2020-text,
    title = "Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for {A}lzheimer{'}s Disease Detection",
    author = "Guo, Zhiqiang  and
      Liu, Zhaoci  and
      Ling, Zhenhua  and
      Wang, Shijin  and
      Jin, Lingjing  and
      Li, Yunxia",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.542",
    doi = "10.18653/v1/2020.coling-main.542",
    pages = "6161--6171",
    abstract = "Data scarcity is always a constraint on analyzing speech transcriptions for automatic Alzheimer{'}s disease (AD) detection, especially when the subjects are non-English speakers. To deal with this issue, this paper first proposes a contrastive learning method to obtain effective representations for text classification based on monolingual embeddings of BERT. Furthermore, a cross-lingual data augmentation method is designed by building autoencoders to learn the text representations shared by both languages. Experiments on a Mandarin AD corpus show that the contrastive learning method can achieve better detection accuracy than conventional CNN-based and BERTbased methods. Our cross-lingual data augmentation method also outperforms other compared methods when using another English AD corpus for augmentation. Finally, a best detection accuracy of 81.6{\%} is obtained by our proposed methods on the Mandarin AD corpus.",
}
@inproceedings{mutuvi-etal-2020-multilingual,
    title = "Multilingual Epidemiological Text Classification: A Comparative Study",
    author = {Mutuvi, Stephen  and
      Boros, Emanuela  and
      Doucet, Antoine  and
      Jatowt, Adam  and
      Lejeune, Ga{\"e}l  and
      Odeo, Moses},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.543",
    doi = "10.18653/v1/2020.coling-main.543",
    pages = "6172--6183",
    abstract = "In this paper, we approach the multilingual text classification task in the context of the epidemiological field. Multilingual text classification models tend to perform differently across different languages (low- or high-resourced), more particularly when the dataset is highly imbalanced, which is the case for epidemiological datasets. We conduct a comparative study of different machine and deep learning text classification models using a dataset comprising news articles related to epidemic outbreaks from six languages, four low-resourced and two high-resourced, in order to analyze the influence of the nature of the language, the structure of the document, and the size of the data. Our findings indicate that the performance of the models based on fine-tuned language models exceeds by more than 50{\%} the chosen baseline models that include a specialized epidemiological news surveillance system and several machine learning models. Also, low-resource languages are highly influenced not only by the typology of the languages on which the models have been pre-trained or/and fine-tuned but also by their size. Furthermore, we discover that the beginning and the end of documents provide the most salient features for this task and, as expected, the performance of the models was proportionate to the training data size.",
}
@inproceedings{du-etal-2020-pointing,
    title = "Pointing to Select: A Fast Pointer-{LSTM} for Long Text Classification",
    author = "Du, Jinhua  and
      Huang, Yan  and
      Moilanen, Karo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.544",
    doi = "10.18653/v1/2020.coling-main.544",
    pages = "6184--6193",
    abstract = "Recurrent neural networks (RNNs) suffer from well-known limitations and complications which include slow inference and vanishing gradients when processing long sequences in text classification. Recent studies have attempted to accelerate RNNs via various ad hoc mechanisms to skip irrelevant words in the input. However, word skipping approaches proposed to date effectively stop at each or a given time step to decide whether or not a given input word should be skipped, breaking the coherence of input processing in RNNs. Furthermore, current methods cannot change skip rates during inference and are consequently unable to support different skip rates in demanding real-world conditions. To overcome these limitations, we propose Pointer- LSTM, a novel LSTM framework which relies on a pointer network to select important words for target prediction. The model maintains a coherent input process for the LSTM modules and makes it possible to change the skip rate during inference. Our evaluation on four public data sets demonstrates that Pointer-LSTM (a) is 1.1x∼3.5x faster than the standard LSTM architecture; (b) is more accurate than Leap-LSTM (the state-of-the-art LSTM skipping model) at high skip rates; and (c) reaches robust accuracy levels even when the skip rate is changed during inference.",
}
@inproceedings{ostendorff-etal-2020-aspect,
    title = "Aspect-based Document Similarity for Research Papers",
    author = "Ostendorff, Malte  and
      Ruas, Terry  and
      Blume, Till  and
      Gipp, Bela  and
      Rehm, Georg",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.545",
    doi = "10.18653/v1/2020.coling-main.545",
    pages = "6194--6206",
    abstract = "Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of applications like recommender systems that rely on document similarity. In this paper, we extend similarity with aspect information by performing a pairwise document classification task. We evaluate our aspect-based document similarity approach for research papers. Paper citations indicate the aspect-based similarity, i.e., the title of a section in which a citation occurs acts as a label for the pair of citing and cited paper. We apply a series of Transformer models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172,073 research paper pairs from the ACL Anthology and CORD-19 corpus. According to our results, SciBERT is the best performing system with F1-scores of up to 0.83. A qualitative analysis validates our quantitative results and indicates that aspect-based document similarity indeed leads to more fine-grained recommendations.",
}
@inproceedings{liao-etal-2020-explainable,
    title = "Explainable and Sparse Representations of Academic Articles for Knowledge Exploration",
    author = "Liao, Keng-Te  and
      Shen, Zhihong  and
      Huang, Chiyuan  and
      Wu, Chieh-Han  and
      Chen, PoChun  and
      Wang, Kuansan  and
      Lin, Shou-de",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.546",
    doi = "10.18653/v1/2020.coling-main.546",
    pages = "6207--6216",
    abstract = "We focus on a recently deployed system built for summarizing academic articles by concept tagging. The system has shown great coverage and high accuracy of concept identification which could be contributed by the knowledge acquired from millions of publications. Provided with the interpretable concepts and knowledge encoded in a pre-trained neural model, we investigate whether the tagged concepts can be applied to a broader class of applications. We propose transforming the tagged concepts into sparse vectors as representations of academic documents. The effectiveness of the representations is analyzed theoretically by a proposed framework. We also empirically show that the representations can have advantages on academic topic discovery and paper recommendation. On these applications, we reveal that the knowledge encoded in the tagging system can be effectively utilized and can help infer additional features from data with limited information.",
}
@inproceedings{kovatchev-etal-2020-mind,
    title = "{``}What is on your mind?{''} Automated Scoring of Mindreading in Childhood and Early Adolescence",
    author = "Kovatchev, Venelin  and
      Smith, Phillip  and
      Lee, Mark  and
      Grumley Traynor, Imogen  and
      Luque Aguilera, Irene  and
      Devine, Rory",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.547",
    doi = "10.18653/v1/2020.coling-main.547",
    pages = "6217--6228",
    abstract = "In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence. We create MIND-CA, a new corpus of 11,311 question-answer pairs in English from 1,066 children aged from 7 to 14. We perform machine learning experiments and carry out extensive quantitative and qualitative evaluation. We obtain promising results, demonstrating the applicability of state-of-the-art NLP solutions to a new domain and task.",
}
@inproceedings{rosso-mateus-etal-2020-deep,
    title = "A Deep Metric Learning Method for Biomedical Passage Retrieval",
    author = "Rosso-Mateus, Andr{\'e}s  and
      Gonz{\'a}lez, Fabio A.  and
      Montes-y-G{\'o}mez, Manuel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.548",
    doi = "10.18653/v1/2020.coling-main.548",
    pages = "6229--6239",
    abstract = "Passage retrieval is the task of identifying text snippets that are valid answers for a natural language posed question. One way to address this problem is to look at it as a metric learning problem, where we want to induce a metric between questions and passages that assign smaller distances to more relevant passages. In this work, we present a novel method for passage retrieval that learns a metric for questions and passages based on their internal semantic interactions. The method uses a similar approach to that of triplet networks, where the training samples are composed of one anchor (the question) and two positive and negative samples (passages). However,and in contrast with triplet networks, the proposed method uses a novel deep architecture that better exploits the particularities of text and takes into consideration complementary relatedness measures. Besides, the paper presents a sampling strategy that selects both easy and hard negative samples which improves the accuracy of the trained model. The method is particularly well suited for domain-specific passage retrieval where it is very important to take into account different sources of information. The proposed approach was evaluated in a biomedical passage retrieval task, the BioASQ challenge, outperforming standard triplet loss substantially by 10{\%},and state-of-the-art performance by 26{\%}.",
}
@inproceedings{hazem-etal-2020-hierarchical,
    title = "Hierarchical Text Segmentation for Medieval Manuscripts",
    author = "Hazem, Amir  and
      Daille, Beatrice  and
      Stutzmann, Dominique  and
      Kermorvant, Christopher  and
      Chevalier, Louis",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.549",
    doi = "10.18653/v1/2020.coling-main.549",
    pages = "6240--6251",
    abstract = "In this paper, we address the segmentation of books of hours, Latin devotional manuscripts of the late Middle Ages, that exhibit challenging issues: a complex hierarchical entangled structure, variable content, noisy transcriptions with no sentence markers, and strong correlations between sections for which topical information is no longer sufficient to draw segmentation boundaries. We show that the main state-of-the-art segmentation methods are either inefficient or inapplicable for books of hours and propose a bottom-up greedy approach that considerably enhances the segmentation results. We stress the importance of such hierarchical segmentation of books of hours for historians to explore their overarching differences underlying conception about Church.",
}
@inproceedings{suwaileh-etal-2020-ready,
    title = "Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets",
    author = "Suwaileh, Reem  and
      Imran, Muhammad  and
      Elsayed, Tamer  and
      Sajjad, Hassan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.550",
    doi = "10.18653/v1/2020.coling-main.550",
    pages = "6252--6263",
    abstract = "The widespread usage of Twitter during emergencies has provided a new opportunity and timely resource to crisis responders for various disaster management tasks. Geolocation information of pertinent tweets is crucial for gaining situational awareness and delivering aid. However, the majority of tweets do not come with geoinformation. In this work, we focus on the task of location mention recognition from crisis-related tweets. Specifically, we investigate the influence of different types of labeled training data on the performance of a BERT-based classification model. We explore several training settings such as combing in- and out-domain data from news articles and general-purpose and crisis-related tweets. Furthermore, we investigate the effect of geospatial proximity while training on near or far-away events from the target event. Using five different datasets, our extensive experiments provide answers to several critical research questions that are useful for the research community to foster research in this important direction. For example, results show that, for training a location mention recognition model, Twitter-based data is preferred over general-purpose data; and crisis-related data is preferred over general-purpose Twitter data. Furthermore, training on data from geographically-nearby disaster events to the target event boosts the performance compared to training on distant events.",
}
@inproceedings{nouri-etal-2020-mining,
    title = "Mining Crowdsourcing Problems from Discussion Forums of Workers",
    author = "Nouri, Zahra  and
      Wachsmuth, Henning  and
      Engels, Gregor",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.551",
    doi = "10.18653/v1/2020.coling-main.551",
    pages = "6264--6276",
    abstract = "Crowdsourcing is used in academia and industry to solve tasks that are easy for humans but hard for computers, in natural language processing mostly to annotate data. The quality of annotations is affected by problems in the task design, task operation, and task evaluation that workers face with requesters in crowdsourcing processes. To learn about the major problems, we provide a short but comprehensive survey based on two complementary studies: (1) a literature review where we collect and organize problems known from interviews with workers, and (2) an empirical data analysis where we use topic modeling to mine workers{'} complaints from a new English corpus of workers{'} forum discussions. While literature covers all process phases, problems in the task evaluation are prevalent, including unfair rejections, late payments, and unjustified blockings of workers. According to the data, however, poor task design in terms of malfunctioning environments, bad workload estimation, and privacy violations seems to bother the workers most. Our findings form the basis for future research on how to improve crowdsourcing processes.",
}
@inproceedings{chandra-etal-2020-abuseanalyzer,
    title = "{A}buse{A}nalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts",
    author = "Chandra, Mohit  and
      Pathak, Ashwin  and
      Dutta, Eesha  and
      Jain, Paryul  and
      Gupta, Manish  and
      Shrivastava, Manish  and
      Kumaraguru, Ponnurangam",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.552",
    doi = "10.18653/v1/2020.coling-main.552",
    pages = "6277--6283",
    abstract = "While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate speech, offensive language, sexist and racist opinions, etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying user posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this paper, we present a first of the kind dataset with 7,601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior. We also propose a system to address these tasks, obtaining an accuracy of ∼80{\%} for abuse presence, ∼82{\%} for abuse target prediction, and ∼65{\%} for abuse severity prediction.",
}
@inproceedings{stajner-yenikent-2020-survey,
    title = "A Survey of Automatic Personality Detection from Texts",
    author = "Stajner, Sanja  and
      Yenikent, Seren",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.553",
    doi = "10.18653/v1/2020.coling-main.553",
    pages = "6284--6295",
    abstract = "Personality profiling has long been used in psychology to predict life outcomes. Recently, automatic detection of personality traits from written messages has gained significant attention in computational linguistics and natural language processing communities, due to its applicability in various fields. In this survey, we show the trajectory of research towards automatic personality detection from purely psychology approaches, through psycholinguistics, to the recent purely natural language processing approaches on large datasets automatically extracted from social media. We point out what has been gained and what lost during that trajectory, and show what can be realistic expectations in the field.",
}
@inproceedings{rahman-etal-2020-mama,
    title = "Mama/Papa, Is this Text for Me?",
    author = "Rahman, Rashedur  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      {\'E}tienne, Aline  and
      Battistelli, Delphine  and
      B{\'e}chet, Nicolas  and
      Chevelu, Jonathan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.554",
    doi = "10.18653/v1/2020.coling-main.554",
    pages = "6296--6301",
    abstract = "Children have less linguistic skills than adults, which makes it more difficult for them to understand some texts, for instance when browsing the Internet. In this context, we present a novel method which predicts the minimal age from which a text can be understood. This method analyses each sentence of a text using a recurrent neural network, and then aggregates this information to provide the text-level prediction. Different approaches are proposed and compared to baseline models, at sentence and text levels. Experiments are carried out on a corpus of 1, 500 texts and 160K sentences. Our best model, based on LSTMs, outperforms state-of-the-art results and achieves mean absolute errors of 1.86 and 2.28, at sentence and text levels, respectively.",
}
@inproceedings{deng-etal-2020-hierarchical,
    title = "Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation",
    author = "Deng, Zhongfen  and
      Peng, Hao  and
      Xia, Congying  and
      Li, Jianxin  and
      He, Lifang  and
      Yu, Philip",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.555",
    doi = "10.18653/v1/2020.coling-main.555",
    pages = "6302--6314",
    abstract = "Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing. However, most existing methods either use hand-crafted features or learn features using deep learning with simple text corpus as input for review rating prediction, ignoring the hierarchies among data. In this paper, we propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation, which can serve as an effective decision-making tool for the academic paper review process. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three). Each encoder first derives contextual representation of each level, then generates a higher-level representation, and after the learning process, we are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers. Furthermore, we introduce two new metrics to evaluate models in data imbalance situations. Extensive experiments on a publicly available dataset (PeerRead) and our own collected dataset (OpenReview) demonstrate the superiority of the proposed approach compared with state-of-the-art methods.",
}
@inproceedings{van-den-berg-markert-2020-context,
    title = "Context in Informational Bias Detection",
    author = "van den Berg, Esther  and
      Markert, Katja",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.556",
    doi = "10.18653/v1/2020.coling-main.556",
    pages = "6315--6326",
    abstract = "Informational bias is bias conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers{'} opinions towards entities. By nature, informational bias is context-dependent, but previous work on informational bias detection has not explored the role of context beyond the sentence. In this paper, we explore four kinds of context for informational bias in English news articles: neighboring sentences, the full article, articles on the same event from other news publishers, and articles from the same domain (but potentially different events). We find that integrating event context improves classification performance over a very strong baseline. In addition, we perform the first error analysis of models on this task. We find that the best-performing context-inclusive model outperforms the baseline on longer sentences, and sentences from politically centrist articles.",
}
@inproceedings{cao-lee-2020-hategan,
    title = "{H}ate{GAN}: Adversarial Generative-Based Data Augmentation for Hate Speech Detection",
    author = "Cao, Rui  and
      Lee, Roy Ka-Wei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.557",
    doi = "10.18653/v1/2020.coling-main.557",
    pages = "6327--6338",
    abstract = "Academia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training. This results in the methods{'} poor detection performance of the hate speech class as the training datasets are highly imbalanced. In this paper, we propose HateGAN, a deep generative reinforcement learning model, which addresses the challenge of imbalance class by augmenting the dataset with hateful tweets. We conduct extensive experiments to augment two commonly-used hate speech detection datasets with the HateGAN generated tweets. Our experiment results show that HateGAN improves the detection performance of the hate speech class regardless of the classifiers and datasets used in the detection task. Specifically, we observe an average 5{\%} improvement for the hate class F1 scores across all state-of-the-art hate speech classifiers. We also conduct case studies to empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.",
}
@inproceedings{guderlei-assenmacher-2020-evaluating,
    title = "Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News",
    author = "Guderlei, Maike  and
      A{\ss}enmacher, Matthias",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.558",
    doi = "10.18653/v1/2020.coling-main.558",
    pages = "6339--6349",
    abstract = "Our goal is to evaluate the usefulness of unsupervised representation learning techniques for detecting stances of Fake News. Therefore we examine several pre-trained language models with respect to their performance on two Fake News related data sets, both consisting of instances with a headline, an associated news article and the stance of the article towards the respective headline. Specifically, the aim is to understand how much hyperparameter tuning is necessary when fine-tuning the pre-trained architectures, how well transfer learning works in this specific case of stance detection and how sensitive the models are to changes in hyperparameters like batch size, learning rate (schedule), sequence length as well as the freezing technique. The results indicate that the computationally more expensive autoregression approach of XLNet (Yanget al., 2019) is outperformed by BERT-based models, notably by RoBERTa (Liu et al., 2019).While the learning rate seems to be the most important hyperparameter, experiments with different freezing techniques indicate that all evaluated architectures had already learned powerful language representations that pose a good starting point for fine-tuning them.",
}
@inproceedings{glavas-etal-2020-xhate,
    title = "{XH}ate-999: Analyzing and Detecting Abusive Language Across Domains and Languages",
    author = "Glava{\v{s}}, Goran  and
      Karan, Mladen  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.559",
    doi = "10.18653/v1/2020.coling-main.559",
    pages = "6350--6365",
    abstract = "We present XHate-999, a multi-domain and multilingual evaluation data set for abusive language detection. By aligning test instances across six typologically diverse languages, XHate-999 for the first time allows for disentanglement of the domain transfer and language transfer effects in abusive language detection. We conduct a series of domain- and language-transfer experiments with state-of-the-art monolingual and multilingual transformer models, setting strong baseline results and profiling XHate-999 as a comprehensive evaluation resource for abusive language detection. Finally, we show that domain- and language-adaption, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved abusive language detection in the target language in the zero-shot transfer setups.",
}
@inproceedings{wang-etal-2020-detect,
    title = "Detect All Abuse! Toward Universal Abusive Language Detection Models",
    author = "Wang, Kunze  and
      Lu, Dong  and
      Han, Caren  and
      Long, Siqu  and
      Poon, Josiah",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.560",
    doi = "10.18653/v1/2020.coling-main.560",
    pages = "6366--6376",
    abstract = "Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years. Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain. In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains. Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user{'}s linguistic behaviour. Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language. Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.",
}
@inproceedings{chen-etal-2020-modeling,
    title = "Modeling Evolution of Message Interaction for Rumor Resolution",
    author = "Chen, Lei  and
      Wei, Zhongyu  and
      Li, Jing  and
      Zhou, Baohua  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.561",
    doi = "10.18653/v1/2020.coling-main.561",
    pages = "6377--6387",
    abstract = "Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately. However, how local interactive pattern affects global information assemblage has not been explored. In this paper, we attempt to address the problem by learning evolution of message interaction. We model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. Moreover, we capture the variation of message interaction using a hierarchical framework to better integrate information flow of a rumor cascade. Experiments on PHEME dataset demonstrate our proposed model achieves higher accuracy than existing methods.",
}
@inproceedings{liu-etal-2020-regularized,
    title = "Regularized Attentive Capsule Network for Overlapped Relation Extraction",
    author = "Liu, Tianyi  and
      Lin, Xiangyu  and
      Jia, Weijia  and
      Zhou, Mingliang  and
      Zhao, Wei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.562",
    doi = "10.18653/v1/2020.coling-main.562",
    pages = "6388--6398",
    abstract = "Distantly supervised relation extraction has been widely applied in knowledge base construction due to its less requirement of human efforts. However, the automatically established training datasets in distant supervision contain low-quality instances with noisy words and overlapped relations, introducing great challenges to the accurate extraction of relations. To address this problem, we propose a novel Regularized Attentive Capsule Network (RA-CapNet) to better identify highly overlapped relations in each informal sentence. To discover multiple relation features in an instance, we embed multi-head attention into the capsule network as the low-level capsules, where the subtraction of two entities acts as a new form of relation query to select salient features regardless of their positions. To further discriminate overlapped relation features, we devise disagreement regularization to explicitly encourage the diversity among both multiple attention heads and low-level capsules. Extensive experiments conducted on widely used datasets show that our model achieves significant improvements in relation extraction.",
}
@inproceedings{yu-etal-2020-bridging,
    title = "Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction",
    author = "Yu, Haiyang  and
      Zhang, Ningyu  and
      Deng, Shumin  and
      Ye, Hongbin  and
      Zhang, Wei  and
      Chen, Huajun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.563",
    doi = "10.18653/v1/2020.coling-main.563",
    pages = "6399--6410",
    abstract = "Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood. Unlike previous single-task few-shot problems, relational triple extraction is more challenging as the entities and relations have implicit correlations. In this paper, We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples, namely, entity pairs and corresponding relations. To be specific, we design a hybrid prototypical learning mechanism that bridges text and knowledge concerning both entities and relations. Thus, implicit correlations between entities and relations are injected. Additionally, we propose a prototype-aware regularization to learn more representative prototypes. Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.",
}
@inproceedings{jung-shim-2020-dual,
    title = "Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation",
    author = "Jung, Woohwan  and
      Shim, Kyuseok",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.564",
    doi = "10.18653/v1/2020.coling-main.564",
    pages = "6411--6423",
    abstract = "Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models on either distantly supervised data or human-annotated data. To take advantage of the high accuracy of human annotation and the cheap cost of distant supervision, we propose the dual supervision framework which effectively utilizes both types of data. However, simply combining the two types of data to train a RE model may decrease the prediction accuracy since distant supervision has labeling bias. We employ two separate prediction networks HA-Net and DS-Net to predict the labels by human annotation and distant supervision, respectively, to prevent the degradation of accuracy by the incorrect labeling of distant supervision. Furthermore, we propose an additional loss term called disagreement penalty to enable HA-Net to learn from distantly supervised labels. In addition, we exploit additional networks to adaptively assess the labeling bias by considering contextual information. Our performance study on sentence-level and document-level REs confirms the effectiveness of the dual supervision framework.",
}
@inproceedings{mandya-etal-2020-graph,
    title = "Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction",
    author = "Mandya, Angrosh  and
      Bollegala, Danushka  and
      Coenen, Frans",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.565",
    doi = "10.18653/v1/2020.coling-main.565",
    pages = "6424--6435",
    abstract = "We propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked to entities in the dependency parse is proposed. Graph convolution operation is performed over the resulting multiple sub-graphs to obtain more informative features useful for relation extraction. Our experimental results show that the proposed method achieves superior performance over the existing GCN-based models achieving state-of-the-art performance on cross-sentence n-ary relation extraction dataset and SemEval 2010 Task 8 sentence-level relation extraction dataset. Our model also achieves a comparable performance to the SoTA on the TACRED dataset.",
}
@inproceedings{zhu-etal-2020-towards,
    title = "Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction",
    author = "Zhu, Tong  and
      Wang, Haitao  and
      Yu, Junjie  and
      Zhou, Xiabing  and
      Chen, Wenliang  and
      Zhang, Wei  and
      Zhang, Min",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.566",
    doi = "10.18653/v1/2020.coling-main.566",
    pages = "6436--6447",
    abstract = "In recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-scale annotated data by aligning entity pairs from Knowledge Bases (KB) to sentences. However, these DS-generated datasets inevitably have wrong labels that result in incorrect evaluation scores during testing, which may mislead the researchers. To solve this problem, we build a new dataset NYTH, where we use the DS-generated data as training data and hire annotators to label test data. Compared with the previous datasets, NYT-H has a much larger test set and then we can perform more accurate and consistent evaluation. Finally, we present the experimental results of several widely used systems on NYT-H. The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different. This indicates that our human-annotated data is necessary for evaluation of distantly-supervised relation extraction.",
}
@inproceedings{chen-etal-2020-multi,
    title = "Multi-choice Relational Reasoning for Machine Reading Comprehension",
    author = "Chen, Wuya  and
      Quan, Xiaojun  and
      Kit, Chunyu  and
      Min, Zhengcheng  and
      Wang, Jiahai",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.567",
    doi = "10.18653/v1/2020.coling-main.567",
    pages = "6448--6458",
    abstract = "This paper presents our study of cloze-style reading comprehension by imitating human reading comprehension, which normally involves tactical comparing and reasoning over candidates while choosing the best answer. We propose a multi-choice relational reasoning (McR$^2$) model with an aim to enable relational reasoning on candidates based on fusion representations of document, query and candidates. For the fusion representations, we develop an efficient encoding architecture by integrating the schemes of bidirectional attention flow, self-attention and document-gated query reading. Then, comparing and inferring over candidates are executed by a novel relational reasoning network. We conduct extensive experiments on four datasets derived from two public corpora, Children{'}s Book Test and Who DiD What, to verify the validity and advantages of our model. The results show that it outperforms all baseline models significantly on the four benchmark datasets. The effectiveness of its key components is also validated by an ablation study.",
}
@inproceedings{pang-etal-2020-fastmatch,
    title = "{FASTMATCH}: Accelerating the Inference of {BERT}-based Text Matching",
    author = "Pang, Shuai  and
      Ma, Jianqiang  and
      Yan, Zeyu  and
      Zhang, Yang  and
      Shen, Jianping",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.568",
    doi = "10.18653/v1/2020.coling-main.568",
    pages = "6459--6469",
    abstract = "Recently, pre-trained language models such as BERT have shown state-of-the-art accuracies in text matching. When being applied to IR (or QA), the BERT-based matching models need to online calculate the representations and interactions for all query-candidate pairs. The high inference cost has prohibited the deployments of BERT-based matching models in many practical applications. To address this issue, we propose a novel BERT-based text matching model, in which the representations and the interactions are decoupled. Then, the representations of the candidates can be calculated and stored offline, and directly retrieved during the online matching phase. To conduct the interactions and generate final matching scores, a lightweight attention network is designed. Experiments based on several large scale text matching datasets show that the proposed model, called FASTMATCH, can achieve up to 100X speed-up to BERT and RoBERTa at the online matching phase, while keeping more up to 98.7{\%} of the performance.",
}
@inproceedings{wu-etal-2020-dt,
    title = "{DT}-{QDC}: A Dataset for Question Comprehension in Online Test",
    author = "Wu, Sijin  and
      Yang, Yujiu  and
      Yung, Nicholas  and
      Shen, Zhengchen  and
      Lei, Zeyang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.569",
    doi = "10.18653/v1/2020.coling-main.569",
    pages = "6470--6480",
    abstract = "With the transformation of education from the traditional classroom environment to online education and assessment, it is more and more important to accurately assess the difficulty of questions than ever. As teachers may not be able to follow the student{'}s performance and learning behavior closely, a well-defined method to measure the difficulty of questions to guide learning is necessary. In this paper, we explore the concept of question difficulty and provide our new Chinese DT-QDC dataset. This is currently the largest and only Chinese question dataset, and it also has enriched attributes and difficulty labels. Additional attributes such as keywords, chapter, and question type would allow models to understand questions more precisely. We proposed the MTMS-BERT and ORMS-BERT, which can improve the judgment of difficulty from different views. The proposed methods outperforms different baselines by 7.79{\%} on F1-score and 15.92{\%} on MAE, 28.26{\%} on MSE on the new DT-QDC dataset, laying the foundation for the question difficulty comprehension task.",
}
@inproceedings{fenogenova-etal-2020-read,
    title = "Read and Reason with {M}u{S}e{RC} and {R}u{C}o{S}: Datasets for Machine Reading Comprehension for {R}ussian",
    author = "Fenogenova, Alena  and
      Mikhailov, Vladislav  and
      Shevelev, Denis",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.570",
    doi = "10.18653/v1/2020.coling-main.570",
    pages = "6481--6497",
    abstract = "The paper introduces two Russian machine reading comprehension (MRC) datasets, called MuSeRC and RuCoS, which require reasoning over multiple sentences and commonsense knowledge to infer the answer. The former follows the design of MultiRC, while the latter is a counterpart of the ReCoRD dataset. The datasets are included in RussianSuperGLUE, the Russian general language understanding benchmark. We provide a comparative analysis and demonstrate that the proposed tasks are relatively more complex as compared to the original ones for English. Besides, performance results of human solvers and BERT-based models show that MuSeRC and RuCoS represent a challenge for recent advanced neural models. We thus hope to facilitate research in the field of MRC for Russian and prompt the study of multi-hop reasoning in a cross-lingual scenario.",
}
@inproceedings{wang-etal-2020-knowledge,
    title = "Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs",
    author = "Wang, Zikang  and
      Li, Linjing  and
      Zeng, Daniel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.571",
    doi = "10.18653/v1/2020.coling-main.571",
    pages = "6498--6508",
    abstract = "Natural Language Inference (NLI) is a vital task in natural language processing. It aims to identify the logical relationship between two sentences. Most of the existing approaches make such inference based on semantic knowledge obtained through training corpus. The adoption of background knowledge is rarely seen or limited to a few specific types. In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI. KGNLI model consists of three components: a semantic-relation representation module, a knowledge-relation representation module, and a label prediction module. Different from previous methods, various kinds of background knowledge can be flexibly combined in the proposed KGNLI model. Experiments on four benchmarks, SNLI, MultiNLI, SciTail, and BNLI, validate the effectiveness of our model.",
}
@inproceedings{pinter-etal-2020-nytwit,
    title = "{NYTWIT}: A Dataset of Novel Words in the {N}ew {Y}ork {T}imes",
    author = "Pinter, Yuval  and
      Jacobs, Cassandra L.  and
      Bittker, Max",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.572",
    doi = "10.18653/v1/2020.coling-main.572",
    pages = "6509--6515",
    abstract = "We present the New York Times Word Innovation Types dataset, or NYTWIT, a collection of over 2,500 novel English words published in the New York Times between November 2017 and March 2019, manually annotated for their class of novelty (such as lexical derivation, dialectal variation, blending, or compounding). We present baseline results for both uncontextual and contextual prediction of novelty class, showing that there is room for improvement even for state-of-the-art NLP systems. We hope this resource will prove useful for linguists and NLP practitioners by providing a real-world environment of novel word appearance.",
}
@inproceedings{yoshimura-etal-2020-reference,
    title = "{SOME}: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction",
    author = "Yoshimura, Ryoma  and
      Kaneko, Masahiro  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.573",
    doi = "10.18653/v1/2020.coling-main.573",
    pages = "6516--6522",
    abstract = "We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC). Previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluations of the system outputs because no dataset of the system output exists with manual evaluation. This study manually evaluates outputs of GEC systems to optimize the metrics. Experimental results show that the proposed metric improves correlation with the manual evaluation in both system- and sentence-level meta-evaluation. Our dataset and metric will be made publicly available.",
}
@inproceedings{biesialska-etal-2020-continual,
    title = "Continual Lifelong Learning in Natural Language Processing: A Survey",
    author = "Biesialska, Magdalena  and
      Biesialska, Katarzyna  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.574",
    doi = "10.18653/v1/2020.coling-main.574",
    pages = "6523--6541",
    abstract = "Continual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge. Furthermore, CL is particularly challenging for language learning, as natural language is ambiguous: it is discrete, compositional, and its meaning is context-dependent. In this work, we look at the problem of CL through the lens of various NLP tasks. Our survey discusses major challenges in CL and current methods applied in neural network models. We also provide a critical review of the existing CL evaluation methods and datasets in NLP. Finally, we present our outlook on future research directions.",
}
@inproceedings{ohman-etal-2020-xed,
    title = "{XED}: A Multilingual Dataset for Sentiment Analysis and Emotion Detection",
    author = {{\"O}hman, Emily  and
      P{\`a}mies, Marc  and
      Kajava, Kaisla  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.575",
    doi = "10.18653/v1/2020.coling-main.575",
    pages = "6542--6552",
    abstract = "We introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages. We use Plutchik{'}s core emotions to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.",
}
@inproceedings{bhardwaj-etal-2020-human,
    title = "Human or Neural Translation?",
    author = "Bhardwaj, Shivendra  and
      Alfonso Hermelo, David  and
      Langlais, Phillippe  and
      Bernier-Colborne, Gabriel  and
      Goutte, Cyril  and
      Simard, Michel",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.576",
    doi = "10.18653/v1/2020.coling-main.576",
    pages = "6553--6564",
    abstract = "Deep neural models tremendously improved machine translation. In this context, we investigate whether distinguishing machine from human translations is still feasible. We trained and applied 18 classifiers under two settings: a monolingual task, in which the classifier only looks at the translation; and a bilingual task, in which the source text is also taken into consideration. We report on extensive experiments involving 4 neural MT systems (Google Translate, DeepL, as well as two systems we trained) and varying the domain of texts. We show that the bilingual task is the easiest one and that transfer-based deep-learning classifiers perform best, with mean accuracies around 85{\%} in-domain and 75{\%} out-of-domain .",
}
@inproceedings{schulz-etal-2020-biomedical,
    title = "Biomedical Concept Relatedness {--} A large {EHR}-based benchmark",
    author = "Schulz, Claudia  and
      Levy-Kramer, Josh  and
      Van Assel, Camille  and
      Kepes, Miklos  and
      Hammerla, Nils",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.577",
    doi = "10.18653/v1/2020.coling-main.577",
    pages = "6565--6575",
    abstract = "A promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a consultation or to recruit suitable patients for a study. This requires search capabilities far beyond simple string matching, including the retrieval of concepts (diagnoses, symptoms, medications, etc.) related to the one in question. The suitability of AI methods for such applications is tested by predicting the relatedness of concepts with known relatedness scores. However, all existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. We open-source a novel concept relatedness benchmark overcoming these issues: it is six times larger than existing datasets and concept pairs are chosen based on co-occurrence in EHRs, ensuring their relevance for the application of interest. We present an in-depth analysis of our new dataset and compare it to existing ones, highlighting that it is not only larger but also complements existing datasets in terms of the types of concepts included. Initial experiments with state-of-the-art embedding methods show that our dataset is a challenging new benchmark for testing concept relatedness models.",
}
@inproceedings{islam-etal-2020-domain,
    title = "Domain-Specific Sentiment Lexicons Induced from Labeled Documents",
    author = "Islam, SM Mazharul  and
      Dong, Xin  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.578",
    doi = "10.18653/v1/2020.coling-main.578",
    pages = "6576--6587",
    abstract = "Sentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies. Although supervised learning algorithms have advanced considerably in recent years, in many settings it remains more practical to apply an unsupervised technique. The latter are oftentimes based on sentiment lexicons. However, existing sentiment lexicons reflect an abstract notion of polarity and do not do justice to the substantial differences of word polarities between different domains. In this work, we draw on a collection of domain-specific data to induce a set of 24 domain-specific sentiment lexicons. We rely on initial linear models to induce initial word intensity scores, and then train new deep models based on word vector representations to overcome the scarcity of the original seed data. Our analysis shows substantial differences between domains, which make domain-specific sentiment lexicons a promising form of lexical resource in downstream tasks, and the predicted lexicons indeed perform effectively on tasks such as review classification and cross-lingual word sentiment prediction.",
}
@inproceedings{caswell-etal-2020-language,
    title = "Language {ID} in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus",
    author = "Caswell, Isaac  and
      Breiner, Theresa  and
      van Esch, Daan  and
      Bapna, Ankur",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.579",
    doi = "10.18653/v1/2020.coling-main.579",
    pages = "6588--6608",
    abstract = "Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context. LangID is largely treated as solved in the literature, with models reported that achieve over 90{\%} average F1 on as many as 1,366 languages. We train LangID models on up to 1,629 languages with comparable quality on held-out test sets, but find that human-judged LangID accuracy for web-crawl text corpora created using these models is only around 5{\%} for many lower-resource languages, suggesting a need for more robust evaluation. Further analysis revealed a variety of error modes, arising from domain mismatch, class imbalance, language similarity, and insufficiently expressive models. We propose two classes of techniques to mitigate these errors: wordlist-based tunable-precision filters (for which we release curated lists in about 500 languages) and transformer-based semi-supervised LangID models, which increase median dataset precision from 5.5{\%} to 71.2{\%}. These techniques enable us to create an initial data set covering 100K or more relatively clean sentences in each of 500+ languages, paving the way towards a 1,000-language web text corpus.",
}
@inproceedings{ho-etal-2020-constructing,
    title = "Constructing A Multi-hop {QA} Dataset for Comprehensive Evaluation of Reasoning Steps",
    author = "Ho, Xanh  and
      Duong Nguyen, Anh-Khoa  and
      Sugawara, Saku  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.580",
    doi = "10.18653/v1/2020.coling-main.580",
    pages = "6609--6625",
    abstract = "A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, current datasets do not provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies revealed that many examples in existing multi-hop datasets do not require multi-hop reasoning to answer a question. In this study, we present a new multi-hop QA dataset, called 2WikiMultiHopQA, which uses structured and unstructured data. In our dataset, we introduce the evidence information containing a reasoning path for multi-hop questions. The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model. We carefully design a pipeline and a set of templates when generating a question-answer pair that guarantees the multi-hop steps and the quality of the questions. We also exploit the structured format in Wikidata and use logical rules to create questions that are natural but still require multi-hop reasoning. Through experiments, we demonstrate that our dataset is challenging for multi-hop models and it ensures that multi-hop reasoning is required.",
}
@inproceedings{masala-etal-2020-robert,
    title = "{R}o{BERT} {--} A {R}omanian {BERT} Model",
    author = "Masala, Mihai  and
      Ruseti, Stefan  and
      Dascalu, Mihai",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.581",
    doi = "10.18653/v1/2020.coling-main.581",
    pages = "6626--6637",
    abstract = "Deep pre-trained language models tend to become ubiquitous in the field of Natural Language Processing (NLP). These models learn contextualized representations by using a huge amount of unlabeled text data and obtain state of the art results on a multitude of NLP tasks, by enabling efficient transfer learning. For other languages besides English, there are limited options of such models, most of which are trained only on multi-lingual corpora. In this paper we introduce a Romanian-only pre-trained BERT model {--} RoBERT {--} and compare it with different multi-lingual models on seven Romanian specific NLP tasks grouped into three categories, namely: sentiment analysis, dialect and cross-dialect topic identification, and diacritics restoration. Our model surpasses the multi-lingual models, as well as a another mono-lingual implementation of BERT, on all tasks.",
}
@inproceedings{bella-etal-2020-exploring,
    title = "Exploring the Language of Data",
    author = "Bella, G{\'a}bor  and
      Gremes, Linda  and
      Giunchiglia, Fausto",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.582",
    doi = "10.18653/v1/2020.coling-main.582",
    pages = "6638--6648",
    abstract = "We set out to uncover the unique grammatical properties of an important yet so far under-researched type of natural language text: that of short labels typically found within structured datasets. We show that such labels obey a specific type of abbreviated grammar that we call the Language of Data, with properties significantly different from the kinds of text typically addressed in computational linguistics and NLP, such as {`}standard{'} written language or social media messages. We analyse orthography, parts of speech, and syntax over a large, bilingual, hand-annotated corpus of data labels collected from a variety of domains. We perform experiments on tokenisation, part-of-speech tagging, and named entity recognition over real-world structured data, demonstrating that models adapted to the Language of Data outperform those trained on standard text. These observations point in a new direction to be explored as future research, in order to develop new NLP tools and models dedicated to the Language of Data.",
}
@inproceedings{plank-etal-2020-dan,
    title = "{D}a{N}+: {D}anish Nested Named Entities and Lexical Normalization",
    author = "Plank, Barbara  and
      Jensen, Kristian N{\o}rgaard  and
      van der Goot, Rob",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.583",
    doi = "10.18653/v1/2020.coling-main.583",
    pages = "6649--6662",
    abstract = "This paper introduces DAN+, a new multi-domain corpus and annotation guidelines for Dan-ish nested named entities (NEs) and lexical normalization to support research on cross-lingualcross-domain learning for a less-resourced language. We empirically assess three strategies tomodel the two-layer Named Entity Recognition (NER) task. We compare transfer capabilitiesfrom German versus in-language annotation from scratch. We examine language-specific versusmultilingual BERT, and study the effect of lexical normalization on NER. Our results show that 1) the most robust strategy is multi-task learning which is rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to domain shifts, and 3) in-language BERT and lexicalnormalization are the most beneficial on the least canonical data. Our results also show that anout-of-domain setup remains challenging, while performance on news plateaus quickly. Thishighlights the importance of cross-domain evaluation of cross-lingual transfer.",
}
@inproceedings{piskorski-etal-2020-new,
    title = "New Benchmark Corpus and Models for Fine-grained Event Classification: To {BERT} or not to {BERT}?",
    author = "Piskorski, Jakub  and
      Haneczok, Jacek  and
      Jacquet, Guillaume",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.584",
    doi = "10.18653/v1/2020.coling-main.584",
    pages = "6663--6678",
    abstract = "We introduce a new set of benchmark datasets derived from ACLED data for fine-grained event classification and compare the performance of various state-of-the-art models on these datasets, including SVM based on TF-IDF character n-grams and neural context-free embeddings (GLOVE and FASTTEXT) as well as deep learning-based BERT with its contextual embeddings. The best results in terms of micro (94.3-94.9{\%}) and macro F1 (86.0-88.9{\%}) were obtained using BERT transformer, with simpler TF-IDF character n-gram based SVM being an interesting alternative. Further, we discuss the pros and cons of the considered benchmark models in terms of their robustness and the dependence of the classification performance on the size of training data.",
}
@inproceedings{meng-wattenhofer-2020-geometry,
    title = "A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples",
    author = "Meng, Zhao  and
      Wattenhofer, Roger",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.585",
    doi = "10.18653/v1/2020.coling-main.585",
    pages = "6679--6689",
    abstract = "Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths. In this paper, we propose a geometry-inspired attack for generating natural language adversarial examples. Our attack generates adversarial examples by iteratively approximating the decision boundary of Deep Neural Networks (DNNs). Experiments on two datasets with two different models show that our attack fools natural language models with high success rates, while only replacing a few words. Human evaluation shows that adversarial examples generated by our attack are hard for humans to recognize. Further experiments show that adversarial training can improve model robustness against our attack.",
}
@inproceedings{betti-etal-2020-expert,
    title = "Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains",
    author = "Betti, Arianna  and
      Reynaert, Martin  and
      Ossenkoppele, Thijs  and
      Oortwijn, Yvette  and
      Salway, Andrew  and
      Bloem, Jelke",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.586",
    doi = "10.18653/v1/2020.coling-main.586",
    pages = "6690--6702",
    abstract = "We present a novel, domain expert-controlled, replicable procedure for the construction of concept-modeling ground truths with the aim of evaluating the application of word embeddings. In particular, our method is designed to evaluate the application of word and paragraph embeddings in concept-focused textual domains, where a generic ontology does not provide enough information. We illustrate the procedure, and validate it by describing the construction of an expert ground truth, QuiNE-GT. QuiNE-GT is built to answer research questions concerning the concept of naturalized epistemology in QUINE, a 2-million-token, single-author, 20th-century English philosophy corpus of outstanding quality, cleaned up and enriched for the purpose. To the best of our ken, expert concept-modeling ground truths are extremely rare in current literature, nor has the theoretical methodology behind their construction ever been explicitly conceptualised and properly systematised. Expert-controlled concept-modeling ground truths are however essential to allow proper evaluation of word embeddings techniques, and increase their trustworthiness in specialised domains in which the detection of concepts through their expression in texts is important. We highlight challenges, requirements, and prospects for future work.",
}
@inproceedings{appidi-etal-2020-creation,
    title = "Creation of Corpus and analysis in Code-Mixed {K}annada-{E}nglish {T}witter data for Emotion Prediction",
    author = "Appidi, Abhinav Reddy  and
      Srirangam, Vamshi Krishna  and
      Suhas, Darsi  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.587",
    doi = "10.18653/v1/2020.coling-main.587",
    pages = "6703--6709",
    abstract = "Emotion prediction is a critical task in the field of Natural Language Processing (NLP). There has been a significant amount of work done in emotion prediction for resource-rich languages. There has been work done on code-mixed social media corpus but not on emotion prediction of Kannada-English code-mixed Twitter data. In this paper, we analyze the problem of emotion prediction on corpus obtained from code-mixed Kannada-English extracted from Twitter annotated with their respective {`}Emotion{'} for each tweet. We experimented with machine learning prediction models using features like Character N-Grams, Word N-Grams, Repetitive characters, and others on SVM and LSTM on our corpus, which resulted in an accuracy of 30{\%} and 32{\%} respectively.",
}
@inproceedings{tutubalina-etal-2020-fair,
    title = "Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for {BERT}-based Models",
    author = "Tutubalina, Elena  and
      Kadurin, Artur  and
      Miftahutdinov, Zulfat",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.588",
    doi = "10.18653/v1/2020.coling-main.588",
    pages = "6710--6716",
    abstract = "Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation. A large number of biomedical corpora and state-of-the-art models have been introduced in the past five years. However, there are no general guidelines regarding the evaluation of models on these corpora in single- and cross-terminology settings. In this work, we perform a comparative evaluation of various benchmarks and study the efficiency of state-of-the-art neural architectures based on Bidirectional Encoder Representations from Transformers (BERT) for linking of three entity types across three domains: research abstracts, drug labels, and user-generated texts on drug therapy in English. We have made the source code and results available at https://github.com/insilicomedicine/Fair-Evaluation-BERT.",
}
@inproceedings{cui-etal-2020-sentence,
    title = "A Sentence Cloze Dataset for {C}hinese Machine Reading Comprehension",
    author = "Cui, Yiming  and
      Liu, Ting  and
      Yang, Ziqing  and
      Chen, Zhipeng  and
      Ma, Wentao  and
      Che, Wanxiang  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.589",
    doi = "10.18653/v1/2020.coling-main.589",
    pages = "6717--6723",
    abstract = "Owing to the continuous efforts by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available. To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the passage that has several blanks. We built a Chinese dataset called CMRC 2019 to evaluate the difficulty of the SC-MRC task. Moreover, to add more difficulties, we also made fake candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which was originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on the pre-trained models, and the results show that the state-of-the-art model still underperforms human performance by a large margin. We release the dataset and baseline system to further facilitate our community. Resources available through https://github.com/ymcui/cmrc2019",
}
@inproceedings{lyu-etal-2020-improving,
    title = "Improving Document-Level Sentiment Analysis with User and Product Context",
    author = "Lyu, Chenyang  and
      Foster, Jennifer  and
      Graham, Yvette",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.590",
    doi = "10.18653/v1/2020.coling-main.590",
    pages = "6724--6729",
    abstract = "Past work that improves document-level sentiment analysis by encoding user and product in- formation has been limited to considering only the text of the current review. We investigate incorporating additional review text available at the time of sentiment prediction that may prove meaningful for guiding prediction. Firstly, we incorporate all available historical review text belonging to the author of the review in question. Secondly, we investigate the inclusion of his- torical reviews associated with the current product (written by other users). We achieve this by explicitly storing representations of reviews written by the same user and about the same product and force the model to memorize all reviews for one particular user and product. Additionally, we drop the hierarchical architecture used in previous work to enable words in the text to directly attend to each other. Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.",
}
@inproceedings{liu-etal-2020-multilingual-neural,
    title = "Multilingual Neural {RST} Discourse Parsing",
    author = "Liu, Zhengyuan  and
      Shi, Ke  and
      Chen, Nancy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.591",
    doi = "10.18653/v1/2020.coling-main.591",
    pages = "6730--6738",
    abstract = "Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data. In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and (2) adopting segment-level translation of the source content. Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing on all sub-tasks.",
}
@inproceedings{wachsmuth-werner-2020-intrinsic,
    title = "Intrinsic Quality Assessment of Arguments",
    author = "Wachsmuth, Henning  and
      Werner, Till",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.592",
    doi = "10.18653/v1/2020.coling-main.592",
    pages = "6739--6745",
    abstract = "Several quality dimensions of natural language arguments have been investigated. Some are likely to be reflected in linguistic features (e.g., an argument{'}s arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability). In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument{'}s text. In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions. Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity. We also find that human assessors differ more clearly to each other than to our approach.",
}
@inproceedings{li-xiao-2020-tree,
    title = "Tree Representations in Transition System for {RST} Parsing",
    author = "Li, Jinfen  and
      Xiao, Lu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.593",
    doi = "10.18653/v1/2020.coling-main.593",
    pages = "6746--6751",
    abstract = "The transition-based systems in the past studies propose a series of actions, to build a right-heavy binarized tree for the RST parsing. However, the nodes of the binary-nuclear relations (e.g., Contrast) have the same nuclear type with those of the multi-nuclear relations (e.g., Joint) in the binary tree structure. In addition, the reduce action only construct binary trees instead of multi-branch trees, which is the original RST tree structure. In our paper, we design a new nuclear type for the multi-nuclear relations, and a new action to construct a multi-branch tree. We enrich the feature set by extracting additional refined dependency feature of texts from the Bi-Affine model. We also compare the performance of two approaches for RST parsing in the transition-based system: a joint action of reduce-shift and nuclear type (i.e., Reduce-SN) vs a separate one that applies Reduce action first and then assigns nuclear type. We find that the new devised nuclear type and action are more capable of capturing the multi-nuclear relation and the joint action is more suitable than the separate one. Our multi-branch tree structure obtains the state-of-the-art performance for all the 18 coarse relations.",
}
@inproceedings{jeon-strube-2020-incremental,
    title = "Incremental Neural Lexical Coherence Modeling",
    author = "Jeon, Sungho  and
      Strube, Michael",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.594",
    doi = "10.18653/v1/2020.coling-main.594",
    pages = "6752--6758",
    abstract = "Pretrained language models, neural models pretrained on massive amounts of data, have established the state of the art in a range of NLP tasks. They are based on a modern machine-learning technique, the Transformer which relates all items simultaneously to capture semantic relations in sequences. However, it differs from what humans do. Humans read sentences one-by-one, incrementally. Can neural models benefit by interpreting texts incrementally as humans do? We investigate this question in coherence modeling. We propose a coherence model which interprets sentences incrementally to capture lexical relations between them. We compare the state of the art in each task, simple neural models relying on a pretrained language model, and our model in two downstream tasks. Our findings suggest that interpreting texts incrementally as humans could be useful to design more advanced models.",
}
@inproceedings{bladier-etal-2020-statistical,
    title = "Statistical Parsing of Tree Wrapping Grammars",
    author = "Bladier, Tatiana  and
      Waszczuk, Jakub  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.595",
    doi = "10.18653/v1/2020.coling-main.595",
    pages = "6759--6766",
    abstract = "We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG). TWG is a tree-rewriting formalism which includes the tree-combination operations of substitution, sister-adjunction and tree-wrapping substitution. TWGs can be extracted from constituency treebanks and aim at representing long distance dependencies (LDDs) in a linguistically adequate way. We present a parsing algorithm for TWGs based on neural supertagging and A* parsing. We extract a TWG for English from the treebanks for Role and Reference Grammar and discuss first parsing results with this grammar.",
}
@inproceedings{heck-etal-2020-task,
    title = "Out-of-Task Training for Dialog State Tracking Models",
    author = "Heck, Michael  and
      Geishauser, Christian  and
      Lin, Hsien-chin  and
      Lubis, Nurul  and
      Moresi, Marco  and
      van Niekerk, Carel  and
      Gasic, Milica",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.596",
    doi = "10.18653/v1/2020.coling-main.596",
    pages = "6767--6774",
    abstract = "Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.",
}
@inproceedings{zhou-etal-2020-resource,
    title = "Resource Constrained Dialog Policy Learning Via Differentiable Inductive Logic Programming",
    author = "Zhou, Zhenpeng  and
      Beirami, Ahmad  and
      Crook, Paul  and
      Shah, Pararth  and
      Subba, Rajen  and
      Geramifard, Alborz",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.597",
    doi = "10.18653/v1/2020.coling-main.597",
    pages = "6775--6787",
    abstract = "Motivated by the needs of resource constrained dialog policy learning, we introduce dialog policy via differentiable inductive logic (DILOG). We explore the tasks of one-shot learning and zero-shot domain transfer with DILOG on SimDial and MultiWoZ. Using a single representative dialog from the restaurant domain, we train DILOG on the SimDial dataset and obtain 99+{\%} in-domain test accuracy. We also show that the trained DILOG zero-shot transfers to all other domains with 99+{\%} accuracy, proving the suitability of DILOG to slot-filling dialogs. We further extend our study to the MultiWoZ dataset achieving 90+{\%} inform and success metrics. We also observe that these metrics are not capturing some of the shortcomings of DILOG in terms of false positives, prompting us to measure an auxiliary Action F1 score. We show that DILOG is 100x more data efficient than state-of-the-art neural approaches on MultiWoZ while achieving similar performance metrics. We conclude with a discussion on the strengths and weaknesses of DILOG.",
}
@inproceedings{chan-etal-2020-germans,
    title = "{G}erman{'}s Next Language Model",
    author = {Chan, Branden  and
      Schweter, Stefan  and
      M{\"o}ller, Timo},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.598",
    doi = "10.18653/v1/2020.coling-main.598",
    pages = "6788--6796",
    abstract = "In this work we present the experiments which lead to the creation of our BERT and ELECTRA based German language models, GBERT and GELECTRA. By varying the input training data, model size, and the presence of Whole Word Masking (WWM) we were able to attain SoTA performance across a set of document classification and named entity recognition (NER) tasks for both models of base and large size. We adopt an evaluation driven approach in training these models and our results indicate that both adding more data and utilizing WWM improve model performance. By benchmarking against existing German models, we show that these models are the best German models to date. All trained models will be made publicly available to the research community.",
}
@inproceedings{nedelchev-etal-2020-language,
    title = "Language Model Transformers as Evaluators for Open-domain Dialogues",
    author = "Nedelchev, Rostislav  and
      Lehmann, Jens  and
      Usbeck, Ricardo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.599",
    doi = "10.18653/v1/2020.coling-main.599",
    pages = "6797--6808",
    abstract = "Computer-based systems for communication with humans are a cornerstone of AI research since the 1950s. So far, the most effective way to assess the quality of the dialogues produced by these systems is to use resource-intensive manual labor instead of automated means. In this work, we investigate whether language models (LM) based on transformer neural networks can indicate the quality of a conversation. In a general sense, language models are methods that learn to predict one or more words based on an already given context. Due to their unsupervised nature, they are candidates for efficient, automatic indication of dialogue quality. We demonstrate that human evaluators have a positive correlation between the output of the language models and scores. We also provide some insights into their behavior and inner-working in a conversational context.",
}
@inproceedings{xu-etal-2020-embedding,
    title = "Embedding Dynamic Attributed Networks by Modeling the Evolution Processes",
    author = "Xu, Zenan  and
      Ou, Zijing  and
      Su, Qinliang  and
      Yu, Jianxing  and
      Quan, Xiaojun  and
      Lin, ZhenKun",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.600",
    doi = "10.18653/v1/2020.coling-main.600",
    pages = "6809--6819",
    abstract = "Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks.",
}
@inproceedings{liu-etal-2020-learning-distributed,
    title = "Learning distributed sentence vectors with bi-directional 3{D} convolutions",
    author = "Liu, Bin  and
      Wang, Liang  and
      Yin, Guosheng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.601",
    doi = "10.18653/v1/2020.coling-main.601",
    pages = "6820--6830",
    abstract = "We propose to learn distributed sentence representation using text{'}s visual features as input. Different from the existing methods that render the words or characters of a sentence into images separately, we further fold these images into a 3-dimensional sentence tensor. Then, multiple 3-dimensional convolutions with different lengths (the third dimension) are applied to the sentence tensor, which act as bi-gram, tri-gram, quad-gram, and even five-gram detectors jointly. Similar to the Bi-LSTM, these n-gram detectors learn both forward and backward distributional semantic knowledge from the sentence tensor. That is, the proposed model using bi-directional convolutions to learn text embedding according to the semantic order of words. The feature maps from the two directions are concatenated for final sentence embedding learning. Our model involves only a single-layer of convolution which makes it easy and fast to train. Finally, we evaluate the sentence embeddings on several downstream Natural Language Processing (NLP) tasks, which demonstrate a surprisingly excellent performance of the proposed model.",
}
@inproceedings{pedinotti-lenci-2020-dont,
    title = "Don{'}t Invite {BERT} to Drink a Bottle: Modeling the Interpretation of Metonymies Using {BERT} and Distributional Representations",
    author = "Pedinotti, Paolo  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.602",
    doi = "10.18653/v1/2020.coling-main.602",
    pages = "6831--6837",
    abstract = "In this work, we carry out two experiments in order to assess the ability of BERT to capture the meaning shift associated with metonymic expressions. We test the model on a new dataset that is representative of the most common types of metonymy. We compare BERT with the Structured Distributional Model (SDM), a model for the representation of words in context which is based on the notion of Generalized Event Knowledge. The results reveal that, while BERT ability to deal with metonymy is quite limited, SDM is good at predicting the meaning of metonymic expressions, providing support for an account of metonymy based on event knowledge.",
}
@inproceedings{ramponi-plank-2020-neural,
    title = "Neural Unsupervised Domain Adaptation in {NLP}{---}{A} Survey",
    author = "Ramponi, Alan  and
      Plank, Barbara",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.603",
    doi = "10.18653/v1/2020.coling-main.603",
    pages = "6838--6855",
    abstract = "Deep neural networks excel at learning from labeled data and achieve state-of-the-art results on a wide array of Natural Language Processing tasks. In contrast, learning from unlabeled data, especially under domain shift, remains a challenge. Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data. This is a more challenging yet a more widely applicable setup. We outline methods, from early traditional non-neural methods to pre-trained model transfer. We also revisit the notion of domain, and we uncover a bias in the type of Natural Language Processing tasks which received most attention. Lastly, we outline future directions, particularly the broader need for out-of-distribution generalization of future NLP.",
}
@inproceedings{welch-etal-2020-exploring,
    title = "Exploring the Value of Personalized Word Embeddings",
    author = "Welch, Charles  and
      Kummerfeld, Jonathan K.  and
      P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.604",
    doi = "10.18653/v1/2020.coling-main.604",
    pages = "6856--6862",
    abstract = "In this paper, we introduce personalized word embeddings, and examine their value for language modeling. We compare the performance of our proposed prediction model when using personalized versus generic word representations, and study how these representations can be leveraged for improved performance. We provide insight into what types of words can be more accurately predicted when building personalized models. Our results show that a subset of words belonging to specific psycholinguistic categories tend to vary more in their representations across users and that combining generic and personalized word embeddings yields the best performance, with a 4.7{\%} relative reduction in perplexity. Additionally, we show that a language model using personalized word embeddings can be effectively used for authorship attribution.",
}
@inproceedings{shwartz-choi-2020-neural,
    title = "Do Neural Language Models Overcome Reporting Bias?",
    author = "Shwartz, Vered  and
      Choi, Yejin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.605",
    doi = "10.18653/v1/2020.coling-main.605",
    pages = "6863--6870",
    abstract = "Mining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language models overcome this issue. We find that while their generalization capacity allows them to better estimate the plausibility of frequent but unspoken of actions, outcomes, and properties, they also tend to overestimate that of the very rare, amplifying the bias that already exists in their training corpus.",
}
@inproceedings{sarkhel-etal-2020-interpretable,
    title = "Interpretable Multi-headed Attention for Abstractive Summarization at Controllable Lengths",
    author = "Sarkhel, Ritesh  and
      Keymanesh, Moniba  and
      Nandi, Arnab  and
      Parthasarathy, Srinivasan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.606",
    doi = "10.18653/v1/2020.coling-main.606",
    pages = "6871--6882",
    abstract = "Abstractive summarization at controllable lengths is a challenging task in natural language processing. It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is not known beforehand. At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical. We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths. The key enabler of our method is an interpretable multi-headed attention mechanism that computes attention distribution over an input document using an array of timestep independent semantic kernels. Each kernel optimizes a human-interpretable syntactic or semantic property. Exhaustive experiments on two low-resource datasets in English show that MLS outperforms strong baselines by up to 14.70{\%} in the METEOR score. Human evaluation of the summaries also suggests that they capture the key concepts of the document at various length-budgets.",
}
@inproceedings{park-etal-2020-scale,
    title = "Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model",
    author = "Park, Sungrae  and
      Kim, Geewook  and
      Lee, Junyeop  and
      Cha, Junbum  and
      Kim, Ji-Hoon  and
      Lee, Hwalsuk",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.607",
    doi = "10.18653/v1/2020.coling-main.607",
    pages = "6883--6893",
    abstract = "This paper introduces a method that efficiently reduces the computational cost and parameter size of Transformer. The proposed model, refer to as Group-Transformer, splits feature space into multiple groups, factorizes the calculation paths, and reduces computations for the group interaction. Extensive experiments on two benchmark tasks, enwik8 and text8, prove our model{'}s effectiveness and efficiency in small-scale Transformers. To the best of our knowledge, Group-Transformer is the first attempt to design Transformer with the group strategy, widely used for efficient CNN architectures.",
}
@inproceedings{sonkar-etal-2020-attention,
    title = "Attention Word Embedding",
    author = "Sonkar, Shashank  and
      Waters, Andrew  and
      Baraniuk, Richard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.608",
    doi = "10.18653/v1/2020.coling-main.608",
    pages = "6894--6902",
    abstract = "Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it. A limitation of CBOW is that it equally weights the context words when making a prediction, which is inefficient, since some words have higher predictive value than others. We tackle this inefficiency by introducing the Attention Word Embedding (AWE) model, which integrates the attention mechanism into the CBOW model. We also propose AWE-S, which incorporates subword information. We demonstrate that AWE and AWE-S outperform the state-of-the-art word embedding models both on a variety of word similarity datasets and when used for initialization of NLP models.",
}
@inproceedings{el-boukkouri-etal-2020-characterbert,
    title = "{C}haracter{BERT}: Reconciling {ELM}o and {BERT} for Word-Level Open-Vocabulary Representations From Characters",
    author = "El Boukkouri, Hicham  and
      Ferret, Olivier  and
      Lavergne, Thomas  and
      Noji, Hiroshi  and
      Zweigenbaum, Pierre  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.609",
    doi = "10.18653/v1/2020.coling-main.609",
    pages = "6903--6915",
    abstract = "Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not being intrinsically linked to the notion of Transformers. While this system is thought to achieve a good balance between the flexibility of characters and the efficiency of full words, using predefined wordpiece vocabularies from the general domain is not always suitable, especially when building models for specialized domains (e.g., the medical domain). Moreover, adopting a wordpiece tokenization shifts the focus from the word level to the subword level, making the models conceptually more complex and arguably less convenient in practice. For these reasons, we propose CharacterBERT, a new variant of BERT that drops the wordpiece system altogether and uses a Character-CNN module instead to represent entire words by consulting their characters. We show that this new model improves the performance of BERT on a variety of medical domain tasks while at the same time producing robust, word-level, and open-vocabulary representations.",
}
@inproceedings{cartuyvels-etal-2020-autoregressive,
    title = "Autoregressive Reasoning over Chains of Facts with Transformers",
    author = "Cartuyvels, Ruben  and
      Spinks, Graham  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.610",
    doi = "10.18653/v1/2020.coling-main.610",
    pages = "6916--6930",
    abstract = "This paper proposes an iterative inference algorithm for multi-hop explanation regeneration, that retrieves relevant factual evidence in the form of text snippets, given a natural language question and its answer. Combining multiple sources of evidence or facts for multi-hop reasoning becomes increasingly hard when the number of sources needed to make an inference grows. Our algorithm copes with this by decomposing the selection of facts from a corpus autoregressively, conditioning the next iteration on previously selected facts. This allows us to use a pairwise learning-to-rank loss. We validate our method on datasets of the TextGraphs 2019 and 2020 Shared Tasks for explanation regeneration. Existing work on this task either evaluates facts in isolation or artificially limits the possible chains of facts, thus limiting multi-hop inference. We demonstrate that our algorithm, when used with a pre-trained transformer model, outperforms the previous state-of-the-art in terms of precision, training time and inference efficiency.",
}
@inproceedings{jindal-etal-2020-augmenting,
    title = "Augmenting {NLP} models using Latent Feature Interpolations",
    author = "Jindal, Amit  and
      Ghosh Chowdhury, Arijit  and
      Didolkar, Aniket  and
      Jin, Di  and
      Sawhney, Ramit  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.611",
    doi = "10.18653/v1/2020.coling-main.611",
    pages = "6931--6936",
    abstract = "Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution. We introduce Emix, a data augmentation method that uses interpolations of word embeddings and hidden layer representations to construct virtual examples. We show that Emix shows significant improvements over previously used interpolation based regularizers and data augmentation techniques. We also demonstrate how our proposed method is more robust to sparsification. We highlight the merits of our proposed methodology by performing thorough quantitative and qualitative assessments.",
}
@inproceedings{lei-etal-2020-neural,
    title = "Neural Language Modeling for Named Entity Recognition",
    author = "Lei, Zhihong  and
      Wang, Weiyue  and
      Dugast, Christian  and
      Ney, Hermann",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.612",
    doi = "10.18653/v1/2020.coling-main.612",
    pages = "6937--6941",
    abstract = "Named entity recognition is a key component in various natural language processing systems, and neural architectures provide significant improvements over conventional approaches. Regardless of different word embedding and hidden layer structures of the networks, a conditional random field layer is commonly used for the output. This work proposes to use a neural language model as an alternative to the conditional random field layer, which is more flexible for the size of the corpus. Experimental results show that the proposed system has a significant advantage in terms of training speed, with a marginal performance degradation.",
}
@proceedings{coling-2020-international-linguistics-industry,
    title = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    editor = "Clifton, Ann  and
      Napoles, Courtney",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.0",
}
@inproceedings{tan-golovneva-2020-evaluating,
    title = "Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models",
    author = "Tan, Lizhen  and
      Golovneva, Olga",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.1",
    doi = "10.18653/v1/2020.coling-industry.1",
    pages = "1--9",
    abstract = "With the recent explosion in popularity of voice assistant devices, there is a growing interest in making them available to user populations in additional countries and languages. However, to provide the highest accuracy and best performance for specific user populations, most existing voice assistant models are developed individually for each region or language, which requires linear investment of effort. In this paper, we propose a general multilingual model framework for Natural Language Understanding (NLU) models, which can help bootstrap new language models faster and reduce the amount of effort required to develop each language separately. We explore how different deep learning architectures affect multilingual NLU model performance. Our experimental results show that these multilingual models can reach same or better performance compared to monolingual models across language-specific test data while require less effort in creating features and model maintenance.",
}
@inproceedings{jolly-etal-2020-data,
    title = "Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems",
    author = "Jolly, Shailza  and
      Falke, Tobias  and
      Tirkaz, Caglar  and
      Sorokin, Daniil",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.2",
    doi = "10.18653/v1/2020.coling-industry.2",
    pages = "10--20",
    abstract = "Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling. However, in evolving real-world dialog systems, where new functionality is regularly added, a major additional challenge is the lack of annotated training data for such new functionality, as the necessary data collection efforts are laborious and time-consuming. A potential solution to reduce the effort is to augment initial seed data by paraphrasing existing utterances automatically. In this paper, we propose a new, data-efficient approach following this idea. Using an interpretation-to-text model for paraphrase generation, we are able to rely on existing dialog system training data, and, in combination with shuffling-based sampling techniques, we can obtain diverse and novel paraphrases from small amounts of seed data. In experiments on a public dataset and with a real-world dialog system, we observe improvements for both intent classification and slot labeling, demonstrating the usefulness of our approach.",
}
@inproceedings{falke-etal-2020-leveraging,
    title = "Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances",
    author = "Falke, Tobias  and
      Boese, Markus  and
      Sorokin, Daniil  and
      Tirkaz, Caglar  and
      Lehnen, Patrick",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.3",
    doi = "10.18653/v1/2020.coling-industry.3",
    pages = "21--32",
    abstract = "In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives. Handling the full range of this distribution is challenging, in particular when relying on manual annotations. However, the same users also provide useful implicit feedback as they often paraphrase an utterance if the dialog system failed to understand it. We propose MARUPA, a method to leverage this type of feedback by creating annotated training examples from it. MARUPA creates new data in a fully automatic way, without manual intervention or effort from annotators, and specifically for currently failing utterances. By re-training the dialog system on this new data, accuracy and coverage for long-tail utterances can be improved. In experiments, we study the effectiveness of this approach in a commercial dialog system across various domains and three languages.",
}
@inproceedings{zhang-etal-2020-query,
    title = "Query Distillation: {BERT}-based Distillation for Ensemble Ranking",
    author = "Zhang, Wangshu  and
      Liu, Junhong  and
      Wen, Zujie  and
      Wang, Yafang  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.4",
    doi = "10.18653/v1/2020.coling-industry.4",
    pages = "33--43",
    abstract = "Recent years have witnessed substantial progress in the development of neural ranking networks, but also an increasingly heavy computational burden due to growing numbers of parameters and the adoption of model ensembles. Knowledge Distillation (KD) is a common solution to balance the effectiveness and efficiency. However, it is not straightforward to apply KD to ranking problems. Ranking Distillation (RD) has been proposed to address this issue, but only shows effectiveness on recommendation tasks. We present a novel two-stage distillation method for ranking problems that allows a smaller student model to be trained while benefitting from the better performance of the teacher model, providing better control of the inference latency and computational burden. We design a novel BERT-based ranking model structure for list-wise ranking to serve as our student model. All ranking candidates are fed to the BERT model simultaneously, such that the self-attention mechanism can enable joint inference to rank the document list. Our experiments confirm the advantages of our method, not just with regard to the inference latency but also in terms of higher-quality rankings compared to the original teacher model.",
}
@inproceedings{palumbo-etal-2020-semantic,
    title = "Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems",
    author = "Palumbo, Enrico  and
      Mezzalira, Andrea  and
      Marco, Cristina  and
      Manzotti, Alessandro  and
      Amberti, Daniele",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.5",
    doi = "10.18653/v1/2020.coling-industry.5",
    pages = "44--49",
    abstract = "The quality of Natural Language Understanding (NLU) models is typically evaluated using aggregated metrics on a large number of utterances. In a dialog system, though, the manual analysis of failures on specific utterances is a time-consuming and yet critical endeavor to guarantee a high-quality customer experience. A crucial question for this analysis is how to create a test set of utterances that covers a diversity of possible customer requests. In this paper, we introduce the task of generating a test set with high semantic diversity for NLU evaluation in dialog systems and we describe an approach to address it. The approach starts by extracting high-traffic utterance patterns. Then, for each pattern, it achieves high diversity selecting utterances from different regions of the utterance embedding space. We compare three selection strategies based on clustering of utterances in the embedding space, on solving the maximum distance optimization problem and on simple heuristics such as random uniform sampling and popularity. The evaluation shows that the highest semantic and lexicon diversity is obtained by a greedy maximum sum of distance solver in a comparable runtime with the clustering and the heuristics approaches.",
}
@inproceedings{bujnowski-etal-2020-empirical,
    title = "An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation",
    author = "Bujnowski, Pawel  and
      Ryzhova, Kseniia  and
      Choi, Hyungtak  and
      Witkowska, Katarzyna  and
      Piersa, Jaroslaw  and
      Krumholc, Tymoteusz  and
      Beksa, Katarzyna",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.6",
    doi = "10.18653/v1/2020.coling-industry.6",
    pages = "50--63",
    abstract = "The topic of this paper is neural multi-task training for text style transfer. We present an efficient method for neutral-to-style transformation using the transformer framework. We demonstrate how to prepare a robust model utilizing large paraphrases corpora together with a small parallel style transfer corpus. We study how much style transfer data is needed for a model on the example of two transformations: neutral-to-cute on internal corpus and modern-to-antique on publicly available Bible corpora. Additionally, we propose a synthetic measure for the automatic evaluation of style transfer models. We hope our research is a step towards replacing common but limited rule-based style transfer systems by more flexible machine learning models for both public and commercial usage.",
}
@inproceedings{arun-etal-2020-best,
    title = "Best Practices for Data-Efficient Modeling in {NLG}:How to Train Production-Ready Neural Models with Less Data",
    author = "Arun, Ankit  and
      Batra, Soumya  and
      Bhardwaj, Vikas  and
      Challa, Ashwini  and
      Donmez, Pinar  and
      Heidari, Peyman  and
      Inan, Hakan  and
      Jain, Shashank  and
      Kumar, Anuj  and
      Mei, Shawn  and
      Mohan, Karthik  and
      White, Michael",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.7",
    doi = "10.18653/v1/2020.coling-industry.7",
    pages = "64--77",
    abstract = "Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of such model-based solutions has been challenging due to high latency, correctness issues, and high data needs. In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production. We describe a family of sampling and modeling techniques to attain production quality with light-weight neural network models using only a fraction of the data that would be necessary otherwise, and show a thorough comparison between each. Our results show that domain complexity dictates the appropriate approach to achieve high data efficiency. Finally, we distill the lessons from our experimental findings into a list of best practices for production-level NLG model development, and present them in a brief runbook. Importantly, the end products of all of the techniques are small sequence-to-sequence models ({\textasciitilde}2Mb) that we can reliably deploy in production. These models achieve the same quality as large pretrained models ({\textasciitilde}1Gb) as judged by human raters.",
}
@inproceedings{hu-etal-2020-interactive,
    title = "Interactive Question Clarification in Dialogue via Reinforcement Learning",
    author = "Hu, Xiang  and
      Wen, Zujie  and
      Wang, Yafang  and
      Li, Xiaolong  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.8",
    doi = "10.18653/v1/2020.coling-industry.8",
    pages = "78--89",
    abstract = "Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.",
}
@inproceedings{chakravarti-etal-2020-towards,
    title = "Towards building a Robust Industry-scale Question Answering System",
    author = "Chakravarti, Rishav  and
      Ferritto, Anthony  and
      Iyer, Bhavani  and
      Pan, Lin  and
      Florian, Radu  and
      Roukos, Salim  and
      Sil, Avi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.9",
    doi = "10.18653/v1/2020.coling-industry.9",
    pages = "90--101",
    abstract = "Industry-scale NLP systems necessitate two features. 1. Robustness: {``}zero-shot transfer learning{''} (ZSTL) performance has to be commendable and 2. Efficiency: systems have to train efficiently and respond instantaneously. In this paper, we introduce the development of a production model called GAAMA (Go Ahead Ask Me Anything) which possess the above two characteristics. For robustness, it trains on the recently introduced Natural Questions (NQ) dataset. NQ poses additional challenges over older datasets like SQuAD: (a) QA systems need to read and comprehend an entire Wikipedia article rather than a small passage, and (b) NQ does not suffer from observation bias during construction, resulting in less lexical overlap between the question and the article. GAAMA consists of Attention-over-Attention, diversity among attention heads, hierarchical transfer learning, and synthetic data augmentation while being computationally inexpensive. Building on top of the powerful BERTQA model, GAAMA provides a ∼2.0{\%} absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ. Further, we show that GAAMA transfers zero-shot to unseen real life and important domains as it yields respectable performance on two benchmarks: the BioASQ and the newly introduced CovidQA datasets.",
}
@inproceedings{yu-etal-2020-delexicalized,
    title = "Delexicalized Paraphrase Generation",
    author = "Yu, Boya  and
      Arkoudas, Konstantine  and
      Hamza, Wael",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.10",
    doi = "10.18653/v1/2020.coling-industry.10",
    pages = "102--112",
    abstract = "We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number of reference paraphrases. These sets of reference paraphrases represent a weak type of semantic equivalence based on annotated slots and intents. To understand semantics from different types of slots, other than anonymizing slots, we apply convolutional neural networks (CNN) prior to pooling on slot values and use pointers to locate slots in the output. We show empirically that the generated paraphrases are of high quality, leading to an additional 1.29{\%} exact match on live utterances. We also show that natural language understanding (NLU) tasks, such as intent classification and named entity recognition, can benefit from data augmentation using automatically generated paraphrases.",
}
@inproceedings{li-etal-2020-multi-task,
    title = "Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention",
    author = "Li, Mingda  and
      Liu, Xinyue  and
      Ruan, Weitong  and
      Soldaini, Luca  and
      Hamza, Wael  and
      Su, Chengwei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.11",
    doi = "10.18653/v1/2020.coling-industry.11",
    pages = "113--123",
    abstract = "Currently, in spoken language understanding (SLU) systems, the automatic speech recognition (ASR) module produces multiple interpretations (or hypotheses) for the input audio signal and the natural language understanding (NLU) module takes the one with the highest confidence score for domain or intent classification. However, the interpretations can be noisy, and solely relying on one interpretation can cause information loss. To address the problem, many research works attempt to rerank the interpretations for a better choice while some recent works get better performance by integrating all the hypotheses during prediction. In this paper, we follow the way of integrating hypotheses but strengthen the training mode by involving more tasks, some of which may be not in existing tasks of NLU but relevant, via multi-task learning or transfer learning. Moreover, we propose the Hierarchical Attention Mechanism (HAM) to further improve the performance with the acoustic-model features like confidence scores, which are ignored in the current hypotheses integration models. The experimental results show that compared to the standard estimation with one hypothesis, the multi-task learning with HAM can improve the domain and intent classification by relatively 19{\%} and 37{\%}, which are much higher than improvements with current integration or reranking methods. To illustrate the cause of improvements brought by our model, we decode the hidden representations of some utterance examples and compare the generated texts with hypotheses and transcripts. The comparison shows that our model could recover the transcription by integrating the fragmented information among hypotheses and identifying the frequent error patterns of the ASR module, and even rewrite the query for a better understanding, which reveals the characteristic of multi-task learning of broadcasting knowledge.",
}
@inproceedings{nagaraj-rao-shen-2020-misspelling,
    title = "Misspelling Detection from Noisy Product Images",
    author = "Nagaraj Rao, Varun  and
      Shen, Mingwei",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.12",
    doi = "10.18653/v1/2020.coling-industry.12",
    pages = "124--135",
    abstract = "Misspellings are introduced on products either due to negligence or as an attempt to deliberately deceive stakeholders. This leads to a revenue loss for online sellers and fosters customer mistrust. Existing spelling research has primarily focused on advancement in misspelling correction and the approach for misspelling detection has remained the use of a large dictionary. The dictionary lookup results in the incorrect detection of several non-dictionary words as misspellings. In this paper, we propose a method to automatically detect misspellings from product images in an attempt to reduce false positive detections. We curate a large scale corpus, define a rich set of features and propose a novel model that leverages importance weighting to account for within class distributional variance. Finally, we experimentally validate this approach on both the curated corpus and an out-of-domain public dataset and show that it leads to a relative improvement of up to 20{\%} in F1 score. The approach thus creates a more robust, generalized deployable solution and reduces reliance on large scale custom dictionaries used today.",
}
@inproceedings{makhija-etal-2020-hinglishnorm,
    title = "hinglish{N}orm - A Corpus of {H}indi-{E}nglish Code Mixed Sentences for Text Normalization",
    author = "Makhija, Piyush  and
      Kumar, Ankit  and
      Gupta, Anuj",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.13",
    doi = "10.18653/v1/2020.coling-industry.13",
    pages = "136--145",
    abstract = "We present hinglishNorm - a human annotated corpus of Hindi-English code-mixed sentences for text normalization task. Each sentence in the corpus is aligned to its corresponding human annotated normalized form. To the best of our knowledge, there is no corpus of Hindi-English code-mixed sentences for text normalization task that is publicly available. Our work is the first attempt in this direction. The corpus contains 13494 segments annotated for text normalization. Further, we present baseline normalization results on this corpus. We obtain a Word Error Rate (WER) of 15.55, BiLingual Evaluation Understudy (BLEU) score of 71.2, and Metric for Evaluation of Translation with Explicit ORdering (METEOR) score of 0.50.",
}
@inproceedings{xu-etal-2020-assessing,
    title = "Assessing Social License to Operate from the Public Discourse on Social Media",
    author = "Xu, Chang  and
      Paris, Cecile  and
      Sparks, Ross  and
      Nepal, Surya  and
      VanderLinden, Keith",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.14",
    doi = "10.18653/v1/2020.coling-industry.14",
    pages = "146--159",
    abstract = "Organisations are monitoring their Social License to Operate (SLO) with increasing regularity. SLO, the level of support organisations gain from the public, is typically assessed through surveys or focus groups, which require expensive manual efforts and yield quickly-outdated results. In this paper, we present SIRTA (Social Insight via Real-Time Text Analytics), a novel real-time text analytics system for assessing and monitoring organisations{'} SLO levels by analysing the public discourse from social posts. To assess SLO levels, our insight is to extract and transform peoples{'} stances towards an organisation into SLO levels. SIRTA achieves this by performing a chain of three text classification tasks, where it identifies task-relevant social posts, discovers key SLO risks discussed in the posts, and infers stances specific to the SLO risks. We leverage recent language understanding techniques (e.g., BERT) for building our classifiers. To monitor SLO levels over time, SIRTA employs quality control mechanisms to reliably identify SLO trends and variations of multiple organisations in a market. These are derived from the smoothed time series of their SLO levels based on exponentially-weighted moving average (EWMA) calculation. Our experimental results show that SIRTA is highly effective in distilling stances from social posts for SLO level assessment, and that the continuous monitoring of SLO levels afforded by SIRTA enables the early detection of critical SLO changes.",
}
@inproceedings{mysore-sathyendra-etal-2020-extreme,
    title = "Extreme Model Compression for On-device Natural Language Understanding",
    author = "Mysore Sathyendra, Kanthashree  and
      Choudhary, Samridhi  and
      Nicolich-Henkin, Leah",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.15",
    doi = "10.18653/v1/2020.coling-industry.15",
    pages = "160--171",
    abstract = "In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a large-scale, commercial NLU system trained on a varied set of intents with huge vocabulary sizes. Our approach outperforms a range of baselines and achieves a compression rate of 97.4{\%} with less than 3.7{\%} degradation in predictive performance. Our analysis indicates that the signal from the downstream task is important for effective compression with minimal degradation in performance.",
}
@inproceedings{el-kurdi-etal-2020-scalable,
    title = "Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers",
    author = "El-Kurdi, Yousef  and
      Kanayama, Hiroshi  and
      Sarioglu Kayi, Efsun  and
      Castelli, Vittorio  and
      Ward, Todd  and
      Florian, Radu",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.16",
    doi = "10.18653/v1/2020.coling-industry.16",
    pages = "172--178",
    abstract = "We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text. We introduce a data augmentation technique that uses synthetic treebanks to improve production-grade parsers. The synthetic treebanks are generated using a state-of-the-art biaffine parser adapted with pretrained Transformer models, such as Multilingual BERT (M-BERT). The new parser improves LAS by up to two points on seven languages. The production models{'} LAS performance improves as the augmented treebanks scale in size, surpassing performance of production models trained on originally annotated UD treebanks.",
}
@inproceedings{zhang-etal-2020-industry,
    title = "An Industry Evaluation of Embedding-based Entity Alignment",
    author = "Zhang, Ziheng  and
      Liu, Hualuo  and
      Chen, Jiaoyan  and
      Chen, Xi  and
      Liu, Bo  and
      Xiang, YueJia  and
      Zheng, Yefeng",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.17",
    doi = "10.18653/v1/2020.coling-industry.17",
    pages = "179--189",
    abstract = "Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage. In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored. Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications. The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.",
}
@inproceedings{roy-etal-2020-learning,
    title = "Learning Domain Terms - Empirical Methods to Enhance Enterprise Text Analytics Performance",
    author = "Roy, Gargi  and
      Dey, Lipika  and
      Shakir, Mohammad  and
      Dasgupta, Tirthankar",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.18",
    doi = "10.18653/v1/2020.coling-industry.18",
    pages = "190--201",
    abstract = "Performance of standard text analytics algorithms are known to be substantially degraded on consumer generated data, which are often very noisy. These algorithms also do not work well on enterprise data which has a very different nature from News repositories, storybooks or Wikipedia data. Text cleaning is a mandatory step which aims at noise removal and correction to improve performance. However, enterprise data need special cleaning methods since it contains many domain terms which appear to be noise against a standard dictionary, but in reality are not so. In this work we present detailed analysis of characteristics of enterprise data and suggest unsupervised methods for cleaning these repositories after domain terms have been automatically segregated from true noise terms. Noise terms are thereafter corrected in a contextual fashion. The effectiveness of the method is established through careful manual evaluation of error corrections over several standard data sets, including those available for hate speech detection, where there is deliberate distortion to avoid detection. We also share results to show enhancement in classification accuracy after noise correction.",
}
@inproceedings{tayal-etal-2020-model,
    title = "Model-agnostic Methods for Text Classification with Inherent Noise",
    author = "Tayal, Kshitij  and
      Ghosh, Rahul  and
      Kumar, Vipin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.19",
    doi = "10.18653/v1/2020.coling-industry.19",
    pages = "202--213",
    abstract = "Text classification is a fundamental problem, and recently, deep neural networks (DNN) have shown promising results in many natural language tasks. However, their human-level performance relies on high-quality annotations, which are time-consuming and expensive to collect. As we move towards large inexpensive datasets, the inherent label noise degrades the generalization of DNN. While most machine learning literature focuses on building complex networks to handle noise, in this work, we evaluate model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption. Specifically, we conduct a point-by-point comparative study between several noise-robust methods on three datasets encompassing three popular classification models. To our knowledge, this is the first time such a comprehensive study in text classification encircling popular models and model-agnostic loss methods has been conducted. In this study, we describe our learning and demonstrate the application of our approach, which outperformed baselines by up to 10 {\%} in classification accuracy while requiring no network modifications.",
}
@inproceedings{patra-etal-2020-scopeit,
    title = "{S}cope{I}t: Scoping Task Relevant Sentences in Documents",
    author = "Patra, Barun  and
      Suryanarayanan, Vishwas  and
      Fufa, Chala  and
      Bhattacharya, Pamela  and
      Lee, Charles",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.20",
    doi = "10.18653/v1/2020.coling-industry.20",
    pages = "214--227",
    abstract = "A prominent problem faced by conversational agents working with large documents (Eg: email-based assistants) is the frequent presence of information in the document that is irrelevant to the assistant. This in turn makes it harder for the agent to accurately detect intents, extract entities relevant to those intents and perform the desired action. To address this issue we present a neural model for scoping relevant information for the agent from a large document. We show that when used as the first step in a popularly used email-based assistant for helping users schedule meetings, our proposed model helps improve the performance of the intent detection and entity extraction tasks required by the agent for correctly scheduling meetings: across a suite of 6 downstream tasks, by using our proposed method, we observe an average gain of 35{\%} in precision without any drop in recall. Additionally, we demonstrate that the same approach can be used for component level analysis in large documents, such as signature block identification.",
}
@inproceedings{liu-etal-2020-uncertainty,
    title = "Uncertainty Modeling for Machine Comprehension Systems using Efficient {B}ayesian Neural Networks",
    author = "Liu, Zhengyuan  and
      Krishnaswamy, Pavitra  and
      Aw, Ai Ti  and
      Chen, Nancy",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.21",
    doi = "10.18653/v1/2020.coling-industry.21",
    pages = "228--235",
    abstract = "While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education. Quantifying uncertainty helps pave the way towards more interpretable neural networks. In classification and regression tasks, Bayesian neural networks have been effective in estimating model uncertainty. However, inference time increases linearly due to the required sampling process in Bayesian neural networks. Thus speed becomes a bottleneck in tasks with high system complexity such as question-answering or dialogue generation. In this work, we propose a hybrid neural architecture to quantify model uncertainty using Bayesian weight approximation but boosts up the inference speed by 80{\%} relative at test time, and apply it for a clinical dialogue comprehension task. The proposed approach is also used to enable active learning so that an updated model can be trained more optimally with new incoming data by selecting samples that are not well-represented in the current training scheme.",
}
@inproceedings{tayal-etal-2020-regularized,
    title = "Regularized Graph Convolutional Networks for Short Text Classification",
    author = "Tayal, Kshitij  and
      Rao, Nikhil  and
      Agarwal, Saurabh  and
      Jia, Xiaowei  and
      Subbian, Karthik  and
      Kumar, Vipin",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-industry.22",
    doi = "10.18653/v1/2020.coling-industry.22",
    pages = "236--242",
    abstract = "Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce. The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning. Simpler methods that rely on bag-of-words representations tend to perform on par with complex deep learning methods. To tackle the limitations of textual features in short text, we propose a Graph-regularized Graph Convolution Network (GR-GCN), which augments graph convolution networks by incorporating label dependencies in the output space. Our model achieves state-of-the-art results on both proprietary and external datasets, outperforming several baseline methods by up to 6{\%} . Furthermore, we show that compared to baseline methods, GR-GCN is more robust to noise in textual features.",
}
@proceedings{coling-2020-international-linguistics,
    title = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    editor = "Ptaszynski, Michal  and
      Ziolko, Bartosz",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.0",
}
@inproceedings{alnajjar-etal-2020-verdd,
    title = "Ve{'}rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource {NLP} and Community Involvement",
    author = {Alnajjar, Khalid  and
      H{\"a}m{\"a}l{\"a}inen, Mika  and
      Rueter, Jack  and
      Partanen, Niko},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.1",
    doi = "10.18653/v1/2020.coling-demos.1",
    pages = "1--6",
    abstract = "We present an open-source online dictionary editing system, Ve′rdd, that offers a chance to re-evaluate and edit grassroots dictionaries that have been exposed to multiple amateur editors. The idea is to incorporate community activities into a state-of-the-art finite-state language description of a seriously endangered minority language, Skolt Sami. Problems involve getting the community to take part in things above the pencil-and-paper level. At times, it seems that the native speakers and the dictionary oriented are lacking technical understanding to utilize the infrastructures which might make their work more meaningful in the future, i.e. multiple reuse of all of their input. Therefore, our system integrates with the existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.",
}
@inproceedings{akhbardeh-etal-2020-maintnet,
    title = "{M}aint{N}et: A Collaborative Open-Source Library for Predictive Maintenance Language Resources",
    author = "Akhbardeh, Farhad  and
      Desell, Travis  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.2",
    doi = "10.18653/v1/2020.coling-demos.2",
    pages = "7--11",
    abstract = "Maintenance record logbooks are an emerging text type in NLP. An important part of them typically consist of free text with many domain specific technical terms, abbreviations, and non-standard spelling and grammar. This poses difficulties for NLP pipelines trained on standard corpora. Analyzing and annotating such documents is of particular importance in the development of predictive maintenance systems, which aim to improve operational efficiency, reduce costs, prevent accidents, and save lives. In order to facilitate and encourage research in this area, we have developed MaintNet, a collaborative open-source library of technical and domain-specific language resources. MaintNet provides novel logbook data from the aviation, automotive, and facility maintenance domains along with tools to aid in their (pre-)processing and clustering. Furthermore, it provides a way to encourage discussion on and sharing of new datasets and tools for logbook data analysis.",
}
@inproceedings{chang-etal-2020-dart,
    title = "{DART}: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool",
    author = "Chang, Ernie  and
      Caplinger, Jeriah  and
      Marin, Alex  and
      Shen, Xiaoyu  and
      Demberg, Vera",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.3",
    doi = "10.18653/v1/2020.coling-demos.3",
    pages = "12--17",
    abstract = "We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an interactive application that reduces human efforts in annotating large quantities of structured data, e.g. in the format of a table or tree structure. By using a backend sequence-to-sequence model, our system iteratively analyzes the annotated labels in order to better sample unlabeled data. In a simulation experiment performed on annotating large quantities of structured data, DART has been shown to reduce the total number of annotations needed with active learning and automatically suggesting relevant labels.",
}
@inproceedings{estevez-velarde-etal-2020-demo,
    title = "Demo Application for the {A}uto{GOAL} Framework",
    author = "Estevez-Velarde, Suilan  and
      Piad-Morffis, Alejandro  and
      Guti{\'e}rrez, Yoan  and
      Montoyo, Andres  and
      Mu{\~n}oz-Guillena, Rafael  and
      Almeida Cruz, Yudivi{\'a}n",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.4",
    doi = "10.18653/v1/2020.coling-demos.4",
    pages = "18--22",
    abstract = "This paper introduces a web demo that showcases the main characteristics of the AutoGOAL framework. AutoGOAL is a framework in Python for automatically finding the best way to solve a given task. It has been designed mainly for automatic machine learning(AutoML) but it can be used in any scenario where several possible strategies are available to solve a given computational task. In contrast with alternative frameworks, AutoGOAL can be applied seamlessly to Natural Language Processing as well as structured classification problems. This paper presents an overview of the framework{'}s design and experimental evaluation in several machine learning problems, including two recent NLP challenges. The accompanying software demo is available online (https://autogoal.github.io/demo) and full source code is provided under the MIT open-source license (https://autogoal.github.io).",
}
@inproceedings{nguyen-etal-2020-fast,
    title = "Fast Word Predictor for On-Device Application",
    author = "Nguyen, Huy Tien  and
      Nguyen, Khoi Tuan  and
      Nguyen, Anh Tuan  and
      Tran, Thanh Lac Thi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.5",
    doi = "10.18653/v1/2020.coling-demos.5",
    pages = "23--27",
    abstract = "Learning on large text corpora, deep neural networks achieve promising results in the next word prediction task. However, deploying these huge models on devices has to deal with constraints of low latency and a small binary size. To address these challenges, we propose a fast word predictor performing efficiently on mobile devices. Compared with a standard neural network which has a similar word prediction rate, the proposed model obtains 60{\%} reduction in memory size and 100X faster inference time on a middle-end mobile device. The method is developed as a feature for a chat application which serves more than 100 million users.",
}
@inproceedings{farmanbar-etal-2020-semantic,
    title = "Semantic search with domain-specific word-embedding and production monitoring in Fintech",
    author = "Farmanbar, Mojtaba  and
      Van Ommeren, Nikki  and
      Zhao, Boyang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.6",
    doi = "10.18653/v1/2020.coling-demos.6",
    pages = "28--33",
    abstract = "We present an end-to-end information retrieval system with domain-specific custom language models for accurate search terms expansion. The text mining pipeline tackles several challenges faced in an industry-setting, including multi-lingual jargon-rich unstructured text and privacy compliance. Combined with a novel statistical approach for word embedding evaluations, the models can be monitored in a production setting. Our approach is used in the real world in risk management in the financial sector and has wide applicability to other domains.",
}
@inproceedings{hollenstein-etal-2020-cognival,
    title = "{C}ogni{V}al in Action: An Interface for Customizable Cognitive Word Embedding Evaluation",
    author = "Hollenstein, Nora  and
      van der Lek, Adrian  and
      Zhang, Ce",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.7",
    doi = "10.18653/v1/2020.coling-demos.7",
    pages = "34--40",
    abstract = "We demonstrate the functionalities of the new user interface for CogniVal. CogniVal is a framework for the cognitive evaluation of English word embeddings, which evaluates the quality of the embeddings based on their performance to predict human lexical representations from cognitive language processing signals from various sources. In this paper, we present an easy-to-use command line interface for CogniVal with multiple improvements over the original work, including the possibility to evaluate custom embeddings against custom cognitive data sources.",
}
@inproceedings{ferritto-etal-2020-multilingual,
    title = "A Multilingual Reading Comprehension System for more than 100 Languages",
    author = "Ferritto, Anthony  and
      Rosenthal, Sara  and
      Bornea, Mihaela  and
      Hasan, Kazi  and
      Chakravarti, Rishav  and
      Roukos, Salim  and
      Florian, Radu  and
      Sil, Avi",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.8",
    doi = "10.18653/v1/2020.coling-demos.8",
    pages = "41--47",
    abstract = "This paper presents M-GAAMA, a Multilingual Question Answering architecture and demo system. This is the first multilingual machine reading comprehension (MRC) demo which is able to answer questions in over 100 languages. M-GAAMA answers questions from a given passage in the same or different language. It incorporates several existing multilingual models that can be used interchangeably in the demo such as M-BERT and XLM-R. The M-GAAMA demo also improves language accessibility by incorporating the IBM Watson machine translation widget to provide additional capabilities to the user to see an answer in their desired language. We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019). We experiment with our system architecture on the Multi-Lingual Question Answering (MLQA) and the COVID-19 CORD (Wang et al., 2020; Tang et al., 2020) datasets to provide insights into the performance of the system.",
}
@inproceedings{kalouli-etal-2020-xplainli,
    title = "{X}plai{NLI}: Explainable Natural Language Inference through Visual Analytics",
    author = "Kalouli, Aikaterini-Lida  and
      Sevastjanova, Rita  and
      de Paiva, Valeria  and
      Crouch, Richard  and
      El-Assady, Mennatallah",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.9",
    doi = "10.18653/v1/2020.coling-demos.9",
    pages = "48--52",
    abstract = "Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is. Recent research has revealed some heuristics and biases of these models. However, to date, there is no systematic effort to capitalize on those insights through a system that uses these to explain the NLI decisions. To this end, we propose XplaiNLI, an eXplainable, interactive, visualization interface that computes NLI with different methods and provides explanations for the decisions made by the different approaches.",
}
@inproceedings{lugini-etal-2020-discussion,
    title = "Discussion Tracker: Supporting Teacher Learning about Students{'} Collaborative Argumentation in High School Classrooms",
    author = "Lugini, Luca  and
      Olshefski, Christopher  and
      Singh, Ravneet  and
      Litman, Diane  and
      Godley, Amanda",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.10",
    doi = "10.18653/v1/2020.coling-demos.10",
    pages = "53--58",
    abstract = "Teaching collaborative argumentation is an advanced skill that many K-12 teachers struggle to develop. To address this, we have developed Discussion Tracker, a classroom discussion analytics system based on novel algorithms for classifying argument moves, specificity, and collaboration. Results from a classroom deployment indicate that teachers found the analytics useful, and that the underlying classifiers perform with moderate to substantial agreement with humans.",
}
@inproceedings{jiang-etal-2020-online,
    title = "An Online Readability Leveled {A}rabic Thesaurus",
    author = "Jiang, Zhengyang  and
      Habash, Nizar  and
      Al Khalil, Muhamed",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.11",
    doi = "10.18653/v1/2020.coling-demos.11",
    pages = "59--63",
    abstract = "This demo paper introduces the online Readability Leveled Arabic Thesaurus interface. For a given user input word, this interface provides the word{'}s possible lemmas, roots, English glosses, related Arabic words and phrases, and readability on a five-level readability scale. This interface builds on and connects multiple existing Arabic resources and processing tools. This one-of-a-kind system enables Arabic speakers and learners to benefit from advances in Arabic computational linguistics technologies. Feedback from users of the system will help the developers to identify lexical coverage gaps and errors. A live link to the demo is available at: http://samer.camel-lab.com/.",
}
@inproceedings{oberhauser-etal-2020-trainx,
    title = "{T}rain{X} {--} Named Entity Linking with Active Sampling and Bi-Encoders",
    author = {Oberhauser, Tom  and
      Bischoff, Tim  and
      Brendel, Karl  and
      Menke, Maluna  and
      Klatt, Tobias  and
      Siu, Amy  and
      Gers, Felix Alexander  and
      L{\"o}ser, Alexander},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.12",
    doi = "10.18653/v1/2020.coling-demos.12",
    pages = "64--69",
    abstract = "We demonstrate TrainX, a system for Named Entity Linking for medical experts. It combines state-of-the-art entity recognition and linking architectures, such as Flair and fine-tuned Bi-Encoders based on BERT, with an easy-to-use interface for healthcare professionals. We support medical experts in annotating training data by using active sampling strategies to forward informative samples to the annotator. We demonstrate that our model is capable of linking against large knowledge bases, such as UMLS (3.6 million entities), and supporting zero-shot cases, where the linker has never seen the entity before. Those zero-shot capabilities help to mitigate the problem of rare and expensive training data that is a common issue in the medical domain.",
}
@inproceedings{salawu-etal-2020-bullstop,
    title = "{B}ull{S}top: A Mobile App for Cyberbullying Prevention",
    author = "Salawu, Semiu  and
      He, Yulan  and
      Lumsden, Jo",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.13",
    doi = "10.18653/v1/2020.coling-demos.13",
    pages = "70--74",
    abstract = "Social media has become the new playground for bullies. Young people are now regularly exposed to a wide range of abuse online. In response to the increasing prevalence of cyberbullying, online social networks have increased efforts to clamp down on online abuse but unfortunately, the nature, complexity and sheer volume of cyberbullying means that many cyberbullying incidents go undetected. BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms. It uses deep learning models to identify instances of cyberbullying and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user. Our system not only achieves impressive prediction results but also demonstrates excellent potential for use in real-world scenarios and is freely available on the Google Play Store.",
}
@inproceedings{poswiata-perelkiewicz-2020-annobot,
    title = "Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot",
    author = "Po{\'s}wiata, Rafa{\l}  and
      Pere{\l}kiewicz, Micha{\l}",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.14",
    doi = "10.18653/v1/2020.coling-demos.14",
    pages = "75--79",
    abstract = "In this paper, we introduce Annobot: a platform for annotating and creating datasets through conversation with a chatbot. This natural form of interaction has allowed us to create a more accessible and flexible interface, especially for mobile devices. Our solution has a wide range of applications such as data labelling for binary, multi-class/label classification tasks, preparing data for regression problems, or creating sets for issues such as machine translation, question answering or text summarization. Additional features include pre-annotation, active sampling, online learning and real-time inter-annotator agreement. The system is integrated with the popular messaging platform: Facebook Messanger. Usability experiment showed the advantages of the proposed platform compared to other labelling tools. The source code of Annobot is available under the GNU LGPL license at https://github.com/rafalposwiata/annobot.",
}
@inproceedings{mubarak-etal-2020-arabic,
    title = "{A}rabic Curriculum Analysis",
    author = "Mubarak, Hamdy  and
      Amer, Shimaa  and
      Abdelali, Ahmed  and
      Darwish, Kareem",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.15",
    doi = "10.18653/v1/2020.coling-demos.15",
    pages = "80--86",
    abstract = "Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes. In this paper, we present a system to analyze Arabic curricula and provide insights into their content. It allows users to explore word presence, surface-forms used, as well as contrasting statistics between different countries from which the curricula were selected. Also, it provides a facility to grade text in reference to given grade-level and gives users feedback about the complexity or difficulty of words used in a text.",
}
@inproceedings{ryu-ptaszynski-2020-epistolary,
    title = "Epistolary Education in 21st Century: A System to Support Composition of {E}-mails by Students to Superiors in {J}apanese",
    author = "Ryu, Kenji  and
      Ptaszynski, Michal",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://www.aclweb.org/anthology/2020.coling-demos.16",
    doi = "10.18653/v1/2020.coling-demos.16",
    pages = "87--92",
    abstract = "E-mail is a communication tool widely used by people of all ages on the Internet today, often in business and formal situations, especially in Japan. Moreover, Japanese E-mail communication has a set of specific rules taught using specialized guidebooks. E-mail literacy education for many Japanese students is typically provided in a traditional, yet inefficient lecture-based way. We propose a system to support Japanese students in writing E-mails to superiors (teachers, job hunting representatives, etc.). We firstly make an investigation into the importance of formal E-mails in Japan, and what is needed to successfully write a formal E-mail. Next, we develop the system with accordance to those rules. Finally, we evaluated the system twofold. The results, although performed on a small number of samples, were generally positive, and clearly indicated additional ways to improve the system.",
}
@proceedings{cogalex-2020-cognitive,
    title = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    editor = "Zock, Michael  and
      Chersoni, Emmanuele  and
      Lenci, Alessandro  and
      Santus, Enrico",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.0",
}
@inproceedings{hofmann-etal-2020-individual,
    title = "Individual corpora predict fast memory retrieval during reading",
    author = {Hofmann, Markus J.  and
      M{\"u}ller, Lara  and
      R{\"o}lke, Andre  and
      Radach, Ralph  and
      Biemann, Chris},
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.1",
    pages = "1--11",
    abstract = "The corpus, from which a predictive language model is trained, can be considered the experience of a semantic system. We recorded everyday reading of two participants for two months on a tablet, generating individual corpus samples of 300/500K tokens. Then we trained word2vec models from individual corpora and a 70 million-sentence newspaper corpus to obtain individual and norm-based long-term memory structure. To test whether individual corpora can make better predictions for a cognitive task of long-term memory retrieval, we generated stimulus materials consisting of 134 sentences with uncorrelated individual and norm-based word probabilities. For the subsequent eye tracking study 1-2 months later, our regression analyses revealed that individual, but not norm-corpus-based word probabilities can account for first-fixation duration and first-pass gaze duration. Word length additionally affected gaze duration and total viewing duration. The results suggest that corpora representative for an individual{'}s long-term memory structure can better explain reading performance than a norm corpus, and that recently acquired information is lexically accessed rapidly.",
}
@inproceedings{cao-etal-2020-investigating,
    title = "Investigating Rich Feature Sources for Conceptual Representation Encoding",
    author = "Cao, Lu  and
      Chen, Yulong  and
      Huang, Dandan  and
      Zhang, Yue",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.2",
    pages = "12--22",
    abstract = "Functional Magnetic Resonance Imaging (fMRI) provides a means to investigate human conceptual representation in cognitive and neuroscience studies, where researchers predict the fMRI activations with elicited stimuli inputs. Previous work mainly uses a single source of features, particularly linguistic features, to predict fMRI activations. However, relatively little work has been done on investigating rich-source features for conceptual representation. In this paper, we systematically compare the linguistic, visual as well as auditory input features in conceptual representation, and further introduce associative conceptual features, which are obtained from Small World of Words game, to predict fMRI activations. Our experimental results show that those rich-source features can enhance performance in predicting the fMRI activations. Our analysis indicates that information from rich sources is present in the conceptual representation of human brains. In particular, the visual feature weights the most on conceptual representation, which is consistent with the recent cognitive science study.",
}
@inproceedings{tjuka-2020-general,
    title = "General patterns and language variation: Word frequencies across {E}nglish, {G}erman, and {C}hinese",
    author = "Tjuka, Annika",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.3",
    pages = "23--32",
    abstract = "Cross-linguistic studies of concepts provide valuable insights for the investigation of the mental lexicon. Recent developments of cross-linguistic databases facilitate an exploration of a diverse set of languages on the basis of comparative concepts. These databases make use of a well-established reference catalog, the Concepticon, which is built from concept lists published in linguistics. A recently released feature of the Concepticon includes data on norms, ratings, and relations for words and concepts. The present study used data on word frequencies to test two hypotheses. First, I examined the assumption that related languages (i.e., English and German) share concepts with more similar frequencies than non-related languages (i.e., English and Chinese). Second, the variation of frequencies across both language pairs was explored to answer the question of whether the related languages share fewer concepts with a large difference between the frequency than the non-related languages. The findings indicate that related languages experience less variation in their frequencies. If there is variation, it seems to be due to cultural and structural differences. The implications of this study are far-reaching in that it exemplifies the use of cross-linguistic data for the study of the mental lexicon.",
}
@inproceedings{yang-etal-2020-less,
    title = "Less is Better: A cognitively inspired unsupervised model for language segmentation",
    author = "Yang, Jinbiao  and
      Frank, Stefan L.  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.4",
    pages = "33--45",
    abstract = "Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels. Although we can do such unitization/segmentation easily, its cognitive mechanism is still not clear. This paper proposes an unsupervised model, Less-is-Better (LiB), to simulate the human cognitive process with respect to language unitization/segmentation. LiB follows the principle of least effort and aims to build a lexicon which minimizes the number of unit tokens (alleviating the effort of analysis) and number of unit types (alleviating the effort of storage) at the same time on any given corpus. LiB{'}s workflow is inspired by empirical cognitive phenomena. The design makes the mechanism of LiB cognitively plausible and the computational requirement light-weight. The lexicon generated by LiB performs the best among different types of lexicons (e.g. ground-truth words) both from an information-theoretical view and a cognitive view, which suggests that the LiB lexicon may be a plausible proxy of the mental lexicon.",
}
@inproceedings{xiang-etal-2020-cogalex,
    title = "The {C}og{AL}ex Shared Task on Monolingual and Multilingual Identification of Semantic Relations",
    author = "Xiang, Rong  and
      Chersoni, Emmanuele  and
      Iacoponi, Luca  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.5",
    pages = "46--53",
    abstract = "The shared task of the CogALex-VI workshop focuses on the monolingual and multilingual identification of semantic relations. We provided training and validation data for the following languages: English, German and Chinese. Given a word pair, systems had to be trained to identify which relation holds between them, with possible choices being synonymy, antonymy, hypernymy and no relation at all. Two test sets were released for evaluating the participating systems. One containing pairs for each of the training languages (systems were evaluated in a monolingual fashion) and the other proposing a surprise language to test the crosslingual transfer capabilities of the systems. Among the submitted systems, top performance was achieved by a transformer-based model in both the monolingual and in the multilingual setting, for all the tested languages, proving the potentials of this recently-introduced neural architecture. The shared task description and the results are available at https://sites.google.com/site/cogalexvisharedtask/.",
}
@inproceedings{colson-2020-extracting,
    title = "Extracting meaning by idiomaticity: Description of the {HS}em{ID} system at {C}og{AL}ex {VI} (2020)",
    author = "Colson, Jean-Pierre",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.6",
    pages = "54--58",
    abstract = "The HSemID system, submitted to the CogALex VI Shared Task is a hybrid system relying mainly on metric clusters measured in large web corpora, complemented by a vector space model using cosine similarity to detect semantic associations. Although the system reached ra-ther weak results for the subcategories of synonyms, antonyms and hypernyms, with some dif-ferences from one language to another, it is able to measure general semantic associations (as being random or not-random) with an F1 score close to 0.80. The results strongly suggest that idiomatic constructions play a fundamental role in semantic associations. Further experiments are necessary in order to fine-tune the model to the subcategories of synonyms, antonyms, hy-pernyms and to explain surprising differences across languages. 1 Introduction",
}
@inproceedings{wachowiak-etal-2020-cogalex,
    title = "{C}og{AL}ex-{VI} Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation Identification",
    author = "Wachowiak, Lennart  and
      Lang, Christian  and
      Heinisch, Barbara  and
      Gromann, Dagmar",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.7",
    pages = "59--64",
    abstract = "We describe our submission to the CogALex-VI shared task on the identification of multilingual paradigmatic relations building on XLM-RoBERTa (XLM-R), a robustly optimized and multilingual BERT model. In spite of several experiments with data augmentation, data addition and ensemble methods with a Siamese Triple Net, Translrelation, the XLM-R model with a linear classifier adapted to this specific task, performed best in testing and achieved the best results in the final evaluation of the shared task, even for a previously unseen language.",
}
@inproceedings{karmakar-mccrae-2020-cogalex,
    title = "{C}og{AL}ex-{VI} Shared Task: Bidirectional Transformer based Identification of Semantic Relations",
    author = "Karmakar, Saurav  and
      McCrae, John P.",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.8",
    pages = "65--71",
    abstract = "This paper presents a bidirectional transformer based approach for recognising semantic relationships between a pair of words as proposed by CogALex VI shared task in 2020. The system presented here works by employing BERT embeddings of the words and passing the same over tuned neural network to produce a learning model for the pair of words and their relationships. Afterwards the very same model is used for the relationship between unknown words from the test set. CogALex VI provided Subtask 1 as the identification of relationship of three specific categories amongst English pair of words and the presented system opts to work on that. The resulted relationships of the unknown words are analysed here which shows a balanced performance in overall characteristics with some scope for improvement.",
}
@inproceedings{hashempour-villavicencio-2020-leveraging,
    title = "Leveraging Contextual Embeddings and Idiom Principle for Detecting Idiomaticity in Potentially Idiomatic Expressions",
    author = "Hashempour, Reyhaneh  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.9",
    pages = "72--80",
    abstract = "The majority of studies on detecting idiomatic expressions have focused on discovering potentially idiomatic expressions overlooking the context. However, many idioms like blow the whistle could be interpreted idiomatically or literally depending on the context. In this work, we leverage the Idiom Principle (Sinclair et al., 1991) and contextualized word embeddings (CWEs), focusing on Context2Vec (Melamud et al., 2016) and BERT (Devlin et al., 2019) to distinguish between literal and idiomatic senses of such expressions in context. We also experiment with a non-contextualized word embedding baseline, in this case word2Vec (Mikolov et al., 2013) and compare its performance with that of CWEs. The results show that CWEs outperform the non-CWEs, especially when the Idiom Principle is applied, as it improves the results by 6{\%}. We further show that the Context2Vec model, trained based on Idiom Principle, can place potentially idiomatic expressions into distinct {`}sense{'} (idiomatic/literal) regions of the embedding space, whereas Word2Vec and BERT seem to lack this capacity. The model is also capable of producing suitable substitutes for ambiguous expressions in context which is promising for downstream tasks like text simplification.",
}
@inproceedings{roig-mirapeix-etal-2020-definition,
    title = "Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions",
    author = "Roig Mirapeix, Mireia  and
      Espinosa Anke, Luis  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.10",
    pages = "81--91",
    abstract = "Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri. In this paper, we present an in-depth analytical study on the main features relevant to the task of definition extraction. Our main goal is to study whether linguistic structures from canonical (the Aristotelian or genus et differentia model) can be leveraged to retrieve definitions from corpora in different domains of knowledge and textual genres alike. To this end, we develop a simple linear classifier and analyze the contribution of several (sets of) linguistic features. Finally, as a result of our experiments, we also shed light on the particularities of existing benchmarks as well as the most challenging aspects of the task.",
}
@inproceedings{sen-2020-speech,
    title = "Speech Disfluencies occur at Higher Perplexities",
    author = "Sen, Priyanka",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.11",
    pages = "92--97",
    abstract = "Speech disfluencies have been hypothesized to occur before words that are less predictable and therefore more cognitively demanding. In this paper, we revisit this hypothesis by using OpenAI{'}s GPT-2 to calculate predictability of words as language model perplexity. Using the Switchboard corpus, we find that 51{\%} of disfluencies occur at the highest, second highest, or within one token of the highest perplexity, and this distribution is not random. We also show that disfluencies precede words with significantly higher perplexity than fluent contexts. Based on our results, we offer new evidence that disfluencies are more likely to occur before less predictable words.",
}
@inproceedings{fleisig-2020-bilingual,
    title = "Bilingual Lexical Access and Cognate Idiom Comprehension",
    author = "Fleisig, Eve",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.12",
    pages = "98--106",
    abstract = "Language transfer can facilitate learning L2 words whose form and meaning are similar to L1 words, or hinder speakers when the languages differ. L2 idioms introduce another layer of challenge, as language transfer could occur on the literal or figurative level of meaning. Thus, the mechanics of language transfer for idiom processing shed light on how literal and figurative meaning is stored in the bilingual lexicon. Three factors appear to influence how language transfer affects idiom comprehension: bilingual fluency, processing of literal-figurative vs. figurative cognate idioms (idioms with the same wording and meaning in both languages, or the same meaning only), and comprehension of literal vs. figurative meaning of a given idiom. To examine the relationship between these factors, this study investigated English-Spanish bilinguals{'} reaction time on a lexical decision task examining literal-figurative and figurative cognate idioms. The results suggest that fluency increases processing speed rather than slow it down due to language transfer, and that language transfer from L1 to L2 occurs on the level of figurative meaning in L1-dominant bilinguals.",
}
@inproceedings{juzek-haeussler-2020-schwa,
    title = "Schwa-deletion in {G}erman noun-noun compounds",
    author = "Juzek, Tom S  and
      Haeussler, Jana",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.13",
    pages = "107--111",
    abstract = "We report ongoing research on linking elements in German compounds, with a focus on noun-noun compounds in which the first constituent is ending in schwa. We present a corpus of about 3000 nouns ending in schwa, annotated for various phonological and morpho-syntactic features, and critically, the dominant linking strategy. The corpus analysis is complemented by an unsuccessful attempt to train neural networks and by a pilot experiment asking native speakers to indicate their preferred linking strategy. In addition to existing nouns, the experimental stimuli included nonce words, also ending in schwa. While neither the corpus study nor the experiment offer a clear picture, the results nevertheless provide interesting insights into the intricacies of German compounding. Overall, we find a predominance of the paradigmatic linking element -n for feminine and masculine nouns. At the same time, the results for nonce words show that -n is not a default strategy.",
}
@inproceedings{kwong-2020-translating,
    title = "Translating Collocations: The Need for Task-driven Word Associations",
    author = "Kwong, Oi Yee",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.14",
    pages = "112--116",
    abstract = "Existing dictionaries may help collocation translation by suggesting associated words in the form of collocations, thesaurus, and example sentences. We propose to enhance them with task-driven word associations, illustrating the need by a few scenarios and outlining a possible approach based on word embedding. An example is given, using pre-trained word embedding, while more extensive investigation with more refined methods and resources is underway.",
}
@inproceedings{aguirre-celis-miikkulainen-2020-characterizing,
    title = "Characterizing Dynamic Word Meaning Representations in the Brain",
    author = "Aguirre-Celis, Nora  and
      Miikkulainen, Risto",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.15",
    pages = "117--128",
    abstract = "During sentence comprehension, humans adjust word meanings according to the combination of the concepts that occur in the sentence. This paper presents a neural network model called CEREBRA (Context-dEpendent meaning REpresentation in the BRAin) that demonstrates this process based on fMRI sentence patterns and the Concept Attribute Rep-resentation (CAR) theory. In several experiments, CEREBRA is used to quantify conceptual combination effect and demonstrate that it matters to humans. Such context-based representations could be used in future natural language processing systems allowing them to mirror human performance more accurately.",
}
@inproceedings{nair-etal-2020-contextualized,
    title = "Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge",
    author = "Nair, Sathvik  and
      Srinivasan, Mahesh  and
      Meylan, Stephan",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.16",
    pages = "129--141",
    abstract = "Understanding context-dependent variation in word meanings is a key aspect of human language comprehension supported by the lexicon. Lexicographic resources (e.g., WordNet) capture only some of this context-dependent variation; for example, they often do not encode how closely senses, or discretized word meanings, are related to one another. Our work investigates whether recent advances in NLP, specifically contextualized word embeddings, capture human-like distinctions between English word senses, such as polysemy and homonymy. We collect data from a behavioral, web-based experiment, in which participants provide judgments of the relatedness of multiple WordNet senses of a word in a two-dimensional spatial arrangement task. We find that participants{'} judgments of the relatedness between senses are correlated with distances between senses in the BERT embedding space. Specifically, homonymous senses (e.g., bat as mammal vs. bat as sports equipment) are reliably more distant from one another in the embedding space than polysemous ones (e.g., chicken as animal vs. chicken as meat). Our findings point towards the potential utility of continuous-space representations of sense meanings.",
}
@inproceedings{reyes-magana-etal-2020-automatic,
    title = "Automatic Word Association Norms ({AWAN})",
    author = "Reyes-Maga{\~n}a, Jorge  and
      Sierra Mart{\'\i}nez, Gerardo  and
      Bel-Enguix, Gemma  and
      Gomez-Adorno, Helena",
    booktitle = "Proceedings of the Workshop on the Cognitive Aspects of the Lexicon",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cogalex-1.17",
    pages = "142--153",
    abstract = "Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.",
}
@proceedings{codi-2020-approaches,
    title = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    editor = "Braud, Chlo{\'e}  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Louis, Annie  and
      Strube, Michael",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.0",
}
@inproceedings{cao-2020-discourse,
    title = "How does discourse affect {S}panish-{C}hinese Translation? A case study based on a {S}panish-{C}hinese parallel corpus",
    author = "Cao, Shuyuan",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.1",
    doi = "10.18653/v1/2020.codi-1.1",
    pages = "1--10",
    abstract = "With their huge speaking populations in the world, Spanish and Chinese occupy important positions in linguistic studies. Since the two languages come from different language systems, the translation between Spanish and Chinese is complicated. A comparative study for the language pair can discover the discourse differences between Spanish and Chinese, and can benefit the Spanish-Chinese translation. In this work, based on a Spanish-Chinese parallel corpus annotated with discourse information, we compare the annotation results between the language pair and analyze how discourse affects Spanish-Chinese translation. The research results in our study can help human translators who work with the language pair.",
}
@inproceedings{maitreyee-2020-beyond,
    title = "Beyond Adjacency Pairs: Hierarchical Clustering of Long Sequences for Human-Machine Dialogues",
    author = "Maitreyee, Maitreyee",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.2",
    doi = "10.18653/v1/2020.codi-1.2",
    pages = "11--19",
    abstract = "This work proposes a framework to predict sequences in dialogues, using turn based syntactic features and dialogue control functions. Syntactic features were extracted using dependency parsing, while dialogue control functions were manually labelled. These features were transformed using tf-idf and word embedding; feature selection was done using Principal Component Analysis (PCA). We ran experiments on six combinations of features to predict sequences with Hierarchical Agglomerative Clustering. An analysis of the clustering results indicate that using word embeddings and syntactic features, significantly improved the results.",
}
@inproceedings{khosla-rose-2020-using,
    title = "Using Type Information to Improve Entity Coreference Resolution",
    author = "Khosla, Sopan  and
      Rose, Carolyn",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.3",
    doi = "10.18653/v1/2020.codi-1.3",
    pages = "20--31",
    abstract = "Coreference resolution (CR) is an essential part of discourse analysis. Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms. So far none of the published neural models leverage external semantic knowledge such as type information. This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types. In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions. Our evaluation covers two different grain sizes of types over four different benchmark corpora.",
}
@inproceedings{kahardipraja-etal-2020-exploring,
    title = "Exploring Span Representations in Neural Coreference Resolution",
    author = "Kahardipraja, Patrick  and
      Vyshnevska, Olena  and
      Lo{\'a}iciga, Sharid",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.4",
    doi = "10.18653/v1/2020.codi-1.4",
    pages = "32--41",
    abstract = "In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.",
}
@inproceedings{li-2020-supporting,
    title = "Supporting Comedy Writers: Predicting Audience{'}s Response from Sketch Comedy and Crosstalk Scripts",
    author = "Li, Maolin",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.5",
    doi = "10.18653/v1/2020.codi-1.5",
    pages = "42--52",
    abstract = "Sketch comedy and crosstalk are two popular types of comedy. They can relieve people{'}s stress and thus benefit their mental health, especially when performances and scripts are high-quality. However, writing a script is time-consuming and its quality is difficult to achieve. In order to minimise the time and effort needed for producing an excellent script, we explore ways of predicting the audience{'}s response from the comedy scripts. For this task, we present a corpus of annotated scripts from popular television entertainment programmes in recent years. Annotations include a) text classification labels, indicating which actor{'}s lines made the studio audience laugh; b) information extraction labels, i.e. the text spans that made the audience laughed immediately after the performers said them. The corpus will also be useful for dialogue systems and discourse analysis, since our annotations are based on entire scripts. In addition, we evaluate different baseline algorithms. Experimental results demonstrate that BERT models can achieve the best predictions among all the baseline methods. Furthermore, we conduct an error analysis and investigate predictions across scripts with different styles.",
}
@inproceedings{lapshinova-koltunski-kunz-2020-exploring,
    title = "Exploring Coreference Features in Heterogeneous Data",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Kunz, Kerstin",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.6",
    doi = "10.18653/v1/2020.codi-1.6",
    pages = "53--64",
    abstract = "The present paper focuses on variation phenomena in coreference chains. We address the hypothesis that the degree of structural variation between chain elements depends on language-specific constraints and preferences and, even more, on the communicative situation of language production. We define coreference features that also include reference to abstract entities and events. These features are inspired through several sources {--} cognitive parameters, pragmatic factors and typological status. We pay attention to the distributions of these features in a dataset containing English and German texts of spoken and written discourse mode, which can be classified into seven different registers. We apply text classification and feature selection to find out how these variational dimensions (language, mode and register) impact on coreference features. Knowledge on the variation under analysis is valuable for contrastive linguistics, translation studies and multilingual natural language processing (NLP), e.g. machine translation or cross-lingual coreference resolution.",
}
@inproceedings{knaebel-stede-2020-contextualized,
    title = "Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing",
    author = "Knaebel, Ren{\'e}  and
      Stede, Manfred",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.7",
    doi = "10.18653/v1/2020.codi-1.7",
    pages = "65--75",
    abstract = "This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specific embeddings. Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time. The success of our approach is supported by state-of-the-art results.",
}
@inproceedings{chernyavskiy-ilvovsky-2020-dsndm,
    title = "{DSNDM}: Deep {S}iamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking",
    author = "Chernyavskiy, Alexander  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.8",
    doi = "10.18653/v1/2020.codi-1.8",
    pages = "76--85",
    abstract = "In this paper, the utility and advantages of the discourse analysis for text pairs categorization and ranking are investigated. We consider two tasks in which discourse structure seems useful and important: automatic verification of political statements, and ranking in question answering systems. We propose a neural network based approach to learn the match between pairs of discourse tree structures. To this end, the neural TreeLSTM model is modified to effectively encode discourse trees and DSNDM model based on it is suggested to analyze pairs of texts. In addition, the integration of the attention mechanism in the model is proposed. Moreover, different ranking approaches are investigated for the second task. In the paper, the comparison with state-of-the-art methods is given. Experiments illustrate that combination of neural networks and discourse structure in DSNDM is effective since it reaches top results in the assigned tasks. The evaluation also demonstrates that discourse analysis improves quality for the processing of longer texts.",
}
@inproceedings{huber-etal-2020-sentence,
    title = "Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?",
    author = "Huber, Laurine  and
      Memmadi, Chaker  and
      Dargnat, Mathilde  and
      Toussaint, Yannick",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.9",
    doi = "10.18653/v1/2020.codi-1.9",
    pages = "86--95",
    abstract = "We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.",
}
@inproceedings{chen-etal-2020-joint-modeling,
    title = "Joint Modeling of Arguments for Event Understanding",
    author = "Chen, Yunmo  and
      Chen, Tongfei  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.10",
    doi = "10.18653/v1/2020.codi-1.10",
    pages = "96--101",
    abstract = "We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.",
}
@inproceedings{farag-etal-2020-analyzing,
    title = "Analyzing Neural Discourse Coherence Models",
    author = "Farag, Youmna  and
      Valvoda, Josef  and
      Yannakoudakis, Helen  and
      Briscoe, Ted",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.11",
    doi = "10.18653/v1/2020.codi-1.11",
    pages = "102--112",
    abstract = "In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics. We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence. We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further. Finally, we make our datasets publicly available as a resource for researchers to use to test discourse coherence models.",
}
@inproceedings{same-van-deemter-2020-computational,
    title = "Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse",
    author = "Same, Fahime  and
      van Deemter, Kees",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.12",
    doi = "10.18653/v1/2020.codi-1.12",
    pages = "113--123",
    abstract = "First, we discuss the most common linguistic perspectives on the concept of recency and propose a taxonomy of recency metrics employed in Machine Learning studies for choosing the form of referring expressions in discourse context. We then report on a Multi-Layer Perceptron study and a Sequential Forward Search experiment, followed by Bayes Factor analysis of the outcomes. The results suggest that recency metrics counting paragraphs and sentences contribute to referential choice prediction more than other recency-related metrics. Based on the results of our analysis, we argue that, sensitivity to discourse structure is important for recency metrics used in determining referring expression forms.",
}
@inproceedings{xiao-etal-2020-really,
    title = "Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !",
    author = "Xiao, Wen  and
      Huber, Patrick  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.13",
    doi = "10.18653/v1/2020.codi-1.13",
    pages = "124--134",
    abstract = "The multi-head self-attention of popular transformer models is widely used within Natural Language Processing (NLP), including for the task of extractive summarization. With the goal of analyzing and pruning the parameter-heavy self-attention mechanism, there are multiple approaches proposing more parameter-light self-attention alternatives. In this paper, we present a novel parameter-lean self-attention mechanism using discourse priors. Our new tree self-attention is based on document-level discourse information, extending the recently proposed {``}Synthesizer{''} framework with another lightweight alternative. We show empirical results that our tree self-attention approach achieves competitive ROUGE-scores on the task of extractive summarization. When compared to the original single-head transformer model, the tree attention approach reaches similar performance on both, EDU and sentence level, despite the significant reduction of parameters in the attention component. We further significantly outperform the 8-head transformer model on sentence level when applying a more balanced hyper-parameter setting, requiring an order of magnitude less parameters.",
}
@inproceedings{liang-etal-2020-extending,
    title = "Extending Implicit Discourse Relation Recognition to the {PDTB}-3",
    author = "Liang, Li  and
      Zhao, Zheng  and
      Webber, Bonnie",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.14",
    doi = "10.18653/v1/2020.codi-1.14",
    pages = "135--147",
    abstract = "The PDTB-3 contains many more Implicit discourse relations than the previous PDTB-2. This is in part because implicit relations have now been annotated within sentences as well as between them. In addition, some now co-occur with explicit discourse relations, instead of standing on their own. Here we show that while this can complicate the problem of identifying the location of implicit discourse relations, it can in turn simplify the problem of identifying their senses. We present data to support this claim, as well as methods that can serve as a non-trivial baseline for future state-of-the-art recognizers for implicit discourse relations.",
}
@inproceedings{kurfali-etal-2020-ted,
    title = "{TED}-{MDB} Lexicons: Tr-{E}n{C}onn{L}ex, Pt-{E}n{C}onn{L}ex",
    author = "Kurfal{\i}, Murathan  and
      Ozer, Sibel  and
      Zeyrek, Deniz  and
      Mendes, Am{\'a}lia",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.15",
    doi = "10.18653/v1/2020.codi-1.15",
    pages = "148--153",
    abstract = "In this work, we present two new bilingual discourse connective lexicons, namely, for Turkish-English and European Portuguese-English created automatically using the existing discourse relation-aligned TED-MDB corpus. In their current form, the Pt-En lexicon includes 95 entries, whereas the Tr-En lexicon contains 133 entries. The lexicons constitute the first step of a larger project of developing a multilingual discourse connective lexicon.",
}
@inproceedings{chai-etal-2020-evaluation,
    title = "Evaluation of Coreference Resolution Systems Under Adversarial Attacks",
    author = "Chai, Haixia  and
      Zhao, Wei  and
      Eger, Steffen  and
      Strube, Michael",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.16",
    doi = "10.18653/v1/2020.codi-1.16",
    pages = "154--159",
    abstract = "A substantial overlap of coreferent mentions in the CoNLL dataset magnifies the recent progress on coreference resolution. This is because the CoNLL benchmark fails to evaluate the ability of coreference resolvers that requires linking novel mentions unseen at train time. In this work, we create a new dataset based on CoNLL, which largely decreases mention overlaps in the entire dataset and exposes the limitations of published resolvers on two aspects{---}lexical inference ability and understanding of low-level orthographic noise. Our findings show (1) the requirements for embeddings, used in resolvers, and for coreference resolutions are, by design, in conflict and (2) adversarial approaches are sometimes not legitimate to mitigate the obstacles, as they may falsely introduce mention overlaps in adversarial training and test sets, thus giving an inflated impression for the improvements.",
}
@inproceedings{guz-carenini-2020-coreference,
    title = "Coreference for Discourse Parsing: A Neural Approach",
    author = "Guz, Grigorii  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the First Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.codi-1.17",
    doi = "10.18653/v1/2020.codi-1.17",
    pages = "160--167",
    abstract = "We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver. In particular, starting with a strong baseline neural parser unaware of any coreference information, we compare a parser which utilizes only the output of a neural coreference resolver, with a more sophisticated model, where discourse parsing and coreference resolution are jointly learned in a neural multitask fashion. Results indicate that these initial attempts to incorporate coreference information do not boost the performance of discourse parsing in a statistically significant way.",
}
@proceedings{cmlc-2020-challenges,
    title = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    editor = {Ba{\'n}ski, Piotr  and
      Barbaresi, Adrien  and
      Clematide, Simon  and
      Kupietz, Marc  and
      L{\"u}ngen, Harald  and
      Pisetta, Ines},
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.0",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{arnold-etal-2020-addressing,
    title = "Addressing Cha(lle)nges in Long-Term Archiving of Large Corpora",
    author = "Arnold, Denis  and
      Fisseni, Bernhard  and
      Kamocki, Pawel  and
      Schonefeld, Oliver  and
      Kupietz, Marc  and
      Schmidt, Thomas",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.1",
    pages = "1--9",
    abstract = "This paper addresses long-term archival for large corpora. Three aspects specific to language resources are focused, namely (1) the removal of resources for legal reasons, (2) versioning of (unchanged) objects in constantly growing resources, especially where objects can be part of multiple releases but also part of different collections, and (3) the conversion of data to new formats for digital preservation. It is motivated why language resources may have to be changed, and why formats may need to be converted. As a solution, the use of an intermediate proxy object called a signpost is suggested. The approach will be exemplified with respect to the corpora of the Leibniz Institute for the German Language in Mannheim, namely the German Reference Corpus (DeReKo) and the Archive for Spoken German (AGD).",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{fankhauser-etal-2020-evaluating,
    title = "Evaluating a Dependency Parser on {D}e{R}e{K}o",
    author = "Fankhauser, Peter  and
      Do, Bich-Ngoc  and
      Kupietz, Marc",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.2",
    pages = "10--14",
    abstract = "We evaluate a graph-based dependency parser on DeReKo, a large corpus of contemporary German. The dependency parser is trained on the German dataset from the SPMRL 2014 Shared Task which contains text from the news domain, whereas DeReKo also covers other domains including fiction, science, and technology. To avoid the need for costly manual annotation of the corpus, we use the parser{'}s probability estimates for unlabeled and labeled attachment as main evaluation criterion. We show that these probability estimates are highly correlated with the actual attachment scores on a manually annotated test set. On this basis, we compare estimated parsing scores for the individual domains in DeReKo, and show that the scores decrease with increasing distance of a domain to the training corpus.",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{popa-fabre-etal-2020-french,
    title = "{F}rench Contextualized Word-Embeddings with a sip of {C}a{B}e{R}net: a New {F}rench Balanced Reference Corpus",
    author = "Popa-Fabre, Murielle  and
      Ortiz Su{\'a}rez, Pedro Javier  and
      Sagot, Beno{\^\i}t  and
      de la Clergerie, {\'E}ric",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.3",
    pages = "15--23",
    abstract = "This paper investigates the impact of different types and size of training corpora on language models. By asking the fundamental question of quality versus quantity, we compare four French corpora by pre-training four different ELMos and evaluating them on dependency parsing, POS-tagging and Named Entities Recognition downstream tasks. We present and asses the relevance of a new balanced French corpus, CaBeRnet, that features a representative range of language usage, including a balanced variety of genres (oral transcriptions, newspapers, popular magazines, technical reports, fiction, academic texts), in oral and written styles. We hypothesize that a linguistically representative corpus will allow the language models to be more efficient, and therefore yield better evaluation scores on different evaluation sets and tasks. This paper offers three main contributions: (1) two newly built corpora: (a) CaBeRnet, a French Balanced Reference Corpus and (b) CBT-fr a domain-specific corpus having both oral and written style in youth literature, (2) five versions of ELMo pre-trained on differently built corpora, and (3) a whole array of computational results on downstream tasks that deepen our understanding of the effects of corpus balance and register in NLP evaluation.",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{filgueira-etal-2020-geoparsing,
    title = "Geoparsing the historical Gazetteers of {S}cotland: accurately computing location in mass digitised texts",
    author = "Filgueira, Rosa  and
      Grover, Claire  and
      Terras, Melissa  and
      Alex, Beatrice",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.4",
    pages = "24--30",
    abstract = "This paper describes work in progress on devising automatic and parallel methods for geoparsing large digital historical textual data by combining the strengths of three natural language processing (NLP) tools, the Edinburgh Geoparser, spaCy and defoe, and employing different tokenisation and named entity recognition (NER) techniques. We apply these tools to a large collection of nineteenth century Scottish geographical dictionaries, and describe preliminary results obtained when processing this data.",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{gartner-2020-corpus,
    title = "The Corpus Query Middleware of Tomorrow {--} A Proposal for a Hybrid Corpus Query Architecture",
    author = {G{\"a}rtner, Markus},
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.5",
    pages = "31--39",
    abstract = "Development of dozens of specialized corpus query systems and languages over the past decades has let to a diverse but also fragmented landscape. Today we are faced with a plethora of query tools that each provide unique features, but which are also not interoperable and often rely on very specific database back-ends or formats for storage. This severely hampers usability both for end users that want to query different corpora and also for corpus designers that wish to provide users with an interface for querying and exploration. We propose a hybrid corpus query architecture as a first step to overcoming this issue. It takes the form of a middleware system between user front-ends and optional database or text indexing solutions as back-ends. At its core is a custom query evaluation engine for index-less processing of corpus queries. With a flexible JSON-LD query protocol the approach allows communication with back-end systems to partially solve queries and offset some of the performance penalties imposed by the custom evaluation engine. This paper outlines the details of our first draft of aforementioned architecture.",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{frick-schmidt-2020-using,
    title = "Using full text indices for querying spoken language data",
    author = "Frick, Elena  and
      Schmidt, Thomas",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.6",
    pages = "40--46",
    abstract = "As a part of the ZuMult-project, we are currently modelling a backend architecture that should provide query access to corpora from the Archive of Spoken German (AGD) at the Leibniz-Institute for the German Language (IDS). We are exploring how to reuse existing search engine frameworks providing full text indices and allowing to query corpora by one of the corpus query languages (QLs) established and actively used in the corpus research community. For this purpose, we tested MTAS - an open source Lucene-based search engine for querying on text with multilevel annotations. We applied MTAS on three oral corpora stored in the TEI-based ISO standard for transcriptions of spoken language (ISO 24624:2016). These corpora differ from the corpus data that MTAS was developed for, because they include interactions with two and more speakers and are enriched, inter alia, with timeline-based annotations. In this contribution, we report our test results and address issues that arise when search frameworks originally developed for querying written corpora are being transferred into the field of spoken language.",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{biber-2020-challenges,
    title = "Challenges for Making Use of a Large Text Corpus such as the {`}{AAC} {--} {A}ustrian Academy Corpus{'} for Digital Literary Studies",
    author = "Biber, Hanno",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.7",
    pages = "47--51",
    abstract = "The challenges for making use of a large text corpus such as the {`}AAC {--} Austrian Academy Corpus{'} for the purposes of digital literary studies will be addressed in this presentation. The research question of how to use a digital text corpus of considerable size for such a specific research purpose is of interest for corpus research in general as it is of interest for digital literary text studies which rely to a large extent on large digital text corpora. The observations of the usage of lexical entities such as words, word forms, multi word units and many other linguistic units determine the way in which texts are being studied and explored. Larger entities have to be taken into account as well, which is why questions of semantic analysis and larger structures come into play. The texts of the AAC {--} Austrian Academy Corpus which was founded in 2001 are German language texts of historical and cultural significance from the time between 1848 and 1989. The aim of this study is to present possible research questions for corpus-based methodological approaches for the digital study of literary texts and to give examples of early experiments and experiences with making use of a large text corpus for these research purposes.",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{kren-2020-czech,
    title = "{C}zech National Corpus in 2020: Recent Developments and Future Outlook",
    author = "Kren, Michal",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.8",
    pages = "52--57",
    abstract = "The paper overviews the state of implementation of the Czech National Corpus (CNC) in all the main areas of its operation: corpus compilation, annotation, application development and user services. As the focus is on the recent development, some of the areas are described in more detail than the others. Close attention is paid to the data collection and, in particular, to the description of web application development. This is not only because CNC has recently seen a significant progress in this area, but also because we believe that end-user web applications shape the way linguists and other scholars think about the language data and about the range of possibilities they offer. This consideration is even more important given the variability of the CNC corpora.",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@inproceedings{scutelnicu-etal-2020-adding,
    title = "Adding a Syntactic Annotation Level to the Corpus of Contemporary {R}omanian Language",
    author = "Scutelnicu, Andrei  and
      Maranduc, Catalina  and
      Cristea, Dan",
    booktitle = "Proceedings of the 8th Workshop on Challenges in the Management of Large Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Ressources Association",
    url = "https://www.aclweb.org/anthology/2020.cmlc-1.9",
    pages = "58--62",
    abstract = "In this paper we present an experiment of augmenting the Corpus of Contemporary Romanian Language (CoRoLa) with the syntactic level of annotations, which would allow users to address queries about the syntax of Romanian sentences, in the Universal Dependency model. After a short introduction of CoRoLa, we describe the treebanks used to train the dependency parser, we show the evaluation results and the process of upgrading CoRoLa with the new level of annotations. The parser displaying the best accuracy with respect to recognition of heads and relations, out of three variants trained on manually built treebanks, was chosen. Keywords: Syntactic annotation, treebank, corpus, maltparser",
    language = "English",
    ISBN = "979-10-95546-61-0",
}
@proceedings{cmcl-2020-cognitive,
    title = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    editor = "Chersoni, Emmanuele  and
      Jacobs, Cassandra  and
      Oseki, Yohei  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.0",
}
@inproceedings{sharma-etal-2020-determines,
    title = "What Determines the Order of Verbal Dependents in {H}indi? Effects of Efficiency in Comprehension and Production",
    author = "Sharma, Kartik  and
      Futrell, Richard  and
      Husain, Samar",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.1",
    doi = "10.18653/v1/2020.cmcl-1.1",
    pages = "1--10",
    abstract = "Word order flexibility is one of the distinctive features of SOV languages. In this work, we investigate whether the order and relative distance of preverbal dependents in Hindi, an SOV language, is affected by factors motivated by efficiency considerations during comprehension/production. We investigate the influence of Head{--}Dependent Mutual Information (HDMI), similarity-based interference, accessibility and case-marking. Results show that preverbal dependents remain close to the verbal head when the HDMI between the verb and its dependent is high. This demonstrates the influence of locality constraints on dependency distance and word order in an SOV language. Additionally, dependency distance were found to be longer when the dependent was animate, when it was case-marked and when it was semantically similar to other preverbal dependents. Together the results highlight the crosslinguistic generalizability of these factors and provide evidence for a functionally motivated account of word order in SOV languages such as Hindi.",
}
@inproceedings{regneri-etal-2020-images,
    title = "Images and Imagination: Automated Analysis of Priming Effects Related to Autism Spectrum Disorder and Developmental Language Disorder",
    author = "Regneri, Michaela  and
      King, Diane  and
      Walji, Fahreen  and
      Palikara, Olympia",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.2",
    doi = "10.18653/v1/2020.cmcl-1.2",
    pages = "11--27",
    abstract = "Different aspects of language processing have been shown to be sensitive to priming but the findings of studies examining priming effects in adolescents with Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD) have been inconclusive. We present a study analysing visual and implicit semantic priming in adolescents with ASD and DLD. Based on a dataset of fictional and script-like narratives, we evaluate how often and how extensively, content of two different priming sources is used by the participants. The first priming source was visual, consisting of images shown to the participants to assist them with their storytelling. The second priming source originated from commonsense knowledge, using crowdsourced data containing prototypical script elements. Our results show that individuals with ASD are less sensitive to both types of priming, but show typical usage of primed cues when they use them at all. In contrast, children with DLD show mostly average priming sensitivity, but exhibit an over-proportional use of the priming cues.",
}
@inproceedings{brasoveanu-dotlacil-2020-production,
    title = "Production-based Cognitive Models as a Test Suite for Reinforcement Learning Algorithms",
    author = "Brasoveanu, Adrian  and
      Dotlacil, Jakub",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.3",
    doi = "10.18653/v1/2020.cmcl-1.3",
    pages = "28--37",
    abstract = "We introduce a framework in which production-rule based computational cognitive modeling and Reinforcement Learning can systematically interact and inform each other. We focus on linguistic applications because the sophisticated rule-based cognitive models needed to capture linguistic behavioral data promise to provide a stringent test suite for RL algorithms, connecting RL algorithms to both accuracy and reaction-time experimental data. Thus, we open a path towards assembling an experimentally rigorous and cognitively realistic benchmark for RL algorithms. We extend our previous work on lexical decision tasks and tabular RL algorithms (Brasoveanu and Dotla{\v{c}}il, 2020b) with a discussion of neural-network based approaches, and a discussion of how parsing can be formalized as an RL problem.",
}
@inproceedings{alhama-etal-2020-evaluating,
    title = "Evaluating Word Embeddings for Language Acquisition",
    author = "Alhama, Raquel G.  and
      Rowland, Caroline  and
      Kidd, Evan",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.4",
    doi = "10.18653/v1/2020.cmcl-1.4",
    pages = "38--42",
    abstract = "Continuous vector word representations (or word embeddings) have shown success in capturing semantic relations between words, as evidenced with evaluation against behavioral data of adult performance on semantic tasks (Pereira et al. 2016). Adult semantic knowledge is the endpoint of a language acquisition process; thus, a relevant question is whether these models can also capture emerging word representations of young language learners. However, the data of semantic knowledge of children is scarce or non-existent for some age groups. In this paper, we propose to bridge this gap by using Age of Acquisition norms to evaluate word embeddings learnt from child-directed input. We present two methods that evaluate word embeddings in terms of (a) the semantic neighbourhood density of learnt words, and (b) the convergence to adult word associations. We apply our methods to bag-of-words models, and we find that (1) children acquire words with fewer semantic neighbours earlier, and (2) young learners only attend to very local context. These findings provide converging evidence for validity of our methods in understanding the prerequisite features for a distributional model of word learning.",
}
@inproceedings{russo-2020-guessing,
    title = "Guessing the Age of Acquisition of {I}talian Lemmas through Linear Regression",
    author = "Russo, Irene",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.5",
    doi = "10.18653/v1/2020.cmcl-1.5",
    pages = "43--48",
    abstract = "The age of acquisition of a word is a psycholinguistic variable concerning the age at which a word is typically learned. It correlates with other psycholinguistic variables such as familiarity, concreteness, and imageability. Existing datasets for multiple languages also include linguistic variables such as the length and the frequency of lemmas in different corpora. There are substantial sets of normative values for English, but for other languages, such as Italian, the coverage is scarce. In this paper,a set of regression experiments investigates whether it is possible to guess the age of acquisition of Italian lemmas that have not been previously rated by humans. An intrinsic evaluation is proposed, correlating estimated Italian lemmas{'} AoA with English lemmas{'} AoA. An extrinsic evaluation - using AoA values as features for the classification of literary excerpts labeled by age appropriateness - shows how es-sential is lexical coverage for this task.",
}
@inproceedings{fourtassi-2020-word,
    title = "Word Co-occurrence in Child-directed Speech Predicts Children{'}s Free Word Associations",
    author = "Fourtassi, Abdellah",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.6",
    doi = "10.18653/v1/2020.cmcl-1.6",
    pages = "49--53",
    abstract = "The free association task has been very influential both in cognitive science and in computational linguistics. However, little research has been done to study how free associations develop in childhood. The current work focuses on the developmental hypothesis according to which free word associations emerge by mirroring the co-occurrence distribution of children{'}s linguistic environment. I trained a distributional semantic model on a large corpus of child language and I tested if it could predict children{'}s responses. The results largely supported the hypothesis: Co-occurrence-based similarity was a strong predictor of children{'}s associative behavior even controlling for other possible predictors such as phonological similarity, word frequency, and word length. I discuss the findings in the light of theories of conceptual development.",
}
@inproceedings{misiek-etal-2020-development,
    title = "Development of Multi-level Linguistic Alignment in Child-adult Conversations",
    author = "Misiek, Thomas  and
      Favre, Benoit  and
      Fourtassi, Abdellah",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.7",
    doi = "10.18653/v1/2020.cmcl-1.7",
    pages = "54--58",
    abstract = "Interactive alignment is a major mechanism of linguistic coordination. Here we study the way this mechanism emerges in development across the lexical, syntactic, and conceptual levels. We leverage NLP tools to analyze a large-scale corpus of child-adult conversations between 2 and 5 years old. We found that, across development, children align consistently to adults above chance and that adults align consistently more to children than vice versa (even controlling for language production abilities). Besides these consistencies, we found a diversity of developmental trajectories across linguistic levels. These corpus-based findings provide strong support for an early onset of multi-level linguistic alignment in children and invites new experimental work.",
}
@inproceedings{mccurdy-etal-2020-conditioning,
    title = "Conditioning, but on Which Distribution? Grammatical Gender in {G}erman Plural Inflection",
    author = "McCurdy, Kate  and
      Lopez, Adam  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.8",
    doi = "10.18653/v1/2020.cmcl-1.8",
    pages = "59--65",
    abstract = "Grammatical gender is a consistent and informative cue to the plural class of German nouns. We find that neural encoder-decoder models learn to rely on this cue to predict plural class, but adult speakers are relatively insensitive to it. This suggests that the neural models are not an effective cognitive model of German plural formation.",
}
@inproceedings{ma-etal-2020-learning,
    title = "Learning Pronoun Case from Distributional Cues: Flexible Frames for Case Acquisition",
    author = "Ma, Xiaomeng  and
      Chodorow, Martin  and
      Valian, Virginia",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.9",
    doi = "10.18653/v1/2020.cmcl-1.9",
    pages = "66--74",
    abstract = "Case is an abstract grammatical feature that indicates argument relationship in a sentence. In English, cases are expressed on pronouns, as nominative case (e.g. I, he), accusative case (e.g. me, him) and genitive case (e.g. my, his). Children correctly use cased pronouns at a very young age. How do they acquire abstract case in the first place, when different cases are not associated with different meanings? This paper proposes that the distributional patterns in parents{'} input could be used to distinguish grammatical cases in English.",
}
@inproceedings{hao-etal-2020-probabilistic,
    title = "Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling",
    author = "Hao, Yiding  and
      Mendelsohn, Simon  and
      Sterneck, Rachel  and
      Martinez, Randi  and
      Frank, Robert",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.cmcl-1.10",
    doi = "10.18653/v1/2020.cmcl-1.10",
    pages = "75--86",
    abstract = "By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model{'}s ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell{'}s analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.",
}
@proceedings{clssts-2020-cross,
    title = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    editor = "McKeown, Kathy  and
      Oard, Douglas W.  and
      {Elizabeth}  and
      Schwartz, Richard",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.0",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{rubino-2020-effect,
    title = "The Effect of Linguistic Parameters in {CLIR} Performance",
    author = "Rubino, Carl",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.1",
    pages = "1--6",
    abstract = "This paper will detail how IARPA{'}s MATERIAL Cross-Language Information Retrieval (CLIR) program investigated certain linguistic parameters to guide language choice, data collection and partitioning, and understand evaluation results. Discerning which linguistic parameters correlated with overall performance enabled the evaluation of progress when different languages were measured, and also was an important factor in determining the most effective CLIR pipeline design, customized to handle language-specific properties deemed necessary to address.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{zavorin-etal-2020-corpora,
    title = "Corpora for Cross-Language Information Retrieval in Six Less-Resourced Languages",
    author = "Zavorin, Ilya  and
      Bills, Aric  and
      Corey, Cassian  and
      Morrison, Michelle  and
      Tong, Audrey  and
      Tong, Richard",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.2",
    pages = "7--13",
    abstract = "The Machine Translation for English Retrieval of Information in Any Language (MATERIAL) research program, sponsored by the Intelligence Advanced Research Projects Activity (IARPA), focuses on rapid development of end-to-end systems capable of retrieving foreign language speech and text documents relevant to different types of English queries that may be further restricted by domain. Those systems also provide evidence of relevance of the retrieved content in the form of English summaries. The program focuses on Less-Resourced Languages and provides its performer teams very limited amounts of annotated training data. This paper describes the corpora that were created for system development and evaluation for the six languages released by the program to date: Tagalog, Swahili, Somali, Lithuanian, Bulgarian and Pashto. The corpora include build packs to train Machine Translation and Automatic Speech Recognition systems; document sets in three text and three speech genres annotated for domain and partitioned for analysis, development and evaluation; and queries of several types together with corresponding binary relevance judgments against the entire set of documents. The paper also describes a detection metric called Actual Query Weighted Value developed by the program to evaluate end-to-end system performance.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{galuscakova-etal-2020-materializing,
    title = "{MATERIAL}izing Cross-Language Information Retrieval: A Snapshot",
    author = "Galuscakova, Petra  and
      Oard, Douglas  and
      Barrow, Joe  and
      Nair, Suraj  and
      Han-Chin, Shing  and
      Zotkina, Elena  and
      Eskander, Ramy  and
      Zhang, Rui",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.3",
    pages = "14--21",
    abstract = "At about the midpoint of the IARPA MATERIAL program in October 2019, an evaluation was conducted on systems{'} abilities to find Lithuanian documents based on English queries. Subsequently, both the Lithuanian test collection and results from all three teams were made available for detailed analysis. This paper capitalizes on that opportunity to begin to look at what{'}s working well at this stage of the program, and to identify some promising directions for future work.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{barry-etal-2020-searcher,
    title = "{SEARCHER}: Shared Embedding Architecture for Effective Retrieval",
    author = "Barry, Joel  and
      Boschee, Elizabeth  and
      Freedman, Marjorie  and
      Miller, Scott",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.4",
    pages = "22--25",
    abstract = "We describe an approach to cross lingual information retrieval that does not rely on explicit translation of either document or query terms. Instead, both queries and documents are mapped into a shared embedding space where retrieval is performed. We discuss potential advantages of the approach in handling polysemy and synonymy. We present a method for training the model, and give details of the model implementation. We present experimental results for two cases: Somali-English and Bulgarian-English CLIR.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{jiang-etal-2020-cross,
    title = "Cross-lingual Information Retrieval with {BERT}",
    author = "Jiang, Zhuolin  and
      El-Jaroudi, Amro  and
      Hartmann, William  and
      Karakos, Damianos  and
      Zhao, Lingjun",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.5",
    pages = "26--31",
    abstract = "Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents in the task of cross-lingual information retrieval. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision, using home-made CLIR training data derived from parallel corpora. Experimental results of the retrieval of Lithuanian documents against short English queries show that our model is effective and outperforms the competitive baseline approaches.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{zosa-etal-2020-comparison,
    title = "A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval",
    author = "Zosa, Elaine  and
      Granroth-Wilding, Mark  and
      Pivovarova, Lidia",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.6",
    pages = "32--37",
    abstract = "We address the problem of linking related documents across languages in a multilingual collection. We evaluate three diverse unsupervised methods to represent and compare documents: (1) multilingual topic model; (2) cross-lingual document embeddings; and (3) Wasserstein distance.We test the performance of these methods in retrieving news articles in Swedish that are known to be related to a given Finnish article.The results show that ensembles of the methods outperform the stand-alone methods, suggesting that they capture complementary characteristics of the documents",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{karakos-etal-2020-reformulating,
    title = "Reformulating Information Retrieval from Speech and Text as a Detection Problem",
    author = "Karakos, Damianos  and
      Zbib, Rabih  and
      Hartmann, William  and
      Schwartz, Richard  and
      Makhoul, John",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.7",
    pages = "38--43",
    abstract = "In the IARPA MATERIAL program, information retrieval (IR) is treated as a hard detection problem; the system has to output a single global ranking over all queries, and apply a hard threshold on this global list to come up with all the hypothesized relevant documents. This means that how queries are ranked relative to each other can have a dramatic impact on performance. In this paper, we study such a performance measure, the Average Query Weighted Value (AQWV), which is a combination of miss and false alarm rates. AQWV requires that the same detection threshold is applied to all queries. Hence, detection scores of different queries should be comparable, and, to do that, a score normalization technique (commonly used in keyword spotting from speech) should be used. We describe unsupervised methods for score normalization, which are borrowed from the speech field and adapted accordingly for IR, and demonstrate that they greatly improve AQWV on the task of cross-language information retrieval (CLIR), on three low-resource languages used in MATERIAL. We also present a novel supervised score normalization approach which gives additional gains.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{zhang-etal-2020-2019,
    title = "The 2019 {BBN} Cross-lingual Information Retrieval System",
    author = "Zhang, Le  and
      Karakos, Damianos  and
      Hartmann, William  and
      Srivastava, Manaj  and
      Tarlin, Lee  and
      Akodes, David  and
      Gouda, Sanjay Krishna  and
      Bathool, Numra  and
      Zhao, Lingjun  and
      Jiang, Zhuolin  and
      Schwartz, Richard  and
      Makhoul, John",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.8",
    pages = "44--51",
    abstract = "In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English. Foreign audio documents are first transcribed by a state-of-the-art pretrained multilingual speech recognition model that is finetuned to the target language. For text documents, we use multiple multilingual neural machine translation (MT) models to achieve good translation results, especially for low/medium resource languages. The processed documents and queries are then scored using a probabilistic CLIR model that makes use of the probability of translation from GIZA translation tables and scores from a Neural Network Lexical Translation Model (NNLTM). Additionally, advanced score normalization, combination, and thresholding schemes are employed to maximize the Average Query Weighted Value (AQWV) scores. The CLIR output, together with multiple translation renderings, are selected and translated into English snippets via a summarization model. Our turnkey system is language agnostic and can be quickly trained for a new low-resource language in few days.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{schwartz-etal-2020-set,
    title = "What Set of Documents to Present to an Analyst?",
    author = "Schwartz, Richard  and
      Makhoul, John  and
      Tarlin, Lee  and
      Karakos, Damianos",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.9",
    pages = "52--57",
    abstract = "We describe the human triage scenario envisioned in the Cross-Lingual Information Retrieval (CLIR) problem of the [REDUCT] Program. The overall goal is to maximize the quality of the set of documents that is given to a bilingual analyst, as measured by the AQWV score. The initial set of source documents that are retrieved by the CLIR system is summarized in English and presented to human judges who attempt to remove the irrelevant documents (false alarms); the resulting documents are then presented to the analyst. First, we describe the AQWV performance measure and show that, in our experience, if the acceptance threshold of the CLIR component has been optimized to maximize AQWV, the loss in AQWV due to false alarms is relatively constant across many conditions, which also limits the possible gain that can be achieved by any post filter (such as human judgments) that removes false alarms. Second, we analyze the likely benefits for the triage operation as a function of the initial CLIR AQWV score and the ability of the human judges to remove false alarms without removing relevant documents. Third, we demonstrate that we can increase the benefit for human judgments by combining the human judgment scores with the original document scores returned by the automatic CLIR system.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{arora-etal-2020-investigative,
    title = "An Investigative Study of Multi-Modal Cross-Lingual Retrieval",
    author = "Arora, Piyush  and
      Shterionov, Dimitar  and
      Moriya, Yasufumi  and
      Kaushik, Abhishek  and
      Dzendzik, Daria  and
      Jones, Gareth",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.10",
    pages = "58--67",
    abstract = "We describe work from our investigations of the novel area of multi-modal cross-lingual retrieval (MMCLIR) under low-resource conditions. We study the challenges associated with MMCLIR relating to: (i) data conversion between different modalities, for example speech and text, (ii) overcoming the language barrier between source and target languages; (iii) effectively scoring and ranking documents to suit the retrieval task; and (iv) handling low resource constraints that prohibit development of heavily tuned machine translation (MT) and automatic speech recognition (ASR) systems. We focus on the use case of retrieving text and speech documents in Swahili, using English queries which was the main focus of the OpenCLIR shared task. Our work is developed within the scope of this task. In this paper we devote special attention to the automatic translation (AT) component which is crucial for the overall quality of the MMCLIR system. We exploit a combination of dictionaries and phrase-based statistical machine translation (MT) systems to tackle effectively the subtask of query translation. We address each MMCLIR challenge individually, and develop separate components for automatic translation (AT), speech processing (SP) and information retrieval (IR). We find that results with respect to cross-lingual text retrieval are quite good relative to the task of cross-lingual speech retrieval. Overall we find that the task of MMCLIR and specifically cross-lingual speech retrieval is quite complex. Further we pinpoint open issues related to handling cross-lingual audio and text retrieval for low resource languages that need to be addressed in future research.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@inproceedings{wan-etal-2020-subtitles,
    title = "Subtitles to Segmentation: Improving Low-Resource Speech-to-{T}ext{T}ranslation Pipelines",
    author = "Wan, David  and
      Jiang, Zhengping  and
      Kedzie, Chris  and
      Turcan, Elsbeth  and
      Bell, Peter  and
      McKeown, Kathy",
    booktitle = "Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.clssts-1.11",
    pages = "68--73",
    abstract = "In this work, we focus on improving ASR output segmentation in the context of low-resource language speech-to-text translation. ASR output segmentation is crucial, as ASR systems segment the input audio using purely acoustic information and are not guaranteed to output sentence-like segments. Since most MT systems expect sentences as input, feeding in longer unsegmented passages can lead to sub-optimal performance. We explore the feasibility of using datasets of subtitles from TV shows and movies to train better ASR segmentation models. We further incorporate part-of-speech (POS) tag and dependency label information (derived from the unsegmented ASR outputs) into our segmentation model. We show that this noisy syntactic information can improve model accuracy. We evaluate our models intrinsically on segmentation quality and extrinsically on downstream MT performance, as well as downstream tasks including cross-lingual information retrieval (CLIR) tasks and human relevance assessments. Our model shows improved performance on downstream tasks for Lithuanian and Bulgarian.",
    language = "English",
    ISBN = "979-10-95546-55-9",
}
@proceedings{cllrd-2020-lrec,
    title = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    editor = "Fiumara, James  and
      Cieri, Christopher  and
      Liberman, Mark  and
      Callison-Burch, Chris",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.0",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{fiumara-etal-2020-languagearc,
    title = "{L}anguage{ARC}: Developing Language Resources Through Citizen Linguistics",
    author = "Fiumara, James  and
      Cieri, Christopher  and
      Wright, Jonathan  and
      Liberman, Mark",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.1",
    pages = "1--6",
    abstract = "This paper introduces the citizen science platform, LanguageARC, developed within the NIEUW (Novel Incentives and Workflows) project supported by the National Science Foundation under Grant No. 1730377. LanguageARC is a community-oriented online platform bringing together researchers and {``}citizen linguists{''} with the shared goal of contributing to linguistic research and language technology development. Like other Citizen Science platforms and projects, LanguageARC harnesses the power and efforts of volunteers who are motivated by the incentives of contributing to science, learning and discovery, and belonging to a community dedicated to social improvement. Citizen linguists contribute language data and judgments by participating in research tasks such as classifying regional accents from audio clips, recording audio of picture descriptions and answering personality questionnaires to create baseline data for NLP research into autism and neurodegenerative conditions. Researchers can create projects on Language ARC without any coding or HTML required using our Project Builder Toolkit.",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{heinisch-2020-developing,
    title = "Developing Language Resources with Citizen Linguistics in {A}ustria {--} A Case Study",
    author = "Heinisch, Barbara",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.2",
    pages = "7--14",
    abstract = "Language resources are a major ingredient for the advancement of language technologies. Citizen linguistics can help to create language resources and annotate language resources, not only for the improvement of language technologies, such as machine translation but also for the advancement of linguistic research. The (language) resources covered in this article are a corpus related to the Question of the Month project strand, which was initially aimed at co-creation in citizen linguistics and a partially annotated database of pictures of written text in different languages found in the public sphere. The number of participants in these project strands differed significantly. Especially those activities that were related to data collection (and analysis) had a significantly higher number of contributions per participant. This especially held true for the activities with (prize) incentives. Nevertheless, the activities of the Question of the Month could reach a higher number of participants, even after the co-creation approach was no longer followed. In addition, the Question of the Month brought research gaps and new knowledge to light and challenged existing paradigms and practices. These are especially important for the advancement of scholarly research. Citizen linguistics can help gather and analyze linguistic data, including language resources, in a short period of time. Thus, it may help increase the access to and availability of language resources.",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{haralabopoulos-etal-2020-objective,
    title = "Objective Assessment of Subjective Tasks in Crowdsourcing Applications",
    author = "Haralabopoulos, Giannis  and
      Tsikandilakis, Myron  and
      Torres Torres, Mercedes  and
      McAuley, Derek",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.3",
    pages = "15--25",
    abstract = "Labelling, or annotation, is the process by which we assign labels to an item with regards to a task. In some Artificial Intelligence problems, such as Computer Vision tasks, the goal is to obtain objective labels. However, in problems such as text and sentiment analysis, subjective labelling is often required. More so when the sentiment analysis deals with actual emotions instead of polarity (positive/negative) . Scientists employ human experts to create these labels, but it is costly and time consuming. Crowdsourcing enables researchers to utilise non-expert knowledge for scientific tasks. From image analysis to semantic annotation, interested researchers can gather a large sample of answers via crowdsourcing platforms in a timely manner. However, non-expert contributions often need to be thoroughly assessed, particularly so when a task is subjective. Researchers have traditionally used {`}Gold Standard{'}, {`}Thresholding{'} and {`}Majority Voting{'} as methods to filter non-expert contributions. We argue that these methods are unsuitable for subjective tasks, such as lexicon acquisition and sentiment analysis. We discuss subjectivity in human centered tasks and present a filtering method that defines quality contributors, based on a set of objectively infused terms in a lexicon acquisition task. We evaluate our method against an established lexicon, the diversity of emotions - i.e. subjectivity- and the exclusion of contributions. Our proposed objective evaluation method can be used to assess contributors in subjective tasks that will provide domain agnostic, quality results, with at least 7{\%} improvement over traditional methods.",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{chamberlain-etal-2020-speaking,
    title = "Speaking Outside the Box: Exploring the Benefits of Unconstrained Input in Crowdsourcing and Citizen Science Platforms",
    author = "Chamberlain, Jon  and
      Kruschwitz, Udo  and
      Poesio, Massimo",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.4",
    pages = "26--34",
    abstract = "Crowdsourcing approaches provide a difficult design challenge for developers. There is a trade-off between the efficiency of the task to be done and the reward given to the user for participating, whether it be altruism, social enhancement, entertainment or money. This paper explores how crowdsourcing and citizen science systems collect data and complete tasks, illustrated by a case study from the online language game-with-a-purpose Phrase Detectives. The game was originally developed to be a constrained interface to prevent player collusion, but subsequently benefited from posthoc analysis of over 76k unconstrained inputs from users. Understanding the interface design and task deconstruction are critical for enabling users to participate in such systems and the paper concludes with a discussion of the idea that social networks can be viewed as form of citizen science platform with both constrained and unconstrained inputs making for a highly complex dataset.",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{martin-etal-2020-leveraging,
    title = "Leveraging Non-Specialists for Accurate and Time Efficient {AMR} Annotation",
    author = "Martin, Mary  and
      Mauceri, Cecilia  and
      Palmer, Martha  and
      Heckman, Christoffer",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.5",
    pages = "35--39",
    abstract = "Abstract Meaning Representations (AMRs), a syntax-free representation of phrase semantics are useful for capturing the meaning of a phrase and reflecting the relationship between concepts that are referred to. However, annotating AMRs are time consuming and expensive. The existing annotation process requires expertly trained workers who have knowledge of an extensive set of guidelines for parsing phrases. In this paper, we propose a cost-saving two-step process for the creation of a corpus of AMR-phrase pairs for spatial referring expressions. The first step uses non-specialists to perform simple annotations that can be leveraged in the second step to accelerate the annotation performed by the experts. We hypothesize that our process will decrease the cost per annotation and improve consistency across annotators. Few corpora of spatial referring expressions exist and the resulting language resource will be valuable for referring expression comprehension and generation modeling.",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{stenger-etal-2020-incomslav,
    title = "The {INCOMSLAV} Platform: Experimental Website with Integrated Methods for Measuring Linguistic Distances and Asymmetries in Receptive Multilingualism",
    author = "Stenger, Irina  and
      Jagrova, Klara  and
      Avgustinova, Tania",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.6",
    pages = "40--48",
    abstract = "We report on a web-based resource for conducting intercomprehension experiments with native speakers of Slavic languages and present our methods for measuring linguistic distances and asymmetries in receptive multilingualism. Through a website which serves as a platform for online testing, a large number of participants with different linguistic backgrounds can be targeted. A statistical language model is used to measure information density and to gauge how language users master various degrees of (un)intelligibilty. The key idea is that intercomprehension should be better when the model adapted for understanding the unknown language exhibits relatively low average distance and surprisal. All obtained intelligibility scores together with distance and asymmetry measures for the different language pairs and processing directions are made available as an integrated online resource in the form of a Slavic intercomprehension matrix (SlavMatrix).",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{cole-2020-identifications,
    title = "Identifications of Speaker Ethnicity in {S}outh-{E}ast {E}ngland: Multicultural {L}ondon {E}nglish as a Divisible Perceptual Variety",
    author = "Cole, Amanda",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.7",
    pages = "49--57",
    abstract = "This study uses crowdsourcing through LanguageARC to collect data on levels of accuracy in the identification of speakers{'} ethnicities. Ten participants (5 US; 5 South-East England) classified lexically identical speech stimuli from a corpus of 227 speakers aged 18-33yrs from South-East England into the main {``}ethnic{''} groups in Britain: White British, Black British and Asian British. Firstly, the data reveals that there is no significant geographic proximity effect on performance between US and British participants. Secondly, results contribute to recent work suggesting that despite the varying heritages of young, ethnic minority speakers in London, they speak an innovative and emerging variety: Multicultural London English (MLE) (e.g. Cheshire et al., 2011). Countering this, participants found perceptual linguistic differences between speakers of all 3 ethnicities (80.7{\%} accuracy). The highest rate of accuracy (96{\%}) was when identifying the ethnicity of Black British speakers from London whose speech seems to form a distinct, perceptual category. Participants also perform substantially better than chance at identifying Black British and Asian British speakers who are not from London (80{\%} and 60{\%} respectively). This suggests that MLE is not a single, homogeneous variety but instead, there are perceptual linguistic differences by ethnicity which transcend the borders of London.",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@inproceedings{cieri-fiumara-2020-languagearc,
    title = "{L}anguage{ARC} - a tutorial",
    author = "Cieri, Christopher  and
      Fiumara, James",
    booktitle = "Proceedings of the LREC 2020 Workshop on ``Citizen Linguistics in Language Resource Development''",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.cllrd-1.8",
    pages = "58--64",
    abstract = "LanguageARC is a portal that offers citizen linguists opportunities to contribute to language related research. It also provides researchers with infrastructure for easily creating data collection and annotation tasks on the portal and potentially connecting with contributors. This document describes LanguageARC{'}s main features and operation for researchers interested in creating new projects and or using the resulting data.",
    language = "English",
    ISBN = "979-10-95546-59-7",
}
@proceedings{clinicalnlp-2020-clinical,
    title = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    editor = "Rumshisky, Anna  and
      Roberts, Kirk  and
      Bethard, Steven  and
      Naumann, Tristan",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.0",
}
@inproceedings{heo-etal-2020-various,
    title = "Various Approaches for Predicting Stroke Prognosis using Magnetic Resonance Imaging Text Records",
    author = "Heo, Tak-Sung  and
      Kim, Chulho  and
      Choi, Jeong-Myeong  and
      Jeong, Yeong-Seok  and
      Kim, Yu-Seop",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.1",
    doi = "10.18653/v1/2020.clinicalnlp-1.1",
    pages = "1--6",
    abstract = "Stroke is one of the leading causes of death and disability worldwide. Stroke is treatable, but it is prone to disability after treatment and must be prevented. To grasp the degree of disability caused by stroke, we use magnetic resonance imaging text records to predict stroke and measure the performance according to the document-level and sentence-level representation. As a result of the experiment, the document-level representation shows better performance.",
}
@inproceedings{dcosta-etal-2020-multiple,
    title = "Multiple Sclerosis Severity Classification From Clinical Text",
    author = "D{'}Costa, Alister  and
      Denkovski, Stefan  and
      Malyska, Michal  and
      Moon, Sae Young  and
      Rufino, Brandon  and
      Yang, Zhen  and
      Killian, Taylor  and
      Ghassemi, Marzyeh",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.2",
    doi = "10.18653/v1/2020.clinicalnlp-1.2",
    pages = "7--23",
    abstract = "Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and recorded in unstructured text in the form of a neurology consult note. An EDSS measurement contains an overall {`}EDSS{'} score and several functional subscores. Typically, expert knowledge is required to interpret consult notes and generate these scores. Previous approaches used limited context length Word2Vec embeddings and keyword searches to predict scores given a consult note, but often failed when scores were not explicitly stated. In this work, we present MS-BERT, the first publicly available transformer model trained on real clinical data other than MIMIC. Next, we present MSBC, a classifier that applies MS-BERT to generate embeddings and predict EDSS and functional subscores. Lastly, we explore combining MSBC with other models through the use of Snorkel to generate scores for unlabelled consult notes. MSBC achieves state-of-the-art performance on all metrics and prediction tasks and outperforms the models generated from the Snorkel ensemble. We improve Macro-F1 by 0.12 (to 0.88) for predicting EDSS and on average by 0.29 (to 0.63) for predicting functional subscores over previous Word2Vec CNN and rule-based approaches.",
}
@inproceedings{zhang-etal-2020-bert,
    title = "{BERT}-{XML}: Large Scale Automated {ICD} Coding Using {BERT} Pretraining",
    author = "Zhang, Zachariah  and
      Liu, Jingshu  and
      Razavian, Narges",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.3",
    doi = "10.18653/v1/2020.clinicalnlp-1.3",
    pages = "24--34",
    abstract = "ICD coding is the task of classifying and cod-ing all diagnoses, symptoms and proceduresassociated with a patient{'}s visit. The process isoften manual, extremely time-consuming andexpensive for hospitals as clinical interactionsare usually recorded in free text medical notes.In this paper, we propose a machine learningmodel, BERT-XML, for large scale automatedICD coding of EHR notes, utilizing recentlydeveloped unsupervised pretraining that haveachieved state of the art performance on a va-riety of NLP tasks. We train a BERT modelfrom scratch on EHR notes, learning with vo-cabulary better suited for EHR tasks and thusoutperform off-the-shelf models. We furtheradapt the BERT architecture for ICD codingwith multi-label attention. We demonstratethe effectiveness of BERT-based models on thelarge scale ICD code classification task usingmillions of EHR notes to predict thousands ofunique codes.",
}
@inproceedings{ding-etal-2020-incorporating,
    title = "Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries",
    author = "Ding, Xiyu  and
      Hall, Mei-Hua  and
      Miller, Timothy",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.4",
    doi = "10.18653/v1/2020.clinicalnlp-1.4",
    pages = "35--40",
    abstract = "Reducing rates of early hospital readmission has been recognized and identified as a key to improve quality of care and reduce costs. There are a number of risk factors that have been hypothesized to be important for understanding re-admission risk, including such factors as problems with substance abuse, ability to maintain work, relations with family. In this work, we develop Roberta-based models to predict the sentiment of sentences describing readmission risk factors in discharge summaries of patients with psychosis. We improve substantially on previous results by a scheme that shares information across risk factors while also allowing the model to learn risk factor-specific information.",
}
@inproceedings{pougue-biyong-etal-2020-information,
    title = "Information Extraction from {S}wedish Medical Prescriptions with Sig-Transformer Encoder",
    author = "Pougu{\'e} Biyong, John  and
      Wang, Bo  and
      Lyons, Terry  and
      Nevado-Holgado, Alejo",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.5",
    doi = "10.18653/v1/2020.clinicalnlp-1.5",
    pages = "41--54",
    abstract = "Relying on large pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT) for encoding and adding a simple prediction layer has led to impressive performance in many clinical natural language processing (NLP) tasks. In this work, we present a novel extension to the Transformer architecture, by incorporating signature transform with the self-attention model. This architecture is added between embedding and prediction layers. Experiments on a new Swedish prescription data show the proposed architecture to be superior in two of the three information extraction tasks, comparing to baseline models. Finally, we evaluate two different embedding approaches between applying Multilingual BERT and translating the Swedish text to English then encode with a BERT model pretrained on clinical notes.",
}
@inproceedings{narayanan-etal-2020-evaluation,
    title = "Evaluation of Transfer Learning for Adverse Drug Event ({ADE}) and Medication Entity Extraction",
    author = "Narayanan, Sankaran  and
      Mannam, Kaivalya  and
      Rajan, Sreeranga P  and
      Rangan, P Venkat",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.6",
    doi = "10.18653/v1/2020.clinicalnlp-1.6",
    pages = "55--64",
    abstract = "We evaluate several biomedical contextual embeddings (based on BERT, ELMo, and Flair) for the detection of medication entities such as Drugs and Adverse Drug Events (ADE) from Electronic Health Records (EHR) using the 2018 ADE and Medication Extraction (Track 2) n2c2 data-set. We identify best practices for transfer learning, such as language-model fine-tuning and scalar mix. Our transfer learning models achieve strong performance in the overall task (F1=92.91{\%}) as well as in ADE identification (F1=53.08{\%}). Flair-based embeddings out-perform in the identification of context-dependent entities such as ADE. BERT-based embeddings out-perform in recognizing clinical terminology such as Drug and Form entities. ELMo-based embeddings deliver competitive performance in all entities. We develop a sentence-augmentation method for enhanced ADE identification benefiting BERT-based and ELMo-based models by up to 3.13{\%} in F1 gains. Finally, we show that a simple ensemble of these models out-paces most current methods in ADE extraction (F1=55.77{\%}).",
}
@inproceedings{schneider-etal-2020-biobertpt,
    title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition",
    author = "Schneider, Elisa Terumi Rubel  and
      de Souza, Jo{\~a}o Vitor Andrioli  and
      Knafou, Julien  and
      Oliveira, Lucas Emanuel Silva e  and
      Copara, Jenny  and
      Gumiel, Yohan Bonescki  and
      Oliveira, Lucas Ferro Antunes de  and
      Paraiso, Emerson Cabrera  and
      Teodoro, Douglas  and
      Barra, Cl{\'a}udia Maria Cabral Moro",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7",
    doi = "10.18653/v1/2020.clinicalnlp-1.7",
    pages = "65--72",
    abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.",
}
@inproceedings{ji-etal-2020-dilated,
    title = "Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text",
    author = "Ji, Shaoxiong  and
      Cambria, Erik  and
      Marttinen, Pekka",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.8",
    doi = "10.18653/v1/2020.clinicalnlp-1.8",
    pages = "73--78",
    abstract = "Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems. The emergence of deep models in natural language processing has boosted the development of automatic assignment methods. However, recent advanced neural architectures with flat convolutions or multi-channel feature concatenation ignore the sequential causal constraint within a text sequence and may not learn meaningful clinical text representations, especially for lengthy clinical notes with long-term sequential dependency. This paper proposes a Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size. Experiments on a real-world clinical dataset empirically show that our model improves the state of the art.",
}
@inproceedings{pilan-etal-2020-classification,
    title = "Classification of Syncope Cases in {N}orwegian Medical Records",
    author = "Pilan, Ildiko  and
      Brekke, P{\aa}l H.  and
      Dahl, Fredrik A.  and
      Gundersen, Tore  and
      Husby, Haldor  and
      Nytr{\o}, {\O}ystein  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.9",
    doi = "10.18653/v1/2020.clinicalnlp-1.9",
    pages = "79--84",
    abstract = "Loss of consciousness, so-called syncope, is a commonly occurring symptom associated with worse prognosis for a number of heart-related diseases. We present a comparison of methods for a diagnosis classification task in Norwegian clinical notes, targeting syncope, i.e. fainting cases. We find that an often neglected baseline with keyword matching constitutes a rather strong basis, but more advanced methods do offer some improvement in classification performance, especially a convolutional neural network model. The developed pipeline is planned to be used for quantifying unregistered syncope cases in Norway.",
}
@inproceedings{langton-etal-2020-comparison,
    title = "Comparison of Machine Learning Methods for Multi-label Classification of Nursing Education and Licensure Exam Questions",
    author = "Langton, John  and
      Srihasam, Krishna  and
      Jiang, Junlin",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.10",
    doi = "10.18653/v1/2020.clinicalnlp-1.10",
    pages = "85--93",
    abstract = "In this paper, we evaluate several machine learning methods for multi-label classification of text questions. Every nursing student in the United States must pass the National Council Licensure Examination (NCLEX) to begin professional practice. NCLEX defines a number of competencies on which students are evaluated. By labeling test questions with NCLEX competencies, we can score students according to their performance in each competency. This information helps instructors measure how prepared students are for the NCLEX, as well as which competencies they may need help with. A key challenge is that questions may be related to more than one competency. Labeling questions with NCLEX competencies, therefore, equates to a multi-label, text classification problem where each competency is a label. Here we present an evaluation of several methods to support this use case along with a proposed approach. While our work is grounded in the nursing education domain, the methods described here can be used for any multi-label, text classification use case.",
}
@inproceedings{huang-etal-2020-clinical,
    title = "Clinical {XLN}et: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation",
    author = "Huang, Kexin  and
      Singh, Abhishek  and
      Chen, Sitong  and
      Moseley, Edward  and
      Deng, Chih-Ying  and
      George, Naomi  and
      Lindvall, Charolotta",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.11",
    doi = "10.18653/v1/2020.clinicalnlp-1.11",
    pages = "94--100",
    abstract = "Clinical notes contain rich information, which is relatively unexploited in predictive modeling compared to structured data. In this work, we developed a new clinical text representation Clinical XLNet that leverages the temporal information of the sequence of the notes. We evaluated our models on prolonged mechanical ventilation prediction problem and our experiments demonstrated that Clinical XLNet outperforms the best baselines consistently. The models and scripts are made publicly available.",
}
@inproceedings{peng-etal-2020-automatic,
    title = "Automatic recognition of abdominal lymph nodes from clinical text",
    author = "Peng, Yifan  and
      Lee, Sungwon  and
      Elton, Daniel C.  and
      Shen, Thomas  and
      Tang, Yu-xing  and
      Chen, Qingyu  and
      Wang, Shuai  and
      Zhu, Yingying  and
      Summers, Ronald  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.12",
    doi = "10.18653/v1/2020.clinicalnlp-1.12",
    pages = "101--110",
    abstract = "Lymph node status plays a pivotal role in the treatment of cancer. The extraction of lymph nodes from radiology text reports enables large-scale training of lymph node detection on MRI. In this work, we first propose an ontology of 41 types of abdominal lymph nodes with a hierarchical relationship. We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports. We demonstrate the superior performance of a model fine-tuned on MRI reports using BlueBERT, called MriBERT. We find that MriBERT outperforms the rule-based labeler (0.957 vs 0.644 in micro weighted F1-score) as well as other BERT-based variations (0.913 - 0.928). We make the code and MriBERT publicly available at https://github.com/ncbi-nlp/bluebert, with the hope that this method can facilitate the development of medical report annotators to produce labels from scratch at scale.",
}
@inproceedings{moon-fan-2020-ask,
    title = "How You Ask Matters: The Effect of Paraphrastic Questions to {BERT} Performance on a Clinical {SQ}u{AD} Dataset",
    author = "Moon, Sungrim (Riea)  and
      Fan, Jungwei",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.13",
    doi = "10.18653/v1/2020.clinicalnlp-1.13",
    pages = "111--116",
    abstract = "Reading comprehension style question-answering (QA) based on patient-specific documents represents a growing area in clinical NLP with plentiful applications. Bidirectional Encoder Representations from Transformers (BERT) and its derivatives lead the state-of-the-art accuracy on the task, but most evaluation has treated the data as a pre-mixture without systematically looking into the potential effect of imperfect train/test questions. The current study seeks to address this gap by experimenting with full versus partial train/test data consisting of paraphrastic questions. Our key findings include 1) training with all pooled question variants yielded best accuracy, 2) the accuracy varied widely, from 0.74 to 0.80, when trained with each single question variant, and 3) questions of similar lexical/syntactic structure tended to induce identical answers. The results suggest that how you ask questions matters in BERT-based QA, especially at the training stage.",
}
@inproceedings{dupuis-etal-2020-relative,
    title = "Relative and Incomplete Time Expression Anchoring for Clinical Text",
    author = "Dupuis, Louise  and
      Bergou, Nicol  and
      Tissot, Hegler  and
      Velupillai, Sumithra",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.14",
    doi = "10.18653/v1/2020.clinicalnlp-1.14",
    pages = "117--129",
    abstract = "Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline. Relative and incomplete time expressions (RI-Timexes) are expressions that require additional information for their temporal anchor to be resolved, but few studies have addressed this challenge specifically. In this study, we aimed to reproduce and verify a classification approach for identifying anchor dates and relations in clinical text, and propose a novel relation classification approach for this task.",
}
@inproceedings{wen-etal-2020-medal,
    title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
    author = "Wen, Zhi  and
      Lu, Xing Han  and
      Reddy, Siva",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
    doi = "10.18653/v1/2020.clinicalnlp-1.15",
    pages = "130--135",
    abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
}
@inproceedings{luo-etal-2020-knowledge,
    title = "Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks",
    author = "Luo, Hongyin  and
      Li, Shang-Wen  and
      Glass, James",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.16",
    doi = "10.18653/v1/2020.clinicalnlp-1.16",
    pages = "136--145",
    abstract = "In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through dialog to detect and collect clinical symptoms automatically, which can save a doctor{'}s time interviewing the patient. Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis. After getting the reply from the patient for each question, the system also decides whether current information is enough for a human doctor to make a diagnosis. To achieve this goal, we propose two neural models and a training pipeline for the multi-step reasoning task. We also build a knowledge graph as additional inputs to further improve model performance. Experiments show that our model significantly outperforms the baseline by 4{\%}, discovering 67{\%} of implicit symptoms on average with a limited number of questions.",
}
@inproceedings{lewis-etal-2020-pretrained,
    title = "Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art",
    author = "Lewis, Patrick  and
      Ott, Myle  and
      Du, Jingfei  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.17",
    doi = "10.18653/v1/2020.clinicalnlp-1.17",
    pages = "146--157",
    abstract = "A large array of pretrained models are available to the biomedical NLP (BioNLP) community. Finding the best model for a particular task can be difficult and time-consuming. For many applications in the biomedical and clinical domains, it is crucial that models can be built quickly and are highly accurate. We present a large-scale study across 18 established biomedical and clinical NLP tasks to determine which of several popular open-source biomedical and clinical NLP models work well in different settings. Furthermore, we apply recent advances in pretraining to train new biomedical language models, and carefully investigate the effect of various design choices on downstream performance. Our best models perform well in all of our benchmarks, and set new State-of-the-Art in 9 tasks. We release these models in the hope that they can help the community to speed up and increase the accuracy of BioNLP and text mining applications.",
}
@inproceedings{abadeer-2020-assessment,
    title = "Assessment of {D}istil{BERT} performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts",
    author = "Abadeer, Macarious",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.18",
    doi = "10.18653/v1/2020.clinicalnlp-1.18",
    pages = "158--167",
    abstract = "Bidirectional Encoder Representations from Transformers (BERT) models achieve state-of-the-art performance on a number of Natural Language Processing tasks. However, their model size on disk often exceeds 1 GB and the process of fine-tuning them and using them to run inference consumes significant hardware resources and runtime. This makes them hard to deploy to production environments. This paper fine-tunes DistilBERT, a lightweight deep learning model, on medical text for the named entity recognition task of Protected Health Information (PHI) and medical concepts. This work provides a full assessment of the performance of DistilBERT in comparison with BERT models that were pre-trained on medical text. For Named Entity Recognition task of PHI, DistilBERT achieved almost the same results as medical versions of BERT in terms of F1 score at almost half the runtime and consuming approximately half the disk space. On the other hand, for the detection of medical concepts, DistilBERT{'}s F1 score was lower by 4 points on average than medical BERT variants.",
}
@inproceedings{wang-etal-2020-distinguishing,
    title = "Distinguishing between Dementia with Lewy bodies ({DLB}) and {A}lzheimer{'}s Disease ({AD}) using Mental Health Records: a Classification Approach",
    author = "Wang, Zixu  and
      Ive, Julia  and
      Moylett, Sinead  and
      Mueller, Christoph  and
      Cardinal, Rudolf  and
      Velupillai, Sumithra  and
      O{'}Brien, John  and
      Stewart, Robert",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.19",
    doi = "10.18653/v1/2020.clinicalnlp-1.19",
    pages = "168--177",
    abstract = "While Dementia with Lewy Bodies (DLB) is the second most common type of neurodegenerative dementia following Alzheimer{'}s Disease (AD), it is difficult to distinguish from AD. We propose a method for DLB detection by using mental health record (MHR) documents from a (3-month) period before a patient has been diagnosed with DLB or AD. Our objective is to develop a model that could be clinically useful to differentiate between DLB and AD across datasets from different healthcare institutions. We cast this as a classification task using Convolutional Neural Network (CNN), an efficient neural model for text classification. We experiment with different representation models, and explore the features that contribute to model performances. In addition, we apply temperature scaling, a simple but efficient model calibration method, to produce more reliable predictions. We believe the proposed method has important potential for clinical applications using routine healthcare records, and for generalising to other relevant clinical record datasets. To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.",
}
@inproceedings{patel-etal-2020-weakly,
    title = "Weakly Supervised Medication Regimen Extraction from Medical Conversations",
    author = "Patel, Dhruvesh  and
      Konam, Sandeep  and
      Prabhakar, Sai",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.20",
    doi = "10.18653/v1/2020.clinicalnlp-1.20",
    pages = "178--193",
    abstract = "Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors. In this paper, we focus on extracting spans for frequency, route and change, corresponding to medications discussed in the conversation. We first describe a unique dataset of annotated doctor-patient conversations and then present a weakly supervised model architecture that can perform span extraction using noisy classification data. The model utilizes an attention bottleneck inside a classification model to perform the extraction. We experiment with several variants of attention scoring and projection functions and propose a novel transformer-based attention scoring function (TAScore). The proposed combination of TAScore and Fusedmax projection achieves a 10 point increase in Longest Common Substring F1 compared to the baseline of additive scoring plus softmax projection.",
}
@inproceedings{bitterman-etal-2020-extracting,
    title = "Extracting Relations between Radiotherapy Treatment Details",
    author = "Bitterman, Danielle  and
      Miller, Timothy  and
      Harris, David  and
      Lin, Chen  and
      Finan, Sean  and
      Warner, Jeremy  and
      Mak, Raymond  and
      Savova, Guergana",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.21",
    doi = "10.18653/v1/2020.clinicalnlp-1.21",
    pages = "194--200",
    abstract = "We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records. Radiotherapy is a central component of the treatment of most solid cancers. Its details are described in non-standardized fashions using jargon not found in other medical specialties, complicating the already difficult task of manual data extraction. We examine the performance of several state-of-the-art neural methods for relation extraction of radiotherapy treatment details, with a goal of automating detailed information extraction. The neural systems perform at 0.82-0.88 macro-average F1, which approximates or in some cases exceeds the inter-annotator agreement. To the best of our knowledge, this is the first effort to develop models for radiotherapy relation extraction and one of the few efforts for relation extraction to describe cancer treatment in general.",
}
@inproceedings{lin-etal-2020-cancer,
    title = "Cancer Registry Information Extraction via Transfer Learning",
    author = "Lin, Yan-Jie  and
      Dai, Hong-Jie  and
      Zhang, You-Chen  and
      Wu, Chung-Yang  and
      Chang, Yu-Cheng  and
      Lu, Pin-Jou  and
      Huang, Chih-Jen  and
      Wang, Yu-Tsang  and
      Hsieh, Hui-Min  and
      Chao, Kun-San  and
      Liu, Tsang-Wu  and
      Chang, I-Shou  and
      Yang, Yi-Hsin Connie  and
      Wang, Ti-Hao  and
      Liu, Ko-Jiunn  and
      Chen, Li-Tzong  and
      Yang, Sheau-Fang",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.22",
    doi = "10.18653/v1/2020.clinicalnlp-1.22",
    pages = "201--208",
    abstract = "A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.",
}
@inproceedings{yue-zhou-2020-phicon,
    title = "{PHICON}: Improving Generalization of Clinical Text De-identification Models via Data Augmentation",
    author = "Yue, Xiang  and
      Zhou, Shuang",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.23",
    doi = "10.18653/v1/2020.clinicalnlp-1.23",
    pages = "209--214",
    abstract = "De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i2b2 2006 and 2014 de-identification challenge datasets show that PHICON can help three selected de-identification models boost F1-score (by at most 8.6{\%}) on cross-dataset test setting. We also discuss how much augmentation to use and how each augmentation method influences the performance.",
}
@inproceedings{michalopoulos-etal-2020-wheres,
    title = "Where{'}s the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data",
    author = "Michalopoulos, George  and
      Chen, Helen  and
      Wong, Alexander",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.24",
    doi = "10.18653/v1/2020.clinicalnlp-1.24",
    pages = "215--226",
    abstract = "In most clinical practice settings, there is no rigorous reviewing of the clinical documentation, resulting in inaccurate information captured in the patient medical records. The gold standard in clinical data capturing is achieved via {``}expert-review{''}, where clinicians can have a dialogue with a domain expert (reviewers) and ask them questions about data entry rules. Automatically identifying {``}real questions{''} in these dialogues could uncover ambiguities or common problems in data capturing in a given clinical setting. In this study, we proposed a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions that expect an answer (information or help) about an issue from sentences that are not questions, as well as from questions referring to an issue mentioned in a nearby sentence (e.g., can you clarify this?), which we will refer as {``}c-questions{''}. We conducted a comprehensive performance comparison analysis of the proposed multi-channel deep convolutional neural network against other deep neural networks. Furthermore, we evaluated the performance of traditional rule-based and learning-based methods for detecting question sentences. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.",
}
@inproceedings{wang-etal-2020-learning-unlabelled,
    title = "Learning from Unlabelled Data for Clinical Semantic Textual Similarity",
    author = "Wang, Yuxia  and
      Verspoor, Karin  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.25",
    doi = "10.18653/v1/2020.clinicalnlp-1.25",
    pages = "227--233",
    abstract = "Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning. To overcome this, we propose to utilise domain unlabelled data by assigning pseudo labels from a general model. We evaluate the approach on two clinical STS datasets, and achieve r= 0.80 on N2C2-STS. Further investigation reveals that if the data distribution of unlabelled sentence pairs is closer to the test data, we can obtain better performance. By leveraging a large general-purpose STS dataset and small-scale in-domain training data, we obtain further improvements to r= 0.90, a new SOTA.",
}
@inproceedings{chen-etal-2020-joint-learning,
    title = "Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics",
    author = "Chen, Miao  and
      Lan, Ganhui  and
      Du, Fang  and
      Lobanov, Victor",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.26",
    doi = "10.18653/v1/2020.clinicalnlp-1.26",
    pages = "234--242",
    abstract = "In drug development, protocols define how clinical trials are conducted, and are therefore of paramount importance. They contain key patient-, investigator-, medication-, and study-related information, often elaborated in different sections in the protocol texts. Granular-level parsing on large quantity of existing protocols can accelerate clinical trial design and provide actionable insights into trial optimization. Here, we report our progresses in using deep learning NLP algorithms to enable automated protocol analytics. In particular, we combined a pre-trained BERT transformer model with joint-learning strategies to simultaneously identify clinically relevant entities (i.e. Named Entity Recognition) and extract the syntactic relations between these entities (i.e. Relation Extraction) from the eligibility criteria section in protocol texts. When comparing to standalone NER and RE models, our joint-learning strategy can effectively improve the performance of RE task while retaining similarly high NER performance, likely due to the synergy of optimizing toward both tasks{'} objectives via shared parameters. The derived NLP model provides an end-to-end solution to convert unstructured protocol texts into structured data source, which will be embedded into a comprehensive clinical analytics workflow for downstream trial design missions such like patient population extraction, patient enrollment rate estimation, and protocol amendment prediction.",
}
@inproceedings{dasgupta-etal-2020-extracting,
    title = "Extracting Semantic Aspects for Structured Representation of Clinical Trial Eligibility Criteria",
    author = "Dasgupta, Tirthankar  and
      Mondal, Ishani  and
      Naskar, Abir  and
      Dey, Lipika",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.27",
    doi = "10.18653/v1/2020.clinicalnlp-1.27",
    pages = "243--248",
    abstract = "Eligibility criteria in the clinical trials specify the characteristics that a patient must or must not possess in order to be treated according to a standard clinical care guideline. As the process of manual eligibility determination is time-consuming, automatic structuring of the eligibility criteria into various semantic categories or aspects is the need of the hour. Existing methods use hand-crafted rules and feature-based statistical machine learning methods to dynamically induce semantic aspects. However, in order to deal with paucity of aspect-annotated clinical trials data, we propose a novel weakly-supervised co-training based method which can exploit a large pool of unlabeled criteria sentences to augment the limited supervised training data, and consequently enhance the performance. Experiments with 0.2M criteria sentences show that the proposed approach outperforms the competitive supervised baselines by 12{\%} in terms of micro-averaged F1 score for all the aspects. Probing deeper into analysis, we observe domain-specific information boosts up the performance by a significant margin.",
}
@inproceedings{pourreza-shahri-etal-2020-ensemble,
    title = "An Ensemble Approach for Automatic Structuring of Radiology Reports",
    author = "Pourreza Shahri, Morteza  and
      Tahmasebi, Amir  and
      Ye, Bingyang  and
      Zhu, Henghui  and
      Aslam, Javed  and
      Ferris, Timothy",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.28",
    doi = "10.18653/v1/2020.clinicalnlp-1.28",
    pages = "249--258",
    abstract = "Automatic structuring of electronic medical records is of high demand for clinical workflow solutions to facilitate extraction, storage, and querying of patient care information. However, developing a scalable solution is extremely challenging, specifically for radiology reports, as most healthcare institutes use either no template or department/institute specific templates. Moreover, radiologists{'} reporting style varies from one to another as sentences are written in a telegraphic format and do not follow general English grammar rules. In this work, we present an ensemble method that consolidates the predictions of three models, capturing various attributes of textual information for automatic labeling of sentences with section labels. These three models are: 1) Focus Sentence model, capturing context of the target sentence; 2) Surrounding Context model, capturing the neighboring context of the target sentence; and finally, 3) Formatting/Layout model, aimed at learning report formatting cues. We utilize Bi-directional LSTMs, followed by sentence encoders, to acquire the context. Furthermore, we define several features that incorporate the structure of reports. We compare our proposed approach against multiple baselines and state-of-the-art approaches on a proprietary dataset as well as 100 manually annotated radiology notes from the MIMIC-III dataset, which we are making publicly available. Our proposed approach significantly outperforms other approaches by achieving 97.1{\%} accuracy.",
}
@inproceedings{wang-etal-2020-utilizing,
    title = "Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries",
    author = "Wang, Wenjie  and
      Park, Youngja  and
      Lee, Taesung  and
      Molloy, Ian  and
      Tang, Pengfei  and
      Xiong, Li",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.29",
    doi = "10.18653/v1/2020.clinicalnlp-1.29",
    pages = "259--268",
    abstract = "Recent studies have shown that adversarial examples can be generated by applying small perturbations to the inputs such that the well- trained deep learning models will misclassify. With the increasing number of safety and security-sensitive applications of deep learn- ing models, the robustness of deep learning models has become a crucial topic. The robustness of deep learning models for health- care applications is especially critical because the unique characteristics and the high financial interests of the medical domain make it more sensitive to adversarial attacks. Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies. As few works studied adversarial threats on clinical summaries, in this work we first apply adversarial attack to clinical summaries of electronic health records (EHR) to show the text-based deep learning systems are vulnerable to adversarial examples. Secondly, benefiting from the multi-modality of the EHR dataset, we propose a novel defense method, MATCH (Multimodal feATure Consistency cHeck), which leverages the consistency between multiple modalities in the data to defend against adversarial examples on a single modality. Our experiments demonstrate the effectiveness of MATCH on a hospital readmission prediction task comparing with baseline methods.",
}
@inproceedings{min-etal-2020-advancing,
    title = "Advancing Seq2seq with Joint Paraphrase Learning",
    author = "Min, So Yeon  and
      Raghavan, Preethi  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.30",
    doi = "10.18653/v1/2020.clinicalnlp-1.30",
    pages = "269--279",
    abstract = "We address the problem of model generalization for sequence to sequence (seq2seq) architectures. We propose going beyond data augmentation via paraphrase-optimized multi-task learning and observe that it is useful in correctly handling unseen sentential paraphrases as inputs. Our models greatly outperform SOTA seq2seq models for semantic parsing on diverse domains (Overnight - up to 3.2{\%} and emrQA - 7{\%}) and Nematus, the winning solution for WMT 2017, for Czech to English translation (CzENG 1.6 - 1.5 BLEU).",
}
@inproceedings{lamare-etal-2020-diminishing,
    title = "On the diminishing return of labeling clinical reports",
    author = "Lamare, Jean-Baptiste  and
      Olatunji, Oloruntobiloba  and
      Yao, Li",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.31",
    doi = "10.18653/v1/2020.clinicalnlp-1.31",
    pages = "280--290",
    abstract = "Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains. Whether the same holds true for medical NLP has by far not been thoroughly investigated. This work shows that this is indeed not always the case. We reveal the somehow counter-intuitive observation that performant medical NLP models may be obtained with small amount of labeled data, quite the opposite to the common belief, most likely due to the domain specificity of the problem. We show quantitatively the effect of training data size on a fixed test set composed of two of the largest public chest x-ray radiology report datasets on the task of abnormality classification. The trained models not only make use of the training data efficiently, but also outperform the current state-of-the-art rule-based systems by a significant margin.",
}
@inproceedings{baez-etal-2020-chilean,
    title = "The {C}hilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in {S}panish",
    author = "B{\'a}ez, Pablo  and
      Villena, Fabi{\'a}n  and
      Rojas, Mat{\'\i}as  and
      Dur{\'a}n, Manuel  and
      Dunstan, Jocelyn",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.32",
    doi = "10.18653/v1/2020.clinicalnlp-1.32",
    pages = "291--300",
    abstract = "In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 900 referrals was manually annotated with 9,029 entities, 385 attributes, and 284 pairs of relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2{\%} of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.",
}
@inproceedings{chen-etal-2020-exploring,
    title = "Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical {NLP}",
    author = "Chen, John  and
      Berlot-Attwell, Ian  and
      Wang, Xindi  and
      Hossain, Safwan  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.33",
    doi = "10.18653/v1/2020.clinicalnlp-1.33",
    pages = "301--312",
    abstract = "Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as free text. We propose a novel task of exploring \textit{fairness} on a multimodal clinical dataset, adopting \textit{equalized odds} for the downstream medical prediction tasks. To this end, we investigate a modality-agnostic fairness algorithm - equalized odds post processing - and compare it to a text-specific fairness algorithm: debiased clinical word embeddings. Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance classical notions of fairness. Our work opens the door for future work at the critical intersection of clinical NLP and fairness.",
}
@article{cl-2020--3,
    title = "",
    journal = "Computational Linguistics",
    volume = "46",
    number = "3",
    month = sep,
    year = "2020",
}
@article{wedekind-kaplan-2020-tractable,
    title = "Tractable {L}exical-{F}unctional {G}rammar",
    author = {Wedekind, J{\"u}rgen  and
      Kaplan, Ronald M.},
    journal = "Computational Linguistics",
    volume = "46",
    number = "3",
    month = sep,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-3.1",
    doi = "10.1162/coli_a_00384",
    pages = "515--569",
    abstract = "The formalism for Lexical-Functional Grammar (LFG) was introduced in the 1980s as one of the first constraint-based grammatical formalisms for natural language. It has led to substantial contributions to the linguistic literature and to the construction of large-scale descriptions of particular languages. Investigations of its mathematical properties have shown that, without further restrictions, the recognition, emptiness, and generation problems are undecidable, and that they are intractable in the worst case even with commonly applied restrictions. However, grammars of real languages appear not to invoke the full expressive power of the formalism, as indicated by the fact that algorithms and implementations for recognition and generation have been developed that run{---}even for broad-coverage grammars{---}in typically polynomial time. This article formalizes some restrictions on the notation and its interpretation that are compatible with conventions and principles that have been implicit or informally stated in linguistic theory. We show that LFG grammars that respect these restrictions, while still suitable for the description of natural languages, are equivalent to linear context-free rewriting systems and allow for tractable computation.",
}
@article{beinborn-choenni-2020-semantic,
    title = "Semantic Drift in Multilingual Representations",
    author = "Beinborn, Lisa  and
      Choenni, Rochelle",
    journal = "Computational Linguistics",
    volume = "46",
    number = "3",
    month = sep,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-3.2",
    doi = "10.1162/coli_a_00382",
    pages = "571--603",
    abstract = "Multilingual representations have mostly been evaluated based on their performance on specific tasks. In this article, we look beyond engineering goals and analyze the relations between languages in computational representations. We introduce a methodology for comparing languages based on their organization of semantic concepts. We propose to conduct an adapted version of representational similarity analysis of a selected set of concepts in computational multilingual representations. Using this analysis method, we can reconstruct a phylogenetic tree that closely resembles those assumed by linguistic experts. These results indicate that multilingual distributional representations that are only trained on monolingual text and bilingual dictionaries preserve relations between languages without the need for any etymological information. In addition, we propose a measure to identify semantic drift between language families. We perform experiments on word-based and sentence-based multilingual models and provide both quantitative results and qualitative examples. Analyses of semantic drift in multilingual representations can serve two purposes: They can indicate unwanted characteristics of the computational models and they provide a quantitative means to study linguistic phenomena across languages.",
}
@article{zabokrtsky-etal-2020-sentence,
    title = "Sentence Meaning Representations Across Languages: What Can We Learn from Existing Frameworks?",
    author = "{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Zeman, Daniel  and
      {\v{S}}ev{\v{c}}{\'\i}kov{\'a}, Magda",
    journal = "Computational Linguistics",
    volume = "46",
    number = "3",
    month = sep,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-3.3",
    doi = "10.1162/coli_a_00385",
    pages = "605--665",
    abstract = "This article gives an overview of how sentence meaning is represented in eleven deep-syntactic frameworks, ranging from those based on linguistic theories elaborated for decades to rather lightweight NLP-motivated approaches. We outline the most important characteristics of each framework and then discuss how particular language phenomena are treated across those frameworks, while trying to shed light on commonalities as well as differences.",
}
@article{oved-etal-2020-predicting,
    title = "Predicting In-Game Actions from Interviews of {NBA} Players",
    author = "Oved, Nadav  and
      Feder, Amir  and
      Reichart, Roi",
    journal = "Computational Linguistics",
    volume = "46",
    number = "3",
    month = sep,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-3.4",
    doi = "10.1162/coli_a_00383",
    pages = "667--712",
    abstract = "Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. Although there is an abundance of computational work on player metrics prediction based on past performance, very few attempts to incorporate out-of-game signals have been made. Specifically, it was previously unclear whether linguistic signals gathered from players{'} interviews can add information that does not appear in performance metrics. To bridge that gap, we define text classification tasks of predicting deviations from mean in NBA players{'} in-game actions, which are associated with strategic choices, player behavior, and risk, using their choice of language prior to the game. We collected a data set of transcripts from key NBA players{'} pre-game interviews and their in-game performance metrics, totalling 5,226 interview-metric pairs. We design neural models for players{'} action prediction based on increasingly more complex aspects of the language signals in their open-ended interviews. Our models can make their predictions based on the textual signal alone, or on a combination of that signal with signals from past-performance metrics. Our text-based models outperform strong baselines trained on performance metrics only, demonstrating the importance of language usage for action prediction. Moreover, the models that utilize both textual input and past-performance metrics produced the best results. Finally, as neural networks are notoriously difficult to interpret, we propose a method for gaining further insight into what our models have learned. Particularly, we present a latent Dirichlet allocation{--}based analysis, where we interpret model predictions in terms of correlated topics. We find that our best performing textual model is most associated with topics that are intuitively related to each prediction task and that better models yield higher correlation with more informative topics.1",
}
@article{cl-2020--2,
    title = "",
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
}
@article{costa-jussa-etal-2020-multilingual,
    title = "Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction",
    author = "Costa-juss{\`a}, Marta R.  and
      Espa{\~n}a-Bonet, Cristina  and
      Fung, Pascale  and
      Smith, Noah A.",
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.1",
    doi = "10.1162/coli_a_00373",
    pages = "249--255",
    abstract = "We introduce the Computational Linguistics special issue on Multilingual and Interlingual Semantic Representations for Natural Language Processing. We situate the special issue{'}s five articles in the context of our fast-changing field, explaining our motivation for this project. We offer a brief summary of the work in the issue, which includes developments on lexical and sentential semantic representations, from symbolic and neural perspectives.",
}
@article{mohiuddin-joty-2020-unsupervised,
    title = "Unsupervised Word Translation with Adversarial Autoencoder",
    author = "Mohiuddin, Tasnim  and
      Joty, Shafiq",
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.2",
    doi = "10.1162/coli_a_00374",
    pages = "257--288",
    abstract = "Crosslingual word embeddings learned from monolingual embeddings have a crucial role in many downstream tasks, ranging from machine translation to transfer learning. Adversarial training has shown impressive success in learning crosslingual embeddings and the associated word translation task without any parallel data by mapping monolingual embeddings to a shared space. However, recent work has shown superior performance for non-adversarial methods in more challenging language pairs. In this article, we investigate adversarial autoencoder for unsupervised word translation and propose two novel extensions to it that yield more stable training and improved results. Our method includes regularization terms to enforce cycle consistency and input reconstruction, and puts the target encoders as an adversary against the corresponding discriminator. We use two types of refinement procedures sequentially after obtaining the trained encoders and mappings from the adversarial training, namely, refinement with Procrustes solution and refinement with symmetric re-weighting. Extensive experimentations with high- and low-resource languages from two different data sets show that our method achieves better performance than existing adversarial and non-adversarial approaches and is also competitive with the supervised system. Along with performing comprehensive ablation studies to understand the contribution of different components of our adversarial model, we also conduct a thorough analysis of the refinement procedures to understand their effects.",
}
@article{colla-etal-2020-lesslex,
    title = "{L}ess{L}ex: Linking Multilingual Embeddings to {S}en{S}e Representations of {LEX}ical Items",
    author = "Colla, Davide  and
      Mensa, Enrico  and
      Radicioni, Daniele P.",
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.3",
    doi = "10.1162/coli_a_00375",
    pages = "289--333",
    abstract = "We present LESSLEX, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for each term we have thus the {``}blended{''} terminological vector along with those describing all senses associated to that term. LESSLEX has been tested on three tasks relevant to lexical semantics: conceptual similarity, contextual similarity, and semantic text similarity. We experimented over the principal data sets for such tasks in their multilingual and crosslingual variants, improving on or closely approaching state-of-the-art results. We conclude by arguing that LESSLEX vectors may be relevant for practical applications and for research on conceptual and lexical access and competence.",
}
@article{sahin-etal-2020-linspector,
    title = "{LINSPECTOR}: Multilingual Probing Tasks for Word Representations",
    author = {{\c{S}}ahin, G{\"o}zde G{\"u}l  and
      Vania, Clara  and
      Kuznetsov, Ilia  and
      Gurevych, Iryna},
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.4",
    doi = "10.1162/coli_a_00376",
    pages = "335--385",
    abstract = "Despite an ever-growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models. Such insights would help the community to get an estimate of the downstream task performance, as well as to design more informed neural architectures, while avoiding extensive experimentation that requires substantial computational resources not all researchers have access to. A recent development in NLP is to use simple classification tasks, also called probing tasks, that test for a single linguistic feature such as part-of-speech. Existing studies mostly focus on exploring the linguistic information encoded by the continuous representations of English text. However, from a typological perspective the morphologically poor English is rather an outlier: The information encoded by the word order and function words in English is often stored on a subword, morphological level in other languages. To address this, we introduce 15 type-level probing tasks such as case marking, possession, word length, morphological tag count, and pseudoword identification for 24 languages. We present a reusable methodology for creation and evaluation of such tests in a multilingual setting, which is challenging because of a lack of resources, lower quality of tools, and differences among languages. We then present experiments on several diverse multilingual word embedding models, in which we relate the probing task performance for a diverse set of languages to a range of five classic NLP tasks: POS-tagging, dependency parsing, semantic role labeling, named entity recognition, and natural language inference. We find that a number of probing tests have significantly high positive correlation to the downstream tasks, especially for morphologically rich languages. We show that our tests can be used to explore word embeddings or black-box neural models for linguistic cues in a multilingual setting. We release the probing data sets and the evaluation suite LINSPECTOR with https://github.com/UKPLab/linspector.",
}
@article{vazquez-etal-2020-systematic,
    title = "A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation",
    author = {V{\'a}zquez, Ra{\'u}l  and
      Raganato, Alessandro  and
      Creutz, Mathias  and
      Tiedemann, J{\"o}rg},
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.5",
    doi = "10.1162/coli_a_00377",
    pages = "387--424",
    abstract = "Neural machine translation has considerably improved the quality of automatic translations by learning good representations of input sentences. In this article, we explore a multilingual translation model capable of producing fixed-size sentence representations by incorporating an intermediate crosslingual shared layer, which we refer to as attention bridge. This layer exploits the semantics from each language and develops into a language-agnostic meaning representation that can be efficiently used for transfer learning. We systematically study the impact of the size of the attention bridge and the effect of including additional languages in the model. In contrast to related previous work, we demonstrate that there is no conflict between translation performance and the use of sentence representations in downstream tasks. In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks. Nevertheless, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks. Similarly, we show that trainable downstream tasks benefit from multilingual models, whereas additional language signals do not improve performance in non-trainable benchmarks. This is an important insight that helps to properly design models for specific applications. Finally, we also include an in-depth analysis of the proposed attention bridge and its ability to encode linguistic properties. We carefully analyze the information that is captured by individual attention heads and identify interesting patterns that explain the performance of specific settings in linguistic probing tasks.",
}
@article{ranta-etal-2020-abstract,
    title = "Abstract Syntax as Interlingua: Scaling Up the Grammatical Framework from Controlled Languages to Robust Pipelines",
    author = "Ranta, Aarne  and
      Angelov, Krasimir  and
      Gruzitis, Normunds  and
      Kolachina, Prasanth",
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.6",
    doi = "10.1162/coli_a_00378",
    pages = "425--486",
    abstract = "Abstract syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF.",
}
@article{nissim-etal-2020-fair,
    title = "Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor",
    author = "Nissim, Malvina  and
      van Noord, Rik  and
      van der Goot, Rob",
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.7",
    doi = "10.1162/coli_a_00379",
    pages = "487--497",
    abstract = "Analogies such as man is to king as woman is to X are often used to illustrate the amazing power of word embeddings. Concurrently, they have also been used to expose how strongly human biases are encoded in vector spaces trained on natural language, with examples like man is to computer programmer as woman is to homemaker. Recent work has shown that analogies are in fact not an accurate diagnostic for bias, but this does not mean that they are not used anymore, or that their legacy is fading. Instead of focusing on the intrinsic problems of the analogy task as a bias detection tool, we discuss a series of issues involving implementation as well as subjective choices that might have yielded a distorted picture of bias in word embeddings. We stand by the truth that human biases are present in word embeddings, and, of course, the need to address them. But analogies are not an accurate tool to do so, and the way they have been most often used has exacerbated some possibly non-existing biases and perhaps hidden others. Because they are still widely popular, and some of them have become classics within and outside the NLP community, we deem it important to provide a series of clarifications that should put well-known, and potentially new analogies, into the right perspective.",
}
@article{schuster-etal-2020-limitations,
    title = "The Limitations of Stylometry for Detecting Machine-Generated Fake News",
    author = "Schuster, Tal  and
      Schuster, Roei  and
      Shah, Darsh J.  and
      Barzilay, Regina",
    journal = "Computational Linguistics",
    volume = "46",
    number = "2",
    month = jun,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-2.8",
    doi = "10.1162/coli_a_00380",
    pages = "499--510",
    abstract = "Recent developments in neural language models (LMs) have raised concerns about their potential misuse for automatically spreading misinformation. In light of these concerns, several studies have proposed to detect machine-generated fake news by capturing their stylistic differences from human-written text. These approaches, broadly termed stylometry, have found success in source attribution and misinformation detection in human-written texts. However, in this work, we show that stylometry is limited against machine-generated misinformation. Whereas humans speak differently when trying to deceive, LMs generate stylistically consistent text, regardless of underlying motive. Thus, though stylometry can successfully prevent impersonation by identifying text provenance, it fails to distinguish legitimate LM applications from those that introduce false information. We create two benchmarks demonstrating the stylistic similarity between malicious and legitimate uses of LMs, utilized in auto-completion and editing-assistance settings.1 Our findings highlight the need for non-stylometry approaches in detecting machine-generated misinformation, and open up the discussion on the desired evaluation benchmarks.",
}
@article{cl-2020-,
    title = "",
    journal = "Computational Linguistics",
    volume = "46",
    number = "1",
    month = mar,
    year = "2020",
}
@article{belinkov-etal-2020-linguistic,
    title = "On the Linguistic Representational Power of Neural Machine Translation Models",
    author = "Belinkov, Yonatan  and
      Durrani, Nadir  and
      Dalvi, Fahim  and
      Sajjad, Hassan  and
      Glass, James",
    journal = "Computational Linguistics",
    volume = "46",
    number = "1",
    month = mar,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-1.1",
    doi = "10.1162/coli_a_00367",
    pages = "1--52",
    abstract = "Despite the recent success of deep neural networks in natural language processing and other spheres of artificial intelligence, their interpretability remains a challenge. We analyze the representations learned by neural machine translation (NMT) models at various levels of granularity and evaluate their quality through relevant extrinsic properties. In particular, we seek answers to the following questions: (i) How accurately is word structure captured within the learned representations, which is an important aspect in translating morphologically rich languages? (ii) Do the representations capture long-range dependencies, and effectively handle syntactically divergent languages? (iii) Do the representations capture lexical semantics? We conduct a thorough investigation along several parameters: (i) Which layers in the architecture capture each of these linguistic phenomena; (ii) How does the choice of translation unit (word, character, or subword unit) impact the linguistic properties captured by the underlying representations? (iii) Do the encoder and decoder learn differently and independently? (iv) Do the representations learned by multilingual NMT models capture the same amount of linguistic information as their bilingual counterparts? Our data-driven, quantitative evaluation illuminates important aspects in NMT models and their ability to capture various linguistic phenomena. We show that deep NMT models trained in an end-to-end fashion, without being provided any direct supervision during the training process, learn a non-trivial amount of linguistic information. Notable findings include the following observations: (i) Word morphology and part-of-speech information are captured at the lower layers of the model; (ii) In contrast, lexical semantics or non-local syntactic and semantic dependencies are better represented at the higher layers of the model; (iii) Representations learned using characters are more informed about word-morphology compared to those learned using subword units; and (iv) Representations learned by multilingual models are richer compared to bilingual models.",
}
@article{zhou-etal-2020-design,
    title = "The Design and Implementation of {X}iao{I}ce, an Empathetic Social Chatbot",
    author = "Zhou, Li  and
      Gao, Jianfeng  and
      Li, Di  and
      Shum, Heung-Yeung",
    journal = "Computational Linguistics",
    volume = "46",
    number = "1",
    month = mar,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-1.2",
    doi = "10.1162/coli_a_00368",
    pages = "53--93",
    abstract = "This article describes the development of Microsoft XiaoIce, the most popular social chatbot in the world. XiaoIce is uniquely designed as an artifical intelligence companion with an emotional connection to satisfy the human need for communication, affection, and social belonging. We take into account both intelligent quotient and emotional quotient in system design, cast human{--}machine social chat as decision-making over Markov Decision Processes, and optimize XiaoIce for long-term user engagement, measured in expected Conversation-turns Per Session (CPS). We detail the system architecture and key components, including dialogue manager, core chat, skills, and an empathetic computing module. We show how XiaoIce dynamically recognizes human feelings and states, understands user intent, and responds to user needs throughout long conversations. Since the release in 2014, XiaoIce has communicated with over 660 million active users and succeeded in establishing long-term relationships with many of them. Analysis of large-scale online logs shows that XiaoIce has achieved an average CPS of 23, which is significantly higher than that of other chatbots and even human conversations.",
}
@article{hao-paul-2020-empirical,
    title = "An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models",
    author = "Hao, Shudong  and
      Paul, Michael J.",
    journal = "Computational Linguistics",
    volume = "46",
    number = "1",
    month = mar,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-1.3",
    doi = "10.1162/coli_a_00369",
    pages = "95--134",
    abstract = "Probabilistic topic modeling is a common first step in crosslingual tasks to enable knowledge transfer and extract multilingual features. Although many multilingual topic models have been developed, their assumptions about the training corpus are quite varied, and it is not clear how well the different models can be utilized under various training conditions. In this article, the knowledge transfer mechanisms behind different multilingual topic models are systematically studied, and through a broad set of experiments with four models on ten languages, we provide empirical insights that can inform the selection and future development of multilingual topic models.",
}
@article{alva-manchego-etal-2020-data,
    title = "Data-Driven Sentence Simplification: Survey and Benchmark",
    author = "Alva-Manchego, Fernando  and
      Scarton, Carolina  and
      Specia, Lucia",
    journal = "Computational Linguistics",
    volume = "46",
    number = "1",
    month = mar,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-1.4",
    doi = "10.1162/coli_a_00370",
    pages = "135--187",
    abstract = "Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand. In order to do so, several rewriting transformations can be performed such as replacement, reordering, and splitting. Executing these transformations while keeping sentences grammatical, preserving their main idea, and generating simpler output, is a challenging and still far from solved problem. In this article, we survey research on SS, focusing on approaches that attempt to learn how to simplify using corpora of aligned original-simplified sentence pairs in English, which is the dominant paradigm nowadays. We also include a benchmark of different approaches on common data sets so as to compare them and highlight their strengths and limitations. We expect that this survey will serve as a starting point for researchers interested in the task and help spark new ideas for future developments.",
}
@article{jimenez-zafra-etal-2020-corpora,
    title = "Corpora Annotated with Negation: An Overview",
    author = "Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Morante, Roser  and
      Mart{\'\i}n-Valdivia, Mar{\'\i}a Teresa  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    journal = "Computational Linguistics",
    volume = "46",
    number = "1",
    month = mar,
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.cl-1.5",
    doi = "10.1162/coli_a_00371",
    pages = "1--52",
    abstract = "Negation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet, such as Chinese or Spanish, is greater every day. In this study, we present a review of the corpora annotated with negation information in several languages with the goal of evaluating what aspects of negation have been annotated and how compatible the corpora are. We conclude that it is very difficult to merge the existing corpora because we found differences in the annotation schemes used, and most importantly, in the annotation guidelines: the way in which each corpus was tokenized and the negation elements that have been annotated. Differently than for other well established tasks like semantic role labeling or parsing, for negation there is no standard annotation scheme nor guidelines, which hampers progress in its treatment.",
}
@proceedings{challenge-hml-2020-grand,
    title = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    editor = "Zadeh, Amir  and
      Morency, Louis-Philippe  and
      Liang, Paul Pu  and
      Poria, Soujanya",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.0",
}
@inproceedings{delbrouck-etal-2020-transformer,
    title = "A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis",
    author = "Delbrouck, Jean-Benoit  and
      Tits, No{\'e}  and
      Brousmiche, Mathilde  and
      Dupont, St{\'e}phane",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.1",
    doi = "10.18653/v1/2020.challengehml-1.1",
    pages = "1--7",
    abstract = "Understanding expressed sentiment and emotions are two crucial factors in human multimodal language. This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities. The proposed solution has also been submitted to the ACL20: Second Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI dataset. The code to replicate the presented experiments is open-source .",
}
@inproceedings{marrese-taylor-etal-2020-multi,
    title = "A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews",
    author = "Marrese-Taylor, Edison  and
      Rodriguez, Cristian  and
      Balazs, Jorge  and
      Gould, Stephen  and
      Matsuo, Yutaka",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.2",
    doi = "10.18653/v1/2020.challengehml-1.2",
    pages = "8--18",
    abstract = "Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents.We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.",
}
@inproceedings{shenoy-sardana-2020-multilogue,
    title = "Multilogue-Net: A Context-Aware {RNN} for Multi-modal Emotion Detection and Sentiment Analysis in Conversation",
    author = "Shenoy, Aman  and
      Sardana, Ashish",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.3",
    doi = "10.18653/v1/2020.challengehml-1.3",
    pages = "19--28",
    abstract = "Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions. Multi-modal Emotion Detection and Sentiment Analysis can be particularly useful, as applications will be able to use specific subsets of available modalities, as per the available data. Current systems dealing with Multi-modal functionality fail to leverage and capture - the context of the conversation through all modalities, the dependency between the listener(s) and speaker emotional states, and the relevance and relationship between the available modalities. In this paper, we propose an end to end RNN architecture that attempts to take into account all the mentioned drawbacks. Our proposed model, at the time of writing, out-performs the state of the art on a benchmark dataset on a variety of accuracy and regression metrics.",
}
@inproceedings{sahay-etal-2020-low,
    title = "Low Rank Fusion based Transformers for Multimodal Sequences",
    author = "Sahay, Saurav  and
      Okur, Eda  and
      H Kumar, Shachi  and
      Nachman, Lama",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.4",
    doi = "10.18653/v1/2020.challengehml-1.4",
    pages = "29--34",
    abstract = "Our senses individually work in a coordinated fashion to express our emotional intentions. In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers. The low-rank factorization of multimodal fusion amongst the modalities helps represent approximate multiplicative latent signal interactions. Motivated by the work of{\textasciitilde}(CITATION) and{\textasciitilde}(CITATION), we present our transformer-based cross-fusion architecture without any over-parameterization of the model. The low-rank fusion helps represent the latent signal interactions while the modality-specific attention helps focus on relevant parts of the signal. We present two methods for the Multimodal Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP datasets and show that our models have lesser parameters, train faster and perform comparably to many larger fusion-based architectures.",
}
@inproceedings{roesler-2020-unsupervised,
    title = "Unsupervised Online Grounding of Natural Language during Human-Robot Interactions",
    author = "Roesler, Oliver",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.5",
    doi = "10.18653/v1/2020.challengehml-1.5",
    pages = "35--45",
    abstract = "Allowing humans to communicate through natural language with robots requires connections between words and percepts. The process of creating these connections is called symbol grounding and has been studied for nearly three decades. Although many studies have been conducted, not many considered grounding of synonyms and the employed algorithms either work only offline or in a supervised manner. In this paper, a cross-situational learning based grounding framework is proposed that allows grounding of words and phrases through corresponding percepts without human supervision and online, i.e. it does not require any explicit training phase, but instead updates the obtained mappings for every new encountered situation. The proposed framework is evaluated through an interaction experiment between a human tutor and a robot, and compared to an existing unsupervised grounding framework. The results show that the proposed framework is able to ground words through their corresponding percepts online and in an unsupervised manner, while outperforming the baseline framework.",
}
@inproceedings{agrawal-etal-2020-leveraging,
    title = "Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback",
    author = "Agrawal, Anumeha  and
      Anil George, Rosa  and
      Ravi, Selvan Sunitha  and
      Kamath S, Sowmya  and
      Kumar, Anand",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.6",
    doi = "10.18653/v1/2020.challengehml-1.6",
    pages = "46--54",
    abstract = "Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee{'}s suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Candidates, therefore, need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee{'}s facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results.",
}
@inproceedings{okur-etal-2020-audio,
    title = "Audio-Visual Understanding of Passenger Intents for In-Cabin Conversational Agents",
    author = "Okur, Eda  and
      H Kumar, Shachi  and
      Sahay, Saurav  and
      Nachman, Lama",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.7",
    doi = "10.18653/v1/2020.challengehml-1.7",
    pages = "55--59",
    abstract = "Building multimodal dialogue understanding capabilities situated in the in-cabin context is crucial to enhance passenger comfort in autonomous vehicle (AV) interaction systems. To this end, understanding passenger intents from spoken interactions and vehicle vision systems is an important building block for developing contextual and visually grounded conversational agents for AV. Towards this goal, we explore AMIE (Automated-vehicle Multimodal In-cabin Experience), the in-cabin agent responsible for handling multimodal passenger-vehicle interactions. In this work, we discuss the benefits of multimodal understanding of in-cabin utterances by incorporating verbal/language input together with the non-verbal/acoustic and visual input from inside and outside the vehicle. Our experimental results outperformed text-only baselines as we achieved improved performances for intent detection with multimodal approach.",
}
@inproceedings{singh-jv-2020-ai,
    title = "{AI} {S}ensing for Robotics using Deep Learning based Visual and Language Modeling",
    author = "Singh, Yuvaram  and
      JV, Kameshwar Rao",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.8",
    doi = "10.18653/v1/2020.challengehml-1.8",
    pages = "60--63",
    abstract = "An artificial intelligence(AI) system should be capable of processing the sensory inputs to extract both task-specific and general information about its environment. However, most of the existing algorithms extract only task specific information. In this work, an innovative approach to address the problem of processing visual sensory data is presented by utilizing convolutional neural network (CNN). It recognizes and represents the physical and semantic nature of the surrounding in both human readable and machine processable format. This work utilizes the image captioning model to capture the semantics of the input image and a modular design to generate a probability distribution for semantic topics. It gives any autonomous system the ability to process visual information in a human-like way and generates more insights which are hardly possible with a conventional algorithm. Here a model and data collection method are proposed.",
}
@inproceedings{halbe-2020-exploring,
    title = "Exploring Weaknesses of {VQA} Models through Attribution Driven Insights",
    author = "Halbe, Shaunak",
    booktitle = "Second Grand-Challenge and Workshop on Multimodal Language (Challenge-HML)",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.challengehml-1.9",
    doi = "10.18653/v1/2020.challengehml-1.9",
    pages = "64--68",
    abstract = "Deep Neural Networks have been successfully used for the task of Visual Question Answering for the past few years owing to the availability of relevant large scale datasets. However these datasets are created in artificial settings and rarely reflect the real world scenario. Recent research effectively applies these VQA models for answering visual questions for the blind. Despite achieving high accuracy these models appear to be susceptible to variation in input questions.We analyze popular VQA models through the lens of attribution (input{'}s influence on predictions) to gain valuable insights. Further, We use these insights to craft adversarial attacks which inflict significant damage to these systems with negligible change in meaning of the input questions. We believe this will enhance development of systems more robust to the possible variations in inputs when deployed to assist the visually impaired.",
}
@proceedings{ccl-2020-chinese,
    title = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    editor = "Sun, Maosong  and
      Li, Sujian  and
      Zhang, Yue  and
      Liu, Yang",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.0",
}
@inproceedings{wang-2020-ji,
    title = "基于规则的双重否定识别{---}{---}以{``}不v1不v2{''}为例(Double Negative Recognition Based on Rules{---}{---}Taking {``}不v1不v2{''} as an Example)",
    author = "Wang, Yu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.1",
    pages = "1--9",
    abstract = "{``}不v1不v2{''}是汉语中典型的双重否定结构形式之一,它包括{``}不+助动词+不+v2{''}(不得不去)、{``}不+是+不v2{''}(不是不好)、述宾结构{``}不v1...不v2{''}(不认为他不去)等多种双重否定结构,情况复杂。本文以{``}不v1不v2{''}为例,结合{``}元语否定{''}、{``}动词叙实性{''}、{``}否定焦点{''}等概念,对{``}不v1不v2{''}进行了全面的考察,制定了{``}不v1不v2{''}双重否定结构的识别策略。根据识别策略,设计了双重否定自动识别程序,并在此过程中补充了助动词表、非叙实动词表等词库。最终,对28033句语料进行了识别,识别正确率为97.87{\%},召回率约为93.10{\%}。",
    language = "Chinese",
}
@inproceedings{zhang-etal-2020-ji,
    title = "基于语料库的武侠与仙侠网络小说文体、词汇及主题对比分析(A Corpus-based Contrastive Analysis of Style, Vocabulary and Theme of Wuxia and Xianxia {I}nternet Novels)",
    author = "Zhang, Sanle  and
      Liu, Pengyuan  and
      Zhang, Hu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.2",
    pages = "10--19",
    abstract = "网络文学在我国发展迅猛,其数量和影响力呈现逐年上升的趋势,但目前尚无公开的较大规模网络文学作品语料库,鲜见基于语料库对网络文学具体类别作品的定量研究。本文初步建立了一个网络文学语料库,其中包括武侠和仙侠网络小说,使用文本计量、词频统计以及主题挖掘的方法对两类小说的文体风格、具体词汇使用和小说主题进行对比分析。通过比较,我们发现两类小说的文体风格大致相同,它们在词汇的使用和主题上既有共性又各具特色。从微观到宏观,从表面到内容,将定量统计和定性分析相结合,多角度、多层次的对武侠和仙侠网络小说进行比较。",
    language = "Chinese",
}
@inproceedings{du-etal-2020-ji,
    title = "基于计量的百年中国人名用字性别特征研究(A Quantified Research on Gender Characteristics of {C}hinese Names in A Century)",
    author = "Du, Bingjie  and
      Liu, Pengyuan  and
      Tian, Yongsheng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.3",
    pages = "20--30",
    abstract = "本文构建了一个包含11万以上条目规模的中国名人人名数据库,每条数据含有人名、性别、出生地等社会文化标签,同时含有拼音、笔画、偏旁等文字信息标签,这是目前已知最大的可用于研究的汉语真人人名数据库。基于该数据库,本文从中选择1919年至今的人名,用定性与定量结合的方法探究人名中汉字的特征和其性别差异以及历时变化。从人名长度来看,男性人名比女性人名长;从人名用字的难易度来看,女性用字比男性更复杂;从用字丰富度来看,人名用字越来越单一和集中化,男性人名的用字丰富度大于女性人名。计算人名用字的性别偏度后发现女性人名的专用自更多。两性用字意象有明显的不同,用字的意象随着时间发生改变,但改变最明显的时间节点是改革开放前后,其中女性的变化比男性显著。除此之外,我们还得出人名中的性别极性字表、各个阶段的高频字表、用字变化趋势表等。",
    language = "Chinese",
}
@inproceedings{zhu-liu-2020-wei,
    title = "伟大的男人和倔强的女人:基于语料库的形容词性别偏度历时研究(Great Males and Stubborn Females: A Diachronic Study of Corpus-Based Gendered Skewness in {C}hinese Adjectives)",
    author = "Zhu, Shucheng  and
      Liu, Pengyuan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.4",
    pages = "31--42",
    abstract = "性别偏见现象是社会语言学和计算语学学者均关注的研究热点,但目前大多数研究都是基于英语的,鲜有对汉语中性别偏见现象,特别是基于形容词的研究缺乏。而形容词是衡量社会对男性和女性角色规约的有力抓手。本文首先利用调查问卷的方法,构建了一个含有466个形容词的数据集,定义性别偏度为特定形容词词义和男性或女性群体相匹配的程度,并计算了数据集中每个形容词的性别偏度。然后基于DCC语料库,研究了《人民日报》的形容词性别偏度的历时总体变化,并考察了和姓名搭配的形容词的历时变化。发现《人民日报》所使用的形容词随时间的推移整体呈现中性化趋势,但在文化大革命期间呈现非常男性化的特征,和男性姓名搭配的形容词整体呈现中性化趋势。",
    language = "Chinese",
}
@inproceedings{song-liu-2020-yong,
    title = "用计量风格学方法考察《水浒传》的作者争议问题{---}{---}以罗贯中《平妖传》为参照(Quantitive Stylistics Based Research on the Controversy of the Author of {``}{T}ales of the {M}arshes{''}: Comparing with {``}{P}ingyaozhuan{''} of {L}uo {G}uanzhong)",
    author = "Song, Li  and
      Liu, Ying",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.5",
    pages = "43--53",
    abstract = "《水浒传》是独著还是合著,施耐庵和罗贯中是何关系一直存在争议。本文将其作者争议粗略归纳为施耐庵作、罗贯中作、施作罗续、罗作他续、施作罗改五种情况,以罗贯中的《平妖传》为参照,用假设检验、文本聚类、文本分类、波动风格计量等方法,结合对文本内容的分析,考察《水浒传》的写作风格,试图为其作者身份认定提供参考。结果显示,只有罗作他续的可能性大,即前70回为罗贯中所作,后由他人续写,其他四种情况可能性都较小。",
    language = "Chinese",
}
@inproceedings{huang-etal-2020-duo,
    title = "多轮对话的篇章级抽象语义表示标注体系研究(Research on Discourse-level {A}bstract {M}eaning {R}epresentation Annotation framework in Multi-round Dialogue)",
    author = "Huang, Tong  and
      Li, Bin  and
      Yan, Peiyi  and
      Ji, Tingting  and
      Qu, Weiguang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.6",
    pages = "54--65",
    abstract = "对话分析是智能客服、聊天机器人等自然语言对话应用的基础课题,而对话语料与常规书面语料有较大差异,存在大量的称谓、情感短语、省略、语序颠倒、冗余等复杂现象,对句法和语义分析器的影响较大,对话自动分析的准确率相对书面语料一直不高。其主要原因在于对多轮对话缺乏严整的形式化描写方式,不利于后续的分析计算。因此,本文在梳理国内外针对对话的标注体系和语料库的基础上,提出了基于抽象语义表示的篇章级多轮对话标注体系。具体探讨了了篇章级别的语义结构标注方法,给出了词语和概念关系的对齐方案,针对称谓语和情感短语增加了相应的语义关系和概念,调整了表示主观情感词语的论元结构,并对对话中一些特殊现象进行了规定,设计了人工标注平台,为大规模的多轮对话语料库标注与计算研究奠定基础。",
    language = "Chinese",
}
@inproceedings{guo-xie-2020-fa,
    title = "发音属性优化建模及其在偏误检测的应用(Speech attributes optimization modeling and application in mispronunciation detection)",
    author = "Guo, Minghao  and
      Xie, Yanlu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.7",
    pages = "66--76",
    abstract = "近年来,发音属性常常被用于计算机辅助发音训练系统(CAPT)中。本文针对使用发音属性的一些难点,提出了一种建模细颗粒度发音属性(FSA)的方法,并在跨语言属性识别、发音偏误检测中进行测试。最终,我们得到了最优平均识别准确率约为95{\%}的属性检测器组;在两个二语测试集上的偏误检测,相比基线,基于FSA方法均获得了超过1{\%}的性能提升。此外,我们还根据发音属性的跨语言特性设置了对照实验,并在上述任务中测试和分析。",
    language = "Chinese",
}
@inproceedings{yan-etal-2020-ji,
    title = "基于抽象语义表示的汉语疑问句的标注与分析({C}hinese Interrogative Sentences Annotation and Analysis Based on the {A}bstract {M}eaning {R}epresentation)",
    author = "Yan, Peiyi  and
      Li, Bin  and
      Huang, Tong  and
      Huo, Kairui  and
      Chen, Jin  and
      Qu, Weiguang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.8",
    pages = "77--87",
    abstract = "疑问句的句法语义分析在搜索引擎、信息抽取和问答系统等领域有着广泛的应用。计算语言学多采取问句分类和句法分析相结合的方式来处理疑问句,精度和效率还不理想。而疑问句的语言学研究成果丰富,比如疑问句的结构类型、疑问焦点和疑问代词的非疑问用法等,但缺乏系统的形式化表示。本文致力于解决这一难题,采用基于图结构的汉语句子语义的整体表示方法{---}中文抽象语义表示(CAMR)来标注疑问句的语义结构,将疑问焦点和整句语义一体化表示出来。然后选取了宾州中文树库CTB8.0网络媒体语料、小学语文教材以及《小王子》中文译本的2万句语料中共计2071句疑问句,统计了疑问句的主要特点。统计表明,各种疑问代词都可以通过疑问概念amr-unknown和语义关系的组合来表示,能够完整地表示出疑问句的关键信息、疑问焦点和语义结构。最后,根据疑问代词所关联的语义关系,统计了疑问焦点的概率分布,其中原因、修饰语和受事的占比最高,分别占26.53{\%}、16.73{\%}以及16.44{\%}。基于抽象语义表示的疑问句标注与分析可以为汉语疑问句研究提供基础理论与资源。",
    language = "Chinese",
}
@inproceedings{ma-2020-yu,
    title = "语用视角下复述句生成方式的类型考察(A Pragmatic Study of Generation Method of Paraphrase Sentence)",
    author = "Ma, Tianhuan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.9",
    pages = "88--96",
    abstract = "本文将汉语母语者的160份复述文本与其原文进行以小句为单位的逐句比对,发现其中出现了6484对复述句对。从其生成的方式来看,可以分为改换词语和重铸整句两大类。以语用学原理对这些复述句进行分析,发现与以往研究的复述现象不同的是:句对间往往不具有相同的逻辑语义真值,但在特定语境下却能传达同一个语用意义,具有等效的语用功能。这说明在自然语言处理中,识别进入真实交际中的复述句不仅依赖语法、语义知识库,还需要借助含有语用知识和语境信息的知识库。",
    language = "Chinese",
}
@inproceedings{zhang-etal-2020-mian,
    title = "面向汉语作为第二语言学习的个性化语法纠错(Personalizing Grammatical Error Correction for {C}hinese as a Second Language)",
    author = "Zhang, Shengsheng  and
      Pang, Guina  and
      Yang, Liner  and
      Wang, Chencheng  and
      Du, Yongping  and
      Yang, Erhong  and
      Huang, Yaping",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.10",
    pages = "97--106",
    abstract = "语法纠错任务旨在通过自然语言处理技术自动检测并纠正文本中的语序、拼写等语法错误。当前许多针对汉语的语法纠错方法已取得较好的效果,但往往忽略了学习者的个性化特征,如二语等级、母语背景等。因此,本文面向汉语作为第二语言的学习者,提出个性化语法纠错,对不同特征的学习者所犯的错误分别进行纠正,并构建了不同领域汉语学习者的数据集进行实验。实验结果表明,将语法纠错模型适应到学习者的各个领域后,性能得到明显提升。",
    language = "Chinese",
}
@inproceedings{li-rao-2020-zhong,
    title = "中文问句的形式分类和资源建设(Formal classification and resource construction of {C}hinese questions)",
    author = "Li, Jiangtao  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.11",
    pages = "107--116",
    abstract = "本文归纳了问句形式在问句语料筛选中的作用,探索了问句分类必需的形式特征,同时通过人工标注建设了中文问句分类语料库,并在此基础上进行了基于规则和统计的分类实验,通过多轮实验迭代优化特征组合形成特征规则集,为当前问答提供形式上的分类基础。实验中,基于优化特征规则集的有限状态自动机可实现宏平均F1值为0.94;统计机器学习中随机森林模型的分类效果较好,F1值宏平均达到0.98,表明问句形式分类具有相当可行性和准确性。",
    language = "Chinese",
}
@inproceedings{qian-etal-2020-ji,
    title = "基于组块分析的汉语块依存语法({C}hinese Chunk-Based Dependency Grammar)",
    author = "Qian, Qingqing  and
      Wang, Chengwen  and
      Rao, Gaoqi  and
      Xun, Endong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.12",
    pages = "117--127",
    abstract = "基于词单元的经典依存语法在面向中文的句子分析中遇到诸多汉语特性引起的困难。为此,本文提出汉语的块依存语法,以谓词为核心,以组块为研究对象,在句内和句间寻找谓词所支配的组块,构建句群级别的句法分析框架。这一操作不仅仅是提升叶子节点的语言单位,而且还针对汉语语义特点进行了分析方式和分析规则上的创新,能够较好地解决微观层次的逻辑结构知识,并为中观论元知识和宏观篇章知识打好铺垫。本文主要介绍了块依存语法理念、表示、分析方法及特点,并简要介绍了块依存树库的构建情况。截至目前为止,树库规模为187万字符(超过4万复句、10万小句),其中包含67{\%}新闻文本和32{\%}百科文本。",
    language = "Chinese",
}
@inproceedings{lu-2020-xin,
    title = "新支话题的句法成分和语义角色研究(A Study of Syntactic Constituent and Semantic Role of New Branch Topic)",
    author = "Lu, Dawei",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.13",
    pages = "128--140",
    abstract = "话题的延续和转换是篇章中重要的语用功能。本文从句首话题共享的角度对话题延续和转换进行了分类,分为句首话题延续、句中子话题延续、完全话题转换、兼语话题转换、新支话题转换五种,进而对话题转换的特殊情况{---}{---}新支话题展开研究。基于33万字的广义话题结构语料库,本文对新支话题的句法成分、语义角色进行了统计和分析,发现能够成为新支话题的成分绝大多数是有关具体事物的体词性短语;句法成分方面,宾语从句或补语从句主语、主谓谓语句小主语、状语起始句的主语、句末宾语、连谓句非句末宾语、兼语句兼语、介词宾语甚至状语等都能成为新支话题,从而引出新支句,其中句末宾语做新支话题的情况最多,但未发现间接宾语作为新支话题的情况;语义角色方面,大部分主体论元(施事、感事、经事、主事)和客体论元(受事、系事、结果、对象、与事),及少数凭借论元(工具、方式,材料)和环境论元(处所、终点、路径)能成为新支话题引出新支句。其中,系事和受事成为新支话题情况最显著,施事、结果和对象次之。本文的研究揭示了句法、语义对话题转换这一语用现象的一种可能的约束途径。这将有助于人和计算机更深入地理解汉语篇章的话题转换机制,以期将这种语用现象逐步落实到语义直至句法的形式中,最终实现计算机对话题转换的自动分析。",
    language = "Chinese",
}
@inproceedings{shan-zhou-2020-yan,
    title = "眼动记录与主旨结构标注的关联性分析研究(Research on the correlation between eye movement feature and thematic structure label)",
    author = "Shan, Haocong  and
      Zhou, Qiang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.14",
    pages = "141--152",
    abstract = "给定包含主旨概括句的汉语句群,针对该句群的内部结构标注是基于语言学的分析结果,而阅读句群时的眼动轨迹则蕴含着人的心理认知,两者的信息融合和内在关联性分析是该文主要工作。该文使用基于径向基函数支持向量机和递归特征消除的分类模型,根据标点小句片段对应的眼动指标数据预测该片段是否为包含主旨内容的关键信息,达到了0.76的准确率,并通过分析关键片段上眼动数据的分布特点,提取出对句群主旨概括信息区分度较好的眼动指标。",
    language = "Chinese",
}
@inproceedings{zhang-su-2020-yi,
    title = "汉语竞争类多人游戏语言中疑问句的形式与功能(The Form and Function of Interrogatives in Multi-party {C}hinese Competitive Game Conversation)",
    author = "Zhang, Wenxian  and
      Su, Qi",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.15",
    pages = "153--161",
    abstract = "本文基于自建的竞争类多人游戏对话语料库对汉语疑问句的形式与功能进行了考察。文章首先在前人研究的基础上将疑问句的类型分为五大类,然后考察不同类型的疑问句在对话中出现的位置与功能。研究显示,是非问(包括反复问)与特指问是最常见的类型,选择问使用频率最低。大部分疑问句会引起话轮转换,具有询问功能,此外,否定与指出事实也是疑问句的主要功能。特指问的否定功能与附加问指出事实的 功能比较突出。",
    language = "Chinese",
}
@inproceedings{zhu-li-2020-rong,
    title = "融合目标端句法的{AMR}-to-Text生成({AMR}-to-Text Generation with Target Syntax)",
    author = "Zhu, Jie  and
      Li, Junhui",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.16",
    pages = "162--171",
    abstract = "抽象语义表示到文本(AMR-to-Text)生成的任务是给定AMR图,生成相同语义表示的文本。可以把此任务当作一个从源端AMR图到目标端句子的机器翻译任务。目前存在的一些方法都在探索如何更好的对图结构进行建模。然而,它们都存在一个未限定的问题,因为在生成阶段许多句法的决策并不受语义图的约束,从而忽略了句子内部潜藏的句法信息。为了明确考虑这一不足,该文提出一种直接而有效的方法,显示的在AMR-to-Text生成的任务中融入句法信息,并在Transformer和目前该任务最优性能的模型上进行了实验。实验结果表明,在现存的两份标准英文数据集LDC2018E86和LDC2017T10上,都取得了显著的提升,达到了新的最高性能。",
    language = "Chinese",
}
@inproceedings{sun-etal-2020-ji,
    title = "基于神经网络的连动句识别(Recognition of serial-verb sentences based on Neural Network)",
    author = "Sun, Chao  and
      Qu, Weiguang  and
      Wei, Tingxin  and
      Gu, Yanhui  and
      Li, Bin  and
      Zhou, Junsheng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.17",
    pages = "172--182",
    abstract = "连动句是具有连动结构的句子,是汉语中的特殊句法结构,在现代汉语中十分常见且使用频繁。连动句语法结构和语义关系都很复杂,在识别中存在许多问题,对此本文针对连动句的识别问题进行了研究,提出了一种基于神经网络的连动句识别方法。本方法分两步:第一步,运用简单的规则对语料进行预处理;第二步,用文本分类的思想,使用BERT编码,利用多层CNN与BiLSTM模型联合提取特征进行分类,进而完成连动句识别任务。在人工标注的语料上进行实验,实验结果达到92.71{\%}的准确率,F1值为87.41{\%}。",
    language = "Chinese",
}
@inproceedings{fan-etal-2020-rong,
    title = "融合全局和局部信息的汉语宏观篇章结构识别(Combining Global and Local Information to Recognize {C}hinese Macro Discourse Structure)",
    author = "Fan, Yaxin  and
      Jiang, Feng  and
      Chu, Xiaomin  and
      Li, Peifeng  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.18",
    pages = "183--194",
    abstract = "作为宏观篇章分析中的基础任务,篇章结构识别任务的目的是识别相邻篇章单元之间的结构,并层次化构建篇章结构树。已有的工作只考虑局部的结构和语义信息或只考虑全局信息。因此,本文提出了一种融合全局和局部信息的指针网络模型,该模型在考虑全局的语义信息同时,又考虑局部段落间的语义关系密切程度,从而有效地提高宏观篇章结构识别的能力。在汉语宏观篇章树库(MCDTB)的实验结果表明,本文所提出的模型性能优于目前性能最好的模型。",
    language = "Chinese",
}
@inproceedings{wang-etal-2020-ji,
    title = "基于图神经网络的汉语依存分析和语义组合计算联合模型(Joint Learning {C}hinese Dependency Parsing and Semantic Composition based on Graph Neural Network)",
    author = "Wang, Kai  and
      Liu, Mingtong  and
      Chen, Yuanmeng  and
      Zhang, Yujie  and
      Xu, Jinan  and
      Chen, Yufeng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.19",
    pages = "195--206",
    abstract = "组合原则表明句子的语义由其构成成分的语义按照一定规则组合而成, 由此基于句法结构的语义组合计算一直是一个重要的探索方向,其中采用树结构的组合计算方法最具有代表性。但是该方法难以应用于大规模数据处理,主要问题是其语义组合的顺序依赖于具体树的结构,无法实现并行处理。本文提出一种基于图的依存句法分析和语义组合计算的联合框架,并借助复述识别任务训练语义组合模型和句法分析模型。一方面图模型可以在训练和预测阶段采用并行处理,极大缩短计算时间;另一方面联合句法分析的语义组合框架不必依赖外部句法分析器,同时两个任务的联合学习可使语义表示同时学习句法结构和语义的上下文信息。我们在公开汉语复述识别数据集LCQMC上进行评测,实验结果显示准确率接近树结构组合方法,达到79.54{\%},而预测速度提升高达30倍。",
    language = "Chinese",
}
@inproceedings{wang-etal-2020-ji-yu,
    title = "基于强负采样的词嵌入优化算法(Word Embedding Optimization Based on Hard Negative Sampling)",
    author = "Wang, Yuchen  and
      Lin, Miaozhe  and
      Zhan, Jiefan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.20",
    pages = "207--214",
    abstract = "word2vec是自然语言处理领域重要的词嵌入算法之一,为了解决随机负采样作为优化目标可能出现的样本贡献消失问题,提出了可以应用在CBOW和Skip-gram框架上的以余弦距离为度量的强负采样方法:HNS-CBOW和HNS-SG。将原随机负采样过程拆解为两个步骤,首先,计算随机负样本与目标词的余弦距离,然后,再使用距离较近的强负样本更新参数。以英文维基百科数据作为实验语料,在公开的语义-语法数据集上对优化算法的效果进行了定量分析,实验表明,优化后的词嵌入质量显著优于原方法。同时,与GloVe等公开发布的预训练词向量相比,可以在更小的语料库上获得更高的准确性。",
    language = "Chinese",
}
@inproceedings{chen-etal-2020-lian,
    title = "联合依存分析的汉语语义组合模型({C}hinese Semantic Composition Model with Dependency Parsing)",
    author = "Chen, Yuanmeng  and
      Zhang, Yujie  and
      Xu, Jinan  and
      Chen, Yufeng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.21",
    pages = "215--224",
    abstract = "在语义组合方法中,结构化方法强调以结构信息指导词义表示的组合方式。现有结构化语义组合方法使用外部分析器获取句法结构信息,导致句法分析与语义组合相互割裂,句法分析的精度严重制约语义组合模型的性能,且训练数据领域不一致等问题会进一步加剧性能的下降。对此,本文提出联合依存分析的语义组合模型,将依存分析与语义组合进行联合,一方面在训练语义组合模型时对依存分析模型进行微调,使其能够更适应语义组合模型使用的训练数据的领域特点;另一方面,在语义组合部分加入依存分析的中间信息表示,获取更丰富的结构信息和语义信息,以此来降低语义组合模型对依存分析错误结果的敏感度,提升模型的鲁棒性。我们以汉语为具体研究对象,将语义组合模型用于复述识别任务,并在CTB5汉语依存分析数据和LCQMC汉语复述识别数据上验证本文提出的模型。实验结果显示,本文所提方法在复述识别任务上的预测正确率和F1值上分别达到76.81{\%}和78.03{\%};我们进一步设计实验对联合学习和中间信息利用的有效性进行验证,并与相关代表性工作进行了对比分析。",
    language = "Chinese",
}
@inproceedings{guan-etal-2020-ji,
    title = "基于对话约束的回复生成研究(Research on Response Generation via Dialogue Constraints)",
    author = "Guan, Mengyu  and
      Wang, Zhongqing  and
      Li, Shoushan  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.22",
    pages = "225--235",
    abstract = "现有的对话系统中存在着生成{``}好的{''}、{``}我不知道{''}等无意义的安全回复问题。日常对话中,对话者通常围绕特定的主题进行讨论且每句话都有明显的情感和意图。因此该文提出了基于对话约束的回复生成模型,即在Seq2Seq模型的基础上,结合对对话的主题、情感、意图的识别。该方法对生成回复的主题、情感和意图进行约束,从而生成具有合理的情感和意图且与对话主题相关的回复。实验证明,该文提出的方法能有效地提高生成回复的质量。",
    language = "Chinese",
}
@inproceedings{ji-etal-2020-duo,
    title = "多模块联合的阅读理解候选句抽取(Evidence sentence extraction for reading comprehension based on multi-module)",
    author = "Ji, Yu  and
      Wang, Xiaoyue  and
      Li, Ru  and
      Guo, Shaoru  and
      Guan, Yong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.23",
    pages = "236--245",
    abstract = "机器阅读理解作为自然语言理解的关键任务,受到国内外学者广泛关注。针对多项选择型阅读理解中无线索标注且涉及多步推理致使候选句抽取困难的问题,本文提出一种基于多模块联合的候选句抽取模型。首先采用部分标注数据微调预训练模型;其次通过TF-IDF递归式抽取多跳推理问题中的候选句;最后结合无监督方式进一步筛选模型预测结果降低冗余性。本文在高考语文选择题及RACE数据集上进行验证,在候选句抽取中,本文方法相比于最优基线模型F1值提升3.44{\%},在下游答题任务中采用候选句作为模型输入较全文输入时准确率分别提高3.68{\%}和3.6{\%},上述结果证实本文所提方法有效性。",
    language = "Chinese",
}
@inproceedings{li-zhou-2020-ji,
    title = "基于层次化语义框架的知识库属性映射方法(Property Mapping in Knowledge Base Under the Hierarchical Semantic Framework)",
    author = "Li, Yu  and
      Zhou, Guangyou",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.24",
    pages = "246--255",
    abstract = "面向知识库的自动问答是自然语言处理的一项重要任务,它旨在对用户提出的自然语言形式问题给出精炼、准确的回复。目前由于缺少数据集、特征不一致等因素,导致难以使用通用的数据和方法实现领域知识库问答。因此,本文将{``}问题意图{''}视作不同领域问答可能存在的共同特征,将{``}问题{''}与三元组知识库中{``}关系谓词{''}的映射过程作为问答核心工作。为了考虑多种层次的语义避免重要信息的损失,本文分别将{``}基于门控卷积的深层语义{''}和{``}基于交互注意力机制的浅层语义{''}两个方面通过门控感知机制相融合。我们在NLPCC-ICCPOL 2016 KBQA数据集上的实验表明,本文提出的方法与现有的基于CDSSM和BDSSM相比,效能有明显的提升。此外,本文通过构造天文常识知识库,将问题与关系谓词映射模型移植到特定领域,结合Bi-LSTM-CRF模型构建了天文常识自动问答系统。",
    language = "Chinese",
}
@inproceedings{lv-etal-2020-mian,
    title = "面向垂直领域的阅读理解数据增强方法(Method for reading comprehension data enhancement in vertical field)",
    author = "Lv, Zhengwei  and
      Yang, Lei  and
      Shi, Zhizhong  and
      Liang, Xiao  and
      Lei, Tao  and
      Liu, Duoxing",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.25",
    pages = "256--265",
    abstract = "阅读理解问答系统是利用语义理解等自然语言处理技术,根据输入问题,对非结构化文档数据进行分析,生成一个答案,具有很高的研究和应用价值。在垂直领域应用过程中,阅读理解问答数据标注成本高且用户问题表达复杂多样,使得阅读理解问答系统准确率低、鲁棒性差。针对这一问题,本文提出一种面向垂直领域的阅读理解问答数据的增强方法,该方法基于真实用户问题,构造阅读理解训练数据,一方面降低标注成本,另一方面增加训练数据多样性,提升模型的准确率和鲁棒性。本文用汽车领域数据对该方法进行实验验证,其结果表明该方法对垂直领域阅读理解模型的准确率和鲁棒性均能有效提升。",
    language = "Chinese",
}
@inproceedings{si-kong-2020-rong,
    title = "融入对话上文整体信息的层次匹配回应选择(Learning Overall Dialogue Information for Dialogue Response Selection)",
    author = "Si, Bowen  and
      Kong, Fang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.26",
    pages = "266--276",
    abstract = "对话是一个顺序交互的过程,回应选择旨在根据已有对话上文选择合适的回应,是自然语言处理领域的研究热点。已有研究取得了一定的成功,但仍然存在两个突出的问题。一是现有的编码器在挖掘对话文本语义信息上尚存在不足;二是只考虑每一回合对话与备选回应之间的关系,忽视了对话上文的整体语义信息。针对问题一,本文借助多头自注意力机制有效捕捉对话文本的语义信息;针对问题二,整合对话上文的整体语义信息,分别从单词、句子以及整体对话上文三个层次与备选回应进行匹配,充分保证匹配信息的完整。在Ubuntu Corpus V1和Douban Conversation Corpus数据集上的对比实验表明了本文给出方法的有效性。",
    language = "Chinese",
}
@inproceedings{ding-etal-2020-yi,
    title = "一种结合话语伪标签注意力的人机对话意图分类方法(A Human-machine Dialogue Intent Classification Method using Utterance Pseudo Label Attention)",
    author = "Ding, Jiande  and
      Huang, Peijie  and
      Xu, Jiabao  and
      Peng, Youming",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.27",
    pages = "277--287",
    abstract = "在人机对话中,系统需要通过意图分类判断用户意图,再触发相应的业务类型。由于多轮人机对话具有口语化、长文本和特征稀疏等特点,现有的文本分类方法在人机对话意图分类上还存在较大困难。本文在层次注意力网络(hierarchical attention networks, HAN)基础上,提出了一种结合话语伪标签注意力的层次注意力网络模型PLA-HAN (HAN with utterance pseudo label attention)。PLA-HAN通过优选伪标签集、构建单句话语意图识别模型以及设计话语伪标签注意力机制,识别单句话语意图伪标签,并计算话语伪标签注意力。进而将单句话语伪标签注意力嵌入到HAN的层级结构中,与HAN中的句子级别注意力相融合。融合了单句话语意图信息的句子级注意力使模型整体性能得到进一步的提升。我们在中国中文信息学会主办的{``}客服领域用户意图分类评测比赛{''}的评测语料上进行实验,实验结果证明PLA-HAN模型取得了优于HAN等对比方法的意图分类性能。",
    language = "Chinese",
}
@inproceedings{xiang-etal-2020-ji,
    title = "基于{BERTCA}的新闻实体与正文语义相关度计算模型(Semantic Relevance Computing Model of News Entity and Text based on {BERTCA})",
    author = "Xiang, Junyi  and
      Hu, Huijun  and
      Mao, Ruibin  and
      Liu, Maofu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.28",
    pages = "288--300",
    abstract = "目前的搜索引擎仍然存在{``}重形式,轻语义{''}的问题,无法做到对搜索关键词和文本的深层次语义理解,因此语义检索成为当代搜索引擎中亟需解决的问题。为了提高搜索引擎的语义理解能力,提出一种语义相关度的计算方法。首先标注金融类新闻标题实体与新闻正文语义相关度语料1万条,然后建立新闻实体与正文语义相关度计算的BERTCA(Bidirectional Encoder Representation from Transformers Co-Attention)模型,通过使用BERT预训练模型,综合考虑细粒度的实体和粗粒度的正文的语义信息,然后经过协同注意力,实现实体与正文的语义匹配,不仅能计算出金融新闻实体与新闻正文之间的相关度,还能根据相关度阈值来判定相关度类别,实验表明该模型在1万条标注语料上准确率超过95{\%},优于目前主流模型,最后通过具体搜索示例展现该模型的优秀性能。",
    language = "Chinese",
}
@inproceedings{qian-etal-2020-ji-yu,
    title = "基于多任务学习的生成式阅读理解(Generative Reading Comprehension via Multi-task Learning)",
    author = "Qian, Jin  and
      Huang, Rongtao  and
      Zou, Bowei  and
      Hong, Yu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.29",
    pages = "301--312",
    abstract = "生成式阅读理解是机器阅读理解领域一项新颖且极具挑战性的研究。与主流的抽取式阅读理解相比,生成式阅读理解模型不再局限于从段落中抽取答案,而是能结合问题和段落生成自然和完整的表述作为答案。然而,现有的生成式阅读理解模型缺乏对答案在段落中的边界信息以及对问题类型信息的理解。为解决上述问题,本文提出一种基于多任务学习的生成式阅读理解模型。该模型在训练阶段将答案生成任务作为主任务,答案抽取和问题分类任务作为辅助任务进行多任务学习,同时学习和优化模型编码层参数;在测试阶段加载模型编码层进行解码生成答案。实验结果表明,答案抽取模型和问题分类模型能够有效提升生成式阅读理解模型的性能。",
    language = "Chinese",
}
@inproceedings{qin-etal-2020-ji,
    title = "基于多头注意力和{B}i{LSTM}改进{DAM}模型的中文问答匹配方法({C}hinese question answering method based on multi-head attention and {B}i{LSTM} improved {DAM} model)",
    author = "Qin, Hanzhong  and
      Yu, Chongchong  and
      Jiang, Weijie  and
      Zhao, Xia",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.30",
    pages = "313--323",
    abstract = "针对目前检索式多轮对话深度注意力机制模型DAM(Deep Attention Matching Network)候选回复细节不匹配和语义混淆的问题,本文提出基于多头注意力和双向长短时记忆网络(BiLSTM)改进DAM模型的中文问答匹配方法,该方法采用多头注意力机制,使模型有能力建模较长的多轮对话,更好的处理目标回复与上下文的匹配关系。此外,本文在特征融合过程中采用BiLSTM模型,通过捕获多轮对话中的序列依赖关系,进一步提升选择目标候选回复的准确率。本文在豆瓣和电商两个开放数据集上进行实验,实验性能均优于DAM基线模型,R10@1指标在含有词向量增强的情况下提升了1.5{\%}。",
    language = "Chinese",
}
@inproceedings{hu-zhou-2020-ji,
    title = "基于Graph Transformer的知识库问题生成(Question Generation from Knowledge Base with Graph Transformer)",
    author = "Hu, Yue  and
      Zhou, Guangyou",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.31",
    pages = "324--335",
    abstract = "知识库问答依靠知识库推断答案需大量带标注信息的问答对,但构建大规模且精准的数据集不仅代价昂贵,还受领域等因素限制。为缓解数据标注问题,面向知识库的问题生成任务引起了研究者关注,该任务是利用知识库三元组自动生成问题。现有方法仅由一个三元组生成的问题简短且缺乏多样性。为生成信息量丰富且多样化的问题,本文采用Graph Transformer和BERT两个编码层来加强三元组多粒度语义表征以获取背景信息。在SimpleQuestions上的实验结果证明了该方法有效性。",
    language = "Chinese",
}
@inproceedings{fan-etal-2020-ji,
    title = "基于{BERT}与柱搜索的中文释义生成({C}hinese Definition Modeling Based on {BERT} and Beam Seach)",
    author = "Fan, Qinan  and
      Kong, Cunliang  and
      Yang, Liner  and
      Yang, Erhong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.32",
    pages = "336--348",
    abstract = "释义生成任务是指为一个目标词生成相应的释义。前人研究中文释义生成任务时未考虑目标词的上下文,本文首次在中文释义生成任务中使用了目标词的上下文信息,并提出了一个基于BERT与柱搜索的释义生成模型。本文构建了包含上下文的CWN中文数据集用于开展实验,除了BLEU指标之外,还使用语义相似度作为额外的自动评价指标,实验结果显示本文模型在中文CWN数据集和英文Oxford数据集上均有显著提升,人工评价结果也与自动评价结果一致。最后,本文对生成实例进行了深入分析。",
    language = "Chinese",
}
@inproceedings{xia-etal-2020-ji,
    title = "基于深度学习的实体关系抽取研究综述(Review of Entity Relation Extraction based on deep learning)",
    author = "Xia, Zhentao  and
      Qu, Weiguang  and
      Gu, Yanhui  and
      Zhou, Junsheng  and
      Li, Bin",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.33",
    pages = "349--362",
    abstract = "作为信息抽取的一项核心子任务,实体关系抽取对于知识图谱、智能问答、语义搜索等自然语言处理应用都十分重要。关系抽取在于从非结构化文本中自动地识别实体之间具有的某种语义关系。该文聚焦句子级别的关系抽取研究,介绍用于关系抽取的主要数据集并对现有的技术作了阐述,主要分为:有监督的关系抽取、远程监督的关系抽取和实体关系联合抽取。我们对比用于该任务的各种模型,分析它们的贡献与缺 陷。最后介绍中文实体关系抽取的研究现状和方法。",
    language = "Chinese",
}
@inproceedings{hu-liu-2020-xiao,
    title = "小样本关系分类研究综述(Few-Shot Relation Classification: A Survey)",
    author = "Hu, Han  and
      Liu, Pengyuan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.34",
    pages = "363--375",
    abstract = "关系分类作为构建结构化知识的重要一环,在自然语言处理领域备受关注。但在很多应用领域中(医疗、金融领域),收集充足的用于训练关系分类模型的数据是十分困难的。近年来,仅需要少量训练样本的小样本学习研究逐渐新兴于各大领域。本文对近期小样本关系分类模型与方法进行了系统的综述。根据度量方法的不同,将现有方法分为原型式和分布式两大类。根据是否利用额外信息,将模型分为预训练和非预训练两大类。此外,除了常规设定下的小样本学习,本文还梳理了跨领域和稀缺资源场景下的小样本学习,并探讨了目前小样本关系分类方法的局限性,分析了跨领域小样本 学习面临的技术挑战。最后,展望了小样本关系分类未来的发展方向。",
    language = "Chinese",
}
@inproceedings{chen-etal-2020-ji,
    title = "基于阅读理解框架的中文事件论元抽取({C}hinese Event Argument Extraction using Reading Comprehension Framework)",
    author = "Chen, Min  and
      Wu, Fan  and
      Wang, Zhongqing  and
      Li, Peifeng  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.35",
    pages = "376--389",
    abstract = "传统的事件论元抽取方法把该任务当作句子中实体提及的多分类或序列标注任务,论元角色的类别在这些方法中只能作为向量表示,而忽略了论元角色的先验信息。实际上,论元角色的语义和论元本身有很大关系。对此,本文提议将其当作机器阅读理解任务,把论元角色表述为自然语言描述的问题,通过在上下文中回答这些问题来抽取论元。该方法更好地利用了论元角色类别的先验信息,在ACE2005中文语料上的实验证明了该方法的有效性。",
    language = "Chinese",
}
@inproceedings{zhang-etal-2020-ji-yu,
    title = "基于{BERT}的端到端中文篇章事件抽取(A {BERT}-based End-to-End Model for {C}hinese Document-level Event Extraction)",
    author = "Zhang, Hongkuan  and
      Song, Hui  and
      Wang, Shuyi  and
      Xu, Bo",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.36",
    pages = "390--401",
    abstract = "篇章级事件抽取研究从整篇文档中检测事件,识别出事件包含的元素并赋予每个元素特定的角色。本文针对限定领域的中文文档提出了基于BERT的端到端模型,在模型的元素和角色识别中依次引入前序层输出的事件类型以及实体嵌入表示,增强文本的事件、元素和角色关联表示,提高篇章中各事件所属元素的识别精度。在此基础上利用标题信息和事件五元组的嵌入式表示,实现主从事件的划分及元素融合。实验证明本文的方法与现有工作相比具有明显的提升。",
    language = "Chinese",
}
@inproceedings{chen-etal-2020-mian,
    title = "面向微博文本的融合字词信息的轻量级命名实体识别(Lightweight Named Entity Recognition for {W}eibo Based on Word and Character)",
    author = "Chen, Chun  and
      Li, Mingyang  and
      Kong, Fang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.37",
    pages = "402--413",
    abstract = "中文社交媒体命名实体识别由于其领域特殊性,一直广受关注。非正式且无结构的微博文本存在以下两个问题:一是词语边界模糊;二是语料规模有限。针对问题一,本文将同维度的字词进行融合,获得丰富的文本序列表征;针对问题二,提出了基于Star-Transformer框架的命名实体识别模型,借助星型拓扑结构更好地捕获动态特征;同时利用高速网络优化Star-Transformer中的信息桥接,提升模型的鲁棒性。本文提出的轻量级命名实体识别模型取得了目前Weibo语料上最好的效果。",
    language = "Chinese",
}
@inproceedings{luo-li-2020-yin,
    title = "引入源端信息的机器译文自动评价方法研究(Research on Incorporating the Source Information to Automatic Evaluation of Machine Translation)",
    author = "Luo, Qi  and
      Li, Maoxi",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.38",
    pages = "414--423",
    abstract = "机器译文自动评价是机器翻译中的一个重要任务。针对目前译文自动评价中完全忽略源语言句子信息,仅利用人工参考译文度量翻译质量的不足,该文提出了引入源语言句子信息的机器译文自动评价方法:从机器译文与其源语言句子组成的二元组中提取描述翻译质量的质量向量,并将其与基于语境词向量的译文自动评价方法利用深度神经网络进行融合。在WMT{'}19译文自动评价任务数据集上的实验结果表明,所提出的方法能够有效增强机器译文自动评价与人工评价的相关性。深入的实验分析进一步揭示了源语言句子信息在译文自动评价中发挥着重要的作用。",
    language = "Chinese",
}
@inproceedings{qiu-etal-2020-xi,
    title = "{``}细粒度英汉机器翻译错误分析语料库{''}的构建与思考(Construction of Fine-Grained Error Analysis Corpus of {E}nglish-{C}hinese Machine Translation and Its Implications)",
    author = "Qiu, Bailian  and
      Wang, Mingwen  and
      Li, Maoxi  and
      Chen, Cong  and
      Xu, Fan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.39",
    pages = "424--433",
    abstract = "机器翻译错误分析旨在找出机器译文中存在的错误,包括错误类型、错误分布等,它在机器翻译研究和应用中起着重要作用。该文将人工译后编辑与错误分析结合起来,对译后编辑操作进行错误标注,采用自动标注和人工标注相结合的方法,构建了一个细粒度英汉机器翻译错误分析语料库,其中每一个标注样本包括源语言句子、机器译文、人工参考译文、译后编辑译文、词错误率和错误类型标注;标注的错误类型包括增词、漏词、错词、词序错误、未译和命名实体翻译错误等。标注的一致性检验表明了标注的有效性;对标注语料的统计分析结果能有效地指导机器翻译系统的开发和人工译员的后编辑。",
    language = "Chinese",
}
@inproceedings{chen-etal-2020-ceng,
    title = "层次化结构全局上下文增强的篇章级神经机器翻译(Hierarchical Global Context Augmented Document-level Neural Machine Translation)",
    author = "Chen, Linqing  and
      Li, Junhui  and
      Gong, Zhengxian",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.40",
    pages = "434--445",
    abstract = "如何有效利用篇章上下文信息一直是篇章级神经机器翻译研究领域的一大挑战。本文提出利用来源于整个篇章的层次化全局上下文提高篇章级神经机器翻译性能。为了实现该目标,本文模型分别获取当前句内单词与篇章内所有句子及单词之间的依赖关系,结合不同层次的依赖关系以获取含有层次化篇章信息的全局上下文。最终源语言当前句子中的每个单词都能获取其独有的综合词和句级别依赖关系的上下文。为了充分利用平行句对语料在训练中的优势本文使用两步训练法,在句子级语料训练模型的基础上使用含有篇章信息的语料进行二次训练以获得捕获全局上下文的能力。在若干基准语料数据集上的实验表明本文提出的模型与若干强基准模型相比取得了有意义的翻译质量提升。实验进一步表明,结合层次化篇章信息的上下文比仅使用词级别上下文更具优势。除此之外,本文尝试通过不同方式将全局上下文与翻译模型结合并观察其对模型性能的影响,并初步探究篇章翻译中全局上下文在篇章中的分布情况。",
    language = "Chinese",
}
@inproceedings{man-etal-2020-ji,
    title = "基于多语言联合训练的汉-英-缅神经机器翻译方法({C}hinese-{E}nglish-{B}urmese Neural Machine Translation Method Based on Multilingual Joint Training)",
    author = "Man, Zhibo  and
      Mao, Cunli  and
      Yu, Zhengtao  and
      Li, Xunyu  and
      Gao, Shengxiang  and
      Zhu, Junguo",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.41",
    pages = "446--456",
    abstract = "多语言神经机器翻译是解决低资源神经机器翻译的有效方法,现有方法通常依靠共享词表的方式解决英语、法语以及德语相似语言之间的多语言翻译问题。缅甸语属于一种典型的低资源语言,汉语、英语以及缅甸语之间的语言结构差异性较大,为了缓解由于差异性引起的共享词表大小受限制的问题,提出一种基于多语言联合训练的汉英缅神经机器翻译方法。在Transformer框架下将丰富的汉英平行语料与汉缅、英缅的语料进行联合训练,模型训练过程中分别在编码端和解码端将汉英缅映射在同一语义空间降低汉英缅语言结构差异性对共享词表的影响,通过共享汉英语料训练参数来弥补汉缅数据缺失的问题。实验表明在一对多、多对多的翻译场景下,提出方法相比基线模型的汉-英、英-缅以及汉-缅的BLEU值有明显的提升。",
    language = "Chinese",
}
@inproceedings{liu-etal-2020-ji,
    title = "基于跨语言双语预训练及{B}i-{LSTM}的汉-越平行句对抽取方法({C}hinese-{V}ietnamese Parallel Sentence Pair Extraction Method Based on Cross-lingual Bilingual Pre-training and {B}i-{LSTM})",
    author = "Liu, Chang  and
      Gao, Shengxiang  and
      Yu, Zhengtao  and
      Huang, Yuxin  and
      You, Congcong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.42",
    pages = "457--466",
    abstract = "汉越平行句对抽取是缓解汉越平行语料库数据稀缺的重要方法。平行句对抽取可转换为同一语义空间下的句子相似性分类任务,其核心在于双语语义空间对齐。传统语义空间对齐方法依赖于大规模的双语平行语料,越南语作为低资源语言获取大规模平行语料相对困难。针对这个问题本文提出一种利用种子词典进行跨语言双语预训练及Bi-LSTM(Bi-directional Long Short-Term Memory)的汉-越平行句对抽取方法。预训练中仅需要大量的汉越单语和一个汉越种子词典,通过利用汉越种子词典将汉越双语映射到公共语义空间进行词对齐。再利用Bi-LSTM和CNN(Convolutional Neural Networks)分别提取句子的全局特征和局部特征从而最大化表示汉-越句对之间的语义相关性。实验结果表明,本文模型在F1得分上提升7.1{\%},优于基线模型。",
    language = "Chinese",
}
@inproceedings{yan-xie-2020-ji,
    title = "基于子词级别词向量和指针网络的朝鲜语句子排序({K}orean Sentence Ordering Based on Sub Word Level Word Vector and Pointer Network)",
    author = "Yan, Xiaodong  and
      Xie, Xiaoqing",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.43",
    pages = "467--478",
    abstract = "句子排序是多文档摘要系统和机器阅读理解中重要的任务之一,排序的质量将直接 影响摘要和答案的连贯性与可读性。因此,本文采用在中英文上大规模使用的深度 学习方法,同时结合朝鲜语词语形态变化丰富的特点,提出了一种基于子词级别词 向量和指针网络的朝鲜语句子排序模型,其目的是解决传统方法无法挖掘深层语义 信息问题。 本文提出基于形态素拆分的词向量训练方法(MorV),同时对比子词n元 词向量训练方法(SG),得到朝鲜语词向量;采用了两种句向量方法:基于卷积神经网 络(CNN)、基于长短时记忆网络(LSTM),结合指针网络分别进行实验。结果表明本文 采用MorV和LSTM的句向量结合方法可以更好地捕获句子间的语义逻辑关系,提升句 子排序的效果。 关键词: 词向量 ;形态素拆分 ;指针网络 ;句子排序",
    language = "Chinese",
}
@inproceedings{yan-etal-2020-ji-yu,
    title = "基于统一模型的藏文新闻摘要(Abstractive Summarization of {T}ibetan News Based on Hybrid Model)",
    author = "Yan, Xiaodong  and
      Xie, Xiaoqing  and
      Zou, Yu  and
      Li, Wei",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.44",
    pages = "479--490",
    abstract = "Seq2seq神经网络模型在中英文文本摘要的研究中取得了良好的效果,但在低资源语言的文本摘要研究还处于探索阶段,尤其是在藏语中。此外,目前还没有大规模的标注语料库进行摘要提取。本文提出了一种生成藏文新闻摘要的统一模型。利用TextRank算法解决了藏语标注训练数据不足的问题。然后,采用两层双GRU神经网络提取代表原始新闻的句子,减少冗余信息。最后,使用基于注意力机制的Seq2Seq来生成理解式摘要。同时,我们加入了指针网络来处理未登录词的问题。实验结果表明,ROUGE-1评分比传统模型提高了2{\%}。 关键词:文本摘要;藏文;TextRank; 指针网络;Bi-GRU",
    language = "Chinese",
}
@inproceedings{bai-hu-2020-meng,
    title = "蒙古文拼写形式多样化现象研究(A Study of Spelling Variety of {M}ongolian)",
    author = "Bai, Shuangcheng  and
      Hu, Sile",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.45",
    pages = "491--498",
    abstract = "蒙古文文本中存在一个有别于多数其他文字的特别现象──看到的单词字形正确但其内码序列不正确,或者说单词{``}变形显现字形{''}序列正确但{``}名义字符{''}序列不正确的现象,我们称其为蒙古文的拼写形式多样化现象。本文先定义该现象及相关概念,再通过简单图示、例词拼写形式穷举、新闻语料统计分析和基于整篇文章标注统计等多方式、多角度论证这一现象的事实性和严重性,分析导致这一现象的深层原因并指出拼写形式多样化对蒙古文信息处理和应用方面的严重影响,最后提出通过推广普及录入规范和标准提高用户意识、使用智能输入法避免误录、使用校对纠错工具后纠正、基于生语料的统计学习方法为补充等多途径解决方法。本文对蒙古文标准编码的推广普及具有较好的参考价值。",
    language = "Chinese",
}
@inproceedings{sha-etal-2020-mian,
    title = "面向司法领域的高质量开源藏汉平行语料库构建(A High-quality Open Source {T}ibetan-{C}hinese Parallel Corpus Construction of Judicial Domain)",
    author = "Sha, Jiu  and
      Zhou, Luqin  and
      Feng, Chong  and
      Li, Hongzheng  and
      Zhang, Tianfu  and
      Hui, Hui",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.46",
    pages = "499--508",
    abstract = "面向司法领域的藏汉机器翻译面临严重的数据稀疏问题。本文将从两个方面展录研究:第一,相比于通用领域,司法领域的藏语要有更严谨的逻辑表达和更多的专业术语。然而,目前藏语资源在司法领域内缺乏对应的语料,稀缺专业术语词以及句法结构。第二,藏语的特殊词汇表达方式和特定句法结构使得通用语料构建方法难以构建藏汉平行语料库。为此,本文提出仺种针对司法领域藏汉平行语料的轻量级构建方法。首先,我们采取人工标注获取一个中等规模的司法领域藏汉专业术语表作为先验知识库,以避免领域越界而产生的语料逻辑表达问题和领域术语缺失问题;其次,我们从全国的地方法庭官网采集实例语料数据,例如裁判文书。我们优先寻找藏文实例数据,其次是汉语,以避免后续构造藏语句子而丢失特殊的词汇表达和句式结构。我们基于以上原则采集藏汉语料构建高质量的藏汉平行语料库,具体方法包括:爬虫获取语料,规则断章对齐检测,语句边界识别,语料库自动清洗。朂终,我们构建了16万级规模的藏汉司法领域语料库,并通过多种翻译模型和交叉实验验证了构建的语料库的高质量特点和鲁棒性。另外,此语料库会弚源以便于相关研究人员用于科研工作。",
    language = "Chinese",
}
@inproceedings{jiayang-etal-2020-yi,
    title = "一种基于相似度的藏文词同现网络构建及特征分析(A Research on Construction and Feature Analysis of Similarity-based {T}ibetan Word Co-occurrence Networks)",
    author = "Jiayang, Dongzhou  and
      Cai, Zhijie  and
      Cairang, Zhuoma  and
      San, Maocuo",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.47",
    pages = "509--517",
    abstract = "语言文字是人类智慧和文明的结晶,是经过漫长演化形成的复杂系统。语言同现网络采 用复杂网络技术研究语言的特征,揭示语言文字的内部结构关系。文章分析相似性同 现网络构建模块结构,提出一种基于相似度的藏文词同现网络构建方法,该方法以词 为网络节点,以相似词间连边构造词同现网络。基于相似度藏文词同现网络构建方法, 在大、中、小三类文档上建立了词同现网络,并分析了它们的统计特征,实验数据表明 建立的藏文词同现网络都具有小世界效应和无标度特征。",
    language = "Chinese",
}
@inproceedings{yuan-cao-2020-dong,
    title = "《动词句法语义信息词典》知识内容说明书(An Introduction to the Syntactic-Semantic Knowledge-Base of {C}hinese Verbs)",
    author = "Yuan, Yulin  and
      Cao, Hong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.48",
    pages = "518--527",
    abstract = "本文首先介绍《实词信息词典》的研制目标与结构内容,重点介绍其中的《动词信息词典》的体系结构与理论背景;然后,介绍《动词信息词典》所区分的8种动词小类及其定义,其为动词所设置的22种语义角色及其定义,由这些语义角色的不同配置而造成的20来种句法格式及其例句,其所考察的动词的9种主要的语法功能及其对于该词类的隶属度;最后,给出《动词信息词典》中检索系统的界面截图,交代其相应的纸质版本的情况。",
    language = "Chinese",
}
@inproceedings{hou-etal-2020-mian,
    title = "面向中文{AMR}标注体系的兼语语料库构建及识别研究(Research on the Construction and Recognition of Concurrent corpus for {C}hinese {AMR} Annotation System)",
    author = "Hou, Wenhui  and
      Qu, Weiguang  and
      Wei, Tingxin  and
      Li, Bin  and
      Gu, Yanhui  and
      Zhou, Junsheng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.49",
    pages = "528--538",
    abstract = "兼语结构是汉语中常见的一种动词结构,由述宾短语与主谓短语共享兼语,结构复杂,给句法分析造成困难,因此兼语语料库构建及识别工作对于语义解析及下游任务都具有重要意义。但现存兼语语料库较少,面向中文AMR标注体系的兼语语料库构建仍处于空白阶段。针对这一现状,本文总结了一套兼语语料库标注规范,并构建了一定数量面向中文AMR标注体系的兼语语料库。基于构建的语料库,采用基于字符的神经网络模型识别兼语结构,并对识别结果以及未来的改进方向进行分析总结。",
    language = "Chinese",
}
@inproceedings{wang-etal-2020-mian,
    title = "面向人工智能伦理计算的中文道德词典构建方法研究(Construction of a {C}hinese Moral Dictionary for Artificial Intelligence Ethical Computing)",
    author = "Wang, Hongrui  and
      Liu, Chang  and
      Yu, Dong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.50",
    pages = "539--549",
    abstract = "道德词典资源的建设是人工智能伦理计算的一个研究重点。由于道德行为复杂多样,现有的英文道德词典分类体系并不完善,而中文方面目前尚未有相关的词典资源,理论体系和构建方法仍待探究。针对以上问题,该文提出了面向人工智能伦理计算的中文道德词典构建任务,设计了四类标签和四种类型,得到包含25,012个词的中文道德词典资源。实验结果表明,该词典资源不仅能够使机器学会道德知识,判断词的道德标签和类型,而且能够为句子级别的道德文本分析提供数据支持。",
    language = "Chinese",
}
@inproceedings{sheng-etal-2020-yi,
    title = "汉语否定焦点识别研究:数据集与基线系统(Research on {C}hinese Negative Focus Identification: Dataset and Baseline)",
    author = "Sheng, Jiaxuan  and
      Zou, Bowei  and
      Shen, Longxiang  and
      Ye, Jing  and
      Hong, Yu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.51",
    pages = "550--560",
    abstract = "自然语言文本中存在大量否定语义表达,否定焦点识别任务作为更细粒度的否定语义分析,近年来开始受到自然语言处理学者的关注。该任务旨在识别句子中被否定词修饰和强调的文本片段,其对自然语言处理的下游任务,如情感分析、观点挖掘等具有重要意义。与英语相比,目前面向汉语的否定焦点识别研究彶展缓慢,其主要原因是尚未有中文数据集为模型提供训练和测试数据。为解决上述问题,本文在汉语否定与不确定语料库上进行了否定焦点的标注工作,初步探索了否定焦点在汉语上的语言现象,并构建了一个包含5,762个样本的数据集。同时,本文还提出了一个基于神经网络模型的基线系统,为后续相关研究提供参照。",
    language = "Chinese",
}
@inproceedings{zhang-etal-2020-mian-xiang,
    title = "面向医学文本处理的医学实体标注规范(Medical Entity Annotation Standard for Medical Text Processing)",
    author = "Zhang, Huan  and
      Zong, Yuan  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Zan, Hongying  and
      Zhang, Kunli",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.52",
    pages = "561--571",
    abstract = "随着智慧医疗的普及,利用自然语言处理技术识别医学信息的需求日益增长。目前,针对医学实体而言,医学共享语料库仍处于空白状态,这对医学文本信息处理各项任务的进展造成了巨大阻力。如何判断不同的医学实体类别?如何界定不同实体间的涵盖范围?这些问题导致缺乏类似通用场景的大规模规范标注的医学文本数据。针对上述问题,该文参考了UMLS中定义的语义类型,提出面向医学文本信息处理的医学实体标注规范,涵盖了疾病、临床表现、医疗程序、医疗设备等9种医学实体,以及基于规范构建医学实体标注语料库。该文综述了标注规范的描述体系、分类原则、混淆处理、语料标注过程以及医学实体自动标注基线实验等相关问题,希望能为医学实体语料库的构建提供可参考的标注规范,以及为医学实体识别提供语料支持。",
    language = "Chinese",
}
@inproceedings{qian-wang-2020-yi,
    title = "汉语块依存语法与树库构建({C}hinese Chunk-Based Dependency Grammar and Treebank construction)",
    author = "Qian, Qingqing  and
      Wang, Chengwen",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.53",
    pages = "572--580",
    abstract = "本研究依据以谓词为核心的块依存语法构建块依存树库,在句内和句间寻找谓词所支配的组块,利用汉语中组块和组块间的依存关系补全缺省部分,明确谓词支配关系。目前共标注2199篇文本,涵盖百科、新闻两个领域,共约187万字语料。本文简述了块依存语法的原则,并对组块及其依存关系进行了定义。将详细介绍标注流程、标注一致率、数据分布等情况。基于现有的树库,本研究发现汉语中有约25{\%}的小句是非自足的,约有88{\%}的核心谓词可支配1{\textasciitilde}3个从属成分。",
    language = "Chinese",
}
@inproceedings{shi-etal-2020-yi,
    title = "汉语学习者依存句法树库构建(Construction of a Treebank of Learner {C}hinese)",
    author = "Shi, Jialu  and
      Luo, Xinyu  and
      Yang, Liner  and
      Xiao, Dan  and
      Hu, Zhengsheng  and
      Wang, Yijun  and
      Yuan, Jiaxin  and
      Jingsi, Yu  and
      Yang, Erhong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.54",
    pages = "581--592",
    abstract = "汉语学习者依存句法树库为非母语者语料提供依存句法分析,可以支持第二语言教学与研究,也对面向第二语言的句法分析、语法改错等相关研究具有重要意义。然而,现有的汉语学习者依存句法树库数量较少,且在标注方面仍存在一些问题。为此,本文改进依存句法标注规范,搭建在线标注平台,并开展汉语学习者依存句法标注。本文重点介绍了数据选取、标注流程等问题,并对标注结果进行质量分析,探索二语偏误对标注质量与句法分析的影响。",
    language = "Chinese",
}
@inproceedings{liu-etal-2020-cdcpp,
    title = "{CDCPP}:跨领域中文标点符号预测({CDCPP}: Cross-Domain {C}hinese Punctuation Prediction)",
    author = "Liu, Pengyuan  and
      Wang, Weikang  and
      Qiu, Likun  and
      Du, Bingjie",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.55",
    pages = "593--603",
    abstract = "标点符号对文本理解起很大作用。但目前,在中文文本特别是在社交媒体及问答领域文本中的标点符号使用存在非常多的错误或缺失的情况,这严重影响对其进行语义分析及机器翻译等各项自然语言处理的效果。当前对标点符号进行预测的相关研究多集中于英文对话的语音转写文本,缺少对社交媒体及问答领域文本进行标点预测的相关研究,也没有这些领域公开的数据集。本文首先提出跨领域中文标点符号预测任务,该任务是要利用标点符号基本规范正确的大规模新闻领域文本,建立标点符号预测模型,然后在标点符号标注不规范的社交媒体及问答领域,进行跨领域标点符号预测。随后构建了新闻、社交媒体及问答三个领域的相应数据集。最后还实现了一个基于BERT的标点符号预测基线模型,并在该数据集上进行了实验与分析。实验结果表明,直接利用新闻领域训练的模型,在社交媒体及问答领域上进行标点符号预测的性能均有所下降,在问答领域下降较小,在微博领域下降较大,超过20{\%},跨领域标点符号预测任务具有一定的挑战性。",
    language = "Chinese",
}
@inproceedings{liu-etal-2020-duo,
    title = "多目标情感分类中文数据集构建及分析研究(Construction and Analysis of {C}hinese Multi-Target Sentiment Classification Dataset)",
    author = "Liu, Pengyuan  and
      Tian, Yongsheng  and
      Du, Chengyu  and
      Qiu, Likun",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.56",
    pages = "604--615",
    abstract = "目标级情感分类任务是要得到句子中特定评价目标的情感倾向。一个评论句中往往存在多个目标,多个目标的情感可能一致,也可能不一致。但在已有针对目标级情感分类的评测数据集中:1)大多数是一个句子一个目标;2)在少数有多个目标的句子中,多个目标情感倾向分布很不均衡,多个目标情感一致的情形占较大优势。数据集本身的缺陷限制了模型针对多个目标进行情感分类的提升空间。针对以上问题,本文构建了一个针对多目标情感分类的中文数据集,人工标注了6339个评价目标,共2071条数据。该数据集:1)评价目标个数分布平衡;2)情感正负极性分布平衡;3)多目标情感倾向分布平衡。随后,本文利用多个目标情感分类的主流模型在该数据集上进行了实验与比较分析。结果表明现有主流模型尚不能对存在多个目标且目标情感倾向性不一致实例中的目标进行很好的分类,尤其是目标的情感倾向为中性时。多目标情感分类任务具有一定的难度与挑战性。",
    language = "Chinese",
}
@inproceedings{wang-etal-2020-ji-yu-self,
    title = "基于Self-Attention的句法感知汉语框架语义角色标注(Syntax-Aware {C}hinese Frame Semantic Role Labeling Based on Self-Attention)",
    author = "Wang, Xiaohui  and
      Li, Ru  and
      Wang, Zhiqiang  and
      Chai, Qinghua  and
      Han, Xiaoqi",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.57",
    pages = "616--623",
    abstract = "框架语义角色标注(Frame Semantic Role Labeling, FSRL)是基于FrameNet标注体系的语义分析任务。语义角色标注通常对句法有很强的依赖性,目前的语义角色标注模型大多基于双向长短时记忆网络Bi-LSTM,虽然可以获取句子中的长距离依赖信息,但无法很好获取句子中的句法信息。因此,引入self-attention机制来捕获句子中每个词的句法信息。实验结果表明,该模型在CFN(Chinese FrameNet,汉语框架网)数据集上的F1达到83.77{\%},提升了近11{\%}。",
    language = "Chinese",
}
@inproceedings{yang-etal-2020-ji,
    title = "基于词语聚类的汉语口语教材自动推送素材研究(Study on Automatic Push Material of Oral {C}hinese Textbook Based on Word Clustering)",
    author = "Yang, Bingbing  and
      Zhao, Huizhou  and
      Wang, Zhimin",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.58",
    pages = "624--633",
    abstract = "新冠肺炎的蔓延使得线上移动教学成为教育发展的必然趋势,本文以适合汉语教材自动推送的口语素材为研究对象,基于10341条生活类口语语料,对词汇的整体特点进行计量分析,在此基础上使用词向量模型及Kmeans算法对全部词语进行聚类,参考词语聚类结果及对口语语料话题和场景的考察,构建了一个包含15个一级话题、102个二级话题及81个交际场景的汉语口语话题-场景素材库。同时对各级话题常用词进行了总结。本文可为教材自动定制的素材库提供资源支持。",
    language = "Chinese",
}
@inproceedings{guo-etal-2020-ji,
    title = "基于半监督学习的中文社交文本事件聚类方法(Semi-supervised Method to Cluster {C}hinese Events on Social Streams)",
    author = "Guo, Hengrui  and
      Wang, Zhongqing  and
      Li, Peifeng  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.59",
    pages = "634--644",
    abstract = "面向社交媒体的事件聚类旨在根据事件特征对短文本聚类。目前,事件聚类模型主要分为无监督模型和有监督模型。无监督模型聚类效果较差,有监督模型依赖大量标注数据。基于此,本文提出了一种半监督事件聚类模型(SemiEC),该模型在小规模标注数据的基础上,利用LSTM表征事件,利用线性模型计算文本相似度,进行增量聚类,利用增量聚类产生的标注数据对模型再训练,结束后对不确定样本再聚类。实验表明,SemiEC的性能相比其他模型均有所提高。",
    language = "Chinese",
}
@inproceedings{zhang-etal-2020-ji-yu-duo,
    title = "基于多粒度语义交互理解网络的幽默等级识别(A Multi-Granularity Semantic Interaction Understanding Network for Humor Level Recognition)",
    author = "Zhang, Jinhui  and
      Zhang, Shaowu  and
      Fan, Xiaochao  and
      Yang, Liang  and
      Lin, Hongfei",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.60",
    pages = "645--655",
    abstract = "幽默在人们日常交流中发挥着重要作用。随着人工智能的快速发展,幽默等级识别成为自然语言处理领域的热点研究问题之一。已有的幽默等级识别研究往往将幽默文本看作一个整体,忽视了幽默文本内部的语义关系。本文将幽默等级识别视为自然语言推理任务,将幽默文本划分为{``}铺垫{''}和{``}笑点{''}两个部分,分别对其语义和语义关系进行建模,提出了一种多粒度语义交互理解网络,从单词和子句两个粒度捕获幽默文本中语义的关联和交互。本文在Reddit公开幽默数据集上进行了实验,相比之前最优结果,模型在语料上的准确率提升了1.3{\%}。实验表明,引入幽默内部的语义关系信息可以提高模型幽默识别的性能,而本文提出的模型也可以很好地建模这种语义关系。",
    language = "Chinese",
}
@inproceedings{naren-xu-2020-wen,
    title = "文本情感分析中的重叠现象研究(A Study on Repetition in Text-based Sentiment Analysis)",
    author = "Naren, Tuya  and
      Xu, Xiaoyin",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.61",
    pages = "656--666",
    abstract = "汉语中的重叠现象丰富,文本情感分析任务中应该密切关注语篇空间内的重叠现象及其交互状态。本文就重叠在文本中的样态、特点及情感标记功能进行了理论探讨;重点就构词性重叠、结构性重叠的表现形式及情感语义进行了分析;据此研究的基础上,本文就重叠现象在文本情感分析上的实际应用从几个方面进行了讨论。",
    language = "Chinese",
}
@inproceedings{hu-etal-2020-ji,
    title = "基于{B}i{LSTM}-{CRF}的社会突发事件研判方法(Social Emergency Event Judgement based on {B}i{LSTM}-{CRF})",
    author = "Hu, Huijun  and
      Wang, Cong  and
      Dai, Jianhua  and
      Liu, Maofu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.62",
    pages = "667--675",
    abstract = "社会突发事件的分类和等级研判作为应急处置中的一环,其重要性不言而喻。然而,目前研究多数采用人工或规则的方法识别证据进行研判,由于社会突发事件的构成的复杂性和语言描述的灵活性,这对于研判证据识别有很大局限性。本文参考{``}事件抽取{''}思想,事件类型和研判证据作为事件中元素,以BiLSTM-CRF方法细粒度的识别,并将二者结合,分类结果作为等级研判的输入,识别出研判证据。最终将识别结果结合注意力机制进行等级研判,通过对研判证据的精准识别从而来增强等级研判的准确性。实验表明,相比人工或规则识别研判证据,本文提出的方法有着更好的鲁棒性,社会突发事件研判时也达到了较好的效果。 关键词:事件分类 ;研判证据识别 ;等级研判 ;BiLSTM-CRF",
    language = "Chinese",
}
@inproceedings{zhu-etal-2020-jie,
    title = "结合金融领域情感词典和注意力机制的细粒度情感分析(Attention-based Recurrent Network Combined with Financial Lexicon for Aspect-level Sentiment Classification)",
    author = "Zhu, Qinglin  and
      Liang, Bin  and
      Han, Liuyu  and
      Chen, Yi  and
      Xu, Ruifeng  and
      Mao, Ruibin",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.63",
    pages = "676--687",
    abstract = "针对在金融领域实体级情感分析任务中,往往缺乏足够的标注语料,以及通用的情感分析模型难以有效处理金融文本等问题。本文构建一个百万级别的金融领域实体情感分析语料库,并标注五千余个金融领域情感词作为金融领域情感词典。同时,基于该金融领域数据集,提出一种结合金融领域情感词典和注意力机制的金融文本细粒度情感分析模型。该模型使用两个LSTM网络分别提取词级别的语义信息和基于情感词典分类后的词类级别信息,能有效获取金融领域词语的特征信息。此外,为了让文本中金融领域情感词获得更多关注,提出一种基于金融领域情感词典的注意力机制来为不同实体获取重要的情感信息。最终在构建的金融领域实体级语料库上进行实验,取得了比对比模型更好的效果。",
    language = "Chinese",
}
@inproceedings{feng-etal-2020-ji,
    title = "基于层次注意力机制和门机制的属性级别情感分析(Aspect-level Sentiment Analysis Based on Hierarchical Attention and Gate Networks)",
    author = "Feng, Chao  and
      Li, Haihui  and
      Zhao, Hongya  and
      Xue, Yun  and
      Tang, Jingyao",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.64",
    pages = "688--697",
    abstract = "近年来,作为细粒度的属性级别情感分析在商业界和学术界受到越来越多的关注,其目的在于识别一个句子中多个属性词所对应的情感极性。目前,在解决属性级别情感分析问题的绝大多数工作都集中在注意力机制的设计上,以此突出上下文和属性词中不同词对于属性级别情感分析的贡献,同时使上下文和属性词之间相互关联。本文提出使用层次注意力机制和门机制处理属性级别情感分析任务,在得到属性词的隐藏状态之后,通过注意力机制得到属性词新的表示,然后利用属性词新的表示和注意力机制进一步得到上下文新的表示,层次注意力机制的设计使得上下文和属性词的表达更加准确;同时通过门机制选择对属性词而言上下文中有用的信息,以此丰富上下文的表达,在SemEval 2014 Task4和Twitter数据集上的实验结果表明本文提出模型的有效性。",
    language = "Chinese",
}
@inproceedings{luo-etal-2020-ji,
    title = "基于循环交互注意力网络的问答立场分析(A Recurrent Interactive Attention Network for Answer Stance Analysis)",
    author = "Luo, Wangda  and
      Liu, Yuhan  and
      Liang, Bin  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.65",
    pages = "698--706",
    abstract = "针对问答立场任务中,现有方法难以提取问答文本间的依赖关系问题,本文提出一种基于循环交互注意力(Recurrent Interactive Attention, RIA)网络的问答立场分析方法。该方法通过模仿人类阅读理解时的思维方式,基于交互注意力机制和循环迭代方法,有效地从问题和答案的相互联系中挖掘问答文本的立场信息。此外,该方法将问题进行陈述化表示,有效地解决疑问句表述下问题文本无法明确表达自身立场的问题。实验结果表明,本文方法取得了比现有模型方法更好的效果,同时证明该方法能有效拟合问答立场分析任务中的问答对依赖关系。",
    language = "Chinese",
}
@inproceedings{liang-etal-2020-xin,
    title = "新型冠状病毒肺炎相关的推特主题与情感研究(Exploring {COVID}-19-related {T}witter Topic Dynamics across Countries)",
    author = "Liang, Shuailong  and
      Wong, Derek F.  and
      Zhang, Yue",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.66",
    pages = "707--718",
    abstract = "我们基于从2020年1月22日至2020年4月30日在推特社交平台上抓取的不同国家和地区发布的50万条推文,研究了有关 2019新型冠状病毒肺炎相关的主题和人们的观点,发现了不同国家之间推特用户的普遍关切和看法之间存在着异同,并且对不同议题的情感态度也有所不同。我们发现大部分推文中包含了强烈的情感,其中表达爱与支持的推文比较普遍。总体来看,人们的情感随着时间的推移逐渐正向增强。",
    language = "Chinese",
}
@inproceedings{wang-etal-2020-rong,
    title = "融入多尺度特征注意力的胶囊神经网络及其在文本分类中的应用(Incorporating Multi-scale Feature Attention into Capsule Network and its Application in Text Classification)",
    author = "Wang, Chaofan  and
      Ju, Shenggen  and
      Sun, Jieping  and
      Chen, Run",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.67",
    pages = "719--730",
    abstract = "近些年来,胶囊神经网络(Capsnets)由于拥有强大的文本特征学习能力已被应用到了文本分类任务当中。目前的研究工作大部分都将提取到的文本多元语法特征视为同等重要,而忽略了单词所对应各个多元语法特征的重要程度应该是由具体上下文决定的这一问题,这将直接影响到模型对整个文本的语义理解。针对上述问题,本文提出了多尺度特征部分连接胶囊网络(MulPart-Capsnets)。该方法将多尺度特征注意力融入到Capsnets中,多尺度特征注意力能够自动选择不同尺度的多元语法特征,通过对其进行加权求和,就能为每个单词精确捕捉到丰富的多元语法特征。同时,为了减少子胶囊与父胶囊之间的冗余信息传递,本文同时也对路由算法进行了改进。 本文提出的算法在文本分类任务上针对七个著名的数据集进行了有效性验证,和现有的研究工作相比,性能显著提高,说明了本文的算法能够捕获文本中更丰富的多元语法特征,具有更加强大的文本特征学习能力。",
    language = "Chinese",
}
@inproceedings{tang-yu-2020-jie,
    title = "结合深度学习和语言难度特征的句子可读性计算方法(The method of calculating sentence readability combined with deep learning and language difficulty characteristics)",
    author = "Tang, Yuling  and
      Yu, Dong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.68",
    pages = "731--742",
    abstract = "本文提出了可读性语料库构建的改进方法,基于该方法,构建了规模更大的汉语句子可读性语料库。该语料库在句子绝对难度评估任务上的准确率达到0.7869,相对前人工作提升了0.15以上,证明了改进方法的有效性。将深度学习方法应用于汉语可读性评估,探究了不同深度学习方法自动捕获难度特征的能力,并进仛步探究了向深度学习特征中融入不同层面的语难度特征对模型整体性能的影响。实验结果显示,不同深度学习模型的难度特征捕获能力不尽相同,语言难度特征可以不同程度地提高深度学习模型的难度表征能力。",
    language = "Chinese",
}
@inproceedings{liu-etal-2020-ji-yu,
    title = "基于预训练语言模型的案件要素识别方法(A Method for Case Factor Recognition Based on Pre-trained Language Models)",
    author = "Liu, Haishun  and
      Wang, Lei  and
      Chen, Yanguang  and
      Zhang, Shuchen  and
      Sun, Yuanyuan  and
      Lin, Hongfei",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.69",
    pages = "743--753",
    abstract = "案件要素识别指将案件描述中重要事实描述自动抽取出来,并根据领域专家设计的要素体系进行分类,是智慧司法领域的重要研究内容。基于传统神经网络的文本编码难以提取深层次特征,基于阈值的多标签分类难以捕获标签间依赖关系,因此本文提出了基于预训练语言模型的多标签文本分类模型。该模型采用以Layer-attentive策略进行特征融合的语言模型作为编码器,使用基于LSTM的序列生成模型作为解码器。在{``}CAIL2019{''}数据集上进行实验,该方法比基于循环神经网络的算法在F1值上最高可提升7.6{\%},在相同超参数设置下比基础语言模型(BERT)提升约3.2{\%}。",
    language = "Chinese",
}
@inproceedings{liang-etal-2020-ji,
    title = "基于拼音约束联合学习的汉语语音识别({C}hinese Speech Recognition Based on {P}inyin Constraint Joint Learning)",
    author = "Liang, Renfeng  and
      Yu, Zhengtao  and
      Gao, Shengxiang  and
      Huang, Yuxin  and
      Guo, Junjun  and
      Xu, Shuli",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.70",
    pages = "754--760",
    abstract = "当前的语音识别模型在英语、法语等表音文字中已经取得很好的效果。然而,汉语是 一种典型的表意文字,汉字与语音没有直接的对应关系,但拼音作为汉字读音的标注 符号,与汉字存在相互转换的内在联系。因此,在汉语语音识别中利用拼音作为解码 约束,引入一种更接近语音的归纳偏置。基于多任务学习框架,提出一种基于拼音约 束联合学习的汉语语音识别方法,以端到端的汉字语音识别为主任务,以拼音语音识 别为辅助任务,通过共享编码器,同时利用汉字与拼音识别结果作为监督信号,增强 编码器对汉语语音的表达能力。实验结果表明,相比基线模型,提出方法取得更优的 识别效果,词错误率WER降低了2.24个百分点",
    language = "Chinese",
}
@inproceedings{xie-etal-2020-ji,
    title = "基于数据增强和多任务特征学习的中文语法错误检测方法({C}hinese Grammar Error Detection based on Data Enhancement and Multi-task Feature Learning)",
    author = "Xie, Haihua  and
      Chen, Zhiyou  and
      Cheng, Jing  and
      Lyu, Xiaoqing  and
      Tang, Zhi",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.71",
    pages = "761--770",
    abstract = "由于中文语法的复杂性,中文语法错误检测(CGED)的难度较大,而训练语料和相关研究的缺乏,使得CGED的效果还远达不到能够实用的程度。本文提出一种CGED模型,采用数据增强、预训练语言模型和基于语言学特征多任务学习的方式,弥补训练语料稀缺的不足。数据增强能够有效地扩充训练集,预训练语言模型蕴含丰富的语义信息有助于语法分析,基于语言学特征多任务学习对语言模型进行微调则可以使语言模型学习到跟语法错误检测相关的语言学特征。本文提出的方法在NLPTEA的CGED数据集进行测试,取得了优于其他模型的结果。",
    language = "Chinese",
}
@inproceedings{zhao-etal-2020-ji,
    title = "基于有向异构图的发票明细税收分类方法(Tax Classification of Invoice Details Based on Directed Heterogeneous Graph)",
    author = "Zhao, Peiyao  and
      Zheng, Qinghua  and
      Dong, Bo  and
      Ruan, Jianfei  and
      Luo, Minnan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.72",
    pages = "771--782",
    abstract = "税收是国家赖以生存的物质基础。为加快税收现代化,方便纳税人便捷、规范开具增值税发票,国税总局规定纳税人在税控系统开票前选择发票明细对应的税收分类才可正常开具发票。提高税收分类的准确度,是构建税收风险指标和分析纳税人行为特征的重要基础。基于此,本文提出了一种基于有向异构图的短文本分类模型(Heterogeneous Directed Graph Attenton Network,HDGAT),利用发票明细间的有向信息建模,引入外部知识,显著地提高了发票明细的税收分类准确度。",
    language = "Chinese",
}
@inproceedings{mao-etal-2020-ban,
    title = "半监督跨领域语义依存分析技术研究(Semi-supervised Domain Adaptation for Semantic Dependency Parsing)",
    author = "Mao, Dazhan  and
      Li, Huayong  and
      Shao, Yanqiu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.73",
    pages = "783--794",
    abstract = "近年来,尽管深度学习给语义依存分析带来了长足的进步,但由于语义依存分析数据标注代价非常高昂,并且在单领域上性能较好的依存分析器迁移到其他领域时,其性能会大幅度下降。因此为了使其走向实用,就必须解决领域适应问题。本文提出一个新的基于对抗学习的领域适应依存分析模型,我们提出了基于对抗学习的共享双编码器结构,并引入领域私有辅助任务和正交约束,同时也探究了多种预训练模型在跨领域依存分析任务上的效果和性能。",
    language = "Chinese",
}
@inproceedings{li-etal-2020-yi,
    title = "汉英篇章衔接对齐语料构建研究(Research on the Construction of {C}hinese-{E}nglish Discourse Cohesion Alignment Corpus)",
    author = "Li, Yancui  and
      Feng, Jike  and
      Lai, Chunxiao  and
      Feng, Hongyu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.74",
    pages = "795--806",
    abstract = "篇章衔接性分析是理解篇章的基础,汉语和英语在指代、连接和省略等主要衔接方式上存在差异。本文旨在创建汉英篇章衔接对齐语料库,给出包括子句、连接词、指代和省略的汉英篇章衔接对齐标注策略,建立包含相应信息的对齐信息的语料库资源,最后对标注语料进行评估并讨论了标注中的难点问题及解决方法。对语料库标注质量评估及简单实验结果表明,本文研究语料标注策略方法切实可行,所标注的资源一致性满足实际需要。",
    language = "Chinese",
}
@inproceedings{zhang-zhang-2020-cross,
    title = "Cross-Lingual Dependency Parsing via Self-Training",
    author = "Zhang, Meishan  and
      Zhang, Yue",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.75",
    pages = "807--819",
    abstract = "Recent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings. Thus self-training, which is effective for these settings, could be possibly beneficial to cross-lingual as well. This paper presents the first comprehensive study for self-training in cross-lingual dependency parsing. Three instance selection strategies are investigated, where two of which are based on the baseline dependency parsing model, and the third one adopts an auxiliary cross-lingual POS tagging model as evidence. We conduct experiments on the universal dependencies for eleven languages. Results show that self-training can boost the dependency parsing performances on the target languages. In addition, the POS tagger assistant instance selection can achieve further improvements consistently. Detailed analysis is conducted to examine the potentiality of self-training in-depth.",
    language = "English",
}
@inproceedings{li-etal-2020-joint,
    title = "A Joint Model for Graph-based {C}hinese Dependency Parsing",
    author = "Li, Xingchen  and
      Liu, Mingtong  and
      Zhang, Yujie  and
      Xu, Jinan  and
      Chen, Yufeng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.76",
    pages = "820--830",
    abstract = "In Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where the transition-based model with feature templates maintains the best performance. Recently, the graph-based joint model (Yan et al., 2019) on word segmentation and dependency parsing has achieved better performance, demonstrating the advantages of the graph-based models. However, this work can not provide POS information for downstream tasks, and the POS tagging task was proved to be helpful to the dependency parsing according to the research of the transition-based model. Therefore, we propose a graph-based joint model for Chinese word segmentation, POS tagging and dependency parsing. We designed a charater-level POS tagging task, and then train it jointly with the model of Yan et al. (2019). We adopt two methods of joint POS tagging task, one is by sharing parameters, the other is by using tag attention mechanism, which enables the three tasks to better share intermediate information and improve each other{'}s performance. The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0.38{\%} on dependency parsing than the model of Yan et al. (2019). Compared with the best transition-based joint model, our model improved by 0.18{\%}, 0.35{\%} and 5.99{\%} respectively in terms of word segmentation, POS tagging and dependency parsing.",
    language = "English",
}
@inproceedings{zhang-etal-2020-semantic,
    title = "Semantic-aware {C}hinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser",
    author = "Zhang, Lanqiu  and
      Shen, Zizhuo  and
      Shao, Yanqiu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.77",
    pages = "831--841",
    abstract = "Deep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well integrated linguistic information into the deep learningbased Chinese zero pronoun resolution model.This paper adopts the idea based on the pre-trained model, and integrates the semantic representations in the pre-trained Chinese semantic dependency graph parser into the Chinese zero pronoun resolution model. The experimental results on OntoNotes-5.0 dataset show that our proposed Chinese zero pronoun resolution model with pretrained Chinese semantic dependency parser improves the F-score by 0.4{\%} compared with our baseline model, and obtains better results than other deep learning-based Chinese zero pronoun resolution models. In addition, we integrate the BERT representations into our model so that the performance of our model was improved by 0.7{\%} compared with our baseline model.",
    language = "English",
}
@inproceedings{wang-etal-2020-improving,
    title = "Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning",
    author = "Wang, Yanfei  and
      Chen, Yangdong  and
      Zhang, Yuejie",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.78",
    pages = "842--852",
    abstract = "Neural network based models have achieved impressive results on the sentence classification task. However, most of previous work focuses on designing more sophisticated network or effective learning paradigms on monolingual data, which often suffers from insufficient discriminative knowledge for classification. In this paper, we investigate to improve sentence classification by multilingual data augmentation and consensus learning. Comparing to previous methods, our model can make use of multilingual data generated by machine translation and mine their language-share and language-specific knowledge for better representation and classification. We evaluate our model using English (i.e., source language) and Chinese (i.e., target language) data on several sentence classification tasks. Very positive classification performance can be achieved by our proposed model.",
    language = "English",
}
@inproceedings{hua-etal-2020-attention,
    title = "Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding",
    author = "Hua, Yuan  and
      Huang, Zheng  and
      Guo, Jie  and
      Qiu, Weidong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.79",
    pages = "853--862",
    abstract = "Information extraction from documents such as receipts or invoices is a fundamental and crucial step for office automation. Many approaches focus on extracting entities and relationships from plain texts, however, when it comes to document images, such demand becomes quite challenging since visual and layout information are also of great significance to help tackle this problem. In this work, we propose the attention-based graph neural network to combine textual and visual information from document images.Moreover, the global node is introduced in our graph construction algorithm which is used as a virtual hub to collect the information from all the nodes and edges to help improve the performance. Extensive experiments on real-world datasets show that our method outperforms baseline methods by significant margins.",
    language = "English",
}
@inproceedings{jing-etal-2020-combining,
    title = "Combining Impression Feature Representation for Multi-turn Conversational Question Answering",
    author = "Jing, Shaoling  and
      Hong, Shibo  and
      Zhao, Dongyan  and
      Xie, Haihua  and
      Tang, Zhi",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.80",
    pages = "863--873",
    abstract = "Multi-turn conversational Question Answering (ConvQA) is a practical task that requires the understanding of conversation history, such as previous QA pairs, the passage context, and current question. It can be applied to a variety of scenarios with human-machine dialogue. The major challenge of this task is to require the model to consider the relevant conversation history while understanding the passage. Existing methods usually simply prepend the history to the current question, or use the complicated mechanism to model the history. This article proposes an impression feature, which use the word-level inter attention mechanism to learn multi-oriented information from conversation history to the input sequence, including attention from history tokens to each token of the input sequence, and history turn inter attention from different history turns to each token of the input sequence, and self-attention within input sequence, where the input sequence contains a current question and a passage. Then a feature selection method is designed to enhance the useful history turns of conversation and weaken the unnecessary information. Finally, we demonstrate the effectiveness of the proposed method on the QuAC dataset, analyze the impact of different feature selection methods, and verify the validity and reliability of the proposed features through visualization and human evaluation.",
    language = "English",
}
@inproceedings{li-etal-2020-chinese,
    title = "{C}hinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches",
    author = "Li, Lin  and
      van Deemter, Kees  and
      Paperno, Denis",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.81",
    pages = "874--880",
    abstract = "This paper presents our work in long and short form choice, a significant question of lexical choice, which plays an important role in many Natural Language Understanding tasks. Long and short form sharing at least one identical word meaning but with different number of syllables is a highly frequent linguistic phenomenon in Chinese like \textit{老虎-虎(laohu-hu, tiger)}",
    language = "English",
}
@inproceedings{guan-etal-2020-refining,
    title = "Refining Data for Text Generation",
    author = "Guan, Wenyu  and
      Liu, Qianying  and
      Li, Tianyi  and
      Li, Sujian",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.82",
    pages = "881--891",
    abstract = "Recent work on data-to-text generation has made progress under the neural encoder-decoder architectures. However, the data input size is often enormous, while not all data records are important for text generation and inappropriate input may bring noise into the final output. To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data. Here we propose a learning to rank model to rank the importance of each record which is supervised by a relation extractor. With the noise-reduced data as input, we implement a text generator which sequentially models the input data records and emits a summary. Experiments on the ROTOWIRE dataset verifies the effectiveness of our proposed method in both performance and efficiency.",
    language = "English",
}
@inproceedings{wang-etal-2020-plan,
    title = "Plan-{CVAE}: A Planning-based Conditional Variational Autoencoder for Story Generation",
    author = "Wang, Lin  and
      Li, Juntao  and
      Yan, Rui  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.83",
    pages = "892--902",
    abstract = "Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings. Although many approaches have been proposed and obvious progress has been made on this task, there is still a large room for improvement, especially for improving thematic consistency and wording diversity. To mitigate the gap between generated stories and those written by human writers, in this paper, we propose a planning-based conditional variational autoencoder, namely Plan-CVAE, which first plans a keyword sequence and then generates a story based on the keyword sequence. In our method, the keywords planning strategy is used to improve thematic consistency while the CVAE module allows enhancing wording diversity. Experimental results on a benchmark dataset confirm that our proposed method can generate stories with both thematic consistency and wording novelty, and outperforms state-of-the-art methods on both automatic metrics and human evaluations.",
    language = "English",
}
@inproceedings{zuo-etal-2020-towards,
    title = "Towards Causal Explanation Detection with Pyramid Salient-Aware Network",
    author = "Zuo, Xinyu  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.84",
    pages = "903--914",
    abstract = "Causal explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In this paper, we focus on Causal Explanation Detection, an important subtask of causal explanation analysis, which determines whether a causal explanation exists in one message. We design a Pyramid Salient-Aware Network (PSAN) to detect causal explanations on messages. PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8{\%} F1 value on the Causal Explanation Detection task.",
    language = "English",
}
@inproceedings{wu-etal-2020-named,
    title = "Named Entity Recognition with Context-Aware Dictionary Knowledge",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Qi, Tao  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.85",
    pages = "915--926",
    abstract = "Named entity recognition (NER) is an important task in the natural language processing field. Existing NER methods heavily rely on labeled data for model training, and their performance on rare entities is usually unsatisfactory. Entity dictionaries can cover many entities including both popular ones and rare ones, and are useful for NER. However, many entity names are context-dependent and it is not optimal to directly apply dictionaries without considering the context. In this paper, we propose a neural NER approach which can exploit dictionary knowledge with contextual information. We propose to learn context-aware dictionary knowledge by modeling the interactions between the entities in dictionaries and their contexts via context-dictionary attention. In addition, we propose an auxiliary term classification task to predict the types of the matched entity names, and jointly train it with the NER model to fuse both contexts and dictionary knowledge into NER. Extensive experiments on the CoNLL-2003 benchmark dataset validate the effectiveness of our approach in exploiting entity dictionaries to improve the performance of various NER models.",
    language = "English",
}
@inproceedings{cao-etal-2020-chinese,
    title = "{C}hinese Named Entity Recognition via Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism",
    author = "Cao, Pengfei  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.86",
    pages = "927--938",
    abstract = "Named entity recognition (NER) aims to identify text spans that mention named entities and classify them into pre-defined categories. For Chinese NER task, most of the existing methods are character-based sequence labeling models and achieve great success. However, these methods usually ignore lexical knowledge, which leads to false prediction of entity boundaries. Moreover, these methods have difficulties in capturing tag dependencies. In this paper, we propose an Adaptive Multi-pass Memory Network with Hierarchical Tagging Mechanism (AMMNHT) to address all above problems. Specifically, to reduce the errors of predicting entity boundaries, we propose an adaptive multi-pass memory network to exploit lexical knowledge. In addition, we propose a hierarchical tagging layer to learn tag dependencies. Experimental results on three widely used Chinese NER datasets demonstrate that our proposed model significantly outperforms other state-of-the-art methods.",
    language = "English",
}
@inproceedings{xiao-etal-2020-practice,
    title = "A Practice of Tourism Knowledge Graph Construction based on Heterogeneous Information",
    author = "Xiao, Dinghe  and
      Wang, Nannan  and
      Yu, Jiangang  and
      Zhang, Chunhong  and
      Wu, Jiaqi",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.87",
    pages = "939--949",
    abstract = "The increasing amount of semi-structured and unstructured data on tourism websites brings a need for information extraction (IE) so as to construct a Tourism-domain Knowledge Graph (TKG), which is helpful to manage tourism information and develop downstream applications such as tourism search engine, recommendation and Q {\&} A. However, the existing TKG is deficient, and there are few open methods to promote the construction and widespread application of TKG. In this paper, we present a systematic framework to build a TKG for Hainan, collecting data from popular tourism websites and structuring it into triples. The data is multi-source and heterogeneous, which raises a great challenge for processing it. So we develop two pipelines of processing methods for semi-structured data and unstructured data respectively. We refer to tourism InfoBox for semi-structured knowledge extraction and leverage deep learning algorithms to extract entities and relations from unstructured travel notes, which are colloquial and high-noise, and then we fuse the extracted knowledge from two sources. Finally, a TKG with 13 entity types and 46 relation types is established, which totally contains 34,079 entities and 441,371 triples. The systematic procedure proposed by this paper can construct a TKG from tourism websites, which can further applied to many scenarios and provide detailed reference for the construction of other domain-specific knowledge graphs.",
    language = "English",
}
@inproceedings{xu-etal-2020-novel,
    title = "A Novel Joint Framework for Multiple {C}hinese Events Extraction",
    author = "Xu, Nuo  and
      Xie, Haihua  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.88",
    pages = "950--961",
    abstract = "Event extraction is an essential yet challenging task in information extraction. Previous approaches have paid little attention to the problem of roles overlap which is a common phenomenon in practice. To solve this problem, this paper defines event relation triple to explicitly represent relations among triggers, arguments and roles which are incorporated into the model to learn their inter-dependencies. The task of argument extraction is converted to event relation triple extraction. A novel joint framework for multiple Chinese event extraction is proposed which jointly performs predictions for event triggers and arguments based on shared feature representations from pre-trained language model. Experimental comparison with state-of-the-art baselines on ACE 2005 dataset shows the superiority of the proposed method in both trigger classification and argument classification.",
    language = "English",
}
@inproceedings{zhao-etal-2020-entity,
    title = "Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction",
    author = "Zhao, Tianyang  and
      Yan, Zhao  and
      Cao, Yunbo  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.89",
    pages = "962--973",
    abstract = "Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps. Among existing studies, the Multi-Head Selection (MHS) framework is efficient in extracting entities and relations simultaneously. However, the method is weak for its limited performance. In this paper, we propose several effective insights to address this problem. First, we propose an entity-specific Relative Position Representation (eRPR) to allow the model to fully leverage the distance information between entities and context tokens. Second, we introduce an auxiliary Global Relation Classification (GRC) to enhance the learning of local contextual features. Moreover, we improve the semantic representation by adopting a pre-trained language model BERT as the feature encoder. Finally, these new keypoints are closely integrated with the multi-head selection framework and optimized jointly. Extensive experiments on two benchmark datasets demonstrate that our approach overwhelmingly outperforms previous works in terms of all evaluation metrics, achieving significant improvements for relation F1 by +2.40{\%} on CoNLL04 and +1.90{\%} on ACE05, respectively.",
    language = "English",
}
@inproceedings{lu-etal-2020-mixed,
    title = "A Mixed Learning Objective for Neural Machine Translation",
    author = "Lu, Wenjie  and
      Zhou, Leiying  and
      Liu, Gongshen  and
      Zhang, Quanhai",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.90",
    pages = "974--983",
    abstract = "Evaluation discrepancy and overcorrection phenomenon are two common problems in neural machine translation (NMT). NMT models are generally trained with word-level learning objective, but evaluated by sentence-level metrics. Moreover, the cross-entropy loss function discourages model to generate synonymous predictions and overcorrect them to ground truth words. To address these two drawbacks, we adopt multi-task learning and propose a mixed learning objective (MLO) which combines the strength of word-level and sentence-level evaluation without modifying model structure. At word-level, it calculates semantic similarity between predicted and ground truth words. At sentence-level, it computes probabilistic n-gram matching scores of generated translations. We also combine a loss-sensitive scheduled sampling decoding strategy with MLO to explore its extensibility. Experimental results on IWSLT 2016 German-English and WMT 2019 English-Chinese datasets demonstrate that our methodology can significantly promote translation quality. The ablation study shows that both word-level and sentence-level learning objectives can improve BLEU scores. Furthermore, MLO is consistent with state-of-the-art scheduled sampling methods and can achieve further promotion.",
    language = "English",
}
@inproceedings{sun-etal-2020-multi,
    title = "Multi-Reward based Reinforcement Learning for Neural Machine Translation",
    author = "Sun, Shuo  and
      Hou, Hongxu  and
      Wu, Nier  and
      Guo, Ziyue  and
      Zhang, Chaowei",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.91",
    pages = "984--993",
    abstract = "Reinforcement learning (RL) has made remarkable progress in neural machine translation (NMT). However, it exists the problems with uneven sampling distribution, sparse rewards and high variance in training phase. Therefore, we propose a multi-reward reinforcement learning training strategy to decouple action selection and value estimation. Meanwhile, our method combines with language model rewards to jointly optimize model parameters. In addition, we add Gumbel noise in sampling to obtain more effective semantic information. To verify the robustness of our method, we not only conducted experiments on large corpora, but also performed on low-resource languages. Experimental results show that our work is superior to the baselines in WMT14 English-German, LDC2014 Chinese-English and CWMT2018 Mongolian-Chinese tasks, which fully certificates the effectiveness of our method.",
    language = "English",
}
@inproceedings{li-etal-2020-low,
    title = "Low-Resource Text Classification via Cross-lingual Language Model Fine-tuning",
    author = "Li, Xiuhong  and
      Li, Zhe  and
      Sheng, Jiabao  and
      Slamu, Wushour",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.92",
    pages = "994--1005",
    abstract = "Text classification tends to be difficult when data are inadequate considering the amount of manually labeled text corpora. For low-resource agglutinative languages including Uyghur, Kazakh, and Kyrgyz (UKK languages), in which words are manufactured via stems concatenated with several suffixes and stems are used as the representation of text content, this feature allows infinite derivatives vocabulary that leads to high uncertainty of writing forms and huge redundant features. There are major challenges of low-resource agglutinative text classification the lack of labeled data in a target domain and morphologic diversity of derivations in language structures. It is an effective solution which fine-tuning a pre-trained language model to provide meaningful and favorable-to-use feature extractors for downstream text classification tasks. To this end, we propose a low-resource agglutinative language model fine-tuning AgglutiFiT, specifically, we build a low-noise fine-tuning dataset by morphological analysis and stem extraction, then fine-tune the cross-lingual pre-training model on this dataset. Moreover, we propose an attention-based fine-tuning strategy that better selects relevant semantic and syntactic information from the pre-trained language model and uses those features on downstream text classification tasks. We evaluate our methods on nine Uyghur, Kazakh, and Kyrgyz classification datasets, where they have significantly better performance compared with several strong baselines.",
    language = "English",
}
@inproceedings{azmat-etal-2020-constructing,
    title = "Constructing {U}yghur Name Entity Recognition System using Neural Machine Translation Tag Projection",
    author = "Azmat, Anwar  and
      Xiao, Li  and
      Yating, Yang  and
      Rui, Dong  and
      Turghun, Osman",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.93",
    pages = "1006--1016",
    abstract = "Although named entity recognition achieved great success by introducing the neural networks, it is challenging to apply these models to low resource languages including Uyghur while it depends on a large amount of annotated training data. Constructing a well-annotated named entity corpus manually is very time-consuming and labor-intensive. Most existing methods based on the parallel corpus combined with the word alignment tools. However, word alignment methods introduce alignment errors inevitably. In this paper, we address this problem by a named entity tag transfer method based on the common neural machine translation. The proposed method marks the entity boundaries in Chinese sentence and translates the sentences to Uyghur by neural machine translation system, hope that neural machine translation will align the source and target entity by the self-attention mechanism. The experimental results show that the Uyghur named entity recognition system trained by the constructed corpus achieve good performance on the test set, with 73.80{\%} F1 score(3.79{\%} improvement by baseline)",
    language = "English",
}
@inproceedings{feiyang-etal-2020-recognition,
    title = "Recognition Method of Important Words in {K}orean Text based on Reinforcement Learning",
    author = "Feiyang, Yang  and
      Yahui, Zhao  and
      Rongyi, Cui",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.94",
    pages = "1017--1025",
    abstract = "The manual labeling work for constructing the Korean corpus is too time-consuming and laborious. It is difficult for low-minority languages to integrate resources. As a result, the research progress of Korean language information processing is slow. From the perspective of representation learning, reinforcement learning was combined with traditional deep learning methods. Based on the Korean text classification effect as a benchmark, and studied how to extract important Korean words in sentences. A structured model Information Distilled of Korean (IDK) was proposed. The model recognizes the words in Korean sentences and retains important words and deletes non-important words. Thereby transforming the reconstruction of the sentence into a sequential decision problem. So you can introduce the Policy Gradient method in reinforcement learning to solve the conversion problem. The results show that the model can identify the important words in Korean instead of manual annotation for representation learning. Furthermore, compared with traditional text classification methods, the model also improves the effect of Korean text classification.",
    language = "English",
}
@inproceedings{wang-etal-2020-mongolian,
    title = "{M}ongolian Questions Classification Based on Mulit-Head Attention",
    author = "Wang, Guangyi  and
      Bao, Feilong  and
      Wang, Weihua",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.95",
    pages = "1026--1034",
    abstract = "Question classification is a crucial subtask in question answering system. Mongolian is a kind of few resource language. It lacks public labeled corpus. And the complex morphological structure of Mongolian vocabulary makes the data-sparse problem. This paper proposes a classification model, which combines the Bi-LSTM model with the Multi-Head Attention mechanism. The Multi-Head Attention mechanism extracts relevant information from different dimensions and representation subspace. According to the characteristics of Mongolian word-formation, this paper introduces Mongolian morphemes representation in the embedding layer. Morpheme vector focuses on the semantics of the Mongolian word. In this paper, character vector and morpheme vector are concatenated to get word vector, which sends to the Bi-LSTM getting context representation. Finally, the Multi-Head Attention obtains global information for classification. The model experimented on the Mongolian corpus. Experimental results show that our proposed model significantly outperforms baseline systems.",
    language = "English",
}
@inproceedings{ge-etal-2020-annotation,
    title = "The Annotation Scheme of {E}nglish-{C}hinese Clause Alignment Corpus",
    author = "Ge, Shili  and
      Lin, Xiaopin  and
      Song, Rou",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.96",
    pages = "1035--1044",
    abstract = "A clause complex consists of clauses, which are connected by component sharing relations and logic-semantic relations. Hence, clause-complex level structural transformations in translation are concerned with the expression adjustment of these two types of relations. In this paper, a formal scheme for tagging structural transformations in English-Chinese translation is designed. The annotation scheme include 3 steps operated on two grammatical levels: parsing an English clause complex into constructs and assembling construct translations on the clause complex level; translating constructs independently on the clause level. The assembling step involves 2 operations: performing operation functions and inserting Chinese words. The corpus annotation shows that it is feasible to divide structural transformations in English-Chinese translation into 2 levels. The corpus, which unfolds formally the operations of clause-complex level structural transformations, would help to improve the end-to-end translation of complicated sentences.",
    language = "English",
}
@inproceedings{tang-shen-2020-categorizing,
    title = "Categorizing Offensive Language in Social Networks: A {C}hinese Corpus, Systems and an Explainable Tool",
    author = "Tang, Xiangru  and
      Shen, Xianjun",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.97",
    pages = "1045--1056",
    abstract = "Recently, more and more data have been generated in the online world, filled with offensive language such as threats, swear words or straightforward insults. It is disgraceful for a progressive society, and then the question arises on how language resources and technologies can cope with this challenge. However, previous work only analyzes the problem as a whole but fails to detect particular types of offensive content in a more fine-grained way, mainly because of the lack of annotated data. In this work, we present a densely annotated data-set COLA",
    language = "English",
}
@inproceedings{qianying-etal-2020-liveqa,
    title = "{L}ive{QA}: A Question Answering Dataset over Sports Live",
    author = "Qianying, Liu  and
      Sicong, Jiang  and
      Yizhong, Wang  and
      Sujian, Li",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.98",
    pages = "1057--1067",
    abstract = "In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu1 website. Derived from the characteristics of sports games, LiveQA can potentially test the reasoning ability across timeline-based live broadcasts, which is challenging compared to the existing datasets. In LiveQA, the questions require understanding the timeline, tracking events or doing mathematical computations. Our preliminary experiments show that the dataset introduces a challenging problem for question answering models, and a strong baseline model only achieves the accuracy of 53.1{\%} and cannot beat the dominant option rule. We release the code and data of this paper for future research.",
    language = "English",
}
@inproceedings{yancui-etal-2020-chinese,
    title = "{C}hinese and {E}nglish Elementary Discourse Units Segmentation based on {B}i-{LSTM}-{CRF} Model",
    author = "Yancui, Li  and
      Chunxiao, Lai  and
      Jike, Feng  and
      Hongyu, Feng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.99",
    pages = "1068--1078",
    abstract = "Elementary Discourse Unit (EDU) recognition is the basic task of discourse analysis, and the Chinese and English discourse alignment corpus is helpful to the studies of EDU recognition. This paper first builds Chinese-English parallel discourse corpus, in which EDUs are annotated and aligned. Then, we present the framework of Bi-LSTM-CRF EDUs recognition model using word embedding, POS and syntactic features, which can combine the advantage of CRF and Bi-LSTM. The results show that F1 is about 2{\%} higher than the traditional method. Compared with CRF and Bi-LSTM, the Bi-LSTM-CRF model can combine the advantages of them and obtains satisfactory results for Chinese and English EDUs recognition. The experiment of feature contribution shows that using all features together can get best result, the syntactic feature outperforms than other features.",
    language = "English",
}
@inproceedings{yuncong-etal-2020-better,
    title = "Better Queries for Aspect-Category Sentiment Classification",
    author = "Yuncong, Li  and
      Cunxiang, Yin  and
      Sheng-hua, Zhong  and
      Huiqiang, Zhong  and
      Jinchang, Luo  and
      Siqi, Xu  and
      Xiaohui, Wu",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.100",
    pages = "1079--1088",
    abstract = "Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM.",
    language = "English",
}
@inproceedings{li-chen-2020-multimodal,
    title = "Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language",
    author = "Li, Xia  and
      Chen, Minping",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.101",
    pages = "1089--1100",
    abstract = "Multimodal sentiment analysis aims to learn a joint representation of multiple features. As demonstrated by previous studies, it is shown that the language modality may contain more semantic information than that of other modalities. Based on this observation, we propose a Multi-perspective Fusion Network(MPFN) focusing on Sense Attentive Language for multimodal sentiment analysis. Different from previous studies, we use the language modality as the main part of the final joint representation, and propose a multi-stage and uni-stage fusion strategy to get the fusion representation of the multiple modalities to assist the final language-dominated multimodal representation. In our model, a Sense-Level Attention Network is proposed to dynamically learn the word representation which is guided by the fusion of the multiple modalities. As in turn, the learned language representation can also help the multi-stage and uni-stage fusion of the different modalities. In this way, the model can jointly learn a well integrated final representation focusing on the language and the interactions between the multiple modalities both on multi-stage and uni-stage. Several experiments are carried on the CMU-MOSI, the CMU-MOSEI and the YouTube public datasets. The experiments show that our model performs better or competitive results compared with the baseline models.",
    language = "English",
}
@inproceedings{jiang-etal-2020-gru,
    title = "{CAN}-{GRU}: a Hierarchical Model for Emotion Recognition in Dialogue",
    author = "Jiang, Ting  and
      Xu, Bing  and
      Zhao, Tiejun  and
      Li, Sheng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.102",
    pages = "1101--1111",
    abstract = "Emotion recognition in dialogue systems has gained attention in the field of natural language processing recent years, because it can be applied in opinion mining from public conversational data on social media. In this paper, we propose a hierarchical model to recognize emotions in the dialogue. In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN). Convolution is used to capture n-gram information and attention mechanism is used to obtain the relevant semantic information among words in the utterance. In the second layer, a GRU-based network helps to capture contextual information in the conversation. Furthermore, we discuss the effects of unidirectional and bidirectional networks. We conduct experiments on Friends dataset and EmotionPush dataset. The results show that our proposed model(CAN-GRU) and its variants achieve better performance than baselines.",
    language = "English",
}
@inproceedings{li-etal-2020-joint-model,
    title = "A Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer",
    author = "Li, Yuncong  and
      Yang, Zhe  and
      Yin, Cunxiang  and
      Pan, Xu  and
      Cui, Lunan  and
      Huang, Qiang  and
      Wei, Ting",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.103",
    pages = "1112--1121",
    abstract = "Aspect-category sentiment analysis (ACSA) aims to predict the aspect categories mentioned in texts and their corresponding sentiment polarities. Some joint models have been proposed to address this task. Given a text, these joint models detect all the aspect categories mentioned in the text and predict the sentiment polarities toward them at once. Although these joint models obtain promising performances, they train separate parameters for each aspect category and therefore suffer from data deficiency of some aspect categories. To solve this problem, we propose a novel joint model which contains a shared sentiment prediction layer. The shared sentiment prediction layer transfers sentiment knowledge between aspect categories and alleviates the problem caused by data deficiency. Experiments conducted on SemEval-2016 Datasets demonstrate the effectiveness of our model.",
    language = "English",
}
@inproceedings{chen-etal-2020-compress,
    title = "Compress Polyphone Pronunciation Prediction Model with Shared Labels",
    author = "Chen, Pengfei  and
      Wang, Lina  and
      Di, Hui  and
      Ouchi, Kazushige  and
      Wang, Lvhong",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.104",
    pages = "1122--1131",
    abstract = "It is well known that deep learning model has huge parameters and is computationally expensive, especially for embedded and mobile devices. Polyphone pronunciations selection is a basic function for Chinese Text-to-Speech (TTS) application. Recurrent neural network (RNN) is a good sequence labeling solution for polyphone pronunciation selection. However, huge parameters and computation make compression needed to alleviate its disadvantage. In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection. The basic idea is to compress large number of target labels into a few label clusters, which will share the parameters of fully-connected layer. Furthermore, we combine it with other methods to further compress the polyphone pronunciation selection model. The experimental result shows that for Bi-LSTM (Bidirectional Long Short Term Memory) based polyphone selection, shared labels model decreases about 52{\%} of original model size and accelerates prediction by 44{\%} almost without performance loss. It is worth mentioning that the proposed method can be applied for other tasks to compress the model and accelerate the calculation.",
    language = "English",
}
@inproceedings{zhuopeng-etal-2020-multi,
    title = "Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge",
    author = "Zhuopeng, Xu  and
      Xia, Li  and
      Yinlin, Li  and
      Zihan, Wang  and
      Yujie, Fanxu  and
      Xiaoyan, Lai",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.105",
    pages = "1132--1142",
    abstract = "Legal Judgement Prediction has attracted more and more attention in recent years. One of the challenges is how to design a model with better interpretable prediction results. Previous studies have proposed different interpretable models based on the generation of court views and the extraction of charge keywords. Different from previous work, we propose a multi-task legal judgement prediction model which combines a subtask of the seriousness of charges. By introducing this subtask, our model can capture the attention weights of different terms of penalty corresponding to the charges and give more attention to the correct terms of penalty in the fact descriptions. Meanwhile, our model also incorporates the position of defendant making it capable of giving attention to the contextual information of the defendant. We carry several experiments on the public CAIL2018 dataset. Experimental results show that our model achieves better or comparable performance on three subtasks compared with the baseline models. Moreover, we also analyze the interpretable contribution of our model.",
    language = "English",
}
@inproceedings{wu-etal-2020-clickbait,
    title = "Clickbait Detection with Style-aware Title Modeling and Co-attention",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Qi, Tao  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.106",
    pages = "1143--1154",
    abstract = "Clickbait is a form of web content designed to attract attention and entice users to click on specific hyperlinks. The detection of clickbaits is an important task for online platforms to improve the quality of web content and the satisfaction of users. Clickbait detection is typically formed as a binary classification task based on the title and body of a webpage, and existing methods are mainly based on the content of title and the relevance between title and body. However, these methods ignore the stylistic patterns of titles, which can provide important clues on identifying clickbaits. In addition, they do not consider the interactions between the contexts within title and body, which are very important for measuring their relevance for clickbait detection. In this paper, we propose a clickbait detection approach with style-aware title modeling and co-attention. Specifically, we use Transformers to learn content representations of title and body, and respectively compute two content-based clickbait scores for title and body based on their representations. In addition, we propose to use a character-level Transformer to learn a style-aware title representation by capturing the stylistic patterns of title, and we compute a title stylistic score based on this representation. Besides, we propose to use a co-attention network to model the relatedness between the contexts within title and body, and further enhance their representations by encoding the interaction information. We compute a title-body matching score based on the representations of title and body enhanced by their interactions. The final clickbait score is predicted by a weighted summation of the aforementioned four kinds of scores. Extensive experiments on two benchmark datasets show that our approach can effectively improve the performance of clickbait detection and consistently outperform many baseline methods.",
    language = "English",
}
@inproceedings{zhang-etal-2020-konwledge,
    title = "Konwledge-Enabled Diagnosis Assistant Based on Obstetric {EMR}s and Knowledge Graph",
    author = "Zhang, Kunli  and
      Zhao, Xu  and
      Zhuang, Lei  and
      Xie, Qi  and
      Zan, Hongying",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.107",
    pages = "1155--1165",
    abstract = "The obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant. We utilize the numerical information in EMRs and the external knowledge from Chinese Obstetric Knowledge Graph (COKG) to enhance the text representation of EMRs. Specifically, the bidirectional maximum matching method and similarity-based approach are used to obtain the entities set contained in EMRs and linked to the COKG. The final knowledge representation is obtained by a weight-based disease prediction algorithm, and it is fused with the text representation through a linear weighting method. Experiment results show that our approach can bring about +3.53 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.",
    language = "English",
}
@inproceedings{duan-etal-2020-reusable,
    title = "Reusable Phrase Extraction Based on Syntactic Parsing",
    author = {Duan, Xuemin  and
      Hongying, Zan  and
      Bai, Xiaojing  and
      Z{\"a}hner, Christoph},
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.108",
    pages = "1166--1171",
    abstract = "Academic Phrasebank is an important resource for academic writers. Student writers use the phrases of Academic Phrasebank organizing their research article to improve their writing ability. Due to the limited size of Academic Phrasebank, it can not meet all the academic writing needs. There are still a large number of academic phraseology in the authentic research article. In this paper, we proposed an academic phraseology extraction model based on constituency parsing and dependency parsing, which can automatically extract the academic phraseology similar to phrases of Academic Phrasebank from an unlabelled research article. We divided the proposed model into three main components including an academic phraseology corpus module, a sentence simplification module, and a syntactic parsing module. We created a corpus of academic phraseology of 2,129 words to help judge whether a word is neutral and general, and created two datasets under two scenarios to verify the feasibility of the proposed model.",
    language = "English",
}
@inproceedings{zhang-etal-2020-wae,
    title = "{WAE}{\_}{RN}: Integrating {W}asserstein Autoencoder and Relational Network for Text Sequence",
    author = "Zhang, Xinxin  and
      Liu, Xiaoming  and
      Yang, Guan  and
      Li, Fangfang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://www.aclweb.org/anthology/2020.ccl-1.109",
    pages = "1172--1182",
    abstract = "One challenge in Natural Language Processing (NLP) area is to learn semantic representation in different contexts. Recent works on pre-trained language model have received great attentions and have been proven as an effective technique. In spite of the success of pre-trained language model in many NLP tasks, the learned text representation only contains the correlation among the words in the sentence itself and ignores the implicit relationship between arbitrary tokens in the sequence. To address this problem, we focus on how to make our model effectively learn word representations that contain the relational information between any tokens of text sequences. In this paper, we propose to integrate the relational network(RN) into a Wasserstein autoencoder(WAE). Specifically, WAE and RN are used to better keep the semantic structurse and capture the relational information, respectively. Extensive experiments demonstrate that our proposed model achieves significant improvements over the traditional Seq2Seq baselines.",
    language = "English",
}
@proceedings{calcs-2020-approaches,
    title = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    editor = "Solorio, Thamar  and
      Choudhury, Monojit  and
      Bali, Kalika  and
      Sitaram, Sunayana  and
      Das, Amitava  and
      Diab, Mona",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.0",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{alvarez-mellado-2020-annotated,
    title = "An Annotated Corpus of Emerging Anglicisms in {S}panish Newspaper Headlines",
    author = "Alvarez-Mellado, Elena",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.1",
    pages = "1--8",
    abstract = "The extraction of anglicisms (lexical borrowings from English) is relevant both for lexicographic purposes and for NLP downstream tasks. We introduce a corpus of European Spanish newspaper headlines annotated with anglicisms and a baseline model for anglicism extraction. In this paper we present: (1) a corpus of 21,570 newspaper headlines written in European Spanish annotated with emergent anglicisms and (2) a conditional random field baseline model with handcrafted features for anglicism extraction. We present the newspaper headlines corpus, describe the annotation tagset and guidelines and introduce a CRF model that can serve as baseline for the task of detecting anglicisms. The presented work is a first step towards the creation of an anglicism extractor for Spanish newswire.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{khanuja-etal-2020-new,
    title = "A New Dataset for Natural Language Inference from Code-mixed Conversations",
    author = "Khanuja, Simran  and
      Dandapat, Sandipan  and
      Sitaram, Sunayana  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.2",
    pages = "9--16",
    abstract = "Natural Language Inference (NLI) is the task of inferring the logical relationship, typically entailment or contradiction, between a premise and hypothesis. Code-mixing is the use of more than one language in the same conversation or utterance, and is prevalent in multilingual communities all over the world. In this paper, we present the first dataset for code-mixed NLI, in which both the premises and hypotheses are in code-mixed Hindi-English. We use data from Hindi movies (Bollywood) as premises, and crowd-source hypotheses from Hindi-English bilinguals. We conduct a pilot annotation study and describe the final annotation protocol based on observations from the pilot. Currently, the data collected consists of 400 premises in the form of code-mixed conversation snippets and 2240 code-mixed hypotheses. We conduct an extensive analysis to infer the linguistic phenomena commonly observed in the dataset obtained. We evaluate the dataset using a standard mBERT-based pipeline for NLI and report results.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{adouane-bernardy-2020-multi,
    title = "When is Multi-task Learning Beneficial for Low-Resource Noisy Code-switched User-generated {A}lgerian Texts?",
    author = "Adouane, Wafia  and
      Bernardy, Jean-Philippe",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.3",
    pages = "17--25",
    abstract = "We investigate when is it beneficial to simultaneously learn representations for several tasks, in low-resource settings. For this, we work with noisy user-generated texts in Algerian, a low-resource non-standardised Arabic variety. That is, to mitigate the problem of the data scarcity, we experiment with jointly learning progressively 4 tasks, namely code-switch detection, named entity recognition, spell normalisation and correction, and identifying users{'} sentiments. The selection of these tasks is motivated by the lack of labelled data for automatic morpho-syntactic or semantic sequence-tagging tasks for Algerian, in contrast to the case of much multi-task learning for NLP. Our empirical results show that multi-task learning is beneficial for some tasks in particular settings, and that the effect of each task on another, the order of the tasks, and the size of the training data of the task with more data do matter. Moreover, the data augmentation that we performed with no external resources has been shown to be beneficial for certain tasks.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{rizal-stymne-2020-evaluating,
    title = "Evaluating Word Embeddings for {I}ndonesian{--}{E}nglish Code-Mixed Text Based on Synthetic Data",
    author = "Rizal, Arra{'}Di Nur  and
      Stymne, Sara",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.4",
    pages = "26--35",
    abstract = "Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora. In this paper, we explore and evaluate different types of word embeddings for Indonesian{--}English code-mixed text. We propose the use of code-mixed embeddings, i.e. embeddings trained on code-mixed text. Because large corpora of code-mixed text are required to train embeddings, we describe a method for synthesizing a code-mixed corpus, grounded in literature and a survey. Using sentiment analysis as a case study, we show that code-mixed embeddings trained on synthesized data are at least as good as cross-lingual embeddings and better than monolingual embeddings.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{srivastava-etal-2020-understanding,
    title = "Understanding Script-Mixing: A Case Study of {H}indi-{E}nglish Bilingual {T}witter Users",
    author = "Srivastava, Abhishek  and
      Bali, Kalika  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.5",
    pages = "36--44",
    abstract = "In a multi-lingual and multi-script society such as India, many users resort to code-mixing while typing on social media. While code-mixing has received a lot of attention in the past few years, it has mostly been studied within a single-script scenario. In this work, we present a case study of Hindi-English bilingual Twitter users while considering the nuances that come with the intermixing of different scripts. We present a concise analysis of how scripts and languages interact in communities and cultures where code-mixing is rampant and offer certain insights into the findings. Our analysis shows that both intra-sentential and inter-sentential script-mixing are present on Twitter and show different behavior in different contexts. Examples suggest that script can be employed as a tool for emphasizing certain phrases within a sentence or disambiguating the meaning of a word. Script choice can also be an indicator of whether a word is borrowed or not. We present our analysis along with examples that bring out the nuances of the different cases.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{singh-lefever-2020-sentiment,
    title = "Sentiment Analysis for {H}inglish Code-mixed Tweets by means of Cross-lingual Word Embeddings",
    author = "Singh, Pranaydeep  and
      Lefever, Els",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.6",
    pages = "45--51",
    abstract = "This paper investigates the use of unsupervised cross-lingual embeddings for solving the problem of code-mixed social media text understanding. We specifically investigate the use of these embeddings for a sentiment analysis task for Hinglish Tweets, viz. English combined with (transliterated) Hindi. In a first step, baseline models, initialized with monolingual embeddings obtained from large collections of tweets in English and code-mixed Hinglish, were trained. In a second step, two systems using cross-lingual embeddings were researched, being (1) a supervised classifier and (2) a transfer learning approach trained on English sentiment data and evaluated on code-mixed data. We demonstrate that incorporating cross-lingual embeddings improves the results (F1-score of 0.635 versus a monolingual baseline of 0.616), without any parallel data required to train the cross-lingual embeddings. In addition, the results show that the cross-lingual embeddings not only improve the results in a fully supervised setting, but they can also be used as a base for distant supervision, by training a sentiment model in one of the source languages and evaluating on the other language projected in the same space. The transfer learning experiments result in an F1-score of 0.556, which is almost on par with the supervised settings and speak to the robustness of the cross-lingual embeddings approach.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{biswas-etal-2020-semi-supervised,
    title = "Semi-supervised acoustic and language model training for {E}nglish-isi{Z}ulu code-switched speech recognition",
    author = "Biswas, Astik  and
      De Wet, Febe  and
      Van der westhuizen, Ewald  and
      Niesler, Thomas",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.7",
    pages = "52--56",
    abstract = "We present an analysis of semi-supervised acoustic and language model training for English-isiZulu code-switched (CS) ASR using soap opera speech. Approximately 11 hours of untranscribed multilingual speech was transcribed automatically using four bilingual CS transcription systems operating in English-isiZulu, English-isiXhosa, English-Setswana and English-Sesotho. These transcriptions were incorporated into the acoustic and language model training sets. Results showed that the TDNN-F acoustic models benefit from the additional semi-supervised data and that even better performance could be achieved by including additional CNN layers. Using these CNN-TDNN-F acoustic models, a first iteration of semi-supervised training achieved an absolute mixed-language WER reduction of 3.44{\%}, and a further 2.18{\%} after a second iteration. Although the languages in the untranscribed data were unknown, the best results were obtained when all automatically transcribed data was used for training and not just the utterances classified as English-isiZulu. Despite perplexity improvements, the semi-supervised language model was not able to improve the ASR performance.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{srinivasan-etal-2020-code,
    title = "Code-mixed parse trees and how to find them",
    author = "Srinivasan, Anirudh  and
      Dandapat, Sandipan  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.8",
    pages = "57--64",
    abstract = "In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into {``}pseudo constituency trees{''} and find that a parser trained on synthetically generated trees is able to decently parse these as well.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@inproceedings{cho-etal-2020-towards,
    title = "Towards an Efficient Code-Mixed Grapheme-to-Phoneme Conversion in an Agglutinative Language: A Case Study on To-{K}orean Transliteration",
    author = "Cho, Won Ik  and
      Kim, Seok Min  and
      Kim, Nam Soo",
    booktitle = "Proceedings of the The 4th Workshop on Computational Approaches to Code Switching",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.calcs-1.9",
    pages = "65--70",
    abstract = "Code-mixed grapheme-to-phoneme (G2P) conversion is a crucial issue for modern speech recognition and synthesis task, but has been seldom investigated in sentence-level in literature. In this study, we construct a system that performs precise and efficient multi-stage code-mixed G2P conversion, for a less studied agglutinative language, Korean. The proposed system undertakes a sentence-level transliteration that is effective in the accurate processing of Korean text. We formulate the underlying philosophy that supports our approach and demonstrate how it fits with the contemporary document.",
    language = "English",
    ISBN = "979-10-95546-66-5",
}
@proceedings{bucc-2020-building,
    title = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    editor = "Rapp, Reinhard  and
      Zweigenbaum, Pierre  and
      Sharoff, Serge",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.0",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{skeppstedt-etal-2020-line,
    title = "Line-a-line: A Tool for Annotating Word-Alignments",
    author = "Skeppstedt, Maria  and
      Ahltorp, Magnus  and
      Eriksson, Gunnar  and
      Domeij, Rickard",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.1",
    pages = "1--5",
    abstract = "We here describe line-a-line, a web-based tool for manual annotation of word-alignments in sentence-aligned parallel corpora. The graphical user interface, which builds on a design template from the Jigsaw system for investigative analysis, displays the words from each sentence pair that is to be annotated as elements in two vertical lists. An alignment between two words is annotated by drag-and-drop, i.e. by dragging an element from the left-hand list and dropping it on an element in the right-hand list. The tool indicates that two words are aligned by lines that connect them and by highlighting associated words when the mouse is hovered over them. Line-a-line uses the efmaral library for producing pre-annotated alignments, on which the user can base the manual annotation. The tool is mainly planned to be used on moderately under-resourced languages, for which resources in the form of parallel corpora are scarce. The automatic word-alignment functionality therefore also incorporates information derived from non-parallel resources, in the form of pre-trained multilingual word embeddings from the MUSE library.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{rapp-etal-2020-overview,
    title = "Overview of the Fourth {BUCC} Shared Task: Bilingual Dictionary Induction from Comparable Corpora",
    author = "Rapp, Reinhard  and
      Zweigenbaum, Pierre  and
      Sharoff, Serge",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.2",
    pages = "6--13",
    abstract = "The shared task of the 13th Workshop on Building and Using Comparable Corpora was devoted to the induction of bilingual dictionaries from comparable rather than parallel corpora. In this task, for a number of language pairs involving Chinese, English, French, German, Russian and Spanish, the participants were supposed to determine automatically the target language translations of several thousand source language test words of three frequency ranges. We describe here some background, the task definition, the training and test data sets and the evaluation used for ranking the participating systems. We also summarize the approaches used and present the results of the evaluation. In conclusion, the outcome of the competition are the results of a number of systems which provide surprisingly good solutions to the ambitious problem.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{mubarak-etal-2020-constructing,
    title = "Constructing a Bilingual Corpus of Parallel Tweets",
    author = "Mubarak, Hamdy  and
      Hassan, Sabit  and
      Abdelali, Ahmed",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.3",
    pages = "14--21",
    abstract = "In a bid to reach a larger and more diverse audience, Twitter users often post parallel tweets{---}tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. In this paper, we introduce a generic method for collecting parallel tweets. Using this method, we collect a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabictweets regularly. Since our method is generic, it can also be used for collecting parallel tweets that cover less-resourced languages such as Serbian and Urdu. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets. This latter information can also be useful for author profiling tasks.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{hirabayashi-etal-2020-automatic,
    title = "Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding",
    author = "Hirabayashi, Teruo  and
      Komiya, Kanako  and
      Asahara, Masayuki  and
      Shinnou, Hiroyuki",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.4",
    pages = "22--28",
    abstract = "In this paper, we show how to use bilingual word embeddings (BWE) to automatically create a corresponding table of meaning tags from two dictionaries in one language and examine the effectiveness of the method. To do this, we had a problem: the meaning tags do not always correspond one-to-one because the granularities of the word senses and the concepts are different from each other. Therefore, we regarded the concept tag that corresponds to a word sense the most as the correct concept tag corresponding the word sense. We used two BWE methods, a linear transformation matrix and VecMap. We evaluated the most frequent sense (MFS) method and the corpus concatenation method for comparison. The accuracies of the proposed methods were higher than the accuracy of the random baseline but lower than those of the MFS and corpus concatenation methods. However, because our method utilized the embedding vectors of the word senses, the relations of the sense tags corresponding to concept tags could be examined by mapping the sense embeddings to the vector space of the concept tags. Also, our methods could be performed when we have only concept or word sense embeddings whereas the MFS method requires a parallel corpus and the corpus concatenation method needs two tagged corpora.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{vintar-etal-2020-mining,
    title = "Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings",
    author = "Vintar, {\v{S}}pela  and
      Gr{\v{c}}i{\'c} Simeunovi{\'c}, Larisa  and
      Martinc, Matej  and
      Pollak, Senja  and
      Stepi{\v{s}}nik, Uro{\v{s}}",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.5",
    pages = "29--34",
    abstract = "We report an experiment aimed at extracting words expressing a specific semantic relation using intersections of word embeddings. In a multilingual frame-based domain model, specific features of a concept are typically described through a set of non-arbitrary semantic relations. In karstology, our domain of choice which we are exploring though a comparable corpus in English and Croatian, karst phenomena such as landforms are usually described through their FORM, LOCATION, CAUSE, FUNCTION and COMPOSITION. We propose an approach to mine words pertaining to each of these relations by using a small number of seed adjectives, for which we retrieve closest words using word embeddings and then use intersections of these neighbourhoods to refine our search. Such cross-language expansion of semantically-rich vocabulary is a valuable aid in improving the coverage of a multilingual knowledge base, but also in exploring differences between languages in their respective conceptualisations of the domain.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{guntara-etal-2020-benchmarking,
    title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation",
    author = "Guntara, Tri Wahyu  and
      Aji, Alham Fikri  and
      Prasojo, Radityo Eko",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.6",
    pages = "35--43",
    abstract = "In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic. In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and conversation, to train and benchmark some variants of transformer-based NMT models across the domains. We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models, and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on \url{https://github.com/gunnxx/indonesian-mt-data}",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{cardon-grabar-2020-reducing,
    title = "Reducing the Search Space for Parallel Sentences in Comparable Corpora",
    author = "Cardon, R{\'e}mi  and
      Grabar, Natalia",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.7",
    pages = "44--48",
    abstract = "This paper describes and evaluates simple techniques for reducing the research space for parallel sentences in monolingual comparable corpora. Initially, when searching for parallel sentences between two comparable documents, all the possible sentence pairs between the documents have to be considered, which introduces a great degree of imbalance between parallel pairs and non-parallel pairs. This is a problem because even with a high performing algorithm, a lot of noise will be present in the extracted results, thus introducing a need for an extensive and costly manual check phase. We work on a manually annotated subset obtained from a French comparable corpus and show how we can drastically reduce the number of sentence pairs that have to be fed to a classifier so that the results can be manually handled.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{severini-etal-2020-lmu,
    title = "{LMU} Bilingual Dictionary Induction System with Word Surface Similarity Scores for {BUCC} 2020",
    author = {Severini, Silvia  and
      Hangya, Viktor  and
      Fraser, Alexander  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.8",
    pages = "49--55",
    abstract = "The task of Bilingual Dictionary Induction (BDI) consists of generating translations for source language words which is important in the framework of machine translation (MT). The aim of the BUCC 2020 shared task is to perform BDI on various language pairs using comparable corpora. In this paper, we present our approach to the task of English-German and English-Russian language pairs. Our system relies on Bilingual Word Embeddings (BWEs) which are often used for BDI when only a small seed lexicon is available making them particularly effective in a low-resource setting. On the other hand, they perform well on high frequency words only. In order to improve the performance on rare words as well, we combine BWE based word similarity with word surface similarity methods, such as orthography In addition to the often used top-n translation method, we experiment with a margin based approach aiming for dynamic number of translations for each source word. We participate in both the open and closed tracks of the shared task and we show improved results of our method compared to simple vector similarity based approaches. Our system was ranked in the top-3 teams and achieved the best results for English-Russian.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{laville-etal-2020-taln,
    title = "{TALN}/{LS}2{N} Participation at the {BUCC} Shared Task: Bilingual Dictionary Induction from Comparable Corpora",
    author = "Laville, Martin  and
      Hazem, Amir  and
      Morin, Emmanuel",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.9",
    pages = "56--60",
    abstract = "This paper describes the TALN/LS2N system participation at the Building and Using Comparable Corpora (BUCC) shared task. We first introduce three strategies: (i) a word embedding approach based on fastText embeddings; (ii) a concatenation approach using both character Skip-Gram and character CBOW models, and finally (iii) a cognates matching approach based on an exact match string similarity. Then, we present the applied strategy for the shared task which consists in the combination of the embeddings concatenation and the cognates matching approaches. The covered languages are French, English, German, Russian and Spanish. Overall, our system mixing embeddings concatenation and perfect cognates matching obtained the best results while compared to individual strategies, except for English-Russian and Russian-English language pairs for which the concatenation approach was preferred.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{jp-etal-2020-centam,
    title = "c{E}n{T}am: Creation and Validation of a New {E}nglish-{T}amil Bilingual Corpus",
    author = "JP, Sanjanasri  and
      B, Premjith  and
      Menon, Vijay Krishna  and
      KP, Soman",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.10",
    pages = "61--64",
    abstract = "Natural Language Processing (NLP), is the field of artificial intelligence that gives the computer the ability to interpret, perceive and extract appropriate information from human languages. Contemporary NLP is predominantly a data driven process. It employs machine learning and statistical algorithms to learn language structures from textual corpus. While application of NLP in English, certain European languages such as Spanish, German, etc. and Chinese, Arabic has been tremendous, it is not so, in many Indian languages. There are obvious advantages in creating aligned bilingual and multilingual corpora. Machine translation, cross-lingual information retrieval, content availability and linguistic comparison are a few of the most sought after applications of such parallel corpora. This paper explains and validates a parallel corpus we created for English-Tamil bilingual pair.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@inproceedings{jp-etal-2020-bucc2020,
    title = "{BUCC}2020: Bilingual Dictionary Induction using Cross-lingual Embedding",
    author = "JP, Sanjanasri  and
      Menon, Vijay Krishna  and
      KP, Soman",
    booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.aclweb.org/anthology/2020.bucc-1.11",
    pages = "65--68",
    abstract = "This paper presents a deep learning system for the BUCC 2020 shared task: Bilingual dictionary induction from comparable corpora. We have submitted two runs for this shared Task, German (de) and English (en) language pair for {``}closed track{''} and Tamil (ta) and English (en) for the {``}open track{''}. Our core approach focuses on quantifying the semantics of the language pairs, so that semantics of two different language pairs can be compared or transfer learned. With the advent of word embeddings, it is possible to quantify this. In this paper, we propose a deep learning approach which makes use of the supplied training data, to generate cross-lingual embedding. This is later used for inducting bilingual dictionary from comparable corpora.",
    language = "English",
    ISBN = "979-10-95546-42-9",
}
@proceedings{blackboxnlp-2020-blackboxnlp,
    title = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    editor = "Alishahi, Afra  and
      Belinkov, Yonatan  and
      Chrupa{\l}a, Grzegorz  and
      Hupkes, Dieuwke  and
      Pinter, Yuval  and
      Sajjad, Hassan",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.0",
}
@inproceedings{gangal-hovy-2020-bertering,
    title = "{BERT}ering {RAMS}: What and How Much does {BERT} Already Know About Event Arguments? - A Study on the {RAMS} Dataset",
    author = "Gangal, Varun  and
      Hovy, Eduard",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.1",
    doi = "10.18653/v1/2020.blackboxnlp-1.1",
    pages = "1--10",
    abstract = "Using the attention map based probing framework from (Clark et al., 2019), we observe that, on the RAMS dataset (Ebner et al., 2020), BERT{'}s attention heads have modest but well above-chance ability to spot event arguments sans any training or domain finetuning, varying from a low of 17.77{\%} for Place to a high of 51.61{\%} for Artifact. Next, we find that linear combinations of these heads, estimated with approx. 11{\%} of available total event argument detection supervision, can push performance well higher for some roles {---} highest two being Victim (68.29{\%} Accuracy) and Artifact (58.82{\%} Accuracy). Furthermore, we investigate how well our methods do for cross-sentence event arguments. We propose a procedure to isolate {``}best heads{''} for cross-sentence argument detection separately of those for intra-sentence arguments. The heads thus estimated have superior cross-sentence performance compared to their jointly estimated equivalents, albeit only under the unrealistic assumption that we already know the argument is present in another sentence. Lastly, we seek to isolate to what extent our numbers stem from lexical frequency based associations between gold arguments and roles. We propose NONCE, a scheme to create adversarial test examples by replacing gold arguments with randomly generated {``}nonce{''} words. We find that learnt linear combinations are robust to NONCE, though individual best heads can be more sensitive.",
}
@inproceedings{kharitonov-baroni-2020-emergent,
    title = "Emergent Language Generalization and Acquisition Speed are not tied to Compositionality",
    author = "Kharitonov, Eugene  and
      Baroni, Marco",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.2",
    doi = "10.18653/v1/2020.blackboxnlp-1.2",
    pages = "11--15",
    abstract = "Studies of discrete languages emerging when neural agents communicate to solve a joint task often look for evidence of compositional structure. This stems for the expectation that such a structure would allow languages to be acquired faster by the agents and enable them to generalize better. We argue that these beneficial properties are only loosely connected to compositionality. In two experiments, we demonstrate that, depending on the task, non-compositional languages might show equal, or better, generalization performance and acquisition speed than compositional ones. Further research in the area should be clearer about what benefits are expected from compositionality, and how the latter would lead to them.",
}
@inproceedings{zhu-etal-2020-examining,
    title = "Examining the rhetorical capacities of neural language models",
    author = "Zhu, Zining  and
      Pan, Chuer  and
      Abdalla, Mohamed  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.3",
    doi = "10.18653/v1/2020.blackboxnlp-1.3",
    pages = "16--32",
    abstract = "Recently, neural language models (LMs) have demonstrated impressive abilities in generating high-quality discourse. While many recent papers have analyzed the syntactic aspects encoded in LMs, there has been no analysis to date of the inter-sentential, rhetorical knowledge. In this paper, we propose a method that quantitatively evaluates the rhetorical capacities of neural LMs. We examine the capacities of neural LMs understanding the rhetoric of discourse by evaluating their abilities to encode a set of linguistic features derived from Rhetorical Structure Theory (RST). Our experiments show that BERT-based LMs outperform other Transformer LMs, revealing the richer discourse knowledge in their intermediate layer representations. In addition, GPT-2 and XLNet apparently encode less rhetorical knowledge, and we suggest an explanation drawing from linguistic philosophy. Our method shows an avenue towards quantifying the rhetorical capacities of neural LMs.",
}
@inproceedings{merchant-etal-2020-happens,
    title = "What Happens To {BERT} Embeddings During Fine-tuning?",
    author = "Merchant, Amil  and
      Rahimtoroghi, Elahe  and
      Pavlick, Ellie  and
      Tenney, Ian",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.4",
    doi = "10.18653/v1/2020.blackboxnlp-1.4",
    pages = "33--44",
    abstract = "While much recent work has examined how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks. Using a suite of analysis techniques{---}supervised probing, unsupervised similarity analysis, and layer-based ablations{---}we investigate how fine-tuning affects the representations of the BERT model. We find that while fine-tuning necessarily makes some significant changes, there is no catastrophic forgetting of linguistic phenomena. We instead find that fine-tuning is a conservative process that primarily affects the top layers of BERT, albeit with noteworthy variation across tasks. In particular, dependency parsing reconfigures most of the model, whereas SQuAD and MNLI involve much shallower processing. Finally, we also find that fine-tuning has a weaker effect on representations of out-of-domain sentences, suggesting room for improvement in model generalization.",
}
@inproceedings{gonen-etal-2020-greek,
    title = "It{'}s not {G}reek to m{BERT}: Inducing Word-Level Translations from Multilingual {BERT}",
    author = "Gonen, Hila  and
      Ravfogel, Shauli  and
      Elazar, Yanai  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.5",
    doi = "10.18653/v1/2020.blackboxnlp-1.5",
    pages = "45--56",
    abstract = "Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages. We study the word-level translation information embedded in mBERT and present two simple methods that expose remarkable translation capabilities with no fine-tuning. The results suggest that most of this information is encoded in a non-linear way, while some of it can also be recovered with purely linear tools. As part of our analysis, we test the hypothesis that mBERT learns representations which contain both a language-encoding component and an abstract, cross-lingual component, and explicitly identify an empirical language-identity subspace within mBERT representations.",
}
@inproceedings{nizar-kobren-2020-leveraging,
    title = "Leveraging Extracted Model Adversaries for Improved Black Box Attacks",
    author = "Nizar, Naveen Jafer  and
      Kobren, Ari",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.6",
    doi = "10.18653/v1/2020.blackboxnlp-1.6",
    pages = "57--67",
    abstract = "We present a method for adversarial input generation against black box models for reading comprehension based question answering. Our approach is composed of two steps. First, we approximate a victim black box model via model extraction. Second, we use our own white box method to generate input perturbations that cause the approximate model to fail. These perturbed inputs are used against the victim. In experiments we find that our method improves on the efficacy of the ADDANY{---}a white box attack{---}performed on the approximate model by 25{\%} F1, and the ADDSENT attack{---}a black box attack{---}by 11{\%} F1.",
}
@inproceedings{mosbach-etal-2020-interplay-fine,
    title = "On the Interplay Between Fine-tuning and Sentence-Level Probing for Linguistic Knowledge in Pre-Trained Transformers",
    author = "Mosbach, Marius  and
      Khokhlova, Anna  and
      Hedderich, Michael A.  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.7",
    doi = "10.18653/v1/2020.blackboxnlp-1.7",
    pages = "68--82",
    abstract = "Fine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline. At the same time, probing has emerged as a way to investigate the linguistic knowledge captured by pre-trained models. Very little is, however, understood about how fine-tuning affects the representations of pre-trained models and thereby the linguistic knowledge they encode. This paper contributes towards closing this gap. We study three different pre-trained models: BERT, RoBERTa, and ALBERT, and investigate through sentence-level probing how fine-tuning affects their representations. We find that for some probing tasks fine-tuning leads to substantial changes in accuracy, possibly suggesting that fine-tuning introduces or even removes linguistic knowledge from a pre-trained model. These changes, however, vary greatly across different models, fine-tuning and probing tasks. Our analysis reveals that while fine-tuning indeed changes the representations of a pre-trained model and these changes are typically larger for higher layers, only in very few cases, fine-tuning has a positive effect on probing accuracy that is larger than just using the pre-trained model with a strong pooling method. Based on our findings, we argue that both positive and negative effects of fine-tuning on probing require a careful interpretation.",
}
@inproceedings{muttenthaler-etal-2020-unsupervised,
    title = "Unsupervised Evaluation for Question Answering with Transformers",
    author = "Muttenthaler, Lukas  and
      Augenstein, Isabelle  and
      Bjerva, Johannes",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.8",
    doi = "10.18653/v1/2020.blackboxnlp-1.8",
    pages = "83--90",
    abstract = "It is challenging to automatically evaluate the answer of a QA model at inference time. Although many models provide confidence scores, and simple heuristics can go a long way towards indicating answer correctness, such measures are heavily dataset-dependent and are unlikely to generalise. In this work, we begin by investigating the hidden representations of questions, answers, and contexts in transformer-based QA architectures. We observe a consistent pattern in the answer representations, which we show can be used to automatically evaluate whether or not a predicted answer span is correct. Our method does not require any labelled data and outperforms strong heuristic baselines, across 2 datasets and 7 domains. We are able to predict whether or not a model{'}s answer is correct with 91.37{\%} accuracy on SQuAD, and 80.7{\%} accuracy on SubjQA. We expect that this method will have broad applications, e.g., in semi-automatic development of QA datasets.",
}
@inproceedings{ravfogel-etal-2020-unsupervised,
    title = "Unsupervised Distillation of Syntactic Information from Contextualized Word Representations",
    author = "Ravfogel, Shauli  and
      Elazar, Yanai  and
      Goldberger, Jacob  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.9",
    doi = "10.18653/v1/2020.blackboxnlp-1.9",
    pages = "91--106",
    abstract = "Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic task. In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the contextualized vectors, that discards the lexical semantics, but keeps the structural information. To this end, we automatically generate groups of sentences which are structurally similar but semantically different, and use metric-learning approach to learn a transformation that emphasizes the structural component that is encoded in the vectors. We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics. Finally, we demonstrate the utility of our distilled representations by showing that they outperform the original contextualized representations in a few-shot parsing setting.",
}
@inproceedings{treviso-martins-2020-explanation,
    title = "The Explanation Game: Towards Prediction Explainability through Sparse Communication",
    author = "Treviso, Marcos  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.10",
    doi = "10.18653/v1/2020.blackboxnlp-1.10",
    pages = "107--118",
    abstract = "Explainability is a topic of growing importance in NLP. In this work, we provide a unified perspective of explainability as a communication problem between an explainer and a layperson about a classifier{'}s decision. We use this framework to compare several explainers, including gradient methods, erasure, and attention mechanisms, in terms of their communication success. In addition, we reinterpret these methods in the light of classical feature selection, and use this as inspiration for new embedded explainers, through the use of selective, sparse attention. Experiments in text classification and natural language inference, using different configurations of explainers and laypeople (including both machines and humans), reveal an advantage of attention-based explainers over gradient and erasure methods, and show that selective attention is a simpler alternative to stochastic rationalizers. Human experiments show strong results on text classification with post-hoc explainers trained to optimize communication success.",
}
@inproceedings{zhang-2020-latent,
    title = "Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?",
    author = "Zhang, Yian",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.11",
    doi = "10.18653/v1/2020.blackboxnlp-1.11",
    pages = "119--125",
    abstract = "Recent latent tree learning models can learn constituency parsing without any exposure to human-annotated tree structures. One such model is ON-LSTM (Shen et al., 2019), which is trained on language modelling and has near-state-of-the-art performance on unsupervised parsing. In order to better understand the performance and consistency of the model as well as how the parses it generates are different from gold-standard PTB parses, we replicate the model with different restarts and examine their parses. We find that (1) the model has reasonably consistent parsing behaviors across different restarts, (2) the model struggles with the internal structures of complex noun phrases, (3) the model has a tendency to overestimate the height of the split points right before verbs. We speculate that both problems could potentially be solved by adopting a different training task other than unidirectional language modelling.",
}
@inproceedings{li-etal-2020-linguistically,
    title = "Linguistically-Informed Transformations ({LIT}): A Method for Automatically Generating Contrast Sets",
    author = "Li, Chuanrong  and
      Shengshuo, Lin  and
      Liu, Zeyu  and
      Wu, Xinyi  and
      Zhou, Xuhui  and
      Steinert-Threlkeld, Shane",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.12",
    doi = "10.18653/v1/2020.blackboxnlp-1.12",
    pages = "126--135",
    abstract = "Although large-scale pretrained language models, such as BERT and RoBERTa, have achieved superhuman performance on in-distribution test sets, their performance suffers on out-of-distribution test sets (e.g., on contrast sets). Building contrast sets often requires human-expert annotation, which is expensive and hard to create on a large scale. In this work, we propose a Linguistically-Informed Transformation (LIT) method to automatically generate contrast sets, which enables practitioners to explore linguistic phenomena of interests as well as compose different phenomena. Experimenting with our method on SNLI and MNLI shows that current pretrained language models, although being claimed to contain sufficient linguistic knowledge, struggle on our automatically generated contrast sets. Furthermore, we improve models{'} performance on the contrast sets by applying LIT to augment the training data, without affecting performance on the original data.",
}
@inproceedings{celikkanat-etal-2020-controlling,
    title = "Controlling the Imprint of Passivization and Negation in Contextualized Representations",
    author = {Celikkanat, Hande  and
      Virpioja, Sami  and
      Tiedemann, J{\"o}rg  and
      Apidianaki, Marianna},
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.13",
    doi = "10.18653/v1/2020.blackboxnlp-1.13",
    pages = "136--148",
    abstract = "Contextualized word representations encode rich information about syntax and semantics, alongside specificities of each context of use. While contextual variation does not always reflect actual meaning shifts, it can still reduce the similarity of embeddings for word instances having the same meaning. We explore the imprint of two specific linguistic alternations, namely passivization and negation, on the representations generated by neural models trained with two different objectives: masked language modeling and translation. Our exploration methodology is inspired by an approach previously proposed for removing societal biases from word vectors. We show that passivization and negation leave their traces on the representations, and that neutralizing this information leads to more similar embeddings for words that should preserve their meaning in the transformation. We also find clear differences in how the respective features generalize across datasets.",
}
@inproceedings{bastings-filippova-2020-elephant,
    title = "The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?",
    author = "Bastings, Jasmijn  and
      Filippova, Katja",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.14",
    doi = "10.18653/v1/2020.blackboxnlp-1.14",
    pages = "149--155",
    abstract = "There is a recent surge of interest in using attention as explanation of model predictions, with mixed evidence on whether attention can be used as such. While attention conveniently gives us one weight per input token and is easily extracted, it is often unclear toward what goal it is used as explanation. We find that often that goal, whether explicitly stated or not, is to find out what input tokens are the most relevant to a prediction, and that the implied user for the explanation is a model developer. For this goal and user, we argue that input saliency methods are better suited, and that there are no compelling reasons to use attention, despite the coincidence that it provides a weight for each input. With this position paper, we hope to shift some of the recent focus on attention to saliency methods, and for authors to clearly state the goal and user for their explanations.",
}
@inproceedings{yenicelik-etal-2020-bert,
    title = "How does {BERT} capture semantics? A closer look at polysemous words",
    author = "Yenicelik, David  and
      Schmidt, Florian  and
      Kilcher, Yannic",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.15",
    doi = "10.18653/v1/2020.blackboxnlp-1.15",
    pages = "156--162",
    abstract = "The recent paradigm shift to contextual word embeddings has seen tremendous success across a wide range of down-stream tasks. However, little is known on how the emergent relation of context and semantics manifests geometrically. We investigate polysemous words as one particularly prominent instance of semantic organization. Our rigorous quantitative analysis of linear separability and cluster organization in embedding vectors produced by BERT shows that semantics do not surface as isolated clusters but form seamless structures, tightly coupled with sentiment and syntax.",
}
@inproceedings{geiger-etal-2020-neural,
    title = "Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation",
    author = "Geiger, Atticus  and
      Richardson, Kyle  and
      Potts, Christopher",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.16",
    doi = "10.18653/v1/2020.blackboxnlp-1.16",
    pages = "163--173",
    abstract = "We address whether neural models for Natural Language Inference (NLI) can learn the compositional interactions between lexical entailment and negation, using four methods: the behavioral evaluation methods of (1) challenge test sets and (2) systematic generalization tasks, and the structural evaluation methods of (3) probes and (4) interventions. To facilitate this holistic evaluation, we present Monotonicity NLI (MoNLI), a new naturalistic dataset focused on lexical entailment and negation. In our behavioral evaluations, we find that models trained on general-purpose NLI datasets fail systematically on MoNLI examples containing negation, but that MoNLI fine-tuning addresses this failure. In our structural evaluations, we look for evidence that our top-performing BERT-based model has learned to implement the monotonicity algorithm behind MoNLI. Probes yield evidence consistent with this conclusion, and our intervention experiments bolster this, showing that the causal dynamics of the model mirror the causal dynamics of this algorithm on subsets of MoNLI. This suggests that the BERT model at least partially embeds a theory of lexical entailment and negation at an algorithmic level.",
}
@inproceedings{singh-etal-2020-bertnesia,
    title = "{BERT}nesia: Investigating the capture and forgetting of knowledge in {BERT}",
    author = "Singh, Jaspreet  and
      Wallat, Jonas  and
      Anand, Avishek",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.17",
    doi = "10.18653/v1/2020.blackboxnlp-1.17",
    pages = "174--183",
    abstract = "Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this paper, we probe BERT specifically to understand and measure the relational knowledge it captures. We utilize knowledge base completion tasks to probe every layer of pre-trained as well as fine-tuned BERT (ranking, question answering, NER). Our findings show that knowledge is not just contained in BERT{'}s final layers. Intermediate layers contribute a significant amount (17-60{\%}) to the total knowledge found. Probing intermediate layers also reveals how different types of knowledge emerge at varying rates. When BERT is fine-tuned, relational knowledge is forgotten but the extent of forgetting is impacted by the fine-tuning objective but not the size of the dataset. We found that ranking models forget the least and retain more knowledge in their final layer.",
}
@inproceedings{johnson-etal-2020-probing,
    title = "Probing for Multilingual Numerical Understanding in Transformer-Based Language Models",
    author = "Johnson, Devin  and
      Mak, Denise  and
      Barker, Andrew  and
      Loessberg-Zahl, Lexi",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.18",
    doi = "10.18653/v1/2020.blackboxnlp-1.18",
    pages = "184--192",
    abstract = "Natural language numbers are an example of compositional structures, where larger numbers are composed of operations on smaller numbers. Given that compositional reasoning is a key to natural language understanding, we propose novel multilingual probing tasks tested on DistilBERT, XLM, and BERT to investigate for evidence of compositional reasoning over numerical data in various natural language number systems. By using both grammaticality judgment and value comparison classification tasks in English, Japanese, Danish, and French, we find evidence that the information encoded in these pretrained models{'} embeddings is sufficient for grammaticality judgments but generally not for value comparisons. We analyze possible reasons for this and discuss how our tasks could be extended in further studies.",
}
@inproceedings{movva-zhao-2020-dissecting,
    title = "Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation",
    author = "Movva, Rajiv  and
      Zhao, Jason",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.19",
    doi = "10.18653/v1/2020.blackboxnlp-1.19",
    pages = "193--203",
    abstract = "Recent work on the lottery ticket hypothesis has produced highly sparse Transformers for NMT while maintaining BLEU. However, it is unclear how such pruning techniques affect a model{'}s learned representations. By probing Transformers with more and more low-magnitude weights pruned away, we find that complex semantic information is first to be degraded. Analysis of internal activations reveals that higher layers diverge most over the course of pruning, gradually becoming less complex than their dense counterparts. Meanwhile, early layers of sparse models begin to perform more encoding. Attention mechanisms remain remarkably consistent as sparsity increases.",
}
@inproceedings{runge-hovy-2020-exploring,
    title = "Exploring Neural Entity Representations for Semantic Information",
    author = "Runge, Andrew  and
      Hovy, Eduard",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.20",
    doi = "10.18653/v1/2020.blackboxnlp-1.20",
    pages = "204--216",
    abstract = "Neural methods for embedding entities are typically extrinsically evaluated on downstream tasks and, more recently, intrinsically using probing tasks. Downstream task-based comparisons are often difficult to interpret due to differences in task structure, while probing task evaluations often look at only a few attributes and models. We address both of these issues by evaluating a diverse set of eight neural entity embedding methods on a set of simple probing tasks, demonstrating which methods are able to remember words used to describe entities, learn type, relationship and factual information, and identify how frequently an entity is mentioned. We also compare these methods in a unified framework on two entity linking tasks and discuss how they generalize to different model architectures and datasets.",
}
@inproceedings{mccoy-etal-2020-berts,
    title = "{BERT}s of a feather do not generalize together: Large variability in generalization across models with similar test set performance",
    author = "McCoy, R. Thomas  and
      Min, Junghyun  and
      Linzen, Tal",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.21",
    doi = "10.18653/v1/2020.blackboxnlp-1.21",
    pages = "217--227",
    abstract = "If the same neural network architecture is trained multiple times on the same dataset, will it make similar linguistic generalizations across runs? To study this question, we fine-tuned 100 instances of BERT on the Multi-genre Natural Language Inference (MNLI) dataset and evaluated them on the HANS dataset, which evaluates syntactic generalization in natural language inference. On the MNLI development set, the behavior of all instances was remarkably consistent, with accuracy ranging between 83.6{\%} and 84.8{\%}. In stark contrast, the same models varied widely in their generalization performance. For example, on the simple case of subject-object swap (e.g., determining that {``}the doctor visited the lawyer{''} does not entail {``}the lawyer visited the doctor{''}), accuracy ranged from 0.0{\%} to 66.2{\%}. Such variation is likely due to the presence of many local minima in the loss surface that are equally attractive to a low-bias learner such as a neural network; decreasing the variability may therefore require models with stronger inductive biases.",
}
@inproceedings{morris-2020-second,
    title = "Second-Order {NLP} Adversarial Examples",
    author = "Morris, John",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.22",
    doi = "10.18653/v1/2020.blackboxnlp-1.22",
    pages = "228--237",
    abstract = "Adversarial example generation methods in NLP rely on models like language models or sentence encoders to determine if potential adversarial examples are valid. In these methods, a valid adversarial example fools the model being attacked, and is determined to be semantically or syntactically valid by a second model. Research to date has counted all such examples as errors by the attacked model. We contend that these adversarial examples may not be flaws in the attacked model, but flaws in the model that determines validity. We term such invalid inputs second-order adversarial examples. We propose the constraint robustness curve, and associated metric ACCS, as tools for evaluating the robustness of a constraint to second-order adversarial examples. To generate this curve, we design an adversarial attack to run directly on the semantic similarity models. We test on two constraints, the Universal Sentence Encoder (USE) and BERTScore. Our findings indicate that such second-order examples exist, but are typically less common than first-order adversarial examples in state-of-the-art models. They also indicate that USE is effective as constraint on NLP adversarial examples, while BERTScore is nearly ineffectual. Code for running the experiments in this paper is available here.",
}
@inproceedings{soulos-etal-2020-discovering,
    title = "Discovering the Compositional Structure of Vector Representations with Role Learning Networks",
    author = "Soulos, Paul  and
      McCoy, R. Thomas  and
      Linzen, Tal  and
      Smolensky, Paul",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.23",
    doi = "10.18653/v1/2020.blackboxnlp-1.23",
    pages = "238--254",
    abstract = "How can neural networks perform so well on compositional tasks even though they lack explicit compositional representations? We use a novel analysis technique called ROLE to show that recurrent neural networks perform well on such tasks by converging to solutions which implicitly represent symbolic structure. This method uncovers a symbolic structure which, when properly embedded in vector space, closely approximates the encodings of a standard seq2seq network trained to perform the compositional SCAN task. We verify the causal importance of the discovered symbolic structure by showing that, when we systematically manipulate hidden embeddings based on this symbolic structure, the model{'}s output is changed in the way predicted by our analysis.",
}
@inproceedings{wu-etal-2020-structured,
    title = "Structured Self-{A}ttention{W}eights Encode Semantics in Sentiment Analysis",
    author = "Wu, Zhengxuan  and
      Nguyen, Thanh-Son  and
      Ong, Desmond",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.24",
    doi = "10.18653/v1/2020.blackboxnlp-1.24",
    pages = "255--264",
    abstract = "Neural attention, especially the self-attention made popular by the Transformer, has become the workhorse of state-of-the-art natural language processing (NLP) models. Very recent work suggests that the self-attention in the Transformer encodes syntactic information; Here, we show that self-attention scores encode semantics by considering sentiment analysis tasks. In contrast to gradient-based feature attribution methods, we propose a simple and effective Layer-wise Attention Tracing (LAT) method to analyze structured attention weights. We apply our method to Transformer models trained on two tasks that have surface dissimilarities, but share common semantics{---}sentiment analysis of movie reviews and time-series valence prediction in life story narratives. Across both tasks, words with high aggregated attention weights were rich in emotional semantics, as quantitatively validated by an emotion lexicon labeled by human annotators. Our results show that structured attention weights encode rich semantics in sentiment analysis, and match human interpretations of semantics.",
}
@inproceedings{thrush-etal-2020-investigating,
    title = "Investigating Novel Verb Learning in {BERT}: Selectional Preference Classes and Alternation-Based Syntactic Generalization",
    author = "Thrush, Tristan  and
      Wilcox, Ethan  and
      Levy, Roger",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.25",
    doi = "10.18653/v1/2020.blackboxnlp-1.25",
    pages = "265--275",
    abstract = "Previous studies investigating the syntactic abilities of deep learning models have not targeted the relationship between the strength of the grammatical generalization and the amount of evidence to which the model is exposed during training. We address this issue by deploying a novel word-learning paradigm to test BERT{'}s few-shot learning capabilities for two aspects of English verbs: alternations and classes of selectional preferences. For the former, we fine-tune BERT on a single frame in a verbal-alternation pair and ask whether the model expects the novel verb to occur in its sister frame. For the latter, we fine-tune BERT on an incomplete selectional network of verbal objects and ask whether it expects unattested but plausible verb/object pairs. We find that BERT makes robust grammatical generalizations after just one or two instances of a novel word in fine-tuning. For the verbal alternation tests, we find that the model displays behavior that is consistent with a transitivity bias: verbs seen few times are expected to take direct objects, but verbs seen with direct objects are not expected to occur intransitively.",
}
@inproceedings{newman-etal-2020-eos,
    title = "The {EOS} Decision and Length Extrapolation",
    author = "Newman, Benjamin  and
      Hewitt, John  and
      Liang, Percy  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.26",
    doi = "10.18653/v1/2020.blackboxnlp-1.26",
    pages = "276--291",
    abstract = "Extrapolation to unseen sequence lengths is a challenge for neural generative models of language. In this work, we characterize the effect on length extrapolation of a modeling decision often overlooked: predicting the end of the generative process through the use of a special end-of-sequence (EOS) vocabulary item. We study an oracle setting - forcing models to generate to the correct sequence length at test time - to compare the length-extrapolative behavior of networks trained to predict EOS (+EOS) with networks not trained to (-EOS). We find that -EOS substantially outperforms +EOS, for example extrapolating well to lengths 10 times longer than those seen at training time in a bracket closing task, as well as achieving a 40{\%} improvement over +EOS in the difficult SCAN dataset length generalization task. By comparing the hidden states and dynamics of -EOS and +EOS models, we observe that +EOS models fail to generalize because they (1) unnecessarily stratify their hidden states by their linear position is a sequence (structures we call length manifolds) or (2) get stuck in clusters (which we refer to as length attractors) once the EOS token is the highest-probability prediction.",
}
@inproceedings{zhang-etal-2020-language-embeddings-capture,
    title = "Do Language Embeddings capture Scales?",
    author = "Zhang, Xikun  and
      Ramachandran, Deepak  and
      Tenney, Ian  and
      Elazar, Yanai  and
      Roth, Dan",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.27",
    doi = "10.18653/v1/2020.blackboxnlp-1.27",
    pages = "292--299",
    abstract = "Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.",
}
@inproceedings{hao-2020-evaluating,
    title = "Evaluating Attribution Methods using White-Box {LSTM}s",
    author = "Hao, Yiding",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.28",
    doi = "10.18653/v1/2020.blackboxnlp-1.28",
    pages = "300--313",
    abstract = "Interpretability methods for neural networks are difficult to evaluate because we do not understand the black-box models typically used to test them. This paper proposes a framework in which interpretability methods are evaluated using manually constructed networks, which we call white-box networks, whose behavior is understood a priori. We evaluate five methods for producing attribution heatmaps by applying them to white-box LSTM classifiers for tasks based on formal languages. Although our white-box classifiers solve their tasks perfectly and transparently, we find that all five attribution methods fail to produce the expected model explanations.",
}
@inproceedings{verma-etal-2020-defining,
    title = "Defining Explanation in an {AI} Context",
    author = "Verma, Tejaswani  and
      Lingenfelder, Christoph  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.29",
    doi = "10.18653/v1/2020.blackboxnlp-1.29",
    pages = "314--322",
    abstract = "With the increase in the use of AI systems, a need for explanation systems arises. Building an explanation system requires a definition of explanation. However, the natural language term explanation is difficult to define formally as it includes multiple perspectives from different domains such as psychology, philosophy, and cognitive sciences. We study multiple perspectives and aspects of explainability of recommendations or predictions made by AI systems, and provide a generic definition of explanation. The proposed definition is ambitious and challenging to apply. With the intention to bridge the gap between theory and application, we also propose a possible architecture of an automated explanation system based on our definition of explanation.",
}
@inproceedings{yoo-etal-2020-searching,
    title = "Searching for a Search Method: Benchmarking Search Algorithms for Generating {NLP} Adversarial Examples",
    author = "Yoo, Jin Yong  and
      Morris, John  and
      Lifland, Eli  and
      Qi, Yanjun",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.30",
    doi = "10.18653/v1/2020.blackboxnlp-1.30",
    pages = "323--332",
    abstract = "We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks. We perform a fine-grained analysis of three elements relevant to search: search algorithm, search space, and search budget. When new search algorithms are proposed in past work, the attack search space is often modified alongside the search algorithm. Without ablation studies benchmarking the search algorithm change with the search space held constant, one cannot tell if an increase in attack success rate is a result of an improved search algorithm or a less restrictive search space. Additionally, many previous studies fail to properly consider the search algorithms{'} run-time cost, which is essential for downstream tasks like adversarial training. Our experiments provide a reproducible benchmark of search algorithms across a variety of search spaces and query budgets to guide future research in adversarial NLP. Based on our experiments, we recommend greedy attacks with word importance ranking when under a time constraint or attacking long inputs, and either beam search or particle swarm optimization otherwise.",
}
@inproceedings{haley-2020-bert,
    title = "This is a {BERT}. Now there are several of them. Can they generalize to novel words?",
    author = "Haley, Coleman",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.31",
    doi = "10.18653/v1/2020.blackboxnlp-1.31",
    pages = "333--341",
    abstract = "Recently, large-scale pre-trained neural network models such as BERT have achieved many state-of-the-art results in natural language processing. Recent work has explored the linguistic capacities of these models. However, no work has focused on the ability of these models to generalize these capacities to novel words. This type of generalization is exhibited by humans, and is intimately related to morphology{---}humans are in many cases able to identify inflections of novel words in the appropriate context. This type of morphological capacity has not been previously tested in BERT models, and is important for morphologically-rich languages, which are under-studied in the literature regarding BERT{'}s linguistic capacities. In this work, we investigate this by considering monolingual and multilingual BERT models{'} abilities to agree in number with novel plural words in English, French, German, Spanish, and Dutch. We find that many models are not able to reliably determine plurality of novel words, suggesting potential deficiencies in the morphological capacities of BERT models.",
}
@inproceedings{jumelet-2020-diagnnose,
    title = "diag{NN}ose: A Library for Neural Activation Analysis",
    author = "Jumelet, Jaap",
    booktitle = "Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.blackboxnlp-1.32",
    doi = "10.18653/v1/2020.blackboxnlp-1.32",
    pages = "342--350",
    abstract = "In this paper we introduce diagNNose, an open source library for analysing the activations of deep neural networks. diagNNose contains a wide array of interpretability techniques that provide fundamental insights into the inner workings of neural networks. We demonstrate the functionality of diagNNose with a case study on subject-verb agreement within language models. diagNNose is available at https://github.com/i-machine-think/diagnnose.",
}
@proceedings{bionlp-2020-sigbiomed,
    title = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.0",
}
@inproceedings{rios-etal-2020-quantifying,
    title = "Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings",
    author = "Rios, Anthony  and
      Joshi, Reenam  and
      Shin, Hejin",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.1",
    doi = "10.18653/v1/2020.bionlp-1.1",
    pages = "1--13",
    abstract = "Gender bias in biomedical research can have an adverse impact on the health of real people. For example, there is evidence that heart disease-related funded research generally focuses on men. Health disparities can form between men and at-risk groups of women (i.e., elderly and low-income) if there is not an equal number of heart disease-related studies for both genders. In this paper, we study temporal bias in biomedical research articles by measuring gender differences in word embeddings. Specifically, we address multiple questions, including, How has gender bias changed over time in biomedical research, and what health-related concepts are the most biased? Overall, we find that traditional gender stereotypes have reduced over time. However, we also find that the embeddings of many medical conditions are as biased today as they were 60 years ago (e.g., concepts related to drug addiction and body dysmorphia).",
}
@inproceedings{das-etal-2020-sequence,
    title = "Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical {IR} using Self-Attention",
    author = "Das, Manirupa  and
      Li, Juanxi  and
      Fosler-Lussier, Eric  and
      Lin, Simon  and
      Rust, Steve  and
      Huang, Yungui  and
      Ramnath, Rajiv",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.2",
    doi = "10.18653/v1/2020.bionlp-1.2",
    pages = "14--27",
    abstract = "Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature. These may not explicitly refer to entities or canonical concept forms occurring in a fact-based knowledge source, e.g. the UMLS ontology. Moreover, hidden associations between related concepts meaningful in the current context, may not exist within a single document, but across documents in the collection. Predicting semantic concept tags of documents can therefore serve to associate documents related in unseen contexts, or categorize them, in information filtering or retrieval scenarios. Thus, inspired by the success of sequence-to-sequence neural models, we develop a novel sequence-to-set framework with attention, for learning document representations in a unique unsupervised setting, using no human-annotated document labels or external knowledge resources and only corpus-derived term statistics to drive the training, that can effect term transfer within a corpus for semantically tagging a large collection of documents. Our sequence-to-set modeling approach to predict semantic tags, gives to the best of our knowledge, the state-of-the-art for both, an unsupervised query expansion (QE) task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25{--}based document retrieval system; and also over the MLTM system baseline baseline (Soleimani and Miller, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del.icio.us and Ohsumed datasets. We make our code and data publicly available.",
}
@inproceedings{taub-tabib-etal-2020-interactive,
    title = "Interactive Extractive Search over Biomedical Corpora",
    author = "Taub Tabib, Hillel  and
      Shlain, Micah  and
      Sadde, Shoval  and
      Lahav, Dan  and
      Eyal, Matan  and
      Cohen, Yaara  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.3",
    doi = "10.18653/v1/2020.bionlp-1.3",
    pages = "28--37",
    abstract = "We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to dependency-based search, we introduce a light-weight query language that does not require the user to know the details of the underlying linguistic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due to efficient linguistic graph-indexing and retrieval engine. This allows for rapid exploration, development and refinement of user queries. We demonstrate the system using example workflows over two corpora: the PubMed corpus including 14,446,243 PubMed abstracts and the CORD-19 dataset, a collection of over 45,000 research papers focused on COVID-19 research. The system is publicly available at https://allenai.github.io/spike",
}
@inproceedings{paullada-etal-2020-improving,
    title = "Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies",
    author = "Paullada, Amandalynne  and
      Percha, Bethany  and
      Cohen, Trevor",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.4",
    doi = "10.18653/v1/2020.bionlp-1.4",
    pages = "38--48",
    abstract = "Inferring the nature of the relationships between biomedical entities from text is an important problem due to the difficulty of maintaining human-curated knowledge bases in rapidly evolving fields. Neural word embeddings have earned attention for an apparent ability to encode relational information. However, word embedding models that disregard syntax during training are limited in their ability to encode the structural relationships fundamental to cognitive theories of analogy. In this paper, we demonstrate the utility of encoding dependency structure in word embeddings in a model we call Embedding of Structural Dependencies (ESD) as a way to represent biomedical relationships in two analogical retrieval tasks: a relationship retrieval (RR) task, and a literature-based discovery (LBD) task meant to hypothesize plausible relationships between pairs of entities unseen in training. We compare our model to skip-gram with negative sampling (SGNS), using 19 databases of biomedical relationships as our evaluation data, with improvements in performance on 17 (LBD) and 18 (RR) of these sets. These results suggest embeddings encoding dependency path information are of value for biomedical analogy retrieval.",
}
@inproceedings{koroleva-etal-2020-despin,
    title = "{D}e{S}pin: a prototype system for detecting spin in biomedical publications",
    author = "Koroleva, Anna  and
      Kamath, Sanjay  and
      Bossuyt, Patrick  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.5",
    doi = "10.18653/v1/2020.bionlp-1.5",
    pages = "49--59",
    abstract = "Improving the quality of medical research reporting is crucial to reduce avoidable waste in research and to improve the quality of health care. Despite various initiatives aiming at improving research reporting {--} guidelines, checklists, authoring aids, peer review procedures, etc. {--} overinterpretation of research results, also known as spin, is still a serious issue in research reporting. In this paper, we propose a Natural Language Processing (NLP) system for detecting several types of spin in biomedical articles reporting randomized controlled trials (RCTs). We use a combination of rule-based and machine learning approaches to extract important information on trial design and to detect potential spin. The proposed spin detection system includes algorithms for text structure analysis, sentence classification, entity and relation extraction, semantic similarity assessment. Our algorithms achieved operational performance for the these tasks, F-measure ranging from 79,42 to 97.86{\%} for different tasks. The most difficult task is extracting reported outcomes. Our tool is intended to be used as a semi-automated aid tool for assisting both authors and peer reviewers to detect potential spin. The tool incorporates a simple interface that allows to run the algorithms and visualize their output. It can also be used for manual annotation and correction of the errors in the outputs. The proposed tool is the first tool for spin detection. The tool and the annotated dataset are freely available.",
}
@inproceedings{kovaleva-etal-2020-towards,
    title = "Towards Visual Dialog for Radiology",
    author = "Kovaleva, Olga  and
      Shivade, Chaitanya  and
      Kashyap, Satyananda  and
      Kanjaria, Karina  and
      Wu, Joy  and
      Ballah, Deddeh  and
      Coy, Adam  and
      Karargyris, Alexandros  and
      Guo, Yufan  and
      Beymer, David Beymer  and
      Rumshisky, Anna  and
      Mukherjee, Vandana Mukherjee",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.6",
    doi = "10.18653/v1/2020.bionlp-1.6",
    pages = "60--69",
    abstract = "Current research in machine learning for radiology is focused mostly on images. There exists limited work in investigating intelligent interactive systems for radiology. To address this limitation, we introduce a realistic and information-rich task of Visual Dialog in radiology, specific to chest X-ray images. Using MIMIC-CXR, an openly available database of chest X-ray images, we construct both a synthetic and a real-world dataset and provide baseline scores achieved by state-of-the-art models. We show that incorporating medical history of the patient leads to better performance in answering questions as opposed to conventional visual question answering model which looks only at the image. While our experiments show promising results, they indicate that the task is extremely challenging with significant scope for improvement. We make both the datasets (synthetic and gold standard) and the associated code publicly available to the research community.",
}
@inproceedings{lin-etal-2020-bert,
    title = "A {BERT}-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction",
    author = "Lin, Chen  and
      Miller, Timothy  and
      Dligach, Dmitriy  and
      Sadeque, Farig  and
      Bethard, Steven  and
      Savova, Guergana",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.7",
    doi = "10.18653/v1/2020.bionlp-1.7",
    pages = "70--75",
    abstract = "Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is inefficient as it requires multiple passes through each input sequence. We extend a recently-proposed one-pass model for relation classification to a one-pass model for relation extraction. We augment this framework by introducing global embeddings to help with long-distance relation inference, and by multi-task learning to increase model performance and generalizability. Our proposed model produces results on par with the state-of-the-art in temporal relation extraction on the THYME corpus and is much {``}greener{''} in computational cost.",
}
@inproceedings{searle-etal-2020-experimental,
    title = "Experimental Evaluation and Development of a Silver-Standard for the {MIMIC}-{III} Clinical Coding Dataset",
    author = "Searle, Thomas  and
      Ibrahim, Zina  and
      Dobson, Richard",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.8",
    doi = "10.18653/v1/2020.bionlp-1.8",
    pages = "76--85",
    abstract = "Clinical coding is currently a labour-intensive, error-prone, but a critical administrative process whereby hospital patient episodes are manually assigned codes by qualified staff from large, standardised taxonomic hierarchies of codes. Automating clinical coding has a long history in NLP research and has recently seen novel developments setting new benchmark results. A popular dataset used in this task is MIMIC-III, a large database of clinical free text notes and their associated codes amongst other data. We argue for the reconsideration of the validity MIMIC-III{'}s assigned codes, as MIMIC-III has not undergone secondary validation. This work presents an open-source, reproducible experimental methodology for assessing the validity of EHR discharge summaries. We exemplify the methodology with MIMIC-III discharge summaries and show the most frequently assigned codes in MIMIC-III are undercoded up to 35{\%}.",
}
@inproceedings{mascio-etal-2020-comparative,
    title = "Comparative Analysis of Text Classification Approaches in Electronic Health Records",
    author = "Mascio, Aurelie  and
      Kraljevic, Zeljko  and
      Bean, Daniel  and
      Dobson, Richard  and
      Stewart, Robert  and
      Bendayan, Rebecca  and
      Roberts, Angus",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.9",
    doi = "10.18653/v1/2020.bionlp-1.9",
    pages = "86--94",
    abstract = "Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However these present specific challenges compared to other classification tasks, notably due to the particular nature of the medical lexicon and language used in clinical records. Recent advances in embedding methods have shown promising results for several clinical tasks, yet there is no exhaustive comparison of such approaches with other commonly used word representations and classification models. In this work, we analyse the impact of various word representations, text pre-processing and classification algorithms on the performance of four different text classification tasks. The results show that traditional approaches, when tailored to the specific language and structure of the text inherent to the classification task, can achieve or exceed the performance of more recent ones based on contextual embeddings such as BERT.",
}
@inproceedings{xu-etal-2020-noise,
    title = "Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning",
    author = "Xu, Liyan  and
      Hogan, Julien  and
      Patzer, Rachel E.  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.10",
    doi = "10.18653/v1/2020.bionlp-1.10",
    pages = "95--104",
    abstract = "This paper presents a reinforcement learning approach to extract noise in long clinical documents for the task of readmission prediction after kidney transplant. We face the challenges of developing robust models on a small dataset where each document may consist of over 10K tokens with full of noise including tabular text and task-irrelevant sentences. We first experiment four types of encoders to empirically decide the best document representation, and then apply reinforcement learning to remove noisy text from the long documents, which models the noise extraction process as a sequential decision problem. Our results show that the old bag-of-words encoder outperforms deep learning-based encoders on this task, and reinforcement learning is able to improve upon baseline while pruning out 25{\%} text segments. Our analysis depicts that reinforcement learning is able to identify both typical noisy tokens and task-specific noisy text.",
}
@inproceedings{wang-etal-2020-evaluating,
    title = "Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity",
    author = "Wang, Yuxia  and
      Liu, Fei  and
      Verspoor, Karin  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.11",
    doi = "10.18653/v1/2020.bionlp-1.11",
    pages = "105--111",
    abstract = "In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain. In low-resource setting of clinical STS, these large models tend to be impractical and prone to overfitting. Building on BERT, we study the impact of a number of model design choices, namely different fine-tuning and pooling strategies. We observe that the impact of domain-specific fine-tuning on clinical STS is much less than that in the general domain, likely due to the concept richness of the domain. Based on this, we propose two data augmentation techniques. Experimental results on N2C2-STS 1 demonstrate substantial improvements, validating the utility of the proposed methods.",
}
@inproceedings{rawat-etal-2020-entity,
    title = "Entity-Enriched Neural Models for Clinical Question Answering",
    author = "Rawat, Bhanu Pratap Singh  and
      Weng, Wei-Hung  and
      Min, So Yeon  and
      Raghavan, Preethi  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.12",
    doi = "10.18653/v1/2020.bionlp-1.12",
    pages = "112--122",
    abstract = "We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time. We enable this by learning to predict logical forms as an auxiliary task along with the main task of answer span detection. The predicted logical forms also serve as a rationale for the answer. Further, we also incorporate medical entity information in these models via the ERNIE architecture. We train our models on the large-scale emrQA dataset and observe that our multi-task entity-enriched models generalize to paraphrased questions {\textasciitilde}5{\%} better than the baseline BERT model.",
}
@inproceedings{deyoung-etal-2020-evidence,
    title = "Evidence Inference 2.0: More Data, Better Models",
    author = "DeYoung, Jay  and
      Lehman, Eric  and
      Nye, Benjamin  and
      Marshall, Iain  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.13",
    doi = "10.18653/v1/2020.bionlp-1.13",
    pages = "123--132",
    abstract = "How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no such database exists; clinical trial results are instead disseminated primarily via lengthy natural language articles. Perusing all such articles would be prohibitively time-consuming for healthcare practitioners; they instead tend to depend on manually compiled \textit{systematic reviews} of medical literature to inform care. NLP may speed this process up, and eventually facilitate immediate consult of published evidence. The \textit{Evidence Inference} dataset was recently released to facilitate research toward this end. This task entails inferring the comparative performance of two treatments, with respect to a given outcome, from a particular article (describing a clinical trial) and identifying supporting evidence. For instance: Does this article report that \textit{chemotherapy} performed better than \textit{surgery} for \textit{five-year survival rates} of operable cancers? In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25{\%}, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality. We also release an \textit{abstract only} (as opposed to full-texts) version of the task for rapid model prototyping. The updated corpus, documentation, and code for new baselines and evaluations are available at \url{http://evidence-inference.ebm-nlp.com/}.",
}
@inproceedings{wang-etal-2020-personalized,
    title = "Personalized Early Stage {A}lzheimer{'}s Disease Detection: A Case Study of President {R}eagan{'}s Speeches",
    author = "Wang, Ning  and
      Luo, Fan  and
      Peddagangireddy, Vishal  and
      Subbalakshmi, Koduvayur  and
      Chandramouli, Rajarathnam",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.14",
    doi = "10.18653/v1/2020.bionlp-1.14",
    pages = "133--139",
    abstract = "Alzheimer{'}s disease (AD)-related global healthcare cost is estimated to be {\$}1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer{'}s disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer{'}s disease. We demonstrate this approach on 10 year{'}s (1980 to 1989) of President Ronald Reagan{'}s speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer{'}s sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique. The proposed technique also identifies the exact speeches that reflect linguistic biomarkers for early stage AD.",
}
@inproceedings{pappas-etal-2020-biomrc,
    title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
    author = "Pappas, Dimitris  and
      Stavropoulos, Petros  and
      Androutsopoulos, Ion  and
      McDonald, Ryan",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
    doi = "10.18653/v1/2020.bionlp-1.15",
    pages = "140--149",
    abstract = "We introduceBIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.",
}
@inproceedings{bleiweiss-2020-neural,
    title = "Neural Transduction of Letter Position Dyslexia using an Anagram Matrix Representation",
    author = "Bleiweiss, Avi",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.16",
    doi = "10.18653/v1/2020.bionlp-1.16",
    pages = "150--155",
    abstract = "Research on analyzing reading patterns of dyslectic children has mainly been driven by classifying dyslexia types offline. We contend that a framework to remedy reading errors inline is more far-reaching and will help to further advance our understanding of this impairment. In this paper, we propose a simple and intuitive neural model to reinstate migrating words that transpire in letter position dyslexia, a visual analysis deficit to the encoding of character order within a word. Introduced by the anagram matrix representation of an input verse, the novelty of our work lies in the expansion from one to a two dimensional context window for training. This warrants words that only differ in the disposition of letters to remain interpreted semantically similar in the embedding space. Subject to the apparent constraints of the self-attention transformer architecture, our model achieved a unigram BLEU score of 40.6 on our reconstructed dataset of the Shakespeare sonnets.",
}
@inproceedings{hur-etal-2020-domain,
    title = "Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes",
    author = "Hur, Brian  and
      Baldwin, Timothy  and
      Verspoor, Karin  and
      Hardefeldt, Laura  and
      Gilkerson, James",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.17",
    doi = "10.18653/v1/2020.bionlp-1.17",
    pages = "156--166",
    abstract = "Identifying the reasons for antibiotic administration in veterinary records is a critical component of understanding antimicrobial usage patterns. This informs antimicrobial stewardship programs designed to fight antimicrobial resistance, a major health crisis affecting both humans and animals in which veterinarians have an important role to play. We propose a document classification approach to determine the reason for administration of a given drug, with particular focus on domain adaptation from one drug to another, and instance selection to minimize annotation effort.",
}
@inproceedings{chang-etal-2020-benchmark,
    title = "Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings",
    author = "Chang, David  and
      Bala{\v{z}}evi{\'c}, Ivana  and
      Allen, Carl  and
      Chawla, Daniel  and
      Brandt, Cynthia  and
      Taylor, Andrew",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.18",
    doi = "10.18653/v1/2020.bionlp-1.18",
    pages = "167--176",
    abstract = "Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text and medical codes. There is a wealth of expert-curated biomedical domain knowledge stored in knowledge bases and ontologies, but the lack of reliable methods for learning knowledge representation has limited their usefulness in machine learning applications. While text-based representation learning has significantly improved in recent years through advances in natural language processing, attempts to learn biomedical concept embeddings so far have been lacking. A recent family of models called knowledge graph embeddings have shown promising results on general domain knowledge graphs, and we explore their capabilities in the biomedical domain. We train several state-of-the-art knowledge graph embedding models on the SNOMED-CT knowledge graph, provide a benchmark with comparison to existing methods and in-depth discussion on best practices, and make a case for the importance of leveraging the multi-relational nature of knowledge graphs for learning biomedical knowledge representation. The embeddings, code, and materials will be made available to the community.",
}
@inproceedings{nejadgholi-etal-2020-extensive,
    title = "Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience",
    author = "Nejadgholi, Isar  and
      Fraser, Kathleen C.  and
      de Bruijn, Berry",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.19",
    doi = "10.18653/v1/2020.bionlp-1.19",
    pages = "177--186",
    abstract = "When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span mismatch. Here we focus on span mismatch and show that its severity can vary from a serious error to a fully acceptable entity extraction due to the subjectivity of span annotations. For a domain-specific BERT-based NER system, we showed that 25{\%} of the errors have the same labels and overlapping span with gold standard entities. We collected expert judgement which shows more than 90{\%} of these mismatches are accepted or partially accepted by the user. Using the training set of the NER system, we built a fast and lightweight entity classifier to approximate the user experience of such mismatches through accepting or rejecting them. The decisions made by this classifier are used to calculate a learning-based F-score which is shown to be a better approximation of a forgiving user{'}s experience than the relaxed F-score. We demonstrated the results of applying the proposed evaluation metric for a variety of deep learning medical entity recognition models trained with two datasets.",
}
@inproceedings{amin-etal-2020-data,
    title = "A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction",
    author = "Amin, Saadullah  and
      Dunfield, Katherine Ann  and
      Vechkaeva, Anna  and
      Neumann, Guenter",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.20",
    doi = "10.18653/v1/2020.bionlp-1.20",
    pages = "187--194",
    abstract = "Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive. Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data. We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction. Our approach further encodes knowledge about the direction of relation triples, allowing for increased focus on relation learning by reducing noise and alleviating the need for joint learning with knowledge graph completion.",
}
@inproceedings{shafieibavani-etal-2020-global,
    title = "Global Locality in Biomedical Relation and Event Extraction",
    author = "ShafieiBavani, Elaheh  and
      Jimeno Yepes, Antonio  and
      Zhong, Xu  and
      Martinez Iraola, David",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.21",
    doi = "10.18653/v1/2020.bionlp-1.21",
    pages = "195--204",
    abstract = "Due to the exponential growth of biomedical literature, event and relation extraction are important tasks in biomedical text mining. Most work only focus on relation extraction, and detect a single entity pair mention on a short span of text, which is not ideal due to long sentences that appear in biomedical contexts. We propose an approach to both relation and event extraction, for simultaneously predicting relationships between all mention pairs in a text. We also perform an empirical study to discuss different network setups for this purpose. The best performing model includes a set of multi-head attentions and convolutions, an adaptation of the transformer architecture, which offers self-attention the ability to strengthen dependencies among related elements, and models the interaction between features extracted by multiple attention heads. Experiment results demonstrate that our approach outperforms the state of the art on a set of benchmark biomedical corpora including BioNLP 2009, 2011, 2013 and BioCreative 2017 shared tasks.",
}
@inproceedings{peng-etal-2020-empirical,
    title = "An Empirical Study of Multi-Task Learning on {BERT} for Biomedical Text Mining",
    author = "Peng, Yifan  and
      Chen, Qingyu  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.22",
    doi = "10.18653/v1/2020.bionlp-1.22",
    pages = "205--214",
    abstract = "Multi-task learning (MTL) has achieved remarkable success in natural language processing applications. In this work, we study a multi-task learning model with multiple decoders on varieties of biomedical and clinical natural language processing tasks such as text similarity, relation extraction, named entity recognition, and text inference. Our empirical results demonstrate that the MTL fine-tuned models outperform state-of-the-art transformer models (e.g., BERT and its variants) by 2.0{\%} and 1.3{\%} in biomedical and clinical domain adaptation, respectively. Pairwise MTL further demonstrates more details about which tasks can improve or decrease others. This is particularly helpful in the context that researchers are in the hassle of choosing a suitable model for new problems. The code and models are publicly available at https://github.com/ncbi-nlp/bluebert.",
}
@proceedings{bea-2020-innovative,
    title = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    editor = "Burstein, Jill  and
      Kochmar, Ekaterina  and
      Leacock, Claudia  and
      Madnani, Nitin  and
      Pil{\'a}n, Ildik{\'o}  and
      Yannakoudakis, Helen  and
      Zesch, Torsten",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.0",
}
@inproceedings{deutsch-etal-2020-linguistic,
    title = "Linguistic Features for Readability Assessment",
    author = "Deutsch, Tovly  and
      Jasbi, Masoud  and
      Shieber, Stuart",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.1",
    doi = "10.18653/v1/2020.bea-1.1",
    pages = "1--17",
    abstract = "Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing this question. Evaluating on two large readability corpora, we find that, given sufficient training data, augmenting deep learning models with linguistically motivated features does not improve state-of-the-art performance. Our results provide preliminary evidence for the hypothesis that the state-of-the-art deep learning models represent linguistic features of the text related to readability. Future research on the nature of representations formed in these models can shed light on the learned features and their relations to linguistically motivated ones hypothesized in traditional approaches.",
}
@inproceedings{loukina-etal-2020-using,
    title = "Using {PRMSE} to evaluate automated scoring systems in the presence of label noise",
    author = "Loukina, Anastassia  and
      Madnani, Nitin  and
      Cahill, Aoife  and
      Yao, Lili  and
      Johnson, Matthew S.  and
      Riordan, Brian  and
      McCaffrey, Daniel F.",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.2",
    doi = "10.18653/v1/2020.bea-1.2",
    pages = "18--29",
    abstract = "The effect of noisy labels on the performance of NLP systems has been studied extensively for system training. In this paper, we focus on the effect that noisy labels have on system evaluation. Using automated scoring as an example, we demonstrate that the quality of human ratings used for system evaluation have a substantial impact on traditional performance metrics, making it impossible to compare system evaluations on labels with different quality. We propose that a new metric, PRMSE, developed within the educational measurement community, can help address this issue, and provide practical guidelines on using PRMSE.",
}
@inproceedings{hellman-etal-2020-multiple,
    title = "Multiple Instance Learning for Content Feedback Localization without Annotation",
    author = "Hellman, Scott  and
      Murray, William  and
      Wiemerslage, Adam  and
      Rosenstein, Mark  and
      Foltz, Peter  and
      Becker, Lee  and
      Derr, Marcia",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.3",
    doi = "10.18653/v1/2020.bea-1.3",
    pages = "30--40",
    abstract = "Automated Essay Scoring (AES) can be used to automatically generate holistic scores with reliability comparable to human scoring. In addition, AES systems can provide formative feedback to learners, typically at the essay level. In contrast, we are interested in providing feedback specialized to the content of the essay, and specifically for the content areas required by the rubric. A key objective is that the feedback should be localized alongside the relevant essay text. An important step in this process is determining where in the essay the rubric designated points and topics are discussed. A natural approach to this task is to train a classifier using manually annotated data; however, collecting such data is extremely resource intensive. Instead, we propose a method to predict these annotation spans without requiring any labeled annotation data. Our approach is to consider AES as a Multiple Instance Learning (MIL) task. We show that such models can both predict content scores and localize content by leveraging their sentence-level score predictions. This capability arises despite never having access to annotation training data. Implications are discussed for improving formative feedback and explainable AES models.",
}
@inproceedings{raina-etal-2020-complementary,
    title = "Complementary Systems for Off-Topic Spoken Response Detection",
    author = "Raina, Vatsal  and
      Gales, Mark  and
      Knill, Kate",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.4",
    doi = "10.18653/v1/2020.bea-1.4",
    pages = "41--51",
    abstract = "Increased demand to learn English for business and education has led to growing interest in automatic spoken language assessment and teaching systems. With this shift to automated approaches it is important that systems reliably assess all aspects of a candidate{'}s responses. This paper examines one form of spoken language assessment; whether the response from the candidate is relevant to the prompt provided. This will be referred to as off-topic spoken response detection. Two forms of previously proposed approaches are examined in this work: the hierarchical attention-based topic model (HATM); and the similarity grid model (SGM). The work focuses on the scenario when the prompt, and associated responses, have not been seen in the training data, enabling the system to be applied to new test scripts without the need to collect data or retrain the model. To improve the performance of the systems for unseen prompts, data augmentation based on easy data augmentation (EDA) and translation based approaches are applied. Additionally for the HATM, a form of prompt dropout is described. The systems were evaluated on both seen and unseen prompts from Linguaskill Business and General English tests. For unseen data the performance of the HATM was improved using data augmentation, in contrast to the SGM where no gains were obtained. The two approaches were found to be complementary to one another, yielding a combined F0.5 score of 0.814 for off-topic response detection where the prompts have not been seen in training.",
}
@inproceedings{stasaski-etal-2020-cima,
    title = "{CIMA}: A Large Open Access Dialogue Dataset for Tutoring",
    author = "Stasaski, Katherine  and
      Kao, Kimberly  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.5",
    doi = "10.18653/v1/2020.bea-1.5",
    pages = "52--64",
    abstract = "One-to-one tutoring is often an effective means to help students learn, and recent experiments with neural conversation systems are promising. However, large open datasets of tutoring conversations are lacking. To remedy this, we propose a novel asynchronous method for collecting tutoring dialogue via crowdworkers that is both amenable to the needs of deep learning algorithms and reflective of pedagogical concerns. In this approach, extended conversations are obtained between crowdworkers role-playing as both students and tutors. The CIMA collection, which we make publicly available, is novel in that students are exposed to overlapping grounded concepts between exercises and multiple relevant tutoring responses are collected for the same input. CIMA contains several compelling properties from an educational perspective: student role-players complete exercises in fewer turns during the course of the conversation and tutor players adopt strategies that conform with some educational conversational norms, such as providing hints versus asking questions in appropriate contexts. The dataset enables a model to be trained to generate the next tutoring utterance in a conversation, conditioned on a provided action strategy.",
}
@inproceedings{kerz-etal-2020-becoming,
    title = "Becoming Linguistically Mature: Modeling {E}nglish and {G}erman Children{'}s Writing Development Across School Grades",
    author = {Kerz, Elma  and
      Qiao, Yu  and
      Wiechmann, Daniel  and
      Str{\"o}bel, Marcus},
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.6",
    doi = "10.18653/v1/2020.bea-1.6",
    pages = "65--74",
    abstract = "In this paper we employ a novel approach to advancing our understanding of the development of writing in English and German children across school grades using classification tasks. The data used come from two recently compiled corpora: The English data come from the the GiC corpus (983 school children in second-, sixth-, ninth- and eleventh-grade) and the German data are from the FD-LEX corpus (930 school children in fifth- and ninth-grade). The key to this paper is the combined use of what we refer to as {`}complexity contours{'}, i.e. series of measurements that capture the progression of linguistic complexity within a text, and Recurrent Neural Network (RNN) classifiers that adequately capture the sequential information in those contours. Our experiments demonstrate that RNN classifiers trained on complexity contours achieve higher classification accuracy than one trained on text-average complexity scores. In a second step, we determine the relative importance of the features from four distinct categories through a Sensitivity-Based Pruning approach.",
}
@inproceedings{afrin-etal-2020-annotation,
    title = "Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing",
    author = "Afrin, Tazin  and
      Wang, Elaine Lin  and
      Litman, Diane  and
      Matsumura, Lindsay Clare  and
      Correnti, Richard",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.7",
    doi = "10.18653/v1/2020.bea-1.7",
    pages = "75--84",
    abstract = "Automated writing evaluation systems can improve students{'} writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e.g., surface vs. content) rather than the extent to which revisions actually respond to the feedback provided and improve the essay. We introduce an annotation scheme to capture the nature of sentence-level revisions of evidence use and reasoning (the {`}RER{'} scheme) and apply it to 5th- and 6th-grade students{'} argumentative essays. We show that reliable manual annotation can be achieved and that revision annotations correlate with a holistic assessment of essay improvement in line with the feedback provided. Furthermore, we explore the feasibility of automatically classifying revisions according to our scheme.",
}
@inproceedings{mathias-bhattacharyya-2020-neural,
    title = "Can Neural Networks Automatically Score Essay Traits?",
    author = "Mathias, Sandeep  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.8",
    doi = "10.18653/v1/2020.bea-1.8",
    pages = "85--91",
    abstract = "Essay traits are attributes of an essay that can help explain how well written (or badly written) the essay is. Examples of traits include Content, Organization, Language, Sentence Fluency, Word Choice, etc. A lot of research in the last decade has dealt with automatic holistic essay scoring - where a machine rates an essay and gives a score for the essay. However, writers need feedback, especially if they want to improve their writing - which is why trait-scoring is important. In this paper, we show how a deep-learning based system can outperform feature-based machine learning systems, as well as a string kernel system in scoring essay traits.",
}
@inproceedings{miaschi-etal-2020-tracking,
    title = "Tracking the Evolution of Written Language Competence in {L}2 {S}panish Learners",
    author = "Miaschi, Alessio  and
      Davidson, Sam  and
      Brunato, Dominique  and
      Dell{'}Orletta, Felice  and
      Sagae, Kenji  and
      Sanchez-Gutierrez, Claudia Helena  and
      Venturi, Giulia",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.9",
    doi = "10.18653/v1/2020.bea-1.9",
    pages = "92--101",
    abstract = "In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students{'} written productions. Beyond reporting classification results for different scenarios, we explore the connection between the most predictive features and the teaching curriculum, finding that our set of linguistic features often reflect the explicit instructions that students receive during each course.",
}
@inproceedings{gao-etal-2020-distractor,
    title = "Distractor Analysis and Selection for Multiple-Choice Cloze Questions for Second-Language Learners",
    author = "Gao, Lingyu  and
      Gimpel, Kevin  and
      Jensson, Arnar",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.10",
    doi = "10.18653/v1/2020.bea-1.10",
    pages = "102--114",
    abstract = "We consider the problem of automatically suggesting distractors for multiple-choice cloze questions designed for second-language learners. We describe the creation of a dataset including collecting manual annotations for distractor selection. We assess the relationship between the choices of the annotators and features based on distractors and the correct answers, both with and without the surrounding passage context in the cloze questions. Simple features of the distractor and correct answer correlate with the annotations, though we find substantial benefit to additionally using large-scale pretrained models to measure the fit of the distractor in the context. Based on these analyses, we propose and train models to automatically select distractors, and measure the importance of model components quantitatively.",
}
@inproceedings{gonzalez-lopez-etal-2020-assisting,
    title = "Assisting Undergraduate Students in Writing {S}panish Methodology Sections",
    author = "Gonz{\'a}lez-L{\'o}pez, Samuel  and
      Bethard, Steven  and
      Lopez-Lopez, Aurelio",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.11",
    doi = "10.18653/v1/2020.bea-1.11",
    pages = "115--123",
    abstract = "In undergraduate theses, a good methodology section should describe the series of steps that were followed in performing the research. To assist students in this task, we develop machine-learning models and an app that uses them to provide feedback while students write. We construct an annotated corpus that identifies sentences representing methodological steps and labels when a methodology contains a logical sequence of such steps. We train machine-learning models based on language modeling and lexical features that can identify sentences representing methodological steps with 0.939 f-measure, and identify methodology sections containing a logical sequence of steps with an accuracy of 87{\%}. We incorporate these models into a Microsoft Office Add-in, and show that students who improved their methodologies according to the model feedback received better grades on their methodologies.",
}
@inproceedings{maxwelll-smith-etal-2020-applications,
    title = "Applications of Natural Language Processing in Bilingual Language Teaching: An {I}ndonesian-{E}nglish Case Study",
    author = "Maxwelll-Smith, Zara  and
      Gonz{\'a}lez Ochoa, Sim{\'o}n  and
      Foley, Ben  and
      Suominen, Hanna",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.12",
    doi = "10.18653/v1/2020.bea-1.12",
    pages = "124--134",
    abstract = "Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. In this paper, we set out methodological considerations of using automated speech recognition to build a corpus of teacher speech in an Indonesian language classroom. Our preliminary results (64{\%} word error rate) suggest these tools have the potential to speed data collection in this context. We provide practical examples of our data structure, details of our piloted computer-assisted processes, and fine-grained error analysis. Our study is informed and directed by genuine research questions and discussion in both the education and computational linguistics fields. We highlight some of the benefits and risks of using these emerging technologies to analyze the complex work of language teachers and in education more generally.",
}
@inproceedings{riordan-etal-2020-empirical,
    title = "An empirical investigation of neural methods for content scoring of science explanations",
    author = "Riordan, Brian  and
      Bichler, Sarah  and
      Bradford, Allison  and
      King Chen, Jennifer  and
      Wiley, Korah  and
      Gerard, Libby  and
      C. Linn, Marcia",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.13",
    doi = "10.18653/v1/2020.bea-1.13",
    pages = "135--144",
    abstract = "With the widespread adoption of the Next Generation Science Standards (NGSS), science teachers and online learning environments face the challenge of evaluating students{'} integration of different dimensions of science learning. Recent advances in representation learning in natural language processing have proven effective across many natural language processing tasks, but a rigorous evaluation of the relative merits of these methods for scoring complex constructed response formative assessments has not previously been carried out. We present a detailed empirical investigation of feature-based, recurrent neural network, and pre-trained transformer models on scoring content in real-world formative assessment data. We demonstrate that recent neural methods can rival or exceed the performance of feature-based methods. We also provide evidence that different classes of neural models take advantage of different learning cues, and pre-trained transformer models may be more robust to spurious, dataset-specific learning cues, better reflecting scoring rubrics.",
}
@inproceedings{ghosh-etal-2020-exploratory,
    title = "An Exploratory Study of Argumentative Writing by Young Students: A transformer-based Approach",
    author = "Ghosh, Debanjan  and
      Beigman Klebanov, Beata  and
      Song, Yi",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.14",
    doi = "10.18653/v1/2020.bea-1.14",
    pages = "145--150",
    abstract = "We present a computational exploration of argument critique writing by young students. Middle school students were asked to criticize an argument presented in the prompt, focusing on identifying and explaining the reasoning flaws. This task resembles an established college-level argument critique task. Lexical and discourse features that utilize detailed domain knowledge to identify critiques exist for the college task but do not perform well on the young students{'} data. Instead, transformer-based architecture (e.g., BERT) fine-tuned on a large corpus of critique essays from the college task performs much better (over 20{\%} improvement in F1 score). Analysis of the performance of various configurations of the system suggests that while children{'}s writing does not exhibit the standard discourse structure of an argumentative essay, it does share basic local sequential structures with the more mature writers.",
}
@inproceedings{mayfield-black-2020-fine,
    title = "Should You Fine-Tune {BERT} for Automated Essay Scoring?",
    author = "Mayfield, Elijah  and
      Black, Alan W",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.15",
    doi = "10.18653/v1/2020.bea-1.15",
    pages = "151--162",
    abstract = "Most natural language processing research now recommends large Transformer-based models with fine-tuning for supervised classification tasks; older strategies like bag-of-words features and linear models have fallen out of favor. Here we investigate whether, in automated essay scoring (AES) research, deep neural models are an appropriate technological choice. We find that fine-tuning BERT produces similar performance to classical models at significant additional cost. We argue that while state-of-the-art strategies do match existing best results, they come with opportunity costs in computational resources. We conclude with a review of promising areas for research on student essays where the unique characteristics of Transformers may provide benefits over classical methods to justify the costs.",
}
@inproceedings{omelianchuk-etal-2020-gector,
    title = "{GECT}o{R} {--} Grammatical Error Correction: Tag, Not Rewrite",
    author = "Omelianchuk, Kostiantyn  and
      Atrasevych, Vitaliy  and
      Chernodub, Artem  and
      Skurzhanskyi, Oleksandr",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.16",
    doi = "10.18653/v1/2020.bea-1.16",
    pages = "163--170",
    abstract = "In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an F{\_}0.5 of 65.3/66.5 on CONLL-2014 (test) and F{\_}0.5 of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system.",
}
@inproceedings{ehara-2020-interpreting,
    title = "Interpreting Neural {CWI} Classifiers{'} Weights as Vocabulary Size",
    author = "Ehara, Yo",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.17",
    doi = "10.18653/v1/2020.bea-1.17",
    pages = "171--176",
    abstract = "Complex Word Identification (CWI) is a task for the identification of words that are challenging for second-language learners to read. Even though the use of neural classifiers is now common in CWI, the interpretation of their parameters remains difficult. This paper analyzes neural CWI classifiers and shows that some of their parameters can be interpreted as vocabulary size. We present a novel formalization of vocabulary size measurement methods that are practiced in the applied linguistics field as a kind of neural classifier. We also contribute to building a novel dataset for validating vocabulary testing and readability via crowdsourcing.",
}
@inproceedings{wang-etal-2020-automated,
    title = "Automated Scoring of Clinical Expressive Language Evaluation Tasks",
    author = "Wang, Yiyi  and
      Prud{'}hommeaux, Emily  and
      Asgari, Meysam  and
      Dolata, Jill",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.18",
    doi = "10.18653/v1/2020.bea-1.18",
    pages = "177--185",
    abstract = "Many clinical assessment instruments used to diagnose language impairments in children include a task in which the subject must formulate a sentence to describe an image using a specific target word. Because producing sentences in this way requires the speaker to integrate syntactic and semantic knowledge in a complex manner, responses are typically evaluated on several different dimensions of appropriateness yielding a single composite score for each response. In this paper, we present a dataset consisting of non-clinically elicited responses for three related sentence formulation tasks, and we propose an approach for automatically evaluating their appropriateness. We use neural machine translation to generate correct-incorrect sentence pairs in order to create synthetic data to increase the amount and diversity of training data for our scoring model. Our scoring model uses transfer learning to facilitate automatic sentence appropriateness evaluation. We further compare custom word embeddings with pre-trained contextualized embeddings serving as features for our scoring model. We find that transfer learning improves scoring accuracy, particularly when using pretrained contextualized embeddings.",
}
@inproceedings{cahill-etal-2020-context,
    title = "Context-based Automated Scoring of Complex Mathematical Responses",
    author = "Cahill, Aoife  and
      Fife, James H  and
      Riordan, Brian  and
      Vajpayee, Avijit  and
      Galochkin, Dmytro",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.19",
    doi = "10.18653/v1/2020.bea-1.19",
    pages = "186--192",
    abstract = "The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately. In this paper we propose a method for automatically scoring responses that contain both text and algebraic expressions. Our method not only achieves high agreement with human raters, but also links explicitly to the scoring rubric {--} essentially providing explainable models and a way to potentially provide feedback to students in the future.",
}
@inproceedings{xue-etal-2020-predicting,
    title = "Predicting the Difficulty and Response Time of Multiple Choice Questions Using Transfer Learning",
    author = "Xue, Kang  and
      Yaneva, Victoria  and
      Runyon, Christopher  and
      Baldwin, Peter",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.20",
    doi = "10.18653/v1/2020.bea-1.20",
    pages = "193--197",
    abstract = "This paper investigates whether transfer learning can improve the prediction of the difficulty and response time parameters for 18,000 multiple-choice questions from a high-stakes medical exam. The type the signal that best predicts difficulty and response time is also explored, both in terms of representation abstraction and item component used as input (e.g., whole item, answer options only, etc.). The results indicate that, for our sample, transfer learning can improve the prediction of item difficulty when response time is used as an auxiliary task but not the other way around. In addition, difficulty was best predicted using signal from the item stem (the description of the clinical case), while all parts of the item were important for predicting the response time.",
}
@inproceedings{white-rozovskaya-2020-comparative,
    title = "A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction",
    author = "White, Max  and
      Rozovskaya, Alla",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.21",
    doi = "10.18653/v1/2020.bea-1.21",
    pages = "198--208",
    abstract = "Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text. Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of annotated training data, which can be expensive or impractical to obtain. This research compares techniques for generating synthetic data utilized by the two highest scoring submissions to the restricted and low-resource tracks in the BEA-2019 Shared Task on Grammatical Error Correction.",
}
@proceedings{autosimtrans-2020-automatic,
    title = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    editor = "Wu, Hua  and
      Cherry, Collin  and
      Huang, Liang  and
      He, Zhongjun  and
      Liberman, Mark  and
      Cross, James  and
      Liu, Yang",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.autosimtrans-1.0",
}
@inproceedings{zhang-zhang-2020-dynamic,
    title = "Dynamic Sentence Boundary Detection for Simultaneous Translation",
    author = "Zhang, Ruiqing  and
      Zhang, Chuanqiang",
    booktitle = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.autosimtrans-1.1",
    doi = "10.18653/v1/2020.autosimtrans-1.1",
    pages = "1--9",
    abstract = "Simultaneous Translation is a great challenge in which translation starts before the source sentence finished. Most studies take transcription as input and focus on balancing translation quality and latency for each sentence. However, most ASR systems can not provide accurate sentence boundaries in realtime. Thus it is a key problem to segment sentences for the word streaming before translation. In this paper, we propose a novel method for sentence boundary detection that takes it as a multi-class classification task under the end-to-end pre-training framework. Experiments show significant improvements both in terms of translation quality and latency.",
}
@inproceedings{li-etal-2020-end,
    title = "End-to-End Speech Translation with Adversarial Training",
    author = "Li, Xuancai  and
      Kehai, Chen  and
      Zhao, Tiejun  and
      Yang, Muyun",
    booktitle = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.autosimtrans-1.2",
    doi = "10.18653/v1/2020.autosimtrans-1.2",
    pages = "10--14",
    abstract = "End-to-End speech translation usually leverages audio-to-text parallel data to train an available speech translation model which has shown impressive results on various speech translation tasks. Due to the artificial cost of collecting audio-to-text parallel data, the speech translation is a natural low-resource translation scenario, which greatly hinders its improvement. In this paper, we proposed a new adversarial training method to leverage target monolingual data to relieve the low-resource shortcoming of speech translation. In our method, the existing speech translation model is considered as a Generator to gain a target language output, and another neural Discriminator is used to guide the distinction between outputs of speech translation model and true target monolingual sentences. Experimental results on the CCMT 2019-BSTC dataset speech translation task demonstrate that the proposed methods can significantly improve the performance of the End-to-End speech translation system.",
}
@inproceedings{xue-etal-2020-robust,
    title = "Robust Neural Machine Translation with {ASR} Errors",
    author = "Xue, Haiyang  and
      Feng, Yang  and
      Gu, Shuhao  and
      Chen, Wei",
    booktitle = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.autosimtrans-1.3",
    doi = "10.18653/v1/2020.autosimtrans-1.3",
    pages = "15--23",
    abstract = "In many practical applications, neural machine translation systems have to deal with the input from automatic speech recognition (ASR) systems which may contain a certain number of errors. This leads to two problems which degrade translation performance. One is the discrepancy between the training and testing data and the other is the translation error caused by the input errors may ruin the whole translation. In this paper, we propose a method to handle the two problems so as to generate robust translation to ASR errors. First, we simulate ASR errors in the training data so that the data distribution in the training and test is consistent. Second, we focus on ASR errors on homophone words and words with similar pronunciation and make use of their pronunciation information to help the translation model to recover from the input errors. Experiments on two Chinese-English data sets show that our method is more robust to input errors and can outperform the strong Transformer baseline significantly.",
}
@inproceedings{zhou-etal-2020-improving-autoregressive,
    title = "Improving Autoregressive {NMT} with Non-Autoregressive Model",
    author = "Zhou, Long  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.autosimtrans-1.4",
    doi = "10.18653/v1/2020.autosimtrans-1.4",
    pages = "24--29",
    abstract = "Autoregressive neural machine translation (NMT) models are often used to teach non-autoregressive models via knowledge distillation. However, there are few studies on improving the quality of autoregressive translation (AT) using non-autoregressive translation (NAT). In this work, we propose a novel Encoder-NAD-AD framework for NMT, aiming at boosting AT with global information produced by NAT model. Specifically, under the semantic guidance of source-side context captured by the encoder, the non-autoregressive decoder (NAD) first learns to generate target-side hidden state sequence in parallel. Then the autoregressive decoder (AD) performs translation from left to right, conditioned on source-side and target-side hidden states. Since AD has global information generated by low-latency NAD, it is more likely to produce a better translation with less time delay. Experiments on WMT14 En-De, WMT16 En-Ro, and IWSLT14 De-En translation tasks demonstrate that our framework achieves significant improvements with only 8{\%} speed degeneration over the autoregressive NMT.",
}
@inproceedings{chen-etal-2020-modeling-discourse,
    title = "Modeling Discourse Structure for Document-level Neural Machine Translation",
    author = "Chen, Junxuan  and
      Li, Xiang  and
      Zhang, Jiarui  and
      Zhou, Chulun  and
      Cui, Jianwei  and
      Wang, Bin  and
      Su, Jinsong",
    booktitle = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.autosimtrans-1.5",
    doi = "10.18653/v1/2020.autosimtrans-1.5",
    pages = "30--36",
    abstract = "Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation. Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks. In this paper, we propose to improve document-level NMT with the aid of discourse structure information. Our encoder is based on a hierarchical attention network (HAN) (Miculicich et al., 2018). Specifically, we first parse the input document to obtain its discourse structure. Then, we introduce a Transformer-based path encoder to embed the discourse structure information of each word. Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder. Experimental results on the English-to-German dataset show that our model can significantly outperform both Transformer and Transformer+HAN.",
}
@inproceedings{li-etal-2020-bits,
    title = "{BIT}{'}s system for the {A}uto{S}im{T}rans 2020",
    author = "Li, Minqin  and
      Cheng, Haodong  and
      Wang, Yuanjie  and
      Zhang, Sijia  and
      Wu, Liting  and
      Guo, Yuhang",
    booktitle = "Proceedings of the First Workshop on Automatic Simultaneous Translation",
    month = jul,
    year = "2020",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.autosimtrans-1.6",
    doi = "10.18653/v1/2020.autosimtrans-1.6",
    pages = "37--44",
    abstract = "This paper describes our machine translation systems for the streaming Chinese-to-English translation task of AutoSimTrans 2020. We present a sentence length based method and a sentence boundary detection model based method for the streaming input segmentation. Experimental results of the transcription and the ASR output translation on the development data sets show that the translation system with the detection model based method outperforms the one with the length based method in BLEU score by 1.19 and 0.99 respectively under similar or better latency.",
}
@proceedings{argmining-2020-argument,
    title = "Proceedings of the 7th Workshop on Argument Mining",
    editor = "Cabrio, Elena  and
      Villata, Serena",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.0",
}
@inproceedings{roush-balaji-2020-debatesum,
    title = "{D}ebate{S}um: A large-scale argument mining and summarization dataset",
    author = "Roush, Allen  and
      Balaji, Arvind",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.1",
    pages = "1--7",
    abstract = "Prior work in Argument Mining frequently alludes to its potential applications in automatic debating systems. Despite this focus, almost no datasets or models exist which apply natural language processing techniques to problems found within competitive formal debate. To remedy this, we present the DebateSum dataset. DebateSum consists of 187,386 unique pieces of evidence with corresponding argument and extractive summaries. DebateSum was made using data compiled by competitors within the National Speech and Debate Association over a 7year period. We train several transformer summarization models to benchmark summarization performance on DebateSum. We also introduce a set of fasttext word-vectors trained on DebateSum called debate2vec. Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today. The DebateSum search engine is available to the public here: http://www.debate.cards",
}
@inproceedings{bauwelinck-lefever-2020-annotating,
    title = "Annotating Topics, Stance, Argumentativeness and Claims in {D}utch Social Media Comments: A Pilot Study",
    author = "Bauwelinck, Nina  and
      Lefever, Els",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.2",
    pages = "8--18",
    abstract = "One of the major challenges currently facing the field of argumentation mining is the lack of consensus on how to analyse argumentative user-generated texts such as online comments. The theoretical motivations underlying the annotation guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. This pilot study reports on the annotation of a corpus of 100 Dutch user comments made in response to politically-themed news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement scores for argumentativeness detection (0.76 Fleiss{'} kappa) and moderate agreement for claim labelling (0.45 Fleiss{'} kappa). We provide a clear justification of the theories and definitions underlying the design of our guidelines. Our analysis of the annotations signal the importance of adjusting our guidelines to include allowances for missing context information and defining the concept of argumentativeness in connection with stance. Our annotated corpus and associated guidelines are made publicly available.",
}
@inproceedings{dorsch-wachsmuth-2020-semi,
    title = "Semi-Supervised Cleansing of Web Argument Corpora",
    author = "Dorsch, Jonas  and
      Wachsmuth, Henning",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.3",
    pages = "19--29",
    abstract = "Debate portals and similar web platforms constitute one of the main text sources in computational argumentation research and its applications. While the corpora built upon these sources are rich of argumentatively relevant content and structure, they also include text that is irrelevant, or even detrimental, to their purpose. In this paper, we present a precision-oriented approach to detecting such irrelevant text in a semi-supervised way. Given a few seed examples, the approach automatically learns basic lexical patterns of relevance and irrelevance and then incrementally bootstraps new patterns from sentences matching the patterns. In the existing args.me corpus with 400k argumentative texts, our approach detects almost 87k irrelevant sentences, at a precision of 0.97 according to manual evaluation. With low effort, the approach can be adapted to other web argument corpora, providing a generic way to improve corpus quality.",
}
@inproceedings{kobbe-etal-2020-exploring,
    title = "Exploring Morality in Argumentation",
    author = "Kobbe, Jonathan  and
      Rehbein, Ines  and
      Hulpu{\textcommabelow{s}}, Ioana  and
      Stuckenschmidt, Heiner",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.4",
    pages = "30--40",
    abstract = "Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated testset. We then apply our models to investigate how moral values in arguments relate to argument quality, stance and audience reactions.",
}
@inproceedings{trautmann-2020-aspect,
    title = "Aspect-Based Argument Mining",
    author = "Trautmann, Dietrich",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.5",
    pages = "41--52",
    abstract = "Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The tools to extract argument units are increasingly available and further open problems can be addressed. In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first instance, we create and release an annotated corpus with aspect information on the token-level. We consider aspects as the main point(s) argument units are addressing. This information is important for further downstream tasks such as argument ranking, argument summarization and generation, as well as the search for counter-arguments on the aspect-level. We present several experiments using state-of-the-art supervised architectures and demonstrate their performance for both of the subtasks. The annotated benchmark is available at https://github.com/trtm/ABAM.",
}
@inproceedings{schaefer-stede-2020-annotation,
    title = "Annotation and Detection of Arguments in Tweets",
    author = "Schaefer, Robin  and
      Stede, Manfred",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.6",
    pages = "53--58",
    abstract = "Notwithstanding the increasing role Twitter plays in modern political and social discourse, resources built for conducting argument mining on tweets remain limited. In this paper, we present a new corpus of German tweets annotated for argument components. To the best of our knowledge, this is the first corpus containing not only annotated full tweets but also argumentative spans within tweets. We further report first promising results using supervised classification (F1: 0.82) and sequence labeling (F1: 0.72) approaches.",
}
@inproceedings{carlebach-etal-2020-news,
    title = "News Aggregation with Diverse Viewpoint Identification Using Neural Embeddings and Semantic Understanding Models",
    author = "Carlebach, Mark  and
      Cheruvu, Ria  and
      Walker, Brandon  and
      Ilharco Magalhaes, Cesar  and
      Jaume, Sylvain",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.7",
    pages = "59--66",
    abstract = "Today{'}s news volume makes it impractical for readers to get a diverse and comprehensive view of published articles written from opposing viewpoints. We introduce a transformer-based news aggregation system, composed of topic modeling, semantic clustering, claim extraction, and textual entailment that identifies viewpoints presented in articles within a semantic cluster and classifies them into positive, neutral and negative entailments. Our novel embedded topic model using BERT-based embeddings outperforms baseline topic modeling algorithms by an 11{\%} relative improvement. We compare recent semantic similarity models in the context of news aggregation, evaluate transformer-based models for claim extraction on news data, and demonstrate the use of textual entailment models for diverse viewpoint identification.",
}
@inproceedings{poudyal-etal-2020-echr,
    title = "{ECHR}: Legal Corpus for Argument Mining",
    author = "Poudyal, Prakash  and
      Savelka, Jaromir  and
      Ieven, Aagje  and
      Moens, Marie Francine  and
      Goncalves, Teresa  and
      Quaresma, Paulo",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.8",
    pages = "67--75",
    abstract = "In this paper, we publicly release an annotated corpus of 42 decisions of the European Court of Human Rights (ECHR). The corpus is annotated in terms of three types of clauses useful in argument mining: premise, conclusion, and non-argument parts of the text. Furthermore, relationships among the premises and conclusions are mapped. We present baselines for three tasks that lead from unstructured texts to structured arguments. The tasks are argument clause recognition, clause relation prediction, and premise/conclusion recognition. Despite a straightforward application of the bidirectional encoders from Transformers (BERT), we obtained very promising results F1 0.765 on argument recognition, 0.511 on relation prediction, and 0.859/0.628 on premise/conclusion recognition). The results suggest the usefulness of pre-trained language models based on deep neural network architectures in argument mining. Because of the simplicity of the baselines, there is ample space for improvement in future work based on the released corpus.",
}
@inproceedings{spliethover-wachsmuth-2020-argument,
    title = "Argument from Old Man{'}s View: Assessing Social Bias in Argumentation",
    author = {Splieth{\"o}ver, Maximilian  and
      Wachsmuth, Henning},
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.9",
    pages = "76--87",
    abstract = "Social bias in language - towards genders, ethnicities, ages, and other social groups - poses a problem with ethical impact for many NLP applications. Recent research has shown that machine learning models trained on respective data may not only adopt, but even amplify the bias. So far, however, little attention has been paid to bias in computational argumentation. In this paper, we study the existence of social biases in large English debate portals. In particular, we train word embedding models on portal-specific corpora and systematically evaluate their bias using WEAT, an existing metric to measure bias in word embeddings. In a word co-occurrence analysis, we then investigate causes of bias. The results suggest that all tested debate corpora contain unbalanced and biased data, mostly in favor of male people with European-American names. Our empirical insights contribute towards an understanding of bias in argumentative data sources.",
}
@inproceedings{moser-mercer-2020-use,
    title = "Use of Claim Graphing and Argumentation Schemes in Biomedical Literature: A Manual Approach to Analysis",
    author = "Moser, Eli  and
      Mercer, Robert E.",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.10",
    pages = "88--99",
    abstract = "Argumentation in an experimental life science paper consists of a main claim being supported with reasoned argumentative steps based on the data garnered from the experiments that were carried out. In this paper we report on an investigation of the large scale argumentation structure found when examining five biochemistry journal publications. One outcome of this investigation of biochemistry articles suggests that argumentation schemes originally designed for genetic research articles may transfer to experimental biomedical literature in general. Our use of these argumentation schemes shows that claims depend not only on experimental data but also on other claims. The tendency for claims to use other claims as their supporting evidence in addition to the experimental data led to two novel models that have provided a better understanding of the large scale argumentation structure of a complete biochemistry paper. First, the claim graph displays the claims within a paper, their interactions, and their evidence. Second, another aspect of this argumentation network is further illustrated by the Model of Informational Hierarchy (MIH) which visualizes at a meta-level the flow of reasoning provided by the authors of the paper and also connects the main claim to the paper{'}s title. Together, these models, which have been produced by a manual examination of the biochemistry articles, would be likely candidates for a computational method that analyzes the large scale argumentation structure.",
}
@inproceedings{lindahl-2020-annotating,
    title = "Annotating argumentation in {S}wedish social media",
    author = "Lindahl, Anna",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.11",
    pages = "100--105",
    abstract = "This paper presents a small study of annotating argumentation in Swedish social media. Annotators were asked to annotate spans of argumentation in 9 threads from two discussion forums. At the post level, Cohen{'}s k and Krippendorff{'}s alpha 0.48 was achieved. When manually inspecting the annotations the annotators seemed to agree when conditions in the guidelines were explicitly met, but implicit argumentation and opinions, resulting in annotators having to interpret what{'}s missing in the text, caused disagreements.",
}
@inproceedings{al-khatib-etal-2020-style,
    title = "Style Analysis of Argumentative Texts by Mining Rhetorical Devices",
    author = "Al Khatib, Khalid  and
      Morari, Viorel  and
      Stein, Benno",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.12",
    pages = "106--116",
    abstract = "Using the appropriate style is key for writing a high-quality text. Reliable computational style analysis is hence essential for the automation of nearly all kinds of text synthesis tasks. Research on style analysis focuses on recognition problems such as authorship identification; the respective technology (e.g., n-gram distribution divergence quantification) showed to be effective for discrimination, but inappropriate for text synthesis since the {``}essence of a style{''} remains implicit. This paper contributes right here: it studies the automatic analysis of style at the knowledge-level based on rhetorical devices. To this end, we developed and evaluated a grammar-based approach for identifying 26 syntax-based devices. Then, we employed that approach to distinguish various patterns of style in selected sets of argumentative articles and presidential debates. The patterns reveal several insights into the style used there, while being adequate for integration in text synthesis systems.",
}
@inproceedings{ng-etal-2020-creating,
    title = "Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment",
    author = "Ng, Lily  and
      Lauscher, Anne  and
      Tetreault, Joel  and
      Napoles, Courtney",
    booktitle = "Proceedings of the 7th Workshop on Argument Mining",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.argmining-1.13",
    pages = "117--126",
    abstract = "Computational models of argument quality (AQ) have focused primarily on assessing the overall quality or just one specific characteristic of an argument, such as its convincingness or its clarity. However, previous work has claimed that assessment based on theoretical dimensions of argumentation could benefit writers, but developing such models has been limited by the lack of annotated data. In this work, we describe GAQCorpus, the first large, domain-diverse annotated corpus of theory-based AQ. We discuss how we designed the annotation task to reliably collect a large number of judgments with crowdsourcing, formulating theory-based guidelines that helped make subjective judgments of AQ more objective. We demonstrate how to identify arguments and adapt the annotation task for three diverse domains. Our work will inform research on theory-based argumentation annotation and enable the creation of more diverse corpora to support computational AQ assessment.",
}
@proceedings{amta-2020-association-machine,
    title = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    editor = "Campbell, Janice  and
      Genzel, Dmitriy  and
      Huyck, Ben  and
      O{'}Neill-Brown, Patricia",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.0",
}
@inproceedings{urban-rodriguez-molina-2020-operationalizing,
    title = "Operationalizing {MT} Quality Estimation",
    author = "Urban, Miklos  and
      Rodr{\'\i}guez Molina, Maribel",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.1",
    pages = "1--31",
}
@inproceedings{guerrero-2020-search,
    title = "In search of an acceptability/unacceptability threshold in machine translation post-editing automated metrics",
    author = "Guerrero, Luc{\'\i}a",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.2",
    pages = "32--47",
}
@inproceedings{diaz-etal-2020-survey,
    title = "A Survey of Qualitative Error Analysis for Neural Machine Translation Systems",
    author = "D{\'\i}az, Denise  and
      Cross, James  and
      Chaudhary, Vishrav  and
      Kishky, Ahmed  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.3",
    pages = "48--77",
}
@inproceedings{stewart-etal-2020-comet,
    title = "{COMET} - Deploying a New State-of-the-art {MT} Evaluation Metric in Production",
    author = "Stewart, Craig  and
      Rei, Ricardo  and
      Farinha, Catarina  and
      Lavie, Alon",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.4",
    pages = "78--109",
}
@inproceedings{schmidtke-2020-scaling,
    title = "Scaling up automatic translation for software: reduction of post-editing volume with well-defined customer impact",
    author = "Schmidtke, Dag",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.5",
    pages = "110--123",
}
@inproceedings{xiao-jin-2020-auto,
    title = "Auto {MT} Quality Prediction Solution and Best Practice",
    author = "Xiao, Martin Lei  and
      Jin, York",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.6",
    pages = "124--137",
}
@inproceedings{picinini-bittlingmayer-2020-language,
    title = "A language comparison of Human Evaluation and Quality Estimation",
    author = "Picinini, Silvio  and
      Bittlingmayer, Adam",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.7",
    pages = "138--161",
}
@inproceedings{renduchintala-genzel-2020-machine,
    title = "Machine Translation quality across demographic dialectal variation in Social Media.",
    author = "Renduchintala, Adithya  and
      Genzel, Dmitriy",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.8",
    pages = "162--189",
}
@inproceedings{cristina-2020-making,
    title = "Making the business case for adopting {MT}",
    author = "Cristina, Rodrigo",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.9",
    pages = "190--203",
}
@inproceedings{matusov-etal-2020-flexible,
    title = "Flexible Customization of a Single Neural Machine Translation System with Multi-dimensional Metadata Inputs",
    author = "Matusov, Evgeny  and
      Wilken, Patrick  and
      Herold, Christian",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.10",
    pages = "204--216",
}
@inproceedings{martinez-dominguez-etal-2020-customized,
    title = "Customized Neural Machine Translation Systems for the {S}wiss Legal Domain",
    author = "Mart{\'\i}nez-Dom{\'\i}nguez, Rub{\'e}n  and
      Rikters, Mat{\=\i}ss  and
      Vasi{\c{l}}evskis, Art{\=u}rs  and
      Pinnis, M{\=a}rcis  and
      Reichenberg, Paula",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.11",
    pages = "217--223",
}
@inproceedings{ritzdorf-2020-machine,
    title = "Machine Translation Hype, Crash-Tested by Translation Students",
    author = "Ritzdorf, Jon",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.12",
    pages = "224--236",
}
@inproceedings{klein-2020-use,
    title = "Use {MT} to Simplify and Speed Up Your Alignment for {TM} Creation",
    author = "Klein, Judith",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.13",
    pages = "237--269",
}
@inproceedings{tamchyna-2020-selection,
    title = "Selection of {MT} Systems in Translation Workflows",
    author = "Tamchyna, Ale{\v{s}}",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.14",
    pages = "270--291",
}
@inproceedings{yanishevsky-2020-beyond,
    title = "Beyond {MT}: Opening Doors for an {NLP} Pipeline",
    author = "Yanishevsky, Alex",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.15",
    pages = "292--308",
}
@inproceedings{savenkov-2020-building,
    title = "Building Multi-Purpose {MT} Portfolio",
    author = "Savenkov, Konstantin",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.16",
    pages = "309--339",
}
@inproceedings{pitman-2020-simultaneous,
    title = "Simultaneous Speech Translation in {G}oogle {T}ranslate",
    author = "Pitman, Jeff",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.17",
    pages = "340--381",
}
@inproceedings{maczynski-2020-understanding,
    title = "Understanding Challenges to Enterprise Machine Translation Adoption",
    author = "Maczynski, Bart",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.18",
    pages = "382--408",
}
@inproceedings{odowd-2020-lexically,
    title = "Lexically Constrained Decoding for Sequence Generation",
    author = "O{'}Dowd, Tony",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.19",
    pages = "409--435",
}
@inproceedings{hashimoto-etal-2020-building,
    title = "Building Salesforce Neural Machine Translation System",
    author = "Hashimoto, Kazuma  and
      Buschiazzo, Raffaella  and
      Xiong, Caiming  and
      Marxhall, Teresa",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.20",
    pages = "436--451",
}
@inproceedings{baker-2020-successful,
    title = "Successful Tech Transfer of {MT} Research in Government",
    author = "Baker, Kathy",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.21",
    pages = "452--468",
}
@inproceedings{miller-etal-2020-plugging,
    title = "Plugging into Trados: Augmenting Translation in the Enclave",
    author = "Miller, Corey  and
      Higgins, Chiara  and
      Havens, Paige  and
      Van Guilder, Steven  and
      Morris, Rodney  and
      Silverman, Danielle",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.22",
    pages = "469--483",
}
@inproceedings{boukhvalov-block-2020-pemt,
    title = "{PEMT} for the Public Sector: Discovery, Scoping, and Delivery",
    author = "Boukhvalov, Konstantine  and
      Block, Eileen",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.23",
    pages = "484--513",
}
@inproceedings{larocca-2020-shareable,
    title = "Shareable {TTS} Components",
    author = "LaRocca, Steve",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.24",
    pages = "514--524",
}
@inproceedings{pinnis-etal-2020-tale,
    title = "A Tale of Eight Countries or the {EU} Council Presidency Translator in Retrospect",
    author = "Pinnis, M{\=a}rcis  and
      Bergmanis, Toms  and
      Metuz{\=a}le, Krist{\=\i}ne  and
      {\v{S}}ics, Valters  and
      Vasi{\c{l}}evskis, Art{\=u}rs  and
      Vasi{\c{l}}jevs, Andrejs",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.25",
    pages = "525--546",
}
@inproceedings{oneill-brown-2020-american,
    title = "{A}merican {S}ign {L}anguage ({ASL}) to {E}nglish Machine Translation",
    author = "O{'}Neill-Brown, Patricia",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.26",
    pages = "547--562",
}
@inproceedings{decamp-2020-hard,
    title = "Why is it so Hard to Develop Comparable Translation Evaluations and How Can Standards Help?",
    author = "DeCamp, Jennifer",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.27",
    pages = "563--576",
}
@inproceedings{ruopp-2020-using,
    title = "Using Contemporary {US} Government Data to Train Custom {MT} for {COVID}-19",
    author = "Ruopp, Achim",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.28",
    pages = "577--592",
}
@proceedings{amta-2020-association,
    title = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    editor = "Denkowski, Michael  and
      Federmann, Christian",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.0",
}
@inproceedings{ahmadnia-dorr-2020-new,
    title = "A New Approach to Parameter-Sharing in Multilingual Neural Machine Translation",
    author = "Ahmadnia, Benyamin  and
      Dorr, Bonnie",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.1",
    pages = "1--6",
}
@inproceedings{bahar-etal-2020-investigation,
    title = "Investigation of Transformer-based Latent Attention Models for Neural Machine Translation",
    author = "Bahar, Parnia  and
      Makarov, Nikita  and
      Ney, Hermann",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.2",
    pages = "7--20",
}
@inproceedings{niehues-2020-machine,
    title = "Machine Translation with Unsupervised Length-Constraints",
    author = "Niehues, Jan",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.3",
    pages = "21--35",
}
@inproceedings{xie-way-2020-constraining,
    title = "Constraining the Transformer {NMT} Model with Heuristic Grid Beam Search",
    author = "Xie, Guodong  and
      Way, Andy",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.4",
    pages = "36--49",
}
@inproceedings{naradowsky-etal-2020-machine,
    title = "Machine Translation System Selection from Bandit Feedback",
    author = "Naradowsky, Jason  and
      Zhang, Xuan  and
      Duh, Kevin",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.5",
    pages = "50--63",
}
@inproceedings{ngo-ho-yvon-2020-generative,
    title = "Generative latent neural models for automatic word alignment",
    author = "Ngo Ho, Anh Khoa  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.6",
    pages = "64--77",
}
@inproceedings{poncelas-etal-2020-impact,
    title = "The Impact of Indirect Machine Translation on Sentiment Classification",
    author = "Poncelas, Alberto  and
      Lohar, Pintu  and
      Hadley, James  and
      Way, Andy",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.7",
    pages = "78--88",
}
@inproceedings{hangya-fraser-2020-towards,
    title = "Towards Handling Compositionality in Low-Resource Bilingual Word Induction",
    author = "Hangya, Viktor  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.8",
    pages = "89--101",
}
@inproceedings{klein-etal-2020-opennmt,
    title = "The {O}pen{NMT} Neural Machine Translation Toolkit: 2020 Edition",
    author = "Klein, Guillaume  and
      Hernandez, Fran{\c{c}}ois  and
      Nguyen, Vincent  and
      Senellart, Jean",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.9",
    pages = "102--109",
}
@inproceedings{domhan-etal-2020-sockeye,
    title = "The Sockeye 2 Neural Machine Translation Toolkit at {AMTA} 2020",
    author = "Domhan, Tobias  and
      Denkowski, Michael  and
      Vilar, David  and
      Niu, Xing  and
      Hieber, Felix  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.10",
    pages = "110--115",
}
@inproceedings{tan-etal-2020-thumt,
    title = "{THUMT}: An Open-Source Toolkit for Neural Machine Translation",
    author = "Tan, Zhixing  and
      Zhang, Jiacheng  and
      Huang, Xuancheng  and
      Chen, Gang  and
      Wang, Shuo  and
      Sun, Maosong  and
      Luan, Huanbo  and
      Liu, Yang",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.11",
    pages = "116--122",
}
@inproceedings{yao-haddow-2020-dynamic,
    title = "Dynamic Masking for Improved Stability in Online Spoken Language Translation",
    author = "Yao, Yuekun  and
      Haddow, Barry",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.12",
    pages = "123--136",
}
@inproceedings{di-gangi-etal-2020-target,
    title = "On Target Segmentation for Direct Speech Translation",
    author = "Di Gangi, Mattia A.  and
      Gaido, Marco  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.13",
    pages = "137--150",
}
@inproceedings{muller-etal-2020-domain,
    title = "Domain Robustness in Neural Machine Translation",
    author = {M{\"u}ller, Mathias  and
      Rios, Annette  and
      Sennrich, Rico},
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.14",
    pages = "151--164",
}
@inproceedings{le-sadat-2020-low,
    title = "Low-Resource {NMT}: an Empirical Study on the Effect of Rich Morphological Word Segmentation on {I}nuktitut",
    author = "Le, Tan Ngoc  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-research.15",
    pages = "165--172",
}
@proceedings{amta-2020-post,
    title = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    editor = "Ortega, John E.  and
      Federico, Marcello  and
      Orasan, Constantin  and
      Popovic, Maja",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.0",
}
@inproceedings{moran-2020-keynote,
    title = "Keynote: Evaluating {MT} based on translation speed - a review of the status quo and a proposal for the future",
    author = "Moran, John",
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.1",
    pages = "1--15",
}
@inproceedings{su-2020-help,
    title = "Help Me! Those Free and/or Open Source Machine Translation Tools!",
    author = "Su, Serene",
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.2",
    pages = "16--37",
}
@inproceedings{reid-mccrae-2020-mt,
    title = "{MT} Quality Estimator - {QUEST}",
    author = "Reid, Christopher  and
      McCrae, Patrick",
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.3",
    pages = "38--55",
}
@inproceedings{ramos-2020-mt,
    title = "{MT} Post Editing challenges: Training happy and successful posteditors",
    author = "Ramos, Luciana",
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.4",
    pages = "56--60",
}
@inproceedings{mutal-etal-2020-copeco,
    title = "{COPECO}: a Collaborative Post-Editing Corpus in Pedagogical Context",
    author = "Mutal, Jonathan  and
      Bouillon, Pierrette  and
      Schumacher, Perrine  and
      Gerlach, Johanna",
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.5",
    pages = "61--78",
}
@inproceedings{koponen-etal-2020-mt-subtitling,
    title = "{MT} for Subtitling: Investigating professional translators{'} user experience and feedback",
    author = {Koponen, Maarit  and
      Sulubacak, Umut  and
      Vitikainen, Kaisa  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.6",
    pages = "79--92",
}
@inproceedings{herbig-etal-2020-improving,
    title = "Improving the Multi-Modal Post-Editing ({MMPE}) {CAT} Environment based on Professional Translators{'} Feedback",
    author = {Herbig, Nico  and
      Pal, Santanu  and
      D{\"u}wel, Tim  and
      Shenoy, Raksha  and
      Kr{\"u}ger, Antonio  and
      van Genabith, Josef},
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.7",
    pages = "93--108",
}
@inproceedings{stasimioti-sosoni-2020-translation,
    title = "Translation vs Post-editing of {NMT} Output: Insights from the {E}nglish-{G}reek language pair",
    author = "Stasimioti, Maria  and
      Sosoni, Vilelmini",
    booktitle = "Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-pemdt.8",
    pages = "109--124",
}
@proceedings{amta-2020-impact,
    title = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    editor = "O'Brien, Sharon  and
      Simard, Michel",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.0",
}
@inproceedings{whitaker-2020-machine,
    title = "The Machine is Blind: Bottom-Up Feedback on the Impact of {MT} on Human Translation Performance",
    author = "Whitaker, Rhett",
    booktitle = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.1",
    pages = "1--17",
}
@inproceedings{martindale-2020-responsible,
    title = "Responsible {`}Gist{'} {MT} Use in the Age of Neural {MT}",
    author = "Martindale, Marianna J.",
    booktitle = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.2",
    pages = "18--45",
}
@inproceedings{briva-iglesias-2020-different,
    title = "A Different, Ethical Machine Translation is Possible: {E}nglish-{C}atalan Free/Open-Source Neural Machine Translation",
    author = "Briva-Iglesias, Vicent",
    booktitle = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.3",
    pages = "46--61",
}
@inproceedings{rescigno-etal-2020-case,
    title = "A Case Study of Natural Gender Phenomena in Translation: A Comparison of {G}oogle {T}ranslate, Bing {M}icrosoft Translator and {D}eep{L} for {E}nglish to {I}talian, {F}rench and {S}panish",
    author = "Rescigno, Argentina Anna  and
      Monti, Johanna  and
      Way, Andy  and
      Vanmassenhove, Eva",
    booktitle = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.4",
    pages = "62--90",
}
@inproceedings{oktem-etal-2020-empowering,
    title = "Empowering translators of marginalized languages through the use of language technology",
    author = {{\"O}ktem, Alp  and
      Locria, Manuel  and
      Paquin, Eric  and
      Tang, Grace},
    booktitle = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.5",
    pages = "91--122",
}
@inproceedings{vashee-2020-exploring,
    title = "Exploring Greater Impact: Business Translation Beyond Localization?",
    author = "Vashee, Kirti",
    booktitle = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.6",
    pages = "123--151",
}
@inproceedings{kovacs-2020-predictive,
    title = "Predictive Translation Memory in the Wild - A Study of Interactive Machine Translation Use on Lilt",
    author = "Kovacs, Geza",
    booktitle = "Workshop on the Impact of Machine Translation (iMpacT 2020)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-impact.7",
    pages = "152--216",
}
@proceedings{alw-2020-online,
    title = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    editor = "Akiwowo, Seyi  and
      Vidgen, Bertie  and
      Prabhakaran, Vinodkumar  and
      Waseem, Zeerak",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.0",
}
@inproceedings{prabhakaran-etal-2020-online,
    title = "Online Abuse and Human Rights: {WOAH} Satellite Session at {R}ights{C}on 2020",
    author = "Prabhakaran, Vinodkumar  and
      Waseem, Zeerak  and
      Akiwowo, Seyi  and
      Vidgen, Bertie",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.1",
    doi = "10.18653/v1/2020.alw-1.1",
    pages = "1--6",
    abstract = "In 2020 The Workshop on Online Abuse and Harms (WOAH) held a satellite panel at RightsCons 2020, an international human rights conference. Our aim was to bridge the gap between human rights scholarship and Natural Language Processing (NLP) research communities in tackling online abuse. We report on the discussions that took place, and present an analysis of four key issues which emerged: Problems in tackling online abuse, Solutions, Meta concerns and the Ecosystem of content moderation and research. We argue there is a pressing need for NLP research communities to engage with human rights perspectives, and identify four key ways in which NLP research into online abuse could immediately be enhanced to create better and more ethical solutions.",
}
@inproceedings{arora-etal-2020-novel,
    title = "A Novel Methodology for Developing Automatic Harassment Classifiers for {T}witter",
    author = "Arora, Ishaan  and
      Guo, Julia  and
      Levitan, Sarah Ita  and
      McGregor, Susan  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.2",
    doi = "10.18653/v1/2020.alw-1.2",
    pages = "7--15",
    abstract = "Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes. These are typically labeled by crowd-sourced annotators {--} not the targets of the abuse themselves. While this method of data collection supports fast development of machine learning classifiers, the models built on them often fail in the context of real-world harassment and abuse, which contain nuances less easily identified by non-targets. Here, we present a mixed-methods approach to create classifiers for abuse and harassment which leverages direct engagement with the target group in order to achieve high quality and ecological validity of data sets and labels, and to generate deeper insights into the key tactics of bad actors. We use women journalists{'} experience on Twitter as an initial community of focus. We identify several structural mechanisms of abuse that we believe will generalize to other target communities.",
}
@inproceedings{isaksen-gamback-2020-using,
    title = "Using Transfer-based Language Models to Detect Hateful and Offensive Language Online",
    author = {Isaksen, Vebj{\o}rn  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.3",
    doi = "10.18653/v1/2020.alw-1.3",
    pages = "16--27",
    abstract = "Distinguishing hate speech from non-hate offensive language is challenging, as hate speech not always includes offensive slurs and offensive language not always express hate. Here, four deep learners based on the Bidirectional Encoder Representations from Transformers (BERT), with either general or domain-specific language models, were tested against two datasets containing tweets labelled as either {`}Hateful{'}, {`}Normal{'} or {`}Offensive{'}. The results indicate that the attention-based models profoundly confuse hate speech with offensive and normal language. However, the pre-trained models outperform state-of-the-art results in terms of accurately predicting the hateful instances.",
}
@inproceedings{ozler-etal-2020-fine,
    title = "Fine-tuning for multi-domain and multi-label uncivil language detection",
    author = "Ozler, Kadir Bulut  and
      Kenski, Kate  and
      Rains, Steve  and
      Shmargad, Yotam  and
      Coe, Kevin  and
      Bethard, Steven",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.4",
    doi = "10.18653/v1/2020.alw-1.4",
    pages = "28--33",
    abstract = "Incivility is a problem on social media, and it comes in many forms (name-calling, vulgarity, threats, etc.) and domains (microblog posts, online news comments, Wikipedia edits, etc.). Training machine learning models to detect such incivility must handle the multi-label and multi-domain nature of the problem. We present a BERT-based model for incivility detection and propose several approaches for training it for multi-label and multi-domain datasets. We find that individual binary classifiers outperform a joint multi-label classifier, and that simply combining multiple domains of training data outperforms other recently-proposed fine tuning strategies. We also establish new state-of-the-art performance on several incivility detection datasets.",
}
@inproceedings{koufakou-etal-2020-hurtbert,
    title = "{H}urt{BERT}: Incorporating Lexical Features with {BERT} for the Detection of Abusive Language",
    author = "Koufakou, Anna  and
      Pamungkas, Endang Wahyu  and
      Basile, Valerio  and
      Patti, Viviana",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.5",
    doi = "10.18653/v1/2020.alw-1.5",
    pages = "34--43",
    abstract = "The detection of abusive or offensive remarks in social texts has received significant attention in research. In several related shared tasks, BERT has been shown to be the state-of-the-art. In this paper, we propose to utilize lexical features derived from a hate lexicon towards improving the performance of BERT in such tasks. We explore different ways to utilize the lexical features in the form of lexicon-based encodings at the sentence level or embeddings at the word level. We provide an extensive dataset evaluation that addresses in-domain as well as cross-domain detection of abusive content to render a complete picture. Our results indicate that our proposed models combining BERT with lexical features help improve over a baseline BERT model in many of our in-domain and cross-domain experiments.",
}
@inproceedings{narang-brew-2020-abusive,
    title = "Abusive Language Detection using Syntactic Dependency Graphs",
    author = "Narang, Kanika  and
      Brew, Chris",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.6",
    doi = "10.18653/v1/2020.alw-1.6",
    pages = "44--53",
    abstract = "Automated detection of abusive language online has become imperative. Current sequential models (LSTM) do not work well for long and complex sentences while bi-transformer models (BERT) are not computationally efficient for the task. We show that classifiers based on syntactic structure of the text, dependency graphical convolutional networks (DepGCNs) can achieve state-of-the-art performance on abusive language datasets. The overall performance is at par with of strong baselines such as fine-tuned BERT. Further, our GCN-based approach is much more efficient than BERT at inference time making it suitable for real-time detection.",
}
@inproceedings{wich-etal-2020-impact,
    title = "Impact of Politically Biased Data on Hate Speech Classification",
    author = "Wich, Maximilian  and
      Bauer, Jan  and
      Groh, Georg",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.7",
    doi = "10.18653/v1/2020.alw-1.7",
    pages = "54--64",
    abstract = "One challenge that social media platforms are facing nowadays is hate speech. Hence, automatic hate speech detection has been increasingly researched in recent years - in particular with the rise of deep learning. A problem of these models is their vulnerability to undesirable bias in training data. We investigate the impact of political bias on hate speech classification by constructing three politically-biased data sets (left-wing, right-wing, politically neutral) and compare the performance of classifiers trained on them. We show that (1) political bias negatively impairs the performance of hate speech classifiers and (2) an explainable machine learning model can help to visualize such bias within the training data. The results show that political bias in training data has an impact on hate speech classification and can become a serious issue.",
}
@inproceedings{zueva-etal-2020-reducing,
    title = "Reducing Unintended Identity Bias in {R}ussian Hate Speech Detection",
    author = "Zueva, Nadezhda  and
      Kabirova, Madina  and
      Kalaidin, Pavel",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.8",
    doi = "10.18653/v1/2020.alw-1.8",
    pages = "65--69",
    abstract = "Toxicity has become a grave problem for many online communities, and has been growing across many languages, including Russian. Hate speech creates an environment of intimidation, discrimination, and may even incite some real-world violence. Both researchers and social platforms have been focused on developing models to detect toxicity in online communication for a while now. A common problem of these models is the presence of bias towards some words (e.g. woman, black, jew or женщина, черный, еврей) that are not toxic, but serve as triggers for the classifier due to model caveats. In this paper, we describe our efforts towards classifying hate speech in Russian, and propose simple techniques of reducing unintended bias, such as generating training data with language models using terms and words related to protected identities as context and applying word dropout to such words.",
}
@inproceedings{razo-kubler-2020-investigating,
    title = "Investigating Sampling Bias in Abusive Language Detection",
    author = {Razo, Dante  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.9",
    doi = "10.18653/v1/2020.alw-1.9",
    pages = "70--78",
    abstract = "Abusive language detection is becoming increasingly important, but we still understand little about the biases in our datasets for abusive language detection, and how these biases affect the quality of abusive language detection. In the work reported here, we reproduce the investigation of Wiegand et al. (2019) to determine differences between different sampling strategies. They compared boosted random sampling, where abusive posts are upsampled, and biased topic sampling, which focuses on topics that are known to cause abusive language. Instead of comparing individual datasets created using these sampling strategies, we use the sampling strategies on a single, large dataset, thus eliminating the textual source of the dataset as a potential confounding factor. We show that differences in the textual source can have more effect than the chosen sampling strategy.",
}
@inproceedings{safi-samghabadi-etal-2020-attending,
    title = "Attending the Emotions to Detect Online Abusive Language",
    author = "Safi Samghabadi, Niloofar  and
      Hatami, Afsheen  and
      Shafaei, Mahsa  and
      Kar, Sudipta  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.10",
    doi = "10.18653/v1/2020.alw-1.10",
    pages = "79--88",
    abstract = "In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus for the task of abusive language detection that is collected from a semi-anonymous online platform, and unlike the majority of other available resources, is not created based on a specific list of bad words. We also develop computational models to incorporate emotions into textual cues to improve aggression identification. We evaluate our proposed methods on a set of corpora related to the task and show promising results with respect to abusive language detection.",
}
@inproceedings{rathnayake-etal-2020-enhancing,
    title = "Enhancing the Identification of Cyberbullying through Participant Roles",
    author = "Rathnayake, Gathika  and
      Atapattu, Thushari  and
      Herath, Mahen  and
      Zhang, Georgia  and
      Falkner, Katrina",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.11",
    doi = "10.18653/v1/2020.alw-1.11",
    pages = "89--94",
    abstract = "Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.",
}
@inproceedings{davidson-etal-2020-developing,
    title = "Developing a New Classifier for Automated Identification of Incivility in Social Media",
    author = "Davidson, Sam  and
      Sun, Qiusi  and
      Wojcieszak, Magdalena",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.12",
    doi = "10.18653/v1/2020.alw-1.12",
    pages = "95--101",
    abstract = "Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, and the platforms themselves. Given the prevalence and effects of online incivility, and the challenges involved in human-based incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments. This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments. The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter. Our best performing model achieves an F1 of 0.802 on our Reddit test set. The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and - as such - ready to be used on vast volumes of online data. All trained models and annotated data are made available to the research community.",
}
@inproceedings{garland-etal-2020-countering,
    title = "Countering hate on social media: Large scale classification of hate and counter speech",
    author = "Garland, Joshua  and
      Ghazi-Zahedi, Keyan  and
      Young, Jean-Gabriel  and
      H{\'e}bert-Dufresne, Laurent  and
      Galesic, Mirta",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.13",
    doi = "10.18653/v1/2020.alw-1.13",
    pages = "102--112",
    abstract = "Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage with hate speech to restore civil non-polarized discourse. However, its actual effectiveness in curbing the spread of hatred is unknown and hard to quantify. One major obstacle to researching this question is a lack of large labeled data sets for training automated classifiers to identify counter speech. Here we use a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech. We use an ensemble learning algorithm which pairs a variety of paragraph embeddings with regularized logistic regression functions to classify both hate and counter speech in a corpus of millions of relevant tweets from these two groups. Our pipeline achieves macro F1 scores on out of sample balanced test sets ranging from 0.76 to 0.97{---}accuracy in line and even exceeding the state of the art. We then use the classifier to discover hate and counter speech in more than 135,000 fully-resolved Twitter conversations occurring from 2013 to 2018 and study their frequency and interaction. Altogether, our results highlight the potential of automated methods to evaluate the impact of coordinated counter speech in stabilizing conversations on social media.",
}
@inproceedings{pershan-2020-moderating,
    title = "Moderating Our (Dis)Content: Renewing the Regulatory Approach",
    author = "Pershan, Claire",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.14",
    doi = "10.18653/v1/2020.alw-1.14",
    pages = "113",
    abstract = "As online platforms become central to our democracies, the problem of toxic content threatens the free flow of information and the enjoyment of fundamental rights. But effective policy response to toxic content must grasp the idiosyncrasies and interconnectedness of content moderation across a fragmented online landscape. This report urges regulators and legislators to consider a range of platforms and moderation approaches in the regulation. In particular, it calls for a holistic, process-oriented regulatory approach that accounts for actors beyond the handful of dominant platforms that currently shape public debate.",
}
@inproceedings{price-etal-2020-six,
    title = "Six Attributes of Unhealthy Conversations",
    author = "Price, Ilan  and
      Gifford-Moore, Jordan  and
      Flemming, Jory  and
      Musker, Saul  and
      Roichman, Maayan  and
      Sylvain, Guillaume  and
      Thain, Nithum  and
      Dixon, Lucas  and
      Sorensen, Jeffrey",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.15",
    doi = "10.18653/v1/2020.alw-1.15",
    pages = "114--124",
    abstract = "We present a new dataset of approximately 44000 comments labeled by crowdworkers. Each comment is labelled as either {`}healthy{'} or {`}unhealthy{'}, in addition to binary labels for the presence of six potentially {`}unhealthy{'} sub-attributes: (1) hostile; (2) antagonistic, insulting, provocative or trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic; and/or (6) an unfair generalisation. Each label also has an associated confidence score. We argue that there is a need for datasets which enable research based on a broad notion of {`}unhealthy online conversation{'}. We build this typology to encompass a substantial proportion of the individual comments which contribute to unhealthy online conversation. For some of these attributes, this is the first publicly available dataset of this scale. We explore the quality of the dataset, present some summary statistics and initial models to illustrate the utility of this data, and highlight limitations and directions for further research.",
}
@inproceedings{banko-etal-2020-unified,
    title = "A Unified Taxonomy of Harmful Content",
    author = "Banko, Michele  and
      MacKeen, Brendon  and
      Ray, Laurie",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.16",
    doi = "10.18653/v1/2020.alw-1.16",
    pages = "125--137",
    abstract = "The ability to recognize harmful content within online communities has come into focus for researchers, engineers and policy makers seeking to protect users from abuse. While the number of datasets aiming to capture forms of abuse has grown in recent years, the community has not standardized around how various harmful behaviors are defined, creating challenges for reliable moderation, modeling and evaluation. As a step towards attaining shared understanding of how online abuse may be modeled, we synthesize the most common types of abuse described by industry, policy, community and health experts into a unified typology of harmful content, with detailed criteria and exceptions for each type of abuse.",
}
@inproceedings{kurrek-etal-2020-towards,
    title = "Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage",
    author = "Kurrek, Jana  and
      Saleem, Haji Mohammad  and
      Ruths, Derek",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.17",
    doi = "10.18653/v1/2020.alw-1.17",
    pages = "138--149",
    abstract = "Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs.",
}
@inproceedings{madukwe-etal-2020-data,
    title = "In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets",
    author = "Madukwe, Kosisochukwu  and
      Gao, Xiaoying  and
      Xue, Bing",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.18",
    doi = "10.18653/v1/2020.alw-1.18",
    pages = "150--161",
    abstract = "Recently, a few studies have discussed the limitations of datasets collected for the task of detecting hate speech from different viewpoints. We intend to contribute to the conversation by providing a consolidated overview of these issues pertaining to the data that debilitate research in this area. Specifically, we discuss how the varying pre-processing steps and the format for making data publicly available result in highly varying datasets that make an objective comparison between studies difficult and unfair. There is currently no study (to the best of our knowledge) focused on comparing the attributes of existing datasets for hate speech detection, outlining their limitations and recommending approaches for future research. This work intends to fill that gap and become the one-stop shop for information regarding hate speech datasets.",
}
@inproceedings{vidgen-etal-2020-detecting,
    title = "Detecting {E}ast {A}sian Prejudice on Social Media",
    author = "Vidgen, Bertie  and
      Hale, Scott  and
      Guest, Ella  and
      Margetts, Helen  and
      Broniatowski, David  and
      Waseem, Zeerak  and
      Botelho, Austin  and
      Hall, Matthew  and
      Tromble, Rebekah",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.19",
    doi = "10.18653/v1/2020.alw-1.19",
    pages = "162--172",
    abstract = "During COVID-19 concerns have heightened about the spread of aggressive and hateful language online, especially hostility directed against East Asia and East Asian people. We report on a new dataset and the creation of a machine learning classifier that categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice, and a neutral class. The classifier achieves a macro-F1 score of 0.83. We then conduct an in-depth ground-up error analysis and show that the model struggles with edge cases and ambiguous content. We provide the 20,000 tweet training dataset (annotated by experienced analysts), which also contains several secondary categories and additional flags. We also provide the 40,000 original annotations (before adjudication), the full codebook, annotations for COVID-19 relevance and East Asian relevance and stance for 1,000 hashtags, and the final model.",
}
@inproceedings{nejadgholi-kiritchenko-2020-cross,
    title = "On Cross-Dataset Generalization in Automatic Detection of Online Abuse",
    author = "Nejadgholi, Isar  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.20",
    doi = "10.18653/v1/2020.alw-1.20",
    pages = "173--183",
    abstract = "NLP research has attained high performances in abusive language detection as a supervised classification task. While in research settings, training and test datasets are usually obtained from similar data samples, in practice systems are often applied on data that are different from the training set in topic and class distributions. Also, the ambiguity in class definitions inherited in this task aggravates the discrepancies between source and target datasets. We explore the topic bias and the task formulation bias in cross-dataset generalization. We show that the benign examples in the Wikipedia Detox dataset are biased towards platform-specific topics. We identify these examples using unsupervised topic modeling and manual inspection of topics{'} keywords. Removing these topics increases cross-dataset generalization, without reducing in-domain classification performance. For a robust dataset design, we suggest applying inexpensive unsupervised methods to inspect the collected data and downsize the non-generalizable content before manually annotating for class labels.",
}
@inproceedings{al-kuwatly-etal-2020-identifying,
    title = "Identifying and Measuring Annotator Bias Based on Annotators{'} Demographic Characteristics",
    author = "Al Kuwatly, Hala  and
      Wich, Maximilian  and
      Groh, Georg",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.21",
    doi = "10.18653/v1/2020.alw-1.21",
    pages = "184--190",
    abstract = "Machine learning is recently used to detect hate speech and other forms of abusive language in online platforms. However, a notable weakness of machine learning models is their vulnerability to bias, which can impair their performance and fairness. One type is annotator bias caused by the subjective perception of the annotators. In this work, we investigate annotator bias using classification models trained on data from demographically distinct annotator groups. To do so, we sample balanced subsets of data that are labeled by demographically distinct annotators. We then train classifiers on these subsets, analyze their performances on similarly grouped test sets, and compare them statistically. Our findings show that the proposed approach successfully identifies bias and that demographic features, such as first language, age, and education, correlate with significant performance differences.",
}
@inproceedings{wich-etal-2020-investigating,
    title = "Investigating Annotator Bias with a Graph-Based Approach",
    author = "Wich, Maximilian  and
      Al Kuwatly, Hala  and
      Groh, Georg",
    booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alw-1.22",
    doi = "10.18653/v1/2020.alw-1.22",
    pages = "191--199",
    abstract = "A challenge that many online platforms face is hate speech or any other form of online abuse. To cope with this, hate speech detection systems are developed based on machine learning to reduce manual work for monitoring these platforms. Unfortunately, machine learning is vulnerable to unintended bias in training data, which could have severe consequences, such as a decrease in classification performance or unfair behavior (e.g., discriminating minorities). In the scope of this study, we want to investigate annotator bias {---} a form of bias that annotators cause due to different knowledge in regards to the task and their subjective perception. Our goal is to identify annotation bias based on similarities in the annotation behavior from annotators. To do so, we build a graph based on the annotations from the different annotators, apply a community detection algorithm to group the annotators, and train for each group classifiers whose performances we compare. By doing so, we are able to identify annotator bias within a data set. The proposed method and collected insights can contribute to developing fairer and more reliable hate speech classification models.",
}
@proceedings{alvr-2020-advances,
    title = "Proceedings of the First Workshop on Advances in Language and Vision Research",
    editor = "Wang, Xin  and
      Thomason, Jesse  and
      Hu, Ronghang  and
      Chen, Xinlei  and
      Anderson, Peter  and
      Wu, Qi  and
      Celikyilmaz, Asli  and
      Baldridge, Jason  and
      Wang, William Yang",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alvr-1.0",
}
@inproceedings{alsudais-2020-extending,
    title = "Extending {I}mage{N}et to {A}rabic using {A}rabic {W}ord{N}et",
    author = "Alsudais, Abdulkareem",
    booktitle = "Proceedings of the First Workshop on Advances in Language and Vision Research",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alvr-1.1",
    doi = "10.18653/v1/2020.alvr-1.1",
    pages = "1--6",
    abstract = "ImageNet has millions of images that are labeled with English WordNet synsets. This paper investigates the extension of ImageNet to Arabic using Arabic WordNet. The objective is to discover if Arabic synsets can be found for synsets used in ImageNet. The primary finding is the identification of Arabic synsets for 1,219 of the 21,841 synsets used in ImageNet, which represents 1.1 million images. By leveraging the parent-child structure of synsets in ImageNet, this dataset is extended to 10,462 synsets (and 7.1 million images) that have an Arabic label, which is either a match or a direct hypernym, and to 17,438 synsets (and 11 million images) when a hypernym of a hypernym is included. When all hypernyms for a node are considered, an Arabic synset is found for all but four synsets. This represents the major contribution of this work: a dataset of images that have Arabic labels for 99.9{\%} of the images in ImageNet.",
}
@inproceedings{choi-etal-2020-toward,
    title = "Toward General Scene Graph: Integration of Visual Semantic Knowledge with Entity Synset Alignment",
    author = "Choi, Woo Suk  and
      On, Kyoung-Woon  and
      Heo, Yu-Jung  and
      Zhang, Byoung-Tak",
    booktitle = "Proceedings of the First Workshop on Advances in Language and Vision Research",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alvr-1.2",
    doi = "10.18653/v1/2020.alvr-1.2",
    pages = "7--11",
    abstract = "Scene graph is a graph representation that explicitly represents high-level semantic knowledge of an image such as objects, attributes of objects and relationships between objects. Various tasks have been proposed for the scene graph, but the problem is that they have a limited vocabulary and biased information due to their own hypothesis. Therefore, results of each task are not generalizable and difficult to be applied to other down-stream tasks. In this paper, we propose Entity Synset Alignment(ESA), which is a method to create a general scene graph by aligning various semantic knowledge efficiently to solve this bias problem. The ESA uses a large-scale lexical database, WordNet and Intersection of Union (IoU) to align the object labels in multiple scene graphs/semantic knowledge. In experiment, the integrated scene graph is applied to the image-caption retrieval task as a down-stream task. We confirm that integrating multiple scene graphs helps to get better representations of images.",
}
@inproceedings{sarrouti-etal-2020-visual,
    title = "Visual Question Generation from Radiology Images",
    author = "Sarrouti, Mourad  and
      Ben Abacha, Asma  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the First Workshop on Advances in Language and Vision Research",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alvr-1.3",
    doi = "10.18653/v1/2020.alvr-1.3",
    pages = "12--18",
    abstract = "Visual Question Generation (VQG), the task of generating a question based on image contents, is an increasingly important area that combines natural language processing and computer vision. Although there are some recent works that have attempted to generate questions from images in the open domain, the task of VQG in the medical domain has not been explored so far. In this paper, we introduce an approach to generation of visual questions about radiology images called VQGR, i.e. an algorithm that is able to ask a question when shown an image. VQGR first generates new training data from the existing examples, based on contextual word embeddings and image augmentation techniques. It then uses the variational auto-encoders model to encode images into a latent space and decode natural language questions. Experimental automatic evaluations performed on the VQA-RAD dataset of clinical visual questions show that VQGR achieves good performances compared with the baseline system. The source code is available at https://github.com/sarrouti/vqgr.",
}
@inproceedings{mazuecos-etal-2020-role,
    title = "On the role of effective and referring questions in {G}uess{W}hat?!",
    author = "Mazuecos, Mauricio  and
      Testoni, Alberto  and
      Bernardi, Raffaella  and
      Benotti, Luciana",
    booktitle = "Proceedings of the First Workshop on Advances in Language and Vision Research",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alvr-1.4",
    doi = "10.18653/v1/2020.alvr-1.4",
    pages = "19--25",
    abstract = "Task success is the standard metric used to evaluate referential visual dialogue systems. In this paper we propose two new metrics that evaluate how each question contributes to the goal. First, we measure how effective each question is by evaluating whether the question discards objects that are not the referent. Second, we define referring questions as those that univocally identify one object in the image. We report the new metrics for human dialogues and for state of the art publicly available models on GuessWhat?!. Regarding our first metric, we find that successful dialogues do not have a higher percentage of effective questions for most models. With respect to the second metric, humans make questions at the end of the dialogue that are referring, confirming their guess before guessing. Human dialogues that use this strategy have a higher task success but models do not seem to learn it.",
}
@inproceedings{rajaby-faghihi-etal-2020-latent,
    title = "Latent Alignment of Procedural Concepts in Multimodal Recipes",
    author = "Rajaby Faghihi, Hossein  and
      Mirzaee, Roshanak  and
      Paliwal, Sudarshan  and
      Kordjamshidi, Parisa",
    booktitle = "Proceedings of the First Workshop on Advances in Language and Vision Research",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.alvr-1.5",
    doi = "10.18653/v1/2020.alvr-1.5",
    pages = "26--31",
    abstract = "We propose a novel alignment mechanism to deal with procedural reasoning on a newly released multimodal QA dataset, named RecipeQA. Our model is solving the textual cloze task which is a reading comprehension on a recipe containing images and instructions. We exploit the power of attention networks, cross-modal representations, and a latent alignment space between instructions and candidate answers to solve the problem. We introduce constrained max-pooling which refines the max pooling operation on the alignment matrix to impose disjoint constraints among the outputs of the model. Our evaluation result indicates a 19{\%} improvement over the baselines.",
}
@proceedings{alta-2020-australasian,
    title = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    editor = "Kim, Maria  and
      Beck, Daniel  and
      Mistica, Meladel",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.0",
}
@inproceedings{moghimifar-etal-2020-domain,
    title = "Domain Adaptative Causality Encoder",
    author = "Moghimifar, Farhad  and
      Haffari, Gholamreza  and
      Baktashmotlagh, Mahsa",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.1",
    pages = "1--10",
    abstract = "Automated discovery of causal relationships from text is a challenging task. Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform poorly when applied to a distributionally different domain for which labelled data did not exist at the time of training. To overcome this limitation, in this paper, we leverage the characteristics of dependency trees and adversarial learning to address the tasks of adaptive causality identification and localisation. The term adaptive is used since the training and test data come from two distributionally different datasets, which to the best of our knowledge, this work is the first to address. Moreover, we present a new causality dataset, namely MedCaus, which integrates all types of causality in the text. Our experiments on four different benchmark causality datasets demonstrate the superiority of our approach over the existing baselines, by up to 7{\%} improvement, on the tasks of identification and localisation of the causal relations from the text.",
}
@inproceedings{atapattu-etal-2020-automated,
    title = "Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability",
    author = "Atapattu, Thushari  and
      Herath, Mahen  and
      Zhang, Georgia  and
      Falkner, Katrina",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.2",
    pages = "11--20",
    abstract = "Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5 (HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistiBERT has achieved 0.73 and 0.74 of F1 score in the task of classifying hate speech (Task A) and aggressiveness and target (Task B) respectively. We adapt the ensemble model developed for Task A to classify offensive language in external datasets and achieved {\textasciitilde}0.7 of F1 score using three benchmark datasets, enabling promising results for cross-domain adaptability. We conduct a qualitative analysis of misclassified tweets to provide insightful recommendations for future cyberbullying research.",
}
@inproceedings{ishihara-2020-influence,
    title = "The Influence of Background Data Size on the Performance of a Score-based Likelihood Ratio System: A Case of Forensic Text Comparison",
    author = "Ishihara, Shunichi",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.3",
    pages = "21--31",
    abstract = "This study investigates the robustness and stability of a likelihood ratio{--}based (LR-based) forensic text comparison (FTC) system against the size of background population data. Focus is centred on a score-based approach for estimating authorship LRs. Each document is represented with a bag-of-words model, and the Cosine distance is used as the score-generating function. A set of population data that differed in the number of scores was synthesised 20 times using the Monte-Carol simulation technique. The FTC system{'}s performance with different population sizes was evaluated by a gradient metric of the log{--}LR cost (Cllr). The experimental results revealed two outcomes: 1) that the score-based approach is rather robust against a small population size{---}in that, with the scores obtained from the 40{\textasciitilde}60 authors in the database, the stability and the performance of the system become fairly comparable to the system with a maximum number of authors (720); and 2) that poor performance in terms of Cllr, which occurred because of limited background population data, is largely due to poor calibration. The results also indicated that the score-based approach is more robust against data scarcity than the feature-based approach; however, this finding obliges further study.",
}
@inproceedings{carne-ishihara-2020-feature,
    title = "Feature-Based Forensic Text Comparison Using a {P}oisson Model for Likelihood Ratio Estimation",
    author = "Carne, Michael  and
      Ishihara, Shunichi",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.4",
    pages = "32--42",
    abstract = "Score- and feature-based methods are the two main ones for estimating a forensic likelihood ratio (LR) quantifying the strength of evidence. In this forensic text comparison (FTC) study, a score-based method using the Cosine distance is compared with a feature-based method built on a Poisson model with texts collected from 2,157 authors. Distance measures (e.g. Burrows{'}s Delta, Cosine distance) are a standard tool in authorship attribution studies. Thus, the implementation of a score-based method using a distance measure is naturally the first step for estimating LRs for textual evidence. However, textual data often violates the statistical assumptions underlying distance-based models. Furthermore, such models only assess the similarity, not the typicality, of the objects (i.e. documents) under comparison. A Poisson model is theoretically more appropriate than distance-based measures for authorship attribution, but it has never been tested with linguistic text evidence within the LR framework. The log-LR cost (Cllr) was used to assess the performance of the two methods. This study demonstrates that: (1) the feature-based method outperforms the score-based method by a Cllr value of ca. 0.09 under the best-performing settings and; (2) the performance of the feature-based method can be further improved by feature selection.",
}
@inproceedings{muradoglu-etal-2020-modelling,
    title = "Modelling Verbal Morphology in {N}en",
    author = "Muradoglu, Saliha  and
      Evans, Nicholas  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.5",
    pages = "43--53",
    abstract = "Nen verbal morphology is particularly complex; a transitive verb can take up to 1,740 unique forms. The combined effect of having a large combinatoric space and a low-resource setting amplifies the need for NLP tools. Nen morphology utilises distributed exponence - a non-trivial means of mapping form to meaning. In this paper, we attempt to model Nen verbal morphology using state-of-the-art machine learning models for morphological reinflection. We explore and categorise the types of errors these systems generate. Our results show sensitivity to training data composition; different distributions of verb type yield different accuracies (patterning with E-complexity). We also demonstrate the types of patterns that can be inferred from the training data, through the case study of sycretism.",
}
@inproceedings{chao-etal-2020-automatic,
    title = "An Automatic Vowel Space Generator for Language Learner Pronunciation Acquisition and Correction",
    author = "Chao, Xinyuan  and
      El-Khaissi, Charbel  and
      Kuo, Nicholas  and
      John, Priscilla Kan  and
      Suominen, Hanna",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.6",
    pages = "54--64",
    abstract = "Speech visualisations are known to help language learners to acquire correct pronunciation and promote a better study experience. We present a two-step approach based on two established techniques to display tongue tip movements of an acoustic speech signal on a vowel space plot. First we use Energy Entropy Ratio to extract vowels; and then we apply Linear Predictive Coding root method to estimate Formant 1 and Formant 2. We invited and collected acoustic data from one Modern Standard Arabic (MSA) lecture and four MSA students. Our proof of concept was able to reflect differences between the tongue tip movements in a native MSA speaker to those of a MSA language learner. This paper addresses principle methods for generating features that reflect bio-physiological features of speech and thus, facilitates an approach that can be generally adapted to languages other than MSA.",
}
@inproceedings{das-zhang-2020-absa,
    title = "{ABSA}-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research",
    author = "Das, Abhishek  and
      Zhang, Wei Emma",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.7",
    pages = "65--71",
    abstract = "Aspect-Based Sentiment Analysis (ABSA)has gained much attention in recent years. It is the task of identifying fine-grained opinionpolarity towards a specific aspect associated with a given target. However, there is a lack of benchmarking platform to provide a unified environment under consistent evaluation criteria for ABSA, resulting in the difficulties for fair comparisons. In this work, we address this issue and define a benchmark, ABSA-Bench, by unifying the evaluation protocols and the pre-processed publicly available datasets in a Web-based platform. ABSA-Bench provides two means of evaluations for participants to submit their predictions or models for online evaluation. Performances are ranked in the leader board and a discussion forum is supported to serve as a collaborative platform for academics and researchers to discuss queries.",
}
@inproceedings{chen-etal-2020-machine,
    title = "A machine-learning based model to identify {P}h{D}-level skills in job ads",
    author = "Chen, Li{'}An  and
      Mewburn, Inger  and
      Suonimen, Hanna",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.8",
    pages = "72--80",
    abstract = "Around 60{\%} of doctoral graduates worldwide ended up working in industry rather than academia. There have been calls to more closely align the PhD curriculum with the needs of industry, but an evidence base is lacking to inform these changes. We need to find better ways to understand what industry employers really want from doctoral graduates. One good source of data is job advertisements where employers provide a {`}wish list{'} of skills and expertise. In this paper, a machine learning-natural language processing (ML-NLP) based approach was used to explore and extract skill requirements from research intensive job advertisements, suitable for PhD graduates. The model developed for detecting skill requirements in job ads was driven by SVM. The experiment results showed that ML-NLP approach had the potential to replicate manual efforts in understanding job requirements of PhD graduates. Our model offers a new perspective to look at PhD-level job skill requirements.",
}
@inproceedings{moghimifar-etal-2020-learning,
    title = "Learning Causal {B}ayesian Networks from Text",
    author = "Moghimifar, Farhad  and
      Rahimi, Afshin  and
      Baktashmotlagh, Mahsa  and
      Li, Xue",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.9",
    pages = "81--85",
    abstract = "Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.",
}
@inproceedings{guo-etal-2020-benchmarking,
    title = "Benchmarking of Transformer-Based Pre-Trained Models on Social Media Text Classification Datasets",
    author = "Guo, Yuting  and
      Dong, Xiangjue  and
      Al-Garadi, Mohammed Ali  and
      Sarker, Abeed  and
      Paris, Cecile  and
      Aliod, Diego Moll{\'a}",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.10",
    pages = "86--91",
    abstract = "Free text data from social media is now widely used in natural language processing research, and one of the most common machine learning tasks performed on this data is classification. Generally speaking, performances of supervised classification algorithms on social media datasets are lower than those on texts from other sources, but recently-proposed transformer-based models have considerably improved upon legacy state-of-the-art systems. Currently, there is no study that compares the performances of different variants of transformer-based models on a wide range of social media text classification datasets. In this paper, we benchmark the performances of transformer-based pre-trained models on 25 social media text classification datasets, 6 of which are health-related. We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy. Our experiments show that RoBERTa-base and BERTweet perform comparably on most datasets, and considerably better than ClinicalBioBERT, even on health-related datasets.",
}
@inproceedings{nguyen-etal-2020-pandemic,
    title = "Pandemic Literature Search: Finding Information on {COVID}-19",
    author = "Nguyen, Vincent  and
      Rybinski, Maciek  and
      Karimi, Sarvnaz  and
      Xing, Zhenchang",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.11",
    pages = "92--97",
    abstract = "Finding information related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. We investigate how to better rank information for pandemic information retrieval. We experiment with different ranking algorithms and propose a novel end-to-end method for neural retrieval, and demonstrate its effectiveness on the TREC COVID search. This work could lead to a search system that aids scientists, clinicians, policymakers and others in finding reliable answers from the scientific literature.",
}
@inproceedings{mistica-etal-2020-information,
    title = "Information Extraction from Legal Documents: A Study in the Context of Common Law Court Judgements",
    author = "Mistica, Meladel  and
      Zhang, Geordie Z.  and
      Chia, Hui  and
      Shrestha, Kabir Manandhar  and
      Gupta, Rohit Kumar  and
      Khandelwal, Saket  and
      Paterson, Jeannie  and
      Baldwin, Timothy  and
      Beck, Daniel",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.12",
    pages = "98--103",
    abstract = "{`}Common Law{'} judicial systems follow the doctrine of precedent, which means the legal principles articulated in court judgements are binding in subsequent cases in lower courts. For this reason, lawyers must search prior judgements for the legal principles that are relevant to their case. The difficulty for those within the legal profession is that the information that they are looking for may be contained within a few paragraphs or sentences, but those few paragraphs may be buried within a hundred-page document. In this study, we create a schema based on the relevant information that legal professionals seek within judgements and perform text classification based on it, with the aim of not only assisting lawyers in researching cases, but eventually enabling large-scale analysis of legal judgements to find trends in court outcomes over time.",
}
@inproceedings{keesing-etal-2020-convolutional,
    title = "Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition",
    author = "Keesing, Aaron  and
      Watson, Ian  and
      Witbrock, Michael",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.13",
    pages = "104--109",
    abstract = "We test four models proposed in the speech emotion recognition (SER) literature on 15 public and academic licensed datasets in speaker-independent cross-validation. Results indicate differences in the performance of the models which is partly dependent on the dataset and features used. We also show that a standard utterance-level feature set still performs competitively with neural models on some datasets. This work serves as a starting point for future model comparisons, in addition to open-sourcing the testing code.",
}
@inproceedings{kitayama-etal-2020-popularity,
    title = "Popularity Prediction of Online Petitions using a Multimodal {D}eep{R}egression Model",
    author = "Kitayama, Kotaro  and
      Subramanian, Shivashankar  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.14",
    pages = "110--114",
    abstract = "Online petitions offer a mechanism for peopleto initiate a request for change and gather sup-port from others to demonstrate support for thecause. In this work, we model the task of peti-tion popularity using both text and image rep-resentations across four different languages,and including petition metadata. We evaluateour proposed approach using a dataset of 75kpetitions from Avaaz.org, and find strong com-plementarity between text and images.",
}
@inproceedings{shcherbakov-etal-2020-exploring,
    title = "Exploring Looping Effects in {RNN}-based Architectures",
    author = "Shcherbakov, Andrei  and
      Muradoglu, Saliha  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.15",
    pages = "115--120",
    abstract = "The paper investigates repetitive loops, a common problem in contemporary text generation (such as machine translation, language modelling, morphological inflection) systems. More specifically, we conduct a study on neural models with recurrent units by explicitly altering their decoder internal state. We use a task of morphological reinflection task as a proxy to study the effects of the changes. Our results show that the probability of the occurrence of repetitive loops is significantly reduced by introduction of an extra neural decoder output. The output should be specifically trained to produce gradually increasing value upon generation of each character of a given sequence. We also explored variations of the technique and found that feeding the extra output back to the decoder amplifies the positive effects.",
}
@inproceedings{ferraro-suominen-2020-transformer,
    title = "Transformer Semantic Parsing",
    author = "Ferraro, Gabriela  and
      Suominen, Hanna",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.16",
    pages = "121--126",
    abstract = "In neural semantic parsing, sentences are mapped to meaning representations using encoder-decoder frameworks. In this paper, we propose to apply the Transformer architecture, instead of recurrent neural networks, to this task. Experiments in two data sets from different domains and with different levels of difficulty show that our model achieved better results than strong baselines in certain settings and competitive results across all our experiments.",
}
@inproceedings{molla-2020-overview,
    title = "Overview of the 2020 {ALTA} Shared Task: Assess Human Behaviour",
    author = "Moll{\'a}, Diego",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.17",
    pages = "127--130",
    abstract = "The 2020 ALTA shared task is the 11th in stance of a series of shared tasks organised by ALTA since 2010. The task is to classify texts posted in social media according to human judgements expressed in them. The data used for this task is a subset of SemEval 2018 AIT DISC, which has been annotated by domain experts for this task. In this paper we introduce the task, describe the data and present the results of participating systems.",
}
@inproceedings{aroyehun-gelbukh-2020-automatically,
    title = "Automatically Predicting Judgement Dimensions of Human Behaviour",
    author = "Aroyehun, Segun Taofeek  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.18",
    pages = "131--134",
    abstract = "This paper describes our submission to the ALTA-2020 shared task on assessing behaviour from short text, We evaluate the effectiveness of traditional machine learning and recent transformers pre-trained models. Our submission with the Roberta-large model and prediction threshold achieved first place on the private leaderboard.",
}
@inproceedings{parameswaran-etal-2020-classifying,
    title = "Classifying Judgements using Transfer Learning",
    author = "Parameswaran, Pradeesh  and
      Trotman, Andrew  and
      Liesaputra, Veronica  and
      Eyers, David",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.19",
    pages = "135--139",
    abstract = "We describe our method for classifying short texts into the APPRAISAL framework, work we conducted as part of the ALTA 2020 shared task. We tackled this problem using transfer learning. Our team, {``}orangutanV2{''} placed equal first in the shared task, with a mean F1-score of 0.1026 on the private data set.",
}
@inproceedings{khilji-etal-2020-human,
    title = "Human Behavior Assessment using Ensemble Models",
    author = "Khilji, Abdullah Faiz Ur Rahman  and
      Khaund, Rituparna  and
      Sinha, Utkarsh",
    booktitle = "Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2020",
    address = "Virtual Workshop",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/2020.alta-1.20",
    pages = "140--144",
    abstract = "Behavioral analysis is a pertinent step in today{'}s automated age. It is important to judge a statement on a variety of parameters before reaching a valid conclusion. In today{'}s world of technology and automation, Natural language processing tools have benefited from growing access to data in order to analyze the context and scenario. A better understanding of human behaviors would empower a range of automated tools to provide users a customized experience. For precise analysis, behavior understanding is important. We have experimented with various machine learning techniques, and have obtained a maximum private score of 0.1033 with a public score of 0.1733. The methods are described as part of the ALTA 2020 shared task. In this work, we have enlisted our results and the challenges faced to solve the problem of the human behavior assessment.",
}
@proceedings{ai4hi-2020-international,
    title = "Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access",
    editor = "Abgaz, Yalemisew  and
      Dorn, Amelie  and
      Diaz, Jose Luis Preza  and
      Koch, Gerda",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.ai4hi-1.0",
    ISBN = "979-10-95546-63-4",
}
@inproceedings{arnold-tilton-2020-enriching,
    title = "Enriching Historic Photography with Structured Data using Image Region Segmentation",
    author = "Arnold, Taylor  and
      Tilton, Lauren",
    booktitle = "Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.ai4hi-1.1",
    pages = "1--10",
    abstract = "Cultural institutions such as galleries, libraries, archives and museums continue to make commitments to large scale digitization of collections. An ongoing challenge is how to increase discovery and access through structured data and the semantic web. In this paper we describe a method for using computer vision algorithms that automatically detect regions of {``}stuff{''} {---} such as the sky, water, and roads {---} to produce rich and accurate structured data triples for describing the content of historic photography. We apply our method to a collection of 1610 documentary photographs produced in the 1930s and 1940 by the FSA-OWI division of the U.S. federal government. Manual verification of the extracted annotations yields an accuracy rate of 97.5{\%}, compared to 70.7{\%} for relations extracted from object detection and 31.5{\%} for automatically generated captions. Our method also produces a rich set of features, providing more unique labels (1170) than either the captions (1040) or object detection (178) methods. We conclude by describing directions for a linguistically-focused ontology of region categories that can better enrich historical image data. Open source code and the extracted metadata from our corpus are made available as external resources.",
    language = "English",
    ISBN = "979-10-95546-63-4",
}
@inproceedings{breit-2020-interlinking,
    title = "Interlinking Iconclass Data with Concepts of Art {\&} Architecture Thesaurus",
    author = "Breit, Anna",
    booktitle = "Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.ai4hi-1.2",
    pages = "11--15",
    abstract = "Iconclass, being a a well established classification system, could benefit from interconnections with other ontologies in order to semantically enrich its content. This work presents a disambiguating and interlinking approach which is used to map Iconclass Subjects to concepts of the Art and Architecture Thesaurus. In a preliminary evaluation, the system is able to produce promising predictions, though the task is highly challenging due to conceptual and schema heterogeneity. Several algorithmic improvements for this specific interlinking task, as well as and future research directions are suggestions. The produced mappings, as well as the source code and additional information can be found at https://github.com/annabreit/taxonomy-interlinking.",
    language = "English",
    ISBN = "979-10-95546-63-4",
}
@inproceedings{roberto-etal-2020-toward,
    title = "Toward the Automatic Retrieval and Annotation of Outsider Art images: A Preliminary Statement",
    author = "Roberto, John  and
      Ortego, Diego  and
      Davis, Brian",
    booktitle = "Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.ai4hi-1.3",
    pages = "16--22",
    abstract = "The aim of this position paper is to establish an initial approach to the automatic classification of digital images about the Outsider Art style of painting. Specifically, we explore whether is it possible to classify non-traditional artistic styles by using the same features that are used for classifying traditional styles? Our research question is motivated by two facts. First, art historians state that non-traditional styles are influenced by factors {``}outside{''} of the world of art. Second, some studies have shown that several artistic styles confound certain classification techniques. Following current approaches to style prediction, this paper utilises Deep Learning methods to encode image features. Our preliminary experiments have provided motivation to think that, as is the case with traditional styles, Outsider Art can be computationally modelled with objective means by using training datasets and CNN models. Nevertheless, our results are not conclusive due to the lack of a large available dataset on Outsider Art. Therefore, at the end of the paper, we have mapped future lines of action, which include the compilation of a large dataset of Outsider Art images and the creation of an ontology of Outsider Art.",
    language = "English",
    ISBN = "979-10-95546-63-4",
}
@inproceedings{bartz-etal-2020-automatic,
    title = "Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis",
    author = "Bartz, Christian  and
      Jain, Nitisha  and
      Krestel, Ralf",
    booktitle = "Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.ai4hi-1.4",
    pages = "23--28",
    abstract = "Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic text exploration for digital cultural resources. However, this information is contained in images of artworks, as well as textual descriptions or annotations accompanied with the images. During the digitization of such resources, the valuable associations between the images and texts are frequently lost. In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives. To this end, we use machine learning to generate text descriptions for the extracted images on the one hand, and to detect descriptive phrases and titles of images from the text on the other hand. Finally, we use embeddings to align both, the descriptions and the images.",
    language = "English",
    ISBN = "979-10-95546-63-4",
}
@inproceedings{abgaz-etal-2020-towards,
    title = "Towards a Comprehensive Assessment of the Quality and Richness of the Europeana Metadata of food-related Images",
    author = "Abgaz, Yalemisew  and
      Dorn, Amelie  and
      Preza Diaz, Jose Luis  and
      Koch, Gerda",
    booktitle = "Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.ai4hi-1.5",
    pages = "29--33",
    abstract = "Semantic enrichment of historical images to build interactive AI systems for the Digital Humanities domain has recently gained significant attention. However, before implementing any semantic enrichment tool for building AI systems, it is also crucial to analyse the quality and richness of the existing datasets and understand the areas where semantic enrichment is most required. Here, we propose an approach to conducting a preliminary analysis of selected historical images from the Europeana platform using existing linked data quality assessment tools. The analysis targets food images by collecting metadata provided from curators such as Galleries, Libraries, Archives and Museums (GLAMs) and cultural aggregators such as Europeana. We identified metrics to evaluate the quality of the metadata associated with food-related images which are harvested from the Europeana platform. In this paper, we present the food-image dataset, the associated metadata and our proposed method for the assessment. The results of our assessment will be used to guide the current effort to semantically enrich the images and build high-quality metadata using Computer Vision.",
    language = "English",
    ISBN = "979-10-95546-63-4",
}
@proceedings{aespen-2020-automated,
    title = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Y{\"o}r{\"u}k, Erdem  and
      Zavarella, Vanni  and
      Tanev, Hristo},
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.0",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{hurriyetoglu-etal-2020-automated,
    title = "Automated Extraction of Socio-political Events from News ({AESPEN}): Workshop and Shared Task Report",
    author = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Zavarella, Vanni  and
      Tanev, Hristo  and
      Y{\"o}r{\"u}k, Erdem  and
      Safaya, Ali  and
      Mutlu, Osman},
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.1",
    pages = "1--6",
    abstract = "We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020). We believe the event extraction studies in computational linguistics and social and political sciences should further support each other in order to enable large scale socio-political event information collection across sources, countries, and languages. The event consists of regular research papers and a shared task, which is about event sentence coreference identification (ESCI), tracks. All submissions were reviewed by five members of the program committee. The workshop attracted research papers related to evaluation of machine learning methodologies, language resources, material conflict forecasting, and a shared task participation report in the scope of socio-political event information collection. It has shown us the volume and variety of both the data sources and event information collection approaches related to socio-political events and the need to fill the gap between automated text processing techniques and requirements of social and political sciences.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{raleigh-2020-keynote,
    title = "Keynote Abstract: Too soon? The limitations of {AI} for event data",
    author = "Raleigh, Clionadh",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.2",
    pages = "7",
    abstract = "Not all conflict datasets offer equal levels of coverage, depth, use-ability, and content. A review of the inclusion criteria, methodology, and sourcing of leading publicly available conflict datasets demonstrates that there are significant discrepancies in the output produced by ostensibly similar projects. This keynote will question the presumption of substantial overlap between datasets, and identify a number of important gaps left by deficiencies across core criteria for effective conflict data collection and analysis.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{schrodt-2020-keynote,
    title = "Keynote Abstract: Current Open Questions for Operational Event Data",
    author = "Schrodt, Philip A.",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.3",
    pages = "8",
    abstract = "In this brief keynote, I will address what I see as five majorissues in terms of development for operational event datasets (that is, event data intended for real time monitoringand forecasting, rather than purely for academic research).",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{buyukoz-etal-2020-analyzing,
    title = "Analyzing {ELM}o and {D}istil{BERT} on Socio-political News Classification",
    author = {B{\"u}y{\"u}k{\"o}z, Berfu  and
      H{\"u}rriyeto{\u{g}}lu, Ali  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.4",
    pages = "9--18",
    abstract = "This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews. A {''}cross-context{''} setting is enabled using test sets that are distinct from the training data. The models are fine-tuned and fed into a Feed-Forward Neural Network (FFNN) and a Bidirectional Long Short Term Memory network (BiLSTM). Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) are used as traditional baselines. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also 30{\%} smaller and 83{\%} faster than ELMo, which suggests superiority for smaller computational training budgets. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional machine learning (ML) algorithms can still be considered as more economic alternatives to deep language representations.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{olsson-etal-2020-text,
    title = "Text Categorization for Conflict Event Annotation",
    author = "Olsson, Fredrik  and
      Sahlgren, Magnus  and
      ben Abdesslem, Fehmi  and
      Ekgren, Ariel  and
      Eck, Kristine",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.5",
    pages = "19--25",
    abstract = "We cast the problem of event annotation as one of text categorization, and compare state of the art text categorization techniques on event data produced within the Uppsala Conflict Data Program (UCDP). Annotating a single text involves assigning the labels pertaining to at least 17 distinct categorization tasks, e.g., who were the attacking organization, who was attacked, and where did the event take place. The text categorization techniques under scrutiny are a classical Bag-of-Words approach; character-based contextualized embeddings produced by ELMo; embeddings produced by the BERT base model, and a version of BERT base fine-tuned on UCDP data; and a pre-trained and fine-tuned classifier based on ULMFiT. The categorization tasks are very diverse in terms of the number of classes to predict as well as the skeweness of the distribution of classes. The categorization results exhibit a large variability across tasks, ranging from 30.3{\%} to 99.8{\%} F-score.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{piskorski-jacquet-2020-tf,
    title = "{TF}-{IDF} Character {N}-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary Study",
    author = "Piskorski, Jakub  and
      Jacquet, Guillaume",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.6",
    pages = "26--34",
    abstract = "Automating the detection of event mentions in online texts and their classification vis-a-vis domain-specific event type taxonomies has been acknowledged by many organisations worldwide to be of paramount importance in order to facilitate the process of intelligence gathering. This paper reports on some preliminary experiments of comparing various linguistically-lightweight approaches for fine-grained event classification based on short text snippets reporting on events. In particular, we compare the performance of a TF-IDF-weighted character n-gram SVM-based model versus SVMs trained on various of-the-shelf pre-trained word embeddings (GloVe, BERT, FastText) as features. We exploit a relatively large event corpus consisting of circa 610K short text event descriptions classified using a 25-event categories that cover political violence and protest events. The best results, i.e., 83.5{\%} macro and 92.4{\%} micro F1 score, were obtained using the TF-IDF-weighted character n-gram model.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{radford-2020-seeing,
    title = "Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes",
    author = "Radford, Benjamin",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.7",
    pages = "35--41",
    abstract = "Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references{---}recognize singular events across multiple sentences or documents. A separate literature in computational linguistics on event coreference resolution attempts to link known events to one another within (and across) documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events. The data set, Headlines of War, is built on the Militarized Interstate Disputes data set and offers headlines classified by dispute status and headline pairs labeled with coreference indicators. Additionally, I introduce a model capable of accomplishing both tasks. The multi-task convolutional neural network is shown to be capable of recognizing events and event coreferences given the headlines{'} texts and publication dates.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{halkia-etal-2020-conflict,
    title = "Conflict Event Modelling: Research Experiment and Event Data Limitations",
    author = "Halkia, Matina  and
      Ferri, Stefano  and
      Papazoglou, Michail  and
      Van Damme, Marie-Sophie  and
      Thomakos, Dimitrios",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.8",
    pages = "42--48",
    abstract = "This paper presents the conflict event modelling experiment, conducted at the Joint Research Centre of the European Commission, particularly focusing on the limitations of the input data. This model is under evaluation as to potentially complement the Global Conflict Risk Index (GCRI), a conflict risk model supporting the design of European Union{'}s conflict prevention strategies. The model aims at estimating the occurrence of material conflict events, under the assumption that an increase in material conflict events goes along with a decrease in material and verbal cooperation. It adopts a Long-Short Term Memory Cell Recurrent Neural Network on country-level actor-based event datasets that indicate potential triggers to violent conflict such as demonstrations, strikes, or elections-related violence. The observed data and the outcome of the model predictions consecutively, consolidate an early warning alarm system that signals abnormal social unrest upheavals, and appears promising as an approach towards a conflict trigger model. However, event-based systems still require overcoming certain obstacles related to the quality of the input data and the event classification method.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{osorio-etal-2020-supervised,
    title = "Supervised Event Coding from Text Written in {A}rabic: Introducing Hadath",
    author = "Osorio, Javier  and
      Reyes, Alejandro  and
      Beltr{\'a}n, Alejandro  and
      Ahmadzai, Atal",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.9",
    pages = "49--56",
    abstract = "This article introduces Hadath, a supervised protocol for coding event data from text written in Arabic. Hadath contributes to recent efforts in advancing multi-language event coding using computer-based solutions. In this application, we focus on extracting event data about the conflict in Afghanistan from 2008 to 2018 using Arabic information sources. The implementation relies first on a Machine Learning algorithm to classify news stories relevant to the Afghan conflict. Then, using Hadath, we implement the Natural Language Processing component for event coding from Arabic script. The output database contains daily geo-referenced information at the district level on who did what to whom, when and where in the Afghan conflict. The data helps to identify trends in the dynamics of violence, the provision of governance, and traditional conflict resolution in Afghanistan for different actors over time and across space.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{papanikolaou-papageorgiou-2020-protest,
    title = "Protest Event Analysis: A Longitudinal Analysis for {G}reece",
    author = "Papanikolaou, Konstantina  and
      Papageorgiou, Haris",
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.10",
    pages = "57--62",
    abstract = "The advent of Big Data has shifted social science research towards computational methods. The volume of data that is nowadays available has brought a radical change in traditional approaches due to the cost and effort needed for processing. Knowledge extraction from heterogeneous and ample data is not an easy task to tackle. Thus, interdisciplinary approaches are necessary, combining experts of both social and computer science. This paper aims to present a work in the context of protest analysis, which falls into the scope of Computational Social Science. More specifically, the contribution of this work is to describe a Computational Social Science methodology for Event Analysis. The presented methodology is generic in the sense that it can be applied in every event typology and moreover, it is innovative and suitable for interdisciplinary tasks as it incorporates the human-in-the-loop. Additionally, a case study is presented concerning Protest Analysis in Greece over the last two decades. The conceptual foundation lies mainly upon claims analysis, and newspaper data were used in order to map, document and discuss protests in Greece in a longitudinal perspective.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@inproceedings{ors-etal-2020-event,
    title = "Event Clustering within News Articles",
    author = {{\"O}rs, Faik Kerem  and
      Yeniterzi, S{\"u}veyda  and
      Yeniterzi, Reyyan},
    booktitle = "Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/2020.aespen-1.11",
    pages = "63--68",
    abstract = "This paper summarizes our group{'}s efforts in the event sentence coreference identification shared task, which is organized as part of the Automated Extraction of Socio-Political Events from News (AESPEN) Workshop. Our main approach consists of three steps. We initially use a transformer based model to predict whether a pair of sentences refer to the same event or not. Later, we use these predictions as the initial scores and recalculate the pair scores by considering the relation of sentences in a pair with respect to other sentences. As the last step, final scores between these sentences are used to construct the clusters, starting with the pairs with the highest scores. Our proposed approach outperforms the baseline approach across all evaluation metrics.",
    language = "English",
    ISBN = "979-10-95546-50-4",
}
@proceedings{acl-2020-association-linguistics-tutorial,
    title = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    editor = "Savary, Agata  and
      Zhang, Yue",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.0",
}
@inproceedings{belinkov-etal-2020-interpretability,
    title = "Interpretability and Analysis in Neural {NLP}",
    author = "Belinkov, Yonatan  and
      Gehrmann, Sebastian  and
      Pavlick, Ellie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.1",
    doi = "10.18653/v1/2020.acl-tutorials.1",
    pages = "1--5",
    abstract = "While deep learning has transformed the natural language processing (NLP) field and impacted the larger computational linguistics community, the rise of neural networks is stained by their opaque nature: It is challenging to interpret the inner workings of neural network models, and explicate their behavior. Therefore, in the last few years, an increasingly large body of work has been devoted to the analysis and interpretation of neural network models in NLP. This body of work is so far lacking a common framework and methodology. Moreover, approaching the analysis of modern neural networks can be difficult for newcomers to the field. This tutorial aims to fill this gap and introduce the nascent field of interpretability and analysis of neural networks in NLP. The tutorial will cover the main lines of analysis work, such as structural analyses using probing classifiers, behavioral studies and test suites, and interactive visualizations. We will highlight not only the most commonly applied analysis methods, but also the specific limitations and shortcomings of current approaches, in order to inform participants where to focus future efforts.",
}
@inproceedings{bender-etal-2020-integrating,
    title = "Integrating Ethics into the {NLP} Curriculum",
    author = "Bender, Emily M.  and
      Hovy, Dirk  and
      Schofield, Alexandra",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.2",
    doi = "10.18653/v1/2020.acl-tutorials.2",
    pages = "6--9",
    abstract = "To raise awareness among future NLP practitioners and prevent inertia in the field, we need to place ethics in the curriculum for all NLP students{---}not as an elective, but as a core part of their education. Our goal in this tutorial is to empower NLP researchers and practitioners with tools and resources to teach others about how to ethically apply NLP techniques. We will present both high-level strategies for developing an ethics-oriented curriculum, based on experience and best practices, as well as specific sample exercises that can be brought to a classroom. This highly interactive work session will culminate in a shared online resource page that pools lesson plans, assignments, exercise ideas, reading suggestions, and ideas from the attendees. Though the tutorial will focus particularly on examples for university classrooms, we believe these ideas can extend to company-internal workshops or tutorials in a variety of organizations. In this setting, a key lesson is that there is no single approach to ethical NLP: each project requires thoughtful consideration about what steps can be taken to best support people affected by that project. However, we can learn (and teach) what issues to be aware of, what questions to ask, and what strategies are available to mitigate harm.",
}
@inproceedings{alikhani-stone-2020-achieving,
    title = "Achieving Common Ground in Multi-modal Dialogue",
    author = "Alikhani, Malihe  and
      Stone, Matthew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.3",
    doi = "10.18653/v1/2020.acl-tutorials.3",
    pages = "10--15",
    abstract = "All communication aims at achieving common ground (grounding): interlocutors can work together effectively only with mutual beliefs about what the state of the world is, about what their goals are, and about how they plan to make their goals a reality. Computational dialogue research offers some classic results on grouding, which unfortunately offer scant guidance to the design of grounding modules and behaviors in cutting-edge systems. In this tutorial, we focus on three main topic areas: 1) grounding in human-human communication; 2) grounding in dialogue systems; and 3) grounding in multi-modal interactive systems, including image-oriented conversations and human-robot interactions. We highlight a number of achievements of recent computational research in coordinating complex content, show how these results lead to rich and challenging opportunities for doing grounding in more flexible and powerful ways, and canvass relevant insights from the literature on human{--}human conversation. We expect that the tutorial will be of interest to researchers in dialogue systems, computational semantics and cognitive modeling, and hope that it will catalyze research and system building that more directly explores the creative, strategic ways conversational agents might be able to seek and offer evidence about their understanding of their interlocutors.",
}
@inproceedings{cohen-etal-2020-reviewing,
    title = "Reviewing Natural Language Processing Research",
    author = {Cohen, Kevin  and
      Fort, Kar{\"e}n  and
      Mieskes, Margot  and
      N{\'e}v{\'e}ol, Aur{\'e}lie},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.4",
    doi = "10.18653/v1/2020.acl-tutorials.4",
    pages = "16--18",
    abstract = "This tutorial will cover the theory and practice of reviewing research in natural language processing. Heavy reviewing burdens on natural language processing researchers have made it clear that our community needs to increase the size of our pool of potential reviewers. Simultaneously, notable {``}false negatives{''}---rejection by our conferences of work that was later shown to be tremendously important after acceptance by other conferences{---}have raised awareness of the fact that our reviewing practices leave something to be desired. We do not often talk about {``}false positives{''} with respect to conference papers, but leaders in the field have noted that we seem to have a publication bias towards papers that report high performance, with perhaps not much else of interest in them. It need not be this way. Reviewing is a learnable skill, and you will learn it here via lectures and a considerable amount of hands-on practice.",
}
@inproceedings{mou-vechtomova-2020-stylized,
    title = "Stylized Text Generation: Approaches and Applications",
    author = "Mou, Lili  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.5",
    doi = "10.18653/v1/2020.acl-tutorials.5",
    pages = "19--22",
    abstract = "Text generation has played an important role in various applications of natural language processing (NLP), and kn recent studies, researchers are paying increasing attention to modeling and manipulating the style of the generation text, which we call stylized text generation. In this tutorial, we will provide a comprehensive literature review in this direction. We start from the definition of style and different settings of stylized text generation, illustrated with various applications. Then, we present different settings of stylized generation, such as style-conditioned generation, style-transfer generation, and style-adversarial generation. In each setting, we delve deep into machine learning methods, including embedding learning techniques to represent style, adversarial learning, and reinforcement learning with cycle consistency to match content but to distinguish different styles. We also introduce current approaches to evaluating stylized text generation systems. We conclude our tutorial by presenting the challenges of stylized text generation and discussing future directions, such as small-data training, non-categorical style modeling, and a generalized scope of style transfer (e.g., controlling the syntax as a style).",
}
@inproceedings{dong-etal-2020-multi,
    title = "Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web",
    author = "Dong, Xin Luna  and
      Hajishirzi, Hannaneh  and
      Lockard, Colin  and
      Shiralkar, Prashant",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.6",
    doi = "10.18653/v1/2020.acl-tutorials.6",
    pages = "23--26",
    abstract = "The World Wide Web contains vast quantities of textual information in several forms: unstructured text, template-based semi-structured webpages (which present data in key-value pairs and lists), and tables. Methods for extracting information from these sources and converting it to a structured form have been a target of research from the natural language processing (NLP), data mining, and database communities. While these researchers have largely separated extraction from web data into different problems based on the modality of the data, they have faced similar problems such as learning with limited labeled data, defining (or avoiding defining) ontologies, making use of prior knowledge, and scaling solutions to deal with the size of the Web. In this tutorial we take a holistic view toward information extraction, exploring the commonalities in the challenges and solutions developed to address these different forms of text. We will explore the approaches targeted at unstructured text that largely rely on learning syntactic or semantic textual patterns, approaches targeted at semi-structured documents that learn to identify structural patterns in the template, and approaches targeting web tables which rely heavily on entity linking and type information. While these different data modalities have largely been considered separately in the past, recent research has started taking a more inclusive approach toward textual extraction, in which the multiple signals offered by textual, layout, and visual clues are combined into a single extraction model made possible by new deep learning approaches. At the same time, trends within purely textual extraction have shifted toward full-document understanding rather than considering sentences as independent units. With this in mind, it is worth considering the information extraction problem as a whole to motivate solutions that harness textual semantics along with visual and semi-structured layout information. We will discuss these approaches and suggest avenues for future work.",
}
@inproceedings{sap-etal-2020-commonsense,
    title = "Commonsense Reasoning for Natural Language Processing",
    author = "Sap, Maarten  and
      Shwartz, Vered  and
      Bosselut, Antoine  and
      Choi, Yejin  and
      Roth, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.7",
    doi = "10.18653/v1/2020.acl-tutorials.7",
    pages = "27--33",
    abstract = "Commonsense knowledge, such as knowing that {``}bumping into people annoys them{''} or {``}rain makes the road slippery{''}, helps humans navigate everyday situations seamlessly. Yet, endowing machines with such human-like commonsense reasoning capabilities has remained an elusive goal of artificial intelligence research for decades. In recent years, commonsense knowledge and reasoning have received renewed attention from the natural language processing (NLP) community, yielding exploratory studies in automated commonsense understanding. We organize this tutorial to provide researchers with the critical foundations and recent advances in commonsense representation and reasoning, in the hopes of casting a brighter light on this promising area of future research. In our tutorial, we will (1) outline the various types of commonsense (e.g., physical, social), and (2) discuss techniques to gather and represent commonsense knowledge, while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). We will then (3) discuss the types of commonsense knowledge captured by modern NLP systems (e.g., large pretrained language models), and (4) present ways to measure systems{'} commonsense reasoning abilities. We will finish with (5) a discussion of various ways in which commonsense reasoning can be used to improve performance on NLP tasks, exemplified by an (6) interactive session on integrating commonsense into a downstream task.",
}
@inproceedings{chen-yih-2020-open,
    title = "Open-Domain Question Answering",
    author = "Chen, Danqi  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-tutorials.8",
    doi = "10.18653/v1/2020.acl-tutorials.8",
    pages = "34--37",
    abstract = "This tutorial provides a comprehensive and coherent overview of cutting-edge research in open-domain question answering (QA), the task of answering questions using a large collection of documents of diversified topics. We will start by first giving a brief historical background, discussing the basic setup and core technical challenges of the research problem, and then describe modern datasets with the common evaluation metrics and benchmarks. The focus will then shift to cutting-edge models proposed for open-domain QA, including two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever-free methods. Finally, we will cover some hybrid approaches using both text and large knowledge bases and conclude the tutorial with important open questions. We hope that the tutorial will not only help the audience to acquire up-to-date knowledge but also provide new perspectives to stimulate the advances of open-domain QA research in the next phase.",
}
@proceedings{acl-2020-association-linguistics-student,
    title = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    editor = "Rijhwani, Shruti  and
      Liu, Jiangming  and
      Wang, Yizhong  and
      Dror, Rotem",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.0",
}
@inproceedings{bhargava-2020-adaptive,
    title = "Adaptive Transformers for Learning Multimodal Representations",
    author = "Bhargava, Prajjwal",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.1",
    doi = "10.18653/v1/2020.acl-srw.1",
    pages = "1--7",
    abstract = "The usage of transformers has grown from learning about language semantics to forming meaningful visiolinguistic representations. These architectures are often over-parametrized, requiring large amounts of computation. In this work, we extend adaptive approaches to learn more about model interpretability and computational efficiency. Specifically, we study attention spans, sparse, and structured dropout methods to help understand how their attention mechanism extends for vision and language tasks. We further show that these approaches can help us learn more about how the network perceives the complexity of input sequences, sparsity preferences for different modalities, and other related phenomena.",
}
@inproceedings{qian-2020-story,
    title = "Story-level Text Style Transfer: A Proposal",
    author = "Qian, Yusu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.2",
    doi = "10.18653/v1/2020.acl-srw.2",
    pages = "8--12",
    abstract = "Text style transfer aims to change the style of the input text to the target style while preserving the content to some extent. Previous works on this task are on the sentence level. We aim to work on story-level text style transfer to generate stories that preserve the plot of the input story while exhibiting a strong target style. The challenge in this task compared to previous work is that the structure of the input story, consisting of named entities and their relations with each other, needs to be preserved, and that the generated story needs to be consistent after adding flavors. We plan to explore three methods including the BERT-based method, the Story Realization method, and the Graph-based method.",
}
@inproceedings{pai-etal-2020-unsupervised,
    title = "Unsupervised Paraphasia Classification in Aphasic Speech",
    author = "Pai, Sharan  and
      Sachdeva, Nikhil  and
      Sachdeva, Prince  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.3",
    doi = "10.18653/v1/2020.acl-srw.3",
    pages = "13--19",
    abstract = "Aphasia is a speech and language disorder which results from brain damage, often characterized by word retrieval deficit (anomia) resulting in naming errors (paraphasia). Automatic paraphasia detection has many benefits for both treatment and diagnosis of Aphasia and its type. But supervised learning methods cant be properly utilized as there is a lack of aphasic speech data. In this paper, we describe our novel unsupervised method which can be implemented without the need for labeled paraphasia data. Our evaluations show that our method outperforms previous work based on supervised learning and transfer learning approaches for English. We demonstrate the utility of our method as an essential first step in developing augmentative and alternative communication (AAC) devices for patients suffering from aphasia in any language.",
}
@inproceedings{yu-etal-2020-hgcn4mesh,
    title = "{HGCN}4{M}e{SH}: Hybrid Graph Convolution Network for {M}e{SH} Indexing",
    author = "Yu, Miaomiao  and
      Yang, Yujiu  and
      Li, Chenhui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.4",
    doi = "10.18653/v1/2020.acl-srw.4",
    pages = "20--26",
    abstract = "Recently deep learning has been used in Medical subject headings (MeSH) indexing to reduce the time and monetary cost by manual annotation, including DeepMeSH, TextCNN, etc. However, these models still suffer from failing to capture the complex correlations between MeSH terms. To this end, we introduce Graph Convolution Network (GCN) to learn the relationship between these terms, and present a novel Hybrid Graph Convolution Net for MeSH index (HGCN4MeSH). Basically, we utilize two BiGRUs to learn the embedding representation of the abstract and the title of the MeSH index text respectively. At the same time, we establish the adjacency matrix of MeSH terms based on the co-occurrence relationships in Corpus, which is easy to apply for GCN representation learning. On the basis of learning the mixed representation, the prediction problem of the MeSH index keywords is transformed into an extreme multi-label classification problem after the attention layer operation. Experimental results on two datasets show that HGCN4MeSH is competitive compared with the state-of-the-art methods.",
}
@inproceedings{takahashi-etal-2020-grammatical,
    title = "Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner{'}s Error Tendency",
    author = "Takahashi, Yujin  and
      Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.5",
    doi = "10.18653/v1/2020.acl-srw.5",
    pages = "27--32",
    abstract = "Recently, several studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data. However, a large amount of pseudo data are required to train an accurate GEC model. To address the limitations of language and computational resources, we assume that introducing pseudo errors into sentences similar to those written by the language learners is more efficient, rather than incorporating random pseudo errors into monolingual data. In this regard, we study the effect of pseudo data on GEC task performance using two approaches. First, we extract sentences that are similar to the learners{'} sentences from monolingual data. Second, we generate realistic pseudo errors by considering error types that learners often make. Based on our comparative results, we observe that F0.5 scores for the Russian GEC task are significantly improved.",
}
@inproceedings{akdemir-2020-research,
    title = "Research on Task Discovery for Transfer Learning in Deep Neural Networks",
    author = "Akdemir, Arda",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.6",
    doi = "10.18653/v1/2020.acl-srw.6",
    pages = "33--41",
    abstract = "Deep neural network based machine learning models are shown to perform poorly on unseen or out-of-domain examples by numerous recent studies. Transfer learning aims to avoid overfitting and to improve generalizability by leveraging the information obtained from multiple tasks. Yet, the benefits of transfer learning depend largely on task selection and finding the right method of sharing. In this thesis, we hypothesize that current deep neural network based transfer learning models do not achieve their fullest potential for various tasks and there are still many task combinations that will benefit from transfer learning that are not considered by the current models. To this end, we started our research by implementing a novel multi-task learner with relaxed annotated data requirements and obtained a performance improvement on two NLP tasks. We will further devise models to tackle tasks from multiple areas of machine learning, such as Bioinformatics and Computer Vision, in addition to NLP.",
}
@inproceedings{zhou-etal-2020-rpd,
    title = "{RPD}: A Distance Function Between Word Embeddings",
    author = "Zhou, Xuhui  and
      Huang, Shujian  and
      Zheng, Zaixiang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.7",
    doi = "10.18653/v1/2020.acl-srw.7",
    pages = "42--50",
    abstract = "It is well-understood that different algorithms, training processes, and corpora produce different word embeddings. However, less is known about the relation between different embedding spaces, i.e. how far different sets of em-beddings deviate from each other. In this paper, we propose a novel metric called Relative Pairwise Inner Product Distance (RPD) to quantify the distance between different sets of word embeddings. This unitary-invariant metric has a unified scale for comparing different sets of word embeddings. Based on the properties of RPD, we study the relations of word embeddings of different algorithms systematically and investigate the influence of different training processes and corpora. The results shed light on the poorly understood word embeddings and justify RPD as a measure of the distance of embedding space.",
}
@inproceedings{ishibashi-etal-2020-reflection,
    title = "Reflection-based Word Attribute Transfer",
    author = "Ishibashi, Yoichi  and
      Sudoh, Katsuhito  and
      Yoshino, Koichiro  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.8",
    doi = "10.18653/v1/2020.acl-srw.8",
    pages = "51--58",
    abstract = "Word embeddings, which often represent such analogic relations as king - man + woman queen, can be used to change a word{'}s attribute, including its gender. For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male. However, developing such knowledge is very costly for words and attributes. In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation. Experimental results show that our proposed method can transfer the word attributes of the given words without changing the words that do not have the target attributes.",
}
@inproceedings{veselova-vorontsov-2020-topic,
    title = "Topic Balancing with Additive Regularization of Topic Models",
    author = "Veselova, Eugeniia  and
      Vorontsov, Konstantin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.9",
    doi = "10.18653/v1/2020.acl-srw.9",
    pages = "59--65",
    abstract = "This article proposes a new approach for building topic models on unbalanced collections in topic modelling, based on the existing methods and our experiments with such methods. Real-world data collections contain topics in various proportions, and often documents of the relatively small theme become distributed all over the larger topics instead of being grouped into one topic. To address this issue, we design a new regularizer for Theta and Phi matrices in probabilistic Latent Semantic Analysis (pLSA) model. We make sure this regularizer increases the quality of topic models, trained on unbalanced collections. Besides, we conceptually support this regularizer by our experiments.",
}
@inproceedings{casas-etal-2020-combining,
    title = "Combining Subword Representations into Word-level Representations in the Transformer Architecture",
    author = "Casas, Noe  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.10",
    doi = "10.18653/v1/2020.acl-srw.10",
    pages = "66--71",
    abstract = "In Neural Machine Translation, using word-level tokens leads to degradation in translation quality. The dominant approaches use subword-level tokens, but this increases the length of the sequences and makes it difficult to profit from word-level information such as POS tags or semantic dependencies. We propose a modification to the Transformer model to combine subword-level representations into word-level ones in the first layers of the encoder, reducing the effective length of the sequences in the following layers and providing a natural point to incorporate extra word-level information. Our experiments show that this approach maintains the translation quality with respect to the normal Transformer model when no extra word-level information is injected and that it is superior to the currently dominant method for incorporating word-level source language information to models based on subword-level vocabularies.",
}
@inproceedings{kim-etal-2020-zero,
    title = "Zero-shot {N}orth {K}orean to {E}nglish Neural Machine Translation by Character Tokenization and Phoneme Decomposition",
    author = "Kim, Hwichan  and
      Hirasawa, Tosho  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.11",
    doi = "10.18653/v1/2020.acl-srw.11",
    pages = "72--78",
    abstract = "The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translation accuracy cannot be achieved. To address this problem, we propose a zero-shot approach using South Korean data, which are remarkably similar to North Korean data. We train a neural machine translation model after tokenizing a South Korean text at the character level and decomposing characters into phonemes.We demonstrate that our method can effectively learn North Korean to English translation and improve the BLEU scores by +1.01 points in comparison with the baseline.",
}
@inproceedings{hamborg-2020-media,
    title = "Media Bias, the Social Sciences, and {NLP}: Automating Frame Analyses to Identify Bias by Word Choice and Labeling",
    author = "Hamborg, Felix",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.12",
    doi = "10.18653/v1/2020.acl-srw.12",
    pages = "79--87",
    abstract = "Media bias can strongly impact the public perception of topics reported in the news. A difficult to detect, yet powerful form of slanted news coverage is called bias by word choice and labeling (WCL). WCL bias can occur, for example, when journalists refer to the same semantic concept by using different terms that frame the concept differently and consequently may lead to different assessments by readers, such as the terms {``}freedom fighters{''} and {``}terrorists,{''} or {``}gun rights{''} and {``}gun control.{''} In this research project, I aim to devise methods that identify instances of WCL bias and estimate the frames they induce, e.g., not only is {``}terrorists{''} of negative polarity but also ascribes to aggression and fear. To achieve this, I plan to research methods using natural language processing and deep learning while employing models and using analysis concepts from the social sciences, where researchers have studied media bias for decades. The first results indicate the effectiveness of this interdisciplinary research approach. My vision is to devise a system that helps news readers to become aware of the differences in media coverage caused by bias.",
}
@inproceedings{malireddy-etal-2020-scar,
    title = "{SCAR}: Sentence Compression using Autoencoders for Reconstruction",
    author = "Malireddy, Chanakya  and
      Maniar, Tirth  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.13",
    doi = "10.18653/v1/2020.acl-srw.13",
    pages = "88--94",
    abstract = "Sentence compression is the task of shortening a sentence while retaining its meaning. Most methods proposed for this task rely on labeled or paired corpora (containing pairs of verbose and compressed sentences), which is often expensive to collect. To overcome this limitation, we present a novel unsupervised deep learning framework (SCAR) for deletion-based sentence compression. SCAR is primarily composed of two encoder-decoder pairs: a compressor and a reconstructor. The compressor masks the input, and the reconstructor tries to regenerate it. The model is entirely trained on unlabeled data and does not require additional inputs such as explicit syntactic information or optimal compression length. SCAR{'}s merit lies in the novel Linkage Loss function, which correlates the compressor and its effect on reconstruction, guiding it to drop inferable tokens. SCAR achieves higher ROUGE scores on benchmark datasets than the existing state-of-the-art methods and baselines. We also conduct a user study to demonstrate the application of our model as a text highlighting system. Using our model to underscore salient information facilitates speed-reading and reduces the time required to skim a document.",
}
@inproceedings{park-etal-2020-feature,
    title = "{F}eature {D}ifference {M}akes {S}ense: {A} medical image captioning model exploiting feature difference and tag information",
    author = "Park, Hyeryun  and
      Kim, Kyungmo  and
      Yoon, Jooyoung  and
      Park, Seongkeun  and
      Choi, Jinwook",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.14",
    doi = "10.18653/v1/2020.acl-srw.14",
    pages = "95--102",
    abstract = "Medical image captioning can reduce the workload of physicians and save time and expense by automatically generating reports. However, current datasets are small and limited, creating additional challenges for researchers. In this study, we propose a feature difference and tag information combined long short-term memory (LSTM) model for chest x-ray report generation. A feature vector extracted from the image conveys visual information, but its ability to describe the image is limited. Other image captioning studies exhibited improved performance by exploiting feature differences, so the proposed model also utilizes them. First, we propose a difference and tag (DiTag) model containing the difference between the patient and normal images. Then, we propose a multi-difference and tag (mDiTag) model that also contains information about low-level differences, such as contrast, texture, and localized area. Evaluation of the proposed models demonstrates that the mDiTag model provides more information to generate captions and outperforms all other models.",
}
@inproceedings{pan-etal-2020-multi,
    title = "Multi-Task Neural Model for Agglutinative Language Translation",
    author = "Pan, Yirong  and
      Li, Xiao  and
      Yang, Yating  and
      Dong, Rui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.15",
    doi = "10.18653/v1/2020.acl-srw.15",
    pages = "103--110",
    abstract = "Neural machine translation (NMT) has achieved impressive performance recently by using large-scale parallel corpora. However, it struggles in the low-resource and morphologically-rich scenarios of agglutinative language translation task. Inspired by the finding that monolingual data can greatly improve the NMT performance, we propose a multi-task neural model that jointly learns to perform bi-directional translation and agglutinative language stemming. Our approach employs the shared encoder and decoder to train a single model without changing the standard NMT architecture but instead adding a token before each source-side sentence to specify the desired target outputs of the two different tasks. Experimental results on Turkish-English and Uyghur-Chinese show that our proposed approach can significantly improve the translation performance on agglutinative languages by using a small amount of monolingual data.",
}
@inproceedings{harbecke-alt-2020-considering,
    title = "Considering Likelihood in {NLP} Classification Explanations with Occlusion and Language Modeling",
    author = "Harbecke, David  and
      Alt, Christoph",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.16",
    doi = "10.18653/v1/2020.acl-srw.16",
    pages = "111--117",
    abstract = "Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occlusion is a well established method that provides explanations on discrete language data, e.g. by removing a language unit from an input and measuring the impact on a model{'}s decision. We argue that current occlusion-based methods often produce invalid or syntactically incorrect language data, neglecting the improved abilities of recent NLP models. Furthermore, gradient-based explanation methods disregard the discrete distribution of data in NLP. Thus, we propose OLM: a novel explanation method that combines occlusion and language models to sample valid and syntactically correct replacements with high likelihood, given the context of the original input. We lay out a theoretical foundation that alleviates these weaknesses of other explanation methods in NLP and provide results that underline the importance of considering data likelihood in occlusion-based explanation.",
}
@inproceedings{luu-malamud-2020-non,
    title = "Non-Topical Coherence in Social Talk: A Call for Dialogue Model Enrichment",
    author = "Luu, Alex  and
      Malamud, Sophia A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.17",
    doi = "10.18653/v1/2020.acl-srw.17",
    pages = "118--133",
    abstract = "Current models of dialogue mainly focus on utterances within a topically coherent discourse segment, rather than new-topic utterances (NTUs), which begin a new topic not correlating with the content of prior discourse. As a result, these models may sufficiently account for discourse context of task-oriented but not social conversations. We conduct a pilot annotation study of NTUs as a first step towards a model capable of rationalizing conversational coherence in social talk. We start with the naturally occurring social dialogues in the Disco-SPICE corpus, annotated with discourse relations in the Penn Discourse Treebank and Cognitive approach to Coherence Relations frameworks. We first annotate content-based coherence relations that are not available in Disco-SPICE, and then heuristically identify NTUs, which lack a coherence relation to prior discourse. Based on the interaction between NTUs and their discourse context, we construct a classification for NTUs that actually convey certain non-topical coherence in social talk. This classification introduces new sequence-based social intents that traditional taxonomies of speech acts do not capture. The new findings advocates the development of a Bayesian game-theoretic model for social talk.",
}
@inproceedings{sommerauer-2020-penguin,
    title = "Why is penguin more similar to polar bear than to sea gull? Analyzing conceptual knowledge in distributional models",
    author = "Sommerauer, Pia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.18",
    doi = "10.18653/v1/2020.acl-srw.18",
    pages = "134--142",
    abstract = "What do powerful models of word mean- ing created from distributional data (e.g. Word2vec (Mikolov et al., 2013) BERT (Devlin et al., 2019) and ELMO (Peters et al., 2018)) represent? What causes words to be similar in the semantic space? What type of information is lacking? This thesis proposal presents a framework for investigating the information encoded in distributional semantic models. Several analysis methods have been suggested, but they have been shown to be limited and are not well understood. This approach pairs observations made on actual corpora with insights obtained from data manipulation experiments. The expected outcome is a better understanding of (1) the semantic information we can infer purely based on linguistic co-occurrence patterns and (2) the potential of distributional semantic models to pick up linguistic evidence.",
}
@inproceedings{nallani-etal-2020-simple,
    title = "A Simple and Effective Dependency Parser for {T}elugu",
    author = "Nallani, Sneha  and
      Shrivastava, Manish  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.19",
    doi = "10.18653/v1/2020.acl-srw.19",
    pages = "143--149",
    abstract = "We present a simple and effective dependency parser for Telugu, a morphologically rich, free word order language. We propose to replace the rich linguistic feature templates used in the past approaches with a minimal feature function using contextual vector representations. We train a BERT model on the Telugu Wikipedia data and use vector representations from this model to train the parser. Each sentence token is associated with a vector representing the token in the context of that sentence and the feature vectors are constructed by concatenating two token representations from the stack and one from the buffer. We put the feature representations through a feedforward network and train with a greedy transition based approach. The resulting parser has a very simple architecture with minimal feature engineering and achieves state-of-the-art results for Telugu.",
}
@inproceedings{chen-etal-2020-pointwise,
    title = "Pointwise Paraphrase Appraisal is Potentially Problematic",
    author = "Chen, Hannah  and
      Ji, Yangfeng  and
      Evans, David",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.20",
    doi = "10.18653/v1/2020.acl-srw.20",
    pages = "150--155",
    abstract = "The prevailing approach for training and evaluating paraphrase identification models is constructed as a binary classification problem: the model is given a pair of sentences, and is judged by how accurately it classifies pairs as either paraphrases or non-paraphrases. This pointwise-based evaluation method does not match well the objective of most real world applications, so the goal of our work is to understand how models which perform well under pointwise evaluation may fail in practice and find better methods for evaluating paraphrase identification models. As a first step towards that goal, we show that although the standard way of fine-tuning BERT for paraphrase identification by pairing two sentences as one sequence results in a model with state-of-the-art performance, that model may perform poorly on simple tasks like identifying pairs with two identical sentences. Moreover, we show that these models may even predict a pair of randomly-selected sentences with higher paraphrase score than a pair of identical ones.",
}
@inproceedings{goyal-etal-2020-efficient,
    title = "Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages",
    author = "Goyal, Vikrant  and
      Kumar, Sourav  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.22",
    doi = "10.18653/v1/2020.acl-srw.22",
    pages = "162--168",
    abstract = "A large percentage of the world{'}s population speaks a language of the Indian subcontinent, comprising languages from both Indo-Aryan (e.g. Hindi, Punjabi, Gujarati, etc.) and Dravidian (e.g. Tamil, Telugu, Malayalam, etc.) families. A universal characteristic of Indian languages is their complex morphology, which, when combined with the general lack of sufficient quantities of high-quality parallel data, can make developing machine translation (MT) systems for these languages difficult. Neural Machine Translation (NMT) is a rapidly advancing MT paradigm and has shown promising results for many language pairs, especially in large training data scenarios. Since the condition of large parallel corpora is not met for Indian-English language pairs, we present our efforts towards building efficient NMT systems between Indian languages (specifically Indo-Aryan languages) and English via efficiently exploiting parallel data from the related languages. We propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related language pairs. We also propose a Multilingual Transfer Learning technique to leverage parallel data from multiple related languages to assist translation for low resource language pair of interest. Our experiments demonstrate an overall average improvement of 5 BLEU points over the standard Transformer-based NMT baselines.",
}
@inproceedings{tang-etal-2020-exploring,
    title = "Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder",
    author = "Tang, Zheng  and
      Hahn-Powell, Gus  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.23",
    doi = "10.18653/v1/2020.acl-srw.23",
    pages = "169--175",
    abstract = "We propose an interpretable approach for event extraction that mitigates the tension between generalization and interpretability by jointly training for the two goals. Our approach uses an encoder-decoder architecture, which jointly trains a classifier for event extraction, and a rule decoder that generates syntactico-semantic rules that explain the decisions of the event classifier. We evaluate the proposed approach on three biomedical events and show that the decoder generates interpretable rules that serve as accurate explanations for the event classifier{'}s decisions, and, importantly, that the joint training generally improves the performance of the event classifier. Lastly, we show that our approach can be used for semi-supervised learning, and that its performance improves when trained on automatically-labeled data generated by a rule-based system.",
}
@inproceedings{meaney-2020-crossing,
    title = "Crossing the Line: Where do Demographic Variables Fit into Humor Detection?",
    author = "Meaney, J. A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.24",
    doi = "10.18653/v1/2020.acl-srw.24",
    pages = "176--181",
    abstract = "Recent humor classification shared tasks have struggled with two issues: either the data comprises a highly constrained genre of humor which does not broadly represent humor, or the data is so indiscriminate that the inter-annotator agreement on its humor content is drastically low. These tasks typically average over all annotators{'} judgments, in spite of the fact that humor is a highly subjective phenomenon. We argue that demographic factors influence whether a text is perceived as humorous or not. We propose the addition of demographic information about the humor annotators in order to bin ratings more sensibly. We also suggest the addition of an {`}offensive{'} label to distinguish between different generations, in terms of humor. This would allow for more nuanced shared tasks and could lead to better performance on downstream tasks, such as content moderation.",
}
@inproceedings{steingrimsson-etal-2020-effectively,
    title = "Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions",
    author = "Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Loftsson, Hrafn  and
      Way, Andy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.25",
    doi = "10.18653/v1/2020.acl-srw.25",
    pages = "182--190",
    abstract = "Parallel corpora are key to developing good machine translation systems. However, abundant parallel data are hard to come by, especially for languages with a low number of speakers. When rich morphology exacerbates the data sparsity problem, it is imperative to have accurate alignment and filtering methods that can help make the most of what is available by maximising the number of correctly translated segments in a corpus and minimising noise by removing incorrect translations and segments containing extraneous data. This paper sets out a research plan for improving alignment and filtering methods for parallel texts in low-resource settings. We propose an effective unsupervised alignment method to tackle the alignment problem. Moreover, we propose a strategy to supplement state-of-the-art models with automatically extracted information using basic NLP tools to effectively handle rich morphology.",
}
@inproceedings{lebanoff-etal-2020-understanding,
    title = "Understanding Points of Correspondence between Sentences for Abstractive Summarization",
    author = "Lebanoff, Logan  and
      Muchovej, John  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Wang, Lidan  and
      Chang, Walter  and
      Liu, Fei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.26",
    doi = "10.18653/v1/2020.acl-srw.26",
    pages = "191--198",
    abstract = "Fusing sentences containing disparate content is a remarkable human ability that helps create informative and succinct summaries. Such a simple task for humans has remained challenging for modern abstractive summarizers, substantially restricting their applicability in real-world scenarios. In this paper, we present an investigation into fusing sentences drawn from a document by introducing the notion of points of correspondence, which are cohesive devices that tie any two sentences together into a coherent text. The types of points of correspondence are delineated by text cohesion theory, covering pronominal and nominal referencing, repetition and beyond. We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences. Our dataset bridges the gap between coreference resolution and summarization. It is publicly shared to serve as a basis for future work to measure the success of sentence fusion systems.",
}
@inproceedings{yuma-etal-2020-ubleu,
    title = "u{BLEU}: Uncertainty-Aware Automatic Evaluation Method for Open-Domain Dialogue Systems",
    author = "Yuma, Tsuta  and
      Yoshinaga, Naoki  and
      Toyoda, Masashi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.27",
    doi = "10.18653/v1/2020.acl-srw.27",
    pages = "199--206",
    abstract = "Because open-domain dialogues allow diverse responses, basic reference-based metrics such as BLEU do not work well unless we prepare a massive reference set of high-quality responses for input utterances. To reduce this burden, a human-aided, uncertainty-aware metric, ΔBLEU, has been proposed; it embeds human judgment on the quality of reference outputs into the computation of multiple-reference BLEU. In this study, we instead propose a fully automatic, uncertainty-aware evaluation method for open-domain dialogue systems, υBLEU. This method first collects diverse reference responses from massive dialogue data and then annotates their quality judgments by using a neural network trained on automatically collected training data. Experimental results on massive Twitter data confirmed that υBLEU is comparable to ΔBLEU in terms of its correlation with human judgment and that the state of the art automatic evaluation method, RUBER, is improved by integrating υBLEU.",
}
@inproceedings{muradoglu-etal-2020-compress,
    title = "To compress or not to compress? A Finite-State approach to {N}en verbal morphology",
    author = "Muradoglu, Saliha  and
      Evans, Nicholas  and
      Suominen, Hanna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.28",
    doi = "10.18653/v1/2020.acl-srw.28",
    pages = "207--213",
    abstract = "This paper describes the development of a verbal morphological parser for an under-resourced Papuan language, Nen. Nen verbal morphology is particularly complex, with a transitive verb taking up to 1,740 unique features. The structural properties exhibited by Nen verbs raises interesting choices for analysis. Here we compare two possible methods of analysis: {`}Chunking{'} and decomposition. {`}Chunking{'} refers to the concept of collating morphological segments into one, whereas the decomposition model follows a more classical linguistic approach. Both models are built using the Finite-State Transducer toolkit foma. The resultant architecture shows differences in size and structural clarity. While the {`}Chunking{'} model is under half the size of the full de-composed counterpart, the decomposition displays higher structural order. In this paper, we describe the challenges encountered when modelling a language exhibiting distributed exponence and present the first morphological analyser for Nen, with an overall accuracy of 80.3{\%}.",
}
@inproceedings{daif-etal-2020-aradic,
    title = "{A}ra{DIC}: {A}rabic Document Classification Using Image-Based Character Embeddings and Class-Balanced Loss",
    author = "Daif, Mahmoud  and
      Kitada, Shunsuke  and
      Iyatomi, Hitoshi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.29",
    doi = "10.18653/v1/2020.acl-srw.29",
    pages = "214--221",
    abstract = "Classical and some deep learning techniques for Arabic text classification often depend on complex morphological analysis, word segmentation, and hand-crafted feature engineering. These could be eliminated by using character-level features. We propose a novel end-to-end Arabic document classification framework, Arabic document image-based classifier (AraDIC), inspired by the work on image-based character embeddings. AraDIC consists of an image-based character encoder and a classifier. They are trained in an end-to-end fashion using the class balanced loss to deal with the long-tailed data distribution problem. To evaluate the effectiveness of AraDIC, we created and published two datasets, the Arabic Wikipedia title (AWT) dataset and the Arabic poetry (AraP) dataset. To the best of our knowledge, this is the first image-based character embedding framework addressing the problem of Arabic text classification. We also present the first deep learning-based text classifier widely evaluated on modern standard Arabic, colloquial Arabic, and Classical Arabic. AraDIC shows performance improvement over classical and deep learning baselines by 12.29{\%} and 23.05{\%} for the micro and macro F-score, respectively.",
}
@inproceedings{kato-etal-2020-embeddings,
    title = "Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition",
    author = "Kato, Takuma  and
      Abe, Kaori  and
      Ouchi, Hiroki  and
      Miyawaki, Shumpei  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.30",
    doi = "10.18653/v1/2020.acl-srw.30",
    pages = "222--229",
    abstract = "In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.",
}
@inproceedings{tanaka-etal-2020-building,
    title = "Building a {J}apanese Typo Dataset from {W}ikipedia{'}s Revision History",
    author = "Tanaka, Yu  and
      Murawaki, Yugo  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.31",
    doi = "10.18653/v1/2020.acl-srw.31",
    pages = "230--236",
    abstract = "User generated texts contain many typos for which correction is necessary for NLP systems to work. Although a large number of typo{--}correction pairs are needed to develop a data-driven typo correction system, no such dataset is available for Japanese. In this paper, we extract over half a million Japanese typo{--}correction pairs from Wikipedia{'}s revision history. Unlike other languages, Japanese poses unique challenges: (1) Japanese texts are unsegmented so that we cannot simply apply a spelling checker, and (2) the way people inputting kanji logographs results in typos with drastically different surface forms from correct ones. We address them by combining character-based extraction rules, morphological analyzers to guess readings, and various filtering methods. We evaluate the dataset using crowdsourcing and run a baseline seq2seq model for typo correction.",
}
@inproceedings{funayama-etal-2020-preventing,
    title = "Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation",
    author = "Funayama, Hiroaki  and
      Sasaki, Shota  and
      Matsubayashi, Yuichiroh  and
      Mizumoto, Tomoya  and
      Suzuki, Jun  and
      Mita, Masato  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.32",
    doi = "10.18653/v1/2020.acl-srw.32",
    pages = "237--243",
    abstract = "Many recent Short Answer Scoring (SAS) systems have employed Quadratic Weighted Kappa (QWK) as the evaluation measure of their systems. However, we hypothesize that QWK is unsatisfactory for the evaluation of the SAS systems when we consider measuring their effectiveness in actual usage. We introduce a new task formulation of SAS that matches the actual usage. In our formulation, the SAS systems should extract as many scoring predictions that are not critical scoring errors (CSEs). We conduct the experiments in our new task formulation and demonstrate that a typical SAS system can predict scores with zero CSE for approximately 50{\%} of test data at maximum by filtering out low-reliablility predictions on the basis of a certain confidence estimation. This result directly indicates the possibility of reducing half the scoring cost of human raters, which is more preferable for the evaluation of SAS systems.",
}
@inproceedings{bhatt-etal-2020-much,
    title = "How much complexity does an {RNN} architecture need to learn syntax-sensitive dependencies?",
    author = "Bhatt, Gantavya  and
      Bansal, Hritik  and
      Singh, Rishubh  and
      Agarwal, Sumeet",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.33",
    doi = "10.18653/v1/2020.acl-srw.33",
    pages = "244--254",
    abstract = "Long short-term memory (LSTM) networks and their variants are capable of encapsulating long-range dependencies, which is evident from their performance on a variety of linguistic tasks. On the other hand, simple recurrent networks (SRNs), which appear more biologically grounded in terms of synaptic connections, have generally been less successful at capturing long-range dependencies as well as the loci of grammatical errors in an unsupervised setting. In this paper, we seek to develop models that bridge the gap between biological plausibility and linguistic competence. We propose a new architecture, the Decay RNN, which incorporates the decaying nature of neuronal activations and models the excitatory and inhibitory connections in a population of neurons. Besides its biological inspiration, our model also shows competitive performance relative to LSTMs on subject-verb agreement, sentence grammaticality, and language modeling tasks. These results provide some pointers towards probing the nature of the inductive biases required for RNN architectures to model linguistic phenomena successfully.",
}
@inproceedings{kvapilikova-etal-2020-unsupervised,
    title = "Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining",
    author = "Kvapil{\'\i}kov{\'a}, Ivana  and
      Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.34",
    doi = "10.18653/v1/2020.acl-srw.34",
    pages = "255--262",
    abstract = "Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingual data. We first produce a synthetic parallel corpus using unsupervised machine translation, and use it to fine-tune a pretrained cross-lingual masked language model (XLM) to derive the multilingual sentence representations. The quality of the representations is evaluated on two parallel corpus mining tasks with improvements of up to 22 F1 points over vanilla XLM. In addition, we observe that a single synthetic bilingual corpus is able to improve results for other language pairs.",
}
@inproceedings{haruta-etal-2020-logical,
    title = "Logical Inferences with Comparatives and Generalized Quantifiers",
    author = "Haruta, Izumi  and
      Mineshima, Koji  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.35",
    doi = "10.18653/v1/2020.acl-srw.35",
    pages = "263--270",
    abstract = "Comparative constructions pose a challenge in Natural Language Inference (NLI), which is the task of determining whether a text entails a hypothesis. Comparatives are structurally complex in that they interact with other linguistic phenomena such as quantifiers, numerals, and lexical antonyms. In formal semantics, there is a rich body of work on comparatives and gradable expressions using the notion of degree. However, a logical inference system for comparatives has not been sufficiently developed for use in the NLI task. In this paper, we present a compositional semantics that maps various comparative constructions in English to semantic representations via Combinatory Categorial Grammar (CCG) parsers and combine it with an inference system based on automated theorem proving. We evaluate our system on three NLI datasets that contain complex logical inferences with comparatives, generalized quantifiers, and numerals. We show that the system outperforms previous logic-based systems as well as recent deep learning-based models.",
}
@inproceedings{biesialska-etal-2020-enhancing,
    title = "Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources",
    author = "Biesialska, Magdalena  and
      Rafieian, Bardia  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.36",
    doi = "10.18653/v1/2020.acl-srw.36",
    pages = "271--278",
    abstract = "In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.",
}
@inproceedings{song-etal-2020-pre,
    title = "Pre-training via Leveraging Assisting Languages for Neural Machine Translation",
    author = "Song, Haiyue  and
      Dabre, Raj  and
      Mao, Zhuoyuan  and
      Cheng, Fei  and
      Kurohashi, Sadao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.37",
    doi = "10.18653/v1/2020.acl-srw.37",
    pages = "279--285",
    abstract = "Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks. However, large monolingual corpora might not always be available for the languages of interest (LOI). Thus, we propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the LOI. We utilize script mapping (Chinese to Japanese) to increase the similarity (number of cognates) between the monolingual corpora of helping languages and LOI. An empirical case study of low-resource Japanese-English neural machine translation (NMT) reveals that leveraging large Chinese and French monolingual corpora can help overcome the shortage of Japanese and English monolingual corpora, respectively, for S2S pre-training. Using only Chinese and French monolingual corpora, we were able to improve Japanese-English translation quality by up to 8.5 BLEU in low-resource scenarios.",
}
@inproceedings{pandramish-sharma-2020-checkpoint,
    title = "Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems",
    author = "Pandramish, Vinay  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.38",
    doi = "10.18653/v1/2020.acl-srw.38",
    pages = "286--291",
    abstract = "In this paper, we propose a method of re-ranking the outputs of Neural Machine Translation (NMT) systems. After the decoding process, we select a few last iteration outputs in the training process as the $N$-best list. After training a Neural Machine Translation (NMT) baseline system, it has been observed that these iteration outputs have an oracle score higher than baseline up to 1.01 BLEU points compared to the last iteration of the trained system.We come up with a ranking mechanism by solely focusing on the decoder{'}s ability to generate distinct tokens and without the usage of any language model or data. With this method, we achieved a translation improvement up to +0.16 BLEU points over baseline.We also evaluate our approach by applying the coverage penalty to the training process.In cases of moderate coverage penalty, the oracle scores are higher than the final iteration up to +0.99 BLEU points, and our algorithm gives an improvement up to +0.17 BLEU points.With excessive penalty, there is a decrease in translation quality compared to the baseline system. Still, an increase in oracle scores up to +1.30 is observed with the re-ranking algorithm giving an improvement up to +0.15 BLEU points is found in case of excessive penalty.The proposed re-ranking method is a generic one and can be extended to other language pairs as well.",
}
@inproceedings{ray-chowdhury-etal-2020-cross,
    title = "Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup",
    author = "Ray Chowdhury, Jishnu  and
      Caragea, Cornelia  and
      Caragea, Doina",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.39",
    doi = "10.18653/v1/2020.acl-srw.39",
    pages = "292--298",
    abstract = "Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management. For this purpose, we compile a multilingual dataset of over 130K samples for multi-label classification of disaster-related tweets. We present a masking-based loss function for partially labelled samples and demonstrate the effectiveness of Manifold Mixup in the text domain. Our main model is based on Multilingual BERT, which we further improve with Manifold Mixup. We show that our model generalizes to unseen disasters in the test set. Furthermore, we analyze the capability of our model for zero-shot generalization to new languages. Our code, dataset, and other resources are available on Github.",
}
@inproceedings{zhang-nie-2020-inducing,
    title = "Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition",
    author = "Zhang, Yuhui  and
      Nie, Allen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.40",
    doi = "10.18653/v1/2020.acl-srw.40",
    pages = "299--305",
    abstract = "The principle of compositionality has deep roots in linguistics: the meaning of an expression is determined by its structure and the meanings of its constituents. However, modern neural network models such as long short-term memory network process expressions in a linear fashion and do not seem to incorporate more complex compositional patterns. In this work, we show that we can explicitly induce grammar by tracing the computational process of a long short-term memory network. We show: (i) the multiplicative nature of long short-term memory network allows complex interaction beyond sequential linear combination; (ii) we can generate compositional trees from the network without external linguistic knowledge; (iii) we evaluate the syntactic difference between the generated trees, randomly generated trees and gold reference trees produced by constituency parsers; (iv) we evaluate whether the generated trees contain the rich semantic information.",
}
@inproceedings{zhuang-riloff-2020-exploring,
    title = "Exploring the Role of Context to Distinguish Rhetorical and Information-Seeking Questions",
    author = "Zhuang, Yuan  and
      Riloff, Ellen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.41",
    doi = "10.18653/v1/2020.acl-srw.41",
    pages = "306--312",
    abstract = "Social media posts often contain questions, but many of the questions are rhetorical and do not seek information. Our work studies the problem of distinguishing rhetorical and information-seeking questions on Twitter. Most work has focused on features of the question itself, but we hypothesize that the prior context plays a role too. This paper introduces a new dataset containing questions in tweets paired with their prior tweets to provide context. We create classification models to assess the difficulty of distinguishing rhetorical and information-seeking questions, and experiment with different properties of the prior context. Our results show that the prior tweet and topic features can improve performance on this task.",
}
@inproceedings{russin-etal-2020-compositional,
    title = "Compositional Generalization by Factorizing Alignment and Translation",
    author = "Russin, Jacob  and
      Jo, Jason  and
      O{'}Reilly, Randall  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.42",
    doi = "10.18653/v1/2020.acl-srw.42",
    pages = "313--327",
    abstract = "Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution. However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words. Inspired by work in cognitive science suggesting a functional distinction between systems for syntactic and semantic processing, we implement a modification to an existing approach in neural machine translation, imposing an analogous separation between alignment and translation. The resulting architecture substantially outperforms standard recurrent networks on the SCAN dataset, a compositional generalization task, without any additional supervision. Our work suggests that learning to align and to translate in separate modules may be a useful heuristic for capturing compositional structure.",
}
@inproceedings{suvarna-bhalla-2020-notawhore,
    title = "{\#}{N}ot{AW}hore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media",
    author = "Suvarna, Ashima  and
      Bhalla, Grusha",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-srw.43",
    doi = "10.18653/v1/2020.acl-srw.43",
    pages = "328--335",
    abstract = "The recent surge in online forums and movements supporting sexual assault survivors has led to the emergence of a {`}virtual bubble{'} where survivors can recount their stories. However, this also makes the survivors vulnerable to bullying, trolling and victim blaming. Specifically, victim blaming has been shown to have acute psychological effects on the survivors and further discourage formal reporting of such crimes. Therefore, it is important to devise computationally relevant methods to identify and prevent victim blaming to protect the victims. In our work, we discuss the drastic effects of victim blaming through a short case study and then propose a single step transfer-learning based classification method to identify victim blaming language on Twitter. Finally, we compare the performance of our proposed model against various deep learning and machine learning models on a manually annotated domain-specific dataset.",
}
@proceedings{acl-2020-association,
    title = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.0",
}
@inproceedings{gelderloos-etal-2020-learning,
    title = "Learning to Understand Child-directed and Adult-directed Speech",
    author = "Gelderloos, Lieke  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.1",
    doi = "10.18653/v1/2020.acl-main.1",
    pages = "1--6",
    abstract = "Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation. Human language acquisition research indicates that child-directed speech helps language learners. This study explores the effect of child-directed speech when learning to extract semantic information from speech directly. We compare the task performance of models trained on adult-directed speech (ADS) and child-directed speech (CDS). We find indications that CDS helps in the initial stages of learning, but eventually, models trained on ADS reach comparable task performance, and generalize better. The results suggest that this is at least partially due to linguistic rather than acoustic properties of the two registers, as we see the same pattern when looking at models trained on acoustically comparable synthetic speech.",
}
@inproceedings{rinaldi-etal-2020-predicting,
    title = "Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts",
    author = "Rinaldi, Alex  and
      Fox Tree, Jean  and
      Chaturvedi, Snigdha",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.2",
    doi = "10.18653/v1/2020.acl-main.2",
    pages = "7--18",
    abstract = "Accurately diagnosing depression is difficult{--} requiring time-intensive interviews, assessments, and analysis. Hence, automated methods that can assess linguistic patterns in these interviews could help psychiatric professionals make faster, more informed decisions about diagnosis. We propose JLPC, a model that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. This latent categorization allows the model to define high-level conversational contexts that influence patterns of language in depressed individuals. We show that the proposed model not only outperforms competitive baselines, but that its latent prompt categories provide psycholinguistic insights about depression.",
}
@inproceedings{liu-etal-2020-coach,
    title = "{C}oach: A Coarse-to-Fine Approach for Cross-domain Slot Filling",
    author = "Liu, Zihan  and
      Winata, Genta Indra  and
      Xu, Peng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.3",
    doi = "10.18653/v1/2020.acl-main.3",
    pages = "19--25",
    abstract = "As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-domain slot filling has naturally arisen to cope with this data scarcity problem. In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling. Our model first learns the general pattern of slot entities by detecting whether the tokens are slot entities or not. It then predicts the specific types for the slot entities. In addition, we propose a template regularization approach to improve the adaptation robustness by regularizing the representation of utterances based on utterance templates. Experimental results show that our model significantly outperforms state-of-the-art approaches in slot filling. Furthermore, our model can also be applied to the cross-domain named entity recognition task, and it achieves better adaptation performance than other existing baselines. The code is available at https://github.com/zliucr/coach.",
}
@inproceedings{zhao-etal-2020-designing,
    title = "Designing Precise and Robust Dialogue Response Evaluators",
    author = "Zhao, Tianyu  and
      Lala, Divesh  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.4",
    doi = "10.18653/v1/2020.acl-main.4",
    pages = "26--33",
    abstract = "Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate correlation with human judgement and they are not robust. In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models. Experimental results demonstrate that the proposed evaluator achieves a strong correlation ({\textgreater} 0.6) with human judgement and generalizes robustly to diverse responses and corpora. We open-source the code and data in https://github.com/ZHAOTING/dialog-processing.",
}
@inproceedings{ouyang-etal-2020-dialogue,
    title = "Dialogue State Tracking with Explicit Slot Connection Modeling",
    author = "Ouyang, Yawen  and
      Chen, Moxin  and
      Dai, Xinyu  and
      Zhao, Yinggong  and
      Huang, Shujian  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.5",
    doi = "10.18653/v1/2020.acl-main.5",
    pages = "34--40",
    abstract = "Recent proposed approaches have made promising progress in dialogue state tracking (DST). However, in multi-domain scenarios, ellipsis and reference are frequently adopted by users to express values that have been mentioned by slots from other domains. To handle these phenomena, we propose a Dialogue State Tracking with Slot Connections (DST-SC) model to explicitly consider slot correlations across different domains. Given a target slot, the slot connecting mechanism in DST-SC can infer its source slot and copy the source slot value directly, thus significantly reducing the difficulty of learning and reasoning. Experimental results verify the benefits of explicit slot connection modeling, and our model achieves state-of-the-art performance on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.",
}
@inproceedings{lin-etal-2020-generating,
    title = "Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy",
    author = "Lin, Xiexiong  and
      Jian, Weiyu  and
      He, Jianshan  and
      Wang, Taifeng  and
      Chu, Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.6",
    doi = "10.18653/v1/2020.acl-main.6",
    pages = "41--52",
    abstract = "Knowledge-driven conversation approaches have achieved remarkable research attention recently. However, generating an informative response with multiple relevant knowledge without losing fluency and coherence is still one of the main challenges. To address this issue, this paper proposes a method that uses recurrent knowledge interaction among response decoding steps to incorporate appropriate knowledge. Furthermore, we introduce a knowledge copy mechanism using a knowledge-aware pointer network to copy words from external knowledge according to knowledge attention distribution. Our joint neural conversation model which integrates recurrent Knowledge-Interaction and knowledge Copy (KIC) performs well on generating informative responses. Experiments demonstrate that our model with fewer parameters yields significant improvements over competitive baselines on two datasets Wizard-of-Wikipedia(average Bleu +87{\%}; abs.: 0.034) and DuConv(average Bleu +20{\%}; abs.: 0.047)) with different knowledge formats (textual {\&} structured) and different languages (English {\&} Chinese).",
}
@inproceedings{wu-etal-2020-guiding,
    title = "Guiding Variational Response Generator to Exploit Persona",
    author = "Wu, Bowen  and
      Li, MengYuan  and
      Wang, Zongsheng  and
      Chen, Yifu  and
      Wong, Derek F.  and
      Feng, Qihang  and
      Huang, Junhong  and
      Wang, Baoxun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.7",
    doi = "10.18653/v1/2020.acl-main.7",
    pages = "53--65",
    abstract = "Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progress achieved by recent studies in this field, persona information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the persona can be involved via End-to-End learning. This paper proposes to adopt the personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various persona modeling approaches, this paper further presents three direct persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of persona-aware response generation, and the metrics are reasonable to evaluate the results.",
}
@inproceedings{boyd-etal-2020-large,
    title = "Large Scale Multi-Actor Generative Dialog Modeling",
    author = "Boyd, Alex  and
      Puri, Raul  and
      Shoeybi, Mohammad  and
      Patwary, Mostofa  and
      Catanzaro, Bryan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.8",
    doi = "10.18653/v1/2020.acl-main.8",
    pages = "66--84",
    abstract = "Non-goal oriented dialog agents (i.e. chatbots) aim to produce varying and engaging conversations with a user; however, they typically exhibit either inconsistent personality across conversations or the average personality of all users. This paper addresses these issues by controlling an agent{'}s persona upon generation via conditioning on prior conversations of a target actor. In doing so, we are able to utilize more abstract patterns within a person{'}s speech and better emulate them in generated responses. This work introduces the Generative Conversation Control model, an augmented and fine-tuned GPT-2 language model that conditions on past reference conversations to probabilistically model multi-turn conversations in the actor{'}s persona. We introduce an accompanying data collection procedure to obtain 10.3M conversations from 6 months worth of Reddit comments. We demonstrate that scaling model sizes from 117M to 8.3B parameters yields an improvement from 23.14 to 13.14 perplexity on 1.7M held out Reddit conversations. Increasing model scale yielded similar improvements in human evaluations that measure preference of model samples to the held out target distribution in terms of realism (31{\%} increased to 37{\%} preference), style matching (37{\%} to 42{\%}), grammar and content quality (29{\%} to 42{\%}), and conversation coherency (32{\%} to 40{\%}). We find that conditionally modeling past conversations improves perplexity by 0.47 in automatic evaluations. Through human trials we identify positive trends between conditional modeling and style matching and outline steps to further improve persona control.",
}
@inproceedings{bao-etal-2020-plato,
    title = "{PLATO}: Pre-trained Dialogue Generation Model with Discrete Latent Variable",
    author = "Bao, Siqi  and
      He, Huang  and
      Wang, Fan  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.9",
    doi = "10.18653/v1/2020.acl-main.9",
    pages = "85--96",
    abstract = "Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In this framework, we adopt flexible attention mechanisms to fully leverage the bi-directional context and the uni-directional characteristic of language generation. We also introduce discrete latent variables to tackle the inherent one-to-many mapping problem in response generation. Two reciprocal tasks of response generation and latent act recognition are designed and carried out simultaneously within a shared network. Comprehensive experiments on three publicly available datasets verify the effectiveness and superiority of the proposed framework.",
}
@inproceedings{li-etal-2020-slot,
    title = "Slot-consistent {NLG} for Task-oriented Dialogue Systems with Iterative Rectification Network",
    author = "Li, Yangming  and
      Yao, Kaisheng  and
      Qin, Libo  and
      Che, Wanxiang  and
      Li, Xiaolong  and
      Liu, Ting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.10",
    doi = "10.18653/v1/2020.acl-main.10",
    pages = "97--106",
    abstract = "Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value. Prior works refer this to hallucination phenomenon. In this paper, we study slot consistency for building reliable NLG systems with all slot values of input dialogue act (DA) properly generated in output sentences. We propose Iterative Rectification Network (IRN) for improving general NLG systems to produce both correct and fluent responses. It applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training. Comprehensive studies have been conducted on multiple benchmark datasets, showing that the proposed methods have significantly reduced the slot error rate (ERR) for all strong baselines. Human evaluations also have confirmed its effectiveness.",
}
@inproceedings{coope-etal-2020-span,
    title = "{S}pan-{ConveRT}: {F}ew-shot Span Extraction for Dialog with Pretrained Conversational Representations",
    author = "Coope, Samuel  and
      Farghly, Tyler  and
      Gerz, Daniela  and
      Vuli{\'c}, Ivan  and
      Henderson, Matthew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.11",
    doi = "10.18653/v1/2020.acl-main.11",
    pages = "107--121",
    abstract = "We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task. This formulation allows for a simple integration of conversational knowledge coded in large pretrained conversational models such as ConveRT (Henderson et al., 2019). We show that leveraging such knowledge in Span-ConveRT is especially useful for few-shot learning scenarios: we report consistent gains over 1) a span extractor that trains representations from scratch in the target domain, and 2) a BERT-based span extractor. In order to inspire more work on span extraction for the slot-filling task, we also release RESTAURANTS-8K, a new challenging data set of 8,198 utterances, compiled from actual conversations in the restaurant booking domain.",
}
@inproceedings{campagna-etal-2020-zero,
    title = "Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking",
    author = "Campagna, Giovanni  and
      Foryciarz, Agata  and
      Moradshahi, Mehrad  and
      Lam, Monica",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.12",
    doi = "10.18653/v1/2020.acl-main.12",
    pages = "122--132",
    abstract = "Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new zero-short transfer learning technique for dialogue state tracking where the in-domain training data are all synthesized from an abstract dialogue model and the ontology of the domain. We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2.1 dataset. We show training with only synthesized in-domain data on the SUMBT model can reach about 2/3 of the accuracy obtained with the full training dataset. We improve the zero-shot learning state of the art on average across domains by 21{\%}.",
}
@inproceedings{hung-etal-2020-complete,
    title = "A Complete Shift-Reduce {C}hinese Discourse Parser with Robust Dynamic Oracle",
    author = "Hung, Shyh-Shiun  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.13",
    doi = "10.18653/v1/2020.acl-main.13",
    pages = "133--138",
    abstract = "This work proposes a standalone, complete Chinese discourse parser for practical applications. We approach Chinese discourse parsing from a variety of aspects and improve the shift-reduce parser not only by integrating the pre-trained text encoder, but also by employing novel training strategies. We revise the dynamic-oracle procedure for training the shift-reduce parser, and apply unsupervised data augmentation to enhance rhetorical relation recognition. Experimental results show that our Chinese discourse parser achieves the state-of-the-art performance.",
}
@inproceedings{he-etal-2020-transs,
    title = "{T}rans{S}-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition",
    author = "He, Ruifang  and
      Wang, Jian  and
      Guo, Fengyu  and
      Han, Yugui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.14",
    doi = "10.18653/v1/2020.acl-main.14",
    pages = "139--148",
    abstract = "Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propose a novel TransS-driven joint learning architecture to address the issues. Specifically, based on the multi-level encoder, we 1) translate discourse relations in low-dimensional embedding space (called TransS), which could mine the latent geometric structure information of argument-relation instances; 2) further exploit the semantic features of arguments to assist discourse understanding; 3) jointly learn 1) and 2) to mutually reinforce each other to obtain the better argument representations, so as to improve the performance of the task. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.",
}
@inproceedings{ren-etal-2020-study,
    title = "A Study of Non-autoregressive Model for Sequence Generation",
    author = "Ren, Yi  and
      Liu, Jinglin  and
      Tan, Xu  and
      Zhao, Zhou  and
      Zhao, Sheng  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.15",
    doi = "10.18653/v1/2020.acl-main.15",
    pages = "149--159",
    abstract = "Non-autoregressive (NAR) models generate all the tokens of a sequence in parallel, resulting in faster generation speed compared to their autoregressive (AR) counterparts but at the cost of lower accuracy. Different techniques including knowledge distillation and source-target alignment have been proposed to bridge the gap between AR and NAR models in various tasks such as neural machine translation (NMT), automatic speech recognition (ASR), and text to speech (TTS). With the help of those techniques, NAR models can catch up with the accuracy of AR models in some tasks but not in some others. In this work, we conduct a study to understand the difficulty of NAR sequence generation and try to answer: (1) Why NAR models can catch up with AR models in some tasks but not all? (2) Why techniques like knowledge distillation and source-target alignment can help NAR models. Since the main difference between AR and NAR models is that NAR models do not use dependency among target tokens while AR models do, intuitively the difficulty of NAR sequence generation heavily depends on the strongness of dependency among target tokens. To quantify such dependency, we propose an analysis model called CoMMA to characterize the difficulty of different NAR sequence generation tasks. We have several interesting findings: 1) Among the NMT, ASR and TTS tasks, ASR has the most target-token dependency while TTS has the least. 2) Knowledge distillation reduces the target-token dependency in target sequence and thus improves the accuracy of NAR models. 3) Source-target alignment constraint encourages dependency of a target token on source tokens and thus eases the training of NAR models.",
}
@inproceedings{thapliyal-soricut-2020-cross,
    title = "{C}ross-modal {L}anguage {G}eneration using {P}ivot {S}tabilization for {W}eb-scale {L}anguage {C}overage",
    author = "Thapliyal, Ashish V.  and
      Soricut, Radu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.16",
    doi = "10.18653/v1/2020.acl-main.16",
    pages = "160--170",
    abstract = "Cross-modal language generation tasks such as image captioning are directly hurt in their ability to support non-English languages by the trend of data-hungry models combined with the lack of non-English annotations. We investigate potential solutions for combining existing language-generation annotations in English with translation capabilities in order to create solutions at web-scale in both domain and language coverage. We describe an approach called Pivot-Language Generation Stabilization (PLuGS), which leverages directly at training time both existing English annotations (gold data) as well as their machine-translated versions (silver data); at run-time, it generates first an English caption and then a corresponding target-language caption. We show that PLuGS models outperform other candidate solutions in evaluations performed over 5 different target languages, under a large-domain testset using images from the Open Images dataset. Furthermore, we find an interesting effect where the English captions generated by the PLuGS models are better than the captions generated by the original, monolingual English model.",
}
@inproceedings{iso-etal-2020-fact,
    title = "{F}act-based {T}ext {E}diting",
    author = "Iso, Hayate  and
      Qiao, Chao  and
      Li, Hang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.17",
    doi = "10.18653/v1/2020.acl-main.17",
    pages = "171--182",
    abstract = "We propose a novel text editing task, referred to as \textit{fact-based text editing}, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, we propose a method for automatically generating a dataset for research on fact-based text editing, where each instance consists of a draft text, a revised text, and several facts represented in triples. We apply the method into two public table-to-text datasets, obtaining two new datasets consisting of 233k and 37k instances, respectively. Next, we propose a new neural network architecture for fact-based text editing, called FactEditor, which edits a draft text by referring to given facts using a buffer, a stream, and a memory. A straightforward approach to address the problem would be to employ an encoder-decoder model. Our experimental results on the two datasets show that FactEditor outperforms the encoder-decoder approach in terms of fidelity and fluency. The results also show that FactEditor conducts inference faster than the encoder-decoder approach.",
}
@inproceedings{chen-etal-2020-shot,
    title = "Few-Shot {NLG} with Pre-Trained Language Model",
    author = "Chen, Zhiyu  and
      Eavani, Harini  and
      Chen, Wenhu  and
      Liu, Yinyin  and
      Wang, William Yang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.18",
    doi = "10.18653/v1/2020.acl-main.18",
    pages = "183--190",
    abstract = "Neural-based end-to-end approaches to natural language generation (NLG) from structured data or knowledge are data-hungry, making their adoption for real-world applications difficult with limited data. In this work, we propose the new task of few-shot natural language generation. Motivated by how humans tend to summarize tabular data, we propose a simple yet effective approach and show that it not only demonstrates strong performance but also provides good generalization across domains. The design of the model architecture is based on two aspects: content selection from input data and language modeling to compose coherent sentences, which can be acquired from prior knowledge. With just 200 training examples, across multiple domains, we show that our approach achieves very reasonable performances and outperforms the strongest baseline by an average of over 8.0 BLEU points improvement. Our code and data can be found at https://github.com/czyssrs/Few-Shot-NLG",
}
@inproceedings{baheti-etal-2020-fluent,
    title = "Fluent Response Generation for Conversational Question Answering",
    author = "Baheti, Ashutosh  and
      Ritter, Alan  and
      Small, Kevin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.19",
    doi = "10.18653/v1/2020.acl-main.19",
    pages = "191--207",
    abstract = "Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language generation (NLG) aspect of high-quality conversational agents. In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. From a technical perspective, we use data augmentation to generate training data for an end-to-end system. Specifically, we develop Syntactic Transformations (STs) to produce question-specific candidate answer responses and rank them using a BERT-based classifier (Devlin et al., 2019). Human evaluation on SQuAD 2.0 data (Rajpurkar et al., 2018) demonstrate that the proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. We further show our model{'}s scalability by conducting tests on the CoQA dataset. The code and data are available at https://github.com/abaheti95/QADialogSystem.",
}
@inproceedings{lee-etal-2020-generating,
    title = "Generating Diverse and Consistent {QA} pairs from Contexts with Information-Maximizing Hierarchical Conditional {VAE}s",
    author = "Lee, Dong Bok  and
      Lee, Seanie  and
      Jeong, Woo Tae  and
      Kim, Donghwan  and
      Hwang, Sung Ju",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.20",
    doi = "10.18653/v1/2020.acl-main.20",
    pages = "208--224",
    abstract = "One of the most crucial challenges in question answering (QA) is the scarcity of labeled data, since it is costly to obtain question-answer (QA) pairs for a target text domain with human annotation. An alternative approach to tackle the problem is to use automatically generated QA pairs from either the problem context or from large amount of unstructured texts (e.g. Wikipedia). In this work, we propose a hierarchical conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QA pairs to ensure their consistency. We validate our Information Maximizing Hierarchical Conditional Variational AutoEncoder (Info-HCVAE) on several benchmark datasets by evaluating the performance of the QA model (BERT-base) using only the generated QA pairs (QA-based evaluation) or by using both the generated and human-labeled pairs (semi-supervised learning) for training, against state-of-the-art baseline models. The results show that our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.",
}
@inproceedings{chai-wan-2020-learning,
    title = "Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction",
    author = "Chai, Zi  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.21",
    doi = "10.18653/v1/2020.acl-main.21",
    pages = "225--237",
    abstract = "Traditional Question Generation (TQG) aims to generate a question given an input passage and an answer. When there is a sequence of answers, we can perform Sequential Question Generation (SQG) to produce a series of interconnected questions. Since the frequently occurred information omission and coreference between questions, SQG is rather challenging. Prior works regarded SQG as a dialog generation task and recurrently produced each question. However, they suffered from problems caused by error cascades and could only capture limited context dependencies. To this end, we generate questions in a semi-autoregressive way. Our model divides questions into different groups and generates each group of them in parallel. During this process, it builds two graphs focusing on information from passages, answers respectively and performs dual-graph interaction to get information for generation. Besides, we design an answer-aware attention mechanism and the coarse-to-fine generation scenario. Experiments on our new dataset containing 81.9K questions show that our model substantially outperforms prior works.",
}
@inproceedings{goyal-durrett-2020-neural,
    title = "Neural Syntactic Preordering for Controlled Paraphrase Generation",
    author = "Goyal, Tanya  and
      Durrett, Greg",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.22",
    doi = "10.18653/v1/2020.acl-main.22",
    pages = "238--252",
    abstract = "Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner. Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly {``}reorder{''} the source sentence and guide our neural paraphrasing model. First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model. This model operates over a partially lexical, partially syntactic view of the sentence and can reorder big chunks. Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order. Our evaluation, both automatic and human, shows that the proposed system retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases.",
}
@inproceedings{duan-etal-2020-pre,
    title = "Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders",
    author = "Duan, Yu  and
      Xu, Canwen  and
      Pei, Jiaxin  and
      Han, Jialong  and
      Li, Chenliang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.23",
    doi = "10.18653/v1/2020.acl-main.23",
    pages = "253--262",
    abstract = "Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents. Current conditional generation models cannot handle emerging conditions due to their joint end-to-end learning fashion. When a new condition added, these techniques require full retraining. In this paper, we present a new framework named Pre-train and Plug-in Variational Auto-Encoder (PPVAE) towards flexible conditional text generation. PPVAE decouples the text generation module from the condition representation module to allow {``}one-to-many{''} conditional generation. When a fresh condition emerges, only a lightweight network needs to be trained and works as a plug-in for PPVAE, which is efficient and desirable for real-world applications. Extensive experiments demonstrate the superiority of PPVAE against the existing alternatives with better conditionality and diversity but less training effort.",
}
@inproceedings{liao-etal-2020-probabilistically,
    title = "Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order",
    author = "Liao, Yi  and
      Jiang, Xin  and
      Liu, Qun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.24",
    doi = "10.18653/v1/2020.acl-main.24",
    pages = "263--274",
    abstract = "Masked language model and autoregressive language model are two types of language models. While pretrained masked language models such as BERT overwhelm the line of natural language understanding (NLU) tasks, autoregressive language models such as GPT are especially capable in natural language generation (NLG). In this paper, we propose a probabilistic masking scheme for the masked language model, which we call probabilistically masked language model (PMLM). We implement a specific PMLM with a uniform prior distribution on the masking ratio named u-PMLM. We prove that u-PMLM is equivalent to an autoregressive permutated language model. One main advantage of the model is that it supports text generation in arbitrary order with surprisingly good quality, which could potentially enable new applications over traditional unidirectional generation. Besides, the pretrained u-PMLM also outperforms BERT on a bunch of downstream NLU tasks.",
}
@inproceedings{tay-etal-2020-reverse,
    title = "Reverse Engineering Configurations of Neural Text Generation Models",
    author = "Tay, Yi  and
      Bahri, Dara  and
      Zheng, Che  and
      Brunk, Clifford  and
      Metzler, Donald  and
      Tomkins, Andrew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.25",
    doi = "10.18653/v1/2020.acl-main.25",
    pages = "275--279",
    abstract = "Recent advances in neural text generation modeling have resulted in a number of societal concerns related to how such approaches might be used in malicious ways. It is therefore desirable to develop a deeper understanding of the fundamental properties of such models. The study of artifacts that emerge in machine generated text as a result of modeling choices is a nascent research area. To this end, the extent and degree to which these artifacts surface in generated text is still unclear. In the spirit of better understanding generative text models and their artifacts, we propose the new task of distinguishing which of several variants of a given model generated some piece of text. Specifically, we conduct an extensive suite of diagnostic tests to observe whether modeling choices (e.g., sampling methods, top-k probabilities, model architectures, etc.) leave detectable artifacts in the text they generate. Our key finding, which is backed by a rigorous set of experiments, is that such artifacts are present and that different modeling choices can be inferred by looking at generated text alone. This suggests that neural text generators may actually be more sensitive to various modeling choices than previously thought.",
}
@inproceedings{yu-etal-2020-review,
    title = "Review-based Question Generation with Adaptive Instance Transfer and Augmentation",
    author = "Yu, Qian  and
      Bing, Lidong  and
      Zhang, Qiong  and
      Lam, Wai  and
      Si, Luo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.26",
    doi = "10.18653/v1/2020.acl-main.26",
    pages = "280--290",
    abstract = "While online reviews of products and services become an important information source, it remains inefficient for potential consumers to exploit verbose reviews for fulfilling their information need. We propose to explore question generation as a new way of review information exploitation, namely generating questions that can be answered by the corresponding review sentences. One major challenge of this generation task is the lack of training data, i.e. explicit mapping relation between the user-posed questions and review sentences. To obtain proper training instances for the generation model, we propose an iterative learning framework with adaptive instance transfer and augmentation. To generate to the point questions about the major aspects in reviews, related features extracted in an unsupervised manner are incorporated without the burden of aspect annotation. Experiments on data from various categories of a popular E-commerce site demonstrate the effectiveness of the framework, as well as the potentials of the proposed review-based question generation task.",
}
@inproceedings{cai-etal-2020-tag,
    title = "{TAG} : Type Auxiliary Guiding for Code Comment Generation",
    author = "Cai, Ruichu  and
      Liang, Zhihao  and
      Xu, Boyan  and
      Li, Zijian  and
      Hao, Yuexing  and
      Chen, Yao",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.27",
    doi = "10.18653/v1/2020.acl-main.27",
    pages = "291--301",
    abstract = "Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc. However, introducing the type information into the existing framework is non-trivial due to the hierarchical dependence among the type information. In order to address the issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for the code comment generation task which considers the source code as an N-ary tree with type information associated with each node. Specifically, our framework is featured with a Type-associated Encoder and a Type-restricted Decoder which enables adaptive summarization of the source code. We further propose a hierarchical reinforcement learning method to resolve the training difficulties of our proposed framework. Extensive evaluations demonstrate the state-of-the-art performance of our framework with both the auto-evaluated metrics and case studies.",
}
@inproceedings{liu-etal-2020-unsupervised,
    title = "Unsupervised Paraphrasing by Simulated Annealing",
    author = "Liu, Xianggen  and
      Mou, Lili  and
      Meng, Fandong  and
      Zhou, Hao  and
      Zhou, Jie  and
      Song, Sen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.28",
    doi = "10.18653/v1/2020.acl-main.28",
    pages = "302--312",
    abstract = "We propose UPSA, a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing. We model paraphrase generation as an optimization problem and propose a sophisticated objective function, involving semantic similarity, expression diversity, and language fluency of paraphrases. UPSA searches the sentence space towards this objective by performing a sequence of local editing. We evaluate our approach on various datasets, namely, Quora, Wikianswers, MSCOCO, and Twitter. Extensive results show that UPSA achieves the state-of-the-art performance compared with previous unsupervised methods in terms of both automatic and human evaluations. Further, our approach outperforms most existing domain-adapted supervised models, showing the generalizability of UPSA.",
}
@inproceedings{barrow-etal-2020-joint,
    title = "A Joint Model for Document Segmentation and Segment Labeling",
    author = "Barrow, Joe  and
      Jain, Rajiv  and
      Morariu, Vlad  and
      Manjunatha, Varun  and
      Oard, Douglas  and
      Resnik, Philip",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.29",
    doi = "10.18653/v1/2020.acl-main.29",
    pages = "313--322",
    abstract = "Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce Segment Pooling LSTM (S-LSTM), which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30{\%} on average, while also improving segment labeling.",
}
@inproceedings{mekala-shang-2020-contextualized,
    title = "Contextualized Weak Supervision for Text Classification",
    author = "Mekala, Dheeraj  and
      Shang, Jingbo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.30",
    doi = "10.18653/v1/2020.acl-main.30",
    pages = "323--333",
    abstract = "Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers. Existing methods mainly generate pseudo-labels in a context-free manner (e.g., string matching), therefore, the ambiguous, context-dependent nature of human language has been long overlooked. In this paper, we propose a novel framework ConWea, providing contextualized weak supervision for text classification. Specifically, we leverage contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of the same word, and thus create a contextualized corpus. This contextualized corpus is further utilized to train the classifier and expand seed words in an iterative manner. This process not only adds new contextualized, highly label-indicative keywords but also disambiguates initial seed words, making our weak supervision fully contextualized. Extensive experiments and case studies on real-world datasets demonstrate the necessity and significant advantages of using contextualized weak supervision, especially when the class labels are fine-grained.",
}
@inproceedings{zhang-etal-2020-every,
    title = "Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks",
    author = "Zhang, Yufeng  and
      Yu, Xueli  and
      Cui, Zeyu  and
      Wu, Shu  and
      Wen, Zhongzhen  and
      Wang, Liang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.31",
    doi = "10.18653/v1/2020.acl-main.31",
    pages = "334--339",
    abstract = "Text classification is fundamental in natural language processing (NLP) and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. Therefore in this work, to overcome such problems, we propose TextING for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structure, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are aggregated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.",
}
@inproceedings{wang-etal-2020-neural-topic,
    title = "Neural Topic Modeling with Bidirectional Adversarial Training",
    author = "Wang, Rui  and
      Hu, Xuemeng  and
      Zhou, Deyu  and
      He, Yulan  and
      Xiong, Yuxuan  and
      Ye, Chenchen  and
      Xu, Haiyang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.32",
    doi = "10.18653/v1/2020.acl-main.32",
    pages = "340--350",
    abstract = "Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6{\%} is observed in accuracy.",
}
@inproceedings{ohashi-etal-2020-text,
    title = "Text Classification with Negative Supervision",
    author = "Ohashi, Sora  and
      Takayama, Junya  and
      Kajiwara, Tomoyuki  and
      Chu, Chenhui  and
      Arase, Yuki",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.33",
    doi = "10.18653/v1/2020.acl-main.33",
    pages = "351--357",
    abstract = "Advanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks. However, the discrepancy between the semantic similarity of texts and labelling standards affects classifiers, i.e. leading to lower performance in cases where classifiers should assign different labels to semantically similar texts. To address this problem, we propose a simple multitask learning model that uses negative supervision. Specifically, our model encourages texts with different labels to have distinct representations. Comprehensive experiments show that our model outperforms the state-of-the-art pre-trained model on both single- and multi-label classifications, sentence and document classifications, and classifications in three different languages.",
}
@inproceedings{chen-etal-2020-content,
    title = "Content Word Aware Neural Machine Translation",
    author = "Chen, Kehai  and
      Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.34",
    doi = "10.18653/v1/2020.acl-main.34",
    pages = "358--364",
    abstract = "Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word. However, NMT does not consider the importance of word in the sentence meaning, for example, some words (i.e., content words) express more important meaning than others (i.e., function words). To address this limitation, we first utilize word frequency information to distinguish between content and function words in a sentence, and then design a content word-aware NMT to improve translation performance. Empirical results on the WMT14 English-to-German, WMT14 English-to-French, and WMT17 Chinese-to-English translation tasks show that the proposed methods can significantly improve the performance of Transformer-based NMT.",
}
@inproceedings{li-etal-2020-evaluating,
    title = "Evaluating Explanation Methods for Neural Machine Translation",
    author = "Li, Jierui  and
      Liu, Lemao  and
      Li, Huayang  and
      Li, Guanlin  and
      Huang, Guoping  and
      Shi, Shuming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.35",
    doi = "10.18653/v1/2020.acl-main.35",
    pages = "365--375",
    abstract = "Recently many efforts have been devoted to interpreting the black-box NMT models, but little progress has been made on metrics to evaluate explanation methods. Word Alignment Error Rate can be used as such a metric that matches human understanding, however, it can not measure explanation methods on those target words that are not aligned to any source word. This paper thereby makes an initial attempt to evaluate explanation methods from an alternative viewpoint. To this end, it proposes a principled metric based on fidelity in regard to the predictive behavior of the NMT model. As the exact computation for this metric is intractable, we employ an efficient approach as its approximation. On six standard translation tasks, we quantitatively evaluate several explanation methods in terms of the proposed metric and we reveal some valuable findings for these explanation methods in our experiments.",
}
@inproceedings{guo-etal-2020-jointly,
    title = "Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation",
    author = "Guo, Junliang  and
      Xu, Linli  and
      Chen, Enhong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.36",
    doi = "10.18653/v1/2020.acl-main.36",
    pages = "376--385",
    abstract = "The masked language model has received remarkable attention due to its effectiveness on various natural language processing tasks. However, few works have adopted this technique in the sequence-to-sequence models. In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-autoregressive neural machine translation{\textasciitilde}(NAT). Specifically, we first empirically study the functionalities of the encoder and the decoder in NAT models, and find that the encoder takes a more important role than the decoder regarding the translation quality. Therefore, we propose to train the encoder more rigorously by masking the encoder input while training. As for the decoder, we propose to train it based on the consecutive masking of the decoder input with an $n$-gram loss function to alleviate the problem of translating duplicate words. The two types of masks are applied to the model jointly at the training stage. We conduct experiments on five benchmark machine translation tasks, and our model can achieve 27.69/32.24 BLEU scores on WMT14 English-German/German-English tasks with $5+$ times speed up compared with an autoregressive model.",
}
@inproceedings{xu-etal-2020-learning,
    title = "Learning Source Phrase Representations for Neural Machine Translation",
    author = "Xu, Hongfei  and
      van Genabith, Josef  and
      Xiong, Deyi  and
      Liu, Qiuhui  and
      Zhang, Jingyi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.37",
    doi = "10.18653/v1/2020.acl-main.37",
    pages = "386--396",
    abstract = "The Transformer translation model (Vaswani et al., 2017) based on a multi-head attention mechanism can be computed effectively in parallel and has significantly pushed forward the performance of Neural Machine Translation (NMT). Though intuitively the attentional network can connect distant words via shorter network paths than RNNs, empirical analysis demonstrates that it still has difficulty in fully capturing long-distance dependencies (Tang et al., 2018). Considering that modeling phrases instead of words has significantly improved the Statistical Machine Translation (SMT) approach through the use of larger translation blocks ({``}phrases{''}) and its reordering ability, modeling NMT at phrase level is an intuitive proposal to help the model capture long-distance relationships. In this paper, we first propose an attentive phrase representation generation mechanism which is able to generate phrase representations from corresponding token representations. In addition, we incorporate the generated phrase representations into the Transformer translation model to enhance its ability to capture long-distance relationships. In our experiments, we obtain significant improvements on the WMT 14 English-German and English-French tasks on top of the strong Transformer baseline, which shows the effectiveness of our approach. Our approach helps Transformer Base models perform at the level of Transformer Big models, and even significantly better for long sentences, but with substantially fewer parameters and training steps. The fact that phrase representations help even in the big setting further supports our conjecture that they make a valuable contribution to long-distance relations.",
}
@inproceedings{xu-etal-2020-lipschitz,
    title = "Lipschitz Constrained Parameter Initialization for Deep Transformers",
    author = "Xu, Hongfei  and
      Liu, Qiuhui  and
      van Genabith, Josef  and
      Xiong, Deyi  and
      Zhang, Jingyi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.38",
    doi = "10.18653/v1/2020.acl-main.38",
    pages = "397--402",
    abstract = "The Transformer translation model employs residual connection and layer normalization to ease the optimization difficulties caused by its multi-layer encoder/decoder structure. Previous research shows that even with residual connection and layer normalization, deep Transformers still have difficulty in training, and particularly Transformer models with more than 12 encoder/decoder layers fail to converge. In this paper, we first empirically demonstrate that a simple modification made in the official implementation, which changes the computation order of residual connection and layer normalization, can significantly ease the optimization of deep Transformers. We then compare the subtle differences in computation order in considerable detail, and present a parameter initialization method that leverages the Lipschitz constraint on the initialization of Transformer parameters that effectively ensures training convergence. In contrast to findings in previous research we further demonstrate that with Lipschitz parameter initialization, deep Transformers with the original computation order can converge, and obtain significant BLEU improvements with up to 24 layers. In contrast to previous research which focuses on deep encoders, our approach additionally enables Transformers to also benefit from deep decoders.",
}
@inproceedings{dubois-etal-2020-location,
    title = "{L}ocation {A}ttention for {E}xtrapolation to {L}onger {S}equences",
    author = "Dubois, Yann  and
      Dagan, Gautier  and
      Hupkes, Dieuwke  and
      Bruni, Elia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.39",
    doi = "10.18653/v1/2020.acl-main.39",
    pages = "403--413",
    abstract = "Neural networks are surprisingly good at interpolating and perform remarkably well when the training set examples resemble those in the test set. However, they are often unable to extrapolate patterns beyond the seen data, even when the abstractions required for such patterns are simple. In this paper, we first review the notion of extrapolation, why it is important and how one could hope to tackle it. We then focus on a specific type of extrapolation which is especially useful for natural language processing: generalization to sequences that are longer than the training ones. We hypothesize that models with a separate content- and location-based attention are more likely to extrapolate than those with common attention mechanisms. We empirically support our claim for recurrent seq2seq models with our proposed attention on variants of the Lookup Table task. This sheds light on some striking failures of neural models for sequences and on possible methods to approaching such issues.",
}
@inproceedings{wei-etal-2020-multiscale,
    title = "Multiscale Collaborative Deep Models for Neural Machine Translation",
    author = "Wei, Xiangpeng  and
      Yu, Heng  and
      Hu, Yue  and
      Zhang, Yue  and
      Weng, Rongxiang  and
      Luo, Weihua",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.40",
    doi = "10.18653/v1/2020.acl-main.40",
    pages = "414--426",
    abstract = "Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train. In this paper, we present a MultiScale Collaborative (MSC) framework to ease the training of NMT models that are substantially deeper than those used previously. We explicitly boost the gradient back-propagation from top to bottom levels by introducing a block-scale collaboration mechanism into deep NMT models. Then, instead of forcing the whole encoder stack directly learns a desired representation, we let each encoder block learns a fine-grained representation and enhance it by encoding spatial dependencies using a context-scale collaboration. We provide empirical evidence showing that the MSC nets are easy to optimize and can obtain improvements of translation quality from considerably increased depth. On IWSLT translation tasks with three translation directions, our extremely deep models (with 72-layer encoders) surpass strong baselines by +2.2{\textasciitilde}+3.1 BLEU points. In addition, our deep MSC achieves a BLEU score of 30.56 on WMT14 English-to-German task that significantly outperforms state-of-the-art deep NMT models. We have included the source code in supplementary materials.",
}
@inproceedings{liu-etal-2020-norm,
    title = "Norm-Based Curriculum Learning for Neural Machine Translation",
    author = "Liu, Xuebo  and
      Lai, Houtim  and
      Wong, Derek F.  and
      Chao, Lidia S.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.41",
    doi = "10.18653/v1/2020.acl-main.41",
    pages = "427--436",
    abstract = "A neural machine translation (NMT) system is expensive to train, especially with high-resource settings. As the NMT architectures become deeper and wider, this issue gets worse and worse. In this paper, we aim to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method. We use the norm (aka length or module) of a word embedding as a measure of 1) the difficulty of the sentence, 2) the competence of the model, and 3) the weight of the sentence. The norm-based sentence difficulty takes the advantages of both linguistically motivated and model-based sentence difficulties. It is easy to determine and contains learning-dependent features. The norm-based model competence makes NMT learn the curriculum in a fully automated way, while the norm-based sentence weight further enhances the learning of the vector representation of the NMT. Experimental results for the WMT{'}14 English-German and WMT{'}17 Chinese-English translation tasks demonstrate that the proposed method outperforms strong baselines in terms of BLEU score (+1.17/+1.56) and training speedup (2.22x/3.33x).",
}
@inproceedings{zheng-etal-2020-opportunistic,
    title = "Opportunistic Decoding with Timely Correction for Simultaneous Translation",
    author = "Zheng, Renjie  and
      Ma, Mingbo  and
      Zheng, Baigong  and
      Liu, Kaibo  and
      Huang, Liang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.42",
    doi = "10.18653/v1/2020.acl-main.42",
    pages = "437--442",
    abstract = "Simultaneous translation has many important application scenarios and attracts much attention from both academia and industry recently. Most existing frameworks, however, have difficulties in balancing between the translation quality and latency, i.e., the decoding policy is usually either too aggressive or too conservative. We propose an opportunistic decoding technique with timely correction ability, which always (over-)generates a certain mount of extra words at each step to keep the audience on track with the latest information. At the same time, it also corrects, in a timely fashion, the mistakes in the former overgenerated words when observing more source context to ensure high translation quality. Experiments show our technique achieves substantial reduction in latency and up to +3.1 increase in BLEU, with revision rate under 8{\%} in Chinese-to-English and English-to-Chinese translation.",
}
@inproceedings{merrill-etal-2020-formal,
    title = "A Formal Hierarchy of {RNN} Architectures",
    author = "Merrill, William  and
      Weiss, Gail  and
      Goldberg, Yoav  and
      Schwartz, Roy  and
      Smith, Noah A.  and
      Yahav, Eran",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.43",
    doi = "10.18653/v1/2020.acl-main.43",
    pages = "443--459",
    abstract = "We develop a formal hierarchy of the expressive capacity of RNN architectures. The hierarchy is based on two formal properties: space complexity, which measures the RNN{'}s memory, and rational recurrence, defined as whether the recurrent update can be described by a weighted finite-state machine. We place several RNN variants within this hierarchy. For example, we prove the LSTM is not rational, which formally separates it from the related QRNN (Bradbury et al., 2016). We also show how these models{'} expressive capacity is expanded by stacking multiple layers or composing them with different pooling functions. Our results build on the theory of {``}saturated{''} RNNs (Merrill, 2019). While formally extending these findings to unsaturated RNNs is left to future work, we hypothesize that the practical learnable capacity of unsaturated RNNs obeys a similar hierarchy. We provide empirical results to support this conjecture. Experimental findings from training unsaturated networks on formal languages support this conjecture.",
}
@inproceedings{ding-etal-2020-three,
    title = "A Three-Parameter Rank-Frequency Relation in Natural Languages",
    author = "Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.44",
    doi = "10.18653/v1/2020.acl-main.44",
    pages = "460--464",
    abstract = "We present that, the rank-frequency relation in textual data follows $f \propto r^{-\alpha}(r+\gamma)^{-\beta}$, where $f$ is the token frequency and $r$ is the rank by frequency, with ($\alpha$, $\beta$, $\gamma$) as parameters. The formulation is derived based on the empirical observation that $d^2 (x+y)/dx^2$ is a typical impulse function, where $(x,y)=(\log r, \log f)$. The formulation is the power law when $\beta=0$ and the Zipf{--}Mandelbrot law when $\alpha=0$. We illustrate that $\alpha$ is related to the analytic features of syntax and $\beta+\gamma$ to those of morphology in natural languages from an investigation of multilingual corpora.",
}
@inproceedings{li-etal-2020-dice,
    title = "Dice Loss for Data-imbalanced {NLP} Tasks",
    author = "Li, Xiaoya  and
      Sun, Xiaofei  and
      Meng, Yuxian  and
      Liang, Junjun  and
      Wu, Fei  and
      Li, Jiwei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.45",
    doi = "10.18653/v1/2020.acl-main.45",
    pages = "465--476",
    abstract = "Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of easy-negative examples overwhelms the training. The most commonly used cross entropy (CE) criteria is actually an accuracy-oriented objective, and thus creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function, while at test time F1 score concerns more about positive examples. In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks. Dice loss is based on the S{\o}rensen--Dice coefficient or Tversky index , which attaches similar importance to false positives and false negatives, and is more immune to the data-imbalance issue. To further alleviate the dominating influence from easy-negative examples in training, we propose to associate training examples with dynamically adjusted weights to deemphasize easy-negative examples. Theoretical analysis shows that this strategy narrows down the gap between the F1 score in evaluation and the dice loss in training. With the proposed training objective, we observe significant performance boost on a wide range of data imbalanced NLP tasks. Notably, we are able to achieve SOTA results on CTB5, CTB6 and UD1.4 for the part of speech tagging task; SOTA results on CoNLL03, OntoNotes5.0, MSRA and OntoNotes4.0 for the named entity recognition task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification.",
}
@inproceedings{bailly-gabor-2020-emergence,
    title = "Emergence of Syntax Needs Minimal Supervision",
    author = {Bailly, Rapha{\"e}l  and
      G{\'a}bor, Kata},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.46",
    doi = "10.18653/v1/2020.acl-main.46",
    pages = "477--487",
    abstract = "This paper is a theoretical contribution to the debate on the learnability of syntax from a corpus without explicit syntax-specific guidance. Our approach originates in the observable structure of a corpus, which we use to define and isolate grammaticality (syntactic information) and meaning/pragmatics information. We describe the formal characteristics of an autonomous syntax and show that it becomes possible to search for syntax-based lexical categories with a simple optimization process, without any prior hypothesis on the form of the model.",
}
@inproceedings{kuribayashi-etal-2020-language,
    title = "Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in {J}apanese",
    author = "Kuribayashi, Tatsuki  and
      Ito, Takumi  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.47",
    doi = "10.18653/v1/2020.acl-main.47",
    pages = "488--504",
    abstract = "We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parallels between LMs and human word order preference, and (ii) consistency of the results obtained using the LM-based method with previous linguistic studies. Through our experiments, we tentatively conclude that LMs display sufficient word order knowledge for usage as an analysis tool. Finally, using the LM-based method, we demonstrate the relationship between the canonical word order and topicalization, which had yet to be analyzed by large-scale experiments.",
}
@inproceedings{lu-li-2020-gcan,
    title = "{GCAN}: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media",
    author = "Lu, Yi-Ju  and
      Li, Cheng-Te",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.48",
    doi = "10.18653/v1/2020.acl-main.48",
    pages = "505--514",
    abstract = "This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16{\%} in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.",
}
@inproceedings{zhong-etal-2020-integrating,
    title = "Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection",
    author = "Zhong, Lei  and
      Cao, Juan  and
      Sheng, Qiang  and
      Guo, Junbo  and
      Wang, Ziang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.49",
    doi = "10.18653/v1/2020.acl-main.49",
    pages = "515--526",
    abstract = "Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views. However, existing methods fail to 1) effectively incorporate the semantic information from content-related posts; 2) preserve the structural information for reply relationship modeling; 3) properly handle posts from topics dissimilar to those in the training set. To overcome the first two limitations, we propose Topic-Post-Comment Graph Convolutional Network (TPC-GCN), which integrates the information from the graph structure and content of topics, posts, and comments for post-level controversy detection. As to the third limitation, we extend our model to Disentangled TPC-GCN (DTPC-GCN), to disentangle topic-related and topic-unrelated features and then fuse dynamically. Extensive experiments on two real-world datasets demonstrate that our models outperform existing methods. Analysis of the results and cases proves that our models can integrate both semantic and structural information with significant generalizability.",
}
@inproceedings{stefanov-etal-2020-predicting,
    title = "Predicting the Topical Stance and Political Leaning of Media using Tweets",
    author = "Stefanov, Peter  and
      Darwish, Kareem  and
      Atanasov, Atanas  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.50",
    doi = "10.18653/v1/2020.acl-main.50",
    pages = "527--537",
    abstract = "Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers. Many supervised solutions exist for determining viewpoints, but manually annotating training data is costly. In this paper, we propose a cascaded method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior; then, it uses supervised learning based on user labels to characterize both the general political leaning of online media and of popular Twitter users, as well as their stance with respect to the target polarizing topic. We evaluate the model by comparing its predictions to gold labels from the Media Bias/Fact Check website, achieving 82.6{\%} accuracy.",
}
@inproceedings{gonen-etal-2020-simple,
    title = "Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora",
    author = "Gonen, Hila  and
      Jawahar, Ganesh  and
      Seddah, Djam{\'e}  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.51",
    doi = "10.18653/v1/2020.acl-main.51",
    pages = "538--555",
    abstract = "The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science. This is commonly approached by training word embeddings on each corpus, aligning the vector spaces, and looking for words whose cosine distance in the aligned space is large. However, these methods often require extensive filtering of the vocabulary to perform well, and - as we show in this work - result in unstable, and hence less reliable, results. We propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word. The method is simple, interpretable and stable. We demonstrate its effectiveness in 9 different setups, considering different corpus splitting criteria (age, gender and profession of tweet authors, time of tweet) and different languages (English, French and Hebrew).",
}
@inproceedings{shen-feng-2020-cdl,
    title = "{CDL}: Curriculum Dual Learning for Emotion-Controllable Response Generation",
    author = "Shen, Lei  and
      Feng, Yang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.52",
    doi = "10.18653/v1/2020.acl-main.52",
    pages = "556--566",
    abstract = "Emotion-controllable response generation is an attractive and valuable task that aims to make open-domain conversations more empathetic and engaging. Existing methods mainly enhance the emotion expression by adding regularization terms to standard cross-entropy loss and thus influence the training process. However, due to the lack of further consideration of content consistency, the common problem of response generation tasks, safe response, is intensified. Besides, query emotions that can help model the relationship between query and response are simply ignored in previous models, which would further hurt the coherence. To alleviate these problems, we propose a novel framework named Curriculum Dual Learning (CDL) which extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively. CDL utilizes two rewards focusing on emotion and content to improve the duality. Additionally, it applies curriculum learning to gradually generate high-quality responses based on the difficulties of expressing various emotions. Experimental results show that CDL significantly outperforms the baselines in terms of coherence, diversity, and relation to emotion factors.",
}
@inproceedings{kim-etal-2020-efficient,
    title = "Efficient Dialogue State Tracking by Selectively Overwriting Memory",
    author = "Kim, Sungdong  and
      Yang, Sohee  and
      Kim, Gyuwan  and
      Lee, Sang-Woo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.53",
    doi = "10.18653/v1/2020.acl-main.53",
    pages = "567--582",
    abstract = "Recent works in dialogue state tracking (DST) focus on an open vocabulary-based setting to resolve scalability and generalization issues of the predefined ontology-based approaches. However, they are inefficient in that they predict the dialogue state at every turn from scratch. Here, we consider dialogue state as an explicit fixed-sized memory and propose a selectively overwriting mechanism for more efficient DST. This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations. Our method decomposes DST into two sub-tasks and guides the decoder to focus only on one of the tasks, thus reducing the burden of the decoder. This enhances the effectiveness of training and DST performance. Our SOM-DST (Selectively Overwriting Memory for Dialogue State Tracking) model achieves state-of-the-art joint goal accuracy with 51.72{\%} in MultiWOZ 2.0 and 53.01{\%} in MultiWOZ 2.1 in an open vocabulary-based DST setting. In addition, we analyze the accuracy gaps between the current and the ground truth-given situations and suggest that it is a promising direction to improve state operation prediction to boost the DST performance.",
}
@inproceedings{ham-etal-2020-end,
    title = "End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using {GPT}-2",
    author = "Ham, Donghoon  and
      Lee, Jeong-Gwan  and
      Jang, Youngsoo  and
      Kim, Kee-Eung",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.54",
    doi = "10.18653/v1/2020.acl-main.54",
    pages = "583--592",
    abstract = "The goal-oriented dialogue system needs to be optimized for tracking the dialogue flow and carrying out an effective conversation under various situations to meet the user goal. The traditional approach to build such a dialogue system is to take a pipelined modular architecture, where its modules are optimized individually. However, such an optimization scheme does not necessarily yield the overall performance improvement of the whole system. On the other hand, end-to-end dialogue systems with monolithic neural architecture are often trained only with input-output utterances, without taking into account the entire annotations available in the corpus. This scheme makes it difficult for goal-oriented dialogues where the system needs to integrate with external systems or to provide interpretable information about why the system generated a particular response. In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above. In the human evaluation, our dialogue system achieved the success rate of 68.32{\%}, the language understanding score of 4.149, and the response appropriateness score of 4.287, which ranked the system at the top position in the end-to-end multi-domain dialogue system task in the 8th dialogue systems technology challenge (DSTC8).",
}
@inproceedings{sato-etal-2020-evaluating,
    title = "Evaluating Dialogue Generation Systems via Response Selection",
    author = "Sato, Shiki  and
      Akama, Reina  and
      Ouchi, Hiroki  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.55",
    doi = "10.18653/v1/2020.acl-main.55",
    pages = "593--599",
    abstract = "Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation. We focus on evaluating response generation systems via response selection. To evaluate systems properly via response selection, we propose a method to construct response selection test sets with well-chosen false candidates. Specifically, we propose to construct test sets filtering out some types of false candidates: (i) those unrelated to the ground-truth response and (ii) those acceptable as appropriate responses. Through experiments, we demonstrate that evaluating systems via response selection with the test set developed by our method correlates more strongly with human evaluation, compared with widely used automatic evaluation metrics such as BLEU.",
}
@inproceedings{zha-etal-2020-gated,
    title = "Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection",
    author = "Zha, Yefei  and
      Li, Ruobing  and
      Lin, Hui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.56",
    doi = "10.18653/v1/2020.acl-main.56",
    pages = "600--608",
    abstract = "Off-topic spoken response detection, the task aiming at predicting whether a response is off-topic for the corresponding prompt, is important for an automated speaking assessment system. In many real-world educational applications, off-topic spoken response detectors are required to achieve high recall for off-topic responses not only on seen prompts but also on prompts that are unseen during training. In this paper, we propose a novel approach for off-topic spoken response detection with high off-topic recall on both seen and unseen prompts. We introduce a new model, Gated Convolutional Bidirectional Attention-based Model (GCBiA), which applies bi-attention mechanism and convolutions to extract topic words of prompts and key-phrases of responses, and introduces gated unit and residual connections between major layers to better represent the relevance of responses and prompts. Moreover, a new negative sampling method is proposed to augment training data. Experiment results demonstrate that our novel approach can achieve significant improvements in detecting off-topic responses with extremely high on-topic recall, for both seen and unseen prompts.",
}
@inproceedings{dai-etal-2020-learning,
    title = "Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment",
    author = "Dai, Yinpei  and
      Li, Hangyu  and
      Tang, Chengguang  and
      Li, Yongbin  and
      Sun, Jian  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.57",
    doi = "10.18653/v1/2020.acl-main.57",
    pages = "609--618",
    abstract = "Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90{\%} per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.",
}
@inproceedings{he-etal-2020-learning,
    title = "Learning to Tag {OOV} Tokens by Integrating Contextual Representation and Background Knowledge",
    author = "He, Keqing  and
      Yan, Yuanmeng  and
      Xu, Weiran",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.58",
    doi = "10.18653/v1/2020.acl-main.58",
    pages = "619--624",
    abstract = "Neural-based context-aware models for slot tagging have achieved state-of-the-art performance. However, the presence of OOV(out-of-vocab) words significantly degrades the performance of neural-based models, especially in a few-shot scenario. In this paper, we propose a novel knowledge-enhanced slot tagging model to integrate contextual representation of input text and the large-scale lexical background knowledge. Besides, we use multi-level graph attention to explicitly model lexical relations. The experiments show that our proposed knowledge integration mechanism achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets.",
}
@inproceedings{takanobu-etal-2020-multi,
    title = "Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition",
    author = "Takanobu, Ryuichi  and
      Liang, Runze  and
      Huang, Minlie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.59",
    doi = "10.18653/v1/2020.acl-main.59",
    pages = "625--638",
    abstract = "Many studies have applied reinforcement learning to train a dialog policy and show great promise these years. One common approach is to employ a user simulator to obtain a large number of simulated user experiences for reinforcement learning algorithms. However, modeling a realistic user simulator is challenging. A rule-based simulator requires heavy domain expertise for complex tasks, and a data-driven simulator requires considerable data and it is even unclear how to evaluate a simulator. To avoid explicitly building a user simulator beforehand, we propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents. Two agents interact with each other and are jointly learned simultaneously. The method uses the actor-critic framework to facilitate pretraining and improve scalability. We also propose Hybrid Value Network for the role-aware reward decomposition to integrate role-specific domain knowledge of each agent in the task-oriented dialog. Results show that our method can successfully build a system policy and a user policy simultaneously, and two agents can achieve a high task success rate through conversational interaction.",
}
@inproceedings{gao-etal-2020-paraphrase,
    title = "Paraphrase Augmented Task-Oriented Dialog Generation",
    author = "Gao, Silin  and
      Zhang, Yichi  and
      Ou, Zhijian  and
      Yu, Zhou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.60",
    doi = "10.18653/v1/2020.acl-main.60",
    pages = "639--649",
    abstract = "Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real world settings. We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model to improve the dialog generation performance. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels. PARG is applicable to various dialog generation models, such as TSCP (Lei et al., 2018) and DAMD (Zhang et al., 2019). Experimental results show that the proposed framework improves these state-of-the-art dialog models further on CamRest676 and MultiWOZ. PARG also outperforms other data augmentation methods significantly in dialog generation tasks, especially under low resource settings.",
}
@inproceedings{tian-etal-2020-response,
    title = "Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation",
    author = "Tian, Zhiliang  and
      Bi, Wei  and
      Lee, Dongkyu  and
      Xue, Lanqing  and
      Song, Yiping  and
      Liu, Xiaojiang  and
      Zhang, Nevin L.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.61",
    doi = "10.18653/v1/2020.acl-main.61",
    pages = "650--659",
    abstract = "Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory. In this paper, we propose to create the document memory with some anticipated responses in mind. This is achieved using a teacher-student framework. The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information. The student learns to construct a response-anticipated document memory from the first two sources, and teacher{'}s insight on memory creation. Empirical results show that our model outperforms the previous state-of-the-art for the CbR task.",
}
@inproceedings{huang-etal-2020-semi,
    title = "Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation",
    author = "Huang, Xinting  and
      Qi, Jianzhong  and
      Sun, Yu  and
      Zhang, Rui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.62",
    doi = "10.18653/v1/2020.acl-main.62",
    pages = "660--670",
    abstract = "Dialogue policy optimization often obtains feedback until task completion in task-oriented dialogue systems. This is insufficient for training intermediate dialogue turns since supervision signals (or rewards) are only provided at the end of dialogues. To address this issue, reward learning has been introduced to learn from state-action pairs of an optimal policy to provide turn-by-turn rewards. This approach requires complete state-action annotations of human-to-human dialogues (i.e., expert demonstrations), which is labor intensive. To overcome this limitation, we propose a novel reward learning approach for semi-supervised policy learning. The proposed approach learns a dynamics model as the reward function which models dialogue progress (i.e., state-action sequences) based on expert demonstrations, either with or without annotations. The dynamics model computes rewards by predicting whether the dialogue progress is consistent with expert demonstrations. We further propose to learn action embeddings for a better generalization of the reward function. The proposed approach outperforms competitive policy learning baselines on MultiWOZ, a benchmark multi-domain dataset.",
}
@inproceedings{su-etal-2020-towards,
    title = "Towards Unsupervised Language Understanding and Generation by Joint Dual Learning",
    author = "Su, Shang-Yu  and
      Huang, Chao-Wei  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.63",
    doi = "10.18653/v1/2020.acl-main.63",
    pages = "671--680",
    abstract = "In modular dialogue systems, natural language understanding (NLU) and natural language generation (NLG) are two critical components, where NLU extracts the semantics from the given texts and NLG is to construct corresponding natural language sentences based on the input semantic representations. However, the dual property between understanding and generation has been rarely explored. The prior work is the first attempt that utilized the duality between NLU and NLG to improve the performance via a dual supervised learning framework. However, the prior work still learned both components in a supervised manner; instead, this paper introduces a general learning framework to effectively exploit such duality, providing flexibility of incorporating both supervised and unsupervised learning algorithms to train language understanding and generation models in a joint fashion. The benchmark experiments demonstrate that the proposed approach is capable of boosting the performance of both NLU and NLG. The source code is available at: https://github.com/MiuLab/DuaLUG.",
}
@inproceedings{mehri-eskenazi-2020-usr,
    title = "{USR}: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation",
    author = "Mehri, Shikib  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.64",
    doi = "10.18653/v1/2020.acl-main.64",
    pages = "681--707",
    abstract = "The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a reference-free metric that trains unsupervised models to measure several desirable qualities of dialog. USR is shown to strongly correlate with human judgment on both Topical-Chat (turn-level: 0.42, system-level: 1.0) and PersonaChat (turn-level: 0.48 and system-level: 1.0). USR additionally produces interpretable measures for several desirable properties of dialog.",
}
@inproceedings{li-etal-2020-explicit,
    title = "Explicit Semantic Decomposition for Definition Generation",
    author = "Li, Jiahuan  and
      Bao, Yu  and
      Huang, Shujian  and
      Dai, Xinyu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.65",
    doi = "10.18653/v1/2020.acl-main.65",
    pages = "708--717",
    abstract = "Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works hardly consider explicitly modeling the {``}components{''} of definitions, leading to under-specific generation results. In this paper, we propose ESD, namely Explicit Semantic Decomposition for definition Generation, which explicitly decomposes the meaning of words into semantic components, and models them with discrete latent variables for definition generation. Experimental results show that achieves top results on WordNet and Oxford benchmarks, outperforming strong previous baselines.",
}
@inproceedings{kang-hashimoto-2020-improved,
    title = "Improved Natural Language Generation via Loss Truncation",
    author = "Kang, Daniel  and
      Hashimoto, Tatsunori",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.66",
    doi = "10.18653/v1/2020.acl-main.66",
    pages = "718--731",
    abstract = "Neural language models are usually trained to match the distributional properties of large-scale corpora by minimizing the log loss. While straightforward to optimize, this approach forces the model to reproduce all variations in the dataset, including noisy and invalid references (e.g., misannotations and hallucinated facts). Even a small fraction of noisy data can degrade the performance of log loss. As an alternative, prior work has shown that minimizing the distinguishability of generated samples is a principled and robust loss that can handle invalid references. However, distinguishability has not been used in practice due to challenges in optimization and estimation. We propose loss truncation: a simple and scalable procedure which adaptively removes high log loss examples as a way to optimize for distinguishability. Empirically, we demonstrate that loss truncation outperforms existing baselines on distinguishability on a summarization task. Furthermore, we show that samples generated by the loss truncation model have factual accuracy ratings that exceed those of baselines and match human references.",
}
@inproceedings{zhao-etal-2020-line,
    title = "Line Graph Enhanced {AMR}-to-Text Generation with Mix-Order Graph Attention Networks",
    author = "Zhao, Yanbin  and
      Chen, Lu  and
      Chen, Zhi  and
      Cao, Ruisheng  and
      Zhu, Su  and
      Yu, Kai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.67",
    doi = "10.18653/v1/2020.acl-main.67",
    pages = "732--741",
    abstract = "Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation {--} A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR). Existing graph-to-sequence approaches generally utilize graph neural networks as their encoders, which have two limitations: 1) The message propagation process in AMR graphs is only guided by the first-order adjacency information. 2) The relationships between labeled edges are not fully considered. In this work, we propose a novel graph encoding framework which can effectively explore the edge relations. We also adopt graph attention networks with higher-order neighborhood information to encode the rich structure in AMR graphs. Experiment results show that our approach obtains new state-of-the-art performance on English AMR benchmark datasets. The ablation analyses also demonstrate that both edge relations and higher-order information are beneficial to graph-to-sequence modeling.",
}
@inproceedings{li-etal-2020-rigid,
    title = "Rigid Formats Controlled Text Generation",
    author = "Li, Piji  and
      Zhang, Haisong  and
      Liu, Xiaojiang  and
      Shi, Shuming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.68",
    doi = "10.18653/v1/2020.acl-main.68",
    pages = "742--751",
    abstract = "Neural text generation has made tremendous progress in various tasks. One common characteristic of most of the tasks is that the texts are not restricted to some rigid formats when generating. However, we may confront some special text paradigms such as Lyrics (assume the music score is given), Sonnet, SongCi (classical Chinese poetry of the Song dynasty), etc. The typical characteristics of these texts are in three folds: (1) They must comply fully with the rigid predefined formats. (2) They must obey some rhyming schemes. (3) Although they are restricted to some formats, the sentence integrity must be guaranteed. To the best of our knowledge, text generation based on the predefined rigid formats has not been well investigated. Therefore, we propose a simple and elegant framework named SongNet to tackle this problem. The backbone of the framework is a Transformer-based auto-regressive language model. Sets of symbols are tailor-designed to improve the modeling performance especially on format, rhyme, and sentence integrity. We improve the attention mechanism to impel the model to capture some future information on the format. A pre-training and fine-tuning framework is designed to further improve the generation quality. Extensive experiments conducted on two collected corpora demonstrate that our proposed framework generates significantly better results in terms of both automatic metrics and the human evaluation.",
}
@inproceedings{dhole-manning-2020-syn,
    title = "Syn-{QG}: Syntactic and Shallow Semantic Rules for Question Generation",
    author = "Dhole, Kaustubh  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.69",
    doi = "10.18653/v1/2020.acl-main.69",
    pages = "752--765",
    abstract = "Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form. We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into question-answer pairs. We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems. In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules. A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions.",
}
@inproceedings{kumar-etal-2020-online,
    title = "An Online Semantic-enhanced {D}irichlet Model for Short Text Stream Clustering",
    author = "Kumar, Jay  and
      Shao, Junming  and
      Uddin, Salah  and
      Ali, Wazir",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.70",
    doi = "10.18653/v1/2020.acl-main.70",
    pages = "766--776",
    abstract = "Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution. Existing approaches often exploit short text streams in a batch way. However, determine the optimal batch size is usually a difficult task since we have no priori knowledge when the topics evolve. In addition, traditional independent word representation in graphical model tends to cause {``}term ambiguity{''} problem in short text clustering. Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short sext stream clustering, called OSDM, which integrates the word-occurance semantic information (i.e., context) into a new graphical model and clusters each arriving short text automatically in an online way. Extensive results have demonstrated that OSDM has better performance compared to many state-of-the-art algorithms on both synthetic and real-world data sets.",
}
@inproceedings{zheng-etal-2020-generative,
    title = "Generative Semantic Hashing Enhanced via {B}oltzmann Machines",
    author = "Zheng, Lin  and
      Su, Qinliang  and
      Shen, Dinghan  and
      Chen, Changyou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.71",
    doi = "10.18653/v1/2020.acl-main.71",
    pages = "777--788",
    abstract = "Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint. For the tractability of training, existing generative-hashing methods mostly assume a factorized form for the posterior distribution, enforcing independence among the bits of hash codes. From the perspectives of both model representation and code space size, independence is always not the best assumption. In this paper, to introduce correlations among the bits of hash codes, we propose to employ the distribution of Boltzmann machine as the variational posterior. To address the intractability issue of training, we first develop an approximate method to reparameterize the distribution of a Boltzmann machine by augmenting it as a hierarchical concatenation of a Gaussian-like distribution and a Bernoulli distribution. Based on that, an asymptotically-exact lower bound is further derived for the evidence lower bound (ELBO). With these novel techniques, the entire model can be optimized efficiently. Extensive experimental results demonstrate that by effectively modeling correlations among different bits within a hash code, our model can achieve significant performance gains.",
}
@inproceedings{kohita-etal-2020-interactive,
    title = "Interactive Construction of User-Centric Dictionary for Text Analytics",
    author = "Kohita, Ryosuke  and
      Yoshida, Issei  and
      Kanayama, Hiroshi  and
      Nasukawa, Tetsuya",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.72",
    doi = "10.18653/v1/2020.acl-main.72",
    pages = "789--799",
    abstract = "We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis. This paper introduces the first formulation of interactive dictionary construction to address this issue. To optimize the interaction, we propose a new algorithm that effectively captures an analyst{'}s intention starting from only a small number of sample terms. Along with the algorithm, we also design an automatic evaluation framework that provides a systematic assessment of any interactive method for the dictionary creation task. Experiments using real scenario based corpora and dictionaries show that our algorithm outperforms baseline methods, and works even with a small number of interactions.",
}
@inproceedings{isonuma-etal-2020-tree,
    title = "{T}ree-{S}tructured {N}eural {T}opic {M}odel",
    author = "Isonuma, Masaru  and
      Mori, Junichiro  and
      Bollegala, Danushka  and
      Sakata, Ichiro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.73",
    doi = "10.18653/v1/2020.acl-main.73",
    pages = "800--806",
    abstract = "This paper presents a tree-structured neural topic model, which has a topic distribution over a tree with an infinite number of branches. Our model parameterizes an unbounded ancestral and fraternal topic distribution by applying doubly-recurrent neural networks. With the help of autoencoding variational Bayes, our model improves data scalability and achieves competitive performance when inducing latent topics and tree structures, as compared to a prior tree-structured topic model (Blei et al., 2010). This work extends the tree-structured topic model such that it can be incorporated with neural models for downstream tasks.",
}
@inproceedings{mass-etal-2020-unsupervised,
    title = "Unsupervised {FAQ} Retrieval with Question Generation and {BERT}",
    author = "Mass, Yosi  and
      Carmeli, Boaz  and
      Roitman, Haggai  and
      Konopnicki, David",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.74",
    doi = "10.18653/v1/2020.acl-main.74",
    pages = "807--812",
    abstract = "We focus on the task of Frequently Asked Questions (FAQ) retrieval. A given user query can be matched against the questions and/or the answers in the FAQ. We present a fully unsupervised method that exploits the FAQ pairs to train two BERT models. The two models match user queries to FAQ answers and questions, respectively. We alleviate the missing labeled data of the latter by automatically generating high-quality question paraphrases. We show that our model is on par and even outperforms supervised models on existing datasets.",
}
@inproceedings{zhou-etal-2020-boating,
    title = "{``}The Boating Store Had Its Best Sail Ever{''}: Pronunciation-attentive Contextualized Pun Recognition",
    author = "Zhou, Yichao  and
      Jiang, Jyun-Yu  and
      Zhao, Jieyu  and
      Chang, Kai-Wei  and
      Wang, Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.75",
    doi = "10.18653/v1/2020.acl-main.75",
    pages = "813--822",
    abstract = "Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.",
}
@inproceedings{shin-etal-2020-fast,
    title = "Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning",
    author = "Shin, Joongbo  and
      Lee, Yoonhyung  and
      Yoon, Seunghyun  and
      Jung, Kyomin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.76",
    doi = "10.18653/v1/2020.acl-main.76",
    pages = "823--835",
    abstract = "Even though BERT has achieved successful performance improvements in various supervised learning tasks, BERT is still limited by repetitive inferences on unsupervised tasks for the computation of contextual language representations. To resolve this limitation, we propose a novel deep bidirectional language model called a Transformer-based Text Autoencoder (T-TA). The T-TA computes contextual language representations without repetition and displays the benefits of a deep bidirectional architecture, such as that of BERT. In computation time experiments in a CPU environment, the proposed T-TA performs over six times faster than the BERT-like model on a reranking task and twelve times faster on a semantic similarity task. Furthermore, the T-TA shows competitive or even better accuracies than those of BERT on the above tasks. Code is available at https://github.com/joongbo/tta.",
}
@inproceedings{wang-etal-2020-fine,
    title = "Fine-grained Interest Matching for Neural News Recommendation",
    author = "Wang, Heyuan  and
      Wu, Fangzhao  and
      Liu, Zheng  and
      Xie, Xing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.77",
    doi = "10.18653/v1/2020.acl-main.77",
    pages = "836--845",
    abstract = "Personalized news recommendation is a critical technology to improve users{'} online news reading experience. The core of news recommendation is accurate matching between user{'}s interests and candidate news. The same user usually has diverse interests that are reflected in different news she has browsed. Meanwhile, important semantic features of news are implied in text segments of different granularities. Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. In this paper, we propose FIM, a Fine-grained Interest Matching method for neural news recommendation. Instead of aggregating user{'}s all historical browsed news into a unified vector, we hierarchically construct multi-level representations for each news via stacked dilated convolutions. Then we perform fine-grained matching between segment pairs of each browsed news and the candidate news at each semantic level. High-order salient signals are then identified by resembling the hierarchy of image recognition for final click prediction. Extensive experiments on a real-world dataset from MSN news validate the effectiveness of our model on news recommendation.",
}
@inproceedings{zhou-etal-2020-interpretable,
    title = "Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder",
    author = "Zhou, Fan  and
      Zhang, Shengming  and
      Yang, Yi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.78",
    doi = "10.18653/v1/2020.acl-main.78",
    pages = "846--852",
    abstract = "Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC). We empirically evaluate the framework on a real-world dataset. The results demonstrate that our method can better utilize unlabeled data and learn visually interpretable document representations. SemiORC also outperforms other baseline methods on operational risk classification.",
}
@inproceedings{zhong-etal-2020-interpreting,
    title = "Interpreting {T}witter User Geolocation",
    author = "Zhong, Ting  and
      Wang, Tianliang  and
      Zhou, Fan  and
      Trajcevski, Goce  and
      Zhang, Kunpeng  and
      Yang, Yi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.79",
    doi = "10.18653/v1/2020.acl-main.79",
    pages = "853--859",
    abstract = "Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users. This methodology helps with providing meaningful explanations on prediction results. Furthermore, it also initiates an attempt to uncover the so-called {``}black-box{''} GNN-based models by investigating the effect of individual nodes.",
}
@inproceedings{lee-li-2020-modeling,
    title = "Modeling Code-Switch Languages Using Bilingual Parallel Corpus",
    author = "Lee, Grandee  and
      Li, Haizhou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.80",
    doi = "10.18653/v1/2020.acl-main.80",
    pages = "860--870",
    abstract = "Language modeling is the technique to estimate the probability of a sequence of words. A bilingual language model is expected to model the sequential dependency for words across languages, which is difficult due to the inherent lack of suitable training data as well as diverse syntactic structure across languages. We propose a bilingual attention language model (BALM) that simultaneously performs language modeling objective with a quasi-translation objective to model both the monolingual as well as the cross-lingual sequential dependency. The attention mechanism learns the bilingual context from a parallel corpus. BALM achieves state-of-the-art performance on the SEAME code-switch database by reducing the perplexity of 20.5{\%} over the best-reported result. We also apply BALM in bilingual lexicon induction, and language normalization tasks to validate the idea.",
}
@inproceedings{cheng-etal-2020-spellgcn,
    title = "{S}pell{GCN}: Incorporating Phonological and Visual Similarities into Language Models for {C}hinese Spelling Check",
    author = "Cheng, Xingyi  and
      Xu, Weidi  and
      Chen, Kunlong  and
      Jiang, Shaohua  and
      Wang, Feng  and
      Wang, Taifeng  and
      Chu, Wei  and
      Qi, Yuan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.81",
    doi = "10.18653/v1/2020.acl-main.81",
    pages = "871--881",
    abstract = "Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. However, they take the similarity knowledge as either an external input resource or just heuristic rules. This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN). The model builds a graph over the characters, and SpellGCN is learned to map this graph into a set of inter-dependent character classifiers. These classifiers are applied to the representations extracted by another network, such as BERT, enabling the whole network to be end-to-end trainable. Experiments are conducted on three human-annotated datasets. Our method achieves superior performance against previous models by a large margin.",
}
@inproceedings{zhang-etal-2020-spelling,
    title = "Spelling Error Correction with Soft-Masked {BERT}",
    author = "Zhang, Shaohua  and
      Huang, Haoran  and
      Liu, Jicong  and
      Li, Hang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.82",
    doi = "10.18653/v1/2020.acl-main.82",
    pages = "882--890",
    abstract = "Spelling error correction is an important yet challenging task because a satisfactory solution of it essentially needs human-level language understanding ability. Without loss of generality we consider Chinese spelling error correction (CSC) in this paper. A state-of-the-art method for the task selects a character from a list of candidates for correction (including non-correction) at each position of the sentence on the basis of BERT, the language representation model. The accuracy of the method can be sub-optimal, however, because BERT does not have sufficient capability to detect whether there is an error at each position, apparently due to the way of pre-training it using mask language modeling. In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique. Our method of using {`}Soft-Masked BERT{'} is general, and it may be employed in other language detection-correction problems. Experimental results on two datasets, including one large dataset which we create and plan to release, demonstrate that the performance of our proposed method is significantly better than the baselines including the one solely based on BERT.",
}
@inproceedings{guo-etal-2020-frame,
    title = "A Frame-based Sentence Representation for Machine Reading Comprehension",
    author = "Guo, Shaoru  and
      Li, Ru  and
      Tan, Hongye  and
      Li, Xiaoli  and
      Guan, Yong  and
      Zhao, Hongyan  and
      Zhang, Yueping",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.83",
    doi = "10.18653/v1/2020.acl-main.83",
    pages = "891--896",
    abstract = "Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings can leverage their semantic knowledge. To bridge the gap, we proposed a novel Frame-based Sentence Representation (FSR) method, which employs frame semantic knowledge to facilitate sentence modelling. Specifically, different from existing methods that only model lexical units (LUs), Frame Representation Models, which utilize both LUs in frame and Frame-to-Frame (F-to-F) relations, are designed to model frames and sentences with attention schema. Our proposed FSR method is able to integrate multiple-frame semantic information to get much better sentence representations. Our extensive experimental results show that it performs better than state-of-the-art technologies on machine reading comprehension task.",
}
@inproceedings{deriu-etal-2020-methodology,
    title = "A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation",
    author = {Deriu, Jan  and
      Mlynchyk, Katsiaryna  and
      Schl{\"a}pfer, Philippe  and
      Rodrigo, Alvaro  and
      von Gr{\"u}nigen, Dirk  and
      Kaiser, Nicolas  and
      Stockinger, Kurt  and
      Agirre, Eneko  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.84",
    doi = "10.18653/v1/2020.acl-main.84",
    pages = "897--911",
    abstract = "In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. For this, we introduce an intermediate representation that is based on the logical query plan in a database, called Operation Trees (OT). This representation allows us to invert the annotation process without loosing flexibility in the types of queries that we generate. Furthermore, it allows for fine-grained alignment of the tokens to the operations. Thus, we randomly generate OTs from a context free grammar and annotators just have to write the appropriate question and assign the tokens. We compare our corpus OTTA (Operation Trees and Token Assignment), a large semantic parsing corpus for evaluating natural language interfaces to databases, to Spider and LC-QuaD 2.0 and show that our methodology more than triples the annotation speed while maintaining the complexity of the queries. Finally, we train a state-of-the-art semantic parsing model on our data and show that our dataset is a challenging dataset and that the token alignment can be leveraged to significantly increase the performance.",
}
@inproceedings{lee-etal-2020-contextualized,
    title = "Contextualized Sparse Representations for Real-Time Open-Domain Question Answering",
    author = "Lee, Jinhyuk  and
      Seo, Minjoon  and
      Hajishirzi, Hannaneh  and
      Kang, Jaewoo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.85",
    doi = "10.18653/v1/2020.acl-main.85",
    pages = "912--919",
    abstract = "Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models. In this paper, we aim to improve the quality of each phrase embedding by augmenting it with a contextualized sparse representation (Sparc). Unlike previous sparse vectors that are term-frequency-based (e.g., tf-idf) or directly learned (only few thousand dimensions), we leverage rectified self-attention to indirectly learn sparse vectors in n-gram vocabulary space. By augmenting the previous phrase retrieval model (Seo et al., 2019) with Sparc, we show 4{\%}+ improvement in CuratedTREC and SQuAD-Open. Our CuratedTREC score is even better than the best known retrieve {\&} read model with at least 45x faster inference speed.",
}
@inproceedings{gottumukkala-etal-2020-dynamic,
    title = "Dynamic Sampling Strategies for Multi-Task Reading Comprehension",
    author = "Gottumukkala, Ananth  and
      Dua, Dheeru  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.86",
    doi = "10.18653/v1/2020.acl-main.86",
    pages = "920--924",
    abstract = "Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community. Prior work has focused on model architecture or generalization to held out datasets, and largely passed over the particulars of the multi-task learning set up. We show that a simple dynamic sampling strategy, selecting instances for training proportional to the multi-task model{'}s current performance on a dataset relative to its single task performance, gives substantive gains over prior multi-task sampling strategies, mitigating the catastrophic forgetting that is common in multi-task learning. We also demonstrate that allowing instances of different tasks to be interleaved as much as possible between each epoch and batch has a clear benefit in multitask performance over forcing task homogeneity at the epoch or batch level. Our final model shows greatly increased performance over the best model on ORB, a recently-released multitask reading comprehension benchmark.",
}
@inproceedings{yuan-etal-2020-enhancing,
    title = "Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension",
    author = "Yuan, Fei  and
      Shou, Linjun  and
      Bai, Xuanyu  and
      Gong, Ming  and
      Liang, Yaobo  and
      Duan, Nan  and
      Fu, Yan  and
      Jiang, Daxin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.87",
    doi = "10.18653/v1/2020.acl-main.87",
    pages = "925--934",
    abstract = "Multilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine Reading Comprehension (MRC) is significantly worse than sentence classification tasks mainly due to the requirement of MRC to detect the word level answer boundary. In this paper, we propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision: (1) A mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; (2) A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web. Besides, extensive experiments on two cross-lingual MRC datasets show the effectiveness of our proposed approach.",
}
@inproceedings{gao-etal-2020-explicit,
    title = "Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading",
    author = "Gao, Yifan  and
      Wu, Chien-Sheng  and
      Joty, Shafiq  and
      Xiong, Caiming  and
      Socher, Richard  and
      King, Irwin  and
      Lyu, Michael  and
      Hoi, Steven C.H.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.88",
    doi = "10.18653/v1/2020.acl-main.88",
    pages = "935--945",
    abstract = "The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions. Existing approaches are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision. Moreover, our framework generates clarification questions by adopting a coarse-to-fine reasoning strategy, utilizing sentence-level entailment scores to weight token-level distributions. On the ShARC benchmark (blind, held-out) testset, EMT achieves new state-of-the-art results of 74.6{\%} micro-averaged decision accuracy and 49.5 BLEU4. We also show that EMT is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. Code and models are released at https://github.com/Yifan-Gao/explicit{\_}memory{\_}tracker.",
}
@inproceedings{geva-etal-2020-injecting,
    title = "Injecting Numerical Reasoning Skills into Language Models",
    author = "Geva, Mor  and
      Gupta, Ankit  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.89",
    doi = "10.18653/v1/2020.acl-main.89",
    pages = "946--958",
    abstract = "Large pre-trained language models (LMs) are known to encode substantial amounts of linguistic information. However, high-level reasoning skills, such as numerical reasoning, are difficult to learn from a language-modeling objective only. Consequently, existing models for numerical reasoning have used specialized architectures with limited flexibility. In this work, we show that numerical reasoning is amenable to automatic data generation, and thus one can inject this skill into pre-trained LMs, by generating large amounts of data, and training in a multi-task setup. We show that pre-training our model, GenBERT, on this data, dramatically improves performance on DROP (49.3 {--}{\textgreater} 72.3 F1), reaching performance that matches state-of-the-art models of comparable size, while using a simple and general-purpose encoder-decoder architecture. Moreover, GenBERT generalizes well to math word problem datasets, while maintaining high performance on standard RC tasks. Our approach provides a general recipe for injecting skills into large pre-trained LMs, whenever the skill is amenable to automatic data augmentation.",
}
@inproceedings{kundu-etal-2020-learning,
    title = "Learning to Identify Follow-Up Questions in Conversational Question Answering",
    author = "Kundu, Souvik  and
      Lin, Qian  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.90",
    doi = "10.18653/v1/2020.acl-main.90",
    pages = "959--968",
    abstract = "Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up question of the current conversation, for more effective answer finding subsequently. In this paper, we introduce a new follow-up question identification task. We propose a three-way attentive pooling network that determines the suitability of a follow-up question by capturing pair-wise interactions between the associated passage, the conversation history, and a candidate follow-up question. It enables the model to capture topic continuity and topic shift while scoring a particular candidate follow-up question. Experiments show that our proposed three-way attentive pooling network outperforms all baseline systems by significant margins.",
}
@inproceedings{lan-jiang-2020-query,
    title = "Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases",
    author = "Lan, Yunshi  and
      Jiang, Jing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.91",
    doi = "10.18653/v1/2020.acl-main.91",
    pages = "969--974",
    abstract = "Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations. In this paper, we handle both types of complexity at the same time. Motivated by the observation that early incorporation of constraints into query graphs can more effectively prune the search space, we propose a modified staged query graph generation method with more flexible ways to generate query graphs. Our experiments clearly show that our method achieves the state of the art on three benchmark KBQA datasets.",
}
@inproceedings{miao-etal-2020-diverse,
    title = "A Diverse Corpus for Evaluating and Developing {E}nglish Math Word Problem Solvers",
    author = "Miao, Shen-yun  and
      Liang, Chao-Chun  and
      Su, Keh-Yih",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.92",
    doi = "10.18653/v1/2020.acl-main.92",
    pages = "975--984",
    abstract = "We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.",
}
@inproceedings{yi-etal-2020-improving,
    title = "Improving Image Captioning Evaluation by Considering Inter References Variance",
    author = "Yi, Yanzhi  and
      Deng, Hangyu  and
      Hu, Jinglu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.93",
    doi = "10.18653/v1/2020.acl-main.93",
    pages = "985--994",
    abstract = "Evaluating image captions is very challenging partially due to the fact that there are multiple correct captions for every single image. Most of the existing one-to-one metrics operate by penalizing mismatches between reference and generative caption without considering the intrinsic variance between ground truth captions. It usually leads to over-penalization and thus a bad correlation to human judgment. Recently, the latest one-to-one metric BERTScore can achieve high human correlation in system-level tasks while some issues can be fixed for better performance. In this paper, we propose a novel metric based on BERTScore that could handle such a challenge and extend BERTScore with a few new features appropriately for image captioning evaluation. The experimental results show that our metric achieves state-of-the-art human judgment correlation.",
}
@inproceedings{ri-tsuruoka-2020-revisiting,
    title = "Revisiting the Context Window for Cross-lingual Word Embeddings",
    author = "Ri, Ryokan  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.94",
    doi = "10.18653/v1/2020.acl-main.94",
    pages = "995--1005",
    abstract = "Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar. The structures of embedding spaces largely depend on the co-occurrence statistics of each word, which the choice of context window determines. Despite this obvious connection between the context window and mapping-based cross-lingual embeddings, their relationship has been underexplored in prior work. In this work, we provide a thorough evaluation, in various languages, domains, and tasks, of bilingual embeddings trained with different context windows. The highlight of our findings is that increasing the size of both the source and target window sizes improves the performance of bilingual lexicon induction, especially the performance on frequent nouns.",
}
@inproceedings{blevins-zettlemoyer-2020-moving,
    title = "Moving Down the Long Tail of Word Sense Disambiguation with Gloss Informed Bi-encoders",
    author = "Blevins, Terra  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.95",
    doi = "10.18653/v1/2020.acl-main.95",
    pages = "1006--1017",
    abstract = "A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not uniformly distributed, causing existing models to generally perform poorly on senses that are either rare or unseen during training. We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context and (2) the dictionary definition, or gloss, of each sense. The encoders are jointly optimized in the same representation space, so that sense disambiguation can be performed by finding the nearest sense embedding for each target word embedding. Our system outperforms previous state-of-the-art models on English all-words WSD; these gains predominantly come from improved performance on rare senses, leading to a 31.1{\%} error reduction on less frequent senses over prior work. This demonstrates that rare senses can be more effectively disambiguated by modeling their definitions.",
}
@inproceedings{bansal-etal-2020-code,
    title = "Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream {NLP} Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection",
    author = "Bansal, Srijan  and
      Garimella, Vishal  and
      Suhane, Ayush  and
      Patro, Jasabanta  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.96",
    doi = "10.18653/v1/2020.acl-main.96",
    pages = "1018--1023",
    abstract = "In this paper, we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode various switching features to improve humour, sarcasm and hate speech detection tasks. We believe that this simple linguistic observation can also be potentially helpful in improving other similar NLP applications.",
}
@inproceedings{wu-etal-2020-dtca,
    title = "{DTCA}: Decision Tree-based Co-Attention Networks for Explainable Claim Verification",
    author = "Wu, Lianwei  and
      Rao, Yuan  and
      Zhao, Yongqiang  and
      Liang, Hao  and
      Nazir, Ambreen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.97",
    doi = "10.18653/v1/2020.acl-main.97",
    pages = "1024--1035",
    abstract = "Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence is aimed at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-attention Self-attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence; and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-the-art performance, boosting the F1-score by more than 3.11{\%}, 2.41{\%}, respectively.",
}
@inproceedings{liu-etal-2020-towards,
    title = "Towards Conversational Recommendation over Multi-Type Dialogs",
    author = "Liu, Zeming  and
      Wang, Haifeng  and
      Niu, Zheng-Yu  and
      Wu, Hua  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.98",
    doi = "10.18653/v1/2020.acl-main.98",
    pages = "1036--1049",
    abstract = "We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user{'}s interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), where there are multiple sequential dialogs for a pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies.",
}
@inproceedings{yan-etal-2020-unknown,
    title = "Unknown Intent Detection Using {G}aussian Mixture Model with an Application to Zero-shot Intent Classification",
    author = "Yan, Guangfeng  and
      Fan, Lu  and
      Li, Qimai  and
      Liu, Han  and
      Zhang, Xiaotong  and
      Wu, Xiao-Ming  and
      Lam, Albert Y.S.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.99",
    doi = "10.18653/v1/2020.acl-main.99",
    pages = "1050--1060",
    abstract = "User intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the study has just begun. This paper proposes a semantic-enhanced Gaussian mixture model (SEG) for unknown intent detection. In particular, we model utterance embeddings with a Gaussian mixture distribution and inject dynamic class semantic information into Gaussian means, which enables learning more class-concentrated embeddings that help to facilitate downstream outlier detection. Coupled with a density-based outlier detection algorithm, SEG achieves competitive results on three real task-oriented dialogue datasets in two languages for unknown intent detection. On top of that, we propose to integrate SEG as an unknown intent identifier into existing generalized zero-shot intent classification models to improve their performance. A case study on a state-of-the-art method, ReCapsNet, shows that SEG can push the classification performance to a significantly higher level.",
}
@inproceedings{cao-etal-2020-expertise,
    title = "Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen",
    author = "Cao, Yixin  and
      Shui, Ruihao  and
      Pan, Liangming  and
      Kan, Min-Yen  and
      Liu, Zhiyuan  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.100",
    doi = "10.18653/v1/2020.acl-main.100",
    pages = "1061--1071",
    abstract = "The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at https://srhthu.github.io/expertise-style-transfer/.",
}
@inproceedings{wang-etal-2020-towards,
    title = "Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints",
    author = "Wang, Zhenyi  and
      Wang, Xiaoyang  and
      An, Bang  and
      Yu, Dong  and
      Chen, Changyou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.101",
    doi = "10.18653/v1/2020.acl-main.101",
    pages = "1072--1086",
    abstract = "Text generation from a knowledge base aims to translate knowledge triples to natural language descriptions. Most existing methods ignore the faithfulness between a generated text description and the original table, leading to generated information that goes beyond the content of the table. In this paper, for the first time, we propose a novel Transformer-based generation framework to achieve the goal. The core techniques in our method to enforce faithfulness include a new table-text optimal-transport matching loss and a table-text embedding similarity loss based on the Transformer model. Furthermore, to evaluate faithfulness, we propose a new automatic metric specialized to the table-to-text generation problem. We also provide detailed analysis on each component of our model in our experiments. Automatic and human evaluations show that our framework can significantly outperform state-of-the-art by a large margin.",
}
@inproceedings{geng-etal-2020-dynamic,
    title = "Dynamic Memory Induction Networks for Few-Shot Text Classification",
    author = "Geng, Ruiying  and
      Li, Binhua  and
      Li, Yongbin  and
      Sun, Jian  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.102",
    doi = "10.18653/v1/2020.acl-main.102",
    pages = "1087--1094",
    abstract = "This paper proposes Dynamic Memory Induction Networks (DMIN) for few-short text classification. The model develops a dynamic routing mechanism over static memory, enabling it to better adapt to unseen classes, a critical capability for few-short classification. The model also expands the induction process with supervised learning weights and query information to enhance the generalization ability of meta-learning. The proposed model brings forward the state-of-the-art performance significantly by 2{\textasciitilde}4{\%} improvement on the miniRCV1 and ODIC datasets. Detailed analysis is further performed to show how the proposed network achieves the new performance.",
}
@inproceedings{chen-etal-2020-exclusive,
    title = "Exclusive Hierarchical Decoding for Deep Keyphrase Generation",
    author = "Chen, Wang  and
      Chan, Hou Pong  and
      Li, Piji  and
      King, Irwin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.103",
    doi = "10.18653/v1/2020.acl-main.103",
    pages = "1095--1105",
    abstract = "Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of keyphrases. A new setting is recently introduced into this problem, in which, given a document, the model needs to predict a set of keyphrases and simultaneously determine the appropriate number of keyphrases to produce. Previous work in this setting employs a sequential decoding process to generate keyphrases. However, such a decoding method ignores the intrinsic hierarchical compositionality existing in the keyphrase set of a document. Moreover, previous work tends to generate duplicated keyphrases, which wastes time and computing resources. To overcome these limitations, we propose an exclusive hierarchical decoding framework that includes a hierarchical decoding process and either a soft or a hard exclusion mechanism. The hierarchical decoding process is to explicitly model the hierarchical compositionality of a keyphrase set. Both the soft and the hard exclusion mechanisms keep track of previously-predicted keyphrases within a window size to enhance the diversity of the generated keyphrases. Extensive experiments on multiple KG benchmark datasets demonstrate the effectiveness of our method to generate less duplicated and more accurate keyphrases.",
}
@inproceedings{zhou-etal-2020-hierarchy,
    title = "Hierarchy-Aware Global Model for Hierarchical Text Classification",
    author = "Zhou, Jie  and
      Ma, Chunping  and
      Long, Dingkun  and
      Xu, Guangwei  and
      Ding, Ning  and
      Zhang, Haoyu  and
      Xie, Pengjun  and
      Liu, Gongshen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.104",
    doi = "10.18653/v1/2020.acl-main.104",
    pages = "1106--1117",
    abstract = "Hierarchical text classification is an essential yet challenging subtask of multi-label text classification with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure in a global view. Furthermore, they cannot make full use of the mutual interactions between the text feature space and the label space. In this paper, we formulate the hierarchy as a directed graph and introduce hierarchy-aware structure encoders for modeling label dependencies. Based on the hierarchy encoder, we propose a novel end-to-end hierarchy-aware global model (HiAGM) with two variants. A multi-label attention variant (HiAGM-LA) learns hierarchy-aware label embeddings through the hierarchy encoder and conducts inductive fusion of label-aware text features. A text feature propagation model (HiAGM-TP) is proposed as the deductive variant that directly feeds text features into hierarchy encoders. Compared with previous works, both HiAGM-LA and HiAGM-TP achieve significant and consistent improvements on three benchmark datasets.",
}
@inproceedings{boudin-etal-2020-keyphrase,
    title = "Keyphrase Generation for Scientific Document Retrieval",
    author = "Boudin, Florian  and
      Gallina, Ygor  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.105",
    doi = "10.18653/v1/2020.acl-main.105",
    pages = "1118--1126",
    abstract = "Sequence-to-sequence models have lead to significant progress in keyphrase generation, but it remains unknown whether they are reliable enough to be beneficial for document retrieval. This study provides empirical evidence that such models can significantly improve retrieval performance, and introduces a new extrinsic evaluation framework that allows for a better understanding of the limitations of keyphrase generation models. Using this framework, we point out and discuss the difficulties encountered with supplementing documents with -not present in text- keyphrases, and generalizing models across domains. Our code is available at https://github.com/boudinfl/ir-using-kg",
}
@inproceedings{hofmann-etal-2020-graph,
    title = "A Graph Auto-encoder Model of Derivational Morphology",
    author = {Hofmann, Valentin  and
      Sch{\"u}tze, Hinrich  and
      Pierrehumbert, Janet},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.106",
    doi = "10.18653/v1/2020.acl-main.106",
    pages = "1127--1138",
    abstract = "There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics. We present a graph auto-encoder that learns embeddings capturing information about the compatibility of affixes and stems in derivation. The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.",
}
@inproceedings{seddah-etal-2020-building,
    title = "Building a User-Generated Content {N}orth-{A}frican {A}rabizi Treebank: Tackling Hell",
    author = "Seddah, Djam{\'e}  and
      Essaidi, Farah  and
      Fethi, Amal  and
      Futeral, Matthieu  and
      Muller, Benjamin  and
      Ortiz Su{\'a}rez, Pedro Javier  and
      Sagot, Beno{\^\i}t  and
      Srivastava, Abhishek",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.107",
    doi = "10.18653/v1/2020.acl-main.107",
    pages = "1139--1150",
    abstract = "We introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect known for its frequent usage of code-switching. Made of 1500 sentences, fully annotated in morpho-syntax and Universal Dependency syntax, with full translation at both the word and the sentence levels, this treebank is made freely available. It is supplemented with 50k unlabeled sentences collected from Common Crawl and web-crawled data using intensive data-mining techniques. Preliminary experiments demonstrate its usefulness for POS tagging and dependency parsing. We believe that what we present in this paper is useful beyond the low-resource language community. This is the first time that enough unlabeled and annotated data is provided for an emerging user-generated content dialectal language with rich morphology and code switching, making it an challenging test-bed for most recent NLP approaches.",
}
@inproceedings{bevendorff-etal-2020-crawling,
    title = "Crawling and Preprocessing Mailing Lists At Scale for Dialog Analysis",
    author = "Bevendorff, Janek  and
      Al Khatib, Khalid  and
      Potthast, Martin  and
      Stein, Benno",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.108",
    doi = "10.18653/v1/2020.acl-main.108",
    pages = "1151--1158",
    abstract = "This paper introduces the Webis Gmane Email Corpus 2019, the largest publicly available and fully preprocessed email corpus to date. We crawled more than 153 million emails from 14,699 mailing lists and segmented them into semantically consistent components using a new neural segmentation model. With 96{\%} accuracy on 15 classes of email segments, our model achieves state-of-the-art performance while being more efficient to train than previous ones. All data, code, and trained models are made freely available alongside the paper.",
}
@inproceedings{nikolaev-etal-2020-fine,
    title = "Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences",
    author = "Nikolaev, Dmitry  and
      Arviv, Ofir  and
      Karidi, Taelin  and
      Kenneth, Neta  and
      Mitnik, Veronika  and
      Saeboe, Lilja Maria  and
      Abend, Omri",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.109",
    doi = "10.18653/v1/2020.acl-main.109",
    pages = "1159--1176",
    abstract = "The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer. Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs. We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study. We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.",
}
@inproceedings{tekiroglu-etal-2020-generating,
    title = "Generating Counter Narratives against Online Hate Speech: Data and Strategies",
    author = "Tekiro{\u{g}}lu, Serra Sinem  and
      Chung, Yi-Ling  and
      Guerini, Marco",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.110",
    doi = "10.18653/v1/2020.acl-main.110",
    pages = "1177--1190",
    abstract = "Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned limitations, we present a study on how to collect responses to hate effectively, employing large scale unsupervised language models such as GPT-2 for the generation of silver data, and the best annotation strategies/neural architectures that can be used for data filtering before expert validation/post-editing.",
}
@inproceedings{rybak-etal-2020-klej,
    title = "{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding",
    author = "Rybak, Piotr  and
      Mroczkowski, Robert  and
      Tracz, Janusz  and
      Gawlik, Ireneusz",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.111",
    doi = "10.18653/v1/2020.acl-main.111",
    pages = "1191--1201",
    abstract = "In recent years, a series of Transformer-based models unlocked major improvements in general natural language understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language, which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based models.",
}
@inproceedings{buechel-etal-2020-learning,
    title = "Learning and Evaluating Emotion Lexicons for 91 Languages",
    author = {Buechel, Sven  and
      R{\"u}cker, Susanna  and
      Hahn, Udo},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.112",
    doi = "10.18653/v1/2020.acl-main.112",
    pages = "1202--1217",
    abstract = "Emotion lexicons describe the affective meaning of words and thus constitute a centerpiece for advanced sentiment and emotion analysis. Yet, manually curated lexicons are only available for a handful of languages, leaving most languages of the world without such a precious resource for downstream applications. Even worse, their coverage is often limited both in terms of the lexical units they contain and the emotional variables they feature. In order to break this bottleneck, we here introduce a methodology for creating almost arbitrarily large emotion lexicons for any target language. Our approach requires nothing but a source language emotion lexicon, a bilingual word translation model, and a target language embedding model. Fulfilling these requirements for 91 languages, we are able to generate representationally rich high-coverage lexicons comprising eight emotional variables with more than 100k lexical entries each. We evaluated the automatically generated lexicons against human judgment from 26 datasets, spanning 12 typologically diverse languages, and found that our approach produces results in line with state-of-the-art monolingual approaches to lexicon creation and even surpasses human reliability for some languages and variables. Code and data are available at https://github.com/JULIELab/MEmoLon archived under DOI 10.5281/zenodo.3779901.",
}
@inproceedings{fomicheva-etal-2020-multi,
    title = "Multi-Hypothesis Machine Translation Evaluation",
    author = "Fomicheva, Marina  and
      Specia, Lucia  and
      Guzm{\'a}n, Francisco",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.113",
    doi = "10.18653/v1/2020.acl-main.113",
    pages = "1218--1232",
    abstract = "Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem. One of the main challenges is the fact that multiple outputs can be equally valid. Attempts to minimise this issue include metrics that relax the matching of MT output and reference strings, and the use of multiple references. The latter has been shown to significantly improve the performance of evaluation metrics. However, collecting multiple references is expensive and in practice a single reference is generally used. In this paper, we propose an alternative approach: instead of modelling linguistic variation in human reference we exploit the MT model uncertainty to generate multiple diverse translations and use these: (i) as surrogates to reference translations; (ii) to obtain a quantification of translation variability to either complement existing metric scores or (iii) replace references altogether. We show that for a number of popular evaluation metrics our variability estimates lead to substantial improvements in correlation with human judgements of quality by up 15{\%}.",
}
@inproceedings{okabe-etal-2020-multimodal,
    title = "Multimodal Quality Estimation for Machine Translation",
    author = "Okabe, Shu  and
      Blain, Fr{\'e}d{\'e}ric  and
      Specia, Lucia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.114",
    doi = "10.18653/v1/2020.acl-main.114",
    pages = "1233--1240",
    abstract = "We propose approaches to Quality Estimation (QE) for Machine Translation that explore both text and visual modalities for Multimodal QE. We compare various multimodality integration and fusion strategies. For both sentence-level and document-level predictions, we show that state-of-the-art neural and feature-based QE frameworks obtain better results when using the additional modality.",
}
@inproceedings{sahin-etal-2020-puzzling,
    title = "{P}uzz{L}ing {M}achines: {A} {C}hallenge on {L}earning {F}rom {S}mall {D}ata",
    author = {{\c{S}}ahin, G{\"o}zde G{\"u}l  and
      Kementchedjhieva, Yova  and
      Rust, Phillip  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.115",
    doi = "10.18653/v1/2020.acl-main.115",
    pages = "1241--1254",
    abstract = "Deep neural models have repeatedly proved excellent at memorizing surface patterns from large datasets for various ML and NLP benchmarks. They struggle to achieve human-like thinking, however, because they lack the skill of iterative reasoning upon knowledge. To expose this problem in a new light, we introduce a challenge on learning from small data, PuzzLing Machines, which consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students. These puzzles are carefully designed to contain only the minimal amount of parallel text necessary to deduce the form of unseen expressions. Solving them does not require external information (e.g., knowledge bases, visual signals) or linguistic expertise, but meta-linguistic awareness and deductive skills. Our challenge contains around 100 puzzles covering a wide range of linguistic phenomena from 81 languages. We show that both simple statistical algorithms and state-of-the-art deep neural models perform inadequately on this challenge, as expected. We hope that this benchmark, available at https://ukplab.github.io/PuzzLing-Machines/, inspires further efforts towards a new paradigm in NLP{---}one that is grounded in human-like reasoning and understanding.",
}
@inproceedings{friedrich-etal-2020-sofc,
    title = "The {SOFC}-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain",
    author = "Friedrich, Annemarie  and
      Adel, Heike  and
      Tomazic, Federico  and
      Hingerl, Johannes  and
      Benteau, Renou  and
      Marusczyk, Anika  and
      Lange, Lukas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.116",
    doi = "10.18653/v1/2020.acl-main.116",
    pages = "1255--1268",
    abstract = "This paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications, such as involved materials and measurement conditions. With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition and slot filling tasks as well as high annotation quality. We also present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set. On all tasks, using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network on top seems beneficial. Our models will serve as competitive baselines in future work, and analysis of their performance highlights difficult cases when modeling the data and suggests promising research directions.",
}
@inproceedings{castelli-etal-2020-techqa,
    title = "The {T}ech{QA} Dataset",
    author = "Castelli, Vittorio  and
      Chakravarti, Rishav  and
      Dana, Saswati  and
      Ferritto, Anthony  and
      Florian, Radu  and
      Franz, Martin  and
      Garg, Dinesh  and
      Khandelwal, Dinesh  and
      McCarley, Scott  and
      McCawley, Michael  and
      Nasr, Mohamed  and
      Pan, Lin  and
      Pendus, Cezar  and
      Pitrelli, John  and
      Pujar, Saurabh  and
      Roukos, Salim  and
      Sakrajda, Andrzej  and
      Sil, Avi  and
      Uceda-Sosa, Rosario  and
      Ward, Todd  and
      Zhang, Rong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.117",
    doi = "10.18653/v1/2020.acl-main.117",
    pages = "1269--1278",
    abstract = "We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size {--} 600 training, 310 dev, and 490 evaluation question/answer pairs {--} thus reflecting the cost of creating large labeled datasets with actual data. Hence, TECHQA is meant to stimulate research in domain adaptation rather than as a resource to build QA systems from scratch. TECHQA was obtained by crawling the IBMDeveloper and DeveloperWorks forums for questions with accepted answers provided in an IBM Technote{---}a technical document that addresses a specific technical issue. We also release a collection of the 801,998 Technotes available on the web as of April 4, 2019 as a companion resource that can be used to learn representations of the IT domain language.",
}
@inproceedings{oprea-magdy-2020-isarcasm,
    title = "i{S}arcasm: A Dataset of Intended Sarcasm",
    author = "Oprea, Silviu  and
      Magdy, Walid",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.118",
    doi = "10.18653/v1/2020.acl-main.118",
    pages = "1279--1289",
    abstract = "We consider the distinction between intended and perceived sarcasm in the context of textual sarcasm detection. The former occurs when an utterance is sarcastic from the perspective of its author, while the latter occurs when the utterance is interpreted as sarcastic by the audience. We show the limitations of previous labelling methods in capturing intended sarcasm and introduce the iSarcasm dataset of tweets labeled for sarcasm directly by their authors. Examining the state-of-the-art sarcasm detection models on our dataset showed low performance compared to previously studied datasets, which indicates that these datasets might be biased or obvious and sarcasm could be a phenomenon under-studied computationally thus far. By providing the iSarcasm dataset, we aim to encourage future NLP research to develop methods for detecting sarcasm in text as intended by the authors of the text, not as labeled under assumptions that we demonstrate to be sub-optimal.",
}
@inproceedings{cai-lam-2020-amr,
    title = "{AMR} Parsing via Graph-Sequence Iterative Inference",
    author = "Cai, Deng  and
      Lam, Wai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.119",
    doi = "10.18653/v1/2020.acl-main.119",
    pages = "1290--1301",
    abstract = "We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. At each time step, our model performs multiple rounds of attention, reasoning, and composition that aim to answer two critical questions: (1) which part of the input \textit{sequence} to abstract; and (2) where in the output \textit{graph} to construct the new concept. We show that the answers to these two questions are mutually causalities. We design a model based on iterative inference that helps achieve better answers in both perspectives, leading to greatly improved parsing accuracy. Our experimental results significantly outperform all previously reported Smatch scores by large margins. Remarkably, without the help of any large-scale pre-trained language model (e.g., BERT), our model already surpasses previous state-of-the-art using BERT. With the help of BERT, we can push the state-of-the-art results to 80.2{\%} on LDC2017T10 (AMR 2.0) and 75.4{\%} on LDC2014T12 (AMR 1.0).",
}
@inproceedings{gholipour-ghalandari-etal-2020-large,
    title = "A Large-Scale Multi-Document Summarization Dataset from the {W}ikipedia Current Events Portal",
    author = "Gholipour Ghalandari, Demian  and
      Hokamp, Chris  and
      Pham, Nghia The  and
      Glover, John  and
      Ifrim, Georgiana",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.120",
    doi = "10.18653/v1/2020.acl-main.120",
    pages = "1302--1308",
    abstract = "Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation. However, there is a lack of datasets that realistically address such use cases at a scale large enough for training supervised models for this task. This work presents a new dataset for MDS that is large both in the total number of document clusters and in the size of individual clusters. We build this dataset by leveraging the Wikipedia Current Events Portal (WCEP), which provides concise and neutral human-written summaries of news events, with links to external source articles. We also automatically extend these source articles by looking for related articles in the Common Crawl archive. We provide a quantitative analysis of the dataset and empirical results for several state-of-the-art MDS techniques.",
}
@inproceedings{zhu-etal-2020-attend,
    title = "Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization",
    author = "Zhu, Junnan  and
      Zhou, Yu  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.121",
    doi = "10.18653/v1/2020.acl-main.121",
    pages = "1309--1321",
    abstract = "Cross-lingual summarization aims at summarizing a document in one language (e.g., Chinese) into another language (e.g., English). In this paper, we propose a novel method inspired by the translation pattern in the process of obtaining a cross-lingual summary. We first attend to some words in the source text, then translate them into the target language, and summarize to get the final summary. Specifically, we first employ the encoder-decoder attention distribution to attend to the source words. Second, we present three strategies to acquire the translation probability, which helps obtain the translation candidates for each source word. Finally, each summary word is generated either from the neural distribution or from the translation candidates of source words. Experimental results on Chinese-to-English and English-to-Chinese summarization tasks have shown that our proposed method can significantly outperform the baselines, achieving comparable performance with the state-of-the-art.",
}
@inproceedings{gholipour-ghalandari-ifrim-2020-examining,
    title = "Examining the State-of-the-Art in News Timeline Summarization",
    author = "Gholipour Ghalandari, Demian  and
      Ifrim, Georgiana",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.122",
    doi = "10.18653/v1/2020.acl-main.122",
    pages = "1322--1334",
    abstract = "Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task. In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the stateof-the-art on all tested benchmarks. For a more robust evaluation, we also present a new TLS dataset, which is larger and spans longer time periods than previous datasets.",
}
@inproceedings{matsumaru-etal-2020-improving,
    title = "Improving Truthfulness of Headline Generation",
    author = "Matsumaru, Kazuki  and
      Takase, Sho  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.123",
    doi = "10.18653/v1/2020.acl-main.123",
    pages = "1335--1346",
    abstract = "Most studies on abstractive summarization report ROUGE scores between system and reference summaries. However, we have a concern about the truthfulness of generated summaries: whether all facts of a generated summary are mentioned in the source text. This paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder model, we show that the model sometimes generates untruthful headlines. We conjecture that one of the reasons lies in untruthful supervision data used for training the model. In order to quantify the truthfulness of article-headline pairs, we consider the textual entailment of whether an article entails its headline. After confirming quite a few untruthful instances in the datasets, this study hypothesizes that removing untruthful instances from the supervision data may remedy the problem of the untruthful behaviors of the model. Building a binary classifier that predicts an entailment relation between an article and its headline, we filter out untruthful instances from the supervision data. Experimental results demonstrate that the headline generation model trained on filtered supervision data shows no clear difference in ROUGE scores but remarkable improvements in automatic and manual evaluations of the generated headlines.",
}
@inproceedings{gao-etal-2020-supert,
    title = "{SUPERT}: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization",
    author = "Gao, Yang  and
      Zhao, Wei  and
      Eger, Steffen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.124",
    doi = "10.18653/v1/2020.acl-main.124",
    pages = "1347--1354",
    abstract = "We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18- 39{\%}. Furthermore, we use SUPERT as rewards to guide a neural-based reinforcement learning summarizer, yielding favorable performance compared to the state-of-the-art unsupervised summarizers. All source code is available at https://github.com/yg211/acl20-ref-free-eval.",
}
@inproceedings{xu-etal-2020-self,
    title = "Self-Attention Guided Copy Mechanism for Abstractive Summarization",
    author = "Xu, Song  and
      Li, Haoran  and
      Yuan, Peng  and
      Wu, Youzheng  and
      He, Xiaodong  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.125",
    doi = "10.18653/v1/2020.acl-main.125",
    pages = "1355--1362",
    abstract = "Copy module has been widely equipped in the recent abstractive summarization models, which facilitates the decoder to extract words from the source into the summary. Generally, the encoder-decoder attention is served as the copy distribution, while how to guarantee that important words in the source are copied remains a challenge. In this work, we propose a Transformer-based model to enhance the copy mechanism. Specifically, we identify the importance of each source word based on the degree centrality with a directed graph built by the self-attention layer in the Transformer. We use the centrality of each source word to guide the copy process explicitly. Experimental results show that the self-attention graph provides useful guidance for the copy distribution. Our proposed models significantly outperform the baseline methods on the CNN/Daily Mail dataset and the Gigaword dataset.",
}
@inproceedings{liang-etal-2020-beyond,
    title = "Beyond User Self-Reported {L}ikert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation",
    author = "Liang, Weixin  and
      Zou, James  and
      Yu, Zhou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.126",
    doi = "10.18653/v1/2020.acl-main.126",
    pages = "1363--1374",
    abstract = "Open Domain dialog system evaluation is one of the most important challenges in dialog research. Existing automatic evaluation metrics, such as BLEU are mostly reference-based. They calculate the difference between the generated response and a limited number of available references. Likert-score based self-reported user rating is widely adopted by social conversational systems, such as Amazon Alexa Prize chatbots. However, self-reported user rating suffers from bias and variance among different users. To alleviate this problem, we formulate dialog evaluation as a comparison task. We also propose an automatic evaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) that automatically cleans self-reported user ratings as it trains on them. Specifically, we first use a self-supervised method to learn better dialog feature representation, and then use KNN and Shapley to remove confusing samples. Our experiments show that CMADE achieves 89.2{\%} accuracy in the dialog comparison task.",
}
@inproceedings{ma-etal-2020-conversational,
    title = "{C}onversational {W}ord {E}mbedding for {R}etrieval-{B}ased {D}ialog {S}ystem",
    author = "Ma, Wentao  and
      Cui, Yiming  and
      Liu, Ting  and
      Wang, Dong  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.127",
    doi = "10.18653/v1/2020.acl-main.127",
    pages = "1375--1380",
    abstract = "Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs {\textless}post, reply{\textgreater} to learn word embedding. Different from previous works, PR-Embedding uses the vectors from two different semantic spaces to represent the words in post and reply.To catch the information among the pair, we first introduce the word alignment model from statistical machine translation to generate the cross-sentence window, then train the embedding on word-level and sentence-level.We evaluate the method on single-turn and multi-turn response selection tasks for retrieval-based dialog systems.The experiment results show that PR-Embedding can improve the quality of the selected response.",
}
@inproceedings{hou-etal-2020-shot,
    title = "Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network",
    author = "Hou, Yutai  and
      Che, Wanxiang  and
      Lai, Yongkui  and
      Zhou, Zhihan  and
      Liu, Yijia  and
      Liu, Han  and
      Liu, Ting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.128",
    doi = "10.18653/v1/2020.acl-main.128",
    pages = "1381--1393",
    abstract = "In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other fewshot classification problems as it calls for modeling the dependencies between labels. But it is hard to apply previously learned label dependencies to an unseen domain, due to the discrepancy of label sets. To tackle this, we introduce a collapsed dependency transfer mechanism into the conditional random field (CRF) to transfer abstract label dependency patterns as transition scores. In the few-shot setting, the emission score of CRF can be calculated as a word{'}s similarity to the representation of each label. To calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model {--} TapNet, by leveraging label name semantics in representing labels. Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.",
}
@inproceedings{gordon-hall-etal-2020-learning,
    title = "{L}earning {D}ialog {P}olicies from {W}eak {D}emonstrations",
    author = "Gordon-Hall, Gabriel  and
      Gorinski, Philip John  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.129",
    doi = "10.18653/v1/2020.acl-main.129",
    pages = "1394--1405",
    abstract = "Deep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems. Building upon Deep Q-learning from Demonstrations (DQfD), an algorithm that scores highly in difficult Atari games, we leverage dialog data to guide the agent to successfully respond to a user{'}s requests. We make progressively fewer assumptions about the data needed, using labeled, reduced-labeled, and even unlabeled data to train expert demonstrators. We introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to overcome the domain gap between the datasets and the environment. Experiments in a challenging multi-domain dialog system framework validate our approaches, and get high success rates even when trained on out-of-domain data.",
}
@inproceedings{cui-etal-2020-mutual,
    title = "{M}u{T}ual: A Dataset for Multi-Turn Dialogue Reasoning",
    author = "Cui, Leyang  and
      Wu, Yu  and
      Liu, Shujie  and
      Zhang, Yue  and
      Zhou, Ming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.130",
    doi = "10.18653/v1/2020.acl-main.130",
    pages = "1406--1416",
    abstract = "Non-task oriented dialogue systems have achieved great success in recent years due to largely accessible conversation data and the development of deep learning techniques. Given a context, current systems are able to yield a relevant and fluent response, but sometimes make logical mistakes because of weak reasoning capabilities. To facilitate the conversation reasoning research, we introduce MuTual, a novel dataset for Multi-Turn dialogue Reasoning, consisting of 8,860 manually annotated dialogues based on Chinese student English listening comprehension exams. Compared to previous benchmarks for non-task oriented dialogue systems, MuTual is much more challenging since it requires a model that be able to handle various reasoning problems. Empirical results show that state-of-the-art methods only reach 71{\%}, which is far behind human performance of 94{\%}, indicating that there is ample room for improving reasoning ability.",
}
@inproceedings{liu-etal-2020-impress,
    title = "You Impress Me: Dialogue Generation via Mutual Persona Perception",
    author = "Liu, Qian  and
      Chen, Yihong  and
      Chen, Bei  and
      Lou, Jian-Guang  and
      Chen, Zixuan  and
      Zhou, Bin  and
      Zhang, Dongmei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.131",
    doi = "10.18653/v1/2020.acl-main.131",
    pages = "1417--1427",
    abstract = "Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P{\^{}}2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, P{\^{}}2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations.",
}
@inproceedings{hou-2020-bridging,
    title = "Bridging Anaphora Resolution as Question Answering",
    author = "Hou, Yufang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.132",
    doi = "10.18653/v1/2020.acl-main.132",
    pages = "1428--1438",
    abstract = "Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given. In this paper, we cast bridging anaphora resolution as question answering based on context. This allows us to find the antecedent for a given anaphor without knowing any gold mention information (except the anaphor itself). We present a question answering framework (BARQA) for this task, which leverages the power of transfer learning. Furthermore, we propose a novel method to generate a large amount of {``}quasi-bridging{''} training data. We show that our model pre-trained on this dataset and fine-tuned on a small amount of in-domain dataset achieves new state-of-the-art results for bridging anaphora resolution on two bridging corpora (ISNotes (Markert et al., 2012) and BASHI (Ro ̈siger, 2018)).",
}
@inproceedings{mesgar-etal-2020-dialogue,
    title = "Dialogue Coherence Assessment Without Explicit Dialogue Act Labels",
    author = {Mesgar, Mohsen  and
      B{\"u}cker, Sebastian  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.133",
    doi = "10.18653/v1/2020.acl-main.133",
    pages = "1439--1450",
    abstract = "Recent dialogue coherence models use the coherence features designed for monologue texts, e.g. nominal entities, to represent utterances and then explicitly augment them with dialogue-relevant features, e.g., dialogue act labels. It indicates two drawbacks, (a) semantics of utterances are limited to entity mentions, and (b) the performance of coherence models strongly relies on the quality of the input dialogue act labels. We address these issues by introducing a novel approach to dialogue coherence assessment. We use dialogue act prediction as an auxiliary task in a multi-task learning scenario to obtain informative utterance representations for coherence assessment. Our approach alleviates the need for explicit dialogue act labels during evaluation. The results of our experiments show that our model substantially (more than 20 accuracy points) outperforms its strong competitors on the DailyDialogue corpus, and performs on par with them on the SwitchBoard corpus for ranking dialogues concerning their coherence. We release our source code.",
}
@inproceedings{yu-etal-2020-fast,
    title = "Fast and Accurate Non-Projective Dependency Tree Linearization",
    author = "Yu, Xiang  and
      Tannert, Simon  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.134",
    doi = "10.18653/v1/2020.acl-main.134",
    pages = "1451--1462",
    abstract = "We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree. We then design a transition system as post-processing, inspired by non-projective transition-based parsing, to obtain non-projective sentences. Our proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.",
}
@inproceedings{pan-etal-2020-semantic,
    title = "Semantic Graphs for Generating Deep Questions",
    author = "Pan, Liangming  and
      Xie, Yuxi  and
      Feng, Yansong  and
      Chua, Tat-Seng  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.135",
    doi = "10.18653/v1/2020.acl-main.135",
    pages = "1463--1475",
    abstract = "This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information about the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework that first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterward, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at https://github.com/WING-NUS/SG-Deep-Question-Generation.",
}
@inproceedings{wei-etal-2020-novel,
    title = "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction",
    author = "Wei, Zhepei  and
      Su, Jianlin  and
      Wang, Yue  and
      Tian, Yuan  and
      Chang, Yi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.136",
    doi = "10.18653/v1/2020.acl-main.136",
    pages = "1476--1488",
    abstract = "Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same entities. In this work, we introduce a fresh perspective to revisit the relational triple extraction task and propose a novel cascade binary tagging framework (CasRel) derived from a principled problem formulation. Instead of treating relations as discrete labels as in previous works, our new framework models relations as functions that map subjects to objects in a sentence, which naturally handles the overlapping problem. Experiments show that the CasRel framework already outperforms state-of-the-art methods even when its encoder module uses a randomly initialized BERT encoder, showing the power of the new tagging framework. It enjoys further performance boost when employing a pre-trained BERT encoder, outperforming the strongest baseline by 17.5 and 30.2 absolute gain in F1-score on two public datasets NYT and WebNLG, respectively. In-depth analysis on different scenarios of overlapping triples shows that the method delivers consistent performance gain across all these scenarios. The source code and data are released online.",
}
@inproceedings{kruiper-etal-2020-laymans,
    title = "In Layman{'}s Terms: Semi-Open Relation Extraction from Scientific Texts",
    author = "Kruiper, Ruben  and
      Vincent, Julian  and
      Chen-Burger, Jessica  and
      Desmulliez, Marc  and
      Konstas, Ioannis",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.137",
    doi = "10.18653/v1/2020.acl-main.137",
    pages = "1489--1500",
    abstract = "Information Extraction (IE) from scientific texts can be used to guide readers to the central information in scientific documents. But narrow IE systems extract only a fraction of the information captured, and Open IE systems do not perform well on the long and complex sentences encountered in scientific texts. In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain. First, we present the Focused Open Biological Information Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow scientific IE system to extract trade-off relations and arguments that are central to biology texts. We then run both the narrow IE system and a state-of-the-art Open IE system on a corpus of 10K open-access scientific biological texts. We show that a significant amount (65{\%}) of erroneous and uninformative Open IE extractions can be filtered using narrow IE extractions. Furthermore, we show that the retained extractions are significantly more often informative to a reader.",
}
@inproceedings{namysl-etal-2020-nat,
    title = "{NAT}: Noise-Aware Training for Robust Neural Sequence Labeling",
    author = {Namysl, Marcin  and
      Behnke, Sven  and
      K{\"o}hler, Joachim},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.138",
    doi = "10.18653/v1/2020.acl-main.138",
    pages = "1501--1517",
    abstract = "Sequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs{---}as these systems often process user-generated text or follow an error-prone upstream component. To this end, we formulate the noisy sequence labeling problem, where the input may undergo an unknown noising process and propose two Noise-Aware Training (NAT) objectives that improve robustness of sequence labeling performed on perturbed input: Our data augmentation method trains a neural model using a mixture of clean and noisy samples, whereas our stability training algorithm encourages the model to create a noise-invariant latent representation. We employ a vanilla noise model at training time. For evaluation, we use both the original data and its variants perturbed with real OCR errors and misspellings. Extensive experiments on English and German named entity recognition benchmarks confirmed that NAT consistently improved robustness of popular sequence labeling models, preserving accuracy on the original input. We make our code and data publicly available for the research community.",
}
@inproceedings{lison-etal-2020-named,
    title = "Named Entity Recognition without Labelled Data: A Weak Supervision Approach",
    author = "Lison, Pierre  and
      Barnes, Jeremy  and
      Hubin, Aliaksandr  and
      Touileb, Samia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.139",
    doi = "10.18653/v1/2020.acl-main.139",
    pages = "1518--1533",
    abstract = "Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain. But what should one do when there is no hand-labelled data for the target domain? This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision. The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain. These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions. A sequence labelling model can finally be trained on the basis of this unified annotation. We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F1 scores compared to an out-of-domain neural NER model.",
}
@inproceedings{alt-etal-2020-probing,
    title = "Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction",
    author = "Alt, Christoph  and
      Gabryszak, Aleksandra  and
      Hennig, Leonhard",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.140",
    doi = "10.18653/v1/2020.acl-main.140",
    pages = "1534--1545",
    abstract = "Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, conditioned on the entity mentions, before classifying the relation. However, the complexity of the task makes it difficult to understand how encoder architecture and supporting linguistic knowledge affect the features learned by the encoder. We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance. For example, adding contextualized word representations greatly increases performance on probing tasks with a focus on named entity and part-of-speech information, and yields better results in RE. In contrast, entity masking improves RE, but considerably lowers performance on entity type related probing tasks.",
}
@inproceedings{nan-etal-2020-reasoning,
    title = "Reasoning with Latent Structure Refinement for Document-Level Relation Extraction",
    author = "Nan, Guoshun  and
      Guo, Zhijiang  and
      Sekulic, Ivan  and
      Lu, Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.141",
    doi = "10.18653/v1/2020.acl-main.141",
    pages = "1546--1557",
    abstract = "Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities. However, effective aggregation of relevant information in the document remains a challenging research question. Existing approaches construct static document-level graphs based on syntactic trees, co-references or heuristics from the unstructured text to model the dependencies. Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph. We further develop a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning. Specifically, our model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results, and also yields new state-of-the-art results on the CDR and GDA dataset. Furthermore, extensive analyses show that the model is able to discover more accurate inter-sentence relations.",
}
@inproceedings{alt-etal-2020-tacred,
    title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task",
    author = "Alt, Christoph  and
      Gabryszak, Aleksandra  and
      Hennig, Leonhard",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.142",
    doi = "10.18653/v1/2020.acl-main.142",
    pages = "1558--1569",
    abstract = "TACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training and knowledge enhanced neural RE, models still show a high error rate. In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement? And how do crowd annotations, dataset, and models contribute to this error rate? To answer these questions, we first validate the most challenging 5K examples in the development and test sets using trained annotators. We find that label errors account for 8{\%} absolute F1 test error, and that more than 50{\%} of the examples need to be relabeled. On the relabeled test set the average F1 score of a large baseline model set improves from 62.1 to 70.1. After validation, we analyze misclassifications on the challenging instances, categorize them into linguistically motivated error groups, and verify the resulting error hypotheses on three state-of-the-art RE models. We show that two groups of ambiguous relations are responsible for most of the remaining errors and that models may adopt shallow heuristics on the dataset when entities are not masked.",
}
@inproceedings{duan-etal-2020-bilingual,
    title = "Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences",
    author = "Duan, Xiangyu  and
      Ji, Baijun  and
      Jia, Hao  and
      Tan, Min  and
      Zhang, Min  and
      Chen, Boxing  and
      Luo, Weihua  and
      Zhang, Yue",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.143",
    doi = "10.18653/v1/2020.acl-main.143",
    pages = "1570--1579",
    abstract = "In this paper, we propose a new task of machine translation (MT), which is based on no parallel sentences but can refer to a ground-truth bilingual dictionary. Motivated by the ability of a monolingual speaker learning to translate via looking up the bilingual dictionary, we propose the task to see how much potential an MT system can attain using the bilingual dictionary and large scale monolingual corpora, while is independent on parallel sentences. We propose anchored training (AT) to tackle the task. AT uses the bilingual dictionary to establish anchoring points for closing the gap between source language and target language. Experiments on various language pairs show that our approaches are significantly better than various baselines, including dictionary-based word-by-word translation, dictionary-supervised cross-lingual word embedding transformation, and unsupervised MT. On distant language pairs that are hard for unsupervised MT to perform well, AT performs remarkably better, achieving performances comparable to supervised SMT trained on more than 4M parallel sentences.",
}
@inproceedings{xu-etal-2020-boosting,
    title = "Boosting Neural Machine Translation with Similar Translations",
    author = "Xu, Jitao  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.144",
    doi = "10.18653/v1/2020.acl-main.144",
    pages = "1580--1590",
    abstract = "This paper explores data augmentation methods for training Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches. In particular, we show how we can simply present the neural model with information of both source and target sides of the fuzzy matches, we also extend the similarity to include semantically related translations retrieved using sentence distributed representations. We show that translations based on fuzzy matching provide the model with {``}copy{''} information while translations based on embedding similarities tend to extend the translation {``}context{''}. Results indicate that the effect from both similar sentences are adding up to further boost accuracy, combine naturally with model fine-tuning and are providing dynamic adaptation for unseen translation pairs. Tests on multiple data sets and domains show consistent accuracy improvements. To foster research around these techniques, we also release an Open-Source toolkit with efficient and flexible fuzzy-match implementation.",
}
@inproceedings{gao-etal-2020-character,
    title = "Character-Level Translation with Self-attention",
    author = "Gao, Yingqiang  and
      Nikolov, Nikola I.  and
      Hu, Yuhuang  and
      Hahnloser, Richard H.R.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.145",
    doi = "10.18653/v1/2020.acl-main.145",
    pages = "1591--1604",
    abstract = "We explore the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolutions. We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual translation to English using up to three input languages (French, Spanish, and Chinese). Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments.",
}
@inproceedings{zenkel-etal-2020-end,
    title = "End-to-End Neural Word Alignment Outperforms {GIZA}++",
    author = "Zenkel, Thomas  and
      Wuebker, Joern  and
      DeNero, John",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.146",
    doi = "10.18653/v1/2020.acl-main.146",
    pages = "1605--1617",
    abstract = "Word alignment was once a core unsupervised learning task in natural language processing because of its essential role in training statistical machine translation (MT) models. Although unnecessary for training neural MT models, word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. While statistical MT methods have been replaced by neural approaches with superior performance, the twenty-year-old GIZA++ toolkit remains a key component of state-of-the-art word alignment systems. Prior work on neural word alignment has only been able to outperform GIZA++ by using its output during training. We present the first end-to-end neural word alignment method that consistently outperforms GIZA++ on three data sets. Our approach repurposes a Transformer model trained for supervised translation to also serve as an unsupervised word alignment model in a manner that is tightly integrated and does not affect translation quality.",
}
@inproceedings{bugliarello-okazaki-2020-enhancing,
    title = "Enhancing Machine Translation with Dependency-Aware Self-Attention",
    author = "Bugliarello, Emanuele  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.147",
    doi = "10.18653/v1/2020.acl-main.147",
    pages = "1618--1627",
    abstract = "Most neural machine translation models only rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. In this work, we investigate different approaches to incorporate syntactic knowledge in the Transformer model and also propose a novel, parameter-free, dependency-aware self-attention mechanism that improves its translation quality, especially for long sentences and in low-resource scenarios. We show the efficacy of each approach on WMT English-German and English-Turkish, and WAT English-Japanese translation tasks.",
}
@inproceedings{zhang-etal-2020-improving,
    title = "Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation",
    author = "Zhang, Biao  and
      Williams, Philip  and
      Titov, Ivan  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.148",
    doi = "10.18653/v1/2020.acl-main.148",
    pages = "1628--1639",
    abstract = "Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations. In this paper, we explore ways to improve them. We argue that multilingual NMT requires stronger modeling capacity to support language pairs with varying typological characteristics, and overcome this bottleneck via language-specific components and deepening NMT architectures. We identify the off-target translation issue (i.e. translating into a wrong target language) as the major source of the inferior zero-shot performance, and propose random online backtranslation to enforce the translation of unseen training language pairs. Experiments on OPUS-100 (a novel multilingual dataset with 100 languages) show that our approach substantially narrows the performance gap with bilingual models in both one-to-many and many-to-many settings, and improves zero-shot performance by {\textasciitilde}10 BLEU, approaching conventional pivot-based methods.",
}
@inproceedings{bugliarello-etal-2020-easier,
    title = "It{'}s Easier to Translate out of {E}nglish than into it: {M}easuring Neural Translation Difficulty by Cross-Mutual Information",
    author = "Bugliarello, Emanuele  and
      Mielke, Sabrina J.  and
      Anastasopoulos, Antonios  and
      Cotterell, Ryan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.149",
    doi = "10.18653/v1/2020.acl-main.149",
    pages = "1640--1649",
    abstract = "The performance of neural machine translation systems is commonly evaluated in terms of BLEU. However, due to its reliance on target language properties and generation, the BLEU metric does not allow an assessment of which translation directions are more difficult to model. In this paper, we propose cross-mutual information (XMI): an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models. XMI allows us to better evaluate the difficulty of translating text into the target language while controlling for the difficulty of the target-side generation component independent of the translation task. We then present the first systematic and controlled study of cross-lingual translation difficulties using modern neural translation systems. Code for replicating our experiments is available online at https://github.com/e-bug/nmt-difficulty.",
}
@inproceedings{zhu-etal-2020-language,
    title = "Language-aware Interlingua for Multilingual Neural Machine Translation",
    author = "Zhu, Changfeng  and
      Yu, Heng  and
      Cheng, Shanbo  and
      Luo, Weihua",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.150",
    doi = "10.18653/v1/2020.acl-main.150",
    pages = "1650--1655",
    abstract = "Multilingual neural machine translation (NMT) has led to impressive accuracy improvements in low-resource scenarios by sharing common linguistic information across languages. However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained. In this paper, we incorporate a language-aware interlingua into the Encoder-Decoder architecture. The interlingual network enables the model to learn a language-independent representation from the semantic spaces of different languages, while still allowing for language-specific specialization of a particular language-pair. Experiments show that our proposed method achieves remarkable improvements over state-of-the-art multilingual NMT baselines and produces comparable performance with strong individual models.",
}
@inproceedings{zhao-etal-2020-limitations,
    title = "On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation",
    author = "Zhao, Wei  and
      Glava{\v{s}}, Goran  and
      Peyrard, Maxime  and
      Gao, Yang  and
      West, Robert  and
      Eger, Steffen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.151",
    doi = "10.18653/v1/2020.acl-main.151",
    pages = "1656--1671",
    abstract = "Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual similarity. In this paper, we concern ourselves with reference-free machine translation (MT) evaluation where we directly compare source texts to (sometimes low-quality) system translations, which represents a natural adversarial setup for multilingual encoders. Reference-free evaluation holds the promise of web-scale comparison of MT systems. We systematically investigate a range of metrics based on state-of-the-art cross-lingual semantic representations obtained with pretrained M-BERT and LASER. We find that they perform poorly as semantic encoders for reference-free MT evaluation and identify their two key limitations, namely, (a) a semantic mismatch between representations of mutual translations and, more prominently, (b) the inability to punish {``}translationese{''}, i.e., low-quality literal translations. We propose two partial remedies: (1) post-hoc re-alignment of the vector spaces and (2) coupling of semantic-similarity based metrics with target-side language modeling. In segment-level MT evaluation, our best metric surpasses reference-based BLEU by 5.7 correlation points.",
}
@inproceedings{chen-etal-2020-parallel,
    title = "Parallel Sentence Mining by Constrained Decoding",
    author = "Chen, Pinzhen  and
      Bogoychev, Nikolay  and
      Heafield, Kenneth  and
      Kirefu, Faheem",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.152",
    doi = "10.18653/v1/2020.acl-main.152",
    pages = "1672--1678",
    abstract = "We present a novel method to extract parallel sentences from two monolingual corpora, using neural machine translation. Our method relies on translating sentences in one corpus, but constraining the decoding by a prefix tree built on the other corpus. We argue that a neural machine translation system by itself can be a sentence similarity scorer and it efficiently approximates pairwise comparison with a modified beam search. When benchmarked on the BUCC shared task, our method achieves results comparable to other submissions.",
}
@inproceedings{ding-etal-2020-self,
    title = "Self-Attention with Cross-Lingual Position Representation",
    author = "Ding, Liang  and
      Wang, Longyue  and
      Tao, Dacheng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.153",
    doi = "10.18653/v1/2020.acl-main.153",
    pages = "1679--1685",
    abstract = "Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences. However, in cross-lingual scenarios, machine translation, the PEs of source and target sentences are modeled independently. Due to word order divergences in different languages, modeling the cross-lingual positional relationships might help SANs tackle this problem. In this paper, we augment SANs with \textit{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence. Specifically, we utilize bracketing transduction grammar (BTG)-based reordering information to encourage SANs to learn bilingual diagonal alignments. Experimental results on WMT{'}14 English$\Rightarrow$German, WAT{'}17 Japanese$\Rightarrow$English, and WMT{'}17 Chinese$\Leftrightarrow$English translation tasks demonstrate that our approach significantly and consistently improves translation quality over strong baselines. Extensive analyses confirm that the performance gains come from the cross-lingual information.",
}
@inproceedings{hovy-etal-2020-sound,
    title = "{``}You Sound Just Like Your Father{''} Commercial Machine Translation Systems Include Stylistic Biases",
    author = "Hovy, Dirk  and
      Bianchi, Federico  and
      Fornaciari, Tommaso",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.154",
    doi = "10.18653/v1/2020.acl-main.154",
    pages = "1686--1690",
    abstract = "The main goal of machine translation has been to convey the correct content. Stylistic considerations have been at best secondary. We show that as a consequence, the output of three commercial machine translation systems (Bing, DeepL, Google) make demographically diverse samples from five languages {``}sound{''} older and more male than the original. Our findings suggest that translation models reflect demographic bias in the training data. This opens up interesting new research avenues in machine translation to take stylistic considerations into account.",
}
@inproceedings{herbig-etal-2020-mmpe,
    title = "{MMPE}: {A} {M}ulti-{M}odal {I}nterface for {P}ost-{E}diting {M}achine {T}ranslation",
    author = {Herbig, Nico  and
      D{\"u}wel, Tim  and
      Pal, Santanu  and
      Meladaki, Kalliopi  and
      Monshizadeh, Mahsa  and
      Kr{\"u}ger, Antonio  and
      van Genabith, Josef},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.155",
    doi = "10.18653/v1/2020.acl-main.155",
    pages = "1691--1702",
    abstract = "Current advances in machine translation (MT) increase the need for translators to switch from traditional translation to post-editing (PE) of machine-translated text, a process that saves time and reduces errors. This affects the design of translation interfaces, as the task changes from mainly generating text to correcting errors within otherwise helpful translation proposals. Since this paradigm shift offers potential for modalities other than mouse and keyboard, we present MMPE, the first prototype to combine traditional input modes with pen, touch, and speech modalities for PE of MT. The results of an evaluation with professional translators suggest that pen and touch interaction are suitable for deletion and reordering tasks, while they are of limited use for longer insertions. On the other hand, speech and multi-modal combinations of select {\&} speech are considered suitable for replacements and insertions but offer less potential for deletion and reordering. Overall, participants were enthusiastic about the new modalities and saw them as good extensions to mouse {\&} keyboard, but not as a complete substitute.",
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
    title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
    author = "Ortiz Su{\'a}rez, Pedro Javier  and
      Romary, Laurent  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.156",
    doi = "10.18653/v1/2020.acl-main.156",
    pages = "1703--1714",
    abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{conforti-etal-2020-will,
    title = "Will-They-Won{'}t-They: A Very Large Dataset for Stance Detection on {T}witter",
    author = "Conforti, Costanza  and
      Berndt, Jakob  and
      Pilehvar, Mohammad Taher  and
      Giannitsarou, Chryssi  and
      Toxvaerd, Flavio  and
      Collier, Nigel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.157",
    doi = "10.18653/v1/2020.acl-main.157",
    pages = "1715--1724",
    abstract = "We present a new challenging stance detection dataset, called Will-They-Won{'}t-They (WT--WT), which contains 51,284 tweets in English, making it by far the largest available dataset of the type. All the annotations are carried out by experts; therefore, the dataset constitutes a high-quality and reliable benchmark for future research in stance detection. Our experiments with a wide range of recent state-of-the-art stance detection systems show that the dataset poses a strong challenge to existing models in this domain.",
}
@inproceedings{hu-etal-2020-systematic,
    title = "A Systematic Assessment of Syntactic Generalization in Neural Language Models",
    author = "Hu, Jennifer  and
      Gauthier, Jon  and
      Qian, Peng  and
      Wilcox, Ethan  and
      Levy, Roger",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.158",
    doi = "10.18653/v1/2020.acl-main.158",
    pages = "1725--1744",
    abstract = "While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage predictive performance leads to human-like syntactic knowledge. Furthermore, existing work has not provided a clear picture about the model properties required to produce proper syntactic generalizations. We present a systematic evaluation of the syntactic knowledge of neural language models, testing 20 combinations of model types and data sizes on a set of 34 English-language syntactic test suites. We find substantial differences in syntactic generalization performance by model architecture, with sequential models underperforming other architectures. Factorially manipulating model architecture and training dataset size (1M-40M words), we find that variability in syntactic generalization performance is substantially greater by architecture than by dataset size for the corpora tested in our experiments. Our results also reveal a dissociation between perplexity and syntactic generalization performance.",
}
@inproceedings{mccurdy-etal-2020-inflecting,
    title = "Inflecting When There{'}s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for {G}erman Plurals",
    author = "McCurdy, Kate  and
      Goldwater, Sharon  and
      Lopez, Adam",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.159",
    doi = "10.18653/v1/2020.acl-main.159",
    pages = "1745--1756",
    abstract = "Can artificial neural networks learn to represent inflectional morphology and generalize to new words as human speakers do? Kirov and Cotterell (2018) argue that the answer is yes: modern Encoder-Decoder (ED) architectures learn human-like behavior when inflecting English verbs, such as extending the regular past tense form /-(e)d/ to novel words. However, their work does not address the criticism raised by Marcus et al. (1995): that neural models may learn to extend not the regular, but the most frequent class {---} and thus fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized. To investigate this question, we first collect a new dataset from German speakers (production and ratings of plural forms for novel nouns) that is designed to avoid sources of information unavailable to the ED model. The speaker data show high variability, and two suffixes evince {`}regular{'} behavior, appearing more often with phonologically atypical inputs. Encoder-decoder models do generalize the most frequently produced plural class, but do not show human-like variability or {`}regular{'} extension of these other plural markers. We conclude that modern neural models may still struggle with minority-class generalization.",
}
@inproceedings{kodner-gupta-2020-overestimation,
    title = "Overestimation of Syntactic Representation in Neural Language Models",
    author = "Kodner, Jordan  and
      Gupta, Nitish",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.160",
    doi = "10.18653/v1/2020.acl-main.160",
    pages = "1757--1762",
    abstract = "With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful. Several testing methodologies have been developed to probe models{'} syntactic representations. One popular method for determining a model{'}s ability to induce syntactic structure trains a model on strings generated according to a template then tests the model{'}s ability to distinguish such strings from superficially similar ones with different syntax. We illustrate a fundamental problem with this approach by reproducing positive results from a recent paper with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs.",
}
@inproceedings{wilmot-keller-2020-modelling,
    title = "Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation",
    author = "Wilmot, David  and
      Keller, Frank",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.161",
    doi = "10.18653/v1/2020.acl-main.161",
    pages = "1763--1788",
    abstract = "Suspense is a crucial ingredient of narrative fiction, engaging readers and making stories compelling. While there is a vast theoretical literature on suspense, it is computationally not well understood. We compare two ways for modelling suspense: surprise, a backward-looking measure of how unexpected the current state is given the story so far; and uncertainty reduction, a forward-looking measure of how unexpected the continuation of the story is. Both can be computed either directly over story representations or over their probability distributions. We propose a hierarchical language model that encodes stories and computes surprise and uncertainty reduction. Evaluating against short stories annotated with human suspense judgements, we find that uncertainty reduction over representations is the best predictor, resulting in near human accuracy. We also show that uncertainty reduction can be used to predict suspenseful events in movie synopses.",
}
@inproceedings{weller-etal-2020-dont,
    title = "You Don{'}t Have Time to Read This: An Exploration of Document Reading Time Prediction",
    author = "Weller, Orion  and
      Hildebrandt, Jordan  and
      Reznik, Ilya  and
      Challis, Christopher  and
      Tass, E. Shannon  and
      Snell, Quinn  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.162",
    doi = "10.18653/v1/2020.acl-main.162",
    pages = "1789--1794",
    abstract = "Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time. However, previous work has dealt with a limited number of participants as well as word level only predictions (i.e. predicting the time to read a single word). We seek to extend these works by examining whether or not document level predictions are effective, given additional information such as subject matter, font characteristics, and readability metrics. We perform a novel experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. We then employ a large number of machine learning methods to predict a user{'}s reading time. We find that despite extensive research showing that word level reading time can be most effectively predicted by neural networks, larger scale text can be easily and most accurately predicted by one factor, the number of words.",
}
@inproceedings{tseng-etal-2020-generative,
    title = "A Generative Model for Joint Natural Language Understanding and Generation",
    author = "Tseng, Bo-Hsiang  and
      Cheng, Jianpeng  and
      Fang, Yimai  and
      Vandyke, David",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.163",
    doi = "10.18653/v1/2020.acl-main.163",
    pages = "1795--1807",
    abstract = "Natural language understanding (NLU) and natural language generation (NLG) are two fundamental and related tasks in building task-oriented dialogue systems with opposite objectives: NLU tackles the transformation from natural language to formal representations, whereas NLG does the reverse. A key to success in either task is parallel training data which is expensive to obtain at a large scale. In this work, we propose a generative model which couples NLU and NLG through a shared latent variable. This approach allows us to explore both spaces of natural language and formal representations, and facilitates information sharing through the latent space to eventually benefit NLU and NLG. Our model achieves state-of-the-art performance on two dialogue datasets with both flat and tree-structured formal representations. We also show that the model can be trained in a semi-supervised fashion by utilising unlabelled data to boost its performance.",
}
@inproceedings{ippolito-etal-2020-automatic,
    title = "Automatic Detection of Generated Text is Easiest when Humans are Fooled",
    author = "Ippolito, Daphne  and
      Duckworth, Daniel  and
      Callison-Burch, Chris  and
      Eck, Douglas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.164",
    doi = "10.18653/v1/2020.acl-main.164",
    pages = "1808--1822",
    abstract = "Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their decisions. Here, we perform careful benchmarking and analysis of three popular sampling-based decoding strategies{---}top-{\_}k{\_}, nucleus sampling, and untruncated random sampling{---}and show that improvements in decoding methods have primarily optimized for fooling humans. This comes at the expense of introducing statistical abnormalities that make detection easy for automatic systems. We also show that though both human and automatic detector performance improve with longer excerpt length, even multi-sentence excerpts can fool expert human raters over 30{\%} of the time. Our findings reveal the importance of using both human and automatic detectors to assess the humanness of text generation systems.",
}
@inproceedings{jiang-etal-2020-multi-domain,
    title = "Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing",
    author = "Jiang, Haoming  and
      Liang, Chen  and
      Wang, Chong  and
      Zhao, Tuo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.165",
    doi = "10.18653/v1/2020.acl-main.165",
    pages = "1823--1834",
    abstract = "Many multi-domain neural machine translation (NMT) models achieve knowledge transfer by enforcing one encoder to learn shared embedding across domains. However, this design lacks adaptation to individual domains. To overcome this limitation, we propose a novel multi-domain NMT model using individual modules for each domain, on which we apply word-level, adaptive and layer-wise domain mixing. We first observe that words in a sentence are often related to multiple domains. Hence, we assume each word has a domain proportion, which indicates its domain preference. Then word representations are obtained by mixing their embedding in individual domains based on their domain proportions. We show this can be achieved by carefully designing multi-head dot-product attention modules for different domains, and eventually taking weighted averages of their parameters by word-level layer-wise domain proportions. Through this, we can achieve effective domain knowledge sharing and capture fine-grained domain-specific knowledge as well. Our experiments show that our proposed model outperforms existing ones in several NMT tasks.",
}
@inproceedings{xu-etal-2020-conversational,
    title = "Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation",
    author = "Xu, Jun  and
      Wang, Haifeng  and
      Niu, Zheng-Yu  and
      Wu, Hua  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.166",
    doi = "10.18653/v1/2020.acl-main.166",
    pages = "1835--1845",
    abstract = "To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog. To this end, we first construct a conversational graph (CG) from dialog corpora, in which there are vertices to represent {``}what to say{''} and {``}how to say{''}, and edges to represent natural transition between a message (the last utterance in a dialog context) and its response. We then present a novel CG grounded policy learning framework that conducts dialog flow planning by graph traversal, which learns to identify a what-vertex and a how-vertex from the CG at each turn to guide response generation. In this way, we effectively leverage the CG to facilitate policy learning as follows: (1) it enables more effective long-term reward design, (2) it provides high-quality candidate actions, and (3) it gives us more control over the policy. Results on two benchmark corpora demonstrate the effectiveness of this framework.",
}
@inproceedings{mager-etal-2020-gpt,
    title = "{GPT}-too: A Language-Model-First Approach for {AMR}-to-Text Generation",
    author = "Mager, Manuel  and
      Fernandez Astudillo, Ram{\'o}n  and
      Naseem, Tahira  and
      Sultan, Md Arafat  and
      Lee, Young-Suk  and
      Florian, Radu  and
      Roukos, Salim",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.167",
    doi = "10.18653/v1/2020.acl-main.167",
    pages = "1846--1852",
    abstract = "Abstract Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10 dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.",
}
@inproceedings{panthaplackel-etal-2020-learning,
    title = "Learning to Update Natural Language Comments Based on Code Changes",
    author = "Panthaplackel, Sheena  and
      Nie, Pengyu  and
      Gligoric, Milos  and
      Li, Junyi Jessy  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.168",
    doi = "10.18653/v1/2020.acl-main.168",
    pages = "1853--1868",
    abstract = "We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. We train and evaluate our model using a dataset that we collected from commit histories of open-source software projects, with each example consisting of a concurrent update to a method and its corresponding comment. We compare our approach against multiple baselines using both automatic metrics and human evaluation. Results reflect the challenge of this task and that our model outperforms baselines with respect to making edits.",
}
@inproceedings{madaan-etal-2020-politeness,
    title = "Politeness Transfer: A Tag and Generate Approach",
    author = "Madaan, Aman  and
      Setlur, Amrith  and
      Parekh, Tanmay  and
      Poczos, Barnabas  and
      Neubig, Graham  and
      Yang, Yiming  and
      Salakhutdinov, Ruslan  and
      Black, Alan W  and
      Prabhumoye, Shrimai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.169",
    doi = "10.18653/v1/2020.acl-main.169",
    pages = "1869--1881",
    abstract = "This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate.",
}
@inproceedings{provilkov-etal-2020-bpe,
    title = "{BPE}-Dropout: Simple and Effective Subword Regularization",
    author = "Provilkov, Ivan  and
      Emelianenko, Dmitrii  and
      Voita, Elena",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.170",
    doi = "10.18653/v1/2020.acl-main.170",
    pages = "1882--1892",
    abstract = "Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with the same vocabulary, BPE splits words into unique sequences; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors. So far, the only way to overcome this BPE imperfection, its deterministic nature, was to create another subword segmentation algorithm (Kudo, 2018). In contrast, we show that BPE itself incorporates the ability to produce multiple segmentations of the same word. We introduce BPE-dropout - simple and effective subword regularization method based on and compatible with conventional BPE. It stochastically corrupts the segmentation procedure of BPE, which leads to producing multiple segmentations within the same fixed BPE framework. Using BPE-dropout during training and the standard BPE during inference improves translation quality up to 2.3 BLEU compared to BPE and up to 0.9 BLEU compared to the previous subword regularization.",
}
@inproceedings{zhou-keung-2020-improving,
    title = "Improving Non-autoregressive Neural Machine Translation with Monolingual Data",
    author = "Zhou, Jiawei  and
      Keung, Phillip",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.171",
    doi = "10.18653/v1/2020.acl-main.171",
    pages = "1893--1898",
    abstract = "Non-autoregressive (NAR) neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model. Under this framework, we leverage large monolingual corpora to improve the NAR model{'}s performance, with the goal of transferring the AR model{'}s generalization ability while preventing overfitting. On top of a strong NAR baseline, our experimental results on the WMT14 En-De and WMT16 En-Ro news translation tasks confirm that monolingual data augmentation consistently improves the performance of the NAR model to approach the teacher AR model{'}s performance, yields comparable or better results than the best non-iterative NAR methods in the literature and helps reduce overfitting in the training process.",
}
@inproceedings{sotudeh-gharebagh-etal-2020-attend,
    title = "Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization",
    author = "Sotudeh Gharebagh, Sajad  and
      Goharian, Nazli  and
      Filice, Ross",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.172",
    doi = "10.18653/v1/2020.acl-main.172",
    pages = "1899--1905",
    abstract = "Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer. Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of ROUGE metrics (with improvements: 2.9{\%} RG-1, 2.5{\%} RG-2, 1.9{\%} RG-L), in the healthcare domain where any range of improvement impacts patients{'} welfare.",
}
@inproceedings{maynez-etal-2020-faithfulness,
    title = "On Faithfulness and Factuality in Abstractive Summarization",
    author = "Maynez, Joshua  and
      Narayan, Shashi  and
      Bohnet, Bernd  and
      McDonald, Ryan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.173",
    doi = "10.18653/v1/2020.acl-main.173",
    pages = "1906--1919",
    abstract = "It is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling and story generation. In this paper we have analyzed limitations of these models for abstractive document summarization and found that these models are highly prone to hallucinate content that is unfaithful to the input document. We conducted a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. Our human annotators found substantial amounts of hallucinated content in all model generated summaries. However, our analysis does show that pretrained models are better summarizers not only in terms of raw metrics, i.e., ROUGE, but also in generating faithful and factual summaries as evaluated by humans. Furthermore, we show that textual entailment measures better correlate with faithfulness than standard metrics, potentially leading the way to automatic evaluation metrics as well as training and decoding criteria.",
}
@inproceedings{papalampidi-etal-2020-screenplay,
    title = "Screenplay Summarization Using Latent Narrative Structure",
    author = "Papalampidi, Pinelopi  and
      Keller, Frank  and
      Frermann, Lea  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.174",
    doi = "10.18653/v1/2020.acl-main.174",
    pages = "1920--1933",
    abstract = "Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased on position and often perform a smart selection of sentences from the beginning of the document. When summarizing long narratives, which have complex structure and present information piecemeal, simple position heuristics are not sufficient. In this paper, we propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays (i.e., extract an optimal sequence of scenes). Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode and improve summarization performance over general extractive algorithms leading to more complete and diverse summaries.",
}
@inproceedings{amplayo-lapata-2020-unsupervised,
    title = "Unsupervised Opinion Summarization with Noising and Denoising",
    author = "Amplayo, Reinald Kim  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.175",
    doi = "10.18653/v1/2020.acl-main.175",
    pages = "1934--1945",
    abstract = "The supervised training of high-capacity models on large datasets containing hundreds of thousands of document-summary pairs is critical to the recent success of deep learning techniques for abstractive summarization. Unfortunately, in most domains (other than news) such training data is not available and cannot be easily sourced. In this paper we enable the use of supervised learning for the setting where there are only documents available (e.g., product or business reviews) without ground truth summaries. We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input. We introduce several linguistically motivated noise generation functions and a summarization model which learns to denoise the input and generate the original review. At test time, the model accepts genuine reviews and generates a summary containing salient opinions, treating those that do not reach consensus as noise. Extensive automatic and human evaluation shows that our model brings substantial improvements over both abstractive and extractive baselines.",
}
@inproceedings{cohen-pakhomov-2020-tale,
    title = "A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the {A}lzheimer{'}s Type",
    author = "Cohen, Trevor  and
      Pakhomov, Serguei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.176",
    doi = "10.18653/v1/2020.acl-main.176",
    pages = "1946--1957",
    abstract = "In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls. The difference between perplexity estimates from two neural language models (LMs) - one trained on transcripts of speech produced by healthy participants and one trained on those with dementia - as a single feature for diagnostic classification of unseen transcripts has been shown to produce state-of-the-art performance. However, little is known about why this approach is effective, and on account of the lack of case/control matching in the most widely-used evaluation set of transcripts (DementiaBank), it is unclear if these approaches are truly diagnostic, or are sensitive to other variables. In this paper, we interrogate neural LMs trained on participants with and without dementia by using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency. We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that using a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.",
}
@inproceedings{goodwin-etal-2020-probing,
    title = "Probing Linguistic Systematicity",
    author = "Goodwin, Emily  and
      Sinha, Koustuv  and
      O{'}Donnell, Timothy J.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.177",
    doi = "10.18653/v1/2020.acl-main.177",
    pages = "1958--1969",
    abstract = "Recently, there has been much interest in the question of whether deep natural language understanding (NLU) models exhibit systematicity, generalizing such that units like words make consistent contributions to the meaning of the sentences in which they appear. There is accumulating evidence that neural models do not learn systematically. We examine the notion of systematicity from a linguistic perspective, defining a set of probing tasks and a set of metrics to measure systematic behaviour. We also identify ways in which network architectures can generalize non-systematically, and discuss why such forms of generalization may be unsatisfying. As a case study, we perform a series of experiments in the setting of natural language inference (NLI). We provide evidence that current state-of-the-art NLU systems do not generalize systematically, despite overall high performance.",
}
@inproceedings{sap-etal-2020-recollection,
    title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models",
    author = "Sap, Maarten  and
      Horvitz, Eric  and
      Choi, Yejin  and
      Smith, Noah A.  and
      Pennebaker, James",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.178",
    doi = "10.18653/v1/2020.acl-main.178",
    pages = "1970--1978",
    abstract = "We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.",
}
@inproceedings{davis-van-schijndel-2020-recurrent,
    title = "Recurrent Neural Network Language Models Always Learn {E}nglish-Like Relative Clause Attachment",
    author = "Davis, Forrest  and
      van Schijndel, Marten",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.179",
    doi = "10.18653/v1/2020.acl-main.179",
    pages = "1979--1990",
    abstract = "A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence). Our work uses ambiguous relative clause attachment to extend such evaluations to cases of multiple simultaneous valid interpretations, where stark grammaticality differences are absent. We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish. Thus, English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences. We conclude by relating these results to broader concerns about the relationship between comprehension (i.e. typical language model use cases) and production (which generates the training data for language models), suggesting that necessary linguistic biases are not present in the training signal at all.",
}
@inproceedings{meinhardt-etal-2020-speakers,
    title = "Speakers enhance contextually confusable words",
    author = "Meinhardt, Eric  and
      Bakovic, Eric  and
      Bergen, Leon",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.180",
    doi = "10.18653/v1/2020.acl-main.180",
    pages = "1991--2002",
    abstract = "Recent work has found evidence that natural languages are shaped by pressures for efficient communication {---} e.g. the more contextually predictable a word is, the fewer speech sounds or syllables it has (Piantadosi et al. 2011). Research on the degree to which speech and language are shaped by pressures for effective communication {---} robustness in the face of noise and uncertainty {---} has been more equivocal. We develop a measure of contextual confusability during word recognition based on psychoacoustic data. Applying this measure to naturalistic speech corpora, we find evidence suggesting that speakers alter their productions to make contextually more confusable words easier to understand.",
}
@inproceedings{futrell-etal-2020-determines,
    title = "What determines the order of adjectives in {E}nglish? Comparing efficiency-based theories using dependency treebanks",
    author = "Futrell, Richard  and
      Dyer, William  and
      Scontras, Greg",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.181",
    doi = "10.18653/v1/2020.acl-main.181",
    pages = "2003--2012",
    abstract = "We take up the scientific question of what determines the preferred order of adjectives in English, in phrases such as big blue box where multiple adjectives modify a following noun. We implement and test four quantitative theories, all of which are theoretically motivated in terms of efficiency in human language production and comprehension. The four theories we test are subjectivity (Scontras et al., 2017), information locality (Futrell, 2019), integration cost (Dyer, 2017), and information gain, which we introduce. We evaluate theories based on their ability to predict orders of unseen adjectives in hand-parsed and automatically-parsed dependency treebanks. We find that subjectivity, information locality, and information gain are all strong predictors, with some evidence for a two-factor account, where subjectivity and information gain reflect a factor involving semantics, and information locality reflects collocational preferences.",
}
@inproceedings{feng-etal-2020-none,
    title = "{``}None of the Above{''}: Measure Uncertainty in Dialog Response Retrieval",
    author = "Feng, Yulan  and
      Mehri, Shikib  and
      Eskenazi, Maxine  and
      Zhao, Tiancheng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.182",
    doi = "10.18653/v1/2020.acl-main.182",
    pages = "2013--2020",
    abstract = "This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks and presents our experimental results on uncertainty classification on the processed Ubuntu Dialog Corpus. We show that instead of retraining models for this specific purpose, we can capture the original retrieval model{'}s underlying confidence concerning the best prediction using trivial additional computation.",
}
@inproceedings{smith-etal-2020-put,
    title = "Can You Put it All Together: Evaluating Conversational Agents{'} Ability to Blend Skills",
    author = "Smith, Eric Michael  and
      Williamson, Mary  and
      Shuster, Kurt  and
      Weston, Jason  and
      Boureau, Y-Lan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.183",
    doi = "10.18653/v1/2020.acl-main.183",
    pages = "2021--2030",
    abstract = "Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow. In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.",
}
@inproceedings{zhang-etal-2020-grounded,
    title = "Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs",
    author = "Zhang, Houyu  and
      Liu, Zhenghao  and
      Xiong, Chenyan  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.184",
    doi = "10.18653/v1/2020.acl-main.184",
    pages = "2031--2043",
    abstract = "Human conversations naturally evolve around related concepts and hop to distant concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows. By grounding conversations to the concept space, ConceptFlow represents the potential conversation flow as traverses in the concept space along commonsense relations. The traverse is guided by graph attentions in the concept graph, moving towards more meaningful directions in the concept space, in order to generate more semantic and informative responses. Experiments on Reddit conversations demonstrate ConceptFlow{'}s effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70{\%} fewer parameters, confirming the advantage of explicit modeling conversation structures. All source codes of this work are available at https://github.com/thunlp/ConceptFlow.",
}
@inproceedings{he-glass-2020-negative,
    title = "Negative Training for Neural Dialogue Response Generation",
    author = "He, Tianxing  and
      Glass, James",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.185",
    doi = "10.18653/v1/2020.acl-main.185",
    pages = "2044--2058",
    abstract = "Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses and generic (boring) responses. In this work, we propose a framework named {``}Negative Training{''} to minimize such behaviors. Given a trained model, the framework will first find generated samples that exhibit the undesirable behavior, and then use them to feed negative training signals for fine-tuning the model. Our experiments show that negative training can significantly reduce the hit rate of malicious responses, or discourage frequent responses and improve response diversity.",
}
@inproceedings{gangadharaiah-narayanaswamy-2020-recursive,
    title = "Recursive Template-based Frame Generation for Task Oriented Dialog",
    author = "Gangadharaiah, Rashmi  and
      Narayanaswamy, Balakrishnan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.186",
    doi = "10.18653/v1/2020.acl-main.186",
    pages = "2059--2064",
    abstract = "The Natural Language Understanding (NLU) component in task oriented dialog systems processes a user{'}s request and converts it into structured information that can be consumed by downstream components such as the Dialog State Tracker (DST). This information is typically represented as a semantic frame that captures the intent and slot-labels provided by the user. We first show that such a shallow representation is insufficient for complex dialog scenarios, because it does not capture the recursive nature inherent in many domains. We propose a recursive, hierarchical frame-based representation and show how to learn it from data. We formulate the frame generation task as a template-based tree decoding task, where the decoder recursively generates a template and then fills slot values into the template. We extend local tree-based loss functions with terms that provide global supervision and show how to optimize them end-to-end. We achieve a small improvement on the widely used ATIS dataset and a much larger improvement on a more complex dataset we describe here.",
}
@inproceedings{elgohary-etal-2020-speak,
    title = "Speak to your Parser: Interactive Text-to-{SQL} with Natural Language Feedback",
    author = "Elgohary, Ahmed  and
      Hosseini, Saghar  and
      Hassan Awadallah, Ahmed",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.187",
    doi = "10.18653/v1/2020.acl-main.187",
    pages = "2065--2077",
    abstract = "We study the task of semantic parse correction with natural language feedback. Given a natural language utterance, most semantic parsing systems pose the problem as one-shot translation where the utterance is mapped to a corresponding logical form. In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance. We focus on natural language to SQL systems and construct, SPLASH, a dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback. We compare various reference models for the correction task and show that incorporating such a rich form of feedback can significantly improve the overall semantic parsing accuracy while retaining the flexibility of natural language interaction. While we estimated human correction accuracy is 81.5{\%}, our best model achieves only 25.1{\%}, which leaves a large gap for improvement in future research. SPLASH is publicly available at https://aka.ms/Splash{\_}dataset.",
}
@inproceedings{jagannatha-yu-2020-calibrating,
    title = "Calibrating Structured Output Predictors for Natural Language Processing",
    author = "Jagannatha, Abhyuday  and
      Yu, Hong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.188",
    doi = "10.18653/v1/2020.acl-main.188",
    pages = "2078--2092",
    abstract = "We address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition and question answering produce calibrated confidence scores for their predictions, especially if the applications are to be deployed in a safety-critical domain such as healthcare. However the output space of such structured prediction models are often too large to directly adapt binary or multi-class calibration methods. In this study, we propose a general calibration scheme for output entities of interest in neural network based structured prediction models. Our proposed method can be used with any binary class calibration scheme and a neural network model. Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements. We show that our method outperforms current calibration techniques for Named Entity Recognition, Part-of-speech tagging and Question Answering systems. We also observe an improvement in model performance from our decoding step across several tasks and benchmark datasets. Our method improves the calibration and model performance on out-of-domain test scenarios as well.",
}
@inproceedings{brantley-etal-2020-active,
    title = "Active Imitation Learning with Noisy Guidance",
    author = "Brantley, Kiant{\'e}  and
      Sharaf, Amr  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.189",
    doi = "10.18653/v1/2020.acl-main.189",
    pages = "2093--2105",
    abstract = "Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies. Such algorithms assume training-time access to an expert that can provide the optimal action at any queried state; unfortunately, the number of such queries is often prohibitive, frequently rendering these approaches impractical. To combat this query complexity, we consider an active learning setting in which the learning algorithm has additional access to a much cheaper noisy heuristic that provides noisy guidance. Our algorithm, LEAQI, learns a difference classifier that predicts when the expert is likely to disagree with the heuristic, and queries the expert only when necessary. We apply LEAQI to three sequence labelling tasks, demonstrating significantly fewer queries to the expert and comparable (or better) accuracies over a passive approach.",
}
@inproceedings{murty-etal-2020-expbert,
    title = "{E}xp{BERT}: Representation Engineering with Natural Language Explanations",
    author = "Murty, Shikhar  and
      Koh, Pang Wei  and
      Liang, Percy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.190",
    doi = "10.18653/v1/2020.acl-main.190",
    pages = "2106--2113",
    abstract = "Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow model developers to specify these types of inductive biases as natural language explanations. We use BERT fine-tuned on MultiNLI to {``}interpret{''} these explanations with respect to the input sentence, producing explanation-guided representations of the input. Across three relation extraction tasks, our method, ExpBERT, matches a BERT baseline but with 3{--}20x less labeled data and improves on the baseline by 3{--}10 F1 points with the same amount of labeled data.",
}
@inproceedings{croce-etal-2020-gan,
    title = "{GAN}-{BERT}: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples",
    author = "Croce, Danilo  and
      Castellucci, Giuseppe  and
      Basili, Roberto",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.191",
    doi = "10.18653/v1/2020.acl-main.191",
    pages = "2114--2119",
    abstract = "Recent Transformer-based architectures, e.g., BERT, provide impressive results in many Natural Language Processing tasks. However, most of the adopted benchmarks are made of (sometimes hundreds of) thousands of examples. In many real scenarios, obtaining high- quality annotated data is expensive and time consuming; in contrast, unlabeled examples characterizing the target task can be, in general, easily collected. One promising method to enable semi-supervised learning has been proposed in image processing, based on Semi- Supervised Generative Adversarial Networks. In this paper, we propose GAN-BERT that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. Experimental results show that the requirement for annotated examples can be drastically reduced (up to only 50-100 annotated examples), still obtaining good performances in several sentence classification tasks.",
}
@inproceedings{jiang-etal-2020-generalizing,
    title = "Generalizing Natural Language Analysis through Span-relation Representations",
    author = "Jiang, Zhengbao  and
      Xu, Wei  and
      Araki, Jun  and
      Neubig, Graham",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.192",
    doi = "10.18653/v1/2020.acl-main.192",
    pages = "2120--2133",
    abstract = "Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks. We perform extensive experiments to test this insight on 10 disparate tasks spanning dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving performance comparable to state-of-the-art specialized models. We further demonstrate benefits of multi-task learning, and also show that the proposed method makes it easy to analyze differences and similarities in how the model handles different tasks. Finally, we convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.",
}
@inproceedings{lan-etal-2020-learning,
    title = "Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling",
    author = "Lan, Ouyu  and
      Huang, Xiao  and
      Lin, Bill Yuchen  and
      Jiang, He  and
      Liu, Liyuan  and
      Ren, Xiang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.193",
    doi = "10.18653/v1/2020.acl-main.193",
    pages = "2134--2146",
    abstract = "Sequence labeling is a fundamental task for a range of natural language processing problems. When used in practice, its performance is largely influenced by the annotation quality and quantity, and meanwhile, obtaining ground truth labels is often costly. In many cases, ground truth labels do not exist, but noisy annotations or annotations from different domains are accessible. In this paper, we propose a novel framework Consensus Network (ConNet) that can be trained on annotations from multiple sources (e.g., crowd annotation, cross-domain data). It learns individual representation for every source and dynamically aggregates source-specific knowledge by a context-aware attention module. Finally, it leads to a model reflecting the agreement (consensus) among multiple sources. We evaluate the proposed framework in two practical settings of multi-source learning: learning with crowd annotations and unsupervised cross-domain model adaptation. Extensive experimental results show that our model achieves significant improvements over existing methods in both settings. We also demonstrate that the method can apply to various tasks and cope with different encoders.",
}
@inproceedings{chen-etal-2020-mixtext,
    title = "{M}ix{T}ext: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification",
    author = "Chen, Jiaao  and
      Yang, Zichao  and
      Yang, Diyi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.194",
    doi = "10.18653/v1/2020.acl-main.194",
    pages = "2147--2157",
    abstract = "This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess low-entropy labels for unlabeled data, hence making them as easy to use as labeled data. By mixing labeled, unlabeled and augmented data, MixText significantly outperformed current pre-trained and fined-tuned models and other state-of-the-art semi-supervised learning methods on several text classification benchmarks. The improvement is especially prominent when supervision is extremely limited. We have publicly released our code at https://github.com/GT-SALT/MixText.",
}
@inproceedings{sun-etal-2020-mobilebert,
    title = "{M}obile{BERT}: a Compact Task-Agnostic {BERT} for Resource-Limited Devices",
    author = "Sun, Zhiqing  and
      Yu, Hongkun  and
      Song, Xiaodan  and
      Liu, Renjie  and
      Yang, Yiming  and
      Zhou, Denny",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.195",
    doi = "10.18653/v1/2020.acl-main.195",
    pages = "2158--2170",
    abstract = "Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT{\_}LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT{\_}LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT{\_}BASE while achieving competitive results on well-known benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves a GLUE score of 77.7 (0.6 lower than BERT{\_}BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of 90.0/79.2 (1.5/2.1 higher than BERT{\_}BASE).",
}
@inproceedings{logan-iv-etal-2020-importance,
    title = "On Importance Sampling-Based Evaluation of Latent Language Models",
    author = "Logan IV, Robert L.  and
      Gardner, Matt  and
      Singh, Sameer",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.196",
    doi = "10.18653/v1/2020.acl-main.196",
    pages = "2171--2176",
    abstract = "Language models that use additional latent structures (e.g., syntax trees, coreference chains, knowledge graph links) provide several advantages over traditional language models. However, likelihood-based evaluation of these models is often intractable as it requires marginalizing over the latent space. Existing works avoid this issue by using importance sampling. Although this approach has asymptotic guarantees, analysis is rarely conducted on the effect of decisions such as sample size and choice of proposal distribution on the reported estimates. In this paper, we carry out this analysis for three models: RNNG, EntityNLM, and KGLM. In addition, we elucidate subtle differences in how importance sampling is applied in these works that can have substantial effects on the final estimates, as well as provide theoretical results which reinforce the validity of this technique.",
}
@inproceedings{jiang-etal-2020-smart,
    title = "{SMART}: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization",
    author = "Jiang, Haoming  and
      He, Pengcheng  and
      Chen, Weizhu  and
      Liu, Xiaodong  and
      Gao, Jianfeng  and
      Zhao, Tuo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.197",
    doi = "10.18653/v1/2020.acl-main.197",
    pages = "2177--2190",
    abstract = "Transfer learning has fundamentally changed the landscape of natural language processing (NLP). Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to overfit the training data of downstream tasks and fail to generalize to unseen data. To address such an issue in a principled manner, we propose a new learning framework for robust and efficient fine-tuning for pre-trained models to attain better generalization performance. The proposed framework contains two important ingredients: 1. Smoothness-inducing regularization, which effectively manages the complexity of the model; 2. Bregman proximal point optimization, which is an instance of trust-region methods and can prevent aggressive updating. Our experiments show that the proposed framework achieves new state-of-the-art performance on a number of NLP tasks including GLUE, SNLI, SciTail and ANLI. Moreover, it also outperforms the state-of-the-art T5 model, which is the largest pre-trained model containing 11 billion parameters, on GLUE.",
}
@inproceedings{demeter-etal-2020-stolen,
    title = "Stolen Probability: A Structural Weakness of Neural Language Models",
    author = "Demeter, David  and
      Kimmel, Gregory  and
      Downey, Doug",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.198",
    doi = "10.18653/v1/2020.acl-main.198",
    pages = "2191--2197",
    abstract = "Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word vectors in a high-dimensional embedding space. The dot-product distance metric forms part of the inductive bias of NNLMs. Although NNLMs optimize well with this inductive bias, we show that this results in a sub-optimal ordering of the embedding space that structurally impoverishes some words at the expense of others when assigning probability. We present numerical, theoretical and empirical analyses which show that words on the interior of the convex hull in the embedding space have their probability bounded by the probabilities of the words on the hull.",
}
@inproceedings{shang-etal-2020-taxonomy,
    title = "Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer",
    author = "Shang, Chao  and
      Dash, Sarthak  and
      Chowdhury, Md. Faisal Mahbub  and
      Mihindukulasooriya, Nandana  and
      Gliozzo, Alfio",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.199",
    doi = "10.18653/v1/2020.acl-main.199",
    pages = "2198--2208",
    abstract = "Extracting lexico-semantic relations as graph-structured taxonomies, also known as taxonomy construction, has been beneficial in a variety of NLP applications. Recently Graph Neural Network (GNN) has shown to be powerful in successfully tackling many tasks. However, there has been no attempt to exploit GNN to create taxonomies. In this paper, we propose Graph2Taxo, a GNN-based cross-domain transfer framework for the taxonomy construction task. Our main contribution is to learn the latent features of taxonomy construction from existing domains to guide the structure learning of an unseen domain. We also propose a novel method of directed acyclic graph (DAG) generation for taxonomy construction. Specifically, our proposed Graph2Taxo uses a noisy graph constructed from automatically extracted noisy hyponym hypernym candidate pairs, and a set of taxonomies for some known domains for training. The learned model is then used to generate taxonomy for a new unknown domain given a set of terms for that domain. Experiments on benchmark datasets from science and environment domains show that our approach attains significant improvements correspondingly over the state of the art.",
}
@inproceedings{wang-etal-2020-pretrain,
    title = "To Pretrain or Not to Pretrain: Examining the Benefits of Pretrainng on Resource Rich Tasks",
    author = "Wang, Sinong  and
      Khabsa, Madian  and
      Ma, Hao",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.200",
    doi = "10.18653/v1/2020.acl-main.200",
    pages = "2209--2213",
    abstract = "Pretraining NLP models with variants of Masked Language Model (MLM) objectives has recently led to a significant improvements on many tasks. This paper examines the benefits of pretrained models as a function of the number of training samples used in the downstream task. On several text classification tasks, we show that as the number of training examples grow into the millions, the accuracy gap between finetuning BERT-based model and training vanilla LSTM from scratch narrows to within 1{\%}. Our findings indicate that MLM-based models might reach a diminishing return point as the supervised data size increases significantly.",
}
@inproceedings{zhang-etal-2020-overfitting,
    title = "Why Overfitting Isn{'}t Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries",
    author = "Zhang, Mozhi  and
      Fujinuma, Yoshinari  and
      Paul, Michael J.  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.201",
    doi = "10.18653/v1/2020.acl-main.201",
    pages = "2214--2220",
    abstract = "Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI). Recent CLWE methods use linear projections, which underfit the training dictionary, to generalize on BLI. However, underfitting can hinder generalization to other downstream tasks that rely on words from the training dictionary. We address this limitation by retrofitting CLWE to the training dictionary, which pulls training translation pairs closer in the embedding space and overfits the training dictionary. This simple post-processing step often improves accuracy on two downstream tasks, despite lowering BLI test accuracy. We also retrofit to both the training dictionary and a synthetic dictionary induced from CLWE, which sometimes generalizes even better on downstream tasks. Our results confirm the importance of fully exploiting training dictionary in downstream tasks and explains why BLI is a flawed CLWE evaluation.",
}
@inproceedings{mukherjee-hassan-awadallah-2020-xtremedistil,
    title = "{X}treme{D}istil: Multi-stage Distillation for Massive Multilingual Models",
    author = "Mukherjee, Subhabrata  and
      Hassan Awadallah, Ahmed",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.202",
    doi = "10.18653/v1/2020.acl-main.202",
    pages = "2221--2234",
    abstract = "Deep and large pre-trained language models are the state-of-the-art for various natural language processing tasks. However, the huge size of these models could be a deterrent to using them in practice. Some recent works use knowledge distillation to compress these huge models into shallow ones. In this work we study knowledge distillation with a focus on multilingual Named Entity Recognition (NER). In particular, we study several distillation strategies and propose a stage-wise optimization scheme leveraging teacher internal representations, that is agnostic of teacher architecture, and show that it outperforms strategies employed in prior works. Additionally, we investigate the role of several factors like the amount of unlabeled data, annotation resources, model architecture and inference latency to name a few. We show that our approach leads to massive compression of teacher models like mBERT by upto 35x in terms of parameters and 51x in terms of latency for batch inference while retaining 95{\%} of its F1-score for NER over 41 languages.",
}
@inproceedings{mahmood-etal-2020-girl,
    title = "A Girl Has A Name: Detecting Authorship Obfuscation",
    author = "Mahmood, Asad  and
      Shafiq, Zubair  and
      Srinivasan, Padmini",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.203",
    doi = "10.18653/v1/2020.acl-main.203",
    pages = "2235--2245",
    abstract = "Authorship attribution aims to identify the author of a text based on the stylometric analysis. Authorship obfuscation, on the other hand, aims to protect against authorship attribution by modifying a text{'}s style. In this paper, we evaluate the stealthiness of state-of-the-art authorship obfuscation methods under an adversarial threat model. An obfuscator is stealthy to the extent an adversary finds it challenging to detect whether or not a text modified by the obfuscator is obfuscated {--} a decision that is key to the adversary interested in authorship attribution. We show that the existing authorship obfuscation methods are not stealthy as their obfuscated texts can be identified with an average F1 score of 0.87. The reason for the lack of stealthiness is that these obfuscators degrade text smoothness, as ascertained by neural language models, in a detectable manner. Our results highlight the need to develop stealthy authorship obfuscation methods that can better protect the identity of an author seeking anonymity.",
}
@inproceedings{xin-etal-2020-deebert,
    title = "{D}ee{BERT}: Dynamic Early Exiting for Accelerating {BERT} Inference",
    author = "Xin, Ji  and
      Tang, Raphael  and
      Lee, Jaejun  and
      Yu, Yaoliang  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.204",
    doi = "10.18653/v1/2020.acl-main.204",
    pages = "2246--2251",
    abstract = "Large-scale pre-trained language models such as BERT have brought significant improvements to NLP applications. However, they are also notorious for being slow in inference, which makes them difficult to deploy in real-time applications. We propose a simple but effective method, DeeBERT, to accelerate BERT inference. Our approach allows samples to exit earlier without passing through the entire model. Experiments show that DeeBERT is able to save up to {\textasciitilde}40{\%} inference time with minimal degradation in model quality. Further analyses show different behaviors in the BERT transformer layers and also reveal their redundancy. Our work provides new ideas to efficiently apply deep transformer-based models to downstream tasks. Code is available at https://github.com/castorini/DeeBERT.",
}
@inproceedings{rivas-rojas-etal-2020-efficient,
    title = "Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks",
    author = "Rivas Rojas, Kervy  and
      Bustamante, Gina  and
      Oncevay, Arturo  and
      Sobrevilla Cabezudo, Marco Antonio",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.205",
    doi = "10.18653/v1/2020.acl-main.205",
    pages = "2252--2257",
    abstract = "In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the studies have focused on developing novels neural network architectures to deal with the hierarchical structure, but we prefer to look for efficient ways to strengthen a baseline model. We first define the task as a sequence-to-sequence problem. Afterwards, we propose an auxiliary synthetic task of bottom-up-classification. Then, from external dictionaries, we retrieve textual definitions for the classes of all the hierarchy{'}s layers, and map them into the word vector space. We use the class-definition embeddings as an additional input to condition the prediction of the next layer and in an adapted beam search. Whereas the modified search did not provide large gains, the combination of the auxiliary task and the additional input of class-definitions significantly enhance the classification accuracy. With our efficient approaches, we outperform previous studies, using a drastically reduced number of parameters, in two well-known English datasets.",
}
@inproceedings{craighead-etal-2020-investigating,
    title = "Investigating the effect of auxiliary objectives for the automated grading of learner {E}nglish speech transcriptions",
    author = "Craighead, Hannah  and
      Caines, Andrew  and
      Buttery, Paula  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.206",
    doi = "10.18653/v1/2020.acl-main.206",
    pages = "2258--2269",
    abstract = "We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level of non-native English speakers by taking advantage of inductive transfer between the main task (grading) and auxiliary prediction tasks: morpho-syntactic labeling, language modeling, and native language identification (L1). We encode the transcriptions with both bi-directional recurrent neural networks and with bi-directional representations from transformers, compare against a feature-rich baseline, and analyse performance at different proficiency levels and with transcriptions of varying error rates. Our best performance comes from a transformer encoder with L1 prediction as an auxiliary task. We discuss areas for improvement and potential applications for text-only speech scoring.",
}
@inproceedings{cohan-etal-2020-specter,
    title = "{SPECTER}: Document-level Representation Learning using Citation-informed Transformers",
    author = "Cohan, Arman  and
      Feldman, Sergey  and
      Beltagy, Iz  and
      Downey, Doug  and
      Weld, Daniel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.207",
    doi = "10.18653/v1/2020.acl-main.207",
    pages = "2270--2282",
    abstract = "Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.",
}
@inproceedings{zhong-etal-2020-semantic,
    title = "Semantic Scaffolds for Pseudocode-to-Code Generation",
    author = "Zhong, Ruiqi  and
      Stern, Mitchell  and
      Klein, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.208",
    doi = "10.18653/v1/2020.acl-main.208",
    pages = "2283--2295",
    abstract = "We propose a method for program generation based on semantic scaffolds, lightweight structures representing the high-level semantic and syntactic composition of a program. By first searching over plausible scaffolds then using these as constraints for a beam search over programs, we achieve better coverage of the search space when compared with existing techniques. We apply our hierarchical search method to the SPoC dataset for pseudocode-to-code generation, in which we are given line-level natural language pseudocode annotations and aim to produce a program satisfying execution-based test cases. By using semantic scaffolds during inference, we achieve a 10{\%} absolute improvement in top-100 accuracy over the previous state-of-the-art. Additionally, we require only 11 candidates to reach the top-3000 performance of the previous best approach when tested against unseen problems, demonstrating a substantial improvement in efficiency.",
}
@inproceedings{broscheit-etal-2020-predict,
    title = "Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction",
    author = "Broscheit, Samuel  and
      Gashteovski, Kiril  and
      Wang, Yanjie  and
      Gemulla, Rainer",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.209",
    doi = "10.18653/v1/2020.acl-main.209",
    pages = "2296--2308",
    abstract = "Open Information Extraction systems extract ({``}subject text{''}, {``}relation text{''}, {``}object text{''}) triples from raw text. Some triples are textual versions of facts, i.e., non-canonicalized mentions of entities and relations. In this paper, we investigate whether it is possible to infer new facts directly from the open knowledge graph without any canonicalization or any supervision from curated knowledge. For this purpose, we propose the open link prediction task,i.e., predicting test facts by completing ({``}subject text{''}, {``}relation text{''}, ?) questions. An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained. For example, facts can appear in different paraphrased textual variants, which can lead to test leakage. To this end, we propose an evaluation protocol and a methodology for creating the open link prediction benchmark OlpBench. We performed experiments with a prototypical knowledge graph embedding model for openlink prediction. While the task is very challenging, our results suggests that it is possible to predict genuinely new facts, which can not be trivially explained.",
}
@inproceedings{gupta-etal-2020-infotabs,
    title = "{INFOTABS}: Inference on Tables as Semi-structured Data",
    author = "Gupta, Vivek  and
      Mehta, Maitrey  and
      Nokhiz, Pegah  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.210",
    doi = "10.18653/v1/2020.acl-main.210",
    pages = "2309--2324",
    abstract = "In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them. We argue that such data can prove as a testing ground for understanding how we reason about information. To study this, we introduce a new dataset called INFOTABS, comprising of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. Our analysis shows that the semi-structured, multi-domain and heterogeneous nature of the premises admits complex, multi-faceted reasoning. Experiments reveal that, while human annotators agree on the relationships between a table-hypothesis pair, several standard modeling strategies are unsuccessful at the task, suggesting that reasoning about tables can pose a difficult modeling challenge.",
}
@inproceedings{yuan-etal-2020-interactive,
    title = "Interactive Machine Comprehension with Information Seeking Agents",
    author = "Yuan, Xingdi  and
      Fu, Jie  and
      C{\^o}t{\'e}, Marc-Alexandre  and
      Tay, Yi  and
      Pal, Chris  and
      Trischler, Adam",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.211",
    doi = "10.18653/v1/2020.acl-main.211",
    pages = "2325--2338",
    abstract = "Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of MRC datasets: most of these are static environments wherein the supporting documents and all necessary information are fully observed. In this paper, we propose a simple method that reframes existing MRC datasets as interactive, partially observable environments. Specifically, we {``}occlude{''} the majority of a document{'}s text and add context-sensitive commands that reveal {``}glimpses{''} of the hidden text to a model. We repurpose SQuAD and NewsQA as an initial case study, and then show how the interactive corpora can be used to train a model that seeks relevant information through sequential decision making. We believe that this setting can contribute in scaling models to web-level QA scenarios.",
}
@inproceedings{min-etal-2020-syntactic,
    title = "Syntactic Data Augmentation Increases Robustness to Inference Heuristics",
    author = "Min, Junghyun  and
      McCoy, R. Thomas  and
      Das, Dipanjan  and
      Pitler, Emily  and
      Linzen, Tal",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.212",
    doi = "10.18653/v1/2020.acl-main.212",
    pages = "2339--2352",
    abstract = "Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets. We hypothesize that this issue is not primarily caused by the pretrained model{'}s limitations, but rather by the paucity of crowdsourced NLI examples that might convey the importance of syntactic structure at the fine-tuning stage. We explore several methods to augment standard training sets with syntactically informative examples, generated by applying syntactic transformations to sentences from the MNLI corpus. The best-performing augmentation method, subject/object inversion, improved BERT{'}s accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73, without affecting performance on the MNLI test set. This improvement generalized beyond the particular construction used for data augmentation, suggesting that augmentation causes BERT to recruit abstract syntactic representations.",
}
@inproceedings{chung-glass-2020-improved,
    title = "Improved Speech Representations with Multi-Target Autoregressive Predictive Coding",
    author = "Chung, Yu-An  and
      Glass, James",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.213",
    doi = "10.18653/v1/2020.acl-main.213",
    pages = "2353--2358",
    abstract = "Training objectives based on predictive coding have recently been shown to be very effective at learning meaningful representations from unlabeled speech. One example is Autoregressive Predictive Coding (Chung et al., 2019), which trains an autoregressive RNN to generate an unseen future frame given a context such as recent past frames. The basic hypothesis of these approaches is that hidden states that can accurately predict future frames are a useful representation for many downstream tasks. In this paper we extend this hypothesis and aim to enrich the information encoded in the hidden states by training the model to make more accurate future predictions. We propose an auxiliary objective that serves as a regularization to improve generalization of the future frame prediction task. Experimental results on phonetic classification, speech recognition, and speech translation not only support the hypothesis, but also demonstrate the effectiveness of our approach in learning representations that contain richer phonetic content.",
}
@inproceedings{rahman-etal-2020-integrating,
    title = "Integrating Multimodal Information in Large Pretrained Transformers",
    author = "Rahman, Wasifur  and
      Hasan, Md Kamrul  and
      Lee, Sangwu  and
      Bagher Zadeh, AmirAli  and
      Mao, Chengfeng  and
      Morency, Louis-Philippe  and
      Hoque, Ehsan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.214",
    doi = "10.18653/v1/2020.acl-main.214",
    pages = "2359--2369",
    abstract = "Recent Transformer-based contextual word representations, including BERT and XLNet, have shown state-of-the-art performance in multiple disciplines within NLP. Fine-tuning the trained contextual models on task-specific datasets has been the key to achieving superior performance downstream. While fine-tuning these pre-trained models is straightforward for lexical applications (applications with only language modality), it is not trivial for multimodal language (a growing area in NLP focused on modeling face-to-face communication). More specifically, this is due to the fact that pre-trained models don{'}t have the necessary components to accept two extra modalities of vision and acoustic. In this paper, we proposed an attachment to BERT and XLNet called Multimodal Adaptation Gate (MAG). MAG allows BERT and XLNet to accept multimodal nonverbal data during fine-tuning. It does so by generating a shift to internal representation of BERT and XLNet; a shift that is conditioned on the visual and acoustic modalities. In our experiments, we study the commonly used CMU-MOSI and CMU-MOSEI datasets for multimodal sentiment analysis. Fine-tuning MAG-BERT and MAG-XLNet significantly boosts the sentiment analysis performance over previous baselines as well as language-only fine-tuning of BERT and XLNet. On the CMU-MOSI dataset, MAG-XLNet achieves human-level multimodal sentiment analysis performance for the first time in the NLP community.",
}
@inproceedings{d-havtorn-etal-2020-multiqt,
    title = "{M}ulti{QT}: Multimodal learning for real-time question tracking in speech",
    author = "D. Havtorn, Jakob  and
      Latko, Jan  and
      Edin, Joakim  and
      Maal{\o}e, Lars  and
      Borgholt, Lasse  and
      Belgrano, Lorenzo  and
      Jacobsen, Nicolai  and
      Sdun, Regitze  and
      Agi{\'c}, {\v{Z}}eljko",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.215",
    doi = "10.18653/v1/2020.acl-main.215",
    pages = "2370--2380",
    abstract = "We address a challenging and practical task of labeling questions in speech in real time during telephone calls to emergency medical services in English, which embeds within a broader decision support system for emergency call-takers. We propose a novel multimodal approach to real-time sequence labeling in speech. Our model treats speech and its own textual representation as two separate modalities or views, as it jointly learns from streamed audio and its noisy transcription into text via automatic speech recognition. Our results show significant gains of jointly learning from the two modalities when compared to text or audio only, under adverse noise and limited volume of training data. The results generalize to medical symptoms detection where we observe a similar pattern of improvements with multimodal learning.",
}
@inproceedings{paraskevopoulos-etal-2020-multimodal,
    title = "Multimodal and Multiresolution Speech Recognition with Transformers",
    author = "Paraskevopoulos, Georgios  and
      Parthasarathy, Srinivas  and
      Khare, Aparna  and
      Sundaram, Shiva",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.216",
    doi = "10.18653/v1/2020.acl-main.216",
    pages = "2381--2387",
    abstract = "This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture. We particularly focus on the scene context provided by the visual information, to ground the ASR. We extract representations for audio features in the encoder layers of the transformer and fuse video features using an additional crossmodal multihead attention layer. Additionally, we incorporate a multitask training criterion for multiresolution ASR, where we train the model to generate both character and subword level transcriptions. Experimental results on the How2 dataset, indicate that multiresolution training can speed up convergence by around 50{\%} and relatively improves word error rate (WER) performance by upto 18{\%} over subword prediction models. Further, incorporating visual information improves performance with relative gains upto 3.76{\%} over audio only models. Our results are comparable to state-of-the-art Listen, Attend and Spell-based architectures.",
}
@inproceedings{salesky-black-2020-phone,
    title = "Phone Features Improve Speech Translation",
    author = "Salesky, Elizabeth  and
      Black, Alan W",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.217",
    doi = "10.18653/v1/2020.acl-main.217",
    pages = "2388--2397",
    abstract = "End-to-end models for speech translation (ST) more tightly couple speech recognition (ASR) and machine translation (MT) than a traditional cascade of separate ASR and MT models, with simpler model architectures and the potential for reduced error propagation. Their performance is often assumed to be superior, though in many conditions this is not yet the case. We compare cascaded and end-to-end models across high, medium, and low-resource conditions, and show that cascades remain stronger baselines. Further, we introduce two methods to incorporate phone features into ST models. We show that these features improve both architectures, closing the gap between end-to-end models and cascades, and outperforming previous academic work {--} by up to 9 BLEU on our low-resource setting.",
}
@inproceedings{cho-may-2020-grounding,
    title = "Grounding Conversations with Improvised Dialogues",
    author = "Cho, Hyundong  and
      May, Jonathan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.218",
    doi = "10.18653/v1/2020.acl-main.218",
    pages = "2398--2413",
    abstract = "Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people. Modern dialogue systems are not explicitly trained to build common ground, and therefore overlook this important aspect of communication. Improvisational theater (improv) intrinsically contains a high proportion of dialogue focused on building common ground, and makes use of the yes-and principle, a strong grounding speech act, to establish coherence and an actionable objective reality. We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier. We fine-tune chit-chat dialogue systems with our corpus to encourage more grounded, relevant conversation and confirm these findings with human evaluations.",
}
@inproceedings{shuster-etal-2020-image,
    title = "Image-Chat: Engaging Grounded Conversations",
    author = "Shuster, Kurt  and
      Humeau, Samuel  and
      Bordes, Antoine  and
      Weston, Jason",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.219",
    doi = "10.18653/v1/2020.acl-main.219",
    pages = "2414--2429",
    abstract = "To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a setup naturally appealing to humans (Hu et al., 2014). In this work we study large-scale architectures and datasets for this goal. We test a set of neural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019). Our dataset, Image-Chat, consists of 202k dialogues over 202k images using 215 possible style traits. Automatic metrics and human evaluations of engagingness show the efficacy of our approach; in particular, we obtain state-of-the-art performance on the existing IGC task, and our best performing model is almost on par with humans on the Image-Chat test set (preferred 47.7{\%} of the time).",
}
@inproceedings{sinha-etal-2020-learning,
    title = "Learning an Unreferenced Metric for Online Dialogue Evaluation",
    author = "Sinha, Koustuv  and
      Parthasarathi, Prasanna  and
      Wang, Jasmine  and
      Lowe, Ryan  and
      Hamilton, William L.  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.220",
    doi = "10.18653/v1/2020.acl-main.220",
    pages = "2430--2441",
    abstract = "Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to develop automatic dialogue evaluation metrics, but most of them do not generalize to unseen datasets and/or need a human-generated reference response during inference, making it infeasible for online evaluation. Here, we propose an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances, and leverages the temporal transitions that exist between them. We show that our model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference.",
}
@inproceedings{roddy-harte-2020-neural,
    title = "Neural Generation of Dialogue Response Timings",
    author = "Roddy, Matthew  and
      Harte, Naomi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.221",
    doi = "10.18653/v1/2020.acl-main.221",
    pages = "2442--2452",
    abstract = "The timings of spoken response offsets in human dialogue have been shown to vary based on contextual elements of the dialogue. We propose neural models that simulate the distributions of these response offsets, taking into account the response turn as well as the preceding turn. The models are designed to be integrated into the pipeline of an incremental spoken dialogue system (SDS). We evaluate our models using offline experiments as well as human listening tests. We show that human listeners consider certain response timings to be more natural based on the dialogue context. The introduction of these models into SDS pipelines could increase the perceived naturalness of interactions.",
}
@inproceedings{shuster-etal-2020-dialogue,
    title = "The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents",
    author = "Shuster, Kurt  and
      Ju, Da  and
      Roller, Stephen  and
      Dinan, Emily  and
      Boureau, Y-Lan  and
      Weston, Jason",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.222",
    doi = "10.18653/v1/2020.acl-main.222",
    pages = "2453--2470",
    abstract = "We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images. By multi-tasking on such a broad large-scale set of data, we hope to both move towards and measure progress in producing a single unified agent that can perceive, reason and converse with humans in an open-domain setting. We show that such multi-tasking improves over a BERT pre-trained baseline, largely due to multi-tasking with very large dialogue datasets in a similar domain, and that the multi-tasking in general provides gains to both text and image-based tasks using several metrics in both the fine-tune and task transfer settings. We obtain state-of-the-art results on many of the tasks, providing a strong baseline for this challenge.",
}
@inproceedings{van-de-cruys-2020-automatic,
    title = "Automatic Poetry Generation from Prosaic Text",
    author = "Van de Cruys, Tim",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.223",
    doi = "10.18653/v1/2020.acl-main.223",
    pages = "2471--2480",
    abstract = "In the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language. In particular, language models based on neural networks have improved the state of the art with regard to predictive language modeling, while topic models are successful at capturing clear-cut, semantic dimensions. In this paper, we will explore how these approaches can be adapted and combined to model the linguistic and literary aspects needed for poetry generation. The system is exclusively trained on standard, non-poetic text, and its output is constrained in order to confer a poetic character to the generated verse. The framework is applied to the generation of poems in both English and French, and is equally evaluated for both languages. Even though it only uses standard, non-poetic text as input, the system yields state of the art results for poetry generation.",
}
@inproceedings{zhao-etal-2020-bridging,
    title = "Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation",
    author = "Zhao, Chao  and
      Walker, Marilyn  and
      Chaturvedi, Snigdha",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.224",
    doi = "10.18653/v1/2020.acl-main.224",
    pages = "2481--2491",
    abstract = "Generating sequential natural language descriptions from graph-structured data (e.g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text. Hence, popular sequence-to-sequence models, which require serialized input, are not a natural fit for this task. Graph neural networks, on the other hand, can better encode the input graph but broaden the structural gap between the encoder and decoder, making faithful generation difficult. To narrow this gap, we propose DualEnc, a dual encoding model that can not only incorporate the graph structure, but can also cater to the linear structure of the output text. Empirical comparisons with strong single-encoder baselines demonstrate that dual encoding can significantly improve the quality of the generated text.",
}
@inproceedings{donahue-etal-2020-enabling,
    title = "Enabling Language Models to Fill in the Blanks",
    author = "Donahue, Chris  and
      Lee, Mina  and
      Liang, Percy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.225",
    doi = "10.18653/v1/2020.acl-main.225",
    pages = "2492--2501",
    abstract = "We present a simple approach for \textit{text infilling}, the task of predicting missing spans of text at any position in a document. While infilling could enable rich functionality especially for writing assistance tools, more attention has been devoted to language modeling{---}a special case of infilling where text is predicted at the end of a document. In this paper, we aim to extend the capabilities of language models (LMs) to the more general task of infilling. To this end, we train (or fine tune) off-the-shelf LMs on sequences containing the concatenation of artificially-masked text and the text which was masked. We show that this approach, which we call \textit{infilling by language modeling}, can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics. Furthermore, we show that humans have difficulty identifying sentences infilled by our approach as machine-generated in the domain of short stories.",
}
@inproceedings{huang-etal-2020-inset,
    title = "{INSET}: Sentence Infilling with {IN}ter-{SE}ntential Transformer",
    author = "Huang, Yichen  and
      Zhang, Yizhe  and
      Elachqar, Oussama  and
      Cheng, Yu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.226",
    doi = "10.18653/v1/2020.acl-main.226",
    pages = "2502--2515",
    abstract = "Missing sentence generation (or sentence in-filling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion. This task asks the model to generate intermediate missing sentences that can syntactically and semantically bridge the surrounding context. Solving the sentence infilling task requires techniques in natural language processing ranging from understanding to discourse-level planning to generation. In this paper, we propose a framework to decouple the challenge and address these three aspects respectively, leveraging the power of existing large-scale pre-trained models such as BERT and GPT-2. We empirically demonstrate the effectiveness of our model in learning a sentence representation for generation and further generating a missing sentence that fits the context.",
}
@inproceedings{zhang-etal-2020-improving-adversarial,
    title = "Improving Adversarial Text Generation by Modeling the Distant Future",
    author = "Zhang, Ruiyi  and
      Chen, Changyou  and
      Gan, Zhe  and
      Wang, Wenlin  and
      Shen, Dinghan  and
      Wang, Guoyin  and
      Wen, Zheng  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.227",
    doi = "10.18653/v1/2020.acl-main.227",
    pages = "2516--2531",
    abstract = "Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.",
}
@inproceedings{hossain-etal-2020-simple,
    title = "Simple and Effective Retrieve-Edit-Rerank Text Generation",
    author = "Hossain, Nabil  and
      Ghazvininejad, Marjan  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.228",
    doi = "10.18653/v1/2020.acl-main.228",
    pages = "2532--2538",
    abstract = "Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output. We propose to extend this framework with a simple and effective post-generation ranking approach. Our framework (i) retrieves several potentially relevant outputs for each input, (ii) edits each candidate independently, and (iii) re-ranks the edited candidates to select the final output. We use a standard editing model with simple task-specific re-ranking approaches, and we show empirically that this approach outperforms existing, significantly more complex methodologies. Experiments on two machine translation (MT) datasets show new state-of-art results. We also achieve near state-of-art performance on the Gigaword summarization dataset, where our analyses show that there is significant room for performance improvement with better candidate output selection in future work.",
}
@inproceedings{zhu-etal-2020-babywalk,
    title = "{B}aby{W}alk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps",
    author = "Zhu, Wang  and
      Hu, Hexiang  and
      Chen, Jiacheng  and
      Deng, Zhiwei  and
      Jain, Vihan  and
      Ie, Eugene  and
      Sha, Fei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.229",
    doi = "10.18653/v1/2020.acl-main.229",
    pages = "2539--2556",
    abstract = "Learning to follow instructions is of fundamental importance to autonomous agents for vision-and-language navigation (VLN). In this paper, we study how an agent can navigate long paths when learning from a corpus that consists of shorter ones. We show that existing state-of-the-art agents do not generalize well. To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially. A special design memory buffer is used by the agent to turn its past experiences into contexts for future steps. The learning process is composed of two phases. In the first phase, the agent uses imitation learning from demonstration to accomplish BabySteps. In the second phase, the agent uses curriculum-based reinforcement learning to maximize rewards on navigation tasks with increasingly longer instructions. We create two new benchmark datasets (of long navigation tasks) and use them in conjunction with existing ones to examine BabyWalk{'}s generalization ability. Empirical results show that BabyWalk achieves state-of-the-art results on several metrics, in particular, is able to follow long instructions better. The codes and the datasets are released on our project page: https://github.com/Sha-Lab/babywalk.",
}
@inproceedings{li-etal-2020-cross,
    title = "Cross-media Structured Common Space for Multimedia Event Extraction",
    author = "Li, Manling  and
      Zareian, Alireza  and
      Zeng, Qi  and
      Whitehead, Spencer  and
      Lu, Di  and
      Ji, Heng  and
      Chang, Shih-Fu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.230",
    doi = "10.18653/v1/2020.acl-main.230",
    pages = "2557--2568",
    abstract = "We introduce a new task, MultiMedia Event Extraction, which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments. We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space. The structures are aligned across modalities by employing a weakly supervised training strategy, which enables exploiting available resources without explicit cross-media annotation. Compared to uni-modal state-of-the-art methods, our approach achieves 4.0{\%} and 9.8{\%} absolute F-score gains on text event argument role labeling and visual event extraction. Compared to state-of-the-art multimedia unstructured representations, we achieve 8.3{\%} and 5.0{\%} absolute F-score gains on multimedia event extraction and argument role labeling, respectively. By utilizing images, we extract 21.4{\%} more event mentions than traditional text-only methods.",
}
@inproceedings{fried-etal-2020-learning,
    title = "Learning to Segment Actions from Observation and Narration",
    author = "Fried, Daniel  and
      Alayrac, Jean-Baptiste  and
      Blunsom, Phil  and
      Dyer, Chris  and
      Clark, Stephen  and
      Nematzadeh, Aida",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.231",
    doi = "10.18653/v1/2020.acl-main.231",
    pages = "2569--2588",
    abstract = "We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality.",
}
@inproceedings{jayannavar-etal-2020-learning,
    title = "Learning to execute instructions in a {M}inecraft dialogue",
    author = "Jayannavar, Prashant  and
      Narayan-Chen, Anjali  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.232",
    doi = "10.18653/v1/2020.acl-main.232",
    pages = "2589--2602",
    abstract = "The Minecraft Collaborative Building Task is a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated Blocks World Environment. We define the subtask of predicting correct action sequences (block placements and removals) in a given game context, and show that capturing B{'}s past actions as well as B{'}s perspective leads to a significant improvement in performance on this challenging language understanding problem.",
}
@inproceedings{lei-etal-2020-mart,
    title = "{MART}: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning",
    author = "Lei, Jie  and
      Wang, Liwei  and
      Shen, Yelong  and
      Yu, Dong  and
      Berg, Tamara  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.233",
    doi = "10.18653/v1/2020.acl-main.233",
    pages = "2603--2614",
    abstract = "Generating multi-sentence descriptions for videos is one of the most challenging captioning tasks due to its high requirements for not only visual relevance but also discourse-based coherence across the sentences in the paragraph. Towards this goal, we propose a new approach called Memory-Augmented Recurrent Transformer (MART), which uses a memory module to augment the transformer architecture. The memory module generates a highly summarized memory state from the video segments and the sentence history so as to help better prediction of the next sentence (w.r.t. coreference and repetition aspects), thus encouraging coherent paragraph generation. Extensive experiments, human evaluations, and qualitative analyses on two popular datasets ActivityNet Captions and YouCookII show that MART generates more coherent and less repetitive paragraph captions than baseline methods, while maintaining relevance to the input video events.",
}
@inproceedings{kojima-etal-2020-learned,
    title = "What is Learned in Visually Grounded Neural Syntax Acquisition",
    author = "Kojima, Noriyuki  and
      Averbuch-Elor, Hadar  and
      Rush, Alexander  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.234",
    doi = "10.18653/v1/2020.acl-main.234",
    pages = "2615--2635",
    abstract = "Visual features are a promising signal for learning bootstrap textual models. However, blackbox learning models make it difficult to isolate the specific contribution of visual components. In this analysis, we consider the case study of the Visually Grounded Neural Syntax Learner (Shi et al., 2019), a recent approach for learning syntax from a visual training signal. By constructing simplified versions of the model, we isolate the core factors that yield the model{'}s strong performance. Contrary to what the model might be capable of learning, we find significantly less expressive versions produce similar predictions and perform just as well, or even better. We also find that a simple lexical signal of noun concreteness plays the main role in the model{'}s predictions as opposed to more complex syntactic reasoning.",
}
@inproceedings{zhu-etal-2020-batch,
    title = "A Batch Normalized Inference Network Keeps the {KL} Vanishing Away",
    author = "Zhu, Qile  and
      Bi, Wei  and
      Liu, Xiaojiang  and
      Ma, Xiyao  and
      Li, Xiaolin  and
      Wu, Dapeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.235",
    doi = "10.18653/v1/2020.acl-main.235",
    pages = "2636--2649",
    abstract = "Variational Autoencoder (VAE) is widely used as a generative model to approximate a model{'}s posterior on latent variables by combining the amortized variational inference and deep neural networks. However, when paired with strong autoregressive decoders, VAE often converges to a degenerated local optimum known as {``}posterior collapse{''}. Previous approaches consider the Kullback{--}Leibler divergence (KL) individual for each datapoint. We propose to let the KL follow a distribution across the whole dataset, and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL{'}s distribution positive. Then we propose Batch Normalized-VAE (BN-VAE), a simple but effective approach to set a lower bound of the expectation by regularizing the distribution of the approximate posterior{'}s parameters. Without introducing any new model component or modifying the objective, our approach can avoid the posterior collapse effectively and efficiently. We further show that the proposed BN-VAE can be extended to conditional VAE (CVAE). Empirically, our approach surpasses strong autoregressive baselines on language modeling, text classification and dialogue generation, and rivals more complex approaches while keeping almost the same training time as VAE.",
}
@inproceedings{arora-etal-2020-contextual,
    title = "Contextual Embeddings: When Are They Worth It?",
    author = "Arora, Simran  and
      May, Avner  and
      Zhang, Jian  and
      R{\'e}, Christopher",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.236",
    doi = "10.18653/v1/2020.acl-main.236",
    pages = "2650--2663",
    abstract = "We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline{---}random word embeddings{---}focusing on the impact of the training set size and the linguistic properties of the task. Surprisingly, we find that both of these simpler baselines can match contextual embeddings on industry-scale data, and often perform within 5 to 10{\%} accuracy (absolute) on benchmark tasks. Furthermore, we identify properties of data for which contextual embeddings give particularly large gains: language containing complex structure, ambiguous word usage, and words unseen in training.",
}
@inproceedings{yu-etal-2020-interactive,
    title = "Interactive Classification by Asking Informative Questions",
    author = "Yu, Lili  and
      Chen, Howard  and
      Wang, Sida I.  and
      Lei, Tao  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.237",
    doi = "10.18653/v1/2020.acl-main.237",
    pages = "2664--2680",
    abstract = "We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multi-choice questions. At each turn, our system decides between asking the most informative question or making the final classification pre-diction. The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowd-sourcing tasks. We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction.",
}
@inproceedings{sachan-2020-knowledge,
    title = "Knowledge Graph Embedding Compression",
    author = "Sachan, Mrinmaya",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.238",
    doi = "10.18653/v1/2020.acl-main.238",
    pages = "2681--2691",
    abstract = "Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings consume a large amount of storage and memory. This is problematic and prohibits the deployment of these techniques in many real world settings. Thus, we propose an approach that compresses the KG embedding layer by representing each entity in the KG as a vector of discrete codes and then composes the embeddings from these codes. The approach can be trained end-to-end with simple modifications to any existing KG embedding technique. We evaluate the approach on various standard KG embedding evaluations and show that it achieves 50-1000x compression of embeddings with a minor loss in performance. The compressed embeddings also retain the ability to perform various reasoning tasks such as KG inference.",
}
@inproceedings{perl-etal-2020-low,
    title = "Low Resource Sequence Tagging using Sentence Reconstruction",
    author = "Perl, Tal  and
      Chaudhury, Sriram  and
      Giryes, Raja",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.239",
    doi = "10.18653/v1/2020.acl-main.239",
    pages = "2692--2698",
    abstract = "This work revisits the task of training sequence tagging models with limited resources using transfer learning. We investigate several proposed approaches introduced in recent works and suggest a new loss that relies on sentence reconstruction from normalized embeddings. Specifically, our method demonstrates how by adding a decoding layer for sentence reconstruction, we can improve the performance of various baselines. We show improved results on the CoNLL02 NER and UD 1.2 POS datasets and demonstrate the power of the method for transfer learning with low-resources achieving 0.6 F1 score in Dutch using only one sample from it.",
}
@inproceedings{salazar-etal-2020-masked,
    title = "Masked Language Model Scoring",
    author = "Salazar, Julian  and
      Liang, Davis  and
      Nguyen, Toan Q.  and
      Kirchhoff, Katrin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.240",
    doi = "10.18653/v1/2020.acl-main.240",
    pages = "2699--2712",
    abstract = "Pretrained masked language models (MLMs) require finetuning for most NLP tasks. Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one. We show that PLLs outperform scores from autoregressive language models like GPT-2 in a variety of tasks. By rescoring ASR and NMT hypotheses, RoBERTa reduces an end-to-end LibriSpeech model{'}s WER by 30{\%} relative and adds up to +1.7 BLEU on state-of-the-art baselines for low-resource translation pairs, with further gains from domain adaptation. We attribute this success to PLL{'}s unsupervised expression of linguistic acceptability without a left-to-right bias, greatly improving on scores from GPT-2 (+10 points on island effects, NPI licensing in BLiMP). One can finetune MLMs to give scores without masking, enabling computation in a single inference pass. In all, PLLs and their associated pseudo-perplexities (PPPLs) enable plug-and-play use of the growing number of pretrained MLMs; e.g., we use a single cross-lingual model to rescore translations in multiple languages. We release our library for language model scoring at https://github.com/awslabs/mlm-scoring.",
}
@inproceedings{tang-etal-2020-orthogonal,
    title = "Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding",
    author = "Tang, Yun  and
      Huang, Jing  and
      Wang, Guangtao  and
      He, Xiaodong  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.241",
    doi = "10.18653/v1/2020.acl-main.241",
    pages = "2713--2722",
    abstract = "Distance-based knowledge graph embeddings have shown substantial improvement on the knowledge graph link prediction task, from TransE to the latest state-of-the-art RotatE. However, complex relations such as N-to-1, 1-to-N and N-to-N still remain challenging to predict. In this work, we propose a novel distance-based approach for knowledge graph link prediction. First, we extend the RotatE from 2D complex domain to high dimensional space with orthogonal transforms to model relations. The orthogonal transform embedding for relations keeps the capability for modeling symmetric/anti-symmetric, inverse and compositional relations while achieves better modeling capacity. Second, the graph context is integrated into distance scoring functions directly. Specifically, graph context is explicitly modeled via two directed context representations. Each node embedding in knowledge graph is augmented with two context representations, which are computed from the neighboring outgoing and incoming nodes/edges respectively. The proposed approach improves prediction accuracy on the difficult N-to-1, 1-to-N and N-to-N cases. Our experimental results show that it achieves state-of-the-art results on two common benchmarks FB15k-237 and WNRR-18, especially on FB15k-237 which has many high in-degree nodes.",
}
@inproceedings{jung-etal-2020-posterior,
    title = "Posterior Calibrated Training on Sentence Classification Tasks",
    author = "Jung, Taehee  and
      Kang, Dongyeop  and
      Cheng, Hua  and
      Mentch, Lucas  and
      Schaaf, Thomas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.242",
    doi = "10.18653/v1/2020.acl-main.242",
    pages = "2723--2730",
    abstract = "Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability. In many settings however, the quality of posterior probability itself (e.g., 65{\%} chance having diabetes), gives more reliable information than the final predicted class alone. When these methods are shown to be poorly calibrated, most fixes to date have relied on posterior calibration, which rescales the predicted probabilities but often has little impact on final classifications. Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities.We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives. Our PosCal achieves about 2.5{\%} of task performance gain and 16.1{\%} of calibration error reduction on GLUE (Wang et al., 2018) compared to the baseline. We achieved the comparable task performance with 13.2{\%} calibration error reduction on xSLUE (Kang and Hovy, 2019), but not outperforming the two-stage calibration baseline. PosCal training can be easily extendable to any types of classification tasks as a form of regularization term. Also, PosCal has the advantage that it incrementally tracks needed statistics for the calibration objective during the training process, making efficient use of large training sets.",
}
@inproceedings{li-rush-2020-posterior,
    title = "Posterior Control of Blackbox Generation",
    author = "Li, Xiang Lisa  and
      Rush, Alexander",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.243",
    doi = "10.18653/v1/2020.acl-main.243",
    pages = "2731--2743",
    abstract = "Text generation often requires high-precision output that obeys task-specific rules. This fine-grained control is difficult to enforce with off-the-shelf deep learning models. In this work, we consider augmenting neural generation models with discrete control states learned through a structured latent-variable approach. Under this formulation, task-specific knowledge can be encoded through a range of rich, posterior constraints that are effectively trained into the model. This approach allows users to ground internal model decisions based on prior knowledge, without sacrificing the representational power of neural generative models. Experiments consider applications of this approach for text generation. We find that this method improves over standard benchmarks, while also providing fine-grained control.",
}
@inproceedings{hendrycks-etal-2020-pretrained,
    title = "Pretrained Transformers Improve Out-of-Distribution Robustness",
    author = "Hendrycks, Dan  and
      Liu, Xiaoyuan  and
      Wallace, Eric  and
      Dziedzic, Adam  and
      Krishnan, Rishabh  and
      Song, Dawn",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.244",
    doi = "10.18653/v1/2020.acl-main.244",
    pages = "2744--2751",
    abstract = "Although pretrained Transformers such as BERT achieve high accuracy on in-distribution examples, do they generalize to new distributions? We systematically measure out-of-distribution (OOD) generalization for seven NLP datasets by constructing a new robustness benchmark with realistic distribution shifts. We measure the generalization of previous models including bag-of-words models, ConvNets, and LSTMs, and we show that pretrained Transformers{'} performance declines are substantially smaller. Pretrained transformers are also more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. We examine which factors affect robustness, finding that larger models are not necessarily more robust, distillation can be harmful, and more diverse pretraining data can enhance robustness. Finally, we show where future work can improve OOD robustness.",
}
@inproceedings{jones-etal-2020-robust,
    title = "Robust Encodings: A Framework for Combating Adversarial Typos",
    author = "Jones, Erik  and
      Jia, Robin  and
      Raghunathan, Aditi  and
      Liang, Percy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.245",
    doi = "10.18653/v1/2020.acl-main.245",
    pages = "2752--2765",
    abstract = "Despite excellent performance on many tasks, NLP systems are easily fooled by small adversarial perturbations of inputs. Existing procedures to defend against such perturbations are either (i) heuristic in nature and susceptible to stronger attacks or (ii) provide guaranteed robustness to worst-case attacks, but are incompatible with state-of-the-art models like BERT. In this work, we introduce robust encodings (RobEn): a simple framework that confers guaranteed robustness, without making compromises on model architecture. The core component of RobEn is an encoding function, which maps sentences to a smaller, discrete space of encodings. Systems using these encodings as a bottleneck confer guaranteed robustness with standard training, and the same encodings can be used across multiple tasks. We identify two desiderata to construct robust encoding functions: perturbations of a sentence should map to a small set of encodings (stability), and models using encodings should still perform well (fidelity). We instantiate RobEn to defend against a large family of adversarial typos. Across six tasks from GLUE, our instantiation of RobEn paired with BERT achieves an average robust accuracy of 71.3{\%} against all adversarial typos in the family considered, while previous work using a typo-corrector achieves only 35.3{\%} accuracy against a simple greedy attack.",
}
@inproceedings{tang-etal-2020-showing,
    title = "Showing Your Work Doesn{'}t Always Work",
    author = "Tang, Raphael  and
      Lee, Jaejun  and
      Xin, Ji  and
      Liu, Xinyu  and
      Yu, Yaoliang  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.246",
    doi = "10.18653/v1/2020.acl-main.246",
    pages = "2766--2772",
    abstract = "In natural language processing, a recently popular line of work explores how to best report the experimental results of neural networks. One exemplar publication, titled {``}Show Your Work: Improved Reporting of Experimental Results{''} (Dodge et al., 2019), advocates for reporting the expected validation effectiveness of the best-tuned model, with respect to the computational budget. In the present work, we critically examine this paper. As far as statistical generalizability is concerned, we find unspoken pitfalls and caveats with this approach. We analytically show that their estimator is biased and uses error-prone assumptions. We find that the estimator favors negative errors and yields poor bootstrapped confidence intervals. We derive an unbiased alternative and bolster our claims with empirical evidence from statistical simulation. Our codebase is at https://github.com/castorini/meanmax.",
}
@inproceedings{glass-etal-2020-span,
    title = "Span Selection Pre-training for Question Answering",
    author = "Glass, Michael  and
      Gliozzo, Alfio  and
      Chakravarti, Rishav  and
      Ferritto, Anthony  and
      Pan, Lin  and
      Bhargav, G P Shrivatsa  and
      Garg, Dinesh  and
      Sil, Avi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.247",
    doi = "10.18653/v1/2020.acl-main.247",
    pages = "2773--2782",
    abstract = "BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pretrained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding. Span Selection PreTraining (SSPT) poses cloze-like training instances, but rather than draw the answer from the model{'}s parameters, it is selected from a relevant passage. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple Machine Reading Comprehension (MRC) datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also show significant impact in HotpotQA, improving answer prediction F1 by 4 points and supporting fact prediction F1 by 1 point and outperforming the previous best system. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.",
}
@inproceedings{prabhumoye-etal-2020-topological,
    title = "Topological Sort for Sentence Ordering",
    author = "Prabhumoye, Shrimai  and
      Salakhutdinov, Ruslan  and
      Black, Alan W",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.248",
    doi = "10.18653/v1/2020.acl-main.248",
    pages = "2783--2792",
    abstract = "Sentence ordering is the task of arranging the sentences of a given text in the correct order. Recent work using deep neural networks for this task has framed it as a sequence prediction problem. In this paper, we propose a new framing of this task as a constraint solving problem and introduce a new technique to solve it. Additionally, we propose a human evaluation for this task. The results on both automatic and human metrics across four different datasets show that this new technique is better at capturing coherence in documents.",
}
@inproceedings{kurita-etal-2020-weight,
    title = "Weight Poisoning Attacks on Pretrained Models",
    author = "Kurita, Keita  and
      Michel, Paul  and
      Neubig, Graham",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.249",
    doi = "10.18653/v1/2020.acl-main.249",
    pages = "2793--2806",
    abstract = "Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show that it is possible to construct {``}weight poisoning{''} attacks where pre-trained weights are injected with vulnerabilities that expose {``}backdoors{''} after fine-tuning, enabling the attacker to manipulate the model prediction simply by injecting an arbitrary keyword. We show that by applying a regularization method which we call RIPPLe and an initialization procedure we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure. Our experiments on sentiment classification, toxicity detection, and spam detection show that this attack is widely applicable and poses a serious threat. Finally, we outline practical defenses against such attacks.",
}
@inproceedings{khetan-karnin-2020-schubert,
    title = "schu{BERT}: Optimizing Elements of {BERT}",
    author = "Khetan, Ashish  and
      Karnin, Zohar",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.250",
    doi = "10.18653/v1/2020.acl-main.250",
    pages = "2807--2818",
    abstract = "Transformers have gradually become a key component for many state-of-the-art natural language representation models. A recent Transformer based model- BERTachieved state-of-the-art results on various natural language processing tasks, including GLUE, SQuAD v1.1, and SQuAD v2.0. This model however is computationally prohibitive and has a huge number of parameters. In this work we revisit the architecture choices of BERT in efforts to obtain a lighter model. We focus on reducing the number of parameters yet our methods can be applied towards other objectives such FLOPs or latency. We show that much efficient light BERT models can be obtained by reducing algorithmically chosen correct architecture design dimensions rather than reducing the number of Transformer encoder layers. In particular, our schuBERT gives 6.6{\%} higher average accuracy on GLUE and SQuAD datasets as compared to BERT with three encoder layers while having the same number of parameters.",
}
@inproceedings{tu-etal-2020-engine,
    title = "{ENGINE}: Energy-Based Inference Networks for Non-Autoregressive Machine Translation",
    author = "Tu, Lifu  and
      Pang, Richard Yuanzhe  and
      Wiseman, Sam  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.251",
    doi = "10.18653/v1/2020.acl-main.251",
    pages = "2819--2826",
    abstract = "We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model. In particular, we view our non-autoregressive translation system as an inference network (Tu and Gimpel, 2018) trained to minimize the autoregressive teacher energy. This contrasts with the popular approach of training a non-autoregressive model on a distilled corpus consisting of the beam-searched outputs of such a teacher model. Our approach, which we call ENGINE (ENerGy-based Inference NEtworks), achieves state-of-the-art non-autoregressive results on the IWSLT 2014 DE-EN and WMT 2016 RO-EN datasets, approaching the performance of autoregressive models.",
}
@inproceedings{siddhant-etal-2020-leveraging,
    title = "Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation",
    author = "Siddhant, Aditya  and
      Bapna, Ankur  and
      Cao, Yuan  and
      Firat, Orhan  and
      Chen, Mia  and
      Kudugunta, Sneha  and
      Arivazhagan, Naveen  and
      Wu, Yonghui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.252",
    doi = "10.18653/v1/2020.acl-main.252",
    pages = "2827--2835",
    abstract = "Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The second direction employs monolingual data with self-supervision to pre-train translation models, followed by fine-tuning on small amounts of supervised data. In this work, we join these two lines of research and demonstrate the efficacy of monolingual data with self-supervision in multilingual NMT. We offer three major results: (i) Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. (ii) Self-supervision improves zero-shot translation quality in multilingual models. (iii) Leveraging monolingual data with self-supervision provides a viable path towards adding new languages to multilingual models, getting up to 33 BLEU on ro-en translation without any parallel data or back-translation.",
}
@inproceedings{edunov-etal-2020-evaluation,
    title = "On The Evaluation of Machine Translation Systems Trained With Back-Translation",
    author = "Edunov, Sergey  and
      Ott, Myle  and
      Ranzato, Marc{'}Aurelio  and
      Auli, Michael",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.253",
    doi = "10.18653/v1/2020.acl-main.253",
    pages = "2836--2846",
    abstract = "Back-translation is a widely used data augmentation technique which leverages target monolingual data. However, its effectiveness has been challenged since automatic metrics such as BLEU only show significant improvements for test examples where the source itself is a translation, or translationese. This is believed to be due to translationese inputs better matching the back-translated training data. In this work, we show that this conjecture is not empirically supported and that back-translation improves translation quality of both naturally occurring text as well as translationese according to professional human translators. We provide empirical evidence to support the view that back-translation is preferred by humans because it produces more fluent outputs. BLEU cannot capture human preferences because references are translationese when source sentences are natural text. We recommend complementing BLEU with a language model score to measure fluency.",
}
@inproceedings{zheng-etal-2020-simultaneous,
    title = "Simultaneous Translation Policies: From Fixed to Adaptive",
    author = "Zheng, Baigong  and
      Liu, Kaibo  and
      Zheng, Renjie  and
      Ma, Mingbo  and
      Liu, Hairong  and
      Huang, Liang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.254",
    doi = "10.18653/v1/2020.acl-main.254",
    pages = "2847--2853",
    abstract = "Adaptive policies are better than fixed policies for simultaneous translation, since they can flexibly balance the tradeoff between translation quality and latency based on the current context information. But previous methods on obtaining adaptive policies either rely on complicated training process, or underperform simple fixed policies. We design an algorithm to achieve adaptive policies via a simple heuristic composition of a set of fixed policies. Experiments on Chinese -{\textgreater} English and German -{\textgreater} English show that our adaptive policies can outperform fixed ones by up to 4 BLEU points for the same latency, and more surprisingly, it even surpasses the BLEU score of full-sentence translation in the greedy mode (and very close to beam mode), but with much lower latency.",
}
@inproceedings{bevilacqua-navigli-2020-breaking,
    title = "Breaking Through the 80{\%} Glass Ceiling: {R}aising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information",
    author = "Bevilacqua, Michele  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.255",
    doi = "10.18653/v1/2020.acl-main.255",
    pages = "2854--2864",
    abstract = "Neural architectures are the current state of the art in Word Sense Disambiguation (WSD). However, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKB). We present Enhanced WSD Integrating Synset Embeddings and Relations (EWISER), a neural supervised architecture that is able to tap into this wealth of knowledge by embedding information from the LKB graph within the neural architecture, and to exploit pretrained synset embeddings, enabling the network to predict synsets that are not in the training set. As a result, we set a new state of the art on almost all the evaluation settings considered, also breaking through, for the first time, the 80{\%} ceiling on the concatenation of all the standard all-words English WSD evaluation benchmarks. On multilingual all-words WSD, we report state-of-the-art results by training on nothing but English.",
}
@inproceedings{chen-etal-2020-glyph2vec,
    title = "{G}lyph2{V}ec: Learning {C}hinese Out-of-Vocabulary Word Embedding from Glyphs",
    author = "Chen, Hong-You  and
      Yu, Sz-Han  and
      Lin, Shou-de",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.256",
    doi = "10.18653/v1/2020.acl-main.256",
    pages = "2865--2871",
    abstract = "Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus. We show that characters{'} written form, \textit{Glyphs}, in ideographic languages could carry rich semantics. We present a multi-modal model, \textit{Glyph2Vec}, to tackle Chinese out-of-vocabulary word embedding problem. Glyph2Vec extracts visual features from word glyphs to expand current word embedding space for out-of-vocabulary word embedding, without the need of accessing any corpus, which is useful for improving Chinese NLP systems, especially for low-resource scenarios. Experiments across different applications show the significant effectiveness of our model.",
}
@inproceedings{gerz-etal-2020-multidirectional,
    title = "Multidirectional Associative Optimization of Function-Specific Word Representations",
    author = "Gerz, Daniela  and
      Vuli{\'c}, Ivan  and
      Rei, Marek  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.257",
    doi = "10.18653/v1/2020.acl-main.257",
    pages = "2872--2882",
    abstract = "We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95{\%}.",
}
@inproceedings{hatty-etal-2020-predicting,
    title = "Predicting Degrees of Technicality in Automatic Terminology Extraction",
    author = {H{\"a}tty, Anna  and
      Schlechtweg, Dominik  and
      Dorna, Michael  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.258",
    doi = "10.18653/v1/2020.acl-main.258",
    pages = "2883--2889",
    abstract = "While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on technicality prediction. When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings. We suggest two novel models to exploit general- vs. domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally. Both models outperform previous approaches, with the multi-channel model performing best.",
}
@inproceedings{rohanian-etal-2020-verbal,
    title = "Verbal Multiword Expressions for Identification of Metaphor",
    author = "Rohanian, Omid  and
      Rei, Marek  and
      Taslimipoor, Shiva  and
      Ha, Le An",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.259",
    doi = "10.18653/v1/2020.acl-main.259",
    pages = "2890--2895",
    abstract = "Metaphor is a linguistic device in which a concept is expressed by mentioning another. Identifying metaphorical expressions, therefore, requires a non-compositional understanding of semantics. Multiword Expressions (MWEs), on the other hand, are linguistic phenomena with varying degrees of semantic opacity and their identification poses a challenge to computational models. This work is the first attempt at analysing the interplay of metaphor and MWEs processing through the design of a neural architecture whereby classification of metaphors is enhanced by informing the model of the presence of MWEs. To the best of our knowledge, this is the first {``}MWE-aware{''} metaphor identification system paving the way for further experiments on the complex interactions of these phenomena. The results and analyses show that this proposed architecture reach state-of-the-art on two different established metaphor datasets.",
}
@inproceedings{zhao-etal-2020-gender,
    title = "Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer",
    author = "Zhao, Jieyu  and
      Mukherjee, Subhabrata  and
      Hosseini, Saghar  and
      Chang, Kai-Wei  and
      Hassan Awadallah, Ahmed",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.260",
    doi = "10.18653/v1/2020.acl-main.260",
    pages = "2896--2907",
    abstract = "Multilingual representations embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language. These embeddings have been widely used in various settings, such as cross-lingual transfer, where a natural language processing (NLP) model trained on one language is deployed to another language. While the cross-lingual transfer techniques are powerful, they carry gender bias from the source to target languages. In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations from both the intrinsic and extrinsic perspectives. Experimental results show that the magnitude of bias in the multilingual representations changes differently when we align the embeddings to different target spaces and that the alignment direction can also have an influence on the bias in transfer learning. We further provide recommendations for using the multilingual word representations for downstream tasks.",
}
@inproceedings{leins-etal-2020-give,
    title = "Give Me Convenience and Give Her Death: Who Should Decide What Uses of {NLP} are Appropriate, and on What Basis?",
    author = "Leins, Kobi  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.261",
    doi = "10.18653/v1/2020.acl-main.261",
    pages = "2908--2913",
    abstract = "As part of growing NLP capabilities, coupled with an awareness of the ethical dimensions of research, questions have been raised about whether particular datasets and tasks should be deemed off-limits for NLP research. We examine this question with respect to a paper on automatic legal sentencing from EMNLP 2019 which was a source of some debate, in asking whether the paper should have been allowed to be published, who should have been charged with making such a decision, and on what basis. We focus in particular on the role of data statements in ethically assessing research, but also discuss the topic of dual use, and examine the outcomes of similar debates in other scientific disciplines.",
}
@inproceedings{ethayarajh-2020-classifier,
    title = "Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds",
    author = "Ethayarajh, Kawin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.262",
    doi = "10.18653/v1/2020.acl-main.262",
    pages = "2914--2919",
    abstract = "Most NLP datasets are not annotated with protected attributes such as gender, making it difficult to measure classification bias using standard measures of fairness (e.g., equal opportunity). However, manually annotating a large dataset with a protected attribute is slow and expensive. Instead of annotating all the examples, can we annotate a subset of them and use that sample to estimate the bias? While it is possible to do so, the smaller this annotated sample is, the less certain we are that the estimate is close to the true bias. In this work, we propose using Bernstein bounds to represent this uncertainty about the bias estimate as a confidence interval. We provide empirical evidence that a 95{\%} confidence interval derived this way consistently bounds the true bias. In quantifying this uncertainty, our method, which we call Bernstein-bounded unfairness, helps prevent classifiers from being deemed biased or unbiased when there is insufficient evidence to make either claim. Our findings suggest that the datasets currently used to measure specific biases are too small to conclusively identify bias except in the most egregious cases. For example, consider a co-reference resolution system that is 5{\%} more accurate on gender-stereotypical sentences {--} to claim it is biased with 95{\%} confidence, we need a bias-specific dataset that is 3.8 times larger than WinoBias, the largest available.",
}
@inproceedings{tan-etal-2020-morphin,
    title = "It{'}s Morphin{'} Time! {C}ombating Linguistic Discrimination with Inflectional Perturbations",
    author = "Tan, Samson  and
      Joty, Shafiq  and
      Kan, Min-Yen  and
      Socher, Richard",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.263",
    doi = "10.18653/v1/2020.acl-main.263",
    pages = "2920--2935",
    abstract = "Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.",
}
@inproceedings{jia-etal-2020-mitigating,
    title = "Mitigating Gender Bias Amplification in Distribution by Posterior Regularization",
    author = "Jia, Shengyu  and
      Meng, Tao  and
      Zhao, Jieyu  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.264",
    doi = "10.18653/v1/2020.acl-main.264",
    pages = "2936--2942",
    abstract = "Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., (CITATION) show that these techniques inadvertently capture the societal bias hidden in the corpus and further amplify it. However, their analysis is conducted only on models{'} top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.",
}
@inproceedings{gaut-etal-2020-towards,
    title = "Towards Understanding Gender Bias in Relation Extraction",
    author = "Gaut, Andrew  and
      Sun, Tony  and
      Tang, Shirlyn  and
      Huang, Yuxin  and
      Qian, Jing  and
      ElSherief, Mai  and
      Zhao, Jieyu  and
      Mirza, Diba  and
      Belding, Elizabeth  and
      Chang, Kai-Wei  and
      Wang, William Yang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.265",
    doi = "10.18653/v1/2020.acl-main.265",
    pages = "2943--2953",
    abstract = "Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10{\%} human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse-of and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birthDate or birthPlace. We also analyze how existing bias mitigation techniques, such as name anonymization, word embedding debiasing, and data augmentation affect the NRE system in terms of maintaining the test performance and reducing biases. Unfortunately, due to NRE models rely heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in NRE.",
}
@inproceedings{goyal-etal-2020-probabilistic,
    title = "A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing",
    author = "Goyal, Kartik  and
      Dyer, Chris  and
      Warren, Christopher  and
      G{'}Sell, Maxwell  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.266",
    doi = "10.18653/v1/2020.acl-main.266",
    pages = "2954--2960",
    abstract = "We propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents. We focus on clustering extracted glyph images into underlying templates in the presence of multiple confounding sources of variance. Our approach introduces a neural editor model that first generates well-understood printing phenomena like spatial perturbations from template parameters via interpertable latent variables, and then modifies the result by generating a non-interpretable latent vector responsible for inking variations, jitter, noise from the archiving process, and other unforeseen phenomena associated with Early Modern printing. Critically, by introducing an inference network whose input is restricted to the visual residual between the observation and the interpretably-modified template, we are able to control and isolate what the vector-valued latent variable captures. We show that our approach outperforms rigid interpretable clustering baselines (c.f. Ocular) and overly-flexible deep generative models (VAE) alike on the task of completely unsupervised discovery of typefaces in mixed-fonts documents.",
}
@inproceedings{wu-etal-2020-attentive,
    title = "Attentive Pooling with Learnable Norms for Text Representation",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Qi, Tao  and
      Cui, Xiaohui  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.267",
    doi = "10.18653/v1/2020.acl-main.267",
    pages = "2961--2970",
    abstract = "Pooling is an important technique for learning text representations in many neural NLP models. In conventional pooling methods such as average, max and attentive pooling, text representations are weighted summations of the L1 or L{\mbox{$\infty$}} norm of input features. However, their pooling norms are always fixed and may not be optimal for learning accurate text representations in different tasks. In addition, in many popular pooling methods such as max and attentive pooling some features may be over-emphasized, while other useful ones are not fully exploited. In this paper, we propose an Attentive Pooling with Learnable Norms (APLN) approach for text representation. Different from existing pooling methods that use a fixed pooling norm, we propose to learn the norm in an end-to-end manner to automatically find the optimal ones for text representation in different tasks. In addition, we propose two methods to ensure the numerical stability of the model training. The first one is scale limiting, which re-scales the input to ensure non-negativity and alleviate the risk of exponential explosion. The second one is re-formulation, which decomposes the exponent operation to avoid computing the real-valued powers of the input and further accelerate the pooling operation. Experimental results on four benchmark datasets show that our approach can effectively improve the performance of attentive pooling.",
}
@inproceedings{schroder-biemann-2020-estimating,
    title = "Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks",
    author = {Schr{\"o}der, Fynn  and
      Biemann, Chris},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.268",
    doi = "10.18653/v1/2020.acl-main.268",
    pages = "2971--2985",
    abstract = "Multi-task learning (MTL) and transfer learning (TL) are techniques to overcome the issue of data scarcity when training state-of-the-art neural networks. However, finding beneficial auxiliary datasets for MTL or TL is a time- and resource-consuming trial-and-error approach. We propose new methods to automatically assess the similarity of sequence tagging datasets to identify beneficial auxiliary data for MTL or TL setups. Our methods can compute the similarity between any two sequence tagging datasets, they do not need to be annotated with the same tagset or multiple labels in parallel. Additionally, our methods take tokens and their labels into account, which is more robust than only using either of them as an information source, as conducted in prior work. We empirically show that our similarity measures correlate with the change in test score of neural networks that use the auxiliary dataset for MTL to increase the main task performance. We provide an efficient, open-source implementation.",
}
@inproceedings{geng-etal-2020-selective,
    title = "How Does Selective Mechanism Improve Self-Attention Networks?",
    author = "Geng, Xinwei  and
      Wang, Longyue  and
      Wang, Xing  and
      Qin, Bing  and
      Liu, Ting  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.269",
    doi = "10.18653/v1/2020.acl-main.269",
    pages = "2986--2995",
    abstract = "Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words. However, the underlying reasons for their strong performance have not been well explained. In this paper, we bridge the gap by assessing the strengths of selective SANs (SSANs), which are implemented with a flexible and universal Gumbel-Softmax. Experimental results on several representative NLP tasks, including natural language inference, semantic role labelling, and machine translation, show that SSANs consistently outperform the standard SANs. Through well-designed probing experiments, we empirically validate that the improvement of SSANs can be attributed in part to mitigating two commonly-cited weaknesses of SANs: word order encoding and structure modeling. Specifically, the selective mechanism improves SANs by paying more attention to content words that contribute to the meaning of the sentence.",
}
@inproceedings{press-etal-2020-improving,
    title = "Improving Transformer Models by Reordering their Sublayers",
    author = "Press, Ofir  and
      Smith, Noah A.  and
      Levy, Omer",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.270",
    doi = "10.18653/v1/2020.acl-main.270",
    pages = "2996--3005",
    abstract = "Multilayer transformer networks consist of interleaved self-attention and feedforward sublayers. Could ordering the sublayers in a different pattern lead to better performance? We generate randomly ordered transformers and train them with the language modeling objective. We observe that some of these models are able to achieve better performance than the interleaved baseline, and that those successful variants tend to have more self-attention at the bottom and more feedforward sublayers at the top. We propose a new transformer pattern that adheres to this property, the sandwich transformer, and show that it improves perplexity on multiple word-level and character-level language modeling benchmarks, at no cost in parameters, memory, or training time. However, the sandwich reordering pattern does not guarantee performance gains across every task, as we demonstrate on machine translation models. Instead, we suggest that further exploration of task-specific sublayer reorderings is needed in order to unlock additional gains.",
}
@inproceedings{kuwabara-etal-2020-single,
    title = "Single Model Ensemble using Pseudo-Tags and Distinct Vectors",
    author = "Kuwabara, Ryosuke  and
      Suzuki, Jun  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.271",
    doi = "10.18653/v1/2020.acl-main.271",
    pages = "3006--3013",
    abstract = "Model ensemble techniques often increase task performance in neural networks; however, they require increased time, memory, and management effort. In this study, we propose a novel method that replicates the effects of a model ensemble with a single model. Our approach creates K-virtual models within a single parameter space using K-distinct pseudo-tags and K-distinct vectors. Experiments on text classification and sequence labeling tasks on several datasets demonstrate that our method emulates or outperforms a traditional model ensemble with 1/K-times fewer parameters.",
}
@inproceedings{ye-etal-2020-zero,
    title = "Zero-shot Text Classification via Reinforced Self-training",
    author = "Ye, Zhiquan  and
      Geng, Yuxia  and
      Chen, Jiaoyan  and
      Chen, Jingmin  and
      Xu, Xiaoxiao  and
      Zheng, SuHang  and
      Wang, Feng  and
      Zhang, Jun  and
      Chen, Huajun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.272",
    doi = "10.18653/v1/2020.acl-main.272",
    pages = "3014--3024",
    abstract = "Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data. Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets. We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection. Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification",
}
@inproceedings{yin-etal-2020-novel,
    title = "A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation",
    author = "Yin, Yongjing  and
      Meng, Fandong  and
      Su, Jinsong  and
      Zhou, Chulun  and
      Yang, Zhengyuan  and
      Zhou, Jie  and
      Luo, Jiebo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.273",
    doi = "10.18653/v1/2020.acl-main.273",
    pages = "3025--3035",
    abstract = "Multi-modal neural machine translation (NMT) aims to translate source sentences into a target language paired with images. However, dominant multi-modal NMT models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have potential to refine multi-modal representation learning. To deal with this issue, in this paper, we propose a novel graph-based multi-modal fusion encoder for NMT. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between multi-modal semantic units (words and visual objects). We then stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations. Finally, these representations provide an attention-based context vector for the decoder. We evaluate our proposed encoder on the Multi30K datasets. Experimental results and in-depth analysis show the superiority of our multi-modal NMT model.",
}
@inproceedings{zhao-etal-2020-relaxed,
    title = "A Relaxed Matching Procedure for Unsupervised {BLI}",
    author = "Zhao, Xu  and
      Wang, Zihao  and
      Zhang, Yong  and
      Wu, Hao",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.274",
    doi = "10.18653/v1/2020.acl-main.274",
    pages = "3036--3041",
    abstract = "Recently unsupervised Bilingual Lexicon Induction(BLI) without any parallel corpus has attracted much research interest. One of the crucial parts in methods for the BLI task is the matching procedure. Previous works impose a too strong constraint on the matching and lead to many counterintuitive translation pairings. Thus We propose a relaxed matching procedure to find a more precise matching between two languages. We also find that aligning source and target language embedding space bidirectionally will bring significant improvement. We follow the previous iterative framework to conduct experiments. Results on standard benchmark demonstrate the effectiveness of our proposed method, which substantially outperforms previous unsupervised methods.",
}
@inproceedings{he-etal-2020-dynamic,
    title = "Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation",
    author = "He, Xuanli  and
      Haffari, Gholamreza  and
      Norouzi, Mohammad",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.275",
    doi = "10.18653/v1/2020.acl-main.275",
    pages = "3042--3051",
    abstract = "This paper introduces Dynamic Programming Encoding (DPE), a new segmentation algorithm for tokenizing sentences into subword units. We view the subword segmentation of output sentences as a latent variable that should be marginalized out for learning and inference. A mixed character-subword transformer is proposed, which enables exact log marginal likelihood estimation and exact MAP inference to find target segmentations with maximum posterior probability. DPE uses a lightweight mixed character-subword transformer as a means of pre-processing parallel data to segment output sentences using dynamic programming. Empirical results on machine translation suggest that DPE is effective for segmenting output sentences and can be combined with BPE dropout for stochastic segmentation of source sentences. DPE achieves an average improvement of 0.9 BLEU over BPE (Sennrich et al., 2016) and an average improvement of 0.55 BLEU over BPE dropout (Provilkov et al., 2019) on several WMT datasets including English {\textless}={\textgreater} (German, Romanian, Estonian, Finnish, Hungarian).",
}
@inproceedings{jawanpuria-etal-2020-geometry,
    title = "Geometry-aware domain adaptation for unsupervised alignment of word embeddings",
    author = "Jawanpuria, Pratik  and
      Meghwanshi, Mayank  and
      Mishra, Bamdev",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.276",
    doi = "10.18653/v1/2020.acl-main.276",
    pages = "3052--3058",
    abstract = "We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages. Our approach formulates the alignment learning problem as a domain adaptation problem over the manifold of doubly stochastic matrices. This viewpoint arises from the aim to align the second order information of the two language spaces. The rich geometry of the doubly stochastic manifold allows to employ efficient Riemannian conjugate gradient algorithm for the proposed formulation. Empirically, the proposed approach outperforms state-of-the-art optimal transport based approach on the bilingual lexicon induction task across several language pairs. The performance improvement is more significant for distant language pairs.",
}
@inproceedings{ran-etal-2020-learning,
    title = "Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation",
    author = "Ran, Qiu  and
      Lin, Yankai  and
      Li, Peng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.277",
    doi = "10.18653/v1/2020.acl-main.277",
    pages = "3059--3069",
    abstract = "Non-autoregressive neural machine translation (NAT) predicts the entire target sequence simultaneously and significantly accelerates inference process. However, NAT discards the dependency information in a sentence, and thus inevitably suffers from the multi-modality problem: the target tokens may be provided by different possible translations, often causing token repetitions or missing. To alleviate this problem, we propose a novel semi-autoregressive model RecoverSAT in this work, which generates a translation as a sequence of segments. The segments are generated simultaneously while each segment is predicted token-by-token. By dynamically determining segment length and deleting repetitive segments, RecoverSAT is capable of recovering from repetitive and missing token errors. Experimental results on three widely-used benchmark datasets show that our proposed model achieves more than 4 times speedup while maintaining comparable performance compared with the corresponding autoregressive model.",
}
@inproceedings{wang-etal-2020-inference,
    title = "On the Inference Calibration of Neural Machine Translation",
    author = "Wang, Shuo  and
      Tu, Zhaopeng  and
      Shi, Shuming  and
      Liu, Yang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.278",
    doi = "10.18653/v1/2020.acl-main.278",
    pages = "3070--3079",
    abstract = "Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer useful indicators of translation errors in the generated output. While prior studies have shown that NMT models trained with label smoothing are well-calibrated on the ground-truth training data, we find that miscalibration still remains a severe challenge for NMT during inference due to the discrepancy between training and inference. By carefully designing experiments on three language pairs, our work provides in-depth analyses of the correlation between calibration and translation performance as well as linguistic properties of miscalibration and reports a number of interesting findings that might help humans better analyze, understand and improve NMT models. Based on these observations, we further propose a new graduated label smoothing method that can improve both inference calibration and translation performance.",
}
@inproceedings{jiang-etal-2020-camouflaged,
    title = "Camouflaged {C}hinese Spam Content Detection with Semi-supervised Generative Active Learning",
    author = "Jiang, Zhuoren  and
      Gao, Zhe  and
      Duan, Yu  and
      Kang, Yangyang  and
      Sun, Changlong  and
      Zhang, Qiong  and
      Liu, Xiaozhong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.279",
    doi = "10.18653/v1/2020.acl-main.279",
    pages = "3080--3085",
    abstract = "We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task. A {``}self-diversity{''} criterion is proposed for measuring the {``}worthiness{''} of a candidate for annotation. A semi-supervised variational autoencoder with masked attention learning approach and a character variation graph-enhanced augmentation procedure are proposed for data augmentation. The preliminary experiment demonstrates the proposed SIGNAL model is not only sensitive to spam sample selection, but also can improve the performance of a series of conventional active learning models for Chinese spam detection task. To the best of our knowledge, this is the first work to integrate active learning and semi-supervised generative learning for text spam detection.",
}
@inproceedings{xu-etal-2020-distinguish,
    title = "Distinguish Confusing Law Articles for Legal Judgment Prediction",
    author = "Xu, Nuo  and
      Wang, Pinghui  and
      Chen, Long  and
      Pan, Li  and
      Wang, Xiaoyan  and
      Zhao, Junzhou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.280",
    doi = "10.18653/v1/2020.acl-main.280",
    pages = "3086--3095",
    abstract = "Legal Judgement Prediction (LJP) is the task of automatically predicting a law case{'}s judgment results given a text describing the case{'}s facts, which has great prospects in judicial assistance systems and handy services for the public. In practice, confusing charges are often presented, because law cases applicable to similar law articles are easily misjudged. To address this issue, existing work relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network, GDL, to automatically learn subtle differences between confusing law articles, and also design a novel attention mechanism that fully exploits the learned differences to attentively extract effective discriminative features from fact descriptions. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN.",
}
@inproceedings{liu-etal-2020-hiring,
    title = "Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation",
    author = "Liu, Liting  and
      Liu, Jie  and
      Zhang, Wenzheng  and
      Chi, Ziming  and
      Shi, Wenxuan  and
      Huang, Yalou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.281",
    doi = "10.18653/v1/2020.acl-main.281",
    pages = "3096--3104",
    abstract = "Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think. It is challenging to specify the level of education, experience, relevant skills per the company information and job description. To this end, we propose a novel task of Job Posting Generation (JPG) which is cast as a conditional text generation problem to generate job requirements according to the job descriptions. To deal with this task, we devise a data-driven global Skill-Aware Multi-Attention generation model, named SAMA. Specifically, to model the complex mapping relationships between input and output, we design a hierarchical decoder that we first label the job description with multiple skills, then we generate a complete text guided by the skill labels. At the same time, to exploit the prior knowledge about the skills, we further construct a skill knowledge graph to capture the global prior knowledge of skills and refine the generated results. The proposed approach is evaluated on real-world job posting data. Experimental results clearly demonstrate the effectiveness of the proposed method.",
}
@inproceedings{cao-etal-2020-hypercore,
    title = "{H}yper{C}ore: Hyperbolic and Co-graph Representation for Automatic {ICD} Coding",
    author = "Cao, Pengfei  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun  and
      Liu, Shengping  and
      Chong, Weifeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.282",
    doi = "10.18653/v1/2020.acl-main.282",
    pages = "3105--3114",
    abstract = "The International Classification of Diseases (ICD) provides a standardized way for classifying diseases, which endows each disease with a unique code. ICD coding aims to assign proper ICD codes to a medical record. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, most of existing methods independently predict each code, ignoring two important characteristics: Code Hierarchy and Code Co-occurrence. In this paper, we propose a Hyperbolic and Co-graph Representation method (HyperCore) to address the above problem. Specifically, we propose a hyperbolic representation method to leverage the code hierarchy. Moreover, we propose a graph convolutional network to utilize the code co-occurrence. Experimental results on two widely used datasets demonstrate that our proposed model outperforms previous state-of-the-art methods.",
}
@inproceedings{chen-etal-2020-hyperbolic,
    title = "Hyperbolic Capsule Networks for Multi-Label Classification",
    author = "Chen, Boli  and
      Huang, Xin  and
      Xiao, Lin  and
      Jing, Liping",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.283",
    doi = "10.18653/v1/2020.acl-main.283",
    pages = "3115--3124",
    abstract = "Although deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g. pooling). Such operations limit the performance. For instance, a multi-label document may contain several concepts. In this case, one vector can not sufficiently capture its salient and discriminative content. Thus, we propose Hyperbolic Capsule Networks (HyperCaps) for Multi-Label Classification (MLC), which have two merits. First, hyperbolic capsules are designed to capture fine-grained document information for each label, which has the ability to characterize complicated structures among labels and documents. Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks. To efficiently handle large-scale MLC datasets, we additionally present a new routing method to adaptively adjust the capsule number during routing. Extensive experiments are conducted on four benchmark datasets. Compared with the state-of-the-art methods, HyperCaps significantly improves the performance of MLC especially on tail labels.",
}
@inproceedings{chauhan-gupta-2020-improving,
    title = "Improving Segmentation for Technical Support Problems",
    author = "Chauhan, Kushal  and
      Gupta, Abhirut",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.284",
    doi = "10.18653/v1/2020.acl-main.284",
    pages = "3125--3134",
    abstract = "Technical support problems are often long and complex. They typically contain user descriptions of the problem, the setup, and steps for attempted resolution. Often they also contain various non-natural language text elements like outputs of commands, snippets of code, error messages or stack traces. These elements contain potentially crucial information for problem resolution. However, they cannot be correctly parsed by tools designed for natural language. In this paper, we address the problem of segmentation for technical support questions. We formulate the problem as a sequence labelling task, and study the performance of state of the art approaches. We compare this against an intuitive contextual sentence-level classification baseline, and a state of the art supervised text-segmentation approach. We also introduce a novel component of combining contextual embeddings from multiple language models pre-trained on different data sources, which achieves a marked improvement over using embeddings from a single pre-trained language model. Finally, we also demonstrate the usefulness of such segmentation with improvements on the downstream task of answer retrieval.",
}
@inproceedings{yu-etal-2020-mooccube,
    title = "{MOOCC}ube: A Large-scale Data Repository for {NLP} Applications in {MOOC}s",
    author = "Yu, Jifan  and
      Luo, Gan  and
      Xiao, Tong  and
      Zhong, Qingyang  and
      Wang, Yuquan  and
      Feng, Wenzheng  and
      Luo, Junyi  and
      Wang, Chenyu  and
      Hou, Lei  and
      Li, Juanzi  and
      Liu, Zhiyuan  and
      Tang, Jie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.285",
    doi = "10.18653/v1/2020.acl-main.285",
    pages = "3135--3142",
    abstract = "The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e.g., course concept extraction, prerequisite relation discovery, etc. However, the publicly available datasets of MOOC are limited in size with few types of data, which hinders advanced models and novel attempts in related topics. Therefore, we present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. Moreover, we conduct a prerequisite discovery task as an example application to show the potential of MOOCCube in facilitating relevant research. The data repository is now available at http://moocdata.cn/data/MOOCCube.",
}
@inproceedings{chen-etal-2020-towards,
    title = "Towards Interpretable Clinical Diagnosis with {B}ayesian Network Ensembles Stacked on Entity-Aware {CNN}s",
    author = "Chen, Jun  and
      Dai, Xiaoya  and
      Yuan, Quan  and
      Lu, Chao  and
      Huang, Haifeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.286",
    doi = "10.18653/v1/2020.acl-main.286",
    pages = "3143--3153",
    abstract = "The automatic text-based diagnosis remains a challenging task for clinical use because it requires appropriate balance between accuracy and interpretability. In this paper, we attempt to propose a solution by introducing a novel framework that stacks Bayesian Network Ensembles on top of Entity-Aware Convolutional Neural Networks (CNN) towards building an accurate yet interpretable diagnosis system. The proposed framework takes advantage of the high accuracy and generality of deep neural networks as well as the interpretability of Bayesian Networks, which is critical for AI-empowered healthcare. The evaluation conducted on the real Electronic Medical Record (EMR) documents from hospitals and annotated by professional doctors proves that, the proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable.",
}
@inproceedings{el-baff-etal-2020-analyzing,
    title = "{A}nalyzing the {P}ersuasive {E}ffect of {S}tyle in {N}ews {E}ditorial {A}rgumentation",
    author = "El Baff, Roxanne  and
      Wachsmuth, Henning  and
      Al Khatib, Khalid  and
      Stein, Benno",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.287",
    doi = "10.18653/v1/2020.acl-main.287",
    pages = "3154--3160",
    abstract = "News editorials argue about political issues in order to challenge or reinforce the stance of readers with different ideologies. Previous research has investigated such persuasive effects for argumentative content. In contrast, this paper studies how important the style of news editorials is to achieve persuasion. To this end, we first compare content- and style-oriented classifiers on editorials from the liberal NYTimes with ideology-specific effect annotations. We find that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content. Focusing on liberals, we then cluster the leads, bodies, and endings of editorials, in order to learn about writing style patterns of effective argumentation.",
}
@inproceedings{ding-etal-2020-ecpe,
    title = "{ECPE}-2{D}: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction",
    author = "Ding, Zixiang  and
      Xia, Rui  and
      Yu, Jianfei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.288",
    doi = "10.18653/v1/2020.acl-main.288",
    pages = "3161--3170",
    abstract = "In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis. It aims at extracting the potential pairs of emotions and their corresponding causes in a document. To solve this task, the existing research employed a two-step framework, which first extracts individual emotion set and cause set, and then pair the corresponding emotions and causes. However, such a pipeline of two steps contains some inherent flaws: 1) the modeling does not aim at extracting the final emotion-cause pair directly; 2) the errors from the first step will affect the performance of the second step. To address these shortcomings, in this paper we propose a new end-to-end approach, called ECPE-Two-Dimensional (ECPE-2D), to represent the emotion-cause pairs by a 2D representation scheme. A 2D transformer module and two variants, window-constrained and cross-road 2D transformers, are further proposed to model the interactions of different emotion-cause pairs. The 2D representation, interaction, and prediction are integrated into a joint framework. In addition to the advantages of joint modeling, the experimental results on the benchmark emotion cause corpus show that our approach improves the F1 score of the state-of-the-art from 61.28{\%} to 68.89{\%}.",
}
@inproceedings{wei-etal-2020-effective,
    title = "Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction",
    author = "Wei, Penghui  and
      Zhao, Jiahao  and
      Mao, Wenji",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.289",
    doi = "10.18653/v1/2020.acl-main.289",
    pages = "3171--3181",
    abstract = "Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.",
}
@inproceedings{tulkens-van-cranenburgh-2020-embarrassingly,
    title = "Embarrassingly Simple Unsupervised Aspect Extraction",
    author = "Tulkens, St{\'e}phan  and
      van Cranenburgh, Andreas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.290",
    doi = "10.18653/v1/2020.acl-main.290",
    pages = "3182--3187",
    abstract = "We present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply to new domains and languages. We introduce Contrastive Attention (CAt), a novel single-head attention mechanism based on an RBF kernel, which gives a considerable boost in performance and makes the model interpretable. Previous work relied on syntactic features and complex neural models. We show that given the simplicity of current benchmark datasets for aspect extraction, such complex models are not needed. The code to reproduce the experiments reported in this paper is available at https://github.com/clips/cat.",
}
@inproceedings{zhang-etal-2020-enhancing,
    title = "Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge",
    author = "Zhang, Bowen  and
      Yang, Min  and
      Li, Xutao  and
      Ye, Yunming  and
      Xu, Xiaofei  and
      Dai, Kuai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.291",
    doi = "10.18653/v1/2020.acl-main.291",
    pages = "3188--3197",
    abstract = "Stance detection is an important task, which aims to classify the attitude of an opinionated text towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, annotating sufficient data is labor-intensive, which establishes significant barriers for generalizing the stance classifier to the data with new targets. In this paper, we proposed a Semantic-Emotion Knowledge Transferring (SEKT) model for cross-target stance detection, which uses the external knowledge (semantic and emotion lexicons) as a bridge to enable knowledge transfer across different targets. Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags. Then, the learned semantic-emotion graph representation, which serves as prior knowledge bridging the gap between the source and target domains, is fully integrated into the bidirectional long short-term memory (BiLSTM) stance classifier by adding a novel knowledge-aware memory unit to the BiLSTM cell. Extensive experiments on a large real-world dataset demonstrate the superiority of SEKT against the state-of-the-art baseline methods.",
}
@inproceedings{ghosal-etal-2020-kingdom,
    title = "{K}in{GDOM}: {K}nowledge-{G}uided {DOM}ain {A}daptation for {S}entiment {A}nalysis",
    author = "Ghosal, Deepanway  and
      Hazarika, Devamanyu  and
      Roy, Abhinaba  and
      Majumder, Navonil  and
      Mihalcea, Rada  and
      Poria, Soujanya",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.292",
    doi = "10.18653/v1/2020.acl-main.292",
    pages = "3198--3210",
    abstract = "Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework.",
}
@inproceedings{phan-ogunbona-2020-modelling,
    title = "Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis",
    author = "Phan, Minh Hieu  and
      Ogunbona, Philip O.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.293",
    doi = "10.18653/v1/2020.acl-main.293",
    pages = "3211--3220",
    abstract = "The aspect-based sentiment analysis (ABSA) consists of two conceptual tasks, namely an aspect extraction and an aspect sentiment classification. Rather than considering the tasks separately, we build an end-to-end ABSA solution. Previous works in ABSA tasks did not fully leverage the importance of syntactical information. Hence, the aspect extraction model often failed to detect the boundaries of multi-word aspect terms. On the other hand, the aspect sentiment classifier was unable to account for the syntactical correlation between aspect terms and the context words. This paper explores the grammatical aspect of the sentence and employs the self-attention mechanism for syntactical learning. We combine part-of-speech embeddings, dependency-based embeddings and contextualized embeddings (e.g. BERT, RoBERTa) to enhance the performance of the aspect extractor. We also propose the syntactic relative distance to de-emphasize the adverse effects of unrelated words, having weak syntactic connection with the aspect terms. This increases the accuracy of the aspect sentiment classifier. Our solutions outperform the state-of-the-art models on SemEval-2014 dataset in both two subtasks.",
}
@inproceedings{zhang-etal-2020-parallel,
    title = "Parallel Data Augmentation for Formality Style Transfer",
    author = "Zhang, Yi  and
      Ge, Tao  and
      Sun, Xu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.294",
    doi = "10.18653/v1/2020.acl-main.294",
    pages = "3221--3228",
    abstract = "The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data. In this paper, we study how to augment parallel data and propose novel and simple data augmentation methods for this task to obtain useful sentence pairs with easily accessible models and systems. Experiments demonstrate that our augmented parallel data largely helps improve formality style transfer when it is used to pre-train the model, leading to the state-of-the-art results in the GYAFC benchmark dataset.",
}
@inproceedings{wang-etal-2020-relational,
    title = "Relational Graph Attention Network for Aspect-based Sentiment Analysis",
    author = "Wang, Kai  and
      Shen, Weizhou  and
      Yang, Yunyi  and
      Quan, Xiaojun  and
      Wang, Rui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.295",
    doi = "10.18653/v1/2020.acl-main.295",
    pages = "3229--3238",
    abstract = "Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.",
}
@inproceedings{zhao-etal-2020-spanmlt,
    title = "{S}pan{M}lt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction",
    author = "Zhao, He  and
      Huang, Longtao  and
      Zhang, Rong  and
      Lu, Quan  and
      Xue, Hui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.296",
    doi = "10.18653/v1/2020.acl-main.296",
    pages = "3239--3248",
    abstract = "Aspect terms extraction and opinion terms extraction are two key problems of fine-grained Aspect Based Sentiment Analysis (ABSA). The aspect-opinion pairs can provide a global profile about a product or service for consumers and opinion mining systems. However, traditional methods can not directly output aspect-opinion pairs without given aspect terms or opinion terms. Although some recent co-extraction methods have been proposed to extract both terms jointly, they fail to extract them as pairs. To this end, this paper proposes an end-to-end method to solve the task of Pair-wise Aspect and Opinion Terms Extraction (PAOTE). Furthermore, this paper treats the problem from a perspective of joint term and relation extraction rather than under the sequence tagging formulation performed in most prior works. We propose a multi-task learning framework based on shared spans, where the terms are extracted under the supervision of span boundaries. Meanwhile, the pair-wise relations are jointly identified using the span representations. Extensive experiments show that our model consistently outperforms state-of-the-art methods.",
}
@inproceedings{zhang-etal-2020-syntax,
    title = "Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks",
    author = "Zhang, Bo  and
      Zhang, Yue  and
      Wang, Rui  and
      Li, Zhenghua  and
      Zhang, Min",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.297",
    doi = "10.18653/v1/2020.acl-main.297",
    pages = "3249--3258",
    abstract = "Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer {``}who expressed what kind of sentiment towards what?{''}. Due to the scarcity of labeled data, ORL remains challenging for data-driven methods. In this work, we try to enhance neural ORL models with syntactic knowledge by comparing and integrating different representations. We also propose dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels. In order to compensate for parser inaccuracy and reduce error propagation, we introduce multi-task learning (MTL) to train the parser and the ORL model simultaneously. We verify our methods on the benchmark MPQA corpus. The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9.29 over the syntax-agnostic baseline. In addition, we find that the contributions from syntactic knowledge do not fully overlap with contextualized word representations (BERT). Our best model achieves 4.34 higher F1 score than the current state-ofthe-art.",
}
@inproceedings{morio-etal-2020-towards,
    title = "Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization",
    author = "Morio, Gaku  and
      Ozaki, Hiroaki  and
      Morishita, Terufumi  and
      Koreeda, Yuta  and
      Yanai, Kohsuke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.298",
    doi = "10.18653/v1/2020.acl-main.298",
    pages = "3259--3266",
    abstract = "State-of-the-art argument mining studies have advanced the techniques for predicting argument structures. However, the technology for capturing non-tree-structured arguments is still in its infancy. In this paper, we focus on non-tree argument mining with a neural model. We jointly predict proposition types and edges between propositions. Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges. Experimental results show that both TSP and PLBA boost edge prediction performance compared to baselines.",
}
@inproceedings{wei-etal-2020-span,
    title = "A Span-based Linearization for Constituent Trees",
    author = "Wei, Yang  and
      Wu, Yuanbin  and
      Lan, Man",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.299",
    doi = "10.18653/v1/2020.acl-main.299",
    pages = "3267--3277",
    abstract = "We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.",
}
@inproceedings{li-etal-2020-empirical,
    title = "An Empirical Comparison of Unsupervised Constituency Parsing Methods",
    author = "Li, Jun  and
      Cao, Yifan  and
      Cai, Jiong  and
      Jiang, Yong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.300",
    doi = "10.18653/v1/2020.acl-main.300",
    pages = "3278--3283",
    abstract = "Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations. While many methods have been proposed to tackle the problem, including statistical and neural methods, their experimental results are often not directly comparable due to discrepancies in datasets, data preprocessing, lexicalization, and evaluation metrics. In this paper, we first examine experimental settings used in previous work and propose to standardize the settings for better comparability between methods. We then empirically compare several existing methods, including decade-old and newly proposed ones, under the standardized settings on English and Japanese, two languages with different branching tendencies. We find that recent models do not show a clear advantage over decade-old models in our experiments. We hope our work can provide new insights into existing methods and facilitate future empirical evaluation of unsupervised constituency parsing.",
}
@inproceedings{nguyen-etal-2020-efficient,
    title = "Efficient Constituency Parsing by Pointing",
    author = "Nguyen, Thanh-Tung  and
      Nguyen, Xuan-Phi  and
      Joty, Shafiq  and
      Li, Xiaoli",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.301",
    doi = "10.18653/v1/2020.acl-main.301",
    pages = "3284--3294",
    abstract = "We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-the-art while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.",
}
@inproceedings{zhang-etal-2020-efficient,
    title = "Efficient Second-Order {T}ree{CRF} for Neural Dependency Parsing",
    author = "Zhang, Yu  and
      Li, Zhenghua  and
      Zhang, Min",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.302",
    doi = "10.18653/v1/2020.acl-main.302",
    pages = "3295--3305",
    abstract = "In the deep learning (DL) era, parsing models are extremely simplified with little hurt on performance, thanks to the remarkable capability of multi-layer BiLSTMs in context representation. As the most popular graph-based dependency parser due to its high efficiency and performance, the biaffine parser directly scores single dependencies under the arc-factorization assumption, and adopts a very simple local token-wise cross-entropy training loss. This paper for the first time presents a second-order TreeCRF extension to the biaffine parser. For a long time, the complexity and inefficiency of the inside-outside algorithm hinder the popularity of TreeCRF. To address this issue, we propose an effective way to batchify the inside and Viterbi algorithms for direct large matrix operation on GPUs, and to avoid the complex outside algorithm via efficient back-propagation. Experiments and analysis on 27 datasets from 13 languages clearly show that techniques developed before the DL era, such as structural learning (global TreeCRF loss) and high-order modeling are still useful, and can further boost parsing performance over the state-of-the-art biaffine parser, especially for partially annotated training data. We release our code at https://github.com/yzhangcs/crfpar.",
}
@inproceedings{lepori-etal-2020-representations,
    title = "Representations of Syntax {[MASK]} Useful: {E}ffects of Constituency and Dependency Structure in Recursive {LSTM}s",
    author = "Lepori, Michael  and
      Linzen, Tal  and
      McCoy, R. Thomas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.303",
    doi = "10.18653/v1/2020.acl-main.303",
    pages = "3306--3316",
    abstract = "Sequence-based neural networks show significant sensitivity to syntactic structure, but they still perform less well on syntactic tasks than tree-based networks. Such tree-based networks can be provided with a constituency parse, a dependency parse, or both. We evaluate which of these two representational schemes more effectively introduces biases for syntactic structure that increase performance on the subject-verb agreement prediction task. We find that a constituency-based network generalizes more robustly than a dependency-based one, and that combining the two types of structure does not yield further improvement. Finally, we show that the syntactic robustness of sequential models can be substantially improved by fine-tuning on a small amount of constructed data, suggesting that data augmentation is a viable alternative to explicit constituency structure for imparting the syntactic biases that sequential models are lacking.",
}
@inproceedings{wang-etal-2020-structure,
    title = "Structure-Level Knowledge Distillation For Multilingual Sequence Labeling",
    author = "Wang, Xinyu  and
      Jiang, Yong  and
      Bach, Nguyen  and
      Wang, Tao  and
      Huang, Fei  and
      Tu, Kewei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.304",
    doi = "10.18653/v1/2020.acl-main.304",
    pages = "3317--3330",
    abstract = "Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages. Compared with relying on multiple monolingual models, using a multilingual model has the benefit of a smaller model size, easier in online serving, and generalizability to low-resource languages. However, current multilingual models still underperform individual monolingual models significantly due to model capacity limitations. In this paper, we propose to reduce the gap between monolingual models and the unified multilingual model by distilling the structural knowledge of several monolingual models (teachers) to the unified multilingual model (student). We propose two novel KD methods based on structure-level information: (1) approximately minimizes the distance between the student{'}s and the teachers{'} structure-level probability distributions, (2) aggregates the structure-level knowledge to local distributions and minimizes the distance between two local probability distributions. Our experiments on 4 multilingual tasks with 25 datasets show that our approaches outperform several strong baselines and have stronger zero-shot generalizability than both the baseline model and teacher models.",
}
@inproceedings{zeng-etal-2020-dynamic,
    title = "Dynamic Online Conversation Recommendation",
    author = "Zeng, Xingshan  and
      Li, Jing  and
      Wang, Lu  and
      Mao, Zhiming  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.305",
    doi = "10.18653/v1/2020.acl-main.305",
    pages = "3331--3341",
    abstract = "Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner. Here we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests. While most prior work assumes static user interests, our model is able to capture the temporal aspects of user interests, and further handle future conversations that are unseen during training time. Concretely, we propose a neural architecture to exploit changes of user interactions and interests over time, to predict which discussions they are likely to enter. We conduct experiments on large-scale collections of Reddit conversations, and results on three subreddits show that our model significantly outperforms state-of-the-art models that make a static assumption of user interests. We further evaluate on handling {``}cold start{''}, and observe consistently better performance by our model when considering various degrees of sparsity of user{'}s chatting history and conversation contexts. Lastly, analyses on our model outputs indicate user interest change, explaining the advantage and efficacy of our approach.",
}
@inproceedings{yu-etal-2020-improving,
    title = "Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer",
    author = "Yu, Jianfei  and
      Jiang, Jing  and
      Yang, Li  and
      Xia, Rui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.306",
    doi = "10.18653/v1/2020.acl-main.306",
    pages = "3342--3352",
    abstract = "In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts. Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their word representations are insensitive to the visual context; (2) most of them ignore the bias brought by the visual context. To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations. To alleviate the visual bias, we further propose to leverage purely text-based entity span detection as an auxiliary module, and design a Unified Multimodal Transformer to guide the final predictions with the entity span predictions. Experiments show that our unified approach achieves the new state-of-the-art performance on two benchmark datasets.",
}
@inproceedings{du-tanaka-ishii-2020-stock,
    title = "Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization",
    author = "Du, Xin  and
      Tanaka-Ishii, Kumiko",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.307",
    doi = "10.18653/v1/2020.acl-main.307",
    pages = "3353--3363",
    abstract = "Previous works that integrated news articles to better process stock prices used a variety of neural networks to predict price movements. The textual and price information were both encoded in the neural network, and it is therefore difficult to apply this approach in situations other than the original framework of the notoriously hard problem of price prediction. In contrast, this paper presents a method to encode the influence of news articles through a vector representation of stocks called a \textit{stock embedding}. The stock embedding is acquired with a deep learning framework using both news articles and price history. Because the embedding takes the operational form of a vector, it is applicable to other financial problems besides price prediction. As one example application, we show the results of portfolio optimization using Reuters {\&} Bloomberg headlines, producing a capital gain 2.8 times larger than that obtained with a baseline method using only stock price data. This suggests that the proposed stock embedding can leverage textual financial semantics to solve financial prediction problems.",
}
@inproceedings{baly-etal-2020-written,
    title = "What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context",
    author = "Baly, Ramy  and
      Karadzhov, Georgi  and
      An, Jisun  and
      Kwak, Haewoon  and
      Dinkov, Yoan  and
      Ali, Ahmed  and
      Glass, James  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.308",
    doi = "10.18653/v1/2020.acl-main.308",
    pages = "3364--3374",
    abstract = "Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim, either manually or automatically. Thus, it has been proposed to profile entire news outlets and to look for those that are likely to publish fake or biased content. This makes it possible to detect likely {``}fake news{''} the moment they are published, by simply checking the reliability of their source. From a practical perspective, political bias and factuality of reporting have a linguistic aspect but also a social context. Here, we study the impact of both, namely (i) what was written (i.e., what was published by the target medium, and how it describes itself in Twitter) vs. (ii) who reads it (i.e., analyzing the target medium{'}s audience on social media). We further study (iii) what was written about the target medium (in Wikipedia). The evaluation results show that what was written matters most, and we further show that putting all information sources together yields huge improvements over the current state-of-the-art.",
}
@inproceedings{noji-takamura-2020-analysis,
    title = "An Analysis of the Utility of Explicit Negative Examples to Improve the Syntactic Abilities of Neural Language Models",
    author = "Noji, Hiroshi  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.309",
    doi = "10.18653/v1/2020.acl-main.309",
    pages = "3375--3385",
    abstract = "We explore the utilities of explicit negative examples in training neural language models. Negative examples here are incorrect words in a sentence, such as \textit{barks} in *\textit{The dogs barks}. Neural language models are commonly trained only on positive examples, a set of sentences in the training data, but recent studies suggest that the models trained in this way are not capable of robustly handling complex syntactic constructions, such as long-distance agreement. In this paper, we first demonstrate that appropriately using negative examples about particular constructions (e.g., subject-verb agreement) will boost the model{'}s robustness on them in English, with a negligible loss of perplexity. The key to our success is an additional margin loss between the log-likelihoods of a correct word and an incorrect word. We then provide a detailed analysis of the trained models. One of our findings is the difficulty of object-relative clauses for RNNs. We find that even with our direct learning signals the models still suffer from resolving agreement across an object-relative clause. Augmentation of training sentences involving the constructions somewhat helps, but the accuracy still does not reach the level of subject-relative clauses. Although not directly cognitively appealing, our method can be a tool to analyze the true architectural limitation of neural models on challenging linguistic constructions.",
}
@inproceedings{yin-etal-2020-robustness,
    title = "On the Robustness of Language Encoders against Grammatical Errors",
    author = "Yin, Fan  and
      Long, Quanyu  and
      Meng, Tao  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.310",
    doi = "10.18653/v1/2020.acl-main.310",
    pages = "3386--3403",
    abstract = "We conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. We use this approach to facilitate debugging models on downstream applications. Results confirm that the performance of all tested models is affected but the degree of impact varies. To interpret model behaviors, we further design a linguistic acceptability task to reveal their abilities in identifying ungrammatical sentences and the position of errors. We find that fixed contextual encoders with a simple classifier trained on the prediction of sentence correctness are able to locate error positions. We also design a cloze test for BERT and discover that BERT captures the interaction between errors and specific tokens in context. Our results shed light on understanding the robustness and behaviors of language encoders against grammatical errors.",
}
@inproceedings{jo-myaeng-2020-roles,
    title = "Roles and Utilization of Attention Heads in Transformer-based Neural Language Models",
    author = "Jo, Jae-young  and
      Myaeng, Sung-Hyon",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.311",
    doi = "10.18653/v1/2020.acl-main.311",
    pages = "3404--3417",
    abstract = "Sentence encoders based on the transformer architecture have shown promising results on various natural language tasks. The main impetus lies in the pre-trained neural language models that capture long-range dependencies among words, owing to multi-head attention that is unique in the architecture. However, little is known for how linguistic properties are processed, represented, and utilized for downstream tasks among hundreds of attention heads inside the pre-trained transformer-based model. For the initial goal of examining the roles of attention heads in handling a set of linguistic features, we conducted a set of experiments with ten probing tasks and three downstream tasks on four pre-trained transformer families (GPT, GPT2, BERT, and ELECTRA). Meaningful insights are shown through the lens of heat map visualization and utilized to propose a relatively simple sentence representation method that takes advantage of most influential attention heads, resulting in additional performance improvements on the downstream tasks.",
}
@inproceedings{sun-lu-2020-understanding,
    title = "Understanding Attention for Text Classification",
    author = "Sun, Xiaobing  and
      Lu, Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.312",
    doi = "10.18653/v1/2020.acl-main.312",
    pages = "3418--3428",
    abstract = "Attention has been proven successful in many natural language processing (NLP) tasks. Recently, many researchers started to investigate the interpretability of attention on NLP tasks. Many existing approaches focused on examining whether the local attention weights could reflect the importance of input representations. In this work, we present a study on understanding the internal mechanism of attention by looking into the gradient update process, checking its behavior when approaching a local minimum during training. We propose to analyze for each word token the following two quantities: its polarity score and its attention score, where the latter is a global assessment on the token{'}s significance. We discuss conditions under which the attention mechanism may become more (or less) interpretable, and show how the interplay between the two quantities can contribute towards model performance.",
}
@inproceedings{nguyen-etal-2020-relational,
    title = "A Relational Memory-based Embedding Model for Triple Classification and Search Personalization",
    author = "Nguyen, Dai Quoc  and
      Nguyen, Tu  and
      Phung, Dinh",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.313",
    doi = "10.18653/v1/2020.acl-main.313",
    pages = "3429--3435",
    abstract = "Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To this end, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to encode potential dependencies in relationship triples. R-MeN considers each triple as a sequence of 3 input vectors that recurrently interact with a memory using a transformer self-attention mechanism. Thus R-MeN encodes new information from interactions between the memory and each input vector to return a corresponding vector. Consequently, R-MeN feeds these 3 returned vectors to a convolutional neural network-based decoder to produce a scalar score for the triple. Experimental results show that our proposed R-MeN obtains state-of-the-art results on SEARCH17 for the search personalization task, and on WN11 and FB13 for the triple classification task.",
}
@inproceedings{miao-etal-2020-right,
    title = "Do you have the right scissors? Tailoring Pre-trained Language Models via {M}onte-{C}arlo Methods",
    author = "Miao, Ning  and
      Song, Yuxuan  and
      Zhou, Hao  and
      Li, Lei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.314",
    doi = "10.18653/v1/2020.acl-main.314",
    pages = "3436--3441",
    abstract = "It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model on a small dataset may lead to over- and/or under-estimate problem. In this paper, we propose MC-Tailor, a novel method to alleviate the above issue in text generation tasks by truncating and transferring the probability mass from over-estimated regions to under-estimated ones. Experiments on a variety of text generation datasets show that MC-Tailor consistently and significantly outperforms the fine-tuning approach.",
}
@inproceedings{li-etal-2020-enhancing,
    title = "Enhancing Pre-trained {C}hinese Character Representation with Word-aligned Attention",
    author = "Li, Yanzeng  and
      Yu, Bowen  and
      Mengge, Xue  and
      Liu, Tingwen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.315",
    doi = "10.18653/v1/2020.acl-main.315",
    pages = "3442--3448",
    abstract = "Most Chinese pre-trained models take character as the basic unit and learn representation according to character{'}s external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese. Hence, we propose a novel word-aligned attention to exploit explicit word information, which is complementary to various character-based Chinese pre-trained language models. Specifically, we devise a pooling mechanism to align the character-level attention to the word level and propose to alleviate the potential issue of segmentation error propagation by multi-source information fusion. As a result, word and character information are explicitly integrated at the fine-tuning procedure. Experimental results on five Chinese NLP benchmark tasks demonstrate that our method achieves significant improvements against BERT, ERNIE and BERT-wwm.",
}
@inproceedings{wu-etal-2020-encoder,
    title = "On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond",
    author = "Wu, Chen  and
      Wang, Prince Zizhuang  and
      Wang, William Yang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.316",
    doi = "10.18653/v1/2020.acl-main.316",
    pages = "3449--3464",
    abstract = "Variational autoencoders (VAEs) combine latent variables with amortized variational inference, whose optimization usually converges into a trivial local optimum termed posterior collapse, especially in text modeling. By tracking the optimization dynamics, we observe the encoder-decoder incompatibility that leads to poor parameterizations of the data manifold. We argue that the trivial local optimum may be avoided by improving the encoder and decoder parameterizations since the posterior network is part of a transition map between them. To this end, we propose Coupled-VAE, which couples a VAE model with a deterministic autoencoder with the same structure and improves the encoder and decoder parameterizations via encoder weight sharing and decoder signal matching. We apply the proposed Coupled-VAE approach to various VAE models with different regularization, posterior family, decoder structure, and optimization strategy. Experiments on benchmark datasets (i.e., PTB, Yelp, and Yahoo) show consistently improved results in terms of probability estimation and richness of the latent space. We also generalize our method to conditional language modeling and propose Coupled-CVAE, which largely improves the diversity of dialogue generation on the Switchboard dataset.",
}
@inproceedings{ye-etal-2020-safer,
    title = "{SAFER}: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions",
    author = "Ye, Mao  and
      Gong, Chengyue  and
      Liu, Qiang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.317",
    doi = "10.18653/v1/2020.acl-main.317",
    pages = "3465--3475",
    abstract = "State-of-the-art NLP models can often be fooled by human-unaware transformations such as synonymous word substitution. For security reasons, it is of critical importance to develop models with certified robustness that can provably guarantee that the prediction is can not be altered by any possible synonymous word substitution. In this work, we propose a certified robust method based on a new randomized smoothing technique, which constructs a stochastic ensemble by applying random word substitutions on the input sentences, and leverage the statistical properties of the ensemble to provably certify the robustness. Our method is simple and structure-free in that it only requires the black-box queries of the model outputs, and hence can be applied to any pre-trained models (such as BERT) and any types of models (world-level or subword-level). Our method significantly outperforms recent state-of-the-art methods for certified robustness on both IMDB and Amazon text classification tasks. To the best of our knowledge, we are the first work to achieve certified robustness on large systems such as BERT with practically meaningful certified accuracy.",
}
@inproceedings{ren-etal-2020-graph,
    title = "A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction",
    author = "Ren, Shuo  and
      Liu, Shujie  and
      Zhou, Ming  and
      Ma, Shuai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.318",
    doi = "10.18653/v1/2020.acl-main.318",
    pages = "3476--3485",
    abstract = "Unsupervised bilingual lexicon induction is the task of inducing word translations from monolingual corpora of two languages. Recent methods are mostly based on unsupervised cross-lingual word embeddings, the key to which is to find initial solutions of word translations, followed by the learning and refinement of mappings between the embedding spaces of two languages. However, previous methods find initial solutions just based on word-level information, which may be (1) limited and inaccurate, and (2) prone to contain some noise introduced by the insufficiently pre-trained embeddings of some words. To deal with those issues, in this paper, we propose a novel graph-based paradigm to induce bilingual lexicons in a coarse-to-fine way. We first build a graph for each language with its vertices representing different words. Then we extract word cliques from the graphs and map the cliques of two languages. Based on that, we induce the initial word translation solution with the central words of the aligned cliques. This coarse-to-fine approach not only leverages clique-level information, which is richer and more accurate, but also effectively reduces the bad effect of the noise in the pre-trained embeddings. Finally, we take the initial solution as the seed to learn cross-lingual embeddings, from which we induce bilingual lexicons. Experiments show that our approach improves the performance of bilingual lexicon induction compared with previous methods.",
}
@inproceedings{zou-etal-2020-reinforced,
    title = "A Reinforced Generation of Adversarial Examples for Neural Machine Translation",
    author = "Zou, Wei  and
      Huang, Shujian  and
      Xie, Jun  and
      Dai, Xinyu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.319",
    doi = "10.18653/v1/2020.acl-main.319",
    pages = "3486--3497",
    abstract = "Neural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of these systems{---}fathoming how and when neural-based systems fail in such cases is critical for industrial maintenance. Instead of collecting and analyzing bad cases using limited handcrafted error features, here we investigate this issue by generating adversarial examples via a new paradigm based on reinforcement learning. Our paradigm could expose pitfalls for a given performance metric, e.g., BLEU, and could target any given neural machine translation architecture. We conduct experiments of adversarial attacks on two mainstream neural machine translation architectures, RNN-search, and Transformer. The results show that our method efficiently produces stable attacks with meaning-preserving adversarial examples. We also present a qualitative and quantitative analysis for the preference pattern of the attack, demonstrating its capability of pitfall exposure.",
}
@inproceedings{ren-etal-2020-retrieve,
    title = "A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation",
    author = "Ren, Shuo  and
      Wu, Yu  and
      Liu, Shujie  and
      Zhou, Ming  and
      Ma, Shuai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.320",
    doi = "10.18653/v1/2020.acl-main.320",
    pages = "3498--3504",
    abstract = "The commonly used framework for unsupervised machine translation builds initial translation models of both translation directions, and then performs iterative back-translation to jointly boost their translation performance. The initialization stage is very important since bad initialization may wrongly squeeze the search space, and too much noise introduced in this stage may hurt the final performance. In this paper, we propose a novel retrieval and rewriting based method to better initialize unsupervised translation models. We first retrieve semantically comparable sentences from monolingual corpora of two languages and then rewrite the target side to minimize the semantic gap between the source and retrieved targets with a designed rewriting model. The rewritten sentence pairs are used to initialize SMT models which are used to generate pseudo data for two NMT models, followed by the iterative back-translation. Experiments show that our method can build better initial unsupervised translation models and improve the final translation performance by over 4 BLEU scores. Our code is released at https://github.com/Imagist-Shuo/RRforUNMT.git.",
}
@inproceedings{ma-etal-2020-simple,
    title = "A Simple and Effective Unified Encoder for Document-Level Machine Translation",
    author = "Ma, Shuming  and
      Zhang, Dongdong  and
      Zhou, Ming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.321",
    doi = "10.18653/v1/2020.acl-main.321",
    pages = "3505--3511",
    abstract = "Most of the existing models for document-level machine translation adopt dual-encoder structures. The representation of the source sentences and the document-level contexts are modeled with two separate encoders. Although these models can make use of the document-level contexts, they do not fully model the interaction between the contexts and the source sentences, and can not directly adapt to the recent pre-training models (e.g., BERT) which encodes multiple sentences with a single encoder. In this work, we propose a simple and effective unified encoder that can outperform the baseline models of dual-encoder models in terms of BLEU and METEOR scores. Moreover, the pre-training models can further boost the performance of our proposed model.",
}
@inproceedings{li-etal-2020-multi-encoder,
    title = "Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation",
    author = "Li, Bei  and
      Liu, Hui  and
      Wang, Ziyang  and
      Jiang, Yufan  and
      Xiao, Tong  and
      Zhu, Jingbo  and
      Liu, Tongran  and
      Li, Changliang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.322",
    doi = "10.18653/v1/2020.acl-main.322",
    pages = "3512--3518",
    abstract = "In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in document-level neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.",
}
@inproceedings{xu-etal-2020-dynamically,
    title = "Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change",
    author = "Xu, Hongfei  and
      van Genabith, Josef  and
      Xiong, Deyi  and
      Liu, Qiuhui",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.323",
    doi = "10.18653/v1/2020.acl-main.323",
    pages = "3519--3524",
    abstract = "The choice of hyper-parameters affects the performance of neural models. While much previous research (Sutskever et al., 2013; Duchi et al., 2011; Kingma and Ba, 2015) focuses on accelerating convergence and reducing the effects of the learning rate, comparatively few papers concentrate on the effect of batch size. In this paper, we analyze how increasing batch size affects gradient direction, and propose to evaluate the stability of gradients with their angle change. Based on our observations, the angle change of gradient direction first tends to stabilize (i.e. gradually decrease) while accumulating mini-batches, and then starts to fluctuate. We propose to automatically and dynamically determine batch sizes by accumulating gradients of mini-batches and performing an optimization step at just the time when the direction of gradients starts to fluctuate. To improve the efficiency of our approach for large models, we propose a sampling approach to select gradients of parameters sensitive to the batch size. Our approach dynamically determines proper and efficient batch sizes during training. In our experiments on the WMT 14 English to German and English to French tasks, our approach improves the Transformer with a fixed 25k batch size by +0.73 and +0.82 BLEU respectively.",
}
@inproceedings{sun-etal-2020-knowledge,
    title = "Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation",
    author = "Sun, Haipeng  and
      Wang, Rui  and
      Chen, Kehai  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.324",
    doi = "10.18653/v1/2020.acl-main.324",
    pages = "3525--3535",
    abstract = "Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs. However, it can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. That is, research on multilingual UNMT has been limited. In this paper, we empirically introduce a simple method to translate between thirteen languages using a single encoder and a single decoder, making use of multilingual data to improve UNMT for all language pairs. On the basis of the empirical findings, we propose two knowledge distillation methods to further enhance multilingual UNMT performance. Our experiments on a dataset with English translated to and from twelve other languages (including three language families and six language branches) show remarkable results, surpassing strong unsupervised individual baselines while achieving promising performance between non-English language pairs in zero-shot translation scenarios and alleviating poor performance in low-resource language pairs.",
}
@inproceedings{susanto-etal-2020-lexically,
    title = "Lexically Constrained Neural Machine Translation with {L}evenshtein Transformer",
    author = "Susanto, Raymond Hendy  and
      Chollampatt, Shamil  and
      Tan, Liling",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.325",
    doi = "10.18653/v1/2020.acl-main.325",
    pages = "3536--3543",
    abstract = "This paper proposes a simple and effective algorithm for incorporating lexical constraints in neural machine translation. Previous work either required re-training existing models with the lexical constraints or incorporating them during beam search decoding with significantly higher computational overheads. Leveraging the flexibility and speed of a recently proposed Levenshtein Transformer model (Gu et al., 2019), our method injects terminology constraints at inference time without any impact on decoding speed. Our method does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries. Experiments on English-German WMT datasets show that our approach improves an unconstrained baseline and previous approaches.",
}
@inproceedings{wang-sennrich-2020-exposure,
    title = "On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation",
    author = "Wang, Chaojun  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.326",
    doi = "10.18653/v1/2020.acl-main.326",
    pages = "3544--3552",
    abstract = "The standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is under debate. In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift. In experiments on three datasets with multiple test domains, we show that exposure bias is partially to blame for hallucinations, and that training with Minimum Risk Training, which avoids exposure bias, can mitigate this. Our analysis explains why exposure bias is more problematic under domain shift, and also links exposure bias to the beam search problem, i.e. performance deterioration with increasing beam size. Our results provide a new justification for methods that reduce exposure bias: even if they do not increase performance on in-domain test sets, they can increase model robustness to domain shift.",
}
@inproceedings{takahashi-etal-2020-automatic,
    title = "Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model",
    author = "Takahashi, Kosuke  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.327",
    doi = "10.18653/v1/2020.acl-main.327",
    pages = "3553--3558",
    abstract = "We propose an automatic evaluation method of machine translation that uses source language sentences regarded as additional pseudo references. The proposed method evaluates a translation hypothesis in a regression model. The model takes the paired source, reference, and hypothesis sentence all together as an input. A pretrained large scale cross-lingual language model encodes the input to sentence-pair vectors, and the model predicts a human evaluation score with those vectors. Our experiments show that our proposed method using Cross-lingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences. Additionally, using source sentences in our proposed method is confirmed to improve the evaluation performance.",
}
@inproceedings{shao-nakashole-2020-chartdialogs,
    title = "{C}hart{D}ialogs: {P}lotting from {N}atural {L}anguage {I}nstructions",
    author = "Shao, Yutong  and
      Nakashole, Ndapa",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.328",
    doi = "10.18653/v1/2020.acl-main.328",
    pages = "3559--3574",
    abstract = "This paper presents the problem of conversational plotting agents that carry out plotting actions from natural language instructions. To facilitate the development of such agents, we introduce ChartDialogs, a new multi-turn dialog dataset, covering a popular plotting library, matplotlib. The dataset contains over 15,000 dialog turns from 3,200 dialogs covering the majority of matplotlib plot types. Extensive experiments show the best-performing method achieving 61{\%} plotting accuracy, demonstrating that the dataset presents a non-trivial challenge for future research on this task.",
}
@inproceedings{khanuja-etal-2020-gluecos,
    title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
    author = "Khanuja, Simran  and
      Dandapat, Sandipan  and
      Srinivasan, Anirudh  and
      Sitaram, Sunayana  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.329",
    doi = "10.18653/v1/2020.acl-main.329",
    pages = "3575--3585",
    abstract = "Code-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on cross-lingual and multilingual tasks. We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish. Specifically, our evaluation benchmark includes Language Identification from text, POS tagging, Named Entity Recognition, Sentiment Analysis, Question Answering and a new task for code-switching, Natural Language Inference. We present results on all these tasks using cross-lingual word embedding models and multilingual models. In addition, we fine-tune multilingual models on artificially generated code-switched data. Although multilingual models perform significantly better than cross-lingual models, our results show that in most tasks, across both language pairs, multilingual models fine-tuned on code-switched data perform best, showing that multilingual models can be further optimized for code-switching tasks.",
}
@inproceedings{xu-etal-2020-matinf,
    title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
    author = "Xu, Canwen  and
      Pei, Jiaxin  and
      Wu, Hongtao  and
      Liu, Yiyu  and
      Li, Chenliang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.330",
    doi = "10.18653/v1/2020.acl-main.330",
    pages = "3586--3596",
    abstract = "Recently, large-scale datasets have vastly facilitated the development in nearly all domains of Natural Language Processing. However, there is currently no cross-task dataset in NLP, which hinders the development of multi-task learning. We propose MATINF, the first jointly labeled large-scale dataset for classification, question answering and summarization. MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the merits held by MATINF.",
}
@inproceedings{wu-etal-2020-mind,
    title = "{MIND}: A Large-scale Dataset for News Recommendation",
    author = "Wu, Fangzhao  and
      Qiao, Ying  and
      Chen, Jiun-Hung  and
      Wu, Chuhan  and
      Qi, Tao  and
      Lian, Jianxun  and
      Liu, Danyang  and
      Xie, Xing  and
      Gao, Jianfeng  and
      Wu, Winnie  and
      Zhou, Ming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.331",
    doi = "10.18653/v1/2020.acl-main.331",
    pages = "3597--3606",
    abstract = "News recommendation is an important technique for personalized news service. Compared with product and movie recommendations which have been comprehensively studied, the research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark dataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from the user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good testbed for news recommendation through a comparative study of several state-of-the-art news recommendation methods which are originally developed on different proprietary datasets. Our results show the performance of news recommendation highly relies on the quality of news content understanding and user interest modeling. Many natural language processing techniques such as effective text representation methods and pre-trained language models can effectively improve the performance of news recommendation. The MIND dataset will be available at https://msnews.github.io.",
}
@inproceedings{shaar-etal-2020-known,
    title = "That is a Known Lie: Detecting Previously Fact-Checked Claims",
    author = "Shaar, Shaden  and
      Babulkov, Nikolay  and
      Da San Martino, Giovanni  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.332",
    doi = "10.18653/v1/2020.acl-main.332",
    pages = "3607--3618",
    abstract = "The recent proliferation of {''}fake news{''} has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives. As a result and over time, a large number of fact-checked claims have been accumulated, which increases the likelihood that a new claim in social media or a new statement by a politician might have already been fact-checked by some trusted fact-checking organization, as viral claims often come back after a while in social media, and politicians like to repeat their favorite statements, true or false, over and over again. As manual fact-checking is very time-consuming (and fully automatic fact-checking has credibility issues), it is important to try to save this effort and to avoid wasting time on claims that have already been fact-checked. Interestingly, despite the importance of the task, it has been largely ignored by the research community so far. Here, we aim to bridge this gap. In particular, we formulate the task and we discuss how it relates to, but also differs from, previous work. We further create a specialized dataset, which we release to the research community. Finally, we present learning-to-rank experiments that demonstrate sizable improvements over state-of-the-art retrieval and textual similarity approaches.",
}
@inproceedings{pang-etal-2020-towards,
    title = "Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation",
    author = "Pang, Bo  and
      Nijkamp, Erik  and
      Han, Wenjuan  and
      Zhou, Linqi  and
      Liu, Yixian  and
      Tu, Kewei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.333",
    doi = "10.18653/v1/2020.acl-main.333",
    pages = "3619--3629",
    abstract = "Open-domain dialogue generation has gained increasing attention in Natural Language Processing. Its evaluation requires a holistic means. Human ratings are deemed as the gold standard. As human evaluation is inefficient and costly, an automated substitute is highly desirable. In this paper, we propose holistic evaluation metrics that capture different aspects of open-domain dialogues. Our metrics consist of (1) GPT-2 based context coherence between sentences in a dialogue, (2) GPT-2 based fluency in phrasing, (3) $n$-gram based diversity in responses to augmented queries, and (4) textual-entailment-inference based logical self-consistency. The empirical validity of our metrics is demonstrated by strong correlations with human judgments. We open source the code and relevant materials.",
}
@inproceedings{wang-he-2020-birre,
    title = "{B}i{RRE}: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection",
    author = "Wang, Chengyu  and
      He, Xiaofeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.334",
    doi = "10.18653/v1/2020.acl-main.334",
    pages = "3630--3640",
    abstract = "The hypernymy detection task has been addressed under various frameworks. Previously, the design of unsupervised hypernymy scores has been extensively studied. In contrast, supervised classifiers, especially distributional models, leverage the global contexts of terms to make predictions, but are more likely to suffer from {``}lexical memorization{''}. In this work, we revisit supervised distributional models for hypernymy detection. Rather than taking embeddings of two terms as classification inputs, we introduce a representation learning framework named Bidirectional Residual Relation Embeddings (BiRRE). In this model, a term pair is represented by a BiRRE vector as features for hypernymy classification, which models the possibility of a term being mapped to another in the embedding space by hypernymy relations. A Latent Projection Model with Negative Regularization (LPMNR) is proposed to simulate how hypernyms and hyponyms are generated by neural language models, and to generate BiRRE vectors based on bidirectional residuals of projections. Experiments verify BiRRE outperforms strong baselines over various evaluation frameworks.",
}
@inproceedings{sung-etal-2020-biomedical,
    title = "Biomedical Entity Representations with Synonym Marginalization",
    author = "Sung, Mujeen  and
      Jeon, Hwisang  and
      Lee, Jinhyuk  and
      Kang, Jaewoo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.335",
    doi = "10.18653/v1/2020.acl-main.335",
    pages = "3641--3650",
    abstract = "Biomedical named entities often play important roles in many biomedical text mining tools. However, due to the incompleteness of provided synonyms and numerous variations in their surface forms, normalization of biomedical entities is very challenging. In this paper, we focus on learning representations of biomedical entities solely based on the synonyms of entities. To learn from the incomplete synonyms, we use a model-based candidate selection and maximize the marginal likelihood of the synonyms present in top candidates. Our model-based candidates are iteratively updated to contain more difficult negative samples as our model evolves. In this way, we avoid the explicit pre-selection of negative samples from more than 400K candidates. On four biomedical entity normalization datasets having three different entity types (disease, chemical, adverse reaction), our model BioSyn consistently outperforms previous state-of-the-art models almost reaching the upper bound on each dataset.",
}
@inproceedings{yu-etal-2020-hypernymy,
    title = "Hypernymy Detection for Low-Resource Languages via Meta Learning",
    author = "Yu, Changlong  and
      Han, Jialong  and
      Zhang, Haisong  and
      Ng, Wilfred",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.336",
    doi = "10.18653/v1/2020.acl-main.336",
    pages = "3651--3656",
    abstract = "Hypernymy detection, a.k.a, lexical entailment, is a fundamental sub-task of many natural language understanding tasks. Previous explorations mostly focus on monolingual hypernymy detection on high-resource languages, e.g., English, but few investigate the low-resource scenarios. This paper addresses the problem of low-resource hypernymy detection by combining high-resource languages. We extensively compare three joint training paradigms and for the first time propose applying meta learning to relieve the low-resource issue. Experiments demonstrate the superiority of our method among the three settings, which substantially improves the performance of extremely low-resource languages by preventing over-fitting on small datasets.",
}
@inproceedings{sasano-korhonen-2020-investigating,
    title = "Investigating Word-Class Distributions in Word Vector Spaces",
    author = "Sasano, Ryohei  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.337",
    doi = "10.18653/v1/2020.acl-main.337",
    pages = "3657--3666",
    abstract = "This paper presents an investigation on the distribution of word vectors belonging to a certain word class in a pre-trained word vector space. To this end, we made several assumptions about the distribution, modeled the distribution accordingly, and validated each assumption by comparing the goodness of each model. Specifically, we considered two types of word classes {--} the semantic class of direct objects of a verb and the semantic class in a thesaurus {--} and tried to build models that properly estimate how likely it is that a word in the vector space is a member of a given word class. Our results on selectional preference and WordNet datasets show that the centroid-based model will fail to achieve good enough performance, the geometry of the distribution and the existence of subgroups will have limited impact, and also the negative instances need to be considered for adequate modeling of the distribution. We further investigated the relationship between the scores calculated by each model and the degree of membership and found that discriminative learning-based models are best in finding the boundaries of a class, while models based on the offset between positive and negative instances perform best in determining the degree of membership.",
}
@inproceedings{chen-etal-2020-aspect,
    title = "Aspect Sentiment Classification with Document-level Sentiment Preference Modeling",
    author = "Chen, Xiao  and
      Sun, Changlong  and
      Wang, Jingjing  and
      Li, Shoushan  and
      Si, Luo  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.338",
    doi = "10.18653/v1/2020.acl-main.338",
    pages = "3667--3677",
    abstract = "In the literature, existing studies always consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect, which largely ignore the document-level sentiment preference information, though obviously such information is crucial for alleviating the information deficiency problem in ASC. In this paper, we explore two kinds of sentiment preference information inside a document, i.e., contextual sentiment consistency w.r.t. the same aspect (namely intra-aspect sentiment consistency) and contextual sentiment tendency w.r.t. all the related aspects (namely inter-aspect sentiment tendency). On the basis, we propose a Cooperative Graph Attention Networks (CoGAN) approach for cooperatively learning the aspect-related sentence representation. Specifically, two graph attention networks are leveraged to model above two kinds of document-level sentiment preference information respectively, followed by an interactive mechanism to integrate the two-fold preference. Detailed evaluation demonstrates the great advantage of the proposed approach to ASC over the state-of-the-art baselines. This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.",
}
@inproceedings{wei-etal-2020-dont,
    title = "Don{'}t Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction",
    author = "Wei, Zhenkai  and
      Hong, Yu  and
      Zou, Bowei  and
      Cheng, Meng  and
      Yao, Jianmin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.339",
    doi = "10.18653/v1/2020.acl-main.339",
    pages = "3678--3684",
    abstract = "The current aspect extraction methods suffer from boundary errors. In general, these errors lead to a relatively minor difference between the extracted aspects and the ground-truth. However, they hurt the performance severely. In this paper, we propose to utilize a pointer network for repositioning the boundaries. Recycling mechanism is used, which enables the training data to be collected without manual intervention. We conduct the experiments on the benchmark datasets SE14 of laptop and SE14-16 of restaurant. Experimental results show that our method achieves substantial improvements over the baseline, and outperforms state-of-the-art methods.",
}
@inproceedings{chen-qian-2020-relation,
    title = "Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis",
    author = "Chen, Zhuang  and
      Qian, Tieyun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.340",
    doi = "10.18653/v1/2020.acl-main.340",
    pages = "3685--3694",
    abstract = "Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still under-exploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is \textit{{``}delicious{''}}, the aspect term must be \textit{{``}food{''}} rather than \textit{{``}place{''}}. In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task.",
}
@inproceedings{yin-etal-2020-sentibert,
    title = "{S}enti{BERT}: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics",
    author = "Yin, Da  and
      Meng, Tao  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.341",
    doi = "10.18653/v1/2020.acl-main.341",
    pages = "3695--3706",
    abstract = "We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics. The model incorporates contextualized representation with binary constituency parse tree to capture semantic composition. Comprehensive experiments demonstrate that SentiBERT achieves competitive performance on phrase-level sentiment classification. We further demonstrate that the sentiment composition learned from the phrase-level annotations on SST can be transferred to other sentiment analysis tasks as well as related tasks, such as emotion classification tasks. Moreover, we conduct ablation studies and design visualization methods to understand SentiBERT. We show that SentiBERT is better than baseline approaches in capturing negation and the contrastive relation and model the compositional sentiment semantics.",
}
@inproceedings{fan-etal-2020-transition,
    title = "Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction",
    author = "Fan, Chuang  and
      Yuan, Chaofa  and
      Du, Jiachen  and
      Gui, Lin  and
      Yang, Min  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.342",
    doi = "10.18653/v1/2020.acl-main.342",
    pages = "3707--3717",
    abstract = "Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71{\%} (p{\textless}0.01) in F1 measure.",
}
@inproceedings{yu-etal-2020-ch,
    title = "{CH}-{SIMS}: A {C}hinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality",
    author = "Yu, Wenmeng  and
      Xu, Hua  and
      Meng, Fanyang  and
      Zhu, Yilin  and
      Ma, Yixiao  and
      Wu, Jiele  and
      Zou, Jiyun  and
      Yang, Kaicheng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.343",
    doi = "10.18653/v1/2020.acl-main.343",
    pages = "3718--3727",
    abstract = "Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single- and multi-modal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at https://github.com/thuiar/MMSA.",
}
@inproceedings{wang-etal-2020-curriculum,
    title = "Curriculum Pre-training for End-to-End Speech Translation",
    author = "Wang, Chengyi  and
      Wu, Yu  and
      Liu, Shujie  and
      Zhou, Ming  and
      Yang, Zhenglu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.344",
    doi = "10.18653/v1/2020.acl-main.344",
    pages = "3728--3738",
    abstract = "End-to-end speech translation poses a heavy burden on the encoder because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful encoder, traditional methods pre-train it on ASR data to capture speech features. However, we argue that pre-training the encoder only through simple speech recognition is not enough, and high-level linguistic knowledge should be considered. Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. The difficulty of these courses is gradually increasing. Experiments show that our curriculum pre-training method leads to significant improvements on En-De and En-Fr speech translation benchmarks.",
}
@inproceedings{prasad-jyothi-2020-accents,
    title = "How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems",
    author = "Prasad, Archiki  and
      Jyothi, Preethi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.345",
    doi = "10.18653/v1/2020.acl-main.345",
    pages = "3739--3753",
    abstract = "In this work, we present a detailed analysis of how accent information is reflected in the internal representation of speech in an end-to-end automatic speech recognition (ASR) system. We use a state-of-the-art end-to-end ASR system, comprising convolutional and recurrent layers, that is trained on a large amount of US-accented English speech and evaluate the model on speech samples from seven different English accents. We examine the effects of accent on the internal representation using three main probing techniques: a) Gradient-based explanation methods, b) Information-theoretic measures, and c) Outputs of accent and phone classifiers. We find different accents exhibiting similar trends irrespective of the probing technique used. We also find that most accent information is encoded within the first recurrent layer, which is suggestive of how one could adapt such an end-to-end model to learn representations that are invariant to accents.",
}
@inproceedings{jamshid-lou-johnson-2020-improving,
    title = "Improving Disfluency Detection by Self-Training a Self-Attentive Model",
    author = "Jamshid Lou, Paria  and
      Johnson, Mark",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.346",
    doi = "10.18653/v1/2020.acl-main.346",
    pages = "3754--3763",
    abstract = "Self-attentive neural syntactic parsers using contextualized word embeddings (e.g. ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts. Since the contextualized word embeddings are pre-trained on a large amount of unlabeled data, using additional unlabeled data to train a neural model might seem redundant. However, we show that self-training {---} a semi-supervised technique for incorporating unlabeled data {---} sets a new state-of-the-art for the self-attentive parser on disfluency detection, demonstrating that self-training provides benefits orthogonal to the pre-trained contextualized word representations. We also show that ensembling self-trained parsers provides further gains for disfluency detection.",
}
@inproceedings{huang-chen-2020-learning,
    title = "Learning Spoken Language Representations with Neural Lattice Language Modeling",
    author = "Huang, Chao-Wei  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.347",
    doi = "10.18653/v1/2020.acl-main.347",
    pages = "3764--3769",
    abstract = "Pre-trained language models have achieved huge improvement on many NLP tasks. However, these methods are usually designed for written text, so they do not consider the properties of spoken language. Therefore, this paper aims at generalizing the idea of language model pre-training to lattices generated by recognition systems. We propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks. The proposed two-stage pre-training approach reduces the demands of speech data and has better efficiency. Experiments on intent detection and dialogue act recognition datasets demonstrate that our proposed method consistently outperforms strong baselines when evaluated on spoken inputs. The code is available at https://github.com/MiuLab/Lattice-ELMo.",
}
@inproceedings{winata-etal-2020-meta,
    title = "Meta-Transfer Learning for Code-Switched Speech Recognition",
    author = "Winata, Genta Indra  and
      Cahyawijaya, Samuel  and
      Lin, Zhaojiang  and
      Liu, Zihan  and
      Xu, Peng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.348",
    doi = "10.18653/v1/2020.acl-main.348",
    pages = "3770--3776",
    abstract = "An increasing number of people in the world today speak a mixed-language as a result of being multilingual. However, building a speech recognition system for code-switching remains difficult due to the availability of limited resources and the expense and significant effort required to collect mixed-language data. We therefore propose a new learning method, meta-transfer learning, to transfer learn on a code-switched speech recognition system in a low-resource setting by judiciously extracting information from high-resource monolingual datasets. Our model learns to recognize individual languages, and transfer them so as to better recognize mixed-language speech by conditioning the optimization on the code-switching data. Based on experimental results, our model outperforms existing baselines on speech recognition and language modeling tasks, and is faster to converge.",
}
@inproceedings{xu-etal-2020-reasoning,
    title = "Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association",
    author = "Xu, Nan  and
      Zeng, Zhixiong  and
      Mao, Wenji",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.349",
    doi = "10.18653/v1/2020.acl-main.349",
    pages = "3777--3786",
    abstract = "Sarcasm is a sophisticated linguistic phenomenon to express the opposite of what one really means. With the rapid growth of social media, multimodal sarcastic tweets are widely posted on various social platforms. In multimodal context, sarcasm is no longer a pure linguistic phenomenon, and due to the nature of social media short text, the opposite is more often manifested via cross-modality expressions. Thus traditional text-based methods are insufficient to detect multimodal sarcasm. To reason with multimodal sarcastic tweets, in this paper, we propose a novel method for modeling cross-modality contrast in the associated context. Our method models both cross-modality contrast and semantic association by constructing the Decomposition and Relation Network (namely D{\&}R Net). The decomposition network represents the commonality and discrepancy between image and text, and the relation network models the semantic association in cross-modality context. Experimental results on a public dataset demonstrate the effectiveness of our model in multimodal sarcasm detection.",
}
@inproceedings{ren-etal-2020-simulspeech,
    title = "{S}imul{S}peech: End-to-End Simultaneous Speech to Text Translation",
    author = "Ren, Yi  and
      Liu, Jinglin  and
      Tan, Xu  and
      Zhang, Chen  and
      Qin, Tao  and
      Zhao, Zhou  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.350",
    doi = "10.18653/v1/2020.acl-main.350",
    pages = "3787--3796",
    abstract = "In this work, we develop SimulSpeech, an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently. SimulSpeech consists of a speech encoder, a speech segmenter and a text decoder, where 1) the segmenter builds upon the encoder and leverages a connectionist temporal classification (CTC) loss to split the input streaming speech in real time, 2) the encoder-decoder attention adopts a wait-$k$ strategy for simultaneous translation. SimulSpeech is more challenging than previous cascaded systems (with simultaneous automatic speech recognition (ASR) and simultaneous neural machine translation (NMT)). We introduce two novel knowledge distillation methods to ensure the performance: 1) Attention-level knowledge distillation transfers the knowledge from the multiplication of the attention matrices of simultaneous NMT and ASR models to help the training of the attention mechanism in SimulSpeech; 2) Data-level knowledge distillation transfers the knowledge from the full-sentence NMT model and also reduces the complexity of data distribution to help on the optimization of SimulSpeech. Experiments on MuST-C English-Spanish and English-German spoken language translation datasets show that SimulSpeech achieves reasonable BLEU scores and lower delay compared to full-sentence end-to-end speech to text translation (without simultaneous translation), and better performance than the two-stage cascaded simultaneous translation model in terms of BLEU scores and translation delay.",
}
@inproceedings{singla-etal-2020-towards,
    title = "Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations",
    author = "Singla, Karan  and
      Chen, Zhuohao  and
      Atkins, David  and
      Narayanan, Shrikanth",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.351",
    doi = "10.18653/v1/2020.acl-main.351",
    pages = "3797--3803",
    abstract = "Spoken language understanding tasks usually rely on pipelines involving complex processing blocks such as voice activity detection, speaker diarization and Automatic speech recognition (ASR). We propose a novel framework for predicting utterance level labels directly from speech features, thus removing the dependency on first generating transcripts, and transcription free behavioral coding. Our classifier uses a pretrained Speech-2-Vector encoder as bottleneck to generate word-level representations from speech features. This pretrained encoder learns to encode speech features for a word using an objective similar to Word2Vec. Our proposed approach just uses speech features and word segmentation information for predicting spoken utterance-level target labels. We show that our model achieves competitive results to other state-of-the-art approaches which use transcribed text for the task of predicting psychotherapy-relevant behavior codes.",
}
@inproceedings{zhu-etal-2020-neural-temporal,
    title = "Neural Temporal Opinion Modelling for Opinion Prediction on {T}witter",
    author = "Zhu, Lixing  and
      He, Yulan  and
      Zhou, Deyu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.352",
    doi = "10.18653/v1/2020.acl-main.352",
    pages = "3804--3810",
    abstract = "Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users{'} tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user{'}s historical tweet sequence and tweets posted by their neighbours. We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately compared to a number of competitive baselines.",
}
@inproceedings{peskov-etal-2020-takes,
    title = "It Takes Two to Lie: One to Lie, and One to Listen",
    author = "Peskov, Denis  and
      Cheng, Benny  and
      Elgohary, Ahmed  and
      Barrow, Joe  and
      Danescu-Niculescu-Mizil, Cristian  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.353",
    doi = "10.18653/v1/2020.acl-main.353",
    pages = "3811--3854",
    abstract = "Trust is implicit in many online text conversations{---}striking up new friendships, or asking for tech support. But trust can be betrayed through deception. We study the language and dynamics of deception in the negotiation-based game Diplomacy, where seven players compete for world domination by forging and breaking alliances with each other. Our study with players from the Diplomacy community gathers 17,289 messages annotated by the sender for their intended truthfulness and by the receiver for their perceived truthfulness. Unlike existing datasets, this captures deception in long-lasting relationships, where the interlocutors strategically combine truth with lies to advance objectives. A model that uses power dynamics and conversational contexts can predict when a lie occurs nearly as well as human players.",
}
@inproceedings{padhi-etal-2020-learning,
    title = "Learning Implicit Text Generation via Feature Matching",
    author = "Padhi, Inkit  and
      Dognin, Pierre  and
      Bai, Ke  and
      Nogueira dos Santos, C{\'\i}cero  and
      Chenthamarakshan, Vijil  and
      Mroueh, Youssef  and
      Das, Payel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.354",
    doi = "10.18653/v1/2020.acl-main.354",
    pages = "3855--3863",
    abstract = "Generative feature matching network (GFMN) is an approach for training state-of-the-art implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.",
}
@inproceedings{shahidi-etal-2020-two,
    title = "Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data",
    author = "Shahidi, Hamidreza  and
      Li, Ming  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.355",
    doi = "10.18653/v1/2020.acl-main.355",
    pages = "3864--3870",
    abstract = "A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks. In this work, we show that this is also the case for text generation from structured and unstructured data. We consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively. Table-to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models. Experimental results demonstrate that a basic attention-based seq2seq model trained with the exponential moving average technique achieves the state of the art in both tasks. Code is available at https://github.com/h-shahidi/2birds-gen.",
}
@inproceedings{barkan-etal-2020-bayesian,
    title = "{B}ayesian Hierarchical Words Representation Learning",
    author = "Barkan, Oren  and
      Rejwan, Idan  and
      Caciularu, Avi  and
      Koenigstein, Noam",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.356",
    doi = "10.18653/v1/2020.acl-main.356",
    pages = "3871--3877",
    abstract = "This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.",
}
@inproceedings{tamborrino-etal-2020-pre,
    title = "Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning",
    author = "Tamborrino, Alexandre  and
      Pellican{\`o}, Nicola  and
      Pannier, Baptiste  and
      Voitot, Pascal  and
      Naudin, Louise",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.357",
    doi = "10.18653/v1/2020.acl-main.357",
    pages = "3878--3887",
    abstract = "Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. Most of the existing approaches rely on a randomly initialized classifier on top of such networks. We argue that this fine-tuning procedure is sub-optimal as the pre-trained model has no prior on the specific classifier labels, while it might have already learned an intrinsic textual representation of the task. In this paper, we introduce a new scoring method that casts a plausibility ranking task in a full-text format and leverages the masked language modeling head tuned during the pre-training phase. We study commonsense reasoning tasks where the model must rank a set of hypotheses given a premise, focusing on the COPA, Swag, HellaSwag and CommonsenseQA datasets. By exploiting our scoring method without fine-tuning, we are able to produce strong baselines (e.g. 80{\%} test accuracy on COPA) that are comparable to supervised approaches. Moreover, when fine-tuning directly on the proposed scoring function, we show that our method provides a much more stable training phase across random restarts (e.g x10 standard deviation reduction on COPA test accuracy) and requires less annotated data than the standard classifier approach to reach equivalent performances.",
}
@inproceedings{xu-etal-2020-seek,
    title = "{SEEK}: Segmented Embedding of Knowledge Graphs",
    author = "Xu, Wentao  and
      Zheng, Shun  and
      He, Liang  and
      Shao, Bin  and
      Yin, Jian  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.358",
    doi = "10.18653/v1/2020.acl-main.358",
    pages = "3888--3897",
    abstract = "In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at \url{https://github.com/Wentao-Xu/SEEK}.",
}
@inproceedings{soto-etal-2020-selecting,
    title = "Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation",
    author = "Soto, Xabier  and
      Shterionov, Dimitar  and
      Poncelas, Alberto  and
      Way, Andy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.359",
    doi = "10.18653/v1/2020.acl-main.359",
    pages = "3898--3908",
    abstract = "Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation. Combining backtranslated data from different sources has led to better results than when using such data in isolation. In this work we analyse the impact that data translated with rule-based, phrase-based statistical and neural MT systems has on new MT systems. We use a real-world low-resource use-case (Basque-to-Spanish in the clinical domain) as well as a high-resource language pair (German-to-English) to test different scenarios with backtranslation and employ data selection to optimise the synthetic corpora. We exploit different data selection strategies in order to reduce the amount of data used, while at the same time maintaining high-quality MT systems. We further tune the data selection method by taking into account the quality of the MT systems used for backtranslation and lexical diversity of the resulting corpora. Our experiments show that incorporating backtranslated data from different sources can be beneficial, and that availing of data selection can yield improved performance.",
}
@inproceedings{brix-etal-2020-successfully,
    title = "Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture",
    author = "Brix, Christopher  and
      Bahar, Parnia  and
      Ney, Hermann",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.360",
    doi = "10.18653/v1/2020.acl-main.360",
    pages = "3909--3915",
    abstract = "Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computations using neural networks. The stabilized lottery ticket hypothesis states that networks can be pruned after none or few training iterations, using a mask computed based on the unpruned converged model. On the transformer architecture and the WMT 2014 English-to-German and English-to-French tasks, we show that stabilized lottery ticket pruning performs similar to magnitude pruning for sparsity levels of up to 85{\%}, and propose a new combination of pruning techniques that outperforms all other techniques for even higher levels of sparsity. Furthermore, we confirm that the parameter{'}s initial sign and not its specific value is the primary factor for successful training, and show that magnitude pruning cannot be used to find winning lottery tickets.",
}
@inproceedings{niu-etal-2020-self,
    title = "A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction",
    author = "Niu, Yilin  and
      Jiao, Fangkai  and
      Zhou, Mantong  and
      Yao, Ting  and
      Xu, Jingfang  and
      Huang, Minlie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.361",
    doi = "10.18653/v1/2020.acl-main.361",
    pages = "3916--3927",
    abstract = "Neural models have achieved great success on machine reading comprehension (MRC), many of which typically consist of two components: an evidence extractor and an answer predictor. The former seeks the most relevant information from a reference text, while the latter is to locate or generate answers from the extracted evidence. Despite the importance of evidence labels for training the evidence extractor, they are not cheaply accessible, particularly in many non-extractive MRC tasks such as YES/NO question answering and multi-choice MRC. To address this problem, we present a Self-Training method (STM), which supervises the evidence extractor with auto-generated evidence labels in an iterative process. At each iteration, a base MRC model is trained with golden answers and noisy evidence labels. The trained model will predict pseudo evidence labels as extra supervision in the next iteration. We evaluate STM on seven datasets over three MRC tasks. Experimental results demonstrate the improvement on existing MRC models, and we also analyze how and why such a self-training method works in MRC.",
}
@inproceedings{zhang-etal-2020-graph,
    title = "Graph-to-Tree Learning for Solving Math Word Problems",
    author = "Zhang, Jipeng  and
      Wang, Lei  and
      Lee, Roy Ka-Wei  and
      Bin, Yi  and
      Wang, Yan  and
      Shao, Jie  and
      Lim, Ee-Peng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.362",
    doi = "10.18653/v1/2020.acl-main.362",
    pages = "3928--3937",
    abstract = "While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by effectively representing the relationships and order information among the quantities in MWPs. We conduct extensive experiments on two available datasets. Our experiment results show that Graph2Tree outperforms the state-of-the-art baselines on two benchmark datasets significantly. We also discuss case studies and empirically examine Graph2Tree{'}s effectiveness in translating the MWP text into solution expressions.",
}
@inproceedings{amigo-etal-2020-effectiveness,
    title = "An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results",
    author = "Amigo, Enrique  and
      Gonzalo, Julio  and
      Mizzaro, Stefano  and
      Carrillo-de-Albornoz, Jorge",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.363",
    doi = "10.18653/v1/2020.acl-main.363",
    pages = "3938--3949",
    abstract = "In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as {``}positive{''}, {``}neutral{''}, {``}negative{''} in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.",
}
@inproceedings{kim-etal-2020-adaptive,
    title = "Adaptive Compression of Word Embeddings",
    author = "Kim, Yeachan  and
      Kim, Kang-Min  and
      Lee, SangKeun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.364",
    doi = "10.18653/v1/2020.acl-main.364",
    pages = "3950--3959",
    abstract = "Distributed representations of words have been an indispensable component for natural language processing (NLP) tasks. However, the large memory footprint of word embeddings makes it challenging to deploy NLP models to memory-constrained devices (e.g., self-driving cars, mobile devices). In this paper, we propose a novel method to adaptively compress word embeddings. We fundamentally follow a code-book approach that represents words as discrete codes such as (8, 5, 2, 4). However, unlike prior works that assign the same length of codes to all words, we adaptively assign different lengths of codes to each word by learning downstream tasks. The proposed method works in two steps. First, each word directly learns to select its code length in an end-to-end manner by applying the Gumbel-softmax tricks. After selecting the code length, each word learns discrete codes through a neural network with a binary constraint. To showcase the general applicability of the proposed method, we evaluate the performance on four different downstream tasks. Comprehensive evaluation results clearly show that our method is effective and makes the highly compressed word embeddings without hurting the task accuracy. Moreover, we show that our model assigns word to each code-book by considering the significance of tasks.",
}
@inproceedings{giulianelli-etal-2020-analysing,
    title = "Analysing Lexical Semantic Change with Contextualised Word Representations",
    author = "Giulianelli, Mario  and
      Del Tredici, Marco  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.365",
    doi = "10.18653/v1/2020.acl-main.365",
    pages = "3960--3973",
    abstract = "This paper presents the first unsupervised approach to lexical semantic change that makes use of contextualised word representations. We propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements. Our extensive qualitative analysis demonstrates that our method captures a variety of synchronic and diachronic linguistic phenomena. We expect our work to inspire further research in this direction.",
}
@inproceedings{chiu-etal-2020-autoencoding,
    title = "Autoencoding Keyword Correlation Graph for Document Clustering",
    author = "Chiu, Billy  and
      Sahu, Sunil Kumar  and
      Thomas, Derek  and
      Sengupta, Neha  and
      Mahdy, Mohammady",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.366",
    doi = "10.18653/v1/2020.acl-main.366",
    pages = "3974--3981",
    abstract = "Document clustering requires a deep understanding of the complex structure of long-text; in particular, the intra-sentential (local) and inter-sentential features (global). Existing representation learning models do not fully capture these features. To address this, we present a novel graph-based representation for document clustering that builds a \textit{graph autoencoder} (GAE) on a Keyword Correlation Graph. The graph is constructed with topical keywords as nodes and multiple local and global features as edges. A GAE is employed to aggregate the two sets of features by learning a latent representation which can jointly reconstruct them. Clustering is then performed on the learned representations, using vector dimensions as features for inducing document classes. Extensive experiments on two datasets show that the features learned by our approach can achieve better clustering performance than other existing features, including term frequency-inverse document frequency and average embedding.",
}
@inproceedings{emerson-2020-autoencoding,
    title = "Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics",
    author = "Emerson, Guy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.367",
    doi = "10.18653/v1/2020.acl-main.367",
    pages = "3982--3995",
    abstract = "Functional Distributional Semantics provides a linguistically interpretable framework for distributional semantics, by representing the meaning of a word as a function (a binary classifier), instead of a vector. However, the large number of latent variables means that inference is computationally expensive, and training a model is therefore slow to converge. In this paper, I introduce the Pixie Autoencoder, which augments the generative model of Functional Distributional Semantics with a graph-convolutional neural network to perform amortised variational inference. This allows the model to be trained more effectively, achieving better results on two tasks (semantic similarity in context and semantic composition), and outperforming BERT, a large pre-trained language model.",
}
@inproceedings{schick-schutze-2020-bertram,
    title = "{BERTRAM}: Improved Word Embeddings Have Big Impact on Contextualized Model Performance",
    author = {Schick, Timo  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.368",
    doi = "10.18653/v1/2020.acl-main.368",
    pages = "3996--4007",
    abstract = {Pretraining deep language models has led to large performance gains in NLP. Despite this success, Schick and Sch{\"u}tze (2020) recently showed that these models struggle to understand rare words. For static word embeddings, this problem has been addressed by separately learning representations for rare words. In this work, we transfer this idea to pretrained language models: We introduce BERTRAM, a powerful architecture based on BERT that is capable of inferring high-quality embeddings for rare words that are suitable as input representations for deep language models. This is achieved by enabling the surface form and contexts of a word to interact with each other in a deep architecture. Integrating BERTRAM into BERT leads to large performance increases due to improved representations of rare and medium frequency words on both a rare word probing task and three downstream tasks.},
}
@inproceedings{pasini-etal-2020-clubert,
    title = "{C}lu{BERT}: {A} Cluster-Based Approach for Learning Sense Distributions in Multiple Languages",
    author = "Pasini, Tommaso  and
      Scozzafava, Federico  and
      Scarlini, Bianca",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.369",
    doi = "10.18653/v1/2020.acl-main.369",
    pages = "4008--4018",
    abstract = "Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly. However, the scarcity of sense-annotated data makes it difficult to induce a reliable and high-coverage distribution of the meanings in a language vocabulary. To address this issue, in this paper we present CluBERT, an automatic and multilingual approach for inducing the distributions of word senses from a corpus of raw sentences. Our experiments show that CluBERT learns distributions over English senses that are of higher quality than those extracted by alternative approaches. When used to induce the MFS of a lemma, CluBERT attains state-of-the-art results on the English Word Sense Disambiguation tasks and helps to improve the disambiguation performance of two off-the-shelf WSD models. Moreover, our distributions also prove to be effective in other languages, beating all their alternatives for computing the MFS on the multilingual WSD tasks. We release our sense distributions in five different languages at https://github.com/SapienzaNLP/clubert.",
}
@inproceedings{du-etal-2020-adversarial,
    title = "Adversarial and Domain-Aware {BERT} for Cross-Domain Sentiment Analysis",
    author = "Du, Chunning  and
      Sun, Haifeng  and
      Wang, Jingyu  and
      Qi, Qi  and
      Liao, Jianxin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.370",
    doi = "10.18653/v1/2020.acl-main.370",
    pages = "4019--4028",
    abstract = "Cross-domain sentiment classification aims to address the lack of massive amounts of labeled data. It demands to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain. In this paper, we investigate how to efficiently apply the pre-training language model BERT on the unsupervised domain adaptation. Due to the pre-training task and corpus, BERT is task-agnostic, which lacks domain awareness and can not distinguish the characteristic of source and target domain when transferring knowledge. To tackle these problems, we design a post-training procedure, which contains the target domain masked language model task and a novel domain-distinguish pre-training task. The post-training procedure will encourage BERT to be domain-aware and distill the domain-specific features in a self-supervised way. Based on this, we could then conduct the adversarial training to derive the enhanced domain-invariant features. Extensive experiments on Amazon dataset show that our model outperforms state-of-the-art methods by a large margin. The ablation study demonstrates that the remarkable improvement is not only from BERT but also from our method.",
}
@inproceedings{bar-haim-etal-2020-arguments,
    title = "From Arguments to Key Points: {T}owards Automatic Argument Summarization",
    author = "Bar-Haim, Roy  and
      Eden, Lilach  and
      Friedman, Roni  and
      Kantor, Yoav  and
      Lahav, Dan  and
      Slonim, Noam",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.371",
    doi = "10.18653/v1/2020.acl-main.371",
    pages = "4029--4039",
    abstract = "Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed \textit{key points}, each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance.",
}
@inproceedings{demszky-etal-2020-goemotions,
    title = "{G}o{E}motions: A Dataset of Fine-Grained Emotions",
    author = "Demszky, Dorottya  and
      Movshovitz-Attias, Dana  and
      Ko, Jeongwoo  and
      Cowen, Alan  and
      Nemade, Gaurav  and
      Ravi, Sujith",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.372",
    doi = "10.18653/v1/2020.acl-main.372",
    pages = "4040--4054",
    abstract = "Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement.",
}
@inproceedings{chiril-etal-2020-said,
    title = "He said {``}who{'}s gonna take care of your children when you are at {ACL}?{''}: Reported Sexist Acts are Not Sexist",
    author = "Chiril, Patricia  and
      Moriceau, V{\'e}ronique  and
      Benamara, Farah  and
      Mari, Alda  and
      Origgi, Gloria  and
      Coulomb-Gully, Marl{\`e}ne",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.373",
    doi = "10.18653/v1/2020.acl-main.373",
    pages = "4055--4066",
    abstract = "In a context of offensive content mediation on social media now regulated by European laws, it is important not only to be able to automatically detect sexist content but also to identify if a message with a sexist content is really sexist or is a story of sexism experienced by a woman. We propose: (1) a new characterization of sexist content inspired by speech acts theory and discourse analysis studies, (2) the first French dataset annotated for sexism detection, and (3) a set of deep learning experiments trained on top of a combination of several tweet{'}s vectorial representations (word embeddings, linguistic features, and various generalization strategies). Our results are encouraging and constitute a first step towards offensive content moderation.",
}
@inproceedings{tian-etal-2020-skep,
    title = "{SKEP}: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis",
    author = "Tian, Hao  and
      Gao, Can  and
      Xiao, Xinyan  and
      Liu, Hao  and
      He, Bolei  and
      Wu, Hua  and
      Wang, Haifeng  and
      Wu, Feng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.374",
    doi = "10.18653/v1/2020.acl-main.374",
    pages = "4067--4076",
    abstract = "Recently, sentiment analysis has seen remarkable advance with the help of pre-training approaches. However, sentiment knowledge, such as sentiment words and aspect-sentiment pairs, is ignored in the process of pre-training, despite the fact that they are widely used in traditional sentiment analysis approaches. In this paper, we introduce Sentiment Knowledge Enhanced Pre-training (SKEP) in order to learn a unified sentiment representation for multiple sentiment analysis tasks. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. Experiments on three kinds of sentiment tasks show that SKEP significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets. We release our code at https://github.com/baidu/Senta.",
}
@inproceedings{kulmizev-etal-2020-neural,
    title = "Do Neural Language Models Show Preferences for Syntactic Formalisms?",
    author = "Kulmizev, Artur  and
      Ravishankar, Vinit  and
      Abdou, Mostafa  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.375",
    doi = "10.18653/v1/2020.acl-main.375",
    pages = "4077--4091",
    abstract = "Recent work on the interpretability of deep neural language models has concluded that many properties of natural language syntax are encoded in their representational spaces. However, such studies often suffer from limited scope by focusing on a single language and a single linguistic formalism. In this study, we aim to investigate the extent to which the semblance of syntactic structure captured by language models adheres to a surface-syntactic or deep syntactic style of analysis, and whether the patterns are consistent across different languages. We apply a probe for extracting directed dependency trees to BERT and ELMo models trained on 13 different languages, probing for two different syntactic annotation styles: Universal Dependencies (UD), prioritizing deep syntactic relations, and Surface-Syntactic Universal Dependencies (SUD), focusing on surface structure. We find that both models exhibit a preference for UD over SUD {---} with interesting variations across languages and layers {---} and that the strength of this preference is correlated with differences in tree shape.",
}
@inproceedings{fernandez-gonzalez-gomez-rodriguez-2020-enriched,
    title = "Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.376",
    doi = "10.18653/v1/2020.acl-main.376",
    pages = "4092--4099",
    abstract = "Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce actions, have achieved the best accuracy to date. In this paper, we show that these results can be improved by using an in-order linearization instead. Based on this observation, we implement an enriched in-order shift-reduce linearization inspired by Vinyals et al. (2015){'}s approach, achieving the best accuracy to date on the English PTB dataset among fully-supervised single-model sequence-to-sequence constituent parsers. Finally, we apply deterministic attention mechanisms to match the speed of state-of-the-art transition-based parsers, thus showing that sequence-to-sequence models can match them, not only in accuracy, but also in speed.",
}
@inproceedings{ye-sun-2020-exact,
    title = "Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis",
    author = "Ye, Yajie  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.377",
    doi = "10.18653/v1/2020.acl-main.377",
    pages = "4100--4110",
    abstract = "A key problem in processing graph-based meaning representations is graph parsing, i.e. computing all possible derivations of a given graph according to a (competence) grammar. We demonstrate, for the first time, that exact graph parsing can be efficient for large graphs and with large Hyperedge Replacement Grammars (HRGs). The advance is achieved by exploiting locality as terminal edge-adjacency in HRG rules. In particular, we highlight the importance of 1) a terminal edge-first parsing strategy, 2) a categorization of a subclass of HRG, i.e. what we call Weakly Regular Graph Grammar, and 3) distributing argument-structures to both lexical and phrasal rules.",
}
@inproceedings{stanojevic-steedman-2020-max,
    title = "Max-Margin Incremental {CCG} Parsing",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Steedman, Mark",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.378",
    doi = "10.18653/v1/2020.acl-main.378",
    pages = "4111--4122",
    abstract = "Incremental syntactic parsing has been an active research area both for cognitive scientists trying to model human sentence processing and for NLP researchers attempting to combine incremental parsing with language modelling for ASR and MT. Most effort has been directed at designing the right transition mechanism, but less has been done to answer the question of what a probabilistic model for those transition parsers should look like. A very incremental transition mechanism of a recently proposed CCG parser when trained in straightforward locally normalised discriminative fashion produces very bad results on English CCGbank. We identify three biases as the causes of this problem: label bias, exposure bias and imbalanced probabilities bias. While known techniques for tackling these biases improve results, they still do not make the parser state of the art. Instead, we tackle all of these three biases at the same time using an improved version of beam search optimisation that minimises all beam search violations instead of minimising only the biggest violation. The new incremental parser gives better results than all previously published incremental CCG parsers, and outperforms even some widely used non-incremental CCG parsers.",
}
@inproceedings{do-rehbein-2020-neural,
    title = "Neural Reranking for Dependency Parsing: An Evaluation",
    author = "Do, Bich-Ngoc  and
      Rehbein, Ines",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.379",
    doi = "10.18653/v1/2020.acl-main.379",
    pages = "4123--4133",
    abstract = "Recent work has shown that neural rerankers can improve results for dependency parsing over the top k trees produced by a base parser. However, all neural rerankers so far have been evaluated on English and Chinese only, both languages with a configurational word order and poor morphology. In the paper, we re-assess the potential of successful neural reranking models from the literature on English and on two morphologically rich(er) languages, German and Czech. In addition, we introduce a new variation of a discriminative reranker based on graph convolutional networks (GCNs). We show that the GCN not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech. We explain the differences in reranking performance based on an analysis of a) the gold tree ratio and b) the variety in the k-best lists.",
}
@inproceedings{zhang-etal-2020-demographics,
    title = "Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting",
    author = "Zhang, Guanhua  and
      Bai, Bing  and
      Zhang, Junqi  and
      Bai, Kun  and
      Zhu, Conghui  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.380",
    doi = "10.18653/v1/2020.acl-main.380",
    pages = "4134--4145",
    abstract = "With the recent proliferation of the use of text classifications, researchers have found that there are certain unintended biases in text classification datasets. For example, texts containing some demographic identity-terms (e.g., {``}gay{''}, {``}black{''}) are more likely to be abusive in existing abusive language detection datasets. As a result, models trained with these datasets may consider sentences like {``}She makes me happy to be gay{''} as abusive simply because of the word {``}gay.{''} In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution. Based on this formalization, we further propose a model-agnostic debiasing training framework by recovering the non-discrimination distribution using instance weighting, which does not require any extra resources or annotations apart from a pre-defined set of demographic identity-terms. Experiments demonstrate that our method can effectively alleviate the impacts of the unintended biases without significantly hurting models{'} generalization ability.",
}
@inproceedings{chrupala-etal-2020-analyzing,
    title = "Analyzing analytical methods: The case of phonology in neural models of spoken language",
    author = "Chrupa{\l}a, Grzegorz  and
      Higy, Bertrand  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.381",
    doi = "10.18653/v1/2020.acl-main.381",
    pages = "4146--4156",
    abstract = "Given the fast development of analysis techniques for NLP and speech processing systems, few systematic studies have been conducted to compare the strengths and weaknesses of each method. As a step in this direction we study the case of representations of phonology in neural network models of spoken language. We use two commonly applied analytical techniques, diagnostic classifiers and representational similarity analysis, to quantify to what extent neural activation patterns encode phonemes and phoneme sequences. We manipulate two factors that can affect the outcome of analysis. First, we investigate the role of learning by comparing neural activations extracted from trained versus randomly-initialized models. Second, we examine the temporal scope of the activations by probing both local activations corresponding to a few milliseconds of the speech signal, and global activations pooled over the whole utterance. We conclude that reporting analysis results with randomly initialized models is crucial, and that global-scope methods tend to yield more consistent and interpretable results and we recommend their use as a complement to local-scope diagnostic methods.",
}
@inproceedings{camburu-etal-2020-make,
    title = "Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations",
    author = "Camburu, Oana-Maria  and
      Shillingford, Brendan  and
      Minervini, Pasquale  and
      Lukasiewicz, Thomas  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.382",
    doi = "10.18653/v1/2020.acl-main.382",
    pages = "4157--4165",
    abstract = "To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for their predictions. In this work, we show that such models are nonetheless prone to generating mutually inconsistent explanations, such as {''}Because there is a dog in the image.{''} and {''}Because there is no dog in the [same] image.{''}, exposing flaws in either the decision-making process of the model or in the generation of the explanations. We introduce a simple yet effective adversarial framework for sanity checking models against the generation of inconsistent natural language explanations. Moreover, as part of the framework, we address the problem of adversarial attacks with full target sequences, a scenario that was not previously addressed in sequence-to-sequence attacks. Finally, we apply our framework on a state-of-the-art neural natural language inference model that provides natural language explanations for its predictions. Our framework shows that this model is capable of generating a significant number of inconsistent explanations.",
}
@inproceedings{wu-etal-2020-perturbed,
    title = "Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting {BERT}",
    author = "Wu, Zhiyong  and
      Chen, Yun  and
      Kao, Ben  and
      Liu, Qun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.383",
    doi = "10.18653/v1/2020.acl-main.383",
    pages = "4166--4176",
    abstract = "By introducing a small set of additional parameters, a \textit{probe} learns to solve specific linguistic tasks (e.g., dependency parsing) in a supervised manner using feature representations (e.g., contextualized embeddings). The effectiveness of such \textit{probing} tasks is taken as evidence that the pre-trained model encodes linguistic knowledge. However, this approach of evaluating a language model is undermined by the uncertainty of the amount of knowledge that is learned by the probe itself. Complementary to those works, we propose a parameter-free probing technique for analyzing pre-trained language models (e.g., BERT). Our method does not require direct supervision from the probing tasks, nor do we introduce additional parameters to the probing process. Our experiments on BERT show that syntactic trees recovered from BERT using our method are significantly better than linguistically-uninformed baselines. We further feed the empirically induced dependency structures into a downstream sentiment classification task and find its improvement compatible with or even superior to a human-designed dependency schema.",
}
@inproceedings{sorodoc-etal-2020-probing,
    title = "Probing for Referential Information in Language Models",
    author = "Sorodoc, Ionut-Teodor  and
      Gulordava, Kristina  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.384",
    doi = "10.18653/v1/2020.acl-main.384",
    pages = "4177--4189",
    abstract = "Language models keep track of complex information about the preceding context {--} including, e.g., syntactic relations in a sentence. We investigate whether they also capture information beneficial for resolving pronominal anaphora in English. We analyze two state of the art models with LSTM and Transformer architectures, via probe tasks and analysis on a coreference annotated corpus. The Transformer outperforms the LSTM in all analyses. Our results suggest that language models are more successful at learning grammatical constraints than they are at learning truly referential information, in the sense of capturing the fact that we use language to refer to entities in the world. However, we find traces of the latter aspect, too.",
}
@inproceedings{abnar-zuidema-2020-quantifying,
    title = "Quantifying Attention Flow in Transformers",
    author = "Abnar, Samira  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.385",
    doi = "10.18653/v1/2020.acl-main.385",
    pages = "4190--4197",
    abstract = "In the Transformer model, {``}self-attention{''} combines information from attended embeddings into the representation of the focal embedding in the next layer. Thus, across layers of the Transformer, information originating from different tokens gets increasingly mixed. This makes attention weights unreliable as explanations probes. In this paper, we consider the problem of quantifying this flow of information through self-attention. We propose two methods for approximating the attention to input tokens given attention weights, attention rollout and attention flow, as post hoc methods when we use attention weights as the relative relevance of the input tokens. We show that these methods give complementary views on the flow of information, and compared to raw attention, both yield higher correlations with importance scores of input tokens obtained using an ablation method and input gradients.",
}
@inproceedings{jacovi-goldberg-2020-towards,
    title = "Towards Faithfully Interpretable {NLP} Systems: How Should We Define and Evaluate Faithfulness?",
    author = "Jacovi, Alon  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.386",
    doi = "10.18653/v1/2020.acl-main.386",
    pages = "4198--4205",
    abstract = "With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this opinion piece we reflect on the current state of interpretability evaluation research. We call for more clearly differentiating between different desired criteria an interpretation should satisfy, and focus on the faithfulness criteria. We survey the literature with respect to faithfulness evaluation, and arrange the current approaches around three assumptions, providing an explicit form to how faithfulness is {``}defined{''} by the community. We provide concrete guidelines on how evaluation of interpretation methods should and should not be conducted. Finally, we claim that the current binary definition for faithfulness sets a potentially unrealistic bar for being considered faithful. We call for discarding the binary notion of faithfulness in favor of a more graded one, which we believe will be of greater practical utility.",
}
@inproceedings{mohankumar-etal-2020-towards,
    title = "Towards Transparent and Explainable Attention Models",
    author = "Mohankumar, Akash Kumar  and
      Nema, Preksha  and
      Narasimhan, Sharan  and
      Khapra, Mitesh M.  and
      Srinivasan, Balaji Vasan  and
      Ravindran, Balaraman",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.387",
    doi = "10.18653/v1/2020.acl-main.387",
    pages = "4206--4216",
    abstract = "Recent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model{'}s predictions. Attention distributions can be considered a faithful explanation if a higher attention weight implies a greater impact on the model{'}s prediction. They can be considered a plausible explanation if they provide a human-understandable justification for the model{'}s predictions. In this work, we first explain why current attention mechanisms in LSTM based encoders can neither provide a faithful nor a plausible explanation of the model{'}s predictions. We observe that in LSTM based encoders the hidden representations at different time-steps are very similar to each other (high conicity) and attention weights in these situations do not carry much meaning because even a random permutation of the attention weights does not affect the model{'}s predictions. Based on experiments on a wide variety of tasks and datasets, we observe attention distributions often attribute the model{'}s predictions to unimportant words such as punctuation and fail to offer a plausible explanation for the predictions. To make attention mechanisms more faithful and plausible, we propose a modified LSTM cell with a diversity-driven training objective that ensures that the hidden representations learned at different time steps are diverse. We show that the resulting attention distributions offer more transparency as they (i) provide a more precise importance ranking of the hidden states (ii) are better indicative of words important for the model{'}s predictions (iii) correlate better with gradient-based attribution methods. Human evaluations indicate that the attention distributions learned by our model offer a plausible explanation of the model{'}s predictions. Our code has been made publicly available at https://github.com/akashkm99/Interpretable-Attention",
}
@inproceedings{mao-etal-2020-tchebycheff,
    title = "Tchebycheff Procedure for Multi-task Text Classification",
    author = "Mao, Yuren  and
      Yun, Shuang  and
      Liu, Weiwei  and
      Du, Bo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.388",
    doi = "10.18653/v1/2020.acl-main.388",
    pages = "4217--4226",
    abstract = "Multi-task Learning methods have achieved great progress in text classification. However, existing methods assume that multi-task text classification problems are convex multiobjective optimization problems, which is unrealistic in real-world applications. To address this issue, this paper presents a novel Tchebycheff procedure to optimize the multi-task classification problems without convex assumption. The extensive experiments back up our theoretical analysis and validate the superiority of our proposals.",
}
@inproceedings{weller-di-marco-fraser-2020-modeling,
    title = "Modeling Word Formation in {E}nglish{--}{G}erman Neural Machine Translation",
    author = "Weller-Di Marco, Marion  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.389",
    doi = "10.18653/v1/2020.acl-main.389",
    pages = "4227--4232",
    abstract = "This paper studies strategies to model word formation in NMT using rich linguistic information, namely a word segmentation approach that goes beyond splitting into substrings by considering fusional morphology. Our linguistically sound segmentation is combined with a method for target-side inflection to accommodate modeling word formation. The best system variants employ source-side morphological analysis and model complex target-side words, improving over a standard system.",
}
@inproceedings{lee-etal-2020-empowering,
    title = "{E}mpowering {A}ctive {L}earning to {J}ointly {O}ptimize {S}ystem and {U}ser {D}emands",
    author = "Lee, Ji-Ung  and
      Meyer, Christian M.  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.390",
    doi = "10.18653/v1/2020.acl-main.390",
    pages = "4233--4247",
    abstract = "Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.",
}
@inproceedings{kaneko-etal-2020-encoder,
    title = "Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction",
    author = "Kaneko, Masahiro  and
      Mita, Masato  and
      Kiyono, Shun  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.391",
    doi = "10.18653/v1/2020.acl-main.391",
    pages = "4248--4254",
    abstract = "This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correction (GEC). The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC. For example, the distribution of the inputs to a GEC model can be considerably different (erroneous, clumsy, etc.) from that of the corpora used for pre-training MLMs; however, this issue is not addressed in the previous methods. Our experiments show that our proposed method, where we first fine-tune a MLM with a given GEC corpus and then use the output of the fine-tuned MLM as additional features in the GEC model, maximizes the benefit of the MLM. The best-performing model achieves state-of-the-art performances on the BEA-2019 and CoNLL-2014 benchmarks. Our code is publicly available at: https://github.com/kanekomasahiro/bert-gec.",
}
@inproceedings{hu-etal-2020-graph,
    title = "Graph Neural News Recommendation with Unsupervised Preference Disentanglement",
    author = "Hu, Linmei  and
      Xu, Siyong  and
      Li, Chen  and
      Yang, Cheng  and
      Shi, Chuan  and
      Duan, Nan  and
      Xie, Xing  and
      Zhou, Ming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.392",
    doi = "10.18653/v1/2020.acl-main.392",
    pages = "4255--4264",
    abstract = "With the explosion of news information, personalized news recommendation has become very important for users to quickly find their interested contents. Most existing methods usually learn the representations of users and news from news contents for recommendation. However, they seldom consider high-order connectivity underlying the user-news interactions. Moreover, existing methods failed to disentangle a user{'}s latent preference factors which cause her clicks on different news. In this paper, we model the user-news interactions as a bipartite graph and propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD. Our model can encode high-order relationships into user and news representations by information propagation along the graph. Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability. A preference regularizer is also designed to force each disentangled subspace to independently reflect an isolated preference, improving the quality of the disentangled representations. Experimental results on real-world news datasets demonstrate that our proposed model can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.",
}
@inproceedings{hu-etal-2020-identifying,
    title = "Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description",
    author = "Hu, Yakun  and
      Luo, Zhunchen  and
      Chao, Wenhan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.393",
    doi = "10.18653/v1/2020.acl-main.393",
    pages = "4265--4269",
    abstract = "In this paper, we study the problem of identifying the principals and accessories from the fact description with multiple defendants in a criminal case. We treat the fact descriptions as narrative texts and the defendants as roles over the narrative story. We propose to model the defendants with \textit{behavioral semantic information} and \textit{statistical characteristics}, then learning the importances of defendants within a learning-to-rank framework. Experimental results on a real-world dataset demonstrate the behavior analysis can effectively model the defendants{'} impacts in a complex case.",
}
@inproceedings{rajamanickam-etal-2020-joint,
    title = "Joint Modelling of Emotion and Abusive Language Detection",
    author = "Rajamanickam, Santhosh  and
      Mishra, Pushkar  and
      Yannakoudakis, Helen  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.394",
    doi = "10.18653/v1/2020.acl-main.394",
    pages = "4270--4279",
    abstract = "The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online. Aiming to tackle this problem, the natural language processing (NLP) community has experimented with a range of techniques for abuse detection. While achieving substantial success, these methods have so far only focused on modelling the linguistic properties of the comments and the online communities of users, disregarding the emotional state of the users and how this might affect their language. The latter is, however, inextricably linked to abusive behaviour. In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other. Our results demonstrate that incorporating affective features leads to significant improvements in abuse detection performance across datasets.",
}
@inproceedings{weigelt-etal-2020-programming,
    title = "{P}rogramming in {N}atural {L}anguage with fu{SE}: {S}ynthesizing {M}ethods from {S}poken {U}tterances {U}sing {D}eep {N}atural {L}anguage {U}nderstanding",
    author = "Weigelt, Sebastian  and
      Steurer, Vanessa  and
      Hey, Tobias  and
      Tichy, Walter F.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.395",
    doi = "10.18653/v1/2020.acl-main.395",
    pages = "4280--4295",
    abstract = "The key to effortless end-user programming is natural language. We examine how to teach intelligent systems new functions, expressed in natural language. As a first step, we collected 3168 samples of teaching efforts in plain English. Then we built fuSE, a novel system that translates English function descriptions into code. Our approach is three-tiered and each task is evaluated separately. We first classify whether an intent to teach new functionality is present in the utterance (accuracy: 97.7{\%} using BERT). Then we analyze the linguistic structure and construct a semantic model (accuracy: 97.6{\%} using a BiLSTM). Finally, we synthesize the signature of the method, map the intermediate steps (instructions in the method body) to API calls and inject control structures (F1: 67.0{\%} with information retrieval and knowledge-based methods). In an end-to-end evaluation on an unseen dataset fuSE synthesized 84.6{\%} of the method signatures and 79.2{\%} of the API calls correctly.",
}
@inproceedings{pavlopoulos-etal-2020-toxicity,
    title = "Toxicity Detection: Does Context Really Matter?",
    author = "Pavlopoulos, John  and
      Sorensen, Jeffrey  and
      Dixon, Lucas  and
      Thain, Nithum  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.396",
    doi = "10.18653/v1/2020.acl-main.396",
    pages = "4296--4305",
    abstract = "Moderation is crucial to promoting healthy online discussions. Although several {`}toxicity{'} detection datasets and models have been published, most of them ignore the context of the posts, implicitly assuming that comments may be judged independently. We investigate this assumption by focusing on two questions: (a) does context affect the human judgement, and (b) does conditioning on context improve performance of toxicity detection systems? We experiment with Wikipedia conversations, limiting the notion of context to the previous post in the thread and the discussion title. We find that context can both amplify or mitigate the perceived toxicity of posts. Moreover, a small but significant subset of manually labeled posts (5{\%} in one of our experiments) end up having the opposite toxicity labels if the annotators are not provided with context. Surprisingly, we also find no evidence that context actually improves the performance of toxicity classifiers, having tried a range of classifiers and mechanisms to make them context aware. This points to the need for larger datasets of comments annotated in context. We make our code and data publicly available.",
}
@inproceedings{zhou-etal-2020-amr,
    title = "{AMR} Parsing with Latent Structural Information",
    author = "Zhou, Qiji  and
      Zhang, Yue  and
      Ji, Donghong  and
      Tang, Hao",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.397",
    doi = "10.18653/v1/2020.acl-main.397",
    pages = "4306--4319",
    abstract = "Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. We investigate parsing AMR with explicit dependency structures and interpretable latent structures. We generate the latent soft structure without additional annotations, and fuse both dependency and latent structure via an extended graph neural networks. The fused structural information helps our experiments results to achieve the best reported results on both AMR 2.0 (77.5{\%} Smatch F1 on LDC2017T10) and AMR 1.0 ((71.8{\%} Smatch F1 on LDC2014T12).",
}
@inproceedings{herzig-etal-2020-tapas,
    title = "{T}a{P}as: Weakly Supervised Table Parsing via Pre-training",
    author = {Herzig, Jonathan  and
      Nowak, Pawel Krzysztof  and
      M{\"u}ller, Thomas  and
      Piccinno, Francesco  and
      Eisenschlos, Julian},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.398",
    doi = "10.18653/v1/2020.acl-main.398",
    pages = "4320--4333",
    abstract = "Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TaPas, an approach to question answering over tables without generating logical forms. TaPas trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TaPas extends BERT{'}s architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with three different semantic parsing datasets, and find that TaPas outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WikiSQL and WikiTQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WikiSQL to WikiTQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.",
}
@inproceedings{alshomary-etal-2020-target,
    title = "Target Inference in Argument Conclusion Generation",
    author = "Alshomary, Milad  and
      Syed, Shahbaz  and
      Potthast, Martin  and
      Wachsmuth, Henning",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.399",
    doi = "10.18653/v1/2020.acl-main.399",
    pages = "4334--4345",
    abstract = "In argumentation, people state premises to reason towards a conclusion. The conclusion conveys a stance towards some target, such as a concept or statement. Often, the conclusion remains implicit, though, since it is self-evident in a discussion or left out for rhetorical reasons. However, the conclusion is key to understanding an argument and, hence, to any application that processes argumentation. We thus study the question to what extent an argument{'}s conclusion can be reconstructed from its premises. In particular, we argue here that a decisive step is to infer a conclusion{'}s target, and we hypothesize that this target is related to the premises{'} targets. We develop two complementary target inference approaches: one ranks premise targets and selects the top-ranked target as the conclusion target, the other finds a new conclusion target in a learned embedding space using a triplet neural network. Our evaluation on corpora from two domains indicates that a hybrid of both approaches is best, outperforming several strong baselines. According to human annotators, we infer a reasonably adequate conclusion target in 89{\%} of the cases.",
}
@inproceedings{yao-wan-2020-multimodal,
    title = "Multimodal Transformer for Multimodal Machine Translation",
    author = "Yao, Shaowei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.400",
    doi = "10.18653/v1/2020.acl-main.400",
    pages = "4346--4350",
    abstract = "Multimodal Machine Translation (MMT) aims to introduce information from other modality, generally static images, to improve the translation quality. Previous works propose various incorporation methods, but most of them do not consider the relative importance of multiple modalities. Equally treating all modalities may encode too much useless information from less important modalities. In this paper, we introduce the multimodal self-attention in Transformer to solve the issues above in MMT. The proposed method learns the representation of images based on the text, which avoids encoding irrelevant information in images. Experiments and visualization analysis demonstrate that our model benefits from visual information and substantially outperforms previous works and competitive baselines in terms of various metrics.",
}
@inproceedings{chauhan-etal-2020-sentiment,
    title = "Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis",
    author = "Chauhan, Dushyant Singh  and
      S R, Dhanush  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.401",
    doi = "10.18653/v1/2020.acl-main.401",
    pages = "4351--4360",
    abstract = "In this paper, we hypothesize that sarcasm is closely related to sentiment and emotion, and thereby propose a multi-task deep learning framework to solve all these three problems simultaneously in a multi-modal conversational scenario. We, at first, manually annotate the recently released multi-modal MUStARD sarcasm dataset with sentiment and emotion classes, both implicit and explicit. For multi-tasking, we propose two attention mechanisms, viz. Inter-segment Inter-modal Attention (Ie-Attention) and Intra-segment Inter-modal Attention (Ia-Attention). The main motivation of Ie-Attention is to learn the relationship between the different segments of the sentence across the modalities. In contrast, Ia-Attention focuses within the same segment of the sentence across the modalities. Finally, representations from both the attentions are concatenated and shared across the five classes (i.e., sarcasm, implicit sentiment, explicit sentiment, implicit emotion, explicit emotion) for multi-tasking. Experimental results on the extended version of the MUStARD dataset show the efficacy of our proposed approach for sarcasm detection over the existing state-of-the-art systems. The evaluation also shows that the proposed multi-task framework yields better performance for the primary task, i.e., sarcasm detection, with the help of two secondary tasks, emotion and sentiment analysis.",
}
@inproceedings{saha-etal-2020-towards,
    title = "Towards Emotion-aided Multi-modal Dialogue Act Classification",
    author = "Saha, Tulika  and
      Patra, Aditya  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.402",
    doi = "10.18653/v1/2020.acl-main.402",
    pages = "4361--4372",
    abstract = "The task of Dialogue Act Classification (DAC) that purports to capture communicative intent has been studied extensively. But these studies limit themselves to text. Non-verbal features (change of tone, facial expressions etc.) can provide cues to identify DAs, thus stressing the benefit of incorporating multi-modal inputs in the task. Also, the emotional state of the speaker has a substantial effect on the choice of the dialogue act, since conversations are often influenced by emotions. Hence, the effect of emotion too on automatic identification of DAs needs to be studied. In this work, we address the role of \textit{both} multi-modality and emotion recognition (ER) in DAC. DAC and ER help each other by way of multi-task learning. One of the major contributions of this work is a new dataset- multimodal Emotion aware Dialogue Act dataset called EMOTyDA, collected from open-sourced dialogue datasets. To demonstrate the utility of EMOTyDA, we build an attention based (self, inter-modal, inter-task) multi-modal, multi-task Deep Neural Network (DNN) for joint learning of DAs and emotions. We show empirically that multi-modality and multi-tasking achieve better performance of DAC compared to uni-modal and single task DAC variants.",
}
@inproceedings{maronikolakis-etal-2020-analyzing,
    title = "Analyzing Political Parody in Social Media",
    author = "Maronikolakis, Antonios  and
      S{\'a}nchez Villegas, Danae  and
      Preotiuc-Pietro, Daniel  and
      Aletras, Nikolaos",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.403",
    doi = "10.18653/v1/2020.acl-main.403",
    pages = "4373--4384",
    abstract = "Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts. In this paper, we present the first computational study of parody. We introduce a new publicly available data set of tweets from real politicians and their corresponding parody accounts. We run a battery of supervised machine learning models for automatically detecting parody tweets with an emphasis on robustness by testing on tweets from accounts unseen in training, across different genders and across countries. Our results show that political parody tweets can be predicted with an accuracy up to 90{\%}. Finally, we identify the markers of parody through a linguistic analysis. Beyond research in linguistics and political communication, accurately and automatically detecting parody is important to improving fact checking for journalists and analytics such as sentiment analysis through filtering out parodical utterances.",
}
@inproceedings{dayanik-pado-2020-masking,
    title = "Masking Actor Information Leads to Fairer Political Claims Detection",
    author = "Dayanik, Erenay  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.404",
    doi = "10.18653/v1/2020.acl-main.404",
    pages = "4385--4391",
    abstract = "A central concern in Computational Social Sciences (CSS) is fairness: where the role of NLP is to scale up text analysis to large corpora, the quality of automatic analyses should be as independent as possible of textual properties. We analyze the performance of a state-of-the-art neural model on the task of political claims detection (i.e., the identification of forward-looking statements made by political actors) and identify a strong frequency bias: claims made by frequent actors are recognized better. We propose two simple debiasing methods which mask proper names and pronouns during training of the model, thus removing personal information bias. We find that (a) these methods significantly decrease frequency bias while keeping the overall performance stable; and (b) the resulting models improve when evaluated in an out-of-domain setting.",
}
@inproceedings{joseph-morgan-2020-word,
    title = "When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?",
    author = "Joseph, Kenneth  and
      Morgan, Jonathan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.405",
    doi = "10.18653/v1/2020.acl-main.405",
    pages = "4392--4415",
    abstract = "Social biases are encoded in word embeddings. This presents a unique opportunity to study society historically and at scale, and a unique danger when embeddings are used in downstream applications. Here, we investigate the extent to which publicly-available word embeddings accurately reflect beliefs about certain kinds of people as measured via traditional survey methods. We find that biases found in word embeddings do, on average, closely mirror survey data across seventeen dimensions of social meaning. However, we also find that biases in embeddings are much more reflective of survey data for some dimensions of meaning (e.g. gender) than others (e.g. race), and that we can be highly confident that embedding-based measures reflect survey data only for the most salient biases.",
}
@inproceedings{zhang-etal-2020-said,
    title = "{``}Who said it, and Why?{''} Provenance for Natural Language Claims",
    author = "Zhang, Yi  and
      Ives, Zachary  and
      Roth, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.406",
    doi = "10.18653/v1/2020.acl-main.406",
    pages = "4416--4426",
    abstract = "In an era where generating content and publishing it is so easy, we are bombarded with information and are exposed to all kinds of claims, some of which do not always rank high on the truth scale. This paper suggests that the key to a longer-term, holistic, and systematic approach to navigating this information pollution is capturing the provenance of claims. To do that, we develop a formal definition of provenance graph for a given natural language claim, aiming to understand where the claim may come from and how it has evolved. To construct the graph, we model provenance inference, formulated mainly as an information extraction task and addressed via a textual entailment model. We evaluate our approach using two benchmark datasets, showing initial success in capturing the notion of provenance and its effectiveness on the application of claim verification.",
}
@inproceedings{chaabouni-etal-2020-compositionality,
    title = "Compositionality and Generalization In Emergent Languages",
    author = "Chaabouni, Rahma  and
      Kharitonov, Eugene  and
      Bouchacourt, Diane  and
      Dupoux, Emmanuel  and
      Baroni, Marco",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.407",
    doi = "10.18653/v1/2020.acl-main.407",
    pages = "4427--4442",
    abstract = "Natural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as compositionality. In this paper, we study whether the language emerging in deep multi-agent simulations possesses a similar ability to refer to novel primitive combinations, and whether it accomplishes this feat by strategies akin to human-language compositionality. Equipped with new ways to measure compositionality in emergent languages inspired by disentanglement in representation learning, we establish three main results: First, given sufficiently large input spaces, the emergent language will naturally develop the ability to refer to novel composite concepts. Second, there is no correlation between the degree of compositionality of an emergent language and its ability to generalize. Third, while compositionality is not necessary for generalization, it provides an advantage in terms of language transmission: The more compositional a language is, the more easily it will be picked up by new learners, even when the latter differ in architecture from the original agents. We conclude that compositionality does not arise from simple generalization pressure, but if an emergent language does chance upon it, it will be more likely to survive and thrive.",
}
@inproceedings{deyoung-etal-2020-eraser,
    title = "{ERASER}: {A} Benchmark to Evaluate Rationalized {NLP} Models",
    author = "DeYoung, Jay  and
      Jain, Sarthak  and
      Rajani, Nazneen Fatema  and
      Lehman, Eric  and
      Xiong, Caiming  and
      Socher, Richard  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.408",
    doi = "10.18653/v1/2020.acl-main.408",
    pages = "4443--4458",
    abstract = "State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased interest in designing more interpretable deep models for NLP that reveal the {`}reasoning{'} behind model outputs. But work in this direction has been conducted on different datasets and tasks with correspondingly unique aims and metrics; this makes it difficult to track progress. We propose the \textbf{E}valuating \textbf{R}ationales \textbf{A}nd \textbf{S}imple \textbf{E}nglish \textbf{R}easoning (\textbf{ERASER} a benchmark to advance research on interpretable models in NLP. This benchmark comprises multiple datasets and tasks for which human annotations of {``}rationales{''} (supporting evidence) have been collected. We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how \textit{faithful} these rationales are (i.e., the degree to which provided rationales influenced the corresponding predictions). Our hope is that releasing this benchmark facilitates progress on designing more interpretable NLP systems. The benchmark, code, and documentation are available at \url{https://www.eraserbenchmark.com/}",
}
@inproceedings{jain-etal-2020-learning,
    title = "{L}earning to Faithfully Rationalize by Construction",
    author = "Jain, Sarthak  and
      Wiegreffe, Sarah  and
      Pinter, Yuval  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.409",
    doi = "10.18653/v1/2020.acl-main.409",
    pages = "4459--4473",
    abstract = "In many settings it is important for one to be able to understand why a model made a particular prediction. In NLP this often entails extracting snippets of an input text {`}responsible for{'} corresponding model output; when such a snippet comprises tokens that indeed informed the model{'}s prediction, it is a faithful explanation. In some settings, faithfulness may be critical to ensure transparency. Lei et al. (2016) proposed a model to produce faithful rationales for neural text classification by defining independent snippet extraction and prediction modules. However, the discrete selection over input tokens performed by this method complicates training, leading to high variance and requiring careful hyperparameter tuning. We propose a simpler variant of this approach that provides faithful explanations by construction. In our scheme, named FRESH, arbitrary feature importance scores (e.g., gradients from a trained model) are used to induce binary labels over token inputs, which an extractor can be trained to predict. An independent classifier module is then trained exclusively on snippets provided by the extractor; these snippets thus constitute faithful explanations, even if the classifier is arbitrarily complex. In both automatic and manual evaluations we find that variants of this simple framework yield predictive performance superior to {`}end-to-end{'} approaches, while being more general and easier to train. Code is available at https://github.com/successar/FRESH.",
}
@inproceedings{yue-etal-2020-clinical,
    title = "Clinical Reading Comprehension: A Thorough Analysis of the emr{QA} Dataset",
    author = "Yue, Xiang  and
      Jimenez Gutierrez, Bernal  and
      Sun, Huan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.410",
    doi = "10.18653/v1/2020.acl-main.410",
    pages = "4474--4486",
    abstract = "Machine reading comprehension has made great progress in recent years owing to large-scale annotated datasets. In the clinical domain, however, creating such datasets is quite difficult due to the domain expertise required for annotation. Recently, Pampari et al. (EMNLP{'}18) tackled this issue by using expert-annotated question templates and existing i2b2 annotations to create emrQA, the first large-scale dataset for question answering (QA) based on clinical notes. In this paper, we provide an in-depth analysis of this dataset and the clinical reading comprehension (CliniRC) task. From our qualitative analysis, we find that (i) emrQA answers are often incomplete, and (ii) emrQA questions are often answerable without using domain knowledge. From our quantitative experiments, surprising results include that (iii) using a small sampled subset (5{\%}-20{\%}), we can obtain roughly equal performance compared to the model trained on the entire dataset, (iv) this performance is close to human expert{'}s performance, and (v) BERT models do not beat the best performing base model. Following our analysis of the emrQA, we further explore two desired aspects of CliniRC systems: the ability to utilize clinical domain knowledge and to generalize to unseen questions and contexts. We argue that both should be considered when creating future datasets.",
}
@inproceedings{cao-etal-2020-deformer,
    title = "{D}e{F}ormer: Decomposing Pre-trained Transformers for Faster Question Answering",
    author = "Cao, Qingqing  and
      Trivedi, Harsh  and
      Balasubramanian, Aruna  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.411",
    doi = "10.18653/v1/2020.acl-main.411",
    pages = "4487--4497",
    abstract = "Transformer-based QA models use input-wide self-attention {--} i.e. across both the question and the input passage {--} at all layers, causing them to be slow and memory-intensive. It turns out that we can get by without input-wide self-attention at all layers, especially in the lower layers. We introduce DeFormer, a decomposed transformer, which substitutes the full self-attention with question-wide and passage-wide self-attentions in the lower layers. This allows for question-independent processing of the input text representations, which in turn enables pre-computing passage representations reducing runtime compute drastically. Furthermore, because DeFormer is largely similar to the original model, we can initialize DeFormer with the pre-training weights of a standard transformer, and directly fine-tune on the target QA dataset. We show DeFormer versions of BERT and XLNet can be used to speed up QA by over 4.3x and with simple distillation-based losses they incur only a 1{\%} drop in accuracy. We open source the code at https://github.com/StonyBrookNLP/deformer.",
}
@inproceedings{saxena-etal-2020-improving,
    title = "Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings",
    author = "Saxena, Apoorv  and
      Tripathi, Aditay  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.412",
    doi = "10.18653/v1/2020.acl-main.412",
    pages = "4498--4507",
    abstract = "Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) task is to answer natural language queries posed over the KG. Multi-hop KGQA requires reasoning over multiple edges of the KG to arrive at the right answer. KGs are often incomplete with many missing links, posing additional challenges for KGQA, especially for multi-hop KGQA. Recent research on multi-hop KGQA has attempted to handle KG sparsity using relevant external text, which isn{'}t always readily available. In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction. Such KG embedding methods, even though highly relevant, have not been explored for multi-hop KGQA so far. We fill this gap in this paper and propose EmbedKGQA. EmbedKGQA is particularly effective in performing multi-hop KGQA over sparse KGs. EmbedKGQA also relaxes the requirement of answer selection from a pre-specified neighborhood, a sub-optimal constraint enforced by previous multi-hop KGQA methods. Through extensive experiments on multiple benchmark datasets, we demonstrate EmbedKGQA{'}s effectiveness over other state-of-the-art baselines.",
}
@inproceedings{fabbri-etal-2020-template,
    title = "Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering",
    author = "Fabbri, Alexander  and
      Ng, Patrick  and
      Wang, Zhiguo  and
      Nallapati, Ramesh  and
      Xiang, Bing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.413",
    doi = "10.18653/v1/2020.acl-main.413",
    pages = "4508--4513",
    abstract = "Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. This paradigm, however, relies on scarce, and costly to obtain, large-scale human-labeled data. We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance by allowing the model to learn more complex context-question relationships. Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14{\%}, and 20{\%} when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.",
}
@inproceedings{yadav-etal-2020-unsupervised,
    title = "Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering",
    author = "Yadav, Vikas  and
      Bethard, Steven  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.414",
    doi = "10.18653/v1/2020.acl-main.414",
    pages = "4514--4525",
    abstract = "Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the QA method. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) stops when the terms in the given question and candidate answers are covered by the retrieved justifications. Despite its simplicity, our approach outperforms all the previous methods (including supervised methods) on the evidence selection task on two datasets: MultiRC and QASC. When these evidence sentences are fed into a RoBERTa answer classification component, we achieve state-of-the-art QA performance on these two datasets.",
}
@inproceedings{salesky-etal-2020-corpus,
    title = "A Corpus for Large-Scale Phonetic Typology",
    author = "Salesky, Elizabeth  and
      Chodroff, Eleanor  and
      Pimentel, Tiago  and
      Wiesner, Matthew  and
      Cotterell, Ryan  and
      Black, Alan W  and
      Eisner, Jason",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.415",
    doi = "10.18653/v1/2020.acl-main.415",
    pages = "4526--4546",
    abstract = "A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions. We present VoxClamantis v1.0, the first large-scale corpus for phonetic typology, with aligned segments and estimated phoneme-level labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants. Access to such data can greatly facilitate investigation of phonetic typology at a large scale and across many languages. However, it is non-trivial and computationally intensive to obtain such alignments for hundreds of languages, many of which have few to no resources presently available. We describe the methodology to create our corpus, discuss caveats with current methods and their impact on the utility of this data, and illustrate possible research directions through a series of case studies on the 48 highest-quality readings. Our corpus and scripts are publicly available for non-commercial use at https://voxclamantisproject.github.io.",
}
@inproceedings{liu-etal-2020-dscorer,
    title = "{D}scorer: A Fast Evaluation Metric for Discourse Representation Structure Parsing",
    author = "Liu, Jiangming  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.416",
    doi = "10.18653/v1/2020.acl-main.416",
    pages = "4547--4554",
    abstract = "Discourse representation structures (DRSs) are scoped semantic representations for texts of arbitrary length. Evaluating the accuracy of predicted DRSs plays a key role in developing semantic parsers and improving their performance. DRSs are typically visualized as boxes which are not straightforward to process automatically. Counter transforms DRSs to clauses and measures clause overlap by searching for variable mappings between two DRSs. However, this metric is computationally costly (with respect to memory and CPU time) and does not scale with longer texts. We introduce Dscorer, an efficient new metric which converts box-style DRSs to graphs and then measures the overlap of n-grams. Experiments show that Dscorer computes accuracy scores that are correlated with Counter at a fraction of the time.",
}
@inproceedings{banon-etal-2020-paracrawl,
    title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora",
    author = "Ba{\~n}{\'o}n, Marta  and
      Chen, Pinzhen  and
      Haddow, Barry  and
      Heafield, Kenneth  and
      Hoang, Hieu  and
      Espl{\`a}-Gomis, Miquel  and
      Forcada, Mikel L.  and
      Kamran, Amir  and
      Kirefu, Faheem  and
      Koehn, Philipp  and
      Ortiz Rojas, Sergio  and
      Pla Sempere, Leopoldo  and
      Ram{\'\i}rez-S{\'a}nchez, Gema  and
      Sarr{\'\i}as, Elsa  and
      Strelec, Marek  and
      Thompson, Brian  and
      Waites, William  and
      Wiggins, Dion  and
      Zaragoza, Jaume",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.417",
    doi = "10.18653/v1/2020.acl-main.417",
    pages = "4555--4567",
    abstract = "We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems.",
}
@inproceedings{cao-daume-iii-2020-toward,
    title = "Toward Gender-Inclusive Coreference Resolution",
    author = "Cao, Yang Trista  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.418",
    doi = "10.18653/v1/2020.acl-main.418",
    pages = "4568--4595",
    abstract = "Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systemic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and develop two new datasets for interrogating bias in crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we build systems that lead to many potential harms.",
}
@inproceedings{sen-etal-2020-human,
    title = "Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?",
    author = "Sen, Cansu  and
      Hartvigsen, Thomas  and
      Yin, Biao  and
      Kong, Xiangnan  and
      Rundensteiner, Elke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.419",
    doi = "10.18653/v1/2020.acl-main.419",
    pages = "4596--4608",
    abstract = "Motivated by human attention, computational attention mechanisms have been designed to help neural networks adjust their focus on specific parts of the input data. While attention mechanisms are claimed to achieve interpretability, little is known about the actual relationships between machine and human attention. In this work, we conduct the first quantitative assessment of human versus computational attention mechanisms for the text classification task. To achieve this, we design and conduct a large-scale crowd-sourcing study to collect human attention maps that encode the parts of a text that humans focus on when conducting text classification. Based on this new resource of human attention dataset for text classification, YELP-HAT, collected on the publicly available YELP dataset, we perform a quantitative comparative analysis of machine attention maps created by deep learning models and human attention maps. Our analysis offers insights into the relationships between human versus machine attention maps along three dimensions: overlap in word selections, distribution over lexical categories, and context-dependency of sentiment polarity. Our findings open promising future research opportunities ranging from supervised attention to the design of human-centric attention-based explanations.",
}
@inproceedings{pimentel-etal-2020-information,
    title = "Information-Theoretic Probing for Linguistic Structure",
    author = "Pimentel, Tiago  and
      Valvoda, Josef  and
      Hall Maudslay, Rowan  and
      Zmigrod, Ran  and
      Williams, Adina  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.420",
    doi = "10.18653/v1/2020.acl-main.420",
    pages = "4609--4622",
    abstract = "The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually {``}know{''} about natural language. Probes are a natural way of assessing this. When probing, a researcher chooses a linguistic task and trains a supervised model to predict annotations in that linguistic task from the network{'}s learned representations. If the probe does well, the researcher may conclude that the representations encode knowledge related to the task. A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task itself. We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation. The experimental portion of our paper focuses on empirically estimating the mutual information between a linguistic property and BERT, comparing these estimates to several baselines. We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research{---}plus English{---}totalling eleven languages. Our implementation is available in https://github.com/rycolab/info-theoretic-probing.",
}
@inproceedings{artetxe-etal-2020-cross,
    title = "On the Cross-lingual Transferability of Monolingual Representations",
    author = "Artetxe, Mikel  and
      Ruder, Sebastian  and
      Yogatama, Dani",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.421",
    doi = "10.18653/v1/2020.acl-main.421",
    pages = "4623--4637",
    abstract = "State-of-the-art unsupervised multilingual models (e.g., multilingual BERT) have been shown to generalize in a zero-shot cross-lingual setting. This generalization ability has been attributed to the use of a shared subword vocabulary and joint training across multiple languages giving rise to deep multilingual abstractions. We evaluate this hypothesis by designing an alternative approach that transfers a monolingual model to new languages at the lexical level. More concretely, we first train a transformer-based masked language model on one language, and transfer it to a new language by learning a new embedding matrix with the same masked language modeling objective, freezing parameters of all other layers. This approach does not rely on a shared vocabulary or joint training. However, we show that it is competitive with multilingual BERT on standard cross-lingual classification benchmarks and on a new Cross-lingual Question Answering Dataset (XQuAD). Our results contradict common beliefs of the basis of the generalization ability of multilingual models and suggest that deep monolingual models learn some abstractions that generalize across languages. We also release XQuAD as a more comprehensive cross-lingual benchmark, which comprises 240 paragraphs and 1190 question-answer pairs from SQuAD v1.1 translated into ten languages by professional translators.",
}
@inproceedings{wu-etal-2020-similarity,
    title = "Similarity Analysis of Contextual Word Representation Models",
    author = "Wu, John  and
      Belinkov, Yonatan  and
      Sajjad, Hassan  and
      Durrani, Nadir  and
      Dalvi, Fahim  and
      Glass, James",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.422",
    doi = "10.18653/v1/2020.acl-main.422",
    pages = "4638--4655",
    abstract = "This paper investigates contextual word representation models from the lens of similarity analysis. Given a collection of trained models, we measure the similarity of their internal representations and attention. Critically, these models come from vastly different architectures. We use existing and novel similarity measures that aim to gauge the level of localization of information in the deep models, and facilitate the investigation of which design factors affect model similarity, without requiring any external linguistic annotation. The analysis reveals that models within the same family are more similar to one another, as may be expected. Surprisingly, different architectures have rather similar representations, but different individual neurons. We also observed differences in information localization in lower and higher layers and found that higher layers are more affected by fine-tuning on downstream tasks.",
}
@inproceedings{levine-etal-2020-sensebert,
    title = "{S}ense{BERT}: Driving Some Sense into {BERT}",
    author = "Levine, Yoav  and
      Lenz, Barak  and
      Dagan, Or  and
      Ram, Ori  and
      Padnos, Dan  and
      Sharir, Or  and
      Shalev-Shwartz, Shai  and
      Shashua, Amnon  and
      Shoham, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.423",
    doi = "10.18653/v1/2020.acl-main.423",
    pages = "4656--4667",
    abstract = "The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision techniques operate at the word form level, which serves as a surrogate for the underlying semantic content. This paper proposes a method to employ weak-supervision directly at the word sense level. Our model, named SenseBERT, is pre-trained to predict not only the masked words but also their WordNet supersenses. Accordingly, we attain a lexical-semantic level language model, without the use of human annotation. SenseBERT achieves significantly improved lexical understanding, as we demonstrate by experimenting on SemEval Word Sense Disambiguation, and by attaining a state of the art result on the {`}Word in Context{'} task.",
}
@inproceedings{alva-manchego-etal-2020-asset,
    title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
    author = "Alva-Manchego, Fernando  and
      Martin, Louis  and
      Bordes, Antoine  and
      Scarton, Carolina  and
      Sagot, Beno{\^\i}t  and
      Specia, Lucia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.424",
    doi = "10.18653/v1/2020.acl-main.424",
    pages = "4668--4679",
    abstract = "In order to simplify a sentence, human editors perform multiple rewriting transformations: they split it into several shorter sentences, paraphrase words (i.e. replacing complex words or phrases by simpler synonyms), reorder components, and/or delete information deemed unnecessary. Despite these varied range of possible text alterations, current models for automatic sentence simplification are evaluated using datasets that are focused on a single transformation, such as lexical paraphrasing or splitting. This makes it impossible to understand the ability of simplification models in more realistic settings. To alleviate this limitation, this paper introduces ASSET, a new dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations. Through quantitative and qualitative experiments, we show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task. Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.",
}
@inproceedings{calabrese-etal-2020-fatality,
    title = "Fatality Killed the Cat or: {B}abel{P}ic, a Multimodal Dataset for Non-Concrete Concepts",
    author = "Calabrese, Agostina  and
      Bevilacqua, Michele  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.425",
    doi = "10.18653/v1/2020.acl-main.425",
    pages = "4680--4686",
    abstract = "Thanks to the wealth of high-quality annotated images available in popular repositories such as ImageNet, multimodal language-vision research is in full bloom. However, events, feelings and many other kinds of concepts which can be visually grounded are not well represented in current datasets. Nevertheless, we would expect a wide-coverage language understanding system to be able to classify images depicting recess and remorse, not just cats, dogs and bridges. We fill this gap by presenting BabelPic, a hand-labeled dataset built by cleaning the image-synset association found within the BabelNet Lexical Knowledge Base (LKB). BabelPic explicitly targets non-concrete concepts, thus providing refreshing new data for the community. We also show that pre-trained language-vision systems can be used to further expand the resource by exploiting natural language knowledge available in the LKB. BabelPic is available for download at http://babelpic.org.",
}
@inproceedings{gaonkar-etal-2020-modeling,
    title = "Modeling Label Semantics for Predicting Emotional Reactions",
    author = "Gaonkar, Radhika  and
      Kwon, Heeyoung  and
      Bastan, Mohaddeseh  and
      Balasubramanian, Niranjan  and
      Chambers, Nathanael",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.426",
    doi = "10.18653/v1/2020.acl-main.426",
    pages = "4687--4692",
    abstract = "Predicting how events induce emotions in the characters of a story is typically seen as a standard multi-label classification task, which usually treats labels as anonymous classes to predict. They ignore information that may be conveyed by the emotion labels themselves. We propose that the semantics of emotion labels can guide a model{'}s attention when representing the input story. Further, we observe that the emotions evoked by an event are often related: an event that evokes joy is unlikely to also evoke sadness. In this work, we explicitly model label classes via label embeddings, and add mechanisms that track label-label correlations both during training and inference. We also introduce a new semi-supervision strategy that regularizes for the correlations on unlabeled data. Our empirical evaluations show that modeling label semantics yields consistent benefits, and we advance the state-of-the-art on an emotion inference task.",
}
@inproceedings{srinet-etal-2020-craftassist,
    title = "{C}raft{A}ssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant",
    author = "Srinet, Kavya  and
      Jernite, Yacine  and
      Gray, Jonathan  and
      Szlam, Arthur",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.427",
    doi = "10.18653/v1/2020.acl-main.427",
    pages = "4693--4714",
    abstract = "We propose a semantic parsing dataset focused on instruction-driven communication with an agent in the game Minecraft. The dataset consists of 7K human utterances and their corresponding parses. Given proper world state, the parses can be interpreted and executed in game. We report the performance of baseline models, and analyze their successes and failures.",
}
@inproceedings{li-etal-2020-dont,
    title = "Don{'}t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training",
    author = "Li, Margaret  and
      Roller, Stephen  and
      Kulikov, Ilia  and
      Welleck, Sean  and
      Boureau, Y-Lan  and
      Cho, Kyunghyun  and
      Weston, Jason",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.428",
    doi = "10.18653/v1/2020.acl-main.428",
    pages = "4715--4728",
    abstract = "Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address. They tend to produce generations that (i) rely too much on copying from the context, (ii) contain repetitions within utterances, (iii) overuse frequent words, and (iv) at a deeper level, contain logical flaws.In this work we show how all of these problems can be addressed by extending the recently introduced unlikelihood loss (Welleck et al., 2019) to these cases. We show that appropriate loss functions which regularize generated outputs to match human distributions are effective for the first three issues. For the last important general issue, we show applying unlikelihood to collected data of what a model should not do is effective for improving logical consistency, potentially paving the way to generative models with greater reasoning ability. We demonstrate the efficacy of our approach across several dialogue tasks.",
}
@inproceedings{zhao-bethard-2020-berts,
    title = "How does {BERT}{'}s attention change when you fine-tune? An analysis methodology and a case study in negation scope",
    author = "Zhao, Yiyun  and
      Bethard, Steven",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.429",
    doi = "10.18653/v1/2020.acl-main.429",
    pages = "4729--4747",
    abstract = "Large pretrained language models like BERT, after fine-tuning to a downstream task, have achieved high performance on a variety of NLP problems. Yet explaining their decisions is difficult despite recent work probing their internal representations. We propose a procedure and analysis methods that take a hypothesis of how a transformer-based model might encode a linguistic phenomenon, and test the validity of that hypothesis based on a comparison between knowledge-related downstream tasks with downstream control tasks, and measurement of cross-dataset consistency. We apply this methodology to test BERT and RoBERTa on a hypothesis that some attention heads will consistently attend from a word in negation scope to the negation cue. We find that after fine-tuning BERT and RoBERTa on a negation scope task, the average attention head improves its sensitivity to negation and its attention consistency across negation datasets compared to the pre-trained models. However, only the base models (not the large models) improve compared to a control task, indicating there is evidence for a shallow encoding of negation only in the base models.",
}
@inproceedings{lu-etal-2020-influence,
    title = "Influence Paths for Characterizing Subject-Verb Number Agreement in {LSTM} Language Models",
    author = "Lu, Kaiji  and
      Mardziel, Piotr  and
      Leino, Klas  and
      Fredrikson, Matt  and
      Datta, Anupam",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.430",
    doi = "10.18653/v1/2020.acl-main.430",
    pages = "4748--4757",
    abstract = "LSTM-based recurrent neural networks are the state-of-the-art for many natural language processing (NLP) tasks. Despite their performance, it is unclear whether, or how, LSTMs learn structural features of natural languages such as subject-verb number agreement in English. Lacking this understanding, the generality of LSTM performance on this task and their suitability for related tasks remains uncertain. Further, errors cannot be properly attributed to a lack of structural capability, training data omissions, or other exceptional faults. We introduce *influence paths*, a causal account of structural properties as carried by paths across gates and neurons of a recurrent neural network. The approach refines the notion of influence (the subject{'}s grammatical number has influence on the grammatical number of the subsequent verb) into a set of gate or neuron-level paths. The set localizes and segments the concept (e.g., subject-verb agreement), its constituent elements (e.g., the subject), and related or interfering elements (e.g., attractors). We exemplify the methodology on a widely-studied multi-layer LSTM language model, demonstrating its accounting for subject-verb number agreement. The results offer both a finer and a more complete view of an LSTM{'}s handling of this structural aspect of the English language than prior results based on diagnostic classifiers and ablation.",
}
@inproceedings{bommasani-etal-2020-interpreting,
    title = "{I}nterpreting {P}retrained {C}ontextualized {R}epresentations via {R}eductions to {S}tatic {E}mbeddings",
    author = "Bommasani, Rishi  and
      Davis, Kelly  and
      Cardie, Claire",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.431",
    doi = "10.18653/v1/2020.acl-main.431",
    pages = "4758--4781",
    abstract = "Contextualized representations (e.g. ELMo, BERT) have become the default pretrained representations for downstream NLP applications. In some settings, this transition has rendered their static embedding predecessors (e.g. Word2Vec, GloVe) obsolete. As a side-effect, we observe that older interpretability methods for static embeddings {---} while more diverse and mature than those available for their dynamic counterparts {---} are underutilized in studying newer contextualized representations. Consequently, we introduce simple and fully general methods for converting from contextualized representations to static lookup-table embeddings which we apply to 5 popular pretrained models and 9 sets of pretrained weights. Our analysis of the resulting static embeddings notably reveals that pooling over many contexts significantly improves representational quality under intrinsic evaluation. Complementary to analyzing representational quality, we consider social biases encoded in pretrained representations with respect to gender, race/ethnicity, and religion and find that bias is encoded disparately across pretrained models and internal layers even for models with the same training data. Concerningly, we find dramatic inconsistencies between social bias estimators for word embeddings.",
}
@inproceedings{pruthi-etal-2020-learning,
    title = "Learning to Deceive with Attention-Based Explanations",
    author = "Pruthi, Danish  and
      Gupta, Mansi  and
      Dhingra, Bhuwan  and
      Neubig, Graham  and
      Lipton, Zachary C.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.432",
    doi = "10.18653/v1/2020.acl-main.432",
    pages = "4782--4793",
    abstract = "Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing. In addition to yielding gains in predictive accuracy, attention weights are often claimed to confer interpretability, purportedly useful both for providing insights to practitioners and for explaining why a model makes its decisions to stakeholders. We call the latter use of attention mechanisms into question by demonstrating a simple method for training models to produce deceptive attention masks. Our method diminishes the total weight assigned to designated impermissible tokens, even when the models can be shown to nevertheless rely on these features to drive predictions. Across multiple models and tasks, our approach manipulates attention weights while paying surprisingly little cost in accuracy. Through a human study, we show that our manipulated attention-based explanations deceive people into thinking that predictions from a model biased against gender minorities do not rely on the gender. Consequently, our results cast doubt on attention{'}s reliability as a tool for auditing algorithms in the context of fairness and accountability.",
}
@inproceedings{geffen-lan-etal-2020-spontaneous,
    title = "{O}n the {S}pontaneous {E}mergence of {D}iscrete and {C}ompositional {S}ignals",
    author = "Geffen Lan, Nur  and
      Chemla, Emmanuel  and
      Steinert-Threlkeld, Shane",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.433",
    doi = "10.18653/v1/2020.acl-main.433",
    pages = "4794--4800",
    abstract = "We propose a general framework to study language emergence through signaling games with neural agents. Using a continuous latent space, we are able to (i) train using backpropagation, (ii) show that discrete messages nonetheless naturally emerge. We explore whether categorical perception effects follow and show that the messages are not compositional.",
}
@inproceedings{klafka-ettinger-2020-spying,
    title = "Spying on Your Neighbors: Fine-grained Probing of Contextual Embeddings for Information about Surrounding Words",
    author = "Klafka, Josef  and
      Ettinger, Allyson",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.434",
    doi = "10.18653/v1/2020.acl-main.434",
    pages = "4801--4811",
    abstract = "Although models using contextual word embeddings have achieved state-of-the-art results on a host of NLP tasks, little is known about exactly what information these embeddings encode about the context words that they are understood to reflect. To address this question, we introduce a suite of probing tasks that enable fine-grained testing of contextual embeddings for encoding of information about surrounding words. We apply these tasks to examine the popular BERT, ELMo and GPT contextual encoders, and find that each of our tested information types is indeed encoded as contextual information across tokens, often with near-perfect recoverability{---}but the encoders vary in which features they distribute to which tokens, how nuanced their distributions are, and how robust the encoding of each feature is to distance. We discuss implications of these results for how different types of models break down and prioritize word-level context information when constructing token embeddings.",
}
@inproceedings{kim-etal-2020-dense,
    title = "Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in {V}ideo{QA}",
    author = "Kim, Hyounghun  and
      Tang, Zineng  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.435",
    doi = "10.18653/v1/2020.acl-main.435",
    pages = "4812--4822",
    abstract = "Videos convey rich information. Dynamic spatio-temporal relationships between people/objects, and diverse multimodal events are present in a video clip. Hence, it is important to develop automated models that can accurately extract such information from videos. Answering questions on videos is one of the tasks which can evaluate such AI abilities. In this paper, we propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. Specifically, we first employ dense image captions to help identify objects and their detailed salient regions and actions, and hence give the model useful extra information (in explicit textual format to allow easier matching) for answering questions. Moreover, our model is also comprised of dual-level attention (word/object and frame level), multi-head self/cross-integration for different sources (video and dense captions), and gates which pass more relevant information to the classifier. Finally, we also cast the frame selection problem as a multi-label classification task and introduce two loss functions, In-andOut Frame Score Margin (IOFSM) and Balanced Binary Cross-Entropy (BBCE), to better supervise the model with human importance annotations. We evaluate our model on the challenging TVQA dataset, where each of our model components provides significant gains, and our overall model outperforms the state-of-the-art by a large margin (74.09{\%} versus 70.52{\%}). We also present several word, object, and frame level visualization studies.",
}
@inproceedings{mu-etal-2020-shaping,
    title = "Shaping Visual Representations with Language for Few-Shot Classification",
    author = "Mu, Jesse  and
      Liang, Percy  and
      Goodman, Noah",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.436",
    doi = "10.18653/v1/2020.acl-main.436",
    pages = "4823--4830",
    abstract = "By describing the features and abstractions of our world, language is a crucial tool for human learning and a promising source of supervision for machine learning models. We use language to improve few-shot visual classification in the underexplored scenario where natural language task descriptions are available during training, but unavailable for novel tasks at test time. Existing models for this setting sample new descriptions at test time and use those to classify images. Instead, we propose language-shaped learning (LSL), an end-to-end model that regularizes visual representations to predict language. LSL is conceptually simpler, more data efficient, and outperforms baselines in two challenging few-shot domains.",
}
@inproceedings{jin-etal-2020-discrete,
    title = "Discrete Latent Variable Representations for Low-Resource Text Classification",
    author = "Jin, Shuning  and
      Wiseman, Sam  and
      Stratos, Karl  and
      Livescu, Karen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.437",
    doi = "10.18653/v1/2020.acl-main.437",
    pages = "4831--4842",
    abstract = "While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient. We consider several approaches to learning discrete latent variable models for text in the case where exact marginalization over these variables is intractable. We compare the performance of the learned representations as features for low-resource document and sentence classification. Our best models outperform the previous best reported results with continuous representations in these low-resource settings, while learning significantly more compressed representations. Interestingly, we find that an amortized variant of Hard EM performs particularly well in the lowest-resource regimes.",
}
@inproceedings{pan-etal-2020-learning,
    title = "Learning Constraints for Structured Prediction Using Rectifier Networks",
    author = "Pan, Xingyuan  and
      Mehta, Maitrey  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.438",
    doi = "10.18653/v1/2020.acl-main.438",
    pages = "4843--4858",
    abstract = "Various natural language processing tasks are structured prediction problems where outputs are constructed with multiple interdependent decisions. Past work has shown that domain knowledge, framed as constraints over the output space, can help improve predictive accuracy. However, designing good constraints often relies on domain expertise. In this paper, we study the problem of learning such constraints. We frame the problem as that of training a two-layer rectifier network to identify valid structures or substructures, and show a construction for converting a trained network into a system of linear constraints over the inference variables. Our experiments on several NLP tasks show that the learned constraints can improve the prediction accuracy, especially when the number of training examples is small.",
}
@inproceedings{iter-etal-2020-pretraining,
    title = "Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models",
    author = "Iter, Dan  and
      Guu, Kelvin  and
      Lansing, Larry  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.439",
    doi = "10.18653/v1/2020.acl-main.439",
    pages = "4859--4870",
    abstract = "Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations. We propose Conpono, an inter-sentence objective for pretraining language models that models discourse coherence and the distance between sentences. Given an anchor sentence, our model is trained to predict the text k sentences away using a sampled-softmax objective where the candidates consist of neighboring sentences and sentences randomly sampled from the corpus. On the discourse representation benchmark DiscoEval, our model improves over the previous state-of-the-art by up to 13{\%} and on average 4{\%} absolute across 7 tasks. Our model is the same size as BERT-Base, but outperforms the much larger BERT-Large model and other more recent approaches that incorporate discourse. We also show that Conpono yields gains of 2{\%}-6{\%} absolute even for tasks that do not explicitly evaluate discourse: textual entailment (RTE), common sense reasoning (COPA) and reading comprehension (ReCoRD).",
}
@inproceedings{lin-etal-2020-recipe,
    title = "A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks",
    author = "Lin, Angela  and
      Rao, Sudha  and
      Celikyilmaz, Asli  and
      Nouri, Elnaz  and
      Brockett, Chris  and
      Dey, Debadeepta  and
      Dolan, Bill",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.440",
    doi = "10.18653/v1/2020.acl-main.440",
    pages = "4871--4884",
    abstract = "Many high-level procedural tasks can be decomposed into sequences of instructions that vary in their order and choice of tools. In the cooking domain, the web offers many, partially-overlapping, text and video recipes (i.e. procedures) that describe how to make the same dish (i.e. high-level task). Aligning instructions for the same dish across different sources can yield descriptive visual explanations that are far richer semantically than conventional textual instructions, providing commonsense insight into how real-world procedures are structured. Learning to align these different instruction sets is challenging because: a) different recipes vary in their order of instructions and use of ingredients; and b) video instructions can be noisy and tend to contain far more information than text instructions. To address these challenges, we use an unsupervised alignment algorithm that learns pairwise alignments between instructions of different recipes for the same dish. We then use a graph algorithm to derive a joint alignment between multiple text and multiple video recipes for the same dish. We release the Microsoft Research Multimodal Aligned Recipe Corpus containing {\textasciitilde}150K pairwise alignments between recipes across 4262 dishes with rich commonsense information.",
}
@inproceedings{nie-etal-2020-adversarial,
    title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding",
    author = "Nie, Yixin  and
      Williams, Adina  and
      Dinan, Emily  and
      Bansal, Mohit  and
      Weston, Jason  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.441",
    doi = "10.18653/v1/2020.acl-main.441",
    pages = "4885--4901",
    abstract = "We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that training models on this new dataset leads to state-of-the-art performance on a variety of popular NLI benchmarks, while posing a more difficult challenge with its new test set. Our analysis sheds light on the shortcomings of current state-of-the-art models, and shows that non-expert annotators are successful at finding their weaknesses. The data collection method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate.",
}
@inproceedings{ribeiro-etal-2020-beyond,
    title = "Beyond Accuracy: Behavioral Testing of {NLP} Models with {C}heck{L}ist",
    author = "Ribeiro, Marco Tulio  and
      Wu, Tongshuang  and
      Guestrin, Carlos  and
      Singh, Sameer",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.442",
    doi = "10.18653/v1/2020.acl-main.442",
    pages = "4902--4912",
    abstract = "Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.",
}
@inproceedings{tabassum-etal-2020-code,
    title = "Code and Named Entity Recognition in {S}tack{O}verflow",
    author = "Tabassum, Jeniya  and
      Maddela, Mounica  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.443",
    doi = "10.18653/v1/2020.acl-main.443",
    pages = "4913--4926",
    abstract = "There is an increasing interest in studying natural language and computer code together, as large corpora of programming texts become readily available on the Internet. For example, StackOverflow currently has over 15 million programming related questions written by 8.5 million users. Meanwhile, there is still a lack of fundamental NLP techniques for identifying code tokens or software-related named entities that appear within natural language sentences. In this paper, we introduce a new named entity recognition (NER) corpus for the computer programming domain, consisting of 15,372 sentences annotated with 20 fine-grained entity types. We trained in-domain BERT representations (BERTOverflow) on 152 million sentences from StackOverflow, which lead to an absolute increase of +10 F1 score over off-the-shelf BERT. We also present the SoftNER model which achieves an overall 79.10 F-1 score for code and named entity recognition on StackOverflow data. Our SoftNER model incorporates a context-independent code token classifier with corpus-level features to improve the BERT-based tagging model. Our code and data are available at: https://github.com/jeniyat/StackOverflowNER/",
}
@inproceedings{yu-etal-2020-dialogue,
    title = "Dialogue-Based Relation Extraction",
    author = "Yu, Dian  and
      Sun, Kai  and
      Cardie, Claire  and
      Yu, Dong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.444",
    doi = "10.18653/v1/2020.acl-main.444",
    pages = "4927--4940",
    abstract = "We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE as a platform for studying cross-sentence RE as most facts span multiple sentences. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Considering the timeliness of communication in a dialogue, we design a new metric to evaluate the performance of RE methods in a conversational setting and investigate the performance of several representative RE methods on DialogRE. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings. DialogRE is available at https://dataset.org/dialogre/.",
}
@inproceedings{mao-etal-2020-facet,
    title = "Facet-Aware Evaluation for Extractive Summarization",
    author = "Mao, Yuning  and
      Liu, Liyuan  and
      Zhu, Qi  and
      Ren, Xiang  and
      Han, Jiawei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.445",
    doi = "10.18653/v1/2020.acl-main.445",
    pages = "4941--4957",
    abstract = "Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries. Specifically, we treat each sentence in the reference summary as a \textit{facet}, identify the sentences in the document that express the semantics of each facet as \textit{support sentences} of the facet, and automatically evaluate extractive summarization methods by comparing the indices of extracted sentences and support sentences of all the facets in the reference summary. To facilitate this new evaluation setup, we construct an extractive version of the CNN/Daily Mail dataset and perform a thorough quantitative investigation, through which we demonstrate that facet-aware evaluation manifests better correlation with human judgment than ROUGE, enables fine-grained evaluation as well as comparative analysis, and reveals valuable insights of state-of-the-art summarization methods. Data can be found at \url{https://github.com/morningmoni/FAR}.",
}
@inproceedings{stasaski-etal-2020-diverse,
    title = "More Diverse Dialogue Datasets via Diversity-Informed Data Collection",
    author = "Stasaski, Katherine  and
      Yang, Grace Hui  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.446",
    doi = "10.18653/v1/2020.acl-main.446",
    pages = "4958--4968",
    abstract = "Automated generation of conversational dialogue using modern neural architectures has made notable advances. However, these models are known to have a drawback of often producing uninteresting, predictable responses; this is known as the diversity problem. We introduce a new strategy to address this problem, called Diversity-Informed Data Collection. Unlike prior approaches, which modify model architectures to solve the problem, this method uses dynamically computed corpus-level statistics to determine which conversational participants to collect data from. Diversity-Informed Data Collection produces significantly more diverse data than baseline data collection methods, and better results on two downstream tasks: emotion classification and dialogue generation. This method is generalizable and can be used with other corpus-level metrics.",
}
@inproceedings{lo-etal-2020-s2orc,
    title = "{S}2{ORC}: The Semantic Scholar Open Research Corpus",
    author = "Lo, Kyle  and
      Wang, Lucy Lu  and
      Neumann, Mark  and
      Kinney, Rodney  and
      Weld, Daniel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.447",
    doi = "10.18653/v1/2020.acl-main.447",
    pages = "4969--4983",
    abstract = "We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text is annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. In S2ORC, we aggregate papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable academic text to date. We hope this resource will facilitate research and development of tools and tasks for text mining over academic text.",
}
@inproceedings{mathur-etal-2020-tangled,
    title = "Tangled up in {BLEU}: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics",
    author = "Mathur, Nitika  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.448",
    doi = "10.18653/v1/2020.acl-main.448",
    pages = "4984--4997",
    abstract = "Automatic metrics are fundamental for the development and evaluation of machine translation systems. Judging whether, and to what extent, automatic metrics concur with the gold standard of human evaluation is not a straightforward problem. We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, which often leads to falsely confident conclusions about a metric{'}s efficacy. Finally, we turn to pairwise system ranking, developing a method for thresholding performance improvement under an automatic metric against human judgements, which allows quantification of type I versus type II errors incurred, i.e., insignificant human differences in system quality that are accepted, and significant human differences that are rejected. Together, these findings suggest improvements to the protocols for metric evaluation and system performance evaluation in machine translation.",
}
@inproceedings{ahmad-etal-2020-transformer,
    title = "A Transformer-based Approach for Source Code Summarization",
    author = "Ahmad, Wasi  and
      Chakraborty, Saikat  and
      Ray, Baishakhi  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.449",
    doi = "10.18653/v1/2020.acl-main.449",
    pages = "4998--5007",
    abstract = "Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies. In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin. We perform extensive analysis and ablation studies that reveal several important findings, e.g., the absolute encoding of source code tokens{'} position hinders, while relative encoding significantly improves the summarization performance. We have made our code publicly available to facilitate future research.",
}
@inproceedings{wang-etal-2020-asking,
    title = "Asking and Answering Questions to Evaluate the Factual Consistency of Summaries",
    author = "Wang, Alex  and
      Cho, Kyunghyun  and
      Lewis, Mike",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.450",
    doi = "10.18653/v1/2020.acl-main.450",
    pages = "5008--5020",
    abstract = "Practical applications of abstractive summarization models are limited by frequent factual inconsistencies with respect to their input. Existing automatic evaluation metrics for summarization are largely insensitive to such errors. We propose QAGS (pronounced {``}kags{''}), an automatic evaluation protocol that is designed to identify factual inconsistencies in a generated summary. QAGS is based on the intuition that if we ask questions about a summary and its source, we will receive similar answers if the summary is factually consistent with the source. To evaluate QAGS, we collect human judgments of factual consistency on model-generated summaries for the CNN/DailyMail (Hermann et al., 2015) and XSUM (Narayan et al., 2018) summarization datasets. QAGS has substantially higher correlations with these judgments than other automatic evaluation metrics. Also, QAGS offers a natural form of interpretability: The answers and questions generated while computing QAGS indicate which tokens of a summary are inconsistent and why. We believe QAGS is a promising tool in automatically generating usable and factually consistent text. Code for QAGS will be available at https://github.com/W4ngatang/qags.",
}
@inproceedings{xu-etal-2020-discourse,
    title = "Discourse-Aware Neural Extractive Text Summarization",
    author = "Xu, Jiacheng  and
      Gan, Zhe  and
      Cheng, Yu  and
      Liu, Jingjing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.451",
    doi = "10.18653/v1/2020.acl-main.451",
    pages = "5021--5031",
    abstract = "Recently BERT has been adopted for document encoding in state-of-the-art text summarization models. However, sentence-based extractive models often result in redundant or uninformative phrases in the extracted summaries. Also, long-range dependencies throughout a document are not well captured by BERT, which is pre-trained on sentence pairs instead of documents. To address these issues, we present a discourse-aware neural summarization model - DiscoBert. DiscoBert extracts sub-sentential discourse units (instead of sentences) as candidates for extractive selection on a finer granularity. To capture the long-range dependencies among discourse units, structural discourse graphs are constructed based on RST trees and coreference mentions, encoded with Graph Convolutional Networks. Experiments show that the proposed model outperforms state-of-the-art methods by a significant margin on popular summarization benchmarks compared to other BERT-base models.",
}
@inproceedings{schumann-etal-2020-discrete,
    title = "Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction",
    author = "Schumann, Raphael  and
      Mou, Lili  and
      Lu, Yao  and
      Vechtomova, Olga  and
      Markert, Katja",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.452",
    doi = "10.18653/v1/2020.acl-main.452",
    pages = "5032--5042",
    abstract = "Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information. A good summary is characterized by language fluency and high information overlap with the source sentence. We model these two aspects in an unsupervised objective function, consisting of language modeling and semantic similarity metrics. We search for a high-scoring summary by discrete optimization. Our proposed method achieves a new state-of-the art for unsupervised sentence summarization according to ROUGE scores. Additionally, we demonstrate that the commonly reported ROUGE F1 metric is sensitive to summary length. Since this is unwillingly exploited in recent work, we emphasize that future evaluation should explicitly group summarization systems by output length brackets.",
}
@inproceedings{ladhak-etal-2020-exploring,
    title = "Exploring Content Selection in Summarization of Novel Chapters",
    author = "Ladhak, Faisal  and
      Li, Bryan  and
      Al-Onaizan, Yaser  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.453",
    doi = "10.18653/v1/2020.acl-main.453",
    pages = "5043--5054",
    abstract = "We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis.",
}
@inproceedings{durmus-etal-2020-feqa,
    title = "{FEQA}: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization",
    author = "Durmus, Esin  and
      He, He  and
      Diab, Mona",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.454",
    doi = "10.18653/v1/2020.acl-main.454",
    pages = "5055--5070",
    abstract = "Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful. Existing automatic metrics do not capture such mistakes effectively. We tackle the problem of evaluating faithfulness of a generated summary given its source document. We first collected human annotations of faithfulness for outputs from numerous models on two datasets. We find that current models exhibit a trade-off between abstractiveness and faithfulness: outputs with less word overlap with the source document are more likely to be unfaithful. Next, we propose an automatic question answering (QA) based metric for faithfulness, FEQA, which leverages recent advances in reading comprehension. Given question-answer pairs generated from the summary, a QA model extracts answers from the document; non-matched answers indicate unfaithful information in the summary. Among metrics based on word overlap, embedding similarity, and learned language understanding models, our QA-based metric has significantly higher correlation with human faithfulness scores, especially on highly abstractive summaries.",
}
@inproceedings{xu-etal-2020-fact,
    title = "Fact-based Content Weighting for Evaluating Abstractive Summarisation",
    author = "Xu, Xinnuo  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Li, Jingyi  and
      Rieser, Verena  and
      Konstas, Ioannis",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.455",
    doi = "10.18653/v1/2020.acl-main.455",
    pages = "5071--5081",
    abstract = "Abstractive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient. We introduce a new evaluation metric which is based on fact-level content weighting, i.e. relating the facts of the document to the facts of the summary. We fol- low the assumption that a good summary will reflect all relevant facts, i.e. the ones present in the ground truth (human-generated refer- ence summary). We confirm this hypothe- sis by showing that our weightings are highly correlated to human perception and compare favourably to the recent manual highlight- based metric of Hardy et al. (2019).",
}
@inproceedings{jin-etal-2020-hooks,
    title = "Hooks in the Headline: Learning to Generate Headlines with Controlled Styles",
    author = "Jin, Di  and
      Jin, Zhijing  and
      Zhou, Joey Tianyi  and
      Orii, Lisa  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.456",
    doi = "10.18653/v1/2020.acl-main.456",
    pages = "5082--5093",
    abstract = "Current summarization systems only produce plain, factual headlines, far from the practical needs for the exposure and memorableness of the articles. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), thus attracting more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates stylistic headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines outperforms the state-of-the-art summarization model by 9.68{\%}, even outperforming human-written references.",
}
@inproceedings{huang-etal-2020-knowledge,
    title = "Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward",
    author = "Huang, Luyang  and
      Wu, Lingfei  and
      Wang, Lu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.457",
    doi = "10.18653/v1/2020.acl-main.457",
    pages = "5094--5107",
    abstract = "Sequence-to-sequence models for abstractive summarization have been studied extensively, yet the generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, the summarizer should acquire semantic interpretation over input, e.g., via structured representation, to allow the generation of more informative summaries. In this paper, we present ASGARD, a novel framework for Abstractive Summarization with Graph-Augmentation and semantic-driven RewarD. We propose the use of dual encoders{---}a sequential document encoder and a graph-structured encoder{---}to maintain the global context and local characteristics of entities, complementing each other. We further design a reward based on a multiple choice cloze test to drive the model to better capture entity interactions. Results show that our models produce significantly higher ROUGE scores than a variant without knowledge graph as input on both New York Times and CNN/Daily Mail datasets. We also obtain better or comparable performance compared to systems that are fine-tuned from large pretrained language models. Human judges further rate our model outputs as more informative and containing fewer unfaithful errors.",
}
@inproceedings{zhang-etal-2020-optimizing,
    title = "Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports",
    author = "Zhang, Yuhao  and
      Merck, Derek  and
      Tsai, Emily  and
      Manning, Christopher D.  and
      Langlotz, Curtis",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.458",
    doi = "10.18653/v1/2020.acl-main.458",
    pages = "5108--5120",
    abstract = "Neural abstractive summarization models are able to generate summaries which have high overlap with human references. However, existing models are not optimized for factual correctness, a critical metric in real-world applications. In this work, we develop a general framework where we evaluate the factual correctness of a generated summary by fact-checking it automatically against its reference using an information extraction module. We further propose a training strategy which optimizes a neural summarization model with a factual correctness reward via reinforcement learning. We apply the proposed method to the summarization of radiology reports, where factual correctness is a key requirement. On two separate datasets collected from hospitals, we show via both automatic and human evaluation that the proposed approach substantially improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of human-authored ones.",
}
@inproceedings{rameshkumar-bailey-2020-storytelling,
    title = "Storytelling with Dialogue: {A} {Critical Role Dungeons and Dragons Dataset}",
    author = "Rameshkumar, Revanth  and
      Bailey, Peter",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.459",
    doi = "10.18653/v1/2020.acl-main.459",
    pages = "5121--5134",
    abstract = "This paper describes the Critical Role Dungeons and Dragons Dataset (CRD3) and related analyses. Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game. The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail, and semantic ties to the previous dialogues. In addition, we provide a data augmentation method that produces 34,243 summary-dialogue chunk pairs to support current neural ML approaches, and we provide an abstractive summarization benchmark and evaluation.",
}
@inproceedings{laban-etal-2020-summary,
    title = "The Summary Loop: Learning to Write Abstractive Summaries Without Examples",
    author = "Laban, Philippe  and
      Hsi, Andrew  and
      Canny, John  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.460",
    doi = "10.18653/v1/2020.acl-main.460",
    pages = "5135--5150",
    abstract = "This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint. It introduces a novel method that encourages the inclusion of key terms from the original document into the summary: key terms are masked out of the original document and must be filled in by a coverage model using the current generated summary. A novel unsupervised training procedure leverages this coverage model along with a fluency model to generate and score summaries. When tested on popular news summarization datasets, the method outperforms previous unsupervised methods by more than 2 R-1 points, and approaches results of competitive supervised methods. Our model attains higher levels of abstraction with copied passages roughly two times shorter than prior work, and learns to compress and merge sentences without supervision.",
}
@inproceedings{brazinskas-etal-2020-unsupervised,
    title = "Unsupervised Opinion Summarization as Copycat-Review Generation",
    author = "Bra{\v{z}}inskas, Arthur  and
      Lapata, Mirella  and
      Titov, Ivan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.461",
    doi = "10.18653/v1/2020.acl-main.461",
    pages = "5151--5169",
    abstract = "Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the model generate novel sentences and hence produce abstractive summaries. Recent progress in summarization has seen the development of supervised models which rely on large quantities of document-summary pairs. Since such training data is expensive to acquire, we instead consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a review collection which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, we should be able to control the {``}amount of novelty{''} going into the new review or, equivalently, vary the extent to which it deviates from the input. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and the products they correspond to are associated with stochastic latent codes, and the review generator ({``}decoder{''}) has direct access to the text of input reviews through the pointer-generator mechanism. Experiments on Amazon and Yelp datasets, show that setting at test time the review{'}s latent code to its mean, allows the model to produce fluent and coherent summaries reflecting common opinions.",
}
@inproceedings{trott-etal-2020-construing,
    title = "(Re)construing Meaning in {NLP}",
    author = "Trott, Sean  and
      Torrent, Tiago Timponi  and
      Chang, Nancy  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.462",
    doi = "10.18653/v1/2020.acl-main.462",
    pages = "5170--5184",
    abstract = "Human speakers have an extensive toolkit of ways to express themselves. In this paper, we engage with an idea largely absent from discussions of meaning in natural language understanding{---}namely, that the way something is expressed reflects different ways of conceptualizing or construing the information being conveyed. We first define this phenomenon more precisely, drawing on considerable prior work in theoretical cognitive semantics and psycholinguistics. We then survey some dimensions of construed meaning and show how insights from construal could inform theoretical and practical work in NLP.",
}
@inproceedings{bender-koller-2020-climbing,
    title = "Climbing towards {NLU}: {On} Meaning, Form, and Understanding in the Age of Data",
    author = "Bender, Emily M.  and
      Koller, Alexander",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.463",
    doi = "10.18653/v1/2020.acl-main.463",
    pages = "5185--5198",
    abstract = "The success of the large neural language models on many NLP tasks is exciting. However, we find that these successes sometimes lead to hype in which these models are being described as {``}understanding{''} language or capturing {``}meaning{''}. In this position paper, we argue that a system trained only on form has a priori no way to learn meaning. In keeping with the ACL 2020 theme of {``}Taking Stock of Where We{'}ve Been and Where We{'}re Going{''}, we argue that a clear understanding of the distinction between form and meaning will help guide the field towards better science around natural language understanding.",
}
@inproceedings{mohammad-2020-examining,
    title = "Examining Citations of Natural Language Processing Literature",
    author = "Mohammad, Saif M.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.464",
    doi = "10.18653/v1/2020.acl-main.464",
    pages = "5199--5209",
    abstract = "We extracted information from the ACL Anthology (AA) and Google Scholar (GS) to examine trends in citations of NLP papers. We explore questions such as: how well cited are papers of different types (journal articles, conference papers, demo papers, etc.)? how well cited are papers from different areas of within NLP? etc. Notably, we show that only about 56{\%} of the papers in AA are cited ten or more times. CL Journal has the most cited papers, but its citation dominance has lessened in recent years. On average, long papers get almost three times as many citations as short papers; and papers on sentiment classification, anaphora resolution, and entity recognition have the highest median citations. The analyses presented here, and the associated dataset of NLP papers mapped to citations, have a number of uses including: understanding how the field is growing and quantifying the impact of different types of papers.",
}
@inproceedings{linzen-2020-accelerate,
    title = "How Can We Accelerate Progress Towards Human-like Linguistic Generalization?",
    author = "Linzen, Tal",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.465",
    doi = "10.18653/v1/2020.acl-main.465",
    pages = "5210--5217",
    abstract = "This position paper describes and critiques the Pretraining-Agnostic Identically Distributed (PAID) evaluation paradigm, which has become a central tool for measuring progress in natural language understanding. This paradigm consists of three stages: (1) pre-training of a word prediction model on a corpus of arbitrary size; (2) fine-tuning (transfer learning) on a training set representing a classification task; (3) evaluation on a test set drawn from the same distribution as that training set. This paradigm favors simple, low-bias architectures, which, first, can be scaled to process vast amounts of data, and second, can capture the fine-grained statistical properties of a particular data set, regardless of whether those properties are likely to generalize to examples of the task outside the data set. This contrasts with humans, who learn language from several orders of magnitude less data than the systems favored by this evaluation paradigm, and generalize to new tasks in a consistent way. We advocate for supplementing or replacing PAID with paradigms that reward architectures that generalize as quickly and robustly as humans.",
}
@inproceedings{zhong-etal-2020-nlp,
    title = "How Does {NLP} Benefit Legal System: A Summary of Legal Artificial Intelligence",
    author = "Zhong, Haoxi  and
      Xiao, Chaojun  and
      Tu, Cunchao  and
      Zhang, Tianyang  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.466",
    doi = "10.18653/v1/2020.acl-main.466",
    pages = "5218--5230",
    abstract = "Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solve tasks from rule-based and symbol-based methods, while NLP researchers concentrate more on data-driven and embedding methods. In this paper, we introduce the history, the current state, and the future directions of research in LegalAI. We illustrate the tasks from the perspectives of legal professionals and NLP researchers and show several representative applications in LegalAI. We conduct experiments and provide an in-depth analysis of the advantages and disadvantages of existing works to explore possible future directions. You can find the implementation of our work from https://github.com/thunlp/CLAIM.",
}
@inproceedings{pruksachatkun-etal-2020-intermediate,
    title = "Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?",
    author = "Pruksachatkun, Yada  and
      Phang, Jason  and
      Liu, Haokun  and
      Htut, Phu Mon  and
      Zhang, Xiaoyi  and
      Pang, Richard Yuanzhe  and
      Vania, Clara  and
      Kann, Katharina  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.467",
    doi = "10.18653/v1/2020.acl-main.467",
    pages = "5231--5247",
    abstract = "While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a target task. However, it is still poorly understood when and why intermediate-task training is beneficial for a given target task. To investigate this, we perform a large-scale study on the pretrained RoBERTa model with 110 intermediate-target task combinations. We further evaluate all trained models with 25 probing tasks meant to reveal the specific skills that drive transfer. We observe that intermediate tasks requiring high-level inference and reasoning abilities tend to work best. We also observe that target task performance is strongly correlated with higher-level abilities such as coreference resolution. However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks. We also observe evidence that the forgetting of knowledge learned during pretraining may limit our analysis, highlighting the need for further work on transfer learning methods in these settings.",
}
@inproceedings{shah-etal-2020-predictive,
    title = "Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview",
    author = "Shah, Deven Santosh  and
      Schwartz, H. Andrew  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.468",
    doi = "10.18653/v1/2020.acl-main.468",
    pages = "5248--5264",
    abstract = "An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks.",
}
@inproceedings{li-etal-2020-bert,
    title = "What Does {BERT} with Vision Look At?",
    author = "Li, Liunian Harold  and
      Yatskar, Mark  and
      Yin, Da  and
      Hsieh, Cho-Jui  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.469",
    doi = "10.18653/v1/2020.acl-main.469",
    pages = "5265--5275",
    abstract = "Pre-trained visually grounded language models such as ViLBERT, LXMERT, and UNITER have achieved significant performance improvement on vision-and-language tasks but what they learn during pre-training remains unclear. In this work, we demonstrate that certain attention heads of a visually grounded language model actively ground elements of language to image regions. Specifically, some heads can map entities to image regions, performing the task known as entity grounding. Some heads can even detect the syntactic relations between non-entity words and image regions, tracking, for example, associations between verbs and regions corresponding to their arguments. We denote this ability as \textit{syntactic grounding}. We verify grounding both quantitatively and qualitatively, using Flickr30K Entities as a testbed.",
}
@inproceedings{zhang-danescu-niculescu-mizil-2020-balancing,
    title = "Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards",
    author = "Zhang, Justine  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.470",
    doi = "10.18653/v1/2020.acl-main.470",
    pages = "5276--5289",
    abstract = "Throughout a conversation, participants make choices that can orient the flow of the interaction. Such choices are particularly salient in the consequential domain of crisis counseling, where a difficulty for counselors is balancing between two key objectives: advancing the conversation towards a resolution, and empathetically addressing the crisis situation. In this work, we develop an unsupervised methodology to quantify how counselors manage this balance. Our main intuition is that if an utterance can only receive a narrow range of appropriate replies, then its likely aim is to advance the conversation forwards, towards a target within that range. Likewise, an utterance that can only appropriately follow a narrow range of possible utterances is likely aimed backwards at addressing a specific situation within that range. By applying this intuition, we can map each utterance to a continuous orientation axis that captures the degree to which it is intended to direct the flow of the conversation forwards or backwards. This unsupervised method allows us to characterize counselor behaviors in a large dataset of crisis counseling conversations, where we show that known counseling strategies intuitively align with this axis. We also illustrate how our measure can be indicative of a conversation{'}s progress, as well as its effectiveness.",
}
@inproceedings{desai-etal-2020-detecting,
    title = "Detecting Perceived Emotions in Hurricane Disasters",
    author = "Desai, Shrey  and
      Caragea, Cornelia  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.471",
    doi = "10.18653/v1/2020.acl-main.471",
    pages = "5290--5305",
    abstract = "Natural disasters (e.g., hurricanes) affect millions of people each year, causing widespread destruction in their wake. People have recently taken to social media websites (e.g., Twitter) to share their sentiments and feelings with the larger community. Consequently, these platforms have become instrumental in understanding and perceiving emotions at scale. In this paper, we introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria. We present a comprehensive study of fine-grained emotions and propose classification tasks to discriminate between coarse-grained emotion groups. Our best BERT model, even after task-guided pre-training which leverages unlabeled Twitter data, achieves only 68{\%} accuracy (averaged across all groups). HurricaneEmo serves not only as a challenging benchmark for models but also as a valuable resource for analyzing emotions in disaster-centric domains.",
}
@inproceedings{lynn-etal-2020-hierarchical,
    title = "Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention",
    author = "Lynn, Veronica  and
      Balasubramanian, Niranjan  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.472",
    doi = "10.18653/v1/2020.acl-main.472",
    pages = "5306--5316",
    abstract = "Not all documents are equally important. Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. In this paper, we present a novel model that uses message-level attention to learn the relative weight of users{'} social media posts for assessing their five factor personality traits. We demonstrate that models with message-level attention outperform those with word-level attention, and ultimately yield state-of-the-art accuracies for all five traits by using both word and message attention in combination with past approaches (an average increase in Pearson r of 2.5{\%}). In addition, examination of the high-signal posts identified by our model provides insight into the relationship between language and personality, helping to inform future work.",
}
@inproceedings{zong-etal-2020-measuring,
    title = "Measuring Forecasting Skill from Text",
    author = "Zong, Shi  and
      Ritter, Alan  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.473",
    doi = "10.18653/v1/2020.acl-main.473",
    pages = "5317--5331",
    abstract = "People vary in their ability to make accurate predictions about the future. Prior studies have shown that some individuals can predict the outcome of future events with consistently better accuracy. This leads to a natural question: what makes some forecasters better than others? In this paper we explore connections between the language people use to describe their predictions and their forecasting skill. Datasets from two different forecasting domains are explored: (1) geopolitical forecasts from Good Judgment Open, an online prediction forum and (2) a corpus of company earnings forecasts made by financial analysts. We present a number of linguistic metrics which are computed over text associated with people{'}s predictions about the future including: uncertainty, readability, and emotion. By studying linguistic factors associated with predictions, we are able to shed some light on the approach taken by skilled forecasters. Furthermore, we demonstrate that it is possible to accurately predict forecasting skill using a model that is based solely on language. This could potentially be useful for identifying accurate predictions or potentially skilled forecasters earlier.",
}
@inproceedings{keith-etal-2020-text,
    title = "Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates",
    author = "Keith, Katherine  and
      Jensen, David  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.474",
    doi = "10.18653/v1/2020.acl-main.474",
    pages = "5332--5344",
    abstract = "Many applications of computational social science aim to infer causal conclusions from non-experimental data. Such observational data often contains confounders, variables that influence both potential causes and potential effects. Unmeasured or latent confounders can bias causal estimates, and this has motivated interest in measuring potential confounders from observed text. For example, an individual{'}s entire history of social media posts or the content of a news article could provide a rich measurement of multiple confounders.Yet, methods and applications for this problem are scattered across different communities and evaluation practices are inconsistent.This review is the first to gather and categorize these examples and provide a guide to data-processing and evaluation decisions. Despite increased attention on adjusting for confounding using text, there are still many open problems, which we highlight in this paper.",
}
@inproceedings{vafa-etal-2020-text,
    title = "Text-Based Ideal Points",
    author = "Vafa, Keyon  and
      Naidu, Suresh  and
      Blei, David",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.475",
    doi = "10.18653/v1/2020.acl-main.475",
    pages = "5345--5357",
    abstract = "Ideal point models analyze lawmakers{'} votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also give speeches, release press statements, and post tweets. In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. We demonstrate the TBIP with two types of politicized text data: U.S. Senate speeches and senator tweets. Though the model does not analyze their votes or political affiliations, the TBIP separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. One benefit of analyzing texts, as opposed to votes, is that the TBIP can estimate ideal points of anyone who authors political texts, including non-voting actors. To this end, we use it to study tweets from the 2020 Democratic presidential candidates. Using only the texts of their tweets, it identifies them along an interpretable progressive-to-moderate spectrum.",
}
@inproceedings{davoodi-etal-2020-understanding,
    title = "Understanding the Language of Political Agreement and Disagreement in Legislative Texts",
    author = "Davoodi, Maryam  and
      Waltenburg, Eric  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.476",
    doi = "10.18653/v1/2020.acl-main.476",
    pages = "5358--5368",
    abstract = "While national politics often receive the spotlight, the overwhelming majority of legislation proposed, discussed, and enacted is done at the state level. Despite this fact, there is little awareness of the dynamics that lead to adopting these policies. In this paper, we take the first step towards a better understanding of these processes and the underlying dynamics that shape them, using data-driven methods. We build a new large-scale dataset, from multiple data sources, connecting state bills and legislator information, geographical information about their districts, and donations and donors{'} information. We suggest a novel task, predicting the legislative body{'}s vote breakdown for a given bill, according to different criteria of interest, such as gender, rural-urban and ideological splits. Finally, we suggest a shared relational embedding model, representing the interactions between the text of the bill and the legislative context in which it is presented. Our experiments show that providing this context helps improve the prediction over strong text-based models.",
}
@inproceedings{tay-etal-2020-rather,
    title = "Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences",
    author = "Tay, Yi  and
      Ong, Donovan  and
      Fu, Jie  and
      Chan, Alvin  and
      Chen, Nancy  and
      Luu, Anh Tuan  and
      Pal, Chris",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.477",
    doi = "10.18653/v1/2020.acl-main.477",
    pages = "5369--5373",
    abstract = "Understanding human preferences, along with cultural and social nuances, lives at the heart of natural language understanding. Concretely, we present a new task and corpus for learning alignments between machine and human preferences. Our newly introduced problem is concerned with predicting the preferable options from two sentences describing scenarios that may involve social and cultural situations. Our problem is framed as a natural language inference task with crowd-sourced preference votes by human players, obtained from a gamified voting platform. We benchmark several state-of-the-art neural models, along with BERT and friends on this task. Our experimental results show that current state-of-the-art NLP models still leave much room for improvement.",
}
@inproceedings{choubey-etal-2020-discourse,
    title = "Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event",
    author = "Choubey, Prafulla Kumar  and
      Lee, Aaron  and
      Huang, Ruihong  and
      Wang, Lu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.478",
    doi = "10.18653/v1/2020.acl-main.478",
    pages = "5374--5386",
    abstract = "Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event. To enable computational modeling of news structures, we apply an existing theory of functional discourse structure for news articles that revolves around the main event and create a human-annotated corpus of 802 documents spanning over four domains and three media sources. Next, we propose several document-level neural-network models to automatically construct news content structures. Finally, we demonstrate that incorporating system predicted news structures yields new state-of-the-art performance for event coreference resolution. The news documents we annotated are openly available and the annotations are publicly released for future research.",
}
@inproceedings{schuster-etal-2020-harnessing,
    title = "Harnessing the linguistic signal to predict scalar inferences",
    author = "Schuster, Sebastian  and
      Chen, Yuxing  and
      Degen, Judith",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.479",
    doi = "10.18653/v1/2020.acl-main.479",
    pages = "5387--5403",
    abstract = "Pragmatic inferences often subtly depend on the presence or absence of linguistic features. For example, the presence of a partitive construction (of the) increases the strength of a so-called scalar inference: listeners perceive the inference that Chris did not eat all of the cookies to be stronger after hearing {``}Chris ate some of the cookies{''} than after hearing the same utterance without a partitive, {``}Chris ate some cookies{''}. In this work, we explore to what extent neural network sentence encoders can learn to predict the strength of scalar inferences. We first show that an LSTM-based sentence encoder trained on an English dataset of human inference strength ratings is able to predict ratings with high accuracy (r = 0.78). We then probe the model{'}s behavior using manually constructed minimal sentence pairs and corpus data. We first that the model inferred previously established associations between linguistic features and inference strength, suggesting that the model learns to use linguistic features to predict pragmatic inferences.",
}
@inproceedings{kim-etal-2020-implicit,
    title = "Implicit Discourse Relation Classification: We Need to Talk about Evaluation",
    author = "Kim, Najoung  and
      Feng, Song  and
      Gunasekara, Chulaka  and
      Lastras, Luis",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.480",
    doi = "10.18653/v1/2020.acl-main.480",
    pages = "5404--5414",
    abstract = "Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations. However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature. In this work, we highlight these inconsistencies and propose an improved evaluation protocol. Paired with this protocol, we report strong baseline results from pretrained sentence encoders, which set the new state-of-the-art for PDTB 2.0. Furthermore, this work is the first to explore fine-grained relation classification on PDTB 3.0. We expect our work to serve as a point of comparison for future work, and also as an initiative to discuss models of larger context and possible data augmentations for downstream transferability.",
}
@inproceedings{toshniwal-etal-2020-petra,
    title = "{P}e{T}ra: {A} {S}parsely {S}upervised {M}emory {M}odel for {P}eople {T}racking",
    author = "Toshniwal, Shubham  and
      Ettinger, Allyson  and
      Gimpel, Kevin  and
      Livescu, Karen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.481",
    doi = "10.18653/v1/2020.acl-main.481",
    pages = "5415--5428",
    abstract = "We propose PeTra, a memory-augmented neural network designed to track entities in its memory slots. PeTra is trained using sparse annotation from the GAP pronoun resolution dataset and outperforms a prior memory model on the task while using a simpler architecture. We empirically compare key modeling choices, finding that we can simplify several aspects of the design of the memory module while retaining strong performance. To measure the people tracking capability of memory models, we (a) propose a new diagnostic evaluation based on counting the number of unique entities in text, and (b) conduct a small scale human evaluation to compare evidence of people tracking in the memory logs of PeTra relative to a previous approach. PeTra is highly effective in both evaluations, demonstrating its ability to track people in its memory despite being trained with limited annotation.",
}
@inproceedings{song-etal-2020-zpr2,
    title = "{ZPR}2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and {BERT}",
    author = "Song, Linfeng  and
      Xu, Kun  and
      Zhang, Yue  and
      Chen, Jianshu  and
      Yu, Dong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.482",
    doi = "10.18653/v1/2020.acl-main.482",
    pages = "5429--5434",
    abstract = "Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively. We propose to better explore their interaction by solving both tasks together, while the previous work treats them separately. For zero pronoun resolution, we study this task in a more realistic setting, where no parsing trees or only automatic trees are available, while most previous work assumes gold trees. Experiments on two benchmarks show that joint modeling significantly outperforms our baseline that already beats the previous state of the arts.",
}
@inproceedings{kennedy-etal-2020-contextualizing,
    title = "Contextualizing Hate Speech Classifiers with Post-hoc Explanation",
    author = "Kennedy, Brendan  and
      Jin, Xisen  and
      Mostafazadeh Davani, Aida  and
      Dehghani, Morteza  and
      Ren, Xiang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.483",
    doi = "10.18653/v1/2020.acl-main.483",
    pages = "5435--5442",
    abstract = "Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like {``}gay{''} or {``}black{''} are used in offensive or prejudiced ways. Such biases manifest in false positives when these identifiers are present, due to models{'} inability to learn the contexts which constitute a hateful usage of identifiers. We extract post-hoc explanations from fine-tuned BERT classifiers to detect bias towards identity terms. Then, we propose a novel regularization technique based on these explanations that encourages models to learn from the context of group identifiers in addition to the identifiers themselves. Our approach improved over baselines in limiting false positives on out-of-domain data while maintaining and in cases improving in-domain performance.",
}
@inproceedings{wang-etal-2020-double,
    title = "Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation",
    author = "Wang, Tianlu  and
      Lin, Xi Victoria  and
      Rajani, Nazneen Fatema  and
      McCann, Bryan  and
      Ordonez, Vicente  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.484",
    doi = "10.18653/v1/2020.acl-main.484",
    pages = "5443--5453",
    abstract = "Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models. Some commonly adopted debiasing approaches, including the seminal Hard Debias algorithm, apply post-processing procedures that project pre-trained word embeddings into a subspace orthogonal to an inferred gender subspace. We discover that semantic-agnostic corpus regularities such as word frequency captured by the word embeddings negatively impact the performance of these algorithms. We propose a simple but effective technique, Double Hard Debias, which purifies the word embeddings against such corpus regularities prior to inferring and removing the gender subspace. Experiments on three bias mitigation benchmarks show that our approach preserves the distributional semantics of the pre-trained word embeddings while reducing gender bias to a significantly larger degree than prior approaches.",
}
@inproceedings{blodgett-etal-2020-language,
    title = "Language (Technology) is Power: A Critical Survey of {``}Bias{''} in {NLP}",
    author = "Blodgett, Su Lin  and
      Barocas, Solon  and
      Daum{\'e} III, Hal  and
      Wallach, Hanna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.485",
    doi = "10.18653/v1/2020.acl-main.485",
    pages = "5454--5476",
    abstract = "We survey 146 papers analyzing {``}bias{''} in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing {``}bias{''} is an inherently normative process. We further find that these papers{'} proposed quantitative techniques for measuring or mitigating {``}bias{''} are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing {``}bias{''} in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of {``}bias{''}---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements{---}and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.",
}
@inproceedings{sap-etal-2020-social,
    title = "Social Bias Frames: Reasoning about Social and Power Implications of Language",
    author = "Sap, Maarten  and
      Gabriel, Saadia  and
      Qin, Lianhui  and
      Jurafsky, Dan  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.486",
    doi = "10.18653/v1/2020.acl-main.486",
    pages = "5477--5490",
    abstract = "Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people{'}s judgments about others. For example, given a statement that {``}we shouldn{'}t lower our standards to hire more women,{''} most listeners will infer the implicature intended by the speaker - that {``}women (candidates) are less qualified.{''} Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80{\%} F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.",
}
@inproceedings{hutchinson-etal-2020-social,
    title = "Social Biases in {NLP} Models as Barriers for Persons with Disabilities",
    author = "Hutchinson, Ben  and
      Prabhakaran, Vinodkumar  and
      Denton, Emily  and
      Webster, Kellie  and
      Zhong, Yu  and
      Denuyl, Stephen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.487",
    doi = "10.18653/v1/2020.acl-main.487",
    pages = "5491--5501",
    abstract = "Building equitable and inclusive NLP technologies demands consideration of whether and how social attitudes are represented in ML models. In particular, representations encoded in models often inadvertently perpetuate undesirable social biases from the data on which they are trained. In this paper, we present evidence of such undesirable biases towards mentions of disability in two different English language models: toxicity prediction and sentiment analysis. Next, we demonstrate that the neural embeddings that are the critical first step in most NLP pipelines similarly contain undesirable biases towards mentions of disability. We end by highlighting topical biases in the discourse about disability which may contribute to the observed model biases; for instance, gun violence, homelessness, and drug addiction are over-represented in texts discussing mental illness.",
}
@inproceedings{liang-etal-2020-towards,
    title = "Towards Debiasing Sentence Representations",
    author = "Liang, Paul Pu  and
      Li, Irene Mengze  and
      Zheng, Emily  and
      Lim, Yao Chong  and
      Salakhutdinov, Ruslan  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.488",
    doi = "10.18653/v1/2020.acl-main.488",
    pages = "5502--5515",
    abstract = "As natural language processing methods are increasingly deployed in real-world scenarios such as healthcare, legal systems, and social science, it becomes necessary to recognize the role they potentially play in shaping social biases and stereotypes. Previous work has revealed the presence of social biases in widely used word embeddings involving gender, race, religion, and other social constructs. While some methods were proposed to debias these word-level embeddings, there is a need to perform debiasing at the sentence-level given the recent shift towards new contextualized sentence representations such as ELMo and BERT. In this paper, we investigate the presence of social biases in sentence-level representations and propose a new method, Sent-Debias, to reduce these biases. We show that Sent-Debias is effective in removing biases, and at the same time, preserves performance on sentence-level downstream tasks such as sentiment analysis, linguistic acceptability, and natural language understanding. We hope that our work will inspire future research on characterizing and removing social biases from widely adopted sentence representations for fairer NLP.",
}
@inproceedings{sun-etal-2020-evaluation,
    title = "A Re-evaluation of Knowledge Graph Completion Methods",
    author = "Sun, Zhiqing  and
      Vashishth, Shikhar  and
      Sanyal, Soumya  and
      Talukdar, Partha  and
      Yang, Yiming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.489",
    doi = "10.18653/v1/2020.acl-main.489",
    pages = "5516--5522",
    abstract = "Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report performance of several existing methods using our protocol. The reproducible code has been made publicly available.",
}
@inproceedings{mueller-etal-2020-cross,
    title = "Cross-Linguistic Syntactic Evaluation of Word Prediction Models",
    author = "Mueller, Aaron  and
      Nicolai, Garrett  and
      Petrou-Zeniou, Panayiota  and
      Talmina, Natalia  and
      Linzen, Tal",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.490",
    doi = "10.18653/v1/2020.acl-main.490",
    pages = "5523--5539",
    abstract = "A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatical sentences with high accuracy. However, these studies are based primarily on monolingual evidence from English. To investigate how these models{'} ability to learn syntax varies by language, we introduce CLAMS (Cross-Linguistic Assessment of Models on Syntax), a syntactic evaluation suite for monolingual and multilingual models. CLAMS includes subject-verb agreement challenge sets for English, French, German, Hebrew and Russian, generated from grammars we develop. We use CLAMS to evaluate LSTM language models as well as monolingual and multilingual BERT. Across languages, monolingual LSTMs achieved high accuracy on dependencies without attractors, and generally poor accuracy on agreement across object relative clauses. On other constructions, agreement accuracy was generally higher in languages with richer morphology. Multilingual models generally underperformed monolingual models. Multilingual BERT showed high syntactic accuracy on English, but noticeable deficiencies in other languages.",
}
@inproceedings{hase-bansal-2020-evaluating,
    title = "Evaluating Explainable {AI}: Which Algorithmic Explanations Help Users Predict Model Behavior?",
    author = "Hase, Peter  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.491",
    doi = "10.18653/v1/2020.acl-main.491",
    pages = "5540--5552",
    abstract = "Algorithmic approaches to interpreting machine learning models have proliferated in recent years. We carry out human subject tests that are the first of their kind to isolate the effect of algorithmic explanations on a key aspect of model interpretability, simulatability, while avoiding important confounding experimental factors. A model is simulatable when a person can predict its behavior on new inputs. Through two kinds of simulation tests involving text and tabular data, we evaluate five explanations methods: (1) LIME, (2) Anchor, (3) Decision Boundary, (4) a Prototype model, and (5) a Composite approach that combines explanations from each method. Clear evidence of method effectiveness is found in very few cases: LIME improves simulatability in tabular classification, and our Prototype method is effective in counterfactual simulation tests. We also collect subjective ratings of explanations, but we do not find that ratings are predictive of how helpful explanations are. Our results provide the first reliable and comprehensive estimates of how explanations influence simulatability across a variety of explanation methods and data domains. We show that (1) we need to be careful about the metrics we use to evaluate explanation methods, and (2) there is significant room for improvement in current methods.",
}
@inproceedings{han-etal-2020-explaining,
    title = "Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions",
    author = "Han, Xiaochuang  and
      Wallace, Byron C.  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.492",
    doi = "10.18653/v1/2020.acl-main.492",
    pages = "5553--5563",
    abstract = "Modern deep learning models for NLP are notoriously opaque. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. While this might be useful for tasks where decisions are explicitly influenced by individual tokens in the input, we suspect that such highlighting is not suitable for tasks where model decisions should be driven by more complex reasoning. In this work, we investigate the use of influence functions for NLP, providing an alternative approach to interpreting neural text classifiers. Influence functions explain the decisions of a model by identifying influential training examples. Despite the promise of this approach, influence functions have not yet been extensively evaluated in the context of NLP, a gap addressed by this work. We conduct a comparison between influence functions and common word-saliency methods on representative tasks. As suspected, we find that influence functions are particularly useful for natural language inference, a task in which {`}saliency maps{'} may not have clear interpretation. Furthermore, we develop a new quantitative measure based on influence functions that can reveal artifacts in training data.",
}
@inproceedings{chi-etal-2020-finding,
    title = "Finding Universal Grammatical Relations in Multilingual {BERT}",
    author = "Chi, Ethan A.  and
      Hewitt, John  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.493",
    doi = "10.18653/v1/2020.acl-main.493",
    pages = "5564--5577",
    abstract = "Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting that some aspects of its representations are shared cross-lingually. To better understand this overlap, we extend recent work on finding syntactic trees in neural networks{'} internal representations to the multilingual setting. We show that subspaces of mBERT representations recover syntactic tree distances in languages other than English, and that these subspaces are approximately shared across languages. Motivated by these results, we present an unsupervised analysis method that provides evidence mBERT learns representations of syntactic dependency labels, in the form of clusters which largely agree with the Universal Dependencies taxonomy. This evidence suggests that even without explicit supervision, multilingual masked language models learn certain linguistic universals.",
}
@inproceedings{chen-etal-2020-generating,
    title = "Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection",
    author = "Chen, Hanjie  and
      Zheng, Guangtao  and
      Ji, Yangfeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.494",
    doi = "10.18653/v1/2020.acl-main.494",
    pages = "5578--5593",
    abstract = "Generating explanations for neural networks has become crucial for their applications in real-world with respect to reliability and trustworthiness. In natural language processing, existing methods usually provide important features which are words or phrases selected from an input text as an explanation, but ignore the interactions between them. It poses challenges for humans to interpret an explanation and connect it to model prediction. In this work, we build hierarchical explanations by detecting feature interactions. Such explanations visualize how words and phrases are combined at different levels of the hierarchy, which can help users understand the decision-making of black-box models. The proposed method is evaluated with three neural text classifiers (LSTM, CNN, and BERT) on two benchmark datasets, via both automatic and human evaluations. Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models and interpretable to humans.",
}
@inproceedings{subramanian-etal-2020-obtaining,
    title = "Obtaining Faithful Interpretations from Compositional Neural Networks",
    author = "Subramanian, Sanjay  and
      Bogin, Ben  and
      Gupta, Nitish  and
      Wolfson, Tomer  and
      Singh, Sameer  and
      Berant, Jonathan  and
      Gardner, Matt",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.495",
    doi = "10.18653/v1/2020.acl-main.495",
    pages = "5594--5608",
    abstract = "Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However, prior work implicitly assumed that the structure of the network modules, describing the abstract reasoning process, provides a faithful explanation of the model{'}s reasoning; that is, that all modules perform their intended behaviour. In this work, we propose and conduct a systematic evaluation of the intermediate outputs of NMNs on NLVR2 and DROP, two datasets which require composing multiple reasoning steps. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour. To remedy that, we train the model with auxiliary supervision and propose particular choices for module architecture that yield much better faithfulness, at a minimal cost to accuracy.",
}
@inproceedings{swanson-etal-2020-rationalizing,
    title = "Rationalizing Text Matching: {L}earning Sparse Alignments via Optimal Transport",
    author = "Swanson, Kyle  and
      Yu, Lili  and
      Lei, Tao",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.496",
    doi = "10.18653/v1/2020.acl-main.496",
    pages = "5609--5626",
    abstract = "Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction. Our approach employs optimal transport (OT) to find a minimal cost alignment between the inputs. However, directly applying OT often produces dense and therefore uninterpretable alignments. To overcome this limitation, we introduce novel constrained variants of the OT problem that result in highly sparse alignments with controllable sparsity. Our model is end-to-end differentiable using the Sinkhorn algorithm for OT and can be trained without any alignment annotations. We evaluate our model on the StackExchange, MultiNews, e-SNLI, and MultiRC datasets. Our model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models.",
}
@inproceedings{dua-etal-2020-benefits,
    title = "Benefits of Intermediate Annotations in Reading Comprehension",
    author = "Dua, Dheeru  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.497",
    doi = "10.18653/v1/2020.acl-main.497",
    pages = "5627--5634",
    abstract = "Complex compositional reading comprehension datasets require performing latent sequential decisions that are learned via supervision from the final answer. A large combinatorial space of possible decision paths that result in the same answer, compounded by the lack of intermediate supervision to help choose the right path, makes the learning particularly hard for this task. In this work, we study the benefits of collecting intermediate reasoning supervision along with the answer during data collection. We find that these intermediate annotations can provide two-fold benefits. First, we observe that for any collection budget, spending a fraction of it on intermediate annotations results in improved model performance, for two complex compositional datasets: DROP and Quoref. Second, these annotations encourage the model to learn the correct latent reasoning steps, helping combat some of the biases introduced during the data collection process.",
}
@inproceedings{yu-etal-2020-crossing,
    title = "Crossing Variational Autoencoders for Answer Retrieval",
    author = "Yu, Wenhao  and
      Wu, Lingfei  and
      Zeng, Qingkai  and
      Tao, Shu  and
      Deng, Yu  and
      Jiang, Meng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.498",
    doi = "10.18653/v1/2020.acl-main.498",
    pages = "5635--5641",
    abstract = "Answer retrieval is to find the most aligned answer from a large set of candidates given a question. Learning vector representations of questions/answers is the key factor. Question-answer alignment and question/answer semantics are two important signals for learning the representations. Existing methods learned semantic representations with dual encoders or dual variational auto-encoders. The semantic information was learned from language models or question-to-question (answer-to-answer) generative processes. However, the alignment and semantics were too separate to capture the aligned semantics between question and answer. In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions. Experiments show that our method outperforms the state-of-the-art answer retrieval method on SQuAD.",
}
@inproceedings{asai-hajishirzi-2020-logic,
    title = "Logic-Guided Data Augmentation and Regularization for Consistent Question Answering",
    author = "Asai, Akari  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.499",
    doi = "10.18653/v1/2020.acl-main.499",
    pages = "5642--5650",
    abstract = "Many natural language questions require qualitative, quantitative or logical comparisons between two entities or events. This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models. Our method leverages logical and linguistic knowledge to augment labeled training data and then uses a consistency-based regularizer to train the model. Improving the global consistency of predictions, our approach achieves large improvements over previous methods in a variety of question answering (QA) tasks, including multiple-choice qualitative reasoning, cause-effect reasoning, and extractive machine reading comprehension. In particular, our method significantly improves the performance of RoBERTa-based models by 1-5{\%} across datasets. We advance state of the art by around 5-8{\%} on WIQA and QuaRel and reduce consistency violations by 58{\%} on HotpotQA. We further demonstrate that our approach can learn effectively from limited data.",
}
@inproceedings{sultan-etal-2020-importance,
    title = "On the Importance of Diversity in Question Generation for {QA}",
    author = "Sultan, Md Arafat  and
      Chandel, Shubham  and
      Fernandez Astudillo, Ram{\'o}n  and
      Castelli, Vittorio",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.500",
    doi = "10.18653/v1/2020.acl-main.500",
    pages = "5651--5656",
    abstract = "Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.",
}
@inproceedings{cheng-etal-2020-probabilistic,
    title = "Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering",
    author = "Cheng, Hao  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.501",
    doi = "10.18653/v1/2020.acl-main.501",
    pages = "5657--5667",
    abstract = "We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings. We compare previously used probability space and distant supervision assumptions (assumptions on the correspondence between the weak answer string labels and possible answer mention spans). We show that these assumptions interact, and that different configurations provide complementary benefits. We demonstrate that a multi-objective model can efficiently combine the advantages of multiple assumptions and outperform the best individual formulation. Our approach outperforms previous state-of-the-art models by 4.3 points in F1 on TriviaQA-Wiki and 1.7 points in Rouge-L on NarrativeQA summaries.",
}
@inproceedings{kong-etal-2020-scde,
    title = "{SCDE}: Sentence Cloze Dataset with High Quality Distractors From Examinations",
    author = "Kong, Xiang  and
      Gangal, Varun  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.502",
    doi = "10.18653/v1/2020.acl-main.502",
    pages = "5668--5683",
    abstract = "We introduce SCDE, a dataset to evaluate the performance of computational models through sentence prediction. SCDE is a human created sentence cloze dataset, collected from public school English examinations. Our task requires a model to fill up multiple blanks in a passage from a shared candidate set with distractors designed by English teachers. Experimental results demonstrate that this task requires the use of non-local, discourse-level context beyond the immediate sentence neighborhood. The blanks require joint solving and significantly impair each other{'}s context. Furthermore, through ablations, we show that the distractors are of high quality and make the task more challenging. Our experiments show that there is a significant performance gap between advanced models (72{\%}) and humans (87{\%}), encouraging future models to bridge this gap.",
}
@inproceedings{kamath-etal-2020-selective,
    title = "Selective Question Answering under Domain Shift",
    author = "Kamath, Amita  and
      Jia, Robin  and
      Liang, Percy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.503",
    doi = "10.18653/v1/2020.acl-main.503",
    pages = "5684--5696",
    abstract = "To avoid giving wrong answers, question answering (QA) models need to know when to abstain from answering. Moreover, users often ask questions that diverge from the model{'}s training data, making errors more likely and thus abstention more critical. In this work, we propose the setting of selective question answering under domain shift, in which a QA model is tested on a mixture of in-domain and out-of-domain data, and must answer (i.e., not abstain on) as many questions as possible while maintaining high accuracy. Abstention policies based solely on the model{'}s softmax probabilities fare poorly, since models are overconfident on out-of-domain inputs. Instead, we train a calibrator to identify inputs on which the QA model errs, and abstain when it predicts an error is likely. Crucially, the calibrator benefits from observing the model{'}s behavior on out-of-domain data, even if from a different domain than the test data. We combine this method with a SQuAD-trained QA model and evaluate on mixtures of SQuAD and five other QA datasets. Our method answers 56{\%} of questions while maintaining 80{\%} accuracy; in contrast, directly using the model{'}s probabilities only answers 48{\%} at 80{\%} accuracy.",
}
@inproceedings{soldaini-moschitti-2020-cascade,
    title = "The Cascade Transformer: an Application for Efficient Answer Sentence Selection",
    author = "Soldaini, Luca  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.504",
    doi = "10.18653/v1/2020.acl-main.504",
    pages = "5697--5708",
    abstract = "Large transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates. While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference. In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. Each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time. Partial encodings from the transformer model are shared among rerankers, providing further speed-up. When compared to a state-of-the-art transformer model, our approach reduces computation by 37{\%} with almost no impact on accuracy, as measured on two English Question Answering datasets.",
}
@inproceedings{li-choi-2020-transformers,
    title = "Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering",
    author = "Li, Changmao  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.505",
    doi = "10.18653/v1/2020.acl-main.505",
    pages = "5709--5714",
    abstract = "We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token- and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for better understanding in dialogue contexts. Then, multi-task learning between the utterance prediction and the token span prediction is applied to fine-tune for span-based question answering (QA). Our approach is evaluated on the FriendsQA dataset and shows improvements of 3.8{\%} and 1.4{\%} over the two state-of-the-art transformer models, BERT and RoBERTa, respectively.",
}
@inproceedings{sadeqi-azer-etal-2020-claims,
    title = "Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses",
    author = "Sadeqi Azer, Erfan  and
      Khashabi, Daniel  and
      Sabharwal, Ashish  and
      Roth, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.506",
    doi = "10.18653/v1/2020.acl-main.506",
    pages = "5715--5725",
    abstract = "Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues. While alternative proposals have been well-debated and adopted in other fields, they remain rarely discussed or used within the NLP community. We address this gap by contrasting various hypothesis assessment techniques, especially those not commonly used in the field (such as evaluations based on Bayesian inference). Since these statistical techniques differ in the hypotheses they can support, we argue that practitioners should first decide their target hypothesis before choosing an assessment method. This is crucial because common fallacies, misconceptions, and misinterpretation surrounding hypothesis assessment methods often stem from a discrepancy between what one would like to claim versus what the method used actually assesses. Our survey reveals that these issues are omnipresent in the NLP research community. As a step forward, we provide best practices and guidelines tailored to NLP research, as well as an easy-to-use package for Bayesian assessment of hypotheses, complementing existing tools.",
}
@inproceedings{berzak-etal-2020-starc,
    title = "{STARC}: Structured Annotations for Reading Comprehension",
    author = "Berzak, Yevgeni  and
      Malmaud, Jonathan  and
      Levy, Roger",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.507",
    doi = "10.18653/v1/2020.acl-main.507",
    pages = "5726--5735",
    abstract = "We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions. Our framework introduces a principled structure for the answer choices and ties them to textual span annotations. The framework is implemented in OneStopQA, a new high-quality dataset for evaluation and analysis of reading comprehension in English. We use this dataset to demonstrate that STARC can be leveraged for a key new application for the development of SAT-like reading comprehension materials: automatic annotation quality probing via span ablation experiments. We further show that it enables in-depth analyses and comparisons between machine and human reading comprehension behavior, including error distributions and guessing ability. Our experiments also reveal that the standard multiple choice dataset in NLP, RACE, is limited in its ability to measure reading comprehension. 47{\%} of its questions can be guessed by machines without accessing the passage, and 18{\%} are unanimously judged by humans as not having a unique correct answer. OneStopQA provides an alternative test set for reading comprehension which alleviates these shortcomings and has a substantially higher human ceiling performance.",
}
@inproceedings{zhang-etal-2020-winowhy,
    title = "{W}ino{W}hy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering {W}inograd Schema Challenge",
    author = "Zhang, Hongming  and
      Zhao, Xinran  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.508",
    doi = "10.18653/v1/2020.acl-main.508",
    pages = "5736--5745",
    abstract = "In this paper, we present the first comprehensive categorization of essential commonsense knowledge for answering the Winograd Schema Challenge (WSC). For each of the questions, we invite annotators to first provide reasons for making correct decisions and then categorize them into six major knowledge categories. By doing so, we better understand the limitation of existing methods (i.e., what kind of knowledge cannot be effectively represented or inferred with existing methods) and shed some light on the commonsense knowledge that we need to acquire in the future for better commonsense reasoning. Moreover, to investigate whether current WSC models can understand the commonsense or they simply solve the WSC questions based on the statistical bias of the dataset, we leverage the collected reasons to develop a new task called WinoWhy, which requires models to distinguish plausible reasons from very similar but wrong reasons for all WSC questions. Experimental results prove that even though pre-trained language representation models have achieved promising progress on the original WSC dataset, they are still struggling at WinoWhy. Further experiments show that even though supervised models can achieve better performance, the performance of these models can be sensitive to the dataset distribution. WinoWhy and all codes are available at: https://github.com/HKUST-KnowComp/WinoWhy.",
}
@inproceedings{sirrianni-etal-2020-agreement,
    title = "Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity",
    author = "Sirrianni, Joseph  and
      Liu, Xiaoqing  and
      Adams, Douglas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.509",
    doi = "10.18653/v1/2020.acl-main.509",
    pages = "5746--5758",
    abstract = "In online debates, users express different levels of agreement/disagreement with one another{'}s arguments and ideas. Often levels of agreement/disagreement are implicit in the text, and must be predicted to analyze collective opinions. Existing stance detection methods predict the polarity of a post{'}s stance toward a topic or post, but don{'}t consider the stance{'}s degree of intensity. We introduce a new research problem, stance polarity and intensity prediction in response relationships between posts. This problem is challenging because differences in stance intensity are often subtle and require nuanced language understanding. Cyber argumentation research has shown that incorporating both stance polarity and intensity data in online debates leads to better discussion analysis. We explore five different learning models: Ridge-M regression, Ridge-S regression, SVR-RF-R, pkudblab-PIP, and T-PAN-PIP for predicting stance polarity and intensity in argumentation. These models are evaluated using a new dataset for stance polarity and intensity prediction collected using a cyber argumentation platform. The SVR-RF-R model performs best for prediction of stance polarity with an accuracy of 70.43{\%} and intensity with RMSE of 0.596. This work is the first to train models for predicting a post{'}s stance polarity and intensity in one combined value in cyber argumentation with reasonably good accuracy.",
}
@inproceedings{fei-li-2020-cross,
    title = "Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning",
    author = "Fei, Hongliang  and
      Li, Ping",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.510",
    doi = "10.18653/v1/2020.acl-main.510",
    pages = "5759--5771",
    abstract = "Recent neural network models have achieved impressive performance on sentiment classification in English as well as other languages. Their success heavily depends on the availability of a large amount of labeled data or parallel corpus. In this paper, we investigate an extreme scenario of cross-lingual sentiment classification, in which the low-resource language does not have any labels or parallel corpus. We propose an unsupervised cross-lingual sentiment classification model named multi-view encoder-classifier (MVEC) that leverages an unsupervised machine translation (UMT) system and a language discriminator. Unlike previous language model (LM) based fine-tuning approaches that adjust parameters solely based on the classification error on training data, we employ the encoder-decoder framework of a UMT as a regularization component on the shared network parameters. In particular, the cross-lingual encoder of our model learns a shared representation, which is effective for both reconstructing input sentences of two languages and generating more representative views from the input for classification. Extensive experiments on five language pairs verify that our model significantly outperforms other models for 8/11 sentiment classification tasks.",
}
@inproceedings{gienapp-etal-2020-efficient,
    title = "Efficient Pairwise Annotation of Argument Quality",
    author = "Gienapp, Lukas  and
      Stein, Benno  and
      Hagen, Matthias  and
      Potthast, Martin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.511",
    doi = "10.18653/v1/2020.acl-main.511",
    pages = "5772--5781",
    abstract = "We present an efficient annotation framework for argument quality, a feature difficult to be measured reliably as per previous work. A stochastic transitivity model is combined with an effective sampling strategy to infer high-quality labels with low effort from crowdsourced pairwise judgments. The model{'}s capabilities are showcased by compiling Webis-ArgQuality-20, an argument quality corpus that comprises scores for rhetorical, logical, dialectical, and overall quality inferred from a total of 41,859 pairwise judgments among 1,271 arguments. With up to 93{\%} cost savings, our approach significantly outperforms existing annotation procedures. Furthermore, novel insight into argument quality is provided through statistical analysis, and a new aggregation method to infer overall quality from individual quality dimensions is proposed.",
}
@inproceedings{ma-etal-2020-entity,
    title = "Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification",
    author = "Ma, Nianzu  and
      Mazumder, Sahisnu  and
      Wang, Hao  and
      Liu, Bing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.512",
    doi = "10.18653/v1/2020.acl-main.512",
    pages = "5782--5788",
    abstract = "This paper studies the task of comparative preference classification (CPC). Given two entities in a sentence, our goal is to classify whether the first (or the second) entity is preferred over the other or no comparison is expressed at all between the two entities. Existing works either do not learn entity-aware representations well and fail to deal with sentences involving multiple entity pairs or use sequential modeling approaches that are unable to capture long-range dependencies between the entities. Some also use traditional machine learning approaches that do not generalize well. This paper proposes a novel Entity-aware Dependency-based Deep Graph Attention Network (ED-GAT) that employs a multi-hop graph attention over a dependency graph sentence representation to leverage both the semantic information from word embeddings and the syntactic information from the dependency graph to solve the problem. Empirical evaluation shows that the proposed model achieves the state-of-the-art performance in comparative preference classification.",
}
@inproceedings{suhara-etal-2020-opiniondigest,
    title = "{O}pinion{D}igest: A Simple Framework for Opinion Summarization",
    author = "Suhara, Yoshihiko  and
      Wang, Xiaolan  and
      Angelidis, Stefanos  and
      Tan, Wang-Chiew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.513",
    doi = "10.18653/v1/2020.acl-main.513",
    pages = "5789--5798",
    abstract = "We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time, we merge extractions from multiple reviews and select the most popular ones. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries, tailored to specific user needs, by filtering the selected opinions according to their aspect and/or sentiment. Automatic evaluation on Yelp data shows that our framework outperforms competitive baselines. Human studies on two corpora verify that OpinionDigest produces informative summaries and shows promising customization capabilities.",
}
@inproceedings{babanejad-etal-2020-comprehensive,
    title = "A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks",
    author = "Babanejad, Nastaran  and
      Agrawal, Ameeta  and
      An, Aijun  and
      Papagelis, Manos",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.514",
    doi = "10.18653/v1/2020.acl-main.514",
    pages = "5799--5810",
    abstract = "Affective tasks such as sentiment analysis, emotion classification, and sarcasm detection have been popular in recent years due to an abundance of user-generated data, accurate computational linguistic models, and a broad range of relevant applications in various domains. At the same time, many studies have highlighted the importance of text preprocessing, as an integral step to any natural language processing prediction model and downstream task. While preprocessing in affective systems is well-studied, preprocessing in word vector-based models applied to affective systems, is not. To address this limitation, we conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models. Our analysis is the first of its kind and provides useful insights of the importance of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase.",
}
@inproceedings{wu-etal-2020-diverse,
    title = "Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness",
    author = "Wu, Sixing  and
      Li, Ying  and
      Zhang, Dawei  and
      Zhou, Yang  and
      Wu, Zhonghai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.515",
    doi = "10.18653/v1/2020.acl-main.515",
    pages = "5811--5820",
    abstract = "Generative dialogue systems tend to produce generic responses, which often leads to boring conversations. For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs. While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context. Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations. To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI. We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI. We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation. Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.",
}
@inproceedings{song-etal-2020-generate,
    title = "Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation",
    author = "Song, Haoyu  and
      Wang, Yan  and
      Zhang, Wei-Nan  and
      Liu, Xiaojiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.516",
    doi = "10.18653/v1/2020.acl-main.516",
    pages = "5821--5831",
    abstract = "Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines. The persona-based dialogue generation task is thus introduced to tackle the personality-inconsistent problem by incorporating explicit persona text into dialogue generation models. Despite the success of existing persona-based models on generating human-like responses, their one-stage decoding framework can hardly avoid the generation of inconsistent persona words. In this work, we introduce a three-stage framework that employs a generate-delete-rewrite mechanism to delete inconsistent words from a generated response prototype and further rewrite it to a personality-consistent one. We carry out evaluations by both human and automatic metrics. Experiments on the Persona-Chat dataset show that our approach achieves good performance.",
}
@inproceedings{song-etal-2020-learning,
    title = "Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks",
    author = "Song, Yiping  and
      Liu, Zequn  and
      Bi, Wei  and
      Yan, Rui  and
      Zhang, Ming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.517",
    doi = "10.18653/v1/2020.acl-main.517",
    pages = "5832--5841",
    abstract = "Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning framework which pre-trains the parameters on all non-target tasks then fine-tunes on the target task. However, fine-tuning distinguishes tasks from the parameter perspective but ignores the model-structure perspective, resulting in similar dialogue models for different tasks. In this paper, we propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting. In our approach, each dialogue model consists of a shared module, a gating module, and a private module. The first two modules are shared among all the tasks, while the third one will differentiate into different network structures to better capture the characteristics of the corresponding task. The extensive experiments on two datasets show that our method outperforms all the baselines in terms of task consistency, response quality, and diversity.",
}
@inproceedings{le-hoi-2020-video,
    title = "Video-Grounded Dialogues with Pretrained Generation Language Models",
    author = "Le, Hung  and
      Hoi, Steven C.H.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.518",
    doi = "10.18653/v1/2020.acl-main.518",
    pages = "5842--5848",
    abstract = "Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natural responses. In this paper, we leverage the power of pre-trained language models for improving video-grounded dialogue, which is very challenging and involves complex features of different dynamics: (1) Video features which can extend across both spatial and temporal dimensions; and (2) Dialogue features which involve semantic dependencies over multiple dialogue turns. We propose a framework by extending GPT-2 models to tackle these challenges by formulating video-grounded dialogue tasks as a sequence-to-sequence task, combining both visual and textual representation into a structured sequence, and fine-tuning a large pre-trained GPT-2 network. Our framework allows fine-tuning language models to capture dependencies across multiple modalities over different levels of information: spatio-temporal level in video and token-sentence level in dialogue context. We achieve promising improvement on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark from DSTC7, which supports a potential direction in this line of research.",
}
@inproceedings{li-etal-2020-unified,
    title = "A Unified {MRC} Framework for Named Entity Recognition",
    author = "Li, Xiaoya  and
      Feng, Jingrong  and
      Meng, Yuxian  and
      Han, Qinghong  and
      Wu, Fei  and
      Li, Jiwei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.519",
    doi = "10.18653/v1/2020.acl-main.519",
    pages = "5849--5859",
    abstract = "The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not.Models are usually separately developed for the two tasks, since sequence labeling models, the most widely used backbone for flat NER, are only able to assign a single label to a particular token, which is unsuitable for nested NER where a token may be assigned several labels. In this paper, we propose a unified framework that is capable of handling both flat and nested NER tasks. Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task. For example, extracting entities with the per label is formalized as extracting answer spans to the question {``}\textit{which person is mentioned in the text}''.This formulation naturally tackles the entity overlapping issue in nested NER: the extraction of two overlapping entities with different categories requires answering two independent questions. Additionally, since the query encodes informative prior knowledge, this strategy facilitates the process of entity extraction, leading to better performances for not only nested NER, but flat NER. We conduct experiments on both nested and flat NER datasets.Experiment results demonstrate the effectiveness of the proposed formulation. We are able to achieve a vast amount of performance boost over current SOTA models on nested NER datasets, i.e., +1.28, +2.55, +5.44, +6.37,respectively on ACE04, ACE05, GENIA and KBP17, along with SOTA results on flat NER datasets, i.e., +0.24, +1.95, +0.21, +1.49 respectively on English CoNLL 2003, English OntoNotes 5.0, Chinese MSRA and Chinese OntoNotes 4.0.",
}
@inproceedings{dai-etal-2020-effective,
    title = "An Effective Transition-based Model for Discontinuous {NER}",
    author = "Dai, Xiang  and
      Karimi, Sarvnaz  and
      Hachey, Ben  and
      Paris, Cecile",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.520",
    doi = "10.18653/v1/2020.acl-main.520",
    pages = "5860--5870",
    abstract = "Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions. We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER. Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.",
}
@inproceedings{kolluru-etal-2020-imojie,
    title = "{IM}o{JIE}: Iterative Memory-Based Joint Open Information Extraction",
    author = "Kolluru, Keshav  and
      Aggarwal, Samarth  and
      Rathore, Vipul  and
      {Mausam}  and
      Chakrabarti, Soumen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.521",
    doi = "10.18653/v1/2020.acl-main.521",
    pages = "5871--5886",
    abstract = "While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task. Our work builds upon CopyAttention, a sequence generation OpenIE model (Cui et. al. 18). Our analysis reveals that CopyAttention produces a constant number of extractions per sentence, and its extracted tuples often express redundant information. We present IMoJIE, an extension to CopyAttention, which produces the next extraction conditioned on all previously extracted tuples. This approach overcomes both shortcomings of CopyAttention, resulting in a variable number of diverse extractions per sentence. We train IMoJIE on training data bootstrapped from extractions of several non-neural systems, which have been automatically filtered to reduce redundancy and noise. IMoJIE outperforms CopyAttention by about 18 F1 pts, and a BERT-based strong baseline by 2 F1 pts, establishing a new state of the art for the task.",
}
@inproceedings{tong-etal-2020-improving,
    title = "Improving Event Detection via Open-domain Trigger Knowledge",
    author = "Tong, Meihan  and
      Xu, Bin  and
      Wang, Shuai  and
      Cao, Yixin  and
      Hou, Lei  and
      Li, Juanzi  and
      Xie, Jun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.522",
    doi = "10.18653/v1/2020.acl-main.522",
    pages = "5887--5897",
    abstract = "Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.",
}
@inproceedings{kruengkrai-etal-2020-improving,
    title = "Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling",
    author = "Kruengkrai, Canasai  and
      Nguyen, Thien Hai  and
      Aljunied, Sharifah Mahani  and
      Bing, Lidong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.523",
    doi = "10.18653/v1/2020.acl-main.523",
    pages = "5898--5905",
    abstract = "Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate. Current models for jointly learning sentence and token labeling are limited to binary classification. We present a joint model that supports multi-class classification and introduce a simple variant of self-attention that allows the model to learn scaling factors. Our model produces 3.78{\%}, 4.20{\%}, 2.08{\%} improvements in F1 over the BiLSTM-CRF baseline on e-commerce product titles in three different low-resource languages: Vietnamese, Thai, and Indonesian, respectively.",
}
@inproceedings{jia-zhang-2020-multi,
    title = "Multi-Cell Compositional {LSTM} for {NER} Domain Adaptation",
    author = "Jia, Chen  and
      Zhang, Yue",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.524",
    doi = "10.18653/v1/2020.acl-main.524",
    pages = "5906--5917",
    abstract = "Cross-domain NER is a challenging yet practical problem. Entity mentions can be highly different across domains. However, the correlations between entity types can be relatively more stable across domains. We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state. With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level. Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer. Empirically, experiments on four few-shot and zero-shot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.",
}
@inproceedings{wang-etal-2020-pyramid,
    title = "{P}yramid: A Layered Model for Nested Named Entity Recognition",
    author = "Wang, Jue  and
      Shou, Lidan  and
      Chen, Ke  and
      Chen, Gang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.525",
    doi = "10.18653/v1/2020.acl-main.525",
    pages = "5918--5928",
    abstract = "This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER layers, from bottom to top, stacked in a pyramid shape. Each time an embedding passes through a layer of the pyramid, its length is reduced by one. Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention. We also design an inverse pyramid to allow bidirectional interaction between layers. The proposed method achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE-2005, GENIA, and NNE, which are 80.27, 79.42, 77.78, and 93.70 with conventional embeddings, and 87.74, 86.34, 79.31, and 94.68 with pre-trained contextualized embeddings. In addition, our model can be used for the more general task of Overlapping Named Entity Recognition. A preliminary experiment confirms the effectiveness of our method in overlapping NER.",
}
@inproceedings{xie-etal-2020-reinceptione,
    title = "{R}e{I}nception{E}: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding",
    author = "Xie, Zhiwen  and
      Zhou, Guangyou  and
      Liu, Jin  and
      Huang, Jimmy Xiangji",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.526",
    doi = "10.18653/v1/2020.acl-main.526",
    pages = "5929--5939",
    abstract = "The goal of Knowledge graph embedding (KGE) is to learn how to represent the low dimensional vectors for entities and relations based on the observed triples. The conventional shallow models are limited to their expressiveness. ConvE (Dettmers et al., 2018) takes advantage of CNN and improves the expressive power with parameter efficient operators by increasing the interactions between head and relation embeddings. However, there is no structural information in the embedding space of ConvE, and the performance is still limited by the number of interactions. The recent KBGAT (Nathani et al., 2019) provides another way to learn embeddings by adaptively utilizing structural information. In this paper, we take the benefits of ConvE and KBGAT together and propose a Relation-aware Inception network with joint local-global structural information for knowledge graph Embedding (ReInceptionE). Specifically, we first explore the Inception network to learn query embedding, which aims to further increase the interactions between head and relation embeddings. Then, we propose to use a relation-aware attention mechanism to enrich the query embedding with the local neighborhood and global entity information. Experimental results on both WN18RR and FB15k-237 datasets demonstrate that ReInceptionE achieves competitive performance compared with state-of-the-art methods.",
}
@inproceedings{chen-etal-2020-relabel,
    title = "Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents",
    author = "Chen, Daoyuan  and
      Li, Yaliang  and
      Lei, Kai  and
      Shen, Ying",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.527",
    doi = "10.18653/v1/2020.acl-main.527",
    pages = "5940--5950",
    abstract = "Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts. In this paper, we consider the problem of shifted label distribution, which is caused by the inconsistency between the noisy-labeled training set subject to external knowledge graph and the human-annotated test set, and exacerbated by the pipelined entity-then-relation extraction manner with noise propagation. We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents. To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective; To leverage the correlations between these two extraction tasks, a confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels. Further, the confidences are used to adjust the training losses of extractors. Experimental results on two real-world datasets verify the benefits of re-labeling noisy instance, and show that the proposed model significantly outperforms the state-of-the-art entity and relation extraction methods.",
}
@inproceedings{ma-etal-2020-simplify,
    title = "Simplify the Usage of Lexicon in {C}hinese {NER}",
    author = "Ma, Ruotian  and
      Peng, Minlong  and
      Zhang, Qi  and
      Wei, Zhongyu  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.528",
    doi = "10.18653/v1/2020.acl-main.528",
    pages = "5951--5960",
    abstract = "Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM has achieved new benchmark results on several public Chinese NER datasets. However, Lattice-LSTM has a complex model architecture. This limits its application in many industrial areas where real-time NER responses are needed. In this work, we propose a simple but effective method for incorporating the word lexicon into the character representations. This method avoids designing a complicated sequence modeling architecture, and for any neural NER model, it requires only subtle adjustment of the character representation layer to introduce the lexicon information. Experimental studies on four benchmark Chinese NER datasets show that our method achieves an inference speed up to 6.15 times faster than those of state-of-the-art methods, along with a better performance. The experimental results also show that the proposed method can be easily incorporated with pre-trained models like BERT.",
}
@inproceedings{cheng-etal-2020-advaug,
    title = "{A}dv{A}ug: Robust Adversarial Augmentation for Neural Machine Translation",
    author = "Cheng, Yong  and
      Jiang, Lu  and
      Macherey, Wolfgang  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.529",
    doi = "10.18653/v1/2020.acl-main.529",
    pages = "5961--5970",
    abstract = "In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT). The main idea is to minimize the vicinal risk over virtual sentences sampled from two vicinity distributions, in which the crucial one is a novel vicinity distribution for adversarial sentences that describes a smooth interpolated embedding space centered around observed training sentence pairs. We then discuss our approach, AdvAug, to train NMT models using the embeddings of virtual sentences in sequence-to-sequence learning. Experiments on Chinese-English, English-French, and English-German translation benchmarks show that AdvAug achieves significant improvements over theTransformer (up to 4.9 BLEU points), and substantially outperforms other data augmentation techniques (e.g.back-translation) without using extra corpora.",
}
@inproceedings{wong-etal-2020-contextual,
    title = "Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns",
    author = "Wong, KayYen  and
      Maruf, Sameen  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.530",
    doi = "10.18653/v1/2020.acl-main.530",
    pages = "5971--5978",
    abstract = "The advent of context-aware NMT has resulted in promising improvements in the overall translation quality and specifically in the translation of discourse phenomena such as pronouns. Previous works have mainly focused on the use of past sentences as context with a focus on anaphora translation. In this work, we investigate the effect of future sentences as context by comparing the performance of a contextual NMT model trained with the future context to the one trained with the past context. Our experiments and evaluation, using generic and pronoun-focused automatic metrics, show that the use of future context not only achieves significant improvements over the context-agnostic Transformer, but also demonstrates comparable and in some cases improved performance over its counterpart trained on past context. We also perform an evaluation on a targeted cataphora test suite and report significant gains over the context-agnostic Transformer in terms of BLEU.",
}
@inproceedings{yang-etal-2020-improving,
    title = "Improving Neural Machine Translation with Soft Template Prediction",
    author = "Yang, Jian  and
      Ma, Shuming  and
      Zhang, Dongdong  and
      Li, Zhoujun  and
      Zhou, Ming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.531",
    doi = "10.18653/v1/2020.acl-main.531",
    pages = "5979--5989",
    abstract = "Although neural machine translation (NMT) has achieved significant progress in recent years, most previous NMT models only depend on the source text to generate translation. Inspired by the success of template-based and syntax-based approaches in other fields, we propose to use extracted templates from tree structures as soft target templates to guide the translation procedure. In order to learn the syntactic structure of the target sentences, we adopt constituency-based parse tree to generate candidate templates. We incorporate the template information into the encoder-decoder framework to jointly utilize the templates and source text. Experiments show that our model significantly outperforms the baseline models on four benchmarks and demonstrates the effectiveness of soft target templates.",
}
@inproceedings{marie-etal-2020-tagged,
    title = "Tagged Back-translation Revisited: Why Does It Really Work?",
    author = "Marie, Benjamin  and
      Rubino, Raphael  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.532",
    doi = "10.18653/v1/2020.acl-main.532",
    pages = "5990--5997",
    abstract = "In this paper, we show that neural machine translation (NMT) systems trained on large back-translated data overfit some of the characteristics of machine-translated texts. Such NMT systems better translate human-produced translations, i.e., translationese, but may largely worsen the translation quality of original texts. Our analysis reveals that adding a simple tag to back-translations prevents this quality degradation and improves on average the overall translation quality by helping the NMT system to distinguish back-translated data from original parallel data during training. We also show that, in contrast to high-resource configurations, NMT systems trained in low-resource settings are much less vulnerable to overfit back-translations. We conclude that the back-translations in the training data should always be tagged especially when the origin of the text to be translated is unknown.",
}
@inproceedings{chuang-etal-2020-worse,
    title = "Worse {WER}, but Better {BLEU}? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation",
    author = "Chuang, Shun-Po  and
      Sung, Tzu-Wei  and
      Liu, Alexander H.  and
      Lee, Hung-yi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.533",
    doi = "10.18653/v1/2020.acl-main.533",
    pages = "5998--6003",
    abstract = "Speech translation (ST) aims to learn transformations from speech in the source language to the text in the target language. Previous works show that multitask learning improves the ST performance, in which the recognition decoder generates the text of the source language, and the translation decoder obtains the final translations based on the output of the recognition decoder. Because whether the output of the recognition decoder has the correct semantics is more critical than its accuracy, we propose to improve the multitask ST model by utilizing word embedding as the intermediate.",
}
@inproceedings{tan-etal-2020-neural,
    title = "Neural-{DINF}: A Neural Network based Framework for Measuring Document Influence",
    author = "Tan, Jie  and
      Yang, Changlin  and
      Li, Ying  and
      Tang, Siliang  and
      Huang, Chen  and
      Zhuang, Yueting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.534",
    doi = "10.18653/v1/2020.acl-main.534",
    pages = "6004--6009",
    abstract = "Measuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the word frequency change. In this paper, we use both frequency changes and word semantic shifts to measure document influence by developing a neural network framework. Our model has three steps. Firstly, we train the word embeddings for different time periods. Subsequently, we propose an unsupervised method to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our model outperforms DIM.",
}
@inproceedings{kazemnejad-etal-2020-paraphrase,
    title = "Paraphrase Generation by Learning How to Edit from Samples",
    author = "Kazemnejad, Amirhossein  and
      Salehi, Mohammadreza  and
      Soleymani Baghshah, Mahdieh",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.535",
    doi = "10.18653/v1/2020.acl-main.535",
    pages = "6010--6021",
    abstract = "Neural sequence to sequence text generation has been proved to be a viable approach to paraphrase generation. Despite promising results, paraphrases generated by these models mostly suffer from lack of quality and diversity. To address these problems, we propose a novel retrieval-based method for paraphrase generation. Our model first retrieves a paraphrase pair similar to the input sentence from a pre-defined index. With its novel editor module, the model then paraphrases the input sequence by editing it using the extracted relations between the retrieved pair of sentences. In order to have fine-grained control over the editing process, our model uses the newly introduced concept of Micro Edit Vectors. It both extracts and exploits these vectors using the attention mechanism in the Transformer architecture. Experimental results show the superiority of our paraphrase generation method in terms of both automatic metrics, and human evaluation of relevance, grammaticality, and diversity of generated paraphrases.",
}
@inproceedings{conneau-etal-2020-emerging,
    title = "Emerging Cross-lingual Structure in Pretrained Language Models",
    author = "Conneau, Alexis  and
      Wu, Shijie  and
      Li, Haoran  and
      Zettlemoyer, Luke  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.536",
    doi = "10.18653/v1/2020.acl-main.536",
    pages = "6022--6034",
    abstract = "We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from monolingual BERT models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked language modeling, these symmetries are automatically discovered and aligned during the joint training process.",
}
@inproceedings{liu-etal-2020-fastbert,
    title = "{F}ast{BERT}: a Self-distilling {BERT} with Adaptive Inference Time",
    author = "Liu, Weijie  and
      Zhou, Peng  and
      Wang, Zhiruo  and
      Zhao, Zhe  and
      Deng, Haotang  and
      Ju, Qi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.537",
    doi = "10.18653/v1/2020.acl-main.537",
    pages = "6035--6044",
    abstract = "Pre-trained language models like BERT have proven to be highly performant. However, they are often computationally expensive in many practical scenarios, for such heavy models can hardly be readily implemented with limited resources. To improve their efficiency with an assured model performance, we propose a novel speed-tunable FastBERT with adaptive inference time. The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided. Moreover, this model adopts a unique self-distillation mechanism at fine-tuning, further enabling a greater computational efficacy with minimal loss in performance. Our model achieves promising results in twelve English and Chinese datasets. It is able to speed up by a wide range from 1 to 12 times than BERT if given different speedup thresholds to make a speed-performance tradeoff.",
}
@inproceedings{xu-etal-2020-incorporating,
    title = "Incorporating External Knowledge through Pre-training for Natural Language to Code Generation",
    author = "Xu, Frank F.  and
      Jiang, Zhengbao  and
      Yin, Pengcheng  and
      Vasilescu, Bogdan  and
      Neubig, Graham",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.538",
    doi = "10.18653/v1/2020.acl-main.538",
    pages = "6045--6052",
    abstract = "Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2{\%} absolute BLEU score on the code generation testbed CoNaLa. The code and resources are available at https://github.com/neulab/external-knowledge-codegen.",
}
@inproceedings{zhong-etal-2020-logicalfactchecker,
    title = "{L}ogical{F}act{C}hecker: Leveraging Logical Operations for Fact Checking with Graph Module Network",
    author = "Zhong, Wanjun  and
      Tang, Duyu  and
      Feng, Zhangyin  and
      Duan, Nan  and
      Zhou, Ming  and
      Gong, Ming  and
      Shou, Linjun  and
      Jiang, Daxin  and
      Wang, Jiahai  and
      Yin, Jian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.539",
    doi = "10.18653/v1/2020.acl-main.539",
    pages = "6053--6065",
    abstract = "Verifying the correctness of a textual statement requires not only semantic reasoning about the meaning of words, but also symbolic reasoning about logical operations like count, superlative, aggregation, etc. In this work, we propose LogicalFactChecker, a neural network approach capable of leveraging logical operations for fact checking. It achieves the state-of-the-art performance on TABFACT, a large-scale, benchmark dataset built for verifying a textual statement with semi-structured tables. This is achieved by a graph module network built upon the Transformer-based architecture. With a textual statement and a table as the input, LogicalFactChecker automatically derives a program (a.k.a. logical form) of the statement in a semantic parsing manner. A heterogeneous graph is then constructed to capture not only the structures of the table and the program, but also the connections between inputs with different modalities. Such a graph reveals the related contexts of each word in the statement, the table and the program. The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture. After that, a program-driven module network is further introduced to exploit the hierarchical structure of the program, where semantic compositionality is dynamically modeled along the program structure with a set of function-specific modules. Ablation experiments suggest that both the heterogeneous graph and the module network are important to obtain strong results.",
}
@inproceedings{zang-etal-2020-word,
    title = "Word-level Textual Adversarial Attacking as Combinatorial Optimization",
    author = "Zang, Yuan  and
      Qi, Fanchao  and
      Yang, Chenghao  and
      Liu, Zhiyuan  and
      Zhang, Meng  and
      Liu, Qun  and
      Sun, Maosong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.540",
    doi = "10.18653/v1/2020.acl-main.540",
    pages = "6066--6080",
    abstract = "Adversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change to the original input. Word-level attacking, which can be regarded as a combinatorial optimization problem, is a well-studied class of textual attack methods. However, existing word-level attack models are far from perfect, largely because unsuitable search space reduction methods and inefficient optimization algorithms are employed. In this paper, we propose a novel attack model, which incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately. We conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM and BERT on three benchmark datasets. Experimental results demonstrate that our model consistently achieves much higher attack success rates and crafts more high-quality adversarial examples as compared to baseline methods. Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training. All the code and data of this paper can be obtained on https://github.com/thunlp/SememePSO-Attack.",
}
@inproceedings{ye-etal-2020-benchmarking,
    title = "Benchmarking Multimodal Regex Synthesis with Complex Structures",
    author = "Ye, Xi  and
      Chen, Qiaochu  and
      Dillig, Isil  and
      Durrett, Greg",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.541",
    doi = "10.18653/v1/2020.acl-main.541",
    pages = "6081--6094",
    abstract = "Existing datasets for regular expression (regex) generation from natural language are limited in complexity; compared to regex tasks that users post on StackOverflow, the regexes in these datasets are simple, and the language used to describe them is not diverse. We introduce StructuredRegex, a new regex synthesis dataset differing from prior ones in three aspects. First, to obtain structurally complex and realistic regexes, we generate the regexes using a probabilistic grammar with pre-defined macros observed from real-world StackOverflow posts. Second, to obtain linguistically diverse natural language descriptions, we show crowdworkers abstract depictions of the underlying regex and ask them to describe the pattern they see, rather than having them paraphrase synthetic language. Third, we augment each regex example with a collection of strings that are and are not matched by the ground truth regex, similar to how real users give examples. Our quantitative and qualitative analysis demonstrates the advantages of StructuredRegex over prior datasets. Further experimental results using various multimodal synthesis techniques highlight the challenge presented by our dataset, including non-local constraints and multi-modal inputs.",
}
@inproceedings{xu-etal-2020-curriculum,
    title = "Curriculum Learning for Natural Language Understanding",
    author = "Xu, Benfeng  and
      Zhang, Licheng  and
      Mao, Zhendong  and
      Wang, Quan  and
      Xie, Hongtao  and
      Zhang, Yongdong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.542",
    doi = "10.18653/v1/2020.acl-main.542",
    pages = "6095--6104",
    abstract = "With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks. At the fine-tune stage, target task data is usually introduced in a completely random order and treated equally. However, examples in NLU tasks can vary greatly in difficulty, and similar to human learning procedure, language models can benefit from an easy-to-difficult curriculum. Based on this idea, we propose our Curriculum Learning approach. By reviewing the trainset in a crossed way, we are able to distinguish easy examples from difficult ones, and arrange a curriculum for language models. Without any manual model architecture design or use of external data, our Curriculum Learning approach obtains significant and universal performance improvements on a wide range of NLU tasks.",
}
@inproceedings{yanaka-etal-2020-neural,
    title = "Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?",
    author = "Yanaka, Hitomi  and
      Mineshima, Koji  and
      Bekki, Daisuke  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.543",
    doi = "10.18653/v1/2020.acl-main.543",
    pages = "6105--6117",
    abstract = "Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. In this paper, we introduce a method for evaluating whether neural models can learn systematicity of monotonicity inference in natural language, namely, the regularity for performing arbitrary inferences with generalization on composition. We consider four aspects of monotonicity inferences and test whether the models can systematically interpret lexical and logical phenomena on different training/test splits. A series of experiments show that three neural models systematically draw inferences on unseen combinations of lexical and logical phenomena when the syntactic structures of the sentences are similar between the training and test sets. However, the performance of the models significantly decreases when the structures are slightly changed in the test set while retaining all vocabularies and constituents already appearing in the training set. This indicates that the generalization ability of neural models is limited to cases where the syntactic structures are nearly the same as those in the training set.",
}
@inproceedings{guo-etal-2020-evidence,
    title = "Evidence-Aware Inferential Text Generation with Vector Quantised Variational {A}uto{E}ncoder",
    author = "Guo, Daya  and
      Tang, Duyu  and
      Duan, Nan  and
      Yin, Jian  and
      Jiang, Daxin  and
      Zhou, Ming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.544",
    doi = "10.18653/v1/2020.acl-main.544",
    pages = "6118--6129",
    abstract = "Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoderdecoder manner and is equipped with Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2mind and Atomic datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.",
}
@inproceedings{jia-etal-2020-ask,
    title = "How to Ask Good Questions? Try to Leverage Paraphrases",
    author = "Jia, Xin  and
      Zhou, Wenjie  and
      Sun, Xu  and
      Wu, Yunfang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.545",
    doi = "10.18653/v1/2020.acl-main.545",
    pages = "6130--6140",
    abstract = "Given a sentence and its relevant answer, how to ask good questions is a challenging task, which has many real applications. Inspired by human{'}s paraphrasing capability to ask questions of the same meaning but with diverse expressions, we propose to incorporate paraphrase knowledge into question generation(QG) to generate human-like questions. Specifically, we present a two-hand hybrid model leveraging a self-built paraphrase resource, which is automatically conducted by a simple back-translation method. On the one hand, we conduct multi-task learning with sentence-level paraphrase generation (PG) as an auxiliary task to supplement paraphrase knowledge to the task-share encoder. On the other hand, we adopt a new loss function for diversity training to introduce more question patterns to QG. Extensive experimental results show that our proposed model obtains obvious performance gain over several strong baselines, and further human evaluation validates that our model can ask questions of high quality by leveraging paraphrase knowledge.",
}
@inproceedings{guan-etal-2020-neuinfer,
    title = "{N}eu{I}nfer: Knowledge Inference on {N}-ary Facts",
    author = "Guan, Saiping  and
      Jin, Xiaolong  and
      Guo, Jiafeng  and
      Wang, Yuanzhuo  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.546",
    doi = "10.18653/v1/2020.acl-main.546",
    pages = "6141--6151",
    abstract = "Knowledge inference on knowledge graph has attracted extensive attention, which aims to find out connotative valid facts in knowledge graph and is very helpful for improving the performance of many downstream applications. However, researchers have mainly poured attention to knowledge inference on binary facts. The studies on n-ary facts are relatively scarcer, although they are also ubiquitous in the real world. Therefore, this paper addresses knowledge inference on n-ary facts. We represent each n-ary fact as a primary triple coupled with a set of its auxiliary descriptive attribute-value pair(s). We further propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. Besides handling the common task to infer an unknown element in a whole fact, NeuInfer can cope with a new type of task, flexible knowledge inference. It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s). Experimental results demonstrate the remarkable superiority of NeuInfer.",
}
@inproceedings{chen-etal-2020-neural,
    title = "Neural Graph Matching Networks for {C}hinese Short Text Matching",
    author = "Chen, Lu  and
      Zhao, Yanbin  and
      Lyu, Boer  and
      Jin, Lesheng  and
      Chen, Zhi  and
      Zhu, Su  and
      Yu, Kai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.547",
    doi = "10.18653/v1/2020.acl-main.547",
    pages = "6152--6158",
    abstract = "Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance. To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.",
}
@inproceedings{wu-etal-2020-neural,
    title = "Neural Mixed Counting Models for Dispersed Topic Discovery",
    author = "Wu, Jiemin  and
      Rao, Yanghui  and
      Zhang, Zusheng  and
      Xie, Haoran  and
      Li, Qing  and
      Wang, Fu Lee  and
      Chen, Ziye",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.548",
    doi = "10.18653/v1/2020.acl-main.548",
    pages = "6159--6169",
    abstract = "Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics. However, the existing parameter inference method like Monte Carlo sampling is quite time-consuming. In this paper, we propose two efficient neural mixed counting models, i.e., the Negative Binomial-Neural Topic Model (NB-NTM) and the Gamma Negative Binomial-Neural Topic Model (GNB-NTM) for dispersed topic discovery. Neural variational inference algorithms are developed to infer model parameters by using the reparameterization of Gamma distribution and the Gaussian approximation of Poisson distribution. Experiments on real-world datasets indicate that our models outperform state-of-the-art baseline models in terms of perplexity and topic coherence. The results also validate that both NB-NTM and GNB-NTM can produce explainable intermediate variables by generating dispersed proportions of document topics.",
}
@inproceedings{zhong-etal-2020-reasoning,
    title = "Reasoning Over Semantic-Level Graph for Fact Checking",
    author = "Zhong, Wanjun  and
      Xu, Jingjing  and
      Tang, Duyu  and
      Xu, Zenan  and
      Duan, Nan  and
      Zhou, Ming  and
      Wang, Jiahai  and
      Yin, Jian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.549",
    doi = "10.18653/v1/2020.acl-main.549",
    pages = "6170--6180",
    abstract = "Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about the semantic-level structure of evidence. Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling. We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet. Specifically, using XLNet as the backbone, we first utilize the graph structure to re-define the relative distances of words, with the intuition that semantically related words should have short distances. Then, we adopt graph convolutional network and graph attention network to propagate and aggregate information from neighboring nodes on the graph. We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy. Our model is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score.",
}
@inproceedings{xing-etal-2020-automatic,
    title = "Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study",
    author = "Xing, Xinyu  and
      Fan, Xiaosheng  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.550",
    doi = "10.18653/v1/2020.acl-main.550",
    pages = "6181--6190",
    abstract = "In this paper, we study the challenging problem of automatic generation of citation texts in scholarly papers. Given the context of a citing paper A and a cited paper B, the task aims to generate a short text to describe B in the given context of A. One big challenge for addressing this task is the lack of training data. Usually, explicit citation texts are easy to extract, but it is not easy to extract implicit citation texts from scholarly papers. We thus first train an implicit citation extraction model based on BERT and leverage the model to construct a large training dataset for the citation text generation task. Then we propose and train a multi-source pointer-generator network with cross attention mechanism for citation text generation. Empirical evaluation results on a manually labeled test dataset verify the efficacy of our model. This pilot study confirms the feasibility of automatically generating citation texts in scholarly papers and the technique has the great potential to help researchers prepare their scientific papers.",
}
@inproceedings{li-etal-2020-composing,
    title = "Composing Elementary Discourse Units in Abstractive Summarization",
    author = "Li, Zhenwen  and
      Wu, Wenhao  and
      Li, Sujian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.551",
    doi = "10.18653/v1/2020.acl-main.551",
    pages = "6191--6196",
    abstract = "In this paper, we argue that elementary discourse unit (EDU) is a more appropriate textual unit of content selection than the sentence unit in abstractive summarization. To well handle the problem of composing EDUs into an informative and fluent summary, we propose a novel summarization method that first designs an EDU selection model to extract and group informative EDUs and then an EDU fusion model to fuse the EDUs in each group into one sentence. We also design the reinforcement learning mechanism to use EDU fusion results to reward the EDU selection action, boosting the final summarization performance. Experiments on CNN/Daily Mail have demonstrated the effectiveness of our model.",
}
@inproceedings{zhong-etal-2020-extractive,
    title = "Extractive Summarization as Text Matching",
    author = "Zhong, Ming  and
      Liu, Pengfei  and
      Chen, Yiran  and
      Wang, Danqing  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.552",
    doi = "10.18653/v1/2020.acl-main.552",
    pages = "6197--6208",
    abstract = "This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling the relationship between sentences, we formulate the extractive summarization task as a semantic text matching problem, in which a source document and candidate summaries will be (extracted from the original text) matched in a semantic space. Notably, this paradigm shift to semantic matching framework is well-grounded in our comprehensive analysis of the inherent gap between sentence-level and summary-level extractors based on the property of the dataset. Besides, even instantiating the framework with a simple form of a matching model, we have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1). Experiments on the other five datasets also show the effectiveness of the matching framework. We believe the power of this matching-based summarization framework has not been fully exploited. To encourage more instantiations in the future, we have released our codes, processed dataset, as well as generated summaries in \url{https://github.com/maszhongming/MatchSum}.",
}
@inproceedings{wang-etal-2020-heterogeneous,
    title = "Heterogeneous Graph Neural Networks for Extractive Document Summarization",
    author = "Wang, Danqing  and
      Liu, Pengfei  and
      Zheng, Yining  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.553",
    doi = "10.18653/v1/2020.acl-main.553",
    pages = "6209--6219",
    abstract = "As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. Besides, our graph structure is flexible in natural extension from a single-document setting to multi-document via introducing document nodes. To our knowledge, we are the first one to introduce different types of nodes into graph-based neural networks for extractive document summarization and perform a comprehensive qualitative analysis to investigate their benefits. The code will be released on Github.",
}
@inproceedings{cao-etal-2020-jointly,
    title = "Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization",
    author = "Cao, Yue  and
      Liu, Hui  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.554",
    doi = "10.18653/v1/2020.acl-main.554",
    pages = "6220--6231",
    abstract = "Cross-lingual summarization is the task of generating a summary in one language given a text in a different language. Previous works on cross-lingual summarization mainly focus on using pipeline methods or training an end-to-end model using the translated parallel data. However, it is a big challenge for the model to directly learn cross-lingual summarization as it requires learning to understand different languages and learning how to summarize at the same time. In this paper, we propose to ease the cross-lingual summarization training by jointly learning to align and summarize. We design relevant loss functions to train this framework and propose several methods to enhance the isomorphism and cross-lingual transfer between languages. Experimental results show that our model can outperform competitive models in most cases. In addition, we show that our model even has the ability to generate cross-lingual summaries without access to any cross-lingual corpus.",
}
@inproceedings{li-etal-2020-leveraging,
    title = "Leveraging Graph to Improve Abstractive Multi-Document Summarization",
    author = "Li, Wei  and
      Xiao, Xinyan  and
      Liu, Jiachen  and
      Wu, Hua  and
      Wang, Haifeng  and
      Du, Junping",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.555",
    doi = "10.18653/v1/2020.acl-main.555",
    pages = "6232--6243",
    abstract = "Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.",
}
@inproceedings{jin-etal-2020-multi,
    title = "Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization",
    author = "Jin, Hanqi  and
      Wang, Tianming  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.556",
    doi = "10.18653/v1/2020.acl-main.556",
    pages = "6244--6254",
    abstract = "In this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used to generate an abstractive summary while the sentence representations are used to produce an extractive summary. We employ attention mechanisms to interact between different granularity of semantic representations, which helps to capture multi-granularity key information and improves the performance of both abstractive and extractive summarization. Experiment results show that our proposed model substantially outperforms all strong baseline methods and achieves the best results on the Multi-News dataset.",
}
@inproceedings{kitaev-klein-2020-tetra,
    title = "Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference",
    author = "Kitaev, Nikita  and
      Klein, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.557",
    doi = "10.18653/v1/2020.acl-main.557",
    pages = "6255--6261",
    abstract = "We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word{'}s tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phase extracts a tree in (empirically) linear time. Our parser achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.",
}
@inproceedings{sun-etal-2020-estimating,
    title = "Are we Estimating or Guesstimating Translation Quality?",
    author = "Sun, Shuo  and
      Guzm{\'a}n, Francisco  and
      Specia, Lucia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.558",
    doi = "10.18653/v1/2020.acl-main.558",
    pages = "6262--6267",
    abstract = "Recent advances in pre-trained multilingual language models lead to state-of-the-art results on the task of quality estimation (QE) for machine translation. A carefully engineered ensemble of such models won the QE shared task at WMT19. Our in-depth analysis, however, shows that the success of using pre-trained language models for QE is over-estimated due to three issues we observed in current QE datasets: (i) The distributions of quality scores are imbalanced and skewed towards good quality scores; (iii) QE models can perform well on these datasets while looking at only source or translated sentences; (iii) They contain statistical artifacts that correlate well with human-annotated QE labels. Our findings suggest that although QE models might capture fluency of translated sentences and complexity of source sentences, they cannot model adequacy of translations effectively.",
}
@inproceedings{tamari-etal-2020-language,
    title = "{L}anguage (Re)modelling: {T}owards Embodied Language Understanding",
    author = "Tamari, Ronen  and
      Shani, Chen  and
      Hope, Tom  and
      Petruck, Miriam R L  and
      Abend, Omri  and
      Shahaf, Dafna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.559",
    doi = "10.18653/v1/2020.acl-main.559",
    pages = "6268--6281",
    abstract = "While natural language understanding (NLU) is advancing rapidly, today{'}s technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization. This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL). According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction. This position paper argues that the use of grounding by metaphoric reasoning and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.",
}
@inproceedings{joshi-etal-2020-state,
    title = "The State and Fate of Linguistic Diversity and Inclusion in the {NLP} World",
    author = "Joshi, Pratik  and
      Santy, Sebastin  and
      Budhiraja, Amar  and
      Bali, Kalika  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.560",
    doi = "10.18653/v1/2020.acl-main.560",
    pages = "6282--6293",
    abstract = "Language technologies contribute to promoting multilingualism and linguistic diversity around the world. However, only a very small number of the over 7000 languages of the world are represented in the rapidly evolving language technologies and applications. In this paper we look at the relation between the types of languages, resources, and their representation in NLP conferences to understand the trajectory that different languages have followed over time. Our quantitative investigation underlines the disparity between languages, especially in terms of their resources, and calls into question the {``}language agnostic{''} status of current models and systems. Through this paper, we attempt to convince the ACL community to prioritise the resolution of the predicaments highlighted here, so that no language is left behind.",
}
@inproceedings{henderson-2020-unstoppable,
    title = "The Unstoppable Rise of Computational Linguistics in Deep Learning",
    author = "Henderson, James",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.561",
    doi = "10.18653/v1/2020.acl-main.561",
    pages = "6294--6306",
    abstract = "In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of variable binding and its instantiation in attention-based models, and argue that Transformer is not a sequence model but an induced-structure model. This perspective leads to predictions of the challenges facing research in deep learning architectures for natural language understanding.",
}
@inproceedings{gartner-jung-2020-boldly,
    title = "To Boldly Query What No One Has Annotated Before? {T}he Frontiers of Corpus Querying",
    author = {G{\"a}rtner, Markus  and
      Jung, Kerstin},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.562",
    doi = "10.18653/v1/2020.acl-main.562",
    pages = "6307--6321",
    abstract = "Corpus query systems exist to address the multifarious information needs of any person interested in the content of annotated corpora. In this role they play an important part in making those resources usable for a wider audience. Over the past decades, several such query systems and languages have emerged, varying greatly in their expressiveness and technical details. This paper offers a broad overview of the history of corpora and corpus query tools. It focusses strongly on the query side and hints at exciting directions for future development.",
}
@inproceedings{shan-etal-2020-contextual,
    title = "A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking",
    author = "Shan, Yong  and
      Li, Zekang  and
      Zhang, Jinchao  and
      Meng, Fandong  and
      Feng, Yang  and
      Niu, Cheng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.563",
    doi = "10.18653/v1/2020.acl-main.563",
    pages = "6322--6333",
    abstract = "Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs. However, most of them have limitations to efficiently exploit relevant context due to the lack of a powerful mechanism for modeling interactions between the slot and the dialogue history. Besides, existing methods usually ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots and eventually hurts overall performance. In this paper, we propose to enhance the DST through employing a contextual hierarchical attention network to not only discern relevant information at both word level and turn level but also learn contextual representations. We further propose an adaptive objective to alleviate the slot imbalance problem by dynamically adjust weights of different slots during training. Experimental results show that our approach reaches 52.68{\%} and 58.55{\%} joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets respectively and achieves new state-of-the-art performance with considerable improvements (+1.24{\%} and +5.98{\%}).",
}
@inproceedings{cai-etal-2020-data,
    title = "Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight",
    author = "Cai, Hengyi  and
      Chen, Hongshen  and
      Song, Yonghao  and
      Zhang, Cheng  and
      Zhao, Xiaofang  and
      Yin, Dawei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.564",
    doi = "10.18653/v1/2020.acl-main.564",
    pages = "6334--6343",
    abstract = "Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model. However, due to the open-ended nature of human conversations, the quality of user-generated training data varies greatly, and effective training samples are typically insufficient while noisy samples frequently appear. This impedes the learning of those data-driven neural dialogue models. Therefore, effective dialogue learning requires not only more reliable learning samples, but also fewer noisy samples. In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously. In particular, the data manipulation model selectively augments the training samples and assigns an importance weight to each instance to reform the training data. Note that, the proposed data manipulation framework is fully data-driven and learnable. It not only manipulates training samples to optimize the dialogue generation model, but also learns to increase its manipulation skills through gradient descent with validation samples. Extensive experiments show that our framework can improve the dialogue generation performance with respect to various automatic evaluation metrics and human judgments.",
}
@inproceedings{qin-etal-2020-dynamic,
    title = "Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog",
    author = "Qin, Libo  and
      Xu, Xiao  and
      Che, Wanxiang  and
      Zhang, Yue  and
      Liu, Ting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.565",
    doi = "10.18653/v1/2020.acl-main.565",
    pages = "6344--6354",
    abstract = "Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled data. However, there has been relatively little research on how to effectively use data from all domains to improve the performance of each domain and also unseen domains. To this end, we investigate methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge. In addition, we propose a novel Dynamic Fusion Network (DF-Net) which automatically exploit the relevance between the target domain and each domain. Results show that our models outperforms existing methods on multi-domain dialogue, giving the state-of-the-art in the literature. Besides, with little training data, we show its transferability by outperforming prior best model by 13.9{\%} on average.",
}
@inproceedings{wang-etal-2020-learning,
    title = "Learning Efficient Dialogue Policy from Demonstrations through Shaping",
    author = "Wang, Huimin  and
      Peng, Baolin  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.566",
    doi = "10.18653/v1/2020.acl-main.566",
    pages = "6355--6365",
    abstract = "Training a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users. Human demonstrations can be used to accelerate learning progress. However, how to effectively leverage demonstrations to learn dialogue policy remains less explored. In this paper, we present S{\^{}}2Agent that efficiently learns dialogue policy from demonstrations through policy shaping and reward shaping. We use an imitation model to distill knowledge from demonstrations, based on which policy shaping estimates feedback on how the agent should act in policy space. Reward shaping is then incorporated to bonus state-actions similar to demonstrations explicitly in value space encouraging better exploration. The effectiveness of the proposed S{\^{}}2Agentt is demonstrated in three dialogue domains and a challenging domain adaptation task with both user simulator evaluation and human evaluation.",
}
@inproceedings{hu-etal-2020-sas,
    title = "{SAS}: Dialogue State Tracking via Slot Attention and Slot Information Sharing",
    author = "Hu, Jiaying  and
      Yang, Yan  and
      Chen, Chencai  and
      He, Liang  and
      Yu, Zhou",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.567",
    doi = "10.18653/v1/2020.acl-main.567",
    pages = "6366--6375",
    abstract = "Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information{'}s interference and improve long dialogue context tracking. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a slot information sharing module. Our model yields a significantly improved performance compared to previous state-of the-art models on the MultiWOZ dataset.",
}
@inproceedings{bak-oh-2020-speaker,
    title = "Speaker Sensitive Response Evaluation Model",
    author = "Bak, JinYeong  and
      Oh, Alice",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.568",
    doi = "10.18653/v1/2020.acl-main.568",
    pages = "6376--6385",
    abstract = "Automatic evaluation of open-domain dialogue response generation is very challenging because there are many appropriate responses for a given context. Existing evaluation models merely compare the generated response with the ground truth response and rate many of the appropriate responses as inappropriate if they deviate from the ground truth. One approach to resolve this problem is to consider the similarity of the generated response with the conversational context. In this paper, we propose an automatic evaluation model based on that idea and learn the model parameters from an unlabeled conversation corpus. Our approach considers the speakers in defining the different levels of similar context. We use a Twitter conversation corpus that contains many speakers and conversations to test our evaluation model. Experiments show that our model outperforms the other existing evaluation metrics in terms of high correlation with human annotation scores. We also show that our model trained on Twitter can be applied to movie dialogues without any additional training. We provide our code and the learned parameters so that they can be used for automatic evaluation of dialogue response generation models.",
}
@inproceedings{zhang-etal-2020-top,
    title = "A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure",
    author = "Zhang, Longyin  and
      Xing, Yuqing  and
      Kong, Fang  and
      Li, Peifeng  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.569",
    doi = "10.18653/v1/2020.acl-main.569",
    pages = "6386--6395",
    abstract = "Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years. However, all the previous studies on text-level discourse parsing adopt bottom-up approaches, which much limit the DRS determination on local information and fail to well benefit from global information of the overall discourse. In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for text-level DRS parsing. On the basis, we propose a top-down neural architecture toward text-level DRS parsing. In particular, we cast discourse parsing as a recursive split point ranking task, where a split point is classified to different levels according to its rank and the elementary discourse units (EDUs) associated with it are arranged accordingly. In this way, we can determine the complete DRS as a hierarchical tree structure via an encoder-decoder with an internal stack. Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus shows the great effectiveness of our proposed top-down approach towards text-level DRS parsing.",
}
@inproceedings{dutta-saha-2020-amalgamation,
    title = "Amalgamation of protein sequence, structure and textual information for improving protein-protein interaction identification",
    author = "Dutta, Pratik  and
      Saha, Sriparna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.570",
    doi = "10.18653/v1/2020.acl-main.570",
    pages = "6396--6407",
    abstract = "An in-depth exploration of protein-protein interactions (PPI) is essential to understand the metabolism in addition to the regulations of biological entities like proteins, carbohydrates, and many more. Most of the recent PPI tasks in BioNLP domain have been carried out solely using textual data. In this paper, we argue that incorporating multimodal cues can improve the automatic identification of PPI. As a first step towards enabling the development of multimodal approaches for PPI identification, we have developed two multi-modal datasets which are extensions and multi-modal versions of two popular benchmark PPI corpora (BioInfer and HRPD50). Besides, existing textual modalities, two new modalities, 3D protein structure and underlying genomic sequence, are also added to each instance. Further, a novel deep multi-modal architecture is also implemented to efficiently predict the protein interactions from the developed datasets. A detailed experimental analysis reveals the superiority of the multi-modal approach in comparison to the strong baselines including unimodal approaches and state-of the-art methods over both the generated multi-modal datasets. The developed multi-modal datasets are available for use at https://github.com/sduttap16/MM{\_}PPI{\_}NLP.",
}
@inproceedings{luo-zhao-2020-bipartite,
    title = "Bipartite Flat-Graph Network for Nested Named Entity Recognition",
    author = "Luo, Ying  and
      Zhao, Hai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.571",
    doi = "10.18653/v1/2020.acl-main.571",
    pages = "6408--6418",
    abstract = "In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional network (GCN) are adopted to jointly learn flat entities and their inner dependencies. Different from previous models, which only consider the unidirectional delivery of information from innermost layers to outer ones (or outside-to-inside), our model effectively captures the bidirectional interaction between them. We first use the entities recognized by the flat NER module to construct an entity graph, which is fed to the next graph module. The richer representation learned from graph module carries the dependencies of inner entities and can be exploited to improve outermost entity predictions. Experimental results on three standard nested NER datasets demonstrate that our BiFlaG outperforms previous state-of-the-art models.",
}
@inproceedings{zhao-etal-2020-connecting,
    title = "Connecting Embeddings for Knowledge Graph Entity Typing",
    author = "Zhao, Yu  and
      Zhang, Anxiang  and
      Xie, Ruobing  and
      Liu, Kang  and
      Wang, Xiaojie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.572",
    doi = "10.18653/v1/2020.acl-main.572",
    pages = "6419--6428",
    abstract = "Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model via connecting them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: https://github.com/Adam1679/ConnectE .",
}
@inproceedings{han-etal-2020-continual,
    title = "Continual Relation Learning via Episodic Memory Activation and Reconsolidation",
    author = "Han, Xu  and
      Dai, Yi  and
      Gao, Tianyu  and
      Lin, Yankai  and
      Liu, Zhiyuan  and
      Li, Peng  and
      Sun, Maosong  and
      Zhou, Jie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.573",
    doi = "10.18653/v1/2020.acl-main.573",
    pages = "6429--6440",
    abstract = "Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations. Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent training is an effective solution for such a challenging problem. However, these memory-based methods usually suffer from overfitting the few memorized examples of old relations, which may gradually cause inevitable confusion among existing relations. Inspired by the mechanism in human long-term memory formation, we introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning. Every time neural models are activated to learn both new and memorized data, EMAR utilizes relation prototypes for memory reconsolidation exercise to keep a stable understanding of old relations. The experimental results show that EMAR could get rid of catastrophically forgetting old relations and outperform the state-of-the-art continual learning models.",
}
@inproceedings{li-etal-2020-handling,
    title = "Handling Rare Entities for Neural Sequence Labeling",
    author = "Li, Yangming  and
      Li, Han  and
      Yao, Kaisheng  and
      Li, Xiaolong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.574",
    doi = "10.18653/v1/2020.acl-main.574",
    pages = "6441--6451",
    abstract = "One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases. Most of test set entities appear only few times and are even unseen in training corpus, yielding large number of out-of-vocabulary (OOV) and low-frequency (LF) entities during evaluation. In this work, we propose approaches to address this problem. For OOV entities, we introduce local context reconstruction to implicitly incorporate contextual information into their representations. For LF entities, we present delexicalized entity identification to explicitly extract their frequency-agnostic and entity-type-specific representations. Extensive experiments on multiple benchmark datasets show that our model has significantly outperformed all previous methods and achieved new start-of-the-art results. Notably, our methods surpass the model fine-tuned on pre-trained language models without external resource.",
}
@inproceedings{ouchi-etal-2020-instance,
    title = "Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition",
    author = "Ouchi, Hiroki  and
      Suzuki, Jun  and
      Kobayashi, Sosuke  and
      Yokoi, Sho  and
      Kuribayashi, Tatsuki  and
      Konno, Ryuto  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.575",
    doi = "10.18653/v1/2020.acl-main.575",
    pages = "6452--6459",
    abstract = "Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.",
}
@inproceedings{zhang-etal-2020-mie,
    title = "{MIE}: A Medical Information Extractor towards Medical Dialogues",
    author = "Zhang, Yuanzhe  and
      Jiang, Zhongtao  and
      Zhang, Tao  and
      Liu, Shiwan  and
      Cao, Jiarun  and
      Liu, Kang  and
      Liu, Shengping  and
      Zhao, Jun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.576",
    doi = "10.18653/v1/2020.acl-main.576",
    pages = "6460--6469",
    abstract = "Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues.",
}
@inproceedings{yu-etal-2020-named,
    title = "Named Entity Recognition as Dependency Parsing",
    author = "Yu, Juntao  and
      Bohnet, Bernd  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.577",
    doi = "10.18653/v1/2020.acl-main.577",
    pages = "6470--6476",
    abstract = "Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.",
}
@inproceedings{wu-etal-2020-neighborhood,
    title = "Neighborhood Matching Network for Entity Alignment",
    author = "Wu, Yuting  and
      Liu, Xiao  and
      Feng, Yansong  and
      Wang, Zheng  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.578",
    doi = "10.18653/v1/2020.acl-main.578",
    pages = "6477--6487",
    abstract = "Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge. NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference. It provides two innovative components for better learning representations for entity alignment. It first uses a novel graph sampling method to distill a discriminative neighborhood for each entity. It then adopts a cross-graph neighborhood matching module to jointly encode the neighborhood difference for a given entity pair. Such strategies allow NMN to effectively construct matching-oriented entity representations while ignoring noisy neighbors that have a negative impact on the alignment task. Extensive experiments performed on three entity alignment datasets show that NMN can well estimate the neighborhood similarity in more tough cases and significantly outperforms 12 previous state-of-the-art methods.",
}
@inproceedings{shahbazi-etal-2020-relation,
    title = "Relation Extraction with Explanation",
    author = "Shahbazi, Hamed  and
      Fern, Xiaoli  and
      Ghaeini, Reza  and
      Tadepalli, Prasad",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.579",
    doi = "10.18653/v1/2020.acl-main.579",
    pages = "6488--6494",
    abstract = "Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Efforts thus far have focused on improving extraction accuracy but little is known about their explanability. In this work we annotate a test set with ground-truth sentence-level explanations to evaluate the quality of explanations afforded by the relation extraction models. We demonstrate that replacing the entity mentions in the sentences with their fine-grained entity types not only enhances extraction accuracy but also improves explanation. We also propose to automatically generate {``}distractor{''} sentences to augment the bags and train the model to ignore the distractors. Evaluations on the widely used FB-NYT dataset show that our methods achieve new state-of-the-art accuracy while improving model explanability.",
}
@inproceedings{majumder-etal-2020-representation,
    title = "Representation Learning for Information Extraction from Form-like Documents",
    author = "Majumder, Bodhisattwa Prasad  and
      Potti, Navneet  and
      Tata, Sandeep  and
      Wendt, James Bradley  and
      Zhao, Qi  and
      Najork, Marc",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.580",
    doi = "10.18653/v1/2020.acl-main.580",
    pages = "6495--6504",
    abstract = "We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images. We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains but are also interpretable, as we show using loss cases.",
}
@inproceedings{wu-etal-2020-single,
    title = "Single-/Multi-Source Cross-Lingual {NER} via Teacher-Student Learning on Unlabeled Data in Target Language",
    author = {Wu, Qianhui  and
      Lin, Zijia  and
      Karlsson, B{\"o}rje  and
      Lou, Jian-Guang  and
      Huang, Biqing},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.581",
    doi = "10.18653/v1/2020.acl-main.581",
    pages = "6505--6514",
    abstract = "To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer. However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language. In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language. The proposed method works for both single-source and multi-source cross-lingual NER. For the latter, we further propose a similarity measuring method to better weight the supervision from different teacher models. Extensive experiments for 3 target languages on benchmark datasets well demonstrate that our method outperforms existing state-of-the-art methods for both single-source and multi-source cross-lingual NER.",
}
@inproceedings{chen-etal-2020-synchronous,
    title = "Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction",
    author = "Chen, Shaowei  and
      Liu, Jie  and
      Wang, Yu  and
      Zhang, Wenzheng  and
      Chi, Ziming",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.582",
    doi = "10.18653/v1/2020.acl-main.582",
    pages = "6515--6524",
    abstract = "Opinion entity extraction is a fundamental task in fine-grained opinion mining. Related studies generally extract aspects and/or opinion expressions without recognizing the relations between them. However, the relations are crucial for downstream tasks, including sentiment classification, opinion summarization, etc. In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs. To deal with this task, we propose Synchronous Double-channel Recurrent Network (SDRN) mainly consisting of an opinion entity extraction unit, a relation detection unit, and a synchronization unit. The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously. Furthermore, within the synchronization unit, we design Entity Synchronization Mechanism (ESM) and Relation Synchronization Mechanism (RSM) to enhance the mutual benefit on the above two channels. To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks. Extensive experiments demonstrate that SDRN achieves state-of-the-art performances.",
}
@inproceedings{alikhani-etal-2020-cross,
    title = "Cross-modal Coherence Modeling for Caption Generation",
    author = "Alikhani, Malihe  and
      Sharma, Piyush  and
      Li, Shengjie  and
      Soricut, Radu  and
      Stone, Matthew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.583",
    doi = "10.18653/v1/2020.acl-main.583",
    pages = "6525--6535",
    abstract = "We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image{--}caption coherence relations, we annotate 10,000 instances from publicly-available image{--}caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.",
}
@inproceedings{schuz-zarriess-2020-knowledge,
    title = "{K}nowledge Supports Visual Language Grounding: {A} Case Study on Colour Terms",
    author = {Sch{\"u}z, Simeon  and
      Zarrie{\ss}, Sina},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.584",
    doi = "10.18653/v1/2020.acl-main.584",
    pages = "6536--6542",
    abstract = "In human cognition, world knowledge supports the perception of object colours: knowing that trees are typically green helps to perceive their colour in certain contexts. We go beyond previous studies on colour terms using isolated colour swatches and study visual grounding of colour terms in realistic objects. Our models integrate processing of visual information and object-specific knowledge via hard-coded (late) or learned (early) fusion. We find that both models consistently outperform a bottom-up baseline that predicts colour terms solely from visual inputs, but show interesting differences when predicting atypical colours of so-called colour diagnostic objects. Our models also achieve promising results when tested on new object categories not seen during training.",
}
@inproceedings{zhang-etal-2020-span,
    title = "Span-based Localizing Network for Natural Language Video Localization",
    author = "Zhang, Hao  and
      Sun, Aixin  and
      Jing, Wei  and
      Zhou, Joey Tianyi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.585",
    doi = "10.18653/v1/2020.acl-main.585",
    pages = "6543--6554",
    abstract = "Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple and yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods; and adopting span-based QA framework is a promising direction to solve NLVL.",
}
@inproceedings{akula-etal-2020-words,
    title = "Words Aren{'}t Enough, Their Order Matters: On the Robustness of Grounding Visual Referring Expressions",
    author = "Akula, Arjun  and
      Gella, Spandana  and
      Al-Onaizan, Yaser  and
      Zhu, Song-Chun  and
      Reddy, Siva",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.586",
    doi = "10.18653/v1/2020.acl-main.586",
    pages = "6555--6565",
    abstract = "Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We critically examine RefCOCOg, a standard benchmark for this task, using a human study and show that 83.7{\%} of test instances do not require reasoning on linguistic structure, i.e., words are enough to identify the target object, the word order doesn{'}t matter. To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn{'}t. Additionally, we create an out-of-distribution dataset Ref-Adv by asking crowdworkers to perturb in-domain examples such that the target object changes. Using these datasets, we empirically show that existing methods fail to exploit linguistic structure and are 12{\%} to 23{\%} lower in performance than the established progress for this task. We also propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase the robustness of ViLBERT, the current state-of-the-art model for this task. Our datasets are publicly available at https://github.com/aws/aws-refcocog-adv.",
}
@inproceedings{peng-etal-2020-mixture,
    title = "A Mixture of h - 1 Heads is Better than h Heads",
    author = "Peng, Hao  and
      Schwartz, Roy  and
      Li, Dianqi  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.587",
    doi = "10.18653/v1/2020.acl-main.587",
    pages = "6566--6577",
    abstract = "Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks. Evidence has shown that they are overparameterized; attention heads can be pruned without significant performance loss. In this work, we instead {``}reallocate{''} them{---}the model learns to activate different heads on different inputs. Drawing connections between multi-head attention and mixture of experts, we propose the mixture of attentive experts model (MAE). MAE is trained using a block coordinate descent algorithm that alternates between updating (1) the responsibilities of the experts and (2) their parameters. Experiments on machine translation and language modeling show that MAE outperforms strong baselines on both tasks. Particularly, on the WMT14 English to German translation dataset, MAE improves over {``}transformer-base{''} by 0.8 BLEU, with a comparable number of parameters. Our analysis shows that our model learns to specialize different experts to different inputs.",
}
@inproceedings{tang-etal-2020-dependency,
    title = "Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification",
    author = "Tang, Hao  and
      Ji, Donghong  and
      Li, Chenliang  and
      Zhou, Qiji",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.588",
    doi = "10.18653/v1/2020.acl-main.588",
    pages = "6578--6588",
    abstract = "Aspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a specific aspect. One sentence may contain various sentiments for different aspects. Many sophisticated methods such as attention mechanism and Convolutional Neural Networks (CNN) have been widely employed for handling this challenge. Recently, semantic dependency tree implemented by Graph Convolutional Networks (GCN) is introduced to describe the inner connection between aspects and the associated emotion words. But the improvement is limited due to the noise and instability of dependency trees. To this end, we propose a dependency graph enhanced dual-transformer network (named DGEDT) by jointly considering the flat representations learnt from Transformer and graph-based representations learnt from the corresponding dependency graph in an iterative interaction manner. Specifically, a dual-transformer structure is devised in DGEDT to support mutual reinforcement between the flat representation learning and graph-based representation learning. The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa. The results on five datasets demonstrate that the proposed DGEDT outperforms all state-of-the-art alternatives with a large margin.",
}
@inproceedings{nguyen-etal-2020-differentiable,
    title = "Differentiable Window for Dynamic Local Attention",
    author = "Nguyen, Thanh-Tung  and
      Nguyen, Xuan-Phi  and
      Joty, Shafiq  and
      Li, Xiaoli",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.589",
    doi = "10.18653/v1/2020.acl-main.589",
    pages = "6589--6599",
    abstract = "We propose Differentiable Window, a new neural module and general purpose component for dynamic window selection. While universally applicable, we demonstrate a compelling use case of utilizing Differentiable Window to improve standard attention modules by enabling more focused attentions over the input regions. We propose two variants of Differentiable Window, and integrate them within the Transformer architecture in two novel ways. We evaluate our proposed approach on a myriad of NLP tasks, including machine translation, sentiment analysis, subject-verb agreement and language modeling. Our experimental results demonstrate consistent and sizable improvements across all tasks.",
}
@inproceedings{zheng-etal-2020-evaluating,
    title = "Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples",
    author = "Zheng, Xiaoqing  and
      Zeng, Jiehang  and
      Zhou, Yi  and
      Hsieh, Cho-Jui  and
      Cheng, Minhao  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.590",
    doi = "10.18653/v1/2020.acl-main.590",
    pages = "6600--6610",
    abstract = "Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples. Previously studies along this line mainly focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. In this study, we show that adversarial examples also exist in dependency parsing: we propose two approaches to study where and how parsers make mistakes by searching over perturbations to existing texts at sentence and phrase levels, and design algorithms to construct such examples in both of the black-box and white-box settings. Our experiments with one of state-of-the-art parsers on the English Penn Treebank (PTB) show that up to 77{\%} of input examples admit adversarial perturbations, and we also show that the robustness of parsing models can be improved by crafting high-quality adversaries and including them in the training stage, while suffering little to no performance drop on the clean input data.",
}
@inproceedings{du-etal-2020-exploiting,
    title = "Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach",
    author = "Du, Wenyu  and
      Lin, Zhouhan  and
      Shen, Yikang  and
      O{'}Donnell, Timothy J.  and
      Bengio, Yoshua  and
      Zhang, Yue",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.591",
    doi = "10.18653/v1/2020.acl-main.591",
    pages = "6611--6628",
    abstract = "It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called {``}syntactic distances{''}, where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.",
}
@inproceedings{li-etal-2020-learning,
    title = "Learning Architectures from an Extended Search Space for Language Modeling",
    author = "Li, Yinqiao  and
      Hu, Chi  and
      Zhang, Yuhao  and
      Xu, Nuo  and
      Jiang, Yufan  and
      Xiao, Tong  and
      Zhu, Jingbo  and
      Liu, Tongran  and
      Li, Changliang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.592",
    doi = "10.18653/v1/2020.acl-main.592",
    pages = "6629--6639",
    abstract = "Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. In this paper, we extend the search space of NAS. In particular, we present a general approach to learn both intra-cell and inter-cell architectures (call it ESS). For a better search result, we design a joint learning method to perform intra-cell and inter-cell NAS simultaneously. We implement our model in a differentiable architecture search system. For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and WikiText data, with a new state-of-the-art on PTB. Moreover, the learned architectures show good transferability to other systems. E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity recognition (NER) tasks and CoNLL chunking task, indicating a promising line of research on large-scale pre-learned architectures.",
}
@inproceedings{schwartz-etal-2020-right,
    title = "The Right Tool for the Job: Matching Model and Instance Complexities",
    author = "Schwartz, Roy  and
      Stanovsky, Gabriel  and
      Swayamdipta, Swabha  and
      Dodge, Jesse  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.593",
    doi = "10.18653/v1/2020.acl-main.593",
    pages = "6640--6651",
    abstract = "As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference budget, we propose a modification to contextual representation fine-tuning which, during inference, allows for an early (and fast) {``}exit{''} from neural network calculations for simple instances, and late (and accurate) exit for hard instances. To achieve this, we add classifiers to different layers of BERT and use their calibrated confidence scores to make early exit decisions. We test our proposed modification on five different datasets in two tasks: three text classification datasets and two natural language inference benchmarks. Our method presents a favorable speed/accuracy tradeoff in almost all cases, producing models which are up to five times faster than the state of the art, while preserving their accuracy. Our method also requires almost no additional training resources (in either time or parameters) compared to the baseline BERT model. Finally, our method alleviates the need for costly retraining of multiple models at different levels of efficiency; we allow users to control the inference speed/accuracy tradeoff using a single trained model, by setting a single variable at inference time. We publicly release our code.",
}
@inproceedings{lane-bird-2020-bootstrapping,
    title = "Bootstrapping Techniques for Polysynthetic Morphological Analysis",
    author = "Lane, William  and
      Bird, Steven",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.594",
    doi = "10.18653/v1/2020.acl-main.594",
    pages = "6652--6661",
    abstract = "Polysynthetic languages have exceptionally large and sparse vocabularies, thanks to the number of morpheme slots and combinations in a word. This complexity, together with a general scarcity of written data, poses a challenge to the development of natural language technologies. To address this challenge, we offer linguistically-informed approaches for bootstrapping a neural morphological analyzer, and demonstrate its application to Kunwinjku, a polysynthetic Australian language. We generate data from a finite state transducer to train an encoder-decoder model. We improve the model by {``}hallucinating{''} missing linguistic structure into the training data, and by resampling from a Zipf distribution to simulate a more natural distribution of morphemes. The best model accounts for all instances of reduplication in the test set and achieves an accuracy of 94.7{\%} overall, a 10 percentage point improvement over the FST baseline. This process demonstrates the feasibility of bootstrapping a neural morph analyzer from minimal resources.",
}
@inproceedings{ding-etal-2020-coupling,
    title = "Coupling Distant Annotation and Adversarial Training for Cross-Domain {C}hinese Word Segmentation",
    author = "Ding, Ning  and
      Long, Dingkun  and
      Xu, Guangwei  and
      Zhu, Muhua  and
      Xie, Pengjun  and
      Wang, Xiaobin  and
      Zheng, Haitao",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.595",
    doi = "10.18653/v1/2020.acl-main.595",
    pages = "6662--6671",
    abstract = "Fully supervised neural approaches have achieved significant progress in the task of Chinese word segmentation (CWS). Nevertheless, the performance of supervised models always drops gravely if the domain shifts due to the distribution gap across domains and the out of vocabulary (OOV) problem. In order to simultaneously alleviate the issues, this paper intuitively couples distant annotation and adversarial training for cross-domain CWS. 1) We rethink the essence of {``}Chinese words{''} and design an automatic distant annotation mechanism, which does not need any supervision or pre-defined dictionaries on the target domain. The method could effectively explore domain-specific words and distantly annotate the raw texts for the target domain. 2) We further develop a sentence-level adversarial training procedure to perform noise reduction and maximum utilization of the source domain information. Experiments on multiple real-world datasets across various domains show the superiority and robustness of our model, significantly outperforming previous state-of-the-arts cross-domain CWS methods.",
}
@inproceedings{xu-etal-2020-modeling,
    title = "Modeling Morphological Typology for Unsupervised Learning of Language Morphology",
    author = "Xu, Hongzhi  and
      Kodner, Jordan  and
      Marcus, Mitchell  and
      Yang, Charles",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.596",
    doi = "10.18653/v1/2020.acl-main.596",
    pages = "6672--6681",
    abstract = "This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology. By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology. The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems. We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.",
}
@inproceedings{williams-etal-2020-predicting,
    title = "Predicting Declension Class from Form and Meaning",
    author = "Williams, Adina  and
      Pimentel, Tiago  and
      Blix, Hagen  and
      McCarthy, Arya D.  and
      Chodroff, Eleanor  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.597",
    doi = "10.18653/v1/2020.acl-main.597",
    pages = "6682--6695",
    abstract = "The noun lexica of many natural languages are divided into several declension classes with characteristic morphological properties. Class membership is far from deterministic, but the phonological form of a noun and/or its meaning can often provide imperfect clues. Here, we investigate the strength of those clues. More specifically, we operationalize this by measuring how much information, in bits, we can glean about declension class from knowing the form and/or meaning of nouns. We know that form and meaning are often also indicative of grammatical gender{---}which, as we quantitatively verify, can itself share information with declension class{---}so we also control for gender. We find for two Indo-European languages (Czech and German) that form and meaning respectively share significant amounts of information with class (and contribute additional information above and beyond gender). The three-way interaction between class, form, and meaning (given gender) is also significant. Our study is important for two reasons: First, we introduce a new method that provides additional quantitative support for a classic linguistic finding that form and meaning are relevant for the classification of nouns into declensions. Secondly, we show not only that individual declensions classes vary in the strength of their clues within a language, but also that these variations themselves vary across languages.",
}
@inproceedings{jin-etal-2020-unsupervised,
    title = "Unsupervised Morphological Paradigm Completion",
    author = "Jin, Huiming  and
      Cai, Liwei  and
      Peng, Yihui  and
      Xia, Chen  and
      McCarthy, Arya  and
      Kann, Katharina",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.598",
    doi = "10.18653/v1/2020.acl-main.598",
    pages = "6696--6707",
    abstract = "We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas. From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators. From a cognitive science perspective, this can shed light on how children acquire morphological knowledge. We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation. We perform an evaluation on 14 typologically diverse languages. Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems.",
}
@inproceedings{zheng-etal-2020-document,
    title = "Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension",
    author = "Zheng, Bo  and
      Wen, Haoyang  and
      Liang, Yaobo  and
      Duan, Nan  and
      Che, Wanxiang  and
      Jiang, Daxin  and
      Zhou, Ming  and
      Liu, Ting",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.599",
    doi = "10.18653/v1/2020.acl-main.599",
    pages = "6708--6718",
    abstract = "Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers, which are a long answer (typically a paragraph) and a short answer (one or more entities inside the long answer). Despite the effectiveness of existing methods on this benchmark, they treat these two sub-tasks individually during training while ignoring their dependencies. To address this issue, we present a novel multi-grained machine reading comprehension framework that focuses on modeling documents at their hierarchical nature, which are different levels of granularity: documents, paragraphs, sentences, and tokens. We utilize graph attention networks to obtain different levels of representations so that they can be learned simultaneously. The long and short answers can be extracted from paragraph-level representation and token-level representation, respectively. In this way, we can model the dependencies between the two-grained answers to provide evidence for each other. We jointly train the two sub-tasks, and our experiments show that our approach significantly outperforms previous systems at both long and short answer criteria.",
}
@inproceedings{li-etal-2020-harvesting,
    title = "Harvesting and Refining Question-Answer Pairs for Unsupervised {QA}",
    author = "Li, Zhongli  and
      Wang, Wenhui  and
      Dong, Li  and
      Wei, Furu  and
      Xu, Ke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.600",
    doi = "10.18653/v1/2020.acl-main.600",
    pages = "6719--6728",
    abstract = "Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to extend these successes to the settings with few or no labeled data available. In this work, we introduce two approaches to improve unsupervised QA. First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA). Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA. We conduct experiments on SQuAD 1.1, and NewsQA by fine-tuning BERT without access to manually annotated data. Our approach outperforms previous unsupervised approaches by a large margin, and is competitive with early supervised models. We also show the effectiveness of our approach in the few-shot learning setting.",
}
@inproceedings{yu-etal-2020-low,
    title = "Low-Resource Generation of Multi-hop Reasoning Questions",
    author = "Yu, Jianxing  and
      Liu, Wei  and
      Qiu, Shuang  and
      Su, Qinliang  and
      Wang, Kai  and
      Quan, Xiaojun  and
      Yin, Jian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.601",
    doi = "10.18653/v1/2020.acl-main.601",
    pages = "6729--6739",
    abstract = "This paper focuses on generating multi-hop reasoning questions from the raw text in a low resource circumstance. Such questions have to be syntactically valid and need to logically correlate with the answers by deducing over multiple relations on several sentences in the text. Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text. Since the labeled data is limited and insufficient for training, we propose to learn the model with the help of a large scale of unlabeled data that is much easier to obtain. Such data contains rich expressive forms of the questions with structural patterns on syntax and semantics. These patterns can be estimated by the neural hidden semi-Markov model using latent variables. With latent patterns as a prior, we can regularize the generation model and produce the optimal results. Experimental results on the HotpotQA data set demonstrate the effectiveness of our model. Moreover, we apply the generated results to the task of machine reading comprehension and achieve significant performance improvements.",
}
@inproceedings{inoue-etal-2020-r4c,
    title = "{R}4{C}: A Benchmark for Evaluating {RC} Systems to Get the Right Answer for the Right Reason",
    author = "Inoue, Naoya  and
      Stenetorp, Pontus  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.602",
    doi = "10.18653/v1/2020.acl-main.602",
    pages = "6740--6750",
    abstract = "Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This prevents the community from reliably measuring the progress of RC systems. To address this issue, we introduce R4C, a new task for evaluating RC systems{'} internal reasoning. R4C requires giving not only answers but also derivations: explanations that justify predicted answers. We present a reliable, crowdsourced framework for scalably annotating RC datasets with derivations. We create and publicly release the R4C dataset, the first, quality-assured dataset consisting of 4.6k questions, each of which is annotated with 3 reference derivations (i.e. 13.8k derivations). Experiments show that our automatic evaluation metrics using multiple reference derivations are reliable, and that R4C assesses different skills from an existing benchmark.",
}
@inproceedings{gong-etal-2020-recurrent,
    title = "Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension",
    author = "Gong, Hongyu  and
      Shen, Yelong  and
      Yu, Dian  and
      Chen, Jianshu  and
      Yu, Dong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.603",
    doi = "10.18653/v1/2020.acl-main.603",
    pages = "6751--6761",
    abstract = "In this paper, we study machine reading comprehension (MRC) on long texts: where a model takes as inputs a lengthy document and a query, extracts a text span from the document as an answer. State-of-the-art models (e.g., BERT) tend to use a stack of transformer layers that are pre-trained from a large number of unlabeled language corpora to encode the joint contextual information of query and document. However, these transformer models can only take as input a fixed-length (e.g., 512) text. To deal with even longer text inputs, previous approaches usually chunk them into \textit{equally-spaced} segments and predict answers based on each segment independently without considering the information from other segments. As a result, they may form segments that fail to cover complete answers or retain insufficient contexts around the correct answer required for question answering. Moreover, they are less capable of answering questions that need cross-segment information. We propose to let a model learn to chunk in a more flexible way via reinforcement learning: a model can decide the next segment that it wants to process in either direction. We also apply recurrent mechanisms to enable information to flow across segments. Experiments on three MRC tasks {--} CoQA, QuAC, and TriviaQA {--} demonstrate the effectiveness of our proposed recurrent chunking mechanisms: we can obtain segments that are more likely to contain complete answers and at the same time provide sufficient contexts around the ground truth answers for better predictions.",
}
@inproceedings{liu-etal-2020-rikinet,
    title = "{R}iki{N}et: Reading {W}ikipedia Pages for Natural Question Answering",
    author = "Liu, Dayiheng  and
      Gong, Yeyun  and
      Fu, Jie  and
      Yan, Yu  and
      Chen, Jiusheng  and
      Jiang, Daxin  and
      Lv, Jiancheng  and
      Duan, Nan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.604",
    doi = "10.18653/v1/2020.acl-main.604",
    pages = "6762--6771",
    abstract = "Reading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question answering. RikiNet contains a dynamic paragraph dual-attention reader and a multi-level cascaded answer predictor. The reader dynamically represents the document and question by utilizing a set of complementary attention mechanisms. The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner. On the Natural Questions (NQ) dataset, a single RikiNet achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks. To our best knowledge, it is the first single model that outperforms the single human performance. Furthermore, an ensemble RikiNet obtains 76.1 F1 and 61.3 F1 on long-answer and short-answer tasks, achieving the best performance on the official NQ leaderboard.",
}
@inproceedings{chen-sun-2020-parsing,
    title = "Parsing into Variable-in-situ Logico-Semantic Graphs",
    author = "Chen, Yufei  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.605",
    doi = "10.18653/v1/2020.acl-main.605",
    pages = "6772--6782",
    abstract = "We propose variable-in-situ logico-semantic graphs to bridge the gap between semantic graph and logical form parsing. The new type of graph-based meaning representation allows us to include analysis for scope-related phenomena, such as quantification, negation and modality, in a way that is consistent with the state-of-the-art underspecification approach. Moreover, the well-formedness of such a graph is clear, since model-theoretic interpretation is available. We demonstrate the effectiveness of this new perspective by developing a new state-of-the-art semantic parser for English Resource Semantics. At the core of this parser is a novel neural graph rewriting system which combines the strengths of Hyperedge Replacement Grammar, a knowledge-intensive model, and Graph Neural Networks, a data-intensive model. Our parser achieves an accuracy of 92.39{\%} in terms of elementary dependency match, which is a 2.88 point improvement over the best data-driven model in the literature. The output of our parser is highly coherent: at least 91{\%} graphs are valid, in that they allow at least one sound scope-resolved logical form.",
}
@inproceedings{zhao-etal-2020-semantic,
    title = "Semantic Parsing for {E}nglish as a Second Language",
    author = "Zhao, Yuanyuan  and
      Sun, Weiwei  and
      Cao, Junjie  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.606",
    doi = "10.18653/v1/2020.acl-main.606",
    pages = "6783--6794",
    abstract = "This paper is concerned with semantic parsing for English as a second language (ESL). Motivated by the theoretical emphasis on the learning challenges that occur at the syntax-semantics interface during second language acquisition, we formulate the task based on the divergence between literal and intended meanings. We combine the complementary strengths of English Resource Grammar, a linguistically-precise hand-crafted deep grammar, and TLE, an existing manually annotated ESL UD-TreeBank with a novel reranking model. Experiments demonstrate that in comparison to human annotations, our method can obtain a very promising SemBanking quality. By means of the newly created corpus, we evaluate state-of-the-art semantic parsing as well as grammatical error correction models. The evaluation profiles the performance of neural NLP techniques for handling ESL data and suggests some research directions.",
}
@inproceedings{jia-etal-2020-semi,
    title = "Semi-Supervised Semantic Dependency Parsing Using {CRF} Autoencoders",
    author = "Jia, Zixia  and
      Ma, Youmi  and
      Cai, Jiong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.607",
    doi = "10.18653/v1/2020.acl-main.607",
    pages = "6795--6805",
    abstract = "Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence. Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph. Our model is arc-factored and therefore parsing and learning are both tractable. Experiments show our model achieves significant and consistent improvement over the supervised baseline.",
}
@inproceedings{cao-etal-2020-unsupervised,
    title = "Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing",
    author = "Cao, Ruisheng  and
      Zhu, Su  and
      Yang, Chenyu  and
      Liu, Chen  and
      Ma, Rao  and
      Zhao, Yanbin  and
      Chen, Lu  and
      Yu, Kai",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.608",
    doi = "10.18653/v1/2020.acl-main.608",
    pages = "6806--6817",
    abstract = "One daunting problem for semantic parsing is the scarcity of annotation. Aiming to reduce nontrivial human labor, we propose a two-stage semantic parsing framework, where the first stage utilizes an unsupervised paraphrase model to convert an unlabeled natural language utterance into the canonical utterance. The downstream naive semantic parser accepts the intermediate output and returns the target logical form. Furthermore, the entire training process is split into two phases: pre-training and cycle learning. Three tailored self-supervised tasks are introduced throughout training to activate the unsupervised paraphrase model. Experimental results on benchmarks Overnight and GeoGranno demonstrate that our framework is effective and compatible with supervised training.",
}
@inproceedings{fu-etal-2020-drts,
    title = "{DRTS} Parsing with Structure-Aware Encoding and Decoding",
    author = "Fu, Qiankun  and
      Zhang, Yue  and
      Liu, Jiangming  and
      Zhang, Meishan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.609",
    doi = "10.18653/v1/2020.acl-main.609",
    pages = "6818--6828",
    abstract = "Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the tree construction as an incremental sequence generation problem. Structural information such as input syntax and the intermediate skeleton of the partial output has been ignored in the model, which could be potentially useful for the DRTS parsing. In this work, we propose a structural-aware model at both the encoder and decoder phase to integrate the structural information, where graph attention network (GAT) is exploited for effectively modeling. Experimental results on a benchmark dataset show that our proposed model is effective and can obtain the best performance in the literature.",
}
@inproceedings{kushilevitz-etal-2020-two,
    title = "A Two-Stage Masked {LM} Method for Term Set Expansion",
    author = "Kushilevitz, Guy  and
      Markovitch, Shaul  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.610",
    doi = "10.18653/v1/2020.acl-main.610",
    pages = "6829--6835",
    abstract = "We tackle the task of Term Set Expansion (TSE): given a small seed set of example terms from a semantic class, finding more members of that class. The task is of great practical utility, and also of theoretical utility as it requires generalization from few examples. Previous approaches to the TSE task can be characterized as either distributional or pattern-based. We harness the power of neural masked language models (MLM) and propose a novel TSE algorithm, which combines the pattern-based and distributional approaches. Due to the small size of the seed set, fine-tuning methods are not effective, calling for more creative use of the MLM. The gist of the idea is to use the MLM to first mine for informative patterns with respect to the seed set, and then to obtain more members of the seed class by generalizing these patterns. Our method outperforms state-of-the-art TSE algorithms. Implementation is available at: https://github.com/ guykush/TermSetExpansion-MPB/",
}
@inproceedings{li-etal-2020-flat,
    title = "{FLAT}: {C}hinese {NER} Using Flat-Lattice Transformer",
    author = "Li, Xiaonan  and
      Yan, Hang  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.611",
    doi = "10.18653/v1/2020.acl-main.611",
    pages = "6836--6842",
    abstract = "Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, since the lattice structure is complex and dynamic, the lattice-based models are hard to fully utilize the parallel computation of GPUs and usually have a low inference speed. In this paper, we propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the lattice structure into a flat structure consisting of spans. Each span corresponds to a character or latent word and its position in the original lattice. With the power of Transformer and well-designed position encoding, FLAT can fully leverage the lattice information and has an excellent parallel ability. Experiments on four datasets show FLAT outperforms other lexicon-based models in performance and efficiency.",
}
@inproceedings{hou-etal-2020-improving,
    title = "Improving Entity Linking through Semantic Reinforced Entity Embeddings",
    author = "Hou, Feng  and
      Wang, Ruili  and
      He, Jun  and
      Zhou, Yi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.612",
    doi = "10.18653/v1/2020.acl-main.612",
    pages = "6843--6848",
    abstract = "Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned from canonical Wikipedia articles and local contexts surrounding target entities. Such entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality. FGS2EE first uses the embeddings of semantic type words to generate semantic embeddings, and then combines them with existing entity embeddings through linear aggregation. Extensive experiments show the effectiveness of such embeddings. Based on our entity embeddings, we achieved new sate-of-the-art performance on entity linking.",
}
@inproceedings{saleh-pecina-2020-document,
    title = "Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain",
    author = "Saleh, Shadi  and
      Pecina, Pavel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.613",
    doi = "10.18653/v1/2020.acl-main.613",
    pages = "6849--6860",
    abstract = "We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation (QT). Our experiments are conducted using the cross-lingual test collection produced within the CLEF eHealth information retrieval tasks in 2013{--}2015 containing English documents and queries in several European languages. We exploit the Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) paradigms and train several domain-specific and task-specific machine translation systems to translate the non-English queries into English (for the QT approach) and the English documents to all the query languages (for the DT approach). The results show that the quality of QT by SMT is sufficient enough to outperform the retrieval results of the DT approach for all the languages. NMT then further boosts translation quality and retrieval quality for both QT and DT for most languages, but still, QT provides generally better retrieval results than DT.",
}
@inproceedings{nguyen-etal-2020-learning,
    title = "Learning Robust Models for e-Commerce Product Search",
    author = "Nguyen, Thanh  and
      Rao, Nikhil  and
      Subbian, Karthik",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.614",
    doi = "10.18653/v1/2020.acl-main.614",
    pages = "6861--6869",
    abstract = "Showing items that do not match search query intent degrades customer experience in e-commerce. These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs. Mitigating the problem requires a large labeled dataset, which is expensive and time-consuming to obtain. In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier. We train the model end-to-end by introducing a latent variable into the cross-entropy loss that alternates between using the real and generated samples. This not only makes the classifier more robust but also boosts the overall ranking performance. Our model achieves a relative gain compared to baselines by over 26{\%} in F-score, and over 17{\%} in Area Under PR curve. On live search traffic, our model gains significant improvement in multiple countries.",
}
@inproceedings{meister-etal-2020-generalized,
    title = "Generalized Entropy Regularization or: There{'}s Nothing Special about Label Smoothing",
    author = "Meister, Clara  and
      Salesky, Elizabeth  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.615",
    doi = "10.18653/v1/2020.acl-main.615",
    pages = "6870--6886",
    abstract = "Prior work has explored directly regularizing the output distributions of probabilistic models to alleviate peaky (i.e. over-confident) predictions, a common sign of overfitting. This class of techniques, of which label smoothing is one, has a connection to entropy regularization. Despite the consistent success of label smoothing across architectures and data sets in language generation tasks, two problems remain open: (1) there is little understanding of the underlying effects entropy regularizers have on models, and (2) the full space of entropy regularization techniques is largely unexplored. We introduce a parametric family of entropy regularizers, which includes label smoothing as a special case, and use it to gain a better understanding of the relationship between the entropy of a model and its performance on language generation tasks. We also find that variance in model performance can be explained largely by the resulting entropy of the model. Lastly, we find that label smoothing provably does not allow for sparsity in an output distribution, an undesirable property for language generation models, and therefore advise the use of other entropy regularization methods in its place.",
}
@inproceedings{chai-etal-2020-highway,
    title = "Highway Transformer: Self-Gating Enhanced Self-Attentive Networks",
    author = "Chai, Yekun  and
      Jin, Shuo  and
      Hou, Xinwen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.616",
    doi = "10.18653/v1/2020.acl-main.616",
    pages = "6887--6900",
    abstract = "Self-attention mechanisms have made striking state-of-the-art (SOTA) progress in various sequence learning tasks, standing on the multi-headed dot product attention by attending to all the global contexts at different locations. Through a pseudo information highway, we introduce a gated component self-dependency units (SDU) that incorporates LSTM-styled gating units to replenish internal semantic importance within the multi-dimensional latent space of individual representations. The subsidiary content-based SDU gates allow for the information flow of modulated latent embeddings through skipped connections, leading to a clear margin of convergence speed with gradient descent algorithms. We may unveil the role of gating mechanism to aid in the context-based Transformer modules, with hypothesizing that SDU gates, especially on shallow layers, could push it faster to step towards suboptimal points during the optimization process.",
}
@inproceedings{chami-etal-2020-low,
    title = "Low-Dimensional Hyperbolic Knowledge Graph Embeddings",
    author = "Chami, Ines  and
      Wolf, Adva  and
      Juan, Da-Cheng  and
      Sala, Frederic  and
      Ravi, Sujith  and
      R{\'e}, Christopher",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.617",
    doi = "10.18653/v1/2020.acl-main.617",
    pages = "6901--6914",
    abstract = "Knowledge graph (KG) embeddings learn low- dimensional representations of entities and relations to predict missing facts. KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space. For hierarchical data, hyperbolic embedding methods have shown promise for high-fidelity and parsimonious representations. However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs. In this work, we introduce a class of hyperbolic KG embedding models that simultaneously capture hierarchical and logical patterns. Our approach combines hyperbolic reflections and rotations with attention to model complex relational patterns. Experimental results on standard KG benchmarks show that our method improves over previous Euclidean- and hyperbolic-based efforts by up to 6.1{\%} in mean reciprocal rank (MRR) in low dimensions. Furthermore, we observe that different geometric transformations capture different types of relations while attention- based transformations generalize to multiple relations. In high dimensions, our approach yields new state-of-the-art MRRs of 49.6{\%} on WN18RR and 57.7{\%} on YAGO3-10.",
}
@inproceedings{karan-etal-2020-classification,
    title = "Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction",
    author = "Karan, Mladen  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.618",
    doi = "10.18653/v1/2020.acl-main.618",
    pages = "6915--6922",
    abstract = "Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure. It gradually expands the initial small seed dictionary to learn improved cross-lingual mappings. In this work, we present ClassyMap, a classification-based approach to self-learning, yielding a more robust and a more effective induction of projection-based CLWEs. Unlike prior self-learning methods, our approach allows for integration of diverse features into the iterative process. We show the benefits of ClassyMap for bilingual lexicon induction: we report consistent improvements in a weakly supervised setup (500 seed translation pairs) on a benchmark with 28 language pairs.",
}
@inproceedings{bentivogli-etal-2020-gender,
    title = "Gender in Danger? Evaluating Speech Translation Technology on the {M}u{ST}-{SHE} Corpus",
    author = "Bentivogli, Luisa  and
      Savoldi, Beatrice  and
      Negri, Matteo  and
      Di Gangi, Mattia A.  and
      Cattoni, Roldano  and
      Turchi, Marco",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.619",
    doi = "10.18653/v1/2020.acl-main.619",
    pages = "6923--6933",
    abstract = "Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).",
}
@inproceedings{zhou-etal-2020-uncertainty,
    title = "Uncertainty-Aware Curriculum Learning for Neural Machine Translation",
    author = "Zhou, Yikai  and
      Yang, Baosong  and
      Wong, Derek F.  and
      Wan, Yu  and
      Chao, Lidia S.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.620",
    doi = "10.18653/v1/2020.acl-main.620",
    pages = "6934--6944",
    abstract = "Neural machine translation (NMT) has proven to be facilitated by curriculum learning which presents examples in an easy-to-hard order at different training stages. The keys lie in the assessment of data difficulty and model competence. We propose uncertainty-aware curriculum learning, which is motivated by the intuition that: 1) the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and 2) the end of the decline in model uncertainty indicates the completeness of current training stage. Specifically, we serve cross-entropy of an example as its data difficulty and exploit the variance of distributions over the weights of the network to present the model uncertainty. Extensive experiments on various translation tasks reveal that our approach outperforms the strong baseline and related methods on both translation quality and convergence speed. Quantitative analyses reveal that the proposed strategy offers NMT the ability to automatically govern its learning schedule.",
}
@inproceedings{lange-etal-2020-closing,
    title = "Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain",
    author = {Lange, Lukas  and
      Adel, Heike  and
      Str{\"o}tgen, Jannik},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.621",
    doi = "10.18653/v1/2020.acl-main.621",
    pages = "6945--6952",
    abstract = "Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only in isolation and does not study the effects of de-identification on other tasks. In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data and investigating joint models for de-identification and concept extraction. In particular, we propose a stacked model with restricted access to privacy sensitive information and a multitask model. We set the new state of the art on benchmark datasets in English (96.1{\%} F1 for de-identification and 88.9{\%} F1 for concept extraction) and Spanish (91.4{\%} F1 for concept extraction).",
}
@inproceedings{wu-etal-2020-corefqa,
    title = "{C}oref{QA}: Coreference Resolution as Query-based Span Prediction",
    author = "Wu, Wei  and
      Wang, Fei  and
      Yuan, Arianna  and
      Wu, Fei  and
      Li, Jiwei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.622",
    doi = "10.18653/v1/2020.acl-main.622",
    pages = "6953--6963",
    abstract = "In this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in question answering: A query is generated for each candidate mention using its surrounding context, and a span prediction module is employed to extract the text spans of the coreferences within the document using the generated query. This formulation comes with the following key advantages: (1) The span prediction strategy provides the flexibility of retrieving mentions left out at the mention proposal stage; (2) In the question answering framework, encoding the mention and its context explicitly in a query makes it possible to have a deep and thorough examination of cues embedded in the context of coreferent mentions; and (3) A plethora of existing question answering datasets can be used for data augmentation to improve the model{'}s generalization capability. Experiments demonstrate significant performance boost over previous models, with 83.1 (+3.5) F1 score on the CoNLL-2012 benchmark and 87.5 (+2.5) F1 score on the GAP benchmark.",
}
@inproceedings{kochkina-liakata-2020-estimating,
    title = "Estimating predictive uncertainty for rumour verification models",
    author = "Kochkina, Elena  and
      Liakata, Maria",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.623",
    doi = "10.18653/v1/2020.acl-main.623",
    pages = "6964--6981",
    abstract = "The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds.",
}
@inproceedings{klie-etal-2020-zero,
    title = "{F}rom {Z}ero to {H}ero: {H}uman-{I}n-{T}he-{L}oop {E}ntity {L}inking in {L}ow {R}esource {D}omains",
    author = "Klie, Jan-Christoph  and
      Eckart de Castilho, Richard  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.624",
    doi = "10.18653/v1/2020.acl-main.624",
    pages = "6982--6993",
    abstract = "Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). It is crucial in a considerable number of fields like humanities, technical writing and biomedical sciences to enrich texts with semantics and discover more knowledge. The use of EL in such domains requires handling noisy texts, low resource settings and domain-specific KBs. Existing approaches are mostly inappropriate for this, as they depend on training data. However, in the above scenario, there exists hardly annotated data, and it needs to be created from scratch. We therefore present a novel domain-agnostic Human-In-The-Loop annotation approach: we use recommenders that suggest potential concepts and adaptive candidate ranking, thereby speeding up the overall annotation process and making it less tedious for users. We evaluate our ranking approach in a simulation on difficult texts and show that it greatly outperforms a strong baseline in ranking accuracy. In a user study, the annotation speed improves by 35{\%} compared to annotating without interactive support; users report that they strongly prefer our system. An open-source and ready-to-use implementation based on the text annotation platform INCEpTION (https://inception-project.github.io) is made available.",
}
@inproceedings{jin-etal-2020-language,
    title = "Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions",
    author = "Jin, Tian  and
      Liu, Zhun  and
      Yan, Shengjia  and
      Eichenberger, Alexandre  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.625",
    doi = "10.18653/v1/2020.acl-main.625",
    pages = "6994--7007",
    abstract = "Transfer learning using ImageNet pre-trained models has been the de facto approach in a wide range of computer vision tasks. However, fine-tuning still requires task-specific training data. In this paper, we propose \textbf{N3} (\textbf{N}eural \textbf{N}etworks from \textbf{N}atural Language) - a new paradigm of synthesizing task-specific neural networks from language descriptions and a generic pre-trained model. \textbf{N3} leverages language descriptions to generate parameter adaptations as well as a new task-specific classification layer for a pre-trained neural network, effectively {``}fine-tuning{''} the network for a new task using only language descriptions as input. To the best of our knowledge, \textbf{N3} is the first method to synthesize entire neural networks from natural language. Experimental results show that \textbf{N3} can out-perform previous natural-language based zero-shot learning methods across 4 different zero-shot image classification benchmarks. We also demonstrate a simple method to help identify keywords in language descriptions leveraged by \textbf{N3} when synthesizing model parameters.",
}
@inproceedings{roit-etal-2020-controlled,
    title = "Controlled Crowdsourcing for High-Quality {QA}-{SRL} Annotation",
    author = "Roit, Paul  and
      Klein, Ayal  and
      Stepanov, Daniela  and
      Mamou, Jonathan  and
      Michael, Julian  and
      Stanovsky, Gabriel  and
      Zettlemoyer, Luke  and
      Dagan, Ido",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.626",
    doi = "10.18653/v1/2020.acl-main.626",
    pages = "7008--7013",
    abstract = "Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations.",
}
@inproceedings{fei-etal-2020-cross,
    title = "Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus",
    author = "Fei, Hao  and
      Zhang, Meishan  and
      Ji, Donghong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.627",
    doi = "10.18653/v1/2020.acl-main.627",
    pages = "7014--7026",
    abstract = "Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.",
}
@inproceedings{poerner-etal-2020-sentence,
    title = "Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity",
    author = {Poerner, Nina  and
      Waltinger, Ulli  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.628",
    doi = "10.18653/v1/2020.acl-main.628",
    pages = "7027--7034",
    abstract = {We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings. We apply, extend and evaluate different meta-embedding methods from the word embedding literature at the sentence level, including dimensionality reduction (Yin and Sch{\"u}tze, 2016), generalized Canonical Correlation Analysis (Rastogi et al., 2015) and cross-view auto-encoders (Bollegala and Bao, 2018). Our sentence meta-embeddings set a new unsupervised State of The Art (SoTA) on the STS Benchmark and on the STS12-STS16 datasets, with gains of between 3.7{\%} and 6.4{\%} Pearson{'}s r over single-source systems.},
}
@inproceedings{fernandez-gonzalez-gomez-rodriguez-2020-transition,
    title = "Transition-based Semantic Dependency Parsing with Pointer Networks",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.629",
    doi = "10.18653/v1/2020.acl-main.629",
    pages = "7035--7046",
    abstract = "Transition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task. In order to further test the capabilities of these powerful neural networks on a harder NLP problem, we propose a transition system that, thanks to Pointer Networks, can straightforwardly produce labelled directed acyclic graphs and perform semantic dependency parsing. In addition, we enhance our approach with deep contextualized word embeddings extracted from BERT. The resulting system not only outperforms all existing transition-based models, but also matches the best fully-supervised accuracy to date on the SemEval 2015 Task 18 datasets among previous state-of-the-art graph-based parsers.",
}
@inproceedings{peinelt-etal-2020-tbert,
    title = "t{BERT}: Topic Models and {BERT} Joining Forces for Semantic Similarity Detection",
    author = "Peinelt, Nicole  and
      Nguyen, Dong  and
      Liakata, Maria",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.630",
    doi = "10.18653/v1/2020.acl-main.630",
    pages = "7047--7055",
    abstract = "Semantic similarity detection is a fundamental task in natural language understanding. Adding topic information has been useful for previous feature-engineered semantic similarity models as well as neural models for other tasks. There is currently no standard way of combining topics with pretrained contextual representations such as BERT. We propose a novel topic-informed BERT-based architecture for pairwise semantic similarity detection and show that our model improves performance over strong neural baselines across a variety of English language datasets. We find that the addition of topics to BERT helps particularly with resolving domain-specific cases.",
}
@inproceedings{li-etal-2020-conditional,
    title = "Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation",
    author = "Li, Kun  and
      Chen, Chengbo  and
      Quan, Xiaojun  and
      Ling, Qing  and
      Song, Yan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.631",
    doi = "10.18653/v1/2020.acl-main.631",
    pages = "7056--7066",
    abstract = "Aspect term extraction aims to extract aspect terms from review texts as opinion targets for sentiment analysis. One of the big challenges with this task is the lack of sufficient annotated data. While data augmentation is potentially an effective technique to address the above issue, it is uncontrollable as it may change aspect words and aspect labels unexpectedly. In this paper, we formulate the data augmentation as a conditional generation task: generating a new sentence while preserving the original opinion targets and labels. We propose a masked sequence-to-sequence method for conditional augmentation of aspect term extraction. Unlike existing augmentation approaches, ours is controllable and allows to generate more diversified sentences. Experimental results confirm that our method alleviates the data scarcity problem significantly. It also effectively boosts the performances of several current models for aspect term extraction.",
}
@inproceedings{al-khatib-etal-2020-exploiting,
    title = "Exploiting Personal Characteristics of Debaters for Predicting Persuasiveness",
    author = {Al Khatib, Khalid  and
      V{\"o}lske, Michael  and
      Syed, Shahbaz  and
      Kolyada, Nikolay  and
      Stein, Benno},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.632",
    doi = "10.18653/v1/2020.acl-main.632",
    pages = "7067--7072",
    abstract = "Predicting the persuasiveness of arguments has applications as diverse as writing assistance, essay scoring, and advertising. While clearly relevant to the task, the personal characteristics of an argument{'}s source and audience have not yet been fully exploited toward automated persuasiveness prediction. In this paper, we model debaters{'} prior beliefs, interests, and personality traits based on their previous activity, without dependence on explicit user profiles or questionnaires. Using a dataset of over 60,000 argumentative discussions, comprising more than three million individual posts collected from the subreddit r/ChangeMyView, we demonstrate that our modeling of debater{'}s characteristics enhances the prediction of argument persuasiveness as well as of debaters{'} resistance to persuasion.",
}
@inproceedings{orbach-etal-2020-echo,
    title = "Out of the Echo Chamber: {D}etecting Countering Debate Speeches",
    author = "Orbach, Matan  and
      Bilu, Yonatan  and
      Toledo, Assaf  and
      Lahav, Dan  and
      Jacovi, Michal  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.633",
    doi = "10.18653/v1/2020.acl-main.633",
    pages = "7073--7086",
    abstract = "An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in {``}echo chambers{''} and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns {--} that of detecting articles that most effectively counter the arguments {--} and not just the stance {--} made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.",
}
@inproceedings{su-etal-2020-diversifying,
    title = "Diversifying Dialogue Generation with Non-Conversational Text",
    author = "Su, Hui  and
      Shen, Xiaoyu  and
      Zhao, Sanqiang  and
      Xiao, Zhou  and
      Hu, Pengwei  and
      Zhong, Randy  and
      Niu, Cheng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.634",
    doi = "10.18653/v1/2020.acl-main.634",
    pages = "7087--7097",
    abstract = "Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective. In this paper, we propose a new perspective to diversify dialogue generation by leveraging \textit{non-conversational} text. Compared with bilateral conversations, non-conversational text are easier to obtain, more diverse and cover a much broader range of topics. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets. We further present a training paradigm to effectively incorporate these text via iterative back translation. The resulting model is tested on two conversational datasets from different domains and is shown to produce significantly more diverse responses without sacrificing the relevance with context.",
}
@inproceedings{zhou-etal-2020-kdconv,
    title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
    author = "Zhou, Hao  and
      Zheng, Chujie  and
      Huang, Kaili  and
      Huang, Minlie  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.635",
    doi = "10.18653/v1/2020.acl-main.635",
    pages = "7098--7108",
    abstract = "The research of knowledge-driven conversational systems is largely limited due to the lack of dialog data which consists of multi-turn conversations on multiple topics and with knowledge annotations. In this paper, we propose a Chinese multi-domain knowledge-driven conversation dataset, KdConv, which grounds the topics in multi-turn conversations to knowledge graphs. Our corpus contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics. To facilitate the following research on this corpus, we provide several benchmark models. Comparative results show that the models can be enhanced by introducing background knowledge, yet there is still a large space for leveraging knowledge to model multi-turn conversations for further research. Results also show that there are obvious performance differences between different domains, indicating that it is worth further explore transfer learning and domain adaptation. The corpus and benchmark models are publicly available.",
}
@inproceedings{huang-etal-2020-meta,
    title = "Meta-Reinforced Multi-Domain State Generator for Dialogue Systems",
    author = "Huang, Yi  and
      Feng, Junlan  and
      Hu, Min  and
      Wu, Xiaoting  and
      Du, Xiaoyu  and
      Ma, Shuo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.636",
    doi = "10.18653/v1/2020.acl-main.636",
    pages = "7109--7118",
    abstract = "A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system. Tremendous progress has been made in recent years. However, the major challenges remain. The state-of-the-art accuracy for DST is below 50{\%} for a multi-domain dialogue task. A learnable DST for any new domain requires a large amount of labeled in-domain data and training from scratch. In this paper, we propose a Meta-Reinforced Multi-Domain State Generator (MERET). Our first contribution is to improve the DST accuracy. We enhance a neural model based DST generator with a reward manager, which is built on policy gradient reinforcement learning (RL) to fine-tune the generator. With this change, we are able to improve the joint accuracy of DST from 48.79{\%} to 50.91{\%} on the MultiWOZ corpus. Second, we explore to train a DST meta-learning model with a few domains as source domains and a new domain as target domain. We apply the model-agnostic meta-learning algorithm (MAML) to DST and the obtained meta-learning model is used for new domain adaptation. Our experimental results show this solution is able to outperform the traditional training approach with extremely less training data in target domain.",
}
@inproceedings{quan-xiong-2020-modeling,
    title = "Modeling Long Context for Task-Oriented Dialogue State Generation",
    author = "Quan, Jun  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.637",
    doi = "10.18653/v1/2020.acl-main.637",
    pages = "7119--7124",
    abstract = "Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. By enabling the model to learn a better representation of the long dialogue context, our approaches attempt to solve the problem that the performance of the baseline significantly drops when the input dialogue context sequence is long. In our experiments, our proposed model achieves a 7.03{\%} relative improvement over the baseline, establishing a new state-of-the-art joint goal accuracy of 52.04{\%} on the MultiWOZ 2.0 dataset.",
}
@inproceedings{wang-etal-2020-multi,
    title = "Multi-Domain Dialogue Acts and Response Co-Generation",
    author = "Wang, Kai  and
      Tian, Junfeng  and
      Wang, Rui  and
      Quan, Xiaojun  and
      Yu, Jianxing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.638",
    doi = "10.18653/v1/2020.acl-main.638",
    pages = "7125--7134",
    abstract = "Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of multi-domain dialogue acts are neglected. Second, the semantic associations between acts and responses are not taken into account for response generation. To address these issues, we propose a neural co-generation model that generates dialogue acts and responses concurrently. Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed. We train the two modules jointly using an uncertainty loss to adjust their task weights adaptively. Extensive experiments are conducted on the large-scale MultiWOZ dataset and the results show that our model achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.",
}
@inproceedings{zhou-etal-2020-exploring,
    title = "Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer",
    author = "Zhou, Chulun  and
      Chen, Liangyu  and
      Liu, Jiachen  and
      Xiao, Xinyan  and
      Su, Jinsong  and
      Guo, Sheng  and
      Wu, Hua",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.639",
    doi = "10.18653/v1/2020.acl-main.639",
    pages = "7135--7144",
    abstract = "Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data. In current dominant approaches, owing to the lack of fine-grained control on the influence from the target style, they are unable to yield desirable output sentences. In this paper, we propose a novel attentional sequence-to-sequence (Seq2seq) model that dynamically exploits the relevance of each output word to the target style for unsupervised style transfer. Specifically, we first pretrain a style classifier, where the relevance of each input word to the original style can be quantified via layer-wise relevance propagation. In a denoising auto-encoding manner, we train an attentional Seq2seq model to reconstruct input sentences and repredict word-level previously-quantified style relevance simultaneously. In this way, this model is endowed with the ability to automatically predict the style relevance of each output word. Then, we equip the decoder of this model with a neural style component to exploit the predicted wordlevel style relevance for better style transfer. Particularly, we fine-tune this model using a carefully-designed objective function involving style transfer, style relevance consistency, content preservation and fluency modeling loss terms. Experimental results show that our proposed model achieves state-of-the-art performance in terms of both transfer accuracy and content preservation.",
}
@inproceedings{yao-etal-2020-heterogeneous,
    title = "Heterogeneous Graph Transformer for Graph-to-Sequence Learning",
    author = "Yao, Shaowei  and
      Wang, Tianming  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.640",
    doi = "10.18653/v1/2020.acl-main.640",
    pages = "7145--7154",
    abstract = "The graph-to-sequence (Graph2Seq) learning aims to transduce graph-structured representations to word sequences for text generation. Recent studies propose various models to encode graph structure. However, most previous works ignore the indirect relations between distance nodes, or treat indirect relations and direct relations in the same way. In this paper, we propose the Heterogeneous Graph Transformer to independently model the different relations in the individual subgraphs of the original graph, including direct relations, indirect relations and multiple possible relations between nodes. Experimental results show that our model strongly outperforms the state of the art on all four standard benchmarks of AMR-to-text generation and syntax-based neural machine translation.",
}
@inproceedings{shen-etal-2020-neural,
    title = "Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence",
    author = "Shen, Xiaoyu  and
      Chang, Ernie  and
      Su, Hui  and
      Niu, Cheng  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.641",
    doi = "10.18653/v1/2020.acl-main.641",
    pages = "7155--7165",
    abstract = "The neural attention model has achieved great success in data-to-text generation tasks. Though usually excelling at producing fluent text, it suffers from the problem of information missing, repetition and {``}hallucination{''}. Due to the black-box nature of the neural attention architecture, avoiding these problems in a systematic way is non-trivial. To address this concern, we propose to explicitly segment target text into fragment units and align them with their data correspondences. The segmentation and correspondence are jointly learned as latent variables without any human annotations. We further impose a soft statistical constraint to regularize the segmental granularity. The resulting architecture maintains the same expressive power as neural attention models, while being able to generate fully interpretable outputs with several times less computational cost. On both E2E and WebNLG benchmarks, we show the proposed model consistently outperforms its neural attention counterparts.",
}
@inproceedings{huang-etal-2020-aligned,
    title = "Aligned Dual Channel Graph Convolutional Network for Visual Question Answering",
    author = "Huang, Qingbao  and
      Wei, Jielong  and
      Cai, Yi  and
      Zheng, Changmeng  and
      Chen, Junying  and
      Leung, Ho-fung  and
      Li, Qing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.642",
    doi = "10.18653/v1/2020.acl-main.642",
    pages = "7166--7176",
    abstract = "Visual question answering aims to answer the natural language question about a given image. Existing graph-based methods only focus on the relations between objects in an image and neglect the importance of the syntactic dependency relations between words in a question. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. The DC-GCN model consists of three parts: an I-GCN module to capture the relations between objects in an image, a Q-GCN module to capture the syntactic dependency relations between words in a question, and an attention alignment module to align image representations and question representations. Experimental results show that our model achieves comparable performance with the state-of-the-art approaches.",
}
@inproceedings{khademi-2020-multimodal,
    title = "Multimodal Neural Graph Memory Networks for Visual Question Answering",
    author = "Khademi, Mahmoud",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.643",
    doi = "10.18653/v1/2020.acl-main.643",
    pages = "7177--7188",
    abstract = "We introduce a new neural network architecture, Multimodal Neural Graph Memory Networks (MN-GMN), for visual question answering. The MN-GMN uses graph structure with different region features as node attributes and applies a recently proposed powerful graph neural network model, Graph Network (GN), to reason about objects and their interactions in an image. The input module of the MN-GMN generates a set of visual features plus a set of encoded region-grounded captions (RGCs) for the image. The RGCs capture object attributes and their relationships. Two GNs are constructed from the input module using the visual features and encoded RGCs. Each node of the GNs iteratively computes a question-guided contextualized representation of the visual/textual information assigned to it. Then, to combine the information from both GNs, the nodes write the updated representations to an external spatial memory. The final states of the memory cells are fed into an answer module to predict an answer. Experiments show MN-GMN rivals the state-of-the-art models on Visual7W, VQA-v2.0, and CLEVR datasets.",
}
@inproceedings{cirik-etal-2020-refer360,
    title = "Refer360$^{\circ}$: A Referring Expression Recognition Dataset in 360$^{\circ}$ Images",
    author = "Cirik, Volkan  and
      Berg-Kirkpatrick, Taylor  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.644",
    doi = "10.18653/v1/2020.acl-main.644",
    pages = "7189--7202",
    abstract = "We propose a novel large-scale referring expression recognition dataset, Refer360{\mbox{$^\circ$}}, consisting of 17,137 instruction sequences and ground-truth actions for completing these instructions in 360{\mbox{$^\circ$}} scenes. Refer360{\mbox{$^\circ$}} differs from existing related datasets in three ways. First, we propose a more realistic scenario where instructors and the followers have partial, yet dynamic, views of the scene {--} followers continuously modify their field-of-view (FoV) while interpreting instructions that specify a final target location. Second, instructions to find the target location consist of multiple steps for followers who will start at random FoVs. As a result, intermediate instructions are strongly grounded in object references, and followers must identify intermediate FoVs to find the final target location correctly. Third, the target locations are neither restricted to predefined objects nor chosen by annotators; instead, they are distributed randomly across scenes. This {``}point anywhere{''} approach leads to more linguistically complex instructions, as shown in our analyses. Our examination of the dataset shows that Refer360{\mbox{$^\circ$}} manifests linguistically rich phenomena in a language grounding task that poses novel challenges for computational modeling of language, vision, and navigation.",
}
@inproceedings{martin-etal-2020-camembert,
    title = "{C}amem{BERT}: a Tasty {F}rench Language Model",
    author = "Martin, Louis  and
      Muller, Benjamin  and
      Ortiz Su{\'a}rez, Pedro Javier  and
      Dupont, Yoann  and
      Romary, Laurent  and
      de la Clergerie, {\'E}ric  and
      Seddah, Djam{\'e}  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.645",
    doi = "10.18653/v1/2020.acl-main.645",
    pages = "7203--7219",
    abstract = "Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models {--}in all languages except English{--} very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks.",
}
@inproceedings{pelsmaeker-aziz-2020-effective,
    title = "Effective Estimation of Deep Generative Language Models",
    author = "Pelsmaeker, Tom  and
      Aziz, Wilker",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.646",
    doi = "10.18653/v1/2020.acl-main.646",
    pages = "7220--7236",
    abstract = "Advances in variational inference enable parameterisation of probabilistic models by deep neural networks. This combines the statistical transparency of the probabilistic modelling framework with the representational power of deep learning. Yet, due to a problem known as posterior collapse, it is difficult to estimate such models in the context of language modelling effectively. We concentrate on one such model, the variational auto-encoder, which we argue is an important building block in hierarchical probabilistic models of language. This paper contributes a sober view of the problem, a survey of techniques to address it, novel techniques, and extensions to the model. To establish a ranking of techniques, we perform a systematic comparison using Bayesian optimisation and find that many techniques perform reasonably similar, given enough resources. Still, a favourite can be named based on convenience. We also make several empirical observations and recommendations of best practices that should help researchers interested in this exciting field.",
}
@inproceedings{ravfogel-etal-2020-null,
    title = "Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection",
    author = "Ravfogel, Shauli  and
      Elazar, Yanai  and
      Gonen, Hila  and
      Twiton, Michael  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.647",
    doi = "10.18653/v1/2020.acl-main.647",
    pages = "7237--7256",
    abstract = "The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We present Iterative Null-space Projection (INLP), a novel method for removing information from neural representations. Our method is based on repeated training of linear classifiers that predict a certain property we aim to remove, followed by projection of the representations on their null-space. By doing so, the classifiers become oblivious to that target property, making it hard to linearly separate the data according to it. While applicable for multiple uses, we evaluate our method on bias and fairness use-cases, and show that our method is able to mitigate bias in word embeddings, as well as to increase fairness in a setting of multi-class classification.",
}
@inproceedings{a-augenstein-2020-2kenize,
    title = "2kenize: Tying Subword Sequences for {C}hinese Script Conversion",
    author = "A, Pranav  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.648",
    doi = "10.18653/v1/2020.acl-main.648",
    pages = "7257--7272",
    abstract = "Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have insufficient performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method{'}s particular strengths are in dealing with code mixing and named entities.",
}
@inproceedings{hofmann-etal-2020-predicting,
    title = "Predicting the Growth of Morphological Families from Social and Linguistic Factors",
    author = {Hofmann, Valentin  and
      Pierrehumbert, Janet  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.649",
    doi = "10.18653/v1/2020.acl-main.649",
    pages = "7273--7283",
    abstract = "We present the first study that examines the evolution of morphological families, i.e., sets of morphologically related words such as {``}trump{''}, {``}antitrumpism{''}, and {``}detrumpify{''}, in social media. We introduce the novel task of Morphological Family Expansion Prediction (MFEP) as predicting the increase in the size of a morphological family. We create a ten-year Reddit corpus as a benchmark for MFEP and evaluate a number of baselines on this benchmark. Our experiments demonstrate very good performance on MFEP.",
}
@inproceedings{makarov-clematide-2020-semi,
    title = "Semi-supervised Contextual Historical Text Normalization",
    author = "Makarov, Peter  and
      Clematide, Simon",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.650",
    doi = "10.18653/v1/2020.acl-main.650",
    pages = "7284--7295",
    abstract = "Historical text normalization, the task of mapping historical word forms to their modern counterparts, has recently attracted a lot of interest (Bollmann, 2019; Tang et al., 2018; Lusetti et al., 2018; Bollmann et al., 2018;Robertson and Goldwater, 2018; Bollmannet al., 2017; Korchagina, 2017). Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation. By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data. In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.",
}
@inproceedings{kumar-black-2020-clarq,
    title = "{C}lar{Q}: A large-scale and diverse dataset for Clarification Question Generation",
    author = "Kumar, Vaibhav  and
      Black, Alan W",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.651",
    doi = "10.18653/v1/2020.acl-main.651",
    pages = "7296--7301",
    abstract = "Question answering and conversational systems are often baffled and need help clarifying certain ambiguities. However, limitations of existing datasets hinder the development of large-scale models capable of generating and utilising clarification questions. In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange. The framework utilises a neural network based architecture for classifying clarification questions. It is a two-step method where the first aims to increase the precision of the classifier and second aims to increase its recall. We quantitatively demonstrate the utility of the newly created dataset by applying it to the downstream task of question-answering. The final dataset, ClarQ, consists of {\textasciitilde}2M examples distributed across 173 domains of stackexchange. We release this dataset in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems.",
}
@inproceedings{campos-etal-2020-doqa,
    title = "{D}o{QA} - Accessing Domain-Specific {FAQ}s via Conversational {QA}",
    author = "Campos, Jon Ander  and
      Otegi, Arantxa  and
      Soroa, Aitor  and
      Deriu, Jan  and
      Cieliebak, Mark  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.652",
    doi = "10.18653/v1/2020.acl-main.652",
    pages = "7302--7314",
    abstract = "The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing. Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain. In addition, we introduce a more realistic information retrieval (IR) scenario where the system needs to find the answer in any of the FAQ documents. The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without in-domain training data. The good results carry over into the more challenging IR scenario. In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.",
}
@inproceedings{lewis-etal-2020-mlqa,
    title = "{MLQA}: Evaluating Cross-lingual Extractive Question Answering",
    author = "Lewis, Patrick  and
      Oguz, Barlas  and
      Rinott, Ruty  and
      Riedel, Sebastian  and
      Schwenk, Holger",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.653",
    doi = "10.18653/v1/2020.acl-main.653",
    pages = "7315--7330",
    abstract = "Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making building QA systems that work well in other languages challenging. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA has over 12K instances in English and 5K in each other language, with each instance parallel between 4 languages on average. We evaluate state-of-the-art cross-lingual models and machine-translation-based baselines on MLQA. In all cases, transfer results are shown to be significantly behind training-language performance.",
}
@inproceedings{yan-etal-2020-multi,
    title = "Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering",
    author = "Yan, Ming  and
      Zhang, Hao  and
      Jin, Di  and
      Zhou, Joey Tianyi",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.654",
    doi = "10.18653/v1/2020.acl-main.654",
    pages = "7331--7341",
    abstract = "Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations. Unfortunately, most existing MCQA datasets are small in size, which increases the difficulty of model learning and generalization. To address this challenge, we propose a multi-source meta transfer (MMT) for low-resource MCQA. In this framework, we first extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains. To bridge the distribution gap between training sources and the target, we further introduce the meta transfer that can be integrated into the multi-source meta training. More importantly, the proposed MMT is independent of backbone language models. Extensive experiments demonstrate the superiority of MMT over state-of-the-arts, and continuous improvements can be achieved on different backbone networks on both supervised and unsupervised domain adaptation settings.",
}
@inproceedings{liu-etal-2020-fine,
    title = "Fine-grained Fact Verification with Kernel Graph Attention Network",
    author = "Liu, Zhenghao  and
      Xiong, Chenyan  and
      Sun, Maosong  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.655",
    doi = "10.18653/v1/2020.acl-main.655",
    pages = "7342--7351",
    abstract = "Fact Verification requires fine-grained natural language inference capability that finds subtle clues to identify the syntactical and semantically correct but not well-supported claims. This paper presents Kernel Graph Attention Network (KGAT), which conducts more fine-grained fact verification with kernel-based attentions. Given a claim and a set of potential evidence sentences that form an evidence graph, KGAT introduces node kernels, which better measure the importance of the evidence node, and edge kernels, which conduct fine-grained evidence propagation in the graph, into Graph Attention Networks for more accurate fact verification. KGAT achieves a 70.38{\%} FEVER score and significantly outperforms existing fact verification models on FEVER, a large-scale benchmark for fact verification. Our analyses illustrate that, compared to dot-product attentions, the kernel-based attention concentrates more on relevant evidence sentences and meaningful clues in the evidence graph, which is the main source of KGAT{'}s effectiveness. All source codes of this work are available at https://github.com/thunlp/KernelGAT.",
}
@inproceedings{atanasova-etal-2020-generating,
    title = "Generating Fact Checking Explanations",
    author = "Atanasova, Pepa  and
      Simonsen, Jakob Grue  and
      Lioma, Christina  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.656",
    doi = "10.18653/v1/2020.acl-main.656",
    pages = "7352--7364",
    abstract = "Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process {--} generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.",
}
@inproceedings{ferreira-freitas-2020-premise,
    title = "Premise Selection in Natural Language Mathematical Texts",
    author = "Ferreira, Deborah  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.657",
    doi = "10.18653/v1/2020.acl-main.657",
    pages = "7365--7374",
    abstract = "The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement. We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks. This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.",
}
@inproceedings{artetxe-etal-2020-call,
    title = "A Call for More Rigor in Unsupervised Cross-lingual Learning",
    author = "Artetxe, Mikel  and
      Ruder, Sebastian  and
      Yogatama, Dani  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.658",
    doi = "10.18653/v1/2020.acl-main.658",
    pages = "7375--7388",
    abstract = "We review motivations, definition, approaches, and methodology for unsupervised cross-lingual learning and call for a more rigorous position in each of them. An existing rationale for such research is based on the lack of parallel data for many of the world{'}s languages. However, we argue that a scenario without any parallel data and abundant monolingual data is unrealistic in practice. We also discuss different training signals that have been used in previous work, which depart from the pure unsupervised setting. We then describe common methodological issues in tuning and evaluation of unsupervised cross-lingual models and present best practices. Finally, we provide a unified outlook for different types of research in this area (i.e., cross-lingual word embeddings, deep multilingual pretraining, and unsupervised machine translation) and argue for comparable evaluation of these models.",
}
@inproceedings{hall-maudslay-etal-2020-tale,
    title = "A Tale of a Probe and a Parser",
    author = "Hall Maudslay, Rowan  and
      Valvoda, Josef  and
      Pimentel, Tiago  and
      Williams, Adina  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.659",
    doi = "10.18653/v1/2020.acl-main.659",
    pages = "7389--7395",
    abstract = "Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training {``}probes{''}{---}supervised models designed to extract linguistic structure from another model{'}s output. One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations. The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious. To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation. The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English). Under a second less common metric, however, there is the opposite trend{---}the structural probe outperforms the parser. This begs the question: which metric should we prefer?",
}
@inproceedings{tsarfaty-etal-2020-spmrl,
    title = "From {SPMRL} to {NMRL}: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages ({MRL}s)?",
    author = "Tsarfaty, Reut  and
      Bareket, Dan  and
      Klein, Stav  and
      Seker, Amit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.660",
    doi = "10.18653/v1/2020.acl-main.660",
    pages = "7396--7408",
    abstract = "It has been exactly a decade since the first establishment of SPMRL, a research initiative unifying multiple research efforts to address the peculiar challenges of Statistical Parsing for Morphologically-Rich Languages (MRLs). Here we reflect on parsing MRLs in that decade, highlight the solutions and lessons learned for the architectural, modeling and lexical challenges in the pre-neural era, and argue that similar challenges re-emerge in neural architectures for MRLs. We then aim to offer a climax, suggesting that incorporating symbolic ideas proposed in SPMRL terms into nowadays neural architectures has the potential to push NLP for MRLs to a new level. We sketch a strategies for designing Neural Models for MRLs (NMRL), and showcase preliminary support for these strategies via investigating the task of multi-tagging in Hebrew, a morphologically-rich, high-fusion, language.",
}
@inproceedings{sperber-paulik-2020-speech,
    title = "Speech Translation and the End-to-End Promise: Taking Stock of Where We Are",
    author = "Sperber, Matthias  and
      Paulik, Matthias",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.661",
    doi = "10.18653/v1/2020.acl-main.661",
    pages = "7409--7421",
    abstract = "Over its three decade history, speech translation has experienced several shifts in its primary research themes; moving from loosely coupled cascades of speech recognition and machine translation, to exploring questions of tight coupling, and finally to end-to-end models that have recently attracted much attention. This paper provides a brief survey of these developments, along with a discussion of the main challenges of traditional approaches which stem from committing to intermediate representations from the speech recognizer, and from training cascaded models separately towards different objectives. Recent end-to-end modeling techniques promise a principled way of overcoming these issues by allowing joint training of all model components and removing the need for explicit intermediate representations. However, a closer look reveals that many end-to-end models fall short of solving these issues, due to compromises made to address data scarcity. This paper provides a unifying categorization and nomenclature that covers both traditional and recent approaches and that may help researchers by highlighting both trade-offs and open research questions.",
}
@inproceedings{boyd-graber-borschinger-2020-question,
    title = "What Question Answering can Learn from Trivia Nerds",
    author = {Boyd-Graber, Jordan  and
      B{\"o}rschinger, Benjamin},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.662",
    doi = "10.18653/v1/2020.acl-main.662",
    pages = "7422--7435",
    abstract = "In addition to the traditional task of machines answering questions, question answering (QA) research creates interesting, challenging questions that help systems how to answer questions and reveal the best systems. We argue that creating a QA dataset{---}and the ubiquitous leaderboard that goes with it{---}closely resembles running a trivia tournament: you write questions, have agents (either humans or machines) answer the questions, and declare a winner. However, the research community has ignored the hard-learned lessons from decades of the trivia community creating vibrant, fair, and effective question answering competitions. After detailing problems with existing QA datasets, we outline the key lessons{---}removing ambiguity, discriminating skill, and adjudicating disputes{---}that can transfer to QA research and how they might be implemented.",
}
@inproceedings{emerson-2020-goals,
    title = "What are the Goals of Distributional Semantics?",
    author = "Emerson, Guy",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.663",
    doi = "10.18653/v1/2020.acl-main.663",
    pages = "7436--7453",
    abstract = "Distributional semantic models have become a mainstay in NLP, providing useful features for downstream tasks. However, assessing long-term progress requires explicit long-term goals. In this paper, I take a broad linguistic perspective, looking at how well current models can deal with various semantic challenges. Given stark differences between models proposed in different subfields, a broad perspective is needed to see how we could integrate them. I conclude that, while linguistic insights can guide the design of model architectures, future progress will require balancing the often conflicting demands of linguistic expressiveness and computational tractability.",
}
@inproceedings{shi-etal-2020-improving,
    title = "Improving Image Captioning with Better Use of Caption",
    author = "Shi, Zhan  and
      Zhou, Xu  and
      Qiu, Xipeng  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.664",
    doi = "10.18653/v1/2020.acl-main.664",
    pages = "7454--7464",
    abstract = "Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. The representation is then enhanced with neighbouring and contextual nodes with their textual and visual features. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences. We perform extensive experiments on the MSCOCO dataset, showing that the proposed framework significantly outperforms the baselines, resulting in the state-of-the-art performance under a wide range of evaluation metrics. The code of our paper has been made publicly available.",
}
@inproceedings{elder-etal-2020-shape,
    title = "{S}hape of Synth to Come: {W}hy We Should Use Synthetic Data for {E}nglish Surface Realization",
    author = "Elder, Henry  and
      Burke, Robert  and
      O{'}Connor, Alexander  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.665",
    doi = "10.18653/v1/2020.acl-main.665",
    pages = "7465--7471",
    abstract = "The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language Generation shared tasks with the goal of exploring approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages. In the 2018 shared task there was very little difference in the absolute performance of systems trained with and without additional, synthetically created data, and a new rule prohibiting the use of synthetic data was introduced for the 2019 shared task. Contrary to the findings of the 2018 shared task, we show, in experiments on the English 2018 dataset, that the use of synthetic data can have a substantial positive effect {--} an improvement of almost 8 BLEU points for a previously state-of-the-art system. We analyse the effects of synthetic data, and we argue that its use should be encouraged rather than prohibited so that future research efforts continue to explore systems that can take advantage of such data.",
}
@inproceedings{ippolito-etal-2020-toward,
    title = "Toward Better Storylines with Sentence-Level Language Models",
    author = "Ippolito, Daphne  and
      Grangier, David  and
      Eck, Douglas  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.666",
    doi = "10.18653/v1/2020.acl-main.666",
    pages = "7472--7478",
    abstract = "We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. Rather than dealing with individual words, our method treats the story so far as a list of pre-trained sentence embeddings and predicts an embedding for the next sentence, which is more efficient than predicting word embeddings. Notably this allows us to consider a large number of candidates for the next sentence during training. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task and with promising results on larger-scale next sentence prediction tasks.",
}
@inproceedings{zhang-etal-2020-two,
    title = "A Two-Step Approach for Implicit Event Argument Detection",
    author = "Zhang, Zhisong  and
      Kong, Xiang  and
      Liu, Zhengzhong  and
      Ma, Xuezhe  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.667",
    doi = "10.18653/v1/2020.acl-main.667",
    pages = "7479--7485",
    abstract = "In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task.",
}
@inproceedings{honovich-etal-2020-machine,
    title = "Machine Reading of Historical Events",
    author = "Honovich, Or  and
      Torroba Hennigen, Lucas  and
      Abend, Omri  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.668",
    doi = "10.18653/v1/2020.acl-main.668",
    pages = "7486--7497",
    abstract = "Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it. Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of task-specific and general-purpose feature embeddings for the classification. Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.",
}
@inproceedings{tran-etal-2020-revisiting,
    title = "Revisiting Unsupervised Relation Extraction",
    author = "Tran, Thy Thy  and
      Le, Phong  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.669",
    doi = "10.18653/v1/2020.acl-main.669",
    pages = "7498--7505",
    abstract = "Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstrate that by using only named entities to induce relation types, we can outperform existing methods on two popular datasets. We conduct a comparison and evaluation of our findings with other URE techniques, to ascertain the important features in URE. We conclude that entity types provide a strong inductive bias for URE.",
}
@inproceedings{jain-etal-2020-scirex,
    title = "{S}ci{REX}: {A} Challenge Dataset for Document-Level Information Extraction",
    author = "Jain, Sarthak  and
      van Zuylen, Madeleine  and
      Hajishirzi, Hannaneh  and
      Beltagy, Iz",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.670",
    doi = "10.18653/v1/2020.acl-main.670",
    pages = "7506--7516",
    abstract = "Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https://github.com/allenai/SciREX .",
}
@inproceedings{klein-nabi-2020-contrastive,
    title = "Contrastive Self-Supervised Learning for Commonsense Reasoning",
    author = "Klein, Tassilo  and
      Nabi, Moin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.671",
    doi = "10.18653/v1/2020.acl-main.671",
    pages = "7517--7523",
    abstract = "We propose a self-supervised method to solve Pronoun Disambiguation and Winograd Schema Challenge problems. Our approach exploits the characteristic structure of training corpora related to so-called {``}trigger{''} words, which are responsible for flipping the answer in pronoun disambiguation. We achieve such commonsense reasoning by constructing pair-wise contrastive auxiliary predictions. To this end, we leverage a mutual exclusive loss regularized by a contrastive margin. Our architecture is based on the recently introduced transformer networks, BERT, that exhibits strong performance on many NLP benchmarks. Empirical results show that our method alleviates the limitation of current supervised approaches for commonsense reasoning. This study opens up avenues for exploiting inexpensive self-supervision to achieve performance gain in commonsense reasoning tasks.",
}
@inproceedings{rae-razavi-2020-transformers,
    title = "Do Transformers Need Deep Long-Range Memory?",
    author = "Rae, Jack  and
      Razavi, Ali",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.672",
    doi = "10.18653/v1/2020.acl-main.672",
    pages = "7524--7529",
    abstract = "Deep attention models have advanced the modelling of sequential data across many domains. For language modelling in particular, the Transformer-XL {---} a Transformer augmented with a long-range memory of past activations {---} has been shown to be state-of-the-art across a variety of well-studied benchmarks. The Transformer-XL incorporates a long-range memory at every layer of the network, which renders its state to be thousands of times larger than RNN predecessors. However it is unclear whether this is necessary. We perform a set of interventions to show that comparable performance can be obtained with 6X fewer long range memories and better performance can be obtained by limiting the range of attention in lower layers of the network.",
}
@inproceedings{cheng-etal-2020-improving,
    title = "Improving Disentangled Text Representation Learning with Information-Theoretic Guidance",
    author = "Cheng, Pengyu  and
      Min, Martin Renqiang  and
      Shen, Dinghan  and
      Malon, Christopher  and
      Zhang, Yizhe  and
      Li, Yitong  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.673",
    doi = "10.18653/v1/2020.acl-main.673",
    pages = "7530--7541",
    abstract = "Learning disentangled representations of natural language is essential for many NLP tasks, e.g., conditional text generation, style transfer, personalized dialogue systems, etc. Similar problems have been studied extensively for other forms of data, such as images and videos. However, the discrete nature of natural language makes the disentangling of textual representations more challenging (e.g., the manipulation over the data space cannot be easily achieved). Inspired by information theory, we propose a novel method that effectively manifests disentangled representations of text, without any supervision on semantics. A new mutual information upper bound is derived and leveraged to measure dependence between style and content. By minimizing this upper bound, the proposed method induces style and content embeddings into two independent low-dimensional spaces. Experiments on both conditional text generation and text-style transfer demonstrate the high quality of our disentangled representation in terms of content and style preservation.",
}
@inproceedings{kalra-etal-2020-understanding,
    title = "Understanding Advertisements with {BERT}",
    author = "Kalra, Kanika  and
      Kurma, Bhargav  and
      Vadakkeeveetil Sreelatha, Silpa  and
      Patwardhan, Manasi  and
      Karande, Shirish",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.674",
    doi = "10.18653/v1/2020.acl-main.674",
    pages = "7542--7547",
    abstract = "We consider a task based on CVPR 2018 challenge dataset on advertisement (Ad) understanding. The task involves detecting the viewer{'}s interpretation of an Ad image captured as text. Recent results have shown that the embedded scene-text in the image holds a vital cue for this task. Motivated by this, we fine-tune the base BERT model for a sentence-pair classification task. Despite utilizing the scene-text as the only source of visual information, we could achieve a hit-or-miss accuracy of 84.95{\%} on the challenge test data. To enable BERT to process other visual information, we append image captions to the scene-text. This achieves an accuracy of 89.69{\%}, which is an improvement of 4.7{\%}. This is the best reported result for this task.",
}
@inproceedings{glavas-vulic-2020-non,
    title = "Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces",
    author = "Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.675",
    doi = "10.18653/v1/2020.acl-main.675",
    pages = "7548--7555",
    abstract = "We present InstaMap, an instance-based method for learning projection-based cross-lingual word embeddings. Unlike prior work, it deviates from learning a single global linear projection. InstaMap is a non-parametric model that learns a non-linear projection by iteratively: (1) finding a globally optimal rotation of the source embedding space relying on the Kabsch algorithm, and then (2) moving each point along an instance-specific translation vector estimated from the translation vectors of the point{'}s nearest neighbours in the training dictionary. We report performance gains with InstaMap over four representative state-of-the-art projection-based models on bilingual lexicon induction across a set of 28 diverse language pairs. We note prominent improvements, especially for more distant language pairs (i.e., languages with non-isomorphic monolingual spaces).",
}
@inproceedings{andreas-2020-good,
    title = "Good-Enough Compositional Data Augmentation",
    author = "Andreas, Jacob",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.676",
    doi = "10.18653/v1/2020.acl-main.676",
    pages = "7556--7566",
    abstract = "We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that appear in at least one similar environment. The protocol is model-agnostic and useful for a variety of tasks. Applied to neural sequence-to-sequence models, it reduces error rate by as much as 87{\%} on diagnostic tasks from the SCAN dataset and 16{\%} on a semantic parsing task. Applied to n-gram language models, it reduces perplexity by roughly 1{\%} on small corpora in several languages.",
}
@inproceedings{wang-etal-2020-rat,
    title = "{RAT-SQL}: Relation-Aware Schema Encoding and Linking for Text-to-{SQL} Parsers",
    author = "Wang, Bailin  and
      Shin, Richard  and
      Liu, Xiaodong  and
      Polozov, Oleksandr  and
      Richardson, Matthew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.677",
    doi = "10.18653/v1/2020.acl-main.677",
    pages = "7567--7578",
    abstract = "When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database schemas. The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query. We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder. On the challenging Spider dataset this framework boosts the exact match accuracy to 57.2{\%}, surpassing its best counterparts by 8.7{\%} absolute improvement. Further augmented with BERT, it achieves the new state-of-the-art performance of 65.6{\%} on the Spider leaderboard. In addition, we observe qualitative improvements in the model{'}s understanding of schema linking and alignment. Our implementation will be open-sourced at https://github.com/Microsoft/rat-sql.",
}
@inproceedings{zhou-etal-2020-temporal,
    title = "Temporal Common Sense Acquisition with Minimal Supervision",
    author = "Zhou, Ben  and
      Ning, Qiang  and
      Khashabi, Daniel  and
      Roth, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.678",
    doi = "10.18653/v1/2020.acl-main.678",
    pages = "7579--7589",
    abstract = "Temporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in text, and human annotation on such concepts is costly. This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TacoLM, a temporal common sense language model. Our method is shown to give quality predictions of various dimensions of temporal common sense (on UDST and a newly collected dataset from RealNews). It also produces representations of events for relevant tasks such as duration comparison, parent-child relations, event coreference and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the standard BERT. Thus, it will be an important component of temporal NLP.",
}
@inproceedings{abdou-etal-2020-sensitivity,
    title = "The Sensitivity of Language Models and Humans to {W}inograd Schema Perturbations",
    author = "Abdou, Mostafa  and
      Ravishankar, Vinit  and
      Barrett, Maria  and
      Belinkov, Yonatan  and
      Elliott, Desmond  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.679",
    doi = "10.18653/v1/2020.acl-main.679",
    pages = "7590--7604",
    abstract = "Large-scale pretrained language models are the major driving force behind recent improvements in perfromance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones.",
}
@inproceedings{rijhwani-preotiuc-pietro-2020-temporally,
    title = "Temporally-Informed Analysis of Named Entity Recognition",
    author = "Rijhwani, Shruti  and
      Preotiuc-Pietro, Daniel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.680",
    doi = "10.18653/v1/2020.acl-main.680",
    pages = "7605--7617",
    abstract = "Natural language processing models often have to make predictions on text data that evolves over time as a result of changes in language use or the information described in the text. However, evaluation results on existing data sets are seldom reported by taking the timestamp of the document into account. We analyze and propose methods that make better use of temporally-diverse training data, with a focus on the task of named entity recognition. To support these experiments, we introduce a novel data set of English tweets annotated with named entities. We empirically demonstrate the effect of temporal drift on performance, and how the temporal information of documents can be used to obtain better models compared to those that disregard temporal information. Our analysis gives insights into why this information is useful, in the hope of informing potential avenues of improvement for named entity recognition as well as other NLP tasks under similar experimental setups.",
}
@inproceedings{naik-rose-2020-towards,
    title = "Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation",
    author = "Naik, Aakanksha  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.681",
    doi = "10.18653/v1/2020.acl-main.681",
    pages = "7618--7624",
    abstract = "We tackle the task of building supervised event trigger identification models which can generalize better across domains. Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance. ADA uses adversarial training to construct representations that are predictive for trigger identification, but not predictive of the example{'}s domain. It requires no labeled data from the target domain, making it completely unsupervised. Experiments with two domains (English literature and news) show that ADA leads to an average F1 score improvement of 3.9 on out-of-domain data. Our best performing model (BERT-A) reaches 44-49 F1 across both domains, using no labeled target data. Preliminary experiments reveal that finetuning on 1{\%} labeled data, followed by self-training leads to substantial improvement, reaching 51.5 and 67.2 F1 on literature and news respectively.",
}
@inproceedings{suglia-etal-2020-compguesswhat,
    title = "{C}omp{G}uess{W}hat?!: A Multi-task Evaluation Framework for Grounded Language Learning",
    author = "Suglia, Alessandro  and
      Konstas, Ioannis  and
      Vanzo, Andrea  and
      Bastianelli, Emanuele  and
      Elliott, Desmond  and
      Frank, Stella  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.682",
    doi = "10.18653/v1/2020.acl-main.682",
    pages = "7625--7641",
    abstract = "Approaches to Grounded Language Learning are commonly focused on a single task-based final performance measure which may not depend on desirable properties of the learned hidden representations, such as their ability to predict object attributes or generalize to unseen situations. To remedy this, we present GroLLA, an evaluation framework for Grounded Language Learning with Attributes based on three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation. We also propose a new dataset CompGuessWhat?! as an instance of this framework for evaluating the quality of learned neural representations, in particular with respect to attribute grounding. To this end, we extend the original GuessWhat?! dataset by including a semantic layer on top of the perceptual one. Specifically, we enrich the VisualGenome scene graphs associated with the GuessWhat?! images with several attributes from resources such as VISA and ImSitu. We then compare several hidden state representations from current state-of-the-art approaches to Grounded Language Learning. By using diagnostic classifiers, we show that current models{'} learned representations are not expressive enough to encode object attributes (average F1 of 44.27). In addition, they do not learn strategies nor representations that are robust enough to perform well when novel scenes or objects are involved in gameplay (zero-shot best accuracy 50.06{\%}).",
}
@inproceedings{zheng-etal-2020-cross,
    title = "Cross-Modality Relevance for Reasoning on Language and Vision",
    author = "Zheng, Chen  and
      Guo, Quan  and
      Kordjamshidi, Parisa",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.683",
    doi = "10.18653/v1/2020.acl-main.683",
    pages = "7642--7651",
    abstract = "This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR). We design a novel cross-modality relevance module that is used in an end-to-end framework to learn the relevance representation between components of various input modalities under the supervision of a target task, which is more generalizable to unobserved data compared to merely reshaping the original representation space. In addition to modeling the relevance between the textual entities and visual entities, we model the higher-order relevance between entity relations in the text and object relations in the image. Our proposed approach shows competitive performance on two different language and vision tasks using public benchmarks and improves the state-of-the-art published results. The learned alignments of input spaces and their relevance representations by NLVR task boost the training efficiency of VQA task.",
}
@inproceedings{srivastava-etal-2020-learning,
    title = "Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context",
    author = "Srivastava, Shashank  and
      Polozov, Oleksandr  and
      Jojic, Nebojsa  and
      Meek, Christopher",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.684",
    doi = "10.18653/v1/2020.acl-main.684",
    pages = "7652--7662",
    abstract = "We explore learning web-based tasks from a human teacher through natural language explanations and a single demonstration. Our approach investigates a new direction for semantic parsing that models explaining a demonstration in a context, rather than mapping explanations to demonstrations. By leveraging the idea of inverse semantics from program synthesis to reason backwards from observed demonstrations, we ensure that all considered interpretations are consistent with executable actions in any context, thus simplifying the problem of search over logical forms. We present a dataset of explanations paired with demonstrations for web-based tasks. Our methods show better task completion rates than a supervised semantic parsing baseline (40{\%} relative improvement on average), and are competitive with simple exploration-and-demonstration based methods, while requiring no exploration of the environment. In learning to align explanations with demonstrations, basic properties of natural language syntax emerge as learned behavior. This is an interesting example of pragmatic language acquisition without any linguistic annotation.",
}
@inproceedings{lazaridou-etal-2020-multi,
    title = "Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning",
    author = "Lazaridou, Angeliki  and
      Potapenko, Anna  and
      Tieleman, Olivier",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.685",
    doi = "10.18653/v1/2020.acl-main.685",
    pages = "7663--7674",
    abstract = "We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning, with an end goal of teaching agents to communicate with humans in natural language. Our starting point is a language model that has been trained on generic, not task-specific language data. We then place this model in a multi-agent self-play environment that generates task-specific rewards used to adapt or modulate the model, turning it into a task-conditional language model. We introduce a new way for combining the two types of learning based on the idea of reranking language model samples, and show that this method outperforms others in communicating with humans in a visual referential communication task. Finally, we present a taxonomy of different types of language drift that can occur alongside a set of measures to detect them.",
}
@inproceedings{wang-etal-2020-hat,
    title = "{HAT}: Hardware-Aware Transformers for Efficient Natural Language Processing",
    author = "Wang, Hanrui  and
      Wu, Zhanghao  and
      Liu, Zhijian  and
      Cai, Han  and
      Zhu, Ligeng  and
      Gan, Chuang  and
      Han, Song",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.686",
    doi = "10.18653/v1/2020.acl-main.686",
    pages = "7675--7688",
    abstract = "Transformers are ubiquitous in Natural Language Processing (NLP) tasks, but they are difficult to be deployed on hardware due to the intensive computation. To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search. We first construct a large design space with arbitrary encoder-decoder attention and heterogeneous layers. Then we train a SuperTransformer that covers all candidates in the design space, and efficiently produces many SubTransformers with weight sharing. Finally, we perform an evolutionary search with a hardware latency constraint to find a specialized SubTransformer dedicated to run fast on the target hardware. Extensive experiments on four machine translation tasks demonstrate that HAT can discover efficient models for different hardware (CPU, GPU, IoT device). When running WMT{'}14 translation task on Raspberry Pi-4, HAT can achieve 3{\mbox{$\times$}} speedup, 3.7{\mbox{$\times$}} smaller size over baseline Transformer; 2.7{\mbox{$\times$}} speedup, 3.6{\mbox{$\times$}} smaller size over Evolved Transformer with 12,041{\mbox{$\times$}} less search cost and no performance loss. HAT is open-sourced at https://github.com/mit-han-lab/hardware-aware-transformers.",
}
@inproceedings{you-etal-2020-hard,
    title = "Hard-Coded {G}aussian Attention for Neural Machine Translation",
    author = "You, Weiqiu  and
      Sun, Simeng  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.687",
    doi = "10.18653/v1/2020.acl-main.687",
    pages = "7689--7700",
    abstract = "Recent work has questioned the importance of the Transformer{'}s multi-headed attention for achieving high translation quality. We push further in this direction by developing a {``}hard-coded{''} attention variant without any learned parameters. Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs. However, additionally, hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention. Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer. Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.",
}
@inproceedings{aji-etal-2020-neural,
    title = "In Neural Machine Translation, What Does Transfer Learning Transfer?",
    author = "Aji, Alham Fikri  and
      Bogoychev, Nikolay  and
      Heafield, Kenneth  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.688",
    doi = "10.18653/v1/2020.acl-main.688",
    pages = "7701--7710",
    abstract = "Transfer learning improves quality for low-resource machine translation, but it is unclear what exactly it transfers. We perform several ablation studies that limit information transfer, then measure the quality impact across three language pairs to gain a black-box understanding of transfer learning. Word embeddings play an important role in transfer learning, particularly if they are properly aligned. Although transfer learning can be performed without embeddings, results are sub-optimal. In contrast, transferring only the embeddings but nothing else yields catastrophic results. We then investigate diagonal alignments with auto-encoders over real languages and randomly generated sequences, finding even randomly generated sequences as parents yield noticeable but smaller gains. Finally, transfer learning can eliminate the need for a warm-up phase when training transformer models in high resource language pairs.",
}
@inproceedings{wang-etal-2020-learning-multi,
    title = "Learning a Multi-Domain Curriculum for Neural Machine Translation",
    author = "Wang, Wei  and
      Tian, Ye  and
      Ngiam, Jiquan  and
      Yang, Yinfei  and
      Caswell, Isaac  and
      Parekh, Zarana",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.689",
    doi = "10.18653/v1/2020.acl-main.689",
    pages = "7711--7723",
    abstract = "Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.",
}
@inproceedings{saunders-byrne-2020-reducing,
    title = "Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem",
    author = "Saunders, Danielle  and
      Byrne, Bill",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.690",
    doi = "10.18653/v1/2020.acl-main.690",
    pages = "7724--7736",
    abstract = "Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men. In Neural Machine Translation (NMT) gender bias has been shown to reduce translation quality, particularly when the target language has grammatical gender. The recent WinoMT challenge set allows us to measure this effect directly (Stanovsky et al, 2019) Ideally we would reduce system bias by simply debiasing all data prior to training, but achieving this effectively is itself a challenge. Rather than attempt to create a {`}balanced{'} dataset, we use transfer learning on a small set of trusted, gender-balanced examples. This approach gives strong and consistent improvements in gender debiasing with much less computational cost than training from scratch. A known pitfall of transfer learning on new domains is {`}catastrophic forgetting{'}, which we address at adaptation and inference time. During adaptation we show that Elastic Weight Consolidation allows a performance trade-off between general translation quality and bias reduction. At inference time we propose a lattice-rescoring scheme which outperforms all systems evaluated in Stanovsky et al, 2019 on WinoMT with no degradation of general test set BLEU. We demonstrate our approach translating from English into three languages with varied linguistic properties and data availability.",
}
@inproceedings{riley-etal-2020-translationese,
    title = "Translationese as a Language in {``}Multilingual{''} {NMT}",
    author = "Riley, Parker  and
      Caswell, Isaac  and
      Freitag, Markus  and
      Grangier, David",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.691",
    doi = "10.18653/v1/2020.acl-main.691",
    pages = "7737--7746",
    abstract = "Machine translation has an undesirable propensity to produce {``}translationese{''} artifacts, which can lead to higher BLEU scores while being liked less by human raters. Motivated by this, we model translationese and original (i.e. natural) text as separate languages in a multilingual model, and pose the question: can we perform zero-shot translation between original source text and original target text? There is no data with original source and original target, so we train a sentence-level classifier to distinguish translationese from original target text, and use this classifier to tag the training data for an NMT model. Using this technique we bias the model to produce more natural outputs at test time, yielding gains in human evaluation scores on both accuracy and fluency. Additionally, we demonstrate that it is possible to bias the model to produce translationese and game the BLEU score, increasing it while decreasing human-rated quality. We analyze these outputs using metrics measuring the degree of translationese, and present an analysis of the volatility of heuristic-based train-data tagging.",
}
@inproceedings{aharoni-goldberg-2020-unsupervised,
    title = "Unsupervised Domain Clusters in Pretrained Language Models",
    author = "Aharoni, Roee  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.692",
    doi = "10.18653/v1/2020.acl-main.692",
    pages = "7747--7763",
    abstract = "The notion of {``}in-domain data{''} in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality. In addition, domain labels are many times unavailable, making it challenging to build domain-specific systems. We show that massive pre-trained language models implicitly learn sentence representations that cluster by domains without supervision {--} suggesting a simple data-driven definition of domains in textual data. We harness this property and propose domain data selection methods based on such models, which require only a small set of in-domain monolingual data. We evaluate our data selection methods for neural machine translation across five diverse domains, where they outperform an established approach as measured by both BLEU and precision and recall with respect to an oracle selection.",
}
@inproceedings{saunders-etal-2020-using,
    title = "Using Context in Neural Machine Translation Training Objectives",
    author = "Saunders, Danielle  and
      Stahlberg, Felix  and
      Byrne, Bill",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.693",
    doi = "10.18653/v1/2020.acl-main.693",
    pages = "7764--7770",
    abstract = "We present Neural Machine Translation (NMT) training using document-level metrics with batch-level documents. Previous sequence-objective approaches to NMT training focus exclusively on sentence-level metrics like sentence BLEU which do not correspond to the desired evaluation metric, typically document BLEU. Meanwhile research into document-level NMT training focuses on data or model architecture rather than training procedure. We find that each of these lines of research has a clear space in it for the other, and propose merging them with a scheme that allows a document-level evaluation metric to be used in the NMT training objective. We first sample pseudo-documents from sentence samples. We then approximate the expected document BLEU gradient with Monte Carlo sampling for use as a cost function in Minimum Risk Training (MRT). This two-level sampling procedure gives NMT performance gains over sequence MRT and maximum-likelihood training. We demonstrate that training is more robust for document-level metrics than with sequence metrics. We further demonstrate improvements on NMT with TER and Grammatical Error Correction (GEC) using GLEU, both metrics used at the document level for evaluations.",
}
@inproceedings{setiawan-etal-2020-variational,
    title = "Variational Neural Machine Translation with Normalizing Flows",
    author = "Setiawan, Hendra  and
      Sperber, Matthias  and
      Nallasamy, Udhyakumar  and
      Paulik, Matthias",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.694",
    doi = "10.18653/v1/2020.acl-main.694",
    pages = "7771--7777",
    abstract = "Variational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on some latent random variables. The latent variable modeling may introduce useful statistical dependencies that can improve translation accuracy. Unfortunately, learning informative latent variables is non-trivial, as the latent space can be prohibitively large, and the latent codes are prone to be ignored by many translation models at training time. Previous works impose strong assumptions on the distribution of the latent code and limit the choice of the NMT architecture. In this paper, we propose to apply the VNMT framework to the state-of-the-art Transformer and introduce a more flexible approximate posterior based on normalizing flows. We demonstrate the efficacy of our proposal under both in-domain and out-of-domain conditions, significantly outperforming strong baselines.",
}
@inproceedings{erdmann-etal-2020-paradigm,
    title = "The Paradigm Discovery Problem",
    author = "Erdmann, Alexander  and
      Elsner, Micha  and
      Wu, Shijie  and
      Cotterell, Ryan  and
      Habash, Nizar",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.695",
    doi = "10.18653/v1/2020.acl-main.695",
    pages = "7778--7790",
    abstract = "This work treats the paradigm discovery problem (PDP), the task of learning an inflectional morphological system from unannotated sentences. We formalize the PDP and develop evaluation metrics for judging systems. Using currently available resources, we construct datasets for the task. We also devise a heuristic benchmark for the PDP and report empirical results on five diverse languages. Our benchmark system first makes use of word embeddings and string similarity to cluster forms by cell and by paradigm. Then, we bootstrap a neural transducer on top of the clustered data to predict words to realize the empty paradigm slots. An error analysis of our system suggests clustering by cell across different inflection classes is the most pressing challenge for future work.",
}
@inproceedings{arora-etal-2020-supervised,
    title = "Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in {H}indi and {P}unjabi",
    author = "Arora, Aryaman  and
      Gessler, Luke  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.696",
    doi = "10.18653/v1/2020.acl-main.696",
    pages = "7791--7795",
    abstract = "Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one exception: whether a schwa represented in the orthography is pronounced or unpronounced (deleted). Previous work has attempted to predict schwa deletion in a rule-based fashion using prosodic or phonetic analysis. We present the first statistical schwa deletion classifier for Hindi, which relies solely on the orthography as the input and outperforms previous approaches. We trained our model on a newly-compiled pronunciation lexicon extracted from various online dictionaries. Our best Hindi model achieves state of the art performance, and also achieves good performance on a closely related language, Punjabi, without modification.",
}
@inproceedings{beigman-klebanov-madnani-2020-automated,
    title = "Automated Evaluation of Writing {--} 50 Years and Counting",
    author = "Beigman Klebanov, Beata  and
      Madnani, Nitin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.697",
    doi = "10.18653/v1/2020.acl-main.697",
    pages = "7796--7810",
    abstract = "In this theme paper, we focus on Automated Writing Evaluation (AWE), using Ellis Page{'}s seminal 1966 paper to frame the presentation. We discuss some of the current frontiers in the field and offer some thoughts on the emergent uses of this technology.",
}
@inproceedings{kassner-schutze-2020-negated,
    title = "Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly",
    author = {Kassner, Nora  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.698",
    doi = "10.18653/v1/2020.acl-main.698",
    pages = "7811--7818",
    abstract = "Building on Petroni et al. 2019, we propose two new probing tasks analyzing factual knowledge stored in Pretrained Language Models (PLMs). (1) Negation. We find that PLMs do not distinguish between negated ({`}{`}Birds cannot [MASK]{''}) and non-negated ({`}{`}Birds can [MASK]{''}) cloze questions. (2) Mispriming. Inspired by priming methods in human psychology, we add {``}misprimes{''} to cloze questions ({`}{`}Talk? Birds can [MASK]{''}). We find that PLMs are easily distracted by misprimes. These results suggest that PLMs still have a long way to go to adequately learn human-like factual knowledge.",
}
@inproceedings{bollmann-elliott-2020-forgetting,
    title = "On Forgetting to Cite Older Papers: An Analysis of the {ACL} {A}nthology",
    author = "Bollmann, Marcel  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.699",
    doi = "10.18653/v1/2020.acl-main.699",
    pages = "7819--7827",
    abstract = "The field of natural language processing is experiencing a period of unprecedented growth, and with it a surge of published papers. This represents an opportunity for us to take stock of how we cite the work of other researchers, and whether this growth comes at the expense of {``}forgetting{''} about older literature. In this paper, we address this question through bibliographic analysis. By looking at the age of outgoing citations in papers published at selected ACL venues between 2010 and 2019, we find that there is indeed a tendency for recent papers to cite more recent work, but the rate at which papers older than 15 years are cited has remained relatively stable.",
}
@inproceedings{flek-2020-returning,
    title = "Returning the {N} to {NLP}: {T}owards Contextually Personalized Classification Models",
    author = "Flek, Lucie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.700",
    doi = "10.18653/v1/2020.acl-main.700",
    pages = "7828--7838",
    abstract = "Most NLP models today treat language as universal, even though socio- and psycholingustic research shows that the communicated message is influenced by the characteristics of the speaker as well as the target audience. This paper surveys the landscape of personalization in natural language processing and related fields, and offers a path forward to mitigate the decades of deviation of the NLP tools from sociolingustic findings, allowing to flexibly process the {``}natural{''} language of each user rather than enforcing a uniform NLP treatment. It outlines a possible direction to incorporate these aspects into neural NLP models by means of socially contextual personalization, and proposes to shift the focus of our evaluation strategies accordingly.",
}
@inproceedings{dunietz-etal-2020-test,
    title = "To Test Machine Comprehension, Start by Defining Comprehension",
    author = "Dunietz, Jesse  and
      Burnham, Greg  and
      Bharadwaj, Akash  and
      Rambow, Owen  and
      Chu-Carroll, Jennifer  and
      Ferrucci, Dave",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.701",
    doi = "10.18653/v1/2020.acl-main.701",
    pages = "7839--7859",
    abstract = "Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key contributions. First, we argue that existing approaches do not adequately define comprehension; they are too unsystematic about what content is tested. Second, we present a detailed definition of comprehension{---}a {``}Template of Understanding{''}{---}for a widely useful class of texts, namely short narratives. We then conduct an experiment that strongly suggests existing systems are not up to the task of narrative understanding as we define it.",
}
@inproceedings{mohammad-2020-gender,
    title = "Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations",
    author = "Mohammad, Saif M.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.702",
    doi = "10.18653/v1/2020.acl-main.702",
    pages = "7860--7870",
    abstract = "Disparities in authorship and citations across gender can have substantial adverse consequences not just on the disadvantaged genders, but also on the field of study as a whole. Measuring gender gaps is a crucial step towards addressing them. In this work, we examine female first author percentages and the citations to their papers in Natural Language Processing (1965 to 2019). We determine aggregate-level statistics using an existing manually curated author--gender list as well as first names strongly associated with a gender. We find that only about 29{\%} of first authors are female and only about 25{\%} of last authors are female. Notably, this percentage has not improved since the mid 2000s. We also show that, on average, female first authors are cited less than male first authors, even when controlling for experience and area of research. Finally, we discuss the ethical considerations involved in automatic demographic analysis.",
}
@inproceedings{lewis-etal-2020-bart,
    title = "{BART}: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension",
    author = "Lewis, Mike  and
      Liu, Yinhan  and
      Goyal, Naman  and
      Ghazvininejad, Marjan  and
      Mohamed, Abdelrahman  and
      Levy, Omer  and
      Stoyanov, Veselin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.703",
    doi = "10.18653/v1/2020.acl-main.703",
    pages = "7871--7880",
    abstract = "We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance.",
}
@inproceedings{sellam-etal-2020-bleurt,
    title = "{BLEURT}: Learning Robust Metrics for Text Generation",
    author = "Sellam, Thibault  and
      Das, Dipanjan  and
      Parikh, Ankur",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.704",
    doi = "10.18653/v1/2020.acl-main.704",
    pages = "7881--7892",
    abstract = "Text generation has made significant advances in the last few years. Yet, evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU and ROUGE) may correlate poorly with human judgment. We propose BLEURT, a learned evaluation metric for English based on BERT. BLEURT can model human judgment with a few thousand possibly biased training examples. A key aspect of our approach is a novel pre-training scheme that uses millions of synthetic examples to help the model generalize. BLEURT provides state-of-the-art results on the last three years of the WMT Metrics shared task and the WebNLG data set. In contrast to a vanilla BERT-based approach, it yields superior results even when the training data is scarce and out-of-distribution.",
}
@inproceedings{chen-etal-2020-distilling,
    title = "Distilling Knowledge Learned in {BERT} for Text Generation",
    author = "Chen, Yen-Chun  and
      Gan, Zhe  and
      Cheng, Yu  and
      Liu, Jingzhou  and
      Liu, Jingjing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.705",
    doi = "10.18653/v1/2020.acl-main.705",
    pages = "7893--7905",
    abstract = "Large-scale pre-trained language model such as BERT has achieved great success in language understanding tasks. However, it remains an open question how to utilize BERT for language generation. In this paper, we present a novel approach, Conditional Masked Language Modeling (C-MLM), to enable the finetuning of BERT on target generation tasks. The finetuned BERT (teacher) is exploited as extra supervision to improve conventional Seq2Seq models (student) for better text generation performance. By leveraging BERT{'}s idiosyncratic bidirectional nature, distilling knowledge learned in BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation. Experiments show that the proposed approach significantly outperforms strong Transformer baselines on multiple language generation tasks such as machine translation and text summarization. Our proposed model also achieves new state of the art on IWSLT German-English and English-Vietnamese MT datasets.",
}
@inproceedings{rajani-etal-2020-esprit,
    title = "{ESPRIT}: Explaining Solutions to Physical Reasoning Tasks",
    author = "Rajani, Nazneen Fatema  and
      Zhang, Rui  and
      Tan, Yi Chern  and
      Zheng, Stephan  and
      Weiss, Jeremy  and
      Vyas, Aadit  and
      Gupta, Abhijit  and
      Xiong, Caiming  and
      Socher, Richard  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.706",
    doi = "10.18653/v1/2020.acl-main.706",
    pages = "7906--7917",
    abstract = "Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at \url{https://github.com/salesforce/esprit}.",
}
@inproceedings{kumar-etal-2020-iterative,
    title = "Iterative Edit-Based Unsupervised Sentence Simplification",
    author = "Kumar, Dhruv  and
      Mou, Lili  and
      Golab, Lukasz  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.707",
    doi = "10.18653/v1/2020.acl-main.707",
    pages = "7918--7928",
    abstract = "We present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation. Then, we iteratively perform word and phrase-level edits on the complex sentence. Compared with previous approaches, our model does not require a parallel training set, but is more controllable and interpretable. Experiments on Newsela and WikiLarge datasets show that our approach is nearly as effective as state-of-the-art supervised approaches.",
}
@inproceedings{chen-etal-2020-logical,
    title = "Logical Natural Language Generation from Open-Domain Tables",
    author = "Chen, Wenhu  and
      Chen, Jianshu  and
      Su, Yu  and
      Chen, Zhiyu  and
      Wang, William Yang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.708",
    doi = "10.18653/v1/2020.acl-main.708",
    pages = "7929--7942",
    abstract = "Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis on logical inference, an important aspect of human thinking and language. In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \textit{logically entailed} by the facts in an open-domain semi-structured table. To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset{\textasciitilde}(CITATION) featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w.r.t. logical inference. The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order. In our experiments, we comprehensively survey different generation architectures (LSTM, Transformer, Pre-Trained LM) trained with different algorithms (RL, Adversarial Training, Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained LM can significantly boost both the fluency and logical fidelity metrics, 2) RL and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine generation can help partially alleviate the fidelity issue while maintaining high language fluency. The code and data are available at \url{https://github.com/wenhuchen/LogicNLG}.",
}
@inproceedings{jiang-etal-2020-neural,
    title = "Neural {CRF} Model for Sentence Alignment in Text Simplification",
    author = "Jiang, Chao  and
      Maddela, Mounica  and
      Lan, Wuwei  and
      Zhong, Yang  and
      Xu, Wei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.709",
    doi = "10.18653/v1/2020.acl-main.709",
    pages = "7943--7960",
    abstract = "The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles. To evaluate and improve sentence alignment quality, we create two manually annotated sentence-aligned datasets from two commonly used text simplification corpora, Newsela and Wikipedia. We propose a novel neural CRF alignment model which not only leverages the sequential nature of sentences in parallel documents but also utilizes a neural sentence pair model to capture semantic similarity. Experiments demonstrate that our proposed approach outperforms all the previous work on monolingual sentence alignment task by more than 5 points in F1. We apply our CRF aligner to construct two new text simplification datasets, Newsela-Auto and Wiki-Auto, which are much larger and of better quality compared to the existing datasets. A Transformer-based seq2seq model trained on our datasets establishes a new state-of-the-art for text simplification in both automatic and human evaluation.",
}
@inproceedings{yuan-etal-2020-one,
    title = "One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases",
    author = "Yuan, Xingdi  and
      Wang, Tong  and
      Meng, Rui  and
      Thaker, Khushboo  and
      Brusilovsky, Peter  and
      He, Daqing  and
      Trischler, Adam",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.710",
    doi = "10.18653/v1/2020.acl-main.710",
    pages = "7961--7975",
    abstract = "Different texts shall by nature correspond to different number of keyphrases. This desideratum is largely missing from existing neural keyphrase generation models. In this study, we address this problem from both modeling and evaluation perspectives. We first propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences. Generation diversity is further enhanced with two novel techniques by manipulating decoder hidden states. In contrast to previous approaches, our model is capable of generating diverse keyphrases and controlling number of outputs. We further propose two evaluation metrics tailored towards the variable-number generation. We also introduce a new dataset StackEx that expands beyond the only existing genre (i.e., academic writing) in keyphrase generation tasks. With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.",
}
@inproceedings{chakrabarty-etal-2020-r,
    title = "{R}{\^{}}3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge",
    author = "Chakrabarty, Tuhin  and
      Ghosh, Debanjan  and
      Muresan, Smaranda  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.711",
    doi = "10.18653/v1/2020.acl-main.711",
    pages = "7976--7986",
    abstract = "We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than humans 34{\%} of the time, and better than a reinforced hybrid baseline 90{\%} of the time.",
}
@inproceedings{song-etal-2020-structural,
    title = "Structural Information Preserving for Graph-to-Text Generation",
    author = "Song, Linfeng  and
      Wang, Ante  and
      Su, Jinsong  and
      Zhang, Yue  and
      Xu, Kun  and
      Ge, Yubin  and
      Yu, Dong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.712",
    doi = "10.18653/v1/2020.acl-main.712",
    pages = "7987--7998",
    abstract = "The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline.",
}
@inproceedings{lin-etal-2020-joint,
    title = "A Joint Neural Model for Information Extraction with Global Features",
    author = "Lin, Ying  and
      Ji, Heng  and
      Huang, Fei  and
      Wu, Lingfei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.713",
    doi = "10.18653/v1/2020.acl-main.713",
    pages = "7999--8009",
    abstract = "Most existing joint neural models for Information Extraction (IE) use local task-specific classifiers to predict labels for individual instances (e.g., trigger, relation) regardless of their interactions. For example, a victim of a die event is likely to be a victim of an attack event in the same sentence. In order to capture such cross-subtask and cross-instance inter-dependencies, we propose a joint neural framework, OneIE, that aims to extract the globally optimal IE result as a graph from an input sentence. OneIE performs end-to-end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder. At the decoding stage, we incorporate global features to capture the cross-subtask and cross-instance interactions. Experiments show that adding global features improves the performance of our model and achieves new state of-the-art on all subtasks. In addition, as OneIE does not use any language-specific feature, we prove it can be easily applied to new languages or trained in a multilingual manner.",
}
@inproceedings{du-cardie-2020-document,
    title = "Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding",
    author = "Du, Xinya  and
      Cardie, Claire",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.714",
    doi = "10.18653/v1/2020.acl-main.714",
    pages = "8010--8020",
    abstract = "Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models{'} performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task.",
}
@inproceedings{pouran-ben-veyseh-etal-2020-exploiting,
    title = "Exploiting the Syntax-Model Consistency for Neural Relation Extraction",
    author = "Pouran Ben Veyseh, Amir  and
      Dernoncourt, Franck  and
      Dou, Dejing  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.715",
    doi = "10.18653/v1/2020.acl-main.715",
    pages = "8021--8032",
    abstract = "This paper studies the task of Relation Extraction (RE) that aims to identify the semantic relations between two entity mentions in text. In the deep learning models for RE, it has been beneficial to incorporate the syntactic structures from the dependency trees of the input sentences. In such models, the dependency trees are often used to directly structure the network architectures or to obtain the dependency relations between the word pairs to inject the syntactic information into the models via multi-task learning. The major problem with these approaches is the lack of generalization beyond the syntactic structures in the training data or the failure to capture the syntactic importance of the words for RE. In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization. In particular, we leverage Ordered-Neuron Long-Short Term Memory Networks (ON-LSTM) to infer the model-based importance scores for RE for every word in the sentences that are then regulated to be consistent with the syntax-based scores to enable syntactic information injection. We perform extensive experiments to demonstrate the effectiveness of the proposed method, leading to the state-of-the-art performance on three RE benchmark datasets.",
}
@inproceedings{aguilar-solorio-2020-english,
    title = "From {E}nglish to Code-Switching: Transfer Learning with Strong Morphological Clues",
    author = "Aguilar, Gustavo  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.716",
    doi = "10.18653/v1/2020.acl-main.716",
    pages = "8033--8044",
    abstract = "Linguistic Code-switching (CS) is still an understudied phenomenon in natural language processing. The NLP community has mostly focused on monolingual and multi-lingual scenarios, but little attention has been given to CS in particular. This is partly because of the lack of resources and annotated data, despite its increasing occurrence in social media platforms. In this paper, we aim at adapting monolingual models to code-switched text in various tasks. Specifically, we transfer English knowledge from a pre-trained ELMo model to different code-switched language pairs (i.e., Nepali-English, Spanish-English, and Hindi-English) using the task of language identification. Our method, CS-ELMo, is an extension of ELMo with a simple yet effective position-aware attention mechanism inside its character convolutions. We show the effectiveness of this transfer learning step by outperforming multilingual BERT and homologous CS-unaware ELMo models and establishing a new state of the art in CS tasks, such as NER and POS tagging. Our technique can be expanded to more English-paired code-switched languages, providing more resources to the CS community.",
}
@inproceedings{zhang-etal-2020-learning-interpretable,
    title = "Learning Interpretable Relationships between Entities, Relations and Concepts via {B}ayesian Structure Learning on Open Domain Facts",
    author = "Zhang, Jingyuan  and
      Sun, Mingming  and
      Feng, Yue  and
      Li, Ping",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.717",
    doi = "10.18653/v1/2020.acl-main.717",
    pages = "8045--8056",
    abstract = "Concept graphs are created as universal taxonomies for text understanding in the open-domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to concepts, showing that an entity is an instance of a concept. In this paper, we propose the task of learning interpretable relationships from open-domain facts to enrich and refine concept graphs. The Bayesian network structures are learned from open-domain facts as the interpretable relationships between relations of facts and concepts of entities. We conduct extensive experiments on public English and Chinese datasets. Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.",
}
@inproceedings{ebner-etal-2020-multi,
    title = "Multi-Sentence Argument Linking",
    author = "Ebner, Seth  and
      Xia, Patrick  and
      Culkin, Ryan  and
      Rawlins, Kyle  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.718",
    doi = "10.18653/v1/2020.acl-main.718",
    pages = "8057--8077",
    abstract = "We present a novel document-level model for finding argument spans that fill an event{'}s roles, connecting related ideas in sentence-level semantic role labeling and coreference resolution. Because existing datasets for cross-sentence linking are small, development of our neural model is supported through the creation of a new resource, Roles Across Multiple Sentences (RAMS), which contains 9,124 annotated events across 139 types. We demonstrate strong performance of our model on RAMS and other event-related datasets.",
}
@inproceedings{wang-etal-2020-rationalizing,
    title = "Rationalizing Medical Relation Prediction from Corpus-level Statistics",
    author = "Wang, Zhen  and
      Lee, Jennifer  and
      Lin, Simon  and
      Sun, Huan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.719",
    doi = "10.18653/v1/2020.acl-main.719",
    pages = "8078--8092",
    abstract = "Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain. Aiming to shed some light on how to rationalize medical relation prediction, we present a new interpretable framework inspired by existing theories on how human memory works, e.g., theories of recall and recognition. Given the corpus-level statistics, i.e., a global co-occurrence graph of a clinical text corpus, to predict the relations between two entities, we first recall rich contexts associated with the target entities, and then recognize relational interactions between these contexts to form model rationales, which will contribute to the final prediction. We conduct experiments on a real-world public clinical dataset and show that our framework can not only achieve competitive predictive performance against a comprehensive list of neural baseline models, but also present rationales to justify its prediction. We further collaborate with medical experts deeply to verify the usefulness of our model rationales for clinical decision making.",
}
@inproceedings{mueller-etal-2020-sources,
    title = "Sources of Transfer in Multilingual Named Entity Recognition",
    author = "Mueller, David  and
      Andrews, Nicholas  and
      Dredze, Mark",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.720",
    doi = "10.18653/v1/2020.acl-main.720",
    pages = "8093--8104",
    abstract = "Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider \textit{polyglot} named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are \textit{fine-tuned} on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.",
}
@inproceedings{lockard-etal-2020-zeroshotceres,
    title = "{Z}ero{S}hot{C}eres: Zero-Shot Relation Extraction from Semi-Structured Webpages",
    author = "Lockard, Colin  and
      Shiralkar, Prashant  and
      Dong, Xin Luna  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.721",
    doi = "10.18653/v1/2020.acl-main.721",
    pages = "8105--8117",
    abstract = "In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for {``}zero-shot{''} open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31{\%} F1 gain over a baseline for zero-shot extraction in a new subject vertical.",
}
@inproceedings{rijhwani-etal-2020-soft,
    title = "Soft Gazetteers for Low-Resource Named Entity Recognition",
    author = "Rijhwani, Shruti  and
      Zhou, Shuyan  and
      Neubig, Graham  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.722",
    doi = "10.18653/v1/2020.acl-main.722",
    pages = "8118--8123",
    abstract = "Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of {``}soft gazetteers{''} that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.",
}
@inproceedings{shing-etal-2020-prioritization,
    title = "A Prioritization Model for Suicidality Risk Assessment",
    author = "Shing, Han-Chin  and
      Resnik, Philip  and
      Oard, Douglas",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.723",
    doi = "10.18653/v1/2020.acl-main.723",
    pages = "8124--8137",
    abstract = "We reframe suicide risk assessment from social media as a ranking problem whose goal is maximizing detection of severely at-risk individuals given the time available. Building on measures developed for resource-bounded document retrieval, we introduce a well founded evaluation paradigm, and demonstrate using an expert-annotated test collection that meaningful improvements over plausible cascade model baselines can be achieved using an approach that jointly ranks individuals and their social media posts.",
}
@inproceedings{viegas-etal-2020-cluhtm,
    title = "{C}lu{HTM} - Semantic Hierarchical Topic Modeling based on {C}lu{W}ords",
    author = "Viegas, Felipe  and
      Cunha, Washington  and
      Gomes, Christian  and
      Pereira, Ant{\^o}nio  and
      Rocha, Leonardo  and
      Goncalves, Marcos",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.724",
    doi = "10.18653/v1/2020.acl-main.724",
    pages = "8138--8150",
    abstract = "Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. Despite advantages over traditional topic modeling, HTM poses its own challenges, such as (1) topic incoherence, (2) unreasonable (hierarchical) structure, and (3) issues related to the definition of the {``}ideal{''} number of topics and depth of the hierarchy. In this paper, we advance the state-of-the-art on HTM by means of the design and evaluation of CluHTM, a novel non-probabilistic hierarchical matrix factorization aimed at solving the specific issues of HTM. CluHTM{'}s novel contributions include: (i) the exploration of richer text representation that encapsulates both, global (dataset level) and local semantic information {--} when combined, these pieces of information help to solve the topic incoherence problem as well as issues related to the unreasonable structure; (ii) the exploitation of a stability analysis metric for defining the number of topics and the {``}shape{''} the hierarchical structure. In our evaluation, considering twelve datasets and seven state-of-the-art baselines, CluHTM outperformed the baselines in the vast majority of the cases, with gains of around 500{\%} over the strongest state-of-the-art baselines. We also provide qualitative and quantitative statistical analyses of why our solution works so well.",
}
@inproceedings{zhang-etal-2020-empower,
    title = "Empower Entity Set Expansion via Language Model Probing",
    author = "Zhang, Yunyi  and
      Shen, Jiaming  and
      Shang, Jingbo  and
      Han, Jiawei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.725",
    doi = "10.18653/v1/2020.acl-main.725",
    pages = "8151--8160",
    abstract = "Entity set expansion, aiming at expanding a small seed entity set with new entities belonging to the same semantic class, is a critical task that benefits many downstream NLP and IR applications, such as question answering, query understanding, and taxonomy construction. Existing set expansion methods bootstrap the seed entity set by adaptively selecting context features and extracting new entities. A key challenge for entity set expansion is to avoid selecting ambiguous context features which will shift the class semantics and lead to accumulative errors in later iterations. In this study, we propose a novel iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. In each iteration, we select one positive and several negative class names by probing a pre-trained language model, and further score each candidate entity based on selected class names. Experiments on two datasets show that our framework generates high-quality class names and outperforms previous state-of-the-art methods significantly.",
}
@inproceedings{qin-etal-2020-feature,
    title = "Feature Projection for Improved Text Classification",
    author = "Qin, Qi  and
      Hu, Wenpeng  and
      Liu, Bing",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.726",
    doi = "10.18653/v1/2020.acl-main.726",
    pages = "8161--8171",
    abstract = "In classification, there are usually some good features that are indicative of class labels. For example, in sentiment classification, words like good and nice are indicative of the positive sentiment and words like bad and terrible are indicative of the negative sentiment. However, there are also many common features (e.g., words) that are not indicative of any specific class (e.g., voice and screen, which are common to both sentiment classes and are not discriminative for classification). Although deep learning has made significant progresses in generating discriminative features through its powerful representation learning, we believe there is still room for improvement. In this paper, we propose a novel angle to further improve this representation learning, i.e., feature projection. This method projects existing features into the orthogonal space of the common features. The resulting projection is thus perpendicular to the common features and more discriminative for classification. We apply this new method to improve CNN, RNN, Transformer, and Bert based text classification and obtain markedly better results.",
}
@inproceedings{shrestha-etal-2020-negative,
    title = "A negative case analysis of visual grounding methods for {VQA}",
    author = "Shrestha, Robik  and
      Kafle, Kushal  and
      Kanan, Christopher",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.727",
    doi = "10.18653/v1/2020.acl-main.727",
    pages = "8172--8181",
    abstract = "Existing Visual Question Answering (VQA) methods tend to exploit dataset biases and spurious statistical correlations, instead of producing right answers for the right reasons. To address this issue, recent bias mitigation methods for VQA propose to incorporate visual cues (e.g., human attention maps) to better ground the VQA models, showcasing impressive gains. However, we show that the performance improvements are not a result of improved visual grounding, but a regularization effect which prevents over-fitting to linguistic priors. For instance, we find that it is not actually necessary to provide proper, human-based cues; random, insensible cues also result in similar improvements. Based on this observation, we propose a simpler regularization scheme that does not require any external annotations and yet achieves near state-of-the-art performance on VQA-CPv2.",
}
@inproceedings{agarwal-etal-2020-history,
    title = "History for Visual Dialog: Do we really need it?",
    author = "Agarwal, Shubham  and
      Bui, Trung  and
      Lee, Joon-Young  and
      Konstas, Ioannis  and
      Rieser, Verena",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.728",
    doi = "10.18653/v1/2020.acl-main.728",
    pages = "8182--8197",
    abstract = "Visual Dialogue involves {``}understanding{''} the dialogue history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to accurately generate the correct response. In this paper, we show that co-attention models which explicitly encode dialoh history outperform models that don{'}t, achieving state-of-the-art performance (72 {\%} NDCG on val set). However, we also expose shortcomings of the crowdsourcing dataset collection procedure, by showing that dialogue history is indeed only required for a small amount of the data, and that the current evaluation metric encourages generic replies. To that end, we propose a challenging subset (VisdialConv) of the VisdialVal set and the benchmark NDCG of 63{\%}.",
}
@inproceedings{li-etal-2020-mapping,
    title = "Mapping Natural Language Instructions to Mobile {UI} Action Sequences",
    author = "Li, Yang  and
      He, Jiacong  and
      Zhou, Xin  and
      Zhang, Yuan  and
      Baldridge, Jason",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.729",
    doi = "10.18653/v1/2020.acl-main.729",
    pages = "8198--8210",
    abstract = "We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PixelHelp, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the language and action data by (a) annotating action phrase spans in How-To instructions and (b) synthesizing grounded descriptions of actions for mobile user interfaces. We use a Transformer to extract action phrase tuples from long-range natural language instructions. A grounding Transformer then contextually represents UI objects using both their content and screen position and connects them to object descriptions. Given a starting screen and instruction, our model achieves 70.59{\%} accuracy on predicting complete ground-truth action sequences in PixelHelp.",
}
@inproceedings{lei-etal-2020-tvqa,
    title = "{TVQA}+: Spatio-Temporal Grounding for Video Question Answering",
    author = "Lei, Jie  and
      Yu, Licheng  and
      Berg, Tamara  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.730",
    doi = "10.18653/v1/2020.acl-main.730",
    pages = "8211--8225",
    abstract = "We present the task of Spatio-Temporal Video Question Answering, which requires intelligent systems to simultaneously retrieve relevant moments and detect referenced visual concepts (people and objects) to answer natural language questions about videos. We first augment the TVQA dataset with 310.8K bounding boxes, linking depicted objects to visual concepts in questions and answers. We name this augmented version as TVQA+. We then propose Spatio-Temporal Answerer with Grounded Evidence (STAGE), a unified framework that grounds evidence in both spatial and temporal domains to answer questions about videos. Comprehensive experiments and analyses demonstrate the effectiveness of our framework and how the rich annotations in our TVQA+ dataset can contribute to the question answering task. Moreover, by performing this joint task, our model is able to produce insightful and interpretable spatio-temporal attention visualizations.",
}
@inproceedings{huang-etal-2020-unsupervised,
    title = "Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting",
    author = "Huang, Po-Yao  and
      Hu, Junjie  and
      Chang, Xiaojun  and
      Hauptmann, Alexander",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.731",
    doi = "10.18653/v1/2020.acl-main.731",
    pages = "8226--8237",
    abstract = "Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when images are not available at the testing time.",
}
@inproceedings{alqahtani-etal-2020-multitask,
    title = "A Multitask Learning Approach for Diacritic Restoration",
    author = "Alqahtani, Sawsan  and
      Mishra, Ajay  and
      Diab, Mona",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.732",
    doi = "10.18653/v1/2020.acl-main.732",
    pages = "8238--8247",
    abstract = "In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possible pronunciations and meanings for a word. This results in a more ambiguous text making computational processing on such text more difficult. Diacritic restoration is the task of restoring missing diacritics in the written text. Most state-of-the-art diacritic restoration models are built on character level information which helps generalize the model to unseen data, but presumably lose useful information at the word level. Thus, to compensate for this loss, we investigate the use of multi-task learning to jointly optimize diacritic restoration with related NLP problems namely word segmentation, part-of-speech tagging, and syntactic diacritization. We use Arabic as a case study since it has sufficient data resources for tasks that we consider in our joint modeling. Our joint models significantly outperform the baselines and are comparable to the state-of-the-art models that are more complex relying on morphological analyzers and/or a lot more data (e.g. dialectal data).",
}
@inproceedings{erdmann-etal-2020-frugal,
    title = "Frugal Paradigm Completion",
    author = "Erdmann, Alexander  and
      Kenter, Tom  and
      Becker, Markus  and
      Schallhart, Christian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.733",
    doi = "10.18653/v1/2020.acl-main.733",
    pages = "8248--8273",
    abstract = "Lexica distinguishing all morphologically related forms of each lexeme are crucial to many language technologies, yet building them is expensive. We propose a frugal paradigm completion approach that predicts all related forms in a morphological paradigm from as few manually provided forms as possible. It induces typological information during training which it uses to determine the best sources at test time. We evaluate our language-agnostic approach on 7 diverse languages. Compared to popular alternative approaches, ours reduces manual labor by 16-63{\%} and is the most robust to typological variation.",
}
@inproceedings{tian-etal-2020-improving,
    title = "Improving {C}hinese Word Segmentation with Wordhood Memory Networks",
    author = "Tian, Yuanhe  and
      Song, Yan  and
      Xia, Fei  and
      Zhang, Tong  and
      Wang, Yonggang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.734",
    doi = "10.18653/v1/2020.acl-main.734",
    pages = "8274--8285",
    abstract = "Contextual features always play an important role in Chinese word segmentation (CWS). Wordhood information, being one of the contextual features, is proved to be useful in many conventional character-based segmenters. However, this feature receives less attention in recent neural models and it is also challenging to design a framework that can properly integrate wordhood information from different wordhood measures to existing neural frameworks. In this paper, we therefore propose a neural framework, WMSeg, which uses memory networks to incorporate wordhood information with several popular encoder-decoder combinations for CWS. Experimental results on five benchmark datasets indicate the memory mechanism successfully models wordhood information for neural segmenters and helps WMSeg achieve state-of-the-art performance on all those datasets. Further experiments and analyses also demonstrate the robustness of our proposed framework with respect to different wordhood measures and the efficiency of wordhood information in cross-domain experiments.",
}
@inproceedings{tian-etal-2020-joint,
    title = "Joint {C}hinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge",
    author = "Tian, Yuanhe  and
      Song, Yan  and
      Ao, Xiang  and
      Xia, Fei  and
      Quan, Xiaojun  and
      Zhang, Tong  and
      Wang, Yonggang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.735",
    doi = "10.18653/v1/2020.acl-main.735",
    pages = "8286--8296",
    abstract = "Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks. Previous studies for joint CWS and POS tagging mainly follow the character-based tagging paradigm with introducing contextual information such as n-gram features or sentential representations from recurrent neural models. However, for many cases, the joint tagging needs not only modeling from context features but also knowledge attached to them (e.g., syntactic relations among words); limited efforts have been made by existing research to meet such needs. In this paper, we propose a neural model named TwASP for joint CWS and POS tagging following the character-based sequence labeling paradigm, where a two-way attention mechanism is used to incorporate both context feature and their corresponding syntactic knowledge for each input character. Particularly, we use existing language processing toolkits to obtain the auto-analyzed syntactic knowledge for the context, and the proposed attention module can learn and benefit from them although their quality may not be perfect. Our experiments illustrate the effectiveness of the two-way attentions for joint CWS and POS tagging, where state-of-the-art performance is achieved on five benchmark datasets.",
}
@inproceedings{zalmout-habash-2020-joint,
    title = "Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging",
    author = "Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.736",
    doi = "10.18653/v1/2020.acl-main.736",
    pages = "8297--8307",
    abstract = "The written forms of Semitic languages are both highly ambiguous and morphologically rich: a word can have multiple interpretations and is one of many inflected forms of the same concept or lemma. This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices. However, the different modeling granularity can make joint modeling more difficult. Our approach models the different features jointly, whether lexicalized (on the character-level), or non-lexicalized (on the word-level). We use Arabic as a test case, and achieve state-of-the-art results for Modern Standard Arabic with 20{\%} relative error reduction, and Egyptian Arabic with 11{\%} relative error reduction.",
}
@inproceedings{ryskina-etal-2020-phonetic,
    title = "Phonetic and Visual Priors for Decipherment of Informal {R}omanization",
    author = "Ryskina, Maria  and
      Gormley, Matthew R.  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.737",
    doi = "10.18653/v1/2020.acl-main.737",
    pages = "8308--8319",
    abstract = "Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. Character substitution choices differ between users but have been shown to be governed by the same main principles observed across a variety of languages{---}namely, character pairs are often associated through phonetic or visual similarity. We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. We train our model directly on romanized data from two languages: Egyptian Arabic and Russian. We demonstrate that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model{'}s performance on both languages, yielding results much closer to the supervised skyline. Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments.",
}
@inproceedings{li-etal-2020-active,
    title = "Active Learning for Coreference Resolution using Discrete Annotation",
    author = "Li, Belinda Z.  and
      Stanovsky, Gabriel  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.738",
    doi = "10.18653/v1/2020.acl-main.738",
    pages = "8320--8331",
    abstract = "We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent. This simple modification, when combined with a novel mention clustering algorithm for selecting which examples to label, is much more efficient in terms of the performance obtained per annotation budget. In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour. Future work can use our annotation protocol to effectively develop coreference models for new domains. Our code is publicly available.",
}
@inproceedings{chinnappa-etal-2020-beyond,
    title = "Beyond Possession Existence: Duration and Co-Possession",
    author = "Chinnappa, Dhivya  and
      Murugan, Srikala  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.739",
    doi = "10.18653/v1/2020.acl-main.739",
    pages = "8332--8341",
    abstract = "This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of corpora annotating possession existence and experimental results. Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds. Regarding co-possessions, we use a binary label. Cohen{'}s kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.",
}
@inproceedings{gururangan-etal-2020-dont,
    title = "Don{'}t Stop Pretraining: Adapt Language Models to Domains and Tasks",
    author = "Gururangan, Suchin  and
      Marasovi{\'c}, Ana  and
      Swayamdipta, Swabha  and
      Lo, Kyle  and
      Beltagy, Iz  and
      Downey, Doug  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.740",
    doi = "10.18653/v1/2020.acl-main.740",
    pages = "8342--8360",
    abstract = "Language models pretrained on text from a wide variety of sources form the foundation of today{'}s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task{'}s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.",
}
@inproceedings{zhelezniak-etal-2020-estimating,
    title = "Estimating Mutual Information Between Dense Word Embeddings",
    author = "Zhelezniak, Vitalii  and
      Savkov, Aleksandar  and
      Hammerla, Nils",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.741",
    doi = "10.18653/v1/2020.acl-main.741",
    pages = "8361--8371",
    abstract = "Word embedding-based similarity measures are currently among the top-performing methods on unsupervised semantic textual similarity (STS) tasks. Recent work has increasingly adopted a statistical view on these embeddings, with some of the top approaches being essentially various correlations (which include the famous cosine similarity). Another excellent candidate for a similarity measure is mutual information (MI), which can capture arbitrary dependencies between the variables and has a simple and intuitive expression. Unfortunately, its use in the context of dense word embeddings has so far been avoided due to difficulties with estimating MI for continuous data. In this work we go through a vast literature on estimating MI in such cases and single out the most promising methods, yielding a simple and elegant similarity measure for word embeddings. We show that mutual information is a viable alternative to correlations, gives an excellent signal that correlates well with human judgements of similarity and rivals existing state-of-the-art unsupervised methods.",
}
@inproceedings{suhr-etal-2020-exploring,
    title = "Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing",
    author = "Suhr, Alane  and
      Chang, Ming-Wei  and
      Shaw, Peter  and
      Lee, Kenton",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.742",
    doi = "10.18653/v1/2020.acl-main.742",
    pages = "8372--8388",
    abstract = "We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training. Recently, several datasets, including Spider, were proposed to support development of XSP systems. We propose a challenging evaluation setup for cross-database semantic parsing, focusing on variation across database schemas and in-domain language use. We re-purpose eight semantic parsing datasets that have been well-studied in the setting where in-domain training data is available, and instead use them as additional evaluation data for XSP systems instead. We build a system that performs well on Spider, and find that it struggles to generalize to our re-purposed set. Our setup uncovers several generalization challenges for cross-database semantic parsing, demonstrating the need to use and develop diverse training and evaluation datasets.",
}
@inproceedings{hossain-etal-2020-predicting,
    title = "Predicting the Focus of Negation: Model and Error Analysis",
    author = "Hossain, Md Mosharaf  and
      Hamilton, Kathleen  and
      Palmer, Alexis  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.743",
    doi = "10.18653/v1/2020.acl-main.743",
    pages = "8389--8401",
    abstract = "The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.",
}
@inproceedings{li-etal-2020-structured,
    title = "Structured Tuning for Semantic Role Labeling",
    author = "Li, Tao  and
      Jawale, Parth Anand  and
      Palmer, Martha  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.744",
    doi = "10.18653/v1/2020.acl-main.744",
    pages = "8402--8412",
    abstract = "Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.",
}
@inproceedings{yin-etal-2020-tabert,
    title = "{T}a{BERT}: Pretraining for Joint Understanding of Textual and Tabular Data",
    author = "Yin, Pengcheng  and
      Neubig, Graham  and
      Yih, Wen-tau  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.745",
    doi = "10.18653/v1/2020.acl-main.745",
    pages = "8413--8426",
    abstract = "Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider.",
}
@inproceedings{stengel-eskin-etal-2020-universal,
    title = "Universal Decompositional Semantic Parsing",
    author = "Stengel-Eskin, Elias  and
      White, Aaron Steven  and
      Zhang, Sheng  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.746",
    doi = "10.18653/v1/2020.acl-main.746",
    pages = "8427--8439",
    abstract = "We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups.",
}
@inproceedings{conneau-etal-2020-unsupervised,
    title = "Unsupervised Cross-lingual Representation Learning at Scale",
    author = "Conneau, Alexis  and
      Khandelwal, Kartikay  and
      Goyal, Naman  and
      Chaudhary, Vishrav  and
      Wenzek, Guillaume  and
      Guzm{\'a}n, Francisco  and
      Grave, Edouard  and
      Ott, Myle  and
      Zettlemoyer, Luke  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.747",
    doi = "10.18653/v1/2020.acl-main.747",
    pages = "8440--8451",
    abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.",
}
@inproceedings{xu-etal-2020-generate,
    title = "A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization",
    author = "Xu, Dongfang  and
      Zhang, Zeyu  and
      Bethard, Steven",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.748",
    doi = "10.18653/v1/2020.acl-main.748",
    pages = "8452--8464",
    abstract = "Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large. In most cases, annotated datasets cover only a small sample of the concepts, yet concept normalizers are expected to predict all concepts in the ontology. In this paper, we propose an architecture consisting of a candidate generator and a list-wise ranker based on BERT. The ranker considers pairings of concept mentions and candidate concepts, allowing it to make predictions for any concept, not just those seen during training. We further enhance this list-wise approach with a semantic type regularizer that allows the model to incorporate semantic type information from the ontology during training. Our proposed concept normalization framework achieves state-of-the-art performance on multiple datasets.",
}
@inproceedings{chen-etal-2020-hierarchical,
    title = "Hierarchical Entity Typing via Multi-level Learning to Rank",
    author = "Chen, Tongfei  and
      Chen, Yunmo  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.749",
    doi = "10.18653/v1/2020.acl-main.749",
    pages = "8465--8475",
    abstract = "We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). Our approach significantly outperform prior work on strict accuracy, demonstrating the effectiveness of our method.",
}
@inproceedings{wang-etal-2020-multi-domain,
    title = "Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference",
    author = "Wang, Jing  and
      Kulkarni, Mayank  and
      Preotiuc-Pietro, Daniel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.750",
    doi = "10.18653/v1/2020.acl-main.750",
    pages = "8476--8488",
    abstract = "Named entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input. However, domain transfer of NER models with data from multiple genres has not been widely studied. To this end, we conduct NER experiments in three predictive setups on data from: a) multiple domains; b) multiple domains where the genre label is unknown at inference time; c) domains not encountered in training. We introduce a new architecture tailored to this task by using shared and private domain parameters and multi-task learning. This consistently outperforms all other baseline and competitive methods on all three experimental setups, with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. These results illustrate the challenges that need to be taken into account when building real-world NLP applications that are robust to various types of text and the methods that can help, at least partially, alleviate these issues.",
}
@inproceedings{karamanolakis-etal-2020-txtract,
    title = "{TX}tract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories",
    author = "Karamanolakis, Giannis  and
      Ma, Jun  and
      Dong, Xin Luna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.751",
    doi = "10.18653/v1/2020.acl-main.751",
    pages = "8489--8502",
    abstract = "Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for a single category of product, and thus do not apply to real-life e-Commerce scenarios, which often contain thousands of diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge extraction model that applies to thousands of product categories organized in a hierarchical taxonomy. Through category conditional self-attention and multi-task learning, our approach is both scalable, as it trains a single model for thousands of categories, and effective, as it extracts category-specific attribute values. Experiments on products from a taxonomy with 4,000 categories show that TXtract outperforms state-of-the-art approaches by up to 10{\%} in F1 and 15{\%} in coverage across all categories.",
}
@inproceedings{lin-etal-2020-triggerner,
    title = "{T}rigger{NER}: Learning with Entity Triggers as Explanations for Named Entity Recognition",
    author = "Lin, Bill Yuchen  and
      Lee, Dong-Ho  and
      Shen, Ming  and
      Moreno, Ryan  and
      Huang, Xiao  and
      Shiralkar, Prashant  and
      Ren, Xiang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.752",
    doi = "10.18653/v1/2020.acl-main.752",
    pages = "8503--8511",
    abstract = "Training neural models for named entity recognition (NER) in a new domain often requires additional human annotations (e.g., tens of thousands of labeled instances) that are usually expensive and time-consuming to collect. Thus, a crucial research question is how to obtain supervision in a cost-effective way. In this paper, we introduce {``}entity triggers,{''} an effective proxy of human explanations for facilitating label-efficient learning of NER models. An entity trigger is defined as a group of words in a sentence that helps to explain why humans would recognize an entity in the sentence. We crowd-sourced 14k entity triggers for two well-studied NER datasets. Our proposed model, Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging. Our framework is significantly more cost-effective than the traditional neural NER frameworks. Experiments show that using only 20{\%} of the trigger-annotated sentences results in a comparable performance as using 70{\%} of conventional annotated sentences.",
}
@inproceedings{mccarthy-etal-2020-addressing,
    title = "Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation",
    author = "McCarthy, Arya D.  and
      Li, Xian  and
      Gu, Jiatao  and
      Dong, Ning",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.753",
    doi = "10.18653/v1/2020.acl-main.753",
    pages = "8512--8525",
    abstract = "This paper proposes a simple and effective approach to address the problem of posterior collapse in conditional variational autoencoders (CVAEs). It thus improves performance of machine translation models that use noisy or monolingual data, as well as in conventional settings. Extending Transformer and conditional VAEs, our proposed latent variable model measurably prevents posterior collapse by (1) using a modified evidence lower bound (ELBO) objective which promotes mutual information between the latent variable and the target, and (2) guiding the latent variable with an auxiliary bag-of-words prediction task. As a result, the proposed model yields improved translation quality compared to existing variational NMT models on WMT Ro↔En and De↔En. With latent variables being effectively utilized, our model demonstrates improved robustness over non-latent Transformer in handling uncertainty: exploiting noisy source-side monolingual data (up to +3.2 BLEU), and training with weakly aligned web-mined parallel data (up to +4.7 BLEU).",
}
@inproceedings{wang-etal-2020-balancing,
    title = "Balancing Training for Multilingual Neural Machine Translation",
    author = "Wang, Xinyi  and
      Tsvetkov, Yulia  and
      Neubig, Graham",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.754",
    doi = "10.18653/v1/2020.acl-main.754",
    pages = "8526--8537",
    abstract = "When training multilingual machine translation (MT) models that can translate to/from multiple languages, we are faced with imbalanced training sets: some languages have much more training data than others. Standard practice is to up-sample less resourced languages to increase representation, and the degree of up-sampling has a large effect on the overall performance. In this paper, we propose a method that instead automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages. Experiments on two sets of languages under both one-to-many and many-to-one MT settings show our method not only consistently outperforms heuristic baselines in terms of average performance, but also offers flexible control over the performance of which languages are optimized.",
}
@inproceedings{niu-etal-2020-evaluating,
    title = "Evaluating Robustness to Input Perturbations for Neural Machine Translation",
    author = "Niu, Xing  and
      Mathur, Prashant  and
      Dinu, Georgiana  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.755",
    doi = "10.18653/v1/2020.acl-main.755",
    pages = "8538--8544",
    abstract = "Neural Machine Translation (NMT) models are sensitive to small perturbations in the input. Robustness to such perturbations is typically measured using translation quality metrics such as BLEU on the noisy input. This paper proposes additional metrics which measure the relative degradation and changes in translation when small perturbations are added to the input. We focus on a class of models employing subword regularization to address robustness and perform extensive evaluations of these models using the robustness measures proposed. Results show that our proposed metrics reveal a clear trend of improved robustness to perturbations when subword regularization methods are used.",
}
@inproceedings{zhang-etal-2020-parallel-corpus,
    title = "Parallel Corpus Filtering via Pre-trained Language Models",
    author = "Zhang, Boliang  and
      Nagesh, Ajay  and
      Knight, Kevin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.756",
    doi = "10.18653/v1/2020.acl-main.756",
    pages = "8545--8554",
    abstract = "Web-crawled data provides a good source of parallel corpora for training machine translation models. It is automatically obtained, but extremely noisy, and recent work shows that neural machine translation systems are more sensitive to noise than traditional statistical machine translation methods. In this paper, we propose a novel approach to filter out noisy sentence pairs from web-crawled corpora via pre-trained language models. We measure sentence parallelism by leveraging the multilingual capability of BERT and use the Generative Pre-training (GPT) language model as a domain filter to balance data domains. We evaluate the proposed method on the WMT 2018 Parallel Corpus Filtering shared task, and on our own web-crawled Japanese-Chinese parallel corpus. Our method significantly outperforms baselines and achieves a new state-of-the-art. In an unsupervised setting, our method achieves comparable performance to the top-1 supervised method. We also evaluate on a web-crawled Japanese-Chinese parallel corpus that we make publicly available.",
}
@inproceedings{li-etal-2020-regularized,
    title = "Regularized Context Gates on Transformer for Machine Translation",
    author = "Li, Xintong  and
      Liu, Lemao  and
      Wang, Rui  and
      Huang, Guoping  and
      Meng, Max",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.757",
    doi = "10.18653/v1/2020.acl-main.757",
    pages = "8555--8562",
    abstract = "Context gates are effective to control the contributions from the source and target contexts in the recurrent neural network (RNN) based neural machine translation (NMT). However, it is challenging to extend them into the advanced Transformer architecture, which is more complicated than RNN. This paper first provides a method to identify source and target contexts and then introduce a gate mechanism to control the source and target contributions in Transformer. In addition, to further reduce the bias problem in the gate mechanism, this paper proposes a regularization method to guide the learning of the gates with supervision automatically generated using pointwise mutual information. Extensive experiments on 4 translation datasets demonstrate that the proposed model obtains an averaged gain of 1.0 BLEU score over a strong Transformer baseline.",
}
@inproceedings{haldar-etal-2020-multi,
    title = "A Multi-Perspective Architecture for Semantic Code Search",
    author = "Haldar, Rajarshi  and
      Wu, Lingfei  and
      Xiong, JinJun  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.758",
    doi = "10.18653/v1/2020.acl-main.758",
    pages = "8563--8568",
    abstract = "The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code{--}text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.",
}
@inproceedings{zhang-litman-2020-automated,
    title = "Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring",
    author = "Zhang, Haoran  and
      Litman, Diane",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.759",
    doi = "10.18653/v1/2020.acl-main.759",
    pages = "8569--8584",
    abstract = "While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.",
}
@inproceedings{schumacher-etal-2020-clinical,
    title = "Clinical Concept Linking with Contextualized Neural Representations",
    author = "Schumacher, Elliot  and
      Mulyar, Andriy  and
      Dredze, Mark",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.760",
    doi = "10.18653/v1/2020.acl-main.760",
    pages = "8585--8592",
    abstract = "In traditional approaches to entity linking, linking decisions are based on three sources of information {--} the similarity of the mention string to an entity{'}s name, the similarity of the context of the document to the entity, and broader information about the knowledge base (KB). In some domains, there is little contextual information present in the KB and thus we rely more heavily on mention string similarity. We consider one example of this, concept linking, which seeks to link mentions of medical concepts to a medical concept ontology. We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name. We find a neural ranking approach paired with contextualized embeddings provides gains over a competitive baseline (Leaman et al. 2013). Additionally, we find that a pre-training step using synonyms from the ontology offers a useful initialization for the ranker.",
}
@inproceedings{hidey-etal-2020-deseption,
    title = "{D}e{S}e{P}tion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking",
    author = "Hidey, Christopher  and
      Chakrabarty, Tuhin  and
      Alhindi, Tariq  and
      Varia, Siddharth  and
      Krstovski, Kriste  and
      Diab, Mona  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.761",
    doi = "10.18653/v1/2020.acl-main.761",
    pages = "8593--8606",
    abstract = "The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extraction and VERification (FEVER) dataset provides such a resource for evaluating endto- end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. We show that current systems for FEVER are vulnerable to three categories of realistic challenges for fact-checking {--} multiple propositions, temporal reasoning, and ambiguity and lexical variation {--} and introduce a resource with these types of claims. Then we present a system designed to be resilient to these {``}attacks{''} using multiple pointer networks for document selection and jointly modeling a sequence of evidence sentences and veracity relation predictions. We find that in handling these attacks we obtain state-of-the-art results on FEVER, largely due to improved evidence retrieval.",
}
@inproceedings{shirani-etal-2020-choose,
    title = "Let Me Choose: From Verbal Context to Font Selection",
    author = "Shirani, Amirreza  and
      Dernoncourt, Franck  and
      Echevarria, Jose  and
      Asente, Paul  and
      Lipka, Nedim  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.762",
    doi = "10.18653/v1/2020.acl-main.762",
    pages = "8607--8613",
    abstract = "In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the only visual element in the document. We introduce a new dataset, containing examples of different topics in social media posts and ads, labeled through crowd-sourcing. Due to the subjective nature of the task, multiple fonts might be perceived as acceptable for an input text, which makes this problem challenging. To this end, we investigate different end-to-end models to learn label distributions on crowd-sourced data, to capture inter-subjectivity across all annotations.",
}
@inproceedings{akyurek-etal-2020-multi,
    title = "Multi-Label and Multilingual News Framing Analysis",
    author = {Aky{\"u}rek, Afra Feyza  and
      Guo, Lei  and
      Elanwar, Randa  and
      Ishwar, Prakash  and
      Betke, Margrit  and
      Wijaya, Derry Tanti},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.763",
    doi = "10.18653/v1/2020.acl-main.763",
    pages = "8614--8624",
    abstract = "News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation. In NLP, although recent works have studied framing in English news, few have studied how the analysis can be extended to other languages and in a multi-label setting. In this work, we explore multilingual transfer learning to detect multiple frames from just the news headline in a genuinely low-resource context where there are few/no frame annotations in the target language. We propose a novel method that can leverage elementary resources consisting of a dictionary and few annotations to detect frames in the target language. Our method performs comparably or better than translating the entire target language headline to the source language for which we have annotated data. This work opens up an exciting new capability of scaling up frame analysis to many languages, even those without existing translation technologies. Lastly, we apply our method to detect frames on the issue of U.S. gun violence in multiple languages and obtain exciting insights on the relationship between different frames of the same problem across different countries with different languages.",
}
@inproceedings{xia-etal-2020-predicting,
    title = "Predicting Performance for Natural Language Processing Tasks",
    author = "Xia, Mengzhou  and
      Anastasopoulos, Antonios  and
      Xu, Ruochen  and
      Yang, Yiming  and
      Neubig, Graham",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.764",
    doi = "10.18653/v1/2020.acl-main.764",
    pages = "8625--8646",
    abstract = "Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting. In this work, we attempt to explore the possibility of gaining plausible judgments of how well an NLP model can perform under an experimental setting, \textit{without actually training or testing the model}. To do so, we build regression models to predict the evaluation score of an NLP experiment given the experimental settings as input. Experimenting on{\textasciitilde}9 different NLP tasks, we find that our predictors can produce meaningful predictions over unseen languages and different modeling architectures, outperforming reasonable baselines as well as human experts. {\%}we represent experimental settings using an array of features. Going further, we outline how our predictor can be used to find a small subset of representative experiments that should be run in order to obtain plausible predictions for all other experimental settings.",
}
@inproceedings{zhu-etal-2020-scriptwriter,
    title = "{S}cript{W}riter: Narrative-Guided Script Generation",
    author = "Zhu, Yutao  and
      Song, Ruihua  and
      Dou, Zhicheng  and
      Nie, Jian-Yun  and
      Zhou, Jin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.765",
    doi = "10.18653/v1/2020.acl-main.765",
    pages = "8647--8657",
    abstract = "It is appealing to have a system that generates a story or scripts automatically from a storyline, even though this is still out of our reach. In dialogue systems, it would also be useful to drive dialogues by a dialogue plan. In this paper, we address a key problem involved in these applications - guiding a dialogue by a narrative. The proposed model ScriptWriter selects the best response among the candidates that fit the context as well as the given narrative. It keeps track of what in the narrative has been said and what is to be said. A narrative plays a different role than the context (i.e., previous utterances), which is generally used in current dialogue systems. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end- users can upload their narratives freely when watching a movie. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.",
}
@inproceedings{anastasopoulos-neubig-2020-cross,
    title = "Should All Cross-Lingual Embeddings Speak {E}nglish?",
    author = "Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.766",
    doi = "10.18653/v1/2020.acl-main.766",
    pages = "8658--8679",
    abstract = "Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting. With this work, however, we challenge these practices. First, we show that the choice of hub language can significantly impact downstream lexicon induction zero-shot POS tagging performance. Second, we both expand a standard English-centered evaluation dictionary collection to include all language pairs using triangulation, and create new dictionaries for under-represented languages. Evaluating established methods over all these language pairs sheds light into their suitability for aligning embeddings from distant languages and presents new challenges for the field. Finally, in our analysis we identify general guidelines for strong cross-lingual embedding baselines, that extend to language pairs that do not include English.",
}
@inproceedings{mukherjee-etal-2020-smart,
    title = "Smart To-Do: Automatic Generation of To-Do Items from Emails",
    author = "Mukherjee, Sudipto  and
      Mukherjee, Subhabrata  and
      Hasegawa, Marcello  and
      Hassan Awadallah, Ahmed  and
      White, Ryen",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.767",
    doi = "10.18653/v1/2020.acl-main.767",
    pages = "8680--8689",
    abstract = "Intelligent features in email service applications aim to increase productivity by helping people organize their folders, compose their emails and respond to pending tasks. In this work, we explore a new application, Smart-To-Do, that helps users with task management over emails. We introduce a new task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. We design a two-stage process leveraging recent advances in neural text generation and sequence-to-sequence learning, obtaining BLEU and ROUGE scores of 0.23 and 0.63 for this task. To the best of our knowledge, this is the first work to address the problem of composing To-Do items from emails.",
}
@inproceedings{jeretic-etal-2020-natural,
    title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
    author = "Jeretic, Paloma  and
      Warstadt, Alex  and
      Bhooshan, Suvrat  and
      Williams, Adina",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.768",
    doi = "10.18653/v1/2020.acl-main.768",
    pages = "8690--8705",
    abstract = "Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by {``}some{''} as entailments. For some presupposition triggers like {``}only{''}, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.",
}
@inproceedings{karimi-mahabadi-etal-2020-end,
    title = "End-to-End Bias Mitigation by Modelling Biases in Corpora",
    author = "Karimi Mahabadi, Rabeeh  and
      Belinkov, Yonatan  and
      Henderson, James",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.769",
    doi = "10.18653/v1/2020.acl-main.769",
    pages = "8706--8716",
    abstract = "Several recent studies have shown that strong natural language understanding (NLU) models are prone to relying on unwanted dataset biases without learning the underlying task, resulting in models that fail to generalize to out-of-domain datasets and are likely to perform poorly in real-world scenarios. We propose two learning strategies to train neural models, which are more robust to such biases and transfer better to out-of-domain datasets. The biases are specified in terms of one or more bias-only models, which learn to leverage the dataset biases. During training, the bias-only models{'} predictions are used to adjust the loss of the base model to reduce its reliance on biases by down-weighting the biased examples and focusing the training on the hard examples. We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data. Results show that our debiasing methods greatly improve robustness in all settings and better transfer to other textual entailment datasets. Our code and data are publicly available in \url{https://github.com/rabeehk/robust-nli}.",
}
@inproceedings{utama-etal-2020-mind,
    title = "Mind the Trade-off: Debiasing {NLU} Models without Degrading the In-distribution Performance",
    author = "Utama, Prasetya Ajie  and
      Moosavi, Nafise Sadat  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.770",
    doi = "10.18653/v1/2020.acl-main.770",
    pages = "8717--8729",
    abstract = "Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution. Recently, several proposed debiasing methods are shown to be very effective in improving out-of-distribution performance. However, their improvements come at the expense of performance drop when models are evaluated on the in-distribution data, which contain examples with higher diversity. This seemingly inevitable trade-off may not tell us much about the changes in the reasoning and understanding capabilities of the resulting models on broader types of examples beyond the small subset represented in the out-of-distribution data. In this paper, we address this trade-off by introducing a novel debiasing method, called confidence regularization, which discourage models from exploiting biases while enabling them to receive enough incentive to learn from all the training examples. We evaluate our method on three NLU tasks and show that, in contrast to its predecessors, it improves the performance on out-of-distribution datasets (e.g., 7pp gain on HANS dataset) while maintaining the original in-distribution accuracy.",
}
@inproceedings{kumar-talukdar-2020-nile,
    title = "{NILE} : Natural Language Inference with Faithful Natural Language Explanations",
    author = "Kumar, Sawan  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.771",
    doi = "10.18653/v1/2020.acl-main.771",
    pages = "8730--8742",
    abstract = "The recent growth in the popularity and success of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanation of the predicted labels. Such generated natural language (NL) explanations are expected to be faithful, i.e., they should correlate well with the model{'}s internal decision making. In this work, we focus on the task of natural language inference (NLI) and address the following question: can we build NLI systems which produce labels with high accuracy, while also generating faithful explanations of its decisions? We propose Natural-language Inference over Label-specific Explanations (NILE), a novel NLI method which utilizes auto-generated label-specific NL explanations to produce labels along with its faithful explanation. We demonstrate NILE{'}s effectiveness over previously reported methods through automated and human evaluation of the produced labels and explanations. Our evaluation of NILE also supports the claim that accurate systems capable of providing testable explanations of their decisions can be designed. We discuss the faithfulness of NILE{'}s explanations in terms of sensitivity of the decisions to the corresponding explanations. We argue that explicit evaluation of faithfulness, in addition to label and explanation accuracy, is an important step in evaluating model{'}s explanations. Further, we demonstrate that task-specific probes are necessary to establish such sensitivity.",
}
@inproceedings{he-etal-2020-quase,
    title = "{Q}u{ASE}: Question-Answer Driven Sentence Encoding",
    author = "He, Hangfeng  and
      Ning, Qiang  and
      Roth, Dan",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.772",
    doi = "10.18653/v1/2020.acl-main.772",
    pages = "8743--8758",
    abstract = "Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, \textit{can we use QAMR (Michael et al., 2017) to improve named entity recognition?} We suggest that simply further pre-training BERT is often not the best option, and propose the \textit{question-answer driven sentence encoding (QuASE)} framework. QuASE learns representations from QA data, using BERT or other state-of-the-art contextual language models. In particular, we observe the need to distinguish between two types of sentence encodings, depending on whether the target task is a single- or multi-sentence input; in both cases, the resulting encoding is shown to be an easy-to-use plugin for many downstream tasks. This work may point out an alternative way to supervise NLP tasks.",
}
@inproceedings{zhou-bansal-2020-towards,
    title = "Towards Robustifying {NLI} Models Against Lexical Dataset Biases",
    author = "Zhou, Xiang  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.773",
    doi = "10.18653/v1/2020.acl-main.773",
    pages = "8759--8771",
    abstract = "While deep learning models are making fast progress on the task of Natural Language Inference, recent studies have also shown that these models achieve high accuracy by exploiting several dataset biases, and without deep understanding of the language semantics. Using contradiction-word bias and word-overlapping bias as our two bias examples, this paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases. First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method. Next, we also compare two ways of directly debiasing the model without knowing what the dataset biases are in advance. The first approach aims to remove the label bias at the embedding level. The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features by forcing orthogonality between these two sub-models. We performed evaluations on new balanced datasets extracted from the original MNLI dataset as well as the NLI stress tests, and show that the orthogonality approach is better at debiasing the model while maintaining competitive overall accuracy.",
}
@inproceedings{chen-etal-2020-uncertain,
    title = "Uncertain Natural Language Inference",
    author = "Chen, Tongfei  and
      Jiang, Zhengping  and
      Poliak, Adam  and
      Sakaguchi, Keisuke  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.774",
    doi = "10.18653/v1/2020.acl-main.774",
    pages = "8772--8779",
    abstract = "We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.",
}
@inproceedings{shi-lee-2020-extracting,
    title = "Extracting Headless {MWE}s from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches",
    author = "Shi, Tianze  and
      Lee, Lillian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.775",
    doi = "10.18653/v1/2020.acl-main.775",
    pages = "8780--8794",
    abstract = "An interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities ({``}Wells Fargo{''}) and dates ({``}July 5, 2020{''}) as well as certain productive constructions ({``}blow for blow{''}, {``}day after day{''}). Despite their special status and prevalence, current dependency-annotation schemes require treating such flat structures as if they had internal syntactic heads, and most current parsers handle them in the same fashion as headed constructions. Meanwhile, outside the context of parsing, taggers are typically used for identifying MWEs, but taggers might benefit from structural information. We empirically compare these two common strategies{---}parsing and tagging{---}for predicting flat MWEs. Additionally, we propose an efficient joint decoding algorithm that combines scores from both strategies. Experimental results on the MWE-Aware English Dependency Corpus and on six non-English dependency treebanks with frequent flat structures show that: (1) tagging is more accurate than parsing for identifying flat-structure MWEs, (2) our joint decoder reconciles the two different views and, for non-BERT features, leads to higher accuracies, and (3) most of the gains result from feature sharing between the parsers and taggers.",
}
@inproceedings{fonseca-martins-2020-revisiting,
    title = "Revisiting Higher-Order Dependency Parsers",
    author = "Fonseca, Erick  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.776",
    doi = "10.18653/v1/2020.acl-main.776",
    pages = "8795--8800",
    abstract = "Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parsers. This has led to a belief that neural encoders can implicitly encode structural constraints, such as siblings and grandparents in a tree. We tested this hypothesis and found that neural parsers may benefit from higher-order features, even when employing a powerful pre-trained encoder, such as BERT. While the gains of higher-order features are small in the presence of a powerful encoder, they are consistent for long-range dependencies and long sentences. In particular, higher-order models are more accurate on full sentence parses and on the exact match of modifier lists, indicating that they deal better with larger, more complex structures.",
}
@inproceedings{chen-etal-2020-seqvat,
    title = "{S}eq{VAT}: Virtual Adversarial Training for Semi-Supervised Sequence Labeling",
    author = "Chen, Luoxin  and
      Ruan, Weitong  and
      Liu, Xinyue  and
      Lu, Jianhua",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.777",
    doi = "10.18653/v1/2020.acl-main.777",
    pages = "8801--8811",
    abstract = "Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.",
}
@inproceedings{wagner-etal-2020-treebank,
    title = "Treebank Embedding Vectors for Out-of-Domain Dependency Parsing",
    author = "Wagner, Joachim  and
      Barry, James  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.778",
    doi = "10.18653/v1/2020.acl-main.778",
    pages = "8812--8818",
    abstract = "A recent advance in monolingual dependency parsing is the idea of a treebank embedding vector, which allows all treebanks for a particular language to be used as training data while at the same time allowing the model to prefer training data from one treebank over others and to select the preferred treebank at test time. We build on this idea by 1) introducing a method to predict a treebank vector for sentences that do not come from a treebank used in training, and 2) exploring what happens when we move away from predefined treebank embedding vectors during test time and instead devise tailored interpolations. We show that 1) there are interpolated vectors that are superior to the predefined ones, and 2) treebank vectors can be predicted with sufficient accuracy, for nine out of ten test languages, to match the performance of an oracle approach that knows the most suitable predefined treebank embedding for the test set.",
}
@proceedings{acl-2020-association-linguistics,
    title = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    editor = "Celikyilmaz, Asli  and
      Wen, Tsung-Hsien",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.0",
}
@inproceedings{xu-etal-2020-xiaomingbot,
    title = "{X}iaomingbot: {A} {M}ultilingual {R}obot {N}ews {R}eporter",
    author = "Xu, Runxin  and
      Cao, Jun  and
      Wang, Mingxuan  and
      Chen, Jiaze  and
      Zhou, Hao  and
      Zeng, Ying  and
      Wang, Yuping  and
      Chen, Li  and
      Yin, Xiang  and
      Zhang, Xijin  and
      Jiang, Songcheng  and
      Wang, Yuxuan  and
      Li, Lei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.1",
    doi = "10.18653/v1/2020.acl-demos.1",
    pages = "1--8",
    abstract = "This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four inte- gral capabilities: news generation, news translation, news reading and avatar animation. Its system summarizes Chinese news that it automatically generates from data tables. Next, it translates the summary or the full article into multiple languages, and reads the multi- lingual rendition through synthesized speech. Notably, Xiaomingbot utilizes a voice cloning technology to synthesize the speech trained from a real person{'}s voice data in one input language. The proposed system enjoys several merits: it has an animated avatar, and is able to generate and read multilingual news. Since it was put into practice, Xiaomingbot has written over 600,000 articles, and gained over 150,000 followers on social media platforms.",
}
@inproceedings{yang-etal-2020-textbrewer,
    title = "{T}ext{B}rewer: {A}n {O}pen-{S}ource {K}nowledge {D}istillation {T}oolkit for {N}atural {L}anguage {P}rocessing",
    author = "Yang, Ziqing  and
      Cui, Yiming  and
      Chen, Zhipeng  and
      Che, Wanxiang  and
      Liu, Ting  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.2",
    doi = "10.18653/v1/2020.acl-demos.2",
    pages = "9--16",
    abstract = "In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and supports various kinds of supervised learning tasks, such as text classification, reading comprehension, sequence labeling. TextBrewer provides a simple and uniform workflow that enables quick setting up of distillation experiments with highly flexible configurations. It offers a set of predefined distillation methods and can be extended with custom code. As a case study, we use TextBrewer to distill BERT on several typical NLP tasks. With simple configurations, we achieve results that are comparable with or even higher than the public distilled BERT models with similar numbers of parameters.",
}
@inproceedings{shlain-etal-2020-syntactic,
    title = "Syntactic Search by Example",
    author = "Shlain, Micah  and
      Taub-Tabib, Hillel  and
      Sadde, Shoval  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.3",
    doi = "10.18653/v1/2020.acl-demos.3",
    pages = "17--23",
    abstract = "We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs. In contrast to previous attempts to this effect, we introduce a light-weight query language that does not require the user to know the details of the underlying syntactic representations, and instead to query the corpus by providing an example sentence coupled with simple markup. Search is performed at an interactive speed due to efficient linguistic graph-indexing and retrieval engine. This allows for rapid exploration, development and refinement of syntax-based queries. We demonstrate the system using queries over two corpora: the English wikipedia, and a collection of English pubmed abstracts. A demo of the wikipedia system is available at https://allenai.github.io/spike/ .",
}
@inproceedings{bernard-2020-tabouid,
    title = "{T}abouid: a {W}ikipedia-based word guessing game",
    author = "Bernard, Timoth{\'e}e",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.4",
    doi = "10.18653/v1/2020.acl-demos.4",
    pages = "24--29",
    abstract = "We present Tabouid, a word-guessing game automatically generated from Wikipedia. Tabouid contains 10,000 (virtual) cards in English, and as many in French, covering not only words and linguistic expressions but also a variety of topics including artists, historical events or scientific concepts. Each card corresponds to a Wikipedia article, and conversely, any article could be turned into a card. A range of relatively simple NLP and machine-learning techniques are effectively integrated into a two-stage process. First, a large subset of Wikipedia articles are scored - this score estimates the difficulty, or alternatively, the playability of the page. Then, the best articles are turned into cards by selecting, for each of them, a list of banned words based on its content. We believe that the game we present is more than mere entertainment and that, furthermore, this paper has pedagogical potential.",
}
@inproceedings{zhao-lee-2020-talk,
    title = "Talk to Papers: Bringing Neural Question Answering to Academic Search",
    author = "Zhao, Tiancheng  and
      Lee, Kyusong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.5",
    doi = "10.18653/v1/2020.acl-demos.5",
    pages = "30--36",
    abstract = "We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It{'}s designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers. We present a large improvement over classic search engine baseline on several standard QA datasets and provide the community a collaborative data collection tool to curate the first natural language processing research QA dataset via a community effort.",
}
@inproceedings{scozzafava-etal-2020-personalized,
    title = "Personalized {P}age{R}ank with Syntagmatic Information for Multilingual Word Sense Disambiguation",
    author = "Scozzafava, Federico  and
      Maru, Marco  and
      Brignone, Fabrizio  and
      Torrisi, Giovanni  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.6",
    doi = "10.18653/v1/2020.acl-demos.6",
    pages = "37--46",
    abstract = "Exploiting syntagmatic information is an encouraging research focus to be pursued in an effort to close the gap between knowledge-based and supervised Word Sense Disambiguation (WSD) performance. We follow this direction in our next-generation knowledge-based WSD system, SyntagRank, which we make available via a Web interface and a RESTful API. SyntagRank leverages the disambiguated pairs of co-occurring words included in SyntagNet, a lexical-semantic combination resource, to perform state-of-the-art knowledge-based WSD in a multilingual setting. Our service provides both a user-friendly interface, available at http://syntagnet.org/, and a RESTful endpoint to query the system programmatically (accessible at http://api.syntagnet.org/).",
}
@inproceedings{tiktinsky-etal-2020-pybart,
    title = "py{BART}: Evidence-based Syntactic Transformations for {IE}",
    author = "Tiktinsky, Aryeh  and
      Goldberg, Yoav  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.7",
    doi = "10.18653/v1/2020.acl-demos.7",
    pages = "47--55",
    abstract = "Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications. Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs. However, they are not available to Python users, and are also limited in coverage. We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation. The library can work as a standalone package or be integrated within a spaCy NLP pipeline. When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns.",
}
@inproceedings{wang-etal-2020-evidenceminer,
    title = "{EVIDENCEMINER}: Textual Evidence Discovery for Life Sciences",
    author = "Wang, Xuan  and
      Guan, Yingjun  and
      Liu, Weili  and
      Chauhan, Aabhas  and
      Jiang, Enyi  and
      Li, Qi  and
      Liem, David  and
      Sigdel, Dibakar  and
      Caufield, John  and
      Ping, Peipei  and
      Han, Jiawei",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.8",
    doi = "10.18653/v1/2020.acl-demos.8",
    pages = "56--62",
    abstract = "Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. We present EVIDENCEMINER, a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation. It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The entities and patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization. EVIDENCEMINER can help scientists uncover important research issues, leading to more effective research and more in-depth quantitative analysis. The system of EVIDENCEMINER is available at https://evidenceminer.firebaseapp.com/.",
}
@inproceedings{nye-etal-2020-trialstreamer,
    title = "{T}rialstreamer: Mapping and Browsing Medical Evidence in Real-Time",
    author = "Nye, Benjamin  and
      Nenkova, Ani  and
      Marshall, Iain  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.9",
    doi = "10.18653/v1/2020.acl-demos.9",
    pages = "63--69",
    abstract = "We introduce Trialstreamer, a living database of clinical trial reports. Here we mainly describe the evidence extraction component; this extracts from biomedical abstracts key pieces of information that clinicians need when appraising the literature, and also the relations between these. Specifically, the system extracts descriptions of trial participants, the treatments compared in each arm (the interventions), and which outcomes were measured. The system then attempts to infer which interventions were reported to work best by determining their relationship with identified trial outcome measures. In addition to summarizing individual trials, these extracted data elements allow automatic synthesis of results across many trials on the same topic. We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic. We make all code and models freely available alongside a demonstration of the web interface.",
}
@inproceedings{gauthier-etal-2020-syntaxgym,
    title = "{S}yntax{G}ym: An Online Platform for Targeted Evaluation of Language Models",
    author = "Gauthier, Jon  and
      Hu, Jennifer  and
      Wilcox, Ethan  and
      Qian, Peng  and
      Levy, Roger",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.10",
    doi = "10.18653/v1/2020.acl-demos.10",
    pages = "70--76",
    abstract = "Targeted syntactic evaluations have yielded insights into the generalizations learned by neural network language models. However, this line of research requires an uncommon confluence of skills: both the theoretical knowledge needed to design controlled psycholinguistic experiments, and the technical proficiency needed to train and deploy large-scale language models. We present SyntaxGym, an online platform designed to make targeted evaluations accessible to both experts in NLP and linguistics, reproducible across computing environments, and standardized following the norms of psycholinguistic experimental design. This paper releases two tools of independent value for the computational linguistics community: 1. A website, syntaxgym.org, which centralizes the process of targeted syntactic evaluation and provides easy tools for analysis and visualization; 2. Two command-line tools, {`}syntaxgym{`} and {`}lm-zoo{`}, which allow any user to reproduce targeted syntactic evaluations and general language model inference on their own machine.",
}
@inproceedings{li-etal-2020-gaia,
    title = "{GAIA}: A Fine-grained Multimedia Knowledge Extraction System",
    author = "Li, Manling  and
      Zareian, Alireza  and
      Lin, Ying  and
      Pan, Xiaoman  and
      Whitehead, Spencer  and
      Chen, Brian  and
      Wu, Bo  and
      Ji, Heng  and
      Chang, Shih-Fu  and
      Voss, Clare  and
      Napierski, Daniel  and
      Freedman, Marjorie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.11",
    doi = "10.18653/v1/2020.acl-demos.11",
    pages = "77--86",
    abstract = "We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology. Our system, GAIA, enables seamless search of complex graph queries, and retrieves multimedia evidence including text, images and videos. GAIA achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. The system is publicly available at GitHub and DockerHub, with a narrated video that documents the system.",
}
@inproceedings{yang-etal-2020-multilingual,
    title = "Multilingual Universal Sentence Encoder for Semantic Retrieval",
    author = "Yang, Yinfei  and
      Cer, Daniel  and
      Ahmad, Amin  and
      Guo, Mandy  and
      Law, Jax  and
      Constant, Noah  and
      Hernandez Abrego, Gustavo  and
      Yuan, Steve  and
      Tar, Chris  and
      Sung, Yun-hsuan  and
      Strope, Brian  and
      Kurzweil, Ray",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.12",
    doi = "10.18653/v1/2020.acl-demos.12",
    pages = "87--94",
    abstract = "We present easy-to-use retrieval focused multilingual sentence embedding models, made available on TensorFlow Hub. The models embed text from 16 languages into a shared semantic space using a multi-task trained dual-encoder that learns tied cross-lingual representations via translation bridge tasks (Chidambaram et al., 2018). The models achieve a new state-of-the-art in performance on monolingual and cross-lingual semantic retrieval (SR). Competitive performance is obtained on the related tasks of translation pair bitext retrieval (BR) and retrieval question answering (ReQA). On transfer learning tasks, our multilingual embeddings approach, and in some cases exceed, the performance of English only sentence embeddings.",
}
@inproceedings{jin-etal-2020-bento,
    title = "{BENTO}: A Visual Platform for Building Clinical {NLP} Pipelines Based on {C}oda{L}ab",
    author = "Jin, Yonghao  and
      Li, Fei  and
      Yu, Hong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.13",
    doi = "10.18653/v1/2020.acl-demos.13",
    pages = "95--100",
    abstract = "CodaLab is an open-source web-based platform for collaborative computational research. Although CodaLab has gained popularity in the research community, its interface has limited support for creating reusable tools that can be easily applied to new datasets and composed into pipelines. In clinical domain, natural language processing (NLP) on medical notes generally involves multiple steps, like tokenization, named entity recognition, etc. Since these steps require different tools which are usually scattered in different publications, it is not easy for researchers to use them to process their own datasets. In this paper, we present BENTO, a workflow management platform with a graphic user interface (GUI) that is built on top of CodaLab, to facilitate the process of building clinical NLP pipelines. BENTO comes with a number of clinical NLP tools that have been pre-trained using medical notes and expert annotations and can be readily used for various clinical NLP tasks. It also allows researchers and developers to create their custom tools (e.g., pre-trained NLP models) and use them in a controlled and reproducible way. In addition, the GUI interface enables researchers with limited computer background to compose tools into NLP pipelines and then apply the pipelines on their own datasets in a {``}what you see is what you get{''} (WYSIWYG) way. Although BENTO is designed for clinical NLP applications, the underlying architecture is flexible to be tailored to any other domains.",
}
@inproceedings{qi-etal-2020-stanza,
    title = "{S}tanza: A Python Natural Language Processing Toolkit for Many Human Languages",
    author = "Qi, Peng  and
      Zhang, Yuhao  and
      Zhang, Yuhui  and
      Bolton, Jason  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.14",
    doi = "10.18653/v1/2020.acl-demos.14",
    pages = "101--108",
    abstract = "We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages. Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition. We have trained Stanza on a total of 112 datasets, including the Universal Dependencies treebanks and other multilingual corpora, and show that the same neural architecture generalizes well and achieves competitive performance on all languages tested. Additionally, Stanza includes a native Python interface to the widely used Java Stanford CoreNLP software, which further extends its functionality to cover other tasks such as coreference resolution and relation extraction. Source code, documentation, and pretrained models for 66 languages are available at https://stanfordnlp.github.io/stanza/.",
}
@inproceedings{pruksachatkun-etal-2020-jiant,
    title = "jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models",
    author = "Pruksachatkun, Yada  and
      Yeres, Phil  and
      Liu, Haokun  and
      Phang, Jason  and
      Htut, Phu Mon  and
      Wang, Alex  and
      Tenney, Ian  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.15",
    doi = "10.18653/v1/2020.acl-demos.15",
    pages = "109--117",
    abstract = "We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration driven experimentation with state-of-the-art models and a broad set of tasks for probing, transfer learning, and multitask training experiments. jiant implements over 50 NLU tasks, including all GLUE and SuperGLUE benchmark tasks. We demonstrate that jiant reproduces published performance on a variety of tasks and models, e.g., RoBERTa and BERT.",
}
@inproceedings{liu-etal-2020-microsoft,
    title = "The {M}icrosoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding",
    author = "Liu, Xiaodong  and
      Wang, Yu  and
      Ji, Jianshu  and
      Cheng, Hao  and
      Zhu, Xueyun  and
      Awa, Emmanuel  and
      He, Pengcheng  and
      Chen, Weizhu  and
      Poon, Hoifung  and
      Cao, Guihong  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.16",
    doi = "10.18653/v1/2020.acl-demos.16",
    pages = "118--126",
    abstract = "We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn.",
}
@inproceedings{tsai-etal-2020-lingglewrite,
    title = "{L}inggle{W}rite: a Coaching System for Essay Writing",
    author = "Tsai, Chung-Ting  and
      Chen, Jhih-Jie  and
      Yang, Ching-Yu  and
      Chang, Jason S.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.17",
    doi = "10.18653/v1/2020.acl-demos.17",
    pages = "127--133",
    abstract = "This paper presents LinggleWrite, a writing coach that provides writing suggestions, assesses writing proficiency levels, detects grammatical errors, and offers corrective feedback in response to user{'}s essay. The method involves extracting grammar patterns, training models for automated essay scoring (AES) and grammatical error detection (GED), and finally retrieving plausible corrections from a n-gram search engine. Experiments on public test sets indicate that both AES and GED models achieve state-of-the-art performance. These results show that LinggleWrite is potentially useful in helping learners improve their writing skills.",
}
@inproceedings{sun-etal-2020-clireval,
    title = "{CLIR}eval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task",
    author = "Sun, Shuo  and
      Sia, Suzanna  and
      Duh, Kevin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.18",
    doi = "10.18653/v1/2020.acl-demos.18",
    pages = "134--141",
    abstract = "We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR). Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset. Instead, it automatically transforms translations and references used in MT evaluations into a synthetic CLIR dataset; it then sets up a standard search engine (Elasticsearch) and computes various information retrieval metrics (e.g., mean average precision) by treating the translations as documents to be retrieved. The idea is to gauge the quality of MT by its impact on the document translation approach to CLIR. As a case study, we run CLIReval on the {``}metrics shared task{''} of WMT2019; while this extrinsic metric is not intended to replace popular intrinsic metrics such as BLEU, results suggest CLIReval is competitive in many language pairs in terms of correlation to human judgments of quality. CLIReval is publicly available at https://github.com/ssun32/CLIReval.",
}
@inproceedings{zhu-etal-2020-convlab,
    title = "{C}onv{L}ab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems",
    author = "Zhu, Qi  and
      Zhang, Zheng  and
      Fang, Yan  and
      Li, Xiang  and
      Takanobu, Ryuichi  and
      Li, Jinchao  and
      Peng, Baolin  and
      Gao, Jianfeng  and
      Zhu, Xiaoyan  and
      Huang, Minlie",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.19",
    doi = "10.18653/v1/2020.acl-demos.19",
    pages = "142--149",
    abstract = "We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab, ConvLab-2 inherits ConvLab{'}s framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides an user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.",
}
@inproceedings{aulamo-etal-2020-opusfilter,
    title = "{O}pus{F}ilter: A Configurable Parallel Corpus Filtering Toolbox",
    author = {Aulamo, Mikko  and
      Virpioja, Sami  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.20",
    doi = "10.18653/v1/2020.acl-demos.20",
    pages = "150--156",
    abstract = "This paper introduces OpusFilter, a flexible and modular toolbox for filtering parallel corpora. It implements a number of components based on heuristic filters, language identification libraries, character-based language models, and word alignment tools, and it can easily be extended with custom filters. Bitext segments can be ranked according to their quality or domain match using single features or a logistic regression model that can be trained without manually labeled training data. We demonstrate the effectiveness of OpusFilter on the example of a Finnish-English news translation task based on noisy web-crawled training data. Applying our tool leads to improved translation quality while significantly reducing the size of the training data, also clearly outperforming an alternative ranking given in the crawled data set. Furthermore, we show the ability of OpusFilter to perform data selection for domain adaptation.",
}
@inproceedings{desmond-etal-2020-label,
    title = "Label Noise in Context",
    author = "Desmond, Michael  and
      Finegan-Dollak, Catherine  and
      Boston, Jeff  and
      Arnold, Matt",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.21",
    doi = "10.18653/v1/2020.acl-demos.21",
    pages = "157--186",
    abstract = "Label noise{---}incorrectly or ambiguously labeled training examples{---}can negatively impact model performance. Although noise detection techniques have been around for decades, practitioners rarely apply them, as manual noise remediation is a tedious process. Examples incorrectly flagged as noise waste reviewers{'} time, and correcting label noise without guidance can be difficult. We propose LNIC, a noise-detection method that uses an example{'}s neighborhood within the training set to (a) reduce false positives and (b) provide an explanation as to why the ex- ample was flagged as noise. We demonstrate on several short-text classification datasets that LNIC outperforms the state of the art on measures of precision and F0.5-score. We also show how LNIC{'}s training set context helps a reviewer to understand and correct label noise in a dataset. The LNIC tool lowers the barriers to label noise remediation, increasing its utility for NLP practitioners.",
}
@inproceedings{hoover-etal-2020-exbert,
    title = "ex{BERT}: {A} {V}isual {A}nalysis {T}ool to {E}xplore {L}earned {R}epresentations in {T}ransformer {M}odels",
    author = "Hoover, Benjamin  and
      Strobelt, Hendrik  and
      Gehrmann, Sebastian",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.22",
    doi = "10.18653/v1/2020.acl-demos.22",
    pages = "187--196",
    abstract = "Large Transformer-based language models can route and reshape complex information via their multi-headed attention mechanism. Although the attention never receives explicit supervision, it can exhibit recognizable patterns following linguistic or positional information. Analyzing the learned representations and attentions is paramount to furthering our understanding of the inner workings of these models. However, analyses have to catch up with the rapid release of new models and the growing diversity of investigation techniques. To support analysis for a wide variety of models, we introduce exBERT, a tool to help humans conduct flexible, interactive investigations and formulate hypotheses for the model-internal reasoning process. exBERT provides insights into the meaning of the contextual representations and attention by matching a human-specified input to similar contexts in large annotated datasets. By aggregating the annotations of the matched contexts, exBERT can quickly replicate findings from literature and extend them to previously not analyzed models.",
}
@inproceedings{shmidman-etal-2020-nakdan,
    title = "{N}akdan: Professional {H}ebrew Diacritizer",
    author = "Shmidman, Avi  and
      Shmidman, Shaltiel  and
      Koppel, Moshe  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.23",
    doi = "10.18653/v1/2020.acl-demos.23",
    pages = "197--203",
    abstract = "We present a system for automatic diacritization of Hebrew Text. The system combines modern neural models with carefully curated declarative linguistic knowledge and comprehensive manually constructed tables and dictionaries. Besides providing state of the art diacritization accuracy, the system also supports an interface for manual editing and correction of the automatic output, and has several features which make it particularly useful for preparation of scientific editions of historical Hebrew texts. The system supports Modern Hebrew, Rabbinic Hebrew and Poetic Hebrew. The system is freely accessible for all use at http://nakdanpro.dicta.org.il",
}
@inproceedings{zeng-etal-2020-photon,
    title = "{P}hoton: A Robust Cross-Domain Text-to-{SQL} System",
    author = "Zeng, Jichuan  and
      Lin, Xi Victoria  and
      Hoi, Steven C.H.  and
      Socher, Richard  and
      Xiong, Caiming  and
      Lyu, Michael  and
      King, Irwin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.24",
    doi = "10.18653/v1/2020.acl-demos.24",
    pages = "204--214",
    abstract = "Natural language interfaces to databases(NLIDB) democratize end user access to relational data. Due to fundamental differences between natural language communication and programming, it is common for end users to issue questions that are ambiguous to the system or fall outside the semantic scope of its underlying query language. We present PHOTON, a robust, modular, cross-domain NLIDB that can flag natural language input to which a SQL mapping cannot be immediately determined. PHOTON consists of a strong neural semantic parser (63.2{\%} structure accuracy on the Spider dev benchmark), a human-in-the-loop question corrector, a SQL executor and a response generator. The question corrector isa discriminative neural sequence editor which detects confusion span(s) in the input question and suggests rephrasing until a translatable input is given by the user or a maximum number of iterations are conducted. Experiments on simulated data show that the proposed method effectively improves the robustness of text-to-SQL system against untranslatable user input.The live demo of our system is available at http://www.naturalsql.com",
}
@inproceedings{li-etal-2020-interactive,
    title = "Interactive Task Learning from {GUI}-Grounded Natural Language Instructions and Demonstrations",
    author = "Li, Toby Jia-Jun  and
      Mitchell, Tom  and
      Myers, Brad",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.25",
    doi = "10.18653/v1/2020.acl-demos.25",
    pages = "215--223",
    abstract = "We show SUGILITE, an intelligent task automation agent that can learn new tasks and relevant associated concepts interactively from the user{'}s natural language instructions and demonstrations, using the graphical user interfaces (GUIs) of third-party mobile apps. This system provides several interesting features: (1) it allows users to teach new task procedures and concepts through verbal instructions together with demonstration of the steps of a script using GUIs; (2) it supports users in clarifying their intents for demonstrated actions using GUI-grounded verbal instructions; (3) it infers parameters of tasks and their possible values in utterances using the hierarchical structures of the underlying app GUIs; and (4) it generalizes taught concepts to different contexts and task domains. We describe the architecture of the SUGILITE system, explain the design and implementation of its key features, and show a prototype in the form of a conversational assistant on Android.",
}
@inproceedings{gao-etal-2020-mixingboard,
    title = "{M}ixing{B}oard: a Knowledgeable Stylized Integrated Text Generation Platform",
    author = "Gao, Xiang  and
      Galley, Michel  and
      Dolan, Bill",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.26",
    doi = "10.18653/v1/2020.acl-demos.26",
    pages = "224--231",
    abstract = "We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation. We unify existing text generation algorithms in a shared codebase and further adapt earlier algorithms for constrained generation. To borrow advantages from different models, we implement strategies for cross-model integration, from the token probability level to the latent space level. An interface to external knowledge is provided via a module that retrieves, on-the-fly, relevant knowledge from passages on the web or a document collection. A user interface for local development, remote webpage access, and a RESTful API are provided to make it simple for users to build their own demos.",
}
@inproceedings{mohammad-2020-nlp-scholar,
    title = "{NLP} Scholar: An Interactive Visual Explorer for Natural Language Processing Literature",
    author = "Mohammad, Saif M.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.27",
    doi = "10.18653/v1/2020.acl-demos.27",
    pages = "232--255",
    abstract = "As part of the NLP Scholar project, we created a single unified dataset of NLP papers and their meta-information (including citation numbers), by extracting and aligning information from the ACL Anthology and Google Scholar. In this paper, we describe several interconnected interactive visualizations (dashboards) that present various aspects of the data. Clicking on an item within a visualization or entering query terms in the search boxes filters the data in all visualizations in the dashboard. This allows users to search for papers in the area of their interest, published within specific time periods, published by specified authors, etc. The interactive visualizations presented here, and the associated dataset of papers mapped to citations, have additional uses as well including understanding how the field is growing (both overall and across sub-areas), as well as quantifying the impact of different types of papers on subsequent publications.",
}
@inproceedings{hossain-etal-2020-stimulating,
    title = "Stimulating Creativity with {F}un{L}ines: A Case Study of Humor Generation in Headlines",
    author = "Hossain, Nabil  and
      Krumm, John  and
      Sajed, Tanvir  and
      Kautz, Henry",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.28",
    doi = "10.18653/v1/2020.acl-demos.28",
    pages = "256--262",
    abstract = "Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where they rate the funniness of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding and educational, keeping players engaged and providing humor data at a very low cost compared to traditional crowdsourcing approaches. FunLines offers useful performance feedback, assisting players in getting better over time at generating and assessing humor, as our analysis shows. This helps to further increase the quality of the generated dataset. We show the effectiveness of this data by training humor classification models that outperform a previous benchmark, and we release this dataset to the public.",
}
@inproceedings{swanson-smus-2020-usnea,
    title = "{U}snea: An Authorship Tool for Interactive Fiction using Retrieval Based Semantic Parsing",
    author = "Swanson, Ben  and
      Smus, Boris",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.29",
    doi = "10.18653/v1/2020.acl-demos.29",
    pages = "263--269",
    abstract = "The reader of a choose your own adventure novel and the user of a modern virtual assistant have a subtle similarity; both may, through the right lens, be viewed as engaging with a work of Interactive Fiction. This literary form emerged in the 1970s and has grown like a vine along the branch of modern technology, one guided by the advances of the other. In this work we weave together threads from the Interactive Fiction community and neural semantic parsing for dialog systems, defining the data model and necessary algorithms for a novel type of Interactive Fiction and open sourcing its accompanying authoring tool. Specifically, our work integrates retrieval based semantic parsing predicates into the branching story structures well known to the Interactive Fiction community, relaxing the relatively strict lexical options of preexisting systems.",
}
@inproceedings{zhang-etal-2020-dialogpt,
    title = "{DIALOGPT} : Large-Scale Generative Pre-training for Conversational Response Generation",
    author = "Zhang, Yizhe  and
      Sun, Siqi  and
      Galley, Michel  and
      Chen, Yen-Chun  and
      Brockett, Chris  and
      Gao, Xiang  and
      Gao, Jianfeng  and
      Liu, Jingjing  and
      Dolan, Bill",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.30",
    doi = "10.18653/v1/2020.acl-demos.30",
    pages = "270--278",
    abstract = "We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.",
}
@inproceedings{li-etal-2020-adviser,
    title = "{ADVISER}: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents",
    author = {Li, Chia-Yu  and
      Ortega, Daniel  and
      V{\"a}th, Dirk  and
      Lux, Florian  and
      Vanderlyn, Lindsey  and
      Schmidt, Maximilian  and
      Neumann, Michael  and
      V{\"o}lkel, Moritz  and
      Denisov, Pavel  and
      Jenne, Sabrina  and
      Kacarevic, Zorica  and
      Vu, Ngoc Thang},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.31",
    doi = "10.18653/v1/2020.acl-demos.31",
    pages = "279--286",
    abstract = "We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e.g. emotion recognition, engagement level prediction and backchanneling) conversational agents. The final Python-based implementation of our toolkit is flexible, easy to use, and easy to extend not only for technically experienced users, such as machine learning researchers, but also for less technically experienced users, such as linguists or cognitive scientists, thereby providing a flexible platform for collaborative research.",
}
@inproceedings{da-san-martino-etal-2020-prta,
    title = "{P}rta: A System to Support the Analysis of Propaganda Techniques in the News",
    author = "Da San Martino, Giovanni  and
      Shaar, Shaden  and
      Zhang, Yifan  and
      Yu, Seunghak  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.32",
    doi = "10.18653/v1/2020.acl-demos.32",
    pages = "287--293",
    abstract = "Recent events, such as the 2016 US Presidential Campaign, Brexit and the COVID-19 {``}infodemic{''}, have brought into the spotlight the dangers of online disinformation. There has been a lot of research focusing on fact-checking and disinformation detection. However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages. Revealing the use of such techniques can help promote media literacy and critical thinking, and eventually contribute to limiting the impact of {``}fake news{''} and disinformation campaigns. Prta (Propaganda Persuasion Techniques Analyzer) allows users to explore the articles crawled on a regular basis by highlighting the spans in which propaganda techniques occur and to compare them on the basis of their use of propaganda techniques. The system further reports statistics about the use of such techniques, overall and over time, or according to filtering criteria specified by the user based on time interval, keywords, and/or political orientation of the media. Moreover, it allows users to analyze any text or URL through a dedicated interface or via an API. The system is available online: https://www.tanbih.org/prta.",
}
@inproceedings{cao-etal-2020-clinical,
    title = "Clinical-Coder: Assigning Interpretable {ICD}-10 Codes to {C}hinese Clinical Notes",
    author = "Cao, Pengfei  and
      Yan, Chenwei  and
      Fu, Xiangling  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun  and
      Liu, Shengping  and
      Chong, Weifeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.33",
    doi = "10.18653/v1/2020.acl-demos.33",
    pages = "294--301",
    abstract = "In this paper, we introduce Clinical-Coder, an online system aiming to assign ICD codes to Chinese clinical notes. ICD coding has been a research hotspot of clinical medicine, but the interpretability of prediction hinders its practical application. We exploit a Dilated Convolutional Attention network with N-gram Matching mechanism (DCANM) to capture semantic features for non-continuous words and continuous n-gram words, concentrating on explaining the reason why each ICD code to be predicted. The experiments demonstrate that our approach is effective and that our system is able to provide supporting information in clinical decision making.",
}
@inproceedings{inaguma-etal-2020-espnet,
    title = "{ESP}net-{ST}: All-in-One Speech Translation Toolkit",
    author = "Inaguma, Hirofumi  and
      Kiyono, Shun  and
      Duh, Kevin  and
      Karita, Shigeki  and
      Yalta, Nelson  and
      Hayashi, Tomoki  and
      Watanabe, Shinji",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.34",
    doi = "10.18653/v1/2020.acl-demos.34",
    pages = "302--311",
    abstract = "We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at https://github.com/espnet/espnet.",
}
@inproceedings{goodman-2020-penman,
    title = "{P}enman: An Open-Source Library and Tool for {AMR} Graphs",
    author = "Goodman, Michael Wayne",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.35",
    doi = "10.18653/v1/2020.acl-demos.35",
    pages = "312--319",
    abstract = "Abstract Meaning Representation (AMR) (Banarescu et al., 2013) is a framework for semantic dependencies that encodes its rooted and directed acyclic graphs in a format called PENMAN notation. The format is simple enough that users of AMR data often write small scripts or libraries for parsing it into an internal graph representation, but there is enough complexity that these users could benefit from a more sophisticated and well-tested solution. The open-source Python library Penman provides a robust parser, functions for graph inspection and manipulation, and functions for formatting graphs into PENMAN notation. Many functions are also available in a command-line tool, thus extending its utility to non-Python setups.",
}
@inproceedings{gokce-etal-2020-embedding,
    title = "Embedding-based Scientific Literature Discovery in a Text Editor Application",
    author = {G{\"o}k{\c{c}}e, Onur  and
      Prada, Jonathan  and
      Nikolov, Nikola I.  and
      Gu, Nianlong  and
      Hahnloser, Richard H.R.},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.36",
    doi = "10.18653/v1/2020.acl-demos.36",
    pages = "320--326",
    abstract = "Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. To define comprehensive search queries requires strong motivation from authors, irrespective of their familiarity with the research field. Moreover, switching between independent applications for literature discovery, bibliography management, reading papers, and writing text burdens authors further and interrupts their creative process. Here, we present a web application that combines text editing and literature discovery in an interactive user interface. The application is equipped with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings, providing a discovery experience tuned to an author{'}s manuscript and his interests. Our application aims to take a step towards more enjoyable and effortless academic writing. The demo of the application (https://SciEditorDemo2020.herokuapp.com) and a short video tutorial (https://youtu.be/pkdVU60IcRc) are available online.",
}
@inproceedings{herbig-etal-2020-mmpe-multi,
    title = "{MMPE}: {A} {M}ulti-{M}odal {I}nterface using {H}andwriting, {T}ouch {R}eordering, and {S}peech {C}ommands for {P}ost-{E}diting {M}achine {T}ranslation",
    author = {Herbig, Nico  and
      Pal, Santanu  and
      D{\"u}wel, Tim  and
      Meladaki, Kalliopi  and
      Monshizadeh, Mahsa  and
      Hnatovskiy, Vladislav  and
      Kr{\"u}ger, Antonio  and
      van Genabith, Josef},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.37",
    doi = "10.18653/v1/2020.acl-demos.37",
    pages = "327--334",
    abstract = "The shift from traditional translation to post-editing (PE) of machine-translated (MT) text can save time and reduce errors, but it also affects the design of translation interfaces, as the task changes from mainly generating text to correcting errors within otherwise helpful translation proposals. Since this paradigm shift offers potential for modalities other than mouse and keyboard, we present MMPE, the first prototype to combine traditional input modes with pen, touch, and speech modalities for PE of MT. Users can directly cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place. All text manipulations are logged in an easily interpretable format to simplify subsequent translation process research. The results of an evaluation with professional translators suggest that pen and touch interaction are suitable for deletion and reordering tasks, while speech and multi-modal combinations of select {\&} speech are considered suitable for replacements and insertions. Overall, experiment participants were enthusiastic about the new modalities and saw them as useful extensions to mouse {\&} keyboard, but not as a complete substitute.",
}
@inproceedings{rush-2020-torch,
    title = "Torch-Struct: Deep Structured Prediction Library",
    author = "Rush, Alexander",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.38",
    doi = "10.18653/v1/2020.acl-demos.38",
    pages = "335--342",
    abstract = "The literature on structured prediction for NLP describes a rich collection of distributions and algorithms over sequences, segmentations, alignments, and trees; however, these algorithms are difficult to utilize in deep learning frameworks. We introduce Torch-Struct, a library for structured prediction designed to take advantage of and integrate with vectorized, auto-differentiation based frameworks. Torch-Struct includes a broad collection of probabilistic structures accessed through a simple and flexible distribution-based API that connects to any deep learning model. The library utilizes batched, vectorized operations and exploits auto-differentiation to produce readable, fast, and testable code. Internally, we also include a number of general-purpose optimizations to provide cross-algorithm efficiency. Experiments show significant performance gains over fast baselines and case-studies demonstrate the benefits of the library. Torch-Struct is available at https://github.com/harvardnlp/pytorch-struct.",
}
@inproceedings{shukla-etal-2020-conversation,
    title = "{C}onversation {L}earner - A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems",
    author = "Shukla, Swadheen  and
      Liden, Lars  and
      Shayandeh, Shahin  and
      Kamal, Eslam  and
      Li, Jinchao  and
      Mazzola, Matt  and
      Park, Thomas  and
      Peng, Baolin  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.39",
    doi = "10.18653/v1/2020.acl-demos.39",
    pages = "343--349",
    abstract = "Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows. While dialog flows are intuitively interpretable and good for simple scenarios, they fall short of performance in terms of the flexibility needed to handle complex dialogs. On the other hand, purely machine-learned models can handle complex dialogs, but they are considered to be black boxes and require large amounts of training data. In this demonstration, we showcase Conversation Learner, a machine teaching tool for building dialog managers. It combines the best of both approaches by enabling dialog authors to create a dialog flow using familiar tools, converting the dialog flow into a parametric model (e.g., neural networks), and allowing dialog authors to improve the dialog manager (i.e., the parametric model) over time by leveraging user-system dialog logs as training data through a machine teaching interface.",
}
@inproceedings{bambrick-etal-2020-nstm,
    title = "{NSTM}: Real-Time Query-Driven News Overview Composition at {B}loomberg",
    author = "Bambrick, Joshua  and
      Xu, Minjie  and
      Almonte, Andy  and
      Malioutov, Igor  and
      Perarnau, Guim  and
      Selo, Vittorio  and
      Chan, Iat Chong",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.40",
    doi = "10.18653/v1/2020.acl-demos.40",
    pages = "350--361",
    abstract = "Millions of news articles from hundreds of thousands of sources around the globe appear in news aggregators every day. Consuming such a volume of news presents an almost insurmountable challenge. For example, a reader searching on Bloomberg{'}s system for news about the U.K. would find 10,000 articles on a typical day. Apple Inc., the world{'}s most journalistically covered company, garners around 1,800 news articles a day. We realized that a new kind of summarization engine was needed, one that would condense large volumes of news into short, easy to absorb points. The system would filter out noise and duplicates to identify and summarize key news about companies, countries or markets. When given a user query, Bloomberg{'}s solution, Key News Themes (or NSTM), leverages state-of-the-art semantic clustering techniques and novel summarization methods to produce comprehensive, yet concise, digests to dramatically simplify the news consumption process. NSTM is available to hundreds of thousands of readers around the world and serves thousands of requests daily with sub-second latency. At ACL 2020, we will present a demo of NSTM.",
}
@inproceedings{wang-etal-2020-supp,
    title = "{SUPP}.{AI}: finding evidence for supplement-drug interactions",
    author = "Wang, Lucy  and
      Tafjord, Oyvind  and
      Cohan, Arman  and
      Jain, Sarthak  and
      Skjonsberg, Sam  and
      Schoenick, Carissa  and
      Botner, Nick  and
      Ammar, Waleed",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.41",
    doi = "10.18653/v1/2020.acl-demos.41",
    pages = "362--371",
    abstract = "Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI, an application for browsing evidence of supplement-drug interactions (SDIs) extracted from the biomedical literature. We train a model to automatically extract supplement information and identify such interactions from the scientific literature. To address the lack of labeled data for SDI identification, we use labels of the closely related task of identifying drug-drug interactions (DDIs) for supervision. We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions. We extract 195k evidence sentences from 22M articles (P=0.82, R=0.58, F1=0.68) for 60k interactions. We create the SUPP.AI application for users to search evidence sentences extracted by our model. SUPP.AI is an attempt to close the information gap on dietary supplements by making up-to-date evidence on SDIs more discoverable for researchers, clinicians, and consumers. An informational video on how to use SUPP.AI is available at: https://youtu.be/dR0ucKdORwc",
}
@inproceedings{lee-etal-2020-lean,
    title = "{LEAN}-{LIFE}: A Label-Efficient Annotation Framework Towards Learning from Explanation",
    author = "Lee, Dong-Ho  and
      Khanna, Rahul  and
      Lin, Bill Yuchen  and
      Lee, Seyeon  and
      Ye, Qinyuan  and
      Boschee, Elizabeth  and
      Neves, Leonardo  and
      Ren, Xiang",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.42",
    doi = "10.18653/v1/2020.acl-demos.42",
    pages = "372--379",
    abstract = "Successfully training a deep neural network demands a huge corpus of labeled data. However, each label only provides limited information to learn from, and collecting the requisite number of labels involves massive human effort. In this work, we introduce LEAN-LIFE, a web-based, Label-Efficient AnnotatioN framework for sequence labeling and classification tasks, with an easy-to-use UI that not only allows an annotator to provide the needed labels for a task but also enables LearnIng From Explanations for each labeling decision. Such explanations enable us to generate useful additional labeled data from unlabeled instances, bolstering the pool of available training data. On three popular NLP tasks (named entity recognition, relation extraction, sentiment analysis), we find that using this enhanced supervision allows our models to surpass competitive baseline F1 scores by more than 5-10 percentage points, while using 2X times fewer labeled instances. Our framework is the first to utilize this enhanced supervision technique and does so for three important tasks {--} thus providing improved annotation recommendations to users and an ability to build datasets of (data, label, explanation) triples instead of the regular (data, label) pair.",
}
@inproceedings{laban-etal-2020-whats,
    title = "What{'}s The Latest? A Question-driven News Chatbot",
    author = "Laban, Philippe  and
      Canny, John  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-demos.43",
    doi = "10.18653/v1/2020.acl-demos.43",
    pages = "380--387",
    abstract = "This work describes an automatic news chatbot that draws content from a diverse set of news articles and creates conversations with a user about the news. Key components of the system include the automatic organization of news articles into topical chatrooms, integration of automatically generated questions into the conversation, and a novel method for choosing which questions to present which avoids repetitive suggestions. We describe the algorithmic framework and present the results of a usability study that shows that news readers using the system successfully engage in multi-turn conversations about specific news stories.",
}
@proceedings{aacl-2020-asia-pacific,
    title = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    editor = "Shmueli, Boaz  and
      Huang, Yin Jou",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.0",
}
@inproceedings{aoki-etal-2020-text,
    title = "Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation",
    author = "Aoki, Takumi  and
      Kitada, Shunsuke  and
      Iyatomi, Hitoshi",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.1",
    pages = "1--7",
    abstract = "We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a β-variational auto-encoder (β -VAE) that learns the proposed glyph-aware disentangled character embedding (GDCE). Since our GDCE provides zero-mean unit-variance character embeddings that are dimensionally independent, it is applicable for our interpretable data augmentation, namely, semantic sub-character augmentation (SSA). In this paper, we evaluated our framework using Japanese text classification tasks at the document- and sentence-level. We confirmed that our GDCE and SSA not only provided embedding interpretability but also improved the classification performance. Our proposal achieved a competitive result to the state-of-the-art model while also providing model interpretability.",
}
@inproceedings{li-jiang-2020-two,
    title = "Two-Headed Monster and Crossed Co-Attention Networks",
    author = "Li, Yaoyiran  and
      Jiang, Jing",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.2",
    pages = "8--15",
    abstract = "This paper investigates a new co-attention mechanism in neural transduction models for machine translation tasks. We propose a paradigm, termed Two-Headed Monster (THM), which consists of two symmetric encoder modules and one decoder module connected with co-attention. As a specific and concrete implementation of THM, Crossed Co-Attention Networks (CCNs) are designed based on the Transformer model. We test CCNs on WMT 2014 EN-DE and WMT 2016 EN-FI translation tasks and show both advantages and disadvantages of the proposed method. Our model outperforms the strong Transformer baseline by 0.51 (big) and 0.74 (base) BLEU points on EN-DE and by 0.17 (big) and 0.47 (base) BLEU points on EN-FI but the epoch time increases by circa 75{\%}.",
}
@inproceedings{laverghetta-jr-etal-2020-towards,
    title = "Towards a Task-Agnostic Model of Difficulty Estimation for Supervised Learning Tasks",
    author = "Laverghetta Jr., Antonio  and
      Mirzakhalov, Jamshidbek  and
      Licato, John",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.3",
    pages = "16--23",
    abstract = "Curriculum learning, a training strategy where training data are ordered based on their difficulty, has been shown to improve performance and reduce training time on various NLP tasks. While much work over the years has developed novel approaches for generating curricula, these strategies are typically only suited for the task they were designed for. This work explores developing a task-agnostic model for problem difficulty and applying it to the Stanford Natural Language Inference (SNLI) dataset. Using the human responses that come with the dev set of SNLI, we train both regression and classification models to predict how many annotators will answer a question correctly and then project the difficulty estimates onto the full SNLI train set to create the curriculum. We argue that our curriculum is effectively capturing difficulty for this task through various analyses of both the model and the predicted difficulty scores.",
}
@inproceedings{shi-etal-2020-siamese,
    title = "A {S}iamese {CNN} Architecture for Learning {C}hinese Sentence Similarity",
    author = "Shi, Haoxiang  and
      Wang, Cen  and
      Sakai, Tetsuya",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.4",
    pages = "24--29",
    abstract = "This paper presents a deep neural architecture which applies the siamese convolutional neural network sharing model parameters for learning a semantic similarity metric between two sentences. In addition, two different similarity metrics (i.e., the Cosine Similarity and Manhattan similarity) are compared based on this architecture. Our experiments in binary similarity classification for Chinese sentence pairs show that the proposed siamese convolutional architecture with Manhattan similarity outperforms the baselines (i.e., the siamese Long Short-Term Memory architecture and the siamese Bidirectional Long Short-Term Memory architecture) by 8.7 points in accuracy.",
}
@inproceedings{wilson-wun-2020-automatic,
    title = "Automatic Classification of Students on {T}witter Using Simple Profile Information",
    author = "Wilson, Lili-Michal  and
      Wun, Christopher",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.5",
    pages = "30--36",
    abstract = "Obtaining social media demographic information using machine learning is important for efficient computational social science research. Automatic age classification has been accomplished with relative success and allows for the study of youth populations, but student classification{---}determining which users are currently attending an academic institution{---}has not been thoroughly studied. Previous work (He et al., 2016) proposes a model which utilizes 3 tweet-content features to classify users as students or non-students. This model achieves an accuracy of 84{\%}, but is restrictive and time intensive because it requires accessing and processing many user tweets. In this study, we propose classification models which use 7 numerical features and 10 text-based features drawn from simple profile information. These profile-based features allow for faster, more accessible data collection and enable the classification of users without needing access to their tweets. Compared to previous models, our models identify students with greater accuracy; our best model obtains an accuracy of 88.1{\%} and an F1 score of .704. This improved student identification tool has the potential to facilitate research on topics ranging from professional networking to the impact of education on Twitter behaviors.",
}
@inproceedings{pant-dadu-2020-towards,
    title = "Towards Code-switched Classification Exploiting Constituent Language Resources",
    author = "Pant, Kartikey  and
      Dadu, Tanvi",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.6",
    pages = "37--43",
    abstract = "Code-switching is a commonly observed communicative phenomenon denoting a shift from one language to another within the same speech exchange. The analysis of code-switched data often becomes an assiduous task, owing to the limited availability of data. In this work, we propose converting code-switched data into its constituent high resource languages for exploiting both monolingual and cross-lingual settings. This conversion allows us to utilize the higher resource availability for its constituent languages for multiple downstream tasks. We perform experiments for two downstream tasks, sarcasm detection and hate speech detection in the English-Hindi code-switched setting. These experiments show an increase in 22{\%} and 42.5{\%} in F1-score for sarcasm detection and hate speech detection, respectively, compared to the state-of-the-art.",
}
@inproceedings{shah-etal-2020-hindi,
    title = "{H}indi History Note Generation with Unsupervised Extractive Summarization",
    author = "Shah, Aayush  and
      Ramasubbu, Dhineshkumar  and
      Mathew, Dhruv  and
      Gadoya, Meet Chetan",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.7",
    pages = "44--49",
    abstract = "In this work, the task of extractive single document summarization applied to an education setting to generate summaries of chapters from grade 10 Hindi history textbooks is undertaken. Unsupervised approaches to extract summaries are employed and evaluated. TextRank, LexRank, Luhn and KLSum are used to extract summaries. When evaluated intrinsically, Luhn and TextRank summaries have the highest ROUGE scores. When evaluated extrinsically, the effective measure of a summary in answering exam questions, TextRank summaries performs the best.",
}
@inproceedings{yadav-etal-2020-unbiasing,
    title = "Unbiasing Review Ratings with Tendency Based Collaborative Filtering",
    author = "Yadav, Pranshi  and
      Yadav, Priya  and
      Nokhiz, Pegah  and
      Gupta, Vivek",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.8",
    pages = "50--56",
    abstract = "User-generated contents{'} score-based prediction and item recommendation has become an inseparable part of the online recommendation systems. The ratings allow people to express their opinions and may affect the market value of items and consumer confidence in e-commerce decisions. A major problem with the models designed for user review prediction is that they unknowingly neglect the rating bias occurring due to personal user bias preferences. We propose a tendency-based approach that models the user and item tendency for score prediction along with text review analysis with respect to ratings.",
}
@inproceedings{ashida-etal-2020-building,
    title = "Building a Part-of-Speech Tagged Corpus for Drenjongke (Bhutia)",
    author = "Ashida, Mana  and
      Lee, Seunghun  and
      Namgyal, Kunzang",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.9",
    pages = "57--63",
    abstract = "This research paper reports on the generation of the first Drenjongke corpus based on texts taken from a phrase book for beginners, written in the Tibetan script. A corpus of sentences was created after correcting errors in the text scanned through optical character reading (OCR). A total of 34 Part-of-Speech (PoS) tags were defined based on manual annotation performed by the three authors, one of whom is a native speaker of Drenjongke. The first corpus of the Drenjongke language comprises 275 sentences and 1379 tokens, which we plan to expand with other materials to promote further studies of this language.",
}
@inproceedings{khairunnisa-etal-2020-towards,
    title = "Towards a Standardized Dataset on {I}ndonesian Named Entity Recognition",
    author = "Khairunnisa, Siti Oryza  and
      Imankulova, Aizhan  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.10",
    pages = "64--71",
    abstract = "In recent years, named entity recognition (NER) tasks in the Indonesian language have undergone extensive development. There are only a few corpora for Indonesian NER; hence, recent Indonesian NER studies have used diverse datasets. Although an open dataset is available, it includes only approximately 2,000 sentences and contains inconsistent annotations, thereby preventing accurate training of NER models without reliance on pre-trained models. Therefore, we re-annotated the dataset and compared the two annotations{'} performance using the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) approach. Fixing the annotation yielded a more consistent result for the organization tag and improved the prediction score by a large margin. Moreover, to take full advantage of pre-trained models, we compared different feature embeddings to determine their impact on the NER task for the Indonesian language.",
}
@inproceedings{khanganba-jha-2020-formal,
    title = "Formal {S}anskrit Syntax: A Specification for Programming Language",
    author = "Khanganba, K. Kabi  and
      Jha, Girish",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.11",
    pages = "72--78",
    abstract = "The paper discusses the syntax of the primary statements of the Sanskritam, a programming language specification based on natural Sanskrit under a doctoral thesis. By a statement, we mean a syntactic unit regardless of its computational operations of variable declarations, program executions or evaluations of Boolean expressions etc. We have selected six common primary statements of declaration, assignment, inline initialization, if-then-else, for loop and while loop. The specification partly overlaps the ideas of natural language programming, Controlled Natural Language (Kunh, 2013), and Natural Language subset. The practice and application of structured natural language set in a discourse are deeply rooted in the theoretical text tradition of Sanskrit, like the s{\=u}tra-based disciplines and Navya-Ny{\=a}ya (NN) formal language, etc. The effort is a kind of continuation and application of such traditions and their techniques in the modern field of Sanskrit NLP.",
}
@inproceedings{rani-anwar-2020-resource,
    title = "Resource Creation and Evaluation of Aspect Based Sentiment Analysis in {U}rdu",
    author = "Rani, Sadaf  and
      Anwar, Muhammad Waqas",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.12",
    pages = "79--84",
    abstract = "Along with the rise of people generated content on social sites, sentiment analysis has gained more importance. Aspect Based Sentiment Analysis (ABSA) is a task of identifying the sentiment at aspect level. It has more importance than sentiment analysis from commercial point of view. To the best of our knowledge, there is very few work on ABSA in Urdu language. Recent work on ABSA has limitations. Only predefined aspects are identified in a specific domain. So our focus is on the creation and evaluation of dataset for ABSA in Urdu language which will support multiple aspects. This dataset will provide a baseline evaluation for ABSA systems.",
}
@inproceedings{prabhu-kann-2020-making,
    title = "Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies",
    author = "Prabhu, Nikhil  and
      Kann, Katharina",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.13",
    pages = "85--92",
    abstract = "Explicit mechanisms for copying have improved the performance of neural models for sequence-to-sequence tasks in the low-resource setting. However, they rely on an overlap between source and target vocabularies. Here, we propose a model that does not: a pointer-generator transformer for disjoint vocabularies. We apply our model to a low-resource version of the grapheme-to-phoneme conversion (G2P) task, and show that it outperforms a standard transformer by an average of 5.1 WER over 15 languages. While our model does not beat the the best performing baseline, we demonstrate that it provides complementary information to it: an oracle that combines the best outputs of the two models improves over the strongest baseline by 7.7 WER on average in the low-resource setting. In the high-resource setting, our model performs comparably to a standard transformer.",
}
@inproceedings{moradi-etal-2020-training,
    title = "Training with Adversaries to Improve Faithfulness of Attention in Neural Machine Translation",
    author = "Moradi, Pooya  and
      Kambhatla, Nishant  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.14",
    pages = "93--100",
    abstract = "Can we trust that the attention heatmaps produced by a neural machine translation (NMT) model reflect its true internal reasoning? We isolate and examine in detail the notion of faithfulness in NMT models. We provide a measure of faithfulness for NMT based on a variety of stress tests where model parameters are perturbed and measuring faithfulness based on how often the model output changes. We show that our proposed faithfulness measure for NMT models can be improved using a novel differentiable objective that rewards faithful behaviour by the model through probability divergence. Our experimental results on multiple language pairs show that our objective function is effective in increasing faithfulness and can lead to a useful analysis of NMT model behaviour and more trustworthy attention heatmaps. Our proposed objective improves faithfulness without reducing the translation quality and it also seems to have a useful regularization effect on the NMT model and can even improve translation quality in some cases.",
}
@inproceedings{guo-nguyen-2020-document,
    title = "Document-Level Neural Machine Translation Using {BERT} as Context Encoder",
    author = "Guo, Zhiyu  and
      Nguyen, Minh Le",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.15",
    pages = "101--107",
    abstract = "Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. The methods of incorporating BERT into document-level machine translation are still being explored. BERT is able to understand sentence relationships since BERT is pre-trained using the next sentence prediction task. In our work, we leverage this property to improve document-level machine translation. In our proposed model, BERT performs as a context encoder to achieve document-level contextual information, which is then integrated into both the encoder and decoder. Experiment results show that our proposed method can significantly outperform strong document-level machine translation baselines on BLEU score. Moreover, the ablation study shows our method can capture document-level context information to boost translation performance.",
}
@inproceedings{gan-etal-2020-review,
    title = "A Review of Cross-Domain Text-to-{SQL} Models",
    author = "Gan, Yujian  and
      Purver, Matthew  and
      Woodward, John R.",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.16",
    pages = "108--115",
    abstract = "WikiSQL and Spider, the large-scale cross-domain text-to-SQL datasets, have attracted much attention from the research community. The leaderboards of WikiSQL and Spider show that many researchers propose their models trying to solve the text-to-SQL problem. This paper first divides the top models in these two leaderboards into two paradigms. We then present details not mentioned in their original paper by evaluating the key components, including schema linking, pretrained word embeddings, and reasoning assistance modules. Based on the analysis of these models, we want to promote understanding of the text-to-SQL field and find out some interesting future works, for example, it is worth studying the text-to-SQL problem in an environment where it is more challenging to build schema linking and also worth studying combing the advantage of each model toward text-to-SQL.",
}
@inproceedings{muangkammuen-fukumoto-2020-multi,
    title = "Multi-task Learning for Automated Essay Scoring with Sentiment Analysis",
    author = "Muangkammuen, Panitan  and
      Fukumoto, Fumiyo",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.17",
    pages = "116--123",
    abstract = "Automated Essay Scoring (AES) is a process that aims to alleviate the workload of graders and improve the feedback cycle in educational systems. Multi-task learning models, one of the deep learning techniques that have recently been applied to many NLP tasks, demonstrate the vast potential for AES. In this work, we present an approach for combining two tasks, sentiment analysis, and AES by utilizing multi-task learning. The model is based on a hierarchical neural network that learns to predict a holistic score at the document-level along with sentiment classes at the word-level and sentence-level. The sentiment features extracted from opinion expressions can enhance a vanilla holistic essay scoring, which mainly focuses on lexicon and text semantics. Our approach demonstrates that sentiment features are beneficial for some essay prompts, and the performance is competitive to other deep learning models on the Automated StudentAssessment Prize (ASAP) benchmark. TheQuadratic Weighted Kappa (QWK) is used to measure the agreement between the human grader{'}s score and the model{'}s prediction. Ourmodel produces a QWK of 0.763.",
}
@inproceedings{mai-zhang-2020-aspect,
    title = "Aspect Extraction Using Coreference Resolution and Unsupervised Filtering",
    author = "Mai, Deon  and
      Zhang, Wei Emma",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.18",
    pages = "124--129",
    abstract = "Aspect extraction is a widely researched field of natural language processing in which aspects are identified from the text as a means for information. For example, in aspect-based sentiment analysis (ABSA), aspects need to be first identified. Previous studies have introduced various approaches to increasing accuracy, although leaving room for further improvement. In a practical situation where the examined dataset is lacking labels, to fine-tune the process a novel unsupervised approach is proposed, combining a lexical rule-based approach with coreference resolution. The model increases accuracy through the recognition and removal of coreferring aspects. Experimental evaluations are performed on two benchmark datasets, demonstrating the greater performance of our approach to extracting coherent aspects through outperforming the baseline approaches.",
}
@inproceedings{horne-etal-2020-grubert,
    title = "{GRUBERT}: A {GRU}-Based Method to Fuse {BERT} Hidden Layers for {T}witter Sentiment Analysis",
    author = "Horne, Leo  and
      Matti, Matthias  and
      Pourjafar, Pouya  and
      Wang, Zuowen",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.19",
    pages = "130--138",
    abstract = "In this work, we introduce a GRU-based architecture called GRUBERT that learns to map the different BERT hidden layers to fused embeddings with the aim of achieving high accuracy on the Twitter sentiment analysis task. Tweets are known for their highly diverse language, and by exploiting different linguistic information present across BERT hidden layers, we can capture the full extent of this language at the embedding level. Our method can be easily adapted to other embeddings capturing different linguistic information. We show that our method outperforms well-known heuristics of using BERT (e.g. using only the last layer) and other embeddings such as ELMo. We observe potential label noise resulting from the data acquisition process and employ early stopping as well as a voting classifier to overcome it.",
}
@inproceedings{khullar-2020-exploring,
    title = "Exploring Statistical and Neural Models for Noun Ellipsis Detection and Resolution in {E}nglish",
    author = "Khullar, Payal",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.20",
    pages = "139--145",
    abstract = "Computational approaches to noun ellipsis resolution has been sparse, with only a naive rule-based approach that uses syntactic feature constraints for marking noun ellipsis licensors and selecting their antecedents. In this paper, we further the ellipsis research by exploring several statistical and neural models for both the subtasks involved in the ellipsis resolution process and addressing the representation and contribution of manual features proposed in previous research. Using the best performing models, we build an end-to-end supervised Machine Learning (ML) framework for this task that improves the existing F1 score by 16.55{\%} for the detection and 14.97{\%} for the resolution subtask. Our experiments demonstrate robust scores through pretrained BERT (Bidirectional Encoder Representations from Transformers) embeddings for word representation, and more so the importance of manual features{--} once again highlighting the syntactic and semantic characteristics of the ellipsis phenomenon. For the classification decision, we notice that a simple Multilayar Perceptron (MLP) works well for the detection of ellipsis; however, Recurrent Neural Networks (RNN) are a better choice for the much harder resolution step.",
}
@inproceedings{li-etal-2020-mrc,
    title = "{MRC} Examples Answerable by {BERT} without a Question Are Less Effective in {MRC} Model Training",
    author = "Li, Hongyu  and
      Chen, Tengyang  and
      Bai, Shuting  and
      Utsuro, Takehito  and
      Kawada, Yasuhide",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.21",
    pages = "146--152",
    abstract = "Models developed for Machine Reading Comprehension (MRC) are asked to predict an answer from a question and its related context. However, there exist cases that can be correctly answered by an MRC model using BERT, where only the context is provided without including the question. In this paper, these types of examples are referred to as {``}easy to answer{''}, while others are as {``}hard to answer{''}, i.e., unanswerable by an MRC model using BERT without being provided the question. Based on classifying examples as answerable or unanswerable by BERT without the given question, we propose a method based on BERT that splits the training examples from the MRC dataset SQuAD1.1 into those that are {``}easy to answer{''} or {``}hard to answer{''}. Experimental evaluation from a comparison of two models, one trained only with {``}easy to answer{''} examples and the other with {``}hard to answer{''} examples demonstrates that the latter outperforms the former.",
}
@inproceedings{nakamachi-etal-2020-text,
    title = "Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity",
    author = "Nakamachi, Akifumi  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.22",
    pages = "153--159",
    abstract = "We optimize rewards of reinforcement learning in text simplification using metrics that are highly correlated with human-perspectives. To address problems of exposure bias and loss-evaluation mismatch, text-to-text generation tasks employ reinforcement learning that rewards task-specific metrics. Previous studies in text simplification employ the weighted sum of sub-rewards from three perspectives: grammaticality, meaning preservation, and simplicity. However, the previous rewards do not align with human-perspectives for these perspectives. In this study, we propose to use BERT regressors fine-tuned for grammaticality, meaning preservation, and simplicity as reward estimators to achieve text simplification conforming to human-perspectives. Experimental results show that reinforcement learning with our rewards balances meaning preservation and simplicity. Additionally, human evaluation confirmed that simplified texts by our method are preferred by humans compared to previous studies.",
}
@inproceedings{chen-etal-2020-label,
    title = "Label Representations in Modeling Classification as Text Generation",
    author = "Chen, Xinyi  and
      Xu, Jingxian  and
      Wang, Alex",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.23",
    pages = "160--164",
    abstract = "Several recent state-of-the-art transfer learning methods model classification tasks as text generation, where labels are represented as strings for the model to generate. We investigate the effect that the choice of strings used to represent labels has on how effectively the model learns the task. For four standard text classification tasks, we design a diverse set of possible string representations for labels, ranging from canonical label definitions to random strings. We experiment with T5 on these tasks, varying the label representations as well as the amount of training data. We find that, in the low data setting, label representation impacts task performance on some tasks, with task-related labels being most effective, but fails to have an impact on others. In the full data setting, our results are largely negative: Different label representations do not affect overall task performance.",
}
@inproceedings{sonawane-etal-2020-generating,
    title = "Generating Inflectional Errors for Grammatical Error Correction in {H}indi",
    author = "Sonawane, Ankur  and
      Vishwakarma, Sujeet Kumar  and
      Srivastava, Bhavana  and
      Kumar Singh, Anil",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-srw.24",
    pages = "165--171",
    abstract = "Automated grammatical error correction has been explored as an important research problem within NLP, with the majority of the work being done on English and similar resource-rich languages. Grammar correction using neural networks is a data-heavy task, with the recent state of the art models requiring datasets with millions of annotated sentences for proper training. It is difficult to find such resources for Indic languages due to their relative lack of digitized content and complex morphology, compared to English. We address this problem by generating a large corpus of artificial inflectional errors for training GEC models. Moreover, to evaluate the performance of models trained on this dataset, we create a corpus of real Hindi errors extracted from Wikipedia edits. Analyzing this dataset with a modified version of the ERRANT error annotation toolkit, we find that inflectional errors are very common in this language. Finally, we produce the initial baseline results using state of the art methods developed for English.",
}
@proceedings{aacl-2020-asia,
    title = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    editor = "Wong, Kam-Fai  and
      Knight, Kevin  and
      Wu, Hua",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.0",
}
@inproceedings{wang-etal-2020-touch,
    title = "Touch Editing: A Flexible One-Time Interaction Approach for Translation",
    author = "Wang, Qian  and
      Zhang, Jiajun  and
      Liu, Lemao  and
      Huang, Guoping  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.1",
    pages = "1--11",
    abstract = "We propose a touch-based editing method for translation, which is more flexible than traditional keyboard-mouse-based translation postediting. This approach relies on touch actions that users perform to indicate translation errors. We present a dual-encoder model to handle the actions and generate refined translations. To mimic the user feedback, we adopt the TER algorithm comparing between draft translations and references to automatically extract the simulated actions for training data construction. Experiments on translation datasets with simulated editing actions show that our method significantly improves original translation of Transformer (up to 25.31 BLEU) and outperforms existing interactive translation methods (up to 16.64 BLEU). We also conduct experiments on post-editing dataset to further prove the robustness and effectiveness of our method.",
}
@inproceedings{chi-etal-2020-monolingual,
    title = "Can Monolingual Pretrained Models Help Cross-Lingual Classification?",
    author = "Chi, Zewen  and
      Dong, Li  and
      Wei, Furu  and
      Mao, Xianling  and
      Huang, Heyan",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.2",
    pages = "12--17",
    abstract = "Multilingual pretrained language models (such as multilingual BERT) have achieved impressive results for cross-lingual transfer. However, due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. In this work, we present two approaches to improve zero-shot cross-lingual classification, by transferring the knowledge from monolingual pretrained models to multilingual ones. Experimental results on two cross-lingual classification benchmarks show that our methods outperform vanilla multilingual fine-tuning.",
}
@inproceedings{sujana-etal-2020-rumor,
    title = "Rumor Detection on {T}witter Using Multiloss Hierarchical {B}i{LSTM} with an Attenuation Factor",
    author = "Sujana, Yudianto  and
      Li, Jiawen  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.3",
    pages = "18--26",
    abstract = "Social media platforms such as Twitter have become a breeding ground for unverified information or rumors. These rumors can threaten people{'}s health, endanger the economy, and affect the stability of a country. Many researchers have developed models to classify rumors using traditional machine learning or vanilla deep learning models. However, previous studies on rumor detection have achieved low precision and are time consuming. Inspired by the hierarchical model and multitask learning, a multiloss hierarchical BiLSTM model with an attenuation factor is proposed in this paper. The model is divided into two BiLSTM modules: post level and event level. By means of this hierarchical structure, the model can extract deep information from limited quantities of text. Each module has a loss function that helps to learn bilateral features and reduce the training time. An attenuation factor is added at the post level to increase the accuracy. The results on two rumor datasets demonstrate that our model achieves better performance than that of state-of-the-art machine learning and vanilla deep learning models.",
}
@inproceedings{yuan-etal-2020-graph,
    title = "Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis",
    author = "Yuan, Li  and
      Wang, Jin  and
      Yu, Liang-Chih  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.4",
    pages = "27--36",
    abstract = "Aspect-level sentiment analysis(ASC) predicts each specific aspect term{'}s sentiment polarity in a given text or review. Recent studies used attention-based methods that can effectively improve the performance of aspect-level sentiment analysis. These methods ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntactically unrelated words mistakenly. One proposed solution, the graph convolutional network (GCN), cannot completely avoid the problem. While it does incorporate useful information about syntax, it assigns equal weight to all the edges between connected words. It may still incorrectly associate unrelated words to the target aspect through the iterations of graph convolutional propagation. In this study, a graph attention network with memory fusion is proposed to extend GCN{'}s idea by assigning different weights to edges. Syntactic constraints can be imposed to block the graph convolutional propagation of unrelated words. A convolutional layer and a memory fusion were applied to learn and exploit multiword relations and draw different weights of words to improve performance further. Experimental results on five datasets show that the proposed method yields better performance than existing methods.",
}
@inproceedings{guo-etal-2020-fernet,
    title = "{FERN}et: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues",
    author = "Guo, Yingmei  and
      Wu, Zhiyong  and
      Xu, Mingxing",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.5",
    pages = "37--43",
    abstract = "Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information. All present methods model historical utterances without considering the content of the target utterance. However, different parts of a historical utterance may contribute differently to emotion inference of different target utterances. Therefore we propose Fine-grained Extraction and Reasoning Network (FERNet) to generate target-specific historical utterance representations. The reasoning module effectively handles both local and global sequential dependencies to reason over context, and updates target utterance representations to more informed vectors. Experiments on two benchmarks show that our method achieves competitive performance compared with previous methods.",
}
@inproceedings{wu-etal-2020-sentirec,
    title = "{S}enti{R}ec: Sentiment Diversity-aware Neural News Recommendation",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Qi, Tao  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.6",
    pages = "44--53",
    abstract = "Personalized news recommendation is important for online news services. Many news recommendation methods recommend news based on their relevance to users{'} historical browsed news, and the recommended news usually have similar sentiment with browsed news. However, if browsed news is dominated by certain kinds of sentiment, the model may intensively recommend news with the same sentiment orientation, making it difficult for users to receive diverse opinions and news events. In this paper, we propose a sentiment diversity-aware neural news recommendation approach, which can recommend news with more diverse sentiment. In our approach, we propose a sentiment-aware news encoder, which is jointly trained with an auxiliary sentiment prediction task, to learn sentiment-aware news representations. We learn user representations from browsed news representations, and compute click scores based on user and candidate news representations. In addition, we propose a sentiment diversity regularization method to penalize the model by combining the overall sentiment orientation of browsed news as well as the click and sentiment scores of candidate news. Extensive experiments on real-world dataset show that our approach can effectively improve the sentiment diversity in news recommendation without performance sacrifice.",
}
@inproceedings{wenjie-etal-2020-bcth,
    title = "{BCTH}: A Novel Text Hashing Approach via {B}ayesian Clustering",
    author = "Wenjie, Ying  and
      Le, Yuquan  and
      Xiong, Hantao",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.7",
    pages = "54--62",
    abstract = "Similarity search is to find the most similar items for a certain target item. The ability of similarity search at large scale plays a significant role in many information retrieval applications, and thus has received much attention. Text hashing is a promising strategy, which utilizes binary encoding to represent documents, obtaining attractive performance. This paper makes the first attempt to utilize Bayesian Clustering for Text Hashing, dubbed as BCTH. Specifically, BCTH is able to map documents to binary codes by utilizing multiple Bayesian Clusterings in parallel, where each Bayesian Clustering is responsible for one bit. Our approach employs the bit-balanced constraint to maximize the amount of information in each bit. Meanwhile, the bit-uncorrected constraint is adopted to keep the independence among all bits. The time complexity of BCTH is linear, where the hash codes and hash function are jointly learned. The experimental results, based on four widely-used datasets, demonstrate that BCTH is competitive, compared with currently competitive baselines in the perspective of both precision and training speed.",
}
@inproceedings{oh-kim-2020-lightweight,
    title = "Lightweight Text Classifier using Sinusoidal Positional Encoding",
    author = "Oh, Byoung-Doo  and
      Kim, Yu-Seop",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.8",
    pages = "63--69",
    abstract = "Large and complex models have recently been developed that require many parameters and much time to solve various problems in natural language processing. This paper explores an efficient way to avoid models being too complicated and ensure nearly equal performance to models showing the state-of-the-art. We propose a single convolutional neural network (CNN) using the sinusoidal positional encoding (SPE) in text classification. The SPE provides useful position information of a word and can construct a more efficient model architecture than before in a CNN-based approach. Our model can significantly reduce the parameter size (at least 67{\%}) and training time (up to 85{\%}) while maintaining similar performance to the CNN-based approach on multiple benchmark datasets.",
}
@inproceedings{wu-etal-2020-towards,
    title = "Towards Non-task-specific Distillation of {BERT} via Sentence Representation Approximation",
    author = "Wu, Bowen  and
      Zhang, Huan  and
      Li, MengYuan  and
      Wang, Zongsheng  and
      Feng, Qihang  and
      Huang, Junhong  and
      Wang, Baoxun",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.9",
    pages = "70--79",
    abstract = "Recently, BERT has become an essential ingredient of various NLP deep models due to its effectiveness and universal-usability. However, the online deployment of BERT is often blocked by its large-scale parameters and high computational cost. There are plenty of studies showing that the knowledge distillation is efficient in transferring the knowledge from BERT into the model with a smaller size of parameters. Nevertheless, current BERT distillation approaches mainly focus on task-specified distillation, such methodologies lead to the loss of the general semantic knowledge of BERT for universal-usability. In this paper, we propose a sentence representation approximating oriented distillation framework that can distill the pre-trained BERT into a simple LSTM based model without specifying tasks. Consistent with BERT, our distilled model is able to perform transfer learning via fine-tuning to adapt to any sentence-level downstream task. Besides, our model can further cooperate with task-specific distillation procedures. The experimental results on multiple NLP tasks from the GLUE benchmark show that our approach outperforms other task-specific distillation methods or even much larger models, i.e., ELMO, with efficiency well-improved.",
}
@inproceedings{feng-etal-2020-simple,
    title = "A Simple and Effective Usage of Word Clusters for {CBOW} Model",
    author = "Feng, Yukun  and
      Hu, Chenlong  and
      Kamigaito, Hidetaka  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.10",
    pages = "80--86",
    abstract = "We propose a simple and effective method for incorporating word clusters into the Continuous Bag-of-Words (CBOW) model. Specifically, we propose to replace infrequent input and output words in CBOW model with their clusters. The resulting cluster-incorporated CBOW model produces embeddings of frequent words and a small amount of cluster embeddings, which will be fine-tuned in downstream tasks. We empirically show our replacing method works well on several downstream tasks. Through our analysis, we show that our method might be also useful for other similar models which produce word embeddings.",
}
@inproceedings{hao-etal-2020-investigating,
    title = "Investigating Learning Dynamics of {BERT} Fine-Tuning",
    author = "Hao, Yaru  and
      Dong, Li  and
      Wei, Furu  and
      Xu, Ke",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.11",
    pages = "87--92",
    abstract = "The recently introduced pre-trained language model BERT advances the state-of-the-art on many NLP tasks through the fine-tuning approach, but few studies investigate how the fine-tuning process improves the model performance on downstream tasks. In this paper, we inspect the learning dynamics of BERT fine-tuning with two indicators. We use JS divergence to detect the change of the attention mode and use SVCCA distance to examine the change to the feature extraction mode during BERT fine-tuning. We conclude that BERT fine-tuning mainly changes the attention mode of the last layers and modifies the feature extraction mode of the intermediate and last layers. Moreover, we analyze the consistency of BERT fine-tuning between different random seeds and different datasets. In summary, we provide a distinctive understanding of the learning dynamics of BERT fine-tuning, which sheds some light on improving the fine-tuning results.",
}
@inproceedings{wang-tu-2020-second,
    title = "Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training",
    author = "Wang, Xinyu  and
      Tu, Kewei",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.12",
    pages = "93--99",
    abstract = "In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the accuracy of very recent state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in both training and testing. We also empirically show the advantage of second-order parsing over first-order parsing and observe that the usefulness of the head-selection structured constraint vanishes when using BERT embedding.",
}
@inproceedings{fei-etal-2020-high,
    title = "High-order Refining for End-to-end {C}hinese Semantic Role Labeling",
    author = "Fei, Hao  and
      Ren, Yafeng  and
      Ji, Donghong",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.13",
    pages = "100--105",
    abstract = "Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.",
}
@inproceedings{li-wu-2020-exploiting,
    title = "Exploiting {W}ord{N}et Synset and Hypernym Representations for Answer Selection",
    author = "Li, Weikang  and
      Wu, Yunfang",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.14",
    pages = "106--115",
    abstract = "Answer selection (AS) is an important subtask of document-based question answering (DQA). In this task, the candidate answers come from the same document, and each answer sentence is semantically related to the given question, which makes it more challenging to select the true answer. WordNet provides powerful knowledge about concepts and their semantic relations so we employ WordNet to enrich the abilities of paraphrasing and reasoning of the network-based question answering model. Specifically, we exploit the synset and hypernym concepts to enrich the word representation and incorporate the similarity scores of two concepts that share the synset or hypernym relations into the attention mechanism. The proposed WordNet-enhanced hierarchical model (WEHM) consists of four modules, including WordNet-enhanced word representation, sentence encoding, WordNet-enhanced attention mechanism, and hierarchical document encoding. Extensive experiments on the public WikiQA and SelQA datasets demonstrate that our proposed model significantly improves the baseline system and outperforms all existing state-of-the-art methods by a large margin.",
}
@inproceedings{sasaki-etal-2020-simple,
    title = "A Simple Text-based Relevant Location Prediction Method using Knowledge Base",
    author = "Sasaki, Mei  and
      Okura, Shumpei  and
      Ono, Shingo",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.15",
    pages = "116--121",
    abstract = "In this paper, we propose a simple method to predict salient locations from news article text using a knowledge base (KB). The proposed method uses a dictionary of locations created from the KB to identify occurrences of locations in the text and uses the hierarchical information between entities in the KB for assigning appropriate saliency scores to regions. It allows prediction at arbitrary region units and has only a few hyperparameters that need to be tuned. We show using manually annotated news articles that the proposed method improves the f-measure by {\textgreater} 0.12 compared to multiple baselines.",
}
@inproceedings{zhang-etal-2020-learning,
    title = "Learning Goal-oriented Dialogue Policy with opposite Agent Awareness",
    author = "Zhang, Zheng  and
      Liao, Lizi  and
      Zhu, Xiaoyan  and
      Chua, Tat-Seng  and
      Liu, Zitao  and
      Huang, Yan  and
      Huang, Minlie",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.16",
    pages = "122--132",
    abstract = "Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treats the opposite agent policy as part of the environment. While in real-world scenarios, the behavior of an opposite agent often exhibits certain patterns or underlies hidden policies, which can be inferred and utilized by the target agent to facilitate its own decision making. This strategy is common in human mental simulation by first imaging a specific action and the probable results before really acting it. We therefore propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent{'}s policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy. We evaluate our model on both cooperative and competitive dialogue tasks, showing superior performance over state-of-the-art baselines.",
}
@inproceedings{park-etal-2020-empirical,
    title = "An Empirical Study of Tokenization Strategies for Various {K}orean {NLP} Tasks",
    author = "Park, Kyubyong  and
      Lee, Joohong  and
      Jang, Seongbo  and
      Jung, Dawoon",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.17",
    pages = "133--142",
    abstract = "Typically, tokenization is the very first step in most text processing works. As a token serves as an atomic unit that embeds the contextual information of text, how to define a token plays a decisive role in the performance of a model. Even though Byte Pair Encoding (BPE) has been considered the de facto standard tokenization method due to its simplicity and universality, it still remains unclear whether BPE works best across all languages and tasks. In this paper, we test several tokenization strategies in order to answer our primary research question, that is, {``}What is the best tokenization strategy for Korean NLP tasks?{''} Experimental results demonstrate that a hybrid approach of morphological segmentation followed by BPE works best in Korean to/from English machine translation and natural language understanding tasks such as KorNLI, KorSTS, NSMC, and PAWS-X. As an exception, for KorQuAD, the Korean extension of SQuAD, BPE segmentation turns out to be the most effective. Our code and pre-trained models are publicly available at https://github.com/kakaobrain/kortok.",
}
@inproceedings{pugoy-kao-2020-bert,
    title = "{BERT}-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation",
    author = "Pugoy, Reinald Adrian  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.18",
    pages = "143--153",
    abstract = "We propose a novel, accurate, and explainable recommender model (BENEFICT) that addresses two drawbacks that most review-based recommender systems face. First is their utilization of traditional word embeddings that could influence prediction performance due to their inability to model the word semantics{'} dynamic characteristic. Second is their black-box nature that makes the explanations behind every prediction obscure. Our model uniquely integrates three key elements: BERT, multilayer perceptron, and maximum subarray problem to derive contextualized review features, model user-item interactions, and generate explanations, respectively. Our experiments show that BENEFICT consistently outperforms other state-of-the-art models by an average improvement gain of nearly 7{\%}. Based on the human judges{'} assessment, the BENEFICT-produced explanations can capture the essence of the customer{'}s preference and help future customers make purchasing decisions. To the best of our knowledge, our model is one of the first recommender models to utilize BERT for neural collaborative filtering.",
}
@inproceedings{omote-etal-2020-transformer,
    title = "Transformer-based Approach for Predicting Chemical Compound Structures",
    author = "Omote, Yutaro  and
      Matsushita, Kyoumoto  and
      Iwakura, Tomoya  and
      Tamura, Akihiro  and
      Ninomiya, Takashi",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.19",
    pages = "154--162",
    abstract = "By predicting chemical compound structures from their names, we can better comprehend chemical compounds written in text and identify the same chemical compound given different notations for database creation. Previous methods have predicted the chemical compound structures from their names and represented them by Simplified Molecular Input Line Entry System (SMILES) strings. However, these methods mainly apply handcrafted rules, and cannot predict the structures of chemical compound names not covered by the rules. Instead of handcrafted rules, we propose Transformer-based models that predict SMILES strings from chemical compound names. We improve the conventional Transformer-based model by introducing two features: (1) a loss function that constrains the number of atoms of each element in the structure, and (2) a multi-task learning approach that predicts both SMILES strings and InChI strings (another string representation of chemical compound structures). In evaluation experiments, our methods achieved higher F-measures than previous rule-based approaches (Open Parser for Systematic IUPAC Nomenclature and two commercially used products), and the conventional Transformer-based model. We release the dataset used in this paper as a benchmark for the future research.",
}
@inproceedings{wang-etal-2020-chinese,
    title = "{C}hinese Grammatical Correction Using {BERT}-based Pre-trained Model",
    author = "Wang, Hongfei  and
      Kurosawa, Michiki  and
      Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.20",
    pages = "163--168",
    abstract = "In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization. In this study, we verify the effectiveness of two methods that incorporate a pre-trained model into an encoder-decoder model on Chinese grammatical error correction tasks. We also analyze the error type and conclude that sentence-level errors are yet to be addressed.",
}
@inproceedings{wang-etal-2020-neural,
    title = "{N}eural {G}ibbs {S}ampling for {J}oint {E}vent {A}rgument {E}xtraction",
    author = "Wang, Xiaozhi  and
      Jia, Shengyu  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Li, Juanzi  and
      Li, Peng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.21",
    pages = "169--180",
    abstract = "Event Argument Extraction (EAE) aims at predicting event argument roles of entities in text, which is a crucial subtask and bottleneck of event extraction. Existing EAE methods either extract each event argument roles independently or sequentially, which cannot adequately model the joint probability distribution among event arguments and their roles. In this paper, we propose a Bayesian model named Neural Gibbs Sampling (NGS) to jointly extract event arguments. Specifically, we train two neural networks to model the prior distribution and conditional distribution over event arguments respectively and then use Gibbs sampling to approximate the joint distribution with the learned distributions. For overcoming the shortcoming of the high complexity of the original Gibbs sampling algorithm, we further apply simulated annealing to efficiently estimate the joint probability distribution over event arguments and make predictions. We conduct experiments on the two widely-used benchmark datasets ACE 2005 and TAC KBP 2016. The Experimental results show that our NGS model can achieve comparable results to existing state-of-the-art EAE methods. The source code can be obtained from https://github.com/THU-KEG/NGS.",
}
@inproceedings{chen-etal-2020-named,
    title = "Named Entity Recognition in Multi-level Contexts",
    author = "Chen, Yubo  and
      Wu, Chuhan  and
      Qi, Tao  and
      Yuan, Zhigang  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.22",
    pages = "181--190",
    abstract = "Named entity recognition is a critical task in the natural language processing field. Most existing methods for this task can only exploit contextual information within a sentence. However, their performance on recognizing entities in limited or ambiguous sentence-level contexts is usually unsatisfactory. Fortunately, other sentences in the same document can provide supplementary document-level contexts to help recognize these entities. In addition, words themselves contain word-level contextual information since they usually have different preferences of entity type and relative position from named entities. In this paper, we propose a unified framework to incorporate multi-level contexts for named entity recognition. We use TagLM as our basic model to capture sentence-level contexts. To incorporate document-level contexts, we propose to capture interactions between sentences via a multi-head self attention network. To mine word-level contexts, we propose an auxiliary task to predict the type of each word to capture its type preference. We jointly train our model in entity recognition and the auxiliary classification task via multi-task learning. The experimental results on several benchmark datasets validate the effectiveness of our method.",
}
@inproceedings{chen-etal-2020-general,
    title = "A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation",
    author = "Chen, Yun  and
      Li, Liangyou  and
      Jiang, Xin  and
      Chen, Xiao  and
      Liu, Qun",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.23",
    pages = "191--200",
    abstract = "Despite the success of neural machine translation (NMT), simultaneous neural machine translation (SNMT), the task of translating in real time before a full sentence has been observed, remains challenging due to the syntactic structure difference and simultaneity requirements. In this paper, we propose a general framework for adapting neural machine translation to translate simultaneously. Our framework contains two parts: prefix translation that utilizes a consecutive NMT model to translate source prefixes and a stopping criterion that determines when to stop the prefix translation. Experiments on three translation corpora and two language pairs show the efficacy of the proposed framework on balancing the quality and latency in adapting NMT to perform simultaneous translation.",
}
@inproceedings{xu-etal-2020-unihanlm,
    title = "{U}nihan{LM}: Coarse-to-Fine {C}hinese-{J}apanese Language Model Pretraining with the Unihan Database",
    author = "Xu, Canwen  and
      Ge, Tao  and
      Li, Chenliang  and
      Wei, Furu",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.24",
    pages = "201--211",
    abstract = "Chinese and Japanese share many characters with similar surface morphology. To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japanese pretrained masked language model (MLM) with a novel two-stage coarse-to-fine training approach. We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters. The resulting clusters are used to replace the original characters in sentences for the coarse-grained pretraining of the MLM. Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters. We conduct extensive experiments on a variety of Chinese and Japanese NLP benchmarks, showing that our proposed UnihanLM is effective on both mono- and cross-lingual Chinese and Japanese tasks, shedding light on a new path to exploit the homology of languages.",
}
@inproceedings{gao-etal-2020-towards,
    title = "Towards a Better Understanding of Label Smoothing in Neural Machine Translation",
    author = "Gao, Yingbo  and
      Wang, Weiyue  and
      Herold, Christian  and
      Yang, Zijian  and
      Ney, Hermann",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.25",
    pages = "212--223",
    abstract = "In order to combat overfitting and in pursuit of better generalization, label smoothing is widely applied in modern neural machine translation systems. The core idea is to penalize over-confident outputs and regularize the model so that its outputs do not diverge too much from some prior distribution. While training perplexity generally gets worse, label smoothing is found to consistently improve test performance. In this work, we aim to better understand label smoothing in the context of neural machine translation. Theoretically, we derive and explain exactly what label smoothing is optimizing for. Practically, we conduct extensive experiments by varying which tokens to smooth, tuning the probability mass to be deducted from the true targets and considering different prior distributions. We show that label smoothing is theoretically well-motivated, and by carefully choosing hyperparameters, the practical performance of strong neural machine translation systems can be further improved.",
}
@inproceedings{rambelli-etal-2020-comparing,
    title = "Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation",
    author = "Rambelli, Giulia  and
      Chersoni, Emmanuele  and
      Lenci, Alessandro  and
      Blache, Philippe  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.26",
    pages = "224--234",
    abstract = "In linguistics and cognitive science, Logical metonymies are defined as type clashes between an event-selecting verb and an entity-denoting noun (e.g. The editor finished the article), which are typically interpreted by inferring a hidden event (e.g. reading) on the basis of contextual cues. This paper tackles the problem of logical metonymy interpretation, that is, the retrieval of the covert event via computational methods. We compare different types of models, including the probabilistic and the distributional ones previously introduced in the literature on the topic. For the first time, we also tested on this task some of the recent Transformer-based models, such as BERT, RoBERTa, XLNet, and GPT-2. Our results show a complex scenario, in which the best Transformer-based models and some traditional distributional models perform very similarly. However, the low performance on some of the testing datasets suggests that logical metonymy is still a challenging phenomenon for computational modeling.",
}
@inproceedings{opitz-2020-amr,
    title = "{AMR} Quality Rating with a Lightweight {CNN}",
    author = "Opitz, Juri",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.27",
    pages = "235--247",
    abstract = "Structured semantic sentence representations such as Abstract Meaning Representations (AMRs) are potentially useful in various NLP tasks. However, the quality of automatic parses can vary greatly and jeopardizes their usefulness. This can be mitigated by models that can accurately rate AMR quality in the absence of costly gold data, allowing us to inform downstream systems about an incorporated parse{'}s trustworthiness or select among different candidate parses. In this work, we propose to transfer the AMR graph to the domain of images. This allows us to create a simple convolutional neural network (CNN) that imitates a human judge tasked with rating graph quality. Our experiments show that the method can rate quality more accurately than strong baselines, in several quality dimensions. Moreover, the method proves to be efficient and reduces the incurred energy consumption.",
}
@inproceedings{ji-etal-2020-generating,
    title = "Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths",
    author = "Ji, Haozhe  and
      Ke, Pei  and
      Huang, Shaohan  and
      Wei, Furu  and
      Huang, Minlie",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.28",
    pages = "248--257",
    abstract = "Commonsense explanation generation aims to empower the machine{'}s sense-making capability by generating plausible explanations to statements against commonsense. While this task is easy to human, the machine still struggles to generate reasonable and informative explanations. In this work, we propose a method that first extracts the underlying concepts which are served as bridges in the reasoning chain and then integrates these concepts to generate the final explanation. To facilitate the reasoning process, we utilize external commonsense knowledge to build the connection between a statement and the bridge concepts by extracting and pruning multi-hop paths to build a subgraph. We design a bridge concept extraction model that first scores the triples, routes the paths in the subgraph, and further selects bridge concepts with weak supervision at both the triple level and the concept level. We conduct experiments on the commonsense explanation generation task and our model outperforms the state-of-the-art baselines in both automatic and human evaluation.",
}
@inproceedings{fu-etal-2020-unsupervised,
    title = "Unsupervised {KB}-to-Text Generation with Auxiliary Triple Extraction using Dual Learning",
    author = "Fu, Zihao  and
      Shi, Bei  and
      Bing, Lidong  and
      Lam, Wai",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.29",
    pages = "258--268",
    abstract = "KB-to-text task aims at generating texts based on the given KB triples. Traditional methods usually map KB triples to sentences via a supervised seq-to-seq model. However, existing annotated datasets are very limited and human labeling is very expensive. In this paper, we propose a method which trains the generation model in a completely unsupervised way with unaligned raw text data and KB triples. Our method exploits a novel dual training framework which leverages the inverse relationship between the KB-to-text generation task and an auxiliary triple extraction task. In our architecture, we reconstruct KB triples or texts via a closed-loop framework via linking a generator and an extractor. Therefore the loss function that accounts for the reconstruction error of KB triples and texts can be used to train the generator and extractor. To resolve the cold start problem in training, we propose a method using a pseudo data generator which generates pseudo texts and KB triples for learning an initial model. To resolve the multiple-triple problem, we design an allocated reinforcement learning component to optimize the reconstruction loss. The experimental results demonstrate that our model can outperform other unsupervised generation methods and close to the bound of supervised methods.",
}
@inproceedings{dai-etal-2020-modality,
    title = "Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition",
    author = "Dai, Wenliang  and
      Liu, Zihan  and
      Yu, Tiezheng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.30",
    pages = "269--280",
    abstract = "Despite the recent achievements made in the multi-modal emotion recognition task, two problems still exist and have not been well investigated: 1) the relationship between different emotion categories are not utilized, which leads to sub-optimal performance; and 2) current models fail to cope well with low-resource emotions, especially for unseen emotions. In this paper, we propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. We use pre-trained word embeddings to represent emotion categories for textual data. Then, two mapping functions are learned to transfer these embeddings into visual and acoustic spaces. For each modality, the model calculates the representation distance between the input sequence and target emotions and makes predictions based on the distances. By doing so, our model can directly adapt to the unseen emotions in any modality since we have their pre-trained embeddings and modality mapping functions. Experiments show that our model achieves state-of-the-art performance on most of the emotion categories. Besides, our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.",
}
@inproceedings{chauhan-etal-2020-one,
    title = "All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes",
    author = "Chauhan, Dushyant Singh  and
      S R, Dhanush  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.31",
    pages = "281--290",
    abstract = "In this paper, we aim at learning the relationships and similarities of a variety of tasks, such as humour detection, sarcasm detection, offensive content detection, motivational content detection and sentiment analysis on a somewhat complicated form of information, i.e., memes. We propose a multi-task, multi-modal deep learning framework to solve multiple tasks simultaneously. For multi-tasking, we propose two attention-like mechanisms viz., Inter-task Relationship Module (iTRM) and Inter-class Relationship Module (iCRM). The main motivation of iTRM is to learn the relationship between the tasks to realize how they help each other. In contrast, iCRM develops relations between the different classes of tasks. Finally, representations from both the attentions are concatenated and shared across the five tasks (i.e., humour, sarcasm, offensive, motivational, and sentiment) for multi-tasking. We use the recently released dataset in the Memotion Analysis task @ SemEval 2020, which consists of memes annotated for the classes as mentioned above. Empirical results on Memotion dataset show the efficacy of our proposed approach over the existing state-of-the-art systems (Baseline and SemEval 2020 winner). The evaluation also indicates that the proposed multi-task framework yields better performance over the single-task learning.",
}
@inproceedings{kano-etal-2020-identifying,
    title = "Identifying Implicit Quotes for Unsupervised Extractive Summarization of Conversations",
    author = "Kano, Ryuji  and
      Miura, Yasuhide  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.32",
    pages = "291--302",
    abstract = "We propose Implicit Quote Extractor, an end-to-end unsupervised extractive neural summarization model for conversational texts. When we reply to posts, quotes are used to highlight important part of texts. We aim to extract quoted sentences as summaries. Most replies do not explicitly include quotes, so it is difficult to use quotes as supervision. However, even if it is not explicitly shown, replies always refer to certain parts of texts; we call them implicit quotes. Implicit Quote Extractor aims to extract implicit quotes as summaries. The training task of the model is to predict whether a reply candidate is a true reply to a post. For prediction, the model has to choose a few sentences from the post. To predict accurately, the model learns to extract sentences that replies frequently refer to. We evaluate our model on two email datasets and one social media dataset, and confirm that our model is useful for extractive summarization. We further discuss two topics; one is whether quote extraction is an important factor for summarization, and the other is whether our model can capture salient sentences that conventional methods cannot.",
}
@inproceedings{narayanan-sundararaman-etal-2020-unsupervised,
    title = "Unsupervised Aspect-Level Sentiment Controllable Style Transfer",
    author = "Narayanan Sundararaman, Mukuntha  and
      Ahmad, Zishan  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.33",
    pages = "303--312",
    abstract = "Unsupervised style transfer in text has previously been explored through the sentiment transfer task. The task entails inverting the overall sentiment polarity in a given input sentence, while preserving its content. From the Aspect-Based Sentiment Analysis (ABSA) task, we know that multiple sentiment polarities can often be present together in a sentence with multiple aspects. In this paper, the task of aspect-level sentiment controllable style transfer is introduced, where each of the aspect-level sentiments can individually be controlled at the output. To achieve this goal, a BERT-based encoder-decoder architecture with saliency weighted polarity injection is proposed, with unsupervised training strategies, such as ABSA masked-language-modelling. Through both automatic and manual evaluation, we show that the system is successful in controlling aspect-level sentiments.",
}
@inproceedings{shang-etal-2020-energy,
    title = "Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding",
    author = "Shang, Guokan  and
      Tixier, Antoine  and
      Vazirgiannis, Michalis  and
      Lorr{\'e}, Jean-Pierre",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.34",
    pages = "313--327",
    abstract = "Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly summarized by a common abstractive sentence. This paper provides a novel approach to this task. We first introduce a neural contextual utterance encoder featuring three types of self-attention mechanisms. We then train it using the siamese and triplet energy-based meta-architectures. Experiments on the AMI corpus show that our system outperforms multiple energy-based and non-energy based baselines from the state-of-the-art. Code and data are publicly available.",
}
@inproceedings{zhang-etal-2020-intent,
    title = "Intent Detection with {W}iki{H}ow",
    author = "Zhang, Li  and
      Lyu, Qing  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.35",
    pages = "328--333",
    abstract = "Modern task-oriented dialog systems need to reliably understand users{'} intents. Intent detection is even more challenging when moving to new domains or new languages, since there is little annotated data. To address this challenge, we present a suite of pretrained intent detection models which can predict a broad range of intended goals from many actions because they are trained on wikiHow, a comprehensive instructional website. Our models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. Our models also demonstrate strong zero- and few-shot performance, reaching over 75{\%} accuracy using only 100 training examples in all datasets.",
}
@inproceedings{nadeem-etal-2020-systematic,
    title = "A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation",
    author = "Nadeem, Moin  and
      He, Tianxing  and
      Cho, Kyunghyun  and
      Glass, James",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.36",
    pages = "334--346",
    abstract = "This work studies the widely adopted ancestral sampling algorithms for auto-regressive language models. We use the quality-diversity (Q-D) trade-off to investigate three popular sampling methods (top-k, nucleus and tempered sampling). We focus on the task of open-ended language generation, and first show that the existing sampling algorithms have similar performance. By carefully inspecting the transformations defined by different sampling algorithms, we identify three key properties that are shared among them: entropy reduction, order preservation, and slope preservation. To validate the importance of the identified properties, we design two sets of new sampling methods: one set in which each algorithm satisfies all three properties, and one set in which each algorithm violates at least one of the properties. We compare their performance with existing algorithms, and find that violating the identified properties could lead to drastic performance degradation, as measured by the Q-D trade-off. On the other hand, we find that the set of sampling algorithms that satisfy these properties performs on par with the existing sampling algorithms.",
}
@inproceedings{ding-etal-2020-chinese,
    title = "{C}hinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels",
    author = "Ding, Yuning  and
      Horbach, Andrea  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.37",
    pages = "347--357",
    abstract = "In this paper, we analyse the challenges of Chinese content scoring in comparison to English. As a review of prior work for Chinese content scoring shows a lack of open-access data in the field, we present two short-answer data sets for Chinese. The Chinese Educational Short Answers data set (CESA) contains 1800 student answers for five science-related questions. As a second data set, we collected ASAP-ZH with 942 answers by re-using three existing prompts from the ASAP data set. We adapt a state-of-the-art content scoring system for Chinese and evaluate it in several settings on these data sets. Results show that features on lower segmentation levels such as character n-grams tend to have better performance than features on token level.",
}
@inproceedings{li-etal-2020-analysis,
    title = "Analysis of Hierarchical Multi-Content Text Classification Model on {B}-{SHARP} Dataset for Early Detection of {A}lzheimer{'}s Disease",
    author = "Li, Renxuan Albert  and
      Hajjar, Ihab  and
      Goldstein, Felicia  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.38",
    pages = "358--365",
    abstract = "This paper presents a new dataset, B-SHARP, that can be used to develop NLP models for the detection of Mild Cognitive Impairment (MCI) known as an early sign of Alzheimer{'}s disease. Our dataset contains 1-2 min speech segments from 326 human subjects for 3 topics, (1) daily activity, (2) room environment, and (3) picture description, and their transcripts so that a total of 650 speech segments are collected. Given the B-SHARP dataset, several hierarchical text classification models are developed that jointly learn combinatory features across all 3 topics. The best performance of 74.1{\%} is achieved by an ensemble model that adapts 3 types of transformer encoders. To the best of our knowledge, this is the first work that builds deep learning-based text classification models on multiple contents for the detection of MCI.",
}
@inproceedings{sun-etal-2020-exploratory,
    title = "An Exploratory Study on Multilingual Quality Estimation",
    author = "Sun, Shuo  and
      Fomicheva, Marina  and
      Blain, Fr{\'e}d{\'e}ric  and
      Chaudhary, Vishrav  and
      El-Kishky, Ahmed  and
      Renduchintala, Adithya  and
      Guzm{\'a}n, Francisco  and
      Specia, Lucia",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.39",
    pages = "366--377",
    abstract = "Predicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared across languages. An obvious disadvantage of this approach is the need for labelled data for each given language pair. We challenge this assumption by exploring different approaches to multilingual Quality Estimation (QE), including using scores from translation models. We show that these outperform single-language models, particularly in less balanced quality label distributions and low-resource settings. In the extreme case of zero-shot QE, we show that it is possible to accurately predict quality for any given new language from models trained on other languages. Our findings indicate that state-of-the-art neural QE models based on powerful pre-trained representations generalise well across languages, making them more applicable in real-world settings.",
}
@inproceedings{he-cohen-2020-english,
    title = "{E}nglish-to-{C}hinese Transliteration with Phonetic Auxiliary Task",
    author = "He, Yuan  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.40",
    pages = "378--388",
    abstract = "Approaching named entities transliteration as a Neural Machine Translation (NMT) problem is common practice. While many have applied various NMT techniques to enhance machine transliteration models, few focus on the linguistic features particular to the relevant languages. In this paper, we investigate the effect of incorporating phonetic features for English-to-Chinese transliteration under the multi-task learning (MTL) setting{---}where we define a phonetic auxiliary task aimed to improve the generalization performance of the main transliteration task. In addition to our system, we also release a new English-to-Chinese dataset and propose a novel evaluation metric which considers multiple possible transliterations given a source name. Our results show that the multi-task model achieves similar performance as the previous state of the art with a model of a much smaller size.",
}
@inproceedings{yang-etal-2020-predicting,
    title = "Predicting and Using Target Length in Neural Machine Translation",
    author = "Yang, Zijian  and
      Gao, Yingbo  and
      Wang, Weiyue  and
      Ney, Hermann",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.41",
    pages = "389--395",
    abstract = "Attention-based encoder-decoder models have achieved great success in neural machine translation tasks. However, the lengths of the target sequences are not explicitly predicted in these models. This work proposes length prediction as an auxiliary task and set up a sub-network to obtain the length information from the encoder. Experimental results show that the length prediction sub-network brings improvements over the strong baseline system and that the predicted length can be used as an alternative to length normalization during decoding.",
}
@inproceedings{jin-schuler-2020-grounded,
    title = "Grounded {PCFG} Induction with Images",
    author = "Jin, Lifeng  and
      Schuler, William",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.42",
    pages = "396--408",
    abstract = "Recent work in unsupervised parsing has tried to incorporate visual information into learning, but results suggest that these models need linguistic bias to compete against models that only rely on text. This work proposes grammar induction models which use visual information from images for labeled parsing, and achieve state-of-the-art results on grounded grammar induction on several languages. Results indicate that visual information is especially helpful in languages where high frequency words are more broadly distributed. Comparison between models with and without visual information shows that the grounded models are able to use visual information for proposing noun phrases, gathering useful information from images for unknown words, and achieving better performance at prepositional phrase attachment prediction.",
}
@inproceedings{li-etal-2020-heads,
    title = "Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads",
    author = "Li, Bowen  and
      Kim, Taeuk  and
      Amplayo, Reinald Kim  and
      Keller, Frank",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.43",
    pages = "409--424",
    abstract = "Transformer-based pre-trained language models (PLMs) have dramatically improved the state of the art in NLP across many tasks. This has led to substantial interest in analyzing the syntactic knowledge PLMs learn. Previous approaches to this question have been limited, mostly using test suites or probes. Here, we propose a novel fully unsupervised parsing approach that extracts constituency trees from PLM attention heads. We rank transformer attention heads based on their inherent properties, and create an ensemble of high-ranking heads to produce the final tree. Our method is adaptable to low-resource languages, as it does not rely on development sets, which can be expensive to annotate. Our experiments show that the proposed method often outperform existing approaches if there is no development set present. Our unsupervised parser can also be used as a tool to analyze the grammars PLMs learn implicitly. For this, we use the parse trees induced by our method to train a neural PCFG and compare it to a grammar derived from a human-annotated treebank.",
}
@inproceedings{biester-etal-2020-building,
    title = "Building Location Embeddings from Physical Trajectories and Textual Representations",
    author = "Biester, Laura  and
      Banea, Carmen  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.44",
    pages = "425--434",
    abstract = "Word embedding methods have become the de-facto way to represent words, having been successfully applied to a wide array of natural language processing tasks. In this paper, we explore the hypothesis that embedding methods can also be effectively used to represent spatial locations. Using a new dataset consisting of the location trajectories of 729 students over a seven month period and text data related to those locations, we implement several strategies to create location embeddings, which we then use to create embeddings of the sequences of locations a student has visited. To identify the surface level properties captured in the representations, we propose a number of probing tasks such as the presence of a specific location in a sequence or the type of activities that take place at a location. We then leverage the representations we generated and employ them in more complex downstream tasks ranging from predicting a student{'}s area of study to a student{'}s depression level, showing the effectiveness of these location embeddings.",
}
@inproceedings{hernandez-abrego-etal-2020-self,
    title = "Self-Supervised Learning for Pairwise Data Refinement",
    author = "Hernandez Abrego, Gustavo  and
      Liang, Bowen  and
      Wang, Wei  and
      Parekh, Zarana  and
      Yang, Yinfei  and
      Sung, Yunhsuan",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.45",
    pages = "435--446",
    abstract = "Pairwise data automatically constructed from weakly supervised signals has been widely used for training deep learning models. Pairwise datasets such as parallel texts can have uneven quality levels overall, but usually contain data subsets that are more useful as learning examples. We present two methods to refine data that are aimed to obtain that kind of subsets in a self-supervised way. Our methods are based on iteratively training dual-encoder models to compute similarity scores. We evaluate our methods on de-noising parallel texts and training neural machine translation models. We find that: (i) The self-supervised refinement achieves most machine translation gains in the first iteration, but following iterations further improve its intrinsic evaluation. (ii) Machine translations can improve the de-noising performance when combined with selection steps. (iii) Our methods are able to reach the performance of a supervised method. Being entirely self-supervised, our methods are well-suited to handle pairwise data without the need of prior knowledge or human annotations.",
}
@inproceedings{danilevsky-etal-2020-survey,
    title = "A Survey of the State of Explainable {AI} for Natural Language Processing",
    author = "Danilevsky, Marina  and
      Qian, Kun  and
      Aharonov, Ranit  and
      Katsis, Yannis  and
      Kawas, Ban  and
      Sen, Prithviraj",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.46",
    pages = "447--459",
    abstract = "Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable AI (XAI), considered within the domain of Natural Language Processing (NLP). We discuss the main categorization of explanations, as well as the various ways explanations can be arrived at and visualized. We detail the operations and explainability techniques currently available for generating explanations for NLP model predictions, to serve as a resource for model developers in the community. Finally, we point out the current gaps and encourage directions for future work in this important research area.",
}
@inproceedings{garg-etal-2020-beyond,
    title = "Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer",
    author = "Garg, Siddhant  and
      Sharma, Rohit Kumar  and
      Liang, Yingyu",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.47",
    pages = "460--469",
    abstract = "Fine-tuning (FT) pre-trained sentence embedding models on small datasets has been shown to have limitations. In this paper we show that concatenating the embeddings from the pre-trained model with those from a simple sentence embedding model trained only on the target data, can improve over the performance of FT for few-sample tasks. To this end, a linear classifier is trained on the combined embeddings, either by freezing the embedding model weights or training the classifier and embedding models end-to-end. We perform evaluation on seven small datasets from NLP tasks and show that our approach with end-to-end training outperforms FT with negligible computational overhead. Further, we also show that sophisticated combination techniques like CCA and KCCA do not work as well in practice as concatenation. We provide theoretical analysis to explain this empirical observation.",
}
@inproceedings{huang-etal-2020-multimodal,
    title = "Multimodal Pretraining for Dense Video Captioning",
    author = "Huang, Gabriel  and
      Pang, Bo  and
      Zhu, Zhenhai  and
      Rivera, Clara  and
      Soricut, Radu",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.48",
    pages = "470--490",
    abstract = "Learning specific hands-on skills such as cooking, car maintenance, and home repairs increasingly happens via instructional videos. The user experience with such videos is known to be improved by meta-information such as time-stamped annotations for the main steps involved. Generating such annotations automatically is challenging, and we describe here two relevant contributions. First, we construct and release a new dense video captioning dataset, Video Timeline Tags (ViTT), featuring a variety of instructional videos together with time-stamped annotations. Second, we explore several multimodal sequence-to-sequence pretraining strategies that leverage large unsupervised datasets of videos and caption-like texts. We pretrain and subsequently finetune dense video captioning models using both YouCook2 and ViTT. We show that such models generalize well and are robust over a wide variety of instructional videos.",
}
@inproceedings{gao-etal-2020-systematic,
    title = "Systematic Generalization on g{SCAN} with Language Conditioned Embedding",
    author = "Gao, Tong  and
      Huang, Qi  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.49",
    pages = "491--503",
    abstract = "Systematic Generalization refers to a learning algorithm{'}s ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. Therefore, we propose a novel method that learns objects{'} contextualized embeddings with dynamic message passing conditioned on the input natural language and end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.",
}
@inproceedings{milewski-etal-2020-scene,
    title = "Are Scene Graphs Good Enough to Improve Image Captioning?",
    author = "Milewski, Victor Siemen Janusz  and
      Moens, Marie-Francine  and
      Calixto, Iacer",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.50",
    pages = "504--515",
    abstract = "Many top-performing image captioning models rely solely on object features computed with an object detection model to generate image descriptions. However, recent studies propose to directly use scene graphs to introduce information about object relations into captioning, hoping to better describe interactions between objects. In this work, we thoroughly investigate the use of scene graphs in image captioning. We empirically study whether using additional scene graph encoders can lead to better image descriptions and propose a conditional graph attention network (C-GAT), where the image captioning decoder state is used to condition the graph updates. Finally, we determine to what extent noise in the predicted scene graphs influence caption quality. Overall, we find no significant difference between models that use scene graph features and models that only use object detection features across different captioning metrics, which suggests that existing scene graph generation models are still too noisy to be useful in image captioning. Moreover, although the quality of predicted scene graphs is very low in general, when using high quality scene graphs we obtain gains of up to 3.3 CIDEr compared to a strong Bottom-Up Top-Down baseline.",
}
@inproceedings{xiao-carenini-2020-systematically,
    title = "Systematically Exploring Redundancy Reduction in Summarizing Long Documents",
    author = "Xiao, Wen  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.51",
    pages = "516--528",
    abstract = "Our analysis of large summarization datasets indicates that redundancy is a very serious problem when summarizing long documents. Yet, redundancy reduction has not been thoroughly investigated in neural summarization. In this work, we systematically explore and compare different ways to deal with redundancy when summarizing long documents. Specifically, we organize existing methods into categories based on when and how the redundancy is considered. Then, in the context of these categories, we propose three additional methods balancing non-redundancy and importance in a general and flexible way. In a series of experiments, we show that our proposed methods achieve the state-of-the-art with respect to ROUGE scores on two scientific paper datasets, Pubmed and arXiv, while reducing redundancy significantly.",
}
@inproceedings{lebanoff-etal-2020-cascade,
    title = "A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion",
    author = "Lebanoff, Logan  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Chang, Walter  and
      Liu, Fei",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.52",
    pages = "529--535",
    abstract = "We present an empirical study in favor of a cascade architecture to neural text summarization. Summarization practices vary widely but few other than news summarization can provide a sufficient amount of training data enough to meet the requirement of end-to-end neural abstractive systems which perform content selection and surface realization jointly to generate abstracts. Such systems also pose a challenge to summarization evaluation, as they force content selection to be evaluated along with text generation, yet evaluation of the latter remains an unsolved problem. In this paper, we present empirical results showing that the performance of a cascaded pipeline that separately identifies important content pieces and stitches them together into a coherent text is comparable to or outranks that of end-to-end systems, whereas a pipeline architecture allows for flexible content selection. We finally discuss how we can take advantage of a cascaded pipeline in neural text summarization and shed light on important directions for future research.",
}
@inproceedings{xu-etal-2020-mixed,
    title = "Mixed-Lingual Pre-training for Cross-lingual Summarization",
    author = "Xu, Ruochen  and
      Zhu, Chenguang  and
      Shi, Yu  and
      Zeng, Michael  and
      Huang, Xuedong",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.53",
    pages = "536--541",
    abstract = "Cross-lingual Summarization (CLS) aims at producing a summary in the target language for an article in the source language. Traditional solutions employ a two-step approach, i.e. translate -{\textgreater} summarize or summarize -{\textgreater} translate. Recently, end-to-end models have achieved better results, but these approaches are mostly limited by their dependence on large-scale labeled data. We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. Thus, our model can leverage the massive monolingual data to enhance its modeling of language. Moreover, the architecture has no task-specific components, which saves memory and increases optimization efficiency. We show in experiments that this pre-training scheme can effectively boost the performance of cross-lingual summarization. In NCLS dataset, our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.",
}
@inproceedings{yuan-etal-2020-point,
    title = "Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation",
    author = "Yuan, Yifei  and
      Zhou, Jingbo  and
      Lam, Wai",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.54",
    pages = "542--550",
    abstract = "Point-of-Interest (POI) oriented question answering (QA) aims to return a list of POIs given a question issued by a user. Recent advances in intelligent virtual assistants have opened the possibility of engaging the client software more actively in the provision of location-based services, thereby showing great promise for automatic POI retrieval. Some existing QA methods can be adopted on this task such as QA similarity calculation and semantic parsing using pre-defined rules. The returned results, however, are subject to inherent limitations due to the lack of the ability for handling some important POI related information, including tags, location entities, and proximity-related terms (e.g. {``}nearby{''}, {``}close{''}). In this paper, we present a novel deep learning framework integrated with joint inference to capture both tag semantic and geographic correlation between question and POIs. One characteristic of our model is to propose a special cross attention question embedding neural network structure to obtain question-to-POI and POI-to-question information. Besides, we utilize a skewed distribution to simulate the spatial relationship between questions and POIs. By measuring the results offered by the model against existing methods, we demonstrate its robustness and practicability, and supplement our conclusions with empirical evidence.",
}
@inproceedings{du-etal-2020-leveraging,
    title = "Leveraging Structured Metadata for Improving Question Answering on the Web",
    author = "Du, Xinya  and
      Awadallah, Ahmed Hassan  and
      Fourney, Adam  and
      Sim, Robert  and
      Bennett, Paul  and
      Cardie, Claire",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.55",
    pages = "551--556",
    abstract = "We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding{'}s advantage over other strategies for encoding the metadata.",
}
@inproceedings{phang-etal-2020-english,
    title = "{E}nglish Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too",
    author = "Phang, Jason  and
      Calixto, Iacer  and
      Htut, Phu Mon  and
      Pruksachatkun, Yada  and
      Liu, Haokun  and
      Vania, Clara  and
      Kann, Katharina  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.56",
    pages = "557--575",
    abstract = "Intermediate-task training{---}fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task{---}often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tasks and moderate improvements on question-answering target tasks. MNLI, SQuAD and HellaSwag achieve the best overall results as intermediate tasks, while multi-task intermediate offers small additional improvements. Using our best intermediate-task models for each target task, we obtain a 5.4 point improvement over XLM-R Large on the XTREME benchmark, setting the state of the art as of June 2020. We also investigate continuing multilingual MLM during intermediate-task training and using machine-translated intermediate-task data, but neither consistently outperforms simply performing English intermediate-task training.",
}
@inproceedings{fitzgerald-2020-stil,
    title = "{STIL} - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using m{BART} on {M}ulti{ATIS}++",
    author = "FitzGerald, Jack",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.57",
    pages = "576--581",
    abstract = "Slot-filling, Translation, Intent classification, and Language identification, or STIL, is a newly-proposed task for multilingual Natural Language Understanding (NLU). By performing simultaneous slot filling and translation into a single output language (English in this case), some portion of downstream system components can be monolingual, reducing development and maintenance cost. Results are given using the multilingual BART model (Liu et al., 2020) fine-tuned on 7 languages using the MultiATIS++ dataset. When no translation is performed, mBART{'}s performance is comparable to the current state of the art system (Cross-Lingual BERT by Xu et al. (2020)) for the languages tested, with better average intent classification accuracy (96.07{\%} versus 95.50{\%}) but worse average slot F1 (89.87{\%} versus 90.81{\%}). When simultaneous translation is performed, average intent classification accuracy degrades by only 1.7{\%} relative and average slot F1 degrades by only 1.2{\%} relative.",
}
@inproceedings{ma-etal-2020-simulmt,
    title = "{S}imul{MT} to {S}imul{ST}: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation",
    author = "Ma, Xutai  and
      Pino, Juan  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.58",
    pages = "582--587",
    abstract = "We investigate how to adapt simultaneous text translation methods such as wait-$k$ and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.",
}
@inproceedings{brahman-etal-2020-cue,
    title = "Cue Me In: Content-Inducing Approaches to Interactive Story Generation",
    author = "Brahman, Faeze  and
      Petrusca, Alexandru  and
      Chaturvedi, Snigdha",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.59",
    pages = "588--597",
    abstract = "Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user. Here, we instead focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions in the form of cue phrases during the generation process. This provides an interface for human users to guide the story generation. We present two content-inducing approaches to effectively incorporate this additional information. Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories compared to baseline methods.",
}
@inproceedings{koto-etal-2020-liputan6,
    title = "Liputan6: A Large-scale {I}ndonesian Dataset for Text Summarization",
    author = "Koto, Fajri  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.60",
    pages = "598--608",
    abstract = "In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from Liputan6.com, an online news portal, and obtain 215,827 document{--}summary pairs. We leverage pre-trained language models to develop benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have low ROUGE scores, and expose both issues with ROUGE itself, as well as with extractive and abstractive summarization models.",
}
@inproceedings{huang-etal-2020-generating,
    title = "Generating Sports News from Live Commentary: A {C}hinese Dataset for Sports Game Summarization",
    author = "Huang, Kuan-Hao  and
      Li, Chen  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.61",
    pages = "609--615",
    abstract = "Sports game summarization focuses on generating news articles from live commentaries. Unlike traditional summarization tasks, the source documents and the target summaries for sports game summarization tasks are written in quite different writing styles. In addition, live commentaries usually contain many named entities, which makes summarizing sports games precisely very challenging. To deeply study this task, we present SportsSum, a Chinese sports game summarization dataset which contains 5,428 soccer games of live commentaries and the corresponding news articles. Additionally, we propose a two-step summarization model consisting of a selector and a rewriter for SportsSum. To evaluate the correctness of generated sports summaries, we design two novel score metrics: name matching score and event matching score. Experimental results show that our model performs better than other summarization baselines on ROUGE scores as well as the two designed scores.",
}
@inproceedings{el-kishky-guzman-2020-massively,
    title = "Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover{'}s Distance",
    author = "El-Kishky, Ahmed  and
      Guzm{\'a}n, Francisco",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.62",
    pages = "616--625",
    abstract = "Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. Such aligned data can be used for a variety of NLP tasks from training cross-lingual representations to mining parallel data for machine translation. In this paper we develop an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. These semantic distances are then used to guide a document alignment algorithm to properly pair cross-lingual web documents across a variety of low, mid, and high-resource language pairs. Recognizing that our proposed scoring function and other state of the art methods are computationally intractable for long web documents, we utilize a more tractable greedy algorithm that performs comparably. We experimentally demonstrate that our distance metric performs better alignment than current baselines outperforming them by 7{\%} on high-resource language pairs, 15{\%} on mid-resource language pairs, and 22{\%} on low-resource language pairs.",
}
@inproceedings{xing-etal-2020-improving,
    title = "Improving Context Modeling in Neural Topic Segmentation",
    author = "Xing, Linzi  and
      Hackinen, Brad  and
      Carenini, Giuseppe  and
      Trebbi, Francesco",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.63",
    pages = "626--636",
    abstract = "Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention. Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets. We also the robustness of our proposed model in domain transfer setting by training a model on a large-scale dataset and testing it on four challenging real-world benchmarks. Furthermore, we apply our proposed strategy to two other languages (German and Chinese), and show its effectiveness in multilingual scenarios.",
}
@inproceedings{chen-etal-2020-contextualized,
    title = "Contextualized End-to-End Neural Entity Linking",
    author = "Chen, Haotian  and
      Li, Xi  and
      Zukov Gregoric, Andrej  and
      Wadhwa, Sahil",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.64",
    pages = "637--642",
    abstract = "We propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED). Our model applies task-specific heads on top of shared BERT contextualized embeddings. We achieve state-of-the-art results across a standard EL dataset using our model; we also study our model{'}s performance under the setting when hand-crafted entity candidate sets are not available and find that the model performs well under such a setting too.",
}
@inproceedings{vijayaraghavan-etal-2020-dapper,
    title = "{DAPPER}: Learning Domain-Adapted Persona Representation Using Pretrained {BERT} and External Memory",
    author = "Vijayaraghavan, Prashanth  and
      Chu, Eric  and
      Roy, Deb",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.65",
    pages = "643--652",
    abstract = "Research in building intelligent agents have emphasized the need for understanding characteristic behavior of people. In order to reflect human-like behavior, agents require the capability to comprehend the context, infer individualized persona patterns and incrementally learn from experience. In this paper, we present a model called DAPPER that can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas. To this end, we implement a text encoding strategy that leverages a pretrained language model and an external memory to produce domain-adapted persona representations. Further, we evaluate the transferability of these embeddings by simulating low-resource scenarios. Our comparative study demonstrates the capability of our method over other approaches towards learning rich transferable persona embeddings. Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection.",
}
@inproceedings{lu-ng-2020-event,
    title = "Event Coreference Resolution with Non-Local Information",
    author = "Lu, Jing  and
      Ng, Vincent",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.66",
    pages = "653--663",
    abstract = "We present two extensions to a state-of-theart joint model for event coreference resolution, which involve incorporating (1) a supervised topic model for improving trigger detection by providing global context, and (2) a preprocessing module that seeks to improve event coreference by discarding unlikely candidate antecedents of an event mention using discourse contexts computed based on salient entities. The resulting model yields the best results reported to date on the KBP 2017 English and Chinese datasets.",
}
@inproceedings{guz-etal-2020-neural,
    title = "Neural {RST}-based Evaluation of Discourse Coherence",
    author = "Guz, Grigorii  and
      Bateni, Peyman  and
      Muglich, Darius  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.67",
    pages = "664--671",
    abstract = "This paper evaluates the utility of Rhetorical Structure Theory (RST) trees and relations in discourse coherence evaluation. We show that incorporating silver-standard RST features can increase accuracy when classifying coherence. We demonstrate this through our tree-recursive neural model, namely RST-Recursive, which takes advantage of the text{'}s RST features produced by a state of the art RST parser. We evaluate our approach on the Grammarly Corpus for Discourse Coherence (GCDC) and show that when ensembled with the current state of the art, we can achieve the new state of the art accuracy on this benchmark. Furthermore, when deployed alone, RST-Recursive achieves competitive accuracy while having 62{\%} fewer parameters.",
}
@inproceedings{vania-etal-2020-asking,
    title = "{A}sking {C}rowdworkers to {W}rite {E}ntailment {E}xamples: {T}he {B}est of {B}ad Options",
    author = "Vania, Clara  and
      Chen, Ruijie  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.68",
    pages = "672--686",
    abstract = "Large-scale natural language inference (NLI) datasets such as SNLI or MNLI have been created by asking crowdworkers to read a premise and write three new hypotheses, one for each possible semantic relationships (entailment, contradiction, and neutral). While this protocol has been used to create useful benchmark data, it remains unclear whether the writing-based annotation protocol is optimal for any purpose, since it has not been evaluated directly. Furthermore, there is ample evidence that crowdworker writing can introduce artifacts in the data. We investigate two alternative protocols which automatically create candidate (premise, hypothesis) pairs for annotators to label. Using these protocols and a writing-based baseline, we collect several new English NLI datasets of over 3k examples each, each using a fixed amount of annotator time, but a varying number of examples to fit that time budget. Our experiments on NLI and transfer learning show negative results: None of the alternative protocols outperforms the baseline in evaluations of generalization within NLI or on transfer to outside target tasks. We conclude that crowdworker writing still the best known option for entailment data, highlighting the need for further data collection work to focus on improving writing-based annotation processes.",
}
@inproceedings{luo-etal-2020-map,
    title = "{M}a{P}: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension",
    author = "Luo, Huaishao  and
      Shi, Yu  and
      Gong, Ming  and
      Shou, Linjun  and
      Li, Tianrui",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.69",
    pages = "687--695",
    abstract = "Span extraction is an essential problem in machine reading comprehension. Most of the existing algorithms predict the start and end positions of an answer span in the given corresponding context by generating two probability vectors. In this paper, we propose a novel approach that extends the probability vector to a probability matrix. Such a matrix can cover more start-end position pairs. Precisely, to each possible start index, the method always generates an end probability vector. Besides, we propose a sampling-based training strategy to address the computational cost and memory issue in the matrix training phase. We evaluate our method on SQuAD 1.1 and three other question answering benchmarks. Leveraging the most competitive models BERT and BiDAF as the backbone, our proposed approach can get consistent improvements in all datasets, demonstrating the effectiveness of the proposed method.",
}
@inproceedings{zhang-etal-2020-answering,
    title = "Answering Product-related Questions with Heterogeneous Information",
    author = "Zhang, Wenxuan  and
      Yu, Qian  and
      Lam, Wai",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.70",
    pages = "696--705",
    abstract = "Providing instant response for product-related questions in E-commerce question answering platforms can greatly improve users{'} online shopping experience. However, existing product question answering (PQA) methods only consider a single information source such as user reviews and/or require large amounts of labeled data. In this paper, we propose a novel framework to tackle the PQA task via exploiting heterogeneous information including natural language text and attribute-value pairs from two information sources of the concerned product, namely product details and user reviews. A heterogeneous information encoding component is then designed for obtaining unified representations of information with different formats. The sources of the candidate snippets are also incorporated when measuring the question-snippet relevance. Moreover, the framework is trained with a specifically designed weak supervision paradigm making use of available answers in the training phase. Experiments on a real-world dataset show that our proposed framework achieves superior performance over state-of-the-art models.",
}
@inproceedings{uppal-etal-2020-two,
    title = "Two-Step Classification using Recasted Data for Low Resource Settings",
    author = "Uppal, Shagun  and
      Gupta, Vivek  and
      Swaminathan, Avinash  and
      Zhang, Haimin  and
      Mahata, Debanjan  and
      Gosangi, Rakesh  and
      Shah, Rajiv Ratn  and
      Stent, Amanda",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
    pages = "706--719",
    abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
@inproceedings{liao-etal-2020-explaining,
    title = "Explaining Word Embeddings via Disentangled Representation",
    author = "Liao, Keng-Te  and
      Lee, Cheng-Syuan  and
      Huang, Zhong-Yu  and
      Lin, Shou-de",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.72",
    pages = "720--725",
    abstract = "Disentangled representations have attracted increasing attention recently. However, how to transfer the desired properties of disentanglement to word representations is unclear. In this work, we propose to transform typical dense word vectors into disentangled embeddings featuring improved interpretability via encoding polysemous semantics separately. We also found the modular structure of our disentangled word embeddings helps generate more efficient and effective features for natural language processing tasks.",
}
@inproceedings{jiang-etal-2020-multi,
    title = "Multi-view Classification Model for Knowledge Graph Completion",
    author = "Jiang, Wenbin  and
      Guo, Mengfei  and
      Chen, Yufeng  and
      Li, Ying  and
      Xu, Jinan  and
      Lyu, Yajuan  and
      Zhu, Yong",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.73",
    pages = "726--734",
    abstract = "Most previous work on knowledge graph completion conducted single-view prediction or calculation for candidate triple evaluation, based only on the content information of the candidate triples. This paper describes a novel multi-view classification model for knowledge graph completion, where multiple classification views are performed based on both content and context information for candidate triple evaluation. Each classification view evaluates the validity of a candidate triple from a specific viewpoint, based on the content information inside the candidate triple and the context information nearby the triple. These classification views are implemented by a unified neural network and the classification predictions are weightedly integrated to obtain the final evaluation. Experiments show that, the multi-view model brings very significant improvements over previous methods, and achieves the new state-of-the-art on two representative datasets. We believe that, the flexibility and the scalability of the multi-view classification model facilitates the introduction of additional information and resources for better performance.",
}
@inproceedings{feng-etal-2020-knowledge,
    title = "Knowledge-Enhanced Named Entity Disambiguation for Short Text",
    author = "Feng, Zhifan  and
      Wang, Qi  and
      Jiang, Wenbin  and
      Lyu, Yajuan  and
      Zhu, Yong",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.74",
    pages = "735--744",
    abstract = "Named entity disambiguation is an important task that plays the role of bridge between text and knowledge. However, the performance of existing methods drops dramatically for short text, which is widely used in actual application scenarios, such as information retrieval and question answering. In this work, we propose a novel knowledge-enhanced method for named entity disambiguation. Considering the problem of information ambiguity and incompleteness for short text, two kinds of knowledge, factual knowledge graph and conceptual knowledge graph, are introduced to provide additional knowledge for the semantic matching between candidate entity and mention context. Our proposed method achieves significant improvement over previous methods on a large manually annotated short-text dataset, and also achieves the state-of-the-art on three standard datasets. The short-text dataset and the proposed model will be publicly available for research use.",
}
@inproceedings{han-etal-2020-data,
    title = "More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction",
    author = "Han, Xu  and
      Gao, Tianyu  and
      Lin, Yankai  and
      Peng, Hao  and
      Yang, Yaoliang  and
      Xiao, Chaojun  and
      Liu, Zhiyuan  and
      Li, Peng  and
      Zhou, Jie  and
      Sun, Maosong",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.75",
    pages = "745--758",
    abstract = "Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require {``}more{''} from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.",
}
@inproceedings{zhang-etal-2020-robustness,
    title = "Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs",
    author = "Zhang, Haiyang  and
      Sneyd, Alison  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.76",
    pages = "759--769",
    abstract = "It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pairs to measure bias and extract biased analogies. We show that the reliance on these gendered pairs has strong limitations: bias measures based off of them are not robust and cannot identify common types of real-world bias, whilst analogies utilising them are unsuitable indicators of bias. In particular, the well-known analogy {``}man is to computer-programmer as woman is to homemaker{''} is due to word similarity rather than bias. This has important implications for work on measuring bias in embeddings and related work debiasing embeddings.",
}
@inproceedings{yu-etal-2020-expanrl,
    title = "{E}xpan{RL}: Hierarchical Reinforcement Learning for Course Concept Expansion in {MOOC}s",
    author = "Yu, Jifan  and
      Wang, Chenyu  and
      Luo, Gan  and
      Hou, Lei  and
      Li, Juanzi  and
      Tang, Jie  and
      Huang, Minlie  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.77",
    pages = "770--780",
    abstract = "Within the prosperity of Massive Open Online Courses (MOOCs), the education applications that automatically provide extracurricular knowledge for MOOC users become rising research topics. However, MOOC courses{'} diversity and rapid updates make it more challenging to find suitable new knowledge for students. In this paper, we present ExpanRL, an end-to-end hierarchical reinforcement learning (HRL) model for concept expansion in MOOCs. Employing a two-level HRL mechanism of seed selection and concept expansion, ExpanRL is more feasible to adjust the expansion strategy to find new concepts based on the students{'} feedback on expansion results. Our experiments on nine novel datasets from real MOOCs show that ExpanRL achieves significant improvements over existing methods and maintain competitive performance under different settings.",
}
@inproceedings{kumar-etal-2020-vocabulary,
    title = "Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation",
    author = "Kumar, Vishwajeet  and
      Joshi, Manish  and
      Ramakrishnan, Ganesh  and
      Li, Yuan-Fang",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.78",
    pages = "781--785",
    abstract = "Question generation (QG) has recently attracted considerable attention. Most of the current neural models take as input only one or two sentences, and perform poorly when multiple sentences or complete paragraphs are given as input. However, in real-world scenarios it is very important to be able to generate high-quality questions from complete paragraphs. In this paper, we present a simple yet effective technique for answer-aware question generation from paragraphs. We augment a basic sequence-to-sequence QG model with dynamic, paragraph-specific dictionary and copy attention that is persistent across the corpus, without requiring features generated by sophisticated NLP pipelines or handcrafted rules. Our evaluation on SQuAD shows that our model significantly outperforms current state-of-the-art systems in question generation from paragraphs in both automatic and human evaluation. We achieve a 6-point improvement over the best system on BLEU-4, from 16.38 to 22.62.",
}
@inproceedings{eger-benz-2020-hero,
    title = "From Hero to Z{\'e}roe: A Benchmark of Low-Level Adversarial Attacks",
    author = "Eger, Steffen  and
      Benz, Yannik",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.79",
    pages = "786--803",
    abstract = "Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mostly focused on high-level attack scenarios such as paraphrasing input texts. We argue that these are less realistic in typical application scenarios such as in social media, and instead focus on low-level attacks on the character-level. Guided by human cognitive abilities and human robustness, we propose the first large-scale catalogue and benchmark of low-level adversarial attacks, which we dub Z{\'e}roe, encompassing nine different attack modes including visual and phonetic adversaries. We show that RoBERTa, NLP{'}s current workhorse, fails on our attacks. Our dataset provides a benchmark for testing robustness of future more human-like NLP models.",
}
@inproceedings{sanchez-villegas-etal-2020-point,
    title = "Point-of-Interest Type Inference from Social Media Text",
    author = "S{\'a}nchez Villegas, Danae  and
      Preotiuc-Pietro, Daniel  and
      Aletras, Nikolaos",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.80",
    pages = "804--810",
    abstract = "Physical places help shape how we perceive the experiences we have there. We study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of {\textasciitilde}200,000 English tweets published from 2,761 different points-of-interest in the U.S., enriched with place type information. We train classifiers to predict the type of the location a tweet was sent from that reach a macro F1 of 43.67 across eight classes and uncover the linguistic markers associated with each type of place. The ability to predict semantic place information from a tweet has applications in recommendation systems, personalization services and cultural geography.",
}
@inproceedings{chen-etal-2020-reconstructing,
    title = "Reconstructing Event Regions for Event Extraction via Graph Attention Networks",
    author = "Chen, Pei  and
      Yang, Hang  and
      Liu, Kang  and
      Huang, Ruihong  and
      Chen, Yubo  and
      Wang, Taifeng  and
      Zhao, Jun",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.81",
    pages = "811--820",
    abstract = "Event information is usually scattered across multiple sentences within a document. The local sentence-level event extractors often yield many noisy event role filler extractions in the absence of a broader view of the document-level context. Filtering spurious extractions and aggregating event information in a document remains a challenging problem. Following the observation that a document has several relevant event regions densely populated with event role fillers, we build graphs with candidate role filler extractions enriched by sentential embeddings as nodes, and use graph attention networks to identify event regions in a document and aggregate event information. We characterize edges between candidate extractions in a graph into rich vector representations to facilitate event region identification. The experimental results on two datasets of two languages show that our approach yields new state-of-the-art performance for the challenging event extraction task.",
}
@inproceedings{jiang-etal-2020-recipe,
    title = "Recipe Instruction Semantics Corpus ({RIS}e{C}): {R}esolving Semantic Structure and Zero Anaphora in Recipes",
    author = "Jiang, Yiwei  and
      Zaporojets, Klim  and
      Deleu, Johannes  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.82",
    pages = "821--826",
    abstract = "We propose a newly annotated dataset for information extraction on recipes. Unlike previous approaches to machine comprehension of procedural texts, we avoid a priori pre-defining domain-specific predicates to recognize (e.g., the primitive instructionsin MILK) and focus on basic understanding of the expressed semantics rather than directly reduce them to a simplified state representation (e.g., ProPara). We thus frame the semantic comprehension of procedural text such as recipes, as fairly generic NLP subtasks, covering (i) entity recognition (ingredients, tools and actions), (ii) relation extraction (what ingredients and tools are involved in the actions), and (iii) zero anaphora resolution (link actions to implicit arguments, e.g., results from previous recipe steps). Further, our Recipe Instruction Semantic Corpus (RISeC) dataset includes textual descriptions for the zero anaphora, to facilitate language generation thereof. Besides the dataset itself, we contribute a pipeline neural architecture that addresses entity and relation extractionas well an identification of zero anaphora. These basic building blocks can facilitate more advanced downstream applications (e.g., question answering, conversational agents).",
}
@inproceedings{katsumata-komachi-2020-stronger,
    title = "Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model",
    author = "Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.83",
    pages = "827--832",
    abstract = "Studies on grammatical error correction (GEC) have reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining of GEC because of the size of the pseudodata. In this study, we explored the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC.",
}
@inproceedings{xiang-etal-2020-sina,
    title = "Sina {M}andarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource",
    author = "Xiang, Rong  and
      Wan, Mingyu  and
      Su, Qi  and
      Huang, Chu-Ren  and
      Lu, Qin",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.84",
    pages = "833--842",
    abstract = "Mandarin Alphabetical Word (MAW) is one indispensable component of Modern Chinese that demonstrates unique code-mixing idiosyncrasies influenced by language exchanges. Yet, this interesting phenomenon has not been properly addressed and is mostly excluded from the Chinese language system. This paper addresses the core problem of MAW identification and proposes to construct a large collection of MAWs from Sina Weibo (SMAW) using an automatic web-based technique which includes rule-based identification, informatics-based extraction, as well as Baidu search engine validation. A collection of 16,207 qualified SMAWs are obtained using this technique along with an annotated corpus of more than 200,000 sentences for linguistic research and applicable inquiries.",
}
@inproceedings{wilie-etal-2020-indonlu,
    title = "{I}ndo{NLU}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Understanding",
    author = "Wilie, Bryan  and
      Vincentio, Karissa  and
      Winata, Genta Indra  and
      Cahyawijaya, Samuel  and
      Li, Xiaohong  and
      Lim, Zhi Yuan  and
      Soleman, Sidik  and
      Mahendra, Rahmad  and
      Fung, Pascale  and
      Bahar, Syafri  and
      Purwarianti, Ayu",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.85",
    pages = "843--857",
    abstract = "Although Indonesian is known to be the fourth most frequently used language over the internet, the research progress on this language in natural language processing (NLP) is slow-moving due to a lack of available resources. In response, we introduce the first-ever vast resource for training, evaluation, and benchmarking on Indonesian natural language understanding (IndoNLU) tasks. IndoNLU includes twelve tasks, ranging from single sentence classification to pair-sentences sequence labeling with different levels of complexity. The datasets for the tasks lie in different domains and styles to ensure task diversity. We also provide a set of Indonesian pre-trained models (IndoBERT) trained from a large and clean Indonesian dataset (Indo4B) collected from publicly available sources such as social media texts, blogs, news, and websites. We release baseline models for all twelve tasks, as well as the framework for benchmark evaluation, thus enabling everyone to benchmark their system performances.",
}
@inproceedings{mathias-etal-2020-happy,
    title = "Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour",
    author = "Mathias, Sandeep  and
      Murthy, Rudra  and
      Kanojia, Diptesh  and
      Mishra, Abhijit  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.86",
    pages = "858--872",
    abstract = "The gaze behaviour of a reader is helpful in solving several NLP tasks such as automatic essay grading. However, collecting gaze behaviour from readers is costly in terms of time and money. In this paper, we propose a way to improve automatic essay grading using gaze behaviour, which is learnt at run time using a multi-task learning framework. To demonstrate the efficacy of this multi-task learning based approach to automatic essay grading, we collect gaze behaviour for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the essays, numbering over 7000 essays. Using the learnt gaze behaviour, we can achieve a statistically significant improvement in performance over the state-of-the-art system for the essay sets where we have gaze data. We also achieve a statistically significant improvement for 4 other essay sets, numbering about 6000 essays, where we have no gaze behaviour data available. Our approach establishes that learning gaze behaviour improves automatic essay grading.",
}
@inproceedings{cui-bollegala-2020-multi,
    title = "Multi-Source Attention for Unsupervised Domain Adaptation",
    author = "Cui, Xia  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.87",
    pages = "873--883",
    abstract = "We model source-selection in multi-source Unsupervised Domain Adaptation (UDA) as an attention-learning problem, where we learn attention over the sources per given target instance. We first independently learn source-specific classification models, and a relatedness map between sources and target domains using pseudo-labelled target domain instances. Next, we learn domain-attention scores over the sources for aggregating the predictions of the source-specific models. Experimental results on two cross-domain sentiment classification datasets show that the proposed method reports consistently good performance across domains, and at times outperforming more complex prior proposals. Moreover, the computed domain-attention scores enable us to find explanations for the predictions made by the proposed method.",
}
@inproceedings{ben-noach-goldberg-2020-compressing,
    title = "Compressing Pre-trained Language Models by Matrix Decomposition",
    author = "Ben Noach, Matan  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.88",
    pages = "884--889",
    abstract = "Large pre-trained language models reach state-of-the-art results on many different NLP tasks when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI). We propose a two-stage model-compression method to reduce a model{'}s inference time cost. We first decompose the matrices in the model into smaller matrices and then perform feature distillation on the internal representation to recover from the decomposition. This approach has the benefit of reducing the number of parameters while preserving much of the information within the model. We experimented on BERT-base model with the GLUE benchmark dataset and show that we can reduce the number of parameters by a factor of 0.4x, and increase inference speed by a factor of 1.45x, while maintaining a minimal loss in metric performance.",
}
@inproceedings{kanouchi-etal-2020-may,
    title = "You May Like This Hotel Because ...: Identifying Evidence for Explainable Recommendations",
    author = "Kanouchi, Shin  and
      Neishi, Masato  and
      Hayashibe, Yuta  and
      Ouchi, Hiroki  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.89",
    pages = "890--899",
    abstract = "Explainable recommendation is a good way to improve user satisfaction. However, explainable recommendation in dialogue is challenging since it has to handle natural language as both input and output. To tackle the challenge, this paper proposes a novel and practical task to explain evidences in recommending hotels given vague requests expressed freely in natural language. We decompose the process into two subtasks on hotel reviews: Evidence Identification and Evidence Explanation. The former predicts whether or not a sentence contains evidence that expresses why a given request is satisfied. The latter generates a recommendation sentence given a request and an evidence sentence. In order to address these subtasks, we build an Evidence-based Explanation dataset, which is the largest dataset for explaining evidences in recommending hotels for vague requests. The experimental results demonstrate that the BERT model can find evidence sentences with respect to various vague requests and that the LSTM-based model can generate recommendation sentences.",
}
@inproceedings{gupta-etal-2020-unified,
    title = "A Unified Framework for Multilingual and Code-Mixed Visual Question Answering",
    author = "Gupta, Deepak  and
      Lenka, Pabitra  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.90",
    pages = "900--913",
    abstract = "In this paper, we propose an effective deep learning framework for multilingual and code- mixed visual question answering. The pro- posed model is capable of predicting answers from the questions in Hindi, English or Code- mixed (Hinglish: Hindi-English) languages. The majority of the existing techniques on Vi- sual Question Answering (VQA) focus on En- glish questions only. However, many applica- tions such as medical imaging, tourism, visual assistants require a multilinguality-enabled module for their widespread usages. As there is no available dataset in English-Hindi VQA, we firstly create Hindi and Code-mixed VQA datasets by exploiting the linguistic properties of these languages. We propose a robust tech- nique capable of handling the multilingual and code-mixed question to provide the answer against the visual information (image). To better encode the multilingual and code-mixed questions, we introduce a hierarchy of shared layers. We control the behaviour of these shared layers by an attention-based soft layer sharing mechanism, which learns how shared layers are applied in different ways for the dif- ferent languages of the question. Further, our model uses bi-linear attention with a residual connection to fuse the language and image fea- tures. We perform extensive evaluation and ablation studies for English, Hindi and Code- mixed VQA. The evaluation shows that the proposed multilingual model achieves state-of- the-art performance in all these settings.",
}
@inproceedings{leite-etal-2020-toxic,
    title = "Toxic Language Detection in Social Media for {B}razilian {P}ortuguese: New Dataset and Multilingual Analysis",
    author = "Leite, Jo{\~a}o Augusto  and
      Silva, Diego  and
      Bontcheva, Kalina  and
      Scarton, Carolina",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.91",
    pages = "914--924",
    abstract = "Hate speech and toxic comments are a common concern of social media platform users. Although these comments are, fortunately, the minority in these platforms, they are still capable of causing harm. Therefore, identifying these comments is an important task for studying and preventing the proliferation of toxicity in social media. Previous work in automatically detecting toxic comments focus mainly in English, with very few work in languages like Brazilian Portuguese. In this paper, we propose a new large-scale dataset for Brazilian Portuguese with tweets annotated as either toxic or non-toxic or in different types of toxicity. We present our dataset collection and annotation process, where we aimed to select candidates covering multiple demographic groups. State-of-the-art BERT models were able to achieve 76{\%} macro-F1 score using monolingual data in the binary case. We also show that large-scale monolingual data is still needed to create more accurate models, despite recent advances in multilingual approaches. An error analysis and experiments with multi-label classification show the difficulty of classifying certain types of toxic comments that appear less frequently in our data and highlights the need to develop models that are aware of different categories of toxicity.",
}
@inproceedings{scarton-etal-2020-measuring,
    title = "Measuring What Counts: The Case of Rumour Stance Classification",
    author = "Scarton, Carolina  and
      Silva, Diego  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-main.92",
    pages = "925--932",
    abstract = "Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies towards a given rumour, namely support, deny, question, or comment. Numerous methods have been proposed and their performance compared in the RumourEval shared tasks in 2017 and 2019. Results demonstrated that this is a challenging problem since naturally occurring rumour stance data is highly imbalanced. This paper specifically questions the evaluation metrics used in these shared tasks. We re-evaluate the systems submitted to the two RumourEval tasks and show that the two widely adopted metrics {--} accuracy and macro-F1 {--} are not robust for the four-class imbalanced task of rumour stance classification, as they wrongly favour systems with highly skewed accuracy towards the majority class. To overcome this problem, we propose new evaluation metrics for rumour stance detection. These are not only robust to imbalanced data but also score higher systems that are capable of recognising the two most informative minority classes (support and deny).",
}
@proceedings{aacl-2020-asia-pacific-chapter,
    title = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    editor = "Wong, Derek  and
      Kiela, Douwe",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.0",
}
@inproceedings{francois-etal-2020-amesure,
    title = "{AM}esure: A Web Platform to Assist the Clear Writing of Administrative Texts",
    author = {Fran{\c{c}}ois, Thomas  and
      M{\"u}ller, Adeline  and
      Rolin, Eva  and
      Norr{\'e}, Magali},
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.1",
    pages = "1--7",
    abstract = "This article presents the AMesure platform, which aims to assist writers of French administrative texts in simplifying their writing. This platform includes a readability formula specialized for administrative texts and it also uses various natural language processing (NLP) tools to analyze texts and highlight a number of linguistic phenomena considered difficult to read. Finally, based on the difficulties identified, it offers pieces of advice coming from official plain language guides to users. This paper describes the different components of the system and reports an evaluation of these components.",
}
@inproceedings{le-etal-2020-autonlu,
    title = "{A}uto{NLU}: An On-demand Cloud-based Natural Language Understanding System for Enterprises",
    author = "Le, Nham  and
      Lai, Tuan  and
      Bui, Trung  and
      Kim, Doo Soon",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.2",
    pages = "8--13",
    abstract = "With the renaissance of deep learning, neural networks have achieved promising results on many natural language understanding (NLU) tasks. Even though the source codes of many neural network models are publicly available, there is still a large gap from open-sourced models to solving real-world problems in enterprises. Therefore, to fill this gap, we introduce AutoNLU, an on-demand cloud-based system with an easy-to-use interface that covers all common use-cases and steps in developing an NLU model. AutoNLU has supported many product teams within Adobe with different use-cases and datasets, quickly delivering them working models. To demonstrate the effectiveness of AutoNLU, we present two case studies. i) We build a practical NLU model for handling various image-editing requests in Photoshop. ii) We build powerful keyphrase extraction models that achieve state-of-the-art results on two public benchmarks. In both cases, end users only need to write a small amount of code to convert their datasets into a common format used by AutoNLU.",
}
@inproceedings{lai-etal-2020-isa,
    title = "{ISA}: An Intelligent Shopping Assistant",
    author = "Lai, Tuan  and
      Bui, Trung  and
      Lipka, Nedim",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.3",
    pages = "14--19",
    abstract = "Despite the growth of e-commerce, brick-and-mortar stores are still the preferred destinations for many people. In this paper, we present ISA, a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. ISA assists users by leveraging advanced techniques in computer vision, speech processing, and natural language processing. An in-store user only needs to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product. The assistant can also guide the user through the purchase process or recommend other similar products to the user. We take a data-driven approach in building the engines of ISA{'}s natural language processing component, and the engines achieve good performance.",
}
@inproceedings{liu-etal-2020-metacat,
    title = "meta{CAT}: A Metadata-based Task-oriented Chatbot Annotation Tool",
    author = "Liu, Ximing  and
      Xue, Wei  and
      Su, Qi  and
      Nie, Weiran  and
      Peng, Wei",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.4",
    pages = "20--25",
    abstract = "Creating high-quality annotated dialogue corpora is challenging. It is essential to develop practical annotation tools to support humans in this time-consuming and error-prone task. We present metaCAT, which is an open-source web-based annotation tool designed specifically for developing task-oriented dialogue data. To the best of our knowledge, metaCAT is the first annotation tool that provides comprehensive metadata annotation coverage to the domain, intent, and span information. The data annotation quality is enhanced by a real-time annotation constraint-checking mechanism. An Automatic Speech Recognition (ASR) function is implemented to allow users to paraphrase and create more diversified annotated utterances. metaCAT is publicly available for the community.",
}
@inproceedings{akhbardeh-etal-2020-nlp,
    title = "{NLP} Tools for Predictive Maintenance Records in {M}aint{N}et",
    author = "Akhbardeh, Farhad  and
      Desell, Travis  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.5",
    pages = "26--32",
    abstract = "Processing maintenance logbook records is an important step in the development of predictive maintenance systems. Logbooks often include free text fields with domain specific terms, abbreviations, and non-standard spelling posing challenges to off-the-shelf NLP pipelines trained on standard contemporary corpora. Despite the importance of this data type, processing predictive maintenance data is still an under-explored topic in NLP. With the goal of providing more datasets and resources to the community, in this paper we present a number of new resources available in MaintNet, a collaborative open-source library and data repository of predictive maintenance language datasets. We describe novel annotated datasets from multiple domains such as aviation, automotive, and facility maintenance domains and new tools for segmentation, spell checking, POS tagging, clustering, and classification.",
}
@inproceedings{wang-etal-2020-fairseq,
    title = "Fairseq {S}2{T}: Fast Speech-to-Text Modeling with Fairseq",
    author = "Wang, Changhan  and
      Tang, Yun  and
      Ma, Xutai  and
      Wu, Anne  and
      Okhonko, Dmytro  and
      Pino, Juan",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.6",
    pages = "33--39",
    abstract = "We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq{'}s careful design for scalability and extensibility. We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. We implement state-of-the-art RNN-based as well as Transformer-based models and open-source detailed training recipes. Fairseq{'}s machine translation models and language models can be seamlessly integrated into S2T workflows for multi-task learning or transfer learning. Fairseq S2T is available at https://github.com/pytorch/fairseq/tree/master/examples/speech{\_}to{\_}text.",
}
@inproceedings{zhu-etal-2020-nlpstattest,
    title = "{NLPS}tat{T}est: A Toolkit for Comparing {NLP} System Performance",
    author = "Zhu, Haotian  and
      Mak, Denise  and
      Gioannini, Jesse  and
      Xia, Fei",
    booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.aacl-demo.7",
    pages = "40--46",
    abstract = "Statistical significance testing centered on p-values is commonly used to compare NLP system performance, but p-values alone are insufficient because statistical significance differs from practical significance. The latter can be measured by estimating effect size. In this pa-per, we propose a three-stage procedure for comparing NLP system performance and provide a toolkit, NLPStatTest, that automates the process. Users can upload NLP system evaluation scores and the toolkit will analyze these scores, run appropriate significance tests, estimate effect size, and conduct power analysis to estimate Type II error. The toolkit provides a convenient and systematic way to compare NLP system performance that goes beyond statistical significance testing.",
}
@proceedings{ws-2019-language,
    title = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    editor = "Cristina Vertan, University of Hamburg  and
      Petya Osenova, Bulgarian cdemy of Sciences  and
      of Sofia, St. Kliment Ohridski University  and
      Dimitar Iliev, St. Kliment Ohridski University of Sofia",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9000",
}
@inproceedings{gonzalez-martinez-2019-graphemic,
    title = "Graphemic ambiguous queries on {A}rabic-scripted historical corpora",
    author = "Gonz{\'a}lez Mart{\'\i}nez, Alicia",
    booktitle = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9001",
    doi = "10.26615/978-954-452-059-5_001",
    pages = "1--2",
    abstract = "",
}
@inproceedings{pivovarova-etal-2019-word,
    title = "Word Clustering for Historical Newspapers Analysis",
    author = "Pivovarova, Lidia  and
      Zosa, Elaine  and
      Marjanen, Jani",
    booktitle = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9002",
    doi = "10.26615/978-954-452-059-5_002",
    pages = "3--10",
    abstract = "This paper is a part of a collaboration between computer scientists and historians aimed at development of novel tools and methods to improve analysis of historical newspapers. We present a case study of ideological terms ending with -ism suffix in nineteenth century Finnish newspapers. We propose a two-step procedure to trace differences in word usages over time: training of diachronic embeddings on several time slices and when clustering embeddings of selected words together with their neighbours to obtain historical context. The obtained clusters turn out to be useful for historical studies. The paper also discuss specific difficulties related to development historian-oriented tools.",
}
@inproceedings{kew-etal-2019-geotagging,
    title = "Geotagging a Diachronic Corpus of Alpine Texts: Comparing Distinct Approaches to Toponym Recognition",
    author = "Kew, Tannon  and
      Shaitarova, Anastassia  and
      Meraner, Isabel  and
      Goldzycher, Janis  and
      Clematide, Simon  and
      Volk, Martin",
    booktitle = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9003",
    doi = "10.26615/978-954-452-059-5_003",
    pages = "11--18",
    abstract = "Geotagging historic and cultural texts provides valuable access to heritage data, enabling location-based searching and new geographically related discoveries. In this paper, we describe two distinct approaches to geotagging a variety of fine-grained toponyms in a diachronic corpus of alpine texts. By applying a traditional gazetteer-based approach, aided by a few simple heuristics, we attain strong high-precision annotations. Using the output of this earlier system, we adopt a state-of-the-art neural approach in order to facilitate the detection of new toponyms on the basis of context. Additionally, we present the results of preliminary experiments on integrating a small amount of crowdsourced annotations to improve overall performance of toponym recognition in our heritage corpus.",
}
@inproceedings{vertan-2019-controlled,
    title = "Controlled Semi-automatic Annotation of Classical Ethiopic",
    author = "Vertan, Cristina",
    booktitle = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9004",
    doi = "10.26615/978-954-452-059-5_004",
    pages = "19--23",
    abstract = "Preservation of the cultural heritage by means of digital methods became extremely popular during last years. After intensive digitization campaigns the focus moves slowly from the genuine preservation (i.e digital archiving together with standard search mechanisms) to research-oriented usage of materials available electronically. This usage is intended to go far beyond simple reading of digitized materials; researchers should be able to gain new insigts in materials, discover new facts by means of tools relying on innovative algorithms. In this article we will describe the workflow necessary for the annotation of a dichronic corpus of classical Ethiopic, language of essential importance for the study of Early Christianity",
}
@inproceedings{gelati-2019-implementing,
    title = "Implementing an archival, multilingual and Semantic Web-compliant taxonomy by means of {SKOS} (Simple Knowledge Organization System)",
    author = "Gelati, Francesco",
    booktitle = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9005",
    doi = "10.26615/978-954-452-059-5_005",
    pages = "24--27",
    abstract = "The paper shows how a multilingual hierarchical thesaurus, or taxonomy, can be created and implemented in compliance with Semantic Web requirements by means of the data model SKOS (Simple Knowledge Organization System). It takes the EHRI (European Holocaust Research Infrastructure) portal as an example, and shows how open-source software like SKOS Play! can facilitate the task.",
}
@inproceedings{stambolieva-2019-eu,
    title = "{EU} 4 {U}: An educational platform for the cultural heritage of the {EU}",
    author = "Stambolieva, Maria",
    booktitle = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9006",
    doi = "10.26615/978-954-452-059-5_006",
    pages = "28--33",
    abstract = "The paper presents an ongoing project of the NBU Laboratory for Language Technology aiming to create a multilingual, CEFR-graded electronic didactic resource for online learning, centered on the history and cultural heritage of the EU (e-EULearn). The resource is developed within the e-Platform of the NBU Laboratory for Language Technology and re-uses the rich corpus of educational material created at the Laboratory for the needs of NBU program modules, distance and blended learning language courses and other projects. Focus being not just on foreign language tuition, but above all on people, places and events in the history and culture of the EU member states, the annotation modules of the e-Platform have been accordingly extended. Current and upcoming activities are directed at: 1/ enriching the English corpus of didactic materials on EU history and culture, 2/ translating the texts into (the) other official EU languages and aligning the translations with the English texts; 3/ developing new test modules. In the process of developing this resource, a database on important people, places, objects and events in the cultural history of the EU will be created.",
}
@inproceedings{vertan-2019-modelling,
    title = "Modelling linguistic vagueness and uncertainty in historical texts",
    author = "Vertan, Cristina",
    booktitle = "Proceedings of the Workshop on Language Technology for Digital Historical Archives",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-9007",
    doi = "10.26615/978-954-452-059-5_007",
    pages = "34--38",
    abstract = "Many applications in Digital Humanities (DH) rely on annotations of the raw material. These annotations (inferred automatically or done manually) assume that labelled facts are either true or false, thus all inferences started on such annotations us boolean logic. This contradicts hermeneutic principles used by humanites in which most part of the knowledge has a degree of truth which varies depending on the experience and the world knowledge of the interpreter. In this paper we will show how uncertainty and vagueness, two main features of any historical text can be encoded in annotations and thus be considered by DH applications.",
}
@proceedings{ws-2019-multiling,
    title = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    editor = "Giannakopoulos, George",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8900",
}
@inproceedings{litvak-etal-2019-ranlp,
    title = "{RANLP} 2019 Multilingual Headline Generation Task Overview",
    author = "Litvak, Marina  and
      Conroy, John M.  and
      Rankel, Peter A.",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8901",
    doi = "10.26615/978-954-452-058-8_001",
    pages = "1--5",
    abstract = "The objective of the 2019 RANLP Multilingual Headline Generation (HG) Task is to explore some of the challenges highlighted by current state of the art approaches on creating informative headlines to news articles: non-descriptive headlines, out-of-domain training data, generating headlines from long documents which are not well represented by the head heuristic, and dealing with multilingual domain. This tasks makes available a large set of training data for headline generation and provides an evaluation methods for the task. Our data sets are drawn from Wikinews as well as Wikipedia. Participants were required to generate headlines for at least 3 languages, which were evaluated via automatic methods. A key aspect of the task is multilinguality. The task measures the performance of multilingual headline generation systems using the Wikipedia and Wikinews articles in multiple languages. The objective is to assess the performance of automatic headline generation techniques on text documents covering a diverse range of languages and topics outside the news domain.",
}
@inproceedings{el-haj-2019-multiling,
    title = "{M}ulti{L}ing 2019: Financial Narrative Summarisation",
    author = "El-Haj, Mahmoud",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8902",
    doi = "10.26615/978-954-452-058-8_002",
    pages = "6--10",
    abstract = "The Financial Narrative Summarisation task at MultiLing 2019 aims to demonstrate the value and challenges of applying automatic text summarisation to financial text written in English, usually referred to as financial narrative disclosures. The task dataset has been extracted from UK annual reports published in PDF file format. The participants were asked to provide structured summaries, based on real-world, publicly available financial annual reports of UK firms by extracting information from different key sections. Participants were asked to generate summaries that reflects the analysis and assessment of the financial trend of the business over the past year, as provided by annual reports. The evaluation of the summaries was performed using AutoSummENG and Rouge automatic metrics. This paper focuses mainly on the data creation process.",
}
@inproceedings{giannakopoulos-pittaras-2019-summary,
    title = "The Summary Evaluation Task in the {M}ulti{L}ing - {RANLP} 2019 Workshop",
    author = "Giannakopoulos, George  and
      Pittaras, Nikiforos",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8903",
    doi = "10.26615/978-954-452-058-8_003",
    pages = "11--16",
    abstract = "This report covers the summarization evaluation task, proposed to the summarization community via the MultiLing 2019 Workshop of the RANLP 2019 conference. The task aims to encourage the development of automatic summarization evaluation methods closely aligned with manual, human-authored summary grades and judgements. A multilingual setting is adopted, building upon a corpus of Wikinews articles across 6 languages (English, Arabic, Romanian, Greek, Spanish and Czech). The evaluation utilizes human (golden) and machine-generated (peer) summaries, which have been assigned human evaluation scores from previous MultiLing tasks. Using these resources, the original corpus is augmented with synthetic data, combining summary texts under three different strategies (reorder, merge and replace), each engineered to introduce noise in the summary in a controlled and quantifiable way. We estimate that the utilization of such data can extract and highlight useful attributes of summary quality estimation, aiding the creation of data-driven automatic methods with an increased correlation to human summary evaluations across domains and languages. This paper provides a brief description of the summary evaluation task, the data generation protocol and the resources made available by the MultiLing community, towards improving automatic summarization evaluation.",
}
@inproceedings{liu-etal-2019-multi-lingual,
    title = "Multi-lingual {W}ikipedia Summarization and Title Generation On Low Resource Corpus",
    author = "Liu, Wei  and
      Li, Lei  and
      Huang, Zuying  and
      Liu, Yinan",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8904",
    doi = "10.26615/978-954-452-058-8_004",
    pages = "17--25",
    abstract = "MultiLing 2019 Headline Generation Task on Wikipedia Corpus raised a critical and practical problem: multilingual task on low resource corpus. In this paper we proposed QDAS extractive summarization model enhanced by sentence2vec and try to apply transfer learning based on large multilingual pre-trained language model for Wikipedia Headline Generation task. We treat it as sequence labeling task and develop two schemes to handle with it. Experimental results have shown that large pre-trained model can effectively utilize learned knowledge to extract certain phrase using low resource supervised data.",
}
@inproceedings{gialitsis-etal-2019-topic,
    title = "A topic-based sentence representation for extractive text summarization",
    author = "Gialitsis, Nikolaos  and
      Pittaras, Nikiforos  and
      Stamatopoulos, Panagiotis",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8905",
    doi = "10.26615/978-954-452-058-8_005",
    pages = "26--34",
    abstract = "In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topic-based representations can prove beneficial to the extractive summarization process in terms of F1, ROUGE-L and ROUGE-W scores, compared to a TF-IDF baseline, with QDA-based analysis providing the best results.",
}
@inproceedings{panagiotopoulos-etal-2019-study,
    title = "A Study on Game Review Summarization",
    author = "Panagiotopoulos, George  and
      Giannakopoulos, George  and
      Liapis, Antonios",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8906",
    doi = "10.26615/978-954-452-058-8_006",
    pages = "35--43",
    abstract = "Game reviews have constituted a unique means of interaction between players and companies for many years. The dynamics appearing through online publishing have significantly grown the number of comments per game, giving rise to very interesting communities. The growth has, in turn, led to a difficulty in dealing with the volume and varying quality of the comments as a source of information. This work studies whether and how game reviews can be summarized, based on the notions pre-existing in aspect-based summarization and sentiment analysis. The work provides suggested pipeline of analysis, also offering preliminary findings on whether aspects detected in a set of comments can be consistently evaluated by human users.",
}
@inproceedings{tsekouras-etal-2019-social,
    title = "Social Web Observatory: An entity-driven, holistic information summarization platform across sources",
    author = "Tsekouras, Leonidas  and
      Petasis, Georgios  and
      Kosmopoulos, Aris",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8907",
    doi = "10.26615/978-954-452-058-8_007",
    pages = "44--52",
    abstract = "The Social Web Observatory is an entity-driven, sentiment-aware, event summarization web platform, combining various methods and tools to overview trends across social media and news sources in Greek. SWO crawls, clusters and summarizes information following an entity-centric view of text streams, allowing to monitor the public sentiment towards a specific person, organization or other entity. In this paper, we overview the platform, outline the analysis pipeline and describe a user study aimed to quantify the usefulness of the system and especially the meaningfulness and coherence of discovered events.",
}
@inproceedings{nn-2019-easy,
    title = "{EASY}-{M}: Evaluation System for Multilingual Summarizers",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8908",
    doi = "10.26615/978-954-452-058-8_008",
    pages = "53--62",
    abstract = "Automatic text summarization aims at producing a shorter version of a document (or a document set). Evaluation of summarization quality is a challenging task. Because human evaluations are expensive and evaluators often disagree between themselves, many researchers prefer to evaluate their systems automatically, with help of software tools. Such a tool usually requires a point of reference in the form of one or more human-written summaries for each text in the corpus. Then, a system-generated summary is compared to one or more human-written summaries, according to selected metrics. However, a single metric cannot reflect all quality-related aspects of a summary. In this paper we present the EvAluation SYstem for Multilingual Summarization (EASY-M), which enables the evaluation of system-generated summaries in 17 different languages with several quality measures, based on comparison with their human-generated counterparts. The system also provides comparative results with two built-in baselines. The source code and both online and offline versions of EASY-M is freely available for the NLP community.",
}
@inproceedings{pittaras-karkaletsis-2019-study,
    title = "A study of semantic augmentation of word embeddings for extractive summarization",
    author = "Pittaras, Nikiforos  and
      Karkaletsis, Vangelis",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8909",
    doi = "10.26615/978-954-452-058-8_009",
    pages = "63--72",
    abstract = "In this study we examine the effect of semantic augmentation approaches on extractive text summarization. Wordnet hypernym relations are used to extract term-frequency concept information, subsequently concatenated to sentence-level representations produced by aggregated deep neural word embeddings. Multiple dimensionality reduction techniques and combination strategies are examined via feature transformation and clustering methods. An experimental evaluation on the MultiLing 2015 MSS dataset illustrates that semantic information can introduce benefits to the extractive summarization process in terms of F1, ROUGE-1 and ROUGE-2 scores, with LSA-based post-processing introducing the largest improvements.",
}
@inproceedings{litvak-etal-2019-hevas,
    title = "{HE}v{AS}: Headline Evaluation and Analysis System",
    author = "Litvak, Marina  and
      Vanetik, Natalia  and
      Eretz Kdosha, Itzhak",
    booktitle = "Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W19-8910",
    doi = "10.26615/978-954-452-058-8_010",
    pages = "73--80",
    abstract = "Automatic headline generation is a subtask of one-line summarization with many reported applications. Evaluation of systems generating headlines is a very challenging and undeveloped area. We introduce the Headline Evaluation and Analysis System (HEvAS) that performs automatic evaluation of systems in terms of a quality of the generated headlines. HEvAS provides two types of metrics{--} one which measures the informativeness of a headline, and another that measures its readability. The results of evaluation can be compared to the results of baseline methods which are implemented in HEvAS. The system also performs the statistical analysis of the evaluation results and provides different visualization charts. This paper describes all evaluation metrics, baselines, analysis, and architecture, utilized by our system.",
}
@proceedings{ws-2019-human,
    title = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8700",
}
@inproceedings{baldo-de-brebisson-2019-comparison,
    title = "Comparison between Automatic and Human Subtitling: A Case Study with Game of Thrones",
    author = "Baldo de Br{\'e}bisson, Sabrina",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8701",
    doi = "10.26615/issn.2683-0078.2019_001",
    pages = "1--10",
    abstract = "In this submission, I would like to share my experiences with the software DeepL and the comparison analysis I have made with human subtitling offered by the DVD version of the corpus I have chosen as the topic of my study {--} the eight Seasons of Game of Thrones. The idea is to study if the version proposed by an automatic translation program could be used as a first draft for the professional subtitler. It is expected that the latter would work on the form of the subtitles, that is to say mainly on their length, in a second step.",
}
@inproceedings{brkic-bakaric-lalli-pacelat-2019-parallel,
    title = "Parallel Corpus of {C}roatian-{I}talian Administrative Texts",
    author = "Brkic Bakaric, Marija  and
      Lalli Pacelat, Ivana",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8702",
    doi = "10.26615/issn.2683-0078.2019_002",
    pages = "11--18",
    abstract = "Parallel corpora constitute a unique re-source for providing assistance to human translators. The selection and preparation of the parallel corpora also conditions the quality of the resulting MT engine. Since Croatian is a national language and Italian is officially recognized as a minority lan-guage in seven cities and twelve munici-palities of Istria County, a large amount of parallel texts is produced on a daily basis. However, there have been no attempts in using these texts for compiling a parallel corpus. A domain-specific sentence-aligned parallel Croatian-Italian corpus of administrative texts would be of high value in creating different language tools and resources. The aim of this paper is, therefore, to explore the value of parallel documents which are publicly available mostly in pdf format and to investigate the use of automatically-built dictionaries in corpus compilation. The effects that a document format and, consequently sentence splitting, and the dictionary input have on the sentence alignment process are manually evaluated.",
}
@inproceedings{castilho-etal-2019-influences,
    title = "What Influences the Features of Post-editese? A Preliminary Study",
    author = "Castilho, Sheila  and
      Resende, Nat{\'a}lia  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8703",
    doi = "10.26615/issn.2683-0078.2019_003",
    pages = "19--27",
    abstract = "While a number of studies have shown evidence of translationese phenomena, that is, statistical differences between original texts and translated texts (Gellerstam, 1986), results of studies searching for translationese features in postedited texts (what has been called {''}posteditese{''} (Daems et al., 2017)) have presented mixed results. This paper reports a preliminary study aimed at identifying the presence of post-editese features in machine-translated post-edited texts and at understanding how they differ from translationese features. We test the influence of factors such as post-editing (PE) levels (full vs. light), translation proficiency (professionals vs. students) and text domain (news vs. literary). Results show evidence of post-editese features, especially in light PE texts and in certain domains.",
}
@inproceedings{czulo-etal-2019-designing,
    title = "Designing a Frame-Semantic Machine Translation Evaluation Metric",
    author = "Czulo, Oliver  and
      Torrent, Tiago Timponi  and
      Matos, Ely Edison da Silva  and
      Diniz da Costa, Alexandre  and
      Kar, Debanjana",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8704",
    doi = "10.26615/issn.2683-0078.2019_004",
    pages = "28--35",
    abstract = "We propose a metric for machine translation evaluation based on frame semantics which does not require the use of reference translations or human corrections, but is aimed at comparing original and translated output directly. The metrics is described on the basis of an existing manual frame-semantic annotation of a parallel corpus with an English original and a Brazilian Portuguese and a German translation. We discuss implications of our metrics design, including the potential of scaling it for multiple languages.",
}
@inproceedings{escribe-2019-human,
    title = "Human Evaluation of Neural Machine Translation: The Case of Deep Learning",
    author = "Escribe, Marie",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8705",
    doi = "10.26615/issn.2683-0078.2019_005",
    pages = "36--46",
    abstract = "Recent advances in artificial neural networks now have a great impact on translation technology. A considerable achievement was reached in this field with the publication of L{'}Apprentissage Profond. This book, originally written in English (Deep Learning), was entirely machine-translated into French and post-edited by several experts. In this context, it appears essential to have a clear vision of the performance of MT tools. Providing an evaluation of NMT is precisely the aim of the present research paper. To accomplish this objective, a framework for error categorisation was built and a comparative analysis of the raw translation output and the post-edited version was performed with the purpose of identifying recurring patterns of errors. The findings showed that even though some grammatical errors were spotted, the output was generally correct from a linguistic point of view. The most recurring errors are linked to the specialised terminology employed in this book. Further errors include parts of text that were not translated as well as edits based on stylistic preferences. The major part of the output was not acceptable as such and required several edits per segment, but some sentences were of publishable quality and were therefore left untouched in the final version.",
}
@inproceedings{kunilovskaya-lapshinova-koltunski-2019-translationese,
    title = "Translationese Features as Indicators of Quality in {E}nglish-{R}ussian Human Translation",
    author = "Kunilovskaya, Maria  and
      Lapshinova-Koltunski, Ekaterina",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8706",
    doi = "10.26615/issn.2683-0078.2019_006",
    pages = "47--56",
    abstract = "We use a range of morpho-syntactic features inspired by research in register studies (e.g. Biber, 1995; Neumann, 2013) and translation studies (e.g. Ilisei et al., 2010; Zanettin, 2013; Kunilovskaya and Kutuzov, 2018) to reveal the association between translationese and human translation quality. Translationese is understood as any statistical deviations of translations from non-translations (Baker, 1993) and is assumed to affect the fluency of translations, rendering them foreign-sounding and clumsy of wording and structure. This connection is often posited or implied in the studies of translationese or translational varieties (De Sutter et al., 2017), but is rarely directly tested. Our 45 features include frequencies of selected morphological forms and categories, some types of syntactic structures and relations, as well as several overall text measures extracted from Universal Dependencies annotation. The research corpora include English-to-Russian professional and student translations of informational or argumentative newspaper texts and a comparable corpus of non-translated Russian. Our results indicate lack of direct association between translationese and quality in our data: while our features distinguish translations and non-translations with the near perfect accuracy, the performance of the same algorithm on the quality classes barely exceeds the chance level.",
}
@inproceedings{miller-2019-punsters,
    title = "The Punster{'}s Amanuensis: The Proper Place of Humans and Machines in the Translation of Wordplay",
    author = "Miller, Tristan",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8707",
    doi = "10.26615/issn.2683-0078.2019_007",
    pages = "57--65",
    abstract = "The translation of wordplay is one of the most extensively researched problems in translation studies, but it has attracted little attention in the fields of natural language processing and machine translation. This is because today{'}s language technologies treat anomalies and ambiguities in the input as things that must be resolved in favour of a single {``}correct{''} interpretation, rather than preserved and interpreted in their own right. But if computers cannot yet process such creative language on their own, can they at least provide specialized support to translation professionals? In this paper, I survey the state of the art relevant to computational processing of humorous wordplay and put forth a vision of how existing theories, resources, and technologies could be adapted and extended to support interactive, computer-assisted translation.",
}
@inproceedings{mouratidis-kermanidis-2019-comparing,
    title = "Comparing a Hand-crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation",
    author = "Mouratidis, Despoina  and
      Kermanidis, Katia Lida",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8708",
    doi = "10.26615/issn.2683-0078.2019_008",
    pages = "66--74",
    abstract = "The automatic evaluation of machine translation (MT) has proven to be a very significant research topic. Most automatic evaluation methods focus on the evaluation of the output of MT as they compute similarity scores that represent translation quality. This work targets on the performance of MT evaluation. We present a general scheme for learning to classify parallel translations, using linguistic information, of two MT model outputs and one human (reference) translation. We present three experiments to this scheme using neural networks (NN). One using string based hand-crafted features (Exp1), the second using automatically trained embeddings from the reference and the two MT outputs (one from a statistical machine translation (SMT) model and the other from a neural ma-chine translation (NMT) model), which are learned using NN (Exp2), and the third experiment (Exp3) that combines information from the other two experiments. The languages involved are English (EN), Greek (GR) and Italian (IT) segments are educational in domain. The proposed language-independent learning scheme which combines information from the two experiments (experiment 3) achieves higher classification accuracy compared with models using BLEU score information as well as other classification approaches, such as Random Forest (RF) and Support Vector Machine (SVM).",
}
@inproceedings{mutal-etal-2019-differences,
    title = "Differences between {SMT} and {NMT} Output - a Translators{'} Point of View",
    author = "Mutal, Jonathan  and
      Volkart, Lise  and
      Bouillon, Pierrette  and
      Girletti, Sabrina  and
      Estrella, Paula",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8709",
    doi = "10.26615/issn.2683-0078.2019_009",
    pages = "75--81",
    abstract = "In this study, we compare the output quality of two MT systems, a statistical (SMT) and a neural (NMT) engine, customised for Swiss Post{'}s Language Service using the same training data. We focus on the point of view of professional translators and investigate how they perceive the differences between the MT output and a human reference (namely deletions, substitutions, insertions and word order). Our findings show that translators more frequently consider these differences to be errors in SMT than NMT, and that deletions are the most serious errors in both architectures. We also observe lower agreement on differences to be corrected in NMT than in SMT, suggesting that errors are easier to identify in SMT. These findings confirm the ability of NMT to produce correct paraphrases, which could also explain why BLEU is often considered as an inadequate metric to evaluate the performance of NMT systems.",
}
@inproceedings{pan-2019-chinese,
    title = "The {C}hinese/{E}nglish Political Interpreting Corpus ({CEPIC}): A New Electronic Resource for Translators and Interpreters",
    author = "Pan, Jun",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8710",
    doi = "10.26615/issn.2683-0078.2019_010",
    pages = "82--88",
    abstract = "The Chinese/English Political Interpreting Corpus (CEPIC) is a new electronic and open access resource developed for translators and interpreters, especially those working with political text types. Over 6 million word tokens in size, the online corpus consists of transcripts of Chinese (Cantonese {\&} Putonghua) / English political speeches and their translated and interpreted texts. It includes rich meta-data and is POS-tagged and annotated with prosodic and paralinguistic features that are of concern to spoken language and interpreting. The online platform of the CEPIC features main functions including Keyword Search, Word Collocation and Expanded Keyword in Context, which are illustrated in the paper. The CEPIC can shed light on online translation and interpreting corpora development in the future.",
}
@inproceedings{petrova-2019-translation,
    title = "Translation Quality Assessment Tools and Processes in Relation to {CAT} Tools",
    author = "Petrova, Viktoriya",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8711",
    doi = "10.26615/issn.2683-0078.2019_011",
    pages = "89--97",
    abstract = "Modern translation QA tools are the latest attempt to overcome the inevitable subjective component of human revisers. This paper analyzes the current situation in the translation industry in respect to those tools and their relationship with CAT tools. The adoption of international standards has set the basic frame that defines {``}quality{''}. Because of the clear impossibility to develop a universal QA tool, all of the existing ones have in common a wide variety of settings for the user to choose from. A brief comparison is made between most popular standalone QA tools. In order to verify their results in practice, QA outputs from two of those tools have been compared. Polls that cover a period of 12 years have been collected. Their participants explained what practices they adopted in order to guarantee quality.",
}
@inproceedings{stambolieva-2019-corpus,
    title = "Corpus Linguistics, Translation and Error Analysis",
    author = "Stambolieva, Maria",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8712",
    doi = "10.26615/issn.2683-0078.2019_012",
    pages = "98--104",
    abstract = "The paper presents a study of the French Imparfait and its functional equivalents in Bulgarian and English in view of applications in machine translation and error analysis. The aims of the study are: 1/ based on the analysis of a corpus of text, to validate/revise earlier research on the values of the French Imparfait, 2/ to define the contextual factors pointing to the realisation of one or another value of the forms, 3/ based on the analysis of aligned translations, to identify the translation equivalents of these values, 4/ to formulate translation rules, 5/ based on the analysis of the translation rules, to refine the annotation modules of the environment used {--} the NBU e-Platform for language teaching and research.",
}
@inproceedings{temnikova-etal-2019-human,
    title = "Human-Informed Speakers and Interpreters Analysis in the {WAW} Corpus and an Automatic Method for Calculating Interpreters{'} D{\'e}calage",
    author = "Temnikova, Irina  and
      Abdelali, Ahmed  and
      Djabri, Souhila  and
      Hedaya, Samy",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8713",
    doi = "10.26615/issn.2683-0078.2019_013",
    pages = "105--115",
    abstract = "This article presents a multi-faceted analysis of a subset of interpreted conference speeches from the WAW corpus for the English-Arabic language pair. We analyze several speakers and interpreters variables via manual annotation and automatic methods. We propose a new automatic method for calculating interpreters{'} d{\'e}calage based on Automatic Speech Recognition (ASR) and automatic alignment of named entities and content words between speaker and interpreter. The method is evaluated by two human annotators who have expertise in interpreting and Interpreting Studies and shows highly satisfactory results, accompanied with a high inter-annotator agreement. We provide insights about the relations of speakers{'} variables, interpreters{'} variables and d{\'e}calage and discuss them from Interpreting Studies and interpreting practice point of view. We had interesting findings about interpreters behavior which need to be extended to a large number of conference sessions in our future research.",
}
@inproceedings{tsou-etal-2019-towards,
    title = "Towards a Proactive {MWE} Terminological Platform for Cross-Lingual Mediation in the Age of Big Data",
    author = "Tsou, Benjamin K.  and
      Chow, Kapo  and
      Nie, Junru  and
      Yuan, Yuan",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8714",
    doi = "10.26615/issn.2683-0078.2019_014",
    pages = "116--121",
    abstract = "The emergence of China as a global economic power in the 21st Century has brought about surging needs for cross-lingual and cross-cultural mediation, typically performed by translators. Advances in Artificial Intelligence and Language Engineering have been bolstered by Machine learning and suitable Big Data cultivation. They have helped to meet some of the translator{'}s needs, though the technical specialists have not kept pace with the practical and expanding requirements in language mediation. One major technical and linguistic hurdle involves words outside the vocabulary of the translator or the lexical database he/she consults, especially Multi-Word Expressions (Compound Words) in technical subjects. A further problem is in the multiplicity of renditions of a term in the target language. This paper discusses a proactive approach following the successful extraction and application of sizable bilingual Multi-Word Expressions (Compound Words) for language mediation in technical subjects, which do not fall within the expertise of typical translators, who have inadequate appreciation of the range of new technical tools available to help him/her. Our approach draws on the personal reflections of translators and teachers of translation and is based on the prior R{\&}D efforts relating to 300,000 comparable Chinese-English patents. The subsequent protocol we have developed aims to be proactive in meeting four identified practical challenges in technical translation (e.g. patents). It has broader economic implication in the Age of Big Data (Tsou et al, 2015) and Trade War, as the workload, if not, the challenges, increasingly cannot be met by currently available front-line translators. We shall demonstrate how new tools can be harnessed to spearhead the application of language technology not only in language mediation but also in the {``}teaching{''} and {``}learning{''} of translation. It shows how a better appreciation of their needs may enhance the contributions of the technical specialists, and thus enhance the resultant synergetic benefits.",
}
@inproceedings{ustaszewski-2019-exploring,
    title = "Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross-Language Aligned Word Embeddings",
    author = "Ustaszewski, Michael",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8715",
    doi = "10.26615/issn.2683-0078.2019_015",
    pages = "122--128",
    abstract = "Neural machine translation (NMT) was shown to produce more fluent output than phrase-based statistical (PBMT) and rule-based machine translation (RBMT). However, improved fluency makes it more difficult for post editors to identify and correct adequacy errors, because unlike RBMT and SMT, in NMT adequacy errors are frequently not anticipated by fluency errors. Omissions and additions of content in otherwise flawlessly fluent NMT output are the most prominent types of such adequacy errors, which can only be detected with reference to source texts. This contribution explores the degree of semantic similarity between source texts, NMT output and post edited output. In this way, computational semantic similarity scores (cosine similarity) are related to human quality judgments. The analyses are based on publicly available NMT post editing data annotated for errors in three language pairs (EN-DE, EN-LV, EN-HR) with the Multidimensional Quality Metrics (MQM). Methodologically, this contribution tests whether cross-language aligned word embeddings as the sole source of semantic information mirror human error annotation.",
}
@inproceedings{ilieva-kancheva-2019-success,
    title = "The Success Story of Mitra Translations",
    author = "Ilieva, Mina  and
      Kancheva, Mariya",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8716",
    doi = "10.26615/issn.2683-0078.2019_016",
    pages = "129--133",
    abstract = "Technologies and their constant updates and innovative nature drastically and irreversibly transformed this small business into a leading brand on the translation market, along with just few other LSPs integrating translation software solutions. Now, we are constantly following the new developments in software updates and online platforms and we are successfully keeping up with any new trend in the field of translation, localization, transcreation, revision, post-editing, etc. Ultimately, we are positive that proper implementation of technology (with focus on quality, cost and time) and hard work are the stepping stones in the way to become a trusted translation services provider.",
}
@inproceedings{sgourou-2019-four,
    title = "The Four Stages of Machine Translation Acceptance in a Freelancer{'}s Life",
    author = "Sgourou, Maria",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8717",
    doi = "10.26615/issn.2683-0078.2019_017",
    pages = "134--135",
    abstract = "Technology is a big challenge and raises many questions and issues when it comes to its application in the translation process, but translation{'}s biggest problem is not technology; it is rather how technology is perceived by translators. MT developers and researchers should take into account this perception and move towards a more democratized approach to include the base of the translation industry and perhaps its more valuable asset, the translators.",
}
@inproceedings{zaretskaya-2019-optimising,
    title = "Optimising the Machine Translation Post-editing Workflow",
    author = "Zaretskaya, Anna",
    booktitle = "Proceedings of the Human-Informed Translation and Interpreting Technology Workshop (HiT-IT 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "Incoma Ltd., Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W19-8718",
    doi = "10.26615/issn.2683-0078.2019_018",
    pages = "136--139",
    abstract = "In this article, we describe how machine translation is used for post-editing at TransPerfect and the ways in which we optimise the workflow. This includes MT evaluation, MT engine customisation, leveraging MT suggestions compared to TM matches, and the lessons learnt from implementing MT at a large scale.",
}
@proceedings{ws-2019-international-natural-language,
    title = "Proceedings of the 12th International Conference on Natural Language Generation",
    editor = "van Deemter, Kees  and
      Lin, Chenghua  and
      Takamura, Hiroya",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8600",
}
@inproceedings{kohn-koller-2019-talking,
    title = "Talking about what is not there: Generating indefinite referring expressions in {M}inecraft",
    author = {K{\"o}hn, Arne  and
      Koller, Alexander},
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8601",
    doi = "10.18653/v1/W19-8601",
    pages = "1--10",
    abstract = "When generating technical instructions, it is often necessary to describe an object that does not exist yet. For example, an NLG system which explains how to build a house needs to generate sentences like {``}build *a wall of height five to your left*{''} and {``}now build *a wall on the other side*.{''} Generating (indefinite) referring expressions to objects that do not exist yet is fundamentally different from generating the usual definite referring expressions, because the new object must be distinguished from an infinite set of possible alternatives. We formalize this problem and present an algorithm for generating such expressions, in the context of generating building instructions within the Minecraft video game.",
}
@inproceedings{briggs-harner-2019-generating,
    title = "Generating Quantified Referring Expressions with Perceptual Cost Pruning",
    author = "Briggs, Gordon  and
      Harner, Hillary",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8602",
    doi = "10.18653/v1/W19-8602",
    pages = "11--18",
    abstract = "We model the production of quantified referring expressions (QREs) that identify collections of visual items. To address this task, we propose a method of perceptual cost pruning, which consists of two steps: (1) determine what subset of quantity information can be perceived given a time limit t, and (2) apply a preference order based REG algorithm (e.g., the Incremental Algorithm) to this reduced set of information. We demonstrate that this method successfully improves the human-likeness of the IA in the QRE generation task and successfully models human-generated language in most cases.",
}
@inproceedings{langner-2019-case,
    title = "A case study on context-bound referring expression generation",
    author = "Langner, Maurice",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8603",
    doi = "10.18653/v1/W19-8603",
    pages = "19--23",
    abstract = "In recent years, Bayesian models of referring expression generation have gained prominence in order to produce situationally more adequate referring expressions. Basically, these models enable the integration of different parameters into the decision process for using a specific referring expression like the cardinality of the object set, the configuration and complexity of the visual field, and the discriminatory power of available attributes that need to be combined with visual salience and personal preference. This paper describes and discusses the results of an empirical study on the production of referring expressions in visual fields with different object configurations of varying complexity and different contextual premises for using a referring expression. The visual fields are set up using data from the TUNA experiment with plain random or pragmatically enriched configurations which allow for target inference. Different categories of the situational contexts, in which the referring expressions are produced, provide different degrees of cooperativeness, so that generation quality and its relations to contextual user intention can be observed. The results of the study suggest that Bayesian approaches must integrate individual generation preference and the cooperativeness of the situational task in order to model the broad variance between speakers more adequately.",
}
@inproceedings{fu-etal-2019-rethinking,
    title = "Rethinking Text Attribute Transfer: A Lexical Analysis",
    author = "Fu, Yao  and
      Zhou, Hao  and
      Chen, Jiaze  and
      Li, Lei",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8604",
    doi = "10.18653/v1/W19-8604",
    pages = "24--33",
    abstract = "Text attribute transfer is modifying certain linguistic attributes (e.g. sentiment, style, author-ship, etc.) of a sentence and transforming them from one type to another. In this paper, we aim to analyze and interpret what is changed during the transfer process. We start from the observation that in many existing models and datasets, certain words within a sentence play important roles in determining the sentence attribute class. These words are referred as the Pivot Words. Based on these pivot words, we propose a lexical analysis framework, the Pivot Analysis, to quantitatively analyze the effects of these words in text attribute classification and transfer. We apply this framework to existing datasets and models and show that: (1) the pivot words are strong features for the classification of sentence attributes; (2) to change the attribute of a sentence, many datasets only requires to change certain pivot words; (3) consequently, many transfer models only perform the lexical-level modification,while leaving higher-level sentence structures unchanged. Our work provides an in-depth understanding of linguistic attribute transfer and further identifies the future requirements and challenges of this task",
}
@inproceedings{li-etal-2019-choosing,
    title = "Choosing between Long and Short Word Forms in {M}andarin",
    author = "Li, Lin  and
      van Deemter, Kees  and
      Paperno, Denis  and
      Fan, Jingyu",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8605",
    doi = "10.18653/v1/W19-8605",
    pages = "34--39",
    abstract = "Between 80{\%} and 90{\%} of all Chinese words have long and short form such as 老虎/虎 (lao-hu/hu , tiger) (Duanmu:2013). Consequently, the choice between long and short forms is a key problem for lexical choice across NLP and NLG. Following an earlier work on abbreviations in English (Mahowald et al, 2013), we bring a probabilistic perspective to these questions, using both a behavioral and a corpus-based approach. We hypothesized that there is a higher probability of choosing short form in supportive context than in neutral context in Mandarin. Consistent with our prediction, our findings revealed that predictability of contexts makes effect on speakers{'} long and short form choice.",
}
@inproceedings{ito-etal-2019-diamonds,
    title = "Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance",
    author = "Ito, Takumi  and
      Kuribayashi, Tatsuki  and
      Kobayashi, Hayato  and
      Brassard, Ana  and
      Hagiwara, Masato  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8606",
    doi = "10.18653/v1/W19-8606",
    pages = "40--53",
    abstract = "The writing process consists of several stages such as drafting, revising, editing, and proofreading. Studies on writing assistance, such as grammatical error correction (GEC), have mainly focused on sentence editing and proofreading, where surface-level issues such as typographical errors, spelling errors, or grammatical errors should be corrected. We broaden this focus to include the earlier revising stage, where sentences require adjustment to the information included or major rewriting and propose Sentence-level Revision (SentRev) as a new writing assistance task. Well-performing systems in this task can help inexperienced authors by producing fluent, complete sentences given their rough, incomplete drafts. We build a new freely available crowdsourced evaluation dataset consisting of incomplete sentences authored by non-native writers paired with their final versions extracted from published academic papers for developing and evaluating SentRev models. We also establish baseline performance on SentRev using our newly built evaluation dataset.",
}
@inproceedings{el-baff-etal-2019-computational,
    title = "Computational Argumentation Synthesis as a Language Modeling Task",
    author = "El Baff, Roxanne  and
      Wachsmuth, Henning  and
      Al Khatib, Khalid  and
      Stede, Manfred  and
      Stein, Benno",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8607",
    doi = "10.18653/v1/W19-8607",
    pages = "54--64",
    abstract = "Synthesis approaches in computational argumentation so far are restricted to generating claim-like argument units or short summaries of debates. Ultimately, however, we expect computers to generate whole new arguments for a given stance towards some topic, backing up claims following argumentative and rhetorical considerations. In this paper, we approach such an argumentation synthesis as a language modeling task. In our language model, argumentative discourse units are the {``}words{''}, and arguments represent the {``}sentences{''}. Given a pool of units for any unseen topic-stance pair, the model selects a set of unit types according to a basic rhetorical strategy (logos vs. pathos), arranges the structure of the types based on the units{'} argumentative roles, and finally {``}phrases{''} an argument by instantiating the structure with semantically coherent units from the pool. Our evaluation suggests that the model can, to some extent, mimic the human synthesis of strategy-specific arguments.",
}
@inproceedings{yi-etal-2019-towards,
    title = "Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators",
    author = "Yi, Sanghyun  and
      Goel, Rahul  and
      Khatri, Chandra  and
      Cervone, Alessandra  and
      Chung, Tagyoung  and
      Hedayatnia, Behnam  and
      Venkatesh, Anu  and
      Gabriel, Raefer  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8608",
    doi = "10.18653/v1/W19-8608",
    pages = "65--75",
    abstract = "Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood (MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., {``}Maybe, I don{'}t know.{''} Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.",
}
@inproceedings{kulikov-etal-2019-importance,
    title = "Importance of Search and Evaluation Strategies in Neural Dialogue Modeling",
    author = "Kulikov, Ilia  and
      Miller, Alexander  and
      Cho, Kyunghyun  and
      Weston, Jason",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8609",
    doi = "10.18653/v1/W19-8609",
    pages = "76--87",
    abstract = "We investigate the impact of search strategies in neural dialogue modeling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate responses. We evaluate these strategies in realistic full conversations with humans and propose a model-based Bayesian calibration to address annotator bias. These conversations are analyzed using two automatic metrics: log-probabilities assigned by the model and utterance diversity. Our experiments reveal that better search algorithms lead to higher rated conversations. However, finding the optimal selection mechanism to choose from a more diverse set of candidates is still an open question.",
}
@inproceedings{santhanam-shaikh-2019-towards,
    title = "Towards Best Experiment Design for Evaluating Dialogue System Output",
    author = "Santhanam, Sashank  and
      Shaikh, Samira",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8610",
    doi = "10.18653/v1/W19-8610",
    pages = "88--94",
    abstract = "To overcome the limitations of automated metrics (e.g. BLEU, METEOR) for evaluating dialogue systems, researchers typically use human judgments to provide convergent evidence. While it has been demonstrated that human judgments can suffer from the inconsistency of ratings, extant research has also found that the design of the evaluation task affects the consistency and quality of human judgments. We conduct a between-subjects study to understand the impact of four experiment conditions on human ratings of dialogue system output. In addition to discrete and continuous scale ratings, we also experiment with a novel application of Best-Worst scaling to dialogue evaluation. Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design. Additionally, we find that factors such as time taken to complete the task and no prior experience of participating in similar studies of rating dialogue system output positively impact consistency and agreement amongst raters.",
}
@inproceedings{rao-etal-2019-tree,
    title = "A Tree-to-Sequence Model for Neural {NLG} in Task-Oriented Dialog",
    author = "Rao, Jinfeng  and
      Upasani, Kartikeya  and
      Balakrishnan, Anusha  and
      White, Michael  and
      Kumar, Anuj  and
      Subba, Rajen",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8611",
    doi = "10.18653/v1/W19-8611",
    pages = "95--100",
    abstract = "Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. Sequence-to-sequence models on flat meaning representations (MR) have been dominant in this task, for example in the E2E NLG Challenge. Previous work has shown that a tree-structured MR can improve the model for better discourse-level structuring and sentence-level planning. In this work, we propose a tree-to-sequence model that uses a tree-LSTM encoder to leverage the tree structures in the input MR, and further enhance the decoding by a structure-enhanced attention mechanism. In addition, we explore combining these enhancements with constrained decoding to improve semantic correctness. Our experiments not only show significant improvements over standard seq2seq baselines, but also is more data-efficient and generalizes better to hard scenarios.",
}
@inproceedings{iwama-kano-2019-multiple,
    title = "Multiple News Headlines Generation using Page Metadata",
    author = "Iwama, Kango  and
      Kano, Yoshinobu",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8612",
    doi = "10.18653/v1/W19-8612",
    pages = "101--105",
    abstract = "Multiple headlines of a newspaper article have an important role to express the content of the article accurately and concisely. A headline depends on the content and intent of their article. While a single headline expresses the whole corresponding article, each of multiple headlines expresses different information individually. We suggest automatic generation method of such a diverse multiple headlines in a newspaper. Our generation method is based on the Pointer-Generator Network, using page metadata on a newspaper which can change headline generation behavior. This page metadata includes headline location, headline size, article page number, etc. In a previous related work, ensemble of three different generation models was performed to obtain a single headline, where each generation model generates a single headline candidate. In contrast, we use a single model to generate multiple headlines. We conducted automatic evaluations for generated headlines. The results show that our method improved ROUGE-1 score by 4.32 points higher than baseline. These results suggest that our model using page metadata can generate various multiple headlines for an article In better performance.",
}
@inproceedings{sasazawa-etal-2019-neural,
    title = "Neural Question Generation using Interrogative Phrases",
    author = "Sasazawa, Yuichi  and
      Takase, Sho  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8613",
    doi = "10.18653/v1/W19-8613",
    pages = "106--111",
    abstract = "Question Generation (QG) is the task of generating questions from a given passage. One of the key requirements of QG is to generate a question such that it results in a target answer. Previous works used a target answer to obtain a desired question. However, we also want to specify how to ask questions and improve the quality of generated questions. In this study, we explore the use of interrogative phrases as additional sources to control QG. By providing interrogative phrases, we expect that QG can generate a more reliable sequence of words subsequent to an interrogative phrase. We present a baseline sequence-to-sequence model with the attention, copy, and coverage mechanisms, and show that the simple baseline achieves state-of-the-art performance. The experiments demonstrate that interrogative phrases contribute to improving the performance of QG. In addition, we report the superiority of using interrogative phrases in human evaluation. Finally, we show that a question answering system can provide target answers more correctly when the questions are generated with interrogative phrases.",
}
@inproceedings{colin-gardent-2019-generating,
    title = "Generating Text from Anonymised Structures",
    author = "Colin, Emilie  and
      Gardent, Claire",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8614",
    doi = "10.18653/v1/W19-8614",
    pages = "112--117",
    abstract = "Surface realisation (SR) consists in generating a text from a meaning representations (MR). In this paper, we introduce a new parallel dataset of deep meaning representations (MR) and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model{'}s ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation.",
}
@inproceedings{niklaus-etal-2019-minwikisplit,
    title = "{M}in{W}iki{S}plit: A Sentence Splitting Corpus with Minimal Propositions",
    author = "Niklaus, Christina  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8615",
    doi = "10.18653/v1/W19-8615",
    pages = "118--123",
    abstract = "We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences. Contrary to previously proposed text simplification corpora, which contain only a small number of split examples, we present a dataset where each input sentence is broken down into a set of minimal propositions, i.e. a sequence of sound, self-contained utterances with each of them presenting a minimal semantic unit that cannot be further decomposed into meaningful propositions. This corpus is useful for developing sentence splitting approaches that learn how to transform sentences with a complex linguistic structure into a fine-grained representation of short sentences that present a simple and more regular structure which is easier to process for downstream applications and thus facilitates and improves their performance.",
}
@inproceedings{chen-etal-2019-qtuna,
    title = "{QTUNA}: A Corpus for Understanding How Speakers Use Quantification",
    author = "Chen, Guanyi  and
      van Deemter, Kees  and
      Pagliaro, Silvia  and
      Smalbil, Louk  and
      Lin, Chenghua",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8616",
    doi = "10.18653/v1/W19-8616",
    pages = "124--129",
    abstract = "A prominent strand of work in formal semantics investigates the ways in which human languages quantify over the elements of a set, as when we say {``}\textit{All \textit{A} are \textit{B} }{''}, {``}\textit{All except two \textit{A} are \textit{B} }{''}, {``}\textit{Only a few of the \textit{A} are \textit{B} }{''} and so on. Our aim is to build Natural Language Generation algorithms that mimic humans{'} use of quantified expressions. To inform these algorithms, we conducted on a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions. We discuss how these experiments were conducted and what corpora they gave rise to. We conduct an informal analysis of the corpora, and offer an initial assessment of the challenges that these corpora pose for Natural Language Generation. The dataset is available at: \url{https://github.com/a-quei/qtuna}.",
}
@inproceedings{gallina-etal-2019-kptimes,
    title = "{KPT}imes: A Large-Scale Dataset for Keyphrase Generation on News Documents",
    author = "Gallina, Ygor  and
      Boudin, Florian  and
      Daille, Beatrice",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8617",
    doi = "10.18653/v1/W19-8617",
    pages = "130--135",
    abstract = "Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https:// github.com/ygorg/KPTimes.",
}
@inproceedings{han-zarriess-2019-sketch,
    title = "Sketch Me if You Can: Towards Generating Detailed Descriptions of Object Shape by Grounding in Images and Drawings",
    author = "Han, Ting  and
      Zarrie{\ss}, Sina",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8618",
    doi = "10.18653/v1/W19-8618",
    pages = "136--140",
    abstract = "A lot of recent work in Language {\&} Vision has looked at generating descriptions or referring expressions for objects in scenes of real-world images, though focusing mostly on relatively simple language like object names, color and location attributes (e.g., brown chair on the left). This paper presents work on Draw-and-Tell, a dataset of detailed descriptions for common objects in images where annotators have produced fine-grained attribute-centric expressions distinguishing a target object from a range of similar objects. Additionally, the dataset comes with hand-drawn sketches for each object. As Draw-and-Tell is medium-sized and contains a rich vocabulary, it constitutes an interesting challenge for CNN-LSTM architectures used in state-of-the-art image captioning models. We explore whether the additional modality given through sketches can help such a model to learn to accurately ground detailed language referring expressions to object shapes. Our results are encouraging.",
}
@inproceedings{nie-etal-2019-encoder,
    title = "An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation",
    author = "Nie, Feng  and
      Wang, Jinpeng  and
      Pan, Rong  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8619",
    doi = "10.18653/v1/W19-8619",
    pages = "141--146",
    abstract = "Data-to-text generation aims to generate descriptions given a structured input data (i.e., a table with multiple records). Existing neural methods for encoding input data can be divided into two categories: a) pooling based encoders which ignore dependencies between input records or b) recurrent encoders which model only sequential dependencies between input records. In our investigation, although the recurrent encoder generally outperforms the pooling based encoder by learning the sequential dependencies, it is sensitive to the order of the input records (i.e., performance decreases when injecting the random shuffling noise over input data). To overcome this problem, we propose to adopt the self-attention mechanism to learn dependencies between arbitrary input records. Experimental results show the proposed method achieves comparable results and remains stable under random shuffling over input data.",
}
@inproceedings{raunak-etal-2019-leveraging,
    title = "On Leveraging the Visual Modality for Neural Machine Translation",
    author = "Raunak, Vikas  and
      Choe, Sang Keun  and
      Lu, Quanyang  and
      Xu, Yi  and
      Metze, Florian",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8620",
    doi = "10.18653/v1/W19-8620",
    pages = "147--151",
    abstract = "Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics. Recently, Caglayan et al. posit that the observed gains are limited mainly due to the very simple, short, repetitive sentences of the Multi30k dataset (the only multimodal MT dataset available at the time), which renders the source text sufficient for context. In this work, we further investigate this hypothesis on a new large scale multimodal Machine Translation (MMT) dataset, How2, which has 1.57 times longer mean sentence length than Multi30k and no repetition. We propose and evaluate three novel fusion techniques, each of which is designed to ensure the utilization of visual context at different stages of the Sequence-to-Sequence transduction pipeline, even under full linguistic context. However, we still obtain only marginal gains under full linguistic context and posit that visual embeddings extracted from deep vision models (ResNet for Multi30k, ResNext for How2) do not lend themselves to increasing the discriminativeness between the vocabulary elements at token level prediction in NMT. We demonstrate this qualitatively by analyzing attention distribution and quantitatively through Principal Component Analysis, arriving at the conclusion that it is the quality of the visual embeddings rather than the length of sentences, which need to be improved in existing MMT datasets.",
}
@inproceedings{ilinykh-etal-2019-tell,
    title = "Tell Me More: A Dataset of Visual Scene Description Sequences",
    author = "Ilinykh, Nikolai  and
      Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8621",
    doi = "10.18653/v1/W19-8621",
    pages = "152--157",
    abstract = "We present a dataset consisting of what we call image description sequences, which are multi-sentence descriptions of the contents of an image. These descriptions were collected in a pseudo-interactive setting, where the describer was told to describe the given image to a listener who needs to identify the image within a set of images, and who successively asks for more information. As we show, this setup produced nicely structured data that, we think, will be useful for learning models capable of planning and realising such description discourses.",
}
@inproceedings{chen-yao-2019-closer,
    title = "A Closer Look at Recent Results of Verb Selection for Data-to-Text {NLG}",
    author = "Chen, Guanyi  and
      Yao, Jin-Ge",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8622",
    doi = "10.18653/v1/W19-8622",
    pages = "158--163",
    abstract = "Automatic natural language generation systems need to use the contextually-appropriate verbs when describing different kinds of facts or events, which has triggered research interest on verb selection for data-to-text generation. In this paper, we discuss a few limitations of the current task settings and the evaluation metrics. We also provide two simple, efficient, interpretable baseline approaches for statistical selection of trend verbs, which give a strong performance on both previously used evaluation metrics and our new evaluation.",
}
@inproceedings{juraska-etal-2019-viggo,
    title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation",
    author = "Juraska, Juraj  and
      Bowden, Kevin  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8623",
    doi = "10.18653/v1/W19-8623",
    pages = "164--172",
    abstract = "The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models. The existing data-to-text datasets are, however, aimed at task-oriented dialogue systems, and often thus limited in diversity and versatility. They are typically crowdsourced, with much of the noise left in them. Moreover, current neural NLG models do not take full advantage of large training data, and due to their strong generalizing properties produce sentences that look template-like regardless. We therefore present a new corpus of 7K samples, which (1) is clean despite being crowdsourced, (2) has utterances of 9 generalizable and conversational dialogue act types, making it more suitable for open-domain dialogue systems, and (3) explores the domain of video games, which is new to dialogue systems despite having excellent potential for supporting rich conversations.",
}
@inproceedings{chan-fan-2019-bert,
    title = "{BERT} for Question Generation",
    author = "Chan, Ying-Hong  and
      Fan, Yao-Chung",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8624",
    doi = "10.18653/v1/W19-8624",
    pages = "173--177",
    abstract = "In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks. We introduce two neural architectures built on top of BERT for question generation tasks. The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. And, the second one remedies the first one by restructuring the BERT employment into a sequential manner for taking information from previous decoded results. Our models are trained and evaluated on the question-answering dataset SQuAD. Experiment results show that our best model yields state-of-the-art performance which advances the BLEU4 score of existing best models from 16.85 to 18.91.",
}
@inproceedings{jafaritazehjani-etal-2019-visually,
    title = "Visually grounded generation of entailments from premises",
    author = "Jafaritazehjani, Somayeh  and
      Gatt, Albert  and
      Tanti, Marc",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8625",
    doi = "10.18653/v1/W19-8625",
    pages = "178--188",
    abstract = "Natural Language Inference (NLI) is the task of determining the semantic relationship between a premise and a hypothesis. In this paper, we focus on the generation of hypotheses from premises in a multimodal setting, to generate a sentence (hypothesis) given an image and/or its description (premise) as the input. The main goals of this paper are (a) to investigate whether it is reasonable to frame NLI as a generation task; and (b) to consider the degree to which grounding textual premises in visual information is beneficial to generation. We compare different neural architectures, showing through automatic and human evaluation that entailments can indeed be generated successfully. We also show that multimodal models outperform unimodal models in this task, albeit marginally",
}
@inproceedings{nguyen-son-etal-2019-detecting,
    title = "Detecting Machine-Translated Text using Back Translation",
    author = "Nguyen-Son, Hoang-Quoc  and
      Tran Phuong, Thao  and
      Hidano, Seira  and
      Kiyomoto, Shinsaku",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8626",
    doi = "10.18653/v1/W19-8626",
    pages = "189--197",
    abstract = "Machine-translated text plays a crucial role in the communication of people using different languages. However, adversaries can use such text for malicious purposes such as plagiarism and fake review. The existing methods detected a machine-translated text only using the text{'}s intrinsic content, but they are unsuitable for classifying the machine-translated and human-written texts with the same meanings. We have proposed a method to extract features used to distinguish machine/human text based on the similarity between the intrinsic text and its back-translation. The evaluation of detecting translated sentences with French shows that our method achieves 75.0{\%} of both accuracy and F-score. It outperforms the existing methods whose the best accuracy is 62.8{\%} and the F-score is 62.7{\%}. The proposed method even detects more efficiently the back-translated text with 83.4{\%} of accuracy, which is higher than 66.7{\%} of the best previous accuracy. We also achieve similar results not only with F-score but also with similar experiments related to Japanese. Moreover, we prove that our detector can recognize both machine-translated and machine-back-translated texts without the language information which is used to generate these machine texts. It demonstrates the persistence of our method in various applications in both low- and rich-resource languages.",
}
@inproceedings{kawano-etal-2019-neural,
    title = "Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label-aware Objective",
    author = "Kawano, Seiya  and
      Yoshino, Koichiro  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8627",
    doi = "10.18653/v1/W19-8627",
    pages = "198--207",
    abstract = "Building a controllable neural conversation model (NCM) is an important task. In this paper, we focus on controlling the responses of NCMs by using dialogue act labels of responses as conditions. We introduce an adversarial learning framework for the task of generating conditional responses with a new objective to a discriminator, which explicitly distinguishes sentences by using labels. This change strongly encourages the generation of label-conditioned sentences. We compared the proposed method with some existing methods for generating conditional responses. The experimental results show that our proposed method has higher controllability for dialogue acts even though it has higher or comparable naturalness to existing methods.",
}
@inproceedings{gero-etal-2019-low,
    title = "Low Level Linguistic Controls for Style Transfer and Content Preservation",
    author = "Gero, Katy  and
      Kedzie, Chris  and
      Reeve, Jonathan  and
      Chilton, Lydia",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8628",
    doi = "10.18653/v1/W19-8628",
    pages = "208--218",
    abstract = "Despite the success of style transfer in image processing, it has seen limited progress in natural language generation. Part of the problem is that content is not as easily decoupled from style in the text domain. Curiously, in the field of stylometry, content does not figure prominently in practical methods of discriminating stylistic elements, such as authorship and genre. Rather, syntax and function words are the most salient features. Drawing on this work, we model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions. We train a neural encoder-decoder model to reconstruct reference sentences given only content words and the setting of the controls. We perform style transfer by keeping the content words fixed while adjusting the controls to be indicative of another style. In experiments, we show that the model reliably responds to the linguistic controls and perform both automatic and manual evaluations on style transfer. We find we can fool a style classifier 84{\%} of the time, and that our model produces highly diverse and stylistically distinctive outputs. This work introduces a formal, extendable model of style that can add control to any neural text generation system.",
}
@inproceedings{poncelas-way-2019-selecting,
    title = "Selecting Artificially-Generated Sentences for Fine-Tuning Neural Machine Translation",
    author = "Poncelas, Alberto  and
      Way, Andy",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8629",
    doi = "10.18653/v1/W19-8629",
    pages = "219--228",
    abstract = "Neural Machine Translation (NMT) models tend to achieve the best performances when larger sets of parallel sentences are provided for training. For this reason, augmenting the training set with artificially-generated sentence pair can boost the performance. Nonetheless, the performance can also be improved with a small number of sentences if they are in the same domain as the test set. Accordingly, we want to explore the use of artificially-generated sentence along with data-selection algorithms to improve NMT models trained solely with authentic data. In this work, we show how artificially-generated sentences can be more beneficial than authentic pairs and what are their advantages when used in combination with data-selection algorithms.",
}
@inproceedings{cano-bojar-2019-efficiency,
    title = "Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study",
    author = "{\c{C}}ano, Erion  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8630",
    doi = "10.18653/v1/W19-8630",
    pages = "229--239",
    abstract = "Using data-driven models for solving text summarization or similar tasks has become very common in the last years. Yet most of the studies report basic accuracy scores only, and nothing is known about the ability of the proposed models to improve when trained on more data. In this paper, we define and propose three data efficiency metrics: data score efficiency, data time deficiency and overall data efficiency. We also propose a simple scheme that uses those metrics and apply it for a more comprehensive evaluation of popular methods on text summarization and title generation tasks. For the latter task, we process and release a huge collection of 35 million abstract-title pairs from scientific articles. Our results reveal that among the tested models, the Transformer is the most efficient on both tasks.",
}
@inproceedings{kolkey-etal-2019-nlg,
    title = "An {NLG} System for Constituent Correspondence: Personality, Affect, and Alignment",
    author = "Kolkey, William  and
      Dong, Jian  and
      Bybee, Greg",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8631",
    doi = "10.18653/v1/W19-8631",
    pages = "240--243",
    abstract = "Roughly 30{\%} of congressional staffers in the United States report spending a {``}great deal{''} of time writing responses to constituent letters. Letters often solicit an update on the status of legislation and a description of a congressman{'}s vote record or vote intention {---} structurable data that can be leveraged by a natural language generation (NLG) system to create a coherent letter response. This paper describes how PoliScribe, a pipeline-architectured NLG platform, constructs personalized responses to constituents inquiring about legislation. Emphasis will be placed on adapting NLG methodologies to the political domain, which entails special attention to affect, discursive variety, and rhetorical strategies that align a speaker with their interlocutor, even in cases of policy disagreement.",
}
@inproceedings{rizvi-etal-2019-margin,
    title = "Margin Call: an Accessible Web-based Text Viewer with Generated Paragraph Summaries in the Margin",
    author = "Rizvi, Naba  and
      Gehrmann, Sebastian  and
      Wang, Lidan  and
      Dernoncourt, Franck",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8632",
    doi = "10.18653/v1/W19-8632",
    pages = "244--246",
    abstract = "We present Margin Call, a web-based text viewer that automatically generates short summaries for each paragraph of the text and displays the summaries in the margin of the text next to the corresponding paragraph. On the back-end, the summarizer first identifies the most important sentence for each paragraph in the text file uploaded by the user. The selected sentence is then automatically compressed to produce the short summary. The resulting summary is a few words long. The displayed summaries can help the user understand and retrieve information faster from the text, while increasing the retention of information.",
}
@inproceedings{xu-etal-2019-privacy,
    title = "Privacy-Aware Text Rewriting",
    author = "Xu, Qiongkai  and
      Qu, Lizhen  and
      Xu, Chenchen  and
      Cui, Ran",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8633",
    doi = "10.18653/v1/W19-8633",
    pages = "247--257",
    abstract = "Biased decisions made by automatic systems have led to growing concerns in research communities. Recent work from the NLP community focuses on building systems that make fair decisions based on text. Instead of relying on unknown decision systems or human decision-makers, we argue that a better way to protect data providers is to remove the trails of sensitive information before publishing the data. In light of this, we propose a new privacy-aware text rewriting task and explore two privacy-aware back-translation methods for the task, based on adversarial training and approximate fairness risk. Our extensive experiments on three real-world datasets with varying demographical attributes show that our methods are effective in obfuscating sensitive attributes. We have also observed that the fairness risk method retains better semantics and fluency, while the adversarial training method tends to leak less sensitive information.",
}
@inproceedings{lee-yeung-2019-personalized,
    title = "Personalized Substitution Ranking for Lexical Simplification",
    author = "Lee, John  and
      Yeung, Chak Yan",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8634",
    doi = "10.18653/v1/W19-8634",
    pages = "258--267",
    abstract = "A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user{'}s vocabulary proficiency, and always aim for the simplest candidate. This approach may overlook less-simple candidates that the user can understand, and that are semantically closer to the original word. We propose a personalized approach for Substitution Ranking to identify the candidate that is the closest synonym and is non-complex for the user. In experiments on learners of English at different proficiency levels, we show that this approach enhances the semantic faithfulness of the output, at the cost of a relatively small increase in the number of complex words.",
}
@inproceedings{puzikov-etal-2019-revisiting,
    title = "Revisiting the Binary Linearization Technique for Surface Realization",
    author = "Puzikov, Yevgeniy  and
      Gardent, Claire  and
      Dagan, Ido  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8635",
    doi = "10.18653/v1/W19-8635",
    pages = "268--278",
    abstract = "End-to-end neural approaches have achieved state-of-the-art performance in many natural language processing (NLP) tasks. Yet, they often lack transparency of the underlying decision-making process, hindering error analysis and certain model improvements. In this work, we revisit the binary linearization approach to surface realization, which exhibits more interpretable behavior, but was falling short in terms of prediction accuracy. We show how enriching the training data to better capture word order constraints almost doubles the performance of the system. We further demonstrate that encoding both local and global prediction contexts yields another considerable performance boost. With the proposed modifications, the system which ranked low in the latest shared task on multilingual surface realization now achieves best results in five out of ten languages, while being on par with the state-of-the-art approaches in others.",
}
@inproceedings{yu-etal-2019-head,
    title = "Head-First Linearization with Tree-Structured Representation",
    author = "Yu, Xiang  and
      Falenska, Agnieszka  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8636",
    doi = "10.18653/v1/W19-8636",
    pages = "279--289",
    abstract = "We present a dependency tree linearization model with two novel components: (1) a tree-structured encoder based on bidirectional Tree-LSTM that propagates information first bottom-up then top-down, which allows each token to access information from the entire tree; and (2) a linguistically motivated head-first decoder that emphasizes the central role of the head and linearizes the subtree by incrementally attaching the dependents on both sides of the head. With the new encoder and decoder, we reach state-of-the-art performance on the Surface Realization Shared Task 2018 dataset, outperforming not only the shared tasks participants, but also previous state-of-the-art systems (Bohnet et al., 2011; Puduppully et al., 2016). Furthermore, we analyze the power of the tree-structured encoder with a probing task and show that it is able to recognize the topological relation between any pair of tokens in a tree.",
}
@inproceedings{hamalainen-alnajjar-2019-lets,
    title = "Let{'}s {FACE} it. {F}innish Poetry Generation with Aesthetics and Framing",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8637",
    doi = "10.18653/v1/W19-8637",
    pages = "290--300",
    abstract = "We present a creative poem generator for the morphologically rich Finnish language. Our method falls into the master-apprentice paradigm, where a computationally creative genetic algorithm teaches a BRNN model to generate poetry. We model several parts of poetic aesthetics in the fitness function of the genetic algorithm, such as sonic features, semantic coherence, imagery and metaphor. Furthermore, we justify the creativity of our method based on the FACE theory on computational creativity and take additional care in evaluating our system by automatic metrics for concepts together with human evaluation for aesthetics, framing and expressions.",
}
@inproceedings{manjavacas-etal-2019-generation,
    title = "Generation of Hip-Hop Lyrics with Hierarchical Modeling and Conditional Templates",
    author = "Manjavacas, Enrique  and
      Kestemont, Mike  and
      Karsdorp, Folgert",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8638",
    doi = "10.18653/v1/W19-8638",
    pages = "301--310",
    abstract = "This paper addresses Hip-Hop lyric generation with conditional Neural Language Models. We develop a simple yet effective mechanism to extract and apply conditional templates from text snippets, and show{---}on the basis of a large-scale crowd-sourced manual evaluation{---}that these templates significantly improve the quality and realism of the generated snippets. Importantly, the proposed approach enables end-to-end training, targeting formal properties of text such as rhythm and rhyme, which are central characteristics of rap texts. Additionally, we explore how generating text at different scales (e.g. character-level or word-level) affects the quality of the output. We find that a hybrid form{---}a hierarchical model that aims to integrate Language Modeling at both word and character-level scales{---}yields significant improvements in text quality, yet surprisingly, cannot exploit conditional templates to their fullest extent. Our findings highlight that text generation models based on Recurrent Neural Networks (RNN) are sensitive to the modeling scale and call for further research on the observed differences in effectiveness of the conditioning mechanism at different scales.",
}
@inproceedings{wang-2019-revisiting,
    title = "Revisiting Challenges in Data-to-Text Generation with Fact Grounding",
    author = "Wang, Hongmin",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8639",
    doi = "10.18653/v1/W19-8639",
    pages = "311--322",
    abstract = "Data-to-text generation models face challenges in ensuring data fidelity by referring to the correct input source. To inspire studies in this area, Wiseman et al. (2017) introduced the RotoWire corpus on generating NBA game summaries from the box- and line-score tables. However, limited attempts have been made in this direction and the challenges remain. We observe a prominent bottleneck in the corpus where only about 60{\%} of the summary contents can be grounded to the boxscore records. Such information deficiency tends to misguide a conditioned language model to produce unconditioned random facts and thus leads to factual hallucinations. In this work, we restore the information balance and revamp this task to focus on fact-grounded data-to-text generation. We introduce a purified and larger-scale dataset, RotoWire-FG (Fact-Grounding), with 50{\%} more data from the year 2017-19 and enriched input tables, and hope to attract research focuses in this direction. Moreover, we achieve improved data fidelity over the state-of-the-art models by integrating a new form of table reconstruction as an auxiliary task to boost the generation quality.",
}
@inproceedings{aoki-etal-2019-controlling,
    title = "Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels",
    author = "Aoki, Kasumi  and
      Miyazawa, Akira  and
      Ishigaki, Tatsuya  and
      Aoki, Tatsuya  and
      Noji, Hiroshi  and
      Goshima, Keiichi  and
      Kobayashi, Ichiro  and
      Takamura, Hiroya  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8640",
    doi = "10.18653/v1/W19-8640",
    pages = "323--332",
    abstract = "We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, because depending on users, it differs what they are interested in, so it is necessary to develop a method to generate various summaries according to users{'} interests. We develop a model to generate various summaries and to control their contents by providing the explicit targets for a reference to the model as controllable factors. In the experiments, we used five-minute or one-hour charts of 9 indicators (e.g., Nikkei225), as time-series data, and daily summaries of Nikkei Quick News as textual data. We conducted comparative experiments using two pieces of information: human-designed topic labels indicating the contents of a sentence and automatically extracted keywords as the referential information for generation.",
}
@inproceedings{hitomi-etal-2019-large,
    title = "A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation",
    author = "Hitomi, Yuta  and
      Taguchi, Yuya  and
      Tamori, Hideaki  and
      Kikuta, Ko  and
      Nishitoba, Jiro  and
      Okazaki, Naoaki  and
      Inui, Kentaro  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8641",
    doi = "10.18653/v1/W19-8641",
    pages = "333--343",
    abstract = "Browsing news articles on multiple devices is now possible. The lengths of news article headlines have precise upper bounds, dictated by the size of the display of the relevant device or interface. Therefore, controlling the length of headlines is essential when applying the task of headline generation to news production. However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths. In this paper, we introduce two corpora, which are Japanese News Corpus (JNC) and JApanese MUlti-Length Headline Corpus (JAMUL), to confirm the validity of previous evaluation settings. The JNC provides common supervision data for headline generation. The JAMUL is a large-scale evaluation dataset for headlines of three different lengths composed by professional editors. We report new findings on these corpora; for example, although the longest length reference summary can appropriately evaluate the existing methods controlling output length, this evaluation setting has several problems.",
}
@inproceedings{amidei-etal-2019-agreement,
    title = "Agreement is overrated: A plea for correlation to assess human evaluation reliability",
    author = "Amidei, Jacopo  and
      Piwek, Paul  and
      Willis, Alistair",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8642",
    doi = "10.18653/v1/W19-8642",
    pages = "344--354",
    abstract = "Inter-Annotator Agreement (IAA) is used as a means of assessing the quality of NLG evaluation data, in particular, its reliability. According to existing scales of IAA interpretation {--} see, for example, Lommel et al. (2014), Liu et al. (2016), Sedoc et al. (2018) and Amidei et al. (2018a) {--} most data collected for NLG evaluation fail the reliability test. We confirmed this trend by analysing papers published over the last 10 years in NLG-specific conferences (in total 135 papers that included some sort of human evaluation study). Following Sampson and Babarczy (2008), Lommel et al. (2014), Joshi et al. (2016) and Amidei et al. (2018b), such phenomena can be explained in terms of irreducible human language variability. Using three case studies, we show the limits of considering IAA as the only criterion for checking evaluation reliability. Given human language variability, we propose that for human evaluation of NLG, correlation coefficients and agreement coefficients should be used together to obtain a better assessment of the evaluation data reliability. This is illustrated using the three case studies.",
}
@inproceedings{van-der-lee-etal-2019-best,
    title = "Best practices for the human evaluation of automatically generated text",
    author = "van der Lee, Chris  and
      Gatt, Albert  and
      van Miltenburg, Emiel  and
      Wubben, Sander  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8643",
    doi = "10.18653/v1/W19-8643",
    pages = "355--368",
    abstract = "Currently, there is little agreement as to how Natural Language Generation (NLG) systems should be evaluated. While there is some agreement regarding automatic metrics, there is a high degree of variation in the way that human evaluation is carried out. This paper provides an overview of how human evaluation is currently conducted, and presents a set of best practices, grounded in the literature. With this paper, we hope to contribute to the quality and consistency of human evaluations in NLG.",
}
@inproceedings{dusek-etal-2019-automatic,
    title = "Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Sevegnani, Karin  and
      Konstas, Ioannis  and
      Rieser, Verena",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8644",
    doi = "10.18653/v1/W19-8644",
    pages = "369--376",
    abstract = "We present a recurrent neural network based system for automatic quality estimation of natural language generation (NLG) outputs, which jointly learns to assign numerical ratings to individual outputs and to provide pairwise rankings of two different outputs. The latter is trained using pairwise hinge loss over scores from two copies of the rating network. We use learning to rank and synthetic data to improve the quality of ratings assigned by our system: We synthesise training pairs of distorted system outputs and train the system to rank the less distorted one higher. This leads to a 12{\%} increase in correlation with human ratings over the previous benchmark. We also establish the state of the art on the dataset of relative rankings from the E2E NLG Challenge (Dusek et al., 2019), where synthetic data lead to a 4{\%} accuracy increase over the base model.",
}
@inproceedings{moryossef-etal-2019-improving,
    title = "Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation",
    author = "Moryossef, Amit  and
      Goldberg, Yoav  and
      Dagan, Ido",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8645",
    doi = "10.18653/v1/W19-8645",
    pages = "377--382",
    abstract = "We follow the step-by-step approach to neural data-to-text generation proposed by Moryossef et al (2019), in which the generation process is divided into a text planning stage followed by a plan realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model{'}s ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module. These extensions result in a generation process that is faster, more fluent, and more accurate.",
}
@inproceedings{mori-etal-2019-toward,
    title = "Toward a Better Story End: Collecting Human Evaluation with Reasons",
    author = "Mori, Yusuke  and
      Yamane, Hiroaki  and
      Mukuta, Yusuke  and
      Harada, Tatsuya",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8646",
    doi = "10.18653/v1/W19-8646",
    pages = "383--390",
    abstract = "Creativity is an essential element of human nature used for many activities, such as telling a story. Based on human creativity, researchers have attempted to teach a computer to generate stories automatically or support this creative process. In this study, we undertake the task of story ending generation. This is a relatively new task, in which the last sentence of a given incomplete story is automatically generated. This is challenging because, in order to predict an appropriate ending, the generation method should comprehend the context of events. Despite the importance of this task, no clear evaluation metric has been established thus far; hence, it has remained an open problem. Therefore, we study the various elements involved in evaluating an automatic method for generating story endings. First, we introduce a baseline hierarchical sequence-to-sequence method for story ending generation. Then, we conduct a pairwise comparison against human-written endings, in which annotators choose the preferable ending. In addition to a quantitative evaluation, we conduct a qualitative evaluation by asking annotators to specify the reason for their choice. From the collected reasons, we discuss what elements the evaluation should focus on, to thereby propose effective metrics for the task.",
}
@inproceedings{mahamood-zembrzuski-2019-hotel,
    title = "Hotel Scribe: Generating High Variation Hotel Descriptions",
    author = "Mahamood, Saad  and
      Zembrzuski, Maciej",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8647",
    doi = "10.18653/v1/W19-8647",
    pages = "391--396",
    abstract = "This paper describes the implementation of the Hotel Scribe system. A commercial Natural Language Generation (NLG) system which generates descriptions of hotels from accommodation metadata with a high level of content and linguistic variation in English. It has been deployed live by *Anonymised Company Name* for the purpose of improving coverage of accommodation descriptions and for Search Engine Optimisation (SEO). In this paper, we describe the motivation for building this system, the challenges faced when dealing with limited metadata, and the implementation used to generate the highly variate accommodation descriptions. Additionally, we evaluate the uniqueness of the texts generated by our system against comparable human written accommodation description texts.",
}
@inproceedings{amidei-etal-2019-use,
    title = "The use of rating and {L}ikert scales in Natural Language Generation human evaluation tasks: A review and some recommendations",
    author = "Amidei, Jacopo  and
      Piwek, Paul  and
      Willis, Alistair",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8648",
    doi = "10.18653/v1/W19-8648",
    pages = "397--402",
    abstract = "Rating and Likert scales are widely used in evaluation experiments to measure the quality of Natural Language Generation (NLG) systems. We review the use of rating and Likert scales for NLG evaluation tasks published in NLG specialized conferences over the last ten years (135 papers in total). Our analysis brings to light a number of deviations from good practice in their use. We conclude with some recommendations about the use of such scales. Our aim is to encourage the appropriate use of evaluation methodologies in the NLG community.",
}
@inproceedings{van-miltenburg-etal-2019-task,
    title = "On task effects in {NLG} corpus elicitation: a replication study using mixed effects modeling",
    author = "van Miltenburg, Emiel  and
      van de Kerkhof, Merel  and
      Koolen, Ruud  and
      Goudbeek, Martijn  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8649",
    doi = "10.18653/v1/W19-8649",
    pages = "403--408",
    abstract = "Task effects in NLG corpus elicitation recently started to receive more attention, but are usually not modeled statistically. We present a controlled replication of the study by Van Miltenburg et al. (2018b), contrasting spoken with written descriptions. We collected additional written Dutch descriptions to supplement the spoken data from the DIDEC corpus, and analyzed the descriptions using mixed effects modeling to account for variation between participants and items. Our results show that the effects of modality largely disappear in a controlled setting.",
}
@inproceedings{nishimura-etal-2019-procedural,
    title = "Procedural Text Generation from a Photo Sequence",
    author = "Nishimura, Taichi  and
      Hashimoto, Atsushi  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8650",
    doi = "10.18653/v1/W19-8650",
    pages = "409--414",
    abstract = "Multimedia procedural texts, such as instructions and manuals with pictures, support people to share how-to knowledge. In this paper, we propose a method for generating a procedural text given a photo sequence allowing users to obtain a multimedia procedural text. We propose a single embedding space both for image and text enabling to interconnect them and to select appropriate words to describe a photo. We implemented our method and tested it on cooking instructions, i.e., recipes. Various experimental results showed that our method outperforms standard baselines.",
}
@inproceedings{braun-etal-2019-simplenlg,
    title = "{S}imple{NLG}-{DE}: Adapting {S}imple{NLG} 4 to {G}erman",
    author = "Braun, Daniel  and
      Klimt, Kira  and
      Schneider, Daniela  and
      Matthes, Florian",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8651",
    doi = "10.18653/v1/W19-8651",
    pages = "415--420",
    abstract = "SimpleNLG is a popular open source surface realiser for the English language. For German, however, the availability of open source and non-domain specific realisers is sparse, partly due to the complexity of the German language. In this paper, we present SimpleNLG-DE, an adaption of SimpleNLG to German. We discuss which parts of the German language have been implemented and how we evaluated our implementation using the TIGER Corpus and newly created data-sets.",
}
@inproceedings{dusek-etal-2019-semantic,
    title = "Semantic Noise Matters for Neural Natural Language Generation",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Howcroft, David M.  and
      Rieser, Verena",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8652",
    doi = "10.18653/v1/W19-8652",
    pages = "421--426",
    abstract = "Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97{\%}, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.",
}
@inproceedings{sadler-etal-2019-neural,
    title = "Can Neural Image Captioning be Controlled via Forced Attention?",
    author = "Sadler, Philipp  and
      Scheffler, Tatjana  and
      Schlangen, David",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8653",
    doi = "10.18653/v1/W19-8653",
    pages = "427--431",
    abstract = "Learned dynamic weighting of the conditioning signal (attention) has been shown to improve neural language generation in a variety of settings. The weights applied when generating a particular output sequence have also been viewed as providing a potentially explanatory insight in the internal workings of the generator. In this paper, we reverse the direction of this connection and ask whether through the control of the attention of the model we can control its output. Specifically, we take a standard neural image captioning model that uses attention, and fix the attention to predetermined areas in the image. We evaluate whether the resulting output is more likely to mention the class of the object in that area than the normally generated caption. We introduce three effective methods to control the attention and find that these are producing expected results in up to 27.43{\%} of the cases.",
}
@inproceedings{deriu-cieliebak-2019-towards,
    title = "Towards a Metric for Automated Conversational Dialogue System Evaluation and Improvement",
    author = "Deriu, Jan Milan  and
      Cieliebak, Mark",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8654",
    doi = "10.18653/v1/W19-8654",
    pages = "432--437",
    abstract = "We present {``}AutoJudge{''}, an automated evaluation method for conversational dialogue systems. The method works by first generating dialogues based on self-talk, i.e. dialogue systems talking to itself. Then, it uses human ratings on these dialogues to train an automated judgement model. Our experiments show that AutoJudge correlates well with the human ratings and can be used to automatically evaluate dialogue systems, even in deployed systems. In a second part, we attempt to apply AutoJudge to improve existing systems. This works well for re-ranking a set of candidate utterances. However, our experiments show that AutoJudge cannot be applied as reward for reinforcement learning, although the metric can distinguish good from bad dialogues. We discuss potential reasons, but state here already that this is still an open question for further research.",
}
@inproceedings{nomoto-2019-generating,
    title = "Generating Paraphrases with Lean Vocabulary",
    author = "Nomoto, Tadashi",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8655",
    doi = "10.18653/v1/W19-8655",
    pages = "438--442",
    abstract = "In this work, we examine whether it is possible to achieve the state of the art performance in paraphrase generation with reduced vocabulary. Our approach consists of building a convolution to sequence model (Conv2Seq) partially guided by the reinforcement learning, and training it on the subword representation of the input. The experiment on the Quora dataset, which contains over 140,000 pairs of sentences and corresponding paraphrases, found that with less than 1,000 token types, we were able to achieve performance which exceeded that of the current state of the art.",
}
@inproceedings{hommes-etal-2019-personalized,
    title = "A Personalized Data-to-Text Support Tool for Cancer Patients",
    author = "Hommes, Saar  and
      van der Lee, Chris  and
      Clouth, Felix  and
      Vermunt, Jeroen  and
      Verbeek, Xander  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8656",
    doi = "10.18653/v1/W19-8656",
    pages = "443--452",
    abstract = "In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context of shared decision making. Currently, information on quality of life implications is often not discussed, partly because (until recently) data has been lacking. In our work, we rely on a newly developed prediction model, which assigns patients to scenarios. Furthermore, we use data-to-text techniques to explain these scenario-based predictions in personalized and understandable language. We highlight the possibilities of NLG for personalization, discuss ethical implications and also present the outcomes of a first evaluation with clinicians.",
}
@inproceedings{cervone-etal-2019-natural,
    title = "Natural Language Generation at Scale: A Case Study for Open Domain Question Answering",
    author = "Cervone, Alessandra  and
      Khatri, Chandra  and
      Goel, Rahul  and
      Hedayatnia, Behnam  and
      Venkatesh, Anu  and
      Hakkani-Tur, Dilek  and
      Gabriel, Raefer",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8657",
    doi = "10.18653/v1/W19-8657",
    pages = "453--462",
    abstract = "Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (up to 20 slot types), usually without considering the previous conversation context. Furthermore, these approaches require large amounts of data for each domain, and do not benefit from examples that may be available for other domains. This work explores the feasibility of applying statistical NLG to scenarios requiring larger ontologies, such as multi-domain dialog applications or open-domain question answering (QA) based on knowledge graphs. We model NLG through an Encoder-Decoder framework using a large dataset of interactions between real-world users and a conversational agent for open-domain QA. First, we investigate the impact of increasing the number of slot types on the generation quality and experiment with different partitions of the QA data with progressively larger ontologies (up to 369 slot types). Second, we perform multi-task learning experiments between open-domain QA and task-oriented dialog, and benchmark our model on a popular NLG dataset. Moreover, we experiment with using the conversational context as an additional input to improve response generation quality. Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.",
}
@inproceedings{mazzei-etal-2019-using,
    title = "Using {NLG} for speech synthesis of mathematical sentences",
    author = "Mazzei, Alessandro  and
      Monticone, Michele  and
      Bernareggi, Cristian",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8658",
    doi = "10.18653/v1/W19-8658",
    pages = "463--472",
    abstract = "People with sight impairments can access to a mathematical expression by using its LaTeX source. However, this mechanisms have several drawbacks: (1) it assumes the knowledge of the LaTeX, (2) it is slow, since LaTeX is verbose and (3) it is error-prone since LATEX is a typographical language. In this paper we study the design of a natural language generation system for producing a mathematical sentence, i.e. a natural language sentence expressing the semantics of a mathematical expression. Moreover, we describe the main results of a first human based evaluation experiment of the system for Italian language.",
}
@inproceedings{mille-etal-2019-teaching,
    title = "Teaching {FORG}e to Verbalize {DB}pedia Properties in {S}panish",
    author = "Mille, Simon  and
      Dasiopoulou, Stamatia  and
      Fisas, Beatriz  and
      Wanner, Leo",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8659",
    doi = "10.18653/v1/W19-8659",
    pages = "473--483",
    abstract = "Statistical generators increasingly dominate the research in NLG. However, grammar-based generators that are grounded in a solid linguistic framework remain very competitive, especially for generation from deep knowledge structures. Furthermore, if built modularly, they can be ported to other genres and languages with a limited amount of work, without the need of the annotation of a considerable amount of training data. One of these generators is FORGe, which is based on the Meaning-Text Model. In the recent WebNLG challenge (the first comprehensive task addressing the mapping of RDF triples to text) FORGe ranked first with respect to the overall quality in human evaluation. We extend the coverage of FORGE{'}s open source grammatical and lexical resources for English, so as to further improve the English texts, and port them to Spanish, to achieve a comparable quality. This confirms that, as already observed in the case of SimpleNLG, a robust universal grammar-driven framework and a systematic organization of the linguistic resources can be an adequate choice for NLG applications.",
}
@inproceedings{kasenberg-etal-2019-generating,
    title = "Generating justifications for norm-related agent decisions",
    author = "Kasenberg, Daniel  and
      Roque, Antonio  and
      Thielstrom, Ravenna  and
      Chita-Tegmark, Meia  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8660",
    doi = "10.18653/v1/W19-8660",
    pages = "484--493",
    abstract = "We present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent{'}s rules, actions, and the extent to which the agent violated the rules) as well as {``}why{''} questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules. To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences. We use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.",
}
@inproceedings{zhou-huang-2019-towards,
    title = "Towards Generating Math Word Problems from Equations and Topics",
    author = "Zhou, Qingyu  and
      Huang, Danqing",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8661",
    doi = "10.18653/v1/W19-8661",
    pages = "494--503",
    abstract = "A math word problem is a narrative with a specific topic that provides clues to the correct equation with numerical quantities and variables therein. In this paper, we focus on the task of generating math word problems. Previous works are mainly template-based with pre-defined rules. We propose a novel neural network model to generate math word problems from the given equations and topics. First, we design a fusion mechanism to incorporate the information of both equations and topics. Second, an entity-enforced loss is introduced to ensure the relevance between the generated math problem and the equation. Automatic evaluation results show that the proposed model significantly outperforms the baseline models. In human evaluations, the math word problems generated by our model are rated as being more relevant (in terms of solvability of the given equations and relevance to topics) and natural (i.e., grammaticality, fluency) than the baseline models.",
}
@inproceedings{niklaus-etal-2019-dissim,
    title = "{D}is{S}im: A Discourse-Aware Syntactic Text Simplification Framework for {E}nglish and {G}erman",
    author = "Niklaus, Christina  and
      Cetto, Matthias  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8662",
    doi = "10.18653/v1/W19-8662",
    pages = "504--507",
    abstract = "We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, we turn input sentences into a two-layered semantic hierarchy in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them. In that way, we preserve the coherence structure of the input and, hence, its interpretability for downstream tasks.",
}
@inproceedings{ito-etal-2019-real,
    title = "Real World Voice Assistant System for Cooking",
    author = "Ito, Takahiko  and
      Inuzuka, Shintaro  and
      Yamada, Yoshiaki  and
      Harashima, Jun",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8663",
    doi = "10.18653/v1/W19-8663",
    pages = "508--509",
    abstract = "This study presents a voice assistant system to support cooking by utilizing smart speakers in Japan. This system not only speaks the procedures written in recipes point by point but also answers the common questions from users for the specified recipes. The system applies machine comprehension techniques to millions of recipes for answering the common questions in cooking such as {``}人参はどうしたらよいですか (How should I cook carrots?){''}. Furthermore, numerous machine-learning techniques are applied to generate better responses to users.",
}
@inproceedings{choi-etal-2019-vae,
    title = "{VAE}-{PGN} based Abstractive Model in Multi-stage Architecture for Text Summarization",
    author = "Choi, Hyungtak  and
      Ravuru, Lohith  and
      Dryja{\'n}ski, Tomasz  and
      Rye, Sunghan  and
      Lee, Donghyun  and
      Lee, Hojung  and
      Hwang, Inchul",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8664",
    doi = "10.18653/v1/W19-8664",
    pages = "510--515",
    abstract = "This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms. However, these models suffer from their extractive nature as they learn to copy words from the source text. In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue. We also propose a Unified Summarization Framework for the generation of summaries. Our model eliminates non-critical information at a sentence-level with an extractive summarization module and generates the summary word by word using an abstractive summarization module. To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model. We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries.",
}
@inproceedings{gehrmann-etal-2019-generating,
    title = "Generating Abstractive Summaries with Finetuned Language Models",
    author = "Gehrmann, Sebastian  and
      Ziegler, Zachary  and
      Rush, Alexander",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8665",
    doi = "10.18653/v1/W19-8665",
    pages = "516--522",
    abstract = "Neural abstractive document summarization is commonly approached by models that exhibit a mostly extractive behavior. This behavior is facilitated by a copy-attention which allows models to copy words from a source document. While models in the mostly extractive news summarization domain benefit from this inductive bias, they commonly fail to paraphrase or compress information from the source document. Recent advances in transfer-learning from large pretrained language models give rise to alternative approaches that do not rely on copy-attention and instead learn to generate concise and abstractive summaries. In this paper, as part of the TL;DR challenge, we compare the abstractiveness of summaries from different summarization approaches and show that transfer-learning can be efficiently utilized without any changes to the model architecture. We demonstrate that the approach leads to a higher level of abstraction for a similar performance on the TL;DR challenge tasks, enabling true natural language compression.",
}
@inproceedings{syed-etal-2019-towards,
    title = "Towards Summarization for Social Media - Results of the {TL};{DR} Challenge",
    author = {Syed, Shahbaz  and
      V{\"o}lske, Michael  and
      Lipka, Nedim  and
      Stein, Benno  and
      Sch{\"u}tze, Hinrich  and
      Potthast, Martin},
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8666",
    doi = "10.18653/v1/W19-8666",
    pages = "523--528",
    abstract = "In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.",
}
@inproceedings{chen-etal-2019-generating,
    title = "Generating Quantified Descriptions of Abstract Visual Scenes",
    author = "Chen, Guanyi  and
      van Deemter, Kees  and
      Lin, Chenghua",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8667",
    doi = "10.18653/v1/W19-8667",
    pages = "529--539",
    abstract = "Quantified expressions have always taken up a central position in formal theories of meaning and language use. Yet quantified expressions have so far attracted far less attention from the Natural Language Generation community than, for example, referring expressions. In an attempt to start redressing the balance, we investigate a recently developed corpus in which quantified expressions play a crucial role; the corpus is the result of a carefully controlled elicitation experiment, in which human participants were asked to describe visually presented scenes. Informed by an analysis of this corpus, we propose algorithms that produce computer-generated descriptions of a wider class of visual scenes, and we evaluate the descriptions generated by these algorithms in terms of their correctness, completeness, and human-likeness. We discuss what this exercise can teach us about the nature of quantification and about the challenges posed by the generation of quantified expressions.",
}
@inproceedings{ghanimifard-dobnik-2019-goes,
    title = "What goes into a word: generating image descriptions with top-down spatial knowledge",
    author = "Ghanimifard, Mehdi  and
      Dobnik, Simon",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8668",
    doi = "10.18653/v1/W19-8668",
    pages = "540--551",
    abstract = "Generating grounded image descriptions requires associating linguistic units with their corresponding visual clues. A common method is to train a decoder language model with attention mechanism over convolutional visual features. Attention weights align the stratified visual features arranged by their location with tokens, most commonly words, in the target description. However, words such as spatial relations (e.g. \textit{next to} and \textit{under}) are not directly referring to geometric arrangements of pixels but to complex geometric and conceptual representations. The aim of this paper is to evaluate what representations facilitate generating image descriptions with spatial relations and lead to better grounded language generation. In particular, we investigate the contribution of three different representational modalities in generating relational referring expressions: (i) pre-trained convolutional visual features, (ii) different top-down geometric relational knowledge between objects, and (iii) world knowledge captured by contextual embeddings in language models.",
}
@inproceedings{qader-etal-2019-semi,
    title = "Semi-Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models",
    author = "Qader, Raheel  and
      Portet, Fran{\c{c}}ois  and
      Labb{\'e}, Cyril",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8669",
    doi = "10.18653/v1/W19-8669",
    pages = "552--562",
    abstract = "In Natural Language Generation (NLG), End-to-End (E2E) systems trained through deep learning have recently gained a strong interest. Such deep models need a large amount of carefully annotated data to reach satisfactory performance. However, acquiring such datasets for every new NLG application is a tedious and time-consuming task. In this paper, we propose a semi-supervised deep learning scheme that can learn from non-annotated data and annotated data when available. It uses a NLG and a Natural Language Understanding (NLU) sequence-to-sequence models which are learned jointly to compensate for the lack of annotation. Experiments on two benchmark datasets show that, with limited amount of annotated data, the method can achieve very competitive results while not using any pre-processing or re-scoring tricks. These findings open the way to the exploitation of non-annotated datasets which is the current bottleneck for the E2E NLG system development to new applications.",
}
@inproceedings{dusek-jurcicek-2019-neural,
    title = "Neural Generation for {C}zech: Data and Baselines",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Jur{\v{c}}{\'\i}{\v{c}}ek, Filip",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8670",
    doi = "10.18653/v1/W19-8670",
    pages = "563--574",
    abstract = "We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.",
}
@inproceedings{niehues-pham-2019-modeling,
    title = "Modeling Confidence in Sequence-to-Sequence Models",
    author = "Niehues, Jan  and
      Pham, Ngoc-Quan",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8671",
    doi = "10.18653/v1/W19-8671",
    pages = "575--583",
    abstract = "Recently, significant improvements have been achieved in various natural language processing tasks using neural sequence-to-sequence models. While aiming for the best generation quality is important, ultimately it is also necessary to develop models that can assess the quality of their output. In this work, we propose to use the similarity between training and test conditions as a measure for models{'} confidence. We investigate methods solely using the similarity as well as methods combining it with the posterior probability. While traditionally only target tokens are annotated with confidence measures, we also investigate methods to annotate source tokens with confidence. By learning an internal alignment model, we can significantly improve confidence projection over using state-of-the-art external alignment tools. We evaluate the proposed methods on downstream confidence estimation for machine translation (MT). We show improvements on segment-level confidence estimation as well as on confidence estimation for source tokens. In addition, we show that the same methods can also be applied to other tasks using sequence-to-sequence models. On the automatic speech recognition (ASR) task, we are able to find 60{\%} of the errors by looking at 20{\%} of the data.",
}
@inproceedings{kedzie-mckeown-2019-good,
    title = "A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models",
    author = "Kedzie, Chris  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8672",
    doi = "10.18653/v1/W19-8672",
    pages = "584--593",
    abstract = "Deep neural networks (DNN) are quickly becoming the de facto standard modeling method for many natural language generation (NLG) tasks. In order for such models to truly be useful, they must be capable of correctly generating utterances for novel meaning representations (MRs) at test time. In practice, even sophisticated DNNs with various forms of semantic control frequently fail to generate utterances faithful to the input MR. In this paper, we propose an architecture agnostic self-training method to sample novel MR/text utterance pairs to augment the original training data. Remarkably, after training on the augmented data, even simple encoder-decoder models with greedy decoding are capable of generating semantically correct utterances that are as good as state-of-the-art outputs in both automatic and human evaluations of quality.",
}
@inproceedings{li-etal-2019-stable,
    title = "A Stable Variational Autoencoder for Text Modelling",
    author = "Li, Ruizhe  and
      Li, Xiao  and
      Lin, Chenghua  and
      Collinson, Matthew  and
      Mao, Rui",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
    month = oct # "{--}" # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8673",
    doi = "10.18653/v1/W19-8673",
    pages = "594--599",
    abstract = "Variational Autoencoder (VAE) is a powerful method for learning representations of high-dimensional data. However, VAEs can suffer from an issue known as latent variable collapse (or KL term vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks. Such an issue is particularly prevalent when employing VAE-RNN architectures for text modelling (Bowman et al., 2016; Yang et al., 2017). In this paper, we present a new architecture called Full-Sampling-VAE-RNN, which can effectively avoid latent variable collapse. Compared to the general VAE-RNN architectures, we show that our model can achieve much more stable training process and can generate text with significantly better quality.",
}
@proceedings{ws-2019-international-resources,
    title = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    editor = "{\v{S}}ev{\v{c}}{\'\i}kov{\'a}, Magda  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Litta, Eleonora  and
      Passarotti, Marco",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8500",
}
@inproceedings{kortvelyessy-2019-cross,
    title = "Cross-linguistic research into derivational networks",
    author = "K{\H{o}}rtv{\'e}lyessy, L{\'\i}via",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8501",
    pages = "1--4",
}
@inproceedings{namer-hathout-2019-paradis,
    title = "{P}ara{D}is and D{\'e}monette: From Theory to Resources for Derivational Paradigms",
    author = "Namer, Fiammetta  and
      Hathout, Nabil",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8502",
    pages = "5--14",
}
@inproceedings{sanacore-etal-2019-semantic,
    title = "Semantic descriptions of {F}rench derivational relations in a families-and-paradigms framework",
    author = "Sanacore, Daniele  and
      Hathout, Nabil  and
      Namer, Fiammetta",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8503",
    pages = "15--24",
}
@inproceedings{pultrova-2019-correlation,
    title = "Correlation between the gradability of {L}atin adjectives and the ability to form qualitative abstract nouns",
    author = "Pultrov{\'a}, Lucie",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8504",
    pages = "25--34",
}
@inproceedings{litta-etal-2019-treatment,
    title = "The Treatment of Word Formation in the {L}i{L}a Knowledge Base of Linguistic Resources for {L}atin",
    author = "Litta, Eleonora  and
      Passarotti, Marco  and
      Mambrini, Francesco",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8505",
    pages = "35--43",
}
@inproceedings{steiner-2019-combining,
    title = "Combining Data-Intense and Compute-Intense Methods for Fine-Grained Morphological Analyses",
    author = "Steiner, Petra",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8506",
    pages = "45--54",
}
@inproceedings{osolsobe-2019-tagged,
    title = "The Tagged Corpus ({SYN}2010) as a Help and a Pitfall in the Word-formation Research",
    author = "Osolsob{\v{e}}, Kl{\'a}ra",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8507",
    pages = "55--59",
}
@inproceedings{rosa-zabokrtsky-2019-attempting,
    title = "Attempting to separate inflection and derivation using vector space representations",
    author = "Rosa, Rudolf  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8508",
    pages = "61--70",
}
@inproceedings{filko-etal-2019-redesign,
    title = "Redesign of the {C}roatian derivational lexicon",
    author = "Filko, Matea  and
      {\v{S}}ojat, Kre{\v{s}}imir  and
      {\v{S}}tefanec, Vanja",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8509",
    pages = "71--80",
}
@inproceedings{vidra-etal-2019-derinet,
    title = "{D}eri{N}et 2.0: Towards an All-in-One Word-Formation Resource",
    author = "Vidra, Jon{\'a}{\v{s}}  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      {\v{S}}ev{\v{c}}{\'\i}kov{\'a}, Magda  and
      Kyj{\'a}nek, Luk{\'a}{\v{s}}",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8510",
    pages = "81--89",
}
@inproceedings{haghdoost-etal-2019-building,
    title = "Building a Morphological Network for {P}ersian on Top of a Morpheme-Segmented Lexicon",
    author = "Haghdoost, Hamid  and
      Ansari, Ebrahim  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Nikravesh, Mahshid",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8511",
    pages = "91--100",
}
@inproceedings{kyjanek-etal-2019-universal,
    title = "Universal Derivations Kickoff: A Collection of Harmonized Derivational Resources for Eleven Languages",
    author = "Kyj{\'a}nek, Luk{\'a}{\v{s}}  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      {\v{S}}ev{\v{c}}{\'\i}kov{\'a}, Magda  and
      Vidra, Jon{\'a}{\v{s}}",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8512",
    pages = "101--110",
}
@inproceedings{curtis-2019-parametric,
    title = "A Parametric Approach to Implemented Analyses: Valence-changing Morphology in the {L}in{GO} Grammar Matrix",
    author = "Curtis, Christian",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8513",
    pages = "111--120",
}
@inproceedings{morita-2019-grammaticalization,
    title = "Grammaticalization in Derivational Morphology: Verification of the Process by Innovative Derivatives",
    author = "Morita, Junya",
    booktitle = "Proceedings of the Second International Workshop on Resources and Tools for Derivational Morphology",
    month = sep,
    year = "2019",
    address = "Prague, Czechia",
    publisher = "Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8514",
    pages = "121--130",
}
@proceedings{ws-2019-interactive,
    title = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    editor = "Alonso, Jose M.  and
      Catala, Alejandro",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8400",
}
@inproceedings{mahamood-2019-explainable,
    title = "Explainable Artificial Intelligence and its potential within Industry",
    author = "Mahamood, Saad",
    booktitle = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8401",
    doi = "10.18653/v1/W19-8401",
    pages = "1--2",
}
@inproceedings{reiter-2019-natural,
    title = "Natural Language Generation Challenges for Explainable {AI}",
    author = "Reiter, Ehud",
    booktitle = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8402",
    doi = "10.18653/v1/W19-8402",
    pages = "3--7",
}
@inproceedings{clinciu-hastie-2019-survey,
    title = "A Survey of Explainable {AI} Terminology",
    author = "Clinciu, Miruna-Adriana  and
      Hastie, Helen",
    booktitle = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8403",
    doi = "10.18653/v1/W19-8403",
    pages = "8--13",
}
@inproceedings{baaj-etal-2019-insights,
    title = "Some Insights Towards a Unified Semantic Representation of Explanation for e{X}plainable Artificial Intelligence",
    author = {Baaj, Isma{\"\i}l  and
      Poli, Jean-Philippe  and
      Ouerdane, Wassila},
    booktitle = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8404",
    doi = "10.18653/v1/W19-8404",
    pages = "14--19",
}
@inproceedings{stepin-etal-2019-paving,
    title = "Paving the way towards counterfactual generation in argumentative conversational agents",
    author = "Stepin, Ilia  and
      Catala, Alejandro  and
      Pereira-Fari{\~n}a, Martin  and
      Alonso, Jose M.",
    booktitle = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8405",
    doi = "10.18653/v1/W19-8405",
    pages = "20--25",
}
@inproceedings{kasenberg-etal-2019-engaging,
    title = "Engaging in Dialogue about an Agent{'}s Norms and Behaviors",
    author = "Kasenberg, Daniel  and
      Roque, Antonio  and
      Thielstrom, Ravenna  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8406",
    doi = "10.18653/v1/W19-8406",
    pages = "26--28",
}
@inproceedings{ehara-2019-approach,
    title = "An Approach to Summarize Concordancers{'} Lists Visually to Support Language Learners in {U}nderstanding{W}ord Usages",
    author = "Ehara, Yo",
    booktitle = "Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8407",
    doi = "10.18653/v1/W19-8407",
    pages = "29--31",
}
@proceedings{ws-2019-international-ai,
    title = "Proceedings of the 1st International Workshop of {AI} Werewolf and Dialog System ({AIW}olf{D}ial2019)",
    editor = "Kano, Yoshinobu  and
      Aranha, Claus  and
      Inaba, Michimasa  and
      Toriumi, Fujio  and
      Osawa, Hirotaka  and
      Katagami, Daisuke  and
      Otsuki, Takashi",
    month = oct,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8300",
}
@inproceedings{kano-etal-2019-overview,
    title = "Overview of {AIW}olf{D}ial 2019 Shared Task: Contest of Automatic Dialog Agents to Play the Werewolf Game through Conversations",
    author = "Kano, Yoshinobu  and
      Aranha, Claus  and
      Inaba, Michimasa  and
      Toriumi, Fujio  and
      Osawa, Hirotaka  and
      Katagami, Daisuke  and
      Otsuki, Takashi  and
      Tsunoda, Issei  and
      Nagayama, Shoji  and
      Tellols, Dol{\c{c}}a  and
      Sugawara, Yu  and
      Nakata, Yohei",
    booktitle = "Proceedings of the 1st International Workshop of {AI} Werewolf and Dialog System ({AIW}olf{D}ial2019)",
    month = oct,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8301",
    doi = "10.18653/v1/W19-8301",
    pages = "1--6",
}
@inproceedings{tellols-2019-talkative,
    title = "Are Talkative {AI} Agents More Likely to Win the Werewolf Game?",
    author = "Tellols, Dol{\c{c}}a",
    booktitle = "Proceedings of the 1st International Workshop of {AI} Werewolf and Dialog System ({AIW}olf{D}ial2019)",
    month = oct,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8302",
    doi = "10.18653/v1/W19-8302",
    pages = "11--14",
}
@inproceedings{tsunoda-kano-2019-ai,
    title = "{AI} Werewolf Agent with Reasoning Using Role Patterns and Heuristics",
    author = "Tsunoda, Issei  and
      Kano, Yoshinobu",
    booktitle = "Proceedings of the 1st International Workshop of {AI} Werewolf and Dialog System ({AIW}olf{D}ial2019)",
    month = oct,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8303",
    doi = "10.18653/v1/W19-8303",
    pages = "15--19",
}
@inproceedings{sugawara-2019-data,
    title = "Data Augmentation Based on Distributed Expressions in Text Classification Tasks",
    author = "Sugawara, Yu",
    booktitle = "Proceedings of the 1st International Workshop of {AI} Werewolf and Dialog System ({AIW}olf{D}ial2019)",
    month = oct,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8304",
    doi = "10.18653/v1/W19-8304",
    pages = "7--10",
}
@inproceedings{nagayama-etal-2019-strategies,
    title = "Strategies for an Autonomous Agent Playing the {``}Werewolf game{''} as a Stealth Werewolf",
    author = "Nagayama, Shoji  and
      Abe, Jotaro  and
      Oya, Kosuke  and
      Sakamoto, Kotaro  and
      Shibuki, Hideyuki  and
      Mori, Tatsunori  and
      Kando, Noriko",
    booktitle = "Proceedings of the 1st International Workshop of {AI} Werewolf and Dialog System ({AIW}olf{D}ial2019)",
    month = oct,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8305",
    doi = "10.18653/v1/W19-8305",
    pages = "20--24",
}
@proceedings{ws-2019-discourse-structure,
    title = "Proceedings of the 1st Workshop on Discourse Structure in Neural NLG",
    editor = "Balakrishnan, Anusha  and
      Demberg, Vera  and
      Khatri, Chandra  and
      Rastogi, Abhinav  and
      Scott, Donia  and
      Walker, Marilyn  and
      White, Michael",
    month = nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8100",
}
@inproceedings{harrison-etal-2019-maximizing,
    title = "Maximizing Stylistic Control and Semantic Accuracy in {NLG}: Personality Variation and Discourse Contrast",
    author = "Harrison, Vrindavan  and
      Reed, Lena  and
      Oraby, Shereen  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 1st Workshop on Discourse Structure in Neural NLG",
    month = nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8101",
    doi = "10.18653/v1/W19-8101",
    pages = "1--12",
    abstract = "Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant. While earlier work focused solely on the semantic fidelity of outputs, recent work has started to explore methods for controlling the style of the generated text while simultaneously achieving semantic accuracy. Here we experiment with two stylistic benchmark tasks, generating language that exhibits variation in personality, and generating discourse contrast. We report a huge performance improvement in both stylistic control and semantic accuracy over the state of the art on both of these benchmarks. We test several different models and show that putting stylistic conditioning in the decoder and eliminating the semantic re-ranker used in earlier models results in more than 15 points higher BLEU for Personality, with a reduction of semantic error to near zero. We also report an improvement from .75 to .81 in controlling contrast and a reduction in semantic error from 16{\%} to 2{\%}.",
}
@inproceedings{li-li-2019-incorporating,
    title = "Incorporating Textual Evidence in Visual Storytelling",
    author = "Li, Tianyi  and
      Li, Sujian",
    booktitle = "Proceedings of the 1st Workshop on Discourse Structure in Neural NLG",
    month = nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8102",
    doi = "10.18653/v1/W19-8102",
    pages = "13--17",
    abstract = "Previous work on visual storytelling mainly focused on exploring image sequence as evidence for storytelling and neglected textual evidence for guiding story generation. Motivated by human storytelling process which recalls stories for familiar images, we exploit textual evidence from similar images to help generate coherent and meaningful stories. To pick the images which may provide textual experience, we propose a two-step ranking method based on image object recognition techniques. To utilize textual information, we design an extended Seq2Seq model with two-channel encoder and attention. Experiments on the VIST dataset show that our method outperforms state-of-the-art baseline models without heavy engineering.",
}
@inproceedings{mehta-etal-2019-fine,
    title = "Fine-Grained Control of Sentence Segmentation and Entity Positioning in Neural {NLG}",
    author = "Mehta, Kritika  and
      Qader, Raheel  and
      Labbe, Cyril  and
      Portet, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 1st Workshop on Discourse Structure in Neural NLG",
    month = nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8103",
    doi = "10.18653/v1/W19-8103",
    pages = "18--23",
    abstract = "The move from pipeline Natural Language Generation (NLG) approaches to neural end-to-end approaches led to a loss of control in sentence planning operations owing to the conflation of intermediary micro-planning stages into a single model. Such control is highly necessary when the text should be tailored to respect some constraints such as which entity to be mentioned first, the entity position, the complexity of sentences, etc. In this paper, we introduce fine-grained control of sentence planning in neural data-to-text generation models at two levels - realization of input entities in desired sentences and realization of the input entities in the desired position among individual sentences. We show that by augmenting the input with explicit position identifiers, the neural model can achieve a great control over the output structure while keeping the naturalness of the generated text intact. Since sentence level metrics are not entirely suitable to evaluate this task, we used a metric specific to our task that accounts for the model{'}s ability to achieve control. The results demonstrate that the position identifiers do constraint the neural model to respect the intended output structure which can be useful in a variety of domains that require the generated text to be in a certain structure.",
}
@inproceedings{cheng-li-2019-zero,
    title = "Zero-shot {C}hinese Discourse Dependency Parsing via Cross-lingual Mapping",
    author = "Cheng, Yi  and
      Li, Sujian",
    booktitle = "Proceedings of the 1st Workshop on Discourse Structure in Neural NLG",
    month = nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8104",
    doi = "10.18653/v1/W19-8104",
    pages = "24--29",
    abstract = "Due to the absence of labeled data, discourse parsing still remains challenging in some languages. In this paper, we present a simple and efficient method to conduct zero-shot Chinese text-level dependency parsing by leveraging English discourse labeled data and parsing techniques. We first construct the Chinese-English mapping from the level of sentence and elementary discourse unit (EDU), and then exploit the parsing results of the corresponding English translations to obtain the discourse trees for the Chinese text. This method can automatically conduct Chinese discourse parsing, with no need of a large scale of Chinese labeled data.",
}
@proceedings{ws-2019-universal,
    title = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    editor = "Rademaker, Alexandre  and
      Tyers, Francis",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8000",
}
@inproceedings{przepiorkowski-2019-syntaxfest,
    title = "{S}yntax{F}est 2019 Invited talk - Arguments and adjuncts",
    author = "Przepi{\'o}rkowski, Adam",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8001",
    doi = "10.18653/v1/W19-8001",
    pages = "1--1",
}
@inproceedings{miletic-etal-2019-building,
    title = "Building a treebank for {O}ccitan: what use for {R}omance {UD} corpora?",
    author = "Miletic, Aleksandra  and
      Bras, Myriam  and
      Esher, Louise  and
      Sibille, Jean  and
      Vergez-Couret, Marianne",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8002",
    doi = "10.18653/v1/W19-8002",
    pages = "2--11",
}
@inproceedings{dione-2019-developing,
    title = "Developing {U}niversal {D}ependencies for {W}olof",
    author = "Dione, Cheikh Bamba",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8003",
    doi = "10.18653/v1/W19-8003",
    pages = "12--23",
}
@inproceedings{dobrovoljc-etal-2019-improving,
    title = "Improving {UD} processing via satellite resources for morphology",
    author = "Dobrovoljc, Kaja  and
      Erjavec, Toma{\v{z}}  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8004",
    doi = "10.18653/v1/W19-8004",
    pages = "24--34",
}
@inproceedings{levshina-2019-universal,
    title = "{U}niversal {D}ependencies in a galaxy far, far away... What makes Yoda{'}s {E}nglish truly alien",
    author = "Levshina, Natalia",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8005",
    doi = "10.18653/v1/W19-8005",
    pages = "35--45",
}
@inproceedings{borges-volker-etal-2019-hdt,
    title = "{HDT}-{UD}: A very large {U}niversal {D}ependencies Treebank for {G}erman",
    author = {Borges V{\"o}lker, Emanuel  and
      Wendt, Maximilian  and
      Hennig, Felix  and
      K{\"o}hn, Arne},
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8006",
    doi = "10.18653/v1/W19-8006",
    pages = "46--57",
}
@inproceedings{przepiorkowski-patejuk-2019-nested,
    title = "Nested Coordination in {U}niversal {D}ependencies",
    author = "Przepi{\'o}rkowski, Adam  and
      Patejuk, Agnieszka",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8007",
    doi = "10.18653/v1/W19-8007",
    pages = "58--69",
}
@inproceedings{thomas-2019-universal,
    title = "{U}niversal {D}ependencies for {M}by{\'a} {G}uaran{\'\i}",
    author = "Thomas, Guillaume",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8008",
    doi = "10.18653/v1/W19-8008",
    pages = "70--77",
}
@inproceedings{partanen-rueter-2019-survey,
    title = "Survey of Uralic {U}niversal {D}ependencies development",
    author = "Partanen, Niko  and
      Rueter, Jack",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8009",
    doi = "10.18653/v1/W19-8009",
    pages = "78--86",
}
@inproceedings{heinecke-2019-conllueditor,
    title = "{C}onllu{E}ditor: a fully graphical editor for Universal dependencies treebank files",
    author = "Heinecke, Johannes",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8010",
    doi = "10.18653/v1/W19-8010",
    pages = "87--93",
}
@inproceedings{ahrenberg-2019-towards,
    title = "Towards an adequate account of parataxis in {U}niversal {D}ependencies",
    author = "Ahrenberg, Lars",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8011",
    doi = "10.18653/v1/W19-8011",
    pages = "94--100",
}
@inproceedings{elkaref-bohnet-2019-recursive,
    title = "Recursive {LSTM} Tree Representation for Arc-Standard Transition-Based Dependency Parsing",
    author = "Elkaref, Mohab  and
      Bohnet, Bernd",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8012",
    doi = "10.18653/v1/W19-8012",
    pages = "101--107",
}
@inproceedings{turk-etal-2019-improving,
    title = "Improving the Annotations in the {T}urkish {U}niversal {D}ependency Treebank",
    author = {T{\"u}rk, Utku  and
      Atmaca, Furkan  and
      Bet{\"u}l {\"O}zate{\c{s}}, {\c{S}}aziye  and
      {\"O}zt{\"u}rk Ba{\c{s}}aran, Balk{\i}z  and
      G{\"u}ng{\"o}r, Tunga  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8013",
    doi = "10.18653/v1/W19-8013",
    pages = "108--115",
}
@inproceedings{osenova-simov-2019-towards,
    title = "Towards transferring {B}ulgarian Sentences with Elliptical Elements to {U}niversal {D}ependencies: issues and strategies",
    author = "Osenova, Petya  and
      Simov, Kiril",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8014",
    doi = "10.18653/v1/W19-8014",
    pages = "116--123",
}
@inproceedings{gerdes-etal-2019-rediscovering,
    title = "Rediscovering Greenberg{'}s Word Order Universals in {UD}",
    author = "Gerdes, Kim  and
      Kahane, Sylvain  and
      Chen, Xinying",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8015",
    doi = "10.18653/v1/W19-8015",
    pages = "124--131",
}
@inproceedings{pirinen-2019-building,
    title = "Building minority dependency treebanks, dictionaries and computational grammars at the same time{---}an experiment in {K}arelian treebanking",
    author = "Pirinen, Tommi A",
    booktitle = "Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-8016",
    doi = "10.18653/v1/W19-8016",
    pages = "132--136",
}
@proceedings{ws-2019-quantitative,
    title = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    editor = "Chen, Xinying  and
      Ferrer-i-Cancho, Ramon",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7900",
}
@inproceedings{ferrer-i-cancho-2019-syntaxfest,
    title = "{S}yntax{F}est 2019 Invited talk - Dependency distance minimization: facts, theory and predictions",
    author = "Ferrer-i-Cancho, Ramon",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7901",
    doi = "10.18653/v1/W19-7901",
    pages = "1--1",
}
@inproceedings{futrell-2019-information,
    title = "Information-theoretic locality properties of natural language",
    author = "Futrell, Richard",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7902",
    doi = "10.18653/v1/W19-7902",
    pages = "2--15",
}
@inproceedings{yan-liu-2019-annotation,
    title = "Which annotation scheme is more expedient to measure syntactic difficulty and cognitive demand?",
    author = "Yan, Jianwei  and
      Liu, Haitao",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7903",
    doi = "10.18653/v1/W19-7903",
    pages = "16--24",
}
@inproceedings{chen-liu-2019-quantitative,
    title = "A quantitative probe into the hierarchical structure of written {C}hinese",
    author = "Chen, Heng  and
      Liu, Haitao",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7904",
    doi = "10.18653/v1/W19-7904",
    pages = "25--32",
}
@inproceedings{liu-2019-comparative,
    title = "A Comparative Corpus Analysis of {PP} Ordering in {E}nglish and {C}hinese",
    author = "Liu, Zoey",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7905",
    doi = "10.18653/v1/W19-7905",
    pages = "33--45",
}
@inproceedings{samo-merlo-2019-intervention,
    title = "Intervention effects in object relatives in {E}nglish and {I}talian: a study in quantitative computational syntax",
    author = "Samo, Giuseppe  and
      Merlo, Paola",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7906",
    doi = "10.18653/v1/W19-7906",
    pages = "46--56",
}
@inproceedings{ninio-2019-explanation,
    title = "An explanation of the decisive role of function words in driving syntactic development",
    author = "Ninio, Anat",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7907",
    doi = "10.18653/v1/W19-7907",
    pages = "57--67",
}
@inproceedings{abeille-winckel-2019-extracting,
    title = "Extracting out of the subject in {F}rench: experimental evidence",
    author = "Abeill{\'e}, Anne  and
      Winckel, Elodie",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7908",
    doi = "10.18653/v1/W19-7908",
    pages = "68--74",
}
@inproceedings{chen-gerdes-2019-relation,
    title = "The relation between dependency distance and frequency",
    author = "Chen, Xinying  and
      Gerdes, Kim",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7909",
    doi = "10.18653/v1/W19-7909",
    pages = "75--82",
}
@inproceedings{cech-etal-2019-full,
    title = "Full valency and the position of enclitics in the Old {C}zech",
    author = "Cech, Radek  and
      Kosek, Pavel  and
      Navratilova, Olga  and
      Macutek, Jan",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7910",
    doi = "10.18653/v1/W19-7910",
    pages = "83--88",
}
@inproceedings{yu-etal-2019-dependency,
    title = "Dependency Length Minimization vs. Word Order Constraints: An Empirical Study On 55 Treebanks",
    author = "Yu, Xiang  and
      Falenska, Agnieszka  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7911",
    doi = "10.18653/v1/W19-7911",
    pages = "89--97",
}
@inproceedings{kahane-yan-2019-advantages,
    title = "Advantages of the flux-based interpretation of dependency length minimization",
    author = "Kahane, Sylvain  and
      Yan, Chunxiao",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7912",
    doi = "10.18653/v1/W19-7912",
    pages = "98--109",
}
@inproceedings{macutek-etal-2019-length,
    title = "Length of non-projective sentences: A pilot study using a {C}zech {UD} treebank",
    author = "Macutek, Jan  and
      Cech, Radek  and
      Milicka, Jiri",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7913",
    doi = "10.18653/v1/W19-7913",
    pages = "110--117",
}
@inproceedings{lesage-bonami-2019-gradient,
    title = "Gradient constraints on the use of {E}stonian possessive reflexives",
    author = "Lesage, Suzanne  and
      Bonami, Olivier",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7914",
    doi = "10.18653/v1/W19-7914",
    pages = "118--124",
}
@inproceedings{courtin-yan-2019-learn,
    title = "What can we learn from natural and artificial dependency trees",
    author = "Courtin, Marine  and
      Yan, Chunxiao",
    booktitle = "Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7915",
    doi = "10.18653/v1/W19-7915",
    pages = "125--135",
}
@proceedings{ws-2019-international-treebanks,
    title = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    editor = "Candito, Marie  and
      Evang, Kilian  and
      Oepen, Stephan  and
      Seddah, Djam{\'e}",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7800",
}
@inproceedings{merlo-2019-syntaxfest,
    title = "{S}yntax{F}est 2019 Invited talk - Quantitative Computational Syntax: dependencies, intervention effects and word embeddings",
    author = "Merlo, Paola",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7801",
    doi = "10.18653/v1/W19-7801",
    pages = "1--1",
}
@inproceedings{yadav-etal-2019-formal,
    title = "Are formal restrictions on crossing dependencies epiphenominal?",
    author = "Yadav, Himanshu  and
      Husain, Samar  and
      Futrell, Richard",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7802",
    doi = "10.18653/v1/W19-7802",
    pages = "2--12",
}
@inproceedings{caron-etal-2019-surface,
    title = "A Surface-Syntactic {UD} Treebank for {N}aija",
    author = "Caron, Bernard  and
      Courtin, Marine  and
      Gerdes, Kim  and
      Kahane, Sylvain",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7803",
    doi = "10.18653/v1/W19-7803",
    pages = "13--24",
}
@inproceedings{sharma-etal-2019-greenbergian,
    title = "Can {G}reenbergian universals be induced from language networks?",
    author = "Sharma, Kartik  and
      Swami, Kaivalya  and
      Shete, Aditya  and
      Husain, Samar",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7804",
    doi = "10.18653/v1/W19-7804",
    pages = "25--37",
}
@inproceedings{uresova-etal-2019-parallel,
    title = "Parallel Dependency Treebank Annotated with Interlinked Verbal Synonym Classes and Roles",
    author = "Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}ov{\'a}, Eva  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7805",
    doi = "10.18653/v1/W19-7805",
    pages = "38--50",
}
@inproceedings{hajicova-etal-2019-ordering,
    title = "Ordering of Adverbials of Time and Place in Grammars and in an Annotated {E}nglish-{C}zech Parallel Corpus",
    author = "Haji{\v{c}}ov{\'a}, Eva  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}  and
      Rysov{\'a}, Kate{\v{r}}ina",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7806",
    doi = "10.18653/v1/W19-7806",
    pages = "51--60",
}
@inproceedings{dyer-2019-weighted,
    title = "Weighted posets: Learning surface order from dependency trees",
    author = "Dyer, William",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7807",
    doi = "10.18653/v1/W19-7807",
    pages = "61--73",
}
@inproceedings{mambrini-passarotti-2019-linked,
    title = "Linked Open Treebanks. Interlinking Syntactically Annotated Corpora in the {LiLa} Knowledge Base of Linguistic Resources for {L}atin",
    author = "Mambrini, Francesco  and
      Passarotti, Marco",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7808",
    doi = "10.18653/v1/W19-7808",
    pages = "74--81",
}
@inproceedings{cetinoglu-coltekin-2019-challenges,
    title = "Challenges of Annotating a Code-Switching Treebank",
    author = {{\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7809",
    doi = "10.18653/v1/W19-7809",
    pages = "82--90",
}
@inproceedings{ghosh-etal-2019-dependency,
    title = "Dependency Parser for {B}engali-{E}nglish Code-Mixed Data enhanced with a Synthetic Treebank",
    author = "Ghosh, Urmi  and
      Sharma, Dipti  and
      Khanuja, Simran",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7810",
    doi = "10.18653/v1/W19-7810",
    pages = "91--99",
}
@inproceedings{rehbein-etal-2019-tweede,
    title = "twee{D}e {--} A {U}niversal {D}ependencies treebank for {G}erman tweets",
    author = "Rehbein, Ines  and
      Ruppenhofer, Josef  and
      Do, Bich-Ngoc",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7811",
    doi = "10.18653/v1/W19-7811",
    pages = "100--108",
}
@inproceedings{keersmaekers-etal-2019-creating,
    title = "Creating, Enriching and Valorizing Treebanks of {A}ncient {G}reek",
    author = "Keersmaekers, Alek  and
      Mercelis, Wouter  and
      Swaelens, Colin  and
      Van Hal, Toon",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7812",
    doi = "10.18653/v1/W19-7812",
    pages = "109--117",
}
@inproceedings{domotor-2019-syntax,
    title = "Syntax is clearer on the other side - Using parallel corpus to extract monolingual data",
    author = {D{\"o}m{\"o}t{\"o}r, Andrea},
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7813",
    doi = "10.18653/v1/W19-7813",
    pages = "118--125",
}
@inproceedings{gerdes-etal-2019-improving,
    title = "Improving Surface-syntactic {U}niversal {D}ependencies ({SUD}): {MWE}s and deep syntactic features",
    author = "Gerdes, Kim  and
      Guillaume, Bruno  and
      Kahane, Sylvain  and
      Perrier, Guy",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7814",
    doi = "10.18653/v1/W19-7814",
    pages = "126--132",
}
@inproceedings{anderson-etal-2019-artificially,
    title = "Artificially Evolved Chunks for Morphosyntactic Analysis",
    author = "Anderson, Mark  and
      Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7815",
    doi = "10.18653/v1/W19-7815",
    pages = "133--143",
}
@inproceedings{regnault-etal-2019-challenges,
    title = "Challenges of language change and variation: towards an extended treebank of Medieval {F}rench",
    author = "Regnault, Mathilde  and
      Pr{\'e}vost, Sophie  and
      Villemonte de la Clergerie, Eric",
    booktitle = "Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7816",
    doi = "10.18653/v1/W19-7816",
    pages = "144--150",
}
@proceedings{ws-2019-international-dependency,
    title = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    editor = "Gerdes, Kim  and
      Kahane, Sylvain",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7700",
}
@inproceedings{dupoux-2019-syntaxfest,
    title = "{S}yntax{F}est 2019 Invited talk - Inductive biases and language emergence in communicative agents",
    author = "Dupoux, Emmanuel",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7701",
    doi = "10.18653/v1/W19-7701",
    pages = "1--1",
}
@inproceedings{plank-2019-syntaxfest,
    title = "{S}yntax{F}est 2019 Invited talk - Transferring {NLP} models across languages and domains",
    author = "Plank, Barbara",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7702",
    doi = "10.18653/v1/W19-7702",
    pages = "2--2",
}
@inproceedings{futrell-etal-2019-syntactic,
    title = "Syntactic dependencies correspond to word pairs with high mutual information",
    author = "Futrell, Richard  and
      Qian, Peng  and
      Gibson, Edward  and
      Fedorenko, Evelina  and
      Blank, Idan",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7703",
    doi = "10.18653/v1/W19-7703",
    pages = "3--13",
}
@inproceedings{kettnerova-lopatkova-2019-reflexives,
    title = "Reflexives in {C}zech from a Dependency Perspective",
    author = "Kettnerova, Vaclava  and
      Lopatkova, Marketa",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7704",
    doi = "10.18653/v1/W19-7704",
    pages = "14--25",
}
@inproceedings{patejuk-przepiorkowski-2019-coordination,
    title = "Coordination of Unlike Grammatical Functions",
    author = "Patejuk, Agnieszka  and
      Przepi{\'o}rkowski, Adam",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7705",
    doi = "10.18653/v1/W19-7705",
    pages = "26--37",
}
@inproceedings{osborne-2019-predicate,
    title = "Predicate Catenae: A Dependency Grammar Analysis of It-Clefts",
    author = "Osborne, Timothy",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7706",
    doi = "10.18653/v1/W19-7706",
    pages = "38--48",
}
@inproceedings{osborne-2019-noun,
    title = "Noun Phrases Rooted by Adjectives: A Dependency Grammar Analysis of the Big Mess Construction",
    author = "Osborne, Timothy",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7707",
    doi = "10.18653/v1/W19-7707",
    pages = "49--59",
}
@inproceedings{milicevic-2019-cliticization,
    title = "Cliticization of {S}erbian Personal Pronouns and Auxiliary Verbs. A Dependency-Based Account",
    author = "Milicevic, Jasmina",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7708",
    doi = "10.18653/v1/W19-7708",
    pages = "60--68",
}
@inproceedings{mazziotta-2019-evolution,
    title = "The evolution of spatial rationales in Tesni{\`e}re{'}s stemmas",
    author = "Mazziotta, Nicolas",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7709",
    doi = "10.18653/v1/W19-7709",
    pages = "69--80",
}
@inproceedings{imrenyi-2019-toward,
    title = "Toward a cognitive dependency grammar of {H}ungarian",
    author = "Imr{\'e}nyi, Andr{\'a}s",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7710",
    doi = "10.18653/v1/W19-7710",
    pages = "81--88",
}
@inproceedings{kahane-2019-interpreting,
    title = "Interpreting and defining connections in a dependency structure",
    author = "Kahane, Sylvain",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7711",
    doi = "10.18653/v1/W19-7711",
    pages = "89--99",
}
@inproceedings{metheniti-etal-2019-identifying,
    title = "Identifying Grammar Rules for Language Education with Dependency Parsing in {G}erman",
    author = {Metheniti, Eleni  and
      Park, Pomi  and
      Kolesova, Kristina  and
      Neumann, G{\"u}nter},
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7712",
    doi = "10.18653/v1/W19-7712",
    pages = "100--111",
}
@inproceedings{meechan-maddon-nivre-2019-parse,
    title = "How to Parse Low-Resource Languages: Cross-Lingual Parsing, Target Language Annotation, or Both?",
    author = "Meechan-Maddon, Ailsa  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7713",
    doi = "10.18653/v1/W19-7713",
    pages = "112--120",
}
@inproceedings{kiss-thomas-2019-word,
    title = "Word order variation in {M}by{\'a} {G}uaran{\'\i}",
    author = "Kiss, Angelika  and
      Thomas, Guillaume",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7714",
    doi = "10.18653/v1/W19-7714",
    pages = "121--129",
}
@inproceedings{komori-etal-2019-examining,
    title = "Examining {MDD} and {MHD} as Syntactic Complexity Measures with Intermediate {J}apanese Learner Corpus Data",
    author = "Komori, Saeko  and
      Sugiura, Masatoshi  and
      Li, Wenping",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7715",
    doi = "10.18653/v1/W19-7715",
    pages = "130--135",
}
@inproceedings{lacroix-2019-dependency,
    title = "Dependency Parsing as Sequence Labeling with Head-Based Encoding and Multi-Task Learning",
    author = "Lacroix, Oph{\'e}lie",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7716",
    doi = "10.18653/v1/W19-7716",
    pages = "136--143",
}
@inproceedings{droganova-zeman-2019-towards,
    title = "Towards Deep {U}niversal {D}ependencies",
    author = "Droganova, Kira  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7717",
    doi = "10.18653/v1/W19-7717",
    pages = "144--152",
}
@inproceedings{mikulova-etal-2019-delimiting,
    title = "Delimiting Adverbial Meanings. A corpus-based comparative study on {C}zech spatial prepositions and their {E}nglish equivalents",
    author = "Mikulov{\'a}, Marie  and
      Kol{\'a}{\v{r}}ov{\'a}, Veronika  and
      Panevov{\'a}, Jarmila  and
      Haji{\v{c}}ov{\'a}, Eva",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7718",
    doi = "10.18653/v1/W19-7718",
    pages = "153--159",
}
@inproceedings{barrios-rodriguez-boguslavsky-2019-spanish,
    title = "A {S}panish {E}-dictionary of Collocations",
    author = "Barrios Rodriguez, Maria Auxiliadora  and
      Boguslavsky, Igor",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7719",
    doi = "10.18653/v1/W19-7719",
    pages = "160--167",
}
@inproceedings{galal-etal-2019-exceptive,
    title = "Exceptive constructions. A Dependency-based Analysis",
    author = "Galal, Mohamed  and
      Kahane, Sylvain  and
      Safwat, Yomna",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7720",
    doi = "10.18653/v1/W19-7720",
    pages = "168--174",
}
@inproceedings{liu-xu-2019-quantitative,
    title = "Quantitative Analysis on verb valence evolution of {C}hinese",
    author = "Liu, Bingli  and
      Xu, Chunshan",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7721",
    doi = "10.18653/v1/W19-7721",
    pages = "175--180",
}
@inproceedings{fischer-etal-2019-association,
    title = "Association Metrics in Neural Transition-Based Dependency Parsing",
    author = {Fischer, Patricia  and
      P{\"u}tz, Sebastian  and
      de Kok, Dani{\"e}l},
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7722",
    doi = "10.18653/v1/W19-7722",
    pages = "181--189",
}
@inproceedings{cignarella-etal-2019-presenting,
    title = "Presenting {TWITTIR{\`O}}-{UD}: An {I}talian {T}witter Treebank in {U}niversal {D}ependencies",
    author = "Cignarella, Alessandra Teresa  and
      Bosco, Cristina  and
      Rosso, Paolo",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7723",
    doi = "10.18653/v1/W19-7723",
    pages = "190--197",
}
@inproceedings{kulkarni-sharma-2019-paninian,
    title = "{P}{\=a}ṇinian Syntactico-Semantic Relation Labels",
    author = "Kulkarni, Amba  and
      Sharma, Dipti",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7724",
    doi = "10.18653/v1/W19-7724",
    pages = "198--208",
}
@inproceedings{mityushin-iomdin-2019-experiments,
    title = "Experiments on human incremental parsing",
    author = "Mityushin, Leonid  and
      Iomdin, Leonid",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7725",
    doi = "10.18653/v1/W19-7725",
    pages = "209--215",
}
@inproceedings{li-etal-2019-character,
    title = "Character-level Annotation for {C}hinese Surface-Syntactic {U}niversal {D}ependencies",
    author = "Li, Yixuan  and
      Kim, Gerdes  and
      Chuanming, Dong",
    booktitle = "Proceedings of the Fifth International Conference on Dependency Linguistics (Depling, SyntaxFest 2019)",
    month = aug,
    year = "2019",
    address = "Paris, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7726",
    doi = "10.18653/v1/W19-7726",
    pages = "216--226",
}
@proceedings{ws-2019-machine-translation-summit-xvii-3,
    title = "Proceedings of Machine Translation Summit XVII Volume 3: Tutorial Abstracts",
    editor = "Rossi, Laura",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7600",
}
@inproceedings{o-dowd-2019-unreasonable,
    title = "The unreasonable effectiveness of Neural Models in Language Decoding",
    author = "O'Dowd, Tony",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 3: Tutorial Abstracts",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7601",
    abstract = "This tutorial will provide an in-depth look at the experiments, jointly carried out by KantanMT and eBay during 2018, to determine which Neural Model delivers the best translation performance for eBay Customer Service content. It will lay out the timeline, process and mechanisms used to customise Neural MT models and how these were used in conjunction with Human Based evaluations to determine which approach to Neural MT provided the best translation outcomes.The tutorial will cover the following topics and methods:- Structural differences in Neural Networks and how they assist the language decoding process {--} RNN, CNN and TNN will be covered in detailed.- Customisation of Neural MT using the KantanMT Platform- Using MQM Framework for the evaluation and comparison of Translation Outputs and comparison to Human Translation- Collation and analysis of experimental findings in reaching our decision to standardise on Transformer type networks.Participants of the tutorial will get a clear understanding of Neural Model types and the differences, it will also cover how to customise these models and then how to set up a controlled experiment to determine translation performance.",
}
@inproceedings{popovic-castilho-2019-challenge,
    title = "Challenge Test Sets for {MT} Evaluation",
    author = "Popovi{\'c}, Maja  and
      Castilho, Sheila",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 3: Tutorial Abstracts",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7602",
    abstract = "Most of the test sets used for the evaluation of MT systems reflect the frequency distribution of different phenomena found in naturally occurring data ({''}standard{''} or {''}natural{''} test sets). However, to better understand particular strengths and weaknesses of MT systems, especially those based on neural networks, it is necessary to apply more focused evaluation procedures. Therefore, another type of test sets ({''}challenge{''} test sets, also called {''}test suites{''}) is being increasingly employed in order to highlight points of difficulty which are relevant to model development, training, or using of the given system. This tutorial will be useful for anyone (researchers, developers, users, translators) interested in detailed evaluation and getting a better understanding of machine translation (MT) systems and models. The attendees will learn about the motivation and linguistic background of challenge test sets and a range of testing possibilities applied to the state-of-the-art MT systems, as well as a number of practical aspects and challenges.",
}
@inproceedings{marg-yanishevsky-2019-deep,
    title = "A Deep Learning Curve for Post-Editing 2",
    author = "Marg, Lena  and
      Yanishevsky, Alex{\textgreater}",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 3: Tutorial Abstracts",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7603",
    abstract = "In the last couple of years, machine translation technology has seen major changes with the breakthrough of neural machine translation (NMT), a growing number of providers and translation platforms. Machine Translation generally is experiencing a peak in demand from translation buyers, thanks to Machine Learning and AI being omnipresent in the media and at industry events. At the same time, new models for defining translation quality are becoming more widely adopted. These changes have profound implications for translators, LSPs and translation buyers: translators have to adjust their post-editing approaches, while LSPs and translation buyers are faced with decisions on selecting providers, best approaches for updating MT systems, financial investments, integrating tools, and getting the timing for implementation right for an optimum ROI.In this tutorial on MT and post-editing we would like to continue sharing the latest trends in the field of MT technologies, and discuss their impact on post-editing practices as well as integrating MT on large, multi-language translation programs. We will look at tool compatibility, different use cases of MT and dynamic quality models, and share our experience of measuring performance.",
}
@inproceedings{toral-2019-practical,
    title = "Practical Statistics for Research in Machine Translation and Translation Studies",
    author = "Toral, Antonio",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 3: Tutorial Abstracts",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7604",
    abstract = "The tutorial will introduce a set of very useful statistical tests for conducting analyses in the research areas of Machine Translation (MT) and Translation Studies (TS). For each statistical test, the presenter will: 1) introduce it in the context of a common research example that pertains to the area of MT and/or TS 2) explain the technique behind the test and its assumptions 3) cover common pitfalls when the test is applied in research studies, and 4) conduct a hands-on activity so that attendees can put the knowledge acquired in practice straight-away. All examples and exercises will be in R. The following statistical tests will be covered: t-tests (both parametric and non-parametric), bootstrap resampling, Pearson and Spearman correlation coefficients, linear mixed-effects models.",
}
@proceedings{ws-2019-international-sanskrit,
    title = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    editor = "Goyal, Pawan",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7500",
}
@inproceedings{kulkarni-pai-2019-sanskrit,
    title = "{S}anskrit Sentence Generator",
    author = "Kulkarni, Amba  and
      Pai, Madhusoodana",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7501",
    pages = "1--13",
}
@inproceedings{kulkarni-etal-2019-dependency,
    title = "Dependency Parser for {S}anskrit Verses",
    author = "Kulkarni, Amba  and
      Vikram, Sanal  and
      K, Sriram",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7502",
    pages = "14--27",
}
@inproceedings{sandhan-etal-2019-revisiting,
    title = "Revisiting the Role of Feature Engineering for Compound Type Identification in {S}anskrit",
    author = "Sandhan, Jivnesh  and
      Krishna, Amrith  and
      Goyal, Pawan  and
      Behera, Laxmidhar",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7503",
    pages = "28--44",
}
@inproceedings{b-etal-2019-machine,
    title = "A Machine Learning Approach for Identifying Compound Words from a {S}anskrit Text",
    author = "B, Premjith  and
      V, Chandni Chandran  and
      Bhat, Shriganesh  and
      Kp, Soman  and
      P, Prabaharan",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7504",
    pages = "45--51",
}
@inproceedings{neill-2019-lda,
    title = "{LDA} Topic Modeling for pram{\=a}ṇa Texts: A Case Study in {S}anskrit {NLP} Corpus Building",
    author = "Neill, Tyler",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7505",
    pages = "52--67",
}
@inproceedings{susarla-challa-2019-platform,
    title = "A Platform for Community-sourced Indic Knowledge Processing at Scale",
    author = "Susarla, Sai  and
      Challa, Damodar Reddy",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7506",
    pages = "68--82",
}
@inproceedings{meyer-2019-sanskrit,
    title = "On {S}anskrit and Information Retrieval",
    author = {Meyer, Micha{\"e}l},
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7507",
    pages = "83--96",
}
@inproceedings{terdalkar-bhattacharya-2019-framework,
    title = "Framework for Question-Answering in {S}anskrit through Automated Construction of Knowledge Graphs",
    author = "Terdalkar, Hrishikesh  and
      Bhattacharya, Arnab",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7508",
    pages = "97--116",
}
@inproceedings{kulkarni-etal-2019-introduction,
    title = "Introduction to {S}anskrit Shabdamitra: An Educational Application of {S}anskrit {W}ordnet",
    author = "Kulkarni, Malhar  and
      Joshi, Nilesh  and
      Khare, Sayali  and
      Redkar, Hanumant  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7509",
    pages = "117--133",
}
@inproceedings{vayuvegula-etal-2019-vaijayantikosa,
    title = "Vaijayant{\=\i}ko{\'s}a Knowledge-Net",
    author = "Vayuvegula, Aruna  and
      Kanugovi, Satish  and
      Nair, Sivaja S  and
      V, Shivani",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7510",
    pages = "134--151",
}
@inproceedings{kanojia-etal-2019-utilizing,
    title = "Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of {S}anskrit Texts",
    author = "Kanojia, Diptesh  and
      Dubey, Abhijeet  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak  and
      Haffari, Gholemreza",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7511",
    pages = "152--165",
}
@inproceedings{kanojia-etal-2019-introduction,
    title = "An Introduction to the Textual History Tool",
    author = "Kanojia, Diptesh  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak  and
      Kahrs, Eivind",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7512",
    pages = "166--180",
}
@inproceedings{basapur-etal-2019-pali,
    title = "P{\=a}li Sandhi {--} A computational approach",
    author = "Basapur, Swati  and
      V, Shivani  and
      Nair, Sivaja",
    booktitle = "Proceedings of the 6th International Sanskrit Computational Linguistics Symposium",
    month = oct,
    year = "2019",
    address = "IIT Kharagpur, India",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7513",
    pages = "181--192",
}
@proceedings{ws-2019-international-natural,
    title = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    editor = "Abbas, Mourad  and
      Freihat, Abed Alhakim",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7400",
}
@inproceedings{kosmajac-keselj-2019-twitter,
    title = "{T}witter Bot Detection using Diversity Measures",
    author = "Kosmajac, Dijana  and
      Keselj, Vlado",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7401",
    pages = "1--8",
}
@inproceedings{nair-etal-2019-aligning,
    title = "Aligning the {I}ndo{W}ord{N}et with the {P}rinceton {W}ord{N}et",
    author = "Nair, Nandu Chandran  and
      Velayuthan, Rajendran Sankara  and
      Batsuren, Khuyagbaatar",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7402",
    pages = "9--16",
}
@inproceedings{lindsay-etal-2019-automatic,
    title = "Automatic Data-Driven Approaches for Evaluating the Phonemic Verbal Fluency Task with Healthy Adults",
    author = "Lindsay, Hali  and
      Linz, Nicklas  and
      Troeger, Johannes  and
      Alexandersson, Jan",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7403",
    pages = "17--24",
}
@inproceedings{mensonides-etal-2019-automatic,
    title = "Automatic Detection and Classification of Argument Components using Multi-task Deep Neural Network",
    author = "Mensonides, Jean-Christophe  and
      Harispe, S{\'e}bastien  and
      Montmain, Jacky  and
      Thireau, V{\'e}ronique",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7404",
    pages = "25--33",
}
@inproceedings{jain-etal-2019-multi,
    title = "Multi Sense Embeddings from Topic Models",
    author = "Jain, Shobhit  and
      Bodapati, Sravan Babu  and
      Nallapati, Ramesh  and
      Anandkumar, Anima",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7405",
    pages = "34--41",
}
@inproceedings{qassem-etal-2019-automatic,
    title = "Automatic {A}rabic Text Summarization Based on Fuzzy Logic",
    author = "Qassem, Lamees Al  and
      Wang, Di  and
      Barada, Hassan  and
      Al-Rubaie, Ahmad  and
      Almoosa, Nawaf",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7406",
    pages = "42--48",
}
@inproceedings{lichouri-etal-2019-arabic,
    title = "An {A}rabic Multi-Domain Spoken Language Understanding System",
    author = "Lichouri, Mohamed  and
      Abbas, Mourad  and
      Djeradi, Rachida  and
      Djeradi, Amar",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7407",
    pages = "49--53",
}
@inproceedings{lounnas-etal-2019-building,
    title = "Building a Speech Corpus based on {A}rabic Podcasts for Language and Dialect Identification",
    author = "Lounnas, Khaled  and
      Abbas, Mourad  and
      Lichouri, Mohamed",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7408",
    pages = "54--58",
}
@inproceedings{elnagar-etal-2019-automatic,
    title = "Automatic Text Tagging of {A}rabic News Articles Using Ensemble Deep Learning Models",
    author = "Elnagar, Ashraf  and
      Einea, Omar  and
      Al-Debsi, Ridhwan",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7409",
    pages = "59--66",
}
@inproceedings{kuriakose-2019-probabilistic,
    title = "A Probabilistic Approach for Confidence Scoring in Speech Recognition",
    author = "Kuriakose, Punnoose",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7410",
    pages = "67--74",
}
@inproceedings{zine-etal-2019-crowdsourcing,
    title = "A Crowdsourcing-based Approach for Speech Corpus Transcription Case of {A}rabic {A}lgerian Dialects",
    author = "Zine, Ilyes  and
      Zeghad, Mohamed Cherif  and
      Bougrine, Soumia  and
      Cherroun, Hadda",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7411",
    pages = "75--83",
}
@inproceedings{ahmed-2019-sample,
    title = "Sample Size in {A}rabic Authorship Verification",
    author = "Ahmed, Hossam",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7412",
    pages = "84--91",
}
@inproceedings{wu-li-2019-folksonomy,
    title = "A folksonomy-based approach for profiling human perception on word similarity",
    author = "Wu, Guani  and
      Li, Ker-Chau",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7413",
    pages = "92--99",
}
@inproceedings{hamed-2019-automatic,
    title = "Automatic Diacritization as Prerequisite Towards the Automatic Generation of {A}rabic Lexical Recognition Tests",
    author = "Hamed, Osama",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7414",
    pages = "100--106",
}
@inproceedings{chen-etal-2019-expanding,
    title = "Expanding {E}nglish and {C}hinese Dictionaries by {W}ikipedia Titles",
    author = "Chen, Wei-Ting  and
      Wang, Yu-Te  and
      Lin, Chuan-Jie",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7415",
    pages = "107--113",
}
@inproceedings{koupka-2019-production,
    title = "Production of Voicing Contrast in Children with Cochlear Implants",
    author = "Koupka, Georgia",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7416",
    pages = "114--119",
}
@inproceedings{cheragui-lakhdar-2019-sumsat,
    title = "{S}um{SAT}: Hybrid {A}rabic Text Summarization based on symbolic and numerical Approaches",
    author = "Cheragui, Mohamed Amine  and
      Lakhdar, Said Moulay",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7417",
    pages = "120--127",
}
@inproceedings{boumaraf-merazka-2019-speech,
    title = "Speech Coding Combining Chaos Encryption and Error Recovery for G.722.2 Codec",
    author = "Boumaraf, Messaouda  and
      Merazka, Fatiha",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7418",
    pages = "128--134",
}
@inproceedings{ziai-2019-compositional,
    title = "Compositional pre-training for neural semantic parsing",
    author = "Ziai, Amir",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7419",
    pages = "135--141",
}
@inproceedings{karanikolos-refanidis-2019-encoding,
    title = "Encoding Position Improves Recurrent Neural Text Summarizers",
    author = "Karanikolos, Apostolos  and
      Refanidis, Ioannis",
    booktitle = "Proceedings of the 3rd International Conference on Natural Language and Speech Processing",
    month = sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-7420",
    pages = "142--150",
}
@proceedings{ws-2019-qualities,
    title = "Proceedings of the Qualities of Literary Machine Translation",
    editor = "Hadley, James  and
      Popovi{\'c}, Maja  and
      Afli, Haithem  and
      Way, Andy",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7300",
}
@inproceedings{kuzman-etal-2019-neural,
    title = "Neural Machine Translation of Literary Texts from {E}nglish to {S}lovene",
    author = "Kuzman, Taja  and
      Vintar, {\v{S}}pela  and
      Ar{\v{c}}an, Mihael",
    booktitle = "Proceedings of the Qualities of Literary Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7301",
    pages = "1--9",
}
@inproceedings{matusov-2019-challenges,
    title = "The Challenges of Using Neural Machine Translation for Literature",
    author = "Matusov, Evgeny",
    booktitle = "Proceedings of the Qualities of Literary Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7302",
    pages = "10--19",
}
@inproceedings{murchu-2019-using,
    title = "Using Intergaelic to pre-translate and subsequently post-edit a sci-fi novel from {S}cottish {G}aelic to {I}rish",
    author = "Murch{\'u}, Eoin P. {\'O}",
    booktitle = "Proceedings of the Qualities of Literary Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7303",
    pages = "20--25",
}
@inproceedings{sahin-gurses-2019-mt,
    title = "Would {MT} kill creativity in literary retranslation?",
    author = {{\c{S}}ahin, Mehmet  and
      G{\"u}rses, Sabri},
    booktitle = "Proceedings of the Qualities of Literary Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7304",
    pages = "26--34",
}
@inproceedings{taivalkoski-shilov-2019-free,
    title = "Free indirect discourse: an insurmountable challenge for literary {MT} systems?",
    author = "Taivalkoski-Shilov, Kristiina",
    booktitle = "Proceedings of the Qualities of Literary Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7305",
    pages = "35--39",
}
@inproceedings{tezcan-etal-2019-sport,
    title = "When a {`}sport{'} is a person and other issues for {NMT} of novels",
    author = "Tezcan, Arda  and
      Daems, Joke  and
      Macken, Lieve",
    booktitle = "Proceedings of the Qualities of Literary Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7306",
    pages = "40--49",
}
@proceedings{ws-2019-patent,
    title = "Proceedings of The 8th Workshop on Patent and Scientific Literature Translation",
    editor = "Utsuro, Takehito  and
      Sudoh, Katsuhito  and
      Tsunakawa, Takashi",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7200",
}
@inproceedings{ono-etal-2019-hybrid,
    title = "Hybrid Data-Model Parallel Training for Sequence-to-Sequence Recurrent Neural Network Machine Translation",
    author = "Ono, Junya  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of The 8th Workshop on Patent and Scientific Literature Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7201",
    pages = "4--12",
}
@inproceedings{poncelas-etal-2019-transductive,
    title = "Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation",
    author = "Poncelas, Alberto  and
      Maillette de Buy Wenniger, Gideon  and
      Way, Andy",
    booktitle = "Proceedings of The 8th Workshop on Patent and Scientific Literature Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7202",
    pages = "13--23",
}
@inproceedings{iida-etal-2019-multi,
    title = "A Multi-Hop Attention for {RNN} based Neural Machine Translation",
    author = "Iida, Shohei  and
      Kimura, Ryuichiro  and
      Cui, Hongyi  and
      Hung, Po-Hsuan  and
      Utsuro, Takehito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of The 8th Workshop on Patent and Scientific Literature Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7203",
    pages = "24--31",
}
@inproceedings{nurminen-2019-decision,
    title = "Decision-making, Risk, and Gist Machine Translation in the Work of Patent Professionals",
    author = "Nurminen, Mary",
    booktitle = "Proceedings of The 8th Workshop on Patent and Scientific Literature Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7204",
    pages = "32--42",
}
@proceedings{ws-2019-multilingualism,
    title = "Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation",
    editor = "Arcan, Mihael  and
      Turchi, Marco  and
      Du, Jinhua  and
      Shterionov, Dimitar  and
      Torregrosa, Daniel",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7100",
}
@inproceedings{chakravarthi-etal-2019-wordnet,
    title = "{W}ord{N}et Gloss Translation for Under-resourced Languages using Multilingual Neural Machine Translation",
    author = "Chakravarthi, Bharathi Raja  and
      Arcan, Mihael  and
      McCrae, John P.",
    booktitle = "Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7101",
    pages = "1--7",
}
@inproceedings{soto-etal-2019-leveraging,
    title = "Leveraging {SNOMED} {CT} terms and relations for machine translation of clinical texts from {B}asque to {S}panish",
    author = "Soto, Xabier  and
      Perez-De-Vi{\~n}aspre, Olatz  and
      Oronoz, Maite  and
      Labaka, Gorka",
    booktitle = "Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7102",
    pages = "8--18",
}
@proceedings{ws-2019-memento,
    title = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    editor = "Carl, Michael  and
      Hansen-Schirra, Silvia",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7000",
}
@inproceedings{do-carmo-2019-edit,
    title = "Edit distances do not describe editing, but they can be useful for translation process research",
    author = "do Carmo, F{\'e}lix",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7001",
    pages = "1--2",
}
@inproceedings{vanroy-etal-2019-modelling,
    title = "Modelling word translation entropy and syntactic equivalence with machine learning",
    author = "Vanroy, Bram  and
      De Clercq, Orph{\'e}e  and
      Macken, Lieve",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7002",
    pages = "3--4",
}
@inproceedings{cumbreno-aranberri-2019-comparison,
    title = "Comparison of temporal, technical and cognitive dimension measurements for post-editing effort",
    author = "Cumbreno, Cristina  and
      Aranberri, Nora",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7003",
    pages = "5--6",
}
@inproceedings{vardaro-etal-2019-translation,
    title = "Translation Quality and Effort Prediction in Professional Machine Translation Post-Editing",
    author = "Vardaro, Jennifer  and
      Schaeffer, Moritz  and
      Hansen-Schirra, Silvia",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7004",
    pages = "7--8",
}
@inproceedings{rossi-esperanca-rodier-2019-without,
    title = "With or without post-editing processes? Evidence for a gap in machine translation evaluation",
    author = "Rossi, Caroline  and
      Esperan{\c{c}}a-Rodier, Emmanuelle",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7005",
    pages = "9--10",
}
@inproceedings{almazroei-etal-2019-investigating,
    title = "Investigating Correlations Between Human Translation and {MT} Output",
    author = "Almazroei, Samar A.  and
      Ogawa, Haruka  and
      Gilbert, Devin",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7006",
    pages = "11--13",
}
@inproceedings{sahoo-carl-2019-lexical,
    title = "Lexical Representation {\&} Retrieval on Monolingual Interpretative text production",
    author = "Sahoo, Debasish  and
      Carl, Michael",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7007",
    pages = "14--16",
}
@inproceedings{wei-2019-predicting,
    title = "Predicting Cognitive Effort in Translation Production",
    author = "Wei, Yuxiang",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7008",
    pages = "17--17",
}
@inproceedings{kuang-2019-computerized,
    title = "Computerized Note-taking in Consecutive Interpreting: A Pen-voice Integrated Approach towards Omissions, Additions and Reconstructions in Notes",
    author = "Kuang, Huolingxiao",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7009",
    pages = "18--18",
}
@inproceedings{tardel-etal-2019-automatization,
    title = "Automatization of subprocesses in subtitling",
    author = "Tardel, Anke  and
      Hansen-Schirra, Silvia  and
      Gutermuth, Silke  and
      Schaeffer, Moritz",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7010",
    pages = "19--20",
}
@inproceedings{dardi-2019-correlating,
    title = "Correlating Metaphors to Behavioural Data: A {CRITT} {TPR}-{DB}-based Study",
    author = "Dardi, Faustino",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7011",
    pages = "21--22",
}
@inproceedings{chen-etal-2019-exploring,
    title = "Exploring Cognitive Effort in Written Translation of {C}hinese Neologisms: An Eye-tracking and Keylogging Study",
    author = "Chen, Jinjin  and
      Li, Defeng  and
      Lei, Victoria",
    booktitle = "Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-7012",
    pages = "23--23",
}
@proceedings{ws-2019-celtic,
    title = "Proceedings of the Celtic Language Technology Workshop",
    editor = "Lynn, Teresa  and
      Prys, Delyth  and
      Batchelor, Colin  and
      Tyers, Francis",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6900",
}
@inproceedings{spasic-etal-2019-unsupervised,
    title = "Unsupervised multi-word term recognition in {W}elsh",
    author = "Spasi{\'c}, Irena  and
      Owen, David  and
      Knight, Dawn  and
      Artemiou, Andreas",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6901",
    pages = "1--6",
}
@inproceedings{batchelor-2019-universal,
    title = "Universal dependencies for {S}cottish {G}aelic: syntax",
    author = "Batchelor, Colin",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6902",
    pages = "7--15",
}
@inproceedings{bell-2019-speech,
    title = "Speech technology and Argentinean {W}elsh",
    author = "Bell, Elise",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6903",
    pages = "16--20",
}
@inproceedings{heinecke-tyers-2019-development,
    title = "Development of a {U}niversal {D}ependencies treebank for {W}elsh",
    author = "Heinecke, Johannes  and
      Tyers, Francis M.",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6904",
    pages = "21--31",
}
@inproceedings{lynn-scannell-2019-code,
    title = "Code-switching in {I}rish tweets: A preliminary analysis",
    author = "Lynn, Teresa  and
      Scannell, Kevin",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6905",
    pages = "32--40",
}
@inproceedings{prys-jones-2019-embedding,
    title = "Embedding {E}nglish to {W}elsh {MT} in a Private Company",
    author = "Prys, Myfyr  and
      Jones, Dewi Bryn",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6906",
    pages = "41--47",
}
@inproceedings{mccrae-doyle-2019-adapting,
    title = "Adapting Term Recognition to an Under-Resourced Language: the Case of {I}rish",
    author = "McCrae, John P.  and
      Doyle, Adrian",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6907",
    pages = "48--57",
}
@inproceedings{dowling-etal-2019-leveraging,
    title = "Leveraging backtranslation to improve machine translation for {G}aelic languages",
    author = "Dowling, Meghan  and
      Lynn, Teresa  and
      Way, Andy",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6908",
    pages = "58--62",
}
@inproceedings{o-raghallaigh-etal-2019-improving,
    title = "Improving full-text search results on d{\'u}chas.ie using language technology",
    author = "{\'O} Raghallaigh, Brian  and
      Scannell, Kevin  and
      Dowling, Meghan",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6909",
    pages = "63--69",
}
@inproceedings{doyle-etal-2019-character,
    title = {A Character-Level {LSTM} Network Model for Tokenizing the {O}ld {I}rish text of the {W}{\"u}rzburg Glosses on the {P}auline Epistles},
    author = "Doyle, Adrian  and
      McCrae, John P.  and
      Downey, Clodagh",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6910",
    pages = "70--79",
}
@inproceedings{ward-etal-2019-green,
    title = "A Green Approach for an {I}rish App (Refactor, reuse and keeping it real)",
    author = "Ward, Monica  and
      Mozgovoy, Maxim  and
      Purgina, Marina",
    booktitle = "Proceedings of the Celtic Language Technology Workshop",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6911",
    pages = "80--88",
}
@proceedings{ws-2019-technologies,
    title = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    editor = "Karakanta, Alina  and
      Ojha, Atul Kr.  and
      Liu, Chao-Hong  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Lakew, Surafel Melaku  and
      Malykh, Valentin  and
      Zhao, Xiaobing",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6800",
}
@inproceedings{thottingal-2019-finite,
    title = "Finite State Transducer based Morphology analysis for {M}alayalam Language",
    author = "Thottingal, Santhosh",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6801",
    pages = "1--5",
}
@inproceedings{uddin-uddin-2019-step,
    title = "A step towards {T}orwali machine translation: an analysis of morphosyntactic challenges in a low-resource language",
    author = "Uddin, Naeem  and
      Uddin, Jalal",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6802",
    pages = "6--10",
}
@inproceedings{pirinen-2019-workflows,
    title = "Workflows for kickstarting {RBMT} in virtually No-Resource Situation",
    author = "Pirinen, Tommi A",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6803",
    pages = "11--16",
}
@inproceedings{montoya-etal-2019-continuous,
    title = "A Continuous Improvement Framework of Machine Translation for {S}hipibo-Konibo",
    author = "Montoya, H{\'e}ctor Erasmo G{\'o}mez  and
      Rojas, Kervy Dante Rivas  and
      Oncevay, Arturo",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6804",
    pages = "17--23",
}
@inproceedings{gokirmak-etal-2019-machine,
    title = "Machine Translation for {C}rimean {T}atar to {T}urkish",
    author = {G{\"o}k{\i}rmak, Memduh  and
      Tyers, Francis  and
      Washington, Jonathan},
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6805",
    pages = "24--31",
}
@inproceedings{defauw-etal-2019-developing,
    title = "Developing a Neural Machine Translation system for {I}rish",
    author = "Defauw, Arne  and
      Szoc, Sara  and
      Vanallemeersch, Tom  and
      Bardadym, Anna  and
      Brabers, Joris  and
      Everaert, Frederic  and
      Scholte, Kim  and
      Van Winckel, Koen  and
      Van den Bogaert, Joachim",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6806",
    pages = "32--38",
}
@inproceedings{mueller-lal-2019-sentence,
    title = "Sentence-Level Adaptation for Low-Resource Neural Machine Translation",
    author = "Mueller, Aaron  and
      Lal, Yash Kumar",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6807",
    pages = "39--47",
}
@inproceedings{tracey-etal-2019-corpus,
    title = "Corpus Building for Low Resource Languages in the {DARPA} {LORELEI} Program",
    author = "Tracey, Jennifer  and
      Strassel, Stephanie  and
      Bies, Ann  and
      Song, Zhiyi  and
      Arrigo, Michael  and
      Griffitt, Kira  and
      Delgado, Dana  and
      Graff, Dave  and
      Kulick, Seth  and
      Mott, Justin  and
      Kuster, Neil",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6808",
    pages = "48--55",
}
@inproceedings{chakravarthi-etal-2019-multilingual,
    title = "Multilingual Multimodal Machine Translation for {D}ravidian Languages utilizing Phonetic Transcription",
    author = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Stearns, Bernardo  and
      Jayapal, Arun  and
      S, Sridevy  and
      Arcan, Mihael  and
      Zarrouk, Manel  and
      McCrae, John P",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6809",
    pages = "56--63",
}
@inproceedings{yadav-etal-2019-a3,
    title = "A3-108 Machine Translation System for {L}o{R}es{MT} 2019",
    author = "Yadav, Saumitra  and
      Mujadia, Vandan  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6810",
    pages = "64--67",
}
@inproceedings{bandyopadhyay-2019-factored,
    title = "Factored Neural Machine Translation at {L}o{R}es{MT} 2019",
    author = "Bandyopadhyay, Saptarashmi",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6811",
    pages = "68--71",
}
@inproceedings{mcnamee-2019-jhu,
    title = "{JHU} {L}o{R}es{MT} 2019 Shared Task System Description",
    author = "McNamee, Paul",
    booktitle = "Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6812",
    pages = "72--75",
}
@proceedings{ws-2019-machine-translation-summit-xvii,
    title = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    editor = "Forcada, Mikel  and
      Way, Andy  and
      Tinsley, John  and
      Shterionov, Dimitar  and
      Rico, Celia  and
      Gaspari, Federico",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6700",
}
@inproceedings{vasiljevs-etal-2019-competitiveness,
    title = "Competitiveness Analysis of the {E}uropean Machine Translation Market",
    author = "Vasi{\c{l}}jevs, Andrejs  and
      Skadi{\c{n}}a, Inguna  and
      S{\=a}m{\=\i}te, Indra  and
      Kauli{\c{n}}{\v{s}}, Kaspars  and
      Ajausks, {\=E}riks  and
      Me{\c{l}}{\c{n}}ika, J{\=u}lija  and
      B{\=e}rzi{\c{n}}{\v{s}}, Aivars",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6701",
    pages = "1--7",
}
@inproceedings{vela-etal-2019-improving,
    title = "Improving {CAT} Tools in the Translation Workflow: New Approaches and Evaluation",
    author = "Vela, Mihaela  and
      Pal, Santanu  and
      Zampieri, Marcos  and
      Naskar, Sudip  and
      van Genabith, Josef",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6702",
    pages = "8--15",
}
@inproceedings{lesznyak-2019-hungarian,
    title = "{H}ungarian translators{'} perceptions of Neural Machine Translation in the {E}uropean Commission",
    author = "Leszny{\'a}k, {\'A}gnes",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6703",
    pages = "16--22",
}
@inproceedings{iwai-etal-2019-applying,
    title = "Applying Machine Translation to Psychology: Automatic Translation of Personality Adjectives",
    author = "Iwai, Ritsuko  and
      Kawahara, Daisuke  and
      Kumada, Takatsune  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6704",
    pages = "23--29",
}
@inproceedings{bayon-sanchez-gijon-2019-evaluating,
    title = "Evaluating machine translation in a low-resource language combination: {S}panish-{G}alician.",
    author = "Bay{\'o}n, Mar{\'\i}a Do Campo  and
      S{\'a}nchez-Gij{\'o}n, Pilar",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6705",
    pages = "30--35",
}
@inproceedings{premoli-etal-2019-mtpe,
    title = "{MTPE} in Patents: A Successful Business Story",
    author = "Premoli, Valeria  and
      Murgolo, Elena  and
      Cresceri, Diego",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6706",
    pages = "36--41",
}
@inproceedings{heinisch-lusicky-2019-user,
    title = "User expectations towards machine translation: A case study",
    author = "Heinisch, Barbara  and
      Lu{\v{s}}icky, Vesna",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6707",
    pages = "42--48",
}
@inproceedings{alvarez-etal-2019-nmt,
    title = "Does {NMT} make a difference when post-editing closely related languages? The case of {S}panish-{C}atalan",
    author = "Alvarez, Sergi  and
      Oliver, Antoni  and
      Badia, Toni",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6708",
    pages = "49--56",
}
@inproceedings{nunziatini-2019-machine,
    title = "Machine Translation in the Financial Services Industry: A Case Study",
    author = "Nunziatini, Mara",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6709",
    pages = "57--63",
}
@inproceedings{hiraoka-yamada-2019-pre,
    title = "Pre-editing Plus Neural Machine Translation for Subtitling: Effective Pre-editing Rules for Subtitling of {TED} Talks",
    author = "Hiraoka, Yusuke  and
      Yamada, Masaru",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6710",
    pages = "64--72",
}
@inproceedings{scansani-etal-2019-translator,
    title = "Do translator trainees trust machine translation? An experiment on post-editing and revision",
    author = "Scansani, Randy  and
      Bernardini, Silvia  and
      Ferraresi, Adriano  and
      Bentivogli, Luisa",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6711",
    pages = "73--79",
}
@inproceedings{popovic-2019-reducing,
    title = "On reducing translation shifts in translations intended for {MT} evaluation",
    author = "Popovic, Maja",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6712",
    pages = "80--87",
}
@inproceedings{ovchinnikova-morozova-2019-comparative,
    title = "Comparative Analysis of Errors in {MT} Output and Computer-assisted Translation: Effect of the Human Factor",
    author = "Ovchinnikova, Irina  and
      Morozova, Daria",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6713",
    pages = "88--94",
}
@inproceedings{qian-etal-2019-comparative,
    title = "A Comparative Study of {E}nglish-{C}hinese Translations of Court Texts by Machine and Human Translators and the {W}ord2{V}ec Based Similarity Measure{'}s Ability To Gauge Human Evaluation Biases",
    author = "Qian, Ming  and
      Liu, Jessie  and
      Li, Chaofeng  and
      Pals, Liming",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6714",
    pages = "95--100",
}
@inproceedings{chen-kageura-2019-translating,
    title = "Translating Terminologies: A Comparative Examination of {NMT} and {PBSMT} Systems",
    author = "Chen, Long-Huei  and
      Kageura, Kyo",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6715",
    pages = "101--108",
}
@inproceedings{helle-herranz-2019-nec,
    title = "{NEC TM} Data Project",
    author = "Helle, Alexandre  and
      Herranz, Manuel",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6716",
    pages = "109--109",
}
@inproceedings{van-den-bogaert-etal-2019-ape,
    title = "{APE}-{QUEST}",
    author = "Van den Bogaert, Joachim  and
      Depraetere, Heidi  and
      Szoc, Sara  and
      Vanallemeersch, Tom  and
      Van Winckel, Koen  and
      Everaert, Frederic  and
      Specia, Lucia  and
      Ive, Julia  and
      Khalilov, Maxim  and
      Maroti, Christine  and
      Farah, Eduardo  and
      Ventura, Artur",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6717",
    pages = "110--111",
}
@inproceedings{way-gaspari-2019-principle,
    title = "{PRINCIPLE}: Providing Resources in {I}rish, {N}orwegian, {C}roatian and {I}celandic for the Purposes of Language Engineering",
    author = "Way, Andy  and
      Gaspari, Federico",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6718",
    pages = "112--113",
}
@inproceedings{garcia-martinez-etal-2019-iadaatpa,
    title = "i{ADAATPA} Project: Pangeanic use cases",
    author = "Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Estela, Amando  and
      Bi{\'e}, Laurent  and
      Helle, Alexandre  and
      Herranz, Manuel",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6719",
    pages = "114--115",
}
@inproceedings{van-den-bogaert-etal-2019-mice,
    title = "{MICE}",
    author = "Van den Bogaert, Joachim  and
      Depraetere, Heidi  and
      Vanallemeersch, Tom  and
      Everaert, Frederic  and
      Van Winckel, Koen  and
      Tammsaar, Katri  and
      Vali, Ingmar  and
      Artma, Tambet  and
      Saartee, Piret  and
      Teder, Laura Katariina  and
      Vasi{\c{l}}evskis, Art{\=u}rs  and
      Sics, Valters  and
      Haelterman, Johan  and
      Bienfait, David",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6720",
    pages = "116--117",
}
@inproceedings{espla-etal-2019-paracrawl,
    title = "{P}ara{C}rawl: Web-scale parallel corpora for the languages of the {EU}",
    author = "Espl{\`a}, Miquel  and
      Forcada, Mikel  and
      Ram{\'\i}rez-S{\'a}nchez, Gema  and
      Hoang, Hieu",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6721",
    pages = "118--119",
}
@inproceedings{liu-etal-2019-pivot,
    title = "Pivot Machine Translation in {INTERACT} Project",
    author = "Liu, Chao-Hong  and
      Way, Andy  and
      Silva, Catarina  and
      Martins, Andr{\'e}",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6722",
    pages = "120--121",
}
@inproceedings{birch-etal-2019-global,
    title = "Global Under-Resourced Media Translation ({G}o{URMET})",
    author = "Birch, Alexandra  and
      Haddow, Barry  and
      Tito, Ivan  and
      Barone, Antonio Valerio Miceli  and
      Bawden, Rachel  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Forcada, Mikel L.  and
      Espl{\`a}-Gomis, Miquel  and
      S{\'a}nchez-Cartagena, V{\'\i}ctor  and
      P{\'e}rez-Ortiz, Juan Antonio  and
      Aziz, Wilker  and
      Secker, Andrew  and
      van der Kreeft, Peggy",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6723",
    pages = "122--122",
}
@inproceedings{tukeyev-zhumanov-2019-neural,
    title = "Neural machine translation system for the {K}azakh language",
    author = "Tukeyev, Ualsher  and
      Zhumanov, Zhandos",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6724",
    pages = "123--124",
}
@inproceedings{torregrosa-etal-2019-leveraging,
    title = "Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models",
    author = "Torregrosa, Daniel  and
      Pasricha, Nivranshu  and
      Masoud, Maraim  and
      Chakravarthi, Bharathi Raja  and
      Alonso, Juan  and
      Casas, Noe  and
      Arcan, Mihael",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6725",
    pages = "125--133",
}
@inproceedings{roturier-etal-2019-bootstrapping,
    title = "Bootstrapping a Natural Language Interface to a Cyber Security Event Collection System using a Hybrid Translation Approach",
    author = "Roturier, Johann  and
      Schlatter, Brian  and
      Schlatter, David Silva",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6726",
    pages = "134--141",
}
@inproceedings{gupta-etal-2019-improving-robustness,
    title = "Improving Robustness in Real-World Neural Machine Translation Engines",
    author = "Gupta, Rohit  and
      Lambert, Patrik  and
      Patel, Raj  and
      Tinsley, John",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6727",
    pages = "142--148",
}
@inproceedings{liyanapathirana-etal-2019-surveying,
    title = "Surveying the potential of using speech technologies for post-editing purposes in the context of international organizations: What do professional translators think?",
    author = "Liyanapathirana, Jeevanthi  and
      Bouillon, Pierrette  and
      Mesa-Lao, Bartolom{\'e}",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6728",
    pages = "149--158",
}
@inproceedings{schmidtke-groves-2019-automatic,
    title = "Automatic Translation for Software with Safe Velocity",
    author = "Schmidtke, Dag  and
      Groves, Declan",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6729",
    pages = "159--166",
}
@inproceedings{kosmaczewska-train-2019-application,
    title = "Application of Post-Edited Machine Translation in Fashion e{C}ommerce",
    author = "Kosmaczewska, Kasia  and
      Train, Matt",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6730",
    pages = "167--173",
}
@inproceedings{bernardinello-2019-morphological,
    title = "Morphological Neural Pre- and Post-Processing for {S}lavic Languages",
    author = "Bernardinello, Giorgio",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6731",
    pages = "174--178",
}
@inproceedings{castilho-etal-2019-large,
    title = "Large-scale Machine Translation Evaluation of the i{ADAATPA} Project",
    author = "Castilho, Sheila  and
      Resende, Nat{\'a}lia  and
      Gaspari, Federico  and
      Way, Andy  and
      O{'}Dowd, Tony  and
      Mazur, Marek  and
      Herranz, Manuel  and
      Helle, Alex  and
      Ram{\'\i}rez-S{\'a}nchez, Gema  and
      S{\'a}nchez-Cartagena, V{\'\i}ctor  and
      Pinnis, M{\=a}rcis  and
      {\v{S}}ics, Valters",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6732",
    pages = "179--185",
}
@inproceedings{defauw-etal-2019-collecting,
    title = "Collecting domain specific data for {MT}: an evaluation of the {P}ara{C}rawlpipeline",
    author = "Defauw, Arne  and
      Vanallemeersch, Tom  and
      Szoc, Sara  and
      Everaert, Frederic  and
      Van Winckel, Koen  and
      Scholte, Kim  and
      Brabers, Joris  and
      Van den Bogaert, Joachim",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6733",
    pages = "186--195",
}
@inproceedings{mutal-etal-2019-monolingual,
    title = "Monolingual backtranslation in a medical speech translation system for diagnostic interviews - a {NMT} approach",
    author = "Mutal, Jonathan  and
      Bouillon, Pierrette  and
      Gerlach, Johanna  and
      Estrella, Paula  and
      Spechbach, Herv{\'e}",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6734",
    pages = "196--203",
}
@inproceedings{silva-2019-improving,
    title = "Improving Domain Adaptation for Machine Translation with{T}ranslation Pieces",
    author = "Silva, Catarina",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6735",
    pages = "204--212",
}
@inproceedings{zaretskaya-2019-raising,
    title = "Raising the {TM} Threshold in Neural {MT} Post-Editing: a Case Study on{T}wo Datasets",
    author = "Zaretskaya, Anna",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6736",
    pages = "213--218",
}
@inproceedings{domingo-etal-2019-incremental,
    title = "Incremental Adaptation of {NMT} for Professional Post-editors: A User Study",
    author = "Domingo, Miguel  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Peris, {\'A}lvaro  and
      Helle, Alexandre  and
      Estela, Amando  and
      Bi{\'e}, Laurent  and
      Casacuberta, Francisco  and
      Herranz, Manuel",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6737",
    pages = "219--227",
}
@inproceedings{shterionov-etal-2019-less,
    title = "When less is more in Neural Quality Estimation of Machine Translation. An industry case study",
    author = "Shterionov, Dimitar  and
      Carmo, F{\'e}lix Do  and
      Moorkens, Joss  and
      Paquin, Eric  and
      Schmidtke, Dag  and
      Groves, Declan  and
      Way, Andy",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6738",
    pages = "228--235",
}
@proceedings{ws-2019-machine-translation-summit,
    title = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    editor = "Forcada, Mikel  and
      Way, Andy  and
      Haddow, Barry  and
      Sennrich, Rico",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6600",
}
@inproceedings{wang-etal-2019-online,
    title = "Online Sentence Segmentation for Simultaneous Interpretation using Multi-Shifted Recurrent Neural Network",
    author = "Wang, Xiaolin  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6601",
    pages = "1--11",
}
@inproceedings{yarmohammadi-etal-2019-robust,
    title = "Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings",
    author = "Yarmohammadi, Mahsa  and
      Ma, Xutai  and
      Hisamoto, Sorami  and
      Rahman, Muhammad  and
      Wang, Yiming  and
      Xu, Hainan  and
      Povey, Daniel  and
      Koehn, Philipp  and
      Duh, Kevin",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6602",
    pages = "12--20",
}
@inproceedings{di-gangi-etal-2019-enhancing,
    title = "Enhancing Transformer for End-to-end Speech-to-Text Translation",
    author = "Di Gangi, Mattia Antonino  and
      Negri, Matteo  and
      Cattoni, Roldano  and
      Dessi, Roberto  and
      Turchi, Marco",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6603",
    pages = "21--31",
}
@inproceedings{hirasawa-komachi-2019-debiasing,
    title = "Debiasing Word Embeddings Improves Multimodal Machine Translation",
    author = "Hirasawa, Tosho  and
      Komachi, Mamoru",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6604",
    pages = "32--42",
}
@inproceedings{gois-martins-2019-translator2vec,
    title = "{T}ranslator2{V}ec: Understanding and Representing Human Post-Editors",
    author = "G{\'o}is, Ant{\'o}nio  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6605",
    pages = "43--54",
}
@inproceedings{cuong-2019-domain,
    title = "Domain Adaptation for {MT}: A Study with Unknown and Out-of-Domain Tasks",
    author = "Cuong, Hoang",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6606",
    pages = "55--66",
}
@inproceedings{arenas-etal-2019-impact,
    title = "What is the impact of raw {MT} on {J}apanese users of Word: preliminary results of a usability study using eye-tracking",
    author = "Arenas, Ana Guerberof  and
      Moorkens, Joss  and
      O{'}Brien, Sharon",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6607",
    pages = "67--77",
}
@inproceedings{scansani-etal-2019-magmatic,
    title = "{MAGMAT}ic: A Multi-domain Academic Gold Standard with Manual Annotation of Terminology for Machine Translation Evaluation",
    author = "Scansani, Randy  and
      Bentivogli, Luisa  and
      Bernardini, Silvia  and
      Ferraresi, Adriano",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6608",
    pages = "78--86",
}
@inproceedings{popovic-vilar-2019-automatic,
    title = "Automatic error classification with multiple error labels",
    author = "Popovic, Maja  and
      Vilar, David",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6609",
    pages = "87--95",
}
@inproceedings{lam-etal-2019-interactive,
    title = "Interactive-Predictive Neural Machine Translation through Reinforcement and Imitation",
    author = "Lam, Tsz Kin  and
      Schamoni, Shigehiko  and
      Riezler, Stefan",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6610",
    pages = "96--106",
}
@inproceedings{ghader-monz-2019-intrinsic,
    title = "An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures",
    author = "Ghader, Hamidreza  and
      Monz, Christof",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6611",
    pages = "107--117",
}
@inproceedings{dakwale-monz-2019-improving,
    title = "Improving Neural Machine Translation Using Noisy Parallel Data through Distillation",
    author = "Dakwale, Praveen  and
      Monz, Christof",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6612",
    pages = "118--127",
}
@inproceedings{imankulova-etal-2019-exploiting,
    title = "Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation",
    author = "Imankulova, Aizhan  and
      Dabre, Raj  and
      Fujita, Atsushi  and
      Imamura, Kenji",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6613",
    pages = "128--139",
}
@inproceedings{stojanovski-fraser-2019-improving,
    title = "Improving Anaphora Resolution in Neural Machine Translation Using Curriculum Learning",
    author = "Stojanovski, Dario  and
      Fraser, Alexander",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6614",
    pages = "140--150",
}
@inproceedings{kim-oneill-brown-2019-improving,
    title = "Improving {A}merican {S}ign {L}anguage Recognition with Synthetic Data",
    author = "Kim, Jungi  and
      O{'}Neill-Brown, Patricia",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6615",
    pages = "151--161",
}
@inproceedings{kimura-etal-2019-selecting,
    title = "Selecting Informative Context Sentence by Forced Back-Translation",
    author = "Kimura, Ryuichiro  and
      Iida, Shohei  and
      Cui, Hongyi  and
      Hung, Po-Hsuan  and
      Utsuro, Takehito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6616",
    pages = "162--171",
}
@inproceedings{collier-beel-2019-memory,
    title = "Memory-Augmented Neural Networks for Machine Translation",
    author = "Collier, Mark  and
      Beel, Joeran",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6617",
    pages = "172--181",
}
@inproceedings{post-etal-2019-exploration,
    title = "An Exploration of Placeholding in Neural Machine Translation",
    author = "Post, Matt  and
      Ding, Shuoyang  and
      Martindale, Marianna  and
      Wu, Winston",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6618",
    pages = "182--192",
}
@inproceedings{marchisio-etal-2019-controlling,
    title = "Controlling the Reading Level of Machine Translation Output",
    author = "Marchisio, Kelly  and
      Guo, Jialiang  and
      Lai, Cheng-I  and
      Koehn, Philipp",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6619",
    pages = "193--203",
}
@inproceedings{ding-etal-2019-call,
    title = "A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation",
    author = "Ding, Shuoyang  and
      Renduchintala, Adithya  and
      Duh, Kevin",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6620",
    pages = "204--213",
}
@inproceedings{oudah-etal-2019-impact,
    title = "The Impact of Preprocessing on {A}rabic-{E}nglish Statistical and Neural Machine Translation",
    author = "Oudah, Mai  and
      Almahairi, Amjad  and
      Habash, Nizar",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6621",
    pages = "214--221",
}
@inproceedings{vanmassenhove-etal-2019-lost,
    title = "Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation",
    author = "Vanmassenhove, Eva  and
      Shterionov, Dimitar  and
      Way, Andy",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6622",
    pages = "222--232",
}
@inproceedings{martindale-etal-2019-identifying,
    title = "Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation",
    author = "Martindale, Marianna  and
      Carpuat, Marine  and
      Duh, Kevin  and
      McNamee, Paul",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6623",
    pages = "233--243",
}
@inproceedings{renduchintala-etal-2019-character,
    title = "Character-Aware Decoder for Translation into Morphologically Rich Languages",
    author = "Renduchintala, Adithya  and
      Shapiro, Pamela  and
      Duh, Kevin  and
      Koehn, Philipp",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6624",
    pages = "244--255",
}
@inproceedings{ortega-etal-2019-improving,
    title = "Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing",
    author = "Ortega, John  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Turchi, Marco  and
      Negri, Matteo",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6625",
    pages = "256--266",
}
@inproceedings{laubli-etal-2019-post,
    title = "Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain",
    author = {L{\"a}ubli, Samuel  and
      Amrhein, Chantal  and
      D{\"u}ggelin, Patrick  and
      Gonzalez, Beatriz  and
      Zwahlen, Alena  and
      Volk, Martin},
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6626",
    pages = "267--272",
}
@inproceedings{toral-2019-post,
    title = "Post-editese: an Exacerbated Translationese",
    author = "Toral, Antonio",
    booktitle = "Proceedings of Machine Translation Summit XVII Volume 1: Research Track",
    month = aug,
    year = "2019",
    address = "Dublin, Ireland",
    publisher = "European Association for Machine Translation",
    url = "https://www.aclweb.org/anthology/W19-6627",
    pages = "273--281",
}
@proceedings{ws-2019-nlp-pseudonymisation,
    title = "Proceedings of the Workshop on NLP and Pseudonymisation",
    editor = "Ahrenberg, Lars  and
      Megyesi, Beata",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6500",
}
@inproceedings{adams-etal-2019-anonymate,
    title = "{A}nony{M}ate: A Toolkit for Anonymizing Unstructured Chat Data",
    author = "Adams, Allison  and
      Aili, Eric  and
      Aioanei, Daniel  and
      Jonsson, Rebecca  and
      Mickelsson, Lina  and
      Mikmekova, Dagmar  and
      Roberts, Fred  and
      Valencia, Javier Fernandez  and
      Wechsler, Roger",
    booktitle = "Proceedings of the Workshop on NLP and Pseudonymisation",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6501",
    pages = "1--7",
}
@inproceedings{berg-dalianis-2019-augmenting,
    title = "Augmenting a De-identification System for {S}wedish Clinical Text Using Open Resources and Deep Learning",
    author = "Berg, Hanna  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the Workshop on NLP and Pseudonymisation",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6502",
    pages = "8--15",
}
@inproceedings{dalianis-2019-pseudonymisation,
    title = "Pseudonymisation of {S}wedish Electronic Patient Records Using a Rule-Based Approach",
    author = "Dalianis, Hercules",
    booktitle = "Proceedings of the Workshop on NLP and Pseudonymisation",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6503",
    pages = "16--23",
}
@proceedings{ws-2019-financial-narrative,
    title = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    editor = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Young, Steven  and
      Bouamor, Houda  and
      Ferradans, Sira",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6400",
}
@inproceedings{tian-peng-2019-finance,
    title = "Finance document Extraction Using Data Augmentation and Attention",
    author = "Tian, Ke  and
      Peng, Zi Jun",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6401",
    pages = "1--4",
}
@inproceedings{blumenthal-graf-2019-utilizing,
    title = "Utilizing Pre-Trained Word Embeddings to Learn Classification Lexicons with Little Supervision",
    author = "Blumenthal, Frederick  and
      Graf, Ferdinand",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6402",
    pages = "5--15",
}
@inproceedings{mariam-mokalled-jaber-2019-automated,
    title = "Automated Stock Price Prediction Using Machine Learning",
    author = "Mariam Mokalled, Wassim El-Hajj  and
      Jaber, Mohamad",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6403",
    pages = "16--24",
}
@inproceedings{gooding-briscoe-2019-active,
    title = "Active Learning for Financial Investment Reports",
    author = "Gooding, Sian  and
      Briscoe, Ted",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6404",
    pages = "25--32",
}
@inproceedings{manandise-2019-towards,
    title = "Towards Unlocking the Narrative of the {U}nited {S}tates Income Tax Forms",
    author = "Manandise, Esme",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6405",
    pages = "33--41",
}
@inproceedings{moreno-sandoval-etal-2019-tone,
    title = "Tone Analysis in {S}panish Financial Reporting Narratives",
    author = "Moreno-Sandoval, Antonio  and
      Ana Gisbert, Pablo Alfonso Haya  and
      Guerrero, Marta  and
      Montoro, Helena",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6406",
    pages = "42--50",
}
@inproceedings{juge-etal-2019-fintoc,
    title = "The {F}in{TOC}-2019 Shared Task: Financial Document Structure Extraction",
    author = "Juge, Remi  and
      Bentabet, Imane  and
      Ferradans, Sira",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6407",
    pages = "51--57",
}
@inproceedings{akl-etal-2019-fintoc,
    title = "{F}in{TOC}-2019 Shared Task: Finding Title in Text Blocks",
    author = "Akl, Hanna Abi  and
      Gupta, Anubhav  and
      Mariko, Dominique",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6408",
    pages = "58--62",
}
@inproceedings{giguet-lejeune-2019-daniel,
    title = "Daniel@{F}in{TOC}-2019 Shared Task : {TOC} Extraction and Title Detection",
    author = {Giguet, Emmanuel  and
      Lejeune, Ga{\"e}l},
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6409",
    pages = "63--68",
}
@inproceedings{abreu-etal-2019-findse,
    title = "{F}in{DSE}@{F}in{TOC}-2019 Shared Task",
    author = "Abreu, Carla  and
      Cardoso, Henrique  and
      Oliveira, Eug{\'e}nio",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6410",
    pages = "69--73",
}
@inproceedings{tomas-hercig-2019-uwb,
    title = "{UWB}@{F}in{TOC}-2019 Shared Task: Financial Document Title Detection",
    author = "Tomas Hercig, Pavel Kr{\'a}l",
    booktitle = "Proceedings of the Second Financial Narrative Processing Workshop (FNP 2019)",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6411",
    pages = "74--78",
}
@proceedings{ws-2019-nlp-computer,
    title = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    editor = "Alfter, David  and
      Volodina, Elena  and
      Borin, Lars  and
      Pilan, Ildik{\'o}  and
      Lange, Herbert",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6300",
}
@inproceedings{avdiu-etal-2019-predicting,
    title = "Predicting learner knowledge of individual words using machine learning",
    author = "Avdiu, Drilon  and
      Bui, Vanessa  and
      Klim{\v{c}}{\'\i}kov{\'a}, Kl{\'a}ra Pta{\v{c}}inov{\'a}",
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6301",
    pages = "1--9",
}
@inproceedings{bick-2019-automatic,
    title = "Automatic Generation and Semantic Grading of {E}speranto Sentences in a Teaching Context",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6302",
    pages = "10--19",
}
@inproceedings{creutz-sjoblom-2019-toward,
    title = "Toward automatic improvement of language produced by non-native language learners",
    author = {Creutz, Mathias  and
      Sj{\"o}blom, Eetu},
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6303",
    pages = "20--30",
}
@inproceedings{del-rio-2019-linguistic,
    title = "Linguistic features and proficiency classification in {L}2 {S}panish and {L}2{P}ortuguese.",
    author = "del R{\'\i}o, Iria",
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6304",
    pages = "31--40",
}
@inproceedings{dittrich-etal-2019-integrating,
    title = "Integrating large-scale web data and curated corpus data in a search engine supporting {G}erman literacy education",
    author = {Dittrich, Sabrina  and
      Weiss, Zarah  and
      Schr{\"o}ter, Hannes  and
      Meurers, Detmar},
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6305",
    pages = "41--56",
}
@inproceedings{hatier-etal-2019-formalism,
    title = "Formalism for a language agnostic language learning game and productive grid generation",
    author = "Hatier, Sylvain  and
      Bey, Arnaud  and
      Loiseau, Mathieu",
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6306",
    pages = "57--64",
}
@inproceedings{kerz-etal-2019-understanding,
    title = "Understanding Vocabulary Growth Through An Adaptive Language Learning System",
    author = "Kerz, Elma  and
      Burgdorf, Andreas  and
      Wiechmann, Daniel  and
      Meeger, Stefan  and
      Qiao, Yu  and
      Kohlschein, Christian  and
      Meisen, Tobias",
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6307",
    pages = "65--78",
}
@inproceedings{mieskes-pado-2019-summarization,
    title = "Summarization Evaluation meets Short-Answer Grading",
    author = "Mieskes, Margot  and
      Pad{\'o}, Ulrike",
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6308",
    pages = "79--85",
}
@inproceedings{zhou-etal-2019-experiments,
    title = "Experiments on Non-native Speech Assessment and its Consistency",
    author = "Zhou, Ziwei  and
      Vajjala, Sowmya  and
      Mirnezami, Seyed Vahid",
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6309",
    pages = "86--92",
}
@inproceedings{ziai-etal-2019-impact,
    title = "The Impact of Spelling Correction and Task Context on Short Answer Assessment for Intelligent Tutoring Systems",
    author = {Ziai, Ramon  and
      Nuxoll, Florian  and
      De Kuthy, Kordula  and
      Rudzewitz, Bj{\"o}rn  and
      Meurers, Detmar},
    booktitle = "Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W19-6310",
    pages = "93--99",
}
@proceedings{ws-2019-nlpl,
    title = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    editor = {Nivre, Joakim  and
      Derczynski, Leon  and
      Ginter, Filip  and
      Lindi, Bj{\o}rn  and
      Oepen, Stephan  and
      S{\o}gaard, Anders  and
      Tidemann, J{\"o}rg},
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6200",
}
@inproceedings{mickus-etal-2019-mark,
    title = "Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling",
    author = "Mickus, Timothee  and
      Paperno, Denis  and
      Constant, Matthieu",
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6201",
    pages = "1--11",
    abstract = "Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations. Building on the distributional hypothesis, we argue here that the most natural formalization of definition modeling is to treat it as a sequence-to-sequence task, rather than a word-to-sequence task: given an input sequence with a highlighted word, generate a contextually appropriate definition for it. We implement this approach in a Transformer-based sequence-to-sequence model. Our proposal allows to train contextualization and definition generation in an end-to-end fashion, which is a conceptual improvement over earlier works. We achieve state-of-the-art results both in contextual and non-contextual definition modeling.",
}
@inproceedings{kurtz-etal-2019-improving,
    title = "Improving Semantic Dependency Parsing with Syntactic Features",
    author = "Kurtz, Robin  and
      Roxbo, Daniel  and
      Kuhlmann, Marco",
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6202",
    pages = "12--21",
    abstract = "We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard syntactic information leads to consistent improvements in semantic parsing accuracy, and that the magnitude of these improvements varies with the specific combination of the syntactic and the semantic representation used. In contrast, automatically predicted syntax does not seem to help semantic parsing. Our error analysis suggests that there is a significant overlap between syntactic and semantic representations.",
}
@inproceedings{kutuzov-kuzmenko-2019-lemmatize,
    title = "To Lemmatize or Not to Lemmatize: How Word Normalisation Affects {ELM}o Performance in Word Sense Disambiguation",
    author = "Kutuzov, Andrey  and
      Kuzmenko, Elizaveta",
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6203",
    pages = "22--28",
    abstract = "In this paper, we critically evaluate the widespread assumption that deep learning NLP models do not require lemmatized input. To test this, we trained versions of contextualised word embedding ELMo models on raw tokenized corpora and on the corpora with word tokens replaced by their lemmas. Then, these models were evaluated on the word sense disambiguation task. This was done for the English and Russian languages. The experiments showed that while lemmatization is indeed not necessary for English, the situation is different for Russian. It seems that for rich-morphology languages, using lemmatized training and testing data yields small but consistent improvements: at least for word sense disambiguation. This means that the decisions about text pre-processing before training ELMo should consider the linguistic nature of the language in question.",
}
@inproceedings{ronnqvist-etal-2019-multilingual,
    title = "Is Multilingual {BERT} Fluent in Language Generation?",
    author = {R{\"o}nnqvist, Samuel  and
      Kanerva, Jenna  and
      Salakoski, Tapio  and
      Ginter, Filip},
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6204",
    pages = "29--36",
    abstract = "The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences. We explore how well the model performs on several languages across several tasks: a diagnostic classification probing the embeddings for a particular syntactic property, a cloze task testing the language modelling ability to fill in gaps in a sentence, and a natural language generation task testing for the ability to produce coherent text fitting a given context. We find that the currently available multilingual BERT model is clearly inferior to the monolingual counterparts, and cannot in many cases serve as a substitute for a well-trained monolingual model. We find that the English and German models perform well at generation, whereas the multilingual model is lacking, in particular, for Nordic languages. The code of the experiments in the paper is available at: https://github.com/TurkuNLP/bert-eval",
}
@inproceedings{ravishankar-etal-2019-multilingual,
    title = "Multilingual Probing of Deep Pre-Trained Contextual Encoders",
    author = {Ravishankar, Vinit  and
      G{\"o}k{\i}rmak, Memduh  and
      {\O}vrelid, Lilja  and
      Velldal, Erik},
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6205",
    pages = "37--47",
    abstract = "Encoders that generate representations based on context have, in recent years, benefited from adaptations that allow for pre-training on large text corpora. Earlier work on evaluating fixed-length sentence representations has included the use of {`}probing{'} tasks, that use diagnostic classifiers to attempt to quantify the extent to which these encoders capture specific linguistic phenomena. The principle of probing has also resulted in extended evaluations that include relatively newer word-level pre-trained encoders. We build on probing tasks established in the literature and comprehensively evaluate and analyse {--} from a typological perspective amongst others {--} multilingual variants of existing encoders on probing datasets constructed for 6 non-English languages. Specifically, we probe each layer of a multiple monolingual RNN-based ELMo models, the transformer-based BERT{'}s cased and uncased multilingual variants, and a variant of BERT that uses a cross-lingual modelling scheme (XLM).",
}
@inproceedings{rasmussen-etal-2019-cross,
    title = "Cross-Domain Sentiment Classification using Vector Embedded Domain Representations",
    author = "Rasmussen, Nicolaj Filrup  and
      Jensen, Kristian N{\o}rgaard  and
      Placenti, Marco  and
      Wang, Thai",
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6206",
    pages = "48--57",
    abstract = "Due to the differences between reviews in different product categories, creating a general model for cross-domain sentiment classification can be a difficult task. This paper proposes an architecture that incorporates domain knowledge into a neural sentiment classification model. In addition to providing a cross-domain model, this also provides a quantifiable representation of the domains as numeric vectors. We show that it is possible to cluster the domain vectors and provide qualitative insights into the inter-domain relations. We also a) present a new data set for sentiment classification that includes a domain parameter and preprocessed data points, and b) perform an ablation study in order to determine whether some word groups impact performance.",
}
@inproceedings{damaschk-etal-2019-multiclass,
    title = "Multiclass Text Classification on Unbalanced, Sparse and Noisy Data",
    author = {Damaschk, Matthias  and
      D{\"o}nicke, Tillmann  and
      Lux, Florian},
    booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6207",
    pages = "58--65",
    abstract = "This paper discusses methods to improve the performance of text classification on data that is difficult to classify due to a large number of unbalanced classes with noisy examples. A variety of features are tested, in combination with three different neural-network-based methods with increasing complexity. The classifiers are applied to a songtext{--}artist dataset which is large, unbalanced and noisy. We come to the conclusion that substantial improvement can be obtained by removing unbalancedness and sparsity from the data. This fulfils a classification task unsatisfactorily{---}however, with contemporary methods, it is a practical step towards fairly satisfactory results.",
}
@proceedings{ws-2019-nordic,
    title = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    editor = "Hartmann, Mareike  and
      Plank, Barbara",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6100",
}
@inproceedings{rosales-nunez-etal-2019-comparison,
    title = "Comparison between {NMT} and {PBSMT} Performance for Translating Noisy User-Generated Content",
    author = "Rosales N{\'u}{\~n}ez, Jos{\'e} Carlos  and
      Seddah, Djam{\'e}  and
      Wisniewski, Guillaume",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6101",
    pages = "2--14",
    abstract = "This work compares the performances achieved by Phrase-Based Statistical Machine Translation systems (PB-SMT) and attention-based Neuronal Machine Translation systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English. We show that, contrary to what could be expected, PBSMT outperforms NMT when translating non-canonical inputs. Our error analysis uncovers the specificities of UGC that are problematic for sequential NMT architectures and suggests new avenue for improving NMT models.",
}
@inproceedings{kolachina-ranta-2019-bootstrapping,
    title = "Bootstrapping {UD} treebanks for Delexicalized Parsing",
    author = "Kolachina, Prasanth  and
      Ranta, Aarne",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6102",
    pages = "15--24",
    abstract = "Standard approaches to treebanking traditionally employ a waterfall model (Sommerville, 2010), where annotation guidelines guide the annotation process and insights from the annotation process in turn lead to subsequent changes in the annotation guidelines. This process remains a very expensive step in creating linguistic resources for a target language, necessitates both linguistic expertise and manual effort to develop the annotations and is subject to inconsistencies in the annotation due to human errors. In this paper, we propose an alternative approach to treebanking{---}one that requires writing grammars. This approach is motivated specifically in the context of Universal Dependencies, an effort to develop uniform and cross-lingually consistent treebanks across multiple languages. We show here that a bootstrapping approach to treebanking via interlingual grammars is plausible and useful in a process where grammar engineering and treebanking are jointly pursued when creating resources for the target language. We demonstrate the usefulness of synthetic treebanks in the task of delexicalized parsing. Our experiments reveal that simple models for treebank generation are cheaper than human annotated treebanks, especially in the lower ends of the learning curves for delexicalized parsing, which is relevant in particular in the context of low-resource languages.",
}
@inproceedings{plank-klerke-2019-lexical,
    title = "Lexical Resources for Low-Resource {P}o{S} Tagging in Neural Times",
    author = "Plank, Barbara  and
      Klerke, Sigrid",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6103",
    pages = "25--34",
    abstract = "More and more evidence is appearing that integrating symbolic lexical knowledge into neural models aids learning. This contrasts the widely-held belief that neural networks largely learn their own feature representations. For example, recent work has shows benefits of integrating lexicons to aid cross-lingual part-of-speech (PoS). However, little is known on how complementary such additional information is, and to what extent improvements depend on the coverage and quality of these external resources. This paper seeks to fill this gap by providing a thorough analysis on the contributions of lexical resources for cross-lingual PoS tagging in neural times.",
}
@inproceedings{sahlgren-olsson-2019-gender,
    title = "Gender Bias in Pretrained {S}wedish Embeddings",
    author = "Sahlgren, Magnus  and
      Olsson, Fredrik",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6104",
    pages = "35--43",
    abstract = "This paper investigates the presence of gender bias in pretrained Swedish embeddings. We focus on a scenario where names are matched with occupations, and we demonstrate how a number of standard pretrained embeddings handle this task. Our experiments show some significant differences between the pretrained embeddings, with word-based methods showing the most bias and contextualized language models showing the least. We also demonstrate that the previously proposed debiasing method does not affect the performance of the various embeddings in this scenario.",
}
@inproceedings{van-aggelen-etal-2019-larger,
    title = "A larger-scale evaluation resource of terms and their shift direction for diachronic lexical semantics",
    author = "van Aggelen, Astrid  and
      Fokkens, Antske  and
      Hollink, Laura  and
      van Ossenbruggen, Jacco",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6105",
    pages = "44--54",
    abstract = "Determining how words have changed their meaning is an important topic in Natural Language Processing. However, evaluations of methods to characterise such change have been limited to small, handcrafted resources. We introduce an English evaluation set which is larger, more varied, and more realistic than seen to date, with terms derived from a historical thesaurus. Moreover, the dataset is unique in that it represents change as a shift from the term of interest to a WordNet synset. Using the synset lemmas, we can use this set to evaluate (standard) methods that detect change between word pairs, as well as (adapted) methods that detect the change between a term and a sense overall. We show that performance on the new data set is much lower than earlier reported findings, setting a new standard.",
}
@inproceedings{bizzoni-etal-2019-steps,
    title = "Some steps towards the generation of diachronic {W}ord{N}ets",
    author = "Bizzoni, Yuri  and
      Mosbach, Marius  and
      Klakow, Dietrich  and
      Degaetano-Ortlieb, Stefania",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6106",
    pages = "55--64",
    abstract = "We apply hyperbolic embeddings to trace the dynamics of change of conceptual-semantic relationships in a large diachronic scientific corpus (200 years). Our focus is on emerging scientific fields and the increasingly specialized terminology establishing around them. Reproducing high-quality hierarchical structures such as WordNet on a diachronic scale is a very difficult task. Hyperbolic embeddings can map partial graphs into low dimensional, continuous hierarchical spaces, making more explicit the latent structure of the input. We show that starting from simple lists of word pairs (rather than a list of entities with directional links) it is possible to build diachronic hierarchical semantic spaces which allow us to model a process towards specialization for selected scientific fields.",
}
@inproceedings{horenovska-2019-evaluation,
    title = "An evaluation of {C}zech word embeddings",
    author = "Ho{\v{r}}e{\v{n}}ovsk{\'a}, Karol{\'\i}na",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6107",
    pages = "65--75",
    abstract = "We present an evaluation of Czech low-dimensional distributed word representations, also known as word embeddings. We describe five different approaches to training the models and three different corpora used in training. We evaluate the resulting models on five different datasets, report the results and provide their further analysis.",
}
@inproceedings{ek-etal-2019-language,
    title = "Language Modeling with Syntactic and Semantic Representation for Sentence Acceptability Predictions",
    author = "Ek, Adam  and
      Bernardy, Jean-Philippe  and
      Lappin, Shalom",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6108",
    pages = "76--85",
    abstract = "In this paper, we investigate the effect of enhancing lexical embeddings in LSTM language models (LM) with syntactic and semantic representations. We evaluate the language models using perplexity, and we evaluate the performance of the models on the task of predicting human sentence acceptability judgments. We train LSTM language models on sentences automatically annotated with universal syntactic dependency roles (Nivre, 2016), dependency depth and universal semantic tags (Abzianidze et al., 2017) to predict sentence acceptability judgments. Our experiments indicate that syntactic tags lower perplexity, while semantic tags increase it. Our experiments also show that neither syntactic nor semantic tags improve the performance of LSTM language models on the task of predicting sentence acceptability judgments.",
}
@inproceedings{saied-etal-2019-comparing,
    title = "Comparing linear and neural models for competitive {MWE} identification",
    author = "Saied, Hazem Al  and
      Candito, Marie  and
      Constant, Mathieu",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6109",
    pages = "86--96",
    abstract = "In this paper, we compare the use of linear versus neural classifiers in a greedy transition system for MWE identification. Both our linear and neural models achieve a new state-of-the-art on the PARSEME 1.1 shared task data sets, comprising 20 languages. Surprisingly, our best model is a simple feed-forward network with one hidden layer, although more sophisticated (recurrent) architectures were tested. The feedback from this study is that tuning a SVM is rather straightforward, whereas tuning our neural system revealed more challenging. Given the number of languages and the variety of linguistic phenomena to handle for the MWE identification task, we have designed an accurate tuning procedure, and we show that hyperparameters are better selected by using a majority-vote within random search configurations rather than a simple best configuration selection. Although the performance is rather good (better than both the best shared task system and the average of the best per-language results), further work is needed to improve the generalization power, especially on unseen MWEs.",
}
@inproceedings{cordeiro-candito-2019-syntax,
    title = "Syntax-based identification of light-verb constructions",
    author = "Cordeiro, Silvio Ricardo  and
      Candito, Marie",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6110",
    pages = "97--104",
    abstract = "This paper analyzes results on light-verb construction identification from the PARSEME shared-task, distinguishing between simple cases that could be directly learned from training data from more complex cases that require an extra level of semantic processing. We propose a simple baseline that beats the state of the art for the simple cases, and couple it with another simple baseline to handle the complex cases. We additionally present two other classifiers based on a richer set of features, with results surpassing the state of the art by 8 percentage points.",
}
@inproceedings{santini-etal-2019-comparing,
    title = "Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language",
    author = {Santini, Marina  and
      Danielsson, Benjamin  and
      J{\"o}nsson, Arne},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6111",
    pages = "105--114",
    abstract = "We explore the effectiveness of four feature representations {--} bag-of-words, word embeddings, principal components and autoencoders {--} for the binary categorization of the easy-to-read variety vs standard language. Standard language refers to the ordinary language variety used by a population as a whole or by a community, while the {``}easy-to-read{''} variety is a simpler (or a simplified) version of the standard language. We test the efficiency of these feature representations on three corpora, which differ in size, class balance, unit of analysis, language and topic. We rely on supervised and unsupervised machine learning algorithms. Results show that bag-of-words is a robust and straightforward feature representation for this task and performs well in many experimental settings. Its performance is equivalent or equal to the performance achieved with principal components and autoencorders, whose preprocessing is however more time-consuming. Word embeddings are less accurate than the other feature representations for this classification task.",
}
@inproceedings{persiani-hellstrom-2019-unsupervised,
    title = "Unsupervised Inference of Object Affordance from Text Corpora",
    author = {Persiani, Michele  and
      Hellstr{\"o}m, Thomas},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6112",
    pages = "115--120",
    abstract = "Affordances denote actions that can be performed in the presence of different objects, or possibility of action in an environment. In robotic systems, affordances and actions may suffer from poor semantic generalization capabilities due to the high amount of required hand-crafted specifications. To alleviate this issue, we propose a method to mine for object-action pairs in free text corpora, successively training and evaluating different prediction models of affordance based on word embeddings.",
}
@inproceedings{maehlum-etal-2019-annotating,
    title = "Annotating evaluative sentences for sentiment analysis: a dataset for {N}orwegian",
    author = "M{\ae}hlum, Petter  and
      Barnes, Jeremy  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6113",
    pages = "121--130",
    abstract = "This paper documents the creation of a large-scale dataset of evaluative sentences {--} i.e. both subjective and objective sentences that are found to be sentiment-bearing {--} based on mixed-domain professional reviews from various news-sources. We present both the annotation scheme and first results for classification experiments. The effort represents a step toward creating a Norwegian dataset for fine-grained sentiment analysis.",
}
@inproceedings{moen-etal-2019-unsupervised,
    title = "An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora",
    author = {Moen, Hans  and
      Peltonen, Laura-Maria  and
      Suhonen, Henry  and
      Matinolli, Hanna-Maria  and
      Mieronkoski, Riitta  and
      Telen, Kirsi  and
      Terho, Kirsi  and
      Salakoski, Tapio  and
      Salanter{\"a}, Sanna},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6114",
    pages = "131--139",
    abstract = "We present our work towards developing a system that should find, in a large text corpus, contiguous phrases expressing similar meaning as a query phrase of arbitrary length. Depending on the use case, this task can be seen as a form of (phrase-level) query rewriting. The suggested approach works in a generative manner, is unsupervised and uses a combination of a semantic word n-gram model, a statistical language model and a document search engine. A central component is a distributional semantic model containing word n-grams vectors (or embeddings) which models semantic similarities between n-grams of different order. As data we use a large corpus of PubMed abstracts. The presented experiment is based on manual evaluation of extracted phrases for arbitrary queries provided by a group of evaluators. The results indicate that the proposed approach is promising and that the use of distributional semantic models trained with uni-, bi- and trigrams seems to work better than a more traditional unigram model.",
}
@inproceedings{barkarson-steingrimsson-2019-compiling,
    title = "Compiling and Filtering {P}ar{I}ce: An {E}nglish-{I}celandic Parallel Corpus",
    author = "Barkarson, Starka{\dh}ur  and
      Steingr{\'\i}msson, Stein{\th}{\'o}r",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6115",
    pages = "140--145",
    abstract = "We present ParIce, a new English-Icelandic parallel corpus. This is the first parallel corpus built for the purposes of language technology development and research for Icelandic, although some Icelandic texts can be found in various other multilingual parallel corpora. We map out which Icelandic texts are available for these purposes, collect aligned data and align other bilingual texts we acquired. We describe the alignment process and how we filter the data to weed out noise and bad alignments. In total we collected 43 million Icelandic words in 4.3 million aligned segment pairs, but after filtering, our corpus includes 38.8 million Icelandic words in 3.5 million segment pairs. We estimate that approximately 5{\%} of the corpus data is noise or faulty alignments while more than 50{\%} of the segments we deleted were faulty. We estimate that our filtering process reduced the number of faulty segments in the corpus by more than 60{\%} while only reducing the number of good alignments by approximately 8{\%}.",
}
@inproceedings{bjarnadottir-etal-2019-dim,
    title = "{DIM}: The Database of {I}celandic Morphology",
    author = {Bjarnad{\'o}ttir, Krist{\'\i}n  and
      Hlynsd{\'o}ttir, Krist{\'\i}n Ingibj{\"o}rg  and
      Steingr{\'\i}msson, Stein{\th}{\'o}r},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6116",
    pages = "146--154",
    abstract = "The topic of this paper is The Database of Icelandic Morphology (DIM), a multipurpose linguistic resource, created for use in language technology, as a reference for the general public in Iceland, and for use in research on the Icelandic language. DIM contains inflectional paradigms and analysis of word formation, with a vocabulary of approx. 285,000 lemmas. DIM is based on The Database of Modern Icelandic Inflection, which has been in use since 2004.",
}
@inproceedings{ivanova-etal-2019-tools,
    title = "Tools for supporting language learning for Sakha",
    author = "Ivanova, Sardana  and
      Katinskaia, Anisia  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6117",
    pages = "155--163",
    abstract = "This paper presents an overview of the available linguistic resources for the Sakha language, and presents new tools for supporting language learning for Sakha. The essential resources include a morphological analyzer, digital dictionaries, and corpora of Sakha texts. Based on these resources, we implement a language-learning environment for Sakha in the Revita CALL platform. We extended an earlier, preliminary version of the morphological analyzer/transducer, built on the Apertium finite-state platform. The analyzer currently has an adequate level of coverage, between 86{\%} and 89{\%} on two Sakha corpora. Revita is a freely available online language learning platform for learners beyond the beginner level. We describe the tools for Sakha currently integrated into the Revita platform. To the best of our knowledge, at present, this is the first large-scale project undertaken to support intermediate-advanced learners of a minority Siberian language.",
}
@inproceedings{lilliestrom-etal-2019-inferring,
    title = "Inferring morphological rules from small examples using 0/1 linear programming",
    author = {Lilliestr{\"o}m, Ann  and
      Claessen, Koen  and
      Smallbone, Nicholas},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6118",
    pages = "164--174",
    abstract = "We show how to express the problem of finding an optimal morpheme segmentation from a set of labelled words as a 0/1 linear programming problem, and how to build on this to analyse a language{'}s morphology. The approach works even when there is very little training data available.",
}
@inproceedings{barnes-etal-2019-lexicon,
    title = "Lexicon information in neural sentiment analysis: a multi-task learning approach",
    author = "Barnes, Jeremy  and
      Touileb, Samia  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6119",
    pages = "175--186",
    abstract = "This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.",
}
@inproceedings{hoang-etal-2019-aspect,
    title = "Aspect-Based Sentiment Analysis using {BERT}",
    author = "Hoang, Mickel  and
      Bihorac, Oskar Alija  and
      Rouces, Jacobo",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6120",
    pages = "187--196",
    abstract = "Sentiment analysis has become very popular in both research and business due to the increasing amount of opinionated text from Internet users. Standard sentiment analysis deals with classifying the overall sentiment of a text, but this doesn{'}t include other important information such as towards which entity, topic or aspect within the text the sentiment is directed. Aspect-based sentiment analysis (ABSA) is a more complex task that consists in identifying both sentiments and aspects. This paper shows the potential of using the contextual word representations from the pre-trained language model BERT, together with a fine-tuning method with additional generated text, in order to solve out-of-domain ABSA and outperform previous state-of-the-art results on SemEval-2015 Task 12 subtask 2 and SemEval-2016 Task 5. To the best of our knowledge, no other existing work has been done on out-of-domain ABSA for aspect classification.",
}
@inproceedings{lehmann-derczynski-2019-political,
    title = "Political Stance in {D}anish",
    author = "Lehmann, Rasmus  and
      Derczynski, Leon",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6121",
    pages = "197--207",
    abstract = "The task of stance detection consists of classifying the opinion within a text towards some target. This paper seeks to generate a dataset of quotes from Danish politicians, label this dataset to allow the task of stance detection to be performed, and present annotation guidelines to allow further expansion of the generated dataset. Furthermore, three models based on an LSTM architecture are designed, implemented and optimized to perform the task of stance detection for the generated dataset. Experiments are performed using conditionality and bi-directionality for these models, and using either singular word embeddings or averaged word embeddings for an entire quote, to determine the optimal model design. The simplest model design, applying neither conditionality or bi-directionality, and averaged word embeddings across quotes, yields the strongest results. Furthermore, it was found that inclusion of the quotes politician, and the party affiliation of the quoted politician, greatly improved performance of the strongest model.",
}
@inproceedings{lillie-etal-2019-joint,
    title = "Joint Rumour Stance and Veracity Prediction",
    author = "Lillie, Anders Edelbo  and
      Middelboe, Emil Refsgaard  and
      Derczynski, Leon",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6122",
    pages = "208--221",
    abstract = "The net is rife with rumours that spread through microblogs and social media. Not all the claims in these can be verified. However, recent work has shown that the stances alone that commenters take toward claims can be sufficiently good indicators of claim veracity, using e.g. an HMM that takes conversational stance sequences as the only input. Existing results are monolingual (English) and mono-platform (Twitter). This paper introduces a stance-annotated Reddit dataset for the Danish language, and describes various implementations of stance classification models. Of these, a Linear SVM provides predicts stance best, with 0.76 accuracy / 0.42 macro F1. Stance labels are then used to predict veracity across platforms and also across languages, training on conversations held in one language and using the model on conversations held in another. In our experiments, monolinugal scores reach stance-based veracity accuracy of 0.83 (F1 0.68); applying the model across languages predicts veracity of claims with an accuracy of 0.82 (F1 0.67). This demonstrates the surprising and powerful viability of transferring stance-based veracity prediction across languages.",
}
@inproceedings{johansen-2019-named,
    title = "Named-Entity Recognition for {N}orwegian",
    author = "Johansen, Bjarte",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6123",
    pages = "222--231",
    abstract = "NER is the task of recognizing and demarcating the segments of a document that are part of a name and which type of name it is. We use 4 different categories of names: Locations (LOC), miscellaneous (MISC), organizations (ORG), and persons (PER). Even though we employ state of the art methods{---}including sub-word embeddings{---}that work well for English, we are unable to reproduce the same success for the Norwegian written forms. However, our model performs better than any previous research on Norwegian text. The study also presents the first NER for Nynorsk. Lastly, we find that by combining Nynorsk and Bokm{\aa}l into one training corpus we improve the performance of our model on both languages.",
}
@inproceedings{hou-etal-2019-projecting,
    title = "Projecting named entity recognizers without annotated or parallel corpora",
    author = "Hou, Jue  and
      Koppatz, Maximilian  and
      Quecedo, Jos{\'e} Mar{\'\i}a Hoya  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6124",
    pages = "232--241",
    abstract = "Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpora, such as Finnish. We propose an approach to create a named entity recognizer with no annotated or parallel documents, by leveraging strong NER models that exist for English. We automatically gather a large amount of \textit{chronologically matched} data in two languages, then project named entity annotations from the English documents onto the Finnish ones, by resolving the matches with limited linguistic rules. We use this {``}artificially{''} annotated data to train a BiLSTM-CRF model. Our results show that this method can produce annotated instances with high precision, and the resulting model achieves state-of-the-art performance.",
}
@inproceedings{kanerva-etal-2019-template,
    title = "Template-free Data-to-Text Generation of {F}innish Sports News",
    author = {Kanerva, Jenna  and
      R{\"o}nnqvist, Samuel  and
      Kekki, Riina  and
      Salakoski, Tapio  and
      Ginter, Filip},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6125",
    pages = "242--252",
    abstract = "News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge, interpretation, insight into the game, and quotes that are not present in the official statistics. This poses a challenge for automated data-to-text news generation with real-world news corpora as training data. We report on the development of a corpus of Finnish ice hockey news, edited to be suitable for training of end-to-end news generation methods, as well as demonstrate generation of text, which was judged by journalists to be relatively close to a viable product. The new dataset and system source code are available for research purposes.",
}
@inproceedings{pettersson-megyesi-2019-matching,
    title = "Matching Keys and Encrypted Manuscripts",
    author = "Pettersson, Eva  and
      Megyesi, Beata",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6126",
    pages = "253--261",
    abstract = "Historical cryptology is the study of historical encrypted messages aiming at their decryption by analyzing the mathematical, linguistic and other coding patterns and their historical context. In libraries and archives we can find quite a lot of ciphers, as well as keys describing the method used to transform the plaintext message into a ciphertext. In this paper, we present work on automatically mapping keys to ciphers to reconstruct the original plaintext message, and use language models generated from historical texts to guess the underlying plaintext language.",
}
@inproceedings{rykova-werner-2019-perceptual,
    title = "Perceptual and acoustic analysis of voice similarities between parents and young children",
    author = "Rykova, Evgeniia  and
      Werner, Stefan",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6127",
    pages = "262--271",
    abstract = "Human voice provides the means for verbal communication and forms a part of personal identity. Due to genetic and environmental factors, a voice of a child should resemble the voice of her parent(s), but voice similarities between parents and young children are underresearched. Read-aloud speech of Finnish-speaking and Russian-speaking parent-child pairs was subject to perceptual and multi-step instrumental and statistical analysis. Finnish-speaking listeners could not discriminate family pairs auditorily in an XAB paradigm, but the Russian-speaking listeners{'} mean accuracy of answers reached 72.5{\%}. On average, in both language groups family-internal f0 similarities were stronger than family-external, with parents showing greater family-internal similarities than children. Auditory similarities did not reflect acoustic similarities in a straightforward way.",
}
@inproceedings{alacam-2019-enhancing,
    title = "Enhancing Natural Language Understanding through Cross-Modal Interaction: Meaning Recovery from Acoustically Noisy Speech",
    author = "Alacam, Ozge",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6128",
    pages = "272--280",
    abstract = "Cross-modality between vision and language is a key component for effective and efficient communication, and human language processing mechanism successfully integrates information from various modalities to extract the intended meaning. However, incomplete linguistic input, i.e. due to a noisy environment, is one of the challenges for a successful communication. In that case, an incompleteness in one channel can be compensated by information from another one. In this paper, by conducting visual-world paradigm, we investigated the dynamics between syntactically possible gap fillers and the visual arrangements in incomplete German sentences and their effect on overall sentence interpretation.",
}
@inproceedings{talman-etal-2019-predicting,
    title = "Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations",
    author = {Talman, Aarne  and
      Suni, Antti  and
      Celikkanat, Hande  and
      Kakouros, Sofoklis  and
      Tiedemann, J{\"o}rg  and
      Vainio, Martti},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6129",
    pages = "281--290",
    abstract = "In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text. To our knowledge this will be the largest publicly available dataset with prosodic labels. We describe the dataset construction and the resulting benchmark dataset in detail and train a number of different models ranging from feature-based classifiers to neural network systems for the prediction of discretized prosodic prominence. We show that pre-trained contextualized word representations from BERT outperform the other models even with less than 10{\%} of the training data. Finally we discuss the dataset in light of the results and point to future research and plans for further improving both the dataset and methods of predicting prosodic prominence from text. The dataset and the code for the models will be made publicly available.",
}
@inproceedings{laippala-etal-2019-toward,
    title = "Toward Multilingual Identification of Online Registers",
    author = {Laippala, Veronika  and
      Kyll{\"o}nen, Roosa  and
      Egbert, Jesse  and
      Biber, Douglas  and
      Pyysalo, Sampo},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6130",
    pages = "292--297",
    abstract = "We consider cross- and multilingual text classification approaches to the identification of online registers (genres), i.e. text varieties with specific situational characteristics. Register is the most important predictor of linguistic variation, and register information could improve the potential of online data for many applications. We introduce the first manually annotated non-English corpus of online registers featuring the full range of linguistic variation found online. The data set consists of 2,237 Finnish documents and follows the register taxonomy developed for the Corpus of Online Registers of English (CORE). Using CORE and the newly introduced corpus, we demonstrate the feasibility of cross-lingual register identification using a simple approach based on convolutional neural networks and multilingual word embeddings. We further find that register identification results can be improved through multilingual training even when a substantial number of annotations is available in the target language.",
}
@inproceedings{chatzikyriakidis-bernardy-2019-wide,
    title = "A Wide-Coverage Symbolic Natural Language Inference System",
    author = "Chatzikyriakidis, Stergios  and
      Bernardy, Jean-Philippe",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6131",
    pages = "298--303",
    abstract = "We present a system for Natural Language Inference which uses a dynamic semantics converter from abstract syntax trees to Coq types. It combines the fine-grainedness of a dynamic semantics system with the powerfulness of a state-of-the-art proof assistant, like Coq. We evaluate the system on all sections of the FraCaS test suite, excluding section 6. This is the first system that does a complete run on the anaphora and ellipsis sections of the FraCaS. It has a better overall accuracy than any previous system.",
}
@inproceedings{kylliainen-silfverberg-2019-ensembles,
    title = "Ensembles of Neural Morphological Inflection Models",
    author = {Kylli{\"a}inen, Ilmari  and
      Silfverberg, Miikka},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6132",
    pages = "304--309",
    abstract = "We investigate different ensemble learning techniques for neural morphological inflection using bidirectional LSTM encoder-decoder models with attention. We experiment with weighted and unweighted majority voting and bagging. We find that all investigated ensemble methods lead to improved accuracy over a baseline of a single model. However, contrary to expectation based on earlier work by Najafi et al. (2018) and Silfverberg et al. (2017), weighting does not deliver clear benefits. Bagging was found to underperform plain voting ensembles in general.",
}
@inproceedings{ingolfsdottir-etal-2019-nefnir,
    title = "{N}efnir: A high accuracy lemmatizer for {I}celandic",
    author = "Ing{\'o}lfsd{\'o}ttir, Svanhv{\'\i}t Lilja  and
      Loftsson, Hrafn  and
      Da{\dh}ason, J{\'o}n Fri{\dh}rik  and
      Bjarnad{\'o}ttir, Krist{\'\i}n",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6133",
    pages = "310--315",
    abstract = "Lemmatization, finding the basic morphological form of a word in a corpus, is an important step in many natural language processing tasks when working with morphologically rich languages. We describe and evaluate Nefnir, a new open source lemmatizer for Icelandic. Nefnir uses suffix substitution rules, derived from a large morphological database, to lemmatize tagged text. Evaluation shows that for correctly tagged text, Nefnir obtains an accuracy of 99.55{\%}, and for text tagged with a PoS tagger, the accuracy obtained is 96.88{\%}.",
}
@inproceedings{akerstrom-etal-2019-natural,
    title = "Natural Language Processing in Policy Evaluation: Extracting Policy Conditions from {IMF} Loan Agreements",
    author = {{\AA}kerstr{\"o}m, Joakim  and
      Daoud, Adel  and
      Johansson, Richard},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6134",
    pages = "316--320",
    abstract = "Social science researchers often use text as the raw data in investigations: for instance, when investigating the effects of IMF policies on the development of countries under IMF programs, researchers typically encode structured descriptions of the programs using a time-consuming manual effort. Making this process automatic may open up new opportunities in scaling up such investigations. As a first step towards automatizing this coding process, we describe an experiment where we apply a sentence classifier that automatically detects mentions of policy conditions in IMF loan agreements and divides them into different types. The results show that the classifier is generally able to detect the policy conditions, although some types are hard to distinguish.",
}
@inproceedings{alfter-graen-2019-interconnecting,
    title = "Interconnecting lexical resources and word alignment: How do learners get on with particle verbs?",
    author = {Alfter, David  and
      Gra{\"e}n, Johannes},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6135",
    pages = "321--326",
    abstract = "In this paper, we present a prototype for an online exercise aimed at learners of English and Swedish that serves multiple purposes. The exercise allows learners of the aforementioned languages to train their knowledge of particle verbs receiving clues from the exercise application. The user themselves decide which clue to receive and pay in virtual currency for each, which provides us with valuable information about the utility of the clues that we provide as well as the learners willingness to trade virtual currency versus accuracy of their choice. As resources, we use list with annotated levels from the proficiency scale defined by the Common European Framework of Reference (CEFR) and a multilingual corpus with syntactic dependency relations and word annotation for all language pairs. From the latter resource, we extract translation equivalents for particle verb construction together with a list of parallel corpus examples that can be used as clues in the exercise.",
}
@inproceedings{barriere-fouret-2019-may,
    title = "May {I} Check Again? {---} A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to {F}rench Legal Texts.",
    author = "Barriere, Valentin  and
      Fouret, Amaury",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6136",
    pages = "327--332",
    abstract = "In this paper we present a new method to learn a model robust to typos for a Named Entity Recognition task. Our improvement over existing methods helps the model to take into account the context of the sentence inside a justice decision in order to recognize an entity with a typo. We used state-of-the-art models and enriched the last layer of the neural network with high-level information linked with the potential of the word to be a certain type of entity. More precisely, we utilized the similarities between the word and the potential entity candidates the tagged sentence context. The experiments on a dataset of french justice decisions show a reduction of the relative F1-score error of 32{\%}, upgrading the score obtained with the most competitive fine-tuned state-of-the-art system from 94.85{\%} to 96.52{\%}.",
}
@inproceedings{bernardy-etal-2019-predicates,
    title = "Predicates as Boxes in {B}ayesian Semantics for Natural Language",
    author = "Bernardy, Jean-Philippe  and
      Blanck, Rasmus  and
      Chatzikyriakidis, Stergios  and
      Lappin, Shalom  and
      Maskharashvili, Aleksandre",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6137",
    pages = "333--337",
    abstract = "In this paper, we present a Bayesian approach to natural language semantics. Our main focus is on the inference task in an environment where judgments require probabilistic reasoning. We treat nouns, verbs, adjectives, etc. as unary predicates, and we model them as boxes in a bounded domain. We apply Bayesian learning to satisfy constraints expressed as premises. In this way we construct a model, by specifying boxes for the predicates. The probability of the hypothesis (the conclusion) is evaluated against the model that incorporates the premises as constraints.",
}
@inproceedings{derczynski-kjeldsen-2019-bornholmsk,
    title = "Bornholmsk Natural Language Processing: Resources and Tools",
    author = "Derczynski, Leon  and
      Kjeldsen, Alex Speed",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6138",
    pages = "338--344",
    abstract = {This paper introduces language processing resources and tools for Bornholmsk, a language spoken on the island of Bornholm, with roots in Danish and closely related to Scanian. This presents an overview of the language and available data, and the first NLP models for this living, minority Nordic language. Sammenfattnijng p{\aa} borrijnholmst: D{\ae}jnna artikkelijn introduserer naturspr{\aa}gsresurser {\aa} varktoi for borrijnholmst, ed spr{\aa}g a d{\ae}r snakkes p{\aa} {\"o}n Borrijnholm me r{\o}dder i danst {\aa} i n{\ae}r familia me sk{\aa}nst. Artikkelijn gjer ed {\^a}uersyn {\^a}uer spr{\aa}ged {\aa} di datan som fijnnes, {\aa} di fosste NLP mod{\ae}llarna for d{\ae}tta l{\ae}wenes nordiska minnret{\^a}lsspr{\aa}ged.},
}
@inproceedings{ens-etal-2019-morphosyntactic,
    title = "Morphosyntactic Disambiguation in an Endangered Language Setting",
    author = {Ens, Jeff  and
      H{\"a}m{\"a}l{\"a}inen, Mika  and
      Rueter, Jack  and
      Pasquier, Philippe},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6139",
    pages = "345--349",
    abstract = "Endangered Uralic languages present a high variety of inflectional forms in their morphology. This results in a high number of homonyms in inflections, which introduces a lot of morphological ambiguity in sentences. Previous research has employed constraint grammars to address this problem, however CGs are often unable to fully disambiguate a sentence, and their development is labour intensive. We present an LSTM based model for automatically ranking morphological readings of sentences based on their quality. This ranking can be used to evaluate the existing CG disambiguators or to directly morphologically disambiguate sentences. Our approach works on a morphological abstraction and it can be trained with a very small dataset.",
}
@inproceedings{kasen-etal-2019-tagging,
    title = "Tagging a {N}orwegian Dialect Corpus",
    author = "K{\aa}sen, Andre  and
      N{\o}klestad, Anders  and
      Hagen, Kristin  and
      Priestley, Joel",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6140",
    pages = "350--355",
    abstract = "This paper describes an evaluation of five data-driven part-of-speech (PoS) taggers for spoken Norwegian. The taggers all rely on different machine learning mechanisms: decision trees, hidden Markov models (HMMs), conditional random fields (CRFs), long-short term memory networks (LSTMs), and convolutional neural networks (CNNs). We go into some of the challenges posed by the task of tagging spoken, as opposed to written, language, and in particular a wide range of dialects as is found in the recordings of the LIA (Language Infrastructure made Accessible) project. The results show that the taggers based on either conditional random fields or neural networks perform much better than the rest, with the LSTM tagger getting the highest score.",
}
@inproceedings{kirkedal-etal-2019-lacunae,
    title = "The Lacunae of {D}anish Natural Language Processing",
    author = "Kirkedal, Andreas  and
      Plank, Barbara  and
      Derczynski, Leon  and
      Schluter, Natalie",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6141",
    pages = "356--362",
    abstract = "Danish is a North Germanic language spoken principally in Denmark, a country with a long tradition of technological and scientific innovation. However, the language has received relatively little attention from a technological perspective. In this paper, we review Natural Language Processing (NLP) research, digital resources and tools which have been developed for Danish. We find that availability of models and tools is limited, which calls for work that lifts Danish NLP a step closer to the privileged languages. Dansk abstrakt: Dansk er et nordgermansk sprog, talt prim{\ae}rt i kongeriget Danmark, et land med st{\ae}rk tradition for teknologisk og videnskabelig innovation. Det danske sprog har imidlertid v{\ae}ret genstand for relativt begr{\ae}nset opm{\ae}rksomhed, teknologisk set. I denne artikel gennemg{\aa}r vi sprogteknologi-forskning, -ressourcer og -v{\ae}rkt{\o}jer udviklet for dansk. Vi konkluderer at der eksisterer et f{\aa}tal af modeller og v{\ae}rkt{\o}jer, hvilket indbyder til forskning som l{\o}fter dansk sprogteknologi i niveau med mere priviligerede sprog.",
}
@inproceedings{ingolfsdottir-etal-2019-towards,
    title = "Towards High Accuracy Named Entity Recognition for {I}celandic",
    author = "Ing{\'o}lfsd{\'o}ttir, Svanhv{\'\i}t Lilja  and
      {\TH}orsteinsson, Sigurj{\'o}n  and
      Loftsson, Hrafn",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6142",
    pages = "363--369",
    abstract = "We report on work in progress which consists of annotating an Icelandic corpus for named entities (NEs) and using it for training a named entity recognizer based on a Bidirectional Long Short-Term Memory model. Currently, we have annotated 7,538 NEs appearing in the first 200,000 tokens of a 1 million token corpus, MIM-GOLD, originally developed for serving as a gold standard for part-of-speech tagging. Our best performing model, trained on this subset of MIM-GOLD, and enriched with external word embeddings, obtains an overall F1 score of 81.3{\%} when categorizing NEs into the following four categories: persons, locations, organizations and miscellaneous. Our preliminary results are promising, especially given the fact that 80{\%} of MIM-GOLD has not yet been used for training.",
}
@inproceedings{plank-2019-neural,
    title = "Neural Cross-Lingual Transfer and Limited Annotated Data for Named Entity Recognition in {D}anish",
    author = "Plank, Barbara",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6143",
    pages = "370--375",
    abstract = "Named Entity Recognition (NER) has greatly advanced by the introduction of deep neural architectures. However, the success of these methods depends on large amounts of training data. The scarcity of publicly-available human-labeled datasets has resulted in limited evaluation of existing NER systems, as is the case for Danish. This paper studies the effectiveness of cross-lingual transfer for Danish, evaluates its complementarity to limited gold data, and sheds light on performance of Danish NER.",
}
@inproceedings{boldsen-agirrezabal-2019-seemingly,
    title = "The Seemingly (Un)systematic Linking Element in {D}anish",
    author = "Boldsen, Sidsel  and
      Agirrezabal, Manex",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6144",
    pages = "376--380",
    abstract = "The use of a linking element between compound members is a common phenomenon in Germanic languages. Still, the exact use and conditioning of such elements is a disputed topic in linguistics. In this paper we address the issue of predicting the use of linking elements in Danish. Following previous research that shows how the choice of linking element might be conditioned by phonology, we frame the problem as a language modeling task: Considering the linking elements -s/-∅ the problem becomes predicting what is most probable to encounter next, a syllable boundary or the joining element, {`}s{'}. We show that training a language model on this task reaches an accuracy of 94 {\%}, and in the case of an unsupervised model, the accuracy reaches 80{\%}.",
}
@inproceedings{alfter-etal-2019-legato,
    title = "{LEGATO}: A flexible lexicographic annotation tool",
    author = {Alfter, David  and
      Tiedemann, Therese Lindstr{\"o}m  and
      Volodina, Elena},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6145",
    pages = "382--388",
    abstract = "This article is a report from an ongoing project aiming at analyzing lexical and grammatical competences of Swedish as a Second language (L2). To facilitate lexical analysis, we need access to metalinguistic information about relevant vocabulary that L2 learners can use and understand. The focus of the current article is on the lexical annotation of the vocabulary scope for a range of lexicographical aspects, such as morphological analysis, valency, types of multi-word units, etc. We perform parts of the analysis automatically, and other parts manually. The rationale behind this is that where there is no possibility to add information automatically, manual effort needs to be added. To facilitate the latter, a tool LEGATO has been designed, implemented and currently put to active testing.",
}
@inproceedings{aulamo-tiedemann-2019-opus,
    title = "The {OPUS} Resource Repository: An Open Package for Creating Parallel Corpora and Machine Translation Services",
    author = {Aulamo, Mikko  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6146",
    pages = "389--394",
    abstract = "This paper presents a flexible and powerful system for creating parallel corpora and for running neural machine translation services. Our package provides a scalable data repository backend that offers transparent data pre-processing pipelines and automatic alignment procedures that facilitate the compilation of extensive parallel data sets from a variety of sources. Moreover, we develop a web-based interface that constitutes an intuitive frontend for end-users of the platform. The whole system can easily be distributed over virtual machines and implements a sophisticated permission system with secure connections and a flexible database for storing arbitrary metadata. Furthermore, we also provide an interface for neural machine translation that can run as a service on virtual machines, which also incorporates a connection to the data repository software.",
}
@inproceedings{debess-etal-2019-garnishing,
    title = "Garnishing a phonetic dictionary for {ASR} intake",
    author = "Debess, Iben Nyholm  and
      Lamhauge, Sandra Saxov  and
      Henrichsen, Peter Juel",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6147",
    pages = "395--399",
    abstract = "We present a new method for preparing a lexical-phonetic database as a resource for acoustic model training. The research is an offshoot of the ongoing Project Ravnur (Speech Recognition for Faroese), but the method is language-independent. At NODALIDA 2019 we demonstrate the method (called SHARP) online, showing how a traditional lexical-phonetic dictionary (with a very rich phone inventory) is transformed into an ASR-friendly database (with reduced phonetics, preventing data sparseness). The mapping procedure is informed by a corpus of speech transcripts. We conclude with a discussion on the benefits of a well-thought-out BLARK design (Basic Language Resource Kit), making tools like SHARP possible.",
}
@inproceedings{klang-nugues-2019-docria,
    title = "{D}ocria: Processing and Storing Linguistic Data with {W}ikipedia",
    author = "Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6148",
    pages = "400--405",
    abstract = "The availability of user-generated content has increased significantly over time. Wikipedia is one example of a corpora which spans a huge range of topics and is freely available. Storing and processing these corpora requires flexible documents models as they may contain malicious and incorrect data. Docria is a library which attempts to address this issue by providing a solution which can be used with small to large corpora, from laptops using Python interactively in a Jupyter notebook to clusters running map-reduce frameworks with optimized compiled code. Docria is available as open-source code.",
}
@inproceedings{varab-schluter-2019-uniparse,
    title = "{U}ni{P}arse: A universal graph-based parsing toolkit",
    author = "Varab, Daniel  and
      Schluter, Natalie",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W19-6149",
    pages = "406--410",
    abstract = "This paper describes the design and use of the graph-based parsing framework and toolkit UniParse, released as an open-source python software package. UniParse as a framework novelly streamlines research prototyping, development and evaluation of graph-based dependency parsing architectures. UniParse does this by enabling highly efficient, sufficiently independent, easily readable, and easily extensible implementations for all dependency parser components. We distribute the toolkit with ready-made configurations as re-implementations of all current state-of-the-art first-order graph-based parsers, including even more efficient Cython implementations of both encoders and decoders, as well as the required specialised loss functions.",
}
@proceedings{ws-2019-use,
    title = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    editor = "Arppe, Antti  and
      Good, Jeff  and
      Hulden, Mans  and
      Lachler, Jordan  and
      Palmer, Alexis  and
      Schwartz, Lane  and
      Silfverberg, Miikka",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6000",
}
@inproceedings{everson-etal-2019-online,
    title = "An Online Platform for Community-Based Language Description and Documentation",
    author = "Everson, Rebecca  and
      Honore, Wolf  and
      Grimm, Scott",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6001",
    pages = "1--5",
}
@inproceedings{gessler-2019-developing,
    title = "Developing without developers: choosing labor-saving tools for language documentation apps",
    author = "Gessler, Luke",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6002",
    pages = "6--13",
}
@inproceedings{van-esch-etal-2019-future,
    title = "Future Directions in Technological Support for Language Documentation",
    author = "van Esch, Daan  and
      Foley, Ben  and
      San, Nay",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6003",
    pages = "14--22",
}
@inproceedings{santos-2019-ocr,
    title = "{OCR} evaluation tools for the 21st century",
    author = "Santos, Eddie Antonio",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6004",
    pages = "23--27",
}
@inproceedings{zamaraeva-etal-2019-handling,
    title = "Handling cross-cutting properties in automatic inference of lexical classes: A case study of Chintang",
    author = "Zamaraeva, Olga  and
      Howell, Kristen  and
      Bender, Emily M.",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6005",
    pages = "28--38",
}
@inproceedings{hamalainen-rueter-2019-finding,
    title = "Finding {S}ami Cognates with a Character-Based {NMT} Approach",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Rueter, Jack},
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6006",
    pages = "39--45",
}
@inproceedings{wiechetek-etal-2019-seeing,
    title = "Seeing more than whitespace {---} Tokenisation and disambiguation in a {N}orth {S}{\'a}mi grammar checker",
    author = "Wiechetek, Linda  and
      Moshagen, Sjur N{\o}rsteb{\o}  and
      Unhammer, Kevin Brubeck",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6007",
    pages = "46--55",
}
@inproceedings{arkhangelskiy-2019-corpus,
    title = "Corpus of usage examples: What is it good for?",
    author = "Arkhangelskiy, Timofey",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6008",
    pages = "56--63",
}
@inproceedings{harrigan-etal-2019-preliminary,
    title = "A Preliminary {P}lains {C}ree Speech Synthesizer",
    author = "Harrigan, Atticus  and
      Arppe, Antti  and
      Mills, Timothy",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6009",
    pages = "64--73",
}
@inproceedings{tyers-etal-2019-biscriptual,
    title = "A biscriptual morphological transducer for {C}rimean {T}atar",
    author = {Tyers, Francis M.  and
      Washington, Jonathan  and
      Kavitskaya, Darya  and
      G{\"o}k{\i}rmak, Memduh  and
      Howell, Nick  and
      Berberova, Remziye},
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6010",
    pages = "74--80",
}
@inproceedings{moeller-etal-2019-improving,
    title = "Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers",
    author = "Moeller, Sarah  and
      Kazeminejad, Ghazaleh  and
      Cowell, Andrew  and
      Hulden, Mans",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6011",
    pages = "81--86",
}
@inproceedings{schwartz-etal-2019-bootstrapping,
    title = "Bootstrapping a Neural Morphological Analyzer for {S}t. {L}awrence {I}sland {Y}upik from a Finite-State Transducer",
    author = "Schwartz, Lane  and
      Chen, Emily  and
      Hunt, Benjamin  and
      Schreiner, Sylvia L.R.",
    booktitle = "Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)",
    month = feb,
    year = "2019",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-6012",
    pages = "87--96",
}
@proceedings{ws-2019-sigdial,
    title = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    editor = "Nakamura, Satoshi  and
      Gasic, Milica  and
      Zuckerman, Ingrid  and
      Skantze, Gabriel  and
      Nakano, Mikio  and
      Papangelis, Alexandros  and
      Ultes, Stefan  and
      Yoshino, Koichiro",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5900",
}
@inproceedings{sankar-ravi-2019-deep,
    title = "Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes",
    author = "Sankar, Chinnadhurai  and
      Ravi, Sujith",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5901",
    doi = "10.18653/v1/W19-5901",
    pages = "1--10",
    abstract = "Open domain dialog systems face the challenge of being repetitive and producing generic responses. In this paper, we demonstrate that by conditioning the response generation on interpretable discrete dialog attributes and composed attributes, it helps improve the model perplexity and results in diverse and interesting non-redundant responses. We propose to formulate the dialog attribute prediction as a reinforcement learning (RL) problem and use policy gradients methods to optimize utterance generation using long-term rewards. Unlike existing RL approaches which formulate the token prediction as a policy, our method reduces the complexity of the policy optimization by limiting the action space to dialog attributes, thereby making the policy optimization more practical and sample efficient. We demonstrate this with experimental and human evaluations.",
}
@inproceedings{ultes-2019-improving,
    title = "Improving Interaction Quality Estimation with {B}i{LSTM}s and the Impact on Dialogue Policy Learning",
    author = "Ultes, Stefan",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5902",
    doi = "10.18653/v1/W19-5902",
    pages = "11--20",
    abstract = "Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work which is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we use a reward based on user satisfaction estimation. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. Furthermore, we apply this novel user satisfaction estimation model live in simulated experiments where the satisfaction estimation model is trained on one domain and applied in many other domains which cover a similar task. We show that applying this model results in higher estimated satisfaction, similar task success rates and a higher robustness to noise.",
}
@inproceedings{mazumder-etal-2019-lifelong,
    title = "Lifelong and Interactive Learning of Factual Knowledge in Dialogues",
    author = "Mazumder, Sahisnu  and
      Liu, Bing  and
      Wang, Shuai  and
      Ma, Nianzu",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5903",
    doi = "10.18653/v1/W19-5903",
    pages = "21--31",
    abstract = "Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to help generate quality responses. However, as the KBs are inherently incomplete and remain fixed during conversation, it limits dialogue systems{'} ability to answer questions and to handle questions involving entities or relations that are not in the KB. In this paper, we make an attempt to propose an engine for Continuous and Interactive Learning of Knowledge (CILK) for dialogue systems to give them the ability to continuously and interactively learn and infer new knowledge during conversations. With more knowledge accumulated over time, they will be able to learn better and answer more questions. Our empirical evaluation shows that CILK is promising.",
}
@inproceedings{shalyminov-etal-2019-shot,
    title = "Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach",
    author = "Shalyminov, Igor  and
      Lee, Sungjin  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5904",
    doi = "10.18653/v1/W19-5904",
    pages = "32--39",
    abstract = "Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an ever-growing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable. In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data. We do this by leveraging background knowledge from a larger, more highly represented dialogue source {---} namely, the MetaLWOz dataset. We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains. We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient than it by not requiring any data annotation.",
}
@inproceedings{zhu-etal-2019-sim,
    title = "{SIM}: A Slot-Independent Neural Model for Dialogue State Tracking",
    author = "Zhu, Chenguang  and
      Zeng, Michael  and
      Huang, Xuedong",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5905",
    doi = "10.18653/v1/W19-5905",
    pages = "40--45",
    abstract = "Dialogue state tracking is an important component in task-oriented dialogue systems to identify users{'} goals and requests as a dialogue proceeds. However, as most previous models are dependent on dialogue slots, the model complexity soars when the number of slots increases. In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots. The model utilizes attention mechanisms between user utterance and system actions. SIM achieves state-of-the-art results on WoZ and DSTC2 tasks, with only 20{\%} of the model size of previous models.",
}
@inproceedings{gupta-etal-2019-simple,
    title = "Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems",
    author = "Gupta, Arshit  and
      Hewitt, John  and
      Kirchhoff, Katrin",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5906",
    doi = "10.18653/v1/W19-5906",
    pages = "46--55",
    abstract = "With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting. These systems typically consist of Spoken Language understanding component which, in turn, consists of two tasks - Intent Classification (IC) and Slot Labeling (SL). Generally, these two tasks are modeled together jointly to achieve best performance. However, this joint modeling adds to model obfuscation. In this work, we first design framework for a modularization of joint IC-SL task to enhance architecture transparency. Then, we explore a number of self-attention, convolutional, and recurrent models, contributing a large-scale analysis of modeling paradigms for IC+SL across two datasets. Finally, using this framework, we propose a class of {`}label-recurrent{'} models that otherwise non-recurrent, with a 10-dimensional representation of the label history, and show that our proposed systems are easy to interpret, highly accurate (achieving over 30{\%} error reduction in SL over the state-of-the-art on the Snips dataset), as well as fast, at 2x the inference and 2/3 to 1/2 the training time of comparable recurrent models, thus giving an edge in critical real-world systems.",
}
@inproceedings{conway-lambert-2019-time,
    title = "Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems",
    author = "Conway, Rylan  and
      Lambert, Mathias",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5907",
    doi = "10.18653/v1/W19-5907",
    pages = "56--61",
    abstract = "In a spoken dialogue system, dialogue state tracker (DST) components track the state of the conversation by updating a distribution of values associated with each of the slots being tracked for the current user turn, using the interactions until then. Much of the previous work has relied on modeling the natural order of the conversation, using distance based offsets as an approximation of time. In this work, we hypothesize that leveraging the wall-clock temporal difference between turns is crucial for finer-grained control of dialogue scenarios. We develop a novel approach that applies a time mask, based on the wall-clock time difference, to the associated slot embeddings and empirically demonstrate that our proposed approach outperforms existing approaches that leverage distance offsets, on both an internal benchmark dataset as well as DSTC2.",
}
@inproceedings{vath-vu-2019-combine,
    title = "To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies",
    author = {V{\"a}th, Dirk  and
      Vu, Ngoc Thang},
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5908",
    doi = "10.18653/v1/W19-5908",
    pages = "62--67",
    abstract = "In this paper, we explore state-of-the-art deep reinforcement learning methods for dialog policy training such as prioritized experience replay, double deep Q-Networks, dueling network architectures and distributional learning. Our main findings show that each individual method improves the rewards and the task success rate but combining these methods in a Rainbow agent, which performs best across tasks and environments, is a non-trivial task. We, therefore, provide insights about the influence of each method on the combination and how to combine them to form a Rainbow agent.",
}
@inproceedings{paranjape-neubig-2019-contextualized,
    title = "Contextualized Representations for Low-resource Utterance Tagging",
    author = "Paranjape, Bhargavi  and
      Neubig, Graham",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5909",
    doi = "10.18653/v1/W19-5909",
    pages = "68--74",
    abstract = "Utterance-level analysis of the speaker{'}s intentions and emotions is a core task in conversational understanding. Depending on the end objective of the conversational understanding task, different categorical dialog-act or affect labels are expertly designed to cover specific aspects of the speakers{'} intentions or emotions respectively. Accurately annotating with these labels requires a high level of human expertise, and thus applying this process to a large conversation corpus or new domains is prohibitively expensive. The resulting paucity of data limits the use of sophisticated neural models. In this paper, we tackle these limitations by performing unsupervised training of utterance representations from a large corpus of spontaneous dialogue data. Models initialized with these representations achieve competitive performance on utterance-level dialogue-act recognition and emotion classification, especially in low-resource settings encountered when analyzing conversations in new domains.",
}
@inproceedings{trinh-etal-2019-capturing,
    title = "Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker",
    author = "Trinh, Anh Duong  and
      Ross, Robert J.  and
      Kelleher, John D.",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5910",
    doi = "10.18653/v1/W19-5910",
    pages = "75--84",
    abstract = "Dialogue state tracking requires the population and maintenance of a multi-slot frame representation of the dialogue state. Frequently, dialogue state tracking systems assume independence between slot values within a frame. In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance. To support this argument, the research presented in this paper is structured into three stages: (i) analyzing variable dependencies in dialogue data; (ii) applying an energy-based methodology to model dialogue state tracking as a structured prediction task; and (iii) evaluating the impact of inter-slot relationships on model performance. Overall we demonstrate that modelling the associations between target slots with an energy-based formalism improves dialogue state tracking performance in a number of ways.",
}
@inproceedings{louvan-magnini-2019-leveraging,
    title = "Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?",
    author = "Louvan, Samuel  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5911",
    doi = "10.18653/v1/W19-5911",
    pages = "85--91",
    abstract = "Slot filling is a core operation for utterance understanding in task-oriented dialogue systems. Slots are typically domain-specific, and adding new domains to a dialogue system involves data and time-intensive processes. A popular technique to address the problem is transfer learning, where it is assumed the availability of a large slot filling dataset for the source domain, to be used to help slot filling on the target domain, with fewer data. In this work, instead, we propose to leverage source tasks based on semantically related non-conversational resources (e.g., semantic sequence tagging datasets), as they are both cheaper to obtain and reusable to several slot filling domains. We show that using auxiliary non-conversational tasks in a multi-task learning setup consistently improves low resource slot filling performance.",
}
@inproceedings{papangelis-etal-2019-collaborative,
    title = "Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning",
    author = "Papangelis, Alexandros  and
      Wang, Yi-Chia  and
      Molino, Piero  and
      Tur, Gokhan",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5912",
    doi = "10.18653/v1/W19-5912",
    pages = "92--102",
    abstract = "Some of the major challenges in training conversational agents include the lack of large-scale data of real-world complexity, defining appropriate evaluation measures, and managing meaningful conversations across many topics over long periods of time. Moreover, most works tend to assume that the conversational agent{'}s environment is stationary, a somewhat strong assumption. To remove this assumption and overcome the lack of data, we take a step away from the traditional training pipeline and model the conversation as a stochastic collaborative game. Each agent (player) has a role ({``}assistant{''}, {``}tourist{''}, {``}eater{''}, etc.) and their own objectives, and can only interact via language they generate. Each agent, therefore, needs to learn to operate optimally in an environment with multiple sources of uncertainty (its own LU and LG, the other agent{'}s LU, Policy, and LG). In this work, we present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language and show that they outperform supervised and deep learning baselines.",
}
@inproceedings{ramanarayanan-etal-2019-scoring,
    title = "Scoring Interactional Aspects of Human-Machine Dialog for Language Learning and Assessment using Text Features",
    author = "Ramanarayanan, Vikram  and
      Mulholland, Matthew  and
      Qian, Yao",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5913",
    doi = "10.18653/v1/W19-5913",
    pages = "103--109",
    abstract = "While there has been much work in the language learning and assessment literature on human and automated scoring of essays and short constructed responses, there is little to no work examining text features for scoring of dialog data, particularly interactional aspects thereof, to assess conversational proficiency over and above constructed response skills. Our work bridges this gap by investigating both human and automated approaches towards scoring human{--}machine text dialog in the context of a real-world language learning application. We collected conversational data of human learners interacting with a cloud-based standards-compliant dialog system, triple-scored these data along multiple dimensions of conversational proficiency, and then analyzed the performance trends. We further examined two different approaches to automated scoring of such data and show that these approaches are able to perform at or above par with human agreement for a majority of dimensions of the scoring rubric.",
}
@inproceedings{rojas-barahona-etal-2019-spoken,
    title = "Spoken Conversational Search for General Knowledge",
    author = "Rojas Barahona, Lina M.  and
      Bellec, Pascal  and
      Besset, Benoit  and
      Dossantos, Martinho  and
      Heinecke, Johannes  and
      Asadullah, Munshi  and
      Leblouch, Olivier  and
      Lancien, Jeanyves.  and
      Damnati, Geraldine  and
      Mory, Emmanuel  and
      Herledan, Frederic",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5914",
    doi = "10.18653/v1/W19-5914",
    pages = "110--113",
    abstract = "We present a spoken conversational question answering proof of concept that is able to answer questions about general knowledge from Wikidata. The dialogue agent does not only orchestrate various agents but also solve coreferences and ellipsis.",
}
@inproceedings{bouraoui-etal-2019-graph2bots,
    title = "{G}raph2{B}ots, Unsupervised Assistance for Designing Chatbots",
    author = "Bouraoui, Jean-Leon  and
      Le Meitour, Sonia  and
      Carbou, Romain  and
      Rojas Barahona, Lina M.  and
      Lemaire, Vincent",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5915",
    doi = "10.18653/v1/W19-5915",
    pages = "114--117",
    abstract = "We present Graph2Bots, a tool for assisting conversational agent designers. It extracts a graph representation from human-human conversations by using unsupervised learning. The generated graph contains the main stages of the dialogue and their inner transitions. The graphical user interface (GUI) then allows graph editing.",
}
@inproceedings{galitsky-etal-2019-chatbot-conducting,
    title = "On a Chatbot Conducting Dialogue-in-Dialogue",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry  and
      Goncharova, Elizaveta",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5916",
    doi = "10.18653/v1/W19-5916",
    pages = "118--121",
    abstract = "We demo a chatbot that delivers content in the form of virtual dialogues automatically produced from plain texts extracted and selected from documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions are automatically generated for these answers.",
}
@inproceedings{yavuz-etal-2019-deepcopy,
    title = "{D}eep{C}opy: Grounded Response Generation with Hierarchical Pointer Networks",
    author = "Yavuz, Semih  and
      Rastogi, Abhinav  and
      Chao, Guan-Lin  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5917",
    doi = "10.18653/v1/W19-5917",
    pages = "122--132",
    abstract = "Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation. However, these models are known to have several problems, especially in the context of chit-chat based dialogue systems: they tend to generate short and dull responses that are often too generic. Furthermore, these models do not ground conversational responses on knowledge and facts, resulting in turns that are not accurate, informative and engaging for the users. In this paper, we propose and experiment with a series of response generation models that aim to serve in the general scenario where in addition to the dialogue context, relevant unstructured external knowledge in the form of text is also assumed to be available for models to harness. Our proposed approach extends pointer-generator networks (See et al., 2017) by allowing the decoder to hierarchically attend and copy from external knowledge in addition to the dialogue context. We empirically show the effectiveness of the proposed model compared to several baselines including (Ghazvininejadet al., 2018; Zhang et al., 2018) through both automatic evaluation metrics and human evaluation on ConvAI2 dataset.",
}
@inproceedings{jiang-etal-2019-towards,
    title = "Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability",
    author = "Jiang, Zhuoxuan  and
      Mao, Xian-Ling  and
      Huang, Ziming  and
      Ma, Jie  and
      Li, Shaochun",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5918",
    doi = "10.18653/v1/W19-5918",
    pages = "133--142",
    abstract = "Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues. However, existing methods either take too many manual efforts (e.g. reinforcement learning methods) or cannot guarantee the dialogue efficiency (e.g. sequence-to-sequence methods). In this paper, we address this problem by proposing a novel end-to-end learning model to train a dialogue agent that can look ahead for several future turns and generate an optimal response to make the dialogue efficient. Our method is data-driven and does not require too much manual work for intervention during system design. We evaluate our method on two datasets of different scenarios and the experimental results demonstrate the efficiency of our model.",
}
@inproceedings{xu-etal-2019-unsupervised,
    title = "Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking",
    author = "Xu, Xinnuo  and
      Zhang, Yizhe  and
      Liden, Lars  and
      Lee, Sungjin",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5919",
    doi = "10.18653/v1/W19-5919",
    pages = "143--154",
    abstract = "Although the data-driven approaches of some recent bot building platforms make it possible for a wide range of users to easily create dialogue systems, those platforms don{'}t offer tools for quickly identifying which log dialogues contain problems. This is important since corrections to log dialogues provide a means to improve performance after deployment. A log dialogue ranker, which ranks problematic dialogues higher, is an essential tool due to the sheer volume of log dialogues that could be generated. However, training a ranker typically requires labelling a substantial amount of data, which is not feasible for most users. In this paper, we present a novel unsupervised approach for dialogue ranking using GANs and release a corpus of labelled dialogues for evaluation and comparison with supervised methods. The evaluation result shows that our method compares favorably to supervised methods without any labelled data.",
}
@inproceedings{tseng-etal-2019-tree,
    title = "Tree-Structured Semantic Encoder with Knowledge Sharing for Domain Adaptation in Natural Language Generation",
    author = "Tseng, Bo-Hsiang  and
      Budzianowski, Pawe{\l}  and
      Wu, Yen-chen  and
      Gasic, Milica",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5920",
    doi = "10.18653/v1/W19-5920",
    pages = "155--164",
    abstract = "Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain. This is particularly the case for dialogue systems, where we want to be able to seamlessly include new domains into the conversation. Therefore, it is crucial for generation models to share knowledge across domains for the effective adaptation from one domain to another. In this study, we exploit a tree-structured semantic encoder to capture the internal structure of complex semantic representations required for multi-domain dialogues in order to facilitate knowledge sharing across domains. In addition, a layer-wise attention mechanism between the tree encoder and the decoder is adopted to further improve the model{'}s capability. The automatic evaluation results show that our model outperforms previous methods in terms of the BLEU score and the slot error rate, in particular when the adaptation data is limited. In subjective evaluation, human judges tend to prefer the sentences generated by our model, rating them more highly on informativeness and naturalness than other systems.",
}
@inproceedings{mehri-etal-2019-structured,
    title = "Structured Fusion Networks for Dialog",
    author = "Mehri, Shikib  and
      Srinivasan, Tejas  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5921",
    doi = "10.18653/v1/W19-5921",
    pages = "165--177",
    abstract = "Neural dialog models have exhibited strong performance, however their end-to-end nature lacks a representation of the explicit structure of dialog. This results in a loss of generalizability, controllability and a data-hungry nature. Conversely, more traditional dialog systems do have strong models of explicit structure. This paper introduces several approaches for explicitly incorporating structure into neural models of dialog. Structured Fusion Networks first learn neural dialog modules corresponding to the structured components of traditional dialog systems and then incorporate these modules in a higher-level generative model. Structured Fusion Networks obtain strong results on the MultiWOZ dataset, both with and without reinforcement learning. Structured Fusion Networks are shown to have several valuable properties, including better domain generalizability, improved performance in reduced data scenarios and robustness to divergence during reinforcement learning.",
}
@inproceedings{shu-etal-2019-flexibly,
    title = "Flexibly-Structured Model for Task-Oriented Dialogues",
    author = "Shu, Lei  and
      Molino, Piero  and
      Namazifar, Mahdi  and
      Xu, Hu  and
      Liu, Bing  and
      Zheng, Huaixiu  and
      Tur, Gokhan",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5922",
    doi = "10.18653/v1/W19-5922",
    pages = "178--187",
    abstract = "This paper proposes a novel end-to-end architecture for task-oriented dialogue systems. It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot. The policy engine and language generation tasks are modeled jointly following that. The copy-augmented sequential decoder deals with new or unknown values in the conversation, while the multi-label decoder combined with the sequential decoder ensures the explicit assignment of values to slots. On the generation part, slot binary classifiers are used to improve performance. This architecture is scalable to real-world scenarios and is shown through an empirical evaluation to achieve state-of-the-art performance on both the Cambridge Restaurant dataset and the Stanford in-car assistant dataset.",
}
@inproceedings{yang-choi-2019-friendsqa,
    title = "{F}riends{QA}: Open-Domain Question Answering on {TV} Show Transcripts",
    author = "Yang, Zhengzhe  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5923",
    doi = "10.18653/v1/W19-5923",
    pages = "188--197",
    abstract = "This paper presents FriendsQA, a challenging question answering dataset that contains 1,222 dialogues and 10,610 open-domain questions, to tackle machine comprehension on everyday conversations. Each dialogue, involving multiple speakers, is annotated with several types of questions regarding the dialogue contexts, and the answers are annotated with certain spans in the dialogue. A series of crowdsourcing tasks are conducted to ensure good annotation quality, resulting a high inter-annotator agreement of 81.82{\%}. A comprehensive annotation analytics is provided for a deeper understanding in this dataset. Three state-of-the-art QA systems are experimented, R-Net, QANet, and BERT, and evaluated on this dataset. BERT in particular depicts promising results, an accuracy of 74.2{\%} for answer utterance selection and an F1-score of 64.2{\%} for answer span selection, suggesting that the FriendsQA task is hard yet has a great potential of elevating QA research on multiparty dialogue to another level.",
}
@inproceedings{cohen-2019-foundations,
    title = "Foundations of Collaborative Task-Oriented Dialogue: What{'}s in a Slot?",
    author = "Cohen, Philip",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5924",
    doi = "10.18653/v1/W19-5924",
    pages = "198--209",
    abstract = "In this paper, we examine the foundations of task-oriented dialogues, in which systems are requested to perform tasks for humans. We argue that the way this dialogue task has been framed has limited its applicability to processing simple requests with atomic {``}slot-fillers{''}. However, real task-oriented dialogues can contain more complex utterances that provide non-atomic constraints on slot values. For example, in response to the system{'}s question {``}What time do you want me to reserve the restaurant?{''}, a user should be able to say {``}the earliest time available,{''} which cannot be handled by classic {``}intent + slots{''} approaches that do not incorporate expressive logical form meaning representations. Furthermore, situations for which it would be desirable to build task-oriented dialogue systems, e.g., to engage in mixed-initiative, collaborative or multiparty dialogues, will require a more general approach. In order to overcome these limitations and to provide such an approach, we give a logical analysis of the {``}intent+slot{''} dialogue setting using a modal logic of intention and including a more expansive notion of {``}dialogue state{''}. Finally, we briefly discuss our program of research to build a next generation of plan-based dialogue systems that goes beyond {``}intent + slots{''}.",
}
@inproceedings{kleingarn-etal-2019-speaker,
    title = "Speaker-adapted neural-network-based fusion for multimodal reference resolution",
    author = "Kleingarn, Diana  and
      Nabizadeh, Nima  and
      Heckmann, Martin  and
      Kolossa, Dorothea",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5925",
    doi = "10.18653/v1/W19-5925",
    pages = "210--214",
    abstract = "Humans use a variety of approaches to reference objects in the external world, including verbal descriptions, hand and head gestures, eye gaze or any combination of them. The amount of useful information from each modality, however, may vary depending on the specific person and on several other factors. For this reason, it is important to learn the correct combination of inputs for inferring the best-fitting reference. In this paper, we investigate appropriate speaker-dependent and independent fusion strategies in a multimodal reference resolution task. We show that without any change in the modality models, only through an optimized fusion technique, it is possible to reduce the error rate of the system on a reference resolution task by more than 50{\%}.",
}
@inproceedings{chao-etal-2019-learning,
    title = "Learning Question-Guided Video Representation for Multi-Turn Video Question Answering",
    author = "Chao, Guan-Lin  and
      Rastogi, Abhinav  and
      Yavuz, Semih  and
      Hakkani-Tur, Dilek  and
      Chen, Jindong  and
      Lane, Ian",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5926",
    doi = "10.18653/v1/W19-5926",
    pages = "215--225",
    abstract = "Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans. Video question answering is a specific scenario of such AI-human interaction where an agent generates a natural language response to a question regarding the video of a dynamic scene. Incorporating features from multiple modalities, which often provide supplementary information, is one of the challenging aspects of video question answering. Furthermore, a question often concerns only a small segment of the video, hence encoding the entire video sequence using a recurrent neural network is not computationally efficient. Our proposed question-guided video representation module efficiently generates the token-level video summary guided by each word in the question. The learned representations are then fused with the question to generate the answer. Through empirical evaluation on the Audio Visual Scene-aware Dialog (AVSD) dataset, our proposed models in single-turn and multi-turn question answering achieve state-of-the-art performance on several automatic natural language generation evaluation metrics.",
}
@inproceedings{kurfali-ostling-2019-zero,
    title = "Zero-shot transfer for implicit discourse relation classification",
    author = {Kurfal{\i}, Murathan  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5927",
    doi = "10.18653/v1/W19-5927",
    pages = "226--231",
    abstract = "Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated training data exists only for a small number of languages, such as English and Chinese. We present a new system using zero-shot transfer learning for implicit discourse relation classification, where the only resource used for the target language is unannotated parallel text. This system is evaluated on the discourse-annotated TED-MDB parallel corpus, where it obtains good results for all seven languages using only English training data.",
}
@inproceedings{acharya-etal-2019-quantitative,
    title = "A Quantitative Analysis of Patients{'} Narratives of Heart Failure",
    author = "Acharya, Sabita  and
      Di Eugenio, Barbara  and
      Boyd, Andrew  and
      Cameron, Richard  and
      Dunn Lopez, Karen  and
      Martyn-Nemeth, Pamela  and
      Chattopadhyay, Debaleena  and
      Habibi, Pantea  and
      Dickens, Carolyn  and
      Vatani, Haleh  and
      Ardati, Amer",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5928",
    doi = "10.18653/v1/W19-5928",
    pages = "232--238",
    abstract = "Patients with chronic conditions like heart failure are the most likely to be re-hospitalized. One step towards avoiding re-hospitalization is to devise strategies for motivating patients to take care of their own health. In this paper, we perform a quantitative analysis of patients{'} narratives of their experience with heart failure and explore the different topics that patients talk about. We compare two different groups of patients- those unable to take charge of their illness, and those who make efforts to improve their health. We will use the findings from our analysis to refine and personalize the summaries of hospitalizations that our system automatically generates.",
}
@inproceedings{naik-etal-2019-tddiscourse,
    title = "{TDD}iscourse: A Dataset for Discourse-Level Temporal Ordering of Events",
    author = "Naik, Aakanksha  and
      Breitfeller, Luke  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5929",
    doi = "10.18653/v1/W19-5929",
    pages = "239--249",
    abstract = "Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs. This restricts the ability of systems to learn temporal links between global pairs, since reliance on local syntactic features suffices to achieve reasonable performance on existing datasets. However, systems should be capable of incorporating cues from document-level structure to assign temporal relations. In this work, we take a first step towards discourse-level temporal ordering by creating TDDiscourse, the first dataset focusing specifically on temporal links between event pairs which are more than one sentence apart. We create TDDiscourse by augmenting TimeBank-Dense, a corpus of English news articles, manually annotating global pairs that cannot be inferred automatically from existing annotations. Our annotations double the number of temporal links in TimeBank-Dense, while possessing several desirable properties such as focusing on long-distance pairs and not being automatically inferable. We adapt and benchmark the performance of three state-of-the-art models on TDDiscourse and observe that existing systems indeed find discourse-level temporal ordering harder.",
}
@inproceedings{alloatti-etal-2019-real,
    title = "Real Life Application of a Question Answering System Using {BERT} Language Model",
    author = "Alloatti, Francesca  and
      Di Caro, Luigi  and
      Sportelli, Gianpiero",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5930",
    doi = "10.18653/v1/W19-5930",
    pages = "250--253",
    abstract = "It is often hard to apply the newest advances in research to real life scenarios. They usually require the resolution of some specific task applied to a restricted domain, all the while providing small amounts of data to begin with. In this study we apply one of the newest innovations in Deep Learning to a task of text classification. We created a question answering system in Italian that provides information about a specific subject, e-invoicing and digital billing. Italy recently introduced a new legislation about e-invoicing and people have some legit doubts, therefore a large share of professionals could benefit from this tool.",
}
@inproceedings{vanzo-etal-2019-hierarchical,
    title = "Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational {AI}: {HERMIT} {NLU}",
    author = "Vanzo, Andrea  and
      Bastianelli, Emanuele  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5931",
    doi = "10.18653/v1/W19-5931",
    pages = "254--263",
    abstract = "We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers. We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics. Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domain-specific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an average 4.45{\%} improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.",
}
@inproceedings{gao-etal-2019-dialog,
    title = "Dialog State Tracking: A Neural Reading Comprehension Approach",
    author = "Gao, Shuyang  and
      Sethi, Abhishek  and
      Agarwal, Sanchit  and
      Chung, Tagyoung  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5932",
    doi = "10.18653/v1/W19-5932",
    pages = "264--273",
    abstract = "Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33{\%} on the standard test split, exceeding current state-of-the-art by 11.75{\%}**.",
}
@inproceedings{akhtiamov-etal-2019-cross,
    title = "Cross-Corpus Data Augmentation for Acoustic Addressee Detection",
    author = "Akhtiamov, Oleg  and
      Siegert, Ingo  and
      Karpov, Alexey  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5933",
    doi = "10.18653/v1/W19-5933",
    pages = "274--283",
    abstract = "Acoustic addressee detection (AD) is a modern paralinguistic and dialogue challenge that especially arises in voice assistants. In the present study, we distinguish addressees in two settings (a conversation between several people and a spoken dialogue system, and a conversation between several adults and a child) and introduce the first competitive baseline (unweighted average recall equals 0.891) for the Voice Assistant Conversation Corpus that models the first setting. We jointly solve both classification problems, using three models: a linear support vector machine dealing with acoustic functionals and two neural networks utilising raw waveforms alongside with acoustic low-level descriptors. We investigate how different corpora influence each other, applying the mixup approach to data augmentation. We also study the influence of various acoustic context lengths on AD. Two-second speech fragments turn out to be sufficient for reliable AD. Mixup is shown to be beneficial for merging acoustic data (extracted features but not raw waveforms) from different domains that allows us to reach a higher classification performance on human-machine AD and also for training a multipurpose neural network that is capable of solving both human-machine and adult-child AD problems.",
}
@inproceedings{laskowski-etal-2019-scalable,
    title = "A Scalable Method for Quantifying the Role of Pitch in Conversational Turn-Taking",
    author = "Laskowski, Kornel  and
      Wlodarczak, Marcin  and
      Heldner, Mattias",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5934",
    doi = "10.18653/v1/W19-5934",
    pages = "284--292",
    abstract = "Pitch has long been held as an important signalling channel when planning and deploying speech in conversation, and myriad studies have been undertaken to determine the extent to which it actually plays this role. Unfortunately, these studies have required considerable human investment in data preparation and analysis, and have therefore often been limited to a handful of specific conversational contexts. The current article proposes a framework which addresses these limitations, by enabling a scalable, quantitative characterization of the role of pitch throughout an entire conversation, requiring only the raw signal and speech activity references. The framework is evaluated on the Switchboard dialogue corpus. Experiments indicate that pitch trajectories of both parties are predictive of their incipient speech activity; that pitch should be expressed on a logarithmic scale and Z-normalized, as well as accompanied by a binary voicing variable; and that only the most recent 400 ms of the pitch trajectory are useful in incipient speech activity prediction.",
}
@inproceedings{cohn-etal-2019-large,
    title = "A Large-Scale User Study of an {A}lexa {P}rize Chatbot: Effect of {TTS} Dynamism on Perceived Quality of Social Dialog",
    author = "Cohn, Michelle  and
      Chen, Chun-Yen  and
      Yu, Zhou",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5935",
    doi = "10.18653/v1/W19-5935",
    pages = "293--306",
    abstract = "This study tests the effect of cognitive-emotional expression in an Alexa text-to-speech (TTS) voice on users{'} experience with a social dialog system. We systematically introduced emotionally expressive interjections (e.g., {``}Wow!{''}) and filler words (e.g., {``}um{''}, {``}mhmm{''}) in an Amazon Alexa Prize socialbot, Gunrock. We tested whether these TTS manipulations improved users{'} ratings of their conversation across thousands of real user interactions (n=5,527). Results showed that interjections and fillers each improved users{'} holistic ratings, an improvement that further increased if the system used both manipulations. A separate perception experiment corroborated the findings from the user study, with improved social ratings for conversations including interjections; however, no positive effect was observed for fillers, suggesting that the role of the rater in the conversation{---}as active participant or external listener{---}is an important factor in assessing social dialogs.",
}
@inproceedings{partovi-zukerman-2019-influence,
    title = "Influence of Time and Risk on Response Acceptability in a Simple Spoken Dialogue System",
    author = "Partovi, Andisheh  and
      Zukerman, Ingrid",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5936",
    doi = "10.18653/v1/W19-5936",
    pages = "307--319",
    abstract = "We describe a longitudinal user study conducted in the context of a Spoken Dialogue System for a household robot, where we examined the influence of time displacement and situational risk on users{'} preferred responses. To this effect, we employed a corpus of spoken requests that asked a robot to fetch or move objects in a room. In the first stage of our study, participants selected among four response types to these requests under two risk conditions: low and high. After some time, the same participants rated several responses to the previous requests {---} these responses were instantiated from the four response types. Our results show that participants did not rate highly their own response types; moreover, they rated their own response types similarly to different ones. This suggests that, at least in this context, people{'}s preferences at a particular point in time may not reflect their general attitudes, and that various reasonable response types may be equally acceptable. Our study also reveals that situational risk influences the acceptability of some response types.",
}
@inproceedings{ginzburg-etal-2019-characterizing,
    title = "Characterizing the Response Space of Questions: a Corpus Study for {E}nglish and {P}olish",
    author = "Ginzburg, Jonathan  and
      Yusupujiang, Zulipiye  and
      Li, Chuyuan  and
      Ren, Kexin  and
      {\L}upkowski, Pawe{\l}",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5937",
    doi = "10.18653/v1/W19-5937",
    pages = "320--330",
    abstract = "The main aim of this paper is to provide a characterization of the response space for questions using a taxonomy grounded in a dialogical formal semantics. As a starting point we take the typology for responses in the form of questions provided in (Lupkowski and Ginzburg, 2016). This work develops a wide coverage taxonomy for question/question sequences observable in corpora including the BNC, CHILDES, and BEE, as well as formal modelling of all the postulated classes. Our aim is to extend this work to cover all responses to questions. We present the extended typology of responses to questions based on a corpus studies of BNC, BEE and Maptask with include 506, 262, and 467 question/response pairs respectively. We compare the data for English with data from Polish using the Spokes corpus (205 question/response pairs). We discuss annotation reliability and disagreement analysis. We sketch how each class can be formalized using a dialogical semantics appropriate for dialogue management.",
}
@inproceedings{attari-etal-2019-explainability,
    title = "From Explainability to Explanation: Using a Dialogue Setting to Elicit Annotations with Justifications",
    author = "Attari, Nazia  and
      Heckmann, Martin  and
      Schlangen, David",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5938",
    doi = "10.18653/v1/W19-5938",
    pages = "331--335",
    abstract = "Despite recent attempts in the field of explainable AI to go beyond black box prediction models, typically already the training data for supervised machine learning is collected in a manner that treats the annotator as a {``}black box{''}, the internal workings of which remains unobserved. We present an annotation method where a task is given to a pair of annotators who collaborate on finding the best response. With this we want to shed light on the questions if the collaboration increases the quality of the responses and if this {``}thinking together{''} provides useful information in itself, as it at least partially reveals their reasoning steps. Furthermore, we expect that this setting puts the focus on explanation as a linguistic act, vs. explainability as a property of models. In a crowd-sourcing experiment, we investigated three different annotation tasks, each in a collaborative dialogical (two annotators) and monological (one annotator) setting. Our results indicate that our experiment elicits collaboration and that this collaboration increases the response accuracy. We see large differences in the annotators{'} behavior depending on the task. Similarly, we also observe that the dialog patterns emerging from the collaboration vary significantly with the task.",
}
@inproceedings{lykartsis-kotti-2019-prediction,
    title = "Prediction of User Emotion and Dialogue Success Using Audio Spectrograms and Convolutional Neural Networks",
    author = "Lykartsis, Athanasios  and
      Kotti, Margarita",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5939",
    doi = "10.18653/v1/W19-5939",
    pages = "336--344",
    abstract = "In this paper we aim to predict dialogue success and user satisfaction as well as emotion on a turn level. To achieve this, we investigate the use of spectrogram representations, extracted from audio files, in combination with several types of convolutional neural networks. The experiments were performed on the Let{'}s Go V2 database, comprising 5065 audio files and having labels for subjective and objective dialogue turn success, as well as the emotional state of the user. Results show that by using only audio, it is possible to predict turn success with very high accuracy for all three labels (90{\%}). The best performing input representation were 1s long mel-spectrograms in combination with a CNN with a bottleneck architecture. The resulting system has the potential to be used real-time. Our results significantly surpass the state of the art for dialogue success prediction based only on audio.",
}
@inproceedings{axelsson-skantze-2019-modelling,
    title = "Modelling Adaptive Presentations in Human-Robot Interaction using Behaviour Trees",
    author = "Axelsson, Nils  and
      Skantze, Gabriel",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5940",
    doi = "10.18653/v1/W19-5940",
    pages = "345--352",
    abstract = "In dialogue, speakers continuously adapt their speech to accommodate the listener, based on the feedback they receive. In this paper, we explore the modelling of such behaviours in the context of a robot presenting a painting. A Behaviour Tree is used to organise the behaviour on different levels, and allow the robot to adapt its behaviour in real-time; the tree organises engagement, joint attention, turn-taking, feedback and incremental speech processing. An initial implementation of the model is presented, and the system is evaluated in a user study, where the adaptive robot presenter is compared to a non-adaptive version. The adaptive version is found to be more engaging by the users, although no effects are found on the retention of the presented material.",
}
@inproceedings{radlinski-etal-2019-coached,
    title = "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences",
    author = "Radlinski, Filip  and
      Balog, Krisztian  and
      Byrne, Bill  and
      Krishnamoorthi, Karthik",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5941",
    doi = "10.18653/v1/W19-5941",
    pages = "353--360",
    abstract = "Conversational recommendation has recently attracted significant attention. As systems must understand users{'} preferences, training them has called for conversational corpora, typically derived from task-oriented conversations. We observe that such corpora often do not reflect how people naturally describe preferences. We present a new approach to obtaining user preferences in dialogue: Coached Conversational Preference Elicitation. It allows collection of natural yet structured conversational preferences. Studying the dialogues in one domain, we present a brief quantitative analysis of how people describe movie preferences at scale. Demonstrating the methodology, we release the CCPE-M dataset to the community with over 500 movie preference dialogues expressing over 10,000 preferences.",
}
@inproceedings{cercas-curry-rieser-2019-crowd,
    title = "A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents",
    author = "Cercas Curry, Amanda  and
      Rieser, Verena",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5942",
    doi = "10.18653/v1/W19-5942",
    pages = "361--366",
    abstract = "How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as {``}polite refusal{''}, score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user{'}s perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.",
}
@inproceedings{zhou-etal-2019-dynamic,
    title = "A Dynamic Strategy Coach for Effective Negotiation",
    author = "Zhou, Yiheng  and
      He, He  and
      Black, Alan W  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5943",
    doi = "10.18653/v1/W19-5943",
    pages = "367--378",
    abstract = "Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology. An average person is often far from an expert in negotiation. Our goal is to assist humans to become better negotiators through a machine-in-the-loop approach that combines machine{'}s advantage at data-driven decision-making and human{'}s language generation ability. We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialogue. Our negotiation coach monitors messages between them and recommends strategies in real time to the seller to get a better deal (e.g., {``}reject the proposal and propose a price{''}, {``}talk about your personal experience with the product{''}). The best strategy largely depends on the context (e.g., the current price, the buyer{'}s attitude). Therefore, we first identify a set of negotiation strategies, then learn to predict the best strategy in a given dialogue context from a set of human-human bargaining dialogues. Evaluation on human-human dialogues shows that our coach increases the profits of the seller by almost 60{\%}.",
}
@inproceedings{gupta-etal-2019-investigating,
    title = "Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References",
    author = "Gupta, Prakhar  and
      Mehri, Shikib  and
      Zhao, Tiancheng  and
      Pavel, Amy  and
      Eskenazi, Maxine  and
      Bigham, Jeffrey",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5944",
    doi = "10.18653/v1/W19-5944",
    pages = "379--391",
    abstract = "The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to correlate poorly with human judgement, particularly in open-domain dialog. One alternative is to collect human annotations for evaluation, which can be expensive and time consuming. To demonstrate the effectiveness of multi-reference evaluation, we augment the test set of DailyDialog with multiple references. A series of experiments show that the use of multiple references results in improved correlation between several automatic metrics and human judgement for both the quality and the diversity of system output.",
}
@inproceedings{keizer-etal-2019-user,
    title = "User Evaluation of a Multi-dimensional Statistical Dialogue System",
    author = "Keizer, Simon  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Liu, Xingkun  and
      Rieser, Verena",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5945",
    doi = "10.18653/v1/W19-5945",
    pages = "392--398",
    abstract = "We present the first complete spoken dialogue system driven by a multiimensional statistical dialogue manager. This framework has been shown to substantially reduce data needs by leveraging domain-independent dimensions, such as social obligations or feedback, which (as we show) can be transferred between domains. In this paper, we conduct a user study and show that the performance of a multi-dimensional system, which can be adapted from a source domain, is equivalent to that of a one-dimensional baseline, which can only be trained from scratch.",
}
@inproceedings{anikina-kruijff-korbayova-2019-dialogue,
    title = "Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response",
    author = "Anikina, Tatiana  and
      Kruijff-Korbayova, Ivana",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5946",
    doi = "10.18653/v1/W19-5946",
    pages = "399--410",
    abstract = "We present the results we obtained on the classification of dialogue acts in a corpus of human-human team communication in the domain of robot-assisted disaster response. We annotated dialogue acts according to the ISO 24617-2 standard scheme and carried out experiments using the FastText linear classifier as well as several neural architectures, including feed-forward, recurrent and convolutional neural models with different types of embeddings, context and attention mechanism. The best performance was achieved with a {''}Divide {\&} Merge{''} architecture presented in the paper, using trainable GloVe embeddings and a structured dialogue history. This model learns from the current utterance and the preceding context separately and then combines the two generated representations. Average accuracy of 10-fold cross-validation is 79.8{\%}, F-score 71.8{\%}.",
}
@inproceedings{mcleod-etal-2019-multi,
    title = "Multi-Task Learning of System Dialogue Act Selection for Supervised Pretraining of Goal-Oriented Dialogue Policies",
    author = "McLeod, Sarah  and
      Kruijff-Korbayova, Ivana  and
      Kiefer, Bernd",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5947",
    doi = "10.18653/v1/W19-5947",
    pages = "411--417",
    abstract = "This paper describes the use of Multi-Task Neural Networks (NNs) for system dialogue act selection. These models leverage the representations learned by the Natural Language Understanding (NLU) unit to enable robust initialization/bootstrapping of dialogue policies from medium sized initial data sets. We evaluate the models on two goal-oriented dialogue corpora in the travel booking domain. Results show the proposed models improve over models trained without knowledge of NLU tasks.",
}
@inproceedings{abrams-etal-2019-b,
    title = "{B}. Rex: a dialogue agent for book recommendations",
    author = "Abrams, Mitchell  and
      Gessler, Luke  and
      Marge, Matthew",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5948",
    doi = "10.18653/v1/W19-5948",
    pages = "418--421",
    abstract = "We present B. Rex, a dialogue agent for book recommendations. B. Rex aims to exploit the cognitive ease of natural dialogue and the excitement of a whimsical persona in order to engage users who might not enjoy using more common interfaces for finding new books. B. Rex succeeds in making book recommendations with good quality based on only information revealed by the user in the dialogue.",
}
@inproceedings{kalpakchi-boye-2019-spacerefnet,
    title = "{S}pace{R}ef{N}et: a neural approach to spatial reference resolution in a real city environment",
    author = "Kalpakchi, Dmytro  and
      Boye, Johan",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5949",
    doi = "10.18653/v1/W19-5949",
    pages = "422--431",
    abstract = "Adding interactive capabilities to pedestrian wayfinding systems in the form of spoken dialogue will make them more natural to humans. Such an interactive wayfinding system needs to continuously understand and interpret pedestrian{'}s utterances referring to the spatial context. Achieving this requires the system to identify exophoric referring expressions in the utterances, and link these expressions to the geographic entities in the vicinity. This exophoric spatial reference resolution problem is difficult, as there are often several dozens of candidate referents. We present a neural network-based approach for identifying pedestrian{'}s references (using a network called RefNet) and resolving them to appropriate geographic objects (using a network called SpaceRefNet). Both methods show promising results beating the respective baselines and earlier reported results in the literature.",
}
@inproceedings{roze-etal-2019-aspects,
    title = "Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification",
    author = "Roze, Charlotte  and
      Braud, Chlo{\'e}  and
      Muller, Philippe",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5950",
    doi = "10.18653/v1/W19-5950",
    pages = "432--441",
    abstract = "Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use. We propose to decompose the task into smaller, mostly binary tasks corresponding to various primitive concepts encoded into the discourse relation definitions. More precisely, we translate the discourse relations into a set of values for attributes based on distinctions used in the mappings between discourse frameworks proposed by Sanders et al. (2018). This arguably allows for a more robust representation of discourse relations, and enables us to address usually ignored aspects of discourse relation prediction, namely multiple labels and underspecified annotations. We show experimentally which of the conceptual primitives are harder to learn from the Penn Discourse Treebank English corpus, and propose a correspondence to predict the original labels, with preliminary empirical comparisons with a direct model.",
}
@inproceedings{varia-etal-2019-discourse,
    title = "Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks",
    author = "Varia, Siddharth  and
      Hidey, Christopher  and
      Chakrabarty, Tuhin",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5951",
    doi = "10.18653/v1/W19-5951",
    pages = "442--452",
    abstract = "Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them. We propose an approach to distill knowledge from word pairs for discourse relation classification with convolutional neural networks by incorporating joint learning of implicit and explicit relations. Our novel approach of representing the input as word pairs achieves state-of-the-art results on four-way classification of both implicit and explicit relations as well as one of the binary classification tasks. For explicit relation prediction, we achieve around 20{\%} error reduction on the four-way task. At the same time, compared to a two-layered Bi-LSTM-CRF model, our model is able to achieve these results with half the number of learnable parameters and approximately half the amount of training time.",
}
@proceedings{ws-2019-semantic,
    title = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    editor = "Espinosa-Anke, Luis  and
      Declerck, Thierry  and
      Gromann, Dagmar  and
      Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5800",
}
@inproceedings{loureiro-jorge-2019-liaad,
    title = "{LIAAD} at {S}em{D}eep-5 Challenge: Word-in-Context ({W}i{C})",
    author = "Loureiro, Daniel  and
      Jorge, Al{\'\i}pio",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5801",
    pages = "1--5",
}
@inproceedings{gari-soler-etal-2019-limsi,
    title = "{LIMSI}-{MULTISEM} at the {IJCAI} {S}em{D}eep-5 {W}i{C} Challenge: Context Representations for Word Usage Similarity Estimation",
    author = "Gar{\'\i} Soler, Aina  and
      Apidianaki, Marianna  and
      Allauzen, Alexandre",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5802",
    pages = "6--11",
}
@inproceedings{wang-etal-2019-bridging,
    title = "Bridging the Gap: Improve Part-of-speech Tagging for {C}hinese Social Media Texts with Foreign Words",
    author = "Wang, Dingmin  and
      Fang, Meng  and
      Song, Yan  and
      Li, Juntao",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5803",
    pages = "12--20",
}
@inproceedings{ansell-etal-2019-elmo,
    title = "An {ELM}o-inspired approach to {S}em{D}eep-5{'}s Word-in-Context task",
    author = "Ansell, Alan  and
      Bravo-Marquez, Felipe  and
      Pfahringer, Bernhard",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5804",
    pages = "21--25",
}
@inproceedings{agibetov-etal-2019-using,
    title = "Using hyperbolic large-margin classifiers for biological link prediction",
    author = "Agibetov, Asan  and
      Dorffner, Georg  and
      Samwald, Matthias",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5805",
    pages = "26--30",
}
@inproceedings{hommel-etal-2019-extending,
    title = "Extending Neural Question Answering with Linguistic Input Features",
    author = "Hommel, Fabian  and
      Cimiano, Philipp  and
      Orlikowski, Matthias  and
      Hartung, Matthias",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5806",
    pages = "31--39",
}
@inproceedings{magnolini-etal-2019-use,
    title = "How to Use Gazetteers for Entity Recognition with Neural Models",
    author = "Magnolini, Simone  and
      Piccioni, Valerio  and
      Balaraman, Vevake  and
      Guerini, Marco  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5807",
    pages = "40--49",
}
@inproceedings{zhou-etal-2019-learning,
    title = "Learning Household Task Knowledge from {W}iki{H}ow Descriptions",
    author = "Zhou, Yilun  and
      Shah, Julie  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5808",
    pages = "50--56",
}
@inproceedings{deshmukh-etal-2019-sequence,
    title = "A Sequence Modeling Approach for Structured Data Extraction from Unstructured Text",
    author = "Deshmukh, Jayati  and
      M, Annervaz K  and
      Sengupta, Shubhashis",
    booktitle = "Proceedings of the 5th Workshop on Semantic Deep Learning (SemDeep-5)",
    month = aug,
    year = "2019",
    address = "Macau, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5809",
    pages = "57--66",
}
@proceedings{ws-2019-mathematics,
    title = "Proceedings of the 16th Meeting on the Mathematics of Language",
    editor = "de Groote, Philippe  and
      Drewes, Frank  and
      Penn, Gerald",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5700",
}
@inproceedings{bjorklund-etal-2019-parsing,
    title = "Parsing Weighted Order-Preserving Hyperedge Replacement Grammars",
    author = {Bj{\"o}rklund, Henrik  and
      Drewes, Frank  and
      Ericson, Petter},
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5701",
    doi = "10.18653/v1/W19-5701",
    pages = "1--11",
}
@inproceedings{graf-de-santo-2019-sensing,
    title = "{S}ensing Tree Automata as a Model of Syntactic Dependencies",
    author = "Graf, Thomas  and
      De Santo, Aniello",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5702",
    doi = "10.18653/v1/W19-5702",
    pages = "12--26",
}
@inproceedings{winter-2019-presupposition,
    title = "Presupposition Projection and Repair Strategies in Trivalent Semantics",
    author = "Winter, Yoad",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5703",
    doi = "10.18653/v1/W19-5703",
    pages = "27--39",
}
@inproceedings{zwanziger-2019-dependently,
    title = "Dependently-Typed {M}ontague Semantics in the Proof Assistant Agda-flat",
    author = "Zwanziger, Colin",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5704",
    doi = "10.18653/v1/W19-5704",
    pages = "40--49",
}
@inproceedings{chandlee-jardine-2019-quantifier,
    title = "Quantifier-free least fixed point functions for phonology",
    author = "Chandlee, Jane  and
      Jardine, Adam",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5705",
    doi = "10.18653/v1/W19-5705",
    pages = "50--62",
}
@inproceedings{rogers-lambert-2019-classes,
    title = "Some classes of sets of structures definable without quantifiers",
    author = "Rogers, James  and
      Lambert, Dakotah",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5706",
    doi = "10.18653/v1/W19-5706",
    pages = "63--77",
}
@inproceedings{burness-mcmullin-2019-efficient,
    title = "Efficient learning of Output Tier-based Strictly 2-Local functions",
    author = "Burness, Phillip  and
      McMullin, Kevin",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5707",
    doi = "10.18653/v1/W19-5707",
    pages = "78--90",
}
@inproceedings{chandlee-etal-2019-learning,
    title = "Learning with Partially Ordered Representations",
    author = "Chandlee, Jane  and
      Eyraud, Remi  and
      Heinz, Jeffrey  and
      Jardine, Adam  and
      Rawski, Jonathan",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5708",
    doi = "10.18653/v1/W19-5708",
    pages = "91--101",
}
@inproceedings{shibata-heinz-2019-maximum,
    title = "Maximum Likelihood Estimation of Factored Regular Deterministic Stochastic Languages",
    author = "Shibata, Chihiro  and
      Heinz, Jeffrey",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5709",
    doi = "10.18653/v1/W19-5709",
    pages = "102--113",
}
@inproceedings{borbely-kornai-2019-sentence,
    title = "Sentence Length",
    author = "Borb{\'e}ly, G{\'a}bor  and
      Kornai, Andr{\'a}s",
    booktitle = "Proceedings of the 16th Meeting on the Mathematics of Language",
    month = jul,
    year = "2019",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5710",
    doi = "10.18653/v1/W19-5710",
    pages = "114--125",
}
@proceedings{ws-2019-arabic-corpus,
    title = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    editor = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Atwell, Eric  and
      Alsudias, Lama",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5600",
}
@inproceedings{omer-oakes-2019-computer,
    title = "Computer Stylometric Comparison of Writings by Qassim Amin and Mohammed Abdu on Women{'}s Rights",
    author = "Omer, Ahmed  and
      Oakes, Michael",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5601",
    pages = "1--6",
}
@inproceedings{albaqawi-oakes-2019-compiling,
    title = "Compiling and Analysing a Corpus of Transcribed Spoken Gulf Pidgin {A}rabic Based on Length of Stay in the Gulf",
    author = "Albaqawi, Najah  and
      Oakes, Michael",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5602",
    pages = "7--15",
}
@inproceedings{omer-oakes-2019-writing,
    title = "Writing Styles of Salwa and Al-Qarni",
    author = "Omer, Ahmed  and
      Oakes, Michael",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5603",
    pages = "16--21",
}
@inproceedings{alsudias-rayson-2019-classifying,
    title = "Classifying Information Sources in {A}rabic {T}witter to Support Online Monitoring of Infectious Diseases",
    author = "Alsudias, Lama  and
      Rayson, Paul",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5604",
    pages = "22--30",
}
@inproceedings{altammami-etal-2019-text,
    title = "Text Segmentation Using N-grams to Annotate Hadith Corpus",
    author = "Altammami, Shatha  and
      Atwell, Eric  and
      Alsalka, Ammar",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5605",
    pages = "31--39",
}
@inproceedings{qwaider-etal-2019-modern,
    title = "Can {M}odern {S}tandard {A}rabic Approaches be used for {A}rabic Dialects? Sentiment Analysis as a Case Study",
    author = "Qwaider, Chatrine  and
      Chatzikyriakidis, Stergios  and
      Dobnik, Simon",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5606",
    pages = "40--50",
}
@inproceedings{alshutayri-atwell-2019-classifying,
    title = "Classifying {A}rabic dialect text in the Social Media {A}rabic Dialect Corpus ({SMADC})",
    author = "Alshutayri, Areej  and
      Atwell, Eric",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5607",
    pages = "51--59",
}
@inproceedings{white-lonsdale-2019-verbs,
    title = "Verbs in {E}gyptian {A}rabic: a case for register variation",
    author = "White, Michael Grant  and
      Lonsdale, Deryle W.",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5608",
    pages = "60--71",
}
@inproceedings{alharbi-lee-2019-crisis,
    title = "Crisis Detection from {A}rabic Tweets",
    author = "Alharbi, Alaa  and
      Lee, Mark",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5609",
    pages = "72--79",
}
@inproceedings{al-harthi-alsaif-2019-design,
    title = "The Design of the {S}au{LTC} application for the {E}nglish-{A}rabic Learner Translation Corpus",
    author = "Al-Harthi, Maha  and
      Alsaif, Amal",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5610",
    pages = "80--88",
}
@inproceedings{ahmed-2019-distance,
    title = "Distance-Based Authorship Verification Across {M}odern {S}tandard {A}rabic Genres",
    author = "Ahmed, Hossam",
    booktitle = "Proceedings of the 3rd Workshop on Arabic Corpus Linguistics",
    month = jul,
    year = "2019",
    address = "Cardiff, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5611",
    pages = "89--96",
}
@proceedings{ws-2019-financial,
    title = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    editor = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5500",
}
@inproceedings{bai-etal-2019-business,
    title = "Business Taxonomy Construction Using Concept-Level Hierarchical Clustering",
    author = "Bai, Haodong  and
      Xing, Frank  and
      Cambria, Erik  and
      Huang, Win-Bin",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5501",
    pages = "1--7",
}
@inproceedings{anish-etal-2019-towards,
    title = "Towards Disambiguating Contracts for their Successful Execution - A Case from Finance Domain",
    author = "Anish, Preethu Rose  and
      Sainani, Abhishek  and
      Ramrakhiyani, Nitin  and
      Pawar, Sachin  and
      Palshikar, Girish K  and
      Ghaisas, Smita",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5502",
    pages = "8--13",
}
@inproceedings{ying-duboue-2019-rationale,
    title = "Rationale Classification for Educational Trading Platforms",
    author = "Ying, Annie  and
      Duboue, Pablo",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5503",
    pages = "14--20",
}
@inproceedings{daudert-ahmadi-2019-cofif,
    title = "{C}o{F}i{F}: A Corpus of Financial Reports in {F}rench Language",
    author = "Daudert, Tobias  and
      Ahmadi, Sina",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5504",
    pages = "21--26",
}
@inproceedings{mao-etal-2019-step,
    title = "Step-wise Refinement Classification Approach for Enterprise Legal Litigation",
    author = "Mao, Ying  and
      Wang, Xian  and
      Tang, Jianbo  and
      Li, Changliang",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5505",
    pages = "27--33",
}
@inproceedings{daudert-etal-2019-cosact,
    title = "{C}o{SACT}: A Collaborative Tool for Fine-Grained Sentiment Annotation and Consolidation of Text",
    author = "Daudert, Tobias  and
      Zarrouk, Manel  and
      Davis, Brian",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5506",
    pages = "34--39",
}
@inproceedings{sakaji-etal-2019-financial,
    title = "Financial Text Data Analytics Framework for Business Confidence Indices and Inter-Industry Relations",
    author = "Sakaji, Hiroki  and
      Kuramoto, Ryota  and
      Matsushima, Hiroyasu  and
      Izumi, Kiyoshi  and
      Shimada, Takashi  and
      Sunakawa, Keita",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5507",
    pages = "40--46",
}
@inproceedings{lin-etal-2019-learning,
    title = "Learning to Learn Sales Prediction with Social Media Sentiment",
    author = "Lin, Zhaojiang  and
      Madotto, Andrea  and
      Winata, Genta Indra  and
      Liu, Zihan  and
      Xu, Yan  and
      Gao, Cong  and
      Fung, Pascale",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5508",
    pages = "47--53",
}
@inproceedings{yang-etal-2019-leveraging,
    title = "Leveraging {BERT} to Improve the {FEARS} Index for Stock Forecasting",
    author = "Yang, Linyi  and
      Dong, Ruihai  and
      Ng, Tin Lok James  and
      Xu, Yang",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5509",
    pages = "54--60",
}
@inproceedings{izumi-sakaji-2019-economic,
    title = "Economic Causal-Chain Search using Text Mining Technology",
    author = "Izumi, Kiyoshi  and
      Sakaji, Hiroki",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5510",
    pages = "61--65",
}
@inproceedings{liu-etal-2019-transformer,
    title = "Transformer-Based Capsule Network For Stock Movement Prediction",
    author = "Liu, Jintao  and
      Lin, Hongfei  and
      Liu, Xikai  and
      Xu, Bo  and
      Ren, Yuqi  and
      Diao, Yufeng  and
      Yang, Liang",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5511",
    pages = "66--73",
}
@inproceedings{azzi-etal-2019-finsbd,
    title = "The {F}in{SBD}-2019 Shared Task: Sentence Boundary Detection in {PDF} Noisy Text in the Financial Domain",
    author = "Azzi, Abderrahim Ait  and
      Bouamor, Houda  and
      Ferradans, Sira",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5512",
    pages = "74--80",
}
@inproceedings{du-etal-2019-aig,
    title = "{AIG} {I}nvestments.{AI} at the {F}in{SBD} Task: Sentence Boundary Detection through Sequence Labelling and {BERT} Fine-tuning",
    author = "Du, Jinhua  and
      Huang, Yan  and
      Moilanen, Karo",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5513",
    pages = "81--87",
}
@inproceedings{tian-peng-2019-aiai,
    title = "aiai at {F}in{SBD} task: Sentence Boundary Detection in Noisy Texts From Financial Documents Using Deep Attention Model",
    author = "Tian, Ke  and
      Peng, Zi Jun",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5514",
    pages = "88--92",
}
@inproceedings{chen-etal-2019-pluto,
    title = "{P}luto: A Deep Learning Based Watchdog for Anti Money Laundering",
    author = "Chen, Hao-Yuan  and
      Zou, Shang-Xuan  and
      Sung, Cheng-Lung",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5515",
    pages = "93--95",
}
@inproceedings{crouspeyre-etal-2019-creditworthiness,
    title = "From Creditworthiness to Trustworthiness with Alternative {NLP}/{NLU} Approaches",
    author = "Crouspeyre, Charles  and
      Alesi, Eleonore  and
      Lespinasse, Karine",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5516",
    pages = "96--98",
}
@inproceedings{galitsky-ilvovsky-2019-chatbot,
    title = "On a Chatbot Conducting a Virtual Dialogue in Financial Domain",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5517",
    pages = "99--101",
}
@inproceedings{hirano-etal-2019-mhirano,
    title = "mhirano at the {F}in{SBD} Task: Pointwise Prediction Based on Multi-layer Perceptron for Sentence Boundary Detection",
    author = "Hirano, Masanori  and
      Sakaji, Hiroki  and
      Izumi, Kiyoshi  and
      Matsushima, Hiroyasu",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5518",
    pages = "102--107",
}
@inproceedings{daudert-ahmadi-2019-nuig,
    title = "{NUIG} at the {F}in{SBD} Task: Sentence Boundary Detection for Noisy Financial {PDF}s in {E}nglish and {F}rench",
    author = "Daudert, Tobias  and
      Ahmadi, Sina",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5519",
    pages = "108--114",
}
@inproceedings{fatima-mueller-2019-hits,
    title = "{HITS}-{SBD} at the {F}in{SBD} Task: Machine Learning vs. Rule-based Sentence Boundary Detection",
    author = "Fatima, Mehwish  and
      Mueller, Mark-Christoph",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5520",
    pages = "115--121",
}
@inproceedings{wan-etal-2019-polyu,
    title = "{P}oly{U}{\_}{CBS}-{CFA} at the {F}in{SBD} Task: Sentence Boundary Detection of Financial Data with Domain Knowledge Enhancement and Bilingual Training",
    author = "Wan, Mingyu  and
      Xiang, Rong  and
      Chersoni, Emmanuele  and
      Klyueva, Natalia  and
      Ahrens, Kathleen  and
      Miao, Bin  and
      Broadstock, David  and
      Kang, Jian  and
      Yung, Amos  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5521",
    pages = "122--129",
}
@inproceedings{mathew-guggilla-2019-ai,
    title = "{AI}{\_}{B}lues at {F}in{SBD} Shared Task: {CRF}-based Sentence Boundary Detection in {PDF} Noisy Text in the Financial Domain",
    author = "Mathew, Ditty  and
      Guggilla, Chinnappa",
    booktitle = "Proceedings of the First Workshop on Financial Technology and Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Macao, China",
    url = "https://www.aclweb.org/anthology/W19-5522",
    pages = "130--136",
}
@proceedings{ws-2019-machine-translation-3,
    title = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Turchi, Marco  and
      Verspoor, Karin",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5400",
}
@inproceedings{fonseca-etal-2019-findings,
    title = "Findings of the {WMT} 2019 Shared Tasks on Quality Estimation",
    author = "Fonseca, Erick  and
      Yankovskaya, Lisa  and
      Martins, Andr{\'e} F. T.  and
      Fishel, Mark  and
      Federmann, Christian",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5401",
    doi = "10.18653/v1/W19-5401",
    pages = "1--10",
    abstract = "We report the results of the WMT19 shared task on Quality Estimation, i.e. the task of predicting the quality of the output of machine translation systems given just the source text and the hypothesis translations. The task includes estimation at three granularity levels: word, sentence and document. A novel addition is evaluating sentence-level QE against human judgments: in other words, designing MT metrics that do not need a reference translation. This year we include three language pairs, produced solely by neural machine translation systems. Participating teams from eleven institutions submitted a variety of systems to different task variants and language pairs.",
}
@inproceedings{chatterjee-etal-2019-findings,
    title = "Findings of the {WMT} 2019 Shared Task on Automatic Post-Editing",
    author = "Chatterjee, Rajen  and
      Federmann, Christian  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5402",
    doi = "10.18653/v1/W19-5402",
    pages = "11--28",
    abstract = "We present the results from the 5th round of the WMT task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a {``}black-box{''} machine translation system by learning from human corrections. Keeping the same general evaluation setting of the previous four rounds, this year we focused on two language pairs (English-German and English-Russian) and on domain-specific data (In-formation Technology). For both the language directions, MT outputs were produced by neural systems unknown to par-ticipants. Seven teams participated in the English-German task, with a total of 18 submitted runs. The evaluation, which was performed on the same test set used for the 2018 round, shows a slight progress in APE technology: 4 teams achieved better results than last year{'}s winning system, with improvements up to -0.78 TER and +1.23 BLEU points over the baseline. Two teams participated in theEnglish-Russian task submitting 2 runs each. On this new language direction, characterized by a higher quality of the original translations, the task proved to be particularly challenging. None of the submitted runs improved the very high results of the strong system used to produce the initial translations(16.16 TER, 76.20 BLEU).",
}
@inproceedings{bawden-etal-2019-findings,
    title = "Findings of the {WMT} 2019 Biomedical Translation Shared Task: Evaluation for {MEDLINE} Abstracts and Biomedical Terminologies",
    author = "Bawden, Rachel  and
      Bretonnel Cohen, Kevin  and
      Grozea, Cristian  and
      Jimeno Yepes, Antonio  and
      Kittner, Madeleine  and
      Krallinger, Martin  and
      Mah, Nancy  and
      Neveol, Aurelie  and
      Neves, Mariana  and
      Soares, Felipe  and
      Siu, Amy  and
      Verspoor, Karin  and
      Vicente Navarro, Maika",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5403",
    doi = "10.18653/v1/W19-5403",
    pages = "29--53",
    abstract = "In the fourth edition of the WMT Biomedical Translation task, we considered a total of six languages, namely Chinese (zh), English (en), French (fr), German (de), Portuguese (pt), and Spanish (es). We performed an evaluation of automatic translations for a total of 10 language directions, namely, zh/en, en/zh, fr/en, en/fr, de/en, en/de, pt/en, en/pt, es/en, and en/es. We provided training data based on MEDLINE abstracts for eight of the 10 language pairs and test sets for all of them. In addition to that, we offered a new sub-task for the translation of terms in biomedical terminologies for the en/es language direction. Higher BLEU scores (close to 0.5) were obtained for the es/en, en/es and en/pt test sets, as well as for the terminology sub-task. After manual validation of the primary runs, some submissions were judged to be better than the reference translations, for instance, for de/en, en/es and es/en.",
}
@inproceedings{koehn-etal-2019-findings,
    title = "Findings of the {WMT} 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions",
    author = "Koehn, Philipp  and
      Guzm{\'a}n, Francisco  and
      Chaudhary, Vishrav  and
      Pino, Juan",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5404",
    doi = "10.18653/v1/W19-5404",
    pages = "54--72",
    abstract = "Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2{\%} and 10{\%} of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task.",
}
@inproceedings{bicici-2019-rtm,
    title = "{RTM} Stacking Results for Machine Translation Performance Prediction",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5405",
    doi = "10.18653/v1/W19-5405",
    pages = "73--77",
    abstract = "We obtain new results using referential translation machines with increased number of learning models in the set of results that are stacked to obtain a better mixture of experts prediction. We combine features extracted from the word-level predictions with the sentence- or document-level features, which significantly improve the results on the training sets but decrease the test set results.",
}
@inproceedings{kepler-etal-2019-unbabels,
    title = "Unbabel{'}s Participation in the {WMT}19 Translation Quality Estimation Shared Task",
    author = "Kepler, Fabio  and
      Tr{\'e}nous, Jonay  and
      Treviso, Marcos  and
      Vera, Miguel  and
      G{\'o}is, Ant{\'o}nio  and
      Farajian, M. Amin  and
      Lopes, Ant{\'o}nio V.  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5406",
    doi = "10.18653/v1/W19-5406",
    pages = "78--84",
    abstract = "We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation. We participated on the word, sentence, and document-level tracks, encompassing 3 language pairs: English-German, English-Russian, and English-French. Our submissions build upon the recent OpenKiwi framework: We combine linear, neural, and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models. We compare systems individually and propose new ensemble techniques for word and sentence-level predictions. We also propose a simple technique for converting word labels into document-level predictions. Overall, our submitted systems achieve the best results on all tracks and language pairs by a considerable margin.",
}
@inproceedings{kim-etal-2019-qe,
    title = "{QE} {BERT}: Bilingual {BERT} Using Multi-task Learning for Neural Quality Estimation",
    author = "Kim, Hyun  and
      Lim, Joon-Ho  and
      Kim, Hyun-Ki  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5407",
    doi = "10.18653/v1/W19-5407",
    pages = "85--89",
    abstract = "For translation quality estimation at word and sentence levels, this paper presents a novel approach based on BERT that recently has achieved impressive results on various natural language processing tasks. Our proposed model is re-purposed BERT for the translation quality estimation and uses multi-task learning for the sentence-level task and word-level subtasks (i.e., source word, target word, and target gap). Experimental results on Quality Estimation shared task of WMT19 show that our systems show competitive results and provide significant improvements over the baseline.",
}
@inproceedings{mosyagin-logacheva-2019-mipt,
    title = "{MIPT} System for World-Level Quality Estimation",
    author = "Mosyagin, Mikhail  and
      Logacheva, Varvara",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5408",
    doi = "10.18653/v1/W19-5408",
    pages = "90--94",
    abstract = "We explore different model architectures for the WMT 19 shared task on word-level quality estimation of automatic translation. We start with a model similar to Shef-bRNN, which we modify by using conditional random fields for sequence labelling. Additionally, we use a different approach for labelling gaps and source words. We further develop this model by including features from different sources such as BERT, baseline features for the task and transformer encoders. We evaluate the performance of our models on the English-German dataset for the corresponding shared task.",
}
@inproceedings{qi-2019-nju,
    title = "{NJU} Submissions for the {WMT}19 Quality Estimation Shared Task",
    author = "Qi, Hou",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5409",
    doi = "10.18653/v1/W19-5409",
    pages = "95--100",
    abstract = "In this paper, we describe the submissions of the team from Nanjing University for the WMT19 sentence-level Quality Estimation (QE) shared task on English-German language pair. We develop two approaches based on a two-stage neural QE model consisting of a feature extractor and a quality estimator. More specifically, one of the proposed approaches employs the translation knowledge between the two languages from two different translation directions; while the other one employs extra monolingual knowledge from both source and target sides, obtained by pre-training deep self-attention networks. To efficiently train these two-stage models, a joint learning training method is applied. Experiments show that the ensemble model of the above two models achieves the best results on the benchmark dataset of the WMT17 sentence-level QE shared task and obtains competitive results in WMT19, ranking 3rd out of 10 submissions.",
}
@inproceedings{yankovskaya-etal-2019-quality,
    title = "Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings",
    author = {Yankovskaya, Elizaveta  and
      T{\"a}ttar, Andre  and
      Fishel, Mark},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5410",
    doi = "10.18653/v1/W19-5410",
    pages = "101--105",
    abstract = "We propose the use of pre-trained embeddings as features of a regression model for sentence-level quality estimation of machine translation. In our work we combine freely available BERT and LASER multilingual embeddings to train a neural-based regression model. In the second proposed method we use as an input features not only pre-trained embeddings, but also log probability of any machine translation (MT) system. Both methods are applied to several language pairs and are evaluated both as a classical quality estimation system (predicting the HTER score) as well as an MT metric (predicting human judgements of translation quality).",
}
@inproceedings{zhou-etal-2019-source,
    title = "{SOURCE}: {SOUR}ce-Conditional Elmo-style Model for Machine Translation Quality Estimation",
    author = "Zhou, Junpei  and
      Zhang, Zhisong  and
      Hu, Zecong",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5411",
    doi = "10.18653/v1/W19-5411",
    pages = "106--111",
    abstract = "Quality estimation (QE) of machine translation (MT) systems is a task of growing importance. It reduces the cost of post-editing, allowing machine-translated text to be used in formal occasions. In this work, we describe our submission system in WMT 2019 sentence-level QE task. We mainly explore the utilization of pre-trained translation models in QE and adopt a bi-directional translation-like strategy. The strategy is similar to ELMo, but additionally conditions on source sentences. Experiments on WMT QE dataset show that our strategy, which makes the pre-training slightly harder, can bring improvements for QE. In WMT-2019 QE task, our system ranked in the second place on En-De NMT dataset and the third place on En-Ru NMT dataset.",
}
@inproceedings{lee-etal-2019-transformer,
    title = "Transformer-based Automatic Post-Editing Model with Joint Encoder and Multi-source Attention of Decoder",
    author = "Lee, WonKee  and
      Shin, Jaehun  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5412",
    doi = "10.18653/v1/W19-5412",
    pages = "112--117",
    abstract = "This paper describes POSTECH{'}s submission to the WMT 2019 shared task on Automatic Post-Editing (APE). In this paper, we propose a new multi-source APE model by extending Transformer. The main contributions of our study are that we 1) reconstruct the encoder to generate a joint representation of translation (mt) and its src context, in addition to the conventional src encoding and 2) suggest two types of multi-source attention layers to compute attention between two outputs of the encoder and the decoder state in the decoder. Furthermore, we train our model by applying various teacher-forcing ratios to alleviate exposure bias. Finally, we adopt the ensemble technique across variations of our model. Experiments on the WMT19 English-German APE data set show improvements in terms of both TER and BLEU scores over the baseline. Our primary submission achieves -0.73 in TER and +1.49 in BLEU compare to the baseline.",
}
@inproceedings{lopes-etal-2019-unbabels,
    title = "Unbabel{'}s Submission to the {WMT}2019 {APE} Shared Task: {BERT}-Based Encoder-Decoder for Automatic Post-Editing",
    author = "Lopes, Ant{\'o}nio V.  and
      Farajian, M. Amin  and
      Correia, Gon{\c{c}}alo M.  and
      Tr{\'e}nous, Jonay  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5413",
    doi = "10.18653/v1/W19-5413",
    pages = "118--123",
    abstract = "This paper describes Unbabel{'}s submission to the WMT2019 APE Shared Task for the English-German language pair. Following the recent rise of large, powerful, pre-trained models, we adapt the BERT pretrained model to perform Automatic Post-Editing in an encoder-decoder framework. Analogously to dual-encoder architectures we develop a BERT-based encoder-decoder (BED) model in which a single pretrained BERT encoder receives both the source src and machine translation mt strings. Furthermore, we explore a conservativeness factor to constrain the APE system to perform fewer edits. As the official results show, when trained on a weighted combination of in-domain and artificial training data, our BED system with the conservativeness penalty improves significantly the translations of a strong NMT system by -0.78 and +1.23 in terms of TER and BLEU, respectively. Finally, our submission achieves a new state-of-the-art, ex-aequo, in English-German APE of NMT.",
}
@inproceedings{pal-etal-2019-usaar,
    title = "{USAAR}-{DFKI} {--} The Transference Architecture for {E}nglish{--}{G}erman Automatic Post-Editing",
    author = {Pal, Santanu  and
      Xu, Hongfei  and
      Herbig, Nico  and
      Kr{\"u}ger, Antonio  and
      van Genabith, Josef},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5414",
    doi = "10.18653/v1/W19-5414",
    pages = "124--131",
    abstract = "In this paper we present an English{--}German Automatic Post-Editing (APE) system called transference, submitted to the APE Task organized at WMT 2019. Our transference model is based on a multi-encoder transformer architecture. Unlike previous approaches, it (i) uses a transformer encoder block for src, (ii) followed by a transformer decoder block, but without masking, for self-attention on mt, which effectively acts as second encoder combining src {--}{\textgreater} mt, and (iii) feeds this representation into a final decoder block generating pe. Our model improves over the raw black-box neural machine translation system by 0.9 and 1.0 absolute BLEU points on the WMT 2019 APE development and test set. Our submission ranked 3rd, however compared to the two top systems, performance differences are not statistically significant.",
}
@inproceedings{shterionov-etal-2019-ape,
    title = "{APE} through Neural and Statistical {MT} with Augmented Data. {ADAPT}/{DCU} Submission to the {WMT} 2019 {APE} Shared Task",
    author = "Shterionov, Dimitar  and
      Wagner, Joachim  and
      do Carmo, F{\'e}lix",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5415",
    doi = "10.18653/v1/W19-5415",
    pages = "132--138",
    abstract = "Automatic post-editing (APE) can be reduced to a machine translation (MT) task, where the source is the output of a specific MT system and the target is its post-edited variant. However, this approach does not consider context information that can be found in the original source of the MT system. Thus a better approach is to employ multi-source MT, where two input sequences are considered {--} the one being the original source and the other being the MT output. Extra context information can be introduced in the form of extra tokens that identify certain global property of a group of segments, added as a prefix or a suffix to each segment. Successfully applied in domain adaptation of MT as well as on APE, this technique deserves further attention. In this work we investigate multi-source neural APE (or NPE) systems with training data which has been augmented with two types of extra context tokens. We experiment with authentic and synthetic data provided by WMT 2019 and submit our results to the APE shared task. We also experiment with using statistical machine translation (SMT) methods for APE. While our systems score bellow the baseline, we consider this work a step towards understanding the added value of extra context in the case of APE.",
}
@inproceedings{tebbifakhr-etal-2019-effort,
    title = "Effort-Aware Neural Automatic Post-Editing",
    author = "Tebbifakhr, Amirhossein  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5416",
    doi = "10.18653/v1/W19-5416",
    pages = "139--144",
    abstract = "For this round of the WMT 2019 APE shared task, our submission focuses on addressing the {``}over-correction{''} problem in APE. Over-correction occurs when the APE system tends to rephrase an already correct MT output, and the resulting sentence is penalized by a reference-based evaluation against human post-edits. Our intuition is that this problem can be prevented by informing the system about the predicted quality of the MT output or, in other terms, the expected amount of needed corrections. For this purpose, following the common approach in multilingual NMT, we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing. Following the best submissions to the WMT 2018 APE shared task, our backbone architecture is based on multi-source Transformer to encode both the MT output and the corresponding source text. We participated both in the English-German and English-Russian subtasks. In the first subtask, our best submission improved the original MT output quality up to +0.98 BLEU and -0.47 TER. In the second subtask, where the higher quality of the MT output increases the risk of over-correction, none of our submitted runs was able to improve the MT output.",
}
@inproceedings{xu-etal-2019-uds,
    title = "{U}d{S} Submission for the {WMT} 19 Automatic Post-Editing Task",
    author = "Xu, Hongfei  and
      Liu, Qiuhui  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5417",
    doi = "10.18653/v1/W19-5417",
    pages = "145--150",
    abstract = "In this paper, we describe our submission to the English-German APE shared task at WMT 2019. We utilize and adapt an NMT architecture originally developed for exploiting context information to APE, implement this in our own transformer model and explore joint training of the APE task with a de-noising encoder.",
}
@inproceedings{carrino-etal-2019-terminology,
    title = "Terminology-Aware Segmentation and Domain Feature for the {WMT}19 Biomedical Translation Task",
    author = "Carrino, Casimiro Pio  and
      Rafieian, Bardia  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5418",
    doi = "10.18653/v1/W19-5418",
    pages = "151--155",
    abstract = "In this work, we give a description of the TALP-UPC systems submitted for the WMT19 Biomedical Translation Task. Our proposed strategy is NMT model-independent and relies only on one ingredient, a biomedical terminology list. We first extracted such a terminology list by labelling biomedical words in our training dataset using the BabelNet API. Then, we designed a data preparation strategy to insert the terms information at a token level. Finally, we trained the Transformer model with this terms-informed data. Our best-submitted system ranked 2nd and 3rd for Spanish-English and English-Spanish translation directions, respectively.",
}
@inproceedings{hira-etal-2019-exploring,
    title = "Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation",
    author = "Hira, Noor-e-  and
      Abdul Rauf, Sadaf  and
      Kiani, Kiran  and
      Zafar, Ammara  and
      Nawaz, Raheel",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5419",
    doi = "10.18653/v1/W19-5419",
    pages = "156--163",
    abstract = "Transfer Learning and Selective data training are two of the many approaches being extensively investigated to improve the quality of Neural Machine Translation systems. This paper presents a series of experiments by applying transfer learning and selective data training for participation in the Bio-medical shared task of WMT19. We have used Information Retrieval to selectively choose related sentences from out-of-domain data and used them as additional training data using transfer learning. We also report the effect of tokenization on translation model performance.",
}
@inproceedings{peng-etal-2019-huaweis,
    title = "Huawei{'}s {NMT} Systems for the {WMT} 2019 Biomedical Translation Task",
    author = "Peng, Wei  and
      Liu, Jianfeng  and
      Li, Liangyou  and
      Liu, Qun",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5420",
    doi = "10.18653/v1/W19-5420",
    pages = "164--168",
    abstract = "This paper describes Huawei{'}s neural machine translation systems for the WMT 2019 biomedical translation shared task. We trained and fine-tuned our systems on a combination of out-of-domain and in-domain parallel corpora for six translation directions covering English{--}Chinese, English{--}French and English{--}German language pairs. Our submitted systems achieve the best BLEU scores on English{--}French and English{--}German language pairs according to the official evaluation results. In the English{--}Chinese translation task, our systems are in the second place. The enhanced performance is attributed to more in-domain training and more sophisticated models developed. Development of translation models and transfer learning (or domain adaptation) methods has significantly contributed to the progress of the task.",
}
@inproceedings{saunders-etal-2019-ucam,
    title = "{UCAM} Biomedical Translation at {WMT}19: Transfer Learning Multi-domain Ensembles",
    author = "Saunders, Danielle  and
      Stahlberg, Felix  and
      Byrne, Bill",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5421",
    doi = "10.18653/v1/W19-5421",
    pages = "169--174",
    abstract = "The 2019 WMT Biomedical translation task involved translating Medline abstracts. We approached this using transfer learning to obtain a series of strong neural models on distinct domains, and combining them into multi-domain ensembles. We further experimented with an adaptive language-model ensemble weighting scheme. Our submission achieved the best submitted results on both directions of English-Spanish.",
}
@inproceedings{soares-krallinger-2019-bsc,
    title = "{BSC} Participation in the {WMT} Translation of Biomedical Abstracts",
    author = "Soares, Felipe  and
      Krallinger, Martin",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5422",
    doi = "10.18653/v1/W19-5422",
    pages = "175--178",
    abstract = "This paper describes the machine translation systems developed by the Barcelona Supercomputing (BSC) team for the biomedical translation shared task of WMT19. Our system is based on Neural Machine Translation unsing the OpenNMT-py toolkit and Transformer architecture. We participated in four translation directions for the English/Spanish and English/Portuguese language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources, as well as terminological resources from UMLS.",
}
@inproceedings{baquero-arnal-etal-2019-mllp,
    title = "The {MLLP}-{UPV} {S}panish-{P}ortuguese and {P}ortuguese-{S}panish Machine Translation Systems for {WMT}19 Similar Language Translation Task",
    author = "Baquero-Arnal, Pau  and
      Iranzo-S{\'a}nchez, Javier  and
      Civera, Jorge  and
      Juan, Alfons",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5423",
    doi = "10.18653/v1/W19-5423",
    pages = "179--184",
    abstract = "This paper describes the participation of the MLLP research group of the Universitat Polit{\`e}cnica de Val{\`e}ncia in the WMT 2019 Similar Language Translation Shared Task. We have submitted systems for the Portuguese ↔ Spanish language pair, in both directions. We have submitted systems based on the Transformer architecture as well as an in development novel architecture which we have called 2D alternating RNN. We have carried out domain adaptation through fine-tuning.",
}
@inproceedings{biesialska-etal-2019-talp,
    title = "The {TALP}-{UPC} System for the {WMT} Similar Language Task: Statistical vs Neural Machine Translation",
    author = "Biesialska, Magdalena  and
      Guardia, Lluis  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5424",
    doi = "10.18653/v1/W19-5424",
    pages = "185--191",
    abstract = "Although the problem of similar language translation has been an area of research interest for many years, yet it is still far from being solved. In this paper, we study the performance of two popular approaches: statistical and neural. We conclude that both methods yield similar results; however, the performance varies depending on the language pair. While the statistical approach outperforms the neural one by a difference of 6 BLEU points for the Spanish-Portuguese language pair, the proposed neural model surpasses the statistical one by a difference of 2 BLEU points for Czech-Polish. In the former case, the language similarity (based on perplexity) is much higher than in the latter case. Additionally, we report negative results for the system combination with back-translation. Our TALP-UPC system submission won 1st place for Czech-{\textgreater}Polish and 2nd place for Spanish-{\textgreater}Portuguese in the official evaluation of the 1st WMT Similar Language Translation task.",
}
@inproceedings{chen-avgustinova-2019-machine,
    title = "Machine Translation from an Intercomprehension Perspective",
    author = "Chen, Yu  and
      Avgustinova, Tania",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5425",
    doi = "10.18653/v1/W19-5425",
    pages = "192--196",
    abstract = "Within the first shared task on machine translation between similar languages, we present our first attempts on Czech to Polish machine translation from an intercomprehension perspective. We propose methods based on the mutual intelligibility of the two languages, taking advantage of their orthographic and phonological similarity, in the hope to improve over our baselines. The translation results are evaluated using BLEU. On this metric, none of our proposals could outperform the baselines on the final test set. The current setups are rather preliminary, and there are several potential improvements we can try in the future.",
}
@inproceedings{khatri-bhattacharyya-2019-utilizing,
    title = "Utilizing Monolingual Data in {NMT} for Similar Languages: Submission to Similar Language Translation Task",
    author = "Khatri, Jyotsana  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5426",
    doi = "10.18653/v1/W19-5426",
    pages = "197--201",
    abstract = "This paper describes our submission to Shared Task on Similar Language Translation in Fourth Conference on Machine Translation (WMT 2019). We submitted three systems for Hindi -{\textgreater} Nepali direction in which we have examined the performance of a RNN based NMT system, a semi-supervised NMT system where monolingual data of both languages is utilized using the architecture by and a system trained with extra synthetic sentences generated using copy of source and target sentences without using any additional monolingual data.",
}
@inproceedings{laskar-etal-2019-neural,
    title = "Neural Machine Translation: {H}indi-{N}epali",
    author = "Laskar, Sahinur Rahman  and
      Pakray, Partha  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5427",
    doi = "10.18653/v1/W19-5427",
    pages = "202--207",
    abstract = "With the extensive use of Machine Translation (MT) technology, there is progressively interest in directly translating between pairs of similar languages. Because the main challenge is to overcome the limitation of available parallel data to produce a precise MT output. Current work relies on the Neural Machine Translation (NMT) with attention mechanism for the similar language translation of WMT19 shared task in the context of Hindi-Nepali pair. The NMT systems trained the Hindi-Nepali parallel corpus and tested, analyzed in Hindi ⇔ Nepali translation. The official result declared at WMT19 shared task, which shows that our NMT system obtained Bilingual Evaluation Understudy (BLEU) score 24.6 for primary configuration in Nepali to Hindi translation. Also, we have achieved BLEU score 53.7 (Hindi to Nepali) and 49.1 (Nepali to Hindi) in contrastive system type.",
}
@inproceedings{marie-etal-2019-nicts-machine,
    title = "{NICT}{'}s Machine Translation Systems for the {WMT}19 Similar Language Translation Task",
    author = "Marie, Benjamin  and
      Dabre, Raj  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5428",
    doi = "10.18653/v1/W19-5428",
    pages = "208--212",
    abstract = "This paper presents the NICT{'}s participation in the WMT19 shared Similar Language Translation Task. We participated in the Spanish-Portuguese task. For both translation directions, we prepared state-of-the-art statistical (SMT) and neural (NMT) machine translation systems. Our NMT systems with the Transformer architecture were trained on the provided parallel data enlarged with a large quantity of back-translated monolingual data. Our primary submission to the task is the result of a simple combination of our SMT and NMT systems. According to BLEU, our systems were ranked second and third respectively for the Portuguese-to-Spanish and Spanish-to-Portuguese translation directions. For contrastive experiments, we also submitted outputs generated with an unsupervised SMT system.",
}
@inproceedings{ojha-etal-2019-panlingua,
    title = "Panlingua-{KMI} {MT} System for Similar Language Translation Task at {WMT} 2019",
    author = "Ojha, Atul Kr.  and
      Kumar, Ritesh  and
      Bansal, Akanksha  and
      Rani, Priya",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5429",
    doi = "10.18653/v1/W19-5429",
    pages = "213--218",
    abstract = "The present paper enumerates the development of Panlingua-KMI Machine Translation (MT) systems for Hindi ↔ Nepali language pair, designed as part of the Similar Language Translation Task at the WMT 2019 Shared Task. The Panlingua-KMI team conducted a series of experiments to explore both the phrase-based statistical (PBSMT) and neural methods (NMT). Among the 11 MT systems prepared under this task, 6 PBSMT systems were prepared for Nepali-Hindi, 1 PBSMT for Hindi-Nepali and 2 NMT systems were developed for Nepali↔Hindi. The results show that PBSMT could be an effective method for developing MT systems for closely-related languages. Our Hindi-Nepali PBSMT system was ranked 2nd among the 13 systems submitted for the pair and our Nepali-Hindi PBSMTsystem was ranked 4th among the 12 systems submitted for the task.",
}
@inproceedings{pal-etal-2019-uds,
    title = "{UDS}{--}{DFKI} Submission to the {WMT}2019 {C}zech{--}{P}olish Similar Language Translation Shared Task",
    author = "Pal, Santanu  and
      Zampieri, Marcos  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5430",
    doi = "10.18653/v1/W19-5430",
    pages = "219--223",
    abstract = "In this paper we present the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. The first edition of this shared task featured data from three pairs of similar languages: Czech and Polish, Hindi and Nepali, and Portuguese and Spanish. Participants could choose to participate in any of these three tracks and submit system outputs in any translation direction. We report the results obtained by our system in translating from Czech to Polish and comment on the impact of out-of-domain test data in the performance of our system. UDS-DFKI achieved competitive performance ranking second among ten teams in Czech to Polish translation.",
}
@inproceedings{przystupa-abdul-mageed-2019-neural,
    title = "Neural Machine Translation of Low-Resource and Similar Languages with Backtranslation",
    author = "Przystupa, Michael  and
      Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5431",
    doi = "10.18653/v1/W19-5431",
    pages = "224--235",
    abstract = "We present our contribution to the WMT19 Similar Language Translation shared task. We investigate the utility of neural machine translation on three low-resource, similar language pairs: Spanish {--} Portuguese, Czech {--} Polish, and Hindi {--} Nepali. Since state-of-the-art neural machine translation systems still require large amounts of bitext, which we do not have for the pairs we consider, we focus primarily on incorporating monolingual data into our models with backtranslation. In our analysis, we found Transformer models to work best on Spanish {--} Portuguese and Czech {--} Polish translation, whereas LSTMs with global attention worked best on Hindi {--} Nepali translation.",
}
@inproceedings{scherrer-etal-2019-university,
    title = "The {U}niversity of {H}elsinki Submissions to the {WMT}19 Similar Language Translation Task",
    author = "Scherrer, Yves  and
      V{\'a}zquez, Ra{\'u}l  and
      Virpioja, Sami",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5432",
    doi = "10.18653/v1/W19-5432",
    pages = "236--244",
    abstract = "This paper describes the University of Helsinki Language Technology group{'}s participation in the WMT 2019 similar language translation task. We trained neural machine translation models for the language pairs Czech {\textless}-{\textgreater} Polish and Spanish {\textless}-{\textgreater} Portuguese. Our experiments focused on different subword segmentation methods, and in particular on the comparison of a cognate-aware segmentation method, Cognate Morfessor, with character segmentation and unsupervised segmentation methods for which the data from different languages were simply concatenated. We did not observe major benefits from cognate-aware segmentation methods, but further research may be needed to explore larger parts of the parameter space. Character-level models proved to be competitive for translation between Spanish and Portuguese, but they are slower in training and decoding.",
}
@inproceedings{axelrod-etal-2019-dual,
    title = "Dual Monolingual Cross-Entropy Delta Filtering of Noisy Parallel Data",
    author = "Axelrod, Amittai  and
      Kumar, Anish  and
      Sloto, Steve",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5433",
    doi = "10.18653/v1/W19-5433",
    pages = "245--251",
    abstract = "We introduce a purely monolingual approach to filtering for parallel data from a noisy corpus in a low-resource scenario. Our work is inspired by Junczysdowmunt:2018, but we relax the requirements to allow for cases where no parallel data is available. Our primary contribution is a dual monolingual cross-entropy delta criterion modified from Cynical data selection Axelrod:2017, and is competitive (within 1.8 BLEU) with the best bilingual filtering method when used to train SMT systems. Our approach is featherweight, and runs end-to-end on a standard laptop in three hours.",
}
@inproceedings{bernier-colborne-lo-2019-nrc,
    title = "{NRC} Parallel Corpus Filtering System for {WMT} 2019",
    author = "Bernier-Colborne, Gabriel  and
      Lo, Chi-kiu",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5434",
    doi = "10.18653/v1/W19-5434",
    pages = "252--260",
    abstract = "We describe the National Research Council Canada team{'}s submissions to the parallel corpus filtering task at the Fourth Conference on Machine Translation.",
}
@inproceedings{chaudhary-etal-2019-low,
    title = "Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings",
    author = "Chaudhary, Vishrav  and
      Tang, Yuqing  and
      Guzm{\'a}n, Francisco  and
      Schwenk, Holger  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5435",
    doi = "10.18653/v1/W19-5435",
    pages = "261--266",
    abstract = "In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.",
}
@inproceedings{erdmann-gwinnup-2019-quality,
    title = "Quality and Coverage: The {AFRL} Submission to the {WMT}19 Parallel Corpus Filtering for Low-Resource Conditions Task",
    author = "Erdmann, Grant  and
      Gwinnup, Jeremy",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5436",
    doi = "10.18653/v1/W19-5436",
    pages = "267--270",
    abstract = "The WMT19 Parallel Corpus Filtering For Low-Resource Conditions Task aims to test various methods of filtering a noisy parallel corpora, to make them useful for training machine translation systems. This year the noisy corpora are the relatively low-resource language pairs of Nepali-English and Sinhala-English. This papers describes the Air Force Research Laboratory (AFRL) submissions, including preprocessing methods and scoring metrics. Numerical results indicate a benefit over baseline and the relative benefits of different options.",
}
@inproceedings{gonzalez-rubio-2019-webinterpret,
    title = "Webinterpret Submission to the {WMT}2019 Shared Task on Parallel Corpus Filtering",
    author = "Gonz{\'a}lez-Rubio, Jes{\'u}s",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5437",
    doi = "10.18653/v1/W19-5437",
    pages = "271--276",
    abstract = "This document describes the participation of Webinterpret in the shared task on parallel corpus filtering at the Fourth Conference on Machine Translation (WMT 2019). Here, we describe the main characteristics of our approach and discuss the results obtained on the data sets published for the shared task.",
}
@inproceedings{kurfali-ostling-2019-noisy,
    title = "Noisy Parallel Corpus Filtering through Projected Word Embeddings",
    author = {Kurfal{\i}, Murathan  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5438",
    doi = "10.18653/v1/W19-5438",
    pages = "277--281",
    abstract = "We present a very simple method for parallel text cleaning of low-resource languages, based on projection of word embeddings trained on large monolingual corpora in high-resource languages. In spite of its simplicity, we approach the strong baseline system in the downstream machine translation evaluation.",
}
@inproceedings{parcheta-etal-2019-filtering,
    title = "Filtering of Noisy Parallel Corpora Based on Hypothesis Generation",
    author = "Parcheta, Zuzanna  and
      Sanchis-Trilles, Germ{\'a}n  and
      Casacuberta, Francisco",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5439",
    doi = "10.18653/v1/W19-5439",
    pages = "282--288",
    abstract = "The filtering task of noisy parallel corpora in WMT2019 aims to challenge participants to create filtering methods to be useful for training machine translation systems. In this work, we introduce a noisy parallel corpora filtering system based on generating hypotheses by means of a translation model. We train translation models in both language pairs: Nepali{--}English and Sinhala{--}English using provided parallel corpora. We select the training subset for three language pairs (Nepali, Sinhala and Hindi to English) jointly using bilingual cross-entropy selection to create the best possible translation model for both language pairs. Once the translation models are trained, we translate the noisy corpora and generate a hypothesis for each sentence pair. We compute the smoothed BLEU score between the target sentence and generated hypothesis. In addition, we apply several rules to discard very noisy or inadequate sentences which can lower the translation score. These heuristics are based on sentence length, source and target similarity and source language detection. We compare our results with the baseline published on the shared task website, which uses the Zipporah model, over which we achieve significant improvements in one of the conditions in the shared task. The designed filtering system is domain independent and all experiments are conducted using neural machine translation.",
}
@inproceedings{sen-etal-2019-parallel,
    title = "Parallel Corpus Filtering Based on Fuzzy String Matching",
    author = "Sen, Sukanta  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5440",
    doi = "10.18653/v1/W19-5440",
    pages = "289--293",
    abstract = "In this paper, we describe the IIT Patna{'}s submission to WMT 2019 shared task on parallel corpus filtering. This shared task asks the participants to develop methods for scoring each parallel sentence from a given noisy parallel corpus. Quality of the scoring method is judged based on the quality of SMT and NMT systems trained on smaller set of high-quality parallel sentences sub-sampled from the original noisy corpus. This task has two language pairs. We submit for both the Nepali-English and Sinhala-English language pairs. We define fuzzy string matching score between English and the translated (into English) source based on Levenshtein distance. Based on the scores, we sub-sample two sets (having 1 million and 5 millions English tokens) of parallel sentences from each parallel corpus, and train SMT systems for development purpose only. The organizers publish the official evaluation using both SMT and NMT on the final official test set. Total 10 teams participated in the shared task and according the official evaluation, our scoring method obtains 2nd position in the team ranking for 1-million NepaliEnglish NMT and 5-million Sinhala-English NMT categories.",
}
@inproceedings{vazquez-etal-2019-university,
    title = "The {U}niversity of {H}elsinki Submission to the {WMT}19 Parallel Corpus Filtering Task",
    author = {V{\'a}zquez, Ra{\'u}l  and
      Sulubacak, Umut  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5441",
    doi = "10.18653/v1/W19-5441",
    pages = "294--300",
    abstract = "This paper describes the University of Helsinki Language Technology group{'}s participation in the WMT 2019 parallel corpus filtering task. Our scores were produced using a two-step strategy. First, we individually applied a series of filters to remove the {`}bad{'} quality sentences. Then, we produced scores for each sentence by weighting these features with a classification model. This methodology allowed us to build a simple and reliable system that is easily adaptable to other language pairs.",
}
@proceedings{ws-2019-machine-translation,
    title = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Turchi, Marco  and
      Verspoor, Karin",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5300",
}
@inproceedings{barrault-etal-2019-findings,
    title = "Findings of the 2019 Conference on Machine Translation ({WMT}19)",
    author = {Barrault, Lo{\"\i}c  and
      Bojar, Ond{\v{r}}ej  and
      Costa-juss{\`a}, Marta R.  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Koehn, Philipp  and
      Malmasi, Shervin  and
      Monz, Christof  and
      M{\"u}ller, Mathias  and
      Pal, Santanu  and
      Post, Matt  and
      Zampieri, Marcos},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5301",
    doi = "10.18653/v1/W19-5301",
    pages = "1--61",
    abstract = "This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.",
}
@inproceedings{ma-etal-2019-results,
    title = "Results of the {WMT}19 Metrics Shared Task: Segment-Level and Strong {MT} Systems Pose Big Challenges",
    author = "Ma, Qingsong  and
      Wei, Johnny  and
      Bojar, Ond{\v{r}}ej  and
      Graham, Yvette",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5302",
    doi = "10.18653/v1/W19-5302",
    pages = "62--90",
    abstract = "This paper presents the results of the WMT19 Metrics Shared Task. Participants were asked to score the outputs of the translations systems competing in the WMT19 News Translation Task with automatic metrics. 13 research groups submitted 24 metrics, 10 of which are reference-less {``}metrics{''} and constitute submissions to the joint task with WMT19 Quality Estimation Task, {``}QE as a Metric{''}. In addition, we computed 11 baseline metrics, with 8 commonly applied baselines (BLEU, SentBLEU, NIST, WER, PER, TER, CDER, and chrF) and 3 reimplementations (chrF+, sacreBLEU-BLEU, and sacreBLEU-chrF). Metrics were evaluated on the system level, how well a given metric correlates with the WMT19 official manual ranking, and segment level, how well the metric correlates with human judgements of segment quality. This year, we use direct assessment (DA) as our only form of manual evaluation.",
}
@inproceedings{li-etal-2019-findings,
    title = "Findings of the First Shared Task on Machine Translation Robustness",
    author = "Li, Xian  and
      Michel, Paul  and
      Anastasopoulos, Antonios  and
      Belinkov, Yonatan  and
      Durrani, Nadir  and
      Firat, Orhan  and
      Koehn, Philipp  and
      Neubig, Graham  and
      Pino, Juan  and
      Sajjad, Hassan",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5303",
    doi = "10.18653/v1/W19-5303",
    pages = "91--102",
    abstract = "We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models{'} robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson{'}s r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.",
}
@inproceedings{bawden-etal-2019-university,
    title = "The {U}niversity of {E}dinburgh{'}s Submissions to the {WMT}19 News Translation Task",
    author = "Bawden, Rachel  and
      Bogoychev, Nikolay  and
      Germann, Ulrich  and
      Grundkiewicz, Roman  and
      Kirefu, Faheem  and
      Miceli Barone, Antonio Valerio  and
      Birch, Alexandra",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5304",
    doi = "10.18653/v1/W19-5304",
    pages = "103--115",
    abstract = "The University of Edinburgh participated in the WMT19 Shared Task on News Translation in six language directions: English↔Gujarati, English↔Chinese, German→English, and English→Czech. For all translation directions, we created or used back-translations of monolingual data in the target language as additional synthetic training data. For English↔Gujarati, we also explored semi-supervised MT with cross-lingual language model pre-training, and translation pivoting through Hindi. For translation to and from Chinese, we investigated character-based tokenisation vs. sub-word segmentation of Chinese text. For German→English, we studied the impact of vast amounts of back-translated training data on translation quality, gaining a few additional insights over Edunov et al. (2018). For English→Czech, we compared different preprocessing and tokenisation regimes.",
}
@inproceedings{bei-etal-2019-gtcom,
    title = "{GTCOM} Neural Machine Translation Systems for {WMT}19",
    author = "Bei, Chao  and
      Zong, Hao  and
      Yuan, Conghu  and
      Liu, Qingming  and
      Fan, Baoyong",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5305",
    doi = "10.18653/v1/W19-5305",
    pages = "116--121",
    abstract = "This paper describes the Global Tone Communication Co., Ltd.{'}s submission of the WMT19 shared news translation task. We participate in six directions: English to (Gujarati, Lithuanian and Finnish) and (Gujarati, Lithuanian and Finnish) to English. Further, we get the best BLEU scores in the directions of English to Gujarati and Lithuanian to English (28.2 and 36.3 respectively) among all the participants. The submitted systems mainly focus on back-translation, knowledge distillation and reranking to build a competitive model for this task. Also, we apply language model to filter monolingual data, back-translated data and parallel data. The techniques we apply for data filtering include filtering by rules, language models. Besides, We conduct several experiments to validate different knowledge distillation techniques and right-to-left (R2L) reranking.",
}
@inproceedings{bicici-2019-machine,
    title = "Machine Translation with parfda, {M}oses, kenlm, nplm, and {PRO}",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5306",
    doi = "10.18653/v1/W19-5306",
    pages = "122--128",
    abstract = "We build parfda Moses statistical machine translation (SMT) models for most language pairs in the news translation task. We experiment with a hybrid approach using neural language models integrated into Moses. We obtain the constrained data statistics on the machine translation task, the coverage of the test sets, and the upper bounds on the translation results. We also contribute a new testsuite for the German-English language pair and a new automated key phrase extraction technique for the evaluation of the testsuite translations.",
}
@inproceedings{bougares-etal-2019-liums,
    title = "{LIUM}{'}s Contributions to the {WMT}2019 News Translation Task: Data and Systems for {G}erman-{F}rench Language Pairs",
    author = {Bougares, Fethi  and
      Wottawa, Jane  and
      Baillot, Anne  and
      Barrault, Lo{\"\i}c  and
      Bardet, Adrien},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5307",
    doi = "10.18653/v1/W19-5307",
    pages = "129--133",
    abstract = "This paper describes the neural machine translation (NMT) systems of the LIUM Laboratory developed for the French↔German news translation task of the Fourth Conference onMachine Translation (WMT 2019). The chosen language pair is included for the first time in the WMT news translation task. We de-scribe how the training and the evaluation data was created. We also present our participation in the French↔German translation directions using self-attentional Transformer networks with small and big architectures.",
}
@inproceedings{briakou-carpuat-2019-university,
    title = "The {U}niversity of {M}aryland{'}s {K}azakh-{E}nglish Neural Machine Translation System at {WMT}19",
    author = "Briakou, Eleftheria  and
      Carpuat, Marine",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5308",
    doi = "10.18653/v1/W19-5308",
    pages = "134--140",
    abstract = "This paper describes the University of Maryland{'}s submission to the WMT 2019 Kazakh-English news translation task. We study the impact of transfer learning from another low-resource but related language. We experiment with different ways of encoding lexical units to maximize lexical overlap between the two language pairs, as well as back-translation and ensembling. The submitted system improves over a Kazakh-only baseline by +5.45 BLEU on newstest2019.",
}
@inproceedings{budiwati-etal-2019-dbms,
    title = "{DBMS}-{KU} Interpolation for {WMT}19 News Translation Task",
    author = "Budiwati, Sari Dewi  and
      Siagian, Al Hafiz Akbar Maulana  and
      Fatyanosa, Tirana Noor  and
      Aritsugi, Masayoshi",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5309",
    doi = "10.18653/v1/W19-5309",
    pages = "141--146",
    abstract = "This paper presents the participation of DBMS-KU Interpolation system in WMT19 shared task, namely, Kazakh-English language pair. We examine the use of interpolation method using a different language model order. Our Interpolation system combines a direct translation with Russian as a pivot language. We use 3-gram and 5-gram language model orders to perform the language translation in this work. To reduce noise in the pivot translation process, we prune the phrase table of source-pivot and pivot-target. Our experimental results show that our Interpolation system outperforms the Baseline in terms of BLEU-cased score by +0.5 and +0.1 points in Kazakh-English and English-Kazakh, respectively. In particular, using the 5-gram language model order in our system could obtain better BLEU-cased score than utilizing the 3-gram one. Interestingly, we found that by employing the Interpolation system could reduce the perplexity score of English-Kazakh when using 3-gram language model order.",
}
@inproceedings{burlot-2019-lingua,
    title = "Lingua Custodia at {WMT}{'}19: Attempts to Control Terminology",
    author = "Burlot, Franck",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5310",
    doi = "10.18653/v1/W19-5310",
    pages = "147--154",
    abstract = "This paper describes Lingua Custodia{'}s submission to the WMT{'}19 news shared task for German-to-French on the topic of the EU elections. We report experiments on the adaptation of the terminology of a machine translation system to a specific topic, aimed at providing more accurate translations of specific entities like political parties and person names, given that the shared task provided no in-domain training parallel data dealing with the restricted topic. Our primary submission to the shared task uses backtranslation generated with a type of decoding allowing the insertion of constraints in the output in order to guarantee the correct translation of specific terms that are not necessarily observed in the data.",
}
@inproceedings{casas-etal-2019-talp,
    title = "The {TALP}-{UPC} Machine Translation Systems for {WMT}19 News Translation Task: Pivoting Techniques for Low Resource {MT}",
    author = "Casas, Noe  and
      Fonollosa, Jos{\'e} A. R.  and
      Escolano, Carlos  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5311",
    doi = "10.18653/v1/W19-5311",
    pages = "155--162",
    abstract = "In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English. Given the low amount of parallel training data, we resort to using Russian as pivot language, training subword-based statistical translation systems for Russian-Kazakh and Russian-English that were then used to create two synthetic pseudo-parallel corpora for Kazakh-English and English-Kazakh respectively. Finally, a self-attention model based on the decoder part of the Transformer architecture was trained on the two pseudo-parallel corpora.",
}
@inproceedings{cromieres-kurohashi-2019-kyoto,
    title = "{K}yoto {U}niversity Participation to the {WMT} 2019 News Shared Task",
    author = "Cromieres, Fabien  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5312",
    doi = "10.18653/v1/W19-5312",
    pages = "163--167",
    abstract = "We describe here the experiments we did for the the news translation shared task of WMT 2019. We focused on the new German-to-French language direction, and mostly used current standard approaches to develop a Neural Machine Translation system. We make use of the Tensor2Tensor implementation of the Transformer model. After carefully cleaning the data and noting the importance of the good use of recent monolingual data for the task, we obtain our final result by combining the output of a diverse set of trained models through the use of their {``}checkpoint agreement{''}.",
}
@inproceedings{dabre-etal-2019-nicts,
    title = "{NICT}{'}s Supervised Neural Machine Translation Systems for the {WMT}19 News Translation Task",
    author = "Dabre, Raj  and
      Chen, Kehai  and
      Marie, Benjamin  and
      Wang, Rui  and
      Fujita, Atsushi  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5313",
    doi = "10.18653/v1/W19-5313",
    pages = "168--174",
    abstract = "In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh↔English, Gujarati↔English, Chinese↔English, and English→Finnish translation directions. We focused on leveraging multilingual transfer learning and back-translation for the extremely low-resource language pairs: Kazakh↔English and Gujarati↔English translation. For the Chinese↔English translation, we used the provided parallel data augmented with a large quantity of back-translated monolingual data to train state-of-the-art NMT systems. We then employed techniques that have been proven to be most effective, such as back-translation, fine-tuning, and model ensembling, to generate the primary submissions of Chinese↔English. For English→Finnish, our submission from WMT18 remains a strong baseline despite the increase in parallel corpora for this year{'}s task.",
}
@inproceedings{ding-tao-2019-university,
    title = "The {U}niversity of {S}ydney{'}s Machine Translation System for {WMT}19",
    author = "Ding, Liang  and
      Tao, Dacheng",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5314",
    doi = "10.18653/v1/W19-5314",
    pages = "175--182",
    abstract = "This paper describes the University of Sydney{'}s submission of the WMT 2019 shared news translation task. We participated in the Finnish-{\textgreater}English direction and got the best BLEU(33.0) score among all the participants. Our system is based on the self-attentional Transformer networks, into which we integrated the most recent effective strategies from academic research (e.g., BPE, back translation, multi-features data selection, data augmentation, greedy model ensemble, reranking, ConMBR system combination, and postprocessing). Furthermore, we propose a novel augmentation method Cycle Translation and a data mixture strategy Big/Small parallel construction to entirely exploit the synthetic corpus. Extensive experiments show that adding the above techniques can make continuous improvements of the BLEU scores, and the best result outperforms the baseline (Transformer ensemble model trained with the original parallel corpus) by approximately 5.3 BLEU score, achieving the state-of-the-art performance.",
}
@inproceedings{espana-bonet-ruiter-2019-uds,
    title = "{U}d{S}-{DFKI} Participation at {WMT} 2019: Low-Resource (en-gu) and Coreference-Aware (en-de) Systems",
    author = "Espa{\~n}a-Bonet, Cristina  and
      Ruiter, Dana",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5315",
    doi = "10.18653/v1/W19-5315",
    pages = "183--190",
    abstract = "This paper describes the UdS-DFKI submission to the WMT2019 news translation task for Gujarati{--}English (low-resourced pair) and German{--}English (document-level evaluation). Our systems rely on the on-line extraction of parallel sentences from comparable corpora for the first scenario and on the inclusion of coreference-related information in the training data in the second one.",
}
@inproceedings{goyal-sharma-2019-iiit,
    title = "The {IIIT}-{H} {G}ujarati-{E}nglish Machine Translation System for {WMT}19",
    author = "Goyal, Vikrant  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5316",
    doi = "10.18653/v1/W19-5316",
    pages = "191--195",
    abstract = "This paper describes the Neural Machine Translation system of IIIT-Hyderabad for the Gujarati→English news translation shared task of WMT19. Our system is basedon encoder-decoder framework with attention mechanism. We experimented with Multilingual Neural MT models. Our experiments show that Multilingual Neural Machine Translation leveraging parallel data from related language pairs helps in significant BLEU improvements upto 11.5, for low resource language pairs like Gujarati-English",
}
@inproceedings{guo-etal-2019-kingsofts,
    title = "Kingsoft{'}s Neural Machine Translation System for {WMT}19",
    author = "Guo, Xinze  and
      Liu, Chang  and
      Li, Xiaolong  and
      Wang, Yiran  and
      Li, Guoliang  and
      Wang, Feng  and
      Xu, Zhitao  and
      Yang, Liuyi  and
      Ma, Li  and
      Li, Changliang",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5317",
    doi = "10.18653/v1/W19-5317",
    pages = "196--202",
    abstract = "This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task. We participated in two language directions: English-Chinese and Chinese-English. For both language directions, we trained several variants of Transformer models using the provided parallel data enlarged with a large quantity of back-translated monolingual data. The best translation result was obtained with ensemble and reranking techniques. According to automatic metrics (BLEU) our Chinese-English system reached the second highest score, and our English-Chinese system reached the second highest score for this subtask.",
}
@inproceedings{gwinnup-etal-2019-afrl,
    title = "The {AFRL} {WMT}19 Systems: Old Favorites and New Tricks",
    author = "Gwinnup, Jeremy  and
      Erdmann, Grant  and
      Anderson, Tim",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5318",
    doi = "10.18653/v1/W19-5318",
    pages = "203--208",
    abstract = "This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT19 evaluation campaign. This year, we refine our approach to training popular neural machine translation toolkits, experiment with a new domain adaptation technique and again measure improvements in performance on the Russian{--}English language pair.",
}
@inproceedings{hokamp-etal-2019-evaluating,
    title = "Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models",
    author = "Hokamp, Chris  and
      Glover, John  and
      Gholipour Ghalandari, Demian",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5319",
    doi = "10.18653/v1/W19-5319",
    pages = "209--217",
    abstract = "We study several methods for full or partial sharing of the decoder parameters of multi-lingual NMT models. Using only the WMT 2019 shared task parallel datasets for training, we evaluate both fully supervised and zero-shot translation performance in 110 unique translation directions. We use additional test sets and re-purpose evaluation methods recently used for unsupervised MT in order to evaluate zero-shot translation performance for language pairs where no gold-standard parallel data is available. To our knowledge, this is the largest evaluation of multi-lingual translation yet conducted in terms of the total size of the training data we use, and in terms of the number of zero-shot translation pairs we evaluate. We conduct an in-depth evaluation of the translation performance of different models, highlighting the trade-offs between methods of sharing decoder parameters. We find that models which have task-specific decoder parameters outperform models where decoder parameters are fully shared across all tasks.",
}
@inproceedings{iranzo-sanchez-etal-2019-mllp,
    title = "The {MLLP}-{UPV} Supervised Machine Translation Systems for {WMT}19 News Translation Task",
    author = "Iranzo-S{\'a}nchez, Javier  and
      Garc{\'e}s D{\'\i}az-Mun{\'\i}o, Gon{\c{c}}al  and
      Civera, Jorge  and
      Juan, Alfons",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5320",
    doi = "10.18653/v1/W19-5320",
    pages = "218--224",
    abstract = "This paper describes the participation of the MLLP research group of the Universitat Polit{\`e}cnica de Val{\`e}ncia in the WMT 2019 News Translation Shared Task. In this edition, we have submitted systems for the German ↔ English and German ↔ French language pairs, participating in both directions of each pair. Our submitted systems, based on the Transformer architecture, make ample use of data filtering, synthetic data and domain adaptation through fine-tuning.",
}
@inproceedings{junczys-dowmunt-2019-microsoft,
    title = "{M}icrosoft Translator at {WMT} 2019: Towards Large-Scale Document-Level Neural Machine Translation",
    author = "Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5321",
    doi = "10.18653/v1/W19-5321",
    pages = "225--233",
    abstract = "This paper describes the Microsoft Translator submissions to the WMT19 news translation shared task for English-German. Our main focus is document-level neural machine translation with deep transformer models. We start with strong sentence-level baselines, trained on large-scale data created via data-filtering and noisy back-translation and find that back-translation seems to mainly help with translationese input. We explore fine-tuning techniques, deeper models and different ensembling strategies to counter these effects. Using document boundaries present in the authentic and synthetic parallel data, we create sequences of up to 1000 subword segments and train transformer translation models. We experiment with data augmentation techniques for the smaller authentic data with document-boundaries and for larger authentic data without boundaries. We further explore multi-task training for the incorporation of document-level source language monolingual data via the BERT-objective on the encoder and two-pass decoding for combinations of sentence-level and document-level systems. Based on preliminary human evaluation results, evaluators strongly prefer the document-level systems over our comparable sentence-level system. The document-level systems also seem to score higher than the human references in source-based direct assessment.",
}
@inproceedings{kocmi-bojar-2019-cuni,
    title = "{CUNI} Submission for Low-Resource Languages in {WMT} News 2019",
    author = "Kocmi, Tom  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5322",
    doi = "10.18653/v1/W19-5322",
    pages = "234--240",
    abstract = "This paper describes the CUNI submission to the WMT 2019 News Translation Shared Task for the low-resource languages: Gujarati-English and Kazakh-English. We participated in both language pairs in both translation directions. Our system combines transfer learning from a different high-resource language pair followed by training on backtranslated monolingual data. Thanks to the simultaneous training in both directions, we can iterate the backtranslation process. We are using the Transformer model in a constrained submission.",
}
@inproceedings{kvapilikova-etal-2019-cuni,
    title = "{CUNI} Systems for the Unsupervised News Translation Task in {WMT} 2019",
    author = "Kvapil{\'\i}kov{\'a}, Ivana  and
      Mach{\'a}{\v{c}}ek, Dominik  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5323",
    doi = "10.18653/v1/W19-5323",
    pages = "241--248",
    abstract = "In this paper we describe the CUNI translation system used for the unsupervised news shared task of the ACL 2019 Fourth Conference on Machine Translation (WMT19). We follow the strategy of Artetxe ae at. (2018b), creating a seed phrase-based system where the phrase table is initialized from cross-lingual embedding mappings trained on monolingual data, followed by a neural machine translation system trained on synthetic parallel data. The synthetic corpus was produced from a monolingual corpus by a tuned PBMT model refined through iterative back-translation. We further focus on the handling of named entities, i.e. the part of vocabulary where the cross-lingual embedding mapping suffers most. Our system reaches a BLEU score of 15.3 on the German-Czech WMT19 shared task.",
}
@inproceedings{li-specia-2019-comparison,
    title = "A Comparison on Fine-grained Pre-trained Embeddings for the {WMT}19{C}hinese-{E}nglish News Translation Task",
    author = "Li, Zhenhao  and
      Specia, Lucia",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5324",
    doi = "10.18653/v1/W19-5324",
    pages = "249--256",
    abstract = "This paper describes our submission to the WMT 2019 Chinese-English (zh-en) news translation shared task. Our systems are based on RNN architectures with pre-trained embeddings which utilize character and sub-character information. We compare models with these different granularity levels using different evaluating metics. We find that a finer granularity embeddings can help the model according to character level evaluation and that the pre-trained embeddings can also be beneficial for model performance marginally when the training data is limited.",
}
@inproceedings{li-etal-2019-niutrans,
    title = "The {N}iu{T}rans Machine Translation Systems for {WMT}19",
    author = "Li, Bei  and
      Li, Yinqiao  and
      Xu, Chen  and
      Lin, Ye  and
      Liu, Jiqiang  and
      Liu, Hui  and
      Wang, Ziyang  and
      Zhang, Yuhao  and
      Xu, Nuo  and
      Wang, Zeyang  and
      Feng, Kai  and
      Chen, Hexuan  and
      Liu, Tengbo  and
      Li, Yanyang  and
      Wang, Qiang  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5325",
    doi = "10.18653/v1/W19-5325",
    pages = "257--266",
    abstract = "This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {KK↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN↔DE} among all constrained submissions.",
}
@inproceedings{littell-etal-2019-multi,
    title = "Multi-Source Transformer for {K}azakh-{R}ussian-{E}nglish Neural Machine Translation",
    author = "Littell, Patrick  and
      Lo, Chi-kiu  and
      Larkin, Samuel  and
      Stewart, Darlene",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5326",
    doi = "10.18653/v1/W19-5326",
    pages = "267--274",
    abstract = "We describe the neural machine translation (NMT) system developed at the National Research Council of Canada (NRC) for the Kazakh-English news translation task of the Fourth Conference on Machine Translation (WMT19). Our submission is a multi-source NMT taking both the original Kazakh sentence and its Russian translation as input for translating into English.",
}
@inproceedings{liu-etal-2019-incorporating-word,
    title = "Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring",
    author = "Liu, Zihan  and
      Xu, Yan  and
      Winata, Genta Indra  and
      Fung, Pascale",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5327",
    doi = "10.18653/v1/W19-5327",
    pages = "275--282",
    abstract = "This paper describes CAiRE{'}s submission to the unsupervised machine translation track of the WMT{'}19 news shared task from German to Czech. We leverage a phrase-based statistical machine translation (PBSMT) model and a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NMT models without using any parallel data. We propose to solve the morphological richness problem of languages by training byte-pair encoding (BPE) embeddings for German and Czech separately, and they are aligned using MUSE (Conneau et al., 2018). To ensure the fluency and consistency of translations, a rescoring mechanism is proposed that reuses the pre-trained language model to select the translation candidates generated through beam search. Moreover, a series of pre-processing and post-processing approaches are applied to improve the quality of final translations.",
}
@inproceedings{mahata-etal-2019-jumt,
    title = "{JUMT} at {WMT}2019 News Translation Task: A Hybrid Approach to Machine Translation for {L}ithuanian to {E}nglish",
    author = "Mahata, Sainik Kumar  and
      Garain, Avishek  and
      Rayala, Adityar  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5328",
    doi = "10.18653/v1/W19-5328",
    pages = "283--286",
    abstract = "In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task. The system was created to translate news text from Lithuanian to English. To accomplish the given task, our system used a Word Embedding based Neural Machine Translation model to post edit the outputs generated by a Statistical Machine Translation model. The current paper documents the architecture of our model, descriptions of the various modules and the results produced using the same. Our system garnered a BLEU score of 17.6.",
}
@inproceedings{marchisio-etal-2019-johns,
    title = "{J}ohns {H}opkins {U}niversity Submission for {WMT} News Translation Task",
    author = "Marchisio, Kelly  and
      Lal, Yash Kumar  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5329",
    doi = "10.18653/v1/W19-5329",
    pages = "287--293",
    abstract = "We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019). We submitted systems for both directions of the English-German language pair. The systems combine multiple techniques {--} sampling, filtering, iterative backtranslation, and continued training {--} previously used to improve performance of neural machine translation models. At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019. Post-submission, the score is 38.4 for De-En and 42.8 for En-De. Various experiments conducted in the process are also described.",
}
@inproceedings{marie-etal-2019-nicts,
    title = "{NICT}{'}s Unsupervised Neural and Statistical Machine Translation Systems for the {WMT}19 News Translation Task",
    author = "Marie, Benjamin  and
      Sun, Haipeng  and
      Wang, Rui  and
      Chen, Kehai  and
      Fujita, Atsushi  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5330",
    doi = "10.18653/v1/W19-5330",
    pages = "294--301",
    abstract = "This paper presents the NICT{'}s participation in the WMT19 unsupervised news translation task. We participated in the unsupervised translation direction: German-Czech. Our primary submission to the task is the result of a simple combination of our unsupervised neural and statistical machine translation systems. Our system is ranked first for the German-to-Czech translation task, using only the data provided by the organizers ({``}constraint{'}{''}), according to both BLEU-cased and human evaluation. We also performed contrastive experiments with other language pairs, namely, English-Gujarati and English-Kazakh, to better assess the effectiveness of unsupervised machine translation in for distant language pairs and in truly low-resource conditions.",
}
@inproceedings{molchanov-2019-promt,
    title = "{PROMT} Systems for {WMT} 2019 Shared Translation Task",
    author = "Molchanov, Alexander",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5331",
    doi = "10.18653/v1/W19-5331",
    pages = "302--307",
    abstract = "This paper describes the PROMT submissions for the WMT 2019 Shared News Translation Task. This year we participated in two language pairs and in three directions: English-Russian, English-German and German-English. All our submissions are Marian-based neural systems. We use significantly more data compared to the last year. We also present our improved data filtering pipeline.",
}
@inproceedings{mondal-etal-2019-ju,
    title = "{JU}-{S}aarland Submission to the {WMT}2019 {E}nglish{--}{G}ujarati Translation Shared Task",
    author = "Mondal, Riktim  and
      Nayek, Shankha Raj  and
      Chowdhury, Aditya  and
      Pal, Santanu  and
      Naskar, Sudip Kumar  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5332",
    doi = "10.18653/v1/W19-5332",
    pages = "308--313",
    abstract = "In this paper we describe our joint submission (JU-Saarland) from Jadavpur University and Saarland University in the WMT 2019 news translation shared task for English{--}Gujarati language pair within the translation task sub-track. Our baseline and primary submissions are built using Recurrent neural network (RNN) based neural machine translation (NMT) system which follows attention mechanism. Given the fact that the two languages belong to different language families and there is not enough parallel data for this language pair, building a high quality NMT system for this language pair is a difficult task. We produced synthetic data through back-translation from available monolingual data. We report the translation quality of our English{--}Gujarati and Gujarati{--}English NMT systems trained at word, byte-pair and character encoding levels where RNN at word level is considered as the baseline and used for comparison purpose. Our English{--}Gujarati system ranked in the second position in the shared task.",
}
@inproceedings{ng-etal-2019-facebook,
    title = "{F}acebook {FAIR}{'}s {WMT}19 News Translation Task Submission",
    author = "Ng, Nathan  and
      Yee, Kyra  and
      Baevski, Alexei  and
      Ott, Myle  and
      Auli, Michael  and
      Edunov, Sergey",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5333",
    doi = "10.18653/v1/W19-5333",
    pages = "314--319",
    abstract = "This paper describes Facebook FAIR{'}s submission to the WMT19 shared news translation task. We participate in four language directions, English {\textless}-{\textgreater} German and English {\textless}-{\textgreater} Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system{'}s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English→Russian.",
}
@inproceedings{oravecz-etal-2019-etranslations,
    title = "e{T}ranslation{'}s Submissions to the {WMT} 2019 News Translation Task",
    author = "Oravecz, Csaba  and
      Bontcheva, Katina  and
      Lardilleux, Adrien  and
      Tihanyi, L{\'a}szl{\'o}  and
      Eisele, Andreas",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5334",
    doi = "10.18653/v1/W19-5334",
    pages = "320--326",
    abstract = "This paper describes the submissions of the eTranslation team to the WMT 2019 news translation shared task. The systems have been developed with the aim of identifying and following rather than establishing best practices, under the constraints imposed by a low resource training and decoding environment normally used for our production systems. Thus most of the findings and results are transferable to systems used in the eTranslation service. Evaluations suggest that this approach is able to produce decent models with good performance and speed without the overhead of using prohibitively deep and complex architectures.",
}
@inproceedings{pinnis-etal-2019-tildes,
    title = "Tilde{'}s Machine Translation Systems for {WMT} 2019",
    author = "Pinnis, Marcis  and
      Kri{\v{s}}lauks, Rihards  and
      Rikters, Mat{\=\i}ss",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5335",
    doi = "10.18653/v1/W19-5335",
    pages = "327--334",
    abstract = "The paper describes the development process of Tilde{'}s NMT systems for the WMT 2019 shared task on news translation. We trained systems for the English-Lithuanian and Lithuanian-English translation directions in constrained and unconstrained tracks. We build upon the best methods of the previous year{'}s competition and combine them with recent advancements in the field. We also present a new method to ensure source domain adherence in back-translated data. Our systems achieved a shared first place in human evaluation.",
}
@inproceedings{pirinen-2019-apertium,
    title = "Apertium-fin-eng{--}Rule-based Shallow Machine Translation for {WMT} 2019 Shared Task",
    author = "Pirinen, Tommi",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5336",
    doi = "10.18653/v1/W19-5336",
    pages = "335--341",
    abstract = "In this paper we describe a rule-based, bi-directional machine translation system for the Finnish{---}English language pair. The baseline system was based on the existing data of FinnWordNet, omorfi and apertium-eng. We have built the disambiguation, lexical selection and translation rules by hand. The dictionaries and rules have been developed based on the shared task data. We describe in this article the use of the shared task data as a kind of a test-driven development workflow in RBMT development and show that it suits perfectly to a modern software engineering continuous integration workflow of RBMT and yields big increases to BLEU scores with minimal effort.",
}
@inproceedings{popel-etal-2019-english,
    title = "{E}nglish-{C}zech Systems in {WMT}19: Document-Level Transformer",
    author = "Popel, Martin  and
      Mach{\'a}{\v{c}}ek, Dominik  and
      Auersperger, Michal  and
      Bojar, Ond{\v{r}}ej  and
      Pecina, Pavel",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5337",
    doi = "10.18653/v1/W19-5337",
    pages = "342--348",
    abstract = "We describe our NMT systems submitted to the WMT19 shared task in English→Czech news translation. Our systems are based on the Transformer model implemented in either Tensor2Tensor (T2T) or Marian framework. We aimed at improving the adequacy and coherence of translated documents by enlarging the context of the source and target. Instead of translating each sentence independently, we split the document into possibly overlapping multi-sentence segments. In case of the T2T implementation, this {``}document-level{''}-trained system achieves a +0.6 BLEU improvement (p {\textless} 0.05) relative to the same system applied on isolated sentences. To assess the potential effect document-level models might have on lexical coherence, we performed a semi-automatic analysis, which revealed only a few sentences improved in this aspect. Thus, we cannot draw any conclusions from this week evidence.",
}
@inproceedings{rosendahl-etal-2019-rwth,
    title = "The {RWTH} {A}achen {U}niversity Machine Translation Systems for {WMT} 2019",
    author = "Rosendahl, Jan  and
      Herold, Christian  and
      Kim, Yunsu  and
      Gra{\c{c}}a, Miguel  and
      Wang, Weiyue  and
      Bahar, Parnia  and
      Gao, Yingbo  and
      Ney, Hermann",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5338",
    doi = "10.18653/v1/W19-5338",
    pages = "349--355",
    abstract = "This paper describes the neural machine translation systems developed at the RWTH Aachen University for the German-English, Chinese-English and Kazakh-English news translation tasks of the Fourth Conference on Machine Translation (WMT19). For all tasks, the final submitted system is based on the Transformer architecture. We focus on improving data filtering and fine-tuning as well as systematically evaluating interesting approaches like unigram language model segmentation and transfer learning. For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39.6{\%} BLEU on newstest2019. In the Zh-En task, we show 1.3{\%} BLEU improvement over our last year{'}s submission, which we mostly attribute to the splitting of long sentences during translation. We further report results on the Kazakh-English task where we gain improvements of 11.1{\%} BLEU over our baseline system. On the same task we present a recent transfer learning approach, which uses half of the free parameters of our submission system and performs on par with it.",
}
@inproceedings{sanchez-cartagena-etal-2019-universitat,
    title = "The {U}niversitat d{'}Alacant Submissions to the {E}nglish-to-{K}azakh News Translation Task at {WMT} 2019",
    author = "S{\'a}nchez-Cartagena, V{\'\i}ctor M.  and
      P{\'e}rez-Ortiz, Juan Antonio  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5339",
    doi = "10.18653/v1/W19-5339",
    pages = "356--363",
    abstract = "This paper describes the two submissions of Universitat d{'}Alacant to the English-to-Kazakh news translation task at WMT 2019. Our submissions take advantage of monolingual data and parallel data from other language pairs by means of iterative backtranslation, pivot backtranslation and transfer learning. They also use linguistic information in two ways: morphological segmentation of Kazakh text, and integration of the output of a rule-based machine translation system. Our systems were ranked second in terms of chrF++ despite being built from an ensemble of only 2 independent training runs.",
}
@inproceedings{stahlberg-etal-2019-cued,
    title = "{CUED}@{WMT}19:{EWC}{\&}{LM}s",
    author = "Stahlberg, Felix  and
      Saunders, Danielle  and
      de Gispert, Adri{\`a}  and
      Byrne, Bill",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5340",
    doi = "10.18653/v1/W19-5340",
    pages = "364--373",
    abstract = "Two techniques provide the fabric of the Cambridge University Engineering Department{'}s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs). We report substantial gains by fine-tuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC. A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains. As in previous years, we also extract n-gram probabilities from SMT lattices which can be seen as a source-conditioned n-gram LM.",
}
@inproceedings{sun-etal-2019-baidu,
    title = "{B}aidu Neural Machine Translation Systems for {WMT}19",
    author = "Sun, Meng  and
      Jiang, Bojian  and
      Xiong, Hao  and
      He, Zhongjun  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5341",
    doi = "10.18653/v1/W19-5341",
    pages = "374--381",
    abstract = "In this paper we introduce the systems Baidu submitted for the WMT19 shared task on Chinese{\textless}-{\textgreater}English news translation. Our systems are based on the Transformer architecture with some effective improvements. Data selection, back translation, data augmentation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments. Our Chinese-{\textgreater}English system achieved the highest case-sensitive BLEU score among all constrained submissions, and our English-{\textgreater}Chinese system ranked the second in all submissions.",
}
@inproceedings{tattar-etal-2019-university,
    title = "{U}niversity of {T}artu{'}s Multilingual Multi-domain {WMT}19 News Translation Shared Task Submission",
    author = {T{\"a}ttar, Andre  and
      Korotkova, Elizaveta  and
      Fishel, Mark},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5342",
    doi = "10.18653/v1/W19-5342",
    pages = "382--385",
    abstract = "This paper describes the University of Tartu{'}s submission to the news translation shared task of WMT19, where the core idea was to train a single multilingual system to cover several language pairs of the shared task and submit its results. We only used the constrained data from the shared task. We describe our approach and its results and discuss the technical issues we faced.",
}
@inproceedings{toral-etal-2019-neural,
    title = "Neural Machine Translation for {E}nglish{--}{K}azakh with Morphological Segmentation and Synthetic Data",
    author = "Toral, Antonio  and
      Edman, Lukas  and
      Yeshmagambetova, Galiya  and
      Spenader, Jennifer",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5343",
    doi = "10.18653/v1/W19-5343",
    pages = "386--392",
    abstract = "This paper presents the systems submitted by the University of Groningen to the English{--} Kazakh language pair (both translation directions) for the WMT 2019 news translation task. We explore the potential benefits of (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English{--}Kazakh data and (iii) synthetic data, both for the source and for the target language. Our best submissions ranked second for Kazakh→English and third for English→Kazakh in terms of the BLEU automatic evaluation metric.",
}
@inproceedings{stojanovski-etal-2019-lmu,
    title = "The {LMU} {M}unich Unsupervised Machine Translation System for {WMT}19",
    author = "Stojanovski, Dario  and
      Hangya, Viktor  and
      Huck, Matthias  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5344",
    doi = "10.18653/v1/W19-5344",
    pages = "393--399",
    abstract = "We describe LMU Munich{'}s machine translation system for German→Czech translation which was used to participate in the WMT19 shared task on unsupervised news translation. We train our model using monolingual data only from both languages. The final model is an unsupervised neural model using established techniques for unsupervised translation such as denoising autoencoding and online back-translation. We bootstrap the model with masked language model pretraining and enhance it with back-translations from an unsupervised phrase-based system which is itself bootstrapped using unsupervised bilingual word embeddings.",
}
@inproceedings{stojanovski-fraser-2019-combining,
    title = "Combining Local and Document-Level Context: The {LMU} {M}unich Neural Machine Translation System at {WMT}19",
    author = "Stojanovski, Dario  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5345",
    doi = "10.18653/v1/W19-5345",
    pages = "400--406",
    abstract = "We describe LMU Munich{'}s machine translation system for English→German translation which was used to participate in the WMT19 shared task on supervised news translation. We specifically participated in the document-level MT track. The system used as a primary submission is a context-aware Transformer capable of both rich modeling of limited contextual information and integration of large-scale document-level context with a less rich representation. We train this model by fine-tuning a big Transformer baseline. Our experimental results show that document-level context provides for large improvements in translation quality, and adding a rich representation of the previous sentence provides a small additional gain.",
}
@inproceedings{sen-etal-2019-iitp,
    title = "{IITP}-{MT} System for {G}ujarati-{E}nglish News Translation Task at {WMT} 2019",
    author = "Sen, Sukanta  and
      Gupta, Kamal Kumar  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5346",
    doi = "10.18653/v1/W19-5346",
    pages = "407--411",
    abstract = "We describe our submission to WMT 2019 News translation shared task for Gujarati-English language pair. We submit constrained systems, i.e, we rely on the data provided for this language pair and do not use any external data. We train Transformer based subword-level neural machine translation (NMT) system using original parallel corpus along with synthetic parallel corpus obtained through back-translation of monolingual data. Our primary systems achieve BLEU scores of 10.4 and 8.1 for Gujarati→English and English→Gujarati, respectively. We observe that incorporating monolingual data through back-translation improves the BLEU score significantly over baseline NMT and SMT systems for this language pair.",
}
@inproceedings{talman-etal-2019-university,
    title = "The {U}niversity of {H}elsinki Submissions to the {WMT}19 News Translation Task",
    author = {Talman, Aarne  and
      Sulubacak, Umut  and
      V{\'a}zquez, Ra{\'u}l  and
      Scherrer, Yves  and
      Virpioja, Sami  and
      Raganato, Alessandro  and
      Hurskainen, Arvi  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5347",
    doi = "10.18653/v1/W19-5347",
    pages = "412--423",
    abstract = "In this paper we present the University of Helsinki submissions to the WMT 2019 shared news translation task in three language pairs: English-German, English-Finnish and Finnish-English. This year we focused first on cleaning and filtering the training data using multiple data-filtering approaches, resulting in much smaller and cleaner training sets. For English-German we trained both sentence-level transformer models as well as compared different document-level translation approaches. For Finnish-English and English-Finnish we focused on different segmentation approaches and we also included a rule-based system for English-Finnish.",
}
@inproceedings{xia-etal-2019-microsoft,
    title = "{M}icrosoft {R}esearch {A}sia{'}s Systems for {WMT}19",
    author = "Xia, Yingce  and
      Tan, Xu  and
      Tian, Fei  and
      Gao, Fei  and
      He, Di  and
      Chen, Weicong  and
      Fan, Yang  and
      Gong, Linyuan  and
      Leng, Yichong  and
      Luo, Renqian  and
      Wang, Yiren  and
      Wu, Lijun  and
      Zhu, Jinhua  and
      Qin, Tao  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5348",
    doi = "10.18653/v1/W19-5348",
    pages = "424--433",
    abstract = "We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks. We won the first place for 8 of the 11 directions and the second place for the other three. Our basic systems are built on Transformer, back translation and knowledge distillation. We integrate several of our rececent techniques to enhance the baseline systems: multi-agent dual learning (MADL), masked sequence-to-sequence pre-training (MASS), neural architecture optimization (NAO), and soft contextual data augmentation (SCA).",
}
@inproceedings{yu-2019-en,
    title = "The En-{R}u Two-way Integrated Machine Translation System Based on Transformer",
    author = "Yu, Doron",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5349",
    doi = "10.18653/v1/W19-5349",
    pages = "434--439",
    abstract = "Machine translation is one of the most popular areas in natural language processing. WMT is a conference to assess the level of machine translation capabilities of organizations around the world, which is the evaluation activity we participated in. In this review we participated in a two-way translation track from Russian to English and English to Russian. We used official training data, 38 million parallel corpora, and 10 million monolingual corpora. The overall framework we use is the Transformer neural machine translation model, supplemented by data filtering, post-processing, reordering and other related processing methods. The BLEU value of our final translation result from Russian to English is 38.7, ranking 5th, while from English to Russian is 27.8, ranking 10th.",
}
@inproceedings{zhang-van-genabith-2019-dfki,
    title = "{DFKI}-{NMT} Submission to the {WMT}19 News Translation Task",
    author = "Zhang, Jingyi  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5350",
    doi = "10.18653/v1/W19-5350",
    pages = "440--444",
    abstract = "This paper describes the DFKI-NMT submission to the WMT19 News translation task. We participated in both English-to-German and German-to-English directions. We trained Transformer models and adopted various techniques for effectively training our models, including data selection, back-translation and in-domain fine-tuning. We give a detailed analysis of the performance of our system.",
}
@inproceedings{avramidis-etal-2019-linguistic,
    title = "Linguistic Evaluation of {G}erman-{E}nglish Machine Translation Using a Test Suite",
    author = "Avramidis, Eleftherios  and
      Macketanz, Vivien  and
      Strohriegel, Ursula  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5351",
    doi = "10.18653/v1/W19-5351",
    pages = "445--454",
    abstract = "We present the results of the application of a grammatical test suite for German-to-English MT on the systems submitted at WMT19, with a detailed analysis for 107 phenomena organized in 14 categories. The systems still translate wrong one out of four test items in average. Low performance is indicated for idioms, modals, pseudo-clefts, multi-word expressions and verb valency. When compared to last year, there has been a improvement of function words, non verbal agreement and punctuation. More detailed conclusions about particular systems and phenomena are also presented.",
}
@inproceedings{rysova-etal-2019-test,
    title = "A Test Suite and Manual Evaluation of Document-Level {NMT} at {WMT}19",
    author = "Rysov{\'a}, Kate{\v{r}}ina  and
      Rysov{\'a}, Magdal{\'e}na  and
      Musil, Tom{\'a}{\v{s}}  and
      Pol{\'a}kov{\'a}, Lucie  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5352",
    doi = "10.18653/v1/W19-5352",
    pages = "455--463",
    abstract = "As the quality of machine translation rises and neural machine translation (NMT) is moving from sentence to document level translations, it is becoming increasingly difficult to evaluate the output of translation systems. We provide a test suite for WMT19 aimed at assessing discourse phenomena of MT systems participating in the News Translation Task. We have manually checked the outputs and identified types of translation errors that are relevant to document-level translation.",
}
@inproceedings{popovic-2019-evaluating,
    title = "Evaluating Conjunction Disambiguation on {E}nglish-to-{G}erman and {F}rench-to-{G}erman {WMT} 2019 Translation Hypotheses",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5353",
    doi = "10.18653/v1/W19-5353",
    pages = "464--469",
    abstract = "We present a test set for evaluating an MT system{'}s capability to translate ambiguous conjunctions depending on the sentence structure. We concentrate on the English conjunction {``}but{''} and its French equivalent {``}mais{''} which can be translated into two different German conjunctions. We evaluate all English-to-German and French-to-German submissions to the WMT 2019 shared translation task. The evaluation is done mainly automatically, with additional fast manual inspection of unclear cases. All systems almost perfectly recognise the target conjunction {``}aber{''}, whereas accuracies for the other target conjunction {``}sondern{''} range from 78{\%} to 97{\%}, and the errors are mostly caused by replacing it with the alternative conjunction {``}aber{''}. The best performing system for both language pairs is a multilingual Transformer {``}TartuNLP{''} system trained on all WMT 2019 language pairs which use the Latin script, indicating that the multilingual approach is beneficial for conjunction disambiguation. As for other system features, such as using synthetic back-translated data, context-aware, hybrid, etc., no particular (dis)advantages can be observed. Qualitative manual inspection of translation hypotheses shown that highly ranked systems generally produce translations with high adequacy and fluency, meaning that these systems are not only capable of capturing the right conjunction whereas the rest of the translation hypothesis is poor. On the other hand, the low ranked systems generally exhibit lower fluency and poor adequacy.",
}
@inproceedings{raganato-etal-2019-mucow,
    title = "The {M}u{C}o{W} Test Suite at {WMT} 2019: Automatically Harvested Multilingual Contrastive Word Sense Disambiguation Test Sets for Machine Translation",
    author = {Raganato, Alessandro  and
      Scherrer, Yves  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5354",
    doi = "10.18653/v1/W19-5354",
    pages = "470--480",
    abstract = "Supervised Neural Machine Translation (NMT) systems currently achieve impressive translation quality for many language pairs. One of the key features of a correct translation is the ability to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. Existing evaluation benchmarks on WSD capabilities of translation systems rely heavily on manual work and cover only few language pairs and few word types. We present MuCoW, a multilingual contrastive test suite that covers 16 language pairs with more than 200 thousand contrastive sentence pairs, automatically built from word-aligned parallel corpora and the wide-coverage multilingual sense inventory of BabelNet. We evaluate the quality of the ambiguity lexicons and of the resulting test suite on all submissions from 9 language pairs presented in the WMT19 news shared translation task, plus on other 5 language pairs using NMT pretrained models. The MuCoW test suite is available at http://github.com/Helsinki-NLP/MuCoW.",
}
@inproceedings{vojtechova-etal-2019-sao,
    title = "{SAO} {WMT}19 Test Suite: Machine Translation of Audit Reports",
    author = "Vojt{\v{e}}chov{\'a}, Tereza  and
      Nov{\'a}k, Michal  and
      Klou{\v{c}}ek, Milo{\v{s}}  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5355",
    doi = "10.18653/v1/W19-5355",
    pages = "481--493",
    abstract = "This paper describes a machine translation test set of documents from the auditing domain and its use as one of the {``}test suites{''} in the WMT19 News Translation Task for translation directions involving Czech, English and German. Our evaluation suggests that current MT systems optimized for the general news domain can perform quite well even in the particular domain of audit reports. The detailed manual evaluation however indicates that deep factual knowledge of the domain is necessary. For the naked eye of a non-expert, translations by many systems seem almost perfect and automatic MT evaluation with one reference is practically useless for considering these details. Furthermore, we show on a sample document from the domain of agreements that even the best systems completely fail in preserving the semantics of the agreement, namely the identity of the parties.",
}
@inproceedings{chow-etal-2019-wmdo,
    title = "{WMDO}: Fluency-based Word Mover{'}s Distance for Machine Translation Evaluation",
    author = "Chow, Julian  and
      Specia, Lucia  and
      Madhyastha, Pranava",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5356",
    doi = "10.18653/v1/W19-5356",
    pages = "494--500",
    abstract = "We propose WMDO, a metric based on distance between distributions in the semantic vector space. Matching in the semantic space has been investigated for translation evaluation, but the constraints of a translation{'}s word order have not been fully explored. Building on the Word Mover{'}s Distance metric and various word embeddings, we introduce a fragmentation penalty to account for fluency of a translation. This word order extension is shown to perform better than standard WMD, with promising results against other types of metrics.",
}
@inproceedings{guo-hu-2019-meteor,
    title = "Meteor++ 2.0: Adopt Syntactic Level Paraphrase Knowledge into Machine Translation Evaluation",
    author = "Guo, Yinuo  and
      Hu, Junfeng",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5357",
    doi = "10.18653/v1/W19-5357",
    pages = "501--506",
    abstract = "This paper describes Meteor++ 2.0, our submission to the WMT19 Metric Shared Task. The well known Meteor metric improves machine translation evaluation by introducing paraphrase knowledge. However, it only focuses on the lexical level and utilizes consecutive n-grams paraphrases. In this work, we take into consideration syntactic level paraphrase knowledge, which sometimes may be skip-grams. We describe how such knowledge can be extracted from Paraphrase Database (PPDB) and integrated into Meteor-based metrics. Experiments on WMT15 and WMT17 evaluation datasets show that the newly proposed metric outperforms all previous versions of Meteor.",
}
@inproceedings{lo-2019-yisi,
    title = "{Y}i{S}i - a Unified Semantic {MT} Quality Evaluation and Estimation Metric for Languages with Different Levels of Available Resources",
    author = "Lo, Chi-kiu",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5358",
    doi = "10.18653/v1/W19-5358",
    pages = "507--513",
    abstract = "We present YiSi, a unified automatic semantic machine translation quality evaluation and estimation metric for languages with different levels of available resources. Underneath the interface with different language resources settings, YiSi uses the same representation for the two sentences in assessment. Besides, we show significant improvement in the correlation of YiSi-1{'}s scores with human judgment is made by using contextual embeddings in multilingual BERT{--}Bidirectional Encoder Representations from Transformers to evaluate lexical semantic similarity. YiSi is open source and publicly available.",
}
@inproceedings{stanchev-etal-2019-eed,
    title = "{EED}: Extended Edit Distance Measure for Machine Translation",
    author = "Stanchev, Peter  and
      Wang, Weiyue  and
      Ney, Hermann",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5359",
    doi = "10.18653/v1/W19-5359",
    pages = "514--520",
    abstract = "Over the years a number of machine translation metrics have been developed in order to evaluate the accuracy and quality of machine-generated translations. Metrics such as BLEU and TER have been used for decades. However, with the rapid progress of machine translation systems, the need for better metrics is growing. This paper proposes an extension of the edit distance, which achieves better human correlation, whilst remaining fast, flexible and easy to understand.",
}
@inproceedings{yoshimura-etal-2019-filtering,
    title = "Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation",
    author = "Yoshimura, Ryoma  and
      Shimanaka, Hiroki  and
      Matsumura, Yukio  and
      Yamagishi, Hayahide  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5360",
    doi = "10.18653/v1/W19-5360",
    pages = "521--525",
    abstract = "In this paper, we introduce our participation in the WMT 2019 Metric Shared Task. We propose an improved version of sentence BLEU using filtered pseudo-references. We propose a method to filter pseudo-references by paraphrasing for automatic evaluation of machine translation (MT). We use the outputs of off-the-shelf MT systems as pseudo-references filtered by paraphrasing in addition to a single human reference (gold reference). We use BERT fine-tuned with paraphrase corpus to filter pseudo-references by checking the paraphrasability with the gold reference. Our experimental results of the WMT 2016 and 2017 datasets show that our method achieved higher correlation with human evaluation than the sentence BLEU (SentBLEU) baselines with a single reference and with unfiltered pseudo-references.",
}
@inproceedings{berard-etal-2019-naver,
    title = "Naver Labs {E}urope{'}s Systems for the {WMT}19 Machine Translation Robustness Task",
    author = "Berard, Alexandre  and
      Calapodescu, Ioan  and
      Roux, Claude",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5361",
    doi = "10.18653/v1/W19-5361",
    pages = "526--532",
    abstract = "This paper describes the systems that we submitted to the WMT19 Machine Translation robustness task. This task aims to improve MT{'}s robustness to noise found on social media, like informal language, spelling mistakes and other orthographic variations. The organizers provide parallel data extracted from a social media website in two language pairs: French-English and Japanese-English (one for each language direction). The goal is to obtain the best scores on unseen test sets from the same source, according to automatic metrics (BLEU) and human evaluation. We propose one single and one ensemble system for each translation direction. Our ensemble models ranked first in all language pairs, according to BLEU evaluation. We discuss the pre-processing choices that we made, and present our solutions for robustness to noise and domain adaptation.",
}
@inproceedings{dabre-sumita-2019-nicts,
    title = "{NICT}{'}s Supervised Neural Machine Translation Systems for the {WMT}19 Translation Robustness Task",
    author = "Dabre, Raj  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5362",
    doi = "10.18653/v1/W19-5362",
    pages = "533--536",
    abstract = "In this paper we describe our neural machine translation (NMT) systems for Japanese↔English translation which we submitted to the translation robustness task. We focused on leveraging transfer learning via fine tuning to improve translation quality. We used a fairly well established domain adaptation technique called Mixed Fine Tuning (MFT) (Chu et. al., 2017) to improve translation quality for Japanese↔English. We also trained bi-directional NMT models instead of uni-directional ones as the former are known to be quite robust, especially in low-resource scenarios. However, given the noisy nature of the in-domain training data, the improvements we obtained are rather modest.",
}
@inproceedings{grozea-2019-system,
    title = "System Description: The Submission of {FOKUS} to the {WMT} 19 Robustness Task",
    author = "Grozea, Cristian",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5363",
    doi = "10.18653/v1/W19-5363",
    pages = "537--538",
    abstract = "This paper describes the systems of Fraunhofer FOKUS for the WMT 2019 machine translation robustness task. We have made submissions to the EN-FR, FR-EN, and JA-EN language pairs. The first two were made with a baseline translator, trained on clean data for the WMT 2019 biomedical translation task. These baselines improved over the baselines from the MTNT paper by 2 to 4 BLEU points, but where not trained on the same data. The last one used the same model class and training procedure, with induced typos in the training data to increase the model robustness.",
}
@inproceedings{helcl-etal-2019-cuni,
    title = "{CUNI} System for the {WMT}19 Robustness Task",
    author = "Helcl, Jind{\v{r}}ich  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Popel, Martin",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5364",
    doi = "10.18653/v1/W19-5364",
    pages = "539--543",
    abstract = "We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation. Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTM-based baseline provided by the task organizers. We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain.",
}
@inproceedings{murakami-etal-2019-ntts,
    title = "{NTT}{'}s Machine Translation Systems for {WMT}19 Robustness Task",
    author = "Murakami, Soichiro  and
      Morishita, Makoto  and
      Hirao, Tsutomu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5365",
    doi = "10.18653/v1/W19-5365",
    pages = "544--551",
    abstract = "This paper describes NTT{'}s submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulties from typical translation tasks such as news. Our submission combined techniques including utilization of a synthetic corpus, domain adaptation, and a placeholder mechanism, which significantly improved over the previous baseline. Experimental results revealed the placeholder mechanism, which temporarily replaces the non-standard tokens including emojis and emoticons with special placeholder tokens during translation, improves translation accuracy even with noisy texts.",
}
@inproceedings{post-duh-2019-jhu,
    title = "{JHU} 2019 Robustness Task System Description",
    author = "Post, Matt  and
      Duh, Kevin",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5366",
    doi = "10.18653/v1/W19-5366",
    pages = "552--558",
    abstract = "We describe the JHU submissions to the French{--}English, Japanese{--}English, and English{--}Japanese Robustness Task at WMT 2019. Our goal was to evaluate the performance of baseline systems on both the official noisy test set as well as news data, in order to ensure that performance gains in the latter did not come at the expense of general-domain performance. To this end, we built straightforward 6-layer Transformer models and experimented with a handful of variables including subword processing (FR→EN) and a handful of hyperparameters settings (JA↔EN). As expected, our systems performed reasonably.",
}
@inproceedings{zheng-etal-2019-robust,
    title = "Robust Machine Translation with Domain Sensitive Pseudo-Sources: {B}aidu-{OSU} {WMT}19 {MT} Robustness Shared Task System Report",
    author = "Zheng, Renjie  and
      Liu, Hairong  and
      Ma, Mingbo  and
      Zheng, Baigong  and
      Huang, Liang",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5367",
    doi = "10.18653/v1/W19-5367",
    pages = "559--564",
    abstract = "This paper describes the machine translation system developed jointly by Baidu Research and Oregon State University for WMT 2019 Machine Translation Robustness Shared Task. Translation of social media is a very challenging problem, since its style is very different from normal parallel corpora (e.g. News) and also include various types of noises. To make it worse, the amount of social media parallel corpora is extremely limited. In this paper, we use a domain sensitive training method which leverages a large amount of parallel data from popular domains together with a little amount of parallel data from social media. Furthermore, we generate a parallel dataset with pseudo noisy source sentences which are back-translated from monolingual data using a model trained by a similar domain sensitive way. In this way, we achieve more than 10 BLEU improvement in both En-Fr and Fr-En translation compared with the baseline methods.",
}
@inproceedings{zhou-etal-2019-improving,
    title = "Improving Robustness of Neural Machine Translation with Multi-task Learning",
    author = "Zhou, Shuyan  and
      Zeng, Xiangkai  and
      Zhou, Yingqi  and
      Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5368",
    doi = "10.18653/v1/W19-5368",
    pages = "565--571",
    abstract = "While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multi-task learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text.",
}
@proceedings{ws-2019-machine,
    title = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Martins, Andr{\'e}  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Turchi, Marco  and
      Verspoor, Karin",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5200",
}
@inproceedings{ding-etal-2019-saliency,
    title = "Saliency-driven Word Alignment Interpretation for Neural Machine Translation",
    author = "Ding, Shuoyang  and
      Xu, Hainan  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5201",
    doi = "10.18653/v1/W19-5201",
    pages = "1--12",
    abstract = "Despite their original goal to jointly learn to align and translate, Neural Machine Translation (NMT) models, especially Transformer, are often perceived as not learning interpretable word alignments. In this paper, we show that NMT models do learn interpretable word alignments, which could only be revealed with proper interpretation methods. We propose a series of such methods that are model-agnostic, are able to be applied either offline or online, and do not require parameter update or architectural change. We show that under the force decoding setup, the alignments induced by our interpretation method are of better quality than fast-align for some systems, and when performing free decoding, they agree well with the alignments induced by automatic alignment tools.",
}
@inproceedings{pham-etal-2019-improving,
    title = "Improving Zero-shot Translation with Language-Independent Constraints",
    author = "Pham, Ngoc-Quan  and
      Niehues, Jan  and
      Ha, Thanh-Le  and
      Waibel, Alexander",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5202",
    doi = "10.18653/v1/W19-5202",
    pages = "13--23",
    abstract = "An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one stone by providing an alternative to pivot translation which also allows us to better understand how the model captures information between languages. In this work, we carried out an investigation on this capability of the multilingual NMT models. First, we intentionally create an encoder architecture which is independent with respect to the source language. Such experiments shed light on the ability of NMT encoders to learn multilingual representations, in general. Based on such proof of concept, we were able to design regularization methods into the standard Transformer model, so that the whole architecture becomes more robust in zero-shot conditions. We investigated the behaviour of such models on the standard IWSLT 2017 multilingual dataset. We achieved an average improvement of 2.23 BLEU points across 12 language pairs compared to the zero-shot performance of a state-of-the-art multilingual system. Additionally, we carry out further experiments in which the effect is confirmed even for language pairs with multiple intermediate pivots.",
}
@inproceedings{currey-heafield-2019-incorporating,
    title = "Incorporating Source Syntax into Transformer-Based Neural Machine Translation",
    author = "Currey, Anna  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5203",
    doi = "10.18653/v1/W19-5203",
    pages = "24--33",
    abstract = "Transformer-based neural machine translation (NMT) has recently achieved state-of-the-art performance on many machine translation tasks. However, recent work (Raganato and Tiedemann, 2018; Tang et al., 2018; Tran et al., 2018) has indicated that Transformer models may not learn syntactic structures as well as their recurrent neural network-based counterparts, particularly in low-resource cases. In this paper, we incorporate constituency parse information into a Transformer NMT model. We leverage linearized parses of the source training sentences in order to inject syntax into the Transformer architecture without modifying it. We introduce two methods: a multi-task machine translation and parsing model with a single encoder and decoder, and a mixed encoder model that learns to translate directly from parsed and unparsed source sentences. We evaluate our methods on low-resource translation from English into twenty target languages, showing consistent improvements of 1.3 BLEU on average across diverse target languages for the multi-task technique. We further evaluate the models on full-scale WMT tasks, finding that the multi-task model aids low- and medium-resource NMT but degenerates high-resource English-German translation.",
}
@inproceedings{freitag-etal-2019-ape,
    title = "{APE} at Scale and Its Implications on {MT} Evaluation Biases",
    author = "Freitag, Markus  and
      Caswell, Isaac  and
      Roy, Scott",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5204",
    doi = "10.18653/v1/W19-5204",
    pages = "34--44",
    abstract = "In this work, we train an Automatic Post-Editing (APE) model and use it to reveal biases in standard MT evaluation procedures. The goal of our APE model is to correct typical errors introduced by the translation process, and convert the {``}translationese{''} output into natural text. Our APE model is trained entirely on monolingual data that has been round-trip translated through English, to mimic errors that are similar to the ones introduced by NMT. We apply our model to the output of existing NMT systems, and demonstrate that, while the human-judged quality improves in all cases, BLEU scores drop with forward-translated test sets. We verify these results for the WMT18 English to German, WMT15 English to French, and WMT16 English to Romanian tasks. Furthermore, we selectively apply our APE model on the output of the top submissions of the most recent WMT evaluation campaigns. We see quality improvements on all tasks of up to 2.5 BLEU points.",
}
@inproceedings{graca-etal-2019-generalizing,
    title = "Generalizing Back-Translation in Neural Machine Translation",
    author = "Gra{\c{c}}a, Miguel  and
      Kim, Yunsu  and
      Schamper, Julian  and
      Khadivi, Shahram  and
      Ney, Hermann",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5205",
    doi = "10.18653/v1/W19-5205",
    pages = "45--52",
    abstract = "Back-translation {---} data augmentation by translating target monolingual data {---} is a crucial component in modern neural machine translation (NMT). In this work, we reformulate back-translation in the scope of cross-entropy optimization of an NMT model, clarifying its underlying mathematical assumptions and approximations beyond its heuristic usage. Our formulation covers broader synthetic data generation schemes, including sampling from a target-to-source NMT model. With this formulation, we point out fundamental problems of the sampling-based approaches and propose to remedy them by (i) disabling label smoothing for the target-to-source model and (ii) sampling from a restricted search space. Our statements are investigated on the WMT 2018 German {\textless}-{\textgreater} English news translation task.",
}
@inproceedings{caswell-etal-2019-tagged,
    title = "Tagged Back-Translation",
    author = "Caswell, Isaac  and
      Chelba, Ciprian  and
      Grangier, David",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5206",
    doi = "10.18653/v1/W19-5206",
    pages = "53--63",
    abstract = "Recent work in Neural Machine Translation (NMT) has shown significant quality gains from noised-beam decoding during back-translation, a method to generate synthetic parallel data. We show that the main role of such synthetic noise is not to diversify the source side, as previously suggested, but simply to indicate to the model that the given source is synthetic. We propose a simpler alternative to noising techniques, consisting of tagging back-translated source sentences with an extra token. Our results on WMT outperform noised back-translation in English-Romanian and match performance on English-German, redefining the state-of-the-art on the former.",
}
@inproceedings{guo-etal-2019-hierarchical,
    title = "Hierarchical Document Encoder for Parallel Corpus Mining",
    author = "Guo, Mandy  and
      Yang, Yinfei  and
      Stevens, Keith  and
      Cer, Daniel  and
      Ge, Heming  and
      Sung, Yun-hsuan  and
      Strope, Brian  and
      Kurzweil, Ray",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5207",
    doi = "10.18653/v1/W19-5207",
    pages = "64--72",
    abstract = "We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings generated by simply averaging multilingual sentence embeddings; (ii) a neural bag-of-words (BoW) document encoding model; (iii) a hierarchical multilingual document encoder (HiDE) that builds on our sentence-level model. The results show document embeddings derived from sentence-level averaging are surprisingly effective for clean datasets, but suggest models trained hierarchically at the document-level are more effective on noisy data. Analysis experiments demonstrate our hierarchical models are very robust to variations in the underlying sentence embedding quality. Using document embeddings trained with HiDE achieves the state-of-the-art on United Nations (UN) parallel document mining, 94.9{\%} P@1 for en-fr and 97.3{\%} P@1 for en-es.",
}
@inproceedings{zhang-toral-2019-effect,
    title = "The Effect of Translationese in Machine Translation Test Sets",
    author = "Zhang, Mike  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5208",
    doi = "10.18653/v1/W19-5208",
    pages = "73--81",
    abstract = "The effect of translationese has been studied in the field of machine translation (MT), mostly with respect to training data. We study in depth the effect of translationese on test data, using the test sets from the last three editions of WMT{'}s news shared task, containing 17 translation directions. We show evidence that (i) the use of translationese in test sets results in inflated human evaluation scores for MT systems; (ii) in some cases system rankings do change and (iii) the impact translationese has on a translation direction is inversely correlated to the translation quality attainable by state-of-the-art MT systems for that direction.",
}
@inproceedings{matusov-etal-2019-customizing,
    title = "Customizing Neural Machine Translation for Subtitling",
    author = "Matusov, Evgeny  and
      Wilken, Patrick  and
      Georgakopoulou, Yota",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5209",
    doi = "10.18653/v1/W19-5209",
    pages = "82--93",
    abstract = "In this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment. The neural translation model was adapted to the subtitling content and style and extended by a simple, yet effective technique for utilizing inter-sentence context for short sentences such as dialog turns. The main contribution of the paper is a novel subtitle segmentation algorithm that predicts the end of a subtitle line given the previous word-level context using a recurrent neural network learned from human segmentation decisions. This model is combined with subtitle length and duration constraints established in the subtitling industry. We conducted a thorough human evaluation with two post-editors (English-to-Spanish translation of a documentary and a sitcom). It showed a notable productivity increase of up to 37{\%} as compared to translating from scratch and significant reductions in human translation edit rate in comparison with the post-editing of the baseline non-adapted system without a learned segmentation model.",
}
@inproceedings{saboo-baumann-2019-integration,
    title = "Integration of Dubbing Constraints into Machine Translation",
    author = "Saboo, Ashutosh  and
      Baumann, Timo",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5210",
    doi = "10.18653/v1/W19-5210",
    pages = "94--101",
    abstract = "Translation systems aim to perform a meaning-preserving conversion of linguistic material (typically text but also speech) from a source to a target language (and, to a lesser degree, the corresponding socio-cultural contexts). Dubbing, i.e., the lip-synchronous translation and revoicing of speech adds to this constraints about the close matching of phonetic and resulting visemic synchrony characteristics of source and target material. There is an inherent conflict between a translation{'}s meaning preservation and {`}dubbability{'} and the resulting trade-off can be controlled by weighing the synchrony constraints. We introduce our work, which to the best of our knowledge is the first of its kind, on integrating synchrony constraints into the machine translation paradigm. We present first results for the integration of synchrony constraints into encoder decoder-based neural machine translation and show that considerably more {`}dubbable{'} translations can be achieved with only a small impact on BLEU score, and dubbability improves more steeply than BLEU degrades.",
}
@inproceedings{emelin-etal-2019-widening,
    title = "Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts",
    author = "Emelin, Denis  and
      Titov, Ivan  and
      Sennrich, Rico",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5211",
    doi = "10.18653/v1/W19-5211",
    pages = "102--115",
    abstract = "The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context. Lexical features are fed into the first layer and propagated through a deep network of hidden layers. We argue that the need to represent and propagate lexical features in each layer limits the model{'}s capacity for learning and representing other information relevant to the task. To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder. This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states. We show that the proposed modification yields consistent improvements over a baseline transformer on standard WMT translation tasks in 5 translation directions (0.9 BLEU on average) and reduces the amount of lexical information passed along the hidden layers. We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior.",
}
@inproceedings{hashimoto-etal-2019-high,
    title = "A High-Quality Multilingual Dataset for Structured Documentation Translation",
    author = "Hashimoto, Kazuma  and
      Buschiazzo, Raffaella  and
      Bradbury, James  and
      Marshall, Teresa  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5212",
    doi = "10.18653/v1/W19-5212",
    pages = "116--127",
    abstract = "This paper presents a high-quality multilingual dataset for the documentation domain to advance research on localization of structured text. Unlike widely-used datasets for translation of plain text, we collect XML-structured parallel text segments from the online documentation for an enterprise software platform. These Web pages have been professionally translated from English into 16 languages and maintained by domain experts, and around 100,000 text segments are available for each language pair. We build and evaluate translation models for seven target languages from English, with several different copy mechanisms and an XML-constrained beam search. We also experiment with a non-English pair to show that our dataset has the potential to explicitly enable 17 {\mbox{$\times$}} 16 translation settings. Our experiments show that learning to translate with the XML tags improves translation accuracy, and the beam search accurately generates XML structures. We also discuss trade-offs of using the copy mechanisms by focusing on translation of numerical words and named entities. We further provide a detailed human analysis of gaps between the model output and human translations for real-world applications, including suitability for post-editing.",
}
@proceedings{ws-2019-joint-multiword,
    title = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    editor = "Savary, Agata  and
      Escart{\'\i}n, Carla Parra  and
      Bond, Francis  and
      Mitrovi{\'c}, Jelena  and
      Mititelu, Verginica Barbu",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5100",
}
@inproceedings{villavicencio-2019-whole,
    title = "When the whole is greater than the sum of its parts: Multiword expressions and idiomaticity",
    author = "Villavicencio, Aline",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5101",
    doi = "10.18653/v1/W19-5101",
    pages = "1",
    abstract = "Multiword expressions (MWEs) feature prominently in the mental lexicon of native speakers (Jackendoff, 1997) in all languages and domains, from informal to technical contexts (Biber et al., 1999) with about four MWEs being produced per minute of discourse (Glucksberg, 1989). MWEs come in all shapes and forms, including idioms like rock the boat (as cause problems or disturb a situation) and compound nouns like monkey business (as dishonest behaviour). Their accurate detection and understanding may often require more than knowledge about individual words and how they can be combined (Fillmore, 1979), as they may display various degrees of idiosyncrasy, including lexical, syntactic, semantic and statistical (Sag et al., 2002; Baldwin and Kim, 2010), which provide new challenges and opportunities for language processing (Constant et al., 2017). For instance, while for some combinations the meaning can be inferred from their parts like olive oil (oil made of olives) this is not always the case, as in dark horse (meaning an unknown candidate who unexpectedly succeeds), and when processing a sentence some of the challenges are to identify which words form an expression (Ramisch, 2015), and whether the expression is idiomatic (Cordeiro et al., 2019). In this talk I will give an overview of advances on the identification and treatment of multiword expressions, in particular concentrating on techniques for identifying their degree of idiomaticity.",
}
@inproceedings{barbu-mititelu-etal-2019-hear,
    title = "Hear about Verbal Multiword Expressions in the {B}ulgarian and the {R}omanian Wordnets Straight from the Horse{'}s Mouth",
    author = "Barbu Mititelu, Verginica  and
      Stoyanova, Ivelina  and
      Leseva, Svetlozara  and
      Mitrofan, Maria  and
      Dimitrova, Tsvetana  and
      Todorova, Maria",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5102",
    doi = "10.18653/v1/W19-5102",
    pages = "2--12",
    abstract = "In this paper we focus on verbal multiword expressions (VMWEs) in Bulgarian and Romanian as reflected in the wordnets of the two languages. The annotation of VMWEs relies on the classification defined within the PARSEME Cost Action. After outlining the properties of various types of VMWEs, a cross-language comparison is drawn, aimed to highlight the similarities and the differences between Bulgarian and Romanian with respect to the lexicalization and distribution of VMWEs. The contribution of this work is in outlining essential features of the description and classification of VMWEs and the cross-language comparison at the lexical level, which is essential for the understanding of the need for uniform annotation guidelines and a viable procedure for validation of the annotation.",
}
@inproceedings{barbu-mititelu-etal-2019-romanian,
    title = "The {R}omanian Corpus Annotated with Verbal Multiword Expressions",
    author = "Barbu Mititelu, Verginica  and
      Cristescu, Mihaela  and
      Onofrei, Mihaela",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5103",
    doi = "10.18653/v1/W19-5103",
    pages = "13--21",
    abstract = "This paper reports on the Romanian journalistic corpus annotated with verbal multiword expressions following the PARSEME guidelines. The corpus is sentence split, tokenized, part-of-speech tagged, lemmatized, syntactically annotated and verbal multiword expressions are identified and classified. It offers insights into the frequency of such Romanian word combinations and allows for their characterization. We offer data about the types of verbal multiword expressions in the corpus and some of their characteristics, such as internal structure, diversity in the corpus, average length, productivity of the verbs. This is a language resource that is important per se, as well as for the task of automatic multiword expressions identification, which can be further used in other systems. It was already used as training and test material in the shared tasks for the automatic identification of verbal multiword expressions organized by PARSEME.",
}
@inproceedings{declerck-etal-2019-using,
    title = "Using {O}nto{L}ex-Lemon for Representing and Interlinking {G}erman Multiword Expressions in {O}de{N}et and {MMORPH}",
    author = "Declerck, Thierry  and
      Siegel, Melanie  and
      Racioppa, Stefania",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5104",
    doi = "10.18653/v1/W19-5104",
    pages = "22--29",
    abstract = "We describe work consisting in porting two large German lexical resources into the OntoLex-Lemon model in order to establish complementary interlinkings between them. One resource is OdeNet (Open German WordNet) and the other is a further development of the German version of the MMORPH morphological analyzer. We show how the Multiword Expressions (MWEs) contained in OdeNet can be morphologically specified by the use of the lexical representation and linking features of OntoLex-Lemon, which also support the formulation of restrictions in the usage of such expressions.",
}
@inproceedings{dhar-van-der-plas-2019-learning,
    title = "Learning to Predict Novel Noun-Noun Compounds",
    author = "Dhar, Prajit  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5105",
    doi = "10.18653/v1/W19-5105",
    pages = "30--39",
    abstract = "We introduce temporally and contextually-aware models for the novel task of predicting unseen but plausible concepts, as conveyed by noun-noun compounds in a time-stamped corpus. We train compositional models on observed compounds, more specifically the composed distributed representations of their constituents across a time-stamped corpus, while giving it corrupted instances (where head or modifier are replaced by a random constituent) as negative evidence. The model captures generalisations over this data and learns what combinations give rise to plausible compounds and which ones do not. After training, we query the model for the plausibility of automatically generated novel combinations and verify whether the classifications are accurate. For our best model, we find that in around 85{\%} of the cases, the novel compounds generated are attested in previously unseen data. An additional estimated 5{\%} are plausible despite not being attested in the recent corpus, based on judgments from independent human raters.",
}
@inproceedings{gamallo-garcia-2019-unsupervised,
    title = "Unsupervised Compositional Translation of Multiword Expressions",
    author = "Gamallo, Pablo  and
      Garcia, Marcos",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5106",
    doi = "10.18653/v1/W19-5106",
    pages = "40--48",
    abstract = "This article describes a dependency-based strategy that uses compositional distributional semantics and cross-lingual word embeddings to translate multiword expressions (MWEs). Our unsupervised approach performs translation as a process of word contextualization by taking into account lexico-syntactic contexts and selectional preferences. This strategy is suited to translate phraseological combinations and phrases whose constituent words are lexically restricted by each other. Several experiments in adjective-noun and verb-object compounds show that mutual contextualization (co-compositionality) clearly outperforms other compositional methods. The paper also contributes with a new freely available dataset of English-Spanish MWEs used to validate the proposed compositional strategy.",
}
@inproceedings{garcia-etal-2019-comparison,
    title = "A comparison of statistical association measures for identifying dependency-based collocations in various languages.",
    author = "Garcia, Marcos  and
      Garc{\'\i}a Salido, Marcos  and
      Alonso-Ramos, Margarita",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5107",
    doi = "10.18653/v1/W19-5107",
    pages = "49--59",
    abstract = "This paper presents an exploration of different statistical association measures to automatically identify collocations from corpora in English, Portuguese, and Spanish. To evaluate the impact of the association metrics we manually annotated corpora with three different syntactic patterns of collocations (adjective-noun, verb-object and nominal compounds). We took advantage of the PARSEME 1.1 Shared Task corpora by selecting a subset of 155k tokens in the three referred languages, in which we annotated 1,526 collocations with the corresponding Lexical Functions according to the Meaning-Text Theory. Using the resulting gold-standard, we have carried out a comparison between frequency data and several well-known association measures, both symmetric and asymmetric. The results show that the combination of dependency triples with raw frequency information is as powerful as the best association measures in most syntactic patterns and languages. Furthermore, and despite the asymmetric behaviour of collocations, directional approaches perform worse than the symmetric ones in the extraction of these phraseological combinations.",
}
@inproceedings{kerz-etal-2019-l2,
    title = "{L}2 Processing Advantages of Multiword Sequences: Evidence from Eye-Tracking",
    author = "Kerz, Elma  and
      Heilmann, Arndt  and
      Neumann, Stella",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5108",
    doi = "10.18653/v1/W19-5108",
    pages = "60--69",
    abstract = "A substantial body of research has demonstrated that native speakers are sensitive to the frequencies of multiword sequences (MWS). Here, we ask whether and to what extent intermediate-advanced L2 speakers of English can also develop the sensitivity to the statistics of MWS. To this end, we aimed to replicate the MWS frequency effects found for adult native language speakers based on evidence from self-paced reading and sentence recall tasks in an ecologically more valid eye-tracking study. L2 speakers{'} sensitivity to MWS frequency was evaluated using generalized linear mixed-effects regression with separate models fitted for each of the four dependent measures. Mixed-effects modeling revealed significantly faster processing of sentences containing MWS compared to sentences containing equivalent control items across all eyetracking measures. Taken together, these findings suggest that, in line with emergentist approaches, MWS are important building blocks of language and that similar mechanisms underlie both native and non-native language processing.",
}
@inproceedings{laskova-etal-2019-modeling,
    title = "Modeling {MWE}s in {BTB}-{WN}",
    author = "Laskova, Laska  and
      Osenova, Petya  and
      Simov, Kiril  and
      Radev, Ivajlo  and
      Kancheva, Zara",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5109",
    doi = "10.18653/v1/W19-5109",
    pages = "70--78",
    abstract = "The paper presents the characteristics of the predominant types of MultiWord expressions (MWEs) in the BulTreeBank WordNet {--} BTB-WN. Their distribution in BTB-WN is discussed with respect to the overall hierarchical organization of the lexical resource. Also, a catena-based modeling is proposed for handling the issues of lexical semantics of MWEs.",
}
@inproceedings{savary-etal-2019-without,
    title = "Without lexicons, multiword expression identification will never fly: A position statement",
    author = "Savary, Agata  and
      Cordeiro, Silvio  and
      Ramisch, Carlos",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5110",
    doi = "10.18653/v1/W19-5110",
    pages = "79--91",
    abstract = "Because most multiword expressions (MWEs), especially verbal ones, are semantically non-compositional, their automatic identification in running text is a prerequisite for semantically-oriented downstream applications. However, recent developments, driven notably by the PARSEME shared task on automatic identification of verbal MWEs, show that this task is harder than related tasks, despite recent contributions both in multilingual corpus annotation and in computational models. In this paper, we analyse possible reasons for this state of affairs. They lie in the nature of the MWE phenomenon, as well as in its distributional properties. We also offer a comparative analysis of the state-of-the-art systems, which exhibit particularly strong sensitivity to unseen data. On this basis, we claim that, in order to make strong headway in MWE identification, the community should bend its mind into coupling identification of MWEs with their discovery, via syntactic MWE lexicons. Such lexicons need not necessarily achieve a linguistically complete modelling of MWEs{'} behavior, but they should provide minimal morphosyntactic information to cover some potential uses, so as to complement existing MWE-annotated corpora. We define requirements for such minimal NLP-oriented lexicon, and we propose a roadmap for the MWE community driven by these requirements.",
}
@inproceedings{shwartz-2019-systematic,
    title = "A Systematic Comparison of {E}nglish Noun Compound Representations",
    author = "Shwartz, Vered",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5111",
    doi = "10.18653/v1/W19-5111",
    pages = "92--103",
    abstract = "Building meaningful representations of noun compounds is not trivial since many of them scarcely appear in the corpus. To that end, composition functions approximate the distributional representation of a noun compound by combining its constituent distributional vectors. In the more general case, phrase embeddings have been trained by minimizing the distance between the vectors representing paraphrases. We compare various types of noun compound representations, including distributional, compositional, and paraphrase-based representations, through a series of tasks and analyses, and with an extensive number of underlying word embeddings. We find that indeed, in most cases, composition functions produce higher quality representations than distributional ones, and they improve with computational power. No single function performs best in all scenarios, suggesting that a joint training objective may produce improved representations.",
}
@inproceedings{strakatova-hinrichs-2019-semantic,
    title = "Semantic Modelling of Adjective-Noun Collocations Using {F}rame{N}et",
    author = "Strakatova, Yana  and
      Hinrichs, Erhard",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5112",
    doi = "10.18653/v1/W19-5112",
    pages = "104--113",
    abstract = "In this paper we argue that Frame Semantics (Fillmore, 1982) provides a good framework for semantic modelling of adjective-noun collocations. More specifically, the notion of a frame is rich enough to account for nouns from different semantic classes and to model semantic relations that hold between an adjective and a noun in terms of Frame Elements. We have substantiated these findings by considering a sample of adjective-noun collocations from German such as {``}enger Freund{''} {`}close friend{'} and {``}starker Regen{''} {`}heavy rain{'}. The data sample is taken from different semantic fields identified in the German wordnet GermaNet (Hamp and Feldweg, 1997; Henrich and Hinrichs, 2010). The study is based on the electronic dictionary DWDS (Klein and Geyken, 2010) and uses the collocation extraction tool Wortprofil (Geyken et al., 2009). The FrameNet modelling is based on the online resource available at http://framenet.icsi.berkeley.edu. Since FrameNets are available for a range of typologically different languages, it is feasible to extend the current case study to other languages.",
}
@inproceedings{waszczuk-etal-2019-neural,
    title = "A Neural Graph-based Approach to Verbal {MWE} Identification",
    author = "Waszczuk, Jakub  and
      Ehren, Rafael  and
      Stodden, Regina  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5113",
    doi = "10.18653/v1/W19-5113",
    pages = "114--124",
    abstract = "We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of-the-art (Al Saied et al., 2018).",
}
@inproceedings{hwang-hidey-2019-confirming,
    title = "Confirming the Non-compositionality of Idioms for Sentiment Analysis",
    author = "Hwang, Alyssa  and
      Hidey, Christopher",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5114",
    doi = "10.18653/v1/W19-5114",
    pages = "125--129",
    abstract = "An idiom is defined as a non-compositional multiword expression, one whose meaning cannot be deduced from the definitions of the component words. This definition does not explicitly define the compositionality of an idiom{'}s sentiment; this paper aims to determine whether the sentiment of the component words of an idiom is related to the sentiment of that idiom. We use the Dictionary of Affect in Language augmented by WordNet to give each idiom in the Sentiment Lexicon of IDiomatic Expressions (SLIDE) a component-wise sentiment score and compare it to the phrase-level sentiment label crowdsourced by the creators of SLIDE. We find that there is no discernible relation between these two measures of idiom sentiment. This supports the hypothesis that idioms are not compositional for sentiment along with semantics and motivates further work in handling idioms for sentiment analysis.",
}
@inproceedings{markantonatou-etal-2019-idion,
    title = "{IDION}: A database for {M}odern {G}reek multiword expressions",
    author = "Markantonatou, Stella  and
      Minos, Panagiotis  and
      Zakis, George  and
      Moutzouri, Vassiliki  and
      Chantou, Maria",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5115",
    doi = "10.18653/v1/W19-5115",
    pages = "130--134",
    abstract = "We report on the ongoing development of IDION, a web resource of richly documented multiword expressions (MWEs) of Modern Greek addressed to the human user and to NLP. IDION contains about 2000 verb MWEs (VMWEs) of which about 850 are fully documented as regards their syntactic flexibility, their semantics and the semantic relations with other VMWEs. Sets of synonymous MWEs are defined in a bottom-up manner revealing the conceptual organization of the MG VMWE domain.",
}
@inproceedings{mccrae-2019-identification,
    title = "Identification of Adjective-Noun Neologisms using Pretrained Language Models",
    author = "McCrae, John Philip",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5116",
    doi = "10.18653/v1/W19-5116",
    pages = "135--141",
    abstract = "Neologism detection is a key task in the constructing of lexical resources and has wider implications for NLP, however the identification of multiword neologisms has received little attention. In this paper, we show that we can effectively identify the distinction between compositional and non-compositional adjective-noun pairs by using pretrained language models and comparing this with individual word embeddings. Our results show that the use of these models significantly improves over baseline linguistic features, however the combination with linguistic features still further improves the results, suggesting the strength of a hybrid approach.",
}
@inproceedings{schmitt-constant-2019-neural,
    title = "Neural Lemmatization of Multiword Expressions",
    author = "Schmitt, Marine  and
      Constant, Mathieu",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5117",
    doi = "10.18653/v1/W19-5117",
    pages = "142--148",
    abstract = "This article focuses on the lemmatization of multiword expressions (MWEs). We propose a deep encoder-decoder architecture generating for every MWE word its corresponding part in the lemma, based on the internal context of the MWE. The encoder relies on recurrent networks based on (1) the character sequence of the individual words to capture their morphological properties, and (2) the word sequence of the MWE to capture lexical and syntactic properties. The decoder in charge of generating the corresponding part of the lemma for each word of the MWE is based on a classical character-level attention-based recurrent model. Our model is evaluated for Italian, French, Polish and Portuguese and shows good performances except for Polish.",
}
@inproceedings{sajatovic-etal-2019-evaluating,
    title = "Evaluating Automatic Term Extraction Methods on Individual Documents",
    author = "{\v{S}}ajatovi{\'c}, Antonio  and
      Buljan, Maja  and
      {\v{S}}najder, Jan  and
      Dalbelo Ba{\v{s}}i{\'c}, Bojana",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5118",
    doi = "10.18653/v1/W19-5118",
    pages = "149--154",
    abstract = "Automatic Term Extraction (ATE) extracts terminology from domain-specific corpora. ATE is used in many NLP tasks, including Computer Assisted Translation, where it is typically applied to individual documents rather than the entire corpus. While corpus-level ATE has been extensively evaluated, it is not obvious how the results transfer to document-level ATE. To fill this gap, we evaluate 16 state-of-the-art ATE methods on full-length documents from three different domains, on both corpus and document levels. Unlike existing studies, our evaluation is more realistic as we take into account all gold terms. We show that no single method is best in corpus-level ATE, but C-Value and KeyConceptRelatendess surpass others in document-level ATE.",
}
@inproceedings{taslimipoor-etal-2019-cross,
    title = "Cross-lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions",
    author = "Taslimipoor, Shiva  and
      Rohanian, Omid  and
      Ha, Le An",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5119",
    doi = "10.18653/v1/W19-5119",
    pages = "155--161",
    abstract = "Recent developments in deep learning have prompted a surge of interest in the application of multitask and transfer learning to NLP problems. In this study, we explore for the first time, the application of transfer learning (TRL) and multitask learning (MTL) to the identification of Multiword Expressions (MWEs). For MTL, we exploit the shared syntactic information between MWE and dependency parsing models to jointly train a single model on both tasks. We specifically predict two types of labels: MWE and dependency parse. Our neural MTL architecture utilises the supervision of dependency parsing in lower layers and predicts MWE tags in upper layers. In the TRL scenario, we overcome the scarcity of data by learning a model on a larger MWE dataset and transferring the knowledge to a resource-poor setting in another language. In both scenarios, the resulting models achieved higher performance compared to standard neural approaches.",
}
@inproceedings{walsh-etal-2019-ilfhocail,
    title = "{I}lfhocail: A Lexicon of {I}rish {MWE}s",
    author = "Walsh, Abigail  and
      Lynn, Teresa  and
      Foster, Jennifer",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5120",
    doi = "10.18653/v1/W19-5120",
    pages = "162--168",
    abstract = "This paper describes the categorisation of Irish MWEs, and the construction of the first version of a lexicon of Irish MWEs for NLP purposes (Ilfhocail, meaning {`}Multiwords{'}), collected from a number of resources. For the purposes of quality assurance, 530 entries of this lexicon were examined and manually annotated for POS information and MWE category.",
}
@inproceedings{zampieri-etal-2019-impact,
    title = "The Impact of Word Representations on Sequential Neural {MWE} Identification",
    author = "Zampieri, Nicolas  and
      Ramisch, Carlos  and
      Damnati, Geraldine",
    booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5121",
    doi = "10.18653/v1/W19-5121",
    pages = "169--175",
    abstract = "Recent initiatives such as the PARSEME shared task allowed the rapid development of MWE identification systems. Many of those are based on recent NLP advances, using neural sequence models that take continuous word representations as input. We study two related questions in neural MWE identification: (a) the use of lemmas and/or surface forms as input features, and (b) the use of word-based or character-based embeddings to represent them. Our experiments on Basque, French, and Polish show that character-based representations yield systematically better results than word-based ones. In some cases, character-based representations of surface forms can be used as a proxy for lemmas, depending on the morphological complexity of the language.",
}
@proceedings{ws-2019-bionlp,
    title = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5000",
}
@inproceedings{newman-griffis-etal-2019-classifying,
    title = "Classifying the reported ability in clinical mobility descriptions",
    author = "Newman-Griffis, Denis  and
      Zirikly, Ayah  and
      Divita, Guy  and
      Desmet, Bart",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5001",
    doi = "10.18653/v1/W19-5001",
    pages = "1--10",
    abstract = "Assessing how individuals perform different activities is key information for modeling health states of individuals and populations. Descriptions of activity performance in clinical free text are complex, including syntactic negation and similarities to textual entailment tasks. We explore a variety of methods for the novel task of classifying four types of assertions about activity performance: Able, Unable, Unclear, and None (no information). We find that ensembling an SVM trained with lexical features and a CNN achieves 77.9{\%} macro F1 score on our task, and yields nearly 80{\%} recall on the rare Unclear and Unable samples. Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling new modalities at test time. Our findings establish a strong baseline for this novel task, and identify intriguing areas for further research.",
}
@inproceedings{yuwono-etal-2019-learning,
    title = "Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes",
    author = "Yuwono, Steven Kester  and
      Ng, Hwee Tou  and
      Ngiam, Kee Yuan",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5002",
    doi = "10.18653/v1/W19-5002",
    pages = "11--19",
    abstract = "The objective of this work is to develop an automated diagnosis system that is able to predict the probability of appendicitis given a free-text emergency department (ED) note and additional structured information (e.g., lab test results). Our clinical corpus consists of about 180,000 ED notes based on ten years of patient visits to the Accident and Emergency (A{\&}E) Department of the National University Hospital (NUH), Singapore. We propose a novel neural network approach that learns to diagnose acute appendicitis based on doctors{'} free-text ED notes without any feature engineering. On a test set of 2,000 ED notes with equal number of appendicitis (positive) and non-appendicitis (negative) diagnosis and in which all the negative ED notes only consist of abdominal-related diagnosis, our model is able to achieve a promising F{\_}0.5-score of 0.895 while ED doctors achieve F{\_}0.5-score of 0.900. Visualization shows that our model is able to learn important features, signs, and symptoms of patients from unstructured free-text ED notes, which will help doctors to make better diagnosis.",
}
@inproceedings{soni-roberts-2019-paraphrase,
    title = "A Paraphrase Generation System for {EHR} Question Answering",
    author = "Soni, Sarvesh  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5003",
    doi = "10.18653/v1/W19-5003",
    pages = "20--29",
    abstract = "This paper proposes a dataset and method for automatically generating paraphrases for clinical questions relating to patient-specific information in electronic health records (EHRs). Crowdsourcing is used to collect 10,578 unique questions across 946 semantically distinct paraphrase clusters. This corpus is then used with a deep learning-based question paraphrasing method utilizing variational autoencoder and LSTM encoder/decoder. The ultimate use of such a method is to improve the performance of automatic question answering methods for EHRs.",
}
@inproceedings{chauhan-etal-2019-reflex,
    title = "{RE}flex: Flexible Framework for Relation Extraction in Multiple Domains",
    author = "Chauhan, Geeticka  and
      McDermott, Matthew B.A.  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5004",
    doi = "10.18653/v1/W19-5004",
    pages = "30--47",
    abstract = "Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques. In this work, we build a unifying framework for RE, applying this on three highly used datasets (from the general, biomedical and clinical domains) with the ability to be extendable to new datasets. By performing a systematic exploration of modeling, pre-processing and training methodologies, we find that choices of preprocessing are a large contributor performance and that omission of such information can further hinder fair comparison. Other insights from our exploration allow us to provide recommendations for future research in this area.",
}
@inproceedings{ormerod-etal-2019-analysing,
    title = "Analysing Representations of Memory Impairment in a Clinical Notes Classification Model",
    author = "Ormerod, Mark  and
      Mart{\'\i}nez-del-Rinc{\'o}n, Jes{\'u}s  and
      Robertson, Neil  and
      McGuinness, Bernadette  and
      Devereux, Barry",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5005",
    doi = "10.18653/v1/W19-5005",
    pages = "48--57",
    abstract = "Despite recent advances in the application of deep neural networks to various kinds of medical data, extracting information from unstructured textual sources remains a challenging task. The challenges of training and interpreting document classification models are amplified when dealing with small and highly technical datasets, as are common in the clinical domain. Using a dataset of de-identified clinical letters gathered at a memory clinic, we construct several recurrent neural network models for letter classification, and evaluate them on their ability to build meaningful representations of the documents and predict patients{'} diagnoses. Additionally, we probe sentence embedding models in order to build a human-interpretable representation of the neural network{'}s features, using a simple and intuitive technique based on perturbative approaches to sentence importance. In addition to showing which sentences in a document are most informative about the patient{'}s condition, this method reveals the types of sentences that lead the model to make incorrect diagnoses. Furthermore, we identify clusters of sentences in the embedding space that correlate strongly with importance scores for each clinical diagnosis class.",
}
@inproceedings{peng-etal-2019-transfer,
    title = "Transfer Learning in Biomedical Natural Language Processing: An Evaluation of {BERT} and {ELM}o on Ten Benchmarking Datasets",
    author = "Peng, Yifan  and
      Yan, Shankai  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5006",
    doi = "10.18653/v1/W19-5006",
    pages = "58--65",
    abstract = "Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten datasets that cover both biomedical and clinical texts with different dataset sizes and difficulties. We also evaluate several baselines based on BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results. We make the datasets, pre-trained models, and codes publicly available at https://github.com/ ncbi-nlp/BLUE{\_}Benchmark.",
}
@inproceedings{apostolova-etal-2019-combining,
    title = "Combining Structured and Free-text Electronic Medical Record Data for Real-time Clinical Decision Support",
    author = "Apostolova, Emilia  and
      Wang, Tony  and
      Tschampel, Tim  and
      Koutroulis, Ioannis  and
      Velez, Tom",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5007",
    doi = "10.18653/v1/W19-5007",
    pages = "66--70",
    abstract = "The goal of this work is to utilize Electronic Medical Record (EMR) data for real-time Clinical Decision Support (CDS). We present a deep learning approach to combining in real time available diagnosis codes (ICD codes) and free-text notes: Patient Context Vectors. Patient Context Vectors are created by averaging ICD code embeddings, and by predicting the same from free-text notes via a Convolutional Neural Network. The Patient Context Vectors were then simply appended to available structured data (vital signs and lab results) to build prediction models for a specific condition. Experiments on predicting ARDS, a rare and complex condition, demonstrate the utility of Patient Context Vectors as a means of summarizing the patient history and overall condition, and improve significantly the prediction model results.",
}
@inproceedings{mitrofan-etal-2019-monero,
    title = "{M}o{NER}o: a Biomedical Gold Standard Corpus for the {R}omanian Language",
    author = "Mitrofan, Maria  and
      Barbu Mititelu, Verginica  and
      Mitrofan, Grigorina",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5008",
    doi = "10.18653/v1/W19-5008",
    pages = "71--79",
    abstract = "In an era when large amounts of data are generated daily in various fields, the biomedical field among others, linguistic resources can be exploited for various tasks of Natural Language Processing. Moreover, increasing number of biomedical documents are available in languages other than English. To be able to extract information from natural language free text resources, methods and tools are needed for a variety of languages. This paper presents the creation of the MoNERo corpus, a gold standard biomedical corpus for Romanian, annotated with both part of speech tags and named entities. MoNERo comprises 154,825 morphologically annotated tokens and 23,188 entity annotations belonging to four entity semantic groups corresponding to UMLS Semantic Groups.",
}
@inproceedings{rajagopal-etal-2019-domain,
    title = "Domain Adaptation of {SRL} Systems for Biological Processes",
    author = "Rajagopal, Dheeraj  and
      Vyas, Nidhi  and
      Siddhant, Aditya  and
      Rayasam, Anirudha  and
      Tandon, Niket  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5009",
    doi = "10.18653/v1/W19-5009",
    pages = "80--87",
    abstract = "Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems. Current state-of-the-art methods are typically trained on large-scale datasets, but their performances do not directly transfer to low-resource domain-specific settings. In this paper, we propose two approaches for domain adaptation in the biological domain that involves pre-training LSTM-CRF based on existing large-scale datasets and adapting it for a low-resource corpus of biological processes. Our first approach defines a mapping between the source labels and the target labels, and the other approach modifies the final CRF layer in sequence-labeling neural network architecture. We perform our experiments on ProcessBank dataset which contains less than 200 paragraphs on biological processes. We improve over the previous state-of-the-art system on this dataset by 21 F1 points. We also show that, by incorporating event-event relationship in ProcessBank, we are able to achieve an additional 2.6 F1 gain, giving us possible insights into how to improve SRL systems for biological process using richer annotations.",
}
@inproceedings{jin-etal-2019-deep,
    title = "Deep Contextualized Biomedical Abbreviation Expansion",
    author = "Jin, Qiao  and
      Liu, Jinling  and
      Lu, Xinghua",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5010",
    doi = "10.18653/v1/W19-5010",
    pages = "88--96",
    abstract = "Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Biomedical Abbreviation Expansion (DECBAE) model. DECBAE automatically collects substantial and relatively clean annotated contexts for 950 ambiguous abbreviations from PubMed abstracts using a simple heuristic. Then it utilizes BioELMo to extract the contextualized features of words, and feed those features to abbreviation-specific bidirectional LSTMs, where the hidden states of the ambiguous abbreviations are used to assign the exact definitions. Our DECBAE model outperforms other baselines by large margins, achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It also surpasses human performance for expanding a sample abbreviation, and remains robust in imbalanced, low-resources and clinical settings.",
}
@inproceedings{pylieva-etal-2019-rnn,
    title = "{RNN} Embeddings for Identifying Difficult to Understand Medical Words",
    author = "Pylieva, Hanna  and
      Chernodub, Artem  and
      Grabar, Natalia  and
      Hamon, Thierry",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5011",
    doi = "10.18653/v1/W19-5011",
    pages = "97--104",
    abstract = "Patients and their families often require a better understanding of medical information provided by doctors. We currently address this issue by improving the identification of difficult to understand medical words. We introduce novel embeddings received from RNN - FrnnMUTE (French RNN Medical Understandability Text Embeddings) which allow to reach up to 87.0 F1 score in identification of difficult words. We also note that adding pre-trained FastText word embeddings to the feature set substantially improves the performance of the model which classifies words according to their difficulty. We study the generalizability of different models through three cross-validation scenarios which allow testing classifiers in real-world conditions: understanding of medical words by new users, and classification of new unseen words by the automatic models. The RNN - FrnnMUTE embeddings and the categorization code are being made available for the research.",
}
@inproceedings{norman-etal-2019-distantly,
    title = "A distantly supervised dataset for automated data extraction from diagnostic studies",
    author = "Norman, Christopher  and
      Leeflang, Mariska  and
      Spijker, Ren{\'e}  and
      Kanoulas, Evangelos  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5012",
    doi = "10.18653/v1/W19-5012",
    pages = "105--114",
    abstract = "Systematic reviews are important in evidence based medicine, but are expensive to produce. Automating or semi-automating the data extraction of index test, target condition, and reference standard from articles has the potential to decrease the cost of conducting systematic reviews of diagnostic test accuracy, but relevant training data is not available. We create a distantly supervised dataset of approximately 90,000 sentences, and let two experts manually annotate a small subset of around 1,000 sentences for evaluation. We evaluate the performance of BioBERT and logistic regression for ranking the sentences, and compare the performance for distant and direct supervision. Our results suggest that distant supervision can work as well as, or better than direct supervision on this problem, and that distantly trained models can perform as well as, or better than human annotators.",
}
@inproceedings{bordea-etal-2019-query,
    title = "Query selection methods for automated corpora construction with a use case in food-drug interactions",
    author = "Bordea, Georgeta  and
      Randriatsitohaina, Tsanta  and
      Mougin, Fleur  and
      Grabar, Natalia  and
      Hamon, Thierry",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5013",
    doi = "10.18653/v1/W19-5013",
    pages = "115--124",
    abstract = "In this paper, we address the problem of automatically constructing a relevant corpus of scientific articles about food-drug interactions. There is a growing number of scientific publications that describe food-drug interactions but currently building a high-coverage corpus that can be used for information extraction purposes is not trivial. We investigate several methods for automating the query selection process using an expert-curated corpus of food-drug interactions. Our experiments show that index term features along with a decision tree classifier are the best approach for this task and that feature selection approaches and in particular gain ratio outperform frequency-based methods for query selection.",
}
@inproceedings{chiu-etal-2019-enhancing,
    title = "Enhancing biomedical word embeddings by retrofitting to verb clusters",
    author = "Chiu, Billy  and
      Baker, Simon  and
      Palmer, Martha  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5014",
    doi = "10.18653/v1/W19-5014",
    pages = "125--134",
    abstract = "Verbs play a fundamental role in many biomed-ical tasks and applications such as relation and event extraction. We hypothesize that performance on many downstream tasks can be improved by aligning the input pretrained embeddings according to semantic verb classes.In this work, we show that by using semantic clusters for verbs, a large lexicon of verbclasses derived from biomedical literature, weare able to improve the performance of common pretrained embeddings in downstream tasks by retrofitting them to verb classes. We present a simple and computationally efficient approach using a widely-available {``}off-the-shelf{''} retrofitting algorithm to align pretrained embeddings according to semantic verb clusters. We achieve state-of-the-art results on text classification and relation extraction tasks.",
}
@inproceedings{joshi-etal-2019-comparison,
    title = "A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics",
    author = "Joshi, Aditya  and
      Karimi, Sarvnaz  and
      Sparks, Ross  and
      Paris, Cecile  and
      MacIntyre, C Raina",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5015",
    doi = "10.18653/v1/W19-5015",
    pages = "135--141",
    abstract = "Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as context-based sentence vectors. We compare the two kinds of representations (word versus context) for three classification problems: influenza infection classification, drug usage classification and personal health mention classification. For statistical classifiers trained for each of these problems, context-based representations based on ELMo, Universal Sentence Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe and the two adapted using the MESH ontology. There is an improvement of 2-4{\%} in the accuracy when these context-based representations are used instead of word-based representations.",
}
@inproceedings{fauqueur-etal-2019-constructing,
    title = "Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns",
    author = "Fauqueur, Julien  and
      Thillaisundaram, Ashok  and
      Togia, Theodosia",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5016",
    doi = "10.18653/v1/W19-5016",
    pages = "142--151",
    abstract = "Knowledge base construction is crucial for summarising, understanding and inferring relationships between biomedical entities. However, for many practical applications such as drug discovery, the scarcity of relevant facts (e.g. gene X is therapeutic target for disease Y) severely limits a domain expert{'}s ability to create a usable knowledge base, either directly or by training a relation extraction model. In this paper, we present a simple and effective method of extracting new facts with a pre-specified binary relationship type from the biomedical literature, without requiring any training data or hand-crafted rules. Our system discovers, ranks and presents the most salient patterns to domain experts in an interpretable form. By marking patterns as compatible with the desired relationship type, experts indirectly batch-annotate candidate pairs whose relationship is expressed with such patterns in the literature. Even with a complete absence of seed data, experts are able to discover thousands of high-quality pairs with the desired relationship within minutes. When a small number of relevant pairs do exist - even when their relationship is more general (e.g. gene X is biologically associated with disease Y) than the relationship of interest - our system leverages them in order to i) learn a better ranking of the patterns to be annotated or ii) generate weakly labelled pairs in a fully automated manner. We evaluate our method both intrinsically and via a downstream knowledge base completion task, and show that it is an effective way of constructing knowledge bases when few or no relevant facts are already available.",
}
@inproceedings{campillos-llanos-2019-first,
    title = "First Steps towards Building a Medical Lexicon for {S}panish with Linguistic and Semantic Information",
    author = "Campillos-Llanos, Leonardo",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5017",
    doi = "10.18653/v1/W19-5017",
    pages = "152--164",
    abstract = "We report the work-in-progress of collecting MedLexSp, an unified medical lexicon for the Spanish language, featuring terms and inflected word forms mapped to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs), semantic types and groups. First, we leveraged a list of term lemmas and forms from a previous project, and mapped them to UMLS terms and CUIs. To enrich the lexicon, we used both domain-corpora (e.g. Summaries of Product Characteristics and MedlinePlus) and natural language processing techniques such as string distance methods or generation of syntactic variants of multi-word terms. We also added term variants by mapping their CUIs to missing items available in the Spanish versions of standard thesauri (e.g. Medical Subject Headings and World Health Organization Adverse Drug Reactions terminology). We enhanced the vocabulary coverage by gathering missing terms from resources such as the Anatomical Therapeutical Classification, the National Cancer Institute (NCI) Dictionary of Cancer Terms, OrphaData, or the Nomencl{\'a}tor de Prescripci{\'o}n for drug names. Part-of-Speech information is being included in the lexicon, and the current version amounts up to 76 454 lemmas and 203 043 inflected forms (including conjugated verbs, number and gender variants), corresponding to 30 647 UMLS CUIs. MedLexSp is distributed freely for research purposes.",
}
@inproceedings{wang-mercer-2019-incorporating,
    title = "Incorporating Figure Captions and Descriptive Text in {M}e{SH} Term Indexing",
    author = "Wang, Xindi  and
      Mercer, Robert E.",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5018",
    doi = "10.18653/v1/W19-5018",
    pages = "165--175",
    abstract = "The goal of text classification is to automatically assign categories to documents. Deep learning automatically learns effective features from data instead of adopting human-designed features. In this paper, we focus specifically on biomedical document classification using a deep learning approach. We present a novel multichannel TextCNN model for MeSH term indexing. Beyond the normal use of the text from the abstract and title for model training, we also consider figure and table captions, as well as paragraphs associated with the figures and tables. We demonstrate that these latter text sources are important feature sources for our method. A new dataset consisting of these text segments curated from 257,590 full text articles together with the articles{'} MEDLINE/PubMed MeSH terms is publicly available.",
}
@inproceedings{khachatrian-etal-2019-biorelex,
    title = "{B}io{R}el{E}x 1.0: Biological Relation Extraction Benchmark",
    author = "Khachatrian, Hrant  and
      Nersisyan, Lilit  and
      Hambardzumyan, Karen  and
      Galstyan, Tigran  and
      Hakobyan, Anna  and
      Arakelyan, Arsen  and
      Rzhetsky, Andrey  and
      Galstyan, Aram",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5019",
    doi = "10.18653/v1/W19-5019",
    pages = "176--190",
    abstract = "Automatic extraction of relations and interactions between biological entities from scientific literature remains an extremely challenging problem in biomedical information extraction and natural language processing in general. One of the reasons for slow progress is the relative scarcity of standardized and publicly available benchmarks. In this paper we introduce BioRelEx, a new dataset of fully annotated sentences from biomedical literature that capture \textit{binding} interactions between proteins and/or biomolecules. To foster reproducible research on the interaction extraction task, we define a precise and transparent evaluation process, tools for error analysis and significance tests. Finally, we conduct extensive experiments to evaluate several baselines, including SciIE, a recently introduced neural multi-task architecture that has demonstrated state-of-the-art performance on several tasks.",
}
@inproceedings{goodrum-etal-2019-extraction,
    title = "Extraction of Lactation Frames from Drug Labels and {L}act{M}ed",
    author = "Goodrum, Heath  and
      Gudala, Meghana  and
      Misra, Ankita  and
      Roberts, Kirk",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5020",
    doi = "10.18653/v1/W19-5020",
    pages = "191--200",
    abstract = "This paper describes a natural language processing (NLP) approach to extracting lactation-specific drug information from two sources: FDA-mandated drug labels and the NLM Drugs and Lactation Database (LactMed). A frame semantic approach is utilized, and the paper describes the selected frames, their annotation on a set of 900 sections from drug labels and LactMed articles, and the NLP system to extract such frame instances automatically. The ultimate goal of the project is to use such a system to identify discrepancies in lactation-related drug information between these resources.",
}
@inproceedings{viani-etal-2019-annotating,
    title = "Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions",
    author = "Viani, Natalia  and
      Tissot, Hegler  and
      Bernardino, Ariane  and
      Velupillai, Sumithra",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5021",
    doi = "10.18653/v1/W19-5021",
    pages = "201--210",
    abstract = "To automatically analyse complex trajectory information enclosed in clinical text (e.g. timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time. In the clinical domain, few temporally annotated corpora are currently available. Moreover, underlying annotation schemas - which mainly rely on the TimeML standard - are not necessarily easily applicable for applications such as patient timeline reconstruction. In this work, we investigated how temporal information is documented in clinical text by annotating a corpus of medical reports with time expressions (TIMEXes), based on TimeML. The developed corpus is available to the NLP community. Starting from our annotations, we analysed the suitability of the TimeML TIMEX schema for capturing timeline information, identifying challenges and possible solutions. As a result, we propose a novel annotation schema that could be useful for timeline reconstruction: CALendar EXpression (CALEX).",
}
@inproceedings{gron-etal-2019-leveraging,
    title = "Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms",
    author = {Gr{\"o}n, Leonie  and
      Bertels, Ann  and
      Heylen, Kris},
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5022",
    doi = "10.18653/v1/W19-5022",
    pages = "211--216",
    abstract = "The automatic processing of clinical documents, such as Electronic Health Records (EHRs), could benefit substantially from the enrichment of medical terminologies with terms encountered in clinical practice. To integrate such terms into existing knowledge sources, they must be linked to corresponding concepts. We present a method for the semantic categorization of clinical terms based on their surface form. We find that features based on sublanguage properties can provide valuable cues for the classification of term variants.",
}
@inproceedings{mezaoui-etal-2019-enhancing,
    title = "Enhancing {PIO} Element Detection in Medical Text Using Contextualized Embedding",
    author = "Mezaoui, Hichem  and
      Gunasekara, Isuru  and
      Gontcharov, Aleksandr",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5023",
    doi = "10.18653/v1/W19-5023",
    pages = "217--222",
    abstract = "In this paper, we presented an improved methodology to extract PIO elements, from abstracts of medical papers, that reduces ambiguity. The proposed technique was used to build a dataset of PIO elements that we call PICONET. We further proposed a model of PIO elements classification using state of the art BERT embedding. In addition, we investigated a contextualized embedding, BioBERT, trained on medical corpora. It has been found that using the BioBERT embedding improved the classification accuracy, outperforming the BERT-based model. This result reinforces the idea of the importance of embedding contextualization in subsequent classification tasks in this specific context.Furthermore, to enhance the accuracy of the model, we have investigated an ensemble method based on the LGBM algorithm. We trained the LGBM model, with the above models as base learners, to learn a linear combination of the predicted probabilities for the 3 classes with the TF-IDF score and the QIEF that optimizes the classification. The results indicate that these text features were good features to consider in order to boost the deeply contextualized classification model. We compared the performance of the classifier when using the features with one of the base learners and the case where we combine the base learners along with the features. We obtained the highest score in terms of AUC when we combine the base learners.The present work resulted in the creation of a PIO element dataset, PICONET, and a classification tool. These constitute and important component of our system of automatic mining of medical abstracts. We intend to extend the dataset to full medical articles. The model will be modified to take into account the higher complexity of full text data and more efficient features for model boosting will be investigated.",
}
@inproceedings{lopes-etal-2019-contributions,
    title = "Contributions to Clinical Named Entity Recognition in {P}ortuguese",
    author = "Lopes, F{\'a}bio  and
      Teixeira, C{\'e}sar  and
      Gon{\c{c}}alo Oliveira, Hugo",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5024",
    doi = "10.18653/v1/W19-5024",
    pages = "223--233",
    abstract = "Having in mind that different languages might present different challenges, this paper presents the following contributions to the area of Information Extraction from clinical text, targeting the Portuguese language: a collection of 281 clinical texts in this language, with manually-annotated named entities; word embeddings trained in a larger collection of similar texts; results of using BiLSTM-CRF neural networks for named entity recognition on the annotated collection, including a comparison of using in-domain or out-of-domain word embeddings in this task. Although learned with much less data, performance is higher when using in-domain embeddings. When tested in 20 independent clinical texts, this model achieved better results than a model using larger out-of-domain embeddings.",
}
@inproceedings{yan-etal-2019-character,
    title = "Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?",
    author = "Yan, Zhaodong  and
      Jeblee, Serena  and
      Hirst, Graeme",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5025",
    doi = "10.18653/v1/W19-5025",
    pages = "234--239",
    abstract = "We present two models for combining word and character embeddings for cause-of-death classification of verbal autopsy reports using the text of the narratives. We find that for smaller datasets (500 to 1000 records), adding character information to the model improves classification, making character-based CNNs a promising method for automated verbal autopsy coding.",
}
@inproceedings{wang-etal-2019-artificial,
    title = "Is artificial data useful for biomedical Natural Language Processing algorithms?",
    author = "Wang, Zixu  and
      Ive, Julia  and
      Velupillai, Sumithra  and
      Specia, Lucia",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5026",
    doi = "10.18653/v1/W19-5026",
    pages = "240--249",
    abstract = "A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.",
}
@inproceedings{tian-etal-2019-chimed,
    title = "{C}hi{M}ed: A {C}hinese Medical Corpus for Question Answering",
    author = "Tian, Yuanhe  and
      Ma, Weicheng  and
      Xia, Fei  and
      Song, Yan",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5027",
    doi = "10.18653/v1/W19-5027",
    pages = "250--260",
    abstract = "Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed; second we annotate a small fraction of the corpus to check the quality of the answers; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task. Several benchmark models are applied to the datasets, producing good results for both tasks.",
}
@inproceedings{wiegreffe-etal-2019-clinical,
    title = "Clinical Concept Extraction for Document-Level Coding",
    author = "Wiegreffe, Sarah  and
      Choi, Edward  and
      Yan, Sherry  and
      Sun, Jimeng  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5028",
    doi = "10.18653/v1/W19-5028",
    pages = "261--272",
    abstract = "The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been information extraction: linking spans of text to concepts in a detailed domain ontology. However, recent work has demonstrated the potential of supervised machine learning to extract document-level codes directly from the raw text of clinical notes. We propose to bridge the gap between the two approaches with two novel syntheses: (1) treating extracted concepts as features, which are used to supplement or replace the text of the note; (2) treating extracted concepts as labels, which are used to learn a better representation of the text. Unfortunately, the resulting concepts do not yield performance gains on the document-level clinical coding task. We explore possible explanations and future research directions.",
}
@inproceedings{grouin-etal-2019-clinical,
    title = "Clinical Case Reports for {NLP}",
    author = "Grouin, Cyril  and
      Grabar, Natalia  and
      Claveau, Vincent  and
      Hamon, Thierry",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5029",
    doi = "10.18653/v1/W19-5029",
    pages = "273--282",
    abstract = "Textual data are useful for accessing expert information. Yet, since the texts are representative of distinct language uses, it is necessary to build specific corpora in order to be able to design suitable NLP tools. In some domains, such as medical domain, it may be complicated to access the representative textual data and their semantic annotations, while there exists a real need for providing efficient tools and methods. Our paper presents a corpus of clinical cases written in French, and their semantic annotations. Thus, we manually annotated a set of 717 files into four general categories (age, gender, outcome, and origin) for a total number of 2,835 annotations. The values of age, gender, and outcome are normalized. A subset with 70 files has been additionally manually annotated into 27 categories for a total number of 5,198 annotations.",
}
@inproceedings{liu-etal-2019-two,
    title = "Two-stage Federated Phenotyping and Patient Representation Learning",
    author = "Liu, Dianbo  and
      Dligach, Dmitriy  and
      Miller, Timothy",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5030",
    doi = "10.18653/v1/W19-5030",
    pages = "283--291",
    abstract = "A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent years for automatic information extraction from medical texts. However, algorithms trained on data from a single healthcare provider are not generalizable and error-prone due to the heterogeneity and uniqueness of medical documents. We develop a two-stage federated natural language processing method that enables utilization of clinical notes from different hospitals or clinics without moving the data, and demonstrate its performance using obesity and comorbities phenotyping as medical task. This approach not only improves the quality of a specific clinical task but also facilitates knowledge progression in the whole healthcare system, which is an essential part of learning health system. To the best of our knowledge, this is the first application of federated machine learning in clinical NLP.",
}
@inproceedings{kyriakakis-etal-2019-transfer,
    title = "Transfer Learning for Causal Sentence Detection",
    author = "Kyriakakis, Manolis  and
      Androutsopoulos, Ion  and
      Saudabayev, Artur  and
      Gin{\'e}s i Ametll{\'e}, Joan",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5031",
    doi = "10.18653/v1/W19-5031",
    pages = "292--297",
    abstract = "We consider the task of detecting sentences that express causality, as a step towards mining causal relations from texts. To bypass the scarcity of causal instances in relation extraction datasets, we exploit transfer learning, namely ELMO and BERT, using a bidirectional GRU with self-attention ( BIGRUATT ) as a baseline. We experiment with both generic public relation extraction datasets and a new biomedical causal sentence detection dataset, a subset of which we make publicly available. We find that transfer learning helps only in very small datasets. With larger datasets, BIGRUATT reaches a performance plateau, then larger datasets and transfer learning do not help.",
}
@inproceedings{kotitsas-etal-2019-embedding,
    title = "Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors",
    author = "Kotitsas, Sotiris  and
      Pappas, Dimitris  and
      Androutsopoulos, Ion  and
      McDonald, Ryan  and
      Apidianaki, Marianna",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5032",
    doi = "10.18653/v1/W19-5032",
    pages = "298--308",
    abstract = "Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.",
}
@inproceedings{koptient-etal-2019-simplification,
    title = "Simplification-induced transformations: typology and some characteristics",
    author = {Koptient, Ana{\"\i}s  and
      Cardon, R{\'e}mi  and
      Grabar, Natalia},
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5033",
    doi = "10.18653/v1/W19-5033",
    pages = "309--318",
    abstract = "The purpose of automatic text simplification is to transform technical or difficult to understand texts into a more friendly version. The semantics must be preserved during this transformation. Automatic text simplification can be done at different levels (lexical, syntactic, semantic, stylistic...) and relies on the corresponding knowledge and resources (lexicon, rules...). Our objective is to propose methods and material for the creation of transformation rules from a small set of parallel sentences differentiated by their technicity. We also propose a typology of transformations and quantify them. We work with French-language data related to the medical domain, although we assume that the method can be exploited on texts in any language and from any domain.",
}
@inproceedings{neumann-etal-2019-scispacy,
    title = "{S}cispa{C}y: Fast and Robust Models for Biomedical Natural Language Processing",
    author = "Neumann, Mark  and
      King, Daniel  and
      Beltagy, Iz  and
      Ammar, Waleed",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5034",
    doi = "10.18653/v1/W19-5034",
    pages = "319--327",
    abstract = "Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new Python library and models for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/scispacy/.",
}
@inproceedings{zhai-etal-2019-improving,
    title = "Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings",
    author = "Zhai, Zenan  and
      Nguyen, Dat Quoc  and
      Akhondi, Saber  and
      Thorne, Camilo  and
      Druckenbrodt, Christian  and
      Cohn, Trevor  and
      Gregory, Michelle  and
      Verspoor, Karin",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5035",
    doi = "10.18653/v1/W19-5035",
    pages = "328--338",
    abstract = "Chemical patents are an important resource for chemical information. However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in part to their structural and linguistic complexity. In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word representations and contextualized ELMo word representations for chemical patents. We compare word embeddings pre-trained on biomedical and chemical patent corpora. The effect of tokenizers optimized for the chemical domain on NER performance in chemical patents is also explored. The results on two patent corpora show that contextualized word representations generated from ELMo substantially improve chemical NER performance w.r.t. the current state-of-the-art. We also show that domain-specific resources such as word embeddings trained on chemical patents and chemical-specific tokenizers, have a positive impact on NER performance.",
}
@inproceedings{alhuzali-ananiadou-2019-improving,
    title = "Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning",
    author = "Alhuzali, Hassan  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5036",
    doi = "10.18653/v1/W19-5036",
    pages = "339--347",
    abstract = "The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs). ADR classification helps to identify negative effects of drugs, which can guide health professionals and pharmaceutical companies in making medications safer and advocating patients{'} safety. Based on the observation that in social media, negative sentiment is frequently expressed towards ADRs, this study presents a neural model that combines sentiment analysis with transfer learning techniques to improve ADR detection in social media postings. Our system is firstly trained to classify sentiment in tweets concerning current affairs, using the SemEval17-task4A corpus. We then apply transfer learning to adapt the model to the task of detecting ADRs in social media postings. We show that, in combination with rich representations of words and their contexts, transfer learning is beneficial, especially given the large degree of vocabulary overlap between the current affairs posts in the SemEval17-task4A corpus and posts about ADRs. We compare our results with previous approaches, and show that our model can outperform them by up to 3{\%} F-score.",
}
@inproceedings{vashisth-etal-2019-exploring,
    title = "Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations",
    author = "Vashisth, Gaurav  and
      Voigt-Antons, Jan-Niklas  and
      Mikhailov, Michael  and
      Roller, Roland",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5037",
    doi = "10.18653/v1/W19-5037",
    pages = "348--358",
    abstract = "In research best practices can change over time as new discoveries are made and novel methods are implemented. Scientific publications reporting about the latest facts and current state-of-the-art can be possibly outdated after some years or even proved to be false. A publication usually sheds light only on the knowledge of the period it has been published. Thus, the aspect of time can play an essential role in the reliability of the presented information. In Natural Language Processing many methods focus on information extraction from text, such as detecting entities and their relationship to each other. Those methods mostly focus on the facts presented in the text itself and not on the aspects of knowledge which changes over time. This work instead examines the evolution in biomedical knowledge over time using scientific literature in terms of diachronic change. Mainly the usage of temporal and distributional concept representations are explored and evaluated by a proof-of-concept.",
}
@inproceedings{koroleva-paroubek-2019-extracting,
    title = "Extracting relations between outcomes and significance levels in Randomized Controlled Trials ({RCT}s) publications",
    author = "Koroleva, Anna  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5038",
    doi = "10.18653/v1/W19-5038",
    pages = "359--369",
    abstract = "Randomized controlled trials assess the effects of an experimental intervention by comparing it to a control intervention with regard to some variables - trial outcomes. Statistical hypothesis testing is used to test if the experimental intervention is superior to the control. Statistical significance is typically reported for the measured outcomes and is an important characteristic of the results. We propose a machine learning approach to automatically extract reported outcomes, significance levels and the relation between them. We annotated a corpus of 663 sentences with 2,552 outcome - significance level relations (1,372 positive and 1,180 negative relations). We compared several classifiers, using a manually crafted feature set, and a number of deep learning models. The best performance (F-measure of 94{\%}) was shown by the BioBERT fine-tuned model.",
}
@inproceedings{ben-abacha-etal-2019-overview,
    title = "Overview of the {MEDIQA} 2019 Shared Task on Textual Inference, Question Entailment and Question Answering",
    author = "Ben Abacha, Asma  and
      Shivade, Chaitanya  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5039",
    doi = "10.18653/v1/W19-5039",
    pages = "370--379",
    abstract = "This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems. MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain. 72 teams participated in the challenge, achieving an accuracy of 98{\%} in the NLI task, 74.9{\%} in the RQE task, and 78.3{\%} in the QA task. In this paper, we describe the tasks, the datasets, and the participants{'} approaches and results. We hope that this shared task will attract further research efforts in textual inference, question entailment, and question answering in the medical domain.",
}
@inproceedings{zhu-etal-2019-panlp,
    title = "{PANLP} at {MEDIQA} 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation",
    author = "Zhu, Wei  and
      Zhou, Xiaofeng  and
      Wang, Keqiang  and
      Luo, Xun  and
      Li, Xiepeng  and
      Ni, Yuan  and
      Xie, Guotong",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5040",
    doi = "10.18653/v1/W19-5040",
    pages = "380--388",
    abstract = "This paper describes the models designated for the MEDIQA 2019 shared tasks by the team PANLP. We take advantages of the recent advances in pre-trained bidirectional transformer language models such as BERT (Devlin et al., 2018) and MT-DNN (Liu et al., 2019b). We find that pre-trained language models can significantly outperform traditional deep learning models. Transfer learning from the NLI task to the RQE task is also experimented, which proves to be useful in improving the results of fine-tuning MT-DNN large. A knowledge distillation process is implemented, to distill the knowledge contained in a set of models and transfer it into an single model, whose performance turns out to be comparable with that obtained by the ensemble of that set of models. Finally, for test submissions, model ensemble and a re-ranking process are implemented to boost the performances. Our models participated in all three tasks and ranked the 1st place for the RQE task, and the 2nd place for the NLI task, and also the 2nd place for the QA task.",
}
@inproceedings{pugaliya-etal-2019-pentagon,
    title = "Pentagon at {MEDIQA} 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment",
    author = "Pugaliya, Hemant  and
      Saxena, Karan  and
      Garg, Shefali  and
      Shalini, Sheetal  and
      Gupta, Prashant  and
      Nyberg, Eric  and
      Mitamura, Teruko",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5041",
    doi = "10.18653/v1/W19-5041",
    pages = "389--398",
    abstract = "Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large margin. More recently, pre-trained models from large related datasets have been able to perform well on many downstream tasks by just fine-tuning on domain-specific datasets (similar to transfer learning). However, using powerful models on non-trivial tasks, such as ranking and large document classification, still remains a challenge due to input size limitations of parallel architecture and extremely small datasets (insufficient for fine-tuning). In this work, we introduce an end-to-end system, trained in a multi-task setting, to filter and re-rank answers in the medical domain. We use task-specific pre-trained models as deep feature extractors. Our model achieves the highest Spearman{'}s Rho and Mean Reciprocal Rank of 0.338 and 0.9622 respectively, on the ACL-BioNLP workshop MediQA Question Answering shared-task.",
}
@inproceedings{xu-etal-2019-doubletransfer,
    title = "{D}ouble{T}ransfer at {MEDIQA} 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain",
    author = "Xu, Yichong  and
      Liu, Xiaodong  and
      Li, Chunyuan  and
      Poon, Hoifung  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5042",
    doi = "10.18653/v1/W19-5042",
    pages = "399--405",
    abstract = "This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task.",
}
@inproceedings{nam-etal-2019-surf,
    title = "Surf at {MEDIQA} 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model",
    author = "Nam, Jiin  and
      Yoon, Seunghyun  and
      Jung, Kyomin",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5043",
    doi = "10.18653/v1/W19-5043",
    pages = "406--414",
    abstract = "While deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain. The lack of large datasets and the pervasive use of domain-specific language (i.e. abbreviations and acronyms) in the clinical domain causes slower progress in NLP tasks than that of the general NLP tasks. To fill this gap, we employ word/subword-level based models that adopt large-scale data-driven methods such as pre-trained language models and transfer learning in analyzing text for the clinical domain. Empirical results demonstrate the superiority of the proposed methods by achieving 90.6{\%} accuracy in medical domain natural language inference task. Furthermore, we inspect the independent strengths of the proposed approaches in quantitative and qualitative manners. This analysis will help researchers to select necessary components in building models for the medical domain.",
}
@inproceedings{wu-etal-2019-wtmed,
    title = "{WTMED} at {MEDIQA} 2019: A Hybrid Approach to Biomedical Natural Language Inference",
    author = "Wu, Zhaofeng  and
      Song, Yan  and
      Huang, Sicong  and
      Tian, Yuanhe  and
      Xia, Fei",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5044",
    doi = "10.18653/v1/W19-5044",
    pages = "415--426",
    abstract = "Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings. In this paper, we propose a hybrid approach to biomedical NLI where different types of information are exploited for this task. Our base model includes a pre-trained text encoder as the core component, and a syntax encoder and a feature encoder to capture syntactic and domain-specific information. Then we combine the output of different base models to form more powerful ensemble models. Finally, we design two conflict resolution strategies when the test data contain multiple (premise, hypothesis) pairs with the same premise. We train our models on the MedNLI dataset, yielding the best performance on the test set of the MEDIQA 2019 Task 1.",
}
@inproceedings{cengiz-etal-2019-ku,
    title = "{KU}{\_}ai at {MEDIQA} 2019: Domain-specific Pre-training and Transfer Learning for Medical {NLI}",
    author = "Cengiz, Cemil  and
      Sert, Ula{\c{s}}  and
      Yuret, Deniz",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5045",
    doi = "10.18653/v1/W19-5045",
    pages = "427--436",
    abstract = "In this paper, we describe our system and results submitted for the Natural Language Inference (NLI) track of the MEDIQA 2019 Shared Task. As KU{\_}ai team, we used BERT as our baseline model and pre-processed the MedNLI dataset to mitigate the negative impact of de-identification artifacts. Moreover, we investigated different pre-training and transfer learning approaches to improve the performance. We show that pre-training the language model on rich biomedical corpora has a significant effect in teaching the model domain-specific language. In addition, training the model on large NLI datasets such as MultiNLI and SNLI helps in learning task-specific reasoning. Finally, we ensembled our highest-performing models, and achieved 84.7{\%} accuracy on the unseen test dataset and ranked 10th out of 17 teams in the official results.",
}
@inproceedings{zhou-etal-2019-dut,
    title = "{DUT}-{NLP} at {MEDIQA} 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering",
    author = "Zhou, Huiwei  and
      Li, Xuefei  and
      Yao, Weihong  and
      Lang, Chengkun  and
      Ning, Shixian",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5046",
    doi = "10.18653/v1/W19-5046",
    pages = "437--445",
    abstract = "In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks. AMTN utilizes a pre-trained BioBERT model and an Interactive Transformer to learn the shared semantic representations across different task through parameter sharing mechanism. Meanwhile, an adversarial training strategy is introduced to separate the private features of each task from the shared representations. Experiments on BioNLP 2019 RQE and QA Shared Task datasets show that our model benefits from the shared representations of both tasks provided by multi-task learning and adversarial training, and obtains significant improvements upon the single-task models.",
}
@inproceedings{zhou-etal-2019-dut-bim,
    title = "{DUT}-{BIM} at {MEDIQA} 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering",
    author = "Zhou, Huiwei  and
      Lei, Bizun  and
      Liu, Zhe  and
      Liu, Zhuang",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5047",
    doi = "10.18653/v1/W19-5047",
    pages = "446--452",
    abstract = "In medical domain, given a medical question, it is difficult to manually select the most relevant information from a large number of search results. BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining technology to automatically judge whether a search result is an answer to the medical question. The main challenge of QA task is how to mine the semantic relation between question and answer. We propose BioBERT Transformer model to tackle this challenge, which applies Transformers to extract semantic relation between different words in questions and answers. Furthermore, BioBERT is utilized to encode medical domain-specific contextualized word representations. Our method has reached the accuracy of 76.24{\%} and spearman of 17.12{\%} on the BioNLP 2019 QA task.",
}
@inproceedings{bannihatti-kumar-etal-2019-dr,
    title = "{D}r.{Q}uad at {MEDIQA} 2019: Towards Textual Inference and Question Entailment using contextualized representations",
    author = "Bannihatti Kumar, Vinayshekhar  and
      Srinivasan, Ashwin  and
      Chaudhary, Aditi  and
      Route, James  and
      Mitamura, Teruko  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5048",
    doi = "10.18653/v1/W19-5048",
    pages = "453--461",
    abstract = "This paper presents the submissions by TeamDr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.",
}
@inproceedings{bhaskar-etal-2019-sieg,
    title = "Sieg at {MEDIQA} 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment",
    author = "Bhaskar, Sai Abishek  and
      Rungta, Rashi  and
      Route, James  and
      Nyberg, Eric  and
      Mitamura, Teruko",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5049",
    doi = "10.18653/v1/W19-5049",
    pages = "462--470",
    abstract = "This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.",
}
@inproceedings{sharma-roychowdhury-2019-iit,
    title = "{IIT}-{KGP} at {MEDIQA} 2019: Recognizing Question Entailment using Sci-{BERT} stacked with a Gradient Boosting Classifier",
    author = "Sharma, Prakhar  and
      Roychowdhury, Sumegh",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5050",
    doi = "10.18653/v1/W19-5050",
    pages = "471--477",
    abstract = "Official System Description paper of Team IIT-KGP ranked 1st in the Development phase and 3rd in Testing Phase in MEDIQA 2019 - Recognizing Question Entailment (RQE) Shared Task of BioNLP workshop - ACL 2019. The number of people turning to the Internet to search for a diverse range of health-related subjects continues to grow and with this multitude of information available, duplicate questions are becoming more frequent and finding the most appropriate answers becomes problematic. This issue is important for question answering platforms as it complicates the retrieval of all information relevant to the same topic, particularly when questions similar in essence are expressed differently, and answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper, we present our novel approach to detect question entailment by determining the type of question asked rather than focusing on the type of the ailment given. This unique methodology makes the approach robust towards examples which have different ailment names but are synonyms of each other. Also, it enables us to check entailment at a much more fine-grained level. QSpider is a staged system consisting of state-of-the-art model Sci-BERT used as a multi-class classifier aimed at capturing both question types and semantic relations stacked with a Gradient Boosting Classifier which checks for entailment. QSpider achieves an accuracy score of 68.4{\%} on the Test set which outperforms the baseline model (54.1{\%}) by an accuracy score of 14.3{\%}.",
}
@inproceedings{nguyen-etal-2019-anu,
    title = "{ANU}-{CSIRO} at {MEDIQA} 2019: Question Answering Using Deep Contextual Knowledge",
    author = "Nguyen, Vincent  and
      Karimi, Sarvnaz  and
      Xing, Zhenchang",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5051",
    doi = "10.18653/v1/W19-5051",
    pages = "478--487",
    abstract = "We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA. Textual inference is the task of finding the semantic relationships between pairs of text. Question entailment involves identifying pairs of questions which have similar semantic content. To improve upon medical natural language inference and question entailment approaches to further medical question answering, we propose a system that incorporates open-domain and biomedical domain approaches to improve semantic understanding and ambiguity resolution. Our models achieve 80{\%} accuracy on medical natural language inference (6.5{\%} absolute improvement over the original baseline), 48.9{\%} accuracy on recognising medical question entailment, 0.248 Spearman{'}s rho for question answering ranking and 68.6{\%} accuracy for question answering classification.",
}
@inproceedings{chopra-etal-2019-msit,
    title = "{MSIT}{\_}{SRIB} at {MEDIQA} 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.",
    author = "Chopra, Sahil  and
      Gupta, Ankita  and
      Kaushik, Anupama",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5052",
    doi = "10.18653/v1/W19-5052",
    pages = "488--492",
    abstract = "In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge. Bio-MTDNN utilizes {``}transfer learning{''} based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.",
}
@inproceedings{tawfik-spruit-2019-uu,
    title = "{UU}{\_}{TAILS} at {MEDIQA} 2019: Learning Textual Entailment in the Medical Domain",
    author = "Tawfik, Noha  and
      Spruit, Marco",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5053",
    doi = "10.18653/v1/W19-5053",
    pages = "493--499",
    abstract = "This article describes the participation of the UU{\_}TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP. The challenge consists of 3 tasks: medical language inference (NLI), recognizing textual entailment (RQE) and question answering (QA). Our team participated in tasks 1 and 2 and our best runs achieved a performance accuracy of 0.852 and 0.584 respectively for the test sets. The models proposed for task 1 relied on BERT embeddings and different ensemble techniques. For the RQE task, we trained a traditional multilayer perceptron network based on embeddings generated by the universal sentence encoder.",
}
@inproceedings{kearns-etal-2019-uw,
    title = "{UW}-{BHI} at {MEDIQA} 2019: An Analysis of Representation Methods for Medical Natural Language Inference",
    author = "Kearns, William  and
      Lau, Wilson  and
      Thomas, Jason",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5054",
    doi = "10.18653/v1/W19-5054",
    pages = "500--509",
    abstract = "Recent advances in distributed language modeling have led to large performance increases on a variety of natural language processing (NLP) tasks. However, it is not well understood how these methods may be augmented by knowledge-based approaches. This paper compares the performance and internal representation of an Enhanced Sequential Inference Model (ESIM) between three experimental conditions based on the representation method: Bidirectional Encoder Representations from Transformers (BERT), Embeddings of Semantic Predications (ESP), or Cui2Vec. The methods were evaluated on the Medical Natural Language Inference (MedNLI) subtask of the MEDIQA 2019 shared task. This task relied heavily on semantic understanding and thus served as a suitable evaluation set for the comparison of these representation methods.",
}
@inproceedings{kanakarajan-etal-2019-saama,
    title = "Saama Research at {MEDIQA} 2019: Pre-trained {B}io{BERT} with Attention Visualisation for Medical Natural Language Inference",
    author = "Kanakarajan, Kamal raj  and
      Ramamoorthy, Suriyadeepan  and
      Archana, Vaidheeswaran  and
      Chatterjee, Soham  and
      Sankarasubbu, Malaikannan",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5055",
    doi = "10.18653/v1/W19-5055",
    pages = "510--516",
    abstract = "Natural Language inference is the task of identifying relation between two sentences as entailment, contradiction or neutrality. MedNLI is a biomedical flavour of NLI for clinical domain. This paper explores the use of Bidirectional Encoder Representation from Transformer (BERT) for solving MedNLI. The proposed model, BERT pre-trained on PMC, PubMed and fine-tuned on MIMICIII v1.4, achieves state of the art results on MedNLI (83.45{\%}) and an accuracy of 78.5{\%} in MEDIQA challenge. The authors present an analysis of the attention patterns that emerged as a result of training BERT on MedNLI using a visualization tool, bertviz.",
}
@inproceedings{bandyopadhyay-etal-2019-iitp,
    title = "{IITP} at {MEDIQA} 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering",
    author = "Bandyopadhyay, Dibyanayan  and
      Gain, Baban  and
      Saikh, Tanik  and
      Ekbal, Asif",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5056",
    doi = "10.18653/v1/W19-5056",
    pages = "517--522",
    abstract = "This paper presents the experiments accomplished as a part of our participation in the MEDIQA challenge, an (Abacha et al., 2019) shared task. We participated in all the three tasks defined in this particular shared task. The tasks are viz. i. Natural Language Inference (NLI) ii. Recognizing Question Entailment(RQE) and their application in medical Question Answering (QA). We submitted runs using multiple deep learning based systems (runs) for each of these three tasks. We submitted five system results in each of the NLI and RQE tasks, and four system results for the QA task. The systems yield encouraging results in all the three tasks. The highest performance obtained in NLI, RQE and QA tasks are 81.8{\%}, 53.2{\%}, and 71.7{\%}, respectively.",
}
@inproceedings{lamurias-couto-2019-lasigebiotm,
    title = "{L}asige{B}io{TM} at {MEDIQA} 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition",
    author = "Lamurias, Andre  and
      Couto, Francisco M",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5057",
    doi = "10.18653/v1/W19-5057",
    pages = "523--527",
    abstract = "Biomedical Question Answering (QA) aims at providing automated answers to user questions, regarding a variety of biomedical topics. For example, these questions may ask for related to diseases, drugs, symptoms, or medical procedures. Automated biomedical QA systems could improve the retrieval of information necessary to answer these questions. The MEDIQA challenge consisted of three tasks concerning various aspects of biomedical QA. This challenge aimed at advancing approaches to Natural Language Inference (NLI) and Recognizing Question Entailment (RQE), which would then result in enhanced approaches to biomedical QA. Our approach explored a common Transformer-based architecture that could be applied to each task. This approach shared the same pre-trained weights, but which were then fine-tuned for each task using the provided training data. Furthermore, we augmented the training data with external datasets and enriched the question and answer texts using MER, a named entity recognition tool. Our approach obtained high levels of accuracy, in particular on the NLI task, which classified pairs of text according to their relation. For the QA task, we obtained higher Spearman{'}s rank correlation values using the entities recognized by MER.",
}
@inproceedings{lee-etal-2019-ncuee,
    title = "{NCUEE} at {MEDIQA} 2019: Medical Text Inference Using Ensemble {BERT}-{B}i{LSTM}-Attention Model",
    author = "Lee, Lung-Hao  and
      Lu, Yi  and
      Chen, Po-Han  and
      Lee, Po-Lei  and
      Shyu, Kuo-Kai",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5058",
    doi = "10.18653/v1/W19-5058",
    pages = "528--532",
    abstract = "This study describes the model design of the NCUEE system for the MEDIQA challenge at the ACL-BioNLP 2019 workshop. We use the BERT (Bidirectional Encoder Representations from Transformers) as the word embedding method to integrate the BiLSTM (Bidirectional Long Short-Term Memory) network with an attention mechanism for medical text inferences. A total of 42 teams participated in natural language inference task at MEDIQA 2019. Our best accuracy score of 0.84 ranked the top-third among all submissions in the leaderboard.",
}
@inproceedings{agrawal-etal-2019-ars,
    title = "{ARS}{\_}{NITK} at {MEDIQA} 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System",
    author = "Agrawal, Anumeha  and
      Anil George, Rosa  and
      Ravi, Selvan Suntiha  and
      Kamath S, Sowmya  and
      Kumar, Anand",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-5059",
    doi = "10.18653/v1/W19-5059",
    pages = "533--540",
    abstract = "In this paper, we present three approaches for Natural Language Inference, Question Entailment Recognition and Question-Answering to improve domain-specific Information Retrieval. For addressing the NLI task, the UMLS Metathesaurus was used to find the synonyms of medical terms in given sentences, on which the InferSent model was trained to predict if the given sentence is an entailment, contradictory or neutral. We also introduce a new Extreme gradient boosting model built on PubMed embeddings to perform RQE. Further, a closed-domain Question Answering technique that uses Bi-directional LSTMs trained on the SquAD dataset to determine relevant ranks of answers for a given question is also discussed.",
}
@proceedings{ws-2019-typ,
    title = "Proceedings of TyP-NLP: The First Workshop on Typology for Polyglot NLP",
    editor = "Dubossarsky, Haim  and
      McCarthy, Arya D.  and
      Ponti, Edoardo Maria  and
      Vuli{\'c}, Ivan  and
      Vylomova, Ekaterina  and
      Berzak, Yevgeni  and
      Cotterell, Ryan  and
      Faruqui, Manaal  and
      Korhonen, Anna  and
      Reichart, Roi",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4900",
}
@proceedings{ws-2019-2019-acl,
    title = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    editor = "Linzen, Tal  and
      Chrupa{\l}a, Grzegorz  and
      Belinkov, Yonatan  and
      Hupkes, Dieuwke",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4800",
}
@inproceedings{korrel-etal-2019-transcoding,
    title = "Transcoding Compositionally: Using Attention to Find More Generalizable Solutions",
    author = "Korrel, Kris  and
      Hupkes, Dieuwke  and
      Dankers, Verna  and
      Bruni, Elia",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4801",
    doi = "10.18653/v1/W19-4801",
    pages = "1--11",
    abstract = "While sequence-to-sequence models have shown remarkable generalization power across several natural language tasks, their construct of solutions are argued to be less compositional than human-like generalization. In this paper, we present seq2attn, a new architecture that is specifically designed to exploit attention to find compositional patterns in the input. In seq2attn, the two standard components of an encoder-decoder model are connected via a transcoder, that modulates the information flow between them. We show that seq2attn can successfully generalize, without requiring any additional supervision, on two tasks which are specifically constructed to challenge the compositional skills of neural networks. The solutions found by the model are highly interpretable, allowing easy analysis of both the types of solutions that are found and potential causes for mistakes. We exploit this opportunity to introduce a new paradigm to test compositionality that studies the extent to which a model overgeneralizes when confronted with exceptions. We show that seq2attn exhibits such overgeneralization to a larger degree than a standard sequence-to-sequence model.",
}
@inproceedings{barnes-etal-2019-sentiment,
    title = "Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification",
    author = "Barnes, Jeremy  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4802",
    doi = "10.18653/v1/W19-4802",
    pages = "12--23",
    abstract = "Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences. Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain. In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset. We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.",
}
@inproceedings{schlechtweg-etal-2019-second,
    title = "Second-order Co-occurrence Sensitivity of Skip-Gram with Negative Sampling",
    author = "Schlechtweg, Dominik  and
      Oguz, Cennet  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4803",
    doi = "10.18653/v1/W19-4803",
    pages = "24--30",
    abstract = "We simulate first- and second-order context overlap and show that Skip-Gram with Negative Sampling is similar to Singular Value Decomposition in capturing second-order co-occurrence information, while Pointwise Mutual Information is agnostic to it. We support the results with an empirical study finding that the models react differently when provided with additional second-order information. Our findings reveal a basic property of Skip-Gram with Negative Sampling and point towards an explanation of its success on a variety of tasks.",
}
@inproceedings{yanaka-etal-2019-neural,
    title = "Can Neural Networks Understand Monotonicity Reasoning?",
    author = "Yanaka, Hitomi  and
      Mineshima, Koji  and
      Bekki, Daisuke  and
      Inui, Kentaro  and
      Sekine, Satoshi  and
      Abzianidze, Lasha  and
      Bos, Johan",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4804",
    doi = "10.18653/v1/W19-4804",
    pages = "31--40",
    abstract = "Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures. Since no test set has been developed for monotonicity reasoning with wide coverage, it is still unclear whether neural models can perform monotonicity reasoning in a proper way. To investigate this issue, we introduce the Monotonicity Entailment Dataset (MED). Performance by state-of-the-art NLI models on the new test set is substantially worse, under 55{\%}, especially on downward reasoning. In addition, analysis using a monotonicity-driven data augmentation method showed that these models might be limited in their generalization ability in upward and downward reasoning.",
}
@inproceedings{wang-etal-2019-multi,
    title = "Multi-Granular Text Encoding for Self-Explaining Categorization",
    author = "Wang, Zhiguo  and
      Zhang, Yue  and
      Yu, Mo  and
      Zhang, Wei  and
      Pan, Lin  and
      Song, Linfeng  and
      Xu, Kun  and
      El-Kurdi, Yousef",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4805",
    doi = "10.18653/v1/W19-4805",
    pages = "41--45",
    abstract = "Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence. A popular type of evidence is sub-sequences extracted from the input text which are sufficient for the classifier to make the prediction. In this work, we define multi-granular ngrams as basic units for explanation, and organize all ngrams into a hierarchical structure, so that shorter ngrams can be reused while computing longer ngrams. We leverage the tree-structured LSTM to learn a context-independent representation for each unit via parameter sharing. Experiments on medical disease classification show that our model is more accurate, efficient and compact than the BiLSTM and CNN baselines. More importantly, our model can extract intuitive multi-granular evidence to support its predictions.",
}
@inproceedings{kuhnle-copestake-2019-meaning,
    title = "The Meaning of {``}Most{''} for Visual Question Answering Models",
    author = "Kuhnle, Alexander  and
      Copestake, Ann",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4806",
    doi = "10.18653/v1/W19-4806",
    pages = "46--55",
    abstract = "The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms. For the example of {``}most{''}, we discuss two strategies which rely on fundamentally different cognitive concepts. Our aim is to identify what strategy deep learning models for visual question answering learn when trained on such questions. To this end, we carefully design data to replicate experiments from psycholinguistics where the same question was investigated for humans. Focusing on the FiLM visual question answering model, our experiments indicate that a form of approximate number system emerges whose performance declines with more difficult scenes as predicted by Weber{'}s law. Moreover, we identify confounding factors, like spatial arrangement of the scene, which impede the effectiveness of this system.",
}
@inproceedings{strout-etal-2019-human,
    title = "Do Human Rationales Improve Machine Explanations?",
    author = "Strout, Julia  and
      Zhang, Ye  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4807",
    doi = "10.18653/v1/W19-4807",
    pages = "56--62",
    abstract = "Work on {``}learning with rationales{''} shows that humans providing explanations to a machine learning system can improve the system{'}s predictive accuracy. However, this work has not been connected to work in {``}explainable AI{''} which concerns machines explaining their reasoning to humans. In this work, we show that learning with rationales can also improve the quality of the machine{'}s explanations as evaluated by human judges. Specifically, we present experiments showing that, for CNN-based text classification, explanations generated using {``}supervised attention{''} are judged superior to explanations generated using normal unsupervised attention.",
}
@inproceedings{vig-belinkov-2019-analyzing,
    title = "Analyzing the Structure of Attention in a Transformer Language Model",
    author = "Vig, Jesse  and
      Belinkov, Yonatan",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4808",
    doi = "10.18653/v1/W19-4808",
    pages = "63--76",
    abstract = "The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads.",
}
@inproceedings{gangula-etal-2019-detecting,
    title = "Detecting Political Bias in News Articles Using Headline Attention",
    author = "Gangula, Rama Rohit Reddy  and
      Duggenpudi, Suma Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4809",
    doi = "10.18653/v1/W19-4809",
    pages = "77--84",
    abstract = "Language is a powerful tool which can be used to state the facts as well as express our views and perceptions. Most of the times, we find a subtle bias towards or against someone or something. When it comes to politics, media houses and journalists are known to create bias by shrewd means such as misinterpreting reality and distorting viewpoints towards some parties. This misinterpretation on a large scale can lead to the production of biased news and conspiracy theories. Automating bias detection in newspaper articles could be a good challenge for research in NLP. We proposed a headline attention network for this bias detection. Our model has two distinctive characteristics: (i) it has a structure that mirrors a person{'}s way of reading a news article (ii) it has attention mechanism applied on the article based on its headline, enabling it to attend to more critical content to predict bias. As the required datasets were not available, we created a dataset comprising of 1329 news articles collected from various Telugu newspapers and marked them for bias towards a particular political party. The experiments conducted on it demonstrated that our model outperforms various baseline methods by a substantial margin.",
}
@inproceedings{talman-chatzikyriakidis-2019-testing,
    title = "Testing the Generalization Power of Neural Network Models across {NLI} Benchmarks",
    author = "Talman, Aarne  and
      Chatzikyriakidis, Stergios",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4810",
    doi = "10.18653/v1/W19-4810",
    pages = "85--94",
    abstract = "Neural network models have been very successful in natural language inference, with the best models reaching 90{\%} accuracy in some benchmarks. However, the success of these models turns out to be largely benchmark specific. We show that models trained on a natural language inference dataset drawn from one benchmark fail to perform well in others, even if the notion of inference assumed in these benchmarks is the same or similar. We train six high performing neural network models on different datasets and show that each one of these has problems of generalizing when we replace the original test set with a test set taken from another corpus designed for the same task. In light of these results, we argue that most of the current neural network models are not able to generalize well in the task of natural language inference. We find that using large pre-trained language models helps with transfer learning when the datasets are similar enough. Our results also highlight that the current NLI datasets do not cover the different nuances of inference extensively enough.",
}
@inproceedings{pinter-etal-2019-character,
    title = "Character Eyes: Seeing Language through Character-Level Taggers",
    author = "Pinter, Yuval  and
      Marone, Marc  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4811",
    doi = "10.18653/v1/W19-4811",
    pages = "95--102",
    abstract = "Character-level models have been used extensively in recent years in NLP tasks as both supplements and replacements for closed-vocabulary token-level word representations. In one popular architecture, character-level LSTMs are used to feed token representations into a sequence tagger predicting token-level annotations such as part-of-speech (POS) tags. In this work, we examine the behavior of POS taggers across languages from the perspective of individual hidden units within the character LSTM. We aggregate the behavior of these units into language-level metrics which quantify the challenges that taggers face on languages with different morphological properties, and identify links between synthesis and affixation preference and emergent behavior of the hidden tagger layer. In a comparative experiment, we show how modifying the balance between forward and backward hidden units affects model arrangement and performance in these types of languages.",
}
@inproceedings{wu-mooney-2019-faithful,
    title = "Faithful Multimodal Explanation for Visual Question Answering",
    author = "Wu, Jialin  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4812",
    doi = "10.18653/v1/W19-4812",
    pages = "103--112",
    abstract = "AI systems{'} ability to explain their reasoning is critical to their utility and trustworthiness. Deep neural networks have enabled significant progress on many challenging problems such as visual question answering (VQA). However, most of them are opaque black boxes with limited explanatory capability. This paper presents a novel approach to developing a high-performing VQA system that can elucidate its answers with integrated textual and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations. Extensive experimental evaluation demonstrates the advantages of this approach compared to competing methods using both automated metrics and human evaluation.",
}
@inproceedings{arras-etal-2019-evaluating,
    title = "Evaluating Recurrent Neural Network Explanations",
    author = {Arras, Leila  and
      Osman, Ahmed  and
      M{\"u}ller, Klaus-Robert  and
      Samek, Wojciech},
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4813",
    doi = "10.18653/v1/W19-4813",
    pages = "113--126",
    abstract = "Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network{'}s decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.",
}
@inproceedings{baan-etal-2019-realization,
    title = "On the Realization of Compositionality in Neural Networks",
    author = {Baan, Joris  and
      Leible, Jana  and
      Nikolaus, Mitja  and
      Rau, David  and
      Ulmer, Dennis  and
      Baumg{\"a}rtner, Tim  and
      Hupkes, Dieuwke  and
      Bruni, Elia},
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4814",
    doi = "10.18653/v1/W19-4814",
    pages = "127--137",
    abstract = "We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task. The models are architecturally identical at inference time, but differ in the way that they are trained: our baseline model is trained with a task-success signal only, while the other model receives additional supervision on its attention mechanism (Attentive Guidance), which has shown to be an effective method for encouraging more compositional solutions. We first confirm that the models with attentive guidance indeed infer more compositional solutions than the baseline, by training them on the lookup table task presented by Liska et al. (2019). We then do an in-depth analysis of the structural differences between the two model types, focusing in particular on the organisation of the parameter space and the hidden layer activations and find noticeable differences in both these aspects. Guided networks focus more on the components of the input rather than the sequence as a whole and develop small functional groups of neurons with specific purposes that use their gates more selectively. Results from parameter heat maps, component swapping and graph analysis also indicate that guided networks exhibit a more modular structure with a small number of specialized, strongly connected neurons.",
}
@inproceedings{yu-etal-2019-learning,
    title = "Learning the {D}yck Language with Attention-based {S}eq2{S}eq Models",
    author = "Yu, Xiang  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4815",
    doi = "10.18653/v1/W19-4815",
    pages = "138--146",
    abstract = "The generalized Dyck language has been used to analyze the ability of Recurrent Neural Networks (RNNs) to learn context-free grammars (CFGs). Recent studies draw conflicting conclusions on their performance, especially regarding the generalizability of the models with respect to the depth of recursion. In this paper, we revisit several common models and experimental settings, discuss the potential problems of the tasks and analyses. Furthermore, we explore the use of attention mechanisms within the seq2seq framework to learn the Dyck language, which could compensate for the limited encoding ability of RNNs. Our findings reveal that attention mechanisms still cannot truly generalize over the recursion depth, although they perform much better than other models on the closing bracket tagging task. Moreover, this also suggests that this commonly used task is not sufficient to test a model{'}s understanding of CFGs.",
}
@inproceedings{stadelmaier-pado-2019-modeling,
    title = "Modeling Paths for Explainable Knowledge Base Completion",
    author = "Stadelmaier, Josua  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4816",
    doi = "10.18653/v1/W19-4816",
    pages = "147--157",
    abstract = "A common approach in knowledge base completion (KBC) is to learn representations for entities and relations in order to infer missing facts by generalizing existing ones. A shortcoming of standard models is that they do not explain their predictions to make them verifiable easily to human inspection. In this paper, we propose the Context Path Model (CPM) which generates explanations for new facts in KBC by providing sets of \textit{context paths} as supporting evidence for these triples. For example, a new triple (Theresa May, nationality, Britain) may be explained by the path (Theresa May, born in, Eastbourne, contained in, Britain). The CPM is formulated as a wrapper that can be applied on top of various existing KBC models. We evaluate it for the well-established TransE model. We observe that its performance remains very close despite the added complexity, and that most of the paths proposed as explanations provide meaningful evidence to assess the correctness.",
}
@inproceedings{merlo-2019-probing,
    title = "Probing Word and Sentence Embeddings for Long-distance Dependencies Effects in {F}rench and {E}nglish",
    author = "Merlo, Paola",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4817",
    doi = "10.18653/v1/W19-4817",
    pages = "158--172",
    abstract = "The recent wide-spread and strong interest in RNNs has spurred detailed investigations of the distributed representations they generate and specifically if they exhibit properties similar to those characterising human languages. Results are at present inconclusive. In this paper, we extend previous work on long-distance dependencies in three ways. We manipulate word embeddings to translate them in a space that is attuned to the linguistic properties under study. We extend the work to sentence embeddings and to new languages. We confirm previous negative results: word embeddings and sentence embeddings do not unequivocally encode fine-grained linguistic properties of long-distance dependencies.",
}
@inproceedings{musil-etal-2019-derivational,
    title = "Derivational Morphological Relations in Word Embeddings",
    author = "Musil, Tom{\'a}{\v{s}}  and
      Vidra, Jon{\'a}{\v{s}}  and
      Mare{\v{c}}ek, David",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4818",
    doi = "10.18653/v1/W19-4818",
    pages = "173--180",
    abstract = "Derivation is a type of a word-formation process which creates new words from existing ones by adding, changing or deleting affixes. In this paper, we explore the potential of word embeddings to identify properties of word derivations in the morphologically rich Czech language. We extract derivational relations between pairs of words from DeriNet, a Czech lexical network, which organizes almost one million Czech lemmas into derivational trees. For each such pair, we compute the difference of the embeddings of the two words, and perform unsupervised clustering of the resulting vectors. Our results show that these clusters largely match manually annotated semantic categories of the derivational relations (e.g. the relation {`}bake{--}baker{'} belongs to category {`}actor{'}, and a correct clustering puts it into the same cluster as {`}govern{--}governor{'}).",
}
@inproceedings{wilcox-etal-2019-hierarchical,
    title = "Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations",
    author = "Wilcox, Ethan  and
      Levy, Roger  and
      Futrell, Richard",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4819",
    doi = "10.18653/v1/W19-4819",
    pages = "181--190",
    abstract = "Work using artificial languages as training input has shown that LSTMs are capable of inducing the stack-like data structures required to represent context-free and certain mildly context-sensitive languages {---} formal language classes which correspond in theory to the hierarchical structures of natural language. Here we present a suite of experiments probing whether neural language models trained on linguistic data induce these stack-like data structures and deploy them while incrementally predicting words. We study two natural language phenomena: center embedding sentences and syntactic island constraints on the filler{--}gap dependency. In order to properly predict words in these structures, a model must be able to temporarily suppress certain expectations and then recover those expectations later, essentially pushing and popping these expectations on a stack. Our results provide evidence that models can successfully suppress and recover expectations in many cases, but do not fully recover their previous grammatical state.",
}
@inproceedings{abnar-etal-2019-blackbox,
    title = "Blackbox Meets Blackbox: Representational Similarity {\&} Stability Analysis of Neural Language Models and Brains",
    author = "Abnar, Samira  and
      Beinborn, Lisa  and
      Choenni, Rochelle  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4820",
    doi = "10.18653/v1/W19-4820",
    pages = "191--203",
    abstract = "In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models. ReStA is a variant of the popular representational similarity analysis (RSA) in cognitive neuroscience. While RSA can be used to compare representations in models, model components, and human brains, ReStA compares instances of the same model, while systematically varying single model parameter. Using ReStA, we study four recent and successful neural language models, and evaluate how sensitive their internal representations are to the amount of prior context. Using RSA, we perform a systematic study of how similar the representational spaces in the first and second (or higher) layers of these models are to each other and to patterns of activation in the human brain. Our results reveal surprisingly strong differences between language models, and give insights into where the deep linguistic processing, that integrates information over multiple sentences, is happening in these models. The combination of ReStA and RSA on models and brains allows us to start addressing the important question of what kind of linguistic processes we can hope to observe in fMRI brain imaging data. In particular, our results suggest that the data on story reading from Wehbe et al./ (2014) contains a signal of shallow linguistic processing, but show no evidence on the more interesting deep linguistic processing.",
}
@inproceedings{chowdhury-zamparelli-2019-lstm,
    title = "An {LSTM} Adaptation Study of (Un)grammaticality",
    author = "Chowdhury, Shammur Absar  and
      Zamparelli, Roberto",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4821",
    doi = "10.18653/v1/W19-4821",
    pages = "204--212",
    abstract = "We propose a novel approach to the study of how artificial neural network perceive the distinction between grammatical and ungrammatical sentences, a crucial task in the growing field of synthetic linguistics. The method is based on performance measures of language models trained on corpora and fine-tuned with either grammatical or ungrammatical sentences, then applied to (different types of) grammatical or ungrammatical sentences. The results show that both in the difficult and highly symmetrical task of detecting subject islands and in the more open CoLA dataset, grammatical sentences give rise to better scores than ungrammatical ones, possibly because they can be better integrated within the body of linguistic structural knowledge that the language model has accumulated.",
}
@inproceedings{anastasopoulos-2019-analysis,
    title = "An Analysis of Source-Side Grammatical Errors in {NMT}",
    author = "Anastasopoulos, Antonios",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4822",
    doi = "10.18653/v1/W19-4822",
    pages = "213--223",
    abstract = "The quality of Neural Machine Translation (NMT) has been shown to significantly degrade when confronted with source-side noise. We present the first large-scale study of state-of-the-art English-to-German NMT on real grammatical noise, by evaluating on several Grammar Correction corpora. We present methods for evaluating NMT robustness without true references, and we use them for extensive analysis of the effects that different grammatical errors have on the NMT output. We also introduce a technique for visualizing the divergence distribution caused by a source-side error, which allows for additional insights.",
}
@inproceedings{merrill-etal-2019-finding,
    title = "Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing",
    author = "Merrill, William  and
      Khazan, Lenny  and
      Amsel, Noah  and
      Hao, Yiding  and
      Mendelsohn, Simon  and
      Frank, Robert",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4823",
    doi = "10.18653/v1/W19-4823",
    pages = "224--232",
    abstract = "Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which such a bias is effective, as the operation of the differentiable stack is not always interpretable. In this paper, we attempt to detect the presence of latent representations of hierarchical structure through an exploration of the unsupervised learning of constituency structure. Using a technique due to Shen et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack RNNs trained on language modeling and classification objectives. We find that our models produce parses that reflect natural language syntactic constituencies, demonstrating that stack RNNs do indeed infer linguistically relevant hierarchical structure.",
}
@inproceedings{tsai-etal-2019-adversarial-attack,
    title = "Adversarial Attack on Sentiment Classification",
    author = "Tsai, Yi-Ting  and
      Yang, Min-Chu  and
      Chen, Han-Yu",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4824",
    doi = "10.18653/v1/W19-4824",
    pages = "233--240",
    abstract = "In this paper, we propose a white-box attack algorithm called {``}Global Search{''} method and compare it with a simple misspelling noise and a more sophisticated and common white-box attack approach called {``}Greedy Search{''}. The attack methods are evaluated on the Convolutional Neural Network (CNN) sentiment classifier trained on the IMDB movie review dataset. The attack success rate is used to evaluate the effectiveness of the attack methods and the perplexity of the sentences is used to measure the degree of distortion of the generated adversarial examples. The experiment results show that the proposed {``}Global Search{''} method generates more powerful adversarial examples with less distortion or less modification to the source text.",
}
@inproceedings{lin-etal-2019-open,
    title = "Open Sesame: Getting inside {BERT}{'}s Linguistic Knowledge",
    author = "Lin, Yongjie  and
      Tan, Yi Chern  and
      Frank, Robert",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4825",
    doi = "10.18653/v1/W19-4825",
    pages = "241--253",
    abstract = "How and to what extent does BERT encode syntactically-sensitive hierarchical information or positionally-sensitive linear information? Recent work has shown that contextual representations like BERT perform well on tasks that require sensitivity to linguistic structure. We present here two studies which aim to provide a better understanding of the nature of BERT{'}s representations. The first of these focuses on the identification of structurally-defined elements using diagnostic classifiers, while the second explores BERT{'}s representation of subject-verb agreement and anaphor-antecedent dependencies through a quantitative assessment of self-attention vectors. In both cases, we find that BERT encodes positional information about word tokens well on its lower layers, but switches to a hierarchically-oriented encoding on higher layers. We conclude then that BERT{'}s representations do indeed model linguistically relevant aspects of hierarchical structure, though they do not appear to show the sharp sensitivity to hierarchical structure that is found in human processing of reflexive anaphora.",
}
@inproceedings{gralinski-etal-2019-geval,
    title = "{GE}val: Tool for Debugging {NLP} Datasets and Models",
    author = "Grali{\'n}ski, Filip  and
      Wr{\'o}blewska, Anna  and
      Stanis{\l}awek, Tomasz  and
      Grabowski, Kamil  and
      G{\'o}recki, Tomasz",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4826",
    doi = "10.18653/v1/W19-4826",
    pages = "254--262",
    abstract = "This paper presents a simple but general and effective method to debug the output of machine learning (ML) supervised models, including neural networks. The algorithm looks for features that lower the evaluation metric in such a way that it cannot be ascribed to chance (as measured by their p-values). Using this method {--} implemented as MLEval tool {--} you can find: (1) anomalies in test sets, (2) issues in preprocessing, (3) problems in the ML model itself. It can give you an insight into what can be improved in the datasets and/or the model. The same method can be used to compare ML models or different versions of the same model. We present the tool, the theory behind it and use cases for text-based models of various types.",
}
@inproceedings{marecek-rosa-2019-balustrades,
    title = "From Balustrades to Pierre Vinken: Looking for Syntax in Transformer Self-Attentions",
    author = "Mare{\v{c}}ek, David  and
      Rosa, Rudolf",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4827",
    doi = "10.18653/v1/W19-4827",
    pages = "263--275",
    abstract = "We inspect the multi-head self-attention in Transformer NMT encoders for three source languages, looking for patterns that could have a syntactic interpretation. In many of the attention heads, we frequently find sequences of consecutive states attending to the same position, which resemble syntactic phrases. We propose a transparent deterministic method of quantifying the amount of syntactic information present in the self-attentions, based on automatically building and evaluating phrase-structure trees from the phrase-like sequences. We compare the resulting trees to existing constituency treebanks, both manually and by computing precision and recall.",
}
@inproceedings{clark-etal-2019-bert,
    title = "What Does {BERT} Look at? An Analysis of {BERT}{'}s Attention",
    author = "Clark, Kevin  and
      Khandelwal, Urvashi  and
      Levy, Omer  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4828",
    doi = "10.18653/v1/W19-4828",
    pages = "276--286",
    abstract = "Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (e.g., language model surprisal) or internal vector representations (e.g., probing classifiers). Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT. BERT{'}s attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy. Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT{'}s attention.",
}
@proceedings{ws-2019-international-approaches,
    title = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    editor = "Tahmasebi, Nina  and
      Borin, Lars  and
      Jatowt, Adam  and
      Xu, Yang",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4700",
}
@inproceedings{luo-etal-2019-insanely,
    title = "From Insanely Jealous to Insanely Delicious: Computational Models for the Semantic Bleaching of {E}nglish Intensifiers",
    author = "Luo, Yiwei  and
      Jurafsky, Dan  and
      Levin, Beth",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4701",
    doi = "10.18653/v1/W19-4701",
    pages = "1--13",
    abstract = "We introduce novel computational models for modeling semantic bleaching, a widespread category of change in which words become more abstract or lose elements of meaning, like the development of {``}arrive{''} from its earlier meaning {`}become at shore.{'} We validate our methods on a widespread case of bleaching in English: de-adjectival adverbs that originate as manner adverbs (as in {``}awfully behaved{''}) and later become intensifying adverbs (as in {``}awfully nice{''}). Our methods formally quantify three reflexes of bleaching: decreasing similarity to the source meaning (e.g., {``}awful{''}), increasing similarity to a fully bleached prototype (e.g., {``}very{''}), and increasing productivity (e.g., the breadth of adjectives that an adverb modifies). We also test a new causal model and find evidence that bleaching is initially triggered in contexts such as {``}conspicuously evident{''} and {``}insanely jealous{''}, where an adverb premodifies a semantically similar adjective. These contexts provide a form of {``}bridging context{''} (Evans and Wilkins, 2000) that allow a manner adverb to be reinterpreted as an intensifying adverb similar to {``}very{''}.",
}
@inproceedings{gopidi-alam-2019-computational,
    title = "Computational Analysis of the Historical Changes in Poetry and Prose",
    author = "Gopidi, Amitha  and
      Alam, Aniket",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4702",
    doi = "10.18653/v1/W19-4702",
    pages = "14--22",
    abstract = "The esoteric definitions of poetry are insufficient in enveloping the changes in poetry that the age of mechanical reproduction has witnessed with the widespread proliferation of the use of digital media and artificial intelligence. They are also insufficient in distinguishing between prose and poetry, as the content of both prose and poetry can be poetic. Using quotes as prose considering their poetic, context-free and celebrated nature, stylistic differences between poetry and prose are delved into. Novel features in grammar and meter are justified as distinguishing features. Datasets of popular prose and poetry spanning across 1870-1920 and 1970-2019 have been created, and multiple experiments have been conducted to prove that prose and poetry in the latter period are more alike than they were in the former. The accuracy of classification of poetry and prose of 1970-2019 is significantly lesser than that of 1870-1920, thereby proving the convergence of poetry and prose in 1970-2019.",
}
@inproceedings{zimmermann-2019-studying,
    title = "Studying Semantic Chain Shifts with {W}ord2{V}ec: {FOOD}{\textgreater}{MEAT}{\textgreater}{FLESH}",
    author = "Zimmermann, Richard",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4703",
    doi = "10.18653/v1/W19-4703",
    pages = "23--28",
    abstract = "Word2Vec models are used to study the semantic chain shift FOOD{\textgreater}MEAT{\textgreater}FLESH in the history of English, c. 1425-1925. The development stretches out over a long time, starting before 1500, and may possibly be continuing to this day. The semantic changes likely proceeded as a push chain.",
}
@inproceedings{vylomova-etal-2019-evaluation,
    title = "Evaluation of Semantic Change of Harm-Related Concepts in Psychology",
    author = "Vylomova, Ekaterina  and
      Murphy, Sean  and
      Haslam, Nicholas",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4704",
    doi = "10.18653/v1/W19-4704",
    pages = "29--34",
    abstract = "The paper focuses on diachronic evaluation of semantic changes of harm-related concepts in psychology. More specifically, we investigate a hypothesis that certain concepts such as {``}addiction{''}, {``}bullying{''}, {``}harassment{''}, {``}prejudice{''}, and {``}trauma{''} became broader during the last four decades. We evaluate semantic changes using two models: an LSA-based model from Sagi et al. (2009) and a diachronic adaptation of word2vec from Hamilton et al. (2016), that are trained on a large corpus of journal abstracts covering the period of 1980{--} 2019. Several concepts showed evidence of broadening. {``}Addiction{''} moved from physiological dependency on a substance to include psychological dependency on gaming and the Internet. Similarly, {``}harassment{''} and {``}trauma{''} shifted towards more psychological meanings. On the other hand, {``}bullying{''} has transformed into a more victim-related concept and expanded to new areas such as workplaces.",
}
@inproceedings{jawahar-seddah-2019-contextualized,
    title = "Contextualized Diachronic Word Representations",
    author = "Jawahar, Ganesh  and
      Seddah, Djam{\'e}",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4705",
    doi = "10.18653/v1/W19-4705",
    pages = "35--47",
    abstract = "Diachronic word embeddings play a key role in capturing interesting patterns about how language evolves over time. Most of the existing work focuses on studying corpora spanning across several decades, which is understandably still not a possibility when working on social media-based user-generated content. In this work, we address the problem of studying semantic changes in a large Twitter corpus collected over five years, a much shorter period than what is usually the norm in diachronic studies. We devise a novel attentional model, based on Bernoulli word embeddings, that are conditioned on contextual extra-linguistic (social) features such as network, spatial and socio-economic variables, which are associated with Twitter users, as well as topic-based features. We posit that these social features provide an inductive bias that helps our model to overcome the narrow time-span regime problem. Our extensive experiments reveal that our proposed model is able to capture subtle semantic shifts without being biased towards frequency cues and also works well when certain contextual features are absent. Our model fits the data better than current state-of-the-art dynamic word embedding models and therefore is a promising tool to study diachronic semantic changes over small time periods.",
}
@inproceedings{bowern-2019-semantic,
    title = "Semantic Change and Semantic Stability: Variation is Key",
    author = "Bowern, Claire",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4706",
    doi = "10.18653/v1/W19-4706",
    pages = "48--55",
    abstract = "I survey some recent approaches to studying change in the lexicon, particularly change in meaning across phylogenies. I briefly sketch an evolutionary approach to language change and point out some issues in recent approaches to studying semantic change that rely on temporally stratified word embeddings. I draw illustrations from lexical cognate models in Pama-Nyungan to identify meaning classes most appropriate for lexical phylogenetic inference, particularly highlighting the importance of variation in studying change over time.",
}
@inproceedings{perrone-etal-2019-gasc,
    title = "{GASC}: Genre-Aware Semantic Change for {A}ncient {G}reek",
    author = "Perrone, Valerio  and
      Palma, Marco  and
      Hengchen, Simon  and
      Vatri, Alessandro  and
      Smith, Jim Q.  and
      McGillivray, Barbara",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4707",
    doi = "10.18653/v1/W19-4707",
    pages = "56--66",
    abstract = "Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word{'}s correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian models for semantic change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of semantic change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document time-stamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and semantic change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic semantic change model that leverages categorical metadata about the texts{'} genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.",
}
@inproceedings{ceolin-sayeed-2019-modeling,
    title = "Modeling Markedness with a Split-and-Merger Model of Sound Change",
    author = "Ceolin, Andrea  and
      Sayeed, Ollie",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4708",
    doi = "10.18653/v1/W19-4708",
    pages = "67--70",
    abstract = "The concept of {`}markedness{'} has been influential in phonology for almost a century. Theoretical phonology has found it useful to describe some segments as more {`}marked{'} than others, referring to a cluster of language-internal and -external properties (Jakobson 1968, Haspelmath 2006). We argue, using a simple mathematical model based on Evolutionary Phonology (Blevins 2004), that markedness is an epiphenomenon of phonetically grounded sound change.",
}
@inproceedings{garcia-garcia-salido-2019-method,
    title = "A Method to Automatically Identify Diachronic Variation in Collocations.",
    author = "Garcia, Marcos  and
      Garc{\'\i}a Salido, Marcos",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4709",
    doi = "10.18653/v1/W19-4709",
    pages = "71--80",
    abstract = "This paper introduces a novel method to track collocational variations in diachronic corpora that can identify several changes undergone by these phraseological combinations and to propose alternative solutions found in later periods. The strategy consists of extracting syntactically-related candidates of collocations and ranking them using statistical association measures. Then, starting from the first period of the corpus, the system tracks each combination over time, verifying different types of historical variation such as the loss of one or both lemmas, the disappearance of the collocation, or its diachronic frequency trend. Using a distributional semantics strategy, it also proposes other linguistic structures which convey similar meanings to those extinct collocations. A case study on historical corpora of Portuguese and Spanish shows that the system speeds up and facilitates the finding of some diachronic changes and phraseological shifts that are harder to identify without using automated methods.",
}
@inproceedings{rutherford-thanyawong-2019-written,
    title = "Written on Leaves or in Stones?: Computational Evidence for the Era of Authorship of Old {T}hai Prose",
    author = "Rutherford, Attapol  and
      Thanyawong, Santhawat",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4710",
    doi = "10.18653/v1/W19-4710",
    pages = "81--85",
    abstract = "We aim to provide computational evidence for the era of authorship of two important old Thai texts: Traiphumikatha and Pumratchatham. The era of authorship of these two books is still an ongoing debate among Thai literature scholars. Analysis of old Thai texts present a challenge for standard natural language processing techniques, due to the lack of corpora necessary for building old Thai word and syllable segmentation. We propose an accurate and interpretable model to classify each segment as one of the three eras of authorship (Sukhothai, Ayuddhya, or Rattanakosin) without sophisticated linguistic preprocessing. Contrary to previous hypotheses, our model suggests that both books were written during the Sukhothai era. Moreover, the second half of the Pumratchtham is uncharacteristic of the Sukhothai era, which may have confounded literary scholars in the past. Further, our model reveals that the most indicative linguistic changes stem from unidirectional grammaticalized words and polyfunctional words, which show up as most dominant features in the model.",
}
@inproceedings{boldsen-etal-2019-identifying,
    title = "Identifying Temporal Trends Based on Perplexity and Clustering: Are We Looking at Language Change?",
    author = "Boldsen, Sidsel  and
      Agirrezabal, Manex  and
      Paggio, Patrizia",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4711",
    doi = "10.18653/v1/W19-4711",
    pages = "86--91",
    abstract = "In this work we propose a data-driven methodology for identifying temporal trends in a corpus of medieval charters. We have used perplexities derived from RNNs as a distance measure between documents and then, performed clustering on those distances. We argue that perplexities calculated by such language models are representative of temporal trends. The clusters produced using the K-Means algorithm give an insight of the differences in language in different time periods at least partly due to language change. We suggest that the temporal distribution of the individual clusters might provide a more nuanced picture of temporal trends compared to discrete bins, thus providing better results when used in a classification task.",
}
@inproceedings{wevers-2019-using,
    title = "Using Word Embeddings to Examine Gender Bias in {D}utch Newspapers, 1950-1990",
    author = "Wevers, Melvin",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4712",
    doi = "10.18653/v1/W19-4712",
    pages = "92--97",
    abstract = "Contemporary debates on filter bubbles and polarization in public and social media raise the question to what extent news media of the past exhibited biases. This paper specifically examines bias related to gender in six Dutch national newspapers between 1950 and 1990. We measure bias related to gender by comparing local changes in word embedding models trained on newspapers with divergent ideological backgrounds. We demonstrate clear differences in gender bias and changes within and between newspapers over time. In relation to themes such as sexuality and leisure, we see the bias moving toward women, whereas, generally, the bias shifts in the direction of men, despite growing female employment number and feminist movements. Even though Dutch society became less stratified ideologically (depillarization), we found an increasing divergence in gender bias between religious and social-democratic on the one hand and liberal newspapers on the other. Methodologically, this paper illustrates how word embeddings can be used to examine historical language change. Future work will investigate how fine-tuning deep contextualized embedding models, such as ELMO, might be used for similar tasks with greater contextual information.",
}
@inproceedings{hartmann-2019-predicting,
    title = "Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of {P}roto-{I}ndo-{E}uropean",
    author = "Hartmann, Frederik",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4713",
    doi = "10.18653/v1/W19-4713",
    pages = "98--108",
    abstract = "Traditional historical linguistics lacks the possibility to empirically assess its assumptions regarding the phonetic systems of past languages and language stages since most current methods rely on comparative tools to gain insights into phonetic features of sounds in proto- or ancestor languages. The paper at hand presents a computational method based on deep neural networks to predict phonetic features of historical sounds where the exact quality is unknown and to test the overall coherence of reconstructed historical phonetic features. The method utilizes the principles of coarticulation, local predictability and statistical phonological constraints to predict phonetic features by the features of their immediate phonetic environment. The validity of this method will be assessed using New High German phonetic data and its specific application to diachronic linguistics will be demonstrated in a case study of the phonetic system Proto-Indo-European.",
}
@inproceedings{kalouli-etal-2019-parhistvis,
    title = "{P}ar{H}ist{V}is: Visualization of Parallel Multilingual Historical Data",
    author = "Kalouli, Aikaterini-Lida  and
      Kehlbeck, Rebecca  and
      Sevastjanova, Rita  and
      Kaiser, Katharina  and
      Kaiser, Georg A.  and
      Butt, Miriam",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4714",
    doi = "10.18653/v1/W19-4714",
    pages = "109--114",
    abstract = "The study of language change through parallel corpora can be advantageous for the analysis of complex interactions between time, text domain and language. Often, those advantages cannot be fully exploited due to the sparse but high-dimensional nature of such historical data. To tackle this challenge, we introduce ParHistVis: a novel, free, easy-to-use, interactive visualization tool for parallel, multilingual, diachronic and synchronic linguistic data. We illustrate the suitability of the components of the tool based on a use case of word order change in Romance wh-interrogatives.",
}
@inproceedings{tripodi-etal-2019-tracing,
    title = "Tracing Antisemitic Language Through Diachronic Embedding Projections: {F}rance 1789-1914",
    author = "Tripodi, Rocco  and
      Warglien, Massimo  and
      Levis Sullam, Simon  and
      Paci, Deborah",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4715",
    doi = "10.18653/v1/W19-4715",
    pages = "115--125",
    abstract = "We investigate some aspects of the history of antisemitism in France, one of the cradles of modern antisemitism, using diachronic word embeddings. We constructed a large corpus of French books and periodicals issues that contain a keyword related to Jews and performed a diachronic word embedding over the 1789-1914 period. We studied the changes over time in the semantic spaces of 4 target words and performed embedding projections over 6 streams of antisemitic discourse. This allowed us to track the evolution of antisemitic bias in the religious, economic, socio-politic, racial, ethic and conspiratorial domains. Projections show a trend of growing antisemitism, especially in the years starting in the mid-80s and culminating in the Dreyfus affair. Our analysis also allows us to highlight the peculiar adverse bias towards Judaism in the broader context of other religions.",
}
@inproceedings{schatzle-booth-2019-diahclust,
    title = "{D}ia{HC}lust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change",
    author = {Sch{\"a}tzle, Christin  and
      Booth, Hannah},
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4716",
    doi = "10.18653/v1/W19-4716",
    pages = "126--135",
    abstract = "Language change is often assessed against a set of pre-determined time periods in order to be able to trace its diachronic trajectory. This is problematic, since a pre-determined periodization might obscure significant developments and lead to false assumptions about the data. Moreover, these time periods can be based on factors which are either arbitrary or non-linguistic, e.g., dividing the corpus data into equidistant stages or taking into account language-external events. Addressing this problem, in this paper we present a data-driven approach to periodization: {`}DiaHClust{'}. DiaHClust is based on iterative hierarchical clustering and offers a multi-layered perspective on change from text-level to broader time periods. We demonstrate the usefulness of DiaHClust via a case study investigating syntactic change in Icelandic, modelling the syntactic system of the language in terms of vectors of syntactic change.",
}
@inproceedings{xu-etal-2019-treat,
    title = "Treat the Word As a Whole or Look Inside? Subword Embeddings Model Language Change and Typology",
    author = "Xu, Yang  and
      Zhang, Jiasheng  and
      Reitter, David",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4717",
    doi = "10.18653/v1/W19-4717",
    pages = "136--145",
    abstract = "We use a variant of word embedding model that incorporates subword information to characterize the degree of compositionality in lexical semantics. Our models reveal some interesting yet contrastive patterns of long-term change in multiple languages: Indo-European languages put more weight on subword units in newer words, while conversely Chinese puts less weights on the subwords, but more weight on the word as a whole. Our method provides novel evidence and methodology that enriches existing theories in evolutionary linguistics. The resulting word vectors also has decent performance in NLP-related tasks.",
}
@inproceedings{brandl-lassner-2019-times,
    title = "Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings",
    author = "Brandl, Stephanie  and
      Lassner, David",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4718",
    doi = "10.18653/v1/W19-4718",
    pages = "146--150",
    abstract = "We propose Word Embedding Networks, a novel method that is able to learn word embeddings of individual data slices while simultaneously aligning and ordering them without feeding temporal information a priori to the model. This gives us the opportunity to analyse the dynamics in word embeddings on a large scale in a purely data-driven manner. In experiments on two different newspaper corpora, the New York Times (English) and die Zeit (German), we were able to show that time actually determines the dynamics of semantic change. However, there is by no means a uniform evolution, but instead times of faster and times of slower change.",
}
@inproceedings{korat-2019-rationality,
    title = "The Rationality of Semantic Change",
    author = "Korat, Omer",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4719",
    doi = "10.18653/v1/W19-4719",
    pages = "151--160",
    abstract = "This study investigates the mutual effects over time of semantically related function words on each other{'}s distribution over syntactic environments. Words that can have the same meaning are observed to have opposite trends of change in frequency across different syntactic structures which correspond to the shared meaning. This phenomenon is demonstrated to have a rational basis: it increases communicative efficiency by prioritizing words differently in the environments on which they compete.",
}
@inproceedings{uban-etal-2019-studying,
    title = "Studying Laws of Semantic Divergence across Languages using Cognate Sets",
    author = "Uban, Ana  and
      Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4720",
    doi = "10.18653/v1/W19-4720",
    pages = "161--166",
    abstract = "Semantic divergence in related languages is a key concern of historical linguistics. Intra-lingual semantic shift has been previously studied in computational linguistics, but this can only provide a limited picture of the evolution of word meanings, which often develop in a multilingual environment. In this paper we investigate semantic change across languages by measuring the semantic distance of cognate words in multiple languages. By comparing current meanings of cognates in different languages, we hope to uncover information about their previous meanings, and about how they diverged in their respective languages from their common original etymon. We further study the properties of their semantic divergence, by analyzing how the features of words such as frequency and polysemy are related to the divergence in their meaning, and thus make the first steps towards formulating laws of cross-lingual semantic change.",
}
@inproceedings{merrill-etal-2019-detecting,
    title = "Detecting Syntactic Change Using a Neural Part-of-Speech Tagger",
    author = "Merrill, William  and
      Stark, Gigi  and
      Frank, Robert",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4721",
    doi = "10.18653/v1/W19-4721",
    pages = "167--174",
    abstract = "We train a diachronic long short-term memory (LSTM) part-of-speech tagger on a large corpus of American English from the 19th, 20th, and 21st centuries. We analyze the tagger{'}s ability to implicitly learn temporal structure between years, and the extent to which this knowledge can be transferred to date new sentences. The learned year embeddings show a strong linear correlation between their first principal component and time. We show that temporal information encoded in the model can be used to predict novel sentences{'} years of composition relatively well. Comparisons to a feedforward baseline suggest that the temporal change learned by the LSTM is syntactic rather than purely lexical. Thus, our results suggest that our tagger is implicitly learning to model syntactic change in American English over the course of the 19th, 20th, and early 21st centuries.",
}
@inproceedings{bizzoni-etal-2019-grammar,
    title = "Grammar and Meaning: Analysing the Topology of Diachronic Word Embeddings",
    author = "Bizzoni, Yuri  and
      Degaetano-Ortlieb, Stefania  and
      Menzel, Katrin  and
      Krielke, Pauline  and
      Teich, Elke",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4722",
    doi = "10.18653/v1/W19-4722",
    pages = "175--185",
    abstract = "The paper showcases the application of word embeddings to change in language use in the domain of science, focusing on the Late Modern English period (17-19th century). Historically, this is the period in which many registers of English developed, including the language of science. Our overarching interest is the linguistic development of scientific writing to a distinctive (group of) register(s). A register is marked not only by the choice of lexical words (discourse domain) but crucially by grammatical choices which indicate style. The focus of the paper is on the latter, tracing words with primarily grammatical functions (function words and some selected, poly-functional word forms) diachronically. To this end, we combine diachronic word embeddings with appropriate visualization and exploratory techniques such as clustering and relative entropy for meaningful aggregation of data and diachronic comparison.",
}
@inproceedings{jeszenszky-etal-2019-spatio,
    title = "Spatio-Temporal Prediction of Dialectal Variant Usage",
    author = "Jeszenszky, P{\'e}ter  and
      Siriaraya, Panote  and
      Stoeckle, Philipp  and
      Jatowt, Adam",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4723",
    doi = "10.18653/v1/W19-4723",
    pages = "186--195",
    abstract = "The distribution of most dialectal variants have not only spatial but also temporal patterns. Based on the {`}apparent time hypothesis{'}, much of dialect change is happening through younger speakers accepting innovations. Thus, synchronic diversity can be interpreted diachronically. With the assumption of the {`}contact effect{'}, i.e. contact possibility (contact and isolation) between speaker communities being responsible for language change, and the apparent time hypothesis, we aim to predict the usage of dialectal variants. In this paper we model the contact possibility based on two of the most important factors in sociolinguistics to be affecting language change: age and distance. The first steps of the approach involve modeling contact possibility using a logistic predictor, taking the age of respondents into account. We test the \textit{global}, and the \textit{local} role of age for variation where the local level means spatial subsets around each survey site, chosen based on \textit{k} nearest neighbors. The prediction approach is tested on Swiss German syntactic survey data, featuring multiple respondents from different age cohorts at survey sites. The results show the relative success of the logistic prediction approach and the limitations of the method, therefore further proposals are made to develop the methodology.",
}
@inproceedings{kutuzov-etal-2019-one,
    title = "One-to-{X} Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts",
    author = "Kutuzov, Andrey  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4724",
    doi = "10.18653/v1/W19-4724",
    pages = "196--201",
    abstract = "We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type {`}location:armed-group{'} based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives; this approach is shown to be beneficial on two different corpora. Finally, we publish a ready-to-use test set for one-to-X analogy evaluation on historical armed conflicts data.",
}
@inproceedings{rodina-etal-2019-measuring,
    title = "Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for {E}nglish, {N}orwegian, and {R}ussian",
    author = "Rodina, Julia  and
      Bakshandaeva, Daria  and
      Fomin, Vadim  and
      Kutuzov, Andrey  and
      Touileb, Samia  and
      Velldal, Erik",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4725",
    doi = "10.18653/v1/W19-4725",
    pages = "202--209",
    abstract = "We measure the intensity of diachronic semantic shifts in adjectives in English, Norwegian and Russian across 5 decades. This is done in order to test the hypothesis that evaluative adjectives are more prone to temporal semantic change. To this end, 6 different methods of quantifying semantic change are used. Frequency-controlled experimental results show that, depending on the particular method, evaluative adjectives either do not differ from other types of adjectives in terms of semantic change or appear to actually be less prone to shifting (particularly, to {`}jitter{'}-type shifting). Thus, in spite of many well-known examples of semantically changing evaluative adjectives (like {`}terrific{'} or {`}incredible{'}), it seems that such cases are not specific to this particular type of words.",
}
@inproceedings{abercrombie-batista-navarro-2019-semantic,
    title = "Semantic Change in the Language of {UK} Parliamentary Debates",
    author = "Abercrombie, Gavin  and
      Batista-Navarro, Riza",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4726",
    doi = "10.18653/v1/W19-4726",
    pages = "210--215",
    abstract = "We investigate changes in the meanings of words used in the UK Parliament across two different epochs. We use word embeddings to explore changes in the distribution of words of interest and uncover words that appear to have undergone semantic transformation in the intervening period, and explore different ways of obtaining target words for this purpose. We find that semantic changes are generally in line with those found in other corpora, and little evidence that parliamentary language is more static than general English. It also seems that words with senses that have been recorded in the dictionary as having fallen into disuse do not undergo semantic changes in this domain.",
}
@inproceedings{haider-eger-2019-semantic,
    title = "Semantic Change and Emerging Tropes In a Large Corpus of {N}ew {H}igh {G}erman Poetry",
    author = "Haider, Thomas  and
      Eger, Steffen",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4727",
    doi = "10.18653/v1/W19-4727",
    pages = "216--222",
    abstract = "Due to its semantic succinctness and novelty of expression, poetry is a great test-bed for semantic change analysis. However, so far there is a scarcity of large diachronic corpora. Here, we provide a large corpus of German poetry which consists of about 75k poems with more than 11 million tokens, with poems ranging from the 16th to early 20th century. We then track semantic change in this corpus by investigating the rise of tropes ({`}love is magic{'}) over time and detecting change points of meaning, which we find to occur particularly within the German Romantic period. Additionally, through self-similarity, we reconstruct literary periods and find evidence that the law of linear semantic change also applies to poetry.",
}
@inproceedings{sommerauer-fokkens-2019-conceptual,
    title = "Conceptual Change and Distributional Semantic Models: an Exploratory Study on Pitfalls and Possibilities",
    author = "Sommerauer, Pia  and
      Fokkens, Antske",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4728",
    doi = "10.18653/v1/W19-4728",
    pages = "223--233",
    abstract = "Studying conceptual change using embedding models has become increasingly popular in the Digital Humanities community while critical observations about them have received less attention. This paper investigates what the impact of known pitfalls can be on the conclusions drawn in a digital humanities study through the use case of {``}Racism{''}. In addition, we suggest an approach for modeling a complex concept in terms of words and relations representative of the conceptual system. Our results show that different models created from the same data yield different results, but also indicate that using different model architectures, comparing different corpora and comparing to control words and relations can help to identify which results are solid and which may be due to artefact. We propose guidelines to conduct similar studies, but also note that more work is needed to fully understand how we can distinguish artefacts from actual conceptual changes.",
}
@inproceedings{dhar-etal-2019-measuring,
    title = "Measuring the Compositionality of Noun-Noun Compounds over Time",
    author = "Dhar, Prajit  and
      Pagel, Janis  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4729",
    doi = "10.18653/v1/W19-4729",
    pages = "234--239",
    abstract = "We present work in progress on the temporal progression of compositionality in noun-noun compounds. Previous work has proposed computational methods for determining the compositionality of compounds. These methods try to automatically determine how transparent the meaning of the compound as a whole is with respect to the meaning of its parts. We hypothesize that such a property might change over time. We use the time-stamped Google Books corpus for our diachronic investigations, and first examine whether the vector-based semantic spaces extracted from this corpus are able to predict compositionality ratings, despite their inherent limitations. We find that using temporal information helps predicting the ratings, although correlation with the ratings is lower than reported for other corpora. Finally, we show changes in compositionality over time for a selection of compounds.",
}
@inproceedings{hazem-etal-2019-towards,
    title = "Towards Automatic Variant Analysis of Ancient Devotional Texts",
    author = "Hazem, Amir  and
      Daille, B{\'e}atrice  and
      Stutzmann, Dominique  and
      Currie, Jacob  and
      Jacquin, Christine",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4730",
    doi = "10.18653/v1/W19-4730",
    pages = "240--249",
    abstract = "We address in this paper the issue of text reuse in liturgical manuscripts of the middle ages. More specifically, we study variant readings of the Obsecro Te prayer, part of the devotional Books of Hours often used by Christians as guidance for their daily prayers. We aim at automatically extracting and categorising pairs of words and expressions that exhibit variant relations. For this purpose, we adopt a linguistic classification that allows to better characterize the variants than edit operations. Then, we study the evolution of Obsecro Te texts from a temporal and geographical axis. Finally, we contrast several unsupervised state-of-the-art approaches for the automatic extraction of Obsecro Te variants. Based on the manual observation of 772 Obsecro Te copies which show more than 21,000 variants, we show that the proposed methodology is helpful for an automatic study of variants and may serve as basis to analyze and to depict useful information from devotional texts.",
}
@inproceedings{mahanty-etal-2019-understanding,
    title = "Understanding the Evolution of Circular Economy through Language Change",
    author = "Mahanty, Sampriti  and
      Boons, Frank  and
      Handl, Julia  and
      Theresa Batista-Navarro, Riza",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4731",
    doi = "10.18653/v1/W19-4731",
    pages = "250--253",
    abstract = "In this study, we propose to focus on understanding the evolution of a specific scientific concept{---}that of Circular Economy (CE){---}by analysing how the language used in academic discussions has changed semantically. It is worth noting that the meaning and central theme of this concept has remained the same; however, we hypothesise that it has undergone semantic change by way of additional layers being added to the concept. We have shown that semantic change in language is a reflection of shifts in scientific ideas, which in turn help explain the evolution of a concept. Focusing on the CE concept, our analysis demonstrated that the change over time in the language used in academic discussions of CE is indicative of the way in which the concept evolved and expanded.",
}
@inproceedings{cathcart-2019-gaussian,
    title = "{G}aussian Process Models of Sound Change in {I}ndo-{A}ryan Dialectology",
    author = "Cathcart, Chundra",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4732",
    doi = "10.18653/v1/W19-4732",
    pages = "254--264",
    abstract = "This paper proposes a Gaussian Process model of sound change targeted toward questions in Indo-Aryan dialectology. Gaussian Processes (GPs) provide a flexible means of expressing covariance between outcomes, and can be extended to a wide variety of probability distributions. We find that GP models fare better in terms of some key posterior predictive checks than models that do not express covariance between sound changes, and outline directions for future work.",
}
@inproceedings{bloem-etal-2019-modeling,
    title = "Modeling a Historical Variety of a Low-Resource Language: {L}anguage Contact Effects in the Verbal Cluster of {E}arly-{M}odern {F}risian",
    author = "Bloem, Jelke  and
      Versloot, Arjen  and
      Weerman, Fred",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4733",
    doi = "10.18653/v1/W19-4733",
    pages = "265--271",
    abstract = "Certain phenomena of interest to linguists mainly occur in low-resource languages, such as contact-induced language change. We show that it is possible to study contact-induced language change computationally in a historical variety of a low-resource language, Early-Modern Frisian, by creating a model using features that were established to be relevant in a closely related language, modern Dutch. This allows us to test two hypotheses on two types of language contact that may have taken place between Frisian and Dutch during this time. Our model shows that Frisian verb cluster word orders are associated with different context features than Dutch verb orders, supporting the {`}learned borrowing{'} hypothesis.",
}
@inproceedings{schatzle-etal-2019-visualizing,
    title = "Visualizing Linguistic Change as Dimension Interactions",
    author = {Sch{\"a}tzle, Christin  and
      Dennig, Frederik L.  and
      Blumenschein, Michael  and
      Keim, Daniel A.  and
      Butt, Miriam},
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4734",
    doi = "10.18653/v1/W19-4734",
    pages = "272--278",
    abstract = "Historical change typically is the result of complex interactions between several linguistic factors. Identifying the relevant factors and understanding how they interact across the temporal dimension is the core remit of historical linguistics. With respect to corpus work, this entails a separate annotation, extraction and painstaking pair-wise comparison of the relevant bits of information. This paper presents a significant extension of HistoBankVis, a multilayer visualization system which allows a fast and interactive exploration of complex linguistic data. Linguistic factors can be understood as data dimensions which show complex interrelationships. We model these relationships with the Parallel Sets technique. We demonstrate the powerful potential of this technique by applying the system to understanding the interaction of case, grammatical relations and word order in the history of Icelandic.",
}
@proceedings{ws-2019-arabic,
    title = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    editor = "El-Hajj, Wassim  and
      Belguith, Lamia Hadrich  and
      Bougares, Fethi  and
      Magdy, Walid  and
      Zitouni, Imed  and
      Tomeh, Nadi  and
      El-Haj, Mahmoud",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4600",
}
@inproceedings{kalimuthu-etal-2019-incremental,
    title = "Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings",
    author = "Kalimuthu, Marimuthu  and
      Barz, Michael  and
      Sonntag, Daniel",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4601",
    doi = "10.18653/v1/W19-4601",
    pages = "1--10",
    abstract = "We study the problem of incremental domain adaptation of a generic neural machine translation model with limited resources (e.g., budget and time) for human translations or model training. In this paper, we propose a novel query strategy for selecting {``}unlabeled{''} samples from a new domain based on sentence embeddings for Arabic. We accelerate the fine-tuning process of the generic model to the target domain. Specifically, our approach estimates the informativeness of instances from the target domain by comparing the distance of their sentence embeddings to embeddings from the generic domain. We perform machine translation experiments (Ar-to-En direction) for comparing a random sampling baseline with our new approach, similar to active learning, using two small update sets for simulating the work of human translators. For the prescribed setting we can save more than 50{\%} of the annotation costs without loss in quality, demonstrating the effectiveness of our approach.",
}
@inproceedings{tawfik-etal-2019-morphology,
    title = "Morphology-aware Word-Segmentation in Dialectal {A}rabic Adaptation of Neural Machine Translation",
    author = "Tawfik, Ahmed  and
      Emam, Mahitab  and
      Essam, Khaled  and
      Nabil, Robert  and
      Hassan, Hany",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4602",
    doi = "10.18653/v1/W19-4602",
    pages = "11--17",
    abstract = "Parallel corpora available for building machine translation (MT) models for dialectal Arabic (DA) are rather limited. The scarcity of resources has prompted the use of Modern Standard Arabic (MSA) abundant resources to complement the limited dialectal resource. However, dialectal clitics often differ between MSA and DA. This paper compares morphology-aware DA word segmentation to other word segmentation approaches like Byte Pair Encoding (BPE) and Sub-word Regularization (SR). A set of experiments conducted on Egyptian Arabic (EA), Levantine Arabic (LA), and Gulf Arabic (GA) show that a sufficiently accurate morphology-aware segmentation used in conjunction with BPE outperforms the other word segmentation approaches.",
}
@inproceedings{attia-etal-2019-pos,
    title = "{POS} Tagging for Improving Code-Switching Identification in {A}rabic",
    author = "Attia, Mohammed  and
      Samih, Younes  and
      Elkahky, Ali  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Darwish, Kareem",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4603",
    doi = "10.18653/v1/W19-4603",
    pages = "18--29",
    abstract = "When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language. This paper explores the possibility of utilizing this pattern in improving code-switching identification between Modern Standard Arabic (MSA) and Egyptian Arabic (EA). We try to answer the question of how strong is the POS signal in word-level code-switching identification. We build a deep learning model enriched with linguistic features (including POS tags) that outperforms the state-of-the-art results by 1.9{\%} on the development set and 1.0{\%} on the test set. We also show that in intra-sentential code-switching, the selection of lexical items is constrained by POS categories, where function words tend to come more often from the dialectal language while the majority of content words come from the standard language.",
}
@inproceedings{mulki-etal-2019-syntax,
    title = "Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of {A}rabic Dialects",
    author = "Mulki, Hala  and
      Haddad, Hatem  and
      Gridach, Mourad  and
      Babao{\u{g}}lu, Ismail",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4604",
    doi = "10.18653/v1/W19-4604",
    pages = "30--39",
    abstract = "Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.",
}
@inproceedings{lachraf-etal-2019-arbengvec,
    title = "{A}rb{E}ng{V}ec : {A}rabic-{E}nglish Cross-Lingual Word Embedding Model",
    author = "Lachraf, Raki  and
      Nagoudi, El Moatez Billah  and
      Ayachi, Youcef  and
      Abdelali, Ahmed  and
      Schwab, Didier",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4605",
    doi = "10.18653/v1/W19-4605",
    pages = "40--48",
    abstract = "Word Embeddings (WE) are getting increasingly popular and widely applied in many Natural Language Processing (NLP) applications due to their effectiveness in capturing semantic properties of words; Machine Translation (MT), Information Retrieval (IR) and Information Extraction (IE) are among such areas. In this paper, we propose an open source ArbEngVec which provides several Arabic-English cross-lingual word embedding models. To train our bilingual models, we use a large dataset with more than 93 million pairs of Arabic-English parallel sentences. In addition, we perform both extrinsic and intrinsic evaluations for the different word embedding model variants. The extrinsic evaluation assesses the performance of models on the cross-language Semantic Textual Similarity (STS), while the intrinsic evaluation is based on the Word Translation (WT) task.",
}
@inproceedings{alqahtani-etal-2019-homograph,
    title = "Homograph Disambiguation through Selective Diacritic Restoration",
    author = "Alqahtani, Sawsan  and
      Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4606",
    doi = "10.18653/v1/W19-4606",
    pages = "49--59",
    abstract = "Lexical ambiguity, a challenging phenomenon in all natural languages, is particularly prevalent for languages with diacritics that tend to be omitted in writing, such as Arabic. Omitting diacritics leads to an increase in the number of homographs: different words with the same spelling. Diacritic restoration could theoretically help disambiguate these words, but in practice, the increase in overall sparsity leads to performance degradation in NLP applications. In this paper, we propose approaches for automatically marking a subset of words for diacritic restoration, which leads to selective homograph disambiguation. Compared to full or no diacritic restoration, these approaches yield selectively-diacritized datasets that balance sparsity and lexical disambiguation. We evaluate the various selection strategies extrinsically on several downstream applications: neural machine translation, part-of-speech tagging, and semantic textual similarity. Our experiments on Arabic show promising results, where our devised strategies on selective diacritization lead to a more balanced and consistent performance in downstream applications.",
}
@inproceedings{liu-etal-2019-arabic,
    title = "{A}rabic Named Entity Recognition: What Works and What{'}s Next",
    author = "Liu, Liyuan  and
      Shang, Jingbo  and
      Han, Jiawei",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4607",
    doi = "10.18653/v1/W19-4607",
    pages = "60--67",
    abstract = "This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com. The proposed model integrates various tailored techniques together, including representation learning, feature engineering, sequence labeling, and ensemble learning. The final model achieves a test F{\_}1 score of 75.82{\%} on the AQMAR dataset and outperforms baselines by a large margin. Detailed analyses are conducted to reveal both its strengths and limitations. Specifically, we observe that (1) representation learning modules can significantly boost the performance but requires a proper pre-processing and (2) the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data. All implementations and pre-trained models are made public.",
}
@inproceedings{eljundi-etal-2019-hulmona,
    title = "h{ULM}on{A}: The Universal Language Model in {A}rabic",
    author = "ElJundi, Obeida  and
      Antoun, Wissam  and
      El Droubi, Nour  and
      Hajj, Hazem  and
      El-Hajj, Wassim  and
      Shaban, Khaled",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4608",
    doi = "10.18653/v1/W19-4608",
    pages = "68--77",
    abstract = "Arabic is a complex language with limited resources which makes it challenging to produce accurate text classification tasks such as sentiment analysis. The utilization of transfer learning (TL) has recently shown promising results for advancing accuracy of text classification in English. TL models are pre-trained on large corpora, and then fine-tuned on task-specific datasets. In particular, universal language models (ULMs), such as recently developed BERT, have achieved state-of-the-art results in various NLP tasks in English. In this paper, we hypothesize that similar success can be achieved for Arabic. The work aims at supporting the hypothesis by developing the first Universal Language Model in Arabic (hULMonA - حلمنا meaning our dream), demonstrating its use for Arabic classifications tasks, and demonstrating how a pre-trained multi-lingual BERT can also be used for Arabic. We then conduct a benchmark study to evaluate both ULM successes with Arabic sentiment analysis. Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.",
}
@inproceedings{adouane-etal-2019-neural,
    title = "Neural Models for Detecting Binary Semantic Textual Similarity for {A}lgerian and {MSA}",
    author = "Adouane, Wafia  and
      Bernardy, Jean-Philippe  and
      Dobnik, Simon",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4609",
    doi = "10.18653/v1/W19-4609",
    pages = "78--87",
    abstract = "We explore the extent to which neural networks can learn to identify semantically equivalent sentences from a small variable dataset using an end-to-end training. We collect a new noisy non-standardised user-generated Algerian (ALG) dataset and also translate it to Modern Standard Arabic (MSA) which serves as its regularised counterpart. We compare the performance of various models on both datasets and report the best performing configurations. The results show that relatively simple models composed of 2 LSTM layers outperform by far other more sophisticated attention-based architectures, for both ALG and MSA datasets.",
}
@inproceedings{el-kishky-etal-2019-constrained,
    title = "Constrained Sequence-to-sequence {S}emitic Root Extraction for Enriching Word Embeddings",
    author = "El-Kishky, Ahmed  and
      Fu, Xingyu  and
      Addawood, Aseel  and
      Sobh, Nahil  and
      Voss, Clare  and
      Han, Jiawei",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4610",
    doi = "10.18653/v1/W19-4610",
    pages = "88--96",
    abstract = "In this paper, we tackle the problem of {``}root extraction{''} from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem that arises from their rich internal morphology, where the substructure is inherently non-concatenative and morphemes are interdigitated in word formation. While previous automated methods have relied on human-curated rules or multiclass classification, they have not fully leveraged the various combinations of regular, sequential concatenative morphology within the words and the internal interleaving within templatic stems of roots and patterns. To address this, we propose a constrained sequence-to-sequence root extraction method. Experimental results show our constrained model outperforms a variety of methods at root extraction. Furthermore, by enriching word embeddings with resulting decompositions, we show improved results on word analogy, word similarity, and language modeling tasks.",
}
@inproceedings{alqaisi-okeefe-2019-en,
    title = "En-Ar Bilingual Word Embeddings without Word Alignment: Factors Effects",
    author = "Alqaisi, Taghreed  and
      O{'}Keefe, Simon",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4611",
    doi = "10.18653/v1/W19-4611",
    pages = "97--107",
    abstract = "This paper introduces the first attempt to investigate morphological segmentation on En-Ar bilingual word embeddings using bilingual word embeddings model without word alignment (BilBOWA). We investigate the effect of sentence length and embedding size on the learning process. Our experiment shows that using the D3 segmentation scheme improves the accuracy of learning bilingual word embeddings up to 10 percentage points compared to the ATB and D0 schemes in all different training settings.",
}
@inproceedings{mozannar-etal-2019-neural,
    title = "Neural {A}rabic Question Answering",
    author = "Mozannar, Hussein  and
      Maamary, Elie  and
      El Hajal, Karl  and
      Hajj, Hazem",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4612",
    doi = "10.18653/v1/W19-4612",
    pages = "108--118",
    abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.",
}
@inproceedings{attia-elkahky-2019-segmentation,
    title = "Segmentation for Domain Adaptation in {A}rabic",
    author = "Attia, Mohammed  and
      Elkahky, Ali",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4613",
    doi = "10.18653/v1/W19-4613",
    pages = "119--129",
    abstract = "Segmentation serves as an integral part in many NLP applications including Machine Translation, Parsing, and Information Retrieval. When a model trained on the standard language is applied to dialects, the accuracy drops dramatically. However, there are more lexical items shared by the standard language and dialects than can be found by mere surface word matching. This shared lexicon is obscured by a lot of cliticization, gemination, and character repetition. In this paper, we prove that segmentation and base normalization of dialects can help in domain adaptation by reducing data sparseness. Segmentation will improve a system performance by reducing the number of OOVs, help isolate the differences and allow better utilization of the commonalities. We show that adding a small amount of dialectal segmentation training data reduced OOVs by 5{\%} and remarkably improves POS tagging for dialects by 7.37{\%} f-score, even though no dialect-specific POS training data is included.",
}
@inproceedings{helwe-etal-2019-assessing,
    title = "Assessing {A}rabic Weblog Credibility via Deep Co-learning",
    author = "Helwe, Chadi  and
      Elbassuoni, Shady  and
      Al Zaatari, Ayman  and
      El-Hajj, Wassim",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4614",
    doi = "10.18653/v1/W19-4614",
    pages = "130--136",
    abstract = "Assessing the credibility of online content has garnered a lot of attention lately. We focus on one such type of online content, namely weblogs or blogs for short. Some recent work attempted the task of automatically assessing the credibility of blogs, typically via machine learning. However, in the case of Arabic blogs, there are hardly any datasets available that can be used to train robust machine learning models for this difficult task. To overcome the lack of sufficient training data, we propose deep co-learning, a semi-supervised end-to-end deep learning approach to assess the credibility of Arabic blogs. In deep co-learning, multiple weak deep neural network classifiers are trained using a small labeled dataset, and each using a different view of the data. Each one of these classifiers is then used to classify unlabeled data, and its prediction is used to train the other classifiers in a semi-supervised fashion. We evaluate our deep co-learning approach on an Arabic blogs dataset, and we report significant improvements in performance compared to many baselines including fully-supervised deep learning models as well as ensemble models.",
}
@inproceedings{alshargi-etal-2019-morphologically,
    title = "Morphologically Annotated Corpora for Seven {A}rabic Dialects: Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi and {M}oroccan",
    author = "Alshargi, Faisal  and
      Dibas, Shahd  and
      Alkhereyf, Sakhar  and
      Faraj, Reem  and
      Abdulkareem, Basmah  and
      Yagi, Sane  and
      Kacha, Ouafaa  and
      Habash, Nizar  and
      Rambow, Owen",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4615",
    doi = "10.18653/v1/W19-4615",
    pages = "137--147",
    abstract = "We present a collection of morphologically annotated corpora for seven Arabic dialects: Taizi Yemeni, Sanaani Yemeni, Najdi, Jordanian, Syrian, Iraqi and Moroccan Arabic. The corpora collectively cover over 200,000 words, and are all manually annotated in a common set of standards for orthography, diacritized lemmas, tokenization, morphological units and English glosses. These corpora will be publicly available to serve as benchmarks for training and evaluating systems for Arabic dialect morphological analysis and disambiguation.",
}
@inproceedings{sawalha-etal-2019-construction,
    title = "Construction and Annotation of the {J}ordan Comprehensive Contemporary {A}rabic Corpus ({JCCA})",
    author = "Sawalha, Majdi  and
      Alshargi, Faisal  and
      AlShdaifat, Abdallah  and
      Yagi, Sane  and
      Qudah, Mohammad A.",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4616",
    doi = "10.18653/v1/W19-4616",
    pages = "148--157",
    abstract = "To compile a modern dictionary that catalogues the words in currency, and to study linguistic patterns in the contemporary language, it is necessary to have a corpus of authentic texts that reflect current usage of the language. Although there are numerous Arabic corpora, none claims to be representative of the language in terms of the combination of geographical region, genre, subject matter, mode, and medium. This paper describes a 100-million-word corpus that takes the British National Corpus (BNC) as a model. The aim of the corpus is to be balanced, annotated, comprehensive, and representative of contemporary Arabic as written and spoken in Arab countries today. It will be different from most others in not being heavily-dominated by the news or in mixing the classical with the modern. In this paper is an outline of the methodology adopted for the design, construction, and annotation of this corpus. DIWAN (Alshargi and Rambow, 2015) was used to annotate a one-million-word snapshot of the corpus. DIWAN is a dialectal word annotation tool, but we upgraded it by adding a new tag-set that is based on traditional Arabic grammar and by adding the roots and morphological patterns of nouns and verbs. Moreover, the corpus we constructed covers the major spoken varieties of Arabic.",
}
@inproceedings{durgar-el-kahlout-etal-2019-translating,
    title = "Translating Between Morphologically Rich Languages: An {A}rabic-to-{T}urkish Machine Translation System",
    author = "Durgar El-Kahlout, {\.I}lknur  and
      Bekta{\c{s}}, Emre  and
      Erdem, Naime {\c{S}}eyma  and
      Kaya, Hamza",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4617",
    doi = "10.18653/v1/W19-4617",
    pages = "158--166",
    abstract = "This paper introduces the work on building a machine translation system for Arabic-to-Turkish in the news domain. Our work includes collecting parallel datasets in several ways for a new and low-resourced language pair, building baseline systems with state-of-the-art architectures and developing language specific algorithms for better translation. Parallel datasets are mainly collected three different ways; i) translating Arabic texts into Turkish by professional translators, ii) exploiting the web for open-source Arabic-Turkish parallel texts, iii) using back-translation. We per-formed preliminary experiments for Arabic-to-Turkish machine translation with neural(Marian) machine translation tools with a novel morphologically motivated vocabulary reduction method.",
}
@inproceedings{khaddaj-etal-2019-improved,
    title = "Improved Generalization of {A}rabic Text Classifiers",
    author = "Khaddaj, Alaa  and
      Hajj, Hazem  and
      El-Hajj, Wassim",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4618",
    doi = "10.18653/v1/W19-4618",
    pages = "167--174",
    abstract = "While transfer learning for text has been very active in the English language, progress in Arabic has been slow, including the use of Domain Adaptation (DA). Domain Adaptation is used to generalize the performance of any classifier by trying to balance the classifier{'}s accuracy for a particular task among different text domains. In this paper, we propose and evaluate two variants of a domain adaptation technique: the first is a base model called Domain Adversarial Neural Network (DANN), while the second is a variation that incorporates representational learning. Similar to previous approaches, we propose the use of proxy A-distance as a metric to assess the success of generalization. We make use of ArSentDLEV, a multi-topic dataset collected from the Levantine countries, to test the performance of the models. We show the superiority of the proposed method in accuracy and robustness when dealing with the Arabic language.",
}
@inproceedings{zeroual-etal-2019-osian,
    title = "{OSIAN}: Open Source International {A}rabic News Corpus - Preparation and Integration into the {CLARIN}-infrastructure",
    author = "Zeroual, Imad  and
      Goldhahn, Dirk  and
      Eckart, Thomas  and
      Lakhouaja, Abdelhak",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4619",
    doi = "10.18653/v1/W19-4619",
    pages = "175--182",
    abstract = "The World Wide Web has become a fundamental resource for building large text corpora. Broadcasting platforms such as news websites are rich sources of data regarding diverse topics and form a valuable foundation for research. The Arabic language is extensively utilized on the Web. Still, Arabic is relatively an under-resourced language in terms of availability of freely annotated corpora. This paper presents the first version of the Open Source International Arabic News (OSIAN) corpus. The corpus data was collected from international Arabic news websites, all being freely available on the Web. The corpus consists of about 3.5 million articles comprising more than 37 million sentences and roughly 1 billion tokens. It is encoded in XML; each article is annotated with metadata information. Moreover, each word is annotated with lemma and part-of-speech. the described corpus is processed, archived and published into the CLARIN infrastructure. This publication includes descriptive metadata via OAI-PMH, direct access to the plain text material (available under Creative Commons Attribution-Non-Commercial 4.0 International License - CC BY-NC 4.0), and integration into the WebLicht annotation platform and CLARIN{'}s Federated Content Search FCS.",
}
@inproceedings{algotiml-etal-2019-arabic,
    title = "{A}rabic Tweet-Act: Speech Act Recognition for {A}rabic Asynchronous Conversations",
    author = "Algotiml, Bushra  and
      Elmadany, AbdelRahim  and
      Magdy, Walid",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4620",
    doi = "10.18653/v1/W19-4620",
    pages = "183--191",
    abstract = "Speech acts are the actions that a speaker intends when performing an utterance within conversations. In this paper, we proposed speech act classification for asynchronous conversations on Twitter using multiple machine learning methods including SVM and deep neural networks. We applied the proposed methods on the ArSAS tweets dataset. The obtained results show that superiority of deep learning methods compared to SVMs, where Bi-LSTM managed to achieve an accuracy of 87.5{\%} and a macro-averaged F1 score 61.5{\%}. We believe that our results are the first to be reported on the task of speech-act recognition for asynchronous conversations on Arabic Twitter.",
}
@inproceedings{abu-farha-magdy-2019-mazajak,
    title = "{M}azajak: An Online {A}rabic Sentiment Analyser",
    author = "Abu Farha, Ibrahim  and
      Magdy, Walid",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4621",
    doi = "10.18653/v1/W19-4621",
    pages = "192--198",
    abstract = "Sentiment analysis (SA) is one of the most useful natural language processing applications. Literature is flooding with many papers and systems addressing this task, but most of the work is focused on English. In this paper, we present {``}Mazajak{''}, an online system for Arabic SA. The system is based on a deep learning model, which achieves state-of-the-art results on many Arabic dialect datasets including SemEval 2017 and ASTD. The availability of such system should assist various applications and research that rely on sentiment analysis as a tool.",
}
@inproceedings{bouamor-etal-2019-madar,
    title = "The {MADAR} Shared Task on {A}rabic Fine-Grained Dialect Identification",
    author = "Bouamor, Houda  and
      Hassan, Sabit  and
      Habash, Nizar",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4622",
    doi = "10.18653/v1/W19-4622",
    pages = "199--207",
    abstract = "In this paper, we present the results and findings of the MADAR Shared Task on Arabic Fine-Grained Dialect Identification. This shared task was organized as part of The Fourth Arabic Natural Language Processing Workshop, collocated with ACL 2019. The shared task includes two subtasks: the MADAR Travel Domain Dialect Identification subtask (Subtask 1) and the MADAR Twitter User Dialect Identification subtask (Subtask 2). This shared task is the first to target a large set of dialect labels at the city and country levels. The data for the shared task was created or collected under the Multi-Arabic Dialect Applications and Resources (MADAR) project. A total of 21 teams from 15 countries participated in the shared task.",
}
@inproceedings{priban-taylor-2019-zcu,
    title = "{ZCU}-{NLP} at {MADAR} 2019: Recognizing {A}rabic Dialects",
    author = "P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Taylor, Stephen",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4623",
    doi = "10.18653/v1/W19-4623",
    pages = "208--213",
    abstract = "In this paper, we present our systems for the MADAR Shared Task: Arabic Fine-Grained Dialect Identification. The shared task consists of two subtasks. The goal of Subtask{--} 1 (S-1) is to detect an Arabic city dialect in a given text and the goal of Subtask{--}2 (S-2) is to predict the country of origin of a Twitter user by using tweets posted by the user. In S-1, our proposed systems are based on language modelling. We use language models to extract features that are later used as an input for other machine learning algorithms. We also experiment with recurrent neural networks (RNN), but these experiments showed that simpler machine learning algorithms are more successful. Our system achieves 0.658 macro F1-score and our rank is 6th out of 19 teams in S-1 and 7th in S-2 with 0.475 macro F1-score.",
}
@inproceedings{eltanbouly-etal-2019-simple,
    title = {Simple But Not Na{\"\i}ve: Fine-Grained {A}rabic Dialect Identification Using Only N-Grams},
    author = "Eltanbouly, Sohaila  and
      Bashendy, May  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4624",
    doi = "10.18653/v1/W19-4624",
    pages = "214--218",
    abstract = "This paper presents the participation of Qatar University team in MADAR shared task, which addresses the problem of sentence-level fine-grained Arabic Dialect Identification over 25 different Arabic dialects in addition to the Modern Standard Arabic. Arabic Dialect Identification is not a trivial task since different dialects share some features, e.g., utilizing the same character set and some vocabularies. We opted to adopt a very simple approach in terms of extracted features and classification models; we only utilize word and character n-grams as features, and Na ̈{\i}ve Bayes models as classifiers. Surprisingly, the simple approach achieved non-na ̈{\i}ve performance. The official results, reported on a held-out testing set, show that the dialect of a given sentence can be identified at an accuracy of 64.58{\%} by our best submitted run.",
}
@inproceedings{kchaou-etal-2019-lium,
    title = "{LIUM}-{MIRACL} Participation in the {MADAR} {A}rabic Dialect Identification Shared Task",
    author = "Kchaou, Sam{\'e}h  and
      Bougares, Fethi  and
      Hadrich-Belguith, Lamia",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4625",
    doi = "10.18653/v1/W19-4625",
    pages = "219--223",
    abstract = "This paper describes the joint participation of the LIUM and MIRACL Laboratories at the Arabic dialect identification challenge of the MADAR Shared Task (Bouamor et al., 2019) conducted during the Fourth Arabic Natural Language Processing Workshop (WANLP 2019). We participated to the Travel Domain Dialect Identification subtask. We built several systems and explored different techniques including conventional machine learning methods and deep learning algorithms. Deep learning approaches did not perform well on this task. We experimented several classification systems and we were able to identify the dialect of an input sentence with an F1-score of 65.41{\%} on the official test set using only the training data supplied by the shared task organizers.",
}
@inproceedings{fares-etal-2019-arabic,
    title = "{A}rabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features",
    author = "Fares, Youssef  and
      El-Zanaty, Zeyad  and
      Abdel-Salam, Kareem  and
      Ezzeldin, Muhammed  and
      Mohamed, Aliaa  and
      El-Awaad, Karim  and
      Torki, Marwan",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4626",
    doi = "10.18653/v1/W19-4626",
    pages = "224--228",
    abstract = "Studies on Dialectical Arabic are growing more important by the day as it becomes the primary written and spoken form of Arabic online in informal settings. Among the important problems that should be explored is that of dialect identification. This paper reports different techniques that can be applied towards such goal and reports their performance on the Multi Arabic Dialect Applications and Resources (MADAR) Arabic Dialect Corpora. Our results show that improving on traditional systems using frequency based features and non deep learning classifiers is a challenging task. We propose different models based on different word and document representations. Our top model is able to achieve an F1 macro averaged score of 65.66 on MADAR{'}s small-scale parallel corpus of 25 dialects and Modern Standard Arabic (MSA).",
}
@inproceedings{ghoul-lejeune-2019-michael,
    title = "{MICHAEL}: Mining Character-level Patterns for {A}rabic Dialect Identification ({MADAR} Challenge)",
    author = {Ghoul, Dhaou  and
      Lejeune, Ga{\"e}l},
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4627",
    doi = "10.18653/v1/W19-4627",
    pages = "229--233",
    abstract = "We present MICHAEL, a simple lightweight method for automatic Arabic Dialect Identification on the MADAR travel domain Dialect Identification (DID). MICHAEL uses simple character-level features in order to perform a pre-processing free classification. More precisely, Character N-grams extracted from the original sentences are used to train a Multinomial Naive Bayes classifier. This system achieved an official score (accuracy) of 53.25{\%} with 1{\textless}=N{\textless}=3 but showed a much better result with character 4-grams (62.17{\%} accuracy).",
}
@inproceedings{mishra-mujadia-2019-arabic,
    title = "{A}rabic Dialect Identification for Travel and {T}witter Text",
    author = "Mishra, Pruthwik  and
      Mujadia, Vandan",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4628",
    doi = "10.18653/v1/W19-4628",
    pages = "234--238",
    abstract = "This paper presents the results of the experiments done as a part of MADAR Shared Task in WANLP 2019 on Arabic Fine-Grained Dialect Identification. Dialect Identification is one of the prominent tasks in the field of Natural language processing where the subsequent language modules can be improved based on it. We explored the use of different features like char, word n-gram, language model probabilities, etc on different classifiers. Results show that these features help to improve dialect classification accuracy. Results also show that traditional machine learning classifier tends to perform better when compared to neural network models on this task in a low resource setting.",
}
@inproceedings{talafha-etal-2019-mawdoo3,
    title = "Mawdoo3 {AI} at {MADAR} Shared Task: {A}rabic Tweet Dialect Identification",
    author = "Talafha, Bashar  and
      Farhan, Wael  and
      Altakrouri, Ahmed  and
      Al-Natsheh, Hussein",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4629",
    doi = "10.18653/v1/W19-4629",
    pages = "239--243",
    abstract = "Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user. We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features. Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features. With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84{\%} on MADAR shared subtask-2. Our best submitted model ranked second out of all participating teams.",
}
@inproceedings{ragab-etal-2019-mawdoo3,
    title = "Mawdoo3 {AI} at {MADAR} Shared Task: {A}rabic Fine-Grained Dialect Identification with Ensemble Learning",
    author = "Ragab, Ahmad  and
      Seelawi, Haitham  and
      Samir, Mostafa  and
      Mattar, Abdelrahman  and
      Al-Bataineh, Hesham  and
      Zaghloul, Mohammad  and
      Mustafa, Ahmad  and
      Talafha, Bashar  and
      Freihat, Abed Alhakim  and
      Al-Natsheh, Hussein",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4630",
    doi = "10.18653/v1/W19-4630",
    pages = "244--248",
    abstract = "In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1). We propose an ensemble model of a group of experimentally designed best performing classifiers on a various set of features. Our system achieves an accuracy of 69.3{\%} macro F1-score with an improvement of 1.4{\%} accuracy from the baseline model on the DEV dataset. Our best run submitted model ranked as third out of 19 participating teams on the TEST dataset with only 0.12{\%} macro F1-score behind the top ranked system.",
}
@inproceedings{de-francony-etal-2019-hierarchical,
    title = "Hierarchical Deep Learning for {A}rabic Dialect Identification",
    author = "de Francony, Gael  and
      Guichard, Victor  and
      Joshi, Praveen  and
      Afli, Haithem  and
      Bouchekif, Abdessalam",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4631",
    doi = "10.18653/v1/W19-4631",
    pages = "249--253",
    abstract = "In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 {\%} on the subtask evaluation dataset.",
}
@inproceedings{abu-kwaik-saad-2019-arbdialectid,
    title = "{A}rb{D}ialect{ID} at {MADAR} Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained {A}rabic Dialect Identification",
    author = "Abu Kwaik, Kathrein  and
      Saad, Motaz",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4632",
    doi = "10.18653/v1/W19-4632",
    pages = "254--258",
    abstract = "In this paper, we present a Dialect Identification system (ArbDialectID) that competed at Task 1 of the MADAR shared task, MADARTravel Domain Dialect Identification. We build a course and a fine-grained identification model to predict the label (corresponding to a dialect of Arabic) of a given text. We build two language models by extracting features at two levels (words and characters). We firstly build a coarse identification model to classify each sentence into one out of six dialects, then use this label as a feature for the fine-grained model that classifies the sentence among 26 dialects from different Arab cities, after that we apply ensemble voting classifier on both sub-systems. Our system ranked 1st that achieving an f-score of 67.32{\%}. Both the models and our feature engineering tools are made available to the research community.",
}
@inproceedings{meftouh-etal-2019-smart,
    title = "The {SM}ar{T} Classifier for {A}rabic Fine-Grained Dialect Identification",
    author = "Meftouh, Karima  and
      Abidi, Karima  and
      Harrat, Salima  and
      Smaili, Kamel",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4633",
    doi = "10.18653/v1/W19-4633",
    pages = "259--263",
    abstract = "This paper describes the approach adopted by the SMarT research group to build a dialect identification system in the framework of the Madar shared task on Arabic fine-grained dialect identification. We experimented several approaches, but we finally decided to use a Multinomial Naive Bayes classifier based on word and character ngrams in addition to the language model probabilities. We achieved a score of 67.73{\%} in terms of Macro accuracy and a macro-averaged F1-score of 67.31{\%}",
}
@inproceedings{lippincott-etal-2019-jhu,
    title = "{JHU} System Description for the {MADAR} {A}rabic Dialect Identification Shared Task",
    author = "Lippincott, Tom  and
      Shapiro, Pamela  and
      Duh, Kevin  and
      McNamee, Paul",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4634",
    doi = "10.18653/v1/W19-4634",
    pages = "264--268",
    abstract = "Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel. This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning.",
}
@inproceedings{abbas-etal-2019-st,
    title = "{ST} {MADAR} 2019 Shared Task: {A}rabic Fine-Grained Dialect Identification",
    author = "Abbas, Mourad  and
      Lichouri, Mohamed  and
      Freihat, Abed Alhakim",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4635",
    doi = "10.18653/v1/W19-4635",
    pages = "269--273",
    abstract = "This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task. The proposed solution utilized a set of classifiers that we trained on character and word features. These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC). The system achieved competitive results, with a performance of 62.87 {\%} and 62.12 {\%} for both development and test sets.",
}
@inproceedings{elaraby-zahran-2019-character,
    title = "A Character Level Convolutional {B}i{LSTM} for {A}rabic Dialect Identification",
    author = "Elaraby, Mohamed  and
      Zahran, Ahmed",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4636",
    doi = "10.18653/v1/W19-4636",
    pages = "274--278",
    abstract = "In this paper, we describe CU-RAISA teamcontribution to the 2019Madar shared task2, which focused on Twitter User fine-grained dialect identification.Among par-ticipating teams, our system ranked the4th(with 61.54{\%}) F1-Macro measure.Our sys-tem is trained using a character level convo-lutional bidirectional long-short-term memorynetwork trained on 2k users{'} data. We showthat training on concatenated user tweets asinput is further superior to training on usertweets separately and assign user{'}s label on themode of user{'}s tweets{'} predictions.",
}
@inproceedings{zhang-abdul-mageed-2019-army,
    title = "No Army, No Navy: {BERT} Semi-Supervised Learning of {A}rabic Dialects",
    author = "Zhang, Chiyu  and
      Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4637",
    doi = "10.18653/v1/W19-4637",
    pages = "279--284",
    abstract = "We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification. We develop tweet-level identification models based on GRUs and BERT in supervised and semi-supervised set-tings. We then introduce a simple, yet effective, method of porting tweet-level labels at the level of users. Our system ranks top 1 in the competition, with 71.70{\%} macro F1 score and 77.40{\%} accuracy.",
}
@inproceedings{talafha-etal-2019-team,
    title = "Team {JUST} at the {MADAR} Shared Task on {A}rabic Fine-Grained Dialect Identification",
    author = "Talafha, Bashar  and
      Fadel, Ali  and
      Al-Ayyoub, Mahmoud  and
      Jararweh, Yaser  and
      AL-Smadi, Mohammad  and
      Juola, Patrick",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4638",
    doi = "10.18653/v1/W19-4638",
    pages = "285--289",
    abstract = "In this paper, we describe our team{'}s effort on the MADAR Shared Task on Arabic Fine-Grained Dialect Identification. The task requires building a system capable of differentiating between 25 different Arabic dialects in addition to MSA. Our approach is simple. After preprocessing the data, we use Data Augmentation (DA) to enlarge the training data six times. We then build a language model and extract n-gram word-level and character-level TF-IDF features and feed them into an MNB classifier. Despite its simplicity, the resulting model performs really well producing the 4th highest F-measure and region-level accuracy and the 5th highest precision, recall, city-level accuracy and country-level accuracy among the participating teams.",
}
@inproceedings{samih-etal-2019-qc,
    title = "{QC}-{GO} Submission for {MADAR} Shared Task: {A}rabic Fine-Grained Dialect Identification",
    author = "Samih, Younes  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Attia, Mohammed  and
      Eldesouki, Mohamed  and
      Darwish, Kareem",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4639",
    doi = "10.18653/v1/W19-4639",
    pages = "290--294",
    abstract = "This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification). In our participation in both subtasks, we explored a number of approaches and system combinations to obtain the best performance for both tasks. These include deep neural nets and heuristics. Since individual approaches suffer from various shortcomings, the combination of different approaches was able to fill some of these gaps. Our system achieves F1-Scores of 66.1{\%} and 67.0{\%} on the development sets for Subtasks 1 and 2 respectively.",
}
@proceedings{ws-2019-argument,
    title = "Proceedings of the 6th Workshop on Argument Mining",
    editor = "Stein, Benno  and
      Wachsmuth, Henning",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4500",
}
@inproceedings{petasis-2019-segmentation,
    title = "Segmentation of Argumentative Texts with Contextualised Word Representations",
    author = "Petasis, Georgios",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4501",
    doi = "10.18653/v1/W19-4501",
    pages = "1--10",
    abstract = "The segmentation of argumentative units is an important subtask of argument mining, which is frequently addressed at a coarse granularity, usually assuming argumentative units to be no smaller than sentences. Approaches focusing at the clause-level granularity, typically address the task as sequence labeling at the token level, aiming to classify whether a token begins, is inside, or is outside of an argumentative unit. Most approaches exploit highly engineered, manually constructed features, and algorithms typically used in sequential tagging {--} such as Conditional Random Fields, while more recent approaches try to exploit manually constructed features in the context of deep neural networks. In this context, we examined to what extend recent advances in sequential labelling allow to reduce the need for highly sophisticated, manually constructed features, and whether limiting features to embeddings, pre-trained on large corpora is a promising approach. Evaluation results suggest the examined models and approaches can exhibit comparable performance, minimising the need for feature engineering.",
}
@inproceedings{jo-etal-2019-cascade,
    title = "A Cascade Model for Proposition Extraction in Argumentation",
    author = "Jo, Yohan  and
      Visser, Jacky  and
      Reed, Chris  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4502",
    doi = "10.18653/v1/W19-4502",
    pages = "11--24",
    abstract = "We present a model to tackle a fundamental but understudied problem in computational argumentation: proposition extraction. Propositions are the basic units of an argument and the primary building blocks of most argument mining systems. However, they are usually substituted by argumentative discourse units obtained via surface-level text segmentation, which may yield text segments that lack semantic information necessary for subsequent argument mining processes. In contrast, our cascade model aims to extract complete propositions by handling anaphora resolution, text segmentation, reported speech, questions, imperatives, missing subjects, and revision. We formulate each task as a computational problem and test various models using a corpus of the 2016 U.S. presidential debates. We show promising performance for some tasks and discuss main challenges in proposition extraction.",
}
@inproceedings{opitz-frank-2019-dissecting,
    title = "Dissecting Content and Context in Argumentative Relation Analysis",
    author = "Opitz, Juri  and
      Frank, Anette",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4503",
    doi = "10.18653/v1/W19-4503",
    pages = "25--34",
    abstract = "When assessing relations between argumentative units (e.g., support or attack), computational systems often exploit disclosing indicators or markers that are not part of elementary argumentative units (EAUs) themselves, but are gained from their context (position in paragraph, preceding tokens, etc.). We show that this dependency is much stronger than previously assumed. In fact, we show that by completely masking the EAU text spans and only feeding information from their context, a competitive system may function even better. We argue that an argument analysis system that relies more on discourse context than the argument{'}s content is unsafe, since it can easily be tricked. To alleviate this issue, we separate argumentative units from their context such that the system is forced to model and rely on an EAU{'}s content. We show that the resulting classification system is more robust, and argue that such models are better suited for predicting argumentative relations across documents.",
}
@inproceedings{huber-etal-2019-aligning,
    title = "Aligning Discourse and Argumentation Structures using Subtrees and Redescription Mining",
    author = "Huber, Laurine  and
      Toussaint, Yannick  and
      Roze, Charlotte  and
      Dargnat, Mathilde  and
      Braud, Chlo{\'e}",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4504",
    doi = "10.18653/v1/W19-4504",
    pages = "35--40",
    abstract = "In this paper, we investigate similarities between discourse and argumentation structures by aligning subtrees in a corpus containing both annotations. Contrary to previous works, we focus on comparing sub-structures and not only relations matches. Using data mining techniques, we show that discourse and argumentation most often align well, and the double annotation allows to derive a mapping between structures. Moreover, this approach enables the study of similarities between discourse structures and differences in their expressive power.",
}
@inproceedings{accuosto-saggion-2019-transferring,
    title = "Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts",
    author = "Accuosto, Pablo  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4505",
    doi = "10.18653/v1/W19-4505",
    pages = "41--51",
    abstract = "In this work we propose to leverage resources available with discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task. In particular, we implement and evaluate a transfer learning approach in which contextualized representations learned from discourse parsing tasks are used as input of argument mining models. As a pilot application, we explore the feasibility of using automatically identified argumentative components and relations to predict the acceptance of papers in computer science venues. In order to conduct our experiments, we propose an annotation scheme for argumentative units and relations and use it to enrich an existing corpus with an argumentation layer.",
}
@inproceedings{eide-2019-swedish,
    title = "The {S}wedish {P}oli{G}raph: A Semantic Graph for Argument Mining of {S}wedish Parliamentary Data",
    author = "Eide, Stian R{\o}dven",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4506",
    doi = "10.18653/v1/W19-4506",
    pages = "52--57",
    abstract = "As part of a larger project on argument mining of Swedish parliamentary data, we have created a semantic graph that, together with named entity recognition and resolution (NER), should make it easier to establish connections between arguments in a given debate. The graph is essentially a semantic database that keeps track of Members of Parliament (MPs), in particular their presence in the parliament and activity in debates, but also party affiliation and participation in commissions. The hope is that the Swedish PoliGraph will enable us to perform named entity resolution on debates in the Swedish parliament with a high accuracy, with the aim of determining to whom an argument is directed.",
}
@inproceedings{lavee-etal-2019-towards,
    title = "Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining",
    author = "Lavee, Tamar  and
      Orbach, Matan  and
      Kotlerman, Lili  and
      Kantor, Yoav  and
      Gretz, Shai  and
      Dankin, Lena  and
      Jacovi, Michal  and
      Bilu, Yonatan  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4507",
    doi = "10.18653/v1/W19-4507",
    pages = "58--66",
    abstract = "Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a corpus of news articles containing billions of sentences, and searching for them in a given speech. This raises the question of whether such claims indeed correspond to those made in spoken speeches. To this end, we collected a large dataset of 400 speeches in English discussing 200 controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech. Results show that in the vast majority of speeches debaters indeed make use of such claims. In addition, we present several baselines for the automatic detection of mined claims in speeches, forming the basis for future work. All collected data is freely available for research.",
}
@inproceedings{lin-etal-2019-lexicon,
    title = "Lexicon Guided Attentive Neural Network Model for Argument Mining",
    author = "Lin, Jian-Fu  and
      Huang, Kuo Yu  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4508",
    doi = "10.18653/v1/W19-4508",
    pages = "67--73",
    abstract = "Identification of argumentative components is an important stage of argument mining. Lexicon information is reported as one of the most frequently used features in the argument mining research. In this paper, we propose a methodology to integrate lexicon information into a neural network model by attention mechanism. We conduct experiments on the UKP dataset, which is collected from heterogeneous sources and contains several text types, e.g., microblog, Wikipedia, and news. We explore lexicons from various application scenarios such as sentiment analysis and emotion detection. We also compare the experimental results of leveraging different lexicons.",
}
@inproceedings{spliethover-etal-2019-worth,
    title = "Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation",
    author = {Splieth{\"o}ver, Maximilian  and
      Klaff, Jonas  and
      Heuer, Hendrik},
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4509",
    doi = "10.18653/v1/W19-4509",
    pages = "74--82",
    abstract = "Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new state-of-the-art results. A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing. With this paper, we report a comparative evaluation of attention layers in combination with a bidirectional long short-term memory network, which is the current state-of-the-art approach for the unit segmentation task. We also compare sentence-level contextualized word embeddings to pre-generated ones. Our findings suggest that for this task, the additional attention layer does not improve the performance. In most cases, contextualized embeddings do also not show an improvement on the score achieved by pre-defined embeddings.",
}
@inproceedings{deguchi-yamaguchi-2019-argument,
    title = "Argument Component Classification by Relation Identification by Neural Network and {T}ext{R}ank",
    author = "Deguchi, Mamoru  and
      Yamaguchi, Kazunori",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4510",
    doi = "10.18653/v1/W19-4510",
    pages = "83--91",
    abstract = "In recent years, argumentation mining, which automatically extracts the structure of argumentation from unstructured documents such as essays and debates, is gaining attention. For argumentation mining applications, argument-component classification is an important subtask. The existing methods can be classified into supervised methods and unsupervised methods. Supervised document classification performs classification using a single sentence without relying on the whole document. On the other hand, unsupervised document classification has the advantage of being able to use the whole document, but accuracy of these methods is not so high. In this paper, we propose a method for argument-component classification that combines relation identification by neural networks and TextRank to integrate relation informations (i.e. the strength of the relation). This method can use argumentation-specific knowledge by employing a supervised learning on a corpus while maintaining the advantage of using the whole document. Experiments on two corpora, one consisting of student essay and the other of Wikipedia articles, show the effectiveness of this method.",
}
@inproceedings{liga-2019-argumentative,
    title = "Argumentative Evidences Classification and Argument Scheme Detection Using Tree Kernels",
    author = "Liga, Davide",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4511",
    doi = "10.18653/v1/W19-4511",
    pages = "92--97",
    abstract = "The purpose of this study is to deploy a novel methodology for classifying different argumentative support (supporting evidences) in arguments, without considering the context. The proposed methodology is based on the idea that the use of Tree Kernel algorithms can be a good way to discriminate between different types of argumentative stances without the need of highly engineered features. This can be useful in different Argumentation Mining sub-tasks. This work provides an example of classifier built using a Tree Kernel method, which can discriminate between different kinds of argumentative support with a high accuracy. The ability to distinguish different kinds of support is, in fact, a key step toward Argument Scheme classification.",
}
@inproceedings{hewett-etal-2019-utility,
    title = "The Utility of Discourse Parsing Features for Predicting Argumentation Structure",
    author = "Hewett, Freya  and
      Prakash Rane, Roshan  and
      Harlacher, Nina  and
      Stede, Manfred",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4512",
    doi = "10.18653/v1/W19-4512",
    pages = "98--103",
    abstract = "Research on argumentation mining from text has frequently discussed relationships to discourse parsing, but few empirical results are available so far. One corpus that has been annotated in parallel for argumentation structure and for discourse structure (RST, SDRT) are the {`}argumentative microtexts{'} (Peldszus and Stede, 2016a). While results on perusing the gold RST annotations for predicting argumentation have been published (Peldszus and Stede, 2016b), the step to automatic discourse parsing has not yet been taken. In this paper, we run various discourse parsers (RST, PDTB) on the corpus, compare their results to the gold annotations (for RST) and then assess the contribution of automatically-derived discourse features for argumentation parsing. After reproducing the state-of-the-art Evidence Graph model from Afantenos et al. (2018) for the microtexts, we find that PDTB features can indeed improve its performance.",
}
@inproceedings{niven-kao-2019-detecting,
    title = "Detecting Argumentative Discourse Acts with Linguistic Alignment",
    author = "Niven, Timothy  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4513",
    doi = "10.18653/v1/W19-4513",
    pages = "104--112",
    abstract = "We report the results of preliminary investigations into the relationship between linguistic alignment and dialogical argumentation at the level of discourse acts. We annotated a proof of concept dataset with illocutions and transitions at the comment level based on Inference Anchoring Theory. We estimated linguistic alignment across discourse acts and found significant variation. Alignment features calculated at the dyad level are found to be useful for detecting a range of argumentative discourse acts.",
}
@inproceedings{alliheedi-etal-2019-annotation,
    title = "Annotation of Rhetorical Moves in Biochemistry Articles",
    author = "Alliheedi, Mohammed  and
      Mercer, Robert E.  and
      Cohen, Robin",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4514",
    doi = "10.18653/v1/W19-4514",
    pages = "113--123",
    abstract = "This paper focuses on the real world application of scientific writing and on determining rhetorical moves, an important step in establishing the argument structure of biomedical articles. Using the observation that the structure of scholarly writing in laboratory-based experimental sciences closely follows laboratory procedures, we examine most closely the Methods section of the texts and adopt an approach of identifying rhetorical moves that are procedure-oriented. We also propose a verb-centric frame semantics with an effective set of semantic roles in order to support the analysis. These components are designed to support a computational model that extends a promising proposal of appropriate rhetorical moves for this domain, but one which is merely descriptive. Our work also contributes to the understanding of argument-related annotation schemes. In particular, we conduct a detailed study with human annotators to confirm that our selection of semantic roles is effective in determining the underlying rhetorical structure of existing biomedical articles in an extensive dataset. The annotated dataset that we produce provides the important knowledge needed for our ultimate goal of analyzing biochemistry articles.",
}
@inproceedings{neves-etal-2019-evaluation,
    title = "Evaluation of Scientific Elements for Text Similarity in Biomedical Publications",
    author = "Neves, Mariana  and
      Butzke, Daniel  and
      Grune, Barbara",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4515",
    doi = "10.18653/v1/W19-4515",
    pages = "124--135",
    abstract = "Rhetorical elements from scientific publications provide a more structured view of the document and allow algorithms to focus on particular parts of the text. We surveyed the literature for previously proposed schemes for rhetorical elements and present an overview of its current state of the art. We also searched for available tools using these schemes and applied four tools for our particular task of ranking biomedical abstracts based on text similarity. Comparison of the tools with two strong baselines shows that the predictions provided by the ArguminSci tool can support our use case of mining alternative methods for animal experiments.",
}
@inproceedings{panchenko-etal-2019-categorizing,
    title = "Categorizing Comparative Sentences",
    author = "Panchenko, Alexander  and
      Bondarenko, Alexander  and
      Franzek, Mirco  and
      Hagen, Matthias  and
      Biemann, Chris",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4516",
    doi = "10.18653/v1/W19-4516",
    pages = "136--145",
    abstract = "We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e.g., {``}Python has better NLP libraries than MATLAB{''} → Python, better, MATLAB). To this end, we manually annotate 7,199 sentences for 217 distinct target item pairs from several domains (27{\%} of the sentences contain an oriented comparison in the sense of {``}better{''} or {``}worse{''}). A gradient boosting model based on pre-trained sentence embeddings reaches an F1 score of 85{\%} in our experimental evaluation. The model can be used to extract comparative sentences for pro/con argumentation in comparative / argument search engines or debating technologies.",
}
@inproceedings{potash-etal-2019-ranking,
    title = "Ranking Passages for Argument Convincingness",
    author = "Potash, Peter  and
      Ferguson, Adam  and
      Hazen, Timothy J.",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4517",
    doi = "10.18653/v1/W19-4517",
    pages = "146--155",
    abstract = "In data ranking applications, pairwise annotation is often more consistent than cardinal annotation for learning ranking models. We examine this in a case study on ranking text passages for argument convincingness. Our task is to choose text passages that provide the highest-quality, most-convincing arguments for opposing sides of a topic. Using data from a deployed system within the Bing search engine, we construct a pairwise-labeled dataset for argument convincingness that is substantially more comprehensive in topical coverage compared to existing public resources. We detail the process of extracting topical passages for queries submitted to a search engine, creating annotated sets of passages aligned to different stances on a topic, and assessing argument convincingness of passages using pairwise annotation. Using a state-of-the-art convincingness model, we evaluate several methods for using pairwise-annotated data examples to train models for ranking passages. Our results show pairwise training outperforms training that regresses to a target score for each passage. Our results also show a simple {`}win-rate{'} score is a better regression target than the previously proposed page-rank target. Lastly, addressing the need to filter noisy crowd-sourced annotations when constructing a dataset, we show that filtering for transitivity within pairwise annotations is more effective than filtering based on annotation confidence measures for individual examples.",
}
@inproceedings{kotonya-toni-2019-gradual,
    title = "Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection",
    author = "Kotonya, Neema  and
      Toni, Francesca",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4518",
    doi = "10.18653/v1/W19-4518",
    pages = "156--166",
    abstract = "Stance detection plays a pivot role in fake news detection. The task involves determining the point of view or stance {--} for or against {--} a text takes towards a claim. One very important stage in employing stance detection for fake news detection is the aggregation of multiple stance labels from different text sources in order to compute a prediction for the veracity of a claim. Typically, aggregation is treated as a credibility-weighted average of stance predictions. In this work, we take the novel approach of applying, for aggregation, a gradual argumentation semantics to bipolar argumentation frameworks mined using stance detection. Our empirical evaluation shows that our method results in more accurate veracity predictions.",
}
@inproceedings{longpre-etal-2019-persuasion,
    title = "Persuasion of the Undecided: Language vs. the Listener",
    author = "Longpre, Liane  and
      Durmus, Esin  and
      Cardie, Claire",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4519",
    doi = "10.18653/v1/W19-4519",
    pages = "167--176",
    abstract = "This paper examines the factors that govern persuasion for a priori UNDECIDED versus DECIDED audience members in the context of on-line debates. We separately study two types of influences: linguistic factors {---} features of the language of the debate itself; and audience factors {---} features of an audience member encoding demographic information, prior beliefs, and debate platform behavior. In a study of users of a popular debate platform, we find first that different combinations of linguistic features are critical for predicting persuasion outcomes for UNDECIDED versus DECIDED members of the audience. We additionally find that audience factors have more influence on predicting the side (PRO/CON) that persuaded UNDECIDED users than for DECIDED users that flip their stance to the opposing side. Our results emphasize the importance of considering the undecided and decided audiences separately when studying linguistic factors of persuasion.",
}
@inproceedings{lindahl-etal-2019-towards,
    title = "Towards Assessing Argumentation Annotation - A First Step",
    author = "Lindahl, Anna  and
      Borin, Lars  and
      Rouces, Jacobo",
    booktitle = "Proceedings of the 6th Workshop on Argument Mining",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4520",
    doi = "10.18653/v1/W19-4520",
    pages = "177--186",
    abstract = "This paper presents a first attempt at using Walton{'}s argumentation schemes for annotating arguments in Swedish political text and assessing the feasibility of using this particular set of schemes with two linguistically trained annotators. The texts are not pre-annotated with argumentation structure beforehand. The results show that the annotators differ both in number of annotated arguments and selection of the conclusion and premises which make up the arguments. They also differ in their labeling of the schemes, but grouping the schemes increases their agreement. The outcome from this will be used to develop guidelines for future annotations.",
}
@proceedings{ws-2019-innovative,
    title = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    editor = "Yannakoudakis, Helen  and
      Kochmar, Ekaterina  and
      Leacock, Claudia  and
      Madnani, Nitin  and
      Pil{\'a}n, Ildik{\'o}  and
      Zesch, Torsten",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4400",
}
@inproceedings{loukina-etal-2019-many,
    title = "The many dimensions of algorithmic fairness in educational applications",
    author = "Loukina, Anastassia  and
      Madnani, Nitin  and
      Zechner, Klaus",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4401",
    doi = "10.18653/v1/W19-4401",
    pages = "1--10",
    abstract = "The issues of algorithmic fairness and bias have recently featured prominently in many publications highlighting the fact that training the algorithms for maximum performance may often result in predictions that are biased against various groups. Educational applications based on NLP and speech processing technologies often combine multiple complex machine learning algorithms and are thus vulnerable to the same sources of bias as other machine learning systems. Yet such systems can have high impact on people{'}s lives especially when deployed as part of high-stakes tests. In this paper we discuss different definitions of fairness and possible ways to apply them to educational applications. We then use simulated and real data to consider how test-takers{'} native language backgrounds can affect their automated scores on an English language proficiency assessment. We illustrate that total fairness may not be achievable and that different definitions of fairness may require different solutions.",
}
@inproceedings{ha-etal-2019-predicting,
    title = "Predicting the Difficulty of Multiple Choice Questions in a High-stakes Medical Exam",
    author = "Ha, Le An  and
      Yaneva, Victoria  and
      Baldwin, Peter  and
      Mee, Janet",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4402",
    doi = "10.18653/v1/W19-4402",
    pages = "11--20",
    abstract = "Predicting the construct-relevant difficulty of Multiple-Choice Questions (MCQs) has the potential to reduce cost while maintaining the quality of high-stakes exams. In this paper, we propose a method for estimating the difficulty of MCQs from a high-stakes medical exam, where all questions were deliberately written to a common reading level. To accomplish this, we extract a large number of linguistic features and embedding types, as well as features quantifying the difficulty of the items for an automatic question-answering system. The results show that the proposed approach outperforms various baselines with a statistically significant difference. Best results were achieved when using the full feature set, where embeddings had the highest predictive power, followed by linguistic features. An ablation study of the various types of linguistic features suggested that information from all levels of linguistic processing contributes to predicting item difficulty, with features related to semantic ambiguity and the psycholinguistic properties of words having a slightly higher importance. Owing to its generic nature, the presented approach has the potential to generalize over other exams containing MCQs.",
}
@inproceedings{zhou-etal-2019-intelligent,
    title = "An Intelligent Testing Strategy for Vocabulary Assessment of {C}hinese Second Language Learners",
    author = "Zhou, Wei  and
      Hu, Renfen  and
      Sun, Feipeng  and
      Huang, Ronghuai",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4403",
    doi = "10.18653/v1/W19-4403",
    pages = "21--29",
    abstract = "Vocabulary is one of the most important parts of language competence. Testing of vocabulary knowledge is central to research on reading and language. However, it usually costs a large amount of time and human labor to build an item bank and to test large number of students. In this paper, we propose a novel testing strategy by combining automatic item generation (AIG) and computerized adaptive testing (CAT) in vocabulary assessment for Chinese L2 learners. Firstly, we generate three types of vocabulary questions by modeling both the vocabulary knowledge and learners{'} writing error data. After evaluation and calibration, we construct a balanced item pool with automatically generated items, and implement a three-parameter computerized adaptive test. We conduct manual item evaluation and online student tests in the experiments. The results show that the combination of AIG and CAT can construct test items efficiently and reduce test cost significantly. Also, the test result of CAT can provide valuable feedback to AIG algorithms.",
}
@inproceedings{weiss-etal-2019-computationally,
    title = "Computationally Modeling the Impact of Task-Appropriate Language Complexity and Accuracy on Human Grading of {G}erman Essays",
    author = {Weiss, Zarah  and
      Riemenschneider, Anja  and
      Schr{\"o}ter, Pauline  and
      Meurers, Detmar},
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4404",
    doi = "10.18653/v1/W19-4404",
    pages = "30--45",
    abstract = "Computational linguistic research on the language complexity of student writing typically involves human ratings as a gold standard. However, educational science shows that teachers find it difficult to identify and cleanly separate accuracy, different aspects of complexity, contents, and structure. In this paper, we therefore explore the use of computational linguistic methods to investigate how task-appropriate complexity and accuracy relate to the grading of overall performance, content performance, and language performance as assigned by teachers. Based on texts written by students for the official school-leaving state examination (Abitur), we show that teachers successfully assign higher language performance grades to essays with higher task-appropriate language complexity and properly separate this from content scores. Yet, accuracy impacts teacher assessment for all grading rubrics, also the content score, overemphasizing the role of accuracy. Our analysis is based on broad computational linguistic modeling of German language complexity and an innovative theory- and data-driven feature aggregation method inferring task-appropriate language complexity.",
}
@inproceedings{snajder-etal-2019-analysing,
    title = "Analysing Rhetorical Structure as a Key Feature of Summary Coherence",
    author = "{\v{S}}najder, Jan  and
      Sladoljev-Agejev, Tamara  and
      Koli{\'c} Vehovec, Svjetlana",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4405",
    doi = "10.18653/v1/W19-4405",
    pages = "46--51",
    abstract = "We present a model for automatic scoring of coherence based on comparing the rhetorical structure (RS) of college student summaries in L2 (English) against expert summaries. Coherence is conceptualised as a construct consisting of the rhetorical relation and its arguments. Comparison with expert-assigned scores shows that RS scores correlate with both cohesion and coherence. Furthermore, RS scores improve the accuracy of a regression model for cohesion score prediction.",
}
@inproceedings{bryant-etal-2019-bea,
    title = "The {BEA}-2019 Shared Task on Grammatical Error Correction",
    author = "Bryant, Christopher  and
      Felice, Mariano  and
      Andersen, {\O}istein E.  and
      Briscoe, Ted",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4406",
    doi = "10.18653/v1/W19-4406",
    pages = "52--75",
    abstract = "This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write{\&}Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F{\_}0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.",
}
@inproceedings{flor-etal-2019-benchmark,
    title = "A Benchmark Corpus of {E}nglish Misspellings and a Minimally-supervised Model for Spelling Correction",
    author = "Flor, Michael  and
      Fried, Michael  and
      Rozovskaya, Alla",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4407",
    doi = "10.18653/v1/W19-4407",
    pages = "76--86",
    abstract = "Spelling correction has attracted a lot of attention in the NLP community. However, models have been usually evaluated on artificiallycreated or proprietary corpora. A publiclyavailable corpus of authentic misspellings, annotated in context, is still lacking. To address this, we present and release an annotated data set of 6,121 spelling errors in context, based on a corpus of essays written by English language learners. We also develop a minimallysupervised context-aware approach to spelling correction. It achieves strong results on our data: 88.12{\%} accuracy. This approach can also train with a minimal amount of annotated data (performance reduced by less than 1{\%}). Furthermore, this approach allows easy portability to new domains. We evaluate our model on data from a medical domain and demonstrate that it rivals the performance of a model trained and tuned on in-domain data.",
}
@inproceedings{qiu-park-2019-artificial,
    title = "Artificial Error Generation with Fluency Filtering",
    author = "Qiu, Mengyang  and
      Park, Jungyeul",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4408",
    doi = "10.18653/v1/W19-4408",
    pages = "87--91",
    abstract = "The quantity and quality of training data plays a crucial role in grammatical error correction (GEC). However, due to the fact that obtaining human-annotated GEC data is both time-consuming and expensive, several studies have focused on generating artificial error sentences to boost training data for grammatical error correction, and shown significantly better performance. The present study explores how fluency filtering can affect the quality of artificial errors. By comparing artificial data filtered by different levels of fluency, we find that artificial error sentences with low fluency can greatly facilitate error correction, while high fluency errors introduce more noise.",
}
@inproceedings{johan-berggren-etal-2019-regression,
    title = "Regression or classification? Automated Essay Scoring for {N}orwegian",
    author = "Johan Berggren, Stig  and
      Rama, Taraka  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4409",
    doi = "10.18653/v1/W19-4409",
    pages = "92--102",
    abstract = "In this paper we present first results for the task of Automated Essay Scoring for Norwegian learner language. We analyze a number of properties of this task experimentally and assess (i) the formulation of the task as either regression or classification, (ii) the use of various non-neural and neural machine learning architectures with various types of input representations, and (iii) applying multi-task learning for joint prediction of essay scoring and native language identification. We find that a GRU-based attention model trained in a single-task setting performs best at the AES task.",
}
@inproceedings{bell-etal-2019-context,
    title = "Context is Key: Grammatical Error Detection with Contextual Word Representations",
    author = "Bell, Samuel  and
      Yannakoudakis, Helen  and
      Rei, Marek",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4410",
    doi = "10.18653/v1/W19-4410",
    pages = "103--115",
    abstract = "Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners. Error detection as a purely supervised task can be challenging, as GED datasets are limited in size and the label distributions are highly imbalanced. Contextualized word representations offer a possible solution, as they can efficiently capture compositional information in language and can be optimized on large amounts of unsupervised data. In this paper, we perform a systematic comparison of ELMo, BERT and Flair embeddings (Peters et al., 2017; Devlin et al., 2018; Akbik et al., 2018) on a range of public GED datasets, and propose an approach to effectively integrate such representations in current methods, achieving a new state of the art on GED. We further analyze the strengths and weaknesses of different contextual embeddings for the task at hand, and present detailed analyses of their impact on different types of errors.",
}
@inproceedings{riordan-etal-2019-account,
    title = "How to account for mispellings: Quantifying the benefit of character representations in neural content scoring models",
    author = "Riordan, Brian  and
      Flor, Michael  and
      Pugh, Robert",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4411",
    doi = "10.18653/v1/W19-4411",
    pages = "116--126",
    abstract = "Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in in word token-based input. We examine this claim in neural models for content scoring. We formulate precise hypotheses about the possible effects of adding character representations to word-based models and test these hypotheses on large-scale real world content scoring datasets. We find that, while character representations may provide small performance gains in general, their effectiveness in accounting for spelling variation may be limited. We show that spelling correction can provide larger gains than character representations, and that spelling correction improves the performance of models with character representations. With these insights, we report a new state of the art on the ASAP-SAS content scoring dataset.",
}
@inproceedings{alikaniotis-raheja-2019-unreasonable,
    title = "The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction",
    author = "Alikaniotis, Dimitris  and
      Raheja, Vipul",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4412",
    doi = "10.18653/v1/W19-4412",
    pages = "127--133",
    abstract = "Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits. At the same time, advancements in language modeling have managed to generate linguistic output, which is almost indistinguishable from that of human-generated text. In this paper, we up the ante by exploring the potential of more sophisticated language models in GEC and offer some key insights on their strengths and weaknesses. We show that, in line with recent results in other NLP tasks, Transformer architectures achieve consistently high performance and provide a competitive baseline for future machine learning models.",
}
@inproceedings{katsumata-komachi-2019-almost,
    title = "(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus",
    author = "Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4413",
    doi = "10.18653/v1/W19-4413",
    pages = "134--138",
    abstract = "We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC system through experiments on a low resource track of the shared task at BEA2019. As a result, we achieved an F0.5 score of 28.31 points with the test data.",
}
@inproceedings{kantor-etal-2019-learning,
    title = "Learning to combine Grammatical Error Corrections",
    author = "Kantor, Yoav  and
      Katz, Yoav  and
      Choshen, Leshem  and
      Cohen-Karlik, Edo  and
      Liberman, Naftali  and
      Toledo, Assaf  and
      Menczel, Amir  and
      Slonim, Noam",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4414",
    doi = "10.18653/v1/W19-4414",
    pages = "139--148",
    abstract = "The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-box systems. Our method automatically detects the strength of a system or the combination of several systems per error type, improving precision and recall while optimizing F-score directly. We show consistent improvement over the best standalone system in all the configurations tested. This approach also outperforms average ensembling of different RNN models with random initializations. In addition, we analyze the use of BERT for GEC - reporting promising results on this end. We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking. This paper describes a system submission to Building Educational Applications 2019 Shared Task: Grammatical Error Correction. Combining the output of top BEA 2019 shared task systems using our approach, currently holds the highest reported score in the open phase of the BEA 2019 shared task, improving F-0.5 score by 3.7 points over the best result reported.",
}
@inproceedings{xu-etal-2019-erroneous,
    title = "Erroneous data generation for Grammatical Error Correction",
    author = "Xu, Shuyao  and
      Zhang, Jiehao  and
      Chen, Jin  and
      Qin, Long",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4415",
    doi = "10.18653/v1/W19-4415",
    pages = "149--158",
    abstract = "It has been demonstrated that the utilization of a monolingual corpus in neural Grammatical Error Correction (GEC) systems can significantly improve the system performance. The previous state-of-the-art neural GEC system is an ensemble of four Transformer models pretrained on a large amount of Wikipedia Edits. The Singsound GEC system follows a similar approach but is equipped with a sophisticated erroneous data generating component. Our system achieved an F0:5 of 66.61 in the BEA 2019 Shared Task: Grammatical Error Correction. With our novel erroneous data generating component, the Singsound neural GEC system yielded an M2 of 63.2 on the CoNLL-2014 benchmark (8.4{\%} relative improvement over the previous state-of-the-art system).",
}
@inproceedings{li-etal-2019-laix,
    title = "The {LAIX} Systems in the {BEA}-2019 {GEC} Shared Task",
    author = "Li, Ruobing  and
      Wang, Chuan  and
      Zha, Yefei  and
      Yu, Yonghong  and
      Guo, Shiman  and
      Wang, Qiang  and
      Liu, Yang  and
      Lin, Hui",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4416",
    doi = "10.18653/v1/W19-4416",
    pages = "159--167",
    abstract = "In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task. We investigate competitive classification models with bi-directional recurrent neural networks (Bi-RNN) and neural machine translation (NMT) models. For different tracks, we use ensemble systems to selectively combine the NMT models, the classification models, and some rules, and demonstrate that an ensemble solution can effectively improve GEC performance over single systems. Our GEC systems ranked the first in the Unrestricted Track, and the third in both the Restricted Track and the Low Resource Track.",
}
@inproceedings{stahlberg-byrne-2019-cueds,
    title = "The {CUED}{'}s Grammatical Error Correction Systems for {BEA}-2019",
    author = "Stahlberg, Felix  and
      Byrne, Bill",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4417",
    doi = "10.18653/v1/W19-4417",
    pages = "168--175",
    abstract = "We describe two entries from the Cambridge University Engineering Department to the BEA 2019 Shared Task on grammatical error correction. Our submission to the low-resource track is based on prior work on using finite state transducers together with strong neural language models. Our system for the restricted track is a purely neural system consisting of neural language models and neural machine translation models trained with back-translation and a combination of checkpoint averaging and fine-tuning {--} without the help of any additional tools like spell checkers. The latter system has been used inside a separate system combination entry in cooperation with the Cambridge University Computer Lab.",
}
@inproceedings{asano-etal-2019-aip,
    title = "The {AIP}-Tohoku System at the {BEA}-2019 Shared Task",
    author = "Asano, Hiroki  and
      Mita, Masato  and
      Mizumoto, Tomoya  and
      Suzuki, Jun",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4418",
    doi = "10.18653/v1/W19-4418",
    pages = "176--182",
    abstract = "We introduce the AIP-Tohoku grammatical error correction (GEC) system for the BEA-2019 shared task in Track 1 (Restricted Track) and Track 2 (Unrestricted Track) using the same system architecture. Our system comprises two key components: error generation and sentence-level error detection. In particular, GEC with sentence-level grammatical error detection is a novel and versatile approach, and we experimentally demonstrate that it significantly improves the precision of the base model. Our system is ranked 9th in Track 1 and 2nd in Track 2.",
}
@inproceedings{naplava-straka-2019-cuni,
    title = "{CUNI} System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction",
    author = "N{\'a}plava, Jakub  and
      Straka, Milan",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4419",
    doi = "10.18653/v1/W19-4419",
    pages = "183--190",
    abstract = "Our submitted models are NMT systems based on the Transformer model, which we improve by incorporating several enhancements: applying dropout to whole source and target words, weighting target subwords, averaging model checkpoints, and using the trained model iteratively for correcting the intermediate translations. The system in the Restricted Track is trained on the provided corpora with oversampled {``}cleaner{''} sentences and reaches 59.39 F0.5 score on the test set. The system in the Low-Resource Track is trained from Wikipedia revision histories and reaches 44.13 F0.5 score. Finally, we finetune the system from the Low-Resource Track on restricted data and achieve 64.55 F0.5 score.",
}
@inproceedings{flachs-etal-2019-noisy,
    title = "Noisy Channel for Low Resource Grammatical Error Correction",
    author = "Flachs, Simon  and
      Lacroix, Oph{\'e}lie  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4420",
    doi = "10.18653/v1/W19-4420",
    pages = "191--196",
    abstract = "This paper describes our contribution to the low-resource track of the BEA 2019 shared task on Grammatical Error Correction (GEC). Our approach to GEC builds on the theory of the noisy channel by combining a channel model and language model. We generate confusion sets from the Wikipedia edit history and use the frequencies of edits to estimate the channel model. Additionally, we use two pre-trained language models: 1) Google{'}s BERT model, which we fine-tune for specific error types and 2) OpenAI{'}s GPT-2 model, utilizing that it can operate with previous sentences as context. Furthermore, we search for the optimal combinations of corrections using beam search.",
}
@inproceedings{yang-wang-2019-blcu,
    title = "The {BLCU} System in the {BEA} 2019 Shared Task",
    author = "Yang, Liner  and
      Wang, Chencheng",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4421",
    doi = "10.18653/v1/W19-4421",
    pages = "197--206",
    abstract = "This paper describes the BLCU Group submissions to the Building Educational Applications (BEA) 2019 Shared Task on Grammatical Error Correction (GEC). The task is to detect and correct grammatical errors that occurred in essays. We participate in 2 tracks including the Restricted Track and the Unrestricted Track. Our system is based on a Transformer model architecture. We integrate many effective methods proposed in recent years. Such as, Byte Pair Encoding, model ensemble, checkpoints average and spell checker. We also corrupt the public monolingual data to further improve the performance of the model. On the test data of the BEA 2019 Shared Task, our system yields F0.5 = 58.62 and 59.50, ranking twelfth and fourth respectively.",
}
@inproceedings{kaneko-etal-2019-tmu,
    title = "{TMU} Transformer System Using {BERT} for Re-ranking at {BEA} 2019 Grammatical Error Correction on Restricted Track",
    author = "Kaneko, Masahiro  and
      Hotate, Kengo  and
      Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4422",
    doi = "10.18653/v1/W19-4422",
    pages = "207--212",
    abstract = "We introduce our system that is submitted to the restricted track of the BEA 2019 shared task on grammatical error correction1 (GEC). It is essential to select an appropriate hypothesis sentence from the candidates list generated by the GEC model. A re-ranker can evaluate the naturalness of a corrected sentence using language models trained on large corpora. On the other hand, these language models and language representations do not explicitly take into account the grammatical errors written by learners. Thus, it is not straightforward to utilize language representations trained from a large corpus, such as Bidirectional Encoder Representations from Transformers (BERT), in a form suitable for the learner{'}s grammatical errors. Therefore, we propose to fine-tune BERT on learner corpora with grammatical errors for re-ranking. The experimental results of the W{\&}I+LOCNESS development dataset demonstrate that re-ranking using BERT can effectively improve the correction performance.",
}
@inproceedings{choe-etal-2019-neural,
    title = "A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning",
    author = "Choe, Yo Joong  and
      Ham, Jiyeon  and
      Park, Kyubyong  and
      Yoon, Yeoil",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4423",
    doi = "10.18653/v1/W19-4423",
    pages = "213--227",
    abstract = "Grammatical error correction can be viewed as a low-resource sequence-to-sequence task, because publicly available parallel corpora are limited.To tackle this challenge, we first generate erroneous versions of large unannotated corpora using a realistic noising function. The resulting parallel corpora are sub-sequently used to pre-train Transformer models. Then, by sequentially applying transfer learning, we adapt these models to the domain and style of the test set. Combined with a context-aware neural spellchecker, our system achieves competitive results in both restricted and low resource tracks in ACL 2019 BEAShared Task. We release all of our code and materials for reproducibility.",
}
@inproceedings{yuan-etal-2019-neural,
    title = "Neural and {FST}-based approaches to grammatical error correction",
    author = "Yuan, Zheng  and
      Stahlberg, Felix  and
      Rei, Marek  and
      Byrne, Bill  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4424",
    doi = "10.18653/v1/W19-4424",
    pages = "228--239",
    abstract = "In this paper, we describe our submission to the BEA 2019 shared task on grammatical error correction. We present a system pipeline that utilises both error detection and correction models. The input text is first corrected by two complementary neural machine translation systems: one using convolutional networks and multi-task learning, and another using a neural Transformer-based system. Training is performed on publicly available data, along with artificial examples generated through back-translation. The n-best lists of these two machine translation systems are then combined and scored using a finite state transducer (FST). Finally, an unsupervised re-ranking system is applied to the n-best output of the FST. The re-ranker uses a number of error detection features to re-rank the FST n-best list and identify the final 1-best correction hypothesis. Our system achieves 66.75{\%} F 0.5 on error correction (ranking 4th), and 82.52{\%} F 0.5 on token-level error detection (ranking 2nd) in the restricted track of the shared task.",
}
@inproceedings{qiu-etal-2019-improving,
    title = "Improving Precision of Grammatical Error Correction with a Cheat Sheet",
    author = "Qiu, Mengyang  and
      Chen, Xuejiao  and
      Liu, Maggie  and
      Parvathala, Krishna  and
      Patil, Apurva  and
      Park, Jungyeul",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4425",
    doi = "10.18653/v1/W19-4425",
    pages = "240--245",
    abstract = "In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction for the restricted track of BEA 2019 Shared Task on GEC. Our results show that phrases directly extracted from GEC corpora outperform phrases from statistical machine translation phrase table by a large margin. Appending error+context phrases to the original GEC corpora yields comparably high precision. We also explore the generation of artificial syntactic error sentences using error+context phrases for the unrestricted track. The additional training data greatly facilitates syntactic error correction (e.g., verb form) and contributes to better overall performance.",
}
@inproceedings{didenko-shaptala-2019-multi,
    title = "Multi-headed Architecture Based on {BERT} for Grammatical Errors Correction",
    author = "Didenko, Bohdan  and
      Shaptala, Julia",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4426",
    doi = "10.18653/v1/W19-4426",
    pages = "246--251",
    abstract = "In this paper, we describe our approach to GEC using the BERT model for creation of encoded representation and some of our enhancements, namely, {``}Heads{''} are fully-connected networks which are used for finding the errors and later receive recommendation from the networks on dealing with a highlighted part of the sentence only. Among the main advantages of our solution is increasing the system productivity and lowering the time of processing while keeping the high accuracy of GEC results.",
}
@inproceedings{grundkiewicz-etal-2019-neural,
    title = "Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data",
    author = "Grundkiewicz, Roman  and
      Junczys-Dowmunt, Marcin  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4427",
    doi = "10.18653/v1/W19-4427",
    pages = "252--263",
    abstract = "Considerable effort has been made to address the data sparsity problem in neural grammatical error correction. In this work, we propose a simple and surprisingly effective unsupervised synthetic error generation method based on confusion sets extracted from a spellchecker to increase the amount of training data. Synthetic data is used to pre-train a Transformer sequence-to-sequence model, which not only improves over a strong baseline trained on authentic error-annotated data, but also enables the development of a practical GEC system in a scenario where little genuine error-annotated data is available. The developed systems placed first in the BEA19 shared task, achieving 69.47 and 64.24 F$_{0.5}$ in the restricted and low-resource tracks respectively, both on the W{\&}I+LOCNESS test set. On the popular CoNLL 2014 test set, we report state-of-the-art results of 64.16 M{\mbox{$^2$}} for the submitted system, and 61.30 M{\mbox{$^2$}} for the constrained system trained on the NUCLE and Lang-8 data.",
}
@inproceedings{bhalla-klimcikova-2019-evaluation,
    title = "Evaluation of automatic collocation extraction methods for language learning",
    author = "Bhalla, Vishal  and
      Klimcikova, Klara",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4428",
    doi = "10.18653/v1/W19-4428",
    pages = "264--274",
    abstract = "A number of methods have been proposed to automatically extract collocations, i.e., conventionalized lexical combinations, from text corpora. However, the attempts to evaluate and compare them with a specific application in mind lag behind. This paper compares three end-to-end resources for collocation learning, all of which used the same corpus but different methods. Adopting a gold-standard evaluation method, the results show that the method of dependency parsing outperforms regex-over-pos in collocation identification. The lexical association measures (AMs) used for collocation ranking perform about the same overall but differently for individual collocation types. Further analysis has also revealed that there are considerable differences between other commonly used AMs.",
}
@inproceedings{markov-etal-2019-anglicized,
    title = "Anglicized Words and Misspelled Cognates in Native Language Identification",
    author = "Markov, Ilia  and
      Nastase, Vivi  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4429",
    doi = "10.18653/v1/W19-4429",
    pages = "275--284",
    abstract = "In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second language (L2). In particular, we look at misspelled words that indicate lexical uncertainty on the part of the author, and separate them into three categories: misspelled cognates, {``}L2-ed{''} (in our case, anglicized) words, and all other spelling errors. We test the assumption that such errors contain clues about the native language of an essay{'}s author through the task of native language identification. The results of the experiments show that the information brought by each of these categories is complementary. We also note that while the distribution of such features changes with the proficiency level of the writer, their contribution towards native language identification remains significant at all levels.",
}
@inproceedings{miaschi-etal-2019-linguistically,
    title = "Linguistically-Driven Strategy for Concept Prerequisites Learning on {I}talian",
    author = "Miaschi, Alessio  and
      Alzetta, Chiara  and
      Cardillo, Franco Alberto  and
      Dell{'}Orletta, Felice",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4430",
    doi = "10.18653/v1/W19-4430",
    pages = "285--295",
    abstract = "We present a new concept prerequisite learning method for Learning Object (LO) ordering that exploits only linguistic features extracted from textual educational resources. The method was tested in a cross- and in- domain scenario both for Italian and English. Additionally, we performed experiments based on a incremental training strategy to study the impact of the training set size on the classifier performances. The paper also introduces ITA-PREREQ, to the best of our knowledge the first Italian dataset annotated with prerequisite relations between pairs of educational concepts, and describe the automatic strategy devised to build it.",
}
@inproceedings{arai-etal-2019-grammatical,
    title = "Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of {J}apanese as a Second Language",
    author = "Arai, Mio  and
      Kaneko, Masahiro  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4431",
    doi = "10.18653/v1/W19-4431",
    pages = "296--305",
    abstract = "Existing example retrieval systems do not include grammatically incorrect examples or present only a few examples, if any. Even if a retrieval system has a wide coverage of incorrect examples along with the correct counterpart, learners need to know whether their query includes errors or not. Considering the usability of retrieving incorrect examples, our proposed method uses a large-scale corpus and presents correct expressions along with incorrect expressions using a grammatical error detection system so that the learner do not need to be aware of how to search for the examples. Intrinsic and extrinsic evaluations indicate that our method improves accuracy of example sentence retrieval and quality of learner{'}s writing.",
}
@inproceedings{yoon-etal-2019-toward,
    title = "Toward Automated Content Feedback Generation for Non-native Spontaneous Speech",
    author = "Yoon, Su-Youn  and
      Hsieh, Ching-Ni  and
      Zechner, Klaus  and
      Mulholland, Matthew  and
      Wang, Yuan  and
      Madnani, Nitin",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4432",
    doi = "10.18653/v1/W19-4432",
    pages = "306--315",
    abstract = "In this study, we developed an automated algorithm to provide feedback about the specific content of non-native English speakers{'} spoken responses. The responses were spontaneous speech, elicited using integrated tasks where the language learners listened to and/or read passages and integrated the core content in their spoken responses. Our models detected the absence of key points considered to be important in a spoken response to a particular test question, based on two different models: (a) a model using word-embedding based content features and (b) a state-of-the art short response scoring engine using traditional n-gram based features. Both models achieved a substantially improved performance over the majority baseline, and the combination of the two models achieved a significant further improvement. In particular, the models were robust to automated speech recognition (ASR) errors, and performance based on the ASR word hypotheses was comparable to that based on manual transcriptions. The accuracy and F-score of the best model for the questions included in the train set were 0.80 and 0.68, respectively. Finally, we discussed possible approaches to generating targeted feedback about the content of a language learner{'}s response, based on automatically detected missing key points.",
}
@inproceedings{mizumoto-etal-2019-analytic,
    title = "Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring",
    author = "Mizumoto, Tomoya  and
      Ouchi, Hiroki  and
      Isobe, Yoriko  and
      Reisert, Paul  and
      Nagata, Ryo  and
      Sekine, Satoshi  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4433",
    doi = "10.18653/v1/W19-4433",
    pages = "316--325",
    abstract = "This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts. We first propose and formalize two novel analytical assessment tasks: analytic score prediction and justification identification, and then provide the first dataset created for analytic short answer scoring research. Subsequently, we present a neural baseline model and report our extensive empirical results to demonstrate how our dataset can be used to explore new and intriguing technical challenges in short answer scoring. The dataset is publicly available for research purposes.",
}
@inproceedings{shah-etal-2019-content,
    title = "Content Customization for Micro Learning using Human Augmented {AI} Techniques",
    author = "Shah, Ayush  and
      Abuelsaad, Tamer  and
      Ahn, Jae-Wook  and
      Dey, Prasenjit  and
      Kokku, Ravi  and
      Sharma Mittal, Ruhi  and
      Vempaty, Aditya  and
      Sharma, Mourvi",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4434",
    doi = "10.18653/v1/W19-4434",
    pages = "326--335",
    abstract = "Visual content has been proven to be effective for micro-learning compared to other media. In this paper, we discuss leveraging this observation in our efforts to build audio-visual content for young learners{'} vocabulary learning. We attempt to tackle two major issues in the process of traditional visual curation tasks. Generic learning videos do not necessarily satisfy the unique context of a learner and/or an educator, and hence may not result in maximal learning outcomes. Also, manual video curation by educators is a highly labor-intensive process. To this end, we present a customizable micro-learning audio-visual content curation tool that is designed to reduce the human (educator) effort in creating just-in-time learning videos from a textual description (learning script). This provides educators with control of the content while preparing the learning scripts, and in turn can also be customized to capture the desired learning objectives and outcomes. As a use case, we automatically generate learning videos with British National Corpus{'} (BNC) frequently spoken vocabulary words and evaluate them with experts. They positively recommended the generated learning videos with an average rating of 4.25 on a Likert scale of 5 points. The inter-annotator agreement between the experts for the video quality was substantial (Fleiss Kappa=0.62) with an overall agreement of 81{\%}.",
}
@inproceedings{raamadhurai-etal-2019-curio,
    title = "Curio {S}mart{C}hat : A system for Natural Language Question Answering for Self-Paced K-12 Learning",
    author = "Raamadhurai, Srikrishna  and
      Baker, Ryan  and
      Poduval, Vikraman",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4435",
    doi = "10.18653/v1/W19-4435",
    pages = "336--342",
    abstract = "During learning, students often have questions which they would benefit from responses to in real time. In class, a student can ask a question to a teacher. During homework, or even in class if the student is shy, it can be more difficult to receive a rapid response. In this work, we introduce Curio SmartChat, an automated question answering system for middle school Science topics. Our system has now been used by around 20,000 students who have so far asked over 100,000 questions. We present data on the challenge created by students{'} grammatical errors and spelling mistakes, and discuss our system{'}s approach and degree of effectiveness at disambiguating questions that the system is initially unsure about. We also discuss the prevalence of student {``}small talk{''} not related to science topics, the pluses and minuses of this behavior, and how a system should respond to these conversational acts. We conclude with discussions and point to directions for potential future work.",
}
@inproceedings{gecchele-etal-2019-supporting,
    title = "Supporting content evaluation of student summaries by Idea Unit embedding",
    author = "Gecchele, Marcello  and
      Yamada, Hiroaki  and
      Tokunaga, Takenobu  and
      Sawaki, Yasuyo",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4436",
    doi = "10.18653/v1/W19-4436",
    pages = "343--348",
    abstract = "This paper discusses the computer-assisted content evaluation of summaries. We propose a method to make a correspondence between the segments of the source text and its summary. As a unit of the segment, we adopt {``}Idea Unit (IU){''} which is proposed in Applied Linguistics. Introducing IUs enables us to make a correspondence even for the sentences that contain multiple ideas. The IU correspondence is made based on the similarity between vector representations of IU. An evaluation experiment with two source texts and 20 summaries showed that the proposed method is more robust against rephrased expressions than the conventional ROUGE-based baselines. Also, the proposed method outperformed the baselines in recall. We im-plemented the proposed method in a GUI tool{``}Segment Matcher{''} that aids teachers to estab-lish a link between corresponding IUs acrossthe summary and source text.",
}
@inproceedings{vajjala-lucic-2019-understanding,
    title = "On Understanding the Relation between Expert Annotations of Text Readability and Target Reader Comprehension",
    author = "Vajjala, Sowmya  and
      Lucic, Ivana",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4437",
    doi = "10.18653/v1/W19-4437",
    pages = "349--359",
    abstract = "Automatic readability assessment aims to ensure that readers read texts that they can comprehend. However, computational models are typically trained on texts created from the perspective of the text writer, not the target reader. There is little experimental research on the relationship between expert annotations of readability, reader{'}s language proficiency, and different levels of reading comprehension. To address this gap, we conducted a user study in which over a 100 participants read texts of different reading levels and answered questions created to test three forms of comprehension. Our results indicate that more than readability annotation or reader proficiency, it is the type of comprehension question asked that shows differences between reader responses - inferential questions were difficult for users of all levels of proficiency across reading levels. The data collected from this study will be released with this paper, which will, for the first time, provide a collection of 45 reader bench marked texts to evaluate readability assessment systems developed for adult learners of English. It can also potentially be useful for the development of question generation approaches in intelligent tutoring systems research.",
}
@inproceedings{forti-etal-2019-measuring,
    title = "Measuring Text Complexity for {I}talian as a Second Language Learning Purposes",
    author = "Forti, Luciana  and
      Milani, Alfredo  and
      Piersanti, Luisa  and
      Santarelli, Filippo  and
      Santucci, Valentino  and
      Spina, Stefania",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4438",
    doi = "10.18653/v1/W19-4438",
    pages = "360--368",
    abstract = "The selection of texts for second language learning purposes typically relies on teachers{'} and test developers{'} individual judgment of the observable qualitative properties of a text. Little or no consideration is generally given to the quantitative dimension within an evidence-based framework of reproducibility. This study aims to fill the gap by evaluating the effectiveness of an automatic tool trained to assess text complexity in the context of Italian as a second language learning. A dataset of texts labeled by expert test developers was used to evaluate the performance of three classifier models (decision tree, random forest, and support vector machine), which were trained using linguistic features measured quantitatively and extracted from the texts. The experimental analysis provided satisfactory results, also in relation to which kind of linguistic trait contributed the most to the final outcome.",
}
@inproceedings{renduchintala-etal-2019-simple,
    title = "Simple Construction of Mixed-Language Texts for Vocabulary Learning",
    author = "Renduchintala, Adithya  and
      Koehn, Philipp  and
      Eisner, Jason",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4439",
    doi = "10.18653/v1/W19-4439",
    pages = "369--379",
    abstract = "We present a machine foreign-language teacher that takes documents written in a student{'}s native language and detects situations where it can replace words with their foreign glosses such that new foreign vocabulary can be learned simply through reading the resulting mixed-language text. We show that it is possible to design such a machine teacher without any supervised data from (human) students. We accomplish this by modifying a cloze language model to incrementally learn new vocabulary items, and use this language model as a proxy for the word guessing and learning ability of real students. Our machine foreign-language teacher decides which subset of words to replace by consulting this language model. We evaluate three variants of our student proxy language models through a study on Amazon Mechanical Turk (MTurk). We find that MTurk {``}students{''} were able to guess the meanings of foreign words introduced by the machine teacher with high accuracy for both function words as well as content words in two out of the three models. In addition, we show that students are able to retain their knowledge about the foreign words after they finish reading the document.",
}
@inproceedings{weiss-meurers-2019-analyzing,
    title = "Analyzing Linguistic Complexity and Accuracy in Academic Language Development of {G}erman across Elementary and Secondary School",
    author = "Weiss, Zarah  and
      Meurers, Detmar",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4440",
    doi = "10.18653/v1/W19-4440",
    pages = "380--393",
    abstract = "We track the development of writing complexity and accuracy in German students{'} early academic language development from first to eighth grade. Combining an empirically broad approach to linguistic complexity with the high-quality error annotation included in the Karlsruhe Children{'}s Text corpus (Lavalley et al. 2015) used, we construct models of German academic language development that successfully identify the student{'}s grade level. We show that classifiers for the early years rely more on accuracy development, whereas development in secondary school is better characterized by increasingly complex language in all domains: linguistic system, language use, and human sentence processing characteristics. We demonstrate the generalizability and robustness of models using such a broad complexity feature set across writing topics.",
}
@inproceedings{yoon-lee-2019-content,
    title = "Content Modeling for Automated Oral Proficiency Scoring System",
    author = "Yoon, Su-Youn  and
      Lee, Chong Min",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4441",
    doi = "10.18653/v1/W19-4441",
    pages = "394--401",
    abstract = "We developed an automated oral proficiency scoring system for non-native English speakers{'} spontaneous speech. Automated systems that score holistic proficiency are expected to assess a wide range of performance categories, and the content is one of the core performance categories. In order to assess the quality of the content, we trained a Siamese convolutional neural network (CNN) to model the semantic relationship between key points generated by experts and a test response. The correlation between human scores and Siamese CNN scores was comparable to human-human agreement (r=0.63), and it was higher than the baseline content features. The inclusion of Siamese CNN-based feature to the existing state-of-the-art automated scoring model achieved a small but statistically significant improvement. However, the new model suffered from score inflation for long atypical responses with serious content issues. We investigated the reasons of this score inflation by analyzing the associations with linguistic features and identifying areas strongly associated with the score errors.",
}
@inproceedings{mondal-etal-2019-learning,
    title = "Learning Outcomes and Their Relatedness in a Medical Curriculum",
    author = "Mondal, Sneha  and
      Dhamecha, Tejas  and
      Godbole, Shantanu  and
      Pathak, Smriti  and
      Mendoza, Red  and
      Wijayarathna, K Gayathri  and
      Zary, Nabil  and
      Saha, Swarnadeep  and
      Chetlur, Malolan",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4442",
    doi = "10.18653/v1/W19-4442",
    pages = "402--411",
    abstract = "A typical medical curriculum is organized in a hierarchy of instructional objectives called Learning Outcomes (LOs); a few thousand LOs span five years of study. Gaining a thorough understanding of the curriculum requires learners to recognize and apply related LOs across years, and across different parts of the curriculum. However, given the large scope of the curriculum, manually labeling related LOs is tedious, and almost impossible to scale. In this paper, we build a system that learns relationships between LOs, and we achieve up to human-level performance in the LO relationship extraction task. We then present an application where the proposed system is employed to build a map of related LOs and Learning Resources (LRs) pertaining to a virtual patient case. We believe that our system can help medical students grasp the curriculum better, within classroom as well as in Intelligent Tutoring Systems (ITS) settings.",
}
@inproceedings{benzahra-yvon-2019-measuring,
    title = "Measuring text readability with machine comprehension: a pilot study",
    author = "Benzahra, Marc  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4443",
    doi = "10.18653/v1/W19-4443",
    pages = "412--422",
    abstract = "This article studies the relationship between text readability indice and automatic machine understanding systems. Our hypothesis is that the simpler a text is, the better it should be understood by a machine. We thus expect to a strong correlation between readability levels on the one hand, and performance of automatic reading systems on the other hand. We test this hypothesis with several understanding systems based on language models of varying strengths, measuring this correlation on two corpora of journalistic texts. Our results suggest that this correlation is rather small that existing comprehension systems are far to reproduce the gradual improvement of their performance on texts of decreasing complexity.",
}
@inproceedings{clausen-nastase-2019-metaphors,
    title = "Metaphors in Text Simplification: To change or not to change, that is the question",
    author = "Clausen, Yulia  and
      Nastase, Vivi",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4444",
    doi = "10.18653/v1/W19-4444",
    pages = "423--434",
    abstract = "We present an analysis of metaphors in news text simplification. Using features that capture general and metaphor specific characteristics, we test whether we can automatically identify which metaphors will be changed or preserved, and whether there are features that have different predictive power for metaphors or literal words. The experiments show that the Age of Acquisition is the most distinctive feature for both metaphors and literal words. Features that capture Imageability and Concreteness are useful when used alone, but within the full set of features they lose their impact. Frequency of use seems to be the best feature to differentiate metaphors that should be changed and those to be preserved.",
}
@inproceedings{wang-etal-2019-application,
    title = "Application of an Automatic Plagiarism Detection System in a Large-scale Assessment of {E}nglish Speaking Proficiency",
    author = "Wang, Xinhao  and
      Evanini, Keelan  and
      Mulholland, Matthew  and
      Qian, Yao  and
      Bruno, James V.",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4445",
    doi = "10.18653/v1/W19-4445",
    pages = "435--443",
    abstract = "This study aims to build an automatic system for the detection of plagiarized spoken responses in the context of an assessment of English speaking proficiency for non-native speakers. Classification models were trained to distinguish between plagiarized and non-plagiarized responses with two different types of features: text-to-text content similarity measures, which are commonly used in the task of plagiarism detection for written documents, and speaking proficiency measures, which were specifically designed for spontaneous speech and extracted using an automated speech scoring system. The experiments were first conducted on a large data set drawn from an operational English proficiency assessment across multiple years, and the best classifier on this heavily imbalanced data set resulted in an F1-score of 0.761 on the plagiarized class. This system was then validated on operational responses collected from a single administration of the assessment and achieved a recall of 0.897. The results indicate that the proposed system can potentially be used to improve the validity of both human and automated assessment of non-native spoken English.",
}
@inproceedings{mayfield-etal-2019-equity,
    title = "Equity Beyond Bias in Language Technologies for Education",
    author = "Mayfield, Elijah  and
      Madaio, Michael  and
      Prabhumoye, Shrimai  and
      Gerritsen, David  and
      McLaughlin, Brittany  and
      Dixon-Rom{\'a}n, Ezekiel  and
      Black, Alan W",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4446",
    doi = "10.18653/v1/W19-4446",
    pages = "444--460",
    abstract = "There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other frameworks for teaching and learning, identifying future work on equity in NLP. We present case studies in a range of topics like intelligent tutoring systems, computer-assisted language learning, automated essay scoring, and sentiment analysis in classrooms, and provide an actionable agenda for research.",
}
@inproceedings{huang-etal-2019-receptive,
    title = "From Receptive to Productive: Learning to Use Confusing Words through Automatically Selected Example Sentences",
    author = "Huang, Chieh-Yang  and
      Huang, Yi-Ting  and
      Chen, MeiHua  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4447",
    doi = "10.18653/v1/W19-4447",
    pages = "461--471",
    abstract = "Knowing how to use words appropriately has been a key to improving language proficiency. Previous studies typically discuss how students learn receptively to select the correct candidate from a set of confusing words in the fill-in-the-blank task where specific context is given. In this paper, we go one step further, assisting students to learn to use confusing words appropriately in a productive task: sentence translation. We leverage the GiveMe-Example system, which suggests example sentences for each confusing word, to achieve this goal. In this study, students learn to differentiate the confusing words by reading the example sentences, and then choose the appropriate word(s) to complete the sentence translation task. Results show students made substantial progress in terms of sentence structure. In addition, highly proficient students better managed to learn confusing words. In view of the influence of the first language on learners, we further propose an effective approach to improve the quality of the suggested sentences.",
}
@inproceedings{sakakini-etal-2019-equipping,
    title = "Equipping Educational Applications with Domain Knowledge",
    author = "Sakakini, Tarek  and
      Gong, Hongyu  and
      Lee, Jong Yoon  and
      Schloss, Robert  and
      Xiong, JinJun  and
      Bhat, Suma",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4448",
    doi = "10.18653/v1/W19-4448",
    pages = "472--477",
    abstract = "One of the challenges of building natural language processing (NLP) applications for education is finding a large domain-specific corpus for the subject of interest (e.g., history or science). To address this challenge, we propose a tool, Dexter, that extracts a subject-specific corpus from a heterogeneous corpus, such as Wikipedia, by relying on a small seed corpus and distributed document representations. We empirically show the impact of the generated corpus on language modeling, estimating word embeddings, and consequently, distractor generation, resulting in better performances than while using a general domain corpus, a heuristically constructed domain-specific corpus, and a corpus generated by a popular system: BootCaT.",
}
@inproceedings{htut-tetreault-2019-unbearable,
    title = "The Unbearable Weight of Generating Artificial Errors for Grammatical Error Correction",
    author = "Htut, Phu Mon  and
      Tetreault, Joel",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4449",
    doi = "10.18653/v1/W19-4449",
    pages = "478--483",
    abstract = "In this paper, we investigate the impact of using 4 recent neural models for generating artificial errors to help train the neural grammatical error correction models. We conduct a battery of experiments on the effect of data size, models, and comparison with a rule-based approach.",
}
@inproceedings{nadeem-etal-2019-automated,
    title = "Automated Essay Scoring with Discourse-Aware Neural Models",
    author = "Nadeem, Farah  and
      Nguyen, Huy  and
      Liu, Yang  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4450",
    doi = "10.18653/v1/W19-4450",
    pages = "484--493",
    abstract = "Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering. Neural networks offer an alternative to feature engineering, but they typically require more annotated data. This paper explores network structures, contextualized embeddings and pre-training strategies aimed at capturing discourse characteristics of essays. Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available.",
}
@inproceedings{hou-etal-2019-modeling,
    title = "Modeling language learning using specialized Elo rating",
    author = "Hou, Jue  and
      Maximilian, Koppatz  and
      Hoya Quecedo, Jos{\'e} Mar{\'\i}a  and
      Stoyanova, Nataliya  and
      Yangarber, Roman",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4451",
    doi = "10.18653/v1/W19-4451",
    pages = "494--506",
    abstract = "Automatic assessment of the proficiency levels of the learner is a critical part of Intelligent Tutoring Systems. We present methods for assessment in the context of language learning. We use a specialized Elo formula used in conjunction with educational data mining. We simultaneously obtain ratings for the proficiency of the learners and for the difficulty of the linguistic concepts that the learners are trying to master. From the same data we also learn a graph structure representing a domain model capturing the relations among the concepts. This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts.",
}
@inproceedings{gao-etal-2019-rubric,
    title = "Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays",
    author = "Gao, Yanjun  and
      Driban, Alex  and
      Xavier McManus, Brennan  and
      Musi, Elena  and
      Davies, Patricia  and
      Muresan, Smaranda  and
      Passonneau, Rebecca J.",
    booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4452",
    doi = "10.18653/v1/W19-4452",
    pages = "507--518",
    abstract = "We present a unique dataset of student source-based argument essays to facilitate research on the relations between content, argumentation skills, and assessment. Two classroom writing assignments were given to college students in a STEM major, accompanied by a carefully designed rubric. The paper presents a reliability study of the rubric, showing it to be highly reliable, and initial annotation on content and argumentation annotation of the essays.",
}
@proceedings{ws-2019-representation,
    title = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    editor = "Augenstein, Isabelle  and
      Gella, Spandana  and
      Ruder, Sebastian  and
      Kann, Katharina  and
      Can, Burcu  and
      Welbl, Johannes  and
      Conneau, Alexis  and
      Ren, Xiang  and
      Rei, Marek",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4300",
}
@inproceedings{benton-etal-2019-deep,
    title = "Deep Generalized Canonical Correlation Analysis",
    author = "Benton, Adrian  and
      Khayrallah, Huda  and
      Gujral, Biman  and
      Reisinger, Dee Ann  and
      Zhang, Sheng  and
      Arora, Raman",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4301",
    doi = "10.18653/v1/W19-4301",
    pages = "1--6",
    abstract = "We present Deep Generalized Canonical Correlation Analysis (DGCCA) {--} a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn and evaluate DGCCA representations for three downstream tasks: phonetic transcription from acoustic {\&} articulatory measurements, recommending hashtags and recommending friends on a dataset of Twitter users.",
}
@inproceedings{peters-etal-2019-tune,
    title = "To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks",
    author = "Peters, Matthew E.  and
      Ruder, Sebastian  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4302",
    doi = "10.18653/v1/W19-4302",
    pages = "7--14",
    abstract = "While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction (where the pretrained weights are frozen), and directly fine-tuning the pretrained model. Our empirical results across diverse NLP tasks with two state-of-the-art models show that the relative performance of fine-tuning vs. feature extraction depends on the similarity of the pretraining and target tasks. We explore possible explanations for this finding and provide a set of adaptation guidelines for the NLP practitioner.",
}
@inproceedings{budhkar-etal-2019-generative,
    title = "Generative Adversarial Networks for Text Using Word2vec Intermediaries",
    author = "Budhkar, Akshay  and
      Vishnubhotla, Krishnapriya  and
      Hossain, Safwan  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4303",
    doi = "10.18653/v1/W19-4303",
    pages = "15--26",
    abstract = "Generative adversarial networks (GANs) have shown considerable success, especially in the realistic generation of images. In this work, we apply similar techniques for the generation of text. We propose a novel approach to handle the discrete nature of text, during training, using word embeddings. Our method is agnostic to vocabulary size and achieves competitive results relative to methods with various discrete gradient estimators.",
}
@inproceedings{raganato-etal-2019-evaluation,
    title = "An Evaluation of Language-Agnostic Inner-Attention-Based Representations in Machine Translation",
    author = {Raganato, Alessandro  and
      V{\'a}zquez, Ra{\'u}l  and
      Creutz, Mathias  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4304",
    doi = "10.18653/v1/W19-4304",
    pages = "27--32",
    abstract = "In this paper, we explore a multilingual translation model with a cross-lingually shared layer that can be used as fixed-size sentence representation in different downstream tasks. We systematically study the impact of the size of the shared layer and the effect of including additional languages in the model. In contrast to related previous work, we demonstrate that the performance in translation does correlate with trainable downstream tasks. In particular, we show that larger intermediate layers not only improve translation quality, especially for long sentences, but also push the accuracy of trainable classification tasks. On the other hand, shorter representations lead to increased compression that is beneficial in non-trainable similarity tasks. We hypothesize that the training procedure on the downstream task enables the model to identify the encoded information that is useful for the specific task whereas non-trainable benchmarks can be confused by other types of information also encoded in the representation of a sentence.",
}
@inproceedings{vazquez-etal-2019-multilingual,
    title = "Multilingual {NMT} with a Language-Independent Attention Bridge",
    author = {V{\'a}zquez, Ra{\'u}l  and
      Raganato, Alessandro  and
      Tiedemann, J{\"o}rg  and
      Creutz, Mathias},
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4305",
    doi = "10.18653/v1/W19-4305",
    pages = "33--39",
    abstract = "In this paper, we propose an architecture for machine translation (MT) capable of obtaining multilingual sentence representations by incorporating an intermediate attention bridge that is shared across all languages. We train the model with language-specific encoders and decoders that are connected through an inner-attention layer on the encoder side. The attention bridge exploits the semantics from each language for translation and develops into a language-agnostic meaning representation that can efficiently be used for transfer learning. We present a new framework for the efficient development of multilingual neural machine translation (NMT) using this model and scheduled training. We have tested the approach in a systematic way with a multi-parallel data set. The model achieves substantial improvements over strong bilingual models and performs well for zero-shot translation, which demonstrates its ability of abstraction and transfer learning.",
}
@inproceedings{kodryan-etal-2019-efficient,
    title = "Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks",
    author = "Kodryan, Maxim  and
      Grachev, Artem  and
      Ignatov, Dmitry  and
      Vetrov, Dmitry",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4306",
    doi = "10.18653/v1/W19-4306",
    pages = "40--48",
    abstract = "Reduction of the number of parameters is one of the most important goals in Deep Learning. In this article we propose an adaptation of Doubly Stochastic Variational Inference for Automatic Relevance Determination (DSVI-ARD) for neural networks compression. We find this method to be especially useful in language modeling tasks, where large number of parameters in the input and output layers is often excessive. We also show that DSVI-ARD can be applied together with encoder-decoder weight tying allowing to achieve even better sparsity and performance. Our experiments demonstrate that more than 90{\%} of the weights in both encoder and decoder layers can be removed with a minimal quality loss.",
}
@inproceedings{rethmeier-plank-2019-morty,
    title = "{M}o{RT}y: Unsupervised Learning of Task-specialized Word Embeddings by Autoencoding",
    author = "Rethmeier, Nils  and
      Plank, Barbara",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4307",
    doi = "10.18653/v1/W19-4307",
    pages = "49--54",
    abstract = "Word embeddings have undoubtedly revolutionized NLP. However, pretrained embeddings do not always work for a specific task (or set of tasks), particularly in limited resource setups. We introduce a simple yet effective, self-supervised post-processing method that constructs task-specialized word representations by picking from a menu of reconstructing transformations to yield improved end-task performance (MORTY). The method is complementary to recent state-of-the-art approaches to inductive transfer via fine-tuning, and forgoes costly model architectures and annotation. We evaluate MORTY on a broad range of setups, including different word embedding methods, corpus sizes and end-task semantics. Finally, we provide a surprisingly simple recipe to obtain specialized embeddings that better fit end-tasks.",
}
@inproceedings{eger-etal-2019-pitfalls,
    title = "Pitfalls in the Evaluation of Sentence Embeddings",
    author = {Eger, Steffen  and
      R{\"u}ckl{\'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4308",
    doi = "10.18653/v1/W19-4308",
    pages = "55--60",
    abstract = "Deep learning models continuously break new records across different NLP tasks. At the same time, their success exposes weaknesses of model evaluation. Here, we compile several key pitfalls of evaluation of sentence embeddings, a currently very popular NLP paradigm. These pitfalls include the comparison of embeddings of different sizes, normalization of embeddings, and the low (and diverging) correlations between transfer and probing tasks. Our motivation is to challenge the current evaluation of sentence embeddings and to provide an easy-to-access reference for future research. Based on our insights, we also recommend better practices for better future evaluations of sentence embeddings.",
}
@inproceedings{kim-etal-2019-learning,
    title = "Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron",
    author = "Kim, Yunsu  and
      Rosendahl, Hendrik  and
      Rossenbach, Nick  and
      Rosendahl, Jan  and
      Khadivi, Shahram  and
      Ney, Hermann",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4309",
    doi = "10.18653/v1/W19-4309",
    pages = "61--71",
    abstract = "We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data. Our method connects autoencoding and neural machine translation to force the source and target sentence embeddings to share the same space without the help of a pivot language or an additional transformation. We train a multilayer perceptron on top of the sentence embeddings to extract good bilingual sentence pairs from nonparallel or noisy parallel data. Our approach shows promising performance on sentence alignment recovery and the WMT 2018 parallel corpus filtering tasks with only a single model.",
}
@inproceedings{kamath-etal-2019-specializing,
    title = "Specializing Distributional Vectors of All Words for Lexical Entailment",
    author = "Kamath, Aishwarya  and
      Pfeiffer, Jonas  and
      Ponti, Edoardo Maria  and
      Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4310",
    doi = "10.18653/v1/W19-4310",
    pages = "72--83",
    abstract = "Semantic specialization methods fine-tune distributional word vectors using lexical knowledge from external resources (e.g. WordNet) to accentuate a particular relation between words. However, such post-processing methods suffer from limited coverage as they affect only vectors of words seen in the external resources. We present the first post-processing method that specializes vectors of all vocabulary words {--} including those unseen in the resources {--} for the asymmetric relation of lexical entailment (LE) (i.e., hyponymy-hypernymy relation). Leveraging a partially LE-specialized distributional space, our POSTLE (i.e., post-specialization for LE) model learns an explicit global specialization function, allowing for specialization of vectors of unseen words, as well as word vectors from other languages via cross-lingual transfer. We capture the function as a deep feed-forward neural network: its objective re-scales vector norms to reflect the concept hierarchy while simultaneously attracting hyponymy-hypernymy pairs to better reflect semantic similarity. An extended model variant augments the basic architecture with an adversarial discriminator. We demonstrate the usefulness and versatility of POSTLE models with different input distributional spaces in different scenarios (monolingual LE and zero-shot cross-lingual LE transfer) and tasks (binary and graded LE). We report consistent gains over state-of-the-art LE-specialization methods, and successfully LE-specialize word vectors for languages without any external lexical knowledge.",
}
@inproceedings{kalouli-etal-2019-composing,
    title = "Composing Noun Phrase Vector Representations",
    author = "Kalouli, Aikaterini-Lida  and
      de Paiva, Valeria  and
      Crouch, Richard",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4311",
    doi = "10.18653/v1/W19-4311",
    pages = "84--95",
    abstract = "Vector representations of words have seen an increasing success over the past years in a variety of NLP tasks. While there seems to be a consensus about the usefulness of word embeddings and how to learn them, it is still unclear which representations can capture the meaning of phrases or even whole sentences. Recent work has shown that simple operations outperform more complex deep architectures. In this work, we propose two novel constraints for computing noun phrase vector representations. First, we propose that the semantic and not the syntactic contribution of each component of a noun phrase should be considered, so that the resulting composed vectors express more of the phrase meaning. Second, the composition process of the two phrase vectors should apply suitable dimensions{'} selection in a way that specific semantic features captured by the phrase{'}s meaning become more salient. Our proposed methods are compared to 11 other approaches, including popular baselines and a neural net architecture, and are evaluated across 6 tasks and 2 datasets. Our results show that these constraints lead to more expressive phrase representations and can be applied to other state-of-the-art methods to improve their performance.",
}
@inproceedings{schweter-baiter-2019-towards,
    title = "Towards Robust Named Entity Recognition for Historic {G}erman",
    author = "Schweter, Stefan  and
      Baiter, Johannes",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4312",
    doi = "10.18653/v1/W19-4312",
    pages = "96--103",
    abstract = "In this paper we study the influence of using language model pre-training for named entity recognition for Historic German. We achieve new state-of-the-art results using carefully chosen training data for language models. For a low-resource domain like named entity recognition for Historic German, language model pre-training can be a strong competitor to CRF-only methods. We show that language model pre-training can be more effective than using transfer-learning with labeled datasets. Furthermore, we introduce a new language model pre-training objective, synthetic masked language model pre-training (SMLM), that allows a transfer from one domain (contemporary texts) to another domain (historical texts) by using only the same (character) vocabulary. Results show that using SMLM can achieve comparable results for Historic named entity recognition, even when they are only trained on contemporary texts. Our pre-trained character-based language models improve upon classical CRF-based methods and previous work on Bi-LSTMs by boosting F1 score performance by up to 6{\%}.",
}
@inproceedings{wang-etal-2019-evaluating,
    title = "On Evaluating Embedding Models for Knowledge Base Completion",
    author = "Wang, Yanjie  and
      Ruffinelli, Daniel  and
      Gemulla, Rainer  and
      Broscheit, Samuel  and
      Meilicke, Christian",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4313",
    doi = "10.18653/v1/W19-4313",
    pages = "104--112",
    abstract = "Knowledge graph embedding models have recently received significant attention in the literature. These models learn latent semantic representations for the entities and relations in a given knowledge base; the representations can be used to infer missing knowledge. In this paper, we study the question of how well recent embedding models perform for the task of knowledge base completion, i.e., the task of inferring new facts from an incomplete knowledge base. We argue that the entity ranking protocol, which is currently used to evaluate knowledge graph embedding models, is not suitable to answer this question since only a subset of the model predictions are evaluated. We propose an alternative entity-pair ranking protocol that considers all model predictions as a whole and is thus more suitable to the task. We conducted an experimental study on standard datasets and found that the performance of popular embeddings models was unsatisfactory under the new protocol, even on datasets that are generally considered to be too easy. Moreover, we found that a simple rule-based model often provided superior performance. Our findings suggest that there is a need for more research into embedding models as well as their training strategies for the task of knowledge base completion.",
}
@inproceedings{kogkalidis-etal-2019-constructive,
    title = "Constructive Type-Logical Supertagging With Self-Attention Networks",
    author = "Kogkalidis, Konstantinos  and
      Moortgat, Michael  and
      Deoskar, Tejaswini",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4314",
    doi = "10.18653/v1/W19-4314",
    pages = "113--123",
    abstract = "We propose a novel application of self-attention networks towards grammar induction. We present an attention-based supertagger for a refined type-logical grammar, trained on constructing types inductively. In addition to achieving a high overall type accuracy, our model is able to learn the syntax of the grammar{'}s type system along with its denotational semantics. This lifts the closed world assumption commonly made by lexicalized grammar supertaggers, greatly enhancing its generalization potential. This is evidenced both by its adequate accuracy over sparse word types and its ability to correctly construct complex types never seen during training, which, to the best of our knowledge, was as of yet unaccomplished.",
}
@inproceedings{eikema-aziz-2019-auto,
    title = "Auto-Encoding Variational Neural Machine Translation",
    author = "Eikema, Bryan  and
      Aziz, Wilker",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4315",
    doi = "10.18653/v1/W19-4315",
    pages = "124--141",
    abstract = "We present a deep generative model of bilingual sentence pairs for machine translation. The model generates source and target sentences jointly from a shared latent representation and is parameterised by neural networks. We perform efficient training using amortised variational inference and reparameterised gradients. Additionally, we discuss the statistical implications of joint modelling and propose an efficient approximation to maximum a posteriori decoding for fast test-time predictions. We demonstrate the effectiveness of our model in three machine translation scenarios: in-domain training, mixed-domain training, and learning from a mix of gold-standard and synthetic data. Our experiments show consistently that our joint formulation outperforms conditional modelling (i.e. standard neural machine translation) in all such scenarios.",
}
@inproceedings{shi-etal-2019-learning,
    title = "Learning Bilingual Word Embeddings Using Lexical Definitions",
    author = "Shi, Weijia  and
      Chen, Muhao  and
      Tian, Yingtao  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4316",
    doi = "10.18653/v1/W19-4316",
    pages = "142--147",
    abstract = "Bilingual word embeddings, which represent lexicons of different languages in a shared embedding space, are essential for supporting semantic and knowledge transfers in a variety of cross-lingual NLP tasks. Existing approaches to training bilingual word embeddings require either large collections of pre-defined seed lexicons that are expensive to obtain, or parallel sentences that comprise coarse and noisy alignment. In contrast, we propose BiLex that leverages publicly available lexical definitions for bilingual word embedding learning. Without the need of predefined seed lexicons, BiLex comprises a novel word pairing strategy to automatically identify and propagate the precise fine-grain word alignment from lexical definitions. We evaluate BiLex in word-level and sentence-level translation tasks, which seek to find the cross-lingual counterparts of words and sentences respectively. BiLex significantly outperforms previous embedding methods on both tasks.",
}
@inproceedings{garcia-silva-etal-2019-empirical,
    title = "An Empirical Study on Pre-trained Embeddings and Language Models for Bot Detection",
    author = "Garcia-Silva, Andres  and
      Berrio, Cristian  and
      G{\'o}mez-P{\'e}rez, Jos{\'e} Manuel",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4317",
    doi = "10.18653/v1/W19-4317",
    pages = "148--155",
    abstract = "Fine-tuning pre-trained language models has significantly advanced the state of art in a wide range of NLP downstream tasks. Usually, such language models are learned from large and well-formed text corpora from e.g. encyclopedic resources, books or news. However, a significant amount of the text to be analyzed nowadays is Web data, often from social media. In this paper we consider the research question: How do standard pre-trained language models generalize and capture the peculiarities of rather short, informal and frequently automatically generated text found in social media? To answer this question, we focus on bot detection in Twitter as our evaluation task and test the performance of fine-tuning approaches based on language models against popular neural architectures such as LSTM and CNN combined with pre-trained and contextualized embeddings. Our results also show strong performance variations among the different language model approaches, which suggest further research.",
}
@inproceedings{ravishankar-etal-2019-probing,
    title = "Probing Multilingual Sentence Representations With {X}-Probe",
    author = "Ravishankar, Vinit  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4318",
    doi = "10.18653/v1/W19-4318",
    pages = "156--168",
    abstract = "This paper extends the task of probing sentence representations for linguistic insight in a multilingual domain. In doing so, we make two contributions: first, we provide datasets for multilingual probing, derived from Wikipedia, in five languages, viz. English, French, German, Spanish and Russian. Second, we evaluate six sentence encoders for each language, each trained by mapping sentence representations to English sentence representations, using sentences in a parallel corpus. We discover that cross-lingually mapped representations are often better at retaining certain linguistic information than representations derived from English encoders trained on natural language inference (NLI) as a downstream task.",
}
@inproceedings{lopez-etal-2019-fine,
    title = "Fine-Grained Entity Typing in Hyperbolic Space",
    author = "L{\'o}pez, Federico  and
      Heinzerling, Benjamin  and
      Strube, Michael",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4319",
    doi = "10.18653/v1/W19-4319",
    pages = "169--180",
    abstract = "How can we represent hierarchical information present in large type inventories for entity typing? We study the suitability of hyperbolic embeddings to capture hierarchical relations between mentions in context and their target types in a shared vector space. We evaluate on two datasets and propose two different techniques to extract hierarchical information from the type inventory: from an expert-generated ontology and by automatically mining the dataset. The hyperbolic model shows improvements in some but not all cases over its Euclidean counterpart. Our analysis suggests that the adequacy of this geometry depends on the granularity of the type inventory and the representation of its distribution.",
}
@inproceedings{winata-etal-2019-learning,
    title = "Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition",
    author = "Winata, Genta Indra  and
      Lin, Zhaojiang  and
      Fung, Pascale",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4320",
    doi = "10.18653/v1/W19-4320",
    pages = "181--186",
    abstract = "In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings. MME learns to utilize information from these embeddings via a self-attention mechanism without explicit language identification. We evaluate the proposed embedding method on the code-switching English-Spanish Named Entity Recognition dataset in a multilingual and cross-lingual setting. The experimental results show that our proposed method achieves state-of-the-art performance on the multilingual setting, and it has the ability to generalize to an unseen language task.",
}
@inproceedings{dobrossy-etal-2019-investigating,
    title = "Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order {H}ungarian",
    author = {D{\"o}br{\"o}ssy, B{\'a}lint  and
      Makrai, M{\'a}rton  and
      Tarj{\'a}n, Bal{\'a}zs  and
      Szasz{\'a}k, Gy{\"o}rgy},
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4321",
    doi = "10.18653/v1/W19-4321",
    pages = "187--193",
    abstract = "For morphologically rich languages, word embeddings provide less consistent semantic representations due to higher variance in word forms. Moreover, these languages often allow for less constrained word order, which further increases variance. For the highly agglutinative Hungarian, semantic accuracy of word embeddings measured on word analogy tasks drops by 50-75{\%} compared to English. We observed that embeddings learn morphosyntax quite well instead. Therefore, we explore and evaluate several sub-word unit based embedding strategies {--} character n-grams, lemmatization provided by an NLP-pipeline, and segments obtained in unsupervised learning (morfessor) {--} to boost semantic consistency in Hungarian word vectors. The effect of changing embedding dimension and context window size have also been considered. Morphological analysis based lemmatization was found to be the best strategy to improve embeddings{'} semantic accuracy, whereas adding character n-grams was found consistently counterproductive in this regard.",
}
@inproceedings{hadifar-etal-2019-self,
    title = "A Self-Training Approach for Short Text Clustering",
    author = "Hadifar, Amir  and
      Sterckx, Lucas  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4322",
    doi = "10.18653/v1/W19-4322",
    pages = "194--199",
    abstract = "Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations of the short texts. Low-dimensional continuous representations or embeddings can counter that sparseness problem: their high representational power is exploited in deep clustering algorithms. While deep clustering has been studied extensively in computer vision, relatively little work has focused on NLP. The method we propose, learns discriminative features from both an autoencoder and a sentence embedding, then uses assignments from a clustering algorithm as supervision to update weights of the encoder network. Experiments on three short text datasets empirically validate the effectiveness of our method.",
}
@inproceedings{gupta-etal-2019-improving,
    title = "Improving Word Embeddings Using Kernel {PCA}",
    author = {Gupta, Vishwani  and
      Giesselbach, Sven  and
      R{\"u}ping, Stefan  and
      Bauckhage, Christian},
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4323",
    doi = "10.18653/v1/W19-4323",
    pages = "200--208",
    abstract = "Word-based embedding approaches such as Word2Vec capture the meaning of words and relations between them, particularly well when trained with large text collections; however, they fail to do so with small datasets. Extensions such as fastText reduce the amount of data needed slightly, however, the joint task of learning meaningful morphology, syntactic and semantic representations still requires a lot of data. In this paper, we introduce a new approach to warm-start embedding models with morphological information, in order to reduce training time and enhance their performance. We use word embeddings generated using both word2vec and fastText models and enrich them with morphological information of words, derived from kernel principal component analysis (KPCA) of word similarity matrices. This can be seen as explicitly feeding the network morphological similarities and letting it learn semantic and syntactic similarities. Evaluating our models on word similarity and analogy tasks in English and German, we find that they not only achieve higher accuracies than the original skip-gram and fastText models but also require significantly less training data and time. Another benefit of our approach is that it is capable of generating a high-quality representation of infrequent words as, for example, found in very recent news articles with rapidly changing vocabularies. Lastly, we evaluate the different models on a downstream sentence classification task in which a CNN model is initialized with our embeddings and find promising results.",
}
@inproceedings{ulmer-etal-2019-assessing,
    title = "Assessing Incrementality in Sequence-to-Sequence Models",
    author = "Ulmer, Dennis  and
      Hupkes, Dieuwke  and
      Bruni, Elia",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4324",
    doi = "10.18653/v1/W19-4324",
    pages = "209--217",
    abstract = "Since their inception, encoder-decoder models have successfully been applied to a wide array of problems in computational linguistics. The most recent successes are predominantly due to the use of different variations of attention mechanisms, but their cognitive plausibility is questionable. In particular, because past representations can be revisited at any point in time, attention-centric methods seem to lack an incentive to build up incrementally more informative representations of incoming sentences. This way of processing stands in stark contrast with the way in which humans are believed to process language: continuously and rapidly integrating new information as it is encountered. In this work, we propose three novel metrics to assess the behavior of RNNs with and without an attention mechanism and identify key differences in the way the different model types process sentences.",
}
@inproceedings{gupta-carvalho-2019-committee,
    title = "On Committee Representations of Adversarial Learning Models for Question-Answer Ranking",
    author = "Gupta, Sparsh  and
      Carvalho, Vitor",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4325",
    doi = "10.18653/v1/W19-4325",
    pages = "218--223",
    abstract = "Adversarial training is a process in Machine Learning that explicitly trains models on adversarial inputs (inputs designed to deceive or trick the learning process) in order to make it more robust or accurate. In this paper we investigate how representing adversarial training models as committees can be used to effectively improve the performance of Question-Answer (QA) Ranking. We start by empirically probing the effects of adversarial training over multiple QA ranking algorithms, including the state-of-the-art Multihop Attention Network model. We evaluate these algorithms on several benchmark datasets and observe that, while adversarial training is beneficial to most baseline algorithms, there are cases where it may lead to overfitting and performance degradation. We investigate the causes of such degradation, and then propose a new representation procedure for this adversarial learning problem, based on committee learning, that not only is capable of consistently improving all baseline algorithms, but also outperforms the previous state-of-the-art algorithm by as much as 6{\%} in NDCG.",
}
@inproceedings{obamuyide-vlachos-2019-meta,
    title = "Meta-Learning Improves Lifelong Relation Extraction",
    author = "Obamuyide, Abiola  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4326",
    doi = "10.18653/v1/W19-4326",
    pages = "224--229",
    abstract = "Most existing relation extraction models assume a fixed set of relations and are unable to adapt to exploit newly available supervision data to extract new relations. In order to alleviate such problems, there is the need to develop approaches that make relation extraction models capable of continuous adaptation and learning. We investigate and present results for such an approach, based on a combination of ideas from lifelong learning and optimization-based meta-learning. We evaluate the proposed approach on two recent lifelong relation extraction benchmarks, and demonstrate that it markedly outperforms current state-of-the-art approaches.",
}
@inproceedings{shakurova-etal-2019-best,
    title = "Best Practices for Learning Domain-Specific Cross-Lingual Embeddings",
    author = "Shakurova, Lena  and
      Nyari, Beata  and
      Li, Chao  and
      Rotaru, Mihai",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4327",
    doi = "10.18653/v1/W19-4327",
    pages = "230--234",
    abstract = "Cross-lingual embeddings aim to represent words in multiple languages in a shared vector space by capturing semantic similarities across languages. They are a crucial component for scaling tasks to multiple languages by transferring knowledge from languages with rich resources to low-resource languages. A common approach to learning cross-lingual embeddings is to train monolingual embeddings separately for each language and learn a linear projection from the monolingual spaces into a shared space, where the mapping relies on a small seed dictionary. While there are high-quality generic seed dictionaries and pre-trained cross-lingual embeddings available for many language pairs, there is little research on how they perform on specialised tasks. In this paper, we investigate the best practices for constructing the seed dictionary for a specific domain. We evaluate the embeddings on the sequence labelling task of Curriculum Vitae parsing and show that the size of a bilingual dictionary, the frequency of the dictionary words in the domain corpora and the source of data (task-specific vs generic) influence performance. We also show that the less training data is available in the low-resource language, the more the construction of the bilingual dictionary matters, and demonstrate that some of the choices are crucial in the zero-shot transfer learning case.",
}
@inproceedings{raunak-etal-2019-effective,
    title = "Effective Dimensionality Reduction for Word Embeddings",
    author = "Raunak, Vikas  and
      Gupta, Vivek  and
      Metze, Florian",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4328",
    doi = "10.18653/v1/W19-4328",
    pages = "235--243",
    abstract = "Pre-trained word embeddings are used in several downstream applications as well as for constructing representations for sentences, paragraphs and documents. Recently, there has been an emphasis on improving the pretrained word vectors through post-processing algorithms. One improvement area is reducing the dimensionality of word embeddings. Reducing the size of word embeddings can improve their utility in memory constrained devices, benefiting several real world applications. In this work, we present a novel technique that efficiently combines PCA based dimensionality reduction with a recently proposed post-processing algorithm (Mu and Viswanath, 2018), to construct effective word embeddings of lower dimensions. Empirical evaluations on several benchmarks show that our algorithm efficiently reduces the embedding size while achieving similar or (more often) better performance than original embeddings. We have released the source code along with this paper.",
}
@inproceedings{zobnin-elistratova-2019-learning,
    title = "Learning Word Embeddings without Context Vectors",
    author = "Zobnin, Alexey  and
      Elistratova, Evgenia",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4329",
    doi = "10.18653/v1/W19-4329",
    pages = "244--249",
    abstract = "Most word embedding algorithms such as word2vec or fastText construct two sort of vectors: for words and for contexts. Naive use of vectors of only one sort leads to poor results. We suggest using indefinite inner product in skip-gram negative sampling algorithm. This allows us to use only one sort of vectors without loss of quality. Our {``}context-free{''} cf algorithm performs on par with SGNS on word similarity datasets",
}
@inproceedings{chidambaram-etal-2019-learning,
    title = "Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model",
    author = "Chidambaram, Muthu  and
      Yang, Yinfei  and
      Cer, Daniel  and
      Yuan, Steve  and
      Sung, Yunhsuan  and
      Strope, Brian  and
      Kurzweil, Ray",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4330",
    doi = "10.18653/v1/W19-4330",
    pages = "250--259",
    abstract = "The scarcity of labeled training data across many languages is a significant roadblock for multilingual neural language processing. We approach the lack of in-language training data using sentence embeddings that map text written in different languages, but with similar meanings, to nearby embedding space representations. The representations are produced using a dual-encoder based model trained to maximize the representational similarity between sentence pairs drawn from parallel data. The representations are enhanced using multitask training and unsupervised monolingual corpora. The effectiveness of our multilingual sentence embeddings are assessed on a comprehensive collection of monolingual, cross-lingual, and zero-shot/few-shot learning tasks.",
}
@inproceedings{barezi-fung-2019-modality,
    title = "Modality-based Factorization for Multimodal Fusion",
    author = "Barezi, Elham J.  and
      Fung, Pascale",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4331",
    doi = "10.18653/v1/W19-4331",
    pages = "260--269",
    abstract = "We propose a novel method, Modality-based Redundancy Reduction Fusion (MRRF), for understanding and modulating the relative contribution of each modality in multimodal inference tasks. This is achieved by obtaining an (\textit{M}+1)-way tensor to consider the high-order relationships between \textit{M} modalities and the output layer of a neural network model. Applying a modality-based tensor factorization method, which adopts different factors for different modalities, results in removing information present in a modality that can be compensated by other modalities, with respect to model outputs. This helps to understand the relative utility of information in each modality. In addition it leads to a less complicated model with less parameters and therefore could be applied as a regularizer avoiding overfitting. We have applied this method to three different multimodal datasets in sentiment analysis, personality trait recognition, and emotion recognition. We are able to recognize relationships and relative importance of different modalities in these tasks and achieves a 1{\%} to 4{\%} improvement on several evaluation measures compared to the state-of-the-art for all three tasks.",
}
@inproceedings{ezeani-etal-2019-leveraging,
    title = "Leveraging Pre-Trained Embeddings for {W}elsh Taggers",
    author = "Ezeani, Ignatius  and
      Piao, Scott  and
      Neale, Steven  and
      Rayson, Paul  and
      Knight, Dawn",
    booktitle = "Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4332",
    doi = "10.18653/v1/W19-4332",
    pages = "270--280",
    abstract = "While the application of word embedding models to downstream Natural Language Processing (NLP) tasks has been shown to be successful, the benefits for low-resource languages is somewhat limited due to lack of adequate data for training the models. However, NLP research efforts for low-resource languages have focused on constantly seeking ways to harness pre-trained models to improve the performance of NLP systems built to process these languages without the need to re-invent the wheel. One such language is Welsh and therefore, in this paper, we present the results of our experiments on learning a simple multi-task neural network model for part-of-speech and semantic tagging for Welsh using a pre-trained embedding model from FastText. Our model{'}s performance was compared with those of the existing rule-based stand-alone taggers for part-of-speech and semantic taggers. Despite its simplicity and capacity to perform both tasks simultaneously, our tagger compared very well with the existing taggers.",
}
@proceedings{ws-2019-research,
    title = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    editor = "Nicolai, Garrett  and
      Cotterell, Ryan",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4200",
}
@inproceedings{madsack-weissgraeber-2019-ax,
    title = "{AX} Semantics{'} Submission to the {SIGMORPHON} 2019 Shared Task",
    author = "Madsack, Andreas  and
      Wei{\ss}graeber, Robert",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4201",
    doi = "10.18653/v1/W19-4201",
    pages = "1--5",
    abstract = "This paper describes the AX Semantics{'} submission to the SIGMORPHON 2019 shared task on morphological reinflection. We implemented two systems, both tackling the task for all languages in one codebase, without any underlying language specific features. The first one is an encoder-decoder model using AllenNLP; the second system uses the same model modified by a custom trainer that trains only with the target language resources after a specific threshold. We especially focused on building an implementation using AllenNLP with out-of-the-box methods to facilitate easy operation and reuse.",
}
@inproceedings{hauer-etal-2019-cognate,
    title = "Cognate Projection for Low-Resource Inflection Generation",
    author = "Hauer, Bradley  and
      Habibi, Amir Ahmad  and
      Luan, Yixing  and
      Riyadh, Rashed Rubby  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4202",
    doi = "10.18653/v1/W19-4202",
    pages = "6--11",
    abstract = "We propose cognate projection as a method of crosslingual transfer for inflection generation in the context of the SIGMORPHON 2019 Shared Task. The results on four language pairs show the method is effective when no low-resource training data is available.",
}
@inproceedings{kondratyuk-2019-cross,
    title = "Cross-Lingual Lemmatization and Morphology Tagging with Two-Stage Multilingual {BERT} Fine-Tuning",
    author = "Kondratyuk, Dan",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4203",
    doi = "10.18653/v1/W19-4203",
    pages = "12--18",
    abstract = "We present our CHARLES-SAARLAND system for the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology, in task 2, Morphological Analysis and Lemmatization in Context. We leverage the multilingual BERT model and apply several fine-tuning strategies introduced by UDify demonstrating exceptional evaluation performance on morpho-syntactic tasks. Our results show that fine-tuning multilingual BERT on the concatenation of all available treebanks allows the model to learn cross-lingual information that is able to boost lemmatization and morphology tagging accuracy over fine-tuning it purely monolingually. Unlike UDify, however, we show that when paired with additional character-level and word-level LSTM layers, a second stage of fine-tuning on each treebank individually can improve evaluation even further. Out of all submissions for this shared task, our system achieves the highest average accuracy and f1 score in morphology tagging and places second in average lemmatization accuracy.",
}
@inproceedings{shadikhodjaev-lee-2019-cbnu,
    title = "{CBNU} System for {SIGMORPHON} 2019 Shared Task 2: a Pipeline Model",
    author = "Shadikhodjaev, Uygun  and
      Lee, Jae Sung",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4204",
    doi = "10.18653/v1/W19-4204",
    pages = "19--24",
    abstract = "In this paper we describe our system for morphological analysis and lemmatization in context, using a transformer-based sequence to sequence model and a biaffine attention based BiLSTM model. First, a lemma is produced for a given word, and then both the lemma and the given word are used for morphological analysis. We also make use of character level word encodings and trainable encodings to improve accuracy. Overall, our system ranked fifth in lemmatization and sixth in morphological accuracy among twelve systems, and demonstrated considerable improvements over the baseline in morphological analysis.",
}
@inproceedings{yildiz-tantug-2019-morpheus,
    title = "{M}orpheus: A Neural Network for Jointly Learning Contextual Lemmatization and Morphological Tagging",
    author = {Yildiz, Eray  and
      Tantu{\u{g}}, A. C{\"u}neyd},
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4205",
    doi = "10.18653/v1/W19-4205",
    pages = "25--34",
    abstract = "In this study, we present Morpheus, a joint contextual lemmatizer and morphological tagger. Morpheus is based on a neural sequential architecture where inputs are the characters of the surface words in a sentence and the outputs are the minimum edit operations between surface words and their lemmata as well as the morphological tags assigned to the words. The experiments on the datasets in nearly 100 languages provided by SigMorphon 2019 Shared Task 2 organizers show that the performance of Morpheus is comparable to the state-of-the-art system in terms of lemmatization. In morphological tagging, on the other hand, Morpheus significantly outperforms the SigMorphon baseline. In our experiments, we also show that the neural encoder-decoder architecture trained to predict the minimum edit operations can produce considerably better results than the architecture trained to predict the characters in lemmata directly as in previous studies. According to the SigMorphon 2019 Shared Task 2 results, Morpheus has placed 3rd in lemmatization and reached the 9th place in morphological tagging among all participant teams.",
}
@inproceedings{ustun-etal-2019-multi,
    title = "Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.",
    author = {{\"U}st{\"u}n, Ahmet  and
      van der Goot, Rob  and
      Bouma, Gosse  and
      van Noord, Gertjan},
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4206",
    doi = "10.18653/v1/W19-4206",
    pages = "35--49",
    abstract = "This paper describes our submission to SIGMORPHON 2019 Task 2: Morphological analysis and lemmatization in context. Our model is a multi-task sequence to sequence neural network, which jointly learns morphological tagging and lemmatization. On the encoding side, we exploit character-level as well as contextual information. We introduce a multi-attention decoder to selectively focus on different parts of character and word sequences. To further improve the model, we train on multiple datasets simultaneously and use external embeddings for initialization. Our final model reaches an average morphological tagging F1 score of 94.54 and a lemma accuracy of 93.91 on the test data, ranking respectively 3rd and 6th out of 13 teams in the SIGMORPHON 2019 shared task.",
}
@inproceedings{peters-martins-2019-ist,
    title = "{IT}{--}{IST} at the {SIGMORPHON} 2019 Shared Task: Sparse Two-headed Models for Inflection",
    author = "Peters, Ben  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4207",
    doi = "10.18653/v1/W19-4207",
    pages = "50--56",
    abstract = "This paper presents the Instituto de Telecomunica{\c{c}}{\~o}es{--}Instituto Superior T{\'e}cnico submission to Task 1 of the SIGMORPHON 2019 Shared Task. Our models combine sparse sequence-to-sequence models with a two-headed attention mechanism that learns separate attention distributions for the lemma and inflectional tags. Among submissions to Task 1, our models rank second and third. Despite the low data setting of the task (only 100 in-language training examples), they learn plausible inflection patterns and often concentrate all probability mass into a small set of hypotheses, making beam search exact.",
}
@inproceedings{chaudhary-etal-2019-cmu,
    title = "{CMU}-01 at the {SIGMORPHON} 2019 Shared Task on Crosslinguality and Context in Morphology",
    author = "Chaudhary, Aditi  and
      Salesky, Elizabeth  and
      Bhat, Gayatri  and
      Mortensen, David R.  and
      Carbonell, Jaime  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4208",
    doi = "10.18653/v1/W19-4208",
    pages = "57--70",
    abstract = "This paper presents the submission by the CMU-01 team to the SIGMORPHON 2019 task 2 of Morphological Analysis and Lemmatization in Context. This task requires us to produce the lemma and morpho-syntactic description of each token in a sequence, for 107 treebanks. We approach this task with a hierarchical neural conditional random field (CRF) model which predicts each coarse-grained feature (eg. POS, Case, etc.) independently. However, most treebanks are under-resourced, thus making it challenging to train deep neural models for them. Hence, we propose a multi-lingual transfer training regime where we transfer from multiple related languages that share similar typology.",
}
@inproceedings{coltekin-2019-cross,
    title = "Cross-lingual morphological inflection with explicit alignment",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4209",
    doi = "10.18653/v1/W19-4209",
    pages = "71--79",
    abstract = "This paper describes two related systems for cross-lingual morphological inflection for SIGMORPHON 2019 Shared Task participation. Both sets of results submitted to the shared task for evaluation are obtained using a simple approach of predicting transducer actions based on initial alignments on the training set, where cross-lingual transfer is limited to only using the high-resource language data as additional training set. The performance of the system does not reach the performance of the top two systems in the competition. However, we show that results can be improved with further tuning. We also present further analyses demonstrating that the cross-lingual gain is rather modest.",
}
@inproceedings{oh-etal-2019-thomas,
    title = "{THOMAS}: The Hegemonic {OSU} Morphological Analyzer using Seq2seq",
    author = "Oh, Byung-Doh  and
      Maneriker, Pranav  and
      Jiang, Nanjiang",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4210",
    doi = "10.18653/v1/W19-4210",
    pages = "80--86",
    abstract = "This paper describes the OSU submission to the SIGMORPHON 2019 shared task, Crosslinguality and Context in Morphology. Our system addresses the \textit{contextual morphological analysis} subtask of Task 2, which is to produce the morphosyntactic description (MSD) of each fully inflected word within a given sentence. We frame this as a sequence generation task and employ a neural encoder-decoder (seq2seq) architecture to generate the sequence of MSD tags given the encoded representation of each token. Follow-up analyses reveal that our system most significantly improves performance on morphologically complex languages whose inflected word forms typically have longer MSD tag sequences. In addition, our system seems to capture the structured correlation between MSD tags, such as that between the {``}verb{''} tag and TAM-related tags.",
}
@inproceedings{aiken-etal-2019-sigmorphon,
    title = "Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few",
    author = "Aiken, Brad  and
      Kelly, Jared  and
      Palmer, Alexis  and
      Polat, Suleyman Olcay  and
      Rama, Taraka  and
      Nielsen, Rodney",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4211",
    doi = "10.18653/v1/W19-4211",
    pages = "87--94",
    abstract = "This paper presents the UNT HiLT+Ling system for the Sigmorphon 2019 shared Task 2: Morphological Analysis and Lemmatization in Context. Our core approach focuses on the morphological tagging task; part-of-speech tagging and lemmatization are treated as secondary tasks. Given the highly multilingual nature of the task, we propose an approach which makes minimal use of the supplied training data, in order to be extensible to languages without labeled training data for the morphological inflection task. Specifically, we use a parallel Bible corpus to align contextual embeddings at the verse level. The aligned verses are used to build cross-language translation matrices, which in turn are used to map between embedding spaces for the various languages. Finally, we use sets of inflected forms, primarily from a high-resource language, to induce vector representations for individual UniMorph tags. Morphological analysis is performed by matching vector representations to embeddings for individual tokens. While our system results are dramatically below the average system submitted for the shared task evaluation campaign, our method is (we suspect) unique in its minimal reliance on labeled training data.",
}
@inproceedings{straka-etal-2019-udpipe,
    title = "{UDP}ipe at {SIGMORPHON} 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging",
    author = "Straka, Milan  and
      Strakov{\'a}, Jana  and
      Hajic, Jan",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4212",
    doi = "10.18653/v1/W19-4212",
    pages = "95--103",
    abstract = "We present our contribution to the SIGMORPHON 2019 Shared Task: Crosslinguality and Context in Morphology, Task 2: contextual morphological analysis and lemmatization. We submitted a modification of the UDPipe 2.0, one of best-performing systems of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies and an overall winner of the The 2018 Shared Task on Extrinsic Parser Evaluation. As our first improvement, we use the pretrained contextualized embeddings (BERT) as additional inputs to the network; secondly, we use individual morphological features as regularization; and finally, we merge the selected corpora of the same language. In the lemmatization task, our system exceeds all the submitted systems by a wide margin with lemmatization accuracy 95.78 (second best was 95.00, third 94.46). In the morphological analysis, our system placed tightly second: our morphological analysis accuracy was 93.19, the winning system{'}s 93.23.",
}
@inproceedings{cardenas-etal-2019-cuni,
    title = "{CUNI}{--}{M}alta system at {SIGMORPHON} 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation",
    author = "Cardenas, Ronald  and
      Borg, Claudia  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4213",
    doi = "10.18653/v1/W19-4213",
    pages = "104--112",
    abstract = "This paper presents the submission by the Charles University-University of Malta team to the SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context. We present a lemmatization model based on previous work on neural transducers (Makarov and Clematide, 2018b; Aharoni and Goldberg, 2016). The key difference is that our model transforms the whole word form in every step, instead of consuming it character by character. We propose a merging strategy inspired by Byte-Pair-Encoding that reduces the space of valid operations by merging frequent adjacent operations. The resulting operations not only encode the actions to be performed but the relative position in the word token and how characters need to be transformed. Our morphological tagger is a vanilla biLSTM tagger that operates over operation representations, encoding operations and words in a hierarchical manner. Even though relative performance according to metrics is below the baseline, experiments show that our models capture important associations between interpretable operation labels and fine-grained morpho-syntax labels.",
}
@inproceedings{erdmann-etal-2019-little,
    title = "A Little Linguistics Goes a Long Way: Unsupervised Segmentation with Limited Language Specific Guidance",
    author = "Erdmann, Alexander  and
      Khalifa, Salam  and
      Oudah, Mai  and
      Habash, Nizar  and
      Bouamor, Houda",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4214",
    doi = "10.18653/v1/W19-4214",
    pages = "113--124",
    abstract = "We present de-lexical segmentation, a linguistically motivated alternative to greedy or other unsupervised methods, requiring only minimal language specific input. Our technique involves creating a small grammar of closed-class affixes which can be written in a few hours. The grammar over generates analyses for word forms attested in a raw corpus which are disambiguated based on features of the linguistic base proposed for each form. Extending the grammar to cover orthographic, morpho-syntactic or lexical variation is simple, making it an ideal solution for challenging corpora with noisy, dialect-inconsistent, or otherwise non-standard content. In two evaluations, we consistently outperform competitive unsupervised baselines and approach the performance of state-of-the-art supervised models trained on large amounts of data, providing evidence for the value of linguistic input during preprocessing.",
}
@inproceedings{anttila-etal-2019-equiprobable,
    title = "Equiprobable mappings in weighted constraint grammars",
    author = "Anttila, Arto  and
      Borgeson, Scott  and
      Magri, Giorgio",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4215",
    doi = "10.18653/v1/W19-4215",
    pages = "125--134",
    abstract = "We show that MaxEnt is so rich that it can distinguish between any two different mappings: there always exists a nonnegative weight vector which assigns them different MaxEnt probabilities. Stochastic HG instead does admit equiprobable mappings and we give a complete formal characterization of them.",
}
@inproceedings{hao-andersson-2019-unbounded,
    title = "Unbounded Stress in Subregular Phonology",
    author = "Hao, Yiding  and
      Andersson, Samuel",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4216",
    doi = "10.18653/v1/W19-4216",
    pages = "135--143",
    abstract = "This paper situates culminative unbounded stress systems within the subregular hierarchy for functions. While Baek (2018) has argued that such systems can be uniformly understood as input tier-based strictly local constraints, we show here that default-to-opposite-side and default-to-same-side stress systems belong to distinct subregular classes when they are viewed as functions that assign primary stress to underlying forms. While the former system can be captured by input tier-based input strictly local functions, a subsequential function class that we define here, the latter system is not subsequential, though it is weakly deterministic according to McCollum et al.{'}s (2018) non-interaction criterion. Our results motivate the extension of recently proposed subregular language classes to subregular functions and argue in favor of McCollum et al{'}s definition of weak determinism over that of Heinz and Lai (2013).",
}
@inproceedings{zhang-2019-data,
    title = "Data mining {M}andarin tone contour shapes",
    author = "Zhang, Shuo",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4217",
    doi = "10.18653/v1/W19-4217",
    pages = "144--153",
    abstract = "In spontaneous speech, Mandarin tones that belong to the same tone category may exhibit many different contour shapes. We explore the use of time-series data mining techniques for understanding the variability of tones in a large corpus of Mandarin newscast speech. First, we adapt a graph-based approach to characterize the clusters (fuzzy types) of tone contour shapes observed in each tone n-gram category. Second, we show correlations between these realized contour shape clusters and a bag of automatically extracted linguistic features. We discuss the implications of the current study within the context of phonological and information theory.",
}
@inproceedings{sorokin-2019-convolutional,
    title = "Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?",
    author = "Sorokin, Alexey",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4218",
    doi = "10.18653/v1/W19-4218",
    pages = "154--159",
    abstract = "We apply convolutional neural networks to the task of shallow morpheme segmentation using low-resource datasets for 5 different languages. We show that both in fully supervised and semi-supervised settings our model beats previous state-of-the-art approaches. We argue that convolutional neural networks reflect local nature of morpheme segmentation better than other semi-supervised approaches.",
}
@inproceedings{kolachina-magyar-2019-phone,
    title = "What do phone embeddings learn about Phonology?",
    author = "Kolachina, Sudheer  and
      Magyar, Lilla",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4219",
    doi = "10.18653/v1/W19-4219",
    pages = "160--169",
    abstract = "Recent work has looked at evaluation of phone embeddings using sound analogies and correlations between distinctive feature space and embedding space. It has not been clear what aspects of natural language phonology are learnt by neural network inspired distributed representational models such as word2vec. To study the kinds of phonological relationships learnt by phone embeddings, we present artificial phonology experiments that show that phone embeddings learn paradigmatic relationships such as phonemic and allophonic distribution quite well. They are also able to capture co-occurrence restrictions among vowels such as those observed in languages with vowel harmony. However, they are unable to learn co-occurrence restrictions among the class of consonants.",
}
@inproceedings{oseki-etal-2019-inverting,
    title = "Inverting and Modeling Morphological Inflection",
    author = "Oseki, Yohei  and
      Sudo, Yasutada  and
      Sakai, Hiromu  and
      Marantz, Alec",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4220",
    doi = "10.18653/v1/W19-4220",
    pages = "170--177",
    abstract = "Previous {``}wug{''} tests (Berko, 1958) on Japanese verbal inflection have demonstrated that Japanese speakers, both adults and children, cannot inflect novel present tense forms to {``}correct{''} past tense forms predicted by rules of existent verbs (de Chene, 1982; Vance, 1987, 1991; Klafehn, 2003, 2013), indicating that Japanese verbs are merely stored in the mental lexicon. However, the implicit assumption that present tense forms are bases for verbal inflection should not be blindly extended to morphologically rich languages like Japanese in which both present and past tense forms are morphologically complex without inherent direction (Albright, 2002). Interestingly, there are also independent observations in the acquisition literature to suggest that past tense forms may be bases for verbal inflection in Japanese (Klafehn, 2003; Murasugi et al., 2010; Hirose, 2017; Tatsumi et al., 2018). In this paper, we computationally simulate two directions of verbal inflection in Japanese, Present → Past and Past → Present, with the rule-based computational model called Minimal Generalization Learner (MGL; Albright and Hayes, 2003) and experimentally evaluate the model with the bidirectional {``}wug{''} test where humans inflect novel verbs in two opposite directions. We conclude that Japanese verbs can be computed online via some generalizations and those generalizations do depend on the direction of morphological inflection.",
}
@inproceedings{steiner-2019-augmenting,
    title = "Augmenting a {G}erman Morphological Database by Data-Intense Methods",
    author = "Steiner, Petra",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4221",
    doi = "10.18653/v1/W19-4221",
    pages = "178--188",
    abstract = "This paper deals with the automatic enhancement of a new German morphological database. While there are some databases for flat word segmentation, this is the first available resource which can be directly used for deep parsing of German words. We combine the entries of this morphological database with the morphological tools SMOR and Moremorph and a context-based evaluation method which builds on a large Wikipedia corpus. We describe the state of the art and the essential characteristics of the database and the context method. The approach is tested on an inflight magazine of Lufthansa. We derive over 5,000 new instances of complex words. The coverage for the lemma types reaches up to over 99 percent. The precision of new found complex splits and monomorphemes is between 0.93 and 0.99.",
}
@inproceedings{eskander-etal-2019-unsupervised,
    title = "Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages",
    author = "Eskander, Ramy  and
      Klavans, Judith  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4222",
    doi = "10.18653/v1/W19-4222",
    pages = "189--195",
    abstract = "Polysynthetic languages pose a challenge for morphological analysis due to the root-morpheme complexity and to the word class {``}squish{''}. In addition, many of these polysynthetic languages are low-resource. We propose unsupervised approaches for morphological segmentation of low-resource polysynthetic languages based on Adaptor Grammars (AG) (Eskander et al., 2016). We experiment with four languages from the Uto-Aztecan family. Our AG-based approaches outperform other unsupervised approaches and show promise when compared to supervised methods, outperforming them on two of the four languages.",
}
@inproceedings{lamont-etal-2019-weakly,
    title = "Weakly deterministic transformations are subregular",
    author = "Lamont, Andrew  and
      O{'}Hara, Charlie  and
      Smith, Caitlin",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4223",
    doi = "10.18653/v1/W19-4223",
    pages = "196--205",
    abstract = "Whether phonological transformations in general are subregular is an open question. This is the case for most transformations, which have been shown to be subsequential, but it is not known whether weakly deterministic mappings form a proper subset of the regular functions. This paper demonstrates that there are regular functions that are not weakly deterministic, and, because all attested processes are weakly deterministic, supports the subregular hypothesis.",
}
@inproceedings{jacobs-mailhot-2019-encoder,
    title = "Encoder-decoder models for latent phonological representations of words",
    author = "Jacobs, Cassandra L.  and
      Mailhot, Fred",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4224",
    doi = "10.18653/v1/W19-4224",
    pages = "206--217",
    abstract = "We use sequence-to-sequence networks trained on sequential phonetic encoding tasks to construct compositional phonological representations of words. We show that the output of an encoder network can predict the phonetic durations of American English words better than a number of alternative forms. We also show that the model{'}s learned representations map onto existing measures of words{'} phonological structure (phonological neighborhood density and phonotactic probability).",
}
@inproceedings{hao-bowers-2019-action,
    title = "Action-Sensitive Phonological Dependencies",
    author = "Hao, Yiding  and
      Bowers, Dustin",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4225",
    doi = "10.18653/v1/W19-4225",
    pages = "218--228",
    abstract = "This paper defines a subregular class of functions called the tier-based synchronized strictly local (TSSL) functions. These functions are similar to the the tier-based input-output strictly local (TIOSL) functions, except that the locality condition is enforced not on the input and output streams, but on the computation history of the minimal subsequential finite-state transducer. We show that TSSL functions naturally describe rhythmic syncope while TIOSL functions cannot, and we argue that TSSL functions provide a more restricted characterization of rhythmic syncope than existing treatments within Optimality Theory.",
}
@inproceedings{mccarthy-etal-2019-sigmorphon,
    title = "The {SIGMORPHON} 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection",
    author = "McCarthy, Arya D.  and
      Vylomova, Ekaterina  and
      Wu, Shijie  and
      Malaviya, Chaitanya  and
      Wolf-Sonkin, Lawrence  and
      Nicolai, Garrett  and
      Kirov, Christo  and
      Silfverberg, Miikka  and
      Mielke, Sabrina J.  and
      Heinz, Jeffrey  and
      Cotterell, Ryan  and
      Hulden, Mans",
    booktitle = "Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4226",
    doi = "10.18653/v1/W19-4226",
    pages = "229--244",
    abstract = "The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years{'} inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year{'}s strong baselines or highly ranked systems from previous years{'} shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines.",
}
@proceedings{ws-2019-nlp-conversational,
    title = "Proceedings of the First Workshop on NLP for Conversational AI",
    editor = "Chen, Yun-Nung  and
      Bedrax-Weiss, Tania  and
      Hakkani-Tur, Dilek  and
      Kumar, Anuj  and
      Lewis, Mike  and
      Luong, Thang-Minh  and
      Su, Pei-Hao  and
      Wen, Tsung-Hsien",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4100",
}
@inproceedings{henderson-etal-2019-repository,
    title = "A Repository of Conversational Datasets",
    author = "Henderson, Matthew  and
      Budzianowski, Pawe{\l}  and
      Casanueva, I{\~n}igo  and
      Coope, Sam  and
      Gerz, Daniela  and
      Kumar, Girish  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Spithourakis, Georgios  and
      Su, Pei-Hao  and
      Vuli{\'c}, Ivan  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4101",
    doi = "10.18653/v1/W19-4101",
    pages = "1--10",
    abstract = "Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using 1-of-100 accuracy. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set.",
}
@inproceedings{ohsugi-etal-2019-simple,
    title = "A Simple but Effective Method to Incorporate Multi-turn Context with {BERT} for Conversational Machine Comprehension",
    author = "Ohsugi, Yasuhito  and
      Saito, Itsumi  and
      Nishida, Kyosuke  and
      Asano, Hisako  and
      Tomita, Junji",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4102",
    doi = "10.18653/v1/W19-4102",
    pages = "11--17",
    abstract = "Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue. Using BERT, a pretraining language model, has been successful for single-turn machine comprehension, while modeling multiple turns of question answering with BERT has not been established because BERT has a limit on the number and the length of input sequences. In this paper, we propose a simple but effective method with BERT for CMC. Our method uses BERT to encode a paragraph independently conditioned with each question and each answer in a multi-turn context. Then, the method predicts an answer on the basis of the paragraph representations encoded with BERT. The experiments with representative CMC datasets, QuAC and CoQA, show that our method outperformed recently published methods (+0.8 F1 on QuAC and +2.1 F1 on CoQA). In addition, we conducted a detailed analysis of the effects of the number and types of dialogue history on the accuracy of CMC, and we found that the gold answer history, which may not be given in an actual conversation, contributed to the model performance most on both datasets.",
}
@inproceedings{dziri-etal-2019-augmenting,
    title = "Augmenting Neural Response Generation with Context-Aware Topical Attention",
    author = "Dziri, Nouha  and
      Kamalloo, Ehsan  and
      Mathewson, Kory  and
      Zaiane, Osmar",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4103",
    doi = "10.18653/v1/W19-4103",
    pages = "18--31",
    abstract = "Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation. Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines.",
}
@inproceedings{swanson-etal-2019-building,
    title = "Building a Production Model for Retrieval-Based Chatbots",
    author = "Swanson, Kyle  and
      Yu, Lili  and
      Fox, Christopher  and
      Wohlwend, Jeremy  and
      Lei, Tao",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4104",
    doi = "10.18653/v1/W19-4104",
    pages = "32--41",
    abstract = "Response suggestion is an important task for building human-computer conversation systems. Recent approaches to conversation modeling have introduced new model architectures with impressive results, but relatively little attention has been paid to whether these models would be practical in a production setting. In this paper, we describe the unique challenges of building a production retrieval-based conversation system, which selects outputs from a whitelist of candidate responses. To address these challenges, we propose a dual encoder architecture which performs rapid inference and scales well with the size of the whitelist. We also introduce and compare two methods for generating whitelists, and we carry out a comprehensive analysis of the model and whitelists. Experimental results on a large, proprietary help desk chat dataset, including both offline metrics and a human evaluation, indicate production-quality performance and illustrate key lessons about conversation modeling in practice.",
}
@inproceedings{hamalainen-honkela-2019-co,
    title = "Co-Operation as an Asymmetric Form of Human-Computer Creativity. Case: Peace Machine",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Honkela, Timo},
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4105",
    doi = "10.18653/v1/W19-4105",
    pages = "42--50",
    abstract = "This theoretical paper identifies a need for a definition of asymmetric co-creativity where creativity is expected from the computational agent but not from the human user. Our co-operative creativity framework takes into account that the computational agent has a message to convey in a co-operative fashion, which introduces a trade-off on how creative the computer can be. The requirements of co-operation are identified from an interdisciplinary point of view. We divide co-operative creativity in message creativity, contextual creativity and communicative creativity. Finally these notions are applied in the context of the Peace Machine system concept.",
}
@inproceedings{tanaka-etal-2019-conversational,
    title = "Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding",
    author = "Tanaka, Shohei  and
      Yoshino, Koichiro  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4106",
    doi = "10.18653/v1/W19-4106",
    pages = "51--59",
    abstract = "We propose a novel method for selecting coherent and diverse responses for a given dialogue context. The proposed method re-ranks response candidates generated from conversational models by using event causality relations between events in a dialogue history and response candidates (e.g., {``}be stressed out{''} precedes {``}relieve stress{''}). We use distributed event representation based on the Role Factored Tensor Model for a robust matching of event causality relations due to limited event causality knowledge of the system. Experimental results showed that the proposed method improved coherency and dialogue continuity of system responses.",
}
@inproceedings{gunasekara-etal-2019-dstc7,
    title = "{DSTC}7 Task 1: Noetic End-to-End Response Selection",
    author = "Gunasekara, Chulaka  and
      Kummerfeld, Jonathan K.  and
      Polymenakos, Lazaros  and
      Lasecki, Walter",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4107",
    doi = "10.18653/v1/W19-4107",
    pages = "60--67",
    abstract = "Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems.",
}
@inproceedings{yang-etal-2019-end-end,
    title = "End-to-End Neural Context Reconstruction in {C}hinese Dialogue",
    author = "Yang, Wei  and
      Qiao, Rui  and
      Qin, Haocheng  and
      Sun, Amy  and
      Tan, Luchen  and
      Xiong, Kun  and
      Li, Ming",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4108",
    doi = "10.18653/v1/W19-4108",
    pages = "68--76",
    abstract = "We tackle the problem of context reconstruction in Chinese dialogue, where the task is to replace pronouns, zero pronouns, and other referring expressions with their referent nouns so that sentences can be processed in isolation without context. Following a standard decomposition of the context reconstruction task into referring expression detection and coreference resolution, we propose a novel end-to-end architecture for separately and jointly accomplishing this task. Key features of this model include POS and position encoding using CNNs and a novel pronoun masking mechanism. One perennial problem in building such models is the paucity of training data, which we address by augmenting previously-proposed methods to generate a large amount of realistic training data. The combination of more data and better models yields accuracy higher than the state-of-the-art method in coreference resolution and end-to-end context reconstruction.",
}
@inproceedings{trinh-etal-2019-energy,
    title = "Energy-Based Modelling for Dialogue State Tracking",
    author = "Trinh, Anh Duong  and
      Ross, Robert  and
      Kelleher, John",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4109",
    doi = "10.18653/v1/W19-4109",
    pages = "77--86",
    abstract = "The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels. We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods.",
}
@inproceedings{kuksenok-martyniv-2019-evaluation,
    title = "Evaluation and Improvement of Chatbot Text Classification Data Quality Using Plausible Negative Examples",
    author = "Kuksenok, Kit  and
      Martyniv, Andriy",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4110",
    doi = "10.18653/v1/W19-4110",
    pages = "87--95",
    abstract = "We describe and validate a metric for estimating multi-class classifier performance based on cross-validation and adapted for improvement of small, unbalanced natural-language datasets used in chatbot design. Our experiences draw upon building recruitment chatbots that mediate communication between job-seekers and recruiters by exposing the ML/NLP dataset to the recruiting team. Evaluation approaches must be understandable to various stakeholders, and useful for improving chatbot performance. The metric, nex-cv, uses negative examples in the evaluation of text classification, and fulfils three requirements. First, it is actionable: it can be used by non-developer staff. Second, it is not overly optimistic compared to human ratings, making it a fast method for comparing classifiers. Third, it allows model-agnostic comparison, making it useful for comparing systems despite implementation differences. We validate the metric based on seven recruitment-domain datasets in English and German over the course of one year.",
}
@inproceedings{chen-etal-2019-improving-long,
    title = "Improving Long Distance Slot Carryover in Spoken Dialogue Systems",
    author = "Chen, Tongfei  and
      Naik, Chetan  and
      He, Hua  and
      Rastogi, Pushpendre  and
      Mathias, Lambert",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4111",
    doi = "10.18653/v1/W19-4111",
    pages = "96--105",
    abstract = "Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems. One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn. Previous work on the slot carryover task used models that made independent decisions for each slot. A close analysis of the results show that this approach results in poor performance over longer context dialogues. In this paper, we propose to jointly model the slots. We propose two neural network architectures, one based on pointer networks that incorporate slot ordering information, and the other based on transformer networks that uses self attention mechanism to model the slot interdependencies. Our experiments on an internal dialogue benchmark dataset and on the public DSTC2 dataset demonstrate that our proposed models are able to resolve longer distance slot references and are able to achieve competitive performance.",
}
@inproceedings{gupta-etal-2019-insights,
    title = "Insights from Building an Open-Ended Conversational Agent",
    author = "Gupta, Khyatti  and
      Joshi, Meghana  and
      Chatterjee, Ankush  and
      Damani, Sonam  and
      Narahari, Kedhar Nath  and
      Agrawal, Puneet",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4112",
    doi = "10.18653/v1/W19-4112",
    pages = "106--112",
    abstract = "Dialogue systems and conversational agents are becoming increasingly popular in modern society. We conceptualized one such conversational agent, Microsoft{'}s {``}Ruuh{''} with the promise to be able to talk to its users on any subject they choose. Building an open-ended conversational agent like Ruuh at onset seems like a daunting task, since the agent needs to think beyond the utilitarian notion of merely generating {``}relevant{''} responses and meet a wider range of user social needs, like expressing happiness when user{'}s favourite sports team wins, sharing a cute comment on showing the pictures of the user{'}s pet and so on. The agent also needs to detect and respond to abusive language, sensitive topics and trolling behaviour of the users. Many of these problems pose significant research challenges as well as product design limitations as one needs to circumnavigate the technical limitations to create an acceptable user experience. However, as the product reaches the real users the true test begins, and one realizes the challenges and opportunities that lie in the vast domain of conversations. With over 2.5 million real-world users till date who have generated over 300 million user conversations with Ruuh, there is a plethora of learning, insights and opportunities that we will talk about in this paper.",
}
@inproceedings{nie-etal-2019-learning,
    title = "Learning to Explain: Answering Why-Questions via Rephrasing",
    author = "Nie, Allen  and
      Bennett, Erin  and
      Goodman, Noah",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4113",
    doi = "10.18653/v1/W19-4113",
    pages = "113--120",
    abstract = "Providing plausible responses to why questions is a challenging but critical goal for language based human-machine interaction. Explanations are challenging in that they require many different forms of abstract knowledge and reasoning. Previous work has either relied on human-curated structured knowledge bases or detailed domain representation to generate satisfactory explanations. They are also often limited to ranking pre-existing explanation choices. In our work, we contribute to the under-explored area of generating natural language explanations for general phenomena. We automatically collect large datasets of explanation-phenomenon pairs which allow us to train sequence-to-sequence models to generate natural language explanations. We compare different training strategies and evaluate their performance using both automatic scores and human ratings. We demonstrate that our strategy is sufficient to generate highly plausible explanations for general open-domain phenomena compared to other models trained on different datasets.",
}
@inproceedings{olabiyi-etal-2019-multi,
    title = "Multi-turn Dialogue Response Generation in an Adversarial Learning Framework",
    author = "Olabiyi, Oluwatobi  and
      Salimov, Alan O  and
      Khazane, Anish  and
      Mueller, Erik",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4114",
    doi = "10.18653/v1/W19-4114",
    pages = "121--132",
    abstract = "We propose an adversarial learning approach for generating multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN{'}s generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirectional RNN that shares context and word embeddings with the generator. During inference, noise samples conditioned on the dialogue history are used to perturb the generator{'}s latent space to generate several possible responses. The final response is the one ranked best by the discriminator. The hredGAN shows improved performance over existing methods: (1) it generalizes better than networks trained using only the log-likelihood criterion, and (2) it generates longer, more informative and more diverse responses with high utterance and topic relevance even with limited training data. This performance improvement is demonstrated on the Movie triples and Ubuntu dialogue datasets with both the automatic and human evaluations.",
}
@inproceedings{takayama-arase-2019-relevant,
    title = "Relevant and Informative Response Generation using Pointwise Mutual Information",
    author = "Takayama, Junya  and
      Arase, Yuki",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4115",
    doi = "10.18653/v1/W19-4115",
    pages = "133--138",
    abstract = "A sequence-to-sequence model tends to generate generic responses with little information for input utterances. To solve this problem, we propose a neural model that generates relevant and informative responses. Our model has simple architecture to enable easy application to existing neural dialogue models. Specifically, using positive pointwise mutual information, it first identifies keywords that frequently co-occur in responses given an utterance. Then, the model encourages the decoder to use the keywords for response generation. Experiment results demonstrate that our model successfully diversifies responses relative to previous models.",
}
@inproceedings{chikai-etal-2019-responsive,
    title = "Responsive and Self-Expressive Dialogue Generation",
    author = "Chikai, Kozo  and
      Takayama, Junya  and
      Arase, Yuki",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4116",
    doi = "10.18653/v1/W19-4116",
    pages = "139--149",
    abstract = "A neural conversation model is a promising approach to develop dialogue systems with the ability of chit-chat. It allows training a model in an end-to-end manner without complex rule design nor feature engineering. However, as a side effect, the neural model tends to generate safe but uninformative and insensitive responses like {``}OK{''} and {``}I don{'}t know.{''} Such replies are called generic responses and regarded as a critical problem for user-engagement of dialogue systems. For a more engaging chit-chat experience, we propose a neural conversation model that generates responsive and self-expressive replies. Specifically, our model generates domain-aware and sentiment-rich responses. Experiments empirically confirmed that our model outperformed the sequence-to-sequence model; 68.1{\%} of our responses were domain-aware with sentiment polarities, which was only 2.7{\%} for responses generated by the sequence-to-sequence model.",
}
@proceedings{ws-2019-linguistic,
    title = "Proceedings of the 13th Linguistic Annotation Workshop",
    editor = "Friedrich, Annemarie  and
      Zeyrek, Deniz  and
      Hoek, Jet",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4000",
}
@inproceedings{ulinski-hirschberg-2019-crowdsourced,
    title = "Crowdsourced Hedge Term Disambiguation",
    author = "Ulinski, Morgan  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4001",
    doi = "10.18653/v1/W19-4001",
    pages = "1--5",
    abstract = "We address the issue of acquiring quality annotations of hedging words and phrases, linguistic phenomenona in which words, sounds, or other constructions are used to express ambiguity or uncertainty. Due to the limited availability of existing corpora annotated for hedging, linguists and other language scientists have been constrained as to the extent they can study this phenomenon. In this paper, we introduce a new method of acquiring hedging annotations via crowdsourcing, based on reformulating the task of labeling hedges as a simple word sense disambiguation task. We also introduce a new hedging corpus we have constructed by applying this method, a collection of forum posts annotated using Amazon Mechanical Turk. We found that the crowdsourced judgments we obtained had an inter-annotator agreement of 92.89{\%} (Fleiss{'} Kappa=0.751) and, when comparing a subset of these annotations to an expert-annotated gold standard, an accuracy of 96.65{\%}.",
}
@inproceedings{lechelle-etal-2019-wire57,
    title = "{W}i{R}e57 : A Fine-Grained Benchmark for Open Information Extraction",
    author = "Lechelle, William  and
      Gotti, Fabrizio  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4002",
    doi = "10.18653/v1/W19-4002",
    pages = "6--15",
    abstract = "We build a reference for the task of Open Information Extraction, on five documents. We tentatively resolve a number of issues that arise, including coreference and granularity, and we take steps toward addressing inference, a significant problem. We seek to better pinpoint the requirements for the task. We produce our annotation guidelines specifying what is correct to extract and what is not. In turn, we use this reference to score existing Open IE systems. We address the non-trivial problem of evaluating the extractions produced by systems against the reference tuples, and share our evaluation script. Among seven compared extractors, we find the MinIE system to perform best.",
}
@inproceedings{yung-etal-2019-crowdsourcing,
    title = "Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task",
    author = "Yung, Frances  and
      Demberg, Vera  and
      Scholman, Merel",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4003",
    doi = "10.18653/v1/W19-4003",
    pages = "16--25",
    abstract = "The perspective of being able to crowd-source coherence relations bears the promise of acquiring annotations for new texts quickly, which could then increase the size and variety of discourse-annotated corpora. It would also open the avenue to answering new research questions: Collecting annotations from a larger number of individuals per instance would allow to investigate the distribution of inferred relations, and to study individual differences in coherence relation interpretation. However, annotating coherence relations with untrained workers is not trivial. We here propose a novel two-step annotation procedure, which extends an earlier method by Scholman and Demberg (2017a). In our approach, coherence relation labels are inferred from connectives that workers insert into the text. We show that the proposed method leads to replicable coherence annotations, and analyse the agreement between the obtained relation labels and annotations from PDTB and RSTDT on the same texts.",
}
@inproceedings{gold-etal-2019-annotating,
    title = "Annotating and analyzing the interactions between meaning relations",
    author = "Gold, Darina  and
      Kovatchev, Venelin  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4004",
    doi = "10.18653/v1/W19-4004",
    pages = "26--36",
    abstract = "Pairs of sentences, phrases, or other text pieces can hold semantic relations such as paraphrasing, textual entailment, contradiction, specificity, and semantic similarity. These relations are usually studied in isolation and no dataset exists where they can be compared empirically. Here we present a corpus annotated with these relations and the analysis of these results. The corpus contains 520 sentence pairs, annotated with these relations. We measure the annotation reliability of each individual relation and we examine their interactions and correlations. Among the unexpected results revealed by our analysis is that the traditionally considered direct relationship between paraphrasing and bi-directional entailment does not hold in our data.",
}
@inproceedings{evang-etal-2019-ccgweb,
    title = "{CCG}web: a New Annotation Tool and a First Quadrilingual {CCG} Treebank",
    author = "Evang, Kilian  and
      Abzianidze, Lasha  and
      Bos, Johan",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4005",
    doi = "10.18653/v1/W19-4005",
    pages = "37--42",
    abstract = "We present the first open-source graphical annotation tool for combinatory categorial grammar (CCG), and the first set of detailed guidelines for syntactic annotation with CCG, for four languages: English, German, Italian, and Dutch. We also release a parallel pilot CCG treebank based on these guidelines, with 4x100 adjudicated sentences, 10K single-annotator fully corrected sentences, and 82K single-annotator partially corrected sentences.",
}
@inproceedings{laarmann-quante-etal-2019-making,
    title = "The making of the Litkey Corpus, a richly annotated longitudinal corpus of {G}erman texts written by primary school children",
    author = "Laarmann-Quante, Ronja  and
      Dipper, Stefanie  and
      Belke, Eva",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4006",
    doi = "10.18653/v1/W19-4006",
    pages = "43--55",
    abstract = "To date, corpus and computational linguistic work on written language acquisition has mostly dealt with second language learners who have usually already mastered orthography acquisition in their first language. In this paper, we present the Litkey Corpus, a richly-annotated longitudinal corpus of written texts produced by primary school children in Germany from grades 2 to 4. The paper focuses on the (semi-)automatic annotation procedure at various linguistic levels, which include POS tags, features of the word-internal structure (phonemes, syllables, morphemes) and key orthographic features of the target words as well as a categorization of spelling errors. Comprehensive evaluations show that high accuracy was achieved on all levels, making the Litkey Corpus a useful resource for corpus-based research on literacy acquisition of German primary school children and for developing NLP tools for educational purposes. The corpus is freely available under https://www.linguistics.rub.de/litkeycorpus/.",
}
@inproceedings{mysore-etal-2019-materials,
    title = "The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures",
    author = "Mysore, Sheshera  and
      Jensen, Zachary  and
      Kim, Edward  and
      Huang, Kevin  and
      Chang, Haw-Shiuan  and
      Strubell, Emma  and
      Flanigan, Jeffrey  and
      McCallum, Andrew  and
      Olivetti, Elsa",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4007",
    doi = "10.18653/v1/W19-4007",
    pages = "56--64",
    abstract = "Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems.",
}
@inproceedings{tjuka-etal-2019-tagging,
    title = "Tagging modality in Oceanic languages of Melanesia",
    author = "Tjuka, Annika  and
      Wei{\ss}mann, Lena  and
      von Prince, Kilu",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4008",
    doi = "10.18653/v1/W19-4008",
    pages = "65--70",
    abstract = "Primary data from small, low-resource languages of Oceania have only recently become available through language documentation. In our study, we explore corpus data of five Oceanic languages of Melanesia which are known to be mood-prominent (in the sense of Bhat, 1999). In order to find out more about tense, aspect, modality, and polarity, we tagged these categories in a subset of our corpora. For the category of modality, we developed a novel tag set (MelaTAMP, 2017), which categorizes clauses into factual, possible, and counterfactual. Based on an analysis of the inter-annotator consistency, we argue that our tag set for the modal domain is efficient for our subject languages and might be useful for other languages and purposes.",
}
@inproceedings{mambrini-passarotti-2019-harmonizing,
    title = "Harmonizing Different Lemmatization Strategies for Building a Knowledge Base of Linguistic Resources for {L}atin",
    author = "Mambrini, Francesco  and
      Passarotti, Marco",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4009",
    doi = "10.18653/v1/W19-4009",
    pages = "71--80",
    abstract = "The interoperability between lemmatized corpora of Latin and other resources that use the lemma as indexing key is hampered by the multiple lemmatization strategies that different projects adopt. In this paper we discuss how we tackle the challenges raised by harmonizing different lemmatization criteria in the context of a project that aims to connect linguistic resources for Latin using the Linked Data paradigm. The paper introduces the architecture supporting an open-ended, lemma-based Knowledge Base, built to make textual and lexical resources for Latin interoperable. Particularly, the paper describes the inclusion into the Knowledge Base of its lexical basis, of a word formation lexicon and of a lemmatized and syntactically annotated corpus.",
}
@inproceedings{monsalve-etal-2019-assessing,
    title = "Assessing Back-Translation as a Corpus Generation Strategy for non-{E}nglish Tasks: A Study in Reading Comprehension and Word Sense Disambiguation",
    author = "Monsalve, Fabricio  and
      Rivas Rojas, Kervy  and
      Sobrevilla Cabezudo, Marco Antonio  and
      Oncevay, Arturo",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4010",
    doi = "10.18653/v1/W19-4010",
    pages = "81--89",
    abstract = "Corpora curated by experts have sustained Natural Language Processing mainly in English, but the expensiveness of corpora creation is a barrier for the development in further languages. Thus, we propose a corpus generation strategy that only requires a machine translation system between English and the target language in both directions, where we filter the best translations by computing automatic translation metrics and the task performance score. By studying Reading Comprehension in Spanish and Word Sense Disambiguation in Portuguese, we identified that a more quality-oriented metric has high potential in the corpora selection without degrading the task performance. We conclude that it is possible to systematise the building of quality corpora using machine translation and automatic metrics, besides some prior effort to clean and process the data.",
}
@inproceedings{casey-etal-2019-framework,
    title = "A Framework for Annotating {`}Related Works{'} to Support Feedback to Novice Writers",
    author = "Casey, Arlene  and
      Webber, Bonnie  and
      Glowacka, Dorota",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4011",
    doi = "10.18653/v1/W19-4011",
    pages = "90--99",
    abstract = "Understanding what is expected of academic writing can be difficult for novice writers to assimilate, and recent years have seen several automated tools become available to support academic writing. Our work presents a framework for annotating features of the Related Work section of academic writing, that supports writer feedback.",
}
@inproceedings{lawrence-etal-2019-online,
    title = "An Online Annotation Assistant for Argument Schemes",
    author = "Lawrence, John  and
      Visser, Jacky  and
      Reed, Chris",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4012",
    doi = "10.18653/v1/W19-4012",
    pages = "100--107",
    abstract = "Understanding the inferential principles underpinning an argument is essential to the proper interpretation and evaluation of persuasive discourse. Argument schemes capture the conventional patterns of reasoning appealed to in persuasion. The empirical study of these patterns relies on the availability of data about the actual use of argumentation in communicative practice. Annotated corpora of argument schemes, however, are scarce, small, and unrepresentative. Aiming to address this issue, we present one step in the development of improved datasets by integrating the Argument Scheme Key {--} a novel annotation method based on one of the most popular typologies of argument schemes {--} into the widely used OVA software for argument analysis.",
}
@inproceedings{dobrovoljc-2019-annotating,
    title = "Annotating formulaic sequences in spoken {S}lovenian: structure, function and relevance",
    author = "Dobrovoljc, Kaja",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4013",
    doi = "10.18653/v1/W19-4013",
    pages = "108--112",
    abstract = "This paper presents the identification of formulaic sequences in the reference corpus of spoken Slovenian and their annotation in terms of syntactic structure, pragmatic function and lexicographic relevance. The annotation campaign, specific in terms of setting, subjectivity and the multifunctionality of items under investigation, resulted in a preliminary lexicon of formulaic sequences in spoken Slovenian with immediate potential for future explorations in formulaic language research. This is especially relevant for the notable number of identified multi-word expressions with discourse-structuring and stance-marking functions, which have often been overlooked by traditional phraseology research.",
}
@inproceedings{de-kuthy-etal-2019-annotating,
    title = "Annotating Information Structure in {I}talian: Characteristics and Cross-Linguistic Applicability of a {QUD}-Based Approach",
    author = "De Kuthy, Kordula  and
      Brunetti, Lisa  and
      Berardi, Marta",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4014",
    doi = "10.18653/v1/W19-4014",
    pages = "113--123",
    abstract = "We present a discourse annotation study, in which an annotation method based on Questions under Discussion (QuD) is applied to Italian data. The results of our inter-annotator agreement analysis show that the QUD-based approach, originally spelled out for English and German, can successfully be transferred cross-linguistically, supporting good agreement for the annotation of central information structure notions such as focus and non-at-issueness. Our annotation and interannotator agreement study on Italian authentic data confirms the cross-linguistic applicability of the QuD-based approach.",
}
@inproceedings{spala-etal-2019-deft,
    title = "{DEFT}: A corpus for definition extraction in free- and semi-structured text",
    author = "Spala, Sasha  and
      Miller, Nicholas A.  and
      Yang, Yiming  and
      Dernoncourt, Franck  and
      Dockhorn, Carl",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4015",
    doi = "10.18653/v1/W19-4015",
    pages = "124--131",
    abstract = "Definition extraction has been a popular topic in NLP research for well more than a decade, but has been historically limited to well-defined, structured, and narrow conditions. In reality, natural language is messy, and messy data requires both complex solutions and data that reflects that reality. In this paper, we present a robust English corpus and annotation schema that allows us to explore the less straightforward examples of term-definition structures in free and semi-structured text.",
}
@inproceedings{kalouli-etal-2019-explaining,
    title = "Explaining Simple Natural Language Inference",
    author = "Kalouli, Aikaterini-Lida  and
      Buis, Annebeth  and
      Real, Livy  and
      Palmer, Martha  and
      de Paiva, Valeria",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4016",
    doi = "10.18653/v1/W19-4016",
    pages = "132--143",
    abstract = "The vast amount of research introducing new corpora and techniques for semi-automatically annotating corpora shows the important role that datasets play in today{'}s research, especially in the machine learning community. This rapid development raises concerns about the quality of the datasets created and consequently of the models trained, as recently discussed with respect to the Natural Language Inference (NLI) task. In this work we conduct an annotation experiment based on a small subset of the SICK corpus. The experiment reveals several problems in the annotation guidelines, and various challenges of the NLI task itself. Our quantitative evaluation of the experiment allows us to assign our empirical observations to specific linguistic phenomena and leads us to recommendations for future annotation tasks, for NLI and possibly for other tasks.",
}
@inproceedings{rehbein-2019-role,
    title = "On the role of discourse relations in persuasive texts",
    author = "Rehbein, Ines",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4017",
    doi = "10.18653/v1/W19-4017",
    pages = "144--154",
    abstract = "This paper investigates the use of explicitly signalled discourse relations in persuasive texts. We present a corpus study where we control for speaker and topic and show that the distribution of different discourse connectives varies considerably across different discourse settings. While this variation can be explained by genre differences, we also observe variation regarding the distribution of discourse relations across different settings. This variation, however, cannot be easily explained by genre differences. We argue that the differences regarding the use of discourse relations reflects different strategies of persuasion and that these might be due to audience design.",
}
@inproceedings{indig-etal-2019-one,
    title = "One format to rule them all {--} The emtsv pipeline for {H}ungarian",
    author = "Indig, Bal{\'a}zs  and
      Sass, B{\'a}lint  and
      Simon, Eszter  and
      Mittelholcz, Iv{\'a}n  and
      Vad{\'a}sz, No{\'e}mi  and
      Makrai, M{\'a}rton",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4018",
    doi = "10.18653/v1/W19-4018",
    pages = "155--165",
    abstract = "We present a more efficient version of the e-magyar NLP pipeline for Hungarian called emtsv. It integrates Hungarian NLP tools in a framework whose individual modules can be developed or replaced independently and allows new ones to be added. The design also allows convenient investigation and manual correction of the data flow from one module to another. The improvements we publish include effective communication between the modules and support of the use of individual modules both in the chain and standing alone. Our goals are accomplished using extended tsv (tab separated values) files, a simple, uniform, generic and self-documenting input/output format. Our vision is maintaining the system for a long time and making it easier for external developers to fit their own modules into the system, thus sharing existing competencies in the field of processing Hungarian, a mid-resourced language. The source code is available under LGPL 3.0 license at https://github.com/dlt-rilmta/emtsv .",
}
@inproceedings{turk-etal-2019-turkish,
    title = "{T}urkish Treebanking: Unifying and Constructing Efforts",
    author = {T{\"u}rk, Utku  and
      Atmaca, Furkan  and
      {\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l  and
      K{\"o}ksal, Abdullatif  and
      Ozturk Basaran, Balkiz  and
      Gungor, Tunga  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4019",
    doi = "10.18653/v1/W19-4019",
    pages = "166--177",
    abstract = {In this paper, we present the current version of two different treebanks, the re-annotation of the Turkish PUD Treebank and the first annotation of the Turkish National Corpus Universal Dependency (henceforth TNC-UD). The annotation of both treebanks, the Turkish PUD Treebank and TNC-UD, was carried out based on the decisions concerning linguistic adequacy of re-annotation of the Turkish IMST-UD Treebank (T{\"u}rk et. al., forthcoming). Both of the treebanks were annotated with the same annotation process and morphological and syntactic analyses. The TNC-UD is planned to have 10,000 sentences. In this paper, we will present the first 500 sentences along with the annotation PUD Treebank. Moreover, this paper also offers the parsing results of a graph-based neural parser on the previous and re-annotated PUD, as well as the TNC-UD. In light of the comparisons, even though we observe a slight decrease in the attachment scores of the Turkish PUD treebank, we demonstrate that the annotation of the TNC-UD improves the parsing accuracy of Turkish. In addition to the treebanks, we have also constructed a custom annotation software with advanced filtering and morphological editing options. Both the treebanks, including a full edit-history and the annotation guidelines, and the custom software are publicly available under an open license online.},
}
@inproceedings{pal-sharma-2019-dataset,
    title = "A Dataset for Semantic Role Labelling of {H}indi-{E}nglish Code-Mixed Tweets",
    author = "Pal, Riya  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4020",
    doi = "10.18653/v1/W19-4020",
    pages = "178--188",
    abstract = "We present a data set of 1460 Hindi-English code-mixed tweets consisting of 20,949 tokens labelled with Proposition Bank labels marking their semantic roles. We created verb frames for complex predicates present in the corpus and formulated mappings from Paninian dependency labels to Proposition Bank labels. With the help of these mappings and the dependency tree, we propose a baseline rule based system for Semantic Role Labelling of Hindi-English code-mixed data. We obtain an accuracy of 96.74{\%} for Argument Identification and are able to further classify 73.93{\%} of the labels correctly. While there is relevant ongoing research on Semantic Role Labelling and on building tools for code-mixed social media data, this is the first attempt at labelling semantic roles in code-mixed data, to the best of our knowledge.",
}
@inproceedings{eckart-de-castilho-etal-2019-multi,
    title = "A Multi-Platform Annotation Ecosystem for Domain Adaptation",
    author = "Eckart de Castilho, Richard  and
      Ide, Nancy  and
      Kim, Jin-Dong  and
      Klie, Jan-Christoph  and
      Suderman, Keith",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4021",
    doi = "10.18653/v1/W19-4021",
    pages = "189--194",
    abstract = "This paper describes an ecosystem consisting of three independent text annotation platforms. To demonstrate their ability to work in concert, we illustrate how to use them to address an interactive domain adaptation task in biomedical entity recognition. The platforms and the approach are in general domain-independent and can be readily applied to other areas of science.",
}
@inproceedings{park-tyers-2019-new,
    title = "A New Annotation Scheme for the {S}ejong Part-of-speech Tagged Corpus",
    author = "Park, Jungyeul  and
      Tyers, Francis",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4022",
    doi = "10.18653/v1/W19-4022",
    pages = "195--202",
    abstract = "In this paper we present a new annotation scheme for the Sejong part-of-speech tagged corpus based on Universal Dependencies style annotation. By using a new annotation scheme, we can produce Sejong-style morphological analysis and part-of-speech tagging results which have been the \textit{de facto} standard for Korean language processing. We also explore the possibility of doing named-entity recognition and semantic-role labelling for Korean using the new annotation scheme.",
}
@inproceedings{sezerer-etal-2019-turkish,
    title = "A {T}urkish Dataset for Gender Identification of {T}witter Users",
    author = "Sezerer, Erhan  and
      Polatbilek, Ozan  and
      Tekir, Selma",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4023",
    doi = "10.18653/v1/W19-4023",
    pages = "203--207",
    abstract = "Author profiling is the identification of an author{'}s gender, age, and language from his/her texts. With the increasing trend of using Twitter as a means to express thought, profiling the gender of an author from his/her tweets has become a challenge. Although several datasets in different languages have been released on this problem, there is still a need for multilingualism. In this work, we propose a dataset of tweets of Turkish Twitter users which are labeled with their gender information. The dataset has 3368 users in training set and 1924 users in test set where each user has 100 tweets. The dataset is publicly available.",
}
@inproceedings{gooding-etal-2019-comparative,
    title = "Comparative judgments are more consistent than binary classification for labelling word complexity",
    author = "Gooding, Sian  and
      Kochmar, Ekaterina  and
      Sarkar, Advait  and
      Blackwell, Alan",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4024",
    doi = "10.18653/v1/W19-4024",
    pages = "208--214",
    abstract = "Lexical simplification systems replace complex words with simple ones based on a model of which words are complex in context. We explore how users can help train complex word identification models through labelling more efficiently and reliably. We show that using an interface where annotators make comparative rather than binary judgments leads to more reliable and consistent labels, and explore whether comparative judgments may provide a faster way for collecting labels.",
}
@inproceedings{lohr-etal-2019-continuous,
    title = "Continuous Quality Control and Advanced Text Segment Annotation with {WAT}-{SL} 2.0",
    author = "Lohr, Christina  and
      Kiesel, Johannes  and
      Luther, Stephanie  and
      Hellrich, Johannes  and
      Kolditz, Tobias  and
      Stein, Benno  and
      Hahn, Udo",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4025",
    doi = "10.18653/v1/W19-4025",
    pages = "215--219",
    abstract = "Today{'}s widely used annotation tools were designed for annotating typically short textual mentions of entities or relations, making their interface cumbersome to use for long(er) stretches of text, e.g, sentences running over several lines in a document. They also lack systematic support for hierarchically structured labels, i.e., one label being conceptually more general than another (e.g., anamnesis in relation to family anamnesis). Moreover, as a more fundamental shortcoming of today{'}s tools, they provide no continuous quality con trol mechanisms for the annotation process, an essential feature to intrinsically support iterative cycles in the development of annotation guidelines. We alleviated these problems by developing WAT-SL 2.0, an open-source web-based annotation tool for long-segment labeling, hierarchically structured label sets and built-ins for quality control.",
}
@inproceedings{novak-etal-2019-creation,
    title = "Creation of a corpus with semantic role labels for {H}ungarian",
    author = {Nov{\'a}k, Attila  and
      Laki, L{\'a}szl{\'o}  and
      Nov{\'a}k, Borb{\'a}la  and
      D{\"o}m{\"o}t{\"o}r, Andrea  and
      Ligeti-Nagy, No{\'e}mi  and
      Kalivoda, {\'A}gnes},
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4026",
    doi = "10.18653/v1/W19-4026",
    pages = "220--229",
    abstract = "In this article, an ongoing research is presented, the immediate goal of which is to create a corpus annotated with semantic role labels for Hungarian that can be used to train a parser-based system capable of formulating relevant questions about the text it processes. We briefly describe the objectives of our research, our efforts at eliminating errors in the Hungarian Universal Dependencies corpus, which we use as the base of our annotation effort, at creating a Hungarian verbal argument database annotated with thematic roles, at classifying adjuncts, and at matching verbal argument frames to specific occurrences of verbs and participles in the corpus.",
}
@inproceedings{cruz-blandon-etal-2019-toward,
    title = "Toward Dialogue Modeling: A Semantic Annotation Scheme for Questions and Answers",
    author = "Cruz Bland{\'o}n, Mar{\'\i}a Andrea  and
      Minnema, Gosse  and
      Nourbakhsh, Aria  and
      Boritchev, Maria  and
      Amblard, Maxime",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4027",
    doi = "10.18653/v1/W19-4027",
    pages = "230--235",
    abstract = "The present study proposes an annotation scheme for classifying the content and discourse contribution of question-answer pairs. We propose detailed guidelines for using the scheme and apply them to dialogues in English, Spanish, and Dutch. Finally, we report on initial machine learning experiments for automatic annotation.",
}
@inproceedings{sobrevilla-cabezudo-pardo-2019-towards,
    title = "Towards a General {A}bstract {M}eaning {R}epresentation Corpus for {B}razilian {P}ortuguese",
    author = "Sobrevilla Cabezudo, Marco Antonio  and
      Pardo, Thiago",
    booktitle = "Proceedings of the 13th Linguistic Annotation Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-4028",
    doi = "10.18653/v1/W19-4028",
    pages = "236--244",
    abstract = "Abstract Meaning Representation (AMR) is a recent and prominent semantic representation with good acceptance and several applications in the Natural Language Processing area. For English, there is a large annotated corpus (with approximately 39K sentences) that supports the research with the representation. However, to the best of our knowledge, there is only one restricted corpus for Portuguese, which contains 1,527 sentences. In this context, this paper presents an effort to build a general purpose AMR-annotated corpus for Brazilian Portuguese by translating and adapting AMR English guidelines. Our results show that such approach is feasible, but there are some challenging phenomena to solve. More than this, efforts are necessary to increase the coverage of the corresponding lexical resource that supports the annotation.",
}
@proceedings{ws-2019-deep,
    title = "Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges",
    editor = "Eisner, Jason  and
      Gall{\'e}, Matthias  and
      Heinz, Jeffrey  and
      Quattoni, Ariadna  and
      Rabusseau, Guillaume",
    month = aug,
    year = "2019",
    address = "Florence",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3900",
}
@inproceedings{merrill-2019-sequential,
    title = "Sequential Neural Networks as Automata",
    author = "Merrill, William",
    booktitle = "Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges",
    month = aug,
    year = "2019",
    address = "Florence",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3901",
    doi = "10.18653/v1/W19-3901",
    pages = "1--13",
    abstract = "This work attempts to explain the types of computation that neural networks can perform by relating them to automata. We first define what it means for a real-time network with bounded precision to accept a language. A measure of network memory follows from this definition. We then characterize the classes of languages acceptable by various recurrent networks, attention, and convolutional networks. We find that LSTMs function like counter machines and relate convolutional networks to the subregular hierarchy. Overall, this work attempts to increase our understanding and ability to interpret neural networks through the lens of theory. These theoretical insights help explain neural computation, as well as the relationship between neural networks and natural language grammar.",
}
@inproceedings{xiao-etal-2019-grammatical,
    title = "Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing",
    author = "Xiao, Chunyang  and
      Teichmann, Christoph  and
      Arkoudas, Konstantine",
    booktitle = "Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges",
    month = aug,
    year = "2019",
    address = "Florence",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3902",
    doi = "10.18653/v1/W19-3902",
    pages = "14--23",
    abstract = "While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to grammatically permissible continuations, we arrive at a widely applicable technique for speeding up semantic parsing. The technique leads to a 74{\%} speed-up on an in-house dataset with a large vocabulary, compared to the same neural model without grammatical restrictions",
}
@inproceedings{liza-grzes-2019-relating,
    title = "Relating {RNN} Layers with the Spectral {WFA} Ranks in Sequence Modelling",
    author = "Liza, Farhana Ferdousi  and
      Grzes, Marek",
    booktitle = "Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges",
    month = aug,
    year = "2019",
    address = "Florence",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3903",
    doi = "10.18653/v1/W19-3903",
    pages = "24--33",
    abstract = "We analyse Recurrent Neural Networks (RNNs) to understand the significance of multiple LSTM layers. We argue that the Weighted Finite-state Automata (WFA) trained using a spectral learning algorithm are helpful to analyse RNNs. Our results suggest that multiple LSTM layers in RNNs help learning distributed hidden states, but have a smaller impact on the ability to learn long-term dependencies. The analysis is based on the empirical results, however relevant theory (whenever possible) was discussed to justify and support our conclusions.",
}
@inproceedings{mahalunkar-kelleher-2019-multi,
    title = "Multi-Element Long Distance Dependencies: Using {SP}k Languages to Explore the Characteristics of Long-Distance Dependencies",
    author = "Mahalunkar, Abhijit  and
      Kelleher, John",
    booktitle = "Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges",
    month = aug,
    year = "2019",
    address = "Florence",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3904",
    doi = "10.18653/v1/W19-3904",
    pages = "34--43",
    abstract = "In order to successfully model Long Distance Dependencies (LDDs) it is necessary to under-stand the full-range of the characteristics of the LDDs exhibited in a target dataset. In this paper, we use Strictly k-Piecewise languages to generate datasets with various properties. We then compute the characteristics of the LDDs in these datasets using mutual information and analyze the impact of factors such as (i) k, (ii) length of LDDs, (iii) vocabulary size, (iv) forbidden strings, and (v) dataset size. This analysis reveal that the number of interacting elements in a dependency is an important characteristic of LDDs. This leads us to the challenge of modelling multi-element long-distance dependencies. Our results suggest that attention mechanisms in neural networks may aide in modeling datasets with multi-element long-distance dependencies. However, we conclude that there is a need to develop more efficient attention mechanisms to address this issue.",
}
@inproceedings{suzgun-etal-2019-lstm,
    title = "{LSTM} Networks Can Perform Dynamic Counting",
    author = "Suzgun, Mirac  and
      Belinkov, Yonatan  and
      Shieber, Stuart  and
      Gehrmann, Sebastian",
    booktitle = "Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges",
    month = aug,
    year = "2019",
    address = "Florence",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3905",
    doi = "10.18653/v1/W19-3905",
    pages = "44--54",
    abstract = "In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in our experiments are designed to be small-sized networks both to prevent them from memorizing the training sets and to visualize and interpret their behaviour at test time. Our results demonstrate that the Long Short-Term Memory (LSTM) networks can learn to recognize the well-balanced parenthesis language (Dyck-1) and the shuffles of multiple Dyck-1 languages, each defined over different parenthesis-pairs, by emulating simple real-time k-counter machines. To the best of our knowledge, this work is the first study to introduce the shuffle languages to analyze the computational power of neural networks. We also show that a single-layer LSTM with only one hidden unit is practically sufficient for recognizing the Dyck-1 language. However, none of our recurrent networks was able to yield a good performance on the Dyck-2 language learning task, which requires a model to have a stack-like mechanism for recognition.",
}
@proceedings{ws-2019-gender,
    title = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    editor = "Costa-juss{\`a}, Marta R.  and
      Hardmeier, Christian  and
      Radford, Will  and
      Webster, Kellie",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3800",
}
@inproceedings{webster-etal-2019-gendered,
    title = "Gendered Ambiguous Pronoun ({GAP}) Shared Task at the Gender Bias in {NLP} Workshop 2019",
    author = "Webster, Kellie  and
      Costa-juss{\`a}, Marta R.  and
      Hardmeier, Christian  and
      Radford, Will",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3801",
    doi = "10.18653/v1/W19-3801",
    pages = "1--7",
    abstract = "The 1st ACL workshop on Gender Bias in Natural Language Processing included a shared task on gendered ambiguous pronoun (GAP) resolution. This task was based on the coreference challenge defined in Webster et al. (2018), designed to benchmark the ability of systems to resolve pronouns in real-world contexts in a gender-fair way. 263 teams competed via a Kaggle competition, with the winning system achieving logloss of 0.13667 and near gender parity. We review the approaches of eleven systems with accepted description papers, noting their effective use of BERT (Devlin et al., 2018), both via fine-tuning and for feature extraction, as well as ensembling.",
}
@inproceedings{hitti-etal-2019-proposed,
    title = "Proposed Taxonomy for Gender Bias in Text; A Filtering Methodology for the Gender Generalization Subtype",
    author = "Hitti, Yasmeen  and
      Jang, Eunbee  and
      Moreno, Ines  and
      Pelletier, Carolyne",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3802",
    doi = "10.18653/v1/W19-3802",
    pages = "8--17",
    abstract = "The purpose of this paper is to present an empirical study on gender bias in text. Current research in this field is focused on detecting and correcting for gender bias in existing machine learning models rather than approaching the issue at the dataset level. The underlying motivation is to create a dataset which could enable machines to learn to differentiate bias writing from non-bias writing. A taxonomy is proposed for structural and contextual gender biases which can manifest themselves in text. A methodology is proposed to fetch one type of structural gender bias, Gender Generalization. We explore the IMDB movie review dataset and 9 different corpora from Project Gutenberg. By filtering out irrelevant sentences, the remaining pool of candidate sentences are sent for human validation. A total of 6123 judgments are made on 1627 sentences and after a quality check on randomly selected sentences we obtain an accuracy of 75{\%}. Out of the 1627 sentences, 808 sentence were labeled as Gender Generalizations. The inter-rater reliability amongst labelers was of 61.14{\%}.",
}
@inproceedings{friedman-etal-2019-relating,
    title = "Relating Word Embedding Gender Biases to Gender Gaps: A Cross-Cultural Analysis",
    author = "Friedman, Scott  and
      Schmer-Galunder, Sonja  and
      Chen, Anthony  and
      Rye, Jeffrey",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3803",
    doi = "10.18653/v1/W19-3803",
    pages = "18--24",
    abstract = "Modern models for common NLP tasks often employ machine learning techniques and train on journalistic, social media, or other culturally-derived text. These have recently been scrutinized for racial and gender biases, rooting from inherent bias in their training text. These biases are often sub-optimal and recent work poses methods to rectify them; however, these biases may shed light on actual racial or gender gaps in the culture(s) that produced the training text, thereby helping us understand cultural context through big data. This paper presents an approach for quantifying gender bias in word embeddings, and then using them to characterize statistical gender gaps in education, politics, economics, and health. We validate these metrics on 2018 Twitter data spanning 51 U.S. regions and 99 countries. We correlate state and country word embedding biases with 18 international and 5 U.S.-based statistical gender gaps, characterizing regularities and predictive strength.",
}
@inproceedings{chaloner-maldonado-2019-measuring,
    title = "Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word Categories",
    author = "Chaloner, Kaytlin  and
      Maldonado, Alfredo",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3804",
    doi = "10.18653/v1/W19-3804",
    pages = "25--32",
    abstract = "Prior work has shown that word embeddings capture human stereotypes, including gender bias. However, there is a lack of studies testing the presence of specific gender bias categories in word embeddings across diverse domains. This paper aims to fill this gap by applying the WEAT bias detection method to four sets of word embeddings trained on corpora from four different domains: news, social networking, biomedical and a gender-balanced corpus extracted from Wikipedia (GAP). We find that some domains are definitely more prone to gender bias than others, and that the categories of gender bias present also vary for each set of word embeddings. We detect some gender bias in GAP. We also propose a simple but novel method for discovering new bias categories by clustering word embeddings. We validate this method through WEAT{'}s hypothesis testing mechanism and find it useful for expanding the relatively small set of well-known gender bias word categories commonly used in the literature.",
}
@inproceedings{basta-etal-2019-evaluating,
    title = "Evaluating the Underlying Gender Bias in Contextualized Word Embeddings",
    author = "Basta, Christine  and
      Costa-juss{\`a}, Marta R.  and
      Casas, Noe",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3805",
    doi = "10.18653/v1/W19-3805",
    pages = "33--39",
    abstract = "Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.",
}
@inproceedings{karve-etal-2019-conceptor,
    title = "Conceptor Debiasing of Word Representations Evaluated on {WEAT}",
    author = "Karve, Saket  and
      Ungar, Lyle  and
      Sedoc, Jo{\~a}o",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3806",
    doi = "10.18653/v1/W19-3806",
    pages = "40--48",
    abstract = "Bias in word representations, such as Word2Vec, has been widely reported and investigated, and efforts made to debias them. We apply the debiasing conceptor for post-processing both traditional and contextualized word embeddings. Our method can simultaneously remove racial and gender biases from word representations. Unlike standard debiasing methods, the debiasing conceptor can utilize heterogeneous lists of biased words without loss in performance. Finally, our empirical experiments show that the debiasing conceptor diminishes racial and gender bias of word representations as measured using the Word Embedding Association Test (WEAT) of Caliskan et al. (2017).",
}
@inproceedings{moryossef-etal-2019-filling,
    title = "Filling Gender {\&} Number Gaps in Neural Machine Translation with Black-box Context Injection",
    author = "Moryossef, Amit  and
      Aharoni, Roee  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3807",
    doi = "10.18653/v1/W19-3807",
    pages = "49--54",
    abstract = "When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must {``}guess{''} this missing information, often leading to incorrect translations in the given context. We propose a black-box approach for injecting the missing information to a pre-trained neural machine translation system, allowing to control the morphological variations in the generated translations without changing the underlying model or training data. We evaluate our method on an English to Hebrew translation task, and show that it is effective in injecting the gender and number information and that supplying the correct information improves the translation accuracy in up to 2.3 BLEU on a female-speaker test set for a state-of-the-art online black-box system. Finally, we perform a fine-grained syntactic analysis of the generated translations that shows the effectiveness of our method.",
}
@inproceedings{sedoc-ungar-2019-role,
    title = "The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations",
    author = "Sedoc, Jo{\~a}o  and
      Ungar, Lyle",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3808",
    doi = "10.18653/v1/W19-3808",
    pages = "55--61",
    abstract = "Systemic bias in word embeddings has been widely reported and studied, and efforts made to debias them; however, new contextualized embeddings such as ELMo and BERT are only now being similarly studied. Standard debiasing methods require heterogeneous lists of target words to identify the {``}bias subspace{''}. We show show that using new contextualized word embeddings in conceptor debiasing allows us to more accurately debias word embeddings by breaking target word lists into more homogeneous subsets and then combining ({''}Or{'}ing{''}) the debiasing conceptors of the different subsets.",
}
@inproceedings{bhaskaran-bhallamudi-2019-good,
    title = "Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis",
    author = "Bhaskaran, Jayadev  and
      Bhallamudi, Isha",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3809",
    doi = "10.18653/v1/W19-3809",
    pages = "62--68",
    abstract = "In this work, we investigate the presence of occupational gender stereotypes in sentiment analysis models. Such a task has implications in reducing implicit biases in these models, which are being applied to an increasingly wide variety of downstream tasks. We release a new gender-balanced dataset of 800 sentences pertaining to specific professions and propose a methodology for using it as a test bench to evaluate sentiment analysis models. We evaluate the presence of occupational gender stereotypes in 3 different models using our approach, and explore their relationship with societal perceptions of occupations.",
}
@inproceedings{prost-etal-2019-debiasing,
    title = "Debiasing Embeddings for Reduced Gender Bias in Text Classification",
    author = "Prost, Flavien  and
      Thain, Nithum  and
      Bolukbasi, Tolga",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3810",
    doi = "10.18653/v1/W19-3810",
    pages = "69--75",
    abstract = "(Bolukbasi et al., 2016) demonstrated that pretrained word embeddings can inherit gender bias from the data they were trained on. We investigate how this bias affects downstream classification tasks, using the case study of occupation classification (De-Arteaga et al., 2019). We show that traditional techniques for debiasing embeddings can actually worsen the bias of the downstream classifier by providing a less noisy channel for communicating gender information. With a relatively minor adjustment, however, we show how these same techniques can be used to simultaneously reduce bias and maintain high classification accuracy.",
}
@inproceedings{alfaro-etal-2019-bert,
    title = "{BERT} Masked Language Modeling for Co-reference Resolution",
    author = "Alfaro, Felipe  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3811",
    doi = "10.18653/v1/W19-3811",
    pages = "76--81",
    abstract = "This paper explains the TALP-UPC participation for the Gendered Pronoun Resolution shared-task of the 1st ACL Workshop on Gender Bias for Natural Language Processing. We have implemented two models for mask language modeling using pre-trained BERT adjusted to work for a classification problem. The proposed solutions are based on the word probabilities of the original BERT model, but using common English names to replace the original test names.",
}
@inproceedings{bao-qiao-2019-transfer,
    title = "Transfer Learning from Pre-trained {BERT} for Pronoun Resolution",
    author = "Bao, Xingce  and
      Qiao, Qianqian",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3812",
    doi = "10.18653/v1/W19-3812",
    pages = "82--88",
    abstract = "The paper describes the submission of the team {``}We used bert!{''} to the shared task Gendered Pronoun Resolution (Pair pronouns to their correct entities). Our final submission model based on the fine-tuned BERT (Bidirectional Encoder Representations from Transformers) ranks 14th among 838 teams with a multi-class logarithmic loss of 0.208. In this work, contribution of transfer learning technique to pronoun resolution systems is investigated and the gender bias contained in classification models is evaluated.",
}
@inproceedings{wang-2019-msnet,
    title = "{MS}net: A {BERT}-based Network for Gendered Pronoun Resolution",
    author = "Wang, Zili",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3813",
    doi = "10.18653/v1/W19-3813",
    pages = "89--95",
    abstract = "The pre-trained BERT model achieves a remarkable state of the art across a wide range of tasks in natural language processing. For solving the gender bias in gendered pronoun resolution task, I propose a novel neural network model based on the pre-trained BERT. This model is a type of mention score classifier and uses an attention mechanism with no parameters to compute the contextual representation of entity span, and a vector to represent the triple-wise semantic similarity among the pronoun and the entities. In stage 1 of the gendered pronoun resolution task, a variant of this model, trained in the fine-tuning approach, reduced the multi-class logarithmic loss to 0.3033 in the 5-fold cross-validation of training set and 0.2795 in testing set. Besides, this variant won the 2nd place with a score at 0.17289 in stage 2 of the task. The code in this paper is available at: https://github.com/ziliwang/MSnet-for-Gendered-Pronoun-Resolution",
}
@inproceedings{xu-yang-2019-look,
    title = "Look Again at the Syntax: Relational Graph Convolutional Network for Gendered Ambiguous Pronoun Resolution",
    author = "Xu, Yinchuan  and
      Yang, Junlin",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3814",
    doi = "10.18653/v1/W19-3814",
    pages = "96--101",
    abstract = "Gender bias has been found in existing coreference resolvers. In order to eliminate gender bias, a gender-balanced dataset Gendered Ambiguous Pronouns (GAP) has been released and the best baseline model achieves only 66.9{\%} F1. Bidirectional Encoder Representations from Transformers (BERT) has broken several NLP task records and can be used on GAP dataset. However, fine-tune BERT on a specific task is computationally expensive. In this paper, we propose an end-to-end resolver by combining pre-trained BERT with Relational Graph Convolutional Network (R-GCN). R-GCN is used for digesting structural syntactic information and learning better task-specific embeddings. Empirical results demonstrate that, under explicit syntactic supervision and without the need to fine tune BERT, R-GCN{'}s embeddings outperform the original BERT embeddings on the coreference task. Our work significantly improves the snippet-context baseline F1 score on GAP dataset from 66.9{\%} to 80.3{\%}. We participated in the Gender Bias for Natural Language Processing 2019 shared task, and our codes are available online.",
}
@inproceedings{yang-etal-2019-fill,
    title = "Fill the {GAP}: Exploiting {BERT} for Pronoun Resolution",
    author = "Yang, Kai-Chou  and
      Niven, Timothy  and
      Chou, Tzu Hsuan  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3815",
    doi = "10.18653/v1/W19-3815",
    pages = "102--106",
    abstract = "In this paper, we describe our entry in the gendered pronoun resolution competition which achieved fourth place without data augmentation. Our method is an ensemble system of BERTs which resolves co-reference in an interaction space. We report four insights from our work: BERT{'}s representations involve significant redundancy; modeling interaction effects similar to natural language inference models is useful for this task; there is an optimal BERT layer to extract representations for pronoun resolution; and the difference between the attention weights from the pronoun to the candidate entities was highly correlated with the correct label, with interesting implications for future work.",
}
@inproceedings{abzaliev-2019-gap,
    title = "On {GAP} Coreference Resolution Shared Task: Insights from the 3rd Place Solution",
    author = "Abzaliev, Artem",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3816",
    doi = "10.18653/v1/W19-3816",
    pages = "107--112",
    abstract = "This paper presents the 3rd-place-winning solution to the GAP coreference resolution shared task. The approach adopted consists of two key components: fine-tuning the BERT language representation model (Devlin et al., 2018) and the usage of external datasets during the training process. The model uses hidden states from the intermediate BERT layers instead of the last layer. The resulting system almost eliminates the difference in log loss per gender during the cross-validation, while providing high performance.",
}
@inproceedings{ionita-etal-2019-resolving,
    title = "Resolving Gendered Ambiguous Pronouns with {BERT}",
    author = "Ionita, Matei  and
      Kashnitsky, Yury  and
      Krige, Ken  and
      Larin, Vladimir  and
      Atanasov, Atanas  and
      Logvinenko, Dennis",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3817",
    doi = "10.18653/v1/W19-3817",
    pages = "113--119",
    abstract = "Pronoun resolution is part of coreference resolution, the task of pairing an expression to its referring entity. This is an important task for natural language understanding and a necessary component of machine translation systems, chat bots and assistants. Neural machine learning systems perform far from ideally in this task, reaching as low as 73{\%} F1 scores on modern benchmark datasets. Moreover, they tend to perform better for masculine pronouns than for feminine ones. Thus, the problem is both challenging and important for NLP researchers and practitioners. In this project, we describe our BERT-based approach to solving the problem of gender-balanced pronoun resolution. We are able to reach 92{\%} F1 score and a much lower gender bias on the benchmark dataset shared by Google AI Language team.",
}
@inproceedings{liu-2019-anonymized,
    title = "Anonymized {BERT}: An Augmentation Approach to the Gendered Pronoun Resolution Challenge",
    author = "Liu, Bo",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3818",
    doi = "10.18653/v1/W19-3818",
    pages = "120--125",
    abstract = "We present our 7th place solution to the Gendered Pronoun Resolution challenge, which uses BERT without fine-tuning and a novel augmentation strategy designed for contextual embedding token-level tasks. Our method anonymizes the referent by replacing candidate names with a set of common placeholder names. Besides the usual benefits of effectively increasing training data size, this approach diversifies idiosyncratic information embedded in names. Using same set of common first names can also help the model recognize names better, shorten token length, and remove gender and regional biases associated with names. The system scored 0.1947 log loss in stage 2, where the augmentation contributed to an improvements of 0.04. Post-competition analysis shows that, when using different embedding layers, the system scores 0.1799 which would be third place.",
}
@inproceedings{chada-2019-gendered,
    title = "Gendered Pronoun Resolution using {BERT} and an Extractive Question Answering Formulation",
    author = "Chada, Rakesh",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3819",
    doi = "10.18653/v1/W19-3819",
    pages = "126--133",
    abstract = "The resolution of ambiguous pronouns is a longstanding challenge in Natural Language Understanding. Recent studies have suggested gender bias among state-of-the-art coreference resolution systems. As an example, Google AI Language team recently released a gender-balanced dataset and showed that performance of these coreference resolvers is significantly limited on the dataset. In this paper, we propose an extractive question answering (QA) formulation of pronoun resolution task that overcomes this limitation and shows much lower gender bias (0.99) on their dataset. This system uses fine-tuned representations from the pre-trained BERT model and outperforms the existing baseline by a significant margin (22.2{\%} absolute improvement in F1 score) without using any hand-engineered features. This QA framework is equally performant even without the knowledge of the candidate antecedents of the pronoun. An ensemble of QA and BERT-based multiple choice and sequence classification models further improves the F1 (23.3{\%} absolute improvement upon the baseline). This ensemble model was submitted to the shared task for the 1st ACL workshop on Gender Bias for Natural Language Processing. It ranked 9th on the final official leaderboard.",
}
@inproceedings{attree-2019-gendered,
    title = "Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling",
    author = "Attree, Sandeep",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3820",
    doi = "10.18653/v1/W19-3820",
    pages = "134--146",
    abstract = "This paper presents a strong set of results for resolving gendered ambiguous pronouns on the Gendered Ambiguous Pronouns shared task. The model presented here draws upon the strengths of state-of-the-art language and coreference resolution models, and introduces a novel evidence-based deep learning architecture. Injecting evidence from the coreference models compliments the base architecture, and analysis shows that the model is not hindered by their weaknesses, specifically gender bias. The modularity and simplicity of the architecture make it very easy to extend for further improvement and applicable to other NLP problems. Evaluation on GAP test data results in a state-of-the-art performance at 92.5{\%} F1 (gender bias of 0.97), edging closer to the human performance of 96.6{\%}. The end-to-end solution presented here placed 1st in the Kaggle competition, winning by a significant lead.",
}
@inproceedings{escude-font-costa-jussa-2019-equalizing,
    title = "Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques",
    author = "Escud{\'e} Font, Joel  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3821",
    doi = "10.18653/v1/W19-3821",
    pages = "147--154",
    abstract = "Neural machine translation has significantly pushed forward the quality of the field. However, there are remaining big issues with the output translations and one of them is fairness. Neural models are trained on large text corpora which contain biases and stereotypes. As a consequence, models inherit these social biases. Recent methods have shown results in reducing gender bias in other natural language processing tools such as word embeddings. We take advantage of the fact that word embeddings are used in neural machine translation to propose a method to equalize gender biases in neural machine translation using these representations. Specifically, we propose, experiment and analyze the integration of two debiasing techniques over GloVe embeddings in the Transformer translation architecture. We evaluate our proposed system on the WMT English-Spanish benchmark task, showing gains up to one BLEU point. As for the gender bias evaluation, we generate a test set of occupations and we show that our proposed system learns to equalize existing biases from the baseline system.",
}
@inproceedings{habash-etal-2019-automatic,
    title = "Automatic Gender Identification and Reinflection in {A}rabic",
    author = "Habash, Nizar  and
      Bouamor, Houda  and
      Chung, Christine",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3822",
    doi = "10.18653/v1/W19-3822",
    pages = "155--165",
    abstract = "The impressive progress in many Natural Language Processing (NLP) applications has increased the awareness of some of the biases these NLP systems have with regards to gender identities. In this paper, we propose an approach to extend biased single-output gender-blind NLP systems with gender-specific alternative reinflections. We focus on Arabic, a gender-marking morphologically rich language, in the context of machine translation (MT) from English, and for first-person-singular constructions only. Our contributions are the development of a system-independent gender-awareness wrapper, and the building of a corpus for training and evaluating first-person-singular gender identification and reinflection in Arabic. Our results successfully demonstrate the viability of this approach with 8{\%} relative increase in Bleu score for first-person-singular feminine, and 5.3{\%} comparable increase for first-person-singular masculine on top of a state-of-the-art gender-blind MT system on a held-out test set.",
}
@inproceedings{kurita-etal-2019-measuring,
    title = "Measuring Bias in Contextualized Word Representations",
    author = "Kurita, Keita  and
      Vyas, Nidhi  and
      Pareek, Ayush  and
      Black, Alan W  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3823",
    doi = "10.18653/v1/W19-3823",
    pages = "166--172",
    abstract = "Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks. Since they are optimized to capture the statistical properties of training data, they tend to pick up on and amplify social stereotypes present in the data as well. In this study, we (1) propose a template-based method to quantify bias in BERT; (2) show that this method obtains more consistent results in capturing social biases than the traditional cosine based method; and (3) conduct a case study, evaluating gender bias in a downstream task of Gender Pronoun Resolution. Although our case study focuses on gender bias, the proposed technique is generalizable to unveiling other biases, including in multiclass settings, such as racial and religious biases.",
}
@inproceedings{cho-etal-2019-measuring,
    title = "On Measuring Gender Bias in Translation of Gender-neutral Pronouns",
    author = "Cho, Won Ik  and
      Kim, Ji Won  and
      Kim, Seok Min  and
      Kim, Nam Soo",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3824",
    doi = "10.18653/v1/W19-3824",
    pages = "173--181",
    abstract = "Ethics regarding social bias has recently thrown striking issues in natural language processing. Especially for gender-related topics, the need for a system that reduces the model bias has grown in areas such as image captioning, content recommendation, and automated employment. However, detection and evaluation of gender bias in the machine translation systems are not yet thoroughly investigated, for the task being cross-lingual and challenging to define. In this paper, we propose a scheme for making up a test set that evaluates the gender bias in a machine translation system, with Korean, a language with gender-neutral pronouns. Three word/phrase sets are primarily constructed, each incorporating positive/negative expressions or occupations; all the terms are gender-independent or at least not biased to one side severely. Then, additional sentence lists are constructed concerning formality of the pronouns and politeness of the sentences. With the generated sentence set of size 4,236 in total, we evaluate gender bias in conventional machine translation systems utilizing the proposed measure, which is termed here as translation gender bias index (TGBI). The corpus and the code for evaluation is available on-line.",
}
@proceedings{ws-2019-balto,
    title = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    editor = "Erjavec, Toma{\v{z}}  and
      Marci{\'n}czuk, Micha{\l}  and
      Nakov, Preslav  and
      Piskorski, Jakub  and
      Pivovarova, Lidia  and
      {\v{S}}najder, Jan  and
      Steinberger, Josef  and
      Yangarber, Roman",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3700",
}
@inproceedings{babych-2019-unsupervised,
    title = "Unsupervised Induction of {U}krainian Morphological Paradigms for the New Lexicon: Extending Coverage for Named Entities and Neologisms using Inflection Tables and Unannotated Corpora",
    author = "Babych, Bogdan",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3701",
    doi = "10.18653/v1/W19-3701",
    pages = "1--11",
    abstract = "The paper presents an unsupervised method for quickly extending a Ukrainian lexicon by generating paradigms and morphological feature structures for new Named Entities and neologisms, which are not covered by existing static morphological resources. This approach addresses a practical problem of modelling paradigms for entities created by the dynamic processes in the lexicon: this problem is especially serious for highly-inflected languages in domains with specialised or quickly changing lexicon. The method uses an unannotated Ukrainian corpus and a small fixed set of inflection tables, which can be found in traditional grammar textbooks. The advantage of the proposed approach is that updating the morphological lexicon does not require training or linguistic annotation, allowing fast knowledge-light extension of an existing static lexicon to improve morphological coverage on a specific corpus. The method is implemented in an open-source package on a GitHub repository. It can be applied to other low-resourced inflectional languages which have internet corpora and linguistic descriptions of their inflection system, following the example of inflection tables for Ukrainian. Evaluation results shows consistent improvements in coverage for Ukrainian corpora of different corpus types.",
}
@inproceedings{katinskaia-ivanova-2019-multiple,
    title = "Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning",
    author = "Katinskaia, Anisia  and
      Ivanova, Sardana",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3702",
    doi = "10.18653/v1/W19-3702",
    pages = "12--22",
    abstract = "We present our work on the problem of Multiple Admissibility (MA) in language learning. Multiple Admissibility occurs in many languages when more than one grammatical form of a word fits syntactically and semantically in a given context. In second language (L2) education - in particular, in intelligent tutoring systems/computer-aided language learning (ITS/CALL) systems, which generate exercises automatically - this implies that multiple alternative answers are possible. We treat the problem as a grammaticality judgement task. We train a neural network with an objective to label sentences as grammatical or ungrammatical, using a {``}simulated learner corpus{''}: a dataset with correct text, and with artificial errors generated automatically. While MA occurs commonly in many languages, this paper focuses on learning Russian. We present a detailed classification of the types of constructions in Russian, in which MA is possible, and evaluate the model using a test set built from answers provided by the users of a running language learning system.",
}
@inproceedings{poswiata-perelkiewicz-2019-numbers,
    title = "Numbers Normalisation in the Inflected Languages: a Case Study of {P}olish",
    author = "Po{\'s}wiata, Rafa{\l}  and
      Pere{\l}kiewicz, Micha{\l}",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3703",
    doi = "10.18653/v1/W19-3703",
    pages = "23--28",
    abstract = "Text normalisation in Text-to-Speech systems is a process of converting written expressions to their spoken forms. This task is complicated because in many cases the normalised form depends on the context. Furthermore, when we analysed languages like Croatian, Lithuanian, Polish, Russian or Slovak there is additional difficulty related to their inflected nature. In this paper we want to show how to deal with this problem for one of these languages: Polish, without having a large dedicated data set and using solutions prepared for other NLP tasks. We limited our study to only numbers expressions, which are the most common non-standard words to normalise. The proposed solution is a combination of morphological tagger and transducer supported by a dictionary of numbers in their spoken forms. The data set used for evaluation is based on the part of 1-million word subset of the National Corpus of Polish. The accuracy of the described approach is presented with a comparison to a simple baseline and two commercial systems: Google Cloud Text-to-Speech and Amazon Polly.",
}
@inproceedings{ljubesic-dobrovoljc-2019-neural,
    title = "What does Neural Bring? Analysing Improvements in Morphosyntactic Annotation and Lemmatisation of {S}lovenian, {C}roatian and {S}erbian",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Dobrovoljc, Kaja",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3704",
    doi = "10.18653/v1/W19-3704",
    pages = "29--34",
    abstract = "We present experiments on Slovenian, Croatian and Serbian morphosyntactic annotation and lemmatisation between the former state-of-the-art for these three languages and one of the best performing systems at the CoNLL 2018 shared task, the Stanford NLP neural pipeline. Our experiments show significant improvements in morphosyntactic annotation, especially on categories where either semantic knowledge is needed, available through word embeddings, or where long-range dependencies have to be modelled. On the other hand, on the task of lemmatisation no improvements are obtained with the neural solution, mostly due to the heavy dependence of the task on the lookup in an external lexicon, but also due to obvious room for improvements in the Stanford NLP pipeline{'}s lemmatisation.",
}
@inproceedings{ponomareva-etal-2019-agrr,
    title = "{AGRR} 2019: Corpus for Gapping Resolution in {R}ussian",
    author = "Ponomareva, Maria  and
      Droganova, Kira  and
      Smurov, Ivan  and
      Shavrina, Tatiana",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3705",
    doi = "10.18653/v1/W19-3705",
    pages = "35--43",
    abstract = "This paper provides a comprehensive overview of the gapping dataset for Russian that consists of 7.5k sentences with gapping (as well as 15k relevant negative sentences) and comprises data from various genres: news, fiction, social media and technical texts. The dataset was prepared for the Automatic Gapping Resolution Shared Task for Russian (AGRR-2019) - a competition aimed at stimulating the development of NLP tools and methods for processing of ellipsis. In this paper, we pay special attention to the gapping resolution methods that were introduced within the shared task as well as an alternative test set that illustrates that our corpus is a diverse and representative subset of Russian language gapping sufficient for effective utilization of machine learning techniques.",
}
@inproceedings{shimorina-etal-2019-creating,
    title = "Creating a Corpus for {R}ussian Data-to-Text Generation Using Neural Machine Translation and Post-Editing",
    author = "Shimorina, Anastasia  and
      Khasanova, Elena  and
      Gardent, Claire",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3706",
    doi = "10.18653/v1/W19-3706",
    pages = "44--49",
    abstract = "In this paper, we propose an approach for semi-automatically creating a data-to-text (D2T) corpus for Russian that can be used to learn a D2T natural language generation model. An error analysis of the output of an English-to-Russian neural machine translation system shows that 80{\%} of the automatically translated sentences contain an error and that 53{\%} of all translation errors bear on named entities (NE). We therefore focus on named entities and introduce two post-editing techniques for correcting wrongly translated NEs.",
}
@inproceedings{bosnjak-karan-2019-data,
    title = "Data Set for Stance and Sentiment Analysis from User Comments on {C}roatian News",
    author = "Bo{\v{s}}njak, Mihaela  and
      Karan, Mladen",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3707",
    doi = "10.18653/v1/W19-3707",
    pages = "50--55",
    abstract = "Nowadays it is becoming more important than ever to find new ways of extracting useful information from the evergrowing amount of user-generated data available online. In this paper, we describe the creation of a data set that contains news articles and corresponding comments from Croatian news outlet 24 sata. Our annotation scheme is specifically tailored for the task of detecting stances and sentiment from user comments as well as assessing if commentator claims are verifiable. Through this data, we hope to get a better understanding of the publics viewpoint on various events. In addition, we also explore the potential of applying supervised machine learning models toautomate annotation of more data.",
}
@inproceedings{puzyrev-etal-2019-dataset,
    title = "A Dataset for Noun Compositionality Detection for a {S}lavic Language",
    author = "Puzyrev, Dmitry  and
      Shelmanov, Artem  and
      Panchenko, Alexander  and
      Artemova, Ekaterina",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3708",
    doi = "10.18653/v1/W19-3708",
    pages = "56--62",
    abstract = "This paper presents the first gold-standard resource for Russian annotated with compositionality information of noun compounds. The compound phrases are collected from the Universal Dependency treebanks according to part of speech patterns, such as ADJ+NOUN or NOUN+NOUN, using the gold-standard annotations. Each compound phrase is annotated by two experts and a moderator according to the following schema: the phrase can be either compositional, non-compositional, or ambiguous (i.e., depending on the context it can be interpreted both as compositional or non-compositional). We conduct an experimental evaluation of models and methods for predicting compositionality of noun compounds in unsupervised and supervised setups. We show that methods from previous work evaluated on the proposed Russian-language resource achieve the performance comparable with results on English corpora.",
}
@inproceedings{piskorski-etal-2019-second,
    title = "The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across {S}lavic Languages",
    author = "Piskorski, Jakub  and
      Laskova, Laska  and
      Marci{\'n}czuk, Micha{\l}  and
      Pivovarova, Lidia  and
      P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Steinberger, Josef  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3709",
    doi = "10.18653/v1/W19-3709",
    pages = "63--74",
    abstract = "We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking. The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference. Eight teams participated in the competition, which covered four languages and five entity types. Performance for the named entity recognition task reached 90{\%} F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task. Detailed evaluation information is available on the shared task web page.",
}
@inproceedings{tsygankova-etal-2019-bsnlp2019,
    title = "{BSNLP}2019 Shared Task Submission: Multisource Neural {NER} Transfer",
    author = "Tsygankova, Tatiana  and
      Mayhew, Stephen  and
      Roth, Dan",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3710",
    doi = "10.18653/v1/W19-3710",
    pages = "75--82",
    abstract = "This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop. The final model submitted is a multi-source neural NER system with multilingual BERT embeddings, trained on the concatenation of training data in various Slavic languages (as well as English). The performance of our system on the official testing data suggests that multi-source approaches consistently outperform single-source approaches for this task, even with the noise of mismatching tagsets.",
}
@inproceedings{moreno-etal-2019-tlr,
    title = "{TLR} at {BSNLP}2019: A Multilingual Named Entity Recognition System",
    author = "Moreno, Jose G.  and
      Linhares Pontes, Elvys  and
      Coustaty, Mickael  and
      Doucet, Antoine",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3711",
    doi = "10.18653/v1/W19-3711",
    pages = "83--88",
    abstract = "This paper presents our participation at the shared task on multilingual named entity recognition at BSNLP2019. Our strategy is based on a standard neural architecture for sequence labeling. In particular, we use a mixed model which combines multilingualcontextual and language-specific embeddings. Our only submitted run is based on a voting schema using multiple models, one for each of the four languages of the task (Bulgarian, Czech, Polish, and Russian) and another for English. Results for named entity recognition are encouraging for all languages, varying from 60{\%} to 83{\%} in terms of Strict and Relaxed metrics, respectively.",
}
@inproceedings{arkhipov-etal-2019-tuning,
    title = "Tuning Multilingual Transformers for Language-Specific Named Entity Recognition",
    author = "Arkhipov, Mikhail  and
      Trofimova, Maria  and
      Kuratov, Yuri  and
      Sorokin, Alexey",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3712",
    doi = "10.18653/v1/W19-3712",
    pages = "89--93",
    abstract = "Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish. We solve this task using the BERT model. We use a hundred languages multilingual model as base for transfer to the mentioned Slavic languages. Unsupervised pre-training of the BERT model on these 4 languages allows to significantly outperform baseline neural approaches and multilingual BERT. Additional improvement is achieved by extending BERT with a word-level CRF layer. Our system was submitted to BSNLP 2019 Shared Task on Multilingual Named Entity Recognition and demonstrated top performance in multilingual setting for two competition metrics. We open-sourced NER models and BERT model pre-trained on the four Slavic languages.",
}
@inproceedings{emelyanov-artemova-2019-multilingual,
    title = "Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and {NCRF}",
    author = "Emelyanov, Anton  and
      Artemova, Ekaterina",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3713",
    doi = "10.18653/v1/W19-3713",
    pages = "94--99",
    abstract = "In this paper we tackle multilingual named entity recognition task. We use the BERT Language Model as embeddings with bidirectional recurrent network, attention, and NCRF on the top. We apply multilingual BERT only as embedder without any fine-tuning. We test out model on the dataset of the BSNLP shared task, which consists of texts in Bulgarian, Czech, Polish and Russian languages.",
}
@inproceedings{jacquet-etal-2019-jrc,
    title = "{JRC} {TMA}-{CC}: {S}lavic Named Entity Recognition and Linking. Participation in the {BSNLP}-2019 shared task",
    author = "Jacquet, Guillaume  and
      Piskorski, Jakub  and
      Tanev, Hristo  and
      Steinberger, Ralf",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3714",
    doi = "10.18653/v1/W19-3714",
    pages = "100--104",
    abstract = "We report on the participation of the JRC Text Mining and Analysis Competence Centre (TMA-CC) in the BSNLP-2019 Shared Task, which focuses on named-entity recognition, lemmatisation and cross-lingual linking. We propose a hybrid system combining a rule-based approach and light ML techniques. We use multilingual lexical resources such as JRC-NAMES and BABELNET together with a named entity guesser to recognise names. In a second step, we combine known names with wild cards to increase recognition recall by also capturing inflection variants. In a third step, we increase precision by filtering these name candidates with automatically learnt inflection patterns derived from name occurrences in large news article collections. Our major requirement is to achieve high precision. We achieved an average of 65{\%} F-measure with 93{\%} precision on the four languages.",
}
@inproceedings{lohar-etal-2019-building,
    title = "Building {E}nglish-to-{S}erbian Machine Translation System for {IMD}b Movie Reviews",
    author = "Lohar, Pintu  and
      Popovi{\'c}, Maja  and
      Way, Andy",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3715",
    doi = "10.18653/v1/W19-3715",
    pages = "105--113",
    abstract = "This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic in-domain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this low-resource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically.",
}
@inproceedings{pecar-etal-2019-improving,
    title = "Improving Sentiment Classification in {S}lovak Language",
    author = "Pecar, Samuel  and
      Simko, Marian  and
      Bielikova, Maria",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3716",
    doi = "10.18653/v1/W19-3716",
    pages = "114--119",
    abstract = "Using different neural network architectures is widely spread for many different NLP tasks. Unfortunately, most of the research is performed and evaluated only in English language and minor languages are often omitted. We believe using similar architectures for other languages can show interesting results. In this paper, we present our study on methods for improving sentiment classification in Slovak language. We performed several experiments for two different datasets, one containing customer reviews, the other one general Twitter posts. We show comparison of performance of different neural network architectures and also different word representations. We show that another improvement can be achieved by using a model ensemble. We performed experiments utilizing different methods of model ensemble. Our proposed models achieved better results than previous models for both datasets. Our experiments showed also other potential research areas.",
}
@inproceedings{galeshchuk-etal-2019-sentiment,
    title = "Sentiment Analysis for Multilingual Corpora",
    author = "Galeshchuk, Svitlana  and
      Qiu, Ju  and
      Jourdan, Julien",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3717",
    doi = "10.18653/v1/W19-3717",
    pages = "120--125",
    abstract = "The paper presents a generic approach to the supervised sentiment analysis of social media content in Slavic languages. The method proposes translating the documents from the original language to English with Google{'}s Neural Translation Model. The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pre-trained Word2Vec English model. Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter datasets returns up to 86{\%} of classification accuracy on the out-of-sample data.",
}
@proceedings{ws-2019-2019,
    title = "Proceedings of the 2019 Workshop on Widening NLP",
    editor = "Axelrod, Amittai  and
      Yang, Diyi  and
      Cunha, Rossana  and
      Shaikh, Samira  and
      Waseem, Zeerak",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3600",
}
@inproceedings{ogbuju-onyesolu-2019-development,
    title = "Development of a General Purpose Sentiment Lexicon for {I}gbo Language",
    author = "Ogbuju, Emeka  and
      Onyesolu, Moses",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3601",
    pages = "1",
    abstract = "There are publicly available general purpose sentiment lexicons in some high resource languages but very few exist in the low resource languages. This makes it difficult to directly perform sentiment analysis tasks in such languages. The objective of this work is to create a general purpose sentiment lexicon for Igbo language that can determine the sentiment of documents written in Igbo language without having to translate it to English language. The material used was an automatically translated Liu{'}s lexicon and manual addition of Igbo native words. The result of this work is a general purpose lexicon {--} IgboSentilex. The performance was tested on the BBC Igbo news channel. It returned an average polarity agreement of 95{\%} with other general purpose sentiment lexicons.",
}
@inproceedings{bamutura-ljunglof-2019-towards,
    title = "Towards a Resource Grammar for {R}unyankore and Rukiga",
    author = {Bamutura, David  and
      Ljungl{\"o}f, Peter},
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3602",
    pages = "2--6",
    abstract = "Currently, there is a lack of computational grammar resources for many under-resourced languages which limits the ability to develop Natural Language Processing (NLP) tools and applications such as Multilingual Document Authoring, Computer-Assisted Language Learning (CALL) and Low-Coverage Machine Translation (MT) for these languages. In this paper, we present our attempt to formalise the grammar of two such languages: Runyankore and Rukiga. For this formalisation we use the Grammatical Framework (GF) and its Resource Grammar Library (GF-RGL).",
}
@inproceedings{abera-h-mariam-2019-speech,
    title = "Speech Recognition for {T}igrinya language Using Deep Neural Network Approach",
    author = "Abera, Hafte  and
      H/mariam, Sebsibe",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3603",
    pages = "7--9",
    abstract = "This work presents a speech recognition model for Tigrinya language .The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks, is the primary layer of our neural network model. The 40-dimensional features are MFCC-LDA-MLLT-fMLLR with CMN were used. The acoustic models are trained on features that are obtained by projecting down to 40 dimensions using linear discriminant analysis (LDA). Moreover, speaker adaptive training (SAT) is done using a single feature-space maximum likelihood linear regression (FMLLR) transform estimated per speaker. We train and compare LSTM and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models. Finally, the accuracy of the model is evaluated based on the recognition rate.",
}
@inproceedings{rouhizadeh-etal-2019-knowledge,
    title = "Knowledge-Based Word Sense Disambiguation with Distributional Semantic Expansion",
    author = "Rouhizadeh, Hossein  and
      Shamsfard, Mehrnoush  and
      Rouhizadeh, Masoud",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3604",
    pages = "10",
    abstract = "In this paper, we presented a WSD system that uses LDA topics for semantic expansion of document words. Our system also uses sense frequency information from SemCor to give higher priority to the senses which are more probable to happen.",
}
@inproceedings{tutubalina-etal-2019-aspera,
    title = "{A}spe{R}a: Aspect-Based Rating Prediction Based on User Reviews",
    author = "Tutubalina, Elena  and
      Malykh, Valentin  and
      Nikolenko, Sergey  and
      Alekseev, Anton  and
      Shenbin, Ilya",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3605",
    pages = "11--13",
    abstract = "We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)",
}
@inproceedings{hosseini-etal-2019-recognizing,
    title = "Recognizing Arrow Of Time In The Short Stories",
    author = "Hosseini, Fahimeh  and
      Fooladi, Hosein  and
      Samsami, Mohammad Reza",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3606",
    pages = "14--16",
    abstract = "Recognizing the arrow of time in the context of paragraphs in short stories is a challenging task. i.e., given only two paragraphs (excerpted from a random position in a short story), determining which comes first and which comes next is a difficult task even for humans. In this paper, we have collected and curated a novel dataset for tackling this challenging task. We have shown that a pre-trained BERT architecture achieves reasonable accuracy on the task, and outperforms RNN-based architectures.",
}
@inproceedings{kifle-2019-amharic,
    title = "{A}mharic Word Sequence Prediction",
    author = "Kifle, Nuniyat",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3607",
    pages = "17",
    abstract = "The significance of computers and handheld devices are not deniable in the modern world of today. Texts are entered to these devices using word processing programs as well as other techniques and word prediction is one of the techniques. Word Prediction is the action of guessing or forecasting what word comes after, based on some current information, and it is the main focus of this study. Even though Amharic is used by a large number of populations, no significant work is done on the topic of word sequence prediction. In this study, Amharic word sequence prediction model is developed with statistical methods using Hidden Markov Model by incorporating detailed Part of speech tag. Evaluation of the model is performed using developed prototype and keystroke savings (KSS) as a metrics. According to our experiment, prediction result using a bi-gram with detailed Part of Speech tag model has higher KSS and it is better compared to tri-gram model and better than those without Part of Speech tag. Therefore, statistical approach with Detailed POS has quite good potential on word sequence prediction for Amharic language. This research deals with designing word sequence prediction model in Amharic language. It is a language that is spoken in eastern Africa. One of the needs for Amharic word sequence prediction for mobile use and other digital devices is in order to facilitate data entry and communication in our language. Word sequence prediction is a challenging task for inflected languages. (Arora, 2007) These kinds of languages are morphologically rich and have enormous word forms. i.e. one word can have different forms. As Amharic language is highly inflected language and morphologically rich it shares this problem. (prediction, 2008) This problem makes word prediction system much more difficult and results poor performance. Due to this reason storing all forms in dictionary won{'}t solve the problem as in English and other less inflected languages. But considering other techniques that could help the predictor to suggest the next word like a POS based prediction should be used. Previous researches used dictionary approach with no consideration of context information. Hence storing all forms of words in dictionary for inflected languages such as Amharic language has been less effective. The main goal of this thesis is to implement Amharic word prediction model that works with better prediction speed and with narrowed search space as much as possible. We introduced two models; tags and words and linear interpolation that use part of speech tag information in addition to word n-grams in order to maximize the likelihood of syntactic appropriateness of the suggestions. We believe the results found reflect this. Amharic word sequence prediction using bi-gram model with higher POS weight and detailed Part of speech tag gave better keystroke savings in all scenarios of our experiment. The study followed Design Science Research Methodology (DSRM). Since DSRM includes approaches, techniques, tools, algorithms and evaluation mechanisms in the process, we followed statistical approach with statistical language modeling and built Amharic prediction model based on information from Part of Speech tagger. The statistics included in the systems varies from single word frequencies to part-of-speech tag n-grams. That means it included the statistics of Word frequencies, Word sequence frequencies, Part-of-speech sequence frequencies and other important information. Later on the system was evaluated using Keystroke Savings. (Lindh, 011). Linux mint was used as the main Operation System during the frame work design. We used corpus of 680,000 tagged words that has 31 tag sets, python programming language and its libraries for both the part of speech tagger and the predictor module. Other Tool that was used is the SRILIM (The SRI language modeling toolkit) in order to generate unigram bigram and trigram count as an input for the language model. SRILIM is toolkit that uses to build and apply statistical language modeling. This thesis presented Amharic word sequence prediction model using the statistical approach. We described a combined statistical and lexical word prediction system for handling inflected languages by making use of POS tags to build the language model. We developed Amharic language models of bigram and trigram for the training purpose. We obtained 29{\%} of KSS using bigram model with detailed part ofspeech tag. Hence, Based on the experiments carried out for this study and the results obtained, the following conclusions were made. We concluded that employing syntactic information in the form of Part-of-Speech (POS) n-grams promises more effective predictions. We also can conclude data quantity, performance of POS tagger and data quality highly affects the keystroke savings. Here in our study the tests were done on a small collection of 100 phrases. According to our evaluation better Keystroke saving (KSS) is achieved when using bi-gram model than the tri-gram models. We believe the results obtained using the experiment of detailed Part of speech tags were effective Since speed and search space are the basic issues in word sequence prediction",
}
@inproceedings{chauhan-etal-2019-framework,
    title = "A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains",
    author = "Chauhan, Geeticka  and
      McDermott, Matthew  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3608",
    pages = "18--20",
    abstract = "In this work, we aim to build a unifying framework for relation extraction (RE), applying this on 3 highly used datasets with the ability to be extendable to new datasets. At the moment, the domain suffers from lack of reproducibility as well as a lack of consensus on generalizable techniques. Our framework will be open-sourced and will aid in performing systematic exploration on the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on the 3 datasets to help establish a consensus.",
}
@inproceedings{sharifirad-jacovi-2019-learning,
    title = "Learning and Understanding Different Categories of Sexism Using Convolutional Neural Network{'}s Filters",
    author = "Sharifirad, Sima  and
      Jacovi, Alon",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3609",
    pages = "21--23",
    abstract = "Sexism is very common in social media and makes the boundaries of free speech tighter for female users. Automatically flagging and removing sexist content requires niche identification and description of the categories. In this study, inspired by social science work, we propose three categories of sexism toward women as follows: {``}Indirect sexism{''}, {``}Sexual sexism{''} and {``}Physical sexism{''}. We build classifiers such as Convolutional Neural Network (CNN) to automatically detect different types of sexism and address problems of annotation. Even though inherent non-interpretability of CNN is a challenge for users who detect sexism, as the reason classifying a given speech instance with regard to sexism is difficult to glance from a CNN. However, recent research developed interpretable CNN filters for text data. In a CNN, filters followed by different activation patterns along with global max-pooling can help us tease apart the most important ngrams from the rest. In this paper, we interpret a CNN model trained to classify sexism in order to understand different categories of sexism by detecting semantic categories of ngrams and clustering them. Then, these ngrams in each category are used to improve the performance of the classification task. It is a preliminary work using machine learning and natural language techniques to learn the concept of sexism and distinguishes itself by looking at more precise categories of sexism in social media along with an in-depth investigation of CNN{'}s filters.",
}
@inproceedings{rivas-2019-modeling,
    title = "Modeling Five Sentence Quality Representations by Finding Latent Spaces Produced with Deep Long Short-Memory Models",
    author = "Rivas, Pablo",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3610",
    pages = "24--26",
    abstract = "We present a study in which we train neural models that approximate rules that assess the quality of English sentences. We modeled five rules using deep LSTMs trained over a dataset of sentences whose quality is evaluated under such rules. Preliminary results suggest the neural architecture can model such rules to high accuracy.",
}
@inproceedings{abate-etal-2019-english,
    title = "{E}nglish-{E}thiopian Languages Statistical Machine Translation",
    author = "Abate, Solomon Teferra  and
      Melese, Michael  and
      Tachbelie, Martha Yifiru  and
      Meshesha, Million  and
      Atinafu, Solomon  and
      Mulugeta, Wondwossen  and
      Assabie, Yaregal  and
      Abera, Hafte  and
      Ephrem, Biniyam  and
      Gebreselassie, Tewodros  and
      Tufa, Wondimagegnhue Tsegaye  and
      Lemma, Amanuel  and
      Andargie, Tsegaye  and
      Shifaw, Seifedin",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3611",
    pages = "27--30",
    abstract = "In this paper, we describe an attempt towards the development of parallel corpora for English and Ethiopian Languages, such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge{'}ez. The corpora are used for conducting bi-directional SMT experiments. The BLEU scores of the bi-directional SMT systems show a promising result. The morphological richness of the Ethiopian languages has a great impact on the performance of SMT especially when the targets are Ethiopian languages.",
}
@inproceedings{ozer-zeyrek-2019-automatic,
    title = "An automatic discourse relation alignment experiment on {TED}-{MDB}",
    author = "Ozer, Sibel  and
      Zeyrek, Deniz",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3612",
    pages = "31--34",
    abstract = "This paper describes an automatic discourse relation alignment experiment as an empirical justification of the planned annotation projection approach to enlarge the 3600-word multilingual corpus of TED Multilingual Discourse Bank (TED-MDB). The experiment is carried out on a single language pair (English-Turkish) included in TED-MDB. The paper first describes the creation of a large corpus of English-Turkish bi-sentences, then it presents a sense-based experiment that automatically aligns the relations in the English sentences of TED-MDB with the Turkish sentences. The results are very close to the results obtained from an earlier semi-automatic post-annotation alignment experiment validated by human annotators and are encouraging for future annotation projection tasks.",
}
@inproceedings{xia-xia-2019-design,
    title = "The Design and Construction of the Corpus of {C}hina {E}nglish",
    author = "Xia, Lixin  and
      Xia, Yun",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3613",
    pages = "35--37",
    abstract = "The paper describes the development a corpus of an English variety, i.e. China English, in or-der to provide a linguistic resource for researchers in the field of China English. The Corpus of China English (CCE) was built with due consideration given to its representativeness and authenticity. It was composed of more than 13,962,102 tokens in 15,333 texts evenly divided between the following four genres: newspapers, magazines, fiction and academic writings. The texts cover a wide range of domains, such as news, financial, politics, environment, social, culture, technology, sports, education, philosophy, literary, etc. It is a helpful resource for research on China English, computational linguistics, natural language processing, corpus linguistics and English language education.",
}
@inproceedings{rauf-pado-2019-learning,
    title = "Learning Trilingual Dictionaries for {U}rdu {--} {R}oman {U}rdu {--} {E}nglish",
    author = "Rauf, Moiz  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3614",
    pages = "38--42",
    abstract = "In this paper, we present an effort to generate a joint Urdu, Roman Urdu and English trilingual lexicon using automated methods. We make a case for using statistical machine translation approaches and parallel corpora for dictionary creation. To this purpose, we use word alignment tools on the corpus and evaluate translations using human evaluators. Despite different writing script and considerable noise in the corpus our results show promise with over 85{\%} accuracy of Roman Urdu{--}Urdu and 45{\%} English{--}Urdu pairs.",
}
@inproceedings{rakshit-2019-joint,
    title = "Joint Inference on Bilingual Parse Trees for {PP}-attachment Disambiguation",
    author = "Rakshit, Geetanjali",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3615",
    pages = "43--45",
    abstract = "Prepositional Phrase (PP) attachment is a classical problem in NLP for languages like English, which suffer from structural ambiguity. In this work, we solve this problem with the help of another language free from such ambiguities, using the parse tree of the parallel sentence in the other language, and word alignments. We formulate an optimization framework that encourages agreement between the parse trees for two languages, and solve it using a novel Dual Decomposition (DD) based algorithm. Experiments on the English-Hindi language pair show promising improvements over the baseline.",
}
@inproceedings{sharifirad-matwin-2019-using,
    title = "Using Attention-based Bidirectional {LSTM} to Identify Different Categories of Offensive Language Directed Toward Female Celebrities",
    author = "Sharifirad, Sima  and
      Matwin, Stan",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3616",
    pages = "46--48",
    abstract = "Social media posts reflect the emotions, intentions and mental state of the users. Twitter users who harass famous female figures may do so with different intentions and intensities. Recent studies have published datasets focusing on different types of online harassment, vulgar language, and emotional intensities. We trained, validate and test our proposed model, attention-based bidirectional neural network, on the three datasets:{''}online harassment{''}, {``}vulgar language{''} and {``}valance{''} and achieved state of the art performance in two of the datasets. We report F1 score for each dataset separately along with the final precision, recall and macro-averaged F1 score. In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter. We tested the trained models on ten collected corpuses each related to one famous female figure to predict the type of harassing language, the type of vulgar language and the degree of intensity of language occurring on their social platforms. Interestingly, the achieved results show different patterns of linguistic use targeting different racial background and occupations. The contribution of this study is two-fold. From the technical perspective, our proposed methodology is shown to be effective with a good margin in comparison to the previous state-of-the-art results on one of the two available datasets. From the social perspective, we introduce a methodology which can unlock facts about the nature of offensive language targeting women on online social platforms. The collected dataset will be shared publicly for further investigation.",
}
@inproceedings{mihret-atinaf-2019-sentiment,
    title = "Sentiment Analysis Model for Opinionated {A}wngi Text: Case of Music Reviews",
    author = "Mihret, Melese  and
      Atinaf, Muluneh",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3617",
    pages = "49--51",
    abstract = {Abstract The analysis of sentiments is imperative to make a decision for individuals, organizations, and governments. Due to the rapid growth of Awngi (Agew) text on the web, there is no available corpus annotated for sentiment analysis. In this paper, we present a SA model for the Awngi language spoken in Ethiopia, by using a supervised machine learning approach. We developed our corpus by collecting around 1500 posts from online sources. This research is begun to build and evaluate the model for opinionated Awngi music reviews. Thus, pre-processing techniques have been employed to clean the data, to convert transliterations to the native Ethiopic script for accessibility and convenience to typing and to change the words to their base form by removing the inflectional morphemes. After pre-processing, the corpus is manually annotated by three the language professional for giving polarity, and rate, their level of confidence in their selection and sentiment intensity scale values. To improve the calculation method of feature selection and weighting and proposed a more suitable SA algorithm for feature extraction named CHI and weight calculation named TF IDF, increasing the proportion and weight of sentiment words in the feature words. We employed Support Vector Machines (SVM), Na{\"\i}ve Bayes (NB) and Maximum Entropy (MxEn) machine learning algorithms. Generally, the results are encouraging, despite the morphological challenge in Awngi, the data cleanness and small size of data. We are believed that the results could improve further with a larger corpus.},
}
@inproceedings{boritchev-amblard-2019-compositional,
    title = "A compositional view of questions",
    author = "Boritchev, Maria  and
      Amblard, Maxime",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3618",
    pages = "52",
    abstract = "We present a research on compositional treatment of questions in neo-davidsonian event semantics style. Our work is based on (Champollion, 2011) where only declarative sentences were considered. Our research is based on complex formal examples, paving the way towards further research in this domain and further testing on real-life corpora.",
}
@inproceedings{cao-etal-2019-controlling,
    title = "Controlling the Specificity of Clarification Question Generation",
    author = "Cao, Yang Trista  and
      Rao, Sudha  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3619",
    pages = "53--56",
    abstract = "Unlike comprehension-style questions, clarification questions look for some missing information in a given context. However, without guidance, neural models for question generation, similar to dialog generation models, lead to generic and bland questions that cannot elicit useful information. We argue that controlling the level of specificity of the generated questions can have useful applications and propose a neural clarification question generation model for the same. We first train a classifier that annotates a clarification question with its level of specificity (generic or specific) to the given context. Our results on the Amazon questions dataset demonstrate that training a clarification question generation model on specificity annotated data can generate questions with varied levels of specificity to the given context.",
}
@inproceedings{brantley-etal-2019-non,
    title = "Non-Monotonic Sequential Text Generation",
    author = "Brantley, Kiante  and
      Cho, Kyunghyun  and
      Daum{\'e}, Hal  and
      Welleck, Sean",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3620",
    pages = "57--59",
    abstract = "Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy{'}s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.",
}
@inproceedings{gonen-goldberg-2019-lipstick-pig,
    title = "Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them",
    author = "Gonen, Hila  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3621",
    pages = "60--63",
    abstract = "Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between {``}gender-neutralized{''} words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.",
}
@inproceedings{gonen-etal-2019-grammatical-gender,
    title = "How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?",
    author = "Gonen, Hila  and
      Kementchedjhieva, Yova  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3622",
    pages = "64--67",
    abstract = "Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun{'}s gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While {``}embedding debiasing{''} methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words{'} context when training word embeddings is effective in removing it. Fixing the grammatical gender bias results in a positive effect on the quality of the resulting word embeddings, both in monolingual and cross lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.",
}
@inproceedings{roberson-2019-automatic,
    title = "Automatic Product Categorization for Official Statistics",
    author = "Roberson, Andrea",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3623",
    pages = "68--72",
    abstract = "The North American Product Classification System (NAPCS) is a comprehensive, hierarchical classification system for products (goods and services) that is consistent across the three North American countries. Beginning in 2017, the Economic Census will use NAPCS to produce economy-wide product tabulations. Respondents are asked to report data from a long, pre-specified list of potential products in a given industry, with some lists containing more than 50 potential products. Businesses have expressed the desire to alternatively supply Universal Product Codes (UPC) to the U. S. Census Bureau. Much work has been done around the categorization of products using product descriptions. No study has applied these efforts for the calculation of official statistics (statistics published by government agencies) using only the text of UPC product descriptions. The question we address in this paper is: Given UPC codes and their associated product descriptions, can we accurately predict NAPCS? We tested the feasibility of businesses submitting a spreadsheet with Universal Product Codes and their associated text descriptions. This novel strategy classified text with very high accuracy rates, all of our algorithms surpassed over 90 percent.",
}
@inproceedings{fenglei-etal-2019-online,
    title = "An Online Topic Modeling Framework with Topics Automatically Labeled",
    author = "Fenglei, Jin  and
      Cuiyun, Gao  and
      Michael R., Lyu",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3624",
    pages = "73--76",
    abstract = "In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic. The proposed framework combines the prior topic distributions in a time window during inferring the topics in current time slice, and introduces a new ranking scheme to select most representative phrases and sentences for the inferred topics. Experiments on 7,076 Stack Exchange posts show the effectiveness of IEDL in tracking topic changes.",
}
@inproceedings{weber-steedman-2019-construction,
    title = "Construction and Alignment of Multilingual Entailment Graphs for Semantic Inference",
    author = "Weber, Sabine  and
      Steedman, Mark",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3625",
    pages = "77--79",
    abstract = "This paper presents ongoing work on the construction and alignment of predicate entailment graphs in English and German. We extract predicate-argument pairs from large corpora of monolingual English and German news text and construct monolingual paraphrase clusters and entailment graphs. We use an aligned subset of entities to derive the bilingual alignment of entities and relations, and achieve better than baseline results on a translated subset of a predicate entailment data set (Levy and Dagan, 2016) and the German portion of XNLI (Conneau et al., 2018).",
}
@inproceedings{cui-etal-2019-kb,
    title = "{KB}-{NLG}: From Knowledge Base to Natural Language Generation",
    author = "Cui, Wen  and
      Zhou, Minghui  and
      Zhao, Rongwen  and
      Norouzi, Narges",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3626",
    pages = "80--82",
    abstract = "We perform the natural language generation (NLG) task by mapping sets of Resource Description Framework (RDF) triples into text. First we investigate the impact of increasing the number of entity types in delexicalisaiton on the generation quality. Second we conduct different experiments to evaluate two widely applied language generation systems, encoder-decoder with attention and the Transformer model on a large benchmark dataset. We evaluate different models on automatic metrics, as well as the training time. To our knowledge, we are the first to apply Transformer model to this task.",
}
@inproceedings{gu-chen-2019-acoustic,
    title = "Acoustic Characterization of Singaporean Children{'}s {E}nglish: Comparisons to {A}merican and {B}ritish Counterparts",
    author = "Gu, Yuling  and
      Chen, Nancy",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3627",
    pages = "83--87",
    abstract = "We investigate English pronunciation patterns in Singaporean children in relation to their American and British counterparts by conducting archetypal analysis on selected vowel pairs. Given that Singapore adopts British English as the institutional standard, one might expect Singaporean children to follow British pronunciation patterns, but we observe that Singaporean children also present similar patterns to Americans for TRAP-BATH spilt vowels: (1) British and Singaporean children both produce these vowels with a relatively lowered tongue height. (2) These vowels are more fronted for American and Singaporean children (p {\textless} 0.001). In addition, when comparing /{\ae}/ and /ε/ productions, British speakers show the clearest distinction between the two vowels; Singaporean and American speakers exhibit a higher and more fronted tongue position for /{\ae}/ (p {\textless} 0.001), causing /{\ae}/ to be acoustically more similar to /ε/.",
}
@inproceedings{pimentel-etal-2019-rethinking,
    title = "Rethinking Phonotactic Complexity",
    author = "Pimentel, Tiago  and
      Roark, Brian  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3628",
    pages = "88--90",
    abstract = "In this work, we propose the use of phone-level language models to estimate phonotactic complexity{---}measured in bits per phoneme{---}which makes cross-linguistic comparison straightforward. We compare the entropy across languages using this simple measure, gaining insight on how complex different language{'}s phonotactics are. Finally, we show a very strong negative correlation between phonotactic complexity and the average length of words{---}Spearman rho=-0.744{---}when analysing a collection of 106 languages with 1016 basic concepts each.",
}
@inproceedings{oladipo-rufai-2019-implementing,
    title = "Implementing a Multi-lingual Chatbot for Positive Reinforcement in Young Learners",
    author = "Oladipo, Francisca  and
      Rufai, Abdulmalik",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3629",
    pages = "91",
    abstract = "This is a humanitarian work {--}a counter-terrorism effort. The presentation describes the experiences of developing a multi-lingua, interactive chatbot trained on the corpus of two Nigerian Languages (Hausa and Fulfude), with simultaneous translation to a third (Kanuri), to stimulate conversations, deliver tailored contents to the users thereby aiding in the detection of the probability and degree of radicalization in young learners through data analysis of the games moves and vocabularies. As chatbots have the ability to simulate a human conversation based on rhetorical behavior, the system is able to learn the need of individual user through constant interaction and deliver tailored contents that promote good behavior in Hausa, Fulfulde and Kanuri languages.",
}
@inproceedings{hashempour-2019-deep,
    title = "A Deep Learning Approach to Language-independent Gender Prediction on {T}witter",
    author = "Hashempour, Reyhaneh",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3630",
    pages = "92--94",
    abstract = "This work presents a set of experiments conducted to predict the gender of Twitter users based on language-independent features extracted from the text of the users{'} tweets. The experiments were performed on a version of TwiSty dataset including tweets written by the users of six different languages: Portuguese, French, Dutch, English, German, and Italian. Logistic regression (LR), and feed-forward neural networks (FFNN) with back-propagation were used to build models in two different settings: Inter-Lingual (IL) and Cross-Lingual (CL). In the IL setting, the training and testing were performed on the same language whereas in the CL, Italian and German datasets were set aside and only used as test sets and the rest were combined to compose training and development sets. In the IL, the highest accuracy score belongs to LR whereas, in the CL, FFNN with three hidden layers yields the highest score. The results show that neural network based models underperform traditional models when the size of the training set is small; however, they beat traditional models by a non-trivial margin, when they are fed with large enough data. Finally, the feature analysis confirms that men and women have different writing styles independent of their language.",
}
@inproceedings{nguyen-etal-2019-isolating,
    title = "Isolating the Effects of Modeling Recursive Structures: A Case Study in Pronunciation Prediction of {C}hinese Characters",
    author = "Nguyen, Minh  and
      Ngo, Gia H  and
      Chen, Nancy",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3631",
    pages = "95--97",
    abstract = "Finding that explicitly modeling structures leads to better generalization, we consider the task of predicting Cantonese pronunciations of logographs (Chinese characters) using logographs{'} recursive structures. This task is a suitable case study for two reasons. First, logographs{'} pronunciations depend on structures (i.e. the hierarchies of sub-units in logographs) Second, the quality of logographic structures is consistent since the structures are constructed automatically using a set of rules. Thus, this task is less affected by confounds such as varying quality between annotators. Empirical results show that modeling structures explicitly using treeLSTM outperforms LSTM baseline, reducing prediction error by 6.0{\%} relative.",
}
@inproceedings{abbott-martinus-2019-benchmarking,
    title = "Benchmarking Neural Machine Translation for {S}outhern {A}frican Languages",
    author = "Abbott, Jade  and
      Martinus, Laura",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3632",
    pages = "98--101",
    abstract = "Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared, meaning researchers struggle to reproduce reported results, and almost no publicly available benchmarks or leaderboards for African machine translation models exist. To start to address these problems, we trained neural machine translation models for a subset of Southern African languages on publicly-available datasets. We provide the code for training the models and evaluate the models on a newly released evaluation set, with the aim of starting a leaderboard for Southern African languages and spur future research in the field.",
}
@inproceedings{mieskes-schmunk-2019-ocr,
    title = "{OCR} Quality and {NLP} Preprocessing",
    author = "Mieskes, Margot  and
      Schmunk, Stefan",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3633",
    pages = "102--105",
    abstract = "We present initial experiments to evaluate the performance of tasks such as Part of Speech Tagging on data corrupted by Optical Character Recognition (OCR). Our results, based on English and German data, using artificial experiments as well as initial real OCRed data indicate that already a small drop in OCR quality considerably increases the error rates, which would have a significant impact on subsequent processing steps.",
}
@inproceedings{abdulrahman-etal-2019-developing,
    title = "Developing a Fine-grained Corpus for a Less-resourced Language: the case of {K}urdish",
    author = "Abdulrahman, Roshna  and
      Hassani, Hossein  and
      Ahmadi, Sina",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3634",
    pages = "106--109",
    abstract = "Kurdish is a less-resourced language consisting of different dialects written in various scripts. Approximately 30 million people in different countries speak the language. The lack of corpora is one of the main obstacles in Kurdish language processing. In this paper, we present KTC-the Kurdish Textbooks Corpus, which is composed of 31 K-12 textbooks in Sorani dialect. The corpus is normalized and categorized into 12 educational subjects containing 693,800 tokens (110,297 types). Our resource is publicly available for non-commercial use under the CC BY-NC-SA 4.0 license.",
}
@inproceedings{taffa-libsie-2019-amharic,
    title = "{A}mharic Question Answering for Biography, Definition, and Description Questions",
    author = "Taffa, Tilahun Abedissa  and
      Libsie, Mulugeta",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3635",
    pages = "110--113",
    abstract = "A broad range of information needs can often be stated as a question. Question Answering (QA) systems attempt to provide users concise answer(s) to natural language questions. The existing Amharic QA systems handle fact-based questions that usually take named entities as an answer. To deal with more complex information needs we developed an Amharic non-factoid QA for biography, definition, and description questions. A hybrid approach has been used for the question classification. For document filtering and answer extraction we have used lexical patterns. On the other hand to answer biography questions we have used a summarizer and the generated summary is validated using a text classifier. Our QA system is evaluated and has shown a promising result.",
}
@inproceedings{floyd-etal-2019-polysemous,
    title = "Polysemous Language in Child Directed Speech",
    author = "Floyd, Sammy  and
      Barak, Libby  and
      Goldberg, Adele  and
      Lew-Williams, Casey",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3636",
    pages = "114--117",
    abstract = "Polysemous Language in Child Directed Speech Learning the meaning of words is one of the fundamental building blocks of verbal communication. Models of child language acquisition have generally made the simplifying assumption that each word appears in child-directed speech with a single meaning. To understand naturalistic word learning during childhood, it is essential to know whether children hear input that is in fact constrained to single meaning per word, or whether the environment naturally contains multiple senses.In this study, we use a topic modeling approach to automatically induce word senses from child-directed speech. Our results confirm the plausibility of our automated analysis approach and reveal an increasing rate of using multiple senses in child-directed speech, starting with corpora from children as early as the first year of life.",
}
@inproceedings{prabhumoye-etal-2019-principled,
    title = "Principled Frameworks for Evaluating Ethics in {NLP} Systems",
    author = "Prabhumoye, Shrimai  and
      Mayfield, Elijah  and
      Black, Alan W",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3637",
    pages = "118--121",
    abstract = "We critique recent work on ethics in natural language processing. Those discussions have focused on data collection, experimental design, and interventions in modeling. But we argue that we ought to first understand the frameworks of ethics that are being used to evaluate the fairness and justice of algorithmic systems. Here, we begin that discussion by outlining deontological and consequentialist ethics, and make predictions on the research agenda prioritized by each.",
}
@inproceedings{lee-etal-2019-understanding,
    title = "Understanding the Shades of Sexism in Popular {TV} Series",
    author = "Lee, Nayeon  and
      Bang, Yejin  and
      Shin, Jamin  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3638",
    pages = "122--125",
    abstract = "[Multiple-submission] In the midst of a generation widely exposed to and influenced by media entertainment, the NLP research community has shown relatively little attention on the sexist comments in popular TV series. To understand sexism in TV series, we propose a way of collecting distant supervision dataset using Character Persona information with the psychological theories on sexism. We assume that sexist characters from TV shows are more prone to making sexist comments when talking about women, and show that this hypothesis is valid through experiment. Finally, we conduct an interesting analysis on popular TV show characters and successfully identify different shades of sexism that is often overlooked.",
}
@inproceedings{barak-goldberg-2019-evaluating,
    title = "Evaluating Ways of Adapting Word Similarity",
    author = "Barak, Libby  and
      Goldberg, Adele",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3639",
    pages = "126--128",
    abstract = "People judge pairwise similarity by deciding which aspects of the words{'} meanings are relevant for the comparison of the given pair. However, computational representations of meaning rely on dimensions of the vector representation for similarity comparisons, without considering the specific pairing at hand. Prior work has adapted computational similarity judgments by using the softmax function in order to address this limitation by capturing asymmetry in human judgments. We extend this analysis by showing that a simple modification of cosine similarity offers a better correlation with human judgments over a comprehensive dataset. The modification performs best when the similarity between two words is calculated with reference to other words that are most similar and dissimilar to the pair.",
}
@inproceedings{koufakou-scott-2019-exploring,
    title = "Exploring the Use of Lexicons to aid Deep Learning towards the Detection of Abusive Language",
    author = "Koufakou, Anna  and
      Scott, Jason",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3640",
    pages = "129--131",
    abstract = "Detecting abusive language is a significant research topic, which has received a lot of attention recently. Our work focused on detecting personal attacks in online conversations. State-of-the-art research on this task has largely used deep learning with word embeddings. We explored the use of sentiment lexicons as well as semantic lexicons towards improving the accuracy of the baseline Convolutional Neural Network (CNN) using regular word embeddings. This is a work in progress, limited by time constraints and appropriate infrastructure. Our preliminary results showed promise for utilizing lexicons, especially semantic lexicons, for the task of detecting abusive language.",
}
@inproceedings{alimova-tutubalina-2019-entity,
    title = "Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models",
    author = "Alimova, Ilseyar  and
      Tutubalina, Elena",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3641",
    pages = "132--134",
    abstract = "This paper presents our experimental work on exploring the potential of neural network models developed for aspect-based sentiment analysis for entity-level adverse drug reaction (ADR) classification. Our goal is to explore how to represent local context around ADR mentions and learn an entity representation, interacting with its context. We conducted extensive experiments on various sources of text-based information, including social media, electronic health records, and abstracts of scientific articles from PubMed. The results show that Interactive Attention Neural Network (IAN) outperformed other models on four corpora in terms of macro F-measure. This work is an abridged version of our recent paper accepted to Programming and Computer Software journal in 2019.",
}
@inproceedings{barak-etal-2019-context,
    title = "Context Effects on Human Judgments of Similarity",
    author = "Barak, Libby  and
      Kong-Johnson, Noe  and
      Goldberg, Adele",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3642",
    pages = "135--137",
    abstract = "The semantic similarity of words forms the basis of many natural language processing methods. These computational similarity measures are often based on a mathematical comparison of vector representations of word meanings, while human judgments of similarity differ in lacking geometrical properties, e.g., symmetric similarity and triangular similarity. In this study, we propose a novel task design to further explore human behavior by asking whether a pair of words is deemed more similar depending on an immediately preceding judgment. Results from a crowdsourcing experiment show that people consistently judge words as more similar when primed by a judgment that evokes a relevant relationship. Our analysis further shows that word2vec similarity correlated significantly better with the out-of-context judgments, thus confirming the methodological differences in human-computer judgments, and offering a new testbed for probing the differences.",
}
@inproceedings{gupta-etal-2019-nlp,
    title = "{NLP} Automation to Read Radiological Reports to Detect the Stage of Cancer Among Lung Cancer Patients",
    author = "Gupta, Khushbu  and
      Thammasudjarit, Ratchainant  and
      Thakkinstian, Ammarin",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3643",
    pages = "138--141",
    abstract = "A common challenge in the healthcare industry today is physicians have access to massive amounts of healthcare data but have little time and no appropriate tools. For instance, the risk prediction model generated by logistic regression could predict the probability of diseases occurrence and thus prioritizing patients{'} waiting list for further investigations. However, many medical reports available in current clinical practice system are not yet ready for analysis using either statistics or machine learning as they are in unstructured text format. The complexity of medical information makes the annotation or validation of data very challenging and thus acts as a bottleneck to apply machine learning techniques in medical data. This study is therefore conducted to create such annotations automatically where the computer can read radiological reports for oncologists and mark the staging of lung cancer. This staging information is obtained using the rule-based method implemented using the standards of Tumor Node Metastasis (TNM) staging along with deep learning technology called Long Short Term Memory (LSTM) to extract clinical information from the Computed Tomography (CT) text report. The empirical experiment shows promising results being the accuracy of up to 85{\%}.",
}
@inproceedings{dekhili-etal-2019-augmenting,
    title = "Augmenting Named Entity Recognition with Commonsense Knowledge",
    author = "Dekhili, Gaith  and
      Le, Tan Ngoc  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3644",
    pages = "142",
    abstract = "Commonsense can be vital in some applications like Natural Language Understanding (NLU), where it is often required to resolve ambiguity arising from implicit knowledge and underspecification. In spite of the remarkable success of neural network approaches on a variety of Natural Language Processing tasks, many of them struggle to react effectively in cases that require commonsense knowledge. In the present research, we take advantage of the availability of the open multilingual knowledge graph ConceptNet, by using it as an additional external resource in Named Entity Recognition (NER). Our proposed architecture involves BiLSTM layers combined with a CRF layer that was augmented with some features such as pre-trained word embedding layers and dropout layers. Moreover, apart from using word representations, we used also character-based representation to capture the morphological and the orthographic information. Our experiments and evaluations showed an improvement in the overall performance with +2.86 in the F1-measure. Commonsense reasonnig has been employed in other studies and NLP tasks but to the best of our knowledge, there is no study relating the integration of a commonsense knowledge base in NER.",
}
@inproceedings{lepp-2019-pardon,
    title = "Pardon the Interruption: Automatic Analysis of Gender and Competitive Turn-Taking in {U}nited {S}tates {S}upreme {C}ourt Hearings",
    author = "Lepp, Haley",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3645",
    pages = "143--145",
    abstract = "The United States Supreme Court plays a key role in defining the legal basis for gender discrimination throughout the country, yet there are few checks on gender bias within the court itself. In conversational turn-taking, interruptions have been documented as a marker of bias between speakers of different genders. The goal of this study is to automatically differentiate between respectful and disrespectful conversational turns taken during official hearings, which could help in detecting bias and finding remediation techniques for discourse in the courtroom. In this paper, I present a corpus of turns annotated by legal professionals, and describe the design of a semi-supervised classifier that will use acoustic and lexical features to analyze turn-taking at scale. On completion of annotations, this classifier will be trained to extract the likelihood that turns are respectful or disrespectful for use in studies of speech trends.",
}
@inproceedings{dziri-etal-2019-evaluating-coherence,
    title = "Evaluating Coherence in Dialogue Systems using Entailment",
    author = "Dziri, Nouha  and
      Kamalloo, Ehsan  and
      Mathewson, Kory  and
      Zaiane, Osmar",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3646",
    pages = "146--148",
    abstract = "Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time consuming, and not scalable. Moreover, judges tend to evaluate a small number of dialogues, meaning that minor differences in evaluation configuration may lead to dissimilar results. In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations. Furthermore, we introduce calculable approximations of human judgment based on conversational coherence by adopting state-of-the-art entailment techniques. Results show that our metrics can be used as a surrogate for human judgment, making it easy to evaluate dialogue systems on large-scale datasets and allowing an unbiased estimate for the quality of the responses. This paper has been accepted in NAACL 2019.",
}
@inproceedings{isiaka-2019-exploiting,
    title = "Exploiting machine algorithms in vocalic quantification of {A}frican {E}nglish corpora",
    author = "Isiaka, Lasisi Adeiza",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3647",
    pages = "149--151",
    abstract = "Towards procedural fidelity in the processing of African English speech corpora, this work demonstrates how the adaptation of machine-assisted segmentation of phonemes and automatic extraction of acoustic values can significantly speed up the processing of naturalistic data and make the vocalic analysis of the varieties less impressionistic. Research in African English phonology has, till date, been least data-driven {--} much less the use of comparative corpora for cross-varietal assessments. Using over 30 hours of naturalistic data (from 28 speakers in 5 Nigerian cities), the procedures for segmenting audio files into phonemic units via the Munich Automatic Segmentation System (MAUS), and the extraction of their spectral values in Praat are explained. Evidence from the speech corpora supports a more complex vocalic inventory than attested in previous auditory/manual-based accounts {--} thus reinforcing the resourcefulness of the algorithms for the current data and cognate varieties. Keywords: machine algorithms; naturalistic data; African English phonology; vowel segmentation",
}
@inproceedings{robin-etal-2019-assessing,
    title = "Assessing the Ability of Neural Machine Translation Models to Perform Syntactic Rewriting",
    author = "Robin, Jahkel  and
      Grissom II, Alvin  and
      Roselli, Matthew",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3648",
    pages = "152",
    abstract = "We describe work in progress for evaluating performance of sequence-to-sequence neural networks on the task of syntax-based reordering for rules applicable to simultaneous machine translation. We train models that attempt to rewrite English sentences using rules that are commonly used by human interpreters. We examine the performance of these models to determine which forms of rewriting are more difficult for them to learn and which architectures are the best at learning them.",
}
@inproceedings{cruz-2019-authorship,
    title = "Authorship Recognition with Short-Text using Graph-based Techniques",
    author = "Cruz, Laura",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3649",
    pages = "153--156",
    abstract = "In recent years, studies of authorship recognition has aroused great interest in graph-based analysis. Modeling the writing style of each author using a network of co-occurrence words. However, short texts can generate some changes in the topology of network that cause impact on techniques of feature extraction based on graph topology. In this work, we evaluate the robustness of global-strategy and local-strategy based on complex network measurements comparing with graph2vec a graph embedding technique based on skip-gram model. The experiment consists of evaluating how each modification in the length of text affects the accuracy of authorship recognition on both techniques using cross-validation and machine learning techniques.",
}
@inproceedings{montano-etal-2019-parallel,
    title = "A Parallel Corpus {M}ixtec-{S}panish",
    author = "Monta{\~n}o, Cynthia  and
      Sierra Mart{\'\i}nez, Gerardo  and
      Bel-Enguix, Gemma  and
      Gomez, Helena",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3650",
    pages = "157--159",
    abstract = "This work is about the compilation process of parallel documents Spanish-Mixtec. There are not many Spanish-Mixec parallel texts and most of the sources are non-digital books. Due to this, we need to face the errors when digitizing the sources and difficulties in sentence alignment, as well as the fact that does not exist a standard orthography. Our parallel corpus consists of sixty texts coming from books and digital repositories. These documents belong to different domains: history, traditional stories, didactic material, recipes, ethnographical descriptions of each town and instruction manuals for disease prevention. We have classified this material in five major categories: didactic (6 texts), educative (6 texts), interpretative (7 texts), narrative (39 texts), and poetic (2 texts). The final total of tokens is 49,814 Spanish words and 47,774 Mixtec words. The texts belong to the states of Oaxaca (48 texts), Guerrero (9 texts) and Puebla (3 texts). According to this data, we see that the corpus is unbalanced in what refers to the representation of the different territories. While 55{\%} of speakers are in Oaxaca, 80{\%} of texts come from this region. Guerrero has the 30{\%} of speakers and the 15{\%} of texts and Puebla, with the 15{\%} of the speakers has a representation of the 5{\%} in the corpus.",
}
@inproceedings{mahajan-shaikh-2019-emoji,
    title = "Emoji Usage Across Platforms: A Case Study for the Charlottesville Event",
    author = "Mahajan, Khyati  and
      Shaikh, Samira",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3651",
    pages = "160--162",
    abstract = "We study emoji usage patterns across two social media platforms, one of them considered a fringe community called Gab, and the other Twitter. We find that Gab tends to comparatively use more emotionally charged emoji, but also seems more apathetic towards the violence during the event, while Twitter takes a more empathetic approach to the event.",
}
@inproceedings{gomez-etal-2019-reading,
    title = "Reading {KITTY}: Pitch Range as an Indicator of Reading Skill",
    author = "Gomez, Alfredo  and
      Ngo, Alicia  and
      Otondo, Alessandra  and
      Medero, Julie",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3652",
    pages = "163--165",
    abstract = "While affective outcomes are generally positive for the use of eBooks and computer-based reading tutors in teaching children to read, learning outcomes are often poorer (Korat and Shamir, 2004). We describe the first iteration of Reading Kitty, an iOS application that uses NLP and speech processing to focus children{'}s time on close reading and prosody in oral reading, while maintaining an emphasis on creativity and artifact creation. We also share preliminary results demonstrating that pitch range can be used to automatically predict readers{'} skill level.",
}
@inproceedings{tsai-etal-2019-adversarial,
    title = "Adversarial Attack on Sentiment Classification",
    author = "Tsai, Yi-Ting (Alicia)  and
      Yang, Min-Chu  and
      Chen, Han-Yu",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3653",
    pages = "166--173",
    abstract = "In this paper, we propose a white-box attack algorithm called {``}Global Search{''} method and compare it with a simple misspelling noise and a more sophisticated and common white-box attack approach called {``}Greedy Search{''}. The attack methods are evaluated on the Convolutional Neural Network (CNN) sentiment classifier trained on the IMDB movie review dataset. The attack success rate is used to evaluate the effectiveness of the attack methods and the perplexity of the sentences is used to measure the degree of distortion of the generated adversarial examples. The experiment results show that the proposed {``}Global Search{''} method generates more powerful adversarial examples with less distortion or less modification to the source text.",
}
@inproceedings{bustamante-oncevay-2019-csi,
    title = "{CSI} {P}eru News: finding the culprit, victim and location in news articles",
    author = "Bustamante, Gina  and
      Oncevay, Arturo",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3654",
    pages = "174--176",
    abstract = "We introduce a shift on the DS method over the domain of crime-related news from Peru, attempting to find the culprit, victim and location of a crime description from a RE perspective. Obtained results are highly promising and show that proposed modifications are effective in non-traditional domains.",
}
@inproceedings{lee-etal-2019-exploring,
    title = "Exploring Social Bias in Chatbots using Stereotype Knowledge",
    author = "Lee, Nayeon  and
      Madotto, Andrea  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3655",
    pages = "177--180",
    abstract = "Exploring social bias in chatbot is an important, yet relatively unexplored problem. In this paper, we propose an approach to understand social bias in chatbots by leveraging stereotype knowledge. It allows interesting comparison of bias between chatbots and humans, and provides intuitive analysis of existing chatbots by borrowing the finer-grain concepts of sexism and racism.",
}
@inproceedings{alva-manchego-etal-2019-cross,
    title = "Cross-Sentence Transformations in Text Simplification",
    author = "Alva-Manchego, Fernando  and
      Scarton, Carolina  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3656",
    pages = "181--184",
    abstract = "Current approaches to Text Simplification focus on simplifying sentences individually. However, certain simplification transformations span beyond single sentences (e.g. joining and re-ordering sentences). In this paper, we motivate the need for modelling the simplification task at the document level, and assess the performance of sequence-to-sequence neural models in this setup. We analyse parallel original-simplified documents created by professional editors and show that there are frequent rewriting transformations that are not restricted to sentence boundaries. We also propose strategies to automatically evaluate the performance of a simplification model on these cross-sentence transformations. Our experiments show the inability of standard sequence-to-sequence neural models to learn these transformations, and suggest directions towards document-level simplification.",
}
@proceedings{ws-2019-abusive,
    title = "Proceedings of the Third Workshop on Abusive Language Online",
    editor = "Roberts, Sarah T.  and
      Tetreault, Joel  and
      Prabhakaran, Vinodkumar  and
      Waseem, Zeerak",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3500",
}
@inproceedings{brassard-gourdeau-khoury-2019-subversive,
    title = "Subversive Toxicity Detection using Sentiment Information",
    author = "Brassard-Gourdeau, Eloi  and
      Khoury, Richard",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3501",
    doi = "10.18653/v1/W19-3501",
    pages = "1--10",
    abstract = "The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection.",
}
@inproceedings{yang-etal-2019-exploring-deep,
    title = "Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification",
    author = "Yang, Fan  and
      Peng, Xiaochang  and
      Ghosh, Gargi  and
      Shilon, Reshef  and
      Ma, Hao  and
      Moore, Eider  and
      Predovic, Goran",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3502",
    doi = "10.18653/v1/W19-3502",
    pages = "11--18",
    abstract = "Interactions among users on social network platforms are usually positive, constructive and insightful. However, sometimes people also get exposed to objectionable content such as hate speech, bullying, and verbal abuse etc. Most social platforms have explicit policy against hate speech because it creates an environment of intimidation and exclusion, and in some cases may promote real-world violence. As users{'} interactions on today{'}s social networks involve multiple modalities, such as texts, images and videos, in this paper we explore the challenge of automatically identifying hate speech with deep multimodal technologies, extending previous research which mostly focuses on the text signal alone. We present a number of fusion approaches to integrate text and photo signals. We show that augmenting text with image embedding information immediately leads to a boost in performance, while applying additional attention fusion methods brings further improvement.",
}
@inproceedings{stoop-etal-2019-detecting,
    title = "Detecting harassment in real-time as conversations develop",
    author = "Stoop, Wessel  and
      Kunneman, Florian  and
      van den Bosch, Antal  and
      Miller, Ben",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3503",
    doi = "10.18653/v1/W19-3503",
    pages = "19--24",
    abstract = "We developed a machine-learning-based method to detect video game players that harass teammates or opponents in chat earlier in the conversation. This real-time technology would allow gaming companies to intervene during games, such as issue warnings or muting or banning a player. In a proof-of-concept experiment on League of Legends data we compute and visualize evaluation metrics for a machine learning classifier as conversations unfold, and observe that the optimal precision and recall of detecting toxic players at each moment in the conversation depends on the confidence threshold of the classifier: the threshold should start low, and increase as the conversation unfolds. How fast this sliding threshold should increase depends on the training set size.",
}
@inproceedings{davidson-etal-2019-racial,
    title = "Racial Bias in Hate Speech and Abusive Language Detection Datasets",
    author = "Davidson, Thomas  and
      Bhattacharya, Debasmita  and
      Weber, Ingmar",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3504",
    doi = "10.18653/v1/W19-3504",
    pages = "25--35",
    abstract = "Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect.",
}
@inproceedings{joksimovic-etal-2019-automated,
    title = "Automated Identification of Verbally Abusive Behaviors in Online Discussions",
    author = "Joksimovic, Srecko  and
      Baker, Ryan S.  and
      Ocumpaugh, Jaclyn  and
      Andres, Juan Miguel L.  and
      Tot, Ivan  and
      Wang, Elle Yuan  and
      Dawson, Shane",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3505",
    doi = "10.18653/v1/W19-3505",
    pages = "36--45",
    abstract = "Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings. However, as much as sharing new ideas or asking thoughtful questions contributes learning, verbally abusive behaviors, such as expressing negative emotions in online discussions, could have disproportionate detrimental effects. To provide means for mitigating the potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several well-established automated text analysis tools and build on the common practices for handling highly imbalanced datasets and reducing the sensitivity to overfitting. Although still in its infancy, our approach shows promising results (ROC AUC .73) towards establishing a robust detector of abusive behaviors. We further provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.",
}
@inproceedings{ibrohim-budi-2019-multi,
    title = "Multi-label Hate Speech and Abusive Language Detection in {I}ndonesian {T}witter",
    author = "Ibrohim, Muhammad Okky  and
      Budi, Indra",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3506",
    doi = "10.18653/v1/W19-3506",
    pages = "46--57",
    abstract = "Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.",
}
@inproceedings{shen-rose-2019-discourse,
    title = "The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in {R}eddit{'}s Quarantine Policy",
    author = "Shen, Qinlan  and
      Rose, Carolyn",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3507",
    doi = "10.18653/v1/W19-3507",
    pages = "58--69",
    abstract = "Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users{'} ideological viewpoint. We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits. We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied. Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated.",
}
@inproceedings{chakrabarty-etal-2019-pay,
    title = "Pay {``}Attention{''} to your Context when Classifying Abusive Language",
    author = "Chakrabarty, Tuhin  and
      Gupta, Kilol  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3508",
    doi = "10.18653/v1/W19-3508",
    pages = "70--79",
    abstract = "The goal of any social media platform is to facilitate healthy and meaningful interactions among its users. But more often than not, it has been found that it becomes an avenue for wanton attacks. We propose an experimental study that has three aims: 1) to provide us with a deeper understanding of current data sets that focus on different types of abusive language, which are sometimes overlapping (racism, sexism, hate speech, offensive language, and personal attacks); 2) to investigate what type of attention mechanism (contextual vs. self-attention) is better for abusive language detection using deep learning architectures; and 3) to investigate whether stacked architectures provide an advantage over simple architectures for this task.",
}
@inproceedings{vidgen-etal-2019-challenges,
    title = "Challenges and frontiers in abusive content detection",
    author = "Vidgen, Bertie  and
      Harris, Alex  and
      Nguyen, Dong  and
      Tromble, Rebekah  and
      Hale, Scott  and
      Margetts, Helen",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3509",
    doi = "10.18653/v1/W19-3509",
    pages = "80--93",
    abstract = "Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spanning technical, social and ethical dimensions. These issues constrain the performance, efficiency and generalizability of abusive content detection systems. In this article we delineate and clarify the main challenges and frontiers in the field, critically evaluate their implications and discuss potential solutions. We also highlight ways in which social scientific insights can advance research. We discuss the lack of support given to researchers working with abusive content and provide guidelines for ethical research.",
}
@inproceedings{fortuna-etal-2019-hierarchically,
    title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset",
    author = "Fortuna, Paula  and
      Rocha da Silva, Jo{\~a}o  and
      Soler-Company, Juan  and
      Wanner, Leo  and
      Nunes, S{\'e}rgio",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3510",
    doi = "10.18653/v1/W19-3510",
    pages = "94--104",
    abstract = "Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning are applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels ({`}hate{'} vs. {`}no-hate{'}). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.",
}
@inproceedings{menini-etal-2019-system,
    title = "A System to Monitor Cyberbullying based on Message Classification and Social Network Analysis",
    author = "Menini, Stefano  and
      Moretti, Giovanni  and
      Corazza, Michele  and
      Cabrio, Elena  and
      Tonelli, Sara  and
      Villata, Serena",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3511",
    doi = "10.18653/v1/W19-3511",
    pages = "105--110",
    abstract = "Social media platforms like Twitter and Instagram face a surge in cyberbullying phenomena against young users and need to develop scalable computational methods to limit the negative consequences of this kind of abuse. Despite the number of approaches recently proposed in the Natural Language Processing (NLP) research area for detecting different forms of abusive language, the issue of identifying cyberbullying phenomena at scale is still an unsolved problem. This is because of the need to couple abusive language detection on textual message with network analysis, so that repeated attacks against the same person can be identified. In this paper, we present a system to monitor cyberbullying phenomena by combining message classification and social network analysis. We evaluate the classification module on a data set built on Instagram messages, and we describe the cyberbullying monitoring user interface.",
}
@inproceedings{mulki-etal-2019-l,
    title = "{L}-{HSAB}: A {L}evantine {T}witter Dataset for Hate Speech and Abusive Language",
    author = "Mulki, Hala  and
      Haddad, Hatem  and
      Bechikh Ali, Chedi  and
      Alshabani, Halima",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3512",
    doi = "10.18653/v1/W19-3512",
    pages = "111--118",
    abstract = "Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research. In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents. We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen{'}s Kappa (k) and Krippendorff{'}s alpha (α) indicated the consistency of the annotations.",
}
@inproceedings{eder-etal-2019-lower,
    title = "At the Lower End of {L}anguage{---}{E}xploring the Vulgar and Obscene Side of {G}erman",
    author = "Eder, Elisabeth  and
      Krieg-Holz, Ulrike  and
      Hahn, Udo",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3513",
    doi = "10.18653/v1/W19-3513",
    pages = "119--128",
    abstract = "In this paper, we describe a workflow for the data-driven acquisition and semantic scaling of a lexicon that covers lexical items from the lower end of the German language register{---}terms typically considered as rough, vulgar or obscene. Since the fine semantic representation of grades of obscenity can only inadequately be captured at the categorical level (e.g., obscene vs. non-obscene, or rough vs. vulgar), our main contribution lies in applying best-worst scaling, a rating methodology that has already been shown to be useful for emotional language, to capture the relative strength of obscenity of lexical items. We describe the empirical foundations for bootstrapping such a low-end lexicon for German by starting from manually supplied lexicographic categorizations of a small seed set of rough and vulgar lexical items and automatically enlarging this set by means of distributional semantics. We then determine the degrees of obscenity for the full set of all acquired lexical items by letting crowdworkers comparatively assess their pejorative grade using best-worst scaling. This semi-automatically enriched lexicon already comprises 3,300 lexical items and incorporates 33,000 vulgarity ratings. Using it as a seed lexicon for fully automatic lexical acquisition, we were able to raise its coverage up to slightly more than 11,000 entries.",
}
@inproceedings{karan-snajder-2019-preemptive,
    title = "Preemptive Toxic Language Detection in {W}ikipedia Comments Using Thread-Level Context",
    author = "Karan, Mladen  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3514",
    doi = "10.18653/v1/W19-3514",
    pages = "129--134",
    abstract = "We address the task of automatically detecting toxic content in user generated texts. We fo cus on exploring the potential for preemptive moderation, i.e., predicting whether a particular conversation thread will, in the future, incite a toxic comment. Moreover, we perform preliminary investigation of whether a model that jointly considers all comments in a conversation thread outperforms a model that considers only individual comments. Using an existing dataset of conversations among Wikipedia contributors as a starting point, we compile a new large-scale dataset for this task consisting of labeled comments and comments from their conversation threads.",
}
@inproceedings{bodapati-etal-2019-neural,
    title = "Neural Word Decomposition Models for Abusive Language Detection",
    author = "Bodapati, Sravan  and
      Gella, Spandana  and
      Bhattacharjee, Kasturi  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3515",
    doi = "10.18653/v1/W19-3515",
    pages = "135--145",
    abstract = "The text we see in social media suffers from lots of undesired characterstics like hatespeech, abusive language, insults etc. The nature of this text is also very different compared to the traditional text we see in news with lots of obfuscated words, intended typos. This poses several robustness challenges to many natural language processing (NLP) techniques developed for traditional text. Many techniques proposed in the recent times such as charecter encoding models, subword models, byte pair encoding to extract subwords can aid in dealing with few of these nuances. In our work, we analyze the effectiveness of each of the above techniques, compare and contrast various word decomposition techniques when used in combination with others. We experiment with recent advances of finetuning pretrained language models, and demonstrate their robustness to domain shift. We also show our approaches achieve state of the art performance on Wikipedia attack, toxicity datasets, and Twitter hatespeech dataset.",
}
@inproceedings{meyer-gamback-2019-platform,
    title = "A Platform Agnostic Dual-Strand Hate Speech Detector",
    author = {Meyer, Johannes Skjeggestad  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3516",
    doi = "10.18653/v1/W19-3516",
    pages = "146--156",
    abstract = "Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform. For instance, the information stored about the text{'}s author may differ between services, and so using such data would reduce a system{'}s general applicability. The paper thus focuses on using exclusively text-based input in the detection, in an optimised architecture combining Convolutional Neural Networks and Long Short-Term Memory-networks. The hate speech detector merges two strands with character n-grams and word embeddings to produce the final classification, and is shown to outperform comparable previous approaches.",
}
@inproceedings{srivastava-khurana-2019-detecting,
    title = "Detecting Aggression and Toxicity using a Multi Dimension Capsule Network",
    author = "Srivastava, Saurabh  and
      Khurana, Prerna",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3517",
    doi = "10.18653/v1/W19-3517",
    pages = "157--162",
    abstract = "In the era of social media, hate speech, trolling and verbal abuse have become a common issue. We present an approach to automatically classify such statements, using a new deep learning architecture. Our model comprises of a Multi Dimension Capsule Network that generates the representation of sentences which we use for classification. We further provide an analysis of our model{'}s interpretation of such statements. We compare the results of our model with state-of-art classification algorithms and demonstrate our model{'}s ability. It also has the capability to handle comments that are written in both Hindi and English, which are provided in the TRAC dataset. We also compare results on Kaggle{'}s Toxic comment classification dataset.",
}
@inproceedings{comandini-patti-2019-impossible,
    title = "An Impossible Dialogue! Nominal Utterances and Populist Rhetoric in an {I}talian {T}witter Corpus of Hate Speech against Immigrants",
    author = "Comandini, Gloria  and
      Patti, Viviana",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3518",
    doi = "10.18653/v1/W19-3518",
    pages = "163--171",
    abstract = "The paper proposes an investigation on the role of populist themes and rhetoric in an Italian Twitter corpus of hate speech against immigrants. The corpus had been annotated with four new layers of analysis: Nominal Utterances, that can be seen as consistent with populist rhetoric; In-out-group rhetoric, a very common populist strategy to polarize public opinion; Slogan-like nominal utterances, that may convey the call for severe illiberal policies against immigrants; News, to recognize the role of newspapers (headlines or reference to articles) in the Twitter political discourse on immigration featured by hate speech.",
}
@inproceedings{brooke-2019-condescending,
    title = "{``}Condescending, Rude, Assholes{''}: Framing gender and hostility on {S}tack {O}verflow",
    author = "Brooke, Sian",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3519",
    doi = "10.18653/v1/W19-3519",
    pages = "172--180",
    abstract = "The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.",
}
@inproceedings{weingartner-stahel-2019-online,
    title = "Online aggression from a sociological perspective: An integrative view on determinants and possible countermeasures",
    author = "Weingartner, Sebastian  and
      Stahel, Lea",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3520",
    doi = "10.18653/v1/W19-3520",
    pages = "181--187",
    abstract = "The present paper introduces a theoretical model for explaining aggressive online comments from a sociological perspective. It is innovative as it combines individual, situational, and social-structural determinants of online aggression and tries to theoretically derive their interplay. Moreover, the paper suggests an empirical strategy for testing the model. The main contribution will be to match online commenting data with survey data containing rich background data of non- /aggressive online commentators.",
}
@proceedings{ws-2019-storytelling,
    title = "Proceedings of the Second Workshop on Storytelling",
    editor = "Ferraro, Francis  and
      Huang, Ting-Hao {`}Kenneth{'}  and
      Lukin, Stephanie M.  and
      Mitchell, Margaret",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3400",
}
@inproceedings{qi-etal-2019-composing,
    title = "Composing a Picture Book by Automatic Story Understanding and Visualization",
    author = "Qi, Xiaoyu  and
      Song, Ruihua  and
      Wang, Chunting  and
      Zhou, Jin  and
      Sakai, Tetsuya",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3401",
    doi = "10.18653/v1/W19-3401",
    pages = "1--10",
    abstract = "Pictures can enrich storytelling experiences. We propose a framework that can automatically compose a picture book by understanding story text and visualizing it with painting elements, i.e., characters and backgrounds. For story understanding, we extract key information from a story on both sentence level and paragraph level, including characters, scenes and actions. These concepts are organized and visualized in a way that depicts the development of a story. We collect a set of Chinese stories for children and apply our approach to compose pictures for stories. Extensive experiments are conducted towards story event extraction for visualization to demonstrate the effectiveness of our method.",
}
@inproceedings{chandu-etal-2019-way,
    title = "{``}My Way of Telling a Story{''}: Persona based Grounded Story Generation",
    author = "Chandu, Khyathi  and
      Prabhumoye, Shrimai  and
      Salakhutdinov, Ruslan  and
      Black, Alan W",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3402",
    doi = "10.18653/v1/W19-3402",
    pages = "11--21",
    abstract = "Visual storytelling is the task of generating stories based on a sequence of images. Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas. However, one of the main challenges of performing this task is the lack of a dataset of visual stories in different personas. Having said that, there are independent datasets for both visual storytelling and annotated sentences for various persona. In this paper we describe an approach to overcome this by getting labelled persona data from a different task and leveraging those annotations to perform persona based story generation. We inspect various ways of incorporating personality in both the encoder and the decoder representations to steer the generation in the target direction. To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand. In our experiments we use five different personas to guide the generation process. We find that the models based on our hypotheses perform better at capturing words while generating stories in the target persona.",
}
@inproceedings{yan-etal-2019-using,
    title = "Using Functional Schemas to Understand Social Media Narratives",
    author = "Yan, Xinru  and
      Naik, Aakanksha  and
      Jo, Yohan  and
      Rose, Carolyn",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3403",
    doi = "10.18653/v1/W19-3403",
    pages = "22--33",
    abstract = "We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures. We develop an unsupervised pipeline to extract schemas and apply our method to Reddit posts to detect schematic structures that are characteristic of different subreddits. We validate our schemas through human interpretation and evaluate their utility via a text classification task. Our experiments show that extracted schemas capture distinctive structural patterns in different subreddits, improving classification performance of several models by 2.4{\%} on average. We also observe that these schemas serve as lenses that reveal community norms.",
}
@inproceedings{zhai-etal-2019-hybrid,
    title = "A Hybrid Model for Globally Coherent Story Generation",
    author = "Zhai, Fangzhou  and
      Demberg, Vera  and
      Shkadzko, Pavel  and
      Shi, Wei  and
      Sayeed, Asad",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3404",
    doi = "10.18653/v1/W19-3404",
    pages = "34--45",
    abstract = "Automatically generating globally coherent stories is a challenging problem. Neural text generation models have been shown to perform well at generating fluent sentences from data, but they usually fail to keep track of the overall coherence of the story after a couple of sentences. Existing work that incorporates a text planning module succeeded in generating recipes and dialogues, but appears quite data-demanding. We propose a novel story generation approach that generates globally coherent stories from a fairly small corpus. The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan. Human evaluation showed that our model outperforms various baselines by a wide margin and generates stories which are fluent as well as globally coherent.",
}
@inproceedings{ammanabrolu-etal-2019-guided,
    title = "Guided Neural Language Generation for Automated Storytelling",
    author = "Ammanabrolu, Prithviraj  and
      Tien, Ethan  and
      Cheung, Wesley  and
      Luo, Zhaochen  and
      Ma, William  and
      Martin, Lara  and
      Riedl, Mark",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3405",
    doi = "10.18653/v1/W19-3405",
    pages = "46--55",
    abstract = "Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. Our method outperforms the baseline sequence-to-sequence model. Additionally, we provide results for a full end-to-end automated story generation system, demonstrating how our model works with existing systems designed for the event-to-event problem.",
}
@inproceedings{kim-klinger-2019-analysis,
    title = "An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling",
    author = "Kim, Evgeny  and
      Klinger, Roman",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3406",
    doi = "10.18653/v1/W19-3406",
    pages = "56--64",
    abstract = "Centrality of emotion for the stories told by humans is underpinned by numerous studies in literature and psychology. The research in automatic storytelling has recently turned towards emotional storytelling, in which characters{'} emotions play an important role in the plot development (Theune et al., 2004; y Perez, 2007; Mendez et al., 2016). However, these studies mainly use emotion to generate propositional statements in the form {``}A feels affection towards B{''} or {``}A confronts B{''}. At the same time, emotional behavior does not boil down to such propositional descriptions, as humans display complex and highly variable patterns in communicating their emotions, both verbally and non-verbally. In this paper, we analyze how emotions are expressed non-verbally in a corpus of fan fiction short stories. Our analysis shows that stories written by humans convey character emotions along various non-verbal channels. We find that some non-verbal channels, such as facial expressions and voice characteristics of the characters, are more strongly associated with joy, while gestures and body postures are more likely to occur with trust. Based on our analysis, we argue that automatic storytelling systems should take variability of emotion into account when generating descriptions of characters{'} emotions.",
}
@inproceedings{van-stegeren-theune-2019-narrative,
    title = "{N}arrative {G}eneration in the {W}ild: {M}ethods from {N}a{N}o{G}en{M}o",
    author = {van Stegeren, Judith  and
      Theune, Mari{\"e}t},
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3407",
    doi = "10.18653/v1/W19-3407",
    pages = "65--74",
    abstract = "In text generation, generating long stories is still a challenge. Coherence tends to decrease rapidly as the output length increases. Especially for generated stories, coherence of the narrative is an important quality aspect of the output text. In this paper we examine how narrative coherence is attained in the submissions of NaNoGenMo 2018, an online text generation event where participants are challenged to generate a 50,000 word novel. We list the main approaches that were used to generate coherent narratives and link them to scientific literature. Finally, we give recommendations on when to use which approach.",
}
@inproceedings{flor-somasundaran-2019-lexical,
    title = "Lexical concreteness in narrative",
    author = "Flor, Michael  and
      Somasundaran, Swapna",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3408",
    doi = "10.18653/v1/W19-3408",
    pages = "75--80",
    abstract = "This study explores the relation between lexical concreteness and narrative text quality. We present a methodology to quantitatively measure lexical concreteness of a text. We apply it to a corpus of student stories, scored according to writing evaluation rubrics. Lexical concreteness is weakly-to-moderately related to story quality, depending on story-type. The relation is mostly borne by adjectives and nouns, but also found for adverbs and verbs.",
}
@inproceedings{qureshi-etal-2019-simple,
    title = "A Simple Approach to Classify Fictional and Non-Fictional Genres",
    author = "Qureshi, Mohammed Rameez  and
      Ranjan, Sidharth  and
      Rajkumar, Rajakrishnan  and
      Shah, Kushal",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3409",
    doi = "10.18653/v1/W19-3409",
    pages = "81--89",
    abstract = "In this work, we deploy a logistic regression classifier to ascertain whether a given document belongs to the fiction or non-fiction genre. For genre identification, previous work had proposed three classes of features, viz., low-level (character-level and token counts), high-level (lexical and syntactic information) and derived features (type-token ratio, average word length or average sentence length). Using the Recursive feature elimination with cross-validation (RFECV) algorithm, we perform feature selection experiments on an exhaustive set of nineteen features (belonging to all the classes mentioned above) extracted from Brown corpus text. As a result, two simple features viz., the ratio of the number of adverbs to adjectives and the number of adjectives to pronouns turn out to be the most significant. Subsequently, our classification experiments aimed towards genre identification of documents from the Brown and Baby BNC corpora demonstrate that the performance of a classifier containing just the two aforementioned features is at par with that of a classifier containing the exhaustive feature set.",
}
@inproceedings{wanzare-etal-2019-detecting,
    title = "Detecting Everyday Scenarios in Narrative Texts",
    author = "Wanzare, Lilian Diana Awuor  and
      Roth, Michael  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3410",
    doi = "10.18653/v1/W19-3410",
    pages = "90--106",
    abstract = "Script knowledge consists of detailed information on everyday activities. Such information is often taken for granted in text and needs to be inferred by readers. Therefore, script knowledge is a central component to language comprehension. Previous work on representing scripts is mostly based on extensive manual work or limited to scenarios that can be found with sufficient redundancy in large corpora. We introduce the task of scenario detection, in which we identify references to scripts. In this task, we address a wide range of different scripts (200 scenarios) and we attempt to identify all references to them in a collection of narrative texts. We present a first benchmark data set and a baseline model that tackles scenario detection using techniques from topic segmentation and text classification.",
}
@inproceedings{pizzolli-strapparava-2019-personality,
    title = "Personality Traits Recognition in Literary Texts",
    author = "Pizzolli, Daniele  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3411",
    doi = "10.18653/v1/W19-3411",
    pages = "107--111",
    abstract = "Interesting stories often are built around interesting characters. Finding and detailing what makes an interesting character is a real challenge, but certainly a significant cue is the character personality traits. Our exploratory work tests the adaptability of the current personality traits theories to literal characters, focusing on the analysis of utterances in theatre scripts. And, at the opposite, we try to find significant traits for interesting characters. The preliminary results demonstrate that our approach is reasonable. Using machine learning for gaining insight into the personality traits of fictional characters can make sense.",
}
@inproceedings{andy-etal-2019-winter,
    title = "Winter is here: Summarizing {T}witter Streams related to Pre-Scheduled Events",
    author = "Andy, Anietie  and
      Wijaya, Derry Tanti  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3412",
    doi = "10.18653/v1/W19-3412",
    pages = "112--116",
    abstract = "Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.",
}
@inproceedings{gupta-etal-2019-writerforcing,
    title = "{W}riter{F}orcing: Generating more interesting story endings",
    author = "Gupta, Prakhar  and
      Bannihatti Kumar, Vinayshekhar  and
      Bhutani, Mukul  and
      Black, Alan W",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3413",
    doi = "10.18653/v1/W19-3413",
    pages = "117--126",
    abstract = "We study the problem of generating interesting endings for stories. Neural generative models have shown promising results for various text generation problems. Sequence to Sequence (Seq2Seq) models are typically trained to generate a single output sequence for a given input sequence. However, in the context of a story, multiple endings are possible. Seq2Seq models tend to ignore the context and generate generic and dull responses. Very few works have studied generating diverse and interesting story endings for the same story context. In this paper, we propose models which generate more diverse and interesting outputs by 1) training models to focus attention on important keyphrases of the story, and 2) promoting generating nongeneric words. We show that the combination of the two leads to more interesting endings.",
}
@inproceedings{kim-etal-2019-prediction,
    title = "Prediction of a Movie{'}s Success From Plot Summaries Using Deep Learning Models",
    author = "Kim, You Jin  and
      Cheong, Yun Gyung  and
      Lee, Jung Hoon",
    booktitle = "Proceedings of the Second Workshop on Storytelling",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3414",
    doi = "10.18653/v1/W19-3414",
    pages = "127--135",
    abstract = "As the size of investment for movie production grows bigger, the need for predicting a movie{'}s success in early stages has increased. To address this need, various approaches have been proposed, mostly relying on movie reviews, trailer movie clips, and SNS postings. However, all of these are available only after a movie is produced and released. To enable a more earlier prediction of a movie{'}s performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text. This paper reports the results evaluating the efficacy of the proposed method and concludes with discussions and future work.",
}
@proceedings{ws-2019-international-designing,
    title = "Proceedings of the First International Workshop on Designing Meaning Representations",
    editor = "Xue, Nianwen  and
      Croft, William  and
      Hajic, Jan  and
      Huang, Chu-Ren  and
      Oepen, Stephan  and
      Palmer, Martha  and
      Pustejovksy, James",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3300",
}
@inproceedings{van-gysel-etal-2019-cross,
    title = "Cross-Linguistic Semantic Annotation: Reconciling the Language-Specific and the Universal",
    author = "Van Gysel, Jens E. L.  and
      Vigus, Meagan  and
      Kalm, Pavlina  and
      Lee, Sook-kyung  and
      Regan, Michael  and
      Croft, William",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3301",
    doi = "10.18653/v1/W19-3301",
    pages = "1--14",
    abstract = "Developers of cross-linguistic semantic annotation schemes face a number of issues not encountered in monolingual annotation. This paper discusses four such issues, related to the establishment of annotation labels, and the treatment of languages with more fine-grained, more coarse-grained, and cross-cutting categories. We propose that a lattice-like architecture of the annotation categories can adequately handle all four issues, and at the same time remain both intuitive for annotators and faithful to typological insights. This position is supported by a brief annotation experiment.",
}
@inproceedings{abzianidze-bos-2019-thirty,
    title = "Thirty Musts for Meaning Banking",
    author = "Abzianidze, Lasha  and
      Bos, Johan",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3302",
    doi = "10.18653/v1/W19-3302",
    pages = "15--27",
    abstract = "Meaning banking{---}creating a semantically annotated corpus for the purpose of semantic parsing or generation{---}is a challenging task. It is quite simple to come up with a complex meaning representation, but it is hard to design a simple meaning representation that captures many nuances of meaning. This paper lists some lessons learned in nearly ten years of meaning annotation during the development of the Groningen Meaning Bank (Bos et al., 2017) and the Parallel Meaning Bank (Abzianidze et al., 2017). The paper{'}s format is rather unconventional: there is no explicit related work, no methodology section, no results, and no discussion (and the current snippet is not an abstract but actually an introductory preface). Instead, its structure is inspired by work of Traum (2000) and Bender (2013). The list starts with a brief overview of the existing meaning banks (Section 1) and the rest of the items are roughly divided into three groups: corpus collection (Section 2 and 3, annotation methods (Section 4{--}11), and design of meaning representations (Section 12{--}30). We hope this overview will give inspiration and guidance in creating improved meaning banks in the future",
}
@inproceedings{pustejovsky-etal-2019-modeling,
    title = "Modeling Quantification and Scope in {A}bstract {M}eaning {R}epresentations",
    author = "Pustejovsky, James  and
      Lai, Ken  and
      Xue, Nianwen",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3303",
    doi = "10.18653/v1/W19-3303",
    pages = "28--33",
    abstract = "In this paper, we propose an extension to Abstract Meaning Representations (AMRs) to encode scope information of quantifiers and negation, in a way that overcomes the semantic gaps of the schema while maintaining its cognitive simplicity. Specifically, we address three phenomena not previously part of the AMR specification: quantification, negation (generally), and modality. The resulting representation, which we call {``}Uniform Meaning Representation{''} (UMR), adopts the predicative core of AMR and embeds it under a {``}scope{''} graph when appropriate. UMR representations differ from other treatments of quantification and modal scope phenomena in two ways: (a) they are more transparent; and (b) they specify default scope when possible.{`}",
}
@inproceedings{lin-xue-2019-parsing,
    title = "Parsing Meaning Representations: Is Easier Always Better?",
    author = "Lin, Zi  and
      Xue, Nianwen",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3304",
    doi = "10.18653/v1/W19-3304",
    pages = "34--43",
    abstract = "The parsing accuracy varies a great deal for different meaning representations. In this paper, we compare the parsing performances between Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS), and provide an in-depth analysis of what factors contributed to the discrepancy in their parsing accuracy. By crystalizing the trade-off between representation expressiveness and ease of automatic parsing, we hope our results can help inform the design of the next-generation meaning representations.",
}
@inproceedings{kalouli-etal-2019-gkr,
    title = "{GKR}: Bridging the Gap between Symbolic/structural and Distributional Meaning Representations",
    author = "Kalouli, Aikaterini-Lida  and
      Crouch, Richard  and
      de Paiva, Valeria",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3305",
    doi = "10.18653/v1/W19-3305",
    pages = "44--55",
    abstract = "Three broad approaches have been attempted to combine distributional and structural/symbolic aspects to construct meaning representations: a) injecting linguistic features into distributional representations, b) injecting distributional features into symbolic representations or c) combining structural and distributional features in the final representation. This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features. We propose two extensions of GKR that clearly show this division and empirically test one of the proposals on an NLI dataset with hard compositional pairs.",
}
@inproceedings{kim-etal-2019-generating,
    title = "Generating Discourse Inferences from Unscoped Episodic Logical Formulas",
    author = "Kim, Gene  and
      Kane, Benjamin  and
      Duong, Viet  and
      Mendiratta, Muskaan  and
      McGuire, Graeme  and
      Sackstein, Sophie  and
      Platonov, Georgiy  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3306",
    doi = "10.18653/v1/W19-3306",
    pages = "56--65",
    abstract = "Abstract Unscoped episodic logical form (ULF) is a semantic representation capturing the predicate-argument structure of English within the episodic logic formalism in relation to the syntactic structure, while leaving scope, word sense, and anaphora unresolved. We describe how ULF can be used to generate natural language inferences that are grounded in the semantic and syntactic structure through a small set of rules defined over interpretable predicates and transformations on ULFs. The semantic restrictions placed by ULF semantic types enables us to ensure that the inferred structures are semantically coherent while the nearness to syntax enables accurate mapping to English. We demonstrate these inferences on four classes of conversationally-oriented inferences in a mixed genre dataset with 68.5{\%} precision from human judgments.",
}
@inproceedings{hajicova-2019-plea,
    title = "A Plea for Information Structure as a Part of Meaning Representation",
    author = "Hajicova, Eva",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3307",
    doi = "10.18653/v1/W19-3307",
    pages = "66--72",
    abstract = "The view that the representation of information structure (IS) should be a part of (any type of) representation of meaning is based on the fact that IS is a semantically relevant phenomenon. In the contribution, three arguments supporting this view are briefly summarized, namely, the relation of IS to the interpretation of negation and presupposition, the relevance of IS to the understanding of discourse connectivity and for the establishment and interpretation of coreference relations. Afterwards, possible integration of the description of the main ingredient of IS into a meaning representation is illustrated.",
}
@inproceedings{zeyrek-basibuyuk-2019-tcl,
    title = "{TCL} - a Lexicon of {T}urkish Discourse Connectives",
    author = {Zeyrek, Deniz  and
      Ba{\c{s}}{\i}b{\"u}y{\"u}k, Kezban},
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3308",
    doi = "10.18653/v1/W19-3308",
    pages = "73--81",
    abstract = "It is known that discourse connectives are the most salient indicators of discourse relations. State-of-the-art parsers being developed to predict explicit discourse connectives exploit annotated discourse corpora but a lexicon of discourse connectives is also needed to enable further research in discourse structure and support the development of language technologies that use these structures for text understanding. This paper presents a lexicon of Turkish discourse connectives built by automatic means. The lexicon has the format of the German connective lexicon, DiMLex, where for each discourse connective, information about the connective{`}s orthographic variants, syntactic category and senses are provided along with sample relations. In this paper, we describe the data sources we used and the development steps of the lexicon.",
}
@inproceedings{choi-etal-2019-meta,
    title = "Meta-Semantic Representation for Early Detection of {A}lzheimer{'}s Disease",
    author = "Choi, Jinho D.  and
      Li, Mengmei  and
      Goldstein, Felicia  and
      Hajjar, Ihab",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3309",
    doi = "10.18653/v1/W19-3309",
    pages = "82--91",
    abstract = "This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer{'}s disease by analyzing their language beyond a syntactic or semantic level. Meta-semantic representation consists of three parts, entities, predicate argument structures, and discourse attributes, that derive rich knowledge graphs. For this study, 50 controls and 50 patients with mild cognitive impairment (MCI) are selected, and meta-semantic representation is annotated on their speeches transcribed in text. Inter-annotator agreement scores of 88{\%}, 82{\%}, and 89{\%} are achieved for the three types of annotation, respectively. Five analyses are made using this annotation, depicting clear distinctions between the control and MCI groups. Finally, a neural model is trained on features extracted from those analyses to classify MCI patients from normal controls, showing a high accuracy of 82{\%} that is very promising.",
}
@inproceedings{liu-etal-2019-ellipsis,
    title = "Ellipsis in {C}hinese {AMR} Corpus",
    author = "Liu, Yihuan  and
      Li, Bin  and
      Yan, Peiyi  and
      Song, Li  and
      Qu, Weiguang",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3310",
    doi = "10.18653/v1/W19-3310",
    pages = "92--99",
    abstract = "Ellipsis is very common in language. It{'}s necessary for natural language processing to restore the elided elements in a sentence. However, there{'}s only a few corpora annotating the ellipsis, which draws back the automatic detection and recovery of the ellipsis. This paper introduces the annotation of ellipsis in Chinese sentences, using a novel graph-based representation Abstract Meaning Representation (AMR), which has a good mechanism to restore the elided elements manually. We annotate 5,000 sentences selected from Chinese TreeBank (CTB). We find that 54.98{\%} of sentences have ellipses. 92{\%} of the ellipses are restored by copying the antecedents{'} concepts. and 12.9{\%} of them are the new added concepts. In addition, we find that the elided element is a word or phrase in most cases, but sometimes only the head of a phrase or parts of a phrase, which is rather hard for the automatic recovery of ellipsis.",
}
@inproceedings{kalm-etal-2019-event,
    title = "Event Structure Representation: Between Verbs and Argument Structure Constructions",
    author = "Kalm, Pavlina  and
      Regan, Michael  and
      Croft, William",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3311",
    doi = "10.18653/v1/W19-3311",
    pages = "100--109",
    abstract = "This paper proposes a novel representation of event structure by separating verbal semantics and the meaning of argument structure constructions that verbs occur in. Our model demonstrates how the two meaning representations interact. Our model thus effectively deals with various verb construals in different argument structure constructions, unlike purely verb-based approaches. However, unlike many constructionally-based approaches, we also provide a richer representation of the event structure evoked by the verb meaning.",
}
@inproceedings{rambelli-etal-2019-distributional,
    title = "Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning",
    author = "Rambelli, Giulia  and
      Chersoni, Emmanuele  and
      Blache, Philippe  and
      Huang, Chu-Ren  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3312",
    doi = "10.18653/v1/W19-3312",
    pages = "110--120",
    abstract = "In this paper, we propose a new type of semantic representation of Construction Grammar that combines constructions with the vector representations used in Distributional Semantics. We introduce a new framework, Distributional Construction Grammar, where grammar and meaning are systematically modeled from language use, and finally, we discuss the kind of contributions that distributional models can provide to CxG representation from a linguistic and cognitive perspective.",
}
@inproceedings{petruck-2019-meaning,
    title = "Meaning Representation of Null Instantiated Semantic Roles in {F}rame{N}et",
    author = "Petruck, Miriam R L",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3313",
    doi = "10.18653/v1/W19-3313",
    pages = "121--127",
    abstract = "Humans have the unique ability to infer information about participants in a scene, even if they are not mentioned in a text about that scene. Computer systems cannot do so without explicit information about those participants. This paper addresses the linguistic phenomenon of null-instantiated frame elements, i.e., implicit semantic roles, and their representation in FrameNet (FN). It motivates FN{'}s annotation practice, and illustrates three types of null-instantiated arguments that FrameNet tracks, noting that other lexical resources do not record such semantic-pragmatic information, despite its need in natural language understanding (NLU), and the elaborate efforts to create new datasets. It challenges the community to appeal to FN data to develop more sophisticated techniques for recognizing implicit semantic roles, and creating needed datasets. Although the annotation of null-instantiated roles was lexicographically motivated, FN provides useful information for text processing, and therefore must be considered in the design of any meaning representation for natural language understanding.",
}
@inproceedings{choe-etal-2019-copula,
    title = "Copula and Case-Stacking Annotations for {K}orean {AMR}",
    author = "Choe, Hyonsu  and
      Han, Jiyoon  and
      Park, Hyejin  and
      Kim, Hansaem",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3314",
    doi = "10.18653/v1/W19-3314",
    pages = "128--135",
    abstract = "This paper concerns the application of Abstract Meaning Representation (AMR) to Korean. In this regard, it focuses on the copula construction and its negation and the case-stacking phenomenon thereof. To illustrate this clearly, we reviewed the :domain annotation scheme from various perspectives. In this process, the existing annotation guidelines were improved to devise annotation schemes for each issue under the principle of pursuing consistency and efficiency of annotation without distorting the characteristics of Korean.",
}
@inproceedings{myers-palmer-2019-cleartac,
    title = "{C}lear{TAC}: Verb Tense, Aspect, and Form Classification Using Neural Nets",
    author = "Myers, Skatje  and
      Palmer, Martha",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3315",
    doi = "10.18653/v1/W19-3315",
    pages = "136--140",
    abstract = "This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.",
}
@inproceedings{shalev-etal-2019-preparing,
    title = "Preparing {SNACS} for Subjects and Objects",
    author = "Shalev, Adi  and
      Hwang, Jena D.  and
      Schneider, Nathan  and
      Srikumar, Vivek  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3316",
    doi = "10.18653/v1/W19-3316",
    pages = "141--147",
    abstract = "Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participants{---}including subjects and objects{---}directly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.",
}
@inproceedings{linh-nguyen-2019-case,
    title = "A Case Study on Meaning Representation for {V}ietnamese",
    author = "Linh, Ha  and
      Nguyen, Huyen",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3317",
    doi = "10.18653/v1/W19-3317",
    pages = "148--153",
    abstract = "This paper presents a case study on meaning representation for Vietnamese. Having introduced several existing semantic representation schemes for different languages, we select as basis for our work on Vietnamese AMR (Abstract Meaning Representation). From it, we define a meaning representation label set by adapting the English schema and taking into account the specific characteristics of Vietnamese.",
}
@inproceedings{brown-etal-2019-verbnet,
    title = "{V}erb{N}et Representations: Subevent Semantics for Transfer Verbs",
    author = "Brown, Susan Windisch  and
      Bonn, Julia  and
      Gung, James  and
      Zaenen, Annie  and
      Pustejovsky, James  and
      Palmer, Martha",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3318",
    doi = "10.18653/v1/W19-3318",
    pages = "154--163",
    abstract = "This paper announces the release of a new version of the English lexical resource VerbNet with substantially revised semantic representations designed to facilitate computer planning and reasoning based on human language. We use the transfer of possession and transfer of information event representations to illustrate both the general framework of the representations and the types of nuances the new representations can capture. These representations use a Generative Lexicon-inspired subevent structure to track attributes of event participants across time, highlighting oppositions and temporal and causal relations among the subevents.",
}
@inproceedings{prange-etal-2019-semantically,
    title = "Semantically Constrained Multilayer Annotation: The Case of Coreference",
    author = "Prange, Jakob  and
      Schneider, Nathan  and
      Abend, Omri",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3319",
    doi = "10.18653/v1/W19-3319",
    pages = "164--176",
    abstract = "We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions. We argue that this allows coreference annotators to sidestep some of the challenges faced in other schemes, which do not enforce consistency with predicate-argument structure and vary widely in what kinds of mentions they annotate and how. The proposed approach is examined with a pilot annotation study and compared with annotations from other schemes.",
}
@inproceedings{zhu-etal-2019-towards,
    title = "Towards Universal Semantic Representation",
    author = "Zhu, Huaiyu  and
      Li, Yunyao  and
      Chiticariu, Laura",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3320",
    doi = "10.18653/v1/W19-3320",
    pages = "177--181",
    abstract = "Natural language understanding at the semantic level and independent of language variations is of great practical value. Existing approaches such as semantic role labeling (SRL) and abstract meaning representation (AMR) still have features related to the peculiarities of the particular language. In this work we describe various challenges and possible solutions in designing a semantic representation that is universal across a variety of languages.",
}
@inproceedings{vigus-etal-2019-dependency,
    title = "A Dependency Structure Annotation for Modality",
    author = "Vigus, Meagan  and
      Van Gysel, Jens E. L.  and
      Croft, William",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3321",
    doi = "10.18653/v1/W19-3321",
    pages = "182--198",
    abstract = "This paper presents an annotation scheme for modality that employs a dependency structure. Events and sources (here, conceivers) are represented as nodes and epistemic strength relations characterize the edges. The epistemic strength values are largely based on Saur{\'\i} and Pustejovsky{'}s (2009) FactBank, while the dependency structure mirrors Zhang and Xue{'}s (2018b) approach to temporal relations. Six documents containing 377 events have been annotated by two expert annotators with high levels of agreement.",
}
@inproceedings{bonial-etal-2019-augmenting,
    title = "Augmenting {A}bstract {M}eaning {R}epresentation for Human-Robot Dialogue",
    author = "Bonial, Claire  and
      Donatelli, Lucia  and
      Lukin, Stephanie M.  and
      Tratz, Stephen  and
      Artstein, Ron  and
      Traum, David  and
      Voss, Clare",
    booktitle = "Proceedings of the First International Workshop on Designing Meaning Representations",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3322",
    doi = "10.18653/v1/W19-3322",
    pages = "199--210",
    abstract = "We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance. We propose 36 augmented AMRs that capture speech acts, tense and aspect, and spatial information. This linguistic information is vital for representing important distinctions, for example whether the robot has moved, is moving, or will move. We evaluate two existing AMR parsers for their performance on dialogue data. We also outline a model for graph-to-graph conversion, in which output from AMR parsers is converted into our refined AMRs. The design scheme presented here, though task-specific, is extendable for broad coverage of speech acts using AMR in future task-independent work.",
}
@proceedings{ws-2019-social,
    title = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    editor = "Weissenbacher, Davy  and
      Gonzalez-Hernandez, Graciela",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3200",
}
@inproceedings{he-etal-2019-extracting,
    title = "Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research",
    author = "He, Kai  and
      Wu, Jialun  and
      Ma, Xiaoyong  and
      Zhang, Chong  and
      Huang, Ming  and
      Li, Chen  and
      Yao, Lixia",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3201",
    doi = "10.18653/v1/W19-3201",
    pages = "1--10",
    abstract = "Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research. We identify online obituaries as a new data source and propose a special named entity recognition and relation extraction solution to extract names and kinships from online obituaries. Built on 1,809 annotated obituaries and a novel tagging scheme, our joint neural model achieved macro-averaged precision, recall and F measure of 72.69{\%}, 78.54{\%} and 74.93{\%}, and micro-averaged precision, recall and F measure of 95.74{\%}, 98.25{\%} and 96.98{\%} using 57 kinships with 10 or more examples in a 10-fold cross-validation experiment. The model performance improved dramatically when trained with 34 kinships with 50 or more examples. Leveraging additional information such as age, death date, birth date and residence mentioned by obituaries, we foresee a promising future of supplementing EHR databases with comprehensive and accurate kinship information for genetic research.",
}
@inproceedings{dirkson-etal-2019-lexical,
    title = "Lexical Normalization of User-Generated Medical Text",
    author = "Dirkson, Anne  and
      Verberne, Suzan  and
      Kraaij, Wessel",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3202",
    doi = "10.18653/v1/W19-3202",
    pages = "11--20",
    abstract = "In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature. The extraction of this knowledge is complicated by colloquial language use and misspellings. Yet, lexical normalization of such data has not been addressed properly. This paper presents an unsupervised, data-driven spelling correction module for medical social media. Our method outperforms state-of-the-art spelling correction and can detect mistakes with an F0.5 of 0.888. Additionally, we present a novel corpus for spelling mistake detection and correction on a medical patient forum.",
}
@inproceedings{weissenbacher-etal-2019-overview,
    title = "Overview of the Fourth Social Media Mining for Health ({SMM}4{H}) Shared Tasks at {ACL} 2019",
    author = "Weissenbacher, Davy  and
      Sarker, Abeed  and
      Magge, Arjun  and
      Daughton, Ashlynn  and
      O{'}Connor, Karen  and
      Paul, Michael J.  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3203",
    doi = "10.18653/v1/W19-3203",
    pages = "21--30",
    abstract = "The number of users of social media continues to grow, with nearly half of adults worldwide and two-thirds of all American adults using social networking. Advances in automated data processing, machine learning and NLP present the possibility of utilizing this massive data source for biomedical and public health applications, if researchers address the methodological challenges unique to this media. We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users. For the fourth execution of this challenge, we proposed four different tasks. Task 1 asked participants to distinguish tweets reporting an adverse drug reaction (ADR) from those that do not. Task 2, a follow-up to Task 1, asked participants to identify the span of text in tweets reporting ADRs. Task 3 is an end-to-end task where the goal was to first detect tweets mentioning an ADR and then map the extracted colloquial mentions of ADRs in the tweets to their corresponding standard concept IDs in the MedDRA vocabulary. Finally, Task 4 asked participants to classify whether a tweet contains a personal mention of one{'}s health, a more general discussion of the health issue, or is an unrelated mention. A total of 34 teams from around the world registered and 19 teams from 12 countries submitted a system run. We summarize here the corpora for this challenge which are freely available at https://competitions.codalab.org/competitions/22521, and present an overview of the methods and the results of the competing systems.",
}
@inproceedings{belousov-etal-2019-mednorm,
    title = "{M}ed{N}orm: A Corpus and Embeddings for Cross-terminology Medical Concept Normalisation",
    author = "Belousov, Maksim  and
      Dixon, William G.  and
      Nenadic, Goran",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3204",
    doi = "10.18653/v1/W19-3204",
    pages = "31--39",
    abstract = "The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA. Existing publicly available datasets annotated using different terminologies cannot be simply merged and utilised, and therefore become less valuable when developing machine learning-based concept normalisation systems. To address that, we designed a data harmonisation pipeline and engineered a corpus of 27,979 textual descriptions simultaneously mapped to both MedDRA and SNOMED-CT, sourced from five publicly available datasets across biomedical and social media domains. The pipeline can be used in the future to integrate new datasets into the corpus and also could be applied in relevant data curation tasks. We also described a method to merge different terminologies into a single concept graph preserving their relations and demonstrated that representation learning approach based on random walks on a graph can efficiently encode both hierarchical and equivalent relations and capture semantic similarities not only between concepts inside a given terminology but also between concepts from different terminologies. We believe that making a corpus and embeddings for cross-terminology medical concept normalisation available to the research community would contribute to a better understanding of the task.",
}
@inproceedings{delahunty-etal-2019-passive,
    title = "Passive Diagnosis Incorporating the {PHQ}-4 for Depression and Anxiety",
    author = "Delahunty, Fionn  and
      Johansson, Robert  and
      Arcan, Mihael",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3205",
    doi = "10.18653/v1/W19-3205",
    pages = "40--46",
    abstract = "Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. In this work, we develop and evaluate a multilabel, multidimensional deep neural network designed to predict PHQ-4 scores based on individuals written text. Our system outperforms random baseline metrics and provides a novel approach to how we can predict psychometric scores from written text. Additionally, we explore how this architecture can be applied to analyse social media data.",
}
@inproceedings{chen-etal-2019-hitsz,
    title = "{HITSZ}-{ICRC}: A Report for {SMM}4{H} Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets",
    author = "Chen, Shuai  and
      Huang, Yuanhang  and
      Huang, Xiaowei  and
      Qin, Haoming  and
      Yan, Jun  and
      Tang, Buzhou",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3206",
    doi = "10.18653/v1/W19-3206",
    pages = "47--51",
    abstract = "This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019. The two subtasks are automatic classification and extraction of adverse effect mentions in tweets. The systems for the two subtasks are based on bidirectional encoder representations from transformers (BERT), and achieves promising results. Among the systems we developed for subtask1, the best F1-score was 0.6457, for subtask2, the best relaxed F1-score and the best strict F1-score were 0.614 and 0.407 respectively. Our system ranks first among all systems on subtask1.",
}
@inproceedings{miftahutdinov-etal-2019-kfu,
    title = "{KFU} {NLP} Team at {SMM}4{H} 2019 Tasks: Want to Extract Adverse Drugs Reactions from Tweets? {BERT} to The Rescue",
    author = "Miftahutdinov, Zulfat  and
      Alimova, Ilseyar  and
      Tutubalina, Elena",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3207",
    doi = "10.18653/v1/W19-3207",
    pages = "52--57",
    abstract = "This paper describes a system developed for the Social Media Mining for Health (SMM4H) 2019 shared tasks. Specifically, we participated in three tasks. The goals of the first two tasks are to classify whether a tweet contains mentions of adverse drug reactions (ADR) and extract these mentions, respectively. The objective of the third task is to build an end-to-end solution: first, detect ADR mentions and then map these entities to concepts in a controlled vocabulary. We investigate the use of a language representation model BERT trained to obtain semantic representations of social media texts. Our experiments on a dataset of user reviews showed that BERT is superior to state-of-the-art models based on recurrent neural networks. The BERT-based system for Task 1 obtained an F1 of 57.38{\%}, with improvements up to +7.19{\%} F1 over a score averaged across all 43 submissions. The ensemble of neural networks with a voting scheme for named entity recognition ranked first among 9 teams at the SMM4H 2019 Task 2 and obtained a relaxed F1 of 65.8{\%}. The end-to-end model based on BERT for ADR normalization ranked first at the SMM4H 2019 Task 3 and obtained a relaxed F1 of 43.2{\%}.",
}
@inproceedings{ellendorff-etal-2019-approaching,
    title = "Approaching {SMM}4{H} with Merged Models and Multi-task Learning",
    author = {Ellendorff, Tilia  and
      Furrer, Lenz  and
      Colic, Nicola  and
      Aepli, No{\"e}mi  and
      Rinaldi, Fabio},
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3208",
    doi = "10.18653/v1/W19-3208",
    pages = "58--61",
    abstract = "We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.",
}
@inproceedings{zhao-etal-2019-identifying,
    title = "Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation",
    author = "Zhao, Xinyan  and
      Yu, Deahan  and
      Vydiswaran, V.G.Vinod",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3209",
    doi = "10.18653/v1/W19-3209",
    pages = "62--70",
    abstract = "Identifying mentions of medical concepts in social media is challenging because of high variability in free text. In this paper, we propose a novel neural network architecture, the Collocated LSTM with Attentive Pooling and Aggregated representation (CLAPA), that integrates a bidirectional LSTM model with attention and pooling strategy and utilizes the collocation information from training data to improve the representation of medical concepts. The collocation and aggregation layers improve the model performance on the task of identifying mentions of adverse drug events (ADE) in tweets. Using the dataset made available as part of the workshop shared task, we show that careful selection of neighborhood contexts can help uncover useful local information and improve the overall medical concept representation.",
}
@inproceedings{schneuwly-etal-2019-correlating,
    title = "Correlating {T}witter Language with Community-Level Health Outcomes",
    author = "Schneuwly, Arno  and
      Grubenmann, Ralf  and
      Rion Logean, S{\'e}verine  and
      Cieliebak, Mark  and
      Jaggi, Martin",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3210",
    doi = "10.18653/v1/W19-3210",
    pages = "71--78",
    abstract = "We study how language on social media is linked to mortal diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.",
}
@inproceedings{tortoreto-etal-2019-affective,
    title = "Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups More Therapeutic than {T}witter?",
    author = "Tortoreto, Giuliano  and
      Stepanov, Evgeny  and
      Cervone, Alessandra  and
      Dubiel, Mateusz  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3211",
    doi = "10.18653/v1/W19-3211",
    pages = "79--88",
    abstract = "The increase in the prevalence of mental health problems has coincided with a growing popularity of health related social networking sites. Regardless of their therapeutic potential, on-line support groups (OSGs) can also have negative effects on patients. In this work we propose a novel methodology to automatically verify the presence of therapeutic factors in social networking websites by using Natural Language Processing (NLP) techniques. The methodology is evaluated on on-line asynchronous multi-party conversations collected from an OSG and Twitter. The results of the analysis indicate that therapeutic factors occur more frequently in OSG conversations than in Twitter conversations. Moreover, the analysis of OSG conversations reveals that the users of that platform are supportive, and interactions are likely to lead to the improvement of their emotional state. We believe that our method provides a stepping stone towards automatic analysis of emotional states of users of online platforms. Possible applications of the method include provision of guidelines that highlight potential implications of using such platforms on users{'} mental health, and/or support in the analysis of their impact on specific individuals.",
}
@inproceedings{dirkson-verberne-2019-transfer,
    title = "Transfer Learning for Health-related {T}witter Data",
    author = "Dirkson, Anne  and
      Verberne, Suzan",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3212",
    doi = "10.18653/v1/W19-3212",
    pages = "89--92",
    abstract = "Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data. This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task. Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets. The code and fine-tuned models are publicly available.",
}
@inproceedings{cortes-tejada-etal-2019-nlp,
    title = "{NLP}@{UNED} at {SMM}4{H} 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets",
    author = "Cortes-Tejada, Javier  and
      Martinez-Romo, Juan  and
      Araujo, Lourdes",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3213",
    doi = "10.18653/v1/W19-3213",
    pages = "93--95",
    abstract = "This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.",
}
@inproceedings{ge-etal-2019-detecting,
    title = "Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention",
    author = "Ge, Suyu  and
      Qi, Tao  and
      Wu, Chuhan  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3214",
    doi = "10.18653/v1/W19-3214",
    pages = "96--98",
    abstract = "This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.",
}
@inproceedings{barry-uzuner-2019-deep,
    title = "Deep Learning for Identification of Adverse Effect Mentions In {T}witter Data",
    author = "Barry, Paul  and
      Uzuner, Ozlem",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3215",
    doi = "10.18653/v1/W19-3215",
    pages = "99--101",
    abstract = "Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019). This task features a training data set of 2,367 tweets, in addition to a 1,000 tweet evaluation data set. The solution presented here features a bidirectional Long Short-term Memory Network (bi-LSTM) for the generation of character-level embeddings. It uses a second bi-LSTM trained on both character and token level embeddings to feed a Conditional Random Field (CRF) which provides the final classification. This paper further discusses the deep learning algorithms used in our solution.",
}
@inproceedings{lopez-ubeda-etal-2019-using,
    title = "Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media",
    author = "L{\'o}pez {\'U}beda, Pilar  and
      D{\'\i}az Galiano, Manuel Carlos  and
      Martin, Maite  and
      Urena Lopez, L. Alfonso",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3216",
    doi = "10.18653/v1/W19-3216",
    pages = "102--106",
    abstract = "Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.",
}
@inproceedings{vydiswaran-etal-2019-towards,
    title = "Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: {U}niversity of {M}ichigan @ {SMM}4{H} 2019 Task 1",
    author = "Vydiswaran, V.G.Vinod  and
      Ganzel, Grace  and
      Romas, Bryan  and
      Yu, Deahan  and
      Austin, Amy  and
      Bhomia, Neha  and
      Chan, Socheatha  and
      Hall, Stephanie  and
      Le, Van  and
      Miller, Aaron  and
      Oduyebo, Olawunmi  and
      Song, Aulia  and
      Sondhi, Radhika  and
      Teng, Danny  and
      Tseng, Hao  and
      Vuong, Kim  and
      Zimmerman, Stephanie",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3217",
    doi = "10.18653/v1/W19-3217",
    pages = "107--109",
    abstract = "We participated in Task 1 of the Social Media Mining for Health Applications (SMM4H) 2019 Shared Tasks on detecting mentions of adverse drug events (ADEs) in tweets. Our approach relied on a text processing pipeline for tweets, and training traditional machine learning and deep learning models. Our submitted runs performed above average for the task.",
}
@inproceedings{gondane-2019-neural,
    title = "Neural Network to Identify Personal Health Experience Mention in Tweets Using {B}io{BERT} Embeddings",
    author = "Gondane, Shubham",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3218",
    doi = "10.18653/v1/W19-3218",
    pages = "110--113",
    abstract = "This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively.",
}
@inproceedings{manousogiannis-etal-2019-give,
    title = "Give It a Shot: Few-shot Learning to Normalize {ADR} Mentions in Social Media Posts",
    author = "Manousogiannis, Emmanouil  and
      Mesbah, Sepideh  and
      Bozzon, Alessandro  and
      Baez, Selene  and
      Sips, Robert Jan",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3219",
    doi = "10.18653/v1/W19-3219",
    pages = "114--116",
    abstract = "This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-the art approach, focusing our contribution on the final entity-linking step. For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data. Our system (relaxed F1: 0.337-0.345) outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.",
}
@inproceedings{wang-etal-2019-bigodm,
    title = "{BIGODM} System in the Social Media Mining for Health Applications Shared Task 2019",
    author = "Wang, Chen-Kai  and
      Dai, Hong-Jie  and
      Wang, Bo-Hung",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3220",
    doi = "10.18653/v1/W19-3220",
    pages = "117--119",
    abstract = "In this study, we describe our methods to automatically classify Twitter posts conveying events of adverse drug reaction (ADR). Based on our previous experience in tackling the ADR classification task, we empirically applied the vote-based under-sampling ensemble approach along with linear support vector machine (SVM) to develop our classifiers as part of our participation in ACL 2019 Social Media Mining for Health Applications (SMM4H) shared task 1. The best-performed model on the test sets were trained on a merged corpus consisting of the datasets released by SMM4H 2017 and 2019. By using VUE, the corpus was randomly under-sampled with 2:1 ratio between the negative and positive classes to create an ensemble using the linear kernel trained with features including bag-of-word, domain knowledge, negation and word embedding. The best performing model achieved an F-measure of 0.551 which is about 5{\%} higher than the average F-scores of 16 teams.",
}
@inproceedings{sarabadani-2019-detection,
    title = "Detection of Adverse Drug Reaction Mentions in Tweets Using {ELM}o",
    author = "Sarabadani, Sarah",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3221",
    doi = "10.18653/v1/W19-3221",
    pages = "120--122",
    abstract = "This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.",
}
@inproceedings{bagherzadeh-etal-2019-adverse,
    title = "Adverse Drug Effect and Personalized Health Mentions, {CL}a{C} at {SMM}4{H} 2019, Tasks 1 and 4",
    author = "Bagherzadeh, Parsa  and
      Sheikh, Nadia  and
      Bergler, Sabine",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3222",
    doi = "10.18653/v1/W19-3222",
    pages = "123--126",
    abstract = "CLaC labs participated in Task 1 and 4 of SMM4H 2019. We pursed two main objectives in our submission. First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested. The results seem positively affected by the proposed architectures.",
}
@inproceedings{mahata-etal-2019-midas,
    title = "{MIDAS}@{SMM}4{H}-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from {T}witter",
    author = "Mahata, Debanjan  and
      Anand, Sarthak  and
      Zhang, Haimin  and
      Shahid, Simra  and
      Mehnaz, Laiba  and
      Kumar, Yaman  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3223",
    doi = "10.18653/v1/W19-3223",
    pages = "127--132",
    abstract = "In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.",
}
@inproceedings{aroyehun-gelbukh-2019-detection,
    title = "Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings",
    author = "Aroyehun, Segun Taofeek  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3224",
    doi = "10.18653/v1/W19-3224",
    pages = "133--135",
    abstract = "This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.",
}
@inproceedings{rawal-etal-2019-identification,
    title = "Identification of Adverse Drug Reaction Mentions in Tweets {--} {SMM}4{H} Shared Task 2019",
    author = "Rawal, Samarth  and
      Rawal, Siddharth  and
      Anwar, Saadat  and
      Baral, Chitta",
    booktitle = "Proceedings of the Fourth Social Media Mining for Health Applications ({\#}SMM4H) Workshop {\&} Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3225",
    doi = "10.18653/v1/W19-3225",
    pages = "136--137",
    abstract = "Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of this work can offer insights into pharmacovigilance research to monitor the adverse effects of medications. This research specifically looks into mentions of adverse drug reactions (ADRs) in Twitter data through the Social Media Mining for Health Applications (SMM4H) Shared Task 2019. Adverse drug reactions are undesired harmful effects which can arise from medication or other methods of treatment. The goal of this research is to build accurate models using natural language processing techniques to detect reports of adverse drug reactions in Twitter data and extract these words or phrases.",
}
@proceedings{ws-2019-international-finite,
    title = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    editor = "Vogler, Heiko  and
      Maletti, Andreas",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3100",
}
@inproceedings{ranta-2019-grammatical,
    title = "Grammatical Framework: an Interlingual Grammar Formalism",
    author = "Ranta, Aarne",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3101",
    doi = "10.18653/v1/W19-3101",
    pages = "1--2",
}
@inproceedings{drewes-2019-survey,
    title = "A Survey of Recent Advances in Efficient Parsing for Graph Grammars",
    author = "Drewes, Frank",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3102",
    doi = "10.18653/v1/W19-3102",
    pages = "3",
}
@inproceedings{gebhardt-2019-latent,
    title = "Latent Variable Grammars for Discontinuous Parsing",
    author = "Gebhardt, Kilian",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3103",
    doi = "10.18653/v1/W19-3103",
    pages = "4--6",
}
@inproceedings{bjorklund-etal-2019-bottom,
    title = "Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs",
    author = {Bj{\"o}rklund, Johanna  and
      Cohen, Shay B.  and
      Drewes, Frank  and
      Satta, Giorgio},
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3104",
    doi = "10.18653/v1/W19-3104",
    pages = "7--17",
    abstract = "We propose a formal model for translating unranked syntactic trees, such as dependency trees, into semantic graphs. These tree-to-graph transducers can serve as a formal basis of transition systems for semantic parsing which recently have been shown to perform very well, yet hitherto lack formalization. Our model features {``}extended{''} rules and an arc-factored normal form, comes with an efficient translation algorithm, and can be equipped with weights in a straightforward manner.",
}
@inproceedings{cognetta-etal-2019-compression,
    title = "On the Compression of Lexicon Transducers",
    author = "Cognetta, Marco  and
      Allauzen, Cyril  and
      Riley, Michael",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3105",
    doi = "10.18653/v1/W19-3105",
    pages = "18--26",
    abstract = "In finite-state language processing pipelines, a lexicon is often a key component. It needs to be comprehensive to ensure accuracy, reducing out-of-vocabulary misses. However, in memory-constrained environments (e.g., mobile phones), the size of the component automata must be kept small. Indeed, a delicate balance between comprehensiveness, speed, and memory must be struck to conform to device requirements while providing a good user experience.In this paper, we describe a compression scheme for lexicons when represented as finite-state transducers. We efficiently encode the graph of the transducer while storing transition labels separately. The graph encoding scheme is based on the LOUDS (Level Order Unary Degree Sequence) tree representation, which has constant time tree traversal for queries while being information-theoretically optimal in space. We find that our encoding is near the theoretical lower bound for such graphs and substantially outperforms more traditional representations in space while remaining competitive in latency benchmarks.",
}
@inproceedings{fernando-etal-2019-mso,
    title = "{MSO} with tests and reducts",
    author = "Fernando, Tim  and
      Woods, David  and
      Vogel, Carl",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3106",
    doi = "10.18653/v1/W19-3106",
    pages = "27--36",
    abstract = "Tests added to Kleene algebra (by Kozen and others) are considered within Monadic Second Order logic over strings, where they are likened to statives in natural language. Reducts are formed over tests and non-tests alike, specifying what is observable. Notions of temporal granularity are based on observable change, under the assumption that a finite set bounds what is observable (with the possibility of stretching such bounds by moving to a larger finite set). String projections at different granularities are conjoined by superpositions that provide another variant of concatenation for Booleans.",
}
@inproceedings{janicki-2019-finite,
    title = "Finite State Transducer Calculus for Whole Word Morphology",
    author = "Janicki, Maciej",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3107",
    doi = "10.18653/v1/W19-3107",
    pages = "37--45",
    abstract = "The research on machine learning of morphology often involves formulating morphological descriptions directly on surface forms of words. As the established two-level morphology paradigm requires the knowledge of the underlying structure, it is not widely used in such settings. In this paper, we propose a formalism describing structural relationships between words based on theories of morphology that reject the notions of internal word structure and morpheme. The formalism covers a wide variety of morphological phenomena (including non-concatenative ones like stem vowel alternation) without the need of workarounds and extensions. Furthermore, we show that morphological rules formulated in such way can be easily translated to FSTs, which enables us to derive performant approaches to morphological analysis, generation and automatic rule discovery.",
}
@inproceedings{morbitz-vogler-2019-weighted,
    title = "Weighted parsing for grammar-based language models",
    author = {M{\"o}rbitz, Richard  and
      Vogler, Heiko},
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3108",
    doi = "10.18653/v1/W19-3108",
    pages = "46--55",
    abstract = "We develop a general framework for weighted parsing which is built on top of grammar-based language models and employs flexible weight algebras. It generalizes previous work in that area (semiring parsing, weighted deductive parsing) and also covers applications outside the classical scope of parsing, e.g., algebraic dynamic programming. We show an algorithm which terminates and is correct for a large class of weighted grammar-based language models.",
}
@inproceedings{nederhof-vogler-2019-regular,
    title = "Regular transductions with {MCFG} input syntax",
    author = "Nederhof, Mark-Jan  and
      Vogler, Heiko",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3109",
    doi = "10.18653/v1/W19-3109",
    pages = "56--64",
    abstract = "We show that regular transductions for which the input part is generated by some multiple context-free grammar can be simulated by synchronous multiple context-free grammars. We prove that synchronous multiple context-free grammars are strictly more powerful than this combination of regular transductions and multiple context-free grammars.",
}
@inproceedings{ozturel-etal-2019-syntactically,
    title = "A Syntactically Expressive Morphological Analyzer for {T}urkish",
    author = "Ozturel, Adnan  and
      Kayadelen, Tolga  and
      Demirsahin, Isin",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3110",
    doi = "10.18653/v1/W19-3110",
    pages = "65--75",
    abstract = "We present a broad coverage model of Turkish morphology and an open-source morphological analyzer that implements it. The model captures intricacies of Turkish morphology-syntax interface, thus could be used as a baseline that guides language model development. It introduces a novel fine part-of-speech tagset, a fine-grained affix inventory and represents morphotactics without zero-derivations. The morphological analyzer is freely available. It consists of modular reusable components of human-annotated gold standard lexicons, implements Turkish morphotactics as finite-state transducers using OpenFst and morphophonemic processes as Thrax grammars.",
}
@inproceedings{sarveswaran-etal-2019-using,
    title = "Using Meta-Morph Rules to develop Morphological Analysers: A case study concerning {T}amil",
    author = "Sarveswaran, Kengatharaiyer  and
      Dias, Gihan  and
      Butt, Miriam",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3111",
    doi = "10.18653/v1/W19-3111",
    pages = "76--86",
    abstract = "This paper describes a new and larger coverage Finite-State Morphological Analyser (FSM) and Generator for the Dravidian language Tamil. The FSM has been developed in the context of computational grammar engineering, adhering to the standards of the ParGram effort. Tamil is a morphologically rich language and the interaction between linguistic analysis and formal implementation is complex, resulting in a challenging task. In order to allow the development of the FSM to focus more on the linguistic analysis and less on the formal details, we have developed a system of meta-morph(ology) rules along with a script which translates these rules into FSM processable representations. The introduction of meta-morph rules makes it possible for computationally naive linguists to interact with the system and to expand it in future work. We found that the meta-morph rules help to express linguistic generalisations and reduce the manual effort of writing lexical classes for morphological analysis. Our Tamil FSM currently handles mainly the inflectional morphology of 3,300 verb roots and their 260 forms. Further, it also has a lexicon of approximately 100,000 nouns along with a guesser to handle out-of-vocabulary items. Although the Tamil FSM was primarily developed to be part of a computational grammar, it can also be used as a web or stand-alone application for other NLP tasks, as per general ParGram practice.",
}
@inproceedings{suresh-etal-2019-distilling,
    title = "Distilling weighted finite automata from arbitrary probabilistic models",
    author = "Suresh, Ananda Theertha  and
      Roark, Brian  and
      Riley, Michael  and
      Schogol, Vlad",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3112",
    doi = "10.18653/v1/W19-3112",
    pages = "87--97",
    abstract = "Weighted finite automata (WFA) are often used to represent probabilistic models, such as n-gram language models, since they are efficient for recognition tasks in time and space. The probabilistic source to be represented as a WFA, however, may come in many forms. Given a generic probabilistic model over sequences, we propose an algorithm to approximate it as a weighted finite automaton such that the Kullback-Leibler divergence between the source model and the WFA target model is minimized. The proposed algorithm involves a counting step and a difference of convex optimization, both of which can be performed efficiently. We demonstrate the usefulness of our approach on some tasks including distilling n-gram models from neural models.",
}
@inproceedings{wakabayashi-2019-silent,
    title = "Silent {HMM}s: Generalized Representation of Hidden Semi-{M}arkov Models and Hierarchical {HMM}s",
    author = "Wakabayashi, Kei",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3113",
    doi = "10.18653/v1/W19-3113",
    pages = "98--107",
    abstract = "Modeling sequence data using probabilistic finite state machines (PFSMs) is a technique that analyzes the underlying dynamics in sequences of symbols. Hidden semi-Markov models (HSMMs) and hierarchical hidden Markov models (HHMMs) are PFSMs that have been successfully applied to a wide variety of applications by extending HMMs to make the extracted patterns easier to interpret. However, these models are independently developed with their own training algorithm, so that we cannot combine multiple kinds of structures to build a PFSM for a specific application. In this paper, we prove that silent hidden Markov models (silent HMMs) are flexible models that have more expressive power than HSMMs and HHMMs. Silent HMMs are HMMs that contain silent states, which do not emit any observations. We show that we can obtain silent HMM equivalent to given HSMMs and HHMMs. We believe that these results form a firm foundation to use silent HMMs as a unified representation for PFSM modeling.",
}
@inproceedings{wolf-sonkin-etal-2019-latin,
    title = "{L}atin script keyboards for {S}outh {A}sian languages with finite-state normalization",
    author = "Wolf-Sonkin, Lawrence  and
      Schogol, Vlad  and
      Roark, Brian  and
      Riley, Michael",
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3114",
    doi = "10.18653/v1/W19-3114",
    pages = "108--117",
    abstract = "The use of the Latin script for text entry of South Asian languages is common, even though there is no standard orthography for these languages in the script. We explore several compact finite-state architectures that permit variable spellings of words during mobile text entry. We find that approaches making use of transliteration transducers provide large accuracy improvements over baselines, but that simpler approaches involving a compact representation of many attested alternatives yields much of the accuracy gain. This is particularly important when operating under constraints on model size (e.g., on inexpensive mobile devices with limited storage and memory for keyboard models), and on speed of inference, since people typing on mobile keyboards expect no perceptual delay in keyboard responsiveness.",
}
@inproceedings{yli-jyra-2019-transition,
    title = "Transition-Based Coding and Formal Language Theory for Ordered Digraphs",
    author = {Yli-Jyr{\"a}, Anssi},
    booktitle = "Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing",
    month = sep,
    year = "2019",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3115",
    doi = "10.18653/v1/W19-3115",
    pages = "118--131",
    abstract = "Transition-based parsing of natural language uses transition systems to build directed annotation graphs (digraphs) for sentences. In this paper, we define, for an arbitrary ordered digraph, a unique decomposition and a corresponding linear encoding that are associated bijectively with each other via a new transition system. These results give us an efficient and succinct representation for digraphs and sets of digraphs. Based on the system and our analysis of its syntactic properties, we give structural bounds under which the set of encoded digraphs is restricted and becomes a context-free or a regular string language. The context-free restriction is essentially a superset of the encodings used previously to characterize properties of noncrossing digraphs and to solve maximal subgraphs problems. The regular restriction with a tight bound is shown to capture the Universal Dependencies v2.4 treebanks in linguistics.",
}
@proceedings{ws-2019-linguistics,
    title = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    editor = "Niederhoffer, Kate  and
      Hollingshead, Kristy  and
      Resnik, Philip  and
      Resnik, Rebecca  and
      Loveys, Kate",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3000",
}
@inproceedings{demasi-etal-2019-towards,
    title = "Towards augmenting crisis counselor training by improving message retrieval",
    author = "Demasi, Orianna  and
      Hearst, Marti A.  and
      Recht, Benjamin",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3001",
    doi = "10.18653/v1/W19-3001",
    pages = "1--11",
    abstract = "A fundamental challenge when training counselors is presenting novices with the opportunity to practice counseling distressed individuals without exacerbating a situation. Rather than replacing human empathy with an automated counselor, we propose simulating an individual in crisis so that human counselors in training can practice crisis counseling in a low-risk environment. Towards this end, we collect a dataset of suicide prevention counselor role-play transcripts and make initial steps towards constructing a CRISISbot for humans to counsel while in training. In this data-constrained setting, we evaluate the potential for message retrieval to construct a coherent chat agent in light of recent advances with text embedding methods. Our results show that embeddings can considerably improve retrieval approaches to make them competitive with generative models. By coherently retrieving messages, we can help counselors practice chatting in a low-risk environment.",
}
@inproceedings{lee-etal-2019-identifying,
    title = "Identifying therapist conversational actions across diverse psychotherapeutic approaches",
    author = "Lee, Fei-Tzin  and
      Hull, Derrick  and
      Levine, Jacob  and
      Ray, Bonnie  and
      McKeown, Kathy",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3002",
    doi = "10.18653/v1/W19-3002",
    pages = "12--23",
    abstract = "While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational {``}action{''} each speaker takes at each utterance, such as sympathizing, problem-solving or assumption checking. We propose to apply dialogue act classification to therapy transcripts, using a therapy-specific labeling scheme, in order to gain a high-level understanding of the flow of conversation in therapy sessions. We present a novel annotation scheme that spans multiple psychotherapeutic approaches, apply it to a large and diverse corpus of psychotherapy transcripts, and present and discuss classification results obtained using both SVM and neural network-based models. The results indicate that identifying the structure and flow of therapeutic actions is an obtainable goal, opening up the opportunity in the future to provide therapeutic recommendations tailored to specific client situations.",
}
@inproceedings{zirikly-etal-2019-clpsych,
    title = "{CLP}sych 2019 Shared Task: Predicting the Degree of Suicide Risk in {R}eddit Posts",
    author = {Zirikly, Ayah  and
      Resnik, Philip  and
      Uzuner, {\"O}zlem  and
      Hollingshead, Kristy},
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3003",
    doi = "10.18653/v1/W19-3003",
    pages = "24--33",
    abstract = "The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych{'}19) introduced an assessment of suicide risk based on social media postings, using data from Reddit to identify users at no, low, moderate, or severe risk. Two variations of the task focused on users whose posts to the r/SuicideWatch subreddit indicated they might be at risk; a third task looked at screening users based only on their more everyday (non-SuicideWatch) posts. We received submissions from 15 different teams, and the results provide progress and insight into the value of language signal in helping to predict risk level.",
}
@inproceedings{mohammadi-etal-2019-clac,
    title = "{CL}a{C} at {CLP}sych 2019: Fusion of Neural Features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online Posts",
    author = "Mohammadi, Elham  and
      Amini, Hessam  and
      Kosseim, Leila",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3004",
    doi = "10.18653/v1/W19-3004",
    pages = "34--38",
    abstract = "This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC. The goal of the shared task was to detect and assess suicide risk based on a collection of online posts. For our participation, we used an ensemble method which utilizes 8 neural sub-models to extract neural features and predict class probabilities, which are then used by an SVM classifier. Our team ranked first in 2 out of the 3 tasks (tasks A and C).",
}
@inproceedings{matero-etal-2019-suicide,
    title = "Suicide Risk Assessment with Multi-level Dual-Context Language and {BERT}",
    author = "Matero, Matthew  and
      Idnani, Akash  and
      Son, Youngseo  and
      Giorgi, Salvatore  and
      Vu, Huy  and
      Zamani, Mohammad  and
      Limbachiya, Parth  and
      Guntuku, Sharath Chandra  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3005",
    doi = "10.18653/v1/W19-3005",
    pages = "39--44",
    abstract = "Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with {``}any-risk{''}, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.",
}
@inproceedings{hauser-etal-2019-using,
    title = "Using natural conversations to classify autism with limited data: Age matters",
    author = "Hauser, Michael  and
      Sariyanidi, Evangelos  and
      Tunc, Birkan  and
      Zampella, Casey  and
      Brodkin, Edward  and
      Schultz, Robert  and
      Parish-Morris, Julia",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3006",
    doi = "10.18653/v1/W19-3006",
    pages = "45--54",
    abstract = "Spoken language ability is highly heterogeneous in Autism Spectrum Disorder (ASD), which complicates efforts to identify linguistic markers for use in diagnostic classification, clinical characterization, and for research and clinical outcome measurement. Machine learning techniques that harness the power of multivariate statistics and non-linear data analysis hold promise for modeling this heterogeneity, but many models require enormous datasets, which are unavailable for most psychiatric conditions (including ASD). In lieu of such datasets, good models can still be built by leveraging domain knowledge. In this study, we compare two machine learning approaches: the first approach incorporates prior knowledge about language variation across middle childhood, adolescence, and adulthood to classify 6-minute naturalistic conversation samples from 140 age- and IQ-matched participants (81 with ASD), while the other approach treats all ages the same. We found that individual age-informed models were significantly more accurate than a single model tasked with building a common algorithm across age groups. Furthermore, predictive linguistic features differed significantly by age group, confirming the importance of considering age-related changes in language use when classifying ASD. Our results suggest that limitations imposed by heterogeneity inherent to ASD and from developmental change with age can be (at least partially) overcome using domain knowledge, such as understanding spoken language development from childhood through adulthood.",
}
@inproceedings{fraser-etal-2019-importance,
    title = "The importance of sharing patient-generated clinical speech and language data",
    author = {Fraser, Kathleen C.  and
      Linz, Nicklas  and
      Lindsay, Hali  and
      K{\"o}nig, Alexandra},
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3007",
    doi = "10.18653/v1/W19-3007",
    pages = "55--61",
    abstract = "Increased access to large datasets has driven progress in NLP. However, most computational studies of clinically-validated, patient-generated speech and language involve very few datapoints, as such data are difficult (and expensive) to collect. In this position paper, we argue that we must find ways to promote data sharing across research groups, in order to build datasets of a more appropriate size for NLP and machine learning analysis. We review the benefits and challenges of sharing clinical language data, and suggest several concrete actions by both clinical and NLP researchers to encourage multi-site and multi-disciplinary data sharing. We also propose the creation of a collaborative data sharing platform, to allow NLP researchers to take a more active responsibility for data transcription, annotation, and curation.",
}
@inproceedings{nalabandian-ireland-2019-depressed,
    title = "Depressed Individuals Use Negative Self-Focused Language When Recalling Recent Interactions with Close Romantic Partners but Not Family or {F}riends",
    author = "Nalabandian, Taleen  and
      Ireland, Molly",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3008",
    doi = "10.18653/v1/W19-3008",
    pages = "62--73",
    abstract = "Depression is characterized by a self-focused negative attentional bias, which is often reflected in everyday language use. In a prospective writing study, we explored whether the association between depressive symptoms and negative, self-focused language varies across social contexts. College students (N = 243) wrote about a recent interaction with a person they care deeply about. Depression symptoms positively correlated with negative emotion words and first-person singular pronouns (or negative self-focus) when writing about a recent interaction with romantic partners or, to a lesser extent, friends, but not family members. The pattern of results was more pronounced when participants perceived greater self-other overlap (i.e., interpersonal closeness) with their romantic partner. Findings regarding how the linguistic profile of depression differs by type of relationship may inform more effective methods of clinical diagnosis and treatment.",
}
@inproceedings{zomick-etal-2019-linguistic,
    title = "Linguistic Analysis of Schizophrenia in {R}eddit Posts",
    author = "Zomick, Jonathan  and
      Levitan, Sarah Ita  and
      Serper, Mark",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3009",
    doi = "10.18653/v1/W19-3009",
    pages = "74--83",
    abstract = "We explore linguistic indicators of schizophrenia in Reddit discussion forums. Schizophrenia (SZ) is a chronic mental disorder that affects a person{'}s thoughts and behaviors. Identifying and detecting signs of SZ is difficult given that SZ is relatively uncommon, affecting approximately 1{\%} of the US population, and people suffering with SZ often believe that they do not have the disorder. Linguistic abnormalities are a hallmark of SZ and many of the illness{'}s symptoms are manifested through language. In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study linguistic characteristics of SZ. We collected and analyzed a large corpus of Reddit posts from users claiming to have received a formal diagnosis of SZ and identified several linguistic features that differentiated these users from a control (CTL) group. We compared these results to other findings on social media linguistic analysis and SZ. We also developed a machine learning classifier to automatically identify self-identified users with SZ on Reddit.",
}
@inproceedings{bar-etal-2019-semantic,
    title = "Semantic Characteristics of Schizophrenic Speech",
    author = "Bar, Kfir  and
      Zilberstein, Vered  and
      Ziv, Ido  and
      Baram, Heli  and
      Dershowitz, Nachum  and
      Itzikowitz, Samuel  and
      Vadim Harel, Eiran",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3010",
    doi = "10.18653/v1/W19-3010",
    pages = "84--93",
    abstract = "Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns.",
}
@inproceedings{parish-morris-2019-computational,
    title = "Computational Linguistics for Enhancing Scientific Reproducibility and Reducing Healthcare Inequities",
    author = "Parish-Morris, Julia",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3011",
    doi = "10.18653/v1/W19-3011",
    pages = "94--102",
    abstract = "Computational linguistics holds promise for improving scientific integrity in clinical psychology, and for reducing longstanding inequities in healthcare access and quality. This paper describes how computational linguistics approaches could address the {``}reproducibility crisis{''} facing social science, particularly with regards to reliable diagnosis of neurodevelopmental and psychiatric conditions including autism spectrum disorder (ASD). It is argued that these improvements in scientific integrity are poised to naturally reduce persistent healthcare inequities in neglected subpopulations, such as verbally fluent girls and women with ASD, but that concerted attention to this issue is necessary to avoid reproducing biases built into training data. Finally, it is suggested that computational linguistics is just one component of an emergent digital phenotyping toolkit that could ultimately be used for clinical decision support, to improve clinical care via precision medicine (i.e., personalized intervention planning), granular treatment response monitoring (including remotely), and for gene-brain-behavior studies aiming to pinpoint the underlying biological etiology of otherwise behaviorally-defined conditions like ASD.",
}
@inproceedings{linz-etal-2019-temporal,
    title = "Temporal Analysis of the Semantic Verbal Fluency Task in Persons with Subjective and Mild Cognitive Impairment",
    author = {Linz, Nicklas  and
      Lundholm Fors, Kristina  and
      Lindsay, Hali  and
      Eckerstr{\"o}m, Marie  and
      Alexandersson, Jan  and
      Kokkinakis, Dimitrios},
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3012",
    doi = "10.18653/v1/W19-3012",
    pages = "103--113",
    abstract = "The Semantic Verbal Fluency (SVF) task is a classical neuropsychological assessment where persons are asked to produce words belonging to a semantic category (e.g., animals) in a given time. This paper introduces a novel method of temporal analysis for SVF tasks utilizing time intervals and applies it to a corpus of elderly Swedish subjects (mild cognitive impairment, subjective cognitive impairment and healthy controls). A general decline in word count and lexical frequency over the course of the task is revealed, as well as an increase in word transition times. Persons with subjective cognitive impairment had a higher word count during the last intervals, but produced words of the same lexical frequencies. Persons with MCI had a steeper decline in both word count and lexical frequencies during the third interval. Additional correlations with neuropsychological scores suggest these findings are linked to a person{'}s overall vocabulary size and processing speed, respectively. Classification results improved when adding the novel features (AUC=0.72), supporting their diagnostic value.",
}
@inproceedings{amir-etal-2019-mental,
    title = "Mental Health Surveillance over Social Media with Digital Cohorts",
    author = "Amir, Silvio  and
      Dredze, Mark  and
      Ayers, John W.",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3013",
    doi = "10.18653/v1/W19-3013",
    pages = "114--120",
    abstract = "The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance. However, inferring accurate population-level trends requires representative samples of the underlying population, which can be challenging given the biases inherent in social media data. While previous work has adjusted samples based on demographic estimates, the populations were selected based on specific outcomes, e.g. specific mental health conditions. We depart from these methods, by conducting analyses over demographically representative digital cohorts of social media users. To validated this approach, we constructed a cohort of US based Twitter users to measure the prevalence of depression and PTSD, and investigate how these illnesses manifest across demographic subpopulations. The analysis demonstrates that cohort-based studies can help control for sampling biases, contextualize outcomes, and provide deeper insights into the data.",
}
@inproceedings{johannssen-etal-2019-reviving,
    title = "Reviving a psychometric measure: Classification and prediction of the Operant Motive Test",
    author = "Johann{\ss}en, Dirk  and
      Biemann, Chris  and
      Scheffer, David",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3014",
    doi = "10.18653/v1/W19-3014",
    pages = "121--125",
    abstract = "Implicit motives allow for the characterization of behavior, subsequent success and long-term development. While this has been operationalized in the operant motive test, research on motives has declined mainly due to labor-intensive and costly human annotation. In this study, we analyze over 200,000 labeled data items from 40,000 participants and utilize them for engineering features for training a logistic model tree machine learning model. It captures manually assigned motives well with an F-score of 80{\%}, coming close to the pairwise annotator intraclass correlation coefficient of r = .85. In addition, we found a significant correlation of r = .2 between subsequent academic success and data automatically labeled with our model in an extrinsic evaluation.",
}
@inproceedings{just-etal-2019-coherence,
    title = "Coherence models in schizophrenia",
    author = {Just, Sandra  and
      Haegert, Erik  and
      Ko{\v{r}}{\'a}nov{\'a}, Nora  and
      Br{\"o}cker, Anna-Lena  and
      Nenchev, Ivan  and
      Funcke, Jakob  and
      Montag, Christiane  and
      Stede, Manfred},
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3015",
    doi = "10.18653/v1/W19-3015",
    pages = "126--136",
    abstract = "Incoherent discourse in schizophrenia has long been recognized as a dominant symptom of the mental disorder (Bleuler, 1911/1950). Recent studies have used modern sentence and word embeddings to compute coherence metrics for spontaneous speech in schizophrenia. While clinical ratings always have a subjective element, computational linguistic methodology allows quantification of speech abnormalities. Clinical and empirical knowledge from psychiatry provide the theoretical and conceptual basis for modelling. Our study is an interdisciplinary attempt at improving coherence models in schizophrenia. Speech samples were obtained from healthy controls and patients with a diagnosis of schizophrenia or schizoaffective disorder and different severity of positive formal thought disorder. Interviews were transcribed and coherence metrics derived from different embeddings. One model found higher coherence metrics for controls than patients. All other models remained non-significant. More detailed analysis of the data motivates different approaches to improving coherence models in schizophrenia, e.g. by assessing referential abnormalities.",
}
@inproceedings{chandler-etal-2019-overcoming,
    title = "Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership",
    author = "Chandler, Chelsea  and
      Foltz, Peter W.  and
      Cheng, Jian  and
      Bernstein, Jared C.  and
      Rosenfeld, Elizabeth P.  and
      Cohen, Alex S.  and
      Holmlund, Terje B.  and
      Elvev{\aa}g, Brita",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3016",
    doi = "10.18653/v1/W19-3016",
    pages = "137--147",
    abstract = "Verbal memory is affected by numerous clinical conditions and most neuropsychological and clinical examinations evaluate it. However, a bottleneck exists in such endeavors because traditional methods require expert human review, and usually only a couple of test versions exist, thus limiting the frequency of administration and clinical applications. The present study overcomes this bottleneck by automating the administration, transcription, analysis and scoring of story recall. A large group of healthy participants (n = 120) and patients with mental illness (n = 105) interacted with a mobile application that administered a wide range of assessments, including verbal memory. The resulting speech generated by participants when retelling stories from the memory task was transcribed using automatic speech recognition tools, which was compared with human transcriptions (overall word error rate = 21{\%}). An assortment of surface-level and semantic language-based features were extracted from the verbal recalls. A final set of three features were used to both predict expert human ratings with a ridge regression model (r = 0.88) and to differentiate patients from healthy individuals with an ensemble of logistic regression classifiers (accuracy = 76{\%}). This is the first {`}outside of the laboratory{'} study to showcase the viability of the complete pipeline of automated assessment of verbal memory in naturalistic settings.",
}
@inproceedings{gonzalez-hevia-etal-2019-analyzing,
    title = "Analyzing the use of existing systems for the {CLP}sych 2019 Shared Task",
    author = "Gonz{\'a}lez Hevia, Alejandro  and
      Cerezo Men{\'e}ndez, Rebeca  and
      Gayo-Avello, Daniel",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3017",
    doi = "10.18653/v1/W19-3017",
    pages = "148--151",
    abstract = "In this paper we describe the UniOvi-WESO classification systems proposed for the 2019 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. We explore the use of two systems trained with ReachOut data from the 2016 CLPsych task, and compare them to a baseline system trained with the data provided for this task. All the classifiers were trained with features extracted just from the text of each post, without using any other metadata. We found out that the baseline system performs slightly better than the pretrained systems, mainly due to the differences in labeling between the two tasks. However, they still work reasonably well and can detect if a user is at risk of suicide or not.",
}
@inproceedings{chen-etal-2019-similar,
    title = "Similar Minds Post Alike: Assessment of Suicide Risk Using a Hybrid Model",
    author = "Chen, Lushi  and
      Aldayel, Abeer  and
      Bogoychev, Nikolay  and
      Gong, Tao",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3018",
    doi = "10.18653/v1/W19-3018",
    pages = "152--157",
    abstract = "This paper describes our system submission for the CLPsych 2019 shared task B on suicide risk assessment. We approached the problem with three separate models: a behaviour model; a language model and a hybrid model. For the behavioral model approach, we model each user{'}s behaviour and thoughts with four groups of features: posting behaviour, sentiment, motivation, and content of the user{'}s posting. We use these features as an input in a support vector machine (SVM). For the language model approach, we trained a language model for each risk level using all the posts from the users as the training corpora. Then, we computed the perplexity of each user{'}s posts to determine how likely his/her posts were to belong to each risk level. Finally, we built a hybrid model that combines both the language model and the behavioral model, which demonstrates the best performance in detecting the suicide risk level.",
}
@inproceedings{bitew-etal-2019-predicting,
    title = "Predicting Suicide Risk from Online Postings in {R}eddit The {UG}ent-{IDL}ab submission to the {CLP}ysch 2019 Shared Task A",
    author = "Bitew, Semere Kiros  and
      Bekoulis, Giannis  and
      Deleu, Johannes  and
      Sterckx, Lucas  and
      Zaporojets, Klim  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3019",
    doi = "10.18653/v1/W19-3019",
    pages = "158--161",
    abstract = "This paper describes IDLab{'}s text classification systems submitted to Task A as part of the CLPsych 2019 shared task. The aim of this shared task was to develop automated systems that predict the degree of suicide risk of people based on their posts on Reddit. Bag-of-words features, emotion features and post level predictions are used to derive user-level predictions. Linear models and ensembles of these models are used to predict final scores. We find that predicting fine-grained risk levels is much more difficult than flagging potentially at-risk users. Furthermore, we do not find clear added value from building richer ensembles compared to simple baselines, given the available training data and the nature of the prediction task.",
}
@inproceedings{ruiz-etal-2019-clpsych2019,
    title = "{CLP}sych2019 Shared Task: Predicting Suicide Risk Level from {R}eddit Posts on Multiple Forums",
    author = "Ruiz, Victor  and
      Shi, Lingyun  and
      Quan, Wei  and
      Ryan, Neal  and
      Biernesser, Candice  and
      Brent, David  and
      Tsui, Rich",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3020",
    doi = "10.18653/v1/W19-3020",
    pages = "162--166",
    abstract = {We aimed to predict an individual suicide risk level from longitudinal posts on Reddit discussion forums. Through participating in a shared task competition hosted by CLPsych2019, we received two annotated datasets: a training dataset with 496 users (31,553 posts) and a test dataset with 125 users (9610 posts). We submitted results from our three best-performing machine-learning models: SVM, Na{\"\i}ve Bayes, and an ensemble model. Each model provided a user{'}s suicide risk level in four categories, i.e., no risk, low risk, moderate risk, and severe risk. Among the three models, the ensemble model had the best macro-averaged F1 score 0.379 when tested on the holdout test dataset. The NB model had the best performance in two additional binary-classification tasks, i.e., no risk vs. flagged risk (any risk level other than no risk) with F1 score 0.836 and no or low risk vs. urgent risk (moderate or severe risk) with F1 score 0.736. We conclude that the NB model may serve as a tool for identifying users with flagged or urgent suicide risk based on longitudinal posts on Reddit discussion forums.},
}
@inproceedings{rissola-etal-2019-suicide,
    title = "Suicide Risk Assessment on Social Media: {USI}-{UPF} at the {CLP}sych 2019 Shared Task",
    author = "R{\'\i}ssola, Esteban  and
      Ram{\'\i}rez-Cifuentes, Diana  and
      Freire, Ana  and
      Crestani, Fabio",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3021",
    doi = "10.18653/v1/W19-3021",
    pages = "167--171",
    abstract = "This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psychology Workshop (CLPsych2019). The goal is to assess the degree of suicide risk of social media users given a labelled dataset with their posts. An appropriate suicide risk assessment, with the usage of automated methods, can assist experts on the detection of people at risk and eventually contribute to prevent suicide. We propose a set of machine learning models with features based on lexicons, word embeddings, word level n-grams, and statistics extracted from users{'} posts. The results show that the most effective models for the tasks are obtained integrating lexicon-based features, a selected set of n-grams, and statistical measures.",
}
@inproceedings{ambalavanan-etal-2019-using,
    title = "Using Contextual Representations for Suicide Risk Assessment from {I}nternet Forums",
    author = "Ambalavanan, Ashwin Karthik  and
      Jagtap, Pranjali Dileep  and
      Adhya, Soumya  and
      Devarakonda, Murthy",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3022",
    doi = "10.18653/v1/W19-3022",
    pages = "172--176",
    abstract = "Social media posts may yield clues to the subject{'}s (usually, the writer{'}s) suicide risk and intent, which can be used for timely intervention. This research, motivated by the CLPsych 2019 shared task, developed neural network-based methods for analyzing posts in one or more Reddit forums to assess the subject{'}s suicide risk. One of the technical challenges this task poses is the large amount of text from multiple posts of a single user. Our neural network models use the advanced multi-headed Attention-based autoencoder architecture, called Bidirectional Encoder Representations from Transformers (BERT). Our system achieved the 2nd best performance of 0.477 macro averaged F measure on Task A of the challenge. Among the three different alternatives we developed for the challenge, the single BERT model that processed all of a user{'}s posts performed the best on all three Tasks.",
}
@inproceedings{morales-etal-2019-investigation,
    title = "An Investigation of Deep Learning Systems for Suicide Risk Assessment",
    author = "Morales, Michelle  and
      Dey, Prajjalita  and
      Theisen, Thomas  and
      Belitz, Danny  and
      Chernova, Natalia",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3023",
    doi = "10.18653/v1/W19-3023",
    pages = "177--181",
    abstract = "This work presents the systems explored as part of the CLPsych 2019 Shared Task. More specifically, this work explores the promise of deep learning systems for suicide risk assessment.",
}
@inproceedings{allen-etal-2019-convsent,
    title = "{C}onv{S}ent at {CLP}sych 2019 Task A: Using Post-level Sentiment Features for Suicide Risk Prediction on {R}eddit",
    author = "Allen, Kristen  and
      Bagroy, Shrey  and
      Davis, Alex  and
      Krishnamurti, Tamar",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3024",
    doi = "10.18653/v1/W19-3024",
    pages = "182--187",
    abstract = "This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users{'} suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system{'}s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.",
}
@inproceedings{iserman-etal-2019-dictionaries,
    title = "Dictionaries and Decision Trees for the 2019 {CLP}sych Shared Task",
    author = "Iserman, Micah  and
      Nalabandian, Taleen  and
      Ireland, Molly",
    booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-3025",
    doi = "10.18653/v1/W19-3025",
    pages = "188--194",
    abstract = "In this summary, we discuss our approach to the CLPsych Shared Task and its initial results. For our predictions in each task, we used a recursive partitioning algorithm (decision trees) to select from our set of features, which were primarily dictionary scores and counts of individual words. We focused primarily on Task A, which aimed to predict suicide risk, as rated by a team of expert clinicians (Shing et al., 2018), based on language used in SuicideWatch posts on Reddit. Category-level findings highlight the potential importance of social and moral language categories. Word-level correlates of risk levels underline the value of fine-grained data-driven approaches, revealing both theory-consistent and potentially novel correlates of suicide risk that may motivate future research.",
}
@proceedings{ws-2019-cognitive,
    title = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    editor = "Chersoni, Emmanuele  and
      Jacobs, Cassandra  and
      Lenci, Alessandro  and
      Linzen, Tal  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2900",
}
@inproceedings{hunter-etal-2019-active,
    title = "The Active-Filler Strategy in a Move-Eager Left-Corner {M}inimalist {G}rammar Parser",
    author = "Hunter, Tim  and
      Stanojevi{\'c}, Milo{\v{s}}  and
      Stabler, Edward",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2901",
    doi = "10.18653/v1/W19-2901",
    pages = "1--10",
    abstract = "Recent psycholinguistic evidence suggests that human parsing of moved elements is {`}active{'}, and perhaps even {`}hyper-active{'}: it seems that a leftward-moved object is related to a verbal position rapidly, perhaps even before the transitivity information associated with the verb is available to the listener. This paper presents a formal, sound and complete parser for Minimalist Grammars whose search space contains branching points that we can identify as the locus of the decision to perform this kind of active gap-finding. This brings formal models of parsing into closer contact with recent psycholinguistic theorizing than was previously possible.",
}
@inproceedings{melnick-wasow-2019-priming,
    title = "Priming vs. Inhibition of Optional Infinitival {``}to{''}",
    author = "Melnick, Robin  and
      Wasow, Thomas",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2902",
    doi = "10.18653/v1/W19-2902",
    pages = "11--19",
    abstract = "The word {``}to{''} that precedes verbs in English infinitives is optional in at least two environments: in what Wasow et al. (2015) previously called the {``}do-be{''} construction, and in the complement of {``}help{''}, which we explore in the present work. In the {``}do-be{''} construction, Wasow et al. found that a preceding infinitival {``}to{''} increases the use of following optional {``}to{''}, but the use of {``}to{''} in the complement of help is reduced following {``}to help{''}. We examine two hypotheses regarding why the same function word is primed by prior use in one construction and inhibited in another. We then test predictions made by the two hypotheses, finding support for one of them.",
}
@inproceedings{tsoukala-etal-2019-simulating,
    title = "Simulating {S}panish-{E}nglish Code-Switching: El Modelo Est{\'a} Generating Code-Switches",
    author = "Tsoukala, Chara  and
      Frank, Stefan L.  and
      van den Bosch, Antal  and
      Vald{\'e}s Kroff, Jorge  and
      Broersma, Mirjam",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2903",
    doi = "10.18653/v1/W19-2903",
    pages = "20--29",
    abstract = "Multilingual speakers are able to switch from one language to the other ({``}code-switch{''}) between or within sentences. Because the underlying cognitive mechanisms are not well understood, in this study we use computational cognitive modeling to shed light on the process of code-switching. We employed the Bilingual Dual-path model, a Recurrent Neural Network of bilingual sentence production (Tsoukala et al., 2017), and simulated sentence production in simultaneous Spanish-English bilinguals. Our first goal was to investigate whether the model would code-switch without being exposed to code-switched training input. The model indeed produced code-switches even without any exposure to such input and the patterns of code-switches are in line with earlier linguistic work (Poplack,1980). The second goal of this study was to investigate an auxiliary phrase asymmetry that exists in Spanish-English code-switched production. Using this cognitive model, we examined a possible cause for this asymmetry. To our knowledge, this is the first computational cognitive model that aims to simulate code-switched sentence production.",
}
@inproceedings{ranjan-etal-2019-surprisal,
    title = "Surprisal and Interference Effects of Case Markers in {H}indi Word Order",
    author = "Ranjan, Sidharth  and
      Agarwal, Sumeet  and
      Rajkumar, Rajakrishnan",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2904",
    doi = "10.18653/v1/W19-2904",
    pages = "30--42",
    abstract = "Based on the Production-Distribution-Comprehension (PDC) account of language processing, we formulate two distinct hypotheses about case marking, word order choices and processing in Hindi. Our first hypothesis is that Hindi tends to optimize for processing efficiency at both lexical and syntactic levels. We quantify the role of case markers in this process. For the task of predicting the reference sentence occurring in a corpus (amidst meaning-equivalent grammatical variants) using a machine learning model, surprisal estimates from an artificial version of the language (i.e., Hindi without any case markers) result in lower prediction accuracy compared to natural Hindi. Our second hypothesis is that Hindi tends to minimize interference due to case markers while ordering preverbal constituents. We show that Hindi tends to avoid placing next to each other constituents whose heads are marked by identical case inflections. Our findings adhere to PDC assumptions and we discuss their implications for language production, learning and universals.",
}
@inproceedings{oseki-etal-2019-modeling,
    title = "Modeling Hierarchical Syntactic Structures in Morphological Processing",
    author = "Oseki, Yohei  and
      Yang, Charles  and
      Marantz, Alec",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2905",
    doi = "10.18653/v1/W19-2905",
    pages = "43--52",
    abstract = "Sentences are represented as hierarchical syntactic structures, which have been successfully modeled in sentence processing. In contrast, despite the theoretical agreement on hierarchical syntactic structures within words, words have been argued to be computationally less complex than sentences and implemented by finite-state models as linear strings of morphemes, and even the psychological reality of morphemes has been denied. In this paper, extending the computational models employed in sentence processing to morphological processing, we performed a computational simulation experiment where, given incremental surprisal as a linking hypothesis, five computational models with different representational assumptions were evaluated against human reaction times in visual lexical decision experiments available from the English Lexicon Project (ELP), a {``}shared task{''} in the morphological processing literature. The simulation experiment demonstrated that (i) {``}amorphous{''} models without morpheme units underperformed relative to {``}morphous{''} models, (ii) a computational model with hierarchical syntactic structures, Probabilistic Context-Free Grammar (PCFG), most accurately explained human reaction times, and (iii) this performance was achieved on top of surface frequency effects. These results strongly suggest that morphological processing tracks morphemes incrementally from left to right and parses them into hierarchical syntactic structures, contrary to {``}amorphous{''} and finite-state models of morphological processing.",
}
@inproceedings{cho-lewis-2019-modeling,
    title = "A Modeling Study of the Effects of Surprisal and Entropy in Perceptual Decision Making of an Adaptive Agent",
    author = "Cho, Pyeong Whan  and
      Lewis, Richard",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2906",
    doi = "10.18653/v1/W19-2906",
    pages = "53--61",
    abstract = "Processing difficulty in online language comprehension has been explained in terms of surprisal and entropy reduction. Although both hypotheses have been supported by experimental data, we do not fully understand their relative contributions on processing difficulty. To develop a better understanding, we propose a mechanistic model of perceptual decision making that interacts with a simulated task environment with temporal dynamics. The proposed model collects noisy bottom-up evidence over multiple timesteps, integrates it with its top-down expectation, and makes perceptual decisions, producing processing time data directly without relying on any linking hypothesis. Temporal dynamics in the task environment was determined by a simple finite-state grammar, which was designed to create the situations where the surprisal and entropy reduction hypotheses predict different patterns. After the model was trained to maximize rewards, the model developed an adaptive policy and both surprisal and entropy effects were observed especially in a measure reflecting earlier processing.",
}
@inproceedings{bushong-jaeger-2019-modeling,
    title = "Modeling Long-Distance Cue Integration in Spoken Word Recognition",
    author = "Bushong, Wednesday  and
      Jaeger, T. Florian",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2907",
    doi = "10.18653/v1/W19-2907",
    pages = "62--70",
    abstract = "Cues to linguistic categories are distributed across the speech signal. Optimal categorization thus requires that listeners maintain gradient representations of incoming input in order to integrate that information with later cues. There is now evidence that listeners can and do integrate cues that occur far apart in time. Computational models of this integration have however been lacking. We take a first step at addressing this gap by mathematically formalizing four models of how listeners may maintain and use cue information during spoken language understanding and test them on two perception experiments. In one experiment, we find support for rational integration of cues at long distances. In a second, more memory and attention-taxing experiment, we find evidence in favor of a switching model that avoids maintaining detailed representations of cues in memory. These results are a first step in understanding what kinds of mechanisms listeners use for cue integration under different memory and attentional constraints.",
}
@inproceedings{gaume-etal-2019-toward,
    title = "Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics",
    author = "Gaume, Bruno  and
      Mai Ho-Dac, Lydia  and
      Tanguy, Ludovic  and
      Fabre, C{\'e}cile  and
      Pierrejean, B{\'e}n{\'e}dicte  and
      Hathout, Nabil  and
      Farinas, J{\'e}r{\^o}me  and
      Pinquier, Julien  and
      Danet, Lola  and
      P{\'e}ran, Patrice  and
      De Boissezon, Xavier  and
      Jucla, M{\'e}lanie",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2908",
    doi = "10.18653/v1/W19-2908",
    pages = "71--76",
    abstract = "This paper presents the first results of a multidisciplinary project, the {``}Evolex{''} project, gathering researchers in Psycholinguistics, Neuropsychology, Computer Science, Natural Language Processing and Linguistics. The Evolex project aims at proposing a new data-based inductive method for automatically characterising the relation between pairs of french words collected in psycholinguistics experiments on lexical access. This method takes advantage of several complementary computational measures of semantic similarity. We show that some measures are more correlated than others with the frequency of lexical associations, and that they also differ in the way they capture different semantic relations. This allows us to consider building a multidimensional lexical similarity to automate the classification of lexical associations.",
}
@inproceedings{lopopolo-etal-2019-dependency,
    title = "Dependency Parsing with your Eyes: Dependency Structure Predicts Eye Regressions During Reading",
    author = "Lopopolo, Alessandro  and
      Frank, Stefan L.  and
      van den Bosch, Antal  and
      Willems, Roel",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2909",
    doi = "10.18653/v1/W19-2909",
    pages = "77--85",
    abstract = "Backward saccades during reading have been hypothesized to be involved in structural reanalysis, or to be related to the level of text difficulty. We test the hypothesis that backward saccades are involved in online syntactic analysis. If this is the case we expect that saccades will coincide, at least partially, with the edges of the relations computed by a dependency parser. In order to test this, we analyzed a large eye-tracking dataset collected while 102 participants read three short narrative texts. Our results show a relation between backward saccades and the syntactic structure of sentences.",
}
@inproceedings{yan-white-2019-framework,
    title = "A Framework for Decoding Event-Related Potentials from Text",
    author = "Yan, Shaorong  and
      White, Aaron Steven",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2910",
    doi = "10.18653/v1/W19-2910",
    pages = "86--92",
    abstract = "We propose a novel framework for modeling event-related potentials (ERPs) collected during reading that couples pre-trained convolutional decoders with a language model. Using this framework, we compare the abilities of a variety of existing and novel sentence processing models to reconstruct ERPs. We find that modern contextual word embeddings underperform surprisal-based models but that, combined, the two outperform either on its own.",
}
@inproceedings{de-santo-2019-testing,
    title = "Testing a {M}inimalist {G}rammar Parser on {I}talian Relative Clause Asymmetries",
    author = "De Santo, Aniello",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2911",
    doi = "10.18653/v1/W19-2911",
    pages = "93--104",
    abstract = "Stabler{'}s (2013) top-down parser for Minimalist grammars has been used to account for off-line processing preferences across a variety of seemingly unrelated phenomena cross-linguistically, via complexity metrics measuring {``}memory burden{''}. This paper extends the empirical coverage of the model by looking at the processing asymmetries of Italian relative clauses, as I discuss the relevance of these constructions in evaluating plausible structure-driven models of processing difficulty.",
}
@inproceedings{testoni-etal-2019-quantifiers,
    title = "Quantifiers in a Multimodal World: Hallucinating Vision with Language and Sound",
    author = "Testoni, Alberto  and
      Pezzelle, Sandro  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2912",
    doi = "10.18653/v1/W19-2912",
    pages = "105--116",
    abstract = "Inspired by the literature on multisensory integration, we develop a computational model to ground quantifiers in perception. The model learns to pick, out of nine quantifiers ({`}few{'}, {`}many{'}, {`}all{'}, etc.), the one that is more likely to describe the percent of animals in a visual-auditory input containing both animals and artifacts. We show that relying on concurrent sensory inputs increases model performance on the quantification task. Moreover, we evaluate the model in a situation in which only the auditory modality is given, while the visual one is {`}hallucinanted{'} either from the auditory input itself or from a linguistic caption describing the quantity of entities in the auditory input. This way, the model exploits prior associations between modalities. We show that the model profits from the prior knowledge and outperforms the auditory-only setting.",
}
@inproceedings{dunn-2019-frequency,
    title = "Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar",
    author = "Dunn, Jonathan",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2913",
    doi = "10.18653/v1/W19-2913",
    pages = "117--128",
    abstract = "A usage-based Construction Grammar (CxG) posits that slot-constraints generalize from common exemplar constructions. But what is the best model of constraint generalization? This paper evaluates competing frequency-based and association-based models across eight languages using a metric derived from the Minimum Description Length paradigm. The experiments show that association-based models produce better generalizations across all languages by a significant margin.",
}
@inproceedings{fourtassi-etal-2019-development,
    title = "The Development of Abstract Concepts in Children{'}s Early Lexical Networks",
    author = "Fourtassi, Abdellah  and
      Scheinfeld, Isaac  and
      Frank, Michael",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2914",
    doi = "10.18653/v1/W19-2914",
    pages = "129--133",
    abstract = "How do children learn abstract concepts such as animal vs. artifact? Previous research has suggested that such concepts can partly be derived using cues from the language children hear around them. Following this suggestion, we propose a model where we represent the children{'} developing lexicon as an evolving network. The nodes of this network are based on vocabulary knowledge as reported by parents, and the edges between pairs of nodes are based on the probability of their co-occurrence in a corpus of child-directed speech. We found that several abstract categories can be identified as the dense regions in such networks. In addition, our simulations suggest that these categories develop simultaneously, rather than sequentially, thanks to the children{'}s word learning trajectory which favors the exploration of the global conceptual space.",
}
@inproceedings{sayeed-etal-2019-verb,
    title = "Verb-Second Effect on Quantifier Scope Interpretation",
    author = "Sayeed, Asad  and
      Lindemann, Matthias  and
      Demberg, Vera",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2915",
    doi = "10.18653/v1/W19-2915",
    pages = "134--139",
    abstract = "Sentences like {``}Every child climbed a tree{''} have at least two interpretations depending on the precedence order of the universal quantifier and the indefinite. Previous experimental work explores the role that different mechanisms such as semantic reanalysis and world knowledge may have in enabling each interpretation. This paper discusses a web-based task that uses the verb-second characteristic of German main clauses to estimate the influence of word order variation over world knowledge.",
}
@inproceedings{osullivan-steinert-threlkeld-2019-neural,
    title = "Neural Models of the Psychosemantics of {`}Most{'}",
    author = "O{'}Sullivan, Lewis  and
      Steinert-Threlkeld, Shane",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2916",
    doi = "10.18653/v1/W19-2916",
    pages = "140--151",
    abstract = "How are the meanings of linguistic expressions related to their use in concrete cognitive tasks? Visual identification tasks show human speakers can exhibit considerable variation in their understanding, representation and verification of certain quantifiers. This paper initiates an investigation into neural models of these psycho-semantic tasks. We trained two types of network {--} a convolutional neural network (CNN) model and a recurrent model of visual attention (RAM) {--} on the {``}most{''} verification task from Pietroski2009, manipulating the visual scene and novel notions of task duration. Our results qualitatively mirror certain features of human performance (such as sensitivity to the ratio of set sizes, indicating a reliance on approximate number) while differing in interesting ways (such as exhibiting a subtly different pattern for the effect of image type). We conclude by discussing the prospects for using neural models as cognitive models of this and other psychosemantic tasks.",
}
@inproceedings{feliciano-de-faria-2019-role,
    title = "The Role of Utterance Boundaries and Word Frequencies for Part-of-speech Learning in {B}razilian {P}ortuguese Through Distributional Analysis",
    author = "Feliciano de Faria, Pablo Picasso",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2917",
    doi = "10.18653/v1/W19-2917",
    pages = "152--159",
    abstract = "In this study, we address the problem of part-of-speech (or syntactic category) learning during language acquisition through distributional analysis of utterances. A model based on Redington et al.{'}s (1998) distributional learner is used to investigate the informativeness of distributional information in Brazilian Portuguese (BP). The data provided to the learner comes from two publicly available corpora of child directed speech. We present preliminary results from two experiments. The first one investigates the effects of different assumptions about utterance boundaries when presenting the input data to the learner. The second experiment compares the learner{'}s performance when counting contextual words{'} frequencies versus just acknowledging their co-occurrence with a given target word. In general, our results indicate that explicit boundaries are more informative, frequencies are important, and that distributional information is useful to the child as a source of categorial information. These results are in accordance with Redington et al.{'}s findings for English.",
}
@inproceedings{ebert-pavlick-2019-using,
    title = "Using Grounded Word Representations to Study Theories of Lexical Concepts",
    author = "Ebert, Dylan  and
      Pavlick, Ellie",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2918",
    doi = "10.18653/v1/W19-2918",
    pages = "160--169",
    abstract = "The fields of cognitive science and philosophy have proposed many different theories for how humans represent {``}concepts{''}. Multiple such theories are compatible with state-of-the-art NLP methods, and could in principle be operationalized using neural networks. We focus on two particularly prominent theories{--}Classical Theory and Prototype Theory{--}in the context of visually-grounded lexical representations. We compare when and how the behavior of models based on these theories differs in terms of categorization and entailment tasks. Our preliminary results suggest that Classical-based representations perform better for entailment and Prototype-based representations perform better for categorization. We discuss plans for additional experiments needed to confirm these initial observations.",
}
@proceedings{ws-2019-models,
    title = "Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference",
    editor = "Ogrodniczuk, Maciej  and
      Pradhan, Sameer  and
      Grishina, Yulia  and
      Ng, Vincent",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2800",
}
@inproceedings{agarwal-etal-2019-evaluation,
    title = "Evaluation of named entity coreference",
    author = "Agarwal, Oshin  and
      Subramanian, Sanjay  and
      Nenkova, Ani  and
      Roth, Dan",
    booktitle = "Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2801",
    doi = "10.18653/v1/W19-2801",
    pages = "1--7",
    abstract = "In many NLP applications like search and information extraction for named entities, it is necessary to find all the mentions of a named entity, some of which appear as pronouns (she, his, etc.) or nominals (the professor, the German chancellor, etc.). It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name. We evaluate state-of-the-art coreference resolution systems for the task of resolving all mentions to named entities. Our analysis reveals that standard coreference metrics do not reflect adequately the requirements in this task: they do not penalize systems for not identifying any mentions by name to an entity and they reward systems even if systems find correctly mentions to the same entity but fail to link these to a proper name (she{--}the student{--}no name). We introduce new metrics for evaluating named entity coreference that address these discrepancies and show that for the comparisons of competitive systems, standard coreference evaluations could give misleading results for this task. We are, however, able to confirm that the state-of-the art system according to traditional evaluations also performs vastly better than other systems on the named entity coreference task.",
}
@inproceedings{grobol-2019-neural,
    title = "Neural Coreference Resolution with Limited Lexical Context and Explicit Mention Detection for Oral {F}rench",
    author = {Grobol, Lo{\"\i}c},
    booktitle = "Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2802",
    doi = "10.18653/v1/W19-2802",
    pages = "8--14",
    abstract = "We propose an end-to-end coreference resolution system obtained by adapting neural models that have recently improved the state-of-the-art on the OntoNotes benchmark to make them applicable to other paradigms for this task. We report the performances of our system on ANCOR, a corpus of transcribed oral French, for which it constitutes a new baseline with proper evaluation.",
}
@inproceedings{kunz-hardmeier-2019-entity,
    title = "Entity Decisions in Neural Language Modelling: Approaches and Problems",
    author = "Kunz, Jenny  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2803",
    doi = "10.18653/v1/W19-2803",
    pages = "15--19",
    abstract = "We explore different approaches to explicit entity modelling in language models (LM). We independently replicate two existing models in a controlled setup, introduce a simplified variant of one of the models and analyze their performance in direct comparison. Our results suggest that today{'}s models are limited as several stochastic variables make learning difficult. We show that the most challenging point in the systems is the decision if the next token is an entity token. The low precision and recall for this variable will lead to severe cascading errors. Our own simplified approach dispenses with the need for latent variables and improves the performance in the entity yes/no decision. A standard well-tuned baseline RNN-LM with a larger number of hidden units outperforms all entity-enabled LMs in terms of perplexity.",
}
@inproceedings{blissett-ji-2019-cross,
    title = "Cross-lingual {NIL} Entity Clustering for Low-resource Languages",
    author = "Blissett, Kevin  and
      Ji, Heng",
    booktitle = "Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2804",
    doi = "10.18653/v1/W19-2804",
    pages = "20--25",
    abstract = "Clustering unlinkable entity mentions across documents in multiple languages (cross-lingual NIL Clustering) is an important task as part of Entity Discovery and Linking (EDL). This task has been largely neglected by the EDL community because it is challenging to outperform simple edit distance or other heuristics based baselines. We propose a novel approach based on encoding the orthographic similarity of the mentions using a Recurrent Neural Network (RNN) architecture. Our model adapts a training procedure from the one-shot facial recognition literature in order to achieve this. We also perform several exploratory probing tasks on our name encodings in order to determine what specific types of information are likely to be encoded by our model. Experiments show our approach provides up to a 6.6{\%} absolute CEAFm F-Score improvement over state-of-the-art methods and successfully captures phonological relations across languages.",
}
@inproceedings{lapshinova-koltunski-etal-2019-cross,
    title = "Cross-lingual Incongruences in the Annotation of Coreference",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Lo{\'a}iciga, Sharid  and
      Hardmeier, Christian  and
      Krielke, Pauline",
    booktitle = "Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2805",
    doi = "10.18653/v1/W19-2805",
    pages = "26--34",
    abstract = "In the present paper, we deal with incongruences in English-German multilingual coreference annotation and present automated methods to discover them. More specifically, we automatically detect full coreference chains in parallel texts and analyse discrepancies in their annotations. In doing so, we wish to find out whether the discrepancies rather derive from language typological constraints, from the translation or the actual annotation process. The results of our study contribute to the referential analysis of similarities and differences across languages and support evaluation of cross-lingual coreference annotation. They are also useful for cross-lingual coreference resolution systems and contrastive linguistic studies.",
}
@inproceedings{urbizu-etal-2019-deep,
    title = "Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of {B}asque",
    author = "Urbizu, Gorka  and
      Soraluze, Ander  and
      Arregi, Olatz",
    booktitle = "Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2806",
    doi = "10.18653/v1/W19-2806",
    pages = "35--41",
    abstract = "In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it.",
}
@proceedings{ws-2019-discourse,
    title = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    editor = "Zeldes, Amir  and
      Das, Debopam  and
      Galani, Erick Maziero  and
      Antonio, Juliano Desiderato  and
      Iruskieta, Mikel",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2700",
}
@inproceedings{zeldes-etal-2019-introduction,
    title = "Introduction to Discourse Relation Parsing and Treebanking ({DISRPT}): 7th Workshop on {R}hetorical {S}tructure {T}heory and Related Formalisms",
    author = "Zeldes, Amir  and
      Das, Debopam  and
      Maziero, Erick Galani  and
      Antonio, Juliano  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2701",
    doi = "10.18653/v1/W19-2701",
    pages = "1--6",
    abstract = "This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop{'}s aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.",
}
@inproceedings{bourgonje-zolotarenko-2019-toward,
    title = "Toward Cross-theory Discourse Relation Annotation",
    author = "Bourgonje, Peter  and
      Zolotarenko, Olha",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2702",
    doi = "10.18653/v1/W19-2702",
    pages = "7--11",
    abstract = "In this exploratory study, we attempt to automatically induce PDTB-style relations from RST trees. We work with a German corpus of news commentary articles, annotated for RST trees and explicit PDTB-style relations and we focus on inducing the implicit relations in an automated way. Preliminary results look promising as a high-precision (but low-recall) way of finding implicit relations where there is no shallow structure annotated at all, but mapping proves more difficult in cases where EDUs and relation arguments overlap, yet do not seem to signal the same relation.",
}
@inproceedings{shi-etal-2019-acquiring,
    title = "Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification",
    author = "Shi, Wei  and
      Yung, Frances  and
      Demberg, Vera",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2703",
    doi = "10.18653/v1/W19-2703",
    pages = "12--21",
    abstract = "Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connectives as strong linguistic cues. A principle bottleneck to further improvement is the shortage of training data (ca. 18k instances in the Penn Discourse Treebank (PDTB)). Shi et al. (2017) proposed to acquire additional data by exploiting connectives in translation: human translators mark discourse relations which are implicit in the source language explicitly in the translation. Using back-translations of such explicitated connectives improves discourse relation parsing performance. This paper addresses the open question of whether the choice of the translation language matters, and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.",
}
@inproceedings{ferracane-etal-2019-news,
    title = "From News to Medical: Cross-domain Discourse Segmentation",
    author = "Ferracane, Elisa  and
      Page, Titan  and
      Li, Junyi Jessy  and
      Erk, Katrin",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2704",
    doi = "10.18653/v1/W19-2704",
    pages = "22--29",
    abstract = "The first step in discourse analysis involves dividing a text into segments. We annotate the first high-quality small-scale medical corpus in English with discourse segments and analyze how well news-trained segmenters perform on this domain. While we expectedly find a drop in performance, the nature of the segmentation errors suggests some problems can be addressed earlier in the pipeline, while others would require expanding the corpus to a trainable size to learn the nuances of the medical domain.",
}
@inproceedings{das-2019-nuclearity,
    title = "Nuclearity in {RST} and signals of coherence relations",
    author = "Das, Debopam",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2705",
    doi = "10.18653/v1/W19-2705",
    pages = "30--37",
    abstract = "We investigate the relationship between the notion of nuclearity as proposed in Rhetorical Structure Theory (RST) and the signalling of coherence relations. RST relations are categorized as either mononuclear (comprising a nucleus and a satellite span) or multinuclear (comprising two or more nuclei spans). We examine how mononuclear relations (e.g., Antithesis, Condition) and multinuclear relations (e.g., Contrast, List) are indicated by relational signals, more particularly by discourse markers (e.g., because, however, if, therefore). We conduct a corpus study, examining the distribution of either type of relations in the RST Discourse Treebank (Carlson et al., 2002) and the distribution of discourse markers for those relations in the RST Signalling Corpus (Das et al., 2015). Our results show that discourse markers are used more often to signal multinuclear relations than mononuclear relations. The findings also suggest a complex relationship between the relation types and syntactic categories of discourse markers (subordinating and coordinating conjunctions).",
}
@inproceedings{potter-2019-rhetorical,
    title = "The Rhetorical Structure of Attribution",
    author = "Potter, Andrew",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2706",
    doi = "10.18653/v1/W19-2706",
    pages = "38--49",
    abstract = "The relational status of Attribution in Rhetorical Structure Theory has been a matter of ongoing debate. Although several researchers have weighed in on the topic, and although numerous studies have relied upon attributional structures for their analyses, nothing approaching consensus has emerged. This paper identifies three basic issues that must be resolved to determine the relational status of attributions. These are identified as the Discourse Units Issue, the Nuclearity Issue, and the Relation Identification Issue. These three issues are analyzed from the perspective of classical RST. A finding of this analysis is that the nuclearity and the relational identification of attribution structures are shown to depend on the writer{'}s intended effect, such that attributional relations cannot be considered as a single relation, but rather as attributional instances of other RST relations.",
}
@inproceedings{scheffler-etal-2019-annotating,
    title = "Annotating Shallow Discourse Relations in {T}witter Conversations",
    author = "Scheffler, Tatjana  and
      Akta{\c{s}}, Berfin  and
      Das, Debopam  and
      Stede, Manfred",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2707",
    doi = "10.18653/v1/W19-2707",
    pages = "50--55",
    abstract = "We introduce our pilot study applying PDTB-style annotation to Twitter conversations. Lexically grounded coherence annotation for Twitter threads will enable detailed investigations of the discourse structure of conversations on social media. Here, we present our corpus of 185 threads and annotation, including an inter-annotator agreement study. We discuss our observations as to how Twitter discourses differ from written news text wrt. discourse connectives and relations. We confirm our hypothesis that discourse relations in written social media conversations are expressed differently than in (news) text. We find that in Twitter, connective arguments frequently are not full syntactic clauses, and that a few general connectives expressing EXPANSION and CONTINGENCY make up the majority of the explicit relations in our data.",
}
@inproceedings{gessler-etal-2019-discourse,
    title = "A Discourse Signal Annotation System for {RST} Trees",
    author = "Gessler, Luke  and
      Liu, Yang  and
      Zeldes, Amir",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2708",
    doi = "10.18653/v1/W19-2708",
    pages = "56--61",
    abstract = "This paper presents a new system for open-ended discourse relation signal annotation in the framework of Rhetorical Structure Theory (RST), implemented on top of an online tool for RST annotation. We discuss existing projects annotating textual signals of discourse relations, which have so far not allowed simultaneously structuring and annotating words signaling hierarchical discourse trees, and demonstrate the design and applications of our interface by extending existing RST annotations in the freely available GUM corpus.",
}
@inproceedings{iruskieta-braud-2019-eusdisparser,
    title = "{E}us{D}is{P}arser: improving an under-resourced discourse parser with cross-lingual data",
    author = "Iruskieta, Mikel  and
      Braud, Chlo{\'e}",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2709",
    doi = "10.18653/v1/W19-2709",
    pages = "62--71",
    abstract = "Development of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since the corpus is very small. In this paper, we create the first demonstrator based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser. More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language. We found that our approach to building a system limited to the small set of data available for Basque allowed us to get an improvement over previous approaches making use of many data annotated in other languages. At best, we get 34.78 in F1 for the full discourse structure. More data annotation is necessary in order to improve the results obtained with these techniques. We also describe which relations match with the gold standard, in order to understand these results.",
}
@inproceedings{liu-2019-beyond,
    title = "Beyond The {W}all {S}treet {J}ournal: Anchoring and Comparing Discourse Signals across Genres",
    author = "Liu, Yang",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2710",
    doi = "10.18653/v1/W19-2710",
    pages = "72--81",
    abstract = "Recent research on discourse relations has found that they are cued not only by discourse markers (DMs) but also by other textual signals and that signaling information is indicative of genres. While several corpora exist with discourse relation signaling information such as the Penn Discourse Treebank (PDTB, Prasad et al. 2008) and the Rhetorical Structure Theory Signalling Corpus (RST-SC, Das and Taboada 2018), they both annotate the Wall Street Journal (WSJ) section of the Penn Treebank (PTB, Marcus et al. 1993), which is limited to the news domain. Thus, this paper adapts the signal identification and anchoring scheme (Liu and Zeldes, 2019) to three more genres, examines the distribution of signaling devices across relations and genres, and provides a taxonomy of indicative signals found in this dataset.",
}
@inproceedings{shelmanov-etal-2019-towards,
    title = "Towards the Data-driven System for Rhetorical Parsing of {R}ussian Texts",
    author = "Shelmanov, Artem  and
      Pisarevskaya, Dina  and
      Chistova, Elena  and
      Toldova, Svetlana  and
      Kobozeva, Maria  and
      Smirnov, Ivan",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2711",
    doi = "10.18653/v1/W19-2711",
    pages = "82--87",
    abstract = "Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank {--} first Russian corpus annotated within RST framework {--} are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 {\mbox{$\pm$}} 0.38). We discover that most of the important features for rhetorical relation classification are related to discourse connectives derived from the connectives lexicon for Russian and from other sources.",
}
@inproceedings{wan-etal-2019-rst,
    title = "{RST}-Tace A tool for automatic comparison and evaluation of {RST} trees",
    author = {Wan, Shujun  and
      Kutschbach, Tino  and
      L{\"u}deling, Anke  and
      Stede, Manfred},
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2712",
    doi = "10.18653/v1/W19-2712",
    pages = "88--96",
    abstract = "This paper presents RST-Tace, a tool for automatic comparison and evaluation of RST trees. RST-Tace serves as an implementation of Iruskieta{'}s comparison method, which allows trees to be compared and evaluated without the influence of decisions at lower levels in a tree in terms of four factors: constituent, attachment point, nuclearity as well as relation. RST-Tace can be used regardless of the language or the size of rhetorical trees. This tool aims to measure the agreement between two annotators. The result is reflected by F-measure and inter-annotator agreement. Both the comparison table and the result of the evaluation can be obtained automatically.",
}
@inproceedings{zeldes-etal-2019-disrpt,
    title = "The {DISRPT} 2019 Shared Task on Elementary Discourse Unit Segmentation and Connective Detection",
    author = "Zeldes, Amir  and
      Das, Debopam  and
      Maziero, Erick Galani  and
      Antonio, Juliano  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2713",
    doi = "10.18653/v1/W19-2713",
    pages = "97--104",
    abstract = "In 2019, we organized the first iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task on Elementary Discourse Unit Segmentation and Connective Detection. In this paper we review the data included in the task, which cover 2.6 million manually annotated tokens from 15 datasets in 10 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.",
}
@inproceedings{bourgonje-schafer-2019-multi,
    title = "Multi-lingual and Cross-genre Discourse Unit Segmentation",
    author = {Bourgonje, Peter  and
      Sch{\"a}fer, Robin},
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2714",
    doi = "10.18653/v1/W19-2714",
    pages = "105--114",
    abstract = "We describe a series of experiments applied to data sets from different languages and genres annotated for coherence relations according to different theoretical frameworks. Specifically, we investigate the feasibility of a unified (theory-neutral) approach toward discourse segmentation; a process which divides a text into minimal discourse units that are involved in s coherence relation. We apply a RandomForest and an LSTM based approach for all data sets, and we improve over a simple baseline assuming simple sentence or clause-like segmentation. Performance however varies a lot depending on language, and more importantly genre, with f-scores ranging from 73.00 to 94.47.",
}
@inproceedings{muller-etal-2019-tony,
    title = "{T}o{N}y: Contextual embeddings for accurate multilingual discourse segmentation of full documents",
    author = "Muller, Philippe  and
      Braud, Chlo{\'e}  and
      Morey, Mathieu",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2715",
    doi = "10.18653/v1/W19-2715",
    pages = "115--124",
    abstract = "Segmentation is the first step in building practical discourse parsers, and is often neglected in discourse parsing studies. The goal is to identify the minimal spans of text to be linked by discourse relations, or to isolate explicit marking of discourse relations. Existing systems on English report F1 scores as high as 95{\%}, but they generally assume gold sentence boundaries and are restricted to English newswire texts annotated within the RST framework. This article presents a generic approach and a system, ToNy, a discourse segmenter developed for the DisRPT shared task where multiple discourse representation schemes, languages and domains are represented. In our experiments, we found that a straightforward sequence prediction architecture with pretrained contextual embeddings is sufficient to reach performance levels comparable to existing systems, when separately trained on each corpus. We report performance between 81{\%} and 96{\%} in F1 score. We also observed that discourse segmentation models only display a moderate generalization capability, even within the same language and discourse representation scheme.",
}
@inproceedings{iruskieta-etal-2019-multilingual,
    title = "Multilingual segmentation based on neural networks and pre-trained word embeddings",
    author = "Iruskieta, Mikel  and
      Bengoetxea, Kepa  and
      Atutxa Salazar, Aitziber  and
      Diaz de Ilarraza, Arantza",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2716",
    doi = "10.18653/v1/W19-2716",
    pages = "125--132",
    abstract = "The DISPRT 2019 workshop has organized a shared task aiming to identify cross-formalism and multilingual discourse segments. Elementary Discourse Units (EDUs) are quite similar across different theories. Segmentation is the very first stage on the way of rhetorical annotation. Still, each annotation project adopted several decisions with consequences not only on the annotation of the relational discourse structure but also at the segmentation stage. In this shared task, we have employed pre-trained word embeddings, neural networks (BiLSTM+CRF) to perform the segmentation. We report F1 results for 6 languages: Basque (0.853), English (0.919), French (0.907), German (0.913), Portuguese (0.926) and Spanish (0.868 and 0.769). Finally, we also pursued an error analysis based on clause typology for Basque and Spanish, in order to understand the performance of the segmenter.",
}
@inproceedings{yu-etal-2019-gumdrop,
    title = "{G}um{D}rop at the {DISRPT}2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection",
    author = "Yu, Yue  and
      Zhu, Yilun  and
      Liu, Yang  and
      Liu, Yan  and
      Peng, Siyao  and
      Gong, Mackenzie  and
      Zeldes, Amir",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2717",
    doi = "10.18653/v1/W19-2717",
    pages = "133--143",
    abstract = "In this paper we present GumDrop, Georgetown University{'}s entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.",
}
@inproceedings{alkorta-etal-2019-towards,
    title = "Towards discourse annotation and sentiment analysis of the {B}asque Opinion Corpus",
    author = "Alkorta, Jon  and
      Gojenola, Koldo  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2718",
    doi = "10.18653/v1/W19-2718",
    pages = "144--152",
    abstract = "Discourse information is crucial for a better understanding of the text structure and it is also necessary to describe which part of an opinionated text is more relevant or to decide how a text span can change the polarity (strengthen or weaken) of other span by means of coherence relations. This work presents the first results on the annotation of the Basque Opinion Corpus using Rhetorical Structure Theory (RST). Our evaluation results and analysis show us the main avenues to improve on a future annotation process. We have also extracted the subjectivity of several rhetorical relations and the results show the effect of sentiment words in relations and the influence of each relation in the semantic orientation value.",
}
@inproceedings{wang-etal-2019-using,
    title = "Using {R}hetorical {S}tructure {T}heory to Assess Discourse Coherence for Non-native Spontaneous Speech",
    author = "Wang, Xinhao  and
      Gyawali, Binod  and
      Bruno, James V.  and
      Molloy, Hillary R.  and
      Evanini, Keelan  and
      Zechner, Klaus",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2719",
    doi = "10.18653/v1/W19-2719",
    pages = "153--162",
    abstract = "This study aims to model the discourse structure of spontaneous spoken responses within the context of an assessment of English speaking proficiency for non-native speakers. Rhetorical Structure Theory (RST) has been commonly used in the analysis of discourse organization of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, non-native spontaneous speech. Due to the fact that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we conducted research to obtain RST annotations on non-native spoken responses from a standardized assessment of academic English proficiency. Subsequently, automatic parsers were trained on these annotations to process non-native spontaneous speech. Finally, a set of features were extracted from automatically generated RST trees to evaluate the discourse structure of non-native spontaneous speech, which were then employed to further improve the validity of an automated speech scoring system.",
}
@inproceedings{jiang-etal-2019-applying,
    title = "Applying {R}hetorical {S}tructure {T}heory to Student Essays for Providing Automated Writing Feedback",
    author = "Jiang, Shiyan  and
      Yang, Kexin  and
      Suvarna, Chandrakumari  and
      Casula, Pooja  and
      Zhang, Mingtong  and
      Ros{\'e}, Carolyn",
    booktitle = "Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, MN",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2720",
    doi = "10.18653/v1/W19-2720",
    pages = "163--168",
    abstract = "We present a package of annotation resources, including annotation guideline, flowchart, and an Intelligent Tutoring System for training human annotators. These resources can be used to apply Rhetorical Structure Theory (RST) to essays written by students in K-12 schools. Furthermore, we highlight the great potential of using RST to provide automated feedback for improving writing quality across genres.",
}
@proceedings{ws-2019-extracting,
    title = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    editor = "Nastase, Vivi  and
      Roth, Benjamin  and
      Dietz, Laura  and
      McCallum, Andrew",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2600",
}
@inproceedings{dai-etal-2019-distantly,
    title = "Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention",
    author = "Dai, Qin  and
      Inoue, Naoya  and
      Reisert, Paul  and
      Takahashi, Ryo  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2601",
    doi = "10.18653/v1/W19-2601",
    pages = "1--10",
    abstract = "The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approach to automatically generate large amounts of labelled data with low manual annotation cost. However, distant supervision inevitably accompanies the wrong labelling problem, which will negatively affect the performance of Relation Extraction (RE). To address this issue, (Han et al., 2018) proposes a novel framework for jointly training both RE model and Knowledge Graph Completion (KGC) model to extract structured knowledge from non-scientific dataset. In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset. Secondly, to achieve better performance on the biomedical dataset, we extend the framework with other competitive KGC models. Moreover, we proposed a new end-to-end KGC model to extend the framework. Experimental results not only show the feasibility of the framework on the biomedical dataset, but also indicate the effectiveness of our extensions, because our extended model achieves significant and consistent improvements on distant supervised RE as compared with baselines.",
}
@inproceedings{agrawal-etal-2019-scalable,
    title = "Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature",
    author = "Agrawal, Kritika  and
      Mittal, Aakash  and
      Pudi, Vikram",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2602",
    doi = "10.18653/v1/W19-2602",
    pages = "11--20",
    abstract = "As scientific communities grow and evolve, there is a high demand for improved methods for finding relevant papers, comparing papers on similar topics and studying trends in the research community. All these tasks involve the common problem of extracting structured information from scientific articles. In this paper, we propose a novel, scalable, semi-supervised method for extracting relevant structured information from the vast available raw scientific literature. We extract the fundamental concepts of {``}aim{''}, {''}method{''} and {``}result{''} from scientific articles and use them to construct a knowledge graph. Our algorithm makes use of domain-based word embedding and the bootstrap framework. Our experiments show that our system achieves precision and recall comparable to the state of the art. We also show the domain independence of our algorithm by analyzing the research trends of two distinct communities - computational linguistics and computer vision.",
}
@inproceedings{noriega-atala-etal-2019-understanding,
    title = "Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods",
    author = "Noriega-Atala, Enrique  and
      Liang, Zhengzhong  and
      Bachman, John  and
      Morrison, Clayton  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2603",
    doi = "10.18653/v1/W19-2603",
    pages = "21--30",
    abstract = "An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.",
}
@inproceedings{prasad-etal-2019-dataset,
    title = "Dataset Mention Extraction and Classification",
    author = "Prasad, Animesh  and
      Si, Chenglei  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2604",
    doi = "10.18653/v1/W19-2604",
    pages = "31--36",
    abstract = "Datasets are integral artifacts of empirical scientific research. However, due to natural language variation, their recognition can be difficult and even when identified, can often be inconsistently referred across and within publications. We report our approach to the Coleridge Initiative{'}s Rich Context Competition, which tasks participants with identifying dataset surface forms (dataset mention extraction) and associating the extracted mention to its referred dataset (dataset classification). In this work, we propose various neural baselines and evaluate these model on one-plus and zero-shot classification scenarios. We further explore various joint learning approaches - exploring the synergy between the tasks - and report the issues with such techniques.",
}
@inproceedings{shirai-etal-2019-annotating,
    title = "Annotating with Pros and Cons of Technologies in Computer Science Papers",
    author = "Shirai, Hono  and
      Inoue, Naoya  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2605",
    doi = "10.18653/v1/W19-2605",
    pages = "37--42",
    abstract = "This paper explores a task for extracting a technological expression and its pros/cons from computer science papers. We report ongoing efforts on an annotated corpus of pros/cons and an analysis of the nature of the automatic extraction task. Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study. In order to identify the challenges of the automatic extraction task, we construct a strong baseline model and conduct an error analysis. The experiments show that pros/cons can be consistently annotated by several annotators, and that the task is challenging due to domain-specific knowledge. The annotated dataset is made publicly available for research purposes.",
}
@inproceedings{parikh-etal-2019-browsing,
    title = "Browsing Health: Information Extraction to Support New Interfaces for Accessing Medical Evidence",
    author = "Parikh, Soham  and
      Conrad, Elizabeth  and
      Agarwal, Oshin  and
      Marshall, Iain  and
      Wallace, Byron  and
      Nenkova, Ani",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2606",
    doi = "10.18653/v1/W19-2606",
    pages = "43--47",
    abstract = "Standard paradigms for search do not work well in the medical context. Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search. Instead, we propose faceted-search in which a user specifies a condition and then can browse treatments and outcomes that have been evaluated. Choosing from these, they can access randomized control trials (RCTs) describing individual studies. Realizing such a view of the medical evidence requires information extraction techniques to identify the population, interventions, and outcome measures in an RCT. Patients, health practitioners, and biomedical librarians all stand to benefit from such innovation in search of medical evidence. We present an initial prototype of such an interface applied to pre-registered clinical studies. We also discuss pilot studies into the applicability of information extraction methods to allow for similar access to all published trial results.",
}
@inproceedings{magnusson-dietz-2019-analysis,
    title = "An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications",
    author = "Magnusson, Matthew  and
      Dietz, Laura",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2607",
    doi = "10.18653/v1/W19-2607",
    pages = "48--56",
    abstract = "Toponym detection in scientific papers is an open task and a key first step in place entity enrichment of documents. We examine three common neural architectures in NLP: 1) convolutional neural network, 2) multi-layer perceptron (both applied in a sliding window context) and 3) bidirectional LSTM and apply contextual and non-contextual word embedding layers to these models. We find that deep contextual word embeddings improve the performance of the bi-LSTM with CRF neural architecture achieving the best performance when multiple layers of deep contextual embeddings are concatenated. Our best performing model achieves an average F1 of 0.910 when evaluated on overlap macro exceeding previous state-of-the-art models in the toponym detection task.",
}
@inproceedings{wang-etal-2019-stac,
    title = "{STAC}: Science Toolkit Based on {C}hinese Idiom Knowledge Graph",
    author = "Wang, Meiling  and
      Xiao, Min  and
      Li, Changliang  and
      Guo, Yu  and
      Zhao, Zhixin  and
      Liu, Xiaonan",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2608",
    doi = "10.18653/v1/W19-2608",
    pages = "57--61",
    abstract = "Chinese idioms (Cheng Yu) have seen five thousand years{'} history and culture of China, meanwhile they contain large number of scientific achievement of ancient China. However, existing Chinese online idiom dictionaries have limited function for scientific exploration. In this paper, we first construct a Chinese idiom knowledge graph by extracting domains and dynasties and associating them with idioms, and based on the idiom knowledge graph, we propose a Science Toolkit for Ancient China (STAC) aiming to support scientific exploration. In the STAC toolkit, idiom navigator helps users explore overall scientific progress from idiom perspective with visualization tools, and idiom card and idiom QA shorten action path and avoid thinking being interrupted while users are reading and writing. The current STAC toolkit is deployed at http://120.92.208.22:7476/demo/{\#}/stac.",
}
@inproceedings{tamari-etal-2019-playing,
    title = "Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Text",
    author = "Tamari, Ronen  and
      Shindo, Hiroyuki  and
      Shahaf, Dafna  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2609",
    doi = "10.18653/v1/W19-2609",
    pages = "62--71",
    abstract = "Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from materials science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A learning agent completes the game by executing the procedure correctly in a text-based simulated lab environment. The framework can complement existing approaches and enables richer forms of learning compared to static texts. We discuss potential limitations and advantages of the approach, and release a prototype proof-of-concept, hoping to encourage research in this direction.",
}
@inproceedings{daudaravicius-2019-textual,
    title = "Textual and Visual Characteristics of Mathematical Expressions in Scholar Documents",
    author = "Daudaravicius, Vidas",
    booktitle = "Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2610",
    doi = "10.18653/v1/W19-2610",
    pages = "72--81",
    abstract = "Mathematical expressions (ME) are widely used in scholar documents. In this paper we analyze characteristics of textual and visual MEs characteristics for the image-to-LaTeX translation task. While there are open data-sets of LaTeX files with MEs included it is very complicated to extract these MEs from a document and to compile the list of MEs. Therefore we release a corpus of open-access scholar documents with PDF and JATS-XML parallel files. The MEs in these documents are LaTeX encoded and are document independent. The data contains more than 1.2 million distinct annotated formulae and more than 80 million raw tokens of LaTeX MEs in more than 8 thousand documents. While the variety of textual lengths and visual sizes of MEs are not well defined we found that the task of analyzing MEs in scholar documents can be reduced to the subtask of a particular text length, image width and height bounds, and display MEs can be processed as arrays of partial MEs.",
}
@proceedings{ws-2019-joint,
    title = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    editor = "Alex, Beatrice  and
      Degaetano-Ortlieb, Stefania  and
      Kazantseva, Anna  and
      Reiter, Nils  and
      Szpakowicz, Stan",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2500",
}
@inproceedings{hellrich-etal-2019-modeling,
    title = "Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection",
    author = "Hellrich, Johannes  and
      Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2501",
    doi = "10.18653/v1/W19-2501",
    pages = "1--11",
    abstract = "To understand historical texts, we must be aware that language{---}including the emotional connotation attached to words{---}changes over time. In this paper, we aim at estimating the emotion which is associated with a given word in former language stages of English and German. Emotion is represented following the popular Valence-Arousal-Dominance (VAD) annotation scheme. While being more expressive than polarity alone, existing word emotion induction methods are typically not suited for addressing it. To overcome this limitation, we present adaptations of two popular algorithms to VAD. To measure their effectiveness in diachronic settings, we present the first gold standard for historical word emotions, which was created by scholars with proficiency in the respective language stages and covers both English and German. In contrast to claims in previous work, our findings indicate that hand-selecting small sets of seed words with supposedly stable emotional meaning is actually harm- rather than helpful.",
}
@inproceedings{riedl-etal-2019-clustering,
    title = "Clustering-Based Article Identification in Historical Newspapers",
    author = "Riedl, Martin  and
      Betz, Daniela  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2502",
    doi = "10.18653/v1/W19-2502",
    pages = "12--17",
    abstract = "This article focuses on the problem of identifying articles and recovering their text from within and across newspaper pages when OCR just delivers one text file per page. We frame the task as a segmentation plus clustering step. Our results on a sample of 1912 New York Tribune magazine shows that performing the clustering based on similarities computed with word embeddings outperforms a similarity measure based on character n-grams and words. Furthermore, the automatic segmentation based on the text results in low scores, due to the low quality of some OCRed documents.",
}
@inproceedings{degaetano-ortlieb-piper-2019-scientization,
    title = "The Scientization of Literary Study",
    author = "Degaetano-Ortlieb, Stefania  and
      Piper, Andrew",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2503",
    doi = "10.18653/v1/W19-2503",
    pages = "18--28",
    abstract = "Scholarly practices within the humanities have historically been perceived as distinct from the natural sciences. We look at literary studies, a discipline strongly anchored in the humanities, and hypothesize that over the past half-century literary studies has instead undergone a process of {``}scientization{''}, adopting linguistic behavior similar to the sciences. We test this using methods based on information theory, comparing a corpus of literary studies articles (around 63,400) with a corpus of standard English and scientific English respectively. We show evidence for {``}scientization{''} effects in literary studies, though at a more muted level than scientific English, suggesting that literary studies occupies a middle ground with respect to standard English in the larger space of academic disciplines. More generally, our methodology can be applied to investigate the social positioning and development of language use across different domains (e.g. scientific disciplines, language varieties, registers).",
}
@inproceedings{vishnubhotla-etal-2019-fictional,
    title = "Are Fictional Voices Distinguishable? Classifying Character Voices in Modern Drama",
    author = "Vishnubhotla, Krishnapriya  and
      Hammond, Adam  and
      Hirst, Graeme",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2504",
    doi = "10.18653/v1/W19-2504",
    pages = "29--34",
    abstract = "According to the literary theory of Mikhail Bakhtin, a dialogic novel is one in which characters speak in their own distinct voices, rather than serving as mouthpieces for their authors. We use text classification to determine which authors best achieve dialogism, looking at a corpus of plays from the late nineteenth and early twentieth centuries. We find that the SAGE model of text generation, which highlights deviations from a background lexical distribution, is an effective method of weighting the words of characters{'} utterances. Our results show that it is indeed possible to distinguish characters by their speech in the plays of canonical writers such as George Bernard Shaw, whereas characters are clustered more closely in the works of lesser-known playwrights.",
}
@inproceedings{steinbach-rehbein-2019-automatic,
    title = "Automatic Alignment and Annotation Projection for Literary Texts",
    author = "Steinbach, Uli  and
      Rehbein, Ines",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2505",
    doi = "10.18653/v1/W19-2505",
    pages = "35--45",
    abstract = "This paper presents a modular NLP pipeline for the creation of a parallel literature corpus, followed by annotation transfer from the source to the target language. The test case we use to evaluate our pipeline is the automatic transfer of quote and speaker mention annotations from English to German. We evaluate the different components of the pipeline and discuss challenges specific to literary texts. Our experiments show that after applying a reasonable amount of semi-automatic postprocessing we can obtain high-quality aligned and annotated resources for a new language.",
}
@inproceedings{bethard-etal-2019-inferring,
    title = "Inferring missing metadata from environmental policy texts",
    author = "Bethard, Steven  and
      Laparra, Egoitz  and
      Wang, Sophia  and
      Zhao, Yiyun  and
      Al-Ghezi, Ragheb  and
      Lien, Aaron  and
      L{\'o}pez-Hoffman, Laura",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2506",
    doi = "10.18653/v1/W19-2506",
    pages = "46--51",
    abstract = "The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years. Unfortunately, there is no central database for this information and it is too voluminous to assess manually. We describe our efforts to enable systematic research over US environmental policy by extracting and organizing metadata from the text of NEPA documents. Our contributions include collecting more than 40,000 NEPA-related documents, and evaluating rule-based baselines that establish the difficulty of three important tasks: identifying lead agencies, aligning document versions, and detecting reused text.",
}
@inproceedings{gianitsos-etal-2019-stylometric,
    title = "Stylometric Classification of {A}ncient {G}reek Literary Texts by Genre",
    author = "Gianitsos, Efthimios  and
      Bolt, Thomas  and
      Chaudhuri, Pramit  and
      Dexter, Joseph P.",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2507",
    doi = "10.18653/v1/W19-2507",
    pages = "52--60",
    abstract = "Classification of texts by genre is an important application of natural language processing to literary corpora but remains understudied for premodern and non-English traditions. We develop a stylometric feature set for ancient Greek that enables identification of texts as prose or verse. The set contains over 20 primarily syntactic features, which are calculated according to custom, language-specific heuristics. Using these features, we classify almost all surviving classical Greek literature as prose or verse with {\textgreater}97{\%} accuracy and F1 score, and further classify a selection of the verse texts into the traditional genres of epic and drama.",
}
@inproceedings{glenny-etal-2019-framework,
    title = "A framework for streamlined statistical prediction using topic models",
    author = "Glenny, Vanessa  and
      Tuke, Jonathan  and
      Bean, Nigel  and
      Mitchell, Lewis",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2508",
    doi = "10.18653/v1/W19-2508",
    pages = "61--70",
    abstract = "In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora. With approaches in these fields being grounded in traditional statistical techniques, the need arises for frameworks whereby advanced NLP techniques such as topic modelling may be incorporated within classical methodologies. This paper provides a classical, supervised, statistical learning framework for prediction from text, using topic models as a data reduction method and the topics themselves as predictors, alongside typical statistical tools for predictive modelling. We apply this framework in a Social Sciences context (applied animal behaviour) as well as a Humanities context (narrative analysis) as examples of this framework. The results show that topic regression models perform comparably to their much less efficient equivalents that use individual words as predictors.",
}
@inproceedings{hamalainen-etal-2019-revisiting,
    title = "Revisiting {NMT} for Normalization of Early {E}nglish Letters",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      S{\"a}ily, Tanja  and
      Rueter, Jack  and
      Tiedemann, J{\"o}rg  and
      M{\"a}kel{\"a}, Eetu},
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2509",
    doi = "10.18653/v1/W19-2509",
    pages = "71--75",
    abstract = "This paper studies the use of NMT (neural machine translation) as a normalization method for an early English letter corpus. The corpus has previously been normalized so that only less frequent deviant forms are left out without normalization. This paper discusses different methods for improving the normalization of these deviant forms by using different approaches. Adding features to the training data is found to be unhelpful, but using a lexicographical resource to filter the top candidates produced by the NMT model together with lemmatization improves results.",
}
@inproceedings{lippincott-2019-graph,
    title = "Graph convolutional networks for exploring authorship hypotheses",
    author = "Lippincott, Tom",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2510",
    doi = "10.18653/v1/W19-2510",
    pages = "76--81",
    abstract = "This work considers a task from traditional literary criticism: annotating a structured, composite document with information about its sources. We take the Documentary Hypothesis, a prominent theory regarding the composition of the first five books of the Hebrew bible, extract stylistic features designed to avoid bias or overfitting, and train several classification models. Our main result is that the recently-introduced graph convolutional network architecture outperforms structurally-uninformed models. We also find that including information about the granularity of text spans is a crucial ingredient when employing hidden layers, in contrast to simple logistic regression. We perform error analysis at several levels, noting how some characteristic limitations of the models and simple features lead to misclassifications, and conclude with an overview of future work.",
}
@inproceedings{rahgozar-inkpen-2019-semantics,
    title = "Semantics and Homothetic Clustering of Hafez Poetry",
    author = "Rahgozar, Arya  and
      Inkpen, Diana",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2511",
    doi = "10.18653/v1/W19-2511",
    pages = "82--90",
    abstract = "We have created two sets of labels for Hafez (1315-1390) poems, using unsupervised learning. Our labels are the only semantic clustering alternative to the previously existing, hand-labeled, gold-standard classification of Hafez poems, to be used for literary research. We have cross-referenced, measured and analyzed the agreements of our clustering labels with Houman{'}s chronological classes. Our features are based on topic modeling and word embeddings. We also introduced a similarity of similarities{'} features, we called homothetic clustering approach that proved effective, in case of Hafez{'}s small corpus of ghazals2. Although all our experiments showed different clusters when compared with Houman{'}s classes, we think they were valid in their own right to have provided further insights, and have proved useful as a contrasting alternative to Houman{'}s classes. Our homothetic clusterer and its feature design and engineering framework can be used for further semantic analysis of Hafez{'}s poetry and other similar literary research.",
}
@inproceedings{rim-etal-2019-computational,
    title = "Computational Linguistics Applications for Multimedia Services",
    author = "Rim, Kyeongmin  and
      Lynch, Kelley  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2512",
    doi = "10.18653/v1/W19-2512",
    pages = "91--97",
    abstract = "We present Computational Linguistics Applications for Multimedia Services (CLAMS), a platform that provides access to computational content analysis tools for archival multimedia material that appear in different media, such as text, audio, image, and video. The primary goal of CLAMS is: (1) to develop an interchange format between multimodal metadata generation tools to ensure interoperability between tools; (2) to provide users with a portable, user-friendly workflow engine to chain selected tools to extract meaningful analyses; and (3) to create a public software development kit (SDK) for developers that eases deployment of analysis tools within the CLAMS platform. CLAMS is designed to help archives and libraries enrich the metadata associated with their mass-digitized multimedia collections, that would otherwise be largely unsearchable.",
}
@inproceedings{soni-etal-2019-correcting,
    title = "Correcting Whitespace Errors in Digitized Historical Texts",
    author = "Soni, Sandeep  and
      Klein, Lauren  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2513",
    doi = "10.18653/v1/W19-2513",
    pages = "98--103",
    abstract = "Whitespace errors are common to digitized archives. This paper describes a lightweight unsupervised technique for recovering the original whitespace. Our approach is based on count statistics from Google n-grams, which are converted into a likelihood ratio test computed from interpolated trigram and bigram probabilities. To evaluate this approach, we annotate a small corpus of whitespace errors in a digitized corpus of newspapers from the 19th century United States. Our technique identifies and corrects most whitespace errors while introducing a minimal amount of oversegmentation: it achieves 77{\%} recall at a false positive rate of less than 1{\%}, and 91{\%} recall at a false positive rate of less than 3{\%}.",
}
@inproceedings{manjavacas-etal-2019-feasibility,
    title = "On the Feasibility of Automated Detection of Allusive Text Reuse",
    author = "Manjavacas, Enrique  and
      Long, Brian  and
      Kestemont, Mike",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2514",
    doi = "10.18653/v1/W19-2514",
    pages = "104--114",
    abstract = "The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely {---} commonly based on none or very few shared words. Arguably, lexical semantics can be resorted to since uncovering semantic relations between words has the potential to increase the support underlying the allusion and alleviate the lexical sparsity. A further obstacle is the lack of evaluation benchmark corpora, largely due to the highly interpretative character of the annotation process. In the present paper, we aim to elucidate the feasibility of automated allusion detection. We approach the matter from an Information Retrieval perspective in which referencing texts act as queries and referenced texts as relevant documents to be retrieved, and estimate the difficulty of benchmark corpus compilation by a novel inter-annotator agreement study on query segmentation. Furthermore, we investigate to what extent the integration of lexical semantic information derived from distributional models and ontologies can aid retrieving cases of allusive reuse. The results show that (i) despite low agreement scores, using manual queries considerably improves retrieval performance with respect to a windowing approach, and that (ii) retrieval performance can be moderately boosted with distributional semantics.",
}
@inproceedings{bullock-etal-2019-limits,
    title = "The limits of {S}panglish?",
    author = "Bullock, Barbara  and
      Guzm{\'a}n, Wally  and
      Toribio, Almeida Jacqueline",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2515",
    doi = "10.18653/v1/W19-2515",
    pages = "115--121",
    abstract = "Linguistic code-switching (C-S) is common in oral bilingual vernacular speech. When used in literature, C-S becomes an artistic choice that can mirror the patterns of bilingual interactions. But it can also potentially exceed them. What are the limits of C-S? We model features of C-S in corpora of contemporary U.S. Spanish-English literary and conversational data to analyze why some critics view the {`}Spanglish{'} texts of Ilan Stavans as deviating from a C-S norm.",
}
@inproceedings{born-etal-2019-sign,
    title = "Sign Clustering and Topic Extraction in {P}roto-{E}lamite",
    author = "Born, Logan  and
      Kelley, Kate  and
      Kambhatla, Nishant  and
      Chen, Carolyn  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 3rd Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2516",
    doi = "10.18653/v1/W19-2516",
    pages = "122--132",
    abstract = "We describe a first attempt at using techniques from computational linguistics to analyze the undeciphered proto-Elamite script. Using hierarchical clustering, n-gram frequencies, and LDA topic models, we both replicate results obtained by manual decipherment and reveal previously-unobserved relationships between signs. This demonstrates the utility of these techniques as an aid to manual decipherment.",
}
@proceedings{ws-2019-narrative,
    title = "Proceedings of the First Workshop on Narrative Understanding",
    editor = "Bamman, David  and
      Chaturvedi, Snigdha  and
      Clark, Elizabeth  and
      Fiterau, Madalina  and
      Iyyer, Mohit",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2400",
}
@inproceedings{cho-etal-2019-towards,
    title = "Towards Coherent and Cohesive Long-form Text Generation",
    author = "Cho, Woon Sang  and
      Zhang, Pengchuan  and
      Zhang, Yizhe  and
      Li, Xiujun  and
      Galley, Michel  and
      Brockett, Chris  and
      Wang, Mengdi  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2401",
    doi = "10.18653/v1/W19-2401",
    pages = "1--11",
    abstract = "Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called {`}negative-critical sequence training{'}, which is proposed to eliminate the need of training a separate critic for estimating {`}baseline{'}. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline {--} recurrent attention-based bidirectional MLE-trained neural language model.",
}
@inproceedings{jahan-finlayson-2019-character,
    title = "Character Identification Refined: A Proposal",
    author = "Jahan, Labiba  and
      Finlayson, Mark",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2402",
    doi = "10.18653/v1/W19-2402",
    pages = "12--18",
    abstract = "Characters are a key element of narrative and so character identification plays an important role in automatic narrative understanding. Unfortunately, most prior work that incorporates character identification is not built upon a clear, theoretically grounded concept of character. They either take character identification for granted (e.g., using simple heuristics on referring expressions), or rely on simplified definitions that do not capture important distinctions between characters and other referents in the story. Prior approaches have also been rather complicated, relying, for example, on predefined case bases or ontologies. In this paper we propose a narratologically grounded definition of character for discussion at the workshop, and also demonstrate a preliminary yet straightforward supervised machine learning model with a small set of features that performs well on two corpora. The most important of the two corpora is a set of 46 Russian folktales, on which the model achieves an F1 of 0.81. Error analysis suggests that features relevant to the plot will be necessary for further improvements in performance.",
}
@inproceedings{shree-etal-2019-deep,
    title = "Deep Natural Language Understanding of News Text",
    author = "Shree, Jaya  and
      Liu, Emily  and
      Gordon, Andrew  and
      Hobbs, Jerry",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2403",
    doi = "10.18653/v1/W19-2403",
    pages = "19--27",
    abstract = "Early proposals for the deep understanding of natural language text advocated an approach of {``}interpretation as abduction,{''} where the meaning of a text was derived as an explanation that logically entailed the input words, given a knowledge base of lexical and commonsense axioms. While most subsequent NLP research has instead pursued statistical and data-driven methods, the approach of interpretation as abduction has seen steady advancements in both theory and software implementations. In this paper, we summarize advances in deriving the logical form of the text, encoding commonsense knowledge, and technologies for scalable abductive reasoning. We then explore the application of these advancements to the deep understanding of a paragraph of news text, where the subtle meaning of words and phrases are resolved by backward chaining on a knowledge base of 80 hand-authored axioms.",
}
@inproceedings{palshikar-etal-2019-extraction-message,
    title = "Extraction of Message Sequence Charts from Narrative History Text",
    author = "Palshikar, Girish  and
      Pawar, Sachin  and
      Patil, Sangameshwar  and
      Hingmire, Swapnil  and
      Ramrakhiyani, Nitin  and
      Bedi, Harsimran  and
      Bhattacharyya, Pushpak  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2404",
    doi = "10.18653/v1/W19-2404",
    pages = "28--36",
    abstract = "In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering. We propose algorithms to automatically extract an MSC from a history narrative. For a given narrative, we first identify verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. As a final step in MSC extraction, we employ a state-of-the art algorithm to temporally re-order these interactions. Our evaluation on multiple publicly available narratives shows improvements over four baselines.",
}
@inproceedings{ippolito-etal-2019-unsupervised,
    title = "Unsupervised Hierarchical Story Infilling",
    author = "Ippolito, Daphne  and
      Grangier, David  and
      Callison-Burch, Chris  and
      Eck, Douglas",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2405",
    doi = "10.18653/v1/W19-2405",
    pages = "37--43",
    abstract = "Story infilling involves predicting words to go into a missing span from a story. This challenging task has the potential to transform interactive tools for creative writing. However, state-of-the-art conditional language models have trouble balancing fluency and coherence with novelty and diversity. We address this limitation with a hierarchical model which first selects a set of rare words and then generates text conditioned on that set. By relegating the high entropy task of picking rare words to a word-sampling model, the second-stage model conditioned on those words can achieve high fluency and coherence by searching for likely sentences, without sacrificing diversity.",
}
@inproceedings{roemmele-2019-identifying,
    title = "Identifying Sensible Lexical Relations in Generated Stories",
    author = "Roemmele, Melissa",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2406",
    doi = "10.18653/v1/W19-2406",
    pages = "44--52",
    abstract = "As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to {``}make sense{''} as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the {`}narrative sense{'} of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story.",
}
@proceedings{ws-2019-methods,
    title = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    editor = "Bosselut, Antoine  and
      Celikyilmaz, Asli  and
      Ghazvininejad, Marjan  and
      Iyer, Srinivasan  and
      Khandelwal, Urvashi  and
      Rashkin, Hannah  and
      Wolf, Thomas",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2300",
}
@inproceedings{olabiyi-etal-2019-adversarial,
    title = "An Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model",
    author = "Olabiyi, Oluwatobi  and
      Khazane, Anish  and
      Salimov, Alan  and
      Mueller, Erik",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2301",
    doi = "10.18653/v1/W19-2301",
    pages = "1--10",
    abstract = "In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on. The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator. We also explore two approaches to accomplish the conditional discriminator: (1) phredGANa, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) phredGANd, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance. To demonstrate the superior performance of phredGAN over the persona Seq2Seq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends. Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation. We also explore the trade-offs from using either variant of phredGAN on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).",
}
@inproceedings{cui-etal-2019-dal,
    title = "{DAL}: Dual Adversarial Learning for Dialogue Generation",
    author = "Cui, Shaobo  and
      Lian, Rongzhong  and
      Jiang, Di  and
      Song, Yuanfeng  and
      Bao, Siqi  and
      Jiang, Yong",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2302",
    doi = "10.18653/v1/W19-2302",
    pages = "11--20",
    abstract = "In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe responses and unnatural responses. To alleviate these two problems, we propose a novel framework named Dual Adversarial Learning(DAL) for high-quality response generation. DAL innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses. Additionally, DAL uses adversarial learning to mimic human judges and guides the system to generate natural responses. Experimental results demonstrate that DAL effectively improves both diversity and overall quality of the generated responses. DAL outperforms state-of-the-art methods regarding automatic metrics and human evaluations.",
}
@inproceedings{sun-etal-2019-compare,
    title = "How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature",
    author = "Sun, Simeng  and
      Shapira, Ori  and
      Dagan, Ido  and
      Nenkova, Ani",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2303",
    doi = "10.18653/v1/W19-2303",
    pages = "21--29",
    abstract = "We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths. This is due to a non-linear pattern between ROUGE F1 and summary length. To alleviate the effect of length during evaluation, we have proposed a new method which normalizes the ROUGE F1 scores of a system by that of a random system with same average output length. A pilot human evaluation has shown that humans prefer short summaries in terms of the verbosity of a summary but overall consider longer summaries to be of higher quality. While human evaluations are more expensive in time and resources, it is clear that normalization, such as the one we proposed for automatic evaluation, will make human evaluations more meaningful.",
}
@inproceedings{wang-cho-2019-bert,
    title = "{BERT} has a Mouth, and It Must Speak: {BERT} as a {M}arkov Random Field Language Model",
    author = "Wang, Alex  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2304",
    doi = "10.18653/v1/W19-2304",
    pages = "30--36",
    abstract = "We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high quality, fluent generations. Compared to the generations of a traditional left-to-right language model, BERT generates sentences that are more diverse but of slightly worse quality.",
}
@inproceedings{palmero-aprosio-etal-2019-neural,
    title = "Neural Text Simplification in Low-Resource Conditions Using Weak Supervision",
    author = "Palmero Aprosio, Alessio  and
      Tonelli, Sara  and
      Turchi, Marco  and
      Negri, Matteo  and
      Di Gangi, Mattia A.",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2305",
    doi = "10.18653/v1/W19-2305",
    pages = "37--44",
    abstract = "Neural text simplification has gained increasing attention in the NLP community thanks to recent advancements in deep sequence-to-sequence learning. Most recent efforts with such a data-demanding paradigm have dealt with the English language, for which sizeable training datasets are currently available to deploy competitive models. Similar improvements on less resource-rich languages are conditioned either to intensive manual work to create training data, or to the design of effective automatic generation techniques to bypass the data acquisition bottleneck. Inspired by the machine translation field, in which synthetic parallel pairs generated from monolingual data yield significant improvements to neural models, in this paper we exploit large amounts of heterogeneous data to automatically select simple sentences, which are then used to create synthetic simplification pairs. We also evaluate other solutions, such as oversampling and the use of external word embeddings to be fed to the neural simplification system. Our approach is evaluated on Italian and Spanish, for which few thousand gold sentence pairs are available. The results show that these techniques yield performance improvements over a baseline sequence-to-sequence configuration.",
}
@inproceedings{cho-etal-2019-paraphrase,
    title = "Paraphrase Generation for Semi-Supervised Learning in {NLU}",
    author = "Cho, Eunah  and
      Xie, He  and
      Campbell, William M.",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2306",
    doi = "10.18653/v1/W19-2306",
    pages = "45--54",
    abstract = "Semi-supervised learning is an efficient way to improve performance for natural language processing systems. In this work, we propose Para-SSL, a scheme to generate candidate utterances using paraphrasing and methods from semi-supervised learning. In order to perform paraphrase generation in the context of a dialog system, we automatically extract paraphrase pairs to create a paraphrase corpus. Using this data, we build a paraphrase generation system and perform one-to-many generation, followed by a validation step to select only the utterances with good quality. The paraphrase-based semi-supervised learning is applied to five functionalities in a natural language understanding system. Our proposed method for semi-supervised learning using paraphrase generation does not require user utterances and can be applied prior to releasing a new functionality to a system. Experiments show that we can achieve up to 19{\%} of relative slot error reduction without an access to user utterances, and up to 35{\%} when leveraging live traffic utterances.",
}
@inproceedings{rashid-etal-2019-bilingual,
    title = "Bilingual-{GAN}: A Step Towards Parallel Text Generation",
    author = "Rashid, Ahmad  and
      Do-Omri, Alan  and
      Haidar, Md. Akmal  and
      Liu, Qun  and
      Rezagholizadeh, Mehdi",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2307",
    doi = "10.18653/v1/W19-2307",
    pages = "55--64",
    abstract = "Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic {`}code{'} mimicking the languages{'} shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.",
}
@inproceedings{elder-etal-2019-designing,
    title = "Designing a Symbolic Intermediate Representation for Neural Surface Realization",
    author = "Elder, Henry  and
      Foster, Jennifer  and
      Barry, James  and
      O{'}Connor, Alexander",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2308",
    doi = "10.18653/v1/W19-2308",
    pages = "65--73",
    abstract = "Generated output from neural NLG systems often contain errors such as hallucination, repetition or contradiction. This work focuses on designing a symbolic intermediate representation to be used in multi-stage neural generation with the intention of reducing the frequency of failed outputs. We show that surface realization from this intermediate representation is of high quality and when the full system is applied to the E2E dataset it outperforms the winner of the E2E challenge. Furthermore, by breaking out the surface realization step from typically end-to-end neural systems, we also provide a framework for non-neural based content selection and planning systems to potentially take advantage of semi-supervised pretraining of neural surface realization models.",
}
@inproceedings{lee-etal-2019-neural,
    title = "Neural Text Style Transfer via Denoising and Reranking",
    author = "Lee, Joseph  and
      Xie, Ziang  and
      Wang, Cindy  and
      Drach, Max  and
      Jurafsky, Dan  and
      Ng, Andrew",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2309",
    doi = "10.18653/v1/W19-2309",
    pages = "74--81",
    abstract = "We introduce a simple method for text style transfer that frames style transfer as denoising: we synthesize a noisy corpus and treat the source style as a noisy version of the target style. To control for aspects such as preserving meaning while modifying style, we propose a reranking approach in the data synthesis phase. We evaluate our method on three novel style transfer tasks: transferring between British and American varieties, text genres (formal vs. casual), and lyrics from different musical genres. By measuring style transfer quality, meaning preservation, and the fluency of generated outputs, we demonstrate that our method is able both to produce high-quality output while maintaining the flexibility to suggest syntactically rich stylistic edits.",
}
@inproceedings{ghazarian-etal-2019-better,
    title = "Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings",
    author = "Ghazarian, Sarik  and
      Wei, Johnny  and
      Galstyan, Aram  and
      Peng, Nanyun",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2310",
    doi = "10.18653/v1/W19-2310",
    pages = "82--89",
    abstract = "Despite advances in open-domain dialogue systems, automatic evaluation of such systems is still a challenging problem. Traditional reference-based metrics such as BLEU are ineffective because there could be many valid responses for a given context that share no common words with reference responses. A recent work proposed Referenced metric and Unreferenced metric Blended Evaluation Routine (RUBER) to combine a learning-based metric, which predicts relatedness between a generated response and a given query, with reference-based metric; it showed high correlation with human judgments. In this paper, we explore using contextualized word embeddings to compute more accurate relatedness scores, thus better evaluation metrics. Experiments show that our evaluation metrics outperform RUBER, which is trained on static embeddings.",
}
@inproceedings{alihosseini-etal-2019-jointly,
    title = "Jointly Measuring Diversity and Quality in Text Generation Models",
    author = "Alihosseini, Danial  and
      Montahaei, Ehsan  and
      Soleymani Baghshah, Mahdieh",
    booktitle = "Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2311",
    doi = "10.18653/v1/W19-2311",
    pages = "90--98",
    abstract = "Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and neglecting their diversity. For example, repeatedly generation of only one high quality sentence would result in a high BLEU score. On the other hand, the more recent metric introduced to evaluate the diversity of generated texts known as Self-BLEU ignores the quality of generated texts. In this paper, we propose metrics to evaluate both the quality and diversity simultaneously by approximating the distance of the learned generative model and the real data distribution. For this purpose, we first introduce a metric that approximates this distance using n-gram based measures. Then, a feature-based measure which is based on a recent highly deep model trained on a large text corpus called BERT is introduced. Finally, for oracle training mode in which the generatorʼs density can also be calculated, we propose to use the distance measures between the corresponding explicit distributions. Eventually, the most popular and recent text generation models are evaluated using both the existing and the proposed metrics and the preferences of the proposed metrics are determined.",
}
@proceedings{ws-2019-natural-legal,
    title = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    editor = "Aletras, Nikolaos  and
      Ash, Elliott  and
      Barrett, Leslie  and
      Chen, Daniel  and
      Meyers, Adam  and
      Preotiuc-Pietro, Daniel  and
      Rosenberg, David  and
      Stent, Amanda",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2200",
}
@inproceedings{manor-li-2019-plain,
    title = "Plain {E}nglish Summarization of Contracts",
    author = "Manor, Laura  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2201",
    doi = "10.18653/v1/W19-2201",
    pages = "1--11",
    abstract = "Unilateral legal contracts, such as terms of service, play a substantial role in modern digital life. However, few read these documents before accepting the terms within, as they are too long and the language too complicated. We propose the task of summarizing such legal documents in plain English, which would enable users to have a better understanding of the terms they are accepting. We propose an initial dataset of legal text snippets paired with summaries written in plain English. We verify the quality of these summaries manually, and show that they involve heavy abstraction, compression, and simplification. Initial experiments show that unsupervised extractive summarization methods do not perform well on this task due to the level of abstraction and style differences. We conclude with a call for resource and technique development for simplification and style transfer for legal language.",
}
@inproceedings{ferro-etal-2019-scalable,
    title = "Scalable Methods for Annotating Legal-Decision Corpora",
    author = "Ferro, Lisa  and
      Aberdeen, John  and
      Branting, Karl  and
      Pfeifer, Craig  and
      Yeh, Alexander  and
      Chakraborty, Amartya",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2202",
    doi = "10.18653/v1/W19-2202",
    pages = "12--20",
    abstract = "Recent research has demonstrated that judicial and administrative decisions can be predicted by machine-learning models trained on prior decisions. However, to have any practical application, these predictions must be explainable, which in turn requires modeling a rich set of features. Such approaches face a roadblock if the knowledge engineering required to create these features is not scalable. We present an approach to developing a feature-rich corpus of administrative rulings about domain name disputes, an approach which leverages a small amount of manual annotation and prototypical patterns present in the case documents to automatically extend feature labels to the entire corpus. To demonstrate the feasibility of this approach, we report results from systems trained on this dataset.",
}
@inproceedings{simonson-etal-2019-extent,
    title = "The Extent of Repetition in Contract Language",
    author = "Simonson, Dan  and
      Broderick, Daniel  and
      Herr, Jonathan",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2203",
    doi = "10.18653/v1/W19-2203",
    pages = "21--30",
    abstract = "Contract language is repetitive (Anderson and Manns, 2017), but so is all language (Zipf, 1949). In this paper, we measure the extent to which contract language in English is repetitive compared with the language of other English language corpora. Contracts have much smaller vocabulary sizes compared with similarly sized non-contract corpora across multiple contract types, contain 1/5th as many hapax legomena, pattern differently on a log-log plot, use fewer pronouns, and contain sentences that are about 20{\%} more similar to one another than in other corpora. These suggest that the study of contracts in natural language processing controls for some linguistic phenomena and allows for more in depth study of others.",
}
@inproceedings{sanchez-2019-sentence,
    title = "Sentence Boundary Detection in Legal Text",
    author = "Sanchez, George",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2204",
    doi = "10.18653/v1/W19-2204",
    pages = "31--38",
    abstract = "In this paper, we examined several algorithms to detect sentence boundaries in legal text. Legal text presents challenges for sentence tokenizers because of the variety of punctuations and syntax of legal text. Out-of-the-box algorithms perform poorly on legal text affecting further analysis of the text. A novel and domain-specific approach is needed to detect sentence boundaries to further analyze legal text. We present the results of our investigation in this paper.",
}
@inproceedings{shaffer-mayhew-2019-legal,
    title = "Legal Linking: Citation Resolution and Suggestion in Constitutional Law",
    author = "Shaffer, Robert  and
      Mayhew, Stephen",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2205",
    doi = "10.18653/v1/W19-2205",
    pages = "39--44",
    abstract = "This paper describes a dataset and baseline systems for linking paragraphs from court cases to clauses or amendments in the US Constitution. We implement a rule-based system, a linear model, and a neural architecture for matching pairs of paragraphs, taking training data from online databases in a distantly-supervised fashion. In experiments on a manually-annotated evaluation set, we find that our proposed neural system outperforms a rules-driven baseline. Qualitatively, this performance gap seems largest for abstract or indirect links between documents, which suggests that our system might be useful for answering political science and legal research questions or discovering novel links. We release the dataset along with the manually-annotated evaluation set to foster future work.",
}
@inproceedings{vacek-etal-2019-litigation-analytics,
    title = "Litigation Analytics: Case Outcomes Extracted from {US} Federal Court Dockets",
    author = "Vacek, Thomas  and
      Teo, Ronald  and
      Song, Dezhao  and
      Nugent, Timothy  and
      Cowling, Conner  and
      Schilder, Frank",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2206",
    doi = "10.18653/v1/W19-2206",
    pages = "45--54",
    abstract = "Dockets contain a wealth of information for planning a litigation strategy, but the information is locked up in semi-structured text. Manually deriving the outcomes for each party (e.g., settlement, verdict) would be very labor intensive. Having such information available for every past court case, however, would be very useful for developing a strategy because it potentially reveals tendencies and trends of judges and courts and the opposing counsel. We used Natural Language Processing (NLP) techniques and deep learning methods allowing us to scale the automatic analysis of millions of US federal court dockets. The automatically extracted information is fed into a Litigation Analytics tool that is used by lawyers to plan how they approach concrete litigations.",
}
@inproceedings{rehm-etal-2019-developing,
    title = "Developing and Orchestrating a Portfolio of Natural Legal Language Processing and Document Curation Services",
    author = "Rehm, Georg  and
      Moreno-Schneider, Juli{\'a}n  and
      Gracia, Jorge  and
      Revenko, Artem  and
      Mireles, Victor  and
      Khvalchik, Maria  and
      Kernerman, Ilan  and
      Lagzdins, Andis  and
      Pinnis, Marcis  and
      Vasilevskis, Artus  and
      Leitner, Elena  and
      Milde, Jan  and
      Wei{\ss}enhorn, Pia",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2207",
    doi = "10.18653/v1/W19-2207",
    pages = "55--66",
    abstract = "We present a portfolio of natural legal language processing and document curation services currently under development in a collaborative European project. First, we give an overview of the project and the different use cases, while, in the main part of the article, we focus upon the 13 different processing services that are being deployed in different prototype applications using a flexible and scalable microservices architecture. Their orchestration is operationalised using a content and document curation workflow manager.",
}
@inproceedings{soh-etal-2019-legal,
    title = "Legal Area Classification: A Comparative Study of Text Classifiers on {S}ingapore {S}upreme {C}ourt Judgments",
    author = "Soh, Jerrold  and
      Lim, How Khang  and
      Chai, Ian Ernst",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2208",
    doi = "10.18653/v1/W19-2208",
    pages = "67--77",
    abstract = "This paper conducts a comparative study on the performance of various machine learning approaches for classifying judgments into legal areas. Using a novel dataset of 6,227 Singapore Supreme Court judgments, we investigate how state-of-the-art NLP methods compare against traditional statistical models when applied to a legal corpus that comprised few but lengthy documents. All approaches tested, including topic model, word embedding, and language model-based classifiers, performed well with as little as a few hundred judgments. However, more work needs to be done to optimize state-of-the-art methods for the legal domain.",
}
@inproceedings{chalkidis-etal-2019-extreme,
    title = "Extreme Multi-Label Legal Text Classification: A Case Study in {EU} Legislation",
    author = "Chalkidis, Ilias  and
      Fergadiotis, Emmanouil  and
      Malakasiotis, Prodromos  and
      Aletras, Nikolaos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2209",
    doi = "10.18653/v1/W19-2209",
    pages = "78--87",
    abstract = "We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, the European Union{'}s public document database, annotated with concepts from EUROVOC, a multidisciplinary thesaurus. The dataset is substantially larger than previous EURLEX datasets and suitable for XMTC, few-shot and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with self-attention outperform the current multi-label state-of-the-art methods, which employ label-wise attention. Replacing CNNs with BIGRUs in label-wise attention networks leads to the best overall performance.",
}
@proceedings{ws-2019-natural-language,
    title = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    editor = "Volkova, Svitlana  and
      Jurgens, David  and
      Hovy, Dirk  and
      Bamman, David  and
      Tsur, Oren",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2100",
}
@inproceedings{van-den-berg-etal-2019-president,
    title = "Not My President: How Names and Titles Frame Political Figures",
    author = "van den Berg, Esther  and
      Korfhage, Katharina  and
      Ruppenhofer, Josef  and
      Wiegand, Michael  and
      Markert, Katja",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2101",
    doi = "10.18653/v1/W19-2101",
    pages = "1--6",
    abstract = "Naming and titling have been discussed in sociolinguistics as markers of status or solidarity. However, these functions have not been studied on a larger scale or for social media data. We collect a corpus of tweets mentioning presidents of six G20 countries by various naming forms. We show that naming variation relates to stance towards the president in a way that is suggestive of a framing effect mediated by respectfulness. This confirms sociolinguistic theory of naming and titling as markers of status.",
}
@inproceedings{lauretig-2019-identification,
    title = "Identification, Interpretability, and {B}ayesian Word Embeddings",
    author = "Lauretig, Adam",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2102",
    doi = "10.18653/v1/W19-2102",
    pages = "7--17",
    abstract = "Social scientists have recently turned to analyzing text using tools from natural language processing like word embeddings to measure concepts like ideology, bias, and affinity. However, word embeddings are difficult to use in the regression framework familiar to social scientists: embeddings are are neither identified, nor directly interpretable. I offer two advances on standard embedding models to remedy these problems. First, I develop Bayesian Word Embeddings with Automatic Relevance Determination priors, relaxing the assumption that all embedding dimensions have equal weight. Second, I apply work identifying latent variable models to anchor embeddings, identifying them, and making them interpretable and usable in a regression. I then apply this model and anchoring approach to two cases, the shift in internationalist rhetoric in the American presidents{'} inaugural addresses, and the relationship between bellicosity in American foreign policy decision-makers{'} deliberations. I find that inaugural addresses became less internationalist after 1945, which goes against the conventional wisdom, and that an increase in bellicosity is associated with an increase in hostile actions by the United States, showing that elite deliberations are not cheap talk, and helping confirm the validity of the model.",
}
@inproceedings{lynn-etal-2019-tweet,
    title = "Tweet Classification without the Tweet: An Empirical Examination of User versus Document Attributes",
    author = "Lynn, Veronica  and
      Giorgi, Salvatore  and
      Balasubramanian, Niranjan  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2103",
    doi = "10.18653/v1/W19-2103",
    pages = "18--28",
    abstract = "NLP naturally puts a primary focus on leveraging document language, occasionally considering user attributes as supplemental. However, as we tackle more social scientific tasks, it is possible user attributes might be of primary importance and the document supplemental. Here, we systematically investigate the predictive power of user-level features alone versus document-level features for document-level tasks. We first show user attributes can sometimes carry more task-related information than the document itself. For example, a tweet-level stance detection model using only 13 user-level attributes (i.e. features that did not depend on the specific tweet) was able to obtain a higher F1 than the top-performing SemEval participant. We then consider multiple tasks and a wider range of user attributes, showing the performance of strong document-only models can often be improved (as in stance, sentiment, and sarcasm) with user attributes, particularly benefiting tasks with stable {``}trait-like{''} outcomes (e.g. stance) most relative to frequently changing {``}state-like{''} outcomes (e.g. sentiment). These results not only support the growing work on integrating user factors into predictive systems, but that some of our NLP tasks might be better cast primarily as user-level (or human) tasks.",
}
@inproceedings{halterman-2019-geolocating,
    title = "Geolocating Political Events in Text",
    author = "Halterman, Andrew",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2104",
    doi = "10.18653/v1/W19-2104",
    pages = "29--39",
    abstract = "This work introduces a general method for automatically finding the locations where political events in text occurred. Using a novel set of 8,000 labeled sentences, I create a method to link automatically extracted events and locations in text. The model achieves human level performance on the annotation task and outperforms previous event geolocation systems. It can be applied to most event extraction systems across geographic contexts. I formalize the event{--}location linking task, describe the neural network model, describe the potential uses of such a system in political science, and demonstrate a workflow to answer an open question on the role of conventional military offensives in causing civilian casualties in the Syrian civil war.",
}
@inproceedings{ng-etal-2019-neural,
    title = "Neural Network Prediction of Censorable Language",
    author = "Ng, Kei Yin  and
      Feldman, Anna  and
      Peng, Jing  and
      Leberknight, Chris",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2105",
    doi = "10.18653/v1/W19-2105",
    pages = "40--46",
    abstract = "Internet censorship imposes restrictions on what information can be publicized or viewed on the Internet. According to Freedom House{'}s annual Freedom on the Net report, more than half the world{'}s Internet users now live in a place where the Internet is censored or restricted. China has built the world{'}s most extensive and sophisticated online censorship system. In this paper, we describe a new corpus of censored and uncensored social media tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts that mention {`}sensitive{'} topics or authored by {`}sensitive{'} users. We use this corpus to build a neural network classifier to predict censorship. Our model performs with a 88.50{\%} accuracy using only linguistic features. We discuss these features in detail and hypothesize that they could potentially be used for censorship circumvention.",
}
@inproceedings{courtland-etal-2019-modeling,
    title = "Modeling performance differences on cognitive tests using {LSTM}s and skip-thought vectors trained on reported media consumption.",
    author = "Courtland, Maury  and
      Davani, Aida  and
      Reyes, Melissa  and
      Yeh, Leigh  and
      Leung, Jun  and
      Kennedy, Brendan  and
      Dehghani, Morteza  and
      Zevin, Jason",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2106",
    doi = "10.18653/v1/W19-2106",
    pages = "47--53",
    abstract = "Cognitive tests have traditionally resorted to standardizing testing materials in the name of equality and because of the onerous nature of creating test items. This approach ignores participants{'} diverse language experiences that potentially significantly affect testing outcomes. Here, we seek to explain our prior finding of significant performance differences on two cognitive tests (reading span and SPiN) between clusters of participants based on their media consumption. Here, we model the language contained in these media sources using an LSTM trained on corpora of each cluster{'}s media sources to predict target words. We also model semantic similarity of test items with each cluster{'}s corpus using skip-thought vectors. We find robust, significant correlations between performance on the SPiN test and the LSTMs and skip-thought models we present here, but not the reading span test.",
}
@inproceedings{lukito-etal-2019-using,
    title = "Using time series and natural language processing to identify viral moments in the 2016 {U}.{S}. Presidential Debate",
    author = "Lukito, Josephine  and
      K Sarma, Prathusha  and
      Foley, Jordan  and
      Abhishek, Aman",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2107",
    doi = "10.18653/v1/W19-2107",
    pages = "54--64",
    abstract = "This paper proposes a method for identifying and studying viral moments or highlights during a political debate. Using a combined strategy of time series analysis and domain adapted word embeddings, this study provides an in-depth analysis of several key moments during the 2016 U.S. Presidential election. First, a time series outlier analysis is used to identify key moments during the debate. These moments had to result in a long-term shift in attention towards either Hillary Clinton or Donald Trump (i.e., a transient change outlier or an intervention, resulting in a permanent change in the time series). To assess whether these moments also resulted in a discursive shift, two corpora are produced for each potential viral moment (a pre-viral corpus and post-viral corpus). A domain adaptation layer learns weights to combine a generic and domain-specific (DS) word embedding into a domain adapted (DA) embedding. Words are then classified using a generic encoder+ classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induce discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.",
}
@inproceedings{mayfield-black-2019-stance,
    title = "Stance Classification, Outcome Prediction, and Impact Assessment: {NLP} Tasks for Studying Group Decision-Making",
    author = "Mayfield, Elijah  and
      Black, Alan",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2108",
    doi = "10.18653/v1/W19-2108",
    pages = "65--77",
    abstract = "In group decision-making, the nuanced process of conflict and resolution that leads to consensus formation is closely tied to the quality of decisions made. Behavioral scientists rarely have rich access to process variables, though, as unstructured discussion transcripts are difficult to analyze. Here, we define ways for NLP researchers to contribute to the study of groups and teams. We introduce three tasks alongside a large new corpus of over 400,000 group debates on Wikipedia. We describe the tasks and their importance, then provide baselines showing that BERT contextualized word embeddings consistently outperform other language representations.",
}
@inproceedings{duseja-jhamtani-2019-sociolinguistic,
    title = "A Sociolinguistic Study of Online Echo Chambers on {T}witter",
    author = "Duseja, Nikita  and
      Jhamtani, Harsh",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2109",
    doi = "10.18653/v1/W19-2109",
    pages = "78--83",
    abstract = "Online social media platforms such as Facebook and Twitter are increasingly facing criticism for polarization of users. One particular aspect which has caught the attention of various critics is presence of users in echo chambers - a situation wherein users are exposed mostly to the opinions which are in sync with their own views. In this paper, we perform a sociolinguistic study by comparing the tweets of users in echo chambers with the tweets of users not in echo chambers with similar levels of polarity on a broad topic. Specifically, we carry out a comparative analysis of tweet structure, lexical choices, and focus issues, and provide possible explanations for the results.",
}
@inproceedings{soldner-etal-2019-uphill,
    title = "Uphill from here: Sentiment patterns in videos from left- and right-wing {Y}ou{T}ube news channels",
    author = "Soldner, Felix  and
      Ho, Justin Chun-ting  and
      Makhortykh, Mykola  and
      van der Vegt, Isabelle W.J.  and
      Mozes, Maximilian  and
      Kleinberg, Bennett",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2110",
    doi = "10.18653/v1/W19-2110",
    pages = "84--93",
    abstract = "News consumption exhibits an increasing shift towards online sources, which bring platforms such as YouTube more into focus. Thus, the distribution of politically loaded news is easier, receives more attention, but also raises the concern of forming isolated ideological communities. Understanding how such news is communicated and received is becoming increasingly important. To expand our understanding in this domain, we apply a linguistic temporal trajectory analysis to analyze sentiment patterns in English-language videos from news channels on YouTube. We examine transcripts from videos distributed through eight channels with pro-left and pro-right political leanings. Using unsupervised clustering, we identify seven different sentiment patterns in the transcripts. We found that the use of two sentiment patterns differed significantly depending on political leaning. Furthermore, we used predictive models to examine how different sentiment patterns relate to video popularity and if they differ depending on the channel{'}s political leaning. No clear relations between sentiment patterns and popularity were found. However, results indicate, that videos from pro-right news channels are more popular and that a negative sentiment further increases that popularity, when sentiments are averaged for each video.",
}
@inproceedings{gillani-levy-2019-simple,
    title = "Simple dynamic word embeddings for mapping perceptions in the public sphere",
    author = "Gillani, Nabeel  and
      Levy, Roger",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2111",
    doi = "10.18653/v1/W19-2111",
    pages = "94--99",
    abstract = "Word embeddings trained on large-scale historical corpora can illuminate human biases and stereotypes that perpetuate social inequalities. These embeddings are often trained in separate vector space models defined according to different attributes of interest. In this paper, we introduce a single, unified dynamic embedding model that learns attribute-specific word embeddings and apply it to a novel dataset{---}talk radio shows from around the US{---}to analyze perceptions about refugees. We validate our model on a benchmark dataset and apply it to two corpora of talk radio shows averaging 117 million words produced over one month across 83 stations and 64 cities. Our findings suggest that dynamic word embeddings are capable of identifying nuanced differences in public discourse about contentious topics, suggesting their usefulness as a tool for better understanding how the public perceives and engages with different issues across time, geography, and other dimensions.",
}
@inproceedings{johnson-goldwasser-2019-modeling,
    title = "Modeling Behavioral Aspects of Social Media Discourse for Moral Classification",
    author = "Johnson, Kristen  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2112",
    doi = "10.18653/v1/W19-2112",
    pages = "100--109",
    abstract = "Political discourse on social media microblogs, specifically Twitter, has become an undeniable part of mainstream U.S. politics. Given the length constraint of tweets, politicians must carefully word their statements to ensure their message is understood by their intended audience. This constraint often eliminates the context of the tweet, making automatic analysis of social media political discourse a difficult task. To overcome this challenge, we propose simultaneous modeling of high-level abstractions of political language, such as political slogans and framing strategies, with abstractions of how politicians behave on Twitter. These behavioral abstractions can be further leveraged as forms of supervision in order to increase prediction accuracy, while reducing the burden of annotation. In this work, we use Probabilistic Soft Logic (PSL) to build relational models to capture the similarities in language and behavior that obfuscate political messages on Twitter. When combined, these descriptors reveal the moral foundations underlying the discourse of U.S. politicians online, \textit{across} differing governing administrations, showing how party talking points remain cohesive or change over time.",
}
@proceedings{ws-2019-evaluating,
    title = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    editor = "Rogers, Anna  and
      Drozd, Aleksandr  and
      Rumshisky, Anna  and
      Goldberg, Yoav",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2000",
}
@inproceedings{schwarzenberg-etal-2019-neural,
    title = "Neural Vector Conceptualization for Word Vector Space Interpretation",
    author = "Schwarzenberg, Robert  and
      Raithel, Lisa  and
      Harbecke, David",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2001",
    doi = "10.18653/v1/W19-2001",
    pages = "1--7",
    abstract = "Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, we train a neural model to conceptualize word vectors, which means that it activates higher order concepts it recognizes in a given vector. Contrary to prior approaches, our model operates in the original vector space and is capable of learning non-linear relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.",
}
@inproceedings{whitaker-etal-2019-characterizing,
    title = "Characterizing the Impact of Geometric Properties of Word Embeddings on Task Performance",
    author = "Whitaker, Brendan  and
      Newman-Griffis, Denis  and
      Haldar, Aparajita  and
      Ferhatosmanoglu, Hakan  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2002",
    doi = "10.18653/v1/W19-2002",
    pages = "8--17",
    abstract = "Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four properties of word embedding geometry, namely: position relative to the origin, distribution of features in the vector space, global pairwise distances, and local pairwise distances. We define a sequence of transformations to generate new embeddings that expose subsets of these properties to downstream models and evaluate change in task performance to understand the contribution of each property to NLP models. We transform publicly available pretrained embeddings from three popular toolkits (word2vec, GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model linguistic information in the vector space, and extrinsic tasks, which use vectors as input to machine learning models. We find that intrinsic evaluations are highly sensitive to absolute position, while extrinsic tasks rely primarily on local similarity. Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.",
}
@inproceedings{hellrich-etal-2019-influence,
    title = "The Influence of Down-Sampling Strategies on {SVD} Word Embedding Stability",
    author = "Hellrich, Johannes  and
      Kampe, Bernd  and
      Hahn, Udo",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2003",
    doi = "10.18653/v1/W19-2003",
    pages = "18--26",
    abstract = "The stability of word embedding algorithms, i.e., the consistency of the word representations they reveal when trained repeatedly on the same data set, has recently raised concerns. We here compare word embedding algorithms on three corpora of different sizes, and evaluate both their stability and accuracy. We find strong evidence that down-sampling strategies (used as part of their training procedures) are particularly influential for the stability of SVD-PPMI-type embeddings. This finding seems to explain diverging reports on their stability and lead us to a simple modification which provides superior stability as well as accuracy on par with skip-gram embedding",
}
@inproceedings{nandakumar-etal-2019-well,
    title = "How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions",
    author = "Nandakumar, Navnita  and
      Baldwin, Timothy  and
      Salehi, Bahar",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2004",
    doi = "10.18653/v1/W19-2004",
    pages = "27--34",
    abstract = "In this paper, we apply various embedding methods on multiword expressions to study how well they capture the nuances of non-compositional data. Our results from a pool of word-, character-, and document-level embbedings suggest that Word2vec performs the best, followed by FastText and Infersent. Moreover, we find that recently-proposed contextualised embedding models such as Bert and ELMo are not adept at handling non-compositionality in multiword expressions.",
}
@inproceedings{tiedemann-scherrer-2019-measuring,
    title = "Measuring Semantic Abstraction of Multilingual {NMT} with Paraphrase Recognition and Generation Tasks",
    author = {Tiedemann, J{\"o}rg  and
      Scherrer, Yves},
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2005",
    doi = "10.18653/v1/W19-2005",
    pages = "35--42",
    abstract = "In this paper, we investigate whether multilingual neural translation models learn stronger semantic abstractions of sentences than bilingual ones. We test this hypotheses by measuring the perplexity of such models when applied to paraphrases of the source language. The intuition is that an encoder produces better representations if a decoder is capable of recognizing synonymous sentences in the same language even though the model is never trained for that task. In our setup, we add 16 different auxiliary languages to a bidirectional bilingual baseline model (English-French) and test it with in-domain and out-of-domain paraphrases in English. The results show that the perplexity is significantly reduced in each of the cases, indicating that meaning can be grounded in translation. This is further supported by a study on paraphrase generation that we also include at the end of the paper.",
}
@inproceedings{thawani-etal-2019-swow,
    title = "{SWOW}-8500: Word Association task for Intrinsic Evaluation of Word Embeddings",
    author = "Thawani, Avijit  and
      Srivastava, Biplav  and
      Singh, Anil",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2006",
    doi = "10.18653/v1/W19-2006",
    pages = "43--51",
    abstract = "Downstream evaluation of pretrained word embeddings is expensive, more so for tasks where current state of the art models are very large architectures. Intrinsic evaluation using word similarity or analogy datasets, on the other hand, suffers from several disadvantages. We propose a novel intrinsic evaluation task employing large word association datasets (particularly the Small World of Words dataset). We observe correlations not just between performances on SWOW-8500 and previously proposed intrinsic tasks of word similarity prediction, but also with downstream tasks (eg. Text Classification and Natural Language Inference). Most importantly, we report better confidence intervals for scores on our word association task, with no fall in correlation with downstream performance.",
}
@inproceedings{mckinney-bock-bedrick-2019-classification,
    title = "Classification of Semantic Paraphasias: Optimization of a Word Embedding Model",
    author = "McKinney-Bock, Katy  and
      Bedrick, Steven",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2007",
    doi = "10.18653/v1/W19-2007",
    pages = "52--62",
    abstract = "In clinical assessment of people with aphasia, impairment in the ability to recall and produce words for objects (anomia) is assessed using a confrontation naming task, where a target stimulus is viewed and a corresponding label is spoken by the participant. Vector space word embedding models have had inital results in assessing semantic similarity of target-production pairs in order to automate scoring of this task; however, the resulting models are also highly dependent upon training parameters. To select an optimal family of models, we fit a beta regression model to the distribution of performance metrics on a set of 2,880 grid search models and evaluate the resultant first- and second-order effects to explore how parameterization affects model performance. Comparing to SimLex-999, we show that clinical data can be used in an evaluation task with comparable optimal parameter settings as standard NLP evaluation datasets.",
}
@inproceedings{chen-etal-2019-codah,
    title = "{CODAH}: An Adversarially-Authored Question Answering Dataset for Common Sense",
    author = "Chen, Michael  and
      D{'}Arcy, Mike  and
      Liu, Alisa  and
      Fernandez, Jared  and
      Downey, Doug",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2008",
    doi = "10.18653/v1/W19-2008",
    pages = "63--69",
    abstract = "Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems. Workers are rewarded for submissions that models fail to answer correctly both before and after fine-tuning (in cross-validation). We create 2.8k questions via this procedure and evaluate the performance of multiple state-of-the-art question answering systems on our dataset. We observe a significant gap between human performance, which is 95.3{\%}, and the performance of the best baseline accuracy of 65.3{\%} by the OpenAI GPT model.",
}
@inproceedings{hershcovich-etal-2019-syntactic,
    title = "Syntactic Interchangeability in Word Embedding Models",
    author = "Hershcovich, Daniel  and
      Toledo, Assaf  and
      Halfon, Alon  and
      Slonim, Noam",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2009",
    doi = "10.18653/v1/W19-2009",
    pages = "70--76",
    abstract = "Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters{---}context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.",
}
@inproceedings{romanov-khusainova-2019-evaluation,
    title = "Evaluation of Morphological Embeddings for {E}nglish and {R}ussian Languages",
    author = "Romanov, Vitaly  and
      Khusainova, Albina",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2010",
    doi = "10.18653/v1/W19-2010",
    pages = "77--81",
    abstract = "This paper evaluates morphology-based embeddings for English and Russian languages. Despite the interest and introduction of several morphology based word embedding models in the past and acclaimed performance improvements on word similarity and language modeling tasks, in our experiments, we did not observe any stable preference over two of our baseline models - SkipGram and FastText. The performance exhibited by morphological embeddings is the average of the two baselines mentioned above.",
}
@inproceedings{jin-etal-2019-probing,
    title = "Probing Biomedical Embeddings from Language Models",
    author = "Jin, Qiao  and
      Dhingra, Bhuwan  and
      Cohen, William  and
      Lu, Xinghua",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2011",
    doi = "10.18653/v1/W19-2011",
    pages = "82--89",
    abstract = "Contextualized word embeddings derived from pre-trained language models (LMs) show significant improvements on downstream NLP tasks. Pre-training on domain-specific corpora, such as biomedical articles, further improves their performance. In this paper, we conduct probing experiments to determine what additional information is carried intrinsically by the in-domain trained contextualized embeddings. For this we use the pre-trained LMs as fixed feature extractors and restrict the downstream task models to not have additional sequence modeling layers. We compare BERT (Devlin et al. 2018), ELMo (Peters et al., 2018), BioBERT (Lee et al., 2019) and BioELMo, a biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We use visualization and nearest neighbor analysis to show that better encoding of entity-type and relational information leads to this superiority.",
}
@inproceedings{yamshchikov-etal-2019-dyr,
    title = "Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings",
    author = "Yamshchikov, Ivan P.  and
      Shibaev, Viascheslav  and
      Tikhonov, Alexey",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2012",
    doi = "10.18653/v1/W19-2012",
    pages = "90--94",
    abstract = "This paper explores modern word embeddings in the context of sound symbolism. Using basic properties of the representations space one can construct semantic axes. A method is proposed to measure if the presence of individual sounds in a given word shifts its semantics of that word along a specific axis. It is shown that, in accordance with several experimental and statistical results, word embeddings capture symbolism for certain sounds.",
}
@inproceedings{mamou-etal-2019-multi,
    title = "Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion",
    author = "Mamou, Jonathan  and
      Pereg, Oren  and
      Wasserblat, Moshe  and
      Dagan, Ido",
    booktitle = "Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-2013",
    doi = "10.18653/v1/W19-2013",
    pages = "95--101",
    abstract = "In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We show that, over this dataset, our algorithm provides up to 5 mean average precision points over the best baseline.",
}
@proceedings{ws-2019-clinical,
    title = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    editor = "Rumshisky, Anna  and
      Roberts, Kirk  and
      Bethard, Steven  and
      Naumann, Tristan",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1900",
}
@inproceedings{tao-etal-2019-effective,
    title = "Effective Feature Representation for Clinical Text Concept Extraction",
    author = "Tao, Yifeng  and
      Godefroy, Bruno  and
      Genthial, Guillaume  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1901",
    doi = "10.18653/v1/W19-1901",
    pages = "1--14",
    abstract = "Crucial information about the practice of healthcare is recorded only in free-form text, which creates an enormous opportunity for high-impact NLP. However, annotated healthcare datasets tend to be small and expensive to obtain, which raises the question of how to make maximally efficient uses of the available data. To this end, we develop an LSTM-CRF model for combining unsupervised word representations and hand-built feature representations derived from publicly available healthcare ontologies. We show that this combined model yields superior performance on five datasets of diverse kinds of healthcare text (clinical, social, scientific, commercial). Each involves the labeling of complex, multi-word spans that pick out different healthcare concepts. We also introduce a new labeled dataset for identifying the treatment relations between drugs and diseases.",
}
@inproceedings{jain-etal-2019-analysis,
    title = "An Analysis of Attention over Clinical Notes for Predictive Tasks",
    author = "Jain, Sarthak  and
      Mohammadi, Ramin  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1902",
    doi = "10.18653/v1/W19-1902",
    pages = "15--21",
    abstract = "The shift to electronic medical records (EMRs) has engendered research into machine learning and natural language technologies to analyze patient records, and to predict from these clinical outcomes of interest. Two observations motivate our aims here. First, unstructured notes contained within EMR often contain key information, and hence should be exploited by models. Second, while strong predictive performance is important, interpretability of models is perhaps equally so for applications in this domain. Together, these points suggest that neural models for EMR may benefit from incorporation of attention over notes, which one may hope will both yield performance gains and afford transparency in predictions. In this work we perform experiments to explore this question using two EMR corpora and four different predictive tasks, that: (i) inclusion of attention mechanisms is critical for neural encoder modules that operate over notes fields in order to yield competitive performance, but, (ii) unfortunately, while these boost predictive performance, it is decidedly less clear whether they provide meaningful support for predictions.",
}
@inproceedings{miller-etal-2019-extracting,
    title = "Extracting Adverse Drug Event Information with Minimal Engineering",
    author = "Miller, Timothy  and
      Geva, Alon  and
      Dligach, Dmitriy",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1903",
    doi = "10.18653/v1/W19-1903",
    pages = "22--27",
    abstract = "In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods. We use the 2018 N2C2 shared task data as our gold standard data set for training. We train support vector machine classifiers to detect drug and drug attribute spans, and pair these detected entities as training instances for an SVM relation classifier, with both systems using standard features. We compare to baseline neural methods that use standard contextualized embedding representations for entity and relation extraction. The SVM-based system and a neural system obtain comparable results, with the SVM system doing better on concepts and the neural system performing better on relation extraction tasks. The neural system obtains surprisingly strong results compared to the system based on years of research in developing features for information extraction.",
}
@inproceedings{marinho-etal-2019-hierarchical,
    title = "Hierarchical Nested Named Entity Recognition",
    author = "Marinho, Zita  and
      Mendes, Afonso  and
      Miranda, Sebasti{\~a}o  and
      Nogueira, David",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1904",
    doi = "10.18653/v1/W19-1904",
    pages = "28--34",
    abstract = "In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further introduce a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our proposed model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.",
}
@inproceedings{melamud-shivade-2019-towards,
    title = "Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models",
    author = "Melamud, Oren  and
      Shivade, Chaitanya",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1905",
    doi = "10.18653/v1/W19-1905",
    pages = "35--45",
    abstract = "Large-scale clinical data is invaluable to driving many computational scientific advances today. However, understandable concerns regarding patient privacy hinder the open dissemination of such data and give rise to suboptimal siloed research. De-identification methods attempt to address these concerns but were shown to be susceptible to adversarial attacks. In this work, we focus on the vast amounts of unstructured natural language data stored in clinical notes and propose to automatically generate synthetic clinical notes that are more amenable to sharing using generative models trained on real de-identified records. To evaluate the merit of such notes, we measure both their privacy preservation properties as well as utility in training clinical NLP models. Experiments using neural language models yield notes whose utility is close to that of the real ones in some clinical NLP tasks, yet leave ample room for future improvements.",
}
@inproceedings{liang-etal-2019-novel,
    title = "A Novel System for Extractive Clinical Note Summarization using {EHR} Data",
    author = "Liang, Jennifer  and
      Tsou, Ching-Huei  and
      Poddar, Ananya",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1906",
    doi = "10.18653/v1/W19-1906",
    pages = "46--54",
    abstract = "While much data within a patient{'}s electronic health record (EHR) is coded, crucial information concerning the patient{'}s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present our clinical note processing pipeline, which extends beyond basic medical natural language processing (NLP) with concept recognition and relation detection to also include components specific to EHR data, such as structured data associated with the encounter, sentence-level clinical aspects, and structures of the clinical notes. We report on the use of this pipeline in a disease-specific extractive text summarization task on clinical notes, focusing primarily on progress notes by physicians and nurse practitioners. We show how the addition of EHR-specific components to the pipeline resulted in an improvement in our overall system performance and discuss the potential impact of EHR-specific components on other higher-level clinical NLP tasks.",
}
@inproceedings{pierson-fairon-2019-study,
    title = "Study of lexical aspect in the {F}rench medical language. Development of a lexical resource",
    author = "Pierson, Agathe  and
      Fairon, C{\'e}drick",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1907",
    doi = "10.18653/v1/W19-1907",
    pages = "55--64",
    abstract = "This paper details the development of a linguistic resource designed to improve temporal information extraction systems and to integrate aspectual values. After a brief review of recent works in temporal information extraction for the medical area, we discuss the linguistic notion of aspect and how it got a place in the NLP field. Then, we present our clinical data and describe the five-step approach adopted in this study. Finally, we represent the linguistic resource itself and explain how we elaborated it and which properties were selected for the creation of the tables.",
}
@inproceedings{lin-etal-2019-bert,
    title = "A {BERT}-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction",
    author = "Lin, Chen  and
      Miller, Timothy  and
      Dligach, Dmitriy  and
      Bethard, Steven  and
      Savova, Guergana",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1908",
    doi = "10.18653/v1/W19-1908",
    pages = "65--71",
    abstract = "Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated {``}silver{''} instances.",
}
@inproceedings{alsentzer-etal-2019-publicly,
    title = "Publicly Available Clinical {BERT} Embeddings",
    author = "Alsentzer, Emily  and
      Murphy, John  and
      Boag, William  and
      Weng, Wei-Hung  and
      Jindi, Di  and
      Naumann, Tristan  and
      McDermott, Matthew",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1909",
    doi = "10.18653/v1/W19-1909",
    pages = "72--78",
    abstract = "Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on 3/5 clinical NLP tasks, establishing a new state-of-the-art on the MedNLI dataset. We find that these domain-specific models are not as performant on 2 clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.",
}
@inproceedings{piad-morffis-etal-2019-general,
    title = "A General-Purpose Annotation Model for Knowledge Discovery: Case Study in {S}panish Clinical Text",
    author = "Piad-Morffis, Alejandro  and
      Guit{\'e}rrez, Yoan  and
      Estevez-Velarde, Suilan  and
      Mu{\~n}oz, Rafael",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1910",
    doi = "10.18653/v1/W19-1910",
    pages = "79--88",
    abstract = "Knowledge discovery from text in natural language is a task usually aided by the manual construction of annotated corpora. Specifically in the clinical domain, several annotation models are used depending on the characteristics of the task to solve (e.g., named entity recognition, relation extraction, etc.). However, few general-purpose annotation models exist, that can support a broad range of knowledge extraction tasks. This paper presents an annotation model designed to capture a large portion of the semantics of natural language text. The structure of the annotation model is presented, with examples of annotated sentences and a brief description of each semantic role and relation defined. This research focuses on an application to clinical texts in the Spanish language. Nevertheless, the presented annotation model is extensible to other domains and languages. An example of annotated sentences, guidelines, and suitable configuration files for an annotation tool are also provided for the research community.",
}
@inproceedings{khan-khattak-etal-2019-predicting,
    title = "Predicting {ICU} transfers using text messages between nurses and doctors",
    author = "Khan Khattak, Faiza  and
      Pou-Prom, Chlo{\'e}  and
      Wu, Robert  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1911",
    doi = "10.18653/v1/W19-1911",
    pages = "89--94",
    abstract = "We explore the use of real-time clinical information, i.e., text messages sent between nurses and doctors regarding patient conditions in order to predict transfer to the intensive care unit(ICU). Preliminary results, in data from five hospitals, indicate that, despite being short and full of noise, text messages can augment other visit information to improve the performance of ICU transfer prediction.",
}
@inproceedings{mondal-etal-2019-medical,
    title = "Medical Entity Linking using Triplet Network",
    author = "Mondal, Ishani  and
      Purkayastha, Sukannya  and
      Sarkar, Sudeshna  and
      Goyal, Pawan  and
      Pillai, Jitesh  and
      Bhattacharyya, Amitava  and
      Gattu, Mahanandeeshwar",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1912",
    doi = "10.18653/v1/W19-1912",
    pages = "95--100",
    abstract = "Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin.",
}
@inproceedings{fan-2019-annotating,
    title = "Annotating and Characterizing Clinical Sentences with Explicit Why-{QA} Cues",
    author = "Fan, Jungwei",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1913",
    doi = "10.18653/v1/W19-1913",
    pages = "101--106",
    abstract = "Many clinical information needs can be stated as why-questions. The answers to them represent important clinical reasoning and justification. Clinical notes are a rich source for such why-question answering (why-QA). However, there are few dedicated corpora, and little is known about the characteristics of clinical why-QA narratives. To address this gap, the study performed manual annotation of 277 sentences containing explicit why-QA cues and summarized their quantitative and qualitative properties. The contributions are: 1) sharing a seed corpus that can be used for various QA-related training purposes, 2) adding to our knowledge about the diversity and distribution of clinical why-QA contents.",
}
@inproceedings{hou-etal-2019-extracting,
    title = "Extracting Factual {M}in/Max Age Information from Clinical Trial Studies",
    author = "Hou, Yufang  and
      Ganguly, Debasis  and
      Deleris, L{\'e}a  and
      Bonin, Francesca",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1914",
    doi = "10.18653/v1/W19-1914",
    pages = "107--116",
    abstract = "Population age information is an essential characteristic of clinical trials. In this paper, we focus on extracting minimum and maximum (min/max) age values for the study samples from clinical research articles. Specifically, we investigate the use of a neural network model for question answering to address this information extraction task. The min/max age QA model is trained on the massive structured clinical study records from ClinicalTrials.gov. For each article, based on multiple min and max age values extracted from the QA model, we predict both actual min/max age values for the study samples and filter out non-factual age expressions. Our system improves the results over (i) a passage retrieval based IE system and (ii) a CRF-based system by a large margin when evaluated on an annotated dataset consisting of 50 research papers on smoking cessation.",
}
@inproceedings{holderness-etal-2019-distinguishing,
    title = "Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records",
    author = "Holderness, Eben  and
      Cawkwell, Philip  and
      Bolton, Kirsten  and
      Pustejovsky, James  and
      Hall, Mei-Hua",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1915",
    doi = "10.18653/v1/W19-1915",
    pages = "117--123",
    abstract = "Recently natural language processing (NLP) tools have been developed to identify and extract salient risk indicators in electronic health records (EHRs). Sentiment analysis, although widely used in non-medical areas for improving decision making, has been studied minimally in the clinical setting. In this study, we undertook, to our knowledge, the first domain adaptation of sentiment analysis to psychiatric EHRs by defining psychiatric clinical sentiment, performing an annotation project, and evaluating multiple sentence-level sentiment machine learning (ML) models. Results indicate that off-the-shelf sentiment analysis tools fail in identifying clinically positive or negative polarity, and that the definition of clinical sentiment that we provide is learnable with relatively small amounts of training data. This project is an initial step towards further refining sentiment analysis methods for clinical use. Our long-term objective is to incorporate the results of this project as part of a machine learning model that predicts inpatient readmission risk. We hope that this work will initiate a discussion concerning domain adaptation of sentiment analysis to the clinical setting.",
}
@inproceedings{soares-etal-2019-medical,
    title = "Medical Word Embeddings for {S}panish: Development and Evaluation",
    author = "Soares, Felipe  and
      Villegas, Marta  and
      Gonzalez-Agirre, Aitor  and
      Krallinger, Martin  and
      Armengol-Estap{\'e}, Jordi",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1916",
    doi = "10.18653/v1/W19-1916",
    pages = "124--133",
    abstract = "Word embeddings are representations of words in a dense vector space. Although they are not recent phenomena in Natural Language Processing (NLP), they have gained momentum after the recent developments of neural methods and Word2Vec. Regarding their applications in medical and clinical NLP, they are invaluable resources when training in-domain named entity recognition systems, classifiers or taggers, for instance. Thus, the development of tailored word embeddings for medical NLP is of great interest. However, we identified a gap in the literature which we aim to fill in this paper: the availability of embeddings for medical NLP in Spanish, as well as a standardized form of intrinsic evaluation. Since most work has been done for English, some established datasets for intrinsic evaluation are already available. In this paper, we show the steps we employed to adapt such datasets for the first time to Spanish, of particular relevance due to the considerable volume of EHRs in this language, as well as the creation of in-domain medical word embeddings for the Spanish using the state-of-the-art FastText model. We performed intrinsic evaluation with our adapted datasets, as well as extrinsic evaluation with a named entity recognition systems using a baseline embedding of general-domain. Both experiments proved that our embeddings are suitable for use in medical NLP in the Spanish language, and are more accurate than general-domain ones.",
}
@inproceedings{liu-etal-2019-attention,
    title = "Attention Neural Model for Temporal Relation Extraction",
    author = "Liu, Sijia  and
      Wang, Liwei  and
      Chaudhary, Vipin  and
      Liu, Hongfang",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1917",
    doi = "10.18653/v1/W19-1917",
    pages = "134--139",
    abstract = "Neural network models have shown promise in the temporal relation extraction task. In this paper, we present the attention based neural network model to extract the containment relations within sentences from clinical narratives. The attention mechanism used on top of GRU model outperforms the existing state-of-the-art neural network models on THYME corpus in intra-sentence temporal relation extraction.",
}
@inproceedings{kazi-kahanda-2019-automatically,
    title = "Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations",
    author = "Kazi, Nazmul  and
      Kahanda, Indika",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1918",
    doi = "10.18653/v1/W19-1918",
    pages = "140--148",
    abstract = "Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and non-verbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.",
}
@inproceedings{ehsani-etal-2019-clinical,
    title = "Clinical Data Classification using Conditional Random Fields and Neural Parsing for Morphologically Rich Languages",
    author = "Ehsani, Razieh  and
      Niemi, Tyko  and
      Khullar, Gaurav  and
      Leivo, Tiina",
    booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1919",
    doi = "10.18653/v1/W19-1919",
    pages = "149--155",
    abstract = "Past prescriptions constitute a central element in patient records. These are often written in an unstructured and brief form. Extracting information from such prescriptions enables the development of automated processes in the medical data mining field. This paper presents a Conditional Random Fields (CRFs) based approach to extract relevant information from prescriptions. We focus on Finnish language prescriptions and make use of Finnish language specific features. Our labeling accuracy is 95{\%}, which compares favorably to the current state-of-the-art in English language prescriptions. This, to the best of our knowledge, is the first such work for the Finnish language.",
}
@proceedings{ws-2019-shortcomings,
    title = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    editor = "Bernardi, Raffaella  and
      Fernandez, Raquel  and
      Gella, Spandana  and
      Kafle, Kushal  and
      Kanan, Christopher  and
      Lee, Stefan  and
      Nabi, Moin",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1800",
}
@inproceedings{grand-belinkov-2019-adversarial,
    title = "Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects",
    author = "Grand, Gabriel  and
      Belinkov, Yonatan",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1801",
    doi = "10.18653/v1/W19-1801",
    pages = "1--13",
    abstract = "Visual question answering (VQA) models have been shown to over-rely on linguistic biases in VQA datasets, answering questions {``}blindly{''} without considering visual context. Adversarial regularization (AdvReg) aims to address this issue via an adversary sub-network that encourages the main model to learn a bias-free representation of the question. In this work, we investigate the strengths and shortcomings of AdvReg with the goal of better understanding how it affects inference in VQA models. Despite achieving a new state-of-the-art on VQA-CP, we find that AdvReg yields several undesirable side-effects, including unstable gradients and sharply reduced performance on in-domain examples. We demonstrate that gradual introduction of regularization during training helps to alleviate, but not completely solve, these issues. Through error analyses, we observe that AdvReg improves generalization to binary questions, but impairs performance on questions with heterogeneous answer distributions. Qualitatively, we also find that regularized models tend to over-rely on visual features, while ignoring important linguistic cues in the question. Our results suggest that AdvReg requires further refinement before it can be considered a viable bias mitigation technique for VQA.",
}
@inproceedings{wiriyathammabhum-etal-2019-referring,
    title = "Referring to Objects in Videos Using Spatio-Temporal Identifying Descriptions",
    author = "Wiriyathammabhum, Peratham  and
      Shrivastava, Abhinav  and
      Morariu, Vlad  and
      Davis, Larry",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1802",
    doi = "10.18653/v1/W19-1802",
    pages = "14--25",
    abstract = "This paper presents a new task, the grounding of spatio-temporal identifying descriptions in videos. Previous work suggests potential bias in existing datasets and emphasizes the need for a new data creation schema to better model linguistic structure. We introduce a new data collection scheme based on grammatical constraints for surface realization to enable us to investigate the problem of grounding spatio-temporal identifying descriptions in videos. We then propose a two-stream modular attention network that learns and grounds spatio-temporal identifying descriptions based on appearance and motion. We show that motion modules help to ground motion-related words and also help to learn in appearance modules because modular neural networks resolve task interference between modules. Finally, we propose a future challenge and a need for a robust system arising from replacing ground truth visual annotations with automatic video object detector and temporal event localization.",
}
@inproceedings{pavlopoulos-etal-2019-survey,
    title = "A Survey on Biomedical Image Captioning",
    author = "Pavlopoulos, John  and
      Kougia, Vasiliki  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1803",
    doi = "10.18653/v1/W19-1803",
    pages = "26--36",
    abstract = "Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians. This article is the first survey of biomedical image captioning, discussing datasets, evaluation measures, and state of the art methods. Additionally, we suggest two baselines, a weak and a stronger one; the latter outperforms all current state of the art systems on one of the datasets.",
}
@inproceedings{conser-etal-2019-revisiting,
    title = "Revisiting Visual Grounding",
    author = "Conser, Erik  and
      Hahn, Kennedy  and
      Watson, Chandler  and
      Mitchell, Melanie",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1804",
    doi = "10.18653/v1/W19-1804",
    pages = "37--46",
    abstract = "We revisit a particular visual grounding method: the {``}Image Retrieval Using Scene Graphs{''} (IRSG) system of Johnson et al. Our experiments indicate that the system does not effectively use its learned object-relationship models. We also look closely at the IRSG dataset, as well as the widely used Visual Relationship Dataset (VRD) that is adapted from it. We find that these datasets exhibit bias that allows methods that ignore relationships to perform relatively well. We also describe several other problems with the IRSG dataset, and report on experiments using a subset of the dataset in which the biases and other problems are removed. Our studies contribute to a more general effort: that of better understanding what machine-learning methods that combine language and vision actually learn and what popular datasets actually test.",
}
@inproceedings{modi-parde-2019-steep,
    title = "The Steep Road to Happily Ever after: an Analysis of Current Visual Storytelling Models",
    author = "Modi, Yatri  and
      Parde, Natalie",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1805",
    doi = "10.18653/v1/W19-1805",
    pages = "47--57",
    abstract = "Visual storytelling is an intriguing and complex task that only recently entered the research arena. In this work, we survey relevant work to date, and conduct a thorough error analysis of three very recent approaches to visual storytelling. We categorize and provide examples of common types of errors, and identify key shortcomings in current work. Finally, we make recommendations for addressing these limitations in the future.",
}
@inproceedings{alikhani-stone-2019-caption,
    title = "{``}Caption{''} as a Coherence Relation: Evidence and Implications",
    author = "Alikhani, Malihe  and
      Stone, Matthew",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1806",
    doi = "10.18653/v1/W19-1806",
    pages = "58--67",
    abstract = "We study verbs in image{--}text corpora, contrasting \textit{caption} corpora, where texts are explicitly written to characterize image content, with \textit{depiction} corpora, where texts and images may stand in more general relations. Captions show a distinctively limited distribution of verbs, with strong preferences for specific tense, aspect, lexical aspect, and semantic field. These limitations, which appear in data elicited by a range of methods, restrict the utility of caption corpora to inform image retrieval, multimodal document generation, and perceptually-grounded semantic models. We suggest that these limitations reflect the discourse constraints in play when subjects write texts to accompany imagery, so we argue that future development of image{--}text corpora should work to increase the diversity of event descriptions, while looking explicitly at the different ways text and imagery can be coherently related.",
}
@inproceedings{singhal-etal-2019-learning,
    title = "Learning Multilingual Word Embeddings Using Image-Text Data",
    author = "Singhal, Karan  and
      Raman, Karthik  and
      ten Cate, Balder",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1807",
    doi = "10.18653/v1/W19-1807",
    pages = "68--77",
    abstract = "There has been significant interest recently in learning multilingual word embeddings {--} in which semantically similar words across languages have similar embeddings. State-of-the-art approaches have relied on expensive labeled data, which is unavailable for low-resource languages, or have involved post-hoc unification of monolingual embeddings. In the present paper, we investigate the efficacy of multilingual embeddings learned from weakly-supervised image-text data. In particular, we propose methods for learning multilingual embeddings using image-text data, by enforcing similarity between the representations of the image and that of the text. Our experiments reveal that even without using any expensive labeled data, a bag-of-words-based embedding model trained on image-text data achieves performance comparable to the state-of-the-art on crosslingual semantic similarity tasks.",
}
@inproceedings{lala-etal-2019-grounded,
    title = "Grounded Word Sense Translation",
    author = "Lala, Chiraag  and
      Madhyastha, Pranava  and
      Specia, Lucia",
    booktitle = "Proceedings of the Second Workshop on Shortcomings in Vision and Language",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1808",
    doi = "10.18653/v1/W19-1808",
    pages = "78--85",
    abstract = "Recent work on visually grounded language learning has focused on broader applications of grounded representations, such as visual question answering and multimodal machine translation. In this paper we consider grounded word sense translation, i.e. the task of correctly translating an ambiguous source word given the corresponding textual and visual context. Our main objective is to investigate the extent to which images help improve word-level (lexical) translation quality. We do so by first studying the dataset for this task to understand the scope and challenges of the task. We then explore different data settings, image features, and ways of grounding to investigate the gain from using images in each of the combinations. We find that grounding on the image is specially beneficial in weaker unidirectional recurrent translation models. We observe that adding structured image information leads to stronger gains in lexical translation accuracy.",
}
@proceedings{ws-2019-speech,
    title = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    editor = "Heidi Christensen, University of Sheffield  and
      Kristy Hollingshead, Florida Institute for Human  and
      Cognition, Machine  and
      Emily Prud{'}hommeaux, Boston College  and
      Frank Rudzicz, University of Toronto  and
      Keith Vertanen, Michigan Technological University",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1700",
}
@inproceedings{virkkunen-etal-2019-user,
    title = "A user study to compare two conversational assistants designed for people with hearing impairments",
    author = {Virkkunen, Anja  and
      Lukkarila, Juri  and
      Palom{\"a}ki, Kalle  and
      Kurimo, Mikko},
    booktitle = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1701",
    doi = "10.18653/v1/W19-1701",
    pages = "1--8",
    abstract = "Participating in conversations can be difficult for people with hearing loss, especially in acoustically challenging environments. We studied the preferences the hearing impaired have for a personal conversation assistant based on automatic speech recognition (ASR) technology. We created two prototypes which were evaluated by hearing impaired test users. This paper qualitatively compares the two based on the feedback obtained from the tests. The first prototype was a proof-of-concept system running real-time ASR on a laptop. The second prototype was developed for a mobile device with the recognizer running on a separate server. In the mobile device, augmented reality (AR) was used to help the hearing impaired observe gestures and lip movements of the speaker simultaneously with the transcriptions. Several testers found the systems useful enough to use in their daily lives, with majority preferring the mobile AR version. The biggest concern of the testers was the accuracy of the transcriptions and the lack of speaker identification.",
}
@inproceedings{kafle-etal-2019-modeling,
    title = "Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues",
    author = "Kafle, Sushant  and
      Alm, Cissi Ovesdotter  and
      Huenerfauth, Matt",
    booktitle = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1702",
    doi = "10.18653/v1/W19-1702",
    pages = "9--16",
    abstract = "Prosodic cues in conversational speech aid listeners in discerning a message. We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn. Individuals who are Deaf and Hard of Hearing often rely on real-time captions in live meetings. Word error rate, a traditional metric for evaluating automatic speech recognition (ASR), fails to capture that some words are more important for a system to transcribe correctly than others. We present and evaluate neural architectures that use acoustic features for 3-class word importance prediction. Our model performs competitively against state-of-the-art text-based word-importance prediction models, and it demonstrates particular benefits when operating on imperfect ASR output.",
}
@inproceedings{cao-etal-2019-permanent,
    title = "Permanent Magnetic Articulograph ({PMA}) vs Electromagnetic Articulograph ({EMA}) in Articulation-to-Speech Synthesis for Silent Speech Interface",
    author = "Cao, Beiming  and
      Sebkhi, Nordine  and
      Mau, Ted  and
      Inan, Omer T.  and
      Wang, Jun",
    booktitle = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1703",
    doi = "10.18653/v1/W19-1703",
    pages = "17--23",
    abstract = "Silent speech interfaces (SSIs) are devices that enable speech communication when audible speech is unavailable. Articulation-to-speech (ATS) synthesis is a software design in SSI that directly converts articulatory movement information into audible speech signals. Permanent magnetic articulograph (PMA) is a wireless articulator motion tracking technology that is similar to commercial, wired Electromagnetic Articulograph (EMA). PMA has shown great potential for practical SSI applications, because it is wireless. The ATS performance of PMA, however, is unknown when compared with current EMA. In this study, we compared the performance of ATS using a PMA we recently developed and a commercially available EMA (NDI Wave system). Datasets with same stimuli and size that were collected from tongue tip were used in the comparison. The experimental results indicated the performance of PMA was close to, although not as equally good as that of EMA. Furthermore, in PMA, converting the raw magnetic signals to positional signals did not significantly affect the performance of ATS, which support the future direction in PMA-based ATS can be focused on the use of positional signals to maximize the benefit of spatial analysis.",
}
@inproceedings{wisler-etal-2019-speech,
    title = "Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis",
    author = "Wisler, Alan  and
      Teplansky, Kristin  and
      Green, Jordan  and
      Yunusova, Yana  and
      Campbell, Thomas  and
      Heitzman, Daragh  and
      Wang, Jun",
    booktitle = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1704",
    doi = "10.18653/v1/W19-1704",
    pages = "24--31",
    abstract = "Amyotrophic Lateral Sclerosis (ALS) is a progressive neurological disease that leads to degeneration of motor neurons and, as a result, inhibits the ability of the brain to control muscle movements. Monitoring the progression of ALS is of fundamental importance due to the wide variability in disease outlook that exists across patients. This progression is typically tracked using the ALS functional rating scale - revised (ALSFRS-R), which is the current clinical assessment of a patient{'}s level of functional impairment including speech and other motor tasks. In this paper, we investigated automatic estimation of the ALSFRS-R bulbar subscore from acoustic and articulatory movement samples. Experimental results demonstrated the AFSFRS-R bulbar subscore can be predicted from speech samples, which has clinical implication for automatic monitoring of the disease progression of ALS using speech information.",
}
@inproceedings{sohail-traum-2019-blissymbolics,
    title = "A Blissymbolics Translation System",
    author = "Sohail, Usman  and
      Traum, David",
    booktitle = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1705",
    doi = "10.18653/v1/W19-1705",
    pages = "32--36",
    abstract = "Blissymbolics (Bliss) is a pictographic writing system that is used by people with communication disorders. Bliss attempts to create a writing system that makes words easier to distinguish by using pictographic symbols that encapsulate meaning rather than sound, as the English alphabet does for example. Users of Bliss rely on human interpreters to use Bliss. We created a translation system from Bliss to natural English with the hopes of decreasing the reliance on human interpreters by the Bliss community. We first discuss the basic rules of Blissymbolics. Then we point out some of the challenges associated with developing computer assisted tools for Blissymbolics. Next we talk about our ongoing work in developing a translation system, including current limitations, and future work. We conclude with a set of examples showing the current capabilities of our translation system.",
}
@inproceedings{adhikary-etal-2019-investigating,
    title = "Investigating Speech Recognition for Improving Predictive {AAC}",
    author = "Adhikary, Jiban  and
      Watling, Robbie  and
      Fletcher, Crystal  and
      Stanage, Alex  and
      Vertanen, Keith",
    booktitle = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1706",
    doi = "10.18653/v1/W19-1706",
    pages = "37--43",
    abstract = "Making good letter or word predictions can help accelerate the communication of users of high-tech AAC devices. This is particularly important for real-time person-to-person conversations. We investigate whether per forming speech recognition on the speaking-side of a conversation can improve language model based predictions. We compare the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines (Google and IBM Watson). We found that despite recognition word error rates of 7-16{\%}, our ensemble of N-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts.",
}
@inproceedings{dong-etal-2019-noisy,
    title = "Noisy Neural Language Modeling for Typing Prediction in {BCI} Communication",
    author = "Dong, Rui  and
      Smith, David  and
      Dudy, Shiran  and
      Bedrick, Steven",
    booktitle = "Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1707",
    doi = "10.18653/v1/W19-1707",
    pages = "44--51",
    abstract = "Language models have broad adoption in predictive typing tasks. When the typing history contains numerous errors, as in open-vocabulary predictive typing with brain-computer interface (BCI) systems, we observe significant performance degradation in both n-gram and recurrent neural network language models trained on clean text. In evaluations of ranking character predictions, training recurrent LMs on noisy text makes them much more robust to noisy histories, even when the error model is misspecified. We also propose an effective strategy for combining evidence from multiple ambiguous histories of BCI electroencephalogram measurements.",
}
@proceedings{ws-2019-combined,
    title = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    editor = "Bhatia, Archna  and
      Bisk, Yonatan  and
      Kordjamshidi, Parisa  and
      Thomason, Jesse",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1600",
}
@inproceedings{katsakioris-etal-2019-corpus,
    title = "Corpus of Multimodal Interaction for Collaborative Planning",
    author = "Katsakioris, Miltiadis Marios  and
      Hastie, Helen  and
      Konstas, Ioannis  and
      Laskov, Atanas",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1601",
    doi = "10.18653/v1/W19-1601",
    pages = "1--6",
    abstract = "As autonomous systems become more commonplace, we need a way to easily and naturally communicate to them our goals and collaboratively come up with a plan on how to achieve these goals. To this end, we conducted a Wizard of Oz study to gather data and investigate the way operators would collaboratively make plans via a conversational {`}planning assistant{'} for remote autonomous systems. We present here a corpus of 22 dialogs from expert operators, which can be used to train such a system. Data analysis shows that multimodality is key to successful interaction, measured both quantitatively and qualitatively via user feedback.",
}
@inproceedings{kery-etal-2019-es,
    title = "?`{E}s un pl{\'a}tano? Exploring the Application of a Physically Grounded Language Acquisition System to {S}panish",
    author = "Kery, Caroline  and
      Ferraro, Francis  and
      Matuszek, Cynthia",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1602",
    doi = "10.18653/v1/W19-1602",
    pages = "7--17",
    abstract = "In this paper we describe a multilingual grounded language learning system adapted from an English-only system. This system learns the meaning of words used in crowd-sourced descriptions by grounding them in the physical representations of the objects they are describing. Our work presents a framework to compare the performance of the system when applied to a new language and to identify modifications necessary to attain equal performance, with the goal of enhancing the ability of robots to learn language from a more diverse range of people. We then demonstrate this system with Spanish, through first analyzing the performance of translated Spanish, and then extending this analysis to a new corpus of crowd-sourced Spanish language data. We find that with small modifications, the system is able to learn color, object, and shape words with comparable performance between languages.",
}
@inproceedings{joseph-2019-virtual,
    title = "From Virtual to Real: A Framework for Verbal Interaction with Robots",
    author = "Joseph, Eugene",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1603",
    doi = "10.18653/v1/W19-1603",
    pages = "18--28",
    abstract = "A Natural Language Understanding (NLU) pipeline integrated with a 3D physics-based scene is a flexible way to develop and test language-based human-robot interaction, by virtualizing people, robot hardware and the target 3D environment. Here, interaction means both controlling robots using language and conversing with them about the user{'}s physical environment and her daily life. Such a virtual development framework was initially developed for the Bot Colony videogame launched on Steam in June 2014, and has been undergoing improvements since. The framework is focused of developing intuitive verbal interaction with various types of robots. Key robot functions (robot vision and object recognition, path planning and obstacle avoidance, task planning and constraints, grabbing and inverse kinematics), the human participants in the interaction, and the impact of gravity and other forces on the environment are all simulated using commercial 3D tools. The framework can be used as a robotics testbed: the results of our simulations can be compared with the output of algorithms in real robots, to validate such algorithms. A novelty of our framework is support for social interaction with robots - enabling robots to converse about people and objects in the user{'}s environment, as well as learning about human needs and everyday life topics from their owner.",
}
@inproceedings{can-etal-2019-learning,
    title = "Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations",
    author = "Can, Ozan Arkan  and
      Zuidberg Dos Martires, Pedro  and
      Persson, Andreas  and
      Gaal, Julian  and
      Loutfi, Amy  and
      De Raedt, Luc  and
      Yuret, Deniz  and
      Saffiotti, Alessandro",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1604",
    doi = "10.18653/v1/W19-1604",
    pages = "29--39",
    abstract = "Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot{'}s world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.",
}
@inproceedings{huang-etal-2019-multi-modal,
    title = "Multi-modal Discriminative Model for Vision-and-Language Navigation",
    author = "Huang, Haoshuo  and
      Jain, Vihan  and
      Mehta, Harsh  and
      Baldridge, Jason  and
      Ie, Eugene",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1605",
    doi = "10.18653/v1/W19-1605",
    pages = "40--49",
    abstract = "Vision-and-Language Navigation (VLN) is a natural language grounding task where agents have to interpret natural language instructions in the context of visual scenes in a dynamic environment to achieve prescribed navigation goals. Successful agents must have the ability to parse natural language of varying linguistic styles, ground them in potentially unfamiliar scenes, plan and react with ambiguous environmental feedback. Generalization ability is limited by the amount of human annotated data. In particular, paired vision-language sequence data is expensive to collect. We develop a discriminator that evaluates how well an instruction explains a given path in VLN task using multi-modal alignment. Our study reveals that only a small fraction of the high-quality augmented data from Fried et al., as scored by our discriminator, is useful for training VLN agents with similar performance. We also show that a VLN agent warm-started with pre-trained components from the discriminator outperforms the benchmark success rates of 35.5 by 10{\%} relative measure.",
}
@inproceedings{sarthou-etal-2019-semantic,
    title = "Semantic Spatial Representation: a unique representation of an environment based on an ontology for robotic applications",
    author = "Sarthou, Guillaume  and
      Clodic, Aur{\'e}lie  and
      Alami, Rachid",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1606",
    doi = "10.18653/v1/W19-1606",
    pages = "50--60",
    abstract = "It is important, for human-robot interaction, to endow the robot with the knowledge necessary to understand human needs and to be able to respond to them. We present a formalized and unified representation for indoor environments using an ontology devised for a route description task in which a robot must provide explanations to a person. We show that this representation can be used to choose a route to explain to a human as well as to verbalize it using a route perspective. Based on ontology, this representation has a strong possibility of evolution to adapt to many other applications. With it, we get the semantics of the environment elements while keeping a description of the known connectivity of the environment. This representation and the illustration algorithms, to find and verbalize a route, have been tested in two environments of different scales.",
}
@inproceedings{ulinski-etal-2019-spatialnet,
    title = "{S}patial{N}et: A Declarative Resource for Spatial Relations",
    author = "Ulinski, Morgan  and
      Coyne, Bob  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1607",
    doi = "10.18653/v1/W19-1607",
    pages = "61--70",
    abstract = "This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two resources which use frame semantics to encode lexical meaning. SpatialNet uses a deep semantic representation of spatial relations to provide a formal description of how a language expresses spatial information. This formal representation of the lexical semantics of spatial language also provides a consistent way to represent spatial meaning across multiple languages. In this paper, we describe the structure of SpatialNet, with examples from English and German. We also show how SpatialNet can be combined with other existing NLP tools to create a text-to-scene system for a language.",
}
@inproceedings{ghanimifard-dobnik-2019-neural,
    title = "What a neural language model tells us about spatial relations",
    author = "Ghanimifard, Mehdi  and
      Dobnik, Simon",
    booktitle = "Proceedings of the Combined Workshop on Spatial Language Understanding ({S}p{LU}) and Grounded Communication for Robotics ({R}obo{NLP})",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1608",
    doi = "10.18653/v1/W19-1608",
    pages = "71--81",
    abstract = "Understanding and generating spatial descriptions requires knowledge about what objects are related, their functional interactions, and where the objects are geometrically located. Different spatial relations have different functional and geometric bias. The wide usage of neural language models in different areas including generation of image description motivates the study of what kind of knowledge is encoded in neural language models about individual spatial relations. With the premise that the functional bias of relations is expressed in their word distributions, we construct multi-word distributional vector representations and show that these representations perform well on intrinsic semantic reasoning tasks, thus confirming our premise. A comparison of our vector representations to human semantic judgments indicates that different bias (functional or geometric) is captured in different data collection tasks which suggests that the contribution of the two meaning modalities is dynamic, related to the context of the task.",
}
@proceedings{ws-2019-structured,
    title = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
    editor = "Martins, Andre  and
      Vlachos, Andreas  and
      Kozareva, Zornitsa  and
      Ravi, Sujith  and
      Lampouras, Gerasimos  and
      Niculae, Vlad  and
      Kreutzer, Julia",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1500",
}
@inproceedings{ding-koehn-2019-parallelizable,
    title = "Parallelizable Stack Long Short-Term Memory",
    author = "Ding, Shuoyang  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1501",
    doi = "10.18653/v1/W19-1501",
    pages = "1--6",
    abstract = "Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation.",
}
@inproceedings{gupta-durrett-2019-tracking,
    title = "Tracking Discrete and Continuous Entity State for Process Understanding",
    author = "Gupta, Aditya  and
      Durrett, Greg",
    booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1502",
    doi = "10.18653/v1/W19-1502",
    pages = "7--12",
    abstract = "Procedural text, which describes entities and their interactions as they undergo some process, depicts entities in a uniquely nuanced way. First, each entity may have some observable discrete attributes, such as its state or location; modeling these involves imposing global structure and enforcing consistency. Second, an entity may have properties which are not made explicit but can be effectively induced and tracked by neural networks. In this paper, we propose a structured neural architecture that reflects this dual nature of entity evolution. The model tracks each entity recurrently, updating its hidden continuous representation at each step to contain relevant state information. The global discrete state structure is explicitly modelled with a neural CRF over the changing hidden representation of the entity. This CRF can explicitly capture constraints on entity states over time, enforcing that, for example, an entity cannot move to a location after it is destroyed. We evaluate the performance of our proposed model on QA tasks over process paragraphs in the ProPara dataset and find that our model achieves state-of-the-art results.",
}
@inproceedings{bommasani-etal-2019-sparse,
    title = "{SPARSE}: Structured Prediction using Argument-Relative Structured Encoding",
    author = "Bommasani, Rishi  and
      Katiyar, Arzoo  and
      Cardie, Claire",
    booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1503",
    doi = "10.18653/v1/W19-1503",
    pages = "13--17",
    abstract = "We propose structured encoding as a novel approach to learning representations for relations and events in neural structured prediction. Our approach explicitly leverages the structure of available relation and event metadata to generate these representations, which are parameterized by both the attribute structure of the metadata as well as the learned representation of the arguments of the relations and events. We consider affine, biaffine, and recurrent operators for building hierarchical representations and modelling underlying features. We apply our approach to the second-order structured prediction task studied in the 2016/2017 Belief and Sentiment analysis evaluations (BeSt): given a document and its entities, relations, and events (including metadata and mentions), determine the sentiment of each entity towards every relation and event in the document. Without task-specific knowledge sources or domain engineering, we significantly improve over systems and baselines that neglect the available metadata or its hierarchical structure. We observe across-the-board improvements on the BeSt 2016/2017 sentiment analysis task of at least 2.3 (absolute) and 10.6{\%} (relative) F-measure over the previous state-of-the-art.",
}
@inproceedings{zupon-etal-2019-lightly,
    title = "Lightly-supervised Representation Learning with Global Interpretability",
    author = "Zupon, Andrew  and
      Alexeeva, Maria  and
      Valenzuela-Esc{\'a}rcega, Marco  and
      Nagesh, Ajay  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1504",
    doi = "10.18653/v1/W19-1504",
    pages = "18--28",
    abstract = "We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning. Our algorithm iteratively learns custom embeddings for both the multi-word entities to be extracted and the patterns that match them from a few example entities per category. We demonstrate that this representation-based approach outperforms three other state-of-the-art bootstrapping approaches on two datasets: CoNLL-2003 and OntoNotes. Additionally, using these embeddings, our approach outputs a globally-interpretable model consisting of a decision list, by ranking patterns based on their proximity to the average entity embedding in a given class. We show that this interpretable model performs close to our complete bootstrapping model, proving that representation learning can be used to produce interpretable models with small loss in performance. This decision list can be edited by human experts to mitigate some of that loss and in some cases outperform the original model.",
}
@inproceedings{luo-etal-2019-semi,
    title = "Semi-Supervised Teacher-Student Architecture for Relation Extraction",
    author = "Luo, Fan  and
      Nagesh, Ajay  and
      Sharp, Rebecca  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Third Workshop on Structured Prediction for {NLP}",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1505",
    doi = "10.18653/v1/W19-1505",
    pages = "29--37",
    abstract = "Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used). On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training data. In this paper, we adapt Mean Teacher (Tarvainen and Valpola, 2017), a denoising SSL framework to extract semantic relations between pairs of entities. We explore the sweet spot of amount of supervision required for good performance on this binary relation extraction task. Additionally, different syntax representations are incorporated into our models to enhance the learned representation of sentences. We evaluate our approach on the Google-IISc Distant Supervision (GDS) dataset, which removes test data noise present in all previous distance supervision datasets, which makes it a reliable evaluation benchmark (Jat et al., 2017). Our results show that the SSL Mean Teacher approach nears the performance of fully-supervised approaches even with only 10{\%} of the labeled corpus. Further, the syntax-aware model outperforms other syntax-free approaches across all levels of supervision.",
}
@proceedings{ws-2019-nlp,
    title = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    editor = {Zampieri, Marcos  and
      Nakov, Preslav  and
      Malmasi, Shervin  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Ali, Ahmed},
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1400",
}
@inproceedings{zampieri-etal-2019-report,
    title = "A Report on the Third {V}ar{D}ial Evaluation Campaign",
    author = "Zampieri, Marcos  and
      Malmasi, Shervin  and
      Scherrer, Yves  and
      Samard{\v{z}}i{\'c}, Tanja  and
      Tyers, Francis  and
      Silfverberg, Miikka  and
      Klyueva, Natalia  and
      Pan, Tung-Le  and
      Huang, Chu-Ren  and
      Ionescu, Radu Tudor  and
      Butnaru, Andrei M.  and
      Jauhiainen, Tommi",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1401",
    doi = "10.18653/v1/W19-1401",
    pages = "1--16",
    abstract = "In this paper, we present the findings of the Third VarDial Evaluation Campaign organized as part of the sixth edition of the workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2019. This year, the campaign included five shared tasks, including one task re-run {--} German Dialect Identification (GDI) {--} and four new tasks {--} Cross-lingual Morphological Analysis (CMA), Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT), Moldavian vs. Romanian Cross-dialect Topic identification (MRC), and Cuneiform Language Identification (CLI). A total of 22 teams submitted runs across the five shared tasks. After the end of the competition, we received 14 system description papers, which are published in the VarDial workshop proceedings and referred to in this report.",
}
@inproceedings{bernier-colborne-etal-2019-improving,
    title = "Improving Cuneiform Language Identification with {BERT}",
    author = "Bernier-Colborne, Gabriel  and
      Goutte, Cyril  and
      L{\'e}ger, Serge",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1402",
    doi = "10.18653/v1/W19-1402",
    pages = "17--25",
    abstract = "We describe the systems developed by the National Research Council Canada for the Cuneiform Language Identification (CLI) shared task at the 2019 VarDial evaluation campaign. We compare a state-of-the-art baseline relying on character n-grams and a traditional statistical classifier, a voting ensemble of classifiers, and a deep learning approach using a Transformer network. We describe how these systems were trained, and analyze the impact of some preprocessing and model estimation decisions. The deep neural network achieved 77{\%} accuracy on the test data, which turned out to be the best performance at the CLI evaluation, establishing a new state-of-the-art for cuneiform language identification.",
}
@inproceedings{riyadh-kondrak-2019-joint,
    title = "Joint Approach to Deromanization of Code-mixed Texts",
    author = "Riyadh, Rashed Rubby  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1403",
    doi = "10.18653/v1/W19-1403",
    pages = "26--34",
    abstract = "The conversion of romanized texts back to the native scripts is a challenging task because of the inconsistent romanization conventions and non-standard language use. This problem is compounded by code-mixing, i.e., using words from more than one language within the same discourse. In this paper, we propose a novel approach for handling these two problems together in a single system. Our approach combines three components: language identification, back-transliteration, and sequence prediction. The results of our experiments on Bengali and Hindi datasets establish the state of the art for the task of deromanization of code-mixed texts.",
}
@inproceedings{chernyak-etal-2019-char,
    title = "Char-{RNN} for Word Stress Detection in {E}ast {S}lavic Languages",
    author = "Chernyak, Ekaterina  and
      Ponomareva, Maria  and
      Milintsevich, Kirill",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1404",
    doi = "10.18653/v1/W19-1404",
    pages = "35--41",
    abstract = "We explore how well a sequence labeling approach, namely, recurrent neural network, is suited for the task of resource-poor and POS tagging free word stress detection in the Russian, Ukranian, Belarusian languages. We present new datasets, annotated with the word stress, for the three languages and compare several RNN models trained on three languages and explore possible applications of the transfer learning for the task. We show that it is possible to train a model in a cross-lingual setting and that using additional languages improves the quality of the results.",
}
@inproceedings{dunn-2019-modeling,
    title = "Modeling Global Syntactic Variation in {E}nglish Using Dialect Classification",
    author = "Dunn, Jonathan",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1405",
    doi = "10.18653/v1/W19-1405",
    pages = "42--53",
    abstract = "This paper evaluates global-scale dialect identification for 14 national varieties of English on both web-crawled data and Twitter data. The paper makes three main contributions: (i) introducing data-driven language mapping as a method for selecting the inventory of national varieties to include in the task; (ii) producing a large and dynamic set of syntactic features using grammar induction rather than focusing on a few hand-selected features such as function words; and (iii) comparing models across both web corpora and social media corpora in order to measure the robustness of syntactic variation across registers.",
}
@inproceedings{wu-etal-2019-language,
    title = "Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation",
    author = {Wu, Nianheng  and
      DeMattos, Eric  and
      So, Kwok Him  and
      Chen, Pin-zhen  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1406",
    doi = "10.18653/v1/W19-1406",
    pages = "54--63",
    abstract = "This paper describes the work done by team tearsofjoy participating in the VarDial 2019 Evaluation Campaign. We developed two systems based on Support Vector Machines: SVM with a flat combination of features and SVM ensembles. We participated in all language/dialect identification tasks, as well as the Moldavian vs. Romanian cross-dialect topic identification (MRC) task. Our team achieved first place in German Dialect identification (GDI) and MRC subtasks 2 and 3, second place in the simplified variant of Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT) as well as Cuneiform Language Identification (CLI), and third and fifth place in DMT traditional and MRC subtask 1 respectively. In most cases, the SVM with a flat combination of features performed better than SVM ensembles. Besides describing the systems and the results obtained by them, we provide a tentative comparison between the feature combination methods, and present additional experiments with a method of adaptation to the test set, which may indicate potential pitfalls with some of the data sets.",
}
@inproceedings{ortmann-dipper-2019-variation,
    title = "Variation between Different Discourse Types: Literate vs. Oral",
    author = "Ortmann, Katrin  and
      Dipper, Stefanie",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1407",
    doi = "10.18653/v1/W19-1407",
    pages = "64--79",
    abstract = "This paper deals with the automatic identification of literate and oral discourse in German texts. A range of linguistic features is selected and their role in distinguishing between literate- and oral-oriented registers is investigated, using a decision-tree classifier. It turns out that all of the investigated features are related in some way to oral conceptuality. Especially simple measures of complexity (average sentence and word length) are prominent indicators of oral and literate discourse. In addition, features of reference and deixis (realized by different types of pronouns) also prove to be very useful in determining the degree of orality of different registers.",
}
@inproceedings{myint-oo-etal-2019-neural,
    title = "Neural Machine Translation between {M}yanmar ({B}urmese) and {R}akhine ({A}rakanese)",
    author = "Myint Oo, Thazin  and
      Kyaw Thu, Ye  and
      Mar Soe, Khin",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1408",
    doi = "10.18653/v1/W19-1408",
    pages = "80--88",
    abstract = "This work explores neural machine translation between Myanmar (Burmese) and Rakhine (Arakanese). Rakhine is a language closely related to Myanmar, often considered a dialect. We implemented three prominent neural machine translation (NMT) systems: recurrent neural networks (RNN), transformer, and convolutional neural networks (CNN). The systems were evaluated on a Myanmar-Rakhine parallel text corpus developed by us. In addition, two types of word segmentation schemes for word embeddings were studied: Word-BPE and Syllable-BPE segmentation. Our experimental results clearly show that the highest quality NMT and statistical machine translation (SMT) performances are obtained with Syllable-BPE segmentation for both types of translations. If we focus on NMT, we find that the transformer with Word-BPE segmentation outperforms CNN and RNN for both Myanmar-Rakhine and Rakhine-Myanmar translation. However, CNN with Syllable-BPE segmentation obtains a higher score than the RNN and transformer.",
}
@inproceedings{jauhiainen-etal-2019-language,
    title = "Language and Dialect Identification of Cuneiform Texts",
    author = "Jauhiainen, Tommi  and
      Jauhiainen, Heidi  and
      Alstola, Tero  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1409",
    doi = "10.18653/v1/W19-1409",
    pages = "89--98",
    abstract = "This article introduces a corpus of cuneiform texts from which the dataset for the use of the Cuneiform Language Identification (CLI) 2019 shared task was derived as well as some preliminary language identification experiments conducted using that corpus. We also describe the CLI dataset and how it was derived from the corpus. In addition, we provide some baseline language identification results using the CLI dataset. To the best of our knowledge, the experiments detailed here represent the first time that automatic language identification methods have been used on cuneiform data.",
}
@inproceedings{alghamdi-diab-2019-leveraging,
    title = "Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data",
    author = "AlGhamdi, Fahad  and
      Diab, Mona",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1410",
    doi = "10.18653/v1/W19-1410",
    pages = "99--109",
    abstract = "Linguistic Code Switching (CS) is a phenomenon that occurs when multilingual speakers alternate between two or more languages/dialects within a single conversation. Processing CS data is especially challenging in intra-sentential data given state-of-the-art monolingual NLP technologies since such technologies are geared toward the processing of one language at a time. In this paper, we address the problem of Part-of-Speech tagging (POS) in the context of linguistic code switching (CS). We explore leveraging multiple neural network architectures to measure the impact of different pre-trained embeddings methods on POS tagging CS data. We investigate the landscape in four CS language pairs, Spanish-English, Hindi-English, Modern Standard Arabic- Egyptian Arabic dialect (MSA-EGY), and Modern Standard Arabic- Levantine Arabic dialect (MSA-LEV). Our results show that multilingual embedding (e.g., MSA-EGY and MSA-LEV) helps closely related languages (EGY/LEV) but adds noise to the languages that are distant (SPA/HIN). Finally, we show that our proposed models outperform state-of-the-art CS taggers for MSA-EGY language pair.",
}
@inproceedings{cathcart-2019-toward,
    title = "Toward a deep dialectological representation of {I}ndo-{A}ryan",
    author = "Cathcart, Chundra",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1411",
    doi = "10.18653/v1/W19-1411",
    pages = "110--119",
    abstract = "This paper presents a new approach to disentangling inter-dialectal and intra-dialectal relationships within one such group, the Indo-Aryan subgroup of Indo-European. We draw upon admixture models and deep generative models to tease apart historic language contact and language-specific behavior in the overall patterns of sound change displayed by Indo-Aryan languages. We show that a {``}deep{''} model of Indo-Aryan dialectology sheds some light on questions regarding inter-relationships among the Indo-Aryan languages, and performs better than a {``}shallow{''} model in terms of certain qualities of the posterior distribution (e.g., entropy of posterior distributions), and outline future pathways for model development.",
}
@inproceedings{yang-xiang-2019-naive,
    title = "Naive {B}ayes and {B}i{LSTM} Ensemble for Discriminating between Mainland and {T}aiwan Variation of {M}andarin {C}hinese",
    author = "Yang, Li  and
      Xiang, Yang",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1412",
    doi = "10.18653/v1/W19-1412",
    pages = "120--127",
    abstract = "Automatic dialect identification is a more challengingctask than language identification, as it requires the ability to discriminate between varieties of one language. In this paper, we propose an ensemble based system, which combines traditional machine learning models trained on bag of n-gram fetures, with deep learning models trained on word embeddings, to solve the Discriminating between Mainland and Taiwan Variation of Mandarin Chinese (DMT) shared task at VarDial 2019. Our experiments show that a character bigram-trigram combination based Naive Bayes is a very strong model for identifying varieties of Mandarin Chinense. Through further ensemble of Navie Bayes and BiLSTM, our system (team: itsalexyang) achived an macro-averaged F1 score of 0.8530 and 0.8687 in two tracks.",
}
@inproceedings{butnaru-2019-bam,
    title = "{BAM}: A combination of deep and shallow models for {G}erman Dialect Identification.",
    author = "Butnaru, Andrei M.",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1413",
    doi = "10.18653/v1/W19-1413",
    pages = "128--137",
    abstract = "*This is a submission for the Third VarDial Evaluation Campaign* In this paper, we present a machine learning approach for the German Dialect Identification (GDI) Closed Shared Task of the DSL 2019 Challenge. The proposed approach combines deep and shallow models, by applying a voting scheme on the outputs resulted from a Character-level Convolutional Neural Networks (Char-CNN), a Long Short-Term Memory (LSTM) network, and a model based on String Kernels. The first model used is the Char-CNN model that merges multiple convolutions computed with kernels of different sizes. The second model is the LSTM network which applies a global max pooling over the returned sequences over time. Both models pass the activation maps to two fully-connected layers. The final model is based on String Kernels, computed on character p-grams extracted from speech transcripts. The model combines two blended kernel functions, one is the presence bits kernel, and the other is the intersection kernel. The empirical results obtained in the shared task prove that the approach can achieve good results. The system proposed in this paper obtained the fourth place with a macro-F1 score of 62.55{\%}",
}
@inproceedings{chifu-2019-r2i,
    title = "The {R}2{I}{\_}{LIS} Team Proposes Majority Vote for {V}ar{D}ial{'}s {MRC} Task",
    author = "Chifu, Adrian-Gabriel",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1414",
    doi = "10.18653/v1/W19-1414",
    pages = "138--143",
    abstract = "This article presents the model that generated the runs submitted by the R2I{\_}LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.",
}
@inproceedings{mikhailov-etal-2019-initial,
    title = "Initial Experiments In Cross-Lingual Morphological Analysis Using Morpheme Segmentation",
    author = "Mikhailov, Vladislav  and
      Tosi, Lorenzo  and
      Khorosheva, Anastasia  and
      Serikov, Oleg",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1415",
    doi = "10.18653/v1/W19-1415",
    pages = "144--152",
    abstract = "The paper describes initial experiments in data-driven cross-lingual morphological analysis of open-category words using a combination of unsupervised morpheme segmentation, annotation projection and an LSTM encoder-decoder model with attention. Our algorithm provides lemmatisation and morphological analysis generation for previously unseen low-resource language surface forms with only annotated data on the related languages given. Despite the inherently lossy annotation projection, we achieved the best lemmatisation F1-score in the VarDial 2019 Shared Task on Cross-Lingual Morphological Analysis for both Karachay-Balkar (Turkic languages, agglutinative morphology) and Sardinian (Romance languages, fusional morphology).",
}
@inproceedings{coltekin-barnes-2019-neural,
    title = "Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Barnes, Jeremy},
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1416",
    doi = "10.18653/v1/W19-1416",
    pages = "153--164",
    abstract = {This paper describes T{\"u}bingen-Oslo team{'}s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign. We participated in the shared task with a standard neural network model. Our model achieved analysis F1-scores of 31.48 and 23.67 on test languages Karachay-Balkar (Turkic) and Sardinian (Romance) respectively. The scores are comparable to the scores obtained by the other participants in both language families, and the analysis score on the Romance data set was also the best result obtained in the shared task. Besides describing the system used in our shared task participation, we describe another, simpler, model based on linear classifiers, and present further analyses using both models. Our analyses, besides revealing some of the difficult cases, also confirm that the usefulness of a source language in this task is highly correlated with the similarity of source and target languages.},
}
@inproceedings{hu-etal-2019-ensemble,
    title = "Ensemble Methods to Distinguish Mainland and {T}aiwan {C}hinese",
    author = "Hu, Hai  and
      Li, Wen  and
      Zhou, He  and
      Tian, Zuoyu  and
      Zhang, Yiwen  and
      Zou, Liang",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1417",
    doi = "10.18653/v1/W19-1417",
    pages = "165--171",
    abstract = "This paper describes the IUCL system at VarDial 2019 evaluation campaign for the task of discriminating between Mainland and Taiwan variation of mandarin Chinese. We first build several base classifiers, including a Naive Bayes classifier with word n-gram as features, SVMs with both character and syntactic features, and neural networks with pre-trained character/word embeddings. Then we adopt ensemble methods to combine output from base classifiers to make final predictions. Our ensemble models achieve the highest F1 score (0.893) in simplified Chinese track and the second highest (0.901) in traditional Chinese track. Our results demonstrate the effectiveness and robustness of the ensemble methods.",
}
@inproceedings{onose-etal-2019-sc,
    title = "{SC}-{UPB} at the {V}ar{D}ial 2019 Evaluation Campaign: {M}oldavian vs. {R}omanian Cross-Dialect Topic Identification",
    author = "Onose, Cristian  and
      Cercel, Dumitru-Clementin  and
      Trausan-Matu, Stefan",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1418",
    doi = "10.18653/v1/W19-1418",
    pages = "172--177",
    abstract = "This paper describes our models for the Moldavian vs. Romanian Cross-Topic Identification (MRC) evaluation campaign, part of the VarDial 2019 workshop. We focus on the three subtasks for MRC: binary classification between the Moldavian (MD) and the Romanian (RO) dialects and two cross-dialect multi-class classification between six news topics, MD to RO and RO to MD. We propose several deep learning models based on long short-term memory cells, Bidirectional Gated Recurrent Unit (BiGRU) and Hierarchical Attention Networks (HAN). We also employ three word embedding models to represent the text as a low dimensional vector. Our official submission includes two runs of the BiGRU and HAN models for each of the three subtasks. The best submitted model obtained the following macro-averaged F1 scores: 0.708 for subtask 1, 0.481 for subtask 2 and 0.480 for the last one. Due to a read error caused by the quoting behaviour over the test file, our final submissions contained a smaller number of items than expected. More than 50{\%} of the submission files were corrupted. Thus, we also present the results obtained with the corrected labels for which the HAN model achieves the following results: 0.930 for subtask 1, 0.590 for subtask 2 and 0.687 for the third one.",
}
@inproceedings{jauhiainen-etal-2019-discriminating,
    title = "Discriminating between {M}andarin {C}hinese and {S}wiss-{G}erman varieties using adaptive language models",
    author = "Jauhiainen, Tommi  and
      Lind{\'e}n, Krister  and
      Jauhiainen, Heidi",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1419",
    doi = "10.18653/v1/W19-1419",
    pages = "178--187",
    abstract = "This paper describes the language identification systems used by the SUKI team in the Discriminating between the Mainland and Taiwan variation of Mandarin Chinese (DMT) and the German Dialect Identification (GDI) shared tasks which were held as part of the third VarDial Evaluation Campaign. The DMT shared task included two separate tracks, one for the simplified Chinese script and one for the traditional Chinese script. We submitted three runs on both tracks of the DMT task as well as on the GDI task. We won the traditional Chinese track using Naive Bayes with language model adaptation, came second on GDI with an adaptive version of the HeLI 2.0 method, and third on the simplified Chinese track using again the adaptive Naive Bayes.",
}
@inproceedings{doostmohammadi-nassajian-2019-investigating,
    title = "Investigating Machine Learning Methods for Language and Dialect Identification of Cuneiform Texts",
    author = "Doostmohammadi, Ehsan  and
      Nassajian, Minoo",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1420",
    doi = "10.18653/v1/W19-1420",
    pages = "188--193",
    abstract = "Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resources and the problem of tokenization. The Cuneiform Language Identification task in VarDial 2019 addresses the problem of identifying seven languages and dialects written in cuneiform; Sumerian and six dialects of Akkadian language: Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian. This paper describes the approaches taken by SharifCL team to this problem in VarDial 2019. The best result belongs to an ensemble of Support Vector Machines and a naive Bayes classifier, both working on character-level features, with macro-averaged F1-score of 72.10{\%}.",
}
@inproceedings{benites-etal-2019-twistbytes,
    title = "{T}wist{B}ytes - Identification of Cuneiform Languages and {G}erman Dialects at {V}ar{D}ial 2019",
    author = {Benites, Fernando  and
      von D{\"a}niken, Pius  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1421",
    doi = "10.18653/v1/W19-1421",
    pages = "194--201",
    abstract = "We describe our approaches for the German Dialect Identification (GDI) and the Cuneiform Language Identification (CLI) tasks at the VarDial Evaluation Campaign 2019. The goal was to identify dialects of Swiss German in GDI and Sumerian and Akkadian in CLI. In GDI, the system should distinguish four dialects from the German-speaking part of Switzerland. Our system for GDI achieved third place out of 6 teams, with a macro averaged F-1 of 74.6{\%}. In CLI, the system should distinguish seven languages written in cuneiform script. Our system achieved third place out of 8 teams, with a macro averaged F-1 of 74.7{\%}.",
}
@inproceedings{tudoreanu-2019-dteam,
    title = "{DT}eam @ {V}ar{D}ial 2019: Ensemble based on skip-gram and triplet loss neural networks for {M}oldavian vs. {R}omanian cross-dialect topic identification",
    author = "Tudoreanu, Diana",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1422",
    doi = "10.18653/v1/W19-1422",
    pages = "202--208",
    abstract = "This paper presents the solution proposed by DTeam in the VarDial 2019 Evaluation Campaign for the Moldavian vs. Romanian cross-topic identification task. The solution proposed is a Support Vector Machines (SVM) ensemble composed of a two character-level neural networks. The first network is a skip-gram classification model formed of an embedding layer, three convolutional layers and two fully-connected layers. The second network has a similar architecture, but is trained using the triplet loss function.",
}
@inproceedings{paetzold-zampieri-2019-experiments,
    title = "Experiments in Cuneiform Language Identification",
    author = "Paetzold, Gustavo Henrique  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1423",
    doi = "10.18653/v1/W19-1423",
    pages = "209--213",
    abstract = "This paper presents methods to discriminate between languages and dialects written in Cuneiform script, one of the first writing systems in the world. We report the results obtained by the PZ team in the Cuneiform Language Identification (CLI) shared task organized within the scope of the VarDial Evaluation Campaign 2019. The task included two languages, Sumerian and Akkadian. The latter is divided into six dialects: Old Babylonian, Middle Babylonian peripheral, Standard Babylonian, Neo Babylonian, Late Babylonian, and Neo Assyrian. We approach the task using a meta-classifier trained on various SVM models and we show the effectiveness of the system for this task. Our submission achieved 0.738 F1 score in discriminating between the seven languages and dialects and it was ranked fourth in the competition among eight teams.",
}
@inproceedings{shapiro-duh-2019-comparing,
    title = "Comparing Pipelined and Integrated Approaches to Dialectal {A}rabic Neural Machine Translation",
    author = "Shapiro, Pamela  and
      Duh, Kevin",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1424",
    doi = "10.18653/v1/W19-1424",
    pages = "214--222",
    abstract = "When translating diglossic languages such as Arabic, situations may arise where we would like to translate a text but do not know which dialect it is. A traditional approach to this problem is to design dialect identification systems and dialect-specific machine translation systems. However, under the recent paradigm of neural machine translation, shared multi-dialectal systems have become a natural alternative. Here we explore under which conditions it is beneficial to perform dialect identification for Arabic neural machine translation versus using a general system for all dialects.",
}
@inproceedings{huck-etal-2019-cross,
    title = "Cross-lingual Annotation Projection Is Effective for Neural Part-of-Speech Tagging",
    author = "Huck, Matthias  and
      Dutka, Diana  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Similar Languages, Varieties and Dialects",
    month = jun,
    year = "2019",
    address = "Ann Arbor, Michigan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1425",
    doi = "10.18653/v1/W19-1425",
    pages = "223--233",
    abstract = "We tackle the important task of part-of-speech tagging using a neural model in the zero-resource scenario, where we have no access to gold-standard POS training data. We compare this scenario with the low-resource scenario, where we have access to a small amount of gold-standard POS training data. Our experiments focus on Ukrainian as a representative of under-resourced languages. Russian is highly related to Ukrainian, so we exploit gold-standard Russian POS tags. We consider four techniques to perform Ukrainian POS tagging: zero-shot tagging and cross-lingual annotation projection (for the zero-resource scenario), and compare these with self-training and multilingual learning (for the low-resource scenario). We find that cross-lingual annotation projection works particularly well in the zero-resource scenario.",
}
@proceedings{ws-2019-approaches,
    title = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    editor = "Balahur, Alexandra  and
      Klinger, Roman  and
      Hoste, Veronique  and
      Strapparava, Carlo  and
      De Clercq, Orphee",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1300",
}
@inproceedings{sane-etal-2019-stance,
    title = "Stance Detection in Code-Mixed {H}indi-{E}nglish Social Media Data using Multi-Task Learning",
    author = "Sane, Sushmitha Reddy  and
      Tripathi, Suraj  and
      Sane, Koushik Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1301",
    doi = "10.18653/v1/W19-1301",
    pages = "1--5",
    abstract = "Social media sites like Facebook, Twitter, and other microblogging forums have emerged as a platform for people to express their opinions and views on different issues and events. It is often observed that people tend to take a stance; in favor, against or neutral towards a particular topic. The task of assessing the stance taken by the individual became significantly important with the emergence in the usage of online social platforms. Automatic stance detection system understands the user{'}s stance by analyzing the standalone texts against a target entity. Due to the limited contextual information a single sentence provides, it is challenging to solve this task effectively. In this paper, we introduce a Multi-Task Learning (MTL) based deep neural network architecture for automatically detecting stance present in the code-mixed corpus. We apply our approach on Hindi-English code-mixed corpus against the target entity - {``}Demonetisation.{''} Our best model achieved the result with a stance prediction accuracy of 63.2{\%} which is a 4.5{\%} overall accuracy improvement compared to the current supervised classification systems developed using the benchmark dataset for code-mixed data stance detection.",
}
@inproceedings{yin-etal-2019-soft,
    title = "A Soft Label Strategy for Target-Level Sentiment Classification",
    author = "Yin, Da  and
      Liu, Xiao  and
      Wu, Xiuyu  and
      Chang, Baobao",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1302",
    doi = "10.18653/v1/W19-1302",
    pages = "6--15",
    abstract = "In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word. We also apply a convolution layer to extract local active features, and introduce positional weights to take relative distance information into consideration. In addition, we obtain more informative target representation by training with context tokens together to make deeper interaction between target and context tokens. We conduct experiments on SemEval 2014 datasets and the experimental results show that our approach significantly outperforms previous models and gives state-of-the-art results on these datasets.",
}
@inproceedings{kumar-etal-2019-online,
    title = "Online abuse detection: the value of preprocessing and neural attention models",
    author = "Kumar, Dhruv  and
      Cohen, Robin  and
      Golab, Lukasz",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1303",
    doi = "10.18653/v1/W19-1303",
    pages = "16--24",
    abstract = "We propose an attention-based neural network approach to detect abusive speech in online social networks. Our approach enables more effective modeling of context and the semantic relationships between words. We also empirically evaluate the value of text pre-processing techniques in addressing the challenge of out-of-vocabulary words in toxic content. Finally, we conduct extensive experiments on the Wikipedia Talk page datasets, showing improved predictive power over the previous state-of-the-art.",
}
@inproceedings{bostan-klinger-2019-exploring,
    title = "Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis",
    author = "Bostan, Laura Ana Maria  and
      Klinger, Roman",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1304",
    doi = "10.18653/v1/W19-1304",
    pages = "25--34",
    abstract = "Adjective phrases like {``}a little bit surprised{''}, {``}completely shocked{''}, or {``}not stunned at all{''} are not handled properly by current state-of-the-art emotion classification and intensity prediction systems. Based on this finding, we analyze differences between embeddings used by these systems in regard to their capability of handling such cases and argue that intensifiers in context of emotion words need special treatment, as is established for sentiment polarity classification, but not for more fine-grained emotion prediction. To resolve this issue, we analyze different aspects of a post-processing pipeline which enriches the word representations of such phrases. This includes expansion of semantic spaces at the phrase level and sub-word level followed by retrofitting to emotion lexicons. We evaluate the impact of these steps with {`}A La Carte and Bag-of-Substrings extensions based on pretrained GloVe,Word2vec, and fastText embeddings against a crowd-sourced corpus of intensity annotations for tweets containing our focus phrases. We show that the fastText-based models do not gain from handling these specific phrases under inspection. For Word2vec embeddings, we show that our post-processing pipeline improves the results by up to 8{\%} on a novel dataset densly populated with intensifiers while it does not decrease the performance on the established EmoInt dataset.",
}
@inproceedings{rezapour-etal-2019-enhancing,
    title = "Enhancing the Measurement of Social Effects by Capturing Morality",
    author = "Rezapour, Rezvaneh  and
      Shah, Saumil H.  and
      Diesner, Jana",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1305",
    doi = "10.18653/v1/W19-1305",
    pages = "35--45",
    abstract = "We investigate the relationship between basic principles of human morality and the expression of opinions in user-generated text data. We assume that people{'}s backgrounds, culture, and values are associated with their perceptions and expressions of everyday topics, and that people{'}s language use reflects these perceptions. While personal values and social effects are abstract and complex concepts, they have practical implications and are relevant for a wide range of NLP applications. To extract human values (in this paper, morality) and measure social effects (morality and stance), we empirically evaluate the usage of a morality lexicon that we expanded via a quality controlled, human in the loop process. As a result, we enhanced the Moral Foundations Dictionary in size (from 324 to 4,636 syntactically disambiguated entries) and scope. We used both lexica for feature-based and deep learning classification (SVM, RF, and LSTM) to test their usefulness for measuring social effects. We find that the enhancement of the original lexicon led to measurable improvements in prediction accuracy for the selected NLP tasks.",
}
@inproceedings{misra-etal-2019-using,
    title = "Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations",
    author = "Misra, Amita  and
      Bhuiyan, Mansurul  and
      Mahmud, Jalal  and
      Tripathy, Saurabh",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1306",
    doi = "10.18653/v1/W19-1306",
    pages = "46--56",
    abstract = "Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of {''}negation{''} in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a combination CNN-LSTM for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and Neural Network methods.",
}
@inproceedings{sane-etal-2019-deep,
    title = "Deep Learning Techniques for Humor Detection in {H}indi-{E}nglish Code-Mixed Tweets",
    author = "Sane, Sushmitha Reddy  and
      Tripathi, Suraj  and
      Sane, Koushik Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1307",
    doi = "10.18653/v1/W19-1307",
    pages = "57--61",
    abstract = "We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed their performance by experimenting with three different neural network models. We propose convolution neural network (CNN) and bidirectional long-short term memory (biLSTM) (with and without Attention) models which take the generated bilingual embeddings as input. We make use of Twitter data to create bilingual word embeddings. All our proposed architectures outperform the state-of-the-art results, and Attention-based bidirectional LSTM model achieved an accuracy of 73.6{\%} which is an increment of more than 4{\%} compared to the current state-of-the-art results.",
}
@inproceedings{fraser-etal-2019-feel,
    title = "How do we feel when a robot dies? Emotions expressed on {T}witter before and after hitch{BOT}{'}s destruction",
    author = "Fraser, Kathleen C.  and
      Zeller, Frauke  and
      Smith, David Harris  and
      Mohammad, Saif  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1308",
    doi = "10.18653/v1/W19-1308",
    pages = "62--71",
    abstract = "In 2014, a chatty but immobile robot called hitchBOT set out to hitchhike across Canada. It similarly made its way across Germany and the Netherlands, and had begun a trip across the USA when it was destroyed by vandals. In this work, we analyze the emotions and sentiments associated with words in tweets posted before and after hitchBOT{'}s destruction to answer two questions: Were there any differences in the emotions expressed across the different countries visited by hitchBOT? And how did the public react to the demise of hitchBOT? Our analyses indicate that while there were few cross-cultural differences in sentiment towards hitchBOT, there was a significant negative emotional reaction to its destruction, suggesting that people had formed an emotional connection with hitchBOT and perceived its destruction as morally wrong. We discuss potential implications of anthropomorphism and emotional attachment to robots from the perspective of robot ethics.",
}
@inproceedings{dubey-etal-2019-numbers,
    title = "{``}When Numbers Matter!{''}: Detecting Sarcasm in Numerical Portions of Text",
    author = "Dubey, Abhijeet  and
      Kumar, Lakshya  and
      Somani, Arpan  and
      Joshi, Aditya  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1309",
    doi = "10.18653/v1/W19-1309",
    pages = "72--80",
    abstract = "Research in sarcasm detection spans almost a decade. However a particular form of sarcasm remains unexplored: sarcasm expressed through numbers, which we estimate, forms about 11{\%} of the sarcastic tweets in our dataset. The sentence {`}Love waking up at 3 am{'} is sarcastic because of the number. In this paper, we focus on detecting sarcasm in tweets arising out of numbers. Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier. The rule-based classifier conveys the crux of the numerical sarcasm problem, namely, incongruity arising out of numbers. The statistical ML classifier uncovers the indicators i.e., features of such sarcasm. The actual system in place, however, are two deep learning (DL) models, CNN and attention network that obtains an F-score of 0.93 and 0.91 on our dataset of tweets containing numbers. To the best of our knowledge, this is the first line of research investigating the phenomenon of sarcasm arising out of numbers, culminating in a detector thereof.",
}
@inproceedings{amini-etal-2019-cross,
    title = "Cross-lingual Subjectivity Detection for Resource Lean Languages",
    author = "Amini, Ida  and
      Karimi, Samane  and
      Shakery, Azadeh",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1310",
    doi = "10.18653/v1/W19-1310",
    pages = "81--90",
    abstract = "Wide and universal changes in the web content due to the growth of web 2 applications increase the importance of user-generated content on the web. Therefore, the related research areas such as sentiment analysis, opinion mining and subjectivity detection receives much attention from the research community. Due to the diverse languages that web-users use to express their opinions and sentiments, research areas like subjectivity detection should present methods which are practicable on all languages. An important prerequisite to effectively achieve this aim is considering the limitations in resource-lean languages. In this paper, cross-lingual subjectivity detection on resource lean languages is investigated using two different approaches: a language-model based and a learning-to-rank approach. Experimental results show the impact of different factors on the performance of subjectivity detection methods using English resources to detect the subjectivity score of Persian documents. The experiments demonstrate that the proposed learning-to-rank method outperforms the baseline method in ranking documents based on their subjectivity degree.",
}
@inproceedings{hayati-muis-2019-analyzing,
    title = "Analyzing Incorporation of Emotion in Emoji Prediction",
    author = "Hayati, Shirley Anugrah  and
      Muis, Aldrian Obaja",
    booktitle = "Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2019",
    address = "Minneapolis, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1311",
    doi = "10.18653/v1/W19-1311",
    pages = "91--99",
    abstract = "In this work, we investigate the impact of incorporating emotion classes on the task of predicting emojis from Twitter texts. More specifically, we first show that there is a correlation between the emotion expressed in the text and the emoji choice of Twitter users. Based on this insight we propose a few simple methods to incorporate emotion information in traditional classifiers. Through automatic metrics, human evaluation, and error analysis, we show that the improvement obtained by incorporating emotion is significant and correlate better with human preferences compared to the baseline models. Through the human ratings that we obtained, we also argue for preference metric to better evaluate the usefulness of an emoji prediction system.",
}
@proceedings{ws-2019-iwcs-shared,
    title = "Proceedings of the {IWCS} Shared Task on Semantic Parsing",
    editor = "Abzianidze, Lasha  and
      van Noord, Rik  and
      Haagsma, Hessel  and
      Bos, Johan",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1200",
}
@inproceedings{abzianidze-etal-2019-first,
    title = "The First Shared Task on Discourse Representation Structure Parsing",
    author = "Abzianidze, Lasha  and
      van Noord, Rik  and
      Haagsma, Hessel  and
      Bos, Johan",
    booktitle = "Proceedings of the {IWCS} Shared Task on Semantic Parsing",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1201",
    doi = "10.18653/v1/W19-1201",
    abstract = "The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from Discourse Representation Theory and represent scoped meaning representations that capture the semantics of negation, modals, quantification, and presupposition triggers. Additionally, concepts and event-participants in DRSs are described with WordNet synsets and the thematic roles from VerbNet. To measure similarity between two DRSs, they are represented in a clausal form, i.e. as a set of tuples. Participant systems were expected to produce DRSs in this clausal form. Taking into account the rich lexical information, explicit scope marking, a high number of shared variables among clauses, and highly-constrained format of valid DRSs, all these makes the DRS parsing a challenging NLP task. The results of the shared task displayed improvements over the existing state-of-the-art parser.",
}
@inproceedings{evang-2019-transition,
    title = "Transition-based {DRS} Parsing Using Stack-{LSTM}s",
    author = "Evang, Kilian",
    booktitle = "Proceedings of the {IWCS} Shared Task on Semantic Parsing",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1202",
    doi = "10.18653/v1/W19-1202",
    abstract = "We present our submission to the IWCS 2019 shared task on semantic parsing, a transition-based parser that uses explicit word-meaning pairings, but no explicit representation of syntax. Parsing decisions are made based on vector representations of parser states, encoded via stack-LSTMs (Ballesteros et al., 2017), as well as some heuristic rules. Our system reaches 70.88{\%} f-score in the competition.",
}
@inproceedings{liu-etal-2019-discourse-representation,
    title = "Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model",
    author = "Liu, Jiangming  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    booktitle = "Proceedings of the {IWCS} Shared Task on Semantic Parsing",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1203",
    doi = "10.18653/v1/W19-1203",
    abstract = "We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8{\%} F1 in the DRS parsing shared task.",
}
@inproceedings{van-noord-2019-neural,
    title = "Neural Boxer at the {IWCS} Shared Task on {DRS} Parsing",
    author = "van Noord, Rik",
    booktitle = "Proceedings of the {IWCS} Shared Task on Semantic Parsing",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1204",
    doi = "10.18653/v1/W19-1204",
    abstract = "This paper describes our participation in the shared task of Discourse Representation Structure parsing. It follows the work of Van Noord et al. (2018), who employed a neural sequence-to-sequence model to produce DRSs, also exploiting linguistic information with multiple encoders. We provide a detailed look in the performance of this model and show that (i) the benefit of the linguistic features is evident across a number of experiments which vary the amount of training data and (ii) the model can be improved by applying a number of postprocessing methods to fix ill-formed output. Our model ended up in second place in the competition, with an F-score of 84.5.",
}
@proceedings{ws-2019-natural,
    title = "Proceedings of the Sixth Workshop on Natural Language and Computer Science",
    editor = "Cooper, Robin  and
      de Paiva, Valeria  and
      Moss, Lawrence S.",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1100",
}
@inproceedings{lucking-etal-2019-distribution,
    title = "Distribution is not enough: going Firther",
    author = {L{\"u}cking, Andy  and
      Cooper, Robin  and
      Larsson, Staffan  and
      Ginzburg, Jonathan},
    booktitle = "Proceedings of the Sixth Workshop on Natural Language and Computer Science",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1101",
    doi = "10.18653/v1/W19-1101",
    pages = "1--10",
    abstract = "Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space. In practice, use is identified with occurrence in text corpora, though there are some efforts to use corpora containing multi-modal information. In this paper we argue that the distributional hypothesis is intrinsically misguided as a self-supporting basis for semantics, as Firth was entirely aware. We mention philosophical arguments concerning the lack of normativity within DH data. Furthermore, we point out the shortcomings of DH as a model of learning, by discussing a variety of linguistic classes that cannot be learnt on a distributional basis, including indexicals, proper names, and wh-phrases. Instead of pursuing DH, we sketch an account of the problematic learning cases by integrating a rich, Firthian notion of dialogue context with interactive learning in signalling games backed by in probabilistic Type Theory with Records. We conclude that the success of the DH in computational semantics rests on a post hoc effect: DS presupposes a referential semantics on the basis of which utterances can be produced, comprehended and analysed in the first place.",
}
@inproceedings{kim-etal-2019-towards,
    title = "Towards Natural Language Story Understanding with Rich Logical Schemas",
    author = "Kim, Gene Louis  and
      Lawley, Lane  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the Sixth Workshop on Natural Language and Computer Science",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1102",
    doi = "10.18653/v1/W19-1102",
    pages = "11--22",
    abstract = "Generating {``}commonsense{'}{'} knowledge for intelligent understanding and reasoning is a difficult, long-standing problem, whose scale challenges the capacity of any approach driven primarily by human input. Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text. The idea of our approach to this problem is to provide a learning system with a {``}head start{''} consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one- or two-year-old could reasonably be presumed to possess. We match these initial schemas to simple children{'}s stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas. Both the initial and generated schemas are specified using a rich, expressive logical form. While modern approaches to schema reasoning often only use slot-and-filler structures, this logical form allows us to specify complex relations and constraints over the slots. Though formal, the representations are language-like, and as such readily relatable to NL text. The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations. To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories. We include a walkthrough of matching a children{'}s story to these schemas and generating inferences from these matches.",
}
@inproceedings{watanabe-etal-2019-questions,
    title = "Questions in Dependent Type Semantics",
    author = "Watanabe, Kazuki  and
      Mineshima, Koji  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the Sixth Workshop on Natural Language and Computer Science",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1103",
    doi = "10.18653/v1/W19-1103",
    pages = "23--33",
    abstract = "Dependent Type Semantics (DTS; Bekki and Mineshima, 2017) is a proof-theoretic compositional dynamic semantics based on Dependent Type Theory. The semantic representations for declarative sentences in DTS are types, based on the propositions-as-types paradigm. While type-theoretic semantics for natural language based on dependent type theory has been developed by many authors, how to assign semantic representations to interrogative sentences has been a non-trivial problem. In this study, we show how to provide the semantics of interrogative sentences in DTS. The basic idea is to assign the same type to both declarative sentences and interrogative sentences, partly building on the recent proposal in Inquisitive Semantics. We use Combinatory Categorial Grammar (CCG) as a syntactic component of DTS and implement our compositional semantics for interrogative sentences using ccg2lambda, a semantic parsing platform based on CCG. Based on the idea that the relationship between questions and answers can be formulated as the task of Recognizing Textual Entailment (RTE), we implement our inference system using proof assistant Coq and show that our system can deal with a wide range of question-answer relationships discussed in the formal semantics literature, including those with polar questions, alternative questions, and wh-questions.",
}
@inproceedings{burke-2019-monads,
    title = "Monads for hyperintensionality? A situation semantics for hyperintensional side effects",
    author = "Burke, Luke",
    booktitle = "Proceedings of the Sixth Workshop on Natural Language and Computer Science",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1104",
    doi = "10.18653/v1/W19-1104",
    pages = "34--43",
    abstract = "We outline a hyperintensional situation semantics in which hyperintensionality is modelled as a {`}side effect{'}, as this term has been understood in natural language semantics and in functional programming. We use monads from category theory in order to {`}upgrade{'} an ordinary intensional semantics to a possible hyperintensional counterpart.",
}
@proceedings{ws-2019-iwcs-2019,
    title = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    editor = "Osswald, Rainer  and
      Retor{\'e}, Christian  and
      Sutton, Peter",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1000",
}
@inproceedings{kubota-etal-2019-underspecification,
    title = "Underspecification and interpretive parallelism in Dependent Type Semantics",
    author = "Kubota, Yusuke  and
      Mineshima, Koji  and
      Levine, Robert  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1001",
    doi = "10.18653/v1/W19-1001",
    pages = "1--9",
}
@inproceedings{pezlar-2019-translating,
    title = "Translating a Fragment of Natural Deduction System for Natural Language into Modern Type Theory",
    author = "Pezlar, Ivo",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1002",
    doi = "10.18653/v1/W19-1002",
    pages = "10--18",
}
@inproceedings{seyffarth-2019-modeling,
    title = "Modeling the Induced Action Alternation and the Caused-Motion Construction with {T}ree {A}djoining {G}rammar ({TAG}) and Semantic Frames",
    author = "Seyffarth, Esther",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1003",
    doi = "10.18653/v1/W19-1003",
    pages = "19--27",
}
@inproceedings{bellingham-2019-complex,
    title = "Complex event representation in a typed feature structure implementation of Role and Reference Grammar",
    author = "Bellingham, Erika",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1004",
    doi = "10.18653/v1/W19-1004",
    pages = "28--36",
}
@inproceedings{loukanova-2019-computational,
    title = "Computational Syntax-Semantics Interface with Type-Theory of Acyclic Recursion for Underspecified Semantics",
    author = "Loukanova, Roussanka",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1005",
    doi = "10.18653/v1/W19-1005",
    pages = "37--48",
}
@inproceedings{kapustin-kapustin-2019-modeling-language,
    title = "Modeling language constructs with compatibility intervals",
    author = "Kapustin, Pavlo  and
      Kapustin, Michael",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1006",
    doi = "10.18653/v1/W19-1006",
    pages = "49--54",
}
@inproceedings{matsson-etal-2019-imagettr,
    title = "{I}mage{TTR}: Grounding Type Theory with Records in Image Classification for Visual Question Answering",
    author = "Matsson, Arild  and
      Dobnik, Simon  and
      Larsson, Staffan",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1007",
    doi = "10.18653/v1/W19-1007",
    pages = "55--64",
}
@inproceedings{maguire-2019-enthymemetic,
    title = "Enthymemetic Conditionals: Topoi as a guide for acceptability",
    author = "Maguire, Eimear",
    booktitle = "Proceedings of the {IWCS} 2019 Workshop on Computing Semantics with Types, Frames and Related Structures",
    month = jun,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-1008",
    doi = "10.18653/v1/W19-1008",
    pages = "65--74",
}
@proceedings{ws-2019-iwcs,
    title = "Proceedings of the {IWCS} Workshop Vector Semantics for Discourse and Dialogue",
    editor = "Sadrzadeh, Mehrnoosh  and
      Purver, Matthew  and
      Eshghi, Arash  and
      Hough, Julian  and
      Kempson, Ruth  and
      Healey, Patrick G. T.",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0900",
}
@proceedings{ws-2019-relations,
    title = "{RELATIONS} - Workshop on meaning relations between phrases and sentences",
    editor = "Kovatchev, Venelin  and
      Gold, Darina  and
      Zesch, Torsten",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0800",
}
@inproceedings{becker-etal-2019-assessing,
    title = "Assessing the Difficulty of Classifying {C}oncept{N}et Relations in a Multi-Label Classification Setting",
    author = "Becker, Maria  and
      Staniek, Michael  and
      Nastase, Vivi  and
      Frank, Anette",
    booktitle = "{RELATIONS} - Workshop on meaning relations between phrases and sentences",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0801",
    doi = "10.18653/v1/W19-0801",
    abstract = "Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in ConceptNet, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the ConceptNet resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.",
}
@inproceedings{khairova-etal-2019-detecting,
    title = "Detecting Collocations Similarity via Logical-Linguistic Model",
    author = "Khairova, Nina  and
      Petrasova, Svitlana  and
      Mamyrbayev, Orken  and
      Mukhsina, Kuralay",
    booktitle = "{RELATIONS} - Workshop on meaning relations between phrases and sentences",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0802",
    doi = "10.18653/v1/W19-0802",
    abstract = "Semantic similarity between collocations, along with words similarity, is one of the main issues of NLP, which must be addressed, in particular, in order to facilitate the automatic thesaurus generation. In the paper, we consider the logical-linguistic model that allows defining the relation of semantic similarity of collocations via the logical-algebraic equations. We provide the model for English, Ukrainian and Russian text corpora. The implementation for each language is slightly different in the equations of the finite predicates algebra and used linguistic resources. As a dataset for our experiment, we use 5801 pairs of sentences of Microsoft Research Paraphrase Corpus for English and more than 1 000 texts of scientific papers for Russian and Ukrainian.",
}
@inproceedings{josi-etal-2019-detecting,
    title = "Detecting Paraphrases of Standard Clause Titles in Insurance Contracts",
    author = "Josi, Frieda  and
      Wartena, Christian  and
      Heid, Ulrich",
    booktitle = "{RELATIONS} - Workshop on meaning relations between phrases and sentences",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0803",
    doi = "10.18653/v1/W19-0803",
    abstract = "For the analysis of contract texts, validated model texts, such as model clauses, can be used to identify reused contract clauses. This paper investigates how to calculate the similarity between titles of model clauses and headings extracted from contracts, and which similarity measure is most suitable for this. For the calculation of the similarities between title pairs we tested various variants of string similarity and token based similarity. We also compare two more semantic similarity measures based on word embeddings using pretrained embeddings and word embeddings trained on contract texts. The identification of the model clause title can be used as a starting point for the mapping of clauses found in contracts to verified clauses.",
}
@inproceedings{mueller-2019-semantic,
    title = "Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications",
    author = "Mueller, Mark-Christoph",
    booktitle = "{RELATIONS} - Workshop on meaning relations between phrases and sentences",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0804",
    doi = "10.18653/v1/W19-0804",
    abstract = "We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities.",
}
@proceedings{ws-2019-international-semantics-student,
    title = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    editor = "Dobnik, Simon  and
      Chatzikyriakidis, Stergios  and
      Demberg, Vera  and
      Abu Kwaik, Kathrein  and
      Maraev, Vladislav",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0600",
}
@inproceedings{lai-pustejovsky-2019-dynamic,
    title = "A Dynamic Semantics for Causal Counterfactuals",
    author = "Lai, Kenneth  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0601",
    doi = "10.18653/v1/W19-0601",
    pages = "1--8",
    abstract = "Under the standard approach to counterfactuals, to determine the meaning of a counterfactual sentence, we consider the {``}closest{''} possible world(s) where the antecedent is true, and evaluate the consequent. Building on the standard approach, some researchers have found that the set of worlds to be considered is dependent on context; it evolves with the discourse. Others have focused on how to define the {``}distance{''} between possible worlds, using ideas from causal modeling. This paper integrates the two ideas. We present a semantics for counterfactuals that uses a distance measure based on causal laws, that can also change over time. We show how our semantics can be implemented in the Haskell programming language.",
}
@inproceedings{utescher-2019-visual,
    title = "Visual {TTR} - Modelling Visual Question Answering in Type Theory with Records",
    author = "Utescher, Ronja",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0602",
    doi = "10.18653/v1/W19-0602",
    pages = "9--14",
    abstract = "In this paper, I will describe a system that was developed for the task of Visual Question Answering. The system uses the rich type universe of Type Theory with Records (TTR) to model both the utterances about the image, the image itself and classifications made related to the two. At its most basic, the decision of whether any given predicate can be assigned to an object in the image is delegated to a CNN. Consequently, images can be judged as evidence for propositions. The end result is a model whose application of perceptual classifiers to a given image is guided by the accompanying utterance.",
}
@inproceedings{kershaw-bober-2019-lexical,
    title = "The Lexical Gap: An Improved Measure of Automated Image Description Quality",
    author = "Kershaw, Austin  and
      Bober, Miroslaw",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0603",
    doi = "10.18653/v1/W19-0603",
    pages = "15--23",
    abstract = "The challenge of automatically describing images and videos has stimulated much research in Computer Vision and Natural Language Processing. In order to test the semantic abilities of new algorithms, we need reliable and objective ways of measuring progress. We show that standard evaluation measures do not take into account the semantic richness of a description, and give the impression that sparse machine descriptions outperform rich human descriptions. We introduce and test a new measure of semantic ability based on relative lexical diversity. We show how our measure can work alongside existing measures to achieve state of the art correlation with human judgement of quality. We also introduce a new dataset: Rich-Sparse Descriptions, which provides 2K human and machine descriptions to stimulate interest into the semantic evaluation of machine descriptions.",
}
@inproceedings{kapustin-kapustin-2019-modeling,
    title = "Modeling language constructs with fuzzy sets: some approaches, examples and interpretations",
    author = "Kapustin, Pavlo  and
      Kapustin, Michael",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0604",
    doi = "10.18653/v1/W19-0604",
    pages = "24--33",
    abstract = "We present and discuss a couple of approaches, including different types of projections, and some examples, discussing the use of fuzzy sets for modeling meaning of certain types of language constructs. We are mostly focusing on words other than adjectives and linguistic hedges as these categories are the most studied from before. We discuss logical and linguistic interpretations of membership functions. We argue that using fuzzy sets for modeling meaning of words and other natural language constructs, along with situations described with natural language is interesting both from purely linguistic perspective, and also as a knowledge representation for problems of computational linguistics and natural language processing.",
}
@inproceedings{savle-etal-2019-topological,
    title = "Topological Data Analysis for Discourse Semantics?",
    author = "Savle, Ketki  and
      Zadrozny, Wlodek  and
      Lee, Minwoo",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0605",
    doi = "10.18653/v1/W19-0605",
    pages = "34--43",
    abstract = "In this paper we present new results on applying topological data analysis to discourse structures. We show that topological information, extracted from the relationships between sentences can be used in inference, namely it can be applied to the very difficult legal entailment given in the COLIEE 2018 data set. Previous results of Doshi and Zadrozny (2018) and Gholizadeh et al. (2018) show that topological features are useful for classification. The applications of computational topology to entailment are novel in our view provide a new set of tools for discourse semantics: computational topology can perhaps provide a bridge between the brittleness of logic and the regression of neural networks. We discuss the advantages and disadvantages of using topological information, and some open problems such as explainability of the classifier decisions.",
}
@inproceedings{alhoshan-etal-2019-semantic,
    title = "Semantic Frame Embeddings for Detecting Relations between Software Requirements",
    author = "Alhoshan, Waad  and
      Batista-Navarro, Riza  and
      Zhao, Liping",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0606",
    doi = "10.18653/v1/W19-0606",
    pages = "44--51",
    abstract = "The early phases of requirements engineering (RE) deal with a vast amount of software requirements (i.e., requirements that define characteristics of software systems), which are typically expressed in natural language. Analysing such unstructured requirements, usually obtained from users{'} inputs, is considered a challenging task due to the inherent ambiguity and inconsistency of natural language. To support such a task, methods based on natural language processing (NLP) can be employed. One of the more recent advances in NLP is the use of word embeddings for capturing contextual information, which can then be applied in word analogy tasks. In this paper, we describe a new resource, i.e., embedding-based representations of semantic frames in FrameNet, which was developed to support the detection of relations between software requirements. Our embeddings, which encapsulate contextual information at the semantic frame level, were trained on a large corpus of requirements (i.e., a collection of more than three million mobile application reviews). The similarity between these frame embeddings is then used as a basis for detecting semantic relatedness between software requirements. Compared with existing resources underpinned by word-level embeddings alone, and frame embeddings built upon pre-trained vectors, our proposed frame embeddings obtained better performance against judgements of an RE expert. These encouraging results demonstrate the strong potential of the resource in supporting RE analysis tasks (e.g., traceability), which we plan to investigate as part of our future work.",
}
@inproceedings{gyllensten-etal-2019-r,
    title = "{R}-grams: Unsupervised Learning of Semantic Units in Natural Language",
    author = "Gyllensten, Amaru Cuba  and
      Ekgren, Ariel  and
      Sahlgren, Magnus",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Student Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0607",
    doi = "10.18653/v1/W19-0607",
    pages = "52--62",
    abstract = "This paper investigates data-driven segmentation using Re-Pair or Byte Pair Encoding-techniques. In contrast to previous work which has primarily been focused on subword units for machine translation, we are interested in the general properties of such segments above the word level. We call these segments r-grams, and discuss their properties and the effect they have on the token frequency distribution. The proposed approach is evaluated by demonstrating its viability in embedding techniques, both in monolingual and multilingual test settings. We also provide a number of qualitative examples of the proposed methodology, demonstrating its viability as a language-invariant segmentation procedure.",
}
@proceedings{ws-2019-international-semantics-short,
    title = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    editor = "Dobnik, Simon  and
      Chatzikyriakidis, Stergios  and
      Demberg, Vera",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0500",
}
@inproceedings{mcgregor-jezek-2019-distributional,
    title = "A Distributional Model of Affordances in Semantic Type Coercion",
    author = "McGregor, Stephen  and
      Jezek, Elisabetta",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0501",
    doi = "10.18653/v1/W19-0501",
    pages = "1--7",
    abstract = "We explore a novel application for interpreting semantic type coercions, motivated by insight into the role that perceptual affordances play in the selection of the semantic roles of artefactual nouns which are observed as arguments for verbs which would stereotypically select for objects of a different type. In order to simulate affordances, which we take to be direct perceptions of context-specific opportunities for action, we preform a distributional analysis dependency relationships between target words and their modifiers and adjuncts. We use these relationships as the basis for generating on-line transformations which project semantic subspaces in which the interpretations of coercive compositions are expected to emerge as salient word-vectors. We offer some preliminary examples of how this model operates on a dataset of sentences involving coercive interactions between verbs and objects specifically designed to evaluate this work.",
}
@inproceedings{hu-etal-2019-natural,
    title = "Natural Language Inference with Monotonicity",
    author = "Hu, Hai  and
      Chen, Qi  and
      Moss, Larry",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0502",
    doi = "10.18653/v1/W19-0502",
    pages = "8--15",
    abstract = "This paper describes a working system which performs natural language inference using polarity-marked parse trees. The system handles all of the instances of monotonicity inference in the FraCaS data set. Except for the initial parse, it is entirely deterministic. It handles multi-premise arguments, and the kind of inference performed is essentially {``}logical{''}, but it goes beyond what is representable in first-order logic. In any case, the system works on surface forms rather than on representations of any kind.",
}
@inproceedings{kuzmenko-herbelot-2019-distributional,
    title = "Distributional Semantics in the Real World: Building Word Vector Representations from a Truth-Theoretic Model",
    author = "Kuzmenko, Elizaveta  and
      Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0503",
    doi = "10.18653/v1/W19-0503",
    pages = "16--23",
    abstract = "Distributional semantics models (DSMs) are known to produce excellent representations of word meaning, which correlate with a range of behavioural data. As lexical representations, they have been said to be fundamentally different from truth-theoretic models of semantics, where meaning is defined as a correspondence relation to the world. There are two main aspects to this difference: a) DSMs are built over corpus data which may or may not reflect {`}what is in the world{'}; b) they are built from word co-occurrences, that is, from lexical types rather than entities and sets. In this paper, we inspect the properties of a distributional model built over a set-theoretic approximation of {`}the real world{'}. To achieve this, we take the annotation a large database of images marked with objects, attributes and relations, convert the data into a representation akin to first-order logic and build several distributional models using various combinations of features. We evaluate those models over both relatedness and similarity datasets, demonstrating their effectiveness in standard evaluations. This allows us to conclude that, despite prior claims, truth-theoretic models are good candidates for building graded lexical representations of meaning.",
}
@inproceedings{van-noord-etal-2019-linguistic,
    title = "Linguistic Information in Neural Semantic Parsing with Multiple Encoders",
    author = "van Noord, Rik  and
      Toral, Antonio  and
      Bos, Johan",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0504",
    doi = "10.18653/v1/W19-0504",
    pages = "24--31",
    abstract = "Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.",
}
@inproceedings{diller-etal-2019-making,
    title = "Making Sense of Conflicting (Defeasible) Rules in the Controlled Natural Language {ACE}: Design of a System with Support for Existential Quantification Using Skolemization",
    author = "Diller, Martin  and
      Wyner, Adam  and
      Strass, Hannes",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0505",
    doi = "10.18653/v1/W19-0505",
    pages = "32--37",
    abstract = "We present the design of a system for making sense of conflicting rules expressed in a fragment of the prominent controlled natural language ACE, yet extended with means of expressing defeasible rules in the form of normality assumptions. The approach we describe is ultimately based on answer-set-programming (ASP); simulating existential quantification by using skolemization in a manner resembling a translation for ASP recently formalized in the context of ∃-ASP. We discuss the advantages of this approach to building on the existing ACE interface to rule-systems, ACERules.",
}
@inproceedings{frassinelli-schulte-im-walde-2019-distributional,
    title = "Distributional Interaction of Concreteness and Abstractness in Verb{--}Noun Subcategorisation",
    author = "Frassinelli, Diego  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0506",
    doi = "10.18653/v1/W19-0506",
    pages = "38--43",
    abstract = "In recent years, both cognitive and computational research has provided empirical analyses of contextual co-occurrence of concrete and abstract words, partially resulting in inconsistent pictures. In this work we provide a more fine-grained description of the distributional nature in the corpus-based interaction of verbs and nouns within subcategorisation, by investigating the concreteness of verbs and nouns that are in a specific syntactic relationship with each other, i.e., subject, direct object, and prepositional object. Overall, our experiments show consistent patterns in the distributional representation of subcategorising and subcategorised concrete and abstract words. At the same time, the studies reveal empirical evidence why contextual abstractness represents a valuable indicator for automatic non-literal language identification.",
}
@inproceedings{krishnaswamy-pustejovsky-2019-generating,
    title = "Generating a Novel Dataset of Multimodal Referring Expressions",
    author = "Krishnaswamy, Nikhil  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0507",
    doi = "10.18653/v1/W19-0507",
    pages = "44--51",
    abstract = "Referring expressions and definite descriptions of objects in space exploit information both about object characteristics and locations. To resolve potential ambiguity, referencing strategies in language can rely on increasingly abstract concepts to distinguish an object in a given location from similar ones elsewhere, yet the description of the intended location may still be imprecise or difficult to interpret. Meanwhile, modalities such as gesture may communicate spatial information such as locations in a more concise manner. In real peer-to-peer communication, humans use language and gesture together to reference entities, with a capacity for mixing and changing modalities where needed. While recent progress in AI and human-computer interaction has created systems where a human can interact with a computer multimodally, computers often lack the capacity to intelligently mix modalities when generating referring expressions. We present a novel dataset of referring expressions combining natural language and gesture, describe its creation and evaluation, and its uses to train computational models for generating and interpreting multimodal referring expressions.",
}
@inproceedings{silva-amarathunga-2019-learning,
    title = "On Learning Word Embeddings From Linguistically Augmented Text Corpora",
    author = "Silva, Amila  and
      Amarathunga, Chathurika",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0508",
    doi = "10.18653/v1/W19-0508",
    pages = "52--58",
    abstract = "Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality embeddings if the text corpus is small and sparse. This paper proposes a method to generate effective word embeddings from limited data. Through experiments, we show that our proposed model outperforms existing works for the classical word similarity task and for a domain-specific application.",
}
@inproceedings{wartena-etal-2019-sentiment,
    title = "Sentiment Independent Topic Detection in Rated Hospital Reviews",
    author = "Wartena, Christian  and
      Sander, Uwe  and
      Patzelt, Christiane",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0509",
    doi = "10.18653/v1/W19-0509",
    pages = "59--64",
    abstract = "We present a simple method to find topics in user reviews that accompany ratings for products or services. Standard topic analysis will perform sub-optimal on such data since the word distributions in the documents are not only determined by the topics but by the sentiment as well. We reduce the influence of the sentiment on the topic selection by adding two explicit topics, representing positive and negative sentiment. We evaluate the proposed method on a set of over 15,000 hospital reviews. We show that the proposed method, Latent Semantic Analysis with explicit word features, finds topics with a much smaller bias for sentiments than other similar methods.",
}
@inproceedings{pierrejean-tanguy-2019-investigating,
    title = "Investigating the Stability of Concrete Nouns in Word Embeddings",
    author = "Pierrejean, B{\'e}n{\'e}dicte  and
      Tanguy, Ludovic",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Short Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0510",
    doi = "10.18653/v1/W19-0510",
    pages = "65--70",
    abstract = "We know that word embeddings trained using neural-based methods (such as word2vec SGNS) are sensitive to stability problems and that across two models trained using the exact same set of parameters, the nearest neighbors of a word are likely to change. All words are not equally impacted by this internal instability and recent studies have investigated features influencing the stability of word embeddings. This stability can be seen as a clue for the reliability of the semantic representation of a word. In this work, we investigate the influence of the degree of concreteness of nouns on the stability of their semantic representation. We show that for English generic corpora, abstract words are more affected by stability problems than concrete words. We also found that to a certain extent, the difference between the degree of concreteness of a noun and its nearest neighbors can partly explain the stability or instability of its neighbors.",
}
@proceedings{ws-2019-international-semantics,
    title = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    editor = "Dobnik, Simon  and
      Chatzikyriakidis, Stergios  and
      Demberg, Vera",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0400",
}
@inproceedings{fernando-2019-projecting,
    title = "Projecting Temporal Properties, Events and Actions",
    author = "Fernando, Tim",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0401",
    doi = "10.18653/v1/W19-0401",
    pages = "1--12",
    abstract = "Temporal notions based on a finite set \textit{A} of properties are represented in strings, on which projections are defined that vary the granularity \textit{A}. The structure of properties in \textit{A} is elaborated to describe statives, events and actions, subject to a distinction in meaning (advocated by Levin and Rappaport Hovav) between what the lexicon prescribes and what a context of use supplies. The projections proposed are deployed as labels for records and record types amenable to finite-state methods.",
}
@inproceedings{kim-schubert-2019-type,
    title = "A Type-coherent, Expressive Representation as an Initial Step to Language Understanding",
    author = "Kim, Gene Louis  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0402",
    doi = "10.18653/v1/W19-0402",
    pages = "13--30",
    abstract = "A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.",
}
@inproceedings{bunt-2019-semantic,
    title = "A Semantic Annotation Scheme for Quantification",
    author = "Bunt, Harry",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0403",
    doi = "10.18653/v1/W19-0403",
    pages = "31--42",
    abstract = "This paper describes in brief the proposal called {`}QuantML{'} which was accepted by the International Organisation for Standards (ISO) last February as a starting point for developing a standard for the interoperable annotation of quantification phenomena in natural language, as part of the ISO 24617 Semantic Annotation Framework. The proposal, firmly rooted in the theory of generalised quantifiers, neo-Davidsonian semantics, and DRT, covers a wide range of quantification phenomena. The QuantML scheme consists of (1) an abstract syntax which defines {`}annotation structures{'} as triples and other set-theoretic constructs; (b) a compositional semantics of annotation structures; (3) an XML representation of annotation structures.",
}
@inproceedings{alokaili-etal-2019-ranking,
    title = "Re-Ranking Words to Improve Interpretability of Automatically Generated Topics",
    author = "Alokaili, Areej  and
      Aletras, Nikolaos  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0404",
    doi = "10.18653/v1/W19-0404",
    pages = "43--54",
    abstract = "Topics models, such as LDA, are widely used in Natural Language Processing. Making their output interpretable is an important area of research with applications to areas such as the enhancement of exploratory search interfaces and the development of interpretable machine learning models. Conventionally, topics are represented by their n most probable words, however, these representations are often difficult for humans to interpret. This paper explores the re-ranking of topic words to generate more interpretable topic representations. A range of approaches are compared and evaluated in two experiments. The first uses crowdworkers to associate topics represented by different word rankings with related documents. The second experiment is an automatic approach based on a document retrieval task applied on multiple domains. Results in both experiments demonstrate that re-ranking words improves topic interpretability and that the most effective re-ranking schemes were those which combine information about the importance of words both within topics and their relative frequency in the entire corpus. In addition, close correlation between the results of the two evaluation approaches suggests that the automatic method proposed here could be used to evaluate re-ranking methods without the need for human judgements.",
}
@inproceedings{blodgett-schneider-2019-improved,
    title = "An Improved Approach for Semantic Graph Composition with {CCG}",
    author = "Blodgett, Austin  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0405",
    doi = "10.18653/v1/W19-0405",
    pages = "55--70",
    abstract = "This paper builds on previous work using Combinatory Categorial Grammar (CCG) to derive a transparent syntax-semantics interface for Abstract Meaning Representation (AMR) parsing. We define new semantics for the CCG combinators that is better suited to deriving AMR graphs. In particular, we define relation-wise alternatives for the application and composition combinators: these require that the two constituents being combined overlap in one AMR relation. We also provide a new semantics for type raising, which is necessary for certain constructions. Using these mechanisms, we suggest an analysis of eventive nouns, which present a challenge for deriving AMR graphs. Our theoretical analysis will facilitate future work on robust and transparent AMR parsing using CCG.",
}
@inproceedings{bick-2019-semantic,
    title = "A Semantic Ontology of {D}anish Adjectives",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0406",
    doi = "10.18653/v1/W19-0406",
    pages = "71--78",
    abstract = "This paper presents a semantic annotation scheme for Danish adjectives, focusing both on prototypical semantic content and semantic collocational restrictions on an adjective{'}s head noun. The core type set comprises about 110 categories ordered in a shallow hierarchy with 14 primary and 25 secondary umbrella categories. In addition, domain information and binary sentiment tags are provided, as well as VerbNet-derived frames and semantic roles for those adjectives governing arguments. The scheme has been almost fully implemented on the lexicon of the Danish VISL parser, DanGram, containing 14,000 adjectives. We discuss the annotation scheme and its applicational perspectives, and present a statistical breakdown and coverage evaluation for three Danish reference corpora.",
}
@inproceedings{fleischhauer-etal-2019-towards,
    title = "Towards a Compositional Analysis of {G}erman Light Verb Constructions ({LVC}s) Combining {L}exicalized {T}ree {A}djoining {G}rammar ({LTAG}) with Frame Semantics",
    author = "Fleischhauer, Jens  and
      Gamerschlag, Thomas  and
      Kallmeyer, Laura  and
      Petitjean, Simon",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0407",
    doi = "10.18653/v1/W19-0407",
    pages = "79--90",
    abstract = "Complex predicates formed of a semantically {`}light{'} verbal head and a noun or verb which contributes the major part of the meaning are frequently referred to as {`}light verb constructions{'} (LVCs). In the paper, we present a case study of LVCs with the German posture verb stehen {`}stand{'}. In our account, we model the syntactic as well as semantic composition of such LVCs by combining Lexicalized Tree Adjoining Grammar (LTAG) with frames. Starting from the analysis of the literal uses of posture verbs, we show how the meaning components of the literal uses are systematically exploited in the interpretation of stehen-LVCs. The paper constitutes an important step towards a compositional and computational analysis of LVCs. We show that LTAG allows us to separate constructional from lexical meaning components and that frames enable elegant generalizations over event types and related constraints.",
}
@inproceedings{czarnowska-etal-2019-words,
    title = "Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs",
    author = "Czarnowska, Paula  and
      Emerson, Guy  and
      Copestake, Ann",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0408",
    doi = "10.18653/v1/W19-0408",
    pages = "91--102",
    abstract = "Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts. Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations. In this work, we propose a new dependency-based DSM. The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label. This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks. In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word.",
}
@inproceedings{kober-etal-2019-temporal,
    title = "Temporal and Aspectual Entailment",
    author = "Kober, Thomas  and
      Bijl de Vroe, Sander  and
      Steedman, Mark",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0409",
    doi = "10.18653/v1/W19-0409",
    pages = "103--119",
    abstract = "Inferences regarding {``}Jane{'}s arrival in London{''} from predications such as {``}Jane is going to London{''} or {``}Jane has gone to London{''} depend on tense and aspect of the predications. Tense determines the temporal location of the predication in the past, present or future of the time of utterance. The aspectual auxiliaries on the other hand specify the internal constituency of the event, i.e. whether the event of {``}going to London{''} is completed and whether its consequences hold at that time or not. While tense and aspect are among the most important factors for determining natural language inference, there has been very little work to show whether modern embedding models capture these semantic concepts. In this paper we propose a novel entailment dataset and analyse the ability of contextualised word representations to perform inference on predications across aspectual types and tenses. We show that they encode a substantial amount of information relating to tense and aspect, but fail to consistently model inferences that require reasoning with these semantic properties.",
}
@inproceedings{westera-boleda-2019-dont,
    title = "Don{'}t Blame Distributional Semantics if it can{'}t do Entailment",
    author = "Westera, Matthijs  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0410",
    doi = "10.18653/v1/W19-0410",
    pages = "120--133",
    abstract = "Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems. However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model? Can it be, on its own, a fully adequate model of the meanings of linguistic expressions? The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality. We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context. In a slogan: words do not refer, speakers do. Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning. Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.",
}
@inproceedings{webber-etal-2019-ambiguity,
    title = "Ambiguity in Explicit Discourse Connectives",
    author = "Webber, Bonnie  and
      Prasad, Rashmi  and
      Lee, Alan",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0411",
    doi = "10.18653/v1/W19-0411",
    pages = "134--141",
    abstract = "Discourse connectives are known to be subject to both usage and sense ambiguity, as has already been discussed in the literature. But discourse connectives are no different from other linguistic expressions in being subject to other types of ambiguity as well. Four are illustrated and discussed here.",
}
@inproceedings{putri-etal-2019-aligning,
    title = "Aligning Open {IE} Relations and {KB} Relations using a {S}iamese Network Based on Word Embedding",
    author = "Putri, Rifki Afina  and
      Hong, Giwon  and
      Myaeng, Sung-Hyon",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0412",
    doi = "10.18653/v1/W19-0412",
    pages = "142--153",
    abstract = "Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text. Given a triple, the relation expresses the type of semantic relation between the entities. Although relations from an Open IE system are more extensible than those used in a traditional Information Extraction system and a Knowledge Base (KB) such as Knowledge Graphs, the former lacks in semantics; an Open IE relation is simply a sequence of words, whereas a KB relation has a predefined meaning. As a way to provide a meaning to an Open IE relation, we attempt to align it with one of the predefined set of relations used in a KB. Our approach is to use a Siamese network that compares two sequences of word embeddings representing an Open IE relation and a predefined KB relation. In order to make the approach practical, we automatically generate a training dataset using a distant supervision approach instead of relying on a hand-labeled dataset. Our experiment shows that the proposed method can capture the relational semantics better than the recent approaches.",
}
@inproceedings{akhtar-etal-2019-language,
    title = "Language-Agnostic Model for Aspect-Based Sentiment Analysis",
    author = "Akhtar, Md Shad  and
      Kumar, Abhishek  and
      Ekbal, Asif  and
      Biemann, Chris  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0413",
    doi = "10.18653/v1/W19-0413",
    pages = "154--164",
    abstract = "In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.",
}
@inproceedings{bizzoni-lappin-2019-effect,
    title = "The Effect of Context on Metaphor Paraphrase Aptness Judgments",
    author = "Bizzoni, Yuri  and
      Lappin, Shalom",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0414",
    doi = "10.18653/v1/W19-0414",
    pages = "165--175",
    abstract = "We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments. The first is an AMT crowd source task in which speakers rank metaphor-paraphrase candidate sentence pairs in short document contexts for paraphrase aptness. In the second we train a composite DNN to predict these human judgments, first in binary classifier mode, and then as gradient ratings. We found that for both mean human judgments and our DNN{'}s predictions, adding document context compresses the aptness scores towards the center of the scale, raising low out-of-context ratings and decreasing high out-of-context scores. We offer a provisional explanation for this compression effect.",
}
@inproceedings{charbonnier-wartena-2019-predicting,
    title = "Predicting Word Concreteness and Imagery",
    author = "Charbonnier, Jean  and
      Wartena, Christian",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0415",
    doi = "10.18653/v1/W19-0415",
    pages = "176--187",
    abstract = "Concreteness of words has been studied extensively in psycholinguistic literature. A number of datasets have been created with average values for perceived concreteness of words. We show that we can train a regression model on these data, using word embeddings and morphological features, that can predict these concreteness values with high accuracy. We evaluate the model on 7 publicly available datasets. Only for a few small subsets of these datasets prediction of concreteness values are found in the literature. Our results clearly outperform the reported results for these datasets.",
}
@inproceedings{shi-demberg-2019-learning,
    title = "Learning to Explicitate Connectives with {S}eq2{S}eq Network for Implicit Discourse Relation Classification",
    author = "Shi, Wei  and
      Demberg, Vera",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0416",
    doi = "10.18653/v1/W19-0416",
    pages = "188--199",
    abstract = "Implicit discourse relation classification is one of the most difficult steps in discourse parsing. The difficulty stems from the fact that the coherence relation must be inferred based on the content of the discourse relational arguments. Therefore, an effective encoding of the relational arguments is of crucial importance. We here propose a new model for implicit discourse relation classification, which consists of a classifier, and a sequence-to-sequence model which is trained to generate a representation of the discourse relational arguments by trying to predict the relational arguments including a suitable implicit connective. Training is possible because such implicit connectives have been annotated as part of the PDTB corpus. Along with a memory network, our model could generate more refined representations for the task. And on the now standard 11-way classification, our method outperforms the previous state of the art systems on the PDTB benchmark on multiple settings including cross validation.",
}
@inproceedings{aminian-etal-2019-cross,
    title = "Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles",
    author = "Aminian, Maryam  and
      Rasooli, Mohammad Sadegh  and
      Diab, Mona",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0417",
    doi = "10.18653/v1/W19-0417",
    pages = "200--210",
    abstract = "We describe a transfer method based on annotation projection to develop a dependency-based semantic role labeling system for languages for which no supervised linguistic information other than parallel data is available. Unlike previous work that presumes the availability of supervised features such as lemmas, part-of-speech tags, and dependency parse trees, we only make use of word and character features. Our deep model considers using character-based representations as well as unsupervised stem embeddings to alleviate the need for supervised features. Our experiments outperform a state-of-the-art method that uses supervised lexico-syntactic features on 6 out of 7 languages in the Universal Proposition Bank.",
}
@inproceedings{shekhar-etal-2019-evaluating,
    title = "Evaluating the Representational Hub of Language and Vision Models",
    author = "Shekhar, Ravi  and
      Takmaz, Ece  and
      Fern{\'a}ndez, Raquel  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0418",
    doi = "10.18653/v1/W19-0418",
    pages = "211--222",
    abstract = "The multimodal models used in the emerging field at the intersection of computational linguistics and computer vision implement the bottom-up processing of the {``}Hub and Spoke{''} architecture proposed in cognitive science to represent how the brain processes and combines multi-sensory inputs. In particular, the Hub is implemented as a neural network encoder. We investigate the effect on this encoder of various vision-and-language tasks proposed in the literature: visual question answering, visual reference resolution, and visually grounded dialogue. To measure the quality of the representations learned by the encoder, we use two kinds of analyses. First, we evaluate the encoder pre-trained on the different vision-and-language tasks on an existing {``}diagnostic task{''} designed to assess multimodal semantic understanding. Second, we carry out a battery of analyses aimed at studying how the encoder merges and exploits the two modalities.",
}
@inproceedings{leonandya-etal-2019-fast,
    title = "The Fast and the Flexible: Training Neural Networks to Learn to Follow Instructions from Small Data",
    author = "Leonandya, Rezka  and
      Hupkes, Dieuwke  and
      Bruni, Elia  and
      Kruszewski, Germ{\'a}n",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0419",
    doi = "10.18653/v1/W19-0419",
    pages = "223--234",
    abstract = "Learning to follow human instructions is a long-pursued goal in artificial intelligence. The task becomes particularly challenging if no prior knowledge of the employed language is assumed while relying only on a handful of examples to learn from. Work in the past has relied on hand-coded components or manually engineered features to provide strong inductive biases that make learning in such situations possible. In contrast, here we seek to establish whether this knowledge can be acquired automatically by a neural network system through a two phase training procedure: A (slow) offline learning stage where the network learns about the general structure of the task and a (fast) online adaptation phase where the network learns the language of a new given speaker. Controlled experiments show that when the network is exposed to familiar instructions but containing novel words, the model adapts very efficiently to the new vocabulary. Moreover, even for human speakers whose language usage can depart significantly from our artificial training language, our network can still make use of its automatically acquired inductive bias to learn to follow instructions more effectively.",
}
@inproceedings{koopman-etal-2019-fast,
    title = "Fast and Discriminative Semantic Embedding",
    author = "Koopman, Rob  and
      Wang, Shenghui  and
      Englebienne, Gwenn",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0420",
    doi = "10.18653/v1/W19-0420",
    pages = "235--246",
    abstract = "The embedding of words and documents in compact, semantically meaningful vector spaces is a crucial part of modern information systems. Deep Learning models are powerful but their hyperparameter selection is often complex and they are expensive to train, and while pre-trained models are available, embeddings trained on general corpora are not necessarily well-suited to domain specific tasks. We propose a novel embedding method which extends random projection by weighting and projecting raw term embeddings orthogonally to an average language vector, thus improving the discriminating power of resulting term embeddings, and build more meaningful document embeddings by assigning appropriate weights to individual terms. We describe how updating the term embeddings online as we process the training data results in an extremely efficient method, in terms of both computational and memory requirements. Our experiments show highly competitive results with various state-of-the-art embedding methods on different tasks, including the standard STS benchmark and a subject prediction task, at a fraction of the computational cost.",
}
@inproceedings{hedderich-etal-2019-using,
    title = "Using Multi-Sense Vector Embeddings for Reverse Dictionaries",
    author = "Hedderich, Michael A.  and
      Yates, Andrew  and
      Klakow, Dietrich  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0421",
    doi = "10.18653/v1/W19-0421",
    pages = "247--258",
    abstract = "Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well.",
}
@inproceedings{segonne-etal-2019-using,
    title = "Using {W}iktionary as a resource for {WSD} : the case of {F}rench verbs",
    author = "Segonne, Vincent  and
      Candito, Marie  and
      Crabb{\'e}, Beno{\^\i}t",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0422",
    doi = "10.18653/v1/W19-0422",
    pages = "259--270",
    abstract = "As opposed to word sense induction, word sense disambiguation (WSD) has the advantage of us-ing interpretable senses, but requires annotated data, which are quite rare for most languages except English (Miller et al. 1993; Fellbaum, 1998). In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French. We first study the usability of Eurosense (Bovi et al. 2017) , a multilingual corpus extracted from Europarl (Kohen, 2005) and automatically annotated with BabelNet (Navigli and Ponzetto, 2010) senses. Such a resource opened up the way to supervised and semi-supervised WSD for resourceless languages like French. While this perspective looked promising, our evaluation on French verbs was inconclusive and showed the annotated senses{'} quality was not sufficient for supervised WSD on French verbs. Instead, we propose to use Wiktionary, a collaboratively edited, multilingual online dictionary, as a resource for WSD. Wiktionary provides both sense inventory and manually sense tagged examples which can be used to train supervised and semi-supervised WSD systems. Yet, because senses{'} distribution differ in lexicographic examples found in Wiktionary with respect to natural text, we then focus on studying the impact on WSD of the training data size and senses{'} distribution. Using state-of-the art semi-supervised systems, we report experiments of Wiktionary-based WSD for French verbs, evaluated on FrenchSemEval (FSE), a new dataset of French verbs manually annotated with wiktionary senses.",
}
@inproceedings{gari-soler-etal-2019-comparison,
    title = "A Comparison of Context-sensitive Models for Lexical Substitution",
    author = "Gar{\'\i} Soler, Aina  and
      Cocos, Anne  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0423",
    doi = "10.18653/v1/W19-0423",
    pages = "271--282",
    abstract = "Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks. Embedding approaches differ as to whether and how they account for the context surrounding a word. We present a comparison of different word and context representations on the task of proposing substitutes for a target word in context (lexical substitution). We also experiment with tuning contextualized word embeddings on a dataset of sense-specific instances for each target word. We show that powerful contextualized word representations, which give high performance in several semantics-related tasks, deal less well with the subtle in-context similarity relationships needed for substitution. This is better handled by models trained with this objective in mind, where the inter-dependence between word and context representations is explicitly modeled during training.",
}
@inproceedings{schlangen-2019-natural,
    title = "Natural Language Semantics With Pictures: Some Language {\&} Vision Datasets and Potential Uses for Computational Semantics",
    author = "Schlangen, David",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0424",
    doi = "10.18653/v1/W19-0424",
    pages = "283--294",
    abstract = "Propelling, and propelled by, the {``}deep learning revolution{''}, recent years have seen the introduction of ever larger corpora of images annotated with natural language expressions. We survey some of these corpora, taking a perspective that reverses the usual directionality, as it were, by viewing the images as semantic annotation of the natural language expressions. We discuss datasets that can be derived from the corpora, and tasks of potential interest for computational semanticists that can be defined on those. In this, we make use of relations provided by the corpora (namely, the link between expression and image, and that between two expressions linked to the same image) and relations that we can add (similarity relations between expressions, or between images). Specifically, we show that in this way we can create data that can be used to learn and evaluate lexical and compositional grounded semantics, and we show that the {``}linked to same image{''} relation tracks a semantic implication relation that is recognisable to annotators even in the absence of the linking image as evidence. Finally, as an example of possible benefits of this approach, we show that an exemplar-model-based approach to implication beats a (simple) distributional space-based one on some derived datasets, while lending itself to explainability.",
}
@inproceedings{sikos-pado-2019-frame,
    title = "Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland",
    author = "Sikos, Jennifer  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
    month = may,
    year = "2019",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0425",
    doi = "10.18653/v1/W19-0425",
    pages = "295--306",
    abstract = "Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization. Even though many tasks in semantics also involve categorization of some kind, theories of categorization do not play a major role in contemporary research in computational linguistics. This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories. The benefit is a space of model families that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions. We instantiate this idea on the task of frame-semantic frame identification. We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input; and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization. We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits. Our prototype-based, fine-tuned model, which combines the best choices for these variables, establishes a new state of the art in frame identification.",
}
@proceedings{ws-2019-international,
    title = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    editor = "Pirinen, Tommi A.  and
      Kaalep, Heiki-Jaan  and
      Tyers, Francis M.",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0300",
}
@inproceedings{silfverberg-tyers-2019-data,
    title = "Data-Driven Morphological Analysis for Uralic Languages",
    author = "Silfverberg, Miikka  and
      Tyers, Francis",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0301",
    doi = "10.18653/v1/W19-0301",
    pages = "1--14",
}
@inproceedings{gronroos-etal-2019-north,
    title = "{N}orth {S}{\'a}mi morphological segmentation with low-resource semi-supervised sequence labeling",
    author = {Gr{\"o}nroos, Stig-Arne  and
      Virpioja, Sami  and
      Kurimo, Mikko},
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0302",
    doi = "10.18653/v1/W19-0302",
    pages = "15--26",
}
@inproceedings{ligeti-nagy-etal-2019-nom,
    title = "What does the Nom say? An algorithm for case disambiguation in {H}ungarian",
    author = {Ligeti-Nagy, No{\'e}mi  and
      D{\"o}m{\"o}t{\"o}r, Andrea  and
      Vad{\'a}sz, No{\'e}mi},
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0303",
    doi = "10.18653/v1/W19-0303",
    pages = "27--41",
}
@inproceedings{robertson-2019-contrastive,
    title = "A Contrastive Evaluation of Word Sense Disambiguation Systems for {F}innish",
    author = "Robertson, Frankie",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0304",
    doi = "10.18653/v1/W19-0304",
    pages = "42--54",
}
@inproceedings{muischnek-torga-2019-elliptical,
    title = "Elliptical Constructions in {E}stonian {UD} Treebank",
    author = "Muischnek, Kadri  and
      Torga, Liisi",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0305",
    doi = "10.18653/v1/W19-0305",
    pages = "55--65",
}
@inproceedings{kettunen-2019-fist,
    title = "{F}i{ST} {--} towards a free Semantic Tagger of modern standard {F}innish",
    author = "Kettunen, Kimmo",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0306",
    doi = "10.18653/v1/W19-0306",
    pages = "66--76",
}
@inproceedings{partanen-riessler-2019-ocr,
    title = "An {OCR} system for the Unified Northern Alphabet",
    author = "Partanen, Niko  and
      Rie{\ss}ler, Michael",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0307",
    doi = "10.18653/v1/W19-0307",
    pages = "77--89",
}
@inproceedings{wilbur-2019-elan,
    title = "{ELAN} as a search engine for hierarchically structured, tagged corpora",
    author = "Wilbur, Joshua",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0308",
    doi = "10.18653/v1/W19-0308",
    pages = "90--103",
}
@inproceedings{pirinen-2019-neural,
    title = "Neural and rule-based {F}innish {NLP} models{---}expectations, experiments and experiences",
    author = "Pirinen, Tommi A",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0309",
    doi = "10.18653/v1/W19-0309",
    pages = "104--114",
}
@inproceedings{arkhangelskiy-etal-2019-uralic,
    title = "Uralic multimedia corpora: {ISO}/{TEI} corpus data in the project {INEL}",
    author = "Arkhangelskiy, Timofey  and
      Ferger, Anne  and
      Hedeland, Hanna",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0310",
    doi = "10.18653/v1/W19-0310",
    pages = "115--124",
}
@inproceedings{arkhangelskiy-2019-corpora,
    title = "Corpora of social media in minority Uralic languages",
    author = "Arkhangelskiy, Timofey",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0311",
    doi = "10.18653/v1/W19-0311",
    pages = "125--140",
}
@inproceedings{wiechetek-etal-2019-end,
    title = "Is this the end? Two-step tokenization of sentence boundaries",
    author = "Wiechetek, Linda  and
      Moshagen, Sjur N{\o}rsteb{\o}  and
      Omma, Thomas",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0312",
    doi = "10.18653/v1/W19-0312",
    pages = "141--153",
}
@inproceedings{huber-spiliopoulou-2019-learning,
    title = "Learning multilingual topics through aspect extraction from monolingual texts",
    author = "Huber, Johannes  and
      Spiliopoulou, Myra",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0313",
    doi = "10.18653/v1/W19-0313",
    pages = "154--183",
}
@inproceedings{ernstreits-2019-electronical,
    title = "Electronical resources for {L}ivonian",
    author = "Ern{\v{s}}treits, Valts",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0314",
    doi = "10.18653/v1/W19-0314",
    pages = "184--191",
}
@inproceedings{kankainen-2019-use,
    title = "The use of Extract Morphology for Automatic Generation of Language Technology for {V}otic",
    author = "Kankainen, Kristian",
    booktitle = "Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2019",
    address = "Tartu, Estonia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W19-0315",
    doi = "10.18653/v1/W19-0315",
    pages = "192--204",
}
@proceedings{ws-2019-society,
    title = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    editor = "Jarosz, Gaja  and
      Nelson, Max  and
      O{'}Connor, Brendan  and
      Pater, Joe",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0100",
    doi = "10.7275/ntf6-xx21",
    pages = "i--ii",
}
@inproceedings{van-schijndel-linzen-2019-entropy,
    title = "Can Entropy Explain Successor Surprisal Effects in Reading?",
    author = "van Schijndel, Marten  and
      Linzen, Tal",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0101",
    doi = "10.7275/qtbb-9d05",
    pages = "1--7",
}
@inproceedings{dolatian-heinz-2019-redtyp,
    title = "{R}ed{T}yp: A Database of Reduplication with Computational Models",
    author = "Dolatian, Hossep  and
      Heinz, Jeffrey",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0102",
    doi = "10.7275/ckx7-s770",
    pages = "8--18",
}
@inproceedings{granroth-wilding-toivonen-2019-unsupervised,
    title = "Unsupervised Learning of Cross-Lingual Symbol Embeddings Without Parallel Data",
    author = "Granroth-Wilding, Mark  and
      Toivonen, Hannu",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0103",
    doi = "10.7275/wx64-ea83",
    pages = "19--28",
}
@inproceedings{danis-jardine-2019-q,
    title = "{Q}-Theory Representations are Logically Equivalent to Autosegmental Representations",
    author = "Danis, Nick  and
      Jardine, Adam",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0104",
    doi = "10.7275/tvj1-k306",
    pages = "29--38",
}
@inproceedings{zamaraeva-etal-2019-modeling,
    title = "Modeling Clausal Complementation for a Grammar Engineering Resource",
    author = "Zamaraeva, Olga  and
      Howell, Kristen  and
      Bender, Emily M.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0105",
    doi = "10.7275/dygn-c796",
    pages = "39--49",
}
@inproceedings{futrell-levy-2019-rnns,
    title = "Do {RNN}s learn human-like abstract word order preferences?",
    author = "Futrell, Richard  and
      Levy, Roger P.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0106",
    doi = "10.7275/jb34-9986",
    pages = "50--59",
}
@inproceedings{nelson-2019-segmentation,
    title = "Segmentation and {UR} Acquisition with {UR} Constraints",
    author = "Nelson, Max",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0107",
    doi = "10.7275/zc9d-pn56",
    pages = "60--68",
}
@inproceedings{moreton-2019-constraint,
    title = "Constraint breeding during on-line incremental learning",
    author = "Moreton, Elliot",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0108",
    doi = "10.7275/6f9x-6411",
    pages = "69--80",
}
@inproceedings{cohn-gordon-etal-2019-incremental,
    title = "An Incremental Iterated Response Model of Pragmatics",
    author = "Cohn-Gordon, Reuben  and
      Goodman, Noah  and
      Potts, Christopher",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0109",
    doi = "10.7275/cprc-8x17",
    pages = "81--90",
}
@inproceedings{hughto-etal-2019-learning,
    title = "Learning Exceptionality and Variation with Lexically Scaled {M}ax{E}nt",
    author = "Hughto, Coral  and
      Lamont, Andrew  and
      Prickett, Brandon  and
      Jarosz, Gaja",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0110",
    doi = "10.7275/y68s-kh12",
    pages = "91--101",
}
@inproceedings{rosen-2019-learning,
    title = "Learning complex inflectional paradigms through blended gradient inputs",
    author = "Rosen, Eric R.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0111",
    doi = "10.7275/ccwf-j606",
    pages = "102--112",
}
@inproceedings{kasai-frank-2019-jabberwocky,
    title = "Jabberwocky Parsing: Dependency Parsing with Lexical Noise",
    author = "Kasai, Jungo  and
      Frank, Robert",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0112",
    doi = "10.7275/h12q-k754",
    pages = "113--123",
}
@inproceedings{hao-2019-learnability,
    title = "Learnability and Overgeneration in Computational Syntax",
    author = "Hao, Yiding",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0113",
    doi = "10.7275/1qmz-bg76",
    pages = "124--134",
}
@inproceedings{burnett-bonami-2019-conceptual,
    title = "A Conceptual Spaces Model of Socially Motivated Language Change",
    author = "Burnett, Heather  and
      Bonami, Olivier",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0114",
    doi = "10.7275/5vmp-cs05",
    pages = "135--145",
}
@inproceedings{seyffarth-2019-identifying,
    title = "Identifying Participation of Individual Verbs or {V}erb{N}et Classes in the Causative Alternation",
    author = "Seyffarth, Esther",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0115",
    doi = "10.7275/efvz-jy59",
    pages = "146--155",
}
@inproceedings{lucy-mendelsohn-2019-using,
    title = "Using Sentiment Induction to Understand Variation in Gendered Online Communities",
    author = "Lucy, Li  and
      Mendelsohn, Julia",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0116",
    doi = "10.7275/11wq-ep51",
    pages = "156--166",
}
@inproceedings{chrupala-etal-2019-difficulty,
    title = "On the difficulty of a distributional semantics of spoken language",
    author = "Chrupa{\l}a, Grzegorz  and
      Gelderloos, Lieke  and
      K{\'a}d{\'a}r, {\'A}kos  and
      Alishahi, Afra",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0117",
    doi = "10.7275/extq-7546",
    pages = "167--173",
}
@inproceedings{mickus-etal-2019-distributional,
    title = "Distributional Effects of Gender Contrasts Across Categories",
    author = "Mickus, Timothee  and
      Bonami, Olivier  and
      Paperno, Denis",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0118",
    doi = "10.7275/g11b-3s25",
    pages = "174--184",
}
@inproceedings{anderson-dillon-2019-guess,
    title = "Guess Who{'}s Coming (and Who{'}s Going): Bringing Perspective to the Rational Speech Acts Framework",
    author = "Anderson, Carolyn Jane  and
      Dillon, Brian W.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0119",
    doi = "10.7275/9bn3-8x38",
    pages = "185--194",
}
@inproceedings{wang-2019-organization,
    title = "The organization of sound inventories: A study on obstruent gaps",
    author = "Wang, Sheng-Fu",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0120",
    doi = "10.7275/pbe8-zf60",
    pages = "195--204",
}
@inproceedings{graf-shafiei-2019-c,
    title = "{C}-Command Dependencies as {TSL} String Constraints",
    author = "Graf, Thomas  and
      Shafiei, Nazila",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0121",
    doi = "10.7275/4rrx-x488",
    pages = "205--215",
}
@inproceedings{barak-etal-2019-modeling,
    title = "Modeling the Acquisition of Words with Multiple Meanings",
    author = "Barak, Libby  and
      Floyd, Sammy  and
      Goldberg, Adele",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0122",
    doi = "10.7275/tr21-m273",
    pages = "216--225",
}
@inproceedings{white-2019-evaluation,
    title = "Evaluation Order Effects in Dynamic Continuized {CCG}: From Negative Polarity Items to Balanced Punctuation",
    author = "White, Michael",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0123",
    doi = "10.7275/kpch-rk05",
    pages = "226--235",
}
@inproceedings{bonial-etal-2019-abstract,
    title = "{A}bstract {M}eaning {R}epresentation for Human-Robot Dialogue",
    author = "Bonial, Claire N.  and
      Donatelli, Lucia  and
      Ervin, Jessica  and
      Voss, Clare R.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0124",
    doi = "10.7275/v3c5-yd35",
    pages = "236--246",
}
@inproceedings{lambert-rogers-2019-logical,
    title = "A Logical and Computational Methodology for Exploring Systems of Phonotactic Constraints",
    author = "Lambert, Dakotah  and
      Rogers, James",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0125",
    doi = "10.7275/t0dv-9t05",
    pages = "247--256",
}
@inproceedings{lalisse-smolensky-2019-augmentic,
    title = "Augmentic Compositional Models for Knowledge Base Completion Using Gradient Representations",
    author = "Lalisse, Matthias R.  and
      Smolensky, Paul",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0126",
    doi = "10.7275/8et8-qd83",
    pages = "257--266",
}
@inproceedings{vu-etal-2019-case,
    title = "Case assignment in {TSL} syntax: a case study",
    author = "Vu, Mai Ha  and
      Shafiei, Nazila  and
      Graf, Thomas",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0127",
    doi = "10.7275/sywz-xw23",
    pages = "267--276",
}
@inproceedings{suzgun-etal-2019-evaluating,
    title = "On Evaluating the Generalization of {LSTM} Models in Formal Languages",
    author = "Suzgun, Mirac  and
      Belinkov, Yonatan  and
      Shieber, Stuart M.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0128",
    doi = "10.7275/s02b-4d91",
    pages = "277--286",
}
@inproceedings{kann-etal-2019-verb,
    title = "Verb Argument Structure Alternations in Word and Sentence Embeddings",
    author = "Kann, Katharina  and
      Warstadt, Alex  and
      Williams, Adina  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2019",
    year = "2019",
    url = "https://www.aclweb.org/anthology/W19-0129",
    doi = "10.7275/q5js-4y86",
    pages = "287--297",
}
@proceedings{alta-2019-australasian,
    title = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    editor = "Mistica, Meladel  and
      Piccardi, Massimo  and
      MacKinlay, Andrew",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1000",
}
@inproceedings{lane-bird-2019-towards,
    title = "Towards A Robust Morphological Analyzer for Kunwinjku",
    author = "Lane, William  and
      Bird, Steven",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1001",
    pages = "1--9",
    abstract = "Kunwinjku is an indigenous Australian language spoken in northern Australia which exhibits agglutinative and polysynthetic properties. Members of the community have expressed interest in co-developing language applications that promote their values and priorities. Modeling the morphology of the Kunwinjku language is an important step towards accomplishing the community{'}s goals. Finite State Transducers have long been the go-to method for modeling morphologically rich languages, and in this paper we discuss some of the distinct modeling challenges present in the morphosyntax of verbs in Kunwinjku. We show that a fairly straightforward implementation using standard features of the foma toolkit can account for much of the verb structure. Continuing challenges include robustness in the face of variation and unseen vocabulary, as well as how to handle complex reduplicative processes. Our future work will build off the baseline and challenges presented here.",
}
@inproceedings{xie-etal-2019-shakespeare,
    title = "From Shakespeare to {L}i-{B}ai: Adapting a Sonnet Model to {C}hinese Poetry",
    author = "Xie, Zhuohan  and
      Lau, Jey Han  and
      Cohn, Trevor",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1002",
    pages = "10--18",
    abstract = "In this paper, we adapt Deep-speare, a joint neural network model for English sonnets, to Chinese poetry. We illustrate characteristics of Chinese quatrain and explain our architecture as well as training and generation procedure, which differs from Shakespeare sonnets in several aspects. We analyse the generated poetry and find that model works well for Chinese poetry, as it can: (1) generate coherent 4-line quatrains of different topics; and (2) capture rhyme automatically (to a certain extent).",
}
@inproceedings{jacob-uitdenbogerd-2019-readability,
    title = "Readability of {T}witter Tweets for Second Language Learners",
    author = "Jacob, Patrick  and
      Uitdenbogerd, Alexandra",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1003",
    pages = "19--27",
    abstract = "Optimal language acquisition via reading requires the learners to read slightly above their current language skill level. Identifying material at the right level is the essential role of automatic readability measurement. Short message platforms such as Twitter offer the opportunity for language practice while reading about current topics and engaging in conversation in small doses, and can be filtered according to linguistic criteria to suit the learner. In this research, we explore how readable tweets are for English language learners and which factors contribute to their readability. With participants from six language groups, we collected 14,659 data points, each representing a tweet from a pool of 4100 tweets, and a judgement of perceived readability. Traditional readability measures and features failed on the data-set, but demographic data showed that judgements were largely genuine and reflected reported language skill, which is consistent with other recent studies. We report on the properties of the data set and implications for future research.",
}
@inproceedings{nguyen-2019-neural,
    title = "A neural joint model for {V}ietnamese word segmentation, {POS} tagging and dependency parsing",
    author = "Nguyen, Dat Quoc",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1004",
    pages = "28--34",
    abstract = "",
}
@inproceedings{di-etal-2019-modelling,
    title = "Modelling {T}ibetan Verbal Morphology",
    author = "Di, Qianji  and
      Vylomova, Ekaterina  and
      Baldwin, Tim",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1005",
    pages = "35--40",
    abstract = "",
}
@inproceedings{poostchi-piccardi-2019-multi,
    title = "A multi-constraint structured hinge loss for named-entity recognition",
    author = "Poostchi, Hanieh  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1006",
    pages = "41--46",
    abstract = "",
}
@inproceedings{shen-etal-2019-feature,
    title = "Feature-guided Neural Model Training for Supervised Document Representation Learning",
    author = "Shen, Aili  and
      Salehi, Bahar  and
      Qi, Jianzhong  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1007",
    pages = "47--51",
    abstract = "",
}
@inproceedings{tay-etal-2019-red,
    title = "Red-faced {ROUGE}: Examining the Suitability of {ROUGE} for Opinion Summary Evaluation",
    author = "Tay, Wenyi  and
      Joshi, Aditya  and
      Zhang, Xiuzhen  and
      Karimi, Sarvnaz  and
      Wan, Stephen",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1008",
    pages = "52--60",
    abstract = "One of the most common metrics to automatically evaluate opinion summaries is ROUGE, a metric developed for text summarisation. ROUGE counts the overlap of word or word units between a candidate summary against reference summaries. This formulation treats all words in the reference summary equally.In opinion summaries, however, not all words in the reference are equally important. Opinion summarisation requires to correctly pair two types of semantic information: (1) aspect or opinion target; and (2) polarity of candidate and reference summaries. We investigate the suitability of ROUGE for evaluating opin-ion summaries of online reviews. Using three simulation-based experiments, we evaluate the behaviour of ROUGE for opinion summarisation on the ability to match aspect and polarity. We show that ROUGE cannot distinguish opinion summaries of similar or opposite polarities for the same aspect. Moreover,ROUGE scores have significant variance under different configuration settings. As a result, we present three recommendations for future work that uses ROUGE to evaluate opinion summarisation.",
}
@inproceedings{khanehzar-etal-2019-modeling,
    title = "Modeling Political Framing Across Policy Issues and Contexts",
    author = "Khanehzar, Shima  and
      Turpin, Andrew  and
      Mikolajczak, Gosia",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1009",
    pages = "61--66",
    abstract = "",
}
@inproceedings{koto-etal-2019-improved,
    title = "Improved Document Modelling with a Neural Discourse Parser",
    author = "Koto, Fajri  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1010",
    pages = "67--76",
    abstract = "Despite the success of attention-based neural models for natural language generation and classification tasks, they are unable to capture the discourse structure of larger documents. We hypothesize that explicit discourse representations have utility for NLP tasks over longer documents or document sequences, which sequence-to-sequence models are unable to capture. For abstractive summarization, for instance, conventional neural models simply match source documents and the summary in a latent space without explicit representation of text structure or relations. In this paper, we propose to use neural discourse representations obtained from a rhetorical structure theory (RST) parser to enhance document representations. Specifically, document representations are generated for discourse spans, known as the elementary discourse units (EDUs). We empirically investigate the benefit of the proposed approach on two different tasks: abstractive summarization and popularity prediction of online petitions. We find that the proposed approach leads to substantial improvements in all cases.",
}
@inproceedings{arora-etal-2019-lstm,
    title = "Does an {LSTM} forget more than a {CNN}? An empirical study of catastrophic forgetting in {NLP}",
    author = "Arora, Gaurav  and
      Rahimi, Afshin  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1011",
    pages = "77--86",
    abstract = "Catastrophic forgetting {---} whereby a model trained on one task is fine-tuned on a second, and in doing so, suffers a {``}catastrophic{''} drop in performance over the first task {---} is a hurdle in the development of better transfer learning techniques. Despite impressive progress in reducing catastrophic forgetting, we have limited understanding of how different architectures and hyper-parameters affect forgetting in a network. With this study, we aim to understand factors which cause forgetting during sequential training. Our primary finding is that CNNs forget less than LSTMs. We show that max-pooling is the underlying operation which helps CNNs alleviate forgetting compared to LSTMs. We also found that curriculum learning, placing a hard task towards the end of task sequence, reduces forgetting. We analysed the effect of fine-tuning contextual embeddings on catastrophic forgetting and found that using embeddings as feature extractor is preferable to fine-tuning in continual learning setup.",
}
@inproceedings{kennardi-etal-2019-domain,
    title = "Domain Adaptation for Low-Resource Neural Semantic Parsing",
    author = "Kennardi, Alvin  and
      Ferraro, Gabriela  and
      Wang, Qing",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1012",
    pages = "87--93",
    abstract = "",
}
@inproceedings{he-etal-2019-pointer,
    title = "A Pointer Network Architecture for Context-Dependent Semantic Parsing",
    author = "He, Xuanli  and
      Tran, Quan  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1013",
    pages = "94--99",
    abstract = "",
}
@inproceedings{yoshikawa-etal-2019-detecting,
    title = "Detecting Chemical Reactions in Patents",
    author = "Yoshikawa, Hiyori  and
      Nguyen, Dat Quoc  and
      Zhai, Zenan  and
      Druckenbrodt, Christian  and
      Thorne, Camilo  and
      Akhondi, Saber A.  and
      Baldwin, Timothy  and
      Verspoor, Karin",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1014",
    pages = "100--110",
    abstract = "Extracting chemical reactions from patents is a crucial task for chemists working on chemical exploration. In this paper we introduce the novel task of detecting the textual spans that describe or refer to chemical reactions within patents. We formulate this task as a paragraph-level sequence tagging problem, where the system is required to return a sequence of paragraphs which contain a description of a reaction. To address this new task, we construct an annotated dataset from an existing proprietary database of chemical reactions manually extracted from patents. We introduce several baseline methods for the task and evaluate them over our dataset. Through error analysis, we discuss what makes the task complex and challenging, and suggest possible directions for future research.",
}
@inproceedings{vu-etal-2019-identifying,
    title = "Identifying Patients with Pain in Emergency Departments using Conventional Machine Learning and Deep Learning",
    author = "Vu, Thanh  and
      Nguyen, Anthony  and
      Brown, Nathan  and
      Hughes, James",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1015",
    pages = "111--119",
    abstract = "Pain is the main symptom that patients present with to the emergency department (ED). Pain management, however, is often poorly done aspect of emergency care and patients with painful conditions can endure long waits before their pain is assessed or treated. To improve pain management quality, identifying whether or not an ED patient presents with pain is an important task and allows for further investigation of the quality of care provided. In this paper, machine learning was utilised to handle the task of automatically detecting patients who present at EDs with pain from retrospective data. Experimental results on a manually annotated dataset show that our proposed machine learning models achieve high performances, in which the highest accuracy and macro-averaged F1 are 91.00{\%} and 90.96{\%}, respectively.",
}
@inproceedings{stead-etal-2019-emerald,
    title = "Emerald 110k: A Multidisciplinary Dataset for Abstract Sentence Classification",
    author = "Stead, Connor  and
      Smith, Stephen  and
      Busch, Peter  and
      Vatanasakdakul, Savanid",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1016",
    pages = "120--125",
    abstract = "",
}
@inproceedings{hossain-etal-2019-cnl,
    title = "{CNL}-{ER}: A Controlled Natural Language for Specifying and Verbalising Entity Relationship Models",
    author = "Hossain, Bayzid Ashik  and
      Rajan, Gayathri  and
      Schwitter, Rolf",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1017",
    pages = "126--135",
    abstract = "The first step towards designing an information system is conceptual modelling where domain experts and knowledge engineers identify the necessary information together to build an information system. Entity relationship modelling is one of the most popular conceptual modelling techniques that represents an information system in terms of entities, attributes and relationships. Entity relationship models are constructed graphically but are often difficult to understand by domain experts. To overcome this problem, we suggest to verbalise these models in a controlled natural language. In this paper, we present CNL-ER, a controlled natural language for specifying and verbalising entity relationship (ER) models that not only solves the verbalisation problem for these models but also provides the benefits of automatic verification and validation, and semantic round-tripping which makes the communication process transparent between the domain experts and the knowledge engineers.",
}
@inproceedings{nguyen-uitdenbogerd-2019-measuring,
    title = "Measuring {E}nglish Readability for {V}ietnamese Speakers",
    author = "Nguyen, Phuoc  and
      Uitdenbogerd, Alexandra",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1018",
    pages = "136--145",
    abstract = "Reading is important for any language learner, but the difficulty level of the text needs to match a reader{'}s level to enable efficient learning of new vocabulary. Many widely used traditional readability measures are not effective for those who speak English as a second or additional language. This study examines English readability for Vietnamese native speakers (VL1). A collection of text difficulty judgements of nearly 100 English text passages was obtained from 12 VL1 participants, using a 5-point Likert scale. Using the same basic features found in traditional English readability measures we found that SVMs and Dale-Chall features were slightly better than linear models using either Flesch or Dale-Chall. VL1 participants{'} text judgements were strongly correlated with their past IELTS test scores. This study introduces a first approximation to readability of English text for VL1, with suggestions for further improvements.",
}
@inproceedings{ferraro-etal-2019-findher,
    title = "{F}ind{H}er: a Filter to Find Women Experts",
    author = "Ferraro, Gabriela  and
      Piper, Zoe  and
      Hinton, Rebecca",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1019",
    pages = "146--150",
    abstract = "",
}
@inproceedings{joshi-etal-2019-multi,
    title = "Does Multi-Task Learning Always Help?: An Evaluation on Health Informatics",
    author = "Joshi, Aditya  and
      Karimi, Sarvnaz  and
      Sparks, Ross  and
      Paris, Cecile  and
      MacIntyre, C Raina",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1020",
    pages = "151--158",
    abstract = "Multi-Task Learning (MTL) has been an attractive approach to deal with limited labeled datasets or leverage related tasks, for a variety of NLP problems. We examine the benefit of MTL for three specific pairs of health informatics tasks that deal with: (a) overlapping symptoms for the same classification problem (personal health mention classification for influenza and for a set of symptoms); (b) overlapping medical concepts for related classification problems (vaccine usage and drug usage detection); and, (c) related classification problems (vaccination intent and vaccination relevance detection). We experiment with a simple neural architecture: a shared layer followed by task-specific dense layers. The novelty of this work is that it compares alternatives for shared layers for these pairs of tasks. While our observations agree with the promise of MTL as compared to single-task learning, for health informatics, we show that the benefit also comes with caveats in terms of the choice of shared layers and the relatedness between the participating tasks.",
}
@inproceedings{yeung-etal-2019-difficulty,
    title = "Difficulty-aware Distractor Generation for Gap-Fill Items",
    author = "Yeung, Chak Yan  and
      Lee, John  and
      Tsou, Benjamin",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1021",
    pages = "159--164",
    abstract = "",
}
@inproceedings{nguyen-etal-2019-investigating,
    title = "Investigating the Effect of Lexical Segmentation in Transformer-based Models on Medical Datasets",
    author = "Nguyen, Vincent  and
      Karimi, Sarvnaz  and
      Xing, Zhenchang",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1022",
    pages = "165--171",
    abstract = "",
}
@inproceedings{nassar-etal-2019-neural,
    title = "Neural Versus Non-Neural Text Simplification: A Case Study",
    author = "Nassar, Islam  and
      Ananda-Rajah, Michelle  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1023",
    pages = "172--177",
    abstract = "",
}
@inproceedings{guan-etal-2019-improved,
    title = "An Improved Coarse-to-Fine Method for Solving Generation Tasks",
    author = "Guan, Wenyv  and
      Liu, Qianying  and
      Han, Guangzhi  and
      Wang, Bin  and
      Li, Sujian",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1024",
    pages = "178--185",
    abstract = "The coarse-to-fine (coarse2fine) methods have recently been widely used in the generation tasks. The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage. However, they usually lack the correction ability when getting a wrong sketch. To solve this problem, in this paper, we propose an improved coarse2fine model with a control mechanism, with which our method can control the influence of the sketch on the final results in the fine stage. Even if the sketch is wrong, our model still has the opportunity to get a correct result. We have experimented our model on the tasks of semantic parsing and math word problem solving. The results have shown the effectiveness of our proposed model.",
}
@inproceedings{shcherbakov-vylomova-2019-string,
    title = "A string-to-graph constructive alignment algorithm for discrete and probabilistic language modeling",
    author = "Shcherbakov, Andrey  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1025",
    pages = "186--191",
    abstract = "",
}
@inproceedings{molla-joshi-2019-overview,
    title = "Overview of the 2019 {ALTA} Shared Task: Sarcasm Target Identification",
    author = "Molla, Diego  and
      Joshi, Aditya",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1026",
    pages = "192--196",
    abstract = "We present an overview of the 2019 ALTA shared task. This is the 10th of the series of shared tasks organised by ALTA since 2010. The task was to detect the target of sarcastic comments posted on social media. We intro- duce the task, describe the data and present the results of baselines and participants. This year{'}s shared task was particularly challenging and no participating systems improved the re- sults of our baseline.",
}
@inproceedings{parameswaran-etal-2019-detecting,
    title = "Detecting Target of Sarcasm using Ensemble Methods",
    author = "Parameswaran, Pradeesh  and
      Trotman, Andrew  and
      Liesaputra, Veronica  and
      Eyers, David",
    booktitle = "Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://www.aclweb.org/anthology/U19-1027",
    pages = "197--203",
    abstract = "We describe our methods in trying to detect the target of sarcasm as part of ALTA 2019 shared task. We use combination of ensemble of clas- sifiers and a rule-based system. Our team ob- tained a Dice-Sorensen Coefficient score of 0.37150, which placed 2nd in the public leader- board. Despite no team beating the baseline score for the private dataset, we present our findings and also some of the challenges and future improvements which can be used in or- der to tackle the problem.",
}
@proceedings{semeval-2019-international,
    title = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2000",
}
@inproceedings{hershcovich-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 1: Cross-lingual Semantic Parsing with {UCCA}",
    author = "Hershcovich, Daniel  and
      Aizenbud, Zohar  and
      Choshen, Leshem  and
      Sulem, Elior  and
      Rappoport, Ari  and
      Abend, Omri",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2001",
    doi = "10.18653/v1/S19-2001",
    pages = "1--10",
    abstract = "We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings. Full results can be found in the task{'}s website \url{https://competitions.codalab.org/competitions/19160}.",
}
@inproceedings{jiang-etal-2019-hlt,
    title = "{HLT}@{SUDA} at {S}em{E}val-2019 Task 1: {UCCA} Graph Parsing as Constituent Tree Parsing",
    author = "Jiang, Wei  and
      Li, Zhenghua  and
      Zhang, Yu  and
      Zhang, Min",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2002",
    doi = "10.18653/v1/S19-2002",
    pages = "11--15",
    abstract = "This paper describes a simple UCCA semantic graph parsing approach. The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery. In this way, we can make use of existing syntactic parsing techniques. Based on the data statistics, we recover discontinuous nodes directly according to the output labels of the constituent parser and use a biaffine classification model to recover the more complex remote edges. The classification model and the constituent parser are simultaneously trained under the multi-task learning framework. We use the multilingual BERT as extra features in the open tracks. Our system ranks the first place in the six English/German closed/open tracks among seven participating systems. For the seventh cross-lingual track, where there is little training data for French, we propose a language embedding approach to utilize English and German training data, and our result ranks the second place.",
}
@inproceedings{qasemizadeh-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 2: Unsupervised Lexical Frame Induction",
    author = "QasemiZadeh, Behrang  and
      Petruck, Miriam R. L.  and
      Stodden, Regina  and
      Kallmeyer, Laura  and
      Candito, Marie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2003",
    doi = "10.18653/v1/S19-2003",
    pages = "16--30",
    abstract = "This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019. Given a set of prespecified syntactic forms in context, the task requires that verbs and their arguments be clustered to resemble semantic frame structures. Results are useful in identifying polysemous words, i.e., those whose frame structures are not easily distinguished, as well as discerning semantic relations of the arguments. Evaluation of unsupervised frame induction methods fell into two tracks: Task A) Verb Clustering based on FrameNet 1.7; and B) Argument Clustering, with B.1) based on FrameNet{'}s core frame elements, and B.2) on VerbNet 3.2 semantic roles. The shared task attracted nine teams, of whom three reported promising results. This paper describes the task and its data, reports on methods and resources that these systems used, and offers a comparison to human annotation.",
}
@inproceedings{arefyev-etal-2019-neural,
    title = "Neural {GRANN}y at {S}em{E}val-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction",
    author = "Arefyev, Nikolay  and
      Sheludko, Boris  and
      Davletov, Adis  and
      Kharchev, Dmitry  and
      Nevidomsky, Alex  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2004",
    doi = "10.18653/v1/S19-2004",
    pages = "31--38",
    abstract = "We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place. The main contributions of this paper are related to the semantic frame induction problem. We propose a combined approach that employs two different types of vector representations: dense representations from hidden layers of a masked language model, and sparse representations based on substitutes for the target word in the context. The first one better groups synonyms, the second one is better at disambiguating homonyms. Extending the context to include nearby sentences improves the results in both cases. New Hearst-like patterns for verbs are introduced that prove to be effective for frame induction. Finally, we propose an approach to selecting the number of clusters in agglomerative clustering.",
}
@inproceedings{chatterjee-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 3: {E}mo{C}ontext Contextual Emotion Detection in Text",
    author = "Chatterjee, Ankush  and
      Narahari, Kedhar Nath  and
      Joshi, Meghana  and
      Agrawal, Puneet",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2005",
    doi = "10.18653/v1/S19-2005",
    pages = "39--48",
    abstract = "In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading {``}Why don{'}t you ever text me!{''} we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.",
}
@inproceedings{huang-etal-2019-ana,
    title = "{ANA} at {S}em{E}val-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical {LSTM}s and {BERT}",
    author = {Huang, Chenyang  and
      Trabelsi, Amine  and
      Za{\"\i}ane, Osmar},
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2006",
    doi = "10.18653/v1/S19-2006",
    pages = "49--53",
    abstract = "This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchi- cal LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational con- text. The results show that, in this task, our HRCLE outperforms the most recent state-of- the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.",
}
@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and
      Bosco, Cristina  and
      Fersini, Elisabetta  and
      Nozza, Debora  and
      Patti, Viviana  and
      Rangel Pardo, Francisco Manuel  and
      Rosso, Paolo  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2007",
    doi = "10.18653/v1/S19-2007",
    pages = "54--63",
    abstract = "The paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and a finer-grained one devoted to identifying further features in hateful contents such as the aggressive attitude and the target harassed, to distinguish if the incitement is against an individual rather than a group. HatEval has been one of the most popular tasks in SemEval-2019 with a total of 108 submitted runs for Subtask A and 70 runs for Subtask B, from a total of 74 different teams. Data provided for the task are described by showing how they have been collected and annotated. Moreover, the paper provides an analysis and discussion about the participant systems and the results they achieved in both subtasks.",
}
@inproceedings{perez-luque-2019-atalaya,
    title = "Atalaya at {S}em{E}val 2019 Task 5: Robust Embeddings for Tweet Classification",
    author = "P{\'e}rez, Juan Manuel  and
      Luque, Franco M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2008",
    doi = "10.18653/v1/S19-2008",
    pages = "64--69",
    abstract = "In this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bag-of-words, bag-of-characters, and word embeddings, and also with recent techniques such as contextualized word representations. In particular, we trained robust task-oriented subword-aware embeddings and computed tweet representations using a weighted-averaging strategy. In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively.",
}
@inproceedings{indurthi-etal-2019-fermi,
    title = "{FERMI} at {S}em{E}val-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in {T}witter",
    author = "Indurthi, Vijayasaradhi  and
      Syed, Bakhtiyar  and
      Shrivastava, Manish  and
      Chakravartula, Nikhil  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2009",
    doi = "10.18653/v1/S19-2009",
    pages = "70--74",
    abstract = "This paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter. We participated in the subtask A for English and ranked first in the evaluation on the test set. We evaluate the quality of multiple sentence embeddings and explore multiple training models to evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team - Fermi{'}s model achieved an accuracy of 65.00{\%} for English language in task A. Our models, which use pretrained Universal Encoder sentence embeddings for transforming the input and SVM (with RBF kernel) for classification, scored first position (among 68) in the leaderboard on the test set for Subtask A in English language. In this paper we provide a detailed description of the approach, as well as the results obtained in the task.",
}
@inproceedings{zampieri-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media ({O}ffens{E}val)",
    author = "Zampieri, Marcos  and
      Malmasi, Shervin  and
      Nakov, Preslav  and
      Rosenthal, Sara  and
      Farra, Noura  and
      Kumar, Ritesh",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2010",
    doi = "10.18653/v1/S19-2010",
    pages = "75--86",
    abstract = "We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report.",
}
@inproceedings{liu-etal-2019-nuli,
    title = "{NULI} at {S}em{E}val-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers",
    author = "Liu, Ping  and
      Li, Wen  and
      Zou, Liang",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2011",
    doi = "10.18653/v1/S19-2011",
    pages = "87--91",
    abstract = "Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e.g., image recognition) and natural language processing (e.g., text classification). One of the benefits of transfer learning is to learn effectively and efficiently from limited labeled data with a pre-trained model. In the shared task of identifying and categorizing offensive language in social media, we preprocess the dataset according to the language behaviors on social media, and then adapt and fine-tune the Bidirectional Encoder Representation from Transformer (BERT) pre-trained by Google AI Language team. Our team NULI wins the first place (1st) in Sub-task A - Offensive Language Identification and is ranked 4th and 18th in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offense Target Identification respectively.",
}
@inproceedings{lyu-etal-2019-cuny,
    title = "{CUNY}-{PKU} Parser at {S}em{E}val-2019 Task 1: Cross-Lingual Semantic Parsing with {UCCA}",
    author = "Lyu, Weimin  and
      Huang, Sheng  and
      Khan, Abdul Rafae  and
      Zhang, Shengqiang  and
      Sun, Weiwei  and
      Xu, Jia",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2012",
    doi = "10.18653/v1/S19-2012",
    pages = "92--96",
    abstract = "This paper describes the systems of the CUNY-PKU team in SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA. We introduce a novel model by applying a cascaded MLP and BiLSTM model. Then, we ensemble multiple system-outputs by reparsing. In particular, we introduce a new decoding algorithm for building the UCCA representation. Our system won the first place in one track (French-20K-Open), second places in four tracks (English-Wiki-Open, English-20K-Open, German-20K-Open, and German-20K-Closed), and third place in one track (English-20K-Closed), among all seven tracks.",
}
@inproceedings{tuan-nguyen-tran-2019-dangnt,
    title = "{DANGNT}@{UIT}.{VNU}-{HCM} at {S}em{E}val 2019 Task 1: Graph Transformation System from {S}tanford Basic Dependencies to {U}niversal {C}onceptual {C}ognitive {A}nnotation ({UCCA})",
    author = "Tuan Nguyen, Dang  and
      Tran, Trung",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2013",
    doi = "10.18653/v1/S19-2013",
    pages = "97--101",
    abstract = "This paper describes the graph transfor-mation system (GT System) for SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cognitive Annotation (UCCA)1. The input of GT System is a pair of text and its unannotated xml, which is a layer 0 part of UCCA form. The output of GT System is the corresponding full UCCA xml. Based on the idea of graph illustration and transformation, we perform four main tasks when building GT System. At the first task, we illustrate the graph form of stanford dependencies2 of input text. We then transform into an intermediate graph in the second task. At the third task, we continue to transform into ouput graph form. Finally, we create the output UCCA xml. The evaluation results show that our method generates good-quality UCCA xml and has a meaningful contribution to the semantic represetation sub-field in Natural Language Processing.",
}
@inproceedings{taslimipoor-etal-2019-gcn,
    title = "{GCN}-Sem at {S}em{E}val-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks",
    author = "Taslimipoor, Shiva  and
      Rohanian, Omid  and
      Mo{\v{z}}e, Sara",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2014",
    doi = "10.18653/v1/S19-2014",
    pages = "102--106",
    abstract = "This paper describes the system submitted to the SemEval 2019 shared task 1 {`}Cross-lingual Semantic Parsing with UCCA{'}. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure of the output along with the types of relations among the nodes. Our proposed neural architecture is composed of Graph Convolution and BiLSTM components. The layers of the system share their weights while predicting dependency links and semantic labels. The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing.",
}
@inproceedings{marzinotto-etal-2019-maskparse,
    title = "{M}ask{P}arse@Deskin at {S}em{E}val-2019 Task 1: Cross-lingual {UCCA} Semantic Parsing using Recursive Masked Sequence Tagging",
    author = "Marzinotto, Gabriel  and
      Heinecke, Johannes  and
      Damnati, G{\'e}raldine",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2015",
    doi = "10.18653/v1/S19-2015",
    pages = "107--112",
    abstract = "This paper describes our recursive system for SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA. Each recursive step consists of two parts. We first perform semantic parsing using a sequence tagger to estimate the probabilities of the UCCA categories in the sentence. Then, we apply a decoding policy which interprets these probabilities and builds the graph nodes. Parsing is done recursively, we perform a first inference on the sentence to extract the main scenes and links and then we recursively apply our model on the sentence using a masking features that reflects the decisions made in previous steps. Process continues until the terminal nodes are reached. We chose a standard neural tagger and we focus on our recursive parsing strategy and on the cross lingual transfer problem to develop a robust model for the French language, using only few training samples",
}
@inproceedings{putz-glocker-2019-tupa,
    title = {T{\"u}pa at {S}em{E}val-2019 Task1: (Almost) feature-free Semantic Parsing},
    author = {P{\"u}tz, Tobias  and
      Glocker, Kevin},
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2016",
    doi = "10.18653/v1/S19-2016",
    pages = "113--118",
    abstract = "Our submission for Task 1 {`}Cross-lingual Semantic Parsing with UCCA{'} at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transition-based directed acyclic graph parser. We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph as an aggregation of their terminal children. We further demonstrate how augmenting data using the baseline systems provides a consistent advantage in all open submission tracks. We submitted results to all open tracks (English, in- and out-of-domain, German in-domain and French in-domain, low-resource). Our system achieves competitive performance in all settings besides the French, where we did not augment the data. Post-evaluation experiments showed that data augmentation is especially crucial in this setting.",
}
@inproceedings{yu-sagae-2019-uc,
    title = "{UC} {D}avis at {S}em{E}val-2019 Task 1: {DAG} Semantic Parsing with Attention-based Decoder",
    author = "Yu, Dian  and
      Sagae, Kenji",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2017",
    doi = "10.18653/v1/S19-2017",
    pages = "119--124",
    abstract = "We present an encoder-decoder model for semantic parsing with UCCA SemEval 2019 Task 1. The encoder is a Bi-LSTM and the decoder uses recursive self-attention. The proposed model alleviates challenges and feature engineering in traditional transition-based and graph-based parsers. The resulting parser is simple and proved to effective on the semantic parsing task.",
}
@inproceedings{anwar-etal-2019-hhmm,
    title = "{HHMM} at {S}em{E}val-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings",
    author = "Anwar, Saba  and
      Ustalov, Dmitry  and
      Arefyev, Nikolay  and
      Ponzetto, Simone Paolo  and
      Biemann, Chris  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2018",
    doi = "10.18653/v1/S19-2018",
    pages = "125--129",
    abstract = "We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (Qasem-iZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.",
}
@inproceedings{ribeiro-etal-2019-l2f,
    title = "{L}2{F}/{INESC}-{ID} at {S}em{E}val-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations",
    author = "Ribeiro, Eug{\'e}nio  and
      Mendon{\c{c}}a, V{\^a}nia  and
      Ribeiro, Ricardo  and
      Martins de Matos, David  and
      Sardinha, Alberto  and
      Santos, Ana L{\'u}cia  and
      Coheur, Lu{\'\i}sa",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2019",
    doi = "10.18653/v1/S19-2019",
    pages = "130--136",
    abstract = "Building large datasets annotated with semantic information, such as FrameNet, is an expensive process. Consequently, such resources are unavailable for many languages and specific domains. This problem can be alleviated by using unsupervised approaches to induce the frames evoked by a collection of documents. That is the objective of the second task of SemEval 2019, which comprises three subtasks: clustering of verbs that evoke the same frame and clustering of arguments into both frame-specific slots and semantic roles. We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments. Using such representations is appropriate in the context of this task, since they provide cues for word-sense disambiguation. Thus, they can be used to identify different frames evoked by the same words. Using this approach we were able to outperform all of the baselines reported for the task on the test set in terms of Purity F1, as well as in terms of BCubed F1 in most cases.",
}
@inproceedings{gratian-2019-brainee,
    title = "{B}rain{EE} at {S}em{E}val-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction",
    author = "Gratian, Vachagan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2020",
    doi = "10.18653/v1/S19-2020",
    pages = "137--141",
    abstract = "The paper describes an ensemble of linear perceptrons trained for emotion classification as part of the SemEval-2019 shared-task 3. The model uses a matrix of probabilities to weight the activations of the base-classifiers and makes a final prediction using the sum rule. The base-classifiers are multi-class perceptrons utilizing character and word n-grams, part-of-speech tags and sentiment polarity scores. The results of our experiments indicate that the ensemble outperforms the base-classifiers, but only marginally. In the best scenario our model attains an F-Micro score of 0.672, whereas the base-classifiers attained scores ranging from 0.636 to 0.666.",
}
@inproceedings{winata-etal-2019-caire,
    title = "{CA}i{RE}{\_}{HKUST} at {S}em{E}val-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification",
    author = "Winata, Genta Indra  and
      Madotto, Andrea  and
      Lin, Zhaojiang  and
      Shin, Jamin  and
      Xu, Yan  and
      Xu, Peng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2021",
    doi = "10.18653/v1/S19-2021",
    pages = "142--147",
    abstract = "Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, we introduce a hierarchical approach to classify emotion, hypothesizing that the current emotional state depends on previous latent emotions. We benchmark several feature-based classifiers using pre-trained word and emotion embeddings, state-of-the-art end-to-end neural network models, and Gaussian processes for automatic hyper-parameter search. In our experiments, hierarchical architectures consistently give significant improvements, and our best model achieves a 76.77{\%} F1-score on the test set.",
}
@inproceedings{bestgen-2019-cecl,
    title = "{CECL} at {S}em{E}val-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations",
    author = "Bestgen, Yves",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2022",
    doi = "10.18653/v1/S19-2022",
    pages = "148--152",
    abstract = "This paper describes the system developed by the Centre for English Corpus Linguistics for the SemEval-2019 Task 3: EmoContext. It aimed at classifying the emotion of a user utterance in a textual conversation as happy, sad, angry or other. It is based on a large number of feature types, mainly unigrams and bigrams, which were extracted by a SAS program. The usefulness of the different feature types was evaluated by means of Monte-Carlo resampling tests. As this system does not rest on any deep learning component, which is currently considered as the state-of-the-art approach, it can be seen as a possible point of comparison for such kind of systems.",
}
@inproceedings{mohammadi-etal-2019-clac-lab,
    title = "{CL}a{C} Lab at {S}em{E}val-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and {SVM}",
    author = "Mohammadi, Elham  and
      Amini, Hessam  and
      Kosseim, Leila",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2023",
    doi = "10.18653/v1/S19-2023",
    pages = "153--158",
    abstract = "This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification.This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12{\%}.",
}
@inproceedings{cummings-wilson-2019-clark,
    title = "{CLARK} at {S}em{E}val-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation",
    author = "Cummings, Joseph  and
      Wilson, Jason",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2024",
    doi = "10.18653/v1/S19-2024",
    pages = "159--163",
    abstract = "With text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naive Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while train-ing the model significantly decreases performance. Our approach has the additional bene-fit that its performance rivals a baseline LSTM model while requiring fewer resources.",
}
@inproceedings{li-xing-2019-clp,
    title = "{CLP} at {S}em{E}val-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection",
    author = "Li, Changjie  and
      Xing, Yun",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2025",
    doi = "10.18653/v1/S19-2025",
    pages = "164--168",
    abstract = "In this paper, we describe the participation of team {''}CLP{''} in SemEval-2019 Task 3 {``}Con- textual Emotion Detection in Text{''} that aims to classify emotion of user utterance in tex- tual conversation. The submitted system is a deep learning architecture based on Hier- archical Attention Networks (HAN) and Em- bedding from Language Model (ELMo). The core of the architecture contains two represen- tation layers. The first one combines the out- puts of ELMo, hand-craft features and Bidi- rectional Long Short-Term Memory with At- tention (Bi-LSTM-Attention) to represent user utterance. The second layer use a Bi-LSTM- Attention encoder to represent the conversa- tion. Our system achieved F1 score of 0.7524 which outperformed the baseline model of the organizers by 0.1656.",
}
@inproceedings{gonzalez-etal-2019-coastal,
    title = "{C}o{AS}ta{L} at {S}em{E}val-2019 Task 3: Affect Classification in Dialogue using Attentive {B}i{LSTM}s",
    author = "Gonz{\'a}lez, Ana Valeria  and
      Petr{\'e}n Bach Hansen, Victor  and
      Bingel, Joachim  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2026",
    doi = "10.18653/v1/S19-2026",
    pages = "169--174",
    abstract = "This work describes the system presented by the CoAStaL Natural Language Processing group at University of Copenhagen. The main system we present uses the same attention mechanism presented in (Yang et al., 2016). Our overall model architecture is also inspired by their hierarchical classification model and adapted to deal with classification in dialogue by encoding information at the turn level. We use different encodings for each turn to create a more expressive representation of dialogue context which is then fed into our classifier.We also define a custom preprocessing step in order to deal with language commonly used in interactions across many social media outlets. Our proposed system achieves a micro F1 score of 0.7340 on the test set and shows significant gains in performance compared to a system using dialogue level encoding.",
}
@inproceedings{poswiata-2019-conssed,
    title = "{C}on{SSED} at {S}em{E}val-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector",
    author = "Po{\'s}wiata, Rafa{\l}",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2027",
    doi = "10.18653/v1/S19-2027",
    pages = "175--179",
    abstract = "This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text. The goal was to for a given textual dialogue, i.e. a user utterance along with two turns of context, identify the emotion of user utterance as one of the emotion classes: Happy, Sad, Angry or Others. Our system: ConSSED is a configurable combination of semantic and sentiment neural models. The official task submission achieved a micro-average F1 score of 75.31 which placed us 16th out of 165 participating systems.",
}
@inproceedings{perelkiewicz-2019-cx,
    title = "{CX}-{ST}-{RNM} at {S}em{E}val-2019 Task 3: Fusion of Recurrent Neural Networks Based on Contextualized and Static Word Representations for Contextual Emotion Detection",
    author = "Pere{\l}kiewicz, Micha{\l}",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2028",
    doi = "10.18653/v1/S19-2028",
    pages = "180--184",
    abstract = "In this paper, I describe a fusion model combining contextualized and static word representations for approaching the EmoContext task in the SemEval 2019 competition. The model is based on two Recurrent Neural Networks, the first one is fed with a state-of-the-art ELMo deep contextualized word representation and the second one is fed with a static Word2Vec embedding augmented with 10-dimensional affective word feature vector. The proposed model is compared with two baseline models based on a static word representation and a contextualized word representation, separately. My approach achieved officially 0.7278 microaveraged F1 score on the test dataset, ranking 47th out of 165 participants.",
}
@inproceedings{jain-etal-2019-paralleldots,
    title = "{P}arallel{D}ots at {S}em{E}val-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis",
    author = "Jain, Akansha  and
      Aggarwal, Ishita  and
      Singh, Ankit",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2029",
    doi = "10.18653/v1/S19-2029",
    pages = "185--189",
    abstract = "This paper describes our proposed system {\&} experiments performed to detect contextual emotion in texts for SemEval 2019 Task 3. We exploit sentiment information, syntactic patterns {\&} semantic relatedness to capture diverse aspects of the text. Word level embeddings such as Glove, FastText, Emoji along with sentence level embeddings like Skip-Thought, DeepMoji {\&} Unsupervised Sentiment Neuron were used as input features to our architecture. We democratize the learning using ensembling of models with different parameters to produce the final output. This paper discusses comparative analysis of the significance of these embeddings and our approach for the task.",
}
@inproceedings{patel-2019-e,
    title = "{E}-{LSTM} at {S}em{E}val-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text",
    author = "Patel, Harsh",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2030",
    doi = "10.18653/v1/S19-2030",
    pages = "190--194",
    abstract = "This paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is {''}Contextual Emotion Detection in Texts{''}. The paper includes the explanation of an architecture that I created by exploiting the embedding layers of Word2Vec and GloVe using LSTMs as memory unit cells which detects approximate emotion of chats between two people in the English language provided in the textual form. The set of emotions on which the model was trained was Happy, Sad, Angry and Others. The paper also includes an analysis of different conventional machine learning algorithms in comparison to E-LSTM.",
}
@inproceedings{gonzalez-etal-2019-elirf,
    title = "{EL}i{RF}-{UPV} at {S}em{E}val-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection",
    author = "Gonz{\'a}lez, Jos{\'e}-{\'A}ngel  and
      Hurtado, Llu{\'\i}s-F.  and
      Pla, Ferran",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2031",
    doi = "10.18653/v1/S19-2031",
    pages = "195--199",
    abstract = "This paper describes the approach developed by the ELiRF-UPV team at SemEval 2019 Task 3: Contextual Emotion Detection in Text. We have developed a Snapshot Ensemble of 1D Hierarchical Convolutional Neural Networks to extract features from 3-turn conversations in order to perform contextual emotion detection in text. This Snapshot Ensemble is obtained by averaging the models selected by a Genetic Algorithm that optimizes the evaluation measure. The proposed ensemble obtains better results than a single model and it obtains competitive and promising results on Contextual Emotion Detection in Text.",
}
@inproceedings{al-omari-etal-2019-emodet,
    title = "{E}mo{D}et at {S}em{E}val-2019 Task 3: Emotion Detection in Text using Deep Learning",
    author = "Al-Omari, Hani  and
      Abdullah, Malak  and
      Bassam, Nabeel",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2032",
    doi = "10.18653/v1/S19-2032",
    pages = "200--204",
    abstract = "Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1-score 0.58).",
}
@inproceedings{chakravartula-indurthi-2019-emominer,
    title = "{EMOMINER} at {S}em{E}val-2019 Task 3: A Stacked {B}i{LSTM} Architecture for Contextual Emotion Detection in Text",
    author = "Chakravartula, Nikhil  and
      Indurthi, Vijayasaradhi",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2033",
    doi = "10.18653/v1/S19-2033",
    pages = "205--209",
    abstract = "This paper describes our participation in the SemEval 2019 Task 3 - Contextual Emotion Detection in Text. This task aims to identify emotions, viz. happiness, anger, sadness in the context of a text conversation. Our system is a stacked Bidirectional LSTM, equipped with attention on top of word embeddings pre-trained on a large collection of Twitter data. In this paper, apart from describing our official submission, we elucidate how different deep learning models respond to this task.",
}
@inproceedings{smetanin-2019-emosense,
    title = "{E}mo{S}ense at {S}em{E}val-2019 Task 3: Bidirectional {LSTM} Network for Contextual Emotion Detection in Textual Conversations",
    author = "Smetanin, Sergey",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2034",
    doi = "10.18653/v1/S19-2034",
    pages = "210--214",
    abstract = "In this paper, we describe a deep-learning system for emotion detection in textual conversations that participated in SemEval-2019 Task 3 {``}EmoContext{''}. We designed a specific architecture of bidirectional LSTM which allows not only to learn semantic and sentiment feature representation, but also to capture user-specific conversation features. To fine-tune word embeddings using distant supervision we additionally collected a significant amount of emotional texts. The system achieved 72.59{\%} micro-average F1 score for emotion classes on the test dataset, thereby significantly outperforming the officially-released baseline. Word embeddings and the source code were released for the research community.",
}
@inproceedings{bouchekif-etal-2019-epita,
    title = "{EPITA}-{ADAPT} at {S}em{E}val-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination",
    author = "Bouchekif, Abdessalam  and
      Joshi, Praveen  and
      Bouchekif, Latifa  and
      Afli, Haithem",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2035",
    doi = "10.18653/v1/S19-2035",
    pages = "215--219",
    abstract = "Messaging platforms like WhatsApp, Facebook Messenger and Twitter have gained recently much popularity owing to their ability in connecting users in real-time. The content of these textual messages can be a useful resource for text mining to discover and unhide various aspects, including emotions. In this paper we present our submission for SemEval 2019 task {`}EmoContext{'}. The task consists of classifying a given textual dialogue into one of four emotion classes: Angry, Happy, Sad and Others. Our proposed system is based on the combination of different deep neural networks techniques. In particular, we use Recurrent Neural Networks (LSTM, B-LSTM, GRU, B-GRU), Convolutional Neural Network (CNN) and Transfer Learning (TL) methodes. Our final system, achieves an F1 score of 74.51{\%} on the subtask evaluation dataset.",
}
@inproceedings{xiao-2019-figure,
    title = "Figure Eight at {S}em{E}val-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection",
    author = "Xiao, Joan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2036",
    doi = "10.18653/v1/S19-2036",
    pages = "220--224",
    abstract = "This paper describes our transfer learning-based approach to contextual emotion detection as part of SemEval-2019 Task 3. We experiment with transfer learning using pre-trained language models (ULMFiT, OpenAI GPT, and BERT) and fine-tune them on this task. We also train a deep learning model from scratch using pre-trained word embeddings and BiLSTM architecture with attention mechanism. The ensembled model achieves competitive result, ranking ninth out of 165 teams. The result reveals that ULMFiT performs best due to its superior fine-tuning techniques. We propose improvements for future work.",
}
@inproceedings{bogdan-2019-gensmt,
    title = "{G}en{SMT} at {S}em{E}val-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approach",
    author = "Bogdan, Dumitru",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2037",
    doi = "10.18653/v1/S19-2037",
    pages = "225--229",
    abstract = "In this paper, we describe our participation in SemEval-2019 Task 3: EmoContext - A Shared Task on Contextual Emotion Detection in Text. We propose a three layer model with a generic, multi-purpose approach that without any task specific optimizations achieve competitive results (f1 score of 0.7096) in the EmoContext task. We describe our development strategy in detail along with an exposition of our results.",
}
@inproceedings{tafreshi-diab-2019-gwu,
    title = "{GWU} {NLP} Lab at {S}em{E}val-2019 Task 3 : {E}mo{C}ontext: Effectiveness of{C}ontextual Information in Models for Emotion Detection in{S}entence-level at Multi-genre Corpus",
    author = "Tafreshi, Shabnam  and
      Diab, Mona",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2038",
    doi = "10.18653/v1/S19-2038",
    pages = "230--235",
    abstract = "In this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : \textit{EmoContext}. Our approach is a Gated Recurrent Neural Network (GRU) model with attention layer is bootstrapped with contextual information and trained with a multigenre corpus, which is combination of several popular emotional data sets. We utilize different word embeddings to empirically select the most suited embedding to represent our features. Our aim is to build a robust emotion classifier that can generalize emotion detection, which is to learn emotion cues in a noisy training environment. To fulfill this aim we train our model with a multigenre emotion corpus, this way we leverage from having more training set. We achieved overall {\%}56.05 f1-score and placed 144. Given our aim and noisy training environment, the results are anticipated.",
}
@inproceedings{pamnani-etal-2019-iit,
    title = "{IIT} {G}andhinagar at {S}em{E}val-2019 Task 3: Contextual Emotion Detection Using Deep Learning",
    author = "Pamnani, Arik  and
      Goel, Rajat  and
      Choudhari, Jayesh  and
      Singh, Mayank",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2039",
    doi = "10.18653/v1/S19-2039",
    pages = "236--240",
    abstract = "Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority of these are text-based messaging platforms. Development of Chatbots that automatically understand latent emotions in the textual message is a challenging task. In this paper, we present an automatic emotion detection system that aims to detect the emotion of a person textually conversing with a chatbot. We explore deep learning techniques such as CNN and LSTM based neural networks and outperformed the baseline score by 14{\%}. The trained model and code are kept in public domain.",
}
@inproceedings{patro-etal-2019-kgpchamps,
    title = "{KGPC}hamps at {S}em{E}val-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.",
    author = "Patro, Jasabanta  and
      Choudhary, Nitin  and
      Chittora, Kalpit  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2040",
    doi = "10.18653/v1/S19-2040",
    pages = "241--246",
    abstract = "This paper describes our approach to solve \textit{Semeval task 3: EmoContext}; where, given a textual dialogue i.e. a user utterance along with two turns of context, we have to classify the emotion associated with the utterance as one of the following emotion classes: \textit{Happy, Sad, Angry} or \textit{Others}. To solve this problem, we experiment with different deep learning models ranging from simple bidirectional LSTM (Long and short term memory) model to comparatively complex attention model. We also experiment with word embedding conceptnet along with word embedding generated from bi-directional LSTM taking input characters. We fine-tune different parameters and hyper-parameters associated with each of our models and report the value of evaluating measure i.e. micro precision along with class wise precision, recall and F1-score of each system. We report the bidirectional LSTM model, along with the input word embedding as the concatenation of word embedding generated from bidirectional LSTM for word characters and conceptnet embedding, as the best performing model with a highest micro-F1 score of 0.7261. We also report class wise precision, recall, and f1-score of best performing model along with other models that we have experimented with.",
}
@inproceedings{alswaidan-menai-2019-ksu,
    title = "{KSU} at {S}em{E}val-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation",
    author = "Alswaidan, Nourah  and
      Menai, Mohamed El Bachir",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2041",
    doi = "10.18653/v1/S19-2041",
    pages = "247--250",
    abstract = "We proposed a model to address emotion recognition in textual conversation based on using automatically extracted features and human engineered features. The proposed model utilizes a fast gated-recurrent-unit backed by CuDNN, and a convolutional neural network to automatically extract features. The human engineered features take the frequency-inverse document frequency of semantic meaning and mood tags extracted from SinticNet.",
}
@inproceedings{ragheb-etal-2019-lirmm,
    title = "{LIRMM}-Advanse at {S}em{E}val-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification",
    author = "Ragheb, Waleed  and
      Az{\'e}, J{\'e}r{\^o}me  and
      Bringay, Sandra  and
      Servajean, Maximilien",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2042",
    doi = "10.18653/v1/S19-2042",
    pages = "251--255",
    abstract = "This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turn-based conversational modeling for classifying the emotions. The approach does not rely on any hand-crafted features or lexicons. Our model was evaluated on the data provided by the SemEval-2019 shared task on contextual emotion detection in text. The model shows very competitive results.",
}
@inproceedings{lee-etal-2019-milab,
    title = "{MILAB} at {S}em{E}val-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis",
    author = "Lee, Yoonhyung  and
      Kim, Yanghoon  and
      Jung, Kyomin",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2043",
    doi = "10.18653/v1/S19-2043",
    pages = "256--260",
    abstract = "This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue. To address this challenge of predicting the emotion considering its context, we propose a Multi-View Turn-by-Turn (MVTT) model. Firstly, MVTT model generates vectors from each utterance using two encoders: word-level Bi-GRU encoder (WLE) and character-level CNN encoder (CLE). Then, MVTT grasps contextual information by combining the vectors and predict the emotion with the contextual information. We conduct experiments on the effect of vector encoding and vector combination. Our final MVTT model achieved 0.7634 microaveraged F1 score.",
}
@inproceedings{bothe-wermter-2019-moongrad,
    title = "{M}oon{G}rad at {S}em{E}val-2019 Task 3: Ensemble {B}i{RNN}s for Contextual Emotion Detection in Dialogues",
    author = "Bothe, Chandrakant  and
      Wermter, Stefan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2044",
    doi = "10.18653/v1/S19-2044",
    pages = "261--265",
    abstract = "When reading {``}I don{'}t want to talk to you any more{''}, we might interpret this as either an angry or a sad emotion in the absence of context. Often, the utterances are shorter, and given a short utterance like {``}Me too!{''}, it is difficult to interpret the emotion without context. The lack of prosodic or visual information makes it a challenging problem to detect such emotions only with text. However, using contextual information in the dialogue is gaining importance to provide a context-aware recognition of linguistic features such as emotion, dialogue act, sentiment etc. The SemEval 2019 Task 3 EmoContext competition provides a dataset of three-turn dialogues labeled with the three emotion classes, i.e. Happy, Sad and Angry, and in addition with Others as none of the aforementioned emotion classes. We develop an ensemble of the recurrent neural model with character- and word-level features as an input to solve this problem. The system performs quite well, achieving a microaveraged F1 score (F1μ) of 0.7212 for the three emotion classes.",
}
@inproceedings{agrawal-suri-2019-nelec,
    title = "{NELEC} at {S}em{E}val-2019 Task 3: Think Twice Before Going Deep",
    author = "Agrawal, Parag  and
      Suri, Anshuman",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2045",
    doi = "10.18653/v1/S19-2045",
    pages = "266--271",
    abstract = "Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc. makes existing deep-learning solutions perform poorly. The inability of deep-learning systems to robustly capture these covariates puts a cap on their performance. We propose NELEC: Neural and Lexical Combiner, a system which elegantly combines textual and deep-learning based methods for sentiment classification. We evaluate our system as part of the third task of {`}Contextual Emotion Detection in Text{'} as part of SemEval-2019. Our system performs significantly better than the baseline, as well as our deep-learning model benchmarks. It achieved a micro-averaged F1 score of 0.7765, ranking 3rd on the test-set leader-board. Our code is available at https://github.com/iamgroot42/nelec",
}
@inproceedings{farkas-lacko-2019-nl,
    title = "{NL}-{FIIT} at {S}em{E}val-2019 Task 3: Emotion Detection From Conversational Triplets Using Hierarchical Encoders",
    author = "Farkas, Michal  and
      Lacko, Peter",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2046",
    doi = "10.18653/v1/S19-2046",
    pages = "272--276",
    abstract = "In this paper, we present our system submission for the EmoContext, the third task of the SemEval 2019 workshop. Our solution is a hierarchical recurrent neural network with ELMo embeddings and regularization through dropout and Gaussian noise. We have mainly experimented with two main model architectures: simple and hierarchical LSTM network. We have also examined ensembling of the models and various variants of an ensemble. We have achieved microF1 score of 0.7481, which is significantly higher than baseline and currently the 19th best submission.",
}
@inproceedings{potamias-siolas-2019-ntua,
    title = "{NTUA}-{ISL}ab at {S}em{E}val-2019 Task 3: Determining emotions in contextual conversations with deep learning",
    author = "Potamias, Rolandos Alexandros  and
      Siolas, Georgios",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2047",
    doi = "10.18653/v1/S19-2047",
    pages = "277--281",
    abstract = "Sentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge amounts of related data. However, capturing emotional states and the sentiment polarity of written excerpts requires knowledge on the events triggering them. Towards this goal, we present a computational end-to-end context-aware SA methodology, which was competed in the context of the SemEval-2019 / EmoContext task (Task 3). The proposed system is founded on the combination of two neural architectures, a deep recurrent neural network, structured by an attentive Bidirectional LSTM, and a deep dense network (DNN). The system achieved 0.745 micro f1-score, and ranked 26/165 (top 20{\%}) teams among the official task submissions.",
}
@inproceedings{zhong-miao-2019-ntuer,
    title = "ntuer at {S}em{E}val-2019 Task 3: Emotion Classification with Word and Sentence Representations in {RCNN}",
    author = "Zhong, Peixiang  and
      Miao, Chunyan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2048",
    doi = "10.18653/v1/S19-2048",
    pages = "282--286",
    abstract = "In this paper we present our model on the task of emotion detection in textual conversations in SemEval-2019. Our model extends the Recurrent Convolutional Neural Network (RCNN) by using external fine-tuned word representations and DeepMoji sentence representations. We also explored several other competitive pre-trained word and sentence representations including ELMo, BERT and InferSent but found inferior performance. In addition, we conducted extensive sensitivity analysis, which empirically shows that our model is relatively robust to hyper-parameters. Our model requires no handcrafted features or emotion lexicons but achieved good performance with a micro-F1 score of 0.7463.",
}
@inproceedings{ma-etal-2019-pkuse,
    title = "{PKUSE} at {S}em{E}val-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network",
    author = "Ma, Luyao  and
      Zhang, Long  and
      Ye, Wei  and
      Hu, Wenhui",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2049",
    doi = "10.18653/v1/S19-2049",
    pages = "287--291",
    abstract = "This paper presents the system in SemEval-2019 Task 3, {``}EmoContext: Contextual Emotion Detection in Text{''}. We propose a deep learning architecture with bidirectional LSTM networks, augmented with an emotion-oriented attention network that is capable of extracting emotion information from an utterance. Experimental results show that our model outperforms its variants and the baseline. Overall, this system has achieved 75.57{\%} for the microaveraged F1 score.",
}
@inproceedings{nguyen-etal-2019-podlab,
    title = "Podlab at {S}em{E}val-2019 Task 3: The Importance of Being Shallow",
    author = "Nguyen, Andrew  and
      South, Tobin  and
      Bean, Nigel  and
      Tuke, Jonathan  and
      Mitchell, Lewis",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2050",
    doi = "10.18653/v1/S19-2050",
    pages = "292--296",
    abstract = "This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92{\%} of the top score for the competition, demonstrating that {``}shallow{''} classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis.",
}
@inproceedings{rebiai-etal-2019-scia,
    title = "{SCIA} at {S}em{E}val-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning",
    author = "Rebiai, Zinedine  and
      Andersen, Simon  and
      Debrenne, Antoine  and
      Lafargue, Victor",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2051",
    doi = "10.18653/v1/S19-2051",
    pages = "297--301",
    abstract = "In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1μ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1μ of 0.7324 on the test dataset.",
}
@inproceedings{anderson-2019-sentim,
    title = "Sentim at {S}em{E}val-2019 Task 3: Convolutional Neural Networks For Sentiment in Conversations",
    author = "Anderson, Jacob",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2052",
    doi = "10.18653/v1/S19-2052",
    pages = "302--306",
    abstract = "In this work convolutional neural networks were used in order to determine the sentiment in a conversational setting. This paper{'}s contributions include a method for handling any sized input and a method for breaking down the conversation into separate parts for easier processing. Finally, clustering was shown to improve results and that such a model for handling sentiment in conversations is both fast and accurate.",
}
@inproceedings{plaza-del-arco-etal-2019-sinai,
    title = "{SINAI} at {S}em{E}val-2019 Task 3: Using affective features for emotion classification in textual conversations",
    author = "Plaza-del-Arco, Flor Miriam  and
      Molina-Gonz{\'a}lez, M. Dolores  and
      Martin, Maite  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2053",
    doi = "10.18653/v1/S19-2053",
    pages = "307--311",
    abstract = "Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using message platforms such as WhatsApp or Telegram. For this reason, it is important to develop systems capable of understanding human emotions in textual conversations. In this paper, we carried out different systems to analyze the emotions of textual dialogue from SemEval-2019 Task 3: EmoContext for English language. Our main contribution is the integration of emotional and sentimental features in the classification using the SVM algorithm.",
}
@inproceedings{bae-etal-2019-snu,
    title = "{SNU} {IDS} at {S}em{E}val-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification",
    author = "Bae, Sanghwan  and
      Choi, Jihun  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2054",
    doi = "10.18653/v1/S19-2054",
    pages = "312--317",
    abstract = "We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between the distribution of prediction and ground truth, they consistently show positive effects on the performance. Also we propose a novel neural architecture which utilizes representation of overall context as well as of each utterance. The combination of the methods and the models achieved micro F1 score of about 0.766 on the final evaluation.",
}
@inproceedings{b-etal-2019-ssn,
    title = "{SSN}{\_}{NLP} at {S}em{E}val-2019 Task 3: Contextual Emotion Identification from Textual Conversation using {S}eq2{S}eq Deep Neural Network",
    author = "B., Senthil Kumar  and
      D., Thenmozhi  and
      Chandrabose, Aravindan  and
      Sharavanan, Srinethe",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2055",
    doi = "10.18653/v1/S19-2055",
    pages = "318--323",
    abstract = "Emotion identification is a process of identifying the emotions automatically from text, speech or images. Emotion identification from textual conversations is a challenging problem due to absence of gestures, vocal intonation and facial expressions. It enables conversational agents, chat bots and messengers to detect and report the emotions to the user instantly for a healthy conversation by avoiding emotional cues and miscommunications. We have adopted a Seq2Seq deep neural network to identify the emotions present in the text sequences. Several layers namely embedding layer, encoding-decoding layer, softmax layer and a loss layer are used to map the sequences from textual conversations to the emotions namely Angry, Happy, Sad and Others. We have evaluated our approach on the EmoContext@SemEval2019 dataset and we have obtained the micro-averaged F1 scores as 0.595 and 0.6568 for the pre-evaluation dataset and final evaluation test set respectively. Our approach improved the base line score by 7{\%} for final evaluation test set.",
}
@inproceedings{polignano-etal-2019-swap,
    title = "{SWAP} at {S}em{E}val-2019 Task 3: Emotion detection in conversations through Tweets, {CNN} and {LSTM} deep neural networks",
    author = "Polignano, Marco  and
      de Gemmis, Marco  and
      Semeraro, Giovanni",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2056",
    doi = "10.18653/v1/S19-2056",
    pages = "324--329",
    abstract = "Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings. A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden relations among embedding features. Meanwhile, a long short-term memory network (LSTM) is used for capturing information shared among words of the sentence. Each conversation has been formalized as a list of word embeddings, in particular during experimental runs pre-trained Glove and Google word embeddings have been evaluated. Surface lexical features have been also considered, but they have been demonstrated to be not usefully for the classification in this specific task. The final system configuration achieved a micro F1 score of 0.7089. The python code of the system is fully available at https://github.com/marcopoli/EmoContext2019",
}
@inproceedings{basile-etal-2019-symantoresearch,
    title = "{S}ymanto{R}esearch at {S}em{E}val-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations",
    author = "Basile, Angelo  and
      Franco-Salvador, Marc  and
      Pawar, Neha  and
      {\v{S}}tajner, Sanja  and
      Chinea Rios, Mara  and
      Benajiba, Yassine",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2057",
    doi = "10.18653/v1/S19-2057",
    pages = "330--334",
    abstract = "In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY) and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.",
}
@inproceedings{maity-2019-tdbot,
    title = "{TDB}ot at {S}em{E}val-2019 Task 3: Context Aware Emotion Detection Using A Conditioned Classification Approach",
    author = "Maity, Sourabh",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2058",
    doi = "10.18653/v1/S19-2058",
    pages = "335--339",
    abstract = "With the system description it is shown how to use the context information while detecting the emotion in a dialogue. Some guidelines about how to handle emojis was also laid out. While developing this system I realized the importance of pre-processing in conversational text data, or in general NLP related tasks; it can not be over emphasized.",
}
@inproceedings{ge-etal-2019-thu,
    title = "{THU}{\_}{NGN} at {S}em{E}val-2019 Task 3: Dialog Emotion Classification using Attentional {LSTM}-{CNN}",
    author = "Ge, Suyu  and
      Qi, Tao  and
      Wu, Chuhan  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2059",
    doi = "10.18653/v1/S19-2059",
    pages = "340--344",
    abstract = "With the development of the Internet, dialog systems are widely used in online platforms to provide personalized services for their users. It is important to understand the emotions through conversations to improve the quality of dialog systems. To facilitate the researches on dialog emotion recognition, the SemEval-2019 Task 3 named EmoContext is proposed. This task aims to classify the emotions of user utterance along with two short turns of dialogues into four categories. In this paper, we propose an attentional LSTM-CNN model to participate in this shared task. We use a combination of convolutional neural networks and long-short term neural networks to capture both local and long-distance contextual information in conversations. In addition, we apply attention mechanism to recognize and attend to important words within conversations. Besides, we propose to use ensemble strategies by combing the variants of our model with different pre-trained word embeddings via weighted voting. Our model achieved 0.7542 micro-F1 score in the final test data, ranking 15{\^{}}th out of 165 teams.",
}
@inproceedings{liang-etal-2019-thu,
    title = "{THU}-{HCSI} at {S}em{E}val-2019 Task 3: Hierarchical Ensemble Classification of Contextual Emotion in Conversation",
    author = "Liang, Xihao  and
      Ma, Ye  and
      Xu, Mingxing",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2060",
    doi = "10.18653/v1/S19-2060",
    pages = "345--349",
    abstract = "In this paper, we describe our hierarchical ensemble system designed for the SemEval-2019 task3, EmoContext. In our system, three sets of classifiers are trained for different sub-targets and the predicted labels of these base classifiers are combined through three steps of voting to make the final prediction. Effective details for developing base classifiers are highlighted.",
}
@inproceedings{yang-etal-2019-tokyotech,
    title = "{T}okyo{T}ech{\_}{NLP} at {S}em{E}val-2019 Task 3: Emotion-related Symbols in Emotion Detection",
    author = "Yang, Zhishen  and
      Vijlbrief, Sam  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2061",
    doi = "10.18653/v1/S19-2061",
    pages = "350--354",
    abstract = "This paper presents our contextual emotion detection system in approaching the SemEval2019 shared task 3: EmoContext: Contextual Emotion Detection in Text. This system cooperates with an emotion detection neural network method (Poria et al., 2017), emoji2vec (Eisner et al., 2016) embedding, word2vec embedding (Mikolov et al., 2013), and our proposed emoticon and emoji preprocessing method. The experimental results demonstrate the usefulness of our emoticon and emoji prepossessing method, and representations of emoticons and emoji contribute model{'}s emotion detection.",
}
@inproceedings{simionescu-etal-2019-uaic,
    title = "{UAIC} at {S}em{E}val-2019 Task 3: Extracting Much from Little",
    author = "Simionescu, Cristian  and
      Stoleru, Ingrid  and
      Lucaci, Diana  and
      Balan, Gheorghe  and
      Bute, Iulian  and
      Iftene, Adrian",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2062",
    doi = "10.18653/v1/S19-2062",
    pages = "355--359",
    abstract = "In this paper, we present a system description for implementing a sentiment analysis agent capable of interpreting the state of an interlocutor engaged in short three message conversations. We present the results and observations of our work and which parts could be further improved in the future.",
}
@inproceedings{li-etal-2019-yun,
    title = "{YUN}-{HPCC} at {S}em{E}val-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation",
    author = "Li, Dawei  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2063",
    doi = "10.18653/v1/S19-2063",
    pages = "360--364",
    abstract = "This paper describes our approach to the sentiment analysis of Twitter textual conversations based on deep learning. We analyze the syntax, abbreviations, and informal-writing of Twitter; and perform perfect data preprocessing on the data to convert them to normative text. We apply a multi-step ensemble strategy to solve the problem of extremely unbalanced data in the training set. This is achieved by taking the GloVe and Elmo word vectors as input into a combination model with four different deep neural networks. The experimental results from the development dataset demonstrate that the proposed model exhibits a strong generalization ability. For evaluation on the best dataset, we integrated the results using the stacking ensemble learning approach and achieved competitive results. According to the final official review, the results of our model ranked 10th out of 165 teams.",
}
@inproceedings{siddiqua-etal-2019-kdehateval,
    title = "{KDEH}at{E}val at {S}em{E}val-2019 Task 5: A Neural Network Model for Detecting Hate Speech in {T}witter",
    author = "Siddiqua, Umme Aymun  and
      Chy, Abu Nowshed  and
      Aono, Masaki",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2064",
    doi = "10.18653/v1/S19-2064",
    pages = "365--370",
    abstract = "In the age of emerging volume of microblog platforms, especially twitter, hate speech propagation is now of great concern. However, due to the brevity of tweets and informal user generated contents, detecting and analyzing hate speech on twitter is a formidable task. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval-2019 Task 5. Our team KDEHatEval employs different neural network models including multi-kernel convolution (MKC), nested LSTMs (NLSTMs), and multi-layer perceptron (MLP) in a unified architecture. Moreover, we utilize the state-of-the-art pre-trained sentence embedding models including DeepMoji, InferSent, and BERT for effective tweet representation. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.",
}
@inproceedings{baruah-etal-2019-abaruah,
    title = "{ABARUAH} at {S}em{E}val-2019 Task 5 : Bi-directional {LSTM} for Hate Speech Detection",
    author = "Baruah, Arup  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2065",
    doi = "10.18653/v1/S19-2065",
    pages = "371--376",
    abstract = "In this paper, we present the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for SemEval-2019 Task 5 titled {''}HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter{''}. This paper presents the results obtained for Subtask A for English language. The results of the BiLSTM and LR models are compared for two different types of preprocessing. One with no stemming performed and no stopwords removed. The other with stemming performed and stopwords removed. The BiLSTM model without attention performed the best for the first test, while the LR model with character n-grams performed the best for the second test. The BiLSTM model obtained an F1 score of 0.51 on the test set and obtained an official ranking of 8/71.",
}
@inproceedings{rozental-biton-2019-amobee,
    title = "{A}mobee at {S}em{E}val-2019 Tasks 5 and 6: Multiple Choice {CNN} Over Contextual Embedding",
    author = "Rozental, Alon  and
      Biton, Dadi",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2066",
    doi = "10.18653/v1/S19-2066",
    pages = "377--381",
    abstract = "This article describes Amobee{'}s participation in {``}HatEval: Multilingual detection of hate speech against immigrants and women in Twitter{''} (task 5) and {``}OffensEval: Identifying and Categorizing Offensive Language in Social Media{''} (task 6). The goal of task 5 was to detect hate speech targeted to women and immigrants. The goal of task 6 was to identify and categorized offensive language in social media, and identify offense target. We present a novel type of convolutional neural network called {``}Multiple Choice CNN{''} (MC- CNN) that we used over our newly developed contextual embedding, Rozental et al. (2019). For both tasks we used this architecture and achieved 4th place out of 69 participants with an F1 score of 0.53 in task 5, in task 6 achieved 2nd place (out of 75) in Sub-task B - automatic categorization of offense types (our model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C respectively in task 6).",
}
@inproceedings{ameer-etal-2019-cic,
    title = "{CIC} at {S}em{E}val-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in {T}witter",
    author = "Ameer, Iqra  and
      Siddiqui, Muhammad Hammad Fahim  and
      Sidorov, Grigori  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2067",
    doi = "10.18653/v1/S19-2067",
    pages = "382--386",
    abstract = "In recent years, the use of social media has in-creased incredibly. Social media permits Inter-net users a friendly platform to express their views and opinions. Along with these nice and distinct communication chances, it also allows bad things like usage of hate speech. Online automatic hate speech detection in various aspects is a significant scientific problem. This paper presents the Instituto Polit{\'e}cnico Nacional (Mexico) approach for the Semeval 2019 Task-5 [Hateval 2019] (Basile et al., 2019) competition for Multilingual Detection of Hate Speech on Twitter. The goal of this paper is to detect (A) Hate speech against immigrants and women, (B) Aggressive behavior and target classification, both for English and Spanish. In the proposed approach, we used a bag of words model with preprocessing (stem-ming and stop words removal). We submitted two different systems with names: (i) CIC-1 and (ii) CIC-2 for Hateval 2019 shared task. We used TF values in the first system and TF-IDF for the second system. The first system, CIC-1 got 2nd rank in subtask B for both English and Spanish languages with EMR score of 0.568 for English and 0.675 for Spanish. The second system, CIC-2 was ranked 4th in sub-task A and 1st in subtask B for Spanish language with a macro-F1 score of 0.727 and EMR score of 0.705 respectively.",
}
@inproceedings{almatarneh-etal-2019-citius,
    title = "{C}i{TIUS}-{COLE} at {S}em{E}val-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual Tweets",
    author = "Almatarneh, Sattam  and
      Gamallo, Pablo  and
      Pena, Francisco J. Ribadas",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2068",
    doi = "10.18653/v1/S19-2068",
    pages = "387--390",
    abstract = "This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classi- fication where the system predicting whether a tweet in English or in Spanish is hateful against women or immigrants or not. The proposed strategy relies on combining linguis- tic features to improve the classifier{'}s perfor- mance. More precisely, the method combines textual and lexical features, embedding words with the bag of words in Term Frequency- Inverse Document Frequency (TF-IDF) repre- sentation. The system performance reaches about 81{\%} F1 when it is applied to the training dataset, but its F1 drops to 36{\%} on the official test dataset for the English and 64{\%} for the Spanish language concerning the hate speech class",
}
@inproceedings{zhang-etal-2019-grunn2019,
    title = "Grunn2019 at {S}em{E}val-2019 Task 5: Shared Task on Multilingual Detection of Hate",
    author = "Zhang, Mike  and
      David, Roy  and
      Graumans, Leon  and
      Timmerman, Gerben",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2069",
    doi = "10.18653/v1/S19-2069",
    pages = "391--395",
    abstract = "Hate speech occurs more often than ever and polarizes society. To help counter this polarization, SemEval 2019 organizes a shared task called the Multilingual Detection of Hate. The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish. In the second task (B), the system is also asked to classify the following sub-tasks: hateful tweets as aggressive or not aggressive, and to identify the target harassed as individual or generic. We evaluate multiple models, and finally combine them in an ensemble setting. This ensemble setting is built of five and three submodels for the English and Spanish task respectively. In the current setup it shows that using a bigger ensemble for English tweets performs mediocre, while a slightly smaller ensemble does work well for detecting hate speech in Spanish tweets. Our results on the test set for English show 0.378 macro F1 on task A and 0.553 macro F1 on task B. For Spanish the results are significantly higher, 0.701 macro F1 on task A and 0.734 macro F1 for task B.",
}
@inproceedings{benito-etal-2019-gsi,
    title = "{GSI}-{UPM} at {S}em{E}val-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on {T}witter",
    author = "Benito, Diego  and
      Araque, Oscar  and
      Iglesias, Carlos A.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2070",
    doi = "10.18653/v1/S19-2070",
    pages = "396--403",
    abstract = "This paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a method based on word embeddings and semantic similarity combined with traditional paradigms, such as n-grams, TF-IDF and POS. This combination of several features is fine-tuned through ablation tests, demonstrating the usefulness of different features. While our approach outperforms baseline classifiers on different sub-tasks, the best of our submitted runs reached the 5th position on the Spanish sub-task A.",
}
@inproceedings{chakravartula-2019-hateminer,
    title = "{HATEMINER} at {S}em{E}val-2019 Task 5: Hate speech detection against Immigrants and Women in {T}witter using a Multinomial Naive {B}ayes Classifier",
    author = "Chakravartula, Nikhil",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2071",
    doi = "10.18653/v1/S19-2071",
    pages = "404--408",
    abstract = "This paper describes our participation in the SemEval 2019 Task 5 - Multilingual Detection of Hate. This task aims to identify hate speech against two specific targets, immigrants and women. We compare and contrast the performance of different word and sentence level embeddings on the state-of-the-art classification algorithms. Our final submission is a Multinomial binarized Naive Bayes model for both the subtasks in the English version.",
}
@inproceedings{nina-alcocer-2019-haterecognizer,
    title = "{HATER}ecognizer at {S}em{E}val-2019 Task 5: Using Features and Neural Networks to Face Hate Recognition",
    author = "Nina-Alcocer, Victor",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2072",
    doi = "10.18653/v1/S19-2072",
    pages = "409--415",
    abstract = "This paper presents a detailed description of our participation in task 5 on SemEval-2019. This task consists of classifying English and Spanish tweets that contain hate towards women or immigrants. We carried out several experiments; for a finer-grained study of the task, we analyzed different features and designing architectures of neural networks. Additionally, to face the lack of hate content in tweets, we include data augmentation as a technique to in- crease hate content in our datasets.",
}
@inproceedings{de-la-pena-2019-gl,
    title = "{GL} at {S}em{E}val-2019 Task 5: Identifying hateful tweets with a deep learning approach.",
    author = "De la Pe{\~n}a, Gretel Liz",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2073",
    doi = "10.18653/v1/S19-2073",
    pages = "416--419",
    abstract = "This paper describes the system we developed for SemEval 2019 on Multilingual detection of hate speech against immigrants and women in Twitter (HatEval - Task 5). We use an approach based on an Attention-based Long Short-Term Memory Recurrent Neural Network. In particular, we build a Bidirectional LSTM to extract information from the word embeddings over the sentence, then apply attention over the hidden states to estimate the importance of each word and finally feed this context vector to another LSTM model to get a representation. Then, the output obtained with this model is used to get the prediction of each of the sub-tasks.",
}
@inproceedings{ribeiro-silva-2019-inf,
    title = "{INF}-{H}at{E}val at {S}em{E}val-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on {T}witter",
    author = "Ribeiro, Alison  and
      Silva, N{\'a}dia",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2074",
    doi = "10.18653/v1/S19-2074",
    pages = "420--425",
    abstract = "In this paper, we describe our approach to detect hate speech against women and immigrants on Twitter in a multilingual context, English and Spanish. This challenge was proposed by the SemEval-2019 Task 5, where participants should develop models for hate speech detection, a two-class classification where systems have to predict whether a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not hateful (Task A), and whether the hate speech is directed at a specific person or a group of individuals (Task B). For this, we implemented a Convolutional Neural Networks (CNN) using pre-trained word embeddings (GloVe and FastText) with 300 dimensions. Our proposed model obtained in Task A 0.488 and 0.696 F1-score for English and Spanish, respectively. For Task B, the CNN obtained 0.297 and 0.430 EMR for English and Spanish, respectively.",
}
@inproceedings{hacohen-kerner-etal-2019-jctdhs,
    title = "{JCTDHS} at {S}em{E}val-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods",
    author = "HaCohen-Kerner, Yaakov  and
      Shayovitz, Elyashiv  and
      Rochman, Shalom  and
      Cahn, Eli  and
      Didi, Gal  and
      Ben-David, Ziv",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2075",
    doi = "10.18653/v1/S19-2075",
    pages = "426--430",
    abstract = "In this paper, we describe our submissions to SemEval-2019 contest. We tackled subtask A - {``}a binary classification where systems have to predict whether a tweet with a given target (women or immigrants) is hateful or not hateful{''}, a part of task 5 {``}Multilingual detection of hate speech against immigrants and women in Twitter (hatEval){''}. Our system JCTDHS (Jerusalem College of Technology Detects Hate Speech) was developed for tweets written in English. We applied various supervised ML methods, various combinations of n-gram features using the TF-IDF scheme and. In addition, we applied various combinations of eight basic preprocessing methods. Our best submission was a special bidirectional RNN, which was ranked at the 11th position out of 68 submissions.",
}
@inproceedings{winter-kern-2019-know,
    title = "Know-Center at {S}em{E}val-2019 Task 5: Multilingual Hate Speech Detection on {T}witter using {CNN}s",
    author = "Winter, Kevin  and
      Kern, Roman",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2076",
    doi = "10.18653/v1/S19-2076",
    pages = "431--435",
    abstract = "This paper presents the Know-Center system submitted for task 5 of the SemEval-2019 workshop. Given a Twitter message in either English or Spanish, the task is to first detect whether it contains hateful speech and second, to determine the target and level of aggression used. For this purpose our system utilizes word embeddings and a neural network architecture, consisting of both dilated and traditional convolution layers. We achieved average F1-scores of 0.57 and 0.74 for English and Spanish respectively.",
}
@inproceedings{bauwelinck-etal-2019-lt3,
    title = "{LT}3 at {S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter (hat{E}val)",
    author = "Bauwelinck, Nina  and
      Jacobs, Gilles  and
      Hoste, V{\'e}ronique  and
      Lefever, Els",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2077",
    doi = "10.18653/v1/S19-2077",
    pages = "436--440",
    abstract = "This paper describes our contribution to the SemEval-2019 Task 5 on the detection of hate speech against immigrants and women in Twitter (hatEval). We considered a supervised classification-based approach to detect hate speech in English tweets, which combines a variety of standard lexical and syntactic features with specific features for capturing offensive language. Our experimental results show good classification performance on the training data, but a considerable drop in recall on the held-out test set.",
}
@inproceedings{zhang-etal-2019-ltl,
    title = "ltl.uni-due at {S}em{E}val-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in {T}witter",
    author = "Zhang, Huangpan  and
      Wojatzki, Michael  and
      Horsmann, Tobias  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2078",
    doi = "10.18653/v1/S19-2078",
    pages = "441--446",
    abstract = "In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexicosemantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39.",
}
@inproceedings{argota-vega-etal-2019-mineriaunam,
    title = "{M}ineria{UNAM} at {S}em{E}val-2019 Task 5: Detecting Hate Speech in {T}witter using Multiple Features in a Combinatorial Framework",
    author = "Argota Vega, Luis Enrique  and
      Reyes-Maga{\~n}a, Jorge Carlos  and
      G{\'o}mez-Adorno, Helena  and
      Bel-Enguix, Gemma",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2079",
    doi = "10.18653/v1/S19-2079",
    pages = "447--452",
    abstract = "This paper presents our approach to the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in Twitter. The task consists of two sub-tasks, in Spanish and English: (A) detection of hate speech and (B) classification of hateful tweets as aggressive or not, and identification of the target harassed as individual or group. We used linguistically motivated features and several types of n-grams (words, characters, functional words, punctuation symbols, POS, among others). For task A, we trained a Support Vector Machine using a combinatorial framework, whereas for task B we followed a multi-labeled approach using the Random Forest classifier. Our approach achieved the highest F1-score in sub-task A for the Spanish language.",
}
@inproceedings{gertner-etal-2019-mitre,
    title = "{MITRE} at {S}em{E}val-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection",
    author = "Gertner, Abigail  and
      Henderson, John  and
      Merkhofer, Elizabeth  and
      Marsh, Amy  and
      Wellner, Ben  and
      Zarrella, Guido",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2080",
    doi = "10.18653/v1/S19-2080",
    pages = "453--459",
    abstract = "This paper describes MITRE{'}s participation in SemEval-2019 Task 5, HatEval: Multilingual detection of hate speech against immigrants and women in Twitter. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention mechanisms. We describe several styles of transfer learning from auxiliary tasks, including a novel method for adapting pre-trained BERT models to Twitter data. Logistic regression ties the systems together into an ensemble submitted for evaluation. The resulting system was used to produce predictions for all four HatEval subtasks, achieving the best mean rank of all teams that participated in all four conditions.",
}
@inproceedings{garibo-i-orts-2019-multilingual,
    title = "Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter at {S}em{E}val-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection",
    author = "Garibo i Orts, {\`O}scar",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2081",
    doi = "10.18653/v1/S19-2081",
    pages = "460--463",
    abstract = "This document describes a text change of representation approach to the task of Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter, as part of SemEval-2019 1 . The task is divided in two sub-tasks. Sub-task A consists in classifying tweets as being hateful or not hateful, whereas sub-task B requires fine tuning the classification by classifying the hateful tweets as being directed to single individuals or generic, if the tweet is aggressive or not. Our approach consists of a change of the space of representation of text into statistical descriptors which characterize the text. In addition, dimensional reduction is performed to 6 characteristics per class in order to make the method suitable for a Big Data environment. Frequency Analysis Interpolation (FAI) is the approach we use to achieve rank 5th in Spanish language and 9th in English language in sub-task B in both cases.",
}
@inproceedings{bojkovsky-pikuliak-2019-stufiit,
    title = "{STUFIIT} at {S}em{E}val-2019 Task 5: Multilingual Hate Speech Detection on {T}witter with {MUSE} and {ELM}o Embeddings",
    author = "Bojkovsk{\'y}, Michal  and
      Pikuliak, Mat{\'u}{\v{s}}",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2082",
    doi = "10.18653/v1/S19-2082",
    pages = "464--468",
    abstract = "We present a number of models used for hate speech detection for Semeval 2019 Task-5: Hateval. We evaluate the viability of multilingual learning for this task. We also experiment with adversarial learning as a means of creating a multilingual model. Ultimately our multilingual models have had worse results than their monolignual counterparts. We find that the choice of word representations (word embeddings) is very crucial for deep learning as a simple switch between MUSE and ELMo embeddings has shown a 3-4{\%} increase in accuracy. This also shows the importance of context when dealing with online content.",
}
@inproceedings{benballa-etal-2019-saagie,
    title = "Saagie at {S}emeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification",
    author = "Benballa, Miriam  and
      Collet, Sebastien  and
      Picot-Clemente, Romain",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2083",
    doi = "10.18653/v1/S19-2083",
    pages = "469--475",
    abstract = "This paper describes our contribution to SemEval 2019 Task 5: Hateval. We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features. For this purpose, we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them.",
}
@inproceedings{plaza-del-arco-etal-2019-sinai-semeval,
    title = "{SINAI} at {S}em{E}val-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in {E}nglish and {S}panish tweets",
    author = "Plaza-del-Arco, Flor Miriam  and
      Molina-Gonz{\'a}lez, M. Dolores  and
      Martin, Maite  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2084",
    doi = "10.18653/v1/S19-2084",
    pages = "476--479",
    abstract = "Misogyny and xenophobia are some of the most important social problems. With the in- crease in the use of social media, this feeling ofhatred towards women and immigrants can be more easily expressed, therefore it can cause harmful effects on social media users. For this reason, it is important to develop systems ca- pable of detecting hateful comments automatically. In this paper, we describe our system to analyze the hate speech in English and Spanish tweets against Immigrants and Women as part of our participation in SemEval-2019 Task 5: hatEval. Our main contribution is the integration of three individual algorithms of predic- tion in a model based on Vote ensemble classifier.",
}
@inproceedings{montejo-raez-etal-2019-sinai,
    title = "{SINAI}-{DL} at {S}em{E}val-2019 Task 5: Recurrent networks and data augmentation by paraphrasing",
    author = "Montejo-R{\'a}ez, Arturo  and
      Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Garc{\'\i}a-Cumbreras, Miguel A.  and
      D{\'\i}az-Galiano, Manuel Carlos",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2085",
    doi = "10.18653/v1/S19-2085",
    pages = "480--483",
    abstract = "This paper describes the participation of the SINAI-DL team at Task 5 in SemEval 2019, called HatEval. We have applied some classic neural network layers, like word embeddings and LSTM, to build a neural classifier for both proposed tasks. Due to the small amount of training data provided compared to what is expected for an adequate learning stage in deep architectures, we explore the use of paraphrasing tools as source for data augmentation. Our results show that this method is promising, as some improvement has been found over non-augmented training sets.",
}
@inproceedings{nourbakhsh-etal-2019-sthruggle,
    title = "sthruggle at {S}em{E}val-2019 Task 5: An Ensemble Approach to Hate Speech Detection",
    author = "Nourbakhsh, Aria  and
      Vermeer, Frida  and
      Wiltvank, Gijs  and
      van der Goot, Rob",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2086",
    doi = "10.18653/v1/S19-2086",
    pages = "484--488",
    abstract = "In this paper, we present our approach to detection of hate speech against women and immigrants in tweets for our participation in the SemEval-2019 Task 5. We trained an SVM and an RF classifier using character bi- and trigram features and a BiLSTM pre-initialized with external word embeddings. We combined the predictions of the SVM, RF and BiLSTM in two different ensemble models. The first was a majority vote of the binary values, and the second used the average of the confidence scores. For development, we got the highest accuracy (75{\%}) by the final ensemble model with majority voting. For testing, all models scored substantially lower and the scores between the classifiers varied more. We believe that these large differences between the higher accuracies in the development phase and the lower accuracies we obtained in the testing phase have partly to do with differences between the training, development and testing data.",
}
@inproceedings{chiril-etal-2019-binary,
    title = "The binary trio at {S}em{E}val-2019 Task 5: Multitarget Hate Speech Detection in Tweets",
    author = "Chiril, Patricia  and
      Benamara Zitoune, Farah  and
      Moriceau, V{\'e}ronique  and
      Kumar, Abhishek",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2087",
    doi = "10.18653/v1/S19-2087",
    pages = "489--493",
    abstract = "The massive growth of user-generated web content through blogs, online forums and most notably, social media networks, led to a large spreading of hatred or abusive messages which have to be moderated. This paper proposes a supervised approach to hate speech detection towards immigrants and women in English tweets. Several models have been developed ranging from feature-engineering approaches to neural ones.",
}
@inproceedings{garain-basu-2019-titans,
    title = "The Titans at {S}em{E}val-2019 Task 5: Detection of hate speech against immigrants and women in {T}witter",
    author = "Garain, Avishek  and
      Basu, Arpan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2088",
    doi = "10.18653/v1/S19-2088",
    pages = "494--497",
    abstract = "This system paper is a description of the system submitted to {''}SemEval-2019 Task 5{''} Task B for the English language, where we had to primarily detect hate speech and then detect aggressive behaviour and its target audience in Twitter. There were two specific target audiences, immigrants and women. The language of the tweets was English. We were required to first detect whether a tweet is containing hate speech. Thereafter we were required to find whether the tweet was showing aggressive behaviour, and then we had to find whether the targeted audience was an individual or a group of people.",
}
@inproceedings{manolescu-etal-2019-tueval,
    title = "{T}u{E}val at {S}em{E}val-2019 Task 5: {LSTM} Approach to Hate Speech Detection in {E}nglish and {S}panish",
    author = {Manolescu, Mihai  and
      L{\"o}fflad, Denise  and
      Mohamed Saber, Adham Nasser  and
      Moradipour Tari, Masoumeh},
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2089",
    doi = "10.18653/v1/S19-2089",
    pages = "498--502",
    abstract = "The detection of hate speech, especially in online platforms and forums, is quickly becoming a hot topic as anti-hate speech legislation begins to be applied to public discourse online. The HatEval shared task was created with this in mind; participants were expected to develop a model capable of determining whether or not input (in this case, Twitter datasets in English and Spanish) could be considered hate speech (designated as Task A), if they were aggressive, and whether the tweet was targeting an individual, or speaking generally (Task B). We approached this task by creating an LSTM model with an embedding layer. We found that our model performed considerably better on English language input when compared to Spanish language input. In English, we achieved an F1-Score of 0.466 for Task A and 0.462 for Task B; In Spanish, we achieved scores of 0.617 and 0.612 on Task A and Task B, respectively.",
}
@inproceedings{mulki-etal-2019-tw,
    title = "Tw-{S}t{AR} at {S}em{E}val-2019 Task 5: N-gram embeddings for Hate Speech Detection in Multilingual Tweets",
    author = "Mulki, Hala  and
      Bechikh Ali, Chedi  and
      Haddad, Hatem  and
      Babao{\u{g}}lu, Ismail",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2090",
    doi = "10.18653/v1/S19-2090",
    pages = "503--507",
    abstract = "In this paper, we describe our contribution in SemEval-2019: subtask A of task 5 {``}Multilingual detection of hate speech against immigrants and women in Twitter (HatEval){''}. We developed two hate speech detection model variants through Tw-StAR framework. While the first model adopted one-hot encoding ngrams to train an NB classifier, the second generated and learned n-gram embeddings within a feedforward neural network. For both models, specific terms, selected via MWT patterns, were tagged in the input data. With two feature types employed, we could investigate the ability of n-gram embeddings to rival one-hot n-grams. Our results showed that in English, n-gram embeddings outperformed one-hot ngrams. However, representing Spanish tweets by one-hot n-grams yielded a slightly better performance compared to that of n-gram embeddings. The official ranking indicated that Tw-StAR ranked 9th for English and 20th for Spanish.",
}
@inproceedings{perello-etal-2019-ua,
    title = "{UA} at {S}em{E}val-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection",
    author = "Perell{\'o}, Carlos  and
      Tom{\'a}s, David  and
      Garcia-Garcia, Alberto  and
      Garcia-Rodriguez, Jose  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2091",
    doi = "10.18653/v1/S19-2091",
    pages = "508--513",
    abstract = "This paper describes the system developed at the University of Alicante (UA) for the SemEval 2019 Task 5: Shared Task on Multilingual Detection of Hate. The purpose of this work is to build a strong baseline for hate speech detection, using a traditional machine learning approach with standard textual features, which could serve in a near future as a reference to compare with deep learning systems. We participated in both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggressive behavior and Target Classification). Despite its simplicity, our system obtained a remarkable F1-score of 72.5 (sixth highest) and an accuracy of 73.6 (second highest) in Spanish (task A), outperforming more complex neural models from a total of 40 participant systems.",
}
@inproceedings{hakimi-parizi-etal-2019-unbnlp,
    title = "{UNBNLP} at {S}em{E}val-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language",
    author = "Hakimi Parizi, Ali  and
      King, Milton  and
      Cook, Paul",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2092",
    doi = "10.18653/v1/S19-2092",
    pages = "514--518",
    abstract = "In this paper we apply a range of approaches to language modeling {--} including word-level n-gram and neural language models, and character-level neural language models {--} to the problem of detecting hate speech and offensive language. Our findings indicate that language models are able to capture knowledge of whether text is hateful or offensive. However, our findings also indicate that more conventional approaches to text classification often perform similarly or better.",
}
@inproceedings{paetzold-etal-2019-utfpr,
    title = "{UTFPR} at {S}em{E}val-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks",
    author = "Paetzold, Gustavo Henrique  and
      Zampieri, Marcos  and
      Malmasi, Shervin",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2093",
    doi = "10.18653/v1/S19-2093",
    pages = "519--523",
    abstract = "In this paper we revisit the problem of automatically identifying hate speech in posts from social media. We approach the task using a system based on minimalistic compositional Recurrent Neural Networks (RNN). We tested our approach on the SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (HatEval) shared task dataset. The dataset made available by the HatEval organizers contained English and Spanish posts retrieved from Twitter annotated with respect to the presence of hateful content and its target. In this paper we present the results obtained by our system in comparison to the other entries in the shared task. Our system achieved competitive performance ranking 7th in sub-task A out of 62 systems in the English track.",
}
@inproceedings{raiyani-etal-2019-vista,
    title = "Vista.ue at {S}em{E}val-2019 Task 5: Single Multilingual Hate Speech Detection Model",
    author = "Raiyani, Kashyap  and
      Gon{\c{c}}alves, Teresa  and
      Quaresma, Paulo  and
      Nogueira, Vitor",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2094",
    doi = "10.18653/v1/S19-2094",
    pages = "524--528",
    abstract = "This paper shares insight from participating in SemEval-2019 Task 5. The main propose of this system-description paper is to facilitate the reader with replicability and to provide insightful analysis of the developed system. Here in Vista.ue, we proposed a single multilingual hate speech detection model. This model was ranked 46/70 for English Task A and 31/43 for English Task B. Vista.ue was able to rank 38/41 for Spanish Task A and 22/25 for Spanish Task B.",
}
@inproceedings{wang-ding-2019-ynu,
    title = "{YNU} {NLP} at {S}em{E}val-2019 Task 5: Attention and Capsule Ensemble for Identifying Hate Speech",
    author = "Wang, Bin  and
      Ding, Haiyan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2095",
    doi = "10.18653/v1/S19-2095",
    pages = "529--534",
    abstract = "This paper describes the system submitted to SemEval 2019 Task 5: Multilingual detection of hate speech against immigrants and women in Twitter (hatEval). Its main purpose is to conduct hate speech detection on Twitter, which mainly includes two specific different targets, immigrants and women. We participate in both subtask A and subtask B for English. In order to address this task, we develope an ensemble of an attention-LSTM model based on HAN and an BiGRU-capsule model. Both models use fastText pre-trained embeddings, and we use this model in both subtasks. In comparison to other participating teams, our system is ranked 16th in the Sub-task A for English, and 12th in the Sub-task B for English.",
}
@inproceedings{ding-etal-2019-ynu,
    title = "{YNU}{\_}{DYX} at {S}em{E}val-2019 Task 5: A Stacked {B}i{GRU} Model Based on Capsule Network in Detection of Hate",
    author = "Ding, Yunxia  and
      Zhou, Xiaobing  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2096",
    doi = "10.18653/v1/S19-2096",
    pages = "535--539",
    abstract = "This paper describes our system designed for SemEval 2019 Task 5 {``}Shared Task on Multilingual Detection of Hate{''}.We only participate in subtask-A in English. To address this task, we present a stacked BiGRU model based on a capsule network system. In or- der to convert the tweets into corresponding vector representations and input them into the neural network, we use the fastText tools to get word representations. Then, the sentence representation is enriched by stacked Bidirectional Gated Recurrent Units (BiGRUs) and used as the input of capsule network. Our system achieves an average F1-score of 0.546 and ranks 3rd in the subtask-A in English.",
}
@inproceedings{sridharan-tr-2019-amrita,
    title = "Amrita School of Engineering - {CSE} at {S}em{E}val-2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and Classification",
    author = "Sridharan, Murali  and
      TR, Swapna",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2097",
    doi = "10.18653/v1/S19-2097",
    pages = "540--546",
    abstract = "With the proliferation and ubiquity of smart gadgets and smart devices, across the world, data generated by them has been growing at exponential rates; in particular social media platforms like Facebook, Twitter and Instagram have been generating voluminous data on a daily basis. According to Twitter{'}s usage statistics, about 500 million tweets are generated each day. While the tweets reflect the users{'} opinions on several events across the world, there are tweets which are offensive in nature that need to be tagged under the hateful conduct policy of Twitter. Offensive tweets have to be identified, captured and processed further, for a variety of reasons, which include i) identifying offensive tweets in order to prevent violent/abusive behavior in Twitter (or any social media for that matter), ii) creating and maintaining a history of offensive tweets for individual users (would be helpful in creating meta-data for user profile), iii) inferring the sentiment of the users on particular event/issue/topic . We have employed neural network models which manipulate attention with Temporal Convolutional Neural Network for the three shared sub-tasks i) ATT-TCN (ATTention based Temporal Convolutional Neural Network) employed for shared sub-task A that yielded a best macro-F1 score of 0.46, ii) SAE-ATT-TCN(Self Attentive Embedding-ATTention based Temporal Convolutional Neural Network) employed for shared sub-task B and sub-task C that yielded best macro-F1 score of 0.61 and 0.51 respectively. Among the two variants ATT-TCN and SAE-ATT-TCN, the latter performed better.",
}
@inproceedings{kumar-etal-2019-bhanodaig,
    title = "bhanodaig at {S}em{E}val-2019 Task 6: Categorizing Offensive Language in social media",
    author = "Kumar, Ritesh  and
      Bhanodai, Guggilla  and
      Pamula, Rajendra  and
      Chennuru, Maheswara Reddy",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2098",
    doi = "10.18653/v1/S19-2098",
    pages = "547--550",
    abstract = "This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards OffensEval i.e. identifying and categorizing offensive language in social media. Out of three sub-tasks, we have participated in sub-task B: automatic categorization of offensive types. We perform the task of categorizing offensive language, whether the tweet is targeted insult or untargeted. We use Linear Support Vector Machine for classification. The official ranking metric is macro-averaged F1. Our system gets the score 0.5282 with accuracy 0.8792. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.",
}
@inproceedings{wu-etal-2019-bnu,
    title = "{BNU}-{HKBU} {UIC} {NLP} Team 2 at {S}em{E}val-2019 Task 6: Detecting Offensive Language Using {BERT} model",
    author = "Wu, Zhenghao  and
      Zheng, Hao  and
      Wang, Jianming  and
      Su, Weifeng  and
      Fong, Jefferson",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2099",
    doi = "10.18653/v1/S19-2099",
    pages = "551--555",
    abstract = "In this study we deal with the problem of identifying and categorizing offensive language in social media. Our group, BNU-HKBU UIC NLP Team2, use supervised classification along with multiple version of data generated by different ways of pre-processing the data. We then use the state-of-the-art model Bidirectional Encoder Representations from Transformers, or BERT (Devlin et al, 2018), to capture linguistic, syntactic and semantic features. Long range dependencies between each part of a sentence can be captured by BERT{'}s bidirectional encoder representations. Our results show 85.12{\%} accuracy and 80.57{\%} F1 scores in Subtask A (offensive language identification), 87.92{\%} accuracy and 50{\%} F1 scores in Subtask B (categorization of offense types), and 69.95{\%} accuracy and 50.47{\%} F1 score in Subtask C (offense target identification). Analysis of the results shows that distinguishing between targeted and untargeted offensive language is not a simple task. More work needs to be done on the unbalance data problem in Subtasks B and C. Some future work is also discussed.",
}
@inproceedings{aglionby-etal-2019-camsterdam,
    title = "{CAM}sterdam at {S}em{E}val-2019 Task 6: Neural and graph-based feature extraction for the identification of offensive tweets",
    author = "Aglionby, Guy  and
      Davis, Chris  and
      Mishra, Pushkar  and
      Caines, Andrew  and
      Yannakoudakis, Helen  and
      Rei, Marek  and
      Shutova, Ekaterina  and
      Buttery, Paula",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2100",
    doi = "10.18653/v1/S19-2100",
    pages = "556--563",
    abstract = "We describe the CAMsterdam team entry to the SemEval-2019 Shared Task 6 on offensive language identification in Twitter data. Our proposed model learns to extract textual features using a multi-layer recurrent network, and then performs text classification using gradient-boosted decision trees (GBDT). A self-attention architecture enables the model to focus on the most relevant areas in the text. In order to enrich input representations, we use node2vec to learn globally optimised embeddings for hashtags, which are then given as additional features to the GBDT classifier. Our best model obtains 78.79{\%} macro F1-score on detecting offensive language (subtask A), 66.32{\%} on categorising offence types (targeted/untargeted; subtask B), and 55.36{\%} on identifying the target of offence (subtask C).",
}
@inproceedings{zhang-etal-2019-cn,
    title = "{CN}-{HIT}-{MI}.{T} at {S}em{E}val-2019 Task 6: Offensive Language Identification Based on {B}i{LSTM} with Double Attention",
    author = "Zhang, Yaojie  and
      Xu, Bing  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2101",
    doi = "10.18653/v1/S19-2101",
    pages = "564--570",
    abstract = "Offensive language has become pervasive in social media. In Offensive Language Identification tasks, it may be difficult to predict accurately only according to the surface words. So we try to dig deeper semantic information of text. This paper presents use an attention-based two layers bidirectional longshort memory neural network (BiLSTM) for semantic feature extraction. Additionally, a residual connection mechanism is used to synthesize two different deep features, and an emoji attention mechanism is used to extract semantic information of emojis in text. We participated in three sub-tasks of SemEval 2019 Task 6 as CN-HIT-MI.T team. Our macro-averaged F1-score in sub-task A is 0.768, ranking 28/103. We got 0.638 in sub-task B, ranking 30/75. In sub-task C, we got 0.549, ranking 22/65. We also tried some other methods of not submitting results.",
}
@inproceedings{pavlopoulos-etal-2019-convai,
    title = "{C}onv{AI} at {S}em{E}val-2019 Task 6: Offensive Language Identification and Categorization with Perspective and {BERT}",
    author = "Pavlopoulos, John  and
      Thain, Nithum  and
      Dixon, Lucas  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2102",
    doi = "10.18653/v1/S19-2102",
    pages = "571--576",
    abstract = "This paper presents the application of two strong baseline systems for toxicity detection and evaluates their performance in identifying and categorizing offensive language in social media. PERSPECTIVE is an API, that serves multiple machine learning models for the improvement of conversations online, as well as a toxicity detection system, trained on a wide variety of comments from platforms across the Internet. BERT is a recently popular language representation model, fine tuned per task and achieving state of the art performance in multiple NLP tasks. PERSPECTIVE performed better than BERT in detecting toxicity, but BERT was much better in categorizing the offensive type. Both baselines were ranked surprisingly high in the SEMEVAL-2019 OFFENSEVAL competition, PERSPECTIVE in detecting an offensive post (12th) and BERT in categorizing it (11th). The main contribution of this paper is the assessment of two strong baselines for the identification (PERSPECTIVE) and the categorization (BERT) of offensive language with little or no additional training data.",
}
@inproceedings{modha-etal-2019-da,
    title = "{DA}-{LD}-{H}ildesheim at {S}em{E}val-2019 Task 6: Tracking Offensive Content with Deep Learning using Shallow Representation",
    author = "Modha, Sandip  and
      Majumder, Prasenjit  and
      Patel, Daksh",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2103",
    doi = "10.18653/v1/S19-2103",
    pages = "577--581",
    abstract = "This paper presents the participation of team DA-LD-Hildesheim of Information Retrieval and Language Processing lab at DA-IICT, India in Semeval-19 OffenEval track. The aim of this shared task is to identify offensive content at fined-grained level granularity. The task is divided into three sub-tasks. The system is required to check whether social media posts contain any offensive or profane content or not, targeted or untargeted towards any entity and classifying targeted posts into the individual, group or other categories. Social media posts suffer from data sparsity problem, Therefore, the distributed word representation technique is chosen over the Bag-of-Words for the text representation. Since limited labeled data was available for the training, pre-trained word vectors are used and fine-tuned on this classification task. Various deep learning models based on LSTM, Bidirectional LSTM, CNN, and Stacked CNN are used for the classification. It has been observed that labeled data was highly affected with class imbalance and our technique to handle the class-balance was not effective, in fact performance was degraded in some of the runs. Macro F1 score is used as a primary evaluation metric for the performance. Our System achieves Macro F1 score = 0.7833 in sub-task A, 0.6456 in the sub-task B and 0.5533 in the sub-task C.",
}
@inproceedings{de-la-pena-rosso-2019-deepanalyzer,
    title = "{D}eep{A}nalyzer at {S}em{E}val-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets",
    author = "De la Pe{\~n}a, Gretel Liz  and
      Rosso, Paolo",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2104",
    doi = "10.18653/v1/S19-2104",
    pages = "582--586",
    abstract = "This paper describes the system we developed for SemEval 2019 on Identifying and Categorizing Offensive Language in Social Media (OffensEval - Task 6). The task focuses on offensive language in tweets. It is organized into three sub-tasks for offensive language identification; automatic categorization of offense types and offense target identification. The approach for the first subtask is a deep learning-based ensemble method which uses a Bidirectional LSTM Recurrent Neural Network and a Convolutional Neural Network. Additionally we use the information from part-of-speech tagging of tweets for target identification and combine previous results for categorization of offense types.",
}
@inproceedings{kapil-etal-2019-nlp,
    title = "{NLP} at {S}em{E}val-2019 Task 6: Detecting Offensive language using Neural Networks",
    author = "Kapil, Prashant  and
      Ekbal, Asif  and
      Das, Dipankar",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2105",
    doi = "10.18653/v1/S19-2105",
    pages = "587--592",
    abstract = "In this paper we built several deep learning architectures to participate in shared task OffensEval: Identifying and categorizing Offensive language in Social media by semEval-2019. The dataset was annotated with three level annotation schemes and task was to detect between offensive and not offensive, categorization and target identification in offensive contents. Deep learning models with POS information as feature were also leveraged for classification. The three best models that performed best on individual sub tasks are stacking of CNN-Bi-LSTM with Attention, BiLSTM with POS information added with word features and Bi-LSTM for third task. Our models achieved a Macro F1 score of 0.7594, 0.5378 and 0.4588 in Task(A,B,C) respectively with rank of 33rd, 54th and 52nd out of 103, 75 and 65 submissions.The three best models that performed best on individual sub task are using Neural Networks.",
}
@inproceedings{pedersen-2019-duluth,
    title = "{D}uluth at {S}em{E}val-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets",
    author = "Pedersen, Ted",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2106",
    doi = "10.18653/v1/S19-2106",
    pages = "593--599",
    abstract = "This paper describes the Duluth systems that participated in SemEval{--}2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine Learning approaches that built classifiers from lexical features found in manually labeled training data. However, our most successful system for classifying a tweet as offensive (or not) was a rule-based black{--}list approach, and we also experimented with combining the training data from two different but related SemEval tasks. Our best systems in each of the three OffensEval tasks placed in the middle of the comparative evaluation, ranking 57th of 103 in task A, 39th of 75 in task B, and 44th of 65 in task C.",
}
@inproceedings{kebriaei-etal-2019-emad,
    title = "Emad at {S}em{E}val-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approaches",
    author = "Kebriaei, Emad  and
      Karimi, Samaneh  and
      Sabri, Nazanin  and
      Shakery, Azadeh",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2107",
    doi = "10.18653/v1/S19-2107",
    pages = "600--603",
    abstract = "In this paper, the used methods and the results obtained by our team, entitled Emad, on the OffensEval 2019 shared task organized at SemEval 2019 are presented. The OffensEval shared task includes three sub-tasks namely Offensive language identification, Automatic categorization of offense types and Offense target identification. We participated in sub-task A and tried various methods including traditional machine learning methods, deep learning methods and also a combination of the first two sets of methods. We also proposed a data augmentation method using word embedding to improve the performance of our methods. The results show that the augmentation approach outperforms other methods in terms of macro-f1.",
}
@inproceedings{pelicon-etal-2019-embeddia,
    title = "Embeddia at {S}em{E}val-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning Approaches",
    author = "Pelicon, Andra{\v{z}}  and
      Martinc, Matej  and
      Kralj Novak, Petra",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2108",
    doi = "10.18653/v1/S19-2108",
    pages = "604--610",
    abstract = "SemEval 2019 Task 6 was OffensEval: Identifying and Categorizing Offensive Language in Social Media. The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification. In this paper, we present the approaches used by the Embeddia team, who qualified as fourth, eighteenth and fifth on the tree sub-tasks. A different model was trained for each sub-task. For the first sub-task, we used a BERT model fine-tuned on the OLID dataset, while for the second and third tasks we developed a custom neural network architecture which combines bag-of-words features and automatically generated sequence-based features. Our results show that combining automatically and manually crafted features fed into a neural architecture outperform transfer learning approach on more unbalanced datasets.",
}
@inproceedings{indurthi-etal-2019-fermi-semeval,
    title = "Fermi at {S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings",
    author = "Indurthi, Vijayasaradhi  and
      Syed, Bakhtiyar  and
      Shrivastava, Manish  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2109",
    doi = "10.18653/v1/S19-2109",
    pages = "611--616",
    abstract = "This paper describes our system (Fermi) for Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media of SemEval-2019. We participated in all the three sub-tasks within Task 6. We evaluate multiple sentence embeddings in conjunction with various supervised machine learning algorithms and evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team Fermi{'}s model achieved an F1-score of 64.40{\%}, 62.00{\%} and 62.60{\%} for sub-task A, B and C respectively on the official leaderboard. Our model for sub-task C which uses pre-trained ELMo embeddings for transforming the input and uses SVM (RBF kernel) for training, scored third position on the official leaderboard. Through the paper we provide a detailed description of the approach, as well as the results obtained for the task.",
}
@inproceedings{doostmohammadi-etal-2019-ghmerti,
    title = "Ghmerti at {S}em{E}val-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification",
    author = "Doostmohammadi, Ehsan  and
      Sameti, Hossein  and
      Saffar, Ali",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2110",
    doi = "10.18653/v1/S19-2110",
    pages = "617--621",
    abstract = "This paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing offensive language in social media in three subtasks; whether or not a content is offensive (subtask A), whether it is targeted (subtask B) towards an individual, a group, or other entities (subtask C). The proposed approach includes character-level Convolutional Neural Network, word-level Recurrent Neural Network, and some preprocessing. The performance achieved by the proposed model is 77.93{\%} macro-averaged F1-score.",
}
@inproceedings{bansal-etal-2019-tubingen,
    title = {{HAD}-{T}{\"u}bingen at {S}em{E}val-2019 Task 6: Deep Learning Analysis of Offensive Language on {T}witter: Identification and Categorization},
    author = "Bansal, Himanshu  and
      Nagel, Daniel  and
      Soloveva, Anita",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2111",
    doi = "10.18653/v1/S19-2111",
    pages = "622--627",
    abstract = {This paper describes the submissions of our team, HAD-T{\"u}bingen, for the SemEval 2019 - Task 6: {``}OffensEval: Identifying and Categorizing Offensive Language in Social Media{''}. We participated in all the three sub-tasks: Sub-task A - {``}Offensive language identification{''}, sub-task B - {``}Automatic categorization of offense types{''} and sub-task C - {``}Offense target identification{''}. As a baseline model we used a Long short-term memory recurrent neural network (LSTM) to identify and categorize offensive tweets. For all the tasks we experimented with external databases in a postprocessing step to enhance the results made by our model. The best macro-average F1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.},
}
@inproceedings{oberstrass-etal-2019-hhu,
    title = "{HHU} at {S}em{E}val-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with {ELM}o",
    author = "Oberstrass, Alexander  and
      Romberg, Julia  and
      Stoll, Anke  and
      Conrad, Stefan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2112",
    doi = "10.18653/v1/S19-2112",
    pages = "628--634",
    abstract = "We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6). Our results show that context embeddings are important features for the three different sub-tasks in connection with classical machine and with deep learning. Our best model reached place 3 of 75 in sub-task B with a macro $F_1$ of 0.719. Our approaches for sub-task A and C perform less well but could also deliver promising results.",
}
@inproceedings{patras-etal-2019-hope,
    title = "Hope at {S}em{E}val-2019 Task 6: Mining social media language to discover offensive language",
    author = "Patras, Gabriel Florentin  and
      Lungu, Diana Florina  and
      Gifu, Daniela  and
      Trandabat, Diana",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2113",
    doi = "10.18653/v1/S19-2113",
    pages = "635--638",
    abstract = "User{'}s content share through social media has reached huge proportions nowadays. However, along with the free expression of thoughts on social media, people risk getting exposed to various aggressive statements. In this paper, we present a system able to identify and classify offensive user-generated content.",
}
@inproceedings{graff-etal-2019-ingeotec,
    title = "{INGEOTEC} at {S}em{E}val-2019 Task 5 and Task 6: A Genetic Programming Approach for Text Classification",
    author = "Graff, Mario  and
      Miranda-Jim{\'e}nez, Sabino  and
      Tellez, Eric  and
      Ochoa, Daniela Alejandra",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2114",
    doi = "10.18653/v1/S19-2114",
    pages = "639--644",
    abstract = "This paper describes our participation in HatEval and OffensEval challenges for English and Spanish languages. We used several approaches, B4MSA, FastText, and EvoMSA. Best results were achieved with EvoMSA, which is a multilingual and domain-independent architecture that combines the prediction of different knowledge sources to solve text classification problems.",
}
@inproceedings{hacohen-kerner-etal-2019-jcticol,
    title = "{JCTICOL} at {S}em{E}val-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods",
    author = "HaCohen-Kerner, Yaakov  and
      Ben-David, Ziv  and
      Didi, Gal  and
      Cahn, Eli  and
      Rochman, Shalom  and
      Shayovitz, Elyashiv",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2115",
    doi = "10.18653/v1/S19-2115",
    pages = "645--651",
    abstract = "In this paper, we describe our submissions to SemEval-2019 task 6 contest. We tackled all three sub-tasks in this task {``}OffensEval - Identifying and Categorizing Offensive Language in Social Media{''}. In our system called JCTICOL (Jerusalem College of Technology Identifies and Categorizes Offensive Language), we applied various supervised ML methods. We applied various combinations of word/character n-gram features using the TF-IDF scheme. In addition, we applied various combinations of seven basic preprocessing methods. Our best submission, an RNN model was ranked at the 25th position out of 65 submissions for the most complex sub-task (C).",
}
@inproceedings{han-etal-2019-jhan014,
    title = "jhan014 at {S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media",
    author = "Han, Jiahui  and
      Wu, Shengtan  and
      Liu, Xinyu",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2116",
    doi = "10.18653/v1/S19-2116",
    pages = "652--656",
    abstract = "In this paper, we present two methods to identify and categorize the offensive language in Twitter. In the first method, we establish a probabilistic model to evaluate the sentence offensiveness level and target level according to different sub-tasks. In the second method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers. In the comparison of two methods, we find both method has its own advantages and drawbacks while they have similar accuracy.",
}
@inproceedings{torres-vaca-2019-jtml,
    title = "{JTML} at {S}em{E}val-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks",
    author = "Torres, Johnny  and
      Vaca, Carmen",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2117",
    doi = "10.18653/v1/S19-2117",
    pages = "657--661",
    abstract = "In this paper, we propose the use of a Convolutional Neural Network (CNN) to identify offensive tweets, as well as the type and target of the offense. We use an end-to-end model (i.e., no preprocessing) and fine-tune pre-trained embeddings (FastText) during training for learning words{'} representation. We compare the proposed CNN model to a baseline model, such as Linear Regression, and several neural models. The results show that CNN outperforms other models, and stands as a simple but strong baseline in comparison to other systems submitted to the Shared Task.",
}
@inproceedings{mukherjee-etal-2019-ju,
    title = "{JU}{\_}{ETCE}{\_}17{\_}21 at {S}em{E}val-2019 Task 6: Efficient Machine Learning and Neural Network Approaches for Identifying and Categorizing Offensive Language in Tweets",
    author = "Mukherjee, Preeti  and
      Pal, Mainak  and
      Banerjee, Somnath  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2118",
    doi = "10.18653/v1/S19-2118",
    pages = "662--667",
    abstract = "This paper describes our system submissions as part of our participation (team name: JU{\_}ETCE{\_}17{\_}21) in the SemEval 2019 shared task 6: {``}OffensEval: Identifying and Catego- rizing Offensive Language in Social Media{''}. We participated in all the three sub-tasks: i) Sub-task A: offensive language identification, ii) Sub-task B: automatic categorization of of- fense types, and iii) Sub-task C: offense target identification. We employed machine learn- ing as well as deep learning approaches for the sub-tasks. We employed Convolutional Neural Network (CNN) and Recursive Neu- ral Network (RNN) Long Short-Term Memory (LSTM) with pre-trained word embeddings. We used both word2vec and Glove pre-trained word embeddings. We obtained the best F1- score using CNN based model for sub-task A, LSTM based model for sub-task B and Lo- gistic Regression based model for sub-task C. Our best submissions achieved 0.7844, 0.5459 and 0.48 F1-scores for sub-task A, sub-task B and sub-task C respectively.",
}
@inproceedings{rani-ojha-2019-kmi,
    title = "{KMI}-Coling at {S}em{E}val-2019 Task 6: Exploring N-grams for Offensive Language detection",
    author = "Rani, Priya  and
      Ojha, Atul Kr.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2119",
    doi = "10.18653/v1/S19-2119",
    pages = "668--671",
    abstract = "In this paper, we present the system description of Offensive language detection tool which is developed by the KMI{\_}Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted in SemEval 2019 workshop. To develop the system, we have explored n-grams up to 8-gram and trained three different namely A, B and C systems for three different subtasks within the OffensEval task which achieves 79.76{\%}, 87.91{\%} and 44.37{\%} accuracy respectively. The task was completed using the dataset provided to us by the OffensEval organisers was the part of OLID dataset. It consists of 13,240 tweets extracted from twitter and were annotated at three levels using crowdsourcing.",
}
@inproceedings{mut-altin-etal-2019-lastus,
    title = "{L}a{STUS}/{TALN} at {S}em{E}val-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based {B}i-{LSTM} model",
    author = "Mut Altin, Lutfiye Seda  and
      Bravo Serrano, {\`A}lex  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2120",
    doi = "10.18653/v1/S19-2120",
    pages = "672--677",
    abstract = "We present a bidirectional Long-Short Term Memory network for identifying offensive language in Twitter. Our system has been developed in the context of the SemEval 2019 Task 6 which comprises three different sub-tasks, namely A: Offensive Language Detection, B: Categorization of Offensive Language, C: Offensive Language Target Identification. We used a pre-trained Word Embeddings in tweet data, including information about emojis and hashtags. Our approach achieves good performance in the three sub-tasks.",
}
@inproceedings{aggarwal-etal-2019-ltl,
    title = "{LTL}-{UDE} at {S}em{E}val-2019 Task 6: {BERT} and Two-Vote Classification for Categorizing Offensiveness",
    author = "Aggarwal, Piush  and
      Horsmann, Tobias  and
      Wojatzki, Michael  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2121",
    doi = "10.18653/v1/S19-2121",
    pages = "678--682",
    abstract = "We present results for Subtask A and C of SemEval 2019 Shared Task 6. In Subtask A, we experiment with an embedding representation of postings and use BERT to categorize postings. Our best result reaches the 10th place (out of 103). In Subtask C, we applied a two-vote classification approach with minority fallback, which is placed on the 19th rank (out of 65).",
}
@inproceedings{mahata-etal-2019-midas-semeval,
    title = "{MIDAS} at {S}em{E}val-2019 Task 6: Identifying Offensive Posts and Targeted Offense from {T}witter",
    author = "Mahata, Debanjan  and
      Zhang, Haimin  and
      Uppal, Karan  and
      Kumar, Yaman  and
      Shah, Rajiv Ratn  and
      Shahid, Simra  and
      Mehnaz, Laiba  and
      Anand, Sarthak",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2122",
    doi = "10.18653/v1/S19-2122",
    pages = "683--690",
    abstract = "In this paper we present our approach and the system description for Sub Task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub Task A involves identifying if a given tweet is offensive and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub Task A is based on an ensemble of Convolutional Neural Network and Bidirectional LSTM, whereas for Sub Task B, we rely on a set of heuristics derived from the training data. We provide detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub Task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants achieving a macro F1 of 0.695.",
}
@inproceedings{nikolov-radivchev-2019-nikolov,
    title = "Nikolov-Radivchev at {S}em{E}val-2019 Task 6: Offensive Tweet Classification with {BERT} and Ensembles",
    author = "Nikolov, Alex  and
      Radivchev, Victor",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2123",
    doi = "10.18653/v1/S19-2123",
    pages = "691--695",
    abstract = "This paper examines different approaches and models towards offensive tweet classification which were used as a part of the OffensEval 2019 competition. It reviews Tweet preprocessing, techniques for overcoming unbalanced class distribution in the provided test data, and comparison of multiple attempted machine learning models.",
}
@inproceedings{swamy-etal-2019-nit,
    title = "{NIT}{\_}{A}gartala{\_}{NLP}{\_}{T}eam at {S}em{E}val-2019 Task 6: An Ensemble Approach to Identifying and Categorizing Offensive Language in {T}witter Social Media Corpora",
    author = {Swamy, Steve Durairaj  and
      Jamatia, Anupam  and
      Gamb{\"a}ck, Bj{\"o}rn  and
      Das, Amitava},
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2124",
    doi = "10.18653/v1/S19-2124",
    pages = "696--703",
    abstract = "The paper describes the systems submitted to OffensEval (SemEval 2019, Task 6) on {`}Identifying and Categorizing Offensive Language in Social Media{'} by the {`}NIT{\_}Agartala{\_}NLP{\_}Team{'}. A Twitter annotated dataset of 13,240 English tweets was provided by the task organizers to train the individual models, with the best results obtained using an ensemble model composed of six different classifiers. The ensemble model produced macro-averaged F1-scores of 0.7434, 0.7078 and 0.4853 on Subtasks A, B, and C, respectively. The paper highlights the overall low predictive nature of various linguistic features and surface level count features, as well as the limitations of a traditional machine learning approach when compared to a Deep Learning counterpart.",
}
@inproceedings{rusert-srinivasan-2019-nlp,
    title = "{NLP}@{UIOWA} at {S}em{E}val-2019 Task 6: Classifying the Crass using Multi-windowed {CNN}s",
    author = "Rusert, Jonathan  and
      Srinivasan, Padmini",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2125",
    doi = "10.18653/v1/S19-2125",
    pages = "704--711",
    abstract = "This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories. Our system is a text based CNN, which learns only from the provided training data. Our system achieves 80 - 90{\%} accuracy for the binary classification problems (offensive vs not offensive and targeted vs untargeted) and 63{\%} accuracy for trinary classification (group vs individual vs other).",
}
@inproceedings{seganti-etal-2019-nlpr,
    title = "{NLPR}@{SRPOL} at {S}em{E}val-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier",
    author = "Seganti, Alessandro  and
      Sobol, Helena  and
      Orlova, Iryna  and
      Kim, Hannam  and
      Staniszewski, Jakub  and
      Krumholc, Tymoteusz  and
      Koziel, Krystian",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2126",
    doi = "10.18653/v1/S19-2126",
    pages = "712--721",
    abstract = "The paper presents a system developed for the SemEval-2019 competition Task 5 hat- Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI{'}s GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.). The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness. In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models.",
}
@inproceedings{mitrovic-etal-2019-nlpup,
    title = "nlp{UP} at {S}em{E}val-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection",
    author = "Mitrovi{\'c}, Jelena  and
      Birkeneder, Bastian  and
      Granitzer, Michael",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2127",
    doi = "10.18653/v1/S19-2127",
    pages = "722--726",
    abstract = "This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language. Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Recurrent Neural Network (RNN). We utilize word2vec to capture the semantic similarities between words. This composition allows us to extract long term dependencies in tweets and distinguish between offensive and non-offensive tweets. In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets. Our model achieved a macro F1-score of 79.40{\%} on the SemEval dataset.",
}
@inproceedings{singh-chand-2019-pardeep,
    title = "Pardeep at {S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Deep Learning",
    author = "Singh, Pardeep  and
      Chand, Satish",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2128",
    doi = "10.18653/v1/S19-2128",
    pages = "727--734",
    abstract = "The rise of social media has made information exchange faster and easier among the people. However, in recent times, the use of offensive language has seen an upsurge in social media. The main challenge for a service provider is to correctly identify such offensive posts and take necessary action to monitor and control their spread. In this work, we try to address this problem by using sophisticated deep learning techniques like LSTM, Bidirectional LSTM and Bidirectional GRU. Our proposed approach solves 3 different Sub-tasks provided in the SemEval-2019 task 6 which incorporates identification of offensive tweets as well as their categorization. We obtain significantly better results in the leader-board for Sub-task B and decent results for Sub-task A and Subtask C validating the fact that the proposed models can be used for automating the offensive post-detection task in social media.",
}
@inproceedings{plaza-del-arco-etal-2019-sinai-semeval-2019,
    title = "{SINAI} at {S}em{E}val-2019 Task 6: Incorporating lexicon knowledge into {SVM} learning to identify and categorize offensive language in social media",
    author = "Plaza-del-Arco, Flor Miriam  and
      Molina-Gonz{\'a}lez, M. Dolores  and
      Martin, Maite  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2129",
    doi = "10.18653/v1/S19-2129",
    pages = "735--738",
    abstract = "Offensive language has an impact across society. The use of social media has aggravated this issue among online users, causing suicides in the worst cases. For this reason, it is important to develop systems capable of identifying and detecting offensive language in text automatically. In this paper, we developed a system to classify offensive tweets as part of our participation in SemEval-2019 Task 6: OffensEval. Our main contribution is the integration of lexical features in the classification using the SVM algorithm.",
}
@inproceedings{d-etal-2019-ssn,
    title = "{SSN}{\_}{NLP} at {S}em{E}val-2019 Task 6: Offensive Language Identification in Social Media using Traditional and Deep Machine Learning Approaches",
    author = "D., Thenmozhi  and
      B., Senthil Kumar  and
      Sharavanan, Srinethe  and
      Chandrabose, Aravindan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2130",
    doi = "10.18653/v1/S19-2130",
    pages = "739--744",
    abstract = "Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that degrades an individual or a group. It is helpful for hate speech detection, flame detection and cyber bullying. Due to immense growth of accessibility to social media, OLI helps to avoid abuse and hurts. In this paper, we present deep and traditional machine learning approaches for OLI. In deep learning approach, we have used bi-directional LSTM with different attention mechanisms to build the models and in traditional machine learning, TF-IDF weighting schemes with classifiers namely Multinomial Naive Bayes and Support Vector Machines with Stochastic Gradient Descent optimizer are used for model building. The approaches are evaluated on the OffensEval@SemEval2019 dataset and our team SSN{\_}NLP submitted runs for three tasks of OffensEval shared task. The best runs of SSN{\_}NLP obtained the F1 scores as 0.53, 0.48, 0.3 and the accuracies as 0.63, 0.84 and 0.42 for the tasks A, B and C respectively. Our approaches improved the base line F1 scores by 12{\%}, 26{\%} and 14{\%} for Task A, B and C respectively.",
}
@inproceedings{fortuna-etal-2019-stop,
    title = "Stop {P}ropag{H}ate at {S}em{E}val-2019 Tasks 5 and 6: Are abusive language classification results reproducible?",
    author = "Fortuna, Paula  and
      Soler-Company, Juan  and
      Nunes, S{\'e}rgio",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2131",
    doi = "10.18653/v1/S19-2131",
    pages = "745--752",
    abstract = "This paper summarizes the participation of Stop PropagHate team at SemEval 2019. Our approach is based on replicating one of the most relevant works on the literature, using word embeddings and LSTM. After circumventing some of the problems of the original code, we found poor results when applying it to the HatEval contest (F1=0.45). We think this is due mainly to inconsistencies in the data of this contest. Finally, for the OffensEval the classifier performed well (F1=0.74), proving to have a better performance for offense detection than for hate speech.",
}
@inproceedings{suseelan-etal-2019-techssn,
    title = "{TECHSSN} at {S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks",
    author = "Suseelan, Angel  and
      S, Rajalakshmi  and
      B, Logesh  and
      S, Harshini  and
      B, Geetika  and
      S, Dyaneswaran  and
      Rajendram, S Milton  and
      T T, Mirnalinee",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2132",
    doi = "10.18653/v1/S19-2132",
    pages = "753--758",
    abstract = "Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google{'}s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.",
}
@inproceedings{garain-basu-2019-titans-semeval,
    title = "The Titans at {S}em{E}val-2019 Task 6: Offensive Language Identification, Categorization and Target Identification",
    author = "Garain, Avishek  and
      Basu, Arpan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2133",
    doi = "10.18653/v1/S19-2133",
    pages = "759--762",
    abstract = "This system paper is a description of the system submitted to {``}SemEval-2019 Task 6{''}, where we had to detect offensive language in Twitter. There were two specific target audiences, immigrants and women. The language of the tweets was English. We were required to first detect whether a tweet contains offensive content, and then we had to find out whether the tweet was targeted against some individual, group or other entity. Finally we were required to classify the targeted audience.",
}
@inproceedings{kannan-stein-2019-tukast,
    title = {{T}{\"u}{K}a{S}t at {S}em{E}val-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification},
    author = "Kannan, Madeeswaran  and
      Stein, Lukas",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2134",
    doi = "10.18653/v1/S19-2134",
    pages = "763--769",
    abstract = {We describe our system (T{\"u}KaSt) submitted for Task 6: Offensive Language Classification, at SemEval 2019. We developed multiple SVM classifier models that used sentence-level dense vector representations of tweets enriched with sentiment information and term-weighting. Our best results achieved F1 scores of 0.734, 0.660 and 0.465 in the first, second and third sub-tasks respectively. We also describe a neural network model that was developed in parallel but not used during evaluation due to time constraints.},
}
@inproceedings{shushkevich-etal-2019-tuvd,
    title = "{TUVD} team at {S}em{E}val-2019 Task 6: Offense Target Identification",
    author = "Shushkevich, Elena  and
      Cardiff, John  and
      Rosso, Paolo",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2135",
    doi = "10.18653/v1/S19-2135",
    pages = "770--774",
    abstract = "This article presents our approach for detecting a target of offensive messages in Twitter, including Individual, Group and Others classes. The model we have created is an ensemble of simpler models, including Logistic Regression, Naive Bayes, Support Vector Machine and the interpolation between Logistic Regression and Naive Bayes with 0.25 coefficient of interpolation. The model allows us to achieve 0.547 macro F1-score.",
}
@inproceedings{rajendran-etal-2019-ubc,
    title = "{UBC}-{NLP} at {S}em{E}val-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data",
    author = "Rajendran, Arun  and
      Zhang, Chiyu  and
      Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2136",
    doi = "10.18653/v1/S19-2136",
    pages = "775--781",
    abstract = "We examine learning offensive content on Twitter with limited, imbalanced data. For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. Among the 75 participating teams in SemEval-2019 sub-task B, our system ranks 6th (with 0.706 macro F1-score). For sub-task C, among the 65 participating teams, our system ranks 9th (with 0.587 macro F1-score).",
}
@inproceedings{wiedemann-etal-2019-uhh,
    title = "{UHH}-{LT} at {S}em{E}val-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection",
    author = "Wiedemann, Gregor  and
      Ruppert, Eugen  and
      Biemann, Chris",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2137",
    doi = "10.18653/v1/S19-2137",
    pages = "782--787",
    abstract = "We present a neural network based approach of transfer learning for offensive language detection. For our system, we compare two types of knowledge transfer: supervised and unsupervised pre-training. Supervised pre-training of our bidirectional GRU-3-CNN architecture is performed as multi-task learning of parallel training of five different tasks. The selected tasks are supervised classification problems from public NLP resources with some overlap to offensive language such as sentiment detection, emoji classification, and aggressive language classification. Unsupervised transfer learning is performed with a thematic clustering of 40M unlabeled tweets via LDA. Based on this dataset, pre-training is performed by predicting the main topic of a tweet. Results indicate that unsupervised transfer from large datasets performs slightly better than supervised training on small {`}near target category{'} datasets. In the SemEval Task, our system ranks 14 out of 103 participants.",
}
@inproceedings{zhu-etal-2019-um,
    title = "{UM}-{IU}@{LING} at {S}em{E}val-2019 Task 6: Identifying Offensive Tweets Using {BERT} and {SVM}s",
    author = {Zhu, Jian  and
      Tian, Zuoyu  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2138",
    doi = "10.18653/v1/S19-2138",
    pages = "788--795",
    abstract = "This paper describes the UM-IU@LING{'}s system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.",
}
@inproceedings{goel-sharma-2019-usf,
    title = "{USF} at {S}em{E}val-2019 Task 6: Offensive Language Detection Using {LSTM} With Word Embeddings",
    author = "Goel, Bharti  and
      Sharma, Ravi",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2139",
    doi = "10.18653/v1/S19-2139",
    pages = "796--800",
    abstract = "In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A). In case a tweet is offensive, our system identifies if a tweet is targeted (insult or threat) or non-targeted like swearing (Sub-task B). In targeted tweets, our system identifies the target as an individual or group (Sub-task C). We used data pre-processing techniques like splitting hashtags into words, removing special characters, stop-word removal, stemming, lemmatization, capitalization, and offensive word dictionary. Later, we used keras tokenizer and word embeddings for feature extraction. For classification, we used the LSTM (Long short-term memory) model of keras framework. Our accuracy scores for Sub-task A, B and C are \textit{0.8128}, \textit{0.8167} and \textit{0.3662} respectively. Our results indicate that fine-grained classification to identify offense target was difficult for the system. Lastly, in the future scope section, we will discuss the ways to improve system performance.",
}
@inproceedings{paetzold-2019-utfpr,
    title = "{UTFPR} at {S}em{E}val-2019 Task 6: Relying on Compositionality to Find Offense",
    author = "Paetzold, Gustavo Henrique",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2140",
    doi = "10.18653/v1/S19-2140",
    pages = "801--805",
    abstract = "We present the UTFPR system for the OffensEval shared task of SemEval 2019: A character-to-word-to-sentence compositional RNN model trained exclusively over the training data provided by the organizers. We find that, although not very competitive for the task at hand, it offers a robust solution to the orthographic irregularity inherent to tweets.",
}
@inproceedings{ramakrishnan-etal-2019-uva,
    title = "{UVA} Wahoos at {S}em{E}val-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning",
    author = "Ramakrishnan, Murugesan  and
      Zadrozny, Wlodek  and
      Tabari, Narges",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2141",
    doi = "10.18653/v1/S19-2141",
    pages = "806--811",
    abstract = "With the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others. We have attempted to detect such tweets and comments that are malicious in intent, which either targets an individual or a group. Our best classifier for identifying offensive tweets for SubTask A (Classifying offensive vs. nonoffensive) has an accuracy of 83.14{\%} and a f1- score of 0.7565 on the actual test data. For SubTask B, to identify if an offensive tweet is targeted (If targeted towards an individual or a group), the classifier performs with an accuracy of 89.17{\%} and f1-score of 0.5885. The paper talks about how we generated linguistic and semantic features to build an ensemble machine learning model. By training with more extracts from different sources (Facebook, and more tweets), the paper shows how the accuracy changes with additional training data.",
}
@inproceedings{zhou-etal-2019-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2019 Task 6: Identifying and Categorising Offensive Language on {T}witter",
    author = "Zhou, Chengjin  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2142",
    doi = "10.18653/v1/S19-2142",
    pages = "812--817",
    abstract = "This document describes the submission of team YNU-HPCC to SemEval-2019 for three Sub-tasks of Task 6: Sub-task A, Sub-task B, and Sub-task C. We have submitted four systems to identify and categorise offensive language. The first subsystem is an attention-based 2-layer bidirectional long short-term memory (BiLSTM). The second subsystem is a voting ensemble of four different deep learning architectures. The third subsystem is a stacking ensemble of four different deep learning architectures. Finally, the fourth subsystem is a bidirectional encoder representations from transformers (BERT) model. Among our models, in Sub-task A, our first subsystem performed the best, ranking 16th among 103 teams; in Sub-task B, the second subsystem performed the best, ranking 12th among 75 teams; in Sub-task C, the fourth subsystem performed best, ranking 4th among 65 teams.",
}
@inproceedings{wang-etal-2019-ynuwb,
    title = "{YNUWB} at {S}em{E}val-2019 Task 6: K-max pooling {CNN} with average meta-embedding for identifying offensive language",
    author = "Wang, Bin  and
      Zhou, Xiaobing  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2143",
    doi = "10.18653/v1/S19-2143",
    pages = "818--822",
    abstract = "This paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019. The task aims to identify and categorize offensive language in social media, we only participate in Sub-task A, which aims to identify offensive language. In order to address this task, we propose a system based on a K-max pooling convolutional neural network model, and use an argument for averaging as a valid meta-embedding technique to get a metaembedding. Finally, we also use a cyclic learning rate policy to improve model performance. Our model achieves a Macro F1-score of 0.802 (ranked 9/103) in the Sub-task A.",
}
@inproceedings{el-zanaty-2019-zeyad,
    title = "Zeyad at {S}em{E}val-2019 Task 6: That{'}s Offensive! An All-Out Search For An Ensemble To Identify And Categorize Offense in Tweets.",
    author = "El-Zanaty, Zeyad",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2144",
    doi = "10.18653/v1/S19-2144",
    pages = "823--828",
    abstract = "The objective of this paper is to provide a description for a classification system built for SemEval-2019 Task 6: OffensEval. This system classifies a tweet as either offensive or not offensive (Sub-task A) and further classifies offensive tweets into categories (Sub-tasks B - C). The system consists of two phases; a brute-force grid search to find the best learners amongst a given set and an ensemble of a subset of these best learners. The system achieved an F1-score of 0.728, ranking in subtask A, an F1-score score of 0.616 in subtask B and an F1-score of 0.509 in subtask C.",
}
@inproceedings{kiesel-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
    author = "Kiesel, Johannes  and
      Mestre, Maria  and
      Shukla, Rishabh  and
      Vincent, Emmanuel  and
      Adineh, Payam  and
      Corney, David  and
      Stein, Benno  and
      Potthast, Martin",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2145",
    doi = "10.18653/v1/S19-2145",
    pages = "829--839",
    abstract = "Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It is an open question how successfully hyperpartisan news detection can be automated, and the goal of this SemEval task was to shed light on the state of the art. We developed new resources for this purpose, including a manually labeled dataset with 1,273 articles, and a second dataset with 754,000 articles, labeled via distant supervision. The interest of the research community in our task exceeded all our expectations: The datasets were downloaded about 1,000 times, 322 teams registered, of which 184 configured a virtual machine on our shared task cloud service TIRA, of which in turn 42 teams submitted a valid run. The best team achieved an accuracy of 0.822 on a balanced sample (yes : no hyperpartisan) drawn from the manually tagged corpus; an ensemble of the submitted systems increased the accuracy by 0.048.",
}
@inproceedings{jiang-etal-2019-team,
    title = "Team Bertha von Suttner at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection using {ELM}o Sentence Representation Convolutional Network",
    author = "Jiang, Ye  and
      Petrak, Johann  and
      Song, Xingyi  and
      Bontcheva, Kalina  and
      Maynard, Diana",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2146",
    doi = "10.18653/v1/S19-2146",
    pages = "840--844",
    abstract = "This paper describes the participation of team {``}bertha-von-suttner{''} in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system uses sentence representations from averaged word embeddings generated from the pre-trained ELMo model with Convolutional Neural Networks and Batch Normalization for predicting hyperpartisan news. The final predictions were generated from the averaged predictions of an ensemble of models. With this architecture, our system ranked in first place, based on accuracy, the official scoring metric.",
}
@inproceedings{gorrell-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 7: {R}umour{E}val, Determining Rumour Veracity and Support for Rumours",
    author = "Gorrell, Genevieve  and
      Kochkina, Elena  and
      Liakata, Maria  and
      Aker, Ahmet  and
      Zubiaga, Arkaitz  and
      Bontcheva, Kalina  and
      Derczynski, Leon",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2147",
    doi = "10.18653/v1/S19-2147",
    pages = "845--854",
    abstract = "Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of {``}fake news{''} has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour{'}s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70{\%} increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.",
}
@inproceedings{li-etal-2019-eventai,
    title = "event{AI} at {S}em{E}val-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation Information",
    author = "Li, Quanzhi  and
      Zhang, Qiong  and
      Si, Luo",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2148",
    doi = "10.18653/v1/S19-2148",
    pages = "855--859",
    abstract = "This paper describes our system for SemEval 2019 RumorEval: Determining rumor veracity and support for rumors (SemEval 2019 Task 7). This track has two tasks: Task A is to determine a user{'}s stance towards the source rumor, and Task B is to detect the veracity of the rumor: true, false or unverified. For stance classification, a neural network model with language features is utilized. For rumor verification, our approach exploits information from different dimensions: rumor content, source credibility, user credibility, user stance, event propagation path, etc. We use an ensemble approach in both tasks, which includes neural network models as well as the traditional classification algorithms. Our system is ranked 1st place in the rumor verification task by both the macro F1 measure and the RMSE measure.",
}
@inproceedings{mihaylova-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 8: Fact Checking in Community Question Answering Forums",
    author = "Mihaylova, Tsvetomila  and
      Karadzhov, Georgi  and
      Atanasova, Pepa  and
      Baly, Ramy  and
      Mohtarami, Mitra  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2149",
    doi = "10.18653/v1/S19-2149",
    pages = "860--869",
    abstract = "We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at http://competitions.codalab.org/competitions/20022.",
}
@inproceedings{lv-etal-2019-autohome,
    title = "{AUTOHOME}-{ORCA} at {S}em{E}val-2019 Task 8: Application of {BERT} for Fact-Checking in Community Forums",
    author = "Lv, Zhengwei  and
      Liu, Duoxing  and
      Sun, Haifeng  and
      Liang, Xiao  and
      Lei, Tao  and
      Shi, Zhizhong  and
      Zhu, Feng  and
      Yang, Lei",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2150",
    doi = "10.18653/v1/S19-2150",
    pages = "870--876",
    abstract = "Fact checking is an important task for maintaining high quality posts and improving user experience in Community Question Answering forums. Therefore, the SemEval-2019 task 8 is aimed to identify factual question (subtask A) and detect true factual information from corresponding answers (subtask B). In order to address this task, we propose a system based on the BERT model with meta information of questions. For the subtask A, the outputs of fine-tuned BERT classification model are combined with the feature of length of questions to boost the performance. For the subtask B, the predictions of several variants of BERT model encoding the meta information are combined to create an ensemble model. Our system achieved competitive results with an accuracy of 0.82 in the subtask A and 0.83 in the subtask B. The experimental results validate the effectiveness of our system.",
}
@inproceedings{negi-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 9: Suggestion Mining from Online Reviews and Forums",
    author = "Negi, Sapna  and
      Daudert, Tobias  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2151",
    doi = "10.18653/v1/S19-2151",
    pages = "877--887",
    abstract = "We present the pilot SemEval task on Suggestion Mining. The task consists of subtasks A and B, where we created labeled data from feedback forum and hotel reviews respectively. Subtask A provides training and test data from the same domain, while Subtask B evaluates the system on a test dataset from a different domain than the available training data. 33 teams participated in the shared task, with a total of 50 members. We summarize the problem definition, benchmark dataset preparation, and methods used by the participating teams, providing details of the methods used by the top ranked systems. The dataset is made freely available to help advance the research in suggestion mining, and reproduce the systems submitted under this task",
}
@inproceedings{yamamoto-sekiya-2019-y,
    title = "m{\_}y at {S}em{E}val-2019 Task 9: Exploring {BERT} for Suggestion Mining",
    author = "Yamamoto, Masahiro  and
      Sekiya, Toshiyuki",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2152",
    doi = "10.18653/v1/S19-2152",
    pages = "888--892",
    abstract = "This paper presents our system to the SemEval-2019 Task 9, Suggestion Mining from Online Reviews and Forums. The goal of this task is to extract suggestions such as the expressions of tips, advice, and recommendations. We explore Bidirectional Encoder Representations from Transformers (BERT) focusing on target domain pre-training in Subtask A which provides training and test datasets in the same domain. In Subtask B, the cross domain suggestion mining task, we apply the idea of distant supervision. Our system obtained the third place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy of our approaches.",
}
@inproceedings{hopkins-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 10: Math Question Answering",
    author = "Hopkins, Mark  and
      Le Bras, Ronan  and
      Petrescu-Prahova, Cristian  and
      Stanovsky, Gabriel  and
      Hajishirzi, Hannaneh  and
      Koncel-Kedziorski, Rik",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2153",
    doi = "10.18653/v1/S19-2153",
    pages = "893--899",
    abstract = "We report on the SemEval 2019 task on math question answering. We provided a question set derived from Math SAT practice exams, including 2778 training questions and 1082 test questions. For a significant subset of these questions, we also provided SMT-LIB logical form annotations and an interpreter that could solve these logical forms. Systems were evaluated based on the percentage of correctly answered questions. The top system correctly answered 45{\%} of the test questions, a considerable improvement over the 17{\%} random guessing baseline.",
}
@inproceedings{liu-etal-2019-aifu,
    title = "{A}i{F}u at {S}em{E}val-2019 Task 10: A Symbolic and Sub-symbolic Integrated System for {SAT} Math Question Answering",
    author = "Liu, Yifan  and
      Ding, Keyu  and
      Zhou, Yi",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2154",
    doi = "10.18653/v1/S19-2154",
    pages = "900--906",
    abstract = "AiFu has won the first place in the SemEval-2019 Task 10 - {''}Math Question Answering{''}competition. This paper is to describe how it works technically and to report and analyze some essential experimental results",
}
@inproceedings{weissenbacher-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 12: Toponym Resolution in Scientific Papers",
    author = "Weissenbacher, Davy  and
      Magge, Arjun  and
      O{'}Connor, Karen  and
      Scotch, Matthew  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2155",
    doi = "10.18653/v1/S19-2155",
    pages = "907--916",
    abstract = "We present the SemEval-2019 Task 12 which focuses on toponym resolution in scientific articles. Given an article from PubMed, the task consists of detecting mentions of names of places, or toponyms, and mapping the mentions to their corresponding entries in GeoNames.org, a database of geospatial locations. We proposed three subtasks. In Subtask 1, we asked participants to detect all toponyms in an article. In Subtask 2, given toponym mentions as input, we asked participants to disambiguate them by linking them to entries in GeoNames. In Subtask 3, we asked participants to perform both the detection and the disambiguation steps for all toponyms. A total of 29 teams registered, and 8 teams submitted a system run. We summarize the corpus and the tools created for the challenge. They are freely available at https://competitions.codalab.org/competitions/19948. We also analyze the methods, the results and the errors made by the competing systems with a focus on toponym disambiguation.",
}
@inproceedings{wang-etal-2019-dm,
    title = "{DM}{\_}{NLP} at {S}em{E}val-2018 Task 12: A Pipeline System for Toponym Resolution",
    author = "Wang, Xiaobin  and
      Ma, Chunping  and
      Zheng, Huafei  and
      Liu, Chu  and
      Xie, Pengjun  and
      Li, Linlin  and
      Si, Luo",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2156",
    doi = "10.18653/v1/S19-2156",
    pages = "917--923",
    abstract = "This paper describes DM-NLP{'}s system for toponym resolution task at Semeval 2019. Our system was developed for toponym detection, disambiguation and end-to-end resolution which is a pipeline of the former two. For toponym detection, we utilized the state-of-the-art sequence labeling model, namely, BiLSTM-CRF model as backbone. A lot of strategies are adopted for further improvement, such as pre-training, model ensemble, model averaging and data augment. For toponym disambiguation, we adopted the widely used searching and ranking framework. For ranking, we proposed several effective features for measuring the consistency between the detected toponym and toponyms in GeoNames. Eventually, our system achieved the best performance among all the submitted results in each sub task.",
}
@inproceedings{papadopoulou-etal-2019-brenda,
    title = "Brenda Starr at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
    author = "Papadopoulou, Olga  and
      Kordopatis-Zilos, Giorgos  and
      Zampoglou, Markos  and
      Papadopoulos, Symeon  and
      Kompatsiaris, Yiannis",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2157",
    doi = "10.18653/v1/S19-2157",
    pages = "924--928",
    abstract = "In the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we experimented with two systems: i) a standard supervised learning approach using superficial text and bag-of-words features from the article title and body, and ii) a deep learning system comprising a four-layer convolutional neural network and max-pooling layers after the embedding layer, feeding the consolidated features to a bi-directional recurrent neural network. We achieved an F-score of 0.712 with our best approach, which corresponds to the mid-range of performance levels in the leaderboard.",
}
@inproceedings{perez-almendros-etal-2019-cardiff,
    title = "{C}ardiff {U}niversity at {S}em{E}val-2019 Task 4: Linguistic Features for Hyperpartisan News Detection",
    author = "P{\'e}rez-Almendros, Carla  and
      Espinosa-Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2158",
    doi = "10.18653/v1/S19-2158",
    pages = "929--933",
    abstract = "This paper summarizes our contribution to the Hyperpartisan News Detection task in SemEval 2019. We experiment with two different approaches: 1) an SVM classifier based on word vector averages and hand-crafted linguistic features, and 2) a BiLSTM-based neural text classifier trained on a filtered training set. Surprisingly, despite their different nature, both approaches achieve an accuracy of 0.74. The main focus of this paper is to further analyze the remarkable fact that a simple feature-based approach can perform on par with modern neural classifiers. We also highlight the effectiveness of our filtering strategy for training the neural network on a large but noisy training set.",
}
@inproceedings{gupta-etal-2019-clark,
    title = "{C}lark {K}ent at {S}em{E}val-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection",
    author = "Gupta, Viresh  and
      Kaur Jolly, Baani Leen  and
      Kaur, Ramneek  and
      Chakraborty, Tanmoy",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2159",
    doi = "10.18653/v1/S19-2159",
    pages = "934--938",
    abstract = "In this paper, we present a news bias prediction system, which we developed as part of a SemEval 2019 task. We developed an XGBoost based system which uses character and word level n-gram features represented using TF-IDF, count vector based correlation matrix, and predicts if an input news article is a hyperpartisan news article. Our model was able to achieve a precision of 68.3{\%} on the test set provided by the contest organizers. We also run our model on the BuzzFeed corpus and find XGBoost with simple character level N-Gram embeddings to be performing well with an accuracy of around 96{\%}.",
}
@inproceedings{isbister-johansson-2019-dick,
    title = "Dick-Preston and Morbo at {S}em{E}val-2019 Task 4: Transfer Learning for Hyperpartisan News Detection",
    author = "Isbister, Tim  and
      Johansson, Fredrik",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2160",
    doi = "10.18653/v1/S19-2160",
    pages = "939--943",
    abstract = "In a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important. We present a deep learning-based approach in which a pre-trained language model has been fine-tuned on domain-specific data and used for classification of news articles, as part of the SemEval-2019 task on hyperpartisan news detection. The suggested approach yields accuracy and F1-scores around 0.8 which places the best performing classifier among the top-5 systems in the competition.",
}
@inproceedings{agerri-2019-doris,
    title = "Doris {M}artin at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features",
    author = "Agerri, Rodrigo",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2161",
    doi = "10.18653/v1/S19-2161",
    pages = "944--948",
    abstract = "In this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed document classification system which consists of a combination of clustering features implemented on top of some simple shallow local features. We show how leveraging distributional features obtained from large in-domain unlabeled data helps to easily and quickly develop a reasonably good performing system for detecting hyperpartisan news. The system and models generated for this task are publicly available.",
}
@inproceedings{sengupta-pedersen-2019-duluth,
    title = "{D}uluth at {S}em{E}val-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector",
    author = "Sengupta, Saptarshi  and
      Pedersen, Ted",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2162",
    doi = "10.18653/v1/S19-2162",
    pages = "949--953",
    abstract = "This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one{--}sided view of a particular story. We developed two variants of our system, the more successful was a Logistic Regression classifier based on unigram features. This was our official entry in the task, and it placed 23rd of 42 participating teams. Our second variant was a Convolutional Neural Network that did not perform as well.",
}
@inproceedings{chakravartula-etal-2019-fermi,
    title = "Fermi at {S}em{E}val-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector",
    author = "Chakravartula, Nikhil  and
      Indurthi, Vijayasaradhi  and
      Syed, Bakhtiyar",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2163",
    doi = "10.18653/v1/S19-2163",
    pages = "954--956",
    abstract = "This paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. We aim to find the right number of features useful for efficient classification and explore multiple training models to evaluate the performance of these text classification algorithms. Our team - Fermi{'}s model achieved an accuracy of 59.10{\%} and an F1 score of 69.5{\%} on the official test data set. In this paper, we provide a detailed description of the approach as well as the results obtained in the task.",
}
@inproceedings{chen-etal-2019-harvey,
    title = "Harvey Mudd College at {S}em{E}val-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector",
    author = "Chen, Celena  and
      Park, Celine  and
      Dwyer, Jason  and
      Medero, Julie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2164",
    doi = "10.18653/v1/S19-2164",
    pages = "957--961",
    abstract = "We use various natural processing and machine learning methods to perform the Hyperpartisan News Detection task. In particular, some of the features we look at are bag-of-words features, the title{'}s length, number of capitalized words in the title, and the sentiment of the sentences and the title. By adding these features, we see improvements in our evaluation metrics compared to the baseline values. We find that sentiment analysis helps improve our evaluation metrics. We do not see a benefit from feature selection. Overall, our system achieves an accuracy of 0.739, finishing 18th out of 42 submissions to the task. From our work, it is evident that both title features and sentiment of articles are meaningful to the hyperpartisanship of news articles.",
}
@inproceedings{drissi-etal-2019-harvey,
    title = "Harvey Mudd College at {S}em{E}val-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector",
    author = "Drissi, Mehdi  and
      Sandoval Segura, Pedro  and
      Ojha, Vivaswat  and
      Medero, Julie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2165",
    doi = "10.18653/v1/S19-2165",
    pages = "962--966",
    abstract = "We investigate the recently developed Bidi- rectional Encoder Representations from Transformers (BERT) model (Devlin et al. 2018) for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two different BERT models using different proportions of the articles is consistently high, with our best-performing model on the validation set achieving 85{\%} accuracy and the best-performing model on the test set achieving 77{\%}. We further determined that our model exhibits strong consistency, labeling independent slices of the same article identically. Finally, we find that randomizing the order of word pieces dramatically reduces validation accuracy (to approximately 60{\%}), but that shuffling groups of four or more word pieces maintains an accuracy of about 80{\%}, indicating the model mainly gains value from local context.",
}
@inproceedings{amason-etal-2019-harvey,
    title = "Harvey Mudd College at {S}em{E}val-2019 Task 4: The {D}.{X}. Beaumont Hyperpartisan News Detector",
    author = "Amason, Evan  and
      Palanker, Jake  and
      Shen, Mary Clare  and
      Medero, Julie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2166",
    doi = "10.18653/v1/S19-2166",
    pages = "967--970",
    abstract = "We use the 600 hand-labelled articles from SemEval Task 4 to hand-tune a classifier with 3000 features for the Hyperpartisan News Detection task. Our final system uses features based on bag-of-words (BoW), analysis of the article title, language complexity, and simple sentiment analysis in a naive Bayes classifier. We trained our final system on the 600,000 articles labelled by publisher. Our final system has an accuracy of 0.653 on the hand-labeled test set. The most effective features are the Automated Readability Index and the presence of certain words in the title. This suggests that hyperpartisan writing uses a distinct writing style, especially in the title.",
}
@inproceedings{nguyen-etal-2019-nlp,
    title = "{NLP}@{UIT} at {S}em{E}val-2019 Task 4: The Paparazzo Hyperpartisan News Detector",
    author = "Nguyen, Duc-Vu  and
      Dang, Thin  and
      Nguyen, Ngan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2167",
    doi = "10.18653/v1/S19-2167",
    pages = "971--975",
    abstract = "This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperpartisan argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. The Paparazzo system, in which we use tri-grams of words and hepta-grams of characters, officially ranks thirteen with an accuracy of 0.747. Another system of ours, which utilizes trigrams of words, tri-grams of characters, trigrams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4.",
}
@inproceedings{knauth-2019-orwellian,
    title = "Orwellian-times at {S}em{E}val-2019 Task 4: A Stylistic and Content-based Classifier",
    author = {Knauth, J{\"u}rgen},
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2168",
    doi = "10.18653/v1/S19-2168",
    pages = "976--980",
    abstract = "While fake news detection received quite a bit of attention in recent years, hyperpartisan news detection is still an underresearched topic. This paper presents our work towards building a classification system for hyperpartisan news detection in the context of the SemEval2019 shared task 4. We experiment with two different approaches - a more stylistic one, and a more content related one - achieving average results.",
}
@inproceedings{moreno-etal-2019-rouletabille,
    title = "Rouletabille at {S}em{E}val-2019 Task 4: Neural Network Baseline for Identification of Hyperpartisan Publishers",
    author = "Moreno, Jose G.  and
      Pitarch, Yoann  and
      Pinel-Sauvagnat, Karen  and
      Hubert, Gilles",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2169",
    doi = "10.18653/v1/S19-2169",
    pages = "981--984",
    abstract = "This paper describes the Rouletabille participation to the Hyperpartisan News Detection task. We propose the use of different text classification methods for this task. Preliminary experiments using a similar collection used in (Potthast et al., 2018) show that neural-based classification methods reach state-of-the art results. Our final submission is composed of a unique run that ranks among all runs at 3/49 position for the by-publisher test dataset and 43/96 for the by-article test dataset in terms of Accuracy.",
}
@inproceedings{alabdulkarim-alhindi-2019-spider,
    title = "Spider-{J}erusalem at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
    author = "Alabdulkarim, Amal  and
      Alhindi, Tariq",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2170",
    doi = "10.18653/v1/S19-2170",
    pages = "985--989",
    abstract = "This paper describes our system for detecting hyperpartisan news articles, which was submitted for the shared task in SemEval 2019 on Hyperpartisan News Detection. We developed a Support Vector Machine (SVM) model that uses TF-IDF of tokens, Language Inquiry and Word Count (LIWC) features, and structural features such as number of paragraphs and hyperlink count in an article. The model was trained on 645 articles from two classes: mainstream and hyperpartisan. Our system was ranked seventeenth out of forty two participating teams in the binary classification task with an accuracy score of 0.742 on the blind test set (the accuracy of the top ranked system was 0.822). We provide a detailed description of our preprocessing steps, discussion of our experiments using different combinations of features, and analysis of our results and prediction errors.",
}
@inproceedings{joo-hwang-2019-steve,
    title = "Steve {M}artin at {S}em{E}val-2019 Task 4: Ensemble Learning Model for Detecting Hyperpartisan News",
    author = "Joo, Youngjun  and
      Hwang, Inchon",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2171",
    doi = "10.18653/v1/S19-2171",
    pages = "990--994",
    abstract = "This paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram CNN model. Finally, we apply the weighted average for effective learning between the two models. Our model achieves an accuracy of 0.745 on the test set in subtask A.",
}
@inproceedings{palic-etal-2019-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
    author = "Pali{\'c}, Niko  and
      Vladika, Juraj  and
      {\v{C}}ubeli{\'c}, Dominik  and
      Lovren{\v{c}}i{\'c}, Ivan  and
      Buljan, Maja  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2172",
    doi = "10.18653/v1/S19-2172",
    pages = "995--998",
    abstract = "In this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is heavily biased towards one side of the political spectrum. Our system receives an article in its raw, textual form, analyzes it, and predicts with moderate accuracy whether the article is hyperpartisan. The learning model used was primarily trained on a manually prelabeled dataset containing news articles. The system relies on the previously constructed SVM model, available in the Python Scikit-Learn library. We ranked 6th in the competition of 42 teams with an accuracy of 79.1{\%} (the winning team had 82.2{\%}).",
}
@inproceedings{cruz-etal-2019-team,
    title = "Team Fernando-Pessa at {S}em{E}val-2019 Task 4: Back to Basics in Hyperpartisan News Detection",
    author = "Cruz, Andr{\'e}  and
      Rocha, Gil  and
      Sousa-Silva, Rui  and
      Lopes Cardoso, Henrique",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2173",
    doi = "10.18653/v1/S19-2173",
    pages = "999--1003",
    abstract = "This paper describes our submission to the SemEval 2019 Hyperpartisan News Detection task. Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining good performance. To this goal, we follow a feature-based approach and perform several experiments with different machine learning classifiers. Additionally, we explore feature importances and distributions among the two classes. On the main task, our model achieved an accuracy of 71.7{\%}, which was improved after the task{'}s end to 72.9{\%}. We also participate on the meta-learning sub-task, for classifying documents with the binary classifications of all submitted systems as input, achieving an accuracy of 89.9{\%}.",
}
@inproceedings{afsarmanesh-etal-2019-team,
    title = "Team Harry Friberg at {S}em{E}val-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics",
    author = "Afsarmanesh, Nazanin  and
      Karlgren, Jussi  and
      Sumbler, Peter  and
      Viereckel, Nina",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2174",
    doi = "10.18653/v1/S19-2174",
    pages = "1004--1006",
    abstract = "This report describes the starting point for a simple rule based hypothesis testing excercise on identifying hyperpartisan news items carried out by the Harry Friberg team from Gavagai. We used manually crafted \textit{metatopics}, topics which often appear in hyperpartisan texts as rant conduits, together with tonality analysis to identify general characteristics of hyperpartisan news items. While the precision of the resulting effort is less than stellar{---} our contribution ranked 37th of the 42 successfully submitted experiments with overly high recall (95{\%}) and low precision (54{\%}){---}we believe we have a model which allows us to continue exploring the underlying features of what the subgenre of hyperpartisan news items is characterised by.",
}
@inproceedings{mutlu-etal-2019-team,
    title = "Team {H}oward {B}eale at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection with {BERT}",
    author = "Mutlu, Osman  and
      Can, Ozan Arkan  and
      Dayanik, Erenay",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2175",
    doi = "10.18653/v1/S19-2175",
    pages = "1007--1011",
    abstract = "This paper describes our system for SemEval-2019 Task 4: Hyperpartisan News Detection (Kiesel et al., 2019). We use pretrained BERT (Devlin et al., 2018) architecture and investigate the effect of different fine tuning regimes on the final classification task. We show that additional pretraining on news domain improves the performance on the Hyperpartisan News Detection task. Our system ranked 8th out of 42 teams with 78.3{\%} accuracy on the held-out test dataset.",
}
@inproceedings{shaprin-etal-2019-team,
    title = "Team Jack Ryder at {S}em{E}val-2019 Task 4: Using {BERT} Representations for Detecting Hyperpartisan News",
    author = "Shaprin, Daniel  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2176",
    doi = "10.18653/v1/S19-2176",
    pages = "1012--1015",
    abstract = "We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection. The task asked participants to predict whether a given article is hyperpartisan, i.e., extreme-left or extreme-right. We proposed an approach based on BERT with fine-tuning, which was ranked 7th out 28 teams on the distantly supervised dataset, where all articles from a hyperpartisan/non-hyperpartisan news outlet are considered to be hyperpartisan/non-hyperpartisan. On a manually annotated test dataset, where human annotators double-checked the labels, we were ranked 29th out of 42 teams.",
}
@inproceedings{anthonio-kloppenburg-2019-team,
    title = "Team Kermit-the-frog at {S}em{E}val-2019 Task 4: Bias Detection Through Sentiment Analysis and Simple Linguistic Features",
    author = "Anthonio, Talita  and
      Kloppenburg, Lennart",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2177",
    doi = "10.18653/v1/S19-2177",
    pages = "1016--1020",
    abstract = "In this paper we describe our participation in the SemEval 2019 shared task on hyperpartisan news detection. We present the system that we submitted for final evaluation and the three approaches that we used: sentiment, bias-laden words and filtered n-gram features. Our submitted model is a Linear SVM that solely relies on the negative sentiment of a document. We achieved an accuracy of 0.621 and a f1 score of 0.694 in the competition, revealing the predictive power of negative sentiment for this task. There was no major improvement by adding or substituting the features of the other two approaches that we tried.",
}
@inproceedings{cramerus-scheffler-2019-team,
    title = "Team Kit Kittredge at {S}em{E}val-2019 Task 4: {LSTM} Voting System",
    author = "Cramerus, Rebekah  and
      Scheffler, Tatjana",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2178",
    doi = "10.18653/v1/S19-2178",
    pages = "1021--1025",
    abstract = "This paper describes the approach of team Kit Kittredge to SemEval-2019 Task 4: Hyperpartisan News Detection. The goal was binary classification of news articles into the categories of {``}biased{''} or {``}unbiased{''}. We had two software submissions: one a simple bag-of-words model, and the second an LSTM (Long Short Term Memory) neural network, which was trained on a subset of the original dataset selected by a voting system of other LSTMs. This method did not prove much more successful than the baseline, however, due to the models{'} tendency to learn publisher-specific traits instead of general bias.",
}
@inproceedings{stevanoski-gievska-2019-team,
    title = "Team {N}ed {L}eeds at {S}em{E}val-2019 Task 4: Exploring Language Indicators of Hyperpartisan Reporting",
    author = "Stevanoski, Bozhidar  and
      Gievska, Sonja",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2179",
    doi = "10.18653/v1/S19-2179",
    pages = "1026--1031",
    abstract = "This paper reports an experiment carried out to investigate the relevance of several syntactic, stylistic and pragmatic features on the task of distinguishing between mainstream and partisan news articles. The results of the evaluation of different feature sets and the extent to which various feature categories could affect the performance metrics are discussed and compared. Among different combinations of features and classifiers, Random Forest classifier using vector representations of the headline and the text of the report, with the inclusion of 8 readability scores and few stylistic features yielded best result, ranking our team at the 9th place at the SemEval 2019 Hyperpartisan News Detection challenge.",
}
@inproceedings{farber-etal-2019-team,
    title = "Team Peter Brinkmann at {S}em{E}val-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks",
    author = {F{\"a}rber, Michael  and
      Qurdina, Agon  and
      Ahmedi, Lule},
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2180",
    doi = "10.18653/v1/S19-2180",
    pages = "1032--1036",
    abstract = "In this paper, we present an approach for classifying news articles as biased (i.e., hyperpartisan) or unbiased, based on a convolutional neural network. We experiment with various embedding methods (pretrained and trained on the training dataset) and variations of the convolutional neural network architecture and compare the results. When evaluating our best performing approach on the actual test data set of the SemEval 2019 Task 4, we obtained relatively low precision and accuracy values, while gaining the highest recall rate among all 42 participating teams.",
}
@inproceedings{ning-etal-2019-team,
    title = "Team Peter-Parker at {S}em{E}val-2019 Task 4: {BERT}-Based Method in Hyperpartisan News Detection",
    author = "Ning, Zhiyuan  and
      Lin, Yuanzhen  and
      Zhong, Ruichao",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2181",
    doi = "10.18653/v1/S19-2181",
    pages = "1037--1040",
    abstract = "This paper describes the team peter-parker{'}s participation in Hyperpartisan News Detection task (SemEval-2019 Task 4), which requires to classify whether a given news article is bias or not. We decided to use JAVA to do the article parsing tool and the BERT-Based model to do the bias prediction. Furthermore, we will show experiment results with analysis.",
}
@inproceedings{saleh-etal-2019-team,
    title = "Team {QCRI}-{MIT} at {S}em{E}val-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection",
    author = "Saleh, Abdelrhman  and
      Baly, Ramy  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Da San Martino, Giovanni  and
      Mohtarami, Mitra  and
      Nakov, Preslav  and
      Glass, James",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2182",
    doi = "10.18653/v1/S19-2182",
    pages = "1041--1046",
    abstract = "We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9{\%} accuracy on the manually annotated testset, and 60.8{\%} on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing.",
}
@inproceedings{zehe-etal-2019-team,
    title = "Team Xenophilius Lovegood at {S}em{E}val-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks",
    author = "Zehe, Albin  and
      Hettinger, Lena  and
      Ernst, Stefan  and
      Hauptmann, Christian  and
      Hotho, Andreas",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2183",
    doi = "10.18653/v1/S19-2183",
    pages = "1047--1051",
    abstract = "This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection. We build on an existing deep learning approach for sentence classification based on a Convolutional Neural Network. Modifying the original model with additional layers to increase its expressiveness and finally building an ensemble of multiple versions of the model, we obtain an accuracy of 67.52{\%} and an F1 score of 73.78{\%} on the main test dataset. We also report on additional experiments incorporating handcrafted features into the CNN and using it as a feature extractor for a linear SVM.",
}
@inproceedings{lee-etal-2019-team,
    title = "Team yeon-zi at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data",
    author = "Lee, Nayeon  and
      Liu, Zihan  and
      Fung, Pascale",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2184",
    doi = "10.18653/v1/S19-2184",
    pages = "1052--1056",
    abstract = "This paper describes our system that has been submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the noise inherent in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the state-of-the-art BERT model. Our model achieves 75.8{\%} accuracy in the final by-article dataset without ensemble learning.",
}
@inproceedings{hanawa-etal-2019-sally,
    title = "The Sally Smedley Hyperpartisan News Detector at {S}em{E}val-2019 Task 4",
    author = "Hanawa, Kazuaki  and
      Sasaki, Shota  and
      Ouchi, Hiroki  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2185",
    doi = "10.18653/v1/S19-2185",
    pages = "1057--1061",
    abstract = "This paper describes our system submitted to the formal run of SemEval-2019 Task 4: Hyperpartisan news detection. Our system is based on a linear classifier using several features, i.e., 1) embedding features based on the pre-trained BERT embeddings, 2) article length features, and 3) embedding features of informative phrases extracted from by-publisher dataset. Our system achieved 80.9{\%} accuracy on the test set for the formal run and got the 3rd place out of 42 teams.",
}
@inproceedings{bestgen-2019-tintin,
    title = "Tintin at {S}em{E}val-2019 Task 4: Detecting Hyperpartisan News Article with only Simple Tokens",
    author = "Bestgen, Yves",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2186",
    doi = "10.18653/v1/S19-2186",
    pages = "1062--1066",
    abstract = "Tintin, the system proposed by the CECL for the Hyperpartisan News Detection task of SemEval 2019, is exclusively based on the tokens that make up the documents and a standard supervised learning procedure. It obtained very contrasting results: poor on the main task, but much more effective at distinguishing documents published by hyperpartisan media outlets from unbiased ones, as it ranked first. An analysis of the most important features highlighted the positive aspects, but also some potential limitations of the approach.",
}
@inproceedings{yeh-etal-2019-tom,
    title = "Tom Jumbo-Grumbo at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection with {G}lo{V}e vectors and {SVM}",
    author = "Yeh, Chia-Lun  and
      Loni, Babak  and
      Schuth, Anne",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2187",
    doi = "10.18653/v1/S19-2187",
    pages = "1067--1071",
    abstract = "In this paper, we describe our attempt to learn bias from news articles. From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels. On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60{\%} to near 80{\%}. Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier. The system ranked 4th in the SemEval-2019 task. The code is released for reproducibility.",
}
@inproceedings{zhang-etal-2019-ubc,
    title = "{UBC}-{NLP} at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection With Attention-Based {B}i-{LSTM}s",
    author = "Zhang, Chiyu  and
      Rajendran, Arun  and
      Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2188",
    doi = "10.18653/v1/S19-2188",
    pages = "1072--1077",
    abstract = "We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3{\%} accuracy) on the {`}labels-by-publisher{'} sub-task and top 24 out of 44 teams (68.3{\%} accuracy) on the {`}labels-by-article{'} sub-task (65.3{\%} accuracy). We also report a model that scores higher than the 8th ranking system (78.5{\%} accuracy) on the {`}labels-by-article{'} sub-task.",
}
@inproceedings{srivastava-etal-2019-vernon,
    title = "Vernon-fenwick at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection using Lexical and Semantic Features",
    author = "Srivastava, Vertika  and
      Gupta, Ankita  and
      Prakash, Divya  and
      Sahoo, Sudeep Kumar  and
      R.R, Rohit  and
      Kim, Yeon Hyang",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2189",
    doi = "10.18653/v1/S19-2189",
    pages = "1078--1082",
    abstract = "In this paper, we present our submission for SemEval-2019 Task 4: Hyperpartisan News Detection. Hyperpartisan news articles are sharply polarized and extremely biased (onesided). It shows blind beliefs, opinions and unreasonable adherence to a party, idea, faction or a person. Through this task, we aim to develop an automated system that can be used to detect hyperpartisan news and serve as a prescreening technique for fake news detection. The proposed system jointly uses a rich set of handcrafted textual and semantic features. Our system achieved 2nd rank on the primary metric (82.0{\%} accuracy) and 1st rank on the secondary metric (82.1{\%} F1-score), among all participating teams. Comparison with the best performing system on the leaderboard shows that our system is behind by only 0.2{\%} absolute difference in accuracy.",
}
@inproceedings{janchevski-gievska-2019-andrejjan,
    title = "{A}ndrej{J}an at {S}em{E}val-2019 Task 7: A Fusion Approach for Exploring the Key Factors pertaining to Rumour Analysis",
    author = "Janchevski, Andrej  and
      Gievska, Sonja",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2190",
    doi = "10.18653/v1/S19-2190",
    pages = "1083--1089",
    abstract = "The viral spread of false, unverified and misleading information on the Internet has attracted a heightened attention of an interdisciplinary research community on the phenomenon. This paper contributes to the research efforts of automatically determining the veracity of rumourous tweets and classifying their replies according to stance. Our research objective was to investigate the interplay between a number of phenomenological and contextual features of rumours, in particular, we explore the extent to which network structural characteristics, metadata and user profiles could complement the linguistic analysis of the written content for the task at hand. The current findings strongly demonstrate that supplementary sources of information play significant role in classifying the veracity and the stance of Twitter interactions deemed to be rumourous.",
}
@inproceedings{yang-etal-2019-blcu,
    title = "{BLCU}{\_}{NLP} at {S}em{E}val-2019 Task 7: An Inference Chain-based {GPT} Model for Rumour Evaluation",
    author = "Yang, Ruoyao  and
      Xie, Wanying  and
      Liu, Chunhua  and
      Yu, Dong",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2191",
    doi = "10.18653/v1/S19-2191",
    pages = "1090--1096",
    abstract = "Researchers have been paying increasing attention to rumour evaluation due to the rapid spread of unsubstantiated rumours on social media platforms, including SemEval 2019 task 7. However, labelled data for learning rumour veracity is scarce, and labels in rumour stance data are highly disproportionate, making it challenging for a model to perform supervised-learning adequately. We propose an inference chain-based system, which fully utilizes conversation structure-based knowledge in the limited data and expand the training data in minority categories to alleviate class imbalance. Our approach obtains 12.6{\%} improvement upon the baseline system for subtask A, ranks 1st among 21 systems in subtask A, and ranks 4th among 12 systems in subtask B.",
}
@inproceedings{fajcik-etal-2019-fit,
    title = "{BUT}-{FIT} at {S}em{E}val-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers",
    author = "Fajcik, Martin  and
      Smrz, Pavel  and
      Burget, Lukas",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2192",
    doi = "10.18653/v1/S19-2192",
    pages = "1097--1104",
    abstract = "This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67 {\%} on the provided test data. Without any hand-crafted feature, the system finished at the 2nd place in the competition, only 0.2 {\%} behind the winner.",
}
@inproceedings{baris-etal-2019-clearumor,
    title = "{CLEAR}umor at {S}em{E}val-2019 Task 7: {C}onvo{L}ving {ELM}o Against Rumors",
    author = "Baris, Ipek  and
      Schmelzeisen, Lukas  and
      Staab, Steffen",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2193",
    doi = "10.18653/v1/S19-2193",
    pages = "1105--1109",
    abstract = "This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous social media post and a reply post as support, query, deny, or comment. The goal of subtask B is to predict the veracity of a given rumor. For subtask A, we implement a CNN-based neural architecture using ELMo embeddings of post text combined with auxiliary features and achieve a F1-score of 44.6{\%}. For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1{\%} (second place in the competition). We provide results and analysis of our system performance and present ablation experiments.",
}
@inproceedings{liu-etal-2019-columbia,
    title = "{C}olumbia at {S}em{E}val-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification",
    author = "Liu, Zhuoran  and
      Goel, Shivali  and
      Yelahanka Raghuprasad, Mukund  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2194",
    doi = "10.18653/v1/S19-2194",
    pages = "1110--1114",
    abstract = "The paper presents Columbia team{'}s participation in the SemEval 2019 Shared Task 7: RumourEval 2019. Detecting rumour on social networks has been a focus of research in recent years. Previous work suffered from data sparsity, which potentially limited the application of more sophisticated neural architecture to this task. We mitigate this problem by proposing a multi-task learning approach together with language model fine-tuning. Our attention-based model allows different tasks to leverage different level of information. Our system ranked 6th overall with an F1-score of 36.25 on stance classification and F1 of 22.44 on rumour verification.",
}
@inproceedings{hamidian-diab-2019-gwu,
    title = "{GWU} {NLP} at {S}em{E}val-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media",
    author = "Hamidian, Sardar  and
      Diab, Mona",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2195",
    doi = "10.18653/v1/S19-2195",
    pages = "1115--1119",
    abstract = "Social media plays a crucial role as the main resource news for information seekers online. However, the unmoderated feature of social media platforms lead to the emergence and spread of untrustworthy contents which harm individuals or even societies. Most of the current automated approaches for automatically determining the veracity of a rumor are not generalizable for novel emerging topics. This paper describes our hybrid system comprising rules and a machine learning model which makes use of replied tweets to identify the veracity of the source tweet. The proposed system in this paper achieved 0.435 F-Macro in stance classification, and 0.262 F-macro and 0.801 RMSE in rumor verification tasks in Task7 of SemEval 2019.",
}
@inproceedings{garcia-cumbreras-etal-2019-sinai,
    title = "{SINAI}-{DL} at {S}em{E}val-2019 Task 7: Data Augmentation and Temporal Expressions",
    author = "Garc{\'\i}a-Cumbreras, Miguel A.  and
      Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Montejo-R{\'a}ez, Arturo  and
      D{\'\i}az-Galiano, Manuel Carlos  and
      Saquete, Estela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2196",
    doi = "10.18653/v1/S19-2196",
    pages = "1120--1124",
    abstract = "This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in SemEval 2019, subtask A: SDQC). SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours. Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours. We have applied data augmentation, temporal expressions labelling and transfer learning with a four-layer neural classifier. We achieve an accuracy of 0.715 with the official run over reply tweets.",
}
@inproceedings{ghanem-etal-2019-upv,
    title = "{UPV}-28-{UNITO} at {S}em{E}val-2019 Task 7: Exploiting Post{'}s Nesting and Syntax Information for Rumor Stance Classification",
    author = "Ghanem, Bilal  and
      Cignarella, Alessandra Teresa  and
      Bosco, Cristina  and
      Rosso, Paolo  and
      Rangel Pardo, Francisco Manuel",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2197",
    doi = "10.18653/v1/S19-2197",
    pages = "1125--1131",
    abstract = "In the present paper we describe the UPV-28-UNITO system{'}s submission to the RumorEval 2019 shared task. The approach we applied for addressing both the subtasks of the contest exploits both classical machine learning algorithms and word embeddings, and it is based on diverse groups of features: stylistic, lexical, emotional, sentiment, meta-structural and Twitter-based. A novel set of features that take advantage of the syntactic information in texts is moreover introduced in the paper.",
}
@inproceedings{xie-etal-2019-blcu,
    title = "{BLCU}{\_}{NLP} at {S}em{E}val-2019 Task 8: A Contextual Knowledge-enhanced {GPT} Model for Fact Checking",
    author = "Xie, Wanying  and
      Que, Mengxi  and
      Yang, Ruoyao  and
      Liu, Chunhua  and
      Yu, Dong",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2198",
    doi = "10.18653/v1/S19-2198",
    pages = "1132--1137",
    abstract = "Since the resources of Community Question Answering are abundant and information sharing becomes universal, it will be increasingly difficult to find factual information for questioners in massive messages. SemEval 2019 task 8 is focusing on these issues. We participate in the task and use Generative Pre-trained Transformer (OpenAI GPT) as our system. Our innovations are data extension, feature extraction, and input transformation. For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task. We demonstrate the effectiveness of our approaches, which achieves 81.95{\%} of subtask A and 61.08{\%} of subtask B in accuracy on the SemEval 2019 task 8.",
}
@inproceedings{avvaru-pandey-2019-codeforthechange,
    title = "{C}ode{F}or{T}he{C}hange at {S}em{E}val-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering",
    author = "Avvaru, Adithya  and
      Pandey, Anupam",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2199",
    doi = "10.18653/v1/S19-2199",
    pages = "1138--1143",
    abstract = "The strengths of the scalable gradient tree boosting algorithm, XGBoost and distributed sentence encoder, Skip-Thought Vectors are not explored yet by the cQA research community. We tried to apply and combine these two effective methods for finding factual nature of the questions and answers. The work also include experimentation with other popular classifier models like AdaBoost Classifier, DecisionTree Classifier, RandomForest Classifier, ExtraTrees Classifier, XGBoost Classifier and Multi-layer Neural Network. In this paper, we present the features used, approaches followed for feature engineering, models experimented with and finally the results.",
}
@inproceedings{chakrabarty-muresan-2019-columbianlp,
    title = "{C}olumbia{NLP} at {S}em{E}val-2019 Task 8: The Answer is Language Model Fine-tuning",
    author = "Chakrabarty, Tuhin  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2200",
    doi = "10.18653/v1/S19-2200",
    pages = "1144--1148",
    abstract = "Community Question Answering forums are very popular nowadays, as they represent effective means for communities to share information around particular topics. But the information shared on these forums are often not authentic. This paper presents the ColumbiaNLP submission for the SemEval-2019 Task 8: Fact-Checking in Community Question Answering Forums. We show how fine-tuning a language model on a large unannotated corpus of old threads from Qatar Living forum helps us to classify question types (factual, opinion, socializing) and to judge the factuality of answers on the shared task labeled data from the same forum. Our system finished 4th and 2nd on Subtask A (question type classification) and B (answer factuality prediction), respectively, based on the official metric of accuracy.",
}
@inproceedings{stammbach-etal-2019-domlin,
    title = "{DOMLIN} at {S}em{E}val-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering Forums",
    author = "Stammbach, Dominik  and
      Varanasi, Stalin  and
      Neumann, Guenter",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2201",
    doi = "10.18653/v1/S19-2201",
    pages = "1149--1154",
    abstract = "In the following, we describe our system developed for the Semeval2019 Task 8. We fine-tuned a BERT checkpoint on the qatar living forum dump and used this checkpoint to train a number of models. Our hand-in for subtask A consists of a fine-tuned classifier from this BERT checkpoint. For subtask B, we first have a classifier deciding whether a comment is factual or non-factual. If it is factual, we retrieve intra-forum evidence and using this evidence, have a classifier deciding the comment{'}s veracity. We trained this classifier on ratings which we crawled from qatarliving.com",
}
@inproceedings{bairaktaris-etal-2019-duth,
    title = "{DUTH} at {S}em{E}val-2019 Task 8: Part-Of-Speech Features for Question Classification",
    author = "Bairaktaris, Anastasios  and
      Symeonidis, Symeon  and
      Arampatzis, Avi",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2202",
    doi = "10.18653/v1/S19-2202",
    pages = "1155--1159",
    abstract = "This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums. Our team dealt only with Subtask A: Question Classification. Our approach was based on shallow natural language processing (NLP) pre-processing techniques to reduce the noise in data, feature selection methods, and supervised machine learning algorithms such as NearestCentroid, Perceptron, and LinearSVC. To determine the essential features, we were aided by exploratory data analysis and visualizations. In order to improve classification accuracy, we developed a customized list of stopwords, retaining some opinion- and fact-denoting common function words which would have been removed by standard stoplisting. Furthermore, we examined the usefulness of part-of-speech (POS) categories for the task; by trying to remove nouns and adjectives, we found some evidence that verbs are a valuable POS category for the opinion question class.",
}
@inproceedings{syed-etal-2019-fermi,
    title = "Fermi at {S}em{E}val-2019 Task 8: An elementary but effective approach to Question Discernment in Community {QA} Forums",
    author = "Syed, Bakhtiyar  and
      Indurthi, Vijayasaradhi  and
      Shrivastava, Manish  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2203",
    doi = "10.18653/v1/S19-2203",
    pages = "1160--1164",
    abstract = "Online Community Question Answering Forums (cQA) have gained massive popularity within recent years. The rise in users for such forums have led to the increase in the need for automated evaluation for question comprehension and fact evaluation of the answers provided by various participants in the forum. Our team, \textbf{Fermi}, participated in sub-task A of Task 8 at SemEval 2019 - which tackles the first problem in the pipeline of factual evaluation in cQA forums, i.e., deciding whether a posed question asks for a factual information, an opinion/advice or is just socializing. This information is highly useful in segregating factual questions from non-factual ones which highly helps in organizing the questions into useful categories and trims down the problem space for the next task in the pipeline for fact evaluation among the available answers. Our system uses the embeddings obtained from Universal Sentence Encoder combined with XGBoost for the classification sub-task A. We also evaluate other combinations of embeddings and off-the-shelf machine learning algorithms to demonstrate the efficacy of the various representations and their combinations. Our results across the evaluation test set gave an accuracy of 84{\%} and received the first position in the final standings judged by the organizers.",
}
@inproceedings{gupta-etal-2019-solomonlab,
    title = "{S}olomon{L}ab at {S}em{E}val-2019 Task 8: Question Factuality and Answer Veracity Prediction in Community Forums",
    author = "Gupta, Ankita  and
      Sahoo, Sudeep Kumar  and
      Prakash, Divya  and
      R.R, Rohit  and
      Srivastava, Vertika  and
      Kim, Yeon Hyang",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2204",
    doi = "10.18653/v1/S19-2204",
    pages = "1165--1171",
    abstract = "We describe our system for SemEval-2019, Task 8 on {``}Fact-Checking in Community Question Answering Forums (cQA){''}. cQA forums are very prevalent nowadays, as they provide an effective means for communities to share knowledge. Unfortunately, this shared information is not always factual and fact-verified. In this task, we aim to identify factual questions posted on cQA and verify the veracity of answers to these questions. Our approach relies on data augmentation and aggregates cues from several dimensions such as semantics, linguistics, syntax, writing style and evidence obtained from trusted external sources. In subtask A, our submission is ranked 3rd, with an accuracy of 83.14{\%}. Our current best solution stands 1st on the leaderboard with 88{\%} accuracy. In subtask B, our present solution is ranked 2nd, with 58.33{\%} MAP score.",
}
@inproceedings{niewinski-etal-2019-tmlab,
    title = "{TML}ab {SRPOL} at {S}em{E}val-2019 Task 8: Fact Checking in Community Question Answering Forums",
    author = "Niewi{\'n}ski, Piotr  and
      Wawer, Aleksander  and
      Pszona, Maria  and
      Janicka, Maria",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2205",
    doi = "10.18653/v1/S19-2205",
    pages = "1172--1175",
    abstract = "The article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums. The systems under discussion participated in Subtask A: decide whether a question asks for factual information, opinion/advice or is just socializing. Our primary submission was ranked as the second one among all participants in the official evaluation phase. The article presents our primary solution: Deeply Regularized Residual Neural Network (DRR NN) with Universal Sentence Encoder embeddings. This is followed by a description of two contrastive solutions based on ensemble methods.",
}
@inproceedings{juhasz-etal-2019-tuefact,
    title = "{T}ue{F}act at {S}em{E}val 2019 Task 8: Fact checking in community question answering forums: context matters",
    author = "Juh{\'a}sz, R{\'e}ka  and
      Linnenschmidt, Franziska Barbara  and
      Roys, Teslin",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2206",
    doi = "10.18653/v1/S19-2206",
    pages = "1176--1179",
    abstract = "The SemEval 2019 Task 8 on Fact-Checking in community question answering forums aimed to classify questions into categories and verify the correctness of answers given on the QatarLiving public forum. The task was divided into two subtasks: the first classifying the question, the second the answers. The TueFact system described in this paper used different approaches for the two subtasks. Subtask A makes use of word vectors based on a bag-of-word-ngram model using up to trigrams. Predictions are done using multi-class logistic regression. The official SemEval result lists an accuracy of 0.60. Subtask B uses vectorized character n-grams up to trigrams instead. Predictions are done using a LSTM model and achieved an accuracy of 0.53 on the final SemEval Task 8 evaluation set.",
}
@inproceedings{liu-etal-2019-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2019 Task 8: Using A {LSTM}-Attention Model for Fact-Checking in Community Forums",
    author = "Liu, Peng  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2207",
    doi = "10.18653/v1/S19-2207",
    pages = "1180--1184",
    abstract = "We propose a system that uses a long short-term memory with attention mechanism (LSTM-Attention) model to complete the task. The LSTM-Attention model uses two LSTM to extract the features of the question and answer pair. Then, each of the features is sequentially composed using the attention mechanism, concatenating the two vectors into one. Finally, the concatenated vector is used as input for the MLP and the MLP{'}s output layer uses the softmax function to classify the provided answers into three categories. This model is capable of extracting the features of the question and answer pair well. The results show that the proposed system outperforms the baseline algorithm.",
}
@inproceedings{fatyanosa-etal-2019-dbms,
    title = "{DBMS}-{KU} at {S}em{E}val-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion",
    author = "Fatyanosa, Tirana  and
      Siagian, Al Hafiz Akbar Maulana  and
      Aritsugi, Masayoshi",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2208",
    doi = "10.18653/v1/S19-2208",
    pages = "1185--1191",
    abstract = "This paper describes the participation of DBMS-KU team in the SemEval 2019 Task 9, that is, suggestion mining from online reviews and forums. To deal with this task, we explore several machine learning approaches, i.e., Random Forest (RF), Logistic Regression (LR), Multinomial Naive Bayes (MNB), Linear Support Vector Classification (LSVC), Sublinear Support Vector Classification (SSVC), Convolutional Neural Network (CNN), and Variable Length Chromosome Genetic Algorithm-Naive Bayes (VLCGA-NB). Our system obtains reasonable results of F1-Score 0.47 and 0.37 on the evaluation data in Subtask A and Subtask B, respectively. In particular, our obtained results outperform the baseline in Subtask A. Interestingly, the results seem to show that our system could perform well in classifying Non-suggestion class.",
}
@inproceedings{cabanski-2019-ds,
    title = "{DS} at {S}em{E}val-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification",
    author = "Cabanski, Tobias",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2209",
    doi = "10.18653/v1/S19-2209",
    pages = "1192--1198",
    abstract = "Suggestion Mining is the task of classifying sentences into suggestions or non-suggestions. SemEval-2019 Task 9 sets the task to mine suggestions from online texts. For each of the two subtasks, the classification has to be applied on a different domain. Subtask A addresses the domain of posts in suggestion online forums and comes with a set of training examples, that is used for supervised training. A combination of LSTM and CNN networks is constructed to create a model which uses BERT word embeddings as input features. For subtask B, the domain of hotel reviews is regarded. In contrast to subtask A, no labeled data for supervised training is provided, so that additional unlabeled data is taken to apply a cross-domain classification. This is done by using adversarial training of the three model parts label classifier, domain classifier and the shared feature representation. For subtask A, the developed model archives a F1-score of 0.7273, which is in the top ten of the leader board. The F1-score for subtask B is 0.8187 and is ranked in the top five of the submissions for that task.",
}
@inproceedings{ezen-can-can-2019-hybrid,
    title = "Hybrid {RNN} at {S}em{E}val-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion Mining",
    author = "Ezen-Can, Aysu  and
      Can, Ethem F.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2210",
    doi = "10.18653/v1/S19-2210",
    pages = "1199--1203",
    abstract = "Social media has an increasing amount of information that both customers and companies can benefit from. These social media posts can include Tweets or be in the form of vocalization of complements and complaints (e.g., reviews) of a product or service. Researchers have been actively mining this invaluable information source to automatically generate insights. Mining sentiments of customer reviews is an example that has gained momentum due to its potential to gather information that customers are not happy about. Instead of reading millions of reviews, companies prefer sentiment analysis to obtain feedback and to improve their products or services. In this work, we aim to identify information that companies can act on, or other customers can utilize for making their own experience better. This is different from identifying if reviews of a product or service is negative, positive, or neutral. To that end, we classify sentences of a given review as suggestion or not suggestion so that readers of the reviews do not have to go through thousands of reviews but instead can focus on actionable items and applicable suggestions. To identify suggestions within reviews, we employ a hybrid approach that utilizes a recurrent neural network (RNN) along with rule-based features to build a domain-independent suggestion mining model. In this way, a model trained on electronics reviews is used to extract suggestions from hotel reviews.",
}
@inproceedings{markov-villemonte-de-la-clergerie-2019-inria,
    title = "{INRIA} at {S}em{E}val-2019 Task 9: Suggestion Mining Using {SVM} with Handcrafted Features",
    author = "Markov, Ilia  and
      Villemonte de la Clergerie, Eric",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2211",
    doi = "10.18653/v1/S19-2211",
    pages = "1204--1207",
    abstract = "We present the INRIA approach to the suggestion mining task at SemEval 2019. The task consists of two subtasks: suggestion mining under single-domain (Subtask A) and cross-domain (Subtask B) settings. We used the Support Vector Machines algorithm trained on handcrafted features, function words, sentiment features, digits, and verbs for Subtask A, and handcrafted features for Subtask B. Our best run archived a F1-score of 51.18{\%} on Subtask A, and ranked in the top ten of the submissions for Subtask B with 73.30{\%} F1-score.",
}
@inproceedings{li-2019-lijunyi,
    title = "Lijunyi at {S}em{E}val-2019 Task 9: An attention-based {LSTM} and ensemble of different models for suggestion mining from online reviews and forums",
    author = "Li, Junyi",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2212",
    doi = "10.18653/v1/S19-2212",
    pages = "1208--1212",
    abstract = "In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from online reviews and forums that can be classified into suggestion and non-suggestion classes. In this task, we combine the attention mechanism with the LSTM model, which is the final system we submitted. The final submission achieves 14th place in Task 9, SubTask A with the accuracy of 0.6776. After the challenge, we train a series of neural network models such as convolutional neural network(CNN), TextCNN, long short-term memory(LSTM) and C-LSTM. Finally, we make an ensemble on the predictions of these models and get a better result.",
}
@inproceedings{anand-etal-2019-midas,
    title = "{MIDAS} at {S}em{E}val-2019 Task 9: Suggestion Mining from Online Reviews using {ULMF}it",
    author = "Anand, Sarthak  and
      Mahata, Debanjan  and
      Aggarwal, Kartik  and
      Mehnaz, Laiba  and
      Shahid, Simra  and
      Zhang, Haimin  and
      Kumar, Yaman  and
      Shah, Rajiv  and
      Uppal, Karan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2213",
    doi = "10.18653/v1/S19-2213",
    pages = "1213--1217",
    abstract = "In this paper we present our approach to tackle the Suggestion Mining from Online Reviews and Forums Sub-Task A. Given a review, we are asked to predict whether the review consists of a suggestion or not. Our model is based on Universal Language Model Fine-tuning for Text Classification. We apply various pre-processing techniques before training the language and the classification model. We further provide analysis of the model. Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011.",
}
@inproceedings{pecar-etal-2019-nl,
    title = "{NL}-{FIIT} at {S}em{E}val-2019 Task 9: Neural Model Ensemble for Suggestion Mining",
    author = "Pecar, Samuel  and
      Simko, Marian  and
      Bielikova, Maria",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2214",
    doi = "10.18653/v1/S19-2214",
    pages = "1218--1223",
    abstract = "In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: {``}Suggestion Mining from Online Reviews and Forums{''}. We participated in both subtasks for domain specific and also cross-domain suggestion mining. We proposed a recurrent neural network architecture that employs Bi-LSTM layers and also self-attention mechanism. Our architecture tries to encode words via word representation using ELMo and ensembles multiple models to achieve better results. We highlight importance of pre-processing of user-generated samples and its contribution to overall results. We performed experiments with different setups of our proposed model involving weighting of prediction classes for loss function. Our best model achieved in official test evaluation score of 0.6816 for subtask A and 0.6850 for subtask B. In official results, we achieved 12th and 10th place in subtasks A and B, respectively.",
}
@inproceedings{potamias-etal-2019-ntua,
    title = "{NTUA}-{ISL}ab at {S}em{E}val-2019 Task 9: Mining Suggestions in the wild",
    author = "Potamias, Rolandos Alexandros  and
      Neofytou, Alexandros  and
      Siolas, Georgios",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2215",
    doi = "10.18653/v1/S19-2215",
    pages = "1224--1230",
    abstract = "As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online. However, systems capable of recognizing suggestions still lack in stability. Suggestion Mining, a novel and challenging field of Natural Language Processing, is increasingly gaining attention, aiming to track user advice on online forums. In this paper, a carefully designed methodology to identify customer-to-company and customer-to-customer suggestions is presented. The methodology implements a rule-based classifier using heuristic, lexical and syntactic patterns. The approach ranked at 5th and 1st position, achieving an f1-score of 0.749 and 0.858 for SemEval-2019/Suggestion Mining sub-tasks A and B, respectively. In addition, we were able to improve performance results by combining the rule-based classifier with a recurrent convolutional neural network, that exhibits an f1-score of 0.79 for subtask A.",
}
@inproceedings{liu-etal-2019-olenet,
    title = "{O}le{N}et at {S}em{E}val-2019 Task 9: {BERT} based Multi-Perspective Models for Suggestion Mining",
    author = "Liu, Jiaxiang  and
      Wang, Shuohuan  and
      Sun, Yu",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2216",
    doi = "10.18653/v1/S19-2216",
    pages = "1231--1236",
    abstract = "This paper describes our system partici- pated in Task 9 of SemEval-2019: the task is focused on suggestion mining and it aims to classify given sentences into sug- gestion and non-suggestion classes in do- main specific and cross domain training setting respectively. We propose a multi- perspective architecture for learning rep- resentations by using different classical models including Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Feed Forward Attention (FFA), etc. To leverage the semantics distributed in large amount of unsupervised data, we also have adopted the pre-trained Bidi- rectional Encoder Representations from Transformers (BERT) model as an en- coder to produce sentence and word rep- resentations. The proposed architecture is applied for both sub-tasks, and achieved f1-score of 0.7812 for subtask A, and 0.8579 for subtask B. We won the first and second place for the two tasks respec- tively in the final competition.",
}
@inproceedings{s-etal-2019-ssn,
    title = "{SSN}-{SPARKS} at {S}em{E}val-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data",
    author = "S, Rajalakshmi  and
      Suseelan, Angel  and
      Rajendram, S Milton  and
      T T, Mirnalinee",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2217",
    doi = "10.18653/v1/S19-2217",
    pages = "1237--1241",
    abstract = "This paper describes the work on mining the suggestions from online reviews and forums. Opinion mining detects whether the comments are positive, negative or neutral, while suggestion mining explores the review content for the possible tips or advice. The system developed by SSN-SPARKS team in SemEval-2019 for task 9 (suggestion mining) uses a rule-based approach for feature selection, SMOTE technique for data augmentation and deep learning technique (Convolutional Neural Network) for classification. We have compared the results with Random Forest classifier (RF) and MultiLayer Perceptron (MLP) model. Results show that the CNN model performs better than other models for both the subtasks.",
}
@inproceedings{ahmed-etal-2019-suggestion,
    title = "Suggestion Miner at {S}em{E}val-2019 Task 9: Suggestion Detection in Online Forum using Word Graph",
    author = "Ahmed, Usman  and
      Liaquat, Humera  and
      Ahmed, Luqman  and
      Hussain, Syed Jawad",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2218",
    doi = "10.18653/v1/S19-2218",
    pages = "1242--1246",
    abstract = "This paper describes the suggestion miner system that participates in SemEval 2019 Task 9 - SubTask A - Suggestion Mining from Online Reviews and Forums. The system participated in the subtasks A. This paper discusses the results of our system in the development, evaluation and post evaluation. Each class in the dataset is represented as directed unweighted graphs. Then, the comparison is carried out with each class graph which results in a vector. This vector is used as features by a machine learning algorithm. The model is evaluated on hold on strategy. The organizers randomly split (8500 instances) training set (provided to the participant in training their system) and testing set (833 instances). The test set is reserved to evaluate the performance of participants systems. During the evaluation, our system ranked 31 in the Coda Lab result of the subtask A (binary class problem). The binary class system achieves evaluation value 0.34, precision 0.87, recall 0.73 and F measure 0.78.",
}
@inproceedings{oostdijk-van-halteren-2019-team,
    title = "Team {T}aurus at {S}em{E}val-2019 Task 9: Expert-informed pattern recognition for suggestion mining",
    author = "Oostdijk, Nelleke  and
      van Halteren, Hans",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2219",
    doi = "10.18653/v1/S19-2219",
    pages = "1247--1253",
    abstract = "This paper presents our submissions to SemEval-2019 Task9, Suggestion Mining. Our system is one in a series of systems in which we compare an approach using expert-defined rules with a comparable one using machine learning. We target tasks with a syntactic or semantic component that might be better described by a human understanding the task than by a machine learner only able to count features. For Semeval-2019 Task 9, the expert rules clearly outperformed our machine learning model when training and testing on equally balanced testsets.",
}
@inproceedings{park-etal-2019-thisiscompetition,
    title = "{T}his{I}s{C}ompetition at {S}em{E}val-2019 Task 9: {BERT} is unstable for out-of-domain samples",
    author = "Park, Cheoneum  and
      Kim, Juae  and
      Lee, Hyeon-gu  and
      Amplayo, Reinald Kim  and
      Kim, Harksoo  and
      Seo, Jungyun  and
      Lee, Changki",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2220",
    doi = "10.18653/v1/S19-2220",
    pages = "1254--1261",
    abstract = "This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT). We include a domain adversarial training module when training for out-of-domain samples. Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-of-domain samples. The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our final models obtained second place with 77.78{\%} F-Score on Subtask A (i.e. in-domain) and achieved an F-Score of 79.59{\%} on Subtask B (i.e. out-of-domain), even without using any additional external data.",
}
@inproceedings{klimaszewski-andruszkiewicz-2019-wut,
    title = "{WUT} at {S}em{E}val-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining",
    author = "Klimaszewski, Mateusz  and
      Andruszkiewicz, Piotr",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2221",
    doi = "10.18653/v1/S19-2221",
    pages = "1262--1266",
    abstract = "We present a system for cross-domain suggestion mining, prepared for the SemEval-2019 Task 9: Suggestion Mining from Online Reviews and Forums (Subtask B). Our submitted solution for this text classification problem explores the idea of treating different suggestions{'} sources as one of the settings of Transfer Learning - Domain Adaptation. Our experiments show that without any labeled target domain examples during training time, we are capable of proposing a system, reaching up to 0.778 in terms of F1 score on test dataset, based on Target Preserving Domain Adversarial Neural Networks.",
}
@inproceedings{zhuang-2019-yimmon,
    title = "Yimmon at {S}em{E}val-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches",
    author = "Zhuang, Yimeng",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2222",
    doi = "10.18653/v1/S19-2222",
    pages = "1267--1271",
    abstract = "Suggestion mining task aims to extract tips, advice, and recommendations from unstructured text. The task includes many challenges, such as class imbalance, figurative expressions, context dependency, and long and complex sentences. This paper gives a detailed system description of our submission in SemEval 2019 Task 9 Subtask A. We transfer Self-Attention Network (SAN), a successful model in machine reading comprehension field, into this task. Our model concentrates on modeling long-term dependency which is indispensable to parse long and complex sentences. Besides, we also adopt techniques, such as contextualized embedding, back-translation, and auxiliary loss, to augment the system. Our model achieves a performance of F1=76.3, and rank 4th among 34 participating systems. Further ablation study shows that the techniques used in our system are beneficial to the performance.",
}
@inproceedings{ding-etal-2019-ynu-dyx,
    title = "{YNU}{\_}{DYX} at {S}em{E}val-2019 Task 9: A Stacked {B}i{LSTM} for Suggestion Mining Classification",
    author = "Ding, Yunxia  and
      Zhou, Xiaobing  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2223",
    doi = "10.18653/v1/S19-2223",
    pages = "1272--1276",
    abstract = "In this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums. We use Word2Vec to learn the distributed representations from sentences. This system is composed of a Stacked Bidirectional Long-Short Memory Network (SBiLSTM) for enriching word representations before and after the sequence relationship with context. We perform an ensemble to improve the effectiveness of our model. Our official submission results achieve an F1-score 0.5659.",
}
@inproceedings{yue-etal-2019-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2019 Task 9: Using a {BERT} and {CNN}-{B}i{LSTM}-{GRU} Model for Suggestion Mining",
    author = "Yue, Ping  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2224",
    doi = "10.18653/v1/S19-2224",
    pages = "1277--1281",
    abstract = "Consumer opinions towards commercial entities are generally expressed through online reviews, blogs, and discussion forums. These opinions largely express positive and negative sentiments towards a given entity,but also tend to contain suggestions for improving the entity. In this task, we extract suggestions from given the unstructured text, compared to the traditional opinion mining systems. Such suggestion mining is more applicability and extends capabilities.",
}
@inproceedings{prasanna-seelan-2019-zoho,
    title = "Zoho at {S}em{E}val-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining",
    author = "Prasanna, Sai  and
      Seelan, Sri Ananda",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2225",
    doi = "10.18653/v1/S19-2225",
    pages = "1282--1286",
    abstract = "This paper describes our submission for the SemEval-2019 Suggestion Mining task. A simple Convolutional Neural Network (CNN) classifier with contextual word representations from a pre-trained language model was used for sentence classification. The model is trained using tri-training, a semi-supervised bootstrapping mechanism for labelling unseen data. Tri-training proved to be an effective technique to accommodate domain shift for cross-domain suggestion mining (Subtask B) where there is no hand labelled training data. For in-domain evaluation (Subtask A), we use the same technique to augment the training set. Our system ranks thirteenth in Subtask A with an F1-score of 68.07 and third in Subtask B with an F1-score of 81.94.",
}
@inproceedings{zhou-etal-2019-zqm,
    title = "{ZQM} at {S}em{E}val-2019 Task9: A Single Layer {CNN} Based on Pre-trained Model for Suggestion Mining",
    author = "Zhou, Qimin  and
      Zhang, Zhengxin  and
      Wu, Hao  and
      Wang, Linmao",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2226",
    doi = "10.18653/v1/S19-2226",
    pages = "1287--1291",
    abstract = "This paper describes our system that competed at SemEval 2019 Task 9 - SubTask A: {''}Sug- gestion Mining from Online Reviews and Forums{''}. Our system fuses the convolutional neural network and the latest BERT model to conduct suggestion mining. In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model. And to enhance the effectiveness of the whole system, the pre-trained BERT model is fine-tuned by provided datasets before the procedure of embedding vectors extraction. Empirical results show the effectiveness of our model which obtained 9th position out of 34 teams with F1 score equals to 0.715.",
}
@inproceedings{luo-etal-2019-problemsolver,
    title = "{P}roblem{S}olver at {S}em{E}val-2019 Task 10: Sequence-to-Sequence Learning and Expression Trees",
    author = "Luo, Xuefeng  and
      Baranova, Alina  and
      Biegert, Jonas",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2227",
    doi = "10.18653/v1/S19-2227",
    pages = "1292--1296",
    abstract = "This paper describes our participation in SemEval-2019 shared task {``}Math Question Answering{''}, where the aim is to create a program that could solve the Math SAT questions automatically as accurately as possible. We went with a dual-pronged approach, building a Sequence-to-Sequence Neural Network pre-trained with augmented data that could answer all categories of questions and a Tree system, which can only answer a certain type of questions. The systems did not perform well on the entire test data given in the task, but did decently on the questions they were actually capable of answering. The Sequence-to-Sequence Neural Network model managed to get slightly better than our baseline of guessing {``}A{''} for every question, while the Tree system additionally improved the results.",
}
@inproceedings{plum-etal-2019-rgcl,
    title = "{RGCL}-{WLV} at {S}em{E}val-2019 Task 12: Toponym Detection",
    author = "Plum, Alistair  and
      Ranasinghe, Tharindu  and
      Calleja, Pablo  and
      Or{\u{a}}san, Constantin  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2228",
    doi = "10.18653/v1/S19-2228",
    pages = "1297--1301",
    abstract = "This article describes the system submitted by the RGCL-WLV team to the SemEval 2019 Task 12: Toponym resolution in scientific papers. The system detects toponyms using a bootstrapped machine learning (ML) approach which classifies names identified using gazetteers extracted from the GeoNames geographical database. The paper evaluates the performance of several ML classifiers, as well as how the gazetteers influence the accuracy of the system. Several runs were submitted. The highest precision achieved for one of the submissions was 89{\%}, albeit it at a relatively low recall of 49{\%}.",
}
@inproceedings{qi-etal-2019-thu,
    title = "{THU}{\_}{NGN} at {S}em{E}val-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers",
    author = "Qi, Tao  and
      Ge, Suyu  and
      Wu, Chuhan  and
      Chen, Yubo  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2229",
    doi = "10.18653/v1/S19-2229",
    pages = "1302--1307",
    abstract = "First name: Tao Last name: Qi Email: taoqi.qt@gmail.com Affiliation: Department of Electronic Engineering, Tsinghua University First name: Suyu Last name: Ge Email: gesy17@mails.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Chuhan Last name: Wu Email: wuch15@mails.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yubo Last name: Chen Email: chen-yb18@mails.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Yongfeng Last name: Huang Email: yfhuang@mail.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University Toponym resolution is an important and challenging task in the neural language processing field, and has wide applications such as emergency response and social media geographical event analysis. Toponym resolution can be roughly divided into two independent steps, i.e., toponym detection and toponym disambiguation. In order to facilitate the study on toponym resolution, the SemEval 2019 task 12 is proposed, which contains three subtasks, i.e., toponym detection, toponym disambiguation and toponym resolution. In this paper, we introduce our system that participated in the SemEval 2019 task 12. For toponym detection, in our approach we use TagLM as the basic model, and explore the use of various features in this task, such as word embeddings extracted from pre-trained language models, POS tags and lexical features extracted from dictionaries. For toponym disambiguation, we propose a heuristics rule-based method using toponym frequency and population. Our systems achieved 83.03{\%} strict macro F1, 74.50 strict micro F1, 85.92 overlap macro F1 and 78.47 overlap micro F1 in toponym detection subtask.",
}
@inproceedings{magnusson-dietz-2019-unh,
    title = "{UNH} at {S}em{E}val-2019 Task 12: Toponym Resolution in Scientific Papers",
    author = "Magnusson, Matthew  and
      Dietz, Laura",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2230",
    doi = "10.18653/v1/S19-2230",
    pages = "1308--1312",
    abstract = "The SemEval-2019 Task 12 is toponym resolution in scientific papers. We focus on Subtask 1: Toponym Detection which is the identification of spans of text for place names mentioned in a document. We propose two methods: 1) sliding window convolutional neural network using ELMo embeddings (cnn-elmo), and 2) sliding window multi-Layer perceptron using ELMo embeddings (mlp-elmo). We also submit Bi-lateral LSTM with Conditional Random Fields (bi-LSTM) as a strong baseline given its state-of-art performance in Named Entity Recognition (NER) task. Our best performing model is cnn-elmo with a F1 of 0.844 which was below bi-LSTM F1 of 0.862 when evaluated on overlap macro detection. Eight teams participated in this subtask with a total of 21 submissions.",
}
@inproceedings{li-etal-2019-unimelb,
    title = "{U}ni{M}elb at {S}em{E}val-2019 Task 12: Multi-model combination for toponym resolution",
    author = "Li, Haonan  and
      Wang, Minghan  and
      Baldwin, Timothy  and
      Tomko, Martin  and
      Vasardani, Maria",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2231",
    doi = "10.18653/v1/S19-2231",
    pages = "1313--1318",
    abstract = "This paper describes our submission to SemEval-2019 Task 12 on toponym resolution over scientific articles. We train separate NER models for toponym detection over text extracted from tables vs. text from the body of the paper, and train another auxiliary model to eliminate misdetected toponyms. For toponym disambiguation, we use an SVM classifier with hand-engineered features. The best setting achieved a strict micro-F1 score of 80.92{\%} and overlap micro-F1 score of 86.88{\%} in the toponym detection subtask, ranking 2nd out of 8 teams on F1 score. For toponym disambiguation and end-to-end resolution, we officially ranked 2nd and 3rd, respectively.",
}
@inproceedings{yadav-etal-2019-university,
    title = "{U}niversity of {A}rizona at {S}em{E}val-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities",
    author = "Yadav, Vikas  and
      Laparra, Egoitz  and
      Wang, Ti-Tai  and
      Surdeanu, Mihai  and
      Bethard, Steven",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2232",
    doi = "10.18653/v1/S19-2232",
    pages = "1319--1323",
    abstract = "We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for task 1, which utilizes only character, word, pre- fix and suffix information for the identification of geolocation entities. Despite using just the training data provided by task organizers and not using any lexicon features, we achieved 78.85{\%} strict micro F-score on task 1. We used the unsupervised population heuristics for task 3 and achieved 52.99{\%} strict micro-F1 score in this task.",
}
@proceedings{semeval-2019-joint,
    title = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    editor = "Mihalcea, Rada  and
      Shutova, Ekaterina  and
      Ku, Lun-Wei  and
      Evang, Kilian  and
      Poria, Soujanya",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1000",
}
@inproceedings{hatty-etal-2019-surel,
    title = "{SUR}el: A Gold Standard for Incorporating Meaning Shifts into Term Extraction",
    author = {H{\"a}tty, Anna  and
      Schlechtweg, Dominik  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1001",
    doi = "10.18653/v1/S19-1001",
    pages = "1--8",
    abstract = "We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts. We show that meaning shifts of term candidates cause errors in term extraction, and demonstrate that the SURel annotation reflects these errors. Furthermore, we illustrate that SURel enables us to assess optimisations of term extraction techniques when incorporating meaning shifts.",
}
@inproceedings{gari-soler-etal-2019-word,
    title = "Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes",
    author = "Gar{\'\i} Soler, Aina  and
      Apidianaki, Marianna  and
      Allauzen, Alexandre",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1002",
    doi = "10.18653/v1/S19-1002",
    pages = "9--21",
    abstract = "Usage similarity estimation addresses the semantic proximity of word instances in different contexts. We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction. Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM. We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity.The best performing models outperform previous methods in both settings.",
}
@inproceedings{zhou-srikumar-2019-beyond,
    title = "Beyond Context: A New Perspective for Word Embeddings",
    author = "Zhou, Yichu  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1003",
    doi = "10.18653/v1/S19-1003",
    pages = "22--32",
    abstract = "Most word embeddings today are trained by optimizing a language modeling goal of scoring words in their context, modeled as a multi-class classification problem. In this paper, we argue that, despite the successes of this assumption, it is incomplete: in addition to its context, orthographical or morphological aspects of words can offer clues about their meaning. We define a new modeling framework for training word embeddings that captures this intuition. Our framework is based on the well-studied problem of multi-label classification and, consequently, exposes several design choices for featurizing words and contexts, loss functions for training and score normalization. Indeed, standard models such as CBOW and fasttext are specific choices along each of these axes. We show via experiments that by combining feature engineering with embedding learning, our method can outperform CBOW using only 10{\%} of the training data in both the standard word embedding evaluations and also text classification experiments.",
}
@inproceedings{sileo-etal-2019-composition,
    title = "Composition of Sentence Embeddings: Lessons from Statistical Relational Learning",
    author = "Sileo, Damien  and
      Van De Cruys, Tim  and
      Pradel, Camille  and
      Muller, Philippe",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1004",
    doi = "10.18653/v1/S19-1004",
    pages = "33--43",
    abstract = "Various NLP problems {--} such as the prediction of sentence similarity, entailment, and discourse relations {--} are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g. concatenation or sum) of those vectors as features for the prediction. At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction.",
}
@inproceedings{zhang-etal-2019-multi-label,
    title = "Multi-Label Transfer Learning for Multi-Relational Semantic Similarity",
    author = "Zhang, Li  and
      Wilson, Steven  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1005",
    doi = "10.18653/v1/S19-1005",
    pages = "44--50",
    abstract = "Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the information provided by the multiple relations, rather than treating them as separate tasks. Not only does this approach outperform the single-task approach and the traditional multi-task learning approach, but it also achieves state-of-the-art performance on all but one relation of the Human Activity Phrase dataset.",
}
@inproceedings{aldarmaki-diab-2019-scalable,
    title = "Scalable Cross-Lingual Transfer of Neural Sentence Embeddings",
    author = "Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1006",
    doi = "10.18653/v1/S19-1006",
    pages = "51--60",
    abstract = "We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.",
}
@inproceedings{liu-etal-2019-second,
    title = "Second-order contexts from lexical substitutes for few-shot learning of word representations",
    author = "Liu, Qianchu  and
      McCarthy, Diana  and
      Korhonen, Anna",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1007",
    doi = "10.18653/v1/S19-1007",
    pages = "61--67",
    abstract = "There is a growing awareness of the need to handle rare and unseen words in word representation modelling. In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts. We introduce a substitute-based context representation technique that can be applied on an existing word embedding space. Previous context-based approaches to modelling unseen words only consider bag-of-word first-order contexts, whereas our method aggregates contexts as second-order substitutes that are produced by a sequence-aware sentence completion model. We experimented with three tasks that aim to test the modelling of emerging concepts. We found that these tasks show different emphasis on first and second order contexts, and our substitute-based method achieves superior performance on naturally-occurring contexts from corpora.",
}
@inproceedings{xu-etal-2019-pre,
    title = "Pre-trained Contextualized Character Embeddings Lead to Major Improvements in Time Normalization: a Detailed Analysis",
    author = "Xu, Dongfang  and
      Laparra, Egoitz  and
      Bethard, Steven",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1008",
    doi = "10.18653/v1/S19-1008",
    pages = "68--74",
    abstract = "Recent studies have shown that pre-trained contextual word embeddings, which assign the same word different vectors in different contexts, improve performance in many tasks. But while contextual embeddings can also be trained at the character level, the effectiveness of such embeddings has not been studied. We derive character-level contextual embeddings from Flair (Akbik et al., 2018), and apply them to a time normalization task, yielding major performance improvements over the previous state-of-the-art: 51{\%} error reduction in news and 33{\%} in clinical notes. We analyze the sources of these improvements, and find that pre-trained contextual character embeddings are more robust to term variations, infrequent terms, and cross-domain changes. We also quantify the size of context that pre-trained contextual character embeddings take advantage of, and show that such embeddings capture features like part-of-speech and capitalization.",
}
@inproceedings{herzig-etal-2019-bot2vec,
    title = "{B}ot2{V}ec: Learning Representations of Chatbots",
    author = "Herzig, Jonathan  and
      Sandbank, Tommy  and
      Shmueli-Scheuer, Michal  and
      Konopnicki, David",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1009",
    doi = "10.18653/v1/S19-1009",
    pages = "75--84",
    abstract = "Chatbots (i.e., bots) are becoming widely used in multiple domains, along with supporting bot programming platforms. These platforms are equipped with novel testing tools aimed at improving the quality of individual chatbots. Doing so requires an understanding of what sort of bots are being built (captured by their underlying conversation graphs) and how well they perform (derived through analysis of conversation logs). In this paper, we propose a new model, Bot2Vec, that embeds bots to a compact representation based on their structure and usage logs. Then, we utilize Bot2Vec representations to improve the quality of two bot analysis tasks. Using conversation data and graphs of over than 90 bots, we show that Bot2Vec representations improve detection performance by more than 16{\%} for both tasks.",
}
@inproceedings{lauscher-glavas-2019-consistently,
    title = "Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors",
    author = "Lauscher, Anne  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1010",
    doi = "10.18653/v1/S19-1010",
    pages = "85--91",
    abstract = "Word embeddings have recently been shown to reflect many of the pronounced societal biases (e.g., gender bias or racial bias). Existing studies are, however, limited in scope and do not investigate the consistency of biases across relevant dimensions like embedding models, types of texts, and different languages. In this work, we present a systematic study of biases encoded in distributional word vector spaces: we analyze how consistent the bias effects are across languages, corpora, and embedding models. Furthermore, we analyze the cross-lingual biases encoded in bilingual embedding spaces, indicative of the effects of bias transfer encompassed in cross-lingual transfer of NLP models. Our study yields some unexpected findings, e.g., that biases can be emphasized or downplayed by different embedding models or that user-generated content may be less biased than encyclopedic text. We hope our work catalyzes bias research in NLP and informs the development of bias reduction techniques.",
}
@inproceedings{venkatesaramani-etal-2019-semantic,
    title = "A Semantic Cover Approach for Topic Modeling",
    author = "Venkatesaramani, Rajagopal  and
      Downey, Doug  and
      Malin, Bradley  and
      Vorobeychik, Yevgeniy",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1011",
    doi = "10.18653/v1/S19-1011",
    pages = "92--102",
    abstract = "We introduce a novel topic modeling approach based on constructing a semantic set cover for clusters of similar documents. Specifically, our approach first clusters documents using their Tf-Idf representation, and then covers each cluster with a set of topic words based on semantic similarity, defined in terms of a word embedding. Computing a topic cover amounts to solving a minimum set cover problem. Our evaluation compares our topic modeling approach to Latent Dirichlet Allocation (LDA) on three metrics: 1) qualitative topic match, measured using evaluations by Amazon Mechanical Turk (MTurk) workers, 2) performance on classification tasks using each topic model as a sparse feature representation, and 3) topic coherence. We find that qualitative judgments significantly favor our approach, the method outperforms LDA on topic coherence, and is comparable to LDA on document classification tasks.",
}
@inproceedings{ostermann-etal-2019-mcscript2,
    title = "{MCS}cript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants",
    author = "Ostermann, Simon  and
      Roth, Michael  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1012",
    doi = "10.18653/v1/S19-1012",
    pages = "103--117",
    abstract = "We introduce MCScript2.0, a machine comprehension corpus for the end-to-end evaluation of script knowledge. MCScript2.0 contains approx. 20,000 questions on approx. 3,500 texts, crowdsourced based on a new collection process that results in challenging questions. Half of the questions cannot be answered from the reading texts, but require the use of commonsense and, in particular, script knowledge. We give a thorough analysis of our corpus and show that while the task is not challenging to humans, existing machine comprehension models fail to perform well on the data, even if they make use of a commonsense knowledge base. The dataset is available at http://www.sfb1102. uni-saarland.de/?page{\_}id=2582",
}
@inproceedings{davis-etal-2019-deconstructing,
    title = "Deconstructing multimodality: visual properties and visual context in human semantic processing",
    author = "Davis, Christopher  and
      Bulat, Luana  and
      Vero, Anita Lilla  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1013",
    doi = "10.18653/v1/S19-1013",
    pages = "118--124",
    abstract = "Multimodal semantic models that extend linguistic representations with additional perceptual input have proved successful in a range of natural language processing (NLP) tasks. Recent research has successfully used neural methods to automatically create visual representations for words. However, these works have extracted visual features from complete images, and have not examined how different kinds of visual information impact performance. In contrast, we construct multimodal models that differentiate between internal visual properties of the objects and their external visual context. We evaluate the models on the task of decoding brain activity associated with the meanings of nouns, demonstrating their advantage over those based on complete images.",
}
@inproceedings{kutuzov-etal-2019-learning,
    title = "Learning Graph Embeddings from {W}ord{N}et-based Similarity Measures",
    author = "Kutuzov, Andrey  and
      Dorgham, Mohammad  and
      Oliynyk, Oleksiy  and
      Biemann, Chris  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1014",
    doi = "10.18653/v1/S19-1014",
    pages = "125--135",
    abstract = "We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities. The model learns representations for nodes in a dense space that approximate a given user-defined graph distance measure, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNet-based similarity measures, show that our approach yields competitive results, outperforming strong graph embedding baselines. The model is computationally efficient, being orders of magnitude faster than the direct computation of graph-based distances.",
}
@inproceedings{huang-paul-2019-neural-user,
    title = "Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics",
    author = "Huang, Xiaolei  and
      Paul, Michael J.",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1015",
    doi = "10.18653/v1/S19-1015",
    pages = "136--146",
    abstract = "Language use varies across different demographic factors, such as gender, age, and geographic location. However, most existing document classification methods ignore demographic variability. In this study, we examine empirically how text data can vary across four demographic factors: gender, age, country, and region. We propose a multitask neural model to account for demographic variations via adversarial training. In experiments on four English-language social media datasets, we find that classification performance improves when adapting for user factors.",
}
@inproceedings{nastase-kotnis-2019-abstract,
    title = "Abstract Graphs and Abstract Paths for Knowledge Graph Completion",
    author = "Nastase, Vivi  and
      Kotnis, Bhushan",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1016",
    doi = "10.18653/v1/S19-1016",
    pages = "147--157",
    abstract = "Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete. Information that we induce from such graphs {--} e.g. entity embeddings, relation representations or patterns {--} will be affected by the imbalance in the information captured in the graph {--} by biasing representations, or causing us to miss potential patterns. To partially compensate for this situation we describe a method for representing knowledge graphs that capture an intensional representation of the original extensional information. This representation is very compact, and it abstracts away from individual links, allowing us to find better path candidates, as shown by the results of link prediction using this information.",
}
@inproceedings{sarabi-etal-2019-corpus,
    title = "A Corpus of Negations and their Underlying Positive Interpretations",
    author = "Sarabi, Zahra  and
      Killian, Erin  and
      Blanco, Eduardo  and
      Palmer, Alexis",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1017",
    doi = "10.18653/v1/S19-1017",
    pages = "158--167",
    abstract = "Negation often conveys implicit positive meaning. In this paper, we present a corpus of negations and their underlying positive interpretations. We work with negations from Simple Wikipedia, automatically generate potential positive interpretations, and then collect manual annotations that effectively rewrite the negation in positive terms. This procedure yields positive interpretations for approximately 77{\%} of negations, and the final corpus includes over 5,700 negations and over 5,900 positive interpretations. We also present baseline results using seq2seq neural models.",
}
@inproceedings{maguire-2019-enthymemetic-conditionals,
    title = "Enthymemetic Conditionals",
    author = "Maguire, Eimear",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1018",
    doi = "10.18653/v1/S19-1018",
    pages = "168--177",
    abstract = "To model conditionals in a way that reflects their acceptability, we must include some means of making judgements about whether antecedent and consequent are meaningfully related or not. Enthymemes are non-logical arguments which do not hold up by themselves, but are acceptable through their relation to a topos, an already-known general principle or pattern for reasoning. This paper uses enthymemes and topoi as a way to model the world-knowledge behind these judgements. In doing so, it provides a reformalisation (in TTR) of enthymemes and topoi as networks rather than functions, and information state update rules for conditionals.",
}
@inproceedings{zhang-xue-2019-acquiring,
    title = "Acquiring Structured Temporal Representation via Crowdsourcing: A Feasibility Study",
    author = "Zhang, Yuchen  and
      Xue, Nianwen",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1019",
    doi = "10.18653/v1/S19-1019",
    pages = "178--185",
    abstract = "Temporal Dependency Trees are a structured temporal representation that represents temporal relations among time expressions and events in a text as a dependency tree structure. Compared to traditional pair-wise temporal relation representations, temporal dependency trees facilitate efficient annotations, higher inter-annotator agreement, and efficient computations. However, annotations on temporal dependency trees so far have only been done by expert annotators, which is costly and time-consuming. In this paper, we introduce a method to crowdsource temporal dependency tree annotations, and show that this representation is intuitive and can be collected with high accuracy and agreement through crowdsourcing. We produce a corpus of temporal dependency trees, and present a baseline temporal dependency parser, trained and evaluated on this new corpus.",
}
@inproceedings{lakshmi-narayan-etal-2019-exploration,
    title = "Exploration of Noise Strategies in Semi-supervised Named Entity Classification",
    author = "Lakshmi Narayan, Pooja  and
      Nagesh, Ajay  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1020",
    doi = "10.18653/v1/S19-1020",
    pages = "186--191",
    abstract = "Noise is inherent in real world datasets and modeling noise is critical during training as it is effective in regularization. Recently, novel semi-supervised deep learning techniques have demonstrated tremendous potential when learning with very limited labeled training data in image processing tasks. A critical aspect of these semi-supervised learning techniques is augmenting the input or the network with noise to be able to learn robust models. While modeling noise is relatively straightforward in continuous domains such as image classification, it is not immediately apparent how noise can be modeled in discrete domains such as language. Our work aims to address this gap by exploring different noise strategies for the semi-supervised named entity classification task, including statistical methods such as adding Gaussian noise to input embeddings, and linguistically-inspired ones such as dropping words and replacing words with their synonyms. We compare their performance on two benchmark datasets (OntoNotes and CoNLL) for named entity classification. Our results indicate that noise strategies that are linguistically informed perform at least as well as statistical approaches, while being simpler and requiring minimal tuning.",
}
@inproceedings{subramanian-roth-2019-improving,
    title = "Improving Generalization in Coreference Resolution via Adversarial Training",
    author = "Subramanian, Sanjay  and
      Roth, Dan",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1021",
    doi = "10.18653/v1/S19-1021",
    pages = "192--197",
    abstract = "In order for coreference resolution systems to be useful in practice, they must be able to generalize to new text. In this work, we demonstrate that the performance of the state-of-the-art system decreases when the names of PER and GPE named entities in the CoNLL dataset are changed to names that do not occur in the training set. We use the technique of adversarial gradient-based training to retrain the state-of-the-art system and demonstrate that the retrained system achieves higher performance on the CoNLL dataset (both with and without the change of named entities) and the GAP dataset.",
}
@inproceedings{ding-etal-2019-improving,
    title = "Improving Human Needs Categorization of Events with Semantic Classification",
    author = "Ding, Haibo  and
      Riloff, Ellen  and
      Feng, Zhe",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1022",
    doi = "10.18653/v1/S19-1022",
    pages = "198--204",
    abstract = "Human Needs categories have been used to characterize the reason why an affective event is positive or negative. For example, {``}I got the flu{''} and {``}I got fired{''} are both negative (undesirable) events, but getting the flu is a Health problem while getting fired is a Financial problem. Previous work created learning models to assign events to Human Needs categories based on their words and contexts. In this paper, we introduce an intermediate step that assigns words to relevant semantic concepts. We create lightly supervised models that learn to label words with respect to 10 semantic concepts associated with Human Needs categories, and incorporate these labels as features for event categorization. Our results show that recognizing relevant semantic concepts improves both the recall and precision of Human Needs categorization for events.",
}
@inproceedings{agarwal-etal-2019-word,
    title = "Word Embeddings (Also) Encode Human Personality Stereotypes",
    author = "Agarwal, Oshin  and
      Durup{\i}nar, Funda  and
      Badler, Norman I.  and
      Nenkova, Ani",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1023",
    doi = "10.18653/v1/S19-1023",
    pages = "205--211",
    abstract = "Word representations trained on text reproduce human implicit bias related to gender, race and age. Methods have been developed to remove such bias. Here, we present results that show that human stereotypes exist even for much more nuanced judgments such as personality, for a variety of person identities beyond the typically legally protected attributes and that these are similarly captured in word representations. Specifically, we collected human judgments about a person{'}s Big Five personality traits formed solely from information about the occupation, nationality or a common noun description of a hypothetical person. Analysis of the data reveals a large number of statistically significant stereotypes in people. We then demonstrate the bias captured in lexical representations is statistically significantly correlated with the documented human bias. Our results, showing bias for a large set of person descriptors for such nuanced traits put in doubt the feasibility of broadly and fairly applying debiasing methods and call for the development of new methods for auditing language technology systems and resources.",
}
@inproceedings{opitz-frank-2019-automatic,
    title = "Automatic Accuracy Prediction for {AMR} Parsing",
    author = "Opitz, Juri  and
      Frank, Anette",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1024",
    doi = "10.18653/v1/S19-1024",
    pages = "212--223",
    abstract = "Abstract Meaning Representation (AMR) represents sentences as directed, acyclic and rooted graphs, aiming at capturing their meaning in a machine readable format. AMR parsing converts natural language sentences into such graphs. However, evaluating a parser on new data by means of comparison to manually created AMR graphs is very costly. Also, we would like to be able to detect parses of questionable quality, or preferring results of alternative systems by selecting the ones for which we can assess good quality. We propose AMR accuracy prediction as the task of predicting several metrics of correctness for an automatically generated AMR parse {--} in absence of the corresponding gold parse. We develop a neural end-to-end multi-output regression model and perform three case studies: firstly, we evaluate the model{'}s capacity of predicting AMR parse accuracies and test whether it can reliably assign high scores to gold parses. Secondly, we perform parse selection based on predicted parse accuracies of candidate parses from alternative systems, with the aim of improving overall results. Finally, we predict system ranks for submissions from two AMR shared tasks on the basis of their predicted parse accuracy averages. All experiments are carried out across two different domains and show that our method is effective.",
}
@inproceedings{opitz-frank-2019-argument,
    title = "An Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling",
    author = "Opitz, Juri  and
      Frank, Anette",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1025",
    doi = "10.18653/v1/S19-1025",
    pages = "224--234",
    abstract = "Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition of roles, following Dowty{'}s feature-based view of proto-roles. This theory determines agenthood vs. patienthood based on a participant{'}s instantiation of more or less typical agent vs. patient properties, such as, for example, volition in an event. To perform SPRL, we develop an ensemble of hierarchical models with self-attention and concurrently learned predicate-argument markers. Our method is competitive with the state-of-the art, overall outperforming previous work in two formulations of the task (multi-label and multi-variate Likert scale pre- diction). In contrast to previous work, our results do not depend on gold argument heads derived from supplementary gold tree banks.",
}
@inproceedings{kim-etal-2019-probing,
    title = "Probing What Different {NLP} Tasks Teach Machines about Function Word Comprehension",
    author = "Kim, Najoung  and
      Patel, Roma  and
      Poliak, Adam  and
      Xia, Patrick  and
      Wang, Alex  and
      McCoy, Tom  and
      Tenney, Ian  and
      Ross, Alexis  and
      Linzen, Tal  and
      Van Durme, Benjamin  and
      Bowman, Samuel R.  and
      Pavlick, Ellie",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1026",
    doi = "10.18653/v1/S19-1026",
    pages = "235--249",
    abstract = "We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific types of function words (e.g., prepositions, wh-words). Using these probing tasks, we explore the effects of various pretraining objectives for sentence encoders (e.g., language modeling, CCG supertagging and natural language inference (NLI)) on the learned representations. Our results show that pretraining on CCG{---}our most syntactic objective{---}performs the best on average across our probing tasks, suggesting that syntactic knowledge helps function word comprehension. Language modeling also shows strong performance, supporting its widespread use for pretraining state-of-the-art NLP models. Overall, no pretraining objective dominates across the board, and our function word probing tasks highlight several intuitive differences between pretraining objectives, e.g., that NLI helps the comprehension of negation.",
}
@inproceedings{yanaka-etal-2019-help,
    title = "{HELP}: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning",
    author = "Yanaka, Hitomi  and
      Mineshima, Koji  and
      Bekki, Daisuke  and
      Inui, Kentaro  and
      Sekine, Satoshi  and
      Abzianidze, Lasha  and
      Bos, Johan",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1027",
    doi = "10.18653/v1/S19-1027",
    pages = "250--255",
    abstract = "Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neural models have a hard time capturing logical inferences, including those licensed by phrase replacements, so-called monotonicity reasoning. Since no large dataset has been developed for monotonicity reasoning, it is still unclear whether the main obstacle is the size of datasets or the model architectures themselves. To investigate this issue, we introduce a new dataset, called HELP, for handling entailments with lexical and logical phenomena. We add it to training data for the state-of-the-art neural models and evaluate them on test sets for monotonicity phenomena. The results showed that our data augmentation improved the overall accuracy. We also find that the improvement is better on monotonicity inferences with lexical replacements than on downward inferences with disjunction and modification. This suggests that some types of inferences can be improved by our data augmentation while others are immune to it.",
}
@inproceedings{belinkov-etal-2019-adversarial,
    title = "On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference",
    author = "Belinkov, Yonatan  and
      Poliak, Adam  and
      Shieber, Stuart  and
      Van Durme, Benjamin  and
      Rush, Alexander",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1028",
    doi = "10.18653/v1/S19-1028",
    pages = "256--262",
    abstract = "Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.",
}
@inproceedings{bernardy-etal-2019-bayesian,
    title = "{B}ayesian Inference Semantics: A Modelling System and A Test Suite",
    author = "Bernardy, Jean-Philippe  and
      Blanck, Rasmus  and
      Chatzikyriakidis, Stergios  and
      Lappin, Shalom  and
      Maskharashvili, Aleksandre",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1029",
    doi = "10.18653/v1/S19-1029",
    pages = "263--272",
    abstract = "We present BIS, a Bayesian Inference Semantics, for probabilistic reasoning in natural language. The current system is based on the framework of Bernardy et al. (2018), but departs from it in important respects. BIS makes use of Bayesian learning for inferring a hypothesis from premises. This involves estimating the probability of the hypothesis, given the data supplied by the premises of an argument. It uses a syntactic parser to generate typed syntactic structures that serve as input to a model generation system. Sentences are interpreted compositionally to probabilistic programs, and the corresponding truth values are estimated using sampling methods. BIS successfully deals with various probabilistic semantic phenomena, including frequency adverbs, generalised quantifiers, generics, and vague predicates. It performs well on a number of interesting probabilistic reasoning tasks. It also sustains most classically valid inferences (instantiation, de Morgan{'}s laws, etc.). To test BIS we have built an experimental test suite with examples of a range of probabilistic and classical inference patterns.",
}
@inproceedings{subramanian-etal-2019-target,
    title = "Target Based Speech Act Classification in Political Campaign Text",
    author = "Subramanian, Shivashankar  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1030",
    doi = "10.18653/v1/S19-1030",
    pages = "273--282",
    abstract = "We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance. We propose a new annotation schema incorporating domain-specific speech acts, such as commissive-action, and present a novel annotated corpus of media releases and speech transcripts from the 2016 Australian election cycle. We show how speech acts and target referents can be modeled as sequential classification, and evaluate several techniques, exploiting contextualized word representations, semi-supervised learning, task dependencies and speaker meta-data.",
}
@inproceedings{sadeque-etal-2019-incivility,
    title = "Incivility Detection in Online Comments",
    author = "Sadeque, Farig  and
      Rains, Stephen  and
      Shmargad, Yotam  and
      Kenski, Kate  and
      Coe, Kevin  and
      Bethard, Steven",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1031",
    doi = "10.18653/v1/S19-1031",
    pages = "283--291",
    abstract = "Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively. In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section. We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like namecalling cannot be accurately detected by simple models like logistic regression. We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset, and find that despite the change of domain, the model makes accurate predictions.",
}
@inproceedings{zhang-etal-2019-generating-animations,
    title = "Generating Animations from Screenplays",
    author = "Zhang, Yeyao  and
      Tsipidi, Eleftheria  and
      Schriber, Sasha  and
      Kapadia, Mubbasir  and
      Gross, Markus  and
      Modi, Ashutosh",
    booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-1032",
    doi = "10.18653/v1/S19-1032",
    pages = "292--307",
    abstract = "Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and public safety. However, translating natural language text into animation is a challenging task. Existing text-to-animation systems can handle only very simple sentences, which limits their applications. In this paper, we develop a text-to-animation system which is capable of handling complex sentences. We achieve this by introducing a text simplification step into the process. Building on an existing animation generation system for screenwriting, we create a robust NLP pipeline to extract information from screenplays and map them to the system{'}s knowledge base. We develop a set of linguistic transformation rules that simplify complex sentences. Information extracted from the simplified sentences is used to generate a rough storyboard and video depicting the text. Our sentence simplification module outperforms existing systems in terms of BLEU and SARI metrics.We further evaluated our system via a user study: 68{\%} participants believe that our system generates reasonable animation from input screenplays.",
}
@proceedings{ranlp-2019-student,
    title = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    editor = "Kovatchev, Venelin  and
      Temnikova, Irina  and
      {\v{S}}andrih, Branislava  and
      Nikolova, Ivelina",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2000",
}
@inproceedings{abdussaitova-amangeldiyeva-2019-normalization,
    title = "Normalization of {K}azakh Texts",
    author = "Abdussaitova, Assina  and
      Amangeldiyeva, Alina",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2001",
    doi = "10.26615/issn.2603-2821.2019_001",
    pages = "1--6",
    abstract = "Kazakh language, like other agglutinative languages, has specific difficulties on both recognition of wrong words and generation the corrections for misspelt words. The main goal of this work is to develop a better algorithm for the normalization of Kazakh texts based on traditional and Machine Learning methods, as well as the new approach which is also considered in this paper. The procedure of election among methods of normalization has been conducted in a manner of comparative analysis. The results of the comparative analysis turned up successful and are shown in details.",
}
@inproceedings{aggarwal-2019-classification,
    title = "Classification Approaches to Identify Informative Tweets",
    author = "Aggarwal, Piush",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2002",
    doi = "10.26615/issn.2603-2821.2019_002",
    pages = "7--15",
    abstract = "Social media platforms have become prime forums for reporting news, with users sharing what they saw, heard or read on social media. News from social media is potentially useful for various stakeholders including aid organizations, news agencies, and individuals. However, social media also contains a vast amount of non-news content. For users to be able to draw on benefits from news reported on social media it is necessary to reliably identify news content and differentiate it from non-news. In this paper, we tackle the challenge of classifying a social post as news or not. To this end, we provide a new manually annotated dataset containing 2,992 tweets from 5 different topical categories. Unlike earlier datasets, it includes postings posted by personal users who do not promote a business or a product and are not affiliated with any organization. We also investigate various baseline systems and evaluate their performance on the newly generated dataset. Our results show that the best classifiers are the SVM and BERT models.",
}
@inproceedings{dorn-2019-dialect,
    title = "Dialect-Specific Models for Automatic Speech Recognition of {A}frican {A}merican {V}ernacular {E}nglish",
    author = "Dorn, Rachel",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2003",
    doi = "10.26615/issn.2603-2821.2019_003",
    pages = "16--20",
    abstract = "African American Vernacular English (AAVE) is a widely-spoken dialect of English, yet it is under-represented in major speech corpora. As a result, speakers of this dialect are often misunderstood by NLP applications. This study explores the effect on transcription accuracy of an automatic voice recognition system when AAVE data is used. Models trained on AAVE data and on Standard American English data were compared to a baseline model trained on a combination of the two dialects. The accuracy for both dialect-specific models was significantly higher than the baseline model, with the AAVE model showing over 18{\%} improvement. By isolating the effect of having AAVE speakers in the training data, this study highlights the importance of increasing diversity in the field of natural language processing.",
}
@inproceedings{baumann-2019-multilingual,
    title = "Multilingual Language Models for Named Entity Recognition in {G}erman and {E}nglish",
    author = "Baumann, Antonia",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2004",
    doi = "10.26615/issn.2603-2821.2019_004",
    pages = "21--27",
    abstract = "We assess the language specificity of recent language models by exploring the potential of a multilingual language model. In particular, we evaluate Google{'}s multilingual BERT (mBERT) model on Named Entity Recognition (NER) in German and English. We expand the work on language model fine-tuning by Howard and Ruder (2018), applying it to the BERT architecture. We successfully reproduce the NER results published by Devlin et al. (2019).Our results show that the multilingual language model generalises well for NER in the chosen languages, matching the native model in English and comparing well with recent approaches for German. However, it does not benefit from the added fine-tuning methods.",
}
@inproceedings{l-r-etal-2019-parts,
    title = "Parts of Speech Tagging for {K}annada",
    author = "L R, Swaroop  and
      Gowda G S, Rakshith  and
      U, Sourabh  and
      Hegde, Shriram",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2005",
    doi = "10.26615/issn.2603-2821.2019_005",
    pages = "28--31",
    abstract = "Parts of speech (POS) tagging is the process of assigning the part of speech tag to each and every word in a sentence. In this paper, we have presented POS tagger for Kannada, a low resource south Asian language, using Condition Random Fields. POS tagger developed in the work uses novel features native to Kannada language. The novel features include Sandhi splitting, where a compound word is broken down into two or more meaningful constituent words. The proposed model is trained and tested on the tagged dataset which contains 21 thousand sentences and achieves a highest accuracy of 94.56{\%}.",
}
@inproceedings{kancheva-2019-cross,
    title = "Cross-Lingual Coreference: The Case of {B}ulgarian and {E}nglish",
    author = "Kancheva, Zara",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2006",
    doi = "10.26615/issn.2603-2821.2019_006",
    pages = "32--38",
    abstract = "The paper presents several common approaches towards cross- and multi-lingual coreference resolution in a search of the most effective practices to be applied within the work on Bulgarian-English manual coreference annotation of a short story. The work aims at outlining the typology of the differences in the annotated parallel texts. The results of the research prove to be comparable with the tendencies observed in similar works on other Slavic languages and show surprising differences between the types of markables and their frequency in Bulgarian and English.",
}
@inproceedings{geneva-shopov-2019-towards,
    title = "Towards Accurate Text Verbalization for {ASR} Based on Audio Alignment",
    author = "Geneva, Diana  and
      Shopov, Georgi",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2007",
    doi = "10.26615/issn.2603-2821.2019_007",
    pages = "39--47",
    abstract = "Verbalization of non-lexical linguistic units plays an important role in language modeling for automatic speech recognition systems. Most verbalization methods require valuable resources such as ground truth, large training corpus and expert knowledge which are often unavailable. On the other hand a considerable amount of audio data along with its transcribed text are freely available on the Internet and could be utilized for the task of verbalization. This paper presents a methodology for accurate verbalization of audio transcriptions based on phone-level alignment between the transcriptions and their corresponding audio recordings. Comparing this approach to a more general rule-based verbalization method shows a significant improvement in ASR recognition of non-lexical units. In the process of evaluating this approach we also expose the indirect influence of verbalization accuracy on the quality of acoustic models trained on automatically derived speech corpora.",
}
@inproceedings{marinova-2019-evaluation,
    title = "Evaluation of Stacked Embeddings for {B}ulgarian on the Downstream Tasks {POS} and {NERC}",
    author = "Marinova, Iva",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2008",
    doi = "10.26615/issn.2603-2821.2019_008",
    pages = "48--54",
    abstract = "This paper reports on experiments with different stacks of word embeddings and evaluation of their usefulness for Bulgarian downstream tasks such as Named Entity Recognition and Classification (NERC) and Part-of-speech (POS) Tagging. Word embeddings stay in the core of the development of NLP, with several key language models being created over the last two years like FastText (CITATION), ElMo (CITATION), BERT (CITATION) and Flair (CITATION). Stacking or combining different word embeddings is another technique used in this paper and still not reported for Bulgarian NERC. Well-established architecture is used for the sequence tagging task such as BI-LSTM-CRF, and different pre-trained language models are combined in the embedding layer to decide which combination of them scores better.",
}
@inproceedings{berbatova-2019-overview,
    title = "Overview on {NLP} Techniques for Content-based Recommender Systems for Books",
    author = "Berbatova, Melania",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2009",
    doi = "10.26615/issn.2603-2821.2019_009",
    pages = "55--61",
    abstract = "Recommender systems are an essential part of today{'}s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-based filtering. It relies on analysing product metadata, a great part of which is textual data. Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders. In this paper, we will first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for GoodReads{'} website. We will give critiques on existing methods and suggest how natural language techniques can be employed for the improvement of content-based recommenders.",
}
@inproceedings{vukovic-etal-2019-corpora,
    title = "Corpora and Processing Tools for Non-standard Contemporary and Diachronic {B}alkan {S}lavic",
    author = {Vukovic, Teodora  and
      Muheim, Nora  and
      Winist{\"o}rfer, Olivier  and
      {\v{S}}imko, Ivan  and
      Makarova, Anastasia  and
      Bradjan, Sanja},
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2010",
    doi = "10.26615/issn.2603-2821.2019_010",
    pages = "62--68",
    abstract = "The paper describes three corpora of different varieties of BS that are currently being developed with the goal of providing data for the analysis of the diatopic and diachronic variation in non-standard Balkan Slavic. The corpora includes spoken materials from Torlak, Macedonian dialects, as well as the manuscripts of pre-standardized Bulgarian. Apart from the texts, tools for PoS annotation and lemmatization for all varieties are being created, as well as syntactic parsing for Torlak and Bulgarian varieties. The corpora are built using a unified methodology, relying on the pest practices and state-of-the-art methods from the field. The uniform methodology allows the contrastive analysis of the data from different varieties. The corpora under construction can be considered a crucial contribution to the linguistic research on the languages in the Balkans as they provide the lacking data needed for the studies of linguistic variation in the Balkan Slavic, and enable the comparison of the said varieties with other neighbouring languages.",
}
@inproceedings{alqifari-2019-question,
    title = "Question Answering Systems Approaches and Challenges",
    author = "Alqifari, Reem",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2011",
    doi = "10.26615/issn.2603-2821.2019_011",
    pages = "69--75",
    abstract = "Question answering (QA) systems permit the user to ask a question using natural language, and the system provides a concise and correct answer. QA systems can be implemented for different types of datasets, structured or unstructured. In this paper, some of the recent studies will be reviewed and the limitations will be discussed. Consequently, the current issues are analyzed with the proposed solutions.",
}
@inproceedings{radev-2019-adding,
    title = "Adding Linguistic Knowledge to {NLP} Tasks for {B}ulgarian: The Verb Paradigm Patterns",
    author = "Radev, Ivaylo",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2012",
    doi = "10.26615/issn.2603-2821.2019_012",
    pages = "76--82",
    abstract = "This paper discusses some possible usages of one unexplored lexical language resource containing Bulgarian verb paradigms and their English translations. This type of data can be used for machine translation, generation of pseudo corpora/language exercises, and evaluation of parsers. Upon completion, the resource will be linked with other existing resources such as the morphological lexicon, valency lexicon, as well as BTB-WordNet.",
}
@inproceedings{sheang-2019-multilingual,
    title = "Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features",
    author = "Sheang, Kim Cheng",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2013",
    doi = "10.26615/issn.2603-2821.2019_013",
    pages = "83--89",
    abstract = "The paper is about our experiments with Complex Word Identification system using deep learning approach with word embeddings and engineered features.",
}
@inproceedings{smirnov-2019-neural,
    title = "Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension",
    author = "Smirnov, Denis",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2019",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-2014",
    doi = "10.26615/issn.2603-2821.2019_014",
    pages = "90--94",
    abstract = "State-of-the-art machine reading comprehension models are capable of producing answers for factual questions about a given piece of text. However, some type of questions requires commonsense knowledge which cannot be inferred from the given text passage. Thus, external semantic information could enhance the performance of these models. This PhD research proposal provides a brief overview of some existing machine reading comprehension datasets and models and outlines possible ways of their improvement.",
}
@proceedings{ranlp-2019-international,
    title = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1000",
}
@inproceedings{adiga-etal-2019-table,
    title = "Table Structure Recognition Based on Cell Relationship, a Bottom-Up Approach",
    author = "Adiga, Darshan  and
      Bhat, Shabir Ahmad  and
      Shah, Muzaffar Bashir  and
      Vyeth, Viveka",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1001",
    doi = "10.26615/978-954-452-056-4_001",
    pages = "1--8",
    abstract = "In this paper, we present a relationship extraction based methodology for table structure recognition in PDF documents. The proposed deep learning-based method takes a bottom-up approach to table recognition in PDF documents. We outline the shortcomings of conventional approaches based on heuristics and machine learning-based top-down approaches. In this work, we explain how the task of table structure recognition can be modeled as a cell relationship extraction task and the importance of the bottom-up approach in recognizing the table cells. We use Multilayer Feedforward Neural Network for table structure recognition and compare the results of three feature sets. To gauge the performance of the proposed method, we prepared a training dataset using 250 tables in PDF documents, carefully selecting the table structures that are most commonly found in the documents. Our model achieves an overall accuracy of 97.95{\%} and an F1-Score of 92.62{\%} on the test dataset.",
}
@inproceedings{aggarwal-aker-2019-identification,
    title = "Identification of Good and Bad News on {T}witter",
    author = "Aggarwal, Piush  and
      Aker, Ahmet",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1002",
    doi = "10.26615/978-954-452-056-4_002",
    pages = "9--17",
    abstract = "Social media plays a great role in news dissemination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature and that this influence is more in bad news. An ideal situation would be that we have a tool that can help to filter out the type of news we do not want to consume. In this paper, we provide the basis for such a tool. In our work, we focus on Twitter. We release a manually annotated dataset containing 6,853 tweets from 5 different topical categories. Each tweet is annotated with good and bad labels. We also investigate various machine learning systems and features and evaluate their performance on the newly generated dataset. We also perform a comparative analysis with sentiments showing that sentiment alone is not enough to distinguish between good and bad news.",
}
@inproceedings{ahmadnia-dorr-2019-bilingual,
    title = "Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of {P}ersian-{S}panish",
    author = "Ahmadnia, Benyamin  and
      Dorr, Bonnie",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1003",
    doi = "10.26615/978-954-452-056-4_003",
    pages = "18--24",
    abstract = "The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality. We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario. Experimental results demonstrate that this competitive approach outperforms the baselines.",
}
@inproceedings{ahmadnia-dorr-2019-enhancing,
    title = "Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network",
    author = "Ahmadnia, Benyamin  and
      Dorr, Bonnie",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1004",
    doi = "10.26615/978-954-452-056-4_004",
    pages = "25--32",
    abstract = "Phrases play a key role in Machine Translation (MT). In this paper, we apply a Long Short-Term Memory (LSTM) model over conventional Phrase-Based Statistical MT (PBSMT). The core idea is to use an LSTM encoder-decoder to score the phrase table generated by the PBSMT decoder. Given a source sequence, the encoder and decoder are jointly trained in order to maximize the conditional probability of a target sequence. Analytically, the performance of a PBSMT system is enhanced by using the conditional probabilities of phrase pairs computed by an LSTM encoder-decoder as an additional feature in the existing log-linear model. We compare the performance of the phrase tables in the PBSMT to the performance of the proposed LSTM and observe its positive impact on translation quality. We construct a PBSMT model using the Moses decoder and enrich the Language Model (LM) utilizing an external dataset. We then rank the phrase tables using an LSTM-based encoder-decoder. This method produces a gain of up to 3.14 BLEU score on the test set.",
}
@inproceedings{ak-yildiz-2019-automatic,
    title = "Automatic {P}ropbank Generation for {T}urkish",
    author = "AK, Koray  and
      Y{\i}ld{\i}z, Olcay Taner",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1005",
    doi = "10.26615/978-954-452-056-4_005",
    pages = "33--41",
    abstract = "Semantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears a semantic role. It provides an extensive dataset to enhance NLP applications such as information retrieval, machine translation, information extraction, and question answering. However, creating SRL models are difficult. Even in some languages, it is infeasible to create SRL models that have predicate-argument structure due to lack of linguistic resources. In this paper, we present our method to create an automatic Turkish PropBank by exploiting parallel data from the translated sentences of English PropBank. Experiments show that our method gives promising results.",
}
@inproceedings{aleksandrova-etal-2019-multilingual,
    title = "Multilingual sentence-level bias detection in {W}ikipedia",
    author = "Aleksandrova, Desislava  and
      Lareau, Fran{\c{c}}ois  and
      M{\'e}nard, Pierre Andr{\'e}",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1006",
    doi = "10.26615/978-954-452-056-4_006",
    pages = "42--51",
    abstract = "We propose a multilingual method for the extraction of biased sentences from Wikipedia, and use it to create corpora in Bulgarian, French and English. Sifting through the revision history of the articles that at some point had been considered biased and later corrected, we retrieve the last tagged and the first untagged revisions as the before/after snapshots of what was deemed a violation of Wikipedia{'}s neutral point of view policy. We extract the sentences that were removed or rewritten in that edit. The approach yields sufficient data even in the case of relatively small Wikipedias, such as the Bulgarian one, where 62k articles produced 5k biased sentences. We evaluate our method by manually annotating 520 sentences for Bulgarian and French, and 744 for English. We assess the level of noise and analyze its sources. Finally, we exploit the data with well-known classification methods to detect biased sentences. Code and datasets are hosted at https://github.com/crim-ca/wiki-bias.",
}
@inproceedings{ansari-etal-2019-supervised,
    title = "Supervised Morphological Segmentation Using Rich Annotated Lexicon",
    author = "Ansari, Ebrahim  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Mahmoudi, Mohammad  and
      Haghdoost, Hamid  and
      Vidra, Jon{\'a}{\v{s}}",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1007",
    doi = "10.26615/978-954-452-056-4_007",
    pages = "52--61",
    abstract = "Morphological segmentation of words is the process of dividing a word into smaller units called morphemes; it is tricky especially when a morphologically rich or polysynthetic language is under question. In this work, we designed and evaluated several Recurrent Neural Network (RNN) based models as well as various other machine learning based approaches for the morphological segmentation task. We trained our models using annotated segmentation lexicons. To evaluate the effect of the training data size on our models, we decided to create a large hand-annotated morphologically segmented corpus of Persian words, which is, to the best of our knowledge, the first and the only segmentation lexicon for the Persian language. In the experimental phase, using the hand-annotated Persian lexicon and two smaller similar lexicons for Czech and Finnish languages, we evaluated the effect of the training data size, different hyper-parameters settings as well as different RNN-based models.",
}
@inproceedings{arefyev-etal-2019-combining,
    title = "Combining Lexical Substitutes in Neural Word Sense Induction",
    author = "Arefyev, Nikolay  and
      Sheludko, Boris  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1008",
    doi = "10.26615/978-954-452-056-4_008",
    pages = "62--70",
    abstract = "Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning. In this work, we improve the approach to WSI proposed by Amrami and Goldberg (2018) based on clustering of lexical substitutes for an ambiguous word in a particular context obtained from neural language models. Namely, we propose methods for combining information from left and right context and similarity to the ambiguous word, which result in generating more accurate substitutes than the original approach. Our simple yet efficient improvement establishes a new state-of-the-art on WSI datasets for two languages. Besides, we show improvements to the original approach on a lexical substitution dataset.",
}
@inproceedings{arikan-etal-2019-detecting,
    title = "Detecting Clitics Related Orthographic Errors in {T}urkish",
    author = "Arikan, Ugurcan  and
      Gungor, Onur  and
      Uskudarli, Suzan",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1009",
    doi = "10.26615/978-954-452-056-4_009",
    pages = "71--76",
    abstract = "For the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account. However, such tools are fairly immature, and, worse, non-existent for many low resource languages. Checking only if a word is well-formed with respect to the morphological rules of a language may produce false negatives due to the ambiguity resulting from the presence of numerous homophonic words. In this work, we propose an approach to detect and correct the {``}de/da{''} clitic errors in Turkish text. Our model is a neural sequence tagger trained with a synthetically constructed dataset consisting of positive and negative samples. The model{'}s performance with this dataset is presented according to different word embedding configurations. The model achieved an F1 score of 86.67{\%} on a synthetically constructed dataset. We also compared the model{'}s performance on a manually curated dataset of challenging samples that proved superior to other spelling correctors with 71{\%} accuracy compared to the second-best (Google Docs) with and accuracy of 34{\%}.",
}
@inproceedings{baisa-etal-2019-benchmark,
    title = "Benchmark Dataset for Propaganda Detection in {C}zech Newspaper Texts",
    author = "Baisa, V{\'\i}t  and
      Herman, Ond{\v{r}}ej  and
      Horak, Ales",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1010",
    doi = "10.26615/978-954-452-056-4_010",
    pages = "77--83",
    abstract = "Propaganda of various pressure groups ranging from big economies to ideological blocks is often presented in a form of objective newspaper texts. However, the real objectivity is here shaded with the support of imbalanced views and distorted attitudes by means of various manipulative stylistic techniques. In the project of Manipulative Propaganda Techniques in the Age of Internet, a new resource for automatic analysis of stylistic mechanisms for influencing the readers{'} opinion is developed. In its current version, the resource consists of 7,494 newspaper articles from four selected Czech digital news servers annotated for the presence of specific manipulative techniques. In this paper, we present the current state of the annotations and describe the structure of the dataset in detail. We also offer an evaluation of bag-of-words classification algorithms for the annotated manipulative techniques.",
}
@inproceedings{basile-etal-2019-diachronic,
    title = "Diachronic Analysis of Entities by Exploiting {W}ikipedia Page revisions",
    author = "Basile, Pierpaolo  and
      Caputo, Annalina  and
      Lawless, Seamus  and
      Semeraro, Giovanni",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1011",
    doi = "10.26615/978-954-452-056-4_011",
    pages = "84--91",
    abstract = "In the last few years, the increasing availability of large corpora spanning several time periods has opened new opportunities for the diachronic analysis of language. This type of analysis can bring to the light not only linguistic phenomena related to the shift of word meanings over time, but it can also be used to study the impact that societal and cultural trends have on this language change. This paper introduces a new resource for performing the diachronic analysis of named entities built upon Wikipedia page revisions. This resource enables the analysis over time of changes in the relations between entities (concepts), surface forms (words), and the contexts surrounding entities and surface forms, by analysing the whole history of Wikipedia internal links. We provide some useful use cases that prove the impact of this resource on diachronic studies and delineate some possible future usage.",
}
@inproceedings{bebeshina-clairet-etal-2019-using,
    title = "Using a Lexical Semantic Network for the Ontology Building",
    author = "Bebeshina-Clairet, Nadia  and
      Despres, Sylvie  and
      Lafourcade, Mathieu",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1012",
    doi = "10.26615/978-954-452-056-4_012",
    pages = "92--101",
    abstract = "Building multilingual ontologies is a hard task as ontologies are often data-rich resources. We introduce an approach which allows exploiting structured lexical semantic knowledge for the ontology building. Given a multilingual lexical semantic (non ontological) resource and an ontology model, it allows mining relevant semantic knowledge and make the ontology building and enhancement process faster.",
}
@inproceedings{beloucif-etal-2019-naive,
    title = "Naive Regularizers for Low-Resource Neural Machine Translation",
    author = "Beloucif, Meriem  and
      Gonzalez, Ana Valeria  and
      Bollmann, Marcel  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1013",
    doi = "10.26615/978-954-452-056-4_013",
    pages = "102--111",
    abstract = "Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios. Neural models have to be trained on large amounts of data and have been shown to perform poorly when only limited data is available. We show that using naive regularization methods, based on sentence length, punctuation and word frequencies, to penalize translations that are very different from the input sentences, consistently improves the translation quality across multiple low-resource languages. We experiment with 12 language pairs, varying the training data size between 17k to 230k sentence pairs. Our best regularizer achieves an average increase of 1.5 BLEU score and 1.0 TER score across all the language pairs. For example, we achieve a BLEU score of 26.70 on the IWSLT15 English{--}Vietnamese translation task simply by using relative differences in punctuation as a regularizer.",
}
@inproceedings{beschke-2019-exploring,
    title = "Exploring Graph-Algebraic {CCG} Combinators for Syntactic-Semantic {AMR} Parsing",
    author = "Beschke, Sebastian",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1014",
    doi = "10.26615/978-954-452-056-4_014",
    pages = "112--121",
    abstract = "We describe a new approach to semantic parsing based on Combinatory Categorial Grammar (CCG). The grammar{'}s semantic construction operators are defined in terms of a graph algebra, which allows our system to induce a compact CCG lexicon. We introduce an expectation maximisation algorithm which we use to filter our lexicon down to 2500 lexical templates. Our system achieves a semantic triple (Smatch) precision that is competitive with other CCG-based AMR parsing approaches.",
}
@inproceedings{bevilacqua-navigli-2019-quasi,
    title = "Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation",
    author = "Bevilacqua, Michele  and
      Navigli, Roberto",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1015",
    doi = "10.26615/978-954-452-056-4_015",
    pages = "122--131",
    abstract = "While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet. In this paper, we introduce QBERT, a Transformer-based architecture for contextualized embeddings which makes use of a co-attentive layer to produce more deeply bidirectional representations, better-fitting for the WSD task. As a result, we are able to train a WSD system that beats the state of the art on the concatenation of all evaluation datasets by over 3 points, also outperforming a comparable model using ELMo.",
}
@inproceedings{bloem-etal-2019-evaluating,
    title = "Evaluating the Consistency of Word Embeddings from Small Data",
    author = "Bloem, Jelke  and
      Fokkens, Antske  and
      Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1016",
    doi = "10.26615/978-954-452-056-4_016",
    pages = "132--141",
    abstract = "In this work, we address the evaluation of distributional semantic models trained on smaller, domain-specific texts, specifically, philosophical text. Specifically, we inspect the behaviour of models using a pre-trained background space in learning. We propose a measure of consistency which can be used as an evaluation metric when no in-domain gold-standard data is available. This measure simply computes the ability of a model to learn similar embeddings from different parts of some homogeneous data. We show that in spite of being a simple evaluation, consistency actually depends on various combinations of factors, including the nature of the data itself, the model used to train the semantic space, and the frequency of the learnt terms, both in the background space and in the in-domain data of interest.",
}
@inproceedings{bodirlau-etal-2019-cross,
    title = "Cross-Domain Training for Goal-Oriented Conversational Agents",
    author = "Bod{\^\i}rl{\u{a}}u, Alexandra Maria  and
      Budulan, Stefania  and
      Rebedea, Traian",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1017",
    doi = "10.26615/978-954-452-056-4_017",
    pages = "142--150",
    abstract = "Goal-Oriented Chatbots in fields such as customer support, providing certain information or general help with bookings or reservations, suffer from low performance partly due to the difficulty of obtaining large domain-specific annotated datasets. Given that the problem is closely related to the domain of the conversational agent and the data belonging to a specific domain is difficult to annotate, there have been some attempts at surpassing these challenges such as unsupervised pre-training or transfer learning between different domains. A more thorough analysis of the transfer learning mechanism is justified by the significant improvement of the results demonstrated in the results section. We describe extensive experiments using transfer learning and warm-starting techniques with improvements of more than 5{\%} in relative percentage of success rate in the majority of cases, and up to 10x faster convergence as opposed to training the system without them.",
}
@inproceedings{bohn-etal-2019-learning,
    title = "Learning Sentence Embeddings for Coherence Modelling and Beyond",
    author = "Bohn, Tanner  and
      Hu, Yining  and
      Zhang, Jinhang  and
      Ling, Charles",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1018",
    doi = "10.26615/978-954-452-056-4_018",
    pages = "151--160",
    abstract = "We present a novel and effective technique for performing text coherence tasks while facilitating deeper insights into the data. Despite obtaining ever-increasing task performance, modern deep-learning approaches to NLP tasks often only provide users with the final network decision and no additional understanding of the data. In this work, we show that a new type of sentence embedding learned through self-supervision can be applied effectively to text coherence tasks while serving as a window through which deeper understanding of the data can be obtained. To produce these sentence embeddings, we train a recurrent neural network to take individual sentences and predict their location in a document in the form of a distribution over locations. We demonstrate that these embeddings, combined with simple visual heuristics, can be used to achieve performance competitive with state-of-the-art on multiple text coherence tasks, outperforming more complex and specialized approaches. Additionally, we demonstrate that these embeddings can provide insights useful to writers for improving writing quality and informing document structuring, and assisting readers in summarizing and locating information.",
}
@inproceedings{boytcheva-etal-2019-risk,
    title = "Risk Factors Extraction from Clinical Texts based on Linked Open Data",
    author = "Boytcheva, Svetla  and
      Angelova, Galia  and
      Angelov, Zhivko",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1019",
    doi = "10.26615/978-954-452-056-4_019",
    pages = "161--167",
    abstract = "This paper presents experiments in risk factors analysis based on clinical texts enhanced with Linked Open Data (LOD). The idea is to determine whether a patient has risk factors for a specific disease analyzing only his/her outpatient records. A semantic graph of {``}meta-knowledge{''} about a disease of interest is constructed, with integrated multilingual terms (labels) of symptoms, risk factors etc. coming from Wikidata, PubMed, Wikipedia and MESH, and linked to clinical records of individual patients via ICD{--}10 codes. Then a predictive model is trained to foretell whether patients are at risk to develop the disease of interest. The testing was done using outpatient records from a nation-wide repository available for the period 2011-2016. The results show improvement of the overall performance of all tested algorithms (kNN, Naive Bayes, Tree, Logistic regression, ANN), when the clinical texts are enriched with LOD resources.",
}
@inproceedings{cardon-grabar-2019-parallel,
    title = "Parallel Sentence Retrieval From Comparable Corpora for Biomedical Text Simplification",
    author = "Cardon, R{\'e}mi  and
      Grabar, Natalia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1020",
    doi = "10.26615/978-954-452-056-4_020",
    pages = "168--177",
    abstract = "Parallel sentences provide semantically similar information which can vary on a given dimension, such as language or register. Parallel sentences with register variation (like expert and non-expert documents) can be exploited for the automatic text simplification. The aim of automatic text simplification is to better access and understand a given information. In the biomedical field, simplification may permit patients to understand medical and health texts. Yet, there is currently no such available resources. We propose to exploit comparable corpora which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences. These corpora are in French and are related to the biomedical area. Manually created reference data show 0.76 inter-annotator agreement. Our purpose is to state whether a given pair of specialized and simplified sentences is parallel and can be aligned or not. We treat this task as binary classification (alignment/non-alignment). We perform experiments with a controlled ratio of imbalance and on the highly unbalanced real data. Our results show that the method we present here can be used to automatically generate a corpus of parallel sentences from our comparable corpus.",
}
@inproceedings{casey-etal-2019-classifying,
    title = "Classifying Author Intention for Writer Feedback in Related Work",
    author = "Casey, Arlene  and
      Webber, Bonnie  and
      Glowacka, Dorota",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1021",
    doi = "10.26615/978-954-452-056-4_021",
    pages = "178--187",
    abstract = "The ability to produce high-quality publishable material is critical to academic success but many Post-Graduate students struggle to learn to do so. While recent years have seen an increase in tools designed to provide feedback on aspects of writing, one aspect that has so far been neglected is the Related Work section of academic research papers. To address this, we have trained a supervised classifier on a corpus of 94 Related Work sections and evaluated it against a manually annotated gold standard. The classifier uses novel features pertaining to citation types and co-reference, along with patterns found from studying Related Works. We show that these novel features contribute to classifier performance with performance being favourable compared to other similar works that classify author intentions and consider feedback for academic writing.",
}
@inproceedings{chakraborty-etal-2019-sparse,
    title = "Sparse Victory {--} A Large Scale Systematic Comparison of count-based and prediction-based vectorizers for text classification",
    author = "Chakraborty, Rupak  and
      Elhence, Ashima  and
      Arora, Kapil",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1022",
    doi = "10.26615/978-954-452-056-4_022",
    pages = "188--197",
    abstract = "In this paper we study the performance of several text vectorization algorithms on a diverse collection of 73 publicly available datasets. Traditional sparse vectorizers like Tf-Idf and Feature Hashing have been systematically compared with the latest state of the art neural word embeddings like Word2Vec, GloVe, FastText and character embeddings like ELMo, Flair. We have carried out an extensive analysis of the performance of these vectorizers across different dimensions like classification metrics (.i.e. precision, recall, accuracy), dataset-size, and imbalanced data (in terms of the distribution of the number of class labels). Our experiments reveal that the sparse vectorizers beat the neural word and character embedding models on 61 of the 73 datasets by an average margin of 3-5{\%} (in terms of macro f1 score) and this performance is consistent across the different dimensions of comparison.",
}
@inproceedings{charfi-etal-2019-fine,
    title = "A Fine-Grained Annotated Multi-Dialectal {A}rabic Corpus",
    author = "Charfi, Anis  and
      Zaghouani, Wajdi  and
      Mehdi, Syed Hassan  and
      Mohamed, Esraa",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1023",
    doi = "10.26615/978-954-452-056-4_023",
    pages = "198--204",
    abstract = "We present ARAP-Tweet 2.0, a corpus of 5 million dialectal Arabic tweets and 50 million words of about 3000 Twitter users from 17 Arab countries. Compared to the first version, the new corpus has significant improvements in terms of the data volume and the annotation quality. It is fully balanced with respect to dialect, gender, and three age groups: under 25 years, between 25 and 34, and 35 years and above. This paper describes the process of creating the corpus starting from gathering the dialectal phrases to find the users, to annotating their accounts and retrieving their tweets. We also report on the evaluation of the annotation quality using the inter-annotator agreement measures which were applied to the whole corpus and not just a subset. The obtained results were substantial with average Cohen{'}s Kappa values of 0.99, 0.92, and 0.88 for the annotation of gender, dialect, and age respectively. We also discuss some challenges encountered when developing this corpus.s.",
}
@inproceedings{costa-paraboni-2019-personality,
    title = "Personality-dependent Neural Text Summarization",
    author = "Costa, Pablo  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1024",
    doi = "10.26615/978-954-452-056-4_024",
    pages = "205--212",
    abstract = "In Natural Language Generation systems, personalization strategies - i.e, the use of information about a target author to generate text that (more) closely resembles human-produced language - have long been applied to improve results. The present work addresses one such strategy - namely, the use of Big Five personality information about the target author - applied to the case of abstractive text summarization using neural sequence-to-sequence models. Initial results suggest that having access to personality information does lead to more accurate (or human-like) text summaries, and paves the way for more robust systems of this kind.",
}
@inproceedings{cui-bollegala-2019-self,
    title = "Self-Adaptation for Unsupervised Domain Adaptation",
    author = "Cui, Xia  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1025",
    doi = "10.26615/978-954-452-056-4_025",
    pages = "213--222",
    abstract = "Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches. Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain. Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction. We then use the trained classifier to predict pseudo labels for the target domain unlabelled data. Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels. Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations.",
}
@inproceedings{dalloux-etal-2019-speculation,
    title = "Speculation and Negation detection in {F}rench biomedical corpora",
    author = "Dalloux, Cl{\'e}ment  and
      Claveau, Vincent  and
      Grabar, Natalia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1026",
    doi = "10.26615/978-954-452-056-4_026",
    pages = "223--232",
    abstract = "In this work, we propose to address the detection of negation and speculation, and of their scope, in French biomedical documents. It has been indeed observed that they play an important role and provide crucial clues for other NLP applications. Our methods are based on CRFs and BiLSTM. We reach up to 97.21 {\%} and 91.30 {\%} F-measure for the detection of negation and speculation cues, respectively, using CRFs. For the computing of scope, we reach up to 90.81 {\%} and 86.73 {\%} F-measure on negation and speculation, respectively, using BiLSTM-CRF fed with word embeddings.",
}
@inproceedings{declerck-racioppa-2019-porting,
    title = "Porting Multilingual Morphological Resources to {O}nto{L}ex-Lemon",
    author = "Declerck, Thierry  and
      Racioppa, Stefania",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1027",
    doi = "10.26615/978-954-452-056-4_027",
    pages = "233--238",
    abstract = "We describe work consisting in porting various morphological resources to the OntoLex-Lemon model. A main objective of this work is to offer a uniform representation of different morphological data sets in order to be able to compare and interlink multilingual resources and to cross-check and interlink or merge the content of morphological resources of one and the same language. The results of our work will be published on the Linguistic Linked Open Data cloud.",
}
@inproceedings{deguchi-etal-2019-dependency,
    title = "Dependency-Based Self-Attention for Transformer {NMT}",
    author = "Deguchi, Hiroyuki  and
      Tamura, Akihiro  and
      Ninomiya, Takashi",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1028",
    doi = "10.26615/978-954-452-056-4_028",
    pages = "239--246",
    abstract = "In this paper, we propose a new Transformer neural machine translation (NMT) model that incorporates dependency relations into self-attention on both source and target sides, dependency-based self-attention. The dependency-based self-attention is trained to attend to the modifiee for each token under constraints based on the dependency relations, inspired by Linguistically-Informed Self-Attention (LISA). While LISA is originally proposed for Transformer encoder for semantic role labeling, this paper extends LISA to Transformer NMT by masking future information on words in the decoder-side dependency-based self-attention. Additionally, our dependency-based self-attention operates at sub-word units created by byte pair encoding. The experiments show that our model improves 1.0 BLEU points over the baseline model on the WAT{'}18 Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.",
}
@inproceedings{dinkov-etal-2019-detecting,
    title = "Detecting Toxicity in News Articles: Application to {B}ulgarian",
    author = "Dinkov, Yoan  and
      Koychev, Ivan  and
      Nakov, Preslav",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1029",
    doi = "10.26615/978-954-452-056-4_029",
    pages = "247--258",
    abstract = "Online media aim for reaching ever bigger audience and for attracting ever longer attention span. This competition creates an environment that rewards sensational, fake, and toxic news. To help limit their spread and impact, we propose and develop a news toxicity detector that can recognize various types of toxic content. While previous research primarily focused on English, here we target Bulgarian. We created a new dataset by crawling a website that for five years has been collecting Bulgarian news articles that were manually categorized into eight toxicity groups. Then we trained a multi-class classifier with nine categories: eight toxic and one non-toxic. We experimented with different representations based on ElMo, BERT, and XLM, as well as with a variety of domain-specific features. Due to the small size of our dataset, we created a separate model for each feature type, and we ultimately combined these models into a meta-classifier. The evaluation results show an accuracy of 59.0{\%} and a macro-F1 score of 39.7{\%}, which represent sizable improvements over the majority-class baseline (Acc=30.3{\%}, macro-F1=5.2{\%}).",
}
@inproceedings{eder-etal-2019-de,
    title = "De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a {G}erman Email Corpus",
    author = "Eder, Elisabeth  and
      Krieg-Holz, Ulrike  and
      Hahn, Udo",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1030",
    doi = "10.26615/978-954-452-056-4_030",
    pages = "259--269",
    abstract = "We deal with the pseudonymization of those stretches of text in emails that might allow to identify real individual persons. This task is decomposed into two steps. First, named entities carrying privacy-sensitive information (e.g., names of persons, locations, phone numbers or dates) are identified, and, second, these privacy-bearing entities are replaced by synthetically generated surrogates (e.g., a person originally named {`}John Doe{'} is renamed as {`}Bill Powers{'}). We describe a system architecture for surrogate generation and evaluate our approach on CodeAlltag, a German email corpus.",
}
@inproceedings{eremeev-vorontsov-2019-lexical,
    title = "Lexical Quantile-Based Text Complexity Measure",
    author = "Eremeev, Maksim  and
      Vorontsov, Konstantin",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1031",
    doi = "10.26615/978-954-452-056-4_031",
    pages = "270--275",
    abstract = "This paper introduces a new approach to estimating the text document complexity. Common readability indices are based on average length of sentences and words. In contrast to these methods, we propose to count the number of rare words occurring abnormally often in the document. We use the reference corpus of texts and the quantile approach in order to determine what words are rare, and what frequencies are abnormal. We construct a general text complexity model, which can be adjusted for the specific task, and introduce two special models. The experimental design is based on a set of thematically similar pairs of Wikipedia articles, labeled using crowdsourcing. The experiments demonstrate the competitiveness of the proposed approach.",
}
@inproceedings{estevez-velarde-etal-2019-demo,
    title = "Demo Application for {LETO}: Learning Engine Through Ontologies",
    author = "Estevez-Velarde, Suilan  and
      Montoyo, Andr{\'e}s  and
      Almeida-Cruz, Yudivian  and
      Guti{\'e}rrez, Yoan  and
      Piad-Morffis, Alejandro  and
      Mu{\~n}oz, Rafael",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1032",
    doi = "10.26615/978-954-452-056-4_032",
    pages = "276--284",
    abstract = "The massive amount of multi-formatted information available on the Web necessitates the design of software systems that leverage this information to obtain knowledge that is valid and useful. The main challenge is to discover relevant information and continuously update, enrich and integrate knowledge from various sources of structured and unstructured data. This paper presents the Learning Engine Through Ontologies(LETO) framework, an architecture for the continuous and incremental discovery of knowledge from multiple sources of unstructured and structured data. We justify the main design decision behind LETO{'}s architecture and evaluate the framework{'}s feasibility using the Internet Movie Data Base(IMDB) and Twitter as a practical application.",
}
@inproceedings{evans-orasan-2019-sentence,
    title = "Sentence Simplification for Semantic Role Labelling and Information Extraction",
    author = "Evans, Richard  and
      Orasan, Constantin",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1033",
    doi = "10.26615/978-954-452-056-4_033",
    pages = "285--294",
    abstract = "In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.",
}
@inproceedings{fadhil-aburaed-2019-ollobot,
    title = "{O}llo{B}ot - Towards A Text-Based {A}rabic Health Conversational Agent: Evaluation and Results",
    author = "Fadhil, Ahmed  and
      AbuRa{'}ed, Ahmed",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1034",
    doi = "10.26615/978-954-452-056-4_034",
    pages = "295--303",
    abstract = "We introduce OlloBot, an Arabic conversational agent that assists physicians and supports patients with the care process. It doesn{'}t replace the physicians, instead provides health tracking and support and assists physicians with the care delivery through a conversation medium. The current model comprises healthy diet, physical activity, mental health, in addition to food logging. Not only OlloBot tracks user daily food, it also offers useful tips for healthier living. We will discuss the design, development and testing of OlloBot, and highlight the findings and limitations arose from the testing.",
}
@inproceedings{faggionato-meelen-2019-developing,
    title = "Developing the {O}ld {T}ibetan Treebank",
    author = "Faggionato, Christian  and
      Meelen, Marieke",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1035",
    doi = "10.26615/978-954-452-056-4_035",
    pages = "304--312",
    abstract = "This paper presents a full procedure for the development of a segmented, POS-tagged and chunkparsed corpus of Old Tibetan. As an extremely low-resource language, Old Tibetan poses non-trivial problems in every step towards the development of a searchable treebank. We demonstrate, however, that a carefully developed, semisupervised method of optimising and extending existing tools for Classical Tibetan, as well as creating specific ones for Old Tibetan can address these issues. We thus also present the first very Tibetan Treebank in a variety of formats to facilitate research in the fields of NLP, historical linguistics and Tibetan Studies.",
}
@inproceedings{feijo-moreira-2019-summarizing,
    title = "Summarizing Legal Rulings: Comparative Experiments",
    author = "Feijo, Diego  and
      Moreira, Viviane",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1036",
    doi = "10.26615/978-954-452-056-4_036",
    pages = "313--322",
    abstract = "In the context of text summarization, texts in the legal domain have peculiarities related to their length and to their specialized vocabulary. Recent neural network-based approaches can achieve high-quality scores for text summarization. However, these approaches have been used mostly for generating very short abstracts for news articles. Thus, their applicability to the legal domain remains an open issue. In this work, we experimented with ten extractive and four abstractive models in a real dataset of legal rulings. These models were compared with an extractive baseline based on heuristics to select the most relevant parts of the text. Our results show that abstractive approaches significantly outperform extractive methods in terms of ROUGE scores.",
}
@inproceedings{felice-buttery-2019-entropy,
    title = "Entropy as a Proxy for Gap Complexity in Open Cloze Tests",
    author = "Felice, Mariano  and
      Buttery, Paula",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1037",
    doi = "10.26615/978-954-452-056-4_037",
    pages = "323--327",
    abstract = "This paper presents a pilot study of entropy as a measure of gap complexity in open cloze tests aimed at learners of English. Entropy is used to quantify the information content in each gap, which can be used to estimate complexity. Our study shows that average gap entropy correlates positively with proficiency levels while individual gap entropy can capture contextual complexity. To the best of our knowledge, this is the first unsupervised information-theoretical approach to evaluating the quality of cloze tests.",
}
@inproceedings{fell-etal-2019-song,
    title = "Song Lyrics Summarization Inspired by Audio Thumbnailing",
    author = "Fell, Michael  and
      Cabrio, Elena  and
      Gandon, Fabien  and
      Giboin, Alain",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1038",
    doi = "10.26615/978-954-452-056-4_038",
    pages = "328--337",
    abstract = "Given the peculiar structure of songs, applying generic text summarization methods to lyrics can lead to the generation of highly redundant and incoherent text. In this paper, we propose to enhance state-of-the-art text summarization approaches with a method inspired by audio thumbnailing. Instead of searching for the thumbnail clues in the audio of the song, we identify equivalent clues in the lyrics. We then show how these summaries that take into account the audio nature of the lyrics outperform the generic methods according to both an automatic evaluation and human judgments.",
}
@inproceedings{fell-etal-2019-comparing,
    title = "Comparing Automated Methods to Detect Explicit Content in Song Lyrics",
    author = "Fell, Michael  and
      Cabrio, Elena  and
      Corazza, Michele  and
      Gandon, Fabien",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1039",
    doi = "10.26615/978-954-452-056-4_039",
    pages = "338--344",
    abstract = "The Parental Advisory Label (PAL) is a warning label that is placed on audio recordings in recognition of profanity or inappropriate references, with the intention of alerting parents of material potentially unsuitable for children. Since 2015, digital providers {--} such as iTunes, Spotify, Amazon Music and Deezer {--} also follow PAL guidelines and tag such tracks as {``}explicit{''}. Nowadays, such labelling is carried out mainly manually on voluntary basis, with the drawbacks of being time consuming and therefore costly, error prone and partly a subjective task. In this paper, we compare automated methods ranging from dictionary-based lookup to state-of-the-art deep neural networks to automatically detect explicit contents in English lyrics. We show that more complex models perform only slightly better on this task, and relying on a qualitative analysis of the data, we discuss the inherent hardness and subjectivity of the task.",
}
@inproceedings{franzoi-etal-2019-linguistic,
    title = "Linguistic classification: dealing jointly with irrelevance and inconsistency",
    author = "Franzoi, Laura  and
      Sgarro, Andrea  and
      Dinu, Anca  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1040",
    doi = "10.26615/978-954-452-056-4_040",
    pages = "345--352",
    abstract = "In this paper, we present new methods for language classification which put to good use both syntax and fuzzy tools, and are capable of dealing with irrelevant linguistic features (i.e. features which should not contribute to the classification) and even inconsistent features (which do not make sense for specific languages). We introduce a metric distance, based on the generalized Steinhaus transform, which allows one to deal jointly with irrelevance and inconsistency. To evaluate our methods, we test them on a syntactic data set, due to the linguist G. Longobardi and his school. We obtain phylogenetic trees which sometimes outperform the ones obtained by Atkinson and Gray.",
}
@inproceedings{gafni-2019-corpus,
    title = "Corpus Lexicography in a Wider Context",
    author = "Gafni, Chen",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1041",
    doi = "10.26615/978-954-452-056-4_041",
    pages = "353--359",
    abstract = "This paper describes a set of tools that offers comprehensive solutions for corpus lexicography. The tools perform a range of tasks, including construction of corpus lexicon, integrating information from external dictionaries, internal analysis of the lexicon, and lexical analysis of the corpus. The set of tools is particularly useful for creating dictionaries for under-resourced languages. The tools are integrated in a general-purpose software that includes additional tools for various research tasks, such as linguistic development analysis. Equipped with a user-friendly interface, the described system can be easily incorporated in research in a variety of fields.",
}
@inproceedings{gafni-2019-universal,
    title = "A Universal System for Automatic Text-to-Phonetics Conversion",
    author = "Gafni, Chen",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1042",
    doi = "10.26615/978-954-452-056-4_042",
    pages = "360--366",
    abstract = "This paper describes an automatic text-to-phonetics conversion system. The system was constructed to primarily serve as a research tool. It is implemented in a general-purpose linguistic software, which allows it to be incorporated in a multifaceted linguistic research in essentially any language. The system currently relies on two mechanisms to generate phonetic transcriptions from texts: (i) importing ready-made phonetic word forms from external dictionaries, and (ii) automatic generation of phonetic word forms based on a set of deterministic linguistic rules. The current paper describes the proposed system and its potential application to linguistic research.",
}
@inproceedings{galitsky-ilvovsky-2019-two,
    title = "Two Discourse Tree - Based Approaches to Indexing Answers",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1043",
    doi = "10.26615/978-954-452-056-4_043",
    pages = "367--372",
    abstract = "We explore anatomy of answers with respect to which text fragments from an answer are worth matching with a question and which should not be matched. We apply the Rhetorical Structure Theory to build a discourse tree of an answer and select elementary discourse units that are suitable for indexing. Manual rules for selection of these discourse units as well as automated classification based on web search engine mining are evaluated con-cerning improving search accuracy. We form two sets of question-answer pairs for FAQ and community QA search domains and use them for evaluation of the proposed indexing methodology, which delivers up to 16 percent improvement in search recall.",
}
@inproceedings{galitsky-ilvovsky-2019-discourse,
    title = "Discourse-Based Approach to Involvement of Background Knowledge for Question Answering",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1044",
    doi = "10.26615/978-954-452-056-4_044",
    pages = "373--381",
    abstract = "We introduce a concept of a virtual discourse tree to improve question answering (Q/A) recall for complex, multi-sentence questions. Augmenting the discourse tree of an answer with tree fragments obtained from text corpora playing the role of ontology, we obtain on the fly a canonical discourse representation of this answer that is independent of the thought structure of a given author. This mechanism is critical for finding an answer that is not only relevant in terms of questions entities but also in terms of inter-relations between these entities in an answer and its style. We evaluate the Q/A system enabled with virtual discourse trees and observe a substantial increase of performance answering complex questions such as Yahoo! Answers and www.2carpros.com.",
}
@inproceedings{galitsky-etal-2019-chatbot,
    title = "On a Chatbot Providing Virtual Dialogues",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry  and
      Goncharova, Elizaveta",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1045",
    doi = "10.26615/978-954-452-056-4_045",
    pages = "382--387",
    abstract = "We present a chatbot that delivers content in the form of virtual dialogues automatically produced from the plain texts that are extracted and selected from the documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions that are automatically generated for these answers based on the initial text.",
}
@inproceedings{ghazouani-etal-2019-assessing,
    title = "Assessing socioeconomic status of {T}witter users: A survey",
    author = "Ghazouani, Dhouha  and
      Lancieri, Luigi  and
      Ounelli, Habib  and
      Jebari, Chaker",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1046",
    doi = "10.26615/978-954-452-056-4_046",
    pages = "388--398",
    abstract = "Every day, the emotion and opinion of different people across the world are reflected in the form of short messages using microblogging platforms. Despite the existence of enormous potential introduced by this data source, the Twitter community is still ambiguous and is not fully explored yet. While there are a huge number of studies examining the possibilities of inferring gender and age, there exist hardly researches on socioeconomic status (SES) inference of Twitter users. As socioeconomic status is essential to treating diverse questions linked to human behavior in several fields (sociology, demography, public health, etc.), we conducted a comprehensive literature review of SES studies, inference methods, and metrics. With reference to the research on literature{'}s results, we came to outline the most critical challenges for researchers. To the best of our knowledge, this paper is the first review that introduces the different aspects of SES inference. Indeed, this article provides the benefits for practitioners who aim to process and explore Twitter SES inference.",
}
@inproceedings{gold-zesch-2019-divide,
    title = "Divide and Extract {--} Disentangling Clause Splitting and Proposition Extraction",
    author = "Gold, Darina  and
      Zesch, Torsten",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1047",
    doi = "10.26615/978-954-452-056-4_047",
    pages = "399--408",
    abstract = "Proposition extraction from sentences is an important task for information extraction systems Evaluation of such systems usually conflates two aspects: splitting complex sentences into clauses and the extraction of propositions. It is thus difficult to independently determine the quality of the proposition extraction step. We create a manually annotated proposition dataset from sentences taken from restaurant reviews that distinguishes between clauses that need to be split and those that do not. The resulting proposition evaluation dataset allows us to independently compare the performance of proposition extraction systems on simple and complex clauses. Although performance drastically drops on more complex sentences, we show that the same systems perform best on both simple and complex clauses. Furthermore, we show that specific kinds of subordinate clauses pose difficulties to most systems.",
}
@inproceedings{grzybowski-etal-2019-sparse,
    title = "Sparse Coding in Authorship Attribution for {P}olish Tweets",
    author = "Grzybowski, Piotr  and
      Juralewicz, Ewa  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1048",
    doi = "10.26615/978-954-452-056-4_048",
    pages = "409--417",
    abstract = "The study explores application of a simple Convolutional Neural Network for the problem of authorship attribution of tweets written in Polish. In our solution we use two-step compression of tweets using Byte Pair Encoding algorithm and vectorisation as an input to the distributional model generated for the large corpus of Polish tweets by word2vec algorithm. Our method achieves results comparable to the state-of-the-art approaches for the similar task on English tweets and expresses a very good performance in the classification of Polish tweets. We tested the proposed method in relation to the number of authors and tweets per author. We also juxtaposed results for authors with different topic backgrounds against each other.",
}
@inproceedings{ha-yaneva-2019-automatic,
    title = "Automatic Question Answering for Medical {MCQ}s: Can It go Further than Information Retrieval?",
    author = "Ha, Le An  and
      Yaneva, Victoria",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1049",
    doi = "10.26615/978-954-452-056-4_049",
    pages = "418--422",
    abstract = "We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require that the training data come from the same source as the questions. We evaluate the system performance on a challenging set of university-level medical science multiple-choice questions. Best performance is achieved when combining a neural approach with an IR approach, both of which work independently. Unlike previous approaches, the system achieves statistically significant improvement over the random guess baseline even for questions that are labeled as challenging based on the performance of baseline solvers.",
}
@inproceedings{hahn-choi-2019-self,
    title = "Self-Knowledge Distillation in Natural Language Processing",
    author = "Hahn, Sangchul  and
      Choi, Heeyoul",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1050",
    doi = "10.26615/978-954-452-056-4_050",
    pages = "423--430",
    abstract = "Since deep learning became a key player in natural language processing (NLP), many deep learning models have been showing remarkable performances in a variety of NLP tasks. Such high performance can be explained by efficient knowledge representation of deep learning models. Knowledge distillation from pretrained deep networks suggests that we can use more information from the soft target probability to train other neural networks. In this paper, we propose a self-knowledge distillation method, based on the soft target probabilities of the training model itself, where multimode information is distilled from the word embedding space right below the softmax layer. Due to the time complexity, our method approximates the soft target probabilities. In experiments, we applied the proposed method to two different and fundamental NLP tasks: language model and neural machine translation. The experiment results show that our proposed method improves performance on the tasks.",
}
@inproceedings{hamalainen-hengchen-2019-paft,
    title = "From the Paft to the Fiiture: a Fully Automatic {NMT} and Word Embeddings Method for {OCR} Post-Correction",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Hengchen, Simon},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1051",
    doi = "10.26615/978-954-452-056-4_051",
    pages = "431--436",
    abstract = "A great deal of historical corpora suffer from errors introduced by the OCR (optical character recognition) methods used in the digitization process. Correcting these errors manually is a time-consuming process and a great part of the automatic approaches have been relying on rules or supervised machine learning. We present a fully automatic unsupervised way of extracting parallel data for training a character-based sequence-to-sequence NMT (neural machine translation) model to conduct OCR error correction.",
}
@inproceedings{haque-etal-2019-investigating,
    title = "Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on {E}nglish-to-{H}indi and {H}indi-to-{E}nglish",
    author = "Haque, Rejwanul  and
      Hasanuzzaman, Md  and
      Way, Andy",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1052",
    doi = "10.26615/978-954-452-056-4_052",
    pages = "437--446",
    abstract = "Terminology translation plays a critical role in domain-specific machine translation (MT). In this paper, we conduct a comparative qualitative evaluation on terminology translation in phrase-based statistical MT (PB-SMT) and neural MT (NMT) in two translation directions: English-to-Hindi and Hindi-to-English. For this, we select a test set from a legal domain corpus and create a gold standard for evaluating terminology translation in MT. We also propose an error typology taking the terminology translation errors into consideration. We evaluate the MT systems{'} performance on terminology translation, and demonstrate our findings, unraveling strengths, weaknesses, and similarities of PB-SMT and NMT in the area of term translation.",
}
@inproceedings{hardalov-etal-2019-beyond,
    title = "Beyond {E}nglish-Only Reading Comprehension: Experiments in Zero-shot Multilingual Transfer for {B}ulgarian",
    author = "Hardalov, Momchil  and
      Koychev, Ivan  and
      Nakov, Preslav",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1053",
    doi = "10.26615/978-954-452-056-4_053",
    pages = "447--459",
    abstract = "Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc. This is largely due to the release of pre-trained contextualized representations such as BERT and ELMo, which can be fine-tuned for the target task. Despite those advances and the creation of more challenging datasets, most of the work is still done for English. Here, we study the effectiveness of multilingual BERT fine-tuned on large-scale English datasets for reading comprehension (e.g., for RACE), and we apply it to Bulgarian multiple-choice reading comprehension. We propose a new dataset containing 2,221 questions from matriculation exams for twelfth grade in various subjects {---}history, biology, geography and philosophy{---}, and 412 additional questions from online quizzes in history. While the quiz authors gave no relevant context, we incorporate knowledge from Wikipedia, retrieving documents matching the combination of question + each answer option. Moreover, we experiment with different indexing and pre-training strategies. The evaluation results show accuracy of 42.23{\%}, which is well above the baseline of 24.89{\%}.",
}
@inproceedings{hazem-hernandez-2019-tweaks,
    title = "Tweaks and Tricks for Word Embedding Disruptions",
    author = "Hazem, Amir  and
      Hernandez, Nicolas",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1054",
    doi = "10.26615/978-954-452-056-4_054",
    pages = "460--464",
    abstract = "Word embeddings are established as very effective models used in several NLP applications. If they differ in their architecture and training process, they often exhibit similar properties and remain vector space models with continuously-valued dimensions describing the observed data. The complexity resides in the developed strategies for learning the values within each dimensional space. In this paper, we introduce the concept of disruption which we define as a side effect of the training process of embedding models. Disruptions are viewed as a set of embedding values that are more likely to be noise than effective descriptive features. We show that dealing with disruption phenomenon is of a great benefit to bottom-up sentence embedding representation. By contrasting several in-domain and pre-trained embedding models, we propose two simple but very effective tweaking techniques that yield strong empirical improvements on textual similarity task.",
}
@inproceedings{hazem-hernandez-2019-meta,
    title = "Meta-Embedding Sentence Representation for Textual Similarity",
    author = "Hazem, Amir  and
      Hernandez, Nicolas",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1055",
    doi = "10.26615/978-954-452-056-4_055",
    pages = "465--473",
    abstract = "Word embedding models are now widely used in most NLP applications. Despite their effectiveness, there is no clear evidence about the choice of the most appropriate model. It often depends on the nature of the task and on the quality and size of the used data sets. This remains true for bottom-up sentence embedding models. However, no straightforward investigation has been conducted so far. In this paper, we propose a systematic study of the impact of the main word embedding models on sentence representation. By contrasting in-domain and pre-trained embedding models, we show under which conditions they can be jointly used for bottom-up sentence embeddings. Finally, we propose the first bottom-up meta-embedding representation at the sentence level for textual similarity. Significant improvements are observed in several tasks including question-to-question similarity, paraphrasing and next utterance ranking.",
}
@inproceedings{hettiarachchi-ranasinghe-2019-emoji,
    title = "Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media",
    author = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1056",
    doi = "10.26615/978-954-452-056-4_056",
    pages = "474--480",
    abstract = "This paper describes a novel research approach to detect type and target of offensive posts in social media using a capsule network. The input to the network was character embeddings combined with emoji embeddings. The approach was evaluated on all three subtasks in Task 6 - SemEval 2019: OffensEval: Identifying and Categorizing Offensive Language in Social Media. The evaluation also showed that even though the capsule networks have not been used commonly in natural language processing tasks, they can outperform existing state of the art solutions for offensive language detection in social media.",
}
@inproceedings{hosseini-pour-shamsfard-2019-eoann,
    title = "{E}o{ANN}: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks",
    author = "Hosseini Pour, Rayehe  and
      Shamsfard, Mehrnoush",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1057",
    doi = "10.26615/978-954-452-056-4_057",
    pages = "481--486",
    abstract = "Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs are very time-consuming. Using automatic relation extraction methods can speed up this process. In this study, we exploit an ensemble of lstm and convolutional neural networks in a supervised manner to capture lexico-semantic relations which can either be used directly in NLP applications or compose the edges of wordnets. The whole procedure of learning vector space representation of relations is language independent. We used Princeton WordNet 3.1, FarsNet 3.0 (the Persian wordnet), Root09 and EVALution as golden standards to evaluate the predictive performance of our model and the results are comparable on the two languages. Empirical results demonstrate that our model outperforms the state of the art models.",
}
@inproceedings{huy-tien-etal-2019-opinions,
    title = "Opinions Summarization: Aspect Similarity Recognition Relaxes The Constraint of Predefined Aspects",
    author = "Huy Tien, Nguyen  and
      Tung Thanh, Le  and
      Minh Le, Nguyen",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1058",
    doi = "10.26615/978-954-452-056-4_058",
    pages = "487--496",
    abstract = "Recently research in opinions summarization focuses on rating expressions by aspects and/or sentiments they carry. To extract aspects of an expression, most studies require a predefined list of aspects or at least the number of aspects. Instead of extracting aspects, we rate expressions by aspect similarity recognition (ASR), which evaluates whether two expressions share at least one aspect. This subtask relaxes the limitation of predefining aspects and makes our opinions summarization applicable in domain adaptation. For the ASR subtask, we propose an attention-cell LSTM model, which integrates attention signals into the LSTM gates. According to the experimental results, the attention-cell LSTM works efficiently for learning latent aspects between two sentences in both settings of in-domain and cross-domain. In addition, the proposed extractive summarization method using ASR shows significant improvements over baselines on the Opinosis corpus.",
}
@inproceedings{ishigaki-etal-2019-discourse,
    title = "Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization",
    author = "Ishigaki, Tatsuya  and
      Kamigaito, Hidetaka  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1059",
    doi = "10.26615/978-954-452-056-4_059",
    pages = "497--506",
    abstract = "Discourse relations between sentences are often represented as a tree, and the tree structure provides important information for summarizers to create a short and coherent summary. However, current neural network-based summarizers treat the source document as just a sequence of sentences and ignore the tree-like discourse structure inherent in the document. To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document. Our discourse-aware summarizer can jointly learn the discourse structure and the salience score of a sentence by using novel hierarchical attention modules, which can be trained on automatically parsed discourse dependency trees. Experimental results showed that our model achieved competitive or better performances against state-of-the-art models in terms of ROUGE scores on the DailyMail dataset. We further conducted manual evaluations. The results showed that our approach also gained the coherence of the output summaries.",
}
@inproceedings{janicki-2019-semi,
    title = "Semi-Supervised Induction of {POS}-Tag Lexicons with Tree Models",
    author = "Janicki, Maciej",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1060",
    doi = "10.26615/978-954-452-056-4_060",
    pages = "507--515",
    abstract = "We approach the problem of POS tagging of morphologically rich languages in a setting where only a small amount of labeled training data is available. We show that a bigram HMM tagger benefits from re-training on a larger untagged text using Baum-Welch estimation. Most importantly, this estimation can be significantly improved by pre-guessing tags for OOV words based on morphological criteria. We consider two models for this task: a character-based recurrent neural network, which guesses the tag from the string form of the word, and a recently proposed graph-based model of morphological transformations. In the latter, the unknown POS tags can be modeled as latent variables in a way very similar to Hidden Markov Tree models and an analogue of the Forward-Backward algorithm can be formulated, which enables us to compute expected values over unknown taggings. We evaluate both the quality of the induced tag lexicon and its impact on the HMM{'}s tagging accuracy. In both tasks, the graph-based morphology model performs significantly better than the RNN predictor. This confirms the intuition that morphologically related words provide useful information about an unknown word{'}s POS tag.",
}
@inproceedings{janz-piasecki-2019-word,
    title = "Word Sense Disambiguation based on Constrained Random Walks in Linked Semantic Networks",
    author = "Janz, Arkadiusz  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1061",
    doi = "10.26615/978-954-452-056-4_061",
    pages = "516--525",
    abstract = "Word Sense Disambiguation remains a challenging NLP task. Due to the lack of annotated training data, especially for rare senses, the supervised approaches are usually designed for specific subdomains limited to a narrow subset of identified senses. Recent advances in this area have shown that knowledge-based approaches are more scalable and obtain more promising results in all-words WSD scenarios. In this work we present a faster WSD algorithm based on the Monte Carlo approximation of sense probabilities given a context using constrained random walks over linked semantic networks. We show that the local semantic relatedness is mostly sufficient to successfully identify correct senses when an extensive knowledge base and a proper weighting scheme are used. The proposed methods are evaluated on English (SenseEval, SemEval) and Polish (Sk{\l}adnica, KPWr) datasets.",
}
@inproceedings{joshi-zincir-heywood-2019-classification,
    title = "Classification of Micro-Texts Using Sub-Word Embeddings",
    author = "Joshi, Mihir  and
      Zincir-Heywood, Nur",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1062",
    doi = "10.26615/978-954-452-056-4_062",
    pages = "526--533",
    abstract = "Extracting features and writing styles from short text messages is always a challenge. Short messages, like tweets, do not have enough data to perform statistical authorship attribution. Besides, the vocabulary used in these texts is sometimes improvised or misspelled. Therefore, in this paper, we propose combining four feature extraction techniques namely character n-grams, word n-grams, Flexible Patterns and a new sub-word embedding using the skip-gram model. Our system uses a Multi-Layer Perceptron to utilize these features from tweets to analyze short text messages. This proposed system achieves 85{\%} accuracy, which is a considerable improvement over previous systems.",
}
@inproceedings{khullar-etal-2019-using,
    title = "Using Syntax to Resolve {NPE} in {E}nglish",
    author = "Khullar, Payal  and
      Antony, Allen  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1063",
    doi = "10.26615/978-954-452-056-4_063",
    pages = "534--540",
    abstract = "This paper describes a novel, syntax-based system for automatic detection and resolution of Noun Phrase Ellipsis (NPE) in English. The system takes in free input English text, detects the site of nominal elision, and if present, selects potential antecedent candidates. The rules are built using the syntactic information on ellipsis and its antecedent discussed in previous theoretical linguistics literature on NPE. Additionally, we prepare a curated dataset of 337 sentences from well-known, reliable sources, containing positive and negative samples of NPE. We split this dataset into two parts, and use one part to refine our rules and the other to test the performance of our final system. We get an F1-score of 76.47{\%} for detection and 70.27{\%} for NPE resolution on the testset. To the best of our knowledge, ours is the first system that detects and resolves NPE in English. The curated dataset used for this task, albeit small, covers a wide variety of NPE cases and will be made public for future work.",
}
@inproceedings{kittask-barbu-2019-similarity,
    title = "Is Similarity Visually Grounded? Computational Model of Similarity for the {E}stonian language",
    author = "Kittask, Claudia  and
      Barbu, Eduard",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1064",
    doi = "10.26615/978-954-452-056-4_064",
    pages = "541--549",
    abstract = "Researchers in Computational Linguistics build models of similarity and test them against human judgments. Although there are many empirical studies of the computational models of similarity for the English language, the similarity for other languages is less explored. In this study we are chiefly interested in two aspects. In the first place we want to know how much of the human similarity is grounded in the visual perception. To answer this question two neural computer vision models are used and their correlation with the human derived similarity scores is computed. In the second place we investigate if language influences the similarity computation. To this purpose diverse computational models trained on Estonian resources are evaluated against human judgments",
}
@inproceedings{knauth-2019-language,
    title = "Language-Agnostic {T}witter-Bot Detection",
    author = {Knauth, J{\"u}rgen},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1065",
    doi = "10.26615/978-954-452-056-4_065",
    pages = "550--558",
    abstract = "In this paper we address the problem of detecting Twitter bots. We analyze a dataset of 8385 Twitter accounts and their tweets consisting of both humans and different kinds of bots. We use this data to train machine learning classifiers that distinguish between real and bot accounts. We identify features that are easy to extract while still providing good results. We analyze different feature groups based on account specific, tweet specific and behavioral specific features and measure their performance compared to other state of the art bot detection methods. For easy future portability of our work we focus on language-agnostic features. With AdaBoost, the best performing classifier, we achieve an accuracy of 0.988 and an AUC of 0.995. As the creation of good training data in machine learning is often difficult - especially in the domain of Twitter bot detection - we additionally analyze to what extent smaller amounts of training data lead to useful results by reviewing cross-validated learning curves. Our results indicate that using few but expressive features already has a good practical benefit for bot detection, especially if only a small amount of training data is available.",
}
@inproceedings{kocon-etal-2019-multi-level,
    title = "Multi-level analysis and recognition of the text sentiment on the example of consumer opinions",
    author = "Koco{\'n}, Jan  and
      Za{\'s}ko-Zieli{\'n}ska, Monika  and
      Mi{\l}kowski, Piotr",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1066",
    doi = "10.26615/978-954-452-056-4_066",
    pages = "559--567",
    abstract = "In this article, we present a novel multi-domain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents. The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis). We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).",
}
@inproceedings{kovatchev-etal-2019-qualitative,
    title = "A Qualitative Evaluation Framework for Paraphrase Identification",
    author = "Kovatchev, Venelin  and
      Marti, M. Antonia  and
      Salamo, Maria  and
      Beltran, Javier",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1067",
    doi = "10.26615/978-954-452-056-4_067",
    pages = "568--577",
    abstract = "In this paper, we present a new approach for the evaluation, error analysis, and interpretation of supervised and unsupervised Paraphrase Identification (PI) systems. Our evaluation framework makes use of a PI corpus annotated with linguistic phenomena to provide a better understanding and interpretation of the performance of various PI systems. Our approach allows for a qualitative evaluation and comparison of the PI models using human interpretable categories. It does not require modification of the training objective of the systems and does not place additional burden on the developers. We replicate several popular supervised and unsupervised PI systems. Using our evaluation framework we show that: 1) Each system performs differently with respect to a set of linguistic phenomena and makes qualitatively different kinds of errors; 2) Some linguistic phenomena are more challenging than others across all systems.",
}
@inproceedings{kumar-etal-2019-study,
    title = "Study on Unsupervised Statistical Machine Translation for Backtranslation",
    author = "Kumar, Anush  and
      Nayak, Nihal V.  and
      Chandra, Aditya  and
      Nair, Mydhili K.",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1068",
    doi = "10.26615/978-954-452-056-4_068",
    pages = "578--582",
    abstract = "Machine Translation systems have drastically improved over the years for several language pairs. Monolingual data is often used to generate synthetic sentences to augment the training data which has shown to improve the performance of machine translation models. In our paper, we make use of an Unsupervised Statistical Machine Translation (USMT) to generate synthetic sentences. Our study compares the performance improvements in Neural Machine Translation model when using synthetic sentences from supervised and unsupervised Machine Translation models. Our approach of using USMT for backtranslation shows promise in low resource conditions and achieves an improvement of 3.2 BLEU score over the Neural Machine Translation model.",
}
@inproceedings{kunilovskaya-sharoff-2019-towards,
    title = "Towards Functionally Similar Corpus Resources for Translation",
    author = "Kunilovskaya, Maria  and
      Sharoff, Serge",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1069",
    doi = "10.26615/978-954-452-056-4_069",
    pages = "583--592",
    abstract = "The paper describes a computational approach to produce functionally comparable monolingual corpus resources for translation studies and contrastive analysis. We exploit a text-external approach, based on a set of Functional Text Dimensions to model text functions, so that each text can be represented as a vector in a multidimensional space of text functions. These vectors can be used to find reasonably homogeneous subsets of functionally similar texts across different corpora. Our models for predicting text functions are based on recurrent neural networks and traditional feature-based machine learning approaches. In addition to using the categories of the British National Corpus as our test case, we investigated the functional comparability of the English parts from the two parallel corpora: CroCo (English-German) and RusLTC (English-Russian) and applied our models to define functionally similar clusters in them. Our results show that the Functional Text Dimensions provide a useful description for text categories, while allowing a more flexible representation for texts with hybrid functions.",
}
@inproceedings{kunneman-etal-2019-question,
    title = "Question Similarity in Community Question Answering: A Systematic Exploration of Preprocessing Methods and Models",
    author = "Kunneman, Florian  and
      Ferreira, Thiago Castro  and
      Krahmer, Emiel  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1070",
    doi = "10.26615/978-954-452-056-4_070",
    pages = "593--601",
    abstract = "Community Question Answering forums are popular among Internet users, and a basic problem they encounter is trying to find out if their question has already been posed before. To address this issue, NLP researchers have developed methods to automatically detect question-similarity, which was one of the shared tasks in SemEval. The best performing systems for this task made use of Syntactic Tree Kernels or the SoftCosine metric. However, it remains unclear why these methods seem to work, whether their performance can be improved by better preprocessing methods and what kinds of errors they (and other methods) make. In this paper, we therefore systematically combine and compare these two approaches with the more traditional BM25 and translation-based models. Moreover, we analyze the impact of preprocessing steps (lowercasing, suppression of punctuation and stop words removal) and word meaning similarity based on different distributions (word translation probability, Word2Vec, fastText and ELMo) on the performance of the task. We conduct an error analysis to gain insight into the differences in performance between the system set-ups. The implementation is made publicly available from https://github.com/fkunneman/DiscoSumo/tree/master/ranlp.",
}
@inproceedings{labat-lefever-2019-classification,
    title = "A Classification-Based Approach to Cognate Detection Combining Orthographic and Semantic Similarity Information",
    author = "Labat, Sofie  and
      Lefever, Els",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1071",
    doi = "10.26615/978-954-452-056-4_071",
    pages = "602--610",
    abstract = "This paper presents proof-of-concept experiments for combining orthographic and semantic information to distinguish cognates from non-cognates. To this end, a context-independent gold standard is developed by manually labelling English-Dutch pairs of cognates and false friends in bilingual term lists. These annotated cognate pairs are then used to train and evaluate a supervised binary classification system for the automatic detection of cognates. Two types of information sources are incorporated in the classifier: fifteen string similarity metrics capture form similarity between source and target words, while word embeddings model semantic similarity between the words. The experimental results show that even though the system already achieves good results by only incorporating orthographic information, the performance further improves by including semantic information in the form of embeddings.",
}
@inproceedings{lalitha-devi-2019-resolving,
    title = "Resolving Pronouns for a Resource-Poor Language, {M}alayalam Using Resource-Rich Language, {T}amil.",
    author = "Lalitha Devi, Sobha",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1072",
    doi = "10.26615/978-954-452-056-4_072",
    pages = "611--618",
    abstract = "In this paper we give in detail how a resource rich language can be used for resolving pronouns for a less resource language. The source language, which is resource rich language in this study, is Tamil and the resource poor language is Malayalam, both belonging to the same language family, Dravidian. The Pronominal resolution developed for Tamil uses CRFs. Our approach is to leverage the Tamil language model to test Malayalam data and the processing required for Malayalam data is detailed. The similarity at the syntactic level between the languages is exploited in identifying the features for developing the Tamil language model. The word form or the lexical item is not considered as a feature for training the CRFs. Evaluation on Malayalam Wikipedia data shows that our approach is correct and the results, though not as good as Tamil, but comparable.",
}
@inproceedings{larionov-etal-2019-semantic,
    title = "Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates",
    author = "Larionov, Daniil  and
      Shelmanov, Artem  and
      Chistova, Elena  and
      Smirnov, Ivan",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1073",
    doi = "10.26615/978-954-452-056-4_073",
    pages = "619--628",
    abstract = "We build the first full pipeline for semantic role labelling of Russian texts. The pipeline implements predicate identification, argument extraction, argument classification (labeling), and global scoring via integer linear programming. We train supervised neural network models for argument classification using Russian semantically annotated corpus {--} FrameBank. However, we note that this resource provides annotations only to a very limited set of predicates. We combat the problem of annotation scarcity by introducing two models that rely on different sets of features: one for {``}known{''} predicates that are present in the training set and one for {``}unknown{''} predicates that are not. We show that the model for {``}unknown{''} predicates can alleviate the lack of annotation by using pretrained embeddings. We perform experiments with various types of embeddings including the ones generated by deep pretrained language models: word2vec, FastText, ELMo, BERT, and show that embeddings generated by deep pretrained language models are superior to classical shallow embeddings for argument classification of both {``}known{''} and {``}unknown{''} predicates.",
}
@inproceedings{leseva-stoyanova-2019-structural,
    title = "Structural Approach to Enhancing {W}ord{N}et with Conceptual Frame Semantics",
    author = "Leseva, Svetlozara  and
      Stoyanova, Ivelina",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1074",
    doi = "10.26615/978-954-452-056-4_074",
    pages = "629--637",
    abstract = "This paper outlines procedures for enhancing WordNet with conceptual information from FrameNet. The mapping of the two resources is non-trivial. We define a number of techniques for the validation of the consistency of the mapping and the extension of its coverage which make use of the structure of both resources and the systematic relations between synsets in WordNet and between frames in FrameNet, as well as between synsets and frames). We present a case study on causativity, a relation which provides enhancement complementary to the one using hierarchical relations, by means of linking in a systematic way large parts of the lexicon. We show how consistency checks and denser relations may be implemented on the basis of this relation. We, then, propose new frames based on causative-inchoative correspondences and in conclusion touch on the possibilities for defining new frames based on the types of specialisation that takes place from parent to child synset.",
}
@inproceedings{lewis-2019-compositional,
    title = "Compositional Hyponymy with Positive Operators",
    author = "Lewis, Martha",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1075",
    doi = "10.26615/978-954-452-056-4_075",
    pages = "638--647",
    abstract = "Language is used to describe concepts, and many of these concepts are hierarchical. Moreover, this hierarchy should be compatible with forming phrases and sentences. We use linear-algebraic methods that allow us to encode words as collections of vectors. The representations we use have an ordering, related to subspace inclusion, which we interpret as modelling hierarchical information. The word representations built can be understood within a compositional distributional semantic framework, providing methods for composing words to form phrase and sentence level representations. We show that the resulting representations give competitive results on both word-level hyponymy and sentence-level entailment datasets.",
}
@inproceedings{lima-etal-2019-impact,
    title = "The Impact of Semantic Linguistic Features in Relation Extraction: A Logical Relational Learning Approach",
    author = "Lima, Rinaldo  and
      Espinasse, Bernard  and
      Freitas, Frederico",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1076",
    doi = "10.26615/978-954-452-056-4_076",
    pages = "648--654",
    abstract = "Relation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine learning algorithms. This paper tries to answer important questions concerning both the impact of semantic based features, and the integration of external linguistic knowledge resources on RE performance. For that, a RE system based on a logical and relational learning algorithm was used and evaluated on three reference datasets from two distinct domains. The yielded results confirm that the classifiers induced using the proposed richer feature set outperformed the classifiers built with morphosyntactic features in average 4{\%} (F1-measure).",
}
@inproceedings{lopez-ubeda-etal-2019-detecting,
    title = "Detecting Anorexia in {S}panish Tweets",
    author = "L{\'o}pez {\'U}beda, Pilar  and
      Plaza del Arco, Flor Miriam  and
      D{\'\i}az Galiano, Manuel Carlos  and
      Urena Lopez, L. Alfonso  and
      Martin, Maite",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1077",
    doi = "10.26615/978-954-452-056-4_077",
    pages = "655--663",
    abstract = "Mental health is one of the main concerns of today{'}s society. Early detection of symptoms can greatly help people with mental disorders. People are using social networks more and more to express emotions, sentiments and mental states. Thus, the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders. However, the first step to solving the problem should be to provide a corpus in order to evaluate our systems. In this paper, we specifically focus on detecting anorexia messages on Twitter. Firstly, we have generated a new corpus of tweets extracted from different accounts including anorexia and non-anorexia messages in Spanish. The corpus is called SAD: Spanish Anorexia Detection corpus. In order to validate the effectiveness of the SAD corpus, we also propose several machine learning approaches for automatically detecting anorexia symptoms in the corpus. The good results obtained show that the application of textual classification methods is a promising option for developing this kind of system demonstrating that these tools could be used by professionals to help in the early detection of mental problems.",
}
@inproceedings{luuk-2019-type,
    title = "A type-theoretical reduction of morphological, syntactic and semantic compositionality to a single level of description",
    author = "Luuk, Erkki",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1078",
    doi = "10.26615/978-954-452-056-4_078",
    pages = "664--673",
    abstract = "The paper presents NLC, a new formalism for modeling natural language (NL) compositionality. NLC is a functional type system (i.e. one based on mathematical functions and their types). Its main features include a close correspondence with NL and an integrated modeling of morphological, syntactic and semantic compositionality. The integration is effected with a subclass of compound types (types which are syntactic compounds of multiple types or their terms), while the correspondence is sought with function types and polymorphism. The paper also presents an implementation of NLC in Coq. The implementation formalizes a diverse fragment of NL, with NLC expressions type checking and failing to type check in exactly the same ways that NL expressions pass and fail their acceptability tests. Among other things, this demonstrates the possibility of reducing morphological, syntactic and semantic compositionality to a single level of description. The level is tentatively identified with semantic compositionality {---} an interpretation which, besides being supported by results from language processing, has interesting implications on NL structure and modeling.",
}
@inproceedings{lyding-etal-2019-v,
    title = "v-trel: Vocabulary Trainer for Tracing Word Relations - An Implicit Crowdsourcing Approach",
    author = {Lyding, Verena  and
      Rodosthenous, Christos  and
      Sangati, Federico  and
      ul Hassan, Umair  and
      Nicolas, Lionel  and
      K{\"o}nig, Alexander  and
      Horbacauskiene, Jolita  and
      Katinskaia, Anisia},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1079",
    doi = "10.26615/978-954-452-056-4_079",
    pages = "674--683",
    abstract = "In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource. We performed an empirical evaluation of our approach with 60 non-native speakers over two days, which shows that new entries to expand Concept-Net can efficiently be gathered through vocabulary exercises on word relations. We also report on the feedback gathered from the users and an expert from language teaching, and discuss the potential of the vocabulary trainer application from the user and language learner perspective. The feedback suggests that v-trel has educational potential, while in its current state some shortcomings could be identified.",
}
@inproceedings{maharjan-etal-2019-jointly,
    title = "Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text",
    author = "Maharjan, Suraj  and
      Mave, Deepthi  and
      Shrestha, Prasha  and
      Montes, Manuel  and
      Gonz{\'a}lez, Fabio A.  and
      Solorio, Thamar",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1080",
    doi = "10.26615/978-954-452-056-4_080",
    pages = "684--692",
    abstract = "An author{'}s way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not. In this paper, we learn representations for authors of literary texts together with representations for character n-grams annotated with their functional roles. We train a neural character n-gram based language model using an external corpus of literary texts and transfer learned representations for use in downstream tasks. We show that augmenting the knowledge from external works of authors produces results competitive with other style-based methods for book likability prediction, genre classification, and authorship attribution.",
}
@inproceedings{majumdar-etal-2019-generating,
    title = "Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play",
    author = "Majumdar, Sourabh  and
      Tekiroglu, Serra Sinem  and
      Guerini, Marco",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1081",
    doi = "10.26615/978-954-452-056-4_081",
    pages = "693--702",
    abstract = "End-to-end neural approaches are becoming increasingly common in conversational scenarios due to their promising performances when provided with sufficient amount of data. In this paper, we present a novel methodology to address the interpretability of neural approaches in such scenarios by creating challenge datasets using dialogue self-play over multiple tasks/intents. Dialogue self-play allows generating large amount of synthetic data; by taking advantage of the complete control over the generation process, we show how neural approaches can be evaluated in terms of unseen dialogue patterns. We propose several out-of-pattern test cases each of which introduces a natural and unexpected user utterance phenomenon. As a proof of concept, we built a single and a multiple memory network, and show that these two architectures have diverse performances depending on the peculiar dialogue patterns.",
}
@inproceedings{mammadli-etal-2019-sentiment,
    title = "Sentiment Polarity Detection in {A}zerbaijani Social News Articles",
    author = "Mammadli, Sevda  and
      Huseynov, Shamsaddin  and
      Alkaramov, Huseyn  and
      Jafarli, Ulviyya  and
      Suleymanov, Umid  and
      Rustamov, Samir",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1082",
    doi = "10.26615/978-954-452-056-4_082",
    pages = "703--710",
    abstract = "Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.",
}
@inproceedings{marcinczuk-oleksy-2019-inforex,
    title = "{I}nforex {---} a Collaborative Systemfor Text Corpora Annotation and Analysis Goes Open",
    author = "Marci{\'n}czuk, Micha{\l}  and
      Oleksy, Marcin",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1083",
    doi = "10.26615/978-954-452-056-4_083",
    pages = "711--719",
    abstract = "In the paper we present the latest changes introduce to Inforex {---} a web-based system for qualitative and collaborative text corpora annotation and analysis. One of the most important news is the release of source codes. Now the system is available on the GitHub repository (https://github.com/CLARIN-PL/Inforex) as an open source project. The system can be easily setup and run in a Docker container what simplifies the installation process. The major improvements include: semi-automatic text annotation, multilingual text preprocessing using CLARIN-PL web services, morphological tagging of XML documents, improved editor for annotation attribute, batch annotation attribute editor, morphological disambiguation, extended word sense annotation. This paper contains a brief description of the mentioned improvements. We also present two use cases in which various Inforex features were used and tested in real-life projects.",
}
@inproceedings{masmoudi-etal-2019-semantic,
    title = "Semantic Language Model for {T}unisian Dialect",
    author = "Masmoudi, Abir  and
      Laatar, Rim  and
      Ellouze, Mariem  and
      Hadrich Belguith, Lamia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1084",
    doi = "10.26615/978-954-452-056-4_084",
    pages = "720--729",
    abstract = "In this paper, we describe the process of creating a statistical Language Model (LM) for the Tunisian Dialect. Indeed, this work is part of the realization of Automatic Speech Recognition (ASR) system for the Tunisian Railway Transport Network. Since our eld of work has been limited, there are several words with similar behaviors (semantic for example) but they do not have the same appearance probability; their class groupings will therefore be possible. For these reasons, we propose to build an n-class LM that is based mainly on the integration of purely semantic data. Indeed, each class represents an abstraction of similar labels. In order to improve the sequence labeling task, we proposed to use a discriminative algorithm based on the Conditional Random Field (CRF) model. To better judge our choice of creating an n-class word model, we compared the created model with the 3-gram type model on the same test corpus of evaluation. Additionally, to assess the impact of using the CRF model to perform the semantic labelling task in order to construct semantic classes, we compared the n-class created model with using the CRF in the semantic labelling task and the n- class model without using the CRF in the semantic labelling task. The drawn comparison of the predictive power of the n-class model obtained by applying the CRF model in the semantic labelling is that it is better than the other two models presenting the highest value of its perplexity.",
}
@inproceedings{masmoudi-etal-2019-automatic,
    title = "Automatic diacritization of {T}unisian dialect text using Recurrent Neural Network",
    author = "Masmoudi, Abir  and
      Ellouze, Mariem  and
      Hadrich belguith, Lamia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1085",
    doi = "10.26615/978-954-452-056-4_085",
    pages = "730--739",
    abstract = "The absence of diacritical marks in the Arabic texts generally leads to morphological, syntactic and semantic ambiguities. This can be more blatant when one deals with under-resourced languages, such as the Tunisian dialect, which suffers from unavailability of basic tools and linguistic resources, like sufficient amount of corpora, multilingual dictionaries, morphological and syntactic analyzers. Thus, this language processing faces greater challenges due to the lack of these resources. The automatic diacritization of MSA text is one of the various complex problems that can be solved by deep neural networks today. Since the Tunisian dialect is an under-resourced language of MSA and as there are a lot of resemblance between both languages, we suggest to investigate a recurrent neural network (RNN) for this dialect diacritization problem. This model will be compared to our previous models models CRF and SMT (CITATION) based on the same dialect corpus. We can experimentally show that our model can achieve better outcomes (DER of 10.72{\%}), as compared to the two models CRF (DER of 20.25{\%}) and SMT (DER of 33.15{\%}).",
}
@inproceedings{matos-veliz-etal-2019-comparing,
    title = "Comparing {MT} Approaches for Text Normalization",
    author = "Matos Veliz, Claudia  and
      De Clercq, Orphee  and
      Hoste, Veronique",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1086",
    doi = "10.26615/978-954-452-056-4_086",
    pages = "740--749",
    abstract = "One of the main characteristics of social media data is the use of non-standard language. Since NLP tools have been trained on traditional text material their performance drops when applied to social media data. One way to overcome this is to first perform text normalization. In this work, we apply text normalization to noisy English and Dutch text coming from different social media genres: text messages, message board posts and tweets. We consider the normalization task as a Machine Translation problem and test the two leading paradigms: statistical and neural machine translation. For SMT we explore the added value of varying background corpora for training the language model. For NMT we have a look at data augmentation since the parallel datasets we are working with are limited in size. Our results reveal that when relying on SMT to perform the normalization it is beneficial to use a background corpus that is close to the genre you are normalizing. Regarding NMT, we find that the translations - or normalizations - coming out of this model are far from perfect and that for a low-resource language like Dutch adding additional training data works better than artificially augmenting the data.",
}
@inproceedings{melleng-etal-2019-sentiment,
    title = "Sentiment and Emotion Based Representations for Fake Reviews Detection",
    author = "Melleng, Alimuddin  and
      Jurek-Loughrey, Anna  and
      P, Deepak",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1087",
    doi = "10.26615/978-954-452-056-4_087",
    pages = "750--757",
    abstract = "Fake reviews are increasingly prevalent across the Internet. They can be unethical as well as harmful. They can affect businesses and mislead individual customers. As the opinions on the Web are increasingly used the detection of fake reviews has become more and more critical. In this study, we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake review detection. We perform empirical studies over three real world datasets and demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.",
}
@inproceedings{menard-mougeot-2019-turning,
    title = "Turning silver into gold: error-focused corpus reannotation with active learning",
    author = "M{\'e}nard, Pierre Andr{\'e}  and
      Mougeot, Antoine",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1088",
    doi = "10.26615/978-954-452-056-4_088",
    pages = "758--767",
    abstract = "While high quality gold standard annotated corpora are crucial for most tasks in natural language processing, many annotated corpora published in recent years, created by annotators or tools, contains noisy annotations. These corpora can be viewed as more silver than gold standards, even if they are used in evaluation campaigns or to compare systems{'} performances. As upgrading a silver corpus to gold level is still a challenge, we explore the application of active learning techniques to detect errors using four datasets designed for document classification and part-of-speech tagging. Our results show that the proposed method for the seeding step improves the chance of finding incorrect annotations by a factor of 2.73 when compared to random selection, a 14.71{\%} increase from the baseline methods. Our query method provides an increase in the error detection precision on average by a factor of 1.78 against random selection, an increase of 61.82{\%} compared to other query approaches.",
}
@inproceedings{mieskes-etal-2019-community,
    title = "Community Perspective on Replicability in Natural Language Processing",
    author = {Mieskes, Margot  and
      Fort, Kar{\"e}n  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Grouin, Cyril  and
      Cohen, Kevin},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1089",
    doi = "10.26615/978-954-452-056-4_089",
    pages = "768--775",
    abstract = "With recent efforts in drawing attention to the task of replicating and/or reproducing results, for example in the context of COLING 2018 and various LREC workshops, the question arises how the NLP community views the topic of replicability in general. Using a survey, in which we involve members of the NLP community, we investigate how our community perceives this topic, its relevance and options for improvement. Based on over two hundred participants, the survey results confirm earlier observations, that successful reproducibility requires more than having access to code and data. Additionally, the results show that the topic has to be tackled from the authors{'}, reviewers{'} and community{'}s side.",
}
@inproceedings{millour-fort-2019-unsupervised,
    title = "Unsupervised Data Augmentation for Less-Resourced Languages with no Standardized Spelling",
    author = {Millour, Alice  and
      Fort, Kar{\"e}n},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1090",
    doi = "10.26615/978-954-452-056-4_090",
    pages = "776--784",
    abstract = "Building representative linguistic resources and NLP tools for non-standardized languages is challenging: when spelling is not determined by a norm, multiple written forms can be encountered for a given word, inducing a large proportion of out-of-vocabulary words. To embrace this diversity, we propose a methodology based on crowdsourced alternative spellings we use to extract rules applied to match OOV words with one of their spelling variants. This virtuous process enables the unsupervised augmentation of multi-variant lexicons without expert rule definition. We apply this multilingual methodology on Alsatian, a French regional language and provide an intrinsic evaluation of the correctness of the variants pairs, and an extrinsic evaluation on a downstream task. We show that in a low-resource scenario, 145 inital pairs can lead to the generation of 876 additional variant pairs, and a diminution of OOV words improving the part-of-speech tagging performance by 1 to 4{\%}.",
}
@inproceedings{mohammadi-etal-2019-neural,
    title = "Neural Feature Extraction for Contextual Emotion Detection",
    author = "Mohammadi, Elham  and
      Amini, Hessam  and
      Kosseim, Leila",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1091",
    doi = "10.26615/978-954-452-056-4_091",
    pages = "785--794",
    abstract = "This paper describes a new approach for the task of contextual emotion detection. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based. We evaluated the model with the dataset of the task 3 of SemEval 2019 (EmoContext), which includes short 3-turn conversations, tagged with 4 emotion classes. The best performing setup was achieved using ELMo word embeddings and POS tags as input, bidirectional GRU as hidden units, and an SVM as the final classifier. This configuration reached 69.93{\%} in terms of micro-average F1 score on the main 3 emotion classes, a score that outperformed the baseline system by 11.25{\%}.",
}
@inproceedings{montariol-allauzen-2019-empirical,
    title = "Empirical Study of Diachronic Word Embeddings for Scarce Data",
    author = "Montariol, Syrielle  and
      Allauzen, Alexandre",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1092",
    doi = "10.26615/978-954-452-056-4_092",
    pages = "795--803",
    abstract = "Word meaning change can be inferred from drifts of time-varying word embeddings. However, temporal data may be too sparse to build robust word embeddings and to discriminate significant drifts from noise. In this paper, we compare three models to learn diachronic word embeddings on scarce data: incremental updating of a Skip-Gram from Kim et al. (2014), dynamic filtering from Bamler {\&} Mandt (2017), and dynamic Bernoulli embeddings from Rudolph {\&} Blei (2018). In particular, we study the performance of different initialisation schemes and emphasise what characteristics of each model are more suitable to data scarcity, relying on the distribution of detected drifts. Finally, we regularise the loss of these models to better adapt to scarce data.",
}
@inproceedings{morita-iwakura-2019-fast,
    title = "A Fast and Accurate Partially Deterministic Morphological Analysis",
    author = "Morita, Hajime  and
      Iwakura, Tomoya",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1093",
    doi = "10.26615/978-954-452-056-4_093",
    pages = "804--809",
    abstract = "This paper proposes a partially deterministic morphological analysis method for improved processing speed. Maximum matching is a fast deterministic method for morphological analysis. However, the method tends to decrease performance due to lack of consideration of contextual information. In order to use maximum matching safely, we propose the use of Context Independent Strings (CISs), which are strings that do not have ambiguity in terms of morphological analysis. Our method first identifies CISs in a sentence using maximum matching without contextual information, then analyzes the unprocessed part of the sentence using a bi-gram-based morphological analysis model. We evaluate the method on a Japanese morphological analysis task. The experimental results show a 30{\%} reduction of running time while maintaining improved accuracy.",
}
@inproceedings{mosbach-etal-2019-incom,
    title = "incom.py - A Toolbox for Calculating Linguistic Distances and Asymmetries between Related Languages",
    author = "Mosbach, Marius  and
      Stenger, Irina  and
      Avgustinova, Tania  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1094",
    doi = "10.26615/978-954-452-056-4_094",
    pages = "810--818",
    abstract = "Languages may be differently distant from each other and their mutual intelligibility may be asymmetric. In this paper we introduce incom.py, a toolbox for calculating linguistic distances and asymmetries between related languages. incom.py allows linguist experts to quickly and easily perform statistical analyses and compare those with experimental results. We demonstrate the efficacy of incom.py in an incomprehension experiment on two Slavic languages: Bulgarian and Russian. Using incom.py we were able to validate three methods to measure linguistic distances and asymmetries: Levenshtein distance, word adaptation surprisal, and conditional entropy as predictors of success in a reading intercomprehension experiment.",
}
@inproceedings{ngonga-ngomo-etal-2019-holistic,
    title = "A Holistic Natural Language Generation Framework for the Semantic Web",
    author = {Ngonga Ngomo, Axel-Cyrille  and
      Moussallem, Diego  and
      B{\"u}hmann, Lorenz},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1095",
    doi = "10.26615/978-954-452-056-4_095",
    pages = "819--828",
    abstract = "With the ever-growing generation of data for the Semantic Web comes an increasing demand for this data to be made available to non-semantic Web experts. One way of achieving this goal is to translate the languages of the Semantic Web into natural language. We present LD2NL, a framework that allows verbalizing the three key languages of the Semantic Web, i.e., RDF, OWL, and SPARQL. Our framework is based on a bottom-up approach to verbalization. We evaluated LD2NL in an open survey with 86 persons. Our results suggest that our framework can generate verbalizations that are close to natural languages and that can be easily understood by non-experts. Therewith, it enables non-domain experts to interpret Semantic Web data with more than 91{\%} of the accuracy of domain experts.",
}
@inproceedings{nicula-etal-2019-building,
    title = "Building a Comprehensive {R}omanian Knowledge Base for Drug Administration",
    author = "Nicula, Bogdan  and
      Dascalu, Mihai  and
      S{\^\i}rbu, Maria-Dorinela  and
      Tr{\u{a}}u{\textcommabelow{s}}an-Matu, {\textcommabelow{S}}tefan  and
      Nu{\textcommabelow{t}}{\u{a}}, Alexandru",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1096",
    doi = "10.26615/978-954-452-056-4_096",
    pages = "829--836",
    abstract = "Information on drug administration is obtained traditionally from doctors and pharmacists, as well as leaflets which provide in most cases cumbersome and hard-to-follow details. Thus, the need for medical knowledge bases emerges to provide access to concrete and well-structured information which can play an important role in informing patients. This paper introduces a Romanian medical knowledge base focused on drug-drug interactions, on representing relevant drug information, and on symptom-disease relations. The knowledge base was created by extracting and transforming information using Natural Language Processing techniques from both structured and unstructured sources, together with manual annotations. The resulting Romanian ontologies are aligned with larger English medical ontologies. Our knowledge base supports queries regarding drugs (e.g., active ingredients, concentration, expiration date), drug-drug interaction, symptom-disease relations, as well as drug-symptom relations.",
}
@inproceedings{nikolov-etal-2019-summary,
    title = "Summary Refinement through Denoising",
    author = "Nikolov, Nikola I.  and
      Calmanovici, Alessandro  and
      Hahnloser, Richard",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1097",
    doi = "10.26615/978-954-452-056-4_097",
    pages = "837--843",
    abstract = "We propose a simple method for post-processing the outputs of a text summarization system in order to refine its overall quality. Our approach is to train text-to-text rewriting models to correct information redundancy errors that may arise during summarization. We train on synthetically generated noisy summaries, testing three different types of noise that introduce out-of-context information within each summary. When applied on top of extractive and abstractive summarization baselines, our summary denoising models yield metric improvements while reducing redundancy.",
}
@inproceedings{nikolov-hahnloser-2019-large,
    title = "Large-Scale Hierarchical Alignment for Data-driven Text Rewriting",
    author = "Nikolov, Nikola I.  and
      Hahnloser, Richard",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1098",
    doi = "10.26615/978-954-452-056-4_098",
    pages = "844--853",
    abstract = "We propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers. Our approach does not require a seed parallel corpus, but instead relies solely on hierarchical search over pre-trained embeddings of documents and sentences. We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on text simplification from the normal to the Simple Wikipedia. We show that pseudo-parallel sentences extracted with our method not only supplement existing parallel data, but can even lead to competitive performance on their own.",
}
@inproceedings{omote-etal-2019-dependency,
    title = "Dependency-Based Relative Positional Encoding for Transformer {NMT}",
    author = "Omote, Yutaro  and
      Tamura, Akihiro  and
      Ninomiya, Takashi",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1099",
    doi = "10.26615/978-954-452-056-4_099",
    pages = "854--861",
    abstract = "This paper proposes a new Transformer neural machine translation model that incorporates syntactic distances between two source words into the relative position representations of the self-attention mechanism. In particular, the proposed model encodes pair-wise relative depths on a source dependency tree, which are differences between the depths of the two source words, in the encoder{'}s self-attention. The experiments show that our proposed model achieves 0.5 point gain in BLEU on the Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.",
}
@inproceedings{onita-etal-2019-image,
    title = "From Image to Text in Sentiment Analysis via Regression and Deep Learning",
    author = "Onita, Daniela  and
      Dinu, Liviu P.  and
      Birlutiu, Adriana",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1100",
    doi = "10.26615/978-954-452-056-4_100",
    pages = "862--868",
    abstract = "Images and text represent types of content which are used together for conveying user emotions in online social networks. These contents are usually associated with a sentiment category. In this paper, we investigate an approach for mapping images to text for three types of sentiment categories: positive, neutral and negative. The mapping from images to text is performed using a Kernel Ridge Regression model. We considered two types of image features: i) RGB pixel-values features, and ii) features extracted with a deep learning approach. The experimental evaluation was performed on a Twitter data set containing both text and images and the sentiment associated with these. The experimental results show a difference in performance for different sentiment categories, in particular the mapping that we propose performs better for the positive sentiment category in comparison with the neutral and negative ones. Furthermore, the experimental results show that the more complex deep learning features perform better than the RGB pixel-value features for all sentiment categories and for larger training sets.",
}
@inproceedings{onal-tyers-2019-building,
    title = "Building a Morphological Analyser for {L}az",
    author = "Onal, Esra  and
      Tyers, Francis",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1101",
    doi = "10.26615/978-954-452-056-4_101",
    pages = "869--877",
    abstract = "This study is an attempt to contribute to documentation and revitalization efforts of endangered Laz language, a member of South Caucasian language family mainly spoken on northeastern coastline of Turkey. It constitutes the first steps to create a general computational model for word form recognition and production for Laz by building a rule-based morphological analyser using Helsinki Finite-State Toolkit (HFST). The evaluation results show that the analyser has a 64.9{\%} coverage over a corpus collected for this study with 111,365 tokens. We have also performed an error analysis on randomly selected 100 tokens from the corpus which are not covered by the analyser, and these results show that the errors mostly result from Turkish words in the corpus and missing stems in our lexicon.",
}
@inproceedings{paalman-etal-2019-term,
    title = "Term Based Semantic Clusters for Very Short Text Classification",
    author = "Paalman, Jasper  and
      Mullick, Shantanu  and
      Zervanou, Kalliopi  and
      Zhang, Yingqian",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1102",
    doi = "10.26615/978-954-452-056-4_102",
    pages = "878--887",
    abstract = "Very short texts, such as tweets and invoices, present challenges in classification. Although term occurrences are strong indicators of content, in very short texts, the sparsity of these texts makes it difficult to capture important semantic relationships. A solution calls for a method that not only considers term occurrence, but also handles sparseness well. In this work, we introduce such an approach, the Term Based Semantic Clusters (TBSeC) that employs terms to create distinctive semantic concept clusters. These clusters are ranked using a semantic similarity function which in turn defines a semantic feature space that can be used for text classification. Our method is evaluated in an invoice classification task. Compared to well-known content representation methods the proposed method performs competitively.",
}
@inproceedings{papay-pado-2019-quotation,
    title = "Quotation Detection and Classification with a Corpus-Agnostic Model",
    author = "Papay, Sean  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1103",
    doi = "10.26615/978-954-452-056-4_103",
    pages = "888--894",
    abstract = "The detection of quotations (i.e., reported speech, thought, and writing) has established itself as an NLP analysis task. However, state-of-the-art models have been developed on the basis of specific corpora and incorpo- rate a high degree of corpus-specific assumptions and knowledge, which leads to fragmentation. In the spirit of task-agnostic modeling, we present a corpus-agnostic neural model for quotation detection and evaluate it on three corpora that vary in language, text genre, and structural assumptions. The model (a) approaches the state-of-the-art on the corpora when using established feature sets and (b) shows reasonable performance even when us- ing solely word forms, which makes it applicable for non-standard (i.e., historical) corpora.",
}
@inproceedings{pendyala-etal-2019-validation,
    title = "Validation of Facts Against Textual Sources",
    author = "Pendyala, Vamsi Krishna  and
      Sinha, Simran  and
      Prakash, Satya  and
      Reddy, Shriya  and
      Jamatia, Anupam",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1104",
    doi = "10.26615/978-954-452-056-4_104",
    pages = "895--903",
    abstract = "In today{'}s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a system which would verify a claim against a source and classify the claim to be true, false, out-of-context or an inappropriate claim with respect to the textual source provided to the system. A true label is used if the claim is true, false if it is false, if the claim has no relation with the source then it is classified as out-of-context and if the claim cannot be verified at all then it is classified as inappropriate. This would help us to verify a claim or a fact as well as know about the source or our knowledge base against which we are trying to verify our facts. We used a two-step approach to achieve our goal. At first, we retrieved evidence related to the claims from the textual source using the Term Frequency-Inverse Document Frequency(TF-IDF) vectors. Later we classified the claim-evidence pairs as true, false, inappropriate and out of context using a modified version of textual entailment module. Textual entailment module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information using Bi-LSTM network to assess the veracity of the claim. The accuracy of the best performing system is 64.49{\%}",
}
@inproceedings{piad-morffis-etal-2019-neural,
    title = "A Neural Network Component for Knowledge-Based Semantic Representations of Text",
    author = "Piad-Morffis, Alejandro  and
      Mu{\~n}oz, Rafael  and
      Guti{\'e}rrez, Yoan  and
      Almeida-Cruz, Yudivian  and
      Estevez-Velarde, Suilan  and
      Montoyo, Andr{\'e}s",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1105",
    doi = "10.26615/978-954-452-056-4_105",
    pages = "904--911",
    abstract = "This paper presents Semantic Neural Networks (SNNs), a knowledge-aware component based on deep learning. SNNs can be trained to encode explicit semantic knowledge from an arbitrary knowledge base, and can subsequently be combined with other deep learning architectures. At prediction time, SNNs provide a semantic encoding extracted from the input data, which can be exploited by other neural network components to build extended representation models that can face alternative problems. The SNN architecture is defined in terms of the concepts and relations present in a knowledge base. Based on this architecture, a training procedure is developed. Finally, an experimental setup is presented to illustrate the behaviour and performance of a SNN for a specific NLP problem, in this case, opinion mining for the classification of movie reviews.",
}
@inproceedings{plum-etal-2019-toponym,
    title = "Toponym Detection in the Bio-Medical Domain: A Hybrid Approach with Deep Learning",
    author = "Plum, Alistair  and
      Ranasinghe, Tharindu  and
      Orasan, Constantin",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1106",
    doi = "10.26615/978-954-452-056-4_106",
    pages = "912--921",
    abstract = "This paper compares how different machine learning classifiers can be used together with simple string matching and named entity recognition to detect locations in texts. We compare five different state-of-the-art machine learning classifiers in order to predict whether a sentence contains a location or not. Following this classification task, we use a string matching algorithm with a gazetteer to identify the exact index of a toponym within the sentence. We evaluate different approaches in terms of machine learning classifiers, text pre-processing and location extraction on the SemEval-2019 Task 12 dataset, compiled for toponym resolution in the bio-medical domain. Finally, we compare the results with our system that was previously submitted to the SemEval-2019 task evaluation.",
}
@inproceedings{poncelas-etal-2019-combining,
    title = "Combining {PBSMT} and {NMT} Back-translated Data for Efficient {NMT}",
    author = "Poncelas, Alberto  and
      Popovi{\'c}, Maja  and
      Shterionov, Dimitar  and
      Maillette de Buy Wenniger, Gideon  and
      Way, Andy",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1107",
    doi = "10.26615/978-954-452-056-4_107",
    pages = "922--931",
    abstract = "Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. One of these methods is back-translation, which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model. Generally, NMT models are used for back-translation. In this work, we analyze the performance of models when the training data is extended with synthetic data using different MT approaches. In particular we investigate back-translated data generated not only by NMT but also by Statistical Machine Translation (SMT) models and combinations of both. The results reveal that the models achieve the best performances when the training set is augmented with back-translated data created by merging different MT approaches.",
}
@inproceedings{popov-etal-2019-unsupervised,
    title = "Unsupervised dialogue intent detection via hierarchical topic model",
    author = "Popov, Artem  and
      Bulatov, Victor  and
      Polyudova, Darya  and
      Veselova, Eugenia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1108",
    doi = "10.26615/978-954-452-056-4_108",
    pages = "932--938",
    abstract = "One of the challenges during a task-oriented chatbot development is the scarce availability of the labeled training data. The best way of getting one is to ask the assessors to tag each dialogue according to its intent. Unfortunately, performing labeling without any provisional collection structure is difficult since the very notion of the intent is ill-defined. In this paper, we propose a hierarchical multimodal regularized topic model to obtain a first approximation of the intent set. Our rationale for hierarchical models usage is their ability to take into account several degrees of the dialogues relevancy. We attempt to build a model that can distinguish between subject-based (e.g. medicine and transport topics) and action-based (e.g. filing of an application and tracking application status) similarities. In order to achieve this, we divide set of all features into several groups according to part-of-speech analysis. Various feature groups are treated differently on different hierarchy levels.",
}
@inproceedings{popov-sikos-2019-graph,
    title = "Graph Embeddings for Frame Identification",
    author = "Popov, Alexander  and
      Sikos, Jennifer",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1109",
    doi = "10.26615/978-954-452-056-4_109",
    pages = "939--948",
    abstract = "Lexical resources such as WordNet (Miller, 1995) and FrameNet (Baker et al., 1998) are organized as graphs, where relationships between words are made explicit via the structure of the resource. This work explores how structural information from these lexical resources can lead to gains in a downstream task, namely frame identification. While much of the current work in frame identification uses various neural architectures to predict frames, those neural architectures only use representations of frames based on annotated corpus data. We demonstrate how incorporating knowledge directly from the FrameNet graph structure improves the performance of a neural network-based frame identification system. Specifically, we construct a bidirectional LSTM with a loss function that incorporates various graph- and corpus-based frame embeddings for learning and ultimately achieves strong performance gains with the graph-based embeddings over corpus-based embeddings alone.",
}
@inproceedings{popov-etal-2019-know,
    title = "Know Your Graph. State-of-the-Art Knowledge-Based {WSD}",
    author = "Popov, Alexander  and
      Simov, Kiril  and
      Osenova, Petya",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1110",
    doi = "10.26615/978-954-452-056-4_110",
    pages = "949--958",
    abstract = "This paper introduces several improvements over the current state of the art in knowledge-based word sense disambiguation. Those innovations are the result of modifying and enriching a knowledge base created originally on the basis of WordNet. They reflect several separate but connected strategies: manipulating the shape and the content of the knowledge base, assigning weights over the relations in the knowledge base, and the addition of new relations to it. The main contribution of the paper is to demonstrate that the previously proposed knowledge bases organize linguistic and world knowledge suboptimally for the task of word sense disambiguation. In doing so, the paper also establishes a new state of the art for knowledge-based approaches. Its best models are competitive in the broader context of supervised systems as well.",
}
@inproceedings{popovic-castilho-2019-ambiguous,
    title = "Are ambiguous conjunctions problematic for machine translation?",
    author = "Popovi{\'c}, Maja  and
      Castilho, Sheila",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1111",
    doi = "10.26615/978-954-452-056-4_111",
    pages = "959--966",
    abstract = "The translation of ambiguous words still poses challenges for machine translation. In this work, we carry out a systematic quantitative analysis regarding the ability of different machine translation systems to disambiguate the source language conjunctions {``}but{''} and {``}and{''}. We evaluate specialised test sets focused on the translation of these two conjunctions. The test sets contain source languages that do not distinguish different variants of the given conjunction, whereas the target languages do. In total, we evaluate the conjunction {``}but{''} on 20 translation outputs, and the conjunction {``}and{''} on 10. All machine translation systems almost perfectly recognise one variant of the target conjunction, especially for the source conjunction {``}but{''}. The other target variant, however, represents a challenge for machine translation systems, with accuracy varying from 50{\%} to 95{\%} for {``}but{''} and from 20{\%} to 57{\%} for {``}and{''}. The major error for all systems is replacing the correct target variant with the opposite one.",
}
@inproceedings{prazak-konopik-2019-ulsana,
    title = "{ULSA}na: Universal Language Semantic Analyzer",
    author = "Pra{\v{z}}{\'a}k, Ond{\v{r}}ej  and
      Konopik, Miloslav",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1112",
    doi = "10.26615/978-954-452-056-4_112",
    pages = "967--972",
    abstract = "We present a live cross-lingual system capable of producing shallow semantic annotations of natural language sentences for 51 languages at this time. The domain of the input sentences is in principle unconstrained. The system uses single training data (in English) for all the languages. The resulting semantic annotations are therefore consistent across different languages. We use CoNLL Semantic Role Labeling training data and Universal dependencies as the basis for the system. The system is publicly available and supports processing data in batches; therefore, it can be easily used by the community for the following research tasks.",
}
@inproceedings{priban-etal-2019-machine,
    title = "Machine Learning Approach to Fact-Checking in {W}est {S}lavic Languages",
    author = "P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Hercig, Tom{\'a}{\v{s}}  and
      Steinberger, Josef",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1113",
    doi = "10.26615/978-954-452-056-4_113",
    pages = "973--979",
    abstract = "Fake news detection and closely-related fact-checking have recently attracted a lot of attention. Automatization of these tasks has been already studied for English. For other languages, only a few studies can be found (e.g. (Baly et al., 2018)), and to the best of our knowledge, no research has been conducted for West Slavic languages. In this paper, we present datasets for Czech, Polish, and Slovak. We also ran initial experiments which set a baseline for further research into this area.",
}
@inproceedings{rajendran-etal-2019-ne,
    title = "{NE}-Table: A Neural key-value table for Named Entities",
    author = "Rajendran, Janarthanan  and
      Ganhotra, Jatin  and
      Guo, Xiaoxiao  and
      Yu, Mo  and
      Singh, Satinder  and
      Polymenakos, Lazaros",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1114",
    doi = "10.26615/978-954-452-056-4_114",
    pages = "980--993",
    abstract = "Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task, which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set which are available at - https://github.com/IBM/ne-table-datasets/",
}
@inproceedings{ranasinghe-etal-2019-enhancing,
    title = "Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations",
    author = "Ranasinghe, Tharindu  and
      Orasan, Constantin  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1115",
    doi = "10.26615/978-954-452-056-4_115",
    pages = "994--1003",
    abstract = "Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS methods rely on word embeddings one way or another. The recently introduced contextualised word embeddings have proved more effective than standard word embeddings in many natural language processing tasks. This paper evaluates the impact of several contextualised word embeddings on unsupervised STS methods and compares it with the existing supervised/unsupervised STS methods for different datasets in different languages and different domains",
}
@inproceedings{ranasinghe-etal-2019-semantic,
    title = "Semantic Textual Similarity with {S}iamese Neural Networks",
    author = "Ranasinghe, Tharindu  and
      Orasan, Constantin  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1116",
    doi = "10.26615/978-954-452-056-4_116",
    pages = "1004--1011",
    abstract = "Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods",
}
@inproceedings{rigouts-terryn-etal-2019-analysing,
    title = "Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: {HAMLET} vs {T}ermo{S}tat",
    author = "Rigouts Terryn, Ayla  and
      Drouin, Patrick  and
      Hoste, Veronique  and
      Lefever, Els",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1117",
    doi = "10.26615/978-954-452-056-4_117",
    pages = "1012--1021",
    abstract = "Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues. However, supervised ML approaches are gaining interest. Relatively little is known about the impact of these supervised methodologies; evaluations are often limited to precision, and sometimes recall and f1-scores, without information about the nature of the extracted candidate terms. Therefore, the current paper presents a detailed and elaborate analysis and comparison of a traditional, state-of-the-art system (TermoStat) and a new, supervised ML approach (HAMLET), using the results obtained for the same, manually annotated, Dutch corpus about dressage.",
}
@inproceedings{rusnachenko-etal-2019-distant,
    title = "Distant Supervision for Sentiment Attitude Extraction",
    author = "Rusnachenko, Nicolay  and
      Loukachevitch, Natalia  and
      Tutubalina, Elena",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1118",
    doi = "10.26615/978-954-452-056-4_118",
    pages = "1022--1030",
    abstract = "News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation. In this paper, we describe a new approach to distant supervision for extracting sentiment attitudes between named entities mentioned in texts. Two factors (pair-based and frame-based) were used to automatically label an extensive news collection, dubbed as RuAttitudes. The latter became a basis for adaptation and training convolutional architectures, including piecewise max pooling and full use of information across different sentences. The results show that models, trained with RuAttitudes, outperform ones that were trained with only supervised learning approach and achieve 13.4{\%} increase in F1-score on RuSentRel collection.",
}
@inproceedings{saffar-mehrjardi-etal-2019-self,
    title = "Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems",
    author = "Saffar Mehrjardi, Mansour  and
      Trabelsi, Amine  and
      Zaiane, Osmar R.",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1119",
    doi = "10.26615/978-954-452-056-4_119",
    pages = "1031--1040",
    abstract = "Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrence-based and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism. Self-attentional models have been used in the creation of the state-of-the-art models in many NLP task such as neural machine translation, but their usage has not been explored for the task of training end-to-end task-oriented dialogue generation systems yet. In this study, we apply these models on the DSTC2 dataset for training task-oriented chatbots. Our finding shows that self-attentional models can be exploited to create end-to-end task-oriented chatbots which not only achieve higher evaluation scores compared to recurrence-based models, but also do so more efficiently.",
}
@inproceedings{salawa-etal-2019-learn,
    title = "Whom to Learn From? Graph- vs. Text-based Word Embeddings",
    author = "Salawa, Ma{\l}gorzata  and
      Branco, Ant{\'o}nio  and
      Branco, Ruben  and
      Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Saedi, Chakaveh",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1120",
    doi = "10.26615/978-954-452-056-4_120",
    pages = "1041--1051",
    abstract = "Vectorial representations of meaning can be supported by empirical data from diverse sources and obtained with diverse embedding approaches. This paper aims at screening this experimental space and reports on an assessment of word embeddings supported (i) by data in raw texts vs. in lexical graphs, (ii) by lexical information encoded in association- vs. inference-based graphs, and obtained (iii) by edge reconstruction- vs. matrix factorisation vs. random walk-based graph embedding methods. The results observed with these experiments indicate that the best solutions with graph-based word embeddings are very competitive, consistently outperforming mainstream text-based ones.",
}
@inproceedings{salton-kelleher-2019-persistence,
    title = "Persistence pays off: Paying Attention to What the {LSTM} Gating Mechanism Persists",
    author = "Salton, Giancarlo  and
      Kelleher, John",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1121",
    doi = "10.26615/978-954-452-056-4_121",
    pages = "1052--1059",
    abstract = "Recurrent Neural Network Language Models composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results in Language Modeling. However, these models still struggle to process long sequences which are more likely to contain long-distance dependencies because of information fading. In this paper we demonstrate an effective mechanism for retrieving information in a memory augmented LSTM LM based on attending to information in memory in proportion to the number of timesteps the LSTM gating mechanism persisted the information.",
}
@inproceedings{sandrih-etal-2019-development,
    title = "Development and Evaluation of Three Named Entity Recognition Systems for {S}erbian - The Case of Personal Names",
    author = "{\v{S}}andrih, Branislava  and
      Krstev, Cvetana  and
      Stankovic, Ranka",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1122",
    doi = "10.26615/978-954-452-056-4_122",
    pages = "1060--1068",
    abstract = "In this paper we present a rule- and lexicon-based system for the recognition of Named Entities (NE) in Serbian newspaper texts that was used to prepare a gold standard annotated with personal names. It was further used to prepare training sets for four different levels of annotation, which were further used to train two Named Entity Recognition (NER) systems: Stanford and spaCy. All obtained models, together with a rule- and lexicon-based system were evaluated on two sample texts: a part of the gold standard and an independent newspaper text of approximately the same size. The results show that rule- and lexicon-based system outperforms trained models in all four scenarios (measured by F1), while Stanford models has the highest precision. All systems obtain best results in recognizing full names, while the recognition of first names only is rather poor. The produced models are incorporated into a Web platform NER{\&}Beyond that provides various NE-related functions.",
}
@inproceedings{santos-paraboni-2019-moral,
    title = "Moral Stance Recognition and Polarity Classification from {T}witter and Elicited Text",
    author = "Santos, Wesley  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1123",
    doi = "10.26615/978-954-452-056-4_123",
    pages = "1069--1075",
    abstract = "We introduce a labelled corpus of stances about moral issues for the Brazilian Portuguese language, and present reference results for both the stance recognition and polarity classification tasks. The corpus is built from Twitter and further expanded with data elicited through crowd sourcing and labelled by their own authors. Put together, the corpus and reference results are expected to be taken as a baseline for further studies in the field of stance recognition and polarity classification from text.",
}
@inproceedings{sass-2019-jump,
    title = "The {``}Jump and Stay{''} Method to Discover Proper Verb Centered Constructions in Corpus Lattices",
    author = "Sass, B{\'a}lint",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1124",
    doi = "10.26615/978-954-452-056-4_124",
    pages = "1076--1084",
    abstract = "The research presented here is based on the theoretical model of corpus lattices. We implemented this as an effective data structure, and developed an algorithm based on this structure to discover essential verbal expressions from corpus data. The idea behind the algorithm is the {``}jump and stay{''} principle, which tells us that our target expressions will be found at such places in the lattice where the value of a suitable function (defined on the vertex set of the corpus lattice) significantly increases (jumps) and then remains the same (stays). We evaluated our method on Hungarian data. Evaluation shows that about 75{\%} of the obtained expressions are correct, actual errors are rare. Thus, this paper is 1. a proof of concept concerning the corpus lattice model, opening the way to investigate this structure further through our implementation; and 2. a proof of concept of the {``}jump and stay{''} idea and the algorithm itself, opening the way to apply it further, e.g. for other languages.",
}
@inproceedings{schafer-burtenshaw-2019-offence,
    title = "Offence in Dialogues: A Corpus-Based Study",
    author = {Sch{\"a}fer, Johannes  and
      Burtenshaw, Ben},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1125",
    doi = "10.26615/978-954-452-056-4_125",
    pages = "1085--1093",
    abstract = "In recent years an increasing number of analyses of offensive language has been published, however, dealing mainly with the automatic detection and classification of isolated instances. In this paper we aim to understand the impact of offensive messages in online conversations diachronically, and in particular the change in offensiveness of dialogue turns. In turn, we aim to measure the progression of offence level as well as its direction - For example, whether a conversation is escalating or declining in offence. We present our method of extracting linear dialogues from tree-structured conversations in social media data and make our code publicly available. Furthermore, we discuss methods to analyse this dataset through changes in discourse offensiveness. Our paper includes two main contributions; first, using a neural network to measure the level of offensiveness in conversations; and second, the analysis of conversations around offensive comments using decoupling functions.",
}
@inproceedings{shoeb-etal-2019-emotag,
    title = "{E}mo{T}ag {--} Towards an Emotion-Based Analysis of Emojis",
    author = "Shoeb, Abu Awal Md  and
      Raji, Shahab  and
      de Melo, Gerard",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1126",
    doi = "10.26615/978-954-452-056-4_126",
    pages = "1094--1103",
    abstract = "Despite being a fairly recent phenomenon, emojis have quickly become ubiquitous. Besides their extensive use in social media, they are now also invoked in customer surveys and feedback forms. Hence, there is a need for techniques to understand their sentiment and emotion. In this work, we provide a method to quantify the emotional association of basic emotions such as anger, fear, joy, and sadness for a set of emojis. We collect and process a unique corpus of 20 million emoji-centric tweets, such that we can capture rich emoji semantics using a comparably small dataset. We evaluate the induced emotion profiles of emojis with regard to their ability to predict word affect intensities as well as sentiment scores.",
}
@inproceedings{simeonova-etal-2019-morpho,
    title = "A Morpho-Syntactically Informed {LSTM}-{CRF} Model for Named Entity Recognition",
    author = "Simeonova, Lilia  and
      Simov, Kiril  and
      Osenova, Petya  and
      Nakov, Preslav",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1127",
    doi = "10.26615/978-954-452-056-4_127",
    pages = "1104--1113",
    abstract = "We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizeable improvements over the state-of-the-art for Bulgarian NER.",
}
@inproceedings{sirotina-loukachevitch-2019-named,
    title = "Named Entity Recognition in Information Security Domain for {R}ussian",
    author = "Sirotina, Anastasiia  and
      Loukachevitch, Natalia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1128",
    doi = "10.26615/978-954-452-056-4_128",
    pages = "1114--1120",
    abstract = "In this paper we discuss the named entity recognition task for Russian texts related to cybersecurity. First of all, we describe the problems that arise in course of labeling unstructured texts from information security domain. We introduce guidelines for human annotators, according to which a corpus has been marked up. Then, a CRF-based system and different neural architectures have been implemented and applied to the corpus. The named entity recognition systems have been evaluated and compared to determine the most efficient one.",
}
@inproceedings{soisalon-soininen-granroth-wilding-2019-cross,
    title = "Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages",
    author = "Soisalon-Soininen, Eliel  and
      Granroth-Wilding, Mark",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1129",
    doi = "10.26615/978-954-452-056-4_129",
    pages = "1121--1130",
    abstract = "We address the problem of cognate identification across vocabulary pairs of any set of languages. In particular, we focus on the case where the examined pair of languages are low-resource to the extent that no training data whatsoever in these languages, or even closely related ones, are available for the task. We investigate the extent to which training data from another, unrelated language family can be used instead. Our approach consists of learning a similarity metric from example cognates in Indo-European languages and applying it to low-resource Sami languages of the Uralic family. We apply two models following previous work: a Siamese convolutional neural network (S-CNN) and a support vector machine (SVM), and compare them with a Levenshtein-distance baseline. We test performance on three Sami languages and find that the S-CNN outperforms the other approaches, suggesting that it is better able to learn such general characteristics of cognateness that carry over across language families. We also experiment with fine-tuning the S-CNN model with data from within the language family in order to quantify how well this model can make use of a small amount of target-domain data to adapt.",
}
@inproceedings{sominsky-wintner-2019-automatic,
    title = "Automatic Detection of Translation Direction",
    author = "Sominsky, Ilia  and
      Wintner, Shuly",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1130",
    doi = "10.26615/978-954-452-056-4_130",
    pages = "1131--1140",
    abstract = "Parallel corpora are crucial resources for NLP applications, most notably for machine translation. The direction of the (human) translation of parallel corpora has been shown to have significant implications for the quality of statistical machine translation systems that are trained with such corpora. We describe a method for determining the direction of the (manual) translation of parallel corpora at the sentence-pair level. Using several linguistically-motivated features, coupled with a neural network model, we obtain high accuracy on several language pairs. Furthermore, we demonstrate that the accuracy is correlated with the (typological) distance between the two languages.",
}
@inproceedings{stajner-popovic-2019-automated,
    title = "Automated Text Simplification as a Preprocessing Step for Machine Translation into an Under-resourced Language",
    author = "{\v{S}}tajner, Sanja  and
      Popovi{\'c}, Maja",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1131",
    doi = "10.26615/978-954-452-056-4_131",
    pages = "1141--1150",
    abstract = "In this work, we investigate the possibility of using fully automatic text simplification system on the English source in machine translation (MT) for improving its translation into an under-resourced language. We use the state-of-the-art automatic text simplification (ATS) system for lexically and syntactically simplifying source sentences, which are then translated with two state-of-the-art English-to-Serbian MT systems, the phrase-based MT (PBMT) and the neural MT (NMT). We explore three different scenarios for using the ATS in MT: (1) using the raw output of the ATS; (2) automatically filtering out the sentences with low grammaticality and meaning preservation scores; and (3) performing a minimal manual correction of the ATS output. Our results show improvement in fluency of the translation regardless of the chosen scenario, and difference in success of the three scenarios depending on the MT approach used (PBMT or NMT) with regards to improving translation fluency and post-editing effort.",
}
@inproceedings{steimel-etal-2019-investigating,
    title = "Investigating Multilingual Abusive Language Detection: A Cautionary Tale",
    author = {Steimel, Kenneth  and
      Dakota, Daniel  and
      Chen, Yue  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1132",
    doi = "10.26615/978-954-452-056-4_132",
    pages = "1151--1160",
    abstract = "Abusive language detection has received much attention in the last years, and recent approaches perform the task in a number of different languages. We investigate which factors have an effect on multilingual settings, focusing on the compatibility of data and annotations. In the current paper, we focus on English and German. Our findings show large differences in performance between the two languages. We find that the best performance is achieved by different classification algorithms. Sampling to address class imbalance issues is detrimental for German and beneficial for English. The only similarity that we find is that neither data set shows clear topics when we compare the results of topic modeling to the gold standard. Based on our findings, we can conclude that a multilingual optimization of classifiers is not possible even in settings where comparable data sets are used.",
}
@inproceedings{steingrimsson-etal-2019-augmenting,
    title = "Augmenting a {B}i{LSTM} Tagger with a Morphological Lexicon and a Lexical Category Identification Step",
    author = {Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      K{\'a}rason, {\"O}rvar  and
      Loftsson, Hrafn},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1133",
    doi = "10.26615/978-954-452-056-4_133",
    pages = "1161--1168",
    abstract = "Previous work on using BiLSTM models for PoS tagging has primarily focused on small tagsets. We evaluate BiLSTM models for tagging Icelandic, a morphologically rich language, using a relatively large tagset. Our baseline BiLSTM model achieves higher accuracy than any other previously published tagger, when not taking advantage of a morphological lexicon. When we extend the model by incorporating such data, we outperform the earlier state-of-the-art results by a significant margin. We also report on work in progress that attempts to address the problem of data sparsity inherent to morphologically detailed, fine-grained tagsets. We experiment with training a separate model on only the lexical category and using the coarse-grained output tag as an input into to the main model. This method further increases the accuracy and reduces the tagging errors by 21.3{\%} compared to previous state-of-the-art results. Finally, we train and test our tagger on a new gold standard for Icelandic.",
}
@inproceedings{tagarev-etal-2019-comparison,
    title = "Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies",
    author = "Tagarev, Andrey  and
      Tulechki, Nikola  and
      Boytcheva, Svetla",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1134",
    doi = "10.26615/978-954-452-056-4_134",
    pages = "1169--1175",
    abstract = "This paper addresses the task of categorizing companies within industry classification schemes. The datasets consists of encyclopedic articles about companies and their economic activities. The target classification schema is build by mapping linked open data in a semi-supervised manner. Target classes are build bottom-up from DBpedia. We apply several state of the art text classification techniques, based both on deep-learning and classical vector-space models.",
}
@inproceedings{tamburini-2019-quantum,
    title = "A Quantum-Like Approach to Word Sense Disambiguation",
    author = "Tamburini, Fabio",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1135",
    doi = "10.26615/978-954-452-056-4_135",
    pages = "1176--1185",
    abstract = "This paper presents a novel algorithm for Word Sense Disambiguation (WSD) based on Quantum Probability Theory. The Quantum WSD algorithm requires concepts representations as vectors in the complex domain and thus we have developed a technique for computing complex word and sentence embeddings based on the Paragraph Vectors algorithm. Despite the proposed method is quite simple and that it does not require long training phases, when it is evaluated on a standardized benchmark for this task it exhibits state-of-the-art (SOTA) performances.",
}
@inproceedings{tang-etal-2019-understanding,
    title = "Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models",
    author = "Tang, Gongbo  and
      Sennrich, Rico  and
      Nivre, Joakim",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1136",
    doi = "10.26615/978-954-452-056-4_136",
    pages = "1186--1193",
    abstract = "In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of word embeddings and positional embeddings represent the source. The decoder is a standard Transformer or recurrent neural network that directly attends to embeddings via attention mechanisms. Experimental results show (1) that the attention mechanism in encoder-free models acts as a strong feature extractor, (2) that the word embeddings in encoder-free models are competitive to those in conventional models, (3) that non-contextualized source representations lead to a big performance drop, and (4) that encoder-free models have different effects on alignment quality for German-English and Chinese-English.",
}
@inproceedings{thejas-etal-2019-text,
    title = "Text-Based Joint Prediction of Numeric and Categorical Attributes of Entities in Knowledge Bases",
    author = "Thejas, V  and
      Gupta, Abhijeet  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1137",
    doi = "10.26615/978-954-452-056-4_137",
    pages = "1194--1202",
    abstract = "Collaboratively constructed knowledge bases play an important role in information systems, but are essentially always incomplete. Thus, a large number of models has been developed for Knowledge Base Completion, the task of predicting new attributes of entities given partial descriptions of these entities. Virtually all of these models either concentrate on numeric attributes ({\textless}Italy,GDP,2T{\$}{\textgreater}) or they concentrate on categorical attributes ({\textless}Tim Cook,chairman,Apple{\textgreater}). In this paper, we propose a simple feed-forward neural architecture to jointly predict numeric and categorical attributes based on embeddings learned from textual occurrences of the entities in question. Following insights from multi-task learning, our hypothesis is that due to the correlations among attributes of different kinds, joint prediction improves over separate prediction. Our experiments on seven FreeBase domains show that this hypothesis is true of the two attribute types: we find substantial improvements for numeric attributes in the joint model, while performance remains largely unchanged for categorical attributes. Our analysis indicates that this is the case because categorical attributes, many of which describe membership in various classes, provide useful {`}background knowledge{'} for numeric prediction, while this is true to a lesser degree in the inverse direction.",
}
@inproceedings{tobaili-etal-2019-senzi,
    title = "{S}en{Z}i: A Sentiment Analysis Lexicon for the Latinised {A}rabic ({A}rabizi)",
    author = "Tobaili, Taha  and
      Fernandez, Miriam  and
      Alani, Harith  and
      Sharafeddine, Sanaa  and
      Hajj, Hazem  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1138",
    doi = "10.26615/978-954-452-056-4_138",
    pages = "1203--1211",
    abstract = "Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi.",
}
@inproceedings{tsakalidis-etal-2019-mining,
    title = "Mining the {UK} Web Archive for Semantic Change Detection",
    author = "Tsakalidis, Adam  and
      Bazzi, Marya  and
      Cucuringu, Mihai  and
      Basile, Pierpaolo  and
      McGillivray, Barbara",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1139",
    doi = "10.26615/978-954-452-056-4_139",
    pages = "1212--1221",
    abstract = "Semantic change detection (i.e., identifying words whose meaning has changed over time) started emerging as a growing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social science. However, several obstacles make progress in the domain slow and difficult. These pertain primarily to the lack of well-established gold standard datasets, resources to study the problem at a fine-grained temporal resolution, and quantitative evaluation approaches. In this work, we aim to mitigate these issues by (a) releasing a new labelled dataset of more than 47K word vectors trained on the UK Web Archive over a short time-frame (2000-2013); (b) proposing a variant of Procrustes alignment to detect words that have undergone semantic shift; and (c) introducing a rank-based approach for evaluation purposes. Through extensive numerical experiments and validation, we illustrate the effectiveness of our approach against competitive baselines. Finally, we also make our resources publicly available to further enable research in the domain.",
}
@inproceedings{ustun-etal-2019-cross,
    title = "Cross-Lingual Word Embeddings for Morphologically Rich Languages",
    author = {{\"U}st{\"u}n, Ahmet  and
      Bouma, Gosse  and
      van Noord, Gertjan},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1140",
    doi = "10.26615/978-954-452-056-4_140",
    pages = "1222--1228",
    abstract = "Cross-lingual word embedding models learn a shared vector space for two or more languages so that words with similar meaning are represented by similar vectors regardless of their language. Although the existing models achieve high performance on pairs of morphologically simple languages, they perform very poorly on morphologically rich languages such as Turkish and Finnish. In this paper, we propose a morpheme-based model in order to increase the performance of cross-lingual word embeddings on morphologically rich languages. Our model includes a simple extension which enables us to exploit morphemes for cross-lingual mapping. We applied our model for the Turkish-Finnish language pair on the bilingual word translation task. Results show that our model outperforms the baseline models by 2{\%} in the nearest neighbour ranking.",
}
@inproceedings{vasileva-etal-2019-takes,
    title = "It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction",
    author = "Vasileva, Slavena  and
      Atanasova, Pepa  and
      M{\`a}rquez, Llu{\'\i}s  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Nakov, Preslav",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1141",
    doi = "10.26615/978-954-452-056-4_141",
    pages = "1229--1239",
    abstract = "We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.",
}
@inproceedings{velichkov-etal-2019-deep,
    title = "Deep learning contextual models for prediction of sport event outcome from sportsman{'}s interviews",
    author = "Velichkov, Boris  and
      Koychev, Ivan  and
      Boytcheva, Svetla",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1142",
    doi = "10.26615/978-954-452-056-4_142",
    pages = "1240--1246",
    abstract = "This paper presents an approach for prediction of results for sport events. Usually the sport forecasting approaches are based on structured data. We test the hypothesis that the sports results can be predicted by using natural language processing and machine learning techniques applied over interviews with the players shortly before the sport events. The proposed method uses deep learning contextual models, applied over unstructured textual documents. Several experiments were performed for interviews with players in individual sports like boxing, martial arts, and tennis. The results from the conducted experiment confirmed our initial assumption that an interview from a sportsman before a match contains information that can be used for prediction the outcome from it. Furthermore, the results provide strong evidence in support of our research hypothesis, that is, we can predict the outcome from a sport match analyzing an interview, given before it.",
}
@inproceedings{virk-etal-2019-exploiting,
    title = "Exploiting Frame-Semantics and Frame-Semantic Parsing for Automatic Extraction of Typological Information from Descriptive Grammars of Natural Languages",
    author = "Virk, Shafqat Mumtaz  and
      Sheikh Muhammad, Azam  and
      Borin, Lars  and
      Aslam, Muhammad Irfan  and
      Iqbal, Saania  and
      Khurram, Nazia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1143",
    doi = "10.26615/978-954-452-056-4_143",
    pages = "1247--1256",
    abstract = "We describe a novel system for automatic extraction of typological linguistic information from descriptive grammars of natural languages, applying the theory of frame semantics in the form of frame-semantic parsing. The current proof-of-concept system covers a few selected linguistic features, but the methodology is general and can be extended not only to other typological features but also to descriptive grammars written in languages other than English. Such a system is expected to be a useful assistance for automatic curation of typological databases which otherwise are built manually, a very labor and time consuming as well as cognitively taxing enterprise.",
}
@inproceedings{vita-klimek-2019-exploiting,
    title = "Exploiting Open {IE} for Deriving Multiple Premises Entailment Corpus",
    author = "V{\'\i}ta, Martin  and
      Kl{\'\i}mek, Jakub",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1144",
    doi = "10.26615/978-954-452-056-4_144",
    pages = "1257--1264",
    abstract = "Natural language inference (NLI) is a key part of natural language understanding. The NLI task is defined as a decision problem whether a given sentence {--} hypothesis {--} can be inferred from a given text. Typically, we deal with a text consisting of just a single premise/single sentence, which is called a single premise entailment (SPE) task. Recently, a derived task of NLI from multiple premises (MPE) was introduced together with the first annotated corpus and corresponding several strong baselines. Nevertheless, the further development in MPE field requires accessibility of huge amounts of annotated data. In this paper we introduce a novel method for rapid deriving of MPE corpora from an existing NLI (SPE) annotated data that does not require any additional annotation work. This proposed approach is based on using an open information extraction system. We demonstrate the application of the method on a well known SNLI corpus. Over the obtained corpus, we provide the first evaluations as well as we state a strong baseline.",
}
@inproceedings{vodolazova-lloret-2019-towards,
    title = "Towards Adaptive Text Summarization: How Does Compression Rate Affect Summary Readability of {L}2 Texts?",
    author = "Vodolazova, Tatiana  and
      Lloret, Elena",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1145",
    doi = "10.26615/978-954-452-056-4_145",
    pages = "1265--1274",
    abstract = "This paper addresses the problem of readability of automatically generated summaries in the context of second language learning. For this we experimented with a new corpus of level-annotated simplified English texts. The texts were summarized using a total of 7 extractive and abstractive summarization systems with compression rates of 20{\%}, 40{\%}, 60{\%} and 80{\%}. We analyzed the generated summaries in terms of lexical, syntactic and length-based features of readability, and concluded that summary complexity depends on the compression rate, summarization technique and the nature of the summarized corpus. Our experiments demonstrate the importance of choosing appropriate summarization techniques that align with user{'}s needs and language proficiency.",
}
@inproceedings{vodolazova-lloret-2019-impact,
    title = "The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries",
    author = "Vodolazova, Tatiana  and
      Lloret, Elena",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1146",
    doi = "10.26615/978-954-452-056-4_146",
    pages = "1275--1284",
    abstract = "In this paper we describe how an abstractive text summarization method improved the informativeness of automatic summaries by integrating syntactic text simplification, subject-verb-object concept frequency scoring and a set of rules that transform text into its semantic representation. We analyzed the impact of each component of our approach on the quality of generated summaries and tested it on DUC 2002 dataset. Our experiments showed that our approach outperformed other state-of-the-art abstractive methods while maintaining acceptable linguistic quality and redundancy rate.",
}
@inproceedings{vu-xuan-etal-2019-etnlp,
    title = "{ETNLP}: A Visual-Aided Systematic Approach to Select Pre-Trained Embeddings for a Downstream Task",
    author = "Vu Xuan, Son  and
      Vu, Thanh  and
      Tran, Son  and
      Jiang, Lili",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1147",
    doi = "10.26615/978-954-452-056-4_147",
    pages = "1285--1294",
    abstract = "Given many recent advanced embedding models, selecting pre-trained word representation (i.e., word embedding) models best fit for a specific downstream NLP task is non-trivial. In this paper, we propose a systematic approach to extracting, evaluating, and visualizing multiple sets of pre-trained word embed- dings to determine which embeddings should be used in a downstream task. First, for extraction, we provide a method to extract a subset of the embeddings to be used in the downstream NLP tasks. Second, for evaluation, we analyse the quality of pre-trained embeddings using an input word analogy list. Finally, we visualize the embedding space to explore the embedded words interactively. We demonstrate the effectiveness of the proposed approach on our pre-trained word embedding models in Vietnamese to select which models are suitable for a named entity recogni- tion (NER) task. Specifically, we create a large Vietnamese word analogy list to evaluate and select the pre-trained embedding models for the task. We then utilize the selected embed- dings for the NER task and achieve the new state-of-the-art results on the task benchmark dataset. We also apply the approach to another downstream task of privacy-guaranteed embedding selection, and show that it helps users quickly select the most suitable embeddings. In addition, we create an open-source system using the proposed systematic approach to facilitate similar studies on other NLP tasks. The source code and data are available at https: //github.com/vietnlp/etnlp.",
}
@inproceedings{walentynowicz-etal-2019-tagger,
    title = "Tagger for {P}olish Computer Mediated Communication Texts",
    author = "Walentynowicz, Wiktor  and
      Piasecki, Maciej  and
      Oleksy, Marcin",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1148",
    doi = "10.26615/978-954-452-056-4_148",
    pages = "1295--1303",
    abstract = "In this paper we present a morpho-syntactic tagger dedicated to Computer-mediated Communication texts in Polish. Its construction is based on an expanded RNN-based neural network adapted to the work on noisy texts. Among several techniques, the tagger utilises fastText embedding vectors, sequential character embedding vectors, and Brown clustering for the coarse-grained representation of sentence structures. In addition a set of manually written rules was proposed for post-processing. The system was trained to disambiguate descriptions of words in relation to Parts of Speech tags together with the full morphological information in terms of values for the different grammatical categories. We present also evaluation of several model variants on the gold standard annotated CMC data, comparison to the state-of-the-art taggers for Polish and error analysis. The proposed tagger shows significantly better results in this domain and demonstrates the viability of adaptation.",
}
@inproceedings{walkowiak-gniewkowski-2019-evaluation,
    title = "Evaluation of vector embedding models in clustering of text documents",
    author = "Walkowiak, Tomasz  and
      Gniewkowski, Mateusz",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1149",
    doi = "10.26615/978-954-452-056-4_149",
    pages = "1304--1311",
    abstract = "The paper presents an evaluation of word embedding models in clustering of texts in the Polish language. Authors verified six different embedding models, starting from widely used word2vec, across fastText with character n-grams embedding, to deep learning-based ELMo and BERT. Moreover, four standardisation methods, three distance measures and four clustering methods were evaluated. The analysis was performed on two corpora of texts in Polish classified into subjects. The Adjusted Mutual Information (AMI) metric was used to verify the quality of clustering results. The performed experiments show that Skipgram models with n-grams character embedding, built on KGR10 corpus and provided by Clarin-PL, outperforms other publicly available models for Polish. Moreover, presented results suggest that Yeo{--}Johnson transformation for document vectors standardisation and Agglomerative Clustering with a cosine distance should be used for grouping of text documents.",
}
@inproceedings{wang-etal-2019-bigger,
    title = "Bigger versus Similar: Selecting a Background Corpus for First Story Detection Based on Distributional Similarity",
    author = "Wang, Fei  and
      Ross, Robert J.  and
      Kelleher, John D.",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1150",
    doi = "10.26615/978-954-452-056-4_150",
    pages = "1312--1320",
    abstract = "The current state of the art for First Story Detection (FSD) are nearest neighbour-based models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale of common terms for FSD. As a basis for our analysis we propose a set of metrics to quantitatively measure the scale of common terms and the distributional similarity between corpora. Using these metrics we rank different background corpora relative to a target corpus. We also apply models based on different background corpora to the FSD task. Our results show that term distributional similarity is more predictive of good FSD performance than the scale of common terms; and, thus we demonstrate that a smaller recent domain-related corpus will be more suitable than a very large-scale general corpus for FSD.",
}
@inproceedings{wawer-sobiczewska-2019-predicting,
    title = "Predicting Sentiment of {P}olish Language Short Texts",
    author = "Wawer, Aleksander  and
      Sobiczewska, Julita",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1151",
    doi = "10.26615/978-954-452-056-4_151",
    pages = "1321--1327",
    abstract = "The goal of this paper is to use all available Polish language data sets to seek the best possible performance in supervised sentiment analysis of short texts. We use text collections with labelled sentiment such as tweets, movie reviews and a sentiment treebank, in three comparison modes. In the first, we examine the performance of models trained and tested on the same text collection using standard cross-validation (in-domain). In the second we train models on all available data except the given test collection, which we use for testing (one vs rest cross-domain). In the third, we train a model on one data set and apply it to another one (one vs one cross-domain). We compare wide range of methods including machine learning on bag-of-words representation, bidirectional recurrent neural networks as well as the most recent pre-trained architectures ELMO and BERT. We formulate conclusions as to cross-domain and in-domain performance of each method. Unsurprisingly, BERT turned out to be a strong performer, especially in the cross-domain setting. What is surprising however, is solid performance of the relatively simple multinomial Naive Bayes classifier, which performed equally well as BERT on several data sets.",
}
@inproceedings{weichselbraun-etal-2019-improving,
    title = "Improving Named Entity Linking Corpora Quality",
    author = "Weichselbraun, Albert  and
      Brasoveanu, Adrian M.P.  and
      Kuntschik, Philipp  and
      Nixon, Lyndon J.B.",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1152",
    doi = "10.26615/978-954-452-056-4_152",
    pages = "1328--1337",
    abstract = "Gold standard corpora and competitive evaluations play a key role in benchmarking named entity linking (NEL) performance and driving the development of more sophisticated NEL systems. The quality of the used corpora and the used evaluation metrics are crucial in this process. We, therefore, assess the quality of three popular evaluation corpora, identifying four major issues which affect these gold standards: (i) the use of different annotation styles, (ii) incorrect and missing annotations, (iii) Knowledge Base evolution, (iv) and differences in annotating co-occurrences. This paper addresses these issues by formalizing NEL annotations and corpus versioning which allows standardizing corpus creation, supports corpus evolution, and paves the way for the use of lenses to automatically transform between different corpus configurations. In addition, the use of clearly defined scoring rules and evaluation metrics ensures a better comparability of evaluation results.",
}
@inproceedings{welleck-cho-2019-sequential,
    title = "Sequential Graph Dependency Parser",
    author = "Welleck, Sean  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1153",
    doi = "10.26615/978-954-452-056-4_153",
    pages = "1338--1345",
    abstract = "We propose a method for non-projective dependency parsing by incrementally predicting a set of edges. Since the edges do not have a pre-specified order, we propose a set-based learning method. Our method blends graph, transition, and easy-first parsing, including a prior state of the parser as a special case. The proposed transition-based method successfully parses near the state of the art on both projective and non-projective languages, without assuming a certain parsing order.",
}
@inproceedings{wolf-etal-2019-term,
    title = "Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case",
    author = "Wolf, Martin  and
      Petukhova, Volha  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1154",
    doi = "10.26615/978-954-452-056-4_154",
    pages = "1346--1355",
    abstract = "The processing of medical information is not a trivial task for medical non-experts. The paper presents an artificial assistant designed to facilitate a reliable access to medical online contents. Interactions are modelled as doctor-patient Question Answering sessions within a pre-operative patient education scenario where the system addresses patient{'}s information needs explaining medical events and procedures. This implies an accurate medical information extraction from and reasoning with available medical knowledge and large amounts of unstructured multilingual online data. Bridging the gap between medical knowledge and data, we explore a language-agnostic approach to medical concepts mining from the standard terminologies, and the data-driven collection of the corresponding seed terms in a distant supervision setting for German. Experimenting with different terminologies, features and term matching strategies, we achieved a promising F-score of 0.91 on the medical term extraction task. The concepts and terms are used to search and retrieve definitions from the verified online free resources. The proof-of-concept definition retrieval system is designed and evaluated showing promising results, acceptable by humans in 92{\%} of cases.",
}
@inproceedings{yaneva-etal-2019-survey,
    title = "A Survey of the Perceived Text Adaptation Needs of Adults with Autism",
    author = "Yaneva, Victoria  and
      Orasan, Constantin  and
      Ha, Le An  and
      Ponomareva, Natalia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1155",
    doi = "10.26615/978-954-452-056-4_155",
    pages = "1356--1363",
    abstract = "NLP approaches to automatic text adaptation often rely on user-need guidelines which are generic and do not account for the differences between various types of target groups. One such group are adults with high-functioning autism, who are usually able to read long sentences and comprehend difficult words but whose comprehension may be impeded by other linguistic constructions. This is especially challenging for real-world user-generated texts such as product reviews, which cannot be controlled editorially and are thus a particularly good applcation for automatic text adaptation systems. In this paper we present a mixed-methods survey conducted with 24 adult web-users diagnosed with autism and an age-matched control group of 33 neurotypical participants. The aim of the survey was to identify whether the group with autism experienced any barriers when reading online reviews, what these potential barriers were, and what NLP methods would be best suited to improve the accessibility of online reviews for people with autism. The group with autism consistently reported significantly greater difficulties with understanding online product reviews compared to the control group and identified issues related to text length, poor topic organisation, and the use of irony and sarcasm.",
}
@inproceedings{yildiz-etal-2019-open,
    title = "An Open, Extendible, and Fast {T}urkish Morphological Analyzer",
    author = {Y{\i}ld{\i}z, Olcay Taner  and
      Avar, Beg{\"u}m  and
      Ercan, G{\"o}khan},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1156",
    doi = "10.26615/978-954-452-056-4_156",
    pages = "1364--1372",
    abstract = "In this paper, we present a two-level morphological analyzer for Turkish. The morphological analyzer consists of five main components: finite state transducer, rule engine for suffixation, lexicon, trie data structure, and LRU cache. We use Java language to implement finite state machine logic and rule engine, Xml language to describe the finite state transducer rules of the Turkish language, which makes the morphological analyzer both easily extendible and easily applicable to other languages. Empowered with the comprehensiveness of a lexicon of 54,000 bare-forms including 19,000 proper nouns, our morphological analyzer presents one of the most reliable analyzers produced so far. The analyzer is compared with Turkish morphological analyzers in the literature. By using LRU cache and a trie data structure, the system can analyze 100,000 words per second, which enables users to analyze huge corpora in a few hours.",
}
@inproceedings{yolchuyeva-etal-2019-self,
    title = "Self-Attention Networks for Intent Detection",
    author = "Yolchuyeva, Sevinj  and
      N{\'e}meth, G{\'e}za  and
      Gyires-T{\'o}th, B{\'a}lint",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1157",
    doi = "10.26615/978-954-452-056-4_157",
    pages = "1373--1379",
    abstract = "Self-attention networks (SAN) have shown promising performance in various Natural Language Processing (NLP) scenarios, especially in machine translation. One of the main points of SANs is the strength of capturing long-range and multi-scale dependencies from the data. In this paper, we present a novel intent detection system which is based on a self-attention network and a Bi-LSTM. Our approach shows improvement by using a transformer model and deep averaging network-based universal sentence encoder compared to previous solutions. We evaluate the system on Snips, Smart Speaker, Smart Lights, and ATIS datasets by different evaluation metrics. The performance of the proposed model is compared with LSTM with the same datasets.",
}
@inproceedings{yuksel-etal-2019-turkish,
    title = "{T}urkish Tweet Classification with Transformer Encoder",
    author = {Y{\"u}ksel, At{\i}f Emre  and
      T{\"u}rkmen, Ya{\c{s}}ar Alim  and
      {\"O}zg{\"u}r, Arzucan  and
      Alt{\i}nel, Berna},
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1158",
    doi = "10.26615/978-954-452-056-4_158",
    pages = "1380--1387",
    abstract = "Short-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space. In this study, we aim to analyze and classify Turkish tweets based on their topics. Social media jargon and the agglutinative structure of the Turkish language makes this classification task even harder. As far as we know, this is the first study that uses a Transformer Encoder for short text classification in Turkish. The model is trained in a weakly supervised manner, where the training data set has been labeled automatically. Our results on the test set, which has been manually labeled, show that performing morphological analysis improves the classification performance of the traditional machine learning algorithms Random Forest, Naive Bayes, and Support Vector Machines. Still, the proposed approach achieves an F-score of 89.3 {\%} outperforming those algorithms by at least 5 points.",
}
@inproceedings{zosa-granroth-wilding-2019-multilingual,
    title = "Multilingual Dynamic Topic Model",
    author = "Zosa, Elaine  and
      Granroth-Wilding, Mark",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1159",
    doi = "10.26615/978-954-452-056-4_159",
    pages = "1388--1396",
    abstract = "Dynamic topic models (DTMs) capture the evolution of topics and trends in time series data.Current DTMs are applicable only to monolingual datasets. In this paper we present the multilingual dynamic topic model (ML-DTM), a novel topic model that combines DTM with an existing multilingual topic modeling method to capture cross-lingual topics that evolve across time. We present results of this model on a parallel German-English corpus of news articles and a comparable corpus of Finnish and Swedish news articles. We demonstrate the capability of ML-DTM to track significant events related to a topic and show that it finds distinct topics and performs as well as existing multilingual topic models in aligning cross-lingual topics.",
}
@inproceedings{thorsteinsson-etal-2019-wide,
    title = "A Wide-Coverage Context-Free Grammar for {I}celandic and an Accompanying Parsing System",
    author = "{\TH}orsteinsson, Vilhj{\'a}lmur  and
      {\'O}lad{\'o}ttir, Hulda  and
      Loftsson, Hrafn",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
    month = sep,
    year = "2019",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R19-1160",
    doi = "10.26615/978-954-452-056-4_160",
    pages = "1397--1404",
    abstract = "We present an open-source, wide-coverage context-free grammar (CFG) for Icelandic, and an accompanying parsing system. The grammar has over 5,600 nonterminals, 4,600 terminals and 19,000 productions in fully expanded form, with feature agreement constraints for case, gender, number and person. The parsing system consists of an enhanced Earley-based parser and a mechanism to select best-scoring parse trees from shared packed parse forests. Our parsing system is able to parse about 90{\%} of all sentences in articles published on the main Icelandic news websites. Preliminary evaluation with evalb shows an F-measure of 70.72{\%} on parsed sentences. Our system demonstrates that parsing a morphologically rich language using a wide-coverage CFG can be practical.",
}
@article{tacl-2019-transactions,
    title = "Transactions of the Association for Computational Linguistics, Volume 7",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1000",
}
@article{rozovskaya-roth-2019-grammar,
    title = "Grammar Error Correction in Morphologically Rich Languages: The Case of {R}ussian",
    author = "Rozovskaya, Alla  and
      Roth, Dan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1001",
    doi = "10.1162/tacl_a_00251",
    pages = "1--17",
    abstract = "Until now, most of the research in grammar error correction focused on English, and the problem has hardly been explored for other languages. We address the task of correcting writing mistakes in morphologically rich languages, with a focus on Russian. We present a corrected and error-tagged corpus of Russian learner writing and develop models that make use of existing state-of-the-art methods that have been well studied for English. Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are available. Because annotation is extremely costly, these approaches are not suitable for the majority of domains and languages. We thus focus on methods that use {``}minimal supervision{''}; that is, those that do not rely on large amounts of annotated training data, and show how existing minimal-supervision approaches extend to a highly inflectional language such as Russian. The results demonstrate that these methods are particularly useful for correcting mistakes in grammatical phenomena that involve rich morphology.",
}
@article{song-etal-2019-semantic,
    title = "Semantic Neural Machine Translation Using {AMR}",
    author = "Song, Linfeng  and
      Gildea, Daniel  and
      Zhang, Yue  and
      Wang, Zhiguo  and
      Su, Jinsong",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1002",
    doi = "10.1162/tacl_a_00252",
    pages = "19--31",
    abstract = "It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.",
}
@article{more-etal-2019-joint,
    title = "Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for {MRL}s and a Case Study from {M}odern {H}ebrew",
    author = "More, Amir  and
      Seker, Amit  and
      Basmova, Victoria  and
      Tsarfaty, Reut",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1003",
    doi = "10.1162/tacl_a_00253",
    pages = "33--48",
    abstract = "In standard NLP pipelines, morphological analysis and disambiguation (MA{\&}D) precedes syntactic and semantic downstream tasks. However, for languages with complex and ambiguous word-internal structure, known as morphologically rich languages (MRLs), it has been hypothesized that syntactic context may be crucial for accurate MA{\&}D, and vice versa. In this work we empirically confirm this hypothesis for Modern Hebrew, an MRL with complex morphology and severe word-level ambiguity, in a novel transition-based framework. Specifically, we propose a joint morphosyntactic transition-based framework which formally unifies two distinct transition systems, morphological and syntactic, into a single transition-based system with joint training and joint inference. We empirically show that MA{\&}D results obtained in the joint settings outperform MA{\&}D results obtained by the respective standalone components, and that end-to-end parsing results obtained by our joint system present a new state of the art for Hebrew dependency parsing.",
}
@article{belinkov-glass-2019-analysis,
    title = "Analysis Methods in Neural Language Processing: A Survey",
    author = "Belinkov, Yonatan  and
      Glass, James",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1004",
    doi = "10.1162/tacl_a_00254",
    pages = "49--72",
    abstract = "The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.",
}
@article{coavoux-etal-2019-unlexicalized,
    title = "Unlexicalized Transition-based Discontinuous Constituency Parsing",
    author = "Coavoux, Maximin  and
      Crabb{\'e}, Beno{\^\i}t  and
      Cohen, Shay B.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1005",
    doi = "10.1162/tacl_a_00255",
    pages = "73--89",
    abstract = "Lexicalized parsing models are based on the assumptions that (i) constituents are organized around a lexical head and (ii) bilexical statistics are crucial to solve ambiguities. In this paper, we introduce an unlexicalized transition-based parser for discontinuous constituency structures, based on a structure-label transition system and a bi-LSTM scoring system. We compare it with lexicalized parsing models in order to address the question of lexicalization in the context of discontinuous constituency parsing. Our experiments show that unlexicalized models systematically achieve higher results than lexicalized models, and provide additional empirical evidence that lexicalization is not necessary to achieve strong parsing results. Our best unlexicalized model sets a new state of the art on English and German discontinuous constituency treebanks. We further provide a per-phenomenon analysis of its errors on discontinuous constituents.",
}
@article{zhou-etal-2019-synchronous,
    title = "Synchronous Bidirectional Neural Machine Translation",
    author = "Zhou, Long  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1006",
    doi = "10.1162/tacl_a_00256",
    pages = "91--105",
    abstract = "Existing approaches to neural machine translation (NMT) generate the target language sequence token-by-token from left to right. However, this kind of unidirectional decoding framework cannot make full use of the target-side future contexts which can be produced in a right-to-left decoding direction, and thus suffers from the issue of unbalanced outputs. In this paper, we introduce a synchronous bidirectional{--}neural machine translation (SB-NMT) that predicts its outputs using left-to-right and right-to-left decoding simultaneously and interactively, in order to leverage both of the history and future information at the same time. Specifically, we first propose a new algorithm that enables synchronous bidirectional decoding in a single model. Then, we present an interactive decoding model in which left-to-right (right-to-left) generation does not only depend on its previously generated outputs, but also relies on future contexts predicted by right-to-left (left-to-right) decoding. We extensively evaluate the proposed SB-NMT model on large-scale NIST Chinese{--}English, WMT14 English{--}German, and WMT18 Russian{--}English translation tasks. Experimental results demonstrate that our model achieves significant improvements over the strong Transformer model by 3.92, 1.49, and 1.04 BLEU points, respectively, and obtains the state-of-the-art performance on Chinese{--}English and English{--}German translation tasks.",
}
@article{jawanpuria-etal-2019-learning,
    title = "Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach",
    author = "Jawanpuria, Pratik  and
      Balgovind, Arjun  and
      Kunchukuttan, Anoop  and
      Mishra, Bamdev",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1007",
    doi = "10.1162/tacl_a_00257",
    pages = "107--120",
    abstract = "We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks. We next generalize our framework to represent multiple languages in a common latent space. Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs. We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.",
}
@article{dangovski-etal-2019-rotational,
    title = "Rotational Unit of Memory: A Novel Representation Unit for {RNN}s with Scalable Applications",
    author = "Dangovski, Rumen  and
      Jing, Li  and
      Nakov, Preslav  and
      Tatalovi{\'c}, Mi{\'c}o  and
      Solja{\v{c}}i{\'c}, Marin",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1008",
    doi = "10.1162/tacl_a_00258",
    pages = "121--138",
    abstract = "Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization. Although LSTMs and GRUs were designed to model long-range dependencies more accurately than conventional RNNs, they nevertheless have problems copying or recalling information from the long distant past. Here, we derive a phase-coded representation of the memory state, Rotational Unit of Memory (RUM), that unifies the concepts of unitary learning and associative memory. We show experimentally that RNNs based on RUMs can solve basic sequential tasks such as memory copying and memory recall much better than LSTMs/GRUs. We further demonstrate that by replacing LSTM/GRU with RUM units we can apply neural networks to real-world problems such as language modeling and text summarization, yielding results comparable to the state of the art.",
}
@article{pappas-henderson-2019-gile,
    title = "{GILE}: A Generalized Input-Label Embedding for Text Classification",
    author = "Pappas, Nikolaos  and
      Henderson, James",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1009",
    doi = "10.1162/tacl_a_00259",
    pages = "139--155",
    abstract = "Neural text classification models typically treat output labels as categorical variables that lack description and semantics. This forces their parametrization to be dependent on the label set size, and, hence, they are unable to scale to large label sets and generalize to unseen ones. Existing joint input-label text models overcome these issues by exploiting label descriptions, but they are unable to capture complex label relationships, have rigid parametrization, and their gains on unseen labels happen often at the expense of weak performance on the labels seen during training. In this paper, we propose a new input-label model that generalizes over previous such models, addresses their limitations, and does not compromise performance on seen labels. The model consists of a joint nonlinear input-label embedding with controllable capacity and a joint-space-dependent classification unit that is trained with cross-entropy loss to optimize classification performance. We evaluate models on full-resource and low- or zero-resource text classification of multilingual news and biomedical text with a large label set. Our model outperforms monolingual and multilingual models that do not leverage label semantics and previous joint input-label space models in both scenarios.",
}
@article{chandlee-jardine-2019-autosegmental,
    title = "Autosegmental Input Strictly Local Functions",
    author = "Chandlee, Jane  and
      Jardine, Adam",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1010",
    doi = "10.1162/tacl_a_00260",
    pages = "157--168",
    abstract = "Autosegmental representations (ARs; Goldsmith, 1976) are claimed to enable local analyses of otherwise non-local phenomena Odden (1994). Focusing on the domain of tone, we investigate this ability of ARs using a computationally well-defined notion of locality extended from Chandlee (2014). The result is a more nuanced understanding of the way in which ARs interact with phonological locality.",
}
@article{arnold-etal-2019-sector,
    title = "{SECTOR}: A Neural Model for Coherent Topic Segmentation and Classification",
    author = {Arnold, Sebastian  and
      Schneider, Rudolf  and
      Cudr{\'e}-Mauroux, Philippe  and
      Gers, Felix A.  and
      L{\"o}ser, Alexander},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1011",
    doi = "10.1162/tacl_a_00261",
    pages = "169--184",
    abstract = "When searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates the identification of the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a model to support machine reading systems by segmenting documents into coherent sections and assigning topic labels to each section. Our deep neural network architecture learns a latent topic embedding over the course of a document. This can be leveraged to classify local topics from plain text and segment a document at topic shifts. In addition, we contribute WikiSection, a publicly available data set with 242k labeled sections in English and German from two distinct domains: diseases and cities. From our extensive evaluation of 20 architectures, we report a highest score of 71.6{\%} F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR long short-term memory model with Bloom filter embeddings and bidirectional segmentation. This is a significant improvement of 29.5 points F1 over state-of-the-art CNN classifiers with baseline segmentation.",
}
@article{saha-etal-2019-complex,
    title = "Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs",
    author = "Saha, Amrita  and
      Ansari, Ghulam Ahmed  and
      Laddha, Abhishek  and
      Sankaranarayanan, Karthik  and
      Chakrabarti, Soumen",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1012",
    doi = "10.1162/tacl_a_00262",
    pages = "185--200",
    abstract = "Recent years have seen increasingly complex question-answering on knowledge bases (KBQA) involving logical, quantitative, and comparative reasoning over KB subgraphs. Neural Program Induction (NPI) is a pragmatic approach toward modularizing the reasoning process by translating a complex natural language query into a multi-step executable program. While NPI has been commonly trained with the {`}{`}gold{'}{'} program or its sketch, for realistic KBQA applications such gold programs are expensive to obtain. There, practically only natural language queries and the corresponding answers can be provided for training. The resulting combinatorial explosion in program space, along with extremely sparse rewards, makes NPI for KBQA ambitious and challenging. We present Complex Imperative Program Induction from Terminal Rewards (CIPITR), an advanced neural programmer that mitigates reward sparsity with auxiliary rewards, and restricts the program space to semantically correct programs using high-level constraints, KB schema, and inferred answer type. CIPITR solves complex KBQA considerably more accurately than key-value memory networks and neural symbolic machines (NSM). For moderately complex queries requiring 2- to 5-step programs, CIPITR scores at least 3{\mbox{$\times$}} higher F1 than the competing systems. On one of the hardest class of programs (comparative reasoning) with 5{--}10 steps, CIPITR outperforms NSM by a factor of 89 and memory networks by 9 times.",
}
@article{kim-etal-2019-categorical,
    title = "Categorical Metadata Representation for Customized Text Classification",
    author = "Kim, Jihyeok  and
      Amplayo, Reinald Kim  and
      Lee, Kyungjae  and
      Sung, Sua  and
      Seo, Minji  and
      Hwang, Seung-won",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1013",
    doi = "10.1162/tacl_a_00263",
    pages = "201--215",
    abstract = "The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e.g., using user/product information for sentiment classification. This information has been used to modify parts of the model (e.g., word embeddings, attention mechanisms) such that results can be customized according to the metadata. We observe that current representation methods for categorical metadata, which are devised for human consumption, are not as effective as claimed in popular classification methods, outperformed even by simple concatenation of categorical features in the final layer of the sentence encoder. We conjecture that categorical features are harder to represent for machine use, as available context only indirectly describes the category, and even such context is often scarce (for tail category). To this end, we propose using basis vectors to effectively incorporate categorical metadata on various parts of a neural-based model. This additionally decreases the number of parameters dramatically, especially when the number of categorical features is large. Extensive experiments on various data sets with different properties are performed and show that through our method, we can represent categorical metadata more effectively to customize parts of the model, including unexplored ones, and increase the performance of the model greatly.",
}
@article{sun-etal-2019-dream,
    title = "{DREAM}: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension",
    author = "Sun, Kai  and
      Yu, Dian  and
      Chen, Jianshu  and
      Yu, Dong  and
      Choi, Yejin  and
      Cardie, Claire",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1014",
    doi = "10.1162/tacl_a_00264",
    pages = "217--231",
    abstract = "We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data set contains 10,197 multiple-choice questions for 6,444 dialogues. In contrast to existing reading comprehension data sets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. DREAM is likely to present significant challenges for existing reading comprehension systems: 84{\%} of answers are non-extractive, 85{\%} of questions require reasoning beyond a single sentence, and 34{\%} of questions also involve commonsense knowledge. We apply several popular neural reading comprehension models that primarily exploit surface information within the text and find them to, at best, just barely outperform a rule-based approach. We next investigate the effects of incorporating dialogue structure and different kinds of general world knowledge into both rule-based and (neural and non-neural) machine learning-based reading comprehension models. Experimental results on the DREAM data set show the effectiveness of dialogue structure and general world knowledge. DREAM is available at https://dataset.org/dream/.",
}
@article{jehl-etal-2019-learning,
    title = "Learning Neural Sequence-to-Sequence Models from Weak Feedback with Bipolar Ramp Loss",
    author = "Jehl, Laura  and
      Lawrence, Carolin  and
      Riezler, Stefan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1015",
    doi = "10.1162/tacl_a_00265",
    pages = "233--248",
    abstract = "In many machine learning scenarios, supervision by gold labels is not available and conse quently neural models cannot be trained directly by maximum likelihood estimation. In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract a supervision signal for training. We present several objectives for two separate weakly supervised tasks, machine translation and semantic parsing. We show that objectives should actively discourage negative outputs in addition to promoting a surrogate gold structure. This notion of bipolarity is naturally present in ramp loss objectives, which we adapt to neural models. We show that bipolar ramp loss objectives outperform other non-bipolar ramp loss objectives and minimum risk training on both weakly supervised tasks, as well as on a supervised machine translation task. Additionally, we introduce a novel token-level ramp loss objective, which is able to outperform even the best sequence-level ramp loss on both weakly supervised tasks.",
}
@article{reddy-etal-2019-coqa,
    title = "{C}o{QA}: A Conversational Question Answering Challenge",
    author = "Reddy, Siva  and
      Chen, Danqi  and
      Manning, Christopher D.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1016",
    doi = "10.1162/tacl_a_00266",
    pages = "249--266",
    abstract = "Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets (e.g., coreference and pragmatic reasoning). We evaluate strong dialogue and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4{\%}, which is 23.4 points behind human performance (88.8{\%}), indicating that there is ample room for improvement. We present CoQA as a challenge to the community at https://stanfordnlp.github.io/coqa.",
}
@article{zeng-etal-2019-say,
    title = "What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations",
    author = "Zeng, Jichuan  and
      Li, Jing  and
      He, Yulan  and
      Gao, Cuiyun  and
      Lyu, Michael R.  and
      King, Irwin",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1017",
    doi = "10.1162/tacl_a_00267",
    pages = "267--281",
    abstract = "This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse).1 Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier.Our data sets and code are available at: http://github.com/zengjichuan/Topic{\_}Disc.",
}
@article{nederhof-2019-calculating,
    title = "Calculating the Optimal Step in Shift-Reduce Dependency Parsing: From Cubic to Linear Time",
    author = "Nederhof, Mark-Jan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1018",
    doi = "10.1162/tacl_a_00268",
    pages = "283--296",
    abstract = "We present a new cubic-time algorithm to calculate the optimal next step in shift-reduce dependency parsing, relative to ground truth, commonly referred to as dynamic oracle. Unlike existing algorithms, it is applicable if the training corpus contains non-projective structures. We then show that for a projective training corpus, the time complexity can be improved from cubic to linear.",
}
@article{guo-etal-2019-densely,
    title = "Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning",
    author = "Guo, Zhijiang  and
      Zhang, Yan  and
      Teng, Zhiyang  and
      Lu, Wei",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1019",
    doi = "10.1162/tacl_a_00269",
    pages = "297--312",
    abstract = "We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Network (DCGCN). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMR-to-text generation and syntax-based neural machine translation.",
}
@article{sperber-etal-2019-attention,
    title = "Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation",
    author = "Sperber, Matthias  and
      Neubig, Graham  and
      Niehues, Jan  and
      Waibel, Alex",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1020",
    doi = "10.1162/tacl_a_00270",
    pages = "313--325",
    abstract = "Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation system trained on parallel texts. Several recent works have shown the feasibility of collapsing the cascade into a single, direct model that can be trained in an end-to-end fashion on a corpus of translated speech. However, experiments are inconclusive on whether the cascade or the direct model is stronger, and have only been conducted under the unrealistic assumption that both are trained on equal amounts of data, ignoring other available speech recognition and machine translation corpora. In this paper, we demonstrate that direct speech translation models require more data to perform well than cascaded models, and although they allow including auxiliary data through multi-task training, they are poor at exploiting such data, putting them at a severe disadvantage. As a remedy, we propose the use of end- to-end trainable models with two attention mechanisms, the first establishing source speech to source text alignments, the second modeling source to target text alignment. We show that such models naturally decompose into multi-task{--}trainable recognition and translation tasks and propose an attention-passing technique that alleviates error propagation issues in a previous formulation of a model with two attention stages. Our proposed model outperforms all examined baselines and is able to exploit auxiliary training data much more effectively than direct attentional models.",
}
@article{cotterell-etal-2019-complexity,
    title = "On the Complexity and Typology of Inflectional Morphological Systems",
    author = "Cotterell, Ryan  and
      Kirov, Christo  and
      Hulden, Mans  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1021",
    doi = "10.1162/tacl_a_00271",
    pages = "327--342",
    abstract = "We quantify the linguistic complexity of different languages{'} morphological systems. We verify that there is a statistically significant empirical trade-off between paradigm size and irregularity: A language{'}s inflectional paradigms may be either large in size or highly irregular, but never both. We define a new measure of paradigm irregularity based on the conditional entropy of the surface realization of a paradigm{---} how hard it is to jointly predict all the word forms in a paradigm from the lemma. We estimate irregularity by training a predictive model. Our measurements are taken on large morphological paradigms from 36 typologically diverse languages.",
}
@article{cai-lapata-2019-syntax,
    title = "Syntax-aware Semantic Role Labeling without Parsing",
    author = "Cai, Rui  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1022",
    doi = "10.1162/tacl_a_00272",
    pages = "343--356",
    abstract = "In this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser. The backbone of our model is an LSTM-based semantic role labeler jointly trained with two auxiliary tasks: predicting the dependency label of a word and whether there exists an arc linking it to the predicate. The auxiliary tasks provide syntactic information that is specific to semantic role labeling and are learned from training data (dependency annotations) without relying on existing dependency parsers, which can be noisy (e.g., on out-of-domain data or infrequent constructions). Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.",
}
@article{li-etal-2019-generative,
    title = "A Generative Model for Punctuation in Dependency Trees",
    author = "Li, Xiang Lisa  and
      Wang, Dingquan  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1023",
    doi = "10.1162/tacl_a_00273",
    pages = "357--373",
    abstract = "Treebanks traditionally treat punctuation marks as ordinary words, but linguists have suggested that a tree{'}s {``}true{''} punctuation marks are not observed (Nunberg, 1990). These latent {``}underlying{''} marks serve to delimit or separate constituents in the syntax tree. When the tree{'}s yield is rendered as a written sentence, a string rewriting mechanism transduces the underlying marks into {``}surface{''} marks, which are part of the observed (surface) string but should not be regarded as part of the tree. We formalize this idea in a generative model of punctuation that admits efficient dynamic programming. We train it without observing the underlying marks, by locally maximizing the incomplete data likelihood (similarly to the EM algorithm). When we use the trained model to reconstruct the tree{'}s underlying punctuation, the results appear plausible across 5 languages, and in particular are consistent with Nunberg{'}s analysis of English. We show that our generative model can be used to beat baselines on punctuation restoration. Also, our reconstruction of a sentence{'}s underlying punctuation lets us appropriately render the surface punctuation (via our trained underlying-to-surface mechanism) when we syntactically transform the sentence.",
}
@article{rajendran-etal-2019-learning,
    title = "Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use",
    author = "Rajendran, Janarthanan  and
      Ganhotra, Jatin  and
      Polymenakos, Lazaros C.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1024",
    doi = "10.1162/tacl_a_00274",
    pages = "375--386",
    abstract = "Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their inability to handle new user behavior at deployment has limited their usage in real world. In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems that handles new user behaviors at deployment by transferring the dialog to a human agent intelligently. The proposed method has three goals: 1) maximize user{'}s task success by transferring to human agents, 2) minimize the load on the human agents by transferring to them only when it is essential, and 3) learn online from the human agent{'}s responses to reduce human agents{'} load further. We evaluate our proposed method on a modified-bAbI dialog task, which simulates the scenario of new user behaviors occurring at test time. Experimental results show that our proposed method is effective in achieving the desired goals.",
}
@article{wallace-etal-2019-trick,
    title = "Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering",
    author = "Wallace, Eric  and
      Rodriguez, Pedro  and
      Feng, Shi  and
      Yamada, Ikuya  and
      Boyd-Graber, Jordan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1029",
    doi = "10.1162/tacl_a_00279",
    pages = "387--401",
    abstract = "Adversarial evaluation stress-tests a model{'}s understanding of natural language. Because past approaches expose superficial patterns, the resulting adversarial examples are limited in complexity and diversity. We propose human- in-the-loop adversarial generation, where human authors are guided to break models. We aid the authors with interpretations of model predictions through an interactive user interface. We apply this generation framework to a question answering task called Quizbowl, where trivia enthusiasts craft adversarial questions. The resulting questions are validated via live human{--}computer matches: Although the questions appear ordinary to humans, they systematically stump neural and information retrieval models. The adversarial questions cover diverse phenomena from multi-hop reasoning to entity type distractors, exposing open challenges in robust question answering.",
}
@article{shwartz-dagan-2019-still,
    title = "Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition",
    author = "Shwartz, Vered  and
      Dagan, Ido",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1027",
    doi = "10.1162/tacl_a_00277",
    pages = "403--419",
    abstract = "Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that, as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, consisting of six tasks related to lexical composition effects, can serve future research aiming to improve representations.",
}
@article{azpiazu-pera-2019-multiattentive,
    title = "Multiattentive Recurrent Neural Network Architecture for Multilingual Readability Assessment",
    author = "Azpiazu, Ion Madrazo  and
      Pera, Maria Soledad",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1028",
    doi = "10.1162/tacl_a_00278",
    pages = "421--436",
    abstract = "We present a multiattentive recurrent neural network architecture for automatic multilingual readability assessment. This architecture considers raw words as its main input, but internally captures text structure and informs its word attention process using other syntax- and morphology-related datapoints, known to be of great importance to readability. This is achieved by a multiattentive strategy that allows the neural network to focus on specific parts of a text for predicting its reading level. We conducted an exhaustive evaluation using data sets targeting multiple languages and prediction task types, to compare the proposed model with traditional, state-of-the-art, and other neural network strategies.",
}
@article{dima-etal-2019-word,
    title = "No Word is an {I}sland{---}{A} Transformation Weighting Model for Semantic Composition",
    author = {Dima, Corina  and
      de Kok, Dani{\"e}l  and
      Witte, Neele  and
      Hinrichs, Erhard},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1025",
    doi = "10.1162/tacl_a_00275",
    pages = "437--451",
    abstract = "Composition models of distributional semantics are used to construct phrase representations from the representations of their words. Composition models are typically situated on two ends of a spectrum. They either have a small number of parameters but compose all phrases in the same way, or they perform word-specific compositions at the cost of a far larger number of parameters. In this paper we propose transformation weighting (TransWeight), a composition model that consistently outperforms existing models on nominal compounds, adjective-noun phrases, and adverb-adjective phrases in English, German, and Dutch. TransWeight drastically reduces the number of parameters needed compared with the best model in the literature by composing similar words in the same way.",
}
@article{kwiatkowski-etal-2019-natural,
    title = "Natural Questions: A Benchmark for Question Answering Research",
    author = "Kwiatkowski, Tom  and
      Palomaki, Jennimaria  and
      Redfield, Olivia  and
      Collins, Michael  and
      Parikh, Ankur  and
      Alberti, Chris  and
      Epstein, Danielle  and
      Polosukhin, Illia  and
      Devlin, Jacob  and
      Lee, Kenton  and
      Toutanova, Kristina  and
      Jones, Llion  and
      Kelcey, Matthew  and
      Chang, Ming-Wei  and
      Dai, Andrew M.  and
      Uszkoreit, Jakob  and
      Le, Quoc  and
      Petrov, Slav",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1026",
    doi = "10.1162/tacl_a_00276",
    pages = "452--466",
    abstract = "We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature.",
}
@article{hahn-baroni-2019-tabula,
    title = "Tabula Nearly Rasa: Probing the Linguistic Knowledge of Character-level Neural Language Models Trained on Unsegmented Text",
    author = "Hahn, Michael  and
      Baroni, Marco",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1033",
    doi = "10.1162/tacl_a_00283",
    pages = "467--484",
    abstract = "Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. This has renewed interest in whether these generic sequence processing devices are inducing genuine linguistic knowledge. Nearly all current analytical studies, however, initialize the RNNs with a vocabulary of known words, and feed them tokenized input during training. We present a multi-lingual study of the linguistic knowledge encoded in RNNs trained as character-level language models, on input data with word boundaries removed. These networks face a tougher and more cognitively realistic task, having to discover any useful linguistic unit from scratch based on input statistics. The results show that our {``}near tabula rasa{''} RNNs are mostly able to solve morphological, syntactic and semantic tasks that intuitively presuppose word-level knowledge, and indeed they learned, to some extent, to track word boundaries. Our study opens the door to speculations about the necessity of an explicit, rigid word lexicon in language learning and usage.",
}
@article{banerjee-khapra-2019-graph,
    title = "Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems",
    author = "Banerjee, Suman  and
      Khapra, Mitesh M.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1034",
    doi = "10.1162/tacl_a_00284",
    pages = "485--500",
    abstract = "Domain-specific goal-oriented dialogue systems typically require modeling three types of inputs, namely, (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances, and (iii) the current utterance for which the response needs to be generated. While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context. Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, semantic role labeling, and document dating, we propose a memory-augmented GCN for goal-oriented dialogues. Our model exploits (i) the entity relation graph in a knowledge-base and (ii) the dependency graph associated with an utterance to compute richer representations for words and entities. Further, we take cognizance of the fact that in certain situations, such as when the conversation is in a code-mixed language, dependency parsers may not be available. We show that in such situations we could use the global word co-occurrence graph to enrich the representations of utterances. We experiment with four datasets: (i) the modified DSTC2 dataset, (ii) recently released code-mixed versions of DSTC2 dataset in four languages, (iii) Wizard-of-Oz style CAM676 dataset, and (iv) Wizard-of-Oz style MultiWOZ dataset. On all four datasets our method outperforms existing methods, on a wide range of evaluation metrics.",
}
@article{govindarajan-etal-2019-decomposing,
    title = "Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements",
    author = "Govindarajan, Venkata  and
      Van Durme, Benjamin  and
      White, Aaron Steven",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1035",
    doi = "10.1162/tacl_a_00285",
    pages = "501--517",
    abstract = "We present a novel semantic framework for modeling linguistic expressions of generalization{---} generic, habitual, and episodic statements{---}as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information{---}including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings{---}for predicting expressions of generalization.",
}
@article{elazar-goldberg-2019-wheres,
    title = "Where{'}s My Head? {D}efinition, Data Set, and Models for Numeric Fused-Head Identification and Resolution",
    author = "Elazar, Yanai  and
      Goldberg, Yoav",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1030",
    doi = "10.1162/tacl_a_00280",
    pages = "519--535",
    abstract = "We provide the first computational treatment of fused-heads constructions (FHs), focusing on the numeric fused-heads (NFHs). FHs constructions are noun phrases in which the head noun is missing and is said to be {``}fused{''} with its dependent modifier. This missing information is implicit and is important for sentence understanding. The missing references are easily filled in by humans but pose a challenge for computational models. We formulate the handling of FHs as a two stages process: Identification of the FH construction and resolution of the missing head. We explore the NFH phenomena in large corpora of English text and create (1) a data set and a highly accurate method for NFH identification; (2) a 10k examples (1 M tokens) crowd-sourced data set of NFH resolution; and (3) a neural baseline for the NFH resolution task. We release our code and data set, to foster further research into this challenging problem.",
}
@article{luu-etal-2019-measuring,
    title = "Measuring Online Debaters{'} Persuasive Skill from Text over Time",
    author = "Luu, Kelvin  and
      Tan, Chenhao  and
      Smith, Noah A.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1031",
    doi = "10.1162/tacl_a_00281",
    pages = "537--550",
    abstract = "Online debates allow people to express their persuasive abilities and provide exciting opportunities for understanding persuasion. Prior studies have focused on studying persuasion in debate content, but without accounting for each debater{'}s history or exploring the progression of a debater{'}s persuasive ability. We study debater skill by modeling how participants progress over time in a collection of debates from Debate.org. We build on a widely used model of skill in two-player games and augment it with linguistic features of a debater{'}s content. We show that online debaters{'} skill levels do tend to improve over time. Incorporating linguistic profiles leads to more robust skill estimation than winning records alone. Notably, we find that an interaction feature combining uncertainty cues (hedging) with terms strongly associated with either side of a particular debate (fightin{'} words) is more predictive than either feature on its own, indicating the importance of fine- grained linguistic features.",
}
@article{napoles-etal-2019-enabling,
    title = "Enabling Robust Grammatical Error Correction in New Domains: Data Sets, Metrics, and Analyses",
    author = "Napoles, Courtney  and
      N{\u{a}}dejde, Maria  and
      Tetreault, Joel",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1032",
    doi = "10.1162/tacl_a_00282",
    pages = "551--566",
    abstract = "Until now, grammatical error correction (GEC) has been primarily evaluated on text written by non-native English speakers, with a focus on student essays. This paper enables GEC development on text written by native speakers by providing a new data set and metric. We present a multiple-reference test corpus for GEC that includes 4,000 sentences in two new domains (formal and informal writing by native English speakers) and 2,000 sentences from a diverse set of non-native student writing. We also collect human judgments of several GEC systems on this new test set and perform a meta-evaluation, assessing how reliable automatic metrics are across these domains. We find that commonly used GEC metrics have inconsistent performance across domains, and therefore we propose a new ensemble metric that is robust on all three domains of text.",
}
@article{akyurek-etal-2019-morphological,
    title = "Morphological Analysis Using a Sequence Decoder",
    author = {Aky{\"u}rek, Ekin  and
      Dayan{\i}k, Erenay  and
      Yuret, Deniz},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1036",
    doi = "10.1162/tacl_a_00286",
    pages = "567--579",
    abstract = "We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and to outperform whole-tag models. In addition, generating morphological features as a sequence rather than, for example, an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically complex languages. We obtain state-of-the-art results in nine languages of different morphological complexity under low-resource, high-resource, and transfer learning settings. We also introduce TrMor2018, a new high-accuracy Turkish morphology data set. Our Morse implementation and the TrMor2018 data set are available online to support future research.1See https://github.com/ai-ku/Morse.jl for a Morse implementation in Julia/Knet (Yuret, 2016) and https://github.com/ai-ku/TrMor2018 for the new Turkish data set.",
}
@article{xu-lapata-2019-weakly,
    title = "Weakly Supervised Domain Detection",
    author = "Xu, Yumo  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1037",
    doi = "10.1162/tacl_a_00287",
    pages = "581--596",
    abstract = "In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments that are domain-heavy (i.e., sentences or phrases that are representative of and provide evidence for a given domain) could enhance the robustness and portability of various text classification applications. We propose an encoder-detector framework for domain detection and bootstrap classifiers with multiple instance learning. The model is hierarchically organized and suited to multilabel classification. We demonstrate that despite learning with minimal supervision, our model can be applied to text spans of different granularities, languages, and genres. We also showcase the potential of domain detection for text summarization.",
}
@article{artetxe-schwenk-2019-massively,
    title = "Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond",
    author = "Artetxe, Mikel  and
      Schwenk, Holger",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1038",
    doi = "10.1162/tacl_a_00288",
    pages = "597--610",
    abstract = "We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared byte-pair encoding vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our experiments in cross-lingual natural language inference (XNLI data set), cross-lingual document classification (MLDoc data set), and parallel corpus mining (BUCC data set) show the effectiveness of our approach. We also introduce a new test set of aligned sentences in 112 languages, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low- resource languages. Our implementation, the pre-trained encoder, and the multilingual test set are available at https://github.com/facebookresearch/LASER.",
}
@article{li-etal-2019-efficient,
    title = "Efficient Contextual Representation Learning With Continuous Outputs",
    author = "Li, Liunian Harold  and
      Chen, Patrick H.  and
      Hsieh, Cho-Jui  and
      Chang, Kai-Wei",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1039",
    doi = "10.1162/tacl_a_00289",
    pages = "611--624",
    abstract = "Contextual representation models have achieved great success in improving various downstream natural language processing tasks. However, these language-model-based encoders are difficult to train due to their large parameter size and high computational complexity. By carefully examining the training procedure, we observe that the softmax layer, which predicts a distribution of the target word, often induces significant overhead, especially when the vocabulary size is large. Therefore, we revisit the design of the output layer and consider directly predicting the pre-trained embedding of the target word for a given context. When applied to ELMo, the proposed approach achieves a 4-fold speedup and eliminates 80{\%} trainable parameters while achieving competitive performance on downstream tasks. Further analysis shows that the approach maintains the speed advantage under various settings, even when the sentence encoder is scaled up.",
}
@article{warstadt-etal-2019-neural,
    title = "Neural Network Acceptability Judgments",
    author = "Warstadt, Alex  and
      Singh, Amanpreet  and
      Bowman, Samuel R.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1040",
    doi = "10.1162/tacl_a_00290",
    pages = "625--641",
    abstract = "This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.{'}s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.",
}
@article{doitch-etal-2019-perturbation,
    title = "Perturbation Based Learning for Structured {NLP} Tasks with Application to Dependency Parsing",
    author = "Doitch, Amichay  and
      Yazdi, Ram  and
      Hazan, Tamir  and
      Reichart, Roi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1041",
    doi = "10.1162/tacl_a_00291",
    pages = "643--659",
    abstract = "The best solution of structured prediction models in NLP is often inaccurate because of limited expressive power of the model or to non-exact parameter estimation. One way to mitigate this problem is sampling candidate solutions from the model{'}s solution space, reasoning that effective exploration of this space should yield high-quality solutions. Unfortunately, sampling is often computationally hard and many works hence back-off to sub-optimal strategies, such as extraction of the best scoring solutions of the model, which are not as diverse as sampled solutions. In this paper we propose a perturbation-based approach where sampling from a probabilistic model is computationally efficient. We present a learning algorithm for the variance of the perturbations, and empirically demonstrate its importance. Moreover, while finding the argmax in our model is intractable, we propose an efficient and effective approximation. We apply our framework to cross-lingual dependency parsing across 72 corpora from 42 languages and to lightly supervised dependency parsing across 13 corpora from 12 languages, and demonstrate strong results in terms of both the quality of the entire solution list and of the final solution.1",
}
@article{gu-etal-2019-insertion,
    title = "Insertion-based Decoding with Automatically Inferred Generation Order",
    author = "Gu, Jiatao  and
      Liu, Qi  and
      Cho, Kyunghyun",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1042",
    doi = "10.1162/tacl_a_00292",
    pages = "661--676",
    abstract = "Conventional neural autoregressive decoding commonly assumes a fixed left-to-right generation order, which may be sub-optimal. In this work, we propose a novel decoding algorithm{---} InDIGO{---}which supports flexible sequence generation in arbitrary orders through insertion operations. We extend Transformer, a state-of-the-art sequence generation model, to efficiently implement the proposed approach, enabling it to be trained with either a pre-defined generation order or adaptive orders obtained from beam-search. Experiments on four real-world tasks, including word order recovery, machine translation, image caption, and code generation, demonstrate that our algorithm can generate sequences following arbitrary orders, while achieving competitive or even better performance compared with the conventional left-to-right generation. The generated sequences show that InDIGO adopts adaptive generation orders based on input information.",
}
@article{pavlick-kwiatkowski-2019-inherent,
    title = "Inherent Disagreements in Human Textual Inferences",
    author = "Pavlick, Ellie  and
      Kwiatkowski, Tom",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1043",
    doi = "10.1162/tacl_a_00293",
    pages = "677--694",
    abstract = "We analyze human{'}s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation {``}noise{''}, but rather persist as we collect more ratings and as we vary the amount of context provided to raters. We further show that the type of uncertainty captured by current state-of-the-art models for natural language inference is not reflective of the type of uncertainty present in human disagreements. We discuss implications of our results in relation to the recognizing textual entailment (RTE)/natural language inference (NLI) task. We argue for a refined evaluation objective that requires models to explicitly capture the full distribution of plausible human judgments.",
}
@article{rotman-reichart-2019-deep,
    title = "Deep Contextualized Self-training for Low Resource Dependency Parsing",
    author = "Rotman, Guy  and
      Reichart, Roi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1044",
    doi = "10.1162/tacl_a_00294",
    pages = "695--713",
    abstract = "Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, which is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser{'}s output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods.1",
}
@article{cocos-callison-burch-2019-paraphrase,
    title = "Paraphrase-Sense-Tagged Sentences",
    author = "Cocos, Anne  and
      Callison-Burch, Chris",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/Q19-1045",
    doi = "10.1162/tacl_a_00295",
    pages = "714--728",
    abstract = "Many natural language processing tasks require discriminating the particular meaning of a word in context, but building corpora for developing sense-aware models can be a challenge. We present a large resource of example usages for words having a particular meaning, called Paraphrase-Sense-Tagged Sentences (PSTS). Built on the premise that a word{'}s paraphrases instantiate its fine-grained meanings (i.e., bug has different meanings corresponding to its paraphrases fly and microbe) the resource contains up to 10,000 sentences for each of 3 million target-paraphrase pairs where the target word takes on the meaning of the paraphrase. We describe an automatic method based on bilingual pivoting used to enumerate sentences for PSTS, and present two models for ranking PSTS sentences based on their quality. Finally, we demonstrate the utility of PSTS by using it to build a dataset for the task of hypernym prediction in context. Training a model on this automatically generated dataset produces accuracy that is competitive with a model trained on smaller datasets crafted with some manual effort.",
}
@proceedings{acl-2019-association-linguistics-tutorial,
    title = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    editor = "Nakov, Preslav  and
      Palmer, Alexis",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4000",
}
@inproceedings{martins-etal-2019-latent,
    title = "Latent Structure Models for Natural Language Processing",
    author = "Martins, Andr{\'e} F. T.  and
      Mihaylova, Tsvetomila  and
      Nangia, Nikita  and
      Niculae, Vlad",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4001",
    doi = "10.18653/v1/P19-4001",
    pages = "1--5",
    abstract = "Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines. They are appealing for two main reasons: they allow incorporating structural bias during training, leading to more accurate models; and they allow discovering hidden linguistic structure, which provides better interpretability. This tutorial will cover recent advances in discrete latent structure models. We discuss their motivation, potential, and limitations, then explore in detail three strategies for designing such models: gradient approximation, reinforcement learning, and end-to-end differentiable methods. We highlight connections among all these methods, enumerating their strengths and weaknesses. The models we present and analyze have been applied to a wide variety of NLP tasks, including sentiment analysis, natural language inference, language modeling, machine translation, and semantic parsing. Examples and evaluation will be covered throughout. After attending the tutorial, a practitioner will be better informed about which method is best suited for their problem.",
}
@inproceedings{koller-etal-2019-graph,
    title = "Graph-Based Meaning Representations: Design and Processing",
    author = "Koller, Alexander  and
      Oepen, Stephan  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4002",
    doi = "10.18653/v1/P19-4002",
    pages = "6--11",
    abstract = "This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs. The tutorial will (a) briefly review relevant background in formal and linguistic semantics; (b) semi-formally define a unified abstract view on different flavors of semantic graphs and associated terminology; (c) survey common frameworks for graph-based meaning representation and available graph banks; and (d) offer a technical overview of a representative selection of different parsing approaches.",
}
@inproceedings{joty-etal-2019-discourse,
    title = "Discourse Analysis and Its Applications",
    author = "Joty, Shafiq  and
      Carenini, Giuseppe  and
      Ng, Raymond  and
      Murray, Gabriel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4003",
    doi = "10.18653/v1/P19-4003",
    pages = "12--17",
    abstract = "Discourse processing is a suite of Natural Language Processing (NLP) tasks to uncover linguistic structures from texts at several levels, which can support many downstream applications. This involves identifying the topic structure, the coherence structure, the coreference structure, and the conversation structure for conversational discourse. Taken together, these structures can inform text summarization, machine translation, essay scoring, sentiment analysis, information extraction, question answering, and thread recovery. The tutorial starts with an overview of basic concepts in discourse analysis {--} monologue vs. conversation, synchronous vs. asynchronous conversation, and key linguistic structures in discourse analysis. We also give an overview of linguistic structures and corresponding discourse analysis tasks that discourse researchers are generally interested in, as well as key applications on which these discourse structures have an impact.",
}
@inproceedings{glavas-etal-2019-computational,
    title = "Computational Analysis of Political Texts: Bridging Research Efforts Across Communities",
    author = "Glava{\v{s}}, Goran  and
      Nanni, Federico  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4004",
    doi = "10.18653/v1/P19-4004",
    pages = "18--23",
    abstract = "In the last twenty years, political scientists started adopting and developing natural language processing (NLP) methods more actively in order to exploit text as an additional source of data in their analyses. Over the last decade the usage of computational methods for analysis of political texts has drastically expanded in scope, allowing for a sustained growth of the text-as-data community in political science. In political science, NLP methods have been extensively used for a number of analyses types and tasks, including inferring policy position of actors from textual evidence, detecting topics in political texts, and analyzing stylistic aspects of political texts (e.g., assessing the role of language ambiguity in framing the political agenda). Just like in numerous other domains, much of the work on computational analysis of political texts has been enabled and facilitated by the development of resources such as, the topically coded electoral programmes (e.g., the Manifesto Corpus) or topically coded legislative texts (e.g., the Comparative Agenda Project). Political scientists created resources and used available NLP methods to process textual data largely in isolation from the NLP community. At the same time, NLP researchers addressed closely related tasks such as election prediction, ideology classification, and stance detection. In other words, these two communities have been largely agnostic of one another, with NLP researchers mostly unaware of interesting applications in political science and political scientists not applying cutting-edge NLP methodology to their problems. The main goal of this tutorial is to systematize and analyze the body of research work on political texts from both communities. We aim to provide a gentle, all-round introduction to methods and tasks related to computational analysis of political texts. Our vision is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary research area.",
}
@inproceedings{pasca-2019-wikipedia,
    title = "{W}ikipedia as a Resource for Text Analysis and Retrieval",
    author = "Pasca, Marius",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4005",
    doi = "10.18653/v1/P19-4005",
    pages = "24",
    abstract = "This tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. Text analysis tasks, which take advantage of Wikipedia, include coreference resolution, word sense and entity disambiguation and information extraction. In information retrieval, a better understanding of the structure and meaning of queries helps in matching queries against documents, clustering search results, answer and entity retrieval and retrieving knowledge panels for queries asking about popular entities.",
}
@inproceedings{chien-2019-deep,
    title = "Deep {B}ayesian Natural Language Processing",
    author = "Chien, Jen-Tzung",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4006",
    doi = "10.18653/v1/P19-4006",
    pages = "25--30",
    abstract = "This introductory tutorial addresses the advances in deep Bayesian learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, {``}deep learning{''} is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The {``}semantic structure{''} in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The {``}distribution function{''} in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network, long short-term memory, sequence-to-sequence model, variational auto-encoder, generative adversarial network, attention mechanism, memory-augmented neural network, skip neural network, stochastic neural network, predictive state neural network and policy neural network. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies and domain applications are presented to tackle different issues in deep Bayesian processing, learning and understanding. At last, we will point out a number of directions and outlooks for future studies.",
}
@inproceedings{ruder-etal-2019-unsupervised,
    title = "Unsupervised Cross-Lingual Representation Learning",
    author = "Ruder, Sebastian  and
      S{\o}gaard, Anders  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4007",
    doi = "10.18653/v1/P19-4007",
    pages = "31--38",
    abstract = "In this tutorial, we provide a comprehensive survey of the exciting recent work on cutting-edge weakly-supervised and unsupervised cross-lingual word representations. After providing a brief history of supervised cross-lingual word representations, we focus on: 1) how to induce weakly-supervised and unsupervised cross-lingual word representations in truly resource-poor settings where bilingual supervision cannot be guaranteed; 2) critical examinations of different training conditions and requirements under which unsupervised algorithms can and cannot work effectively; 3) more robust methods for distant language pairs that can mitigate instability issues and low performance for distant language pairs; 4) how to comprehensively evaluate such representations; and 5) diverse applications that benefit from cross-lingual word representations (e.g., MT, dialogue, cross-lingual sequence labeling and structured prediction applications, cross-lingual IR).",
}
@inproceedings{budzynska-reed-2019-advances,
    title = "Advances in Argument Mining",
    author = "Budzynska, Katarzyna  and
      Reed, Chris",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4008",
    doi = "10.18653/v1/P19-4008",
    pages = "39--42",
    abstract = "This course aims to introduce students to an exciting and dynamic area that has witnessed remarkable growth over the past 36 months. Argument mining builds on opinion mining, sentiment analysis and related to tasks to automatically extract not just *what* people think, but *why* they hold the opinions they do. From being largely beyond the state of the art barely five years ago, there are now many hundreds of papers on the topic, millions of dollars of commercial and research investment, and the 6th ACL workshop on the topic will be in Florence in 2019. The tutors have delivered tutorials on argument mining at ACL 2016, at IJCAI 2016 and at ESSLLI 2017; for ACL 2019, we have developed a tutorial that provides a synthesis of the major advances in the area over the past three years.",
}
@inproceedings{mishra-etal-2019-storytelling,
    title = "Storytelling from Structured Data and Knowledge Graphs : An {NLG} Perspective",
    author = "Mishra, Abhijit  and
      Laha, Anirban  and
      Sankaranarayanan, Karthik  and
      Jain, Parag  and
      Krishnan, Saravanan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-4009",
    doi = "10.18653/v1/P19-4009",
    pages = "43--48",
    abstract = "In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language. The attendees of the tutorial will be able to take away from this tutorial, (1) the basic ideas around how modern NLP and NLG techniques could be applied to describe and summarize textual data in format that is non-linguistic in nature or has some structure, and (2) a few interesting open-ended questions, which could lead to significant research contributions in future. The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. Various solutions, starting from traditional rule based/heuristic driven and modern data-driven and ultra-modern deep-neural style architectures will be discussed, followed by a brief discussion on evaluation and quality estimation. A significant portion of the tutorial will be dedicated towards unsupervised, scalable, and adaptable solutions, given that systems for such an important task will never naturally enjoy sustainable large scale domain independent labeled (parallel) data.",
}
@proceedings{acl-2019-association-linguistics-system,
    title = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    editor = "Costa-juss{\`a}, Marta R.  and
      Alfonseca, Enrique",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3000",
}
@inproceedings{arai-etal-2019-sakura,
    title = "{S}akura: Large-scale Incorrect Example Retrieval System for Learners of {J}apanese as a Second Language",
    author = "Arai, Mio  and
      Kodaira, Tomonori  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3001",
    doi = "10.18653/v1/P19-3001",
    pages = "1--6",
    abstract = "This study develops an incorrect example retrieval system, called Sakura, using a large-scale Lang-8 dataset for Japanese language learners. Existing example retrieval systems do not include grammatically incorrect examples or present only a few examples, if any. If a retrieval system has a wide coverage of incorrect examples along with the correct counterpart, learners can revise their composition themselves. Considering the usability of retrieving incorrect examples, our proposed system uses a large-scale corpus to expand the coverage of incorrect examples and presents correct expressions along with incorrect expressions. Our intrinsic and extrinsic evaluations indicate that our system is more useful than a previous system.",
}
@inproceedings{kummerfeld-2019-slate,
    title = "{SLATE}: A Super-Lightweight Annotation Tool for Experts",
    author = "Kummerfeld, Jonathan K.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3002",
    doi = "10.18653/v1/P19-3002",
    pages = "7--12",
    abstract = "Many annotation tools have been developed, covering a wide variety of tasks and providing features like user management, pre-processing, and automatic labeling. However, all of these tools use Graphical User Interfaces, and often require substantial effort to install and configure. This paper presents a new annotation tool that is designed to fill the niche of a lightweight interface for users with a terminal-based workflow. SLATE supports annotation at different scales (spans of characters, tokens, and lines, or a document) and of different types (free text, labels, and links), with easily customisable keybindings, and unicode support. In a user study comparing with other tools it was consistently the easiest to install and use. SLATE fills a need not met by existing systems, and has already been used to annotate two corpora, one of which involved over 250 hours of annotation effort.",
}
@inproceedings{el-assady-etal-2019-lingvis,
    title = "lingvis.io - A Linguistic Visual Analytics Framework",
    author = "El-Assady, Mennatallah  and
      Jentner, Wolfgang  and
      Sperrle, Fabian  and
      Sevastjanova, Rita  and
      Hautli-Janisz, Annette  and
      Butt, Miriam  and
      Keim, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3003",
    doi = "10.18653/v1/P19-3003",
    pages = "13--18",
    abstract = "We present a modular framework for the rapid-prototyping of linguistic, web-based, visual analytics applications. Our framework gives developers access to a rich set of machine learning and natural language processing steps, through encapsulating them into micro-services and combining them into a computational pipeline. This processing pipeline is auto-configured based on the requirements of the visualization front-end, making the linguistic processing and visualization design, detached independent development tasks. This paper describes the constellation and modality of our framework, which continues to support the efficient development of various human-in-the-loop, linguistic visual analytics research techniques and applications.",
}
@inproceedings{boschee-etal-2019-saral,
    title = "{SARAL}: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage",
    author = "Boschee, Elizabeth  and
      Barry, Joel  and
      Billa, Jayadev  and
      Freedman, Marjorie  and
      Gowda, Thamme  and
      Lignos, Constantine  and
      Palen-Michel, Chester  and
      Pust, Michael  and
      Khonglah, Banriskhem Kayang  and
      Madikeri, Srikanth  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3004",
    doi = "10.18653/v1/P19-3004",
    pages = "19--24",
    abstract = "With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.",
}
@inproceedings{zhipeng-etal-2019-jiuge,
    title = "{J}iuge: A Human-Machine Collaborative {C}hinese Classical Poetry Generation System",
    author = "Zhipeng, Guo  and
      Yi, Xiaoyuan  and
      Sun, Maosong  and
      Li, Wenhao  and
      Yang, Cheng  and
      Liang, Jiannan  and
      Chen, Huimin  and
      Zhang, Yuhui  and
      Li, Ruoyu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3005",
    doi = "10.18653/v1/P19-3005",
    pages = "25--30",
    abstract = "Research on the automatic generation of poetry, the treasure of human culture, has lasted for decades. Most existing systems, however, are merely model-oriented, which input some user-specified keywords and directly complete the generation process in one pass, with little user participation. We believe that the machine, being a collaborator or an assistant, should not replace human beings in poetic creation. Therefore, we proposed Jiuge, a human-machine collaborative Chinese classical poetry generation system. Unlike previous systems, Jiuge allows users to revise the unsatisfied parts of a generated poem draft repeatedly. According to the revision, the poem will be dynamically updated and regenerated. After the revision and modification procedure, the user can write a satisfying poem together with Jiuge system collaboratively. Besides, Jiuge can accept multi-modal inputs, such as keywords, plain text or images. By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.",
}
@inproceedings{chan-etal-2019-rapid,
    title = "Rapid Customization for Event Extraction",
    author = "Chan, Yee Seng  and
      Fasching, Joshua  and
      Qiu, Haoling  and
      Min, Bonan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3006",
    doi = "10.18653/v1/P19-3006",
    pages = "31--36",
    abstract = "Extracting events in the form of who is involved in what at when and where from text, is one of the core information extraction tasks that has many applications such as web search and question answering. We present a system for rapidly customizing event extraction capability to find new event types (what happened) and their arguments (who, when, and where). To enable extracting events of new types, we develop a novel approach to allow a user to find, expand and filter event triggers by exploring an unannotated development corpus. The system will then generate mention level event annotation automatically and train a neural network model for finding the corresponding events. To enable extracting arguments for new event types, the system makes novel use of the ACE annotation dataset to train a generic argument attachment model for extracting Actor, Place, and Time. We demonstrate that with less than 10 minutes of human effort per event type, the system achieves good performance for 67 novel event types. Experiments also show that the generic argument attachment model performs well on the novel event types. Our system (code, UI, documentation, demonstration video) is released as open source.",
}
@inproceedings{vig-2019-multiscale,
    title = "A Multiscale Visualization of Attention in the Transformer Model",
    author = "Vig, Jesse",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3007",
    doi = "10.18653/v1/P19-3007",
    pages = "37--42",
    abstract = "The Transformer is a sequence model that forgoes traditional recurrent architectures in favor of a fully attention-based approach. Besides improving performance, an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements. However, the multi-layer, multi-head attention mechanism in the Transformer model can be difficult to decipher. To make the model more accessible, we introduce an open-source tool that visualizes attention at multiple scales, each of which provides a unique perspective on the attention mechanism. We demonstrate the tool on BERT and OpenAI GPT-2 and present three example use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior.",
}
@inproceedings{xu-etal-2019-postac,
    title = "{P}ost{A}c : A Visual Interactive Search, Exploration, and Analysis Platform for {P}h{D} Intensive Job Postings",
    author = "Xu, Chenchen  and
      Mewburn, Inger  and
      Grant, Will J  and
      Suominen, Hanna",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3008",
    doi = "10.18653/v1/P19-3008",
    pages = "43--48",
    abstract = "Over 60{\%} of Australian PhD graduates land their first job after graduation outside academia, but this job market remains largely hidden to these job seekers. Employers{'} low awareness and interest in attracting PhD graduates means that the term {``}PhD{''} is rarely used as a keyword in job advertisements; 80{\%} of companies looking to employ similar researchers do not specifically ask for a PhD qualification. As a result, typing in {``}PhD{''} to a job search engine tends to return mostly academic jobs. We set out to make the market for advanced research skills more visible to job seekers. In this paper, we present PostAc, an online platform of authentic job postings that helps PhD graduates sharpen their career thinking. The platform is underpinned by research on the key factors that identify what an employer is looking for when they want to hire a highly skilled researcher. Its ranking model leverages the free-form text embedded in the job description to quantify the most sought-after PhD skills and educate information seekers about the Australian job-market appetite for PhD skills. The platform makes visible the geographic location, industry sector, job title, working hours, continuity, and wage of the research intensive jobs. This is the first data-driven exploration in this field. Both empirical results and online platform will be presented in this paper.",
}
@inproceedings{duong-etal-2019-adaptable,
    title = "An adaptable task-oriented dialog system for stand-alone embedded devices",
    author = "Duong, Long  and
      Hoang, Vu Cong Duy  and
      Pham, Tuyen Quang  and
      Hong, Yu-Heng  and
      Dovgalecs, Vladislavs  and
      Bashkansky, Guy  and
      Black, Jason  and
      Bleeker, Andrew  and
      Huitouze, Serge Le  and
      Johnson, Mark",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3009",
    doi = "10.18653/v1/P19-3009",
    pages = "49--57",
    abstract = "This paper describes a spoken-language end-to-end task-oriented dialogue system for small embedded devices such as home appliances. While the current system implements a smart alarm clock with advanced calendar scheduling functionality, the system is designed to make it easy to port to other application domains (e.g., the dialogue component factors out domain-specific execution from domain-general actions such as requesting and updating slot values). The system does not require internet connectivity because all components, including speech recognition, natural language understanding, dialogue management, execution and text-to-speech, run locally on the embedded device (our demo uses a Raspberry Pi). This simplifies deployment, minimizes server costs and most importantly, eliminates user privacy risks. The demo video in alarm domain is here youtu.be/N3IBMGocvHU",
}
@inproceedings{lin-etal-2019-alpacatag,
    title = "{A}lpaca{T}ag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging",
    author = "Lin, Bill Yuchen  and
      Lee, Dong-Ho  and
      Xu, Frank F.  and
      Lan, Ouyu  and
      Ren, Xiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3010",
    doi = "10.18653/v1/P19-3010",
    pages = "58--63",
    abstract = "We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER). The distinctive advantages of AlpacaTag are three-fold. 1) Active intelligent recommendation: dynamically suggesting annotations and sampling the most informative unlabeled instances with a back-end active learned model; 2) Automatic crowd consolidation: enhancing real-time inter-annotator agreement by merging inconsistent labels from multiple annotators; 3) Real-time model deployment: users can deploy their models in downstream systems while new annotations are being made. AlpacaTag is a comprehensive solution for sequence labeling tasks, ranging from rapid tagging with recommendations powered by active learning and auto-consolidation of crowd annotations to real-time model deployment.",
}
@inproceedings{lee-etal-2019-convlab,
    title = "{C}onv{L}ab: Multi-Domain End-to-End Dialog System Platform",
    author = "Lee, Sungjin  and
      Zhu, Qi  and
      Takanobu, Ryuichi  and
      Zhang, Zheng  and
      Zhang, Yaoqin  and
      Li, Xiang  and
      Li, Jinchao  and
      Peng, Baolin  and
      Li, Xiujun  and
      Huang, Minlie  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3011",
    doi = "10.18653/v1/P19-3011",
    pages = "64--69",
    abstract = "We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings.",
}
@inproceedings{domingo-etal-2019-demonstration,
    title = "Demonstration of a Neural Machine Translation System with Online Learning for Translators",
    author = "Domingo, Miguel  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Estela Pastor, Amando  and
      Bi{\'e}, Laurent  and
      Helle, Alexander  and
      Peris, {\'A}lvaro  and
      Casacuberta, Francisco  and
      Herranz P{\'e}rez, Manuel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3012",
    doi = "10.18653/v1/P19-3012",
    pages = "70--74",
    abstract = "We present a demonstration of our system, which implements online learning for neural machine translation in a production environment. These techniques allow the system to continuously learn from the corrections provided by the translators. We implemented an end-to-end platform integrating our machine translation servers to one of the most common user interfaces for professional translators: SDL Trados Studio. We pretend to save post-editing effort as the machine is continuously learning from its mistakes and adapting the models to a specific domain or user style.",
}
@inproceedings{tekiroglu-etal-2019-fastdial,
    title = "{FASTD}ial: Abstracting Dialogue Policies for Fast Development of Task Oriented Agents",
    author = "Tekiroglu, Serra Sinem  and
      Magnini, Bernardo  and
      Guerini, Marco",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3013",
    doi = "10.18653/v1/P19-3013",
    pages = "75--80",
    abstract = "We present a novel abstraction framework called FASTDial for designing task oriented dialogue agents, built on top of the OpenDial toolkit. This framework is meant to facilitate prototyping and development of dialogue systems from scratch also by non tech savvy especially when limited training data is available. To this end, we use a generic and simple frame-slots data-structure with pre-defined dialogue policies that allows for fast design and implementation at the price of some flexibility reduction. Moreover, it allows for minimizing programming effort and domain expert training time, by hiding away many implementation details. We provide a system demonstration screencast video in the following link: https://vimeo.com/329840716",
}
@inproceedings{peris-casacuberta-2019-neural,
    title = "A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks",
    author = "Peris, {\'A}lvaro  and
      Casacuberta, Francisco",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3014",
    doi = "10.18653/v1/P19-3014",
    pages = "81--86",
    abstract = "We present a demonstration of a neural interactive-predictive system for tackling multimodal sequence to sequence tasks. The system generates text predictions to different sequence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The system reacts to each correction, providing alternative hypotheses, compelling with the feedback provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which communicates with the neural model, hosted in a local server. From this website, the different tasks can be tackled following the interactive{--}predictive framework. We open-source all the code developed for building this system. The demonstration in hosted in http://casmacat.prhlt.upv.es/interactive-seq2seq.",
}
@inproceedings{liu-etal-2019-neuralclassifier,
    title = "{N}eural{C}lassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit",
    author = "Liu, Liqun  and
      Mu, Funan  and
      Li, Pengyu  and
      Mu, Xin  and
      Tang, Jing  and
      Ai, Xingsheng  and
      Fu, Ran  and
      Wang, Lifeng  and
      Zhou, Xing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3015",
    doi = "10.18653/v1/P19-3015",
    pages = "87--92",
    abstract = "In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification. NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.",
}
@inproceedings{ortega-etal-2019-adviser,
    title = "{ADVISER}: A Dialog System Framework for Education {\&} Research",
    author = {Ortega, Daniel  and
      V{\"a}th, Dirk  and
      Weber, Gianna  and
      Vanderlyn, Lindsey  and
      Schmidt, Maximilian  and
      V{\"o}lkel, Moritz  and
      Karacevic, Zorica  and
      Vu, Ngoc Thang},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3016",
    doi = "10.18653/v1/P19-3016",
    pages = "93--98",
    abstract = "In this paper, we present ADVISER - an open source dialog system framework for education and research purposes. This system supports multi-domain task-oriented conversations in two languages. It additionally provides a flexible architecture in which modules can be arbitrarily combined or exchanged - allowing for easy switching between rules-based and neural network based implementations. Furthermore, ADVISER offers a transparent, user-friendly framework designed for interdisciplinary collaboration: from a flexible back end, allowing easy integration of new features, to an intuitive graphical user interface supporting nontechnical users.",
}
@inproceedings{lin-etal-2019-kcat,
    title = "{KCAT}: A Knowledge-Constraint Typing Annotation Tool",
    author = "Lin, Sheng  and
      Zheng, Luye  and
      Chen, Bo  and
      Tang, Siliang  and
      Chen, Zhigang  and
      Hu, Guoping  and
      Zhuang, Yueting  and
      Wu, Fei  and
      Ren, Xiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3017",
    doi = "10.18653/v1/P19-3017",
    pages = "99--104",
    abstract = "In this paper, we propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool, which further improves the entity typing process through entity linking together with some practical functions.",
}
@inproceedings{blessing-etal-2019-environment,
    title = "An Environment for Relational Annotation of Political Debates",
    author = "Blessing, Andre  and
      Blokker, Nico  and
      Haunss, Sebastian  and
      Kuhn, Jonas  and
      Lapesa, Gabriella  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3018",
    doi = "10.18653/v1/P19-3018",
    pages = "105--110",
    abstract = "This paper describes the MARDY corpus annotation environment developed for a collaboration between political science and computational linguistics. The tool realizes the complete workflow necessary for annotating a large newspaper text collection with rich information about claims (demands) raised by politicians and other actors, including claim and actor spans, relations, and polarities. In addition to the annotation GUI, the tool supports the identification of relevant documents, text pre-processing, user management, integration of external knowledge bases, annotation comparison and merging, statistical analysis, and the incorporation of machine learning models as {``}pseudo-annotators{''}.",
}
@inproceedings{gehrmann-etal-2019-gltr,
    title = "{GLTR}: Statistical Detection and Visualization of Generated Text",
    author = "Gehrmann, Sebastian  and
      Strobelt, Hendrik  and
      Rush, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3019",
    doi = "10.18653/v1/P19-3019",
    pages = "111--116",
    abstract = "The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by non-experts. In this work, we introduce GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across multiple sampling schemes. In a human-subjects study, we show that the annotation scheme provided by GLTR improves the human detection-rate of fake text from 54{\%} to 72{\%} without any prior training. GLTR is open-source and publicly deployed, and has already been widely used to detect generated outputs.",
}
@inproceedings{kepler-etal-2019-openkiwi,
    title = "{O}pen{K}iwi: An Open Source Framework for Quality Estimation",
    author = "Kepler, Fabio  and
      Tr{\'e}nous, Jonay  and
      Treviso, Marcos  and
      Vera, Miguel  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3020",
    doi = "10.18653/v1/P19-3020",
    pages = "117--122",
    abstract = "We introduce OpenKiwi, a Pytorch-based open source framework for translation quality estimation. OpenKiwi supports training and testing of word-level and sentence-level quality estimation systems, implementing the winning systems of the WMT 2015{--}18 quality estimation campaigns. We benchmark OpenKiwi on two datasets from WMT 2018 (English-German SMT and NMT), yielding state-of-the-art performance on the word-level tasks and near state-of-the-art in the sentence-level tasks.",
}
@inproceedings{shiv-etal-2019-microsoft,
    title = "{M}icrosoft Icecaps: An Open-Source Toolkit for Conversation Modeling",
    author = "Shiv, Vighnesh Leonardo  and
      Quirk, Chris  and
      Suri, Anshuman  and
      Gao, Xiang  and
      Shahid, Khuram  and
      Govindarajan, Nithya  and
      Zhang, Yizhe  and
      Gao, Jianfeng  and
      Galley, Michel  and
      Brockett, Chris  and
      Menon, Tulasi  and
      Dolan, Bill",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3021",
    doi = "10.18653/v1/P19-3021",
    pages = "123--128",
    abstract = "The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository. Icecaps wraps TensorFlow functionality in a modular component-based architecture, presenting an intuitive and flexible paradigm for constructing sophisticated learning setups. Capabilities include multitask learning between models with shared parameters, upgraded language model decoding features, a range of built-in architectures, and a user-friendly data processing pipeline. The system is targeted toward conversational tasks, exploring diverse response generation, coherence, and knowledge grounding. Icecaps also provides pre-trained conversational models that can be either used directly or loaded for fine-tuning or bootstrapping other models; these models power an online demo of our framework.",
}
@inproceedings{chen-etal-2019-perspectroscope,
    title = "{P}erspectro{S}cope: A Window to the World of Diverse Perspectives",
    author = "Chen, Sihao  and
      Khashabi, Daniel  and
      Callison-Burch, Chris  and
      Roth, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3022",
    doi = "10.18653/v1/P19-3022",
    pages = "129--134",
    abstract = "This work presents PerspectroScope, a web-based system which lets users query a discussion-worthy natural language claim, and extract and visualize various perspectives in support or against the claim, along with evidence supporting each perspective. The system thus lets users explore various perspectives that could touch upon aspects of the issue at hand.The system is built as a combination of retrieval engines and learned textual-entailment-like classifiers built using a few recent developments in natural language understanding. To make the system more adaptive, expand its coverage, and improve its decisions over time, our platform employs various mechanisms to get corrections from the users. PerspectroScope is available at github.com/CogComp/perspectroscope Web demo link: http://orwell.seas.upenn.edu:4002/ Link to demo video: https://www.youtube.com/watch?v=MXBTR1Sp3Bs",
}
@inproceedings{sen-etal-2019-heidl,
    title = "{HEIDL}: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop",
    author = "Sen, Prithviraj  and
      Li, Yunyao  and
      Kandogan, Eser  and
      Yang, Yiwei  and
      Lasecki, Walter",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3023",
    doi = "10.18653/v1/P19-3023",
    pages = "135--140",
    abstract = "While the role of humans is increasingly recognized in machine learning community, representation of and interaction with models in current human-in-the-loop machine learning (HITL-ML) approaches are too low-level and far-removed from human{'}s conceptual models. We demonstrate HEIDL, a prototype HITL-ML system that exposes the machine-learned model through high-level, explainable linguistic expressions formed of predicates representing semantic structure of text. In HEIDL, human{'}s role is elevated from simply evaluating model predictions to interpreting and even updating the model logic directly by enabling interaction with rule predicates themselves. Raising the currency of interaction to such semantic levels calls for new interaction paradigms between humans and machines that result in improved productivity for text analytics model development process. Moreover, by involving humans in the process, the human-machine co-created models generalize better to unseen data as domain experts are able to instill their expertise by extrapolating from what has been learned by automated algorithms from few labelled data.",
}
@inproceedings{madnani-etal-2019-turn,
    title = "My Turn To Read: An Interleaved {E}-book Reading Tool for Developing and Struggling Readers",
    author = "Madnani, Nitin  and
      Beigman Klebanov, Beata  and
      Loukina, Anastassia  and
      Gyawali, Binod  and
      Lange, Patrick  and
      Sabatini, John  and
      Flor, Michael",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3024",
    doi = "10.18653/v1/P19-3024",
    pages = "141--146",
    abstract = "Literacy is crucial for functioning in modern society. It underpins everything from educational attainment and employment opportunities to health outcomes. We describe My Turn To Read, an app that uses interleaved reading to help developing and struggling readers improve reading skills while reading for meaning and pleasure. We hypothesize that the longer-term impact of the app will be to help users become better, more confident readers with an increased stamina for extended reading. We describe the technology and present preliminary evidence in support of this hypothesis.",
}
@inproceedings{betts-etal-2019-grapal,
    title = "{G}rap{AL}: Connecting the Dots in Scientific Literature",
    author = "Betts, Christine  and
      Power, Joanna  and
      Ammar, Waleed",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3025",
    doi = "10.18653/v1/P19-3025",
    pages = "147--152",
    abstract = "We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature that was semi-automatically constructed using NLP methods. GrapAL fills many informational needs expressed by researchers. At the core of GrapAL is a Neo4j graph database with an intuitive schema and a simple query language. In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities, and computing citation-based metrics. We open source the demo code to help other researchers develop applications that build on GrapAL.",
}
@inproceedings{majithia-etal-2019-claimportal,
    title = "{C}laim{P}ortal: Integrated Monitoring, Searching, Checking, and Analytics of Factual Claims on {T}witter",
    author = "Majithia, Sarthak  and
      Arslan, Fatma  and
      Lubal, Sumeet  and
      Jimenez, Damian  and
      Arora, Priyank  and
      Caraballo, Josue  and
      Li, Chengkai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3026",
    doi = "10.18653/v1/P19-3026",
    pages = "153--158",
    abstract = "We present ClaimPortal, a web-based platform for monitoring, searching, checking, and analyzing English factual claims on Twitter from the American political domain. We explain the architecture of ClaimPortal, its components and functions, and the user interface. While the last several years have witnessed a substantial growth in interests and efforts in the area of computational fact-checking, ClaimPortal is a novel infrastructure in that fact-checkers have largely skipped factual claims in tweets. It can be a highly powerful tool to both general web users and fact-checkers. It will also be an educational resource in helping cultivate a society that is less susceptible to falsehoods.",
}
@inproceedings{hu-etal-2019-texar,
    title = "{T}exar: A Modularized, Versatile, and Extensible Toolkit for Text Generation",
    author = "Hu, Zhiting  and
      Shi, Haoran  and
      Tan, Bowen  and
      Wang, Wentao  and
      Yang, Zichao  and
      Zhao, Tiancheng  and
      He, Junxian  and
      Qin, Lianhui  and
      Wang, Di  and
      Ma, Xuezhe  and
      Liu, Zhengzhong  and
      Liang, Xiaodan  and
      Zhu, Wanrong  and
      Sachan, Devendra  and
      Xing, Eric",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3027",
    doi = "10.18653/v1/P19-3027",
    pages = "159--164",
    abstract = "We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.",
}
@inproceedings{molino-etal-2019-parallax,
    title = "{P}arallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae",
    author = "Molino, Piero  and
      Wang, Yang  and
      Zhang, Jiawei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3028",
    doi = "10.18653/v1/P19-3028",
    pages = "165--180",
    abstract = "Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models. In this paper, we introduce Parallax, a tool explicitly designed for this task. Parallax allows the user to use both state-of-the-art embedding analysis methods (PCA and t-SNE) and a simple yet effective task-oriented approach where users can explicitly define the axes of the projection through algebraic formulae. {\%}consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging. In this approach, embeddings are projected into a semantically meaningful subspace, which enhances interpretability and allows for more fine-grained analysis. We demonstrate the power of the tool and the proposed methodology through a series of case studies and a user study.",
}
@inproceedings{wohlwend-etal-2019-flambe,
    title = "{F}lamb{\'e}: A Customizable Framework for Machine Learning Experiments",
    author = "Wohlwend, Jeremy  and
      Matthews, Nicholas  and
      Itzcovich, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3029",
    doi = "10.18653/v1/P19-3029",
    pages = "181--188",
    abstract = "Flamb{\'e} is a machine learning experimentation framework built to accelerate the entire research life cycle. Flamb{\'e}{'}s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flamb{\'e} achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.",
}
@inproceedings{nyzam-bossard-2019-modular,
    title = "A Modular Tool for Automatic Summarization",
    author = "Nyzam, Valentin  and
      Bossard, Aur{\'e}lien",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3030",
    doi = "10.18653/v1/P19-3030",
    pages = "189--194",
    abstract = "This paper introduces the first fine-grained modular tool for automatic summarization. Open source and written in Java, it is designed to be as straightforward as possible for end-users. Its modular architecture is meant to ease its maintenance and the development and integration of new modules. We hope that it will ease the work of researchers in automatic summarization by providing a reliable baseline for future works as well as an easy way to evaluate methods on different corpora.",
}
@inproceedings{chernodub-etal-2019-targer,
    title = "{TARGER}: Neural Argument Mining at Your Fingertips",
    author = "Chernodub, Artem  and
      Oliynyk, Oleksiy  and
      Heidenreich, Philipp  and
      Bondarenko, Alexander  and
      Hagen, Matthias  and
      Biemann, Chris  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3031",
    doi = "10.18653/v1/P19-3031",
    pages = "195--200",
    abstract = "We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus. The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user{'}s side. The open source code ensures portability to other domains and use cases.",
}
@inproceedings{van-der-goot-2019-monoise,
    title = "{M}o{N}oise: A Multi-lingual and Easy-to-use Lexical Normalization Tool",
    author = "van der Goot, Rob",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3032",
    doi = "10.18653/v1/P19-3032",
    pages = "201--206",
    abstract = "In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages. We further improve this model by using features from the original word for every normalization candidate. For comparison with future work, we propose the bundling of seven datasets in six languages to form a new benchmark, together with a novel evaluation metric which is particularly suitable for cross-dataset comparisons. MoNoise reaches a new state-of-art performance for six out of seven of these datasets. Furthermore, we allow the user to tune the {`}aggressiveness{'} of the normalization, and show how the model can be made more efficient with only a small loss in performance. The online demo can be found on: http://www.robvandergoot.com/monoise and the corresponding code on: https://bitbucket.org/robvanderg/monoise/",
}
@inproceedings{han-etal-2019-level,
    title = "Level-Up: Learning to Improve Proficiency Level of Essays",
    author = "Han, Wen-Bin  and
      Chen, Jhih-Jie  and
      Yang, Chingyu  and
      Chang, Jason",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3033",
    doi = "10.18653/v1/P19-3033",
    pages = "207--212",
    abstract = "We introduce a method for generating suggestions on a given sentence for improving the proficiency level. In our approach, the sentence is transformed into a sequence of grammatical elements aimed at providing suggestions of more advanced grammar elements based on originals. The method involves parsing the sentence, identifying grammatical elements, and ranking related elements to recommend a higher level of grammatical element. We present a prototype tutoring system, Level-Up, that applies the method to English learners{'} essays in order to assist them in writing and reading. Evaluation on a set of essays shows that our method does assist user in writing.",
}
@inproceedings{chen-etal-2019-learning,
    title = "Learning to Link Grammar and Encyclopedic Information of Assist {ESL} Learners",
    author = "Chen, Jhih-Jie  and
      Yang, Chingyu  and
      Ho, Peichen  and
      Tsai, Ming Chiao  and
      Ho, Chia-Fang  and
      Tuan, Kai-Wen  and
      Tsai, Chung-Ting  and
      Han, Wen-Bin  and
      Chang, Jason",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3034",
    doi = "10.18653/v1/P19-3034",
    pages = "213--218",
    abstract = "We introduce a system aimed at improving and expanding second language learners{'} English vocabulary. In addition to word definitions, we provide rich lexical information such as collocations and grammar patterns for target words. We present Linggle Booster that takes an article, identifies target vocabulary, provides lexical information, and generates a quiz on target words. Linggle Booster also links named-entity to corresponding Wikipedia pages. Evaluation on a set of target words shows that the method have reasonably good performance in terms of generating useful and information for learning vocabulary.",
}
@proceedings{acl-2019-association-linguistics,
    title = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    editor = "Alva-Manchego, Fernando  and
      Choi, Eunsol  and
      Khashabi, Daniel",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2000",
}
@inproceedings{kaur-2019-distributed,
    title = "Distributed Knowledge Based Clinical Auto-Coding System",
    author = "Kaur, Rajvir",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2001",
    doi = "10.18653/v1/P19-2001",
    pages = "1--9",
    abstract = "Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) methods and techniques to resolve the problem of manual coding of clinical narratives. Most of the studies are focused on classification systems relevant to the U.S and there is a scarcity of studies relevant to Australian classification systems such as ICD-10-AM and ACHI. Therefore, we aim to develop a knowledge-based clinical auto-coding system, that utilise appropriate NLP and ML techniques to assign ICD-10-AM and ACHI codes to clinical records, while adhering to both local coding standards (Australian Coding Standard) and international guidelines that get updated and validated continuously.",
}
@inproceedings{malykh-2019-robust,
    title = "Robust to Noise Models in Natural Language Processing Tasks",
    author = "Malykh, Valentin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2002",
    doi = "10.18653/v1/P19-2002",
    pages = "10--16",
    abstract = "There are a lot of noise texts surrounding a person in modern life. The traditional approach is to use spelling correction, yet the existing solutions are far from perfect. We propose robust to noise word embeddings model, which outperforms existing commonly used models, like fasttext and word2vec in different tasks. In addition, we investigate the noise robustness of current models in different natural language processing tasks. We propose extensions for modern models in three downstream tasks, i.e. text classification, named entity recognition and aspect extraction, which shows improvement in noise robustness over existing solutions.",
}
@inproceedings{jagfeld-2019-computational,
    title = "A Computational Linguistic Study of Personal Recovery in Bipolar Disorder",
    author = "Jagfeld, Glorianna",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2003",
    doi = "10.18653/v1/P19-2003",
    pages = "17--26",
    abstract = "Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants inWestern countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.",
}
@inproceedings{chen-2019-measuring,
    title = "Measuring the Value of Linguistics: A Case Study from {S}t. {L}awrence {I}sland {Y}upik",
    author = "Chen, Emily",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2004",
    doi = "10.18653/v1/P19-2004",
    pages = "27--33",
    abstract = "The adaptation of neural approaches to NLP is a landmark achievement that has called into question the utility of linguistics in the development of computational systems. This research proposal consequently explores this question in the context of a neural morphological analyzer for a polysynthetic language, St. Lawrence Island Yupik. It asks whether incorporating elements of Yupik linguistics into the implementation of the analyzer can improve performance, both in low-resource settings and in high-resource settings, where rich quantities of data are readily available.",
}
@inproceedings{tay-2019-reviews,
    title = "Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization",
    author = "Tay, Wenyi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2005",
    doi = "10.18653/v1/P19-2005",
    pages = "34--42",
    abstract = "Consumers read online reviews for insights which help them to make decisions. Given the large volumes of reviews, succinct review summaries are important for many applications. Existing research has focused on mining for opinions from only review texts and largely ignores the reviewers. However, reviewers have biases and may write lenient or harsh reviews; they may also have preferences towards some topics over others. Therefore, not all reviews are equal. Ignoring the biases in reviews can generate misleading summaries. We aim for summarization of reviews to include balanced opinions from reviewers of different biases and preferences. We propose to model reviewer biases from their review texts and rating distributions, and learn a bias-aware opinion representation. We further devise an approach for balanced opinion summarization of reviews using our bias-aware opinion representation.",
}
@inproceedings{azin-eryigit-2019-towards,
    title = "Towards {T}urkish {A}bstract {M}eaning {R}epresentation",
    author = {Azin, Zahra  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2006",
    doi = "10.18653/v1/P19-2006",
    pages = "43--47",
    abstract = "Using rooted, directed and labeled graphs, Abstract Meaning Representation (AMR) abstracts away from syntactic features such as word order and does not annotate every constituent in a sentence. AMR has been specified for English and was not supposed to be an Interlingua. However, several studies strived to overcome divergences in the annotations between English AMRs and those of their target languages by refining the annotation specification. Following this line of research, we have started to build the first Turkish AMR corpus by hand-annotating 100 sentences of the Turkish translation of the novel {``}The Little Prince{''} and comparing the results with the English AMRs available for the same corpus. The next step is to prepare the Turkish AMR annotation specification for training future annotators.",
}
@inproceedings{qian-2019-gender,
    title = "Gender Stereotypes Differ between Male and Female Writings",
    author = "Qian, Yusu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2007",
    doi = "10.18653/v1/P19-2007",
    pages = "48--53",
    abstract = "Written language often contains gender stereotypes, typically conveyed unintentionally by the author. To study the difference in how female and male authors portray people of different genders, we quantitatively evaluate and analyze the gender stereotypes in their writings on two different datasets and from multiple aspects. We show that writings by females on average have lower gender stereotype scores. We plan to study and interpret the distributions of gender stereotype scores of individual words, and how they differ between male and female writings. We also plan on using more datasets over the past century to study how the stereotypes in female and male writings evolved over time.",
}
@inproceedings{nguyen-2019-question,
    title = "Question Answering in the Biomedical Domain",
    author = "Nguyen, Vincent",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2008",
    doi = "10.18653/v1/P19-2008",
    pages = "54--63",
    abstract = "Question answering techniques have mainly been investigated in open domains. However, there are particular challenges in extending these open-domain techniques to extend into the biomedical domain. Question answering focusing on patients is less studied. We find that there are some challenges in patient question answering such as limited annotated data, lexical gap and quality of answer spans. We aim to address some of these gaps by extending and developing upon the literature to design a question answering system that can decide on the most appropriate answers for patients attempting to self-diagnose while including the ability to abstain from answering when confidence is low.",
}
@inproceedings{dirkson-2019-knowledge,
    title = "Knowledge Discovery and Hypothesis Generation from Online Patient Forums: A Research Proposal",
    author = "Dirkson, Anne",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2009",
    doi = "10.18653/v1/P19-2009",
    pages = "64--73",
    abstract = "The unprompted patient experiences shared on patient forums contain a wealth of unexploited knowledge. Mining this knowledge and cross-linking it with biomedical literature, could expose novel insights, which could subsequently provide hypotheses for further clinical research. As of yet, automated methods for open knowledge discovery on patient forum text are lacking. Thus, in this research proposal, we outline future research into methods for mining, aggregating and cross-linking patient knowledge from online forums. Additionally, we aim to address how one could measure the credibility of this extracted knowledge.",
}
@inproceedings{hosseini-kivanani-etal-2019-automated,
    title = "Automated Cross-language Intelligibility Analysis of {P}arkinson{'}s Disease Patients Using Speech Recognition Technologies",
    author = {Hosseini-Kivanani, Nina  and
      V{\'a}squez-Correa, Juan Camilo  and
      Stede, Manfred  and
      N{\"o}th, Elmar},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2010",
    doi = "10.18653/v1/P19-2010",
    pages = "74--80",
    abstract = "Speech deficits are common symptoms amongParkinson{'}s Disease (PD) patients. The automatic assessment of speech signals is promising for the evaluation of the neurological state and the speech quality of the patients. Recently, progress has been made in applying machine learning and computational methods to automatically evaluate the speech of PD patients. In the present study, we plan to analyze the speech signals of PD patients and healthy control (HC) subjects in three different languages: German, Spanish, and Czech, with the aim to identify biomarkers to discriminate between PD patients and HC subjects and to evaluate the neurological state of the patients. Therefore, the main contribution of this study is the automatic classification of PD patients and HC subjects in different languages with focusing on phonation, articulation, and prosody. We will focus on an intelligibility analysis based on automatic speech recognition systems trained on these three languages. This is one of the first studies done that considers the evaluation of the speech of PD patients in different languages. The purpose of this research proposal is to build a model that can discriminate PD and HC subjects even when the language used for train and test is different.",
}
@inproceedings{sobrevilla-cabezudo-pardo-2019-natural,
    title = "Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of {B}razilian {P}ortuguese Language",
    author = "Sobrevilla Cabezudo, Marco Antonio  and
      Pardo, Thiago",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2011",
    doi = "10.18653/v1/P19-2011",
    pages = "81--88",
    abstract = "This paper presents a more recent literature review on Natural Language Generation. In particular, we highlight the efforts for Brazilian Portuguese in order to show the available resources and the existent approaches for this language. We also focus on the approaches for generation from semantic representations (emphasizing the Abstract Meaning Representation formalism) as well as their advantages and limitations, including possible future directions.",
}
@inproceedings{bommasani-2019-long,
    title = "Long-Distance Dependencies Don{'}t Have to Be Long: Simplifying through Provably (Approximately) Optimal Permutations",
    author = "Bommasani, Rishi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2012",
    doi = "10.18653/v1/P19-2012",
    pages = "89--99",
    abstract = "Neural models at the sentence level often operate on the constituent words/tokens in a way that encodes the inductive bias of processing the input in a similar fashion to how humans do. However, there is no guarantee that the standard ordering of words is computationally efficient or optimal. To help mitigate this, we consider a dependency parse as a proxy for the inter-word dependencies in a sentence and simplify the sentence with respect to combinatorial objectives imposed on the sentence-parse pair. The associated optimization results in permuted sentences that are provably (approximately) optimal with respect to minimizing dependency parse lengths and that are demonstrably simpler. We evaluate our general-purpose permutations within a fine-tuning schema for the downstream task of subjectivity analysis. Our fine-tuned baselines reflect a new state of the art for the SUBJ dataset and the permutations we introduce lead to further improvements with a 2.0{\%} increase in classification accuracy (absolute) and a 45{\%} reduction in classification error (relative) over the previous state of the art.",
}
@inproceedings{mckillop-2019-predicting,
    title = "Predicting the Outcome of Deliberative Democracy: A Research Proposal",
    author = "McKillop, Conor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2013",
    doi = "10.18653/v1/P19-2013",
    pages = "100--105",
    abstract = "As liberal states across the world face a decline in political participation by citizens, deliberative democracy is a promising solution for the public{'}s decreasing confidence and apathy towards the democratic process. Deliberative dialogue is method of public interaction that is fundamental to the concept of deliberative democracy. The ability to identify and predict consensus in the dialogues could bring greater accessibility and transparency to the face-to-face participatory process. The paper sets out a research plan for the first steps at automatically identifying and predicting consensus in a corpus of German language debates on hydraulic fracking. It proposes the use of a unique combination of lexical, sentiment, durational and further {`}derivative{'} features of adjacency pairs to train traditional classification models. In addition to this, the use of deep learning techniques to improve the accuracy of the classification and prediction tasks is also discussed. Preliminary results at the classification of utterances are also presented, with an F1 between 0.61 and 0.64 demonstrating that the task of recognising agreement is demanding but possible.",
}
@inproceedings{galvan-sosa-2019-active,
    title = "Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy",
    author = "Galv{\'a}n-Sosa, Diana",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2014",
    doi = "10.18653/v1/P19-2014",
    pages = "106--112",
    abstract = "Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader{'}s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.",
}
@inproceedings{zhou-etal-2019-paraphrases,
    title = "Paraphrases as Foreign Languages in Multilingual Neural Machine Translation",
    author = "Zhou, Zhong  and
      Sperber, Matthias  and
      Waibel, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2015",
    doi = "10.18653/v1/P19-2015",
    pages = "113--122",
    abstract = "Paraphrases, rewordings of the same semantic meaning, are useful for improving generalization and translation. Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT). Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines. Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice. Adding the source paraphrases boosts performance better than adding the target ones, while adding both lifts performance further. We achieve a BLEU score of 57.2 for French-to-English translation using 24 corpus-level paraphrases of the Bible, which outperforms the multilingual baselines and is +34.7 above the single-source single-target NMT baseline.",
}
@inproceedings{ji-etal-2019-improving,
    title = "Improving {M}ongolian-{C}hinese Neural Machine Translation with Morphological Noise",
    author = "Ji, Yatu  and
      Hou, Hongxu  and
      Junjie, Chen  and
      Wu, Nier",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2016",
    doi = "10.18653/v1/P19-2016",
    pages = "123--129",
    abstract = "For the translation of agglutinative language such as typical Mongolian, unknown (UNK) words not only come from the quite restricted vocabulary, but also mostly from misunderstanding of the translation model to the morphological changes. In this study, we introduce a new adversarial training model to alleviate the UNK problem in Mongolian-Chinese machine translation. The training process can be described as three adversarial sub models (generator, value screener and discriminator), playing a win-win game. In this game, the added screener plays the role of emphasizing that the discriminator pays attention to the added Mongolian morphological noise in the form of pseudo-data and improving the training efficiency. The experimental results show that the newly emerged Mongolian-Chinese task is state-of-the-art. Under this premise, the training time is greatly shortened.",
}
@inproceedings{varis-bojar-2019-unsupervised,
    title = "Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation",
    author = "Vari{\v{s}}, Du{\v{s}}an  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2017",
    doi = "10.18653/v1/P19-2017",
    pages = "130--135",
    abstract = "This work presents our ongoing research of unsupervised pretraining in neural machine translation (NMT). In our method, we initialize the weights of the encoder and decoder with two language models that are trained with monolingual data and then fine-tune the model on parallel data using Elastic Weight Consolidation (EWC) to avoid forgetting of the original language modeling task. We compare the regularization by EWC with the previous work that focuses on regularization by language modeling objectives. The positive result is that using EWC with the decoder achieves BLEU scores similar to the previous work. However, the model converges 2-3 times faster and does not require the original unlabeled training data during the fine-tuning stage. In contrast, the regularization using EWC is less effective if the original and new tasks are not closely related. We show that initializing the bidirectional NMT encoder with a left-to-right language model and forcing the model to remember the original left-to-right language modeling task limits the learning capacity of the encoder for the whole bidirectional context.",
}
@inproceedings{trye-etal-2019-maori,
    title = "{M}{\=a}{O}ri Loanwords: A Corpus of {N}ew {Z}ealand {E}nglish Tweets",
    author = "Trye, David  and
      Calude, Andreea  and
      Bravo-Marquez, Felipe  and
      Keegan, Te Taka",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2018",
    doi = "10.18653/v1/P19-2018",
    pages = "136--142",
    abstract = "M{\=a}ori loanwords are widely used in New Zealand English for various social functions by New Zealanders within and outside of the M{\=a}ori community. Motivated by the lack of linguistic resources for studying how M{\=a}ori loanwords are used in social media, we present a new corpus of New Zealand English tweets. We collected tweets containing selected M{\=a}ori words that are likely to be known by New Zealanders who do not speak M{\=a}ori. Since over 30{\%} of these words turned out to be irrelevant, we manually annotated a sample of our tweets into relevant and irrelevant categories. This data was used to train machine learning models to automatically filter out irrelevant tweets.",
}
@inproceedings{schricker-scheffler-2019-ranking,
    title = "Ranking of Potential Questions",
    author = "Schricker, Luise  and
      Scheffler, Tatjana",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2019",
    doi = "10.18653/v1/P19-2019",
    pages = "143--148",
    abstract = "Questions are an integral part of discourse. They provide structure and support the exchange of information. One linguistic theory, the Questions Under Discussion model, takes question structures as integral to the functioning of a coherent discourse. This theory has not been tested on the count of its validity for predicting observations in real dialogue data, however. In this submission, a system for ranking explicit and implicit questions by their appropriateness in a dialogue is presented. This system implements constraints and principles put forward in the linguistic literature.",
}
@inproceedings{hotate-etal-2019-controlling,
    title = "Controlling Grammatical Error Correction Using Word Edit Rate",
    author = "Hotate, Kengo  and
      Kaneko, Masahiro  and
      Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2020",
    doi = "10.18653/v1/P19-2020",
    pages = "149--154",
    abstract = "When professional English teachers correct grammatically erroneous sentences written by English learners, they use various methods. The correction method depends on how much corrections a learner requires. In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction. We show that it is possible to actually control the degree of GEC by using new training data annotated with word edit rate. Thereby, diverse corrected sentences is obtained from a single erroneous sentence. Moreover, compared to a GEC model that does not use information on the degree of correction, the proposed method improves correction accuracy.",
}
@inproceedings{minnema-herbelot-2019-brain,
    title = "From Brain Space to Distributional Space: The Perilous Journeys of f{MRI} Decoding",
    author = "Minnema, Gosse  and
      Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2021",
    doi = "10.18653/v1/P19-2021",
    pages = "155--161",
    abstract = "Recent work in cognitive neuroscience has introduced models for predicting distributional word meaning representations from brain imaging data. Such models have great potential, but the quality of their predictions has not yet been thoroughly evaluated from a computational linguistics point of view. Due to the limited size of available brain imaging datasets, standard quality metrics (e.g. similarity judgments and analogies) cannot be used. Instead, we investigate the use of several alternative measures for evaluating the predicted distributional space against a corpus-derived distributional space. We show that a state-of-the-art decoder, while performing impressively on metrics that are commonly used in cognitive neuroscience, performs unexpectedly poorly on our metrics. To address this, we propose strategies for improving the model{'}s performance. Despite returning promising results, our experiments also demonstrate that much work remains to be done before distributional representations can reliably be predicted from brain data.",
}
@inproceedings{kabbach-etal-2019-towards,
    title = "Towards Incremental Learning of Word Embeddings Using Context Informativeness",
    author = "Kabbach, Alexandre  and
      Gulordava, Kristina  and
      Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2022",
    doi = "10.18653/v1/P19-2022",
    pages = "162--168",
    abstract = "In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. We focus on the notion of {`}informativeness{'}, that is, the idea that some content is more valuable to the learning process than other. We further highlight the challenges of online learning and argue that previous systems fall short of implementing incrementality. Concretely, we incorporate informativeness in a previously proposed model of nonce learning, using it for context selection and learning rate modulation. We test our system on the task of learning new words from definitions, as well as on the task of learning new words from potentially uninformative contexts. We demonstrate that informativeness is crucial to obtaining state-of-the-art performance in a truly incremental setup.",
}
@inproceedings{liu-ye-2019-strong,
    title = "A Strong and Robust Baseline for Text-Image Matching",
    author = "Liu, Fangyu  and
      Ye, Rongtian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2023",
    doi = "10.18653/v1/P19-2023",
    pages = "169--176",
    abstract = "We review the current schemes of text-image matching models and propose improvements for both training and inference. First, we empirically show limitations of two popular loss (sum and max-margin loss) widely used in training text-image embeddings and propose a trade-off: a kNN-margin loss which 1) utilizes information from hard negatives and 2) is robust to noise as all K-most hardest samples are taken into account, tolerating pseudo negatives and outliers. Second, we advocate the use of Inverted Softmax (IS) and Cross-modal Local Scaling (CSLS) during inference to mitigate the so-called hubness problem in high-dimensional embedding space, enhancing scores of all metrics by a large margin.",
}
@inproceedings{yamada-etal-2019-incorporating,
    title = "Incorporating Textual Information on User Behavior for Personality Prediction",
    author = "Yamada, Kosuke  and
      Sasano, Ryohei  and
      Takeda, Koichi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2024",
    doi = "10.18653/v1/P19-2024",
    pages = "177--182",
    abstract = "Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.",
}
@inproceedings{srirangam-etal-2019-corpus,
    title = "Corpus Creation and Analysis for Named Entity Recognition in {T}elugu-{E}nglish Code-Mixed Social Media Data",
    author = "Srirangam, Vamshi Krishna  and
      Reddy, Appidi Abhinav  and
      Singh, Vinay  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2025",
    doi = "10.18653/v1/P19-2025",
    pages = "183--189",
    abstract = "Named Entity Recognition(NER) is one of the important tasks in Natural Language Processing(NLP) and also is a subtask of Information Extraction. In this paper we present our work on NER in Telugu-English code-mixed social media data. Code-Mixing, a progeny of multilingualism is a way in which multilingual people express themselves on social media by using linguistics units from different languages within a sentence or speech context. Entity Extraction from social media data such as tweets(twitter) is in general difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. We present a Telugu-English code-mixed corpus with the corresponding named entity tags. The named entities used to tag data are Person({`}Per{'}), Organization({`}Org{'}) and Location({`}Loc{'}). We experimented with the machine learning models Conditional Random Fields(CRFs), Decision Trees and BiLSTMs on our corpus which resulted in a F1-score of 0.96, 0.94 and 0.95 respectively.",
}
@inproceedings{martins-etal-2019-joint,
    title = "Joint Learning of Named Entity Recognition and Entity Linking",
    author = "Martins, Pedro Henrique  and
      Marinho, Zita  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2026",
    doi = "10.18653/v1/P19-2026",
    pages = "190--196",
    abstract = "Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system. For that, we introduce a model inspired by the Stack-LSTM approach. We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives. Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.",
}
@inproceedings{tanaka-etal-2019-dialogue,
    title = "Dialogue-Act Prediction of Future Responses Based on Conversation History",
    author = "Tanaka, Koji  and
      Takayama, Junya  and
      Arase, Yuki",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2027",
    doi = "10.18653/v1/P19-2027",
    pages = "197--202",
    abstract = "Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8{\%} higher F1-score and 3.0{\%} higher accuracy on DA prediction.",
}
@inproceedings{delobelle-etal-2019-computational,
    title = "Computational Ad Hominem Detection",
    author = "Delobelle, Pieter  and
      Cunha, Murilo  and
      Massip Cano, Eric  and
      Peperkamp, Jeroen  and
      Berendt, Bettina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2028",
    doi = "10.18653/v1/P19-2028",
    pages = "203--209",
    abstract = "Fallacies like the personal attack{---}also known as the ad hominem attack{---}are introduced in debates as an easy win, even though they provide no rhetorical contribution. Although their importance in argumentation mining is acknowledged, automated mining and analysis is still lacking. We show TF-IDF approaches are insufficient to detect the ad hominem attack. Therefore we present a machine learning approach for information extraction, which has a recall of 80{\%} for a social media data source. We also demonstrate our approach with an application that uses online learning.",
}
@inproceedings{zhou-etal-2019-multiple,
    title = "Multiple Character Embeddings for {C}hinese Word Segmentation",
    author = "Zhou, Jianing  and
      Wang, Jingkang  and
      Liu, Gongshen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2029",
    doi = "10.18653/v1/P19-2029",
    pages = "210--216",
    abstract = "Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great success with the help of powerful neural networks. However, these works neglect an important clue: Chinese characters incorporate both semantic and phonetic meanings. In this paper, we introduce multiple character embeddings including Pinyin Romanization and Wubi Input, both of which are easily accessible and effective in depicting semantics of characters. We propose a novel shared Bi-LSTM-CRF model to fuse linguistic features efficiently by sharing the LSTM network during the training procedure. Extensive experiments on five corpora show that extra embeddings help obtain a significant improvement in labeling accuracy. Specifically, we achieve the state-of-the-art performance in AS and CityU corpora with F1 scores of 96.9 and 97.3, respectively without leveraging any external lexical resources.",
}
@inproceedings{iida-etal-2019-attention,
    title = "Attention over Heads: A Multi-Hop Attention for Neural Machine Translation",
    author = "Iida, Shohei  and
      Kimura, Ryuichiro  and
      Cui, Hongyi  and
      Hung, Po-Hsuan  and
      Utsuro, Takehito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2030",
    doi = "10.18653/v1/P19-2030",
    pages = "217--222",
    abstract = "In this paper, we propose a multi-hop attention for the Transformer. It refines the attention for an output symbol by integrating that of each head, and consists of two hops. The first hop attention is the scaled dot-product attention which is the same attention mechanism used in the original Transformer. The second hop attention is a combination of multi-layer perceptron (MLP) attention and head gate, which efficiently increases the complexity of the model by adding dependencies between heads. We demonstrate that the translation accuracy of the proposed multi-hop attention outperforms the baseline Transformer significantly, +0.85 BLEU point for the IWSLT-2017 German-to-English task and +2.58 BLEU point for the WMT-2017 German-to-English task. We also find that the number of parameters required for a multi-hop attention is smaller than that for stacking another self-attention layer and the proposed model converges significantly faster than the original Transformer.",
}
@inproceedings{qian-etal-2019-reducing,
    title = "Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function",
    author = "Qian, Yusu  and
      Muaz, Urwa  and
      Zhang, Ben  and
      Hyun, Jae Won",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2031",
    doi = "10.18653/v1/P19-2031",
    pages = "223--228",
    abstract = "Gender bias exists in natural language datasets, which neural language models tend to learn, resulting in biased text generation. In this research, we propose a debiasing approach based on the loss function modification. We introduce a new term to the loss function which attempts to equalize the probabilities of male and female words in the output. Using an array of bias evaluation metrics, we provide empirical evidence that our approach successfully mitigates gender bias in language models without increasing perplexity. In comparison to existing debiasing strategies, data augmentation, and word embedding debiasing, our method performs better in several aspects, especially in reducing gender bias in occupation words. Finally, we introduce a combination of data augmentation and our approach and show that it outperforms existing strategies in all bias evaluation metrics.",
}
@inproceedings{zeng-etal-2019-automatic,
    title = "Automatic Generation of Personalized Comment Based on User Profile",
    author = "Zeng, Wenhuan  and
      Abuduweili, Abulikemu  and
      Li, Lei  and
      Yang, Pengcheng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2032",
    doi = "10.18653/v1/P19-2032",
    pages = "229--235",
    abstract = "Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation (NLG) tasks. Besides, since different user has different expression habits, it is necessary to take the user{'}s profile into consideration when generating comments. In this paper, we introduce the task of automatic generation of personalized comment (AGPC) for social media. Based on tens of thousands of users{'} real comments and corresponding user profiles on weibo, we propose Personalized Comment Generation Network (PCGN) for AGPC. The model utilizes user feature embedding with a gated memory and attends to user description to model personality of users. In addition, external user representation is taken into consideration during the decoding to enhance the comments generation. Experimental results show that our model can generate natural, human-like and personalized comments.",
}
@inproceedings{escolano-etal-2019-bilingual,
    title = "From Bilingual to Multilingual Neural Machine Translation by Incremental Training",
    author = "Escolano, Carlos  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2033",
    doi = "10.18653/v1/P19-2033",
    pages = "236--242",
    abstract = "Multilingual Neural Machine Translation approaches are based on the use of task specific models and the addition of one more language can only be done by retraining the whole system. In this work, we propose a new training schedule that allows the system to scale to more languages without modification of the previous components based on joint training and language-independent encoder/decoder modules allowing for zero-shot translation. This work in progress shows close results to state-of-the-art in the WMT task.",
}
@inproceedings{bouscarrat-etal-2019-strass,
    title = "{STRASS}: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings",
    author = "Bouscarrat, L{\'e}o  and
      Bonnefoy, Antoine  and
      Peel, Thomas  and
      Pereira, C{\'e}cile",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2034",
    doi = "10.18653/v1/P19-2034",
    pages = "243--252",
    abstract = "This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding. The model earns a transformation of the document embedding to minimize the similarity between the extractive summary and the ground truth summary. As the transformation is only composed of a dense layer, the training can be done on CPU, therefore, inexpensive. Moreover, inference time is short and linear according to the number of sentences. As a second contribution, we introduce the French CASS dataset, composed of judgments from the French Court of cassation and their corresponding summaries. On this dataset, our results show that our method performs similarly to the state of the art extractive methods with effective training and inferring time.",
}
@inproceedings{bao-etal-2019-attention,
    title = "Attention and Lexicon Regularized {LSTM} for Aspect-based Sentiment Analysis",
    author = "Bao, Lingxian  and
      Lambert, Patrik  and
      Badia, Toni",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2035",
    doi = "10.18653/v1/P19-2035",
    pages = "253--259",
    abstract = "Abstract Attention based deep learning systems have been demonstrated to be the state of the art approach for aspect-level sentiment analysis, however, end-to-end deep neural networks lack flexibility as one can not easily adjust the network to fix an obvious problem, especially when more training data is not available: e.g. when it always predicts \textit{positive} when seeing the word \textit{disappointed}. Meanwhile, it is less stressed that attention mechanism is likely to {``}over-focus{''} on particular parts of a sentence, while ignoring positions which provide key information for judging the polarity. In this paper, we describe a simple yet effective approach to leverage lexicon information so that the model becomes more flexible and robust. We also explore the effect of regularizing attention vectors to allow the network to have a broader {``}focus{''} on different parts of the sentence. The experimental results demonstrate the effectiveness of our approach.",
}
@inproceedings{nishihara-etal-2019-controllable,
    title = "Controllable Text Simplification with Lexical Constraint Loss",
    author = "Nishihara, Daiki  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2036",
    doi = "10.18653/v1/P19-2036",
    pages = "260--266",
    abstract = "We propose a method to control the level of a sentence in a text simplification task. Text simplification is a monolingual translation task translating a complex sentence into a simpler and easier to understand the alternative. In this study, we use the grade level of the US education system as the level of the sentence. Our text simplification method succeeds in translating an input into a specific grade level by considering levels of both sentences and words. Sentence level is considered by adding the target grade level as input. By contrast, the word level is considered by adding weights to the training loss based on words that frequently appear in sentences of the desired grade level. Although existing models that consider only the sentence level may control the syntactic complexity, they tend to generate words beyond the target level. Our approach can control both the lexical and syntactic complexity and achieve an aggressive rewriting. Experiment results indicate that the proposed method improves the metrics of both BLEU and SARI.",
}
@inproceedings{colakoglu-etal-2019-normalizing,
    title = "Normalizing Non-canonical {T}urkish Texts Using Machine Translation Approaches",
    author = {{\c{C}}olako{\u{g}}lu, Talha  and
      Sulubacak, Umut  and
      Tantu{\u{g}}, Ahmet C{\"u}neyd},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2037",
    doi = "10.18653/v1/P19-2037",
    pages = "267--272",
    abstract = "With the growth of the social web, user-generated text data has reached unprecedented sizes. Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems. The state of the art in Turkish text normalization is composed of a token level pipeline of modules, heavily dependent on external linguistic resources and manually defined rules. Instead, we propose a fully automated, context-aware machine translation approach with fewer stages of processing. Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.",
}
@inproceedings{ghosh-chowdhury-etal-2019-arhnet,
    title = "{ARHN}et - Leveraging Community Interaction for Detection of Religious Hate Speech in {A}rabic",
    author = "Ghosh Chowdhury, Arijit  and
      Didolkar, Aniket  and
      Sawhney, Ramit  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2038",
    doi = "10.18653/v1/P19-2038",
    pages = "273--280",
    abstract = "The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language. Religious Hate Speech, in particular, often leads to unrest and sometimes aggravates to violence against people on the basis of their religious affiliations. The richness of the Arabic morphology and the limited available resources makes this task especially challenging. The current state-of-the-art approaches to detect hate speech in Arabic rely entirely on textual (lexical and semantic) cues. Our proposed methodology contends that leveraging Community-Interaction can better help us profile hate speech content on social media. Our proposed ARHNet (Arabic Religious Hate Speech Net) model incorporates both Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech.",
}
@inproceedings{bhavan-etal-2019-investigating,
    title = "Investigating Political Herd Mentality: A Community Sentiment Based Approach",
    author = "Bhavan, Anjali  and
      Mishra, Rohan  and
      Sinha, Pradyumna Prakhar  and
      Sawhney, Ramit  and
      Shah, Rajiv Ratn",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2039",
    doi = "10.18653/v1/P19-2039",
    pages = "281--287",
    abstract = "Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today. This experiment aims to address this issue by analyzing publicly-available Hansard transcripts of the debates conducted in the UK Parliament. Our proposed approach, which uses community-based graph information to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts, currently surpasses the benchmark results on the same dataset. Such sentiment classification systems could prove to be of great use in today{'}s politically turbulent times, for public knowledge of politicians{'} stands on various relevant issues proves vital for good governance and citizenship. The experiments also demonstrate that continuous feature representations learned from graphs can improve performance on sentiment classification tasks significantly.",
}
@inproceedings{karunanayake-etal-2019-transfer,
    title = "Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages",
    author = "Karunanayake, Yohan  and
      Thayasivam, Uthayasanker  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2040",
    doi = "10.18653/v1/P19-2040",
    pages = "288--294",
    abstract = "Current state-of-the-art speech-based user interfaces use data intense methodologies to recognize free-form speech commands. However, this is not viable for low-resource languages, which lack speech data. This restricts the usability of such interfaces to a limited number of languages. In this paper, we propose a methodology to develop a robust domain-specific speech command classification system for low-resource languages using speech data of a high-resource language. In this transfer learning-based approach, we used a Convolution Neural Network (CNN) to identify a fixed set of intents using an ASR-based character probability map. We were able to achieve significant results for Sinhala and Tamil datasets using an English based ASR, which attests the robustness of the proposed approach.",
}
@inproceedings{el-boukkouri-etal-2019-embedding,
    title = "Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition",
    author = "El Boukkouri, Hicham  and
      Ferret, Olivier  and
      Lavergne, Thomas  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2041",
    doi = "10.18653/v1/P19-2041",
    pages = "295--301",
    abstract = "Using pre-trained word embeddings in conjunction with Deep Learning models has become the {``}de facto{''} approach in Natural Language Processing (NLP). While this usually yields satisfactory results, off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. Moreover, training specialized word representations from scratch is often either impossible or ineffective due to the lack of large enough in-domain data. In this work, we focus on the clinical domain for which we study embedding strategies that rely on general-domain resources only. We show that by combining off-the-shelf contextual embeddings (ELMo) with static word2vec embeddings trained on a small in-domain corpus built from the task data, we manage to reach and sometimes outperform representations learned from a large corpus in the medical domain.",
}
@inproceedings{di-palo-parde-2019-enriching,
    title = "Enriching Neural Models with Targeted Features for Dementia Detection",
    author = "Di Palo, Flavio  and
      Parde, Natalie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2042",
    doi = "10.18653/v1/P19-2042",
    pages = "302--308",
    abstract = "Alzheimers disease is an irreversible brain disease that slowly destroys memory skills andthinking skills leading to the need for full-time care. Early detection of Alzheimer{'}s dis-ease is fundamental to slow down the progress of the disease. In this work we are developing Natural Language Processing techniques to detect linguistic characteristics of patients suffering Alzheimer{'}s Disease and related Dementias. We are proposing a neural model based on a CNN-LSTM architecture that is able to take in consideration both long language samples and hand-crafted linguistic features to distinguish between dementia affected and healthy patients. We are exploring the effects of the introduction of an attention mechanism on both our model and the actual state of the art. Our approach is able to set a new state-of-the art on the DementiaBank dataset achieving an F1 Score of 0.929 in the Dementia patients classification Supplementary material include code to run the experiments.",
}
@inproceedings{dwiastuti-2019-english,
    title = "{E}nglish-{I}ndonesian Neural Machine Translation for Spoken Language Domains",
    author = "Dwiastuti, Meisyarah",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2043",
    doi = "10.18653/v1/P19-2043",
    pages = "309--314",
    abstract = "In this work, we conduct a study on Neural Machine Translation (NMT) for English-Indonesian (EN-ID) and Indonesian-English (ID-EN). We focus on spoken language domains, namely colloquial and speech languages. We build NMT systems using the Transformer model for both translation directions and implement domain adaptation, in which we train our pre-trained NMT systems on speech language (in-domain) data. Moreover, we conduct an evaluation on how the domain-adaptation method in our EN-ID system can result in more formal translation outputs.",
}
@inproceedings{sevgili-etal-2019-improving,
    title = "Improving Neural Entity Disambiguation with Graph Embeddings",
    author = {Sevgili, {\"O}zge  and
      Panchenko, Alexander  and
      Biemann, Chris},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2044",
    doi = "10.18653/v1/P19-2044",
    pages = "315--322",
    abstract = "Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. Current methods have mostly focused on unstructured text data to learn representations of entities, however, there is structured information in the knowledge base itself that should be useful to disambiguate entities. In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from text-based representations. Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-of-the-art neural ED system.",
}
@inproceedings{aly-etal-2019-hierarchical,
    title = "Hierarchical Multi-label Classification of Text with Capsule Networks",
    author = "Aly, Rami  and
      Remus, Steffen  and
      Biemann, Chris",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2045",
    doi = "10.18653/v1/P19-2045",
    pages = "323--330",
    abstract = "Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference. In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text classification and show that they can perform superior to other neural networks, such as CNNs and LSTMs, and non-neural network architectures such as SVMs. For our experiments, we use the established Web of Science (WOS) dataset and introduce a new real-world scenario dataset, the BlurbGenreCollection (BGC). Our results confirm the hypothesis that capsule networks are especially advantageous for rare events and structurally diverse categories, which we attribute to their ability to combine latent encoded information.",
}
@inproceedings{dereli-saraclar-2019-convolutional,
    title = "Convolutional Neural Networks for Financial Text Regression",
    author = "Dereli, Ne{\c{s}}at  and
      Saraclar, Murat",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2046",
    doi = "10.18653/v1/P19-2046",
    pages = "331--337",
    abstract = "Forecasting financial volatility of a publicly-traded company from its annual reports has been previously defined as a text regression problem. Recent studies use a manually labeled lexicon to filter the annual reports by keeping sentiment words only. In order to remove the lexicon dependency without decreasing the performance, we replace bag-of-words model word features by word embedding vectors. Using word vectors increases the number of parameters. Considering the increase in number of parameters and excessive lengths of annual reports, a convolutional neural network model is proposed and transfer learning is applied. Experimental results show that the convolutional neural network model provides more accurate volatility predictions than lexicon based models.",
}
@inproceedings{kolajo-etal-2019-sentiment,
    title = "Sentiment Analysis on {N}aija-Tweets",
    author = "Kolajo, Taiwo  and
      Daramola, Olawande  and
      Adebiyi, Ayodele",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2047",
    doi = "10.18653/v1/P19-2047",
    pages = "338--343",
    abstract = "Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, and spelling error coupled with the availability of real-time content. Moreover, most of the knowledge-based approaches for resolving slang, abbreviation, and acronym do not consider the issue of ambiguity that evolves in the usage of these noisy terms. This research work proposes an improved framework for social media feed pre-processing that leverages on the combination of integrated local knowledge bases and adapted Lesk algorithm to facilitate pre-processing of social media feeds. The results from the experimental evaluation revealed an improvement over existing methods when applied to supervised learning algorithms in the task of extracting sentiments from Nigeria-origin tweets with an accuracy of 99.17{\%}.",
}
@inproceedings{kennedy-etal-2019-fact,
    title = "Fact or Factitious? Contextualized Opinion Spam Detection",
    author = "Kennedy, Stefan  and
      Walsh, Niall  and
      Sloka, Kirils  and
      McCarren, Andrew  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2048",
    doi = "10.18653/v1/P19-2048",
    pages = "344--350",
    abstract = "In this paper we perform an analytic comparison of a number of techniques used to detect fake and deceptive online reviews. We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potential of contextualised embeddings for fake review detection, and lay the groundwork for future research in this area.",
}
@inproceedings{mihaylova-martins-2019-scheduled,
    title = "Scheduled Sampling for Transformers",
    author = "Mihaylova, Tsvetomila  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2049",
    doi = "10.18653/v1/P19-2049",
    pages = "351--356",
    abstract = "Scheduled sampling is a technique for avoiding one of the known problems in sequence-to-sequence generation: exposure bias. It consists of feeding the model a mix of the teacher forced embeddings and the model predictions from the previous step in training time. The technique has been used for improving model performance with recurrent neural networks (RNN). In the Transformer model, unlike the RNN, the generation of a new word attends to the full sentence generated so far, not only to the last word, and it is not straightforward to apply the scheduled sampling technique. We propose some structural changes to allow scheduled sampling to be applied to Transformer architectures, via a two-pass decoding strategy. Experiments on two language pairs achieve performance close to a teacher-forcing baseline and show that this technique is promising for further exploration.",
}
@inproceedings{pathak-srihari-2019-breaking,
    title = "{BREAKING}! Presenting Fake News Corpus for Automated Fact Checking",
    author = "Pathak, Archita  and
      Srihari, Rohini",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2050",
    doi = "10.18653/v1/P19-2050",
    pages = "357--362",
    abstract = "Popular fake news articles spread faster than mainstream articles on the same topic which renders manual fact checking inefficient. At the same time, creating tools for automatic detection is as challenging due to lack of dataset containing articles which present fake or manipulated stories as compelling facts. In this paper, we introduce manually verified corpus of compelling fake and questionable news articles on the USA politics, containing around 700 articles from Aug-Nov, 2016. We present various analyses on this corpus and finally implement classification model based on linguistic features. This work is still in progress as we plan to extend the dataset in the future and use it for our approach towards automated fake news detection.",
}
@inproceedings{pamungkas-patti-2019-cross,
    title = "Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon",
    author = "Pamungkas, Endang Wahyu  and
      Patti, Viviana",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2051",
    doi = "10.18653/v1/P19-2051",
    pages = "363--370",
    abstract = "The development of computational methods to detect abusive language in social media within variable and multilingual contexts has recently gained significant traction. The growing interest is confirmed by the large number of benchmark corpora for different languages developed in the latest years. However, abusive language behaviour is multifaceted and available datasets are featured by different topical focuses. This makes abusive language detection a domain-dependent task, and building a robust system to detect general abusive content a first challenge. Moreover, most resources are available for English, which makes detecting abusive language in low-resource languages a further challenge. We address both challenges by considering ten publicly available datasets across different domains and languages. A hybrid approach with deep learning and a multilingual lexicon to cross-domain and cross-lingual detection of abusive content is proposed and compared with other simpler models. We show that training a system on general abusive language datasets will produce a cross-domain robust system, which can be used to detect other more specific types of abusive content. We also found that using the domain-independent lexicon HurtLex is useful to transfer knowledge between domains and languages. In the cross-lingual experiment, we demonstrate the effectiveness of our jointlearning model also in out-domain scenarios.",
}
@inproceedings{lal-etal-2019-de,
    title = "De-Mixing Sentiment from Code-Mixed Text",
    author = "Lal, Yash Kumar  and
      Kumar, Vaibhav  and
      Dhar, Mrinal  and
      Shrivastava, Manish  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2052",
    doi = "10.18653/v1/P19-2052",
    pages = "371--377",
    abstract = "Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today{'}s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54{\%} accuracy and 0.827 F1 score - on a benchmark dataset.",
}
@inproceedings{mim-etal-2019-unsupervised,
    title = "Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring",
    author = "Mim, Farjana Sultana  and
      Inoue, Naoya  and
      Reisert, Paul  and
      Ouchi, Hiroki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2053",
    doi = "10.18653/v1/P19-2053",
    pages = "378--385",
    abstract = "Existing document embedding approaches mainly focus on capturing sequences of words in documents. However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents. Although some prior approaches consider this issue and utilize discourse structure of text for document classification, these approaches are dependent on computationally expensive parsers. In this paper, we propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require any expensive parser or annotation. Extrinsic evaluation results show that the document representation obtained from our approach improves the performance of essay Organization scoring and Argument Strength scoring.",
}
@inproceedings{suzuki-etal-2019-multimodal,
    title = "Multimodal Logical Inference System for Visual-Textual Entailment",
    author = "Suzuki, Riko  and
      Yanaka, Hitomi  and
      Yoshikawa, Masashi  and
      Mineshima, Koji  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2054",
    doi = "10.18653/v1/P19-2054",
    pages = "386--392",
    abstract = "A large amount of research about multimodal inference across text and vision has been recently developed to obtain visually grounded word and sentence representations. In this paper, we use logic-based representations as unified meaning representations for texts and images and present an unsupervised multimodal logical inference system that can effectively prove entailment relations between them. We show that by combining semantic parsing and theorem proving, the system can handle semantically complex sentences for visual-textual inference.",
}
@inproceedings{miftahutdinov-tutubalina-2019-deep,
    title = "Deep Neural Models for Medical Concept Normalization in User-Generated Texts",
    author = "Miftahutdinov, Zulfat  and
      Tutubalina, Elena",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2055",
    doi = "10.18653/v1/P19-2055",
    pages = "393--399",
    abstract = "In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This is a challenging task since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextualized word representation models trained to obtain semantic representations of social media expressions. Our experimental evaluation over three different benchmarks shows that neural architectures leverage the semantic meaning of the entity mention and significantly outperform existing state of the art models.",
}
@inproceedings{yasui-etal-2019-using,
    title = "Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation",
    author = "Yasui, Go  and
      Tsuruoka, Yoshimasa  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2056",
    doi = "10.18653/v1/P19-2056",
    pages = "400--406",
    abstract = "Traditional model training for sentence generation employs cross-entropy loss as the loss function. While cross-entropy loss has convenient properties for supervised learning, it is unable to evaluate sentences as a whole, and lacks flexibility. We present the approach of training the generation model using the estimated semantic similarity between the output and reference sentences to alleviate the problems faced by the training with cross-entropy loss. We use the BERT-based scorer fine-tuned to the Semantic Textual Similarity (STS) task for semantic similarity estimation, and train the model with the estimated scores through reinforcement learning (RL). Our experiments show that reinforcement learning with semantic similarity reward improves the BLEU scores from the baseline LSTM NMT model.",
}
@inproceedings{thongtan-phienthrakul-2019-sentiment,
    title = "Sentiment Classification Using Document Embeddings Trained with Cosine Similarity",
    author = "Thongtan, Tan  and
      Phienthrakul, Tanasanee",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2057",
    doi = "10.18653/v1/P19-2057",
    pages = "407--414",
    abstract = "In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This paper proposes training document embeddings using cosine similarity instead of dot product. Experiments on the IMDB dataset show that accuracy is improved when using cosine similarity compared to using dot product, while using feature combination with Naive Bayes weighted bag of n-grams achieves a new state of the art accuracy of 97.42{\%}. Code to reproduce all experiments is available at https://github.com/tanthongtan/dv-cosine",
}
@inproceedings{alimova-tutubalina-2019-detecting,
    title = "Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks",
    author = "Alimova, Ilseyar  and
      Tutubalina, Elena",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2058",
    doi = "10.18653/v1/P19-2058",
    pages = "415--421",
    abstract = "Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology. Social media and electronic clinical reports are becoming increasingly popular as a source for obtaining health related information. In this work, we focus on extraction information of adverse drug reactions from various sources of biomedical textbased information, including biomedical literature and social media. We formulate the problem as a binary classification task and compare the performance of four state-of-the-art attention-based neural networks in terms of the F-measure. We show the effectiveness of these methods on four different benchmarks.",
}
@inproceedings{egawa-etal-2019-annotating,
    title = "Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments",
    author = "Egawa, Ryo  and
      Morio, Gaku  and
      Fujita, Katsuhide",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2059",
    doi = "10.18653/v1/P19-2059",
    pages = "422--428",
    abstract = "For analyzing online persuasions, one of the important goals is to semantically understand how people construct comments to persuade others. However, analyzing the semantic role of arguments for online persuasion has been less emphasized. Therefore, in this study, we propose a novel annotation scheme that captures the semantic role of arguments in a popular online persuasion forum, so-called ChangeMyView. Through this study, we have made the following contributions: (i) proposing a scheme that includes five types of elementary units (EUs) and two types of relations. (ii) annotating ChangeMyView which results in 4612 EUs and 2713 relations in 345 posts. (iii) analyzing the semantic role of persuasive arguments. Our analyses captured certain characteristic phenomena for online persuasion.",
}
@inproceedings{aguirre-aguiar-2019-japanese,
    title = "A {J}apanese Word Segmentation Proposal",
    author = "Aguirre, Stalin  and
      Aguiar, Josaf{\'a}",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-2060",
    doi = "10.18653/v1/P19-2060",
    pages = "429--435",
    abstract = "Current Japanese word segmentation methods, that use a morpheme-based approach, may produce different segmentations for the same strings. This occurs when these strings appear in different sentences. The cause is the influence of different contexts around these strings affecting the probabilistic models used in segmentation algorithms. This paper presents an alternative to the current morpheme-based scheme for Japanese word segmentation. The proposed scheme focuses on segmenting inflections as single words instead of separating the auxiliary verbs and other morphemes from the stems. Some morphological segmentation rules are presented for each type of word and these rules are implemented in a program which is properly described. The program is used to generate a segmentation of a sentence corpus, whose consistency is calculated and compared with the current morpheme-based segmentation of the same corpus. The experiments show that this method produces a much more consistent segmentation than the morpheme-based one.",
}
@proceedings{acl-2019-association,
    title = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'\i}s",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1000",
}
@inproceedings{tao-etal-2019-one,
    title = "One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues",
    author = "Tao, Chongyang  and
      Wu, Wei  and
      Xu, Can  and
      Hu, Wenpeng  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1001",
    doi = "10.18653/v1/P19-1001",
    pages = "1--11",
    abstract = "Currently, researchers have paid great attention to retrieval-based dialogues in open-domain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark data sets. State-of-the-art methods require a response to interact with each utterance in a context from the beginning, but the interaction is performed in a shallow way. In this work, we let utterance-response interaction go deep by proposing an interaction-over-interaction network (IoI). The model performs matching by stacking multiple interaction blocks in which residual information from one time of interaction initiates the interaction process again. Thus, matching information within an utterance-response pair is extracted from the interaction of the pair in an iterative fashion, and the information flows along the chain of the blocks via representations. Evaluation results on three benchmark data sets indicate that IoI can significantly outperform state-of-the-art methods in terms of various matching metrics. Through further analysis, we also unveil how the depth of interaction affects the performance of IoI.",
}
@inproceedings{li-etal-2019-incremental,
    title = "Incremental Transformer with Deliberation Decoder for Document Grounded Conversations",
    author = "Li, Zekang  and
      Niu, Cheng  and
      Meng, Fandong  and
      Feng, Yang  and
      Li, Qian  and
      Zhou, Jie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1002",
    doi = "10.18653/v1/P19-1002",
    pages = "12--21",
    abstract = "Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, while existing dialogue models do not exploit this kind of knowledge effectively enough. In this paper, we propose a novel Transformer-based architecture for multi-turn document grounded conversations. In particular, we devise an Incremental Transformer to encode multi-turn utterances along with knowledge in related documents. Motivated by the human cognitive process, we design a two-pass decoder (Deliberation Decoder) to improve context coherence and knowledge correctness. Our empirical study on a real-world Document Grounded Dataset proves that responses generated by our model significantly outperform competitive baselines on both context coherence and knowledge relevance.",
}
@inproceedings{su-etal-2019-improving,
    title = "Improving Multi-turn Dialogue Modelling with Utterance {R}e{W}riter",
    author = "Su, Hui  and
      Shen, Xiaoyu  and
      Zhang, Rongzhi  and
      Sun, Fei  and
      Hu, Pengwei  and
      Niu, Cheng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1003",
    doi = "10.18653/v1/P19-1003",
    pages = "22--31",
    abstract = "Recent research has achieved impressive results in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challenge is the frequently occurred coreference and information omission in our daily conversation, making it hard for machines to understand the real intention. In this paper, we propose rewriting the human utterance as a pre-process to help multi-turn dialgoue modelling. Each utterance is first rewritten to recover all coreferred and omitted information. The next processing steps are then performed based on the rewritten utterance. To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network. We show the proposed architecture achieves remarkably good performance on the utterance rewriting task. The trained utterance rewriter can be easily integrated into online chatbots and brings general improvement over different domains.",
}
@inproceedings{sankar-etal-2019-neural,
    title = "Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study",
    author = "Sankar, Chinnadhurai  and
      Subramanian, Sandeep  and
      Pal, Chris  and
      Chandar, Sarath  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1004",
    doi = "10.18653/v1/P19-1004",
    pages = "32--37",
    abstract = "Neural generative models have been become increasingly popular when building conversational agents. They offer flexibility, can be easily adapted to new domains, and require minimal domain engineering. A common criticism of these systems is that they seldom understand or use the available dialog history effectively. In this paper, we take an empirical approach to understanding how these models use the available dialog history by studying the sensitivity of the models to artificially introduced unnatural changes or perturbations to their context at test time. We experiment with 10 different types of perturbations on 4 multi-turn dialog datasets and find that commonly used neural dialog architectures like recurrent and transformer-based seq2seq models are rarely sensitive to most perturbations such as missing or reordering utterances, shuffling words, etc. Also, by open-sourcing our code, we believe that it will serve as a useful diagnostic tool for evaluating dialog systems in the future.",
}
@inproceedings{du-black-2019-boosting,
    title = "Boosting Dialog Response Generation",
    author = "Du, Wenchao  and
      Black, Alan W",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1005",
    doi = "10.18653/v1/P19-1005",
    pages = "38--43",
    abstract = "Neural models have become one of the most important approaches to dialog response generation. However, they still tend to generate the most common and generic responses in the corpus all the time. To address this problem, we designed an iterative training process and ensemble method based on boosting. We combined our method with different training and decoding paradigms as the base model, including mutual-information-based decoding and reward-augmented maximum likelihood learning. Empirical results show that our approach can significantly improve the diversity and relevance of the responses generated by all base models, backed by objective measurements and human evaluation.",
}
@inproceedings{lu-etal-2019-constructing,
    title = "Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection",
    author = "Lu, Junyu  and
      Zhang, Chenbin  and
      Xie, Zeying  and
      Ling, Guang  and
      Zhou, Tom Chao  and
      Xu, Zenglin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1006",
    doi = "10.18653/v1/P19-1006",
    pages = "44--50",
    abstract = "Response selection plays an important role in fully automated dialogue systems. Given the dialogue context, the goal of response selection is to identify the best-matched next utterance (i.e., response) from multiple candidates. Despite the efforts of many previous useful models, this task remains challenging due to the huge semantic gap and also the large size of candidate set. To address these issues, we propose a Spatio-Temporal Matching network (STM) for response selection. In detail, soft alignment is first used to obtain the local relevance between the context and the response. And then, we construct spatio-temporal features by aggregating attention images in time dimension and make use of 3D convolution and pooling operations to extract matching information. Evaluation on two large-scale multi-turn response selection tasks has demonstrated that our proposed model significantly outperforms the state-of-the-art model. Particularly, visualization analysis shows that the spatio-temporal features enables matching information in segment pairs and time sequences, and have good interpretability for multi-turn text matching.",
}
@inproceedings{cao-etal-2019-semantic,
    title = "Semantic Parsing with Dual Learning",
    author = "Cao, Ruisheng  and
      Zhu, Su  and
      Liu, Chen  and
      Li, Jieyu  and
      Yu, Kai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1007",
    doi = "10.18653/v1/P19-1007",
    pages = "51--64",
    abstract = "Semantic parsing converts natural language queries into structured logical forms. The lack of training data is still one of the most serious problems in this area. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on OVERNIGHT dataset.",
}
@inproceedings{venant-koller-2019-semantic,
    title = "Semantic Expressive Capacity with Bounded Memory",
    author = "Venant, Antoine  and
      Koller, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1008",
    doi = "10.18653/v1/P19-1008",
    pages = "65--79",
    abstract = "We investigate the capacity of mechanisms for compositional semantic parsing to describe relations between sentences and semantic representations. We prove that in order to represent certain relations, mechanisms which are syntactically projective must be able to remember an unbounded number of locations in the semantic representations, where nonprojective mechanisms need not. This is the first result of this kind, and has consequences both for grammar-based and for neural systems.",
}
@inproceedings{zhang-etal-2019-amr,
    title = "{AMR} Parsing as Sequence-to-Graph Transduction",
    author = "Zhang, Sheng  and
      Ma, Xutai  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1009",
    doi = "10.18653/v1/P19-1009",
    pages = "80--94",
    abstract = "We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3{\%} on LDC2017T10) and AMR 1.0 (70.2{\%} on LDC2014T12).",
}
@inproceedings{shaw-etal-2019-generating,
    title = "Generating Logical Forms from Graph Representations of Text and Entities",
    author = "Shaw, Peter  and
      Massey, Philip  and
      Chen, Angelica  and
      Piccinno, Francesco  and
      Altun, Yasemin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1010",
    doi = "10.18653/v1/P19-1010",
    pages = "95--106",
    abstract = "Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.",
}
@inproceedings{shen-etal-2019-learning,
    title = "Learning Compressed Sentence Representations for On-Device Text Processing",
    author = "Shen, Dinghan  and
      Cheng, Pengyu  and
      Sundararaman, Dhanasekar  and
      Zhang, Xinyuan  and
      Yang, Qian  and
      Tang, Meng  and
      Celikyilmaz, Asli  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1011",
    doi = "10.18653/v1/P19-1011",
    pages = "107--116",
    abstract = "Vector representations of sentences, trained on massive text corpora, are widely used as generic sentence embeddings across a variety of NLP problems. The learned representations are generally assumed to be continuous and real-valued, giving rise to a large memory footprint and slow retrieval speed, which hinders their applicability to low-resource (memory and computation) platforms, such as mobile devices. In this paper, we propose four different strategies to transform continuous and generic sentence embeddings into a binarized form, while preserving their rich semantic information. The introduced methods are evaluated across a wide range of downstream tasks, where the binarized sentence embeddings are demonstrated to degrade performance by only about 2{\%} relative to their continuous counterparts, while reducing the storage requirement by over 98{\%}. Moreover, with the learned binary representations, the semantic relatedness of two sentences can be evaluated by simply calculating their Hamming distance, which is more computational efficient compared with the inner product operation between continuous embeddings. Detailed analysis and case study further validate the effectiveness of proposed methods.",
}
@inproceedings{falenska-kuhn-2019-non,
    title = "The (Non-)Utility of Structural Features in {B}i{LSTM}-based Dependency Parsers",
    author = "Falenska, Agnieszka  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1012",
    doi = "10.18653/v1/P19-1012",
    pages = "117--128",
    abstract = "Classical non-neural dependency parsers put considerable effort on the design of feature functions. Especially, they benefit from information coming from structural features, such as features drawn from neighboring tokens in the dependency tree. In contrast, their BiLSTM-based successors achieve state-of-the-art performance without explicit information about the structural context. In this paper we aim to answer the question: How much structural context are the BiLSTM representations able to capture implicitly? We show that features drawn from partial subtrees become redundant when the BiLSTMs are used. We provide a deep insight into information flow in transition- and graph-based neural architectures to demonstrate where the implicit information comes from when the parsers make their decisions. Finally, with model ablations we demonstrate that the structural context is not only present in the models, but it significantly influences their performance.",
}
@inproceedings{yoshikawa-etal-2019-automatic,
    title = "Automatic Generation of High Quality {CCG}banks for Parser Domain Adaptation",
    author = "Yoshikawa, Masashi  and
      Noji, Hiroshi  and
      Mineshima, Koji  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1013",
    doi = "10.18653/v1/P19-1013",
    pages = "129--139",
    abstract = "We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7{\%} to 96.6{\%} on speech conversation, and from 88.5{\%} to 96.8{\%} on math problems.",
}
@inproceedings{beryozkin-etal-2019-joint,
    title = "A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy",
    author = "Beryozkin, Genady  and
      Drori, Yoel  and
      Gilon, Oren  and
      Hartman, Tzvika  and
      Szpektor, Idan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1014",
    doi = "10.18653/v1/P19-1014",
    pages = "140--150",
    abstract = "We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags. This setting occurs when various datasets are created using different annotation schemes. This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset. We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. We compare this model to combining independent models and to a model based on the multitasking approach. Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.",
}
@inproceedings{rahimi-etal-2019-massively,
    title = "Massively Multilingual Transfer for {NER}",
    author = "Rahimi, Afshin  and
      Li, Yuan  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1015",
    doi = "10.18653/v1/P19-1015",
    pages = "151--164",
    abstract = "In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a {``}massive{''} setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model.",
}
@inproceedings{lin-etal-2019-reliability,
    title = "Reliability-aware Dynamic Feature Composition for Name Tagging",
    author = "Lin, Ying  and
      Liu, Liyuan  and
      Ji, Heng  and
      Yu, Dong  and
      Han, Jiawei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1016",
    doi = "10.18653/v1/P19-1016",
    pages = "165--174",
    abstract = "Word embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, rare word embeddings are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and uncommon words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model by 2.7{\%} absolute gain in F-score. In cross-genre experiments on five genres in OntoNotes, our model improves the performance for most genre pairs and obtains up to 5{\%} absolute F-score gain.",
}
@inproceedings{leng-etal-2019-unsupervised,
    title = "Unsupervised Pivot Translation for Distant Languages",
    author = "Leng, Yichong  and
      Tan, Xu  and
      Qin, Tao  and
      Li, Xiang-Yang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1017",
    doi = "10.18653/v1/P19-1017",
    pages = "175--183",
    abstract = "Unsupervised neural machine translation (NMT) has attracted a lot of attention recently. While state-of-the-art methods for unsupervised translation usually perform well between similar languages (e.g., English-German translation), they perform poorly between distant languages, because unsupervised alignment does not work well for distant languages. In this work, we introduce unsupervised pivot translation for distant languages, which translates a language to a distant language through multiple hops, and the unsupervised translation on each hop is relatively easier than the original direct translation. We propose a learning to route (LTR) method to choose the translation path between the source and target languages. LTR is trained on language pairs whose best translation path is available and is applied on the unseen language pairs for path selection. Experiments on 20 languages and 294 distant language pairs demonstrate the advantages of the unsupervised pivot translation for distant languages, as well as the effectiveness of the proposed LTR for path selection. Specifically, in the best case, LTR achieves an improvement of 5.58 BLEU points over the conventional direct unsupervised method.",
}
@inproceedings{patra-etal-2019-bilingual,
    title = "Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces",
    author = "Patra, Barun  and
      Moniz, Joel Ruben Antony  and
      Garg, Sarthak  and
      Gormley, Matthew R.  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1018",
    doi = "10.18653/v1/P19-1018",
    pages = "184--193",
    abstract = "Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) {---} a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don{'}t appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.",
}
@inproceedings{artetxe-etal-2019-effective,
    title = "An Effective Approach to Unsupervised Machine Translation",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1019",
    doi = "10.18653/v1/P19-1019",
    pages = "194--203",
    abstract = "While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.",
}
@inproceedings{sato-etal-2019-effective,
    title = "Effective Adversarial Regularization for Neural Machine Translation",
    author = "Sato, Motoki  and
      Suzuki, Jun  and
      Kiyono, Shun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1020",
    doi = "10.18653/v1/P19-1020",
    pages = "204--210",
    abstract = "A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements. We aim to further leverage this promising methodology into more sophisticated and critical neural models in the natural language processing field, i.e., neural machine translation (NMT) models. However, it is not trivial to apply this methodology to such models. Thus, this paper investigates the effectiveness of several possible configurations of applying the adversarial perturbation and reveals that the adversarial regularization technique can significantly and consistently improve the performance of widely used NMT models, such as LSTM-based and Transformer-based models.",
}
@inproceedings{sennrich-zhang-2019-revisiting,
    title = "Revisiting Low-Resource Neural Machine Translation: A Case Study",
    author = "Sennrich, Rico  and
      Zhang, Biao",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1021",
    doi = "10.18653/v1/P19-1021",
    pages = "211--221",
    abstract = "It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results. In this paper, we re-assess the validity of these results, arguing that they are the result of lack of system adaptation to low-resource settings. We discuss some pitfalls to be aware of when training low-resource NMT systems, and recent techniques that have shown to be especially helpful in low-resource settings, resulting in a set of best practices for low-resource NMT. In our experiments on German{--}English with different amounts of IWSLT14 training data, we show that, without the use of any auxiliary monolingual or multilingual data, an optimized NMT system can outperform PBSMT with far less data than previously claimed. We also apply these techniques to a low-resource Korean{--}English dataset, surpassing previously reported results by 4 BLEU.",
}
@inproceedings{saunders-etal-2019-domain,
    title = "Domain Adaptive Inference for Neural Machine Translation",
    author = "Saunders, Danielle  and
      Stahlberg, Felix  and
      de Gispert, Adri{\`a}  and
      Byrne, Bill",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1022",
    doi = "10.18653/v1/P19-1022",
    pages = "222--228",
    abstract = "We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain. We adapt sequentially across two Spanish-English and three English-German tasks, comparing unregularized fine-tuning, L2 and Elastic Weight Consolidation. We then report a novel scheme for adaptive NMT ensemble decoding by extending Bayesian Interpolation with source information, and report strong improvements across test domains without access to the domain label.",
}
@inproceedings{trisedya-etal-2019-neural,
    title = "Neural Relation Extraction for Knowledge Base Enrichment",
    author = "Trisedya, Bayu Distiawan  and
      Weikum, Gerhard  and
      Qi, Jianzhong  and
      Zhang, Rui",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1023",
    doi = "10.18653/v1/P19-1023",
    pages = "229--240",
    abstract = "We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED errors may cause extraction errors that affect the overall precision and recall.To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based attention model that captures multi-word entity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-the-art baselines by 15.51{\%} and 8.38{\%} in terms of F1 score on two real-world datasets.",
}
@inproceedings{guo-etal-2019-attention,
    title = "Attention Guided Graph Convolutional Networks for Relation Extraction",
    author = "Guo, Zhijiang  and
      Zhang, Yan  and
      Lu, Wei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1024",
    doi = "10.18653/v1/P19-1024",
    pages = "241--251",
    abstract = "Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenging research question. Existing approaches employing rule based hard-pruning strategies for selecting relevant partial dependency structures may not always yield optimal results. In this work, we propose Attention Guided Graph Convolutional Networks (AGGCNs), a novel model which directly takes full dependency trees as inputs. Our model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task. Extensive results on various tasks including cross-sentence n-ary relation extraction and large-scale sentence-level relation extraction show that our model is able to better leverage the structural information of the full dependency trees, giving significantly better results than previous approaches.",
}
@inproceedings{maiti-vucetic-2019-spatial,
    title = "Spatial Aggregation Facilitates Discovery of Spatial Topics",
    author = "Maiti, Aniruddha  and
      Vucetic, Slobodan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1025",
    doi = "10.18653/v1/P19-1025",
    pages = "252--262",
    abstract = "Spatial aggregation refers to merging of documents created at the same spatial location. We show that by spatial aggregation of a large collection of documents and applying a traditional topic discovery algorithm on the aggregated data we can efficiently discover spatially distinct topics. By looking at topic discovery through matrix factorization lenses we show that spatial aggregation allows low rank approximation of the original document-word matrix, in which spatially distinct topics are preserved and non-spatial topics are aggregated into a single topic. Our experiments on synthetic data confirm this observation. Our experiments on 4.7 million tweets collected during the Sandy Hurricane in 2012 show that spatial and temporal aggregation allows rapid discovery of relevant spatial and temporal topics during that period. Our work indicates that different forms of document aggregation might be effective in rapid discovery of various types of distinct topics from large collections of documents.",
}
@inproceedings{xu-li-2019-relation,
    title = "Relation Embedding with Dihedral Group in Knowledge Graph",
    author = "Xu, Canran  and
      Li, Ruijiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1026",
    doi = "10.18653/v1/P19-1026",
    pages = "263--272",
    abstract = "Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. As a family of effective approaches for link predictions, embedding methods try to learn low-rank representations for both entities and relations such that the bilinear form defined therein is a well-behaved scoring function. Despite of their successful performances, existing bilinear forms overlook the modeling of relation compositions, resulting in lacks of interpretability for reasoning on KG. To fulfill this gap, we propose a new model called DihEdral, named after dihedral symmetry group. This new model learns knowledge graph embeddings that can capture relation compositions by nature. Furthermore, our approach models the relation embeddings parametrized by discrete values, thereby decrease the solution space drastically. Our experiments show that DihEdral is able to capture all desired properties such as (skew-) symmetry, inversion and (non-) Abelian composition, and outperforms existing bilinear form based approach and is comparable to or better than deep learning models such as ConvE.",
}
@inproceedings{heinzerling-strube-2019-sequence,
    title = "Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation",
    author = "Heinzerling, Benjamin  and
      Strube, Michael",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1027",
    doi = "10.18653/v1/P19-1027",
    pages = "273--291",
    abstract = "Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack of systematic evaluations makes it difficult for practitioners to choose between them. In this work, we conduct an extensive evaluation comparing non-contextual subword embeddings, namely FastText and BPEmb, and a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. We find that overall, a combination of BERT, BPEmb, and character representations works best across languages and tasks. A more detailed analysis reveals different strengths and weaknesses: Multilingual BERT performs well in medium- to high-resource languages, but is outperformed by non-contextual subword embeddings in a low-resource setting.",
}
@inproceedings{li-srikumar-2019-augmenting,
    title = "Augmenting Neural Networks with First-order Logic",
    author = "Li, Tao  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1028",
    doi = "10.18653/v1/P19-1028",
    pages = "292--302",
    abstract = "Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to perform end-to-end training remains an open question. In this paper, we present a novel framework for introducing declarative knowledge to neural network architectures in order to guide training and prediction. Our framework systematically compiles logical statements into computation graphs that augment a neural network without extra learnable parameters or manual redesign. We evaluate our modeling strategy on three tasks: machine comprehension, natural language inference, and text chunking. Our experiments show that knowledge-augmented networks can strongly improve over baselines, especially in low-data regimes.",
}
@inproceedings{kreutzer-riezler-2019-self,
    title = "Self-Regulated Interactive Sequence-to-Sequence Learning",
    author = "Kreutzer, Julia  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1029",
    doi = "10.18653/v1/P19-1029",
    pages = "303--315",
    abstract = "Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an $\epsilon$-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.",
}
@inproceedings{ahmed-etal-2019-need,
    title = "You Only Need Attention to Traverse Trees",
    author = "Ahmed, Mahtab  and
      Samee, Muhammad Rifayat  and
      Mercer, Robert E.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1030",
    doi = "10.18653/v1/P19-1030",
    pages = "316--322",
    abstract = "In recent NLP research, a topic of interest is universal sentence encoding, sentence representations that can be used in any supervised task. At the word sequence level, fully attention-based models suffer from two problems: a quadratic increase in memory consumption with respect to the sentence length and an inability to capture and use syntactic information. Recursive neural nets can extract very good syntactic information by traversing a tree structure. To this end, we propose Tree Transformer, a model that captures phrase level syntax for constituency trees as well as word-level dependencies for dependency trees by doing recursive traversal only with attention. Evaluation of this model on four tasks gets noteworthy results compared to the standard transformer and LSTM-based models as well as tree-structured LSTMs. Ablation studies to find whether positional information is inherently encoded in the trees and which type of attention is suitable for doing the recursive traversal are provided.",
}
@inproceedings{fried-etal-2019-cross,
    title = "Cross-Domain Generalization of Neural Constituency Parsers",
    author = "Fried, Daniel  and
      Kitaev, Nikita  and
      Klein, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1031",
    doi = "10.18653/v1/P19-1031",
    pages = "323--330",
    abstract = "Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing{---}but to what degree do they generalize to other domains? We present three results about the generalization of neural parsers in a zero-shot setting: training on trees from one corpus and evaluating on out-of-domain corpora. First, neural and non-neural parsers generalize comparably to new domains. Second, incorporating pre-trained encoder representations into neural parsers substantially improves their performance across all domains, but does not give a larger relative improvement for out-of-domain treebanks. Finally, despite the rich input representations they learn, neural parsers still benefit from structured output prediction of output trees, yielding higher exact match accuracy and stronger generalization both to larger text spans and to out-of-domain corpora. We analyze generalization on English and Chinese corpora, and in the process obtain state-of-the-art parsing results for the Brown, Genia, and English Web treebanks.",
}
@inproceedings{sukhbaatar-etal-2019-adaptive,
    title = "Adaptive Attention Span in Transformers",
    author = "Sukhbaatar, Sainbayar  and
      Grave, Edouard  and
      Bojanowski, Piotr  and
      Joulin, Armand",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1032",
    doi = "10.18653/v1/P19-1032",
    pages = "331--335",
    abstract = "We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time. We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters.",
}
@inproceedings{an-etal-2019-neural,
    title = "Neural News Recommendation with Long- and Short-term User Representations",
    author = "An, Mingxiao  and
      Wu, Fangzhao  and
      Wu, Chuhan  and
      Zhang, Kun  and
      Liu, Zheng  and
      Xie, Xing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1033",
    doi = "10.18653/v1/P19-1033",
    pages = "336--345",
    abstract = "Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both long-term preferences and short-term interests. However, existing news recommendation methods usually learn single representations of users, which may be insufficient. In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations. The core of our approach is a news encoder and a user encoder. In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words. In the user encoder, we propose to learn long-term user representations from the embeddings of their IDs.In addition, we propose to learn short-term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine long-term and short-term user representations. The first one is using the long-term user representation to initialize the hidden state of the GRU network in short-term user representation. The second one is concatenating both long- and short-term user representations as a unified user vector. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of neural news recommendation.",
}
@inproceedings{sedinkina-etal-2019-automatic,
    title = "Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes",
    author = {Sedinkina, Marina  and
      Breitkopf, Nikolas  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1034",
    doi = "10.18653/v1/P19-1034",
    pages = "346--359",
    abstract = "In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (i) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert{'}s a priori belief about a word{'}s meaning can be incorrect {--} annotation should be performed based on the word{'}s contexts in the target domain instead.",
}
@inproceedings{lee-etal-2019-manipulating,
    title = "Manipulating the Difficulty of {C}-Tests",
    author = "Lee, Ji-Ung  and
      Schwan, Erik  and
      Meyer, Christian M.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1035",
    doi = "10.18653/v1/P19-1035",
    pages = "360--370",
    abstract = "We propose two novel manipulation strategies for increasing and decreasing the difficulty of C-tests automatically. This is a crucial step towards generating learner-adaptive exercises for self-directed language learning and preparing language assessment tests. To reach the desired difficulty level, we manipulate the size and the distribution of gaps based on absolute and relative gap difficulty predictions. We evaluate our approach in corpus-based experiments and in a user study with 60 participants. We find that both strategies are able to generate C-tests with the desired difficulty level.",
}
@inproceedings{haj-yahia-etal-2019-towards,
    title = "Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings",
    author = "Haj-Yahia, Zied  and
      Sieg, Adrien  and
      Deleris, L{\'e}a A.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1036",
    doi = "10.18653/v1/P19-1036",
    pages = "371--379",
    abstract = "Text classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of labeled documents to reach adequate accuracy. This is particularly true when the number of target categories is in the tens or the hundreds. In this work, we explore an unsupervised approach to classify documents into categories simply described by a label. The proposed method is inspired by the way a human proceeds in this situation: It draws on textual similarity between the most relevant words in each document and a dictionary of keywords for each category reflecting its semantics and lexical field. The novelty of our method hinges on the enrichment of the category labels through a combination of human expertise and language models, both generic and domain specific. Our experiments on 5 standard corpora show that the proposed method increases F1-score over relying solely on human expertise and can also be on par with simple supervised approaches. It thus provides a practical alternative to situations where low cost text categorization is needed, as we illustrate with our application to operational risk incidents classification.",
}
@inproceedings{shardlow-nawaz-2019-neural,
    title = "Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table",
    author = "Shardlow, Matthew  and
      Nawaz, Raheel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1037",
    doi = "10.18653/v1/P19-1037",
    pages = "380--389",
    abstract = "Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medical terminology to simpler vocabulary by mining SNOMED-CT. In an evaluation task using crowdsourcing, we show that the results of our new system are ranked easier to understand (average rank 1.93) than using the original system (2.34) without our phrase table. We also show improvement against baselines including the original text (2.79) and using the phrase table without the neural text simplification software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to provide effective neural text simplification for any domain without the need for costly annotation.",
}
@inproceedings{qin-yang-2019-say,
    title = "What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues",
    author = "Qin, Yu  and
      Yang, Yi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1038",
    doi = "10.18653/v1/P19-1038",
    pages = "390--401",
    abstract = "Predicting financial risk is an essential task in financial market. Prior research has shown that textual information in a firm{'}s financial statement can be used to predict its stock{'}s risk level. Nowadays, firm CEOs communicate information not only verbally through press releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal evidences that CEO{'}s vocal features, such as emotions and voice tones, can reveal the firm{'}s performance. However, how vocal features can be used to predict risk levels, and to what extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual transcripts for S{\&}P 500 companies in recent years. We propose a multimodal deep regression model (MDRM) that jointly model CEO{'}s verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets. The processed earnings conference calls data (text and audio) are released for readers who are interested in reproducing the results or designing trading strategy.",
}
@inproceedings{hu-2019-detecting,
    title = "Detecting Concealed Information in Text and Speech",
    author = "Hu, Shengli",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1039",
    doi = "10.18653/v1/P19-1039",
    pages = "402--412",
    abstract = "Motivated by infamous cheating scandals in the media industry, the wine industry, and political campaigns, we address the problem of detecting concealed information in technical settings. In this work, we explore acoustic-prosodic and linguistic indicators of information concealment by collecting a unique corpus of professionals practicing for oral exams while concealing information. We reveal subtle signs of concealing information in speech and text, compare and contrast them with those in deception detection literature, uncovering the link between concealing information and deception. We then present a series of experiments that automatically detect concealed information from text and speech. We compare the use of acoustic-prosodic, linguistic, and individual feature sets, using different machine learning models. Finally, we present a multi-task learning framework with acoustic, linguistic, and individual features, that outperforms human performance by over 15{\%}.",
}
@inproceedings{zhang-etal-2019-evidence,
    title = "Evidence-based Trustworthiness",
    author = "Zhang, Yi  and
      Ives, Zachary  and
      Roth, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1040",
    doi = "10.18653/v1/P19-1040",
    pages = "413--423",
    abstract = "The information revolution brought with it information pollution. Information retrieval and extraction help us cope with abundant information from diverse sources. But some sources are of anonymous authorship, and some are of uncertain accuracy, so how can we determine what we should actually believe? Not all information sources are equally trustworthy, and simply accepting the majority view is often wrong. This paper develops a general framework for estimating the trustworthiness of information sources in an environment where multiple sources provide claims and supporting evidence, and each claim can potentially be produced by multiple sources. We consider two settings: one in which information sources directly assert claims, and a more realistic and challenging one, in which claims are inferred from evidence provided by sources, via (possibly noisy) NLP techniques. Our key contribution is to develop a family of probabilistic models that jointly estimate the trustworthiness of sources, and the credibility of claims they assert. This is done while accounting for the (possibly noisy) NLP needed to infer claims from evidence supplied by sources. We evaluate our framework on several datasets, showing strong results and significant improvement over baselines.",
}
@inproceedings{john-etal-2019-disentangled,
    title = "Disentangled Representation Learning for Non-Parallel Text Style Transfer",
    author = "John, Vineet  and
      Mou, Lili  and
      Bahuleyan, Hareesh  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1041",
    doi = "10.18653/v1/P19-1041",
    pages = "424--434",
    abstract = "This paper tackles the problem of disentangling the latent representations of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for style prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning can be applied to style transfer on non-parallel corpora. We achieve high performance in terms of transfer accuracy, content preservation, and language fluency, in comparison to various previous approaches.",
}
@inproceedings{chollampatt-etal-2019-cross,
    title = "Cross-Sentence Grammatical Error Correction",
    author = "Chollampatt, Shamil  and
      Wang, Weiqi  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1042",
    doi = "10.18653/v1/P19-1042",
    pages = "435--445",
    abstract = "Automatic grammatical error correction (GEC) research has made remarkable progress in the past decade. However, all existing approaches to GEC correct errors by considering a single sentence alone and ignoring crucial cross-sentence context. Some errors can only be corrected reliably using cross-sentence context and models can also benefit from the additional contextual information in correcting other errors. In this paper, we address this serious limitation of existing approaches and improve strong neural encoder-decoder models by appropriately modeling wider contexts. We employ an auxiliary encoder that encodes previous sentences and incorporate the encoding in the decoder via attention and gating mechanisms. Our approach results in statistically significant improvements in overall GEC performance over strong baselines across multiple test sets. Analysis of our cross-sentence GEC model on a synthetic dataset shows high performance in verb tense corrections that require cross-sentence context.",
}
@inproceedings{zhang-tetreault-2019-email,
    title = "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation",
    author = "Zhang, Rui  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1043",
    doi = "10.18653/v1/P19-1043",
    pages = "446--456",
    abstract = "Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email{'}s content. In this paper, we propose and study the task of \textit{email subject line generation}: automatically generating an email subject line from the email body. We create the first dataset for this task and find that email subject line generation favor extremely abstractive summary which differentiates it from news headline generation or news single document summarization. We then develop a novel deep learning method and compare it to several baselines as well as recent state-of-the-art text summarization systems. We also investigate the efficacy of several automatic metrics based on correlations with human judgments and propose a new automatic evaluation metric. Our system outperforms competitive baselines given both automatic and human evaluations. To our knowledge, this is the first work to tackle the problem of effective email subject line generation.",
}
@inproceedings{dubossarsky-etal-2019-time,
    title = "Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change",
    author = "Dubossarsky, Haim  and
      Hengchen, Simon  and
      Tahmasebi, Nina  and
      Schlechtweg, Dominik",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1044",
    doi = "10.18653/v1/P19-1044",
    pages = "457--470",
    abstract = "State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.",
}
@inproceedings{zhu-etal-2019-adversarial,
    title = "Adversarial Attention Modeling for Multi-dimensional Emotion Regression",
    author = "Zhu, Suyang  and
      Li, Shoushan  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1045",
    doi = "10.18653/v1/P19-1045",
    pages = "471--480",
    abstract = "In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is trained to weight the words in an input sequence. Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator. In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions. Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader{'}s and Writer{'}s multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.",
}
@inproceedings{mai-etal-2019-divide,
    title = "Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing",
    author = "Mai, Sijie  and
      Hu, Haifeng  and
      Xing, Songlong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1046",
    doi = "10.18653/v1/P19-1046",
    pages = "481--492",
    abstract = "We propose a general strategy named {`}divide, conquer and combine{'} for multimodal fusion. Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings. In the {`}divide{'} and {`}conquer{'} stages, we conduct local fusion by exploring the interaction of a portion of the aligned feature vectors across various modalities lying within a sliding window, which ensures that each part of multimodal embeddings are explored sufficiently. On its basis, global fusion is conducted in the {`}combine{'} stage to explore the interconnection across local interactions, via an Attentive Bi-directional Skip-connected LSTM that directly connects distant local interactions and integrates two levels of attention mechanism. In this way, local interactions can exchange information sufficiently and thus obtain an overall view of multimodal information. Our method achieves state-of-the-art performance on multimodal affective computing with higher efficiency.",
}
@inproceedings{keith-stent-2019-modeling,
    title = "Modeling Financial Analysts{'} Decision Making via the Pragmatics and Semantics of Earnings Calls",
    author = "Keith, Katherine  and
      Stent, Amanda",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1047",
    doi = "10.18653/v1/P19-1047",
    pages = "493--503",
    abstract = "Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity re- search reports to help investors make deci- sions. In this paper, we examine analysts{'} decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts{'} questions which we correlate with analysts{'} pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts{'} post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts{'} decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.",
}
@inproceedings{he-etal-2019-interactive,
    title = "An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis",
    author = "He, Ruidan  and
      Lee, Wee Sun  and
      Ng, Hwee Tou  and
      Dahlmeier, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1048",
    doi = "10.18653/v1/P19-1048",
    pages = "504--515",
    abstract = "Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus. In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level. Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.",
}
@inproceedings{gemechu-reed-2019-decompositional,
    title = "Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction",
    author = "Gemechu, Debela  and
      Reed, Chris",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1049",
    doi = "10.18653/v1/P19-1049",
    pages = "516--526",
    abstract = "This work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure. The entities addressed by a proposition are target concepts and the features selected to make a point about the target concepts are aspects. A line of reasoning is followed by providing evidence for the points made about the target concepts via aspects. Opinions on target concepts and opinions on aspects are used to support or attack the ideas expressed by target concepts and aspects. The relations between aspects, target concepts, opinions on target concepts and aspects are used to infer the argument relations. Propositions are connected iteratively to form a graph structure. The approach is generic in that it is not tuned for a specific corpus and evaluated on three different corpora from the literature: AAEC, AMT, US2016G1tv and achieved an F score of 0.79, 0.77 and 0.64, respectively.",
}
@inproceedings{poria-etal-2019-meld,
    title = "{MELD}: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations",
    author = "Poria, Soujanya  and
      Hazarika, Devamanyu  and
      Majumder, Navonil  and
      Naik, Gautam  and
      Cambria, Erik  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1050",
    doi = "10.18653/v1/P19-1050",
    pages = "527--536",
    abstract = "Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github.io.",
}
@inproceedings{hu-etal-2019-open,
    title = "Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification",
    author = "Hu, Minghao  and
      Peng, Yuxing  and
      Huang, Zhen  and
      Li, Dongsheng  and
      Lv, Yiwei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1051",
    doi = "10.18653/v1/P19-1051",
    pages = "537--546",
    abstract = "Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models.",
}
@inproceedings{chen-qian-2019-transfer,
    title = "Transfer Capsule Network for Aspect Level Sentiment Classification",
    author = "Chen, Zhuang  and
      Qian, Tieyun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1052",
    doi = "10.18653/v1/P19-1052",
    pages = "547--556",
    abstract = "Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.",
}
@inproceedings{tang-etal-2019-progressive,
    title = "Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis",
    author = "Tang, Jialong  and
      Lu, Ziyao  and
      Su, Jinsong  and
      Ge, Yubin  and
      Song, Linfeng  and
      Sun, Le  and
      Luo, Jiebo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1053",
    doi = "10.18653/v1/P19-1053",
    pages = "557--566",
    abstract = "In aspect-level sentiment classification (ASC), it is prevalent to equip dominant neural models with attention mechanisms, for the sake of acquiring the importance of each context word on the given aspect. However, such a mechanism tends to excessively focus on a few frequent words with sentiment polarities, while ignoring infrequent ones. In this paper, we propose a progressive self-supervised attention learning approach for neural ASC models, which automatically mines useful attention supervision information from a training corpus to refine attention mechanisms. Specifically, we iteratively conduct sentiment predictions on all training instances. Particularly, at each iteration, the context word with the maximum attention weight is extracted as the one with active/misleading influence on the correct/incorrect prediction of every instance, and then the word itself is masked for subsequent iterations. Finally, we augment the conventional training objective with a regularization term, which enables ASC models to continue equally focusing on the extracted active context words while decreasing weights of those misleading ones. Experimental results on multiple datasets show that our proposed approach yields better attention mechanisms, leading to substantial improvements over the two state-of-the-art neural ASC models. Source code and trained models are available at https://github.com/DeepLearnXMU/PSSAttention.",
}
@inproceedings{reimers-etal-2019-classification,
    title = "Classification and Clustering of Arguments with Contextualized Word Embeddings",
    author = "Reimers, Nils  and
      Schiller, Benjamin  and
      Beck, Tilman  and
      Daxenberger, Johannes  and
      Stab, Christian  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1054",
    doi = "10.18653/v1/P19-1054",
    pages = "567--578",
    abstract = "We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus.",
}
@inproceedings{zhang-goldwasser-2019-sentiment,
    title = "Sentiment Tagging with Partial Labels using Modular Architectures",
    author = "Zhang, Xiao  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1055",
    doi = "10.18653/v1/P19-1055",
    pages = "579--590",
    abstract = "Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label. In this paper we focus on a popular class of learning problems, sequence prediction applied to several sentiment analysis tasks, and suggest a modular learning approach in which different sub-tasks are learned using separate functional modules, combined to perform the final task while sharing information. Our experiments show this approach helps constrain the learning process and can alleviate some of the supervision efforts.",
}
@inproceedings{luo-etal-2019-doer,
    title = "{DOER}: Dual Cross-Shared {RNN} for Aspect Term-Polarity Co-Extraction",
    author = "Luo, Huaishao  and
      Li, Tianrui  and
      Liu, Bing  and
      Zhang, Junbo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1056",
    doi = "10.18653/v1/P19-1056",
    pages = "591--601",
    abstract = "This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction. The former task is to extract aspects of a product or service from an opinion document, and the latter is to identify the polarity expressed in the document about these extracted aspects. Most existing algorithms address them as two separate tasks and solve them one by one, or only perform one task, which can be complicated for real applications. In this paper, we treat these two tasks as two sequence labeling problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate all aspect term-polarity pairs of the input sentence simultaneously. Specifically, DOER involves a dual recurrent neural network to extract the respective representation of each task, and a cross-shared unit to consider the relationship between them. Experimental results demonstrate that the proposed framework outperforms state-of-the-art baselines on three benchmark datasets.",
}
@inproceedings{durmus-cardie-2019-corpus,
    title = "A Corpus for Modeling User and Language Effects in Argumentation on Online Debating",
    author = "Durmus, Esin  and
      Cardie, Claire",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1057",
    doi = "10.18653/v1/P19-1057",
    pages = "602--607",
    abstract = "Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text. They have been much less successful in fostering studies of the effect of {``}user{''} traits {---} characteristics and beliefs of the participants {---} on the debate/argument outcome as this type of user information is generally not available. This paper presents a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.",
}
@inproceedings{xu-etal-2019-topic,
    title = "Topic Tensor Network for Implicit Discourse Relation Recognition in {C}hinese",
    author = "Xu, Sheng  and
      Li, Peifeng  and
      Kong, Fang  and
      Zhu, Qiaoming  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1058",
    doi = "10.18653/v1/P19-1058",
    pages = "608--618",
    abstract = "In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.",
}
@inproceedings{mcdowell-goodman-2019-learning,
    title = "Learning from Omission",
    author = "McDowell, Bill  and
      Goodman, Noah",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1059",
    doi = "10.18653/v1/P19-1059",
    pages = "619--628",
    abstract = "Pragmatic reasoning allows humans to go beyond the literal meaning when interpret- ing language in context. Previous work has shown that such reasoning can improve the performance of already-trained language understanding systems. Here, we explore whether pragmatic reasoning during training can improve the quality of learned meanings. Our experiments on reference game data show that end-to-end pragmatic training produces more accurate utterance interpretation models, especially when data is sparse and language is complex.",
}
@inproceedings{farag-yannakoudakis-2019-multi,
    title = "Multi-Task Learning for Coherence Modeling",
    author = "Farag, Youmna  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1060",
    doi = "10.18653/v1/P19-1060",
    pages = "629--639",
    abstract = "We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a hierarchical neural network trained in a multi-task fashion that learns to predict a document-level coherence score (at the network{'}s top layers) along with word-level grammatical roles (at the bottom layers), taking advantage of inductive transfer between the two tasks. We assess the extent to which our framework generalizes to different domains and prediction tasks, and demonstrate its effectiveness not only on standard binary evaluation coherence tasks, but also on real-world tasks involving the prediction of varying degrees of coherence, achieving a new state of the art.",
}
@inproceedings{badene-etal-2019-data,
    title = "Data Programming for Learning Discourse Structure",
    author = "Badene, Sonia  and
      Thompson, Kate  and
      Lorr{\'e}, Jean-Pierre  and
      Asher, Nicholas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1061",
    doi = "10.18653/v1/P19-1061",
    pages = "640--645",
    abstract = "This paper investigates the advantages and limits of data programming for the task of learning discourse structure. The data programming paradigm implemented in the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the {``}generative step{''} into probability distributions of the class labels given the training candidates. These results are later generalized using a discriminative model. Snorkel{'}s attractive promise to create a large amount of annotated data from a smaller set of training data by unifying the output of a set of heuristics has yet to be used for computationally difficult tasks, such as that of discourse attachment, in which one must decide where a given discourse unit attaches to other units in a text in order to form a coherent discourse structure. Although approaching this problem using Snorkel requires significant modifications to the structure of the heuristics, we show that weak supervision methods can be more than competitive with classical supervised learning approaches to the attachment problem.",
}
@inproceedings{ferracane-etal-2019-evaluating,
    title = "Evaluating Discourse in Structured Text Representations",
    author = "Ferracane, Elisa  and
      Durrett, Greg  and
      Li, Junyi Jessy  and
      Erk, Katrin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1062",
    doi = "10.18653/v1/P19-1062",
    pages = "646--653",
    abstract = "Discourse structure is integral to understanding a text and is helpful in many NLP tasks. Learning latent representations of discourse is an attractive alternative to acquiring expensive labeled discourse data. Liu and Lapata (2018) propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. We examine this model in detail, and evaluate on additional discourse-relevant tasks and datasets, in order to assess whether the structured attention improves performance on the end task and whether it captures a text{'}s discourse structure. We find the learned latent trees have little to no structure and instead focus on lexical cues; even after obtaining more structured trees with proposed model modifications, the trees are still far from capturing discourse structure when compared to discourse dependency trees from an existing discourse parser. Finally, ablation studies show the structured attention provides little benefit, sometimes even hurting performance.",
}
@inproceedings{zarriess-schlangen-2019-know,
    title = "Know What You Don{'}t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1063",
    doi = "10.18653/v1/P19-1063",
    pages = "654--659",
    abstract = "Zero-shot learning in Language {\&} Vision is the task of correctly labelling (or naming) objects of novel categories. Another strand of work in L{\&}V aims at pragmatically informative rather than {``}correct{''} object descriptions, e.g. in reference games. We combine these lines of research and model zero-shot reference games, where a speaker needs to successfully refer to a novel object in an image. Inspired by models of {``}rational speech acts{''}, we extend a neural generator to become a pragmatic speaker reasoning about uncertain object categories. As a result of this reasoning, the generator produces fewer nouns and names of distractor categories as compared to a literal speaker. We show that this conversational strategy for dealing with novel objects often improves communicative success, in terms of resolution accuracy of an automatic listener.",
}
@inproceedings{fei-etal-2019-end,
    title = "End-to-end Deep Reinforcement Learning Based Coreference Resolution",
    author = "Fei, Hongliang  and
      Li, Xu  and
      Li, Dingcheng  and
      Li, Ping",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1064",
    doi = "10.18653/v1/P19-1064",
    pages = "660--665",
    abstract = "Recent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence of local decisions. In this paper, we introduce an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. Specifically, we modify the state-of-the-art higher-order mention ranking approach in Lee et al. (2018) to a reinforced policy gradient model by incorporating the reward associated with a sequence of coreference linking actions. Furthermore, we introduce maximum entropy regularization for adequate exploration to prevent the model from prematurely converging to a bad local optimum. Our proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.",
}
@inproceedings{ma-etal-2019-implicit,
    title = "Implicit Discourse Relation Identification for Open-domain Dialogues",
    author = "Ma, Mingyu Derek  and
      Bowden, Kevin  and
      Wu, Jiaqi  and
      Cui, Wen  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1065",
    doi = "10.18653/v1/P19-1065",
    pages = "666--672",
    abstract = "Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently non-dialogic, i.e., news and journals. This data however is not suitable to handle the nuances of informal dialogue nor is it capable of navigating the plethora of valid topics present in open-domain dialogue. In this paper, we designed a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems. We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse. Moreover, we have taken the first steps to leverage the dialogue features unique to our task to further improve the identification of such relations by performing feature ablation and incorporating dialogue features to enhance the state-of-the-art model.",
}
@inproceedings{kantor-globerson-2019-coreference,
    title = "Coreference Resolution with Entity Equalization",
    author = "Kantor, Ben  and
      Globerson, Amir",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1066",
    doi = "10.18653/v1/P19-1066",
    pages = "673--677",
    abstract = "A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an {``}Entity Equalization{''} mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6{\%}.",
}
@inproceedings{xu-etal-2019-cross,
    title = "A Cross-Domain Transferable Neural Coherence Model",
    author = "Xu, Peng  and
      Saghir, Hamidreza  and
      Kang, Jin Sung  and
      Long, Teng  and
      Bose, Avishek Joey  and
      Cao, Yanshuai  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1067",
    doi = "10.18653/v1/P19-1067",
    pages = "678--687",
    abstract = "Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.",
}
@inproceedings{butnaru-ionescu-2019-moroco,
    title = "{MOROCO}: The {M}oldavian and {R}omanian Dialectal Corpus",
    author = "Butnaru, Andrei  and
      Ionescu, Radu Tudor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1068",
    doi = "10.18653/v1/P19-1068",
    pages = "688--698",
    abstract = "In this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at https://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech. The data set is divided into 21719 samples for training, 5921 samples for validation and another 5924 samples for testing. For each sample, we provide corresponding dialectal and category labels. This allows us to perform empirical studies on several classification tasks such as (i) binary discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect multi-class categorization by topic and (iii) cross-dialect multi-class categorization by topic. We perform experiments using a shallow approach based on string kernels, as well as a novel deep approach based on character-level convolutional neural networks containing Squeeze-and-Excitation blocks. We also present and analyze the most discriminative features of our best performing model, before and after named entity removal.",
}
@inproceedings{scarlini-etal-2019-just,
    title = "Just {``}{O}ne{S}e{C}{''} for Producing Multilingual Sense-Annotated Data",
    author = "Scarlini, Bianca  and
      Pasini, Tommaso  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1069",
    doi = "10.18653/v1/P19-1069",
    pages = "699--709",
    abstract = "The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages. In this paper we formulate the assumption of One Sense per Wikipedia Category and present OneSeC, a language-independent method for the automatic extraction of hundreds of thousands of sentences in which a target word is tagged with its meaning. Our automatically-generated data consistently lead a supervised WSD model to state-of-the-art performance when compared with other automatic and semi-automatic methods. Moreover, our approach outperforms its competitors on multilingual and domain-specific settings, where it beats the existing state of the art on all languages and most domains. All the training data are available for research purposes at http://trainomatic.org/onesec.",
}
@inproceedings{glavas-etal-2019-properly,
    title = "How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions",
    author = "Glava{\v{s}}, Goran  and
      Litschko, Robert  and
      Ruder, Sebastian  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1070",
    doi = "10.18653/v1/P19-1070",
    pages = "710--721",
    abstract = "Cross-lingual word embeddings (CLEs) facilitate cross-lingual transfer of NLP models. Despite their ubiquitous downstream usage, increasingly popular projection-based CLE models are almost exclusively evaluated on bilingual lexicon induction (BLI). Even the BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models. In this work, we take the first step towards a comprehensive evaluation of CLE models: we thoroughly evaluate both supervised and unsupervised CLE models, for a large number of language pairs, on BLI and three downstream tasks, providing new insights concerning the ability of cutting-edge CLE models to support cross-lingual NLP. We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI may hurt downstream performance. We indicate the most robust supervised and unsupervised CLE models and emphasize the need to reassess simple baselines, which still display competitive performance across the board. We hope our work catalyzes further research on CLE evaluation and model analysis.",
}
@inproceedings{zhang-etal-2019-sp,
    title = "{SP}-10{K}: A Large-scale Evaluation Set for Selectional Preference Acquisition",
    author = "Zhang, Hongming  and
      Ding, Hantian  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1071",
    doi = "10.18653/v1/P19-1071",
    pages = "722--731",
    abstract = "Selectional Preference (SP) is a commonly observed language phenomenon and proved to be useful in many natural language processing tasks. To provide a better evaluation method for SP models, we introduce SP-10K, a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. Three representative SP acquisition methods based on pseudo-disambiguation are evaluated with SP-10K. To demonstrate the importance of our dataset, we investigate the relationship between SP-10K and the commonsense knowledge in ConceptNet5 and show the potential of using SP to represent the commonsense knowledge. We also use the Winograd Schema Challenge to prove that the proposed new SP relations are essential for the hard pronoun coreference resolution problem.",
}
@inproceedings{schlechtweg-etal-2019-wind,
    title = "A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains",
    author = {Schlechtweg, Dominik  and
      H{\"a}tty, Anna  and
      Del Tredici, Marco  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1072",
    doi = "10.18653/v1/P19-1072",
    pages = "732--746",
    abstract = "We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains. Our work addresses the superficialness and lack of comparison in assessing models of diachronic lexical change, by bringing together and extending benchmark models on a common state-of-the-art evaluation task. In addition, we demonstrate that the same evaluation task and modelling approaches can successfully be utilised for the synchronic detection of domain-specific sense divergences in the field of term extraction.",
}
@inproceedings{wu-etal-2019-errudite,
    title = "{E}rrudite: Scalable, Reproducible, and Testable Error Analysis",
    author = "Wu, Tongshuang  and
      Ribeiro, Marco Tulio  and
      Heer, Jeffrey  and
      Weld, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1073",
    doi = "10.18653/v1/P19-1073",
    pages = "747--763",
    abstract = "Though error analysis is crucial to understanding and improving NLP models, the common practice of manual, subjective categorization of a small sample of errors can yield biased and incomplete conclusions. This paper codifies model and task agnostic principles for informative error analysis, and presents Errudite, an interactive tool for better supporting this process. First, error groups should be precisely defined for reproducibility; Errudite supports this with an expressive domain-specific language. Second, to avoid spurious conclusions, a large set of instances should be analyzed, including both positive and negative examples; Errudite enables systematic grouping of relevant instances with filtering queries. Third, hypotheses about the cause of errors should be explicitly tested; Errudite supports this via automated counterfactual rewriting. We validate our approach with a user study, finding that Errudite (1) enables users to perform high quality and reproducible error analyses with less effort, (2) reveals substantial ambiguities in prior published error analyses practices, and (3) enhances the error analysis experience by allowing users to test and revise prior beliefs.",
}
@inproceedings{yao-etal-2019-docred,
    title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
    author = "Yao, Yuan  and
      Ye, Deming  and
      Li, Peng  and
      Han, Xu  and
      Lin, Yankai  and
      Liu, Zhenghao  and
      Liu, Zhiyuan  and
      Huang, Lixin  and
      Zhou, Jie  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1074",
    doi = "10.18653/v1/P19-1074",
    pages = "764--777",
    abstract = "Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: (1) DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text; (2) DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document; (3) along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios. In order to verify the challenges of document-level RE, we implement recent state-of-the-art methods for RE and conduct a thorough evaluation of these methods on DocRED. Empirical results show that DocRED is challenging for existing RE methods, which indicates that document-level RE remains an open problem and requires further efforts. Based on the detailed analysis on the experiments, we discuss multiple promising directions for future research. We make DocRED and the code for our baselines publicly available at https://github.com/thunlp/DocRED.",
}
@inproceedings{zheng-etal-2019-chid,
    title = "{C}h{ID}: A Large-scale {C}hinese {ID}iom Dataset for Cloze Test",
    author = "Zheng, Chujie  and
      Huang, Minlie  and
      Sun, Aixin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1075",
    doi = "10.18653/v1/P19-1075",
    pages = "778--787",
    abstract = "Cloze-style reading comprehension in Chinese is still limited due to the lack of various corpora. In this paper we propose a large-scale Chinese cloze test dataset ChID, which studies the comprehension of idiom, a unique language phenomenon in Chinese. In this corpus, the idioms in a passage are replaced by blank symbols and the correct answer needs to be chosen from well-designed candidate idioms. We carefully study how the design of candidate idioms and the representation of idioms affect the performance of state-of-the-art models. Results show that the machine accuracy is substantially worse than that of human, indicating a large space for further research.",
}
@inproceedings{lund-etal-2019-automatic,
    title = "Automatic Evaluation of Local Topic Quality",
    author = "Lund, Jeffrey  and
      Armstrong, Piper  and
      Fearn, Wilson  and
      Cowley, Stephen  and
      Byun, Courtni  and
      Boyd-Graber, Jordan  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1076",
    doi = "10.18653/v1/P19-1076",
    pages = "788--796",
    abstract = "Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) topic assignments. Token-level assignments are important for downstream tasks such as classification. Even recent models, which aim to improve the quality of these token-level topic assignments, have been evaluated only with respect to global metrics. We propose a task designed to elicit human judgments of token-level topic assignments. We use a variety of topic model types and parameters and discover that global metrics agree poorly with human assignments. Since human evaluation is expensive we propose a variety of automated metrics to evaluate topic models at a local level. Finally, we correlate our proposed metrics with human judgments from the task on several datasets. We show that an evaluation based on the percent of topic switches correlates most strongly with human judgment of local topic quality. We suggest that this new metric, which we call consistency, be adopted alongside global metrics such as topic coherence when evaluating new topic models.",
}
@inproceedings{madge-etal-2019-crowdsourcing,
    title = "Crowdsourcing and Aggregating Nested Markable Annotations",
    author = "Madge, Chris  and
      Yu, Juntao  and
      Chamberlain, Jon  and
      Kruschwitz, Udo  and
      Paun, Silviu  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1077",
    doi = "10.18653/v1/P19-1077",
    pages = "797--807",
    abstract = "One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand{--}which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task and language-independent and are thus applicable to a variety of other annotation scenarios.",
}
@inproceedings{wu-etal-2019-transferable,
    title = "Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems",
    author = "Wu, Chien-Sheng  and
      Madotto, Andrea  and
      Hosseini-Asl, Ehsan  and
      Xiong, Caiming  and
      Socher, Richard  and
      Fung, Pascale",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1078",
    doi = "10.18653/v1/P19-1078",
    pages = "808--819",
    abstract = "Over-dependence on domain ontology and lack of sharing knowledge across domains are two practical and yet less studied problems of dialogue state tracking. Existing approaches generally fall short when tracking unknown slot values during inference and often have difficulties in adapting to new domains. In this paper, we propose a Transferable Dialogue State Generator (TRADE) that generates dialogue states from utterances using copy mechanism, facilitating transfer when predicting (domain, slot, value) triplets not encountered during training. Our model is composed of an utterance encoder, a slot gate, and a state generator, which are shared across domains. Empirical results demonstrate that TRADE achieves state-of-the-art 48.62{\%} joint goal accuracy for the five domains of MultiWOZ, a human-human dialogue dataset. In addition, we show the transferring ability by simulating zero-shot and few-shot dialogue state tracking for unseen domains. TRADE achieves 60.58{\%} joint goal accuracy in one of the zero-shot domains, and is able to adapt to few-shot cases without forgetting already trained domains.",
}
@inproceedings{pentyala-etal-2019-multi,
    title = "Multi-Task Networks with Universe, Group, and Task Feature Learning",
    author = "Pentyala, Shiva  and
      Liu, Mengwen  and
      Dreyer, Markus",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1079",
    doi = "10.18653/v1/P19-1079",
    pages = "820--830",
    abstract = "We present methods for multi-task learning that take advantage of natural groupings of related tasks. Task groups may be defined along known properties of the tasks, such as task domain or language. Such task groups represent supervised information at the inter-task level and can be encoded into the model. We investigate two variants of neural network architectures that accomplish this, learning different feature spaces at the levels of individual tasks, task groups, as well as the universe of all tasks: (1) parallel architectures encode each input simultaneously into feature spaces at different levels; (2) serial architectures encode each input successively into feature spaces at different levels in the task hierarchy. We demonstrate the methods on natural language understanding (NLU) tasks, where a grouping of tasks into different task domains leads to improved performance on ATIS, Snips, and a large in-house dataset.",
}
@inproceedings{balakrishnan-etal-2019-constrained,
    title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
    author = "Balakrishnan, Anusha  and
      Rao, Jinfeng  and
      Upasani, Kartikeya  and
      White, Michael  and
      Subba, Rajen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1080",
    doi = "10.18653/v1/P19-1080",
    pages = "831--844",
    abstract = "Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems. Avenues like the E2E NLG Challenge have encouraged the development of neural approaches, particularly sequence-to-sequence (Seq2Seq) models for this problem. The semantic representations used, however, are often underspecified, which places a higher burden on the generation model for sentence planning, and also limits the extent to which generated responses can be controlled in a live system. In this paper, we (1) propose using tree-structured semantic representations, like those used in traditional rule-based NLG systems, for better discourse-level structuring and sentence-level planning; (2) introduce a challenging dataset using this representation for the weather domain; (3) introduce a constrained decoding approach for Seq2Seq models that leverages this representation to improve semantic correctness; and (4) demonstrate promising results on our dataset and the E2E dataset.",
}
@inproceedings{moon-etal-2019-opendialkg,
    title = "{O}pen{D}ial{KG}: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs",
    author = "Moon, Seungwhan  and
      Shah, Pararth  and
      Kumar, Anuj  and
      Subba, Rajen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1081",
    doi = "10.18653/v1/P19-1081",
    pages = "845--854",
    abstract = "We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For this study, we collect a new Open-ended Dialog {\textless}-{\textgreater} KG parallel corpus called OpenDialKG, where each utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts. We then propose the DialKG Walker model that learns the symbolic transitions of dialog contexts as structured traversals over KG, and predicts natural entities to introduce given previous dialog contexts via a novel domain-agnostic, attention-based graph path decoder. Automatic and human evaluations show that our model can retrieve more natural and human-like responses than the state-of-the-art baselines or rule-based models, in both in-domain and cross-domain tasks. The proposed model also generates a KG walk path for each entity retrieved, providing a natural way to explain conversational reasoning.",
}
@inproceedings{guo-etal-2019-coupling,
    title = "Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing",
    author = "Guo, Daya  and
      Tang, Duyu  and
      Duan, Nan  and
      Zhou, Ming  and
      Yin, Jian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1082",
    doi = "10.18653/v1/P19-1082",
    pages = "855--866",
    abstract = "In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned on the class environment. Our approach naturally combines a retrieval model and a meta-learner, where the former learns to find similar datapoints from the training data, and the latter considers retrieved datapoints as a pseudo task for fast adaptation. Specifically, our retriever is a context-aware encoder-decoder model with a latent variable which takes context environment into consideration, and our meta-learner learns to utilize retrieved datapoints in a model-agnostic meta-learning paradigm for fast adaptation. We conduct experiments on CONCODE and CSQA datasets, where the context refers to class environment in JAVA codes and conversational history, respectively. We use sequence-to-action model as the base semantic parser, which performs the state-of-the-art accuracy on both datasets. Results show that both the context-aware retriever and the meta-learning strategy improve accuracy, and our approach performs better than retrieve-and-edit baselines.",
}
@inproceedings{zhang-etal-2019-knowledge,
    title = "Knowledge-aware Pronoun Coreference Resolution",
    author = "Zhang, Hongming  and
      Song, Yan  and
      Song, Yangqiu  and
      Yu, Dong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1083",
    doi = "10.18653/v1/P19-1083",
    pages = "867--876",
    abstract = "Resolving pronoun coreference requires knowledge support, especially for particular domains (e.g., medicine). In this paper, we explore how to leverage different types of knowledge to better resolve pronoun coreference with a neural model. To ensure the generalization ability of our model, we directly incorporate knowledge in the format of triplets, which is the most common format of modern knowledge graphs, instead of encoding it with features or rules as that in conventional approaches. Moreover, since not all knowledge is helpful in certain contexts, to selectively use them, we propose a knowledge attention module, which learns to select and use informative knowledge based on contexts, to enhance our model. Experimental results on two datasets from different domains prove the validity and effectiveness of our model, where it outperforms state-of-the-art baselines by a large margin. Moreover, since our model learns to use external knowledge rather than only fitting the training data, it also demonstrates superior performance to baselines in the cross-domain setting.",
}
@inproceedings{belinkov-etal-2019-dont,
    title = "Don{'}t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference",
    author = "Belinkov, Yonatan  and
      Poliak, Adam  and
      Shieber, Stuart  and
      Van Durme, Benjamin  and
      Rush, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1084",
    doi = "10.18653/v1/P19-1084",
    pages = "877--891",
    abstract = "Natural Language Inference (NLI) datasets often contain hypothesis-only biases{---}artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets.",
}
@inproceedings{zhou-etal-2019-gear,
    title = "{GEAR}: Graph-based Evidence Aggregating and Reasoning for Fact Verification",
    author = "Zhou, Jie  and
      Han, Xu  and
      Yang, Cheng  and
      Liu, Zhiyuan  and
      Wang, Lifeng  and
      Li, Changcheng  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1085",
    doi = "10.18653/v1/P19-1085",
    pages = "892--901",
    abstract = "Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract information from evidence without letting evidence communicate with each other, e.g., merely concatenate the evidence for processing. Therefore, these methods are unable to grasp sufficient relational and logical information among the evidence. To alleviate this issue, we propose a graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information. We further employ BERT, an effective pre-trained language representation model, to improve the performance. Experimental results on a large-scale benchmark dataset FEVER have demonstrated that GEAR could leverage multi-evidence information for FV and thus achieves the promising result with a test FEVER score of 67.10{\%}. Our code is available at https://github.com/thunlp/GEAR.",
}
@inproceedings{schmitt-schutze-2019-sherliic,
    title = "{S}her{LI}i{C}: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference",
    author = {Schmitt, Martin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1086",
    doi = "10.18653/v1/P19-1086",
    pages = "902--914",
    abstract = "We present SherLIiC, a testbed for lexical inference in context (LIiC), consisting of 3985 manually annotated inference rule candidates (InfCands), accompanied by (i) {\textasciitilde}960k unlabeled InfCands, and (ii) {\textasciitilde}190k typed textual relations between Freebase entities extracted from the large entity-linked corpus ClueWeb09. Each InfCand consists of one of these relations, expressed as a lemmatized dependency path, and two argument placeholders, each linked to one or more Freebase types. Due to our candidate selection process based on strong distributional evidence, SherLIiC is much harder than existing testbeds because distributional evidence is of little utility in the classification of InfCands. We also show that, due to its construction, many of SherLIiC{'}s correct InfCands are novel and missing from existing rule bases. We evaluate a large number of strong baselines on SherLIiC, ranging from semantic vector space models to state of the art neural models of natural language inference (NLI). We show that SherLIiC poses a tough challenge to existing NLI systems.",
}
@inproceedings{du-etal-2019-extracting,
    title = "Extracting Symptoms and their Status from Clinical Conversations",
    author = "Du, Nan  and
      Chen, Kai  and
      Kannan, Anjuli  and
      Tran, Linh  and
      Chen, Yuhui  and
      Shafran, Izhak",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1087",
    doi = "10.18653/v1/P19-1087",
    pages = "915--925",
    abstract = "This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly available corpus in this privacy-sensitive domain led us to develop our own corpus, consisting of about 3K conversations annotated by professional medical scribes. We propose two novel deep learning approaches to infer the symptom names and their status: (1) a new hierarchical span-attribute tagging (SA-T) model, trained using curriculum learning, and (2) a variant of sequence-to-sequence model which decodes the symptoms and their status from a few speaker turns within a sliding window over the conversation. This task stems from a realistic application of assisting medical providers in capturing symptoms mentioned by patients from their clinical conversations. To reflect this application, we define multiple metrics. From inter-rater agreement, we find that the task is inherently difficult. We conduct comprehensive evaluations on several contrasting conditions and observe that the performance of the models range from an F-score of 0.5 to 0.8 depending on the condition. Our analysis not only reveals the inherent challenges of the task, but also provides useful directions to improve the models.",
}
@inproceedings{perez-rosas-etal-2019-makes,
    title = "What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Wu, Xinyi  and
      Resnicow, Kenneth  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1088",
    doi = "10.18653/v1/P19-1088",
    pages = "926--935",
    abstract = "The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants{'} turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88{\%}.",
}
@inproceedings{zhang-etal-2019-finding,
    title = "Finding Your Voice: The Linguistic Development of Mental Health Counselors",
    author = "Zhang, Justine  and
      Filbin, Robert  and
      Morrison, Christine  and
      Weiser, Jaclyn  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1089",
    doi = "10.18653/v1/P19-1089",
    pages = "936--947",
    abstract = "Mental health counseling is an enterprise with profound societal importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.",
}
@inproceedings{daniel-etal-2019-towards,
    title = "Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting",
    author = "Daniel, Jeanne E.  and
      Brink, Willie  and
      Eloff, Ryan  and
      Copley, Charles",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1090",
    doi = "10.18653/v1/P19-1090",
    pages = "948--953",
    abstract = "We discuss ongoing work into automating a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. Our anonymized dataset consists of short informal questions, often in low-resource languages, with unreliable language labels, spelling errors and code-mixing, as well as template answers with some inconsistencies. We explore cross-lingual word embeddings, and train parametric and non-parametric models on 90K samples for answer selection from a set of 126 templates. Preliminary results indicate that LSTMs trained end-to-end perform best, with a test accuracy of 62.13{\%} and a recall@5 of 89.56{\%}, and demonstrate that we can accelerate response time by several orders of magnitude.",
}
@inproceedings{bhatia-etal-2019-joint,
    title = "Joint Entity Extraction and Assertion Detection for Clinical Text",
    author = "Bhatia, Parminder  and
      Celikkaya, Busra  and
      Khalilia, Mohammed",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1091",
    doi = "10.18653/v1/P19-1091",
    pages = "954--959",
    abstract = "Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for in-formation extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER)and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.",
}
@inproceedings{vilares-gomez-rodriguez-2019-head,
    title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1092",
    doi = "10.18653/v1/P19-1092",
    pages = "960--966",
    abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
}
@inproceedings{gleize-etal-2019-convinced,
    title = "Are You Convinced? Choosing the More Convincing Evidence with a {S}iamese Network",
    author = "Gleize, Martin  and
      Shnarch, Eyal  and
      Choshen, Leshem  and
      Dankin, Lena  and
      Moshkowich, Guy  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1093",
    doi = "10.18653/v1/P19-1093",
    pages = "967--976",
    abstract = "With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.",
}
@inproceedings{bar-haim-etal-2019-surrogacy,
    title = "From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion",
    author = "Bar-Haim, Roy  and
      Krieger, Dalia  and
      Toledo-Ronen, Orith  and
      Edelstein, Lilach  and
      Bilu, Yonatan  and
      Halfon, Alon  and
      Katz, Yoav  and
      Menczel, Amir  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1094",
    doi = "10.18653/v1/P19-1094",
    pages = "977--990",
    abstract = "When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.",
}
@inproceedings{aguilar-etal-2019-multimodal,
    title = "Multimodal and Multi-view Models for Emotion Recognition",
    author = "Aguilar, Gustavo  and
      Rozgic, Viktor  and
      Wang, Weiran  and
      Wang, Chao",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1095",
    doi = "10.18653/v1/P19-1095",
    pages = "991--1002",
    abstract = "Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings where both modalities are available in training and evaluation. However, in practice, this is not always the case; getting ASR output may represent a bottleneck in a deployment pipeline due to computational complexity or privacy-related constraints. To address this challenge, we study the problem of efficiently combining acoustic and lexical modalities during training while still providing a deployable acoustic model that does not require lexical inputs. We first experiment with multimodal models and two attention mechanisms to assess the extent of the benefits that lexical information can provide. Then, we frame the task as a multi-view learning problem to induce semantic information from a multimodal model into our acoustic-only network using a contrastive loss function. Our multimodal model outperforms the previous state of the art on the USC-IEMOCAP dataset reported on lexical and acoustic information. Additionally, our multi-view-trained acoustic network significantly surpasses models that have been exclusively trained with acoustic features.",
}
@inproceedings{xia-ding-2019-emotion,
    title = "Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts",
    author = "Xia, Rui  and
      Ding, Zixiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1096",
    doi = "10.18653/v1/P19-1096",
    pages = "1003--1012",
    abstract = "Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications. However, it suffers from two shortcomings: 1) the emotion must be annotated before cause extraction in ECE, which greatly limits its applications in real-world scenarios; 2) the way to first annotate emotion and then extract the cause ignores the fact that they are mutually indicative. In this work, we propose a new task: emotion-cause pair extraction (ECPE), which aims to extract the potential pairs of emotions and corresponding causes in a document. We propose a 2-step approach to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering. The experimental results on a benchmark emotion cause corpus prove the feasibility of the ECPE task as well as the effectiveness of our approach.",
}
@inproceedings{bilu-etal-2019-argument,
    title = "Argument Invention from First Principles",
    author = "Bilu, Yonatan  and
      Gera, Ariel  and
      Hershcovich, Daniel  and
      Sznajder, Benjamin  and
      Lahav, Dan  and
      Moshkowich, Guy  and
      Malet, Anael  and
      Gavron, Assaf  and
      Slonim, Noam",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1097",
    doi = "10.18653/v1/P19-1097",
    pages = "1013--1026",
    abstract = "Competitive debaters often find themselves facing a challenging task {--} how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet? What they often do is rely on {''}first principles{''}, commonplace arguments which are relevant to many topics, and which they have refined in past debates. In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic. As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP. The main goal of this work is to show that it is possible to define such a taxonomy. While the taxonomy suggested here should be thought of as a {''}first attempt{''} it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.",
}
@inproceedings{cho-etal-2019-improving,
    title = "Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization",
    author = "Cho, Sangwoo  and
      Lebanoff, Logan  and
      Foroosh, Hassan  and
      Liu, Fei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1098",
    doi = "10.18653/v1/P19-1098",
    pages = "1027--1038",
    abstract = "The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data. These obstacles prevent encoder-decoder models from being used directly, but optimization-based methods such as determinantal point processes (DPPs) are known to handle them well. In this paper we seek to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks. The approach measures redundancy between a pair of sentences based on surface form and semantic information. We show that our DPP system with improved similarity measure performs competitively, outperforming strong summarization baselines on benchmark datasets. Our findings are particularly meaningful for summarizing documents created by multiple authors containing redundant yet lexically diverse expressions.",
}
@inproceedings{makino-etal-2019-global,
    title = "Global Optimization under Length Constraint for Neural Text Summarization",
    author = "Makino, Takuya  and
      Iwakura, Tomoya  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1099",
    doi = "10.18653/v1/P19-1099",
    pages = "1039--1048",
    abstract = "We propose a global optimization method under length constraint (GOLC) for neural text summarization models. GOLC increases the probabilities of generating summaries that have high evaluation scores, ROUGE in this paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a Japanese single document summarization data set of The Mainichi Shimbun Newspapers. The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70{\%} overlength summaries on CNN/Daily and 7.8{\%} on long summary of Mainichi, compared to the approximately 20{\%} to 50{\%} on CNN/Daily Mail and 10{\%} to 30{\%} on Mainichi with the other optimization methods. We also demonstrate the importance of the generation of in-length summaries for post-editing with the dataset Mainich that is created with strict length constraints. The ex- perimental results show approximately 30{\%} to 40{\%} improved post-editing time by use of in-length summaries.",
}
@inproceedings{zhong-etal-2019-searching,
    title = "Searching for Effective Neural Extractive Summarization: What Works and What{'}s Next",
    author = "Zhong, Ming  and
      Liu, Pengfei  and
      Wang, Danqing  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1100",
    doi = "10.18653/v1/P19-1100",
    pages = "1049--1058",
    abstract = "The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or how they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Besides, we find an effective way to improve the current framework and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analysis. Hopefully, our work could provide more hints for future research on extractive summarization.",
}
@inproceedings{peyrard-2019-simple,
    title = "A Simple Theoretical Model of Importance for Summarization",
    author = "Peyrard, Maxime",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1101",
    doi = "10.18653/v1/P19-1101",
    pages = "1059--1073",
    abstract = "Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notion of information Importance remaining latent. We argue that establishing theoretical models of Importance will advance our understanding of the task and help to further improve summarization systems. To this end, we propose simple but rigorous definitions of several concepts that were previously used only intuitively in summarization: Redundancy, Relevance, and Informativeness. Importance arises as a single quantity naturally unifying these concepts. Additionally, we provide intuitions to interpret the proposed quantities and experiments to demonstrate the potential of the framework to inform and guide subsequent works.",
}
@inproceedings{fabbri-etal-2019-multi,
    title = "Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model",
    author = "Fabbri, Alexander  and
      Li, Irene  and
      She, Tianwei  and
      Li, Suyi  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1102",
    doi = "10.18653/v1/P19-1102",
    pages = "1074--1084",
    abstract = "Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. In this paper, we introduce Multi-News, the first large-scale MDS news dataset. Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. We benchmark several methods on Multi-News and hope that this work will promote advances in summarization in the multi-document setting.",
}
@inproceedings{ren-etal-2019-generating,
    title = "Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency",
    author = "Ren, Shuhuai  and
      Deng, Yihe  and
      He, Kun  and
      Che, Wanxiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1103",
    doi = "10.18653/v1/P19-1103",
    pages = "1085--1097",
    abstract = "We address the problem of adversarial attacks on text classification, which is rarely studied comparing to attacks on image classification. The challenge of this task is to generate adversarial examples that maintain lexical correctness, grammatical correctness and semantic similarity. Based on the synonyms substitution strategy, we introduce a new word replacement order determined by both the word saliency and the classification probability, and propose a greedy algorithm called probability weighted word saliency (PWWS) for text adversarial attack. Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate. A human evaluation study shows that our generated adversarial examples maintain the semantic similarity well and are hard for humans to perceive. Performing adversarial training using our perturbed datasets improves the robustness of the models. At last, our method also exhibits a good transferability on the generated adversarial examples.",
}
@inproceedings{bevendorff-etal-2019-heuristic,
    title = "Heuristic Authorship Obfuscation",
    author = "Bevendorff, Janek  and
      Potthast, Martin  and
      Hagen, Matthias  and
      Stein, Benno",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1104",
    doi = "10.18653/v1/P19-1104",
    pages = "1098--1108",
    abstract = "Authorship verification is the task of determining whether two texts were written by the same author. We deal with the adversary task, called authorship obfuscation: preventing verification by altering a to-be-obfuscated text. Our new obfuscation approach (1) models writing style difference as the Jensen-Shannon distance between the character n-gram distributions of texts, and (2) manipulates an author{'}s subconsciously encoded writing style in a sophisticated manner using heuristic search. To obfuscate, we analyze the huge space of textual variants for a paraphrased version of the to-be-obfuscated text that has a sufficient Jensen-Shannon distance at minimal costs in terms of text quality. We analyze, quantify, and illustrate the rationale of this approach, define paraphrasing operators, derive obfuscation thresholds, and develop an effective obfuscation framework. Our authorship obfuscation approach defeats state-of-the-art verification approaches, including unmasking and compression models, while keeping text changes at a minimum.",
}
@inproceedings{shimura-etal-2019-text,
    title = "Text Categorization by Learning Predominant Sense of Words as Auxiliary Task",
    author = "Shimura, Kazuya  and
      Li, Jiyi  and
      Fukumoto, Fumiyo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1105",
    doi = "10.18653/v1/P19-1105",
    pages = "1109--1119",
    abstract = "Distributions of the senses of words are often highly skewed and give a strong influence of the domain of a document. This paper follows the assumption and presents a method for text categorization by leveraging the predominant sense of words depending on the domain, i.e., domain-specific senses. The key idea is that the features learned from predominant senses are possible to discriminate the domain of the document and thus improve the overall performance of text categorization. We propose multi-task learning framework based on the neural network model, transformer, which trains a model to simultaneously categorize documents and predicts a predominant sense for each word. The experimental results using four benchmark datasets show that our method is comparable to the state-of-the-art categorization approach, especially our model works well for categorization of multi-label documents.",
}
@inproceedings{ghosal-etal-2019-deepsentipeer,
    title = "{D}eep{S}enti{P}eer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions",
    author = "Ghosal, Tirthankar  and
      Verma, Rajeev  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1106",
    doi = "10.18653/v1/P19-1106",
    pages = "1120--1130",
    abstract = "Automatically validating a research artefact is one of the frontiers in Artificial Intelligence (AI) that directly brings it close to competing with human intellect and intuition. Although criticised sometimes, the existing peer review system still stands as the benchmark of research validation. The present-day peer review process is not straightforward and demands profound domain knowledge, expertise, and intelligence of human reviewer(s), which is somewhat elusive with the current state of AI. However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration. Here in this work, we investigate the role of reviewer sentiment embedded within peer review texts to predict the peer review outcome. Our proposed deep neural architecture takes into account three channels of information: the paper, the corresponding reviews, and review{'}s polarity to predict the overall recommendation score as well as the final decision. We achieve significant performance improvement over the baselines (∼ 29{\%} error reduction) proposed in a recently released dataset of peer reviews. An AI of this kind could assist the editors/program chairs as an additional layer of confidence, especially when non-responding/missing reviewers are frequent in present day peer review.",
}
@inproceedings{kim-etal-2019-gated,
    title = "Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion",
    author = "Kim, Suyoun  and
      Dalmia, Siddharth  and
      Metze, Florian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1107",
    doi = "10.18653/v1/P19-1107",
    pages = "1131--1141",
    abstract = "We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is consequently better at recognizing long conversations. Specifically, we propose to use text-based external word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework, yielding significant improvement in word error rate with better conversational-context representation. We evaluated the models on the Switchboard conversational speech corpus and show that our model outperforms standard end-to-end speech recognition models.",
}
@inproceedings{iyer-etal-2019-figurative,
    title = "Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection",
    author = "Iyer, Adith  and
      Joshi, Aditya  and
      Karimi, Sarvnaz  and
      Sparks, Ross  and
      Paris, Cecile",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1108",
    doi = "10.18653/v1/P19-1108",
    pages = "1142--1147",
    abstract = "Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work mentions errors in this prediction when symptom words, i.e., names of symptoms of interest, are used in a figurative sense. Therefore, we combine a state-of-the-art figurative usage detection with CNN-based personal health mention detection. To do so, we present two methods: a pipeline-based approach and a feature augmentation-based approach. The introduction of figurative usage detection results in an average improvement of 2.21{\%} F-score of personal health mention detection, in the case of the feature augmentation-based approach. This paper demonstrates the promise of using figurative usage detection to improve personal health mention detection.",
}
@inproceedings{gooding-kochmar-2019-complex,
    title = "Complex Word Identification as a Sequence Labelling Task",
    author = "Gooding, Sian  and
      Kochmar, Ekaterina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1109",
    doi = "10.18653/v1/P19-1109",
    pages = "1148--1153",
    abstract = "Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve text simplification. However, most CWI systems to date address the task on a word-by-word basis, not taking the context into account. In this paper, we present a novel approach to CWI based on sequence modelling. Our system is capable of performing CWI in context, does not require extensive feature engineering and outperforms state-of-the-art systems on this task.",
}
@inproceedings{wu-etal-2019-neural,
    title = "Neural News Recommendation with Topic-Aware News Representation",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      An, Mingxiao  and
      Huang, Yongfeng  and
      Xie, Xing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1110",
    doi = "10.18653/v1/P19-1110",
    pages = "1154--1159",
    abstract = "News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, it is not considered in many existing news recommendation methods. In this paper, we propose a neural news recommendation approach with topic-aware news representations. The core of our approach is a topic-aware news encoder and a user encoder. In the news encoder we learn representations of news from their titles via CNN networks and apply attention networks to select important words. In addition, we propose to learn topic-aware news representations by jointly training the news encoder with an auxiliary topic classification task. In the user encoder we learn the representations of users from their browsed news and use attention networks to select informative news for user representation learning. Extensive experiments on a real-world dataset validate the effectiveness of our approach.",
}
@inproceedings{krishna-etal-2019-poetry,
    title = "Poetry to Prose Conversion in {S}anskrit as a Linearisation Task: A Case for Low-Resource Languages",
    author = "Krishna, Amrith  and
      Sharma, Vishnu  and
      Santra, Bishal  and
      Chakraborty, Aishik  and
      Satuluri, Pavankumar  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1111",
    doi = "10.18653/v1/P19-1111",
    pages = "1160--1166",
    abstract = "The word ordering in a Sanskrit verse is often not aligned with its corresponding prose order. Conversion of the verse to its corresponding prose helps in better comprehension of the construction. Owing to the resource constraints, we formulate this task as a word ordering (linearisation) task. In doing so, we completely ignore the word arrangement at the verse side. k{\=a}vya guru, the approach we propose, essentially consists of a pipeline of two pretraining steps followed by a seq2seq model. The first pretraining step learns task-specific token embeddings from pretrained embeddings. In the next step, we generate multiple possible hypotheses for possible word arrangements of the input {\%}using another pretraining step. We then use them as inputs to a neural seq2seq model for the final prediction. We empirically show that the hypotheses generated by our pretraining step result in predictions that consistently outperform predictions based on the original order in the verse. Overall, k{\=a}vya guru outperforms current state of the art models in linearisation for the poetry to prose conversion task in Sanskrit.",
}
@inproceedings{shirani-etal-2019-learning,
    title = "Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions",
    author = "Shirani, Amirreza  and
      Dernoncourt, Franck  and
      Asente, Paul  and
      Lipka, Nedim  and
      Kim, Seokhwan  and
      Echevarria, Jose  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1112",
    doi = "10.18653/v1/P19-1112",
    pages = "1167--1172",
    abstract = "In visual communication, text emphasis is used to increase the comprehension of written text to convey the author{'}s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author{'}s intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.",
}
@inproceedings{li-etal-2019-rumor,
    title = "Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning",
    author = "Li, Quanzhi  and
      Zhang, Qiong  and
      Si, Luo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1113",
    doi = "10.18653/v1/P19-1113",
    pages = "1173--1179",
    abstract = "In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. We incorporate the user credibility information into the rumor detection layer, and we also apply attention mechanism in the rumor detection process. The attended information include not only the hidden states in the rumor detection layer, but also the hidden states from the stance detection layer. The experiments on two datasets show that our proposed model outperforms the state-of-the-art rumor detection approaches.",
}
@inproceedings{shahrokh-esfahani-etal-2019-context,
    title = "Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements",
    author = "Shahrokh Esfahani, Saeideh  and
      Cafarella, Michael J.  and
      Baran Pouyan, Maziyar  and
      DeAngelo, Gregory  and
      Eneva, Elena  and
      Fano, Andy E.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1114",
    doi = "10.18653/v1/P19-1114",
    pages = "1180--1184",
    abstract = "Human trafficking is a worldwide crisis. Traffickers exploit their victims by anonymously offering sexual services through online advertisements. These ads often contain clues that law enforcement can use to separate out potential trafficking cases from volunteer sex advertisements. The problem is that the sheer volume of ads is too overwhelming for manual processing. Ideally, a centralized semi-automated tool can be used to assist law enforcement agencies with this task. Here, we present an approach using natural language processing to identify trafficking ads on these websites. We propose a classifier by integrating multiple text feature sets, including the publicly available pre-trained textual language model Bi-directional Encoder Representation from transformers (BERT). In this paper, we demonstrate that a classifier using this composite feature set has significantly better performance compared to any single feature set alone.",
}
@inproceedings{sperber-etal-2019-self,
    title = "Self-Attentional Models for Lattice Inputs",
    author = "Sperber, Matthias  and
      Neubig, Graham  and
      Pham, Ngoc-Quan  and
      Waibel, Alex",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1115",
    doi = "10.18653/v1/P19-1115",
    pages = "1185--1197",
    abstract = "Lattices are an efficient and effective method to encode ambiguity of upstream systems in natural language processing tasks, for example to compactly capture multiple speech recognition hypotheses, or to represent multiple linguistic analyses. Previous work has extended recurrent neural networks to model lattice inputs and achieved improvements in various tasks, but these models suffer from very slow computation speeds. This paper extends the recently proposed paradigm of self-attention to handle lattice inputs. Self-attention is a sequence modeling technique that relates inputs to one another by computing pairwise similarities and has gained popularity for both its strong results and its computational efficiency. To extend such models to handle lattices, we introduce probabilistic reachability masks that incorporate lattice structure into the model and support lattice scores if available. We also propose a method for adapting positional embeddings to lattice structures. We apply the proposed model to a speech translation task and find that it outperforms all examined baselines while being much faster to compute than previous neural lattice models during both training and inference.",
}
@inproceedings{voita-etal-2019-good,
    title = "When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion",
    author = "Voita, Elena  and
      Sennrich, Rico  and
      Titov, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1116",
    doi = "10.18653/v1/P19-1116",
    pages = "1198--1212",
    abstract = "Though machine translation errors caused by the lack of context beyond one sentence have long been acknowledged, the development of context-aware NMT systems is hampered by several problems. Firstly, standard metrics are not sensitive to improvements in consistency in document-level translations. Secondly, previous work on context-aware NMT assumed that the sentence-aligned parallel data consisted of complete documents while in most practical scenarios such document-level data constitutes only a fraction of the available parallel data. To address the first issue, we perform a human study on an English-Russian subtitles dataset and identify deixis, ellipsis and lexical cohesion as three main sources of inconsistency. We then create test sets targeting these phenomena. To address the second shortcoming, we consider a set-up in which a much larger amount of sentence-level data is available compared to that aligned at the document level. We introduce a model that is suitable for this scenario and demonstrate major gains over a context-agnostic baseline on our new benchmarks without sacrificing performance as measured with BLEU.",
}
@inproceedings{wang-etal-2019-compact,
    title = "A Compact and Language-Sensitive Multilingual Translation Method",
    author = "Wang, Yining  and
      Zhou, Long  and
      Zhang, Jiajun  and
      Zhai, Feifei  and
      Xu, Jingfang  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1117",
    doi = "10.18653/v1/P19-1117",
    pages = "1213--1223",
    abstract = "Multilingual neural machine translation (Multi-NMT) with one encoder-decoder model has made remarkable progress due to its simple deployment. However, this multilingual translation paradigm does not make full use of language commonality and parameter sharing between encoder and decoder. Furthermore, this kind of paradigm cannot outperform the individual models trained on bilingual corpus in most cases. In this paper, we propose a compact and language-sensitive method for multilingual translation. To maximize parameter sharing, we first present a universal representor to replace both encoder and decoder models. To make the representor sensitive for specific languages, we further introduce language-sensitive embedding, attention, and discriminator with the ability to enhance model performance. We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot. Extensive experiments demonstrate that our proposed methods remarkably outperform strong standard multilingual translation systems on WMT and IWSLT datasets. Moreover, we find that our model is especially helpful in low-resource and zero-shot translation scenarios.",
}
@inproceedings{hangya-fraser-2019-unsupervised,
    title = "Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation",
    author = "Hangya, Viktor  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1118",
    doi = "10.18653/v1/P19-1118",
    pages = "1224--1234",
    abstract = "Mining parallel sentences from comparable corpora is important. Most previous work relies on supervised systems, which are trained on parallel data, thus their applicability is problematic in low-resource scenarios. Recent developments in building unsupervised bilingual word embeddings made it possible to mine parallel sentences based on cosine similarities of source and target language words. We show that relying only on this information is not enough, since sentences often have similar words but different meanings. We detect continuous parallel segments in sentence pair candidates and rely on them when mining parallel sentences. We show better mining accuracy on three language pairs in a standard shared task on artificial data. We also provide the first experiments showing that parallel sentences mined from real life sources improve unsupervised MT. Our code is available, we hope it will be used to support low-resource MT research.",
}
@inproceedings{sun-etal-2019-unsupervised,
    title = "Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation",
    author = "Sun, Haipeng  and
      Wang, Rui  and
      Chen, Kehai  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1119",
    doi = "10.18653/v1/P19-1119",
    pages = "1235--1245",
    abstract = "Unsupervised bilingual word embedding (UBWE), together with other technologies such as back-translation and denoising, has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs. In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT. That is, the training of UBWE and UNMT are separate. In this paper, we first empirically investigate the relationship between UBWE and UNMT. The empirical findings show that the performance of UNMT is significantly affected by the performance of UBWE. Thus, we propose two methods that train UNMT with UBWE agreement. Empirical results on several language pairs show that the proposed methods significantly outperform conventional UNMT.",
}
@inproceedings{kim-etal-2019-effective,
    title = "Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies",
    author = "Kim, Yunsu  and
      Gao, Yingbo  and
      Ney, Hermann",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1120",
    doi = "10.18653/v1/P19-1120",
    pages = "1246--1257",
    abstract = "Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pretrained NMT model to a new, unrelated language without shared vocabularies. We relieve the vocabulary mismatch by using cross-lingual word embedding, train a more language-agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pretraining data without back-translation. Our methods do not require restructuring the vocabulary or retraining the model. We improve plain NMT transfer by up to +5.1{\%} BLEU in five low-resource translation tasks, outperforming multilingual joint training by a large margin. We also provide extensive ablation studies on pretrained embedding, synthetic data, vocabulary size, and parameter freezing for a better understanding of NMT transfer.",
}
@inproceedings{gu-etal-2019-improved,
    title = "Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations",
    author = "Gu, Jiatao  and
      Wang, Yong  and
      Cho, Kyunghyun  and
      Li, Victor O.K.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1121",
    doi = "10.18653/v1/P19-1121",
    pages = "1258--1268",
    abstract = "Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the system in multilingual settings. However, naive training for zero-shot NMT easily fails, and is sensitive to hyper-parameter setting. The performance typically lags far behind the more conventional pivot-based approach which translates twice using a third language as a pivot. In this work, we address the degeneracy problem due to capturing spurious correlations by quantitatively analyzing the mutual information between language IDs of the source and decoded sentences. Inspired by this analysis, we propose to use two simple but effective approaches: (1) decoder pre-training; (2) back-translation. These methods show significant improvement (4 22 BLEU points) over the vanilla zero-shot translation on three challenging multilingual datasets, and achieve similar or better results than the pivot-based approach.",
}
@inproceedings{akoury-etal-2019-syntactically,
    title = "Syntactically Supervised Transformers for Faster Neural Machine Translation",
    author = "Akoury, Nader  and
      Krishna, Kalpesh  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1122",
    doi = "10.18653/v1/P19-1122",
    pages = "1269--1281",
    abstract = "Standard decoders for neural machine translation autoregressively generate a single target token per timestep, which slows inference especially for long outputs. While architectural advances such as the Transformer fully parallelize the decoder computations at training time, inference still proceeds sequentially. Recent developments in non- and semi-autoregressive decoding produce multiple tokens per timestep independently of the others, which improves inference speed but deteriorates translation quality. In this work, we propose the syntactically supervised Transformer (SynST), which first autoregressively predicts a chunked parse tree before generating all of the target tokens in one shot conditioned on the predicted parse. A series of controlled experiments demonstrates that SynST decodes sentences {\textasciitilde}5x faster than the baseline autoregressive Transformer while achieving higher BLEU scores than most competing methods on En-De and En-Fr datasets.",
}
@inproceedings{wang-etal-2019-dynamically,
    title = "Dynamically Composing Domain-Data Selection with Clean-Data Selection by {``}Co-Curricular Learning{''} for Neural Machine Translation",
    author = "Wang, Wei  and
      Caswell, Isaac  and
      Chelba, Ciprian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1123",
    doi = "10.18653/v1/P19-1123",
    pages = "1282--1292",
    abstract = "Noise and domain are important aspects of data quality for neural machine translation. Existing research focus separately on domain-data selection, clean-data selection, or their static combination, leaving the dynamic interaction across them not explicitly examined. This paper introduces a {``}co-curricular learning{''} method to compose dynamic domain-data selection with dynamic clean-data selection, for transfer learning across both capabilities. We apply an EM-style optimization procedure to further refine the {``}co-curriculum{''}. Experiment results and analysis with two domains demonstrate the effectiveness of the method and the properties of data scheduled by the co-curriculum.",
}
@inproceedings{li-etal-2019-word,
    title = "On the Word Alignment from Neural Machine Translation",
    author = "Li, Xintong  and
      Li, Guanlin  and
      Liu, Lemao  and
      Meng, Max  and
      Shi, Shuming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1124",
    doi = "10.18653/v1/P19-1124",
    pages = "1293--1303",
    abstract = "Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models. This paper thereby proposes two methods to induce word alignment which are general and agnostic to specific NMT models. Experiments show that both methods induce much better word alignment than attention. This paper further visualizes the translation through the word alignment induced by NMT. In particular, it analyzes the effect of alignment errors on translation errors at word level and its quantitative analysis over many testing examples consistently demonstrate that alignment errors are likely to lead to translation errors measured by different metrics.",
}
@inproceedings{wei-etal-2019-imitation,
    title = "Imitation Learning for Non-Autoregressive Neural Machine Translation",
    author = "Wei, Bingzhen  and
      Wang, Mingxuan  and
      Zhou, Hao  and
      Lin, Junyang  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1125",
    doi = "10.18653/v1/P19-1125",
    pages = "1304--1312",
    abstract = "Non-autoregressive translation models (NAT) have achieved impressive inference speedup. A potential issue of the existing NAT algorithms, however, is that the decoding is conducted in parallel, without directly considering previous context. In this paper, we propose an imitation learning framework for non-autoregressive machine translation, which still enjoys the fast translation speed but gives comparable translation performance compared to its auto-regressive counterpart. We conduct experiments on the IWSLT16, WMT14 and WMT16 datasets. Our proposed model achieves a significant speedup over the autoregressive models, while keeping the translation quality comparable to the autoregressive models. By sampling sentence length in parallel at inference time, we achieve the performance of 31.85 BLEU on WMT16 Ro$\rightarrow$En and 30.68 BLEU on IWSLT16 En$\rightarrow$De.",
}
@inproceedings{arivazhagan-etal-2019-monotonic,
    title = "Monotonic Infinite Lookback Attention for Simultaneous Machine Translation",
    author = "Arivazhagan, Naveen  and
      Cherry, Colin  and
      Macherey, Wolfgang  and
      Chiu, Chung-Cheng  and
      Yavuz, Semih  and
      Pang, Ruoming  and
      Li, Wei  and
      Raffel, Colin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1126",
    doi = "10.18653/v1/P19-1126",
    pages = "1313--1323",
    abstract = "Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios. Simultaneous systems must carefully schedule their reading of the source sentence to balance quality against latency. We present the first simultaneous translation system to learn an adaptive schedule jointly with a neural machine translation (NMT) model that attends over all source tokens read thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention, which maintains both a hard, monotonic attention head to schedule the reading of the source sentence, and a soft attention head that extends from the monotonic head back to the beginning of the source. We show that MILk{'}s adaptive schedule allows it to arrive at latency-quality trade-offs that are favorable to those of a recently proposed wait-k strategy for many latency values.",
}
@inproceedings{chen-etal-2019-global,
    title = "Global Textual Relation Embedding for Relational Understanding",
    author = "Chen, Zhiyu  and
      Zha, Hanwen  and
      Liu, Honglei  and
      Chen, Wenhu  and
      Yan, Xifeng  and
      Su, Yu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1127",
    doi = "10.18653/v1/P19-1127",
    pages = "1324--1330",
    abstract = "Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose embedding of textual relations, defined as the shortest dependency path between entities. Textual relation embedding provides a level of knowledge between word/phrase level and sentence level, and we show that it can facilitate downstream tasks requiring relational understanding of the text. To learn such an embedding, we create the largest distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. We use global co-occurrence statistics between textual and knowledge base relations as the supervision signal to train the embedding. Evaluation on two relational understanding tasks demonstrates the usefulness of the learned textual relation embedding. The data and code can be found at https://github.com/czyssrs/GloREPlus",
}
@inproceedings{zhu-etal-2019-graph,
    title = "Graph Neural Networks with Generated Parameters for Relation Extraction",
    author = "Zhu, Hao  and
      Lin, Yankai  and
      Liu, Zhiyuan  and
      Fu, Jie  and
      Chua, Tat-Seng  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1128",
    doi = "10.18653/v1/P19-1128",
    pages = "1331--1339",
    abstract = "In this paper, we propose a novel graph neural network with generated parameters (GP-GNNs). The parameters in the propagation module, i.e. the transition matrices used in message passing procedure, are produced by a generator taking natural language sentences as inputs. We verify GP-GNNs in relation extraction from text, both on bag- and instance-settings. Experimental results on a human-annotated dataset and two distantly supervised datasets show that multi-hop reasoning mechanism yields significant improvements. We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.",
}
@inproceedings{li-etal-2019-entity,
    title = "Entity-Relation Extraction as Multi-Turn Question Answering",
    author = "Li, Xiaoya  and
      Yin, Fan  and
      Sun, Zijun  and
      Li, Xiayu  and
      Yuan, Arianna  and
      Chai, Duo  and
      Zhou, Mingxin  and
      Li, Jiwei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1129",
    doi = "10.18653/v1/P19-1129",
    pages = "1340--1350",
    abstract = "In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and elations is transformed to the task of identifying answer spans from the context. This multi-turn QA formalization comes with several key advantages: firstly, the question query encodes important information for the entity/relation class we want to identify; secondly, QA provides a natural way of jointly modeling entity and relation; and thirdly, it allows us to exploit the well developed machine reading comprehension (MRC) models. Experiments on the ACE and the CoNLL04 corpora demonstrate that the proposed paradigm significantly outperforms previous best models. We are able to obtain the state-of-the-art results on all of the ACE04, ACE05 and CoNLL04 datasets, increasing the SOTA results on the three datasets to 49.6 (+1.2), 60.3 (+0.7) and 69.2 (+1.4), respectively. Additionally, we construct and will release a newly developed dataset RESUME, which requires multi-step reasoning to construct entity dependencies, as opposed to the single-step dependency extraction in the triplet exaction in previous datasets. The proposed multi-turn QA model also achieves the best performance on the RESUME dataset.",
}
@inproceedings{ye-etal-2019-exploiting,
    title = "Exploiting Entity {BIO} Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data",
    author = "Ye, Wei  and
      Li, Bo  and
      Xie, Rui  and
      Sheng, Zhonghao  and
      Chen, Long  and
      Zhang, Shikun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1130",
    doi = "10.18653/v1/P19-1130",
    pages = "1351--1360",
    abstract = "In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context (negative instances) usually far exceeds the others (positive instances), which negatively affects a model{'}s performance. To mitigate this problem, we propose a multi-task architecture which jointly trains a model to perform relation identification with cross-entropy loss and relation classification with ranking loss. Meanwhile, we observe that a sentence may have multiple entities and relation mentions, and the patterns in which the entities appear in a sentence may contain useful semantic information that can be utilized to distinguish between positive and negative instances. Thus we further incorporate the embeddings of character-wise/word-wise BIO tag from the named entity recognition task into character/word embeddings to enrich the input representation. Experiment results show that our proposed approach can significantly improve the performance of a baseline model with more than 10{\%} absolute increase in F1-score, and outperform the state-of-the-art models on ACE 2005 Chinese and English corpus. Moreover, BIO tag embeddings are particularly effective and can be used to improve other models as well.",
}
@inproceedings{sun-etal-2019-joint,
    title = "Joint Type Inference on Entities and Relations via Graph Convolutional Networks",
    author = "Sun, Changzhi  and
      Gong, Yeyun  and
      Wu, Yuanbin  and
      Gong, Ming  and
      Jiang, Daxin  and
      Lan, Man  and
      Sun, Shiliang  and
      Duan, Nan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1131",
    doi = "10.18653/v1/P19-1131",
    pages = "1361--1370",
    abstract = "We develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph. By introducing a binary relation classification task, we are able to utilize the structure of entity-relation bipartite graph in a more efficient and interpretable way. Experiments on ACE05 show that our model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.",
}
@inproceedings{wang-etal-2019-extracting,
    title = "Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers",
    author = "Wang, Haoyu  and
      Tan, Ming  and
      Yu, Mo  and
      Chang, Shiyu  and
      Wang, Dakuo  and
      Xu, Kun  and
      Guo, Xiaoxiao  and
      Potdar, Saloni",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1132",
    doi = "10.18653/v1/P19-1132",
    pages = "1371--1377",
    abstract = "Many approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extractions by encoding the paragraph only once. We build our solution upon the pre-trained self-attentive models (Transformer), where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with entity-aware attention. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.",
}
@inproceedings{simon-etal-2019-unsupervised,
    title = "Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses",
    author = "Simon, {\'E}tienne  and
      Guigue, Vincent  and
      Piwowarski, Benjamin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1133",
    doi = "10.18653/v1/P19-1133",
    pages = "1378--1387",
    abstract = "Unsupervised relation extraction aims at extracting relations between entities in text. Previous unsupervised approaches are either generative or discriminative. In a supervised setting, discriminative approaches, such as deep neural network classifiers, have demonstrated substantial improvement. However, these models are hard to train without supervision, and the currently proposed solutions are unstable. To overcome this limitation, we introduce a skewness loss which encourages the classifier to predict a relation with confidence given a sentence, and a distribution distance loss enforcing that all relations are predicted in average. These losses improve the performance of discriminative based models, and enable us to train deep neural networks satisfactorily, surpassing current state of the art on three different datasets.",
}
@inproceedings{alt-etal-2019-fine,
    title = "Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction",
    author = {Alt, Christoph  and
      H{\"u}bner, Marc  and
      Hennig, Leonhard},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1134",
    doi = "10.18653/v1/P19-1134",
    pages = "1388--1398",
    abstract = "Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels. Current relation extraction methods try to alleviate the noise by multi-instance learning and by providing supporting linguistic and contextual information to more efficiently guide the relation classification. While achieving state-of-the-art results, we observed these models to be biased towards recognizing a limited set of relations with high precision, while ignoring those in the long tail. To address this gap, we utilize a pre-trained language model, the OpenAI Generative Pre-trained Transformer (GPT) (Radford et al., 2018). The GPT and similar models have been shown to capture semantic and syntactic features, and also a notable amount of {``}common-sense{''} knowledge, which we hypothesize are important features for recognizing a more diverse set of relations. By extending the GPT to the distantly supervised setting, and fine-tuning it on the NYT10 dataset, we show that it predicts a larger set of distinct relation types with high confidence. Manual and automated evaluation of our model shows that it achieves a state-of-the-art AUC score of 0.422 on the NYT10 dataset, and performs especially well at higher recall levels.",
}
@inproceedings{jia-etal-2019-arnor,
    title = "{ARNOR}: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification",
    author = "Jia, Wei  and
      Dai, Dai  and
      Xiao, Xinyan  and
      Wu, Hua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1135",
    doi = "10.18653/v1/P19-1135",
    pages = "1399--1408",
    abstract = "Distant supervision is widely used in relation classification in order to create large-scale training data by aligning a knowledge base with an unlabeled corpus. However, it also introduces amounts of noisy labels where a contextual sentence actually does not express the labeled relation. In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification. ARNOR assumes that a trustable relation label should be explained by the neural attention model. Specifically, our ARNOR framework iteratively learns an interpretable model and utilizes it to select trustable instances. We first introduce attention regularization to force the model to pay attention to the patterns which explain the relation labels, so as to make the model more interpretable. Then, if the learned model can clearly locate the relation patterns of a candidate instance in the training set, we will select it as a trustable instance for further training step. According to the experiments on NYT data, our ARNOR framework achieves significant improvements over state-of-the-art methods in both relation classification performance and noise reduction effect.",
}
@inproceedings{fu-etal-2019-graphrel,
    title = "{G}raph{R}el: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction",
    author = "Fu, Tsu-Jui  and
      Li, Peng-Hsuan  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1136",
    doi = "10.18653/v1/P19-1136",
    pages = "1409--1418",
    abstract = "In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction between named entities and relations via a 2nd-phase relation-weighted GCN to better extract relations. Linear and dependency structures are both used to extract both sequential and regional features of the text, and a complete word graph is further utilized to extract implicit features among all word pairs of the text. With the graph-based approach, the prediction for overlapping relations is substantially improved over previous sequential approaches. We evaluate GraphRel on two public datasets: NYT and WebNLG. Results show that GraphRel maintains high precision while increasing recall substantially. Also, GraphRel outperforms previous work by 3.2{\%} and 5.8{\%} (F1 score), achieving a new state-of-the-art for relation extraction.",
}
@inproceedings{zheng-etal-2019-diag,
    title = "{DIAG}-{NRE}: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction",
    author = "Zheng, Shun  and
      Han, Xu  and
      Lin, Yankai  and
      Yu, Peilin  and
      Chen, Lu  and
      Huang, Ling  and
      Liu, Zhiyuan  and
      Xu, Wei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1137",
    doi = "10.18653/v1/P19-1137",
    pages = "1419--1429",
    abstract = "Pattern-based labeling methods have achieved promising results in alleviating the inevitable labeling noises of distantly supervised neural relation extraction. However, these methods require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. To ease the labor-intensive workload of pattern writing and enable the quick generalization to new relation types, we propose a neural pattern diagnosis framework, DIAG-NRE, that can automatically summarize and refine high-quality relational patterns from noise data with human experts in the loop. To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.",
}
@inproceedings{xia-etal-2019-multi,
    title = "Multi-grained Named Entity Recognition",
    author = "Xia, Congying  and
      Zhang, Chenwei  and
      Yang, Tao  and
      Li, Yaliang  and
      Du, Nan  and
      Wu, Xian  and
      Fan, Wei  and
      Ma, Fenglong  and
      Yu, Philip",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1138",
    doi = "10.18653/v1/P19-1138",
    pages = "1430--1440",
    abstract = "This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4{\%} in terms of the F1 score among nested/non-overlapping NER tasks.",
}
@inproceedings{zhang-etal-2019-ernie,
    title = "{ERNIE}: Enhanced Language Representation with Informative Entities",
    author = "Zhang, Zhengyan  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Jiang, Xin  and
      Sun, Maosong  and
      Liu, Qun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1139",
    doi = "10.18653/v1/P19-1139",
    pages = "1441--1451",
    abstract = "Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with external knowledge. In this paper, we utilize both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE), which can take full advantage of lexical, syntactic, and knowledge information simultaneously. The experimental results have demonstrated that ERNIE achieves significant improvements on various knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art model BERT on other common NLP tasks. The code and datasets will be available in the future.",
}
@inproceedings{cao-etal-2019-multi,
    title = "Multi-Channel Graph Neural Network for Entity Alignment",
    author = "Cao, Yixin  and
      Liu, Zhiyuan  and
      Li, Chengjiang  and
      Liu, Zhiyuan  and
      Li, Juanzi  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1140",
    doi = "10.18653/v1/P19-1140",
    pages = "1452--1461",
    abstract = "Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. Extensive experiments on five publicly available datasets demonstrate our superior performance (5{\%} Hits@1 up on average). Source code and data used in the experiments can be accessed at https://github.com/thunlp/MuGNN .",
}
@inproceedings{ding-etal-2019-neural,
    title = "A Neural Multi-digraph Model for {C}hinese {NER} with Gazetteers",
    author = "Ding, Ruixue  and
      Xie, Pengjun  and
      Zhang, Xiaoyan  and
      Lu, Wei  and
      Li, Linlin  and
      Si, Luo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1141",
    doi = "10.18653/v1/P19-1141",
    pages = "1462--1467",
    abstract = "Gazetteers were shown to be useful resources for named entity recognition (NER). Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies or handcrafted templates, which may not always lead to optimal effectiveness, especially when multiple gazetteers are involved. This is especially the case for the task of Chinese NER, where the words are not naturally tokenized, leading to additional ambiguities. To automatically learn how to incorporate multiple gazetteers into an NER system, we propose a novel approach based on graph neural networks with a multi-digraph structure that captures the information that the gazetteers offer. Experiments on various datasets show that our model is effective in incorporating rich gazetteer information while resolving ambiguities, outperforming previous approaches.",
}
@inproceedings{brahma-2019-improved,
    title = "Improved Language Modeling by Decoding the Past",
    author = "Brahma, Siddhartha",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1142",
    doi = "10.18653/v1/P19-1142",
    pages = "1468--1476",
    abstract = "Highly regularized LSTMs achieve impressive results on several benchmark datasets in language modeling. We propose a new regularization method based on decoding the last token in the context using the predicted distribution of the next token. This biases the model towards retaining more contextual information, in turn improving its ability to predict the next token. With negligible overhead in the number of parameters and training time, our Past Decode Regularization (PDR) method improves perplexity on the Penn Treebank dataset by up to 1.8 points and by up to 2.3 points on the WikiText-2 dataset, over strong regularized baselines using a single softmax. With a mixture-of-softmax model, we show gains of up to 1.0 perplexity points on these datasets. In addition, our method achieves 1.169 bits-per-character on the Penn Treebank Character dataset for character level language modeling.",
}
@inproceedings{grave-etal-2019-training,
    title = "Training Hybrid Language Models by Marginalizing over Segmentations",
    author = "Grave, Edouard  and
      Sukhbaatar, Sainbayar  and
      Bojanowski, Piotr  and
      Joulin, Armand",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1143",
    doi = "10.18653/v1/P19-1143",
    pages = "1477--1482",
    abstract = "In this paper, we study the problem of hybrid language modeling, that is using models which can predict both characters and larger units such as character ngrams or words. Using such models, multiple potential segmentations usually exist for a given string, for example one using words and one using characters only. Thus, the probability of a string is the sum of the probabilities of all the possible segmentations. Here, we show how it is possible to marginalize over the segmentations efficiently, in order to compute the true probability of a sequence. We apply our technique on three datasets, comprising seven languages, showing improvements over a strong character level language model.",
}
@inproceedings{luo-etal-2019-improving,
    title = "Improving Neural Language Models by Segmenting, Attending, and Predicting the Future",
    author = "Luo, Hongyin  and
      Jiang, Lan  and
      Belinkov, Yonatan  and
      Glass, James",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1144",
    doi = "10.18653/v1/P19-1144",
    pages = "1483--1493",
    abstract = "Common language models typically predict the next word given the context. In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase. The model does not require any linguistic annotation of phrase segmentation. Instead, we define syntactic heights and phrase segmentation rules, enabling the model to automatically induce phrases, recognize their task-specific heads, and generate phrase embeddings in an unsupervised learning manner. Our method can easily be applied to language models with different network architectures since an independent module is used for phrase induction and context-phrase alignment, and no change is required in the underlying language modeling network. Experiments have shown that our model outperformed several strong baseline models on different data sets. We achieved a new state-of-the-art performance of 17.4 perplexity on the Wikitext-103 dataset. Additionally, visualizing the outputs of the phrase induction module showed that our model is able to learn approximate phrase-level structural knowledge without any annotation.",
}
@inproceedings{tay-etal-2019-lightweight,
    title = "Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks",
    author = "Tay, Yi  and
      Zhang, Aston  and
      Luu, Anh Tuan  and
      Rao, Jinfeng  and
      Zhang, Shuai  and
      Wang, Shuohang  and
      Fu, Jie  and
      Hui, Siu Cheung",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1145",
    doi = "10.18653/v1/P19-1145",
    pages = "1494--1503",
    abstract = "Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks. To this end, our models exploit computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly (75{\%}) reduced parameter size due to lesser degrees of freedom in the Hamilton product. We propose Quaternion variants of models, giving rise to new architectures such as the Quaternion attention Model and Quaternion Transformer. Extensive experiments on a battery of NLP tasks demonstrates the utility of proposed Quaternion-inspired models, enabling up to 75{\%} reduction in parameter size without significant loss in performance.",
}
@inproceedings{peters-etal-2019-sparse,
    title = "Sparse Sequence-to-Sequence Models",
    author = "Peters, Ben  and
      Niculae, Vlad  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1146",
    doi = "10.18653/v1/P19-1146",
    pages = "1504--1519",
    abstract = "Sequence-to-sequence models are a powerful workhorse of NLP. Most variants employ a softmax transformation in both their attention mechanism and output layer, leading to dense alignments and strictly positive output probabilities. This density is wasteful, making models less interpretable and assigning probability mass to many implausible outputs. In this paper, we propose sparse sequence-to-sequence models, rooted in a new family of $\alpha$-entmax transformations, which includes softmax and sparsemax as particular cases, and is sparse for any $\alpha > 1$. We provide fast algorithms to evaluate these transformations and their gradients, which scale well for large vocabulary sizes. Our models are able to produce sparse alignments and to assign nonzero probability to a short list of plausible outputs, sometimes rendering beam search exact. Experiments on morphological inflection and machine translation reveal consistent gains over dense models.",
}
@inproceedings{hsieh-etal-2019-robustness,
    title = "On the Robustness of Self-Attentive Models",
    author = "Hsieh, Yu-Lun  and
      Cheng, Minhao  and
      Juan, Da-Cheng  and
      Wei, Wei  and
      Hsu, Wen-Lian  and
      Hsieh, Cho-Jui",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1147",
    doi = "10.18653/v1/P19-1147",
    pages = "1520--1529",
    abstract = "This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self-attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.",
}
@inproceedings{wu-cotterell-2019-exact,
    title = "Exact Hard Monotonic Attention for Character-Level Transduction",
    author = "Wu, Shijie  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1148",
    doi = "10.18653/v1/P19-1148",
    pages = "1530--1537",
    abstract = "Many common character-level, string-to-string transduction tasks, e.g., grapheme-to-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, non-monotonic models, outperform popular monotonic models. In this work, we ask the following question: Is monotonicity really a helpful inductive bias in these tasks? We develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns alignment jointly. With the help of dynamic programming, we are able to compute the exact marginalization over all alignments. Our models achieve state-of-the-art performance on morphological inflection. Furthermore, we find strong performance on two other character-level transduction tasks. Code is available at https://github.com/shijie-wu/neural-transducer.",
}
@inproceedings{zhang-sennrich-2019-lightweight,
    title = "A Lightweight Recurrent Network for Sequence Modeling",
    author = "Zhang, Biao  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1149",
    doi = "10.18653/v1/P19-1149",
    pages = "1538--1548",
    abstract = "Recurrent networks have achieved great success on various sequential tasks with the assistance of complex recurrent units, but suffer from severe computational inefficiency due to weak parallelization. One direction to alleviate this issue is to shift heavy computations outside the recurrence. In this paper, we propose a lightweight recurrent network, or LRN. LRN uses input and forget gates to handle long-range dependencies as well as gradient vanishing and explosion, with all parameter related calculations factored outside the recurrence. The recurrence in LRN only manipulates the weight assigned to each token, tightly connecting LRN with self-attention networks. We apply LRN as a drop-in replacement of existing recurrent units in several neural sequential models. Extensive experiments on six NLP tasks show that LRN yields the best running efficiency with little or no loss in model performance.",
}
@inproceedings{zhao-etal-2019-towards,
    title = "Towards Scalable and Reliable Capsule Networks for Challenging {NLP} Applications",
    author = "Zhao, Wei  and
      Peng, Haiyun  and
      Eger, Steffen  and
      Cambria, Erik  and
      Yang, Min",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1150",
    doi = "10.18653/v1/P19-1150",
    pages = "1549--1559",
    abstract = "Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes. In this paper, we introduce: (i) an agreement score to evaluate the performance of routing processes at instance-level; (ii) an adaptive optimizer to enhance the reliability of routing; (iii) capsule compression and partial routing to improve the scalability of capsule networks. We validate our approach on two NLP tasks, namely: multi-label text classification and question answering. Experimental results show that our approach considerably improves over strong competitors on both tasks. In addition, we gain the best results in low-resource settings with few training instances.",
}
@inproceedings{park-etal-2019-soft,
    title = "Soft Representation Learning for Sparse Transfer",
    author = "Park, Haeju  and
      Yeo, Jinyoung  and
      Wang, Gengyu  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1151",
    doi = "10.18653/v1/P19-1151",
    pages = "1560--1568",
    abstract = "Transfer learning is effective for improving the performance of tasks that are related, and Multi-task learning (MTL) and Cross-lingual learning (CLL) are important instances. This paper argues that hard-parameter sharing, of hard-coding layers shared across different tasks or languages, cannot generalize well, when sharing with a loosely related task. Such case, which we call sparse transfer, might actually hurt performance, a phenomenon known as negative transfer. Our contribution is using adversarial training across tasks, to {``}soft-code{''} shared and private spaces, to avoid the shared space gets too sparse. In CLL, our proposed architecture considers another challenge of dealing with low-quality input.",
}
@inproceedings{liang-etal-2019-learning,
    title = "Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization",
    author = "Liang, Paul Pu  and
      Liu, Zhun  and
      Tsai, Yao-Hung Hubert  and
      Zhao, Qibin  and
      Salakhutdinov, Ruslan  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1152",
    doi = "10.18653/v1/P19-1152",
    pages = "1569--1576",
    abstract = "There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a result of imperfect modalities, missing entries or noise corruption. To address these concerns, we present a regularization method based on tensor rank minimization. Our method is based on the observation that high-dimensional multimodal time series data often exhibit correlations across time and modalities which leads to low-rank tensor representations. However, the presence of noise or incomplete values breaks these correlations and results in tensor representations of higher rank. We design a model to learn such tensor representations and effectively regularize their rank. Experiments on multimodal language data show that our model achieves good results across various levels of imperfection.",
}
@inproceedings{prato-etal-2019-towards,
    title = "Towards Lossless Encoding of Sentences",
    author = "Prato, Gabriele  and
      Duchesneau, Mathieu  and
      Chandar, Sarath  and
      Tapp, Alain",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1153",
    doi = "10.18653/v1/P19-1153",
    pages = "1577--1583",
    abstract = "A lot of work has been done in the field of image compression via machine learning, but not much attention has been given to the compression of natural language. Compressing text into lossless representations while making features easily retrievable is not a trivial task, yet has huge benefits. Most methods designed to produce feature rich sentence embeddings focus solely on performing well on downstream tasks and are unable to properly reconstruct the original sequence from the learned embedding. In this work, we propose a near lossless method for encoding long sequences of texts as well as all of their sub-sequences into feature rich representations. We test our method on sentiment analysis and show good performance across all sub-sentence and sentence embeddings.",
}
@inproceedings{zhang-etal-2019-open,
    title = "Open Vocabulary Learning for Neural {C}hinese {P}inyin {IME}",
    author = "Zhang, Zhuosheng  and
      Huang, Yafang  and
      Zhao, Hai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1154",
    doi = "10.18653/v1/P19-1154",
    pages = "1584--1594",
    abstract = "Pinyin-to-character (P2C) conversion is the core component of pinyin-based Chinese input method engine (IME). However, the conversion is seriously compromised by the ambiguities of Chinese characters corresponding to pinyin as well as the predefined fixed vocabularies. To alleviate such inconveniences, we propose a neural P2C conversion model augmented by an online updated vocabulary with a sampling mechanism to support open vocabulary learning during IME working. Our experiments show that the proposed method outperforms commercial IMEs and state-of-the-art traditional models on standard corpus and true inputting history dataset in terms of multiple metrics and thus the online updated vocabulary indeed helps our IME effectively follows user inputting behavior.",
}
@inproceedings{mirea-bicknell-2019-using,
    title = "Using {LSTM}s to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning",
    author = "Mirea, Nicole  and
      Bicknell, Klinton",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1155",
    doi = "10.18653/v1/P19-1155",
    pages = "1595--1605",
    abstract = "To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not. Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level.",
}
@inproceedings{blevins-zettlemoyer-2019-better,
    title = "Better Character Language Modeling through Morphology",
    author = "Blevins, Terra  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1156",
    doi = "10.18653/v1/P19-1156",
    pages = "1606--1613",
    abstract = "We incorporate morphological supervision into character language models (CLMs) via multitasking and show that this addition improves bits-per-character (BPC) performance across 24 languages, even when the morphology data and language modeling data are disjoint. Analyzing the CLMs shows that inflected words benefit more from explicitly modeling morphology than uninflected words, and that morphological supervision improves performance even as the amount of language modeling data grows. We then transfer morphological supervision across languages to improve performance in the low-resource setting.",
}
@inproceedings{flachs-etal-2019-historical,
    title = "Historical Text Normalization with Delayed Rewards",
    author = "Flachs, Simon  and
      Bollmann, Marcel  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1157",
    doi = "10.18653/v1/P19-1157",
    pages = "1614--1619",
    abstract = "Training neural sequence-to-sequence models with simple token-level log-likelihood is now a standard approach to historical text normalization, albeit often outperformed by phrase-based models. Policy gradient training enables direct optimization for exact matches, and while the small datasets in historical text normalization are prohibitive of from-scratch reinforcement learning, we show that policy gradient fine-tuning leads to significant improvements across the board. Policy gradient training, in particular, leads to more accurate normalizations for long or unseen words.",
}
@inproceedings{hiraoka-etal-2019-stochastic,
    title = "Stochastic Tokenization with a Language Model for Neural Text Classification",
    author = "Hiraoka, Tatsuya  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1158",
    doi = "10.18653/v1/P19-1158",
    pages = "1620--1629",
    abstract = "For unsegmented languages such as Japanese and Chinese, tokenization of a sentence has a significant impact on the performance of text classification. Sentences are usually segmented with words or subwords by a morphological analyzer or byte pair encoding and then encoded with word (or subword) representations for neural networks. However, segmentation is potentially ambiguous, and it is unclear whether the segmented tokens achieve the best performance for the target task. In this paper, we propose a method to simultaneously learn tokenization and text classification to address these problems. Our model incorporates a language model for unsupervised tokenization into a text classifier and then trains both models simultaneously. To make the model robust against infrequent tokens, we sampled segmentation for each sentence stochastically during training, which resulted in improved performance of text classification. We conducted experiments on sentiment analysis as a text classification task and show that our method achieves better performance than previous methods.",
}
@inproceedings{sun-etal-2019-mitigating,
    title = "Mitigating Gender Bias in Natural Language Processing: Literature Review",
    author = "Sun, Tony  and
      Gaut, Andrew  and
      Tang, Shirlyn  and
      Huang, Yuxin  and
      ElSherief, Mai  and
      Zhao, Jieyu  and
      Mirza, Diba  and
      Belding, Elizabeth  and
      Chang, Kai-Wei  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1159",
    doi = "10.18653/v1/P19-1159",
    pages = "1630--1640",
    abstract = "As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.",
}
@inproceedings{kaneko-bollegala-2019-gender,
    title = "Gender-preserving Debiasing for Pre-trained Word Embeddings",
    author = "Kaneko, Masahiro  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1160",
    doi = "10.18653/v1/P19-1160",
    pages = "1641--1650",
    abstract = "Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the down-stream NLP applications that use those word embeddings. Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings. Specifically, we consider four types of information: \textit{feminine}, \textit{masculine}, \textit{gender-neutral} and \textit{stereotypical}, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the gender-related information in feminine and masculine words, (b) preserves the neutrality in gender-neutral words, and (c) removes the biases from stereotypical words. Experimental results on several previously proposed benchmark datasets show that our proposed method can debias pre-trained word embeddings better than existing SoTA methods proposed for debiasing word embeddings while preserving gender-related but non-discriminative information.",
}
@inproceedings{zmigrod-etal-2019-counterfactual,
    title = "Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology",
    author = "Zmigrod, Ran  and
      Mielke, Sabrina J.  and
      Wallach, Hanna  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1161",
    doi = "10.18653/v1/P19-1161",
    pages = "1651--1661",
    abstract = "Gender stereotypes are manifest in most of the world{'}s languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present a novel approach for converting between masculine-inflected and feminine-inflected sentences in such languages. For Spanish and Hebrew, our approach achieves F1 scores of 82{\%} and 73{\%} at the level of tags and accuracies of 90{\%} and 87{\%} at the level of forms. By evaluating our approach using four different languages, we show that, on average, it reduces gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.",
}
@inproceedings{sweeney-najafian-2019-transparent,
    title = "A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings",
    author = "Sweeney, Chris  and
      Najafian, Maryam",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1162",
    doi = "10.18653/v1/P19-1162",
    pages = "1662--1667",
    abstract = "Word embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on vector space based metrics like the Word Embedding Association Test (WEAT). While these approaches offer great geometric insights into unintended biases in the embedding vector space, they fail to offer an interpretable meaning for how the embeddings could cause discrimination in downstream NLP applications. In this work, we present a transparent framework and metric for evaluating discrimination across protected groups with respect to their word embedding bias. Our metric (Relative Negative Sentiment Bias, RNSB) measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. We show that our framework and metric enable useful analysis into the bias in word embeddings.",
}
@inproceedings{sap-etal-2019-risk,
    title = "The Risk of Racial Bias in Hate Speech Detection",
    author = "Sap, Maarten  and
      Card, Dallas  and
      Gabriel, Saadia  and
      Choi, Yejin  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1163",
    doi = "10.18653/v1/P19-1163",
    pages = "1668--1678",
    abstract = "We investigate how annotators{'} insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African American English (AAE) and ratings of toxicity in several widely-used hate speech datasets. Then, we show that models trained on these corpora acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. Finally, we propose *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet{'}s dialect they are significantly less likely to label the tweet as offensive.",
}
@inproceedings{stanovsky-etal-2019-evaluating,
    title = "Evaluating Gender Bias in Machine Translation",
    author = "Stanovsky, Gabriel  and
      Smith, Noah A.  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1164",
    doi = "10.18653/v1/P19-1164",
    pages = "1679--1684",
    abstract = "We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., {``}The doctor asked the nurse to help her in the operation{''}). We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word {``}doctor{''}). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages. Our data and code are publicly available at https://github.com/gabrielStanovsky/mt{\_}gender.",
}
@inproceedings{iacobacci-navigli-2019-lstmembed,
    title = "{LSTME}mbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories",
    author = "Iacobacci, Ignacio  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1165",
    doi = "10.18653/v1/P19-1165",
    pages = "1685--1695",
    abstract = "While word embeddings are now a de facto standard representation of words in most NLP tasks, recently the attention has been shifting towards vector representations which capture the different meanings, i.e., senses, of words. In this paper we explore the capabilities of a bidirectional LSTM model to learn representations of word senses from semantically annotated corpora. We show that the utilization of an architecture that is aware of word order, like an LSTM, enables us to create better representations. We assess our proposed model on various standard benchmarks for evaluating semantic representations, reaching state-of-the-art performance on the SemEval-2014 word-to-sense similarity task. We release the code and the resulting word and sense embeddings at http://lcl.uniroma1.it/LSTMEmbed.",
}
@inproceedings{ethayarajh-etal-2019-understanding,
    title = "Understanding Undesirable Word Embedding Associations",
    author = "Ethayarajh, Kawin  and
      Duvenaud, David  and
      Hirst, Graeme",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1166",
    doi = "10.18653/v1/P19-1166",
    pages = "1696--1705",
    abstract = "Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effective, we use it to derive a new measure of association called the relational inner product association (RIPA). Experiments with RIPA reveal that, on average, skipgram with negative sampling (SGNS) does not make most words any more gendered than they are in the training corpus. However, for gender-stereotyped words, SGNS actually amplifies the gender association in the corpus.",
}
@inproceedings{hoyle-etal-2019-unsupervised,
    title = "Unsupervised Discovery of Gendered Language through Latent-Variable Modeling",
    author = "Hoyle, Alexander Miserlis  and
      Wolf-Sonkin, Lawrence  and
      Wallach, Hanna  and
      Augenstein, Isabelle  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1167",
    doi = "10.18653/v1/P19-1167",
    pages = "1706--1716",
    abstract = "Studying the ways in which language is gendered has long been an area of interest in sociolinguistics. Studies have explored, for example, the speech of male and female characters in film and the language used to describe male and female politicians. In this paper, we aim not to merely study this phenomenon qualitatively, but instead to quantify the degree to which the language used to describe men and women is different and, moreover, different in a positive or negative way. To that end, we introduce a generative latent-variable model that jointly represents adjective (or verb) choice, with its sentiment, given the natural gender of a head (or dependent) noun. We find that there are significant differences between descriptions of male and female nouns and that these differences align with common gender stereotypes: Positive adjectives used to describe women are more often related to their bodies than adjectives used to describe men.",
}
@inproceedings{piratla-etal-2019-topic,
    title = "Topic Sensitive Attention on Generic Corpora Corrects Sense Bias in Pretrained Embeddings",
    author = "Piratla, Vihari  and
      Sarawagi, Sunita  and
      Chakrabarti, Soumen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1168",
    doi = "10.18653/v1/P19-1168",
    pages = "1717--1726",
    abstract = "Given a small corpus D{\_}T pertaining to a limited set of focused topics, our goal is to train embeddings that accurately capture the sense of words in the topic in spite of the limited size of D{\_}T. These embeddings may be used in various tasks involving D{\_}T. A popular strategy in limited data settings is to adapt pretrained embeddings E trained on a large corpus. To correct for sense drift, fine-tuning, regularization, projection, and pivoting have been proposed recently. Among these, regularization informed by a word{'}s corpus frequency performed well, but we improve upon it using a new regularizer based on the stability of its cooccurrence with other words. However, a thorough comparison across ten topics, spanning three tasks, with standardized settings of hyper-parameters, reveals that even the best embedding adaptation strategies provide small gains beyond well-tuned baselines, which many earlier comparisons ignored. In a bold departure from adapting pretrained embeddings, we propose using D{\_}T to probe, attend to, and borrow fragments from any large, topic-rich source corpus (such as Wikipedia), which need not be the corpus used to pretrain embeddings. This step is made scalable and practical by suitable indexing. We reach the surprising conclusion that even limited corpus augmentation is more useful than adapting embeddings, which suggests that non-dominant sense information may be irrevocably obliterated from pretrained embeddings and cannot be salvaged by adaptation.",
}
@inproceedings{wang-etal-2019-spherere,
    title = "{S}phere{RE}: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings",
    author = "Wang, Chengyu  and
      He, Xiaofeng  and
      Zhou, Aoying",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1169",
    doi = "10.18653/v1/P19-1169",
    pages = "1727--1737",
    abstract = "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.",
}
@inproceedings{vargas-etal-2019-multilingual,
    title = "Multilingual Factor Analysis",
    author = "Vargas, Francisco  and
      Brestnichki, Kamen  and
      Papadopoulos Korfiatis, Alex  and
      Hammerla, Nils",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1170",
    doi = "10.18653/v1/P19-1170",
    pages = "1738--1750",
    abstract = "In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary. We model equivalent words in different languages as different views of the same word generated by a common latent variable representing their latent lexical meaning. We explore the task of alignment by querying the fitted model for multilingual embeddings achieving competitive results across a variety of tasks. The proposed model is robust to noise in the embedding space making it a suitable method for distributed representations learned from noisy corpora.",
}
@inproceedings{pimentel-etal-2019-meaning,
    title = "Meaning to Form: Measuring Systematicity as Information",
    author = "Pimentel, Tiago  and
      McCarthy, Arya D.  and
      Blasi, Damian  and
      Roark, Brian  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1171",
    doi = "10.18653/v1/P19-1171",
    pages = "1751--1764",
    abstract = "A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between a word form and its meaning, or does some systematic phenomenon pervade? For instance, does the character bigram {`}gl{'} have any systematic relationship to the meaning of words like {`}glisten{'}, {`}gleam{'} and {`}glow{'}? In this work, we offer a holistic quantification of the systematicity of the sign using mutual information and recurrent neural networks. We employ these in a data-driven and massively multilingual approach to the question, examining 106 languages. We find a statistically significant reduction in entropy when modeling a word form conditioned on its semantic representation. Encouragingly, we also recover well-attested English examples of systematic affixes. We conclude with the meta-point: Our approximate effect size (measured in bits) is quite small{---}despite some amount of systematicity between form and meaning, an arbitrary relationship and its resulting benefits dominate human language.",
}
@inproceedings{nicolai-yarowsky-2019-learning,
    title = "Learning Morphosyntactic Analyzers from the {B}ible via Iterative Annotation Projection across 26 Languages",
    author = "Nicolai, Garrett  and
      Yarowsky, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1172",
    doi = "10.18653/v1/P19-1172",
    pages = "1765--1774",
    abstract = "A large percentage of computational tools are concentrated in a very small subset of the planet{'}s languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simultaneously: leveraging the high accuracy of English taggers and parsers, we project morphological information onto translations of the Bible in 26 varied test languages. Using an iterative discovery, constraint, and training process, we build inflectional lexica in the target languages. Through a combination of iteration, ensembling, and reranking, we see double-digit relative error reductions in lemmatization and morphological analysis over a strong initial system.",
}
@inproceedings{zalmout-habash-2019-adversarial,
    title = "Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling",
    author = "Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1173",
    doi = "10.18653/v1/P19-1173",
    pages = "1775--1786",
    abstract = "Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity. Dialectal variants of morphologically rich languages suffer more as they tend to be more noisy and have less resources. In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging. We use multitask learning for joint morphological modeling for the features within two dialects, and as a knowledge-transfer scheme for cross-dialectal modeling. We use adversarial training to learn dialect invariant features that can help the knowledge-transfer scheme from the high to low-resource variants. We work with two dialectal variants: Modern Standard Arabic (high-resource {``}dialect{'}{''}) and Egyptian Arabic (low-resource dialect) as a case study. Our models achieve state-of-the-art results for both. Furthermore, adversarial training provides more significant improvement when using smaller training datasets in particular.",
}
@inproceedings{chen-etal-2019-neural,
    title = "Neural Machine Translation with Reordering Embeddings",
    author = "Chen, Kehai  and
      Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1174",
    doi = "10.18653/v1/P19-1174",
    pages = "1787--1799",
    abstract = "The reordering model plays an important role in phrase-based statistical machine translation. However, there are few works that exploit the reordering information in neural machine translation. In this paper, we propose a reordering mechanism to learn the reordering embedding of a word based on its contextual information. These learned reordering embeddings are stacked together with self-attention networks to learn sentence representation for machine translation. The reordering mechanism can be easily integrated into both the encoder and the decoder in the Transformer translation system. Experimental results on WMT{'}14 English-to-German, NIST Chinese-to-English, and WAT Japanese-to-English translation tasks demonstrate that the proposed methods can significantly improve the performance of the Transformer.",
}
@inproceedings{bulte-tezcan-2019-neural,
    title = "Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation",
    author = "Bulte, Bram  and
      Tezcan, Arda",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1175",
    doi = "10.18653/v1/P19-1175",
    pages = "1800--1809",
    abstract = "We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems. The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation.",
}
@inproceedings{wang-etal-2019-learning,
    title = "Learning Deep Transformer Models for Machine Translation",
    author = "Wang, Qiang  and
      Li, Bei  and
      Xiao, Tong  and
      Zhu, Jingbo  and
      Li, Changliang  and
      Wong, Derek F.  and
      Chao, Lidia S.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1176",
    doi = "10.18653/v1/P19-1176",
    pages = "1810--1822",
    abstract = "Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT{'}16 English-German and NIST OpenMT{'}12 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.",
}
@inproceedings{shu-etal-2019-generating,
    title = "Generating Diverse Translations with Sentence Codes",
    author = "Shu, Raphael  and
      Nakayama, Hideki  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1177",
    doi = "10.18653/v1/P19-1177",
    pages = "1823--1827",
    abstract = "Users of machine translation systems may desire to obtain multiple candidates translated in different ways. In this work, we attempt to obtain diverse translations by using sentence codes to condition the sentence generation. We describe two methods to extract the codes, either with or without the help of syntax information. For diverse generation, we sample multiple candidates, each of which conditioned on a unique code. Experiments show that the sampled translations have much higher diversity scores when using reasonable sentence codes, where the translation quality is still on par with the baselines even under strong constraint imposed by the codes. In qualitative analysis, we show that our method is able to generate paraphrase translations with drastically different structures. The proposed approach can be easily adopted to existing translation systems as no modification to the model is required.",
}
@inproceedings{ruiter-etal-2019-self,
    title = "Self-Supervised Neural Machine Translation",
    author = "Ruiter, Dana  and
      Espa{\~n}a-Bonet, Cristina  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1178",
    doi = "10.18653/v1/P19-1178",
    pages = "1828--1834",
    abstract = "We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations. This is done in a self-supervised way without parallel data, in such a way that both tasks enhance each other during training. The method is language independent, introduces no additional hyper-parameters, and achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training.",
}
@inproceedings{salesky-etal-2019-exploring,
    title = "Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation",
    author = "Salesky, Elizabeth  and
      Sperber, Matthias  and
      Black, Alan W",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1179",
    doi = "10.18653/v1/P19-1179",
    pages = "1835--1841",
    abstract = "Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text. We show that a naive method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditional frame-level speech features. Specifically, we generate phoneme labels for speech frames and average consecutive frames with the same label to create shorter, higher-level source sequences for translation. We see improvements of up to 5 BLEU on both our high and low resource language pairs, with a reduction in training time of 60{\%}. Our improvements hold across multiple data sizes and two language pairs.",
}
@inproceedings{shi-etal-2019-visually,
    title = "Visually Grounded Neural Syntax Acquisition",
    author = "Shi, Haoyue  and
      Mao, Jiayuan  and
      Gimpel, Kevin  and
      Livescu, Karen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1180",
    doi = "10.18653/v1/P19-1180",
    pages = "1842--1861",
    abstract = "We present the Visually Grounded Neural Syntax Learner (VG-NSL), an approach for learning syntactic representations and structures without any explicit supervision. The model learns by looking at natural images and reading paired captions. VG-NSL generates constituency parse trees of texts, recursively composes representations for constituents, and matches them with images. We define concreteness of constituents by their matching scores with images, and use it to guide the parsing of text. Experiments on the MSCOCO data set show that VG-NSL outperforms various unsupervised parsing approaches that do not use visual grounding, in terms of F1 scores against gold parse trees. We find that VGNSL is much more stable with respect to the choice of random initialization and the amount of training data. We also find that the concreteness acquired by VG-NSL correlates well with a similar measure defined by linguists. Finally, we also apply VG-NSL to multiple languages in the Multi30K data set, showing that our model consistently outperforms prior unsupervised approaches.",
}
@inproceedings{jain-etal-2019-stay,
    title = "Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation",
    author = "Jain, Vihan  and
      Magalhaes, Gabriel  and
      Ku, Alexander  and
      Vaswani, Ashish  and
      Ie, Eugene  and
      Baldridge, Jason",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1181",
    doi = "10.18653/v1/P19-1181",
    pages = "1862--1872",
    abstract = "Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation(VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals. Despite recent progress, current research leaves unclear how much of a role language under-standing plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions. Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al.,2018b) and propose a new metric, Coverage weighted by Length Score (CLS). We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths. We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R). Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion.",
}
@inproceedings{tan-etal-2019-expressing,
    title = "Expressing Visual Relationships via Language",
    author = "Tan, Hao  and
      Dernoncourt, Franck  and
      Lin, Zhe  and
      Bui, Trung  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1182",
    doi = "10.18653/v1/P19-1182",
    pages = "1873--1883",
    abstract = "Describing images with text is a fundamental problem in vision-language research. Current studies in this domain mostly focus on single image captioning. However, in various real applications (e.g., image editing, difference interpretation, and retrieval), generating relational captions for two images, can also be very useful. This important problem has not been explored mostly due to lack of datasets and effective models. To push forward the research in this direction, we first introduce a new language-guided image editing dataset that contains a large number of real image pairs with corresponding editing instructions. We then propose a new relational speaker model based on an encoder-decoder architecture with static relational attention and sequential multi-head attention. We also extend the model with dynamic relational attention, which calculates visual alignment while decoding. Our models are evaluated on our newly collected and two public datasets consisting of image pairs annotated with relationship sentences. Experimental results, based on both automatic and human evaluation, demonstrate that our model outperforms all baselines and existing methods on all the datasets.",
}
@inproceedings{chen-etal-2019-weakly,
    title = "Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video",
    author = "Chen, Zhenfang  and
      Ma, Lin  and
      Luo, Wenhan  and
      Wong, Kwan-Yee Kenneth",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1183",
    doi = "10.18653/v1/P19-1183",
    pages = "1884--1894",
    abstract = "In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video. Specifically, given a natural sentence and a video, we localize a spatio-temporal tube in the video that semantically corresponds to the given sentence, with no reliance on any spatio-temporal annotations during training. First, a set of spatio-temporal tubes, referred to as instances, are extracted from the video. We then encode these instances and the sentence using our newly proposed attentive interactor which can exploit their fine-grained relationships to characterize their matching behaviors. Besides a ranking loss, a novel diversity loss is introduced to train our attentive interactor to strengthen the matching behaviors of reliable instance-sentence pairs and penalize the unreliable ones. We also contribute a dataset, called VID-sentence, based on the ImageNet video object detection dataset, to serve as a benchmark for our task. Results from extensive experiments demonstrate the superiority of our model over the baseline approaches.",
}
@inproceedings{haber-etal-2019-photobook,
    title = "The {P}hoto{B}ook Dataset: Building Common Ground through Visually-Grounded Dialogue",
    author = {Haber, Janosch  and
      Baumg{\"a}rtner, Tim  and
      Takmaz, Ece  and
      Gelderloos, Lieke  and
      Bruni, Elia  and
      Fern{\'a}ndez, Raquel},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1184",
    doi = "10.18653/v1/P19-1184",
    pages = "1895--1910",
    abstract = "This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions. We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected. To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain. Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction.",
}
@inproceedings{pasunuru-bansal-2019-continual,
    title = "Continual and Multi-Task Architecture Search",
    author = "Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1185",
    doi = "10.18653/v1/P19-1185",
    pages = "1911--1922",
    abstract = "Architecture search is the process of automatically learning the neural model or cell structure that best suits the given task. Recently, this approach has shown promising performance improvements (on language modeling and image classification) with reasonable training speed, using a weight sharing strategy called Efficient Neural Architecture Search (ENAS). In our work, we first introduce a novel continual architecture search (CAS) approach, so as to continually evolve the model parameters during the sequential training of several tasks, without losing performance on previously learned tasks (via block-sparsity and orthogonality constraints), thus enabling life-long learning. Next, we explore a multi-task architecture search (MAS) approach over ENAS for finding a unified, single cell structure that performs well across multiple tasks (via joint controller rewards), and hence allows more generalizable transfer of the cell structure knowledge to an unseen new task. We empirically show the effectiveness of our sequential continual learning and parallel multi-task learning based architecture search approaches on diverse sentence-pair classification tasks (GLUE) and multimodal-generation based video captioning tasks. Further, we present several ablations and analyses on the learned cell structures.",
}
@inproceedings{li-etal-2019-semi,
    title = "Semi-supervised Stochastic Multi-Domain Learning using Variational Inference",
    author = "Li, Yitong  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1186",
    doi = "10.18653/v1/P19-1186",
    pages = "1923--1934",
    abstract = "Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient quantity, and moreover, within any domain of text, data is often highly heterogenous. In this paper we propose a method to distill the important domain signal as part of a multi-domain learning system, using a latent variable model in which parts of a neural model are stochastically gated based on the inferred domain. We compare the use of discrete versus continuous latent variables, operating in a domain-supervised or a domain semi-supervised setting, where the domain is known only for a subset of training inputs. We show that our model leads to substantial performance improvements over competitive benchmark domain adaptation methods, including methods using adversarial learning.",
}
@inproceedings{le-titov-2019-boosting,
    title = "Boosting Entity Linking Performance by Leveraging Unlabeled Documents",
    author = "Le, Phong  and
      Titov, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1187",
    doi = "10.18653/v1/P19-1187",
    pages = "1935--1945",
    abstract = "Modern entity linking systems rely on large collections of documents specifically annotated for the task (e.g., AIDA CoNLL). In contrast, we propose an approach which exploits only naturally occurring information: unlabeled documents and Wikipedia. Our approach consists of two stages. First, we construct a high recall list of candidate entities for each mention in an unlabeled document. Second, we use the candidate lists as weak supervision to constrain our document-level entity linking model. The model treats entities as latent variables and, when estimated on a collection of unlabelled texts, learns to choose entities relying both on local context of each mention and on coherence with other entities in the document. The resulting approach rivals fully-supervised state-of-the-art systems on standard test sets. It also approaches their performance in the very challenging setting: when tested on a test set sampled from the data used to estimate the supervised systems. By comparing to Wikipedia-only training of our model, we demonstrate that modeling unlabeled documents is beneficial.",
}
@inproceedings{gaddy-klein-2019-pre,
    title = "Pre-Learning Environment Representations for Data-Efficient Neural Instruction Following",
    author = "Gaddy, David  and
      Klein, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1188",
    doi = "10.18653/v1/P19-1188",
    pages = "1946--1956",
    abstract = "We consider the problem of learning to map from natural language instructions to state transitions (actions) in a data-efficient manner. Our method takes inspiration from the idea that it should be easier to ground language to concepts that have already been formed through pre-linguistic observation. We augment a baseline instruction-following learner with an initial environment-learning phase that uses observations of language-free state transitions to induce a suitable latent representation of actions before processing the instruction-following training data. We show that mapping to pre-learned representations substantially improves performance over systems whose representations are learned from limited instructional data alone.",
}
@inproceedings{liu-etal-2019-reinforced,
    title = "Reinforced Training Data Selection for Domain Adaptation",
    author = "Liu, Miaofeng  and
      Song, Yan  and
      Zou, Hongbin  and
      Zhang, Tong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1189",
    doi = "10.18653/v1/P19-1189",
    pages = "1957--1968",
    abstract = "Supervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance. To solve the problem, training data selection (TDS) has been proven to be a prospective solution for domain adaptation in leveraging appropriate data. However, conventional TDS methods normally requires a predefined threshold which is neither easy to set nor can be applied across tasks, and models are trained separately with the TDS process. To make TDS self-adapted to data and task, and to combine it with model training, in this paper, we propose a reinforcement learning (RL) framework that synchronously searches for training instances relevant to the target domain and learns better representations for them. A selection distribution generator (SDG) is designed to perform the selection and is updated according to the rewards computed from the selected data, where a predictor is included in the framework to ensure a task-specific model can be trained on the selected data and provides feedback to rewards. Experimental results from part-of-speech tagging, dependency parsing, and sentiment analysis, as well as ablation studies, illustrate that the proposed framework is not only effective in data selection and representation, but also generalized to accommodate different NLP tasks.",
}
@inproceedings{li-etal-2019-generating,
    title = "Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding",
    author = "Li, Junyi  and
      Zhao, Wayne Xin  and
      Wen, Ji-Rong  and
      Song, Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1190",
    doi = "10.18653/v1/P19-1190",
    pages = "1969--1979",
    abstract = "Generating long and informative review text is a challenging natural language generation task. Previous work focuses on word-level generation, neglecting the importance of topical and syntactic characteristics from natural languages. In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process. First, we model the aspect transitions to capture the overall content flow. Then, to generate a sentence, an aspect-aware sketch will be predicted using an aspect-aware decoder. Finally, another decoder fills in the semantic slots by generating corresponding words. Our approach is able to jointly utilize aspect semantics, syntactic sketch, and context information. Extensive experiments results have demonstrated the effectiveness of the proposed model.",
}
@inproceedings{wang-etal-2019-paperrobot,
    title = "{P}aper{R}obot: Incremental Draft Generation of Scientific Ideas",
    author = "Wang, Qingyun  and
      Huang, Lifu  and
      Jiang, Zhiying  and
      Knight, Kevin  and
      Ji, Heng  and
      Bansal, Mohit  and
      Luan, Yi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1191",
    doi = "10.18653/v1/P19-1191",
    pages = "1980--1991",
    abstract = "We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30{\%}, 24{\%} and 12{\%} of the time, respectively.",
}
@inproceedings{liu-etal-2019-rhetorically,
    title = "Rhetorically Controlled Encoder-Decoder for {M}odern {C}hinese Poetry Generation",
    author = "Liu, Zhiqiang  and
      Fu, Zuohui  and
      Cao, Jie  and
      de Melo, Gerard  and
      Tam, Yik-Cheung  and
      Niu, Cheng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1192",
    doi = "10.18653/v1/P19-1192",
    pages = "1992--2001",
    abstract = "Rhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics. However, to date, it has not been considered in research on automatic poetry generation. In this paper, we propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation. Our model relies on a continuous latent variable as a rhetoric controller to capture various rhetorical patterns in an encoder, and then incorporates rhetoric-based mixtures while generating modern Chinese poetry. For metaphor and personification, an automated evaluation shows that our model outperforms state-of-the-art baselines by a substantial margin, while human evaluation shows that our model generates better poems than baseline methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics.",
}
@inproceedings{yang-etal-2019-enhancing,
    title = "Enhancing Topic-to-Essay Generation with External Commonsense Knowledge",
    author = "Yang, Pengcheng  and
      Li, Lei  and
      Luo, Fuli  and
      Liu, Tianyu  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1193",
    doi = "10.18653/v1/P19-1193",
    pages = "2002--2012",
    abstract = "Automatic topic-to-essay generation is a challenging task since it requires generating novel, diverse, and topic-consistent paragraph-level text with a set of topics as input. Previous work tends to perform essay generation based solely on the given topics while ignoring massive commonsense knowledge. However, this commonsense knowledge provides additional background information, which can help to generate essays that are more novel and diverse. Towards filling this gap, we propose to integrate commonsense from the external knowledge base into the generator through dynamic memory mechanism. Besides, the adversarial training based on a multi-label discriminator is employed to further improve topic-consistency. We also develop a series of automatic evaluation metrics to comprehensively assess the quality of the generated essay. Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.",
}
@inproceedings{luo-etal-2019-towards,
    title = "Towards Fine-grained Text Sentiment Transfer",
    author = "Luo, Fuli  and
      Li, Peng  and
      Yang, Pengcheng  and
      Zhou, Jie  and
      Tan, Yutong  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1194",
    doi = "10.18653/v1/P19-1194",
    pages = "2013--2022",
    abstract = "In this paper, we focus on the task of fine-grained text sentiment transfer (FGST). This task aims to revise an input sequence to satisfy a given sentiment intensity, while preserving the original semantic content. Different from the conventional sentiment transfer task that only reverses the sentiment polarity (positive/negative) of text, the FTST task requires more nuanced and fine-grained control of sentiment. To remedy this, we propose a novel Seq2SentiSeq model. Specifically, the numeric sentiment intensity value is incorporated into the decoder via a Gaussian kernel layer to finely control the sentiment intensity of the output. Moreover, to tackle the problem of lacking parallel data, we propose a cycle reinforcement learning algorithm to guide the model training. In this framework, the elaborately designed rewards can balance both sentiment transformation and content preservation, while not requiring any ground truth output. Experimental results show that our approach can outperform existing methods by a large margin in both automatic evaluation and human evaluation.",
}
@inproceedings{puduppully-etal-2019-data,
    title = "Data-to-text Generation with Entity Modeling",
    author = "Puduppully, Ratish  and
      Dong, Li  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1195",
    doi = "10.18653/v1/P19-1195",
    pages = "2023--2035",
    abstract = "Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end. These models rely on representation learning to select content appropriately, structure it coherently, and verbalize it grammatically, treating entities as nothing more than vocabulary tokens. In this work we propose an entity-centric neural architecture for data-to-text generation. Our model creates entity-specific representations which are dynamically updated. Text is generated conditioned on the data input and entity memory representations using hierarchical attention at each time step. We present experiments on the RotoWire benchmark and a (five times larger) new dataset on the baseball domain which we create. Our results show that the proposed model outperforms competitive baselines in automatic and human evaluation.",
}
@inproceedings{chen-etal-2019-ensuring,
    title = "Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation",
    author = "Chen, Jiangjie  and
      Wang, Ao  and
      Jiang, Haiyun  and
      Feng, Suo  and
      Li, Chenguang  and
      Xiao, Yanghua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1196",
    doi = "10.18653/v1/P19-1196",
    pages = "2036--2046",
    abstract = "A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.",
}
@inproceedings{ma-etal-2019-key,
    title = "Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation",
    author = "Ma, Shuming  and
      Yang, Pengcheng  and
      Liu, Tianyu  and
      Li, Peng  and
      Zhou, Jie  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1197",
    doi = "10.18653/v1/P19-1197",
    pages = "2047--2057",
    abstract = "Table-to-text generation aims to translate the structured data into the unstructured text. Most existing methods adopt the encoder-decoder framework to learn the transformation, which requires large-scale training samples. However, the lack of large parallel data is a major practical problem for many domains. In this work, we consider the scenario of low resource table-to-text generation, where only limited parallel data is available. We propose a novel model to separate the generation into two stages: key fact prediction and surface realization. It first predicts the key facts from the tables, and then generates the text with the key facts. The training of key fact prediction needs much fewer annotated data, while surface realization can be trained with pseudo parallel corpus. We evaluate our model on a biography generation dataset. Our model can achieve 27.34 BLEU score with only 1,000 parallel data, while the baseline model only obtain the performance of 9.71 BLEU score.",
}
@inproceedings{surya-etal-2019-unsupervised,
    title = "Unsupervised Neural Text Simplification",
    author = "Surya, Sai  and
      Mishra, Abhijit  and
      Laha, Anirban  and
      Jain, Parag  and
      Sankaranarayanan, Karthik",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1198",
    doi = "10.18653/v1/P19-1198",
    pages = "2058--2068",
    abstract = "The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora. The core framework is composed of a shared encoder and a pair of attentional-decoders, crucially assisted by discrimination-based losses and denoising. The framework is trained using unlabeled text collected from en-Wikipedia dump. Our analysis (both quantitative and qualitative involving human evaluators) on public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods. It also outperforms viable unsupervised baselines. Adding a few labeled pairs helps improve the performance further.",
}
@inproceedings{zhang-etal-2019-syntax,
    title = "Syntax-Infused Variational Autoencoder for Text Generation",
    author = "Zhang, Xinyuan  and
      Yang, Yi  and
      Yuan, Siyang  and
      Shen, Dinghan  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1199",
    doi = "10.18653/v1/P19-1199",
    pages = "2069--2078",
    abstract = "We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SIVAE contains two separate latent spaces, for sentences and syntactic trees. The evidence lower bound objective is redesigned correspondingly, by optimizing a joint distribution that accommodates two encoders and two decoders. SIVAE works with long short-term memory architectures to simultaneously generate sentences and syntactic trees. Two versions of SIVAE are proposed: one captures the dependencies between the latent variables through a conditional prior network, and the other treats the latent variables independently such that syntactically-controlled sentence generation can be performed. Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. Finally, we show that the proposed models can be used for unsupervised paraphrasing given different syntactic tree templates.",
}
@inproceedings{shen-etal-2019-towards,
    title = "Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models",
    author = "Shen, Dinghan  and
      Celikyilmaz, Asli  and
      Zhang, Yizhe  and
      Chen, Liqun  and
      Wang, Xin  and
      Gao, Jianfeng  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1200",
    doi = "10.18653/v1/P19-1200",
    pages = "2079--2089",
    abstract = "Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. However, previous works typically focus on synthesizing relatively short sentences (up to 20 words), and the posterior collapse issue has been widely identified in text-VAEs. In this paper, we propose to leverage several multi-level structures to learn a VAE model for generating long, and coherent text. In particular, a hierarchy of stochastic layers between the encoder and decoder networks is employed to abstract more informative and semantic-rich latent codes. Besides, we utilize a multi-level decoder structure to capture the coherent long-term structure inherent in long-form texts, by generating intermediate sentence representations as high-level plan vectors. Extensive experimental results demonstrate that the proposed multi-level VAE model produces more coherent and less repetitive long text compared to baselines as well as can mitigate the posterior-collapse issue.",
}
@inproceedings{ye-etal-2019-jointly,
    title = "Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization",
    author = "Ye, Hai  and
      Li, Wenjie  and
      Wang, Lu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1201",
    doi = "10.18653/v1/P19-1201",
    pages = "2090--2101",
    abstract = "Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing an NL description for some given MRs. Despite this intrinsic connection, the two tasks are often studied separately in prior work. In this paper, we model the duality of these two tasks via a joint learning framework, and demonstrate its effectiveness of boosting the performance on both tasks. Concretely, we propose a novel method of dual information maximization (DIM) to regularize the learning process, where DIM empirically maximizes the variational lower bounds of expected joint distributions of NL and MRs. We further extend DIM to a semi-supervision setup (SemiDIM), which leverages unlabeled data of both tasks. Experiments on three datasets of dialogue management and code generation (and summarization) show that performance on both semantic parsing and NL generation can be consistently improved by DIM, in both supervised and semi-supervised setups.",
}
@inproceedings{iso-etal-2019-learning,
    title = "Learning to Select, Track, and Generate for Data-to-Text",
    author = "Iso, Hayate  and
      Uehara, Yui  and
      Ishigaki, Tatsuya  and
      Noji, Hiroshi  and
      Aramaki, Eiji  and
      Kobayashi, Ichiro  and
      Miyao, Yusuke  and
      Okazaki, Naoaki  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1202",
    doi = "10.18653/v1/P19-1202",
    pages = "2102--2113",
    abstract = "We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our proposed model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generations. Experimental results show that our proposed model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.",
}
@inproceedings{pan-etal-2019-reinforced,
    title = "Reinforced Dynamic Reasoning for Conversational Question Generation",
    author = "Pan, Boyuan  and
      Li, Hao  and
      Yao, Ziyu  and
      Cai, Deng  and
      Sun, Huan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1203",
    doi = "10.18653/v1/P19-1203",
    pages = "2114--2124",
    abstract = "This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs). CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations or test user understanding of a given passage. Towards that end, we propose a new approach named Reinforced Dynamic Reasoning network, which is based on the general encoder-decoder framework but incorporates a reasoning procedure in a dynamic manner to better understand what has been asked and what to ask next about the passage into the general encoder-decoder framework. To encourage producing meaningful questions, we leverage a popular question answering (QA) model to provide feedback and fine-tune the question generator using a reinforcement learning mechanism. Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method in comparison with various baselines and model variants. Moreover, to show the applicability of our method, we also apply it to create multi-turn question-answering conversations for passages in SQuAD.",
}
@inproceedings{lev-etal-2019-talksumm,
    title = "{T}alk{S}umm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks",
    author = "Lev, Guy  and
      Shmueli-Scheuer, Michal  and
      Herzig, Jonathan  and
      Jerbi, Achiya  and
      Konopnicki, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1204",
    doi = "10.18653/v1/P19-1204",
    pages = "2125--2131",
    abstract = "Currently, no large-scale training data is available for the task of scientific paper summarization. In this paper, we propose a novel method that automatically generates summaries for scientific papers, by utilizing videos of talks at scientific conferences. We hypothesize that such talks constitute a coherent and concise description of the papers{'} content, and can form the basis for good summaries. We collected 1716 papers and their corresponding videos, and created a dataset of paper summaries. A model trained on this dataset achieves similar performance as models trained on a dataset of summaries created manually. In addition, we validated the quality of our summaries by human experts.",
}
@inproceedings{you-etal-2019-improving,
    title = "Improving Abstractive Document Summarization with Salient Information Modeling",
    author = "You, Yongjian  and
      Jia, Weijia  and
      Liu, Tianyi  and
      Yang, Wenmian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1205",
    doi = "10.18653/v1/P19-1205",
    pages = "2132--2141",
    abstract = "Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information. To tackle the above difficulties, we propose a Transformer-based encoder-decoder framework with two novel extensions for abstractive document summarization. Specifically, (1) to encode the documents comprehensively, we design a focus-attention mechanism and incorporate it into the encoder. This mechanism models a Gaussian focal bias on attention scores to enhance the perception of local context, which contributes to producing salient and informative summaries. (2) To distinguish salient information precisely, we design an independent saliency-selection network which manages the information flow from encoder to decoder. This network effectively reduces the influences of secondary information on the generated summaries. Experimental results on the popular CNN/Daily Mail benchmark demonstrate that our model outperforms other state-of-the-art baselines on the ROUGE metrics.",
}
@inproceedings{isonuma-etal-2019-unsupervised,
    title = "Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking",
    author = "Isonuma, Masaru  and
      Mori, Junichiro  and
      Sakata, Ichiro",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1206",
    doi = "10.18653/v1/P19-1206",
    pages = "2142--2152",
    abstract = "This paper focuses on the end-to-end abstractive summarization of a single product review without supervision. We assume that a review can be described as a discourse tree, in which the summary is the root, and the child sentences explain their parent in detail. By recursively estimating a parent from its children, our model learns the latent discourse tree without an external parser and generates a concise summary. We also introduce an architecture that ranks the importance of each sentence on the tree to support summary generation focusing on the main review point. The experimental results demonstrate that our model is competitive with or outperforms other unsupervised approaches. In particular, for relatively long reviews, it achieves a competitive or better performance than supervised models. The induced tree shows that the child sentences provide additional information about their parent, and the generated summary abstracts the entire review.",
}
@inproceedings{wang-etal-2019-biset,
    title = "{B}i{SET}: Bi-directional Selective Encoding with Template for Abstractive Summarization",
    author = "Wang, Kai  and
      Quan, Xiaojun  and
      Wang, Rui",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1207",
    doi = "10.18653/v1/P19-1207",
    pages = "2153--2162",
    abstract = "The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset are conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.",
}
@inproceedings{chan-etal-2019-neural,
    title = "Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards",
    author = "Chan, Hou Pong  and
      Chen, Wang  and
      Wang, Lu  and
      King, Irwin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1208",
    doi = "10.18653/v1/P19-1208",
    pages = "2163--2174",
    abstract = "Generating keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Although existing generative models are capable of predicting multiple keyphrases for an input document as well as determining the number of keyphrases to generate, they still suffer from the problem of generating too few keyphrases. To address this problem, we propose a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases. Furthermore, we introduce a new evaluation method that incorporates name variations of the ground-truth keyphrases using the Wikipedia knowledge base. Thus, our evaluation method can more robustly evaluate the quality of predicted keyphrases. Extensive experiments on five real-world datasets of different scales demonstrate that our RL approach consistently and significantly improves the performance of the state-of-the-art generative models with both conventional and new evaluation methods.",
}
@inproceedings{lebanoff-etal-2019-scoring,
    title = "Scoring Sentence Singletons and Pairs for Abstractive Summarization",
    author = "Lebanoff, Logan  and
      Song, Kaiqiang  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Kim, Seokhwan  and
      Chang, Walter  and
      Liu, Fei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1209",
    doi = "10.18653/v1/P19-1209",
    pages = "2175--2189",
    abstract = "When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.",
}
@inproceedings{li-etal-2019-keep,
    title = "Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization",
    author = "Li, Manling  and
      Zhang, Lingyu  and
      Ji, Heng  and
      Radke, Richard J.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1210",
    doi = "10.18653/v1/P19-1210",
    pages = "2190--2196",
    abstract = "Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention across three levels: segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional text features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that the utterance is more important if the speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.",
}
@inproceedings{chen-chen-2019-adversarial,
    title = "Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation",
    author = "Chen, Francine  and
      Chen, Yan-Ying",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1211",
    doi = "10.18653/v1/P19-1211",
    pages = "2197--2203",
    abstract = "A common issue in training a deep learning, abstractive summarization model is lack of a large set of training summaries. This paper examines techniques for adapting from a labeled source domain to an unlabeled target domain in the context of an encoder-decoder model for text generation. In addition to adversarial domain adaptation (ADA), we introduce the use of artificial titles and sequential training to capture the grammatical style of the unlabeled target domain. Evaluation on adapting to/from news articles and Stack Exchange posts indicates that the use of these techniques can boost performance for both unsupervised adaptation as well as fine-tuning with limited target data.",
}
@inproceedings{sharma-etal-2019-bigpatent,
    title = "{BIGPATENT}: A Large-Scale Dataset for Abstractive and Coherent Summarization",
    author = "Sharma, Eva  and
      Li, Chen  and
      Wang, Lu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1212",
    doi = "10.18653/v1/P19-1212",
    pages = "2204--2213",
    abstract = "Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article{'}s global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research.",
}
@inproceedings{falke-etal-2019-ranking,
    title = "Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference",
    author = "Falke, Tobias  and
      Ribeiro, Leonardo F. R.  and
      Utama, Prasetya Ajie  and
      Dagan, Ido  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1213",
    doi = "10.18653/v1/P19-1213",
    pages = "2214--2220",
    abstract = "While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice. In this paper, we evaluate summaries produced by state-of-the-art models via crowdsourcing and show that such errors occur frequently, in particular with more abstractive models. We study whether textual entailment predictions can be used to detect such errors and if they can be reduced by reranking alternative predicted summaries. That leads to an interesting downstream application for entailment models. In our experiments, we find that out-of-the-box entailment models trained on NLI datasets do not yet offer the desired performance for the downstream task and we therefore release our annotations as additional test data for future extrinsic evaluations of NLI.",
}
@inproceedings{wang-etal-2019-self,
    title = "Self-Supervised Learning for Contextualized Extractive Summarization",
    author = "Wang, Hong  and
      Wang, Xin  and
      Xiong, Wenhan  and
      Yu, Mo  and
      Guo, Xiaoxiao  and
      Chang, Shiyu  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1214",
    doi = "10.18653/v1/P19-1214",
    pages = "2221--2227",
    abstract = "Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level. In this paper, we aim to improve this task by introducing three auxiliary pre-training tasks that learn to capture the document-level context in a self-supervised fashion. Experiments on the widely-used CNN/DM dataset validate the effectiveness of the proposed auxiliary tasks. Furthermore, we show that after pre-training, a clean model with simple building blocks is able to outperform previous state-of-the-art that are carefully designed.",
}
@inproceedings{ben-abacha-demner-fushman-2019-summarization,
    title = "On the Summarization of Consumer Health Questions",
    author = "Ben Abacha, Asma  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1215",
    doi = "10.18653/v1/P19-1215",
    pages = "2228--2234",
    abstract = "Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16{\%}. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.",
}
@inproceedings{zhao-etal-2019-unsupervised,
    title = "Unsupervised Rewriter for Multi-Sentence Compression",
    author = "Zhao, Yang  and
      Shen, Xiaoyu  and
      Bi, Wei  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1216",
    doi = "10.18653/v1/P19-1216",
    pages = "2235--2240",
    abstract = "Multi-sentence compression (MSC) aims to generate a grammatical but reduced compression from multiple input sentences while retaining their key information. Previous dominating approach for MSC is the extraction-based word graph approach. A few variants further leveraged lexical substitution to yield more abstractive compression. However, two limitations exist. First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression. Second, lexical substitution is often inappropriate without the consideration of context information. To tackle the above-mentioned issues, we present a neural rewriter for multi-sentence compression that does not need any parallel corpus. Empirical studies have shown that our approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation. A parallel corpus with more than 140,000 (sentence group, compression) pairs is also constructed as a by-product for future research.",
}
@inproceedings{yu-etal-2019-inferential,
    title = "Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text",
    author = "Yu, Jianxing  and
      Zha, Zhengjun  and
      Yin, Jian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1217",
    doi = "10.18653/v1/P19-1217",
    pages = "2241--2251",
    abstract = "This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills. In particular, we first encode the given document, question and options in a context aware way. We then propose a new network to solve the inference problem by decomposing it into a series of attention-based reasoning steps. The result of the previous step acts as the context of next step. To make each step can be directly inferred from the text, we design an operational cell with prior structure. By recursively linking the cells, the inferred results are synthesized together to form the evidence chain for reasoning, where the reasoning direction can be guided by imposing structural constraints to regulate interactions on the cells. Moreover, a termination mechanism is introduced to dynamically determine the uncertain reasoning depth, and the network is trained by reinforcement learning. Experimental results on 3 popular data sets, including MCTest, RACE and MultiRC, demonstrate the effectiveness of our approach.",
}
@inproceedings{zhuang-wang-2019-token,
    title = "Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension",
    author = "Zhuang, Yimeng  and
      Wang, Huadong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1218",
    doi = "10.18653/v1/P19-1218",
    pages = "2252--2262",
    abstract = "Multi-passage reading comprehension requires the ability to combine cross-passage information and reason over multiple passages to infer the answer. In this paper, we introduce the Dynamic Self-attention Network (DynSAN) for multi-passage reading comprehension task, which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. The core module of the dynamic self-attention is a proposed gated token selection mechanism, which dynamically selects important tokens from a sequence. These chosen tokens will attend to each other via a self-attention mechanism to model long-range dependencies. Besides, convolutional layers are combined with the dynamic self-attention to enhance the model{'}s capacity of extracting local semantic. The experimental results show that the proposed DynSAN achieves new state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets. Further ablation study also validates the effectiveness of our model components.",
}
@inproceedings{wang-jiang-2019-explicit,
    title = "Explicit Utilization of General Knowledge in Machine Reading Comprehension",
    author = "Wang, Chao  and
      Jiang, Hui",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1219",
    doi = "10.18653/v1/P19-1219",
    pages = "2263--2272",
    abstract = "To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings. On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair. On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms. Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them. When only a subset (20{\%}-80{\%}) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise.",
}
@inproceedings{nishida-etal-2019-multi,
    title = "Multi-style Generative Reading Comprehension",
    author = "Nishida, Kyosuke  and
      Saito, Itsumi  and
      Nishida, Kosuke  and
      Shinoda, Kazutoshi  and
      Otsuka, Atsushi  and
      Asano, Hisako  and
      Tomita, Junji",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1220",
    doi = "10.18653/v1/P19-1220",
    pages = "2273--2284",
    abstract = "This study tackles generative reading comprehension (RC), which consists of answering questions based on textual evidence and natural language generation (NLG). We propose a multi-style abstractive summarization model for question answering, called Masque. The proposed model has two key characteristics. First, unlike most studies on RC that have focused on extracting an answer span from the provided passages, our model instead focuses on generating a summary from the question and multiple passages. This serves to cover various answer styles required for real-world applications. Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved. This also enables our model to give an answer in the target style. Experiments show that our model achieves state-of-the-art performance on the Q{\&}A task and the Q{\&}A + NLG task of MS MARCO 2.1 and the summary task of NarrativeQA. We observe that the transfer of the style-independent NLG capability to the target style is the key to its success.",
}
@inproceedings{hu-etal-2019-retrieve,
    title = "Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension",
    author = "Hu, Minghao  and
      Peng, Yuxing  and
      Huang, Zhen  and
      Li, Dongsheng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1221",
    doi = "10.18653/v1/P19-1221",
    pages = "2285--2295",
    abstract = "This paper considers the reading comprehension task in which multiple documents are given as input. Prior work has shown that a pipeline of retriever, reader, and reranker can improve the overall performance. However, the pipeline system is inefficient since the input is re-encoded within each module, and is unable to leverage upstream components to help downstream training. In this work, we present RE$^3$QA, a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. Unlike previous pipelined approaches, RE$^3$QA shares contextualized text representation across different components, and is carefully designed to use high-quality upstream outputs (e.g., retrieved context or candidate answers) for directly supervising downstream modules (e.g., the reader or the reranker). As a result, the whole network can be trained end-to-end to avoid the context inconsistency problem. Experiments show that our model outperforms the pipelined baseline and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD.",
}
@inproceedings{feldman-el-yaniv-2019-multi,
    title = "Multi-Hop Paragraph Retrieval for Open-Domain Question Answering",
    author = "Feldman, Yair  and
      El-Yaniv, Ran",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1222",
    doi = "10.18653/v1/P19-1222",
    pages = "2296--2309",
    abstract = "This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, respectively.",
}
@inproceedings{zhong-zettlemoyer-2019-e3,
    title = "{E}3: Entailment-driven Extracting and Editing for Conversational Machine Reading",
    author = "Zhong, Victor  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1223",
    doi = "10.18653/v1/P19-1223",
    pages = "2310--2320",
    abstract = "Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made (e.g. whether they need certain income levels or veteran status). The key challenge is that these rules are only provided in the form of a procedural text (e.g. guidelines from government website) which the system must read to figure out what to ask the user. We present a new conversational machine reading model that jointly extracts a set of decision rules from the procedural text while reasoning about which are entailed by the conversational history and which still need to be edited to create questions for the user. On the recently introduced ShARC conversational machine reading dataset, our Entailment-driven Extract and Edit network (E3) achieves a new state-of-the-art, outperforming existing systems as well as a new BERT-based baseline. In addition, by explicitly highlighting which information still needs to be gathered, E3 provides a more explainable alternative to prior work. We release source code for our models and experiments at https://github.com/vzhong/e3.",
}
@inproceedings{krishna-iyyer-2019-generating,
    title = "Generating Question-Answer Hierarchies",
    author = "Krishna, Kalpesh  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1224",
    doi = "10.18653/v1/P19-1224",
    pages = "2321--2334",
    abstract = "The process of knowledge acquisition can be viewed as a question-answer game between a student and a teacher in which the student typically starts by asking broad, open-ended questions before drilling down into specifics (Hintikka, 1981; Hakkarainen and Sintonen, 2002). This pedagogical perspective motivates a new way of representing documents. In this paper, we present SQUASH (Specificity-controlled Question-Answer Hierarchies), a novel and challenging text generation task that converts an input document into a hierarchy of question-answer pairs. Users can click on high-level questions (e.g., {``}Why did Frodo leave the Fellowship?{''}) to reveal related but more specific questions (e.g., {``}Who did Frodo leave with?{''}). Using a question taxonomy loosely based on Lehnert (1978), we classify questions in existing reading comprehension datasets as either GENERAL or SPECIFIC . We then use these labels as input to a pipelined system centered around a conditional neural language model. We extensively evaluate the quality of the generated QA hierarchies through crowdsourced experiments and report strong empirical results.",
}
@inproceedings{nishida-etal-2019-answering,
    title = "Answering while Summarizing: Multi-task Learning for Multi-hop {QA} with Evidence Extraction",
    author = "Nishida, Kosuke  and
      Nishida, Kyosuke  and
      Nagata, Masaaki  and
      Otsuka, Atsushi  and
      Saito, Itsumi  and
      Asano, Hisako  and
      Tomita, Junji",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1225",
    doi = "10.18653/v1/P19-1225",
    pages = "2335--2345",
    abstract = "Question answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop QA, which requires the system to return the answer with evidence sentences by reasoning and gathering disjoint pieces of the reference texts. It proposes the Query Focused Extractor (QFE) model for evidence extraction and uses multi-task learning with the QA model. QFE is inspired by extractive summarization models; compared with the existing method, which extracts each evidence sentence independently, it sequentially extracts evidence sentences by using an RNN with an attention mechanism on the question sentence. It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence. Experimental results show that QFE with a simple RC baseline model achieves a state-of-the-art evidence extraction score on HotpotQA. Although designed for RC, it also achieves a state-of-the-art evidence extraction score on FEVER, which is a recognizing textual entailment task on a large textual database.",
}
@inproceedings{yang-etal-2019-enhancing-pre,
    title = "Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension",
    author = "Yang, An  and
      Wang, Quan  and
      Liu, Jing  and
      Liu, Kai  and
      Lyu, Yajuan  and
      Wu, Hua  and
      She, Qiaoqiao  and
      Li, Sujian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1226",
    doi = "10.18653/v1/P19-1226",
    pages = "2346--2357",
    abstract = "Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019).",
}
@inproceedings{liu-etal-2019-xqa,
    title = "{XQA}: A Cross-lingual Open-domain Question Answering Dataset",
    author = "Liu, Jiahua  and
      Lin, Yankai  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1227",
    doi = "10.18653/v1/P19-1227",
    pages = "2358--2368",
    abstract = "Open-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension. Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA. However, the success of these models relies on a massive volume of training data (usually in English), which is not available in many other languages, especially for those low-resource languages. Therefore, it is essential to investigate cross-lingual OpenQA. In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research. It consists of a training set in English as well as development and test sets in eight other languages. Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method (multilingual BERT). Experimental results show that the multilingual BERT model achieves the best results in almost all target languages, while the performance of cross-lingual OpenQA is still much lower than that of English. Our analysis indicates that the performance of cross-lingual OpenQA is related to not only how similar the target language and English are, but also how difficult the question set of the target language is. The XQA dataset is publicly available at http://github.com/thunlp/XQA.",
}
@inproceedings{kim-etal-2019-compound,
    title = "Compound Probabilistic Context-Free Grammars for Grammar Induction",
    author = "Kim, Yoon  and
      Dyer, Chris  and
      Rush, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1228",
    doi = "10.18653/v1/P19-1228",
    pages = "2369--2385",
    abstract = "We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context free grammar. In contrast to traditional formulations which learn a single stochastic grammar, our context-free rule probabilities are modulated by a per-sentence continuous latent variable, which induces marginal dependencies beyond the traditional context-free assumptions. Inference in this context-dependent grammar is performed by collapsed variational inference, in which an amortized variational posterior is placed on the continuous variable, and the latent trees are marginalized with dynamic programming. Experiments on English and Chinese show the effectiveness of our approach compared to recent state-of-the-art methods for grammar induction from words with neural language models.",
}
@inproceedings{li-etal-2019-semi-supervised,
    title = "Semi-supervised Domain Adaptation for Dependency Parsing",
    author = "Li, Zhenghua  and
      Peng, Xue  and
      Zhang, Min  and
      Wang, Rui  and
      Si, Luo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1229",
    doi = "10.18653/v1/P19-1229",
    pages = "2386--2395",
    abstract = "During the past decades, due to the lack of sufficient labeled data, most studies on cross-domain parsing focus on unsupervised domain adaptation, assuming there is no target-domain training data. However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing. This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-aware datasets. We propose a simple domain embedding approach to merge the source- and target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning. In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margin.",
}
@inproceedings{zhou-zhao-2019-head,
    title = "{H}ead-{D}riven {P}hrase {S}tructure {G}rammar Parsing on {P}enn {T}reebank",
    author = "Zhou, Junru  and
      Zhao, Hai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1230",
    doi = "10.18653/v1/P19-1230",
    pages = "2396--2408",
    abstract = "Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich contextual syntactic and even semantic meanings. This paper makes the first attempt to formulate a simplified HPSG by integrating constituent and dependency formal representations into head-driven phrase structure. Then two parsing algorithms are respectively proposed for two converted tree representations, division span and joint span. As HPSG encodes both constituent and dependency structure information, the proposed HPSG parsers may be regarded as a sort of joint decoder for both types of structures and thus are evaluated in terms of extracted or converted constituent and dependency parsing trees. Our parser achieves new state-of-the-art performance for both parsing tasks on Penn Treebank (PTB) and Chinese Penn Treebank, verifying the effectiveness of joint learning constituent and dependency structures. In details, we report 95.84 F1 of constituent parsing and 97.00{\%} UAS of dependency parsing on PTB.",
}
@inproceedings{peng-etal-2019-distantly,
    title = "Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning",
    author = "Peng, Minlong  and
      Xing, Xiaoyu  and
      Zhang, Qi  and
      Fu, Jinlan  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1231",
    doi = "10.18653/v1/P19-1231",
    pages = "2409--2419",
    abstract = "In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive-unlabeled (PU) learning problem and accordingly propose a novel PU learning algorithm to perform the task. We prove that the proposed algorithm can unbiasedly and consistently estimate the task loss as if there is fully labeled data. A key feature of the proposed method is that it does not require the dictionaries to label every entity within a sentence, and it even does not require the dictionaries to label all of the words constituting an entity. This greatly reduces the requirement on the quality of the dictionaries and makes our method generalize well with quite simple dictionaries. Empirical studies on four public NER datasets demonstrate the effectiveness of our proposed method. We have published the source code at \url{https://github.com/v-mipeng/LexiconNER}.",
}
@inproceedings{kurita-sogaard-2019-multi,
    title = "Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies",
    author = "Kurita, Shuhei  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1232",
    doi = "10.18653/v1/P19-1232",
    pages = "2420--2430",
    abstract = "In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees. We propose a new iterative predicate selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based and transition-based parsing approaches in order to handle multiple semantic head words. We train the IPS model using a combination of multi-task learning and task-specific policy gradient training. Trained this way, IPS achieves a new state of the art on the SemEval 2015 Task 18 datasets. Furthermore, we observe that policy gradient training learns an easy-first strategy.",
}
@inproceedings{liu-etal-2019-gcdt,
    title = "{GCDT}: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling",
    author = "Liu, Yijin  and
      Meng, Fandong  and
      Zhang, Jinchao  and
      Xu, Jinan  and
      Chen, Yufeng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1233",
    doi = "10.18653/v1/P19-1233",
    pages = "2431--2441",
    abstract = "Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs). However, the shallow connections between consecutive hidden states of RNNs and insufficient modeling of global information restrict the potential performance of those models. In this paper, we try to address these issues, and thus propose a Global Context enhanced Deep Transition architecture for sequence labeling named GCDT. We deepen the state transition path at each position in a sentence, and further assign every token with a global representation learned from the entire sentence. Experiments on two standard sequence labeling tasks show that, given only training data and the ubiquitous word embeddings (Glove), our GCDT achieves 91.96 F1 on the CoNLL03 NER task and 95.43 F1 on the CoNLL2000 Chunking task, which outperforms the best reported results under the same settings. Furthermore, by leveraging BERT as an additional resource, we establish new state-of-the-art results with 93.47 F1 on NER and 97.30 F1 on Chunking.",
}
@inproceedings{jin-etal-2019-unsupervised,
    title = "Unsupervised Learning of {PCFG}s with Normalizing Flow",
    author = "Jin, Lifeng  and
      Doshi-Velez, Finale  and
      Miller, Timothy  and
      Schwartz, Lane  and
      Schuler, William",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1234",
    doi = "10.18653/v1/P19-1234",
    pages = "2442--2452",
    abstract = "Unsupervised PCFG inducers hypothesize sets of compact context-free rules as explanations for sentences. PCFG induction not only provides tools for low-resource languages, but also plays an important role in modeling language acquisition (Bannard et al., 2009; Abend et al. 2017). However, current PCFG induction models, using word tokens as input, are unable to incorporate semantics and morphology into induction, and may encounter issues of sparse vocabulary when facing morphologically rich languages. This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information. Linguistically motivated sparsity and categorical distance constraints are imposed on the inducer as regularization. Experiments show that the PCFG induction model with normalizing flow produces grammars with state-of-the-art accuracy on a variety of different languages. Ablation further shows a positive effect of normalizing flow, context embeddings and proposed regularizers.",
}
@inproceedings{jin-schuler-2019-variance,
    title = "Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised {PCFG} Induction",
    author = "Jin, Lifeng  and
      Schuler, William",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1235",
    doi = "10.18653/v1/P19-1235",
    pages = "2453--2463",
    abstract = "In unsupervised grammar induction, data likelihood is known to be only weakly correlated with parsing accuracy, especially at convergence after multiple runs. In order to find a better indicator for quality of induced grammars, this paper correlates several linguistically- and psycholinguistically-motivated predictors to parsing accuracy on a large multilingual grammar induction evaluation data set. Results show that variance of average surprisal (VAS) better correlates with parsing accuracy than data likelihood and that using VAS instead of data likelihood for model selection provides a significant accuracy boost. Further evidence shows VAS to be a better candidate than data likelihood for predicting word order typology classification. Analyses show that VAS seems to separate content words from function words in natural language grammars, and to better arrange words with different frequencies into separate classes that are more consistent with linguistic theory.",
}
@inproceedings{jia-etal-2019-cross,
    title = "Cross-Domain {NER} using Cross-Domain Language Modeling",
    author = "Jia, Chen  and
      Liang, Xiaobo  and
      Zhang, Yue",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1236",
    doi = "10.18653/v1/P19-1236",
    pages = "2464--2474",
    abstract = "Due to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both the source and target domains. A disadvantage of such methods is that they cannot train for domains without NER data. To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing cross-domain and cross-task knowledge transfer by designing a novel parameter generation network. Results show that our method can effectively extract domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while also giving state-of-the-art results among supervised domain adaptation methods.",
}
@inproceedings{ji-etal-2019-graph,
    title = "Graph-based Dependency Parsing with Graph Neural Networks",
    author = "Ji, Tao  and
      Wu, Yuanbin  and
      Lan, Man",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1237",
    doi = "10.18653/v1/P19-1237",
    pages = "2475--2485",
    abstract = "We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN{'}s updating and aggregation functions. Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0{\%}, 94.3{\%}) among systems without using any external resources.",
}
@inproceedings{torr-etal-2019-wide,
    title = "Wide-Coverage Neural {A}* Parsing for {M}inimalist {G}rammars",
    author = "Torr, John  and
      Stanojevic, Milos  and
      Steedman, Mark  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1238",
    doi = "10.18653/v1/P19-1238",
    pages = "2486--2505",
    abstract = "Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky{'}s (1995) Minimalist Program. This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing. The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014; Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees. We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies. Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is cubic in the length of the sentence. The parser is publicly available.",
}
@inproceedings{cai-etal-2019-multi,
    title = "Multi-Modal Sarcasm Detection in {T}witter with Hierarchical Fusion Model",
    author = "Cai, Yitao  and
      Cai, Huiyu  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1239",
    doi = "10.18653/v1/P19-1239",
    pages = "2506--2515",
    abstract = "Sarcasm is a subtle form of language in which people express the opposite of what is implied. Previous works of sarcasm detection focused on texts. However, more and more social media platforms like Twitter allow users to create multi-modal messages, including texts, images, and videos. It is insufficient to detect sarcasm from multi-model messages based only on texts. In this paper, we focus on multi-modal sarcasm detection for tweets consisting of texts and images in Twitter. We treat text features, image features and image attributes as three modalities and propose a multi-modal hierarchical fusion model to address this task. Our model first extracts image features and attribute features, and then leverages attribute features and bidirectional LSTM network to extract text features. Features of three modalities are then reconstructed and fused into one feature vector for prediction. We create a multi-modal sarcasm detection dataset based on Twitter. Evaluation results on the dataset demonstrate the efficacy of our proposed model and the usefulness of the three modalities.",
}
@inproceedings{wang-etal-2019-topic,
    title = "Topic-Aware Neural Keyphrase Generation for Social Media Language",
    author = "Wang, Yue  and
      Li, Jing  and
      Chan, Hou Pong  and
      King, Irwin  and
      Lyu, Michael R.  and
      Shi, Shuming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1240",
    doi = "10.18653/v1/P19-1240",
    pages = "2516--2526",
    abstract = "A huge volume of user-generated content is daily produced on social media. To facilitate automatic language understanding, we study keyphrase prediction, distilling salient information from massive posts. While most existing methods extract words from source posts to form keyphrases, we propose a sequence-to-sequence (seq2seq) based neural keyphrase generation framework, enabling absent keyphrases to be created. Moreover, our model, being topic-aware, allows joint modeling of corpus-level latent topic representations, which helps alleviate data sparsity widely exhibited in social media language. Experiments on three datasets collected from English and Chinese social media platforms show that our model significantly outperforms both extraction and generation models without exploiting latent topics. Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.",
}
@inproceedings{ghosh-chowdhury-etal-2019-youtoo,
    title = "{\#}{Y}ou{T}oo? Detection of Personal Recollections of Sexual Harassment on Social Media",
    author = "Ghosh Chowdhury, Arijit  and
      Sawhney, Ramit  and
      Shah, Rajiv Ratn  and
      Mahata, Debanjan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1241",
    doi = "10.18653/v1/P19-1241",
    pages = "2527--2537",
    abstract = "The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat- ing to our social lives, particularly our health and well-being. The {\#}MeToo trend has led to people talking about personal experiences of harassment more openly. This work at- tempts to aggregate such experiences of sex- ual abuse to facilitate a better understanding of social media constructs and to bring about social change. It has been found that disclo- sure of abuse has positive psychological im- pacts. Hence, we contend that such informa- tion can leveraged to create better campaigns for social change by analyzing how users react to these stories and to obtain a better insight into the consequences of sexual abuse. We use a three part Twitter-Specific Social Media Lan- guage Model to segregate personal recollec- tions of sexual harassment from Twitter posts. An extensive comparison with state-of-the-art generic and specific models along with a de- tailed error analysis explores the merit of our proposed model.",
}
@inproceedings{maddela-etal-2019-multi,
    title = "Multi-task Pairwise Neural Ranking for Hashtag Segmentation",
    author = "Maddela, Mounica  and
      Xu, Wei  and
      Preo{\c{t}}iuc-Pietro, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1242",
    doi = "10.18653/v1/P19-1242",
    pages = "2538--2549",
    abstract = "Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. Our novel neural approaches demonstrate 24.6{\%} error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6{\%} increase in average recall on the SemEval 2017 sentiment analysis dataset.",
}
@inproceedings{field-tsvetkov-2019-entity,
    title = "Entity-Centric Contextual Affective Analysis",
    author = "Field, Anjalie  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1243",
    doi = "10.18653/v1/P19-1243",
    pages = "2550--2560",
    abstract = "While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.",
}
@inproceedings{ma-etal-2019-sentence,
    title = "Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks",
    author = "Ma, Jing  and
      Gao, Wei  and
      Joty, Shafiq  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1244",
    doi = "10.18653/v1/P19-1244",
    pages = "2561--2571",
    abstract = "Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer attends on sentences that can semantically infer the claim on top of the first attention; and 3) An output layer predicts the verdict based on the embedded evidence. Experimental results on three public benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.",
}
@inproceedings{wilson-mihalcea-2019-predicting,
    title = "Predicting Human Activities from User-Generated Content",
    author = "Wilson, Steven  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1245",
    doi = "10.18653/v1/P19-1245",
    pages = "2572--2582",
    abstract = "The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.",
}
@inproceedings{basile-etal-2019-write,
    title = "You Write like You Eat: Stylistic Variation as a Predictor of Social Stratification",
    author = "Basile, Angelo  and
      Gatt, Albert  and
      Nissim, Malvina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1246",
    doi = "10.18653/v1/P19-1246",
    pages = "2583--2593",
    abstract = "Inspired by Labov{'}s seminal work on stylisticvariation as a function of social stratification,we develop and compare neural models thatpredict a person{'}s presumed socio-economicstatus, obtained through distant supervision,from their writing style on social media. Thefocus of our work is on identifying the mostimportant stylistic parameters to predict socio-economic group. In particular, we show theeffectiveness of morpho-syntactic features aspredictors of style, in contrast to lexical fea-tures, which are good predictors of topic",
}
@inproceedings{li-goldwasser-2019-encoding,
    title = "Encoding Social Information with Graph Convolutional Networks for{P}olitical Perspective Detection in News Media",
    author = "Li, Chang  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1247",
    doi = "10.18653/v1/P19-1247",
    pages = "2594--2604",
    abstract = "Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks. We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents{'} social context. We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.",
}
@inproceedings{wan-etal-2019-fine,
    title = "Fine-Grained Spoiler Detection from Large-Scale Review Corpora",
    author = "Wan, Mengting  and
      Misra, Rishabh  and
      Nakashole, Ndapa  and
      McAuley, Julian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1248",
    doi = "10.18653/v1/P19-1248",
    pages = "2605--2610",
    abstract = "This paper presents computational approaches for automatically detecting critical plot twists in reviews of media products. First, we created a large-scale book review dataset that includes fine-grained spoiler annotations at the sentence-level, as well as book and (anonymized) user information. Second, we carefully analyzed this dataset, and found that: spoiler language tends to be book-specific; spoiler distributions vary greatly across books and review authors; and spoiler sentences tend to jointly appear in the latter part of reviews. Third, inspired by these findings, we developed an end-to-end neural network architecture to detect spoiler sentences in review corpora. Quantitative and qualitative results demonstrate that the proposed method substantially outperforms existing baselines.",
}
@inproceedings{wiegmann-etal-2019-celebrity,
    title = "Celebrity Profiling",
    author = "Wiegmann, Matti  and
      Stein, Benno  and
      Potthast, Martin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1249",
    doi = "10.18653/v1/P19-1249",
    pages = "2611--2618",
    abstract = "Celebrities are among the most prolific users of social media, promoting their personas and rallying followers. This activity is closely tied to genuine writing samples, which makes them worthy research subjects in many respects, not least profiling. With this paper we introduce the Webis Celebrity Corpus 2019. For its construction the Twitter feeds of 71,706 verified accounts have been carefully linked with their respective Wikidata items, crawling both. After cleansing, the resulting profiles contain an average of 29,968 words per profile and up to 239 pieces of personal information. A cross-evaluation that checked the correct association of Twitter account and Wikidata item revealed an error rate of only 0.6{\%}, rendering the profiles highly reliable. Our corpus comprises a wide cross-section of local and global celebrities, forming a unique combination of scale, profile comprehensiveness, and label reliability. We further establish the state of the art{'}s profiling performance by evaluating the winning approaches submitted to the PAN gender prediction tasks in a transfer learning experiment. They are only outperformed by our own deep learning approach, which we also use to exemplify celebrity occupation prediction for the first time.",
}
@inproceedings{fujita-etal-2019-dataset,
    title = "Dataset Creation for Ranking Constructive News Comments",
    author = "Fujita, Soichiro  and
      Kobayashi, Hayato  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1250",
    doi = "10.18653/v1/P19-1250",
    pages = "2619--2626",
    abstract = "Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task. However, most of them considered users{'} positive feedback, such as {``}Like{''}-button clicks, as a quality measure. In this paper, we address directly evaluating the quality of comments on the basis of {``}constructiveness,{''} separately from user feedback. To this end, we create a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores). Our experiments clarify that C-scores are not always related to users{'} positive feedback, and the performance of pairwise ranking models tends to be enhanced by the variation of comments rather than articles.",
}
@inproceedings{jiang-etal-2019-enhancing,
    title = "Enhancing Air Quality Prediction with Social Media and Natural Language Processing",
    author = "Jiang, Jyun-Yu  and
      Sun, Xue  and
      Wang, Wei  and
      Young, Sean",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1251",
    doi = "10.18653/v1/P19-1251",
    pages = "2627--2632",
    abstract = "Accompanied by modern industrial developments, air pollution has already become a major concern for human health. Hence, air quality measures, such as the concentration of PM2.5, have attracted increasing attention. Even some studies apply historical measurements into air quality forecast, the changes of air quality conditions are still hard to monitor. In this paper, we propose to exploit social media and natural language processing techniques to enhance air quality prediction. Social media users are treated as social sensors with their findings and locations. After filtering noisy tweets using word selection and topic modeling, a deep learning model based on convolutional neural networks and over-tweet-pooling is proposed to enhance air quality prediction. We conduct experiments on 7-month real-world Twitter datasets in the five most heavily polluted states in the USA. The results show that our approach significantly improves air quality prediction over the baseline that does not use social media by 6.9{\%} to 17.7{\%} in macro-F1 scores.",
}
@inproceedings{pan-etal-2019-twitter,
    title = "{T}witter Homophily: Network Based Prediction of User{'}s Occupation",
    author = "Pan, Jiaqi  and
      Bhardwaj, Rishabh  and
      Lu, Wei  and
      Chieu, Hai Leong  and
      Pan, Xinghao  and
      Puay, Ni Yi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1252",
    doi = "10.18653/v1/P19-1252",
    pages = "2633--2638",
    abstract = "In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user{'}s occupational class. We show that the content information of a user{'}s tweets, the profile descriptions of a user{'}s follower/following community, and the user{'}s social network provide useful information for classifying a user{'}s occupational group. In our study, we extend an existing data set for this problem, and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work. In our analysis, we found that by using the graph convolutional network to exploit social homophily, we can achieve competitive performance on this data set with just a small fraction of the training data.",
}
@inproceedings{qian-yu-2019-domain,
    title = "Domain Adaptive Dialog Generation via Meta Learning",
    author = "Qian, Kun  and
      Yu, Zhou",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1253",
    doi = "10.18653/v1/P19-1253",
    pages = "2639--2649",
    abstract = "Domain adaptation is an essential task in dialog system building because there are so many new dialog tasks created for different needs every day. Collecting and annotating training data for these new tasks is costly since it involves real user interactions. We propose a domain adaptive dialog generation method based on meta-learning (DAML). DAML is an end-to-end trainable dialog system model that learns from multiple rich-resource tasks and then adapts to new domains with minimal training samples. We train a dialog system model using multiple rich-resource single-domain dialog data by applying the model-agnostic meta-learning algorithm to dialog domain. The model is capable of learning a competitive dialog system on a new domain with only a few training examples in an efficient manner. The two-step gradient updates in DAML enable the model to learn general features across multiple tasks. We evaluate our method on a simulated dialog dataset and achieve state-of-the-art performance, which is generalizable to new tasks.",
}
@inproceedings{fan-etal-2019-strategies,
    title = "Strategies for Structuring Story Generation",
    author = "Fan, Angela  and
      Lewis, Mike  and
      Dauphin, Yann",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1254",
    doi = "10.18653/v1/P19-1254",
    pages = "2650--2660",
    abstract = "Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories.",
}
@inproceedings{hua-etal-2019-argument,
    title = "Argument Generation with Retrieval, Planning, and Realization",
    author = "Hua, Xinyu  and
      Hu, Zhe  and
      Wang, Lu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1255",
    doi = "10.18653/v1/P19-1255",
    pages = "2661--2672",
    abstract = "Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content.",
}
@inproceedings{nie-etal-2019-simple,
    title = "A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation",
    author = "Nie, Feng  and
      Yao, Jin-Ge  and
      Wang, Jinpeng  and
      Pan, Rong  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1256",
    doi = "10.18653/v1/P19-1256",
    pages = "2673--2679",
    abstract = "Recent neural language generation systems often \textit{hallucinate} contents (i.e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text. To mitigate this issue, we propose to integrate a language understanding module for data refinement with self-training iterations to effectively induce strong equivalence between the input data and the paired text. Experiments on the E2E challenge dataset show that our proposed framework can reduce more than 50{\%} relative unaligned noise from the original data-text pairs. A vanilla sequence-to-sequence neural NLG model trained on the refined data has improved on content correctness compared with the current state-of-the-art ensemble generator.",
}
@inproceedings{yang-etal-2019-cross,
    title = "Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information",
    author = "Yang, Pengcheng  and
      Zhang, Zhihan  and
      Luo, Fuli  and
      Li, Lei  and
      Huang, Chengyang  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1257",
    doi = "10.18653/v1/P19-1257",
    pages = "2680--2686",
    abstract = "Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms. Previous work focuses on automatic commenting based solely on textual content. However, in real-scenarios, online articles usually contain multiple modal contents. For instance, graphic news contains plenty of images in addition to text. Contents other than text are also vital because they are not only more attractive to the reader but also may provide critical information. To remedy this, we propose a new task: cross-model automatic commenting (CMAC), which aims to make comments by integrating multiple modal contents. We construct a large-scale dataset for this task and explore several representative methods. Going a step further, an effective co-attention model is presented to capture the dependency between textual and visual information. Evaluation results show that our proposed model can achieve better performance than competitive baselines.",
}
@inproceedings{chen-etal-2019-working,
    title = "A Working Memory Model for Task-oriented Dialog Response Generation",
    author = "Chen, Xiuyi  and
      Xu, Jiaming  and
      Xu, Bo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1258",
    doi = "10.18653/v1/P19-1258",
    pages = "2687--2693",
    abstract = "Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the {``}activated{''} contents from the long-term memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.",
}
@inproceedings{ding-etal-2019-cognitive,
    title = "Cognitive Graph for Multi-Hop Reading Comprehension at Scale",
    author = "Ding, Ming  and
      Zhou, Chang  and
      Chen, Qibin  and
      Yang, Hongxia  and
      Tang, Jie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1259",
    doi = "10.18653/v1/P19-1259",
    pages = "2694--2703",
    abstract = "We propose a new CogQA framework for multi-hop reading comprehension question answering in web-scale documents. Founded on the dual process theory in cognitive science, the framework gradually builds a \textit{cognitive graph} in an iterative process by coordinating an implicit extraction module (System 1) and an explicit reasoning module (System 2). While giving accurate answers, our framework further provides explainable reasoning paths. Specifically, our implementation based on BERT and graph neural network efficiently handles millions of documents for multi-hop reasoning questions in the HotpotQA fullwiki dataset, achieving a winning joint $F_1$ score of 34.9 on the leaderboard, compared to 23.1 of the best competitor.",
}
@inproceedings{tu-etal-2019-multi,
    title = "Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs",
    author = "Tu, Ming  and
      Wang, Guangtao  and
      Huang, Jing  and
      Tang, Yun  and
      He, Xiaodong  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1260",
    doi = "10.18653/v1/P19-1260",
    pages = "2704--2713",
    abstract = "Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi-hop RC problem. We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. The advantage of HDE graph is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning over the HDE graph with nodes representation initialized with co-attention and self-attention based context encoders. We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the blind test set of the Qangaroo WikiHop data set, our HDE graph based single model delivers competitive result, and the ensemble model achieves the state-of-the-art performance.",
}
@inproceedings{jiang-etal-2019-explore,
    title = "Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension",
    author = "Jiang, Yichen  and
      Joshi, Nitish  and
      Chen, Yen-Chun  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1261",
    doi = "10.18653/v1/P19-1261",
    pages = "2714--2725",
    abstract = "Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context. To achieve this, we propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr). First, the Document Explorer iteratively selects relevant documents and represents divergent reasoning chains in a tree structure so as to allow assimilating information from all chains. The Answer Proposer then proposes an answer from every root-to-leaf path in the reasoning tree. Finally, the Evidence Assembler extracts a key sentence containing the proposed answer from every path and combines them to predict the final answer. Intuitively, EPAr approximates the coarse-to-fine-grained comprehension behavior of human readers when facing multiple long documents. We jointly optimize our 3 modules by minimizing the sum of losses from each stage conditioned on the previous stage{'}s output. On two multi-hop reading comprehension datasets WikiHop and MedHop, our EPAr model achieves significant improvements over the baseline and competitive results compared to the state-of-the-art model. We also present multiple reasoning-chain-recovery tests and ablation studies to demonstrate our system{'}s ability to perform interpretable and accurate reasoning.",
}
@inproceedings{jiang-bansal-2019-avoiding,
    title = "Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop {QA}",
    author = "Jiang, Yichen  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1262",
    doi = "10.18653/v1/P19-1262",
    pages = "2726--2736",
    abstract = "Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi-hop HotpotQA (Yang et al., 2018) dataset, the examples often contain reasoning shortcuts through which models can directly locate the answer by word-matching the question with a sentence in the context. We demonstrate this issue by constructing adversarial documents that create contradicting answers to the shortcut but do not affect the validity of the original answer. The performance of strong baseline models drops significantly on our adversarial test, indicating that they are indeed exploiting the shortcuts rather than performing multi-hop reasoning. After adversarial training, the baseline{'}s performance improves but is still limited on the adversarial test. Hence, we use a control unit that dynamically attends to the question at different reasoning hops to guide the model{'}s multi-hop reasoning. We show that our 2-hop model trained on the regular data is more robust to the adversaries than the baseline. After adversarial training, it not only achieves significant improvements over its counterpart trained on regular data, but also outperforms the adversarially-trained baseline significantly. Finally, we sanity-check that these improvements are not obtained by exploiting potential new shortcuts in the adversarial data, but indeed due to robust multi-hop reasoning skills of the models.",
}
@inproceedings{kundu-etal-2019-exploiting,
    title = "Exploiting Explicit Paths for Multi-hop Reading Comprehension",
    author = "Kundu, Souvik  and
      Khot, Tushar  and
      Sabharwal, Ashish  and
      Clark, Peter",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1263",
    doi = "10.18653/v1/P19-1263",
    pages = "2737--2747",
    abstract = "We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text. It generates potential paths through passages and scores them without any direct path supervision. The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path. To capture additional context, PathNet also composes the passage representations along each path to compute a passage-based representation. Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages. We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching state-of-the-art performance.",
}
@inproceedings{clark-etal-2019-sentence,
    title = "Sentence Mover{'}s Similarity: Automatic Evaluation for Multi-Sentence Texts",
    author = "Clark, Elizabeth  and
      Celikyilmaz, Asli  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1264",
    doi = "10.18653/v1/P19-1264",
    pages = "2748--2760",
    abstract = "For evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming. The most common automatic metrics, like BLEU and ROUGE, depend on exact word matching, an inflexible approach for measuring semantic similarity. We introduce methods based on sentence mover{'}s similarity; our automatic metrics evaluate text in a continuous space using word and sentence embeddings. We find that sentence-based metrics correlate with human judgments significantly better than ROUGE, both on machine-generated summaries (average length of 3.4 sentences) and human-authored essays (average length of 7.5). We also show that sentence mover{'}s similarity can be used as a reward when learning a generation model via reinforcement learning; we present both automatic and human evaluations of summaries learned in this way, finding that our approach outperforms ROUGE.",
}
@inproceedings{schulz-etal-2019-analysis,
    title = "Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains",
    author = "Schulz, Claudia  and
      Meyer, Christian M.  and
      Kiesewetter, Jan  and
      Sailer, Michael  and
      Bauer, Elisabeth  and
      Fischer, Martin R.  and
      Fischer, Frank  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1265",
    doi = "10.18653/v1/P19-1265",
    pages = "2761--2772",
    abstract = "Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks.",
}
@inproceedings{dror-etal-2019-deep,
    title = "Deep Dominance - How to Properly Compare Deep Neural Models",
    author = "Dror, Rotem  and
      Shlomov, Segev  and
      Reichart, Roi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1266",
    doi = "10.18653/v1/P19-1266",
    pages = "2773--2785",
    abstract = "Comparing between Deep Neural Network (DNN) models based on their performance on unseen data is crucial for the progress of the NLP field. However, these models have a large number of hyper-parameters and, being non-convex, their convergence point depends on the random values chosen at initialization and during training. Proper DNN comparison hence requires a comparison between their empirical score distributions on unseen data, rather than between single evaluation scores as is standard for more simple, convex models. In this paper, we propose to adapt to this problem a recently proposed test for the Almost Stochastic Dominance relation between two distributions. We define the criteria for a high quality comparison method between DNNs, and show, both theoretically and through analysis of extensive experimental results with leading DNN models for sequence tagging tasks, that the proposed test meets all criteria while previously proposed methods fail to do so. We hope the test we propose here will set a new working practice in the NLP community.",
}
@inproceedings{gorman-bedrick-2019-need,
    title = "We Need to Talk about Standard Splits",
    author = "Gorman, Kyle  and
      Bedrick, Steven",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1267",
    doi = "10.18653/v1/P19-1267",
    pages = "2786--2791",
    abstract = "It is standard practice in speech {\&} language technology to rank systems according to their performance on a test set held out for evaluation. However, few researchers apply statistical tests to determine whether differences in performance are likely to arise by chance, and few examine the stability of system ranking across multiple training-testing splits. We conduct replication and reproduction experiments with nine part-of-speech taggers published between 2000 and 2018, each of which claimed state-of-the-art performance on a widely-used {``}standard split{''}. While we replicate results on the standard split, we fail to reliably reproduce some rankings when we repeat this analysis with randomly generated training-testing splits. We argue that randomly generated splits should be used in system evaluation.",
}
@inproceedings{peinelt-etal-2019-aiming,
    title = "Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets",
    author = "Peinelt, Nicole  and
      Liakata, Maria  and
      Nguyen, Dong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1268",
    doi = "10.18653/v1/P19-1268",
    pages = "2792--2798",
    abstract = "Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty. This paper proposes to distinguish obvious from non-obvious text pairs based on superficial lexical overlap and ground-truth labels. We characterise existing datasets in terms of containing difficult cases and find that recently proposed models struggle to capture the non-obvious cases of semantic similarity. We describe metrics that emphasise cases of similarity which require more complex inference and propose that these are used for evaluating systems for semantic similarity.",
}
@inproceedings{mathur-etal-2019-putting,
    title = "Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation",
    author = "Mathur, Nitika  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1269",
    doi = "10.18653/v1/P19-1269",
    pages = "2799--2808",
    abstract = "Accurate, automatic evaluation of machine translation is critical for system tuning, and evaluating progress in the field. We proposed a simple unsupervised metric, and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences. We find that these models rival or surpass all existing metrics in the WMT 2017 sentence-level and system-level tracks, and our trained model has a substantially higher correlation with human judgements than all existing metrics on the WMT 2017 to-English sentence level dataset.",
}
@inproceedings{zeng-etal-2019-joint,
    title = "Joint Effects of Context and User History for Predicting Online Conversation Re-entries",
    author = "Zeng, Xingshan  and
      Li, Jing  and
      Wang, Lu  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1270",
    doi = "10.18653/v1/P19-1270",
    pages = "2809--2818",
    abstract = "As the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage with each other online, we study a challenging problem of re-entry prediction foreseeing whether a user will come back to a conversation they once participated in. We hypothesize that both the context of the ongoing conversations and the users{'} previous chatting history will affect their continued interests in future engagement. Specifically, we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting history jointly result in their re-entry behavior. We experiment with two large-scale datasets collected from Twitter and Reddit. Results show that our proposed framework with bi-attention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-of-the-art methods from previous work.",
}
@inproceedings{chung-etal-2019-conan,
    title = "{CONAN} - {CO}unter {NA}rratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech",
    author = "Chung, Yi-Ling  and
      Kuzmenko, Elizaveta  and
      Tekiroglu, Serra Sinem  and
      Guerini, Marco",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1271",
    doi = "10.18653/v1/P19-1271",
    pages = "2819--2829",
    abstract = "Although there is an unprecedented effort to provide adequate responses in terms of laws and policies to hate content on social media platforms, dealing with hatred online is still a tough problem. Tackling hate speech in the standard way of content deletion or user suspension may be charged with censorship and overblocking. One alternate strategy, that has received little attention so far by the research community, is to actually oppose hate content with counter-narratives (i.e. informed textual responses). In this paper, we describe the creation of the first large-scale, multilingual, expert-based dataset of hate-speech/counter-narrative pairs. This dataset has been built with the effort of more than 100 operators from three different NGOs that applied their training and expertise to the task. Together with the collected data we also provide additional annotations about expert demographics, hate and response type, and data augmentation through translation and paraphrasing. Finally, we provide initial experiments to assess the quality of our data.",
}
@inproceedings{vempala-preotiuc-pietro-2019-categorizing,
    title = "Categorizing and Inferring the Relationship between the Text and Image of {T}witter Posts",
    author = "Vempala, Alakananda  and
      Preo{\c{t}}iuc-Pietro, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1272",
    doi = "10.18653/v1/P19-1272",
    pages = "2830--2840",
    abstract = "Text in social media posts is frequently accompanied by images in order to provide content, supply context, or to express feelings. This paper studies how the meaning of the entire tweet is composed through the relationship between its textual content and its image. We build and release a data set of image tweets annotated with four classes which express whether the text or the image provides additional information to the other modality. We show that by combining the text and image information, we can build a machine learning approach that accurately distinguishes between the relationship types. Further, we derive insights into how these relationships are materialized through text and image content analysis and how they are impacted by user demographic traits. These methods can be used in several downstream applications including pre-training image tagging models, collecting distantly supervised data for image captioning, and can be directly used in end-user applications to optimize screen estate.",
}
@inproceedings{pado-etal-2019-sides,
    title = "Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates",
    author = "Pad{\'o}, Sebastian  and
      Blessing, Andre  and
      Blokker, Nico  and
      Dayanik, Erenay  and
      Haunss, Sebastian  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1273",
    doi = "10.18653/v1/P19-1273",
    pages = "2841--2847",
    abstract = "Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision making. The vision of computational construction of such discourse networks from newspaper reports brings together political science and natural language processing. This paper presents three contributions towards this goal: (a) a requirements analysis, linking the task to knowledge base population; (b) an annotated pilot corpus of migration claims based on German newspaper reports; (c) initial modeling results.",
}
@inproceedings{preotiuc-pietro-devlin-marier-2019-analyzing,
    title = "Analyzing Linguistic Differences between Owner and Staff Attributed Tweets",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Devlin Marier, Rita",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1274",
    doi = "10.18653/v1/P19-1274",
    pages = "2848--2853",
    abstract = "Research on social media has to date assumed that all posts from an account are authored by the same person. In this study, we challenge this assumption and study the linguistic differences between posts signed by the account owner or attributed to their staff. We introduce a novel data set of tweets posted by U.S. politicians who self-reported their tweets using a signature. We analyze the linguistic topics and style features that distinguish the two types of tweets. Predictive results show that we are able to predict owner and staff attributed tweets with good accuracy, even when not using any training data from that account.",
}
@inproceedings{oprea-magdy-2019-exploring,
    title = "Exploring Author Context for Detecting Intended vs Perceived Sarcasm",
    author = "Oprea, Silviu  and
      Magdy, Walid",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1275",
    doi = "10.18653/v1/P19-1275",
    pages = "2854--2859",
    abstract = "We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.",
}
@inproceedings{liu-etal-2019-open,
    title = "Open Domain Event Extraction Using Neural Latent Variable Models",
    author = "Liu, Xiao  and
      Huang, Heyan  and
      Zhang, Yue",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1276",
    doi = "10.18653/v1/P19-1276",
    pages = "2860--2871",
    abstract = "We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.",
}
@inproceedings{ye-ling-2019-multi,
    title = "Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification",
    author = "Ye, Zhi-Xiu  and
      Ling, Zhen-Hua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1277",
    doi = "10.18653/v1/P19-1277",
    pages = "2872--2881",
    abstract = "This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of the support set for each relation candidate independently. On the contrary, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.",
}
@inproceedings{chen-etal-2019-quantifying,
    title = "Quantifying Similarity between Relations with Fact Distribution",
    author = "Chen, Weize  and
      Zhu, Hao  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1278",
    doi = "10.18653/v1/P19-1278",
    pages = "2882--2894",
    abstract = "We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence between the conditional probability distributions over entity pairs. In this paper, these distributions are parameterized by a very simple neural network. Although computing the exact similarity is in-tractable, we provide a sampling-based method to get a good approximation. We empirically show the outputs of our approach significantly correlate with human judgments. By applying our method to various tasks, we also find that (1) our approach could effectively detect redundant relations extracted by open information extraction (Open IE) models, that (2) even the most competitive models for relational classification still make mistakes among very similar relations, and that (3) our approach could be incorporated into negative sampling and softmax classification to alleviate these mistakes.",
}
@inproceedings{baldini-soares-etal-2019-matching,
    title = "Matching the Blanks: Distributional Similarity for Relation Learning",
    author = "Baldini Soares, Livio  and
      FitzGerald, Nicholas  and
      Ling, Jeffrey  and
      Kwiatkowski, Tom",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1279",
    doi = "10.18653/v1/P19-1279",
    pages = "2895--2905",
    abstract = "General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their surface forms, or which jointly embed surface forms with relations from an existing knowledge graph. However, both of these approaches are limited in their ability to generalize. In this paper, we build on extensions of Harris{'} distributional hypothesis to relations, as well as recent advances in learning text representations (specifically, BERT), to build task agnostic relation representations solely from entity-linked text. We show that these representations significantly outperform previous work on exemplar based relation extraction (FewRel) even without using any of that task{'}s training data. We also show that models initialized with our task agnostic representations, and then tuned on supervised relation extraction datasets, significantly outperform the previous methods on SemEval 2010 Task 8, KBP37, and TACRED",
}
@inproceedings{vashishtha-etal-2019-fine,
    title = "Fine-Grained Temporal Relation Extraction",
    author = "Vashishtha, Siddharth  and
      Van Durme, Benjamin  and
      White, Aaron Steven",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1280",
    doi = "10.18653/v1/P19-1280",
    pages = "2906--2919",
    abstract = "We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.",
}
@inproceedings{moss-etal-2019-fiesta,
    title = "{FIESTA}: Fast {I}d{E}ntification of State-of-The-Art models using adaptive bandit algorithms",
    author = "Moss, Henry  and
      Moore, Andrew  and
      Leslie, David  and
      Rayson, Paul",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1281",
    doi = "10.18653/v1/P19-1281",
    pages = "2920--2930",
    abstract = "We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collections of candidate models. Despite being known to produce unreliable comparisons, it is still common practice to compare model evaluations based on single choices of random seeds. We show that reliable model selection also requires evaluations based on multiple train-test splits (contrary to common practice in many shared tasks). Using bandit theory from the statistics literature, we are able to adaptively determine appropriate numbers of data splits and random seeds used to evaluate each model, focusing computational resources on the evaluation of promising models whilst avoiding wasting evaluations on models with lower performance. Furthermore, our user-friendly Python implementation produces confidence guarantees of correctly selecting the optimal model. We evaluate our algorithms by selecting between 8 target-dependent sentiment analysis methods using dramatically fewer model evaluations than current model selection approaches.",
}
@inproceedings{serrano-smith-2019-attention,
    title = "Is Attention Interpretable?",
    author = "Serrano, Sofia  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1282",
    doi = "10.18653/v1/P19-1282",
    pages = "2931--2951",
    abstract = "Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components{'} representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word tokens). We test whether that assumption holds by manipulating attention weights in already-trained text classification models and analyzing the resulting differences in their predictions. While we observe some ways in which higher attention weights correlate with greater impact on model predictions, we also find many ways in which this does not hold, i.e., where gradient-based rankings of attention weights better predict their effects than their magnitudes. We conclude that while attention noisily predicts input components{'} overall importance to a model, it is by no means a fail-safe indicator.",
}
@inproceedings{chrupala-alishahi-2019-correlating,
    title = "Correlating Neural and Symbolic Representations of Language",
    author = "Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1283",
    doi = "10.18653/v1/P19-1283",
    pages = "2952--2962",
    abstract = "Analysis methods which enable us to better understand the representations and functioning of neural models of language are increasingly needed as deep learning becomes the dominant approach in NLP. Here we present two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to directly quantify how strongly the information encoded in neural activation patterns corresponds to information represented by symbolic structures such as syntax trees. We first validate our methods on the case of a simple synthetic language for arithmetic expressions with clearly defined syntax and semantics, and show that they exhibit the expected pattern of results. We then our methods to correlate neural representations of English sentences with their constituency parse trees.",
}
@inproceedings{bastings-etal-2019-interpretable,
    title = "Interpretable Neural Predictions with Differentiable Binary Variables",
    author = "Bastings, Jasmijn  and
      Aziz, Wilker  and
      Titov, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1284",
    doi = "10.18653/v1/P19-1284",
    pages = "2963--2977",
    abstract = "The success of neural networks comes hand in hand with a desire for more interpretability. We focus on text classifiers and make them more interpretable by having them provide a justification{--}a rationale{--}for their predictions. We approach this problem by jointly training two neural network models: a latent model that selects a rationale (i.e. a short and informative part of the input text), and a classifier that learns from the words in the rationale alone. Previous work proposed to assign binary latent masks to input positions and to promote short selections via sparsity-inducing penalties such as L0 regularisation. We propose a latent model that mixes discrete and continuous behaviour allowing at the same time for binary selections and gradient-based training without REINFORCE. In our formulation, we can tractably compute the expected value of penalties such as L0, which allows us to directly optimise the model towards a pre-specified text selection rate. We show that our approach is competitive with previous work on rationale extraction, and explore further uses in attention mechanisms.",
}
@inproceedings{dai-etal-2019-transformer,
    title = "Transformer-{XL}: Attentive Language Models beyond a Fixed-Length Context",
    author = "Dai, Zihang  and
      Yang, Zhilin  and
      Yang, Yiming  and
      Carbonell, Jaime  and
      Le, Quoc  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1285",
    doi = "10.18653/v1/P19-1285",
    pages = "2978--2988",
    abstract = "Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80{\%} longer than RNNs and 450{\%} longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.",
}
@inproceedings{hu-etal-2019-domain,
    title = "Domain Adaptation of Neural Machine Translation by Lexicon Induction",
    author = "Hu, Junjie  and
      Xia, Mengzhou  and
      Neubig, Graham  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1286",
    doi = "10.18653/v1/P19-1286",
    pages = "2989--3001",
    abstract = "It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized nature of NMT, resulting in failure for sentences with large numbers of unknown words, and lack of supervision for domain-specific words. To remedy this problem, we propose an unsupervised adaptation method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. Specifically, we perform lexicon induction to extract an in-domain lexicon, and construct a pseudo-parallel in-domain corpus by performing word-for-word back-translation of monolingual in-domain target sentences. In five domains over twenty pairwise adaptation settings and two model architectures, our method achieves consistent improvements without using any in-domain parallel sentences, improving up to 14 BLEU over unadapted models, and up to 2 BLEU over strong back-translation baselines.",
}
@inproceedings{fu-etal-2019-reference,
    title = "Reference Network for Neural Machine Translation",
    author = "Fu, Han  and
      Liu, Chenghao  and
      Sun, Jianling",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1287",
    doi = "10.18653/v1/P19-1287",
    pages = "3002--3012",
    abstract = "Neural Machine Translation (NMT) has achieved notable success in recent years. Such a framework usually generates translations in isolation. In contrast, human translators often refer to reference data, either rephrasing the intricate sentence fragments with common terms in source language, or just accessing to the golden translation directly. In this paper, we propose a Reference Network to incorporate referring process into translation decoding of NMT. To construct a reference book, an intuitive way is to store the detailed translation history with extra memory, which is computationally expensive. Instead, we employ Local Coordinates Coding (LCC) to obtain global context vectors containing monolingual and bilingual contextual information for NMT decoding. Experimental results on Chinese-English and English-German tasks demonstrate that our proposed model is effective in improving the translation quality with lightweight computation cost.",
}
@inproceedings{shao-etal-2019-retrieving,
    title = "Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation",
    author = "Shao, Chenze  and
      Feng, Yang  and
      Zhang, Jinchao  and
      Meng, Fandong  and
      Chen, Xilin  and
      Zhou, Jie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1288",
    doi = "10.18653/v1/P19-1288",
    pages = "3013--3024",
    abstract = "Non-Autoregressive Transformer (NAT) aims to accelerate the Transformer model through discarding the autoregressive mechanism and generating target words independently, which fails to exploit the target sequential information. Over-translation and under-translation errors often occur for the above reason, especially in the long sentence translation scenario. In this paper, we propose two approaches to retrieve the target sequential information for NAT to enhance its translation ability while preserving the fast-decoding property. Firstly, we propose a sequence-level training method based on a novel reinforcement algorithm for NAT (Reinforce-NAT) to reduce the variance and stabilize the training procedure. Secondly, we propose an innovative Transformer decoder named FS-decoder to fuse the target sequential information into the top layer of the decoder. Experimental results on three translation tasks show that the Reinforce-NAT surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed and the FS-decoder achieves comparable translation performance to the autoregressive Transformer with considerable speedup.",
}
@inproceedings{ma-etal-2019-stacl,
    title = "{STACL}: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework",
    author = "Ma, Mingbo  and
      Huang, Liang  and
      Xiong, Hao  and
      Zheng, Renjie  and
      Liu, Kaibo  and
      Zheng, Baigong  and
      Zhang, Chuanqiang  and
      He, Zhongjun  and
      Liu, Hairong  and
      Li, Xing  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1289",
    doi = "10.18653/v1/P19-1289",
    pages = "3025--3036",
    abstract = "Simultaneous translation, which translates sentences before they are finished, is use- ful in many scenarios but is notoriously dif- ficult due to word-order differences. While the conventional seq-to-seq framework is only suitable for full-sentence translation, we pro- pose a novel prefix-to-prefix framework for si- multaneous translation that implicitly learns to anticipate in a single translation model. Within this framework, we present a very sim- ple yet surprisingly effective {``}wait-k{''} policy trained to generate the target sentence concur- rently with the source sentence, but always k words behind. Experiments show our strat- egy achieves low latency and reasonable qual- ity (compared to full-sentence translation) on 4 directions: zh↔en and de↔en.",
}
@inproceedings{indurthi-etal-2019-look,
    title = "Look Harder: A Neural Machine Translation Model with Hard Attention",
    author = "Indurthi, Sathish Reddy  and
      Chung, Insoo  and
      Kim, Sangha",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1290",
    doi = "10.18653/v1/P19-1290",
    pages = "3037--3043",
    abstract = "Soft-attention based Neural Machine Translation (NMT) models have achieved promising results on several translation tasks. These models attend all the words in the source sequence for each target token, which makes them ineffective for long sequence translation. In this work, we propose a hard-attention based NMT model which selects a subset of source tokens for each target token to effectively handle long sequence translation. Due to the discrete nature of the hard-attention mechanism, we design a reinforcement learning algorithm coupled with reward shaping strategy to efficiently train it. Experimental results show that the proposed model performs better on long sequences and thereby achieves significant BLEU score improvement on English-German (EN-DE) and English-French (ENFR) translation tasks compared to the soft attention based NMT.",
}
@inproceedings{liu-etal-2019-robust,
    title = "Robust Neural Machine Translation with Joint Textual and Phonetic Embedding",
    author = "Liu, Hairong  and
      Ma, Mingbo  and
      Huang, Liang  and
      Xiong, Hao  and
      He, Zhongjun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1291",
    doi = "10.18653/v1/P19-1291",
    pages = "3044--3049",
    abstract = "Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the \textit{homophone noise}, where words are replaced by other words with similar pronunciations. We propose to improve the robustness of NMT to homophone noises by 1) jointly embedding both textual and phonetic information of source sentences, and 2) augmenting the training dataset with homophone noises. Interestingly, to achieve better translation quality and more robustness, we found that most (though not all) weights should be put on the phonetic rather than textual information. Experiments show that our method not only significantly improves the robustness of NMT to homophone noises, but also surprisingly improves the translation quality on some clean test sets.",
}
@inproceedings{correia-martins-2019-simple,
    title = "A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning",
    author = "Correia, Gon{\c{c}}alo M.  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1292",
    doi = "10.18653/v1/P19-1292",
    pages = "3050--3056",
    abstract = "Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits. APE systems are usually trained by complementing human post-edited data with large, artificial data generated through back-translations, a time-consuming process often no easier than training a MT system from scratch. in this paper, we propose an alternative where we fine-tune pre-trained BERT models on both the encoder and decoder of an APE system, exploring several parameter sharing strategies. By only training on a dataset of 23K sentences for 3 hours on a single GPU we obtain results that are competitive with systems that were trained on 5M artificial sentences. When we add this artificial data our method obtains state-of-the-art results.",
}
@inproceedings{pourdamghani-etal-2019-translating,
    title = "Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation",
    author = "Pourdamghani, Nima  and
      Aldarrab, Nada  and
      Ghazvininejad, Marjan  and
      Knight, Kevin  and
      May, Jonathan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1293",
    doi = "10.18653/v1/P19-1293",
    pages = "3057--3062",
    abstract = "Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then {`}translating{'} the resulting pseudo-translation, or {`}Translationese{'} into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.",
}
@inproceedings{dinu-etal-2019-training,
    title = "Training Neural Machine Translation to Apply Terminology Constraints",
    author = "Dinu, Georgiana  and
      Mathur, Prashant  and
      Federico, Marcello  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1294",
    doi = "10.18653/v1/P19-1294",
    pages = "3063--3068",
    abstract = "This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms. While being effective, these constrained decoding methods add, however, significant computational overhead to the inference step, and, as we show in this paper, can be brittle when tested in realistic conditions. In this paper we approach the problem by training a neural MT system to learn how to use custom terminology when provided with the input. Comparative experiments show that our method is not only more effective than a state-of-the-art implementation of constrained decoding, but is also as fast as constraint-free decoding.",
}
@inproceedings{xu-etal-2019-leveraging,
    title = "Leveraging Local and Global Patterns for Self-Attention Networks",
    author = "Xu, Mingzhou  and
      Wong, Derek F.  and
      Yang, Baosong  and
      Zhang, Yue  and
      Chao, Lidia S.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1295",
    doi = "10.18653/v1/P19-1295",
    pages = "3069--3075",
    abstract = "Self-attention networks have received increasing research attention. By default, the hidden states of each word are hierarchically calculated by attending to all words in the sentence, which assembles global information. However, several studies pointed out that taking all signals into account may lead to overlooking neighboring information (e.g. phrase pattern). To address this argument, we propose a hybrid attention mechanism to dynamically leverage both of the local and global information. Specifically, our approach uses a gating scalar for integrating both sources of the information, which is also convenient for quantifying their contributions. Experiments on various neural machine translation tasks demonstrate the effectiveness of the proposed method. The extensive analyses verify that the two types of contexts are complementary to each other, and our method gives highly effective improvements in their integration.",
}
@inproceedings{yang-etal-2019-sentence,
    title = "Sentence-Level Agreement for Neural Machine Translation",
    author = "Yang, Mingming  and
      Wang, Rui  and
      Chen, Kehai  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhang, Min  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1296",
    doi = "10.18653/v1/P19-1296",
    pages = "3076--3082",
    abstract = "The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references. In NMT, there is a natural correspondence between the source sentence and the target sentence. However, this relationship has only been represented using the entire neural network and the training objective is computed in word-level. In this paper, we propose a sentence-level agreement module to directly minimize the difference between the representation of source and target sentence. The proposed agreement module can be integrated into NMT as an additional training objective function and can also be used to enhance the representation of the source sentences. Empirical results on the NIST Chinese-to-English and WMT English-to-German tasks show the proposed agreement module can significantly improve the NMT performance.",
}
@inproceedings{sen-etal-2019-multilingual,
    title = "Multilingual Unsupervised {NMT} using Shared Encoder and Language-Specific Decoders",
    author = "Sen, Sukanta  and
      Gupta, Kamal Kumar  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1297",
    doi = "10.18653/v1/P19-1297",
    pages = "3083--3089",
    abstract = "In this paper, we propose a multilingual unsupervised NMT scheme which jointly trains multiple languages with a shared encoder and multiple decoders. Our approach is based on denoising autoencoding of each language and back-translating between English and multiple non-English languages. This results in a universal encoder which can encode any language participating in training into an inter-lingual representation, and language-specific decoders. Our experiments using only monolingual corpora show that multilingual unsupervised model performs better than the separately trained bilingual models achieving improvement of up to 1.48 BLEU points on WMT test sets. We also observe that even if we do not train the network for all possible translation directions, the network is still able to translate in a many-to-many fashion leveraging encoder{'}s ability to generate interlingual representation.",
}
@inproceedings{xiao-etal-2019-lattice,
    title = "Lattice-Based Transformer Encoder for Neural Machine Translation",
    author = "Xiao, Fengshun  and
      Li, Jiangtong  and
      Zhao, Hai  and
      Wang, Rui  and
      Chen, Kehai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1298",
    doi = "10.18653/v1/P19-1298",
    pages = "3090--3097",
    abstract = "Neural machine translation (NMT) takes deterministic sequences for source representations. However, either word-level or subword-level segmentations have multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. We hypothesize that the diversity in segmentations may affect the NMT performance. To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training. We propose two methods: 1) lattice positional encoding and 2) lattice-aware self-attention. These two methods can be used together and show complementary to each other to further improve translation performance. Experiment results show superiorities of lattice-based encoders in word-level and subword-level representations over conventional Transformer encoder.",
}
@inproceedings{chen-etal-2019-multi,
    title = "Multi-Source Cross-Lingual Model Transfer: Learning What to Share",
    author = "Chen, Xilun  and
      Awadallah, Ahmed Hassan  and
      Hassan, Hany  and
      Wang, Wei  and
      Cardie, Claire",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1299",
    doi = "10.18653/v1/P19-1299",
    pages = "3098--3112",
    abstract = "Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language-invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. Moreover, when coupled with unsupervised multilingual embeddings, our model can operate in a zero-resource setting where neither target language training data nor cross-lingual resources are available. Our model achieves significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale industry dataset.",
}
@inproceedings{wada-etal-2019-unsupervised,
    title = "Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models",
    author = "Wada, Takashi  and
      Iwata, Tomoharu  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1300",
    doi = "10.18653/v1/P19-1300",
    pages = "3113--3124",
    abstract = "Recently, a variety of unsupervised methods have been proposed that map pre-trained word embeddings of different languages into the same space without any parallel data. These methods aim to find a linear transformation based on the assumption that monolingual word embeddings are approximately isomorphic between languages. However, it has been demonstrated that this assumption holds true only on specific conditions, and with limited resources, the performance of these methods decreases drastically. To overcome this problem, we propose a new unsupervised multilingual embedding method that does not rely on such assumption and performs well under resource-poor scenarios, namely when only a small amount of monolingual data (i.e., 50k sentences) are available, or when the domains of monolingual data are different across languages. Our proposed model, which we call {`}Multilingual Neural Language Models{'}, shares some of the network parameters among multiple languages, and encodes sentences of multiple languages into the same space. The model jointly learns word embeddings of different languages in the same space, and generates multilingual embeddings without any parallel data or pre-training. Our experiments on word alignment tasks have demonstrated that, on the low-resource condition, our model substantially outperforms existing unsupervised and even supervised methods trained with 500 bilingual pairs of words. Our model also outperforms unsupervised methods given different-domain corpora across languages. Our code is publicly available.",
}
@inproceedings{lin-etal-2019-choosing,
    title = "Choosing Transfer Languages for Cross-Lingual Learning",
    author = "Lin, Yu-Hsiang  and
      Chen, Chian-Yu  and
      Lee, Jean  and
      Li, Zirui  and
      Zhang, Yuyan  and
      Xia, Mengzhou  and
      Rijhwani, Shruti  and
      He, Junxian  and
      Zhang, Zhisong  and
      Ma, Xuezhe  and
      Anastasopoulos, Antonios  and
      Littell, Patrick  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1301",
    doi = "10.18653/v1/P19-1301",
    pages = "3125--3135",
    abstract = "Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, given a particular task language, it is not clear which language to transfer from, and the standard strategy is to select languages based on ad hoc criteria, usually the intuition of the experimenter. Since a large number of features contribute to the success of cross-lingual transfer (including phylogenetic similarity, typological properties, lexical overlap, or size of available data), even the most enlightened experimenter rarely considers all these factors for the particular task at hand. In this paper, we consider this task of automatically selecting optimal transfer languages as a ranking problem, and build models that consider the aforementioned features to perform this prediction. In experiments on representative NLP tasks, we demonstrate that our model predicts good transfer languages much better than ad hoc baselines considering single features in isolation, and glean insights on what features are most informative for each different NLP tasks, which may inform future ad hoc selection even without use of our method.",
}
@inproceedings{batsuren-etal-2019-cognet,
    title = "{C}og{N}et: A Large-Scale Cognate Database",
    author = "Batsuren, Khuyagbaatar  and
      Bella, Gabor  and
      Giunchiglia, Fausto",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1302",
    doi = "10.18653/v1/P19-1302",
    pages = "3136--3145",
    abstract = "This paper introduces CogNet, a new, large-scale lexical database that provides cognates -words of common origin and meaning- across languages. The database currently contains 3.1 million cognate pairs across 338 languages using 35 writing systems. The paper also describes the automated method by which cognates were computed from publicly available wordnets, with an accuracy evaluated to 94{\%}. Finally, it presents statistics about the cognate data and some initial insights into it, hinting at a possible future exploitation of the resource by various fields of lingustics.",
}
@inproceedings{luo-etal-2019-neural,
    title = "Neural Decipherment via Minimum-Cost Flow: From {U}garitic to {L}inear {B}",
    author = "Luo, Jiaming  and
      Cao, Yuan  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1303",
    doi = "10.18653/v1/P19-1303",
    pages = "3146--3155",
    abstract = "In this paper we propose a novel neural approach for automatic decipherment of lost languages. To compensate for the lack of strong supervision signal, our model design is informed by patterns in language change documented in historical linguistics. The model utilizes an expressive sequence-to-sequence model to capture character-level correspondences between cognates. To effectively train the model in unsupervised manner, we innovate the training procedure by formalizing it as a minimum-cost flow problem. When applied to decipherment of Ugaritic, we achieve 5{\%} absolute improvement over state-of-the-art results. We also report first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where our model correctly translates 67.3{\%} of cognates.",
}
@inproceedings{xu-etal-2019-cross-lingual,
    title = "Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network",
    author = "Xu, Kun  and
      Wang, Liwei  and
      Yu, Mo  and
      Feng, Yansong  and
      Song, Yan  and
      Wang, Zhiguo  and
      Yu, Dong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1304",
    doi = "10.18653/v1/P19-1304",
    pages = "3156--3161",
    abstract = "Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we introduce the topic entity graph, a local sub-graph of an entity, to represent entities with their contextual information in KG. From this view, the KB-alignment task can be formulated as a graph matching problem; and we further propose a graph-attention based solution, which first matches all entities in two topic entity graphs, and then jointly model the local matching information to derive a graph-level matching vector. Experiments show that our model outperforms previous state-of-the-art methods by a large margin.",
}
@inproceedings{duan-etal-2019-zero,
    title = "Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention",
    author = "Duan, Xiangyu  and
      Yin, Mingming  and
      Zhang, Min  and
      Chen, Boxing  and
      Luo, Weihua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1305",
    doi = "10.18653/v1/P19-1305",
    pages = "3162--3172",
    abstract = "Abstractive Sentence Summarization (ASSUM) targets at grasping the core idea of the source sentence and presenting it as the summary. It is extensively studied using statistical models or neural models based on the large-scale monolingual source-summary parallel corpus. But there is no cross-lingual parallel corpus, whose source sentence language is different to the summary language, to directly train a cross-lingual ASSUM system. We propose to solve this zero-shot problem by using resource-rich monolingual ASSUM system to teach zero-shot cross-lingual ASSUM system on both summary word generation and attention. This teaching process is along with a back-translation process which simulates source-summary pairs. Experiments on cross-lingual ASSUM task show that our proposed method is significantly better than pipeline baselines and previous works, and greatly enhances the cross-lingual performances closer to the monolingual performances.",
}
@inproceedings{zhang-etal-2019-improving,
    title = "Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations",
    author = "Zhang, Rui  and
      Westerfield, Caitlin  and
      Shim, Sungrok  and
      Bingham, Garrett  and
      Fabbri, Alexander  and
      Hu, William  and
      Verma, Neha  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1306",
    doi = "10.18653/v1/P19-1306",
    pages = "3173--3179",
    abstract = "In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cross-lingual word embeddings as input. By including query likelihood scores as extra features, our model effectively learns to rerank the retrieved documents by using a small number of relevance labels for low-resource language pairs. Due to the shared cross-lingual word embedding space, the model can also be directly applied to another language pair without any training label. Experimental results on the Material dataset show that our model outperforms the competitive translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual information retrieval tasks.",
}
@inproceedings{zhang-etal-2019-girls,
    title = "Are Girls Neko or Sh{\=o}jo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization",
    author = "Zhang, Mozhi  and
      Xu, Keyulu  and
      Kawarabayashi, Ken-ichi  and
      Jegelka, Stefanie  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1307",
    doi = "10.18653/v1/P19-1307",
    pages = "3180--3189",
    abstract = "Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings. However, orthogonal mapping only works on language pairs whose embeddings are naturally isomorphic. For non-isomorphic pairs, our method (Iterative Normalization) transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language{'}s average vector is zero. Iterative Normalization consistently improves word translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (from 2{\%} to 44{\%} test accuracy).",
}
@inproceedings{yang-etal-2019-maam,
    title = "{MAAM}: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction",
    author = "Yang, Pengcheng  and
      Luo, Fuli  and
      Chen, Peng  and
      Liu, Tianyu  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1308",
    doi = "10.18653/v1/P19-1308",
    pages = "3190--3196",
    abstract = "The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages. Previous work has shown that morphological variation is an intractable challenge for the UBLI task, where the induced translation in failure case is usually morphologically related to the correct translation. To tackle this challenge, we propose a morphology-aware alignment model for the UBLI task. The proposed model aims to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model. Results show that our approach can substantially outperform several state-of-the-art unsupervised systems, and even achieves competitive performance compared to supervised methods.",
}
@inproceedings{artetxe-schwenk-2019-margin,
    title = "Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings",
    author = "Artetxe, Mikel  and
      Schwenk, Holger",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1309",
    doi = "10.18653/v1/P19-1309",
    pages = "3197--3203",
    abstract = "Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. In contrast to previous approaches, which rely on nearest neighbor retrieval with a hard threshold over cosine similarity, our proposed method accounts for the scale inconsistencies of this measure, considering the margin between a given sentence pair and its closest candidates instead. Our experiments show large improvements over existing methods. We outperform the best published results on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points, respectively. Filtering the English-German ParaCrawl corpus with our approach, we obtain 31.2 BLEU points on newstest2014, an improvement of more than one point over the best official filtered version.",
}
@inproceedings{agic-vulic-2019-jw300,
    title = "{JW}300: A Wide-Coverage Parallel Corpus for Low-Resource Languages",
    author = "Agi{\'c}, {\v{Z}}eljko  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1310",
    doi = "10.18653/v1/P19-1310",
    pages = "3204--3210",
    abstract = "Viable cross-lingual transfer critically depends on the availability of parallel texts. Shortage of such resources imposes a development and evaluation bottleneck in multilingual processing. We introduce JW300, a parallel corpus of over 300 languages with around 100 thousand parallel sentences per language pair on average. In this paper, we present the resource and showcase its utility in experiments with cross-lingual word embedding induction and multi-source part-of-speech projection.",
}
@inproceedings{he-etal-2019-cross,
    title = "Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections",
    author = "He, Junxian  and
      Zhang, Zhisong  and
      Berg-Kirkpatrick, Taylor  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1311",
    doi = "10.18653/v1/P19-1311",
    pages = "3211--3223",
    abstract = "Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available. In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly. The parameters of source model and target model are softly shared through a regularized log likelihood objective. An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect cross-lingual word embedding input. We evaluate our method on two syntactic tasks: part-of-speech (POS) tagging and dependency parsing. On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages. On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2{\%} absolute improvement on POS tagging and 8.3{\%} absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models.",
}
@inproceedings{marie-fujita-2019-unsupervised,
    title = "Unsupervised Joint Training of Bilingual Word Embeddings",
    author = "Marie, Benjamin  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1312",
    doi = "10.18653/v1/P19-1312",
    pages = "3224--3230",
    abstract = "State-of-the-art methods for unsupervised bilingual word embeddings (BWE) train a mapping function that maps pre-trained monolingual word embeddings into a bilingual space. Despite its remarkable results, unsupervised mapping is also well-known to be limited by the original dissimilarity between the word embedding spaces to be mapped. In this work, we propose a new approach that trains unsupervised BWE jointly on synthetic parallel data generated through unsupervised machine translation. We demonstrate that existing algorithms that jointly train BWE are very robust to noisy training data and show that unsupervised BWE jointly trained significantly outperform unsupervised mapped BWE in several cross-lingual NLP tasks.",
}
@inproceedings{le-etal-2019-inferring,
    title = "Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings",
    author = "Le, Matthew  and
      Roller, Stephen  and
      Papaxanthos, Laetitia  and
      Kiela, Douwe  and
      Nickel, Maximilian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1313",
    doi = "10.18653/v1/P19-1313",
    pages = "3231--3241",
    abstract = "We consider the task of inferring {``}is-a{''} relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing {``}is-a{''}-relationships and to correct wrong extractions. Moreover {--} and in contrast with other methods {--} the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.",
}
@inproceedings{li-etal-2019-word-segmentation,
    title = "Is Word Segmentation Necessary for Deep Learning of {C}hinese Representations?",
    author = "Li, Xiaoya  and
      Meng, Yuxian  and
      Sun, Xiaofei  and
      Han, Qinghong  and
      Yuan, Arianna  and
      Li, Jiwei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1314",
    doi = "10.18653/v1/P19-1314",
    pages = "3242--3252",
    abstract = "Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored. In this paper, we ask the fundamental question of whether Chinese word segmentation (CWS) is necessary for deep learning-based Chinese Natural Language Processing. We benchmark neural word-based models which rely on word segmentation against neural char-based models which do not involve word segmentation in four end-to-end NLP benchmark tasks: language modeling, machine translation, sentence matching/paraphrase and text classification. Through direct comparisons between these two types of models, we find that char-based models consistently outperform word-based models. Based on these observations, we conduct comprehensive experiments to study why word-based models underperform char-based models in these deep learning-based NLP tasks. We show that it is because word-based models are more vulnerable to data sparsity and the presence of out-of-vocabulary (OOV) words, and thus more prone to overfitting. We hope this paper could encourage researchers in the community to rethink the necessity of word segmentation in deep learning-based Chinese Natural Language Processing.",
}
@inproceedings{ethayarajh-etal-2019-towards,
    title = "Towards Understanding Linear Word Analogies",
    author = "Ethayarajh, Kawin  and
      Duvenaud, David  and
      Hirst, Graeme",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1315",
    doi = "10.18653/v1/P19-1315",
    pages = "3253--3262",
    abstract = "A surprising property of word vectors is that word analogies can often be solved with vector arithmetic. However, it is unclear why arithmetic operators correspond to non-linear embedding models such as skip-gram with negative sampling (SGNS). We provide a formal explanation of this phenomenon without making the strong assumptions that past theories have made about the vector space and word distribution. Our theory has several implications. Past work has conjectured that linear substructures exist in vector spaces because relations can be represented as ratios; we prove that this holds for SGNS. We provide novel justification for the addition of SGNS word vectors by showing that it automatically down-weights the more frequent word, as weighting schemes do ad hoc. Lastly, we offer an information theoretic interpretation of Euclidean distance in vector spaces, justifying its use in capturing word dissimilarity.",
}
@inproceedings{jana-etal-2019-compositionality,
    title = "On the Compositionality Prediction of Noun Phrases using Poincar{\'e} Embeddings",
    author = "Jana, Abhik  and
      Puzyrev, Dima  and
      Panchenko, Alexander  and
      Goyal, Pawan  and
      Biemann, Chris  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1316",
    doi = "10.18653/v1/P19-1316",
    pages = "3263--3274",
    abstract = "The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical relations. Prediction of (non)-compositionality is a task that has been frequently addressed with distributional semantic models. We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality. In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar{\'e} embeddings in addition to the distributional information to detect compositionality for noun phrases. Using a weighted average of the distributional similarity and a Poincar{\'e} similarity function, we obtain consistent and substantial, statistically significant improvement across three gold standard datasets over state-of-the-art models based on distributional information only. Unlike traditional approaches that solely use an unsupervised setting, we have also framed the problem as a supervised task, obtaining comparable improvements. Further, we publicly release our Poincar{\'e} embeddings, which are trained on the output of handcrafted lexical-syntactic patterns on a large corpus.",
}
@inproceedings{phan-etal-2019-robust,
    title = "Robust Representation Learning of Biomedical Names",
    author = "Phan, Minh C.  and
      Sun, Aixin  and
      Tay, Yi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1317",
    doi = "10.18653/v1/P19-1317",
    pages = "3275--3285",
    abstract = "Biomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering downstream applications inefficacious and/or potentially unreliable. This paper proposes a new framework for learning robust representations of biomedical names and terms. The idea behind our approach is to consider and encode contextual meaning, conceptual meaning, and the similarity between synonyms during the representation learning process. Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks. Moreover, our proposed method is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications.",
}
@inproceedings{camacho-collados-etal-2019-relational,
    title = "Relational Word Embeddings",
    author = "Camacho-Collados, Jose  and
      Espinosa Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1318",
    doi = "10.18653/v1/P19-1318",
    pages = "3286--3296",
    abstract = "While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding. Such strategies may not be optimal, however, as they are limited by the coverage of available resources and conflate similarity with other forms of relatedness. As an alternative, in this paper we propose to encode relational knowledge in a separate word embedding, which is aimed to be complementary to a given standard word embedding. This relational word embedding is still learned from co-occurrence statistics, and can thus be used even when no external knowledge base is available. Our analysis shows that relational word vectors do indeed capture information that is complementary to what is encoded in standard word embeddings.",
}
@inproceedings{etcheverry-wonsever-2019-unraveling,
    title = "Unraveling Antonym{'}s Word Vectors through a {S}iamese-like Network",
    author = "Etcheverry, Mathias  and
      Wonsever, Dina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1319",
    doi = "10.18653/v1/P19-1319",
    pages = "3297--3307",
    abstract = "Discriminating antonyms and synonyms is an important NLP task that has the difficulty that both, antonyms and synonyms, contains similar distributional information. Consequently, pairs of antonyms and synonyms may have similar word vectors. We present an approach to unravel antonymy and synonymy from word vectors based on a siamese network inspired approach. The model consists of a two-phase training of the same base network: a pre-training phase according to a siamese model supervised by synonyms and a training phase on antonyms through a siamese-like model that supports the antitransitivity present in antonymy. The approach makes use of the claim that the antonyms in common of a word tend to be synonyms. We show that our approach outperforms distributional and pattern-based approaches, relaying on a simple feed forward network as base network of the training phases.",
}
@inproceedings{vashishth-etal-2019-incorporating,
    title = "Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks",
    author = "Vashishth, Shikhar  and
      Bhandari, Manik  and
      Yadav, Prateek  and
      Rai, Piyush  and
      Bhattacharyya, Chiranjib  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1320",
    doi = "10.18653/v1/P19-1320",
    pages = "3308--3318",
    abstract = "Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some attempts at utilizing syntactic context of a word, such methods result in an explosion of the vocabulary size. In this paper, we overcome this problem by proposing SynGCN, a flexible Graph Convolution based method for learning word embeddings. SynGCN utilizes the dependency context of a word without increasing the vocabulary size. Word embeddings learned by SynGCN outperform existing methods on various intrinsic and extrinsic tasks and provide an advantage when used with ELMo. We also propose SemGCN, an effective framework for incorporating diverse semantic knowledge for further enhancing learned word representations. We make the source code of both models available to encourage reproducible research.",
}
@inproceedings{jameel-schockaert-2019-word,
    title = "Word and Document Embedding with v{MF}-Mixture Priors on Context Word Vectors",
    author = "Jameel, Shoaib  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1321",
    doi = "10.18653/v1/P19-1321",
    pages = "3319--3328",
    abstract = "Word embedding models typically learn two types of vectors: target word vectors and context word vectors. These vectors are normally learned such that they are predictive of some word co-occurrence statistic, but they are otherwise unconstrained. However, the words from a given language can be organized in various natural groupings, such as syntactic word classes (e.g. nouns, adjectives, verbs) and semantic themes (e.g. sports, politics, sentiment). Our hypothesis in this paper is that embedding models can be improved by explicitly imposing a cluster structure on the set of context word vectors. To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises-Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors. We show that this results in word vectors which are qualitatively different from those obtained with existing word embedding models. We furthermore show that our embedding model can also be used to learn high-quality document representations.",
}
@inproceedings{zhang-etal-2019-delta,
    title = "Delta Embedding Learning",
    author = "Zhang, Xiao  and
      Wu, Ji  and
      Dou, Dejing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1322",
    doi = "10.18653/v1/P19-1322",
    pages = "3329--3334",
    abstract = "Unsupervised word embeddings have become a popular approach of word representation in NLP tasks. However there are limitations to the semantics represented by unsupervised embeddings, and inadequate fine-tuning of embeddings can lead to suboptimal performance. We propose a novel learning technique called Delta Embedding Learning, which can be applied to general NLP tasks to improve performance by optimized tuning of the word embeddings. A structured regularization is applied to the embeddings to ensure they are tuned in an incremental way. As a result, the tuned word embeddings become better word representations by absorbing semantic information from supervision without {``}forgetting.{''} We apply the method to various NLP tasks and see a consistent improvement in performance. Evaluation also confirms the tuned word embeddings have better semantic properties.",
}
@inproceedings{egg-etal-2019-annotation,
    title = "Annotation and Automatic Classification of Aspectual Categories",
    author = "Egg, Markus  and
      Prepens, Helena  and
      Roberts, Will",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1323",
    doi = "10.18653/v1/P19-1323",
    pages = "3335--3341",
    abstract = "We present the first annotated resource for the aspectual classification of German verb tokens in their clausal context. We use aspectual features compatible with the plurality of aspectual classifications in previous work and treat aspectual ambiguity systematically. We evaluate our corpus by using it to train supervised classifiers to automatically assign aspectual categories to verbs in context, permitting favourable comparisons to previous work.",
}
@inproceedings{aina-etal-2019-putting,
    title = "Putting Words in Context: {LSTM} Language Models and Lexical Ambiguity",
    author = "Aina, Laura  and
      Gulordava, Kristina  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1324",
    doi = "10.18653/v1/P19-1324",
    pages = "3342--3348",
    abstract = "In neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, representing the contextually relevant information is not trivial. We investigate how an LSTM language model deals with lexical ambiguity in English, designing a method to probe its hidden representations for lexical and contextual information about words. We find that both types of information are represented to a large extent, but also that there is room for improvement for contextual information.",
}
@inproceedings{kutuzov-etal-2019-making,
    title = "Making Fast Graph-based Algorithms with Graph Metric Embeddings",
    author = "Kutuzov, Andrey  and
      Dorgham, Mohammad  and
      Oliynyk, Oleksiy  and
      Biemann, Chris  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1325",
    doi = "10.18653/v1/P19-1325",
    pages = "3349--3355",
    abstract = "Graph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks.",
}
@inproceedings{yang-etal-2019-embedding,
    title = "Embedding Imputation with Grounded Language Information",
    author = "Yang, Ziyi  and
      Zhu, Chenguang  and
      Sachidananda, Vin  and
      Darve, Eric",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1326",
    doi = "10.18653/v1/P19-1326",
    pages = "3356--3361",
    abstract = "Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical problem in language processing. Embedding imputation involves learning representations for rare or unseen words during the training of an embedding model, often in a post-hoc manner. In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph. This is in contrast to existing approaches which typically make use of vector space properties or subword information. We propose an online method to construct a graph from grounded information and design an algorithm to map from the resulting graphical structure to the space of the pre-trained embeddings. Finally, we evaluate our approach on a range of rare and unseen word tasks across various domains and show that our model can learn better representations. For example, on the Card-660 task our method improves Pearson{'}s and Spearman{'}s correlation coefficients upon the state-of-the-art by 11{\%} and 17.8{\%} respectively using GloVe embeddings.",
}
@inproceedings{held-habash-2019-effectiveness,
    title = "The Effectiveness of Simple Hybrid Systems for Hypernym Discovery",
    author = "Held, William  and
      Habash, Nizar",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1327",
    doi = "10.18653/v1/P19-1327",
    pages = "3362--3367",
    abstract = "Hypernymy modeling has largely been separated according to two paradigms, pattern-based methods and distributional methods. However, recent works utilizing a mix of these strategies have yielded state-of-the-art results. This paper evaluates the contribution of both paradigms to hybrid success by evaluating the benefits of hybrid treatment of baseline models from each paradigm. Even with a simple methodology for each individual system, utilizing a hybrid approach establishes new state-of-the-art results on two domain-specific English hypernym discovery tasks and outperforms all non-hybrid approaches in a general English hypernym discovery task.",
}
@inproceedings{zhou-etal-2019-bert,
    title = "{BERT}-based Lexical Substitution",
    author = "Zhou, Wangchunshu  and
      Ge, Tao  and
      Xu, Ke  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1328",
    doi = "10.18653/v1/P19-1328",
    pages = "3368--3373",
    abstract = "Previous studies on lexical substitution tend to obtain substitute candidates by finding the target word{'}s synonyms from lexical resources (e.g., WordNet) and then rank the candidates based on its contexts. These approaches have two limitations: (1) They are likely to overlook good substitute candidates that are not the synonyms of the target words in the lexical resources; (2) They fail to take into account the substitution{'}s influence on the global context of the sentence. To address these issues, we propose an end-to-end BERT-based lexical substitution approach which can propose and validate substitute candidates without using any annotated data or manually curated resources. Our approach first applies dropout to the target word{'}s embedding for partially masking the word, allowing BERT to take balanced consideration of the target word{'}s semantics and contexts for proposing substitute candidates, and then validates the candidates based on their substitution{'}s influence on the global contextualized representation of the sentence. Experiments show our approach performs well in both proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks.",
}
@inproceedings{naik-etal-2019-exploring,
    title = "Exploring Numeracy in Word Embeddings",
    author = "Naik, Aakanksha  and
      Ravichander, Abhilasha  and
      Rose, Carolyn  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1329",
    doi = "10.18653/v1/P19-1329",
    pages = "3374--3380",
    abstract = "Word embeddings are now pervasive across NLP subfields as the de-facto method of forming text representataions. In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding. Numbers are ubiquitous and frequently appear in text. Inspired by cognitive studies on how humans perceive numbers, we develop an analysis framework to test how well word embeddings capture two essential properties of numbers: magnitude (e.g. 3{\textless}4) and numeration (e.g. 3=three). Our experiments reveal that most models capture an approximate notion of magnitude, but are inadequate at capturing numeration. We hope that our observations provide a starting point for the development of methods which better capture numeracy in NLP systems.",
}
@inproceedings{hardy-etal-2019-highres,
    title = "{H}igh{RES}: Highlight-based Reference-less Evaluation of Summarization",
    author = "Hardy, Hardy  and
      Narayan, Shashi  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1330",
    doi = "10.18653/v1/P19-1330",
    pages = "3381--3392",
    abstract = "There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches. However, manual evaluation of the system generated summaries is inconsistent due to the difficulty the task poses to human non-expert readers. To address this issue, we propose a novel approach for manual evaluation, Highlight-based Reference-less Evaluation of Summarization (HighRES), in which summaries are assessed by multiple annotators against the source document via manually highlighted salient content in the latter. Thus summary assessment on the source document by human judges is facilitated, while the highlights can be used for evaluating multiple systems. To validate our approach we employ crowd-workers to augment with highlights a recently proposed dataset and compare two state-of-the-art systems. We demonstrate that HighRES improves inter-annotator agreement in comparison to using the source document directly, while they help emphasize differences among systems that would be ignored under other evaluation approaches.",
}
@inproceedings{dong-etal-2019-editnts,
    title = "{E}dit{NTS}: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing",
    author = "Dong, Yue  and
      Li, Zichao  and
      Rezagholizadeh, Mehdi  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1331",
    doi = "10.18653/v1/P19-1331",
    pages = "3393--3402",
    abstract = "We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. These methods learn to simplify sentences as a byproduct of the fact that they are trained on complex-simple sentence pairs. By contrast, our neural programmer-interpreter is directly trained to predict explicit edit operations on targeted parts of the input sentence, resembling the way that humans perform simplification and revision. Our model outperforms previous state-of-the-art neural sentence simplification models (without external knowledge) by large margins on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, +1.41 Newsela), and is judged by humans to produce overall better and simpler output sentences.",
}
@inproceedings{li-etal-2019-decomposable,
    title = "Decomposable Neural Paraphrase Generation",
    author = "Li, Zichao  and
      Jiang, Xin  and
      Shang, Lifeng  and
      Liu, Qun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1332",
    doi = "10.18653/v1/P19-1332",
    pages = "3403--3414",
    abstract = "Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level. This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way. Specifically, the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity. The empirical study shows that the decomposition mechanism of DNPG makes paraphrase generation more interpretable and controllable. Based on DNPG, we further develop an unsupervised domain adaptation method for paraphrase generation. Experimental results show that the proposed model achieves competitive in-domain performance compared to state-of-the-art neural models, and significantly better performance when adapting to a new domain.",
}
@inproceedings{niklaus-etal-2019-transforming,
    title = "Transforming Complex Sentences into a Semantic Hierarchy",
    author = "Niklaus, Christina  and
      Cetto, Matthias  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1333",
    doi = "10.18653/v1/P19-1333",
    pages = "3415--3427",
    abstract = "We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346{\%} in precision and 52{\%} in recall. To enable reproducible research, all code is provided online.",
}
@inproceedings{mccoy-etal-2019-right,
    title = "Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference",
    author = "McCoy, Tom  and
      Pavlick, Ellie  and
      Linzen, Tal",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1334",
    doi = "10.18653/v1/P19-1334",
    pages = "3428--3448",
    abstract = "A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area.",
}
@inproceedings{logeswaran-etal-2019-zero,
    title = "Zero-Shot Entity Linking by Reading Entity Descriptions",
    author = "Logeswaran, Lajanugen  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina  and
      Devlin, Jacob  and
      Lee, Honglak",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1335",
    doi = "10.18653/v1/P19-1335",
    pages = "3449--3460",
    abstract = "We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly specialized domains, and so no metadata or alias tables are assumed. In this setting, entities are only identified by text descriptions, and models must rely strictly on language understanding to resolve the new entities. First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities. Second, we propose a simple and effective adaptive pre-training strategy, which we term domain-adaptive pre-training (DAP), to address the domain shift problem associated with linking unseen entities in a new domain. We present experiments on a new dataset that we construct for this task and show that DAP improves over strong pre-training baselines, including BERT. The data and code are available at https://github.com/lajanugen/zeshel.",
}
@inproceedings{zhou-etal-2019-dual,
    title = "Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition",
    author = "Zhou, Joey Tianyi  and
      Zhang, Hao  and
      Jin, Di  and
      Zhu, Hongyuan  and
      Fang, Meng  and
      Goh, Rick Siow Mong  and
      Kwok, Kenneth",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1336",
    doi = "10.18653/v1/P19-1336",
    pages = "3461--3471",
    abstract = "We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model.",
}
@inproceedings{kuncoro-etal-2019-scalable,
    title = "Scalable Syntax-Aware Language Models Using Knowledge Distillation",
    author = "Kuncoro, Adhiguna  and
      Dyer, Chris  and
      Rimell, Laura  and
      Clark, Stephen  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1337",
    doi = "10.18653/v1/P19-1337",
    pages = "3472--3484",
    abstract = "Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential language models. However, their computational complexity renders scaling difficult, and it remains an open question whether structural biases are still necessary when sequential models have access to ever larger amounts of training data. To answer this question, we introduce an efficient knowledge distillation (KD) technique that transfers knowledge from a syntactic language model trained on a small corpus to an LSTM language model, hence enabling the LSTM to develop a more structurally sensitive representation of the larger training data it learns from. On targeted syntactic evaluations, we find that, while sequential LSTMs perform much better than previously reported, our proposed technique substantially improves on this baseline, yielding a new state of the art. Our findings and analysis affirm the importance of structural biases, even in models that learn from large amounts of data.",
}
@inproceedings{li-etal-2019-imitation,
    title = "An Imitation Learning Approach to Unsupervised Parsing",
    author = "Li, Bowen  and
      Mou, Lili  and
      Keller, Frank",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1338",
    doi = "10.18653/v1/P19-1338",
    pages = "3485--3492",
    abstract = "Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the learned trees often do not match actual syntax trees well. Shen et al. (2018) propose a structured attention mechanism for language modeling (PRPN), which induces better syntactic structures but relies on ad hoc heuristics. Also, their model lacks interpretability as it is not grounded in parsing actions. In our work, we propose an imitation learning approach to unsupervised parsing, where we transfer the syntactic knowledge induced by PRPN to a Tree-LSTM model with discrete parsing actions. Its policy is then refined by Gumbel-Softmax training towards a semantically oriented objective. We evaluate our approach on the All Natural Language Inference dataset and show that it achieves a new state of the art in terms of parsing F-score, outperforming our base models, including PRPN.",
}
@inproceedings{garimella-etal-2019-womens,
    title = "Women{'}s Syntactic Resilience and Men{'}s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing",
    author = "Garimella, Aparna  and
      Banea, Carmen  and
      Hovy, Dirk  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1339",
    doi = "10.18653/v1/P19-1339",
    pages = "3493--3498",
    abstract = "Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences. To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles{'} authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women. Further analyses reveal numerous part-of-speech tags and syntactic relations whose prediction performances benefit from the prevalence of a specific gender in the training data. The results underscore the importance of accounting for gendered differences in syntactic tasks, and outline future venues for developing more accurate taggers and parsers. We release our data to the research community.",
}
@inproceedings{kitaev-etal-2019-multilingual,
    title = "Multilingual Constituency Parsing with Self-Attention and Pre-Training",
    author = "Kitaev, Nikita  and
      Cao, Steven  and
      Klein, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1340",
    doi = "10.18653/v1/P19-1340",
    pages = "3499--3505",
    abstract = "We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions. We first compare the benefits of no pre-training, fastText, ELMo, and BERT for English and find that BERT outperforms ELMo, in large part due to increased model capacity, whereas ELMo in turn outperforms the non-contextual fastText embeddings. We also find that pre-training is beneficial across all 11 languages tested; however, large model sizes (more than 100 million parameters) make it computationally expensive to train separate models for each language. To address this shortcoming, we show that joint multilingual pre-training and fine-tuning allows sharing all but a small number of parameters between ten languages in the final model. The 10x reduction in model size compared to fine-tuning one model per language causes only a 3.2{\%} relative error increase in aggregate. We further explore the idea of joint fine-tuning and show that it gives low-resource languages a way to benefit from the larger datasets of other languages. Finally, we demonstrate new state-of-the-art results for 11 languages, including English (95.8 F1) and Chinese (91.8 F1).",
}
@inproceedings{zhao-schutze-2019-multilingual,
    title = "A Multilingual {BPE} Embedding Space for Universal Sentiment Lexicon Induction",
    author = {Zhao, Mengjie  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1341",
    doi = "10.18653/v1/P19-1341",
    pages = "3506--3517",
    abstract = "We present a new method for sentiment lexicon induction that is designed to be applicable to the entire range of typological diversity of the world{'}s languages. We evaluate our method on Parallel Bible Corpus+ (PBC+), a parallel corpus of 1593 languages. The key idea is to use Byte Pair Encodings (BPEs) as basic units for multilingual embeddings. Through zero-shot transfer from English sentiment, we learn a seed lexicon for each language in the domain of PBC+. Through domain adaptation, we then generalize the domain-specific lexicon to a general one. We show {--} across typologically diverse languages in PBC+ {--} good quality of seed and general-domain sentiment lexicons by intrinsic and extrinsic and by automatic and human evaluation. We make freely available our code, seed sentiment lexicons for all 1593 languages and induced general-domain sentiment lexicons for 200 languages.",
}
@inproceedings{zhang-zhang-2019-tree,
    title = "Tree Communication Models for Sentiment Analysis",
    author = "Zhang, Yuan  and
      Zhang, Yue",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1342",
    doi = "10.18653/v1/P19-1342",
    pages = "3518--3527",
    abstract = "Tree-LSTMs have been used for tree-based sentiment analysis over Stanford Sentiment Treebank, which allows the sentiment signals over hierarchical phrase structures to be calculated simultaneously. However, traditional tree-LSTMs capture only the bottom-up dependencies between constituents. In this paper, we propose a tree communication model using graph convolutional neural network and graph recurrent neural network, which allows rich information exchange between phrases constituent tree. Experiments show that our model outperforms existing work on bidirectional tree-LSTMs in both accuracy and efficiency, providing more consistent predictions on phrase-level sentiments.",
}
@inproceedings{samanta-etal-2019-improved,
    title = "Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text",
    author = "Samanta, Bidisha  and
      Ganguly, Niloy  and
      Chakrabarti, Soumen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1343",
    doi = "10.18653/v1/P19-1343",
    pages = "3528--3537",
    abstract = "Multilingual writers and speakers often alternate between two languages in a single discourse. This practice is called {``}code-switching{''}. Existing sentiment detection methods are usually trained on sentiment-labeled monolingual text. Manually labeled code-switched text, especially involving minority languages, is extremely rare. Consequently, the best monolingual methods perform relatively poorly on code-switched text. We present an effective technique for synthesizing labeled code-switched text from labeled monolingual text, which is relatively readily available. The idea is to replace carefully selected subtrees of constituency parses of sentences in the resource-rich language with suitable token spans selected from automatic translations to the resource-poor language. By augmenting the scarce labeled code-switched text with plentiful synthetic labeled code-switched text, we achieve significant improvements in sentiment labeling accuracy (1.5{\%}, 5.11{\%} 7.20{\%}) for three different language pairs (English-Hindi, English-Spanish and English-Bengali). The improvement is even significant in hatespeech detection whereby we achieve a 4{\%} improvement using only synthetic code-switched data (6{\%} with data augmentation).",
}
@inproceedings{ma-etal-2019-exploring,
    title = "Exploring Sequence-to-Sequence Learning in Aspect Term Extraction",
    author = "Ma, Dehong  and
      Li, Sujian  and
      Wu, Fangzhao  and
      Xie, Xing  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1344",
    doi = "10.18653/v1/P19-1344",
    pages = "3538--3547",
    abstract = "Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem. However, sequence labeling based methods cannot make full use of the overall meaning of the whole sentence and have the limitation in processing dependencies between labels. To tackle these problems, we first explore to formalize ATE as a sequence-to-sequence (Seq2Seq) learning task where the source sequence and target sequence are composed of words and labels respectively. At the same time, to make Seq2Seq learning suit to ATE where labels correspond to words one by one, we design the gated unit networks to incorporate corresponding word representation into the decoder, and position-aware attention to pay more attention to the adjacent words of a target word. The experimental results on two datasets show that Seq2Seq learning is effective in ATE accompanied with our proposed gated unit networks and position-aware attention mechanism.",
}
@inproceedings{wang-etal-2019-aspect,
    title = "Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network",
    author = "Wang, Jingjing  and
      Sun, Changlong  and
      Li, Shoushan  and
      Liu, Xiaozhong  and
      Si, Luo  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1345",
    doi = "10.18653/v1/P19-1345",
    pages = "3548--3557",
    abstract = "In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.",
}
@inproceedings{fan-etal-2019-eli5,
    title = "{ELI}5: Long Form Question Answering",
    author = "Fan, Angela  and
      Jernite, Yacine  and
      Perez, Ethan  and
      Grangier, David  and
      Weston, Jason  and
      Auli, Michael",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1346",
    doi = "10.18653/v1/P19-1346",
    pages = "3558--3567",
    abstract = "We introduce the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum {``}Explain Like I{'}m Five{''} (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline.However, our best model is still far from human performance since raters prefer gold responses in over 86{\%} of cases, leaving ample opportunity for future improvement.",
}
@inproceedings{kim-etal-2019-textbook,
    title = "Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension",
    author = "Kim, Daesik  and
      Kim, Seonhoon  and
      Kwak, Nojun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1347",
    doi = "10.18653/v1/P19-1347",
    pages = "3568--3584",
    abstract = "In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of the TQA dataset. First, solving the TQA problems requires to comprehend multi-modal contexts in complicated input data. To tackle this issue of extracting knowledge features from long text lessons and merging them with visual features, we establish a context graph from texts and images, and propose a new module f-GCN based on graph convolutional networks (GCN). Second, scientific terms are not spread over the chapters and subjects are split in the TQA dataset. To overcome this so called {`}out-of-domain{'} issue, before learning QA problems, we introduce a novel self-supervised open-set learning process without any annotations. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.",
}
@inproceedings{wu-etal-2019-generating,
    title = "Generating Question Relevant Captions to Aid Visual Question Answering",
    author = "Wu, Jialin  and
      Hu, Zeyuan  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1348",
    doi = "10.18653/v1/P19-1348",
    pages = "3585--3594",
    abstract = "Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to better VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is trained using an existing caption dataset by automatically determining question-relevant captions using an online gradient-based method. Experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g. 68.4{\%} in the Test-standard set using a single model) by simultaneously generating question-relevant captions.",
}
@inproceedings{huang-etal-2019-multi,
    title = "Multi-grained Attention with Object-level Grounding for Visual Question Answering",
    author = "Huang, Pingping  and
      Huang, Jianhui  and
      Guo, Yuqing  and
      Qiao, Min  and
      Zhu, Yong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1349",
    doi = "10.18653/v1/P19-1349",
    pages = "3595--3600",
    abstract = "Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question. Most approaches train attention models from a coarse-grained association between sentences and images, which tends to fail on small objects or uncommon concepts. To address this problem, this paper proposes a multi-grained attention method. It learns explicit word-object correspondence by two types of word-level attention complementary to the sentence-image association. Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with state-of-the-art models. And the visualized attention maps demonstrate that addition of object-level groundings leads to a better understanding of the images and locates the attended objects more precisely.",
}
@inproceedings{greco-etal-2019-psycholinguistics,
    title = "Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering",
    author = "Greco, Claudio  and
      Plank, Barbara  and
      Fern{\'a}ndez, Raquel  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1350",
    doi = "10.18653/v1/P19-1350",
    pages = "3601--3605",
    abstract = "We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA). Motivated by evidence from psycholinguistics, we devise a set of linguistically-informed VQA tasks, which differ by the types of questions involved (Wh-questions and polar questions). We test what impact task difficulty has on continual learning, and whether the order in which a child acquires question types facilitates computational models. Our results show that dramatic forgetting is at play and that task difficulty and order matter. Two well-known current continual learning methods mitigate the problem only to a limiting degree.",
}
@inproceedings{kim-bansal-2019-improving,
    title = "Improving Visual Question Answering by Referring to Generated Paragraph Captions",
    author = "Kim, Hyounghun  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1351",
    doi = "10.18653/v1/P19-1351",
    pages = "3606--3612",
    abstract = "Paragraph-style image captions describe diverse aspects of an image as opposed to the more common single-sentence captions that only provide an abstract description of the image. These paragraph captions can hence contain substantial information of the image for tasks such as visual question answering. Moreover, this textual information is complementary with visual information present in the image because it can discuss both more abstract concepts and more explicit, intermediate symbolic information about objects, events, and scenes that can directly be matched with the textual question and copied into the textual answer (i.e., via easier modality match). Hence, we propose a combined Visual and Textual Question Answering (VTQA) model which takes as input a paragraph caption as well as the corresponding image, and answers the given question based on both inputs. In our model, the inputs are fused to extract related information by cross-attention (early fusion), then fused again in the form of consensus (late fusion), and finally expected answers are given an extra score to enhance the chance of selection (later fusion). Empirical results show that paragraph captions, even when automatically generated (via an RL-based encoder-decoder model), help correctly answer more visual questions. Overall, our joint model, when trained on the Visual Genome dataset, significantly improves the VQA performance over a strong baseline model.",
}
@inproceedings{liu-etal-2019-shared,
    title = "Shared-Private Bilingual Word Embeddings for Neural Machine Translation",
    author = "Liu, Xuebo  and
      Wong, Derek F.  and
      Liu, Yang  and
      Chao, Lidia S.  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1352",
    doi = "10.18653/v1/P19-1352",
    pages = "3613--3622",
    abstract = "Word embedding is central to neural machine translation (NMT), which has attracted intensive research interest in recent years. In NMT, the source embedding plays the role of the entrance while the target embedding acts as the terminal. These layers occupy most of the model parameters for representation learning. Furthermore, they indirectly interface via a soft-attention mechanism, which makes them comparatively isolated. In this paper, we propose shared-private bilingual word embeddings, which give a closer relationship between the source and target embeddings, and which also reduce the number of model parameters. For similar source and target words, their embeddings tend to share a part of the features and they cooperatively learn these common representation units. Experiments on 5 language pairs belonging to 6 different language families and written in 5 different alphabets demonstrate that the proposed model provides a significant performance boost over the strong baselines with dramatically fewer model parameters.",
}
@inproceedings{sims-etal-2019-literary,
    title = "Literary Event Detection",
    author = "Sims, Matthew  and
      Park, Jong Ho  and
      Bamman, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1353",
    doi = "10.18653/v1/P19-1353",
    pages = "3623--3634",
    abstract = "In this work we present a new dataset of literary events{---}events that are depicted as taking place within the imagined space of a novel. While previous work has focused on event detection in the domain of contemporary news, literature poses a number of complications for existing systems, including complex narration, the depiction of a broad array of mental states, and a strong emphasis on figurative language. We outline the annotation decisions of this new dataset and compare several models for predicting events; the best performing model, a bidirectional LSTM with BERT token representations, achieves an F1 score of 73.9. We then apply this model to a corpus of novels split across two dimensions{---}prestige and popularity{---}and demonstrate that there are statistically significant differences in the distribution of events for prestige.",
}
@inproceedings{yang-etal-2019-assessing,
    title = "Assessing the Ability of Self-Attention Networks to Learn Word Order",
    author = "Yang, Baosong  and
      Wang, Longyue  and
      Wong, Derek F.  and
      Chao, Lidia S.  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1354",
    doi = "10.18653/v1/P19-1354",
    pages = "3635--3644",
    abstract = "Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, e.g. machine translation. Due to the lack of recurrence structure such as recurrent neural networks (RNN), SAN is ascribed to be weak at learning positional information of words for sequence modeling. However, neither this speculation has been empirically confirmed, nor explanations for their strong performances on machine translation tasks when {``}lacking positional information{''} have been explored. To this end, we propose a novel word reordering detection task to quantify how well the word order information learned by SAN and RNN. Specifically, we randomly move one word to another position, and examine whether a trained model can detect both the original and inserted positions. Experimental results reveal that: 1) SAN trained on word reordering detection indeed has difficulty learning the positional information even with the position embedding; and 2) SAN trained on machine translation learns better positional information than its RNN counterpart, in which position embedding plays a critical role. Although recurrence structure make the model more universally-effective on learning word order, learning objectives matter more in the downstream tasks such as machine translation.",
}
@inproceedings{strubell-etal-2019-energy,
    title = "Energy and Policy Considerations for Deep Learning in {NLP}",
    author = "Strubell, Emma  and
      Ganesh, Ananya  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1355",
    doi = "10.18653/v1/P19-1355",
    pages = "3645--3650",
    abstract = "Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computational resources that necessitate similarly substantial energy consumption. As a result these models are costly to train and develop, both financially, due to the cost of hardware and electricity or cloud compute time, and environmentally, due to the carbon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP. Based on these findings, we propose actionable recommendations to reduce costs and improve equity in NLP research and practice.",
}
@inproceedings{jawahar-etal-2019-bert,
    title = "What Does {BERT} Learn about the Structure of Language?",
    author = "Jawahar, Ganesh  and
      Sagot, Beno{\^\i}t  and
      Seddah, Djam{\'e}",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1356",
    doi = "10.18653/v1/P19-1356",
    pages = "3651--3657",
    abstract = "BERT is a recent language representation model that has surprisingly performed well in diverse language understanding benchmarks. This result indicates the possibility that BERT networks capture structural information about language. In this work, we provide novel support for this claim by performing a series of experiments to unpack the elements of English language structure learned by BERT. Our findings are fourfold. BERT{'}s phrasal representation captures the phrase-level information in the lower layers. The intermediate layers of BERT compose a rich hierarchy of linguistic information, starting with surface features at the bottom, syntactic features in the middle followed by semantic features at the top. BERT requires deeper layers while tracking subject-verb agreement to handle long-term dependency problem. Finally, the compositional scheme underlying BERT mimics classical, tree-like structures.",
}
@inproceedings{jurgens-etal-2019-just,
    title = "A Just and Comprehensive Strategy for Using {NLP} to Address Online Abuse",
    author = "Jurgens, David  and
      Hemphill, Libby  and
      Chandrasekharan, Eshwar",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1357",
    doi = "10.18653/v1/P19-1357",
    pages = "3658--3666",
    abstract = "Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse. However, current methods have largely focused on a narrow definition of abuse to detriment of victims who seek both validation and solutions. In this position paper, we argue that the community needs to make three substantive changes: (1) expanding our scope of problems to tackle both more subtle and more serious forms of abuse, (2) developing proactive technologies that counter or inhibit abuse before it harms, and (3) reframing our effort within a framework of justice to promote healthy communities.",
}
@inproceedings{hancock-etal-2019-learning,
    title = "Learning from Dialogue after Deployment: Feed Yourself, Chatbot!",
    author = "Hancock, Braden  and
      Bordes, Antoine  and
      Mazare, Pierre-Emmanuel  and
      Weston, Jason",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1358",
    doi = "10.18653/v1/P19-1358",
    pages = "3667--3684",
    abstract = "The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user{'}s responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot{'}s dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.",
}
@inproceedings{song-etal-2019-generating,
    title = "Generating Responses with a Specific Emotion in Dialog",
    author = "Song, Zhenqiao  and
      Zheng, Xiaoqing  and
      Liu, Lu  and
      Xu, Mu  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1359",
    doi = "10.18653/v1/P19-1359",
    pages = "3685--3695",
    abstract = "It is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction. After a careful investigation of real-life conversation data, we found that there are at least two ways to express emotions with language. One is to describe emotional states by explicitly using strong emotional words; another is to increase the intensity of the emotional experiences by implicitly combining neutral words in distinct ways. We propose an emotional dialogue system (EmoDS) that can generate the meaningful responses with a coherent structure for a post, and meanwhile express the desired emotion explicitly or implicitly within a unified framework. Experimental results showed EmoDS performed better than the baselines in BLEU, diversity and the quality of emotional expression.",
}
@inproceedings{chen-etal-2019-semantically,
    title = "Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention",
    author = "Chen, Wenhu  and
      Chen, Jianshu  and
      Qin, Pengda  and
      Yan, Xifeng  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1360",
    doi = "10.18653/v1/P19-1360",
    pages = "3696--3709",
    abstract = "Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be difficult because the possible combinations of semantic inputs grow exponentially with the number of domains. To alleviate such scalability issue, we exploit the structure of dialog acts to build a multi-layer hierarchical graph, where each act is represented as a root-to-leaf route on the graph. Then, we incorporate such graph structure prior as an inductive bias to build a hierarchical disentangled self-attention network, where we disentangle attention heads to model designated nodes on the dialog act graph. By activating different (disentangled) heads at each layer, combinatorially many dialog act semantics can be modeled to control the neural response generation. On the large-scale Multi-Domain-WOZ dataset, our model can yield a significant improvement over the baselines on various automatic and human evaluation metrics.",
}
@inproceedings{wang-etal-2019-incremental,
    title = "Incremental Learning from Scratch for Task-Oriented Dialogue Systems",
    author = "Wang, Weikang  and
      Zhang, Jiajun  and
      Li, Qian  and
      Hwang, Mei-Yuh  and
      Zong, Chengqing  and
      Li, Zhifei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1361",
    doi = "10.18653/v1/P19-1361",
    pages = "3710--3720",
    abstract = "Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands are taken into account in the design phase. Consequently, existing systems will break down when encountering unconsidered user needs. To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs. Specifically, we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses. If there is high confidence, IDS will provide responses to users. Otherwise, humans will be involved in the dialogue process, and IDS can learn from human intervention through an online learning module. To evaluate our method, we propose a new dataset which simulates unanticipated user needs in the deployment stage. Experiments show that IDS is robust to unconsidered user actions, and can update itself online by smartly selecting only the most effective training data, and hence attains better performance with less annotation cost.",
}
@inproceedings{zhang-etal-2019-recosa,
    title = "{R}e{C}o{S}a: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation",
    author = "Zhang, Hainan  and
      Lan, Yanyan  and
      Pang, Liang  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1362",
    doi = "10.18653/v1/P19-1362",
    pages = "3721--3730",
    abstract = "In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response accordingly. However, the widely used hierarchical recurrent encoder-decoder models just treat all the contexts indiscriminately, which may hurt the following response generation process. Some researchers try to use the cosine similarity or the traditional attention mechanism to find the relevant contexts, but they suffer from either insufficient relevance assumption or position bias problem. In this paper, we propose a new model, named ReCoSa, to tackle this problem. Firstly, a word level LSTM encoder is conducted to obtain the initial representation of each context. Then, the self-attention mechanism is utilized to update both the context and masked response representation. Finally, the attention weights between each context and response representations are computed and used in the further decoding process. Experimental results on both Chinese customer services dataset and English Ubuntu dialogue dataset show that ReCoSa significantly outperforms baseline models, in terms of both metric-based and human evaluations. Further analysis on attention shows that the detected relevant contexts by ReCoSa are highly coherent with human{'}s understanding, validating the correctness and interpretability of ReCoSa.",
}
@inproceedings{welleck-etal-2019-dialogue,
    title = "Dialogue Natural Language Inference",
    author = "Welleck, Sean  and
      Weston, Jason  and
      Szlam, Arthur  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1363",
    doi = "10.18653/v1/P19-1363",
    pages = "3731--3741",
    abstract = "Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model{'}s consistency.",
}
@inproceedings{zhang-etal-2019-budgeted,
    title = "Budgeted Policy Learning for Task-Oriented Dialogue Systems",
    author = "Zhang, Zhirui  and
      Li, Xiujun  and
      Gao, Jianfeng  and
      Chen, Enhong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1364",
    doi = "10.18653/v1/P19-1364",
    pages = "3742--3751",
    abstract = "This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents. BCS consists of (1) a Poisson-based global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences; (3) a user goal sampling module to generate the experiences that are most effective for policy learning. Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-of-the-art baselines given the fixed budget.",
}
@inproceedings{ippolito-etal-2019-comparison,
    title = "Comparison of Diverse Decoding Methods from Conditional Language Models",
    author = "Ippolito, Daphne  and
      Kriz, Reno  and
      Sedoc, Jo{\~a}o  and
      Kustikova, Maria  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1365",
    doi = "10.18653/v1/P19-1365",
    pages = "3752--3762",
    abstract = "While conditional language models have greatly improved in their ability to output high quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that rerank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from a conditional language model. In addition, we present a novel method where we over-sample candidates, then use clustering to remove similar sequences, thus achieving high diversity without sacrificing quality.",
}
@inproceedings{zhu-etal-2019-retrieval,
    title = "Retrieval-Enhanced Adversarial Training for Neural Response Generation",
    author = "Zhu, Qingfu  and
      Cui, Lei  and
      Zhang, Wei-Nan  and
      Wei, Furu  and
      Liu, Ting",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1366",
    doi = "10.18653/v1/P19-1366",
    pages = "3763--3773",
    abstract = "Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the advantages of different models. In this paper, we propose a Retrieval-Enhanced Adversarial Training (REAT) method for neural response generation. Distinct from existing approaches, the REAT method leverages an encoder-decoder framework in terms of an adversarial training paradigm, while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. An empirical study on a large scale public available benchmark dataset shows that the REAT method significantly outperforms the vanilla Seq2Seq model as well as the conventional adversarial training approach.",
}
@inproceedings{liu-etal-2019-vocabulary,
    title = "Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation",
    author = "Liu, Cao  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1367",
    doi = "10.18653/v1/P19-1367",
    pages = "3774--3783",
    abstract = "We study the task of response generation. Conventional methods employ a fixed vocabulary and one-pass decoding, which not only make them prone to safe and general responses but also lack further refining to the first generated raw sequence. To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation. Specifically, the dialogue input and output are represented by multi-level vocabularies which are obtained from hierarchical clustering of raw words. Then, multi-pass encoding and decoding are conducted on the multi-level vocabularies. Since VPN is able to leverage rich encoding and decoding information with multi-level vocabularies, it has the potential to generate better responses. Experiments on English Twitter and Chinese Weibo datasets demonstrate that VPN remarkably outperforms strong baselines.",
}
@inproceedings{ravi-kozareva-2019-device,
    title = "On-device Structured and Context Partitioned Projection Networks",
    author = "Ravi, Sujith  and
      Kozareva, Zornitsa",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1368",
    doi = "10.18653/v1/P19-1368",
    pages = "3784--3793",
    abstract = "A challenging problem in on-device text classification is to build highly accurate neural models that can fit in small memory footprint and have low latency. To address this challenge, we propose an on-device neural network SGNN++ which dynamically learns compact projection vectors from raw text using structured and context-dependent partition projections. We show that this results in accelerated inference and performance improvements. We conduct extensive evaluation on multiple conversational tasks and languages such as English, Japanese, Spanish and French. Our SGNN++ model significantly outperforms all baselines, improves upon existing on-device neural models and even surpasses RNN, CNN and BiLSTM models on dialog act and intent prediction. Through a series of ablation studies we show the impact of the partitioned projections and structured information leading to 10{\%} improvement. We study the impact of the model size on accuracy and introduce quatization-aware training for SGNN++ to further reduce the model size while preserving the same quality. Finally, we show fast inference on mobile phones.",
}
@inproceedings{wu-etal-2019-proactive,
    title = "Proactive Human-Machine Conversation with Explicit Conversation Goal",
    author = "Wu, Wenquan  and
      Guo, Zhen  and
      Zhou, Xiangyang  and
      Wu, Hua  and
      Zhang, Xiyuan  and
      Lian, Rongzhong  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1369",
    doi = "10.18653/v1/P19-1369",
    pages = "3794--3804",
    abstract = "Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, rather than on its own initiatives. In this paper, we take a radical step towards building a human-like conversational agent: endowing it with the ability of proactively leading the conversation (introducing a new topic or maintaining the current topic). To facilitate the development of such conversation systems, we create a new dataset named Konv where one acts as a conversation leader and the other acts as the follower. The leader is provided with a knowledge graph and asked to sequentially change the discussion topics, following the given conversation goal, and meanwhile keep the dialogue as natural and engaging as possible. Konv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph. We establish baseline results on this dataset (about 270K utterances and 30k dialogues) using several state-of-the-art models. Experimental results show that dialogue models that plan over the knowledge graph can make full use of related knowledge to generate more diverse multi-turn conversations. The baseline systems along with the dataset are publicly available.",
}
@inproceedings{feng-etal-2019-learning,
    title = "Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems",
    author = "Feng, Jiazhan  and
      Tao, Chongyang  and
      Wu, Wei  and
      Feng, Yansong  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1370",
    doi = "10.18653/v1/P19-1370",
    pages = "3805--3815",
    abstract = "We study learning of a matching model for response selection in retrieval-based dialogue systems. The problem is equally important with designing the architecture of a model, but is less explored in existing literature. To learn a robust matching model from noisy training data, we propose a general co-teaching framework with three specific teaching strategies that cover both teaching with loss functions and teaching with data curriculum. Under the framework, we simultaneously learn two matching models with independent training sets. In each iteration, one model transfers the knowledge learned from its training set to the other model, and at the same time receives the guide from the other model on how to overcome noise in training. Through being both a teacher and a student, the two models learn from each other and get improved together. Evaluation results on two public data sets indicate that the proposed learning approach can generally and significantly improve the performance of existing matching models.",
}
@inproceedings{tian-etal-2019-learning,
    title = "Learning to Abstract for Memory-augmented Conversational Response Generation",
    author = "Tian, Zhiliang  and
      Bi, Wei  and
      Li, Xiaopeng  and
      Zhang, Nevin L.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1371",
    doi = "10.18653/v1/P19-1371",
    pages = "3816--3825",
    abstract = "Neural generative models for open-domain chit-chat conversations have become an active area of research in recent years. A critical issue with most existing generative models is that the generated responses lack informativeness and diversity. A few researchers attempt to leverage the results of retrieval models to strengthen the generative models, but these models are limited by the quality of the retrieval results. In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation. Our model clusters query-response samples, extracts characteristics of each cluster, and learns to utilize these characteristics for response generation. Experimental results show that our model outperforms other competitive baselines.",
}
@inproceedings{qiu-etal-2019-training,
    title = "Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References",
    author = "Qiu, Lisong  and
      Li, Juntao  and
      Bi, Wei  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1372",
    doi = "10.18653/v1/P19-1372",
    pages = "3826--3835",
    abstract = "Due to its potential applications, open-domain dialogue generation has become popular and achieved remarkable progress in recent years, but sometimes suffers from generic responses. Previous models are generally trained based on 1-to-1 mapping from an input query to its response, which actually ignores the nature of 1-to-n mapping in dialogue that there may exist multiple valid responses corresponding to the same query. In this paper, we propose to utilize the multiple references by considering the correlation of different valid responses and modeling the 1-to-n mapping with a novel two-step generation architecture. The first generation phase extracts the common features of different responses which, combined with distinctive features obtained in the second phase, can generate multiple diverse and appropriate responses. Experimental results show that our proposed model can effectively improve the quality of response and outperform existing neural dialogue models on both automatic and human evaluations.",
}
@inproceedings{mehri-etal-2019-pretraining,
    title = "Pretraining Methods for Dialog Context Representation Learning",
    author = "Mehri, Shikib  and
      Razumovskaia, Evgeniia  and
      Zhao, Tiancheng  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1373",
    doi = "10.18653/v1/P19-1373",
    pages = "3836--3845",
    abstract = "This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. Each pretraining objective is fine-tuned and evaluated on a set of downstream dialog tasks using the MultiWoz dataset and strong performance improvement is observed. Further evaluation shows that our pretraining objectives result in not only better performance, but also better convergence, models that are less data hungry and have better domain generalizability.",
}
@inproceedings{kummerfeld-etal-2019-large,
    title = "A Large-Scale Corpus for Conversation Disentanglement",
    author = "Kummerfeld, Jonathan K.  and
      Gouravajhala, Sai R.  and
      Peper, Joseph J.  and
      Athreya, Vignesh  and
      Gunasekara, Chulaka  and
      Ganhotra, Jatin  and
      Patel, Siva Sankalp  and
      Polymenakos, Lazaros C  and
      Lasecki, Walter",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1374",
    doi = "10.18653/v1/P19-1374",
    pages = "3846--3856",
    abstract = "Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89{\%} of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.",
}
@inproceedings{wu-etal-2019-self,
    title = "Self-Supervised Dialogue Learning",
    author = "Wu, Jiawei  and
      Wang, Xin  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1375",
    doi = "10.18653/v1/P19-1375",
    pages = "3857--3867",
    abstract = "The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations. We consider the order information as a crucial supervised signal for dialogue learning, which, however, has been neglected by many previous dialogue systems. Therefore, in this paper, we introduce a self-supervised learning task, inconsistent order detection, to explicitly capture the flow of conversation in dialogues. Given a sampled utterance pair triple, the task is to predict whether it is ordered or misordered. Then we propose a sampling-based self-supervised network SSN to perform the prediction with sampled triple references from previous dialogue history. Furthermore, we design a joint learning framework where SSN can guide the dialogue systems towards more coherent and relevant dialogue learning through adversarial training. We demonstrate that the proposed methods can be applied to both open-domain and task-oriented dialogue scenarios, and achieve the new state-of-the-art performance on the OpenSubtitiles and Movie-Ticket Booking datasets.",
}
@inproceedings{corkery-etal-2019-yet,
    title = "Are we there yet? Encoder-decoder neural networks as cognitive models of {E}nglish past tense inflection",
    author = "Corkery, Maria  and
      Matusevych, Yevgen  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1376",
    doi = "10.18653/v1/P19-1376",
    pages = "3868--3877",
    abstract = "The cognitive mechanisms needed to account for the English past tense have long been a subject of debate in linguistics and cognitive science. Neural network models were proposed early on, but were shown to have clear flaws. Recently, however, Kirov and Cotterell (2018) showed that modern encoder-decoder (ED) models overcome many of these flaws. They also presented evidence that ED models demonstrate humanlike performance in a nonce-word task. Here, we look more closely at the behaviour of their model in this task. We find that (1) the model exhibits instability across multiple simulations in terms of its correlation with human data, and (2) even when results are aggregated across simulations (treating each simulation as an individual human participant), the fit to the human data is not strong{---}worse than an older rule-based model. These findings hold up through several alternative training regimes and evaluation measures. Although other neural architectures might do better, we conclude that there is still insufficient evidence to claim that neural nets are a good cognitive model for this task.",
}
@inproceedings{hulpus-etal-2019-spreading,
    title = "A Spreading Activation Framework for Tracking Conceptual Complexity of Texts",
    author = "Hulpu{\textcommabelow{s}}, Ioana  and
      {\v{S}}tajner, Sanja  and
      Stuckenschmidt, Heiner",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1377",
    doi = "10.18653/v1/P19-1377",
    pages = "3878--3887",
    abstract = "We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art.",
}
@inproceedings{mao-etal-2019-end,
    title = "End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories",
    author = "Mao, Rui  and
      Lin, Chenghua  and
      Guerin, Frank",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1378",
    doi = "10.18653/v1/P19-1378",
    pages = "3888--3898",
    abstract = "End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification.",
}
@inproceedings{hu-etal-2019-diachronic,
    title = "Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View",
    author = "Hu, Renfen  and
      Li, Shen  and
      Liang, Shichen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1379",
    doi = "10.18653/v1/P19-1379",
    pages = "3899--3908",
    abstract = "Diachronic word embeddings have been widely used in detecting temporal changes. However, existing methods face the meaning conflation deficiency by representing a word as a single vector at each time period. To address this issue, this paper proposes a sense representation and tracking framework based on deep contextualized embeddings, aiming at answering not only what and when, but also how the word meaning changes. The experiments show that our framework is effective in representing fine-grained word senses, and it brings a significant improvement in word change detection task. Furthermore, we model the word change from an ecological viewpoint, and sketch two interesting sense behaviors in the process of language evolution, i.e. sense competition and sense cooperation.",
}
@inproceedings{bouchacourt-baroni-2019-miss,
    title = "Miss Tools and Mr Fruit: Emergent Communication in Agents Learning about Object Affordances",
    author = "Bouchacourt, Diane  and
      Baroni, Marco",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1380",
    doi = "10.18653/v1/P19-1380",
    pages = "3909--3918",
    abstract = "Recent research studies communication emergence in communities of deep network agents assigned a joint task, hoping to gain insights on human language evolution. We propose here a new task capturing crucial aspects of the human environment, such as natural object affordances, and of human conversation, such as full symmetry among the participants. By conducting a thorough pragmatic and semantic analysis of the emergent protocol, we show that the agents solve the shared task through genuine bilateral, referential communication. However, the agents develop multiple idiolects, which makes us conclude that full symmetry is not a sufficient condition for a common language to emerge.",
}
@inproceedings{dessi-baroni-2019-cnns,
    title = "{CNN}s found to jump around more skillfully than {RNN}s: Compositional Generalization in Seq2seq Convolutional Networks",
    author = "Dess{\`\i}, Roberto  and
      Baroni, Marco",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1381",
    doi = "10.18653/v1/P19-1381",
    pages = "3919--3923",
    abstract = "Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of {``}jump around{''} 0-shot from the component words. Recurrent networks (RNNs) were found to completely fail the most challenging generalization cases. We test here a convolutional network (CNN) on these tasks, reporting hugely improved performance with respect to RNNs. Despite the big improvement, the CNN has however not induced systematic rules, suggesting that the difference between compositional and non-compositional behaviour is not clear-cut.",
}
@inproceedings{bjerva-etal-2019-uncovering,
    title = "Uncovering Probabilistic Implications in Typological Knowledge Bases",
    author = "Bjerva, Johannes  and
      Kementchedjhieva, Yova  and
      Cotterell, Ryan  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1382",
    doi = "10.18653/v1/P19-1382",
    pages = "3924--3930",
    abstract = "The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have postpositions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population.",
}
@inproceedings{loukatou-etal-2019-word,
    title = "Is Word Segmentation Child{'}s Play in All Languages?",
    author = "Loukatou, Georgia R.  and
      Moran, Steven  and
      Blasi, Damian  and
      Stoll, Sabine  and
      Cristia, Alejandrina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1383",
    doi = "10.18653/v1/P19-1383",
    pages = "3931--3937",
    abstract = "When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation. Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language. We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages. The results consist evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.",
}
@inproceedings{blasi-etal-2019-distribution,
    title = "On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study",
    author = "Blasi, Damian  and
      Cotterell, Ryan  and
      Wolf-Sonkin, Lawrence  and
      Stoll, Sabine  and
      Bickel, Balthasar  and
      Baroni, Marco",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1384",
    doi = "10.18653/v1/P19-1384",
    pages = "3938--3943",
    abstract = "Embedding a clause inside another ({``}the girl [who likes cars [that run fast]] has arrived{''}) is a fundamental resource that has been argued to be a key driver of linguistic expressiveness. As such, it plays a central role in fundamental debates on what makes human language unique, and how they might have evolved. Empirical evidence on the prevalence and the limits of embeddings has however been based on either laboratory setups or corpus data of relatively limited size. We introduce here a collection of large, dependency-parsed written corpora in 17 languages, that allow us, for the first time, to capture clausal embedding through dependency graphs and assess their distribution. Our results indicate that there is no evidence for hard constraints on embedding depth: the tail of depth distributions is heavy. Moreover, although deeply embedded clauses tend to be shorter, suggesting processing load issues, complex sentences with many embeddings do not display a bias towards less deep embeddings. Taken together, the results suggest that deep embeddings are not disfavoured in written language. More generally, our study illustrates how resources and methods from latest-generation big-data NLP can provide new perspectives on fundamental questions in theoretical linguistics.",
}
@inproceedings{margatina-etal-2019-attention,
    title = "Attention-based Conditioning Methods for External Knowledge Integration",
    author = "Margatina, Katerina  and
      Baziotis, Christos  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1385",
    doi = "10.18653/v1/P19-1385",
    pages = "3944--3951",
    abstract = "In this paper, we present a novel approach for incorporating external knowledge in Recurrent Neural Networks (RNNs). We propose the integration of lexicon features into the self-attention mechanism of RNN-based architectures. This form of conditioning on the attention distribution, enforces the contribution of the most salient words for the task at hand. We introduce three methods, namely attentional concatenation, feature-based gating and affine transformation. Experiments on six benchmark datasets show the effectiveness of our methods. Attentional feature-based gating yields consistent performance improvement across tasks. Our approach is implemented as a simple add-on module for RNN-based models with minimal computational overhead and can be adapted to any deep neural architecture.",
}
@inproceedings{emami-etal-2019-knowref,
    title = "The {K}now{R}ef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution",
    author = "Emami, Ali  and
      Trichelair, Paul  and
      Trischler, Adam  and
      Suleman, Kaheer  and
      Schulz, Hannes  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1386",
    doi = "10.18653/v1/P19-1386",
    pages = "3952--3961",
    abstract = "We introduce a new benchmark for coreference resolution and NLI, KnowRef, that targets common-sense understanding and world knowledge. Previous coreference resolution tasks can largely be solved by exploiting the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of naturally occurring text. We present a corpus of over 8,000 annotated text passages with ambiguous pronominal anaphora. These instances are both challenging and realistic. We show that various coreference systems, whether rule-based, feature-rich, or neural, perform significantly worse on the task than humans, who display high inter-annotator agreement. To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision. We then use problem-specific insights to propose a data-augmentation trick called antecedent switching to alleviate this tendency in models. Finally, we show that antecedent switching yields promising results on other tasks as well: we use it to achieve state-of-the-art results on the GAP coreference task.",
}
@inproceedings{sarkar-etal-2019-stre,
    title = "{S}t{RE}: Self Attentive Edit Quality Prediction in {W}ikipedia",
    author = "Sarkar, Soumya  and
      Reddy, Bhanu Prakash  and
      Sikdar, Sandipan  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1387",
    doi = "10.18653/v1/P19-1387",
    pages = "3962--3972",
    abstract = "Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, in the field of natural language processing toward developing automated tools for content moderation and review. In this paper we propose Self Attentive Revision Encoder (StRE) which leverages orthographic similarity of lexical units toward predicting the quality of new edits. In contrast to existing propositions which primarily employ features like page reputation, editor activity or rule based heuristics, we utilize the textual content of the edits which, we believe contains superior signatures of their quality. More specifically, we deploy deep encoders to generate representations of the edits from its text content, which we then leverage to infer quality. We further contribute a novel dataset containing ∼ 21M revisions across 32K Wikipedia pages and demonstrate that StRE outperforms existing methods by a significant margin {--} at least 17{\%} and at most 103{\%}. Our pre-trained model achieves such result after retraining on a set as small as 20{\%} of the edits in a wikipage. This, to the best of our knowledge, is also the first attempt towards employing deep language models to the enormous domain of automated content moderation and review in Wikipedia.",
}
@inproceedings{elazar-etal-2019-large,
    title = "How Large Are Lions? Inducing Distributions over Quantitative Attributes",
    author = "Elazar, Yanai  and
      Mahabal, Abhijit  and
      Ramachandran, Deepak  and
      Bedrax-Weiss, Tania  and
      Roth, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1388",
    doi = "10.18653/v1/P19-1388",
    pages = "3973--3983",
    abstract = "Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as {``}Is a lion bigger than a wolf?{''}. Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.",
}
@inproceedings{bi-etal-2019-fine,
    title = "Fine-Grained Sentence Functions for Short-Text Conversation",
    author = "Bi, Wei  and
      Gao, Jun  and
      Liu, Xiaojiang  and
      Shi, Shuming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1389",
    doi = "10.18653/v1/P19-1389",
    pages = "3984--3993",
    abstract = "Sentence function is an important linguistic feature referring to a user{'}s purpose in uttering a specific sentence. The use of sentence function has shown promising results to improve the performance of conversation models. However, there is no large conversation dataset annotated with sentence functions. In this work, we collect a new Short-Text Conversation dataset with manually annotated SEntence FUNctions (STC-Sefun). Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query. We later train conversation models conditioned on the sentence functions, including information retrieval-based and neural generative models. Experimental results demonstrate that the use of sentence functions can help improve the quality of the returned responses.",
}
@inproceedings{ke-etal-2019-give,
    title = "Give Me More Feedback {II}: Annotating Thesis Strength and Related Attributes in Student Essays",
    author = "Ke, Zixuan  and
      Inamdar, Hrishikesh  and
      Lin, Hui  and
      Ng, Vincent",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1390",
    doi = "10.18653/v1/P19-1390",
    pages = "3994--4004",
    abstract = "While the vast majority of existing work on automated essay scoring has focused on holistic scoring, researchers have recently begun work on scoring specific dimensions of essay quality. Nevertheless, progress on dimension-specific essay scoring is limited in part by the lack of annotated corpora. To facilitate advances in this area, we design a scoring rubric for scoring a core, yet unexplored dimension of persuasive essay quality, thesis strength, and annotate a corpus of essays with thesis strength scores. We additionally identify the attributes that could impact thesis strength and annotate the essays with the values of these attributes, which, when predicted by computational models, could provide further feedback to students on why her essay receives a particular thesis strength score.",
}
@inproceedings{troiano-etal-2019-crowdsourcing,
    title = "Crowdsourcing and Validating Event-focused Emotion Corpora for {G}erman and {E}nglish",
    author = "Troiano, Enrica  and
      Pad{\'o}, Sebastian  and
      Klinger, Roman",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1391",
    doi = "10.18653/v1/P19-1391",
    pages = "4005--4011",
    abstract = "Sentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on crosslingual modeling and the development of predictive models for other languages. In this paper, we fill this gap for German by constructing deISEAR, a corpus designed in analogy to the well-established English ISEAR emotion dataset. Motivated by Scherer{'}s appraisal theory, we implement a crowdsourcing experiment which consists of two steps. In step 1, participants create descriptions of emotional events for a given emotion. In step 2, five annotators assess the emotion expressed by the texts. We show that transferring an emotion classification model from the original English ISEAR to the German crowdsourced deISEAR via machine translation does not, on average, cause a performance drop.",
}
@inproceedings{garcia-etal-2019-pay,
    title = "Pay Attention when you Pay the Bills. A Multilingual Corpus with Dependency-based and Semantic Annotation of Collocations.",
    author = "Garcia, Marcos  and
      Garc{\'\i}a Salido, Marcos  and
      Sotelo, Susana  and
      Mosqueira, Estela  and
      Alonso-Ramos, Margarita",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1392",
    doi = "10.18653/v1/P19-1392",
    pages = "4012--4019",
    abstract = "This paper presents a new multilingual corpus with semantic annotation of collocations in English, Portuguese, and Spanish. The whole resource contains 155k tokens and 1,526 collocations labeled in context. The annotated examples belong to three syntactic relations (adjective-noun, verb-object, and nominal compounds), and represent 58 lexical functions in the Meaning-Text Theory (e.g., Oper, Magn, Bon, etc.). Each collocation was annotated by three linguists and the final resource was revised by a team of experts. The resulting corpus can serve as a basis to evaluate different approaches for collocation identification, which in turn can be useful for different NLP tasks such as natural language understanding or natural language generation.",
}
@inproceedings{wang-etal-2019-make,
    title = "Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation",
    author = "Wang, Cunxiang  and
      Liang, Shuailong  and
      Zhang, Yue  and
      Li, Xiaonan  and
      Gao, Tian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1393",
    doi = "10.18653/v1/P19-1393",
    pages = "4020--4026",
    abstract = "Introducing common sense to natural language understanding systems has received increasing research attention. It remains a fundamental question on how to evaluate whether a system has the sense-making capability. Existing benchmarks measure common sense knowledge indirectly or without reasoning. In this paper, we release a benchmark to directly test whether a system can differentiate natural language statements that make sense from those that do not make sense. In addition, a system is asked to identify the most crucial reason why a statement does not make sense. We evaluate models trained over large-scale language modeling tasks as well as human performance, showing that there are different challenges for system sense-making.",
}
@inproceedings{blinov-etal-2019-large,
    title = "Large Dataset and Language Model Fun-Tuning for Humor Recognition",
    author = "Blinov, Vladislav  and
      Bolotova-Baranova, Valeria  and
      Braslavski, Pavel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1394",
    doi = "10.18653/v1/P19-1394",
    pages = "4027--4032",
    abstract = "The task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents. We collected a dataset of jokes and funny dialogues in Russian from various online resources and complemented them carefully with unfunny texts with similar lexical properties. The dataset comprises of more than 300,000 short texts, which is significantly larger than any previous humor-related corpus. Manual annotation of 2,000 items proved the reliability of the corpus construction approach. Further, we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 on a test set, which constitutes a considerable gain over baseline methods. The dataset is freely available for research community.",
}
@inproceedings{aghajanyan-etal-2019-towards,
    title = "Towards Language Agnostic Universal Representations",
    author = "Aghajanyan, Armen  and
      Song, Xia  and
      Tiwary, Saurabh",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1395",
    doi = "10.18653/v1/P19-1395",
    pages = "4033--4041",
    abstract = "When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language. However, current representations in machine learning are language dependent. In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion. We learn these representations by taking inspiration from linguistics, specifically the Universal Grammar hypothesis and learn universal latent representations that are language agnostic. We demonstrate the capabilities of these representations by showing that models trained on a single language using language agnostic representations achieve very similar accuracies in other languages.",
}
@inproceedings{zhao-etal-2019-leveraging,
    title = "Leveraging Meta Information in Short Text Aggregation",
    author = "Zhao, He  and
      Du, Lan  and
      Liu, Guanfeng  and
      Buntine, Wray",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1396",
    doi = "10.18653/v1/P19-1396",
    pages = "4042--4049",
    abstract = "Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.",
}
@inproceedings{tang-de-sa-2019-exploiting,
    title = "Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning",
    author = "Tang, Shuai  and
      de Sa, Virginia R.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1397",
    doi = "10.18653/v1/P19-1397",
    pages = "4050--4060",
    abstract = "Encoder-decoder models for unsupervised sentence representation learning using the distributional hypothesis effectively constrain the learnt representation of a sentence to only that needed to reproduce the next sentence. While the decoder is important to constrain the representation, these models tend to discard the decoder after training since only the encoder is needed to map the input sentence into a vector representation. However, parameters learnt in the decoder also contain useful information about the language. In order to utilise the decoder after learning, we present two types of decoding functions whose inverse can be easily derived without expensive inverse calculation. Therefore, the inverse of the decoding function serves as another encoder that produces sentence representations. We show that, with careful design of the decoding functions, the model learns good sentence representations, and the ensemble of the representations produced from the encoder and the inverse of the decoder demonstrate even better generalisation ability and solid transferability.",
}
@inproceedings{ruff-etal-2019-self,
    title = "Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text",
    author = "Ruff, Lukas  and
      Zemlyanskiy, Yury  and
      Vandermeulen, Robert  and
      Schnake, Thomas  and
      Kloft, Marius",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1398",
    doi = "10.18653/v1/P19-1398",
    pages = "4061--4071",
    abstract = "There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words. In this paper we introduce a new anomaly detection method{---}Context Vector Data Description (CVDD){---}which builds upon word embedding models to learn multiple sentence representations that capture multiple semantic contexts via the self-attention mechanism. Modeling multiple contexts enables us to perform contextual anomaly detection of sentences and phrases with respect to the multiple themes and concepts present in an unlabeled text corpus. These contexts in combination with the self-attention weights make our method highly interpretable. We demonstrate the effectiveness of CVDD quantitatively as well as qualitatively on the well-known Reuters, 20 Newsgroups, and IMDB Movie Reviews datasets.",
}
@inproceedings{huang-etal-2019-hubless,
    title = "Hubless Nearest Neighbor Search for Bilingual Lexicon Induction",
    author = "Huang, Jiaji  and
      Qiu, Qiang  and
      Church, Kenneth",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1399",
    doi = "10.18653/v1/P19-1399",
    pages = "4072--4080",
    abstract = "Bilingual Lexicon Induction (BLI) is the task of translating words from corpora in two languages. Recent advances in BLI work by aligning the two word embedding spaces. Following that, a key step is to retrieve the nearest neighbor (NN) in the target space given the source word. However, a phenomenon called hubness often degrades the accuracy of NN. Hubness appears as some data points, called hubs, being extra-ordinarily close to many of the other data points. Reducing hubness is necessary for retrieval tasks. One successful example is Inverted SoFtmax (ISF), recently proposed to improve NN. This work proposes a new method, Hubless Nearest Neighbor (HNN), to mitigate hubness. HNN differs from NN by imposing an additional equal preference assumption. Moreover, the HNN formulation explains why ISF works as well as it does. Empirical results demonstrate that HNN outperforms NN, ISF and other state-of-the-art. For reproducibility and follow-ups, we have published all code.",
}
@inproceedings{le-titov-2019-distant,
    title = "Distant Learning for Entity Linking with Automatic Noise Detection",
    author = "Le, Phong  and
      Titov, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1400",
    doi = "10.18653/v1/P19-1400",
    pages = "4081--4090",
    abstract = "Accurate entity linkers have been produced for domains and languages where annotated data (i.e., texts linked to a knowledge base) is available. However, little progress has been made for the settings where no or very limited amounts of labeled data are present (e.g., legal or most scientific domains). In this work, we show how we can learn to link mentions without having any labeled examples, only a knowledge base and a collection of unannotated texts from the corresponding domain. In order to achieve this, we frame the task as a multi-instance learning problem and rely on surface matching to create initial noisy labels. As the learning signal is weak and our surrogate labels are noisy, we introduce a noise detection component in our model: it lets the model detect and disregard examples which are likely to be noisy. Our method, jointly learning to detect noise and link entities, greatly outperforms the surface matching baseline. For a subset of entity categories, it even approaches the performance of supervised learning.",
}
@inproceedings{vu-etal-2019-learning,
    title = "Learning How to Active Learn by Dreaming",
    author = "Vu, Thuy-Trang  and
      Liu, Ming  and
      Phung, Dinh  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1401",
    doi = "10.18653/v1/P19-1401",
    pages = "4091--4101",
    abstract = "Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. Recent data-driven AL policy learning methods are also restricted to learn from closely related domains. We introduce a new sample-efficient method that learns the AL policy directly on the target domain of interest by using wake and dream cycles. Our approach interleaves between querying the annotation of the selected datapoints to update the underlying student learner and improving AL policy using simulation where the current student learner acts as an imperfect annotator. We evaluate our method on cross-domain and cross-lingual text classification and named entity recognition tasks. Experimental results show that our dream-based AL policy training strategy is more effective than applying the pretrained policy without further fine-tuning and better than the existing strong baseline methods that use heuristics or reinforcement learning.",
}
@inproceedings{hu-etal-2019-shot,
    title = "Few-Shot Representation Learning for Out-Of-Vocabulary Words",
    author = "Hu, Ziniu  and
      Chen, Ting  and
      Chang, Kai-Wei  and
      Sun, Yizhou",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1402",
    doi = "10.18653/v1/P19-1402",
    pages = "4102--4112",
    abstract = "Existing approaches for learning word embedding often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts. However, in real-world scenarios, out-of-vocabulary (a.k.a. OOV) words that do not appear in training corpus emerge frequently. How to learn accurate representations of these words to augment a pre-trained embedding by only a few observations is a challenging research problem. In this paper, we formulate the learning of OOV embedding as a few-shot regression problem by fitting a representation function to predict an oracle embedding vector (defined as embedding trained with abundant observations) based on limited contexts. Specifically, we propose a novel hierarchical attention network-based embedding framework to serve as the neural regression function, in which the context information of a word is encoded and aggregated from K observations. Furthermore, we propose to use Model-Agnostic Meta-Learning (MAML) for adapting the learned model to the new corpus fast and robustly. Experiments show that the proposed approach significantly outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embedding is utilized.",
}
@inproceedings{huang-paul-2019-neural,
    title = "Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models",
    author = "Huang, Xiaolei  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1403",
    doi = "10.18653/v1/P19-1403",
    pages = "4113--4123",
    abstract = "Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations. This paper describes two complementary ways to adapt classifiers to shifts across time. First, we show that diachronic word embeddings, which were originally developed to study language change, can also improve document classification, and we show a simple method for constructing this type of embedding. Second, we propose a time-driven neural classification model inspired by methods for domain adaptation. Experiments on six corpora show how these methods can make classifiers more robust over time.",
}
@inproceedings{bhatt-etal-2019-learning,
    title = "Learning Transferable Feature Representations Using Neural Networks",
    author = "Bhatt, Himanshu Sharad  and
      Roy, Shourya  and
      Rajkumar, Arun  and
      Ramakrishnan, Sriranjani",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1404",
    doi = "10.18653/v1/P19-1404",
    pages = "4124--4134",
    abstract = "Learning representations such that the source and target distributions appear as similar as possible has benefited transfer learning tasks across several applications. Generally it requires labeled data from the source and only unlabeled data from the target to learn such representations. While these representations act like a bridge to transfer knowledge learned in the source to the target; they may lead to negative transfer when the source specific characteristics detract their ability to represent the target data. We present a novel neural network architecture to simultaneously learn a two-part representation which is based on the principle of segregating source specific representation from the common representation. The first part captures the source specific characteristics while the second part captures the truly common representation. Our architecture optimizes an objective function which acts adversarial for the source specific part if it contributes towards the cross-domain learning. We empirically show that two parts of the representation, in different arrangements, outperforms existing learning algorithms on the source learning as well as cross-domain tasks on multiple datasets.",
}
@inproceedings{wang-li-2019-bayes,
    title = "{B}ayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models",
    author = "Wang, Ruibo  and
      Li, Jihong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1405",
    doi = "10.18653/v1/P19-1405",
    pages = "4135--4145",
    abstract = "Direct comparison on point estimation of the precision (P), recall (R), and F1 measure of two natural language processing (NLP) models on a common test corpus is unreasonable and results in less replicable conclusions due to a lack of a statistical test. However, the existing t-tests in cross-validation (CV) for model comparison are inappropriate because the distributions of P, R, F1 are skewed and an interval estimation of P, R, and F1 based on a t-test may exceed [0,1]. In this study, we propose to use a block-regularized 3{\mbox{$\times$}}2 CV (3{\mbox{$\times$}}2 BCV) in model comparison because it could regularize the difference in certain frequency distributions over linguistic units between training and validation sets and yield stable estimators of P, R, and F1. On the basis of the 3{\mbox{$\times$}}2 BCV, we calibrate the posterior distributions of P, R, and F1 and derive an accurate interval estimation of P, R, and F1. Furthermore, we formulate the comparison into a hypothesis testing problem and propose a novel Bayes test. The test could directly compute the probabilities of the hypotheses on the basis of the posterior distributions and provide more informative decisions than the existing significance t-tests. Three experiments with regard to NLP chunking tasks are conducted, and the results illustrate the validity of the Bayes test.",
}
@inproceedings{liu-etal-2019-tigs,
    title = "{TIGS}: An Inference Algorithm for Text Infilling with Gradient Search",
    author = "Liu, Dayiheng  and
      Fu, Jie  and
      Liu, Pengfei  and
      Lv, Jiancheng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1406",
    doi = "10.18653/v1/P19-1406",
    pages = "4146--4156",
    abstract = "Text infilling aims at filling in the missing part of a sentence or paragraph, which has been applied to a variety of real-world natural language generation scenarios. Given a well-trained sequential generative model, it is challenging for its unidirectional decoder to generate missing symbols conditioned on the past and future information around the missing part. In this paper, we propose an iterative inference algorithm based on gradient search, which could be the first inference algorithm that can be broadly applied to any neural sequence generative models for text infilling tasks. Extensive experimental comparisons show the effectiveness and efficiency of the proposed method on three different text infilling tasks with various mask ratios and different mask strategies, comparing with five state-of-the-art methods.",
}
@inproceedings{benmalek-etal-2019-keeping,
    title = "Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder",
    author = "Benmalek, Ryan  and
      Khabsa, Madian  and
      Desu, Suma  and
      Cardie, Claire  and
      Banko, Michele",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1407",
    doi = "10.18653/v1/P19-1407",
    pages = "4157--4167",
    abstract = "We introduce the Scratchpad Mechanism, a novel addition to the sequence-to-sequence (seq2seq) neural network architecture and demonstrate its effectiveness in improving the overall fluency of seq2seq models for natural language generation tasks. By enabling the decoder at each time step to write to all of the encoder output layers, Scratchpad can employ the encoder as a {``}scratchpad{''} memory to keep track of what has been generated so far and thereby guide future generation. We evaluate Scratchpad in the context of three well-studied natural language generation tasks {---} Machine Translation, Question Generation, and Text Summarization {---} and obtain state-of-the-art or comparable performance on standard datasets for each task. Qualitative assessments in the form of human judgements (question generation), attention visualization (MT), and sample output (summarization) provide further evidence of the ability of Scratchpad to generate fluent and expressive output.",
}
@inproceedings{moosavi-etal-2019-using,
    title = "Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection",
    author = "Moosavi, Nafise Sadat  and
      Born, Leo  and
      Poesio, Massimo  and
      Strube, Michael",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1408",
    doi = "10.18653/v1/P19-1408",
    pages = "4168--4178",
    abstract = "The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller corpora. However, this additional annotation is costly and therefore, this solution does not scale to large corpora. In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts. Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift. We have integrated MINA into https://github.com/ns-moosavi/coval for reporting standard coreference scores based on both maximum and automatically detected minimum spans.",
}
@inproceedings{barhom-etal-2019-revisiting,
    title = "Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution",
    author = "Barhom, Shany  and
      Shwartz, Vered  and
      Eirew, Alon  and
      Bugert, Michael  and
      Reimers, Nils  and
      Dagan, Ido",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1409",
    doi = "10.18653/v1/P19-1409",
    pages = "4179--4189",
    abstract = "Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task{'}s importance, research focus was given mostly to within-document entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al. (2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model{'}s success.",
}
@inproceedings{lin-etal-2019-unified,
    title = "A Unified Linear-Time Framework for Sentence-Level Discourse Parsing",
    author = "Lin, Xiang  and
      Joty, Shafiq  and
      Jwalapuram, Prathyusha  and
      Bari, M Saiful",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1410",
    doi = "10.18653/v1/P19-1410",
    pages = "4190--4200",
    abstract = "We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an F1 score of 95.4{\%}, and our parser achieves an F1 score of 81.7{\%} on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 F1).",
}
@inproceedings{nguyen-etal-2019-employing,
    title = "Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings",
    author = "Nguyen, Linh The  and
      Van Ngo, Linh  and
      Than, Khoat  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1411",
    doi = "10.18653/v1/P19-1411",
    pages = "4201--4207",
    abstract = "It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions. In this work, we explore this property in a multi-task learning framework for IDRR in which the relations and the connectives are simultaneously predicted, and the mapping is leveraged to transfer knowledge between the two prediction tasks via the embeddings of relations and connectives. We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).",
}
@inproceedings{jiang-de-marneffe-2019-know,
    title = "Do You Know That Florence Is Packed with Visitors? Evaluating State-of-the-art Models of Speaker Commitment",
    author = "Jiang, Nanjiang  and
      de Marneffe, Marie-Catherine",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1412",
    doi = "10.18653/v1/P19-1412",
    pages = "4208--4213",
    abstract = "When a speaker, Mary, asks {``}Do you know that Florence is packed with visitors?{''}, we take her to believe that Florence is packed with visitors, but not if she asks {``}Do you think that Florence is packed with visitors?{''}. Inferring speaker commitment (aka event factuality) is crucial for information extraction and question answering. Here, we explore the hypothesis that linguistic deficits drive the error patterns of existing speaker commitment models by analyzing the linguistic correlates of model error on a challenging naturalistic dataset. We evaluate two state-of-the-art speaker commitment models on the CommitmentBank, an English dataset of naturally occurring discourses. The CommitmentBank is annotated with speaker commitment towards the content of the complement ({``}Florence is packed with visitors{''} in our example) of clause-embedding verbs ({``}know{''}, {``}think{''}) under four entailment-canceling environments (negation, modal, question, conditional). A breakdown of items by linguistic features reveals asymmetrical error patterns: while the models achieve good performance on some classes (e.g., negation), they fail to generalize to the diverse linguistic constructions (e.g., conditionals) in natural language, highlighting directions for improvement.",
}
@inproceedings{lee-goldwasser-2019-multi,
    title = "Multi-Relational Script Learning for Discourse Relations",
    author = "Lee, I-Ta  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1413",
    doi = "10.18653/v1/P19-1413",
    pages = "4214--4226",
    abstract = "Modeling script knowledge can be useful for a wide range of NLP tasks. Current statistical script learning approaches embed the events, such that their relationships are indicated by their similarity in the embedding. While intuitive, these approaches fall short of representing nuanced relations, needed for downstream tasks. In this paper, we suggest to view learning event embedding as a multi-relational problem, which allows us to capture different aspects of event pairs. We model a rich set of event relations, such as Cause and Contrast, derived from the Penn Discourse Tree Bank. We evaluate our model on three types of tasks, the popular Mutli-Choice Narrative Cloze and its variants, several multi-relational prediction tasks, and a related downstream task{---}implicit discourse sense classification.",
}
@inproceedings{oh-etal-2019-open,
    title = "Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts",
    author = "Oh, Jong-Hoon  and
      Kadowaki, Kazuma  and
      Kloetzer, Julien  and
      Iida, Ryu  and
      Torisawa, Kentaro",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1414",
    doi = "10.18653/v1/P19-1414",
    pages = "4227--4237",
    abstract = "In this paper, we propose a method for why-question answering (why-QA) that uses an adversarial learning framework. Existing why-QA methods retrieve {``}answer passages{''} that usually consist of several sentences. These multi-sentence passages contain not only the reason sought by a why-question and its connection to the why-question, but also redundant and/or unrelated parts. We use our proposed {``}Adversarial networks for Generating compact-answer Representation{''} (AGR) to generate from a passage a vector representation of the non-redundant reason sought by a why-question and exploit the representation for judging whether the passage actually answers the why-question. Through a series of experiments using Japanese why-QA datasets, we show that these representations improve the performance of our why-QA neural model as well as that of a BERT-based why-QA model. We show that they also improve a state-of-the-art distantly supervised open-domain QA (DS-QA) method on publicly available English datasets, even though the target task is not a why-QA.",
}
@inproceedings{zhu-etal-2019-learning,
    title = "Learning to Ask Unanswerable Questions for Machine Reading Comprehension",
    author = "Zhu, Haichao  and
      Dong, Li  and
      Wei, Furu  and
      Wang, Wenhui  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1415",
    doi = "10.18653/v1/P19-1415",
    pages = "4238--4248",
    abstract = "Machine reading comprehension with unanswerable questions is a challenging task. In this work, we propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. We introduce a pair-to-sequence model for unanswerable question generation, which effectively captures the interactions between the question and the paragraph. We also present a way to construct training data for our question generation models by leveraging the existing reading comprehension dataset. Experimental results show that the pair-to-sequence model performs consistently better compared with the sequence-to-sequence baseline. We further use the automatically generated unanswerable questions as a means of data augmentation on the SQuAD 2.0 dataset, yielding 1.9 absolute F1 improvement with BERT-base model and 1.7 absolute F1 improvement with BERT-large model.",
}
@inproceedings{min-etal-2019-compositional,
    title = "Compositional Questions Do Not Necessitate Multi-hop Reasoning",
    author = "Min, Sewon  and
      Wallace, Eric  and
      Singh, Sameer  and
      Gardner, Matt  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1416",
    doi = "10.18653/v1/P19-1416",
    pages = "4249--4257",
    abstract = "Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct large multi-hop RC datasets. For example, even highly compositional questions can be answered with a single hop if they target specific entity types, or the facts needed to answer them are redundant. Our analysis is centered on HotpotQA, where we show that single-hop reasoning can solve much more of the dataset than previously thought. We introduce a single-hop BERT-based RC model that achieves 67 F1{---}comparable to state-of-the-art multi-hop models. We also design an evaluation setting where humans are not shown all of the necessary paragraphs for the intended multi-hop reasoning but can still answer over 80{\%} of questions. Together with detailed error analysis, these results suggest there should be an increasing focus on the role of evidence in multi-hop reasoning and possibly even a shift towards information retrieval style evaluations with large and diverse evidence collections.",
}
@inproceedings{xiong-etal-2019-improving,
    title = "Improving Question Answering over Incomplete {KB}s with Knowledge-Aware Reader",
    author = "Xiong, Wenhan  and
      Yu, Mo  and
      Chang, Shiyu  and
      Guo, Xiaoxiao  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1417",
    doi = "10.18653/v1/P19-1417",
    pages = "4258--4264",
    abstract = "We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets.Under the assumptions that structured data is easier to query and the acquired knowledge can help the understanding of unstructured text, our model first accumulates knowledge ofKB entities from a question-related KB sub-graph; then reformulates the question in the latent space and reads the text with the accumulated entity knowledge at hand. The evidence from KB and text are finally aggregated to predict answers. On the widely-used KBQA benchmark WebQSP, our model achieves consistent improvements across settings with different extents of KB incompleteness.",
}
@inproceedings{zhang-etal-2019-adansp,
    title = "{A}da{NSP}: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing",
    author = "Zhang, Xiang  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1418",
    doi = "10.18653/v1/P19-1418",
    pages = "4265--4270",
    abstract = "Neural semantic parsers utilize the encoder-decoder framework to learn an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens. We instead to propose an adaptive decoding method to avoid such intermediate representations. The decoder is guided by model uncertainty and automatically uses deeper computations when necessary. Thus it can predict tokens adaptively. Our model outperforms the state-of-the-art neural models and does not need any expertise like predefined grammar or sketches in the meantime.",
}
@inproceedings{choshen-etal-2019-language,
    title = "The Language of Legal and Illegal Activity on the {D}arknet",
    author = "Choshen, Leshem  and
      Eldad, Dan  and
      Hershcovich, Daniel  and
      Sulem, Elior  and
      Abend, Omri",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1419",
    doi = "10.18653/v1/P19-1419",
    pages = "4271--4279",
    abstract = "The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well do off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drugs-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.",
}
@inproceedings{du-etal-2019-eliciting,
    title = "Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis",
    author = "Du, Junyi  and
      Jiang, He  and
      Shen, Jiaming  and
      Ren, Xiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1420",
    doi = "10.18653/v1/P19-1420",
    pages = "4280--4291",
    abstract = "Cognitive task analysis (CTA) is a type of analysis in applied psychology aimed at eliciting and representing the knowledge and thought processes of domain experts. In CTA, often heavy human labor is involved to parse the interview transcript into structured knowledge (e.g., flowchart for different actions). To reduce human efforts and scale the process, automated CTA transcript parsing is desirable. However, this task has unique challenges as (1) it requires the understanding of long-range context information in conversational text; and (2) the amount of labeled data is limited and indirect{---}i.e., context-aware, noisy, and low-resource. In this paper, we propose a weakly-supervised information extraction framework for automated CTA transcript parsing. We partition the parsing process into a sequence labeling task and a text span-pair relation extraction task, with distant supervision from human-curated protocol files. To model long-range context information for extracting sentence relations, neighbor sentences are involved as a part of input. Different types of models for capturing context dependency are then applied. We manually annotate real-world CTA transcripts to facilitate the evaluation of the parsing tasks.",
}
@inproceedings{yu-etal-2019-course,
    title = "Course Concept Expansion in {MOOC}s with External Knowledge and Interactive Game",
    author = "Yu, Jifan  and
      Wang, Chenyu  and
      Luo, Gan  and
      Hou, Lei  and
      Li, Juanzi  and
      Liu, Zhiyuan  and
      Tang, Jie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1421",
    doi = "10.18653/v1/P19-1421",
    pages = "4292--4302",
    abstract = "As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drifts and lack of knowledge guidance, existing methods can not effectively expand course concepts in complex MOOC environments. In this paper, we first build a novel boundary during searching for new concepts via external knowledge base and then utilize heterogeneous features to verify the high-quality results. In addition, to involve human efforts in our model, we design an interactive optimization mechanism based on a game. Our experiments on the four datasets from Coursera and XuetangX show that the proposed method achieves significant improvements(+0.19 by MAP) over existing methods.",
}
@inproceedings{dai-cai-2019-towards,
    title = "Towards Near-imperceptible Steganographic Text",
    author = "Dai, Falcon  and
      Cai, Zheng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1422",
    doi = "10.18653/v1/P19-1422",
    pages = "4303--4308",
    abstract = "We show that the imperceptibility of several existing linguistic steganographic systems (Fang et al., 2017; Yang et al., 2018) relies on implicit assumptions on statistical behaviors of fluent text. We formally analyze them and empirically evaluate these assumptions. Furthermore, based on these observations, we propose an encoding algorithm called patient-Huffman with improved near-imperceptible guarantees.",
}
@inproceedings{sahu-etal-2019-inter,
    title = "Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network",
    author = "Sahu, Sunil Kumar  and
      Christopoulou, Fenia  and
      Miwa, Makoto  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1423",
    doi = "10.18653/v1/P19-1423",
    pages = "4309--4316",
    abstract = "Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph. The graph is constructed using various inter- and intra-sentence dependencies to capture local and non-local dependency information. In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring. Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets. Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.",
}
@inproceedings{chalkidis-etal-2019-neural,
    title = "Neural Legal Judgment Prediction in {E}nglish",
    author = "Chalkidis, Ilias  and
      Androutsopoulos, Ion  and
      Aletras, Nikolaos",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1424",
    doi = "10.18653/v1/P19-1424",
    pages = "4317--4323",
    abstract = "Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case{'}s facts. Previous work on using neural models for this task has focused on Chinese; only feature-based models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT{'}s length limitation.",
}
@inproceedings{cheng-etal-2019-robust,
    title = "Robust Neural Machine Translation with Doubly Adversarial Inputs",
    author = "Cheng, Yong  and
      Jiang, Lu  and
      Macherey, Wolfgang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1425",
    doi = "10.18653/v1/P19-1425",
    pages = "4324--4333",
    abstract = "Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input. We propose an approach to improving the robustness of NMT models, which consists of two parts: (1) attack the translation model with adversarial source examples; (2) defend the translation model with adversarial target inputs to improve its robustness against the adversarial source inputs. For the generation of adversarial inputs, we propose a gradient-based method to craft adversarial examples informed by the translation loss over the clean inputs. Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements (2.8 and 1.6 BLEU points) over Transformer on standard clean benchmarks as well as exhibiting higher robustness on noisy data.",
}
@inproceedings{zhang-etal-2019-bridging,
    title = "Bridging the Gap between Training and Inference for Neural Machine Translation",
    author = "Zhang, Wen  and
      Feng, Yang  and
      Meng, Fandong  and
      You, Di  and
      Liu, Qun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1426",
    doi = "10.18653/v1/P19-1426",
    pages = "4334--4343",
    abstract = "Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words. At training time, it predicts with the ground truth words as context while at inference it has to generate the entire sequence from scratch. This discrepancy of the fed context leads to error accumulation among the way. Furthermore, word-level training requires strict matching between the generated sequence and the ground truth sequence which leads to overcorrection over different but reasonable translations. In this paper, we address these issues by sampling context words not only from the ground truth sequence but also from the predicted sequence by the model during training, where the predicted sequence is selected with a sentence-level optimum. Experiment results on Chinese-{\textgreater}English and WMT{'}14 English-{\textgreater}German translation tasks demonstrate that our approach can achieve significant improvements on multiple datasets.",
}
@inproceedings{wieting-etal-2019-beyond,
    title = "Beyond {BLEU}:Training Neural Machine Translation with Semantic Similarity",
    author = "Wieting, John  and
      Berg-Kirkpatrick, Taylor  and
      Gimpel, Kevin  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1427",
    doi = "10.18653/v1/P19-1427",
    pages = "4344--4355",
    abstract = "While most neural machine translation (NMT)systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can significantly improve final translation accuracy. However, training with BLEU has some limitations: it doesn{'}t assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference. In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity. We evaluate on four disparate languages trans-lated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster. Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU",
}
@inproceedings{estevez-velarde-etal-2019-automl,
    title = "{A}uto{ML} Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text",
    author = "Estevez-Velarde, Suilan  and
      Guti{\'e}rrez, Yoan  and
      Montoyo, Andr{\'e}s  and
      Almeida-Cruz, Yudivi{\'a}n",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1428",
    doi = "10.18653/v1/P19-1428",
    pages = "4356--4365",
    abstract = "The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community.",
}
@inproceedings{lu-etal-2019-distilling,
    title = "Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning",
    author = "Lu, Yaojie  and
      Lin, Hongyu  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1429",
    doi = "10.18653/v1/P19-1429",
    pages = "4366--4376",
    abstract = "Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words. Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge. To address this problem, this paper proposes a Delta-learning approach to distill discrimination and generalization knowledge by effectively decoupling, incrementally learning and adaptively fusing event representation. Experiments show that our method significantly outperforms previous approaches on unseen/sparse trigger words, and achieves state-of-the-art performance on both ACE2005 and KBP2017 datasets.",
}
@inproceedings{li-etal-2019-chinese,
    title = "{C}hinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge",
    author = "Li, Ziran  and
      Ding, Ning  and
      Liu, Zhiyuan  and
      Zheng, Haitao  and
      Shen, Ying",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1430",
    doi = "10.18653/v1/P19-1430",
    pages = "4377--4386",
    abstract = "Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge. In this framework, (1) we incorporate word-level information into character sequence inputs so that segmentation errors can be avoided. (2) We also model multiple senses of polysemous words with the help of external linguistic knowledge, so as to alleviate polysemy ambiguity. Experiments on three real-world datasets in distinct domains show consistent and significant superiority and robustness of our model, as compared with other baselines. We will release the source code of this paper in the future.",
}
@inproceedings{bansal-etal-2019-a2n,
    title = "{A}2{N}: Attending to Neighbors for Knowledge Graph Inference",
    author = "Bansal, Trapit  and
      Juan, Da-Cheng  and
      Ravi, Sujith  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1431",
    doi = "10.18653/v1/P19-1431",
    pages = "4387--4392",
    abstract = "State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multi-relational graph which can generalize to infer unseen entity relationships at test time. This can be sub-optimal as it requires memorizing and generalizing to all possible entity relationships using these fixed representations. We thus propose a novel attention-based method to learn query-dependent representation of entities which adaptively combines the relevant graph neighborhood of an entity leading to more accurate KG completion. The proposed method is evaluated on two benchmark datasets for knowledge graph completion, and experimental results show that the proposed model performs competitively or better than existing state-of-the-art, including recent methods for explicit multi-hop reasoning. Qualitative probing offers insight into how the model can reason about facts involving multiple hops in the knowledge graph, through the use of neighborhood attention.",
}
@inproceedings{pouran-ben-veyseh-etal-2019-graph,
    title = "Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures",
    author = "Pouran Ben Veyseh, Amir  and
      Nguyen, Thien Huu  and
      Dou, Dejing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1432",
    doi = "10.18653/v1/P19-1432",
    pages = "4393--4399",
    abstract = "Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP.",
}
@inproceedings{goyal-durrett-2019-embedding,
    title = "Embedding Time Expressions for Deep Temporal Ordering Models",
    author = "Goyal, Tanya  and
      Durrett, Greg",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1433",
    doi = "10.18653/v1/P19-1433",
    pages = "4400--4406",
    abstract = "Data-driven models have demonstrated state-of-the-art performance in inferring the temporal ordering of events in text. However, these models often overlook explicit temporal signals, such as dates and time windows. Rule-based methods can be used to identify the temporal links between these time expressions (timexes), but they fail to capture timexes{'} interactions with events and are hard to integrate with the distributed representations of neural net models. In this paper, we introduce a framework to infuse temporal awareness into such models by learning a pre-trained model to embed timexes. We generate synthetic data consisting of pairs of timexes, then train a character LSTM to learn embeddings and classify the timexes{'} temporal relation. We evaluate the utility of these embeddings in the context of a strong neural model for event temporal ordering, and show a small increase in performance on the MATRES dataset and more substantial gains on an automatically collected dataset with more frequent event-timex interactions.",
}
@inproceedings{han-etal-2019-episodic,
    title = "Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data",
    author = "Han, Moonsu  and
      Kang, Minki  and
      Jung, Hyunwoo  and
      Hwang, Sung Ju",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1434",
    doi = "10.18653/v1/P19-1434",
    pages = "4407--4417",
    abstract = "We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, which is difficult to solve with existing QA methods due to their lack of scalability. To tackle this problem, we propose a novel end-to-end deep network model for reading comprehension, which we refer to as Episodic Memory Reader (EMR) that sequentially reads the input contexts into an external memory, while replacing memories that are less important for answering unseen questions. Specifically, we train an RL agent to replace a memory entry when the memory is full, in order to maximize its QA accuracy at a future timepoint, while encoding the external memory using either the GRU or the Transformer architecture to learn representations that considers relative importance between the memory entries. We validate our model on a synthetic dataset (bAbI) as well as real-world large-scale textual QA (TriviaQA) and video QA (TVQA) datasets, on which it achieves significant improvements over rule based memory scheduling policies or an RL based baseline that independently learns the query-specific importance of each memory.",
}
@inproceedings{zhang-etal-2019-selection,
    title = "Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets",
    author = "Zhang, Guanhua  and
      Bai, Bing  and
      Liang, Jian  and
      Bai, Kun  and
      Chang, Shiyu  and
      Yu, Mo  and
      Zhu, Conghui  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1435",
    doi = "10.18653/v1/P19-1435",
    pages = "4418--4429",
    abstract = "Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However, biased datasets can also hurt the generalization performance of trained models and give untrustworthy evaluation results. For many NLSM datasets, the providers select some pairs of sentences into the datasets, and this sampling procedure can easily bring unintended pattern, i.e., selection bias. One example is the QuoraQP dataset, where some content-independent naive features are unreasonably predictive. Such features are the reflection of the selection bias and termed as the {``}leakage features.{''} In this paper, we investigate the problem of selection bias on six NLSM datasets and find that four out of them are significantly biased. We further propose a training and evaluation framework to alleviate the bias. Experimental results on QuoraQP suggest that the proposed framework can improve the generalization ability of trained models, and give more trustworthy evaluation results for real-world adoptions.",
}
@inproceedings{seo-etal-2019-real,
    title = "Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index",
    author = "Seo, Minjoon  and
      Lee, Jinhyuk  and
      Kwiatkowski, Tom  and
      Parikh, Ankur  and
      Farhadi, Ali  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1436",
    doi = "10.18653/v1/P19-1436",
    pages = "4430--4441",
    abstract = "Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive. In this paper, we introduce query-agnostic indexable representations of document phrases that can drastically speed up open-domain QA. In particular, our dense-sparse phrase encoding effectively captures syntactic, semantic, and lexical information of the phrases and eliminates the pipeline filtering of context documents. Leveraging strategies for optimizing training and inference time, our model can be trained and deployed even in a single 4-GPU server. Moreover, by representing phrases as pointers to their start and end tokens, our model indexes phrases in the entire English Wikipedia (up to 60 billion phrases) using under 2TB. Our experiments on SQuAD-Open show that our model is on par with or more accurate than previous models with 6000x reduced computational cost, which translates into at least 68x faster end-to-end inference benchmark on CPUs. Code and demo are available at nlp.cs.washington.edu/denspi",
}
@inproceedings{zhang-song-2019-language,
    title = "Language Modeling with Shared Grammar",
    author = "Zhang, Yuyu  and
      Song, Le",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1437",
    doi = "10.18653/v1/P19-1437",
    pages = "4442--4453",
    abstract = "Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language. Recent works on structure-aware models have shown promising results on language modeling. However, how to incorporate structure knowledge on corpus without syntactic annotations remains an open problem. In this work, we propose neural variational language model (NVLM), which enables the sharing of grammar knowledge among different corpora. Experimental results demonstrate the effectiveness of our framework on two popular benchmark datasets. With the help of shared grammar, our language model converges significantly faster to a lower perplexity on new training corpus.",
}
@inproceedings{givoli-reichart-2019-zero,
    title = "Zero-Shot Semantic Parsing for Instructions",
    author = "Givoli, Ofer  and
      Reichart, Roi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1438",
    doi = "10.18653/v1/P19-1438",
    pages = "4454--4464",
    abstract = "We consider a zero-shot semantic parsing task: parsing instructions into compositional logical forms, in domains that were not seen during training. We present a new dataset with 1,390 examples from 7 application domains (e.g. a calendar or a file manager), each example consisting of a triplet: (a) the application{'}s initial state, (b) an instruction, to be carried out in the context of that state, and (c) the state of the application after carrying out the instruction. We introduce a new training algorithm that aims to train a semantic parser on examples from a set of source domains, so that it can effectively parse instructions from an unknown target domain. We integrate our algorithm into the floating parser of Pasupat and Liang (2015), and further augment the parser with features and a logical form candidate filtering logic, to support zero-shot adaptation. Our experiments with various zero-shot adaptation setups demonstrate substantial performance gains over a non-adapted parser.",
}
@inproceedings{wang-etal-2019-tell,
    title = "Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling",
    author = "Wang, Alex  and
      Hula, Jan  and
      Xia, Patrick  and
      Pappagari, Raghavendra  and
      McCoy, R. Thomas  and
      Patel, Roma  and
      Kim, Najoung  and
      Tenney, Ian  and
      Huang, Yinghui  and
      Yu, Katherin  and
      Jin, Shuning  and
      Chen, Berlin  and
      Van Durme, Benjamin  and
      Grave, Edouard  and
      Pavlick, Ellie  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1439",
    doi = "10.18653/v1/P19-1439",
    pages = "4465--4476",
    abstract = "Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo{'}s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.",
}
@inproceedings{zhang-etal-2019-complex,
    title = "Complex Question Decomposition for Semantic Parsing",
    author = "Zhang, Haoyu  and
      Cai, Jingjing  and
      Xu, Jianjun  and
      Wang, Ji",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1440",
    doi = "10.18653/v1/P19-1440",
    pages = "4477--4486",
    abstract = "In this work, we focus on complex question semantic parsing and propose a novel Hierarchical Semantic Parsing (HSP) method, which utilizes the decompositionality of complex questions for semantic parsing. Our model is designed within a three-stage parsing architecture based on the idea of decomposition-integration. In the first stage, we propose a question decomposer which decomposes a complex question into a sequence of sub-questions. In the second stage, we design an information extractor to derive the type and predicate information of these questions. In the last stage, we integrate the generated information from previous stages and generate a logical form for the complex question. We conduct experiments on COMPLEXWEBQUESTIONS which is a large scale complex question semantic parsing dataset, results show that our model achieves significant improvement compared to state-of-the-art methods.",
}
@inproceedings{liu-etal-2019-multi,
    title = "Multi-Task Deep Neural Networks for Natural Language Understanding",
    author = "Liu, Xiaodong  and
      He, Pengcheng  and
      Chen, Weizhu  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1441",
    doi = "10.18653/v1/P19-1441",
    pages = "4487--4496",
    abstract = "In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7{\%} (2.2{\%} absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available.",
}
@inproceedings{nie-etal-2019-dissent,
    title = "{D}is{S}ent: Learning Sentence Representations from Explicit Discourse Relations",
    author = "Nie, Allen  and
      Bennett, Erin  and
      Goodman, Noah",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1442",
    doi = "10.18653/v1/P19-1442",
    pages = "4497--4510",
    abstract = "Learning effective representations of sentences is one of the core missions of natural language understanding. Existing models either train on a vast amount of text, or require costly, manually curated sentence relation datasets. We show that with dependency parsing and rule-based rubrics, we can curate a high quality sentence relation task by leveraging explicit discourse relations. We show that our curated dataset provides an excellent signal for learning vector representations of sentence meaning, representing relations that can only be determined when the meanings of two sentences are combined. We demonstrate that the automatically curated corpus allows a bidirectional LSTM sentence encoder to yield high quality sentence embeddings and can serve as a supervised fine-tuning dataset for larger models such as BERT. Our fixed sentence embeddings achieve high performance on a variety of transfer tasks, including SentEval, and we achieve state-of-the-art results on Penn Discourse Treebank{'}s implicit relation prediction task.",
}
@inproceedings{yu-etal-2019-sparc,
    title = "{SP}ar{C}: Cross-Domain Semantic Parsing in Context",
    author = "Yu, Tao  and
      Zhang, Rui  and
      Yasunaga, Michihiro  and
      Tan, Yi Chern  and
      Lin, Xi Victoria  and
      Li, Suyi  and
      Er, Heyang  and
      Li, Irene  and
      Pang, Bo  and
      Chen, Tao  and
      Ji, Emily  and
      Dixit, Shreya  and
      Proctor, David  and
      Shim, Sungrok  and
      Kraft, Jonathan  and
      Zhang, Vincent  and
      Xiong, Caiming  and
      Socher, Richard  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1443",
    doi = "10.18653/v1/P19-1443",
    pages = "4511--4523",
    abstract = "We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2{\%} over all questions and less than10{\%} over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github.io/sparc.",
}
@inproceedings{guo-etal-2019-towards,
    title = "Towards Complex Text-to-{SQL} in Cross-Domain Database with Intermediate Representation",
    author = "Guo, Jiaqi  and
      Zhan, Zecheng  and
      Gao, Yan  and
      Xiao, Yan  and
      Lou, Jian-Guang  and
      Liu, Ting  and
      Zhang, Dongmei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1444",
    doi = "10.18653/v1/P19-1444",
    pages = "4524--4535",
    abstract = "We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) the challenge in predicting columns caused by the large number of out-of-domain words. Instead of end-to-end synthesizing a SQL query, IRNet decomposes the synthesis process into three phases. In the first phase, IRNet performs a schema linking over a question and a database schema. Then, IRNet adopts a grammar-based neural model to synthesize a SemQL query which is an intermediate representation that we design to bridge NL and SQL. Finally, IRNet deterministically infers a SQL query from the synthesized SemQL query with domain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNet achieves 46.7{\%} accuracy, obtaining 19.5{\%} absolute improvement over previous state-of-the-art approaches. At the time of writing, IRNet achieves the first position on the Spider leaderboard.",
}
@inproceedings{kayal-tsatsaronis-2019-eigensent,
    title = "{E}igen{S}ent: Spectral sentence embeddings using higher-order Dynamic Mode Decomposition",
    author = "Kayal, Subhradeep  and
      Tsatsaronis, George",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1445",
    doi = "10.18653/v1/P19-1445",
    pages = "4536--4546",
    abstract = "Distributed representation of words, or word embeddings, have motivated methods for calculating semantic representations of word sequences such as phrases, sentences and paragraphs. Most of the existing methods to do so either use algorithms to learn such representations, or improve on calculating weighted averages of the word vectors. In this work, we experiment with spectral methods of signal representation and summarization as mechanisms for constructing such word-sequence embeddings in an unsupervised fashion. In particular, we explore an algorithm rooted in fluid-dynamics, known as higher-order Dynamic Mode Decomposition, which is designed to capture the eigenfrequencies, and hence the fundamental transition dynamics, of periodic and quasi-periodic systems. It is empirically observed that this approach, which we call EigenSent, can summarize transitions in a sequence of words and generate an embedding that can represent well the sequence itself. To the best of the authors{'} knowledge, this is the first application of a spectral decomposition and signal summarization technique on text, to create sentence embeddings. We test the efficacy of this algorithm in creating sentence embeddings on three public datasets, where it performs appreciably well. Moreover it is also shown that, due to the positive combination of their complementary properties, concatenating the embeddings generated by EigenSent with simple word vector averaging achieves state-of-the-art results.",
}
@inproceedings{song-gildea-2019-sembleu,
    title = "{S}em{B}leu: A Robust Metric for {AMR} Parsing Evaluation",
    author = "Song, Linfeng  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1446",
    doi = "10.18653/v1/P19-1446",
    pages = "4547--4552",
    abstract = "Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs. The major evaluation metric, SMATCH (Cai and Knight, 2013), searches for one-to-one mappings between the nodes of two AMRs with a greedy hill-climbing algorithm, which leads to search errors. We propose SEMBLEU, a robust metric that extends BLEU (Papineni et al., 2002) to AMRs. It does not suffer from search errors and considers non-local correspondences in addition to local ones. SEMBLEU is fully content-driven and punishes situations where a system{'}s output does not preserve most information from the input. Preliminary experiments on both sentence and corpus levels show that SEMBLEU has slightly higher consistency with human judgments than SMATCH. Our code is available at http://github.com/ freesunshine0316/sembleu.",
}
@inproceedings{yin-neubig-2019-reranking,
    title = "Reranking for Neural Semantic Parsing",
    author = "Yin, Pengcheng  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1447",
    doi = "10.18653/v1/P19-1447",
    pages = "4553--4559",
    abstract = "Semantic parsing considers the task of transducing natural language (NL) utterances into machine executable meaning representations (MRs). While neural network-based semantic parsers have achieved impressive improvements over previous methods, results are still far from perfect, and cursory manual inspection can easily identify obvious problems such as lack of adequacy or coherence of the generated MRs. This paper presents a simple approach to quickly iterate and improve the performance of an existing neural semantic parser by reranking an n-best list of predicted MRs, using features that are designed to fix observed problems with baseline models. We implement our reranker in a competitive neural semantic parser and test on four semantic parsing (GEO, ATIS) and Python code generation (Django, CoNaLa) tasks, improving the strong baseline parser by up to 5.7{\%} absolute in BLEU (CoNaLa) and 2.9{\%} in accuracy (Django), outperforming the best published neural parser results on all four datasets.",
}
@inproceedings{bogin-etal-2019-representing,
    title = "Representing Schema Structure with Graph Neural Networks for Text-to-{SQL} Parsing",
    author = "Bogin, Ben  and
      Berant, Jonathan  and
      Gardner, Matt",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1448",
    doi = "10.18653/v1/P19-1448",
    pages = "4560--4565",
    abstract = "Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In spider, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so the structure of the DB schema can inform the predicted SQL query. In this paper, we present an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural network, and this representation is later used at both encoding and decoding time. Evaluation shows that encoding the schema structure improves our parser accuracy from 33.8{\%} to 39.4{\%}, dramatically above the current state of the art, which is at 19.7{\%}.",
}
@inproceedings{nangia-bowman-2019-human,
    title = "Human vs. Muppet: A Conservative Estimate of Human Performance on the {GLUE} Benchmark",
    author = "Nangia, Nikita  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1449",
    doi = "10.18653/v1/P19-1449",
    pages = "4566--4575",
    abstract = "The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at launch to 83.9, state of the art at the time of writing (May 24, 2019). Here, we measure human performance on the benchmark, in order to learn whether significant headroom remains for further progress. We provide a conservative estimate of human performance on the benchmark through crowdsourcing: Our annotators are non-experts who must learn each task from a brief set of instructions and 20 examples. In spite of limited training, these annotators robustly outperform the state of the art on six of the nine GLUE tasks and achieve an average score of 87.1. Given the fast pace of progress however, the headroom we observe is quite limited. To reproduce the data-poor setting that our annotators must learn in, we also train the BERT model (Devlin et al., 2019) in limited-data regimes, and conclude that low-resource sentence classification remains a challenge for modern neural network approaches to text understanding.",
}
@inproceedings{lindemann-etal-2019-compositional,
    title = "Compositional Semantic Parsing across Graphbanks",
    author = "Lindemann, Matthias  and
      Groschwitz, Jonas  and
      Koller, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1450",
    doi = "10.18653/v1/P19-1450",
    pages = "4576--4585",
    abstract = "Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.",
}
@inproceedings{naseem-etal-2019-rewarding,
    title = "Rewarding {S}match: Transition-Based {AMR} Parsing with Reinforcement Learning",
    author = "Naseem, Tahira  and
      Shah, Abhishek  and
      Wan, Hui  and
      Florian, Radu  and
      Roukos, Salim  and
      Ballesteros, Miguel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1451",
    doi = "10.18653/v1/P19-1451",
    pages = "4586--4592",
    abstract = "Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser.",
}
@inproceedings{tenney-etal-2019-bert,
    title = "{BERT} Rediscovers the Classical {NLP} Pipeline",
    author = "Tenney, Ian  and
      Das, Dipanjan  and
      Pavlick, Ellie",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1452",
    doi = "10.18653/v1/P19-1452",
    pages = "4593--4601",
    abstract = "Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the network. We find that the model represents the steps of the traditional NLP pipeline in an interpretable and localizable way, and that the regions responsible for each step appear in the expected sequence: POS tagging, parsing, NER, semantic roles, then coreference. Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically, revising lower-level decisions on the basis of disambiguating information from higher-level representations.",
}
@inproceedings{wieting-etal-2019-simple,
    title = "Simple and Effective Paraphrastic Similarity from Parallel Translations",
    author = "Wieting, John  and
      Gimpel, Kevin  and
      Neubig, Graham  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1453",
    doi = "10.18653/v1/P19-1453",
    pages = "4602--4608",
    abstract = "We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating para-phrase corpora. Further, we show that the resulting model can be applied to cross lingual tasks where it both outperforms and is orders of magnitude faster than more complex state-of-the-art baselines.",
}
@inproceedings{wang-etal-2019-second,
    title = "Second-Order Semantic Dependency Parsing with End-to-End Neural Networks",
    author = "Wang, Xinyu  and
      Huang, Jingxian  and
      Tu, Kewei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1454",
    doi = "10.18653/v1/P19-1454",
    pages = "4609--4618",
    abstract = "Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). We can unfold both algorithms as recurrent layers of a neural network and therefore can train the parser in an end-to-end manner. Our experiments show that our approach achieves state-of-the-art performance.",
}
@inproceedings{castro-etal-2019-towards,
    title = "Towards Multimodal Sarcasm Detection (An {\_}{O}bviously{\_} Perfect Paper)",
    author = "Castro, Santiago  and
      Hazarika, Devamanyu  and
      P{\'e}rez-Rosas, Ver{\'o}nica  and
      Zimmermann, Roger  and
      Mihalcea, Rada  and
      Poria, Soujanya",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1455",
    doi = "10.18653/v1/P19-1455",
    pages = "4619--4629",
    abstract = "Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the recent work in sarcasm detection has been carried out on textual data. In this paper, we argue that incorporating multimodal cues can improve the automatic classification of sarcasm. As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows. MUStARD consists of audiovisual utterances annotated with sarcasm labels. Each utterance is accompanied by its context of historical utterances in the dialogue, which provides additional information on the scenario where the utterance occurs. Our initial results show that the use of multimodal information can reduce the relative error rate of sarcasm detection by up to 12.9{\%} in F-score when compared to the use of individual modalities. The full dataset is publicly available for use at https://github.com/soujanyaporia/MUStARD.",
}
@inproceedings{durmus-etal-2019-determining,
    title = "Determining Relative Argument Specificity and Stance for Complex Argumentative Structures",
    author = "Durmus, Esin  and
      Ladhak, Faisal  and
      Cardie, Claire",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1456",
    doi = "10.18653/v1/P19-1456",
    pages = "4630--4641",
    abstract = "Systems for automatic argument generation and debate require the ability to (1) determine the stance of any claims employed in the argument and (2) assess the specificity of each claim relative to the argument context. Existing work on understanding claim specificity and stance, however, has been limited to the study of argumentative structures that are relatively shallow, most often consisting of a single claim that directly supports or opposes the argument thesis. In this paper, we tackle these tasks in the context of complex arguments on a diverse set of topics. In particular, our dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims; lines of argument are generally of depth 2 to 6. We find that as the distance between a pair of claims increases along the argument path, determining the relative specificity of a pair of claims becomes easier and determining their relative stance becomes harder.",
}
@inproceedings{zhang-etal-2019-latent,
    title = "Latent Variable Sentiment Grammar",
    author = "Zhang, Liwen  and
      Tu, Kewei  and
      Zhang, Yue",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1457",
    doi = "10.18653/v1/P19-1457",
    pages = "4642--4651",
    abstract = "Neural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to encode sentiment class labels. To this end, we investigate two formalisms with deep sentiment representations that capture sentiment subtype expressions by latent variables and Gaussian mixture vectors, respectively. Experiments on Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar over vanilla neural encoders. Using ELMo embeddings, our method gives the best results on this benchmark.",
}
@inproceedings{bataa-wu-2019-investigation,
    title = "An Investigation of Transfer Learning-Based Sentiment Analysis in {J}apanese",
    author = "Bataa, Enkhbold  and
      Wu, Joshua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1458",
    doi = "10.18653/v1/P19-1458",
    pages = "4652--4657",
    abstract = "Text classification approaches have usually required task-specific model architectures and huge labeled datasets. Recently, thanks to the rise of text-based transfer learning techniques, it is possible to pre-train a language model in an unsupervised manner and leverage them to perform effective on downstream tasks. In this work we focus on Japanese and show the potential use of transfer learning techniques in text classification. Specifically, we perform binary and multi-class sentiment classification on the Rakuten product review and Yahoo movie review datasets. We show that transfer learning-based approaches perform better than task-specific models trained on 3 times as much data. Furthermore, these approaches perform just as well for language modeling pre-trained on only 1/30 of the data. We release our pre-trained models and code as open source.",
}
@inproceedings{niven-kao-2019-probing,
    title = "Probing Neural Network Comprehension of Natural Language Arguments",
    author = "Niven, Timothy  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1459",
    doi = "10.18653/v1/P19-1459",
    pages = "4658--4664",
    abstract = "We are surprised to find that BERT{'}s peak performance of 77{\%} on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. However, we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset. We analyze the nature of these cues and demonstrate that a range of models all exploit them. This analysis informs the construction of an adversarial dataset on which all models achieve random accuracy. Our adversarial dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work.",
}
@inproceedings{xu-etal-2019-recognising,
    title = "Recognising Agreement and Disagreement between Stances with Reason Comparing Networks",
    author = "Xu, Chang  and
      Paris, Cecile  and
      Nepal, Surya  and
      Sparks, Ross",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1460",
    doi = "10.18653/v1/P19-1460",
    pages = "4665--4671",
    abstract = "We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialogic features are used for (dis)agreement inference. We extend this scope and seek to detect stance (dis)agreement in a broader setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared. To cope with such non-dialogic utterances, we find that the reasons uttered to back up a specific stance can help predict stance (dis)agreements. We propose a reason comparing network (RCN) to leverage reason information for stance comparison. Empirical results on a well-known stance corpus show that our method can discover useful reason information, enabling it to outperform several baselines in stance (dis)agreement detection.",
}
@inproceedings{otani-hovy-2019-toward,
    title = "Toward Comprehensive Understanding of a Sentiment Based on Human Motives",
    author = "Otani, Naoki  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1461",
    doi = "10.18653/v1/P19-1461",
    pages = "4672--4677",
    abstract = "In sentiment detection, the natural language processing community has focused on determining holders, facets, and valences, but has paid little attention to the reasons for sentiment decisions. Our work considers human motives as the driver for human sentiments and addresses the problem of motive detection as the first step. Following a study in psychology, we define six basic motives that cover a wide range of topics appearing in review texts, annotate 1,600 texts in restaurant and laptop domains with the motives, and report the performance of baseline methods on this new dataset. We also show that cross-domain transfer learning boosts detection performance, which indicates that these universal motives exist across different domains.",
}
@inproceedings{liang-etal-2019-context,
    title = "Context-aware Embedding for Targeted Aspect-based Sentiment Analysis",
    author = "Liang, Bin  and
      Du, Jiachen  and
      Xu, Ruifeng  and
      Li, Binyang  and
      Huang, Hejiao",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1462",
    doi = "10.18653/v1/P19-1462",
    pages = "4678--4683",
    abstract = "Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the context-dependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.",
}
@inproceedings{haddadan-etal-2019-yes,
    title = "Yes, we can! Mining Arguments in 50 Years of {US} Presidential Campaign Debates",
    author = "Haddadan, Shohreh  and
      Cabrio, Elena  and
      Villata, Serena",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1463",
    doi = "10.18653/v1/P19-1463",
    pages = "4684--4690",
    abstract = "Political debates offer a rare opportunity for citizens to compare the candidates{'} positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.",
}
@inproceedings{kuribayashi-etal-2019-empirical,
    title = "An Empirical Study of Span Representations in Argumentation Structure Parsing",
    author = "Kuribayashi, Tatsuki  and
      Ouchi, Hiroki  and
      Inoue, Naoya  and
      Reisert, Paul  and
      Miyoshi, Toshinori  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1464",
    doi = "10.18653/v1/P19-1464",
    pages = "4691--4698",
    abstract = "For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established. With such basis, exploring task-dependent extensions for argumentation structure parsing (ASP) becomes an interesting research direction. This study investigates (i) span representation originally developed for other NLP tasks and (ii) a simple task-dependent extension for ASP. Our extensive experiments and analysis show that these representations yield high performance for ASP and provide some challenging types of instances to be parsed.",
}
@inproceedings{yang-etal-2019-simple,
    title = "Simple and Effective Text Matching with Richer Alignment Features",
    author = "Yang, Runqi  and
      Zhang, Jianhai  and
      Gao, Xing  and
      Ji, Feng  and
      Chen, Haiqing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1465",
    doi = "10.18653/v1/P19-1465",
    pages = "4699--4709",
    abstract = "In this paper, we present a fast and strong neural approach for general purpose text matching applications. We explore what is sufficient to build a fast and well-performed text matching model and propose to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components. We conduct experiments on four well-studied benchmark datasets across tasks of natural language inference, paraphrase identification and answer selection. The performance of our model is on par with the state-of-the-art on all datasets with much fewer parameters and the inference speed is at least 6 times faster compared with similarly performed ones.",
}
@inproceedings{nathani-etal-2019-learning,
    title = "Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs",
    author = "Nathani, Deepak  and
      Chauhan, Jatin  and
      Sharma, Charu  and
      Kaul, Manohar",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1466",
    doi = "10.18653/v1/P19-1466",
    pages = "4710--4723",
    abstract = "The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction). Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention-based feature embedding that captures both entity and relation features in any given entity{'}s neighborhood. Additionally, we also encapsulate relation clusters and multi-hop relations in our model. Our empirical study offers insights into the efficacy of our attention-based model and we show marked performance gains in comparison to state-of-the-art methods on all datasets.",
}
@inproceedings{ouyang-mckeown-2019-neural,
    title = "Neural Network Alignment for Sentential Paraphrases",
    author = "Ouyang, Jessica  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1467",
    doi = "10.18653/v1/P19-1467",
    pages = "4724--4735",
    abstract = "We present a monolingual alignment system for long, sentence- or clause-level alignments, and demonstrate that systems designed for word- or short phrase-based alignment are ill-suited for these longer alignments. Our system is capable of aligning semantically similar spans of arbitrary length. We achieve significantly higher recall on aligning phrases of four or more words and outperform state-of-the- art aligners on the long alignments in the MSR RTE corpus.",
}
@inproceedings{hosseini-etal-2019-duality,
    title = "Duality of Link Prediction and Entailment Graph Induction",
    author = "Hosseini, Mohammad Javad  and
      Cohen, Shay B.  and
      Johnson, Mark  and
      Steedman, Mark",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1468",
    doi = "10.18653/v1/P19-1468",
    pages = "4736--4746",
    abstract = "Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.",
}
@inproceedings{choi-etal-2019-cross,
    title = "A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching",
    author = "Choi, Jihun  and
      Kim, Taeuk  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1469",
    doi = "10.18653/v1/P19-1469",
    pages = "4747--4761",
    abstract = "We present a latent variable model for predicting the relationship between a pair of text sequences. Unlike previous auto-encoding{--}based approaches that consider each sequence separately, our proposed framework utilizes both sequences within a single model by generating a sequence that has a given relationship with a source sequence. We further extend the cross-sentence generating framework to facilitate semi-supervised training. We also define novel semantic constraints that lead the decoder network to generate semantically plausible and diverse sequences. We demonstrate the effectiveness of the proposed model from quantitative and qualitative experiments, while achieving state-of-the-art results on semi-supervised natural language inference and paraphrase identification.",
}
@inproceedings{bosselut-etal-2019-comet,
    title = "{COMET}: Commonsense Transformers for Automatic Knowledge Graph Construction",
    author = "Bosselut, Antoine  and
      Rashkin, Hannah  and
      Sap, Maarten  and
      Malaviya, Chaitanya  and
      Celikyilmaz, Asli  and
      Choi, Yejin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1470",
    doi = "10.18653/v1/P19-1470",
    pages = "4762--4779",
    abstract = "We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017). Contrary to many conventional KBs that store knowledge with canonical templates, commonsense KBs only store loosely structured open-text descriptions of knowledge. We posit that an important step toward automatic commonsense completion is the development of generative models of commonsense knowledge, and propose COMmonsEnse Transformers (COMET) that learn to generate rich and diverse commonsense descriptions in natural language. Despite the challenges of commonsense modeling, our investigation reveals promising results when implicit knowledge from deep pre-trained language models is transferred to generate explicit knowledge in commonsense knowledge graphs. Empirical results demonstrate that COMET is able to generate novel knowledge that humans rate as high quality, with up to 77.5{\%} (ATOMIC) and 91.7{\%} (ConceptNet) precision at top 1, which approaches human performance for these resources. Our findings suggest that using generative commonsense models for automatic commonsense KB completion could soon be a plausible alternative to extractive methods.",
}
@inproceedings{aldawsari-finlayson-2019-detecting,
    title = "Detecting Subevents using Discourse and Narrative Features",
    author = "Aldawsari, Mohammed  and
      Finlayson, Mark",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1471",
    doi = "10.18653/v1/P19-1471",
    pages = "4780--4790",
    abstract = "Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is a subevent of another. Building on prior work, we introduce several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance. Error analysis further demonstrates the utility of these features. We evaluate our model on the only two annotated corpora with event hierarchies: HiEve and the Intelligence Community corpus. No prior system has been evaluated on both corpora. Our model outperforms previous systems on both corpora, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 on the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models.",
}
@inproceedings{zellers-etal-2019-hellaswag,
    title = "{H}ella{S}wag: Can a Machine Really Finish Your Sentence?",
    author = "Zellers, Rowan  and
      Holtzman, Ari  and
      Bisk, Yonatan  and
      Farhadi, Ali  and
      Choi, Yejin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1472",
    doi = "10.18653/v1/P19-1472",
    pages = "4791--4800",
    abstract = "Recent work by Zellers et al. (2018) introduced a new task of commonsense natural language inference: given an event description such as {``}A woman sits at a piano,{''} a machine must select the most likely followup: {``}She sets her fingers on the keys.{''} With the introduction of BERT, near human-level performance was reached. Does this mean that machines can perform human level commonsense inference? In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Though its questions are trivial for humans ({\textgreater}95{\%} accuracy), state-of-the-art models struggle ({\textless}48{\%}). We achieve this via Adversarial Filtering (AF), a data collection paradigm wherein a series of discriminators iteratively select an adversarial set of machine-generated wrong answers. AF proves to be surprisingly robust. The key insight is to scale up the length and complexity of the dataset examples towards a critical {`}Goldilocks{'} zone wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models. Our construction of HellaSwag, and its resulting difficulty, sheds light on the inner workings of deep pretrained models. More broadly, it suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges.",
}
@inproceedings{agrawal-etal-2019-unified,
    title = "Unified Semantic Parsing with Weak Supervision",
    author = "Agrawal, Priyanka  and
      Dalmia, Ayushi  and
      Jain, Parag  and
      Bansal, Abhishek  and
      Mittal, Ashish  and
      Sankaranarayanan, Karthik",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1473",
    doi = "10.18653/v1/P19-1473",
    pages = "4801--4810",
    abstract = "Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains. However, the fundamental challenge lies in obtaining high-quality annotations of (utterance, program) pairs across various domains needed for training such models. To overcome this, we propose a novel framework to build a unified multi-domain enabled semantic parser trained only with weak supervision (denotations). Weakly supervised training is particularly arduous as the program search space grows exponentially in a multi-domain setting. To solve this, we incorporate a multi-policy distillation mechanism in which we first train domain-specific semantic parsers (teachers) using weak supervision in the absence of the ground truth programs, followed by training a single unified parser (student) from the domain specific policies obtained from these teachers. The resultant semantic parser is not only compact but also generalizes better, and generates more accurate programs. It further does not require the user to provide a domain label while querying. On the standard Overnight dataset (containing multiple domains), we demonstrate that the proposed model improves performance by 20{\%} in terms of denotation accuracy in comparison to baseline techniques.",
}
@inproceedings{aly-etal-2019-every,
    title = "Every Child Should Have Parents: A Taxonomy Refinement Algorithm Based on Hyperbolic Term Embeddings",
    author = {Aly, Rami  and
      Acharya, Shantanu  and
      Ossa, Alexander  and
      K{\"o}hn, Arne  and
      Biemann, Chris  and
      Panchenko, Alexander},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1474",
    doi = "10.18653/v1/P19-1474",
    pages = "4811--4817",
    abstract = "We introduce the use of Poincar{\'e} embeddings to improve existing state-of-the-art approaches to domain-specific taxonomy induction from text as a signal for both relocating wrong hyponym terms within a (pre-induced) taxonomy as well as for attaching disconnected terms in a taxonomy. This method substantially improves previous state-of-the-art results on the SemEval-2016 Task 13 on taxonomy extraction. We demonstrate the superiority of Poincar{\'e} embeddings over distributional semantic representations, supporting the hypothesis that they can better capture hierarchical lexical-semantic relationships than embeddings in the Euclidean space.",
}
@inproceedings{li-etal-2019-learning,
    title = "Learning to Rank for Plausible Plausibility",
    author = "Li, Zhongyang  and
      Chen, Tongfei  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1475",
    doi = "10.18653/v1/P19-1475",
    pages = "4818--4823",
    abstract = "Researchers illustrate improvements in contextual encoding strategies via resultant performance on a battery of shared Natural Language Understanding (NLU) tasks. Many of these tasks are of a categorical prediction variety: given a conditioning context (e.g., an NLI premise), provide a label based on an associated prompt (e.g., an NLI hypothesis). The categorical nature of these tasks has led to common use of a cross entropy log-loss objective during training. We suggest this loss is intuitively wrong when applied to plausibility tasks, where the prompt by design is neither categorically entailed nor contradictory given the context. Log-loss naturally drives models to assign scores near 0.0 or 1.0, in contrast to our proposed use of a margin-based loss. Following a discussion of our intuition, we describe a confirmation study based on an extreme, synthetically curated task derived from MultiNLI. We find that a margin-based loss leads to a more plausible model of plausibility. Finally, we illustrate improvements on the Choice Of Plausible Alternative (COPA) task through this change in loss.",
}
@inproceedings{glavas-vulic-2019-generalized,
    title = "Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment",
    author = "Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1476",
    doi = "10.18653/v1/P19-1476",
    pages = "4824--4830",
    abstract = "Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE. Our Generalized Lexical ENtailment model (GLEN) is decoupled from the word embedding model and applicable to any distributional vector space. Yet {--} unlike existing retrofitting models {--} it captures a general specialization function allowing for LE-tuning of the entire distributional space and not only the vectors of words seen in lexical constraints. Coupled with a multilingual embedding space, GLEN seamlessly enables cross-lingual LE detection. We demonstrate the effectiveness of GLEN in graded LE and report large improvements (over 20{\%} in accuracy) over state-of-the-art in cross-lingual LE detection.",
}
@inproceedings{klein-nabi-2019-attention,
    title = "Attention Is (not) All You Need for Commonsense Reasoning",
    author = "Klein, Tassilo  and
      Nabi, Moin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1477",
    doi = "10.18653/v1/P19-1477",
    pages = "4831--4836",
    abstract = "The recently introduced BERT model exhibits strong performance on several language understanding benchmarks. In this paper, we describe a simple re-implementation of BERT for commonsense reasoning. We show that the attentions produced by BERT can be directly utilized for tasks such as the Pronoun Disambiguation Problem and Winograd Schema Challenge. Our proposed attention-guided commonsense reasoning method is conceptually simple yet empirically powerful. Experimental analysis on multiple datasets demonstrates that our proposed system performs remarkably well on all cases while outperforming the previously reported state of the art by a margin. While results suggest that BERT seems to implicitly learn to establish complex relationships between entities, solving commonsense reasoning tasks might require more than unsupervised models learned from huge text corpora.",
}
@inproceedings{kocijan-etal-2019-surprisingly,
    title = "A Surprisingly Robust Trick for the {W}inograd Schema Challenge",
    author = "Kocijan, Vid  and
      Cretu, Ana-Maria  and
      Camburu, Oana-Maria  and
      Yordanov, Yordan  and
      Lukasiewicz, Thomas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1478",
    doi = "10.18653/v1/P19-1478",
    pages = "4837--4842",
    abstract = "The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of three language models on WSC273 consistently and robustly improves when fine-tuned on a similar pronoun disambiguation problem dataset (denoted WSCR). We additionally generate a large unsupervised WSC-like dataset. By fine-tuning the BERT language model both on the introduced and on the WSCR dataset, we achieve overall accuracies of 72.5{\%} and 74.7{\%} on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.8{\%} and 9.6{\%}, respectively. Furthermore, our fine-tuned models are also consistently more accurate on the {``}complex{''} subsets of WSC273, introduced by Trichelair et al. (2018).",
}
@inproceedings{li-etal-2019-coherent,
    title = "Coherent Comments Generation for {C}hinese Articles with a Graph-to-Sequence Model",
    author = "Li, Wei  and
      Xu, Jingjing  and
      He, Yancheng  and
      Yan, ShengLi  and
      Wu, Yunfang  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1479",
    doi = "10.18653/v1/P19-1479",
    pages = "4843--4852",
    abstract = "Automatic article commenting is helpful in encouraging user engagement on online news platforms. However, the news documents are usually too long for models under traditional encoder-decoder frameworks, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to generate coherent and informative comments. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.",
}
@inproceedings{gao-etal-2019-interconnected,
    title = "Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling",
    author = "Gao, Yifan  and
      Li, Piji  and
      King, Irwin  and
      Lyu, Michael R.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1480",
    doi = "10.18653/v1/P19-1480",
    pages = "4853--4862",
    abstract = "We study the problem of generating interconnected questions in question-answering style conversations. Compared with previous works which generate questions based on a single sentence (or paragraph), this setting is different in two major aspects: (1) Questions are highly conversational. Almost half of them refer back to conversation history using coreferences. (2) In a coherent conversation, questions have smooth transitions between turns. We propose an end-to-end neural model with coreference alignment and conversation flow modeling. The coreference alignment modeling explicitly aligns coreferent mentions in conversation history with corresponding pronominal references in generated questions, which makes generated questions interconnected to conversation history. The conversation flow modeling builds a coherent conversation by starting questioning on the first few sentences in a text passage and smoothly shifting the focus to later parts. Extensive experiments show that our system outperforms several baselines and can generate highly conversational questions. The code implementation is released at https://github.com/Evan-Gao/conversaional-QG.",
}
@inproceedings{kumar-etal-2019-cross,
    title = "Cross-Lingual Training for Automatic Question Generation",
    author = "Kumar, Vishwajeet  and
      Joshi, Nitish  and
      Mukherjee, Arijit  and
      Ramakrishnan, Ganesh  and
      Jyothi, Preethi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1481",
    doi = "10.18653/v1/P19-1481",
    pages = "4863--4872",
    abstract = "Automatic question generation (QG) is a challenging problem in natural language understanding. QG systems are typically built assuming access to a large number of training instances where each instance is a question and its corresponding answer. For a new language, such training instances are hard to obtain making the QG problem even more challenging. Using this as our motivation, we study the reuse of an available large QG dataset in a secondary language (e.g. English) to learn a QG model for a primary language (e.g. Hindi) of interest. For the primary language, we assume access to a large amount of monolingual text but only a small QG dataset. We propose a cross-lingual QG model which uses the following training regime: (i) Unsupervised pretraining of language models in both primary and secondary languages and (ii) joint supervised training for QG in both languages. We demonstrate the efficacy of our proposed approach using two different primary languages, Hindi and Chinese. Our proposed framework clearly outperforms a number of baseline models, including a fully-supervised transformer-based model trained on the QG datasets in the primary language. We also create and release a new question answering dataset for Hindi consisting of 6555 sentences.",
}
@inproceedings{wu-etal-2019-hierarchical,
    title = "A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer",
    author = "Wu, Chen  and
      Ren, Xuancheng  and
      Luo, Fuli  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1482",
    doi = "10.18653/v1/P19-1482",
    pages = "4873--4883",
    abstract = "Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision. Existing seq2seq methods face three challenges: 1) the transfer is weakly interpretable, 2) generated outputs struggle in content preservation, and 3) the trade-off between content and style is intractable. To address these challenges, we propose a hierarchical reinforced sequence operation method, named Point-Then-Operate (PTO), which consists of a high-level agent that proposes operation positions and a low-level agent that alters the sentence. We provide comprehensive training objectives to control the fluency, style, and content of the outputs and a mask-based inference algorithm that allows for multi-step revision based on the single-step trained agents. Experimental results on two text style transfer datasets show that our method significantly outperforms recent methods and effectively addresses the aforementioned challenges.",
}
@inproceedings{dhingra-etal-2019-handling,
    title = "Handling Divergent Reference Texts when Evaluating Table-to-Text Generation",
    author = "Dhingra, Bhuwan  and
      Faruqui, Manaal  and
      Parikh, Ankur  and
      Chang, Ming-Wei  and
      Das, Dipanjan  and
      Cohen, William",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1483",
    doi = "10.18653/v1/P19-1483",
    pages = "4884--4895",
    abstract = "Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data. We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge. We propose a new metric, PARENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall. Through a large scale human evaluation study of table-to-text models for WikiBio, we show that PARENT correlates with human judgments better than existing text generation metrics. We also adapt and evaluate the information extraction based evaluation proposed by Wiseman et al (2017), and show that PARENT has comparable correlation to it, while being easier to use. We show that PARENT is also applicable when the reference texts are elicited from humans using the data from the WebNLG challenge.",
}
@inproceedings{lewis-etal-2019-unsupervised,
    title = "Unsupervised Question Answering by Cloze Translation",
    author = "Lewis, Patrick  and
      Denoyer, Ludovic  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1484",
    doi = "10.18653/v1/P19-1484",
    pages = "4896--4910",
    abstract = "Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what extent high quality training data is actually required for Extractive QA, and investigate the possibility of unsupervised Extractive QA. We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically. To generate such triples, we first sample random context paragraphs from a large corpus of documents and then random noun phrases or Named Entity mentions from these paragraphs as answers. Next we convert answers in context to {``}fill-in-the-blank{''} cloze questions and finally translate them into natural questions. We propose and compare various unsupervised ways to perform cloze-to-natural question translation, including training an unsupervised NMT model using non-aligned corpora of natural questions and cloze questions as well as a rule-based approach. We find that modern QA models can learn to answer human questions surprisingly well using only synthetic training data. We demonstrate that, without using the SQuAD training data at all, our approach achieves 56.4 F1 on SQuAD v1 (64.5 F1 when the answer is a Named Entity mention), outperforming early supervised models.",
}
@inproceedings{talmor-berant-2019-multiqa,
    title = "{M}ulti{QA}: An Empirical Investigation of Generalization and Transfer in Reading Comprehension",
    author = "Talmor, Alon  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1485",
    doi = "10.18653/v1/P19-1485",
    pages = "4911--4921",
    abstract = "A large number of reading comprehension (RC) datasets has been created recently, but little analysis has been done on whether they generalize to one another, and the extent to which existing datasets can be leveraged for improving performance on new ones. In this paper, we conduct such an investigation over ten RC datasets, training on one or more source RC datasets, and evaluating generalization, as well as transfer to a target RC dataset. We analyze the factors that contribute to generalization, and show that training on a source RC dataset and transferring to a target dataset substantially improves performance, even in the presence of powerful contextual representations from BERT (Devlin et al., 2019). We also find that training on multiple source RC datasets leads to robust generalization and transfer, and can reduce the cost of example collection for a new RC dataset. Following our analysis, we propose MultiQA, a BERT-based model, trained on multiple RC datasets, which leads to state-of-the-art performance on five RC datasets. We share our infrastructure for the benefit of the research community.",
}
@inproceedings{tay-etal-2019-simple,
    title = "Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives",
    author = "Tay, Yi  and
      Wang, Shuohang  and
      Luu, Anh Tuan  and
      Fu, Jie  and
      Phan, Minh C.  and
      Yuan, Xingdi  and
      Rao, Jinfeng  and
      Hui, Siu Cheung  and
      Zhang, Aston",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1486",
    doi = "10.18653/v1/P19-1486",
    pages = "4922--4931",
    abstract = "This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. This can be interpreted as a form of domain randomization and/or generative pretraining during training. To this end, the usage of the Pointer-Generator softens the requirement of having the answer within the context, enabling us to construct diverse training samples for learning. Additionally, we propose a new Introspective Alignment Layer (IAL), which reasons over decomposed alignments using block-based self-attention. We evaluate our proposed method on the NarrativeQA reading comprehension benchmark, achieving state-of-the-art performance, improving existing baselines by 51{\%} relative improvement on BLEU-4 and 17{\%} relative improvement on Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and CL components.",
}
@inproceedings{rajani-etal-2019-explain,
    title = "Explain Yourself! Leveraging Language Models for Commonsense Reasoning",
    author = "Rajani, Nazneen Fatema  and
      McCann, Bryan  and
      Xiong, Caiming  and
      Socher, Richard",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1487",
    doi = "10.18653/v1/P19-1487",
    pages = "4932--4942",
    abstract = "Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input. We collect human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations (CoS-E). We use CoS-E to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the state-of-the-art by 10{\%} on the challenging CommonsenseQA task. We further study commonsense reasoning in DNNs using both human and auto-generated explanations including transfer to out-of-domain tasks. Empirical results indicate that we can effectively leverage language models for commonsense reasoning.",
}
@inproceedings{sydorova-etal-2019-interpretable,
    title = "Interpretable Question Answering on Knowledge Bases and Text",
    author = "Sydorova, Alona  and
      Poerner, Nina  and
      Roth, Benjamin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1488",
    doi = "10.18653/v1/P19-1488",
    pages = "4943--4951",
    abstract = "Interpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination of knowledge bases (KB) and text documents. We adapt post hoc explanation methods such as LIME and input perturbation (IP) and compare them with the self-explanatory attention mechanism of the model. For this purpose, we propose an automatic evaluation paradigm for explanation methods in the context of QA. We also conduct a study with human annotators to evaluate whether explanations help them identify better QA models. Our results suggest that IP provides better explanations than LIME or attention, according to both automatic and human evaluation. We obtain the same ranking of methods in both experiments, which supports the validity of our automatic evaluation paradigm.",
}
@inproceedings{fujinuma-etal-2019-resource,
    title = "A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity",
    author = "Fujinuma, Yoshinari  and
      Boyd-Graber, Jordan  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1489",
    doi = "10.18653/v1/P19-1489",
    pages = "4952--4962",
    abstract = "Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that word similarity should be independent of language{---}i.e., word vectors within one language should not be more similar to each other than to words in another language. We measure this characteristic using modularity, a network measurement that measures the strength of clusters in a graph. Modularity has a moderate to strong correlation with three downstream tasks, even though modularity is based only on the structure of embeddings and does not require any external resources. We show through experiments that modularity can serve as an intrinsic validation metric to improve unsupervised cross-lingual word embeddings, particularly on distant language pairs in low-resource settings.",
}
@inproceedings{vulic-etal-2019-multilingual,
    title = "Multilingual and Cross-Lingual Graded Lexical Entailment",
    author = "Vuli{\'c}, Ivan  and
      Ponzetto, Simone Paolo  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1490",
    doi = "10.18653/v1/P19-1490",
    pages = "4963--4974",
    abstract = "Grounded in cognitive linguistics, graded lexical entailment (GR-LE) is concerned with fine-grained assertions regarding the directional hierarchical relationships between concepts on a continuous scale. In this paper, we present the first work on cross-lingual generalisation of GR-LE relation. Starting from HyperLex, the only available GR-LE dataset in English, we construct new monolingual GR-LE datasets for three other languages, and combine those to create a set of six cross-lingual GR-LE datasets termed CL-HYPERLEX. We next present a novel method dubbed CLEAR (Cross-Lingual Lexical Entailment Attract-Repel) for effectively capturing graded (and binary) LE, both monolingually in different languages as well as across languages (i.e., on CL-HYPERLEX). Coupled with a bilingual dictionary, CLEAR leverages taxonomic LE knowledge in a resource-rich language (e.g., English) and propagates it to other languages. Supported by cross-lingual LE transfer, CLEAR sets competitive baseline performance on three new monolingual GR-LE datasets and six cross-lingual GR-LE datasets. In addition, we show that CLEAR outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin for diverse language pairs.",
}
@inproceedings{mielke-etal-2019-kind,
    title = "What Kind of Language Is Hard to Language-Model?",
    author = "Mielke, Sabrina J.  and
      Cotterell, Ryan  and
      Gorman, Kyle  and
      Roark, Brian  and
      Eisner, Jason",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1491",
    doi = "10.18653/v1/P19-1491",
    pages = "4975--4989",
    abstract = "How language-agnostic are current state-of-the-art NLP tools? Are there some types of language that are easier to model with current methods? In prior work (Cotterell et al., 2018) we attempted to address this question for language modeling, and observed that recurrent neural network language models do not perform equally well over all the high-resource European languages found in the Europarl corpus. We speculated that inflectional morphology may be the primary culprit for the discrepancy. In this paper, we extend these earlier experiments to cover 69 languages from 13 language families using a multilingual Bible corpus. Methodologically, we introduce a new paired-sample multiplicative mixed-effects model to obtain language difficulty coefficients from at-least-pairwise parallel corpora. In other words, the model is aware of inter-sentence variation and can handle missing data. Exploiting this model, we show that {``}translationese{''} is not any easier to model than natively written language in a fair comparison. Trying to answer the question of what features difficult languages have in common, we try and fail to reproduce our earlier (Cotterell et al., 2018) observation about morphological complexity and instead reveal far simpler statistics of the data that seem to drive complexity in a much larger sample.",
}
@inproceedings{ormazabal-etal-2019-analyzing,
    title = "Analyzing the Limitations of Cross-lingual Word Embedding Mappings",
    author = "Ormazabal, Aitor  and
      Artetxe, Mikel  and
      Labaka, Gorka  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1492",
    doi = "10.18653/v1/P19-1492",
    pages = "4990--4995",
    abstract = "Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations. While several authors have questioned the underlying isomorphism assumption, which states that word embeddings in different languages have approximately the same structure, it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning cross-lingual embeddings. So as to answer this question, we experiment with parallel corpora, which allows us to compare offline mapping to an extension of skip-gram that jointly learns both embedding spaces. We observe that, under these ideal conditions, joint learning yields to more isomorphic embeddings, is less sensitive to hubness, and obtains stronger results in bilingual lexicon induction. We thus conclude that current mapping methods do have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.",
}
@inproceedings{pires-etal-2019-multilingual,
    title = "How Multilingual is Multilingual {BERT}?",
    author = "Pires, Telmo  and
      Schlinger, Eva  and
      Garrette, Dan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1493",
    doi = "10.18653/v1/P19-1493",
    pages = "4996--5001",
    abstract = "In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.",
}
@inproceedings{artetxe-etal-2019-bilingual,
    title = "Bilingual Lexicon Induction through Unsupervised Machine Translation",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1494",
    doi = "10.18653/v1/P19-1494",
    pages = "5002--5007",
    abstract = "A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods. In this paper, we propose an alternative approach to this problem that builds on the recent work on unsupervised machine translation. This way, instead of directly inducing a bilingual lexicon from cross-lingual embeddings, we use them to build a phrase-table, combine it with a language model, and use the resulting machine translation system to generate a synthetic parallel corpus, from which we extract the bilingual lexicon using statistical word alignment techniques. As such, our method can work with any word embedding and cross-lingual mapping technique, and it does not require any additional resource besides the monolingual corpus used to train the embeddings. When evaluated on the exact same cross-lingual embeddings, our proposed method obtains an average improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS retrieval, establishing a new state-of-the-art in the standard MUSE dataset.",
}
@inproceedings{preotiuc-pietro-etal-2019-automatically,
    title = "Automatically Identifying Complaints in Social Media",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Gaman, Mihaela  and
      Aletras, Nikolaos",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1495",
    doi = "10.18653/v1/P19-1495",
    pages = "5008--5019",
    abstract = "Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints. In this paper, we introduce the first systematic analysis of complaints in computational linguistics. We collect a new annotated data set of written complaints expressed on Twitter. We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.",
}
@inproceedings{xiong-etal-2019-tweetqa,
    title = "{TWEETQA}: A Social Media Focused Question Answering Dataset",
    author = "Xiong, Wenhan  and
      Wu, Jiawei  and
      Wang, Hong  and
      Kulkarni, Vivek  and
      Yu, Mo  and
      Chang, Shiyu  and
      Guo, Xiaoxiao  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1496",
    doi = "10.18653/v1/P19-1496",
    pages = "5020--5031",
    abstract = "With social media becoming increasingly popular on which lots of news and real-time events are reported, developing automated question answering systems is critical to the effective-ness of many applications that rely on real-time knowledge. While previous datasets have concentrated on question answering (QA) for formal text like news and Wikipedia, we present the first large-scale dataset for QA over social media data. To ensure that the tweets we collected are useful, we only gather tweets used by journalists to write news articles. We then ask human annotators to write questions and answers upon these tweets. Unlike otherQA datasets like SQuAD in which the answers are extractive, we allow the answers to be abstractive. We show that two recently proposed neural models that perform well on formal texts are limited in their performance when applied to our dataset. In addition, even the fine-tuned BERT model is still lagging behind human performance with a large margin. Our results thus point to the need of improved QA systems targeting social media text.",
}
@inproceedings{chai-etal-2019-asking,
    title = "Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums",
    author = "Chai, Zi  and
      Xing, Xinyu  and
      Wan, Xiaojun  and
      Huang, Bo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1497",
    doi = "10.18653/v1/P19-1497",
    pages = "5032--5046",
    abstract = "Teaching machines to ask questions is an important yet challenging task. Most prior work focused on generating questions with fixed answers. As contents are highly limited by given answers, these questions are often not worth discussing. In this paper, we take the first step on teaching machines to ask open-answered questions from real-world news for open discussion (openQG). To generate high-qualified questions, effective ways for question evaluation are required. We take the perspective that the more answers a question receives, the better it is for open discussion, and analyze how language use affects the number of answers. Compared with other factors, e.g. topic and post time, linguistic factors keep our evaluation from being domain-specific. We carefully perform variable control on 11.5M questions from online forums to get a dataset, OQRanD, and further perform question analysis. Based on these conclusions, several models are built for question evaluation. For openQG task, we construct OQGenD, the first dataset as far as we know, and propose a model based on conditional generative adversarial networks and our question evaluation model. Experiments show that our model can generate questions with higher quality compared with commonly-used text generation methods.",
}
@inproceedings{kumar-carley-2019-tree,
    title = "Tree {LSTM}s with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations",
    author = "Kumar, Sumeet  and
      Carley, Kathleen",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1498",
    doi = "10.18653/v1/P19-1498",
    pages = "5047--5058",
    abstract = "Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve state-of-the-art performance, outperforming the current best model by 12{\%} and 15{\%} on F1-macro for rumor-veracity classification and stance classification tasks respectively.",
}
@inproceedings{zhang-etal-2019-hibert,
    title = "{HIBERT}: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization",
    author = "Zhang, Xingxing  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1499",
    doi = "10.18653/v1/P19-1499",
    pages = "5059--5069",
    abstract = "Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \textit{inaccurate} labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders (Devlin et al., 2018), we propose Hibert (as shorthand for \textbf{HI}erachical \textbf{B}idirectional \textbf{E}ncoder \textbf{R}epresentations from \textbf{T}ransformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained Hibert to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets.",
}
@inproceedings{liu-lapata-2019-hierarchical,
    title = "Hierarchical Transformers for Multi-Document Summarization",
    author = "Liu, Yang  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1500",
    doi = "10.18653/v1/P19-1500",
    pages = "5070--5081",
    abstract = "In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations. Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.",
}
@inproceedings{kouris-etal-2019-abstractive,
    title = "Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization",
    author = "Kouris, Panagiotis  and
      Alexandridis, Georgios  and
      Stafylopatis, Andreas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1501",
    doi = "10.18653/v1/P19-1501",
    pages = "5082--5092",
    abstract = "This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.",
}
@inproceedings{peyrard-2019-studying,
    title = "Studying Summarization Evaluation Metrics in the Appropriate Scoring Range",
    author = "Peyrard, Maxime",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1502",
    doi = "10.18653/v1/P19-1502",
    pages = "5093--5100",
    abstract = "In summarization, automatic evaluation metrics are usually compared based on their ability to correlate with human judgments. Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks. However, modern systems are typically better than the best systems submitted at the time of these shared tasks. We show that, surprisingly, evaluation metrics which behave similarly on these datasets (average-scoring range) strongly disagree in the higher-scoring range in which current systems now operate. It is problematic because metrics disagree yet we can{'}t decide which one to trust. This is a call for collecting human judgments for high-scoring summaries as this would resolve the debate over which metrics to trust. This would also be greatly beneficial to further improve summarization systems and metrics alike.",
}
@inproceedings{zhou-rush-2019-simple,
    title = "Simple Unsupervised Summarization by Contextual Matching",
    author = "Zhou, Jiawei  and
      Rush, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1503",
    doi = "10.18653/v1/P19-1503",
    pages = "5101--5106",
    abstract = "We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific to the target domain. We show that by using a product-of-experts criteria these are enough for maintaining continuous contextual matching while maintaining output fluency. Experiments on both abstractive and extractive sentence summarization data sets show promising results of our method without being exposed to any paired data.",
}
@inproceedings{perez-beltrachini-etal-2019-generating,
    title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders",
    author = "Perez-Beltrachini, Laura  and
      Liu, Yang  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1504",
    doi = "10.18653/v1/P19-1504",
    pages = "5107--5116",
    abstract = "Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automatic and human evaluation demonstrate that our summaries have better content coverage.",
}
@inproceedings{wu-etal-2019-morphological,
    title = "Morphological Irregularity Correlates with Frequency",
    author = "Wu, Shijie  and
      Cotterell, Ryan  and
      O{'}Donnell, Timothy",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1505",
    doi = "10.18653/v1/P19-1505",
    pages = "5117--5126",
    abstract = "We present a study of morphological irregularity. Following recent work, we define an information-theoretic measure of irregularity based on the predictability of forms in a language. Using a neural transduction model, we estimate this quantity for the forms in 28 languages. We first present several validatory and exploratory analyses of irregularity. We then show that our analyses provide evidence for a correlation between irregularity and frequency: higher frequency items are more likely to be irregular and irregular items are more likely be highly frequent. To our knowledge, this result is the first of its breadth and confirms longstanding proposals from the linguistics literature. The correlation is more robust when aggregated at the level of whole paradigms{---}providing support for models of linguistic structure in which inflected forms are unified by abstract underlying stems or lexemes.",
}
@inproceedings{ororbia-etal-2019-like,
    title = "Like a Baby: Visually Situated Neural Language Acquisition",
    author = "Ororbia, Alexander  and
      Mali, Ankur  and
      Kelly, Matthew  and
      Reitter, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1506",
    doi = "10.18653/v1/P19-1506",
    pages = "5127--5136",
    abstract = "We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2{\%} decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5{\%} improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.",
}
@inproceedings{jat-etal-2019-relating,
    title = "Relating Simple Sentence Representations in Deep Neural Networks and the Brain",
    author = "Jat, Sharmistha  and
      Tang, Hao  and
      Talukdar, Partha  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1507",
    doi = "10.18653/v1/P19-1507",
    pages = "5137--5154",
    abstract = "What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain? Is there any correspondence between hidden layers of these recurrent models and brain regions when processing sentences? Can these deep models be used to synthesize brain data which can then be utilized in other extrinsic tasks? We investigate these questions using sentences with simple syntax and semantics (e.g., The bone was eaten by the dog.). We consider multiple neural network architectures, including recently proposed ELMo and BERT. We use magnetoencephalography (MEG) brain recording data collected from human subjects when they were reading these simple sentences. Overall, we find that BERT{'}s activations correlate the best with MEG brain data. We also find that the deep network representation can be used to generate brain data from new sentences to augment existing brain data. To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence. Our exploration is also the first to use deep neural network representations to generate synthetic brain data and to show that it helps in improving subsequent stimuli decoding task accuracy.",
}
@inproceedings{djokic-etal-2019-modeling,
    title = "Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models",
    author = "Djokic, Vesna  and
      Maillard, Jean  and
      Bulat, Luana  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1508",
    doi = "10.18653/v1/P19-1508",
    pages = "5155--5165",
    abstract = "Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings. However, it is yet unclear to what extent such models can be used to study and decode fMRI patterns associated with specific aspects of semantic composition such as the negation function. In this paper, we apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. Our results show reduced decoding (correlation) of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. This supports behavioral and brain imaging studies, suggesting that negation involves reduced access to aspects of the affirmative mental representation. The results pave the way for testing alternate semantic models of negation against human semantic processing in the brain.",
}
@inproceedings{chaabouni-etal-2019-word,
    title = "Word-order Biases in Deep-agent Emergent Communication",
    author = "Chaabouni, Rahma  and
      Kharitonov, Eugene  and
      Lazaric, Alessandro  and
      Dupoux, Emmanuel  and
      Baroni, Marco",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1509",
    doi = "10.18653/v1/P19-1509",
    pages = "5166--5175",
    abstract = "Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to {``}natural{''} word-order constraints. We train models to communicate about paths in a simple gridworld, using miniature languages that reflect or violate various natural language trends, such as the tendency to avoid redundancy or to minimize long-distance dependencies. We study how the controlled characteristics of our miniature languages affect individual learning and their stability across multiple network generations. The results draw a mixed picture. On the one hand, neural networks show a strong tendency to avoid long-distance dependencies. On the other hand, there is no clear preference for the efficient, non-redundant encoding of information that is widely attested in natural language. We thus suggest inoculating a notion of {``}effort{''} into neural networks, as a possible way to make their linguistic behavior more human-like.",
}
@inproceedings{ringland-etal-2019-nne,
    title = "{NNE}: A Dataset for Nested Named Entity Recognition in {E}nglish Newswire",
    author = "Ringland, Nicky  and
      Dai, Xiang  and
      Hachey, Ben  and
      Karimi, Sarvnaz  and
      Paris, Cecile  and
      Curran, James R.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1510",
    doi = "10.18653/v1/P19-1510",
    pages = "5176--5181",
    abstract = "Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions. We describe NNE{---}a fine-grained, nested named entity dataset over the full Wall Street Journal portion of the Penn Treebank (PTB). Our annotation comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. We hope the public release of this large dataset for English newswire will encourage development of new techniques for nested NER.",
}
@inproceedings{lin-etal-2019-sequence,
    title = "Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks",
    author = "Lin, Hongyu  and
      Lu, Yaojie  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1511",
    doi = "10.18653/v1/P19-1511",
    pages = "5182--5192",
    abstract = "Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recognizing nested entity mentions. In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i.e., although a mention can nest other mentions, they will not share the same head word. Specifically, we propose Anchor-Region Networks (ARNs), a sequence-to-nuggets architecture for nested mention detection. ARNs first identify anchor words (i.e., possible head words) of all mentions, and then recognize the mention boundaries for each anchor word by exploiting regular phrase structures. Furthermore, we also design Bag Loss, an objective function which can train ARNs in an end-to-end manner without using any anchor word annotation. Experiments show that ARNs achieve the state-of-the-art performance on three standard nested entity mention detection benchmarks.",
}
@inproceedings{chen-etal-2019-improving,
    title = "Improving Textual Network Embedding with Global Attention via Optimal Transport",
    author = "Chen, Liqun  and
      Wang, Guoyin  and
      Tao, Chenyang  and
      Shen, Dinghan  and
      Cheng, Pengyu  and
      Zhang, Xinyuan  and
      Wang, Wenlin  and
      Zhang, Yizhe  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1512",
    doi = "10.18653/v1/P19-1512",
    pages = "5193--5202",
    abstract = "Constituting highly informative network embeddings is an essential tool for network analysis. It encodes network topology, along with other useful side information, into low dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network embedding problem, and present two novel strategies to improve over traditional attention mechanisms: (i) a content-aware sparse attention module based on optimal transport; and (ii) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.",
}
@inproceedings{hou-etal-2019-identification,
    title = "Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction",
    author = "Hou, Yufang  and
      Jochim, Charles  and
      Gleize, Martin  and
      Bonin, Francesca  and
      Ganguly, Debasis",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1513",
    doi = "10.18653/v1/P19-1513",
    pages = "5203--5213",
    abstract = "While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.",
}
@inproceedings{xu-etal-2019-scaling,
    title = "Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title",
    author = "Xu, Huimin  and
      Wang, Wenting  and
      Mao, Xin  and
      Jiang, Xinyu  and
      Lan, Man",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1514",
    doi = "10.18653/v1/P19-1514",
    pages = "5214--5223",
    abstract = "Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain. Previous studies treat each attribute only as an entity type and build one set of NER tags (e.g., BIO) for each of them, leading to a scalability issue which unfits to the large sized attribute system in real world e-Commerce. In this work, we propose a novel approach to support value extraction scaling up to thousands of attributes without losing performance: (1) We propose to regard attribute as a query and adopt only one global set of BIO tags for any attributes to reduce the burden of attribute tag or model explosion; (2) We explicitly model the semantic representations for attribute and title, and develop an attention mechanism to capture the interactive semantic relations in-between to enforce our framework to be attribute comprehensive. We conduct extensive experiments in real-life datasets. The results show that our model not only outperforms existing state-of-the-art NER tagging models, but also is robust and generates promising results for up to 8,906 attributes.",
}
@inproceedings{zhao-zhang-2019-incorporating,
    title = "Incorporating Linguistic Constraints into Keyphrase Generation",
    author = "Zhao, Jing  and
      Zhang, Yuxiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1515",
    doi = "10.18653/v1/P19-1515",
    pages = "5224--5233",
    abstract = "Keyphrases, that concisely describe the high-level topics discussed in a document, are very useful for a wide range of natural language processing tasks. Though existing keyphrase generation methods have achieved remarkable performance on this task, they generate many overlapping phrases (including sub-phrases or super-phrases) of keyphrases. In this paper, we propose the parallel Seq2Seq network with the coverage attention to alleviate the overlapping phrase problem. Specifically, we integrate the linguistic constraints of keyphrase into the basic Seq2Seq network on the source side, and employ the multi-task learning framework on the target side. In addition, in order to prevent from generating overlapping phrases of keyphrases with correct syntax, we introduce the coverage vector to keep track of the attention history and to decide whether the parts of source text have been covered by existing generated keyphrases. Experimental results show that our method can outperform the state-of-the-art CopyRNN on scientific datasets, and is also more effective in news domain.",
}
@inproceedings{yadav-etal-2019-unified,
    title = "A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining",
    author = "Yadav, Shweta  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1516",
    doi = "10.18653/v1/P19-1516",
    pages = "5234--5245",
    abstract = "The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance. The traditional ways of identifying ADR are reliable but time-consuming, non-scalable and offer a very limited amount of ADR relevant information. With the unprecedented growth of information sources in the forms of social media texts (Twitter, Blogs, Reviews etc.), biomedical literature, and Electronic Medical Records (EMR), it has become crucial to extract the most pertinent ADR related information from these free-form texts. In this paper, we propose a neural network inspired multi- task learning framework that can simultaneously extract ADRs from various sources. We adopt a novel adversarial learning-based approach to learn features across multiple ADR information sources. Unlike the other existing techniques, our approach is capable to extracting fine-grained information (such as {`}Indications{'}, {`}Symptoms{'}, {`}Finding{'}, {`}Disease{'}, {`}Drug{'}) which provide important cues in pharmacovigilance. We evaluate our proposed approach on three publicly available real- world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. Experiments show that our unified framework achieves state-of-the-art performance on individual tasks associated with the different benchmark datasets. This establishes the fact that our proposed approach is generic, which enables it to achieve high performance on the diverse datasets.",
}
@inproceedings{zou-lu-2019-quantity,
    title = "Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems",
    author = "Zou, Yanyan  and
      Lu, Wei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1517",
    doi = "10.18653/v1/P19-1517",
    pages = "5246--5251",
    abstract = "An arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions. This work presents a novel approach, \textit{Quantity Tagger}, that automatically discovers such hidden relations by tagging each quantity with a \textit{sign} corresponding to one type of mathematical operation. For each quantity, we assume there exists a latent, variable-sized \textit{quantity span} surrounding the quantity token in the text, which conveys information useful for determining its sign. Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches.",
}
@inproceedings{yang-etal-2019-deep,
    title = "A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification",
    author = "Yang, Pengcheng  and
      Luo, Fuli  and
      Ma, Shuming  and
      Lin, Junyang  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1518",
    doi = "10.18653/v1/P19-1518",
    pages = "5252--5258",
    abstract = "Multi-label classification (MLC) aims to predict a set of labels for a given instance. Based on a pre-defined label order, the sequence-to-sequence (Seq2Seq) model trained via maximum likelihood estimation method has been successfully applied to the MLC task and shows powerful ability to capture high-order correlations between labels. However, the output labels are essentially an unordered set rather than an ordered sequence. This inconsistency tends to result in some intractable problems, e.g., sensitivity to the label order. To remedy this, we propose a simple but effective sequence-to-set model. The proposed model is trained via reinforcement learning, where reward feedback is designed to be independent of the label order. In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels. Extensive experiments show that our approach can substantially outperform competitive baselines, as well as effectively reduce the sensitivity to the label order.",
}
@inproceedings{zhang-etal-2019-joint,
    title = "Joint Slot Filling and Intent Detection via Capsule Neural Networks",
    author = "Zhang, Chenwei  and
      Li, Yaliang  and
      Du, Nan  and
      Fan, Wei  and
      Yu, Philip",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1519",
    doi = "10.18653/v1/P19-1519",
    pages = "5259--5267",
    abstract = "Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.",
}
@inproceedings{dai-song-2019-neural,
    title = "Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision",
    author = "Dai, Hongliang  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1520",
    doi = "10.18653/v1/P19-1520",
    pages = "5268--5277",
    abstract = "Lack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews. To alleviate this problem, we first propose an algorithm to automatically mine extraction rules from existing training examples based on dependency parsing results. The mined rules are then applied to label a large amount of auxiliary data. Finally, we study training procedures to train a neural model which can learn from both the data automatically labeled by the rules and a small amount of data accurately annotated by human. Experimental results show that although the mined rules themselves do not perform well due to their limited flexibility, the combination of human annotated data and rule labeled auxiliary data can improve the neural model and allow it to achieve performance better than or comparable with the current state-of-the-art.",
}
@inproceedings{lin-etal-2019-cost,
    title = "Cost-sensitive Regularization for Label Confusion-aware Event Detection",
    author = "Lin, Hongyu  and
      Lu, Yaojie  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1521",
    doi = "10.18653/v1/P19-1521",
    pages = "5278--5283",
    abstract = "In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type. To address this label confusion problem, this paper proposes cost-sensitive regularization, which can force the training procedure to concentrate more on optimizing confusing type pairs. Specifically, we introduce a cost-weighted term into the training loss, which penalizes more on mislabeling between confusing label pairs. Furthermore, we also propose two estimators which can effectively measure such label confusion based on instance-level or population-level statistics. Experiments on TAC-KBP 2017 datasets demonstrate that the proposed method can significantly improve the performances of different models in both English and Chinese event detection.",
}
@inproceedings{yang-etal-2019-exploring,
    title = "Exploring Pre-trained Language Models for Event Extraction and Generation",
    author = "Yang, Sen  and
      Feng, Dawei  and
      Qiao, Linbo  and
      Kan, Zhigang  and
      Li, Dongsheng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1522",
    doi = "10.18653/v1/P19-1522",
    pages = "5284--5294",
    abstract = "Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size. Therefore, in addition to the difficulty of event extraction itself, insufficient training data hinders the learning process as well. To promote event extraction, we first propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. Moreover, to address the problem of insufficient training data, we propose a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality. Experiments on the ACE2005 dataset demonstrate that our extraction model can surpass most existing extraction methods. Besides, incorporating our generation method exhibits further significant improvement. It obtains new state-of-the-art results on the event extraction task, including pushing the F1 score of trigger classification to 81.1{\%}, and the F1 score of argument classification to 58.9{\%}.",
}
@inproceedings{jiang-etal-2019-improving,
    title = "Improving Open Information Extraction via Iterative Rank-Aware Learning",
    author = "Jiang, Zhengbao  and
      Yin, Pengcheng  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1523",
    doi = "10.18653/v1/P19-1523",
    pages = "5295--5300",
    abstract = "Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. A key step in open IE is confidence modeling, ranking the extractions based on their estimated quality to adjust precision and recall of extracted assertions. We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences. We propose an additional binary classification loss to calibrate the likelihood to make it more globally comparable, and an iterative learning process, where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. Experiments on OIE2016 demonstrate the effectiveness of our method. Code and data are available at https://github.com/jzbjyb/oie{\_}rank.",
}
@inproceedings{liu-etal-2019-towards,
    title = "Towards Improving Neural Named Entity Recognition with Gazetteers",
    author = "Liu, Tianyu  and
      Yao, Jin-Ge  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1524",
    doi = "10.18653/v1/P19-1524",
    pages = "5301--5307",
    abstract = "Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. This could increase the chance of overfitting since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize beyond the annotated entities. In this work, we show that properly utilizing external gazetteers could benefit segmental neural NER models. We add a simple module on the recently proposed hybrid semi-Markov CRF architecture and observe some promising results.",
}
@inproceedings{dixit-al-onaizan-2019-span,
    title = "Span-Level Model for Relation Extraction",
    author = "Dixit, Kalpit  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1525",
    doi = "10.18653/v1/P19-1525",
    pages = "5308--5314",
    abstract = "Relation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions. Recent approaches for this span-level task have been token-level models which have inherent limitations. They cannot easily define and implement span-level features, cannot model overlapping entity mentions and have cascading errors due to the use of sequential decoding. To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction. We report a new state-of-the-art performance of 62.83 F1 (prev best was 60.49) on the ACE2005 dataset.",
}
@inproceedings{han-etal-2019-enhancing,
    title = "Enhancing Unsupervised Generative Dependency Parser with Contextual Information",
    author = "Han, Wenjuan  and
      Jiang, Yong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1526",
    doi = "10.18653/v1/P19-1526",
    pages = "5315--5325",
    abstract = "Most of the unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse. Probabilistic generative models usually explicit decompose the desired dependency tree into factorized grammar rules, which lack the global features of the entire sentence. In this paper, we propose a novel probabilistic model called discriminative neural dependency model with valence (D-NDMV) that generates a sentence and its parse from a continuous latent representation, which encodes global contextual information of the generated sentence. We propose two approaches to model the latent representation: the first deterministically summarizes the representation from the sentence and the second probabilistically models the representation conditioned on the sentence. Our approach can be regarded as a new type of autoencoder model to unsupervised dependency parsing that combines the benefits of both generative and discriminative techniques. In particular, our approach breaks the context-free independence assumption in previous generative approaches and therefore becomes more expressive. Our extensive experimental results on seventeen datasets from various sources show that our approach achieves competitive accuracy compared with both generative and discriminative state-of-the-art unsupervised dependency parsers.",
}
@inproceedings{strakova-etal-2019-neural,
    title = "Neural Architectures for Nested {NER} through Linearization",
    author = "Strakov{\'a}, Jana  and
      Straka, Milan  and
      Hajic, Jan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1527",
    doi = "10.18653/v1/P19-1527",
    pages = "5326--5331",
    abstract = "We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We encode the nested labels using a linearized scheme. In our first proposed approach, the nested labels are modeled as multilabels corresponding to the Cartesian product of the nested labels in a standard LSTM-CRF architecture. In the second one, the nested NER is viewed as a sequence-to-sequence problem, in which the input sequence consists of the tokens and output sequence of the labels, using hard attention on the word whose label is being predicted. The proposed methods outperform the nested NER state of the art on four corpora: ACE-2004, ACE-2005, GENIA and Czech CNEC. We also enrich our architectures with the recently published contextual embeddings: ELMo, BERT and Flair, reaching further improvements for the four nested entity corpora. In addition, we report flat NER state-of-the-art results for CoNLL-2002 Dutch and Spanish and for CoNLL-2003 English.",
}
@inproceedings{cognetta-etal-2019-online,
    title = "Online Infix Probability Computation for Probabilistic Finite Automata",
    author = "Cognetta, Marco  and
      Han, Yo-Sub  and
      Kwon, Soon Chan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1528",
    doi = "10.18653/v1/P19-1528",
    pages = "5332--5337",
    abstract = "Probabilistic finite automata (PFAs) are com- mon statistical language model in natural lan- guage and speech processing. A typical task for PFAs is to compute the probability of all strings that match a query pattern. An impor- tant special case of this problem is computing the probability of a string appearing as a pre- fix, suffix, or infix. These problems find use in many natural language processing tasks such word prediction and text error correction. Recently, we gave the first incremental algorithm to efficiently compute the infix probabilities of each prefix of a string (Cognetta et al., 2018). We develop an asymptotic improvement of that algorithm and solve the open problem of computing the infix probabilities of PFAs from streaming data, which is crucial when process- ing queries online and is the ultimate goal of the incremental approach.",
}
@inproceedings{wang-etal-2019-best,
    title = "How to Best Use Syntax in Semantic Role Labelling",
    author = "Wang, Yufei  and
      Johnson, Mark  and
      Wan, Stephen  and
      Sun, Yifang  and
      Wang, Wei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1529",
    doi = "10.18653/v1/P19-1529",
    pages = "5338--5343",
    abstract = "There are many different ways in which external information might be used in a NLP task. This paper investigates how external syntactic information can be used most effectively in the Semantic Role Labeling (SRL) task. We evaluate three different ways of encoding syntactic parses and three different ways of injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling model. We show that using a constituency representation as input features improves performance the most, achieving a new state-of-the-art for non-ensemble SRL models on the in-domain CoNLL{'}05 and CoNLL{'}12 benchmarks.",
}
@inproceedings{kato-matsubara-2019-ptb,
    title = "{PTB} Graph Parsing with Tree Approximation",
    author = "Kato, Yoshihide  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1530",
    doi = "10.18653/v1/P19-1530",
    pages = "5344--5349",
    abstract = "The Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies. This paper proposes a method that approximates PTB graph-structured representations by trees. By our approximation method, we can reduce nonlocal dependency identification and constituency parsing into single tree-based parsing. An experimental result demonstrates that our approximation method with an off-the-shelf tree-based constituency parser significantly outperforms the previous methods in nonlocal dependency identification.",
}
@inproceedings{strzyz-etal-2019-sequence,
    title = "Sequence Labeling Parsing by Learning across Representations",
    author = "Strzyz, Michalina  and
      Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1531",
    doi = "10.18653/v1/P19-1531",
    pages = "5350--5357",
    abstract = "We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions.To do so, we cast the problem as multitask learning (MTL). First, we show that adding a parsing paradigm as an auxiliary loss consistently improves the performance on the other paradigm. Secondly, we explore an MTL sequence labeling model that parses both representations, at almost no cost in terms of performance and speed. The results across the board show that on average MTL models with auxiliary losses for constituency parsing outperform single-task ones by 1.05 F1 points, and for dependency parsing by 0.62 UAS points.",
}
@inproceedings{liu-etal-2019-prism,
    title = "A Prism Module for Semantic Disentanglement in Name Entity Recognition",
    author = "Liu, Kun  and
      Li, Shen  and
      Zheng, Daqi  and
      Lu, Zhengdong  and
      Gao, Sheng  and
      Li, Si",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1532",
    doi = "10.18653/v1/P19-1532",
    pages = "5358--5362",
    abstract = "Natural Language Processing has been perplexed for many years by the problem that multiple semantics are mixed inside a word, even with the help of context. To solve this problem, we propose a prism module to disentangle the semantic aspects of words and reduce noise at the input layer of a model. In the prism module, some words are selectively replaced with task-related semantic aspects, then these denoised word representations can be fed into downstream tasks to make them easier. Besides, we also introduce a structure to train this module jointly with the downstream model without additional data. This module can be easily integrated into the downstream model and significantly improve the performance of baselines on named entity recognition (NER) task. The ablation analysis demonstrates the rationality of the method. As a side effect, the proposed method also provides a way to visualize the contribution of each word.",
}
@inproceedings{wiseman-stratos-2019-label,
    title = "Label-Agnostic Sequence Labeling by Copying Nearest Neighbors",
    author = "Wiseman, Sam  and
      Stratos, Karl",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1533",
    doi = "10.18653/v1/P19-1533",
    pages = "5363--5369",
    abstract = "Retrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest. However, much recent work merely conditions on retrieved structures (e.g., in a sequence-to-sequence framework), rather than explicitly manipulating them. We show we can perform accurate sequence labeling by explicitly (and only) copying labels from retrieved neighbors. Moreover, because this copying is label-agnostic, we can achieve impressive performance in zero-shot sequence-labeling tasks. We additionally consider a dynamic programming approach to sequence labeling in the presence of retrieved neighbors, which allows for controlling the number of distinct (copied) segments used to form a prediction, and leads to both more interpretable and accurate predictions.",
}
@inproceedings{rashkin-etal-2019-towards,
    title = "Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset",
    author = "Rashkin, Hannah  and
      Smith, Eric Michael  and
      Li, Margaret  and
      Boureau, Y-Lan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1534",
    doi = "10.18653/v1/P19-1534",
    pages = "5370--5381",
    abstract = "One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others{'} feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model.",
}
@inproceedings{bao-etal-2019-know,
    title = "Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment",
    author = "Bao, Siqi  and
      He, Huang  and
      Wang, Fan  and
      Lian, Rongzhong  and
      Wu, Hua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1535",
    doi = "10.18653/v1/P19-1535",
    pages = "5382--5391",
    abstract = "In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other. For the sake of rational knowledge utilization and coherent conversation flow, a dialogue strategy which controls knowledge selection is instantiated and continuously adapted via reinforcement learning. Under the deployed strategy, knowledge grounded conversations are conducted with two dialogue agents. The generated dialogues are comprehensively evaluated on aspects like informativeness and coherence, which are aligned with our objective and human instinct. These assessments are integrated as a compound reward to guide the evolution of dialogue strategy via policy gradient. Comprehensive experiments have been carried out on the publicly available dataset, demonstrating that the proposed method outperforms the other state-of-the-art approaches significantly.",
}
@inproceedings{henderson-etal-2019-training,
    title = "Training Neural Response Selection for Task-Oriented Dialogue Systems",
    author = "Henderson, Matthew  and
      Vuli{\'c}, Ivan  and
      Gerz, Daniela  and
      Casanueva, I{\~n}igo  and
      Budzianowski, Pawe{\l}  and
      Coope, Sam  and
      Spithourakis, Georgios  and
      Wen, Tsung-Hsien  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Su, Pei-Hao",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1536",
    doi = "10.18653/v1/P19-1536",
    pages = "5392--5404",
    abstract = "Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on five diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.",
}
@inproceedings{narayan-chen-etal-2019-collaborative,
    title = "Collaborative Dialogue in {M}inecraft",
    author = "Narayan-Chen, Anjali  and
      Jayannavar, Prashant  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1537",
    doi = "10.18653/v1/P19-1537",
    pages = "5405--5415",
    abstract = "We wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. Since computer games allow us to simulate such tasks without the need for physical robots, we define a Minecraft-based collaborative building task in which one player (A, the Architect) is shown a target structure and needs to instruct the other player (B, the Builder) to build this structure. Both players interact via a chat interface. A can observe B but cannot place blocks. We present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs. As a first step towards our goal of developing fully interactive agents for this task, we consider the subtask of Architect utterance generation, and show how challenging it is.",
}
@inproceedings{xu-etal-2019-neural,
    title = "Neural Response Generation with Meta-words",
    author = "Xu, Can  and
      Wu, Wei  and
      Tao, Chongyang  and
      Hu, Huang  and
      Schuerman, Matt  and
      Wang, Ying",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1538",
    doi = "10.18653/v1/P19-1538",
    pages = "5416--5426",
    abstract = "We present open domain dialogue generation with meta-words. A meta-word is a structured record that describes attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To incorporate meta-words into generation, we propose a novel goal-tracking memory network that formalizes meta-word expression as a goal in response generation and manages the generation process to achieve the goal with a state memory panel and a state controller. Experimental results from both automatic evaluation and human judgment on two large-scale data sets indicate that our model can significantly outperform state-of-the-art generation models in terms of response relevance, response diversity, and accuracy of meta-word expression.",
}
@inproceedings{qin-etal-2019-conversing,
    title = "Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading",
    author = "Qin, Lianhui  and
      Galley, Michel  and
      Brockett, Chris  and
      Liu, Xiaodong  and
      Gao, Xiang  and
      Dolan, Bill  and
      Choi, Yejin  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1539",
    doi = "10.18653/v1/P19-1539",
    pages = "5427--5436",
    abstract = "Although neural conversational models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output.",
}
@inproceedings{chauhan-etal-2019-ordinal,
    title = "Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System",
    author = "Chauhan, Hardik  and
      Firdaus, Mauajama  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1540",
    doi = "10.18653/v1/P19-1540",
    pages = "5437--5447",
    abstract = "Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems. The state-of-the-art dialogue systems are primarily based on unimodal sources, predominantly the text, and hence cannot capture the information present in the other sources such as videos, audios, images etc. With the availability of large scale multimodal dialogue dataset (MMD) (Saha et al., 2018) on the fashion domain, the visual appearance of the products is essential for understanding the intention of the user. Without capturing the information from both the text and image, the system will be incapable of generating correct and desirable responses. In this paper, we propose a novel position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. Our evaluation shows that the proposed model can generate appropriate responses while preserving the position and attribute information. Experimental results also prove that our proposed approach attains superior performance compared to the baseline models, and outperforms the state-of-the-art approaches on text similarity based evaluation metrics.",
}
@inproceedings{bai-etal-2019-memory,
    title = "Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference",
    author = "Bai, He  and
      Zhou, Yu  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1541",
    doi = "10.18653/v1/P19-1541",
    pages = "5448--5453",
    abstract = "Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations. However, most of the previous models learn the context memory with only one objective to maximizing the SLU performance, leaving the context memory under-exploited. In this paper, we propose a new dialogue logistic inference (DLI) task to consolidate the context memory jointly with SLU in the multi-task framework. DLI is defined as sorting a shuffled dialogue session into its original logical order and shares the same memory encoder and retrieval mechanism as the SLU model. Our experimental results show that various popular contextual SLU models can benefit from our approach, and improvements are quite impressive, especially in slot filling.",
}
@inproceedings{madotto-etal-2019-personalizing,
    title = "Personalizing Dialogue Agents via Meta-Learning",
    author = "Madotto, Andrea  and
      Lin, Zhaojiang  and
      Wu, Chien-Sheng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1542",
    doi = "10.18653/v1/P19-1542",
    pages = "5454--5459",
    abstract = "Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. Collecting such descriptions from existing dialogues is expensive and requires hand-crafted feature designs. In this paper, we propose to extend Model-Agnostic Meta-Learning (MAML) (Finn et al., 2017) to personalized dialogue learning without using any persona descriptions. Our model learns to quickly adapt to new personas by leveraging only a few dialogue samples collected from the same user, which is fundamentally different from conditioning the response on the persona descriptions. Empirical results on Persona-chat dataset (Zhang et al., 2018) indicate that our solution outperforms non-meta-learning baselines using automatic evaluation metrics, and in terms of human-evaluated fluency and consistency.",
}
@inproceedings{liu-chen-2019-reading,
    title = "Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension",
    author = "Liu, Zhengyuan  and
      Chen, Nancy",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1543",
    doi = "10.18653/v1/P19-1543",
    pages = "5460--5466",
    abstract = "Comprehending multi-turn spoken conversations is an emerging research area, presenting challenges different from reading comprehension of passages due to the interactive nature of information exchange from at least two speakers. Unlike passages, where sentences are often the default semantic modeling unit, in multi-turn conversations, a turn is a topically coherent unit embodied with immediately relevant context, making it a linguistically intuitive segment for computationally modeling verbal interactions. Therefore, in this work, we propose a hierarchical attention neural network architecture, combining turn-level and word-level attention mechanisms, to improve spoken dialogue comprehension performance. Experiments are conducted on a multi-turn conversation dataset, where nurses inquire and discuss symptom information with patients. We empirically show that the proposed approach outperforms standard attention baselines, achieves more efficient learning outcomes, and is more robust to lengthy and out-of-distribution test samples.",
}
@inproceedings{e-etal-2019-novel,
    title = "A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling",
    author = "E, Haihong  and
      Niu, Peiqing  and
      Chen, Zhongfu  and
      Song, Meina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1544",
    doi = "10.18653/v1/P19-1544",
    pages = "5467--5471",
    abstract = "A spoken language understanding (SLU) system includes two main tasks, slot filling (SF) and intent detection (ID). The joint model for the two tasks is becoming a tendency in SLU. But the bi-directional interrelated connections between the intent and slots are not established in the existing joint models. In this paper, we propose a novel bi-directional interrelated model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks to help them promote each other mutually. Besides, we design an entirely new iteration mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the relative improvement in the sentence-level semantic frame accuracy of our model is 3.79{\%} and 5.42{\%} on ATIS and Snips datasets, respectively, compared to the state-of-the-art model.",
}
@inproceedings{su-etal-2019-dual,
    title = "Dual Supervised Learning for Natural Language Understanding and Generation",
    author = "Su, Shang-Yu  and
      Huang, Chao-Wei  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1545",
    doi = "10.18653/v1/P19-1545",
    pages = "5472--5477",
    abstract = "Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP and dialogue fields. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in literature. This paper proposes a novel learning framework for natural language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks, demonstrating the effectiveness of the dual relationship.",
}
@inproceedings{lee-etal-2019-sumbt,
    title = "{SUMBT}: Slot-Utterance Matching for Universal and Scalable Belief Tracking",
    author = "Lee, Hwaran  and
      Lee, Jinsik  and
      Kim, Tae-Yoon",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1546",
    doi = "10.18653/v1/P19-1546",
    pages = "5478--5483",
    abstract = "In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn. Previous neural approaches have modeled domain- and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configurations. In this paper, we propose a new approach to universal and scalable belief tracker, called slot-utterance matching belief tracker (SUMBT). The model learns the relations between domain-slot-types and slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and MultiWOZ, the proposed model showed performance improvement in comparison with slot-dependent methods and achieved the state-of-the-art joint accuracy.",
}
@inproceedings{shah-etal-2019-robust,
    title = "Robust Zero-Shot Cross-Domain Slot Filling with Example Values",
    author = "Shah, Darsh  and
      Gupta, Raghav  and
      Fayazi, Amir  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1547",
    doi = "10.18653/v1/P19-1547",
    pages = "5484--5490",
    abstract = "Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites. Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas. We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas. Our approach outperforms state-of-the-art models on two multi-domain datasets, especially in the low-data setting.",
}
@inproceedings{lin-xu-2019-deep,
    title = "Deep Unknown Intent Detection with Margin Loss",
    author = "Lin, Ting-En  and
      Xu, Hua",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1548",
    doi = "10.18653/v1/P19-1548",
    pages = "5491--5496",
    abstract = "Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.",
}
@inproceedings{shen-etal-2019-modeling,
    title = "Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables",
    author = "Shen, Lei  and
      Feng, Yang  and
      Zhan, Haolan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1549",
    doi = "10.18653/v1/P19-1549",
    pages = "5497--5502",
    abstract = "Multi-turn conversations consist of complex semantic structures, and it is still a challenge to generate coherent and diverse responses given previous utterances. It{'}s practical that a conversation takes place under a background, meanwhile, the query and response are usually most related and they are consistent in topic but also different in content. However, little work focuses on such hierarchical relationship among utterances. To address this problem, we propose a Conversational Semantic Relationship RNN (CSRR) model to construct the dependency explicitly. The model contains latent variables in three hierarchies. The discourse-level one captures the global background, the pair-level one stands for the common topic information between query and response, and the utterance-level ones try to represent differences in content. Experimental results show that our model significantly improves the quality of responses in terms of fluency, coherence, and diversity compared to baseline methods.",
}
@inproceedings{niu-penn-2019-rationally,
    title = "Rationally Reappraising {ATIS}-based Dialogue Systems",
    author = "Niu, Jingcheng  and
      Penn, Gerald",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1550",
    doi = "10.18653/v1/P19-1550",
    pages = "5503--5507",
    abstract = "The Air Travel Information Service (ATIS) corpus has been the most common benchmark for evaluating Spoken Language Understanding (SLU) tasks for more than three decades since it was released. Recent state-of-the-art neural models have obtained F1-scores near 98{\%} on the task of slot filling. We developed a rule-based grammar for the ATIS domain that achieves a 95.82{\%} F1-score on our evaluation set. In the process, we furthermore discovered numerous shortcomings in the ATIS corpus annotation, which we have fixed. This paper presents a detailed account of these shortcomings, our proposed repairs, our rule-based grammar and the neural slot-filling architectures associated with ATIS. We also rationally reappraise the motivations for choosing a neural architecture in view of this account. Fixing the annotation errors results in a relative error reduction of between 19.4 and 52{\%} across all architectures. We nevertheless argue that neural models must play a different role in ATIS dialogues because of the latter{'}s lack of variety.",
}
@inproceedings{corro-titov-2019-learning,
    title = "Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming",
    author = "Corro, Caio  and
      Titov, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1551",
    doi = "10.18653/v1/P19-1551",
    pages = "5508--5521",
    abstract = "We treat projective dependency trees as latent variables in our probabilistic model and induce them in such a way as to be beneficial for a downstream task, without relying on any direct tree supervision. Our approach relies on Gumbel perturbations and differentiable dynamic programming. Unlike previous approaches to latent tree learning, we stochastically sample global structures and our parser is fully differentiable. We illustrate its effectiveness on sentiment analysis and natural language inference tasks. We also study its properties on a synthetic structure induction task. Ablation studies emphasize the importance of both stochasticity and constraining latent structures to be projective trees.",
}
@inproceedings{liu-etal-2019-neural,
    title = "Neural-based {C}hinese Idiom Recommendation for Enhancing Elegance in Essay Writing",
    author = "Liu, Yuanchao  and
      Pang, Bo  and
      Liu, Bingquan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1552",
    doi = "10.18653/v1/P19-1552",
    pages = "5522--5526",
    abstract = "Although the proper use of idioms can enhance the elegance of writing, the active use of various expressions is a challenge because remembering idioms is difficult. In this study, we address the problem of idiom recommendation by leveraging a neural machine translation framework, in which we suppose that idioms are written with one pseudo target language. Two types of real-life datasets are collected to support this study. Experimental results show that the proposed approach achieves promising performance compared with other baseline methods.",
}
@inproceedings{kruengkrai-2019-better,
    title = "Better Exploiting Latent Variables in Text Modeling",
    author = "Kruengkrai, Canasai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1553",
    doi = "10.18653/v1/P19-1553",
    pages = "5527--5532",
    abstract = "We show that sampling latent variables multiple times at a gradient step helps in improving a variational autoencoder and propose a simple and effective method to better exploit these latent variables through hidden state averaging. Consistent gains in performance on two different datasets, Penn Treebank and Yahoo, indicate the generalizability of our method.",
}
@inproceedings{feng-etal-2019-misleading,
    title = "Misleading Failures of Partial-input Baselines",
    author = "Feng, Shi  and
      Wallace, Eric  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1554",
    doi = "10.18653/v1/P19-1554",
    pages = "5533--5538",
    abstract = "Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only model for SNLI or question-only model for VQA). A successful partial-input baseline indicates that the dataset is cheatable. But the converse is not necessarily true: failures of partial-input baselines do not mean the dataset is free of artifacts. We first design artificial datasets to illustrate how the trivial patterns that are only visible in the full input can evade any partial-input baseline. Next, we identify such artifacts in the SNLI dataset{---}a hypothesis-only model augmented with trivial patterns in the premise can solve 15{\%} of previously-thought {``}hard{''} examples. Our work provides a caveat for the use and creation of partial-input baselines for datasets.",
}
@inproceedings{gao-etal-2019-soft,
    title = "Soft Contextual Data Augmentation for Neural Machine Translation",
    author = "Gao, Fei  and
      Zhu, Jinhua  and
      Wu, Lijun  and
      Xia, Yingce  and
      Qin, Tao  and
      Cheng, Xueqi  and
      Zhou, Wengang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1555",
    doi = "10.18653/v1/P19-1555",
    pages = "5539--5544",
    abstract = "While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited. In this paper, we present a novel data augmentation method for neural machine translation.Different from previous augmentation methods that randomly drop, swap or replace words with other words in a sentence, we softly augment a randomly chosen word in a sentence by its contextual mixture of multiple related words. More accurately, we replace the one-hot representation of a word by a distribution (provided by a language model) over the vocabulary, i.e., replacing the embedding of this word by a weighted combination of multiple semantically similar words. Since the weights of those words depend on the contextual information of the word to be replaced,the newly generated sentences capture much richer information than previous augmentation methods. Experimental results on both small scale and large scale machine translation data sets demonstrate the superiority of our method over strong baselines.",
}
@inproceedings{kamath-etal-2019-reversing,
    title = "Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks",
    author = "Kamath, Anush  and
      Gupta, Sparsh  and
      Carvalho, Vitor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1556",
    doi = "10.18653/v1/P19-1556",
    pages = "5545--5550",
    abstract = "Adversarial domain adaptation has been recently proposed as an effective technique for textual matching tasks, such as question deduplication. Here we investigate the use of gradient reversal on adversarial domain adaptation to explicitly learn both shared and unshared (domain specific) representations between two textual domains. In doing so, gradient reversal learns features that explicitly compensate for domain mismatch, while still distilling domain specific knowledge that can improve target domain accuracy. We evaluate reversing gradients for adversarial adaptation on multiple domains, and demonstrate that it significantly outperforms other methods on question deduplication as well as on recognizing textual entailment (RTE) tasks, achieving up to 7{\%} absolute boost in base model accuracy on some datasets.",
}
@inproceedings{aghaebrahimian-cieliebak-2019-towards,
    title = "Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks",
    author = "Aghaebrahimian, Ahmad  and
      Cieliebak, Mark",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1557",
    doi = "10.18653/v1/P19-1557",
    pages = "5551--5557",
    abstract = "We report our ongoing work about a new deep architecture working in tandem with a statistical test procedure for jointly training texts and their label descriptions for multi-label and multi-class classification tasks. A statistical hypothesis testing method is used to extract the most informative words for each given class. These words are used as a class description for more label-aware text classification. Intuition is to help the model to concentrate on more informative words rather than more frequent ones. The model leverages the use of label descriptions in addition to the input text to enhance text classification performance. Our method is entirely data-driven, has no dependency on other sources of information than the training data, and is adaptable to different classification problems by providing appropriate training data without major hyper-parameter tuning. We trained and tested our system on several publicly available datasets, where we managed to improve the state-of-the-art on one set with a high margin and to obtain competitive results on all other ones.",
}
@inproceedings{wu-etal-2019-depth,
    title = "Depth Growing for Neural Machine Translation",
    author = "Wu, Lijun  and
      Wang, Yiren  and
      Xia, Yingce  and
      Tian, Fei  and
      Gao, Fei  and
      Qin, Tao  and
      Lai, Jianhuang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1558",
    doi = "10.18653/v1/P19-1558",
    pages = "5558--5563",
    abstract = "While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of the neural machine translation (NMT) models for better translation quality remains a challenging problem. Directly stacking more blocks to the NMT model results in no improvement and even drop in performance. In this work, we propose an effective two-stage approach with three specially designed components to construct deeper NMT models, which result in significant improvements over the strong Transformer baselines on WMT14 English$\to$German and English$\to$French translation tasks.",
}
@inproceedings{zhang-etal-2019-generating,
    title = "Generating Fluent Adversarial Examples for Natural Languages",
    author = "Zhang, Huangzhao  and
      Zhou, Hao  and
      Miao, Ning  and
      Li, Lei",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1559",
    doi = "10.18653/v1/P19-1559",
    pages = "5564--5569",
    abstract = "Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make small perturbations along the direction of gradients. Secondly, the fluency of the generated examples cannot be guaranteed. In this paper, we propose MHA, which addresses both problems by performing Metropolis-Hastings sampling, whose proposal is designed with the guidance of gradients. Experiments on IMDB and SNLI show that our proposed MHAoutperforms the baseline model on attacking capability. Adversarial training with MHA also leads to better robustness and performance.",
}
@inproceedings{liu-etal-2019-towards-explainable,
    title = "Towards Explainable {NLP}: A Generative Explanation Framework for Text Classification",
    author = "Liu, Hui  and
      Yin, Qingyu  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1560",
    doi = "10.18653/v1/P19-1560",
    pages = "5570--5581",
    abstract = "Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions. Existing approaches for explainable machine learning systems tend to focus on interpreting the outputs or the connections between inputs and outputs. However, the fine-grained information (e.g. textual explanations for the labels) is often ignored, and the systems do not explicitly generate the human-readable explanations. To solve this problem, we propose a novel generative explanation framework that learns to make classification decisions and generate fine-grained explanations at the same time. More specifically, we introduce the explainable factor and the minimum risk training approach that learn to generate more reasonable explanations. We construct two new datasets that contain summaries, rating scores, and fine-grained reasons. We conduct experiments on both datasets, comparing with several strong neural network baseline systems. Experimental results show that our method surpasses all baselines on both datasets, and is able to generate concise explanations at the same time.",
}
@inproceedings{pruthi-etal-2019-combating,
    title = "Combating Adversarial Misspellings with Robust Word Recognition",
    author = "Pruthi, Danish  and
      Dhingra, Bhuwan  and
      Lipton, Zachary C.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1561",
    doi = "10.18653/v1/P19-1561",
    pages = "5582--5591",
    abstract = "To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier. Our word recognition models build upon the RNN semi-character architecture, introducing several new backoff strategies for handling rare and unseen words. Trained to recognize words corrupted by random adds, drops, swaps, and keyboard mistakes, our method achieves 32{\%} relative (and 3.3{\%} absolute) error reduction over the vanilla semi-character model. Notably, our pipeline confers robustness on the downstream classifier, outperforming both adversarial training and off-the-shelf spell checkers. Against a BERT model fine-tuned for sentiment analysis, a single adversarially-chosen character attack lowers accuracy from 90.3{\%} to 45.8{\%}. Our defense restores accuracy to 75{\%}. Surprisingly, better word recognition does not always entail greater robustness. Our analysis reveals that robustness also depends upon a quantity that we denote the sensitivity.",
}
@inproceedings{zhang-etal-2019-empirical,
    title = "An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing",
    author = "Zhang, Zhisong  and
      Ma, Xuezhe  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1562",
    doi = "10.18653/v1/P19-1562",
    pages = "5592--5598",
    abstract = "In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017). With evaluations on 14 treebanks, we empirically show that global output-structured models can generally obtain better performance, especially on the metric of sentence-level Complete Match. However, probably because neural models already learn good global views of the inputs, the improvement brought by structured output modeling is modest.",
}
@inproceedings{cao-etal-2019-observing,
    title = "Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes",
    author = "Cao, Jie  and
      Tanana, Michael  and
      Imel, Zac  and
      Poitras, Eric  and
      Atkins, David  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1563",
    doi = "10.18653/v1/P19-1563",
    pages = "5599--5611",
    abstract = "Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue.",
}
@inproceedings{le-etal-2019-multimodal,
    title = "Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems",
    author = "Le, Hung  and
      Sahoo, Doyen  and
      Chen, Nancy  and
      Hoi, Steven",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1564",
    doi = "10.18653/v1/P19-1564",
    pages = "5612--5623",
    abstract = "Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and (2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.) to obtain a comprehensive understanding. Most existing work is based on RNNs and sequence-to-sequence architectures, which are not very effective for capturing complex long-term dependencies (like in videos). To overcome this, we propose Multimodal Transformer Networks (MTN) to encode videos and incorporate information from different modalities. We also propose query-aware attention through an auto-encoder to extract query-aware features from non-text modalities. We develop a training procedure to simulate token-level decoding to improve the quality of generated responses during inference. We get state of the art performance on Dialogue System Technology Challenge 7 (DSTC7). Our model also generalizes to another multimodal visual-grounded dialogue task, and obtains promising performance.",
}
@inproceedings{tang-etal-2019-target,
    title = "Target-Guided Open-Domain Conversation",
    author = "Tang, Jianheng  and
      Zhao, Tiancheng  and
      Xiong, Chenyan  and
      Liang, Xiaodan  and
      Xing, Eric  and
      Hu, Zhiting",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1565",
    doi = "10.18653/v1/P19-1565",
    pages = "5624--5634",
    abstract = "Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches",
}
@inproceedings{wang-etal-2019-persuasion,
    title = "Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good",
    author = "Wang, Xuewei  and
      Shi, Weiyan  and
      Kim, Richard  and
      Oh, Yoojung  and
      Yang, Sijia  and
      Zhang, Jingwen  and
      Yu, Zhou",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1566",
    doi = "10.18653/v1/P19-1566",
    pages = "5635--5649",
    abstract = "Developing intelligent persuasive conversational agents to change people{'}s opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a subset. Based on the annotation, we built a baseline classifier with context information and sentence-level features to predict the 10 persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed the relationships between individuals{'} demographic and psychological backgrounds including personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depending on the individuals{'} personal backgrounds. This work lays the ground for developing a personalized persuasive dialogue system.",
}
@inproceedings{csaky-etal-2019-improving,
    title = "Improving Neural Conversational Models with Entropy-Based Data Filtering",
    author = "Cs{\'a}ky, Rich{\'a}rd  and
      Purgai, Patrik  and
      Recski, G{\'a}bor",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1567",
    doi = "10.18653/v1/P19-1567",
    pages = "5650--5669",
    abstract = "Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. Priors such as persona, emotion, or topic provide additional information to dialog models to aid response generation, but annotating a dataset with priors is expensive and such annotations are rarely available. While previous methods for improving the quality of open-domain response generation focused on either the underlying model or the training objective, we present a method of filtering dialog datasets by removing generic utterances from training data using a simple entropy-based approach that does not require human supervision. We conduct extensive experiments with different variations of our method, and compare dialog models across 17 evaluation metrics to show that training on datasets filtered this way results in better conversational quality as chatbots learn to output more diverse responses.",
}
@inproceedings{kumar-etal-2019-zero,
    title = "Zero-shot Word Sense Disambiguation using Sense Definition Embeddings",
    author = "Kumar, Sawan  and
      Jat, Sharmistha  and
      Saxena, Karan  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1568",
    doi = "10.18653/v1/P19-1568",
    pages = "5670--5681",
    abstract = "Word Sense Disambiguation (WSD) is a long-standing but open problem in Natural Language Processing (NLP). WSD corpora are typically small in size, owing to an expensive annotation process. Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training. This leads to poor performance on rare and unseen senses. To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space. This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zero-shot learning. To obtain target sense embeddings, EWISE utilizes sense definitions. EWISE learns a novel sentence encoder for sense definitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method. We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings. EWISE achieves new state-of-the-art WSD performance.",
}
@inproceedings{loureiro-jorge-2019-language,
    title = "Language Modelling Makes Sense: Propagating Representations through {W}ord{N}et for Full-Coverage Word Sense Disambiguation",
    author = "Loureiro, Daniel  and
      Jorge, Al{\'\i}pio",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1569",
    doi = "10.18653/v1/P19-1569",
    pages = "5682--5691",
    abstract = "Contextual embeddings represent a new generation of semantic representations learned from Neural Language Modelling (NLM) that addresses the issue of meaning conflation hampering traditional word embeddings. In this work, we show that contextual embeddings can be used to achieve unprecedented gains in Word Sense Disambiguation (WSD) tasks. Our approach focuses on creating sense-level embeddings with full-coverage of WordNet, and without recourse to explicit knowledge of sense distributions or task-specific modelling. As a result, a simple Nearest Neighbors (k-NN) method using our representations is able to consistently surpass the performance of previous systems using powerful neural sequencing models. We also analyse the robustness of our approach when ignoring part-of-speech and lemma features, requiring disambiguation against the full sense inventory, and revealing shortcomings to be improved. Finally, we explore applications of our sense embeddings for concept-level analyses of contextual embeddings and their respective NLMs.",
}
@inproceedings{panigrahi-etal-2019-word2sense,
    title = "{W}ord2{S}ense: Sparse Interpretable Word Embeddings",
    author = "Panigrahi, Abhishek  and
      Simhadri, Harsha Vardhan  and
      Bhattacharyya, Chiranjib",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1570",
    doi = "10.18653/v1/P19-1570",
    pages = "5692--5705",
    abstract = "We present an unsupervised method to generate Word2Sense word embeddings that are interpretable {---} each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of the embedding along the j-th dimension represents the relevance of the j-th sense to the word. The underlying LDA-based generative model can be extended to refine the representation of a polysemous word in a short context, allowing us to use the embedings in contextual tasks. On computational NLP tasks, Word2Sense embeddings compare well with other word embeddings generated by unsupervised methods. Across tasks such as word similarity, entailment, sense induction, and contextual interpretation, Word2Sense is competitive with the state-of-the-art method for that task. Word2Sense embeddings are at least as sparse and fast to compute as prior art.",
}
@inproceedings{qi-etal-2019-modeling,
    title = "Modeling Semantic Compositionality with Sememe Knowledge",
    author = "Qi, Fanchao  and
      Huang, Junjie  and
      Yang, Chenghao  and
      Liu, Zhiyuan  and
      Chen, Xiao  and
      Liu, Qun  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1571",
    doi = "10.18653/v1/P19-1571",
    pages = "5706--5715",
    abstract = "Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememe-incorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC.All the code and data of this paper can be obtained on https://github.com/thunlp/Sememe-SC.",
}
@inproceedings{simpson-etal-2019-predicting,
    title = "Predicting Humorousness and Metaphor Novelty with {G}aussian Process Preference Learning",
    author = "Simpson, Edwin  and
      Do Dinh, Erik-L{\^a}n  and
      Miller, Tristan  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1572",
    doi = "10.18653/v1/P19-1572",
    pages = "5716--5728",
    abstract = "The inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding. To address this, we introduce novel tasks for evaluating the creativeness of language{---}namely, scoring and ranking text by humorousness and metaphor novelty. To sidestep the difficulty of assigning discrete labels or numeric scores, we learn from pairwise comparisons between texts. We introduce a Bayesian approach for predicting humorousness and metaphor novelty using Gaussian process preference learning (GPPL), which achieves a Spearman{'}s ρ of 0.56 against gold using word embeddings and linguistic features. Our experiments show that given sparse, crowdsourced annotation data, ranking using GPPL outperforms best{--}worst scaling. We release a new dataset for evaluating humour containing 28,210 pairwise comparisons of 4,030 texts, and make our software freely available.",
}
@inproceedings{krasnowska-kieras-wroblewska-2019-empirical,
    title = "Empirical Linguistic Study of Sentence Embeddings",
    author = "Krasnowska-Kiera{\'s}, Katarzyna  and
      Wr{\'o}blewska, Alina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1573",
    doi = "10.18653/v1/P19-1573",
    pages = "5729--5739",
    abstract = "The purpose of the research is to answer the question whether linguistic information is retained in vector representations of sentences. We introduce a method of analysing the content of sentence embeddings based on universal probing tasks, along with the classification datasets for two contrasting languages. We perform a series of probing and downstream experiments with different types of sentence embeddings, followed by a thorough analysis of the experimental results. Aside from dependency parser-based embeddings, linguistic information is retained best in the recently proposed LASER sentence embeddings.",
}
@inproceedings{yaghoobzadeh-etal-2019-probing,
    title = "Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings",
    author = {Yaghoobzadeh, Yadollah  and
      Kann, Katharina  and
      Hazen, T. J.  and
      Agirre, Eneko  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1574",
    doi = "10.18653/v1/P19-1574",
    pages = "5740--5753",
    abstract = "Word embeddings typically represent different meanings of a word in a single conflated vector. Empirical analysis of embeddings of ambiguous words is currently limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. We present a large dataset based on manual Wikipedia annotations and word senses, where word senses from different words are related by semantic classes. This is the basis for novel diagnostic tests for an embedding{'}s content: we probe word embeddings for semantic classes and analyze the embedding space by classifying embeddings into semantic classes. Our main findings are: (i) Information about a sense is generally represented well in a single-vector embedding {--} if the sense is frequent. (ii) A classifier can accurately predict whether a word is single-sense or multi-sense, based only on its embedding. (iii) Although rare senses are not well represented in single-vector embeddings, this does not have negative impact on an NLP application whose performance depends on frequent senses.",
}
@inproceedings{fiacco-etal-2019-deep,
    title = "Deep Neural Model Inspection and Comparison via Functional Neuron Pathways",
    author = "Fiacco, James  and
      Choudhary, Samridhi  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1575",
    doi = "10.18653/v1/P19-1575",
    pages = "5754--5764",
    abstract = "We introduce a general method for the interpretation and comparison of neural models. The method is used to factor a complex neural model into its functional components, which are comprised of sets of co-firing neurons that cut across layers of the network architecture, and which we call neural pathways. The function of these pathways can be understood by identifying correlated task level and linguistic heuristics in such a way that this knowledge acts as a lens for approximating what the network has learned to apply to its intended task. As a case study for investigating the utility of these pathways, we present an examination of pathways identified in models trained for two standard tasks, namely Named Entity Recognition and Recognizing Textual Entailment.",
}
@inproceedings{espinosa-anke-etal-2019-collocation,
    title = "Collocation Classification with Unsupervised Relation Vectors",
    author = "Espinosa Anke, Luis  and
      Schockaert, Steven  and
      Wanner, Leo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1576",
    doi = "10.18653/v1/P19-1576",
    pages = "5765--5772",
    abstract = "Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations hold. First, we introduce a novel dataset with collocations categorized according to lexical functions. Second, we conduct experiments on a subset of this benchmark, comparing it in particular to the well known DiffVec dataset. In these experiments, in addition to simple word vector arithmetic operations, we also investigate the role of unsupervised relation vectors as a complementary input. While these relation vectors indeed help, we also show that lexical function classification poses a greater challenge than the syntactic and semantic relations that are typically used for benchmarks in the literature.",
}
@inproceedings{loukachevitch-2019-corpus,
    title = "Corpus-based Check-up for Thesaurus",
    author = "Loukachevitch, Natalia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1577",
    doi = "10.18653/v1/P19-1577",
    pages = "5773--5779",
    abstract = "In this paper we discuss the usefulness of applying a checking procedure to existing thesauri. The procedure is based on the analysis of discrepancies of corpus-based and thesaurus-based word similarities. We applied the procedure to more than 30 thousand words of the Russian wordnet and found some serious errors in word sense description, including inaccurate relationships and missing senses of ambiguous words.",
}
@inproceedings{wang-etal-2019-confusionset,
    title = "Confusionset-guided Pointer Networks for {C}hinese Spelling Check",
    author = "Wang, Dingmin  and
      Tay, Yi  and
      Zhong, Li",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1578",
    doi = "10.18653/v1/P19-1578",
    pages = "5780--5785",
    abstract = "This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary. We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20{\%} F1 score, achieving state-of-the-art performance on three datasets.",
}
@inproceedings{xia-etal-2019-generalized,
    title = "Generalized Data Augmentation for Low-Resource Translation",
    author = "Xia, Mengzhou  and
      Kong, Xiang  and
      Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1579",
    doi = "10.18653/v1/P19-1579",
    pages = "5786--5796",
    abstract = "Low-resource language pairs with a paucity of parallel data pose challenges for machine translation in terms of both adequacy and fluency. Data augmentation utilizing a large amount of monolingual data is regarded as an effective way to alleviate the problem. In this paper, we propose a general framework of data augmentation for low-resource machine translation not only using target-side monolingual data, but also by pivoting through a related high-resource language. Specifically, we experiment with a two-step pivoting method to convert high-resource data to the low-resource language, making best use of available resources to better approximate the true distribution of the low-resource language. First, we inject low-resource words into high-resource sentences through an induced bilingual dictionary. Second, we further edit the high-resource data injected with low-resource words using a modified unsupervised machine translation framework. Extensive experiments on four low-resource datasets show that under extreme low-resource settings, our data augmentation techniques improve translation quality by up to 1.5 to 8 BLEU points compared to supervised back-translation baselines.",
}
@inproceedings{voita-etal-2019-analyzing,
    title = "Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned",
    author = "Voita, Elena  and
      Talbot, David  and
      Moiseev, Fedor  and
      Sennrich, Rico  and
      Titov, Ivan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1580",
    doi = "10.18653/v1/P19-1580",
    pages = "5797--5808",
    abstract = "Multi-head self-attention is a key component of the Transformer, a state-of-the-art architecture for neural machine translation. In this work we evaluate the contribution made by individual attention heads to the overall performance of the model and analyze the roles played by them in the encoder. We find that the most important and confident heads play consistent and often linguistically-interpretable roles. When pruning heads using a method based on stochastic gates and a differentiable relaxation of the L0 penalty, we observe that specialized heads are last to be pruned. Our novel pruning method removes the vast majority of heads without seriously affecting performance. For example, on the English-Russian WMT dataset, pruning 38 out of 48 encoder heads results in a drop of only 0.15 BLEU.",
}
@inproceedings{huck-etal-2019-better,
    title = "Better {OOV} Translation with Bilingual Terminology Mining",
    author = "Huck, Matthias  and
      Hangya, Viktor  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1581",
    doi = "10.18653/v1/P19-1581",
    pages = "5809--5815",
    abstract = "Unseen words, also called out-of-vocabulary words (OOVs), are difficult for machine translation. In neural machine translation, byte-pair encoding can be used to represent OOVs, but they are still often incorrectly translated. We improve the translation of OOVs in NMT using easy-to-obtain monolingual data. We look for OOVs in the text to be translated and translate them using simple-to-construct bilingual word embeddings (BWEs). In our MT experiments we take the 5-best candidates, which is motivated by intrinsic mining experiments. Using all five of the proposed target language words as queries we mine target-language sentences. We then back-translate, forcing the back-translation of each of the five proposed target-language OOV-translation-candidates to be the original source-language OOV. We show that by using this synthetic data to fine-tune our system the translation of OOVs can be dramatically improved. In our experiments we use a system trained on Europarl and mine sentences containing medical terms from monolingual data.",
}
@inproceedings{zheng-etal-2019-simultaneous,
    title = "Simultaneous Translation with Flexible Policy via Restricted Imitation Learning",
    author = "Zheng, Baigong  and
      Zheng, Renjie  and
      Ma, Mingbo  and
      Huang, Liang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1582",
    doi = "10.18653/v1/P19-1582",
    pages = "5816--5822",
    abstract = "Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP. Previous work either uses fixed-latency policies, or train a complicated two-staged model using reinforcement learning. We propose a much simpler single model that adds a {``}delay{''} token to the target vocabulary, and design a restricted dynamic oracle to greatly simplify training. Experiments on Chinese {\textless}-{\textgreater} English simultaneous translation show that our work leads to flexible policies that achieve better BLEU scores and lower latencies compared to both fixed and RL-learned policies.",
}
@inproceedings{wang-neubig-2019-target,
    title = "Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation",
    author = "Wang, Xinyi  and
      Neubig, Graham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1583",
    doi = "10.18653/v1/P19-1583",
    pages = "5823--5828",
    abstract = "To improve low-resource Neural Machine Translation (NMT) with multilingual corpus, training on the most related high-resource language only is generally more effective than us- ing all data available (Neubig and Hu, 2018). However, it remains a question whether a smart data selection strategy can further improve low-resource NMT with data from other auxiliary languages. In this paper, we seek to construct a sampling distribution over all multilingual data, so that it minimizes the training loss of the low-resource language. Based on this formulation, we propose and efficient algorithm, (TCS), which first samples a target sentence, and then conditionally samples its source sentence. Experiments show TCS brings significant gains of up to 2 BLEU improvements on three of four languages we test, with minimal training overhead.",
}
@inproceedings{friedrich-etal-2019-adversarial,
    title = "Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records",
    author = {Friedrich, Max  and
      K{\"o}hn, Arne  and
      Wiedemann, Gregor  and
      Biemann, Chris},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1584",
    doi = "10.18653/v1/P19-1584",
    pages = "5829--5839",
    abstract = "De-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHR) to be shared for research. Automatic de-identification classifiers can significantly speed up the sanitization process. However, obtaining a large and diverse dataset to train such a classifier that works well across many types of medical text poses a challenge as privacy laws prohibit the sharing of raw medical records. We introduce a method to create privacy-preserving shareable representations of medical text (i.e. they contain no PHI) that does not require expensive manual pseudonymization. These representations can be shared between organizations to create unified datasets for training de-identification models. Our representation allows training a simple LSTM-CRF de-identification model to an F1 score of 97.4{\%}, which is comparable to a strong baseline that exposes private information in its representation. A robust, widely available de-identification classifier based on our representation could potentially enable studies for which de-identification would otherwise be too costly.",
}
@inproceedings{fisher-vlachos-2019-merge,
    title = "Merge and Label: A Novel Neural Network Architecture for Nested {NER}",
    author = "Fisher, Joseph  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1585",
    doi = "10.18653/v1/P19-1585",
    pages = "5840--5850",
    abstract = "Named entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many applications. In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entities forming nested structures, and then labels each of them independently. Unlike previous work, our merge and label approach predicts real-valued instead of discrete segmentation structures, which allow it to combine word and nested entity embeddings while maintaining differentiability. We evaluate our approach using the ACE 2005 Corpus, where it achieves state-of-the-art F1 of 74.6, further improved with contextual embeddings (BERT) to 82.4, an overall improvement of close to 8 F1 points over previous approaches trained on the same data. Additionally we compare it against BiLSTM-CRFs, the dominant approach for flat NER structures, demonstrating that its ability to predict nested structures does not impact performance in simpler cases.",
}
@inproceedings{kasai-etal-2019-low,
    title = "Low-resource Deep Entity Resolution with Transfer and Active Learning",
    author = "Kasai, Jungo  and
      Qian, Kun  and
      Gurajada, Sairam  and
      Li, Yunyao  and
      Popa, Lucian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1586",
    doi = "10.18653/v1/P19-1586",
    pages = "5851--5861",
    abstract = "Entity resolution (ER) is the task of identifying different representations of the same real-world entities across databases. It is a key step for knowledge base creation and text mining. Recent adaptation of deep learning methods for ER mitigates the need for dataset-specific feature engineering by constructing distributed representations of entity records. While these methods achieve state-of-the-art performance over benchmark data, they require large amounts of labeled data, which are typically unavailable in realistic ER applications. In this paper, we develop a deep learning-based method that targets low-resource settings for ER through a novel combination of transfer learning and active learning. We design an architecture that allows us to learn a transferable model from a high-resource setting to a low-resource one. To further adapt to the target dataset, we incorporate active learning that carefully selects a few informative examples to fine-tune the transferred model. Empirical evaluation demonstrates that our method achieves comparable, if not better, performance compared to state-of-the-art learning-based methods while using an order of magnitude fewer labels.",
}
@inproceedings{arora-etal-2019-semi,
    title = "A Semi-{M}arkov Structured Support Vector Machine Model for High-Precision Named Entity Recognition",
    author = "Arora, Ravneet  and
      Tsai, Chen-Tse  and
      Tsereteli, Ketevan  and
      Kambadur, Prabhanjan  and
      Yang, Yi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1587",
    doi = "10.18653/v1/P19-1587",
    pages = "5862--5866",
    abstract = "Named entity recognition (NER) is the backbone of many NLP solutions. F1 score, the harmonic mean of precision and recall, is often used to select/evaluate the best models. However, when precision needs to be prioritized over recall, a state-of-the-art model might not be the best choice. There is little in literature that directly addresses training-time modifications to achieve higher precision information extraction. In this paper, we propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. The semi-Markov property provides more accurate phrase-level predictions, thereby improving performance. We empirically demonstrate the advantage of our model when high precision is required by comparing against strong baselines based on CRF. In our experiments with the CoNLL 2003 dataset, our model achieves a better precision-recall trade-off at various precision levels.",
}
@inproceedings{zhang-zhang-2019-using,
    title = "Using Human Attention to Extract Keyphrase from Microblog Post",
    author = "Zhang, Yingyi  and
      Zhang, Chengzhi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1588",
    doi = "10.18653/v1/P19-1588",
    pages = "5867--5872",
    abstract = "This paper studies automatic keyphrase extraction on social media. Previous works have achieved promising results on it, but they neglect human reading behavior during keyphrase annotating. The human attention is a crucial element of human reading behavior. It reveals the relevance of words to the main topics of the target text. Thus, this paper aims to integrate human attention into keyphrase extraction models. First, human attention is represented by the reading duration estimated from eye-tracking corpus. Then, we merge human attention with neural network models by an attention mechanism. In addition, we also integrate human attention into unsupervised models. To the best of our knowledge, we are the first to utilize human attention on keyphrase extraction tasks. The experimental results show that our models have significant improvements on two Twitter datasets.",
}
@inproceedings{obamuyide-vlachos-2019-model,
    title = "Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision",
    author = "Obamuyide, Abiola  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1589",
    doi = "10.18653/v1/P19-1589",
    pages = "5873--5879",
    abstract = "In this paper we frame the task of supervised relation classification as an instance of meta-learning. We propose a model-agnostic meta-learning protocol for training relation classifiers to achieve enhanced predictive performance in limited supervision settings. During training, we aim to not only learn good parameters for classifying relations with sufficient supervision, but also learn model parameters that can be fine-tuned to enhance predictive performance for relations with limited supervision. In experiments conducted on two relation classification datasets, we demonstrate that the proposed meta-learning approach improves the predictive performance of two state-of-the-art supervised relation classification models.",
}
@inproceedings{gururangan-etal-2019-variational,
    title = "Variational Pretraining for Semi-supervised Text Classification",
    author = "Gururangan, Suchin  and
      Dang, Tam  and
      Card, Dallas  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1590",
    doi = "10.18653/v1/P19-1590",
    pages = "5880--5894",
    abstract = "We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream classifier. Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings. We also find that fine-tuning to in-domain data is crucial to achieving decent performance from contextual embeddings when working with limited supervision. We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.",
}
@inproceedings{ziser-reichart-2019-task,
    title = "Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation",
    author = "Ziser, Yftah  and
      Reichart, Roi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1591",
    doi = "10.18653/v1/P19-1591",
    pages = "5895--5906",
    abstract = "Pivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach is still challenged by the large pivot detection problem that should be solved, and by the inherent instability of LSTMs. In this paper we propose a Task Refinement Learning (TRL) approach, in order to solve these problems. Our algorithms iteratively train the PBLM model, gradually increasing the information exposed about each pivot. TRL-PBLM achieves stateof- the-art accuracy in six domain adaptation setups for sentiment classification. Moreover, it is much more stable than plain PBLM across model configurations, making the model much better fitted for practical use.",
}
@inproceedings{tam-etal-2019-optimal,
    title = "Optimal Transport-based Alignment of Learned Character Representations for String Similarity",
    author = "Tam, Derek  and
      Monath, Nicholas  and
      Kobren, Ari  and
      Traylor, Aaron  and
      Das, Rajarshi  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1592",
    doi = "10.18653/v1/P19-1592",
    pages = "5907--5917",
    abstract = "String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE{--}a learned model for computing the similarity of two strings. Our approach encodes the characters of each string, aligns the encodings using Sinkhorn Iteration (alignment is posed as an instance of optimal transport) and scores the alignment with a convolutional neural network. We evaluate STANCE{'}s ability to detect whether two strings can refer to the same entity{--}a task we term alias detection. We construct five new alias detection datasets (and make them publicly available). We show that STANCE (or one of its variants) outperforms both state-of-the-art and classic, parameter-free similarity models on four of the five datasets. We also demonstrate STANCE{'}s ability to improve downstream tasks by applying it to an instance of cross-document coreference and show that it leads to a 2.8 point improvement in B{\^{}}3 F1 over the previous state-of-the-art approach.",
}
@inproceedings{liu-etal-2019-referential,
    title = "The Referential Reader: A Recurrent Entity Network for Anaphora Resolution",
    author = "Liu, Fei  and
      Zettlemoyer, Luke  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1593",
    doi = "10.18653/v1/P19-1593",
    pages = "5918--5925",
    abstract = "We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory. The update operation implies coreference with the other mentions that are stored in the same cell; the overwrite operation causes these mentions to be forgotten. By encoding the memory operations as differentiable gates, it is possible to train the model end-to-end, using both a supervised anaphora resolution objective as well as a supplementary language modeling objective. Evaluation on a dataset of pronoun-name anaphora demonstrates strong performance with purely incremental text processing.",
}
@inproceedings{quattoni-carreras-2019-interpolated,
    title = "Interpolated Spectral {NG}ram Language Models",
    author = "Quattoni, Ariadna  and
      Carreras, Xavier",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1594",
    doi = "10.18653/v1/P19-1594",
    pages = "5926--5930",
    abstract = "Spectral models for learning weighted non-deterministic automata have nice theoretical and algorithmic properties. Despite this, it has been challenging to obtain competitive results in language modeling tasks, for two main reasons. First, in order to capture long-range dependencies of the data, the method must use statistics from long substrings, which results in very large matrices that are difficult to decompose. The second is that the loss function behind spectral learning, based on moment matching, differs from the probabilistic metrics used to evaluate language models. In this work we employ a technique for scaling up spectral learning, and use interpolated predictions that are optimized to maximize perplexity. Our experiments in character-based language modeling show that our method matches the performance of state-of-the-art ngram models, while being very fast to train.",
}
@inproceedings{clark-etal-2019-bam,
    title = "{BAM}! Born-Again Multi-Task Networks for Natural Language Understanding",
    author = "Clark, Kevin  and
      Luong, Minh-Thang  and
      Khandelwal, Urvashi  and
      Manning, Christopher D.  and
      Le, Quoc V.",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1595",
    doi = "10.18653/v1/P19-1595",
    pages = "5931--5937",
    abstract = "It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training.",
}
@inproceedings{oraby-etal-2019-curate,
    title = "Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural {NLG}",
    author = "Oraby, Shereen  and
      Harrison, Vrindavan  and
      Ebrahimi, Abteen  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1596",
    doi = "10.18653/v1/P19-1596",
    pages = "5938--5951",
    abstract = "Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years. While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomings of NNLG systems are clear: new tasks require new training data which is not available or straightforward to acquire, and model outputs are simple and may be dull and repetitive. This paper addresses these two critical challenges in NNLG by: (1) scalably (and at no cost) creating training datasets of parallel meaning representations and reference texts with rich style markup by using data from freely available and naturally descriptive user reviews, and (2) systematically exploring how the style markup enables joint control of semantic and stylistic aspects of neural model output. We present YelpNLG, a corpus of 300,000 rich, parallel meaning representations and highly stylistically varied reference texts spanning different restaurant attributes, and describe a novel methodology that can be scalably reused to generate NLG datasets for other domains. The experiments show that the models control important aspects, including lexical choice of adjectives, output length, and sentiment, allowing the models to successfully hit multiple style targets without sacrificing semantics.",
}
@inproceedings{zang-etal-2019-automated,
    title = "Automated Chess Commentator Powered by Neural Chess Engine",
    author = "Zang, Hongyu  and
      Yu, Zhiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1597",
    doi = "10.18653/v1/P19-1597",
    pages = "5952--5961",
    abstract = "In this paper, we explore a new approach for automated chess commentary generation, which aims to generate chess commentary texts in different categories (e.g., \textit{description}, \textit{comparison}, \textit{planning}, etc.). We introduce a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. By jointly training the neural chess engine and the generation models for different categories, the models become more effective. We conduct experiments on 5 categories in a benchmark Chess Commentary dataset and achieve inspiring results in both automatic and human evaluations.",
}
@inproceedings{logan-etal-2019-baracks,
    title = "{B}arack{'}s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling",
    author = "Logan, Robert  and
      Liu, Nelson F.  and
      Peters, Matthew E.  and
      Gardner, Matt  and
      Singh, Sameer",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1598",
    doi = "10.18653/v1/P19-1598",
    pages = "5962--5971",
    abstract = "Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often have difficulty recalling them. To address this, we introduce the knowledge graph language model (KGLM), a neural language model with mechanisms for selecting and copying facts from a knowledge graph that are relevant to the context. These mechanisms enable the model to render information it has never seen before, as well as generate out-of-vocabulary tokens. We also introduce the Linked WikiText-2 dataset, a corpus of annotated text aligned to the Wikidata knowledge graph whose contents (roughly) match the popular WikiText-2 benchmark. In experiments, we demonstrate that the KGLM achieves significantly better performance than a strong baseline language model. We additionally compare different language model{'}s ability to complete sentences requiring factual knowledge, showing that the KGLM outperforms even very large language models in generating facts.",
}
@inproceedings{chen-etal-2019-controllable,
    title = "Controllable Paraphrase Generation with a Syntactic Exemplar",
    author = "Chen, Mingda  and
      Tang, Qingming  and
      Wiseman, Sam  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1599",
    doi = "10.18653/v1/P19-1599",
    pages = "5972--5984",
    abstract = "Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel task, where the syntax of a generated sentence is controlled rather by a sentential exemplar. To evaluate quantitatively with standard metrics, we create a novel dataset with human annotations. We also develop a variational model with a neural module specifically designed for capturing syntactic knowledge and several multitask training objectives to promote disentangled representation learning. Empirically, the proposed model is observed to achieve improvements over baselines and learn to capture desirable characteristics.",
}
@inproceedings{liu-etal-2019-towards-comprehensive,
    title = "Towards Comprehensive Description Generation from Factual Attribute-value Tables",
    author = "Liu, Tianyu  and
      Luo, Fuli  and
      Yang, Pengcheng  and
      Wu, Wei  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1600",
    doi = "10.18653/v1/P19-1600",
    pages = "5985--5996",
    abstract = "The comprehensive descriptions for factual attribute-value tables, which should be accurate, informative and loyal, can be very helpful for end users to understand the structured data in this form. However previous neural generators might suffer from key attributes missing, less informative and groundless information problems, which impede the generation of high-quality comprehensive descriptions for tables. To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing. Furthermore, we propose reinforcement learning for information richness to generate more informative as well as more loyal descriptions for tables. In our experiments, we utilize the widely used WIKIBIO dataset as a benchmark. Besides, we create WB-filter based on WIKIBIO to test our model in the simulated user-oriented scenarios, in which the generated descriptions should accord with particular user interests. Experimental results show that our model outperforms the state-of-the-art baselines on both automatic and human evaluation.",
}
@inproceedings{dai-etal-2019-style,
    title = "Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation",
    author = "Dai, Ning  and
      Liang, Jianze  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1601",
    doi = "10.18653/v1/P19-1601",
    pages = "5997--6007",
    abstract = "Disentangling the content and style in the latent space is prevalent in unpaired text style transfer. However, two major issues exist in most of the current neural models. 1) It is difficult to completely strip the style information from the semantics for a sentence. 2) The recurrent neural network (RNN) based encoder and decoder, mediated by the latent representation, cannot well deal with the issue of the long-term dependency, resulting in poor preservation of non-stylistic semantic content. In this paper, we propose the Style Transformer, which makes no assumption about the latent representation of source sentence and equips the power of attention mechanism in Transformer to achieve better style transfer and better content preservation.",
}
@inproceedings{bao-etal-2019-generating,
    title = "Generating Sentences from Disentangled Syntactic and Semantic Spaces",
    author = "Bao, Yu  and
      Zhou, Hao  and
      Huang, Shujian  and
      Li, Lei  and
      Mou, Lili  and
      Vechtomova, Olga  and
      Dai, Xin-yu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1602",
    doi = "10.18653/v1/P19-1602",
    pages = "6008--6019",
    abstract = "Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the syntactic information. In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces. Our proposed method explicitly models syntactic information in the VAE{'}s latent space by using the linearized tree sequence, leading to better performance of language generation. Additionally, the advantage of sampling in the disentangled syntactic and semantic latent spaces enables us to perform novel applications, such as the unsupervised paraphrase generation and syntax transfer generation. Experimental results show that our proposed model achieves similar or better performance in various tasks, compared with state-of-the-art related work.",
}
@inproceedings{luo-etal-2019-learning,
    title = "Learning to Control the Fine-grained Sentiment for Story Ending Generation",
    author = "Luo, Fuli  and
      Dai, Damai  and
      Yang, Pengcheng  and
      Liu, Tianyu  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Sun, Xu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1603",
    doi = "10.18653/v1/P19-1603",
    pages = "6020--6026",
    abstract = "Automatic story ending generation is an interesting and challenging task in natural language generation. Previous studies are mainly limited to generate coherent, reasonable and diversified story endings, and few works focus on controlling the sentiment of story endings. This paper focuses on generating a story ending which meets the given fine-grained sentiment intensity. There are two major challenges to this task. First is the lack of story corpus which has fine-grained sentiment labels. Second is the difficulty of explicitly controlling sentiment intensity when generating endings. Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges. The sentiment analyzer adopts a series of methods to acquire sentiment intensities of the story dataset. The sentimental generator introduces the sentiment intensity into decoder via a Gaussian Kernel Layer to control the sentiment of the output. To the best of our knowledge, this is the first endeavor to control the fine-grained sentiment for story ending generation without manually annotating sentiment labels. Experiments show that our proposed framework can generate story endings which are not only more coherent and fluent but also able to meet the given sentiment intensity better.",
}
@inproceedings{scialom-etal-2019-self,
    title = "Self-Attention Architectures for Answer-Agnostic Neural Question Generation",
    author = "Scialom, Thomas  and
      Piwowarski, Benjamin  and
      Staiano, Jacopo",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1604",
    doi = "10.18653/v1/P19-1604",
    pages = "6027--6032",
    abstract = "Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks. We explore how Transformers can be adapted to the task of Neural Question Generation without constraining the model to focus on a specific answer passage. We study the effect of several strategies to deal with out-of-vocabulary words such as copy mechanisms, placeholders, and contextual word embeddings. We report improvements obtained over the state-of-the-art on the SQuAD dataset according to automated metrics (BLEU, ROUGE), as well as qualitative human assessments of the system outputs.",
}
@inproceedings{roy-grangier-2019-unsupervised,
    title = "Unsupervised Paraphrasing without Translation",
    author = "Roy, Aurko  and
      Grangier, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1605",
    doi = "10.18653/v1/P19-1605",
    pages = "6033--6039",
    abstract = "Paraphrasing is an important task demonstrating the ability to abstract semantic content from its surface form. Recent literature on automatic paraphrasing is dominated by methods leveraging machine translation as an intermediate step. This contrasts with humans, who can paraphrase without necessarily being bilingual. This work proposes to learn paraphrasing models only from a monolingual corpus. To that end, we propose a residual variant of vector-quantized variational auto-encoder. Our experiments consider paraphrase identification, and paraphrasing for training set augmentation, comparing to supervised and unsupervised translation-based approaches. Monolingual paraphrasing is shown to outperform unsupervised translation in all contexts. The comparison with supervised MT is more mixed: monolingual paraphrasing is interesting for identification and augmentation but supervised MT is superior for generation.",
}
@inproceedings{chandu-etal-2019-storyboarding,
    title = "Storyboarding of Recipes: Grounded Contextual Generation",
    author = "Chandu, Khyathi  and
      Nyberg, Eric  and
      Black, Alan W",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1606",
    doi = "10.18653/v1/P19-1606",
    pages = "6040--6046",
    abstract = "Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context. We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain. The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps. We setup a baseline motivated by the best performing model in terms of human evaluation for the Visual Story Telling (ViST) task. In addition, we introduce two models to incorporate high level structure learnt by a Finite State Machine (FSM) in neural sequential generation process by: (1) Scaffolding Structure in Decoder (SSiD) (2) Scaffolding Structure in Loss (SSiL). Our best performing model (SSiL) achieves a METEOR score of 0.31, which is an improvement of 0.6 over the baseline model. We also conducted human evaluation of the generated grounded recipes, which reveal that 61{\%} found that our proposed (SSiL) model is better than the baseline model in terms of overall recipes. We also discuss analysis of the output highlighting key important NLP issues for prospective directions.",
}
@inproceedings{kajiwara-2019-negative,
    title = "Negative Lexically Constrained Decoding for Paraphrase Generation",
    author = "Kajiwara, Tomoyuki",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1607",
    doi = "10.18653/v1/P19-1607",
    pages = "6047--6052",
    abstract = "Paraphrase generation can be regarded as monolingual translation. Unlike bilingual machine translation, paraphrase generation rewrites only a limited portion of an input sentence. Hence, previous methods based on machine translation often perform conservatively to fail to make necessary rewrites. To solve this problem, we propose a neural model for paraphrase generation that first identifies words in the source sentence that should be paraphrased. Then, these words are paraphrased by the negative lexically constrained decoding that avoids outputting these words as they are. Experiments on text simplification and formality transfer show that our model improves the quality of paraphrasing by making necessary rewrites to an input sentence.",
}
@inproceedings{golovanov-etal-2019-large,
    title = "Large-Scale Transfer Learning for Natural Language Generation",
    author = "Golovanov, Sergey  and
      Kurbanov, Rauf  and
      Nikolenko, Sergey  and
      Truskovskyi, Kyryl  and
      Tselousov, Alexander  and
      Wolf, Thomas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1608",
    doi = "10.18653/v1/P19-1608",
    pages = "6053--6058",
    abstract = "Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. We study how these architectures can be applied and adapted for natural language generation, comparing a number of architectural and training schemes. We focus in particular on open-domain dialog as a typical high entropy generation task, presenting and comparing different architectures for adapting pretrained models with state of the art results.",
}
@inproceedings{ge-etal-2019-automatic,
    title = "Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study",
    author = "Ge, Tao  and
      Zhang, Xingxing  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1609",
    doi = "10.18653/v1/P19-1609",
    pages = "6059--6064",
    abstract = "Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can help improve seq2seq text generation. We conduct experiments across various seq2seq text generation tasks including machine translation, formality style transfer, sentence compression and simplification. Experiments show the state-of-the-art grammatical error correction system can improve the grammaticality of generated text and can bring task-oriented improvements in the tasks where target sentences are in a formal style.",
}
@inproceedings{gan-ng-2019-improving,
    title = "Improving the Robustness of Question Answering Systems to Question Paraphrasing",
    author = "Gan, Wee Chung  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1610",
    doi = "10.18653/v1/P19-1610",
    pages = "6065--6075",
    abstract = "Despite the advancement of question answering (QA) systems and rapid improvements on held-out test sets, their generalizability is a topic of concern. We explore the robustness of QA models to question paraphrasing by creating two test sets consisting of paraphrased SQuAD questions. Paraphrased questions from the first test set are very similar to the original questions designed to test QA models{'} over-sensitivity, while questions from the second test set are paraphrased using context words near an incorrect answer candidate in an attempt to confuse QA models. We show that both paraphrased test sets lead to significant decrease in performance on multiple state-of-the-art QA models. Using a neural paraphrasing model trained to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions, we propose a data augmentation approach that requires no human intervention to re-train the models for improved robustness to question paraphrasing.",
}
@inproceedings{kratzwald-etal-2019-rankqa,
    title = "{R}ank{QA}: Neural Question Answering with Answer Re-Ranking",
    author = "Kratzwald, Bernhard  and
      Eigenmann, Anna  and
      Feuerriegel, Stefan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1611",
    doi = "10.18653/v1/P19-1611",
    pages = "6076--6085",
    abstract = "The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer. However, both stages are largely isolated in the status quo and, hence, information from the two phases is never properly fused. In contrast, this work proposes RankQA: RankQA extends the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. The re-ranking leverages different features that are directly extracted from the QA pipeline, i.e., a combination of retrieval and comprehension features. While our intentionally simple design allows for an efficient, data-sparse estimation, it nevertheless outperforms more complex QA systems by a significant margin: in fact, RankQA achieves state-of-the-art performance on 3 out of 4 benchmark datasets. Furthermore, its performance is especially superior in settings where the size of the corpus is dynamic. Here the answer re-ranking provides an effective remedy against the underlying noise-information trade-off due to a variable corpus size. As a consequence, RankQA represents a novel, powerful, and thus challenging baseline for future research in content-based QA.",
}
@inproceedings{lee-etal-2019-latent,
    title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering",
    author = "Lee, Kenton  and
      Chang, Ming-Wei  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1612",
    doi = "10.18653/v1/P19-1612",
    pages = "6086--6096",
    abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.",
}
@inproceedings{min-etal-2019-multi,
    title = "Multi-hop Reading Comprehension through Question Decomposition and Rescoring",
    author = "Min, Sewon  and
      Zhong, Victor  and
      Zettlemoyer, Luke  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1613",
    doi = "10.18653/v1/P19-1613",
    pages = "6097--6109",
    abstract = "Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models. Since annotations for such decomposition are expensive, we recast subquestion generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as human-authored sub-questions. We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance. Our experiments on HotpotQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.",
}
@inproceedings{prakash-etal-2019-combining,
    title = "Combining Knowledge Hunting and Neural Language Models to Solve the {W}inograd Schema Challenge",
    author = "Prakash, Ashok  and
      Sharma, Arpit  and
      Mitra, Arindam  and
      Baral, Chitta",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1614",
    doi = "10.18653/v1/P19-1614",
    pages = "6110--6119",
    abstract = "Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge. The needed knowledge is not present in the given text. Automatic extraction of the needed knowledge is a bottleneck in solving the challenge. The existing state-of-the-art approach uses the knowledge embedded in their pre-trained language model. However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts. This limits the performance of such models on the WSC problems. In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module. Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53{\%} and 7.7{\%} respectively. Overall our system achieves the state-of-the-art accuracy of 71.06{\%} on the WSC dataset, an improvement of 7.36{\%} over the previous best.",
}
@inproceedings{banerjee-etal-2019-careful,
    title = "Careful Selection of Knowledge to Solve Open Book Question Answering",
    author = "Banerjee, Pratyay  and
      Pal, Kuntal Kumar  and
      Mitra, Arindam  and
      Baral, Chitta",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1615",
    doi = "10.18653/v1/P19-1615",
    pages = "6120--6129",
    abstract = "Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0{\%} accuracy, an 11.6{\%} improvement over the current state of the art.",
}
@inproceedings{wu-etal-2019-learning,
    title = "Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering",
    author = "Wu, Peng  and
      Huang, Shujian  and
      Weng, Rongxiang  and
      Zheng, Zaixiang  and
      Zhang, Jianbing  and
      Yan, Xiaohui  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1616",
    doi = "10.18653/v1/P19-1616",
    pages = "6130--6139",
    abstract = "Relation detection is a core step in many natural language process applications including knowledge base question answering. Previous efforts show that single-fact questions could be answered with high accuracy. However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data. But for unseen relations, the performance will drop rapidly. The main reason for this problem is that the representations for unseen relations are missing. In this paper, we propose a simple mapping method, named representation adapter, to learn the representation mapping for both seen and unseen relations based on previously learned relation embedding. We employ the adversarial objective and the reconstruction objective to improve the mapping performance. We re-organize the popular SimpleQuestion dataset to reveal and evaluate the problem of detecting unseen relations. Experiments show that our method can greatly improve the performance of unseen relations while the performance for those seen part is kept comparable to the state-of-the-art.",
}
@inproceedings{qiu-etal-2019-dynamically,
    title = "Dynamically Fused Graph Network for Multi-hop Reasoning",
    author = "Qiu, Lin  and
      Xiao, Yunxuan  and
      Qu, Yanru  and
      Zhou, Hao  and
      Li, Lei  and
      Zhang, Weinan  and
      Yu, Yong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1617",
    doi = "10.18653/v1/P19-1617",
    pages = "6140--6150",
    abstract = "Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. However, many difficult questions require multiple supporting evidence from scattered text among two or more documents. In this paper, we propose Dynamically Fused Graph Network (DFGN), a novel method to answer those questions requiring multiple scattered evidence and reasoning over them. Inspired by human{'}s step-by-step reasoning behavior, DFGN includes a dynamic fusion layer that starts from the entities mentioned in the given query, explores along the entity graph dynamically built from the text, and gradually finds relevant supporting entities from the given documents. We evaluate DFGN on HotpotQA, a public TBQA dataset requiring multi-hop reasoning. DFGN achieves competitive results on the public board. Furthermore, our analysis shows DFGN produces interpretable reasoning chains.",
}
@inproceedings{weber-etal-2019-nlprolog,
    title = "{NLP}rolog: Reasoning with Weak Unification for Question Answering in Natural Language",
    author = {Weber, Leon  and
      Minervini, Pasquale  and
      M{\"u}nchmeyer, Jannes  and
      Leser, Ulf  and
      Rockt{\"a}schel, Tim},
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1618",
    doi = "10.18653/v1/P19-1618",
    pages = "6151--6161",
    abstract = "Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge. However, such systems are difficult to apply to problems involving natural language, due to its large linguistic variability. In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret. In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language. Specifically, we propose to use an Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders. We fine-tune the representations for the similarity function via backpropagation. This leads to a system that can apply rule-based reasoning to natural language, and induce domain-specific natural language rules from training data. We evaluate the proposed system on two different question answering tasks, showing that it outperforms two baselines {--} BiDAF (Seo et al., 2016a) and FastQA( Weissenborn et al., 2017) on a subset of the WikiHop corpus and achieves competitive results on the MedHop data set (Welbl et al., 2017).",
}
@inproceedings{li-etal-2019-modeling,
    title = "Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions",
    author = "Li, Jierui  and
      Wang, Lei  and
      Zhang, Jipeng  and
      Wang, Yan  and
      Dai, Bing Tian  and
      Zhang, Dongxiang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1619",
    doi = "10.18653/v1/P19-1619",
    pages = "6162--6167",
    abstract = "Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs{'} specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9{\%} to 69.5{\%} on Math23K with training-test split, from 65.8{\%} to 66.9{\%} on Math23K with 5-fold cross-validation and from 69.2{\%} to 76.1{\%} on MAWPS.",
}
@inproceedings{alberti-etal-2019-synthetic,
    title = "Synthetic {QA} Corpora Generation with Roundtrip Consistency",
    author = "Alberti, Chris  and
      Andor, Daniel  and
      Pitler, Emily  and
      Devlin, Jacob  and
      Collins, Michael",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1620",
    doi = "10.18653/v1/P19-1620",
    pages = "6168--6173",
    abstract = "We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1{\%} and 0.4{\%} from human performance on SQuAD2.",
}
@inproceedings{ribeiro-etal-2019-red,
    title = "Are Red Roses Red? Evaluating Consistency of Question-Answering Models",
    author = "Ribeiro, Marco Tulio  and
      Guestrin, Carlos  and
      Singh, Sameer",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1621",
    doi = "10.18653/v1/P19-1621",
    pages = "6174--6184",
    abstract = "Although current evaluation of question-answering systems treats predictions in isolation, we need to consider the relationship between predictions to measure true understanding. A model should be penalized for answering {``}no{''} to {``}Is the rose red?{''} if it answers {``}red{''} to {``}What color is the rose?{''}. We propose a method to automatically extract such implications for instances from two QA datasets, VQA and SQuAD, which we then use to evaluate the consistency of models. Human evaluation shows these generated implications are well formed and valid. Consistency evaluation provides crucial insights into gaps in existing models, while retraining with implication-augmented data improves consistency on both synthetic and human-generated implications.",
}
@inproceedings{zhang-2019-mc,
    title = "{MC}{\^{}}2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension",
    author = "Zhang, Xuanyu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1622",
    doi = "10.18653/v1/P19-1622",
    pages = "6185--6190",
    abstract = "Conversational machine reading comprehension (CMRC) extends traditional single-turn machine reading comprehension (MRC) by multi-turn interactions, which requires machines to consider the history of conversation. Most of models simply combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. To comprehend context profoundly and efficiently from different perspectives, we propose a novel neural network model, Multi-perspective Convolutional Cube (MC{\^{}}2). We regard each conversation as a cube. 1D and 2D convolutions are integrated with RNN in our model. To avoid models previewing the next turn of conversation, we also extend causal convolution partially to 2D. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves state-of-the-art results.",
}
@inproceedings{yang-etal-2019-reducing,
    title = "Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach",
    author = "Yang, Zonghan  and
      Cheng, Yong  and
      Liu, Yang  and
      Sun, Maosong",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1623",
    doi = "10.18653/v1/P19-1623",
    pages = "6191--6196",
    abstract = "While neural machine translation (NMT) has achieved remarkable success, NMT systems are prone to make word omission errors. In this work, we propose a contrastive learning approach to reducing word omission errors in NMT. The basic idea is to enable the NMT model to assign a higher probability to a ground-truth translation and a lower probability to an erroneous translation, which is automatically constructed from the ground-truth translation by omitting words. We design different types of negative examples depending on the number of omitted words, word frequency, and part of speech. Experiments on Chinese-to-English, German-to-English, and Russian-to-English translation tasks show that our approach is effective in reducing word omission errors and achieves better translation performance than three baseline methods.",
}
@inproceedings{wang-etal-2019-exploiting,
    title = "Exploiting Sentential Context for Neural Machine Translation",
    author = "Wang, Xing  and
      Tu, Zhaopeng  and
      Wang, Longyue  and
      Shi, Shuming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1624",
    doi = "10.18653/v1/P19-1624",
    pages = "6197--6203",
    abstract = "In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT). Specifically, we show that a shallow sentential context extracted from the top encoder layer only, can improve translation performance via contextualizing the encoding representations of individual words. Next, we introduce a deep sentential context, which aggregates the sentential context representations from all of the internal layers of the encoder to form a more comprehensive context representation. Experimental results on the WMT14 English-German and English-French benchmarks show that our model consistently improves performance over the strong Transformer model, demonstrating the necessity and effectiveness of exploiting sentential context for NMT.",
}
@inproceedings{ndubuisi-obi-etal-2019-wetin,
    title = "Wetin dey with these comments? Modeling Sociolinguistic Factors Affecting Code-switching Behavior in Nigerian Online Discussions",
    author = "Ndubuisi-Obi, Innocent  and
      Ghosh, Sayan  and
      Jurgens, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1625",
    doi = "10.18653/v1/P19-1625",
    pages = "6204--6214",
    abstract = "Multilingual individuals code switch between languages as a part of a complex communication process. However, most computational studies have examined only one or a handful of contextual factors predictive of switching. Here, we examine Naija-English code switching in a rich contextual environment to understand the social and topical factors eliciting a switch. We introduce a new corpus of 330K articles and accompanying 389K comments labeled for code switching behavior. In modeling whether a comment will switch, we show that topic-driven variation, tribal affiliation, emotional valence, and audience design all play complementary roles in behavior.",
}
@inproceedings{argueta-chiang-2019-accelerating,
    title = "Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units",
    author = "Argueta, Arturo  and
      Chiang, David",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1626",
    doi = "10.18653/v1/P19-1626",
    pages = "6215--6224",
    abstract = "Graphics Processing Units (GPUs) are commonly used to train and evaluate neural networks efficiently. While previous work in deep learning has focused on accelerating operations on dense matrices/tensors on GPUs, efforts have concentrated on operations involving sparse data structures. Operations using sparse structures are common in natural language models at the input and output layers, because these models operate on sequences over discrete alphabets. We present two new GPU algorithms: one at the input layer, for multiplying a matrix by a few-hot vector (generalizing the more common operation of multiplication by a one-hot vector) and one at the output layer, for a fused softmax and top-N selection (commonly used in beam search). Our methods achieve speedups over state-of-the-art parallel GPU baselines of up to 7x and 50x, respectively. We also illustrate how our methods scale on different GPU architectures.",
}
@inproceedings{rama-list-2019-automated,
    title = "An Automated Framework for Fast Cognate Detection and {B}ayesian Phylogenetic Inference in Computational Historical Linguistics",
    author = "Rama, Taraka  and
      List, Johann-Mattis",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1627",
    doi = "10.18653/v1/P19-1627",
    pages = "6225--6235",
    abstract = "We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics.",
}
@inproceedings{zheng-lapata-2019-sentence,
    title = "Sentence Centrality Revisited for Unsupervised Summarization",
    author = "Zheng, Hao  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1628",
    doi = "10.18653/v1/P19-1628",
    pages = "6236--6247",
    abstract = "Single document summarization has enjoyed renewed interest in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this paper we develop an unsupervised approach arguing that it is unrealistic to expect large-scale and high-quality training data to be available or created for different types of summaries, domains, or languages. We revisit a popular graph-based ranking algorithm and modify how node (aka sentence) centrality is computed in two ways: (a) we employ BERT, a state-of-the-art neural representation learning model to better capture sentential meaning and (b) we build graphs with directed edges arguing that the contribution of any two nodes to their respective centrality is influenced by their relative position in a document. Experimental results on three news summarization datasets representative of different languages and writing styles show that our approach outperforms strong baselines by a wide margin.",
}
@inproceedings{liu-etal-2019-discourse,
    title = "Discourse Representation Parsing for Sentences and Documents",
    author = "Liu, Jiangming  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1629",
    doi = "10.18653/v1/P19-1629",
    pages = "6248--6262",
    abstract = "We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993). Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents. We present a general framework for parsing discourse structures of arbitrary length and granularity. We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy. Experimental results on sentence- and document-level benchmarks show that our model outperforms competitive baselines by a wide margin.",
}
@inproceedings{frermann-klementiev-2019-inducing,
    title = "Inducing Document Structure for Aspect-based Summarization",
    author = "Frermann, Lea  and
      Klementiev, Alexandre",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1630",
    doi = "10.18653/v1/P19-1630",
    pages = "6263--6273",
    abstract = "Automatic summarization is typically treated as a 1-to-1 mapping from document to summary. Documents such as news articles, however, are structured and often cover multiple topics or aspects; and readers may be interested in only some of them. We tackle the task of aspect-based summarization, where, given a document and a target aspect, our models generate a summary centered around the aspect. We induce latent document structure jointly with an abstractive summarization objective, and train our models in a scalable synthetic setup. In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (a) learn to accurately segment documents by aspect; (b) can leverage the structure to produce both abstractive and extractive aspect-based summaries; and (c) that structure is particularly advantageous for summarizing long documents. All results transfer from synthetic training documents to natural news articles from CNN/Daily Mail and RCV1.",
}
@inproceedings{liu-avci-2019-incorporating,
    title = "Incorporating Priors with Feature Attribution on Text Classification",
    author = "Liu, Frederick  and
      Avci, Besim",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1631",
    doi = "10.18653/v1/P19-1631",
    pages = "6274--6283",
    abstract = "Feature attribution methods, proposed recently, help users interpret the predictions of complex models. Our approach integrates feature attributions into the objective function to allow machine learning practitioners to incorporate priors in model building. To demonstrate the effectiveness our technique, we apply it to two tasks: (1) mitigating unintended bias in text classifiers by neutralizing identity terms; (2) improving classifier performance in scarce data setting by forcing model to focus on toxic terms. Our approach adds an L2 distance loss between feature attributions and task-specific prior values to the objective. Our experiments show that i) a classifier trained with our technique reduces undesired model biases without a tradeoff on the original task; ii) incorporating prior helps model performance in scarce data settings.",
}
@inproceedings{liu-etal-2019-matching,
    title = "Matching Article Pairs with Graphical Decomposition and Convolutions",
    author = "Liu, Bang  and
      Niu, Di  and
      Wei, Haojie  and
      Lin, Jinghong  and
      He, Yancheng  and
      Lai, Kunfeng  and
      Xu, Yu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1632",
    doi = "10.18653/v1/P19-1632",
    pages = "6284--6294",
    abstract = "Identifying the relationship between two articles, e.g., whether two articles published from different sources describe the same breaking news, is critical to many document understanding tasks. Existing approaches for modeling and matching sentence pairs do not perform well in matching longer documents, which embody more complex interactions between the enclosed entities than a sentence does. To model article pairs, we propose the Concept Interaction Graph to represent an article as a graph of concepts. We then match a pair of articles by comparing the sentences that enclose the same concept vertex through a series of encoding techniques, and aggregate the matching signals through a graph convolutional network. To facilitate the evaluation of long article matching, we have created two datasets, each consisting of about 30K pairs of breaking news articles covering diverse topics in the open domain. Extensive evaluations of the proposed methods on the two datasets demonstrate significant improvements over a wide range of state-of-the-art methods for natural language matching.",
}
@inproceedings{banerjee-etal-2019-hierarchical,
    title = "Hierarchical Transfer Learning for Multi-label Text Classification",
    author = "Banerjee, Siddhartha  and
      Akkaya, Cem  and
      Perez-Sorrosal, Francisco  and
      Tsioutsiouliklis, Kostas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1633",
    doi = "10.18653/v1/P19-1633",
    pages = "6295--6300",
    abstract = "Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy. MLHTC can be formulated by combining multiple binary classification problems with an independent classifier for each category. We propose a novel transfer learning based strategy, HTrans, where binary classifiers at lower levels in the hierarchy are initialized using parameters of the parent classifier and fine-tuned on the child category classification task. In HTrans, we use a Gated Recurrent Unit (GRU)-based deep learning architecture coupled with attention. Compared to binary classifiers trained from scratch, our HTrans approach results in significant improvements of 1{\%} on micro-F1 and 3{\%} on macro-F1 on the RCV1 dataset. Our experiments also show that binary classifiers trained from scratch are significantly better than single multi-label models.",
}
@inproceedings{bevendorff-etal-2019-bias,
    title = "Bias Analysis and Mitigation in the Evaluation of Authorship Verification",
    author = "Bevendorff, Janek  and
      Hagen, Matthias  and
      Stein, Benno  and
      Potthast, Martin",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1634",
    doi = "10.18653/v1/P19-1634",
    pages = "6301--6306",
    abstract = "The PAN series of shared tasks is well known for its continuous and high quality research in the field of digital text forensics. Among others, PAN contributions include original corpora, tailored benchmarks, and standardized experimentation platforms. In this paper we review, theoretically and practically, the authorship verification task and conclude that the underlying experiment design cannot guarantee pushing forward the state of the art{---}in fact, it allows for top benchmarking with a surprisingly straightforward approach. In this regard, we present a {``}Basic and Fairly Flawed{''} (BAFF) authorship verifier that is on a par with the best approaches submitted so far, and that illustrates sources of bias that should be eliminated. We pinpoint these sources in the evaluation chain and present a refined authorship corpus as effective countermeasure.",
}
@inproceedings{chen-etal-2019-numeracy,
    title = "Numeracy-600{K}: Learning Numeracy for Detecting Exaggerated Information in Market Comments",
    author = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1635",
    doi = "10.18653/v1/P19-1635",
    pages = "6307--6313",
    abstract = "In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description. A large benchmark dataset, called Numeracy-600K, is provided for the novel task. We explore several neural network models including CNN, GRU, BiGRU, CRNN, CNN-capsule, GRU-capsule, and BiGRU-capsule in the experiments. The results show that the BiGRU model gets the best micro-averaged F1 score of 80.16{\%}, and the GRU-capsule model gets the best macro-averaged F1 score of 64.71{\%}. Besides discussing the challenges through comprehensive experiments, we also present an important application scenario, i.e., detecting exaggerated information, for the task.",
}
@inproceedings{chalkidis-etal-2019-large,
    title = "Large-Scale Multi-Label Text Classification on {EU} Legislation",
    author = "Chalkidis, Ilias  and
      Fergadiotis, Emmanouil  and
      Malakasiotis, Prodromos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1636",
    doi = "10.18653/v1/P19-1636",
    pages = "6314--6322",
    abstract = "We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EUR-LEX, annotated with ∼4.3k EUROVOC labels, which is suitable for LMTC, few- and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with label-wise attention perform better than other current state of the art methods. Domain-specific WORD2VEC and context-sensitive ELMO embeddings further improve performance. We also find that considering only particular zones of the documents is sufficient. This allows us to bypass BERT{'}s maximum text length limit and fine-tune BERT, obtaining the best results in all but zero-shot learning cases.",
}
@inproceedings{kumar-etal-2019-didnt,
    title = "Why Didn{'}t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models",
    author = "Kumar, Varun  and
      Smith-Renner, Alison  and
      Findlater, Leah  and
      Seppi, Kevin  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1637",
    doi = "10.18653/v1/P19-1637",
    pages = "6323--6330",
    abstract = "To address the lack of comparative evaluation of Human-in-the-Loop Topic Modeling (HLTM) systems, we implement and evaluate three contrasting HLTM modeling approaches using simulation experiments. These approaches extend previously proposed frameworks, including constraints and informed prior-based methods. Users should have a sense of control in HLTM systems, so we propose a control metric to measure whether refinement operations{'} results match users{'} expectations. Informed prior-based methods provide better control than constraints, but constraints yield higher quality topics.",
}
@inproceedings{vu-iyyer-2019-encouraging,
    title = "Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification",
    author = "Vu, Tu  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1638",
    doi = "10.18653/v1/P19-1638",
    pages = "6331--6338",
    abstract = "While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque. In this paper, we investigate a state-of-the-art paragraph embedding method proposed by Zhang et al. (2017) and discover that it cannot reliably tell whether a given sentence occurs in the input paragraph or not. We formulate a sentence content task to probe for this basic linguistic property and find that even a much simpler bag-of-words method has no trouble solving it. This result motivates us to replace the reconstruction-based objective of Zhang et al. (2017) with our sentence content probe objective in a semi-supervised setting. Despite its simplicity, our objective improves over paragraph reconstruction in terms of (1) downstream classification accuracies on benchmark datasets, (2) faster training, and (3) better generalization ability.",
}
@inproceedings{sarwar-etal-2019-multi,
    title = "A Multi-Task Architecture on Relevance-based Neural Query Translation",
    author = "Sarwar, Sheikh Muhammad  and
      Bonab, Hamed  and
      Allan, James",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1639",
    doi = "10.18653/v1/P19-1639",
    pages = "6339--6344",
    abstract = "We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translation process for Cross-lingual Information Retrieval (CLIR) task is usually treated as a black box and it is performed as an independent step. However, an NMT model trained on sentence-level parallel data is not aware of the vocabulary distribution of the retrieval corpus. We address this problem and propose a multi-task learning architecture that achieves 16{\%} improvement over a strong baseline on Italian-English query-document dataset. We show using both quantitative and qualitative analysis that our model generates balanced and precise translations with the regularization effect it achieves from multi-task learning paradigm.",
}
@inproceedings{nan-etal-2019-topic,
    title = "Topic Modeling with {W}asserstein Autoencoders",
    author = "Nan, Feng  and
      Ding, Ran  and
      Nallapati, Ramesh  and
      Xiang, Bing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1640",
    doi = "10.18653/v1/P19-1640",
    pages = "6345--6381",
    abstract = "We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.",
}
@inproceedings{shi-etal-2019-dense,
    title = "Dense Procedure Captioning in Narrated Instructional Videos",
    author = "Shi, Botian  and
      Ji, Lei  and
      Liang, Yaobo  and
      Duan, Nan  and
      Chen, Peng  and
      Niu, Zhendong  and
      Zhou, Ming",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1641",
    doi = "10.18653/v1/P19-1641",
    pages = "6382--6391",
    abstract = "Understanding narrated instructional videos is important for both research and real-world web applications. Motivated by video dense captioning, we propose a model to generate procedure captions from narrated instructional videos which are a sequence of step-wise clips with description. Previous works on video dense captioning learn video segments and generate captions without considering transcripts. We argue that transcripts in narrated instructional videos can enhance video representation by providing fine-grained complimentary and semantic textual information. In this paper, we introduce a framework to (1) extract procedures by a cross-modality module, which fuses video content with the entire transcript; and (2) generate captions by encoding video frames as well as a snippet of transcripts within each extracted procedure. Experiments show that our model can achieve state-of-the-art performance in procedure extraction and captioning, and the ablation studies demonstrate that both the video frames and the transcripts are important for the task.",
}
@inproceedings{calixto-etal-2019-latent,
    title = "Latent Variable Model for Multi-modal Translation",
    author = "Calixto, Iacer  and
      Rios, Miguel  and
      Aziz, Wilker",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1642",
    doi = "10.18653/v1/P19-1642",
    pages = "6392--6405",
    abstract = "In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language. It is used in a target-language decoder and also to predict image features. Importantly, our model formulation utilises visual and textual inputs during training but does not require that images be available at test time. We show that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliott and Kadar, 2017) and a conditional variational auto-encoder approach (Toyama et al., 2016). Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable, and (iii) by training on additional target-language image descriptions (i.e. synthetic data).",
}
@inproceedings{ignat-etal-2019-identifying,
    title = "Identifying Visible Actions in Lifestyle Vlogs",
    author = "Ignat, Oana  and
      Burdick, Laura  and
      Deng, Jia  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1643",
    doi = "10.18653/v1/P19-1643",
    pages = "6406--6417",
    abstract = "We consider the task of identifying human actions visible in online videos. We focus on the widely spread genre of lifestyle vlogs, which consist of videos of people performing actions while verbally describing them. Our goal is to identify if actions mentioned in the speech description of a video are visually present. We construct a dataset with crowdsourced manual annotations of visible actions, and introduce a multimodal algorithm that leverages information derived from visual and linguistic clues to automatically infer which actions are visible in a video.",
}
@inproceedings{suhr-etal-2019-corpus,
    title = "A Corpus for Reasoning about Natural Language Grounded in Photographs",
    author = "Suhr, Alane  and
      Zhou, Stephanie  and
      Zhang, Ally  and
      Zhang, Iris  and
      Bai, Huajun  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1644",
    doi = "10.18653/v1/P19-1644",
    pages = "6418--6428",
    abstract = "We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.",
}
@inproceedings{kawakami-etal-2019-learning,
    title = "Learning to Discover, Ground and Use Words with Segmental Neural Language Models",
    author = "Kawakami, Kazuya  and
      Dyer, Chris  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1645",
    doi = "10.18653/v1/P19-1645",
    pages = "6429--6441",
    abstract = "We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words{'} meanings ground in representations of nonlinguistic modalities. Experiments show that the unconditional model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and that modeling language conditional on visual context improves performance on both.",
}
@inproceedings{shukla-etal-2019-ask,
    title = "What Should {I} Ask? Using Conversationally Informative Rewards for Goal-oriented Visual Dialog.",
    author = "Shukla, Pushkar  and
      Elmadjian, Carlos  and
      Sharan, Richika  and
      Kulkarni, Vivek  and
      Turk, Matthew  and
      Wang, William Yang",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1646",
    doi = "10.18653/v1/P19-1646",
    pages = "6442--6451",
    abstract = "The ability to engage in goal-oriented conversations has allowed humans to gain knowledge, reduce uncertainty, and perform tasks more efficiently. Artificial agents, however, are still far behind humans in having goal-driven conversations. In this work, we focus on the task of goal-oriented visual dialogue, aiming to automatically generate a series of questions about an image with a single objective. This task is challenging since these questions must not only be consistent with a strategy to achieve a goal, but also consider the contextual information in the image. We propose an end-to-end goal-oriented visual dialogue system, that combines reinforcement learning with regularized information gain. Unlike previous approaches that have been proposed for the task, our work is motivated by the Rational Speech Act framework, which models the process of human inquiry to reach a goal. We test the two versions of our model on the GuessWhat?! dataset, obtaining significant results that outperform the current state-of-the-art models in the task of generating questions to find an undisclosed object in an image.",
}
@inproceedings{chrupala-2019-symbolic,
    title = "Symbolic Inductive Bias for Visually Grounded Learning of Spoken Language",
    author = "Chrupa{\l}a, Grzegorz",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1647",
    doi = "10.18653/v1/P19-1647",
    pages = "6452--6462",
    abstract = "A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.",
}
@inproceedings{gan-etal-2019-multi,
    title = "Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog",
    author = "Gan, Zhe  and
      Cheng, Yu  and
      Kholy, Ahmed  and
      Li, Linjie  and
      Liu, Jingjing  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1648",
    doi = "10.18653/v1/P19-1648",
    pages = "6463--6474",
    abstract = "This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image. In each question-answering turn of a dialog, ReDAN infers the answer progressively through multiple reasoning steps. In each step of the reasoning process, the semantic representation of the question is updated based on the image and the previous dialog history, and the recurrently-refined representation is used for further reasoning in the subsequent step. On the VisDial v1.0 dataset, the proposed ReDAN model achieves a new state-of-the-art of 64.47{\%} NDCG score. Visualization on the reasoning process further demonstrates that ReDAN can locate context-relevant visual and textual clues via iterative refinement, which can lead to the correct answer step-by-step.",
}
@inproceedings{zhang-etal-2019-lattice,
    title = "Lattice Transformer for Speech Translation",
    author = "Zhang, Pei  and
      Ge, Niyu  and
      Chen, Boxing  and
      Fan, Kai",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1649",
    doi = "10.18653/v1/P19-1649",
    pages = "6475--6484",
    abstract = "Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results. However, depending on the up-stream systems, e.g., speech recognition, or word segmentation, the input to translation system can vary greatly. The goal of this work is to extend the attention mechanism of the transformer to naturally consume the lattice in addition to the traditional sequential input. We first propose a general lattice transformer for speech translation where the input is the output of the automatic speech recognition (ASR) which contains multiple paths and posterior scores. To leverage the extra information from the lattice structure, we develop a novel controllable lattice attention mechanism to obtain latent representations. On the LDC Spanish-English speech translation corpus, our experiments show that lattice transformer generalizes significantly better and outperforms both a transformer baseline and a lattice LSTM. Additionally, we validate our approach on the WMT 2017 Chinese-English translation task with lattice inputs from different BPE segmentations. In this task, we also observe the improvements over strong baselines.",
}
@inproceedings{zhao-etal-2019-informative,
    title = "Informative Image Captioning with External Sources of Information",
    author = "Zhao, Sanqiang  and
      Sharma, Piyush  and
      Levinboim, Tomer  and
      Soricut, Radu",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1650",
    doi = "10.18653/v1/P19-1650",
    pages = "6485--6494",
    abstract = "An image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact. However, current captioning models are usually trained to generate captions that only contain common object names, thus falling short on an important {``}informativeness{''} dimension. We present a mechanism for integrating image information together with fine-grained labels (assumed to be generated by some upstream models) into a caption that describes the image in a fluent and informative manner. We introduce a multimodal, multi-encoder model based on Transformer that ingests both image features and multiple sources of entity labels. We demonstrate that we can learn to control the appearance of these entity labels in the output, resulting in captions that are both fluent and informative.",
}
@inproceedings{kim-etal-2019-codraw,
    title = "{C}o{D}raw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication",
    author = "Kim, Jin-Hwa  and
      Kitaev, Nikita  and
      Chen, Xinlei  and
      Rohrbach, Marcus  and
      Zhang, Byoung-Tak  and
      Tian, Yuandong  and
      Batra, Dhruv  and
      Parikh, Devi",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1651",
    doi = "10.18653/v1/P19-1651",
    pages = "6495--6513",
    abstract = "In this work, we propose a goal-driven collaborative task that combines language, perception, and action. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art objects. The game involves two players: a Teller and a Drawer. The Teller sees an abstract scene containing multiple clip art pieces in a semantically meaningful configuration, while the Drawer tries to reconstruct the scene on an empty canvas using available clip art pieces. The two players communicate with each other using natural language. We collect the CoDraw dataset of {\textasciitilde}10K dialogs consisting of {\textasciitilde}138K messages exchanged between human players. We define protocols and metrics to evaluate learned agents in this testbed, highlighting the need for a novel {``}crosstalk{''} evaluation condition which pairs agents trained independently on disjoint subsets of the training data. We present models for our task and benchmark them using both fully automated evaluation and by having them play the game live with humans.",
}
@inproceedings{fan-etal-2019-bridging,
    title = "Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning",
    author = "Fan, Zhihao  and
      Wei, Zhongyu  and
      Wang, Siyuan  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1652",
    doi = "10.18653/v1/P19-1652",
    pages = "6514--6524",
    abstract = "Image Captioning aims at generating a short description for an image. Existing research usually employs the architecture of CNN-RNN that views the generation as a sequential decision-making process and the entire dataset vocabulary is used as decoding space. They suffer from generating high frequent n-gram with irrelevant words. To tackle this problem, we propose to construct an image-grounded vocabulary, based on which, captions are generated with limitation and guidance. In specific, a novel hierarchical structure is proposed to construct the vocabulary incorporating both visual information and relations among words. For generation, we propose a word-aware RNN cell incorporating vocabulary information into the decoding process directly. Reinforce algorithm is employed to train the generator using constraint vocabulary as action space. Experimental results on MS COCO and Flickr30k show the effectiveness of our framework compared to some state-of-the-art models.",
}
@inproceedings{ive-etal-2019-distilling,
    title = "Distilling Translations with Visual Awareness",
    author = "Ive, Julia  and
      Madhyastha, Pranava  and
      Specia, Lucia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1653",
    doi = "10.18653/v1/P19-1653",
    pages = "6525--6538",
    abstract = "Previous work on multimodal machine translation has shown that visual information is only needed in very specific cases, for example in the presence of ambiguous words where the textual context is not sufficient. As a consequence, models tend to learn to ignore this information. We propose a translate-and-refine approach to this problem where images are only used by a second stage decoder. This approach is trained jointly to generate a good first draft translation and to improve over this draft by (i) making better use of the target language textual context (both left and right-side contexts) and (ii) making use of visual context. This approach leads to the state of the art results. Additionally, we show that it has the ability to recover from erroneous or missing words in the source language.",
}
@inproceedings{madhyastha-etal-2019-vifidel,
    title = "{VIFIDEL}: Evaluating the Visual Fidelity of Image Descriptions",
    author = "Madhyastha, Pranava  and
      Wang, Josiah  and
      Specia, Lucia",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1654",
    doi = "10.18653/v1/P19-1654",
    pages = "6539--6550",
    abstract = "We address the task of evaluating image description generation systems. We propose a novel image-aware metric for this task: VIFIDEL. It estimates the faithfulness of a generated caption with respect to the content of the actual image, based on the semantic similarity between labels of objects depicted in images and words in the description. The metric is also able to take into account the relative importance of objects mentioned in human reference descriptions during evaluation. Even if these human reference descriptions are not available, VIFIDEL can still reliably evaluate system descriptions. The metric achieves high correlation with human judgments on two well-known datasets and is competitive with metrics that depend on and rely exclusively on human references.",
}
@inproceedings{hu-etal-2019-looking,
    title = "Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation",
    author = "Hu, Ronghang  and
      Fried, Daniel  and
      Rohrbach, Anna  and
      Klein, Dan  and
      Darrell, Trevor  and
      Saenko, Kate",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1655",
    doi = "10.18653/v1/P19-1655",
    pages = "6551--6557",
    abstract = "Vision-and-Language Navigation (VLN) requires grounding instructions, such as {``}turn right and stop at the door{''}, to routes in a visual environment. The actual grounding can connect language to the environment through multiple modalities, e.g. {``}stop at the door{''} might ground into visual objects, while {``}turn right{''} might rely only on the geometric structure of a route. We investigate where the natural language empirically grounds under two recent state-of-the-art VLN models. Surprisingly, we discover that visual features may actually hurt these models: models which only use route structure, ablating visual features, outperform their visual counterparts in unseen new environments on the benchmark Room-to-Room dataset. To better use all the available modalities, we propose to decompose the grounding procedure into a set of expert models with access to different modalities (including object detections) and ensemble them at prediction time, improving the performance of state-of-the-art models on the VLN task.",
}
@inproceedings{tsai-etal-2019-multimodal,
    title = "Multimodal Transformer for Unaligned Multimodal Language Sequences",
    author = "Tsai, Yao-Hung Hubert  and
      Bai, Shaojie  and
      Liang, Paul Pu  and
      Kolter, J. Zico  and
      Morency, Louis-Philippe  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1656",
    doi = "10.18653/v1/P19-1656",
    pages = "6558--6569",
    abstract = "Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. However, two major challenges in modeling such multimodal human language time-series data exist: 1) inherent data non-alignment due to variable sampling rates for the sequences from each modality; and 2) long-range dependencies between elements across modalities. In this paper, we introduce the Multimodal Transformer (MulT) to generically address the above issues in an end-to-end manner without explicitly aligning the data. At the heart of our model is the directional pairwise crossmodal attention, which attends to interactions between multimodal sequences across distinct time steps and latently adapt streams from one modality to another. Comprehensive experiments on both aligned and non-aligned multimodal time-series show that our model outperforms state-of-the-art methods by a large margin. In addition, empirical analysis suggests that correlated crossmodal signals are able to be captured by the proposed crossmodal attention mechanism in MulT.",
}
@inproceedings{jing-etal-2019-show,
    title = "Show, Describe and Conclude: On Exploiting the Structure Information of Chest {X}-ray Reports",
    author = "Jing, Baoyu  and
      Wang, Zeya  and
      Xing, Eric",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1657",
    doi = "10.18653/v1/P19-1657",
    pages = "6570--6580",
    abstract = "Chest X-Ray (CXR) images are commonly used for clinical screening and diagnosis. Automatically writing reports for these images can considerably lighten the workload of radiologists for summarizing descriptive findings and conclusive impressions. The complex structures between and within sections of the reports pose a great challenge to the automatic report generation. Specifically, the section Impression is a diagnostic summarization over the section Findings; and the appearance of normality dominates each section over that of abnormality. Existing studies rarely explore and consider this fundamental structure information. In this work, we propose a novel framework which exploits the structure information between and within report sections for generating CXR imaging reports. First, we propose a two-stage strategy that explicitly models the relationship between Findings and Impression. Second, we design a novel co-operative multi-agent system that implicitly captures the imbalanced distribution between abnormality and normality. Experiments on two CXR report datasets show that our method achieves state-of-the-art performance in terms of various evaluation metrics. Our results expose that the proposed approach is able to generate high-quality medical reports through integrating the structure information.",
}
@inproceedings{hsu-etal-2019-visual,
    title = "Visual Story Post-Editing",
    author = "Hsu, Ting-Yao  and
      Huang, Chieh-Yang  and
      Hsu, Yen-Chia  and
      Huang, Ting-Hao",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1658",
    doi = "10.18653/v1/P19-1658",
    pages = "6581--6586",
    abstract = "We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task. The dataset ,VIST-Edit, includes 14,905 human-edited versions of 2,981 machine-generated visual stories. The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions. We establish baselines for the task, showing how a relatively small set of human edits can be leveraged to boost the performance of large visual storytelling models. We also discuss the weak correlation between automatic evaluation scores and human ratings, motivating the need for new automatic metrics.",
}
@inproceedings{palaskar-etal-2019-multimodal,
    title = "Multimodal Abstractive Summarization for How2 Videos",
    author = "Palaskar, Shruti  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Gella, Spandana  and
      Metze, Florian",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1659",
    doi = "10.18653/v1/P19-1659",
    pages = "6587--6596",
    abstract = "In this paper, we study abstractive summarization for open-domain videos. Unlike the traditional text news summarization, the goal is less to {``}compress{''} text information but rather to provide a fluent textual summary of information that has been collected and fused from different source modalities, in our case video and audio transcripts (or text). We show how a multi-source sequence-to-sequence model with hierarchical attention can integrate information from different modalities into a coherent output, compare various models trained with different modalities and present pilot experiments on the \textit{How2 corpus} of instructional videos. We also propose a new evaluation metric (Content F1) for abstractive summarization task that measures semantic adequacy rather than fluency of the summaries, which is covered by metrics like ROUGE and BLEU.",
}
@inproceedings{garg-etal-2019-learning,
    title = "Learning to Relate from Captions and Bounding Boxes",
    author = "Garg, Sarthak  and
      Moniz, Joel Ruben Antony  and
      Aviral, Anshu  and
      Bollimpalli, Priyatham",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1660",
    doi = "10.18653/v1/P19-1660",
    pages = "6597--6603",
    abstract = "In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision. Our proposed approach uses a top-down attention mechanism to align entities in captions to objects in the image, and then leverage the syntactic structure of the captions to align the relations. We use these alignments to train a relation classification network, thereby obtaining both grounded captions and dense relationships. We demonstrate the effectiveness of our model on the Visual Genome dataset by achieving a recall@50 of 15{\%} and recall@100 of 25{\%} on the relationships present in the image. We also show that the model successfully predicts relations that are not present in the corresponding captions.",
}
@proceedings{naacl-2019-2019-north-american-chapter-association,
    title = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
    editor = "Sarkar, Anoop  and
      Strube, Michael",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-5000",
}
@inproceedings{wang-etal-2019-deep,
    title = "Deep Adversarial Learning for {NLP}",
    author = "Wang, William Yang  and
      Singh, Sameer  and
      Li, Jiwei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-5001",
    doi = "10.18653/v1/N19-5001",
    pages = "1--5",
    abstract = "Adversarial learning is a game-theoretic learning paradigm, which has achieved huge successes in the field of Computer Vision recently. Adversarial learning is also a general framework that enables a variety of learning models, including the popular Generative Adversarial Networks (GANs). Due to the discrete nature of language, designing adversarial learning models is still challenging for NLP problems. In this tutorial, we provide a gentle introduction to the foundation of deep adversarial learning, as well as some practical problem formulations and solutions in NLP. We describe recent advances in deep adversarial learning for NLP, with a special focus on generation, adversarial examples {\&} rules, and dialogue. We provide an overview of the research area, categorize different types of adversarial learning models, and discuss pros and cons, aiming at providing some practical perspectives on the future of adversarial learning for solving real-world NLP problems.",
}
@inproceedings{bowman-zhu-2019-deep,
    title = "Deep Learning for Natural Language Inference",
    author = "Bowman, Samuel  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-5002",
    doi = "10.18653/v1/N19-5002",
    pages = "6--8",
    abstract = "This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting-edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning models for language understanding and reasoning.",
}
@inproceedings{eisenstein-2019-measuring,
    title = "Measuring and Modeling Language Change",
    author = "Eisenstein, Jacob",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-5003",
    doi = "10.18653/v1/N19-5003",
    pages = "9--14",
    abstract = "This tutorial is designed to help researchers answer the following sorts of questions: - Are people happier on the weekend? - What was 1861{'}s word of the year? - Are Democrats and Republicans more different than ever? - When did {``}gay{''} stop meaning {``}happy{''}? - Are gender stereotypes getting weaker, stronger, or just different? - Who is a linguistic leader? - How can we get internet users to be more polite and objective? Such questions are fundamental to the social sciences and humanities, and scholars in these disciplines are increasingly turning to computational techniques for answers. Meanwhile, the ACL community is increasingly engaged with data that varies across time, and with the social insights that can be offered by analyzing temporal patterns and trends. The purpose of this tutorial is to facilitate this convergence in two main ways: 1. By synthesizing recent computational techniques for handling and modeling temporal data, such as dynamic word embeddings, the tutorial will provide a starting point for future computational research. It will also identify useful tools for social scientists and digital humanities scholars. 2. The tutorial will provide an overview of techniques and datasets from the quantitative social sciences and the digital humanities, which are not well-known in the computational linguistics community. These techniques include vector autoregressive models, multiple comparisons corrections for hypothesis testing, and causal inference. Datasets include historical newspaper archives and corpora of contemporary political speech.",
}
@inproceedings{ruder-etal-2019-transfer,
    title = "Transfer Learning in Natural Language Processing",
    author = "Ruder, Sebastian  and
      Peters, Matthew E.  and
      Swayamdipta, Swabha  and
      Wolf, Thomas",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-5004",
    doi = "10.18653/v1/N19-5004",
    pages = "15--18",
    abstract = "The classic supervised machine learning paradigm is based on learning in isolation, a single predictive model for a task using a single dataset. This approach requires a large number of training examples and performs best for well-defined and narrow tasks. Transfer learning refers to a set of methods that extend this approach by leveraging data from additional domains or tasks to train a model with better generalization properties. Over the last two years, the field of Natural Language Processing (NLP) has witnessed the emergence of several transfer learning methods and architectures which significantly improved upon the state-of-the-art on a wide range of NLP tasks. These improvements together with the wide availability and ease of integration of these methods are reminiscent of the factors that led to the success of pretrained word embeddings and ImageNet pretraining in computer vision, and indicate that these methods will likely become a common tool in the NLP landscape as well as an important research direction. We will present an overview of modern transfer learning methods in NLP, how models are pre-trained, what information the representations they learn capture, and review examples and case studies on how these models can be integrated and adapted in downstream NLP tasks.",
}
@inproceedings{nematzadeh-etal-2019-language,
    title = "Language Learning and Processing in People and Machines",
    author = "Nematzadeh, Aida  and
      Futrell, Richard  and
      Levy, Roger",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-5005",
    doi = "10.18653/v1/N19-5005",
    pages = "19--21",
    abstract = "The goal of this tutorial is to bring the fields of computational linguistics and computational cognitive science closer: we will introduce different stages of language acquisition and their parallel problems in NLP. As an example, one of the early challenges children face is mapping the meaning of word labels (such as {``}cat{''}) to their referents (the furry animal in the living room). Word learning is similar to the word alignment problem in machine translation. We explain the current computational models of language acquisition, their limitations, and how the insights from these models can be incorporated into NLP applications. Moreover, we discuss how we can take advantage of the cognitive science of language in computational linguistics: for example, by designing cognitively-motivated evaluations task or buildings language-learning inductive biases into our models.",
}
@inproceedings{wang-etal-2019-applications,
    title = "Applications of Natural Language Processing in Clinical Research and Practice",
    author = "Wang, Yanshan  and
      Tafti, Ahmad  and
      Sohn, Sunghwan  and
      Zhang, Rui",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-5006",
    doi = "10.18653/v1/N19-5006",
    pages = "22--25",
    abstract = "Rapid growth in adoption of electronic health records (EHRs) has led to an unprecedented expansion in the availability of large longitudinal datasets. Large initiatives such as the Electronic Medical Records and Genomics (eMERGE) Network, the Patient-Centered Outcomes Research Network (PCORNet), and the Observational Health Data Science and Informatics (OHDSI) consortium, have been established and have reported successful applications of secondary use of EHRs in clinical research and practice. In these applications, natural language processing (NLP) technologies have played a crucial role as much of detailed patient information in EHRs is embedded in narrative clinical documents. Meanwhile, a number of clinical NLP systems, such as MedLEE, MetaMap/MetaMap Lite, cTAKES, and MedTagger have been developed and utilized to extract useful information from diverse types of clinical text, such as clinical notes, radiology reports, and pathology reports. Success stories in applying these tools have been reported widely. Despite the demonstrated success of NLP in the clinical domain, methodologies and tools developed for the clinical NLP are still underknown and underutilized by students and experts in the general NLP domain, mainly due to the limited exposure to EHR data. Through this tutorial, we would like to introduce NLP methodologies and tools developed in the clinical domain, and showcase the real-world NLP applications in clinical research and practice at Mayo Clinic (the No. 1 national hospital ranked by the U.S. News {\&} World Report) and the University of Minnesota (the No. 41 best global universities ranked by the U.S. News {\&} World Report). We will review NLP techniques in solving clinical problems and facilitating clinical research, the state-of-the art clinical NLP tools, and share collaboration experience with clinicians, as well as publicly available EHR data and medical resources, and finally conclude the tutorial with vast opportunities and challenges of clinical NLP. The tutorial will provide an overview of clinical backgrounds, and does not presume knowledge in medicine or health care. The goal of this tutorial is to encourage NLP researchers in the general domain (as opposed to the specialized clinical domain) to contribute to this burgeoning area. In this tutorial, we will first present an overview of clinical NLP. We will then dive into two subareas of clinical NLP in clinical research, including big data infrastructure for large-scale clinical NLP and advances of NLP in clinical research, and two subareas in clinical practice, including clinical information extraction and patient cohort retrieval using EHRs. Around 70{\%} of the tutorial will review clinical problems, cutting-edge methodologies, and real-world clinical NLP tools while another 30{\%} introduce use cases at Mayo Clinic and the University of Minnesota. Finally, we will conclude the tutorial with challenges and opportunities in this rapidly developing domain.",
}
@proceedings{naacl-2019-2019-north-american-chapter,
    title = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    editor = "Ammar, Waleed  and
      Louis, Annie  and
      Mostafazadeh, Nasrin",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4000",
}
@inproceedings{ciosici-assent-2019-abbreviation,
    title = "Abbreviation Explorer - an interactive system for pre-evaluation of Unsupervised Abbreviation Disambiguation",
    author = "Ciosici, Manuel R.  and
      Assent, Ira",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4001",
    doi = "10.18653/v1/N19-4001",
    pages = "1--5",
    abstract = "We present Abbreviation Explorer, a system that supports interactive exploration of abbreviations that are challenging for Unsupervised Abbreviation Disambiguation (UAD). Abbreviation Explorer helps to identify long-forms that are easily confused, and to pinpoint likely causes such as limitations of normalization, language switching, or inconsistent typing. It can also support determining which long-forms would benefit from additional input text for unsupervised abbreviation disambiguation. The system provides options for creating corrective rules that merge redundant long-forms with identical meaning. The identified rules can be easily applied to the already existing vector spaces used by UAD to improve disambiguation performance, while also avoiding the cost of retraining.",
}
@inproceedings{obeid-etal-2019-adida,
    title = "{ADIDA}: Automatic Dialect Identification for {A}rabic",
    author = "Obeid, Ossama  and
      Salameh, Mohammad  and
      Bouamor, Houda  and
      Habash, Nizar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4002",
    doi = "10.18653/v1/N19-4002",
    pages = "6--11",
    abstract = "This demo paper describes ADIDA, a web-based system for automatic dialect identification for Arabic text. The system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) in addition to Modern Standard Arabic. The results are presented with either a point map or a heat map visualizing the automatic identification probabilities over a geographical map of the Arab World.",
}
@inproceedings{barbosa-etal-2019-enabling,
    title = "Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text",
    author = "Barbosa, George C. G.  and
      Wong, Zechy  and
      Hahn-Powell, Gus  and
      Bell, Dane  and
      Sharp, Rebecca  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4003",
    doi = "10.18653/v1/N19-4003",
    pages = "12--17",
    abstract = "Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62{\%}, which demonstrates the value of mining multilingual scientific information.",
}
@inproceedings{liu-etal-2019-ins,
    title = "{INS}: An Interactive {C}hinese News Synthesis System",
    author = "Liu, Hui  and
      Qin, Wentao  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4004",
    doi = "10.18653/v1/N19-4004",
    pages = "18--23",
    abstract = "Nowadays, we are surrounded by more and more online news articles. Tens or hundreds of news articles need to be read if we wish to explore a hot news event or topic. So it is of vital importance to automatically synthesize a batch of news articles related to the event or topic into a new synthesis article (or overview article) for reader{'}s convenience. It is so challenging to make news synthesis fully automatic that there is no successful solution by now. In this paper, we put forward a novel Interactive News Synthesis system (i.e. INS), which can help generate news overview articles automatically or by interacting with users. More importantly, INS can serve as a tool for editors to help them finish their jobs. In our experiments, INS performs well on both topic representation and synthesis article generation. A user study also demonstrates the usefulness and users{'} satisfaction with the INS tool. A demo video is available at \url{https://youtu.be/7ItteKW3GEk}.",
}
@inproceedings{ho-etal-2019-learning,
    title = "Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings",
    author = "Ho, Chia-Fang  and
      Chang, Jason  and
      Chen, Jhih-Jie  and
      Yang, Chingyu",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4005",
    doi = "10.18653/v1/N19-4005",
    pages = "24--28",
    abstract = "We present a method for learning bilingual word embeddings in order to support second language (L2) learners in finding recurring phrases and example sentences that match mixed-code queries (e.g., {``}接 受 sentence{''}) composed of words in both target language and native language (L1). In our approach, mixed-code queries are transformed into target language queries aimed at maximizing the probability of retrieving relevant target language phrases and sentences. The method involves converting a given parallel corpus into mixed-code data, generating word embeddings from mixed-code data, and expanding queries in target languages based on bilingual word embeddings. We present a prototype search engine, x.Linggle, that applies the method to a linguistic search engine for a parallel corpus. Preliminary evaluation on a list of common word-translation shows that the method performs reasonablly well.",
}
@inproceedings{schwarzenberg-etal-2019-train,
    title = "Train, Sort, Explain: Learning to Diagnose Translation Models",
    author = {Schwarzenberg, Robert  and
      Harbecke, David  and
      Macketanz, Vivien  and
      Avramidis, Eleftherios  and
      M{\"o}ller, Sebastian},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4006",
    doi = "10.18653/v1/N19-4006",
    pages = "29--34",
    abstract = "Evaluating translation models is a trade-off between effort and detail. On the one end of the spectrum there are automatic count-based methods such as BLEU, on the other end linguistic evaluations by humans, which arguably are more informative but also require a disproportionately high effort. To narrow the spectrum, we propose a general approach on how to automatically expose systematic differences between human and machine translations to human experts. Inspired by adversarial settings, we train a neural text classifier to distinguish human from machine translations. A classifier that performs and generalizes well after training should recognize systematic differences between the two classes, which we uncover with neural explainability methods. Our proof-of-concept implementation, DiaMaT, is open source. Applied to a dataset translated by a state-of-the-art neural Transformer model, DiaMaT achieves a classification accuracy of 75{\%} and exposes meaningful differences between humans and the Transformer, amidst the current discussion about human parity.",
}
@inproceedings{neubig-etal-2019-compare,
    title = "compare-mt: A Tool for Holistic Comparison of Language Generation Systems",
    author = "Neubig, Graham  and
      Dou, Zi-Yi  and
      Hu, Junjie  and
      Michel, Paul  and
      Pruthi, Danish  and
      Wang, Xinyi",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4007",
    doi = "10.18653/v1/N19-4007",
    pages = "35--41",
    abstract = "In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic n-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. compare-mt is a pure-Python open source package, that has already proven useful to generate analyses that have been used in our published papers. Demo Video: https://youtu.be/NyJEQT7t2CA",
}
@inproceedings{sharp-etal-2019-eidos,
    title = "Eidos, {INDRA}, {\&} Delphi: From Free Text to Executable Causal Models",
    author = "Sharp, Rebecca  and
      Pyarelal, Adarsh  and
      Gyori, Benjamin  and
      Alcock, Keith  and
      Laparra, Egoitz  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Nagesh, Ajay  and
      Yadav, Vikas  and
      Bachman, John  and
      Tang, Zheng  and
      Lent, Heather  and
      Luo, Fan  and
      Paul, Mithun  and
      Bethard, Steven  and
      Barnard, Kobus  and
      Morrison, Clayton  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4008",
    doi = "10.18653/v1/N19-4008",
    pages = "42--47",
    abstract = "Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.",
}
@inproceedings{ott-etal-2019-fairseq,
    title = "fairseq: A Fast, Extensible Toolkit for Sequence Modeling",
    author = "Ott, Myle  and
      Edunov, Sergey  and
      Baevski, Alexei  and
      Fan, Angela  and
      Gross, Sam  and
      Ng, Nathan  and
      Grangier, David  and
      Auli, Michael",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4009",
    doi = "10.18653/v1/N19-4009",
    pages = "48--53",
    abstract = "fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto",
}
@inproceedings{akbik-etal-2019-flair,
    title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
    author = "Akbik, Alan  and
      Bergmann, Tanja  and
      Blythe, Duncan  and
      Rasul, Kashif  and
      Schweter, Stefan  and
      Vollgraf, Roland",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4010",
    doi = "10.18653/v1/N19-4010",
    pages = "54--59",
    abstract = "We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models. The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings. This effectively hides all embedding-specific engineering complexity and allows researchers to {``}mix and match{''} various embeddings with little effort. The framework also implements standard model training and hyperparameter selection routines, as well as a data fetching module that can download publicly available NLP datasets and convert them into data structures for quick set up of experiments. Finally, FLAIR also ships with a {``}model zoo{''} of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications. This paper gives an overview of the framework and its functionality. The framework is available on GitHub at https://github.com/zalandoresearch/flair .",
}
@inproceedings{sedoc-etal-2019-chateval,
    title = "{C}hat{E}val: A Tool for Chatbot Evaluation",
    author = "Sedoc, Jo{\~a}o  and
      Ippolito, Daphne  and
      Kirubarajan, Arun  and
      Thirani, Jai  and
      Ungar, Lyle  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4011",
    doi = "10.18653/v1/N19-4011",
    pages = "60--65",
    abstract = "Open-domain dialog systems (i.e. chatbots) are difficult to evaluate. The current best practice for analyzing and comparing these dialog systems is the use of human judgments. However, the lack of standardization in evaluation procedures, and the fact that model parameters and code are rarely published hinder systematic human evaluation experiments. We introduce a unified framework for human evaluation of chatbots that augments existing tools and provides a web-based hub for researchers to share and compare their dialog systems. Researchers can submit their trained models to the ChatEval web interface and obtain comparisons with baselines and prior work. The evaluation code is open-source to ensure standardization and transparency. In addition, we introduce open-source baseline models and evaluation datasets. ChatEval can be found at https://chateval.org.",
}
@inproceedings{shi-etal-2019-leafnats,
    title = "{L}eaf{NATS}: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization",
    author = "Shi, Tian  and
      Wang, Ping  and
      Reddy, Chandan K.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4012",
    doi = "10.18653/v1/N19-4012",
    pages = "66--71",
    abstract = "Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia. In this paper, we introduce an open-source toolkit, namely LeafNATS, for training and evaluation of different sequence-to-sequence based models for the NATS task, and for deploying the pre-trained models to real-world applications. The toolkit is modularized and extensible in addition to maintaining competitive performance in the NATS task. A live news blogging system has also been implemented to demonstrate how these models can aid blog/news editors by providing them suggestions of headlines and summaries of their articles.",
}
@inproceedings{yang-etal-2019-end,
    title = "End-to-End Open-Domain Question Answering with {BERT}serini",
    author = "Yang, Wei  and
      Xie, Yuqing  and
      Lin, Aileen  and
      Li, Xingyu  and
      Tan, Luchen  and
      Xiong, Kun  and
      Li, Ming  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4013",
    doi = "10.18653/v1/N19-4013",
    pages = "72--77",
    abstract = "We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion. We report large improvements over previous results on a standard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans.",
}
@inproceedings{nadeem-etal-2019-fakta,
    title = "{FAKTA}: An Automatic End-to-End Fact Checking System",
    author = "Nadeem, Moin  and
      Fang, Wei  and
      Xu, Brian  and
      Mohtarami, Mitra  and
      Glass, James",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4014",
    doi = "10.18653/v1/N19-4014",
    pages = "78--83",
    abstract = "We present FAKTA which is a unified framework that integrates various components of a fact-checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level to explain its predictions.",
}
@inproceedings{lee-etal-2019-icomposer,
    title = "i{C}omposer: An Automatic Songwriting System for {C}hinese Popular Music",
    author = "Lee, Hsin-Pei  and
      Fang, Jhih-Sheng  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4015",
    doi = "10.18653/v1/N19-4015",
    pages = "84--88",
    abstract = "In this paper, we introduce iComposer, an interactive web-based songwriting system designed to assist human creators by greatly simplifying music production. iComposer automatically creates melodies to accompany any given text. It also enables users to generate a set of lyrics given arbitrary melodies. iComposer is based on three sequence-to-sequence models, which are used to predict melody, rhythm, and lyrics, respectively. Songs generated by iComposer are compared with human-composed and randomly-generated ones in a subjective test, the experimental results of which demonstrate the capability of the proposed system to write pleasing melodies and meaningful lyrics at a level similar to that of humans.",
}
@inproceedings{goldfarb-tarrant-etal-2019-plan,
    title = "Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation",
    author = "Goldfarb-Tarrant, Seraphina  and
      Feng, Haining  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4016",
    doi = "10.18653/v1/N19-4016",
    pages = "89--97",
    abstract = "Story composition is a challenging problem for machines and even for humans. We present a neural narrative generation system that interacts with humans to generate stories. Our system has different levels of human interaction, which enables us to understand at what stage of story-writing human collaboration is most productive, both to improving story quality and human engagement in the writing process. We compare different varieties of interaction in story-writing, story-planning, and diversity controls under time constraints, and show that increased types of human collaboration at both planning and writing stages results in a 10-50{\%} improvement in story quality as compared to less interactive baselines. We also show an accompanying increase in user engagement and satisfaction with stories as compared to our own less interactive systems and to previous turn-taking approaches to interaction. Finally, we find that humans tasked with collaboratively improving a particular characteristic of a story are in fact able to do so, which has implications for future uses of human-in-the-loop systems.",
}
@inproceedings{fischer-etal-2019-lt,
    title = "{LT} Expertfinder: An Evaluation Framework for Expert Finding Methods",
    author = "Fischer, Tim  and
      Remus, Steffen  and
      Biemann, Chris",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4017",
    doi = "10.18653/v1/N19-4017",
    pages = "98--104",
    abstract = "Expert finding is the task of ranking persons for a predefined topic or search query. Finding experts for a specified area is an important task and has attracted much attention in the information retrieval community. Most approaches for this task are evaluated in a supervised fashion, which depend on predefined topics of interest as well as gold standard expert rankings. Famous representatives of such datasets are enriched versions of DBLP provided by the ArnetMiner projet or the W3C Corpus of TREC. However, manually ranking experts can be considered highly subjective and detailed rankings are hardly distinguishable. Evaluating these datasets does not necessarily guarantee a good or bad performance of the system. Particularly for dynamic systems, where topics are not predefined but formulated as a search query, we believe a more informative approach is to perform user studies for directly comparing different methods in the same view. In order to accomplish this in a user-friendly way, we present the LT Expert Finder web-application, which is equipped with various query-based expert finding methods that can be easily extended, a detailed expert profile view, detailed evidence in form of relevant documents and statistics, and an evaluation component that allows the qualitative comparison between different rankings.",
}
@inproceedings{shen-etal-2019-skillbot,
    title = "{S}kill{B}ot: Towards Automatic Skill Development via User Demonstration",
    author = "Shen, Yilin  and
      Ray, Avik  and
      Jin, Hongxia  and
      Nama, Sandeep",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4018",
    doi = "10.18653/v1/N19-4018",
    pages = "105--109",
    abstract = "We present SkillBot that takes the first step to enable end users to teach new skills in personal assistants (PA). Unlike existing PA products that need software developers to build new skills via IDE tools, an end user can use SkillBot to build new skills just by naturally demonstrating the task on device screen. SkillBot automatically develops a natural language understanding (NLU) engine and implements the action without the need of coding. On both benchmark and in-house datasets, we validate the competitive performance of SkillBot automatically built NLU. We also observe that it only takes a few minutes for an end user to build a new skill using SkillBot.",
}
@inproceedings{li-etal-2019-multilingual,
    title = "Multilingual Entity, Relation, Event and Human Value Extraction",
    author = "Li, Manling  and
      Lin, Ying  and
      Hoover, Joseph  and
      Whitehead, Spencer  and
      Voss, Clare  and
      Dehghani, Morteza  and
      Ji, Heng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4019",
    doi = "10.18653/v1/N19-4019",
    pages = "110--115",
    abstract = "This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference. It extracts and aggregates knowledge elements across multiple languages and documents as well as provides visualizations of the results along three dimensions: temporal (as displayed in an event timeline), spatial (as displayed in an event heatmap), and relational (as displayed in entity-relation networks). For our system to further support users{'} analyses of causal sequences of events in complex situations, we also integrate a wide range of human moral value measures, independently derived from region-based survey, into the event heatmap. This system is publicly available as a docker container and a live demo.",
}
@inproceedings{vacek-etal-2019-litigation,
    title = "Litigation Analytics: Extracting and querying motions and orders from {US} federal courts",
    author = "Vacek, Thomas  and
      Song, Dezhao  and
      Molina-Salgado, Hugo  and
      Teo, Ronald  and
      Cowling, Conner  and
      Schilder, Frank",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4020",
    doi = "10.18653/v1/N19-4020",
    pages = "116--121",
    abstract = "Legal litigation planning can benefit from statistics collected from past decisions made by judges. Information on the typical duration for a submitted motion, for example, can give valuable clues for developing a successful strategy. Such information is encoded in semi-structured documents called dockets. In order to extract and aggregate this information, we deployed various information extraction and machine learning techniques. The aggregated data can be queried in real time within the Westlaw Edge search engine. In addition to a keyword search for judges, lawyers, law firms, parties and courts, we also implemented a question answering interface that offers targeted questions in order to get to the respective answers quicker.",
}
@inproceedings{hunt-etal-2019-community,
    title = "Community lexical access for an endangered polysynthetic language: An electronic dictionary for {S}t. {L}awrence {I}sland {Y}upik",
    author = "Hunt, Benjamin  and
      Chen, Emily  and
      Schreiner, Sylvia L.R.  and
      Schwartz, Lane",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4021",
    doi = "10.18653/v1/N19-4021",
    pages = "122--126",
    abstract = "In this paper, we introduce a morphologically-aware electronic dictionary for St. Lawrence Island Yupik, an endangered language of the Bering Strait region. Implemented using HTML, Javascript, and CSS, the dictionary is set in an uncluttered interface and permits users to search in Yupik or in English for Yupik root words and Yupik derivational suffixes. For each matching result, our electronic dictionary presents the user with the corresponding entry from the Badten (2008) Yupik-English paper dictionary. Because Yupik is a polysynthetic language, handling of multimorphemic word forms is critical. If a user searches for an inflected Yupik word form, we perform a morphological analysis and return entries for the root word and for any derivational suffixes present in the word. This electronic dictionary should serve not only as a valuable resource for all students and speakers of Yupik, but also for field linguists working towards documentation and conservation of the language.",
}
@inproceedings{lepp-etal-2019-visualizing,
    title = "Visualizing Inferred Morphotactic Systems",
    author = "Lepp, Haley  and
      Zamaraeva, Olga  and
      Bender, Emily M.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4022",
    doi = "10.18653/v1/N19-4022",
    pages = "127--131",
    abstract = "We present a web-based system that facilitates the exploration of complex morphological patterns found in morphologically very rich languages. The need for better understanding of such patterns is urgent for linguistics and important for cross-linguistically applicable natural language processing. In this paper we give an overview of the system architecture and describe a sample case study on Abui [abz], a Trans-New Guinea language spoken in Indonesia.",
}
@inproceedings{marge-etal-2019-research,
    title = "A {R}esearch {P}latform for {M}ulti-{R}obot {D}ialogue with {H}umans",
    author = "Marge, Matthew  and
      Nogar, Stephen  and
      Hayes, Cory J.  and
      Lukin, Stephanie M.  and
      Bloecker, Jesse  and
      Holder, Eric  and
      Voss, Clare",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4023",
    doi = "10.18653/v1/N19-4023",
    pages = "132--137",
    abstract = "This paper presents a research platform that supports spoken dialogue interaction with multiple robots. The demonstration showcases our crafted MultiBot testing scenario in which users can verbally issue search, navigate, and follow instructions to two robotic teammates: a simulated ground robot and an aerial robot. This flexible language and robotic platform takes advantage of existing tools for speech recognition and dialogue management that are compatible with new domains, and implements an inter-agent communication protocol (tactical behavior specification), where verbal instructions are encoded for tasks assigned to the appropriate robot.",
}
@inproceedings{cascante-bonilla-etal-2019-chat,
    title = "Chat-crowd: A Dialog-based Platform for Visual Layout Composition",
    author = "Cascante-Bonilla, Paola  and
      Yin, Xuwang  and
      Ordonez, Vicente  and
      Feng, Song",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-4024",
    doi = "10.18653/v1/N19-4024",
    pages = "138--142",
    abstract = "In this paper we introduce Chat-crowd, an interactive environment for visual layout composition via conversational interactions. Chat-crowd supports multiple agents with two conversational roles: agents who play the role of a designer are in charge of placing objects in an editable canvas according to instructions or commands issued by agents with a director role. The system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and is equipped with comprehensive quality controls on the performance of both types of agents. We expect that this system will be useful to build multimodal goal-oriented dialog tasks that require spatial and geometric reasoning.",
}
@proceedings{naacl-2019-2019-north-american,
    title = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    editor = "Kar, Sudipta  and
      Nadeem, Farah  and
      Burdick, Laura  and
      Durrett, Greg  and
      Han, Na-Rae",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3000",
}
@inproceedings{dzendzik-etal-2019-dish,
    title = "Is It Dish Washer Safe? Automatically Answering {``}Yes/No{''} Questions Using Customer Reviews",
    author = "Dzendzik, Daria  and
      Vogel, Carl  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3001",
    doi = "10.18653/v1/N19-3001",
    pages = "1--6",
    abstract = "It has become commonplace for people to share their opinions about all kinds of products by posting reviews online. It has also become commonplace for potential customers to do research about the quality and limitations of these products by posting questions online. We test the extent to which reviews are useful in question-answering by combining two Amazon datasets and focusing our attention on yes/no questions. A manual analysis of 400 cases reveals that the reviews directly contain the answer to the question just over a third of the time. Preliminary reading comprehension experiments with this dataset prove inconclusive, with accuracy in the range 50-66{\%}.",
}
@inproceedings{bordia-bowman-2019-identifying,
    title = "Identifying and Reducing Gender Bias in Word-Level Language Models",
    author = "Bordia, Shikha  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3002",
    doi = "10.18653/v1/N19-3002",
    pages = "7--15",
    abstract = "Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data. For example, doctor cooccurs more frequently with male pronouns than female pronouns. In this study we (i) propose a metric to measure gender bias; (ii) measure bias in a text corpus and the text generated from a recurrent neural network language model trained on the text corpus; (iii) propose a regularization loss term for the language model that minimizes the projection of encoder-trained embeddings onto an embedding subspace that encodes gender; (iv) finally, evaluate efficacy of our proposed method on reducing gender bias. We find this regularization method to be effective in reducing gender bias up to an optimal weight assigned to the loss term, beyond which the model becomes unstable as the perplexity increases. We replicate this study on three training corpora{---}Penn Treebank, WikiText-2, and CNN/Daily Mail{---}resulting in similar conclusions.",
}
@inproceedings{munot-nenkova-2019-emotion,
    title = "Emotion Impacts Speech Recognition Performance",
    author = "Munot, Rushab  and
      Nenkova, Ani",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3003",
    doi = "10.18653/v1/N19-3003",
    pages = "16--21",
    abstract = "It has been established that the performance of speech recognition systems depends on multiple factors including the lexical content, speaker identity and dialect. Here we use three English datasets of acted emotion to demonstrate that emotional content also impacts the performance of commercial systems. On two of the corpora, emotion is a bigger contributor to recognition errors than speaker identity and on two, neutral speech is recognized considerably better than emotional speech. We further evaluate the commercial systems on spontaneous interactions that contain portions of emotional speech. We propose and validate on the acted datasets, a method that allows us to evaluate the overall impact of emotion on recognition even when manual transcripts are not available. Using this method, we show that emotion in natural spontaneous dialogue is a less prominent but still significant factor in recognition accuracy.",
}
@inproceedings{li-etal-2019-strength,
    title = "The Strength of the Weakest Supervision: Topic Classification Using Class Labels",
    author = "Li, Jiatong  and
      Zheng, Kai  and
      Xu, Hua  and
      Mei, Qiaozhu  and
      Wang, Yue",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3004",
    doi = "10.18653/v1/N19-3004",
    pages = "22--28",
    abstract = "When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels. Ideally, an intelligent classifier can understand these labels right away and start classifying documents. Indeed, a human can confidently tell if an article is about science, politics, sports, or none of the above, after knowing just the class labels. We study the problem of training an initial topic classifier using only class labels. We investigate existing techniques for solving this problem and propose a simple but effective approach. Experiments on a variety of topic classification data sets show that learning from class labels can save significant initial labeling effort, essentially providing a {''}free{''} warm start to the topic classifier.",
}
@inproceedings{paul-etal-2019-handling,
    title = "Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling",
    author = "Paul, Debjit  and
      Singh, Mittul  and
      Hedderich, Michael A.  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3005",
    doi = "10.18653/v1/N19-3005",
    pages = "29--34",
    abstract = "In this paper, we address the problem of effectively self-training neural networks in a low-resource setting. Self-training is frequently used to automatically increase the amount of training data. However, in a low-resource scenario, it is less effective due to unreliable annotations created using self-labeling of unlabeled data. We propose to combine self-training with noise handling on the self-labeled data. Directly estimating noise on the combined clean training set and self-labeled data can lead to corruption of the clean data and hence, performs worse. Thus, we propose the Clean and Noisy Label Neural Network which trains on clean and noisy self-labeled data simultaneously by explicitly modelling clean and noisy labels separately. In our experiments on Chunking and NER, this approach performs more robustly than the baselines. Complementary to this explicit approach, noise can also be handled implicitly with the help of an auxiliary learning task. To such a complementary approach, our method is more beneficial than other baseline methods and together provides the best performance overall.",
}
@inproceedings{han-kando-2019-opinion,
    title = "Opinion Mining with Deep Contextualized Embeddings",
    author = "Han, Wen-Bin  and
      Kando, Noriko",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3006",
    doi = "10.18653/v1/N19-3006",
    pages = "35--42",
    abstract = "Detecting opinion expression is a potential and essential task in opinion mining that can be extended to advanced tasks. In this paper, we considered opinion expression detection as a sequence labeling task and exploited different deep contextualized embedders into the state-of-the-art architecture, composed of bidirectional long short-term memory (BiLSTM) and conditional random field (CRF). Our experimental results show that using different word embeddings can cause contrasting results, and the model can achieve remarkable scores with deep contextualized embeddings. Especially, using BERT embedder can significantly exceed using ELMo embedder.",
}
@inproceedings{chen-etal-2019-bag,
    title = "A Bag-of-concepts Model Improves Relation Extraction in a Narrow Knowledge Domain with Limited Data",
    author = "Chen, Jiyu  and
      Verspoor, Karin  and
      Zhai, Zenan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3007",
    doi = "10.18653/v1/N19-3007",
    pages = "43--52",
    abstract = "This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain. We explore this task with a clinical corpus consisting of 200 breast cancer follow-up treatment letters in which 16 distinct types of relations are annotated. We experiment with an approach to extracting typed relations called window-bounded co-occurrence (WBC), which uses an adjustable context window around entity mentions of a relevant type, and compare its performance with a more typical intra-sentential co-occurrence baseline. We further introduce a new bag-of-concepts (BoC) approach to feature engineering based on the state-of-the-art word embeddings and word synonyms. We demonstrate the competitiveness of BoC by comparing with methods of higher complexity, and explore its effectiveness on this small dataset.",
}
@inproceedings{ahamad-2019-generating,
    title = "Generating Text through Adversarial Training Using Skip-Thought Vectors",
    author = "Ahamad, Afroz",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3008",
    doi = "10.18653/v1/N19-3008",
    pages = "53--60",
    abstract = "GANs have been shown to perform exceedingly well on tasks pertaining to image generation and style transfer. In the field of language modelling, word embeddings such as GLoVe and word2vec are state-of-the-art methods for applying neural network models on textual data. Attempts have been made to utilize GANs with word embeddings for text generation. This study presents an approach to text generation using Skip-Thought sentence embeddings with GANs based on gradient penalty functions and f-measures. The proposed architecture aims to reproduce writing style in the generated text by modelling the way of expression at a sentence level across all the works of an author. Extensive experiments were run in different embedding settings on a variety of tasks including conditional text generation and language generation. The model outperforms baseline text generation networks across several automated evaluation metrics like BLEU-n, METEOR and ROUGE. Further, wide applicability and effectiveness in real life tasks are demonstrated through human judgement scores.",
}
@inproceedings{manning-2019-partially,
    title = "A Partially Rule-Based Approach to {AMR} Generation",
    author = "Manning, Emma",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3009",
    doi = "10.18653/v1/N19-3009",
    pages = "61--70",
    abstract = "This paper presents a new approach to generating English text from Abstract Meaning Representation (AMR). In contrast to the neural and statistical MT approaches used in other AMR generation systems, this one is largely rule-based, supplemented only by a language model and simple statistical linearization models, allowing for more control over the output. We also address the difficulties of automatically evaluating AMR generation systems and the problems with BLEU for this task. We compare automatic metrics to human evaluations and show that while METEOR and TER arguably reflect human judgments better than BLEU, further research into suitable evaluation metrics is needed.",
}
@inproceedings{kabbara-2019-computational,
    title = "Computational Investigations of Pragmatic Effects in Natural Language",
    author = "Kabbara, Jad",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3010",
    doi = "10.18653/v1/N19-3010",
    pages = "71--76",
    abstract = "Semantics and pragmatics are two complimentary and intertwined aspects of meaning in language. The former is concerned with the literal (context-free) meaning of words and sentences, the latter focuses on the intended meaning, one that is context-dependent. While NLP research has focused in the past mostly on semantics, the goal of this thesis is to develop computational models that leverage this pragmatic knowledge in language that is crucial to performing many NLP tasks correctly. In this proposal, we begin by reviewing the current progress in this thesis, namely, on the tasks of definiteness prediction and adverbial presupposition triggering. Then we discuss the proposed research for the remainder of the thesis which builds on this progress towards the goal of building better and more pragmatically-aware natural language generation and understanding systems.",
}
@inproceedings{morabia-etal-2019-sedtwik,
    title = "{SEDTW}ik: Segmentation-based Event Detection from Tweets Using {W}ikipedia",
    author = "Morabia, Keval  and
      Bhanu Murthy, Neti Lalita  and
      Malapati, Aruna  and
      Samant, Surender",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3011",
    doi = "10.18653/v1/N19-3011",
    pages = "77--85",
    abstract = "Event Detection has been one of the research areas in Text Mining that has attracted attention during this decade due to the widespread availability of social media data specifically twitter data. Twitter has become a major source for information about real-world events because of the use of hashtags and the small word limit of Twitter that ensures concise presentation of events. Previous works on event detection from tweets are either applicable to detect localized events or breaking news only or miss out on many important events. This paper presents the problems associated with event detection from tweets and a tweet-segmentation based system for event detection called SEDTWik, an extension to a previous work, that is able to detect newsworthy events occurring at different locations of the world from a wide range of categories. The main idea is to split each tweet and hash-tag into segments, extract bursty segments, cluster them, and summarize them. We evaluated our results on the well-known Events2012 corpus and achieved state-of-the-art results. Keywords: Event detection, Twitter, Social Media, Microblogging, Tweet segmentation, Text Mining, Wikipedia, Hashtag.",
}
@inproceedings{hirasawa-etal-2019-multimodal,
    title = "Multimodal Machine Translation with Embedding Prediction",
    author = "Hirasawa, Tosho  and
      Yamagishi, Hayahide  and
      Matsumura, Yukio  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3012",
    doi = "10.18653/v1/N19-3012",
    pages = "86--91",
    abstract = "Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.",
}
@inproceedings{gupta-dipadova-2019-deep,
    title = "Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks",
    author = "Gupta, Sarah  and
      DiPadova, Anthony",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3013",
    doi = "10.18653/v1/N19-3013",
    pages = "92--96",
    abstract = "Automated extraction methods are widely available for vowels, but automated methods for coding rhoticity have lagged far behind. R-fulness versus r-lessness (in words like park, store, etc.) is a classic and frequently cited variable, but it is still commonly coded by human analysts rather than automated methods. Human-coding requires extensive resources and lacks replicability, making it difficult to compare large datasets across research groups. Can reliable automated methods be developed to aid in coding rhoticity? In this study, we use Neural Networks/Deep Learning, training our model on 208 Boston-area speakers.",
}
@inproceedings{kim-etal-2019-data,
    title = "Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding",
    author = "Kim, Hwa-Yeon  and
      Roh, Yoon-Hyung  and
      Kim, Young-Kil",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3014",
    doi = "10.18653/v1/N19-3014",
    pages = "97--102",
    abstract = "One of the main challenges in Spoken Language Understanding (SLU) is dealing with {`}open-vocabulary{'} slots. Recently, SLU models based on neural network were proposed, but it is still difficult to recognize the slots of unknown words or {`}open-vocabulary{'} slots because of the high cost of creating a manually tagged SLU dataset. This paper proposes data noising, which reflects the characteristics of the {`}open-vocabulary{'} slots, for data augmentation. We applied it to an attention based bi-directional recurrent neural network (Liu and Lane, 2016) and experimented with three datasets: Airline Travel Information System (ATIS), Snips, and MIT-Restaurant. We achieved performance improvements of up to 0.57{\%} and 3.25 in intent prediction (accuracy) and slot filling (f1-score), respectively. Our method is advantageous because it does not require additional memory and it can be applied simultaneously with the training process of the model.",
}
@inproceedings{dammalapati-etal-2019-expectation,
    title = "Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in {E}nglish Speech",
    author = "Dammalapati, Samvit  and
      Rajkumar, Rajakrishnan  and
      Agarwal, Sumeet",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3015",
    doi = "10.18653/v1/N19-3015",
    pages = "103--109",
    abstract = "This study examines the role of three influential theories of language processing, \textit{viz.}, Surprisal Theory, Uniform Information Density (UID) hypothesis and Dependency Locality Theory (DLT), in predicting disfluencies in speech production. To this end, we incorporate features based on lexical surprisal, word duration and DLT integration and storage costs into logistic regression classifiers aimed to predict disfluencies in the Switchboard corpus of English conversational speech. We find that disfluencies occur in the face of upcoming difficulties and speakers tend to handle this by lessening cognitive load before disfluencies occur. Further, we see that reparandums behave differently from disfluent fillers possibly due to the lessening of the cognitive load also happening in the word choice of the reparandum, i.e., in the disfluency itself. While the UID hypothesis does not seem to play a significant role in disfluency prediction, lexical surprisal and DLT costs do give promising results in explaining language production. Further, we also find that as a means to lessen cognitive load for upcoming difficulties speakers take more time on words preceding disfluencies, making duration a key element in understanding disfluencies.",
}
@inproceedings{balazs-matsuo-2019-gating,
    title = "Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study",
    author = "Balazs, Jorge  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3016",
    doi = "10.18653/v1/N19-3016",
    pages = "110--124",
    abstract = "In this paper we study how different ways of combining character and word-level representations affect the quality of both final word and sentence representations. We provide strong empirical evidence that modeling characters improves the learned representations at the word and sentence levels, and that doing so is particularly useful when representing less frequent words. We further show that a feature-wise sigmoid gating mechanism is a robust method for creating representations that encode semantic similarity, as it performed reasonably well in several word similarity datasets. Finally, our findings suggest that properly capturing semantic similarity at the word level does not consistently yield improved performance in downstream sentence-level tasks.",
}
@inproceedings{debnath-shrivastava-2019-pregroup,
    title = "A Pregroup Representation of Word Order Alternation Using {H}indi Syntax",
    author = "Debnath, Alok  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3017",
    doi = "10.18653/v1/N19-3017",
    pages = "125--135",
    abstract = "Pregroup calculus has been used for the representation of free word order languages (Sanskrit and Hungarian), using a construction called precyclicity. However, restricted word order alternation has not been handled before. This paper aims at introducing and formally expressing three methods of representing word order alternation in the pregroup representation of any language. This paper describes the word order alternation patterns of Hindi, and creates a basic pregroup representation for the language. In doing so, the shortcoming of correct reductions for ungrammatical sentences due to the current apparatus is highlighted, and the aforementioned methods are invoked for a grammatically accurate representation of restricted word order alternation. The replicability of these methods is explained in the representation of adverbs and prepositional phrases in English.",
}
@inproceedings{ghosh-chowdhury-etal-2019-speak,
    title = "Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment",
    author = "Ghosh Chowdhury, Arijit  and
      Sawhney, Ramit  and
      Mathur, Puneet  and
      Mahata, Debanjan  and
      Ratn Shah, Rajiv",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3018",
    doi = "10.18653/v1/N19-3018",
    pages = "136--146",
    abstract = "The {\#}MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment. The aggregation and analysis of such disclosures pave the way to development of technology-based prevention of sexual harassment. We contend that the lack of specificity in generic sentence classification models may not be the best way to tackle text subtleties that intrinsically prevail in a classification task as complex as identifying disclosures of sexual harassment. We propose the Disclosure Language Model, a three part ULMFiT architecture, consisting of a Language model, a Medium-Specific (Twitter) model and a Task-Specific classifier to tackle this problem and create a manually annotated real-world dataset to test our technique on this, to show that using a Discourse Language Model often yields better classification performance over (i) Generic deep learning based sentence classification models (ii) existing models that rely on handcrafted stylistic features. An extensive comparison with state-of-the-art generic and specific models along with a detailed error analysis presents the case for our proposed methodology.",
}
@inproceedings{mishra-etal-2019-snap,
    title = "{SNAP}-{BATNET}: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media",
    author = "Mishra, Rohan  and
      Prakhar Sinha, Pradyumn  and
      Sawhney, Ramit  and
      Mahata, Debanjan  and
      Mathur, Puneet  and
      Ratn Shah, Rajiv",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-3019",
    doi = "10.18653/v1/N19-3019",
    pages = "147--156",
    abstract = "Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research. While it has been established that these users share a common set of properties, the current state-of-the-art approaches utilize only text-based (stylistic and semantic) cues. We contend that the use of information from networks in the form of condensed social graph embeddings and author profiling using features from historical data can be combined with an existing set of features to improve the performance. To that end, we experiment on a manually annotated dataset of tweets created using a three-phase strategy and propose SNAP-BATNET, a deep learning based model to extract text-based features and a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings that outperform the current state-of-the-art. We conduct a comprehensive quantitative analysis with baselines, both generic and specific, that presents the case for SNAP-BATNET, along with an error analysis that highlights the limitations and challenges faced paving the way to the future of AI-based suicide ideation detection.",
}
@proceedings{naacl-2019-2019-north,
    title = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    editor = "Loukina, Anastassia  and
      Morales, Michelle  and
      Kumar, Rohit",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2000",
}
@inproceedings{yao-etal-2019-enabling,
    title = "Enabling Real-time Neural {IME} with Incremental Vocabulary Selection",
    author = "Yao, Jiali  and
      Shu, Raphael  and
      Li, Xinjian  and
      Ohtsuki, Katsutoshi  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2001",
    doi = "10.18653/v1/N19-2001",
    pages = "1--8",
    abstract = "Input method editor (IME) converts sequential alphabet key inputs to words in a target language. It is an indispensable service for billions of Asian users. Although the neural-based language model is extensively studied and shows promising results in sequence-to-sequence tasks, applying a neural-based language model to IME was not considered feasible due to high latency when converting words on user devices. In this work, we articulate the bottleneck of neural IME decoding to be the heavy softmax computation over a large vocabulary. We propose an approach that incrementally builds a subset vocabulary from the word lattice. Our approach always computes the probability with a selected subset vocabulary. When the selected vocabulary is updated, the stale probabilities in previous steps are fixed by recomputing the missing logits. The experiments on Japanese IME benchmark shows an over 50x speedup for the softmax computations comparing to the baseline, reaching real-time speed even on commodity CPU without losing conversion accuracy. The approach is potentially applicable to other incremental sequence-to-sequence decoding tasks such as real-time continuous speech recognition.",
}
@inproceedings{lee-etal-2019-locale,
    title = "Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding",
    author = "Lee, Jihwan  and
      Sarikaya, Ruhi  and
      Kim, Young-Bum",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2002",
    doi = "10.18653/v1/N19-2002",
    pages = "9--15",
    abstract = "In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain classifier to a large number of locales. We propose a locale-agnostic universal domain classification model based on selective multi-task learning that learns a joint representation of an utterance over locales with different sets of domains and allows locales to share knowledge selectively depending on the domains. The experimental results demonstrate the effectiveness of our approach on domain classification task in the scenario of multiple locales with imbalanced data and disparate domain sets. The proposed approach outperforms other baselines models especially when classifying locale-specific domains and also low-resourced domains.",
}
@inproceedings{damonte-etal-2019-practical,
    title = "Practical Semantic Parsing for Spoken Language Understanding",
    author = "Damonte, Marco  and
      Goel, Rahul  and
      Chung, Tagyoung",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2003",
    doi = "10.18653/v1/N19-2003",
    pages = "16--23",
    abstract = "Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q{\&}A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q{\&}A and SLU.",
}
@inproceedings{liu-etal-2019-fast,
    title = "Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring",
    author = "Liu, Zhengyuan  and
      Lim, Hazel  and
      Suhaimi, Nur Farah Ain  and
      Tong, Shao Chuen  and
      Ong, Sharon  and
      Ng, Angela  and
      Lee, Sheldon  and
      Macdonald, Michael R.  and
      Ramasamy, Savitha  and
      Krishnaswamy, Pavitra  and
      Chow, Wai Leng  and
      Chen, Nancy F.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2004",
    doi = "10.18653/v1/N19-2004",
    pages = "24--31",
    abstract = "Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this work, we investigate fast prototyping of a dialogue comprehension system by leveraging on minimal nurse-to-patient conversations. We propose a framework inspired by nurse-initiated clinical symptom monitoring conversations to construct a simulated human-human dialogue dataset, embodying linguistic characteristics of spoken interactions like thinking aloud, self-contradiction, and topic drift. We then adopt an established bidirectional attention pointer network on this simulated dataset, achieving more than 80{\%} F1 score on a held-out test set from real-world nurse-to-patient conversations. The ability to automatically comprehend conversations in the healthcare domain by exploiting only limited data has implications for improving clinical workflows through red flag symptom detection and triaging capabilities. We demonstrate the feasibility for efficient and effective extraction, retrieval and comprehension of symptom checking information discussed in multi-turn human-human spoken conversations.",
}
@inproceedings{liu-etal-2019-graph,
    title = "Graph Convolution for Multimodal Information Extraction from Visually Rich Documents",
    author = "Liu, Xiaojing  and
      Gao, Feiyu  and
      Zhang, Qiong  and
      Zhao, Huasha",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2005",
    doi = "10.18653/v1/N19-2005",
    pages = "32--39",
    abstract = "Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.",
}
@inproceedings{deb-etal-2019-diversifying,
    title = "Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder",
    author = "Deb, Budhaditya  and
      Bailey, Peter  and
      Shokouhi, Milad",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2006",
    doi = "10.18653/v1/N19-2006",
    pages = "40--47",
    abstract = "We consider the problem of diversifying automated reply suggestions for a commercial instant-messaging (IM) system (Skype). Our conversation model is a standard matching based information retrieval architecture, which consists of two parallel encoders to project messages and replies into a common feature representation. During inference, we select replies from a fixed response set using nearest neighbors in the feature space. To diversify responses, we formulate the model as a generative latent variable model with Conditional Variational Auto-Encoder (M-CVAE). We propose a constrained-sampling approach to make the variational inference in M-CVAE efficient for our production system. In offline experiments, M-CVAE consistently increased diversity by ∼30−40{\%} without significant impact on relevance. This translated to a ∼5{\%} gain in click-rate in our online production system.",
}
@inproceedings{lu-etal-2019-goal,
    title = "Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting",
    author = "Lu, Yichao  and
      Srivastava, Manisha  and
      Kramer, Jared  and
      Elfardy, Heba  and
      Kahn, Andrea  and
      Wang, Song  and
      Bhardwaj, Vikas",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2007",
    doi = "10.18653/v1/N19-2007",
    pages = "48--55",
    abstract = "End-to-end neural models for goal-oriented conversational systems have become an increasingly active area of research, though results in real-world settings are few. We present real-world results for two issue types in the customer service domain. We train models on historical chat transcripts and test on live contacts using a human-in-the-loop research platform. Additionally, we incorporate customer profile features to assess their impact on model performance. We experiment with two approaches for response generation: (1) sequence-to-sequence generation and (2) template ranking. To test our models, a customer service agent handles live contacts and at each turn we present the top four model responses and allow the agent to select (and optionally edit) one of the suggestions or to type their own. We present results for turn acceptance rate, response coverage, and edit rate based on approximately 600 contacts, as well as qualitative analysis on patterns of turn rejection and edit behavior. Top-4 turn acceptance rate across all models ranges from 63{\%}-80{\%}. Our results suggest that these models are promising for an agent-support application.",
}
@inproceedings{yang-etal-2019-detecting,
    title = "Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features",
    author = "Yang, Wei  and
      Tan, Luchen  and
      Lu, Chunwei  and
      Cui, Anqi  and
      Li, Han  and
      Chen, Xi  and
      Xiong, Kun  and
      Wang, Muzi  and
      Li, Ming  and
      Pei, Jian  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2008",
    doi = "10.18653/v1/N19-2008",
    pages = "56--63",
    abstract = "Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority. This paper tackles the challenge of monitoring ongoing text chat dialogues to identify cases where the customer expresses such an intent, providing triage and prioritization for a separate pool of specialized agents specially trained to handle more complex situations. We describe a hybrid model that tackles this challenge by integrating recurrent neural networks with manually-engineered features. Experiments show that both components are complementary and contribute to overall recall, outperforming competitive baselines. A trial online deployment of our model demonstrates its business value in improving customer service.",
}
@inproceedings{zhang-etal-2019-multi,
    title = "Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile {E}-Commerce",
    author = "Zhang, Jianguo  and
      Zou, Pengcheng  and
      Li, Zhao  and
      Wan, Yao  and
      Pan, Xiuming  and
      Gong, Yu  and
      Yu, Philip S.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2009",
    doi = "10.18653/v1/N19-2009",
    pages = "64--72",
    abstract = "Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe redundant and over-informative product titles to attract attentions from customers, it is important to concisely display short product titles on limited screen of mobile phones. To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation in E-Commerce, which innovatively incorporates image information and attribute tags from product, as well as textual information from original long titles. MM-GAN poses short title generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view. Extensive experiments on a large-scale E-Commerce dataset demonstrate that our algorithm outperforms other state-of-the-art methods. Moreover, we deploy our model into a real-world online E-Commerce environment and effectively boost the performance of click through rate and click conversion rate by 1.66{\%} and 1.87{\%}, respectively.",
}
@inproceedings{murao-etal-2019-case,
    title = "A Case Study on Neural Headline Generation for Editing Support",
    author = "Murao, Kazuma  and
      Kobayashi, Ken  and
      Kobayashi, Hayato  and
      Yatsuka, Taichi  and
      Masuyama, Takeshi  and
      Higurashi, Tatsuru  and
      Tabuchi, Yoshimune",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2010",
    doi = "10.18653/v1/N19-2010",
    pages = "73--82",
    abstract = "There have been many studies on neural headline generation models trained with a lot of (article, headline) pairs. However, there are few situations for putting such models into practical use in the real world since news articles typically already have corresponding headlines. In this paper, we describe a practical use case of neural headline generation in a news aggregator, where dozens of professional editors constantly select important news articles and manually create their headlines, which are much shorter than the original headlines. Specifically, we show how to deploy our model to an editing support tool and report the results of comparing the behavior of the editors before and after the release.",
}
@inproceedings{williams-2019-neural,
    title = "Neural Lexicons for Slot Tagging in Spoken Language Understanding",
    author = "Williams, Kyle",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2011",
    doi = "10.18653/v1/N19-2011",
    pages = "83--89",
    abstract = "We explore the use of lexicons or gazettes in neural models for slot tagging in spoken language understanding. We develop models that encode lexicon information as neural features for use in a Long-short term memory neural network. Experiments are performed on data from 4 domains from an intelligent assistant under conditions that often occur in an industry setting, where there may be: 1) large amounts of training data, 2) limited amounts of training data for new domains, and 3) cross domain training. Results show that the use of neural lexicon information leads to a significant improvement in slot tagging, with improvements in the F-score of up to 12{\%}. Our findings have implications for how lexicons can be used to improve the performance of neural slot tagging models.",
}
@inproceedings{peshterliev-etal-2019-active,
    title = "Active Learning for New Domains in Natural Language Understanding",
    author = "Peshterliev, Stanislav  and
      Kearney, John  and
      Jagannatha, Abhyuday  and
      Kiss, Imre  and
      Matsoukas, Spyros",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2012",
    doi = "10.18653/v1/N19-2012",
    pages = "90--96",
    abstract = "We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system. We propose an algorithm called Majority-CRF that uses an ensemble of classification models to guide the selection of relevant utterances, as well as a sequence labeling model to help prioritize informative examples. Experiments with three domains show that Majority-CRF achieves 6.6{\%}-9{\%} relative error rate reduction compared to random sampling with the same annotation budget, and statistically significant improvements compared to other AL approaches. Additionally, case studies with human-in-the-loop AL on six new domains show 4.6{\%}-9{\%} improvement on an existing NLU system.",
}
@inproceedings{rastogi-etal-2019-scaling,
    title = "Scaling Multi-Domain Dialogue State Tracking via Query Reformulation",
    author = "Rastogi, Pushpendre  and
      Gupta, Arpit  and
      Chen, Tongfei  and
      Lambert, Mathias",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2013",
    doi = "10.18653/v1/N19-2013",
    pages = "97--105",
    abstract = "We present a novel approach to dialogue state tracking and referring expression resolution tasks. Successful contextual understanding of multi-turn spoken dialogues requires resolving referring expressions across turns and tracking the entities relevant to the conversation across turns. Tracking conversational state is particularly challenging in a multi-domain scenario when there exist multiple spoken language understanding (SLU) sub-systems, and each SLU sub-system operates on its domain-specific meaning representation. While previous approaches have addressed the disparate schema issue by learning candidate transformations of the meaning representation, in this paper, we instead model the reference resolution as a dialogue context-aware user query reformulation task {--} the dialog state is serialized to a sequence of natural language tokens representing the conversation. We develop our model for query reformulation using a pointer-generator network and a novel multi-task learning setup. In our experiments, we show a significant improvement in absolute F1 on an internal as well as a, soon to be released, public benchmark respectively.",
}
@inproceedings{meteer-etal-2019-tools,
    title = "Are the Tools up to the Task? an Evaluation of Commercial Dialog Tools in Developing Conversational Enterprise-grade Dialog Systems",
    author = "Meteer, Marie  and
      Hickey, Meghan  and
      Rothberg, Carmi  and
      Nahamoo, David  and
      Eide Kislal, Ellen",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2014",
    doi = "10.18653/v1/N19-2014",
    pages = "106--113",
    abstract = "There has been a significant investment in dialog systems (tools and runtime) for building conversational systems by major companies including Google, IBM, Microsoft, and Amazon. The question remains whether these tools are up to the task of building conversational, task-oriented dialog applications at the enterprise level. In our company, we are exploring and comparing several toolsets in an effort to determine their strengths and weaknesses in meeting our goals for dialog system development: accuracy, time to market, ease of replicating and extending applications, and efficiency and ease of use by developers. In this paper, we provide both quantitative and qualitative results in three main areas: natural language understanding, dialog, and text generation. While existing toolsets were all incomplete, we hope this paper will provide a roadmap of where they need to go to meet the goal of building effective dialog systems.",
}
@inproceedings{afzal-etal-2019-development,
    title = "Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System",
    author = "Afzal, Shazia  and
      Dhamecha, Tejas  and
      Mukhi, Nirmal  and
      Sindhgatta, Renuka  and
      Marvaniya, Smit  and
      Ventura, Matthew  and
      Yarbro, Jessica",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2015",
    doi = "10.18653/v1/N19-2015",
    pages = "114--121",
    abstract = "There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy. These issues are magnified when implementing such a system at scale and across domains. In this paper, we describe and reflect on the design, methods, decisions and assessments that led to the successful deployment of our AI driven DBT currently being used by several hundreds of college level students for practice and self-regulated study in diverse subjects like Sociology, Communications, and American Government.",
}
@inproceedings{godin-etal-2019-learning,
    title = "Learning When Not to Answer: a Ternary Reward Structure for Reinforcement Learning Based Question Answering",
    author = "Godin, Fr{\'e}deric  and
      Kumar, Anjishnu  and
      Mittal, Arpit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2016",
    doi = "10.18653/v1/N19-2016",
    pages = "122--129",
    abstract = "In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications. We examine the performance metrics used by state-of-the-art systems and determine that they are inadequate for such settings. More specifically, they do not evaluate the systems correctly for situations when there is no answer available and thus agents optimized for these metrics are poor at modeling confidence. We introduce a simple new performance metric for evaluating question-answering agents that is more representative of practical usage conditions, and optimize for this metric by extending the binary reward structure used in prior work to a ternary reward structure which also rewards an agent for not answering a question rather than giving an incorrect answer. We show that this can drastically improve the precision of answered questions while only not answering a limited number of previously correctly answered questions. Employing a supervised learning strategy using depth-first-search paths to bootstrap the reinforcement learning algorithm further improves performance.",
}
@inproceedings{palshikar-etal-2019-extraction,
    title = "Extraction of Message Sequence Charts from Software Use-Case Descriptions",
    author = "Palshikar, Girish  and
      Ramrakhiyani, Nitin  and
      Patil, Sangameshwar  and
      Pawar, Sachin  and
      Hingmire, Swapnil  and
      Varma, Vasudeva  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2017",
    doi = "10.18653/v1/N19-2017",
    pages = "130--137",
    abstract = "Software Requirement Specification documents provide natural language descriptions of the core functional requirements as a set of use-cases. Essentially, each use-case contains a set of actors and sequences of steps describing the interactions among them. Goals of use-case reviews and analyses include their correctness, completeness, detection of ambiguities, prototyping, verification, test case generation and traceability. Message Sequence Chart (MSC) have been proposed as a expressive, rigorous yet intuitive visual representation of use-cases. In this paper, we describe a linguistic knowledge-based approach to extract MSCs from use-cases. Compared to existing techniques, we extract richer constructs of the MSC notation such as timers, conditions and alt-boxes. We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques. We also discuss the benefits and limitations of the extracted MSCs to meet the above goals.",
}
@inproceedings{peng-etal-2019-improving,
    title = "Improving Knowledge Base Construction from Robust Infobox Extraction",
    author = "Peng, Boya  and
      Huh, Yejin  and
      Ling, Xiao  and
      Banko, Michele",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2018",
    doi = "10.18653/v1/N19-2018",
    pages = "138--148",
    abstract = "A capable, automatic Question Answering (QA) system can provide more complete and accurate answers using a comprehensive knowledge base (KB). One important approach to constructing a comprehensive knowledge base is to extract information from Wikipedia infobox tables to populate an existing KB. Despite previous successes in the Infobox Extraction (IBE) problem (e.g., DBpedia), three major challenges remain: 1) Deterministic extraction patterns used in DBpedia are vulnerable to template changes; 2) Over-trusting Wikipedia anchor links can lead to entity disambiguation errors; 3) Heuristic-based extraction of unlinkable entities yields low precision, hurting both accuracy and completeness of the final KB. This paper presents a robust approach that tackles all three challenges. We build probabilistic models to predict relations between entity mentions directly from the infobox tables in HTML. The entity mentions are linked to identifiers in an existing KB if possible. The unlinkable ones are also parsed and preserved in the final output. Training data for both the relation extraction and the entity linking models are automatically generated using distant supervision. We demonstrate the empirical effectiveness of the proposed method in both precision and recall compared to a strong IBE baseline, DBpedia, with an absolute improvement of 41.3{\%} in average F1. We also show that our extraction makes the final KB significantly more complete, improving the completeness score of list-value relation types by 61.4{\%}.",
}
@inproceedings{chen-chen-2019-k,
    title = "A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling",
    author = "Chen, Yue  and
      Chen, John",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2019",
    doi = "10.18653/v1/N19-2019",
    pages = "149--156",
    abstract = "In this paper we present a new method for intent recognition for complex dialog management in low resource situations. Complex dialog management is required because our target domain is real world mixed initiative food ordering between agents and their customers, where individual customer utterances may contain multiple intents and refer to food items with complex structure. For example, a customer might say {``}Can I get a deluxe burger with large fries and oh put extra mayo on the burger would you?{''} We approach this task as a multi-level sequence labeling problem, with the constraint of limited real training data. Both traditional methods like HMM, MEMM, or CRF and newer methods like DNN or BiLSTM use only homogeneous feature sets. Newer methods perform better but also require considerably more data. Previous research has done pseudo-data synthesis to obtain the required amounts of training data. We propose to use a k-NN learner with heterogeneous feature set. We used windowed word n-grams, POS tag n-grams and pre-trained word embeddings as features. For the experiments we perform a comparison between using pseudo-data and real world data. We also perform semi-supervised self-training to obtain additional labeled data, in order to better model real world scenarios. Instead of using massive pseudo-data, we show that with only less than 1{\%} of the data size, we can achieve better result than any of the methods above by annotating real world data. We achieve labeled bracketed F-scores of 75.46, 52.84 and 49.66 for the three levels of sequence labeling where each level has a longer word span than its previous level. Overall we achieve 60.71F. In comparison, two previous systems, MEMM and DNN-ELMO, achieved 52.32 and 45.25 respectively.",
}
@inproceedings{poddar-etal-2019-train,
    title = "Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering",
    author = "Poddar, Lahari  and
      Neves, Leonardo  and
      Brendel, William  and
      Marujo, Luis  and
      Tulyakov, Sergey  and
      Karuturi, Pradeep",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2020",
    doi = "10.18653/v1/N19-2020",
    pages = "157--165",
    abstract = "Tracking user reported bugs requires considerable engineering effort in going through many repetitive reports and assigning them to the correct teams. This paper proposes a neural architecture that can jointly (1) detect if two bug reports are duplicates, and (2) aggregate them into latent topics. Leveraging the assumption that learning the topic of a bug is a sub-task for detecting duplicates, we design a loss function that can jointly perform both tasks but needs supervision for only duplicate classification, achieving topic clustering in an unsupervised fashion. We use a two-step attention module that uses self-attention for topic clustering and conditional attention for duplicate detection. We study the characteristics of two types of real world datasets that have been marked for duplicate bugs by engineers and by non-technical annotators. The results demonstrate that our model not only can outperform state-of-the-art methods for duplicate classification on both cases, but can also learn meaningful latent clusters without additional supervision.",
}
@inproceedings{marzinotto-etal-2019-robust,
    title = "Robust Semantic Parsing with Adversarial Learning for Domain Generalization",
    author = "Marzinotto, Gabriel  and
      Damnati, G{\'e}raldine  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Favre, Beno{\^\i}t",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2021",
    doi = "10.18653/v1/N19-2021",
    pages = "166--173",
    abstract = "This paper addresses the issue of generalization for Semantic Parsing in an adversarial framework. Building models that are more robust to inter-document variability is crucial for the integration of Semantic Parsing technologies in real applications. The underlying question throughout this study is whether adversarial learning can be used to train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations. We propose to perform Semantic Parsing with a domain classification adversarial task, covering various use-cases with or without explicit knowledge of the domain. The strategy is first evaluated on a French corpus of encyclopedic documents, annotated with FrameNet, in an information retrieval perspective. This corpus constitutes a new public benchmark, gathering documents from various thematic domains and various sources. We show that adversarial learning yields improved results when using explicit domain classification as the adversarial task. We also propose an unsupervised domain discovery approach that yields equivalent improvements. The latter is also evaluated on a PropBank Semantic Role Labeling task on the CoNLL-2005 benchmark and is shown to increase the model{'}s generalization capabilities on out-of-domain data.",
}
@inproceedings{sun-etal-2019-toi,
    title = "{TOI}-{CNN}: a Solution of Information Extraction on {C}hinese Insurance Policy",
    author = "Sun, Lin  and
      Zhang, Kai  and
      Ji, Fule  and
      Yang, Zhenhua",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2022",
    doi = "10.18653/v1/N19-2022",
    pages = "174--181",
    abstract = "Contract analysis can significantly ease the work for humans using AI techniques. This paper shows a problem of Element Tagging on Insurance Policy (ETIP). A novel Text-Of-Interest Convolutional Neural Network (TOI-CNN) is proposed for the ETIP solution. We introduce a TOI pooling layer to replace traditional pooling layer for processing the nested phrasal or clausal elements in insurance policies. The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes. The computation of backpropagation through TOI pooling is also demonstrated in the paper. We have collected a large Chinese insurance contract dataset and labeled the critical elements of seven categories to test the performance of the proposed method. The results show the promising performance of our method in the ETIP problem.",
}
@inproceedings{johnson-etal-2019-cross,
    title = "Cross-lingual Transfer Learning for {J}apanese Named Entity Recognition",
    author = "Johnson, Andrew  and
      Karanasou, Penny  and
      Gaspers, Judith  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2023",
    doi = "10.18653/v1/N19-2023",
    pages = "182--189",
    abstract = "This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English. A deep neural network model is adopted and the best combination of weights to transfer is extensively investigated. Moreover, a novel approach is presented that overcomes linguistic differences between this language pair by romanizing a portion of the Japanese input. Experiments are conducted on external datasets, as well as internal large-scale real-world ones. Gains with TL are achieved for all evaluated cases. Finally, the influence on TL of the target dataset size and of the target tagset distribution is further investigated.",
}
@inproceedings{mansfield-etal-2019-neural,
    title = "Neural Text Normalization with Subword Units",
    author = {Mansfield, Courtney  and
      Sun, Ming  and
      Liu, Yuzong  and
      Gandhe, Ankur  and
      Hoffmeister, Bj{\"o}rn},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2024",
    doi = "10.18653/v1/N19-2024",
    pages = "190--196",
    abstract = "Text normalization (TN) is an important step in conversational systems. It converts written text to its spoken form to facilitate speech recognition, natural language understanding and text-to-speech synthesis. Finite state transducers (FSTs) are commonly used to build grammars that handle text normalization. However, translating linguistic knowledge into grammars requires extensive effort. In this paper, we frame TN as a machine translation task and tackle it with sequence-to-sequence (seq2seq) models. Previous research focuses on normalizing a word (or phrase) with the help of limited word-level context, while our approach directly normalizes full sentences. We find subword models with additional linguistic features yield the best performance (with a word error rate of 0.17{\%}).",
}
@inproceedings{cohn-etal-2019-audio,
    title = "Audio De-identification - a New Entity Recognition Task",
    author = "Cohn, Ido  and
      Laish, Itay  and
      Beryozkin, Genady  and
      Li, Gang  and
      Shafran, Izhak  and
      Szpektor, Idan  and
      Hartman, Tzvika  and
      Hassidim, Avinatan  and
      Matias, Yossi",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2025",
    doi = "10.18653/v1/N19-2025",
    pages = "197--204",
    abstract = "Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In such recordings, audio spans with personal information should be redacted, similar to the redaction of sensitive character spans in de-ID for written text. The application of NER in the context of audio de-identification has yet to be fully investigated. To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected. We then present our pipeline for this task, which involves Automatic Speech Recognition (ASR), NER on the transcript text, and text-to-audio alignment. Finally, we introduce a novel metric for audio de-ID and a new evaluation benchmark consisting of a large labeled segment of the Switchboard and Fisher audio datasets and detail our pipeline{'}s results on it.",
}
@inproceedings{prateek-etal-2019-news,
    title = "In Other News: a Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data",
    author = "Prateek, Nishant  and
      {\L}ajszczak, Mateusz  and
      Barra-Chicote, Roberto  and
      Drugman, Thomas  and
      Lorenzo-Trueba, Jaime  and
      Merritt, Thomas  and
      Ronanki, Srikanth  and
      Wood, Trevor",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2026",
    doi = "10.18653/v1/N19-2026",
    pages = "205--213",
    abstract = "Neural text-to-speech synthesis (NTTS) models have shown significant progress in generating high-quality speech, however they require a large quantity of training data. This makes creating models for multiple styles expensive and time-consuming. In this paper different styles of speech are analysed based on prosodic variations, from this a model is proposed to synthesise speech in the style of a newscaster, with just a few hours of supplementary data. We pose the problem of synthesising in a target style using limited data as that of creating a bi-style model that can synthesise both neutral-style and newscaster-style speech via a one-hot vector which factorises the two styles. We also propose conditioning the model on contextual word embeddings, and extensively evaluate it against neutral NTTS, and neutral concatenative-based synthesis. This model closes the gap in perceived style-appropriateness between natural recordings for newscaster-style of speech, and neutral speech synthesis by approximately two-thirds.",
}
@inproceedings{challa-etal-2019-generate,
    title = "Generate, Filter, and Rank: Grammaticality Classification for Production-Ready {NLG} Systems",
    author = "Challa, Ashwini  and
      Upasani, Kartikeya  and
      Balakrishnan, Anusha  and
      Subba, Rajen",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2027",
    doi = "10.18653/v1/N19-2027",
    pages = "214--225",
    abstract = "Neural approaches to Natural Language Generation (NLG) have been promising for goal-oriented dialogue. One of the challenges of productionizing these approaches, however, is the ability to control response quality, and ensure that generated responses are acceptable. We propose the use of a generate, filter, and rank framework, in which candidate responses are first filtered to eliminate unacceptable responses, and then ranked to select the best response. While acceptability includes grammatical correctness and semantic correctness, we focus only on grammaticality classification in this paper, and show that existing datasets for grammatical error correction don{'}t correctly capture the distribution of errors that data-driven generators are likely to make. We release a grammatical classification and semantic correctness classification dataset for the weather domain that consists of responses generated by 3 data-driven NLG systems. We then explore two supervised learning approaches (CNNs and GBDTs) for classifying grammaticality. Our experiments show that grammaticality classification is very sensitive to the distribution of errors in the data, and that these distributions vary significantly with both the source of the response as well as the domain. We show that it{'}s possible to achieve high precision with reasonable recall on our dataset.",
}
@inproceedings{davoudi-etal-2019-content,
    title = "Content-based Dwell Time Engagement Prediction Model for News Articles",
    author = "Davoudi, Heidar  and
      An, Aijun  and
      Edall, Gordon",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-2028",
    doi = "10.18653/v1/N19-2028",
    pages = "226--233",
    abstract = "The article dwell time (i.e., expected time that users spend on an article) is among the most important factors showing the article engagement. It is of great interest to predict the dwell time of an article before its release. This allows digital newspapers to make informed decisions and publish more engaging articles. In this paper, we propose a novel content-based approach based on a deep neural network architecture for predicting article dwell times. The proposed model extracts emotion, event and entity features from an article, learns interactions among them, and combines the interactions with the word-based features of the article to learn a model for predicting the dwell time. The experimental results on a real dataset from a major newspaper show that the proposed model outperforms other state-of-the-art baselines.",
}
@proceedings{naacl-2019-2019,
    title = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1000",
}
@inproceedings{hollenstein-zhang-2019-entity,
    title = "Entity Recognition at First Sight: {I}mproving {NER} with Eye Movement Information",
    author = "Hollenstein, Nora  and
      Zhang, Ce",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1001",
    doi = "10.18653/v1/N19-1001",
    pages = "1--10",
    abstract = "Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.",
}
@inproceedings{lakretz-etal-2019-emergence,
    title = "The emergence of number and syntax units in {LSTM} language models",
    author = "Lakretz, Yair  and
      Kruszewski, German  and
      Desbordes, Theo  and
      Hupkes, Dieuwke  and
      Dehaene, Stanislas  and
      Baroni, Marco",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1002",
    doi = "10.18653/v1/N19-1002",
    pages = "11--20",
    abstract = "Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they accomplish this remarkable feat. Some have conjectured it depends on heuristics that do not truly take hierarchical structure into account. We present here a detailed study of the inner mechanics of number tracking in LSTMs at the single neuron level. We discover that long-distance number information is largely managed by two {``}number units{''}. Importantly, the behaviour of these units is partially controlled by other units independently shown to track syntactic structure. We conclude that LSTMs are, to some extent, implementing genuinely syntactic processing mechanisms, paving the way to a more general understanding of grammatical encoding in LSTMs.",
}
@inproceedings{amiri-2019-neural,
    title = "Neural Self-Training through Spaced Repetition",
    author = "Amiri, Hadi",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1003",
    doi = "10.18653/v1/N19-1003",
    pages = "21--31",
    abstract = "Self-training is a semi-supervised learning approach for utilizing unlabeled data to create better learners. The efficacy of self-training algorithms depends on their data sampling techniques. The majority of current sampling techniques are based on predetermined policies which may not effectively explore the data space or improve model generalizability. In this work, we tackle the above challenges by introducing a new data sampling technique based on spaced repetition that dynamically samples informative and diverse unlabeled instances with respect to individual learner and instance characteristics. The proposed model is specifically effective in the context of neural models which can suffer from overfitting and high-variance gradients when trained with small amount of labeled data. Our model outperforms current semi-supervised learning approaches developed for neural networks on publicly-available datasets.",
}
@inproceedings{futrell-etal-2019-neural,
    title = "Neural language models as psycholinguistic subjects: Representations of syntactic state",
    author = "Futrell, Richard  and
      Wilcox, Ethan  and
      Morita, Takashi  and
      Qian, Peng  and
      Ballesteros, Miguel  and
      Levy, Roger",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1004",
    doi = "10.18653/v1/N19-1004",
    pages = "32--42",
    abstract = "We investigate the extent to which the behavior of neural network language models reflects incremental representations of syntactic state. To do so, we employ experimental methodologies which were originally developed in the field of psycholinguistics to study syntactic representation in the human mind. We examine neural network model behavior on sets of artificial sentences containing a variety of syntactically complex structures. These sentences not only test whether the networks have a representation of syntactic state, they also reveal the specific lexical cues that networks use to update these states. We test four models: two publicly available LSTM sequence models of English (Jozefowicz et al., 2016; Gulordava et al., 2018) trained on large datasets; an RNN Grammar (Dyer et al., 2016) trained on a small, parsed dataset; and an LSTM trained on the same small corpus as the RNNG. We find evidence for basic syntactic state representations in all models, but only the models trained on large datasets are sensitive to subtle lexical cues signaling changes in syntactic state.",
}
@inproceedings{schwartz-mitchell-2019-understanding,
    title = "Understanding language-elicited {EEG} data by predicting it from a fine-tuned language model",
    author = "Schwartz, Dan  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1005",
    doi = "10.18653/v1/N19-1005",
    pages = "43--57",
    abstract = "Electroencephalography (EEG) recordings of brain activity taken while participants read or listen to language are widely used within the cognitive neuroscience and psycholinguistics communities as a tool to study language comprehension. Several time-locked stereotyped EEG responses to word-presentations {--} known collectively as event-related potentials (ERPs) {--} are thought to be markers for semantic or syntactic processes that take place during comprehension. However, the characterization of each individual ERP in terms of what features of a stream of language trigger the response remains controversial. Improving this characterization would make ERPs a more useful tool for studying language comprehension. We take a step towards better understanding the ERPs by finetuning a language model to predict them. This new approach to analysis shows for the first time that all of the ERPs are predictable from embeddings of a stream of language. Prior work has only found two of the ERPs to be predictable. In addition to this analysis, we examine which ERPs benefit from sharing parameters during joint training. We find that two pairs of ERPs previously identified in the literature as being related to each other benefit from joint training, while several other pairs of ERPs that benefit from joint training are suggestive of potential relationships. Extensions of this analysis that further examine what kinds of information in the model embeddings relate to each ERP have the potential to elucidate the processes involved in human language comprehension.",
}
@inproceedings{bansal-etal-2019-pre,
    title = "Pre-training on high-resource speech recognition improves low-resource speech-to-text translation",
    author = "Bansal, Sameer  and
      Kamper, Herman  and
      Livescu, Karen  and
      Lopez, Adam  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1006",
    doi = "10.18653/v1/N19-1006",
    pages = "58--68",
    abstract = "We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tune its parameters for ST. We demonstrate that our approach is effective by pre-training on 300 hours of English ASR data to improve Spanish English ST from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data are available. Through an ablation study, we find that the pre-trained encoder (acoustic model) accounts for most of the improvement, despite the fact that the shared language in these tasks is the target language text, not the source language audio. Applying this insight, we show that pre-training on ASR helps ST even when the ASR language differs from both source and target ST languages: pre-training on French ASR also improves Spanish-English ST. Finally, we show that the approach improves performance on a true low-resource task: pre-training on a combination of English ASR and French ASR improves Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1 BLEU.",
}
@inproceedings{shain-elsner-2019-measuring,
    title = "Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders",
    author = "Shain, Cory  and
      Elsner, Micha",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1007",
    doi = "10.18653/v1/N19-1007",
    pages = "69--85",
    abstract = "In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further evaluate the degree to which theory-driven phonological features are encoded in the latent bit patterns, finding that some (e.g. [+-approximant]), are well represented by the network in both languages, while others (e.g. [+-spread glottis]) are less so. Together, these findings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues must eventually be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination. Our results also suggest differences in degree of perceptual availability between features, yielding testable predictions as to which features might depend more or less heavily on top-down cues during child language acquisition.",
}
@inproceedings{zayats-ostendorf-2019-giving,
    title = "Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection",
    author = "Zayats, Vicky  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1008",
    doi = "10.18653/v1/N19-1008",
    pages = "86--95",
    abstract = "Disfluencies in spontaneous speech are known to be associated with prosodic disruptions. However, most algorithms for disfluency detection use only word transcripts. Integrating prosodic cues has proved difficult because of the many sources of variability affecting the acoustic correlates. This paper introduces a new approach to extracting acoustic-prosodic cues using text-based distributional prediction of acoustic cues to derive vector z-score features (innovations). We explore both early and late fusion techniques for integrating text and prosody, showing gains over a high-accuracy text-only model.",
}
@inproceedings{adams-etal-2019-massively,
    title = "Massively Multilingual Adversarial Speech Recognition",
    author = "Adams, Oliver  and
      Wiesner, Matthew  and
      Watanabe, Shinji  and
      Yarowsky, David",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1009",
    doi = "10.18653/v1/N19-1009",
    pages = "96--108",
    abstract = "We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.",
}
@inproceedings{vogler-etal-2019-lost,
    title = "{L}ost in {I}nterpretation: {P}redicting {U}ntranslated {T}erminology in {S}imultaneous {I}nterpretation",
    author = "Vogler, Nikolai  and
      Stewart, Craig  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1010",
    doi = "10.18653/v1/N19-1010",
    pages = "109--118",
    abstract = "Simultaneous interpretation, the translation of speech from one language to another in real-time, is an inherently difficult and strenuous task. One of the greatest challenges faced by interpreters is the accurate translation of difficult terminology like proper names, numbers, or other entities. Intelligent computer-assisted interpreting (CAI) tools that could analyze the spoken word and detect terms likely to be untranslated by an interpreter could reduce translation error and improve interpreter performance. In this paper, we propose a task of predicting which terminology simultaneous interpreters will leave untranslated, and examine methods that perform this task using supervised sequence taggers. We describe a number of task-specific features explicitly designed to indicate when an interpreter may struggle with translating a word. Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus (Shimizu et al., 2014) indicate the promise of our proposed method.",
}
@inproceedings{kim-etal-2019-audiocaps,
    title = "{A}udio{C}aps: Generating Captions for Audios in The Wild",
    author = "Kim, Chris Dongjoo  and
      Kim, Byeongchang  and
      Lee, Hyunmin  and
      Kim, Gunhee",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1011",
    doi = "10.18653/v1/N19-1011",
    pages = "119--132",
    abstract = "We explore the problem of Audio Captioning: generating natural language description for any kind of audio in the wild, which has been surprisingly unexplored in previous research. We contribute a large-scale dataset of 46K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset. Our thorough empirical studies not only show that our collected captions are indeed faithful to audio inputs but also discover what forms of audio representation and captioning models are effective for the audio captioning. From extensive experiments, we also propose two novel components that help improve audio captioning performance: the top-down multi-scale encoder and aligned semantic attention.",
}
@inproceedings{hossain-etal-2019-president,
    title = "{``}President Vows to Cut {\textless}Taxes{\textgreater} Hair{''}: Dataset and Analysis of Creative Text Editing for Humorous Headlines",
    author = "Hossain, Nabil  and
      Krumm, John  and
      Gamon, Michael",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1012",
    doi = "10.18653/v1/N19-1012",
    pages = "133--142",
    abstract = "We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. We carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline. The simple edits, usually just a single word replacement, mean we can apply straightforward analysis techniques to determine what makes our edited headlines humorous. We show how the data support classic theories of humor, such as incongruity, superiority, and setup/punchline. Finally, we develop baseline classifiers that can predict whether or not an edited headline is funny, which is a first step toward automatically generating humorous headlines as an approach to creating topical humor.",
}
@inproceedings{rao-daume-iii-2019-answer,
    title = "{A}nswer-based {A}dversarial {T}raining for {G}enerating {C}larification {Q}uestions",
    author = "Rao, Sudha  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1013",
    doi = "10.18653/v1/N19-1013",
    pages = "143--155",
    abstract = "We present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete. We propose that modeling hypothetical answers (to clarification questions) as latent variables can guide our approach into generating more useful clarification questions. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.",
}
@inproceedings{zhao-etal-2019-improving,
    title = "Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data",
    author = "Zhao, Wei  and
      Wang, Liang  and
      Shen, Kewei  and
      Jia, Ruoyu  and
      Liu, Jingming",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1014",
    doi = "10.18653/v1/N19-1014",
    pages = "156--165",
    abstract = "Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. In this paper, we propose a copy-augmented architecture for the GEC task by copying the unchanged words from the source sentence to the target sentence. Since the GEC suffers from not having enough labeled training data to achieve high accuracy. We pre-train the copy-augmented architecture with a denoising auto-encoder using the unlabeled One Billion Benchmark and make comparisons between the fully pre-trained model and a partially pre-trained model. It is the first time copying words from the source context and fully pre-training a sequence to sequence model are experimented on the GEC task. Moreover, We add token-level and sentence-level multi-task learning for the GEC task. The evaluation results on the CoNLL-2014 test set show that our approach outperforms all recently published state-of-the-art results by a large margin.",
}
@inproceedings{wang-etal-2019-topic-guided,
    title = "Topic-Guided Variational Auto-Encoder for Text Generation",
    author = "Wang, Wenlin  and
      Gan, Zhe  and
      Xu, Hongteng  and
      Zhang, Ruiyi  and
      Wang, Guoyin  and
      Shen, Dinghan  and
      Chen, Changyou  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1015",
    doi = "10.18653/v1/N19-1015",
    pages = "166--177",
    abstract = "We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each mixture component corresponds to a latent topic, which provides a guidance to generate sentences under the topic. The neural topic module and the VAE-based neural sequence module in our model are learned jointly. In particular, a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during the model inference. Experimental results show that our TGVAE outperforms its competitors on both unconditional and conditional text generation, which can also generate semantically-meaningful sentences with various topics.",
}
@inproceedings{ruprecht-denkinger-2019-implementation,
    title = {Implementation of a {C}homsky-Sch{\"u}tzenberger n-best parser for weighted multiple context-free grammars},
    author = "Ruprecht, Thomas  and
      Denkinger, Tobias",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1016",
    doi = "10.18653/v1/N19-1016",
    pages = "178--191",
    abstract = {Constituent parsing has been studied extensively in the last decades. Chomsky-Sch{\"u}tzenberger parsing as an approach to constituent parsing has only been investigated theoretically, yet. It uses the decomposition of a language into a regular language, a homomorphism, and a bracket language to divide the parsing problem into simpler subproblems. We provide the first implementation of Chomsky-Sch{\"u}tzenberger parsing. It employs multiple context-free grammars and incorporates many refinements to achieve feasibility. We compare its performance to state-of-the-art grammar-based parsers.},
}
@inproceedings{dehouck-denis-2019-phylogenic,
    title = "Phylogenic Multi-Lingual Dependency Parsing",
    author = "Dehouck, Mathieu  and
      Denis, Pascal",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1017",
    doi = "10.18653/v1/N19-1017",
    pages = "192--203",
    abstract = "Languages evolve and diverge over time. Their evolutionary history is often depicted in the shape of a phylogenetic tree. Assuming parsing models are representations of their languages grammars, their evolution should follow a structure similar to that of the phylogenetic tree. In this paper, drawing inspiration from multi-task learning, we make use of the phylogenetic tree to guide the learning of multi-lingual dependency parsers leveraging languages structural similarities. Experiments on data from the Universal Dependency project show that phylogenetic training is beneficial to low resourced languages and to well furnished languages families. As a side product of phylogenetic training, our model is able to perform zero-shot parsing of previously unseen languages.",
}
@inproceedings{coavoux-cohen-2019-discontinuous,
    title = "Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle",
    author = "Coavoux, Maximin  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1018",
    doi = "10.18653/v1/N19-1018",
    pages = "204--217",
    abstract = "We introduce a novel transition system for discontinuous constituency parsing. Instead of storing subtrees in a stack {--}i.e. a data structure with linear-time sequential access{--} the proposed system uses a set of parsing items, with constant-time random access. This change makes it possible to construct any discontinuous constituency tree in exactly $4n–2$ transitions for a sentence of length n. At each parsing step, the parser considers every item in the set to be combined with a focus item and to construct a new constituent in a bottom-up fashion. The parsing strategy is based on the assumption that most syntactic structures can be parsed incrementally and that the set {--}the memory of the parser{--} remains reasonably small on average. Moreover, we introduce a provably correct dynamic oracle for the new transition system, and present the first experiments in discontinuous constituency parsing using a dynamic oracle. Our parser obtains state-of-the-art results on three English and German discontinuous treebanks.",
}
@inproceedings{wisniewski-yvon-2019-bad,
    title = "{H}ow {B}ad are {P}o{S} {T}agger in {C}ross-{C}orpora {S}ettings? {E}valuating {A}nnotation {D}ivergence in the {UD} {P}roject.",
    author = "Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1019",
    doi = "10.18653/v1/N19-1019",
    pages = "218--227",
    abstract = "The performance of Part-of-Speech tagging varies significantly across the treebanks of the Universal Dependencies project. This work points out that these variations may result from divergences between the annotation of train and test sets. We show how the annotation variation principle, introduced by Dickinson and Meurers (2003) to automatically detect errors in gold standard, can be used to identify inconsistencies between annotations; we also evaluate their impact on prediction performance.",
}
@inproceedings{stanojevic-steedman-2019-ccg,
    title = "{CCG} Parsing Algorithm with Incremental Tree Rotation",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Steedman, Mark",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1020",
    doi = "10.18653/v1/N19-1020",
    pages = "228--239",
    abstract = "The main obstacle to incremental sentence processing arises from right-branching constituent structures, which are present in the majority of English sentences, as well as optional constituents that adjoin on the right, such as right adjuncts and right conjuncts. In CCG, many right-branching derivations can be replaced by semantically equivalent left-branching incremental derivations. The problem of right-adjunction is more resistant to solution, and has been tackled in the past using revealing-based approaches that often rely either on the higher-order unification over lambda terms (Pareschi and Steedman,1987) or heuristics over dependency representations that do not cover the whole CCGbank (Ambati et al., 2015). We propose a new incremental parsing algorithm for CCG following the same revealing tradition of work but having a purely syntactic approach that does not depend on access to a distinct level of semantic representation. This algorithm can cover the whole CCGbank, with greater incrementality and accuracy than previous proposals.",
}
@inproceedings{fu-etal-2019-cyclical,
    title = "Cyclical Annealing Schedule: A Simple Approach to Mitigating {KL} Vanishing",
    author = "Fu, Hao  and
      Li, Chunyuan  and
      Liu, Xiaodong  and
      Gao, Jianfeng  and
      Celikyilmaz, Asli  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1021",
    doi = "10.18653/v1/N19-1021",
    pages = "240--250",
    abstract = "Variational autoencoders (VAE) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. VAE objective consists of two terms, the KL regularization term and the reconstruction term, balanced by a weighting hyper-parameter $\beta$. One notorious training difficulty is that the KL term tends to vanish. In this paper we study different scheduling schemes for $\beta$, and show that KL vanishing is caused by the lack of good latent codes in training decoder at the beginning of optimization. To remedy the issue, we propose a cyclical annealing schedule, which simply repeats the process of increasing $\beta$ multiple times. This new procedure allows us to learn more meaningful latent codes progressively by leveraging the results of previous learning cycles as warm re-restart. The effectiveness of cyclical annealing schedule is validated on a broad range of NLP tasks, including language modeling, dialog response generation and semi-supervised text classification.",
}
@inproceedings{schluter-2019-recurrent,
    title = "Recurrent models and lower bounds for projective syntactic decoding",
    author = "Schluter, Natalie",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1022",
    doi = "10.18653/v1/N19-1022",
    pages = "251--260",
    abstract = "The current state-of-the-art in neural graph-based parsing uses only approximate decoding at the training phase. In this paper aim to understand this result better. We show how recurrent models can carry out projective maximum spanning tree decoding. This result holds for both current state-of-the-art models for shift-reduce and graph-based parsers, projective or not. We also provide the first proof on the lower bounds of projective maximum spanning tree decoding.",
}
@inproceedings{wijnholds-sadrzadeh-2019-evaluating,
    title = "Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings",
    author = "Wijnholds, Gijs  and
      Sadrzadeh, Mehrnoosh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1023",
    doi = "10.18653/v1/N19-1023",
    pages = "261--271",
    abstract = "Ellipsis is a natural language phenomenon where part of a sentence is missing and its information must be recovered from its surrounding context, as in {``}Cats chase dogs and so do foxes.{''}. Formal semantics has different methods for resolving ellipsis and recovering the missing information, but the problem has not been considered for distributional semantics, where words have vector embeddings and combinations thereof provide embeddings for sentences. In elliptical sentences these combinations go beyond linear as copying of elided information is necessary. In this paper, we develop different models for embedding VP-elliptical sentences. We extend existing verb disambiguation and sentence similarity datasets to ones containing elliptical phrases and evaluate our models on these datasets for a variety of non-linear combinations and their linear counterparts. We compare results of these compositional models to state of the art holistic sentence encoders. Our results show that non-linear addition and a non-linear tensor-based composition outperform the naive non-compositional baselines and the linear models, and that sentence encoders perform well on sentence similarity, but not on verb disambiguation.",
}
@inproceedings{lin-etal-2019-neural,
    title = "Neural Finite-State Transducers: Beyond Rational Relations",
    author = "Lin, Chu-Cheng  and
      Zhu, Hao  and
      Gormley, Matthew R.  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1024",
    doi = "10.18653/v1/N19-1024",
    pages = "272--283",
    abstract = "We introduce neural finite state transducers (NFSTs), a family of string transduction models defining joint and conditional probability distributions over pairs of strings. The probability of a string pair is obtained by marginalizing over all its accepting paths in a finite state transducer. In contrast to ordinary weighted FSTs, however, each path is scored using an arbitrary function such as a recurrent neural network, which breaks the usual conditional independence assumption (Markov property). NFSTs are more powerful than previous finite-state models with neural features (Rastogi et al., 2016.) We present training and inference algorithms for locally and globally normalized variants of NFSTs. In experiments on different transduction tasks, they compete favorably against seq2seq models while offering interpretable paths that correspond to hard monotonic alignments.",
}
@inproceedings{wang-wang-2019-riemannian,
    title = "{R}iemannian Normalizing Flow on Variational {W}asserstein Autoencoder for Text Modeling",
    author = "Wang, Prince Zizhuang  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1025",
    doi = "10.18653/v1/N19-1025",
    pages = "284--294",
    abstract = "Recurrent Variational Autoencoder has been widely used for language modeling and text generation tasks. These models often face a difficult optimization problem, also known as KL vanishing, where the posterior easily collapses to the prior and model will ignore latent codes in generative tasks. To address this problem, we introduce an improved Variational Wasserstein Autoencoder (WAE) with Riemannian Normalizing Flow (RNF) for text modeling. The RNF transforms a latent variable into a space that respects the geometric characteristics of input space, which makes posterior impossible to collapse to the non-informative prior. The Wasserstein objective minimizes the distance between marginal distribution and the prior directly and therefore does not force the posterior to match the prior. Empirical experiments show that our model avoids KL vanishing over a range of datasets and has better performance in tasks such as language modeling, likelihood approximation, and text generation. Through a series of experiments and analysis over latent space, we show that our model learns latent distributions that respect latent space geometry and is able to generate sentences that are more diverse.",
}
@inproceedings{yaghoub-zadeh-fard-etal-2019-study,
    title = "A Study of Incorrect Paraphrases in Crowdsourced User Utterances",
    author = "Yaghoub-Zadeh-Fard, Mohammad-Ali  and
      Benatallah, Boualem  and
      Chai Barukh, Moshe  and
      Zamanirad, Shayan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1026",
    doi = "10.18653/v1/N19-1026",
    pages = "295--306",
    abstract = "Developing bots demands highquality training samples, typically in the form of user utterances and their associated intents. Given the fuzzy nature of human language, such datasets ideally must cover all possible utterances of each single intent. Crowdsourcing has widely been used to collect such inclusive datasets by paraphrasing an initial utterance. However, the quality of this approach often suffers from various issues, particularly language errors produced by unqualified crowd workers. More so, since workers are tasked to write open-ended text, it is very challenging to automatically asses the quality of paraphrased utterances. In this paper, we investigate common crowdsourced paraphrasing issues, and propose an annotated dataset called Para-Quality, for detecting the quality issues. We also investigate existing tools and services to provide baselines for detecting each category of issues. In all, this work presents a data-driven view of incorrect paraphrases during the bot development process, and we pave the way towards automatic detection of unqualified paraphrases.",
}
@inproceedings{abujabal-etal-2019-comqa,
    title = "{C}om{QA}: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters",
    author = "Abujabal, Abdalghani  and
      Saha Roy, Rishiraj  and
      Yahya, Mohamed  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1027",
    doi = "10.18653/v1/N19-1027",
    pages = "307--317",
    abstract = "To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as compositionality, temporal reasoning, and comparisons. ComQA questions come from the WikiAnswers community QA platform, which typically contains questions that are not satisfactorily answerable by existing search engine technology. Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers. ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters. We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing. We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.",
}
@inproceedings{jiang-etal-2019-freebaseqa,
    title = "{F}reebase{QA}: A New Factoid {QA} Data Set Matching Trivia-Style Question-Answer Pairs with {F}reebase",
    author = "Jiang, Kelvin  and
      Wu, Dekun  and
      Jiang, Hui",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1028",
    doi = "10.18653/v1/N19-1028",
    pages = "318--323",
    abstract = "In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove any false positive in these matched triples. Using this method, we are able to efficiently generate over 54K matches from about 28K unique questions with minimal cost. Our analysis shows that this data set is suitable for model training in factoid QA tasks beyond simpler questions since FreebaseQA provides more linguistically sophisticated questions than other existing data sets.",
}
@inproceedings{zhao-etal-2019-simple,
    title = "Simple Question Answering with Subgraph Ranking and Joint-Scoring",
    author = "Zhao, Wenbo  and
      Chung, Tagyoung  and
      Goyal, Anuj  and
      Metallinou, Angeliki",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1029",
    doi = "10.18653/v1/N19-1029",
    pages = "324--334",
    abstract = "Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject{--}relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject{--}relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44{\%} in accuracy) on the SimpleQuestions dataset.",
}
@inproceedings{ni-etal-2019-learning,
    title = "Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering",
    author = "Ni, Jianmo  and
      Zhu, Chenguang  and
      Chen, Weizhu  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1030",
    doi = "10.18653/v1/N19-1030",
    pages = "335--344",
    abstract = "Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets, notably achieving the level of the state-of-the-art on the AI2 Reasoning Challenge (ARC) dataset.",
}
@inproceedings{chen-etal-2019-uhop,
    title = "{UH}op: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering",
    author = "Chen, Zi-Yuan  and
      Chang, Chih-Hung  and
      Chen, Yi-Pei  and
      Nayak, Jijnasa  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1031",
    doi = "10.18653/v1/N19-1031",
    pages = "345--356",
    abstract = "In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called {``}one hop{''}. In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expensive. Therefore, the number of hops is generally restricted to two or three. In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one. We conduct experiments on conventional 1- and 2-hop questions as well as lengthy questions, including datasets such as WebQSP, PathQuestion, and Grid World. Results show that the proposed framework enables the ability to halt, works well with state-of-the-art models, achieves competitive performance without exhaustive searches, and opens the performance gap for long relation paths.",
}
@inproceedings{cao-etal-2019-bag,
    title = "{BAG}: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering",
    author = "Cao, Yu  and
      Fang, Meng  and
      Tao, Dacheng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1032",
    doi = "10.18653/v1/N19-1032",
    pages = "357--362",
    abstract = "Multi-hop reasoning question answering requires deep comprehension of relationships between various documents and queries. We propose a Bi-directional Attention Entity Graph Convolutional Network (BAG), leveraging relationships between nodes in an entity graph and attention information between a query and the entity graph, to solve this task. Graph convolutional networks are used to obtain a relation-aware representation of nodes for entity graphs built from documents with multi-level features. Bidirectional attention is then applied on graphs and queries to generate a query-aware nodes representation, which will be used for the final prediction. Experimental evaluation shows BAG achieves state-of-the-art accuracy performance on the QAngaroo WIKIHOP dataset.",
}
@inproceedings{ionescu-butnaru-2019-vector,
    title = "Vector of Locally-Aggregated Word Embeddings ({VLAWE}): A Novel Document-level Representation",
    author = "Ionescu, Radu Tudor  and
      Butnaru, Andrei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1033",
    doi = "10.18653/v1/N19-1033",
    pages = "363--369",
    abstract = "In this paper, we propose a novel representation for text documents based on aggregating word embedding vectors into document embeddings. Our approach is inspired by the Vector of Locally-Aggregated Descriptors used for image representation, and it works as follows. First, the word embeddings gathered from a collection of documents are clustered by k-means in order to learn a codebook of semnatically-related word embeddings. Each word embedding is then associated to its nearest cluster centroid (codeword). The Vector of Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then computed by accumulating the differences between each codeword vector and each word vector (from the document) associated to the respective codeword. We plug the VLAWE representation, which is learned in an unsupervised manner, into a classifier and show that it is useful for a diverse set of text classification tasks. We compare our approach with a broad range of recent state-of-the-art methods, demonstrating the effectiveness of our approach. Furthermore, we obtain a considerable improvement on the Movie Review data set, reporting an accuracy of 93.3{\%}, which represents an absolute gain of 10{\%} over the state-of-the-art approach.",
}
@inproceedings{akhtar-etal-2019-multi,
    title = "Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis",
    author = "Akhtar, Md Shad  and
      Chauhan, Dushyant  and
      Ghosal, Deepanway  and
      Poria, Soujanya  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1034",
    doi = "10.18653/v1/N19-1034",
    pages = "370--379",
    abstract = "Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e. text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU-MOSEI dataset for multi-modal sentiment and emotion analysis. Evaluation results suggest that multi-task learning framework offers improvement over the single-task framework. The proposed approach reports new state-of-the-art performance for both sentiment analysis and emotion analysis.",
}
@inproceedings{sun-etal-2019-utilizing,
    title = "Utilizing {BERT} for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence",
    author = "Sun, Chi  and
      Huang, Luyao  and
      Qiu, Xipeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1035",
    doi = "10.18653/v1/N19-1035",
    pages = "380--385",
    abstract = "Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.",
}
@inproceedings{zeng-etal-2019-variational,
    title = "A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification",
    author = "Zeng, Ziqian  and
      Zhou, Wenxuan  and
      Liu, Xin  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1036",
    doi = "10.18653/v1/N19-1036",
    pages = "386--396",
    abstract = "In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target-opinion word pairs as {``}supervision.{''} These word pairs can be extracted by using dependency parsers and simple rules. Our objective is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment polarity classifier to predict the sentiment polarity of each aspect given a document. By introducing a latent variable, i.e., the sentiment polarity, to the objective function, we can inject the sentiment polarity classifier to the objective via the variational lower bound. We can learn a sentiment polarity classifier by optimizing the lower bound. We show that our method can outperform weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to the state-of-the-art supervised method with hundreds of labels per aspect.",
}
@inproceedings{jiao-etal-2019-higru,
    title = "{H}i{GRU}: {H}ierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition",
    author = "Jiao, Wenxiang  and
      Yang, Haiqin  and
      King, Irwin  and
      Lyu, Michael R.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1037",
    doi = "10.18653/v1/N19-1037",
    pages = "397--406",
    abstract = "In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to capture the contexts of utterance-level embeddings. Moreover, we promote the framework to two variants, Hi-GRU with individual features fusion (HiGRU-f) and HiGRU with self-attention and features fusion (HiGRU-sf), so that the word/utterance-level individual inputs and the long-range contextual information can be sufficiently utilized. Experiments on three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate that our proposed Hi-GRU models attain at least 8.7{\%}, 7.5{\%}, 6.0{\%} improvement over the state-of-the-art methods on each dataset, respectively. Particularly, by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at least 3.8{\%} improvement over the state-of-the-art conversational memory network (CMN) with the trimodal features of text, video, and audio.",
}
@inproceedings{prollochs-etal-2019-learning,
    title = "Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach",
    author = {Pr{\"o}llochs, Nicolas  and
      Feuerriegel, Stefan  and
      Neumann, Dirk},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1038",
    doi = "10.18653/v1/N19-1038",
    pages = "407--413",
    abstract = "Negation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level. This suffers from two key drawbacks: (1) such granular annotations are costly and (2) highly subjective, since, due to the absence of explicit linguistic resolution rules, human annotators often disagree in the perceived negation scopes. To the best of our knowledge, our work presents the first approach that eliminates the need for world-level negation labels, replacing it instead with document-level sentiment annotations. For this, we present a novel strategy for learning fully interpretable negation rules via weak supervision: we apply reinforcement learning to find a policy that reconstructs negation rules from sentiment predictions at document level. Our experiments demonstrate that our approach for weak supervision can effectively learn negation rules. Furthermore, an out-of-sample evaluation via sentiment analysis reveals consistent improvements (of up to 4.66{\%}) over both a sentiment analysis with (i) no negation handling and (ii) the use of word-level annotations from humans. Moreover, the inferred negation rules are fully interpretable.",
}
@inproceedings{miller-2019-simplified,
    title = "Simplified Neural Unsupervised Domain Adaptation",
    author = "Miller, Timothy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1039",
    doi = "10.18653/v1/N19-1039",
    pages = "414--419",
    abstract = "Unsupervised domain adaptation (UDA) is the task of training a statistical model on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. Existing state-of-the-art UDA approaches use neural networks to learn representations that are trained to predict the values of subset of important features called {``}pivot features{''} on combined data from the source and target domains. In this work, we show that it is possible to improve on existing neural domain adaptation algorithms by 1) jointly training the representation learner with the task learner; and 2) removing the need for heuristically-selected {``}pivot features.{''} Our results show competitive performance with a simpler model.",
}
@inproceedings{feng-wan-2019-learning,
    title = "Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision",
    author = "Feng, Yanlin  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1040",
    doi = "10.18653/v1/N19-1040",
    pages = "420--429",
    abstract = "Word embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. However, these methods are mainly evaluated on tasks of word translation or word similarity. We show that these methods fail to capture the sentiment information and do not perform well enough on cross-lingual sentiment analysis. In this work, we propose UBiSE (Unsupervised Bilingual Sentiment Embeddings), which learns sentiment-specific word representations for two languages in a common space without any cross-lingual supervision. Our method only requires a sentiment corpus in the source language and pretrained monolingual word embeddings of both languages. We evaluate our method on three language pairs for cross-lingual sentiment analysis. Experimental results show that our method outperforms previous unsupervised BWE methods and even supervised BWE methods. Our method succeeds for a distant language pair English-Basque.",
}
@inproceedings{jauregi-unanue-etal-2019-rewe,
    title = "{R}e{WE}: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems",
    author = "Jauregi Unanue, Inigo  and
      Zare Borzeshi, Ehsan  and
      Esmaili, Nazanin  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1041",
    doi = "10.18653/v1/N19-1041",
    pages = "430--436",
    abstract = "Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.4 BLEU points, and also a marked improvement over a state-of-the-art system.",
}
@inproceedings{cohn-gordon-goodman-2019-lost,
    title = "Lost in Machine Translation: A Method to Reduce Meaning Loss",
    author = "Cohn-Gordon, Reuben  and
      Goodman, Noah",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1042",
    doi = "10.18653/v1/N19-1042",
    pages = "437--441",
    abstract = "A desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same sentence in another language. However, state-of-the-art systems often fail in this regard, particularly in cases where the source and target languages partition the {``}meaning space{''} in different ways. For instance, {``}I cut my finger.{''} and {``}I cut my finger off.{''} describe different states of the world but are translated to French (by both Fairseq and Google Translate) as {``}Je me suis coup{\'e} le doigt.{''}, which is ambiguous as to whether the finger is detached. More generally, translation systems are typically many-to-one (non-injective) functions from source to target language, which in many cases results in important distinctions in meaning being lost in translation. Building on Bayesian models of informative utterance production, we present a method to define a less ambiguous translation system in terms of an underlying pre-trained neural sequence-to-sequence model. This method increases injectivity, resulting in greater preservation of meaning as measured by improvement in cycle-consistency, without impeding translation quality (measured by BLEU score).",
}
@inproceedings{niu-etal-2019-bi,
    title = "Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation",
    author = "Niu, Xing  and
      Xu, Weijia  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1043",
    doi = "10.18653/v1/N19-1043",
    pages = "442--448",
    abstract = "We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT). This loss compares original inputs to reconstructed inputs, obtained by back-translating translation hypotheses into the input language. We leverage differentiable sampling and bi-directional NMT to train models end-to-end, without introducing additional parameters. This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states.",
}
@inproceedings{song-etal-2019-code,
    title = "Code-Switching for Enhancing {NMT} with Pre-Specified Translation",
    author = "Song, Kai  and
      Zhang, Yue  and
      Yu, Heng  and
      Luo, Weihua  and
      Wang, Kun  and
      Zhang, Min",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1044",
    doi = "10.18653/v1/N19-1044",
    pages = "449--459",
    abstract = "Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Extensive experiments show that our method achieves consistent improvements over existing approaches, improving translation of constrained words without hurting unconstrained words.",
}
@inproceedings{yehezkel-lubin-etal-2019-aligning,
    title = "Aligning Vector-spaces with Noisy Supervised Lexicon",
    author = "Yehezkel Lubin, Noa  and
      Goldberger, Jacob  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1045",
    doi = "10.18653/v1/N19-1045",
    pages = "460--465",
    abstract = "The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the set of aligned points is allowed to contain an amount of noise, in the form of incorrect lexicon pairs and show that this arises in practice by analyzing the edited dictionaries after the cleaning process. We demonstrate that such noise substantially degrades the accuracy of the learned translation when using current methods. We propose a model that accounts for noisy pairs. This is achieved by introducing a generative model with a compatible iterative EM algorithm. The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces. We demonstrate the effectiveness of our proposed algorithm on two alignment problems: bilingual word embedding translation, and mapping between diachronic embedding spaces for recovering the semantic shifts of words across time periods.",
}
@inproceedings{li-etal-2019-understanding,
    title = "{U}nderstanding and {I}mproving {H}idden {R}epresentations for {N}eural {M}achine {T}ranslation",
    author = "Li, Guanlin  and
      Liu, Lemao  and
      Li, Xintong  and
      Zhu, Conghui  and
      Zhao, Tiejun  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1046",
    doi = "10.18653/v1/N19-1046",
    pages = "466--477",
    abstract = "Multilayer architectures are currently the gold standard for large-scale neural machine translation. Existing works have explored some methods for understanding the hidden representations, however, they have not sought to improve the translation quality rationally according to their understanding. Towards understanding for performance improvement, we first artificially construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks. Based on our understanding, we then propose to regularize the layer-wise representations with all tree-induced tasks. To overcome the computational bottleneck resulting from the large number of regularization terms, we design efficient approximation methods by selecting a few coarse-to-fine tasks for regularization. Extensive experiments on two widely-used datasets demonstrate the proposed methods only lead to small extra overheads in training but no additional overheads in testing, and achieve consistent improvements (up to +1.3 BLEU) compared to the state-of-the-art translation model.",
}
@inproceedings{hershcovich-etal-2019-content,
    title = "Content Differences in Syntactic and Semantic Representation",
    author = "Hershcovich, Daniel  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1047",
    doi = "10.18653/v1/N19-1047",
    pages = "478--488",
    abstract = "Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA{'}s distinction between a Scene and a non-Scene; (2) UCCA{'}s distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.",
}
@inproceedings{schick-schutze-2019-attentive,
    title = "Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts",
    author = {Schick, Timo  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1048",
    doi = "10.18653/v1/N19-1048",
    pages = "489--494",
    abstract = "Learning high-quality embeddings for rare words is a hard problem because of sparse context information. Mimicking (Pinter et al., 2017) has been proposed as a solution: given embeddings learned by a standard algorithm, a model is first trained to reproduce embeddings of frequent words from their surface form and then used to compute embeddings for rare words. In this paper, we introduce attentive mimicking: the mimicking model is given access not only to a word{'}s surface form, but also to all available contexts and learns to attend to the most informative and reliable contexts for computing an embedding. In an evaluation on four tasks, we show that attentive mimicking outperforms previous work for both rare and medium-frequency words. Thus, compared to previous work, attentive mimicking improves embeddings for a much larger part of the vocabulary, including the medium-frequency range.",
}
@inproceedings{mir-etal-2019-evaluating,
    title = "Evaluating Style Transfer for Text",
    author = "Mir, Remi  and
      Felbo, Bjarke  and
      Obradovich, Nick  and
      Rahwan, Iyad",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1049",
    doi = "10.18653/v1/N19-1049",
    pages = "495--504",
    abstract = "Research in the area of style transfer for text is currently bottlenecked by a lack of standard evaluation practices. This paper aims to alleviate this issue by experimentally identifying best practices with a Yelp sentiment dataset. We specify three aspects of interest (style transfer intensity, content preservation, and naturalness) and show how to obtain more reliable measures of them from human evaluation than in previous work. We propose a set of metrics for automated evaluation and demonstrate that they are more strongly correlated and in agreement with human judgment: direction-corrected Earth Mover{'}s Distance, Word Mover{'}s Distance on style-masked texts, and adversarial classification for the respective aspects. We also show that the three examined models exhibit tradeoffs between aspects of interest, demonstrating the importance of evaluating style transfer models at specific points of their tradeoff plots. We release software with our evaluation metrics to facilitate research.",
}
@inproceedings{asaadi-etal-2019-big,
    title = "Big {B}i{RD}: A Large, Fine-Grained, Bigram Relatedness Dataset for Examining Semantic Composition",
    author = "Asaadi, Shima  and
      Mohammad, Saif  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1050",
    doi = "10.18653/v1/N19-1050",
    pages = "505--516",
    abstract = "Bigrams (two-word sequences) hold a special place in semantic composition research since they are the smallest unit formed by composing words. A semantic relatedness dataset that includes bigrams will thus be useful in the development of automatic methods of semantic composition. However, existing relatedness datasets only include pairs of unigrams (single words). Further, existing datasets were created using rating scales and thus suffer from limitations such as in consistent annotations and scale region bias. In this paper, we describe how we created a large, fine-grained, bigram relatedness dataset (BiRD), using a comparative annotation technique called Best{--}Worst Scaling. Each of BiRD{'}s 3,345 English term pairs involves at least one bigram. We show that the relatedness scores obtained are highly reliable (split-half reliability r= 0.937). We analyze the data to obtain insights into bigram semantic relatedness. Finally, we present benchmark experiments on using the relatedness dataset as a testbed to evaluate simple unsupervised measures of semantic composition. BiRD is made freely available to foster further research on how meaning can be represented and how meaning can be composed.",
}
@inproceedings{larson-etal-2019-outlier,
    title = "Outlier Detection for Improved Data Quality and Diversity in Dialog Systems",
    author = "Larson, Stefan  and
      Mahendran, Anish  and
      Lee, Andrew  and
      Kummerfeld, Jonathan K.  and
      Hill, Parker  and
      Laurenzano, Michael A.  and
      Hauswald, Johann  and
      Tang, Lingjia  and
      Mars, Jason",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1051",
    doi = "10.18653/v1/N19-1051",
    pages = "517--527",
    abstract = "In a corpus of data, outliers are either errors: mistakes in the data that are counterproductive, or are unique: informative samples that improve model robustness. Identifying outliers can lead to better datasets by (1) removing noise in datasets and (2) guiding collection of additional data to fill gaps. However, the problem of detecting both outlier types has received relatively little attention in NLP, particularly for dialog systems. We introduce a simple and effective technique for detecting both erroneous and unique samples in a corpus of short texts using neural sentence embeddings combined with distance-based outlier detection. We also present a novel data collection pipeline built atop our detection technique to automatically and iteratively mine unique data samples while discarding erroneous samples. Experiments show that our outlier detection technique is effective at finding errors while our data collection pipeline yields highly diverse corpora that in turn produce more robust intent classification and slot-filling models.",
}
@inproceedings{fu-etal-2019-asking,
    title = "Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives",
    author = "Fu, Liye  and
      Chang, Jonathan P.  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1052",
    doi = "10.18653/v1/N19-1052",
    pages = "528--541",
    abstract = "People often share personal narratives in order to seek advice from others. To properly infer the narrator{'}s intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the advice-seeking goal behind a personal narrative. We formulate this as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative. The main challenge in constructing this task is finding pairs of semantically plausible advice-seeking questions for given narratives. To address this challenge, we devise a method that exploits commonalities in experiences people share online to automatically extract pairs of questions that are appropriate candidates for the cloze task. This results in a dataset of over 20,000 personal narratives, each matched with a pair of related advice-seeking questions: one actually intended by the narrator, and the other one not. The dataset covers a very broad array of human experiences, from dating, to career options, to stolen iPads. We use human annotation to determine the degree to which the task relies on common sense and social intuition in addition to a semantic understanding of the narrative. By introducing several baselines for this new task we demonstrate its feasibility and identify avenues for better modeling the intention of the narrator.",
}
@inproceedings{chen-etal-2019-seeing,
    title = "Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims",
    author = "Chen, Sihao  and
      Khashabi, Daniel  and
      Yin, Wenpeng  and
      Callison-Burch, Chris  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1053",
    doi = "10.18653/v1/N19-1053",
    pages = "542--557",
    abstract = "One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won{'}t suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as {``}animals should have lawful rights{''}, and these responses form a spectrum of perspectives, each with a stance relative to this claim and, ideally, with evidence supporting it. Inherently, this is a natural language understanding task, and we propose to address it as such. Specifically, we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. Each perspective should be substantiated by evidence paragraphs which summarize pertinent results and facts. We construct PERSPECTRUM, a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify our dataset. We use crowd-sourcing to filter out noise and ensure high-quality data. Our dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. We provide a thorough analysis of the dataset to highlight key underlying language understanding challenges, and show that human baselines across multiple subtasks far outperform ma-chine baselines built upon state-of-the-art NLP techniques. This poses a challenge and opportunity for the NLP community to address.",
}
@inproceedings{chakrabarty-etal-2019-imho,
    title = "{IMHO} Fine-Tuning Improves Claim Detection",
    author = "Chakrabarty, Tuhin  and
      Hidey, Christopher  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1054",
    doi = "10.18653/v1/N19-1054",
    pages = "558--563",
    abstract = "Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine-tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.",
}
@inproceedings{gangadharaiah-narayanaswamy-2019-joint,
    title = "Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog",
    author = "Gangadharaiah, Rashmi  and
      Narayanaswamy, Balakrishnan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1055",
    doi = "10.18653/v1/N19-1055",
    pages = "564--569",
    abstract = "Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification. In many real-world scenarios, users have multiple intents in the same utterance, and a token-level slot label can belong to more than one intent. We investigate an attention-based neural network model that performs multi-label classification for identifying multiple intents and produces labels for both intents and slot-labels at the token-level. We show state-of-the-art performance for both intent detection and slot-label identification by comparing against strong, recently proposed models. Our model provides a small but statistically significant improvement of 0.2{\%} on the predominantly single-intent ATIS public data set, and 55{\%} intent accuracy improvement on an internal multi-intent dataset.",
}
@inproceedings{alikhani-etal-2019-cite,
    title = "{CITE}: A Corpus of Image-Text Discourse Relations",
    author = "Alikhani, Malihe  and
      Nag Chowdhury, Sreyasi  and
      de Melo, Gerard  and
      Stone, Matthew",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1056",
    doi = "10.18653/v1/N19-1056",
    pages = "570--575",
    abstract = "This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. Like previous corpora annotating discourse structure between text arguments, such as the Penn Discourse Treebank, our new corpus aids in establishing a better understanding of natural communication and common-sense reasoning, while our findings have implications for a wide range of applications, such as understanding and generation of multimodal documents.",
}
@inproceedings{sharma-etal-2019-improving,
    title = "Improving Dialogue State Tracking by Discerning the Relevant Context",
    author = "Sharma, Sanuj  and
      Choubey, Prafulla Kumar  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1057",
    doi = "10.18653/v1/N19-1057",
    pages = "576--581",
    abstract = "A typical conversation comprises of multiple turns between participants where they go back and forth between different topics. At each user turn, dialogue state tracking (DST) aims to estimate user{'}s goal by processing the current utterance. However, in many turns, users implicitly refer to the previous goal, necessitating the use of relevant dialogue history. Nonetheless, distinguishing relevant history is challenging and a popular method of using dialogue recency for that is inefficient. We, therefore, propose a novel framework for DST that identifies relevant historical context by referring to the past utterances where a particular slot-value changes and uses that together with weighted system utterance to identify the relevant context. Specifically, we use the current user utterance and the most recent system utterance to determine the relevance of a system utterance. Empirical analyses show that our method improves joint goal accuracy by 2.75{\%} and 2.36{\%} on WoZ 2.0 and Multi-WoZ restaurant domain datasets respectively over the previous state-of-the-art GLAD model.",
}
@inproceedings{kottur-etal-2019-clevr,
    title = "{CLEVR}-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog",
    author = "Kottur, Satwik  and
      Moura, Jos{\'e} M. F.  and
      Parikh, Devi  and
      Batra, Dhruv  and
      Rohrbach, Marcus",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1058",
    doi = "10.18653/v1/N19-1058",
    pages = "582--595",
    abstract = "Visual Dialog is a multimodal task of answering a sequence of questions grounded in an image (using the conversation history as context). It entails challenges in vision, language, reasoning, and grounding. However, studying these subtasks in isolation on large, real datasets is infeasible as it requires prohibitively-expensive complete annotation of the {`}state{'} of all images and dialogs. We develop CLEVR-Dialog, a large diagnostic dataset for studying multi-round reasoning in visual dialog. Specifically, we construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset. This combination results in a dataset where all aspects of the visual dialog are fully annotated. In total, CLEVR-Dialog contains 5 instances of 10-round dialogs for about 85k CLEVR images, totaling to 4.25M question-answer pairs. We use CLEVR-Dialog to benchmark performance of standard visual dialog models; in particular, on visual coreference resolution (as a function of the coreference distance). This is the first analysis of its kind for visual dialog models that was not possible without this dataset. We hope the findings from CLEVR-Dialog will help inform the development of future models for visual dialog. Our code and dataset are publicly available.",
}
@inproceedings{mu-etal-2019-learning,
    title = "Learning Outside the Box: Discourse-level Features Improve Metaphor Identification",
    author = "Mu, Jesse  and
      Yannakoudakis, Helen  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1059",
    doi = "10.18653/v1/N19-1059",
    pages = "596--601",
    abstract = "Most current approaches to metaphor identification use restricted linguistic contexts, e.g. by considering only a verb{'}s arguments or the sentence containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for better metaphor identification. We train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without the complex metaphor-specific features or deep neural architectures employed by other systems. A qualitative analysis further confirms the need for broader context in metaphor processing.",
}
@inproceedings{wiegand-etal-2019-detection,
    title = "{D}etection of {A}busive {L}anguage: the {P}roblem of {B}iased {D}atasets",
    author = "Wiegand, Michael  and
      Ruppenhofer, Josef  and
      Kleinbauer, Thomas",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1060",
    doi = "10.18653/v1/N19-1060",
    pages = "602--608",
    abstract = "We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.",
}
@inproceedings{gonen-goldberg-2019-lipstick,
    title = "Lipstick on a Pig: {D}ebiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them",
    author = "Gonen, Hila  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1061",
    doi = "10.18653/v1/N19-1061",
    pages = "609--614",
    abstract = "Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between {``}gender-neutralized{''} words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.",
}
@inproceedings{manzini-etal-2019-black,
    title = "Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings",
    author = "Manzini, Thomas  and
      Yao Chong, Lim  and
      Black, Alan W  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1062",
    doi = "10.18653/v1/N19-1062",
    pages = "615--621",
    abstract = "Online texts - across genres, registers, domains, and styles - are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and propagate biases to machine learning models that use word embeddings as features. In this work, we propose a method to debias word embeddings in multiclass settings such as race and religion, extending the work of (Bolukbasi et al., 2016) from the binary setting, such as binary gender. Next, we propose a novel methodology for the evaluation of multiclass debiasing. We demonstrate that our multiclass debiasing is robust and maintains the efficacy in standard NLP tasks.",
}
@inproceedings{may-etal-2019-measuring,
    title = "On Measuring Social Biases in Sentence Encoders",
    author = "May, Chandler  and
      Wang, Alex  and
      Bordia, Shikha  and
      Bowman, Samuel R.  and
      Rudinger, Rachel",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1063",
    doi = "10.18653/v1/N19-1063",
    pages = "622--628",
    abstract = "The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test{'}s assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders.",
}
@inproceedings{zhao-etal-2019-gender,
    title = "Gender Bias in Contextualized Word Embeddings",
    author = "Zhao, Jieyu  and
      Wang, Tianlu  and
      Yatskar, Mark  and
      Cotterell, Ryan  and
      Ordonez, Vicente  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1064",
    doi = "10.18653/v1/N19-1064",
    pages = "629--634",
    abstract = "In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo{'}s contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) training data for ELMo contains significantly more male than female entities, (2) the trained ELMo embeddings systematically encode gender information and (3) ELMo unequally encodes gender information about male and female entities. Then, we show that a state-of-the-art coreference system that depends on ELMo inherits its bias and demonstrates significant bias on the WinoBias probing corpus. Finally, we explore two methods to mitigate such gender bias and show that the bias demonstrated on WinoBias can be eliminated.",
}
@inproceedings{hoyle-etal-2019-combining,
    title = "{C}ombining {S}entiment {L}exica with a {M}ulti-{V}iew {V}ariational {A}utoencoder",
    author = "Hoyle, Alexander Miserlis  and
      Wolf-Sonkin, Lawrence  and
      Wallach, Hanna  and
      Cotterell, Ryan  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1065",
    doi = "10.18653/v1/N19-1065",
    pages = "635--640",
    abstract = "When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.",
}
@inproceedings{zhang-etal-2019-enhancing,
    title = "Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling",
    author = "Zhang, Meishan  and
      Liang, Peili  and
      Fu, Guohong",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1066",
    doi = "10.18653/v1/N19-1066",
    pages = "641--646",
    abstract = "Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger. The task is highly correlative with semantic role labeling (SRL), which identifies important semantic arguments such as agent and patient for a given predicate. As predicate agents and patients usually correspond to opinion holders and targets respectively, SRL could be valuable for ORL. In this work, we propose a simple and novel method to enhance ORL by utilizing SRL, presenting semantic-aware word representations which are learned from SRL. The representations are then fed into a baseline neural ORL model as basic inputs. We verify the proposed method on a benchmark MPQA corpus. Experimental results show that the proposed method is highly effective. In addition, we compare the method with two representative methods of SRL integration as well, finding that our method can outperform the two methods significantly, achieving 1.47{\%} higher F-scores than the better one.",
}
@inproceedings{kim-klinger-2019-frowning,
    title = "Frowning {F}rodo, Wincing {L}eia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters",
    author = "Kim, Evgeny  and
      Klinger, Roman",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1067",
    doi = "10.18653/v1/N19-1067",
    pages = "647--653",
    abstract = "The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks. In literature analysis, such networks have mostly been analyzed without particular relation types or focusing on roles which the characters take with respect to each other. We argue that an important aspect for the analysis of stories and their development is the emotion between characters. In this paper, we combine these aspects into a unified framework to classify emotional relationships of fictional characters. We formalize it as a new task and describe the annotation of a corpus, based on fan-fiction short stories. The extraction pipeline which we propose consists of character identification (which we treat as given by an oracle here) and the relation classification. For the latter, we provide results using several approaches previously proposed for relation identification with neural methods. The best result of 0.45 F1 is achieved with a GRU with character position indicators on the task of predicting undirected emotion relations in the associated social network graph.",
}
@inproceedings{bevendorff-etal-2019-generalizing,
    title = "Generalizing Unmasking for Short Texts",
    author = "Bevendorff, Janek  and
      Stein, Benno  and
      Hagen, Matthias  and
      Potthast, Martin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1068",
    doi = "10.18653/v1/N19-1068",
    pages = "654--659",
    abstract = "Authorship verification is the problem of inferring whether two texts were written by the same author. For this task, unmasking is one of the most robust approaches as of today with the major shortcoming of only being applicable to book-length texts. In this paper, we present a generalized unmasking approach which allows for authorship verification of texts as short as four printed pages with very high precision at an adjustable recall tradeoff. Our generalized approach therefore reduces the required material by orders of magnitude, making unmasking applicable to authorship cases of more practical proportions. The new approach is on par with other state-of-the-art techniques that are optimized for texts of this length: it achieves accuracies of 75{--}80{\%}, while also allowing for easy adjustment to forensic scenarios that require higher levels of confidence in the classification.",
}
@inproceedings{mchardy-etal-2019-adversarial,
    title = "Adversarial Training for Satire Detection: Controlling for Confounding Variables",
    author = "McHardy, Robert  and
      Adel, Heike  and
      Klinger, Roman",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1069",
    doi = "10.18653/v1/N19-1069",
    pages = "660--665",
    abstract = "The automatic detection of satire vs. regular news is relevant for downstream applications (for instance, knowledge base population) and to improve the understanding of linguistic characteristics of satire. Recent approaches build upon corpora which have been labeled automatically based on article sources. We hypothesize that this encourages the models to learn characteristics for different publication sources (e.g., {``}The Onion{''} vs. {``}The Guardian{''}) rather than characteristics of satire, leading to poor generalization performance to unseen publication sources. We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source. On a large novel data set collected from German news (which we make available to the research community), we observe comparable satire classification performance and, as desired, a considerable drop in publication classification performance with adversarial training. Our analysis shows that the adversarial component is crucial for the model to learn to pay attention to linguistic properties of satire.",
}
@inproceedings{cano-bojar-2019-keyphrase,
    title = "Keyphrase Generation: A Text Summarization Struggle",
    author = "{\c{C}}ano, Erion  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1070",
    doi = "10.18653/v1/N19-1070",
    pages = "666--672",
    abstract = "Authors{'} keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.",
}
@inproceedings{baziotis-etal-2019-seq,
    title = "{SEQ}{\^{}}3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression",
    author = "Baziotis, Christos  and
      Androutsopoulos, Ion  and
      Konstas, Ioannis  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1071",
    doi = "10.18653/v1/N19-1071",
    pages = "673--681",
    abstract = "Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ{\^{}}3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.",
}
@inproceedings{shapira-etal-2019-crowdsourcing,
    title = "Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation",
    author = "Shapira, Ori  and
      Gabay, David  and
      Gao, Yang  and
      Ronen, Hadar  and
      Pasunuru, Ramakanth  and
      Bansal, Mohit  and
      Amsterdamer, Yael  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1072",
    doi = "10.18653/v1/N19-1072",
    pages = "682--687",
    abstract = "Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable, providing objective scores. Yet, due to the high cost of the Pyramid method and the required expertise, researchers resorted to cheaper and less thorough manual evaluation methods, such as Responsiveness and pairwise comparison, attainable via crowdsourcing. We revisit the Pyramid approach, proposing a lightweight sampling-based version that is crowdsourcable. We analyze the performance of our method in comparison to original expert-based Pyramid evaluations, showing higher correlation relative to the common Responsiveness method. We release our crowdsourced Summary-Content-Units, along with all crowdsourcing scripts, for future evaluations.",
}
@inproceedings{hajipoor-etal-2019-serial,
    title = "Serial Recall Effects in Neural Language Modeling",
    author = "Hajipoor, Hassan  and
      Amiri, Hadi  and
      Rahgozar, Maseud  and
      Oroumchian, Farhad",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1073",
    doi = "10.18653/v1/N19-1073",
    pages = "688--694",
    abstract = "Serial recall experiments study the ability of humans to recall words in the order in which they occurred. The following serial recall effects are generally investigated in studies with humans: word length and frequency, primacy and recency, semantic confusion, repetition, and transposition effects. In this research, we investigate neural language models in the context of these serial recall effects. Our work provides a framework to better understand and analyze neural language models and opens a new window to develop accurate language models.",
}
@inproceedings{falke-gurevych-2019-fast,
    title = "Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization",
    author = "Falke, Tobias  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1074",
    doi = "10.18653/v1/N19-1074",
    pages = "695--700",
    abstract = "Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art method suggested a new grouping method that was shown to improve the summary quality, its use of pairwise comparisons leads to polynomial runtime complexity that prohibits the application to large document collections. In this paper, we propose two alternative grouping techniques based on locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm. They exhibit linear and log-linear runtime complexity, making them much more scalable. We report experimental results that confirm the improved runtime behavior while also showing that the quality of the summary concept maps remains comparable.",
}
@inproceedings{kasai-etal-2019-syntax,
    title = "Syntax-aware Neural Semantic Role Labeling with Supertags",
    author = "Kasai, Jungo  and
      Friedman, Dan  and
      Frank, Robert  and
      Radev, Dragomir  and
      Rambow, Owen",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1075",
    doi = "10.18653/v1/N19-1075",
    pages = "701--709",
    abstract = "We introduce a new syntax-aware model for dependency-based semantic role labeling that outperforms syntax-agnostic models for English and Spanish. We use a BiLSTM to tag the text with supertags extracted from dependency parses, and we feed these supertags, along with words and parts of speech, into a deep highway BiLSTM for semantic role labeling. Our model combines the strengths of earlier models that performed SRL on the basis of a full dependency parse with more recent models that use no syntactic information at all. Our local and non-ensemble model achieves state-of-the-art performance on the CoNLL 09 English and Spanish datasets. SRL models benefit from syntactic information, and we show that supertagging is a simple, powerful, and robust way to incorporate syntax into a neural SRL system.",
}
@inproceedings{fernandez-gonzalez-gomez-rodriguez-2019-left,
    title = "Left-to-Right Dependency Parsing with Pointer Networks",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1076",
    doi = "10.18653/v1/N19-1076",
    pages = "710--716",
    abstract = "We propose a novel transition-based algorithm that straightforwardly parses sentences from left to right by building n attachments, with n being the length of the input sentence. Similarly to the recent stack-pointer parser by Ma et al. (2018), we use the pointer network framework that, given a word, can directly point to a position from the sentence. However, our left-to-right approach is simpler than the original top-down stack-pointer parser (not requiring a stack) and reduces transition sequence length in half, from 2n-1 actions to n. This results in a quadratic non-projective parser that runs twice as fast as the original while achieving the best accuracy to date on the English PTB dataset (96.04{\%} UAS, 94.43{\%} LAS) among fully-supervised single-model dependency parsers, and improves over the former top-down transition system in the majority of languages tested.",
}
@inproceedings{strzyz-etal-2019-viable,
    title = "Viable Dependency Parsing as Sequence Labeling",
    author = "Strzyz, Michalina  and
      Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1077",
    doi = "10.18653/v1/N19-1077",
    pages = "717--723",
    abstract = "We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BILSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.",
}
@inproceedings{akbik-etal-2019-pooled,
    title = "Pooled Contextualized Embeddings for Named Entity Recognition",
    author = "Akbik, Alan  and
      Bergmann, Tanja  and
      Vollgraf, Roland",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1078",
    doi = "10.18653/v1/N19-1078",
    pages = "724--728",
    abstract = "Contextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based on character-level language models which treat text as distributions over characters and are capable of generating embeddings for any string of characters within any textual context. However, such purely character-based approaches struggle to produce meaningful embeddings if a rare string is used in a underspecified context. To address this drawback, we propose a method in which we dynamically aggregate contextualized embeddings of each unique string that we encounter. We then use a pooling operation to distill a {''}global{''} word representation from all contextualized instances. We evaluate these {''}pooled contextualized embeddings{''} on common named entity recognition (NER) tasks such as CoNLL-03 and WNUT and show that our approach significantly improves the state-of-the-art for NER. We make all code and pre-trained models available to the research community for use and reproduction.",
}
@inproceedings{jie-etal-2019-better,
    title = "Better Modeling of Incomplete Annotations for Named Entity Recognition",
    author = "Jie, Zhanming  and
      Xie, Pengjun  and
      Lu, Wei  and
      Ding, Ruixue  and
      Li, Linlin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1079",
    doi = "10.18653/v1/N19-1079",
    pages = "729--734",
    abstract = "Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information. However, in practice, annotated data can often be imperfect with one typical issue being the training data may contain incomplete annotations. We highlight several pitfalls associated with learning under such a setup in the context of NER and identify limitations associated with existing approaches, proposing a novel yet easy-to-implement approach for recognizing named entities with incomplete data annotations. We demonstrate the effectiveness of our approach through extensive experiments.",
}
@inproceedings{liu-etal-2019-event,
    title = "Event Detection without Triggers",
    author = "Liu, Shulin  and
      Li, Yang  and
      Zhang, Feng  and
      Yang, Tao  and
      Zhou, Xinpeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1080",
    doi = "10.18653/v1/N19-1080",
    pages = "735--744",
    abstract = "The goal of event detection (ED) is to detect the occurrences of events and categorize them. Previous work solved this task by recognizing and classifying event triggers, which is defined as the word or phrase that most clearly expresses an event occurrence. As a consequence, existing approaches required both annotated triggers and event types in training data. However, triggers are nonessential to event detection, and it is time-consuming for annotators to pick out the {``}most clearly{''} word from a given sentence, especially from a long sentence. The expensive annotation of training corpus limits the application of existing approaches. To reduce manual effort, we explore detecting events without triggers. In this work, we propose a novel framework dubbed as Type-aware Bias Neural Network with Attention Mechanisms (TBNNAM), which encodes the representation of a sentence based on target event types. Experimental results demonstrate the effectiveness. Remarkably, the proposed approach even achieves competitive performances compared with state-of-the-arts that used annotated triggers.",
}
@inproceedings{bekoulis-etal-2019-sub,
    title = "Sub-event detection from twitter streams as a sequence labeling problem",
    author = "Bekoulis, Giannis  and
      Deleu, Johannes  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1081",
    doi = "10.18653/v1/N19-1081",
    pages = "745--750",
    abstract = "This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also directly on the stream level. Current approaches to identify sub-events within a given event, such as a goal during a soccer match, essentially do not exploit the sequential nature of social media streams. We address this shortcoming by framing the sub-event detection problem in social media streams as a sequence labeling task and adopt a neural sequence architecture that explicitly accounts for the chronological order of posts. Specifically, we (i) establish a neural baseline that outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7{\%} micro-F1 improvement), as well as (ii) demonstrate superiority of a recurrent neural network model on the posts sequence level for labeled sub-events (2.4{\%} bin-level F1 improvement over non-sequential models).",
}
@inproceedings{qian-etal-2019-graphie,
    title = "{G}raph{IE}: A Graph-Based Framework for Information Extraction",
    author = "Qian, Yujie  and
      Santus, Enrico  and
      Jin, Zhijing  and
      Guo, Jiang  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1082",
    doi = "10.18653/v1/N19-1082",
    pages = "751--761",
    abstract = "Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this paper, we introduce GraphIE, a framework that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks {---} namely textual, social media and visual information extraction {---} shows that GraphIE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.",
}
@inproceedings{zhang-etal-2019-openki,
    title = "{O}pen{KI}: {I}ntegrating {O}pen {I}nformation {E}xtraction and {K}nowledge {B}ases with {R}elation {I}nference",
    author = "Zhang, Dongxu  and
      Mukherjee, Subhabrata  and
      Lockard, Colin  and
      Dong, Luna  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1083",
    doi = "10.18653/v1/N19-1083",
    pages = "762--772",
    abstract = "In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema and from schema mapping fall in two extremes: either they perform instance-level inference relying on embedding for (subject, object) pairs, thus cannot handle pairs absent in any existing triples; or they perform predicate-level mapping and completely ignore background evidence from individual entities, thus cannot achieve satisfying quality. We propose \textit{OpenKI} to handle sparsity of OpenIE extractions by performing instance-level inference: for each entity, we encode the rich information in its neighborhood in both KB and OpenIE extractions, and leverage this information in relation inference by exploring different methods of aggregation and attention. In order to handle unseen entities, our model is designed without creating entity-specific parameters. Extensive experiments show that this method not only significantly improves state-of-the-art for conventional OpenIE extractions like ReVerb, but also boosts the performance on OpenIE from semi-structured data, where new entity pairs are abundant and data are fairly sparse.",
}
@inproceedings{xiong-etal-2019-imposing,
    title = "Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing",
    author = "Xiong, Wenhan  and
      Wu, Jiawei  and
      Lei, Deren  and
      Yu, Mo  and
      Chang, Shiyu  and
      Guo, Xiaoxiao  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1084",
    doi = "10.18653/v1/N19-1084",
    pages = "773--784",
    abstract = "Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical scenarios where the type set is not restricted by KB schema and includes a vast number of free-form types. To model the underlying label correlations without access to manually annotated label structures, we introduce a novel label-relational inductive bias, represented by a graph propagation layer that effectively encodes both global label co-occurrence statistics and word-level similarities. On a large dataset with over 10,000 free-form types, the graph-enhanced model equipped with an attention-based matching module is able to achieve a much higher recall score while maintaining a high-level precision. Specifically, it achieves a 15.3{\%} relative F1 improvement and also less inconsistency in the outputs. We further show that a simple modification of our proposed graph layer can also improve the performance on a conventional and widely-tested dataset that only includes KB-schema types.",
}
@inproceedings{huang-etal-2019-improving,
    title = "Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data",
    author = "Huang, Yin Jou  and
      Lu, Jing  and
      Kurohashi, Sadao  and
      Ng, Vincent",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1085",
    doi = "10.18653/v1/N19-1085",
    pages = "785--795",
    abstract = "Argument compatibility is a linguistic condition that is frequently incorporated into modern event coreference resolution systems. If two event mentions have incompatible arguments in any of the argument roles, they cannot be coreferent. On the other hand, if these mentions have compatible arguments, then this may be used as information towards deciding their coreferent status. One of the key challenges in leveraging argument compatibility lies in the paucity of labeled data. In this work, we propose a transfer learning framework for event coreference resolution that utilizes a large amount of unlabeled data to learn argument compatibility of event mentions. In addition, we adopt an interactive inference network based model to better capture the compatible and incompatible relations between the context words of event mentions. Our experiments on the KBP 2017 English dataset confirm the effectiveness of our model in learning argument compatibility, which in turn improves the performance of the overall event coreference model.",
}
@inproceedings{wang-etal-2019-sentence,
    title = "Sentence Embedding Alignment for Lifelong Relation Extraction",
    author = "Wang, Hong  and
      Xiong, Wenhan  and
      Yu, Mo  and
      Guo, Xiaoxiao  and
      Chang, Shiyu  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1086",
    doi = "10.18653/v1/N19-1086",
    pages = "796--806",
    abstract = "Conventional approaches to relation extraction usually require a fixed set of pre-defined relations. Such requirement is hard to meet in many real applications, especially when new data and relations are emerging incessantly and it is computationally expensive to store all data and re-train the whole model every time new data and relations come in. We formulate such challenging problem as lifelong relation extraction and investigate memory-efficient incremental learning methods without catastrophically forgetting knowledge learned from previous tasks. We first investigate a modified version of the stochastic gradient methods with a replay memory, which surprisingly outperforms recent state-of-the-art lifelong learning methods. We further propose to improve this approach to alleviate the forgetting problem by anchoring the sentence embedding space. Specifically, we utilize an explicit alignment model to mitigate the sentence embedding distortion of learned model when training on new data and new relations. Experiment results on multiple benchmarks show that our proposed method significantly outperforms the state-of-the-art lifelong learning approaches.",
}
@inproceedings{obeidat-etal-2019-description,
    title = "Description-Based Zero-shot Fine-Grained Entity Typing",
    author = "Obeidat, Rasha  and
      Fern, Xiaoli  and
      Shahbazi, Hamed  and
      Tadepalli, Prasad",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1087",
    doi = "10.18653/v1/N19-1087",
    pages = "807--814",
    abstract = "Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entity typing approach that utilizes the type description available from Wikipedia to build a distributed semantic representation of the types. During training, our system learns to align the entity mentions and their corresponding type representations on the known types. At test time, any new type can be incorporated into the system given its Wikipedia descriptions. We evaluate our approach on FIGER, a public benchmark entity tying dataset. Because the existing test set of FIGER covers only a small portion of the fine-grained types, we create a new test set by manually annotating a portion of the noisy training data. Our experiments demonstrate the effectiveness of the proposed method in recognizing novel types that are not present in the training data.",
}
@inproceedings{romanov-etal-2019-adversarial,
    title = "Adversarial Decomposition of Text Representation",
    author = "Romanov, Alexey  and
      Rumshisky, Anna  and
      Rogers, Anna  and
      Donahue, David",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1088",
    doi = "10.18653/v1/N19-1088",
    pages = "815--825",
    abstract = "In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, each of them responsible for a specific aspect of the input sentence. We evaluate the proposed method on two case studies: the conversion between different social registers and diachronic language change. We show that the proposed method is capable of fine-grained controlled change of these aspects of the input sentence. It is also learning a continuous (rather than categorical) representation of the style of the sentence, which is more linguistically realistic. The model uses adversarial-motivational training and includes a special motivational loss, which acts opposite to the discriminator and encourages a better decomposition. Furthermore, we evaluate the obtained meaning embeddings on a downstream task of paraphrase detection and show that they significantly outperform the embeddings of a regular autoencoder.",
}
@inproceedings{kang-etal-2019-pomo,
    title = "{P}o{M}o: Generating Entity-Specific Post-Modifiers in Context",
    author = "Kang, Jun Seok  and
      Logan, Robert  and
      Chu, Zewei  and
      Chen, Yang  and
      Dua, Dheeru  and
      Gimpel, Kevin  and
      Singh, Sameer  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1089",
    doi = "10.18653/v1/N19-1089",
    pages = "826--838",
    abstract = "We introduce entity post-modifier generation as an instance of a collaborative writing task. Given a sentence about a target entity, the task is to automatically generate a post-modifier phrase that provides contextually relevant information about the entity. For example, for the sentence, {``}Barack Obama, {\_}{\_}{\_}{\_}{\_}{\_}{\_}, supported the {\#}MeToo movement.{''}, the phrase {``}a father of two girls{''} is a contextually relevant post-modifier. To this end, we build PoMo, a post-modifier dataset created automatically from news articles reflecting a journalistic need for incorporating entity information that is relevant to a particular news event. PoMo consists of more than 231K sentences with post-modifiers and associated facts extracted from Wikidata for around 57K unique entities. We use crowdsourcing to show that modeling contextual relevance is necessary for accurate post-modifier generation. We adapt a number of existing generation approaches as baselines for this dataset. Our results show there is large room for improvement in terms of both identifying relevant facts to include (knowing which claims are relevant gives a {\textgreater}20{\%} improvement in BLEU score), and generating appropriate post-modifier text for the context (providing relevant claims is not sufficient for accurate generation). We conduct an error analysis that suggests promising directions for future research.",
}
@inproceedings{hu-etal-2019-improved,
    title = "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting",
    author = "Hu, J. Edward  and
      Khayrallah, Huda  and
      Culkin, Ryan  and
      Xia, Patrick  and
      Chen, Tongfei  and
      Post, Matt  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1090",
    doi = "10.18653/v1/N19-1090",
    pages = "839--850",
    abstract = "Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We describe vectorized dynamic beam allocation, which extends work in lexically-constrained decoding to work with batching, leading to a five-fold improvement in throughput when working with positive constraints. Faster decoding enables faster exploration of constraint strategies: we illustrate this via data augmentation experiments with a monolingual rewriter applied to the tasks of natural language inference, question answering and machine translation, showing improvements in all three.",
}
@inproceedings{golchha-etal-2019-courteously,
    title = "Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network",
    author = "Golchha, Hitesh  and
      Firdaus, Mauajama  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1091",
    doi = "10.18653/v1/N19-1091",
    pages = "851--860",
    abstract = "In this paper, we propose an effective deep learning framework for inducing courteous behavior in customer care responses. The interaction between a customer and the customer care representative contributes substantially to the overall customer experience. Thus it is imperative for customer care agents and chatbots engaging with humans to be personal, cordial and emphatic to ensure customer satisfaction and retention. Our system aims at automatically transforming neutral customer care responses into courteous replies. Along with stylistic transfer (of courtesy), our system ensures that responses are coherent with the conversation history, and generates courteous expressions consistent with the emotional state of the customer. Our technique is based on a reinforced pointer-generator model for the sequence to sequence task. The model is also conditioned on a hierarchically encoded and emotionally aware conversational context. We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: {`}generic{'} and {`}courteous{'}. We perform quantitative and qualitative analyses on established and task-specific metrics, both automatic and human evaluation based. Our evaluation shows that the proposed models can generate emotionally-appropriate courteous expressions while preserving the content. Experimental results also prove that our proposed approach performs better than the baseline models.",
}
@inproceedings{yu-wan-2019-avoid,
    title = "How to Avoid Sentences Spelling Boring? Towards a Neural Approach to Unsupervised Metaphor Generation",
    author = "Yu, Zhiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1092",
    doi = "10.18653/v1/N19-1092",
    pages = "861--871",
    abstract = "Metaphor generation attempts to replicate human creativity with language, which is an attractive but challengeable text generation task. Previous efforts mainly focus on template-based or rule-based methods and result in a lack of linguistic subtlety. In order to create novel metaphors, we propose a neural approach to metaphor generation and explore the shared inferential structure of a metaphorical usage and a literal usage of a verb. Our approach does not require any manually annotated metaphors for training. We extract the metaphorically used verbs with their metaphorical senses in an unsupervised way and train a neural language model from wiki corpus. Then we generate metaphors conveying the assigned metaphorical senses with an improved decoding algorithm. Automatic metrics and human evaluations demonstrate that our approach can generate metaphors with good readability and creativity.",
}
@inproceedings{zhang-etal-2019-incorporating,
    title = "Incorporating Context and External Knowledge for Pronoun Coreference Resolution",
    author = "Zhang, Hongming  and
      Song, Yan  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1093",
    doi = "10.18653/v1/N19-1093",
    pages = "872--881",
    abstract = "Linking pronominal expressions to the correct references requires, in many cases, better analysis of the contextual information and external knowledge. In this paper, we propose a two-layer model for pronoun coreference resolution that leverages both context and external knowledge, where a knowledge attention mechanism is designed to ensure the model leveraging the appropriate source of external knowledge based on different context. Experimental results demonstrate the validity and effectiveness of our model, where it outperforms state-of-the-art models by a large margin.",
}
@inproceedings{wang-etal-2019-unsupervised,
    title = "Unsupervised Deep Structured Semantic Models for Commonsense Reasoning",
    author = "Wang, Shuohang  and
      Zhang, Sheng  and
      Shen, Yelong  and
      Liu, Xiaodong  and
      Liu, Jingjing  and
      Gao, Jianfeng  and
      Jiang, Jing",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1094",
    doi = "10.18653/v1/N19-1094",
    pages = "882--891",
    abstract = "Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.",
}
@inproceedings{yang-etal-2019-recovering,
    title = "Recovering dropped pronouns in {C}hinese conversations via modeling their referents",
    author = "Yang, Jingxuan  and
      Tong, Jianzhuo  and
      Li, Si  and
      Gao, Sheng  and
      Guo, Jun  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1095",
    doi = "10.18653/v1/N19-1095",
    pages = "892--901",
    abstract = "Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context. Recovering dropped pronouns is essential to applications such as Information Extraction where the referents of these dropped pronouns need to be resolved, or Machine Translation when Chinese is the source language. In this work, we present a novel end-to-end neural network model to recover dropped pronouns in conversational data. Our model is based on a structured attention mechanism that models the referents of dropped pronouns utilizing both sentence-level and word-level information. Results on three different conversational genres show that our approach achieves a significant improvement over the current state of the art.",
}
@inproceedings{vasiljeva-etal-2019-problem,
    title = "The problem with probabilistic {DAG} automata for semantic graphs",
    author = "Vasiljeva, Ieva  and
      Gilroy, Sorcha  and
      Lopez, Adam",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1096",
    doi = "10.18653/v1/N19-1096",
    pages = "902--911",
    abstract = "Semantic representations in the form of directed acyclic graphs (DAGs) have been introduced in recent years, and to model them, we need probabilistic models of DAGs. One model that has attracted some attention is the DAG automaton, but it has not been studied as a probabilistic model. We show that some DAG automata cannot be made into useful probabilistic models by the nearly universal strategy of assigning weights to transitions. The problem affects single-rooted, multi-rooted, and unbounded-degree variants of DAG automata, and appears to be pervasive. It does not affect planar variants, but these are problematic for other reasons.",
}
@inproceedings{zhu-etal-2019-systematic,
    title = "A Systematic Study of Leveraging Subword Information for Learning Word Representations",
    author = "Zhu, Yi  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1097",
    doi = "10.18653/v1/N19-1097",
    pages = "912--932",
    abstract = "The use of subword-level information (e.g., characters, character n-grams, morphemes) has become ubiquitous in modern word representation learning. Its importance is attested especially for morphologically rich languages which generate a large number of rare words. Despite a steadily increasing interest in such subword-informed word representations, their systematic comparative analysis across typologically diverse languages and different tasks is still missing. In this work, we deliver such a study focusing on the variation of two crucial components required for subword-level integration into word representation models: 1) segmentation of words into subword units, and 2) subword composition functions to obtain final word representations. We propose a general framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components, also including more advanced techniques based on position embeddings and self-attention. Using the unified framework, we run experiments over a large number of subword-informed word representation configurations (60 in total) on 3 tasks (general and rare word similarity, dependency parsing, fine-grained entity typing) for 5 languages representing 3 language types. Our main results clearly indicate that there is no {``}one-size-fits-all{''} configuration, as performance is both language- and task-dependent. We also show that configurations based on unsupervised segmentation (e.g., BPE, Morfessor) are sometimes comparable to or even outperform the ones based on supervised word segmentation.",
}
@inproceedings{gupta-etal-2019-better,
    title = "Better Word Embeddings by Disentangling Contextual n-Gram Information",
    author = "Gupta, Prakhar  and
      Pagliardini, Matteo  and
      Jaggi, Martin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1098",
    doi = "10.18653/v1/N19-1098",
    pages = "933--939",
    abstract = "Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word embeddings along with higher n-gram embeddings helps in the removal of the contextual information from the unigrams, resulting in better stand-alone word embeddings. We empirically show the validity of our hypothesis by outperforming other competing word representation models by a significant margin on a wide variety of tasks. We make our models publicly available.",
}
@inproceedings{li-etal-2019-integration,
    title = "Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical {D}irichlet Process",
    author = "Li, Dingcheng  and
      Zamani, Siamak  and
      Zhang, Jingyuan  and
      Li, Ping",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1099",
    doi = "10.18653/v1/N19-1099",
    pages = "940--950",
    abstract = "Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. In this paper, we develop \textit{topic modeling with knowledge graph embedding} (TMKGE), a Bayesian nonparametric model to employ knowledge graph (KG) embedding in the context of topic modeling, for extracting more coherent topics. Specifically, we build a hierarchical Dirichlet process (HDP) based model to flexibly borrow information from KG to improve the interpretability of topics. An efficient online variational inference method based on a stick-breaking construction of HDP is developed for TMKGE, making TMKGE suitable for large document corpora and KGs. Experiments on three public datasets illustrate the superior performance of TMKGE in terms of topic coherence and document classification accuracy, compared to state-of-the-art topic modeling methods.",
}
@inproceedings{zhelezniak-etal-2019-correlation,
    title = "Correlation Coefficients and Semantic Textual Similarity",
    author = "Zhelezniak, Vitalii  and
      Savkov, Aleksandar  and
      Shen, April  and
      Hammerla, Nils",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1100",
    doi = "10.18653/v1/N19-1100",
    pages = "951--962",
    abstract = "A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similarity being used unquestionably in the majority of cases. In this work, we illustrate that for all common word vectors, cosine similarity is essentially equivalent to the Pearson correlation coefficient, which provides some justification for its use. We thoroughly characterise cases where Pearson correlation (and thus cosine similarity) is unfit as similarity measure. Importantly, we show that Pearson correlation is appropriate for some word vectors but not others. When it is not appropriate, we illustrate how common non-parametric rank correlation coefficients can be used instead to significantly improve performance. We support our analysis with a series of evaluations on word-level and sentence-level semantic textual similarity benchmarks. On the latter, we show that even the simplest averaged word vectors compared by rank correlation easily rival the strongest deep representations compared by cosine similarity.",
}
@inproceedings{thorne-etal-2019-generating,
    title = "Generating Token-Level Explanations for Natural Language Inference",
    author = "Thorne, James  and
      Vlachos, Andreas  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1101",
    doi = "10.18653/v1/N19-1101",
    pages = "963--969",
    abstract = "The task of Natural Language Inference (NLI) is widely modeled as supervised sentence pair classification. While there has been a lot of work recently on generating explanations of the predictions of classifiers on a single piece of text, there have been no attempts to generate explanations of classifiers operating on pairs of sentences. In this paper, we show that it is possible to generate token-level explanations for NLI without the need for training data explicitly annotated for this purpose. We use a simple LSTM architecture and evaluate both LIME and Anchor explanations for this task. We compare these to a Multiple Instance Learning (MIL) method that uses thresholded attention make token-level predictions. The approach we present in this paper is a novel extension of zero-shot single-sentence tagging to sentence pairs for NLI. We conduct our experiments on the well-studied SNLI dataset that was recently augmented with manually annotation of the tokens that explain the entailment relation. We find that our white-box MIL-based method, while orders of magnitude faster, does not reach the same accuracy as the black-box methods.",
}
@inproceedings{finnimore-etal-2019-strong,
    title = "Strong Baselines for Complex Word Identification across Multiple Languages",
    author = "Finnimore, Pierre  and
      Fritzsch, Elisabeth  and
      King, Daniel  and
      Sneyd, Alison  and
      Ur Rehman, Aneeq  and
      Alva-Manchego, Fernando  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1102",
    doi = "10.18653/v1/N19-1102",
    pages = "970--977",
    abstract = "Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a target audience. The latest CWI Shared Task released data for two settings: monolingual (i.e. train and test in the same language) and cross-lingual (i.e. test in a language not seen during training). The best monolingual models relied on language-dependent features, which do not generalise in the cross-lingual setting, while the best cross-lingual model used neural networks with multi-task learning. In this paper, we present monolingual and cross-lingual CWI models that perform as well as (or better than) most models submitted to the latest CWI Shared Task. We show that carefully selected features and simple learning models can achieve state-of-the-art performance, and result in strong baselines for future development in this area. Finally, we discuss how inconsistencies in the annotation of the data can explain some of the results obtained.",
}
@inproceedings{jiang-etal-2019-adaptive,
    title = "Adaptive Convolution for Multi-Relational Learning",
    author = "Jiang, Xiaotian  and
      Wang, Quan  and
      Wang, Bin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1103",
    doi = "10.18653/v1/N19-1103",
    pages = "978--987",
    abstract = "We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein. Convolutional neural networks have recently shown their superiority for this problem, bringing increased model expressiveness while remaining parameter efficient. Despite the success, previous convolution designs fail to model full interactions between input entities and relations, which potentially limits the performance of link prediction. In this work we introduce ConvR, an adaptive convolutional network designed to maximize entity-relation interactions in a convolutional fashion. ConvR adaptively constructs convolution filters from relation representations, and applies these filters across entity representations to generate convolutional features. As such, ConvR enables rich interactions between entity and relation representations at diverse regions, and all the convolutional features generated will be able to capture such interactions. We evaluate ConvR on multiple benchmark datasets. Experimental results show that: (1) ConvR performs substantially better than competitive baselines in almost all the metrics and on all the datasets; (2) Compared with state-of-the-art convolutional models, ConvR is not only more effective but also more efficient. It offers a 7{\%} increase in MRR and a 6{\%} increase in Hits@10, while saving 12{\%} in parameter storage.",
}
@inproceedings{ebisu-ichise-2019-graph,
    title = "Graph Pattern Entity Ranking Model for Knowledge Graph Completion",
    author = "Ebisu, Takuma  and
      Ichise, Ryutaro",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1104",
    doi = "10.18653/v1/N19-1104",
    pages = "988--997",
    abstract = "Knowledge graphs have been developed rapidly in recent years and shown their usefulness for many artificial intelligence tasks. However, knowledge graphs often have lots of missing facts. To solve this problem, many knowledge graph embedding models to populate knowledge graphs have been developed and have shown outstanding performance these days. However, knowledge graph embedding models are so called-black box. Hence, we actually does not know how information of a knowledge graph is processed and the models are hard to interpret. In this paper, we utilize graph patterns in a knowledge graph to overcome such problems. Our proposed model, graph pattern entity ranking Model (GRank), constructs an entity ranking system for each graph pattern and evaluate them using a measure for a ranking system. By doing so, we can find helpful graph patterns for predicting facts. Then we conduct the link prediction tasks on standard data sets to evaluate GRank. We show our approach outperforms other state-of-the-art approaches such as ComplEx and TorusE on standard metrics such as HITS@n and MRR. Moreover, This model is easily interpretable because output facts are described by graph patterns.",
}
@inproceedings{wang-etal-2019-adversarial,
    title = "Adversarial Training for Weakly Supervised Event Detection",
    author = "Wang, Xiaozhi  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Sun, Maosong  and
      Li, Peng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1105",
    doi = "10.18653/v1/N19-1105",
    pages = "998--1008",
    abstract = "Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data. However, these methods typically rely on sophisticated pre-defined rules as well as existing instances in knowledge bases for automatic annotation and thus suffer from low coverage, topic bias, and data noise. To address these issues, we build a large event-related candidate set with good coverage and then apply an adversarial training mechanism to iteratively identify those informative instances from the candidate set and filter out those noisy ones. The experiments on two real-world datasets show that our candidate selection and adversarial training can cooperate together to obtain more diverse and accurate training data for ED, and significantly outperform the state-of-the-art methods in various weakly supervised scenarios. The datasets and source code can be obtained from https://github.com/thunlp/Adv-ED.",
}
@inproceedings{barezi-etal-2019-submodular,
    title = "A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems",
    author = "Barezi, Elham J.  and
      Wood, Ian D.  and
      Fung, Pascale  and
      Rabiee, Hamid R.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1106",
    doi = "10.18653/v1/N19-1106",
    pages = "1009--1018",
    abstract = "Extreme classification is a classification task on an extremely large number of labels (tags). User generated labels for any type of online data can be sparing per individual user but intractably large among all users. It would be useful to automatically select a smaller, standard set of labels to represent the whole label set. We can then solve efficiently the problem of multi-label learning with an intractably large number of interdependent labels, such as automatic tagging of Wikipedia pages. We propose a submodular maximization framework with linear cost to find informative labels which are most relevant to other labels yet least redundant with each other. A simple prediction model can then be trained on this label subset. Our framework includes both label-label and label-feature dependencies, which aims to find the labels with the most representation and prediction ability. In addition, to avoid information loss, we extract and predict outlier labels with weak dependency on other labels. We apply our model to four standard natural language data sets including Bibsonomy entries with users assigned tags, web pages with user assigned tags, legal texts with EUROVOC descriptors(A topic hierarchy with almost 4000 categories regarding different aspects of European law) and Wikipedia pages with tags from social bookmarking as well as news videos for automated label detection from a lexicon of semantic concepts. Experimental results show that our proposed approach improves label prediction quality, in terms of precision and nDCG, by 3{\%} to 5{\%} in three of the 5 tasks and is competitive in the others, even with a simple linear prediction model. An ablation study shows how different data sets benefit from different aspects of our model, with all aspects contributing substantially to at least one data set.",
}
@inproceedings{yan-etal-2019-relation,
    title = "Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision",
    author = "Yan, Jianhao  and
      He, Lin  and
      Huang, Ruqin  and
      Li, Jian  and
      Liu, Ying",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1107",
    doi = "10.18653/v1/N19-1107",
    pages = "1019--1030",
    abstract = "Distant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these examples to automatically generate the training data. However, the examples collected can be very noisy, and pose significant challenge for obtaining high quality labels. Previous work has made remarkable progress in predicting the relation from distant supervision, but typically ignores the temporal relations among those supervising instances. This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot. For this purpose, we construct a dataset called WIKI-TIME which additionally includes the valid period of a certain relation of two entities in the knowledge base. We propose a novel neural model to incorporate both the temporal information encoding and sequential reasoning. The experimental results show that, compared with the best of existing models, our model achieves better performance in both WIKI-TIME dataset and the well-studied NYT-10 dataset.",
}
@inproceedings{zhang-etal-2019-integrating,
    title = "Integrating Semantic Knowledge to Tackle Zero-shot Text Classification",
    author = "Zhang, Jingqing  and
      Lertvittayakumjorn, Piyawat  and
      Guo, Yike",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1108",
    doi = "10.18653/v1/N19-1108",
    pages = "1031--1040",
    abstract = "Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario.",
}
@inproceedings{sen-etal-2019-word,
    title = "Word-{N}ode2{V}ec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences",
    author = "Sen, Procheta  and
      Ganguly, Debasis  and
      Jones, Gareth",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1109",
    doi = "10.18653/v1/N19-1109",
    pages = "1041--1051",
    abstract = "A standard word embedding algorithm, such as word2vec and glove, makes a strong assumption that words are likely to be semantically related only if they co-occur locally within a window of fixed size. However, this strong assumption may not capture the semantic association between words that co-occur frequently but non-locally within documents. In this paper, we propose a graph-based word embedding method, named {`}word-node2vec{'}. By relaxing the strong constraint of locality, our method is able to capture both the local and non-local co-occurrences. Word-node2vec constructs a graph where every node represents a word and an edge between two nodes represents a combination of both local (e.g. word2vec) and document-level co-occurrences. Our experiments show that word-node2vec outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization.",
}
@inproceedings{briakou-etal-2019-cross,
    title = "Cross-Topic Distributional Semantic Representations Via Unsupervised Mappings",
    author = "Briakou, Eleftheria  and
      Athanasiou, Nikos  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1110",
    doi = "10.18653/v1/N19-1110",
    pages = "1052--1061",
    abstract = "In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation. In this work, we propose a DSM that learns multiple distributional representations of a word based on different topics. First, a separate DSM is trained for each topic and then each of the topic-based DSMs is aligned to a common vector space. Our unsupervised mapping approach is motivated by the hypothesis that words preserving their relative distances in different topic semantic sub-spaces constitute robust semantic anchors that define the mappings between them. Aligned cross-topic representations achieve state-of-the-art results for the task of contextual word similarity. Furthermore, evaluation on NLP downstream tasks shows that multiple topic-based embeddings outperform single-prototype models.",
}
@inproceedings{hayes-2019-just,
    title = "What just happened? {E}valuating retrofitted distributional word vectors",
    author = "Hayes, Dmetri",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1111",
    doi = "10.18653/v1/N19-1111",
    pages = "1062--1072",
    abstract = "Recent work has attempted to enhance vector space representations using information from structured semantic resources. This process, dubbed retrofitting (Faruqui et al., 2015), has yielded improvements in word similarity performance. Research has largely focused on the retrofitting algorithm, or on the kind of structured semantic resources used, but little research has explored why some resources perform better than others. We conducted a fine-grained analysis of the original retrofitting process, and found that the utility of different lexical resources for retrofitting depends on two factors: the coverage of the resource and the evaluation metric. Our assessment suggests that the common practice of using correlation measures to evaluate increases in performance against full word similarity benchmarks 1) obscures the benefits offered by smaller resources, and 2) overlooks incremental gains in word similarity performance. We propose root-mean-square error (RMSE) as an alternative evaluation metric, and demonstrate that correlation measures and RMSE sometimes yield opposite conclusions concerning the efficacy of retrofitting. This point is illustrated by word vectors retrofitted with novel treatments of the FrameNet data (Fillmore and Baker, 2010).",
}
@inproceedings{liu-etal-2019-linguistic,
    title = "Linguistic Knowledge and Transferability of Contextual Representations",
    author = "Liu, Nelson F.  and
      Gardner, Matt  and
      Belinkov, Yonatan  and
      Peters, Matthew E.  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1112",
    doi = "10.18653/v1/N19-1112",
    pages = "1073--1094",
    abstract = "Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture, we study the representations produced by several recent pretrained contextualizers (variants of ELMo, the OpenAI transformer language model, and BERT) with a suite of sixteen diverse probing tasks. We find that linear models trained on top of frozen contextual representations are competitive with state-of-the-art task-specific models in many cases, but fail on tasks requiring fine-grained linguistic knowledge (e.g., conjunct identification). To investigate the transferability of contextual word representations, we quantify differences in the transferability of individual layers within contextualizers, especially between recurrent neural networks (RNNs) and transformers. For instance, higher layers of RNNs are more task-specific, while transformer layers do not exhibit the same monotonic trend. In addition, to better understand what makes contextual word representations transferable, we compare language model pretraining with eleven supervised pretraining tasks. For any given task, pretraining on a closely related task yields better performance than language model pretraining (which is better on average) when the pretraining dataset is fixed. However, language model pretraining on more data gives the best results.",
}
@inproceedings{stratos-2019-mutual,
    title = "Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction",
    author = "Stratos, Karl",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1113",
    doi = "10.18653/v1/N19-1113",
    pages = "1095--1104",
    abstract = "We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent (SGD): a novel generalization of the classical Brown clustering objective and a recently proposed variational lower bound. While both objectives are subject to noise in gradient updates, we show through analysis and experiments that the variational lower bound is robust whereas the generalized Brown objective is vulnerable. We obtain strong performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context.",
}
@inproceedings{kim-etal-2019-unsupervised,
    title = "Unsupervised Recurrent Neural Network Grammars",
    author = "Kim, Yoon  and
      Rush, Alexander  and
      Yu, Lei  and
      Kuncoro, Adhiguna  and
      Dyer, Chris  and
      Melis, G{\'a}bor",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1114",
    doi = "10.18653/v1/N19-1114",
    pages = "1105--1117",
    abstract = "Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. Supervised RNNGs achieve strong language modeling and parsing performance, but require an annotated corpus of parse trees. In this work, we experiment with unsupervised learning of RNNGs. Since directly marginalizing over the space of latent trees is intractable, we instead apply amortized variational inference. To maximize the evidence lower bound, we develop an inference network parameterized as a neural CRF constituency parser. On language modeling, unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese. On constituency grammar induction, they are competitive with recent neural language models that induce tree structures from words through attention mechanisms.",
}
@inproceedings{havrylov-etal-2019-cooperative,
    title = "Cooperative Learning of Disjoint Syntax and Semantics",
    author = "Havrylov, Serhii  and
      Kruszewski, Germ{\'a}n  and
      Joulin, Armand",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1115",
    doi = "10.18653/v1/N19-1115",
    pages = "1118--1128",
    abstract = "There has been considerable attention devoted to models that learn to jointly infer an expression{'}s syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by Choi et al. (2018) that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimisation schemes. Our model does not require any linguistic structure for supervision, and its recursive nature allows for out-of-domain generalisation. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis.",
}
@inproceedings{drozdov-etal-2019-unsupervised,
    title = "Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders",
    author = "Drozdov, Andrew  and
      Verga, Patrick  and
      Yadav, Mohit  and
      Iyyer, Mohit  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1116",
    doi = "10.18653/v1/N19-1116",
    pages = "1129--1141",
    abstract = "We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence. During training we use dynamic programming to consider all possible binary trees over the sentence, and for inference we use the CKY algorithm to extract the highest scoring parse. DIORA outperforms previously reported results for unsupervised binary constituency parsing on the benchmark WSJ dataset.",
}
@inproceedings{liu-etal-2019-knowledge,
    title = "Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition",
    author = "Liu, Angli  and
      Du, Jingfei  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1117",
    doi = "10.18653/v1/N19-1117",
    pages = "1142--1150",
    abstract = "Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., person or location) and have equipped language models with an access to external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information.",
}
@inproceedings{zhang-etal-2019-syntax-enhanced,
    title = "Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations",
    author = "Zhang, Meishan  and
      Li, Zhenghua  and
      Fu, Guohong  and
      Zhang, Min",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1118",
    doi = "10.18653/v1/N19-1118",
    pages = "1151--1161",
    abstract = "Syntax has been demonstrated highly effective in neural machine translation (NMT). Previous NMT models integrate syntax by representing 1-best tree outputs from a well-trained parsing system, e.g., the representative Tree-RNN and Tree-Linearization methods, which may suffer from error propagation. In this work, we propose a novel method to integrate source-side syntax implicitly for NMT. The basic idea is to use the intermediate hidden representations of a well-trained end-to-end dependency parser, which are referred to as syntax-aware word representations (SAWRs). Then, we simply concatenate such SAWRs with ordinary word embeddings to enhance basic NMT models. The method can be straightforwardly integrated into the widely-used sequence-to-sequence (Seq2Seq) NMT models. We start with a representative RNN-based Seq2Seq baseline system, and test the effectiveness of our proposed method on two benchmark datasets of the Chinese-English and English-Vietnamese translation tasks, respectively. Experimental results show that the proposed approach is able to bring significant BLEU score improvements on the two datasets compared with the baseline, 1.74 points for Chinese-English translation and 0.80 point for English-Vietnamese translation, respectively. In addition, the approach also outperforms the explicit Tree-RNN and Tree-Linearization methods.",
}
@inproceedings{platanios-etal-2019-competence,
    title = "Competence-based Curriculum Learning for Neural Machine Translation",
    author = "Platanios, Emmanouil Antonios  and
      Stretcu, Otilia  and
      Neubig, Graham  and
      Poczos, Barnabas  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1119",
    doi = "10.18653/v1/N19-1119",
    pages = "1162--1172",
    abstract = "Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70{\%} decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU.",
}
@inproceedings{wu-etal-2019-extract,
    title = "Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation",
    author = "Wu, Jiawei  and
      Wang, Xin  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1120",
    doi = "10.18653/v1/N19-1120",
    pages = "1173--1183",
    abstract = "The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs. Back-translation has been dominantly used in previous approaches for unsupervised neural machine translation, where pseudo sentence pairs are generated to train the models with a reconstruction loss. However, the pseudo sentences are usually of low quality as translation errors accumulate during training. To avoid this fundamental issue, we propose an alternative but more effective approach, extract-edit, to extract and then edit real sentences from the target monolingual corpora. Furthermore, we introduce a comparative translation loss to evaluate the translated target sentences and thus train the unsupervised translation systems. Experiments show that the proposed approach consistently outperforms the previous state-of-the-art unsupervised machine translation systems across two benchmarks (English-French and English-German) and two low-resource language pairs (English-Romanian and English-Russian) by more than 2 (up to 3.63) BLEU points.",
}
@inproceedings{al-shedivat-parikh-2019-consistency,
    title = "Consistency by Agreement in Zero-Shot Neural Machine Translation",
    author = "Al-Shedivat, Maruan  and
      Parikh, Ankur",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1121",
    doi = "10.18653/v1/N19-1121",
    pages = "1184--1197",
    abstract = "Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus on zero-shot generalization{---}a challenging setup that tests models on translation directions they have not been optimized for at training time. To solve the problem, we (i) reformulate multilingual translation as probabilistic inference, (ii) define the notion of zero-shot consistency and show why standard training often results in models unsuitable for zero-shot tasks, and (iii) introduce a consistent agreement-based training method that encourages the model to produce equivalent translations of parallel sentences in auxiliary languages. We test our multilingual NMT models on multiple public zero-shot translation benchmarks (IWSLT17, UN corpus, Europarl) and show that agreement-based learning often results in 2-3 BLEU zero-shot improvement over strong baselines without any loss in performance on supervised translation directions.",
}
@inproceedings{hao-etal-2019-modeling,
    title = "Modeling Recurrence for Transformer",
    author = "Hao, Jie  and
      Wang, Xing  and
      Yang, Baosong  and
      Wang, Longyue  and
      Zhang, Jinfeng  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1122",
    doi = "10.18653/v1/N19-1122",
    pages = "1198--1207",
    abstract = "Recently, the Transformer model that is based solely on attention mechanisms, has advanced the state-of-the-art on various machine translation tasks. However, recent studies reveal that the lack of recurrence modeling hinders its further improvement of translation capacity. In response to this problem, we propose to directly model recurrence for Transformer with an additional recurrence encoder. In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention models and recurrent networks. Experimental results on the widely-used WMT14 English⇒German and WMT17 Chinese⇒English translation tasks demonstrate the effectiveness of the proposed approach. Our studies also reveal that the proposed model benefits from a short-cut that bridges the source and target sequences with a single recurrent layer, which outperforms its deep counterpart.",
}
@inproceedings{zhao-etal-2019-rethinking,
    title = "Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models",
    author = "Zhao, Tiancheng  and
      Xie, Kaige  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1123",
    doi = "10.18653/v1/N19-1123",
    pages = "1208--1218",
    abstract = "Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use handcrafted dialog acts, or the output vocabulary, e.g. in neural encoder decoders, as the action spaces. Both have their own limitations. This paper proposes a novel latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods in order to induce its own action space from the data. Comprehensive experiments are conducted examining both continuous and discrete action types and two different optimization methods based on stochastic variational inference. Results show that the proposed latent actions achieve superior empirical performance improvement over previous word-level policy gradient methods on both DealOrNoDeal and MultiWoz dialogs. Our detailed analysis also provides insights about various latent variable approaches for policy learning and can serve as a foundation for developing better latent actions in future research.",
}
@inproceedings{cai-etal-2019-skeleton,
    title = "Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory",
    author = "Cai, Deng  and
      Wang, Yan  and
      Bi, Wei  and
      Tu, Zhaopeng  and
      Liu, Xiaojiang  and
      Lam, Wai  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1124",
    doi = "10.18653/v1/N19-1124",
    pages = "1219--1228",
    abstract = "Traditional generative dialogue models generate responses solely from input queries. Such information is insufficient for generating a specific response since a certain query could be answered in multiple ways. Recently, researchers have attempted to fill the information gap by exploiting information retrieval techniques. For a given query, similar dialogues are retrieved from the entire training data and considered as an additional knowledge source. While the use of retrieval may harvest extensive information, the generative models could be overwhelmed, leading to unsatisfactory performance. In this paper, we propose a new framework which exploits retrieval results via a skeleton-to-response paradigm. At first, a skeleton is extracted from the retrieved dialogues. Then, both the generated skeleton and the original query are used for response generation via a novel response generator. Experimental results show that our approach significantly improves the informativeness of the generated responses",
}
@inproceedings{gao-etal-2019-jointly,
    title = "Jointly Optimizing Diversity and Relevance in Neural Response Generation",
    author = "Gao, Xiang  and
      Lee, Sungjin  and
      Zhang, Yizhe  and
      Brockett, Chris  and
      Galley, Michel  and
      Gao, Jianfeng  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1125",
    doi = "10.18653/v1/N19-1125",
    pages = "1229--1238",
    abstract = "Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the output of the conversation model, the improvement often comes at the cost of decreased relevance. In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms. As a result, our approach induces a latent space in which the distance and direction from the predicted response vector roughly match the relevance and diversity, respectively. This property also lends itself well to an intuitive visualization of the latent space. Both automatic and human evaluation results demonstrate that the proposed approach brings significant improvement compared to strong baselines in both diversity and relevance.",
}
@inproceedings{raghu-etal-2019-disentangling,
    title = "{D}isentangling {L}anguage and {K}nowledge in {T}ask-{O}riented {D}ialogs",
    author = "Raghu, Dinesh  and
      Gupta, Nikhil  and
      {Mausam}",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1126",
    doi = "10.18653/v1/N19-1126",
    pages = "1239--1255",
    abstract = "The Knowledge Base (KB) used for real-world applications, such as booking a movie or restaurant reservation, keeps changing over time. End-to-end neural networks trained for these task-oriented dialogs are expected to be immune to any changes in the KB. However, existing approaches breakdown when asked to handle such changes. We propose an encoder-decoder architecture (BoSsNet) with a novel Bag-of-Sequences (BoSs) memory, which facilitates the disentangled learning of the response{'}s language model and its knowledge incorporation. Consequently, the KB can be modified with new knowledge without a drop in interpretability. We find that BoSsNeT outperforms state-of-the-art models, with considerable improvements ({\textgreater}10{\%}) on bAbI OOV test sets and other human-human datasets. We also systematically modify existing datasets to measure disentanglement and show BoSsNeT to be robust to KB modifications.",
}
@inproceedings{shen-etal-2019-tensorized,
    title = "Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together",
    author = "Shen, Tao  and
      Zhou, Tianyi  and
      Long, Guodong  and
      Jiang, Jing  and
      Zhang, Chengqi",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1127",
    doi = "10.18653/v1/N19-1127",
    pages = "1256--1266",
    abstract = "Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, their expressive power and performance can be boosted by using a vector to measure pairwise dependency, but this requires to expand the alignment matrix to a tensor, which results in memory and computation bottlenecks. In this paper, we propose a novel attention mechanism called {``}Multi-mask Tensorized Self-Attention{''} (MTSA), which is as fast and as memory-efficient as a CNN, but significantly outperforms previous CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token) and global (source2token) dependencies by a novel compatibility function composed of dot-product and additive attentions, 2) uses a tensor to represent the feature-wise alignment scores for better expressive power but only requires parallelizable matrix multiplications, and 3) combines multi-head with multi-dimensional attentions, and applies a distinct positional mask to each head (subspace), so the memory and computation can be distributed to multiple heads, each with sequential information encoded independently. The experiments show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or competitive performance on nine NLP benchmarks with compelling memory- and time-efficiency.",
}
@inproceedings{pilehvar-camacho-collados-2019-wic,
    title = "{W}i{C}: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations",
    author = "Pilehvar, Mohammad Taher  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1128",
    doi = "10.18653/v1/N19-1128",
    pages = "1267--1273",
    abstract = "By design, word embeddings are unable to model the dynamic nature of words{'} semantics, i.e., the property of words to correspond to potentially different meanings. To address this limitation, dozens of specialized meaning representation techniques such as sense or contextualized embeddings have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling of the standard evaluation dataset for the purpose, i.e., Stanford Contextual Word Similarity, and highlight its shortcomings. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive representations. WiC is released in https://pilehvar.github.io/wic/.",
}
@inproceedings{gao-etal-2019-rebuttal,
    title = "Does My Rebuttal Matter? Insights from a Major {NLP} Conference",
    author = "Gao, Yang  and
      Eger, Steffen  and
      Kuznetsov, Ilia  and
      Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1129",
    doi = "10.18653/v1/N19-1129",
    pages = "1274--1290",
    abstract = "Peer review is a core element of the scientific process, particularly in conference-centered fields such as ML and NLP. However, only few studies have evaluated its properties empirically. Aiming to fill this gap, we present a corpus that contains over 4k reviews and 1.2k author responses from ACL-2018. We quantitatively and qualitatively assess the corpus. This includes a pilot study on paper weaknesses given by reviewers and on quality of author responses. We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i.e., final) scores from initial reviews and author responses. Although author responses do have a marginal (and statistically significant) influence on the final scores, especially for borderline papers, our results suggest that a reviewer{'}s final score is largely determined by her initial score and the distance to the other reviewers{'} initial scores. In this context, we discuss the conformity bias inherent to peer reviewing, a bias that has largely been overlooked in previous research. We hope our analyses will help better assess the usefulness of the rebuttal phase in NLP conferences.",
}
@inproceedings{wang-iyyer-2019-casting,
    title = "{C}asting {L}ight on {I}nvisible {C}ities: {C}omputationally {E}ngaging with {L}iterary {C}riticism",
    author = "Wang, Shufan  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1130",
    doi = "10.18653/v1/N19-1130",
    pages = "1291--1297",
    abstract = "Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. Applying natural language processing methods to aid in such literary analyses remains a challenge in digital humanities. While most previous work focuses on {``}distant reading{''} by algorithmically discovering high-level patterns from large collections of literary works, here we sharpen the focus of our methods to a single literary theory about Italo Calvino{'}s postmodern novel \textit{Invisible Cities}, which consists of 55 short descriptions of imaginary cities. Calvino has provided a classification of these cities into eleven thematic groups, but literary scholars disagree as to how trustworthy his categorization is. Due to the unique structure of this novel, we can computationally weigh in on this debate: we leverage pretrained contextualized representations to embed each city{'}s description and use unsupervised methods to cluster these embeddings. Additionally, we compare results of our computational approach to similarity judgments generated by human readers. Our work is a first step towards incorporating natural language processing into literary criticism.",
}
@inproceedings{zhang-etal-2019-paws,
    title = "{PAWS}: Paraphrase Adversaries from Word Scrambling",
    author = "Zhang, Yuan  and
      Baldridge, Jason  and
      He, Luheng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1131",
    doi = "10.18653/v1/N19-1131",
    pages = "1298--1308",
    abstract = "Existing paraphrase identification datasets lack sentence pairs that have high lexical overlap without being paraphrases. Models trained on such data fail to distinguish pairs like flights from New York to Florida and flights from Florida to New York. This paper introduces PAWS (Paraphrase Adversaries from Word Scrambling), a new dataset with 108,463 well-formed paraphrase and non-paraphrase pairs with high lexical overlap. Challenging pairs are generated by controlled word swapping and back translation, followed by fluency and paraphrase judgments by human raters. State-of-the-art models trained on existing datasets have dismal performance on PAWS ({\textless}40{\%} accuracy); however, including PAWS training data for these models improves their accuracy to 85{\%} while maintaining performance on existing tasks. In contrast, models that do not capture non-local contextual information fail even with PAWS training examples. As such, PAWS provides an effective instrument for driving further progress on models that better exploit structure, context, and pairwise comparisons.",
}
@inproceedings{mita-etal-2019-cross,
    title = "Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models {---} Is Single-Corpus Evaluation Enough?",
    author = "Mita, Masato  and
      Mizumoto, Tomoya  and
      Kaneko, Masahiro  and
      Nagata, Ryo  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1132",
    doi = "10.18653/v1/N19-1132",
    pages = "1309--1314",
    abstract = "This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models. GEC models have been previously evaluated based on a single commonly applied corpus: the CoNLL-2014 benchmark. However, the evaluation remains incomplete because the task difficulty varies depending on the test corpus and conditions such as the proficiency levels of the writers and essay topics. To overcome this limitation, we evaluate the performance of several GEC models, including NMT-based (LSTM, CNN, and transformer) and an SMT-based model, against various learner corpora (CoNLL-2013, CoNLL-2014, FCE, JFLEG, ICNALE, and KJ). Evaluation results reveal that the models{'} rankings considerably vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models.",
}
@inproceedings{guo-etal-2019-star,
    title = "Star-Transformer",
    author = "Guo, Qipeng  and
      Qiu, Xipeng  and
      Liu, Pengfei  and
      Shao, Yunfan  and
      Xue, Xiangyang  and
      Zhang, Zheng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1133",
    doi = "10.18653/v1/N19-1133",
    pages = "1315--1325",
    abstract = "Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data. In this paper, we present Star-Transformer, a lightweight alternative by careful sparsification. To reduce model complexity, we replace the fully-connected structure with a star-shaped topology, in which every two non-adjacent nodes are connected through a shared relay node. Thus, complexity is reduced from quadratic to linear, while preserving the capacity to capture both local composition and long-range dependency. The experiments on four tasks (22 datasets) show that Star-Transformer achieved significant improvements against the standard Transformer for the modestly sized datasets.",
}
@inproceedings{mohiuddin-etal-2019-adaptation,
    title = "Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation",
    author = "Mohiuddin, Tasnim  and
      Nguyen, Thanh-Tung  and
      Joty, Shafiq",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1134",
    doi = "10.18653/v1/N19-1134",
    pages = "1326--1336",
    abstract = "We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled conversational data and the available labeled data from synchronous domains. We carry out our research in three main steps. First, we introduce a neural architecture based on hierarchical LSTMs and conditional random fields (CRF) for SAR, and show that our method outperforms existing methods when trained on in-domain data only. Second, we improve our initial SAR models by semi-supervised learning in the form of pretrained word embeddings learned from a large unlabeled conversational corpus. Finally, we employ adversarial training to improve the results further by leveraging the labeled data from synchronous domains and by explicitly modeling the distributional shift in two domains.",
}
@inproceedings{zufall-etal-2019-legal,
    title = "From legal to technical concept: Towards an automated classification of {G}erman political {T}witter postings as criminal offenses",
    author = "Zufall, Frederike  and
      Horsmann, Tobias  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1135",
    doi = "10.18653/v1/N19-1135",
    pages = "1337--1347",
    abstract = "Advances in the automated detection of offensive Internet postings make this mechanism very attractive to social media companies, who are increasingly under pressure to monitor and action activity on their sites. However, these advances also have important implications as a threat to the fundamental right of free expression. In this article, we analyze which Twitter posts could actually be deemed offenses under German criminal law. German law follows the deductive method of the Roman law tradition based on abstract rules as opposed to the inductive reasoning in Anglo-American common law systems. This allows us to show how legal conclusions can be reached and implemented without relying on existing court decisions. We present a data annotation schema, consisting of a series of binary decisions, for determining whether a specific post would constitute a criminal offense. This schema serves as a step towards an inexpensive creation of a sufficient amount of data for an automated classification. We find that the majority of posts deemed offensive actually do not constitute a criminal offense and still contribute to public discourse. Furthermore, laymen can provide sufficiently reliable data to an expert reference but are, for instance, more lenient in the interpretation of what constitutes a disparaging statement.",
}
@inproceedings{dong-etal-2019-joint,
    title = "Joint Multi-Label Attention Networks for Social Text Annotation",
    author = "Dong, Hang  and
      Wang, Wei  and
      Huang, Kaizhu  and
      Coenen, Frans",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1136",
    doi = "10.18653/v1/N19-1136",
    pages = "1348--1354",
    abstract = "We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semantic-based loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13{\%}-26{\%} and the Hierarchical Attention Network (HAN) by around 4{\%}-12{\%} on both datasets, with around 10{\%}-30{\%} reduction of training time.",
}
@inproceedings{islam-etal-2019-multi,
    title = "Multi-Channel Convolutional Neural Network for {T}witter Emotion and Sentiment Recognition",
    author = "Islam, Jumayel  and
      Mercer, Robert E.  and
      Xiao, Lu",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1137",
    doi = "10.18653/v1/N19-1137",
    pages = "1355--1365",
    abstract = "The advent of micro-blogging sites has paved the way for researchers to collect and analyze huge volumes of data in recent years. Twitter, being one of the leading social networking sites worldwide, provides a great opportunity to its users for expressing their states of mind via short messages which are called tweets. The urgency of identifying emotions and sentiments conveyed through tweets has led to several research works. It provides a great way to understand human psychology and impose a challenge to researchers to analyze their content easily. In this paper, we propose a novel use of a multi-channel convolutional neural architecture which can effectively use different emotion and sentiment indicators such as hashtags, emoticons and emojis that are present in the tweets and improve the performance of emotion and sentiment identification. We also investigate the incorporation of different lexical features in the neural network model and its effect on the emotion and sentiment identification task. We analyze our model on some standard datasets and compare its effectiveness with existing techniques.",
}
@inproceedings{yagcioglu-etal-2019-detecting,
    title = "Detecting Cybersecurity Events from Noisy Short Text",
    author = "Yagcioglu, Semih  and
      Seyfioglu, Mehmet Saygin  and
      Citamak, Begum  and
      Bardak, Batuhan  and
      Guldamlasioglu, Seren  and
      Yuksel, Azmi  and
      Tatli, Emin Islam",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1138",
    doi = "10.18653/v1/N19-1138",
    pages = "1366--1372",
    abstract = "It is very critical to analyze messages shared over social networks for cyber threat intelligence and cyber-crime prevention. In this study, we propose a method that leverages both domain-specific word embeddings and task-specific features to detect cyber security events from tweets. Our model employs a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network which takes word level meta-embeddings as inputs and incorporates contextual embeddings to classify noisy short text. We collected a new dataset of cyber security related tweets from Twitter and manually annotated a subset of 2K of them. We experimented with this dataset and concluded that the proposed model outperforms both traditional and neural baselines. The results suggest that our method works well for detecting cyber security events from noisy short text.",
}
@inproceedings{gil-etal-2019-white,
    title = "White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks",
    author = "Gil, Yotam  and
      Chai, Yoav  and
      Gorodissky, Or  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1139",
    doi = "10.18653/v1/N19-1139",
    pages = "1373--1379",
    abstract = "Adversarial examples are important for understanding the behavior of neural models, and can improve their robustness through adversarial training. Recent work in natural language processing generated adversarial examples by assuming white-box access to the attacked model, and optimizing the input directly against it (Ebrahimi et al., 2018). In this work, we show that the knowledge implicit in the optimization procedure can be distilled into another more efficient neural network. We train a model to emulate the behavior of a white-box attack and show that it generalizes well across examples. Moreover, it reduces adversarial example generation time by 19x-39x. We also show that our approach transfers to a black-box setting, by attacking The Google Perspective API and exposing its vulnerability. Our attack flips the API-predicted label in 42{\%} of the generated examples, while humans maintain high-accuracy in predicting the gold label.",
}
@inproceedings{zong-etal-2019-analyzing,
    title = "Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media",
    author = "Zong, Shi  and
      Ritter, Alan  and
      Mueller, Graham  and
      Wright, Evan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1140",
    doi = "10.18653/v1/N19-1140",
    pages = "1380--1390",
    abstract = "Breaking cybersecurity events are shared across a range of websites, including security blogs (FireEye, Kaspersky, etc.), in addition to social media platforms such as Facebook and Twitter. In this paper, we investigate methods to analyze the severity of cybersecurity threats based on the language that is used to describe them online. A corpus of 6,000 tweets describing software vulnerabilities is annotated with authors{'} opinions toward their severity. We show that our corpus supports the development of automatic classifiers with high precision for this task. Furthermore, we demonstrate the value of analyzing users{'} opinions about the severity of threats reported online as an early indicator of important software vulnerabilities. We present a simple, yet effective method for linking software vulnerabilities reported in tweets to Common Vulnerabilities and Exposures (CVEs) in the National Vulnerability Database (NVD). Using our predicted severity scores, we show that it is possible to achieve a Precision@50 of 0.86 when forecasting high severity vulnerabilities, significantly outperforming a baseline that is based on tweet volume. Finally we show how reports of severe vulnerabilities online are predictive of real-world exploits.",
}
@inproceedings{nguyen-etal-2019-fake,
    title = "Fake News Detection using Deep {M}arkov Random Fields",
    author = "Nguyen, Duc Minh  and
      Do, Tien Huu  and
      Calderbank, Robert  and
      Deligiannis, Nikos",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1141",
    doi = "10.18653/v1/N19-1141",
    pages = "1391--1400",
    abstract = "Deep-learning-based models have been successfully applied to the problem of detecting fake news on social media. While the correlations among news articles have been shown to be effective cues for online news analysis, existing deep-learning-based methods often ignore this information and only consider each news article individually. To overcome this limitation, we develop a graph-theoretic method that inherits the power of deep learning while at the same time utilizing the correlations among the articles. We formulate fake news detection as an inference problem in a Markov random field (MRF) which can be solved by the iterative mean-field algorithm. We then unfold the mean-field algorithm into hidden layers that are composed of common neural network operations. By integrating these hidden layers on top of a deep network, which produces the MRF potentials, we obtain our deep MRF model for fake news detection. Experimental results on well-known datasets show that the proposed model improves upon various state-of-the-art models.",
}
@inproceedings{hartmann-etal-2019-issue,
    title = "Issue Framing in Online Discussion Fora",
    author = "Hartmann, Mareike  and
      Jansen, Tallulah  and
      Augenstein, Isabelle  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1142",
    doi = "10.18653/v1/N19-1142",
    pages = "1401--1407",
    abstract = "In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.",
}
@inproceedings{amiri-mohtarami-2019-vector,
    title = "Vector of Locally Aggregated Embeddings for Text Representation",
    author = "Amiri, Hadi  and
      Mohtarami, Mitra",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1143",
    doi = "10.18653/v1/N19-1143",
    pages = "1408--1414",
    abstract = "We present Vector of Locally Aggregated Embeddings (VLAE) for effective and, ultimately, lossless representation of textual content. Our model encodes each input text by effectively identifying and integrating the representations of its semantically-relevant parts. The proposed model generates high quality representation of textual content and improves the classification performance of current state-of-the-art deep averaging networks across several text classification tasks.",
}
@inproceedings{zampieri-etal-2019-predicting,
    title = "Predicting the Type and Target of Offensive Posts in Social Media",
    author = "Zampieri, Marcos  and
      Malmasi, Shervin  and
      Nakov, Preslav  and
      Rosenthal, Sara  and
      Farra, Noura  and
      Kumar, Ritesh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1144",
    doi = "10.18653/v1/N19-1144",
    pages = "1415--1420",
    abstract = "As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. We further experiment with and we compare the performance of different machine learning models on OLID.",
}
@inproceedings{li-etal-2019-biomedical,
    title = "Biomedical Event Extraction based on Knowledge-driven Tree-{LSTM}",
    author = "Li, Diya  and
      Huang, Lifu  and
      Ji, Heng  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1145",
    doi = "10.18653/v1/N19-1145",
    pages = "1421--1430",
    abstract = "Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts. To better encode contextual information and external background knowledge, we propose a novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework, incorporating two new types of features: (1) dependency structures to capture wide contexts; (2) entity properties (types and category descriptions) from external ontologies via entity linking. We evaluate our approach on the BioNLP shared task with Genia dataset and achieve a new state-of-the-art result. In addition, both quantitative and qualitative studies demonstrate the advancement of the Tree-LSTM and the external knowledge representation for biomedical event extraction.",
}
@inproceedings{zhu-etal-2019-detecting,
    title = "Detecting cognitive impairments by agreeing on interpretations of linguistic features",
    author = "Zhu, Zining  and
      Novikova, Jekaterina  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1146",
    doi = "10.18653/v1/N19-1146",
    pages = "1431--1441",
    abstract = "Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during training. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.",
}
@inproceedings{singh-bhatia-2019-relation,
    title = "Relation Extraction using Explicit Context Conditioning",
    author = "Singh, Gaurav  and
      Bhatia, Parminder",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1147",
    doi = "10.18653/v1/N19-1147",
    pages = "1442--1447",
    abstract = "Relation extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them. This works well for intra-sentence RE, and we call them first-order relations. However, this methodology can sometimes fail to capture complex and long dependencies. To address this, we hypothesize that at times the target entities can be connected via a context token. We refer to such indirect relations as second-order relations, and describe an efficient implementation for computing them. These second-order relation scores are then combined with first-order relation scores to obtain final relation scores. Our empirical results show that the proposed method leads to state-of-the-art performance over two biomedical datasets.",
}
@inproceedings{park-etal-2019-conversation,
    title = "Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues",
    author = "Park, Sungjoon  and
      Kim, Donghyun  and
      Oh, Alice",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1148",
    doi = "10.18653/v1/N19-1148",
    pages = "1448--1459",
    abstract = "The recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome. With proper anonymization, we collect counselor-client dialogues, define meaningful categories of client utterances with professional counselors, and develop a novel neural network model for classifying the client utterances. The central idea of our model, ConvMFiT, is a pre-trained conversation model which consists of a general language model built from an out-of-domain corpus and two role-specific language models built from unlabeled in-domain dialogues. The classification result shows that ConvMFiT outperforms state-of-the-art comparison models. Further, the attention weights in the learned model confirm that the model finds expected linguistic patterns for each category.",
}
@inproceedings{dai-etal-2019-using,
    title = "Using Similarity Measures to Select Pretraining Data for {NER}",
    author = "Dai, Xiang  and
      Karimi, Sarvnaz  and
      Hachey, Ben  and
      Paris, Cecile",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1149",
    doi = "10.18653/v1/N19-1149",
    pages = "1460--1470",
    abstract = "Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks. However, the measure and impact of similarity between pretraining data and target task data are left to intuition. We propose three cost-effective measures to quantify different aspects of similarity between source pretraining and target task data. We demonstrate that these measures are good predictors of the usefulness of pretrained models for Named Entity Recognition (NER) over 30 data pairs. Results also suggest that pretrained LMs are more effective and more predictable than pretrained word vectors, but pretrained word vectors are better when pretraining data is dissimilar.",
}
@inproceedings{yang-etal-2019-predicting,
    title = "Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction",
    author = "Yang, Yinfei  and
      Agarwal, Oshin  and
      Tar, Chris  and
      Wallace, Byron C.  and
      Nenkova, Ani",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1150",
    doi = "10.18653/v1/N19-1150",
    pages = "1471--1480",
    abstract = "Modern NLP systems require high-quality annotated data. For specialized domains, expert annotations may be prohibitively expensive; the alternative is to rely on crowdsourcing to reduce costs at the risk of introducing noise. In this paper we demonstrate that directly modeling instance difficulty can be used to improve model performance and to route instances to appropriate annotators. Our difficulty prediction model combines two learned representations: a {`}universal{'} encoder trained on out of domain data, and a task-specific encoder. Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing. Further, our experiments confirm the expectation that for such domain-specific tasks expert annotations are of much higher quality and preferable to obtain if practical and that augmenting small amounts of expert data with a larger set of lay annotations leads to further improvements in model performance.",
}
@inproceedings{aragon-etal-2019-detecting,
    title = "Detecting Depression in Social Media using Fine-Grained Emotions",
    author = "Arag{\'o}n, Mario Ezra  and
      L{\'o}pez-Monroy, Adrian Pastor  and
      Gonz{\'a}lez-Gurrola, Luis Carlos  and
      Montes-y-G{\'o}mez, Manuel",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1151",
    doi = "10.18653/v1/N19-1151",
    pages = "1481--1486",
    abstract = "Nowadays social media platforms are the most popular way for people to share information, from work issues to personal matters. For example, people with health disorders tend to share their concerns for advice, support or simply to relieve suffering. This provides a great opportunity to proactively detect these users and refer them as soon as possible to professional help. We propose a new representation called Bag of Sub-Emotions (BoSE), which represents social media documents by a set of fine-grained emotions automatically generated using a lexical resource of emotions and subword embeddings. The proposed representation is evaluated in the task of depression detection. The results are encouraging; the usage of fine-grained emotions improved the results from a representation based on the core emotions and obtained competitive results in comparison to state of the art approaches.",
}
@inproceedings{sousa-etal-2019-silver,
    title = "A Silver Standard Corpus of Human Phenotype-Gene Relations",
    author = "Sousa, Diana  and
      Lamurias, Andre  and
      Couto, Francisco M.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1152",
    doi = "10.18653/v1/N19-1152",
    pages = "1487--1492",
    abstract = "Human phenotype-gene relations are fundamental to fully understand the origin of some phenotypic abnormalities and their associated diseases. Biomedical literature is the most comprehensive source of these relations, however, we need Relation Extraction tools to automatically recognize them. Most of these tools require an annotated corpus and to the best of our knowledge, there is no corpus available annotated with human phenotype-gene relations. This paper presents the Phenotype-Gene Relations (PGR) corpus, a silver standard corpus of human phenotype and gene annotations and their relations. The corpus consists of 1712 abstracts, 5676 human phenotype annotations, 13835 gene annotations, and 4283 relations. We generated this corpus using Named-Entity Recognition tools, whose results were partially evaluated by eight curators, obtaining a precision of 87.01{\%}. By using the corpus we were able to obtain promising results with two state-of-the-art deep learning tools, namely 78.05{\%} of precision. The PGR corpus was made publicly available to the research community.",
}
@inproceedings{manjavacas-etal-2019-improving,
    title = "Improving Lemmatization of Non-Standard Languages with Joint Learning",
    author = "Manjavacas, Enrique  and
      K{\'a}d{\'a}r, {\'A}kos  and
      Kestemont, Mike",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1153",
    doi = "10.18653/v1/N19-1153",
    pages = "1493--1503",
    abstract = "Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map them to a dictionary headword. In the present paper we aim to improve lemmatization performance on a set of non-standard historical languages in which the difficulty is increased by an additional aspect (iii): spelling variation due to lacking orthographic standards. We approach lemmatization as a string-transduction task with an Encoder-Decoder architecture which we enrich with sentence information using a hierarchical sentence encoder. We show significant improvements over the state-of-the-art by fine-tuning the sentence encodings to jointly optimize a bidirectional language model loss. Crucially, our architecture does not require POS or morphological annotations, which are not always available for historical corpora. Additionally, we also test the proposed model on a set of typologically diverse standard languages showing results on par or better than a model without fine-tuned sentence representations and previous state-of-the-art systems. Finally, to encourage future work on processing of non-standard varieties, we release the dataset of non-standard languages underlying the present study, which is based on openly accessible sources.",
}
@inproceedings{durrani-etal-2019-one,
    title = "One Size Does Not Fit All: Comparing {NMT} Representations of Different Granularities",
    author = "Durrani, Nadir  and
      Dalvi, Fahim  and
      Sajjad, Hassan  and
      Belinkov, Yonatan  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1154",
    doi = "10.18653/v1/N19-1154",
    pages = "1504--1516",
    abstract = "Recent work has shown that contextualized word representations derived from neural machine translation are a viable alternative to such from simple word predictions tasks. This is because the internal understanding that needs to be built in order to be able to translate from one language to another is much more comprehensive. Unfortunately, computational and memory limitations as of present prevent NMT models from using large word vocabularies, and thus alternatives such as subword units (BPE and morphological segmentations) and characters have been used. Here we study the impact of using different kinds of units on the quality of the resulting representations when used to model morphology, syntax, and semantics. We found that while representations derived from subwords are slightly better for modeling syntax, character-based representations are superior for modeling morphology and are also more robust to noisy input.",
}
@inproceedings{malaviya-etal-2019-simple,
    title = "A Simple Joint Model for Improved Contextual Neural Lemmatization",
    author = "Malaviya, Chaitanya  and
      Wu, Shijie  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1155",
    doi = "10.18653/v1/N19-1155",
    pages = "1517--1528",
    abstract = "English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these diverse forms back to a canonical one, known as the lemma. We present a simple joint neural model for lemmatization and morphological tagging that achieves state-of-the-art results on 20 languages from the Universal Dependencies corpora. Our paper describes the model in addition to training and decoding procedures. Error analysis indicates that joint morphological tagging and lemmatization is especially helpful in low-resource lemmatization and languages that display a larger degree of morphological complexity.",
}
@inproceedings{bjerva-etal-2019-probabilistic,
    title = "A Probabilistic Generative Model of Linguistic Typology",
    author = "Bjerva, Johannes  and
      Kementchedjhieva, Yova  and
      Cotterell, Ryan  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1156",
    doi = "10.18653/v1/N19-1156",
    pages = "1529--1540",
    abstract = "In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the status of multiple features. The implied covariance between features inspires our probabilisation of this line of linguistic inquiry{---}we develop a generative model of language based on exponential-family matrix factorisation. By modelling all languages and features within the same architecture, we show how structural similarities between languages can be exploited to predict typological features with near-perfect accuracy, outperforming several baselines on the task of predicting held-out features. Furthermore, we show that language embeddings pre-trained on monolingual text allow for generalisation to unobserved languages. This finding has clear practical and also theoretical implications: the results confirm what linguists have hypothesised, i.e. that there are significant correlations between typological features and languages.",
}
@inproceedings{ciosici-etal-2019-quantifying,
    title = "Quantifying the morphosyntactic content of Brown Clusters",
    author = "Ciosici, Manuel R.  and
      Derczynski, Leon  and
      Assent, Ira",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1157",
    doi = "10.18653/v1/N19-1157",
    pages = "1541--1550",
    abstract = "Brown and Exchange word clusters have long been successfully used as word representations in Natural Language Processing (NLP) systems. Their success has been attributed to their seeming ability to represent both semantic and syntactic information. Using corpora representing several language families, we test the hypothesis that Brown and Exchange word clusters are highly effective at encoding morphosyntactic information. Our experiments show that word clusters are highly capable at distinguishing Parts of Speech. We show that increases in Average Mutual Information, the clustering algorithms{'} optimization goal, are highly correlated with improvements in encoding of morphosyntactic information. Our results provide empirical evidence that downstream NLP systems addressing tasks dependent on morphosyntactic information can benefit from word cluster features.",
}
@inproceedings{hao-paul-2019-analyzing,
    title = "Analyzing {B}ayesian Crosslingual Transfer in Topic Models",
    author = "Hao, Shudong  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1158",
    doi = "10.18653/v1/N19-1158",
    pages = "1551--1565",
    abstract = "We introduce a theoretical analysis of crosslingual transfer in probabilistic topic models. By formulating posterior inference through Gibbs sampling as a process of language transfer, we propose a new measure that quantifies the loss of knowledge across languages during this process. This measure enables us to derive a PAC-Bayesian bound that elucidates the factors affecting model quality, both during training and in downstream applications. We provide experimental validation of the analysis on a diverse set of five languages, and discuss best practices for data collection and model design based on our analysis.",
}
@inproceedings{de-lhoneux-etal-2019-recursive,
    title = "Recursive Subtree Composition in {LSTM}-Based Dependency Parsing",
    author = "de Lhoneux, Miryam  and
      Ballesteros, Miguel  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1159",
    doi = "10.18653/v1/N19-1159",
    pages = "1566--1576",
    abstract = "The need for tree structure modelling on top of sequence modelling is an open issue in neural dependency parsing. We investigate the impact of adding a tree layer on top of a sequential model by recursively composing subtree representations (composition) in a transition-based parser that uses features extracted by a BiLSTM. Composition seems superfluous with such a model, suggesting that BiLSTMs capture information about subtrees. We perform model ablations to tease out the conditions under which composition helps. When ablating the backward LSTM, performance drops and composition does not recover much of the gap. When ablating the forward LSTM, performance drops less dramatically and composition recovers a substantial part of the gap, indicating that a forward LSTM and composition capture similar information. We take the backward LSTM to be related to lookahead features and the forward LSTM to the rich history-based features both crucial for transition-based parsers. To capture history-based information, composition is better than a forward LSTM on its own, but it is even better to have a forward LSTM as part of a BiLSTM. We correlate results with language properties, showing that the improved lookahead of a backward LSTM is especially important for head-final languages.",
}
@inproceedings{evang-2019-cross,
    title = "Cross-lingual {CCG} Induction",
    author = "Evang, Kilian",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1160",
    doi = "10.18653/v1/N19-1160",
    pages = "1577--1587",
    abstract = "Combinatory categorial grammars are linguistically motivated and useful for semantic parsing, but costly to acquire in a supervised way and difficult to acquire in an unsupervised way. We propose an alternative making use of cross-lingual learning: an existing source-language parser is used together with a parallel corpus to induce a grammar and parsing model for a target language. On the PASCAL benchmark, cross-lingual CCG induction outperforms CCG induction from gold-standard POS tags on 3 out of 8 languages, and unsupervised CCG induction on 6 out of 8 languages. We also show that cross-lingually induced CCGs reflect syntactic properties of the target languages.",
}
@inproceedings{zhou-etal-2019-density,
    title = "Density Matching for Bilingual Word Embedding",
    author = "Zhou, Chunting  and
      Ma, Xuezhe  and
      Wang, Di  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1161",
    doi = "10.18653/v1/N19-1161",
    pages = "1588--1598",
    abstract = "Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.",
}
@inproceedings{schuster-etal-2019-cross,
    title = "Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing",
    author = "Schuster, Tal  and
      Ram, Ori  and
      Barzilay, Regina  and
      Globerson, Amir",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1162",
    doi = "10.18653/v1/N19-1162",
    pages = "1599--1613",
    abstract = "We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the context-dependent spaces. This mapping readily supports processing of a target language, improving transfer by context-aware embeddings. Our experimental results demonstrate the effectiveness of this approach for zero-shot and few-shot learning of dependency parsing. Specifically, our method consistently outperforms the previous state-of-the-art on 6 tested languages, yielding an improvement of 6.8 LAS points on average.",
}
@inproceedings{zhou-etal-2019-early,
    title = "Early Rumour Detection",
    author = "Zhou, Kaimin  and
      Shu, Chang  and
      Li, Binyang  and
      Lau, Jey Han",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1163",
    doi = "10.18653/v1/N19-1163",
    pages = "1614--1623",
    abstract = "Rumours can spread quickly through social media, and malicious ones can bring about significant economical and social impact. Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them. Although there are numerous studies on rumour detection, few are concerned with the timing of the detection. A successfully-detected malicious rumour can still cause significant damage if it isn{'}t detected in a timely manner, and so timing is crucial. To address this, we present a novel methodology for early rumour detection. Our model treats social media posts (e.g. tweets) as a data stream and integrates reinforcement learning to learn the number minimum number of posts required before we classify an event as a rumour. Experiments on Twitter and Weibo demonstrate that our model identifies rumours earlier than state-of-the-art systems while maintaining a comparable accuracy.",
}
@inproceedings{wang-etal-2019-microblog,
    title = "Microblog Hashtag Generation via Encoding Conversation Contexts",
    author = "Wang, Yue  and
      Li, Jing  and
      King, Irwin  and
      Lyu, Michael R.  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1164",
    doi = "10.18653/v1/N19-1164",
    pages = "1624--1633",
    abstract = "Automatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from limited candidates, or word-level hashtag discovery using topic models. Different from previous work considering hashtags to be inseparable, our work is the first effort to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. Moreover, to address the data sparsity issue in processing short microblog posts, we propose to jointly model the target posts and the conversation contexts initiated by them with bidirectional attention. Extensive experimental results on two large-scale datasets, newly collected from English Twitter and Chinese Weibo, show that our model significantly outperforms state-of-the-art models based on classification. Further studies demonstrate our ability to effectively generate rare and even unseen hashtags, which is however not possible for most existing methods.",
}
@inproceedings{eger-etal-2019-text,
    title = "Text Processing Like Humans Do: Visually Attacking and Shielding {NLP} Systems",
    author = {Eger, Steffen  and
      {\c{S}}ahin, G{\"o}zde G{\"u}l  and
      R{\"u}ckl{\'e}, Andreas  and
      Lee, Ji-Ung  and
      Schulz, Claudia  and
      Mesgar, Mohsen  and
      Swarnkar, Krishnkant  and
      Simpson, Edwin  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1165",
    doi = "10.18653/v1/N19-1165",
    pages = "1634--1647",
    abstract = "Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., {``}!d10t{''}) or as a writing style ({``}1337{''} in {``}leet speak{''}), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual perturbations demonstrate. We investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82{\%}. We then explore three shielding methods{---}visual character embeddings, adversarial training, and rule-based recovery{---}which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.",
}
@inproceedings{hessel-lee-2019-somethings,
    title = "Something{'}s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features",
    author = "Hessel, Jack  and
      Lee, Lillian",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1166",
    doi = "10.18653/v1/N19-1166",
    pages = "1648--1659",
    abstract = "Controversial posts are those that split the preferences of a community, receiving both significant positive and significant negative feedback. Our inclusion of the word {``}community{''} here is deliberate: what is controversial to some audiences may not be so to others. Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts, leveraging features drawn from both the textual content and the tree structure of the early comments that initiate the discussion. We find that even when only a handful of comments are available, e.g., the first 5 comments made within 15 minutes of the original post, discussion features often add predictive capacity to strong content-and- rate only baselines. Additional experiments on domain transfer suggest that conversation- structure features often generalize to other communities better than conversation-content features do.",
}
@inproceedings{han-etal-2019-permanent,
    title = "No Permanent {F}riends or Enemies: Tracking Relationships between Nations from News",
    author = "Han, Xiaochuang  and
      Choi, Eunsol  and
      Tan, Chenhao",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1167",
    doi = "10.18653/v1/N19-1167",
    pages = "1660--1676",
    abstract = "Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nations from news articles. We extend existing models by incorporating shallow linguistics information and propose a new automatic evaluation metric that aligns relationship dynamics with manually annotated key events. As understanding international relations requires carefully analyzing complex relationships, we conduct in-person human evaluations with three groups of participants. Overall, humans prefer the outputs of our model and give insightful feedback that suggests future directions for human-centered models. Furthermore, our model reveals interesting regional differences in news coverage. For instance, with respect to US-China relations, Singaporean media focus more on {``}strengthening{''} and {``}purchasing{''}, while US media focus more on {``}criticizing{''} and {``}denouncing{''}.",
}
@inproceedings{gehrmann-etal-2019-improving,
    title = "Improving Human Text Comprehension through Semi-{M}arkov {CRF}-based Neural Section Title Generation",
    author = "Gehrmann, Sebastian  and
      Layne, Steven  and
      Dernoncourt, Franck",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1168",
    doi = "10.18653/v1/N19-1168",
    pages = "1677--1688",
    abstract = "Titles of short sections within long documents support readers by guiding their focus towards relevant passages and by providing anchor-points that help to understand the progression of the document. The positive effects of section titles are even more pronounced when measured on readers with less developed reading abilities, for example in communities with limited labeled text resources. We, therefore, aim to develop techniques to generate section titles in low-resource environments. In particular, we present an extractive pipeline for section title generation by first selecting the most salient sentence and then applying deletion-based compression. Our compression approach is based on a Semi-Markov Conditional Random Field that leverages unsupervised word-representations such as ELMo or BERT, eliminating the need for a complex encoder-decoder architecture. The results show that this approach leads to competitive performance with sequence-to-sequence models with high resources, while strongly outperforming it with low resources. In a human-subject study across subjects with varying reading abilities, we find that our section titles improve the speed of completing comprehension tasks while retaining similar accuracy.",
}
@inproceedings{hashimoto-etal-2019-unifying,
    title = "Unifying Human and Statistical Evaluation for Natural Language Generation",
    author = "Hashimoto, Tatsunori  and
      Zhang, Hugh  and
      Liang, Percy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1169",
    doi = "10.18653/v1/N19-1169",
    pages = "1689--1701",
    abstract = "How can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models that simply plagiarize from the training set. On the other hand, statistical evaluation (i.e., perplexity) captures diversity but not quality, as models that occasionally emit low quality samples would be insufficiently penalized. In this paper, we propose a unified framework which evaluates both diversity and quality, based on the optimal error rate of predicting whether a sentence is human- or machine-generated. We demonstrate that this error rate can be efficiently estimated by combining human and statistical evaluation, using an evaluation metric which we call HUSE. On summarization and chit-chat dialogue, we show that (i) HUSE detects diversity defects which fool pure human evaluation and that (ii) techniques such as annealing for improving quality actually decrease HUSE due to decreased diversity.",
}
@inproceedings{see-etal-2019-makes,
    title = "What makes a good conversation? How controllable attributes affect human judgments",
    author = "See, Abigail  and
      Roller, Stephen  and
      Kiela, Douwe  and
      Weston, Jason",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1170",
    doi = "10.18653/v1/N19-1170",
    pages = "1702--1723",
    abstract = "A good conversation requires balance {--} between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chit-chat dialogue: repetition, specificity, response-relatedness and question-asking. We conduct a large-scale human evaluation to measure the effect of these control parameters on multi-turn interactive conversations on the PersonaChat task. We provide a detailed analysis of their relationship to high-level aspects of conversation, and show that by controlling combinations of these variables our models obtain clear improvements in human quality judgments.",
}
@inproceedings{goyal-etal-2019-empirical,
    title = "{A}n Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search",
    author = "Goyal, Kartik  and
      Dyer, Chris  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1171",
    doi = "10.18653/v1/N19-1171",
    pages = "1724--1733",
    abstract = "Globally normalized neural sequence models are considered superior to their locally normalized equivalents because they may ameliorate the effects of label bias. However, when considering high-capacity neural parametrizations that condition on the whole input sequence, both model classes are theoretically equivalent in terms of the distributions they are capable of representing. Thus, the practical advantage of global normalization in the context of modern neural methods remains unclear. In this paper, we attempt to shed light on this problem through an empirical study. We extend an approach for search-aware training via a continuous relaxation of beam search (Goyal et al., 2017b) in order to enable training of globally normalized recurrent sequence models through simple backpropagation. We then use this technique to conduct an empirical study of the interaction between global normalization, high-capacity encoders, and search-aware optimization. We observe that in the context of inexact search, globally normalized neural models are still more effective than their locally normalized counterparts. Further, since our training approach is sensitive to warm-starting with pre-trained models, we also propose a novel initialization strategy based on self-normalization for pre-training globally normalized models. We perform analysis of our approach on two tasks: CCG supertagging and Machine Translation, and demonstrate the importance of global normalization under different conditions while using search-aware training.",
}
@inproceedings{he-etal-2019-pun,
    title = "Pun Generation with Surprise",
    author = "He, He  and
      Peng, Nanyun  and
      Liang, Percy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1172",
    doi = "10.18653/v1/N19-1172",
    pages = "1734--1744",
    abstract = "We tackle the problem of generating a pun sentence given a pair of homophones (e.g., {``}died{''} and {``}dyed{''}). Puns are by their very nature statistically anomalous and not amenable to most text generation methods that are supervised by a large corpus. In this paper, we propose an unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle. Specifically, we posit that in a pun sentence, there is a strong association between the pun word (e.g., {``}dyed{''}) and the distant context, but a strong association between the alternative word (e.g., {``}died{''}) and the immediate context. We instantiate the surprisal principle in two ways: (i) as a measure based on the ratio of probabilities given by a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Based on human evaluation, our retrieve-and-edit approach generates puns successfully 30{\%} of the time, doubling the success rate of a neural generation baseline.",
}
@inproceedings{liu-etal-2019-single,
    title = "Single Document Summarization as Tree Induction",
    author = "Liu, Yang  and
      Titov, Ivan  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1173",
    doi = "10.18653/v1/N19-1173",
    pages = "1745--1755",
    abstract = "In this paper, we conceptualize single-document extractive summarization as a tree induction problem. In contrast to previous approaches which have relied on linguistically motivated document representations to generate summaries, our model induces a multi-root dependency tree while predicting the output summary. Each root node in the tree is a summary sentence, and the subtrees attached to it are sentences whose content relates to or explains the summary sentence. We design a new iterative refinement algorithm: it induces the trees through repeatedly refining the structures predicted by previous iterations. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods.",
}
@inproceedings{hidey-mckeown-2019-fixed,
    title = "Fixed That for You: Generating Contrastive Claims with Semantic Edits",
    author = "Hidey, Christopher  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1174",
    doi = "10.18653/v1/N19-1174",
    pages = "1756--1767",
    abstract = "Understanding contrastive opinions is a key component of argument generation. Central to an argument is the claim, a statement that is in dispute. Generating a counter-argument then requires generating a response in contrast to the main claim of the original argument. To generate contrastive claims, we create a corpus of Reddit comment pairs self-labeled by posters using the acronym FTFY (fixed that for you). We then train neural models on these pairs to edit the original claim and produce a new claim with a different view. We demonstrate significant improvement over a sequence-to-sequence baseline in BLEU score and a human evaluation for fluency, coherence, and contrast.",
}
@inproceedings{soldner-etal-2019-box,
    title = "Box of Lies: Multimodal Deception Detection in Dialogues",
    author = "Soldner, Felix  and
      P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1175",
    doi = "10.18653/v1/N19-1175",
    pages = "1768--1777",
    abstract = "Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing the Box of Lies game from The Tonight Show Starring Jimmy Fallon, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69{\%} accuracy in distinguishing deceptive and truthful behaviors.",
}
@inproceedings{poesio-etal-2019-crowdsourced,
    title = "A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation",
    author = "Poesio, Massimo  and
      Chamberlain, Jon  and
      Paun, Silviu  and
      Yu, Juntao  and
      Uma, Alexandra  and
      Kruschwitz, Udo",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1176",
    doi = "10.18653/v1/N19-1176",
    pages = "1778--1789",
    abstract = "We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally, the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of the results is presented.",
}
@inproceedings{miller-etal-2019-streamlined,
    title = "A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd",
    author = "Miller, Tristan  and
      Sukhareva, Maria  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1177",
    doi = "10.18653/v1/N19-1177",
    pages = "1790--1796",
    abstract = "The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality. We present a method that breaks down a popular but relatively complex discourse-level argument annotation scheme into a simpler, iterative procedure that can be applied even by untrained annotators. We apply this method in a crowdsourcing setup and report on the reliability of the annotations obtained. The source code for a tool implementing our annotation method, as well as the sample data we obtained (4909 gold-standard annotations across 982 documents), are freely released to the research community. These are intended to serve the needs of qualitative research into argumentation, as well as of data-driven approaches to argument mining.",
}
@inproceedings{shi-etal-2019-unsupervised,
    title = "Unsupervised Dialog Structure Learning",
    author = "Shi, Weiyan  and
      Zhao, Tiancheng  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1178",
    doi = "10.18653/v1/N19-1178",
    pages = "1797--1807",
    abstract = "Learning a shared dialog structure from a set of task-oriented dialogs is an important challenge in computational linguistics. The learned dialog structure can shed light on how to analyze human dialogs, and more importantly contribute to the design and evaluation of dialog systems. We propose to extract dialog structures using a modified VRNN model with discrete latent vectors. Different from existing HMM-based models, our model is based on variational-autoencoder (VAE). Such model is able to capture more dynamics in dialogs beyond the surface forms of the language. We find that qualitatively, our method extracts meaningful dialog structure, and quantitatively, outperforms previous models on the ability to predict unseen data. We further evaluate the model{'}s effectiveness in a downstream task, the dialog system building task. Experiments show that, by integrating the learned dialog structure into the reward function design, the model converges faster and to a better outcome in a reinforcement learning setting.",
}
@inproceedings{gao-etal-2019-modeling,
    title = "Modeling Document-level Causal Structures for Event Causal Relation Identification",
    author = "Gao, Lei  and
      Choubey, Prafulla Kumar  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1179",
    doi = "10.18653/v1/N19-1179",
    pages = "1808--1817",
    abstract = "We aim to comprehensively identify all the event causal relations in a document, both within a sentence and across sentences, which is important for reconstructing pivotal event structures. The challenges we identified are two: 1) event causal relations are sparse among all possible event pairs in a document, in addition, 2) few causal relations are explicitly stated. Both challenges are especially true for identifying causal relations between events across sentences. To address these challenges, we model rich aspects of document-level causal structures for achieving comprehensive causal relation identification. The causal structures include heavy involvements of document-level main events in causal relations as well as several types of fine-grained constraints that capture implications from certain sentential syntactic relations and discourse relations as well as interactions between event causal relations and event coreference relations. Our experimental results show that modeling the global and fine-grained aspects of causal structures using Integer Linear Programming (ILP) greatly improves the performance of causal relation identification, especially in identifying cross-sentence causal relations.",
}
@inproceedings{wu-etal-2019-hierarchical-user,
    title = "Hierarchical User and Item Representation with Three-Tier Attention for Recommendation",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Liu, Junxin  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1180",
    doi = "10.18653/v1/N19-1180",
    pages = "1818--1826",
    abstract = "Utilizing reviews to learn user and item representations is useful for recommender systems. Existing methods usually merge all reviews from the same user or for the same item into a long document. However, different reviews, sentences and even words usually have different informativeness for modeling users and items. In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation. Our model contains three major components, i.e., a sentence encoder to learn sentence representations from words, a review encoder to learn review representations from sentences, and a user/item encoder to learn user/item representations from reviews. In addition, we incorporate a three-tier attention network in our model to select important words, sentences and reviews. Besides, we combine the user and item representations learned from the reviews with user and item embeddings based on IDs as the final representations to capture the latent factors of individual users and items. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.",
}
@inproceedings{vor-der-bruck-pouly-2019-text,
    title = "Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing",
    author = {vor der Br{\"u}ck, Tim  and
      Pouly, Marc},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1181",
    doi = "10.18653/v1/N19-1181",
    pages = "1827--1836",
    abstract = "The prevalent way to estimate the similarity of two documents based on word embeddings is to apply the cosine similarity measure to the two centroids obtained from the embedding vectors associated with the words in each document. Motivated by an industrial application from the domain of youth marketing, where this approach produced only mediocre results, we propose an alternative way of combining the word vectors using matrix norms. The evaluation shows superior results for most of the investigated matrix norms in comparison to both the classical cosine measure and several other document similarity estimates.",
}
@inproceedings{prasad-kan-2019-glocal,
    title = "{G}local: Incorporating Global Information in Local Convolution for Keyphrase Extraction",
    author = "Prasad, Animesh  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1182",
    doi = "10.18653/v1/N19-1182",
    pages = "1837--1846",
    abstract = "Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes{'} local pairwise importance, they lack the capability to model global importance relative to other nodes of the graph. This causes such models to miss critical information in tasks where global ranking is a key component for the task, such as in keyphrase extraction. We address this shortcoming by allowing the proper incorporation of global information into the GCN family of models through the use of scaled node weights. In the context of keyphrase extraction, incorporating global random walk scores obtained from TextRank boosts performance significantly. With our proposed method, we achieve state-of-the-art results, bettering a strong baseline by an absolute 2{\%} increase in F1 score.",
}
@inproceedings{haponchyk-moschitti-2019-study,
    title = "A Study of Latent Structured Prediction Approaches to Passage Reranking",
    author = "Haponchyk, Iryna  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1183",
    doi = "10.18653/v1/N19-1183",
    pages = "1847--1857",
    abstract = "The structured output framework provides a helpful tool for learning to rank problems. In this paper, we propose a structured output approach which regards rankings as latent variables. Our approach addresses the complex optimization of Mean Average Precision (MAP) ranking metric. We provide an inference procedure to find the max-violating ranking based on the decomposition of the corresponding loss. The results of our experiments on WikiQA and TREC13 datasets show that our reranking based on structured prediction is a promising research direction.",
}
@inproceedings{beltagy-etal-2019-combining,
    title = "Combining Distant and Direct Supervision for Neural Relation Extraction",
    author = "Beltagy, Iz  and
      Lo, Kyle  and
      Ammar, Waleed",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1184",
    doi = "10.18653/v1/N19-1184",
    pages = "1858--1867",
    abstract = "In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model{'}s ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.",
}
@inproceedings{siddiqua-etal-2019-tweet,
    title = "{T}weet Stance Detection Using an Attention based Neural Ensemble Model",
    author = "Siddiqua, Umme Aymun  and
      Chy, Abu Nowshed  and
      Aono, Masaki",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1185",
    doi = "10.18653/v1/N19-1185",
    pages = "1868--1873",
    abstract = "Stance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. To tackle this problem, most of the prior studies have been explored the traditional deep learning models, e.g., LSTM and GRU. However, in compared to these traditional approaches, recently proposed densely connected Bi-LSTM and nested LSTMs architectures effectively address the vanishing-gradient and overfitting problems as well as dealing with long-term dependencies. In this paper, we propose a neural ensemble model that adopts the strengths of these two LSTM variants to learn better long-term dependencies, where each module coupled with an attention mechanism that amplifies the contribution of important elements in the final representation. We also employ a multi-kernel convolution on top of them to extract the higher-level tweet representations. Results of extensive experiments on single and multi-target stance detection datasets show that our proposed method achieves substantial improvement over the current state-of-the-art deep learning based methods.",
}
@inproceedings{echizenya-etal-2019-word,
    title = "Word Embedding-Based Automatic {MT} Evaluation Metric using Word Position Information",
    author = "Echizen{'}ya, Hiroshi  and
      Araki, Kenji  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1186",
    doi = "10.18653/v1/N19-1186",
    pages = "1874--1883",
    abstract = "We propose a new automatic evaluation metric for machine translation. Our proposed metric is obtained by adjusting the Earth Mover{'}s Distance (EMD) to the evaluation task. The EMD measure is used to obtain the distance between two probability distributions consisting of some signatures having a feature and a weight. We use word embeddings, sentence-level tf-idf, and cosine similarity between two word embeddings, respectively, as the features, weight, and the distance between two features. Results show that our proposed metric can evaluate machine translation based on word meaning. Moreover, for distance, cosine similarity and word position information are used to address word-order differences. We designate this metric as Word Embedding-Based automatic MT evaluation using Word Position Information (WE{\_}WPI). A meta-evaluation using WMT16 metrics shared task set indicates that our WE{\_}WPI achieves the highest correlation with human judgment among several representative metrics.",
}
@inproceedings{ma-etal-2019-learning,
    title = "Learning to Stop in Structured Prediction for Neural Machine Translation",
    author = "Ma, Mingbo  and
      Zheng, Renjie  and
      Huang, Liang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1187",
    doi = "10.18653/v1/N19-1187",
    pages = "1884--1889",
    abstract = "Beam search optimization (Wiseman and Rush, 2016) resolves many issues in neural machine translation. However, this method lacks principled stopping criteria and does not learn how to stop during training, and the model naturally prefers longer hypotheses during the testing time in practice since they use the raw score instead of the probability-based score. We propose a novel ranking method which enables an optimal beam search stop- ping criteria. We further introduce a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. Experiments of neural machine translation on both synthetic data and real languages (German→English and Chinese→English) demonstrate our pro- posed methods lead to better length and BLEU score.",
}
@inproceedings{heyman-etal-2019-learning,
    title = "Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs",
    author = "Heyman, Geert  and
      Verreet, Bregt  and
      Vuli{\'c}, Ivan  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1188",
    doi = "10.18653/v1/N19-1188",
    pages = "1890--1902",
    abstract = "Recent research has discovered that a shared bilingual word embedding space can be induced by projecting monolingual word embedding spaces from two languages using a self-learning paradigm without any bilingual supervision. However, it has also been shown that for distant language pairs such fully unsupervised self-learning methods are unstable and often get stuck in poor local optima due to reduced isomorphism between starting monolingual spaces. In this work, we propose a new robust framework for learning unsupervised multilingual word embeddings that mitigates the instability issues. We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space. Through the gradual language addition the method can leverage the interdependencies between the new language and all other languages in the current multilingual space. We find that it is beneficial to project more distant languages later in the iterative process. Our fully unsupervised multilingual embedding spaces yield results that are on par with the state-of-the-art methods in the bilingual lexicon induction (BLI) task, and simultaneously obtain state-of-the-art scores on two downstream tasks: multilingual document classification and multilingual dependency parsing, outperforming even supervised baselines. This finding also accentuates the need to establish evaluation protocols for cross-lingual word embeddings beyond the omnipresent intrinsic BLI task in future work.",
}
@inproceedings{zhang-etal-2019-curriculum,
    title = "Curriculum Learning for Domain Adaptation in Neural Machine Translation",
    author = "Zhang, Xuan  and
      Shapiro, Pamela  and
      Kumar, Gaurav  and
      McNamee, Paul  and
      Carpuat, Marine  and
      Duh, Kevin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1189",
    doi = "10.18653/v1/N19-1189",
    pages = "1903--1915",
    abstract = "We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.",
}
@inproceedings{vaibhav-etal-2019-improving,
    title = "Improving Robustness of Machine Translation with Synthetic Noise",
    author = "Vaibhav, Vaibhav  and
      Singh, Sumeet  and
      Stewart, Craig  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1190",
    doi = "10.18653/v1/N19-1190",
    pages = "1916--1920",
    abstract = "Modern Machine Translation (MT) systems perform remarkably well on clean, in-domain text. However most of the human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of MT. In this paper we propose methods to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system more resilient to naturally occurring noise, partially mitigating loss in accuracy resulting therefrom.",
}
@inproceedings{bapna-firat-2019-non,
    title = "Non-Parametric Adaptation for Neural Machine Translation",
    author = "Bapna, Ankur  and
      Firat, Orhan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1191",
    doi = "10.18653/v1/N19-1191",
    pages = "1921--1931",
    abstract = "Neural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) this results in poor performance on heterogeneous datasets and on sub-tasks like rare phrase translation. On the other hand, non-parametric approaches are immune to forgetting, perfectly complementing the generalization ability of NMT. However, attempts to combine non-parametric or retrieval based approaches with NMT have only been successful on narrow domains, possibly due to over-reliance on sentence level retrieval. We propose a novel n-gram level retrieval approach that relies on local phrase level similarities, allowing us to retrieve neighbors that are useful for translation even when overall sentence similarity is low. We complement this with an expressive neural network, allowing our model to extract information from the noisy retrieved context. We evaluate our Semi-parametric NMT approach on a heterogeneous dataset composed of WMT, IWSLT, JRC-Acquis and OpenSubtitles, and demonstrate gains on all 4 evaluation sets. The Semi-parametric nature of our approach also opens the door for non-parametric domain adaptation, demonstrating strong inference-time adaptation performance on new domains without the need for any parameter updates.",
}
@inproceedings{wei-etal-2019-online,
    title = "Online Distilling from Checkpoints for Neural Machine Translation",
    author = "Wei, Hao-Ran  and
      Huang, Shujian  and
      Wang, Ran  and
      Dai, Xin-yu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1192",
    doi = "10.18653/v1/N19-1192",
    pages = "1932--1941",
    abstract = "Current predominant neural machine translation (NMT) models often have a deep structure with large amounts of parameters, making these models hard to train and easily suffering from over-fitting. A common practice is to utilize a validation set to evaluate the training process and select the best checkpoint. Average and ensemble techniques on checkpoints can lead to further performance improvement. However, as these methods do not affect the training process, the system performance is restricted to the checkpoints generated in original training procedure. In contrast, we propose an online knowledge distillation method. Our method on-the-fly generates a teacher model from checkpoints, guiding the training process to obtain better performance. Experiments on several datasets and language pairs show steady improvement over a strong self-attention-based baseline system. We also provide analysis on data-limited setting against over-fitting. Furthermore, our method leads to an improvement in a machine reading experiment as well.",
}
@inproceedings{muhlgay-etal-2019-value,
    title = "Value-based Search in Execution Space for Mapping Instructions to Programs",
    author = "Muhlgay, Dor  and
      Herzig, Jonathan  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1193",
    doi = "10.18653/v1/N19-1193",
    pages = "1942--1954",
    abstract = "Training models to map natural language instructions to programs, given target world supervision only, requires searching for good programs at training time. Search is commonly done using beam search in the space of partial programs or program trees, but as the length of the instructions grows finding a good program becomes difficult. In this work, we propose a search algorithm that uses the target world state, known at training time, to train a critic network that predicts the expected reward of every search state. We then score search states on the beam by interpolating their expected reward with the likelihood of programs represented by the search state. Moreover, we search not in the space of programs but in a more compressed state of program executions, augmented with recent entities and actions. On the SCONE dataset, we show that our algorithm dramatically improves performance on all three domains compared to standard beam search and other baselines.",
}
@inproceedings{acharya-etal-2019-vqd,
    title = "{VQD}: Visual Query Detection In Natural Scenes",
    author = "Acharya, Manoj  and
      Jariwala, Karan  and
      Kanan, Christopher",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1194",
    doi = "10.18653/v1/N19-1194",
    pages = "1955--1961",
    abstract = "We propose a new visual grounding task called Visual Query Detection (VQD). In VQD, the task is to localize a \textit{variable} number of objects in an image where the objects are specified in natural language. VQD is related to visual referring expression comprehension, where the task is to localize only \textit{one} object. We propose the first algorithms for VQD, and we evaluate them on both visual referring expression datasets and our new VQDv1 dataset.",
}
@inproceedings{mehta-goldwasser-2019-improving,
    title = "Improving Natural Language Interaction with Robots Using Advice",
    author = "Mehta, Nikhil  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1195",
    doi = "10.18653/v1/N19-1195",
    pages = "1962--1967",
    abstract = "Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain. These works typically view this problem as a single-step process, in which a human operator gives an instruction and an automated agent is evaluated on its ability to execute it. In this paper we take the first step towards increasing the bandwidth of this interaction, and suggest a protocol for including advice, high-level observations about the task, which can help constrain the agent{'}s prediction. We evaluate our approach on the blocks world task, and show that even simple advice can help lead to significant performance improvements. To help reduce the effort involved in supplying the advice, we also explore model self-generated advice which can still improve results.",
}
@inproceedings{prokhorov-etal-2019-generating,
    title = "Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models",
    author = "Prokhorov, Victor  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1196",
    doi = "10.18653/v1/N19-1196",
    pages = "1968--1976",
    abstract = "We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem. Specifically, given the encoded state of an input text, our decoder directly predicts paths in the knowledge graph, starting from the root and ending at the the target node following hypernym-hyponym relationships. In this way, and in contrast to other text-to-entity mapping systems, our model outputs hierarchically structured predictions that are fully interpretable in the context of the underlying ontology, in an end-to-end manner. We present a proof-of-concept experiment with encouraging results, comparable to those of state-of-the-art systems.",
}
@inproceedings{thomason-etal-2019-shifting,
    title = "Shifting the Baseline: Single Modality Performance on Visual Navigation {\&} {QA}",
    author = "Thomason, Jesse  and
      Gordon, Daniel  and
      Bisk, Yonatan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1197",
    doi = "10.18653/v1/N19-1197",
    pages = "1977--1983",
    abstract = "We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research. Where existing work often compares against random or majority class baselines, we argue that unimodal approaches better capture and reflect dataset biases and therefore provide an important comparison when assessing the performance of multimodal techniques. We present unimodal ablations on three recent datasets in visual navigation and QA, seeing an up to 29{\%} absolute gain in performance over published baselines.",
}
@inproceedings{ghosh-etal-2019-excl,
    title = "{E}x{CL}: {E}xtractive {C}lip {L}ocalization {U}sing {N}atural {L}anguage {D}escriptions",
    author = "Ghosh, Soham  and
      Agarwal, Anuva  and
      Parekh, Zarana  and
      Hauptmann, Alexander",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1198",
    doi = "10.18653/v1/N19-1198",
    pages = "1984--1990",
    abstract = "The task of retrieving clips within videos based on a given natural language query requires cross-modal reasoning over multiple frames. Prior approaches such as sliding window classifiers are inefficient, while text-clip similarity driven ranking-based approaches such as segment proposal networks are far more complicated. In order to select the most relevant video clip corresponding to the given text description, we propose a novel extractive approach that predicts the start and end frames by leveraging cross-modal interactions between the text and video - this removes the need to retrieve and re-rank multiple proposal segments. Using recurrent networks we encode the two modalities into a joint representation which is then used in different variants of start-end frame predictor networks. Through extensive experimentation and ablative analysis, we demonstrate that our simple and elegant approach significantly outperforms state of the art on two datasets and has comparable performance on a third.",
}
@inproceedings{li-etal-2019-detecting,
    title = "Detecting dementia in {M}andarin {C}hinese using transfer learning from a parallel corpus",
    author = "Li, Bai  and
      Hsu, Yi-Te  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1199",
    doi = "10.18653/v1/N19-1199",
    pages = "1991--1997",
    abstract = "Machine learning has shown promise for automatic detection of Alzheimer{'}s disease (AD) through speech; however, efforts are hampered by a scarcity of data, especially in languages other than English. We propose a method to learn a correspondence between independently engineered lexicosyntactic features in two languages, using a large parallel corpus of out-of-domain movie dialogue data. We apply it to dementia detection in Mandarin Chinese, and demonstrate that our method outperforms both unilingual and machine translation-based baselines. This appears to be the first study that transfers feature domains in detecting cognitive decline.",
}
@inproceedings{gella-etal-2019-cross,
    title = "Cross-lingual Visual Verb Sense Disambiguation",
    author = "Gella, Spandana  and
      Elliott, Desmond  and
      Keller, Frank",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1200",
    doi = "10.18653/v1/N19-1200",
    pages = "1998--2004",
    abstract = "Recent work has shown that visual context improves cross-lingual sense disambiguation for nouns. We extend this line of work to the more challenging task of cross-lingual verb sense disambiguation, introducing the MultiSense dataset of 9,504 images annotated with English, German, and Spanish verbs. Each image in MultiSense is annotated with an English verb and its translation in German or Spanish. We show that cross-lingual verb sense disambiguation models benefit from visual context, compared to unimodal baselines. We also show that the verb sense predicted by our best disambiguation model can improve the results of a text-only machine translation system when used for a multimodal translation task.",
}
@inproceedings{mager-etal-2019-subword,
    title = "Subword-Level Language Identification for Intra-Word Code-Switching",
    author = {Mager, Manuel  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      Kann, Katharina},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1201",
    doi = "10.18653/v1/N19-1201",
    pages = "2005--2011",
    abstract = "Language identification for code-switching (CS), the phenomenon of alternating between two or more languages in conversations, has traditionally been approached under the assumption of a single language per token. However, if at least one language is morphologically rich, a large number of words can be composed of morphemes from more than one language (intra-word CS). In this paper, we extend the language identification task to the subword-level, such that it includes splitting mixed words while tagging each part with a language ID. We further propose a model for this task, which is based on a segmental recurrent neural network. In experiments on a new Spanish{--}Wixarika dataset and on an adapted German{--}Turkish dataset, our proposed model performs slightly better than or roughly on par with our best baseline, respectively. Considering only mixed words, however, it strongly outperforms all baselines.",
}
@inproceedings{di-gangi-etal-2019-must,
    title = "{M}u{ST}-{C}: a {M}ultilingual {S}peech {T}ranslation {C}orpus",
    author = "Di Gangi, Mattia A.  and
      Cattoni, Roldano  and
      Bentivogli, Luisa  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1202",
    doi = "10.18653/v1/N19-1202",
    pages = "2012--2017",
    abstract = "Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora. This problem hinders the adoption of neural end-to-end approaches, which represent the state of the art in the two parent tasks of SLT: automatic speech recognition and machine translation. To fill this gap, we created MuST-C, a multilingual speech translation corpus whose size and quality will facilitate the training of end-to-end systems for SLT from English into 8 languages. For each target language, MuST-C comprises at least 385 hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. Together with a description of the corpus creation methodology (scalable to add new data and cover new languages), we provide an empirical verification of its quality and SLT results computed with a state-of-the-art approach on each language direction.",
}
@inproceedings{vylomova-etal-2019-contextualization,
    title = "Contextualization of Morphological Inflection",
    author = "Vylomova, Ekaterina  and
      Cotterell, Ryan  and
      Cohn, Trevor  and
      Baldwin, Timothy  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1203",
    doi = "10.18653/v1/N19-1203",
    pages = "2018--2024",
    abstract = "Critical to natural language generation is the production of correctly inflected text. In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. Unlike traditional morphological inflection or surface realization, our task input does not provide {``}gold{''} tags that specify what morphological features to realize on each lemmatized word; rather, such features must be inferred from sentential context. We develop a neural hybrid graphical model that explicitly reconstructs morphological features before predicting the inflected forms, and compare this to a system that directly predicts the inflected forms without relying on any morphological annotation. We experiment on several typologically diverse languages from the Universal Dependencies treebanks, showing the utility of incorporating linguistically-motivated latent variables into NLP models.",
}
@inproceedings{ouyang-etal-2019-robust,
    title = "A Robust Abstractive System for Cross-Lingual Summarization",
    author = "Ouyang, Jessica  and
      Song, Boya  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1204",
    doi = "10.18653/v1/N19-1204",
    pages = "2025--2031",
    abstract = "We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.",
}
@inproceedings{ma-etal-2019-improving,
    title = "Improving Neural Machine Translation with Neural Syntactic Distance",
    author = "Ma, Chunpeng  and
      Tamura, Akihiro  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1205",
    doi = "10.18653/v1/N19-1205",
    pages = "2032--2037",
    abstract = "The explicit use of syntactic information has been proved useful for neural machine translation (NMT). However, previous methods resort to either tree-structured neural networks or long linearized sequences, both of which are inefficient. Neural syntactic distance (NSD) enables us to represent a constituent tree using a sequence whose length is identical to the number of words in the sentence. NSD has been used for constituent parsing, but not in machine translation. We propose five strategies to improve NMT with NSD. Experiments show that it is not trivial to improve NMT with NSD; however, the proposed strategies are shown to improve translation performance of the baseline model (+2.1 (En{--}Ja), +1.3 (Ja{--}En), +1.2 (En{--}Ch), and +1.0 (Ch{--}En) BLEU).",
}
@inproceedings{simianer-etal-2019-measuring,
    title = "Measuring Immediate Adaptation Performance for Neural Machine Translation",
    author = "Simianer, Patrick  and
      Wuebker, Joern  and
      DeNero, John",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1206",
    doi = "10.18653/v1/N19-1206",
    pages = "2038--2046",
    abstract = "Incremental domain adaptation, in which a system learns from the correct output for each input immediately after making its prediction for that input, can dramatically improve system performance for interactive machine translation. Users of interactive systems are sensitive to the speed of adaptation and how often a system repeats mistakes, despite being corrected. Adaptation is most commonly assessed using corpus-level BLEU- or TER-derived metrics that do not explicitly take adaptation speed into account. We find that these metrics often do not capture immediate adaptation effects, such as zero-shot and one-shot learning of domain-specific lexical items. To this end, we propose new metrics that directly evaluate immediate adaptation performance for machine translation. We use these metrics to choose the most suitable adaptation method from a range of different adaptation techniques for neural machine translation systems.",
}
@inproceedings{xu-etal-2019-differentiable,
    title = "Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation",
    author = "Xu, Weijia  and
      Niu, Xing  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1207",
    doi = "10.18653/v1/N19-1207",
    pages = "2047--2053",
    abstract = "Despite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in sampled sequences are aligned at each time step. Our new differentiable sampling algorithm addresses this issue by optimizing the probability that the reference can be aligned with the sampled output, based on a soft alignment predicted by the model itself. As a result, the output distribution at each time step is evaluated with respect to the whole predicted sequence. Experiments on IWSLT translation tasks show that our approach improves BLEU compared to maximum likelihood and scheduled sampling baselines. In addition, our approach is simpler to train with no need for sampling schedule and yields models that achieve larger improvements with smaller beam sizes.",
}
@inproceedings{kumar-etal-2019-reinforcement,
    title = "Reinforcement Learning based Curriculum Optimization for Neural Machine Translation",
    author = "Kumar, Gaurav  and
      Foster, George  and
      Cherry, Colin  and
      Krikun, Maxim",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1208",
    doi = "10.18653/v1/N19-1208",
    pages = "2054--2061",
    abstract = "We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, in the course of a single training run. We show that this approach can beat uniform baselines on Paracrawl and WMT English-to-French datasets by +3.4 and +1.3 BLEU respectively. Additionally, we match the performance of strong filtering baselines and hand-designed, state-of-the-art curricula.",
}
@inproceedings{thompson-etal-2019-overcoming,
    title = "Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation",
    author = "Thompson, Brian  and
      Gwinnup, Jeremy  and
      Khayrallah, Huda  and
      Duh, Kevin  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1209",
    doi = "10.18653/v1/N19-1209",
    pages = "2062--2068",
    abstract = "Continued training is an effective method for domain adaptation in neural machine translation. However, in-domain gains from adaptation come at the expense of general-domain performance. In this work, we interpret the drop in general-domain performance as catastrophic forgetting of general-domain knowledge. To mitigate it, we adapt Elastic Weight Consolidation (EWC){---}a machine learning method for learning a new task without forgetting previous tasks. Our method retains the majority of general-domain performance lost in continued training without degrading in-domain performance, outperforming the previous state-of-the-art. We also explore the full range of general-domain performance available when some in-domain degradation is acceptable.",
}
@inproceedings{del-tredici-etal-2019-short,
    title = "Short-Term Meaning Shift: A Distributional Exploration",
    author = "Del Tredici, Marco  and
      Fern{\'a}ndez, Raquel  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1210",
    doi = "10.18653/v1/N19-1210",
    pages = "2069--2075",
    abstract = "We present the first exploration of meaning shift over short periods of time in online communities using distributional representations. We create a small annotated dataset and use it to assess the performance of a standard model for meaning shift detection on short-term meaning shift. We find that the model has problems distinguishing meaning shift from referential phenomena, and propose a measure of contextual variability to remedy this.",
}
@inproceedings{wiegand-etal-2019-detecting,
    title = "{D}etecting {D}erogatory {C}ompounds {--} {A}n {U}nsupervised {A}pproach",
    author = "Wiegand, Michael  and
      Wolf, Maximilian  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1211",
    doi = "10.18653/v1/N19-1211",
    pages = "2076--2081",
    abstract = "We examine the new task of detecting derogatory compounds (e.g. {``}curry muncher{''}). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. {``}idiot{''}) since they are more sparsely represented in lexical resources previously found effective for this task (e.g. Wiktionary). We propose an unsupervised classification approach that incorporates linguistic properties of compounds. It mostly depends on a simple distributional representation. We compare our approach against previously established methods proposed for extracting derogatory unigrams.",
}
@inproceedings{hu-2019-personalized,
    title = "Personalized Neural Embeddings for Collaborative Filtering with Text",
    author = "Hu, Guangneng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1212",
    doi = "10.18653/v1/N19-1212",
    pages = "2082--2088",
    abstract = "Collaborative filtering (CF) is a core technique for recommender systems. Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and views) only and hence they suffer from the data sparsity issue. Items are usually associated with unstructured text such as article abstracts and product reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit both interactions and words seamlessly. We learn such embeddings of users, items, and words jointly, and predict user preferences on items based on these learned representations. PNE estimates the probability that a user will like an item by two terms{---}behavior factors and semantic factors. On two real-world datasets, PNE shows better performance than four state-of-the-art baselines in terms of three metrics. We also show that PNE learns meaningful word embeddings by visualization.",
}
@inproceedings{chronopoulou-etal-2019-embarrassingly,
    title = "An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models",
    author = "Chronopoulou, Alexandra  and
      Baziotis, Christos  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1213",
    doi = "10.18653/v1/N19-1213",
    pages = "2089--2095",
    abstract = "A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora. In this paper we present a conceptually simple and effective transfer learning approach that addresses the problem of catastrophic forgetting. Specifically, we combine the task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. This preserves language regularities captured by language models, while enabling sufficient adaptation for solving the target task. Our method does not require pretraining or finetuning separate components of the network and we train our models end-to-end in a single step. We present results on a variety of challenging affective and text classification tasks, surpassing well established transfer learning methods with greater level of complexity.",
}
@inproceedings{singh-etal-2019-incorporating,
    title = "Incorporating Emoji Descriptions Improves Tweet Classification",
    author = "Singh, Abhishek  and
      Blanco, Eduardo  and
      Jin, Wei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1214",
    doi = "10.18653/v1/N19-1214",
    pages = "2096--2101",
    abstract = "Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.",
}
@inproceedings{oba-etal-2019-modeling,
    title = "Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings",
    author = "Oba, Daisuke  and
      Yoshinaga, Naoki  and
      Sato, Shoetsu  and
      Akasaki, Satoshi  and
      Toyoda, Masashi",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1215",
    doi = "10.18653/v1/N19-1215",
    pages = "2102--2108",
    abstract = "There exist biases in individual{'}s language use; the same word (e.g., cool) is used for expressing different meanings (e.g., temperature range) or different words (e.g., cloudy, hazy) are used for describing the same meaning. In this study, we propose a method of modeling such personal biases in word meanings (hereafter, semantic variations) with personalized word embeddings obtained by solving a task on subjective text while regarding words used by different individuals as different words. To prevent personalized word embeddings from being contaminated by other irrelevant biases, we solve a task of identifying a review-target (objective output) from a given review. To stabilize the training of this extreme multi-class classification, we perform a multi-task learning with metadata identification. Experimental results with reviews retrieved from RateBeer confirmed that the obtained personalized word embeddings improved the accuracy of sentiment analysis as well as the target task. Analysis of the obtained personalized word embeddings revealed trends in semantic variations related to frequent and adjective words.",
}
@inproceedings{baly-etal-2019-multi,
    title = "Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media",
    author = "Baly, Ramy  and
      Karadzhov, Georgi  and
      Saleh, Abdelrhman  and
      Glass, James  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1216",
    doi = "10.18653/v1/N19-1216",
    pages = "2109--2116",
    abstract = "In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyper-partisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.",
}
@inproceedings{zou-lu-2019-joint,
    title = "Joint Detection and Location of {E}nglish Puns",
    author = "Zou, Yanyan  and
      Lu, Wei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1217",
    doi = "10.18653/v1/N19-1217",
    pages = "2117--2123",
    abstract = "A pun is a form of wordplay for an intended humorous or rhetorical effect, where a word suggests two or more meanings by exploiting polysemy (homographic pun) or phonological similarity to another word (heterographic pun). This paper presents an approach that addresses pun detection and pun location jointly from a sequence labeling perspective. We employ a new tagging scheme such that the model is capable of performing such a joint task, where useful structural information can be properly captured. We show that our proposed model is effective in handling both homographic and heterographic puns. Empirical results on the benchmark datasets demonstrate that our approach can achieve new state-of-the-art results.",
}
@inproceedings{vilares-gomez-rodriguez-2019-harry,
    title = "{H}arry {P}otter and the Action Prediction Challenge from Natural Language",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1218",
    doi = "10.18653/v1/N19-1218",
    pages = "2124--2130",
    abstract = "We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions. As a case of study, we consider the world of the Harry Potter fantasy novels and inferring what spell will be cast next given a fragment of a story. Spells act as keywords that abstract actions (e.g. {`}Alohomora{'} to open a door) and denote a response to the environment. This idea is used to automatically build HPAC, a corpus containing 82,836 samples and 85 actions. We then evaluate different baselines. Among the tested models, an LSTM-based approach obtains the best performance for frequent actions and large scene descriptions, but approaches such as logistic regression behave well on infrequent actions.",
}
@inproceedings{hua-etal-2019-argument-mining,
    title = "Argument Mining for Understanding Peer Reviews",
    author = "Hua, Xinyu  and
      Nikolov, Mitko  and
      Badugu, Nikhil  and
      Wang, Lu",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1219",
    doi = "10.18653/v1/N19-1219",
    pages = "2131--2137",
    abstract = "Peer-review plays a critical role in the scientific writing and publication ecosystem. To assess the efficiency and efficacy of the reviewing process, one essential element is to understand and evaluate the reviews themselves. In this work, we study the content and structure of peer reviews under the argument mining framework, through automatically detecting (1) the argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). We first collect 14.2K reviews from major machine learning and natural language processing venues. 400 reviews are annotated with 10,386 propositions and corresponding types of Evaluation, Request, Fact, Reference, or Quote. We then train state-of-the-art proposition segmentation and classification models on the data to evaluate their utilities and identify new challenges for this new domain, motivating future directions for argument mining. Further experiments show that proposition usage varies across venues in amount, type, and topic.",
}
@inproceedings{bamman-etal-2019-annotated,
    title = "An annotated dataset of literary entities",
    author = "Bamman, David  and
      Popat, Sejal  and
      Shen, Sheng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1220",
    doi = "10.18653/v1/N19-1220",
    pages = "2138--2144",
    abstract = "We present a new dataset comprised of 210,532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). These categories include non-named entities (such as {``}the boy{''}, {``}the kitchen{''}) and nested structure (such as [[the cook]{'}s sister]). In contrast to existing datasets built primarily on news (focused on geo-political entities and organizations), literary texts offer strikingly different distributions of entity categories, with much stronger emphasis on people and description of settings. We present empirical results demonstrating the performance of nested entity recognition models in this domain; training natively on in-domain literary data yields an improvement of over 20 absolute points in F-score (from 45.7 to 68.3), and mitigates a disparate impact in performance for male and female entities present in models trained on news data.",
}
@inproceedings{mishra-etal-2019-abusive,
    title = "{A}busive {L}anguage {D}etection with {G}raph {C}onvolutional {N}etworks",
    author = "Mishra, Pushkar  and
      Del Tredici, Marco  and
      Yannakoudakis, Helen  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1221",
    doi = "10.18653/v1/N19-1221",
    pages = "2145--2150",
    abstract = "Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower{--}following relationships. In contrast, working with graph convolutional networks (GCNs), we present the first approach that captures not only the structure of online communities but also the linguistic behavior of the users within them. We show that such a heterogeneous graph-structured modeling of communities significantly advances the current state of the art in abusive language detection.",
}
@inproceedings{pilehvar-2019-importance,
    title = "On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping",
    author = "Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1222",
    doi = "10.18653/v1/N19-1222",
    pages = "2151--2156",
    abstract = "Meaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic understanding of the input text and inevitably hamper the performance. Sense representations target this problem. However, their potential impact has rarely been investigated in downstream NLP applications. Through a set of experiments on a state-of-the-art reverse dictionary system based on neural networks, we show that a simple adjustment aimed at addressing the meaning conflation deficiency can lead to substantial improvements.",
}
@inproceedings{cao-clark-2019-factorising,
    title = "Factorising {AMR} generation through syntax",
    author = "Cao, Kris  and
      Clark, Stephen",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1223",
    doi = "10.18653/v1/N19-1223",
    pages = "2157--2163",
    abstract = "Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. To explicitly account for this variation, we break down generating from AMR into two steps: first generate a syntactic structure, and then generate the surface form. We show that decomposing the generation process this way leads to state-of-the-art single model performance generating from AMR without additional unlabelled data. We also demonstrate that we can generate meaning-preserving syntactic paraphrases of the same AMR graph, as judged by humans.",
}
@inproceedings{dumitrache-etal-2019-crowdsourced,
    title = "A Crowdsourced Frame Disambiguation Corpus with Ambiguity",
    author = "Dumitrache, Anca  and
      Aroyo, Lora  and
      Welty, Chris",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1224",
    doi = "10.18653/v1/N19-1224",
    pages = "2164--2170",
    abstract = "We present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. In contrast to the typical approach of attributing the best single frame to each word, we provide a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. This is based on the idea that inter-annotator disagreement is at least partly caused by ambiguity that is inherent to the text and frames. We have found many examples where the semantics of individual frames overlap sufficiently to make them acceptable alternatives for interpreting a sentence. We have argued that ignoring this ambiguity creates an overly arbitrary target for training and evaluating natural language processing systems - if humans cannot agree, why would we expect the correct answer from a machine to be any different? To process this data we also utilized an expanded lemma-set provided by the Framester system, which merges FN with WordNet to enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs whose lemmas are not part of FN. Finally we present metrics for evaluating frame disambiguation systems that account for ambiguity.",
}
@inproceedings{liu-etal-2019-inoculation,
    title = "Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets",
    author = "Liu, Nelson F.  and
      Schwartz, Roy  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1225",
    doi = "10.18653/v1/N19-1225",
    pages = "2171--2179",
    abstract = "Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared to the original benchmark, it is unclear what particular weaknesses they reveal. For example, a challenge dataset may be difficult because it targets phenomena that current models cannot capture, or because it simply exploits blind spots in a model{'}s specific training set. We introduce inoculation by fine-tuning, a new analysis method for studying challenge datasets by exposing models (the metaphorical patient) to a small amount of data from the challenge dataset (a metaphorical pathogen) and assessing how well they can adapt. We apply our method to analyze the NLI {``}stress tests{''} (Naik et al., 2018) and the Adversarial SQuAD dataset (Jia and Liang, 2017). We show that after slight exposure, some of these datasets are no longer challenging, while others remain difficult. Our results indicate that failures on challenge datasets may lead to very different conclusions about models, training datasets, and the challenge datasets themselves.",
}
@inproceedings{nguyen-etal-2019-capsule,
    title = "A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization",
    author = "Nguyen, Dai Quoc  and
      Vu, Thanh  and
      Nguyen, Tu Dinh  and
      Nguyen, Dat Quoc  and
      Phung, Dinh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1226",
    doi = "10.18653/v1/N19-1226",
    pages = "2180--2189",
    abstract = "In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are reconstructed into corresponding capsules which are then routed to another capsule to produce a continuous vector. The length of this vector is used to measure the plausibility score of the triple. Our proposed CapsE obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17.",
}
@inproceedings{ning-etal-2019-partial,
    title = "Partial Or Complete, That{'}s The Question",
    author = "Ning, Qiang  and
      He, Hangfeng  and
      Fan, Chuchu  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1227",
    doi = "10.18653/v1/N19-1227",
    pages = "2190--2200",
    abstract = "For many structured learning tasks, the data annotation process is complex and costly. Existing annotation schemes usually aim at acquiring completely annotated structures, under the common perception that partial structures are of low quality and could hurt the learning process. This paper questions this common perception, motivated by the fact that structures consist of interdependent sets of variables. Thus, given a fixed budget, partly annotating each structure may provide the same level of supervision, while allowing for more structures to be annotated. We provide an information theoretic formulation for this perspective and use it, in the context of three diverse structured learning tasks, to show that learning from partial structures can sometimes outperform learning from complete ones. Our findings may provide important insights into structured data annotation schemes and could support progress in learning protocols for structured tasks.",
}
@inproceedings{yang-etal-2019-sequential,
    title = "{S}equential {A}ttention with {K}eyword {M}ask {M}odel for {C}ommunity-based {Q}uestion {A}nswering",
    author = "Yang, Jianxin  and
      Rong, Wenge  and
      Shi, Libin  and
      Xiong, Zhang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1228",
    doi = "10.18653/v1/N19-1228",
    pages = "2201--2211",
    abstract = "In Community-based Question Answering system(CQA), Answer Selection(AS) is a critical task, which focuses on finding a suitable answer within a list of candidate answers. For neural network models, the key issue is how to model the representations of QA text pairs and calculate the interactions between them. We propose a Sequential Attention with Keyword Mask model(SAKM) for CQA to imitate human reading behavior. Question and answer text regard each other as context within keyword-mask attention when encoding the representations, and repeat multiple times(hops) in a sequential style. So the QA pairs capture features and information from both question text and answer text, interacting and improving vector representations iteratively through hops. The flexibility of the model allows to extract meaningful keywords from the sentences and enhance diverse mutual information. We perform on answer selection tasks and multi-level answer ranking tasks. Experiment results demonstrate the superiority of our proposed model on community-based QA datasets.",
}
@inproceedings{shi-etal-2019-simple,
    title = "Simple Attention-Based Representation Learning for Ranking Short Social Media Posts",
    author = "Shi, Peng  and
      Rao, Jinfeng  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1229",
    doi = "10.18653/v1/N19-1229",
    pages = "2212--2217",
    abstract = "This paper explores the problem of ranking short social media posts with respect to user queries using neural networks. Instead of starting with a complex architecture, we proceed from the bottom up and examine the effectiveness of a simple, word-level Siamese architecture augmented with attention-based mechanisms for capturing semantic {``}soft{''} matches between query and post tokens. Extensive experiments on datasets from the TREC Microblog Tracks show that our simple models not only achieve better effectiveness than existing approaches that are far more complex or exploit a more diverse set of relevance signals, but are also much faster.",
}
@inproceedings{tokala-etal-2019-attentivechecker,
    title = "{A}ttentive{C}hecker: A Bi-Directional Attention Flow Mechanism for Fact Verification",
    author = "Tokala, Santosh  and
      G, Vishal  and
      Saha, Avirup  and
      Ganguly, Niloy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1230",
    doi = "10.18653/v1/N19-1230",
    pages = "2218--2222",
    abstract = "The recently released FEVER dataset provided benchmark results on a fact-checking task in which given a factual claim, the system must extract textual evidence (sets of sentences from Wikipedia pages) that support or refute the claim. In this paper, we present a completely task-agnostic pipelined system, AttentiveChecker, consisting of three homogeneous Bi-Directional Attention Flow (BIDAF) networks, which are multi-layer hierarchical networks that represent the context at different levels of granularity. We are the first to apply to this task a bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. AttentiveChecker can be used to perform document retrieval, sentence selection, and claim verification. Experiments on the FEVER dataset indicate that AttentiveChecker is able to achieve the state-of-the-art results on the FEVER test set.",
}
@inproceedings{erdmann-etal-2019-practical,
    title = "Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities",
    author = "Erdmann, Alexander  and
      Wrisley, David Joseph  and
      Allen, Benjamin  and
      Brown, Christopher  and
      Cohen-Bod{\'e}n{\`e}s, Sophie  and
      Elsner, Micha  and
      Feng, Yukun  and
      Joseph, Brian  and
      Joyeux-Prunel, B{\'e}atrice  and
      de Marneffe, Marie-Catherine",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1231",
    doi = "10.18653/v1/N19-1231",
    pages = "2223--2234",
    abstract = "Scholars in inter-disciplinary fields like the Digital Humanities are increasingly interested in semantic annotation of specialized corpora. Yet, under-resourced languages, imperfect or noisily structured data, and user-specific classification tasks make it difficult to meet their needs using off-the-shelf models. Manual annotation of large corpora from scratch, meanwhile, can be prohibitively expensive. Thus, we propose an active learning solution for named entity recognition, attempting to maximize a custom model{'}s improvement per additional unit of manual annotation. Our system robustly handles any domain or user-defined label set and requires no external resources, enabling quality named entity recognition for Humanities corpora where such resources are not available. Evaluating on typologically disparate languages and datasets, we reduce required annotation by 20-60{\%} and greatly outperform a competitive active learning baseline.",
}
@inproceedings{zhang-zhu-2019-doc2hash,
    title = "{D}oc2hash: Learning Discrete Latent variables for Documents Retrieval",
    author = "Zhang, Yifei  and
      Zhu, Hao",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1232",
    doi = "10.18653/v1/N19-1232",
    pages = "2235--2240",
    abstract = "Learning to hash via generative model has become a powerful paradigm for fast similarity search in documents retrieval. To get binary representation (i.e., hash codes), the discrete distribution prior (i.e., Bernoulli Distribution) is applied to train the variational autoencoder (VAE). However, the discrete stochastic layer is usually incompatible with the backpropagation in the training stage, and thus causes a gradient flow problem because of non-differentiable operators. The reparameterization trick of sampling from a discrete distribution usually inc non-differentiable operators. In this paper, we propose a method, Doc2hash, that solves the gradient flow problem of the discrete stochastic layer by using continuous relaxation on priors, and trains the generative model in an end-to-end manner to generate hash codes. In qualitative and quantitative experiments, we show the proposed model outperforms other state-of-art methods.",
}
@inproceedings{tevet-etal-2019-evaluating,
    title = "Evaluating Text {GAN}s as Language Models",
    author = "Tevet, Guy  and
      Habib, Gavriel  and
      Shwartz, Vered  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1233",
    doi = "10.18653/v1/N19-1233",
    pages = "2241--2247",
    abstract = "Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of {``}exposure bias{''}. However, A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric. In this work, we propose to approximate the distribution of text generated by a GAN, which permits evaluating them with traditional probability-based LM metrics. We apply our approximation procedure on several GAN-based models and show that they currently perform substantially worse than state-of-the-art LMs. Our evaluation procedure promotes better understanding of the relation between GANs and LMs, and can accelerate progress in GAN-based text generation.",
}
@inproceedings{haidar-etal-2019-latent,
    title = "Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation",
    author = "Haidar, Md. Akmal  and
      Rezagholizadeh, Mehdi  and
      Omri, Alan Do  and
      Rashid, Ahmad",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1234",
    doi = "10.18653/v1/N19-1234",
    pages = "2248--2258",
    abstract = "Text generation with generative adversarial networks (GANs) can be divided into the text-based and code-based categories according to the type of signals used for discrimination. In this work, we introduce a novel text-based approach called Soft-GAN to effectively exploit GAN setup for text generation. We demonstrate how autoencoders (AEs) can be used for providing a continuous representation of sentences, which we will refer to as soft-text. This soft representation will be used in GAN discrimination to synthesize similar soft-texts. We also propose hybrid latent code and text-based GAN (LATEXT-GAN) approaches with one or more discriminators, in which a combination of the latent code and the soft-text is used for GAN discriminations. We perform a number of subjective and objective experiments on two well-known datasets (SNLI and Image COCO) to validate our techniques. We discuss the results using several evaluation metrics and show that the proposed techniques outperform the traditional GAN-based text-generation methods.",
}
@inproceedings{hajdik-etal-2019-neural,
    title = "Neural Text Generation from Rich Semantic Representations",
    author = "Hajdik, Valerie  and
      Buys, Jan  and
      Goodman, Michael Wayne  and
      Bender, Emily M.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1235",
    doi = "10.18653/v1/N19-1235",
    pages = "2259--2266",
    abstract = "We propose neural models to generate high-quality text from structured representations based on Minimal Recursion Semantics (MRS). MRS is a rich semantic representation that encodes more precise semantic detail than other representations such as Abstract Meaning Representation (AMR). We show that a sequence-to-sequence model that maps a linearization of Dependency MRS, a graph-based representation of MRS, to text can achieve a BLEU score of 66.11 when trained on gold data. The performance of the model can be improved further using a high-precision, broad coverage grammar-based parser to generate a large silver training corpus, achieving a final BLEU score of 77.17 on the full test set, and 83.37 on the subset of test data most closely matching the silver data domain. Our results suggest that MRS-based representations are a good choice for applications that need both structured semantics and the ability to produce natural language text as output.",
}
@inproceedings{moryossef-etal-2019-step,
    title = "{S}tep-by-Step: {S}eparating Planning from Realization in Neural Data-to-Text Generation",
    author = "Moryossef, Amit  and
      Goldberg, Yoav  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1236",
    doi = "10.18653/v1/N19-1236",
    pages = "2267--2277",
    abstract = "Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. For training a plan-to-text generator, we present a method for matching reference texts to their corresponding text plans. For inference time, we describe a method for selecting high-quality text plans for new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our results demonstrate that decoupling text planning from neural realization indeed improves the system{'}s reliability and adequacy while maintaining fluent output. We observe improvements both in BLEU scores and in manual evaluations. Another benefit of our approach is the ability to output diverse realizations of the same input, paving the way to explicit control over the generated text structure.",
}
@inproceedings{hosking-riedel-2019-evaluating,
    title = "Evaluating Rewards for Question Generation Models",
    author = "Hosking, Tom  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1237",
    doi = "10.18653/v1/N19-1237",
    pages = "2278--2283",
    abstract = "Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. Models are trained using teacher forcing to optimise only the one-step-ahead prediction. However, at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process (exposure bias). A number of authors have suggested that optimising for rewards less tightly coupled to the training data might counter this mismatch. We therefore optimise directly for various objectives beyond simply replicating the ground truth questions, including a novel approach using an adversarial discriminator that seeks to generate questions that are indistinguishable from real examples. We confirm that training with policy gradient methods leads to increases in the metrics used as rewards. We perform a human evaluation, and show that although these metrics have previously been assumed to be good proxies for question quality, they are poorly aligned with human judgement and the model simply learns to exploit the weaknesses of the reward source.",
}
@inproceedings{koncel-kedziorski-etal-2019-text,
    title = "{T}ext {G}eneration from {K}nowledge {G}raphs with {G}raph {T}ransformers",
    author = "Koncel-Kedziorski, Rik  and
      Bekal, Dhanush  and
      Luan, Yi  and
      Lapata, Mirella  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1238",
    doi = "10.18653/v1/N19-1238",
    pages = "2284--2293",
    abstract = "Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph. Graphical knowledge representations are ubiquitous in computing, but pose a significant challenge for text generation techniques due to their non-hierarchical nature, collapsing of long-distance dependencies, and structural variety. We introduce a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints. Incorporated into an encoder-decoder setup, we provide an end-to-end trainable system for graph-to-text generation that we apply to the domain of scientific text. Automatic and human evaluations show that our technique produces more informative texts which exhibit better document structure than competitive encoder-decoder methods.",
}
@inproceedings{bhutani-etal-2019-open,
    title = "Open Information Extraction from Question-Answer Pairs",
    author = "Bhutani, Nikita  and
      Suhara, Yoshihiko  and
      Tan, Wang-Chiew  and
      Halevy, Alon  and
      Jagadish, H. V.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1239",
    doi = "10.18653/v1/N19-1239",
    pages = "2294--2305",
    abstract = "Open Information Extraction (OpenIE) extracts meaningful structured tuples from free-form text. Most previous work on OpenIE considers extracting data from one sentence at a time. We describe NeurON, a system for extracting tuples from question-answer pairs. One of the main motivations for NeurON is to be able to extend knowledge bases in a way that considers precisely the information that users care about. NeurON addresses several challenges. First, an answer text is often hard to understand without knowing the question, and second, relevant information can span multiple sentences. To address these, NeurON formulates extraction as a multi-source sequence-to-sequence learning task, wherein it combines distributed representations of a question and an answer to generate knowledge facts. We describe experiments on two real-world datasets that demonstrate that NeurON can find a significant number of new and interesting facts to extend a knowledge base compared to state-of-the-art OpenIE methods.",
}
@inproceedings{de-cao-etal-2019-question,
    title = "Question Answering by Reasoning Across Documents with Graph Convolutional Networks",
    author = "De Cao, Nicola  and
      Aziz, Wilker  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1240",
    doi = "10.18653/v1/N19-1240",
    pages = "2306--2317",
    abstract = "Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).",
}
@inproceedings{yatskar-2019-qualitative,
    title = "A Qualitative Comparison of {C}o{QA}, {SQ}u{AD} 2.0 and {Q}u{AC}",
    author = "Yatskar, Mark",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1241",
    doi = "10.18653/v1/N19-1241",
    pages = "2318--2323",
    abstract = "We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (3) abstractive answers.We show that the datasets provide complementary coverage of the first two aspects, but weak coverage of the third.Because of the datasets{'} structural similarity, a single extractive model can be easily adapted to any of the datasets and we show improved baseline results on both SQuAD 2.0 and CoQA. Despite the similarity, models trained on one dataset are ineffective on another dataset, but we find moderate performance improvement through pretraining. To encourage cross-evaluation, we release code for conversion between datasets.",
}
@inproceedings{xu-etal-2019-bert,
    title = "{BERT} Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
    author = "Xu, Hu  and
      Liu, Bing  and
      Shu, Lei  and
      Yu, Philip",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1242",
    doi = "10.18653/v1/N19-1242",
    pages = "2324--2335",
    abstract = "Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions. We call this problem Review Reading Comprehension (RRC). To the best of our knowledge, no existing work has been done on RRC. In this work, we first build an RRC dataset called ReviewRC based on a popular benchmark for aspect-based sentiment analysis. Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC. To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis. Experimental results demonstrate that the proposed post-training is highly effective.",
}
@inproceedings{sakor-etal-2019-old,
    title = "Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text",
    author = {Sakor, Ahmad  and
      Onando Mulang{'}, Isaiah  and
      Singh, Kuldeep  and
      Shekarpour, Saeedeh  and
      Esther Vidal, Maria  and
      Lehmann, Jens  and
      Auer, S{\"o}ren},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1243",
    doi = "10.18653/v1/N19-1243",
    pages = "2336--2346",
    abstract = "Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a light-weight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.",
}
@inproceedings{du-etal-2019-consistent,
    title = "Be Consistent! Improving Procedural Text Comprehension using Label Consistency",
    author = "Du, Xinya  and
      Dalvi, Bhavana  and
      Tandon, Niket  and
      Bosselut, Antoine  and
      Yih, Wen-tau  and
      Clark, Peter  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1244",
    doi = "10.18653/v1/N19-1244",
    pages = "2347--2356",
    abstract = "Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a recipe). This task is challenging as the world is changing throughout the text, and despite recent advances, current systems still struggle with this task. Our approach is to leverage the fact that, for many procedural texts, multiple independent descriptions are readily available, and that predictions from them should be consistent (label consistency). We present a new learning framework that leverages label consistency during training, allowing consistency bias to be built into the model. Evaluation on a standard benchmark dataset for procedural text, ProPara (Dalvi et al., 2018), shows that our approach significantly improves prediction performance (F1) over prior state-of-the-art systems.",
}
@inproceedings{amini-etal-2019-mathqa,
    title = "{M}ath{QA}: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms",
    author = "Amini, Aida  and
      Gabriel, Saadia  and
      Lin, Shanchuan  and
      Koncel-Kedziorski, Rik  and
      Choi, Yejin  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1245",
    doi = "10.18653/v1/N19-1245",
    pages = "2357--2367",
    abstract = "We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver by learning to map problems to their operation programs. Due to annotation challenges, current datasets in this domain have been either relatively small in scale or did not offer precise operational annotations over diverse problem types. We introduce a new representation language to model operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models. Using this representation language, we significantly enhance the AQUA-RAT dataset with fully-specified operational programs. We additionally introduce a neural sequence-to-program model with automatic problem categorization. Our experiments show improvements over competitive baselines in our dataset as well as the AQUA-RAT dataset. The results are still lower than human performance indicating that the dataset poses new challenges for future research. Our dataset is available at https://math-qa.github.io/math-QA/",
}
@inproceedings{dua-etal-2019-drop,
    title = "{DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs",
    author = "Dua, Dheeru  and
      Wang, Yizhong  and
      Dasigi, Pradeep  and
      Stanovsky, Gabriel  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1246",
    doi = "10.18653/v1/N19-1246",
    pages = "2368--2378",
    abstract = "Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be done. We introduce a new reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs. In this crowdsourced, adversarially-created, 55k-question benchmark, a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs, as they remove the paraphrase-and-entity-typing shortcuts available in prior datasets. We apply state-of-the-art methods from both the reading comprehension and semantic parsing literatures on this dataset and show that the best systems only achieve 38.4{\%} F1 on our generalized accuracy metric, while expert human performance is 96{\%}. We additionally present a new model that combines reading comprehension methods with simple numerical reasoning to achieve 51{\%} F1.",
}
@inproceedings{liu-etal-2019-encoding,
    title = "An Encoding Strategy Based Word-Character {LSTM} for {C}hinese {NER}",
    author = "Liu, Wei  and
      Xu, Tongge  and
      Xu, Qinghua  and
      Song, Jiayu  and
      Zu, Yueran",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1247",
    doi = "10.18653/v1/N19-1247",
    pages = "2379--2389",
    abstract = "A recently proposed lattice model has demonstrated that words in character sequence can provide rich word boundary information for character-based Chinese NER model. In this model, word information is integrated into a shortcut path between the start and the end characters of the word. However, the existence of shortcut path may cause the model to degenerate into a partial word-based model, which will suffer from word segmentation errors. Furthermore, the lattice model can not be trained in batches due to its DAG structure. In this paper, we propose a novel word-character LSTM(WC-LSTM) model to add word information into the start or the end character of the word, alleviating the influence of word segmentation errors while obtaining the word boundary information. Four different strategies are explored in our model to encode word information into a fixed-sized representation for efficient batch training. Experiments on benchmark datasets show that our proposed model outperforms other state-of-the-arts models.",
}
@inproceedings{mubarak-etal-2019-highly,
    title = "Highly Effective {A}rabic Diacritization using Sequence to Sequence Modeling",
    author = "Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Sajjad, Hassan  and
      Samih, Younes  and
      Darwish, Kareem",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1248",
    doi = "10.18653/v1/N19-1248",
    pages = "2390--2395",
    abstract = "Arabic text is typically written without short vowels (or diacritics). However, their presence is required for properly verbalizing Arabic and is hence essential for applications such as text to speech. There are two types of diacritics, namely core-word diacritics and case-endings. Most previous works on automatic Arabic diacritic recovery rely on a large number of manually engineered features, particularly for case-endings. In this work, we present a unified character level sequence-to-sequence deep learning model that recovers both types of diacritics without the use of explicit feature engineering. Specifically, we employ a standard neural machine translation setup on overlapping windows of words (broken down into characters), and then we use voting to select the most likely diacritized form of a word. The proposed model outperforms all previous state-of-the-art systems. Our best settings achieve a word error rate (WER) of 4.49{\%} compared to the state-of-the-art of 12.25{\%} on a standard dataset.",
}
@inproceedings{lu-etal-2019-sc,
    title = "{SC}-{LSTM}: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling",
    author = "Lu, Peng  and
      Bai, Ting  and
      Langlais, Philippe",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1249",
    doi = "10.18653/v1/N19-1249",
    pages = "2396--2406",
    abstract = "Multi-task learning (MTL) has been studied recently for sequence labeling. Typically, auxiliary tasks are selected specifically in order to improve the performance of a target task. Jointly learning multiple tasks in a way that benefit all of them simultaneously can increase the utility of MTL. In order to do so, we propose a new LSTM cell which contains both shared parameters that can learn from all tasks, and task-specific parameters that can learn task-specific information. We name it a Shared-Cell Long-Short Term Memory SC-LSTM. Experimental results on three sequence labeling benchmarks (named-entity recognition, text chunking, and part-of-speech tagging) demonstrate the effectiveness of our SC-LSTM cell.",
}
@inproceedings{onoe-durrett-2019-learning,
    title = "Learning to Denoise Distantly-Labeled Data for Entity Typing",
    author = "Onoe, Yasumasa  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1250",
    doi = "10.18653/v1/N19-1250",
    pages = "2407--2417",
    abstract = "Distantly-labeled data can be used to scale up training of statistical models, but it is typically noisy and that noise can vary with the distant labeling technique. In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training. Our denoising approach consists of two parts. First, a filtering function discards examples from the distantly labeled data that are wholly unusable. Second, a relabeling function repairs noisy labels for the retained examples. Each of these components is a model trained on synthetically-noised examples generated from a small manually-labeled set. We investigate this approach on the ultra-fine entity typing task of Choi et al. (2018). Our baseline model is an extension of their model with pre-trained ELMo representations, which already achieves state-of-the-art performance. Adding distant data that has been denoised with our learned models gives further performance gains over this base model, outperforming models trained on raw distant data or heuristically-denoised distant data.",
}
@inproceedings{flachs-etal-2019-simple,
    title = "A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors",
    author = "Flachs, Simon  and
      Lacroix, Oph{\'e}lie  and
      Rei, Marek  and
      Yannakoudakis, Helen  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1251",
    doi = "10.18653/v1/N19-1251",
    pages = "2418--2427",
    abstract = "While rule-based detection of subject-verb agreement (SVA) errors is sensitive to syntactic parsing errors and irregularities and exceptions to the main rules, neural sequential labelers have a tendency to overfit their training data. We observe that rule-based error generation is less sensitive to syntactic parsing errors and irregularities than error detection and explore a simple, yet efficient approach to getting the best of both worlds: We train neural sequential labelers on the combination of large volumes of silver standard data, obtained through rule-based error generation, and gold standard data. We show that our simple protocol leads to more robust detection of SVA errors on both in-domain and out-of-domain data, as well as in the context of other errors and long-distance dependencies; and across four standard benchmarks, the induced model on average achieves a new state of the art.",
}
@inproceedings{cardenas-etal-2019-grounded,
    title = "A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages",
    author = "Cardenas, Ronald  and
      Lin, Ying  and
      Ji, Heng  and
      May, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1252",
    doi = "10.18653/v1/N19-1252",
    pages = "2428--2439",
    abstract = "Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a {`}ciphertext{'} and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger{'}s utility by incorporating it into a true {`}zero-resource{'} variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.",
}
@inproceedings{ahmad-etal-2019-difficulties,
    title = "On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing",
    author = "Ahmad, Wasi  and
      Zhang, Zhisong  and
      Ma, Xuezhe  and
      Hovy, Eduard  and
      Chang, Kai-Wei  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1253",
    doi = "10.18653/v1/N19-1253",
    pages = "2440--2452",
    abstract = "Different languages might have different word orders. In this paper, we investigate crosslingual transfer and posit that an orderagnostic model will perform better when transferring to distant foreign languages. To test our hypothesis, we train dependency parsers on an English corpus and evaluate their transfer performance on 30 other languages. Specifically, we compare encoders and decoders based on Recurrent Neural Networks (RNNs) and modified self-attentive architectures. The former relies on sequential information while the latter is more flexible at modeling word order. Rigorous experiments and detailed analysis shows that RNN-based architectures transfer well to languages that are close to English, while self-attentive models have better overall cross-lingual transferability and perform especially well on distant languages.",
}
@inproceedings{chen-etal-2019-multi-task,
    title = "A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations",
    author = "Chen, Mingda  and
      Tang, Qingming  and
      Wiseman, Sam  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1254",
    doi = "10.18653/v1/N19-1254",
    pages = "2453--2464",
    abstract = "We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We show we can achieve better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. We evaluate our models on standard semantic similarity tasks and novel syntactic similarity tasks. Empirically, we find that the model with the best performing syntactic and semantic representations also gives rise to the most disentangled representations.",
}
@inproceedings{chen-etal-2019-self,
    title = "Self-Discriminative Learning for Unsupervised Document Embedding",
    author = "Chen, Hong-You  and
      Hu, Chin-Hua  and
      Wehbe, Leila  and
      Lin, Shou-De",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1255",
    doi = "10.18653/v1/N19-1255",
    pages = "2465--2474",
    abstract = "Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. Extensive experiments on both small and large public datasets show the competitiveness of the proposed method. In evaluations on standard document classification, our model has errors that are 5 to 13{\%} lower than state-of-the-art unsupervised embedding models. The reduction in error is even more pronounced in scarce label setting.",
}
@inproceedings{choi-etal-2019-adaptive,
    title = "Adaptive Convolution for Text Classification",
    author = "Choi, Byung-Ju  and
      Park, Jun-Hyung  and
      Lee, SangKeun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1256",
    doi = "10.18653/v1/N19-1256",
    pages = "2475--2485",
    abstract = "In this paper, we present an adaptive convolution for text classification to give flexibility to convolutional neural networks (CNNs). Unlike traditional convolutions which utilize the same set of filters regardless of different inputs, the adaptive convolution employs adaptively generated convolutional filters conditioned on inputs. We achieve this by attaching filter-generating networks, which are carefully designed to generate input-specific filters, to convolution blocks in existing CNNs. We show the efficacy of our approach in existing CNNs based on the performance evaluation. Our evaluation indicates that all of our baselines achieve performance improvements with adaptive convolutions as much as up to 2.6 percentage point in seven benchmark text classification datasets.",
}
@inproceedings{jebbara-cimiano-2019-zero,
    title = "{Z}ero-Shot Cross-Lingual Opinion Target Extraction",
    author = "Jebbara, Soufian  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1257",
    doi = "10.18653/v1/N19-1257",
    pages = "2486--2495",
    abstract = "Aspect-based sentiment analysis involves the recognition of so called opinion target expressions (OTEs). To automatically extract OTEs, supervised learning algorithms are usually employed which are trained on manually annotated corpora. The creation of these corpora is labor-intensive and sufficiently large datasets are therefore usually only available for a very narrow selection of languages and domains. In this work, we address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture for OTE extraction. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language. Depending on the source and target language pairs, we reach performances in a zero-shot regime of up to 77{\%} of a model trained on target language data. Furthermore, we can increase this performance up to 87{\%} of a baseline model trained on target language data by performing cross-lingual learning from multiple source languages.",
}
@inproceedings{qu-etal-2019-adversarial,
    title = "Adversarial Category Alignment Network for Cross-domain Sentiment Classification",
    author = "Qu, Xiaoye  and
      Zou, Zhikang  and
      Cheng, Yu  and
      Yang, Yang  and
      Zhou, Pan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1258",
    doi = "10.18653/v1/N19-1258",
    pages = "2496--2508",
    abstract = "Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain. Most existing adversarial learning methods focus on aligning the global marginal distribution by fooling a domain discriminator, without taking category-specific decision boundaries into consideration, which can lead to the mismatch of category-level features. In this work, we propose an adversarial category alignment network (ACAN), which attempts to enhance category consistency between the source domain and the target domain. Specifically, we increase the discrepancy of two polarity classifiers to provide diverse views, locating ambiguous features near the decision boundaries. Then the generator learns to create better features away from the category boundaries by minimizing this discrepancy. Experimental results on benchmark datasets show that the proposed method can achieve state-of-the-art performance and produce more discriminative features.",
}
@inproceedings{fan-etal-2019-target,
    title = "Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling",
    author = "Fan, Zhifang  and
      Wu, Zhen  and
      Dai, Xin-Yu  and
      Huang, Shujian  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1259",
    doi = "10.18653/v1/N19-1259",
    pages = "2509--2518",
    abstract = "Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). Recently, many methods have made progress on these two tasks. However, few works aim at extracting opinion targets and opinion words as pairs. In this paper, we propose a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target. A target-fused sequence labeling neural network model is designed to perform this task. The opinion target information is well encoded into context by an Inward-Outward LSTM. Then left and right contexts of the opinion target and the global context are combined to find the corresponding opinion words. We build four datasets for TOWE based on several popular ABSA benchmarks from laptop and restaurant reviews. The experimental results show that our proposed model outperforms the other compared methods significantly. We believe that our work may not only be helpful for downstream sentiment analysis task, but can also be used for pair-wise opinion summarization.",
}
@inproceedings{kim-etal-2019-abstractive,
    title = "Abstractive Summarization of {R}eddit Posts with Multi-level Memory Networks",
    author = "Kim, Byeongchang  and
      Kim, Hyunwoo  and
      Kim, Gunhee",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1260",
    doi = "10.18653/v1/N19-1260",
    pages = "2519--2531",
    abstract = "We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually located at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models.",
}
@inproceedings{xia-etal-2019-automatic,
    title = "Automatic learner summary assessment for reading comprehension",
    author = "Xia, Menglin  and
      Kochmar, Ekaterina  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1261",
    doi = "10.18653/v1/N19-1261",
    pages = "2532--2542",
    abstract = "Automating the assessment of learner summary provides a useful tool for assessing learner reading comprehension. We present a summarization task for evaluating non-native reading comprehension and propose three novel approaches to automatically assess the learner summaries. We evaluate our models on two datasets we created and show that our models outperform traditional approaches that rely on exact word match on this task. Our best model produces quality assessments close to professional examiners.",
}
@inproceedings{p-v-s-meyer-2019-data,
    title = "Data-efficient Neural Text Compression with Interactive Learning",
    author = "P.V.S, Avinesh  and
      Meyer, Christian M.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1262",
    doi = "10.18653/v1/N19-1262",
    pages = "2543--2554",
    abstract = "Neural sequence-to-sequence models have been successfully applied to text compression. However, these models were trained on huge automatically induced parallel corpora, which are only available for a few domains and tasks. In this paper, we propose a novel interactive setup to neural text compression that enables transferring a model to new domains and compression tasks with minimal human supervision. This is achieved by employing active learning, which intelligently samples from a large pool of unlabeled data. Using this setup, we can successfully adapt a model trained on small data of 40k samples for a headline generation task to a general text compression dataset at an acceptable compression quality with just 500 sampled instances annotated by a human.",
}
@inproceedings{peng-etal-2019-text,
    title = "Text Generation with Exemplar-based Adaptive Decoding",
    author = "Peng, Hao  and
      Parikh, Ankur  and
      Faruqui, Manaal  and
      Dhingra, Bhuwan  and
      Das, Dipanjan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1263",
    doi = "10.18653/v1/N19-1263",
    pages = "2555--2565",
    abstract = "We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e., what to say), and the template influences how to say it. Building on the successful encoder-decoder paradigm, it first encodes the content representation from the given input text; to produce the output, it retrieves exemplar text from the training data as {``}soft templates,{''} which are then used to construct an exemplar-specific decoder. We evaluate the proposed model on abstractive text summarization and data-to-text generation. Empirical results show that this model achieves strong performance and outperforms comparable baselines.",
}
@inproceedings{arumae-liu-2019-guiding,
    title = "Guiding Extractive Summarization with Question-Answering Rewards",
    author = "Arumae, Kristjan  and
      Liu, Fei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1264",
    doi = "10.18653/v1/N19-1264",
    pages = "2566--2577",
    abstract = "Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However, a major obstacle to the development of a supervised summarizer is the lack of ground-truth. Manual annotation of extraction units is cost-prohibitive, whereas acquiring labels by automatically aligning human abstracts and source documents can yield inferior results. In this paper we describe a novel framework to guide a supervised, extractive summarization system with question-answering rewards. We argue that quality summaries should serve as document surrogates to answer important questions, and such question-answer pairs can be conveniently obtained from human abstracts. The system learns to promote summaries that are informative, fluent, and perform competitively on question-answering. Our results compare favorably with those reported by strong summarization baselines as evaluated by automatic metrics and human assessors.",
}
@inproceedings{shekhar-etal-2019-beyond,
    title = "Beyond task success: A closer look at jointly learning to see, ask, and {G}uess{W}hat",
    author = {Shekhar, Ravi  and
      Venkatesh, Aashish  and
      Baumg{\"a}rtner, Tim  and
      Bruni, Elia  and
      Plank, Barbara  and
      Bernardi, Raffaella  and
      Fern{\'a}ndez, Raquel},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1265",
    doi = "10.18653/v1/N19-1265",
    pages = "2578--2587",
    abstract = "We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components. As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify an object in a complex visual scene by asking a sequence of yes/no questions. Our visually-grounded encoder leverages synergies between guessing and asking questions, as it is trained jointly using multi-task learning. We further enrich our model via a cooperative learning regime. We show that the introduction of both the joint architecture and cooperative learning lead to accuracy improvements over the baseline system. We compare our approach to an alternative system which extends the baseline with reinforcement learning. Our in-depth analysis shows that the linguistic skills of the two models differ dramatically, despite approaching comparable performance levels. This points at the importance of analyzing the linguistic output of competing systems beyond numeric comparison solely based on task success.",
}
@inproceedings{yao-etal-2019-world,
    title = "The World in My Mind: Visual Dialog with Adversarial Multi-modal Feature Encoding",
    author = "Yao, Yiqun  and
      Xu, Jiaming  and
      Xu, Bo",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1266",
    doi = "10.18653/v1/N19-1266",
    pages = "2588--2598",
    abstract = "Visual Dialog is a multi-modal task that requires a model to participate in a multi-turn human dialog grounded on an image, and generate correct, human-like responses. In this paper, we propose a novel Adversarial Multi-modal Feature Encoding (AMFE) framework for effective and robust auxiliary training of visual dialog systems. AMFE can force the language-encoding part of a model to generate hidden states in a distribution closely related to the distribution of real-world images, resulting in language features containing general knowledge from both modalities by nature, which can help generate both more correct and more general responses with reasonably low time cost. Experimental results show that AMFE can steadily bring performance gains to different models on different scales of data. Our method outperforms both the supervised learning baselines and other fine-tuning methods, achieving state-of-the-art results on most metrics of VisDial v0.5/v0.9 generative tasks.",
}
@inproceedings{liang-etal-2019-strong,
    title = "Strong and Simple Baselines for Multimodal Utterance Embeddings",
    author = "Liang, Paul Pu  and
      Lim, Yao Chong  and
      Tsai, Yao-Hung Hubert  and
      Salakhutdinov, Ruslan  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1267",
    doi = "10.18653/v1/N19-1267",
    pages = "2599--2609",
    abstract = "Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. Learning representations for these spoken utterances is a complex research problem due to the presence of multiple heterogeneous sources of information. Recent advances in multimodal learning have followed the general trend of building more complex models that utilize various attention, memory and recurrent components. In this paper, we propose two simple but strong baselines to learn embeddings of multimodal utterances. The first baseline assumes a conditional factorization of the utterance into unimodal factors. Each unimodal factor is modeled using the simple form of a likelihood function obtained via a linear transformation of the embedding. We show that the optimal embedding can be derived in closed form by taking a weighted average of the unimodal features. In order to capture richer representations, our second baseline extends the first by factorizing into unimodal, bimodal, and trimodal factors, while retaining simplicity and efficiency during learning and inference. From a set of experiments across two tasks, we show strong performance on both supervised and semi-supervised multimodal prediction, as well as significant (10 times) speedups over neural models during inference. Overall, we believe that our strong baseline models offer new benchmarking options for future research in multimodal learning.",
}
@inproceedings{tan-etal-2019-learning,
    title = "Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout",
    author = "Tan, Hao  and
      Yu, Licheng  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1268",
    doi = "10.18653/v1/N19-1268",
    pages = "2610--2621",
    abstract = "A grand goal in AI is to build a robot that can accurately navigate based on natural language instructions, which requires the agent to perceive the scene, understand and ground language, and act in the real-world environment. One key challenge here is to learn to navigate in new environments that are unseen during training. Most of the existing approaches perform dramatically worse in unseen environments as compared to seen ones. In this paper, we present a generalizable navigational agent. Our agent is trained in two stages. The first stage is training via mixed imitation and reinforcement learning, combining the benefits from both off-policy and on-policy optimization. The second stage is fine-tuning via newly-introduced {`}unseen{'} triplets (environment, path, instruction). To generate these unseen triplets, we propose a simple but effective {`}environmental dropout{'} method to mimic unseen environments, which overcomes the problem of limited seen environment variability. Next, we apply semi-supervised learning (via back-translation) on these dropout environments to generate new paths and instructions. Empirically, we show that our agent is substantially better at generalizability when fine-tuned with these triplets, outperforming the state-of-art approaches by a large margin on the private unseen test set of the Room-to-Room task, and achieving the top rank on the leaderboard.",
}
@inproceedings{prabhumoye-etal-2019-towards,
    title = "Towards Content Transfer through Grounded Text Generation",
    author = "Prabhumoye, Shrimai  and
      Quirk, Chris  and
      Galley, Michel",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1269",
    doi = "10.18653/v1/N19-1269",
    pages = "2622--2632",
    abstract = "Recent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness. However, there has been less work on controlling neural text generation for content. This paper introduces the notion of Content Transfer for long-form text generation, where the task is to generate a next sentence in a document that both fits its context and is grounded in a content-rich external textual source such as a news story. Our experiments on Wikipedia data show significant improvements against competitive baselines. As another contribution of this paper, we release a benchmark dataset of 640k Wikipedia referenced sentences paired with the source articles to encourage exploration of this new task.",
}
@inproceedings{sun-etal-2019-improving,
    title = "Improving Machine Reading Comprehension with General Reading Strategies",
    author = "Sun, Kai  and
      Yu, Dian  and
      Yu, Dong  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1270",
    doi = "10.18653/v1/N19-1270",
    pages = "2633--2643",
    abstract = "Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge. Just as the process of knowledge accumulation is time-consuming for human readers, it is resource-demanding to impart rich general domain knowledge into a deep language model via pre-training. Inspired by reading strategies identified in cognitive science, and given limited computational resources - just a pre-trained model and a fixed number of training instances - we propose three general strategies aimed to improve non-extractive machine reading comprehension (MRC): (i) BACK AND FORTH READING that considers both the original and reverse order of an input sequence, (ii) HIGHLIGHTING, which adds a trainable embedding to the text embedding of tokens that are relevant to the question and candidate answers, and (iii) SELF-ASSESSMENT that generates practice questions and candidate answers directly from the text in an unsupervised manner. By fine-tuning a pre-trained language model (Radford et al., 2018) with our proposed strategies on the largest general domain multiple-choice MRC dataset RACE, we obtain a 5.8{\%} absolute increase in accuracy over the previous best result achieved by the same pre-trained model fine-tuned on RACE without the use of strategies. We further fine-tune the resulting model on a target MRC task, leading to an absolute improvement of 6.2{\%} in average accuracy over previous state-of-the-art approaches on six representative non-extractive MRC datasets from different domains (i.e., ARC, OpenBookQA, MCTest, SemEval-2018 Task 11, ROCStories, and MultiRC). These results demonstrate the effectiveness of our proposed strategies and the versatility and general applicability of our fine-tuned models that incorporate these strategies. Core code is available at https://github.com/nlpdata/strategy/.",
}
@inproceedings{xu-etal-2019-multi,
    title = "Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension",
    author = "Xu, Yichong  and
      Liu, Xiaodong  and
      Shen, Yelong  and
      Liu, Jingjing  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1271",
    doi = "10.18653/v1/N19-1271",
    pages = "2644--2655",
    abstract = "We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains. Inspired by recent ideas of data selection in machine translation, we develop a novel sample re-weighting scheme to assign sample-specific weights to the loss. Empirical study shows that our approach can be applied to many existing MRC models. Combined with contextual representations from pre-trained language models (such as ELMo), we achieve new state-of-the-art results on a set of MRC benchmark datasets. We release our code at \url{https://github.com/xycforgithub/MultiTask-MRC}.",
}
@inproceedings{chiang-chen-2019-semantically,
    title = "Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems",
    author = "Chiang, Ting-Rui  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1272",
    doi = "10.18653/v1/N19-1272",
    pages = "2656--2668",
    abstract = "Solving math word problems is a challenging task that requires accurate natural language understanding to bridge natural language texts and math expressions. Motivated by the intuition about how human generates the equations given the problem texts, this paper presents a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. This paper views the process of generating equation as a bridge between the semantic world and the symbolic world, where the proposed neural math solver is based on an encoder-decoder framework. In the proposed model, the encoder is designed to understand the semantics of problems, and the decoder focuses on tracking semantic meanings of the generated symbols and then deciding which symbol to generate next. The preliminary experiments are conducted in a dataset Math23K, and our model significantly outperforms both the state-of-the-art single model and the best non-retrieval-based model over about 10{\%} accuracy, demonstrating the effectiveness of bridging the symbolic and semantic worlds from math word problems.",
}
@inproceedings{dasigi-etal-2019-iterative,
    title = "Iterative Search for Weakly Supervised Semantic Parsing",
    author = "Dasigi, Pradeep  and
      Gardner, Matt  and
      Murty, Shikhar  and
      Zettlemoyer, Luke  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1273",
    doi = "10.18653/v1/N19-1273",
    pages = "2669--2680",
    abstract = "Training semantic parsers from question-answer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training algorithm that alternates between searching for consistent logical forms and maximizing the marginal likelihood of the retrieved ones. This training scheme lets us iteratively train models that provide guidance to subsequent ones to search for logical forms of increasing complexity, thus dealing with the problem of spuriousness. We evaluate these techniques on two hard datasets: WikiTableQuestions (WTQ) and Cornell Natural Language Visual Reasoning (NLVR), and show that our training algorithm outperforms the previous best systems, on WTQ in a comparable setting, and on NLVR with significantly less supervision.",
}
@inproceedings{yadav-etal-2019-alignment,
    title = "Alignment over Heterogeneous Embeddings for Question Answering",
    author = "Yadav, Vikas  and
      Bethard, Steven  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1274",
    doi = "10.18653/v1/N19-1274",
    pages = "2681--2691",
    abstract = "We propose a simple, fast, and mostly-unsupervised approach for non-factoid question answering (QA) called Alignment over Heterogeneous Embeddings (AHE). AHE simply aligns each word in the question and candidate answer with the most similar word in the retrieved supporting paragraph, and weighs each alignment score with the inverse document frequency of the corresponding question/answer term. AHE{'}s similarity function operates over embeddings that model the underlying text at different levels of abstraction: character (FLAIR), word (BERT and GloVe), and sentence (InferSent), where the latter is the only supervised component in the proposed approach. Despite its simplicity and lack of supervision, AHE obtains a new state-of-the-art performance on the {``}Easy{''} partition of the AI2 Reasoning Challenge (ARC) dataset (64.6{\%} accuracy), top-two performance on the {``}Challenge{''} partition of ARC (34.1{\%}), and top-three performance on the WikiQA dataset (74.08{\%} MRR), outperforming many other complex, supervised approaches. Our error analysis indicates that alignments over character, word, and sentence embeddings capture substantially different semantic information. We exploit this with a simple meta-classifier that learns how much to trust the predictions over each representation, which further improves the performance of unsupervised AHE.",
}
@inproceedings{rohanian-etal-2019-bridging,
    title = "{B}ridging the Gap: {A}ttending to Discontinuity in Identification of Multiword Expressions",
    author = "Rohanian, Omid  and
      Taslimipoor, Shiva  and
      Kouchaki, Samaneh  and
      Ha, Le An  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1275",
    doi = "10.18653/v1/N19-1275",
    pages = "2692--2698",
    abstract = "We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both, through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score.",
}
@inproceedings{higashiyama-etal-2019-incorporating,
    title = "Incorporating Word Attention into Character-Based Word Segmentation",
    author = "Higashiyama, Shohei  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Ideuchi, Masao  and
      Oida, Yoshiaki  and
      Sakamoto, Yohei  and
      Okada, Isaac",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1276",
    doi = "10.18653/v1/N19-1276",
    pages = "2699--2709",
    abstract = "Neural network models have been actively applied to word segmentation, especially Chinese, because of the ability to minimize the effort in feature engineering. Typical segmentation models are categorized as character-based, for conducting exact inference, or word-based, for utilizing word-level information. We propose a character-based model utilizing word information to leverage the advantages of both types of models. Our model learns the importance of multiple candidate words for a character on the basis of an attention mechanism, and makes use of it for segmentation decisions. The experimental results show that our model achieves better performance than the state-of-the-art models on both Japanese and Chinese benchmark datasets.",
}
@inproceedings{sun-etal-2019-vcwe,
    title = "{VCWE}: Visual Character-Enhanced Word Embeddings",
    author = "Sun, Chi  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1277",
    doi = "10.18653/v1/N19-1277",
    pages = "2710--2719",
    abstract = "Chinese is a logographic writing system, and the shape of Chinese characters contain rich syntactic and semantic information. In this paper, we propose a model to learn Chinese word embeddings via three-level composition: (1) a convolutional neural network to extract the intra-character compositionality from the visual shape of a character; (2) a recurrent neural network with self-attention to compose character representation into word embeddings; (3) the Skip-Gram framework to capture non-compositionality directly from the contextual information. Evaluations demonstrate the superior performance of our model on four tasks: word similarity, sentiment analysis, named entity recognition and part-of-speech tagging.",
}
@inproceedings{yang-etal-2019-subword,
    title = "Subword Encoding in Lattice {LSTM} for {C}hinese Word Segmentation",
    author = "Yang, Jie  and
      Zhang, Yue  and
      Liang, Shuailong",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1278",
    doi = "10.18653/v1/N19-1278",
    pages = "2720--2725",
    abstract = "We investigate subword information for Chinese word segmentation, by integrating sub word embeddings trained using byte-pair encoding into a Lattice LSTM (LaLSTM) network over a character sequence. Experiments on standard benchmark show that subword information brings significant gains over strong character-based segmentation models. To our knowledge, this is the first research on the effectiveness of subwords on neural word segmentation.",
}
@inproceedings{ye-etal-2019-improving,
    title = "Improving Cross-Domain {C}hinese Word Segmentation with Word Embeddings",
    author = "Ye, Yuxiao  and
      Li, Weikang  and
      Zhang, Yue  and
      Qiu, Likun  and
      Sun, Jian",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1279",
    doi = "10.18653/v1/N19-1279",
    pages = "2726--2735",
    abstract = "Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0{\%} on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our data and code available on Github.",
}
@inproceedings{kemos-etal-2019-neural,
    title = "Neural Semi-{M}arkov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging",
    author = {Kemos, Apostolos  and
      Adel, Heike  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1280",
    doi = "10.18653/v1/N19-1280",
    pages = "2736--2743",
    abstract = "Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. However, they often still rely on correct token boundaries. In this paper, we propose to eliminate the need for tokenizers with an end-to-end character-level semi-Markov conditional random field. It uses neural networks for its character and segment representations. We demonstrate its effectiveness in multilingual settings and when token boundaries are noisy: It matches state-of-the-art part-of-speech taggers for various languages and significantly outperforms them on a noisy English version of a benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.",
}
@inproceedings{tolmachev-etal-2019-shrinking,
    title = "Shrinking {J}apanese Morphological Analyzers With Neural Networks and Semi-supervised Learning",
    author = "Tolmachev, Arseny  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1281",
    doi = "10.18653/v1/N19-1281",
    pages = "2744--2755",
    abstract = "For languages without natural word boundaries, like Japanese and Chinese, word segmentation is a prerequisite for downstream analysis. For Japanese, segmentation is often done jointly with part of speech tagging, and this process is usually referred to as morphological analysis. Morphological analyzers are trained on data hand-annotated with segmentation boundaries and part of speech tags. A segmentation dictionary or character n-gram information is also provided as additional inputs to the model. Incorporating this extra information makes models large. Modern neural morphological analyzers can consume gigabytes of memory. We propose a compact alternative to these cumbersome approaches which do not rely on any externally provided n-gram or word representations. The model uses only unigram character embeddings, encodes them using either stacked bi-LSTM or a self-attention network, and independently infers both segmentation and part of speech information. The model is trained in an end-to-end and semi-supervised fashion, on labels produced by a state-of-the-art analyzer. We demonstrate that the proposed technique rivals performance of a previous dictionary-based state-of-the-art approach and can even surpass it when training with the combination of human-annotated and automatically-annotated data. Our model itself is significantly smaller than the dictionary-based one: it uses less than 15 megabytes of space.",
}
@inproceedings{jamshid-lou-etal-2019-neural,
    title = "Neural Constituency Parsing of Speech Transcripts",
    author = "Jamshid Lou, Paria  and
      Wang, Yufei  and
      Johnson, Mark",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1282",
    doi = "10.18653/v1/N19-1282",
    pages = "2756--2765",
    abstract = "This paper studies the performance of a neural self-attentive parser on transcribed speech. Speech presents parsing challenges that do not appear in written text, such as the lack of punctuation and the presence of speech disfluencies (including filled pauses, repetitions, corrections, etc.). Disfluencies are especially problematic for conventional syntactic parsers, which typically fail to find any EDITED disfluency nodes at all. This motivated the development of special disfluency detection systems, and special mechanisms added to parsers specifically to handle disfluencies. However, we show here that neural parsers can find EDITED disfluency nodes, and the best neural parsers find them with an accuracy surpassing that of specialized disfluency detection systems, thus making these specialized mechanisms unnecessary. This paper also investigates a modified loss function that puts more weight on EDITED nodes. It also describes tree-transformations that simplify the disfluency detection task by providing alternative encodings of disfluencies and syntactic information.",
}
@inproceedings{kim-metze-2019-acoustic,
    title = "Acoustic-to-Word Models with Conversational Context Information",
    author = "Kim, Suyoun  and
      Metze, Florian",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1283",
    doi = "10.18653/v1/N19-1283",
    pages = "2766--2771",
    abstract = "Conversational context information, higher-level knowledge that spans across sentences, can help to recognize a long conversation. However, existing speech recognition models are typically built at a sentence level, and thus it may not capture important conversational context information. The recent progress in end-to-end speech recognition enables integrating context with other available information (e.g., acoustic, linguistic resources) and directly recognizing words from speech. In this work, we present a direct acoustic-to-word, end-to-end speech recognition model capable of utilizing the conversational context to better process long conversations. We evaluate our proposed approach on the Switchboard conversational speech corpus and show that our system outperforms a standard end-to-end speech recognition system.",
}
@inproceedings{cheng-etal-2019-dynamic,
    title = "A Dynamic Speaker Model for Conversational Interactions",
    author = "Cheng, Hao  and
      Fang, Hao  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1284",
    doi = "10.18653/v1/N19-1284",
    pages = "2772--2785",
    abstract = "Individual differences in speakers are reflected in their language use as well as in their interests and opinions. Characterizing these differences can be useful in human-computer interaction, as well as analysis of human-human conversations. In this work, we introduce a neural model for learning a dynamically updated speaker embedding in a conversational context. Initial model training is unsupervised, using context-sensitive language generation as an objective, with the context being the conversation history. Further fine-tuning can leverage task-dependent supervised training. The learned neural representation of speakers is shown to be useful for content ranking in a socialbot and dialog act prediction in human-human conversations.",
}
@inproceedings{salesky-etal-2019-fluent,
    title = "Fluent Translations from Disfluent Speech in End-to-End Speech Translation",
    author = "Salesky, Elizabeth  and
      Sperber, Matthias  and
      Waibel, Alexander",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1285",
    doi = "10.18653/v1/N19-1285",
    pages = "2786--2792",
    abstract = "Spoken language translation applications for speech suffer due to conversational speech phenomena, particularly the presence of disfluencies. With the rise of end-to-end speech translation models, processing steps such as disfluency removal that were previously an intermediate step between speech recognition and machine translation need to be incorporated into model architectures. We use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected {`}copy-edited{'} references for the Fisher Spanish-English dataset. We are able to directly generate fluent translations and introduce considerations about how to evaluate success on this task. This work provides a baseline for a new task, implicitly removing disfluencies in end-to-end translation of conversational speech.",
}
@inproceedings{tran-etal-2019-relation,
    title = "Relation Classification Using Segment-Level Attention-based {CNN} and Dependency-based {RNN}",
    author = "Tran, Van-Hien  and
      Phi, Van-Thuy  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1286",
    doi = "10.18653/v1/N19-1286",
    pages = "2793--2798",
    abstract = "Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based Recurrent Neural Networks (DepRNNs). While SACNNs allow the model to selectively focus on the important information segment from the raw sequence, DepRNNs help to handle the long-distance relations from the shortest dependency path of relation entities. Experiments on the SemEval-2010 Task 8 dataset show that our model is comparable to the state-of-the-art without using any external lexical features.",
}
@inproceedings{qian-etal-2019-document,
    title = "Document-Level Event Factuality Identification via Adversarial Neural Network",
    author = "Qian, Zhong  and
      Li, Peifeng  and
      Zhu, Qiaoming  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1287",
    doi = "10.18653/v1/N19-1287",
    pages = "2799--2809",
    abstract = "Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP). Previous studies mainly suffer from the scarcity of suitable corpus and effective methods. To solve these two issues, we first construct a corpus annotated with both document- and sentence-level event factuality information on both English and Chinese texts. Then we present an LSTM neural network based on adversarial training with both intra- and inter-sequence attentions to identify document-level event factuality. Experimental results show that our neural network model can outperform various baselines on the constructed corpus.",
}
@inproceedings{ye-ling-2019-distant,
    title = "Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions",
    author = "Ye, Zhi-Xiu  and
      Ling, Zhen-Hua",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1288",
    doi = "10.18653/v1/N19-1288",
    pages = "2810--2819",
    abstract = "This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.",
}
@inproceedings{wang-etal-2019-ranking,
    title = "Ranking-Based Autoencoder for Extreme Multi-label Classification",
    author = "Wang, Bingyu  and
      Chen, Li  and
      Sun, Wei  and
      Qin, Kechen  and
      Li, Kefeng  and
      Zhou, Hui",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1289",
    doi = "10.18653/v1/N19-1289",
    pages = "2820--2830",
    abstract = "Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions. XML is more and more on demand in the Internet industries, accompanied with the increasing business scale / scope and data accumulation. The extremely large label collections yield challenges such as computational complexity, inter-label dependency and noisy labeling. Many methods have been proposed to tackle these challenges, based on different mathematical formulations. In this paper, we propose a deep learning XML method, with a word-vector-based self-attention, followed by a ranking-based AutoEncoder architecture. The proposed method has three major advantages: 1) the autoencoder simultaneously considers the inter-label dependencies and the feature-label dependencies, by projecting labels and features onto a common embedding space; 2) the ranking loss not only improves the training efficiency and accuracy but also can be extended to handle noisy labeled data; 3) the efficient attention mechanism improves feature representation by highlighting feature importance. Experimental results on benchmark datasets show the proposed method is competitive to state-of-the-art methods.",
}
@inproceedings{zhang-etal-2019-posterior,
    title = "Posterior-regularized {REINFORCE} for Instance Selection in Distant Supervision",
    author = "Zhang, Qi  and
      Tang, Siliang  and
      Ren, Xiang  and
      Wu, Fei  and
      Pu, Shiliang  and
      Zhuang, Yueting",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1290",
    doi = "10.18653/v1/N19-1290",
    pages = "2831--2835",
    abstract = "This paper provides a new way to improve the efficiency of the REINFORCE training process. We apply it to the task of instance selection in distant supervision. Modeling the instance selection in one bag as a sequential decision process, a reinforcement learning agent is trained to determine whether an instance is valuable or not and construct a new bag with less noisy instances. However unbiased methods, such as REINFORCE, could usually take much time to train. This paper adopts posterior regularization (PR) to integrate some domain-specific rules in instance selection using REINFORCE. As the experiment results show, this method remarkably improves the performance of the relation classifier trained on cleaned distant supervision dataset as well as the efficiency of the REINFORCE training.",
}
@inproceedings{islam-foulds-2019-scalable,
    title = "Scalable Collapsed Inference for High-Dimensional Topic Models",
    author = "Islam, Rashidul  and
      Foulds, James",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1291",
    doi = "10.18653/v1/N19-1291",
    pages = "2836--2845",
    abstract = "The bigger the corpus, the more topics it can potentially support. To truly make full use of massive text corpora, a topic model inference algorithm must therefore scale efficiently in 1) documents and 2) topics, while 3) achieving accurate inference. Previous methods have achieved two out of three of these criteria simultaneously, but never all three at once. In this paper, we develop an online inference algorithm for topic models which leverages stochasticity to scale well in the number of documents, sparsity to scale well in the number of topics, and which operates in the collapsed representation of the topic model for improved accuracy and run-time performance. We use a Monte Carlo inner loop in the online setting to approximate the collapsed variational Bayes updates in a sparse and efficient way, which we accomplish via the MetropolisHastings Walker method. We showcase our algorithm on LDA and the recently proposed mixed membership skip-gram topic model. Our method requires only amortized $O(k_{d})$ computation per word token instead of $O(K)$ operations, where the number of topics occurring for a particular document $k_{d}\ll$ the total number of topics in the corpus $K$, to converge to a high-quality solution.",
}
@inproceedings{chen-etal-2019-integrated,
    title = "An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction",
    author = "Chen, Wang  and
      Chan, Hou Pong  and
      Li, Piji  and
      Bing, Lidong  and
      King, Irwin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1292",
    doi = "10.18653/v1/N19-1292",
    pages = "2846--2856",
    abstract = "In this paper, we present a novel integrated approach for keyphrase generation (KG). Unlike previous works which are purely extractive or generative, we first propose a new multi-task learning framework that jointly learns an extractive model and a generative model. Besides extracting keyphrases, the output of the extractive model is also employed to rectify the copy probability distribution of the generative model, such that the generative model can better identify important contents from the given document. Moreover, we retrieve similar documents with the given document from training data and use their associated keyphrases as external knowledge for the generative model to produce more accurate keyphrases. For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases. Experiments on the five KG benchmarks demonstrate that our integrated approach outperforms the state-of-the-art methods.",
}
@inproceedings{roy-etal-2019-predicting,
    title = "Predicting Malware Attributes from Cybersecurity Texts",
    author = "Roy, Arpita  and
      Park, Youngja  and
      Pan, Shimei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1293",
    doi = "10.18653/v1/N19-1293",
    pages = "2857--2861",
    abstract = "Text analytics is a useful tool for studying malware behavior and tracking emerging threats. The task of automated malware attribute identification based on cybersecurity texts is very challenging due to a large number of malware attribute labels and a small number of training instances. In this paper, we propose a novel feature learning method to leverage diverse knowledge sources such as small amount of human annotations, unlabeled text and specifications about malware attribute labels. Our evaluation has demonstrated the effectiveness of our method over the state-of-the-art malware attribute prediction systems.",
}
@inproceedings{chen-etal-2019-improving-distantly,
    title = "Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering",
    author = "Chen, Bo  and
      Gu, Xiaotao  and
      Hu, Yufeng  and
      Tang, Siliang  and
      Hu, Guoping  and
      Zhuang, Yueting  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1294",
    doi = "10.18653/v1/N19-1294",
    pages = "2862--2872",
    abstract = "Recently, distant supervision has gained great success on Fine-grained Entity Typing (FET). Despite its efficiency in reducing manual labeling efforts, it also brings the challenge of dealing with false entity type labels, as distant supervision assigns labels in a context-agnostic manner. Existing works alleviated this issue with partial-label loss, but usually suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. In this work, we propose to regularize distantly supervised models with Compact Latent Space Clustering (CLSC) to bypass this problem and effectively utilize noisy data yet. Our proposed method first dynamically constructs a similarity graph of different entity mentions; infer the labels of noisy instances via label propagation. Based on the inferred labels, mention embeddings are updated accordingly to encourage entity mentions with close semantics to form a compact cluster in the embedding space, thus leading to better classification performance. Extensive experiments on standard benchmarks show that our CLSC model consistently outperforms state-of-the-art distantly supervised entity typing systems by a significant margin.",
}
@inproceedings{li-etal-2019-modelling,
    title = "Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks",
    author = {Li, Maolin  and
      Fahlstr{\"o}m Myrman, Arvid  and
      Mu, Tingting  and
      Ananiadou, Sophia},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1295",
    doi = "10.18653/v1/N19-1295",
    pages = "2873--2883",
    abstract = "When constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators. Annotators may provide labels of inconsistent quality due to their varying expertise and reliability in a domain. Previous studies have mostly focused on estimating each annotator{'}s overall reliability on the entire annotation task. However, in practice, the reliability of an annotator may depend on each specific instance. Only a limited number of studies have investigated modelling per-instance reliability and these only considered binary labels. In this paper, we propose an unsupervised model which can handle both binary and multi-class labels. It can automatically estimate the per-instance reliability of each annotator and the correct label for each instance. We specify our model as a probabilistic model which incorporates neural networks to model the dependency between latent variables and instances. For evaluation, the proposed method is applied to both synthetic and real data, including two labelling tasks: text classification and textual entailment. Experimental results demonstrate our novel method can not only accurately estimate the reliability of annotators across different instances, but also achieve superior performance in predicting the correct labels and detecting the least reliable annotators compared to state-of-the-art baselines.",
}
@inproceedings{zhao-etal-2019-review,
    title = "Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations",
    author = "Zhao, Guangxiang  and
      Xu, Jingjing  and
      Zeng, Qi  and
      Ren, Xuancheng  and
      Sun, Xu",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1296",
    doi = "10.18653/v1/N19-1296",
    pages = "2884--2891",
    abstract = "This paper explores a new natural languageprocessing task, review-driven multi-label musicstyle classification. This task requires systemsto identify multiple styles of music basedon its reviews on websites. The biggest challengelies in the complicated relations of musicstyles. To tackle this problem, we proposea novel deep learning approach to automaticallylearn and exploit style correlations.Experiment results show that our approachachieves large improvements over baselines onthe proposed dataset. Furthermore, the visualizedanalysis shows that our approach performswell in capturing style correlations.",
}
@inproceedings{fu-etal-2019-fact,
    title = "Fact Discovery from Knowledge Base via Facet Decomposition",
    author = "Fu, Zihao  and
      Lin, Yankai  and
      Liu, Zhiyuan  and
      Lam, Wai",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1297",
    doi = "10.18653/v1/N19-1297",
    pages = "2892--2901",
    abstract = "During the past few decades, knowledge bases (KBs) have experienced rapid growth. Nevertheless, most KBs still suffer from serious incompletion. Researchers proposed many tasks such as knowledge base completion and relation prediction to help build the representation of KBs. However, there are some issues unsettled towards enriching the KBs. Knowledge base completion and relation prediction assume that we know two elements of the fact triples and we are going to predict the missing one. This assumption is too restricted in practice and prevents it from discovering new facts directly. To address this issue, we propose a new task, namely, fact discovery from knowledge base. This task only requires that we know the head entity and the goal is to discover facts associated with the head entity. To tackle this new problem, we propose a novel framework that decomposes the discovery problem into several facet discovery components. We also propose a novel auto-encoder based facet component to estimate some facets of the fact. Besides, we propose a feedback learning component to share the information between each facet. We evaluate our framework using a benchmark dataset and the experimental results show that our framework achieves promising results. We also conduct an extensive analysis of our framework in discovering different kinds of facts. The source code of this paper can be obtained from https://github.com/thunlp/FFD.",
}
@inproceedings{can-etal-2019-richer,
    title = "A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction",
    author = "Can, Duy-Cat  and
      Le, Hoang-Quynh  and
      Ha, Quang-Thuy  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1298",
    doi = "10.18653/v1/N19-1298",
    pages = "2902--2912",
    abstract = "To extract the relationship between two entities in a sentence, two common approaches are (1) using their shortest dependency path (SDP) and (2) using an attention model to capture a context-based representation of the sentence. Each approach suffers from its own disadvantage of either missing or redundant information. In this work, we propose a novel model that combines the advantages of these two approaches. This is based on the basic information in the SDP enhanced with information selected by several attention mechanisms with kernel filters, namely RbSP (Richer-but-Smarter SDP). To exploit the representation behind the RbSP structure effectively, we develop a combined deep neural model with a LSTM network on word sequences and a CNN on RbSP. Experimental results on the SemEval-2010 dataset demonstrate improved performance over competitive baselines. The data and source code are available at https://github.com/catcd/RbSP.",
}
@inproceedings{chen-etal-2019-bidirectional,
    title = "Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases",
    author = "Chen, Yu  and
      Wu, Lingfei  and
      Zaki, Mohammed J.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1299",
    doi = "10.18653/v1/N19-1299",
    pages = "2913--2923",
    abstract = "When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based methods for knowledge base question answering (KBQA) ignore the subtle inter-relationships between the question and the KB (e.g., entity types, relation paths and context). In this work, we propose to directly model the two-way flow of interactions between the questions and the KB via a novel Bidirectional Attentive Memory Network, called BAMnet. Requiring no external resources and only very few hand-crafted features, on the WebQuestions benchmark, our method significantly outperforms existing information-retrieval based methods, and remains competitive with (hand-crafted) semantic parsing based methods. Also, since we use attention mechanisms, our method offers better interpretability compared to other baselines.",
}
@inproceedings{clark-etal-2019-boolq,
    title = "{B}ool{Q}: Exploring the Surprising Difficulty of Natural Yes/No Questions",
    author = "Clark, Christopher  and
      Lee, Kenton  and
      Chang, Ming-Wei  and
      Kwiatkowski, Tom  and
      Collins, Michael  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1300",
    doi = "10.18653/v1/N19-1300",
    pages = "2924--2936",
    abstract = "In this paper we study yes/no questions that are naturally occurring {---} meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingly, continues to be very beneficial even when starting from massive pre-trained language models such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on our train set. It achieves 80.4{\%} accuracy compared to 90{\%} accuracy of human annotators (and 62{\%} majority-baseline), leaving a significant gap for future work.",
}
@inproceedings{xu-etal-2019-enhancing,
    title = "Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering",
    author = "Xu, Kun  and
      Lai, Yuxuan  and
      Feng, Yansong  and
      Wang, Zhiguo",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1301",
    doi = "10.18653/v1/N19-1301",
    pages = "2937--2947",
    abstract = "Traditional Key-value Memory Neural Networks (KV-MemNNs) are proved to be effective to support shallow reasoning over a collection of documents in domain specific Question Answering or Reading Comprehension tasks. However, extending KV-MemNNs to Knowledge Based Question Answering (KB-QA) is not trivia, which should properly decompose a complex question into a sequence of queries against the memory, and update the query representations to support multi-hop reasoning over the memory. In this paper, we propose a novel mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions. To achieve this, we design a new query updating strategy to mask previously-addressed memory information from the query representations, and introduce a novel STOP strategy to avoid invalid or repeated memory reading without strong annotation signals. This also enables KV-MemNNs to produce structured queries and work in a semantic parsing fashion. Experimental results on benchmark datasets show that our solution, trained with question-answer pairs only, can provide conventional KV-MemNNs models with better reasoning abilities on complex questions, and achieve state-of-art performances.",
}
@inproceedings{trivedi-etal-2019-repurposing,
    title = "Repurposing Entailment for Multi-Hop Question Answering Tasks",
    author = "Trivedi, Harsh  and
      Kwon, Heeyoung  and
      Khot, Tushar  and
      Sabharwal, Ashish  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1302",
    doi = "10.18653/v1/N19-1302",
    pages = "2948--2958",
    abstract = "Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which require reasoning with \textit{multiple} sentences, it remains unclear how best to utilize entailment models pre-trained on large scale datasets such as SNLI, which are based on sentence pairs. We introduce Multee, a general architecture that can effectively use entailment models for multi-hop QA tasks. Multee uses (i) a local module that helps locate important sentences, thereby avoiding distracting information, and (ii) a global module that aggregates information by effectively incorporating importance weights. Importantly, we show that both modules can use entailment functions pre-trained on a large scale NLI datasets. We evaluate performance on MultiRC and OpenBookQA, two multihop QA datasets. When using an entailment function pre-trained on NLI datasets, Multee outperforms QA models trained only on the target QA datasets and the OpenAI transformer models.",
}
@inproceedings{ananya-etal-2019-genderquant,
    title = "{G}ender{Q}uant: Quantifying Mention-Level Genderedness",
    author = "{Ananya}  and
      Parthasarthi, Nitya  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1303",
    doi = "10.18653/v1/N19-1303",
    pages = "2959--2969",
    abstract = "Language is gendered if the context surrounding a mention is suggestive of a particular binary gender for that mention. Detecting the different ways in which language is gendered is an important task since gendered language can bias NLP models (such as for coreference resolution). This task is challenging since genderedness is often expressed in subtle ways. Existing approaches need considerable annotation efforts for each language, domain, and author, and often require handcrafted lexicons and features. Additionally, these approaches do not provide a quantifiable measure of how gendered the text is, nor are they applicable at the fine-grained mention level. In this paper, we use existing NLP pipelines to automatically annotate gender of mentions in the text. On corpora labeled using this method, we train a supervised classifier to predict the gender of any mention from its context and evaluate it on unseen text. The model confidence for a mention{'}s gender can be used as a proxy to indicate the level of genderedness of the context. We test this gendered language detector on movie summaries, movie reviews, news articles, and fiction novels, achieving an AUC-ROC of up to 0.71, and observe that the model predictions agree with human judgments collected for this task. We also provide examples of detected gendered sentences from aforementioned domains.",
}
@inproceedings{demszky-etal-2019-analyzing,
    title = "Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings",
    author = "Demszky, Dorottya  and
      Garg, Nikhil  and
      Voigt, Rob  and
      Zou, James  and
      Shapiro, Jesse  and
      Gentzkow, Matthew  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1304",
    doi = "10.18653/v1/N19-1304",
    pages = "2970--3005",
    abstract = "We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that our approach generates more cohesive topics than traditional LDA-based models. We apply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice. We identify framing devices, such as grounding and the contrasting use of the terms {``}terrorist{''} and {``}crazy{''}, that contribute to polarization. Results pertaining to topic choice, affect and illocutionary force suggest that Republicans focus more on the shooter and event-specific facts (news) while Democrats focus more on the victims and call for policy changes. Our work contributes to a deeper understanding of the way group divisions manifest in language and to computational methods for studying them.",
}
@inproceedings{qian-etal-2019-learning,
    title = "Learning to Decipher Hate Symbols",
    author = "Qian, Jing  and
      ElSherief, Mai  and
      Belding, Elizabeth  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1305",
    doi = "10.18653/v1/N19-1305",
    pages = "3006--3015",
    abstract = "Existing computational models to understand hate speech typically frame the problem as a simple classification task, bypassing the understanding of hate symbols (e.g., 14 words, kigy) and their secret connotations. In this paper, we propose a novel task of deciphering hate symbols. To do this, we leveraged the Urban Dictionary and collected a new, symbol-rich Twitter corpus of hate speech. We investigate neural network latent context models for deciphering hate symbols. More specifically, we study Sequence-to-Sequence models and show how they are able to crack the ciphers based on context. Furthermore, we propose a novel Variational Decipher and show how it can generalize better to unseen hate symbols in a more challenging testing setting.",
}
@inproceedings{zhang-etal-2019-long,
    title = "Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks",
    author = "Zhang, Ningyu  and
      Deng, Shumin  and
      Sun, Zhanlin  and
      Wang, Guanying  and
      Chen, Xi  and
      Zhang, Wei  and
      Chen, Huajun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1306",
    doi = "10.18653/v1/N19-1306",
    pages = "3016--3025",
    abstract = "We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate {``}few-shot{''} models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.",
}
@inproceedings{li-etal-2019-gan,
    title = "{GAN} Driven Semi-distant Supervision for Relation Extraction",
    author = "Li, Pengshuai  and
      Zhang, Xinsong  and
      Jia, Weijia  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1307",
    doi = "10.18653/v1/N19-1307",
    pages = "3026--3035",
    abstract = "Distant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently. However, the automatically constructed datasets comprise numbers of wrongly labeled negative instances due to the incompleteness of knowledge bases, which is neglected by current distant supervised methods resulting in seriously misleading in both training and testing processes. To address this issue, we propose a novel semi-distant supervision approach for relation extraction by constructing a small accurate dataset and properly leveraging numerous instances without relation labels. In our approach, we construct accurate instances by both knowledge base and entity descriptions determined to avoid wrong negative labeling and further utilize unlabeled instances sufficiently using generative adversarial network (GAN) framework. Experimental results on real-world datasets show that our approach can achieve significant improvements in distant supervised relation extraction over strong baselines.",
}
@inproceedings{luan-etal-2019-general,
    title = "A general framework for information extraction using dynamic span graphs",
    author = "Luan, Yi  and
      Wadden, Dave  and
      He, Luheng  and
      Shah, Amy  and
      Ostendorf, Mari  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1308",
    doi = "10.18653/v1/N19-1308",
    pages = "3036--3046",
    abstract = "We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by selecting the most confident entity spans and linking these nodes with confidence-weighted relation types and coreferences. The dynamic span graph allow coreference and relation type confidences to propagate through the graph to iteratively refine the span representations. This is unlike previous multi-task frameworks for information extraction in which the only interaction between tasks is in the shared first-layer LSTM. Our framework significantly outperforms state-of-the-art on multiple information extraction tasks across multiple datasets reflecting different domains. We further observe that the span enumeration approach is good at detecting nested span entities, with significant F1 score improvement on the ACE dataset.",
}
@inproceedings{lockard-etal-2019-openceres,
    title = "{O}pen{C}eres: {W}hen Open Information Extraction Meets the Semi-Structured Web",
    author = "Lockard, Colin  and
      Shiralkar, Prashant  and
      Dong, Xin Luna",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1309",
    doi = "10.18653/v1/N19-1309",
    pages = "3047--3056",
    abstract = "Open Information Extraction (OpenIE), the problem of harvesting triples from natural language text whose predicate relations are not aligned to any pre-defined ontology, has been a popular subject of research for the last decade. However, this research has largely ignored the vast quantity of facts available in semi-structured webpages. In this paper, we define the problem of OpenIE from semi-structured websites to extract such facts, and present an approach for solving it. We also introduce a labeled evaluation dataset to motivate research in this area. Given a semi-structured website and a set of seed facts for some relations existing on its pages, we employ a semi-supervised label propagation technique to automatically create training data for the relations present on the site. We then use this training data to learn a classifier for relation extraction. Experimental results of this method on our new benchmark dataset obtained a precision of over 70{\%}. A larger scale extraction experiment on 31 websites in the movie vertical resulted in the extraction of over 2 million triples.",
}
@inproceedings{bai-ritter-2019-structured,
    title = "{S}tructured {M}inimally {S}upervised {L}earning for {N}eural {R}elation {E}xtraction",
    author = "Bai, Fan  and
      Ritter, Alan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1310",
    doi = "10.18653/v1/N19-1310",
    pages = "3057--3069",
    abstract = "We present an approach to minimally supervised relation extraction that combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from a KB. By explicitly reasoning about missing data during learning, our approach enables large-scale training of 1D convolutional neural networks while mitigating the issue of label noise inherent in distant supervision. Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.",
}
@inproceedings{anastasopoulos-etal-2019-neural,
    title = "Neural Machine Translation of Text from Non-Native Speakers",
    author = "Anastasopoulos, Antonios  and
      Lui, Alison  and
      Nguyen, Toan Q.  and
      Chiang, David",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1311",
    doi = "10.18653/v1/N19-1311",
    pages = "3070--3080",
    abstract = "Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data. In this paper, we show that augmenting training data with sentences containing artificially-introduced grammatical errors can make the system more robust to such errors. In combination with an automatic grammar error correction system, we can recover 1.0 BLEU out of 2.4 BLEU lost due to grammatical errors. We also present a set of Spanish translations of the JFLEG grammar error correction corpus, which allows for testing NMT robustness to real grammatical errors.",
}
@inproceedings{gu-etal-2019-improving,
    title = "Improving Domain Adaptation Translation with Domain Invariant and Specific Information",
    author = "Gu, Shuhao  and
      Feng, Yang  and
      Liu, Qun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1312",
    doi = "10.18653/v1/N19-1312",
    pages = "3081--3091",
    abstract = "In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model the two kinds of information in the encoder-decoder framework so as to exploit out-of-domain data in in-domain training. In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a common encoder and a common decoder shared by all the domains which can only have domain-independent information flow through. Besides, we add a discriminator to the shared encoder and employ adversarial training for the whole model to reinforce the performance of information separation and machine translation simultaneously. Experiment results show that our method can outperform competitive baselines greatly on multiple data sets.",
}
@inproceedings{maruf-etal-2019-selective,
    title = "Selective Attention for Context-aware Neural Machine Translation",
    author = "Maruf, Sameen  and
      Martins, Andr{\'e} F. T.  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1313",
    doi = "10.18653/v1/N19-1313",
    pages = "3092--3102",
    abstract = "Despite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document. Recent works in context-aware NMT consider only a few previous sentences as context and may not scale to entire documents. To this end, we propose a novel and scalable top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context and then attends to key words in those sentences. We also propose single-level attention approaches based on sentence or word-level information in the context. The document-level context representation, produced from these attention modules, is integrated into the encoder or decoder of the Transformer model depending on whether we use monolingual or bilingual context. Our experiments and evaluation on English-German datasets in different document MT settings show that our selective attention approach not only significantly outperforms context-agnostic baselines but also surpasses context-aware baselines in most cases.",
}
@inproceedings{michel-etal-2019-evaluation,
    title = "On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models",
    author = "Michel, Paul  and
      Li, Xian  and
      Neubig, Graham  and
      Pino, Juan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1314",
    doi = "10.18653/v1/N19-1314",
    pages = "3103--3114",
    abstract = "Adversarial examples {---} perturbations to the input of a model that elicit large changes in the output {---} have been shown to be an effective way of assessing the robustness of sequence-to-sequence (seq2seq) models. However, these perturbations only indicate weaknesses in the model if they do not change the input so significantly that it legitimately results in changes in the expected output. This fact has largely been ignored in the evaluations of the growing body of related literature. Using the example of untargeted attacks on machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models that takes the semantic equivalence of the pre- and post-perturbation input into account. Using this framework, we demonstrate that existing methods may not preserve meaning in general, breaking the aforementioned assumption that source side perturbations should not result in changes in the expected output. We further use this framework to demonstrate that adding additional constraints on attacks allows for adversarial perturbations that are more meaning-preserving, but nonetheless largely change the output sequence. Finally, we show that performing untargeted adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness, without hurting test performance. A toolkit implementing our evaluation framework is released at https://github.com/pmichel31415/teapot-nlp.",
}
@inproceedings{hashimoto-tsuruoka-2019-accelerated,
    title = "Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction",
    author = "Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1315",
    doi = "10.18653/v1/N19-1315",
    pages = "3115--3125",
    abstract = "A major obstacle in reinforcement learning-based sentence generation is the large action space whose size is equal to the vocabulary size of the target-side language. To improve the efficiency of reinforcement learning, we present a novel approach for reducing the action space based on dynamic vocabulary prediction. Our method first predicts a fixed-size small vocabulary for each input to generate its target sentence. The input-specific vocabularies are then used at supervised and reinforcement learning steps, and also at test time. In our experiments on six machine translation and two image captioning datasets, our method achieves faster reinforcement learning ({\textasciitilde}2.7x faster) with less GPU memory ({\textasciitilde}2.3x less) than the full-vocabulary counterpart. We also show that our method more effectively receives rewards with fewer iterations of supervised pre-training.",
}
@inproceedings{zhang-etal-2019-mitigating,
    title = "Mitigating Uncertainty in Document Classification",
    author = "Zhang, Xuchao  and
      Chen, Fanglan  and
      Lu, Chang-Tien  and
      Ramakrishnan, Naren",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1316",
    doi = "10.18653/v1/N19-1316",
    pages = "3126--3136",
    abstract = "The uncertainty measurement of classifiers{'} predictions is especially important in applications such as medical diagnoses that need to ensure limited human resources can focus on the most uncertain predictions returned by machine learning models. However, few existing uncertainty models attempt to improve overall prediction accuracy where human resources are involved in the text classification task. In this paper, we propose a novel neural-network-based model that applies a new dropout-entropy method for uncertainty measurement. We also design a metric learning method on feature representations, which can boost the performance of dropout-based uncertainty methods with smaller prediction variance in accurate prediction trials. Extensive experiments on real-world data sets demonstrate that our method can achieve a considerable improvement in overall prediction accuracy compared to existing approaches. In particular, our model improved the accuracy from 0.78 to 0.92 when 30{\%} of the most uncertain predictions were handed over to human experts in {``}20NewsGroup{''} data.",
}
@inproceedings{kriz-etal-2019-complexity,
    title = "Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification",
    author = "Kriz, Reno  and
      Sedoc, Jo{\~a}o  and
      Apidianaki, Marianna  and
      Zheng, Carolina  and
      Kumar, Gaurav  and
      Miltsakaki, Eleni  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1317",
    doi = "10.18653/v1/N19-1317",
    pages = "3137--3147",
    abstract = "Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via reinforcement learning and memory augmentation. One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence, resulting in outputs that are relatively long and complex. We aim to alleviate this issue through the use of two main techniques. First, we incorporate content word complexities, as predicted with a leveled word complexity model, into our loss function during training. Second, we generate a large set of diverse candidate simplifications at test time, and rerank these to promote fluency, adequacy, and simplicity. Here, we measure simplicity through a novel sentence complexity model. These extensions allow our models to perform competitively with state-of-the-art systems while generating simpler sentences. We report standard automatic and human evaluation metrics.",
}
@inproceedings{halder-etal-2019-predicting,
    title = "Predicting Helpful Posts in Open-Ended Discussion Forums: A Neural Architecture",
    author = "Halder, Kishaloy  and
      Kan, Min-Yen  and
      Sugiyama, Kazunari",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1318",
    doi = "10.18653/v1/N19-1318",
    pages = "3148--3157",
    abstract = "Users participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or by sharing their new findings on a certain topic. We find that, unlike Community Question Answering, where questions are mostly factoid based, the threads in a forum are often open-ended (e.g., asking for recommendations from others) without a single correct answer. In this paper, we address the task of identifying helpful posts in a forum thread to help users comprehend long running discussion threads, which often contain repetitive or irrelevant posts. We propose a recurrent neural network based architecture to model (i) the relevance of a post regarding the original post starting the thread and (ii) the novelty it brings to the discussion, compared to the previous posts in the thread. Experimental results on different types of online forum datasets show that our model significantly outperforms the state-of-the-art neural network models for text classification.",
}
@inproceedings{mahabal-etal-2019-text,
    title = "Text Classification with Few Examples using Controlled Generalization",
    author = "Mahabal, Abhijit  and
      Baldridge, Jason  and
      Karagol Ayan, Burcu  and
      Perot, Vincent  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1319",
    doi = "10.18653/v1/N19-1319",
    pages = "3158--3167",
    abstract = "Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems. This means classifiers must generalize from limited evidence, but the manner and extent of generalization is task dependent. Current practice primarily relies on pre-trained word embeddings to map words unseen in training to similar seen ones. Unfortunately, this squishes many components of meaning into highly restricted capacity. Our alternative begins with sparse pre-trained representations derived from unlabeled parsed corpora; based on the available training data, we select features that offers the relevant generalizations. This produces task-specific semantic vectors; here, we show that a feed-forward network over these vectors is especially effective in low-data scenarios, compared to existing state-of-the-art methods. By further pairing this network with a convolutional neural network, we keep this edge in low data scenarios and remain competitive when using full training sets.",
}
@inproceedings{gong-etal-2019-reinforcement,
    title = "Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus",
    author = "Gong, Hongyu  and
      Bhat, Suma  and
      Wu, Lingfei  and
      Xiong, JinJun  and
      Hwu, Wen-mei",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1320",
    doi = "10.18653/v1/N19-1320",
    pages = "3168--3180",
    abstract = "Text style transfer rephrases a text from a source style (e.g., informal) to a target style (e.g., formal) while keeping its original meaning. Despite the success existing works have achieved using a parallel corpus for the two styles, transferring text style has proven significantly more challenging when there is no parallel training corpus. In this paper, we address this challenge by using a reinforcement-learning-based generator-evaluator architecture. Our generator employs an attention-based encoder-decoder to transfer a sentence from the source style to the target style. Our evaluator is an adversarially trained style discriminator with semantic and syntactic constraints that score the generated sentence for style, meaning preservation, and fluency. Experimental results on two different style transfer tasks{--}sentiment transfer, and formality transfer{--}show that our model outperforms state-of-the-art approaches.Furthermore, we perform a manual evaluation that demonstrates the effectiveness of the proposed method using subjective metrics of generated text quality.",
}
@inproceedings{qin-etal-2019-adapting,
    title = "Adapting {RNN} Sequence Prediction Model to Multi-label Set Prediction",
    author = "Qin, Kechen  and
      Li, Cheng  and
      Pavlu, Virgil  and
      Aslam, Javed",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1321",
    doi = "10.18653/v1/N19-1321",
    pages = "3181--3190",
    abstract = "We present an adaptation of RNN sequence models to the problem of multi-label classification for text, where the target is a set of labels, not a sequence. Previous such RNN models define probabilities for sequences but not for sets; attempts to obtain a set probability are after-thoughts of the network design, including pre-specifying the label order, or relating the sequence probability to the set probability in ad hoc ways. Our formulation is derived from a principled notion of set probability, as the sum of probabilities of corresponding permutation sequences for the set. We provide a new training objective that maximizes this set probability, and a new prediction objective that finds the most probable set on a test document. These new objectives are theoretically appealing because they give the RNN model freedom to discover the best label order, which often is the natural one (but different among documents). We develop efficient procedures to tackle the computation difficulties involved in training and prediction. Experiments on benchmark datasets demonstrate that we outperform state-of-the-art methods for this task.",
}
@inproceedings{shubha-etal-2019-customizing,
    title = "Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in {B}angla Language",
    author = "Shubha, Sudipta Saha  and
      Sadeq, Nafis  and
      Ahmed, Shafayat  and
      Islam, Md. Nahidul  and
      Adnan, Muhammad Abdullah  and
      Khan, Md. Yasin Ali  and
      Islam, Mohammad Zuberul",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1322",
    doi = "10.18653/v1/N19-1322",
    pages = "3191--3200",
    abstract = "Grapheme to phoneme (G2P) conversion is an integral part in various text and speech processing systems, such as: Text to Speech system, Speech Recognition system, etc. The existing methodologies for G2P conversion in Bangla language are mostly rule-based. However, data-driven approaches have proved their superiority over rule-based approaches for large-scale G2P conversion in other languages, such as: English, German, etc. As the performance of data-driven approaches for G2P conversion depend largely on pronunciation lexicon on which the system is trained, in this paper, we investigate on developing an improved training lexicon by identifying and categorizing the critical cases in Bangla language and include those critical cases in training lexicon for developing a robust G2P conversion system in Bangla language. Additionally, we have incorporated nasal vowels in our proposed phoneme list. Our methodology outperforms other state-of-the-art approaches for G2P conversion in Bangla language.",
}
@inproceedings{xu-barbosa-2019-connecting,
    title = "Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction",
    author = "Xu, Peng  and
      Barbosa, Denilson",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1323",
    doi = "10.18653/v1/N19-1323",
    pages = "3201--3206",
    abstract = "Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. One way to improve RE is to use KB Embeddings (KBE) for link prediction. However, despite clear connections between RE and KBE, little has been done toward properly unifying these models systematically. We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. The code is available at https://github.com/billy-inn/HRERE.",
}
@inproceedings{kim-etal-2019-segmentation,
    title = "Segmentation-free compositional $n$-gram embedding",
    author = "Kim, Geewook  and
      Fukui, Kazuki  and
      Shimodaira, Hidetoshi",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1324",
    doi = "10.18653/v1/N19-1324",
    pages = "3207--3215",
    abstract = "We propose a new type of representation learning method that models words, phrases and sentences seamlessly. Our method does not depend on word segmentation and any human-annotated resources (e.g., word dictionaries), yet it is very effective for noisy corpora written in unsegmented languages such as Chinese and Japanese. The main idea of our method is to ignore word boundaries completely (i.e., segmentation-free), and construct representations for all character $n$-grams in a raw corpus with embeddings of compositional sub-$n$-grams. Although the idea is simple, our experiments on various benchmarks and real-world datasets show the efficacy of our proposal.",
}
@inproceedings{yang-etal-2019-exploiting,
    title = "Exploiting Noisy Data in Distant Supervision Relation Classification",
    author = "Yang, Kaijia  and
      He, Liang  and
      Dai, Xin-yu  and
      Huang, Shujian  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1325",
    doi = "10.18653/v1/N19-1325",
    pages = "3216--3225",
    abstract = "Distant supervision has obtained great progress on relation classification task. However, it still suffers from noisy labeling problem. Different from previous works that underutilize noisy data which inherently characterize the property of classification, in this paper, we propose RCEND, a novel framework to enhance Relation Classification by Exploiting Noisy Data. First, an instance discriminator with reinforcement learning is designed to split the noisy data into correctly labeled data and incorrectly labeled data. Second, we learn a robust relation classifier in semi-supervised learning way, whereby the correctly and incorrectly labeled data are treated as labeled and unlabeled data respectively. The experimental results show that our method outperforms the state-of-the-art models.",
}
@inproceedings{piktus-etal-2019-misspelling,
    title = "Misspelling Oblivious Word Embeddings",
    author = "Piktus, Aleksandra  and
      Edizel, Necati Bora  and
      Bojanowski, Piotr  and
      Grave, Edouard  and
      Ferreira, Rui  and
      Silvestri, Fabrizio",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1326",
    doi = "10.18653/v1/N19-1326",
    pages = "3226--3234",
    abstract = "In this paper we present a method to learn word embeddings that are resilient to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain a non-negligible amount of out-of-vocabulary words. We propose a method combining FastText with subwords and a supervised task of learning misspelling patterns. In our method, misspellings of each word are embedded close to their correct variants. We train these embeddings on a new dataset we are releasing publicly. Finally, we experimentally show the advantages of this approach on both intrinsic and extrinsic NLP tasks using public test sets.",
}
@inproceedings{rossiello-etal-2019-learning,
    title = "Learning Relational Representations by Analogy using Hierarchical {S}iamese Networks",
    author = "Rossiello, Gaetano  and
      Gliozzo, Alfio  and
      Farrell, Robert  and
      Fauceglia, Nicolas  and
      Glass, Michael",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1327",
    doi = "10.18653/v1/N19-1327",
    pages = "3235--3245",
    abstract = "We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. Following this idea, we collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. We leverage this dataset to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. We evaluate our model in a one-shot learning task by showing a promising generalization capability in order to classify unseen relation types, which makes this approach suitable to perform automatic knowledge base population with minimal supervision. Moreover, the model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a downstream relation extraction task.",
}
@inproceedings{jindal-etal-2019-effective,
    title = "An Effective Label Noise Model for {DNN} Text Classification",
    author = "Jindal, Ishan  and
      Pressel, Daniel  and
      Lester, Brian  and
      Nokleby, Matthew",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1328",
    doi = "10.18653/v1/N19-1328",
    pages = "3246--3256",
    abstract = "Because large, human-annotated datasets suffer from labeling errors, it is crucial to be able to train deep neural networks in the presence of label noise. While training image classification models with label noise have received much attention, training text classification models have not. In this paper, we propose an approach to training deep networks that is robust to label noise. This approach introduces a non-linear processing layer (noise model) that models the statistics of the label noise into a convolutional neural network (CNN) architecture. The noise model and the CNN weights are learned jointly from noisy training data, which prevents the model from overfitting to erroneous labels. Through extensive experiments on several text classification datasets, we show that this approach enables the CNN to learn better sentence representations and is robust even to extreme label noise. We find that proper initialization and regularization of this noise model is critical. Further, by contrast to results focusing on large batch sizes for mitigating label noise for image classification, we find that altering the batch size does not have much effect on classification performance.",
}
@inproceedings{saphra-lopez-2019-understanding,
    title = "Understanding Learning Dynamics Of Language Models with {SVCCA}",
    author = "Saphra, Naomi  and
      Lopez, Adam",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1329",
    doi = "10.18653/v1/N19-1329",
    pages = "3257--3267",
    abstract = "Research has shown that neural models implicitly encode linguistic features, but there has been no research showing \textit{how} these encodings arise as the models are trained. We present the first study on the learning dynamics of neural language models, using a simple and flexible analysis method called Singular Vector Canonical Correlation Analysis (SVCCA), which enables us to compare learned representations across time and across models, without the need to evaluate directly on annotated data. We probe the evolution of syntactic, semantic, and topic representations, finding, for example, that part-of-speech is learned earlier than topic; that recurrent layers become more similar to those of a tagger during training; and embedding layers less similar. Our results and methods could inform better learning algorithms for NLP models, possibly to incorporate linguistic information more effectively.",
}
@inproceedings{yang-etal-2019-using,
    title = "Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models",
    author = "Yang, Yiben  and
      Wang, Ji-Ping  and
      Downey, Doug",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1330",
    doi = "10.18653/v1/N19-1330",
    pages = "3268--3273",
    abstract = "Recurrent neural network language models (RNNLM) form a valuable foundation for many NLP systems, but training the models can be computationally expensive, and may take days to train on a large corpus. We explore a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus. In experiments with the Billion-Word and Wikitext corpora, we show that the technique is effective, and more time-efficient than simply training on a larger sequential corpus. We also introduce new strategies for selecting the most informative n-grams, and show that these boost efficiency.",
}
@inproceedings{liu-etal-2019-continual,
    title = "Continual Learning for Sentence Representations Using Conceptors",
    author = "Liu, Tianlin  and
      Ungar, Lyle  and
      Sedoc, Jo{\~a}o",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1331",
    doi = "10.18653/v1/N19-1331",
    pages = "3274--3279",
    abstract = "Distributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence representations: Given a sequence of corpora, we aim to optimize the sentence encoder with respect to the new corpus while maintaining its accuracy on the old corpora. To address this problem, we propose to initialize sentence encoders with the help of corpus-independent features, and then sequentially update sentence encoders using Boolean operations of conceptor matrices to learn corpus-dependent features. We evaluate our approach on semantic textual similarity tasks and show that our proposed sentence encoder can continually learn features from new corpora while retaining its competence on previously encountered corpora.",
}
@inproceedings{liang-etal-2019-relation,
    title = "Relation Discovery with Out-of-Relation Knowledge Base as Supervision",
    author = "Liang, Yan  and
      Liu, Xin  and
      Zhang, Jianwen  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1332",
    doi = "10.18653/v1/N19-1332",
    pages = "3280--3290",
    abstract = "Unsupervised relation discovery aims to discover new relations from a given text corpus without annotated data. However, it does not consider existing human annotated knowledge bases even when they are relevant to the relations to be discovered. In this paper, we study the problem of how to use out-of-relation knowledge bases to supervise the discovery of unseen relations, where out-of-relation means that relations to discover from the text corpus and those in knowledge bases are not overlapped. We construct a set of constraints between entity pairs based on the knowledge base embedding and then incorporate constraints into the relation discovery by a variational auto-encoder based algorithm. Experiments show that our new approach can improve the state-of-the-art relation discovery performance by a large margin.",
}
@inproceedings{lichtarge-etal-2019-corpora,
    title = "Corpora Generation for Grammatical Error Correction",
    author = "Lichtarge, Jared  and
      Alberti, Chris  and
      Kumar, Shankar  and
      Shazeer, Noam  and
      Parmar, Niki  and
      Tong, Simon",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1333",
    doi = "10.18653/v1/N19-1333",
    pages = "3291--3301",
    abstract = "Grammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, GEC suffers from the lack of plentiful parallel data. We describe two approaches for generating large parallel datasets for GEC using publicly available Wikipedia data. The first method extracts source-target pairs from Wikipedia edit histories with minimal filtration heuristics while the second method introduces noise into Wikipedia sentences via round-trip translation through bridge languages. Both strategies yield similar sized parallel corpora containing around 4B tokens. We employ an iterative decoding strategy that is tailored to the loosely supervised nature of our constructed corpora. We demonstrate that neural GEC models trained using either type of corpora give similar performance. Fine-tuning these models on the Lang-8 corpus and ensembling allows us to surpass the state of the art on both the CoNLL {`}14 benchmark and the JFLEG task. We present systematic analysis that compares the two approaches to data generation and highlights the effectiveness of ensembling.",
}
@inproceedings{wilcox-etal-2019-structural,
    title = "Structural Supervision Improves Learning of Non-Local Grammatical Dependencies",
    author = "Wilcox, Ethan  and
      Qian, Peng  and
      Futrell, Richard  and
      Ballesteros, Miguel  and
      Levy, Roger",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1334",
    doi = "10.18653/v1/N19-1334",
    pages = "3302--3312",
    abstract = "State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail, and have been shown to acquire a number of non-local grammatical dependencies with some success. Here we investigate whether supervision with hierarchical structure enhances learning of a range of grammatical dependencies, a question that has previously been addressed only for subject-verb agreement. Using controlled experimental methods from psycholinguistics, we compare the performance of word-based LSTM models versus Recurrent Neural Network Grammars (RNNGs) (Dyer et al. 2016) which represent hierarchical syntactic structure and use neural control to deploy it in left-to-right processing, on two classes of non-local grammatical dependencies in English{---}Negative Polarity licensing and Filler-Gap Dependencies{---}tested in a range of configurations. Using the same training data for both models, we find that the RNNG outperforms the LSTM on both types of grammatical dependencies and even learns many of the Island Constraints on the filler-gap dependency. Structural supervision thus provides data efficiency advantages over purely string-based training of neural language models in acquiring human-like generalizations about non-local grammatical dependencies.",
}
@inproceedings{tu-gimpel-2019-benchmarking,
    title = "Benchmarking Approximate Inference Methods for Neural Structured Prediction",
    author = "Tu, Lifu  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1335",
    doi = "10.18653/v1/N19-1335",
    pages = "3313--3324",
    abstract = "Exact structured inference with neural network scoring functions is computationally challenging but several methods have been proposed for approximating inference. One approach is to perform gradient descent with respect to the output structure directly (Belanger and McCallum, 2016). Another approach, proposed recently, is to train a neural network (an {``}inference network{''}) to perform inference (Tu and Gimpel, 2018). In this paper, we compare these two families of inference methods on three sequence labeling datasets. We choose sequence labeling because it permits us to use exact inference as a benchmark in terms of speed, accuracy, and search error. Across datasets, we demonstrate that inference networks achieve a better speed/accuracy/search error trade-off than gradient descent, while also being faster than exact inference at similar accuracy levels. We find further benefit by combining inference networks and gradient descent, using the former to provide a warm start for the latter.",
}
@inproceedings{cheng-etal-2019-evaluating,
    title = "Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent",
    author = "Cheng, Minhao  and
      Wei, Wei  and
      Hsieh, Cho-Jui",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1336",
    doi = "10.18653/v1/N19-1336",
    pages = "3325--3335",
    abstract = "Recent research has demonstrated that goal-oriented dialogue agents trained on large datasets can achieve striking performance when interacting with human users. In real world applications, however, it is important to ensure that the agent performs smoothly interacting with not only regular users but also those malicious ones who would attack the system through interactions in order to achieve goals for their own advantage. In this paper, we develop algorithms to evaluate the robustness of a dialogue agent by carefully designed attacks using adversarial agents. Those attacks are performed in both black-box and white-box settings. Furthermore, we demonstrate that adversarial training using our attacks can significantly improve the robustness of a goal-oriented dialogue system. On a case-study of the negotiation agent developed by (Lewis et al., 2017), our attacks reduced the average advantage of rewards between the attacker and the trained RL-based agent from 2.68 to -5.76 on a scale from -10 to 10 for randomized goals. Moreover, we show that with the adversarial training, we are able to improve the robustness of negotiation agents by 1.5 points on average against all our attacks.",
}
@inproceedings{pezeshkpour-etal-2019-investigating,
    title = "Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications",
    author = "Pezeshkpour, Pouya  and
      Tian, Yifan  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1337",
    doi = "10.18653/v1/N19-1337",
    pages = "3336--3347",
    abstract = "Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving accuracy and overlook other aspects such as robustness and interpretability. In this paper, we propose adversarial modifications for link prediction models: identifying the fact to add into or remove from the knowledge graph that changes the prediction for a target fact after the model is retrained. Using these single modifications of the graph, we identify the most influential fact for a predicted link and evaluate the sensitivity of the model to the addition of fake facts. We introduce an efficient approach to estimate the effect of such modifications by approximating the change in the embeddings when the knowledge graph changes. To avoid the combinatorial search over all possible facts, we train a network to decode embeddings to their corresponding graph components, allowing the use of gradient-based optimization to identify the adversarial modification. We use these techniques to evaluate the robustness of link prediction models (by measuring sensitivity to additional facts), study interpretability through the facts most responsible for predictions (by identifying the most influential neighbors), and detect incorrect facts in the knowledge base.",
}
@inproceedings{sankar-etal-2019-transferable,
    title = "Transferable Neural Projection Representations",
    author = "Sankar, Chinnadhurai  and
      Ravi, Sujith  and
      Kozareva, Zornitsa",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1339",
    doi = "10.18653/v1/N19-1339",
    pages = "3355--3360",
    abstract = "Neural word representations are at the core of many state-of-the-art natural language processing models. A widely used approach is to pre-train, store and look up word or character embedding matrices. While useful, such representations occupy huge memory making it hard to deploy on-device and often do not generalize to unknown words due to vocabulary pruning. In this paper, we propose a skip-gram based architecture coupled with Locality-Sensitive Hashing (LSH) projections to learn efficient dynamically computable representations. Our model does not need to store lookup tables as representations are computed on-the-fly and require low memory footprint. The representations can be trained in an unsupervised fashion and can be easily transferred to other NLP tasks. For qualitative evaluation, we analyze the nearest neighbors of the word representations and discover semantically similar words even with misspellings. For quantitative evaluation, we plug our transferable projections into a simple LSTM and run it on multiple NLP tasks and show how our transferable projections achieve better performance compared to prior work.",
}
@inproceedings{guan-etal-2019-semantic,
    title = "Semantic Role Labeling with Associated Memory Network",
    author = "Guan, Chaoyu  and
      Cheng, Yuhao  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1340",
    doi = "10.18653/v1/N19-1340",
    pages = "3361--3371",
    abstract = "Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works were presented. This paper proposes a novel syntax-agnostic SRL model enhanced by the proposed associated memory network (AMN), which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. In detail, we use sentences and their labels from train dataset as an associated memory cue to help label the target sentence. Furthermore, we compare several associated sentences selecting strategies and label merging methods in AMN to find and utilize the label of associated sentences while attending them. By leveraging the attentive memory from known training data, Our full model reaches state-of-the-art on CoNLL-2009 benchmark datasets for syntax-agnostic setting, showing a new effective research line of SRL enhancement other than exploiting external resources such as well pre-trained language models.",
}
@inproceedings{vilares-etal-2019-better,
    title = "Better, Faster, Stronger Sequence Tagging Constituent Parsers",
    author = "Vilares, David  and
      Abdou, Mostafa  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1341",
    doi = "10.18653/v1/N19-1341",
    pages = "3372--3383",
    abstract = "Sequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high error rates around closing brackets of long constituents, (b) large label sets, leading to sparsity, and (c) error propagation arising from greedy decoding. To effectively close brackets, we train a model that learns to switch between tagging schemes. To reduce sparsity, we decompose the label set and use multi-task learning to jointly learn to predict sublabels. Finally, we mitigate issues from greedy decoding through auxiliary losses and sentence-level fine-tuning with policy gradient. Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further. On the SPMRL datasets, we observe even greater improvements across the board, including a new state of the art on Basque, Hebrew, Polish and Swedish.",
}
@inproceedings{zhu-wang-2019-ner,
    title = "{CAN}-{NER}: {C}onvolutional {A}ttention {N}etwork for {C}hinese {N}amed {E}ntity {R}ecognition",
    author = "Zhu, Yuying  and
      Wang, Guoxin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1342",
    doi = "10.18653/v1/N19-1342",
    pages = "3384--3393",
    abstract = "Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.",
}
@inproceedings{teranishi-etal-2019-decomposed,
    title = "Decomposed Local Models for Coordinate Structure Parsing",
    author = "Teranishi, Hiroki  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1343",
    doi = "10.18653/v1/N19-1343",
    pages = "3394--3403",
    abstract = "We propose a simple and accurate model for coordination boundary identification. Our model decomposes the task into three sub-tasks during training; finding a coordinator, identifying inside boundaries of a pair of conjuncts, and selecting outside boundaries of it. For inference, we make use of probabilities of coordinators and conjuncts in the CKY parsing to find the optimal combination of coordinate structures. Experimental results demonstrate that our model achieves state-of-the-art results, ensuring that the global structure of coordinations is consistent.",
}
@inproceedings{omori-komachi-2019-multi,
    title = "Multi-Task Learning for {J}apanese Predicate Argument Structure Analysis",
    author = "Omori, Hikaru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1344",
    doi = "10.18653/v1/N19-1344",
    pages = "3404--3414",
    abstract = "An event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and event-noun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multi-task models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.",
}
@inproceedings{marz-etal-2019-domain,
    title = "Domain adaptation for part-of-speech tagging of noisy user-generated text",
    author = {M{\"a}rz, Luisa  and
      Trautmann, Dietrich  and
      Roth, Benjamin},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1345",
    doi = "10.18653/v1/N19-1345",
    pages = "3415--3420",
    abstract = "The performance of a Part-of-speech (POS) tagger is highly dependent on the domain of the processed text, and for many domains there is no or only very little training data available. This work addresses the problem of POS tagging noisy user-generated text using a neural network. We propose an architecture that trains an out-of-domain model on a large newswire corpus, and transfers those weights by using them as a prior for a model trained on the target domain (a data-set of German Tweets) for which there is very little annotations available. The neural network has a standard bidirectional LSTM at its core. However, we find it crucial to also encode a set of task-specific features, and to obtain reliable (source-domain and target-domain) word representations. Experiments with different regularization techniques such as early stopping, dropout and fine-tuning the domain adaptation prior weights are conducted. Our best model uses external weights from the out-of-domain model, as well as feature embeddings, pre-trained word and sub-word embeddings and achieves a tagging accuracy of slightly over 90{\%}, improving on the previous state of the art for this task.",
}
@inproceedings{li-etal-2019-neural,
    title = "Neural {C}hinese Address Parsing",
    author = "Li, Hao  and
      Lu, Wei  and
      Xie, Pengjun  and
      Li, Linlin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1346",
    doi = "10.18653/v1/N19-1346",
    pages = "3421--3431",
    abstract = "This paper introduces a new task {--} Chinese address parsing {--} the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence labelling approach, our observation is that there exist complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. We investigate neural structured prediction models with latent variables to capture such rich structural information within Chinese addresses. We create and publicly release a new dataset consisting of 15K Chinese addresses, and conduct extensive experiments on the dataset to investigate the model effectiveness and robustness. We release our code and data at http://statnlp.org/research/sp.",
}
@inproceedings{karimi-tang-2019-learning,
    title = "Learning Hierarchical Discourse-level Structure for Fake News Detection",
    author = "Karimi, Hamid  and
      Tang, Jiliang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1347",
    doi = "10.18653/v1/N19-1347",
    pages = "3432--3442",
    abstract = "On the one hand, nowadays, fake news articles are easily propagated through various online media platforms and have become a grand threat to the trustworthiness of information. On the other hand, our understanding of the language of fake news is still minimal. Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured. Nevertheless, this has rarely been investigated in the fake news detection domain and faces tremendous challenges. First, existing methods for capturing discourse-level structure rely on annotated corpora which are not available for fake news datasets. Second, how to extract out useful information from such discovered structures is another challenge. To address these challenges, we propose Hierarchical Discourse-level Structure for Fake news detection. HDSF learns and constructs a discourse-level structure for fake/real news articles in an automated and data-driven manner. Moreover, we identify insightful structure-related properties, which can explain the discovered structures and boost our understating of fake news. Conducted experiments show the effectiveness of the proposed approach. Further structural analysis suggests that real and fake news present substantial differences in the hierarchical discourse-level structures.",
}
@inproceedings{geva-etal-2019-discofuse,
    title = "{D}isco{F}use: A Large-Scale Dataset for Discourse-Based Sentence Fusion",
    author = "Geva, Mor  and
      Malmi, Eric  and
      Szpektor, Idan  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1348",
    doi = "10.18653/v1/N19-1348",
    pages = "3443--3455",
    abstract = "Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically-generating fusion examples from raw text and present DiscoFuse, a large scale dataset for discourse-based sentence fusion. We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences. We apply our approach on two document collections: Wikipedia and Sports articles, yielding 60 million fusion examples annotated with discourse information required to reconstruct the fused text. We develop a sequence-to-sequence model on DiscoFuse and thoroughly analyze its strengths and weaknesses with respect to the various discourse phenomena, using both automatic as well as human evaluation. Finally, we conduct transfer learning experiments with WebSplit, a recent dataset for text simplification. We show that pretraining on DiscoFuse substantially improves performance on WebSplit when viewed as a sentence fusion task.",
}
@inproceedings{ko-etal-2019-linguistically,
    title = "Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation",
    author = "Ko, Wei-Jen  and
      Durrett, Greg  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1349",
    doi = "10.18653/v1/N19-1349",
    pages = "3456--3466",
    abstract = "Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses. Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. In this work, we examine whether specificity is solely a frequency-related notion and find that more linguistically-driven specificity measures are better suited to improving response informativeness. However, we find that forcing a sequence-to-sequence model to be more specific can expose a host of other problems in the responses, including flawed discourse and implausible semantics. We rerank our model{'}s outputs using externally-trained classifiers targeting each of these identified factors. Experiments show that our final model using linguistically motivated specificity and plausibility reranking improves the informativeness, reasonableness, and grammatically of responses.",
}
@inproceedings{ishiwatari-etal-2019-learning,
    title = "Learning to Describe Unknown Phrases with Local and Global Contexts",
    author = "Ishiwatari, Shonosuke  and
      Hayashi, Hiroaki  and
      Yoshinaga, Naoki  and
      Neubig, Graham  and
      Sato, Shoetsu  and
      Toyoda, Masashi  and
      Kitsuregawa, Masaru",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1350",
    doi = "10.18653/v1/N19-1350",
    pages = "3467--3476",
    abstract = "When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. If we humans cannot figure out the meaning of those expressions from the immediate local context, we consult dictionaries for definitions or search documents or the web to find other global context to help in interpretation. Can machines help us do this work? Which type of context is more important for machines to solve the problem? To answer these questions, we undertake a task of describing a given phrase in natural language based on its local and global contexts. To solve this task, we propose a neural description model that consists of two context encoders and a description decoder. In contrast to the existing methods for non-standard English explanation [Ni+ 2017] and definition generation [Noraset+ 2017; Gadetsky+ 2018], our model appropriately takes important clues from both local and global contexts. Experimental results on three existing datasets (including WordNet, Oxford and Urban Dictionaries) and a dataset newly created from Wikipedia demonstrate the effectiveness of our method over previous work.",
}
@inproceedings{sileo-etal-2019-mining,
    title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
    author = "Sileo, Damien  and
      Van De Cruys, Tim  and
      Pradel, Camille  and
      Muller, Philippe",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1351",
    doi = "10.18653/v1/N19-1351",
    pages = "3477--3486",
    abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",
}
@inproceedings{chen-etal-2019-large,
    title = "How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection",
    author = "Chen, Wenhu  and
      Su, Yu  and
      Shen, Yilin  and
      Chen, Zhiyu  and
      Yan, Xifeng  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1352",
    doi = "10.18653/v1/N19-1352",
    pages = "3487--3497",
    abstract = "With the rapid development in deep learning, deep neural networks have been widely adopted in many real-life natural language applications. Under deep neural networks, a pre-defined vocabulary is required to vectorize text inputs. The canonical approach to select pre-defined vocabulary is based on the word frequency, where a threshold is selected to cut off the long tail distribution. However, we observed that such a simple approach could easily lead to under-sized vocabulary or over-sized vocabulary issues. Therefore, we are interested in understanding how the end-task classification accuracy is related to the vocabulary size and what is the minimum required vocabulary size to achieve a specific performance. In this paper, we provide a more sophisticated variational vocabulary dropout (VVD) based on variational dropout to perform vocabulary selection, which can intelligently select the subset of the vocabulary to achieve the required performance. To evaluate different algorithms on the newly proposed vocabulary selection problem, we propose two new metrics: Area Under Accuracy-Vocab Curve and Vocab Size under X{\%} Accuracy Drop. Through extensive experiments on various NLP classification tasks, our variational framework is shown to significantly outperform the frequency-based and other selection baselines on these metrics.",
}
@inproceedings{sasaki-etal-2019-subword,
    title = "{S}ubword-based {C}ompact {R}econstruction of {W}ord {E}mbeddings",
    author = "Sasaki, Shota  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1353",
    doi = "10.18653/v1/N19-1353",
    pages = "3498--3508",
    abstract = "The idea of subword-based word embeddings has been proposed in the literature, mainly for solving the out-of-vocabulary (OOV) word problem observed in standard word-based word embeddings. In this paper, we propose a method of reconstructing pre-trained word embeddings using subword information that can effectively represent a large number of subword embeddings in a considerably small fixed space. The key techniques of our method are twofold: memory-shared embeddings and a variant of the key-value-query self-attention mechanism. Our experiments show that our reconstructed subword-based embeddings can successfully imitate well-trained word embeddings in a small fixed space while preventing quality degradation across several linguistic benchmark datasets, and can simultaneously predict effective embeddings of OOV words. We also demonstrate the effectiveness of our reconstruction method when we apply them to downstream tasks.",
}
@inproceedings{shareghi-etal-2019-bayesian,
    title = "{B}ayesian Learning for Neural Dependency Parsing",
    author = "Shareghi, Ehsan  and
      Li, Yingzhen  and
      Zhu, Yi  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1354",
    doi = "10.18653/v1/N19-1354",
    pages = "3509--3519",
    abstract = "While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data. We demonstrate that in the small data regime, where uncertainty around parameter estimation and model prediction matters the most, Bayesian neural modeling is very effective. In order to overcome the computational and statistical costs of the approximate inference step in this framework, we utilize an efficient sampling procedure via stochastic gradient Langevin dynamics to generate samples from the approximated posterior. Moreover, we show that our Bayesian neural parser can be further improved when integrated into a multi-task parsing and POS tagging framework, designed to minimize task interference via an adversarial procedure. When trained and tested on 6 languages with less than 5k training instances, our parser consistently outperforms the strong bilstm baseline (Kiperwasser and Goldberg, 2016). Compared with the biaffine parser (Dozat et al., 2017) our model achieves an improvement of up to 3{\%} for Vietnames and Irish, while our multi-task model achieves an improvement of up to 9{\%} across five languages: Farsi, Russian, Turkish, Vietnamese, and Irish.",
}
@inproceedings{guo-etal-2019-autosem,
    title = "{A}uto{S}e{M}: Automatic Task Selection and Mixing in Multi-Task Learning",
    author = "Guo, Han  and
      Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1355",
    doi = "10.18653/v1/N19-1355",
    pages = "3520--3531",
    abstract = "Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r.t. the primary task is not known a priori, the success of MTL models depends on the correct choice of these auxiliary tasks and also a balanced mixing ratio of these tasks during alternate training. These two problems could be resolved via manual intuition or hyper-parameter tuning over all combinatorial task choices, but this introduces inductive bias or is not scalable when the number of candidate auxiliary tasks is very large. To address these issues, we present AutoSeM, a two-stage MTL pipeline, where the first stage automatically selects the most useful auxiliary tasks via a Beta-Bernoulli multi-armed bandit with Thompson Sampling, and the second stage learns the training mixing ratio of these selected auxiliary tasks via a Gaussian Process based Bayesian optimization framework. We conduct several MTL experiments on the GLUE language understanding tasks, and show that our AutoSeM framework can successfully find relevant auxiliary tasks and automatically learn their mixing ratio, achieving significant performance boosts on several primary tasks. Finally, we present ablations for each stage of AutoSeM and analyze the learned auxiliary task choices.",
}
@inproceedings{ravfogel-etal-2019-studying,
    title = "Studying the Inductive Biases of {RNN}s with Synthetic Variations of Natural Languages",
    author = "Ravfogel, Shauli  and
      Goldberg, Yoav  and
      Linzen, Tal",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1356",
    doi = "10.18653/v1/N19-1356",
    pages = "3532--3542",
    abstract = "How do typological properties such as word order and morphological case marking affect the ability of neural sequence models to acquire the syntax of a language? Cross-linguistic comparisons of RNNs{'} syntactic performance (e.g., on subject-verb agreement prediction) are complicated by the fact that any two languages differ in multiple typological properties, as well as by differences in training corpus. We propose a paradigm that addresses these issues: we create synthetic versions of English, which differ from English in one or more typological parameters, and generate corpora for those languages based on a parsed English corpus. We report a series of experiments in which RNNs were trained to predict agreement features for verbs in each of those synthetic languages. Among other findings, (1) performance was higher in subject-verb-object order (as in English) than in subject-object-verb order (as in Japanese), suggesting that RNNs have a recency bias; (2) predicting agreement with both subject and object (polypersonal agreement) improves over predicting each separately, suggesting that underlying syntactic knowledge transfers across the two tasks; and (3) overt morphological case makes agreement prediction significantly easier, regardless of word order.",
}
@inproceedings{jain-wallace-2019-attention,
    title = "{A}ttention is not {E}xplanation",
    author = "Jain, Sarthak  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1357",
    doi = "10.18653/v1/N19-1357",
    pages = "3543--3556",
    abstract = "Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equipped with attention provide a distribution over attended-to input units, and this is often presented (at least implicitly) as communicating the relative importance of inputs. However, it is unclear what relationship exists between attention weights and model outputs. In this work we perform extensive experiments across a variety of NLP tasks that aim to assess the degree to which attention weights provide meaningful {``}explanations{''} for predictions. We find that they largely do not. For example, learned attention weights are frequently uncorrelated with gradient-based measures of feature importance, and one can identify very different attention distributions that nonetheless yield equivalent predictions. Our findings show that standard attention modules do not provide meaningful explanations and should not be treated as though they do.",
}
@inproceedings{ammanabrolu-riedl-2019-playing,
    title = "Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning",
    author = "Ammanabrolu, Prithviraj  and
      Riedl, Mark",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1358",
    doi = "10.18653/v1/N19-1358",
    pages = "3557--3565",
    abstract = "Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. This graph is used to prune the action space, enabling more efficient exploration. The question of which action to take can be reduced to a question-answering task, a form of transfer learning that pre-trains certain parts of our architecture. In experiments using the TextWorld framework, we show that our proposed technique can learn a control policy faster than baseline alternatives. We have also open-sourced our code at https://github.com/rajammanabrolu/KG-DQN.",
}
@inproceedings{li-etal-2019-information,
    title = "Information Aggregation for Multi-Head Attention with Routing-by-Agreement",
    author = "Li, Jian  and
      Yang, Baosong  and
      Dou, Zi-Yi  and
      Wang, Xing  and
      Lyu, Michael R.  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1359",
    doi = "10.18653/v1/N19-1359",
    pages = "3566--3575",
    abstract = "Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces. Concerning the information aggregation, a common practice is to use a concatenation followed by a linear transformation, which may not fully exploit the expressiveness of multi-head attention. In this work, we propose to improve the information aggregation for multi-head attention with a more powerful routing-by-agreement algorithm. Specifically, the routing algorithm iteratively updates the proportion of how much a part (i.e. the distinct information learned from a specific subspace) should be assigned to a whole (i.e. the final output representation), based on the agreement between parts and wholes. Experimental results on linguistic probing tasks and machine translation tasks prove the superiority of the advanced information aggregation over the standard linear transformation.",
}
@inproceedings{chen-bunescu-2019-context,
    title = "Context Dependent Semantic Parsing over Temporally Structured Data",
    author = "Chen, Charles  and
      Bunescu, Razvan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1360",
    doi = "10.18653/v1/N19-1360",
    pages = "3576--3585",
    abstract = "We describe a new semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface. Multiple time series belonging to an entity of interest are stored in a database and the user interacts with the system to obtain a better understanding of the entity{'}s state and behavior, entailing sequences of actions and questions whose answers may depend on previous factual or navigational interactions. We design an LSTM-based encoder-decoder architecture that models context dependency through copying mechanisms and multiple levels of attention over inputs and previous outputs. When trained to predict tokens using supervised learning, the proposed architecture substantially outperforms standard sequence generation baselines. Training the architecture using policy gradient leads to further improvements in performance, reaching a sequence-level accuracy of 88.7{\%} on artificial data and 74.8{\%} on real data.",
}
@inproceedings{cohan-etal-2019-structural,
    title = "Structural Scaffolds for Citation Intent Classification in Scientific Publications",
    author = "Cohan, Arman  and
      Ammar, Waleed  and
      van Zuylen, Madeleine  and
      Cady, Field",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1361",
    doi = "10.18653/v1/N19-1361",
    pages = "3586--3596",
    abstract = "Identifying the intent of a citation in scientific papers (e.g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3{\%} absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.",
}
@inproceedings{joshi-etal-2019-pair2vec,
    title = "pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference",
    author = "Joshi, Mandar  and
      Choi, Eunsol  and
      Levy, Omer  and
      Weld, Daniel  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1362",
    doi = "10.18653/v1/N19-1362",
    pages = "3597--3608",
    abstract = "Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This paper proposes new methods for learning and using embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are computed as a compositional function of each word{'}s representation, which is learned by maximizing the pointwise mutual information (PMI) with the contexts in which the the two words co-occur. We add these representations to the cross-sentence attention layer of existing inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or replacing existing word embeddings. Experiments show a gain of 2.7{\%} on the recently released SQuAD 2.0 and 1.3{\%} on MultiNLI. Our representations also aid in better generalization with gains of around 6-7{\%} on adversarial SQuAD datasets, and 8.8{\%} on the adversarial entailment test set by Glockner et al. (2018).",
}
@inproceedings{kumar-etal-2019-submodular,
    title = "Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation",
    author = "Kumar, Ashutosh  and
      Bhattamishra, Satwik  and
      Bhandari, Manik  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1363",
    doi = "10.18653/v1/N19-1363",
    pages = "3609--3619",
    abstract = "Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents. Previous paraphrasing approaches have mainly focused on the issue of generating semantically similar paraphrases while paying little attention towards diversity. In fact, most of the methods rely solely on top-k beam search sequences to obtain a set of paraphrases. The resulting set, however, contains many structurally similar sentences. In this work, we focus on the task of obtaining highly diverse paraphrases while not compromising on paraphrasing quality. We provide a novel formulation of the problem in terms of monotone submodular function maximization, specifically targeted towards the task of paraphrasing. Additionally, we demonstrate the effectiveness of our method for data augmentation on multiple tasks such as intent classification and paraphrase recognition. In order to drive further research, we have made the source code available.",
}
@inproceedings{yang-etal-2019-lets,
    title = "Let{'}s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms",
    author = "Yang, Diyi  and
      Chen, Jiaao  and
      Yang, Zichao  and
      Jurafsky, Dan  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1364",
    doi = "10.18653/v1/N19-1364",
    pages = "3620--3630",
    abstract = "Modeling what makes a request persuasive - eliciting the desired response from a reader - is critical to the study of propaganda, behavioral economics, and advertising. Yet current models can{'}t quantify the persuasiveness of requests or extract successful persuasive strategies. Building on theories of persuasion, we propose a neural network to quantify persuasiveness and identify the persuasive strategies in advocacy requests. Our semi-supervised hierarchical neural network model is supervised by the number of people persuaded to take actions and partially supervised at the sentence level with human-labeled rhetorical strategies. Our method outperforms several baselines, uncovers persuasive strategies - offering increased interpretability of persuasive speech - and has applications for other situations with document-level supervision but only partial sentence supervision.",
}
@inproceedings{cases-etal-2019-recursive,
    title = "Recursive Routing Networks: Learning to Compose Modules for Language Understanding",
    author = "Cases, Ignacio  and
      Rosenbaum, Clemens  and
      Riemer, Matthew  and
      Geiger, Atticus  and
      Klinger, Tim  and
      Tamkin, Alex  and
      Li, Olivia  and
      Agarwal, Sandhini  and
      Greene, Joshua D.  and
      Jurafsky, Dan  and
      Potts, Christopher  and
      Karttunen, Lauri",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1365",
    doi = "10.18653/v1/N19-1365",
    pages = "3631--3648",
    abstract = "We introduce Recursive Routing Networks (RRNs), which are modular, adaptable models that learn effectively in diverse environments. RRNs consist of a set of functions, typically organized into a grid, and a meta-learner decision-making component called the router. The model jointly optimizes the parameters of the functions and the meta-learner{'}s policy for routing inputs through those functions. RRNs can be incorporated into existing architectures in a number of ways; we explore adding them to word representation layers, recurrent network hidden layers, and classifier layers. Our evaluation task is natural language inference (NLI). Using the MultiNLI corpus, we show that an RRN{'}s routing decisions reflect the high-level genre structure of that corpus. To show that RRNs can learn to specialize to more fine-grained semantic distinctions, we introduce a new corpus of NLI examples involving implicative predicates, and show that the model components become fine-tuned to the inferential signatures that are characteristic of these predicates.",
}
@inproceedings{damonte-cohen-2019-structural,
    title = "Structural Neural Encoders for {AMR}-to-text Generation",
    author = "Damonte, Marco  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1366",
    doi = "10.18653/v1/N19-1366",
    pages = "3649--3658",
    abstract = "AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs. Sequence-to-sequence models can be used to this end by converting the AMR graphs to strings. Approaching the problem while working directly with graphs requires the use of graph-to-sequence models that encode the AMR graph into a vector representation. Such encoding has been shown to be beneficial in the past, and unlike sequential encoding, it allows us to explicitly capture reentrant structures in the AMR graphs. We investigate the extent to which reentrancies (nodes with multiple parents) have an impact on AMR-to-text generation by comparing graph encoders to tree encoders, where reentrancies are not preserved. We show that improvements in the treatment of reentrancies and long-range dependencies contribute to higher overall scores for graph encoders. Our best model achieves 24.40 BLEU on LDC2015E86, outperforming the state of the art by 1.1 points and 24.54 BLEU on LDC2017T10, outperforming the state of the art by 1.24 points.",
}
@inproceedings{fraser-etal-2019-multilingual,
    title = "Multilingual prediction of {A}lzheimer{'}s disease through domain adaptation and concept-based language modelling",
    author = {Fraser, Kathleen C.  and
      Linz, Nicklas  and
      Li, Bai  and
      Lundholm Fors, Kristina  and
      Rudzicz, Frank  and
      K{\"o}nig, Alexandra  and
      Alexandersson, Jan  and
      Robert, Philippe  and
      Kokkinakis, Dimitrios},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1367",
    doi = "10.18653/v1/N19-1367",
    pages = "3659--3670",
    abstract = "There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.",
}
@inproceedings{paul-frank-2019-ranking,
    title = "Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs",
    author = "Paul, Debjit  and
      Frank, Anette",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1368",
    doi = "10.18653/v1/N19-1368",
    pages = "3671--3681",
    abstract = "To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character{'}s psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model{'}s performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge.",
}
@inproceedings{olex-etal-2019-nlp,
    title = "{NLP} Whack-A-Mole: {C}hallenges in Cross-Domain Temporal Expression Extraction",
    author = "Olex, Amy  and
      Maffey, Luke  and
      McInnes, Bridget",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1369",
    doi = "10.18653/v1/N19-1369",
    pages = "3682--3692",
    abstract = "Incorporating domain knowledge is vital in building successful natural language processing (NLP) applications. Many times, cross-domain application of a tool results in poor performance as the tool does not account for domain-specific attributes. The clinical domain is challenging in this aspect due to specialized medical terms and nomenclature, shorthand notation, fragmented text, and a variety of writing styles used by different medical units. Temporal resolution is an NLP task that, in general, is domain-agnostic because temporal information is represented using a limited lexicon. However, domain-specific aspects of temporal resolution are present in clinical texts. Here we explore parsing issues that arose when running our system, a tool built on Newswire text, on clinical notes in the THYME corpus. Many parsing issues were straightforward to correct; however, a few code changes resulted in a cascading series of parsing errors that had to be resolved before an improvement in performance was observed, revealing the complexity temporal resolution and rule-based parsing. Our system now outperforms current state-of-the-art systems on the THYME corpus with little change in its performance on Newswire texts.",
}
@inproceedings{jia-etal-2019-document,
    title = "Document-Level N-ary Relation Extraction with Multiscale Representation Learning",
    author = "Jia, Robin  and
      Wong, Cliff  and
      Poon, Hoifung",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1370",
    doi = "10.18653/v1/N19-1370",
    pages = "3693--3704",
    abstract = "Most information extraction methods focus on binary relations expressed within single sentences. In high-value domains, however, n-ary relations are of great demand (e.g., drug-gene-mutation interactions in precision oncology). Such relations often involve entity mentions that are far apart in the document, yet existing work on cross-sentence relation extraction is generally confined to small text spans (e.g., three consecutive sentences), which severely limits recall. In this paper, we propose a novel multiscale neural architecture for document-level n-ary relation extraction. Our system combines representations learned over various text spans throughout the document and across the subrelation hierarchy. Widening the system{'}s purview to the entire document maximizes potential recall. Moreover, by integrating weak signals across the document, multiscale modeling increases precision, even in the presence of noisy labels from distant supervision. Experiments on biomedical machine reading show that our approach substantially outperforms previous n-ary relation extraction methods.",
}
@inproceedings{lehman-etal-2019-inferring,
    title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials",
    author = "Lehman, Eric  and
      DeYoung, Jay  and
      Barzilay, Regina  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1371",
    doi = "10.18653/v1/N19-1371",
    pages = "3705--3717",
    abstract = "How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated in natural language scientific articles, imposing substantial burden on those trying to make sense of them. In this paper, we present a new task and corpus for making this unstructured published scientific evidence actionable. The task entails inferring reported findings from a full-text article describing randomized controlled trials (RCT) with respect to a given intervention, comparator, and outcome of interest, e.g., inferring if a given article provides evidence supporting the use of aspirin to reduce risk of stroke, as compared to placebo. We present a new corpus for this task comprising 10,000+ prompts coupled with full-text articles describing RCTs. Results using a suite of baseline models {---} ranging from heuristic (rule-based) approaches to attentive neural architectures {---} demonstrate the difficulty of the task, which we believe largely owes to the lengthy, technical input texts. To facilitate further work on this important, challenging problem we make the corpus, documentation, a website and leaderboard, and all source code for baselines and evaluation publicly available.",
}
@inproceedings{kim-lee-2019-decay,
    title = "Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding",
    author = "Kim, Jonggu  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1372",
    doi = "10.18653/v1/N19-1372",
    pages = "3718--3726",
    abstract = "To capture salient contextual information for spoken language understanding (SLU) of a dialogue, we propose time-aware models that automatically learn the latent time-decay function of the history without a manual time-decay function. We also propose a method to identify and label the current speaker to improve the SLU accuracy. In experiments on the benchmark dataset used in Dialog State Tracking Challenge 4, the proposed models achieved significantly higher F1 scores than the state-of-the-art contextual models. Finally, we analyze the effectiveness of the introduced models in detail. The analysis demonstrates that the proposed methods were effective to improve SLU accuracy individually.",
}
@inproceedings{raheja-tetreault-2019-dialogue,
    title = "{D}ialogue {A}ct {C}lassification with {C}ontext-{A}ware {S}elf-{A}ttention",
    author = "Raheja, Vipul  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1373",
    doi = "10.18653/v1/N19-1373",
    pages = "3727--3733",
    abstract = "Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.",
}
@inproceedings{colombo-etal-2019-affect,
    title = "Affect-Driven Dialog Generation",
    author = "Colombo, Pierre  and
      Witon, Wojciech  and
      Modi, Ashutosh  and
      Kennedy, James  and
      Kapadia, Mubbasir",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1374",
    doi = "10.18653/v1/N19-1374",
    pages = "3734--3743",
    abstract = "The majority of current systems for end-to-end dialog generation focus on response quality without an explicit control over the affective content of the responses. In this paper, we present an affect-driven dialog system, which generates emotional responses in a controlled manner using a continuous representation of emotions. The system achieves this by modeling emotions at a word and sequence level using: (1) a vector representation of the desired emotion, (2) an affect regularizer, which penalizes neutral words, and (3) an affect sampling method, which forces the neural network to generate diverse words that are emotionally relevant. During inference, we use a re-ranking procedure that aims to extract the most emotionally relevant responses using a human-in-the-loop optimization process. We study the performance of our system in terms of both quantitative (BLEU score and response diversity), and qualitative (emotional appropriateness) measures.",
}
@inproceedings{gangi-reddy-etal-2019-multi,
    title = "Multi-Level Memory for Task Oriented Dialogs",
    author = "Gangi Reddy, Revanth  and
      Contractor, Danish  and
      Raghu, Dinesh  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1375",
    doi = "10.18653/v1/N19-1375",
    pages = "3744--3754",
    abstract = "Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25{\%} increase in both entity F1 and BLEU scores.",
}
@inproceedings{chitkara-etal-2019-topic,
    title = "Topic Spotting using Hierarchical Networks with Self Attention",
    author = "Chitkara, Pooja  and
      Modi, Ashutosh  and
      Avvaru, Pravalika  and
      Janghorbani, Sepehr  and
      Kapadia, Mubbasir",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1376",
    doi = "10.18653/v1/N19-1376",
    pages = "3755--3761",
    abstract = "Success of deep learning techniques have renewed the interest in development of dialogue systems. However, current systems struggle to have consistent long term conversations with the users and fail to build rapport. Topic spotting, the task of automatically inferring the topic of a conversation, has been shown to be helpful in making dialog system more engaging and efficient. We propose a hierarchical model with self attention for topic spotting. Experiments on the Switchboard corpus show the superior performance of our model over previously proposed techniques for topic spotting and deep models for text classification. Additionally, in contrast to offline processing of dialog, we also analyze the performance of our model in a more realistic setting i.e. in an online setting where the topic is identified in real time as the dialog progresses. Results show that our model is able to generalize even with limited information in the online setting.",
}
@inproceedings{du-black-2019-top,
    title = "Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar",
    author = "Du, Wenchao  and
      Black, Alan W",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1377",
    doi = "10.18653/v1/N19-1377",
    pages = "3762--3771",
    abstract = "We consider neural language generation under a novel problem setting: generating the words of a sentence according to the order of their first appearance in its lexicalized PCFG parse tree, in a depth-first, left-to-right manner. Unlike previous tree-based language generation methods, our approach is both (i) top-down and (ii) explicitly generating syntactic structure at the same time. In addition, our method combines neural model with symbolic approach: word choice at each step is constrained by its predicted syntactic function. We applied our model to the task of dialog response generation, and found it significantly improves over sequence-to-sequence baseline, in terms of diversity and relevance. We also investigated the effect of lexicalization on language generation, and found that lexicalization schemes that give priority to content words have certain advantages over those focusing on dependency relations.",
}
@inproceedings{aina-etal-2019-entity,
    title = "What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue",
    author = "Aina, Laura  and
      Silberer, Carina  and
      Sorodoc, Ionut-Teodor  and
      Westera, Matthijs  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1378",
    doi = "10.18653/v1/N19-1378",
    pages = "3772--3783",
    abstract = "Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations have been proposed. Such entity-centric models have shown empirical success, but we still know little about why. In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4). We show that these models outperform the state of the art on this task, and that they do better on lower frequency entities than a counterpart model that is not entity-centric, with the same model size. We argue that making models entity-centric naturally fosters good architectural decisions. However, we also show that these models do not really build entity representations and that they make poor use of linguistic context. These negative results underscore the need for model analysis, to test whether the motivations for particular architectures are borne out in how models behave when deployed.",
}
@inproceedings{li-etal-2019-continuous,
    title = "Continuous Learning for Large-scale Personalized Domain Classification",
    author = "Li, Han  and
      Lee, Jihwan  and
      Mudgal, Sidharth  and
      Sarikaya, Ruhi  and
      Kim, Young-Bum",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1379",
    doi = "10.18653/v1/N19-1379",
    pages = "3784--3794",
    abstract = "Domain classification is the task to map spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants (IPDAs). This is observed in mainstream IPDAs in industry and third-party domains are developed to enhance the capability of the IPDAs. As more and more new domains are developed very frequently, how to continuously accommodate the new domains still remains challenging. Moreover, if one wants to use personalized information dynamically for better domain classification, it is infeasible to directly adopt existing continual learning approaches. In this paper, we propose CoNDA, a neural-based approach for continuous domain adaption with normalization and regularization. Unlike existing methods that often conduct full model parameter update, CoNDA only updates the necessary parameters in the model for the new domains. Empirical evaluation shows that CoNDA achieves high accuracy on both the accommodated new domains and the existing known domains for which input samples come with personal information, and outperforms the baselines by a large margin.",
}
@inproceedings{schuster-etal-2019-cross-lingual,
    title = "Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog",
    author = "Schuster, Sebastian  and
      Gupta, Sonal  and
      Shah, Rushin  and
      Lewis, Mike",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1380",
    doi = "10.18653/v1/N19-1380",
    pages = "3795--3805",
    abstract = "One of the first steps in the utterance interpretation pipeline of many task-oriented conversational AI systems is to identify user intents and the corresponding slots. Since data collection for machine learning models for this task is time-consuming, it is desirable to make use of existing data in a high-resource language to train models in low-resource languages. However, development of such models has largely been hindered by the lack of multilingual training data. In this paper, we present a new data set of 57k annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) across the domains weather, alarm, and reminder. We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations. We find that given several hundred training examples in the the target language, the latter two methods outperform translating the training data. Further, in very low-resource settings, multilingual contextual word representations give better results than using cross-lingual static embeddings. We also compare the cross-lingual methods to using monolingual resources in the form of contextual ELMo representations and find that given just small amounts of target language data, this method outperforms all cross-lingual methods, which highlights the need for more sophisticated cross-lingual methods.",
}
@inproceedings{dziri-etal-2019-evaluating,
    title = "Evaluating Coherence in Dialogue Systems using Entailment",
    author = "Dziri, Nouha  and
      Kamalloo, Ehsan  and
      Mathewson, Kory  and
      Zaiane, Osmar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1381",
    doi = "10.18653/v1/N19-1381",
    pages = "3806--3812",
    abstract = "Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time consuming, and not scalable. Moreover, judges tend to evaluate a small number of dialogues, meaning that minor differences in evaluation configuration may lead to dissimilar results. In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations. Furthermore, we introduce calculable approximations of human judgment based on conversational coherence by adopting state-of-the-art entailment techniques. Results show that our metrics can be used as a surrogate for human judgment, making it easy to evaluate dialogue systems on large-scale datasets and allowing an unbiased estimate for the quality of the responses.",
}
@inproceedings{arora-etal-2019-knowledge,
    title = "On Knowledge distillation from complex networks for response prediction",
    author = "Arora, Siddhartha  and
      Khapra, Mitesh M.  and
      Ramaswamy, Harish G.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1382",
    doi = "10.18653/v1/N19-1382",
    pages = "3813--3822",
    abstract = "Recent advances in Question Answering have lead to the development of very complex models which compute rich representations for query and documents by capturing all pairwise interactions between query and document words. This makes these models expensive in space and time, and in practice one has to restrict the length of the documents that can be fed to these models. Such models have also been recently employed for the task of predicting dialog responses from available background documents (e.g., Holl-E dataset). However, here the documents are longer, thereby rendering these complex models infeasible except in select restricted settings. In order to overcome this, we use standard simple models which do not capture all pairwise interactions, but learn to emulate certain characteristics of a complex teacher network. Specifically, we first investigate the conicity of representations learned by a complex model and observe that it is significantly lower than that of simpler models. Based on this insight, we modify the simple architecture to mimic this characteristic. We go further by using knowledge distillation approaches, where the simple model acts as a student and learns to match the output from the complex teacher network. We experiment with the Holl-E dialog data set and show that by mimicking characteristics and matching outputs from a teacher, even a simple network can give improved performance.",
}
@inproceedings{huang-etal-2019-cross,
    title = "Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging",
    author = "Huang, Lifu  and
      Ji, Heng  and
      May, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1383",
    doi = "10.18653/v1/N19-1383",
    pages = "3823--3833",
    abstract = "We focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual representations or use a shared encoder in a multitask network to transfer knowledge. These approaches inevitably introduce noise to the target language annotation due to mismatched source-target sentence structures. To effectively transfer the resources, we develop a new neural architecture that leverages multi-level adversarial transfer: (1) word-level adversarial training, which projects source language words into the same semantic space as those of the target language without using any parallel corpora or bilingual gazetteers, and (2) sentence-level adversarial training, which yields language-agnostic sequential features. Our neural architecture outperforms previous approaches on CoNLL data sets. Moreover, on 10 low-resource languages, our approach achieves up to 16{\%} absolute F-score gain over all high-performing baselines on cross-lingual transfer without using any target-language resources.",
}
@inproceedings{marie-fujita-2019-unsupervised-extraction,
    title = "Unsupervised Extraction of Partial Translations for Neural Machine Translation",
    author = "Marie, Benjamin  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1384",
    doi = "10.18653/v1/N19-1384",
    pages = "3834--3844",
    abstract = "In neural machine translation (NMT), monolingual data are usually exploited through a so-called back-translation: sentences in the target language are translated into the source language to synthesize new parallel data. While this method provides more training data to better model the target language, on the source side, it only exploits translations that the NMT system is already able to generate using a model trained on existing parallel data. In this work, we assume that new translation knowledge can be extracted from monolingual data, without relying at all on existing parallel data. We propose a new algorithm for extracting from monolingual data what we call partial translations: pairs of source and target sentences that contain sequences of tokens that are translations of each other. Our algorithm is fully unsupervised and takes only source and target monolingual data as input. Our empirical evaluation points out that our partial translations can be used in combination with back-translation to further improve NMT models. Furthermore, while partial translations are particularly useful for low-resource language pairs, they can also be successfully exploited in resource-rich scenarios to improve translation quality.",
}
@inproceedings{rasooli-collins-2019-low,
    title = "Low-Resource Syntactic Transfer with Unsupervised Source Reordering",
    author = "Rasooli, Mohammad Sadegh  and
      Collins, Michael",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1385",
    doi = "10.18653/v1/N19-1385",
    pages = "3845--3856",
    abstract = "We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly used parallel data in transfer methods. We use the concatenation of projected trees from the Bible corpus, and the gold-standard treebanks in multiple source languages along with cross-lingual word representations. We demonstrate that reordering the source treebanks before training on them for a target language improves the accuracy of languages outside the European language family. Our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages. Among them, our experiments on 16 treebanks of 12 non-European languages achieve an average UAS absolute improvement of 3.3{\%} over a state-of-the-art method.",
}
@inproceedings{mohiuddin-joty-2019-revisiting,
    title = "Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training",
    author = "Mohiuddin, Tasnim  and
      Joty, Shafiq",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1386",
    doi = "10.18653/v1/N19-1386",
    pages = "3857--3867",
    abstract = "Adversarial training has shown impressive success in learning bilingual dictionary without any parallel data by mapping monolingual embeddings to a shared space. However, recent work has shown superior performance for non-adversarial methods in more challenging language pairs. In this work, we revisit adversarial autoencoder for unsupervised word translation and propose two novel extensions to it that yield more stable training and improved results. Our method includes regularization terms to enforce cycle consistency and input reconstruction, and puts the target encoders as an adversary against the corresponding discriminator. Extensive experimentations with European, non-European and low-resource languages show that our method is more robust and achieves better performance than recently proposed adversarial and non-adversarial approaches.",
}
@inproceedings{murthy-etal-2019-addressing,
    title = "Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages",
    author = "Murthy, Rudra  and
      Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1387",
    doi = "10.18653/v1/N19-1387",
    pages = "3868--3873",
    abstract = "Transfer learning approaches for Neural Machine Translation (NMT) train a NMT model on an assisting language-target language pair (parent model) which is later fine-tuned for the source language-target language pair of interest (child model), with the target language being the same. In many cases, the assisting language has a different word order from the source language. We show that divergent word order adversely limits the benefits from transfer learning when little to no parallel corpus between the source and target language is available. To bridge this divergence, we propose to pre-order the assisting language sentences to match the word order of the source language and train the parent model. Our experiments on many language pairs show that bridging the word order gap leads to significant improvement in the translation quality in extremely low-resource scenarios.",
}
@inproceedings{aharoni-etal-2019-massively,
    title = "Massively Multilingual Neural Machine Translation",
    author = "Aharoni, Roee  and
      Johnson, Melvin  and
      Firat, Orhan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1388",
    doi = "10.18653/v1/N19-1388",
    pages = "3874--3884",
    abstract = "Multilingual Neural Machine Translation enables training a single model that supports translation from multiple source languages into multiple target languages. We perform extensive experiments in training massively multilingual NMT models, involving up to 103 distinct languages and 204 translation directions simultaneously. We explore different setups for training such models and analyze the trade-offs between translation quality and various modeling decisions. We report results on the publicly available TED talks multilingual corpus where we show that massively multilingual many-to-many models are effective in low resource settings, outperforming the previous state-of-the-art while supporting up to 59 languages in 116 translation directions in a single model. Our experiments on a large-scale dataset with 103 languages, 204 trained directions and up to one million examples per direction also show promising results, surpassing strong bilingual baselines and encouraging future work on massively multilingual NMT.",
}
@inproceedings{bollmann-2019-large,
    title = "A Large-Scale Comparison of Historical Text Normalization Systems",
    author = "Bollmann, Marcel",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1389",
    doi = "10.18653/v1/N19-1389",
    pages = "3885--3898",
    abstract = "There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder{--}decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.",
}
@inproceedings{roth-upadhyay-2019-combining,
    title = "Combining Discourse Markers and Cross-lingual Embeddings for Synonym{--}Antonym Classification",
    author = "Roth, Michael  and
      Upadhyay, Shyam",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1390",
    doi = "10.18653/v1/N19-1390",
    pages = "3899--3905",
    abstract = "It is well-known that distributional semantic approaches have difficulty in distinguishing between synonyms and antonyms (Grefenstette, 1992; Pad{\'o} and Lapata, 2003). Recent work has shown that supervision available in English for this task (e.g., lexical resources) can be transferred to other languages via cross-lingual word embeddings. However, this kind of transfer misses monolingual distributional information available in a target language, such as contrast relations that are indicative of antonymy (e.g. hot ... while ... cold). In this work, we improve the transfer by exploiting monolingual information, expressed in the form of co-occurrences with discourse markers that convey contrast. Our approach makes use of less than a dozen markers, which can easily be obtained for many languages. Compared to a baseline using only cross-lingual embeddings, we show absolute improvements of 4{--}10{\%} F1-score in Vietnamese and Hindi.",
}
@inproceedings{aldarmaki-diab-2019-context,
    title = "Context-Aware Cross-Lingual Mapping",
    author = "Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1391",
    doi = "10.18653/v1/N19-1391",
    pages = "3906--3911",
    abstract = "Cross-lingual word vectors are typically obtained by fitting an orthogonal matrix that maps the entries of a bilingual dictionary from a source to a target vector space. Word vectors, however, are most commonly used for sentence or document-level representations that are calculated as the weighted average of word embeddings. In this paper, we propose an alternative to word-level mapping that better reflects sentence-level cross-lingual similarity. We incorporate context in the transformation matrix by directly mapping the averaged embeddings of aligned sentences in a parallel corpus. We also implement cross-lingual mapping of deep contextualized word embeddings using parallel sentences with word alignments. In our experiments, both approaches resulted in cross-lingual sentence embeddings that outperformed context-independent word mapping in sentence translation retrieval. Furthermore, the sentence-level transformation could be used for word-level mapping without loss in word translation quality.",
}
@inproceedings{mulcaire-etal-2019-polyglot,
    title = "Polyglot Contextual Representations Improve Crosslingual Transfer",
    author = "Mulcaire, Phoebe  and
      Kasai, Jungo  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1392",
    doi = "10.18653/v1/N19-1392",
    pages = "3912--3918",
    abstract = "We introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word representations with those of multilingual representation learning. We produce language models from dissimilar language pairs (English/Arabic and English/Chinese) and use them in dependency parsing, semantic role labeling, and named entity recognition, with comparisons to monolingual and non-contextual variants. Our results provide further evidence for the benefits of polyglot learning, in which representations are shared across multiple languages.",
}
@inproceedings{scholivet-etal-2019-typological,
    title = "Typological Features for Multilingual Delexicalised Dependency Parsing",
    author = "Scholivet, Manon  and
      Dary, Franck  and
      Nasr, Alexis  and
      Favre, Benoit  and
      Ramisch, Carlos",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1393",
    doi = "10.18653/v1/N19-1393",
    pages = "3919--3930",
    abstract = "The existence of universal models to describe the syntax of languages has been debated for decades. The availability of resources such as the Universal Dependencies treebanks and the World Atlas of Language Structures make it possible to study the plausibility of universal grammar from the perspective of dependency parsing. Our work investigates the use of high-level language descriptions in the form of typological features for multilingual dependency parsing. Our experiments on multilingual parsing for 40 languages show that typological information can indeed guide parsers to share information between similar languages beyond simple language identification.",
}
@inproceedings{leclair-mcmillan-2019-recommendations,
    title = "Recommendations for Datasets for Source Code Summarization",
    author = "LeClair, Alexander  and
      McMillan, Collin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1394",
    doi = "10.18653/v1/N19-1394",
    pages = "3931--3937",
    abstract = "Source Code Summarization is the task of writing short, natural language descriptions of source code. The main use for these descriptions is in software documentation e.g. the one-sentence Java method descriptions in JavaDocs. Code summarization is rapidly becoming a popular research problem, but progress is restrained due to a lack of suitable datasets. In addition, a lack of community standards for creating datasets leads to confusing and unreproducible research results {--} we observe swings in performance of more than 33{\%} due only to changes in dataset design. In this paper, we make recommendations for these standards from experimental results. We release a dataset based on prior work of over 2.1m pairs of Java methods and one sentence method descriptions from over 28k Java projects. We describe the dataset and point out key differences from natural language data, to guide and support future researchers.",
}
@inproceedings{eyal-etal-2019-question,
    title = "Question Answering as an Automatic Evaluation Metric for News Article Summarization",
    author = "Eyal, Matan  and
      Baumel, Tal  and
      Elhadad, Michael",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1395",
    doi = "10.18653/v1/N19-1395",
    pages = "3938--3948",
    abstract = "Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. APES utilizes recent progress in the field of reading-comprehension to quantify the ability of a summary to answer a set of manually created questions regarding central entities in the source article. We first analyze the strength of this metric by comparing it to known manual evaluation metrics. We then present an end-to-end neural abstractive model that maximizes APES, while increasing ROUGE scores to competitive results.",
}
@inproceedings{kumar-cheung-2019-understanding,
    title = "{U}nderstanding the {B}ehaviour of {N}eural {A}bstractive {S}ummarizers using {C}ontrastive {E}xamples",
    author = "Kumar, Krtin  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1396",
    doi = "10.18653/v1/N19-1396",
    pages = "3949--3954",
    abstract = "Neural abstractive summarizers generate summary texts using a language model conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarization datasets. We investigate how they achieve this performance with respect to human-written gold-standard abstracts, and whether the systems are able to understand deeper syntactic and semantic structures. We generate a set of contrastive summaries which are perturbed, deficient versions of human-written summaries, and test whether existing neural summarizers score them more highly than the human-written summaries. We analyze their performance on different datasets and find that these systems fail to understand the source text, in a majority of the cases.",
}
@inproceedings{mendes-etal-2019-jointly,
    title = "Jointly Extracting and Compressing Documents with Summary State Representations",
    author = "Mendes, Afonso  and
      Narayan, Shashi  and
      Miranda, Sebasti{\~a}o  and
      Marinho, Zita  and
      Martins, Andr{\'e} F. T.  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1397",
    doi = "10.18653/v1/N19-1397",
    pages = "3955--3966",
    abstract = "We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.",
}
@inproceedings{karn-etal-2019-news,
    title = "News Article Teaser Tweets and How to Generate Them",
    author = {Karn, Sanjeev Kumar  and
      Buckley, Mark  and
      Waltinger, Ulli  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1398",
    doi = "10.18653/v1/N19-1398",
    pages = "3967--3977",
    abstract = "In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers. A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read particular news items. Teasers are one of the main vehicles for transmitting news to social media users. We compile a novel dataset of teasers by systematically accumulating tweets and selecting those that conform to the teaser definition. We have compared a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al. seq2seq with pointer network.",
}
@inproceedings{lund-etal-2019-cross,
    title = "Cross-referencing Using Fine-grained Topic Modeling",
    author = "Lund, Jeffrey  and
      Armstrong, Piper  and
      Fearn, Wilson  and
      Cowley, Stephen  and
      Hales, Emily  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1399",
    doi = "10.18653/v1/N19-1399",
    pages = "3978--3987",
    abstract = "Cross-referencing, which links passages of text to other related passages, can be a valuable study aid for facilitating comprehension of a text. However, cross-referencing requires first, a comprehensive thematic knowledge of the entire corpus, and second, a focused search through the corpus specifically to find such useful connections. Due to this, cross-reference resources are prohibitively expensive and exist only for the most well-studied texts (e.g. religious texts). We develop a topic-based system for automatically producing candidate cross-references which can be easily verified by human annotators. Our system utilizes fine-grained topic modeling with thousands of highly nuanced and specific topics to identify verse pairs which are topically related. We demonstrate that our system can be cost effective compared to having annotators acquire the expertise necessary to produce cross-reference resources unaided.",
}
@inproceedings{akasaki-kaji-2019-conversation,
    title = "Conversation Initiation by Diverse News Contents Introduction",
    author = "Akasaki, Satoshi  and
      Kaji, Nobuhiro",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1400",
    doi = "10.18653/v1/N19-1400",
    pages = "3988--3998",
    abstract = "In our everyday chit-chat, there is a conversation initiator, who proactively casts an initial utterance to start chatting. However, most existing conversation systems cannot play this role. Previous studies on conversation systems assume that the user always initiates conversation, and have placed emphasis on how to respond to the given user{'}s utterance. As a result, existing conversation systems become passive. Namely they continue waiting until being spoken to by the users. In this paper, we consider the system as a conversation initiator and propose a novel task of generating the initial utterance in open-domain non-task-oriented conversation. Here, in order not to make users bored, it is necessary to generate diverse utterances to initiate conversation without relying on boilerplate utterances like greetings. To this end, we propose to generate initial utterance by summarizing and chatting about news articles, which provide fresh and various contents everyday. To address the lack of the training data for this task, we constructed a novel large-scale dataset through crowd-sourcing. We also analyzed the dataset in detail to examine how humans initiate conversations (the dataset will be released to facilitate future research activities). We present several approaches to conversation initiation including information retrieval based and generation based models. Experimental results showed that the proposed models trained on our dataset performed reasonably well and outperformed baselines that utilize automatically collected training data in both automatic and manual evaluation.",
}
@inproceedings{takase-okazaki-2019-positional,
    title = "Positional Encoding to Control Output Sequence Length",
    author = "Takase, Sho  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1401",
    doi = "10.18653/v1/N19-1401",
    pages = "3999--4004",
    abstract = "Neural encoder-decoder models have been successful in natural language generation tasks. However, real applications of abstractive summarization must consider an additional constraint that a generated summary should not exceed a desired length. In this paper, we propose a simple but effective extension of a sinusoidal positional encoding (Vaswani et al., 2017) so that a neural encoder-decoder model preserves the length constraint. Unlike previous studies that learn length embeddings, the proposed method can generate a text of any length even if the target length is unseen in training data. The experimental results show that the proposed method is able not only to control generation length but also improve ROUGE scores.",
}
@inproceedings{wang-jiang-2019-lower,
    title = "The Lower The Simpler: Simplifying Hierarchical Recurrent Models",
    author = "Wang, Chao  and
      Jiang, Hui",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1402",
    doi = "10.18653/v1/N19-1402",
    pages = "4005--4009",
    abstract = "To improve the training efficiency of hierarchical recurrent models without compromising their performance, we propose a strategy named as {``}the lower the simpler{''}, which is to simplify the baseline models by making the lower layers simpler than the upper layers. We carry out this strategy to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. Specifically, we propose Scalar Gated Unit (SGU), which is a simplified variant of GRU, and use it to replace the GRUs at the middle layers of HRED and R-NET. Besides, we also use Fixed-size Ordinally-Forgetting Encoding (FOFE), which is an efficient encoding method without any trainable parameter, to replace the GRUs at the bottom layers of HRED and R-NET. The experimental results show that the simplified HRED and the simplified R-NET contain significantly less trainable parameters, consume significantly less training time, and achieve slightly better performance than their baseline models.",
}
@inproceedings{pujari-goldwasser-2019-using,
    title = "Using Natural Language Relations between Answer Choices for Machine Comprehension",
    author = "Pujari, Rajkumar  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1403",
    doi = "10.18653/v1/N19-1403",
    pages = "4010--4015",
    abstract = "While evaluating an answer choice for Reading Comprehension task, other answer choices available for the question and the answers of related questions about the same paragraph often provide valuable information. In this paper, we propose a method to leverage the natural language relations between the answer choices, such as entailment and contradiction, to improve the performance of machine comprehension. We use a stand-alone question answering (QA) system to perform QA task and a Natural Language Inference (NLI) system to identify the relations between the choice pairs. Then we perform inference using an Integer Linear Programming (ILP)-based relational framework to re-evaluate the decisions made by the standalone QA system in light of the relations identified by the NLI system. We also propose a multitask learning model that learns both the tasks jointly.",
}
@inproceedings{ghaeini-etal-2019-saliency,
    title = "{S}aliency {L}earning: {T}eaching the {M}odel {W}here to {P}ay {A}ttention",
    author = "Ghaeini, Reza  and
      Fern, Xiaoli  and
      Shahbazi, Hamed  and
      Tadepalli, Prasad",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1404",
    doi = "10.18653/v1/N19-1404",
    pages = "4016--4025",
    abstract = "Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model{'}s behaviour and predictions, which are helpful for assessing the reliability of the model{'}s predictions. However, such methods do not improve the model{'}s reliability. In this paper, we aim to teach the model to make the right prediction for the right reason by providing explanation training and ensuring the alignment of the model{'}s explanation with the ground truth explanation. Our experimental results on multiple tasks and datasets demonstrate the effectiveness of the proposed method, which produces more reliable predictions while delivering better results compared to traditionally trained models.",
}
@inproceedings{chen-durrett-2019-understanding,
    title = "Understanding Dataset Design Choices for Multi-hop Reasoning",
    author = "Chen, Jifan  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1405",
    doi = "10.18653/v1/N19-1405",
    pages = "4026--4032",
    abstract = "Learning multi-hop reasoning has been a key challenge for reading comprehension models, leading to the design of datasets that explicitly focus on it. Ideally, a model should not be able to perform well on a multi-hop question answering task without doing multi-hop reasoning. In this paper, we investigate two recently proposed datasets, WikiHop and HotpotQA. First, we explore sentence-factored models for these tasks; by design, these models cannot do multi-hop reasoning, but they are still able to solve a large number of examples in both datasets. Furthermore, we find spurious correlations in the unmasked version of WikiHop, which make it easy to achieve high performance considering only the questions and answers. Finally, we investigate one key difference between these datasets, namely span-based vs. multiple-choice formulations of the QA task. Multiple-choice versions of both datasets can be easily gamed, and two models we examine only marginally exceed a baseline in this setting. Overall, while these datasets are useful testbeds, high-performing models may not be learning as much multi-hop reasoning as previously thought.",
}
@inproceedings{stahlberg-etal-2019-neural,
    title = "Neural Grammatical Error Correction with Finite State Transducers",
    author = "Stahlberg, Felix  and
      Bryant, Christopher  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1406",
    doi = "10.18653/v1/N19-1406",
    pages = "4033--4039",
    abstract = "Grammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet superseded more traditional symbolic models. Hybrid systems combining phrase-based statistical machine translation (SMT) and neural sequence models are currently among the most effective approaches to GEC. However, both SMT and neural sequence-to-sequence models require large amounts of annotated data. Language model based GEC (LM-GEC) is a promising alternative which does not rely on annotated training data. We show how to improve LM-GEC by applying modelling techniques based on finite state transducers. We report further gains by rescoring with neural language models. We show that our methods developed for LM-GEC can also be used with SMT systems if annotated training data is available. Our best system outperforms the best published result on the CoNLL-2014 test set, and achieves far better relative improvements over the SMT baselines than previous hybrid systems.",
}
@inproceedings{yang-etal-2019-convolutional,
    title = "Convolutional Self-Attention Networks",
    author = "Yang, Baosong  and
      Wang, Longyue  and
      Wong, Derek F.  and
      Chao, Lidia S.  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1407",
    doi = "10.18653/v1/N19-1407",
    pages = "4040--4045",
    abstract = "Self-attention networks (SANs) have drawn increasing interest due to their high parallelization in computation and flexibility in modeling dependencies. SANs can be further enhanced with multi-head attention by allowing the model to attend to information from different representation subspaces. In this work, we propose novel convolutional self-attention networks, which offer SANs the abilities to 1) strengthen dependencies among neighboring elements, and 2) model the interaction between features extracted by multiple attention heads. Experimental results of machine translation on different language pairs and model settings show that our approach outperforms both the strong Transformer baseline and other existing models on enhancing the locality of SANs. Comparing with prior studies, the proposed model is parameter free in terms of introducing no more parameters.",
}
@inproceedings{adhikari-etal-2019-rethinking,
    title = "Rethinking Complex Neural Network Architectures for Document Classification",
    author = "Adhikari, Ashutosh  and
      Ram, Achyudh  and
      Tang, Raphael  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1408",
    doi = "10.18653/v1/N19-1408",
    pages = "4046--4051",
    abstract = "Neural network models for many NLP tasks have grown increasingly complex in recent years, making training and deployment more difficult. A number of recent papers have questioned the necessity of such architectures and found that well-executed, simpler models are quite effective. We show that this is also the case for document classification: in a large-scale reproducibility study of several recent neural models, we find that a simple BiLSTM architecture with appropriate regularization yields accuracy and F1 that are either competitive or exceed the state of the art on four standard benchmark datasets. Surprisingly, our simple model is able to achieve these results without attention mechanisms. While these regularization techniques, borrowed from language modeling, are not novel, to our knowledge we are the first to apply them in this context. Our work provides an open-source platform and the foundation for future work in document classification.",
}
@inproceedings{edunov-etal-2019-pre,
    title = "Pre-trained language model representations for language generation",
    author = "Edunov, Sergey  and
      Baevski, Alexei  and
      Auli, Michael",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1409",
    doi = "10.18653/v1/N19-1409",
    pages = "4052--4059",
    abstract = "Pre-trained language model representations have been successful in a wide range of language understanding tasks. In this paper, we examine different strategies to integrate pre-trained representations into sequence to sequence models and apply it to neural machine translation and abstractive summarization. We find that pre-trained representations are most effective when added to the encoder network which slows inference by only 14{\%}. Our experiments in machine translation show gains of up to 5.3 BLEU in a simulated resource-poor setup. While returns diminish with more labeled data, we still observe improvements when millions of sentence-pairs are available. Finally, on abstractive summarization we achieve a new state of the art on the full text version of CNN/DailyMail.",
}
@inproceedings{shen-etal-2019-pragmatically,
    title = "Pragmatically Informative Text Generation",
    author = "Shen, Sheng  and
      Fried, Daniel  and
      Andreas, Jacob  and
      Klein, Dan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1410",
    doi = "10.18653/v1/N19-1410",
    pages = "4060--4067",
    abstract = "We improve the informativeness of models for conditional text generation using techniques from computational pragmatics. These techniques formulate language production as a game between speakers and listeners, in which a speaker should generate output text that a listener can use to correctly identify the original input that the text describes. While such approaches are widely used in cognitive science and grounded language learning, they have received less attention for more standard language generation tasks. We consider two pragmatic modeling methods for text generation: one where pragmatics is imposed by information preservation, and another where pragmatics is imposed by explicit modeling of distractors. We find that these methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations.",
}
@inproceedings{bahuleyan-etal-2019-stochastic,
    title = "Stochastic {W}asserstein Autoencoder for Probabilistic Sentence Generation",
    author = "Bahuleyan, Hareesh  and
      Mou, Lili  and
      Zhou, Hao  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1411",
    doi = "10.18653/v1/N19-1411",
    pages = "4068--4076",
    abstract = "The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) on the latent space and penalizes the Kullback-Leibler (KL) divergence between the posterior and prior. In NLP, VAEs are extremely difficult to train due to the problem of KL collapsing to zero. One has to implement various heuristics such as KL weight annealing and word dropout in a carefully engineered manner to successfully train a VAE for text. In this paper, we propose to use the Wasserstein autoencoder (WAE) for probabilistic sentence generation, where the encoder could be either stochastic or deterministic. We show theoretically and empirically that, in the original WAE, the stochastically encoded Gaussian distribution tends to become a Dirac-delta function, and we propose a variant of WAE that encourages the stochasticity of the encoder. Experimental results show that the latent space learned by WAE exhibits properties of continuity and smoothness as in VAEs, while simultaneously achieving much higher BLEU scores for sentence reconstruction.",
}
@inproceedings{bisk-etal-2019-benchmarking,
    title = "Benchmarking Hierarchical Script Knowledge",
    author = "Bisk, Yonatan  and
      Buys, Jan  and
      Pichotta, Karl  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1412",
    doi = "10.18653/v1/N19-1412",
    pages = "4077--4085",
    abstract = "Understanding procedural language requires reasoning about both hierarchical and temporal relations between events. For example, {``}boiling pasta{''} is a sub-event of {``}making a pasta dish{''}, typically happens before {``}draining pasta,{''} and requires the use of omitted tools (e.g. a strainer, sink...). While people are able to choose when and how to use abstract versus concrete instructions, the NLP community lacks corpora and tasks for evaluating if our models can do the same. In this paper, we introduce KidsCook, a parallel script corpus, as well as a cloze task which matches video captions with missing procedural details. Experimental results show that state-of-the-art models struggle at this task, which requires inducing functional commonsense knowledge not explicitly stated in text.",
}
@inproceedings{shain-2019-large,
    title = "A large-scale study of the effects of word frequency and predictability in naturalistic reading",
    author = "Shain, Cory",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1413",
    doi = "10.18653/v1/N19-1413",
    pages = "4086--4094",
    abstract = "A number of psycholinguistic studies have factorially manipulated words{'} contextual predictabilities and corpus frequencies and shown separable effects of each on measures of human sentence processing, a pattern which has been used to support distinct mechanisms underlying prediction on the one hand and lexical retrieval on the other. This paper examines the generalizability of this finding to more realistic conditions of sentence processing by studying effects of frequency and predictability in three large-scale naturalistic reading corpora. Results show significant effects of word frequency and predictability in isolation but no effect of frequency over and above predictability, and thus do not provide evidence of distinct mechanisms. The non-replication of separable effects in a naturalistic setting raises doubts about the existence of such a distinction in everyday sentence comprehension. Instead, these results are consistent with previous claims that apparent effects of frequency are underlyingly effects of predictability.",
}
@inproceedings{budhkar-rudzicz-2019-augmenting,
    title = "Augmenting word2vec with latent {D}irichlet allocation within a clinical application",
    author = "Budhkar, Akshay  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1414",
    doi = "10.18653/v1/N19-1414",
    pages = "4095--4099",
    abstract = "This paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer{'}s disease from transcripts of picture descriptions. Two of our models get F-scores over the current state-of-the-art using automatic methods on the DementiaBank dataset.",
}
@inproceedings{liu-etal-2019-idiosyncrasies,
    title = "On the Idiosyncrasies of the {M}andarin {C}hinese Classifier System",
    author = "Liu, Shijia  and
      Mei, Hongyuan  and
      Williams, Adina  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1415",
    doi = "10.18653/v1/N19-1415",
    pages = "4100--4106",
    abstract = "While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods. In this paper, we introduce an information-theoretic approach to measuring idiosyncrasy; we examine how much the uncertainty in Mandarin Chinese classifiers can be reduced by knowing semantic information about the nouns that the classifiers modify. Using the empirical distribution of classifiers from the parsed Chinese Gigaword corpus (Graff et al., 2005), we compute the mutual information (in bits) between the distribution over classifiers and distributions over other linguistic quantities. We investigate whether semantic classes of nouns and adjectives differ in how much they reduce uncertainty in classifier choice, and find that it is not fully idiosyncratic; while there are no obvious trends for the majority of semantic classes, shape nouns reduce uncertainty in classifier choice the most.",
}
@inproceedings{meftah-etal-2019-joint,
    title = "Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging",
    author = "Meftah, Sara  and
      Tamaazousti, Youssef  and
      Semmar, Nasredine  and
      Essafi, Hassane  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1416",
    doi = "10.18653/v1/N19-1416",
    pages = "4107--4112",
    abstract = "Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains. In a standard fine-tuning scheme, source and target problems are trained using the same architecture. Although capable of adapting to new domains, pre-trained units struggle with learning uncommon target-specific patterns. In this paper, we propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining the valuable source knowledge. Our experiments on POS tagging of social media texts (Tweets domain) demonstrate that our method achieves state-of-the-art performances on 3 commonly used datasets.",
}
@inproceedings{shareghi-etal-2019-show,
    title = "Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines",
    author = "Shareghi, Ehsan  and
      Gerz, Daniela  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1417",
    doi = "10.18653/v1/N19-1417",
    pages = "4113--4118",
    abstract = "In recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets. While the reasons for their success and their computational demand are well-documented, a comparison between neural models and more recent developments in n-gram models is neglected. In this paper, we examine the recent progress in n-gram literature, running experiments on 50 languages covering all morphological language families. Experimental results illustrate that a simple extension of Modified Kneser-Ney outperforms an lstm language model on 42 languages while a word-level Bayesian n-gram LM (Shareghi et al., 2017) outperforms the character-aware neural model (Kim et al., 2016) on average across all languages, and its extension which explicitly injects linguistic knowledge (Gerz et al., 2018) on 8 languages. Further experiments on larger Europarl datasets for 3 languages indicate that neural architectures are able to outperform computationally much cheaper n-gram models: n-gram training is up to 15,000x quicker. Our experiments illustrate that standalone n-gram models lend themselves as natural choices for resource-lean or morphologically rich languages, while the recent progress has significantly improved their accuracy.",
}
@inproceedings{bergmanis-goldwater-2019-data,
    title = "Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text",
    author = "Bergmanis, Toms  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1418",
    doi = "10.18653/v1/N19-1418",
    pages = "4119--4128",
    abstract = "Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in low-resource languages. In addition (as shown here), in a low-resource setting, a lemmatizer can learn more from n labeled examples of distinct words (types) than from n (contiguous) labeled tokens, since the latter contain far fewer distinct types. To combine the efficiency of type-based learning with the benefits of context, we propose a way to train a context-sensitive lemmatizer with little or no labeled corpus data, using inflection tables from the UniMorph project and raw text examples from Wikipedia that provide sentence contexts for the unambiguous UniMorph examples. Despite these being unambiguous examples, the model successfully generalizes from them, leading to improved results (both overall, and especially on unseen words) in comparison to a baseline that does not use context.",
}
@inproceedings{hewitt-manning-2019-structural,
    title = "{A} Structural Probe for Finding Syntax in Word Representations",
    author = "Hewitt, John  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1419",
    doi = "10.18653/v1/N19-1419",
    pages = "4129--4138",
    abstract = "Recent work has improved our ability to detect linguistic knowledge in word representations. However, current methods for detecting syntactic knowledge do not test whether syntax trees are represented in their entirety. In this work, we propose a structural probe, which evaluates whether syntax trees are embedded in a linear transformation of a neural network{'}s word representation space. The probe identifies a linear transformation under which squared L2 distance encodes the distance between words in the parse tree, and one in which squared L2 norm encodes depth in the parse tree. Using our probe, we show that such transformations exist for both ELMo and BERT but not in baselines, providing evidence that entire syntax trees are embedded implicitly in deep models{'} vector geometry.",
}
@inproceedings{li-etal-2019-cnm,
    title = "{CNM}: An Interpretable Complex-valued Network for Matching",
    author = "Li, Qiuchi  and
      Wang, Benyou  and
      Melucci, Massimo",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1420",
    doi = "10.18653/v1/N19-1420",
    pages = "4139--4148",
    abstract = "This paper seeks to model human language by the mathematical framework of quantum physics. With the well-designed mathematical formulations in quantum physics, this framework unifies different linguistic units in a single complex-valued vector space, e.g. words as particles in quantum states and sentences as mixed systems. A complex-valued network is built to implement this framework for semantic matching. With well-constrained complex-valued components, the network admits interpretations to explicit physical meanings. The proposed complex-valued network for matching (CNM) achieves comparable performances to strong CNN and RNN baselines on two benchmarking question answering (QA) datasets.",
}
@inproceedings{talmor-etal-2019-commonsenseqa,
    title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
    author = "Talmor, Alon  and
      Herzig, Jonathan  and
      Lourie, Nicholas  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1421",
    doi = "10.18653/v1/N19-1421",
    pages = "4149--4158",
    abstract = "When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering. To capture common sense beyond associations, we extract from ConceptNet (Speer et al., 2017) multiple target concepts that have the same semantic relation to a single source concept. Crowd-workers are asked to author multiple-choice questions that mention the source concept and discriminate in turn between each of the target concepts. This encourages workers to create questions with complex semantics that often require prior knowledge. We create 12,247 questions through this procedure and demonstrate the difficulty of our task with a large number of strong baselines. Our best baseline is based on BERT-large (Devlin et al., 2018) and obtains 56{\%} accuracy, well below human performance, which is 89{\%}.",
}
@inproceedings{caglayan-etal-2019-probing,
    title = "Probing the Need for Visual Context in Multimodal Machine Translation",
    author = {Caglayan, Ozan  and
      Madhyastha, Pranava  and
      Specia, Lucia  and
      Barrault, Lo{\"\i}c},
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1422",
    doi = "10.18653/v1/N19-1422",
    pages = "4159--4170",
    abstract = "Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial. We posit that this is a consequence of the very simple, short and repetitive sentences used in the only available dataset for the task (Multi30K), rendering the source text sufficient as context. In the general case, however, we believe that it is possible to combine visual and textual information in order to ground translations. In this paper we probe the contribution of the visual modality to state-of-the-art MMT models by conducting a systematic analysis where we partially deprive the models from source-side textual context. Our results show that under limited textual context, models are capable of leveraging the visual input to generate better translations. This contradicts the current belief that MMT models disregard the visual modality because of either the quality of the image features or the way they are integrated into the model.",
}
@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1423",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).",
}
@inproceedings{romanov-etal-2019-whats,
    title = "What{'}s in a Name? {R}educing Bias in Bios without Access to Protected Attributes",
    author = "Romanov, Alexey  and
      De-Arteaga, Maria  and
      Wallach, Hanna  and
      Chayes, Jennifer  and
      Borgs, Christian  and
      Chouldechova, Alexandra  and
      Geyik, Sahin  and
      Kenthapadi, Krishnaram  and
      Rumshisky, Anna  and
      Kalai, Adam",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1424",
    doi = "10.18653/v1/N19-1424",
    pages = "4187--4195",
    abstract = "There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is often desirable to simultaneously consider multiple protected attributes, as well as their intersections. In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual{'}s true occupation and a word embedding of their name. This method leverages the societal biases that are encoded in word embeddings, eliminating the need for access to protected attributes. Crucially, it only requires access to individuals{'} names at training time and not at deployment time. We evaluate two variations of our proposed method using a large-scale dataset of online biographies. We find that both variations simultaneously reduce race and gender biases, with almost no reduction in the classifier{'}s overall true positive rate.",
}
@proceedings{conll-2019-shared,
    title = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    editor = "Oepen, Stephan  and
      Abend, Omri  and
      Hajic, Jan  and
      Hershcovich, Daniel  and
      Kuhlmann, Marco  and
      O{'}Gorman, Tim  and
      Xue, Nianwen",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2000",
}
@inproceedings{oepen-etal-2019-mrp,
    title = "{MRP} 2019: Cross-Framework Meaning Representation Parsing",
    author = "Oepen, Stephan  and
      Abend, Omri  and
      Hajic, Jan  and
      Hershcovich, Daniel  and
      Kuhlmann, Marco  and
      O{'}Gorman, Tim  and
      Xue, Nianwen  and
      Chun, Jayeol  and
      Straka, Milan  and
      Uresova, Zdenka",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2001",
    doi = "10.18653/v1/K19-2001",
    pages = "1--27",
    abstract = "The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graph were represented in the training and evaluation data for the task, packaged in a uniform abstract graph representation and serialization. The task received submissions from eighteen teams, of which five do not participate in the official ranking because they arrived after the closing deadline, made use of additional training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu",
}
@inproceedings{hershcovich-arviv-2019-tupa,
    title = "{TUPA} at {MRP} 2019: A Multi-Task Baseline System",
    author = "Hershcovich, Daniel  and
      Arviv, Ofir",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2002",
    doi = "10.18653/v1/K19-2002",
    pages = "28--39",
    abstract = "This paper describes the TUPA system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Because it was prepared by one of the task co-organizers, TUPA provides a baseline point of comparison and is not considered in the official ranking of participating systems. While originally developed for UCCA only, TUPA has been generalized to support all MRP frameworks included in the task, and trained using multi-task learning to parse them all with a shared model. It is a transition-based parser with a BiLSTM encoder, augmented with BERT contextualized embeddings.",
}
@inproceedings{oepen-flickinger-2019-erg,
    title = "The {ERG} at {MRP} 2019: Radically Compositional Semantic Dependencies",
    author = "Oepen, Stephan  and
      Flickinger, Dan",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2003",
    doi = "10.18653/v1/K19-2003",
    pages = "40--44",
    abstract = "The English Resource Grammar (ERG) is a broad-coverage computational grammar of English that outputs underspecified logical-form representations of meaning in a framework dubbed English Resource Semantics (ERS). Two of the target representations in the the 2019 Shared Task on Cross-Framework Meaning Representation Parsing (MRP 2019) derive graph-based simplifications of ERS, viz. Elementary Dependency Structures (EDS) and DELPH-IN MRS Bi-Lexical Dependencies (DM). As a point of reference outside the official MRP competition, we parsed the evaluation strings using the ERG and converted the resulting meaning representations to EDS and DM. These graphs yield higher evaluation scores than the purely data-driven parsers in the actual shared task, suggesting that the general-purpose linguistic knowledge about English grammar encoded in the ERG can add value when parsing into these meaning representations.",
}
@inproceedings{li-etal-2019-sjtu,
    title = "{SJTU}-{NICT} at {MRP} 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing",
    author = "Li, Zuchao  and
      Zhao, Hai  and
      Zhang, Zhuosheng  and
      Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2004",
    doi = "10.18653/v1/K19-2004",
    pages = "45--54",
    abstract = "This paper describes our SJTU-NICT{'}s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Our system uses a graph-based approach to model a variety of semantic graph parsing tasks. Our main contributions in the submitted system are summarized as follows: 1. Our model is fully end-to-end and is capable of being trained only on the given training set which does not rely on any other extra training source including the companion data provided by the organizer; 2. We extend our graph pruning algorithm to a variety of semantic graphs, solving the problem of excessive semantic graph search space; 3. We introduce multi-task learning for multiple objectives within the same framework. The evaluation results show that our system achieved second place in the overall $F_1$ score and achieved the best $F_1$ score on the DM framework.",
}
@inproceedings{wang-etal-2019-shanghaitech,
    title = "{S}hanghai{T}ech at {MRP} 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing",
    author = "Wang, Xinyu  and
      Liu, Yixian  and
      Jia, Zixia  and
      Jiang, Chengyue  and
      Tu, Kewei",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2005",
    doi = "10.18653/v1/K19-2005",
    pages = "55--65",
    abstract = "This paper presents the system used in our submission to the CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. Our system is a graph-based parser which combines an extended pointer-generator network that generates nodes and a second-order mean field variational inference module that predicts edges. Our system achieved 1st and 2nd place for the DM and PSD frameworks respectively on the in-framework ranks and achieved 3rd place for the DM framework on the cross-framework ranks.",
}
@inproceedings{donatelli-etal-2019-saarland,
    title = "{S}aarland at {MRP} 2019: Compositional parsing across all graphbanks",
    author = "Donatelli, Lucia  and
      Fowlie, Meaghan  and
      Groschwitz, Jonas  and
      Koller, Alexander  and
      Lindemann, Matthias  and
      Mina, Mario  and
      Wei{\ss}enhorn, Pia",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2006",
    doi = "10.18653/v1/K19-2006",
    pages = "66--75",
    abstract = "We describe the Saarland University submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference on Computational Natural Language Learning (CoNLL).",
}
@inproceedings{che-etal-2019-hit,
    title = "{HIT}-{SCIR} at {MRP} 2019: A Unified Pipeline for Meaning Representation Parsing via Efficient Training and Effective Encoding",
    author = "Che, Wanxiang  and
      Dou, Longxu  and
      Xu, Yang  and
      Wang, Yuxuan  and
      Liu, Yijia  and
      Liu, Ting",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2007",
    doi = "10.18653/v1/K19-2007",
    pages = "76--85",
    abstract = "This paper describes our system (HIT-SCIR) for CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. We extended the basic transition-based parser with two improvements: a) Efficient Training by realizing Stack LSTM parallel training; b) Effective Encoding via adopting deep contextualized word embeddings BERT. Generally, we proposed a unified pipeline to meaning representation parsing, including framework-specific transition-based parsers, BERT-enhanced word representation, and post-processing. In the final evaluation, our system was ranked first according to ALL-F1 (86.2{\%}) and especially ranked first in UCCA framework (81.67{\%}).",
}
@inproceedings{bai-zhao-2019-sjtu,
    title = "{SJTU} at {MRP} 2019: A Transition-Based Multi-Task Parser for Cross-Framework Meaning Representation Parsing",
    author = "Bai, Hongxiao  and
      Zhao, Hai",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2008",
    doi = "10.18653/v1/K19-2008",
    pages = "86--94",
    abstract = "This paper describes the system of our team SJTU for our participation in the CoNLL 2019 Shared Task: Cross-Framework Meaning Representation Parsing. The goal of the task is to advance data-driven parsing into graph-structured representations of sentence meaning. This task includes five meaning representation frameworks: DM, PSD, EDS, UCCA, and AMR. These frameworks have different properties and structures. To tackle all the frameworks in one model, it is needed to find out the commonality of them. In our work, we define a set of the transition actions to once-for-all tackle all the frameworks and train a transition-based model to parse the meaning representation. The adopted multi-task model also can allow learning for one framework to benefit the others. In the final official evaluation of the shared task, our system achieves 42{\%} F1 unified MRP metric score.",
}
@inproceedings{na-etal-2019-jbnu,
    title = "{JBNU} at {MRP} 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing",
    author = "Na, Seung-Hoon  and
      Min, Jinwoon  and
      Park, Kwanghyeon  and
      Shin, Jong-Hun  and
      Kim, Young-Kil",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2009",
    doi = "10.18653/v1/K19-2009",
    pages = "95--103",
    abstract = "This paper describes Jeonbuk National University (JBNU){'}s system for the 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP 2019) at the Conference on Computational Natural Language Learning. Of the five frameworks, we address only the DELPH-IN MRS Bi-Lexical Dependencies (DP), Prague Semantic Dependencies (PSD), and Universal Conceptual Cognitive Annotation (UCCA) frameworks. We propose a unified parsing model using biaffine attention (Dozat and Manning, 2017), consisting of 1) a BERT-BiLSTM encoder and 2) a biaffine attention decoder. First, the BERT-BiLSTM for sentence encoder uses BERT to compose a sentence{'}s wordpieces into word-level embeddings and subsequently applies BiLSTM to word-level representations. Second, the biaffine attention decoder determines the scores for an edge{'}s existence and its labels based on biaffine attention functions between roledependent representations. We also present multi-level biaffine attention models by combining all the role-dependent representations that appear at multiple intermediate layers.",
}
@inproceedings{lai-etal-2019-cuhk,
    title = "{CUHK} at {MRP} 2019: Transition-Based Parser with Cross-Framework Variable-Arity Resolve Action",
    author = "Lai, Sunny  and
      Lo, Chun Hei  and
      Leung, Kwong Sak  and
      Leung, Yee",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2010",
    doi = "10.18653/v1/K19-2010",
    pages = "104--113",
    abstract = "This paper describes our system (RESOLVER) submitted to the CoNLL 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP). Our system implements a transition-based parser with a directed acyclic graph (DAG) to tree preprocessor and a novel cross-framework variable-arity resolve action that generalizes over five different representations. Although we ranked low in the competition, we have shown the current limitations and potentials of including variable-arity action in MRP and concluded with directions for improvements in the future.",
}
@inproceedings{koreeda-etal-2019-hitachi,
    title = "Hitachi at {MRP} 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing",
    author = "Koreeda, Yuta  and
      Morio, Gaku  and
      Morishita, Terufumi  and
      Ozaki, Hiroaki  and
      Yanai, Kohsuke",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2011",
    doi = "10.18653/v1/K19-2011",
    pages = "114--126",
    abstract = "This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for five frameworks, each with different order of {``}abstraction{''} from input tokens. We proposed a unified encoder-to-biaffine network for all five frameworks, which effectively incorporates a shared encoder to extract rich input features, decoder networks to generate anchorless nodes in UCCA and AMR, and biaffine networks to predict edges. Our system was ranked fifth with the macro-averaged MRP F1 score of 0.7604, and outperformed the baseline unified transition-based MRP. Furthermore, post-evaluation experiments showed that we can boost the performance of the proposed system by incorporating multi-task learning, whereas the baseline could not. These imply efficacy of incorporating the biaffine network to the shared architecture for MRP and that learning heterogeneous meaning representations at once can boost the system performance.",
}
@inproceedings{straka-strakova-2019-ufal,
    title = "{{\'U}FAL} {MRP}ipe at {MRP} 2019: {UDP}ipe Goes Semantic in the Meaning Representation Parsing Shared Task",
    author = "Straka, Milan  and
      Strakov{\'a}, Jana",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2012",
    doi = "10.18653/v1/K19-2012",
    pages = "127--137",
    abstract = "We present a system description of our contribution to the CoNLL 2019 shared task, CrossFramework Meaning Representation Parsing (MRP 2019). The proposed architecture is our first attempt towards a semantic parsing extension of the UDPipe 2.0, a lemmatization, POS tagging and dependency parsing pipeline. For the MRP 2019, which features five formally and linguistically different approaches to meaning representation (DM, PSD, EDS, UCCA and AMR), we propose a uniform, language and framework agnostic graph-tograph neural network architecture. Without any knowledge about the graph structure, and specifically without any linguistically or framework motivated features, our system implicitly models the meaning representation graphs. After fixing a human error (we used earlier incorrect version of provided test set analyses), our submission would score third in the competition evaluation. The source code of our system is available at https://github.com/ufal/mrpipe-conll2019.",
}
@inproceedings{cao-etal-2019-amazon,
    title = "{A}mazon at {MRP} 2019: Parsing Meaning Representations with Lexical and Phrasal Anchoring",
    author = "Cao, Jie  and
      Zhang, Yi  and
      Youssef, Adel  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2013",
    doi = "10.18653/v1/K19-2013",
    pages = "138--148",
    abstract = "This paper describes the system submission of our team Amazon to the shared task on Cross Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Via extensive analysis of implicit alignments in AMR, we recategorize five meaning representations (MRs) into two classes: Lexical- Anchoring and Phrasal-Anchoring. Then we propose a unified graph-based parsing framework for the lexical-anchoring MRs, and a phrase-structure parsing for one of the phrasal- anchoring MRs, UCCA. Our system submission ranked 1st in the AMR subtask, and later improvements show promising results on other frameworks as well.",
}
@inproceedings{zhang-etal-2019-suda,
    title = "{SUDA}-{A}libaba at {MRP} 2019: Graph-Based Models with {BERT}",
    author = "Zhang, Yue  and
      Jiang, Wei  and
      Xia, Qingrong  and
      Cao, Junjie  and
      Wang, Rui  and
      Li, Zhenghua  and
      Zhang, Min",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2014",
    doi = "10.18653/v1/K19-2014",
    pages = "149--157",
    abstract = "In this paper, we describe our participating systems in the shared task on Cross- Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). The task includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR. One common characteristic of our systems is that we employ graph-based methods instead of transition-based methods when predicting edges between nodes. For SDP, we jointly perform edge prediction, frame tagging, and POS tagging via multi-task learning (MTL). For UCCA, we also jointly model a constituent tree parsing and a remote edge recovery task. For both EDS and AMR, we produce nodes first and edges second in a pipeline fashion. External resources like BERT are found helpful for all frameworks except AMR. Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.",
}
@inproceedings{droganova-etal-2019-ufal,
    title = "{{\'U}FAL}-{O}slo at {MRP} 2019: Garage Sale Semantic Parsing",
    author = "Droganova, Kira  and
      Kutuzov, Andrey  and
      Mediankin, Nikita  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2015",
    doi = "10.18653/v1/K19-2015",
    pages = "158--165",
    abstract = "This paper describes the {\'U}FAL--Oslo system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP, Oepen et al. 2019). The submission is based on several third-party parsers. Within the official shared task results, the submission ranked 11th out of 13 participating systems.",
}
@inproceedings{chen-etal-2019-peking,
    title = "Peking at {MRP} 2019: Factorization- and Composition-Based Parsing for Elementary Dependency Structures",
    author = "Chen, Yufei  and
      Ye, Yajie  and
      Sun, Weiwei",
    booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-2016",
    doi = "10.18653/v1/K19-2016",
    pages = "166--176",
    abstract = "We design, implement and evaluate two semantic parsers, which represent factorization- and composition-based approaches respectively, for Elementary Dependency Structures (EDS) at the CoNLL 2019 Shared Task on Cross-Framework Meaning Representation Parsing. The detailed evaluation of the two parsers gives us a new perception about parsing into linguistically enriched meaning representations: current neural EDS parsers are able to reach an accuracy at the inter-annotator agreement level in the same-epoch-and-domain setup.",
}
@proceedings{conll-2019-natural,
    title = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    editor = "Bansal, Mohit  and
      Villavicencio, Aline",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1000",
}
@inproceedings{jumelet-etal-2019-analysing,
    title = "Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment",
    author = "Jumelet, Jaap  and
      Zuidema, Willem  and
      Hupkes, Dieuwke",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1001",
    doi = "10.18653/v1/K19-1001",
    pages = "1--11",
    abstract = "Extensive research has recently shown that recurrent neural language models are able to process a wide range of grammatical phenomena. How these models are able to perform these remarkable feats so well, however, is still an open question. To gain more insight into what information LSTMs base their decisions on, we propose a generalisation of Contextual Decomposition (GCD). In particular, this setup enables us to accurately distil which part of a prediction stems from semantic heuristics, which part truly emanates from syntactic cues and which part arise from the model biases themselves instead. We investigate this technique on tasks pertaining to syntactic agreement and co-reference resolution and discover that the model strongly relies on a default reasoning effect to perform these tasks.",
}
@inproceedings{zhu-sarkar-2019-deconstructing,
    title = "Deconstructing Supertagging into Multi-Task Sequence Prediction",
    author = "Zhu, Zhenqi  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1002",
    doi = "10.18653/v1/K19-1002",
    pages = "12--21",
    abstract = "Supertagging is a sequence prediction task where each word is assigned a piece of complex syntactic structure called a supertag. We provide a novel approach to multi-task learning for Tree Adjoining Grammar (TAG) supertagging by deconstructing these complex supertags in order to define a set of related but auxiliary sequence prediction tasks. Our multi-task prediction framework is trained over the exactly same training data used to train the original supertagger where each auxiliary task provides an alternative view on the original prediction task. Our experimental results show that our multi-task approach significantly improves TAG supertagging with a new state-of-the-art accuracy score of 91.39{\%} on the Penn Treebank supertagging dataset.",
}
@inproceedings{sakuma-yoshinaga-2019-multilingual,
    title = "Multilingual Model Using Cross-Task Embedding Projection",
    author = "Sakuma, Jin  and
      Yoshinaga, Naoki",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1003",
    doi = "10.18653/v1/K19-1003",
    pages = "22--32",
    abstract = "We present a method for applying a neural network trained on one (resource-rich) language for a given task to other (resource-poor) languages. We accomplish this by inducing a mapping from pre-trained cross-lingual word embeddings to the embedding layer of the neural network trained on the resource-rich language. To perform element-wise cross-task embedding projection, we invent locally linear mapping which assumes and preserves the local topology across the semantic spaces before and after the projection. Experimental results on topic classification task and sentiment analysis task showed that the fully task-specific multilingual model obtained using our method outperformed the existing multilingual models with embedding layers fixed to pre-trained cross-lingual word embeddings.",
}
@inproceedings{liu-etal-2019-investigating,
    title = "Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation",
    author = "Liu, Qianchu  and
      McCarthy, Diana  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1004",
    doi = "10.18653/v1/K19-1004",
    pages = "33--43",
    abstract = "In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models. We propose a novel and challenging task, Bilingual Token-level Sense Retrieval (BTSR). It specifically evaluates the accurate alignment of words with the same meaning in cross-lingual non-parallel contexts, currently not evaluated by existing tasks such as Bilingual Contextual Word Similarity and Sentence Retrieval. We show how the proposed BTSR task highlights the merits of different alignment methods. In particular, we find that using context average type-level alignment is effective in transferring monolingual contextualized embeddings cross-lingually especially in non-parallel contexts, and at the same time improves the monolingual space. Furthermore, aligning independently trained models yields better performance than aligning multilingual embeddings with shared vocabulary.",
}
@inproceedings{hu-etal-2019-large,
    title = "Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering",
    author = "Hu, J. Edward  and
      Singh, Abhinav  and
      Holzenberger, Nils  and
      Post, Matt  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1005",
    doi = "10.18653/v1/K19-1005",
    pages = "44--54",
    abstract = "Producing diverse paraphrases of a sentence is a challenging task. Natural paraphrase corpora are scarce and limited, while existing large-scale resources are automatically generated via back-translation and rely on beam search, which tends to lack diversity. We describe ParaBank 2, a new resource that contains multiple diverse sentential paraphrases, produced from a bilingual corpus using negative constraints, inference sampling, and clustering.We show that ParaBank 2 significantly surpasses prior work in both lexical and syntactic diversity while being meaning-preserving, as measured by human judgments and standardized metrics. Further, we illustrate how such paraphrastic resources may be used to refine contextualized encoders, leading to improvements in downstream tasks.",
}
@inproceedings{ilharco-etal-2019-large,
    title = "Large-Scale Representation Learning from Visually Grounded Untranscribed Speech",
    author = "Ilharco, Gabriel  and
      Zhang, Yuan  and
      Baldridge, Jason",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1006",
    doi = "10.18653/v1/K19-1006",
    pages = "55--65",
    abstract = "Systems that can associate images with their spoken audio captions are an important step towards visually grounded language learning. We describe a scalable method to automatically generate diverse audio for image captioning datasets. This supports pretraining deep networks for encoding both audio and images, which we do via a dual encoder that learns to align latent representations from both modalities. We show that a masked margin softmax loss for such models is superior to the standard triplet loss. We fine-tune these models on the Flickr8k Audio Captions Corpus and obtain state-of-the-art results{---}improving recall in the top 10 from 29.6{\%} to 49.5{\%}. We also obtain human ratings on retrieval outputs to better assess the impact of incidentally matching image-caption pairs that were not associated in the data, finding that automatic evaluation substantially underestimates the quality of the retrieved results.",
}
@inproceedings{prasad-etal-2019-using,
    title = "Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models",
    author = "Prasad, Grusha  and
      van Schijndel, Marten  and
      Linzen, Tal",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1007",
    doi = "10.18653/v1/K19-1007",
    pages = "66--76",
    abstract = "Neural language models (LMs) perform well on tasks that require sensitivity to syntactic structure. Drawing on the syntactic priming paradigm from psycholinguistics, we propose a novel technique to analyze the representations that enable such success. By establishing a gradient similarity metric between structures, this technique allows us to reconstruct the organization of the LMs{'} syntactic representational space. We use this technique to demonstrate that LSTM LMs{'} representations of different types of sentences with relative clauses are organized hierarchically in a linguistically interpretable manner, suggesting that the LMs track abstract properties of the sentence.",
}
@inproceedings{rabinovich-etal-2019-say,
    title = "Say Anything: Automatic Semantic Infelicity Detection in {L}2 {E}nglish Indefinite Pronouns",
    author = "Rabinovich, Ella  and
      Watson, Julia  and
      Beekhuizen, Barend  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1008",
    doi = "10.18653/v1/K19-1008",
    pages = "77--86",
    abstract = "Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level. We focus instead on acquisition of subtle semantic nuances of English indefinite pronouns by non-native speakers at varying levels of proficiency. We first lay out theoretical, linguistically motivated hypotheses, and supporting empirical evidence, on the nature of the challenges posed by indefinite pronouns to English learners. We then suggest and evaluate an automatic approach for detection of atypical usage patterns, demonstrating that deep learning architectures are promising for this task involving nuanced semantic anomalies.",
}
@inproceedings{nikolaus-etal-2019-compositional,
    title = "Compositional Generalization in Image Captioning",
    author = "Nikolaus, Mitja  and
      Abdou, Mostafa  and
      Lamm, Matthew  and
      Aralikatte, Rahul  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1009",
    doi = "10.18653/v1/K19-1009",
    pages = "87--98",
    abstract = "Image captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts. We study the problem of compositional generalization, which measures how well a model composes unseen combinations of concepts when describing images. State-of-the-art image captioning models show poor generalization performance on this task. We propose a multi-task model to address the poor performance, that combines caption generation and image{--}sentence ranking, and uses a decoding mechanism that re-ranks the captions according their similarity to the image. This model is substantially better at generalizing to unseen combinations of concepts compared to state-of-the-art captioning models.",
}
@inproceedings{azab-etal-2019-representing,
    title = "Representing Movie Characters in Dialogues",
    author = "Azab, Mahmoud  and
      Kojima, Noriyuki  and
      Deng, Jia  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1010",
    doi = "10.18653/v1/K19-1010",
    pages = "99--109",
    abstract = "We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other participants in the dialogue. We evaluate the performance of these new character embeddings on two tasks: (1) character relatedness, using a dataset we introduce consisting of a dense character interaction matrix for 4,378 unique character pairs over 22 hours of dialogue from eighteen movies; and (2) character relation classification, for fine- and coarse-grained relations, as well as sentiment relations. Our experiments show that our model significantly outperforms the traditional Word2Vec continuous bag-of-words and skip-gram models, demonstrating the effectiveness of the character embeddings we introduce. We further show how these embeddings can be used in conjunction with a visual question answering system to improve over previous results.",
}
@inproceedings{merlo-andueza-rodriguez-2019-cross,
    title = "Cross-Lingual Word Embeddings and the Structure of the Human Bilingual Lexicon",
    author = "Merlo, Paola  and
      Andueza Rodriguez, Maria",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1011",
    doi = "10.18653/v1/K19-1011",
    pages = "110--120",
    abstract = "Research on the bilingual lexicon has uncovered fascinating interactions between the lexicons of the native language and of the second language in bilingual speakers. In particular, it has been found that the lexicon of the underlying native language affects the organisation of the second language. In the spirit of interpreting current distributed representations, this paper investigates two models of cross-lingual word embeddings, comparing them to the shared-translation effect and the cross-lingual coactivation effects of false and true friends (cognates) found in humans. We find that the similarity structure of the cross-lingual word embeddings space yields the same effects as the human bilingual lexicon.",
}
@inproceedings{chen-etal-2019-federated,
    title = "Federated Learning of N-Gram Language Models",
    author = "Chen, Mingqing  and
      Suresh, Ananda Theertha  and
      Mathews, Rajiv  and
      Wong, Adeline  and
      Allauzen, Cyril  and
      Beaufays, Fran{\c{c}}oise  and
      Riley, Michael",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1012",
    doi = "10.18653/v1/K19-1012",
    pages = "121--130",
    abstract = "We propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to train global models for portable devices such as smart phones. Federated learning is especially relevant for applications handling privacy-sensitive data, such as virtual keyboards, because training is performed without the users{'} data ever leaving their devices. While the principles of federated learning are fairly generic, its methodology assumes that the underlying models are neural networks. However, virtual keyboards are typically powered by n-gram language models for latency reasons. We propose to train a recurrent neural network language model using the decentralized FederatedAveraging algorithm and to approximate this federated model server-side with an n-gram model that can be deployed to devices for fast inference. Our technical contributions include ways of handling large vocabularies, algorithms to correct capitalization errors in user data, and efficient finite state transducer algorithms to convert word language models to word-piece language models and vice versa. The n-gram language models trained with federated learning are compared to n-grams trained with traditional server-based algorithms using A/B tests on tens of millions of users of a virtual keyboard. Results are presented for two languages, American English and Brazilian Portuguese. This work demonstrates that high-quality n-gram language models can be trained directly on client mobile devices without sensitive training data ever leaving the devices.",
}
@inproceedings{alshaikh-etal-2019-learning,
    title = "Learning Conceptual Spaces with Disentangled Facets",
    author = "Alshaikh, Rana  and
      Bouraoui, Zied  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1013",
    doi = "10.18653/v1/K19-1013",
    pages = "131--139",
    abstract = "Conceptual spaces are geometric representations of meaning that were proposed by G ̈ardenfors (2000). They share many similarities with the vector space embeddings that are commonly used in natural language processing. However, rather than representing entities in a single vector space, conceptual spaces are usually decomposed into several facets, each of which is then modelled as a relatively low dimensional vector space. Unfortunately, the problem of learning such conceptual spaces has thus far only received limited attention. To address this gap, we analyze how, and to what extent, a given vector space embedding can be decomposed into meaningful facets in an unsupervised fashion. While this problem is highly challenging, we show that useful facets can be discovered by relying on word embeddings to group semantically related features.",
}
@inproceedings{gorman-etal-2019-weird,
    title = "Weird Inflects but {OK}: Making Sense of Morphological Generation Errors",
    author = "Gorman, Kyle  and
      McCarthy, Arya D.  and
      Cotterell, Ryan  and
      Vylomova, Ekaterina  and
      Silfverberg, Miikka  and
      Markowska, Magdalena",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1014",
    doi = "10.18653/v1/K19-1014",
    pages = "140--151",
    abstract = "We conduct a manual error analysis of the CoNLL-SIGMORPHON Shared Task on Morphological Reinflection. This task involves natural language generation: systems are given a word in citation form (e.g., hug) and asked to produce the corresponding inflected form (e.g., the simple past hugged). We propose an error taxonomy and use it to annotate errors made by the top two systems across twelve languages. Many of the observed errors are related to inflectional patterns sensitive to inherent linguistic properties such as animacy or affect; many others are failures to predict truly unpredictable inflectional behaviors. We also find nearly one quarter of the residual {``}errors{''} reflect errors in the gold data.",
}
@inproceedings{chen-etal-2019-learning-represent,
    title = "Learning to Represent Bilingual Dictionaries",
    author = "Chen, Muhao  and
      Tian, Yingtao  and
      Chen, Haochen  and
      Chang, Kai-Wei  and
      Skiena, Steven  and
      Zaniolo, Carlo",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1015",
    doi = "10.18653/v1/K19-1015",
    pages = "152--162",
    abstract = "Bilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages. However, the cross-lingual correspondence between sentences and words is less studied, despite that this correspondence can significantly benefit many applications such as crosslingual semantic search and textual inference. To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries. The proposed model is trained to map the lexical definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques. To enhance the learning process on limited resources, our model adopts several critical learning strategies, including multi-task learning on different bridges of languages, and joint learning of the dictionary model with a bilingual word embedding model. We conduct experiments on two new tasks. In the cross-lingual reverse dictionary retrieval task, we demonstrate that our model is capable of comprehending bilingual concepts based on descriptions, and the proposed learning strategies are effective. In the bilingual paraphrase identification task, we show that our model effectively associates sentences in different languages via a shared embedding space, and outperforms existing approaches in identifying bilingual paraphrases.",
}
@inproceedings{tong-etal-2019-improving,
    title = "Improving Natural Language Understanding by Reverse Mapping Bytepair Encoding",
    author = "Tong, Chaodong  and
      Peng, Huailiang  and
      Dai, Qiong  and
      Jiang, Lei  and
      Huang, Jianghua",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1016",
    doi = "10.18653/v1/K19-1016",
    pages = "163--173",
    abstract = "We propose a method called reverse mapping bytepair encoding, which maps named-entity information and other word-level linguistic features back to subwords during the encoding procedure of bytepair encoding (BPE). We employ this method to the Generative Pre-trained Transformer (OpenAI GPT) by adding a weighted linear layer after the embedding layer. We also propose a new model architecture named as the multi-channel separate transformer to employ a training process without parameter-sharing. Evaluation on Stories Cloze, RTE, SciTail and SST-2 datasets demonstrates the effectiveness of our approach.",
}
@inproceedings{prange-etal-2019-made,
    title = "Made for Each Other: Broad-Coverage Semantic Structures Meet Preposition Supersenses",
    author = "Prange, Jakob  and
      Schneider, Nathan  and
      Abend, Omri",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1017",
    doi = "10.18653/v1/K19-1017",
    pages = "174--185",
    abstract = "Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles. We argue that lexicon-free annotation of the semantic roles marked by prepositions, as formulated by Schneider et al. (2018), is complementary and suitable for integration within UCCA. We show empirically for English that the schemes, though annotated independently, are compatible and can be combined in a single semantic graph. A comparison of several approaches to parsing the integrated representation lays the groundwork for future research on this task.",
}
@inproceedings{rosin-radinsky-2019-generating,
    title = "Generating Timelines by Modeling Semantic Change",
    author = "Rosin, Guy D.  and
      Radinsky, Kira",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1018",
    doi = "10.18653/v1/K19-1018",
    pages = "186--195",
    abstract = "Though languages can evolve slowly, they can also react strongly to dramatic world events. By studying the connection between words and events, it is possible to identify which events change our vocabulary and in what way. In this work, we tackle the task of creating timelines - records of historical {``}turning points{''}, represented by either words or events, to understand the dynamics of a target word. Our approach identifies these points by leveraging both static and time-varying word embeddings to measure the influence of words and events. In addition to quantifying changes, we show how our technique can help isolate semantic changes. Our qualitative and quantitative evaluations show that we are able to capture this semantic change and event influence.",
}
@inproceedings{rozen-etal-2019-diversify,
    title = "Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets",
    author = "Rozen, Ohad  and
      Shwartz, Vered  and
      Aharoni, Roee  and
      Dagan, Ido",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1019",
    doi = "10.18653/v1/K19-1019",
    pages = "196--205",
    abstract = "Phenomenon-specific {``}adversarial{''} datasets have been recently designed to perform targeted stress-tests for particular inference types. Recent work (Liu et al., 2019a) proposed that such datasets can be utilized for training NLI and other types of models, often allowing to learn the phenomenon in focus and improve on the challenge dataset, indicating a {``}blind spot{''} in the original training data. Yet, although a model can improve in such a training process, it might still be vulnerable to other challenge datasets targeting the same phenomenon but drawn from a different distribution, such as having a different syntactic complexity level. In this work, we extend this method to drive conclusions about a model{'}s ability to learn and generalize a target phenomenon rather than to {``}learn{''} a dataset, by controlling additional aspects in the adversarial datasets. We demonstrate our approach on two inference phenomena {--} dative alternation and numerical reasoning, elaborating, and in some cases contradicting, the results of Liu et al.. Our methodology enables building better challenge datasets for creating more robust models, and may yield better model understanding and subsequent overarching improvements.",
}
@inproceedings{lo-simard-2019-fully,
    title = "Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on {BERT} for Identifying Parallel Data",
    author = "Lo, Chi-kiu  and
      Simard, Michel",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1020",
    doi = "10.18653/v1/K19-1020",
    pages = "206--215",
    abstract = "We present a fully unsupervised crosslingual semantic textual similarity (STS) metric, based on contextual embeddings extracted from BERT {--} Bidirectional Encoder Representations from Transformers (Devlin et al., 2019). The goal of crosslingual STS is to measure to what degree two segments of text in different languages express the same meaning. Not only is it a key task in crosslingual natural language understanding (XLU), it is also particularly useful for identifying parallel resources for training and evaluating downstream multilingual natural language processing (NLP) applications, such as machine translation. Most previous crosslingual STS methods relied heavily on existing parallel resources, thus leading to a circular dependency problem. With the advent of massively multilingual context representation models such as BERT, which are trained on the concatenation of non-parallel data from each language, we show that the deadlock around parallel resources can be broken. We perform intrinsic evaluations on crosslingual STS data sets and extrinsic evaluations on parallel corpus filtering and human translation equivalence assessment tasks. Our results show that the unsupervised crosslingual STS metric using BERT without fine-tuning achieves performance on par with supervised or weakly supervised approaches.",
}
@inproceedings{zhu-etal-2019-importance,
    title = "On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages",
    author = "Zhu, Yi  and
      Heinzerling, Benjamin  and
      Vuli{\'c}, Ivan  and
      Strube, Michael  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1021",
    doi = "10.18653/v1/K19-1021",
    pages = "216--226",
    abstract = "Recent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value should be even more pronounced in low-data regimes. In this work, we therefore provide a comprehensive analysis focused on the usefulness of subwords for word representation learning in truly low-resource scenarios and for three representative morphological tasks: fine-grained entity typing, morphological tagging, and named entity recognition. We conduct a systematic study that spans several dimensions of comparison: 1) type of data scarcity which can stem from the lack of task-specific training data, or even from the lack of unannotated data required to train word embeddings, or both; 2) language type by working with a sample of 16 typologically diverse languages including some truly low-resource ones (e.g. Rusyn, Buryat, and Zulu); 3) the choice of the subword-informed word representation method. Our main results show that subword-informed models are universally useful across all language types, with large gains over subword-agnostic embeddings. They also suggest that the effective use of subwords largely depends on the language (type) and the task at hand, as well as on the amount of available data for training the embeddings and task-based models, where having sufficient in-task data is a more critical requirement.",
}
@inproceedings{matthews-etal-2019-comparing,
    title = "Comparing Top-Down and Bottom-Up Neural Generative Dependency Models",
    author = "Matthews, Austin  and
      Neubig, Graham  and
      Dyer, Chris",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1022",
    doi = "10.18653/v1/K19-1022",
    pages = "227--237",
    abstract = "Recurrent neural network grammars generate sentences using phrase-structure syntax and perform very well on both parsing and language modeling. To explore whether generative dependency models are similarly effective, we propose two new generative models of dependency syntax. Both models use recurrent neural nets to avoid making explicit independence assumptions, but they differ in the order used to construct the trees: one builds the tree bottom-up and the other top-down, which profoundly changes the estimation problem faced by the learner. We evaluate the two models on three typologically different languages: English, Arabic, and Japanese. While both generative models improve parsing performance over a discriminative baseline, they are significantly less effective than non-syntactic LSTM language models. Surprisingly, little difference between the construction orders is observed for either parsing or language modeling.",
}
@inproceedings{le-roux-etal-2019-representation,
    title = "Representation Learning and Dynamic Programming for Arc-Hybrid Parsing",
    author = "Le Roux, Joseph  and
      Rozenknop, Antoine  and
      Lacroix, Mathieu",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1023",
    doi = "10.18653/v1/K19-1023",
    pages = "238--248",
    abstract = "We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.",
}
@inproceedings{abercrombie-etal-2019-policy,
    title = "Policy Preference Detection in Parliamentary Debate Motions",
    author = "Abercrombie, Gavin  and
      Nanni, Federico  and
      Batista-Navarro, Riza  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1024",
    doi = "10.18653/v1/K19-1024",
    pages = "249--259",
    abstract = "Debate motions (proposals) tabled in the UK Parliament contain information about the stated policy preferences of the Members of Parliament who propose them, and are key to the analysis of all subsequent speeches given in response to them. We attempt to automatically label debate motions with codes from a pre-existing coding scheme developed by political scientists for the annotation and analysis of political parties{'} manifestos. We develop annotation guidelines for the task of applying these codes to debate motions at two levels of granularity and produce a dataset of manually labelled examples. We evaluate the annotation process and the reliability and utility of the labelling scheme, finding that inter-annotator agreement is comparable with that of other studies conducted on manifesto data. Moreover, we test a variety of ways of automatically labelling motions with the codes, ranging from similarity matching to neural classification methods, and evaluate them against the gold standard labels. From these experiments, we note that established supervised baselines are not always able to improve over simple lexical heuristics. At the same time, we detect a clear and evident benefit when employing BERT, a state-of-the-art deep language representation model, even in classification scenarios with over 30 different labels and limited amounts of training data.",
}
@inproceedings{shi-etal-2019-improving,
    title = "Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning",
    author = "Shi, Xuewen  and
      Huang, Heyan  and
      Wang, Wenguan  and
      Jian, Ping  and
      Tang, Yi-Kun",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1025",
    doi = "10.18653/v1/K19-1025",
    pages = "260--270",
    abstract = "Neural Machine Translation (NMT) optimized by Maximum Likelihood Estimation (MLE) lacks the guarantee of translation adequacy. To alleviate this problem, we propose an NMT approach that heightens the adequacy in machine translation by transferring the semantic knowledge learned from bilingual sentence alignment. Specifically, we first design a discriminator that learns to estimate sentence aligning score over translation candidates, and then the learned semantic knowledge is transfered to the NMT model under an adversarial learning framework. We also propose a gated self-attention based encoder for sentence embedding. Furthermore, an N-pair training loss is introduced in our framework to aid the discriminator in better capturing lexical evidence in translation candidates. Experimental results show that our proposed method outperforms baseline NMT models on Chinese-to-English and English-to-German translation tasks. Further analysis also indicates the detailed semantic knowledge transfered from the discriminator to the NMT model.",
}
@inproceedings{winata-etal-2019-code,
    title = "Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences",
    author = "Winata, Genta Indra  and
      Madotto, Andrea  and
      Wu, Chien-Sheng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1026",
    doi = "10.18653/v1/K19-1026",
    pages = "271--280",
    abstract = "Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure. Linguistic constraint theories have been used for decades to generate artificial code-switching sentences to cope with this issue. However, this require external word alignments or constituency parsers that create erroneous results on distant languages. We propose a sequence-to-sequence model using a copy mechanism to generate code-switching data by leveraging parallel monolingual translations from a limited source of code-switching data. The model learns how to combine words from parallel sentences and identifies when to switch one language to the other. Moreover, it captures code-switching constraints by attending and aligning the words in inputs, without requiring any external knowledge. Based on experimental results, the language model trained with the generated sentences achieves state-of-the-art performance and improves end-to-end automatic speech recognition.",
}
@inproceedings{wei-etal-2019-unsupervised,
    title = "Unsupervised Neural Machine Translation with Future Rewarding",
    author = "Wei, Xiangpeng  and
      Hu, Yue  and
      Xing, Luxi  and
      Gao, Li",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1027",
    doi = "10.18653/v1/K19-1027",
    pages = "281--290",
    abstract = "In this paper, we alleviate the local optimality of back-translation by learning a policy (takes the form of an encoder-decoder and is defined by its parameters) with future rewarding under the reinforcement learning framework, which aims to optimize the global word predictions for unsupervised neural machine translation. To this end, we design a novel reward function to characterize high-quality translations from two aspects: n-gram matching and semantic adequacy. The n-gram matching is defined as an alternative for the discrete BLEU metric, and the semantic adequacy is used to measure the adequacy of conveying the meaning of the source sentence to the target. During training, our model strives for earning higher rewards by learning to produce grammatically more accurate and semantically more adequate translations. Besides, a variational inference network (VIN) is proposed to constrain the corresponding sentences in two languages have the same or similar latent semantic code. On the widely used WMT{'}14 English-French, WMT{'}16 English-German and NIST Chinese-to-English benchmarks, our models respectively obtain 27.59/27.15, 19.65/23.42 and 22.40 BLEU points without using any labeled data, demonstrating consistent improvements over previous unsupervised NMT models.",
}
@inproceedings{choshen-abend-2019-automatically,
    title = "Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation",
    author = "Choshen, Leshem  and
      Abend, Omri",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1028",
    doi = "10.18653/v1/K19-1028",
    pages = "291--303",
    abstract = "We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We therefore propose an automatic approach for extracting challenge sets rich with long-distance dependencies, and argue that evaluation using this methodology provides a complementary perspective on system performance. To support our claim, we compile challenge sets for English-German and German-English, which are much larger than any previously released challenge set for MT. The extracted sets are large enough to allow reliable automatic evaluation, which makes the proposed approach a scalable and practical solution for evaluating MT performance on the long-tail of syntactic phenomena.",
}
@inproceedings{mulcaire-etal-2019-low,
    title = "Low-Resource Parsing with Crosslingual Contextualized Representations",
    author = "Mulcaire, Phoebe  and
      Kasai, Jungo  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1029",
    doi = "10.18653/v1/K19-1029",
    pages = "304--315",
    abstract = "Despite advances in dependency parsing, languages with small treebanks still present challenges. We assess recent approaches to multilingual contextual word representations (CWRs), and compare them for crosslingual transfer from a language with a large treebank to a language with a small or nonexistent treebank, by sharing parameters between languages in the parser itself. We experiment with a diverse selection of languages in both simulated and truly low-resource scenarios, and show that multilingual CWRs greatly facilitate low-resource dependency parsing even without crosslingual supervision such as dictionaries or parallel text. Furthermore, we examine the non-contextual part of the learned language models (which we call a {``}decontextual probe{''}) to demonstrate that polyglot language models better encode crosslingual lexical correspondence compared to aligned monolingual language models. This analysis provides further evidence that polyglot training is an effective approach to crosslingual transfer.",
}
@inproceedings{wang-etal-2019-improving,
    title = "Improving Pre-Trained Multilingual Model with Vocabulary Expansion",
    author = "Wang, Hai  and
      Yu, Dian  and
      Sun, Kai  and
      Chen, Jianshu  and
      Yu, Dong",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1030",
    doi = "10.18653/v1/K19-1030",
    pages = "316--327",
    abstract = "Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language. Instead of exhaustively pre-training monolingual language models independently, an alternative solution is to pre-train a powerful multilingual deep language model over large-scale corpora in hundreds of languages. However, the vocabulary size for each language in such a model is relatively small, especially for low-resource languages. This limitation inevitably hinders the performance of these multilingual models on tasks such as sequence labeling, wherein in-depth token-level or sentence-level understanding is essential. In this paper, inspired by previous methods designed for monolingual settings, we investigate two approaches (i.e., joint mapping and mixture mapping) based on a pre-trained multilingual model BERT for addressing the out-of-vocabulary (OOV) problem on a variety of tasks, including part-of-speech tagging, named entity recognition, machine translation quality estimation, and machine reading comprehension. Experimental results show that using mixture mapping is more promising. To the best of our knowledge, this is the first work that attempts to address and discuss the OOV issue in multilingual settings.",
}
@inproceedings{neishi-yoshinaga-2019-relation,
    title = "On the Relation between Position Information and Sentence Length in Neural Machine Translation",
    author = "Neishi, Masato  and
      Yoshinaga, Naoki",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1031",
    doi = "10.18653/v1/K19-1031",
    pages = "328--338",
    abstract = "Long sentences have been one of the major challenges in neural machine translation (NMT). Although some approaches such as the attention mechanism have partially remedied the problem, we found that the current standard NMT model, Transformer, has difficulty in translating long sentences compared to the former standard, Recurrent Neural Network (RNN)-based model. One of the key differences of these NMT models is how the model handles position information which is essential to process sequential data. In this study, we focus on the position information type of NMT models, and hypothesize that relative position is better than absolute position. To examine the hypothesis, we propose RNN-Transformer which replaces positional encoding layer of Transformer by RNN, and then compare RNN-based model and four variants of Transformer. Experiments on ASPEC English-to-Japanese and WMT2014 English-to-German translation tasks demonstrate that relative position helps translating sentences longer than those in the training data. Further experiments on length-controlled training data reveal that absolute position actually causes overfitting to the sentence length.",
}
@inproceedings{havard-etal-2019-word,
    title = "Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech",
    author = "Havard, William N.  and
      Chevrot, Jean-Pierre  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1032",
    doi = "10.18653/v1/K19-1032",
    pages = "339--348",
    abstract = "In this paper, we study how word-like units are represented and activated in a recurrent neural model of visually grounded speech. The model used in our experiments is trained to project an image and its spoken description in a common representation space. We show that a recurrent model trained on spoken sentences implicitly segments its input into word-like units and reliably maps them to their correct visual referents. We introduce a methodology originating from linguistics to analyse the representation learned by neural networks {--} the gating paradigm {--} and show that the correct representation of a word is only activated if the network has access to first phoneme of the target word, suggesting that the network does not rely on a global acoustic pattern. Furthermore, we find out that not all speech frames (MFCC vectors in our case) play an equal role in the final encoded representation of a given word, but that some frames have a crucial effect on it. Finally we suggest that word representation could be activated through a process of lexical competition.",
}
@inproceedings{ravichander-etal-2019-equate,
    title = "{EQUATE}: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference",
    author = "Ravichander, Abhilasha  and
      Naik, Aakanksha  and
      Rose, Carolyn  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1033",
    doi = "10.18653/v1/K19-1033",
    pages = "349--361",
    abstract = "Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle. We present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual Entailment), a new framework for quantitative reasoning in textual entailment. We benchmark the performance of 9 published NLI models on EQUATE, and find that on average, state-of-the-art methods do not achieve an absolute improvement over a majority-class baseline, suggesting that they do not implicitly learn to reason with quantities. We establish a new baseline Q-REAS that manipulates quantities symbolically. In comparison to the best performing NLI model, it achieves success on numerical reasoning tests (+24.2 {\%}), but has limited verbal reasoning capabilities (-8.1 {\%}). We hope our evaluation framework will support the development of models of quantitative reasoning in language understanding.",
}
@inproceedings{stowe-etal-2019-linguistic,
    title = "Linguistic Analysis Improves Neural Metaphor Detection",
    author = "Stowe, Kevin  and
      Moeller, Sarah  and
      Michaelis, Laura  and
      Palmer, Martha",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1034",
    doi = "10.18653/v1/K19-1034",
    pages = "362--371",
    abstract = "In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks. However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications. We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification. This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.",
}
@inproceedings{ahmad-etal-2019-cross,
    title = "Cross-Lingual Dependency Parsing with Unlabeled Auxiliary Languages",
    author = "Ahmad, Wasi Uddin  and
      Zhang, Zhisong  and
      Ma, Xuezhe  and
      Chang, Kai-Wei  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1035",
    doi = "10.18653/v1/K19-1035",
    pages = "372--382",
    abstract = "Cross-lingual transfer learning has become an important weapon to battle the unavailability of annotated resources for low-resource languages. One of the fundamental techniques to transfer across languages is learning \textit{language-agnostic} representations, in the form of word embeddings or contextual encodings. In this work, we propose to leverage unannotated sentences from auxiliary languages to help learning language-agnostic representations. Specifically, we explore adversarial training for learning contextual encoders that produce invariant representations across languages to facilitate cross-lingual transfer. We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages. Experiments on 28 target languages demonstrate that adversarial training significantly improves the overall transfer performances under several different settings. We conduct a careful analysis to evaluate the language-agnostic representations resulted from adversarial training.",
}
@inproceedings{li-etal-2019-dual,
    title = "A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification",
    author = "Li, Ruizhe  and
      Lin, Chenghua  and
      Collinson, Matthew  and
      Li, Xiao  and
      Chen, Guanyi",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1036",
    doi = "10.18653/v1/K19-1036",
    pages = "383--392",
    abstract = "Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets.",
}
@inproceedings{dieter-etal-2019-mimic,
    title = "Mimic and Rephrase: Reflective Listening in Open-Ended Dialogue",
    author = "Dieter, Justin  and
      Wang, Tian  and
      Chaganty, Arun Tejasvi  and
      Angeli, Gabor  and
      Chang, Angel X.",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1037",
    doi = "10.18653/v1/K19-1037",
    pages = "393--403",
    abstract = "Reflective listening{--}demonstrating that you have heard your conversational partner{--}is key to effective communication. Expert human communicators often mimic and rephrase their conversational partner, e.g., when responding to sentimental stories or to questions they don{'}t know the answer to. We introduce a new task and an associated dataset wherein dialogue agents similarly mimic and rephrase a user{'}s request to communicate sympathy (I{'}m sorry to hear that) or lack of knowledge (I do not know that). We study what makes a rephrasal response good against a set of qualitative metrics. We then evaluate three models for generating responses: a syntax-aware rule-based system, a seq2seq LSTM neural models with attention (S2SA), and the same neural model augmented with a copy mechanism (S2SA+C). In a human evaluation, we find that S2SA+C and the rule-based system are comparable and approach human-generated response quality. In addition, experiences with a live deployment of S2SA+C in a customer support setting suggest that this generation task is a practical contribution to real world conversational agents.",
}
@inproceedings{gao-etal-2019-automated,
    title = "Automated Pyramid Summarization Evaluation",
    author = "Gao, Yanjun  and
      Sun, Chen  and
      Passonneau, Rebecca J.",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1038",
    doi = "10.18653/v1/K19-1038",
    pages = "404--418",
    abstract = "Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts. A pyramid lists the distinct units of content found in several reference summaries, weights content units by how many reference summaries they occur in, and produces three scores based on the weighted content of new summaries. We present an automated method that is more efficient, more transparent, and more complete than previous automated pyramid methods. It is tested on a new dataset of student summaries, and historical NIST data from extractive summarizers.",
}
@inproceedings{hessel-etal-2019-case,
    title = "A Case Study on Combining {ASR} and Visual Features for Generating Instructional Video Captions",
    author = "Hessel, Jack  and
      Pang, Bo  and
      Zhu, Zhenhai  and
      Soricut, Radu",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1039",
    doi = "10.18653/v1/K19-1039",
    pages = "419--429",
    abstract = "Instructional videos get high-traffic on video sharing platforms, and prior work suggests that providing time-stamped, subtask annotations (e.g., {``}heat the oil in the pan{''}) improves user experiences. However, current automatic annotation methods based on visual features alone perform only slightly better than constant prediction. Taking cues from prior work, we show that we can improve performance significantly by considering automatic speech recognition (ASR) tokens as input. Furthermore, jointly modeling ASR tokens and visual features results in higher performance compared to training individually on either modality. We find that unstated background information is better explained by visual features, whereas fine-grained distinctions (e.g., {``}add oil{''} vs. {``}add olive oil{''}) are disambiguated more easily via ASR tokens.",
}
@inproceedings{roy-etal-2019-leveraging,
    title = "Leveraging Past References for Robust Language Grounding",
    author = "Roy, Subhro  and
      Noseworthy, Michael  and
      Paul, Rohan  and
      Park, Daehyung  and
      Roy, Nicholas",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1040",
    doi = "10.18653/v1/K19-1040",
    pages = "430--440",
    abstract = "Grounding referring expressions to objects in an environment has traditionally been considered a one-off, ahistorical task. However, in realistic applications of grounding, multiple users will repeatedly refer to the same set of objects. As a result, past referring expressions for objects can provide strong signals for grounding subsequent referring expressions. We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object. The network combines information from vision and past referring expressions to resolve which object is being referred to. Our experiments show that detecting referring expression coreference is an effective way to ground objects described by subtle visual properties, which standard visual grounding models have difficulty capturing. We also show the ability to detect object coreference allows the grounding model to perform well even when it encounters object categories not seen in the training data.",
}
@inproceedings{amac-etal-2019-procedural,
    title = "Procedural Reasoning Networks for Understanding Multimodal Procedures",
    author = "Amac, Mustafa Sercan  and
      Yagcioglu, Semih  and
      Erdem, Aykut  and
      Erdem, Erkut",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1041",
    doi = "10.18653/v1/K19-1041",
    pages = "441--451",
    abstract = "This paper addresses the problem of comprehending procedural commonsense knowledge. This is a challenging task as it requires identifying key entities, keeping track of their state changes, and understanding temporal and causal relations. Contrary to most of the previous work, in this study, we do not rely on strong inductive bias and explore the question of how multimodality can be exploited to provide a complementary semantic signal. Towards this end, we introduce a new entity-aware neural comprehension model augmented with external relational memory units. Our model learns to dynamically update entity states in relation to each other while reading the text instructions. Our experimental analysis on the visual reasoning tasks in the recently proposed RecipeQA dataset reveals that our approach improves the accuracy of the previously reported models by a large margin. Moreover, we find that our model learns effective dynamic representations of entities even though we do not use any supervision at the level of entity states.",
}
@inproceedings{koshorek-etal-2019-limits,
    title = "On the Limits of Learning to Actively Learn Semantic Representations",
    author = "Koshorek, Omri  and
      Stanovsky, Gabriel  and
      Zhou, Yichu  and
      Srikumar, Vivek  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1042",
    doi = "10.18653/v1/K19-1042",
    pages = "452--462",
    abstract = "One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively-learn(LTAL) is a recent paradigm for reducing the amount of labeled data by learning a policy that selects which samples should be labeled. In this work, we examine LTAL for learning semantic representations, such as QA-SRL. We show that even an oracle policy that is allowed to pick examples that maximize performance on the test set (and constitutes an upper bound on the potential of LTAL), does not substantially improve performance compared to a random policy. We investigate factors that could explain this finding and show that a distinguishing characteristic of successful applications of LTAL is the interaction between optimization and the oracle policy selection process. In successful applications of LTAL, the examples selected by the oracle policy do not substantially depend on the optimization procedure, while in our setup the stochastic nature of optimization strongly affects the examples selected by the oracle. We conclude that the current applicability of LTAL for improving data efficiency in learning semantic meaning representations is limited.",
}
@inproceedings{gonen-etal-2019-grammatical,
    title = "How Does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?",
    author = "Gonen, Hila  and
      Kementchedjhieva, Yova  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1043",
    doi = "10.18653/v1/K19-1043",
    pages = "463--471",
    abstract = "Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun{'}s gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While {``}embedding debiasing{''} methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words{'} context when training word embeddings is effective in removing it. Fixing the grammatical gender bias yields a positive effect on the quality of the resulting word embeddings, both in monolingual and cross-lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.",
}
@inproceedings{schumann-rehbein-2019-active,
    title = "Active Learning via Membership Query Synthesis for Semi-Supervised Sentence Classification",
    author = "Schumann, Raphael  and
      Rehbein, Ines",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1044",
    doi = "10.18653/v1/K19-1044",
    pages = "472--481",
    abstract = "Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers. For NLP tasks, pool-based and stream-based sampling techniques have been used to select new instances for AL while gen erating new, artificial instances via Membership Query Synthesis was, up to know, considered to be infeasible for NLP problems. We present the first successfull attempt to use Membership Query Synthesis for generating AL queries, using Variational Autoencoders for query generation. We evaluate our approach in a text classification task and demonstrate that query synthesis shows competitive performance to pool-based AL strategies while substantially reducing annotation time",
}
@inproceedings{deutsch-etal-2019-general,
    title = "A General-Purpose Algorithm for Constrained Sequential Inference",
    author = "Deutsch, Daniel  and
      Upadhyay, Shyam  and
      Roth, Dan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1045",
    doi = "10.18653/v1/K19-1045",
    pages = "482--492",
    abstract = "Inference in structured prediction involves finding the best output structure for an input, subject to certain constraints. Many current approaches use sequential inference, which constructs the output in a left-to-right manner. However, there is no general framework to specify constraints in these approaches. We present a principled approach for incorporating constraints into sequential inference algorithms. Our approach expresses constraints using an automaton, which is traversed in lock-step during inference, guiding the search to valid outputs. We show that automata can express commonly used constraints and are easily incorporated into sequential inference. When it is more natural to represent constraints as a set of automata, our algorithm uses an active set method for demonstrably fast and efficient inference. We experimentally show the benefits of our algorithm on constituency parsing and semantic role labeling. For parsing, unlike unconstrained approaches, our algorithm always generates valid output, incurring only a small drop in performance. For semantic role labeling, imposing constraints using our algorithm corrects common errors, improving F1 by 1.5 points. These benefits increase in low-resource settings. Our active set method achieves a 5.2x relative speed-up over a naive approach.",
}
@inproceedings{hanselowski-etal-2019-richly,
    title = "A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking",
    author = "Hanselowski, Andreas  and
      Stab, Christian  and
      Schulz, Claudia  and
      Li, Zile  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1046",
    doi = "10.18653/v1/K19-1046",
    pages = "493--503",
    abstract = "Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machine learning methods require a large corpus with reliable annotations for the different tasks in the fact-checking process. Having analyzed existing fact-checking corpora, we found that none of them meets these criteria in full. They are either too small in size, do not provide detailed annotations, or are limited to a single domain. Motivated by this gap, we present a new substantially sized mixed-domain corpus with annotations of good quality for the core fact-checking tasks: document retrieval, evidence extraction, stance detection, and claim validation. To aid future corpus construction, we describe our methodology for corpus creation and annotation, and demonstrate that it results in substantial inter-annotator agreement. As baselines for future research, we perform experiments on our corpus with a number of model architectures that reach high performance in similar problem settings. Finally, to support the development of future models, we provide a detailed error analysis for each of the tasks. Our results show that the realistic, multi-domain setting defined by our data poses new challenges for the existing models, providing opportunities for considerable improvement by future systems.",
}
@inproceedings{liu-etal-2019-detecting,
    title = "Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding {U}.{S}. Gun Violence",
    author = "Liu, Siyi  and
      Guo, Lei  and
      Mays, Kate  and
      Betke, Margrit  and
      Wijaya, Derry Tanti",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1047",
    doi = "10.18653/v1/K19-1047",
    pages = "504--514",
    abstract = "Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as {``}frames{''}, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news headlines related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. Our proposed approach sets a new state-of-the-art performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9{\%} absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.",
}
@inproceedings{huang-etal-2019-learning,
    title = "Learning a Unified Named Entity Tagger from Multiple Partially Annotated Corpora for Efficient Adaptation",
    author = "Huang, Xiao  and
      Dong, Li  and
      Boschee, Elizabeth  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1048",
    doi = "10.18653/v1/K19-1048",
    pages = "515--527",
    abstract = "Named entity recognition (NER) identifies typed entity mentions in raw text. While the task is well-established, there is no universally used tagset: often, datasets are annotated for use in downstream applications and accordingly only cover a small set of entity types relevant to a particular task. For instance, in the biomedical domain, one corpus might annotate genes, another chemicals, and another diseases{---}despite the texts in each corpus containing references to all three types of entities. In this paper, we propose a deep structured model to integrate these {``}partially annotated{''} datasets to jointly identify all entity types appearing in the training corpora. By leveraging multiple datasets, the model can learn robust input representations; by building a joint structured model, it avoids potential conflicts caused by combining several models{'} predictions at test time. Experiments show that the proposed model significantly outperforms strong multi-task learning baselines when training on multiple, partially annotated datasets and testing on datasets that contain tags from more than one of the training corpora.",
}
@inproceedings{gillick-etal-2019-learning,
    title = "Learning Dense Representations for Entity Retrieval",
    author = "Gillick, Daniel  and
      Kulkarni, Sayali  and
      Lansing, Larry  and
      Presta, Alessandro  and
      Baldridge, Jason  and
      Ie, Eugene  and
      Garcia-Olano, Diego",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1049",
    doi = "10.18653/v1/K19-1049",
    pages = "528--537",
    abstract = "We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search. Unlike prior work, this setup does not rely on an alias table followed by a re-ranker, and is thus the first fully learned entity retrieval model. We show that our dual encoder, trained using only anchor-text links in Wikipedia, outperforms discrete alias table and BM25 baselines, and is competitive with the best comparable results on the standard TACKBP-2010 dataset. In addition, it can retrieve candidates extremely fast, and generalizes well to a new dataset derived from Wikinews. On the modeling side, we demonstrate the dramatic value of an unsupervised negative mining algorithm for this task.",
}
@inproceedings{hollenstein-etal-2019-cognival,
    title = "{C}ogni{V}al: A Framework for Cognitive Word Embedding Evaluation",
    author = "Hollenstein, Nora  and
      de la Torre, Antonio  and
      Langer, Nicolas  and
      Zhang, Ce",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1050",
    doi = "10.18653/v1/K19-1050",
    pages = "538--549",
    abstract = "An interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain. However, most, if not all, previous works only focus on small datasets and a single modality. In this paper, we present the first multi-modal framework for evaluating English word representations based on cognitive lexical semantics. Six types of word embeddings are evaluated by fitting them to 15 datasets of eye-tracking, EEG and fMRI signals recorded during language processing. To achieve a global score over all evaluation hypotheses, we apply statistical significance testing accounting for the multiple comparisons problem. This framework is easily extensible and available to include other intrinsic and extrinsic evaluation methods. We find strong correlations in the results between cognitive datasets, across recording modalities and to their performance on extrinsic NLP tasks.",
}
@inproceedings{peng-etal-2019-knowsemlm,
    title = "{K}now{S}em{LM}: A Knowledge Infused Semantic Language Model",
    author = "Peng, Haoruo  and
      Ning, Qiang  and
      Roth, Dan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1051",
    doi = "10.18653/v1/K19-1051",
    pages = "550--562",
    abstract = "Story understanding requires developing expectations of what events come next in text. Prior knowledge {--} both statistical and declarative {--} is essential in guiding such expectations. While existing semantic language models (SemLM) capture event co-occurrence information by modeling event sequences as semantic frames, entities, and other semantic units, this paper aims at augmenting them with causal knowledge (i.e., one event is likely to lead to another). Such knowledge is modeled at the frame and entity level, and can be obtained either statistically from text or stated declaratively. The proposed method, KnowSemLM, infuses this knowledge into a semantic LM by joint training and inference, and is shown to be effective on both the event cloze test and story/referent prediction tasks.",
}
@inproceedings{yamada-shindo-2019-neural,
    title = "Neural Attentive Bag-of-Entities Model for Text Classification",
    author = "Yamada, Ikuya  and
      Shindo, Hiroyuki",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1052",
    doi = "10.18653/v1/K19-1052",
    pages = "563--573",
    abstract = "This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for text classification. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec.",
}
@inproceedings{patil-etal-2019-roll,
    title = "Roll Call Vote Prediction with Knowledge Augmented Models",
    author = "Patil, Pallavi  and
      Myer, Kriti  and
      Zala, Ronak  and
      Singh, Arpit  and
      Mysore, Sheshera  and
      McCallum, Andrew  and
      Benton, Adrian  and
      Stent, Amanda",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1053",
    doi = "10.18653/v1/K19-1053",
    pages = "574--581",
    abstract = "The official voting records of United States congresspeople are preserved as roll call votes. Prediction of voting behavior of politicians for whom no voting record exists, such as individuals running for office, is important for forecasting key political decisions. Prior work has relied on past votes cast to predict future votes, and thus fails to predict voting patterns for politicians without voting records. We address this by augmenting a prior state of the art model with multiple sources of external knowledge so as to enable prediction on unseen politicians. The sources of knowledge we use are news text and Freebase, a manually curated knowledge base. We propose augmentations based on unigram features for news text, and a knowledge base embedding method followed by a neural network composition for relations from Freebase. Empirical evaluation of these approaches indicate that the proposed models outperform the prior system for politicians with complete historical voting records by 1.0{\%} point of accuracy (8.7{\%} error reduction) and for politicians without voting records by 33.4{\%} points of accuracy (66.7{\%} error reduction). We also show that the knowledge base augmented approach outperforms the news text augmented approach by 4.2{\%} points of accuracy.",
}
@inproceedings{mota-etal-2019-beamseg,
    title = "{B}eam{S}eg: A Joint Model for Multi-Document Segmentation and Topic Identification",
    author = "Mota, Pedro  and
      Eskenazi, Maxine  and
      Coheur, Lu{\'\i}sa",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1054",
    doi = "10.18653/v1/K19-1054",
    pages = "582--592",
    abstract = "We propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain. The model assumes that lexical cohesion can be observed across documents, meaning that segments describing the same topic use a similar lexical distribution over the vocabulary. The model implements lexical cohesion in an unsupervised Bayesian setting by drawing from the same language model segments with the same topic. Contrary to previous approaches, we assume that language models are not independent, since the vocabulary changes in consecutive segments are expected to be smooth and not abrupt. We achieve this by using a dynamic Dirichlet prior that takes into account data contributions from other topics. BeamSeg also models segment length properties of documents based on modality (textbooks, slides, \textit{etc.}). The evaluation is carried out in three datasets. In two of them, improvements of up to 4.8{\%} and 7.3{\%} are obtained in the segmentation and topic identifications tasks, indicating that both tasks should be jointly modeled.",
}
@inproceedings{chen-etal-2019-mrmep,
    title = "{M}r{M}ep: Joint Extraction of Multiple Relations and Multiple Entity Pairs Based on Triplet Attention",
    author = "Chen, Jiayu  and
      Yuan, Caixia  and
      Wang, Xiaojie  and
      Bai, Ziwei",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1055",
    doi = "10.18653/v1/K19-1055",
    pages = "593--602",
    abstract = "This paper focuses on how to extract multiple relational facts from unstructured text. Neural encoder-decoder models have provided a viable new approach for jointly extracting relations and entity pairs. However, these models either fail to deal with entity overlapping among relational facts, or neglect to produce the whole entity pairs. In this work, we propose a novel architecture that augments the encoder and decoder in two elegant ways. First, we apply a binary CNN classifier for each relation, which identifies all possible relations maintained in the text, while retaining the target relation representation to aid entity pair recognition. Second, we perform a multi-head attention over the text and a triplet attention with the target relation interacting with every token of the text to precisely produce all possible entity pairs in a sequential manner. Experiments on three benchmark datasets show that our proposed method successfully addresses the multiple relations and multiple entity pairs even with complex overlapping and significantly outperforms the state-of-the-art methods.",
}
@inproceedings{nayak-ng-2019-effective,
    title = "Effective Attention Modeling for Neural Relation Extraction",
    author = "Nayak, Tapas  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1056",
    doi = "10.18653/v1/K19-1056",
    pages = "603--612",
    abstract = "Relation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be long and two entities can be located far from each other in a sentence. The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via co-reference. Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence. Also, the words in a sentence do not contribute equally in identifying the relation between the two entities. To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism. Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.",
}
@inproceedings{ji-etal-2019-exploiting,
    title = "Exploiting the Entity Type Sequence to Benefit Event Detection",
    author = "Ji, Yuze  and
      Lin, Youfang  and
      Gao, Jianwei  and
      Wan, Huaiyu",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1057",
    doi = "10.18653/v1/K19-1057",
    pages = "613--623",
    abstract = "Event Detection (ED) is one of the most important task in the field of information extraction. The goal of ED is to find triggers in sentences and classify them into different event types. In previous works, the information of entity types are commonly utilized to benefit event detection. However, the sequential features of entity types have not been well utilized yet in the existing ED methods. In this paper, we propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.",
}
@inproceedings{stanislawek-etal-2019-named,
    title = "Named Entity Recognition - Is There a Glass Ceiling?",
    author = "Stanislawek, Tomasz  and
      Wr{\'o}blewska, Anna  and
      W{\'o}jcicka, Alicja  and
      Ziembicki, Daniel  and
      Biecek, Przemyslaw",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1058",
    doi = "10.18653/v1/K19-1058",
    pages = "624--633",
    abstract = "Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study illustrates weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, training process, and for checking model quality and stability.",
}
@inproceedings{lagus-etal-2019-low,
    title = "Low-Rank Approximations of Second-Order Document Representations",
    author = "Lagus, Jarkko  and
      Sinkkonen, Janne  and
      Klami, Arto",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1059",
    doi = "10.18653/v1/K19-1059",
    pages = "634--644",
    abstract = "Document embeddings, created with methods ranging from simple heuristics to statistical and deep models, are widely applicable. Bag-of-vectors models for documents include the mean and quadratic approaches (Torki, 2018). We present evidence that quadratic statistics alone, without the mean information, can offer superior accuracy, fast document comparison, and compact document representations. In matching news articles to their comment threads, low-rank representations of only 3-4 times the size of the mean vector give most accurate matching, and in standard sentence comparison tasks, results are state of the art despite faster computation. Similarity measures are discussed, and the Frobenius product implicit in the proposed method is contrasted to Wasserstein or Bures metric from the transportation theory. We also shortly demonstrate matching of unordered word lists to documents, to measure topicality or sentiment of documents.",
}
@inproceedings{mayhew-etal-2019-named,
    title = "Named Entity Recognition with Partially Annotated Training Data",
    author = "Mayhew, Stephen  and
      Chaturvedi, Snigdha  and
      Tsai, Chen-Tse  and
      Roth, Dan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1060",
    doi = "10.18653/v1/K19-1060",
    pages = "645--655",
    abstract = "Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated. We study the problem of Named Entity Recognition (NER) with partially annotated training data in which a fraction of the named entities are labeled, and all other tokens, entities or otherwise, are labeled as non-entity by default. In order to train on this noisy dataset, we need to distinguish between the true and false negatives. To this end, we introduce a constraint-driven iterative algorithm that learns to detect false negatives in the noisy set and downweigh them, resulting in a weighted training set. With this set, we train a weighted NER model. We evaluate our algorithm with weighted variants of neural and non-neural NER models on data in 8 languages from several language and script families, showing strong ability to learn from partial data. Finally, to show real-world efficacy, we evaluate on a Bengali NER corpus annotated by non-speakers, outperforming the prior state-of-the-art by over 5 points F1.",
}
@inproceedings{mhamdi-etal-2019-contextualized,
    title = "Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources",
    author = "M{'}hamdi, Meryem  and
      Freedman, Marjorie  and
      May, Jonathan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1061",
    doi = "10.18653/v1/K19-1061",
    pages = "656--665",
    abstract = "Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.",
}
@inproceedings{han-etal-2019-deep,
    title = "Deep Structured Neural Network for Event Temporal Relation Extraction",
    author = "Han, Rujun  and
      Hsu, I-Hung  and
      Yang, Mu  and
      Galstyan, Aram  and
      Weischedel, Ralph  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1062",
    doi = "10.18653/v1/K19-1062",
    pages = "666--106",
    abstract = "We propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, and 2) a structured support vector machine (SSVM) to make joint predictions. The neural network automatically learns representations that account for long-term contexts to provide robust features for the structured model, while the SSVM incorporates domain knowledge such as transitive closure of temporal relations as constraints to make better globally consistent decisions. By jointly training the two components, our model combines the benefits of both data-driven learning and knowledge exploitation. Experimental results on three high-quality event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that incorporated with pre-trained contextualized embeddings, the proposed model achieves significantly better performances than the state-of-the-art methods on all three datasets. We also provide thorough ablation studies to investigate our model.",
}
@inproceedings{broscheit-2019-investigating,
    title = "Investigating Entity Knowledge in {BERT} with Simple Neural End-To-End Entity Linking",
    author = "Broscheit, Samuel",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1063",
    doi = "10.18653/v1/K19-1063",
    pages = "677--685",
    abstract = "A typical architecture for end-to-end entity linking systems consists of three steps: mention detection, candidate generation and entity disambiguation. In this study we investigate the following questions: (a) Can all those steps be learned jointly with a model for contextualized text-representations, i.e. BERT? (b) How much entity knowledge is already contained in pretrained BERT? (c) Does additional entity knowledge improve BERT{'}s performance in downstream tasks? To this end we propose an extreme simplification of the entity linking setup that works surprisingly well: simply cast it as a per token classification over the entire entity vocabulary (over $700K$ classes in our case). We show on an entity linking benchmark that (i) this model improves the entity representations over plain BERT, (ii) that it outperforms entity linking architectures that optimize the tasks separately and (iii) that it only comes second to the current state-of-the-art that does mention detection and entity disambiguation jointly. Additionally, we investigate the usefulness of entity-aware token-representations in the text-understanding benchmark GLUE, as well as the question answering benchmarks SQUAD{\textasciitilde}V2 and SWAG and also the EN-DE WMT14 machine translation benchmark. To our surprise, we find that most of those benchmarks do not benefit from additional entity knowledge, except for a task with very small training data, the RTE task in GLUE, which improves by 2{\%}.",
}
@inproceedings{huang-li-2019-unsupervised,
    title = "Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification",
    author = "Huang, Hsin-Ping  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1064",
    doi = "10.18653/v1/K19-1064",
    pages = "686--695",
    abstract = "Implicit discourse relations are not only more challenging to classify, but also to annotate, than their explicit counterparts. We tackle situations where training data for implicit relations are lacking, and exploit domain adaptation from explicit relations (Ji et al., 2015). We present an unsupervised adversarial domain adaptive network equipped with a reconstruction component. Our system outperforms prior works and other adversarial benchmarks for unsupervised domain adaptation. Additionally, we extend our system to take advantage of labeled data if some are available.",
}
@inproceedings{wang-etal-2019-evidence,
    title = "Evidence Sentence Extraction for Machine Reading Comprehension",
    author = "Wang, Hai  and
      Yu, Dian  and
      Sun, Kai  and
      Chen, Jianshu  and
      Yu, Dong  and
      McAllester, David  and
      Roth, Dan",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1065",
    doi = "10.18653/v1/K19-1065",
    pages = "696--707",
    abstract = "Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor. To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision. We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiple-choice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document. To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC.",
}
@inproceedings{mahgoub-etal-2019-simvecs,
    title = "{S}im{V}ecs: Similarity-Based Vectors for Utterance Representation in Conversational {AI} Systems",
    author = "Mahgoub, Ashraf  and
      Shahin, Youssef  and
      Mansour, Riham  and
      Bagchi, Saurabh",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1066",
    doi = "10.18653/v1/K19-1066",
    pages = "708--717",
    abstract = "Conversational AI systems are gaining a lot of attention recently in both industrial and scientific domains, providing a natural way of interaction between customers and adaptive intelligent systems. A key requirement in these systems is the ability to understand the user{'}s intent and provide adequate responses to them. One of the greatest challenges of language understanding (LU) services is efficient utterance (sentence) representation in vector space, which is an essential step for most ML tasks. In this paper, we propose a novel approach for generating vector space representations of utterances using pair-wise similarity metrics. The proposed approach uses only a few corpora to tune the weights of the similarity metric without relying on external general purpose ontologies. Our experiments confirm that the generated vectors can improve the performance of LU services in unsupervised, semi-supervised and supervised learning tasks.",
}
@inproceedings{liu-etal-2019-incorporating,
    title = "Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots",
    author = "Liu, Cao  and
      Liu, Kang  and
      He, Shizhu  and
      Nie, Zaiqing  and
      Zhao, Jun",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1067",
    doi = "10.18653/v1/K19-1067",
    pages = "718--727",
    abstract = "Conventional chatbots focus on two-party response generation, which simplifies the real dialogue scene. In this paper, we strive toward a novel task of Response Generation on Multi-Party Chatbot (RGMPC), where the generated responses heavily rely on the interlocutors{'} roles (e.g., speaker and addressee) and their utterances. Unfortunately, complex interactions among the interlocutors{'} roles make it challenging to precisely capture conversational contexts and interlocutors{'} information. Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC. Specifically, we employ interactive representations to capture dialogue contexts for different interlocutors. Moreover, we leverage an addressee memory to enhance contextual interlocutor information for the target addressee. Finally, we construct a corpus for RGMPC based on an existing open-access dataset. Automatic and manual evaluations demonstrate that the ICRED remarkably outperforms strong baselines.",
}
@inproceedings{moon-etal-2019-memory,
    title = "Memory Graph Networks for Explainable Memory-grounded Question Answering",
    author = "Moon, Seungwhan  and
      Shah, Pararth  and
      Kumar, Anuj  and
      Subba, Rajen",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1068",
    doi = "10.18653/v1/K19-1068",
    pages = "728--736",
    abstract = "We introduce Episodic Memory QA, the task of answering personal user questions grounded on memory graph (MG), where episodic memories and related entity nodes are connected via relational edges. We create a new benchmark dataset first by generating synthetic memory graphs with simulated attributes, and by composing 100K QA pairs for the generated MG with bootstrapped scripts. To address the unique challenges for the proposed task, we propose Memory Graph Networks (MGN), a novel extension of memory networks to enable dynamic expansion of memory slots through graph traversals, thus able to answer queries in which contexts from multiple linked episodes and external knowledge are required. We then propose the Episodic Memory QA Net with multiple module networks to effectively handle various question types. Empirical results show improvement over the QA baselines in top-k answer prediction accuracy in the proposed task. The proposed model also generates a graph walk path and attention vectors for each predicted answer, providing a natural way to explain its QA reasoning.",
}
@inproceedings{ma-etal-2019-triplenet,
    title = "{T}riple{N}et: Triple Attention Network for Multi-Turn Response Selection in Retrieval-Based Chatbots",
    author = "Ma, Wentao  and
      Cui, Yiming  and
      Shao, Nan  and
      He, Su  and
      Zhang, Wei-Nan  and
      Liu, Ting  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1069",
    doi = "10.18653/v1/K19-1069",
    pages = "737--746",
    abstract = "We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to fully model the task with the triple {\textless}context, query, response{\textgreater} instead of {\textless}context, response {\textgreater} in previous works. The heart of TripleNet is a novel attention mechanism named triple attention to model the relationships within the triple at four levels. The new mechanism updates the representation of each element based on the attention with the other two concurrently and symmetrically.We match the triple {\textless}C, Q, R{\textgreater} centered on the response from char to context level for prediction.Experimental results on two large-scale multi-turn response selection datasets show that the proposed model can significantly outperform the state-of-the-art methods.",
}
@inproceedings{huang-etal-2019-relation,
    title = "Relation Module for Non-Answerable Predictions on Reading Comprehension",
    author = "Huang, Kevin  and
      Tang, Yun  and
      Huang, Jing  and
      He, Xiaodong  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1070",
    doi = "10.18653/v1/K19-1070",
    pages = "747--756",
    abstract = "Machine reading comprehension (MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model{'}s ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both the BiDAF and BERT models as baseline readers. We obtain 1.8{\%} gain of F1 accuracy on top of the BiDAF reader, and 1.0{\%} on top of the BERT base model. These results show the effectiveness of our relation module on MRC.",
}
@inproceedings{kim-etal-2019-slot,
    title = "Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios",
    author = "Kim, Kunho  and
      Jha, Rahul  and
      Williams, Kyle  and
      Marin, Alex  and
      Zitouni, Imed",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1071",
    doi = "10.18653/v1/K19-1071",
    pages = "757--767",
    abstract = "Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants. In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task. Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources. Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation. We also propose ensemble techniques that aggregate these different knowledge sources into a single model. Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09{\%} compared to existing approaches.",
}
@inproceedings{knaebel-etal-2019-window,
    title = "Window-Based Neural Tagging for Shallow Discourse Argument Labeling",
    author = "Knaebel, Ren{\'e}  and
      Stede, Manfred  and
      Stober, Sebastian",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1072",
    doi = "10.18653/v1/K19-1072",
    pages = "768--777",
    abstract = "This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing. Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure. For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model. The model is trained and evaluated on the Penn Discourse Treebank 2 corpus. While it is not as good as knowledge-intense approaches, it clearly outperforms other models that are also trained without additional linguistic features.",
}
@inproceedings{karmaker-santu-etal-2019-tilm,
    title = "{TILM}: Neural Language Models with Evolving Topical Influence",
    author = "Karmaker Santu, Shubhra Kanti  and
      Veeramachaneni, Kalyan  and
      Zhai, Chengxiang",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1073",
    doi = "10.18653/v1/K19-1073",
    pages = "778--788",
    abstract = "Content of text data are often influenced by contextual factors which often evolve over time (e.g., content of social media are often influenced by topics covered in the major news streams). Existing language models do not consider the influence of such related evolving topics, and thus are not optimal. In this paper, we propose to incorporate such topical-influence into a language model to both improve its accuracy and enable cross-stream analysis of topical influences. Specifically, we propose a novel language model called Topical Influence Language Model (TILM), which is a novel extension of a neural language model to capture the influences on the contents in one text stream by the evolving topics in another related (or possibly same) text stream. Experimental results on six different text stream data comprised of conference paper titles show that the incorporation of evolving topical influence into a language model is beneficial and TILM outperforms multiple baselines in a challenging task of text forecasting. In addition to serving as a language model, TILM further enables interesting analysis of topical influence among multiple text streams.",
}
@inproceedings{zhang-etal-2019-pretraining,
    title = "Pretraining-Based Natural Language Generation for Text Summarization",
    author = "Zhang, Haoyu  and
      Cai, Jingjing  and
      Xu, Jianjun  and
      Wang, Ji",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1074",
    doi = "10.18653/v1/K19-1074",
    pages = "789--797",
    abstract = "In this paper, we propose a novel pretraining-based encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner. For the encoder of our model, we encode the input sequence into context representations using BERT. For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence. In the second stage, we mask each word of the draft sequence and feed it to BERT, then by combining the input sequence and the draft representation generated by BERT, we use a Transformer-based decoder to predict the refined word for each masked position. To the best of our knowledge, our approach is the first method which applies the BERT into text generation tasks. As the first step in this direction, we evaluate our proposed method on the text summarization task. Experimental results show that our model achieves new state-of-the-art on both CNN/Daily Mail and New York Times datasets.",
}
@inproceedings{lai-etal-2019-goal,
    title = "Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation",
    author = "Lai, Yi-An  and
      Gupta, Arshit  and
      Zhang, Yi",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1075",
    doi = "10.18653/v1/K19-1075",
    pages = "798--811",
    abstract = "Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.",
}
@inproceedings{kumar-etal-2019-putting,
    title = "Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text",
    author = "Kumar, Vishwajeet  and
      Ramakrishnan, Ganesh  and
      Li, Yuan-Fang",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1076",
    doi = "10.18653/v1/K19-1076",
    pages = "812--821",
    abstract = "Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task. In this work, we attempt to strengthen them significantly by adopting a holistic and novel generator-evaluator framework that directly optimizes objectives that reward semantics and structure. The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated. The generator predicts an answer in the passage that the question can pivot on. Employing the copy and coverage mechanisms, it also acknowledges other contextually important (and possibly rare) keywords in the passage that the question needs to conform to, while not redundantly repeating words. The \textit{evaluator} model evaluates and assigns a reward to each predicted question based on its conformity to the \textit{structure} of ground-truth questions. We propose two novel QG-specific reward functions for text conformity and answer conformity of the generated question. The evaluator also employs structure-sensitive rewards based on evaluation measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. In contrast, most of the previous works only optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation) measures. Our evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark as per both automatic and human evaluation.",
}
@inproceedings{li-etal-2019-conclusion,
    title = "In Conclusion Not Repetition: Comprehensive Abstractive Summarization with Diversified Attention Based on Determinantal Point Processes",
    author = "Li, Lei  and
      Liu, Wei  and
      Litvak, Marina  and
      Vanetik, Natalia  and
      Huang, Zuying",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1077",
    doi = "10.18653/v1/K19-1077",
    pages = "822--832",
    abstract = "Various Seq2Seq learning models designed for machine translation were applied for abstractive summarization task recently. Despite these models provide high ROUGE scores, they are limited to generate comprehensive summaries with a high level of abstraction due to its degenerated attention distribution. We introduce Diverse Convolutional Seq2Seq Model(DivCNN Seq2Seq) using Determinantal Point Processes methods(Micro DPPs and Macro DPPs) to produce attention distribution considering both quality and diversity. Without breaking the end to end architecture, DivCNN Seq2Seq achieves a higher level of comprehensiveness compared to vanilla models and strong baselines. All the reproducible codes and datasets are available online.",
}
@inproceedings{chawla-etal-2019-generating,
    title = "Generating Formality-Tuned Summaries Using Input-Dependent Rewards",
    author = "Chawla, Kushal  and
      Srinivasan, Balaji Vasan  and
      Chhaya, Niyati",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1078",
    doi = "10.18653/v1/K19-1078",
    pages = "833--842",
    abstract = "Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content. Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary. However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries. In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article. Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together. Once trained, the same model can generate formal and informal summary variants. Our automated and qualitative evaluations show the viability of the proposed framework.",
}
@inproceedings{see-etal-2019-massively,
    title = "Do Massively Pretrained Language Models Make Better Storytellers?",
    author = "See, Abigail  and
      Pappu, Aneesh  and
      Saxena, Rohun  and
      Yerukola, Akhila  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1079",
    doi = "10.18653/v1/K19-1079",
    pages = "843--861",
    abstract = "Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these models as Natural Language Generators is less clear. Though anecdotal evidence suggests that these models generate better quality text, there has been no detailed study characterizing their generation abilities. In this work, we compare the performance of an extensively pretrained model, OpenAI GPT2-117 (Radford et al., 2019), to a state-of-the-art neural story generation model (Fan et al., 2018). By evaluating the generated text across a wide variety of automatic metrics, we characterize the ways in which pretrained models do, and do not, make better storytellers. We find that although GPT2-117 conditions more strongly on context, is more sensitive to ordering of events, and uses more unusual words, it is just as likely to produce repetitive and under-diverse text when using likelihood-maximizing decoding algorithms.",
}
@inproceedings{liu-etal-2019-self,
    title = "Self-Adaptive Scaling for Learnable Residual Structure",
    author = "Liu, Fenglin  and
      Gao, Meng  and
      Liu, Yuanxin  and
      Lei, Kai",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1080",
    doi = "10.18653/v1/K19-1080",
    pages = "862--870",
    abstract = "Residual has been widely applied to build deep neural networks with enhanced feature propagation and improved accuracy. In the literature, multiple variants of residual structure are proposed. However, most of them are manually designed for particular tasks and datasets and the combination of existing residual structures has not been well studied. In this work, we propose the Self-Adaptive Scaling (SAS) approach that automatically learns the design of residual structure from data. The proposed approach makes the best of various residual structures, resulting in a general architecture covering several existing ones. In this manner, we construct a learnable residual structure which can be easily integrated into a wide range of residual-based models. We evaluate our approach on various tasks concerning different modalities, including machine translation (IWSLT-2015 EN-VI and WMT-2014 EN-DE, EN-FR), image classification (CIFAR-10 and CIFAR-100), and image captioning (MSCOCO). Empirical results show that the proposed approach consistently improves the residual-based models and exhibits desirable generalization ability. In particular, by incorporating the proposed approach to the Transformer model, we establish new state-of-the-arts on the IWSLT-2015 EN-VI low-resource machine translation dataset.",
}
@inproceedings{ahmed-etal-2019-biofid,
    title = "{BIO}fid Dataset: Publishing a {G}erman Gold Standard for Named Entity Recognition in Historical Biodiversity Literature",
    author = "Ahmed, Sajawel  and
      Stoeckel, Manuel  and
      Driller, Christine  and
      Pachzelt, Adrian  and
      Mehler, Alexander",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1081",
    doi = "10.18653/v1/K19-1081",
    pages = "871--880",
    abstract = "The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23{\%} F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.",
}
@inproceedings{pei-etal-2019-slang,
    title = "Slang Detection and Identification",
    author = "Pei, Zhengqi  and
      Sun, Zhewei  and
      Xu, Yang",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1082",
    doi = "10.18653/v1/K19-1082",
    pages = "881--889",
    abstract = "The prevalence of informal language such as slang presents challenges for natural language systems, particularly in the automatic discovery of flexible word usages. Previous work has explored slang in terms of dictionary construction, sentiment analysis, word formation, and interpretation, but scarce research has attempted the basic problem of slang detection and identification. We examine the extent to which deep learning methods support automatic detection and identification of slang from natural sentences using a combination of bidirectional recurrent neural networks, conditional random field, and multilayer perceptron. We test these models based on a comprehensive set of linguistic features in sentence-level detection and token-level identification of slang. We found that a prominent feature of slang is the surprising use of words across syntactic categories or syntactic shift (e.g., verb-noun). Our best models detect the presence of slang at the sentence level with an F1-score of 0.80 and identify its exact position at the token level with an F1-Score of 0.50.",
}
@inproceedings{kocher-etal-2019-alleviating,
    title = "Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes",
    author = "Kocher, No{\'e}mien  and
      Scuito, Christian  and
      Tarantino, Lorenzo  and
      Lazaridis, Alexandros  and
      Fischer, Andreas  and
      Musat, Claudiu",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1083",
    doi = "10.18653/v1/K19-1083",
    pages = "890--899",
    abstract = "In sequence modeling tasks the token order matters, but this information can be partially lost due to the discretization of the sequence into data points. In this paper, we study the imbalance between the way certain token pairs are included in data points and others are not. We denote this a token order imbalance (TOI) and we link the partial sequence information loss to a diminished performance of the system as a whole, both in text and speech processing tasks. We then provide a mechanism to leverage the full token order information{---}Alleviated TOI{---}by iteratively overlapping the token composition of data points. For recurrent networks, we use prime numbers for the batch size to avoid redundancies when building batches from overlapped data points. The proposed method achieved state of the art performance in both text and speech related tasks.",
}
@inproceedings{parshakova-etal-2019-global,
    title = "Global Autoregressive Models for Data-Efficient Sequence Learning",
    author = "Parshakova, Tetiana  and
      Andreoli, Jean-Marc  and
      Dymetman, Marc",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1084",
    doi = "10.18653/v1/K19-1084",
    pages = "900--909",
    abstract = "Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component with a log-linear component, allowing the use of global \textit{a priori} features to compensate for lack of data. We train these models in two steps. In the first step, we obtain an \textit{unnormalized} GAM that maximizes the likelihood of the data, but is improper for fast inference or evaluation. In the second step, we use this GAM to train (by distillation) a second autoregressive model that approximates the \textit{normalized} distribution associated with the GAM, and can be used for fast inference and evaluation. Our experiments focus on language modelling under synthetic conditions and show a strong perplexity reduction of using the second autoregressive model over the standard one.",
}
@inproceedings{diallo-etal-2019-learning,
    title = "Learning Analogy-Preserving Sentence Embeddings for Answer Selection",
    author = {Diallo, A{\"\i}ssatou  and
      Zopf, Markus  and
      F{\"u}rnkranz, Johannes},
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1085",
    doi = "10.18653/v1/K19-1085",
    pages = "910--919",
    abstract = "Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is that question-answer pairs are often in analogical relation to each other. Using analogical inference as our use case, we propose a framework and a neural network architecture for learning dedicated sentence embeddings that preserve analogical properties in the semantic space. We evaluate the proposed method on benchmark datasets for answer selection and demonstrate that our sentence embeddings indeed capture analogical properties better than conventional embeddings, and that analogy-based question answering outperforms a comparable similarity-based technique.",
}
@inproceedings{feng-etal-2019-simple,
    title = "A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models",
    author = "Feng, Yukun  and
      Kamigaito, Hidetaka  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1086",
    doi = "10.18653/v1/K19-1086",
    pages = "920--928",
    abstract = "We propose a simple and effective method to inject word-level information into character-aware neural language models. Unlike previous approaches which usually inject word-level information at the input of a long short-term memory (LSTM) network, we inject it into the softmax function. The resultant model can be seen as a combination of character-aware language model and simple word-level language model. Our injection method can also be used together with previous methods. Through the experiments on 14 typologically diverse languages, we empirically show that our injection method, when used together with the previous methods, works better than the previous methods, including a gating mechanism, averaging, and concatenation of word vectors. We also provide a comprehensive comparison of these injection methods.",
}
@inproceedings{madhyastha-jain-2019-model,
    title = "On Model Stability as a Function of Random Seed",
    author = "Madhyastha, Pranava  and
      Jain, Rishabh",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1087",
    doi = "10.18653/v1/K19-1087",
    pages = "929--939",
    abstract = "In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general. We specifically perform a controlled study on the effect of random seeds on the behaviour of attention, gradient-based and surrogate model based (LIME) interpretations. Our analysis suggests that random seeds can adversely affect the consistency of models resulting in counterfactual interpretations. We propose a technique called Aggressive Stochastic Weight Averaging (ASWA) and an extension called Norm-filtered Aggressive Stochastic Weight Averaging (NASWA) which improves the stability of models over random seeds. With our ASWA and NASWA based optimization, we are able to improve the robustness of the original model, on average reducing the standard deviation of the model{'}s performance by 72{\%}.",
}
@inproceedings{swamy-etal-2019-studying,
    title = "Studying Generalisability across Abusive Language Detection Datasets",
    author = {Swamy, Steve Durairaj  and
      Jamatia, Anupam  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1088",
    doi = "10.18653/v1/K19-1088",
    pages = "940--950",
    abstract = "Work on Abusive Language Detection has tackled a wide range of subtasks and domains. As a result of this, there exists a great deal of redundancy and non-generalisability between datasets. Through experiments on cross-dataset training and testing, the paper reveals that the preconceived notion of including more non-abusive samples in a dataset (to emulate reality) may have a detrimental effect on the generalisability of a model trained on that data. Hence a hierarchical annotation model is utilised here to reveal redundancies in existing datasets and to help reduce redundancy in future efforts.",
}
@inproceedings{tschuggnall-etal-2019-reduce,
    title = "Reduce {\&} Attribute: Two-Step Authorship Attribution for Large-Scale Problems",
    author = {Tschuggnall, Michael  and
      Murauer, Benjamin  and
      Specht, G{\"u}nther},
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1089",
    doi = "10.18653/v1/K19-1089",
    pages = "951--960",
    abstract = "Authorship attribution is an active research area which has been prevalent for many decades. Nevertheless, the majority of approaches consider problem sizes of a few candidate authors only, making them difficult to apply to recent scenarios incorporating thousands of authors emerging due to the manifold means to digitally share text. In this study, we focus on such large-scale problems and propose to effectively reduce the number of candidate authors before applying common attribution techniques. By utilizing document embeddings, we show on a novel, comprehensive dataset collection that the set of candidate authors can be reduced with high accuracy. Moreover, we show that common authorship attribution methods substantially benefit from a preliminary reduction if thousands of authors are involved.",
}
@inproceedings{cheng-etal-2019-variational,
    title = "Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer",
    author = "Cheng, Xingyi  and
      Xu, Weidi  and
      Wang, Taifeng  and
      Chu, Wei  and
      Huang, Weipeng  and
      Chen, Kunlong  and
      Hu, Junfeng",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1090",
    doi = "10.18653/v1/K19-1090",
    pages = "961--969",
    abstract = "Aspect-term sentiment analysis (ATSA) is a long-standing challenge in natural language process. It requires fine-grained semantical reasoning about a target entity appeared in the text. As manual annotation over the aspects is laborious and time-consuming, the amount of labeled data is limited for supervised learning. This paper proposes a semi-supervised method for the ATSA problem by using the Variational Autoencoder based on Transformer. The model learns the latent distribution via variational inference. By disentangling the latent representation into the aspect-specific sentiment and the lexical context, our method induces the underlying sentiment prediction for the unlabeled data, which then benefits the ATSA classifier. Our method is classifier-agnostic, i.e., the classifier is an independent module and various supervised models can be integrated. Experimental results are obtained on the SemEval 2014 task 4 and show that our method is effective with different the five specific classifiers and outperforms these models by a significant margin.",
}
@inproceedings{hu-etal-2019-learning,
    title = "Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis",
    author = "Hu, Mengting  and
      Zhao, Shiwan  and
      Guo, Honglei  and
      Cheng, Renhong  and
      Su, Zhong",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1091",
    doi = "10.18653/v1/K19-1091",
    pages = "970--979",
    abstract = "Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence. Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations. The attention is expected to concentrate on opinion words for accurate sentiment prediction. However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects. In this paper, we propose an alternative hard-selection approach, which determines the start and end positions of the opinion snippet, and selects the words between these two positions for sentiment prediction. Specifically, we learn deep associations between the sentence and aspect, and the long-term dependencies within the sentence by leveraging the pre-trained BERT model. We further detect the opinion snippet by self-critical reinforcement learning. Especially, experimental results demonstrate the effectiveness of our method and prove that our hard-selection approach outperforms soft-selection approaches when handling multi-aspect sentences.",
}
@inproceedings{kocon-etal-2019-multi,
    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
    author = "Koco{\'n}, Jan  and
      Mi{\l}kowski, Piotr  and
      Za{\'s}ko-Zieli{\'n}ska, Monika",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1092",
    doi = "10.18653/v1/K19-1092",
    pages = "980--991",
    abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).",
}
@inproceedings{guo-etal-2019-personalized,
    title = "A Personalized Sentiment Model with Textual and Contextual Information",
    author = {Guo, Siwen  and
      H{\"o}hn, Sviatlana  and
      Schommer, Christoph},
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1093",
    doi = "10.18653/v1/K19-1093",
    pages = "992--1001",
    abstract = "In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person{'}s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person{'}s past expressions, and offer a better understanding of the sentiment from the expresser{'}s perspective. Additionally, we investigate how a person{'}s sentiment changes over time so that recent incidents or opinions may have more effect on the person{'}s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.",
}
@inproceedings{zhang-etal-2019-cluster,
    title = "Cluster-Gated Convolutional Neural Network for Short Text Classification",
    author = "Zhang, Haidong  and
      Ni, Wancheng  and
      Zhao, Meijing  and
      Lin, Ziqi",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1094",
    doi = "10.18653/v1/K19-1094",
    pages = "1002--1011",
    abstract = "Text classification plays a crucial role for understanding natural language in a wide range of applications. Most existing approaches mainly focus on long text classification (e.g., blogs, documents, paragraphs). However, they cannot easily be applied to short text because of its sparsity and lack of context. In this paper, we propose a new model called cluster-gated convolutional neural network (CGCNN), which jointly explores word-level clustering and text classification in an end-to-end manner. Specifically, the proposed model firstly uses a bi-directional long short-term memory to learn word representations. Then, it leverages a soft clustering method to explore their semantic relation with the cluster centers, and takes linear transformation on text representations. It develops a cluster-dependent gated convolutional layer to further control the cluster-dependent feature flows. Experimental results on five commonly used datasets show that our model outperforms state-of-the-art models.",
}
@inproceedings{gangavarapu-etal-2019-coherence,
    title = "Coherence-based Modeling of Clinical Concepts Inferred from Heterogeneous Clinical Notes for {ICU} Patient Risk Stratification",
    author = "Gangavarapu, Tushaar  and
      Krishnan, Gokul S  and
      Kamath S, Sowmya",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1095",
    doi = "10.18653/v1/K19-1095",
    pages = "1012--1022",
    abstract = "In hospitals, critical care patients are often susceptible to various complications that adversely affect their morbidity and mortality. Digitized patient data from Electronic Health Records (EHRs) can be utilized to facilitate risk stratification accurately and provide prioritized care. Existing clinical decision support systems are heavily reliant on the structured nature of the EHRs. However, the valuable patient-specific data contained in unstructured clinical notes are often manually transcribed into EHRs. The prolific use of extensive medical jargon, heterogeneity, sparsity, rawness, inconsistent abbreviations, and complex structure of the clinical notes poses significant challenges, and also results in a loss of information during the manual conversion process. In this work, we employ two coherence-based topic modeling approaches to model the free-text in the unstructured clinical nursing notes and capture its semantic textual features with the emphasis on human interpretability. Furthermore, we present FarSight, a long-term aggregation mechanism intended to detect the onset of disease with the earliest recorded symptoms and infections. We utilize the predictive capabilities of deep neural models for the clinical task of risk stratification through ICD-9 code group prediction. Our experimental validation on MIMIC-III (v1.4) database underlined the efficacy of FarSight with coherence-based topic modeling, in extracting discriminative clinical features from the unstructured nursing notes. The proposed approach achieved a superior predictive performance when benchmarked against the structured EHR data based state-of-the-art model, with an improvement of 11.50{\%} in AUPRC and 1.16{\%} in AUROC.",
}
@inproceedings{atanasov-etal-2019-predicting,
    title = "Predicting the Role of Political Trolls in Social Media",
    author = "Atanasov, Atanas  and
      De Francisci Morales, Gianmarco  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1096",
    doi = "10.18653/v1/K19-1096",
    pages = "1023--1034",
    abstract = "We investigate the political roles of {``}Internet trolls{''} in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and labor-intensive, thus making it impractical as a first-response tool for newly-discovered troll farms. In this paper, we show how to automate this analysis by using machine learning in a realistic setting. In particular, we show how to classify trolls according to their political role {---}left, news feed, right{---} by using features extracted from social media, i.e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls. Technically, we leverage the community structure and the text of the messages in the online social network of trolls represented as a graph, from which we extract several types of learned representations, i.e., embeddings, for the trolls. Experiments on the {``}IRA Russian Troll{''} dataset show that our methodology improves over the state-of-the-art in the first scenario, while providing a compelling case for the second scenario, which has not been explored in the literature thus far.",
}
@inproceedings{feng-wan-2019-towards,
    title = "Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis",
    author = "Feng, Yanlin  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K19-1097",
    doi = "10.18653/v1/K19-1097",
    pages = "1035--1044",
    abstract = "Sentiment analysis in low-resource languages suffers from the lack of training data. Cross-lingual sentiment analysis (CLSA) aims to improve the performance on these languages by leveraging annotated data from other languages. Recent studies have shown that CLSA can be performed in a fully unsupervised manner, without exploiting either target language supervision or cross-lingual supervision. However, these methods rely heavily on unsupervised cross-lingual word embeddings (CLWE), which has been shown to have serious drawbacks on distant language pairs (e.g. English - Japanese). In this paper, we propose an end-to-end CLSA model by leveraging unlabeled data in multiple languages and multiple domains and eliminate the need for unsupervised CLWE. Our model applies to two CLSA settings: the traditional cross-lingual in-domain setting and the more challenging cross-lingual cross-domain setting. We empirically evaluate our approach on the multilingual multi-domain Amazon review dataset. Experimental results show that our model outperforms the baselines by a large margin despite its minimal resource requirement.",
}
@article{cl-2019--3,
    title = "",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
}
@article{kaplan-2019-computational,
    title = "Computational Psycholinguistics",
    author = "Kaplan, Ronald M.",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-4001",
    doi = "10.1162/coli_a_00359",
    pages = "607--626",
}
@article{sachan-etal-2019-discourse,
    title = "Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks",
    author = "Sachan, Mrinmaya  and
      Dubey, Avinava  and
      Hovy, Eduard H.  and
      Mitchell, Tom M.  and
      Roth, Dan  and
      Xing, Eric P.",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-4002",
    doi = "10.1162/coli_a_00360",
    pages = "627--665",
    abstract = "To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features that can be leveraged for various NLP tasks. In this article, we study some of these discourse features in multimedia text and what communicative function they fulfill in the context. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We conclude that the discourse and text layout features provide information that is complementary to lexical semantic information. Finally, we show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable.",
}
@article{ciobanu-dinu-2019-automatic,
    title = "Automatic Identification and Production of Related Words for Historical Linguistics",
    author = "Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-4003",
    doi = "10.1162/coli_a_00361",
    pages = "667--704",
    abstract = "Language change across space and time is one of the main concerns in historical linguistics. In this article, we develop tools to assist researchers and domain experts in the study of language evolution.First, we introduce a method to automatically determine whether two words are cognates. We propose an algorithm for extracting cognates from electronic dictionaries that contain etymological information. Having built a data set of related words, we further develop machine learning methods based on orthographic alignment for identifying cognates. We use aligned subsequences as features for classification algorithms in order to infer rules for linguistic changes undergone by words when entering new languages and to discriminate between cognates and non-cognates.Second, we extend the method to a finer-grained level, to identify the type of relationship between words. Discriminating between cognates and borrowings provides a deeper insight into the history of a language and allows a better characterization of language relatedness. We show that orthographic features have discriminative power and we analyze the underlying linguistic factors that prove relevant in the classification task. To our knowledge, this is the first attempt of this kind.Third, we develop a machine learning method for automatically producing related words. We focus on reconstructing proto-words, but we also address two related sub-problems, producing modern word forms and producing cognates. The task of reconstructing proto-words consists of recreating the words in an ancient language from its modern daughter languages. Having modern word forms in multiple Romance languages, we infer the form of their common Latin ancestors. Our approach relies on the regularities that occurred when words entered the modern languages. We leverage information from several modern languages, building an ensemble system for reconstructing proto-words. We apply our method to multiple data sets, showing that our approach improves on previous results, also having the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.",
}
@article{wang-pan-2019-syntactically,
    title = "Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction",
    author = "Wang, Wenya  and
      Pan, Sinno Jialin",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-4004",
    doi = "10.1162/coli_a_00362",
    pages = "705--736",
    abstract = "In fine-grained opinion mining, extracting aspect terms (a.k.a. opinion targets) and opinion terms (a.k.a. opinion expressions) from user-generated texts is the most fundamental task in order to generate structured opinion summarization. Existing studies have shown that the syntactic relations between aspect and opinion words play an important role for aspect and opinion terms extraction. However, most of the works either relied on predefined rules or separated relation mining with feature learning. Moreover, these works only focused on single-domain extraction, which failed to adapt well to other domains of interest where only unlabeled data are available. In real-world scenarios, annotated resources are extremely scarce for many domains, motivating knowledge transfer strategies from labeled source domain(s) to any unlabeled target domain. We observe that syntactic relations among target words to be extracted are not only crucial for single-domain extraction, but also serve as invariant {``}pivot{''} information to bridge the gap between different domains. In this article, we explore the constructions of recursive neural networks based on the dependency tree of each sentence for associating syntactic structure with feature learning. Furthermore, we construct transferable recursive neural networks to automatically learn the domain-invariant fine-grained interactions among aspect words and opinion words. The transferability is built on an auxiliary task and a conditional domain adversarial network to reduce domain distribution difference in the hidden spaces effectively in word level through syntactic relations. Specifically, the auxiliary task builds structural correspondences across domains by predicting the dependency relation for each path of the dependency tree in the recursive neural network. The conditional domain adversarial network helps to learn domain-invariant hidden representation for each word conditioned on the syntactic structure. In the end, we integrate the recursive neural network with a sequence labeling classifier on top that models contextual influence in the final predictions. Extensive experiments and analysis are conducted to demonstrate the effectiveness of the proposed model and each component on three benchmark data sets.",
}
@article{laha-etal-2019-scalable,
    title = "Scalable Micro-planned Generation of Discourse from Structured Data",
    author = "Laha, Anirban  and
      Jain, Parag  and
      Mishra, Abhijit  and
      Sankaranarayanan, Karthik",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-4005",
    doi = "10.1162/coli_a_00363",
    pages = "737--763",
    abstract = "We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains.Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.",
}
@article{lawrence-reed-2019-argument,
    title = "Argument Mining: A Survey",
    author = "Lawrence, John  and
      Reed, Chris",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-4006",
    doi = "10.1162/coli_a_00364",
    pages = "765--818",
    abstract = "Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.",
}
@article{wichmann-2019-distinguish,
    title = "How to Distinguish Languages and Dialects",
    author = "Wichmann, S{\o}ren",
    journal = "Computational Linguistics",
    volume = "45",
    number = "4",
    month = dec,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-4007",
    doi = "10.1162/coli_a_00366",
    pages = "823--831",
    abstract = "The terms {``}language{''} and {``}dialect{''} are ingrained, but linguists nevertheless tend to agree that it is impossible to apply a non-arbitrary distinction such that two speech varieties can be identified as either distinct languages or two dialects of one and the same language. A database of lexical information for more than 7,500 speech varieties, however, unveils a strong tendency for linguistic distances to be bimodally distributed. For a given language group the linguistic distances pertaining to either cluster can be teased apart, identifying a mixture of normal distributions within the data and then separating them fitting curves and finding the point where they cross. The thresholds identified are remarkably consistent across data sets, qualifying their mean as a universal criterion for distinguishing between language and dialect pairs. The mean of the thresholds identified translates into a temporal distance of around one to one-and-a-half millennia (1,075{--}1,635 years).",
}
@article{cl-2019--2,
    title = "",
    journal = "Computational Linguistics",
    volume = "45",
    number = "3",
    month = sep,
    year = "2019",
}
@article{gamallo-etal-2019-contextualized,
    title = "Contextualized Translations of Phrasal Verbs with Distributional Compositional Semantics and Monolingual Corpora",
    author = "Gamallo, Pablo  and
      Sotelo, Susana  and
      Pichel, Jos{\'e} Ramom  and
      Artetxe, Mikel",
    journal = "Computational Linguistics",
    volume = "45",
    number = "3",
    month = sep,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-3001",
    doi = "10.1162/coli_a_00353",
    pages = "395--421",
    abstract = "This article describes a compositional distributional method to generate contextualized senses of words and identify their appropriate translations in the target language using monolingual corpora. Word translation is modeled in the same way as contextualization of word meaning, but in a bilingual vector space. The contextualization of meaning is carried out by means of distributional composition within a structured vector space with syntactic dependencies, and the bilingual space is created by means of transfer rules and a bilingual dictionary. A phrase in the source language, consisting of a head and a dependent, is translated into the target language by selecting both the nearest neighbor of the head given the dependent, and the nearest neighbor of the dependent given the head. This process is expanded to larger phrases by means of incremental composition. Experiments were performed on English and Spanish monolingual corpora in order to translate phrasal verbs in context. A new bilingual data set to evaluate strategies aimed at translating phrasal verbs in restricted syntactic domains has been created and released.",
}
@article{ustalov-etal-2019-watset,
    title = "Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction",
    author = "Ustalov, Dmitry  and
      Panchenko, Alexander  and
      Biemann, Chris  and
      Ponzetto, Simone Paolo",
    journal = "Computational Linguistics",
    volume = "45",
    number = "3",
    month = sep,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-3002",
    doi = "10.1162/coli_a_00354",
    pages = "423--479",
    abstract = "We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph, which reflects the {``}ambiguity{''} of its nodes. Then, it uses hard clustering to discover clusters in this {``}disambiguated{''} intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can also be applied to other networks of linguistic data.",
}
@article{takahashi-tanaka-ishii-2019-evaluating,
    title = "Evaluating Computational Language Models with Scaling Properties of Natural Language",
    author = "Takahashi, Shuntaro  and
      Tanaka-Ishii, Kumiko",
    journal = "Computational Linguistics",
    volume = "45",
    number = "3",
    month = sep,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-3003",
    doi = "10.1162/coli_a_00355",
    pages = "481--513",
    abstract = "In this article, we evaluate computational models of natural language with respect to the universal statistical behaviors of natural language. Statistical mechanical analyses have revealed that natural language text is characterized by scaling properties, which quantify the global structure in the vocabulary population and the long memory of a text. We study whether five scaling properties (given by Zipf{'}s law, Heaps{'} law, Ebeling{'}s method, Taylor{'}s law, and long-range correlation analysis) can serve for evaluation of computational models. Specifically, we test n-gram language models, a probabilistic context-free grammar, language models based on Simon/Pitman-Yor processes, neural language models, and generative adversarial networks for text generation. Our analysis reveals that language models based on recurrent neural networks with a gating mechanism (i.e., long short-term memory; a gated recurrent unit; and quasi-recurrent neural networks) are the only computational models that can reproduce the long memory behavior of natural language. Furthermore, through comparison with recently proposed model-based evaluation methods, we find that the exponent of Taylor{'}s law is a good indicator of model quality.",
}
@article{fomicheva-specia-2019-taking,
    title = "Taking {MT} Evaluation Metrics to Extremes: Beyond Correlation with Human Judgments",
    author = "Fomicheva, Marina  and
      Specia, Lucia",
    journal = "Computational Linguistics",
    volume = "45",
    number = "3",
    month = sep,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-3004",
    doi = "10.1162/coli_a_00356",
    pages = "515--558",
    abstract = "Automatic Machine Translation (MT) evaluation is an active field of research, with a handful of new metrics devised every year. Evaluation metrics are generally benchmarked against manual assessment of translation quality, with performance measured in terms of overall correlation with human scores. Much work has been dedicated to the improvement of evaluation metrics to achieve a higher correlation with human judgments. However, little insight has been provided regarding the weaknesses and strengths of existing approaches and their behavior in different settings. In this work we conduct a broad meta-evaluation study of the performance of a wide range of evaluation metrics focusing on three major aspects. First, we analyze the performance of the metrics when faced with different levels of translation quality, proposing a local dependency measure as an alternative to the standard, global correlation coefficient. We show that metric performance varies significantly across different levels of MT quality: Metrics perform poorly when faced with low-quality translations and are not able to capture nuanced quality distinctions. Interestingly, we show that evaluating low-quality translations is also more challenging for humans. Second, we show that metrics are more reliable when evaluating neural MT than the traditional statistical MT systems. Finally, we show that the difference in the evaluation accuracy for different metrics is maintained even if the gold standard scores are based on different criteria.",
}
@article{ponti-etal-2019-modeling,
    title = "Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing",
    author = "Ponti, Edoardo Maria  and
      O{'}Horan, Helen  and
      Berzak, Yevgeni  and
      Vuli{\'c}, Ivan  and
      Reichart, Roi  and
      Poibeau, Thierry  and
      Shutova, Ekaterina  and
      Korhonen, Anna",
    journal = "Computational Linguistics",
    volume = "45",
    number = "3",
    month = sep,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-3005",
    doi = "10.1162/coli_a_00357",
    pages = "559--601",
    abstract = "Linguistic typology aims to capture structural and semantic variation across the world{'}s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.",
}
@article{cl-2019-,
    title = "",
    journal = "Computational Linguistics",
    volume = "45",
    number = "2",
    month = jun,
    year = "2019",
}
@article{murawaki-2019-bayesian,
    title = "{B}ayesian Learning of Latent Representations of Language Structures",
    author = "Murawaki, Yugo",
    journal = "Computational Linguistics",
    volume = "45",
    number = "2",
    month = jun,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-2001",
    doi = "10.1162/coli_a_00346",
    pages = "199--228",
    abstract = "We borrow the concept of representation learning from deep learning research, and we argue that the quest for Greenbergian implicational universals can be reformulated as the learning of good latent representations of languages, or sequences of surface typological features. By projecting languages into latent representations and performing inference in the latent space, we can handle complex dependencies among features in an implicit manner. The most challenging problem in turning the idea into a concrete computational model is the alarmingly large number of missing values in existing typological databases. To address this problem, we keep the number of model parameters relatively small to avoid overfitting, adopt the Bayesian learning framework for its robustness, and exploit phylogenetically and/or spatially related languages as additional clues. Experiments show that the proposed model recovers missing values more accurately than others and that some latent variables exhibit phylogenetic and spatial signals comparable to those of surface features.",
}
@article{sprugnoli-tonelli-2019-novel,
    title = "Novel Event Detection and Classification for Historical Texts",
    author = "Sprugnoli, Rachele  and
      Tonelli, Sara",
    journal = "Computational Linguistics",
    volume = "45",
    number = "2",
    month = jun,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-2002",
    doi = "10.1162/coli_a_00347",
    pages = "229--265",
    abstract = "Event processing is an active area of research in the Natural Language Processing community, but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts Particularly in the current era of massive digitization of historical sources: Research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding light on the complex concept of events when dealing with historical texts. More specifically, we introduce new annotation guidelines for event mentions and types, categorized into 22 classes. Then, we annotate a historical corpus accordingly, and compare two approaches for automatic event detection and classification following this novel scheme. We believe that this work can foster research in a field of inquiry as yet underestimated in the area of Temporal Information Processing. To this end, we release new annotation guidelines, a corpus, and new models for automatic annotation.",
}
@article{eriguchi-etal-2019-incorporating,
    title = "Incorporating Source-Side Phrase Structures into Neural Machine Translation",
    author = "Eriguchi, Akiko  and
      Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    journal = "Computational Linguistics",
    volume = "45",
    number = "2",
    month = jun,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-2003",
    doi = "10.1162/coli_a_00348",
    pages = "267--292",
    abstract = "Neural machine translation (NMT) has shown great success as a new alternative to the traditional Statistical Machine Translation model in multiple languages. Early NMT models are based on sequence-to-sequence learning that encodes a sequence of source words into a vector space and generates another sequence of target words from the vector. In those NMT models, sentences are simply treated as sequences of words without any internal structure. In this article, we focus on the role of the syntactic structure of source sentences and propose a novel end-to-end syntactic NMT model, which we call a tree-to-sequence NMT model, extending a sequence-to-sequence model with the source-side phrase structure. Our proposed model has an attention mechanism that enables the decoder to generate a translated word while softly aligning it with phrases as well as words of the source sentence. We have empirically compared the proposed model with sequence-to-sequence models in various settings on Chinese-to-Japanese and English-to-Japanese translation tasks. Our experimental results suggest that the use of syntactic structure can be beneficial when the training data set is small, but is not as effective as using a bi-directional encoder. As the size of training data set increases, the benefits of using a syntactic tree tends to diminish.",
}
@article{zhang-etal-2019-neural,
    title = "Neural Models of Text Normalization for Speech Applications",
    author = "Zhang, Hao  and
      Sproat, Richard  and
      Ng, Axel H.  and
      Stahlberg, Felix  and
      Peng, Xiaochang  and
      Gorman, Kyle  and
      Roark, Brian",
    journal = "Computational Linguistics",
    volume = "45",
    number = "2",
    month = jun,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-2004",
    doi = "10.1162/coli_a_00349",
    pages = "293--337",
    abstract = "Machine learning, including neural network techniques, have been applied to virtually every domain in natural language processing. One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS). In this application, one must decide, for example, that 123 is verbalized as one hundred twenty three in 123 pages but as one twenty three in 123 King Ave. For this task, state-of-the-art industrial systems depend heavily on hand-written language-specific grammars.We propose neural network models that treat text normalization for TTS as a sequence-to-sequence problem, in which the input is a text token in context, and the output is the verbalization of that token. We find that the most effective model, in accuracy and efficiency, is one where the sentential context is computed once and the results of that computation are combined with the computation of each token in sequence to compute the verbalization. This model allows for a great deal of flexibility in terms of representing the context, and also allows us to integrate tagging and segmentation into the process.These models perform very well overall, but occasionally they will predict wildly inappropriate verbalizations, such as reading 3 cm as three kilometers. Although rare, such verbalizations are a major issue for TTS applications. We thus use finite-state covering grammars to guide the neural models, either during training and decoding, or just during decoding, away from such {``}unrecoverable{''} errors. Such grammars can largely be learned from data.",
}
@article{gildea-etal-2019-ordered,
    title = "Ordered Tree Decomposition for {HRG} Rule Extraction",
    author = "Gildea, Daniel  and
      Satta, Giorgio  and
      Peng, Xiaochang",
    journal = "Computational Linguistics",
    volume = "45",
    number = "2",
    month = jun,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-2005",
    doi = "10.1162/coli_a_00350",
    pages = "339--379",
    abstract = "We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure. The assumption of a fixed order for the vertices of the input graph makes it possible to solve the problem in polynomial time, in contrast to the fact that the problem of finding optimal tree decompositions for a graph is NP-hard. We also present polynomial-time algorithms for parsing based on our HRGs, where the input is a vertex sequence and the output is a graph structure. The intended application of our algorithms is grammar extraction and parsing for semantic representation of natural language. We apply our algorithms to data annotated with Abstract Meaning Representations and report on the characteristics of the resulting grammars.",
}
@article{bjerva-etal-2019-language,
    title = "What Do Language Representations Really Represent?",
    author = {Bjerva, Johannes  and
      {\"O}stling, Robert  and
      Veiga, Maria Han  and
      Tiedemann, J{\"o}rg  and
      Augenstein, Isabelle},
    journal = "Computational Linguistics",
    volume = "45",
    number = "2",
    month = jun,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-2006",
    doi = "10.1162/coli_a_00351",
    pages = "381--389",
    abstract = "A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships{---}a convenient benchmark used for evaluation in previous work{---}appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.",
}
@article{cl-2019-linguistics,
    title = "Computational Linguistics, Volume 45, Issue 1 - March 2019",
    journal = "Computational Linguistics",
    volume = "45",
    number = "1",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-1000",
}
@article{cordeiro-etal-2019-unsupervised,
    title = "Unsupervised Compositionality Prediction of Nominal Compounds",
    author = "Cordeiro, Silvio  and
      Villavicencio, Aline  and
      Idiart, Marco  and
      Ramisch, Carlos",
    journal = "Computational Linguistics",
    volume = "45",
    number = "1",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-1001",
    doi = "10.1162/coli_a_00341",
    pages = "1--57",
    abstract = "Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.",
}
@article{cheng-etal-2019-learning,
    title = "Learning an Executable Neural Semantic Parser",
    author = "Cheng, Jianpeng  and
      Reddy, Siva  and
      Saraswat, Vijay  and
      Lapata, Mirella",
    journal = "Computational Linguistics",
    volume = "45",
    number = "1",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-1002",
    doi = "10.1162/coli_a_00342",
    pages = "59--94",
    abstract = "This article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including fully supervised training where annotated logical forms are given, weakly supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of data sets demonstrate the effectiveness of our parser.",
}
@article{sun-etal-2019-parsing,
    title = "Parsing {C}hinese Sentences with Grammatical Relations",
    author = "Sun, Weiwei  and
      Chen, Yufei  and
      Wan, Xiaojun  and
      Liu, Meichun",
    journal = "Computational Linguistics",
    volume = "45",
    number = "1",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-1003",
    doi = "10.1162/coli_a_00343",
    pages = "95--136",
    abstract = "We report our work on building linguistic resources and data-driven parsers in the grammatical relation (GR) analysis for Mandarin Chinese. Chinese, as an analytic language, encodes grammatical information in a highly configurational rather than morphological way. Accordingly, it is possible and reasonable to represent almost all grammatical relations as bilexical dependencies. In this work, we propose to represent grammatical information using general directed dependency graphs. Both only-local and rich long-distance dependencies are explicitly represented. To create high-quality annotations, we take advantage of an existing TreeBank, namely, Chinese TreeBank (CTB), which is grounded on the Government and Binding theory. We define a set of linguistic rules to explore CTB{'}s implicit phrase structural information and build deep dependency graphs. The reliability of this linguistically motivated GR extraction procedure is highlighted by manual evaluation. Based on the converted corpus, data-driven, including graph- and transition-based, models are explored for Chinese GR parsing. For graph-based parsing, a new perspective, graph merging, is proposed for building flexible dependency graphs: constructing complex graphs via constructing simple subgraphs. Two key problems are discussed in this perspective: (1) how to decompose a complex graph into simple subgraphs, and (2) how to combine subgraphs into a coherent complex graph. For transition-based parsing, we introduce a neural parser based on a list-based transition system. We also discuss several other key problems, including dynamic oracle and beam search for neural transition-based parsing. Evaluation gauges how successful GR parsing for Chinese can be by applying data-driven models. The empirical analysis suggests several directions for future study.",
}
@article{list-2019-automatic,
    title = "Automatic Inference of Sound Correspondence Patterns across Multiple Languages",
    author = "List, Johann-Mattis",
    journal = "Computational Linguistics",
    volume = "45",
    number = "1",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-1004",
    doi = "10.1162/coli_a_00344",
    pages = "137--161",
    abstract = "Sound correspondence patterns play a crucial role for linguistic reconstruction. Linguists use them to prove language relationship, to reconstruct proto-forms, and for classical phylogenetic reconstruction based on shared innovations. Cognate words that fail to conform with expected patterns can further point to various kinds of exceptions in sound change, such as analogy or assimilation of frequent words. Here I present an automatic method for the inference of sound correspondence patterns across multiple languages based on a network approach. The core idea is to represent all columns in aligned cognate sets as nodes in a network with edges representing the degree of compatibility between the nodes. The task of inferring all compatible correspondence sets can then be handled as the well-known minimum clique cover problem in graph theory, which essentially seeks to split the graph into the smallest number of cliques in which each node is represented by exactly one clique. The resulting partitions represent all correspondence patterns that can be inferred for a given data set. By excluding those patterns that occur in only a few cognate sets, the core of regularly recurring sound correspondences can be inferred. Based on this idea, the article presents a method for automatic correspondence pattern recognition, which is implemented as part of a Python library which supplements the article. To illustrate the usefulness of the method, I present how the inferred patterns can be used to predict words that have not been observed before.",
}
@article{wu-etal-2019-sequential,
    title = "A Sequential Matching Framework for Multi-Turn Response Selection in Retrieval-Based Chatbots",
    author = "Wu, Yu  and
      Wu, Wei  and
      Xing, Chen  and
      Xu, Can  and
      Li, Zhoujun  and
      Zhou, Ming",
    journal = "Computational Linguistics",
    volume = "45",
    number = "1",
    month = mar,
    year = "2019",
    url = "https://www.aclweb.org/anthology/J19-1005",
    doi = "10.1162/coli_a_00345",
    pages = "163--197",
    abstract = "We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task involves matching a response candidate with a conversation context, the challenges for which include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. This motivates us to propose a new matching framework that can sufficiently carry important information in contexts to matching and model relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interact with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) that models relationships among utterances. Context-response matching is then calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experiment results show that both models can significantly outperform state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage important information in contexts for matching.",
}
@proceedings{emnlp-2019-fact,
    title = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    editor = "Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6600",
}
@inproceedings{thorne-etal-2019-fever2,
    title = "The {FEVER}2.0 Shared Task",
    author = "Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6601",
    doi = "10.18653/v1/D19-6601",
    pages = "1--6",
    abstract = "We present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. The task challenged participants to both build systems to verify factoid claims using evidence retrieved from Wikipedia and to generate adversarial attacks against other participant{'}s systems. The shared task had three phases: \textit{building, breaking and fixing}. There were 8 systems in the builder{'}s round, three of which were new qualifying submissions for this shared task, and 5 adversaries generated instances designed to induce classification errors and one builder submitted a fixed system which had higher FEVER score and resilience than their first submission. All but one newly submitted systems attained FEVER scores higher than the best performing system from the first shared task and under adversarial evaluation, all systems exhibited losses in FEVER score. There was a great variety in adversarial attack types as well as the techniques used to generate the attacks, In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.",
}
@inproceedings{wawer-etal-2019-fact,
    title = "Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims?",
    author = "Wawer, Aleksander  and
      Wojdyga, Grzegorz  and
      Sarzy{\'n}ska-Wawer, Justyna",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6602",
    doi = "10.18653/v1/D19-6602",
    pages = "7--12",
    abstract = "The goal of our paper is to compare psycholinguistic text features with fact checking approaches to distinguish lies from true statements. We examine both methods using data from a large ongoing study on deception and deception detection covering a mixture of factual and opinionated topics that polarize public opinion. We conclude that fact checking approaches based on Wikipedia are too limited for this task, as only a few percent of sentences from our study has enough evidence to become supported or refuted. Psycholinguistic features turn out to outperform both fact checking and human baselines, but the accuracy is not high. Overall, it appears that deception detection applicable to less-than-obvious topics is a difficult task and a problem to be solved.",
}
@inproceedings{fang-etal-2019-neural,
    title = "Neural Multi-Task Learning for Stance Prediction",
    author = "Fang, Wei  and
      Nadeem, Moin  and
      Mohtarami, Mitra  and
      Glass, James",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6603",
    doi = "10.18653/v1/D19-6603",
    pages = "13--19",
    abstract = "We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.",
}
@inproceedings{niewinski-etal-2019-gem,
    title = "{GEM}: Generative Enhanced Model for adversarial attacks",
    author = "Niewinski, Piotr  and
      Pszona, Maria  and
      Janicka, Maria",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6604",
    doi = "10.18653/v1/D19-6604",
    pages = "20--26",
    abstract = "We present our Generative Enhanced Model (GEM) that we used to create samples awarded the first prize on the FEVER 2.0 Breakers Task. GEM is the extended language model developed upon GPT-2 architecture. The addition of novel target vocabulary input to the already existing context input enabled controlled text generation. The training procedure resulted in creating a model that inherited the knowledge of pretrained GPT-2, and therefore was ready to generate natural-like English sentences in the task domain with some additional control. As a result, GEM generated malicious claims that mixed facts from various articles, so it became difficult to classify their truthfulness.",
}
@inproceedings{mohammadshahi-etal-2019-aligning-multilingual,
    title = "Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task",
    author = "Mohammadshahi, Alireza  and
      Lebret, R{\'e}mi  and
      Aberer, Karl",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6605",
    doi = "10.18653/v1/D19-6605",
    pages = "27--33",
    abstract = "In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model. We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task. Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.",
}
@inproceedings{andrews-witteveen-2019-unsupervised,
    title = "Unsupervised Natural Question Answering with a Small Model",
    author = "Andrews, Martin  and
      Witteveen, Sam",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6606",
    doi = "10.18653/v1/D19-6606",
    pages = "34--38",
    abstract = "The recent demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is being embedded directly within these large models. This short paper describes an architecture through which much smaller models can also answer such questions - by making use of {`}raw{'} external knowledge. The contribution of this work is that the methods presented here rely on unsupervised learning techniques, complementing the unsupervised training of the Language Model. The goal of this line of research is to be able to add knowledge explicitly, without extensive training.",
}
@inproceedings{clancy-etal-2019-scalable,
    title = "Scalable Knowledge Graph Construction from Text Collections",
    author = "Clancy, Ryan  and
      Ilyas, Ihab F.  and
      Lin, Jimmy",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6607",
    doi = "10.18653/v1/D19-6607",
    pages = "39--46",
    abstract = "We present a scalable, open-source platform that {``}distills{''} a potentially large text collection into a knowledge graph. Our platform takes documents stored in Apache Solr and scales out the Stanford CoreNLP toolkit via Apache Spark integration to extract mentions and relations that are then ingested into the Neo4j graph database. The raw knowledge graph is then enriched with facts extracted from an external knowledge graph. The complete product can be manipulated by various applications using Neo4j{'}s native Cypher query language: We present a subgraph-matching approach to align extracted relations with external facts and show that fact verification, locating textual support for asserted facts, detecting inconsistent and missing facts, and extracting distantly-supervised training data can all be performed within the same framework.",
}
@inproceedings{park-kim-2019-relation,
    title = "Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism",
    author = "Park, Seong Sik  and
      Kim, Harksoo",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6608",
    doi = "10.18653/v1/D19-6608",
    pages = "47--51",
    abstract = "Many previous studies on relation extrac-tion have been focused on finding only one relation between two entities in a single sentence. However, we can easily find the fact that multiple entities exist in a single sentence and the entities form multiple relations. To resolve this prob-lem, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations by using a forward de-coder called an object decoder. Then, it finds 1-to-n subject-object relations by using a backward decoder called a sub-ject decoder. In the experiments with the ACE-05 dataset and the NYT dataset, the proposed model achieved the state-of-the-art performances (F1-score of 80.5{\%} in the ACE-05 dataset, F1-score of 78.3{\%} in the NYT dataset)",
}
@inproceedings{jobanputra-2019-unsupervised,
    title = "Unsupervised Question Answering for Fact-Checking",
    author = "Jobanputra, Mayank",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6609",
    doi = "10.18653/v1/D19-6609",
    pages = "52--56",
    abstract = "Recent Deep Learning (DL) models have succeeded in achieving human-level accuracy on various natural language tasks such as question-answering, natural language inference (NLI), and textual entailment. These tasks not only require the contextual knowledge but also the reasoning abilities to be solved efficiently. In this paper, we propose an unsupervised question-answering based approach for a similar task, fact-checking. We transform the FEVER dataset into a Cloze-task by masking named entities provided in the claims. To predict the answer token, we utilize pre-trained Bidirectional Encoder Representations from Transformers (BERT). The classifier computes label based on the correctly answered questions and a threshold. Currently, the classifier is able to classify the claims as {``}SUPPORTS{''} and {``}MANUAL{\_}REVIEW{''}. This approach achieves a label accuracy of 80.2{\%} on the development set and 80.25{\%} on the test set of the transformed dataset.",
}
@inproceedings{singh-etal-2019-improving,
    title = "Improving Evidence Detection by Leveraging Warrants",
    author = "Singh, Keshav  and
      Reisert, Paul  and
      Inoue, Naoya  and
      Kavumba, Pride  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6610",
    doi = "10.18653/v1/D19-6610",
    pages = "57--62",
    abstract = "Recognizing the implicit link between a claim and a piece of evidence (i.e. warrant) is the key to improving the performance of evidence detection. In this work, we explore the effectiveness of automatically extracted warrants for evidence detection. Given a claim and candidate evidence, our proposed method extracts multiple warrants via similarity search from an existing, structured corpus of arguments. We then attentively aggregate the extracted warrants, considering the consistency between the given argument and the acquired warrants. Although a qualitative analysis on the warrants shows that the extraction method needs to be improved, our results indicate that our method can still improve the performance of evidence detection.",
}
@inproceedings{khong-etal-2019-hybrid,
    title = "Hybrid Models for Aspects Extraction without Labelled Dataset",
    author = "Khong, Wai-Howe  and
      Soon, Lay-Ki  and
      Goh, Hui-Ngo",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6611",
    doi = "10.18653/v1/D19-6611",
    pages = "63--68",
    abstract = "One of the important tasks in opinion mining is to extract aspects of the opinion target. Aspects are features or characteristics of the opinion target that are being reviewed, which can be categorised into explicit and implicit aspects. Extracting aspects from opinions is essential in order to ensure accurate information about certain attributes of an opinion target is retrieved. For instance, a professional camera receives a positive feedback in terms of its functionalities in a review, but its overly high price receives negative feedback. Most of the existing solutions focus on explicit aspects. However, sentences in reviews normally do not state the aspects explicitly. In this research, two hybrid models are proposed to identify and extract both explicit and implicit aspects, namely TDM-DC and TDM-TED. The proposed models combine topic modelling and dictionary-based approach. The models are unsupervised as they do not require any labelled dataset. The experimental results show that TDM-DC achieves F1-measure of 58.70{\%}, where it outperforms both the baseline topic model and dictionary-based approach. In comparison to other existing unsupervised techniques, the proposed models are able to achieve higher F1-measure by approximately 3{\%}. Although the supervised techniques perform slightly better, the proposed models are domain-independent, and hence more versatile.",
}
@inproceedings{chernyavskiy-ilvovsky-2019-extract,
    title = "Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification",
    author = "Chernyavskiy, Anton  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6612",
    doi = "10.18653/v1/D19-6612",
    pages = "69--78",
    abstract = "Triggered by Internet development, a large amount of information is published in online sources. However, it is a well-known fact that publications are inundated with inaccurate data. That is why fact-checking has become a significant topic in the last 5 years. It is widely accepted that factual data verification is a challenge even for the experts. This paper presents a domain-independent fact checking system. It can solve the fact verification problem entirely or at the individual stages. The proposed model combines various advanced methods of text data analysis, such as BERT and Infersent. The theoretical and empirical study of the system features is carried out. Based on FEVER and Fact Checking Challenge test-collections, experimental results demonstrate that our model can achieve the score on a par with state-of-the-art models designed by the specificity of particular datasets.",
}
@inproceedings{stahlhut-2019-interactive,
    title = "Interactive Evidence Detection: train state-of-the-art model out-of-domain or simple model interactively?",
    author = "Stahlhut, Chris",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6613",
    doi = "10.18653/v1/D19-6613",
    pages = "79--89",
    abstract = "Finding evidence is of vital importance in research as well as fact checking and an evidence detection method would be useful in speeding up this process. However, when addressing a new topic there is no training data and there are two approaches to get started. One could use large amounts of out-of-domain data to train a state-of-the-art method, or to use the small data that a person creates while working on the topic. In this paper, we address this problem in two steps. First, by simulating users who read source documents and label sentences they can use as evidence, thereby creating small amounts of training data for an interactively trained evidence detection model; and second, by comparing such an interactively trained model against a pre-trained model that has been trained on large out-of-domain data. We found that an interactively trained model not only often out-performs a state-of-the-art model but also requires significantly lower amounts of computational resources. Therefore, especially when computational resources are scarce, e.g. no GPU available, training a smaller model on the fly is preferable to training a well generalising but resource hungry out-of-domain model.",
}
@inproceedings{azevedo-moustafa-2019-veritas,
    title = "Veritas Annotator: Discovering the Origin of a Rumour",
    author = "Azevedo, Lucas  and
      Moustafa, Mohamed",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6614",
    doi = "10.18653/v1/D19-6614",
    pages = "90--98",
    abstract = "Defined as the intentional or unintentionalspread of false information (K et al., 2019)through context and/or content manipulation,fake news has become one of the most seriousproblems associated with online information(Waldrop, 2017). Consequently, it comes asno surprise that Fake News Detection hasbecome one of the major foci of variousfields of machine learning and while machinelearning models have allowed individualsand companies to automate decision-basedprocesses that were once thought to be onlydoable by humans, it is no secret that thereal-life applications of such models are notviable without the existence of an adequatetraining dataset. In this paper we describethe Veritas Annotator, a web application formanually identifying the origin of a rumour.These rumours, often referred as claims,were previously checked for validity byFact-Checking Agencies.",
}
@inproceedings{kim-allan-2019-fever,
    title = "{FEVER} Breaker{'}s Run of Team {N}b{A}uz{D}r{L}qg",
    author = "Kim, Youngwoo  and
      Allan, James",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6615",
    doi = "10.18653/v1/D19-6615",
    pages = "99--104",
    abstract = "We describe our submission for the Breaker phase of the second Fact Extraction and VERification (FEVER) Shared Task. Our adversarial data can be explained by two perspectives. First, we aimed at testing model{'}s ability to retrieve evidence, when appropriate query terms could not be easily generated from the claim. Second, we test model{'}s ability to precisely understand the implications of the texts, which we expect to be rare in FEVER 1.0 dataset. Overall, we suggested six types of adversarial attacks. The evaluation on the submitted systems showed that the systems were only able get both the evidence and label correct in 20{\%} of the data. We also demonstrate our adversarial run analysis in the data development process.",
}
@inproceedings{stammbach-neumann-2019-team,
    title = "Team {DOMLIN}: Exploiting Evidence Enhancement for the {FEVER} Shared Task",
    author = "Stammbach, Dominik  and
      Neumann, Guenter",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6616",
    doi = "10.18653/v1/D19-6616",
    pages = "105--109",
    abstract = "This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge. We propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is not only conditioned on the claim, but also on previously retrieved evidence. We use a publicly available document retrieval module and have fine-tuned BERT checkpoints for sentence se- lection and as the entailment classifier. We report a FEVER score of 68.46{\%} on the blind testset.",
}
@inproceedings{alonso-reina-etal-2019-team,
    title = "Team {GPLSI}. Approach for automated fact checking",
    author = "Alonso-Reina, Aim{\'e}e  and
      Sep{\'u}lveda-Torres, Robiert  and
      Saquete, Estela  and
      Palomar, Manuel",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6617",
    doi = "10.18653/v1/D19-6617",
    pages = "110--114",
    abstract = "Fever Shared 2.0 Task is a challenge meant for developing automated fact checking systems. Our approach for the Fever 2.0 is based on a previous proposal developed by Team Athene UKP TU Darmstadt. Our proposal modifies the sentence retrieval phase, using statement extraction and representation in the form of triplets (subject, object, action). Triplets are extracted from the claim and compare to triplets extracted from Wikipedia articles using semantic similarity. Our results are satisfactory but there is room for improvement.",
}
@proceedings{emnlp-2019-discourse,
    title = "Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)",
    editor = "Popescu-Belis, Andrei  and
      Lo{\'a}iciga, Sharid  and
      Hardmeier, Christian  and
      Xiong, Deyi",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6500",
}
@inproceedings{lapshinova-koltunski-etal-2019-analysing,
    title = "Analysing Coreference in Transformer Outputs",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Espa{\~n}a-Bonet, Cristina  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6501",
    doi = "10.18653/v1/D19-6501",
    pages = "1--12",
    abstract = "We analyse coreference phenomena in three neural machine translation systems trained with different data settings with or without access to explicit intra- and cross-sentential anaphoric information. We compare system performance on two different genres: news and TED talks. To do this, we manually annotate (the possibly incorrect) coreference chains in the MT outputs and evaluate the coreference chain translations. We define an error typology that aims to go further than pronoun translation adequacy and includes types such as incorrect word selection or missing words. The features of coreference chains in automatic translations are also compared to those of the source texts and human translations. The analysis shows stronger potential translationese effects in machine translated outputs than in human translations.",
}
@inproceedings{martinez-garcia-etal-2019-context,
    title = "Context-Aware Neural Machine Translation Decoding",
    author = "Mart{\'\i}nez Garcia, Eva  and
      Creus, Carles  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6502",
    doi = "10.18653/v1/D19-6502",
    pages = "13--23",
    abstract = "This work presents a decoding architecture that fuses the information from a neural translation model and the context semantics enclosed in a semantic space language model based on word embeddings. The method extends the beam search decoding process and therefore can be applied to any neural machine translation framework. With this, we sidestep two drawbacks of current document-level systems: (i) we do not modify the training process so there is no increment in training time, and (ii) we do not require document-level an-notated data. We analyze the impact of the fusion system approach and its parameters on the final translation quality for English{--}Spanish. We obtain consistent and statistically significant improvements in terms of BLEU and METEOR and we observe how the fused systems are able to handle synonyms to propose more adequate translations as well as help the system to disambiguate among several translation candidates for a word.",
}
@inproceedings{kim-etal-2019-document,
    title = "When and Why is Document-level Context Useful in Neural Machine Translation?",
    author = "Kim, Yunsu  and
      Tran, Duc Thanh  and
      Ney, Hermann",
    booktitle = "Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6503",
    doi = "10.18653/v1/D19-6503",
    pages = "24--34",
    abstract = "Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.",
}
@inproceedings{sugiyama-yoshinaga-2019-data,
    title = "Data augmentation using back-translation for context-aware neural machine translation",
    author = "Sugiyama, Amane  and
      Yoshinaga, Naoki",
    booktitle = "Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6504",
    doi = "10.18653/v1/D19-6504",
    pages = "35--44",
    abstract = "A single sentence does not always convey information that is enough to translate it into other languages. Some target languages need to add or specialize words that are omitted or ambiguous in the source languages (e.g, zero pronouns in translating Japanese to English or epicene pronouns in translating English to French). To translate such ambiguous sentences, we need contexts beyond a single sentence, and have so far explored context-aware neural machine translation (NMT). However, a large amount of parallel corpora is not easily available to train accurate context-aware NMT models. In this study, we first obtain large-scale pseudo parallel corpora by back-translating monolingual data, and then investigate its impact on the translation accuracy of context-aware NMT models. We evaluated context-aware NMT models trained with small parallel corpora and the large-scale pseudo parallel corpora on English-Japanese and English-French datasets to demonstrate the large impact of the data augmentation for context-aware NMT models.",
}
@inproceedings{ohtani-etal-2019-context,
    title = "Context-aware Neural Machine Translation with Coreference Information",
    author = "Ohtani, Takumi  and
      Kamigaito, Hidetaka  and
      Nagata, Masaaki  and
      Okumura, Manabu",
    booktitle = "Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6505",
    doi = "10.18653/v1/D19-6505",
    pages = "45--50",
    abstract = "We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly. The graph-based encoder can dynamically encode the source text without attending to all tokens in the text. In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set. Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.",
}
@inproceedings{scherrer-etal-2019-analysing,
    title = "Analysing concatenation approaches to document-level {NMT} in two different domains",
    author = {Scherrer, Yves  and
      Tiedemann, J{\"o}rg  and
      Lo{\'a}iciga, Sharid},
    booktitle = "Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6506",
    doi = "10.18653/v1/D19-6506",
    pages = "51--61",
    abstract = "In this paper, we investigate how different aspects of discourse context affect the performance of recent neural MT systems. We describe two popular datasets covering news and movie subtitles and we provide a thorough analysis of the distribution of various document-level features in their domains. Furthermore, we train a set of context-aware MT models on both datasets and propose a comparative evaluation scheme that contrasts coherent context with artificially scrambled documents and absent context, arguing that the impact of discourse-aware MT models will become visible in this way. Our results show that the models are indeed affected by the manipulation of the test data, providing a different view on document-level translation quality than absolute sentence-level scores.",
}
@proceedings{emnlp-2019-beyond,
    title = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    editor = "Mogadala, Aditya  and
      Klakow, Dietrich  and
      Pezzelle, Sandro  and
      Moens, Marie-Francine",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6400",
}
@inproceedings{pahuja-etal-2019-structure,
    title = "Structure Learning for Neural Module Networks",
    author = "Pahuja, Vardaan  and
      Fu, Jie  and
      Chandar, Sarath  and
      Pal, Christopher",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6401",
    doi = "10.18653/v1/D19-6401",
    pages = "1--10",
    abstract = "Neural Module Networks, originally proposed for the task of visual question answering, are a class of neural network architectures that involve human-specified neural modules, each designed for a specific form of reasoning. In current formulations of such networks only the parameters of the neural modules and/or the order of their execution is learned. In this work, we further expand this approach and also learn the underlying internal structure of modules in terms of the ordering and combination of simple and elementary arithmetic operators. We utilize a minimum amount of prior knowledge from the human-specified neural modules in the form of different input types and arithmetic operators used in these modules. Our results show that one is indeed able to simultaneously learn both internal module structure and module sequencing without extra supervisory signals for module execution sequencing. With this approach, we report performance comparable to models using hand-designed modules. In addition, we do a analysis of sensitivity of the learned modules w.r.t. the arithmetic operations and infer the analytical expressions of the learned modules.",
}
@inproceedings{mohammadshahi-etal-2019-aligning,
    title = "Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task",
    author = "Mohammadshahi, Alireza  and
      Lebret, R{\'e}mi  and
      Aberer, Karl",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6402",
    doi = "10.18653/v1/D19-6402",
    pages = "11--17",
    abstract = "In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space while adapting the alignment of word embeddings between existing languages in our model. We show that our approach enables better generalization, achieving state-of-the-art performance in text-to-image and image-to-text retrieval task, and caption-caption similarity task. Two multimodal multilingual datasets are used for evaluation: Multi30k with German and English captions and Microsoft-COCO with English and Japanese captions.",
}
@inproceedings{pezzelle-fernandez-2019-big,
    title = "Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size",
    author = "Pezzelle, Sandro  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6403",
    doi = "10.18653/v1/D19-6403",
    pages = "18--23",
    abstract = "In this paper, we experiment with a recently proposed visual reasoning task dealing with quantities {--} modeling the multimodal, contextually-dependent meaning of size adjectives ({`}big{'}, {`}small{'}) {--} and explore the impact of varying the training data on the learning behavior of a state-of-art system. In previous work, models have been shown to fail in generalizing to unseen adjective-noun combinations. Here, we investigate whether, and to what extent, seeing some of these cases during training helps a model understand the rule subtending the task, i.e., that being big implies being not small, and vice versa. We show that relatively few examples are enough to understand this relationship, and that developing a specific, mutually exclusive representation of size adjectives is beneficial to the task.",
}
@inproceedings{tseng-hsieh-2019-eigencharacter,
    title = "{E}igencharacter: An Embedding of {C}hinese Character Orthography",
    author = "Tseng, Yu-Hsiang  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6404",
    doi = "10.18653/v1/D19-6404",
    pages = "24--28",
    abstract = "Chinese characters are unique in its logographic nature, which inherently encodes world knowledge through thousands of years evolution. This paper proposes an embedding approach, namely eigencharacter (EC) space, which helps NLP application easily access the knowledge encoded in Chinese orthography. These EC representations are automatically extracted, encode both structural and radical information, and easily integrate with other computational models. We built EC representations of 5,000 Chinese characters, investigated orthography knowledge encoded in ECs, and demonstrated how these ECs identified visually similar characters with both structural and radical information.",
}
@inproceedings{wang-etal-2019-role,
    title = "On the Role of Scene Graphs in Image Captioning",
    author = "Wang, Dalin  and
      Beck, Daniel  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6405",
    doi = "10.18653/v1/D19-6405",
    pages = "29--34",
    abstract = "Scene graphs represent semantic information in images, which can help image captioning system to produce more descriptive outputs versus using only the image as context. Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images. However, those graphs introduce noise and it is unclear the effect of parser errors on captioning accuracy. In this work, we investigate to what extent scene graphs can help image captioning. Our results show that a state-of-the-art scene graph parser can boost performance almost as much as the ground truth graphs, showing that the bottleneck currently resides more on the captioning models than on the performance of the scene graph parser.",
}
@inproceedings{dutta-chowdhury-elliott-2019-understanding,
    title = "Understanding the Effect of Textual Adversaries in Multimodal Machine Translation",
    author = "Dutta Chowdhury, Koel  and
      Elliott, Desmond",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6406",
    doi = "10.18653/v1/D19-6406",
    pages = "35--40",
    abstract = "It is assumed that multimodal machine translation systems are better than text-only systems at translating phrases that have a direct correspondence in the image. This assumption has been challenged in experiments demonstrating that state-of-the-art multimodal systems perform equally well in the presence of randomly selected images, but, more recently, it has been shown that masking entities from the source language sentence during training can help to overcome this problem. In this paper, we conduct experiments with both visual and textual adversaries in order to understand the role of incorrect textual inputs to such systems. Our results show that when the source language sentence contains mistakes, multimodal translation systems do not leverage the additional visual signal to produce the correct translation. We also find that the degradation of translation performance caused by textual adversaries is significantly higher than by visual adversaries.",
}
@inproceedings{kolb-etal-2019-learning,
    title = "Learning to request guidance in emergent language",
    author = "Kolb, Benjamin  and
      Lang, Leon  and
      Bartsch, Henning  and
      Gansekoele, Arwin  and
      Koopmanschap, Raymond  and
      Romor, Leonardo  and
      Speck, David  and
      Mul, Mathijs  and
      Bruni, Elia",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6407",
    doi = "10.18653/v1/D19-6407",
    pages = "41--50",
    abstract = "Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent. The guide achieves this by providing the agent with discrete messages in an emerged language about how to solve the task. We extend this one-directional communication by a one-bit communication channel from the learner back to the guide: It is able to ask the guide for help, and we limit the guidance by penalizing the learner for these requests. During training, the agent learns to control this gate based on its current observation. We find that the amount of requested guidance decreases over time and guidance is requested in situations of high uncertainty. We investigate the agent{'}s performance in cases of open and closed gates and discuss potential motives for the observed gating behavior.",
}
@inproceedings{klerke-plank-2019-glance,
    title = "At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging",
    author = "Klerke, Sigrid  and
      Plank, Barbara",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6408",
    doi = "10.18653/v1/D19-6408",
    pages = "51--61",
    abstract = "Readers{'} eye movements used as part of the training signal have been shown to improve performance in a wide range of Natural Language Processing (NLP) tasks. Previous work uses gaze data either at the type level or at the token level and mostly from a single eye-tracking corpus. In this paper, we analyze type vs token-level integration options with eye tracking data from two corpora to inform two syntactic sequence labeling problems: binary phrase chunking and part-of-speech tagging. We show that using globally-aggregated measures that capture the central tendency or variability of gaze data is more beneficial than proposed local views which retain individual participant information. While gaze data is informative for supervised POS tagging, which complements previous findings on unsupervised POS induction, almost no improvement is obtained for binary phrase chunking, except for a single specific setup. Hence, caution is warranted when using gaze data as signal for NLP, as no single view is robust over tasks, modeling choice and gaze corpus.",
}
@inproceedings{gupta-etal-2019-seeded,
    title = "Seeded self-play for language learning",
    author = "Gupta, Abhinav  and
      Lowe, Ryan  and
      Foerster, Jakob  and
      Kiela, Douwe  and
      Pineau, Joelle",
    booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6409",
    doi = "10.18653/v1/D19-6409",
    pages = "62--66",
    abstract = "How can we teach artificial agents to use human language flexibly to solve problems in real-world environments? We have an example of this in nature: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop. Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn language in this way. An outstanding goal is finding an algorithm with a suitable {`}language learning prior{'} that allows it to learn human language, while minimizing the number of on-policy human interactions. In this paper, we propose to learn such a prior in simulation using an approach we call, Learning to Learn to Communicate (L2C). Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations, including populations speaking human language. Our key insight is that such populations can be obtained via self-play, after pre-training agents with imitation learning on a small amount of off-policy human language data. We call this latter technique Seeded Self-Play (S2P). Our preliminary experiments show that agents trained with L2C and S2P need fewer on-policy samples to learn a compositional language in a Lewis signaling game.",
}
@proceedings{emnlp-2019-multilingual,
    title = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    editor = "Mille, Simon  and
      Belz, Anja  and
      Bohnet, Bernd  and
      Graham, Yvette  and
      Wanner, Leo",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6300",
}
@inproceedings{mille-etal-2019-second,
    title = "The Second Multilingual Surface Realisation Shared Task ({SR}{'}19): Overview and Evaluation Results",
    author = "Mille, Simon  and
      Belz, Anja  and
      Bohnet, Bernd  and
      Graham, Yvette  and
      Wanner, Leo",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6301",
    doi = "10.18653/v1/D19-6301",
    pages = "1--17",
    abstract = "We report results from the SR{'}19 Shared Task, the second edition of a multilingual surface realisation task organised as part of the EMNLP{'}19 Workshop on Multilingual Surface Realisation. As in SR{'}18, the shared task comprised two tracks with different levels of complexity: (a) a shallow track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (b) a deep track where additionally, functional words and morphological information were removed. The shallow track was offered in eleven, and the deep track in three languages. Systems were evaluated (a) automatically, using a range of intrinsic metrics, and (b) by human judges in terms of readability and meaning similarity. This report presents the evaluation results, along with descriptions of the SR{'}19 tracks, data and evaluation methods. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.",
}
@inproceedings{du-black-2019-learning,
    title = "Learning to Order Graph Elements with Application to Multilingual Surface Realization",
    author = "Du, Wenchao  and
      Black, Alan W",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6302",
    doi = "10.18653/v1/D19-6302",
    pages = "18--24",
    abstract = "Recent advances in deep learning have shown promises in solving complex combinatorial optimization problems, such as sorting variable-sized sequences. In this work, we take a step further and tackle the problem of ordering the elements of sequences that come with graph structures. Our solution adopts an encoder-decoder framework, in which the encoder is a graph neural network that learns the representation for each element, and the decoder predicts the ordering of each local neighborhood of the graph in turn. We apply our framework to multilingual surface realization, which is the task of ordering and completing sentences with their dependency parses given but without the ordering of words. Experiments show that our approach is much better for this task than prior works that do not consider graph structures. We participated in 2019 Surface Realization Shared Task (SR{'}19), and we ranked second out of 14 teams while outperforming those teams below by a large margin.",
}
@inproceedings{dyer-2019-depdist,
    title = "{D}ep{D}ist: Surface realization via regex and learned dependency-distance tolerance",
    author = "Dyer, William",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6303",
    doi = "10.18653/v1/D19-6303",
    pages = "25--34",
    abstract = "This paper describes a method of inflecting and linearizing a lemmatized dependency tree by: (1) determining a regular expression and substitution to describe each productive wordform rule; (2) learning the dependency distance tolerance for each head-dependent pair, resulting in an edge-weighted directed acyclic graph (DAG); and (3) topologically sorting the DAG into a surface realization based on edge weight. The method{'}s output for 11 languages across 18 treebanks is competitive with the other submissions to the Second Multilingual Surface Realization Shared Task (SR {`}19).",
}
@inproceedings{kovacs-etal-2019-bme,
    title = "{BME}-{UW} at {SRST}-2019: Surface realization with Interpreted Regular Tree Grammars",
    author = "Kov{\'a}cs, {\'A}d{\'a}m  and
      {\'A}cs, Evelin  and
      {\'A}cs, Judit  and
      Kornai, Andras  and
      Recski, G{\'a}bor",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6304",
    doi = "10.18653/v1/D19-6304",
    pages = "35--40",
    abstract = "The Surface Realization Shared Task involves mapping Universal Dependency graphs to raw text, i.e. restoring word order and inflection from a graph of typed, directed dependencies between lemmas. Interpreted Regular Tree Grammars (IRTGs) encode the correspondence between generations in multiple algebras, and have previously been used for semantic parsing from raw text. Our system induces an IRTG for simultaneously building pairs of surface forms and UD graphs in the SRST training data, then prunes this grammar for each UD graph in the test data for efficient parsing and generation of the surface ordering of lemmas. For the inflection step we use a standard sequence-to-sequence model with a biLSTM encoder and an LSTM decoder with attention. Both components of our system are available on GitHub under an MIT license.",
}
@inproceedings{lapalme-2019-realizing,
    title = "Realizing {U}niversal {D}ependencies Structures",
    author = "Lapalme, Guy",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6305",
    doi = "10.18653/v1/D19-6305",
    pages = "41--49",
    abstract = "We first describe a surface realizer forUniversal Dependencies (UD) structures. The system uses a symbolic approach to transform the dependency tree into a tree of constituents that is transformed into an English sentence by an existing realizer. This approach was then adapted for the two shared tasks of SR{'}19. The system is quite fast and showed competitive results for English sentences using automatic and manual evaluation measures.",
}
@inproceedings{yu-etal-2019-imsurreal,
    title = "{IMS}ur{R}eal: {IMS} at the Surface Realization Shared Task 2019",
    author = "Yu, Xiang  and
      Falenska, Agnieszka  and
      Haid, Marina  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6306",
    doi = "10.18653/v1/D19-6306",
    pages = "50--58",
    abstract = "We introduce the IMS contribution to the Surface Realization Shared Task 2019. Our submission achieves the state-of-the-art performance without using any external resources. The system takes a pipeline approach consisting of five steps: linearization, completion, inflection, contraction, and detokenization. We compare the performance of our linearization algorithm with two external baselines and report results for each step in the pipeline. Furthermore, we perform detailed error analysis revealing correlation between word order freedom and difficulty of the linearization task.",
}
@inproceedings{castro-ferreira-krahmer-2019-surface,
    title = "Surface Realization Shared Task 2019 ({MSR}19): The Team 6 Approach",
    author = "Castro Ferreira, Thiago  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6307",
    doi = "10.18653/v1/D19-6307",
    pages = "59--62",
    abstract = "This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR{'}19) (Mille et al., 2019). Based on Ferreira et al. (2017) and on our 2018 submission Ferreira et al. (2018), the approach generates texts by first preprocessing an input dependency tree into an ordered linearized string, which is then realized using a rule-based and a statistical machine translation (SMT) model. This year our submission is able to realize texts in the 11 languages proposed for the task, different from our last year submission, which covered only 6 Indo-European languages. The model is publicly available.",
}
@inproceedings{farahnak-etal-2019-concordia,
    title = "The Concordia {NLG} Surface Realizer at {SRST} 2019",
    author = "Farahnak, Farhood  and
      Rafiee, Laya  and
      Kosseim, Leila  and
      Fevens, Thomas",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6308",
    doi = "10.18653/v1/D19-6308",
    pages = "63--67",
    abstract = "This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task. The model reconstructs sentences whose word order and word inflections were removed. We divided the problem into two sub-problems: reordering and inflecting. For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules. In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed.",
}
@inproceedings{upasani-etal-2019-osu,
    title = "The {OSU}/{F}acebook Realizer for {SRST} 2019: {S}eq2{S}eq Inflection and Serialized {T}ree2{T}ree Linearization",
    author = "Upasani, Kartikeya  and
      King, David  and
      Rao, Jinfeng  and
      Balakrishnan, Anusha  and
      White, Michael",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6309",
    doi = "10.18653/v1/D19-6309",
    pages = "68--74",
    abstract = "We describe our exploratory system for the shallow surface realization task, which combines morphological inflection using character sequence-to-sequence models with a baseline linearizer that implements a tree-to-tree model using sequence-to-sequence models on serialized trees. Results for morphological inflection were competitive across languages. Due to time constraints, we could only submit complete results (including linearization) for English. Preliminary linearization results were decent, with a small benefit from reranking to prefer valid output trees, but inadequate control over the words in the output led to poor quality on longer sentences.",
}
@inproceedings{hong-etal-2019-improving,
    title = "Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders",
    author = "Hong, Xudong  and
      Chang, Ernie  and
      Demberg, Vera",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6310",
    doi = "10.18653/v1/D19-6310",
    pages = "75--80",
    abstract = "The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the third rank without using data augmentation techniques or additional components (such as a re-ranker).",
}
@inproceedings{mazzei-basile-2019-dipinfounito,
    title = "The {D}ip{I}nfo{U}ni{T}o Realizer at {SRST}{'}19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization",
    author = "Mazzei, Alessandro  and
      Basile, Valerio",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6311",
    doi = "10.18653/v1/D19-6311",
    pages = "81--87",
    abstract = "We describe the system presented at the SR{'}19 shared task by the DipInfoUnito team. Our approach is based on supervised machine learning. In particular, we divide the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. Two neural networks with different architectures run on the same input structure, each producing a partial output which is recombined in the final step in order to produce the predicted surface form. This work is a direct successor of the architecture presented at SR{'}19.",
}
@inproceedings{shimorina-gardent-2019-loria,
    title = "{LORIA} / Lorraine University at Multilingual Surface Realisation 2019",
    author = "Shimorina, Anastasia  and
      Gardent, Claire",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6312",
    doi = "10.18653/v1/D19-6312",
    pages = "88--93",
    abstract = "This paper presents the LORIA / Lorraine University submission at the Multilingual Surface Realisation shared task 2019 for the shallow track. We outline our approach and evaluate it on 11 languages covered by the shared task. We provide a separate evaluation of each component of our pipeline, concluding on some difficulties and suggesting directions for future work.",
}
@inproceedings{sobrevilla-cabezudo-etal-2019-back,
    title = "Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations",
    author = "Sobrevilla Cabezudo, Marco Antonio  and
      Mille, Simon  and
      Pardo, Thiago",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6313",
    doi = "10.18653/v1/D19-6313",
    pages = "94--103",
    abstract = "This paper presents an exploratory study that aims to evaluate the usefulness of back-translation in Natural Language Generation (NLG) from semantic representations for non-English languages. Specifically, Abstract Meaning Representation and Brazilian Portuguese (BP) are chosen as semantic representation and language, respectively. Two methods (focused on Statistical and Neural Machine Translation) are evaluated on two datasets (one automatically generated and another one human-generated) to compare the performance in a real context. Also, several cuts according to quality measures are performed to evaluate the importance (or not) of the data quality in NLG. Results show that there are still many improvements to be made but this is a promising approach.",
}
@proceedings{emnlp-2019-international,
    title = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    editor = "Holderness, Eben  and
      Jimeno Yepes, Antonio  and
      Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Pustejovsky, James  and
      Rinaldi, Fabio",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6200",
}
@inproceedings{wright-bettner-etal-2019-cross,
    title = "Cross-document coreference: An approach to capturing coreference without context",
    author = "Wright-Bettner, Kristin  and
      Palmer, Martha  and
      Savova, Guergana  and
      de Groen, Piet  and
      Miller, Timothy",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6201",
    doi = "10.18653/v1/D19-6201",
    pages = "1--10",
    abstract = "This paper discusses a cross-document coreference annotation schema that was developed to further automatic extraction of timelines in the clinical domain. Lexical senses and coreference choices are determined largely by context, but cross-document work requires reasoning across contexts that are not necessarily coherent. We found that an annotation approach that relies less on context-guided annotator intuitions and more on schematic rules was most effective in creating meaningful and consistent cross-document relations.",
}
@inproceedings{nunez-carenini-2019-comparing,
    title = "Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine",
    author = "Nunez, John-Jose  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6202",
    doi = "10.18653/v1/D19-6202",
    pages = "11--17",
    abstract = "Pre-trained word embeddings are becoming increasingly popular for natural language processing tasks. This includes medical applications, where embeddings are trained for clinical concepts using specific medical data. Recent work continues to improve on these embeddings. However, no one has yet sought to determine whether these embeddings work as well for one field of medicine as they do in others. In this work, we use intrinsic methods to evaluate embeddings from the various fields of medicine as defined by their ICD-9 systems. We find significant differences between fields, and motivate future work to investigate whether extrinsic tasks will follow a similar pattern.",
}
@inproceedings{nguyen-etal-2019-effectiveness,
    title = "On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning",
    author = "Nguyen, Tuan Ngo  and
      Dernoncourt, Franck  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6203",
    doi = "10.18653/v1/D19-6203",
    pages = "18--27",
    abstract = "Deep learning models have achieved state-of-the-art performances on many relation extraction datasets. A common element in these deep learning models involves the pooling mechanisms where a sequence of hidden vectors is aggregated to generate a single representation vector, serving as the features to perform prediction for RE. Unfortunately, the models in the literature tend to employ different strategies to perform pooling for RE, leading to the challenge to determine the best pooling mechanism for this problem, especially in the biomedical domain. In order to answer this question, in this work, we conduct a comprehensive study to evaluate the effectiveness of different pooling mechanisms for the deep learning models in biomedical RE. The experimental results suggest that dependency-based pooling is the best pooling strategy for RE in the biomedical domain, yielding the state-of-the-art performance on two benchmark datasets for this problem.",
}
@inproceedings{li-ji-2019-syntax,
    title = "Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction",
    author = "Li, Diya  and
      Ji, Heng",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6204",
    doi = "10.18653/v1/D19-6204",
    pages = "28--33",
    abstract = "In this paper we tackle two unique challenges in biomedical relation extraction. The first challenge is that the contextual information between two entity mentions often involves sophisticated syntactic structures. We propose a novel graph convolutional networks model that incorporates dependency parsing and contextualized embedding to effectively capture comprehensive contextual information. The second challenge is that most of the benchmark data sets for this task are quite imbalanced because more than 80{\%} mention pairs are negative instances (i.e., no relations). We propose a multi-task learning framework to jointly model relation identification and classification tasks to propagate supervision signals from each other and apply a focal loss to focus training on ambiguous mention pairs. By applying these two strategies, experiments show that our model achieves state-of-the-art F-score on the 2013 drug-drug interaction extraction task.",
}
@inproceedings{basaldella-collier-2019-bioreddit,
    title = "{B}io{R}eddit: Word Embeddings for User-Generated Biomedical {NLP}",
    author = "Basaldella, Marco  and
      Collier, Nigel",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6205",
    doi = "10.18653/v1/D19-6205",
    pages = "34--38",
    abstract = "Word embeddings, in their different shapes and iterations, have changed the natural language processing research landscape in the last years. The biomedical text processing field is no stranger to this revolution; however, scholars in the field largely trained their embeddings on scientific documents only, even when working on user-generated data. In this paper we show how training embeddings from a corpus collected from user-generated text from medical forums heavily influences the performance on downstream tasks, outperforming embeddings trained both on general purpose data or on scientific papers when applied on user-generated content.",
}
@inproceedings{tsai-etal-2019-leveraging,
    title = "Leveraging Hierarchical Category Knowledge for Data-Imbalanced Multi-Label Diagnostic Text Understanding",
    author = "Tsai, Shang-Chi  and
      Chang, Ting-Yun  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6206",
    doi = "10.18653/v1/D19-6206",
    pages = "39--43",
    abstract = "Clinical notes are essential medical documents to record each patient{'}s symptoms. Each record is typically annotated with medical diagnostic codes, which means diagnosis and treatment. This paper focuses on predicting diagnostic codes given the descriptive present illness in electronic health records by leveraging domain knowledge. We investigate various losses in a convolutional model to utilize hierarchical category knowledge of diagnostic codes in order to allow the model to share semantics across different labels under the same category. The proposed model not only considers the external domain knowledge but also addresses the issue about data imbalance. The MIMIC3 benchmark experiments show that the proposed methods can effectively utilize category knowledge and provide informative cues to improve the performance in terms of the top-ranked diagnostic codes which is better than the prior state-of-the-art. The investigation and discussion express the potential of integrating the domain knowledge in the current machine learning based models and guiding future research directions.",
}
@inproceedings{mykowiecka-marciniak-2019-experiments,
    title = "Experiments with ad hoc ambiguous abbreviation expansion",
    author = "Mykowiecka, Agnieszka  and
      Marciniak, Malgorzata",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6207",
    doi = "10.18653/v1/D19-6207",
    pages = "44--53",
    abstract = "The paper addresses experiments to expand ad hoc ambiguous abbreviations in medical notes on the basis of morphologically annotated texts, without using additional domain resources. We work on Polish data but the described approaches can be used for other languages too. We test two methods to select candidates for word abbreviation expansions. The first one automatically selects all words in text which might be an expansion of an abbreviation according to the language rules. The second method uses clustering of abbreviation occurrences to select representative elements which are manually annotated to determine lists of potential expansions. We then train a classifier to assign expansions to abbreviations based on three training sets: automatically obtained, consisting of manual annotation, and concatenation of the two previous ones. The results obtained for the manually annotated training data significantly outperform automatically obtained training data. Adding the automatically obtained training data to the manually annotated data improves the results, in particular for less frequent abbreviations. In this context the proposed a priori data driven selection of possible extensions turned out to be crucial.",
}
@inproceedings{kirinde-gamaarachchige-inkpen-2019-multi,
    title = "Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text",
    author = "Kirinde Gamaarachchige, Prasadith  and
      Inkpen, Diana",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6208",
    doi = "10.18653/v1/D19-6208",
    pages = "54--64",
    abstract = "We investigate the impact of using emotional patterns identified by the clinical practitioners and computational linguists to enhance the prediction capabilities of a mental illness detection (in our case depression and post-traumatic stress disorder) model built using a deep neural network architecture. Over the years, deep learning methods have been successfully used in natural language processing tasks, including a few in the domain of mental illness and suicide ideation detection. We illustrate the effectiveness of using multi-task learning with a multi-channel convolutional neural network as the shared representation and use additional inputs identified by researchers as indicatives in detecting mental disorders to enhance the model predictability. Given the limited amount of unstructured data available for training, we managed to obtain a task-specific AUC higher than 0.90. In comparison to methods such as multi-class classification, we identified multi-task learning with multi-channel convolution neural network and multiple-inputs to be effective in detecting mental disorders.",
}
@inproceedings{jeblee-etal-2019-extracting,
    title = "Extracting relevant information from physician-patient dialogues for automated clinical note taking",
    author = "Jeblee, Serena  and
      Khan Khattak, Faiza  and
      Crampton, Noah  and
      Mamdani, Muhammad  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6209",
    doi = "10.18653/v1/D19-6209",
    pages = "65--74",
    abstract = "We present a system for automatically extracting pertinent medical information from dialogues between clinicians and patients. The system parses each dialogue and extracts entities such as medications and symptoms, using context to predict which entities are relevant. We also classify the primary diagnosis for each conversation. In addition, we extract topic information and identify relevant utterances. This serves as a baseline for a system that extracts information from dialogues and automatically generates a patient note, which can be reviewed and edited by the clinician.",
}
@inproceedings{chakraborty-etal-2019-biomedical,
    title = "Biomedical Relation Classification by single and multiple source domain adaptation",
    author = "Chakraborty, Sinchani  and
      Sarkar, Sudeshna  and
      Goyal, Pawan  and
      Gattu, Mahanandeeshwar",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6210",
    doi = "10.18653/v1/D19-6210",
    pages = "75--80",
    abstract = "Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it takes a lot of effort to label data for a new domain. In this paper, we explore domain adaptation techniques for this task. While past works have focused on single source domain adaptation for bio-medical relation classification, we classify relations in an unlabeled target domain by transferring useful knowledge from one or more related source domains. Our experiments with the model have shown to improve state-of-the-art F1 score on 3 benchmark biomedical corpora for single domain and on 2 out of 3 for multi-domain scenarios. When used with contextualized embeddings, there is further boost in performance outperforming neural-network based domain adaptation baselines for both the cases.",
}
@inproceedings{alvarez-mellado-etal-2019-assessing,
    title = "Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction",
    author = "Alvarez-Mellado, Elena  and
      Holderness, Eben  and
      Miller, Nicholas  and
      Dhang, Fyonn  and
      Cawkwell, Philip  and
      Bolton, Kirsten  and
      Pustejovsky, James  and
      Hall, Mei-Hua",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6211",
    doi = "10.18653/v1/D19-6211",
    pages = "81--86",
    abstract = "Predicting which patients are more likely to be readmitted to a hospital within 30 days after discharge is a valuable piece of information in clinical decision-making. Building a successful readmission risk classifier based on the content of Electronic Health Records (EHRs) has proved, however, to be a challenging task. Previously explored features include mainly structured information, such as sociodemographic data, comorbidity codes and physiological variables. In this paper we assess incorporating additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients.",
}
@inproceedings{van-etal-2019-language,
    title = "What does the language of foods say about us?",
    author = "Van, Hoang  and
      Musa, Ahmad  and
      Chen, Hang  and
      Kobourov, Stephen  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6212",
    doi = "10.18653/v1/D19-6212",
    pages = "87--96",
    abstract = "In this work we investigate the signal contained in the language of food on social media. We experiment with a dataset of 24 million food-related tweets, and make several observations. First,thelanguageoffoodhaspredictive power. We are able to predict if states in the United States (US) are above the medianratesfortype2diabetesmellitus(T2DM), income, poverty, and education {--} outperforming previous work by 4{--}18{\%}. Second, we investigate the effect of socioeconomic factors (income, poverty, and education) on predicting state-level T2DM rates. Socioeconomic factors do improve T2DM prediction, with the greatestimprovementcomingfrompovertyinformation(6{\%}),but,importantly,thelanguage of food adds distinct information that is not captured by socioeconomics. Third, we analyze how the language of food has changed over a five-year period (2013 {--} 2017), which is indicative of the shift in eating habits in the US during that period. We find several food trends, and that the language of food is used differently by different groups such as differentgenders. Last,weprovideanonlinevisualization tool for real-time queries and semantic analysis.",
}
@inproceedings{turcan-mckeown-2019-dreaddit,
    title = "{D}readdit: A {R}eddit Dataset for Stress Analysis in Social Media",
    author = "Turcan, Elsbeth  and
      McKeown, Kathy",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6213",
    doi = "10.18653/v1/D19-6213",
    pages = "97--107",
    abstract = "Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.",
}
@inproceedings{shieh-etal-2019-towards,
    title = "Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation",
    author = "Shieh, Alexander Te-Wei  and
      Chuang, Yung-Sung  and
      Su, Shang-Yu  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6214",
    doi = "10.18653/v1/D19-6214",
    pages = "108--117",
    abstract = "Randomized controlled trials (RCTs) represent the paramount evidence of clinical medicine. Using machines to interpret the massive amount of RCTs has the potential of aiding clinical decision-making. We propose a RCT conclusion generation task from the PubMed 200k RCT sentence classification dataset to examine the effectiveness of sequence-to-sequence models on understanding RCTs. We first build a pointer-generator baseline model for conclusion generation. Then we fine-tune the state-of-the-art GPT-2 language model, which is pre-trained with general domain data, for this new medical domain task. Both automatic and human evaluation show that our GPT-2 fine-tuned models achieve improved quality and correctness in the generated conclusions compared to the baseline pointer-generator model. Further inspection points out the limitations of this current approach and future directions to explore.",
}
@inproceedings{berg-etal-2019-building,
    title = "Building a De-identification System for Real {S}wedish Clinical Text Using Pseudonymised Clinical Text",
    author = "Berg, Hanna  and
      Chomutare, Taridzo  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6215",
    doi = "10.18653/v1/D19-6215",
    pages = "118--125",
    abstract = "This article presents experiments with pseudonymised Swedish clinical text used as training data to de-identify real clinical text with the future aim to transfer non-sensitive training data to other hospitals. Conditional Random Fields (CFR) and Long Short-Term Memory (LSTM) machine learning algorithms were used to train de-identification models. The two models were trained on pseudonymised data and evaluated on real data. For benchmarking, models were also trained on real data, and evaluated on real data as well as trained on pseudonymised data and evaluated on pseudonymised data. CRF showed better performance for some PHI information like Date Part, First Name and Last Name; consistent with some reports in the literature. In contrast, poor performances on Location and Health Care Unit information were noted, partially due to the constrained vocabulary in the pseudonymised training data. It is concluded that it is possible to train transferable models based on pseudonymised Swedish clinical data, but even small narrative and distributional variation could negatively impact performance.",
}
@inproceedings{yim-etal-2019-automatic,
    title = "Automatic rubric-based content grading for clinical notes",
    author = "Yim, Wen-wai  and
      Mills, Ashley  and
      Chun, Harold  and
      Hashiguchi, Teresa  and
      Yew, Justin  and
      Lu, Bryan",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6216",
    doi = "10.18653/v1/D19-6216",
    pages = "126--135",
    abstract = "Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set.",
}
@inproceedings{schoene-etal-2019-dilated,
    title = "Dilated {LSTM} with attention for Classification of Suicide Notes",
    author = "Schoene, Annika M  and
      Lacey, George  and
      Turner, Alexander P  and
      Dethlefs, Nina",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6217",
    doi = "10.18653/v1/D19-6217",
    pages = "136--145",
    abstract = "In this paper we present a dilated LSTM with attention mechanism for document-level classification of suicide notes, last statements and depressed notes. We achieve an accuracy of 87.34{\%} compared to competitive baselines of 80.35{\%} (Logistic Model Tree) and 82.27{\%} (Bi-directional LSTM with Attention). Furthermore, we provide an analysis of both the grammatical and thematic content of suicide notes, last statements and depressed notes. We find that the use of personal pronouns, cognitive processes and references to loved ones are most important. Finally, we show through visualisations of attention weights that the Dilated LSTM with attention is able to identify the same distinguishing features across documents as the linguistic analysis.",
}
@inproceedings{newman-griffis-fosler-lussier-2019-writing,
    title = "Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings",
    author = "Newman-Griffis, Denis  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6218",
    doi = "10.18653/v1/D19-6218",
    pages = "146--156",
    abstract = "Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishing characteristics of medical document types. We present a method for characterizing the usage patterns of clinical concepts among different document types, in order to capture semantic differences beyond the lexical level. By training concept embeddings on clinical documents of different types and measuring the differences in their nearest neighborhood structures, we are able to measure divergences in concept usage while correcting for noise in embedding learning. Experiments on the MIMIC-III corpus demonstrate that our approach captures clinically-relevant differences in concept usage and provides an intuitive way to explore semantic characteristics of clinical document collections.",
}
@inproceedings{nejadgholi-etal-2019-recognizing,
    title = "Recognizing {UMLS} Semantic Types with Deep Learning",
    author = "Nejadgholi, Isar  and
      Fraser, Kathleen C.  and
      De Bruijn, Berry  and
      Li, Muqun  and
      LaPlante, Astha  and
      Zine El Abidine, Khaldoun",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6219",
    doi = "10.18653/v1/D19-6219",
    pages = "157--167",
    abstract = "Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the-art entity recognition approaches on a newly released dataset, MedMentions. This dataset contains over 4000 biomedical abstracts, annotated for UMLS semantic types. In comparison to existing datasets, MedMentions contains a far greater number of entity types, and thus represents a more challenging but realistic scenario in a real-world setting. We explore a number of relevant dimensions, including the use of contextual versus non-contextual word embeddings, general versus domain-specific unsupervised pre-training, and different deep learning architectures. We contrast our results against the well-known i2b2 2010 entity recognition dataset, and propose a new method to combine general and domain-specific information. While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0.90), our results on MedMentions are significantly lower (F1 = 0.63), suggesting there is still plenty of opportunity for improvement on this new data.",
}
@inproceedings{falis-etal-2019-ontological,
    title = "Ontological attention ensembles for capturing semantic concepts in {ICD} code prediction from clinical text",
    author = "Falis, Matus  and
      Pajak, Maciej  and
      Lisowska, Aneta  and
      Schrempf, Patrick  and
      Deckers, Lucas  and
      Mikhael, Shadia  and
      Tsaftaris, Sotirios  and
      O{'}Neil, Alison",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6220",
    doi = "10.18653/v1/D19-6220",
    pages = "168--177",
    abstract = "We present a semantically interpretable system for automated ICD coding of clinical text documents. Our contribution is an ontological attention mechanism which matches the structure of the ICD ontology, in which shared attention vectors are learned at each level of the hierarchy, and combined into label-dependent ensembles. Analysis of the attention heads shows that shared concepts are learned by the lowest common denominator node. This allows child nodes to focus on the differentiating concepts, leading to efficient learning and memory usage. Visualisation of the multi-level attention on the original text allows explanation of the code predictions according to the semantics of the ICD ontology. On the MIMIC-III dataset we achieve a 2.7{\%} absolute (11{\%} relative) improvement from 0.218 to 0.245 macro-F1 score compared to the previous state of the art across 3,912 codes. Finally, we analyse the labelling inconsistencies arising from different coding practices which limit performance on this task.",
}
@inproceedings{sergeeva-etal-2019-neural,
    title = "Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text",
    author = "Sergeeva, Elena  and
      Zhu, Henghui  and
      Tahmasebi, Amir  and
      Szolovits, Peter",
    booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6221",
    doi = "10.18653/v1/D19-6221",
    pages = "178--187",
    abstract = "Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks. Nevertheless, the degree to which the resulting context-aware representations encode information about morpho-syntactic properties of the word/token in a sentence remains unclear. In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cue-conditional speculation and negation scope detection coupled with the independently computed morpho-syntactic information. Through this work, We establish a new state-of-the-art for the BioScope and NegPar corpus. More importantly, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discuss model behavior on different datasets and address the annotation-induced biases in the learned representations.",
}
@proceedings{emnlp-2019-deep,
    title = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    editor = "Cherry, Colin  and
      Durrett, Greg  and
      Foster, George  and
      Haffari, Reza  and
      Khadivi, Shahram  and
      Peng, Nanyun  and
      Ren, Xiang  and
      Swayamdipta, Swabha",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6100",
}
@inproceedings{kumar-etal-2019-closer,
    title = "A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification",
    author = "Kumar, Varun  and
      Glaude, Hadrien  and
      de Lichy, Cyprien  and
      Campbell, Wlliam",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6101",
    doi = "10.18653/v1/D19-6101",
    pages = "1--10",
    abstract = "New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance. We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used to introduce a new intent. In this paper, we study six feature space data augmentation methods to improve classification performance in FSI setting in combination with both supervised and unsupervised representation learning methods such as BERT. Through realistic experiments on two public conversational datasets, SNIPS, and the Facebook Dialog corpus, we show that data augmentation in feature space provides an effective way to improve intent classification performance in few-shot setting beyond traditional transfer learning approaches. In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.",
}
@inproceedings{rocha-lopes-cardoso-2019-comparative,
    title = "A Comparative Analysis of Unsupervised Language Adaptation Methods",
    author = "Rocha, Gil  and
      Lopes Cardoso, Henrique",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6102",
    doi = "10.18653/v1/D19-6102",
    pages = "11--21",
    abstract = "To overcome the lack of annotated resources in less-resourced languages, recent approaches have been proposed to perform unsupervised language adaptation. In this paper, we explore three recent proposals: Adversarial Training, Sentence Encoder Alignment and Shared-Private Architecture. We highlight the differences of these approaches in terms of unlabeled data requirements and capability to overcome additional domain shift in the data. A comparative analysis in two different tasks is conducted, namely on Sentiment Classification and Natural Language Inference. We show that adversarial training methods are more suitable when the source and target language datasets contain other variations in content besides the language shift. Otherwise, sentence encoder alignment methods are very effective and can yield scores on the target language that are close to the source language scores.",
}
@inproceedings{salvatore-etal-2019-logical,
    title = "A logical-based corpus for cross-lingual evaluation",
    author = "Salvatore, Felipe  and
      Finger, Marcelo  and
      Hirata Jr, Roberto",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6103",
    doi = "10.18653/v1/D19-6103",
    pages = "22--30",
    abstract = "At present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail. However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference. We propose a new set of syntactic tasks focused on contradiction detection that require specific capacities over linguistic logical forms such as: Boolean coordination, quantifiers, definite description, and counting operators. We evaluate two kinds of deep learning models that implicitly exploit language structure: recurrent models and the Transformer network BERT. We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators. Since the syntactic tasks can be implemented in different languages, we show a successful case of cross-lingual transfer learning between English and Portuguese.",
}
@inproceedings{van-hautte-etal-2019-bad,
    title = "Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models",
    author = "Van Hautte, Jeroen  and
      Emerson, Guy  and
      Rei, Marek",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6104",
    doi = "10.18653/v1/D19-6104",
    pages = "31--39",
    abstract = "Word embeddings are an essential component in a wide range of natural language processing applications. However, distributional semantic models are known to struggle when only a small number of context sentences are available. Several methods have been proposed to obtain higher-quality vectors for these words, leveraging both this context information and sometimes the word forms themselves through a hybrid approach. We show that the current tasks do not suffice to evaluate models that use word-form information, as such models can easily leverage word forms in the training data that are related to word forms in the test data. We introduce 3 new tasks, allowing for a more balanced comparison between models. Furthermore, we show that hyperparameters that have largely been ignored in previous work can consistently improve the performance of both baseline and advanced models, achieving a new state of the art on 4 out of 6 tasks.",
}
@inproceedings{ebner-etal-2019-bag,
    title = "Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning",
    author = "Ebner, Seth  and
      Wang, Felicity  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6105",
    doi = "10.18653/v1/D19-6105",
    pages = "40--46",
    abstract = "Many architectures for multi-task learning (MTL) have been proposed to take advantage of transfer among tasks, often involving complex models and training procedures. In this paper, we ask if the sentence-level representations learned in previous approaches provide significant benefit beyond that provided by simply improving word-based representations. To investigate this question, we consider three techniques that ignore sequence information: a syntactically-oblivious pooling encoder, pre-trained non-contextual word embeddings, and unigram generative regularization. Compared to a state-of-the-art MTL approach to textual inference, the simple techniques we use yield similar performance on a universe of task combinations while reducing training time and model size.",
}
@inproceedings{singh-etal-2019-bert,
    title = "{BERT} is Not an Interlingua and the Bias of Tokenization",
    author = "Singh, Jasdeep  and
      McCann, Bryan  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6106",
    doi = "10.18653/v1/D19-6106",
    pages = "47--55",
    abstract = "Multilingual transfer learning can benefit both high- and low-resource languages, but the source of these improvements is not well understood. Cananical Correlation Analysis (CCA) of the internal representations of a pre- trained, multilingual BERT model reveals that the model partitions representations for each language rather than using a common, shared, interlingual space. This effect is magnified at deeper layers, suggesting that the model does not progressively abstract semantic con- tent while disregarding languages. Hierarchical clustering based on the CCA similarity scores between languages reveals a tree structure that mirrors the phylogenetic trees hand- designed by linguists. The subword tokenization employed by BERT provides a stronger bias towards such structure than character- and word-level tokenizations. We release a subset of the XNLI dataset translated into an additional 14 languages at https://www.github.com/salesforce/xnli{\_}extension to assist further research into multilingual representations.",
}
@inproceedings{pan-etal-2019-cross,
    title = "Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining",
    author = "Pan, Xiaoman  and
      Gowda, Thamme  and
      Ji, Heng  and
      May, Jonathan  and
      Miller, Scott",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6107",
    doi = "10.18653/v1/D19-6107",
    pages = "56--66",
    abstract = "Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1{\%} absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.",
}
@inproceedings{papanikolaou-etal-2019-deep,
    title = "Deep Bidirectional Transformers for Relation Extraction without Supervision",
    author = "Papanikolaou, Yannis  and
      Roberts, Ian  and
      Pierleoni, Andrea",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6108",
    doi = "10.18653/v1/D19-6108",
    pages = "67--75",
    abstract = "We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base. Our approach leverages syntactic parsing and pre-trained word embeddings to extract few but precise relations, which are then used to annotate a larger corpus, in a manner identical to distant supervision. The resulting data set is employed to fine tune a pre-trained BERT model in order to perform relation extraction. Empirical evaluation on four data sets from the biomedical domain shows that our method significantly outperforms two simple baselines for unsupervised relation extraction and, even if not using any supervision at all, achieves slightly worse results than the state-of-the-art in three out of four data sets. Importantly, we show that it is possible to successfully fine tune a large pretrained language model with noisy data, as opposed to previous works that rely on gold data for fine tuning.",
}
@inproceedings{ma-etal-2019-domain,
    title = "Domain Adaptation with {BERT}-based Domain Classification and Data Selection",
    author = "Ma, Xiaofei  and
      Xu, Peng  and
      Wang, Zhiguo  and
      Nallapati, Ramesh  and
      Xiang, Bing",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6109",
    doi = "10.18653/v1/D19-6109",
    pages = "76--83",
    abstract = "The performance of deep neural models can deteriorate substantially when there is a domain shift between training and test data. For example, the pre-trained BERT model can be easily fine-tuned with just one additional output layer to create a state-of-the-art model for a wide range of tasks. However, the fine-tuned BERT model suffers considerably at zero-shot when applied to a different domain. In this paper, we present a novel two-step domain adaptation framework based on curriculum learning and domain-discriminative data selection. The domain adaptation is conducted in a mostly unsupervised manner using a small target domain validation set for hyper-parameter tuning. We tested the framework on four large public datasets with different domain similarities and task types. Our framework outperforms a popular discrepancy-based domain adaptation method on most transfer tasks while consuming only a fraction of the training budget.",
}
@inproceedings{zeng-etal-2019-empirical,
    title = "Empirical Evaluation of Active Learning Techniques for Neural {MT}",
    author = "Zeng, Xiangkai  and
      Garg, Sarthak  and
      Chatterjee, Rajen  and
      Nallasamy, Udhyakumar  and
      Paulik, Matthias",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6110",
    doi = "10.18653/v1/D19-6110",
    pages = "84--93",
    abstract = "Active learning (AL) for machine translation (MT) has been well-studied for the phrase-based MT paradigm. Several AL algorithms for data sampling have been proposed over the years. However, given the rapid advancement in neural methods, these algorithms have not been thoroughly investigated in the context of neural MT (NMT). In this work, we address this missing aspect by conducting a systematic comparison of different AL methods in a simulated AL framework. Our experimental setup to compare different AL methods uses: i) State-of-the-art NMT architecture to achieve realistic results; and ii) the same dataset (WMT{'}13 English-Spanish) to have fair comparison across different methods. We then demonstrate how recent advancements in unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods. Finally, we propose a neural extension for an AL sampling method used in the context of phrase-based MT - Round Trip Translation Likelihood (RTTL). RTTL uses a bidirectional translation model to estimate the loss of information during translation and outperforms previous methods.",
}
@inproceedings{ray-etal-2019-fast,
    title = "Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention",
    author = "Ray, Avik  and
      Shen, Yilin  and
      Jin, Hongxia",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6111",
    doi = "10.18653/v1/D19-6111",
    pages = "94--103",
    abstract = "Semantic parsers are used to convert user{'}s natural language commands to executable logical form in intelligent personal agents. Labeled datasets required to train such parsers are expensive to collect, and are never comprehensive. As a result, for effective post-deployment domain adaptation and personalization, semantic parsers are continuously retrained to learn new user vocabulary and paraphrase variety. However, state-of-the art attention based neural parsers are slow to retrain which inhibits real time domain adaptation. Secondly, these parsers do not leverage numerous paraphrases already present in the training dataset. Designing parsers which can simultaneously maintain high accuracy and fast retraining time is challenging. In this paper, we present novel paraphrase attention based sequence-to-sequence/tree parsers which support fast near real time retraining. In addition, our parsers often boost accuracy by jointly modeling the semantic dependencies of paraphrases. We evaluate our model on benchmark datasets to demonstrate upto 9X speedup in retraining time compared to existing parsers, as well as achieving state-of-the-art accuracy.",
}
@inproceedings{bollmann-etal-2019-shot,
    title = "Few-Shot and Zero-Shot Learning for Historical Text Normalization",
    author = "Bollmann, Marcel  and
      Korchagina, Natalia  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6112",
    doi = "10.18653/v1/D19-6112",
    pages = "104--114",
    abstract = "Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study of different multi-task learning architectures. This paper evaluates 63 multi-task learning configurations for sequence-to-sequence-based historical text normalization across ten datasets from eight languages, using autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary tasks. We observe consistent, significant improvements across languages when training data for the target task is limited, but minimal or no improvements when training data is abundant. We also show that zero-shot learning outperforms the simple, but relatively strong, identity baseline.",
}
@inproceedings{patidar-etal-2019-monolingual,
    title = "From Monolingual to Multilingual {FAQ} Assistant using Multilingual Co-training",
    author = "Patidar, Mayur  and
      Kumari, Surabhi  and
      Patwardhan, Manasi  and
      Karande, Shirish  and
      Agarwal, Puneet  and
      Vig, Lovekesh  and
      Shroff, Gautam",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6113",
    doi = "10.18653/v1/D19-6113",
    pages = "115--123",
    abstract = "Recent research on cross-lingual transfer show state-of-the-art results on benchmark datasets using pre-trained language representation models (PLRM) like BERT. These results are achieved with the traditional training approaches, such as Zero-shot with no data, Translate-train or Translate-test with machine translated data. In this work, we propose an approach of {``}Multilingual Co-training{''} (MCT) where we augment the expert annotated dataset in the source language (English) with the corresponding machine translations in the target languages (e.g. Arabic, Spanish) and fine-tune the PLRM jointly. We observe that the proposed approach provides consistent gains in the performance of BERT for multiple benchmark datasets (e.g. 1.0{\%} gain on MLDocs, and 1.2{\%} gain on XNLI over translate-train with BERT), while requiring a single model for multiple languages. We further consider a FAQ dataset where the available English test dataset is translated by experts into Arabic and Spanish. On such a dataset, we observe an average gain of 4.9{\%} over all other cross-lingual transfer protocols with BERT. We further observe that domain-specific joint pre-training of the PLRM using HR policy documents in English along with the machine translations in the target languages, followed by the joint finetuning, provides a further improvement of 2.8{\%} in average accuracy.",
}
@inproceedings{melas-kyriazi-etal-2019-generation,
    title = "Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings",
    author = "Melas-Kyriazi, Luke  and
      Han, George  and
      Liang, Celine",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6114",
    doi = "10.18653/v1/D19-6114",
    pages = "124--131",
    abstract = "Over the past year, the emergence of transfer learning with large-scale language models (LM) has led to dramatic performance improvements across a broad range of natural language understanding tasks. However, the size and memory footprint of these large LMs often makes them difficult to deploy in many scenarios (e.g. on mobile phones). Recent research points to knowledge distillation as a potential solution, showing that when training data for a given task is abundant, it is possible to distill a large (teacher) LM into a small task-specific (student) network with minimal loss of performance. However, when such data is scarce, there remains a significant performance gap between large pretrained LMs and smaller task-specific models, even when training via distillation. In this paper, we bridge this gap with a novel training approach, called generation-distillation, that leverages large finetuned LMs in two ways: (1) to generate new (unlabeled) training examples, and (2) to distill their knowledge into a small network using these examples. Across three low-resource text classification datsets, we achieve comparable performance to BERT while using 300 times fewer parameters, and we outperform prior approaches to distillation for text classification while using 3 times fewer parameters.",
}
@inproceedings{he-etal-2019-unlearn,
    title = "Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual",
    author = "He, He  and
      Zha, Sheng  and
      Wang, Haohan",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6115",
    doi = "10.18653/v1/D19-6115",
    pages = "132--142",
    abstract = "Statistical natural language inference (NLI) models are susceptible to learning dataset bias: superficial cues that happen to associate with the label on a particular dataset, but are not useful in general, e.g., negation words indicate contradiction. As exposed by several recent challenge datasets, these models perform poorly when such association is absent, e.g., predicting that {``}I love dogs.{''} contradicts {``}I don{'}t love cats.{''}. Our goal is to design learning algorithms that guard against known dataset bias. We formalize the concept of dataset bias under the framework of distribution shift and present a simple debiasing algorithm based on residual fitting, which we call DRiFt. We first learn a biased model that only uses features that are known to relate to dataset bias. Then, we train a debiased model that fits to the residual of the biased model, focusing on examples that cannot be predicted well by biased features only. We use DRiFt to train three high-performing NLI models on two benchmark datasets, SNLI and MNLI. Our debiased models achieve significant gains over baseline models on two challenge test sets, while maintaining reasonable performance on the original test sets.",
}
@inproceedings{wohlwend-etal-2019-metric,
    title = "Metric Learning for Dynamic Text Classification",
    author = "Wohlwend, Jeremy  and
      Elenberg, Ethan R.  and
      Altschul, Sam  and
      Henry, Shawn  and
      Lei, Tao",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6116",
    doi = "10.18653/v1/D19-6116",
    pages = "143--152",
    abstract = "Traditional text classifiers are limited to predicting over a fixed set of labels. However, in many real-world applications the label set is frequently changing. For example, in intent classification, new intents may be added over time while others are removed. We propose to address the problem of dynamic text classification by replacing the traditional, fixed-size output layer with a learned, semantically meaningful metric space. Here the distances between textual inputs are optimized to perform nearest-neighbor classification across overlapping label sets. Changing the label set does not involve removing parameters, but rather simply adding or removing support points in the metric space. Then the learned metric can be fine-tuned with only a few additional training examples. We demonstrate that this simple strategy is robust to changes in the label space. Furthermore, our results show that learning a non-Euclidean metric can improve performance in the low data regime, suggesting that further work on metric spaces may benefit low-resource research.",
}
@inproceedings{desai-etal-2019-evaluating,
    title = "Evaluating Lottery Tickets Under Distributional Shifts",
    author = "Desai, Shrey  and
      Zhan, Hongyuan  and
      Aly, Ahmed",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6117",
    doi = "10.18653/v1/D19-6117",
    pages = "153--162",
    abstract = "The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better) test accuracy. However, the initialization and generalizability of the obtained sparse subnetworks have been recently called into question. Our work focuses on evaluating the initialization of sparse subnetworks under distributional shifts. Specifically, we investigate the extent to which a sparse subnetwork obtained in a source domain can be re-trained in isolation in a dissimilar, target domain. In addition, we examine the effects of different initialization strategies at transfer-time. Our experiments show that sparse subnetworks obtained through lottery ticket training do not simply overfit to particular domains, but rather reflect an inductive bias of deep neural networks that can be exploited in multiple domains.",
}
@inproceedings{barry-etal-2019-cross,
    title = "Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A {F}aroese Case Study",
    author = "Barry, James  and
      Wagner, Joachim  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6118",
    doi = "10.18653/v1/D19-6118",
    pages = "163--174",
    abstract = "Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. It is a crucial component for inducing dependency parsers in low-resource scenarios where no training data for a language exists. Using Faroese as the target language, we compare two approaches using annotation projection: first, projecting from multiple monolingual source models; second, projecting from a single polyglot model which is trained on the combination of all source languages. Furthermore, we reproduce multi-source projection (Tyers et al., 2018), in which dependency trees of multiple sources are combined. Finally, we apply multi-treebank modelling to the projected treebanks, in addition to or alternatively to polyglot modelling on the source side. We find that polyglot training on the source languages produces an overall trend of better results on the target language but the single best result for the target language is obtained by projecting from monolingual source parsing models and then training multi-treebank POS tagging and parsing models on the target side.",
}
@inproceedings{wang-etal-2019-inject,
    title = "Inject Rubrics into Short Answer Grading System",
    author = "Wang, Tianqi  and
      Inoue, Naoya  and
      Ouchi, Hiroki  and
      Mizumoto, Tomoya  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6119",
    doi = "10.18653/v1/D19-6119",
    pages = "175--182",
    abstract = "Short Answer Grading (SAG) is a task of scoring students{'} answers in examinations. Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance. But they ignore important evaluation criteria such as rubrics, which play a crucial role for evaluating answers in real-world situations. In this paper, we present a method to inject information from rubrics into SAG systems. We implement our approach on top of word-level attention mechanism to introduce the rubric information, in order to locate information in each answer that are highly related to the score. Our experimental results demonstrate that injecting rubric information effectively contributes to the performance improvement and that our proposed model outperforms the state-of-the-art SAG model on the widely used ASAP-SAS dataset under low-resource settings.",
}
@inproceedings{basu-roy-chowdhury-etal-2019-instance,
    title = "Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing",
    author = "Basu Roy Chowdhury, Somnath  and
      M, Annervaz  and
      Dukkipati, Ambedkar",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6120",
    doi = "10.18653/v1/D19-6120",
    pages = "183--191",
    abstract = "Supervised learning models are typically trained on a single dataset and the performance of these models rely heavily on the size of the dataset i.e., the amount of data available with ground truth. Learning algorithms try to generalize solely based on the data that it is presented with during the training. In this work, we propose an inductive transfer learning method that can augment learning models by infusing similar instances from different learning tasks in Natural Language Processing (NLP) domain. We propose to use instance representations from a source dataset, without inheriting anything else from the source learning model. Representations of the instances of source and target datasets are learned, retrieval of relevant source instances is performed using soft-attention mechanism and locality sensitive hashing and then augmented into the model during training on the target dataset. Therefore, while learning from a training data, we also simultaneously exploit and infuse relevant local instance-level information from an external data. Using this approach we have shown significant improvements over the baseline for three major news classification datasets. Experimental evaluations also show that the proposed approach reduces dependency on labeled data by a significant margin for comparable performance. With our proposed cross dataset learning procedure we show that one can achieve competitive/better performance than learning from a single dataset.",
}
@inproceedings{route-etal-2019-multimodal,
    title = "Multimodal, Multilingual Grapheme-to-Phoneme Conversion for Low-Resource Languages",
    author = "Route, James  and
      Hillis, Steven  and
      Czeresnia Etinger, Isak  and
      Zhang, Han  and
      Black, Alan W",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6121",
    doi = "10.18653/v1/D19-6121",
    pages = "192--201",
    abstract = "Grapheme-to-phoneme conversion (g2p) is the task of predicting the pronunciation of words from their orthographic representation. His- torically, g2p systems were transition- or rule- based, making generalization beyond a mono- lingual (high resource) domain impractical. Recently, neural architectures have enabled multilingual systems to generalize widely; however, all systems to date have been trained only on spelling-pronunciation pairs. We hy- pothesize that the sequences of IPA characters used to represent pronunciation do not capture its full nuance, especially when cleaned to fa- cilitate machine learning. We leverage audio data as an auxiliary modality in a multi-task training process to learn a more optimal inter- mediate representation of source graphemes; this is the first multimodal model proposed for multilingual g2p. Our approach is highly ef- fective: on our in-domain test set, our mul- timodal model reduces phoneme error rate to 2.46{\%}, a more than 65{\%} decrease compared to our implementation of a unimodal spelling- pronunciation model{---}which itself achieves state-of-the-art results on the Wiktionary test set. The advantages of the multimodal model generalize to wholly unseen languages, reduc- ing phoneme error rate on our out-of-domain test set to 6.39{\%} from the unimodal 8.21{\%}, a more than 20{\%} relative decrease. Further- more, our training and test sets are composed primarily of low-resource languages, demon- strating that our multimodal approach remains useful when training data are constrained.",
}
@inproceedings{tang-etal-2019-natural,
    title = "Natural Language Generation for Effective Knowledge Distillation",
    author = "Tang, Raphael  and
      Lu, Yao  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6122",
    doi = "10.18653/v1/D19-6122",
    pages = "202--208",
    abstract = "Knowledge distillation can effectively transfer knowledge from BERT, a deep language representation model, to traditional, shallow word embedding-based neural networks, helping them approach or exceed the quality of other heavyweight language representation models. As shown in previous work, critical to this distillation procedure is the construction of an unlabeled transfer dataset, which enables effective knowledge transfer. To create transfer set examples, we propose to sample from pretrained language models fine-tuned on task-specific text. Unlike previous techniques, this directly captures the purpose of the transfer set. We hypothesize that this principled, general approach outperforms rule-based techniques. On four datasets in sentiment classification, sentence similarity, and linguistic acceptability, we show that our approach improves upon previous methods. We outperform OpenAI GPT, a deep pretrained transformer, on three of the datasets, while using a single-layer bidirectional LSTM that runs at least ten times faster.",
}
@inproceedings{kann-etal-2019-neural,
    title = "Neural Unsupervised Parsing Beyond {E}nglish",
    author = "Kann, Katharina  and
      Mohananey, Anhad  and
      Bowman, Samuel R.  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6123",
    doi = "10.18653/v1/D19-6123",
    pages = "209--218",
    abstract = "Recently, neural network models which automatically infer syntactic structure from raw text have started to achieve promising results. However, earlier work on unsupervised parsing shows large performance differences between non-neural models trained on corpora in different languages, even for comparable amounts of data. With that in mind, we train instances of the PRPN architecture (Shen et al., 2018){---}one of these unsupervised neural network parsers{---}for Arabic, Chinese, English, and German. We find that (i) the model strongly outperforms trivial baselines and, thus, acquires at least some parsing ability for all languages; (ii) good hyperparameter values seem to be universal; (iii) how the model benefits from larger training set sizes depends on the corpus, with the model achieving the largest performance gains when increasing the number of sentences from 2,500 to 12,500 for English. In addition, we show that, by sharing parameters between the related languages German and English, we can improve the model{'}s unsupervised parsing F1 score by up to 4{\%} in the low-resource setting.",
}
@inproceedings{joshi-etal-2019-reevaluating,
    title = "Reevaluating Argument Component Extraction in Low Resource Settings",
    author = "Joshi, Anirudh  and
      Baldwin, Timothy  and
      Sinnott, Richard  and
      Paris, Cecile",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6124",
    doi = "10.18653/v1/D19-6124",
    pages = "219--224",
    abstract = "Argument component extraction is a challenging and complex high-level semantic extraction task. As such, it is both expensive to annotate (meaning training data is limited and low-resource by nature), and hard for current-generation deep learning methods to model. In this paper, we reevaluate the performance of state-of-the-art approaches in both single- and multi-task learning settings using combinations of character-level, GloVe, ELMo, and BERT encodings using standard BiLSTM-CRF encoders. We use evaluation metrics that are more consistent with evaluation practice in named entity recognition to understand how well current baselines address this challenge and compare their performance to lower-level semantic tasks such as CoNLL named entity recognition. We find that performance utilizing various pre-trained representations and training methodologies often leaves a lot to be desired as it currently stands, and suggest future pathways for improvement.",
}
@inproceedings{nooralahzadeh-etal-2019-reinforcement,
    title = "Reinforcement-based denoising of distantly supervised {NER} with partial annotation",
    author = "Nooralahzadeh, Farhad  and
      L{\o}nning, Jan Tore  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6125",
    doi = "10.18653/v1/D19-6125",
    pages = "225--233",
    abstract = "Existing named entity recognition (NER) systems rely on large amounts of human-labeled data for supervision. However, obtaining large-scale annotated data is challenging particularly in specific domains like health-care, e-commerce and so on. Given the availability of domain specific knowledge resources, (e.g., ontologies, dictionaries), distant supervision is a solution to generate automatically labeled training data to reduce human effort. The outcome of distant supervision for NER, however, is often noisy. False positive and false negative instances are the main issues that reduce performance on this kind of auto-generated data. In this paper, we explore distant supervision in a supervised setup. We adopt a technique of partial annotation to address false negative cases and implement a reinforcement learning strategy with a neural network policy to identify false positive instances. Our results establish a new state-of-the-art on four benchmark datasets taken from different domains and different languages. We then go on to show that our model reduces the amount of manually annotated data required to perform NER in a new domain.",
}
@inproceedings{duggenpudi-etal-2019-samvaadhana,
    title = "{S}amvaadhana: A {T}elugu Dialogue System in Hospital Domain",
    author = "Duggenpudi, Suma Reddy  and
      Siva Subrahamanyam Varma, Kusampudi  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6126",
    doi = "10.18653/v1/D19-6126",
    pages = "234--242",
    abstract = "In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built. It handles various hospital and doctor related queries. The main aim of this paper is to present an approach for modelling a dialogue system in a resource-poor language by combining linguistic and domain knowledge. Focusing on the question answering aspect of the dialogue system, we identified Question Classification and Query Processing as the two most important parts of the dialogue system. Our method combines deep learning techniques for question classification and computational rule-based analysis for query processing. Human evaluation of the system has been performed as there is no automated evaluation tool for dialogue systems in Telugu. Our system achieves a high overall rating along with a significantly accurate context-capturing method as shown in the results.",
}
@inproceedings{zhou-etal-2019-towards,
    title = "Towards Zero-resource Cross-lingual Entity Linking",
    author = "Zhou, Shuyan  and
      Rijhwani, Shruti  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6127",
    doi = "10.18653/v1/D19-6127",
    pages = "243--252",
    abstract = "Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, many works on XEL have been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are not available in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. We next propose three improvements to both entity candidate generation and disambiguation that make better use of the limited resources we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-20{\%} end-to-end linking accuracy.",
}
@inproceedings{bjerva-etal-2019-transductive,
    title = "Transductive Auxiliary Task Self-Training for Neural Multi-Task Models",
    author = "Bjerva, Johannes  and
      Kann, Katharina  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6128",
    doi = "10.18653/v1/D19-6128",
    pages = "253--258",
    abstract = "Multi-task learning and self-training are two common ways to improve a machine learning model{'}s performance in settings with limited training data. Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated. We perform extensive experiments on 86 combinations of languages and tasks. Our results are that, on average, transductive auxiliary task self-training improves absolute accuracy by up to 9.56{\%} over the pure multi-task model for dependency relation tagging and by up to 13.03{\%} for semantic tagging.",
}
@inproceedings{zhao-etal-2019-weakly,
    title = "Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval",
    author = "Zhao, Lingjun  and
      Zbib, Rabih  and
      Jiang, Zhuolin  and
      Karakos, Damianos  and
      Huang, Zhongqiang",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6129",
    doi = "10.18653/v1/D19-6129",
    pages = "259--264",
    abstract = "We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages. Low resource languages often lack relevance annotations for CLIR, and when available the training data usually has limited coverage for possible queries. In this paper, we design a model which does not require relevance annotations, instead it is trained on samples extracted from translation corpora as weak supervision. This model relies on an attention mechanism to learn spans in the foreign sentence that are relevant to the query. We report experiments on two low resource languages: Swahili and Tagalog, trained on less that 100k parallel sentences each. The proposed model achieves 19 MAP points improvement compared to using CNNs for feature extraction, 12 points improvement from machine translation-based CLIR, and up to 6 points improvement compared to probabilistic CLIR models.",
}
@inproceedings{abdou-etal-2019-x,
    title = "{X}-{W}iki{RE}: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension",
    author = "Abdou, Mostafa  and
      Sas, Cezar  and
      Aralikatte, Rahul  and
      Augenstein, Isabelle  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6130",
    doi = "10.18653/v1/D19-6130",
    pages = "265--274",
    abstract = "Although the vast majority of knowledge bases (KBs) are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.",
}
@inproceedings{blissett-ji-2019-zero,
    title = "Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages",
    author = "Blissett, Kevin  and
      Ji, Heng",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6131",
    doi = "10.18653/v1/D19-6131",
    pages = "275--280",
    abstract = "In this paper we address a challenging cross-lingual name retrieval task. Given an English named entity query, we aim to find all name mentions in documents in low-resource languages. We present a novel method which relies on zero annotation or resources from the target language. By leveraging freely available, cross-lingual resources and a small amount of training data from another language, we are able to perform name retrieval on a new language without any additional training data. Our method proceeds in a multi-step process: first, we pre-train a language-independent orthographic encoder using Wikipedia inter-lingual links from dozens of languages. Next, we gather user expectations about important entities in an English comparable document and compare those expected entities with actual spans of the target language text in order to perform name finding. Our method shows 11.6{\%} absolute F-score improvement over state-of-the-art methods.",
}
@inproceedings{tran-bisazza-2019-zero,
    title = "Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations",
    author = "Tran, Ke  and
      Bisazza, Arianna",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6132",
    doi = "10.18653/v1/D19-6132",
    pages = "281--288",
    abstract = "We investigate whether off-the-shelf deep bidirectional sentence representations (Devlin et al., 2019) trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser. This approach only leverages a mix of monolingual corpora in many languages and does not require any translation data making it applicable to low-resource languages. In our experiments we outperform the best CoNLL 2018 language-specific systems in all of the shared task{'}s six truly low-resource languages while using a single system. However, we also find that (i) parsing accuracy still varies dramatically when changing the training languages and (ii) in some target languages zero-shot transfer fails under all tested conditions, raising concerns on the {`}universality{'} of the whole approach.",
}
@proceedings{emnlp-2019-commonsense,
    title = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    editor = "Ostermann, Simon  and
      Zhang, Sheng  and
      Roth, Michael  and
      Clark, Peter",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6000",
}
@inproceedings{da-kasai-2019-cracking,
    title = "Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations",
    author = "Da, Jeff  and
      Kasai, Jungo",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6001",
    doi = "10.18653/v1/D19-6001",
    pages = "1--12",
    abstract = "Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of the capability of these models. Thus, we investigate and challenge several aspects of BERT{'}s commonsense representation abilities. First, we probe BERT{'}s ability to classify various object attributes, demonstrating that BERT shows a strong ability in encoding various commonsense features in its embedding space, but is still deficient in many areas. Next, we show that, by augmenting BERT{'}s pretraining data with additional data related to the deficient attributes, we are able to improve performance on a downstream commonsense reasoning task while using a minimal amount of data. Finally, we develop a method of fine-tuning knowledge graphs embeddings alongside BERT and show the continued importance of explicit knowledge graphs.",
}
@inproceedings{he-etal-2019-hybrid,
    title = "A Hybrid Neural Network Model for Commonsense Reasoning",
    author = "He, Pengcheng  and
      Liu, Xiaodong  and
      Chen, Weizhu  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6002",
    doi = "10.18653/v1/D19-6002",
    pages = "13--21",
    abstract = "This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89{\%}, the Winograd Schema Challenge (WSC) benchmark to 75.1{\%}, and the PDP60 benchmark to 90.0{\%}. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn.",
}
@inproceedings{ma-etal-2019-towards,
    title = "Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering",
    author = "Ma, Kaixin  and
      Francis, Jonathan  and
      Lu, Quanyang  and
      Nyberg, Eric  and
      Oltramari, Alessandro",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6003",
    doi = "10.18653/v1/D19-6003",
    pages = "22--32",
    abstract = "Non-extractive commonsense QA remains a challenging AI task, as it requires systems to reason about, synthesize, and gather disparate pieces of information, in order to generate responses to queries. Recent approaches on such tasks show increased performance, only when models are either pre-trained with additional information or when domain-specific heuristics are used, without any special consideration regarding the knowledge resource type. In this paper, we perform a survey of recent commonsense QA methods and we provide a systematic analysis of popular knowledge resources and knowledge-integration methods, across benchmarks from multiple commonsense datasets. Our results and analysis show that attention-based injection seems to be a preferable choice for knowledge integration and that the degree of domain overlap, between knowledge bases and datasets, plays a crucial role in determining model success.",
}
@inproceedings{kavumba-etal-2019-choosing,
    title = "When Choosing Plausible Alternatives, Clever Hans can be Clever",
    author = "Kavumba, Pride  and
      Inoue, Naoya  and
      Heinzerling, Benjamin  and
      Singh, Keshav  and
      Reisert, Paul  and
      Inui, Kentaro",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6004",
    doi = "10.18653/v1/D19-6004",
    pages = "33--42",
    abstract = "Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. However, recent work found that many improvements in benchmarks of natural language understanding are not due to models learning the task, but due to their increasing ability to exploit superficial cues, such as tokens that occur more often in the correct answer than the wrong one. Are BERT{'}s and RoBERTa{'}s good performance on COPA also caused by this? We find superficial cues in COPA, as well as evidence that BERT exploits these cues.To remedy this problem, we introduce Balanced COPA, an extension of COPA that does not suffer from easy-to-exploit single token cues. We analyze BERT{'}s and RoBERTa{'}s performance on original and Balanced COPA, finding that BERT relies on superficial cues when they are present, but still achieves comparable performance once they are made ineffective, suggesting that BERT learns the task to a certain degree when forced to. In contrast, RoBERTa does not appear to rely on superficial cues.",
}
@inproceedings{nakashole-2019-commonsense,
    title = "Commonsense about Human Senses: Labeled Data Collection Processes",
    author = "Nakashole, Ndapa",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6005",
    doi = "10.18653/v1/D19-6005",
    pages = "43--52",
    abstract = "We consider the problem of extracting from text commonsense knowledge pertaining to human senses such as sound and smell. First, we consider the problem of recognizing mentions of human senses in text. Our contribution is a method for acquiring labeled data. Experiments show the effectiveness of our proposed data labeling approach when used with standard machine learning models on the task of sense recognition in text. Second, we propose to extract novel, common sense relationships pertaining to sense perception concepts. Our contribution is a process for generating labeled data by leveraging large corpora and crowdsourcing questionnaires.",
}
@inproceedings{thiem-jansen-2019-extracting,
    title = "Extracting Common Inference Patterns from Semi-Structured Explanations",
    author = "Thiem, Sebastian  and
      Jansen, Peter",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6006",
    doi = "10.18653/v1/D19-6006",
    pages = "53--65",
    abstract = "Complex questions often require combining multiple facts to correctly answer, particularly when generating detailed explanations for why those answers are correct. Combining multiple facts to answer questions is often modeled as a {``}multi-hop{''} graph traversal problem, where a given solver must find a series of interconnected facts in a knowledge graph that, taken together, answer the question and explain the reasoning behind that answer. Multi-hop inference currently suffers from semantic drift, or the tendency for chains of reasoning to {``}drift{''}{'} to unrelated topics, and this semantic drift greatly limits the number of facts that can be combined in both free text or knowledge base inference. In this work we present our effort to mitigate semantic drift by extracting large high-confidence multi-hop inference patterns, generated by abstracting large-scale explanatory structure from a corpus of detailed explanations. We represent these inference patterns as sets of generalized constraints over sentences represented as rows in a knowledge base of semi-structured tables. We present a prototype tool for identifying common inference patterns from corpora of semi-structured explanations, and use it to successfully extract 67 inference patterns from a {``}matter{''} subset of standardized elementary science exam questions that span scientific and world knowledge.",
}
@inproceedings{ostermann-etal-2019-commonsense,
    title = "Commonsense Inference in Natural Language Processing ({COIN}) - Shared Task Report",
    author = "Ostermann, Simon  and
      Zhang, Sheng  and
      Roth, Michael  and
      Clark, Peter",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6007",
    doi = "10.18653/v1/D19-6007",
    pages = "66--74",
    abstract = "This paper reports on the results of the shared tasks of the COIN workshop at EMNLP-IJCNLP 2019. The tasks consisted of two machine comprehension evaluations, each of which tested a system{'}s ability to answer questions/queries about a text. Both evaluations were designed such that systems need to exploit commonsense knowledge, for example, in the form of inferences over information that is available in the common ground but not necessarily mentioned in the text. A total of five participating teams submitted systems for the shared tasks, with the best submitted system achieving 90.6{\%} accuracy and 83.7{\%} F1-score on task 1 and task 2, respectively.",
}
@inproceedings{jain-singh-2019-karna,
    title = "{KARNA} at {COIN} Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense",
    author = "Jain, Yash  and
      Singh, Chinmay",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6008",
    doi = "10.18653/v1/D19-6008",
    pages = "75--79",
    abstract = "This paper describes our model for COmmonsense INference in Natural Language Processing (COIN) shared task 1: Commonsense Inference in Everyday Narrations. This paper explores the use of Bidirectional Encoder Representations from Transformers(BERT) along with external relational knowledge from ConceptNet to tackle the problem of commonsense inference. The input passage, question, and answer are augmented with relational knowledge from ConceptNet. Using this technique we are able to achieve an accuracy of 73.3 {\%} on the official test data.",
}
@inproceedings{sharma-roychowdhury-2019-iit-kgp,
    title = "{IIT}-{KGP} at {COIN} 2019: Using pre-trained Language Models for modeling Machine Comprehension",
    author = "Sharma, Prakhar  and
      Roychowdhury, Sumegh",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6009",
    doi = "10.18653/v1/D19-6009",
    pages = "80--84",
    abstract = "In this paper, we describe our system for COIN 2019 Shared Task 1: Commonsense Inference in Everyday Narrations. We show the power of leveraging state-of-the-art pre-trained language models such as BERT(Bidirectional Encoder Representations from Transformers) and XLNet over other Commonsense Knowledge Base Resources such as ConceptNet and NELL for modeling machine comprehension. We used an ensemble of BERT-Large and XLNet-Large. Experimental results show that our model give substantial improvements over the baseline and other systems incorporating knowledge bases. We bagged 2nd position on the final test set leaderboard with an accuracy of 90.5{\%}",
}
@inproceedings{da-2019-jeff,
    title = "Jeff Da at {COIN} - Shared Task: {BIG} {MOOD}: Relating Transformers to Explicit Commonsense Knowledge",
    author = "Da, Jeff",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6010",
    doi = "10.18653/v1/D19-6010",
    pages = "85--92",
    abstract = "We introduce a simple yet effective method of integrating contextual embeddings with commonsense graph embeddings, dubbed BERT Infused Graphs: Matching Over Other embeDdings. First, we introduce a preprocessing method to improve the speed of querying knowledge bases. Then, we develop a method of creating knowledge embeddings from each knowledge base. We introduce a method of aligning tokens between two misaligned tokenization methods. Finally, we contribute a method of contextualizing BERT after combining with knowledge base embeddings. We also show BERTs tendency to correct lower accuracy question types. Our model achieves a higher accuracy than BERT, and we score fifth on the official leaderboard of the shared task and score the highest without any additional language model pretraining.",
}
@inproceedings{li-etal-2019-pingan,
    title = "Pingan Smart Health and {SJTU} at {COIN} - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks",
    author = "Li, Xiepeng  and
      Zhang, Zhexi  and
      Zhu, Wei  and
      Li, Zheng  and
      Ni, Yuan  and
      Gao, Peng  and
      Yan, Junchi  and
      Xie, Guotong",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6011",
    doi = "10.18653/v1/D19-6011",
    pages = "93--98",
    abstract = "To solve the shared tasks of COIN: COmmonsense INference in Natural Language Processing) Workshop in , we need explore the impact of knowledge representation in modeling commonsense knowledge to boost performance of machine reading comprehension beyond simple text matching. There are two approaches to represent knowledge in the low-dimensional space. The first is to leverage large-scale unsupervised text corpus to train fixed or contextual language representations. The second approach is to explicitly express knowledge into a knowledge graph (KG), and then fit a model to represent the facts in the KG. We have experimented both (a) improving the fine-tuning of pre-trained language models on a task with a small dataset size, by leveraging datasets of similar tasks; and (b) incorporating the distributional representations of a KG onto the representations of pre-trained language models, via simply concatenation or multi-head attention. We find out that: (a) for task 1, first fine-tuning on larger datasets like RACE (Lai et al., 2017) and SWAG (Zellersetal.,2018), and then fine-tuning on the target task improve the performance significantly; (b) for task 2, we find out the incorporating a KG of commonsense knowledge, WordNet (Miller, 1995) into the Bert model (Devlin et al., 2018) is helpful, however, it will hurts the performace of XLNET (Yangetal.,2019), a more powerful pre-trained model. Our approaches achieve the state-of-the-art results on both shared task{'}s official test data, outperforming all the other submissions.",
}
@inproceedings{liu-yu-2019-blcu,
    title = "{BLCU}-{NLP} at {COIN}-Shared Task1: Stagewise Fine-tuning {BERT} for Commonsense Inference in Everyday Narrations",
    author = "Liu, Chunhua  and
      Yu, Dong",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6012",
    doi = "10.18653/v1/D19-6012",
    pages = "99--103",
    abstract = "This paper describes our system for COIN Shared Task 1: Commonsense Inference in Everyday Narrations. To inject more external knowledge to better reason over the narrative passage, question and answer, the system adopts a stagewise fine-tuning method based on pre-trained BERT model. More specifically, the first stage is to fine-tune on addi- tional machine reading comprehension dataset to learn more commonsense knowledge. The second stage is to fine-tune on target-task (MCScript2.0) with MCScript (2018) dataset assisted. Experimental results show that our system achieves significant improvements over the baseline systems with 84.2{\%} accuracy on the official test dataset.",
}
@inproceedings{huminski-etal-2019-commonsense,
    title = "Commonsense inference in human-robot communication",
    author = "Huminski, Aliaksandr  and
      Ng, Yan Bin  and
      Kwok, Kenneth  and
      Bond, Francis",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6013",
    doi = "10.18653/v1/D19-6013",
    pages = "104--112",
    abstract = "Natural language communication between machines and humans are still constrained. The article addresses a gap in natural language understanding about actions, specifically that of understanding commands. We propose a new method for commonsense inference (grounding) of high-level natural language commands into specific action commands for further execution by a robotic system. The method allows to build a knowledge base that consists of a large set of commonsense inferences. The preliminary results have been presented.",
}
@inproceedings{kiyomaru-etal-2019-diversity,
    title = "Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction",
    author = "Kiyomaru, Hirokazu  and
      Omura, Kazumasa  and
      Murawaki, Yugo  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6014",
    doi = "10.18653/v1/D19-6014",
    pages = "113--122",
    abstract = "Typical event sequences are an important class of commonsense knowledge. Formalizing the task as the generation of a next event conditioned on a current event, previous work in event prediction employs sequence-to-sequence (seq2seq) models. However, what can happen after a given event is usually diverse, a fact that can hardly be captured by deterministic models. In this paper, we propose to incorporate a conditional variational autoencoder (CVAE) into seq2seq for its ability to represent diverse next events as a probabilistic distribution. We further extend the CVAE-based seq2seq with a reconstruction mechanism to prevent the model from concentrating on highly typical events. To facilitate fair and systematic evaluation of the diversity-aware models, we also extend existing evaluation datasets by tying each current event to multiple next events. Experiments show that the CVAE-based models drastically outperform deterministic models in terms of precision and that the reconstruction mechanism improves the recall of CVAE-based models without sacrificing precision.",
}
@inproceedings{porada-etal-2019-gorilla,
    title = "Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text",
    author = "Porada, Ian  and
      Suleman, Kaheer  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6015",
    doi = "10.18653/v1/D19-6015",
    pages = "123--129",
    abstract = "Modeling semantic plausibility requires commonsense knowledge about the world and has been used as a testbed for exploring various knowledge representations. Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervised setting. At the same time, distributional models, namely large pretrained language models, have led to improved results for many natural language understanding tasks. In this work, we show that these pretrained language models are in fact effective at modeling physical plausibility in the supervised setting. We therefore present the more difficult problem of learning to model physical plausibility directly from text. We create a training set by extracting attested events from a large corpus, and we provide a baseline for training on these attested events in a self-supervised manner and testing on a physical plausibility task. We believe results could be further improved by injecting explicit commonsense knowledge into a distributional model.",
}
@inproceedings{goel-etal-2019-pre,
    title = "How Pre-trained Word Representations Capture Commonsense Physical Comparisons",
    author = "Goel, Pranav  and
      Feng, Shi  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-6016",
    doi = "10.18653/v1/D19-6016",
    pages = "130--135",
    abstract = "Understanding common sense is important for effective natural language reasoning. One type of common sense is how two objects compare on physical properties such as size and weight: e.g., {`}is a house bigger than a person?{'}. We probe whether pre-trained representations capture comparisons and find they, in fact, have higher accuracy than previous approaches. They also generalize to comparisons involving objects not seen during training. We investigate \textit{how} such comparisons are made: models learn a consistent ordering over all the objects in the comparisons. Probing models have significantly higher accuracy than those baseline models which use dataset artifacts: e.g., memorizing some words are larger than any other word.",
}
@proceedings{emnlp-2019-aggregating,
    title = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    editor = "Paun, Silviu  and
      Hovy, Dirk",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5900",
}
@inproceedings{tratz-2019-dependency,
    title = "Dependency Tree Annotation with {M}echanical {T}urk",
    author = "Tratz, Stephen",
    booktitle = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5901",
    doi = "10.18653/v1/D19-5901",
    pages = "1--5",
    abstract = "Crowdsourcing is frequently employed to quickly and inexpensively obtain valuable linguistic annotations but is rarely used for parsing, likely due to the perceived difficulty of the task and the limited training of the available workers. This paper presents what is, to the best of our knowledge, the first published use of Mechanical Turk (or similar platform) to crowdsource parse trees. We pay Turkers to construct unlabeled dependency trees for 500 English sentences using an interactive graphical dependency tree editor, collecting 10 annotations per sentence. Despite not requiring any training, several of the more prolific workers meet or exceed 90{\%} attachment agreement with the Penn Treebank (PTB) portion of our data, and, furthermore, for 72{\%} of these PTB sentences, at least one Turker produces a perfect parse. Thus, we find that, supported with a simple graphical interface, people with presumably no prior experience can achieve surprisingly high degrees of accuracy on this task. To facilitate research into aggregation techniques for complex crowdsourced annotations, we publicly release our annotated corpus.",
}
@inproceedings{asahara-2019-word,
    title = "Word Familiarity Rate Estimation Using a {B}ayesian Linear Mixed Model",
    author = "Asahara, Masayuki",
    booktitle = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5902",
    doi = "10.18653/v1/D19-5902",
    pages = "6--14",
    abstract = "This paper presents research on word familiarity rate estimation using the {`}Word List by Semantic Principles{'}. We collected rating information on 96,557 words in the {`}Word List by Semantic Principles{'} via Yahoo! crowdsourcing. We asked 3,392 subject participants to use their introspection to rate the familiarity of words based on the five perspectives of {`}KNOW{'}, {`}WRITE{'}, {`}READ{'}, {`}SPEAK{'}, and {`}LISTEN{'}, and each word was rated by at least 16 subject participants. We used Bayesian linear mixed models to estimate the word familiarity rates. We also explored the ratings with the semantic labels used in the {`}Word List by Semantic Principles{'}.",
}
@inproceedings{colruyt-etal-2019-leveraging,
    title = "Leveraging syntactic parsing to improve event annotation matching",
    author = "Colruyt, Camiel  and
      De Clercq, Orph{\'e}e  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5903",
    doi = "10.18653/v1/D19-5903",
    pages = "15--23",
    abstract = "Detecting event mentions is the first step in event extraction from text and annotating them is a notoriously difficult task. Evaluating annotator consistency is crucial when building datasets for mention detection. When event mentions are allowed to cover many tokens, annotators may disagree on their span, which means that overlapping annotations may then refer to the same event or to different events. This paper explores different fuzzy-matching functions which aim to resolve this ambiguity. The functions extract the sets of syntactic heads present in the annotations, use the Dice coefficient to measure the similarity between sets and return a judgment based on a given threshold. The functions are tested against the judgment of a human evaluator and a comparison is made between sets of tokens and sets of syntactic heads. The best-performing function is a head-based function that is found to agree with the human evaluator in 89{\%} of cases.",
}
@inproceedings{li-fukumoto-2019-dataset,
    title = "A Dataset of Crowdsourced Word Sequences: Collections and Answer Aggregation for Ground Truth Creation",
    author = "Li, Jiyi  and
      Fukumoto, Fumiyo",
    booktitle = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5904",
    doi = "10.18653/v1/D19-5904",
    pages = "24--28",
    abstract = "The target outputs of many NLP tasks are word sequences. To collect the data for training and evaluating models, the crowd is a cheaper and easier to access than the oracle. To ensure the quality of the crowdsourced data, people can assign multiple workers to one question and then aggregate the multiple answers with diverse quality into a golden one. How to aggregate multiple crowdsourced word sequences with diverse quality is a curious and challenging problem. People need a dataset for addressing this problem. We thus create a dataset (CrowdWSA2019) which contains the translated sentences generated from multiple workers. We provide three approaches as the baselines on the task of extractive word sequence aggregation. Specially, one of them is an original one we propose which models the reliability of workers. We also discuss some issues on ground truth creation of word sequences which can be addressed based on this dataset.",
}
@inproceedings{lavee-etal-2019-crowd,
    title = "Crowd-sourcing annotation of complex {NLU} tasks: A case study of argumentative content annotation",
    author = "Lavee, Tamar  and
      Kotlerman, Lili  and
      Orbach, Matan  and
      Bilu, Yonatan  and
      Jacovi, Michal  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5905",
    doi = "10.18653/v1/D19-5905",
    pages = "29--38",
    abstract = "Recent advancements in machine reading and listening comprehension involve the annotation of long texts. Such tasks are typically time consuming, making crowd-annotations an attractive solution, yet their complexity often makes such a solution unfeasible. In particular, a major concern is that crowd annotators may be tempted to skim through long texts, and answer questions without reading thoroughly. We present a case study of adapting this type of task to the crowd. The task is to identify claims in a several minute long debate speech. We show that sentence-by-sentence annotation does not scale and that labeling only a subset of sentences is insufficient. Instead, we propose a scheme for effectively performing the full, complex task with crowd annotators, allowing the collection of large scale annotated datasets. We believe that the encountered challenges and pitfalls, as well as lessons learned, are relevant in general when collecting data for large scale natural language understanding (NLU) tasks.",
}
@inproceedings{yoon-etal-2019-computer,
    title = "Computer Assisted Annotation of Tension Development in {TED} Talks through Crowdsourcing",
    author = "Yoon, Seungwon  and
      Yang, Wonsuk  and
      Park, Jong",
    booktitle = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5906",
    doi = "10.18653/v1/D19-5906",
    pages = "39--47",
    abstract = "We propose a method of machine-assisted annotation for the identification of tension development, annotating whether the tension is increasing, decreasing, or staying unchanged. We use a neural network based prediction model, whose predicted results are given to the annotators as initial values for the options that they are asked to choose. By presenting such initial values to the annotators, the annotation task becomes an evaluation task where the annotators inspect whether or not the predicted results are correct. To demonstrate the effectiveness of our method, we performed the annotation task in both in-house and crowdsourced environments. For the crowdsourced environment, we compared the annotation results with and without our method of machine-assisted annotation. We find that the results with our method showed a higher agreement to the gold standard than those without, though our method had little effect at reducing the time for annotation. Our codes for the experiment are made publicly available.",
}
@inproceedings{shah-etal-2019-cossat,
    title = "{C}o{SSAT}: Code-Switched Speech Annotation Tool",
    author = "Shah, Sanket  and
      Joshi, Pratik  and
      Santy, Sebastin  and
      Sitaram, Sunayana",
    booktitle = "Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5907",
    doi = "10.18653/v1/D19-5907",
    pages = "48--52",
    abstract = "Code-switching refers to the alternation of two or more languages in a conversation or utterance and is common in multilingual communities across the world. Building code-switched speech and natural language processing systems are challenging due to the lack of annotated speech and text data. We present a speech annotation interface CoSSAT, which helps annotators transcribe code-switched speech faster, more easily and more accurately than a traditional interface, by displaying candidate words from monolingual speech recognizers. We conduct a user study on the transcription of Hindi-English code-switched speech with 10 annotators and describe quantitative and qualitative results.",
}
@proceedings{emnlp-2019-machine,
    title = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    editor = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5800",
}
@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
    abstract = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the rest were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT.",
}
@inproceedings{bao-etal-2019-inspecting,
    title = "Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension",
    author = "Bao, Hangbo  and
      Dong, Li  and
      Wei, Furu  and
      Wang, Wenhui  and
      Yang, Nan  and
      Cui, Lei  and
      Piao, Songhao  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5802",
    doi = "10.18653/v1/D19-5802",
    pages = "14--18",
    abstract = "Most machine reading comprehension (MRC) models separately handle encoding and matching with different network architectures. In contrast, pretrained language models with Transformer layers, such as GPT (Radford et al., 2018) and BERT (Devlin et al., 2018), have achieved competitive performance on MRC. A research question that naturally arises is: apart from the benefits of pre-training, how many performance gain comes from the unified network architecture. In this work, we evaluate and analyze unifying encoding and matching components with Transformer for the MRC task. Experimental results on SQuAD show that the unified model outperforms previous networks that separately treat encoding and matching. We also introduce a metric to inspect whether a Transformer layer tends to perform encoding or matching. The analysis results show that the unified model learns different modeling strategies compared with previous manually-designed models.",
}
@inproceedings{bechet-etal-2019-calor-quest,
    title = "{CALOR}-{QUEST} : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations",
    author = "Bechet, Frederic  and
      Aloui, Cindy  and
      Charlet, Delphine  and
      Damnati, Geraldine  and
      Heinecke, Johannes  and
      Nasr, Alexis  and
      Herledan, Frederic",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5803",
    doi = "10.18653/v1/D19-5803",
    pages = "19--26",
    abstract = "Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document. Recently very large corpora (SQuAD, MS MARCO) containing triplets (document, question, answer) were made available to the scientific community to develop supervised methods based on deep neural networks with promising results. These methods need very large training corpus to be efficient, however such kind of data only exists for English and Chinese at the moment. The aim of this study is the development of such resources for other languages by proposing to generate in a semi-automatic way questions from the semantic Frame analysis of large corpora. The collect of natural questions is reduced to a validation/test set. We applied this method on the CALOR-Frame French corpus to develop the CALOR-QUEST resource presented in this paper.",
}
@inproceedings{pan-etal-2019-improving-question,
    title = "Improving Question Answering with External Knowledge",
    author = "Pan, Xiaoman  and
      Sun, Kai  and
      Yu, Dian  and
      Chen, Jianshu  and
      Ji, Heng  and
      Cardie, Claire  and
      Yu, Dong",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5804",
    doi = "10.18653/v1/D19-5804",
    pages = "27--37",
    abstract = "We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus. In this work, we explore simple yet effective methods for exploiting two sources of external knowledge for subject-area QA. The first enriches the original subject-area reference corpus with relevant text snippets extracted from an open-domain resource (i.e., Wikipedia) that cover potentially ambiguous concepts in the question and answer options. As in other QA research, the second method simply increases the amount of training data by appending additional in-domain subject-area instances. Experiments on three challenging multiple-choice science QA tasks (i.e., ARC-Easy, ARC-Challenge, and OpenBookQA) demonstrate the effectiveness of our methods: in comparison to the previous state-of-the-art, we obtain absolute gains in accuracy of up to 8.1{\%}, 13.0{\%}, and 12.8{\%}, respectively. While we observe consistent gains when we introduce knowledge from Wikipedia, we find that employing additional QA training instances is not uniformly helpful: performance degrades when the added instances exhibit a higher level of difficulty than the original training data. As one of the first studies on exploiting unstructured external knowledge for subject-area QA, we hope our methods, observations, and discussion of the exposed limitations may shed light on further developments in the area.",
}
@inproceedings{li-etal-2019-answer,
    title = "Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension",
    author = "Li, Qian  and
      Su, Hui  and
      Niu, Cheng  and
      Wang, Daling  and
      Li, Zekang  and
      Feng, Shi  and
      Zhang, Yifei",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5805",
    doi = "10.18653/v1/D19-5805",
    pages = "38--47",
    abstract = "In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.",
}
@inproceedings{xiong-etal-2019-simple,
    title = "Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering",
    author = "Xiong, Wenhan  and
      Yu, Mo  and
      Guo, Xiaoxiao  and
      Wang, Hong  and
      Chang, Shiyu  and
      Campbell, Murray  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5806",
    doi = "10.18653/v1/D19-5806",
    pages = "48--52",
    abstract = "A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques to retrieve answer passages, i.e., the passages containing the groundtruth answers. However, IR-based approaches are insufficient for multi-hop questions, as the topic of the second or further hops is not explicitly covered by the question. To resolve this issue, we introduce a new subproblem of open-domain multi-hop QA, which aims to recognize the bridge (i.e., the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model. This model, the bridge reasoner, is trained with a weakly supervised signal and produces the candidate answer passages for the passage reader to extract the answer. On the full-wiki HotpotQA benchmark, we significantly improve the baseline method by 14 point F1. Without using any memory inefficient contextual embeddings, our result is also competitive with the state-of-the-art that applies BERT in multiple modules.",
}
@inproceedings{wu-etal-2019-improving,
    title = "Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior",
    author = "Wu, Bowen  and
      Huang, Haoyang  and
      Wang, Zongsheng  and
      Feng, Qihang  and
      Yu, Jingsong  and
      Wang, Baoxun",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5807",
    doi = "10.18653/v1/D19-5807",
    pages = "53--57",
    abstract = "Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples. To address this issue, we attempt to take sentence syntax as the leverage in the answer predicting process which previously only takes account of phrase-level semantics. Furthermore, to better utilize the sentence syntax and improve the robustness, we propose a Syntactic Leveraging Network, which is designed to deal with adversarial samples by exploiting the syntactic elements of a question. The experiment results indicate that our method is promising for improving the generalization and robustness of MRC models against the influence of adversarial samples, with performance well-maintained.",
}
@inproceedings{lin-etal-2019-reasoning,
    title = "Reasoning Over Paragraph Effects in Situations",
    author = "Lin, Kevin  and
      Tafjord, Oyvind  and
      Clark, Peter  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5808",
    doi = "10.18653/v1/D19-5808",
    pages = "58--62",
    abstract = "A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind of reading, we present ROPES, a challenging benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations. We target expository language describing causes and effects (e.g., {``}animal pollinators increase efficiency of fertilization in flowers{''}), as they have clear implications for new situations. A system is presented a background passage containing at least one of these relations, a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation. We collect background passages from science textbooks and Wikipedia that contain such phenomena, and ask crowd workers to author situations, questions, and answers, resulting in a 14,322 question dataset. We analyze the challenges of this task and evaluate the performance of state-of-the-art reading comprehension models. The best model performs only slightly better than randomly guessing an answer of the correct type, at 61.6{\%} F1, well below the human performance of 89.0{\%}.",
}
@inproceedings{nakanishi-etal-2019-towards,
    title = "Towards Answer-unaware Conversational Question Generation",
    author = "Nakanishi, Mao  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5809",
    doi = "10.18653/v1/D19-5809",
    pages = "63--71",
    abstract = "Conversational question generation is a novel area of NLP research which has a range of potential applications. This paper is first to presents a framework for conversational question generation that is unaware of the corresponding answers. To properly generate a question coherent to the grounding text and the current conversation history, the proposed framework first locates the focus of a question in the text passage, and then identifies the question pattern that leads the sequential generation of the words in a question. The experiments using the CoQA dataset demonstrate that the quality of generated questions greatly improves if the question foci and the question patterns are correctly identified. In addition, it was shown that the question foci, even estimated with a reasonable accuracy, could contribute to the quality improvement. These results established that our research direction may be promising, but at the same time revealed that the identification of question patterns is a challenging issue, and it has to be largely refined to achieve a better quality in the end-to-end automatic question generation.",
}
@inproceedings{egonmwan-etal-2019-cross,
    title = "Cross-Task Knowledge Transfer for Query-Based Text Summarization",
    author = "Egonmwan, Elozino  and
      Castelli, Vittorio  and
      Sultan, Md Arafat",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5810",
    doi = "10.18653/v1/D19-5810",
    pages = "72--77",
    abstract = "We demonstrate the viability of knowledge transfer between two related tasks: machine reading comprehension (MRC) and query-based text summarization. Using an MRC model trained on the SQuAD1.1 dataset as a core system component, we first build an extractive query-based summarizer. For better precision, this summarizer also compresses the output of the MRC model using a novel sentence compression technique. We further leverage pre-trained machine translation systems to abstract our extracted summaries. Our models achieve state-of-the-art results on the publicly available CNN/Daily Mail and Debatepedia datasets, and can serve as simple yet powerful baselines for future systems. We also hope that these results will encourage research on transfer learning from large MRC corpora to query-based summarization.",
}
@inproceedings{angelidis-etal-2019-book,
    title = "Book {QA}: Stories of Challenges and Opportunities",
    author = "Angelidis, Stefanos  and
      Frermann, Lea  and
      Marcheggiani, Diego  and
      Blanco, Roi  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5811",
    doi = "10.18653/v1/D19-5811",
    pages = "78--85",
    abstract = "We present a system for answering questions based on the full text of books (BookQA), which first selects book passages given a question at hand, and then uses a memory network to reason and predict an answer. To improve generalization, we pretrain our memory network using artificial questions generated from book sentences. We experiment with the recently published NarrativeQA corpus, on the subset of Who questions, which expect book characters as answers. We experimentally show that BERT-based retrieval and pretraining improve over baseline results significantly. At the same time, we confirm that NarrativeQA is a highly challenging data set, and that there is need for novel research in order to achieve high-precision BookQA results. We analyze some of the bottlenecks of the current approach, and we argue that more research is needed on text representation, retrieval of relevant passages, and reasoning, including commonsense knowledge.",
}
@inproceedings{yeh-chen-2019-flowdelta,
    title = "{F}low{D}elta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension",
    author = "Yeh, Yi-Ting  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5812",
    doi = "10.18653/v1/D19-5812",
    pages = "86--90",
    abstract = "Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoning for better understanding. This paper proposes to explicitly model the information gain through the dialogue reasoning in order to allow the model to focus on more informative cues. The proposed model achieves the state-of-the-art performance in a conversational QA dataset QuAC and sequential instruction understanding dataset SCONE, which shows the effectiveness of the proposed mechanism and demonstrate its capability of generalization to different QA models and tasks.",
}
@inproceedings{wang-etal-2019-multi-hop,
    title = "Do Multi-hop Readers Dream of Reasoning Chains?",
    author = "Wang, Haoyu  and
      Yu, Mo  and
      Guo, Xiaoxiao  and
      Das, Rajarshi  and
      Xiong, Wenhan  and
      Gao, Tian",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5813",
    doi = "10.18653/v1/D19-5813",
    pages = "91--97",
    abstract = "General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer. In this paper we conduct a systematic analysis to assess such an ability of various existing models proposed for multi-hop QA tasks. Specifically, our analysis investigates that whether providing the full reasoning chain of multiple passages, instead of just one final passage where the answer appears, could improve the performance of the existing QA models. Surprisingly, when using the additional evidence passages, the improvements of all the existing multi-hop reading approaches are rather limited, with the highest error reduction of 5.8{\%} on F1 (corresponding to 1.3{\%} improvement) from the BERT model. To better understand whether the reasoning chains indeed could help find the correct answers, we further develop a co-matching-based method that leads to 13.1{\%} error reduction with passage chains when applied to two of our base readers (including BERT). Our results demonstrate the existence of the potential improvement using explicit multi-hop reasoning and the necessity to develop models with better reasoning abilities.",
}
@inproceedings{takahashi-etal-2019-machine,
    title = "Machine Comprehension Improves Domain-Specific {J}apanese Predicate-Argument Structure Analysis",
    author = "Takahashi, Norio  and
      Shibata, Tomohide  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5814",
    doi = "10.18653/v1/D19-5814",
    pages = "98--104",
    abstract = "To improve the accuracy of predicate-argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable. We focus on a specific domain, specifically Japanese blogs on driving, and construct two wide-coverage datasets as a form of QA using crowdsourcing: a PAS-QA dataset and a reading comprehension QA (RC-QA) dataset. We train a machine comprehension (MC) model based on these datasets to perform PAS analysis. Our experiments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.",
}
@inproceedings{gardner-etal-2019-making,
    title = "On Making Reading Comprehension More Comprehensive",
    author = "Gardner, Matt  and
      Berant, Jonathan  and
      Hajishirzi, Hannaneh  and
      Talmor, Alon  and
      Min, Sewon",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5815",
    doi = "10.18653/v1/D19-5815",
    pages = "105--112",
    abstract = "Machine reading comprehension, the task of evaluating a machine{'}s ability to comprehend a passage of text, has seen a surge in popularity in recent years. There are many datasets that are targeted at reading comprehension, and many systems that perform as well as humans on some of these datasets. Despite all of this interest, there is no work that systematically defines what reading comprehension is. In this work, we justify a question answering approach to reading comprehension and describe the various kinds of questions one might use to more fully test a system{'}s comprehension of a passage, moving beyond questions that only probe local predicate-argument structures. The main pitfall of this approach is that questions can easily have surface cues or other biases that allow a model to shortcut the intended reasoning process. We discuss ways proposed in current literature to mitigate these shortcuts, and we conclude with recommendations for future dataset collection efforts.",
}
@inproceedings{das-etal-2019-multi,
    title = "Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering",
    author = "Das, Rajarshi  and
      Godbole, Ameya  and
      Kavarthapu, Dilip  and
      Gong, Zhiyu  and
      Singhal, Abhishek  and
      Yu, Mo  and
      Guo, Xiaoxiao  and
      Gao, Tian  and
      Zamani, Hamed  and
      Zaheer, Manzil  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5816",
    doi = "10.18653/v1/D19-5816",
    pages = "113--118",
    abstract = "Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \textit{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to {`}\textit{hop}{'} to other relevant evidence. In a setting, with more than \textbf{5 million} Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the benchmark by \textbf{10.59} F1.",
}
@inproceedings{chen-etal-2019-evaluating,
    title = "Evaluating Question Answering Evaluation",
    author = "Chen, Anthony  and
      Stanovsky, Gabriel  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5817",
    doi = "10.18653/v1/D19-5817",
    pages = "119--124",
    abstract = "As the complexity of question answering (QA) datasets evolve, moving away from restricted formats like span extraction and multiple-choice (MC) to free-form answer generation, it is imperative to understand how well current metrics perform in evaluating QA. This is especially important as existing metrics (BLEU, ROUGE, METEOR, and F1) are computed using n-gram similarity and have a number of well-known drawbacks. In this work, we study the suitability of existing metrics in QA. For generative QA, we show that while current metrics do well on existing datasets, converting multiple-choice datasets into free-response datasets is challenging for current metrics. We also look at span-based QA, where F1 is a reasonable metric. We show that F1 may not be suitable for all extractive QA tasks depending on the answer types. Our study suggests that while current metrics may be suitable for existing QA datasets, they limit the complexity of QA datasets that can be created. This is especially true in the context of free-form QA, where we would like our models to be able to generate more complex and abstractive answers, thus necessitating new metrics that go beyond n-gram based matching. As a step towards a better QA metric, we explore using BERTScore, a recently proposed metric for evaluating translation, for QA. We find that although it fails to provide stronger correlation with human judgements, future work focused on tailoring a BERT-based metric to QA evaluation may prove fruitful.",
}
@inproceedings{pugaliya-etal-2019-bend,
    title = "Bend but Don{'}t Break? Multi-Challenge Stress Test for {QA} Models",
    author = "Pugaliya, Hemant  and
      Route, James  and
      Ma, Kaixin  and
      Geng, Yixuan  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5818",
    doi = "10.18653/v1/D19-5818",
    pages = "125--136",
    abstract = "The field of question answering (QA) has seen rapid growth in new tasks and modeling approaches in recent years. Large scale datasets and focus on challenging linguistic phenomena have driven development in neural models, some of which have achieved parity with human performance in limited cases. However, an examination of state-of-the-art model output reveals that a gap remains in reasoning ability compared to a human, and performance tends to degrade when models are exposed to less-constrained tasks. We are interested in more clearly defining the strengths and limitations of leading models across diverse QA challenges, intending to help future researchers with identifying pathways to generalizable performance. We conduct extensive qualitative and quantitative analyses on the results of four models across four datasets and relate common errors to model capabilities. We also illustrate limitations in the datasets we examine and discuss a way forward for achieving generalizable models and datasets that broadly test QA capabilities.",
}
@inproceedings{ahmad-etal-2019-reqa,
    title = "{R}e{QA}: An Evaluation for End-to-End Answer Retrieval Models",
    author = "Ahmad, Amin  and
      Constant, Noah  and
      Yang, Yinfei  and
      Cer, Daniel",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5819",
    doi = "10.18653/v1/D19-5819",
    pages = "137--146",
    abstract = "Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging problem, and places different requirements on the model architecture. There is growing interest in developing scalable answer retrieval models trained end-to-end, bypassing the typical document retrieval step. In this paper, we introduce Retrieval Question-Answering (ReQA), a benchmark for evaluating large-scale sentence-level answer retrieval models. We establish baselines using both neural encoding models as well as classical information retrieval techniques. We release our evaluation code to encourage further work on this challenging task.",
}
@inproceedings{dua-etal-2019-comprehensive,
    title = "Comprehensive Multi-Dataset Evaluation of Reading Comprehension",
    author = "Dua, Dheeru  and
      Gottumukkala, Ananth  and
      Talmor, Alon  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5820",
    doi = "10.18653/v1/D19-5820",
    pages = "147--153",
    abstract = "Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context. Given the availability of many such datasets, comprehensive and reliable evaluation is tedious and time-consuming for researchers working on this problem. We present an evaluation server, ORB, that reports performance on seven diverse reading comprehension datasets, encouraging and facilitating testing a single model{'}s capability in understanding a wide variety of reading phenomena. The evaluation server places no restrictions on how models are trained, so it is a suitable test bed for exploring training paradigms and representation learning for general reading facility. As more suitable datasets are released, they will be added to the evaluation server. We also collect and include synthetic augmentations for these datasets, testing how well models can handle out-of-domain questions.",
}
@inproceedings{chan-fan-2019-recurrent,
    title = "A Recurrent {BERT}-based Model for Question Generation",
    author = "Chan, Ying-Hong  and
      Fan, Yao-Chung",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5821",
    doi = "10.18653/v1/D19-5821",
    pages = "154--162",
    abstract = "In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks. We introduce three neural architectures built on top of BERT for question generation tasks. The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. Accordingly, we propose another two models by restructuring our BERT employment into a sequential manner for taking information from previous decoded results. Our models are trained and evaluated on the recent question-answering dataset SQuAD. Experiment results show that our best model yields state-of-the-art performance which advances the BLEU 4 score of the existing best models from 16.85 to 22.17.",
}
@inproceedings{kang-etal-2019-know,
    title = "Let Me Know What to Ask: Interrogative-Word-Aware Question Generation",
    author = "Kang, Junmo  and
      Puerto San Roman, Haritz  and
      Myaeng, Sung-Hyon",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5822",
    doi = "10.18653/v1/D19-5822",
    pages = "163--171",
    abstract = "Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the generated question. They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question. In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model. The first module predicts the interrogative word that is provided to the second module to create the question. Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in METEOR, and from 44.53 to 46.94 in ROUGE-L.",
}
@inproceedings{frermann-2019-extractive,
    title = "Extractive {N}arrative{QA} with Heuristic Pre-Training",
    author = "Frermann, Lea",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5823",
    doi = "10.18653/v1/D19-5823",
    pages = "172--182",
    abstract = "Although advances in neural architectures for NLP problems as well as unsupervised pre-training have led to substantial improvements on question answering and natural language inference, understanding of and reasoning over long texts still poses a substantial challenge. Here, we consider the task of question answering from full narratives (e.g., books or movie scripts), or their summaries, tackling the NarrativeQA challenge (NQA; Kocisky et al. (2018)). We introduce a heuristic extractive version of the data set, which allows us to approach the more feasible problem of answer extraction (rather than generation). We train systems for passage retrieval as well as answer span prediction using this data set. We use pre-trained BERT embeddings for injecting prior knowledge into our system. We show that our setup leads to state of the art performance on summary-level QA. On QA from full narratives, our model outperforms previous models on the METEOR metric. We analyze the relative contributions of pre-trained embeddings and the extractive training paradigm, and provide a detailed error analysis.",
}
@inproceedings{takahashi-etal-2019-cler,
    title = "{CLER}: Cross-task Learning with Expert Representation to Generalize Reading and Understanding",
    author = "Takahashi, Takumi  and
      Taniguchi, Motoki  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5824",
    doi = "10.18653/v1/D19-5824",
    pages = "183--190",
    abstract = "This paper describes our model for the reading comprehension task of the MRQA shared task. We propose CLER, which stands for Cross-task Learning with Expert Representation for the generalization of reading and understanding. To generalize its capabilities, the proposed model is composed of three key ideas: multi-task learning, mixture of experts, and ensemble. In-domain datasets are used to train and validate our model, and other out-of-domain datasets are used to validate the generalization of our model{'}s performances. In a submission run result, the proposed model achieved an average F1 score of 66.1 {\%} in the out-of-domain setting, which is a 4.3 percentage point improvement over the official BERT baseline model.",
}
@inproceedings{osama-etal-2019-question,
    title = "Question Answering Using Hierarchical Attention on Top of {BERT} Features",
    author = "Osama, Reham  and
      El-Makky, Nagwa  and
      Torki, Marwan",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5825",
    doi = "10.18653/v1/D19-5825",
    pages = "191--195",
    abstract = "The model submitted works as follows. When supplied a question and a passage it makes use of the BERT embedding along with the hierarchical attention model which consists of 2 parts, the co-attention and the self-attention, to locate a continuous span of the passage that is the answer to the question.",
}
@inproceedings{lee-etal-2019-domain,
    title = "Domain-agnostic Question-Answering with Adversarial Training",
    author = "Lee, Seanie  and
      Kim, Donggyu  and
      Park, Jangwon",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5826",
    doi = "10.18653/v1/D19-5826",
    pages = "196--202",
    abstract = "Adapting models to new domain without finetuning is a challenging problem in deep learning. In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task. Our model consists of a conventional QA model and a discriminator. The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features. We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model.",
}
@inproceedings{su-etal-2019-generalizing,
    title = "Generalizing Question Answering System with Pre-trained Language Model Fine-tuning",
    author = "Su, Dan  and
      Xu, Yan  and
      Winata, Genta Indra  and
      Xu, Peng  and
      Kim, Hyeondey  and
      Liu, Zihan  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5827",
    doi = "10.18653/v1/D19-5827",
    pages = "203--211",
    abstract = "With a large number of datasets being released and new techniques being proposed, Question answering (QA) systems have witnessed great breakthroughs in reading comprehension (RC)tasks. However, most existing methods focus on improving in-domain performance, leaving open the research question of how these mod-els and techniques can generalize to out-of-domain and unseen RC tasks. To enhance the generalization ability, we propose a multi-task learning framework that learns the shared representation across different tasks. Our model is built on top of a large pre-trained language model, such as XLNet, and then fine-tuned on multiple RC datasets. Experimental results show the effectiveness of our methods, with an average Exact Match score of 56.59 and an average F1 score of 68.98, which significantly improves the BERT-Large baseline by8.39 and 7.22, respectively",
}
@inproceedings{li-etal-2019-net,
    title = "{D}-{NET}: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension",
    author = "Li, Hongyu  and
      Zhang, Xiyuan  and
      Liu, Yibing  and
      Zhang, Yiming  and
      Wang, Quan  and
      Zhou, Xiangyang  and
      Liu, Jing  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5828",
    doi = "10.18653/v1/D19-5828",
    pages = "212--219",
    abstract = "In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. Our system is built on a framework of pretraining and fine-tuning, namely D-NET. The techniques of pre-trained language models and multi-task learning are explored to improve the generalization of MRC models and we conduct experiments to examine the effectiveness of these strategies. Our system is ranked at top 1 of all the participants in terms of averaged F1 score. Our codes and models will be released at PaddleNLP.",
}
@inproceedings{longpre-etal-2019-exploration,
    title = "An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering",
    author = "Longpre, Shayne  and
      Lu, Yi  and
      Tu, Zhucheng  and
      DuBois, Chris",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5829",
    doi = "10.18653/v1/D19-5829",
    pages = "220--227",
    abstract = "To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation. We find a simple negative sampling technique to be particularly effective, even though it is typically used for datasets that include unanswerable questions, such as SQuAD 2.0. When applied in conjunction with per-domain sampling, our XLNet (Yang et al., 2019)-based submission achieved the second best Exact Match and F1 in the MRQA leaderboard competition.",
}
@proceedings{emnlp-2019-bionlp,
    title = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    editor = "Jin-Dong, Kim  and
      Claire, N{\'e}dellec  and
      Robert, Bossy  and
      Louise, Del{\'e}ger",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5700",
}
@inproceedings{gonzalez-agirre-etal-2019-pharmaconer,
    title = "{P}harma{C}o{NER}: Pharmacological Substances, Compounds and proteins Named Entity Recognition track",
    author = "Gonzalez-Agirre, Aitor  and
      Marimon, Montserrat  and
      Intxaurrondo, Ander  and
      Rabal, Obdulia  and
      Villegas, Marta  and
      Krallinger, Martin",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5701",
    doi = "10.18653/v1/D19-5701",
    pages = "1--10",
    abstract = "One of the biomedical entity types of relevance for medicine or biosciences are chemical compounds and drugs. The correct detection these entities is critical for other text mining applications building on them, such as adverse drug-reaction detection, medication-related fake news or drug-target extraction. Although a significant effort was made to detect mentions of drugs/chemicals in English texts, so far only very limited attempts were made to recognize them in medical documents in other languages. Taking into account the growing amount of medical publications and clinical records written in Spanish, we have organized the first shared task on detecting drug and chemical entities in Spanish medical documents. Additionally, we included a clinical concept-indexing sub-track asking teams to return SNOMED-CT identifiers related to drugs/chemicals for a collection of documents. For this task, named PharmaCoNER, we generated annotation guidelines together with a corpus of 1,000 manually annotated clinical case studies. A total of 22 teams participated in the sub-track 1, (77 system runs), and 7 teams in the sub-track 2 (19 system runs). Top scoring teams used sophisticated deep learning approaches yielding very competitive results with F-measures above 0.91. These results indicate that there is a real interest in promoting biomedical text mining efforts beyond English. We foresee that the PharmaCoNER annotation guidelines, corpus and participant systems will foster the development of new resources for clinical and biomedical text mining systems of Spanish medical data.",
}
@inproceedings{stoeckel-etal-2019-specialization,
    title = "When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in {S}panish",
    author = "Stoeckel, Manuel  and
      Hemati, Wahed  and
      Mehler, Alexander",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5702",
    doi = "10.18653/v1/D19-5702",
    pages = "11--15",
    abstract = "The recognition of pharmacological substances, compounds and proteins is an essential preliminary work for the recognition of relations between chemicals and other biomedically relevant units. In this paper, we describe an approach to Task 1 of the PharmaCoNER Challenge, which involves the recognition of mentions of chemicals and drugs in Spanish medical texts. We train a state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized Embeddings, word and sub-word embeddings using the open-source framework FLAIR. We present a new corpus composed of articles and papers from Spanish health science journals, termed the Spanish Health Corpus, and use it to train domain-specific embeddings which we incorporate in our model training. We achieve a result of 89.76{\%} F1-score using pre-trained embeddings and are able to improve these results to 90.52{\%} F1-score using specialized embeddings.",
}
@inproceedings{suarez-paniagua-2019-vsp,
    title = "{VSP} at {P}harma{C}o{NER} 2019: Recognition of Pharmacological Substances, Compounds and Proteins with Recurrent Neural Networks in {S}panish Clinical Cases",
    author = "Su{\'a}rez-Paniagua, V{\'\i}ctor",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5703",
    doi = "10.18653/v1/D19-5703",
    pages = "16--20",
    abstract = "This paper presents the participation of the VSP team for the PharmaCoNER Tracks from the BioNLP Open Shared Task 2019. The system consists of a neural model for the Named Entity Recognition of drugs, medications and chemical entities in Spanish and the use of the Spanish Edition of SNOMED CT term search engine for the concept normalization of the recognized mentions. The neural network is implemented with two bidirectional Recurrent Neural Networks with LSTM cells that creates a feature vector for each word of the sentences in order to classify the entities. The first layer uses the characters of each word and the resulting vector is aggregated to the second layer together with its word embedding in order to create the feature vector of the word. Besides, a Conditional Random Field layer classifies the vector representation of each word in one of the mention types. The system obtains a performance of 76.29{\%}, and 60.34{\%} in F1 for the classification of the Named Entity Recognition task and the Concept indexing task, respectively. This method presents good results with a basic approach without using pretrained word embeddings or any hand-crafted features.",
}
@inproceedings{lahuerta-etal-2019-ixamed,
    title = "{I}xa{M}ed at {P}harmaco{NER} Challenge 2019",
    author = "Lahuerta, Xabier  and
      Goenaga, Iakes  and
      Gojenola, Koldo  and
      Atutxa Salazar, Aitziber  and
      Oronoz, Maite",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5704",
    doi = "10.18653/v1/D19-5704",
    pages = "21--25",
    abstract = "The aim of this paper is to present our approach (IxaMed) in the PharmacoNER 2019 task. The task consists of identifying chemical, drug, and gene/protein mentions from clinical case studies written in Spanish. The evaluation of the task is divided in two scenarios: one corresponding to the detection of named entities and one corresponding to the indexation of named entities that have been previously identified. In order to identify named entities we have made use of a Bi-LSTM with a CRF on top in combination with different types of word embeddings. We have achieved our best result (86.81 F-Score) combining pretrained word embeddings of Wikipedia and Electronic Health Records (50M words) with contextual string embeddings of Wikipedia and Electronic Health Records. On the other hand, for the indexation of the named entities we have used the Levenshtein distance obtaining a 85.34 F-Score as our best result.",
}
@inproceedings{lange-etal-2019-nlnde,
    title = "{NLNDE}: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection",
    author = {Lange, Lukas  and
      Adel, Heike  and
      Str{\"o}tgen, Jannik},
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5705",
    doi = "10.18653/v1/D19-5705",
    pages = "26--32",
    abstract = "Named entity recognition has been extensively studied on English news texts. However, the transfer to other domains and languages is still a challenging problem. In this paper, we describe the system with which we participated in the first subtrack of the PharmaCoNER competition of the BioNLP Open Shared Tasks 2019. Aiming at pharmacological entity detection in Spanish texts, the task provides a non-standard domain and language setting. However, we propose an architecture that requires neither language nor domain expertise. We treat the task as a sequence labeling task and experiment with attention-based embedding selection and the training on automatically annotated data to further improve our system{'}s performance. Our system achieves promising results, especially by combining the different techniques, and reaches up to 88.6{\%} F1 in the competition.",
}
@inproceedings{xiong-etal-2019-deep,
    title = "A Deep Learning-Based System for {P}harma{C}o{NER}",
    author = "Xiong, Ying  and
      Shen, Yedan  and
      Huang, Yuanhang  and
      Chen, Shuai  and
      Tang, Buzhou  and
      Wang, Xiaolong  and
      Chen, Qingcai  and
      Yan, Jun  and
      Zhou, Yi",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5706",
    doi = "10.18653/v1/D19-5706",
    pages = "33--37",
    abstract = "The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2). We developed a pipeline system based on deep learning methods for this shared task, specifically, a subsystem based on BERT (Bidirectional Encoder Representations from Transformers) for NER offset and entity classification and a subsystem based on Bpool (Bi-LSTM with max/mean pooling) for concept indexing. Evaluation conducted on the shared task data showed that our system achieves a micro-average F1-score of 0.9105 on track 1 and a micro-average F1-score of 0.8391 on track 2.",
}
@inproceedings{rivera-martinez-2019-deep,
    title = "Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in {S}panish clinical text",
    author = "Rivera, Renzo  and
      Mart{\'\i}nez, Paloma",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5707",
    doi = "10.18653/v1/D19-5707",
    pages = "38--46",
    abstract = "In this work, we introduce a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts. We propose a hybrid model approach based on two Bidirectional Long Short-Term Memory (Bi-LSTM) network and Conditional Random Field (CRF) network using character, word, concept and sense embeddings to deal with the extraction of semantic, syntactic and morphological features. The approach was evaluated on the PharmaCoNER Corpus obtaining an F-measure of 85.24{\%} for subtask 1 and 49.36{\%} for subtask2. These results prove that deep learning methods with specific domain embedding representations can outperform the state-of-the-art approaches.",
}
@inproceedings{sohrab-etal-2019-neural,
    title = "A Neural Pipeline Approach for the {P}harma{C}o{NER} Shared Task using Contextual Exhaustive Models",
    author = "Sohrab, Mohammad Golam  and
      Pham, Minh Thang  and
      Miwa, Makoto  and
      Takamura, Hiroya",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5708",
    doi = "10.18653/v1/D19-5708",
    pages = "47--55",
    abstract = "We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on pharmaceutical drugs and chemical entities. We proposed a neural NER model that captures the surrounding semantic information of a given sequence by capturing the forward- and backward-context of bidirectional LSTM (Bi-LSTM) output of a target span using contextual span representation-based exhaustive approach. The NER model enumerates all possible spans as potential entity mentions and classify them into entity types or no entity with deep neural networks. For representing span, we compare several different neural network architectures and their ensembling for the NER model. We then perform dictionary matching for CI and, if there is no matching, we further compute similarity scores between a mention and CUIs using entity embeddings to assign the CUI with the highest score to the mention. We evaluate our approach on the two sub-tasks in the shared task. Among the five submitted runs, the best run for each sub-task achieved the F-score of 86.76{\%} on Sub-task 1 (NER) and the F-score of 79.97{\%} (strict) on Sub-task 2 (CI).",
}
@inproceedings{hakala-pyysalo-2019-biomedical,
    title = "Biomedical Named Entity Recognition with Multilingual {BERT}",
    author = "Hakala, Kai  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5709",
    doi = "10.18653/v1/D19-5709",
    pages = "56--61",
    abstract = "We present the approach of the Turku NLP group to the PharmaCoNER task on Spanish biomedical named entity recognition. We apply a CRF-based baseline approach and multilingual BERT to the task, achieving an F-score of 88{\%} on the development data and 87{\%} on the test set with BERT. Our approach reflects a straightforward application of a state-of-the-art multilingual model that is not specifically tailored to either the language nor the application domain. The source code is available at: https://github.com/chaanim/pharmaconer",
}
@inproceedings{wang-etal-2019-overview,
    title = "An Overview of the Active Gene Annotation Corpus and the {B}io{NLP} {OST} 2019 {AGAC} Track Tasks",
    author = "Wang, Yuxing  and
      Zhou, Kaiyin  and
      Gachloo, Mina  and
      Xia, Jingbo",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5710",
    doi = "10.18653/v1/D19-5710",
    pages = "62--71",
    abstract = "The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. Based on the corpus, the AGAC track of the BioNLP Open Shared Tasks 2019 was organized, to facilitate cross-disciplinary collaboration across BioNLP and Pharmacoinformatics communities, for drug repurposing. The AGAC track consists of three subtasks: 1) named entity recognition, 2) thematic relation extraction, and 3) loss of function (LOF) / gain of function (GOF) topic classification. The AGAC track was participated by five teams, of which the performance are compared and analyzed. The the results revealed a substantial room for improvement in the design of the task, which we analyzed in terms of {``}imbalanced data{''}, {``}selective annotation{''} and {``}latent topic annotation{''}.",
}
@inproceedings{li-etal-2019-trigger,
    title = "Trigger Word Detection and Thematic Role Identification via {BERT} and Multitask Learning",
    author = "Li, Dongfang  and
      Xiong, Ying  and
      Hu, Baotian  and
      Du, Hanyang  and
      Tang, Buzhou  and
      Chen, Qingcai",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5711",
    doi = "10.18653/v1/D19-5711",
    pages = "72--76",
    abstract = "The prediction of the relationship between the disease with genes and its mutations is a very important knowledge extraction task that can potentially help drug discovery. In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019. Task 1 can be regarded as the traditional name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. Task 2 can be regarded as relation extraction which captures the thematic roles between entities. For two tasks, we exploit the pre-trained biomedical language representation model (i.e., BERT) in the pipe of information extraction for the collection of mutation-disease knowledge from PubMed. And also, we design a fine-tuning technique and extra features by using multi-task learning. The experiment results show that our proposed approaches achieve 0.60 (ranks 1) and 0.25 (ranks 2) on task 1 and task 2 respectively in terms of $F_1$ metric.",
}
@inproceedings{liu-etal-2019-deepgenemd,
    title = "{D}eep{G}ene{MD}: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from {P}ub{M}ed Literature",
    author = "Liu, Feifan  and
      Zheng, Xiaoyu  and
      Wang, Bo  and
      Kiefe, Catarina",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5712",
    doi = "10.18653/v1/D19-5712",
    pages = "77--83",
    abstract = "Understanding the pathogenesis of genetic diseases through different gene activities and their relations to relevant diseases is important for new drug discovery and drug repositioning. In this paper, we present a joint deep learning model in a multi-task learning paradigm for gene mutation-disease knowledge extraction, DeepGeneMD, which adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition (NER) and relation extraction (RE) in the context of the AGAC (Active Gene Annotation Corpus) track at 2019 BioNLP Open Shared Tasks (BioNLP-OST). It simultaneously extracts gene mutation related activities, diseases, and their relations from the published scientific literature. In DeepGeneMD, we explore the task decomposition to create auxiliary subtasks so that more interactions between different learning subtasks can be leveraged in model training. Our model achieves the average F1 score of 0.45 on recognizing gene activities and disease entities, ranking 2nd in the AGAC NER task; and the average F1 score of 0.35 on extracting relations, ranking 1st in the AGAC RE task.",
}
@inproceedings{thillaisundaram-togia-2019-biomedical,
    title = "Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture",
    author = "Thillaisundaram, Ashok  and
      Togia, Theodosia",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5713",
    doi = "10.18653/v1/D19-5713",
    pages = "84--89",
    abstract = "This paper presents our participation in the AGAC Track from the 2019 BioNLP Open Shared Tasks. We provide a solution for Task 3, which aims to extract {``}gene - function change - disease{''} triples, where {``}gene{''} and {``}disease{''} are mentions of particular genes and diseases respectively and {``}function change{''} is one of four pre-defined relationship types. Our system extends BERT (Devlin et al., 2018), a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus and whose parameters can be fine-tuned to solve specific tasks with minimal additional architecture. We encode the pair of mentions and their textual context as two consecutive sequences in BERT, separated by a special symbol. We then use a single linear layer to classify their relationship into five classes (four pre-defined, as well as {`}no relation{'}). Despite considerable class imbalance, our system significantly outperforms a random baseline while relying on an extremely simple setup with no specially engineered features.",
}
@inproceedings{ion-etal-2019-racais,
    title = "{RACAI}{'}s System at {P}harma{C}o{NER} 2019",
    author = "Ion, Radu  and
      P{\u{a}}i{\textcommabelow{s}}, Vasile Florian  and
      Mitrofan, Maria",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5714",
    doi = "10.18653/v1/D19-5714",
    pages = "90--99",
    abstract = "This paper describes the Named Entity Recognition system of the Institute for Artificial Intelligence {``}Mihai Dr{\u{a}}g{\u{a}}nescu{''} of the Romanian Academy (RACAI for short). Our best F1 score of 0.84984 was achieved using an ensemble of two systems: a gazetteer-based baseline and a RNN-based NER system, developed specially for PharmaCoNER 2019. We will describe the individual systems and the ensemble algorithm, compare the final system to the current state of the art, as well as discuss our results with respect to the quality of the training data and its annotation strategy. The resulting NER system is language independent, provided that language-dependent resources and preprocessing tools exist, such as tokenizers and POS taggers.",
}
@inproceedings{sun-yang-2019-transfer,
    title = "Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of {BERT} in the {P}harma{C}o{NER} task",
    author = "Sun, Cong  and
      Yang, Zhihao",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5715",
    doi = "10.18653/v1/D19-5715",
    pages = "100--104",
    abstract = "To date, a large amount of biomedical content has been published in non-English texts, especially for clinical documents. Therefore, it is of considerable significance to conduct Natural Language Processing (NLP) research in non-English literature. PharmaCoNER is the first Named Entity Recognition (NER) task to recognize chemical and protein entities from Spanish biomedical texts. Since there have been abundant resources in the NLP field, how to exploit these existing resources to a new task to obtain competitive performance is a meaningful study. Inspired by the success of transfer learning with language models, we introduce the BERT benchmark to facilitate the research of PharmaCoNER task. In this paper, we evaluate two baselines based on Multilingual BERT and BioBERT on the PharmaCoNER corpus. Experimental results show that transferring the knowledge learned from source large-scale datasets to the target domain offers an effective solution for the PharmaCoNER task.",
}
@inproceedings{zhang-etal-2019-multi-task,
    title = "A Multi-Task Learning Framework for Extracting Bacteria Biotope Information",
    author = "Zhang, Qi  and
      Liu, Chao  and
      Chi, Ying  and
      Xie, Xuansong  and
      Hua, Xiansheng",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5716",
    doi = "10.18653/v1/D19-5716",
    pages = "105--109",
    abstract = "This paper presents a novel transfer multi-task learning method for Bacteria Biotope rel+ner task at BioNLP-OST 2019. To alleviate the data deficiency problem in domain-specific information extraction, we use BERT(Bidirectional Encoder Representations from Transformers) and pre-train it using mask language models and next sentence prediction on both general corpus and medical corpus like PubMed. In fine-tuning stage, we fine-tune the relation extraction layer and mention recognition layer designed by us on the top of BERT to extract mentions and relations simultaneously. The evaluation results show that our method achieves the best performance on all metrics (including slot error rate, precision and recall) in the Bacteria Biotope rel+ner subtask.",
}
@inproceedings{li-etal-2019-ynu,
    title = "{YNU}-junyi in {B}io{NLP}-{OST} 2019: Using {CNN}-{LSTM} Model with Embeddings for {S}ee{D}ev Binary Event Extraction",
    author = "Li, Junyi  and
      Zhou, Xiaobing  and
      Wu, Yuhang  and
      Wang, Bin",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5717",
    doi = "10.18653/v1/D19-5717",
    pages = "110--114",
    abstract = "We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task. The model was constructed us- ing convolutional neural networks (CNN) and long short term memory networks (LSTM). The full text information and context information were collected using the advantages of CNN and LSTM. The model consisted of two main modules: distributed semantic representation construction, such as word embedding, distance embedding and entity type embed- ding; and CNN-LSTM model. The F1 value of our participated task on the test data set of all types was 0.342. We achieved the second highest in the task. The results showed that our proposed method performed effectively in the binary relation extraction.",
}
@inproceedings{lopez-ubeda-etal-2019-using-snomed,
    title = "Using Snomed to recognize and index chemical and drug mentions.",
    author = "L{\'o}pez {\'U}beda, Pilar  and
      D{\'\i}az Galiano, Manuel Carlos  and
      Urena Lopez, L. Alfonso  and
      Martin, Maite",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5718",
    doi = "10.18653/v1/D19-5718",
    pages = "115--120",
    abstract = "In this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natural Language Processing tools and techniques have been used, and a dictionary of different sources of information has been built. The results are promising, we obtain 78{\%} in F1 score on the first sub-track and in the second task we map with Snomed correctly 72{\%} of the found entities.",
}
@inproceedings{bossy-etal-2019-bacteria,
    title = "Bacteria Biotope at {B}io{NLP} Open Shared Tasks 2019",
    author = "Bossy, Robert  and
      Del{\'e}ger, Louise  and
      Chaix, Estelle  and
      Ba, Mouhamadou  and
      N{\'e}dellec, Claire",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5719",
    doi = "10.18653/v1/D19-5719",
    pages = "121--131",
    abstract = "This paper presents the fourth edition of the Bacteria Biotope task at BioNLP Open Shared Tasks 2019. The task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts, and the characterization of these entities with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on biodiversity for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, and the challenge organization. We also provide an analysis of the results obtained by participants, and inspect the evolution of the results since the last edition in 2016.",
}
@inproceedings{gupta-etal-2019-linguistically,
    title = "Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in {B}io{NLP}-{OST} 2019",
    author = {Gupta, Pankaj  and
      Yaseen, Usama  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5720",
    doi = "10.18653/v1/D19-5720",
    pages = "132--142",
    abstract = "Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. This paper presents our findings from participating in BioNLP Shared Tasks 2019. We addressed Named Entity Recognition including nested entities extraction, Entity Normalization and Relation Extraction. Our proposed approach of Named Entities can be generalized to different languages and we have shown it{'}s effectiveness for English and Spanish text. We investigated linguistic features, hybrid loss including ranking and Conditional Random Fields (CRF), multi-task objective and token level ensembling strategy to improve NER. We employed dictionary based fuzzy and semantic search to perform Entity Normalization. Finally, our RE system employed Support Vector Machine (SVM) with linguistic features. Our NER submission (team:MIC-CIS) ranked first in BB-2019 norm+NER task with standard error rate (SER) of 0.7159 and showed competitive performance on PharmaCo NER task with F1-score of 0.8662. Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0.3738.",
}
@inproceedings{deng-etal-2019-ensemble,
    title = "An ensemble {CNN} method for biomedical entity normalization",
    author = "Deng, Pan  and
      Chen, Haipeng  and
      Huang, Mengyao  and
      Ruan, Xiaowen  and
      Xu, Liang",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5721",
    doi = "10.18653/v1/D19-5721",
    pages = "143--149",
    abstract = "Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries. The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.",
}
@inproceedings{karadeniz-etal-2019-boun,
    title = "{BOUN}-{ISIK} Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes",
    author = {Karadeniz, {\.I}lknur  and
      Tuna, {\"O}mer Faruk  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5722",
    doi = "10.18653/v1/D19-5722",
    pages = "150--157",
    abstract = "This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.",
}
@inproceedings{xiong-etal-2019-bacteria,
    title = "Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs",
    author = "Xiong, Wuti  and
      Li, Fei  and
      Cheng, Ming  and
      Yu, Hong  and
      Ji, Donghong",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5723",
    doi = "10.18653/v1/D19-5723",
    pages = "158--167",
    abstract = "abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019. This task aims to promote the development of text mining systems that extract relationships between Microorganism, Habitat and Phenotype entities. In this paper, we propose a novel approach for dependency graph construction based on lexical chains, so one dependency graph can represent one or multiple sentences. After that, we propose a neural network model which consists of the bidirectional long short-term memories and an attention graph convolution neural network to learn relation extraction features from the graph. Our approach is able to extract both intra- and inter-sentence relations, and meanwhile utilize syntax information. The results show that our approach achieved the best F1 (66.3{\%}) in the official evaluation participated by 7 teams.",
}
@inproceedings{mao-liu-2019-integration,
    title = "Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature",
    author = "Mao, Jihang  and
      Liu, Wanli",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5724",
    doi = "10.18653/v1/D19-5724",
    pages = "168--173",
    abstract = "In this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the state-of-the-art performance and is among the top two systems in five of all six subtasks.",
}
@inproceedings{baumgartner-etal-2019-craft,
    title = "{CRAFT} Shared Tasks 2019 Overview {---} Integrated Structure, Semantics, and Coreference",
    author = "Baumgartner, William  and
      Bada, Michael  and
      Pyysalo, Sampo  and
      Ciosici, Manuel R.  and
      Hailu, Negacy  and
      Pielke-Lombardo, Harrison  and
      Regan, Michael  and
      Hunter, Lawrence",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5725",
    doi = "10.18653/v1/D19-5725",
    pages = "174--184",
    abstract = "As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks {---} dependency parse construction, coreference resolution, and ontology concept identification {---} over full-text biomedical articles. The structural annotation task requires the automatic generation of dependency parses for each sentence of an article given only the article text. The coreference resolution task focuses on linking coreferring base noun phrase mentions into chains using the symmetrical and transitive identity relation. The ontology concept annotation task involves the identification of concept mentions within text using the classes of ten distinct ontologies in the biomedical domain, both unmodified and augmented with extension classes. This paper provides an overview of each task, including descriptions of the data provided to participants and the evaluation metrics used, and discusses participant results relative to baseline performances for each of the three tasks.",
}
@inproceedings{furrer-etal-2019-uzh,
    title = "{UZH}@{CRAFT}-{ST}: a Sequence-labeling Approach to Concept Recognition",
    author = "Furrer, Lenz  and
      Cornelius, Joseph  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5726",
    doi = "10.18653/v1/D19-5726",
    pages = "185--195",
    abstract = "As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition. We propose two different systems for joint named entity recognition (NER) and normalization (NEN), both of which model the task as a sequence labeling problem. Our first system is a BiLSTM network with two separate outputs for NER and NEN trained from scratch, whereas the second system is an instance of BioBERT fine-tuned on the concept-recognition task. We exploit two strategies for extending concept coverage, ontology pretraining and backoff with a dictionary lookup. Our results show that the backoff strategy effectively tackles the problem of unseen concepts, addressing a major limitation of the chosen design. In the cross-system comparison, BioBERT proves to be a strong basis for creating a concept-recognition system, although some entity types are predicted more accurately by the BiLSTM-based system.",
}
@inproceedings{trieu-etal-2019-coreference,
    title = "Coreference Resolution in Full Text Articles with {BERT} and Syntax-based Mention Filtering",
    author = "Trieu, Hai-Long  and
      Duong Nguyen, Anh-Khoa  and
      Nguyen, Nhung  and
      Miwa, Makoto  and
      Takamura, Hiroya  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5727",
    doi = "10.18653/v1/D19-5727",
    pages = "196--205",
    abstract = "This paper describes our system developed for the coreference resolution task of the CRAFT Shared Tasks 2019. The CRAFT corpus is more challenging than other existing corpora because it contains full text articles. We have employed an existing span-based state-of-theart neural coreference resolution system as a baseline system. We enhance the system with two different techniques to capture longdistance coreferent pairs. Firstly, we filter noisy mentions based on parse trees with increasing the number of antecedent candidates. Secondly, instead of relying on the LSTMs, we integrate the highly expressive language model{--}BERT into our model. Experimental results show that our proposed systems significantly outperform the baseline. The best performing system obtained F-scores of 44{\%}, 48{\%}, 39{\%}, 49{\%}, 40{\%}, and 57{\%} on the test set with B3, BLANC, CEAFE, CEAFM, LEA, and MUC metrics, respectively. Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.",
}
@inproceedings{ngo-etal-2019-neural,
    title = "Neural Dependency Parsing of Biomedical Text: {T}urku{NLP} entry in the {CRAFT} Structural Annotation Task",
    author = "Ngo, Thang Minh  and
      Kanerva, Jenna  and
      Ginter, Filip  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5728",
    doi = "10.18653/v1/D19-5728",
    pages = "206--215",
    abstract = "We present the approach taken by the TurkuNLP group in the CRAFT Structural Annotation task, a shared task on dependency parsing. Our approach builds primarily on the Turku neural parser, a native dependency parser that ranked among the best in the recent CoNLL tasks on parsing Universal Dependencies. To adapt the parser to the biomedical domain, we considered and evaluated a number of approaches, including the generation of custom word embeddings, combination with other in-domain resources, and the incorporation of information from named entity recognition. We achieved a labeled attachment score of 89.7{\%}, the best result among task participants.",
}
@inproceedings{anani-etal-2019-rdoc,
    title = "{RD}o{C} Task at {B}io{NLP}-{OST} 2019",
    author = "Anani, Mohammad  and
      Kazi, Nazmul  and
      Kuntz, Matthew  and
      Kahanda, Indika",
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5729",
    doi = "10.18653/v1/D19-5729",
    pages = "216--226",
    abstract = "BioNLP Open Shared Tasks (BioNLP-OST) is an international competition organized to facilitate development and sharing of computational tasks of biomedical text mining and solutions to them. For BioNLP-OST 2019, we introduced a new mental health informatics task called {``}RDoC Task{''}, which is composed of two subtasks: information retrieval and sentence extraction through National Institutes of Mental Health{'}s Research Domain Criteria framework. Five and four teams around the world participated in the two tasks, respectively. According to the performance on the two tasks, we observe that there is room for improvement for text mining on brain research and mental illness.",
}
@inproceedings{gupta-etal-2019-bionlp,
    title = "{B}io{NLP}-{OST} 2019 {RD}o{C} Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions",
    author = {Gupta, Pankaj  and
      Chaudhary, Yatin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5730",
    doi = "10.18653/v1/D19-5730",
    pages = "227--236",
    abstract = "This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics to behaviour. Non-availability of RDoC labelled dataset and tedious labelling process hinders the use of RDoC framework to reach its full potential in Biomedical research community and Healthcare industry. Therefore, Task-1 aims at retrieval and ranking of PubMed abstracts relevant to a given RDoC construct and Task-2 aims at extraction of the most relevant sentence from a given PubMed abstract. We investigate (1) attention based supervised neural topic model and SVM for retrieval and ranking of PubMed abstracts and, further utilize BM25 and other relevance measures for re-ranking, (2) supervised and unsupervised sentence ranking models utilizing multi-view representations comprising of query-aware attention-based sentence representation (QAR), bag-of-words (BoW) and TF-IDF. Our best systems achieved 1st rank and scored 0.86 mAP and 0.58 macro average accuracy in Task-1 and Task-2 respectively.",
}
@proceedings{emnlp-2019-neural,
    title = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    editor = "Birch, Alexandra  and
      Finch, Andrew  and
      Hayashi, Hiroaki  and
      Konstas, Ioannis  and
      Luong, Thang  and
      Neubig, Graham  and
      Oda, Yusuke  and
      Sudoh, Katsuhito",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5600",
}
@inproceedings{hayashi-etal-2019-findings,
    title = "Findings of the Third Workshop on Neural Generation and Translation",
    author = "Hayashi, Hiroaki  and
      Oda, Yusuke  and
      Birch, Alexandra  and
      Konstas, Ioannis  and
      Finch, Andrew  and
      Luong, Minh-Thang  and
      Neubig, Graham  and
      Sudoh, Katsuhito",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5601",
    doi = "10.18653/v1/D19-5601",
    pages = "1--14",
    abstract = "This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.",
}
@inproceedings{budzianowski-vulic-2019-hello,
    title = "Hello, It{'}s {GPT}-2 - How Can {I} Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems",
    author = "Budzianowski, Pawe{\l}  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5602",
    doi = "10.18653/v1/D19-5602",
    pages = "15--22",
    abstract = "Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, and language generation from absurdly small amounts of task-specific data. In this paper, we demonstrate that recent progress in language modeling pre-training and transfer learning shows promise to overcome this problem. We propose a task-oriented dialogue model that operates solely on text input: it effectively bypasses explicit policy and language generation modules. Building on top of the TransferTransfo framework (Wolf et al., 2019) and generative model pre-training (Radford et al., 2019), we validate the approach on complex multi-domain task-oriented dialogues from the MultiWOZ dataset. Our automatic and human evaluations show that the proposed model is on par with a strong task-specific neural baseline. In the long run, our approach holds promise to mitigate the data scarcity problem, and to support the construction of more engaging and more eloquent task-oriented conversational agents.",
}
@inproceedings{imamura-sumita-2019-recycling,
    title = "Recycling a Pre-trained {BERT} Encoder for Neural Machine Translation",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5603",
    doi = "10.18653/v1/D19-5603",
    pages = "23--31",
    abstract = "In this paper, a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model is applied to Transformer-based neural machine translation (NMT). In contrast to monolingual tasks, the number of unlearned model parameters in an NMT decoder is as huge as the number of learned parameters in the BERT model. To train all the models appropriately, we employ two-stage optimization, which first trains only the unlearned parameters by freezing the BERT model, and then fine-tunes all the sub-models. In our experiments, stable two-stage optimization was achieved, in contrast the BLEU scores of direct fine-tuning were extremely low. Consequently, the BLEU scores of the proposed method were better than those of the Transformer base model and the same model without pre-training. Additionally, we confirmed that NMT with the BERT encoder is more effective in low-resource settings.",
}
@inproceedings{cho-etal-2019-generating,
    title = "Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models",
    author = "Cho, Woon Sang  and
      Zhang, Yizhe  and
      Rao, Sudha  and
      Brockett, Chris  and
      Lee, Sungjin",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5604",
    doi = "10.18653/v1/D19-5604",
    pages = "32--43",
    abstract = "Ambiguous user queries in search engines result in the retrieval of documents that often span multiple topics. One potential solution is for the search engine to generate multiple refined queries, each of which relates to a subset of the documents spanning the same topic. A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents. We propose a new task of generating common question from multiple documents and present simple variant of an existing multi-source encoder-decoder framework, called the Multi-Source Question Generator (MSQG). We first train an RNN-based single encoder-decoder generator from (single document, question) pairs. At test time, given multiple documents, the Distribute step of our MSQG model predicts target word distributions for each document using the trained model. The Aggregate step aggregates these distributions to generate a common question. This simple yet effective strategy significantly outperforms several existing baseline models applied to the new task when evaluated using automated metrics and human judgments on the MS-MARCO-QA dataset.",
}
@inproceedings{tu-etal-2019-generating,
    title = "Generating Diverse Story Continuations with Controllable Semantics",
    author = "Tu, Lifu  and
      Ding, Xiaoan  and
      Yu, Dong  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5605",
    doi = "10.18653/v1/D19-5605",
    pages = "44--58",
    abstract = "We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values; (2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3) controlling generation with semantic frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics. We also conduct a human evaluation to assess the utility of providing multiple suggestions for creative writing, demonstrating promising results for the potential of controllable, diverse generation in a collaborative writing system.",
}
@inproceedings{dou-etal-2019-domain,
    title = "Domain Differential Adaptation for Neural Machine Translation",
    author = "Dou, Zi-Yi  and
      Wang, Xinyi  and
      Hu, Junjie  and
      Neubig, Graham",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5606",
    doi = "10.18653/v1/D19-5606",
    pages = "59--69",
    abstract = "Neural networks are known to be data hungry and domain sensitive, but it is nearly impossible to obtain large quantities of labeled data for every domain we are interested in. This necessitates the use of domain adaptation strategies. One common strategy encourages generalization by aligning the global distribution statistics between source and target domains, but one drawback is that the statistics of different domains or tasks are inherently divergent, and smoothing over these differences can lead to sub-optimal performance. In this paper, we propose the framework of \textit{Domain Differential Adaptation (DDA)}, where instead of smoothing over these differences we embrace them, directly modeling the difference between domains using models in a related task. We then use these learned domain differentials to adapt models for the target task accordingly. Experimental results on domain adaptation for neural machine translation demonstrate the effectiveness of this strategy, achieving consistent improvements over other alternative adaptation strategies in multiple experimental settings.",
}
@inproceedings{egonmwan-chali-2019-transformer,
    title = "Transformer-based Model for Single Documents Neural Summarization",
    author = "Egonmwan, Elozino  and
      Chali, Yllias",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5607",
    doi = "10.18653/v1/D19-5607",
    pages = "70--79",
    abstract = "We propose a system that improves performance on single document summarization task using the CNN/DailyMail and Newsroom datasets. It follows the popular encoder-decoder paradigm, but with an extra focus on the encoder. The intuition is that the probability of correctly decoding an information significantly lies in the pattern and correctness of the encoder. Hence we introduce, encode {--}encode {--} decode. A framework that encodes the source text first with a transformer, then a sequence-to-sequence (seq2seq) model. We find that the transformer and seq2seq model complement themselves adequately, making for a richer encoded vector representation. We also find that paying more attention to the vocabulary of target words during abstraction improves performance. We experiment our hypothesis and framework on the task of extractive and abstractive single document summarization and evaluate using the standard CNN/DailyMail dataset and the recently released Newsroom dataset.",
}
@inproceedings{aji-heafield-2019-making,
    title = "Making Asynchronous Stochastic Gradient Descent Work for Transformers",
    author = "Aji, Alham Fikri  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5608",
    doi = "10.18653/v1/D19-5608",
    pages = "80--89",
    abstract = "Asynchronous stochastic gradient descent (SGD) converges poorly for Transformer models, so synchronous SGD has become the norm for Transformer training. This is unfortunate because asynchronous SGD is faster at raw training speed since it avoids waiting for synchronization. Moreover, the Transformer model is the basis for state-of-the-art models for several tasks, including machine translation, so training speed matters. To understand why asynchronous SGD under-performs, we blur the lines between asynchronous and synchronous methods. We find that summing several asynchronous updates, rather than applying them immediately, restores convergence behavior. With this method, the Transformer attains the same BLEU score 1.36 times as fast.",
}
@inproceedings{malandrakis-etal-2019-controlled,
    title = "Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents",
    author = "Malandrakis, Nikolaos  and
      Shen, Minmin  and
      Goyal, Anuj  and
      Gao, Shuyang  and
      Sethi, Abhishek  and
      Metallinou, Angeliki",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5609",
    doi = "10.18653/v1/D19-5609",
    pages = "90--98",
    abstract = "Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. We investigate the use of text generation techniques to augment the training data of a popular commercial artificial agent across categories of functionality, with the goal of faster development of new functionality. We explore a variety of encoder-decoder generative models for synthetic training data generation and propose using conditional variational auto-encoders. Our approach requires only direct optimization, works well with limited data and significantly outperforms the previous controlled text generation techniques. Further, the generated data are used as additional training samples in an extrinsic intent classification task, leading to improved performance by up to 5{\%} absolute f-score in low-resource cases, validating the usefulness of our approach.",
}
@inproceedings{currey-heafield-2019-zero,
    title = "Zero-Resource Neural Machine Translation with Monolingual Pivot Data",
    author = "Currey, Anna  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5610",
    doi = "10.18653/v1/D19-5610",
    pages = "99--107",
    abstract = "Zero-shot neural machine translation (NMT) is a framework that uses source-pivot and target-pivot parallel data to train a source-target NMT system. An extension to zero-shot NMT is zero-resource NMT, which generates pseudo-parallel corpora using a zero-shot system and further trains the zero-shot system on that data. In this paper, we expand on zero-resource NMT by incorporating monolingual data in the pivot language into training; since the pivot language is usually the highest-resource language of the three, we expect monolingual pivot-language data to be most abundant. We propose methods for generating pseudo-parallel corpora using pivot-language monolingual data and for leveraging the pseudo-parallel corpora to improve the zero-shot NMT system. We evaluate these methods for a high-resource language pair (German-Russian) using English as the pivot. We show that our proposed methods yield consistent improvements over strong zero-shot and zero-resource baselines and even catch up to pivot-based models in BLEU (while not requiring the two-pass inference that pivot models require).",
}
@inproceedings{clinchant-etal-2019-use,
    title = "On the use of {BERT} for Neural Machine Translation",
    author = "Clinchant, Stephane  and
      Jung, Kweon Woo  and
      Nikoulina, Vassilina",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5611",
    doi = "10.18653/v1/D19-5611",
    pages = "108--117",
    abstract = "Exploiting large pretrained models for various NMT tasks have gained a lot of visibility recently. In this work we study how BERT pretrained models could be exploited for supervised Neural Machine Translation. We compare various ways to integrate pretrained BERT model with NMT model and study the impact of the monolingual data used for BERT training on the final translation quality. We use WMT-14 English-German, IWSLT15 English-German and IWSLT14 English-Russian datasets for these experiments. In addition to standard task test set evaluation, we perform evaluation on out-of-domain test sets and noise injected test sets, in order to assess how BERT pretrained representations affect model robustness.",
}
@inproceedings{prokhorov-etal-2019-importance,
    title = "On the Importance of the {K}ullback-{L}eibler Divergence Term in Variational Autoencoders for Text Generation",
    author = "Prokhorov, Victor  and
      Shareghi, Ehsan  and
      Li, Yingzhen  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5612",
    doi = "10.18653/v1/D19-5612",
    pages = "118--127",
    abstract = "Variational Autoencoders (VAEs) are known to suffer from learning uninformative latent representation of the input due to issues such as approximated posterior collapse, or entanglement of the latent space. We impose an explicit constraint on the Kullback-Leibler (KL) divergence term inside the VAE objective function. While the explicit constraint naturally avoids posterior collapse, we use it to further understand the significance of the KL term in controlling the information transmitted through the VAE channel. Within this framework, we explore different properties of the estimated posterior distribution, and highlight the trade-off between the amount of information encoded in a latent code during training, and the generative capacity of the model.",
}
@inproceedings{yamshchikov-etal-2019-decomposing,
    title = "Decomposing Textual Information For Style Transfer",
    author = {Yamshchikov, Ivan P.  and
      Shibaev, Viacheslav  and
      Nagaev, Aleksander  and
      Jost, J{\"u}rgen  and
      Tikhonov, Alexey},
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5613",
    doi = "10.18653/v1/D19-5613",
    pages = "128--137",
    abstract = "This paper focuses on latent representations that could effectively decompose different aspects of textual information. Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. We validate these methods with several state-of-the-art textual style transfer methods. Higher quality of information decomposition corresponds to higher performance in terms of bilingual evaluation understudy (BLEU) between output and human-written reformulations.",
}
@inproceedings{pang-gimpel-2019-unsupervised,
    title = "Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer",
    author = "Pang, Richard Yuanzhe  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5614",
    doi = "10.18653/v1/D19-5614",
    pages = "138--147",
    abstract = "We consider the problem of automatically generating textual paraphrases with modified attributes or properties, focusing on the setting without parallel data (Hu et al., 2017; Shen et al., 2017). This setting poses challenges for evaluation. We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic preservation and fluency as well as a way to combine them into a single overall score. We contribute new loss functions and training strategies to address the different metrics. Semantic preservation is addressed by adding a cyclic consistency loss and a loss based on paraphrase pairs, while fluency is improved by integrating losses based on style-specific language models. We experiment with a Yelp sentiment dataset and a new literature dataset that we propose, using multiple models that extend prior work (Shen et al., 2017). We demonstrate that our metrics correlate well with human judgments, at both the sentence-level and system-level. Automatic and manual evaluation also show large improvements over the baseline method of Shen et al. (2017). We hope that our proposed metrics can speed up system development for new textual transfer tasks while also encouraging the community to address our three complementary aspects of transfer quality.",
}
@inproceedings{gong-etal-2019-enhanced,
    title = "Enhanced Transformer Model for Data-to-Text Generation",
    author = "Gong, Li  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5615",
    doi = "10.18653/v1/D19-5615",
    pages = "148--156",
    abstract = "Neural models have recently shown significant progress on data-to-text generation tasks in which descriptive texts are generated conditioned on database records. In this work, we present a new Transformer-based data-to-text generation model which learns content selection and summary generation in an end-to-end fashion. We introduce two extensions to the baseline transformer model: First, we modify the latent representation of the input, which helps to significantly improve the content correctness of the output summary; Second, we include an additional learning objective that accounts for content selection modelling. In addition, we propose two data augmentation methods that succeed to further improve performance of the resulting generation models. Evaluation experiments show that our final model outperforms current state-of-the-art systems as measured by different metrics: BLEU, content selection precision and content ordering. We made publicly available the transformer extension presented in this paper.",
}
@inproceedings{schmidt-2019-generalization,
    title = "Generalization in Generation: A closer look at Exposure Bias",
    author = "Schmidt, Florian",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5616",
    doi = "10.18653/v1/D19-5616",
    pages = "157--167",
    abstract = "Exposure bias refers to the train-test discrepancy that seemingly arises when an autoregressive generative model uses only ground-truth contexts at training time but generated ones at test time. We separate the contribution of the learning framework and the model to clarify the debate on consequences and review proposed counter-measures. In this light, we argue that generalization is the underlying property to address and propose unconditional generation as its fundamental benchmark. Finally, we combine latent variable modeling with a recent formulation of exploration in reinforcement learning to obtain a rigorous handling of true and generated contexts. Results on language modeling and variational sentence auto-encoding confirm the model{'}s generalization capability.",
}
@inproceedings{berard-etal-2019-machine,
    title = "Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness",
    author = "Berard, Alexandre  and
      Calapodescu, Ioan  and
      Dymetman, Marc  and
      Roux, Claude  and
      Meunier, Jean-Luc  and
      Nikoulina, Vassilina",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5617",
    doi = "10.18653/v1/D19-5617",
    pages = "168--176",
    abstract = "We share a French-English parallel corpus of Foursquare restaurant reviews, and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where better-quality MT would be greatly beneficial. We discuss the challenges of such user-generated content, and train good baseline models that build upon the latest techniques for MT robustness. We also perform an extensive evaluation (automatic and human) that shows significant improvements over existing online systems. Finally, we propose task-specific metrics based on sentiment analysis or translation accuracy of domain-specific polysemous words.",
}
@inproceedings{zaremoodi-haffari-2019-adaptively,
    title = "Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation",
    author = "Zaremoodi, Poorya  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5618",
    doi = "10.18653/v1/D19-5618",
    pages = "177--186",
    abstract = "Neural Machine Translation (NMT), a data-hungry technology, suffers from the lack of bilingual data in low-resource scenarios. Multitask learning (MTL) can alleviate this issue by injecting inductive biases into NMT, using auxiliary syntactic and semantic tasks. However, an effective \textit{training schedule} is required to balance the importance of tasks to get the best use of the training signal. The role of training schedule becomes even more crucial in \textit{biased-MTL} where the goal is to improve one (or a subset) of tasks the most, e.g. translation quality. Current approaches for biased-MTL are based on brittle \textit{hand-engineered} heuristics that require trial and error, and should be (re-)designed for each learning scenario. To the best of our knowledge, ours is the first work on \textit{adaptively} and \textit{dynamically} changing the training schedule in biased-MTL. We propose a rigorous approach for automatically reweighing the training data of the main and auxiliary tasks throughout the training process based on their contributions to the generalisability of the main NMT task. Our experiments on translating from English to Vietnamese/Turkish/Spanish show improvements of up to +1.2 BLEU points, compared to strong baselines. Additionally, our analyses shed light on the dynamic of needs throughout the training of NMT: from syntax to semantic.",
}
@inproceedings{ataman-etal-2019-importance,
    title = "On the Importance of Word Boundaries in Character-level Neural Machine Translation",
    author = "Ataman, Duygu  and
      Firat, Orhan  and
      Di Gangi, Mattia A.  and
      Federico, Marcello  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5619",
    doi = "10.18653/v1/D19-5619",
    pages = "187--193",
    abstract = "Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task. Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks. In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters. We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model.",
}
@inproceedings{li-chan-2019-big,
    title = "Big Bidirectional Insertion Representations for Documents",
    author = "Li, Lala  and
      Chan, William",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5620",
    doi = "10.18653/v1/D19-5620",
    pages = "194--198",
    abstract = "The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring $O(\log_2 n)$ generation steps to generate $n$ tokens. However, modeling long sequences is difficult, as there is more ambiguity captured in the attention mechanism. This work proposes the Big Bidirectional Insertion Representations for Documents (Big BIRD), an insertion-based model for document-level translation tasks. We scale up the insertion-based models to long form documents. Our key contribution is introducing sentence alignment via sentence-positional embeddings between the source and target document. We show an improvement of +4.3 BLEU on the WMT{'}19 English-{\textgreater}German document-level translation task compared with the Insertion Transformer baseline.",
}
@inproceedings{bhat-etal-2019-margin,
    title = "A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation",
    author = "Bhat, Gayatri  and
      Kumar, Sachin  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5621",
    doi = "10.18653/v1/D19-5621",
    pages = "199--205",
    abstract = "Neural models that eliminate the softmax bottleneck by generating word embeddings (rather than multinomial distributions over a vocabulary) attain faster training with fewer learnable parameters. These models are currently trained by maximizing densities of pretrained target embeddings under von Mises-Fisher distributions parameterized by corresponding model-predicted embeddings. This work explores the utility of margin-based loss functions in optimizing such models. We present syn-margin loss, a novel margin-based loss that uses a synthetic negative sample constructed from only the predicted and target embeddings at every step. The loss is efficient to compute, and we use a geometric analysis to argue that it is more consistent and interpretable than other margin-based losses. Empirically, we find that syn-margin provides small but significant improvements over both vMF and standard margin-based losses in continuous-output neural machine translation.",
}
@inproceedings{cui-etal-2019-mixed,
    title = "Mixed Multi-Head Self-Attention for Neural Machine Translation",
    author = "Cui, Hongyi  and
      Iida, Shohei  and
      Hung, Po-Hsuan  and
      Utsuro, Takehito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5622",
    doi = "10.18653/v1/D19-5622",
    pages = "206--214",
    abstract = "Recently, the Transformer becomes a state-of-the-art architecture in the filed of neural machine translation (NMT). A key point of its high-performance is the multi-head self-attention which is supposed to allow the model to independently attend to information from different representation subspaces. However, there is no explicit mechanism to ensure that different attention heads indeed capture different features, and in practice, redundancy has occurred in multiple heads. In this paper, we argue that using the same global attention in multiple heads limits multi-head self-attention{'}s capacity for learning distinct features. In order to improve the expressiveness of multi-head self-attention, we propose a novel Mixed Multi-Head Self-Attention (MMA) which models not only global and local attention but also forward and backward attention in different attention heads. This enables the model to learn distinct representations explicitly among multiple heads. In our experiments on both WAT17 English-Japanese as well as IWSLT14 German-English translation task, we show that, without increasing the number of parameters, our models yield consistent and significant improvements (0.9 BLEU scores on average) over the strong Transformer baseline.",
}
@inproceedings{witteveen-andrews-2019-paraphrasing,
    title = "Paraphrasing with Large Language Models",
    author = "Witteveen, Sam  and
      Andrews, Martin",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5623",
    doi = "10.18653/v1/D19-5623",
    pages = "215--220",
    abstract = "Recently, large language models such as GPT-2 have shown themselves to be extremely adept at text generation and have also been able to achieve high-quality results in many downstream NLP tasks such as text classification, sentiment analysis and question answering with the aid of fine-tuning. We present a useful technique for using a large language model to perform the task of paraphrasing on a variety of texts and subjects. Our approach is demonstrated to be capable of generating paraphrases not only at a sentence level but also for longer spans of text such as paragraphs without needing to break the text into smaller chunks.",
}
@inproceedings{moradi-etal-2019-interrogating,
    title = "Interrogating the Explanatory Power of Attention in Neural Machine Translation",
    author = "Moradi, Pooya  and
      Kambhatla, Nishant  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5624",
    doi = "10.18653/v1/D19-5624",
    pages = "221--230",
    abstract = "Attention models have become a crucial component in neural machine translation (NMT). They are often implicitly or explicitly used to justify the model{'}s decision in generating a specific token but it has not yet been rigorously established to what extent attention is a reliable source of information in NMT. To evaluate the explanatory power of attention for NMT, we examine the possibility of yielding the same prediction but with counterfactual attention models that modify crucial aspects of the trained attention model. Using these counterfactual attention mechanisms we assess the extent to which they still preserve the generation of function and content words in the translation process. Compared to a state of the art attention model, our counterfactual attention models produce 68{\%} of function words and 21{\%} of content words in our German-English dataset. Our experiments demonstrate that attention models by themselves cannot reliably explain the decisions made by a NMT model.",
}
@inproceedings{murray-etal-2019-auto,
    title = "Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation",
    author = "Murray, Kenton  and
      Kinnison, Jeffery  and
      Nguyen, Toan Q.  and
      Scheirer, Walter  and
      Chiang, David",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5625",
    doi = "10.18653/v1/D19-5625",
    pages = "231--240",
    abstract = "Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation. Yet these neural networks are very sensitive to architecture and hyperparameter settings. Optimizing these settings by grid or random search is computationally expensive because it requires many training runs. In this paper, we incorporate architecture search into a single training run through auto-sizing, which uses regularization to delete neurons in a network over the course of training. On very low-resource language pairs, we show that auto-sizing can improve BLEU scores by up to 3.9 points while removing one-third of the parameters from the model.",
}
@inproceedings{park-tsvetkov-2019-learning,
    title = "Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation",
    author = "Park, Chan Young  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5626",
    doi = "10.18653/v1/D19-5626",
    pages = "241--248",
    abstract = "Neural machine translation (NMT) often fails in one-to-many translation, e.g., in the translation of multi-word expressions, compounds, and collocations. To improve the translation of phrases, phrase-based NMT systems have been proposed; these typically combine word-based NMT with external phrase dictionaries or with phrase tables from phrase-based statistical MT systems. These solutions introduce a significant overhead of additional resources and computational costs. In this paper, we introduce a phrase-based NMT model built upon continuous-output NMT, in which the decoder generates embeddings of words or phrases. The model uses a fertility module, which guides the decoder to generate embeddings of sequences of varying lengths. We show that our model learns to translate phrases better, performing on par with state of the art phrase-based NMT. Since our model does not resort to softmax computation over a huge vocabulary of phrases, its training time is about 112x faster than the baseline.",
}
@inproceedings{egonmwan-chali-2019-transformer-seq2seq,
    title = "Transformer and seq2seq model for Paraphrase Generation",
    author = "Egonmwan, Elozino  and
      Chali, Yllias",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5627",
    doi = "10.18653/v1/D19-5627",
    pages = "249--255",
    abstract = "Paraphrase generation aims to improve the clarity of a sentence by using different wording that convey similar meaning. For better quality of generated paraphrases, we propose a framework that combines the effectiveness of two models {--} transformer and sequence-to-sequence (seq2seq). We design a two-layer stack of encoders. The first layer is a transformer model containing 6 stacked identical layers with multi-head self attention, while the second-layer is a seq2seq model with gated recurrent units (GRU-RNN). The transformer encoder layer learns to capture long-term dependencies, together with syntactic and semantic properties of the input sentence. This rich vector representation learned by the transformer serves as input to the GRU-RNN encoder responsible for producing the state vector for decoding. Experimental results on two datasets-QUORA and MSCOCO using our framework, produces a new benchmark for paraphrase generation.",
}
@inproceedings{maruf-haffari-2019-monash,
    title = "Monash University{'}s Submissions to the {WNGT} 2019 Document Translation Task",
    author = "Maruf, Sameen  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5628",
    doi = "10.18653/v1/D19-5628",
    pages = "256--261",
    abstract = "We describe the work of Monash University for the shared task of Rotowire document translation organised by the 3rd Workshop on Neural Generation and Translation (WNGT 2019). We submitted systems for both directions of the English-German language pair. Our main focus is on employing an established document-level neural machine translation model for this task. We achieve a BLEU score of 39.83 (41.46 BLEU per WNGT evaluation) for En-De and 45.06 (47.39 BLEU per WNGT evaluation) for De-En translation directions on the Rotowire test set. All experiments conducted in the process are also described.",
}
@inproceedings{gong-etal-2019-systran,
    title = "{SYSTRAN} @ {WNGT} 2019: {DGT} Task",
    author = "Gong, Li  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5629",
    doi = "10.18653/v1/D19-5629",
    pages = "262--267",
    abstract = "This paper describes SYSTRAN participation to the Document-level Generation and Trans- lation (DGT) Shared Task of the 3rd Workshop on Neural Generation and Translation (WNGT 2019). We participate for the first time using a Transformer network enhanced with modified input embeddings and optimising an additional objective function that considers content selection. The network takes in structured data of basketball games and outputs a summary of the game in natural language.",
}
@inproceedings{puduppully-etal-2019-university,
    title = "{U}niversity of {E}dinburgh{'}s submission to the Document-level Generation and Translation Shared Task",
    author = "Puduppully, Ratish  and
      Mallinson, Jonathan  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5630",
    doi = "10.18653/v1/D19-5630",
    pages = "268--272",
    abstract = "The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages. For the NLG track, we submitted a multilingual system based on the Content Selection and Planning model of Puduppully et al (2019). For the MT track, we submitted Transformer-based Neural Machine Translation models, where out-of-domain parallel data was augmented with in-domain data extracted from monolingual corpora. Our MT+NLG systems disregard the structured input data and instead rely exclusively on the source summaries.",
}
@inproceedings{saleh-etal-2019-naver,
    title = "Naver Labs {E}urope{'}s Systems for the Document-Level Generation and Translation Task at {WNGT} 2019",
    author = "Saleh, Fahimeh  and
      Berard, Alexandre  and
      Calapodescu, Ioan  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5631",
    doi = "10.18653/v1/D19-5631",
    pages = "273--279",
    abstract = "Recently, neural models led to significant improvements in both machine translation (MT) and natural language generation tasks (NLG). However, generation of long descriptive summaries conditioned on structured data remains an open challenge. Likewise, MT that goes beyond sentence-level context is still an open issue (e.g., document-level MT or MT with metadata). To address these challenges, we propose to leverage data from both tasks and do transfer learning between MT, NLG, and MT with source-side metadata (MT+NLG). First, we train document-based MT systems with large amounts of parallel data. Then, we adapt these models to pure NLG and MT+NLG tasks by fine-tuning with smaller amounts of domain-specific data. This end-to-end NLG approach, without data selection and planning, outperforms the previous state of the art on the Rotowire NLG task. We participated to the {``}Document Generation and Translation{''} task at WNGT 2019, and ranked first in all tracks.",
}
@inproceedings{kim-etal-2019-research,
    title = "From Research to Production and Back: Ludicrously Fast Neural Machine Translation",
    author = "Kim, Young Jin  and
      Junczys-Dowmunt, Marcin  and
      Hassan, Hany  and
      Fikri Aji, Alham  and
      Heafield, Kenneth  and
      Grundkiewicz, Roman  and
      Bogoychev, Nikolay",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5632",
    doi = "10.18653/v1/D19-5632",
    pages = "280--288",
    abstract = "This paper describes the submissions of the {``}Marian{''} team to the WNGT 2019 efficiency shared task. Taking our dominating submissions to the previous edition of the shared task as a starting point, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models. For efficient CPU-based decoding, we propose pre-packed 8-bit matrix products, improved batched decoding, cache-friendly student architectures with parameter sharing and light-weight RNN-based decoder architectures. GPU-based decoding benefits from the same architecture changes, from pervasive 16-bit inference and concurrent streams. These modifications together with profiler-based C++ code optimization allow us to push the Pareto frontier established during the 2018 edition towards 24x (CPU) and 14x (GPU) faster models at comparable or higher BLEU values. Our fastest CPU model is more than 4x faster than last year{'}s fastest submission at more than 3 points higher BLEU. Our fastest GPU model at 1.5 seconds translation time is slightly faster than last year{'}s fastest RNN-based submissions, but outperforms them by more than 4 BLEU and 10 BLEU points respectively.",
}
@inproceedings{miculicich-etal-2019-selecting,
    title = "Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation",
    author = "Miculicich, Lesly  and
      Marone, Marc  and
      Hassan, Hany",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5633",
    doi = "10.18653/v1/D19-5633",
    pages = "289--296",
    abstract = "In this paper, we report our system submissions to all 6 tracks of the WNGT 2019 shared task on Document-Level Generation and Translation. The objective is to generate a textual document from either structured data: generation task, or a document in a different language: translation task. For the translation task, we focused on adapting a large scale system trained on WMT data by fine tuning it on the RotoWire data. For the generation task, we participated with two systems based on a selection and planning model followed by (a) a simple language model generation, and (b) a GPT-2 pre-trained language model approach. The selection and planning module chooses a subset of table records in order, and the language models produce text given such a subset.",
}
@inproceedings{murray-etal-2019-efficiency,
    title = "Efficiency through Auto-Sizing: {N}otre {D}ame {NLP}{'}s Submission to the {WNGT} 2019 Efficiency Task",
    author = "Murray, Kenton  and
      DuSell, Brian  and
      Chiang, David",
    booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5634",
    doi = "10.18653/v1/D19-5634",
    pages = "297--301",
    abstract = "This paper describes the Notre Dame Natural Language Processing Group{'}s (NDNLP) submission to the WNGT 2019 shared task (Hayashi et al., 2019). We investigated the impact of auto-sizing (Murray and Chiang, 2015; Murray et al., 2019) to the Transformer network (Vaswani et al., 2017) with the goal of substantially reducing the number of parameters in the model. Our method was able to eliminate more than 25{\%} of the model{'}s parameters while suffering a decrease of only 1.1 BLEU.",
}
@proceedings{emnlp-2019-noisy,
    title = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5500",
}
@inproceedings{karamanolakis-etal-2019-weakly,
    title = "Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health",
    author = "Karamanolakis, Giannis  and
      Hsu, Daniel  and
      Gravano, Luis",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5501",
    doi = "10.18653/v1/D19-5501",
    pages = "1--10",
    abstract = "In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different aspects of the entity in question. However, training supervised models for segment-level classification requires segment labels, which may be more difficult or expensive to obtain than review labels. In this paper, we employ Multiple Instance Learning (MIL) and use only weak supervision in the form of a single label per review. First, we show that when inappropriate MIL aggregation functions are used, then MIL-based networks are outperformed by simpler baselines. Second, we propose a new aggregation function based on the sigmoid attention mechanism and show that our proposed model outperforms the state-of-the-art models for segment-level sentiment classification (by up to 9.8{\%} in F1). Finally, we highlight the importance of fine-grained predictions in an important public-health application: finding actionable reports of foodborne illness. We show that our model achieves 48.6{\%} higher recall compared to previous models, thus increasing the chance of identifying previously unknown foodborne outbreaks.",
}
@inproceedings{czeresnia-etinger-black-2019-formality,
    title = "Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora",
    author = "Czeresnia Etinger, Isak  and
      Black, Alan W",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5502",
    doi = "10.18653/v1/D19-5502",
    pages = "11--16",
    abstract = "Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. As each existing dataset is sourced from a specific domain and context, most use cases will have a sizable mismatch from the vocabulary and sentence structures of any dataset available. This reduces the performance of the style transfer, and is particularly significant for noisy, user-generated text. To solve this problem, we show a technique to derive a dataset of aligned pairs (style-agnostic vs stylistic sentences) from an unlabeled corpus by using an auxiliary dataset, allowing for in-domain training. We test the technique with the Yahoo Formality Dataset and 6 novel datasets we produced, which consist of scripts from 5 popular TV-shows (Friends, Futurama, Seinfeld, Southpark, Stargate SG-1) and the Slate Star Codex online forum. We gather 1080 human evaluations, which show that our method produces a sizable change in formality while maintaining fluency and context; and that it considerably outperforms OpenNMT{'}s Seq2Seq model directly trained on the Yahoo Formality Dataset. Additionally, we publish the full pipeline code and our novel datasets.",
}
@inproceedings{federmann-etal-2019-multilingual,
    title = "Multilingual Whispers: Generating Paraphrases with Translation",
    author = "Federmann, Christian  and
      Elachqar, Oussama  and
      Quirk, Chris",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5503",
    doi = "10.18653/v1/D19-5503",
    pages = "17--26",
    abstract = "Naturally occurring paraphrase data, such as multiple news stories about the same event, is a useful but rare resource. This paper compares translation-based paraphrase gathering using human, automatic, or hybrid techniques to monolingual paraphrasing by experts and non-experts. We gather translations, paraphrases, and empirical human quality assessments of these approaches. Neural machine translation techniques, especially when pivoting through related languages, provide a relatively robust source of paraphrases with diversity comparable to expert human paraphrases. Surprisingly, human translators do not reliably outperform neural systems. The resulting data release will not only be a useful test set, but will also allow additional explorations in translation and paraphrase quality assessments and relationships.",
}
@inproceedings{nadejde-tetreault-2019-personalizing,
    title = "Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and {L}1",
    author = "Nadejde, Maria  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5504",
    doi = "10.18653/v1/D19-5504",
    pages = "27--33",
    abstract = "Grammar error correction (GEC) systems have become ubiquitous in a variety of software applications, and have started to approach human-level performance for some datasets. However, very little is known about how to efficiently personalize these systems to the user{'}s characteristics, such as their proficiency level and first language, or to emerging domains of text. We present the first results on adapting a general purpose neural GEC system to both the proficiency level and the first language of a writer, using only a few thousand annotated sentences. Our study is the broadest of its kind, covering five proficiency levels and twelve different languages, and comparing three different adaptation scenarios: adapting to the proficiency level only, to the first language only, or to both aspects simultaneously. We show that tailoring to both scenarios achieves the largest performance improvement (3.6 F0.5) relative to a strong baseline.",
}
@inproceedings{li-etal-2019-exploiting,
    title = "Exploiting {BERT} for End-to-End Aspect-based Sentiment Analysis",
    author = "Li, Xin  and
      Bing, Lidong  and
      Zhang, Wenxuan  and
      Lam, Wai",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5505",
    doi = "10.18653/v1/D19-5505",
    pages = "34--41",
    abstract = "In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.",
}
@inproceedings{karpukhin-etal-2019-training,
    title = "Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation",
    author = "Karpukhin, Vladimir  and
      Levy, Omer  and
      Eisenstein, Jacob  and
      Ghazvininejad, Marjan",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5506",
    doi = "10.18653/v1/D19-5506",
    pages = "42--47",
    abstract = "Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution.",
}
@inproceedings{chambers-etal-2019-character,
    title = "Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking",
    author = "Chambers, Nathanael  and
      Forman, Timothy  and
      Griswold, Catherine  and
      Lu, Kevin  and
      Khastgir, Yogaish  and
      Steckler, Stephen",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5507",
    doi = "10.18653/v1/D19-5507",
    pages = "48--56",
    abstract = "Illicit activity on the Web often uses noisy text to obscure information between client and seller, such as the seller{'}s phone number. This presents an interesting challenge to language understanding systems; how do we model adversarial noise in a text extraction system? This paper addresses the sex trafficking domain, and proposes some of the first neural network architectures to learn and extract phone numbers from noisy text. We create a new adversarial advertisement dataset, propose several RNN-based models to solve the problem, and most notably propose a visual character language model to interpret unseen unicode characters. We train a CRF jointly with a CNN to improve number recognition by 89{\%} over just a CRF. Through data augmentation in this unique model, we present the first results on characters never seen in training.",
}
@inproceedings{bhandari-armstrong-2019-tkol,
    title = "Tkol, Httt, and r/radiohead: High Affinity Terms in {R}eddit Communities",
    author = "Bhandari, Abhinav  and
      Armstrong, Caitrin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5508",
    doi = "10.18653/v1/D19-5508",
    pages = "57--67",
    abstract = "Language is an important marker of a cultural group, large or small. One aspect of language variation between communities is the employment of highly specialized terms with unique significance to the group. We study these high affinity terms across a wide variety of communities by leveraging the rich diversity of Reddit.com. We provide a systematic exploration of high affinity terms, the often rapid semantic shifts they undergo, and their relationship to subreddit characteristics across 2600 diverse subreddits. Our results show that high affinity terms are effective signals of loyal communities, they undergo more semantic shift than low affinity terms, and that they are partial barrier to entry for new users. We conclude that Reddit is a robust and valuable data source for testing further theories about high affinity terms across communities.",
}
@inproceedings{bonadiman-etal-2019-large,
    title = "Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning",
    author = "Bonadiman, Daniele  and
      Kumar, Anjishnu  and
      Mittal, Arpit",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5509",
    doi = "10.18653/v1/D19-5509",
    pages = "68--75",
    abstract = "The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question. Such a system can be used to understand and answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms. This has large-scale applications for community Question Answering (cQA) and open-domain spoken language question answering systems. In this paper we describe a new QPR system implemented as a Neural Information Retrieval (NIR) system consisting of a neural network sentence encoder and an approximate k-Nearest Neighbour index for efficient vector retrieval. We also describe our mechanism to generate an annotated dataset for question paraphrase retrieval experiments automatically from question-answer logs via distant supervision. We show that the standard loss function in NIR, triplet loss, does not perform well with noisy labels. We propose smoothed deep metric loss (SDML) and with our experiments on two QPR datasets we show that it significantly outperforms triplet loss in the noisy label setting.",
}
@inproceedings{nguyen-hovy-2019-hey,
    title = "Hey {S}iri. Ok {G}oogle. {A}lexa: A topic modeling of user reviews for smart speakers",
    author = "Nguyen, Hanh  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5510",
    doi = "10.18653/v1/D19-5510",
    pages = "76--83",
    abstract = "User reviews provide a significant source of information for companies to understand their market and audience. In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA). However, while there are metrics to choose the {``}best{''} number of topics, it is not clear whether the resulting topics can also provide in-depth, actionable product analysis. Our paper examines this issue by analyzing user reviews from the Best Buy US website for smart speakers. Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns. We find that while coherence scores are a good starting point to identify a number of topics, it still requires manual adaptation based on domain knowledge to provide market insights. We show that the resulting dimensions capture brand performance and differences, and differentiate the market into two distinct groups with different properties.",
}
@inproceedings{athavale-etal-2019-predicting,
    title = "Predicting Algorithm Classes for Programming Word Problems",
    author = "Athavale, Vinayak  and
      Naik, Aayush  and
      Vanjape, Rajas  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5511",
    doi = "10.18653/v1/D19-5511",
    pages = "84--93",
    abstract = "We introduce the task of algorithm class prediction for programming word problems. A programming word problem is a problem written in natural language, which can be solved using an algorithm or a program. We define classes of various programming word problems which correspond to the class of algorithms required to solve the problem. We present four new datasets for this task, two multiclass datasets with 550 and 1159 problems each and two multilabel datasets having 3737 and 3960 problems each. We pose the problem as a text classification problem and train neural network and non-neural network based models on this task. Our best performing classifier gets an accuracy of 62.7 percent for the multiclass case on the five class classification dataset, Codeforces Multiclass-5 (CFMC5). We also do some human-level analysis and compare human performance with that of our text classification models. Our best classifier has an accuracy only 9 percent lower than that of a human on this task. To the best of our knowledge, these are the first reported results on such a task. We make our code and datasets publicly available.",
}
@inproceedings{van-der-lee-etal-2019-automatic,
    title = "Automatic identification of writers{'} intentions: Comparing different methods for predicting relationship goals in online dating profile texts",
    author = "van der Lee, Chris  and
      van der Zanden, Tess  and
      Krahmer, Emiel  and
      Mos, Maria  and
      Schouten, Alexander",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5512",
    doi = "10.18653/v1/D19-5512",
    pages = "94--100",
    abstract = "Psychologically motivated, lexicon-based text analysis methods such as LIWC (Pennebaker et al., 2015) have been criticized by computational linguists for their lack of adaptability, but they have not often been systematically compared with either human evaluations or machine learning approaches. The goal of the current study was to assess the effectiveness and predictive ability of LIWC on a relationship goal classification task. In this paper, we compared the outcomes of (1) LIWC, (2) machine learning, and (3) a human baseline. A newly collected corpus of online dating profile texts (a genre not explored before in the ACL anthology) was used, accompanied by the profile writers{'} self-selected relationship goal (long-term versus date). These three approaches were tested by comparing their performance on identifying both the intended relationship goal and content-related text labels. Results show that LIWC and machine learning models correlate with human evaluations in terms of content-related labels. LIWC{'}s content-related labels corresponded more strongly to humans than those of the classifier. Moreover, all approaches were similarly accurate in predicting the relationship goal.",
}
@inproceedings{ghaddar-langlais-2019-contextualized,
    title = "Contextualized Word Representations from Distant Supervision with and for {NER}",
    author = "Ghaddar, Abbas  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5513",
    doi = "10.18653/v1/D19-5513",
    pages = "101--108",
    abstract = "We describe a special type of deep contextualized word representation that is learned from distant supervision annotations and dedicated to named entity recognition. Our extensive experiments on 7 datasets show systematic gains across all domains over strong baselines, and demonstrate that our representation is complementary to previously proposed embeddings. We report new state-of-the-art results on CONLL and ONTONOTES datasets.",
}
@inproceedings{gu-etal-2019-extract,
    title = "Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template",
    author = "Gu, Yunfan  and
      Yuqiao, Yang  and
      Wei, Zhongyu",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5514",
    doi = "10.18653/v1/D19-5514",
    pages = "109--114",
    abstract = "Question paraphrasing aims to restate a given question with different expressions but keep the original meaning. Recent approaches are mostly based on neural networks following a sequence-to-sequence fashion, however, these models tend to generate unpredictable results. To overcome this drawback, we propose a pipeline model based on templates. It follows three steps, a) identifies template from the input question, b) retrieves candidate templates, c) fills candidate templates with original topic words. Experiment results on two self-constructed datasets show that our model outperforms the sequence-to-sequence model in a large margin and the advantage is more promising when the size of training sample is small.",
}
@inproceedings{van-der-goot-2019-depth,
    title = "An In-depth Analysis of the Effect of Lexical Normalization on the Dependency Parsing of Social Media",
    author = "van der Goot, Rob",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5515",
    doi = "10.18653/v1/D19-5515",
    pages = "115--120",
    abstract = "Existing natural language processing systems have often been designed with standard texts in mind. However, when these tools are used on the substantially different texts from social media, their performance drops dramatically. One solution is to translate social media data to standard language before processing, this is also called normalization. It is well-known that this improves performance for many natural language processing tasks on social media data. However, little is known about which types of normalization replacements have the most effect. Furthermore, it is unknown what the weaknesses of existing lexical normalization systems are in an extrinsic setting. In this paper, we analyze the effect of manual as well as automatic lexical normalization for dependency parsing. After our analysis, we conclude that for most categories, automatic normalization scores close to manually annotated normalization and that small annotation differences are important to take into consideration when exploiting normalization in a pipeline setup.",
}
@inproceedings{dumont-etal-2019-wrote,
    title = "Who wrote this book? A challenge for e-commerce",
    author = "Dumont, B{\'e}ranger  and
      Maggio, Simona  and
      Sidi Said, Ghiles  and
      Au, Quoc-Tien",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5516",
    doi = "10.18653/v1/D19-5516",
    pages = "121--125",
    abstract = "Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via search or browsing. Of particular significance is the book category, where the author name(s) field poses a significant challenge. Indeed, books written by a given author might be listed with different authors{'} names due to abbreviations, spelling variants and mistakes, among others. To solve this problem at scale, we design a composite system involving open data sources for books, as well as deep learning components, such as approximate match with Siamese networks and name correction with sequence-to-sequence networks. We evaluate this approach on product data from the e-commerce website Rakuten France, and find that the top proposal of the system is the normalized author name with 72{\%} accuracy.",
}
@inproceedings{kobayakawa-etal-2019-mining,
    title = "Mining Tweets that refer to {TV} programs with Deep Neural Networks",
    author = "Kobayakawa, Takeshi  and
      Miyazaki, Taro  and
      Okamoto, Hiroki  and
      Clippingdale, Simon",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5517",
    doi = "10.18653/v1/D19-5517",
    pages = "126--130",
    abstract = "The automatic analysis of expressions of opinion has been well studied in the opinion mining area, but a remaining problem is robustness for user-generated texts. Although consumer-generated texts are valuable since they contain a great number and wide variety of user evaluations, spelling inconsistency and the variety of expressions make analysis difficult. In order to tackle such situations, we applied a model that is reported to handle context in many natural language processing areas, to the problem of extracting references to the opinion target from text. Experiments on tweets that refer to television programs show that the model can extract such references with more than 90{\%} accuracy.",
}
@inproceedings{adouane-etal-2019-normalising,
    title = "Normalising Non-standardised Orthography in {A}lgerian Code-switched User-generated Data",
    author = "Adouane, Wafia  and
      Bernardy, Jean-Philippe  and
      Dobnik, Simon",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5518",
    doi = "10.18653/v1/D19-5518",
    pages = "131--140",
    abstract = "We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consisting of user-generated textual data matched with normalised and corrected human annotations following data-driven and our linguistically motivated standard. We use an end-to-end deep neural model designed to deal with context-dependent spelling correction and normalisation. Results indicate that a model with two CNN sub-network encoders and an LSTM decoder performs the best, and that word context matters. Additionally, pre-processing data token-by-token with an edit-distance based aligner significantly improves the performance. We get promising results for the spelling correction and normalisation, as a pre-processing step for downstream tasks, on detecting binary Semantic Textual Similarity.",
}
@inproceedings{partanen-etal-2019-dialect,
    title = "Dialect Text Normalization to Normative Standard {F}innish",
    author = {Partanen, Niko  and
      H{\"a}m{\"a}l{\"a}inen, Mika  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5519",
    doi = "10.18653/v1/D19-5519",
    pages = "141--146",
    abstract = "We compare different LSTMs and transformer models in terms of their effectiveness in normalizing dialectal Finnish into the normative standard Finnish. As dialect is the common way of communication for people online in Finnish, such a normalization is a necessary step to improve the accuracy of the existing Finnish NLP tools that are tailored for normative Finnish text. We work on a corpus consisting of dialectal data of 23 distinct Finnish dialects. The best functioning BRNN approach lowers the initial word error rate of the corpus from 52.89 to 5.73.",
}
@inproceedings{yong-2019-cross,
    title = "A Cross-Topic Method for Supervised Relevance Classification",
    author = "Yong, Jiawei",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5520",
    doi = "10.18653/v1/D19-5520",
    pages = "147--152",
    abstract = "In relevance classification, we hope to judge whether some utterances expressed on a topic are relevant or not. A usual method is to train a specific classifier respectively for each topic. However, in that way, it easily causes an underfitting problem in supervised learning model, since annotated data can be insufficient for every single topic. In this paper, we explore the common features beyond different topics and propose our cross-topic relevance embedding aggregation methodology (CREAM) that can expand the range of training data and apply what has been learned from source topics to a target topic. In our experiment, we show that our proposal could capture common features within a small amount of annotated data and improve the performance of relevance classification compared with other baselines.",
}
@inproceedings{liu-etal-2019-exploring,
    title = "Exploring Multilingual Syntactic Sentence Representations",
    author = "Liu, Chen  and
      De Andrade, Anderson  and
      Osama, Muhammad",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5521",
    doi = "10.18653/v1/D19-5521",
    pages = "153--159",
    abstract = "We study methods for learning sentence embeddings with syntactic structure. We focus on methods of learning syntactic sentence-embeddings by using a multilingual parallel-corpus augmented by Universal Parts-of-Speech tags. We evaluate the quality of the learned embeddings by examining sentence-level nearest neighbours and functional dissimilarity in the embedding space. We also evaluate the ability of the method to learn syntactic sentence-embeddings for low-resource languages and demonstrate strong evidence for transfer learning. Our results show that syntactic sentence-embeddings can be learned while using less training data, fewer model parameters, and resulting in better evaluation metrics than state-of-the-art language models.",
}
@inproceedings{hong-etal-2019-faspell,
    title = "{FASP}ell: A Fast, Adaptable, Simple, Powerful {C}hinese Spell Checker Based On {DAE}-Decoder Paradigm",
    author = "Hong, Yuzhong  and
      Yu, Xianguo  and
      He, Neng  and
      Liu, Nan  and
      Liu, Junhui",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5522",
    doi = "10.18653/v1/D19-5522",
    pages = "160--169",
    abstract = "We propose a Chinese spell checker {--} FASPell based on a new paradigm which consists of a denoising autoencoder (DAE) and a decoder. In comparison with previous state-of-the-art models, the new paradigm allows our spell checker to be Faster in computation, readily Adaptable to both simplified and traditional Chinese texts produced by either humans or machines, and to require much Simpler structure to be as much Powerful in both error detection and correction. These four achievements are made possible because the new paradigm circumvents two bottlenecks. First, the DAE curtails the amount of Chinese spell checking data needed for supervised learning (to {\textless}10k sentences) by leveraging the power of unsupervisedly pre-trained masked language model as in BERT, XLNet, MASS etc. Second, the decoder helps to eliminate the use of confusion set that is deficient in flexibility and sufficiency of utilizing the salient feature of Chinese character similarity.",
}
@inproceedings{heidenreich-williams-2019-latent,
    title = "Latent semantic network induction in the context of linked example senses",
    author = "Heidenreich, Hunter  and
      Williams, Jake",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5523",
    doi = "10.18653/v1/D19-5523",
    pages = "170--180",
    abstract = "The Princeton WordNet is a powerful tool for studying language and developing natural language processing algorithms. With significant work developing it further, one line considers its extension through aligning its expert-annotated structure with other lexical resources. In contrast, this work explores a completely data-driven approach to network construction, forming a wordnet using the entirety of the open-source, noisy, user-annotated dictionary, Wiktionary. Comparing baselines to WordNet, we find compelling evidence that our network induction process constructs a network with useful semantic structure. With thousands of semantically-linked examples that demonstrate sense usage from basic lemmas to multiword expressions (MWEs), we believe this work motivates future research.",
}
@inproceedings{pant-etal-2019-smokeng,
    title = "{S}mok{E}ng: Towards Fine-grained Classification of Tobacco-related Social Media Text",
    author = "Pant, Kartikey  and
      Yanamandra, Venkata Himakar  and
      Debnath, Alok  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5524",
    doi = "10.18653/v1/D19-5524",
    pages = "181--190",
    abstract = "Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a fine-grained classification mechanism. In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification. Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification. This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.",
}
@inproceedings{shen-etal-2019-modelling,
    title = "Modelling Uncertainty in Collaborative Document Quality Assessment",
    author = "Shen, Aili  and
      Beck, Daniel  and
      Salehi, Bahar  and
      Qi, Jianzhong  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5525",
    doi = "10.18653/v1/D19-5525",
    pages = "191--201",
    abstract = "In the context of document quality assessment, previous work has mainly focused on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. To imitate people{'}s disagreement over inherently subjective tasks such as rating the quality of a Wikipedia article, a document quality assessment system should provide not only a prediction of the article quality but also the uncertainty over its predictions. This motivates us to measure the uncertainty in document quality predictions, in addition to making the label prediction. Experimental results show that both Gaussian processes (GPs) and random forests (RFs) can yield competitive results in predicting the quality of Wikipedia articles, while providing an estimate of uncertainty when there is inconsistency in the quality labels from the Wikipedia contributors. We additionally evaluate our methods in the context of a semi-automated document quality class assignment decision-making process, where there is asymmetric risk associated with overestimates and underestimates of document quality. Our experiments suggest that GPs provide more reliable estimates in this context.",
}
@inproceedings{belz-etal-2019-conceptualisation,
    title = "Conceptualisation and Annotation of Drug Nonadherence Information for Knowledge Extraction from Patient-Generated Texts",
    author = "Belz, Anja  and
      Hoile, Richard  and
      Ford, Elizabeth  and
      Mullick, Azam",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5526",
    doi = "10.18653/v1/D19-5526",
    pages = "202--211",
    abstract = "Approaches to knowledge extraction (KE) in the health domain often start by annotating text to indicate the knowledge to be extracted, and then use the annotated text to train systems to perform the KE. This may work for annotat- ing named entities or other contiguous noun phrases (drugs, some drug effects), but be- comes increasingly difficult when items tend to be expressed across multiple, possibly non- contiguous, syntactic constituents (e.g. most descriptions of drug effects in user-generated text). Other issues include that it is not al- ways clear how annotations map to actionable insights, or how they scale up to, or can form part of, more complex KE tasks. This paper reports our efforts in developing an approach to extracting knowledge about drug nonadher- ence from health forums which led us to con- clude that development cannot proceed in sep- arate steps but that all aspects{---}from concep- tualisation to annotation scheme development, annotation, KE system training and knowl- edge graph instantiation{---}are interdependent and need to be co-developed. Our aim in this paper is two-fold: we describe a generally ap- plicable framework for developing a KE ap- proach, and present a specific KE approach, developed with the framework, for the task of gathering information about antidepressant drug nonadherence. We report the conceptual- isation, the annotation scheme, the annotated corpus, and an analysis of annotated texts.",
}
@inproceedings{hayati-etal-2019-sunny,
    title = "What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection",
    author = "Hayati, Shirley Anugrah  and
      Chaudhary, Aditi  and
      Otani, Naoki  and
      Black, Alan W",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5527",
    doi = "10.18653/v1/D19-5527",
    pages = "212--216",
    abstract = "Irony detection is an important task with applications in identification of online abuse and harassment. With the ubiquitous use of non-verbal cues such as emojis in social media, in this work we aim to study the role of these structures in irony detection. Since the existing irony detection datasets have {\textless}10{\%} ironic tweets with emoji, classifiers trained on them are insensitive to emojis. We propose an automated pipeline for creating a more balanced dataset.",
}
@inproceedings{fornaciari-hovy-2019-geolocation,
    title = "Geolocation with Attention-Based Multitask Learning Models",
    author = "Fornaciari, Tommaso  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5528",
    doi = "10.18653/v1/D19-5528",
    pages = "217--223",
    abstract = "Geolocation, predicting the location of a post based on text and other information, has a huge potential for several social media applications. Typically, the problem is modeled as either multi-class classification or regression. In the first case, the classes are geographic areas previously identified; in the second, the models directly predict geographic coordinates. The former requires discretization of the coordinates, but yields better performance. The latter is potentially more precise and true to the nature of the problem, but often results in worse performance. We propose to combine the two approaches in an attentionbased multitask convolutional neural network that jointly predicts both discrete locations and continuous geographic coordinates. We evaluate the multi-task (MTL) model against singletask models and prior work. We find that MTL significantly improves performance, reporting large gains on one data set, but also note that the correlation between labels and coordinates has a marked impact on the effectiveness of including a regression task.",
}
@inproceedings{fornaciari-hovy-2019-dense,
    title = "Dense Node Representation for Geolocation",
    author = "Fornaciari, Tommaso  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5529",
    doi = "10.18653/v1/D19-5529",
    pages = "224--230",
    abstract = "Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of connections, which grow exponentially with the number of users. In order to incorporate this information, we therefore need to limit the network size, in turn limiting performance and risking sample bias. In this paper, we address these limitations by instead using dense network representations. We explore two methods to learn continuous node representations from either 1) the network structure with node2vec (Grover and Leskovec, 2016), or 2) textual user mentions via doc2vec (Le and Mikolov, 2014). We combine both methods with input from social media posts in an attention-based convolutional neural network and evaluate the contribution of each component on geolocation performance. Our method enables us to incorporate arbitrarily large networks in a fixed-length vector, without limiting the network size. Our models achieve competitive results with similar state-of-the-art methods, but with much fewer model parameters, while being applicable to networks of virtually any size.",
}
@inproceedings{fornaciari-hovy-2019-identifying,
    title = "Identifying Linguistic Areas for Geolocation",
    author = "Fornaciari, Tommaso  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5530",
    doi = "10.18653/v1/D19-5530",
    pages = "231--236",
    abstract = "Geolocating social media posts relies on the assumption that language carries sufficient geographic information. However, locations are usually given as continuous latitude/longitude tuples, so we first need to define discrete geographic regions that can serve as labels. Most studies use some form of clustering to discretize the continuous coordinates (Han et al., 2016). However, the resulting regions do not always correspond to existing linguistic areas. Consequently, accuracy at 100 miles tends to be good, but degrades for finer-grained distinctions, when different linguistic regions get lumped together. We describe a new algorithm, Point-to-City (P2C), an iterative k-d tree-based method for clustering geographic coordinates and associating them with towns. We create three sets of labels at different levels of granularity, and compare performance of a state-of-the-art geolocation model trained and tested with P2C labels to one with regular k-d tree labels. Even though P2C results in substantially more labels than the baseline, model accuracy increases significantly over using traditional labels at the fine-grained level, while staying comparable at 100 miles. The results suggest that identifying meaningful linguistic areas is crucial for improving geolocation at a fine-grained level.",
}
@inproceedings{bodapati-etal-2019-robustness,
    title = "Robustness to Capitalization Errors in Named Entity Recognition",
    author = "Bodapati, Sravan  and
      Yun, Hyokun  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5531",
    doi = "10.18653/v1/D19-5531",
    pages = "237--242",
    abstract = "Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such noise.Existing methods to improve robustness to the noise completely discard given orthographic information, which significantly degrades their performance on well-formed text. We propose a simple alternative approach based on data augmentation, which allows the model to learn to utilize or ignore orthographic information depending on its usefulness in the context. It achieves competitive robustness to capitalization errors while making negligible compromise to its performance on well-formed text and significantly improving generalization power on noisy user-generated text. Our experiments clearly and consistently validate our claim across different types of machine learning models, languages, and dataset sizes.",
}
@inproceedings{lai-nguyen-2019-extending,
    title = "Extending Event Detection to New Types with Learning from Keywords",
    author = "Lai, Viet Dac  and
      Nguyen, Thien",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5532",
    doi = "10.18653/v1/D19-5532",
    pages = "243--248",
    abstract = "Traditional event detection classifies a word or a phrase in a given sentence for a set of prede- fined event types. The limitation of such pre- defined set is that it prevents the adaptation of the event detection models to new event types. We study a novel formulation of event detec- tion that describes types via several keywords to match the contexts in documents. This fa- cilitates the operation of the models to new types. We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new for- mulation. Our extensive experiments demon- strate the benefits of the new formulation for new type extension for event detection as well as the proposed attention mechanism for this problem",
}
@inproceedings{xing-luo-2019-distant,
    title = "Distant Supervised Relation Extraction with Separate Head-Tail {CNN}",
    author = "Xing, Rui  and
      Luo, Jie",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5533",
    doi = "10.18653/v1/D19-5533",
    pages = "249--258",
    abstract = "Distant supervised relation extraction is an efficient and effective strategy to find relations between entities in texts. However, it inevitably suffers from mislabeling problem and the noisy data will hinder the performance. In this paper, we propose the Separate Head-Tail Convolution Neural Network (SHTCNN), a novel neural relation extraction framework to alleviate this issue. In this method, we apply separate convolution and pooling to the head and tail entity respectively for extracting better semantic features of sentences, and coarse-to-fine strategy to filter out instances which do not have actual relations in order to alleviate noisy data issues. Experiments on a widely used dataset show that our model achieves significant and consistent improvements in relation extraction compared to statistical and vanilla CNN-based methods.",
}
@inproceedings{ismaeil-etal-2019-discovering,
    title = "Discovering the Functions of Language in Online Forums",
    author = "Ismaeil, Youmna  and
      Balalau, Oana  and
      Mirza, Paramita",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5534",
    doi = "10.18653/v1/D19-5534",
    pages = "259--264",
    abstract = "In this work, we revisit the functions of language proposed by linguist Roman Jakobson and we highlight their potential in analyzing online forum conversations. We investigate the relationship between functions and other properties of comments, such as controversiality. We propose and evaluate a semi-supervised framework for predicting the functions of Reddit comments. To accommodate further research, we release a corpus of 165K comments annotated with their functions of language.",
}
@inproceedings{constantin-etal-2019-incremental,
    title = "Incremental processing of noisy user utterances in the spoken language understanding task",
    author = "Constantin, Stefan  and
      Niehues, Jan  and
      Waibel, Alex",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5535",
    doi = "10.18653/v1/D19-5535",
    pages = "265--274",
    abstract = "The state-of-the-art neural network architectures make it possible to create spoken language understanding systems with high quality and fast processing time. One major challenge for real-world applications is the high latency of these systems caused by triggered actions with high executions times. If an action can be separated into subactions, the reaction time of the systems can be improved through incremental processing of the user utterance and starting subactions while the utterance is still being uttered. In this work, we present a model-agnostic method to achieve high quality in processing incrementally produced partial utterances. Based on clean and noisy versions of the ATIS dataset, we show how to create datasets with our method to create low-latency natural language understanding components. We get improvements of up to 47.91 absolute percentage points in the metric F1-score.",
}
@inproceedings{matos-veliz-etal-2019-benefits,
    title = "Benefits of Data Augmentation for {NMT}-based Text Normalization of User-Generated Content",
    author = "Matos Veliz, Claudia  and
      De Clercq, Orphee  and
      Hoste, Veronique",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5536",
    doi = "10.18653/v1/D19-5536",
    pages = "275--285",
    abstract = "One of the most persistent characteristics of written user-generated content (UGC) is the use of non-standard words. This characteristic contributes to an increased difficulty to automatically process and analyze UGC. Text normalization is the task of transforming lexical variants to their canonical forms and is often used as a pre-processing step for conventional NLP tasks in order to overcome the performance drop that NLP systems experience when applied to UGC. In this work, we follow a Neural Machine Translation approach to text normalization. To train such an encoder-decoder model, large parallel training corpora of sentence pairs are required. However, obtaining large data sets with UGC and their normalized version is not trivial, especially for languages other than English. In this paper, we explore how to overcome this data bottleneck for Dutch, a low-resource language. We start off with a small publicly available parallel Dutch data set comprising three UGC genres and compare two different approaches. The first is to manually normalize and add training data, a money and time-consuming task. The second approach is a set of data augmentation techniques which increase data size by converting existing resources into synthesized non-standard forms. Our results reveal that, while the different approaches yield similar results regarding the normalization issues in the test set, they also introduce a large amount of over-normalizations.",
}
@inproceedings{sun-jiang-2019-contextual,
    title = "Contextual Text Denoising with Masked Language Model",
    author = "Sun, Yifu  and
      Jiang, Haoming",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5537",
    doi = "10.18653/v1/D19-5537",
    pages = "286--290",
    abstract = "Recently, with the help of deep learning models, significant advances have been made in different Natural Language Processing (NLP) tasks. Unfortunately, state-of-the-art models are vulnerable to noisy texts. We propose a new contextual text denoising algorithm based on the ready-to-use masked language model. The proposed algorithm does not require retraining of the model and can be integrated into any NLP system without additional training on paired cleaning training data. We evaluate our method under synthetic noise and natural noise and show that the proposed algorithm can use context information to correct noise text and improve the performance of noisy inputs in several downstream tasks.",
}
@inproceedings{pal-sharma-2019-towards,
    title = "Towards Automated Semantic Role Labelling of {H}indi-{E}nglish Code-Mixed Tweets",
    author = "Pal, Riya  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5538",
    doi = "10.18653/v1/D19-5538",
    pages = "291--296",
    abstract = "We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets. We explore the issues posed by noisy, user generated code-mixed social media data. We also compare the individual effect of various linguistic features used in our system. Our proposed model is a 2-step system for automated labelling which gives an overall accuracy of 84{\%} for Argument Classification, marking a 10{\%} increase over the existing rule-based baseline model. This is the first attempt at building a statistical Semantic Role Labeller for Hindi-English code-mixed data, to the best of our knowledge.",
}
@inproceedings{muller-etal-2019-enhancing,
    title = "Enhancing {BERT} for Lexical Normalization",
    author = "Muller, Benjamin  and
      Sagot, Benoit  and
      Seddah, Djam{\'e}",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5539",
    doi = "10.18653/v1/D19-5539",
    pages = "297--306",
    abstract = "Language model-based pre-trained representations have become ubiquitous in natural language processing. They have been shown to significantly improve the performance of neural models on a great variety of tasks. However, it remains unclear how useful those general models can be in handling non-canonical text. In this article, focusing on User Generated Content (UGC), we study the ability of BERT to perform lexical normalisation. Our contribution is simple: by framing lexical normalisation as a token prediction task, by enhancing its architecture and by carefully fine-tuning it, we show that BERT can be a competitive lexical normalisation model without the need of any UGC resources aside from 3,000 training sentences. To the best of our knowledge, it is the first work done in adapting and analysing the ability of this model to handle noisy UGC data.",
}
@inproceedings{wang-etal-2019-youre,
    title = "No, you{'}re not alone: A better way to find people with similar experiences on {R}eddit",
    author = "Wang, Zhilin  and
      Rastorgueva, Elena  and
      Lin, Weizhe  and
      Wu, Xiaodong",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5540",
    doi = "10.18653/v1/D19-5540",
    pages = "307--315",
    abstract = "We present a probabilistic clustering algorithm that can help Reddit users to find posts that discuss experiences similar to their own. This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{\^{}}2) to O(n) with respect to the number of posts. We demonstrate that such probabilistic clustering can yield a performance better than baseline clustering methods based on Latent Dirichlet Allocation (Blei et al., 2003) and Word2Vec (Mikolov et al., 2013). Furthermore, there is a high degree of coherence between our probabilistic clustering and the exhaustive comparison O(n{\^{}}2) algorithm in which the similarity between every pair of posts is found. This makes the use of the BERT Next Sentence Prediction model more practical for unsupervised clustering tasks due to the high runtime overhead of each BERT computation.",
}
@inproceedings{ying-etal-2019-improving,
    title = "Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge",
    author = "Ying, Wenhao  and
      Xiang, Rong  and
      Lu, Qin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5541",
    doi = "10.18653/v1/D19-5541",
    pages = "316--321",
    abstract = "Deep learning based general language models have achieved state-of-the-art results in many popular tasks such as sentiment analysis and QA tasks. Text in domains like social media has its own salient characteristics. Domain knowledge should be helpful in domain relevant tasks. In this work, we devise a simple method to obtain domain knowledge and further propose a method to integrate domain knowledge with general knowledge based on deep language models to improve performance of emotion classification. Experiments on Twitter data show that even though a deep language model fine-tuned by a target domain data has attained comparable results to that of previous state-of-the-art models, this fine-tuned model can still benefit from our extracted domain knowledge to obtain more improvement. This highlights the importance of making use of domain knowledge in domain-specific applications.",
}
@inproceedings{sekulic-strube-2019-adapting,
    title = "Adapting Deep Learning Methods for Mental Health Prediction on Social Media",
    author = "Sekulic, Ivan  and
      Strube, Michael",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5542",
    doi = "10.18653/v1/D19-5542",
    pages = "322--327",
    abstract = "Mental health poses a significant challenge for an individual{'}s well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users{'} mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model{'}s word-level attention weights.",
}
@inproceedings{li-specia-2019-improving,
    title = "Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation",
    author = "Li, Zhenhao  and
      Specia, Lucia",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5543",
    doi = "10.18653/v1/D19-5543",
    pages = "328--336",
    abstract = "Neural Machine Translation (NMT) models have been proved strong when translating clean texts, but they are very sensitive to noise in the input. Improving NMT models robustness can be seen as a form of {``}domain{''} adaption to noise. The recently created Machine Translation on Noisy Text task corpus provides noisy-clean parallel data for a few language pairs, but this data is very limited in size and diversity. The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. Secondly, we explore the effect of utilizing noise from external data in the form of speech transcripts and show that it could help robustness.",
}
@inproceedings{badlani-etal-2019-ensemble,
    title = "An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews",
    author = "Badlani, Rohan  and
      Asnani, Nishit  and
      Rai, Manan",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5544",
    doi = "10.18653/v1/D19-5544",
    pages = "337--345",
    abstract = "Due to the nature of online user reviews, sentiment analysis on such data requires a deep semantic understanding of the text. Many online reviews are sarcastic, humorous, or hateful. Signals from such language nuances may reinforce or completely alter the sentiment of a review as predicted by a machine learning model that attempts to detect sentiment alone. Thus, having a model that is explicitly aware of these features should help it perform better on reviews that are characterized by them. We propose a composite two-step model that extracts features pertaining to sarcasm, humour, hate speech, as well as sentiment, in the first step, feeding them in conjunction to inform sentiment classification in the second step. We show that this multi-step approach leads to a better empirical performance for sentiment classification than a model that predicts sentiment alone. A qualitative analysis reveals that the conjunctive approach can better capture the nuances of sentiment as expressed in online reviews.",
}
@inproceedings{naplava-straka-2019-grammatical,
    title = "Grammatical Error Correction in Low-Resource Scenarios",
    author = "N{\'a}plava, Jakub  and
      Straka, Milan",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5545",
    doi = "10.18653/v1/D19-5545",
    pages = "346--356",
    abstract = "Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at http://hdl.handle.net/11234/1-3057, and the source code of the GEC model is available at https://github.com/ufal/low-resource-gec-wnut2019.",
}
@inproceedings{grundkiewicz-junczys-dowmunt-2019-minimally,
    title = "Minimally-Augmented Grammatical Error Correction",
    author = "Grundkiewicz, Roman  and
      Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5546",
    doi = "10.18653/v1/D19-5546",
    pages = "357--363",
    abstract = "There has been an increased interest in low-resource approaches to automatic grammatical error correction. We introduce Minimally-Augmented Grammatical Error Correction (MAGEC) that does not require any error-labelled data. Our unsupervised approach is based on a simple but effective synthetic error generation method based on confusion sets from inverted spell-checkers. In low-resource settings, we outperform the current state-of-the-art results for German and Russian GEC tasks by a large margin without using any real error-annotated training data. When combined with labelled data, our method can serve as an efficient pre-training technique",
}
@inproceedings{cortis-davis-2019-social,
    title = "A Social Opinion Gold Standard for the {M}alta Government Budget 2018",
    author = "Cortis, Keith  and
      Davis, Brian",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5547",
    doi = "10.18653/v1/D19-5547",
    pages = "364--369",
    abstract = "We present a gold standard of annotated social opinion for the Malta Government Budget 2018. It consists of over 500 online posts in English and/or the Maltese less-resourced language, gathered from social media platforms, specifically, social networking services and newswires, which have been annotated with information about opinions expressed by the general public and other entities, in terms of sentiment polarity, emotion, sarcasm/irony, and negation. This dataset is a resource for opinion mining based on social data, within the context of politics. It is the first opinion annotated social dataset from Malta, which has very limited language resources available.",
}
@inproceedings{han-etal-2019-fallacy,
    title = "The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in {K}orean Media",
    author = "Han, Jiyoung  and
      Lee, Youngin  and
      Lee, Junbum  and
      Cha, Meeyoung",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5548",
    doi = "10.18653/v1/D19-5548",
    pages = "370--374",
    abstract = "This study analyzes the political slants of user comments on Korean partisan media. We built a BERT-based classifier to detect political leaning of short comments via the use of semi-unsupervised deep learning methods that produced an F1 score of 0.83. As a result of classifying 21.6K comments, we found the high presence of conservative bias on both conservative and liberal news outlets. Moreover, this study discloses an asymmetry across the partisan spectrum in that more liberals (48.0{\%}) than conservatives (23.6{\%}) comment not only on news stories resonating with their political perspectives but also on those challenging their viewpoints. These findings advance the current understanding of online echo chambers.",
}
@inproceedings{stanovsky-tamari-2019-yall,
    title = "{Y}{'}all should read this! Identifying Plurality in Second-Person Personal Pronouns in {E}nglish Texts",
    author = "Stanovsky, Gabriel  and
      Tamari, Ronen",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5549",
    doi = "10.18653/v1/D19-5549",
    pages = "375--380",
    abstract = "Distinguishing between singular and plural {``}you{''} in English is a challenging task which has potential for downstream applications, such as machine translation or coreference resolution. While formal written English does not distinguish between these cases, other languages (such as Spanish), as well as other dialects of English (via phrases such as {``}y{'}all{''}), do make this distinction. We make use of this to obtain distantly-supervised labels for the task on a large-scale in two domains. Following, we train a model to distinguish between the single/plural {`}you{'}, finding that although in-domain training achieves reasonable accuracy ({\mbox{$\geq$}} 77{\%}), there is still a lot of room for improvement, especially in the domain-transfer scenario, which proves extremely challenging. Our code and data are publicly available.",
}
@inproceedings{marrese-taylor-etal-2019-edit,
    title = "An Edit-centric Approach for {W}ikipedia Article Quality Assessment",
    author = "Marrese-Taylor, Edison  and
      Loyola, Pablo  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5550",
    doi = "10.18653/v1/D19-5550",
    pages = "381--386",
    abstract = "We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques. Our model consists of a main classifier equipped with an auxiliary generative module which, for a given edit, jointly provides an estimation of its quality and generates a description in natural language. We performed an empirical study to assess the feasibility of the proposed model and its cost-effectiveness in terms of data and quality requirements.",
}
@inproceedings{seonwoo-etal-2019-additive,
    title = "Additive Compositionality of Word Vectors",
    author = "Seonwoo, Yeon  and
      Park, Sungjoon  and
      Kim, Dongkwan  and
      Oh, Alice",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5551",
    doi = "10.18653/v1/D19-5551",
    pages = "387--396",
    abstract = "Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives. Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution. In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions. We then empirically show our model{'}s improved semantic representation performance on word similarity and noisy sentence similarity.",
}
@inproceedings{ashihara-etal-2019-contextualized,
    title = "Contextualized context2vec",
    author = "Ashihara, Kazuki  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki  and
      Uchida, Satoru",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5552",
    doi = "10.18653/v1/D19-5552",
    pages = "397--406",
    abstract = "Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. There are two major approaches for lexical substitution: (1) generating contextualized word embeddings by assigning multiple embeddings to one word and (2) generating context embeddings using the sentence. Herein we propose a method that combines these two approaches to contextualize word embeddings for lexical substitution. Experiments demonstrate that our method outperforms the current state-of-the-art method. We also create CEFR-LP, a new evaluation dataset for the lexical substitution task. It has a wider coverage of substitution candidates than previous datasets and assigns English proficiency levels to all target words and substitution candidates.",
}
@inproceedings{rosales-nunez-etal-2019-phonetic,
    title = "Phonetic Normalization for Machine Translation of User Generated Content",
    author = "Rosales N{\'u}{\~n}ez, Jos{\'e} Carlos  and
      Seddah, Djam{\'e}  and
      Wisniewski, Guillaume",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5553",
    doi = "10.18653/v1/D19-5553",
    pages = "407--416",
    abstract = "We present an approach to correct noisy User Generated Content (UGC) in French aiming to produce a pretreatement pipeline to improve Machine Translation for this kind of non-canonical corpora. In order to do so, we have implemented a character-based neural model phonetizer to produce IPA pronunciations of words. In this way, we intend to correct grammar, vocabulary and accentuation errors often present in noisy UGC corpora. Our method leverages on the fact that some errors are due to confusion induced by words with similar pronunciation which can be corrected using a phonetic look-up table to produce normalization candidates. These potential corrections are then encoded in a lattice and ranked using a language model to output the most probable corrected phrase. Compare to using other phonetizers, our method boosts a transformer-based machine translation system on UGC.",
}
@inproceedings{barik-etal-2019-normalization,
    title = "Normalization of {I}ndonesian-{E}nglish Code-Mixed {T}witter Data",
    author = "Barik, Anab Maulana  and
      Mahendra, Rahmad  and
      Adriani, Mirna",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5554",
    doi = "10.18653/v1/D19-5554",
    pages = "417--424",
    abstract = "Twitter is an excellent source of data for NLP researches as it offers tremendous amount of textual data. However, processing tweet to extract meaningful information is very challenging, at least for two reasons: (i) using nonstandard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation. Most of the previous works have addressed both issues in isolated different task. In this study, we work on normalization task in code-mixed Twitter data, more specifically in Indonesian-English language. We propose a pipeline that consists of four modules, i.e tokenization, language identification, lexical normalization, and translation. Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.",
}
@inproceedings{zalmout-etal-2019-unsupervised,
    title = "Unsupervised Neologism Normalization Using Embedding Space Mapping",
    author = "Zalmout, Nasser  and
      Thadani, Kapil  and
      Pappu, Aasish",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5555",
    doi = "10.18653/v1/D19-5555",
    pages = "425--430",
    abstract = "This paper presents an approach for detecting and normalizing neologisms in social media content. Neologisms refer to recent expressions that are specific to certain entities or events and are being increasingly used by the public, but have not yet been accepted in mainstream language. Automated methods for handling neologisms are important for natural language understanding and normalization, especially for informal genres with user generated content. We present an unsupervised approach for detecting neologisms and then normalizing them to canonical words without relying on parallel training data. Our approach builds on the text normalization literature and introduces adaptations to fit the specificities of this task, including phonetic and etymological considerations. We evaluate the proposed techniques on a dataset of Reddit comments, with detected neologisms and corresponding normalizations.",
}
@inproceedings{novikova-etal-2019-lexical,
    title = "Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power",
    author = "Novikova, Jekaterina  and
      Balagopalan, Aparna  and
      Shkaruta, Ksenia  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5556",
    doi = "10.18653/v1/D19-5556",
    pages = "431--443",
    abstract = "Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models. In this paper, we investigate how generic language characteristics, such as syntax or the lexicon, are impacted by artificial text alterations. The vulnerability of features is analysed from two perspectives: (1) the level of feature value change, and (2) the level of change of feature predictive power as a result of text modifications. We show that lexical features are more sensitive to text modifications than syntactic ones. However, we also demonstrate that these smaller changes of syntactic features have a stronger influence on classification performance downstream, compared to the impact of changes to lexical features. Results are validated across three datasets representing different text-classification tasks, with different levels of lexical and syntactic complexity of both conversational and written language.",
}
@inproceedings{pang-2019-towards,
    title = "Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation",
    author = "Pang, Richard Yuanzhe",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5557",
    doi = "10.18653/v1/D19-5557",
    pages = "444--445",
    abstract = "Regarding the problem of automatically generating paraphrases with modified styles or attributes, the difficulty lies in the lack of parallel corpora. Numerous advances have been proposed for the generation. However, significant problems remain with the auto-evaluation of style transfer tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019), style transfer evaluations rely on three metrics: post-transfer style classification accuracy, content or semantic similarity, and naturalness or fluency. We elucidate the dangerous current state of style transfer auto-evaluation research. Moreover, we propose ways to aggregate the three metrics into one evaluator. This abstract aims to bring researchers to think about the future of style transfer and style transfer evaluation research.",
}
@inproceedings{rabinovich-etal-2019-codeswitch-reddit,
    title = "{C}ode{S}witch-{R}eddit: Exploration of Written Multilingual Discourse in Online Discussion Forums",
    author = "Rabinovich, Ella  and
      Sultani, Masih  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5558",
    doi = "10.18653/v1/D19-5558",
    pages = "446",
    abstract = "In contrast to many decades of research on oral code-switching, the study of written multilingual productions has only recently enjoyed a surge of interest. Many open questions remain regarding the sociolinguistic underpinnings of written code-switching, and progress has been limited by a lack of suitable resources. We introduce a novel, large, and diverse dataset of written code-switched productions, curated from topical threads of multiple bilingual communities on the Reddit discussion platform, and explore questions that were mainly addressed in the context of spoken language thus far. We investigate whether findings in oral code-switching concerning content and style, as well as speaker proficiency, are carried over into written code-switching in discussion forums. The released dataset can further facilitate a range of research and practical activities.",
}
@proceedings{emnlp-2019-new,
    title = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    editor = "Wang, Lu  and
      Cheung, Jackie Chi Kit  and
      Carenini, Giuseppe  and
      Liu, Fei",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5400",
}
@inproceedings{pal-etal-2019-answering,
    title = "Answering Naturally: Factoid to Full length Answer Generation",
    author = "Pal, Vaishali  and
      Shrivastava, Manish  and
      Bhat, Irshad",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5401",
    doi = "10.18653/v1/D19-5401",
    pages = "1--9",
    abstract = "In recent years, the task of Question Answering over passages, also pitched as a reading comprehension, has evolved into a very active research area. A reading comprehension system extracts a span of text, comprising of named entities, dates, small phrases, etc., which serve as the answer to a given question. However, these spans of text would result in an unnatural reading experience in a conversational system. Usually, dialogue systems solve this issue by using template-based language generation. These systems, though adequate for a domain specific task, are too restrictive and predefined for a domain independent system. In order to present the user with a more conversational experience, we propose a pointer generator based full-length answer generator which can be used with most QA systems. Our system generates a full length answer given a question and the extracted factoid/span answer without relying on the passage from where the answer was extracted. We also present a dataset of 315000 question, factoid answer and full length answer triples. We have evaluated our system using ROUGE-1,2,L and BLEU and achieved 74.05 BLEU score and 86.25 Rogue-L score.",
}
@inproceedings{bae-etal-2019-summary,
    title = "Summary Level Training of Sentence Rewriting for Abstractive Summarization",
    author = "Bae, Sanghwan  and
      Kim, Taeuk  and
      Kim, Jihoon  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5402",
    doi = "10.18653/v1/D19-5402",
    pages = "10--20",
    abstract = "As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary. However, the existing models in this framework mostly rely on sentence-level rewards or suboptimal labels, causing a mismatch between a training objective and evaluation metric. In this paper, we present a novel training signal that directly maximizes summary-level ROUGE scores through reinforcement learning. In addition, we incorporate BERT into our model, making good use of its ability on natural language understanding. In extensive experiments, we show that a combination of our proposed model and training procedure obtains new state-of-the-art performance on both CNN/Daily Mail and New York Times datasets. We also demonstrate that it generalizes better on DUC-2002 test set.",
}
@inproceedings{steen-markert-2019-abstractive,
    title = "Abstractive Timeline Summarization",
    author = "Steen, Julius  and
      Markert, Katja",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5403",
    doi = "10.18653/v1/D19-5403",
    pages = "21--31",
    abstract = "Timeline summarization (TLS) automatically identifies key dates of major events and provides short descriptions of what happened on these dates. Previous approaches to TLS have focused on extractive methods. In contrast, we suggest an abstractive timeline summarization system. Our system is entirely unsupervised, which makes it especially suited to TLS where there are very few gold summaries available for training of supervised systems. In addition, we present the first abstractive oracle experiments for TLS. Our system outperforms extractive competitors in terms of ROUGE when the number of input documents is high and the output requires strong compression. In these cases, our oracle experiments confirm that our approach also has a higher upper bound for ROUGE scores than extractive methods. A study with human judges shows that our abstractive system also produces output that is easy to read and understand.",
}
@inproceedings{antognini-faltings-2019-learning,
    title = "Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization",
    author = "Antognini, Diego  and
      Faltings, Boi",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5404",
    doi = "10.18653/v1/D19-5404",
    pages = "32--41",
    abstract = "Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on hand-crafted features, which are domain-dependent and hard to craft, or additional annotated data, which is costly to gather. To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings: universal embeddings, which are trained on a large unrelated corpus, and domain-specific embeddings, which are learned during training. To this end, we develop SemSentSum, a fully data-driven model able to leverage both types of sentence embeddings by building a sentence semantic relation graph. SemSentSum achieves competitive results on two types of summary, consisting of 665 bytes and 100 words. Unlike other state-of-the-art models, neither hand-crafted features nor additional annotated data are necessary, and the method is easily adaptable for other tasks. To our knowledge, we are the first to use multiple sentence embeddings for the task of multi-document summarization.",
}
@inproceedings{coavoux-etal-2019-unsupervised,
    title = "Unsupervised Aspect-Based Multi-Document Abstractive Summarization",
    author = "Coavoux, Maximin  and
      Elsahar, Hady  and
      Gall{\'e}, Matthias",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5405",
    doi = "10.18653/v1/D19-5405",
    pages = "42--47",
    abstract = "User-generated reviews of products or services provide valuable information to customers. However, it is often impossible to read each of the potentially thousands of reviews: it would therefore save valuable time to provide short summaries of their contents. We address opinion summarization, a multi-document summarization task, with an unsupervised abstractive summarization neural system. Our system is based on (i) a language model that is meant to encode reviews to a vector space, and to generate fluent sentences from the same vector space (ii) a clustering step that groups together reviews about the same aspects and allows the system to generate summary sentences focused on these aspects. Our experiments on the Oposum dataset empirically show the importance of the clustering step.",
}
@inproceedings{kornilova-eidelman-2019-billsum,
    title = "{B}ill{S}um: A Corpus for Automatic Summarization of {US} Legislation",
    author = "Kornilova, Anastassia  and
      Eidelman, Vladimir",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5406",
    doi = "10.18653/v1/D19-5406",
    pages = "48--56",
    abstract = "Automatic summarization methods have been studied on a variety of domains, including news and scientific articles. Yet, legislation has not previously been considered for this task, despite US Congress and state governments releasing tens of thousands of bills every year. In this paper, we introduce BillSum, the first dataset for summarization of US Congressional and California state bills. We explain the properties of the dataset that make it more challenging to process than other domains. Then, we benchmark extractive methods that consider neural sentence representations and traditional contextual features. Finally, we demonstrate that models built on Congressional bills can be used to summarize California billa, thus, showing that methods developed on this dataset can transfer to states without human-written summaries.",
}
@inproceedings{moroshko-etal-2019-editorial,
    title = "An Editorial Network for Enhanced Document Summarization",
    author = "Moroshko, Edward  and
      Feigenblat, Guy  and
      Roitman, Haggai  and
      Konopnicki, David",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5407",
    doi = "10.18653/v1/D19-5407",
    pages = "57--63",
    abstract = "We suggest a new idea of Editorial Network {--} a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. We further suggest an effective way for training the {``}editor{''} based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baselines.",
}
@inproceedings{arumae-etal-2019-towards,
    title = "Towards Annotating and Creating Summary Highlights at Sub-sentence Level",
    author = "Arumae, Kristjan  and
      Bhatia, Parminder  and
      Liu, Fei",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5408",
    doi = "10.18653/v1/D19-5408",
    pages = "64--69",
    abstract = "Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences. They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries. In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same. We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence. This formulation dramatically reduces the task complexity involved in sentence compression. Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.",
}
@inproceedings{gliwa-etal-2019-samsum,
    title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
    author = "Gliwa, Bogdan  and
      Mochol, Iwona  and
      Biesek, Maciej  and
      Wawer, Aleksander",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5409",
    doi = "10.18653/v1/D19-5409",
    pages = "70--79",
    abstract = "This paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue summaries. We investigate the challenges it poses for automated summarization by testing several models and comparing their results with those obtained on a corpus of news articles. We show that model-generated summaries of dialogues achieve higher ROUGE scores than the model-generated summaries of news {--} in contrast with human evaluators{'} judgement. This suggests that a challenging task of abstractive dialogue summarization requires dedicated models and non-standard quality measures. To our knowledge, our study is the first attempt to introduce a high-quality chat-dialogues corpus, manually annotated with abstractive summarizations, which can be used by the research community for further studies.",
}
@inproceedings{zhong-etal-2019-closer,
    title = "A Closer Look at Data Bias in Neural Extractive Summarization Models",
    author = "Zhong, Ming  and
      Wang, Danqing  and
      Liu, Pengfei  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5410",
    doi = "10.18653/v1/D19-5410",
    pages = "80--89",
    abstract = "In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models. Specifically, we first propose several properties of datasets, which matter for the generalization of summarization models. Then we build the connection between priors residing in datasets and model designs, analyzing how different properties of datasets influence the choices of model structure design and training methods. Finally, by taking a typical dataset as an example, we rethink the process of the model design based on the experience of the above analysis. We demonstrate that when we have a deep understanding of the characteristics of datasets, a simple approach can bring significant improvements to the existing state-of-the-art model.",
}
@inproceedings{nguyen-daume-iii-2019-global,
    title = "{G}lobal {V}oices: Crossing Borders in Automatic News Summarization",
    author = "Nguyen, Khanh  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5411",
    doi = "10.18653/v1/D19-5411",
    pages = "90--97",
    abstract = "We construct Global Voices, a multilingual dataset for evaluating cross-lingual summarization methods. We extract social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages. Especially, for the into-English summarization task, we crowd-source a high-quality evaluation dataset based on guidelines that emphasize accuracy, coverage, and understandability. To ensure the quality of this dataset, we collect human ratings to filter out bad summaries, and conduct a survey on humans, which shows that the remaining summaries are preferred over the social-network summaries. We study the effect of translation quality in cross-lingual summarization, comparing a translate-then-summarize approach with several baselines. Our results highlight the limitations of the ROUGE metric that are overlooked in monolingual summarization.",
}
@inproceedings{cho-etal-2019-multi,
    title = "Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations",
    author = "Cho, Sangwoo  and
      Li, Chen  and
      Yu, Dong  and
      Foroosh, Hassan  and
      Liu, Fei",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5412",
    doi = "10.18653/v1/D19-5412",
    pages = "98--103",
    abstract = "Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select a most probable set of summary sentences according to a probabilistic measure defined by respectively modeling sentence prominence and pairwise repulsion. Traditionally, both aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question. Whether, and to what extent, could contextualized sentence representations be used to improve the DPP framework? Our findings suggest that, despite the success of deep semantic representations, it remains necessary to combine them with surface indicators for effective identification of summary-worthy sentences.",
}
@inproceedings{lebanoff-etal-2019-analyzing,
    title = "Analyzing Sentence Fusion in Abstractive Summarization",
    author = "Lebanoff, Logan  and
      Muchovej, John  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Kim, Seokhwan  and
      Chang, Walter  and
      Liu, Fei",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5413",
    doi = "10.18653/v1/D19-5413",
    pages = "104--110",
    abstract = "While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences. In this paper, we analyze the outputs of five state-of-the-art abstractive summarizers, focusing on summary sentences that are formed by sentence fusion. We ask assessors to judge the grammaticality, faithfulness, and method of fusion for summary sentences. Our analysis reveals that system sentences are mostly grammatical, but often fail to remain faithful to the original article.",
}
@inproceedings{handler-etal-2019-summarizing,
    title = "Summarizing Relationships for Interactive Concept Map Browsers",
    author = "Handler, Abram  and
      Ganeshkumar, Premkumar  and
      O{'}Connor, Brendan  and
      AlTantawy, Mohamed",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5414",
    doi = "10.18653/v1/D19-5414",
    pages = "111--115",
    abstract = "Concept maps are visual summaries, structured as directed graphs: important concepts from a dataset are displayed as vertexes, and edges between vertexes show natural language descriptions of the relationships between the concepts on the map. Thus far, preliminary attempts at automatically creating concept maps have focused on building static summaries. However, in interactive settings, users will need to dynamically investigate particular relationships between pairs of concepts. For instance, a historian using a concept map browser might decide to investigate the relationship between two politicians in a news archive. We present a model which responds to such queries by returning one or more short, importance-ranked, natural language descriptions of the relationship between two requested concepts, for display in a visual interface. Our model is trained on a new public dataset, collected for this task.",
}
@inproceedings{liu-chen-2019-exploiting,
    title = "Exploiting Discourse-Level Segmentation for Extractive Summarization",
    author = "Liu, Zhengyuan  and
      Chen, Nancy",
    booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5415",
    doi = "10.18653/v1/D19-5415",
    pages = "116--121",
    abstract = "Extractive summarization selects and concatenates the most essential text spans in a document. Most, if not all, neural approaches use sentences as the elementary unit to select content for summarization. However, semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. In this work, we propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. We investigate how the sub-sentential segmentation improves extractive summarization performance when content selection is modeled through two basic neural network architectures and a deep bi-directional transformer. Experiment results on the CNN/Daily Mail dataset show that discourse-level segmentation is effective in both cases. In particular, we achieve state-of-the-art performance when discourse-level segmentation is combined with our adapted contextual representation model.",
}
@proceedings{emnlp-2019-graph,
    title = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    editor = "Ustalov, Dmitry  and
      Somasundaran, Swapna  and
      Jansen, Peter  and
      Glava{\v{s}}, Goran  and
      Riedl, Martin  and
      Surdeanu, Mihai  and
      Vazirgiannis, Michalis",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5300",
}
@inproceedings{ammanabrolu-riedl-2019-transfer,
    title = "Transfer in Deep Reinforcement Learning Using Knowledge Graphs",
    author = "Ammanabrolu, Prithviraj  and
      Riedl, Mark",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5301",
    doi = "10.18653/v1/D19-5301",
    pages = "1--10",
    abstract = "Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language. Prior work has demonstrated that using a knowledge graph as a state representation and question-answering to pre-train a deep Q-network facilitates faster control policy learning. In this paper, we explore the use of knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. Our methods are tested across multiple computer generated and human authored games, varying in domain and complexity, and demonstrate that our transfer learning methods let us learn a higher-quality control policy faster.",
}
@inproceedings{tagawa-etal-2019-relation,
    title = "Relation Prediction for Unseen-Entities Using Entity-Word Graphs",
    author = "Tagawa, Yuki  and
      Taniguchi, Motoki  and
      Miura, Yasuhide  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko  and
      Yamamoto, Takayuki  and
      Nemoto, Keiichi",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5302",
    doi = "10.18653/v1/D19-5302",
    pages = "11--16",
    abstract = "Knowledge graphs (KGs) are generally used for various NLP tasks. However, as KGs still miss some information, it is necessary to develop Knowledge Graph Completion (KGC) methods. Most KGC researches do not focus on the Out-of-KGs entities (Unseen-entities), we need a method that can predict the relation for the entity pairs containing Unseen-entities to automatically add new entities to the KGs. In this study, we focus on relation prediction and propose a method to learn entity representations via a graph structure that uses Seen-entities, Unseen-entities and words as nodes created from the descriptions of all entities. In the experiments, our method shows a significant improvement in the relation prediction for the entity pairs containing Unseen-entities.",
}
@inproceedings{khalife-vazirgiannis-2019-scalable,
    title = "Scalable graph-based method for individual named entity identification",
    author = "Khalife, Sammy  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5303",
    doi = "10.18653/v1/D19-5303",
    pages = "17--25",
    abstract = "In this paper, we consider the named entity linking (NEL) problem. We assume a set of queries, named entities, that have to be identified within a knowledge base. This knowledge base is represented by a text database paired with a semantic graph, endowed with a classification of entities (ontology). We present state-of-the-art methods in NEL, and propose a new method for individual identification requiring few annotated data samples. We demonstrate its scalability and performance over standard datasets, for several ontology configurations. Our approach is well-motivated for integration in real systems. Indeed, recent deep learning methods, despite their capacity to improve experimental precision, require lots of parameter tuning along with large volume of annotated data.",
}
@inproceedings{beck-etal-2019-neural,
    title = "Neural Speech Translation using Lattice Transformations and Graph Networks",
    author = "Beck, Daniel  and
      Cohn, Trevor  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5304",
    doi = "10.18653/v1/D19-5304",
    pages = "26--31",
    abstract = "Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation. However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios. In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks. Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work.",
}
@inproceedings{zuckerman-last-2019-using,
    title = "Using Graphs for Word Embedding with Enhanced Semantic Relations",
    author = "Zuckerman, Matan  and
      Last, Mark",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5305",
    doi = "10.18653/v1/D19-5305",
    pages = "32--41",
    abstract = "Word embedding algorithms have become a common tool in the field of natural language processing. While some, like Word2Vec, are based on sequential text input, others are utilizing a graph representation of text. In this paper, we introduce a new algorithm, named WordGraph2Vec, or in short WG2V, which combines the two approaches to gain the benefits of both. The algorithm uses a directed word graph to provide additional information for sequential text input algorithms. Our experiments on benchmark datasets show that text classification algorithms are nearly as accurate with WG2V as with other word embedding models while preserving more stable accuracy rankings.",
}
@inproceedings{thayaparan-etal-2019-identifying,
    title = "Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks",
    author = "Thayaparan, Mokanarangan  and
      Valentino, Marco  and
      Schlegel, Viktor  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5306",
    doi = "10.18653/v1/D19-5306",
    pages = "42--51",
    abstract = "Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. However, complex Question Answering (QA) typically requires multi-hop reasoning - i.e. the integration of supporting facts from different sources, to infer the correct answer. This paper proposes Document Graph Network (DGN), a message passing architecture for the identification of supporting facts over a graph-structured representation of text. The evaluation on HotpotQA shows that DGN obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning.",
}
@inproceedings{ma-etal-2019-essentia,
    title = "{E}ssentia: Mining Domain-specific Paraphrases with Word-Alignment Graphs",
    author = "Ma, Danni  and
      Chen, Chen  and
      Golshan, Behzad  and
      Tan, Wang-Chiew",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5307",
    doi = "10.18653/v1/D19-5307",
    pages = "52--57",
    abstract = "Paraphrases are important linguistic resources for a wide variety of NLP applications. Many techniques for automatic paraphrase mining from general corpora have been proposed. While these techniques are successful at discovering generic paraphrases, they often fail to identify domain-specific paraphrases (e.g., {staff, concierge} in the hospitality domain). This is because current techniques are often based on statistical methods, while domain-specific corpora are too small to fit statistical methods. In this paper, we present an unsupervised graph-based technique to mine paraphrases from a small set of sentences that roughly share the same topic or intent. Our system, Essentia, relies on word-alignment techniques to create a word-alignment graph that merges and organizes tokens from input sentences. The resulting graph is then used to generate candidate paraphrases. We demonstrate that our system obtains high quality paraphrases, as evaluated by crowd workers. We further show that the majority of the identified paraphrases are domain-specific and thus complement existing paraphrase databases.",
}
@inproceedings{schwarzenberg-etal-2019-layerwise,
    title = "Layerwise Relevance Visualization in Convolutional Text Graph Classifiers",
    author = {Schwarzenberg, Robert  and
      H{\"u}bner, Marc  and
      Harbecke, David  and
      Alt, Christoph  and
      Hennig, Leonhard},
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5308",
    doi = "10.18653/v1/D19-5308",
    pages = "58--62",
    abstract = "Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain. Graph Convolutional Networks (GCN) allow this projection, but existing explainability methods do not exploit this fact, i.e. do not focus their explanations on intermediate states. In this work, we present a novel method that traces and visualizes features that contribute to a classification decision in the visible and hidden layers of a GCN. Our method exposes hidden cross-layer dynamics in the input graph structure. We experimentally demonstrate that it yields meaningful layerwise explanations for a GCN sentence classifier.",
}
@inproceedings{jansen-ustalov-2019-textgraphs,
    title = "{T}ext{G}raphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration",
    author = "Jansen, Peter  and
      Ustalov, Dmitry",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5309",
    doi = "10.18653/v1/D19-5309",
    pages = "63--77",
    abstract = "While automated question answering systems are increasingly able to retrieve answers to natural language questions, their ability to generate detailed human-readable explanations for their answers is still quite limited. The Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating detailed gold explanations for standardized elementary science exam questions by selecting facts from a knowledge base of semi-structured tables. Each explanation contains between 1 and 16 interconnected facts that form an {``}explanation graph{''} spanning core scientific knowledge and detailed world knowledge. It is expected that successfully combining these facts to generate detailed explanations will require advancing methods in multi-hop inference and information combination, and will make use of the supervised training data provided by the WorldTree explanation corpus. The top-performing system achieved a mean average precision (MAP) of 0.56, substantially advancing the state-of-the-art over a baseline information retrieval model. Detailed extended analyses of all submitted systems showed large relative improvements in accessing the most challenging multi-hop inference problems, while absolute performance remains low, highlighting the difficulty of generating detailed explanations through multi-hop reasoning.",
}
@inproceedings{banerjee-2019-asu,
    title = "{ASU} at {T}ext{G}raphs 2019 Shared Task: Explanation {R}e{G}eneration using Language Models and Iterative Re-Ranking",
    author = "Banerjee, Pratyay",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5310",
    doi = "10.18653/v1/D19-5310",
    pages = "78--84",
    abstract = "In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-the-art language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3{\%} on the test set.",
}
@inproceedings{chia-etal-2019-red,
    title = "Red Dragon {AI} at {T}ext{G}raphs 2019 Shared Task: Language Model Assisted Explanation Generation",
    author = "Chia, Yew Ken  and
      Witteveen, Sam  and
      Andrews, Martin",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5311",
    doi = "10.18653/v1/D19-5311",
    pages = "85--89",
    abstract = "The TextGraphs-13 Shared Task on Explanation Regeneration (Jansen and Ustalov, 2019) asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI{'}s entries used the language of the questions and explanation text directly, rather than a constructing a separate graph-like representation. Our leaderboard submission placed us 3rd in the competition, but we present here three methods of increasing sophistication, each of which scored successively higher on the test set after the competition close.",
}
@inproceedings{dsouza-etal-2019-team,
    title = "Team {SVM}rank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions",
    author = {D{'}Souza, Jennifer  and
      Mulang{'}, Isaiah Onando  and
      Auer, S{\"o}ren},
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5312",
    doi = "10.18653/v1/D19-5312",
    pages = "90--100",
    abstract = "The TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration (MIER-19) tackles explanation generation for answers to elementary science questions. It builds on the AI2 Reasoning Challenge 2018 (ARC-18) which was organized as an advanced question answering task on a dataset of elementary science questions. The ARC-18 questions were shown to be hard to answer with systems focusing on surface-level cues alone, instead requiring far more powerful knowledge and reasoning. To address MIER-19, we adopt a hybrid pipelined architecture comprising a featurerich learning-to-rank (LTR) machine learning model, followed by a rule-based system for reranking the LTR model predictions. Our system was ranked fourth in the official evaluation, scoring close to the second and third ranked teams, achieving 39.4{\%} MAP.",
}
@inproceedings{das-etal-2019-chains,
    title = "Chains-of-Reasoning at {T}ext{G}raphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference",
    author = "Das, Rajarshi  and
      Godbole, Ameya  and
      Zaheer, Manzil  and
      Dhuliawala, Shehzaad  and
      McCallum, Andrew",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5313",
    doi = "10.18653/v1/D19-5313",
    pages = "101--117",
    abstract = "This paper describes our submission to the shared task on {``}Multi-hop Inference Explanation Regeneration{''} in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019). Our system identifies chains of facts relevant to explain an answer to an elementary science examination question. To counter the problem of {`}spurious chains{'} leading to {`}semantic drifts{'}, we train a ranker that uses contextualized representation of facts to score its relevance for explaining an answer to a question. Our system was ranked first w.r.t the mean average precision (MAP) metric outperforming the second best system by 14.95 points.",
}
@inproceedings{daix-moreux-galle-2019-joint,
    title = "Joint Semantic and Distributional Word Representations with Multi-Graph Embeddings",
    author = "Daix-Moreux, Pierre  and
      Gall{\'e}, Matthias",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5314",
    doi = "10.18653/v1/D19-5314",
    pages = "118--123",
    abstract = "Word embeddings continue to be of great use for NLP researchers and practitioners due to their training speed and easiness of use and distribution. Prior work has shown that the representation of those words can be improved by the use of semantic knowledge-bases. In this paper we propose a novel way of combining those knowledge-bases while the lexical information of co-occurrences of words remains. It is conceptually clear, as it consists in mapping both distributional and semantic information into a multi-graph and modifying existing node embeddings techniques to compute word representations. Our experiments show improved results compared to vanilla word embeddings, retrofitting and concatenation techniques using the same information, on a variety of data-sets of word similarities.",
}
@inproceedings{amplayo-etal-2019-evaluating,
    title = "Evaluating Research Novelty Detection: Counterfactual Approaches",
    author = "Amplayo, Reinald Kim  and
      Hwang, Seung-won  and
      Song, Min",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5315",
    doi = "10.18653/v1/D19-5315",
    pages = "124--133",
    abstract = "In this paper, we explore strategies to evaluate models for the task research paper novelty detection: Given all papers released at a given date, which of the papers discuss new ideas and influence future research? We find the novelty is not a singular concept, and thus inherently lacks of ground truth annotations with cross-annotator agreement, which is a major obstacle in evaluating these models. Test-of-time award is closest to such annotation, which can only be made retrospectively and is extremely scarce. We thus propose to compare and evaluate models using counterfactual simulations. First, we ask models if they can differentiate papers at time $t$ and counterfactual paper from future time $t+d$. Second, we ask models if they can predict test-of-time award at $t+d$. These are proxies that can be agreed by human annotators and easily augmented by correlated signals, using which evaluation can be done through four tasks: classification, ranking, correlation and feature selection. We show these proxy evaluation methods complement each other regarding error handling, coverage, interpretability, and scope, and thus altogether contribute to the observation of the relative strength of existing models.",
}
@inproceedings{vaibhav-etal-2019-sentence,
    title = "Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification",
    author = "Vaibhav, Vaibhav  and
      Mandyam, Raghuram  and
      Hovy, Eduard",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5316",
    doi = "10.18653/v1/D19-5316",
    pages = "134--139",
    abstract = "The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https://github.com/MysteryVaibhav/fake{\_}news{\_}semantics.",
}
@inproceedings{zeng-etal-2019-faceted,
    title = "Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method",
    author = "Zeng, Qingkai  and
      Yu, Mengxia  and
      Yu, Wenhao  and
      Xiong, JinJun  and
      Shi, Yiyu  and
      Jiang, Meng",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5317",
    doi = "10.18653/v1/D19-5317",
    pages = "140--150",
    abstract = "On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet {``}type-of{''}. We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.",
}
@inproceedings{qiu-etal-2019-graph,
    title = "Graph-Based Semi-Supervised Learning for Natural Language Understanding",
    author = "Qiu, Zimeng  and
      Cho, Eunah  and
      Ma, Xiaochun  and
      Campbell, William",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5318",
    doi = "10.18653/v1/D19-5318",
    pages = "151--158",
    abstract = "Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph-based semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach{'}s applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5{\%}.",
}
@inproceedings{huo-etal-2019-graph,
    title = "Graph Enhanced Cross-Domain Text-to-{SQL} Generation",
    author = "Huo, Siyu  and
      Ma, Tengfei  and
      Chen, Jie  and
      Chang, Maria  and
      Wu, Lingfei  and
      Witbrock, Michael",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5319",
    doi = "10.18653/v1/D19-5319",
    pages = "159--163",
    abstract = "Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations. Existing deep learning approaches for semantic parsing have shown promise on a variety of benchmark data sets, particularly on text-to-SQL parsing. However, most text-to-SQL parsers do not generalize to unseen data sets in different domains. In this paper, we propose a new cross-domain learning scheme to perform text-to-SQL translation and demonstrate its use on Spider, a large-scale cross-domain text-to-SQL data set. We improve upon a state-of-the-art Spider model, SyntaxSQLNet, by constructing a graph of column names for all databases and using graph neural networks to compute their embeddings. The resulting embeddings offer better cross-domain representations and SQL queries, as evidenced by substantial improvement on the Spider data set compared to SyntaxSQLNet.",
}
@inproceedings{sudhahar-etal-2019-reasoning,
    title = "Reasoning Over Paths via Knowledge Base Completion",
    author = "Sudhahar, Saatviga  and
      Pierleoni, Andrea  and
      Roberts, Ian",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5320",
    doi = "10.18653/v1/D19-5320",
    pages = "164--171",
    abstract = "Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60{\%} of the time within the top 10 ranked paths and achieve 49{\%} mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.",
}
@inproceedings{szubert-steedman-2019-node,
    title = "Node Embeddings for Graph Merging: Case of Knowledge Graph Construction",
    author = "Szubert, Ida  and
      Steedman, Mark",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5321",
    doi = "10.18653/v1/D19-5321",
    pages = "172--176",
    abstract = "Combining two graphs requires merging the nodes which are counterparts of each other. In this process errors occur, resulting in incorrect merging or incorrect failure to merge. We find a high prevalence of such errors when using AskNET, an algorithm for building Knowledge Graphs from text corpora. AskNET node matching method uses string similarity, which we propose to replace with vector embedding similarity. We explore graph-based and word-based embedding models and show an overall error reduction of from 56{\%} to 23.6{\%}, with a reduction of over a half in both types of incorrect node matching.",
}
@inproceedings{schlegel-freitas-2019-dbee,
    title = "{DB}ee: A Database for Creating and Managing Knowledge Graphs and Embeddings",
    author = "Schlegel, Viktor  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5322",
    doi = "10.18653/v1/D19-5322",
    pages = "177--185",
    abstract = "This paper describes DBee, a database to support the construction of data-intensive AI applications. DBee provides a unique data model which operates jointly over large-scale knowledge graphs (KGs) and embedding vector spaces (VSs). This model supports queries which exploit the semantic properties of both types of representations (KGs and VSs). Additionally, DBee aims to facilitate the construction of KGs and VSs, by providing a library of generators, which can be used to create, integrate and transform data into KGs and VSs.",
}
@inproceedings{jiang-diesner-2019-constituency,
    title = "A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications",
    author = "Jiang, Ming  and
      Diesner, Jana",
    booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5323",
    doi = "10.18653/v1/D19-5323",
    pages = "186--191",
    abstract = "We present a simple, rule-based method for extracting entity networks from the abstracts of scientific literature. By taking advantage of selected syntactic features of constituent parsing trees, our method automatically extracts and constructs graphs in which nodes represent text-based entities (in this case, noun phrases) and their relationships (in this case, verb phrases or preposition phrases). We use two benchmark datasets for evaluation and compare with previously presented results for these data. Our evaluation results show that the proposed method leads to accuracy rates that are comparable to or exceed the results achieved with state-of-the-art, learning-based methods in several cases.",
}
@proceedings{emnlp-2019-asian,
    title = "Proceedings of the 6th Workshop on Asian Translation",
    editor = "Nakazawa, Toshiaki  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Doi, Nobushige  and
      Oda, Yusuke  and
      Bojar, Ond{\v{r}}ej  and
      Parida, Shantipriya  and
      Goto, Isao  and
      Mino, Hidaya",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5200",
}
@inproceedings{nakazawa-etal-2019-overview,
    title = "Overview of the 6th Workshop on {A}sian Translation",
    author = "Nakazawa, Toshiaki  and
      Doi, Nobushige  and
      Higashiyama, Shohei  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Mino, Hideya  and
      Goto, Isao  and
      Pa, Win Pa  and
      Kunchukuttan, Anoop  and
      Oda, Yusuke  and
      Parida, Shantipriya  and
      Bojar, Ond{\v{r}}ej  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5201",
    doi = "10.18653/v1/D19-5201",
    pages = "1--35",
    abstract = "This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.",
}
@inproceedings{dai-yamaguchi-2019-compact,
    title = "Compact and Robust Models for {J}apanese-{E}nglish Character-level Machine Translation",
    author = "Dai, Jinan  and
      Yamaguchi, Kazunori",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5202",
    doi = "10.18653/v1/D19-5202",
    pages = "36--44",
    abstract = "Character-level translation has been proved to be able to achieve preferable translation quality without explicit segmentation, but training a character-level model needs a lot of hardware resources. In this paper, we introduced two character-level translation models which are mid-gated model and multi-attention model for Japanese-English translation. We showed that the mid-gated model achieved the better performance with respect to BLEU scores. We also showed that a relatively narrow beam of width 4 or 5 was sufficient for the mid-gated model. As for unknown words, we showed that the mid-gated model could somehow translate the one containing Katakana by coining out a close word. We also showed that the model managed to produce tolerable results for heavily noised sentences, even though the model was trained with the dataset without noise.",
}
@inproceedings{feely-etal-2019-controlling,
    title = "Controlling {J}apanese Honorifics in {E}nglish-to-{J}apanese Neural Machine Translation",
    author = "Feely, Weston  and
      Hasler, Eva  and
      de Gispert, Adri{\`a}",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5203",
    doi = "10.18653/v1/D19-5203",
    pages = "45--53",
    abstract = "In the Japanese language different levels of honorific speech are used to convey respect, deference, humility, formality and social distance. In this paper, we present a method for controlling the level of formality of Japanese output in English-to-Japanese neural machine translation (NMT). By using heuristics to identify honorific verb forms, we classify Japanese sentences as being one of three levels of informal, polite, or formal speech in parallel text. The English source side is marked with a feature that identifies the level of honorific speech present in the Japanese target side. We use this parallel text to train an English-Japanese NMT model capable of producing Japanese translations in different honorific speech styles for the same English input sentence.",
}
@inproceedings{rikters-etal-2019-designing,
    title = "Designing the Business Conversation Corpus",
    author = "Rikters, Mat{\=\i}ss  and
      Ri, Ryokan  and
      Li, Tong  and
      Nakazawa, Toshiaki",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5204",
    doi = "10.18653/v1/D19-5204",
    pages = "54--61",
    abstract = "While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems. In this paper, we aim to boost the machine translation quality of conversational texts by introducing a newly constructed Japanese-English business conversation parallel corpus. A detailed analysis of the corpus is provided along with challenging examples for automatic translation. We also experiment with adding the corpus in a machine translation training scenario and show how the resulting system benefits from its use.",
}
@inproceedings{laskar-etal-2019-english,
    title = "{E}nglish to {H}indi Multi-modal Neural Machine Translation and {H}indi Image Captioning",
    author = "Laskar, Sahinur Rahman  and
      Singh, Rohit Pratap  and
      Pakray, Partha  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5205",
    doi = "10.18653/v1/D19-5205",
    pages = "62--67",
    abstract = "With the widespread use of Machine Trans-lation (MT) techniques, attempt to minimizecommunication gap among people from di-verse linguistic backgrounds. We have par-ticipated in Workshop on Asian Transla-tion 2019 (WAT2019) multi-modal translationtask. There are three types of submissiontrack namely, multi-modal translation, Hindi-only image captioning and text-only transla-tion for English to Hindi translation. The mainchallenge is to provide a precise MT output.The multi-modal concept incorporates textualand visual features in the translation task. Inthis work, multi-modal translation track re-lies on pre-trained convolutional neural net-works (CNN) with Visual Geometry Grouphaving 19 layered (VGG19) to extract imagefeatures and attention-based Neural MachineTranslation (NMT) system for translation.The merge-model of recurrent neural network(RNN) and CNN is used for the Hindi-onlyimage captioning. The text-only translationtrack is based on the transformer model of theNMT system. The official results evaluated atWAT2019 translation task, which shows thatour multi-modal NMT system achieved Bilin-gual Evaluation Understudy (BLEU) score20.37, Rank-based Intuitive Bilingual Eval-uation Score (RIBES) 0.642838, Adequacy-Fluency Metrics (AMFM) score 0.668260 forchallenge test data and BLEU score 40.55,RIBES 0.760080, AMFM score 0.770860 forevaluation test data in English to Hindi multi-modal translation respectively.",
}
@inproceedings{marie-etal-2019-supervised,
    title = "Supervised and Unsupervised Machine Translation for {M}yanmar-{E}nglish and {K}hmer-{E}nglish",
    author = "Marie, Benjamin  and
      Kaing, Hour  and
      Mon, Aye Myat  and
      Ding, Chenchen  and
      Fujita, Atsushi  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5206",
    doi = "10.18653/v1/D19-5206",
    pages = "68--75",
    abstract = "This paper presents the NICT{'}s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cleaned and normalized. Our combination of NMT and SMT performed among the best systems for the four translation directions. We also investigated the feasibility of unsupervised machine translation for low-resource and distant language pairs and confirmed observations of previous work showing that unsupervised MT is still largely unable to deal with them.",
}
@inproceedings{dabre-sumita-2019-nicts-participation,
    title = "{NICT}{'}s participation to {WAT} 2019: Multilingualism and Multi-step Fine-Tuning for Low Resource {NMT}",
    author = "Dabre, Raj  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5207",
    doi = "10.18653/v1/D19-5207",
    pages = "76--80",
    abstract = "In this paper we describe our submissions to WAT 2019 for the following tasks: English{--}Tamil translation and Russian{--}Japanese translation. Our team,{``}NICT-5{''}, focused on multilingual domain adaptation and back-translation for Russian{--}Japanese translation and on simple fine-tuning for English{--}Tamil translation . We noted that multi-stage fine tuning is essential in leveraging the power of multilingualism for an extremely low-resource language like Russian{--}Japanese. Furthermore, we can improve the performance of such a low-resource language pair by exploiting a small but in-domain monolingual corpus via back-translation. We managed to obtain second rank in both tasks for all translation directions.",
}
@inproceedings{park-etal-2019-knu,
    title = "{KNU}-{HYUNDAI}{'}s {NMT} system for Scientific Paper and Patent Tasks on{WAT} 2019",
    author = "Park, Cheoneum  and
      Jung, Young-Jun  and
      Kim, Kihoon  and
      Kim, Geonyeong  and
      Jeon, Jae-Won  and
      Lee, Seongmin  and
      Kim, Junseok  and
      Lee, Changki",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5208",
    doi = "10.18653/v1/D19-5208",
    pages = "81--89",
    abstract = "In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). We participated in all tasks of ASPEC and JPC2, which included those of Chinese-Japanese, English-Japanese, and Korean-{\textgreater}Japanese. We submitted our transformer-based NMT system with built using the following methods: a) relative positioning method for pairwise relationships between the input elements, b) back-translation and multi-source translation for data augmentation, c) right-to-left (r2l)-reranking model robust against error propagation in autoregressive architectures such as decoders, and d) checkpoint ensemble models, which selected the top three models with the best validation bilingual evaluation understudy (BLEU) . We have reported the translation results on the two aforementioned tasks. We performed well in both the tasks and were ranked first in terms of the BLEU scores in all the JPC2 subtasks we participated in.",
}
@inproceedings{wang-etal-2019-english,
    title = "{E}nglish-{M}yanmar Supervised and Unsupervised {NMT}: {NICT}{'}s Machine Translation Systems at {WAT}-2019",
    author = "Wang, Rui  and
      Sun, Haipeng  and
      Chen, Kehai  and
      Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5209",
    doi = "10.18653/v1/D19-5209",
    pages = "90--93",
    abstract = "This paper presents the NICT{'}s participation (team ID: NICT) in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions. We built neural machine translation (NMT) systems for these tasks. Our NMT systems were trained with language model pretraining. Back-translation technology is adopted to NMT. Our NMT systems rank the third in English-to-Myanmar and the second in Myanmar-to-English according to BLEU score.",
}
@inproceedings{thida-etal-2019-ucsmnlp,
    title = "{UCSMNLP}: Statistical Machine Translation for {WAT} 2019",
    author = "Thida, Aye  and
      Han, Nway Nway  and
      Oo, Sheinn Thawtar  and
      Htar, Khin Thet",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5210",
    doi = "10.18653/v1/D19-5210",
    pages = "94--98",
    abstract = "This paper represents UCSMNLP{'}s submission to the WAT 2019 Translation Tasks focusing on the Myanmar-English translation. Phrase based statistical machine translation (PBSMT) system is built by using other resources: Name Entity Recognition (NER) corpus and bilingual dictionary which is created by Google Translate (GT). This system is also adopted with listwise reranking process in order to improve the quality of translation and tuning is done by changing initial distortion weight. The experimental results show that PBSMT using other resources with initial distortion weight (0.4) and listwise reranking function outperforms the baseline system.",
}
@inproceedings{morishita-etal-2019-ntt,
    title = "{NTT} Neural Machine Translation Systems at {WAT} 2019",
    author = "Morishita, Makoto  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5211",
    doi = "10.18653/v1/D19-5211",
    pages = "99--105",
    abstract = "In this paper, we describe our systems that were submitted to the translation shared tasks at WAT 2019. This year, we participated in two distinct types of subtasks, a scientific paper subtask and a timely disclosure subtask, where we only considered English-to-Japanese and Japanese-to-English translation directions. We submitted two systems (En-Ja and Ja-En) for the scientific paper subtask and two systems (Ja-En, texts, items) for the timely disclosure subtask. Three of our four systems obtained the best human evaluation performances. We also confirmed that our new additional web-crawled parallel corpus improves the performance in unconstrained settings.",
}
@inproceedings{mino-etal-2019-neural,
    title = "Neural Machine Translation System using a Content-equivalently Translated Parallel Corpus for the Newswire Translation Tasks at {WAT} 2019",
    author = "Mino, Hideya  and
      Ito, Hitoshi  and
      Goto, Isao  and
      Yamada, Ichiro  and
      Tanaka, Hideki  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5212",
    doi = "10.18653/v1/D19-5212",
    pages = "106--111",
    abstract = "This paper describes NHK and NHK Engineering System (NHK-ES){'}s submission to the newswire translation tasks of WAT 2019 in both directions of Japanese→English and English→Japanese. In addition to the JIJI Corpus that was officially provided by the task organizer, we developed a corpus of 0.22M sentence pairs by manually, translating Japanese news sentences into English content- equivalently. The content-equivalent corpus was effective for improving translation quality, and our systems achieved the best human evaluation scores in the newswire translation tasks at WAT 2019.",
}
@inproceedings{chen-etal-2019-facebook,
    title = "{F}acebook {AI}{'}s {WAT}19 {M}yanmar-{E}nglish Translation Task Submission",
    author = "Chen, Peng-Jen  and
      Shen, Jiajun  and
      Le, Matthew  and
      Chaudhary, Vishrav  and
      El-Kishky, Ahmed  and
      Wenzek, Guillaume  and
      Ott, Myle  and
      Ranzato, Marc{'}Aurelio",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5213",
    doi = "10.18653/v1/D19-5213",
    pages = "112--122",
    abstract = "This paper describes Facebook AI{'}s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.",
}
@inproceedings{eriguchi-etal-2019-combining,
    title = "Combining Translation Memory with Neural Machine Translation",
    author = "Eriguchi, Akiko  and
      Rarrick, Spencer  and
      Matsushita, Hitokazu",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5214",
    doi = "10.18653/v1/D19-5214",
    pages = "123--130",
    abstract = "In this paper, we report our submission systems (geoduck) to the Timely Disclosure task on the 6th Workshop on Asian Translation (WAT) (Nakazawa et al., 2019). Our system employs a combined approach of translation memory and Neural Machine Translation (NMT) models, where we can select final translation outputs from either a translation memory or an NMT system, when the similarity score of a test source sentence exceeds the predefined threshold. We observed that this combination approach significantly improves the translation performance on the Timely Disclosure corpus, as compared to a standalone NMT system. We also conducted source-based direct assessment on the final output, and we discuss the comparison between human references and each system{'}s output.",
}
@inproceedings{philip-etal-2019-cvits,
    title = "{CVIT}{'}s submissions to {WAT}-2019",
    author = "Philip, Jerin  and
      Siripragada, Shashank  and
      Kumar, Upendra  and
      Namboodiri, Vinay  and
      Jawahar, C V",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5215",
    doi = "10.18653/v1/D19-5215",
    pages = "131--136",
    abstract = "This paper describes the Neural Machine Translation systems used by IIIT Hyderabad (CVIT-MT) for the translation tasks part of WAT-2019. We participated in tasks pertaining to Indian languages and submitted results for English-Hindi, Hindi-English, English-Tamil and Tamil-English language pairs. We employ Transformer architecture experimenting with multilingual models and methods for low-resource languages.",
}
@inproceedings{goyal-sharma-2019-ltrc,
    title = "{LTRC}-{MT} Simple {\&} Effective {H}indi-{E}nglish Neural Machine Translation Systems at {WAT} 2019",
    author = "Goyal, Vikrant  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5216",
    doi = "10.18653/v1/D19-5216",
    pages = "137--140",
    abstract = "This paper describes the Neural Machine Translation systems of IIIT-Hyderabad (LTRC-MT) for WAT 2019 Hindi-English shared task. We experimented with both Recurrent Neural Networks {\&} Transformer architectures. We also show the results of our experiments of training NMT models using additional data via backtranslation.",
}
@inproceedings{imamura-sumita-2019-long,
    title = "Long Warm-up and Self-Training: Training Strategies of {NICT}-2 {NMT} System at {WAT}-2019",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5217",
    doi = "10.18653/v1/D19-5217",
    pages = "141--146",
    abstract = "This paper describes the NICT-2 neural machine translation system at the 6th Workshop on Asian Translation. This system employs the standard Transformer model but features the following two characteristics. One is the long warm-up strategy, which performs a longer warm-up of the learning rate at the start of the training than conventional approaches. Another is that the system introduces self-training approaches based on multiple back-translations generated by sampling. We participated in three tasks{---}ASPEC.en-ja, ASPEC.ja-en, and TDDC.ja-en{---}using this system.",
}
@inproceedings{tong-etal-2019-supervised,
    title = "Supervised neural machine translation based on data augmentation and improved training {\&} inference process",
    author = "Tong, Yixuan  and
      Liang, Liang  and
      Liu, Boyan  and
      Jiang, Shanshan  and
      Dong, Bin",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5218",
    doi = "10.18653/v1/D19-5218",
    pages = "147--151",
    abstract = "This is the second time for SRCB to participate in WAT. This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019. We participated in ASPEC tasks and submitted results on English-Japanese, Japanese-English, Chinese-Japanese, and Japanese-Chinese four language pairs. We employed the Transformer model as the baseline and experimented relative position representation, data augmentation, deep layer model, ensemble. Experiments show that all these methods can yield substantial improvements.",
}
@inproceedings{susanto-etal-2019-sarahs,
    title = "Sarah{'}s Participation in {WAT} 2019",
    author = "Susanto, Raymond Hendy  and
      Htun, Ohnmar  and
      Tan, Liling",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5219",
    doi = "10.18653/v1/D19-5219",
    pages = "152--158",
    abstract = "This paper describes our MT systems{'} participation in the of WAT 2019. We participated in the (i) Patent, (ii) Timely Disclosure, (iii) Newswire and (iv) Mixed-domain tasks. Our main focus is to explore how similar Transformer models perform on various tasks. We observed that for tasks with smaller datasets, our best model setup are shallower models with lesser number of attention heads. We investigated practical issues in NMT that often appear in production settings, such as coping with multilinguality and simplifying pre- and post-processing pipeline in deployment.",
}
@inproceedings{yang-ogata-2019-neural,
    title = "Our Neural Machine Translation Systems for {WAT} 2019",
    author = "Yang, Wei  and
      Ogata, Jun",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5220",
    doi = "10.18653/v1/D19-5220",
    pages = "159--164",
    abstract = "In this paper, we describe our Neural Machine Translation (NMT) systems for the WAT 2019 translation tasks we focus on. This year we participate in scientific paper tasks and focus on the language pair between English and Japanese. We use Transformer model through our work in this paper to explore and experience the powerful of the Transformer architecture relying on self-attention mechanism. We use different NMT toolkit/library as the implementation of training the Transformer model. For word segmentation, we use different subword segmentation strategies while using different toolkit/library. We not only give the translation accuracy obtained based on absolute position encodings that introduced in the Transformer model, but also report the the improvements in translation accuracy while replacing absolute position encodings with relative position representations. We also ensemble several independent trained Transformer models to further improve the translation accuracy.",
}
@inproceedings{imankulova-etal-2019-japanese,
    title = "{J}apanese-{R}ussian {TMU} Neural Machine Translation System using Multilingual Model for {WAT} 2019",
    author = "Imankulova, Aizhan  and
      Kaneko, Masahiro  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5221",
    doi = "10.18653/v1/D19-5221",
    pages = "165--170",
    abstract = "We introduce our system that is submitted to the News Commentary task (Japanese{\textless}-{\textgreater}Russian) of the 6th Workshop on Asian Translation. The goal of this shared task is to study extremely low resource situations for distant language pairs. It is known that using parallel corpora of different language pair as training data is effective for multilingual neural machine translation model in extremely low resource scenarios. Therefore, to improve the translation quality of Japanese{\textless}-{\textgreater}Russian language pair, our method leverages other in-domain Japanese-English and English-Russian parallel corpora as additional training data for our multilingual NMT model.",
}
@inproceedings{kumar-singh-2019-nlprl,
    title = "{NLPRL} at {WAT}2019: Transformer-based {T}amil {--} {E}nglish Indic Task Neural Machine Translation System",
    author = "Kumar, Amit  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5222",
    doi = "10.18653/v1/D19-5222",
    pages = "171--174",
    abstract = "This paper describes the Machine Translation system for Tamil-English Indic Task organized at WAT 2019. We use Transformer- based architecture for Neural Machine Translation.",
}
@inproceedings{parida-etal-2019-idiap,
    title = "Idiap {NMT} System for {WAT} 2019 Multimodal Translation Task",
    author = "Parida, Shantipriya  and
      Bojar, Ond{\v{r}}ej  and
      Motlicek, Petr",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5223",
    doi = "10.18653/v1/D19-5223",
    pages = "175--180",
    abstract = "This paper describes the Idiap submission to WAT 2019 for the English-Hindi Multi-Modal Translation Task. We have used the state-of-the-art Transformer model and utilized the IITB English-Hindi parallel corpus as an additional data source. Among the different tracks of the multi-modal task, we have participated in the {``}Text-Only{''} track for the evaluation and challenge test sets. Our submission tops in its track among the competitors in terms of both automatic and manual evaluation. Based on automatic scores, our text-only submission also outperforms systems that consider visual information in the {``}multi-modal translation{''} task.",
}
@inproceedings{sanayai-meetei-etal-2019-wat2019,
    title = "{WAT}2019: {E}nglish-{H}indi Translation on {H}indi Visual Genome Dataset",
    author = "Sanayai Meetei, Loitongbam  and
      Singh, Thoudam Doren  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5224",
    doi = "10.18653/v1/D19-5224",
    pages = "181--188",
    abstract = "A multimodal translation is a task of translating a source language to a target language with the help of a parallel text corpus paired with images that represent the contextual details of the text. In this paper, we carried out an extensive comparison to evaluate the benefits of using a multimodal approach on translating text in English to a low resource language, Hindi as a part of WAT2019 shared task. We carried out the translation of English to Hindi in three separate tasks with both the evaluation and challenge dataset. First, by using only the parallel text corpora, then through an image caption generation approach and, finally with the multimodal approach. Our experiment shows a significant improvement in the result with the multimodal approach than the other approach.",
}
@inproceedings{xu-etal-2019-systran,
    title = "{SYSTRAN} @ {WAT} 2019: {R}ussian-{J}apanese News Commentary task",
    author = "Xu, Jitao  and
      Nguyen, TuAnh  and
      Pham, MinhQuang  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5225",
    doi = "10.18653/v1/D19-5225",
    pages = "189--194",
    abstract = "This paper describes Systran{'}s submissions to WAT 2019 Russian-Japanese News Commentary task. A challenging translation task due to the extremely low resources available and the distance of the language pair. We have used the neural Transformer architecture learned over the provided resources and we carried out synthetic data generation experiments which aim at alleviating the data scarcity problem. Results indicate the suitability of the data augmentation experiments, enabling our systems to rank first according to automatic evaluations.",
}
@inproceedings{shwesin-etal-2019-ucsynlp,
    title = "{UCSYNLP}-Lab Machine Translation Systems for {WAT} 2019",
    author = "ShweSin, Yimon  and
      Pa, Win Pa  and
      Soe, KhinMar",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5226",
    doi = "10.18653/v1/D19-5226",
    pages = "195--199",
    abstract = "This paper describes the UCSYNLP-Lab submission to WAT 2019 for Myanmar-English translation tasks in both direction. We have used the neural machine translation systems with attention model and utilized the UCSY-corpus and ALT corpus. In NMT with attention model, we use the word segmentation level as well as syllable segmentation level. Especially, we made the UCSY-corpus to be cleaned in WAT 2019. Therefore, the UCSY corpus for WAT 2019 is not identical to those used in WAT 2018. Experiments show that the translation systems can produce the substantial improvements.",
}
@inproceedings{si-etal-2019-sentiment,
    title = "Sentiment Aware Neural Machine Translation",
    author = "Si, Chenglei  and
      Wu, Kui  and
      Aw, Ai Ti  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5227",
    doi = "10.18653/v1/D19-5227",
    pages = "200--206",
    abstract = "Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.",
}
@inproceedings{ngo-etal-2019-overcoming,
    title = "Overcoming the Rare Word Problem for low-resource language pairs in Neural Machine Translation",
    author = "Ngo, Thi-Vinh  and
      Ha, Thanh-Le  and
      Nguyen, Phuong-Thai  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5228",
    doi = "10.18653/v1/D19-5228",
    pages = "207--214",
    abstract = "Among the six challenges of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of low-resource languages. In this paper, we propose three solutions to address the rare words in neural machine translation systems. First, we enhance source context to predict the target words by connecting directly the source embeddings to the output of the attention component in NMT. Second, we propose an algorithm to learn morphology of unknown words for English in supervised way in order to minimize the adverse effect of rare-word problem. Finally, we exploit synonymous relation from the WordNet to overcome out-of-vocabulary (OOV) problem of NMT. We evaluate our approaches on two low-resource language pairs: English-Vietnamese and Japanese-Vietnamese. In our experiments, we have achieved significant improvements of up to roughly +1.0 BLEU points in both language pairs.",
}
@inproceedings{fadel-etal-2019-neural,
    title = "Neural {A}rabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation",
    author = "Fadel, Ali  and
      Tuffaha, Ibraheem  and
      Al-Jawarneh, Bara{'}  and
      Al-Ayyoub, Mahmoud",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5229",
    doi = "10.18653/v1/D19-5229",
    pages = "215--225",
    abstract = "In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.",
}
@proceedings{emnlp-2019-economics,
    title = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    editor = "Hahn, Udo  and
      Hoste, V{\'e}ronique  and
      Zhang, Zhu",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5100",
}
@inproceedings{oral-etal-2019-extracting,
    title = "Extracting Complex Relations from Banking Documents",
    author = {Oral, Berke  and
      Emekligil, Erdem  and
      Arslan, Se{\c{c}}il  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5101",
    doi = "10.18653/v1/D19-5101",
    pages = "1--9",
    abstract = "In order to automate banking processes (e.g. payments, money transfers, foreign trade), we need to extract banking transactions from different types of mediums such as faxes, e-mails, and scanners. Banking orders may be considered as complex documents since they contain quite complex relations compared to traditional datasets used in relation extraction research. In this paper, we present our method to extract intersentential, nested and complex relations from banking orders, and introduce a relation extraction method based on maximal clique factorization technique. We demonstrate 11{\%} error reduction over previous methods.",
}
@inproceedings{ein-dor-etal-2019-financial,
    title = "Financial Event Extraction Using {W}ikipedia-Based Weak Supervision",
    author = "Ein-Dor, Liat  and
      Gera, Ariel  and
      Toledo-Ronen, Orith  and
      Halfon, Alon  and
      Sznajder, Benjamin  and
      Dankin, Lena  and
      Bilu, Yonatan  and
      Katz, Yoav  and
      Slonim, Noam",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5102",
    doi = "10.18653/v1/D19-5102",
    pages = "10--15",
    abstract = "Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.",
}
@inproceedings{buechel-etal-2019-time,
    title = "A Time Series Analysis of Emotional Loading in Central Bank Statements",
    author = "Buechel, Sven  and
      Junker, Simon  and
      Schlaak, Thore  and
      Michelsen, Claus  and
      Hahn, Udo",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5103",
    doi = "10.18653/v1/D19-5103",
    pages = "16--21",
    abstract = "We examine the affective content of central bank press statements using emotion analysis. Our focus is on two major international players, the European Central Bank (ECB) and the US Federal Reserve Bank (Fed), covering a time span from 1998 through 2019. We reveal characteristic patterns in the emotional dimensions of valence, arousal, and dominance and find{---}despite the commonly established attitude that emotional wording in central bank communication should be avoided{---}a correlation between the state of the economy and particularly the dominance dimension in the press releases under scrutiny and, overall, an impact of the president in office.",
}
@inproceedings{zhai-zhang-2019-forecasting,
    title = "Forecasting Firm Material Events from 8-K Reports",
    author = "Zhai, Shuang (Sophie)  and
      Zhang, Zhu (Drew)",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5104",
    doi = "10.18653/v1/D19-5104",
    pages = "22--30",
    abstract = "In this paper, we show deep learning models can be used to forecast firm material event sequences based on the contents in the company{'}s 8-K Current Reports. Specifically, we exploit state-of-the-art neural architectures, including sequence-to-sequence (Seq2Seq) architecture and attention mechanisms, in the model. Our 8K-powered deep learning model demonstrates promising performance in forecasting firm future event sequences. The model is poised to benefit various stakeholders, including management and investors, by facilitating risk management and decision making.",
}
@inproceedings{chen-etal-2019-incorporating,
    title = "Incorporating Fine-grained Events in Stock Movement Prediction",
    author = "Chen, Deli  and
      Zou, Yanyan  and
      Harimoto, Keiko  and
      Bao, Ruihan  and
      Ren, Xuancheng  and
      Sun, Xu",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5105",
    doi = "10.18653/v1/D19-5105",
    pages = "31--40",
    abstract = "Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.",
}
@inproceedings{chen-etal-2019-group,
    title = "Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction",
    author = "Chen, Deli  and
      Ma, Shuming  and
      Harimoto, Keiko  and
      Bao, Ruihan  and
      Su, Qi  and
      Sun, Xu",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5106",
    doi = "10.18653/v1/D19-5106",
    pages = "41--50",
    abstract = "Incorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency exchange) market because the associated texts are too redundant. In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement. We firstly group news from different aspects: time, topic and category. Then we extract the most crucial news in each group by the SOTA extractive summarization method. Finally, we conduct interaction between the news and the trade data with attention to predict the forex movement. The experimental results show that the category based method performs best among three grouping methods and outperforms all the baselines. Besides, we study the influence of essential news attributes (category and region) by statistical analysis and summarize the influence patterns for different currency pairs.",
}
@inproceedings{filgueiras-etal-2019-complaint,
    title = "Complaint Analysis and Classification for Economic and Food Safety",
    author = "Filgueiras, Jo{\~a}o  and
      Barbosa, Lu{\'\i}s  and
      Rocha, Gil  and
      Lopes Cardoso, Henrique  and
      Reis, Lu{\'\i}s Paulo  and
      Machado, Jo{\~a}o Pedro  and
      Oliveira, Ana Maria",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5107",
    doi = "10.18653/v1/D19-5107",
    pages = "51--60",
    abstract = "Governmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and machine learning techniques are being used to process citizen feedback. In this paper, we report on the use of such techniques for analyzing and classifying complaints, in the context of the Portuguese Economic and Food Safety Authority. Grounded in its operational process, we address three different classification problems: target economic activity, implied infraction severity level, and institutional competence. We show promising results obtained using feature-based approaches and traditional classifiers, with accuracy scores above 70{\%}, and analyze the shortcomings of our current results and avenues for further improvement, taking into account the intended use of our classifiers in helping human officers to cope with thousands of yearly complaints.",
}
@inproceedings{rivera-etal-2019-annotation,
    title = "Annotation Process for the Dialog Act Classification of a Taglish {E}-commerce {Q}{\&}{A} Corpus",
    author = "Rivera, Jared  and
      Pensica, Jan Caleb Oliver  and
      Valenzuela, Jolene  and
      Secuya, Alfonso  and
      Cheng, Charibeth",
    booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2019",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5108",
    doi = "10.18653/v1/D19-5108",
    pages = "61--68",
    abstract = "With conversational agents or chatbots making up in quantity of replies rather than quality, the need to identify user intent has become a main concern to improve these agents. Dialog act (DA) classification tackles this concern, and while existing studies have already addressed DA classification in general contexts, no training corpora in the context of e-commerce is available to the public. This research addressed the said insufficiency by building a text-based corpus of 7,265 posts from the question and answer section of products on Lazada Philippines. The SWBD-DAMSL tagset for DA classification was modified to 28 tags fitting the categories applicable to e-commerce conversations. The posts were annotated manually by three (3) human annotators and preprocessing techniques decreased the vocabulary size from 6,340 to 1,134. After analysis, the corpus was composed dominantly of single-label posts, with 34{\%} of the corpus having multiple intent tags. The annotated corpus allowed insights toward the structure of posts created with single to multiple intents.",
}
@proceedings{emnlp-2019-natural,
    title = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Brew, Chris  and
      Leberknight, Chris  and
      Nakov, Preslav",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5000",
}
@inproceedings{navaki-arefi-etal-2019-assessing,
    title = "Assessing Post Deletion in {S}ina {W}eibo: Multi-modal Classification of Hot Topics",
    author = "Navaki Arefi, Meisam  and
      Pandi, Rajkumar  and
      Tschantz, Michael Carl  and
      Crandall, Jedidiah R.  and
      Fu, King-wa  and
      Qiu Shi, Dahlia  and
      Sha, Miao",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5001",
    doi = "10.18653/v1/D19-5001",
    pages = "1--9",
    abstract = "Widespread Chinese social media applications such as Weibo are widely known for monitoring and deleting posts to conform to Chinese government requirements. In this paper, we focus on analyzing a dataset of censored and uncensored posts in Weibo. Despite previous work that only considers text content of posts, we take a multi-modal approach that takes into account both text and image content. We categorize this dataset into 14 categories that have the potential to be censored on Weibo, and seek to quantify censorship by topic. Specifically, we investigate how different factors interact to affect censorship. We also investigate how consistently and how quickly different topics are censored. To this end, we have assembled an image dataset with 18,966 images, as well as a text dataset with 994 posts from 14 categories. We then utilized deep learning, CNN localization, and NLP techniques to analyze the target dataset and extract categories, for further analysis to better understand censorship mechanisms in Weibo. We found that sentiment is the only indicator of censorship that is consistent across the variety of topics we identified. Our finding matches with recently leaked logs from Sina Weibo. We also discovered that most categories like those related to anti-government actions (e.g. protest) or categories related to politicians (e.g. Xi Jinping) are often censored, whereas some categories such as crisis-related categories (e.g. rainstorm) are less frequently censored. We also found that censored posts across all categories are deleted in three hours on average.",
}
@inproceedings{lee-etal-2019-detecting,
    title = "Detecting context abusiveness using hierarchical deep learning",
    author = "Lee, Ju-Hyoung  and
      Park, Jun-U  and
      Cha, Jeong-Won  and
      Han, Yo-Sub",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5002",
    doi = "10.18653/v1/D19-5002",
    pages = "10--19",
    abstract = "Abusive text is a serious problem in social media and causes many issues among users as the number of users and the content volume increase. There are several attempts for detecting or preventing abusive text effectively. One simple yet effective approach is to use an abusive lexicon and determine the existence of an abusive word in text. This approach works well even when an abusive word is obfuscated. On the other hand, it is still a challenging problem to determine abusiveness in a text having no explicit abusive words. Especially, it is hard to identify sarcasm or offensiveness in context without any abusive words. We tackle this problem using an ensemble deep learning model. Our model consists of two parts of extracting local features and global features, which are crucial for identifying implicit abusiveness in context level. We evaluate our model using three benchmark data. Our model outperforms all the previous models for detecting abusiveness in a text data without abusive words. Furthermore, we combine our model and an abusive lexicon method. The experimental results show that our model has at least 4{\%} better performance compared with the previous approaches for identifying text abusiveness in case of with/without abusive words.",
}
@inproceedings{nasrin-etal-2019-many,
    title = "How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a {R}ussian Troll Farm",
    author = "Nasrin, Nayeema  and
      Raymond Choo, Kim-Kwang  and
      Ko, Myung  and
      Rios, Anthony",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5003",
    doi = "10.18653/v1/D19-5003",
    pages = "20--30",
    abstract = "Social media has reportedly been (ab)used by Russian troll farms to promote political agendas. Specifically, state-affiliated actors disguise themselves as native citizens of the United States to promote discord and promote their political motives. Therefore, developing methods to automatically detect Russian trolls can ensure fair elections and possibly reduce political extremism by stopping trolls that produce discord. While data exists for some troll organizations (e.g., Internet Research Agency), it is challenging to collect ground-truth accounts for new troll farms in a timely fashion. In this paper, we study the impact the number of labeled troll accounts has on detection performance. We analyze the use of self-supervision with less than 100 troll accounts as training data. We improve classification performance by nearly 4{\%} F1. Furthermore, in combination with self-supervision, we also explore novel features for troll detection grounded in stylometry. Intuitively, we assume that the writing style is consistent across troll accounts because a single troll organization employee may control multiple user accounts. Overall, we improve on models based on words features by {\textasciitilde}9{\%} F1.",
}
@inproceedings{levi-etal-2019-identifying,
    title = "Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues",
    author = "Levi, Or  and
      Hosseini, Pedram  and
      Diab, Mona  and
      Broniatowski, David",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5004",
    doi = "10.18653/v1/D19-5004",
    pages = "31--35",
    abstract = "The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.",
}
@inproceedings{rogers-etal-2019-calls,
    title = "Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential",
    author = "Rogers, Anna  and
      Kovaleva, Olga  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5005",
    doi = "10.18653/v1/D19-5005",
    pages = "36--44",
    abstract = "Calls to action on social media are known to be effective means of mobilization in social movements, and a frequent target of censorship. We investigate the possibility of their automatic detection and their potential for predicting real-world protest events, on historical data of Bolotnaya protests in Russia (2011-2013). We find that political calls to action can be annotated and detected with relatively high accuracy, and that in our sample their volume has a moderate positive correlation with rally attendance.",
}
@inproceedings{hartmann-etal-2019-mapping,
    title = "Mapping (Dis-)Information Flow about the {MH}17 Plane Crash",
    author = "Hartmann, Mareike  and
      Golovchenko, Yevgeniy  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5006",
    doi = "10.18653/v1/D19-5006",
    pages = "45--55",
    abstract = "Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators.",
}
@inproceedings{park-etal-2019-generating,
    title = "Generating Sentential Arguments from Diverse Perspectives on Controversial Topic",
    author = "Park, ChaeHun  and
      Yang, Wonsuk  and
      Park, Jong",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5007",
    doi = "10.18653/v1/D19-5007",
    pages = "56--65",
    abstract = "Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions. A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data. In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem. Our model, ArgDiver (Argument generation model from diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments. At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models. We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.",
}
@inproceedings{li-etal-2019-rumor-detection,
    title = "Rumor Detection on Social Media: Datasets, Methods and Opportunities",
    author = "Li, Quanzhi  and
      Zhang, Qiong  and
      Si, Luo  and
      Liu, Yingchi",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5008",
    doi = "10.18653/v1/D19-5008",
    pages = "66--75",
    abstract = "Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fake news. Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques. This paper gives an overview of the recent studies in the rumor detection field. It provides a comprehensive list of datasets used for rumor detection, and reviews the important studies based on what types of information they exploit and the approaches they take. And more importantly, we also present several new directions for future research.",
}
@inproceedings{chen-etal-2019-unraveling,
    title = "Unraveling the Search Space of Abusive Language in {W}ikipedia with Dynamic Lexicon Acquisition",
    author = "Chen, Wei-Fan  and
      Al Khatib, Khalid  and
      Hagen, Matthias  and
      Wachsmuth, Henning  and
      Stein, Benno",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5009",
    doi = "10.18653/v1/D19-5009",
    pages = "76--82",
    abstract = "Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic. Since an automatic detection of abusive language can support human moderators of online discussion platforms, detecting abusiveness has recently received increased attention. However, the existing approaches simply train one classifier for the whole variety of abusiveness. In contrast, our approach is to distinguish explicitly abusive cases from the more {``}shadowed{''} ones. By dynamically extending a lexicon of abusive terms (e.g., including new obfuscations of abusive terms), our approach can support a moderator with explicit unraveled explanations for why something was flagged as abusive: due to known explicitly abusive terms, due to newly detected (obfuscated) terms, or due to shadowed cases.",
}
@inproceedings{hou-chen-2019-caunlp,
    title = "{CAU}n{LP} at {NLP}4{IF} 2019 Shared Task: Context-Dependent {BERT} for Sentence-Level Propaganda Detection",
    author = "Hou, Wenjun  and
      Chen, Ying",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5010",
    doi = "10.18653/v1/D19-5010",
    pages = "83--86",
    abstract = "The goal of fine-grained propaganda detection is to determine whether a given sentence uses propaganda techniques (sentence-level) or to recognize which techniques are used (fragment-level). This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task. In order to better utilize the document information, we construct context-dependent input pairs (sentence-title pair and sentence- context pair) to fine-tune the pretrained BERT, and we also use the undersampling method to tackle the problem of imbalanced data.",
}
@inproceedings{yoosuf-yang-2019-fine,
    title = "Fine-Grained Propaganda Detection with Fine-Tuned {BERT}",
    author = "Yoosuf, Shehel  and
      Yang, Yin",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5011",
    doi = "10.18653/v1/D19-5011",
    pages = "87--91",
    abstract = "This paper presents the winning solution of the Fragment Level Classification (FLC) task in the Fine Grained Propaganda Detection competition at the NLP4IF{'}19 workshop. The goal of the FLC task is to detect and classify textual segments that correspond to one of the 18 given propaganda techniques in a news articles dataset. The main idea of our solution is to perform word-level classification using fine-tuned BERT, a popular pre-trained language model. Besides presenting the model and its evaluation results, we also investigate the attention heads in the model, which provide insights into what the model learns, as well as aspects for potential improvements.",
}
@inproceedings{gupta-etal-2019-neural,
    title = "Neural Architectures for Fine-Grained Propaganda Detection in News",
    author = {Gupta, Pankaj  and
      Saxena, Khushbu  and
      Yaseen, Usama  and
      Runkler, Thomas  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5012",
    doi = "10.18653/v1/D19-5012",
    pages = "92--97",
    abstract = "This paper describes our system (MIC-CIS) details and results of participation in the fine grained propaganda detection shared task 2019. To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e.g., CNN, LSTM-CRF and BERT) and extract linguistic (e.g., part-of-speech, named entity, readability, sentiment, emotion, etc.), layout and topical features. Specifically, we have designed multi-granularity and multi-tasking neural architectures to jointly perform both the sentence and fragment level propaganda detection. Additionally, we investigate different ensemble schemes such as majority-voting, relax-voting, etc. to boost overall system performance. Compared to the other participating systems, our submissions are ranked 3rd and 4th in FLC and SLC tasks, respectively.",
}
@inproceedings{alhindi-etal-2019-fine,
    title = "Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels",
    author = "Alhindi, Tariq  and
      Pfeiffer, Jonas  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5013",
    doi = "10.18653/v1/D19-5013",
    pages = "98--102",
    abstract = "This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on Fine-Grained Propaganda Detection. Our system finished 5th out of 26 teams on the sentence-level classification task and 5th out of 11 teams on the fragment-level classification task based on our scores on the blind test set. We present our models, a discussion of our ablation studies and experiments, and an analysis of our performance on all eighteen propaganda techniques present in the corpus of the shared task.",
}
@inproceedings{mapes-etal-2019-divisive,
    title = "Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning {BERT}-based Language Models for Binary Classification",
    author = "Mapes, Norman  and
      White, Anna  and
      Medury, Radhika  and
      Dua, Sumeet",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5014",
    doi = "10.18653/v1/D19-5014",
    pages = "103--106",
    abstract = "On the NLP4IF 2019 sentence level propaganda classification task, we used a BERT language model that was pre-trained on Wikipedia and BookCorpus as team ltuorp ranking {\#}1 of 26. It uses deep learning in the form of an attention transformer. We substituted the final layer of the neural network to a linear real valued output neuron from a layer of softmaxes. The backpropagation trained the entire neural network and not just the last layer. Training took 3 epochs and on our computation resources this took approximately one day. The pre-trained model consisted of uncased words and there were 12-layers, 768-hidden neurons with 12-heads for a total of 110 million parameters. The articles used in the training data promote divisive language similar to state-actor-funded influence operations on social media. Twitter shows state-sponsored examples designed to maximize division occurring across political lines, ranging from {``}Obama calls me a clinger, Hillary calls me deplorable, ... and Trump calls me an American{''} oriented to the political right, to Russian propaganda featuring {``}Black Lives Matter{''} material with suggestions of institutional racism in US police forces oriented to the political left. We hope that raising awareness through our work will reduce the polarizing dialogue for the betterment of nations.",
}
@inproceedings{ferreira-cruz-etal-2019-sentence,
    title = "On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings",
    author = "Ferreira Cruz, Andr{\'e}  and
      Rocha, Gil  and
      Lopes Cardoso, Henrique",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5015",
    doi = "10.18653/v1/D19-5015",
    pages = "107--112",
    abstract = "Bias is ubiquitous in most online sources of natural language, from news media to social networks. Given the steady shift in news consumption behavior from traditional outlets to online sources, the automatic detection of propaganda, in which information is shaped to purposefully foster a predetermined agenda, is an increasingly crucial task. To this goal, we explore the task of sentence-level propaganda detection, and experiment with both handcrafted features and learned dense semantic representations. We also experiment with random undersampling of the majority class (non-propaganda) to curb the influence of class distribution on the system{'}s performance, leading to marked improvements on the minority class (propaganda). Our best performing system uses pre-trained ELMo word embeddings, followed by a bidirectional LSTM and an attention layer. We have submitted a 5-model ensemble of our best performing system to the NLP4IF shared task on sentence-level propaganda detection (team LIACC), achieving rank 10 among 25 participants, with 59.5 F1-score.",
}
@inproceedings{al-omari-etal-2019-justdeep,
    title = "{JUSTD}eep at {NLP}4{IF} 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models",
    author = "Al-Omari, Hani  and
      Abdullah, Malak  and
      AlTiti, Ola  and
      Shaikh, Samira",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5016",
    doi = "10.18653/v1/D19-5016",
    pages = "113--118",
    abstract = "The internet and the high use of social media have enabled the modern-day journalism to publish, share and spread news that is difficult to distinguish if it is true or fake. Defining {``}fake news{''} is not well established yet, however, it can be categorized under several labels: false, biased, or framed to mislead the readers that are characterized as propaganda. Digital content production technologies with logical fallacies and emotional language can be used as propaganda techniques to gain more readers or mislead the audience. Recently, several researchers have proposed deep learning (DL) models to address this issue. This research paper provides an ensemble deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda. The proposed model has been applied on the dataset provided by the challenge NLP4IF 2019, Task 1 Sentence Level Classification (SLC) and it shows a significant performance over the baseline model.",
}
@inproceedings{li-etal-2019-detection,
    title = "Detection of Propaganda Using Logistic Regression",
    author = "Li, Jinfen  and
      Ye, Zhihao  and
      Xiao, Lu",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5017",
    doi = "10.18653/v1/D19-5017",
    pages = "119--124",
    abstract = "Various propaganda techniques are used to manipulate peoples perspectives in order to foster a predetermined agenda such as by the use of logical fallacies or appealing to the emotions of the audience. In this paper, we develop a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. We utilize features like TF-IDF, BERT vector, sentence length, readability grade level, emotion feature, LIWC feature and emphatic content feature to help us differentiate these two categories. The linguistic and semantic features combination results in 66.16{\%} of F1 score, which outperforms the baseline hugely.",
}
@inproceedings{tayyar-madabushi-etal-2019-cost,
    title = "Cost-Sensitive {BERT} for Generalisable Sentence Classification on Imbalanced Data",
    author = "Tayyar Madabushi, Harish  and
      Kochkina, Elena  and
      Castelle, Michael",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5018",
    doi = "10.18653/v1/D19-5018",
    pages = "125--134",
    abstract = "The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprising. However, propaganda detection, like other tasks that deal with news documents and other forms of decontextualized social communication (e.g. sentiment analysis), inherently deals with data whose categories are simultaneously imbalanced and dissimilar. We show that BERT, while capable of handling imbalanced classes with no additional data augmentation, does not generalise well when the training and test data are sufficiently dissimilar (as is often the case with news sources, whose topics evolve over time). We show how to address this problem by providing a statistical measure of similarity between datasets and a method of incorporating cost-weighting into BERT when the training and test sets are dissimilar. We test these methods on the Propaganda Techniques Corpus (PTC) and achieve the second highest score on sentence-level propaganda classification.",
}
@inproceedings{hua-2019-understanding,
    title = "Understanding {BERT} performance in propaganda analysis",
    author = "Hua, Yiqing",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5019",
    doi = "10.18653/v1/D19-5019",
    pages = "135--138",
    abstract = "In this paper, we describe our system used in the shared task for fine-grained propaganda analysis at sentence level. Despite the challenging nature of the task, our pretrained BERT model (team YMJA) fine tuned on the training dataset provided by the shared task scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the contest. We present a set of illustrative experiments to better understand the performance of our BERT model on this shared task. Further, we explore beyond the given dataset for false-positive cases that likely to be produced by our system. We show that despite the high performance on the given testset, our system may have the tendency of classifying opinion pieces as propaganda and cannot distinguish quotations of propaganda speech from actual usage of propaganda techniques.",
}
@inproceedings{fadel-etal-2019-pretrained,
    title = "Pretrained Ensemble Learning for Fine-Grained Propaganda Detection",
    author = "Fadel, Ali  and
      Tuffaha, Ibraheem  and
      Al-Ayyoub, Mahmoud",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5020",
    doi = "10.18653/v1/D19-5020",
    pages = "139--142",
    abstract = "In this paper, we describe our team{'}s effort on the fine-grained propaganda detection on sentence level classification (SLC) task of NLP4IF 2019 workshop co-located with the EMNLP-IJCNLP 2019 conference. Our top performing system results come from applying ensemble average on three pretrained models to make their predictions. The first two models use the uncased and cased versions of Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) while the third model uses Universal Sentence Encoder (USE) (Cer et al. 2018). Out of 26 participating teams, our system is ranked in the first place with 68.8312 F1-score on the development dataset and in the sixth place with 61.3870 F1-score on the testing dataset.",
}
@inproceedings{aggarwal-sadana-2019-nsit,
    title = "{NSIT}@{NLP}4{IF}-2019: Propaganda Detection from News Articles using Transfer Learning",
    author = "Aggarwal, Kartik  and
      Sadana, Anubhav",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5021",
    doi = "10.18653/v1/D19-5021",
    pages = "143--147",
    abstract = "In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection. Given a sentence from a news article, the task is to detect whether the sentence contains a propagandistic agenda or not. The main contribution of our work is to evaluate the effectiveness of various transfer learning approaches like ELMo, BERT, and RoBERTa for propaganda detection. We show the use of Document Embeddings on the top of Stacked Embeddings combined with LSTM for identification of propagandistic context in the sentence. We further provide analysis of these models to show the effect of oversampling on the provided dataset. In the final test-set evaluation, our system ranked 21st with F1-score of 0.43 in the SLC Task.",
}
@inproceedings{vlad-etal-2019-sentence,
    title = "Sentence-Level Propaganda Detection in News Articles with Transfer Learning and {BERT}-{B}i{LSTM}-Capsule Model",
    author = "Vlad, George-Alexandru  and
      Tanase, Mircea-Adrian  and
      Onose, Cristian  and
      Cercel, Dumitru-Clementin",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5022",
    doi = "10.18653/v1/D19-5022",
    pages = "148--154",
    abstract = "In recent years, the need for communication increased in online social media. Propaganda is a mechanism which was used throughout history to influence public opinion and it is gaining a new dimension with the rising interest of online social media. This paper presents our submission to NLP4IF-2019 Shared Task SLC: Sentence-level Propaganda Detection in news articles. The challenge of this task is to build a robust binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Our model relies on a unified neural network, which consists of several deep leaning modules, namely BERT, BiLSTM and Capsule, to solve the sentencelevel propaganda classification problem. In addition, we take a pre-training approach on a somewhat similar task (i.e., emotion classification) improving results against the cold-start model. Among the 26 participant teams in the NLP4IF-2019 Task SLC, our solution ranked 12th with an F1-score 0.5868 on the official test data. Our proposed solution indicates promising results since our system significantly exceeds the baseline approach of the organizers by 0.1521 and is slightly lower than the winning system by 0.0454.",
}
@inproceedings{ek-ghanimifard-2019-synthetic,
    title = "Synthetic Propaganda Embeddings To Train A Linear Projection",
    author = "Ek, Adam  and
      Ghanimifard, Mehdi",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5023",
    doi = "10.18653/v1/D19-5023",
    pages = "155--161",
    abstract = "This paper presents a method of detecting fine-grained categories of propaganda in text. Given a sentence, our method aims to identify a span of words and predict the type of propaganda used. To detect propaganda, we explore a method for extracting features of propaganda from contextualized embeddings without fine-tuning the large parameters of the base model. We show that by generating synthetic embeddings we can train a linear function with ReLU activation to extract useful labeled embeddings from an embedding space generated by a general-purpose language model. We also introduce an inference technique to detect continuous spans in sequences of propaganda tokens in sentences. A result of the ensemble model is submitted to the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin. In this paper, we provide additional analysis regarding our method of detecting spans of propaganda with synthetically generated representations.",
}
@inproceedings{da-san-martino-etal-2019-findings,
    title = "Findings of the {NLP}4{IF}-2019 Shared Task on Fine-Grained Propaganda Detection",
    author = "Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-5024",
    doi = "10.18653/v1/D19-5024",
    pages = "162--170",
    abstract = "We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at http://propaganda.qcri.org/nlp4if-shared-task/.",
}
@proceedings{emnlp-2019-2019-empirical,
    title = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    editor = "Pad{\'o}, Sebastian  and
      Huang, Ruihong",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3000",
}
@inproceedings{pereg-etal-2019-absapp,
    title = "{ABSA}pp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System",
    author = "Pereg, Oren  and
      Korat, Daniel  and
      Wasserblat, Moshe  and
      Mamou, Jonathan  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3001",
    doi = "10.18653/v1/D19-3001",
    pages = "1--6",
    abstract = "We present ABSApp, a portable system for weakly-supervised aspect-based sentiment ex- traction. The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different domains in applied setups. The system flow includes three stages: First, it generates domain-specific aspect and opinion lexicons based on an unlabeled dataset; second, it enables the user to view and edit those lexicons (weak supervision); and finally, it enables the user to select an unlabeled target dataset from the same domain, classify it, and generate an aspect-based sentiment report. ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis.",
}
@inproceedings{wallace-etal-2019-allennlp,
    title = "{A}llen{NLP} Interpret: A Framework for Explaining Predictions of {NLP} Models",
    author = "Wallace, Eric  and
      Tuyls, Jens  and
      Wang, Junlin  and
      Subramanian, Sanjay  and
      Gardner, Matt  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3002",
    doi = "10.18653/v1/D19-3002",
    pages = "7--12",
    abstract = "Neural NLP models are increasingly accurate but are imperfect and opaque{---}they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation methods ameliorate this opacity by providing explanations for specific model predictions. Unfortunately, existing interpretation codebases make it difficult to apply these methods to new models and tasks, which hinders adoption for practitioners and burdens interpretability researchers. We introduce AllenNLP Interpret, a flexible framework for interpreting NLP models. The toolkit provides interpretation primitives (e.g., input gradients) for any AllenNLP model and task, a suite of built-in interpretation methods, and a library of front-end visualization components. We demonstrate the toolkit{'}s flexibility and utility by implementing live demos for five interpretation methods (e.g., saliency maps and adversarial attacks) on a variety of models and tasks (e.g., masked language modeling using BERT and reading comprehension using BiDAF). These demos, alongside our code and tutorials, are available at https://allennlp.org/interpret.",
}
@inproceedings{xu-etal-2019-alter,
    title = "{ALTER}: Auxiliary Text Rewriting Tool for Natural Language Generation",
    author = "Xu, Qiongkai  and
      Xu, Chenchen  and
      Qu, Lizhen",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3003",
    doi = "10.18653/v1/D19-3003",
    pages = "13--18",
    abstract = "In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer. Our tool is characterized by two features, i) recording of word-level revision histories and ii) flexible auxiliary edit support and feedback to annotators. The text rewriting assist and traceable rewriting history are potentially beneficial to the future research of natural language generation.",
}
@inproceedings{akkalyoncu-yilmaz-etal-2019-applying,
    title = "Applying {BERT} to Document Retrieval with Birch",
    author = "Akkalyoncu Yilmaz, Zeynep  and
      Wang, Shengjin  and
      Yang, Wei  and
      Zhang, Haotian  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3004",
    doi = "10.18653/v1/D19-3004",
    pages = "19--24",
    abstract = "We present Birch, a system that applies BERT to document retrieval via integration with the open-source Anserini information retrieval toolkit to demonstrate end-to-end search over large document collections. Birch implements simple ranking models that achieve state-of-the-art effectiveness on standard TREC newswire and social media test collections. This demonstration focuses on technical challenges in the integration of NLP and IR capabilities, along with the design rationale behind our approach to tightly-coupled integration between Python (to support neural networks) and the Java Virtual Machine (to support document retrieval using the open-source Lucene search library). We demonstrate integration of Birch with an existing search interface as well as interactive notebooks that highlight its capabilities in an easy-to-understand manner.",
}
@inproceedings{fauceglia-etal-2019-automatic,
    title = "Automatic Taxonomy Induction and Expansion",
    author = "Fauceglia, Nicolas Rodolfo  and
      Gliozzo, Alfio  and
      Dash, Sarthak  and
      Chowdhury, Md. Faisal Mahbub  and
      Mihindukulasooriya, Nandana",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3005",
    doi = "10.18653/v1/D19-3005",
    pages = "25--30",
    abstract = "The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system. One of its main capabilities is to automatically induce taxonomies from input documents using a hybrid approach that takes advantage of linguistic patterns, semantic web and neural networks. KGIS allows the user to semi-automatically curate and expand the induced taxonomy through a component called Smart SpreadSheet by exploiting distributional semantics. In this paper, we describe these taxonomy induction and expansion features of KGIS. A screencast video demonstrating the system is available in https://ibm.box.com/v/emnlp-2019-demo .",
}
@inproceedings{chakravarti-etal-2019-cfo,
    title = "{CFO}: A Framework for Building Production {NLP} Systems",
    author = "Chakravarti, Rishav  and
      Pendus, Cezar  and
      Sakrajda, Andrzej  and
      Ferritto, Anthony  and
      Pan, Lin  and
      Glass, Michael  and
      Castelli, Vittorio  and
      Murdock, J. William  and
      Florian, Radu  and
      Roukos, Salim  and
      Sil, Avi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3006",
    doi = "10.18653/v1/D19-3006",
    pages = "31--36",
    abstract = "This paper introduces a novel orchestration framework, called CFO (Computation Flow Orchestrator), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments. We then demonstrate a question answering system built using this framework which incorporates state-of-the-art BERT based MRC (Machine Reading Com- prehension) with IR components to enable end-to-end answer retrieval. Results from the demo system are shown to be high quality in both academic and industry domain specific settings. Finally, we discuss best practices when (pre-)training BERT based MRC models for production systems. Screencast links: - Short video ({\textless} 3 min): http: //ibm.biz/gaama{\_}demo - Supplementary long video ({\textless} 13 min): http://ibm.biz/gaama{\_}cfo{\_}demo",
}
@inproceedings{song-etal-2019-chameleon,
    title = "{C}hameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech",
    author = "Song, Yuanfeng  and
      Jiang, Di  and
      Zhao, Weiwei  and
      Xu, Qian  and
      Wong, Raymond Chi-Wing  and
      Yang, Qiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3007",
    doi = "10.18653/v1/D19-3007",
    pages = "37--42",
    abstract = "Language model is a vital component in modern automatic speech recognition (ASR) systems. Since {``}one-size-fits-all{''} language model works suboptimally for conversational speeches, language model adaptation (LMA) is considered as a promising solution for solving this problem. In order to compare the state-of-the-art LMA techniques and systematically demonstrate their effect in conversational speech recognition, we develop a novel toolkit named Chameleon, which includes the state-of-the-art cache-based and topic-based LMA techniques. This demonstration does not only vividly visualize underlying working mechanisms of a variety of the state-of-the-art LMA models but also provide an interface for the user to customize the hyperparameters of them. With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion. We wish this demonstration would inspire more research on better language model techniques for ASR.",
}
@inproceedings{shen-etal-2019-controlling,
    title = "Controlling Sequence-to-Sequence Models - A Demonstration on Neural-based Acrostic Generator",
    author = "Shen, Liang-Hsin  and
      Tai, Pei-Lun  and
      Wu, Chao-Chung  and
      Lin, Shou-De",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3008",
    doi = "10.18653/v1/D19-3008",
    pages = "43--48",
    abstract = "An acrostic is a form of writing that the first token of each line (or other recurring features in the text) forms a meaningful sequence. In this paper we present a generalized acrostic generation system that can hide certain message in a flexible pattern specified by the users. Different from previous works that focus on rule-based solutions, here we adopt a neural- based sequence-to-sequence model to achieve this goal. Besides acrostic, users are also allowed to specify the rhyme and length of the output sequences. Based on our knowledge, this is the first neural-based natural language generation system that demonstrates the capability of performing micro-level control over output sentences.",
}
@inproceedings{alva-manchego-etal-2019-easse,
    title = "{EASSE}: Easier Automatic Sentence Simplification Evaluation",
    author = "Alva-Manchego, Fernando  and
      Martin, Louis  and
      Scarton, Carolina  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3009",
    doi = "10.18653/v1/D19-3009",
    pages = "49--54",
    abstract = "We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems. EASSE provides a single access point to a broad range of evaluation resources: standard automatic metrics for assessing SS outputs (e.g. SARI), word-level accuracy scores for certain simplification transformations, reference-independent quality estimation features (e.g. compression ratio), and standard test data for SS evaluation (e.g. TurkCorpus). Finally, EASSE generates easy-to-visualise reports on the various metrics and features above and on how a particular SS output fares against reference simplifications. Through experiments, we show that these functionalities allow for better comparison and understanding of the performance of SS systems.",
}
@inproceedings{kharitonov-etal-2019-egg,
    title = "{EGG}: a toolkit for research on Emergence of lan{G}uage in Games",
    author = "Kharitonov, Eugene  and
      Chaabouni, Rahma  and
      Bouchacourt, Diane  and
      Baroni, Marco",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3010",
    doi = "10.18653/v1/D19-3010",
    pages = "55--60",
    abstract = "There is renewed interest in simulating language emergence among deep neural agents that communicate to jointly solve a task, spurred by the practical aim to develop language-enabled interactive AIs, as well as by theoretical questions about the evolution of human language. However, optimizing deep architectures connected by a discrete communication channel (such as that in which language emerges) is technically challenging. We introduce EGG, a toolkit that greatly simplifies the implementation of emergent-language communication games. EGG{'}s modular design provides a set of building blocks that the user can combine to create new games, easily navigating the optimization and architecture space. We hope that the tool will lower the technical barrier, and encourage researchers from various backgrounds to do original work in this exciting area.",
}
@inproceedings{raghuvanshi-etal-2019-entity,
    title = "Entity resolution for noisy {ASR} transcripts",
    author = "Raghuvanshi, Arushi  and
      Ramakrishnan, Vijay  and
      Embar, Varsha  and
      Carroll, Lucien  and
      Raghunathan, Karthik",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3011",
    doi = "10.18653/v1/D19-3011",
    pages = "61--66",
    abstract = "Large vocabulary domain-agnostic Automatic Speech Recognition (ASR) systems often mistranscribe domain-specific words and phrases. Since these generic ASR systems are the first component of most voice assistants in production, building Natural Language Understanding (NLU) systems that are robust to these errors can be a challenging task. In this paper, we focus on handling ASR errors in named entities, specifically person names, for a voice-based collaboration assistant. We demonstrate an effective method for resolving person names that are mistranscribed by black-box ASR systems, using character and phoneme-based information retrieval techniques and contextual information, which improves accuracy by 40.8{\%} on our production system. We provide a live interactive demo to further illustrate the nuances of this problem and the effectiveness of our solution.",
}
@inproceedings{an-etal-2019-eusp,
    title = "{EUSP}: An Easy-to-Use Semantic Parsing {P}lat{F}orm",
    author = "An, Bo  and
      Bo, Chen  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3012",
    doi = "10.18653/v1/D19-3012",
    pages = "67--72",
    abstract = "Semantic parsing aims to map natural language utterances into structured meaning representations. We present a modular platform, EUSP (Easy-to-Use Semantic Parsing PlatForm), that facilitates developers to build semantic parser from scratch. Instead of requiring a large amount of training data or complex grammar knowledge, in our platform developers can build grammar-based semantic parser or neural-based semantic parser through configure files which specify the modules and components that compose semantic parsing system. A high quality grammar-based semantic parsing system only requires domain lexicons rather than costly training data for a semantic parser. Furthermore, we provide a browser-based method to generate the semantic parsing system to minimize the difficulty of development. Experimental results show that the neural-based semantic parser system achieves competitive performance on semantic parsing task, and grammar-based semantic parsers significantly improve the performance of a business search engine.",
}
@inproceedings{pfeiffer-etal-2019-famulus,
    title = "{FAMULUS}: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning",
    author = "Pfeiffer, Jonas  and
      Meyer, Christian M.  and
      Schulz, Claudia  and
      Kiesewetter, Jan  and
      Zottmann, Jan  and
      Sailer, Michael  and
      Bauer, Elisabeth  and
      Fischer, Frank  and
      Fischer, Martin R.  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3013",
    doi = "10.18653/v1/D19-3013",
    pages = "73--78",
    abstract = "Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diagnosing is an exceptionally difficult skill to obtain but vital for many different professions (e.g., medical doctors, teachers). Previous case simulation systems are limited to multiple-choice questions and thus cannot give constructive individualized feedback on a student{'}s diagnostic reasoning process. Given initially only limited data, we leverage a (replaceable) NLP model to both support experts in their further data annotation with automatic suggestions, and we provide automatic feedback for students. We argue that because the central model consistently improves, our interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation. We show results from two user studies on diagnostic reasoning in medicine and teacher education and outline how our system can be extended to further use cases.",
}
@inproceedings{yu-etal-2019-gunrock,
    title = "{G}unrock: A Social Bot for Complex and Engaging Long Conversations",
    author = "Yu, Dian  and
      Cohn, Michelle  and
      Yang, Yi Mang  and
      Chen, Chun Yen  and
      Wen, Weiming  and
      Zhang, Jiaping  and
      Zhou, Mingyang  and
      Jesse, Kevin  and
      Chau, Austin  and
      Bhowmick, Antara  and
      Iyer, Shreenath  and
      Sreenivasulu, Giritheja  and
      Davidson, Sam  and
      Bhandare, Ashwin  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3014",
    doi = "10.18653/v1/D19-3014",
    pages = "79--84",
    abstract = "Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and related validation analysis. Overall, we found that users produce longer sentences to Gunrock, which are directly related to users{'} engagement (e.g., ratings, number of turns). Additionally, users{'} backstory queries about Gunrock are positively correlated to user satisfaction. Finally, we found dialog flows that interleave facts and personal opinions and stories lead to better user satisfaction.",
}
@inproceedings{newman-griffis-fosler-lussier-2019-hare,
    title = "{HARE}: a Flexible Highlighting Annotator for Ranking and Exploration",
    author = "Newman-Griffis, Denis  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3015",
    doi = "10.18653/v1/D19-3015",
    pages = "85--90",
    abstract = "Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools. Our system is available online at https://github.com/OSU-slatelab/HARE.",
}
@inproceedings{lee-etal-2019-honkling,
    title = "{H}onkling: In-Browser Personalization for Ubiquitous Keyword Spotting",
    author = "Lee, Jaejun  and
      Tang, Raphael  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3016",
    doi = "10.18653/v1/D19-3016",
    pages = "91--96",
    abstract = "Used for simple commands recognition on devices from smart speakers to mobile phones, keyword spotting systems are everywhere. Ubiquitous as well are web applications, which have grown in popularity and complexity over the last decade. However, despite their obvious advantages in natural language interaction, voice-enabled web applications are still few and far between. We attempt to bridge this gap with Honkling, a novel, JavaScript-based keyword spotting system. Purely client-side and cross-device compatible, Honkling can be deployed directly on user devices. Our in-browser implementation enables seamless personalization, which can greatly improve model quality; in the presence of underrepresented, non-American user accents, we can achieve up to an absolute 10{\%} increase in accuracy in the personalized model with only a few examples.",
}
@inproceedings{wang-etal-2019-iflylegal,
    title = "{IF}ly{L}egal: A {C}hinese Legal System for Consultation, Law Searching, and Document Analysis",
    author = "Wang, Ziyue  and
      Wang, Baoxin  and
      Duan, Xingyi  and
      Wu, Dayong  and
      Wang, Shijin  and
      Hu, Guoping  and
      Liu, Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3017",
    doi = "10.18653/v1/D19-3017",
    pages = "97--102",
    abstract = "Legal Tech is developed to help people with legal services and solve legal problems via machines. To achieve this, one of the key requirements for machines is to utilize legal knowledge and comprehend legal context. This can be fulfilled by natural language processing (NLP) techniques, for instance, text representation, text categorization, question answering (QA) and natural language inference, etc. To this end, we introduce a freely available Chinese Legal Tech system (IFlyLegal) that benefits from multiple NLP tasks. It is an integrated system that performs legal consulting, multi-way law searching, and legal document analysis by exploiting techniques such as deep contextual representations and various attention mechanisms. To our knowledge, IFlyLegal is the first Chinese legal system that employs up-to-date NLP techniques and caters for needs of different user groups, such as lawyers, judges, procurators, and clients. Since Jan, 2019, we have gathered 2,349 users and 28,238 page views (till June, 23, 2019).",
}
@inproceedings{santy-etal-2019-inmt,
    title = "{INMT}: Interactive Neural Machine Translation Prediction",
    author = "Santy, Sebastin  and
      Dandapat, Sandipan  and
      Choudhury, Monojit  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3018",
    doi = "10.18653/v1/D19-3018",
    pages = "103--108",
    abstract = "In this paper, we demonstrate an Interactive Machine Translation interface, that assists human translators with on-the-fly hints and suggestions. This makes the end-to-end translation process faster, more efficient and creates high-quality translations. We augment the OpenNMT backend with a mechanism to accept the user input and generate conditioned translations.",
}
@inproceedings{kreutzer-etal-2019-joey,
    title = "Joey {NMT}: A Minimalist {NMT} Toolkit for Novices",
    author = "Kreutzer, Julia  and
      Bastings, Jasmijn  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3019",
    doi = "10.18653/v1/D19-3019",
    pages = "109--114",
    abstract = "We present Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices. Joey NMT provides many popular NMT features in a small and simple code base, so that novices can easily and quickly learn to use it and adapt it to their needs. Despite its focus on simplicity, Joey NMT supports classic architectures (RNNs, transformers), fast beam search, weight tying, and more, and achieves performance comparable to more complex toolkits on standard benchmarks. We evaluate the accessibility of our toolkit in a user study where novices with general knowledge about Pytorch and NMT and experts work through a self-contained Joey NMT tutorial, showing that novices perform almost as well as experts in a subsequent code quiz. Joey NMT is available at \url{https://github.com/joeynmt/joeynmt}.",
}
@inproceedings{karmakharm-etal-2019-journalist,
    title = "Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis",
    author = "Karmakharm, Twin  and
      Aletras, Nikolaos  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3020",
    doi = "10.18653/v1/D19-3020",
    pages = "115--120",
    abstract = "Automatically identifying rumours in social media and assessing their veracity is an important task with downstream applications in journalism. A significant challenge is how to keep rumour analysis tools up-to-date as new information becomes available for particular rumours that spread in a social network. This paper presents a novel open-source web-based rumour analysis tool that can continuous learn from journalists. The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface. The feedback allows the system to improve an underlying state-of-the-art neural network-based rumour classification model. The system can be easily integrated as a service into existing tools and platforms used by journalists using a REST API.",
}
@inproceedings{collins-etal-2019-lida,
    title = "{LIDA}: Lightweight Interactive Dialogue Annotator",
    author = "Collins, Edward  and
      Rozanov, Nikolai  and
      Zhang, Bingbing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3021",
    doi = "10.18653/v1/D19-3021",
    pages = "121--126",
    abstract = "Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them.It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation. With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data. As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data. Furthermore it supports the integration of arbitrary machine learning mod-els as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing an-notations for a dataset. LIDA is fully open source, documented and publicly available.[https://github.com/Wluper/lida] {--}{\textgreater} Screen Cast: https://vimeo.com/329824847",
}
@inproceedings{eichler-etal-2019-linspector,
    title = "{LINSPECTOR} {WEB}: A Multilingual Probing Suite for Word Representations",
    author = {Eichler, Max  and
      {\c{S}}ahin, G{\"o}zde G{\"u}l  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3022",
    doi = "10.18653/v1/D19-3022",
    pages = "127--132",
    abstract = "We present LINSPECTOR WEB , an open source multilingual inspector to analyze word representations. Our system provides researchers working in low-resource settings with an easily accessible web based probing tool to gain quick insights into their word embeddings especially outside of the English language. To do this we employ 16 simple linguistic probing tasks such as gender, case marking, and tense for a diverse set of 28 languages. We support probing of static word embeddings along with pretrained AllenNLP models that are commonly used for NLP downstream tasks such as named entity recognition, natural language inference and dependency parsing. The results are visualized in a polar chart and also provided as a table. LINSPECTOR WEB is available as an offline tool or at https://linspector.ukp.informatik.tu-darmstadt.de.",
}
@inproceedings{jiang-etal-2019-massistant,
    title = "{MA}ssistant: A Personal Knowledge Assistant for {MOOC} Learners",
    author = "Jiang, Lan  and
      Hu, Shuhan  and
      Huang, Mingyu  and
      Wang, Zhichun  and
      Yang, Jinjian  and
      Ye, Xiaoju  and
      Zheng, Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3023",
    doi = "10.18653/v1/D19-3023",
    pages = "133--138",
    abstract = "Massive Open Online Courses (MOOCs) have developed rapidly and attracted large number of learners. In this work, we present MAssistant system, a personal knowledge assistant for MOOC learners. MAssistant helps users to trace the concepts they have learned in MOOCs, and to build their own concept graphs. There are three key components in MAssistant: (i) a large-scale concept graph built from open data sources, which contains concepts in various domains and relations among them; (ii) a browser extension which interacts with learners when they are watching video lectures, and presents important concepts to them; (iii) a web application allowing users to explore their personal concept graphs, which are built based on their learning activities on MOOCs. MAssistant will facilitate the knowledge management task for MOOC learners, and make the learning on MOOCs easier.",
}
@inproceedings{searle-etal-2019-medcattrainer,
    title = "{M}ed{CATT}rainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation",
    author = "Searle, Thomas  and
      Kraljevic, Zeljko  and
      Bendayan, Rebecca  and
      Bean, Daniel  and
      Dobson, Richard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3024",
    doi = "10.18653/v1/D19-3024",
    pages = "139--144",
    abstract = "An interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text, and the efficient collation of accurate research use case specific training data and subsequent model training. Screencast demo available here: https://www.youtube.com/watch?v=lM914DQjvSo",
}
@inproceedings{moon-etal-2019-memory-grounded,
    title = "Memory Grounded Conversational Reasoning",
    author = "Moon, Seungwhan  and
      Shah, Pararth  and
      Subba, Rajen  and
      Kumar, Anuj",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3025",
    doi = "10.18653/v1/D19-3025",
    pages = "145--150",
    abstract = "We demonstrate a conversational system which engages the user through a multi-modal, multi-turn dialog over the user{'}s memories. The system can perform QA over memories by responding to user queries to recall specific attributes and associated media (e.g. photos) of past episodic memories. The system can also make proactive suggestions to surface related events or facts from past memories to make conversations more engaging and natural. To implement such a system, we collect a new corpus of memory grounded conversations, which comprises human-to-human role-playing dialogs given synthetic memory graphs with simulated attributes. Our proof-of-concept system operates on these synthetic memory graphs, however it can be trained and applied to real-world user memory data (e.g. photo albums, etc.) We present the architecture of the proposed conversational system, and example queries that the system supports.",
}
@inproceedings{escolano-etal-2019-multilingual,
    title = "Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations",
    author = "Escolano, Carlos  and
      Costa-juss{\`a}, Marta R.  and
      Lacroux, Elora  and
      V{\'a}zquez, Pere-Pau",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3026",
    doi = "10.18653/v1/D19-3026",
    pages = "151--156",
    abstract = "The main alternatives nowadays to deal with sequences are Recurrent Neural Networks (RNN) architectures and the Transformer. In this context, Both RNN{'}s and Transformer have been used as an encoder-decoder architecture with multiple layers in each module. Far beyond this, these architectures are the basis for the contextual word embeddings which are revolutionizing most natural language downstream applications. However, intermediate representations in either the RNN or Transformer architectures can be difficult to interpret. To make these layer representations more accessible and meaningful, we introduce a web-based tool that visualizes them both at the sentence and token level. We present three use cases. The first analyses gender issues in contextual word embeddings. The second and third are showing multilingual intermediate representations for sentences and tokens and the evolution of these intermediate representations along with the multiple layers of the decoder and in the context of multilingual machine translation.",
}
@inproceedings{ding-etal-2019-akkhara,
    title = "{MY}-{AKKHARA}: A {R}omanization-based {B}urmese ({M}yanmar) Input Method",
    author = "Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3027",
    doi = "10.18653/v1/D19-3027",
    pages = "157--162",
    abstract = "MY-AKKHARA is a method used to input Burmese texts encoded in the Unicode standard, based on commonly accepted Latin transcription. By using this method, arbitrary Burmese strings can be accurately inputted with 26 lowercase Latin letters. Meanwhile, the 26 uppercase Latin letters are designed as shortcuts of lowercase letter sequences. The frequency of Burmese characters is considered in MY-AKKHARA to realize an efficient keystroke distribution on a QWERTY keyboard. Given that the Unicode standard has not been extensively used in digitization of Burmese, we hope that MY-AKKHARA can contribute to the widespread use of Unicode in Myanmar and can provide a platform for smart input methods for Burmese in the future. An implementation of MY-AKKHARA running in Windows is released at http://www2.nict.go.jp/astrec-att/member/ding/my-akkhara.html",
}
@inproceedings{gong-etal-2019-neuronblocks,
    title = "{N}euron{B}locks: Building Your {NLP} {DNN} Models Like Playing {L}ego",
    author = "Gong, Ming  and
      Shou, Linjun  and
      Lin, Wutao  and
      Sang, Zhijie  and
      Yan, Quanjia  and
      Yang, Ze  and
      Cheng, Feixiang  and
      Jiang, Daxin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3028",
    doi = "10.18653/v1/D19-3028",
    pages = "163--168",
    abstract = "Deep Neural Networks (DNN) have been widely employed in industry to address various Natural Language Processing (NLP) tasks. However, many engineers find it a big overhead when they have to choose from multiple frameworks, compare different types of models, and understand various optimization mechanisms. An NLP toolkit for DNN models with both generality and flexibility can greatly improve the productivity of engineers by saving their learning cost and guiding them to find optimal solutions to their tasks. In this paper, we introduce NeuronBlocks, a toolkit encapsulating a suite of neural network modules as building blocks to construct various DNN models with complex architecture. This toolkit empowers engineers to build, train, and test various NLP models through simple configuration of JSON files. The experiments on several NLP datasets such as GLUE, WikiQA and CoNLL-2003 demonstrate the effectiveness of NeuronBlocks. Code: https://github.com/Microsoft/NeuronBlocks Demo: https://youtu.be/x6cOpVSZcdo",
}
@inproceedings{han-etal-2019-opennre,
    title = "{O}pen{NRE}: An Open and Extensible Toolkit for Neural Relation Extraction",
    author = "Han, Xu  and
      Gao, Tianyu  and
      Yao, Yuan  and
      Ye, Deming  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3029",
    doi = "10.18653/v1/D19-3029",
    pages = "169--174",
    abstract = "OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE methods, OpenNRE not only allows developers to train custom models to extract structured relational facts from the plain text but also supports quick model validation for researchers. Besides, OpenNRE provides various functional RE modules based on both TensorFlow and PyTorch to maintain sufficient modularity and extensibility, making it becomes easy to incorporate new models into the framework. Besides the toolkit, we also release an online system to meet real-time extraction without any training and deploying. Meanwhile, the online system can extract facts in various scenarios as well as aligning the extracted facts to Wikidata, which may benefit various downstream knowledge-driven applications (e.g., information retrieval and question answering). More details of the toolkit and online system can be obtained from http://github.com/thunlp/OpenNRE.",
}
@inproceedings{kumar-etal-2019-paraqg,
    title = "{P}ara{QG}: A System for Generating Questions and Answers from Paragraphs",
    author = "Kumar, Vishwajeet  and
      Muneeswaran, Sivaanandh  and
      Ramakrishnan, Ganesh  and
      Li, Yuan-Fang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3030",
    doi = "10.18653/v1/D19-3030",
    pages = "175--180",
    abstract = "Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications. Manual generation is a labour-intensive task, as it requires the reading, parsing and understanding of long passages of text. A number of question generation models based on sequence-to-sequence techniques have recently been proposed. Most of them generate questions from sentences only, and none of them is publicly available as an easy-to-use service. In this paper, we demonstrate ParaQG, a Web-based system for generating questions from sentences and paragraphs. ParaQG incorporates a number of novel functionalities to make the question generation process user-friendly. It provides an interactive interface for a user to select answers with visual insights on generation of questions. It also employs various faceted views to group similar questions as well as filtering techniques to eliminate unanswerable questions.",
}
@inproceedings{henderson-etal-2019-polyresponse,
    title = "{P}oly{R}esponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking",
    author = "Henderson, Matthew  and
      Vuli{\'c}, Ivan  and
      Casanueva, I{\~n}igo  and
      Budzianowski, Pawe{\l}  and
      Gerz, Daniela  and
      Coope, Sam  and
      Spithourakis, Georgios  and
      Wen, Tsung-Hsien  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Su, Pei-Hao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3031",
    doi = "10.18653/v1/D19-3031",
    pages = "181--186",
    abstract = "We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit semantics in the form of task-specific ontologies. The PolyResponse engine is trained on hundreds of millions of examples extracted from real conversations: it learns what responses are appropriate in different conversational contexts. It then ranks a large index of text and visual responses according to their similarity to the given context, and narrows down the list of relevant entities during the multi-turn conversation. We introduce a restaurant search and booking system powered by the PolyResponse engine, currently available in 8 different languages.",
}
@inproceedings{jang-etal-2019-pyopendial,
    title = "{P}y{O}pen{D}ial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules",
    author = "Jang, Youngsoo  and
      Lee, Jongmin  and
      Park, Jaeyoung  and
      Lee, Kyeng-Hun  and
      Lison, Pierre  and
      Kim, Kee-Eung",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3032",
    doi = "10.18653/v1/D19-3032",
    pages = "187--192",
    abstract = "We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems. Recent advances in core components of dialogue systems, such as speech recognition, language understanding, dialogue management, and language generation, harness deep learning to achieve state-of-the-art performance. The original OpenDial, implemented in Java, provides a plugin architecture to integrate external modules, but lacks Python bindings, making it difficult to interface with popular deep learning frameworks such as Tensorflow or PyTorch. To this end, we re-implemented OpenDial in Python and extended the toolkit with a number of novel functionalities for neural dialogue state tracking and action planning. We describe the overall architecture and its extensions, and illustrate their use on an example where the system response model is implemented with a recurrent neural network.",
}
@inproceedings{stewart-etal-2019-redcoat,
    title = "{R}edcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing",
    author = "Stewart, Michael  and
      Liu, Wei  and
      Cardell-Oliver, Rachel",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3033",
    doi = "10.18653/v1/D19-3033",
    pages = "193--198",
    abstract = "We introduce Redcoat, a web-based annotation tool that supports collaborative hierarchical entity typing. As an annotation tool, Redcoat also facilitates knowledge elicitation by allowing the creation and continuous refinement of concept hierarchies during annotation. It aims to minimise not only annotation time but the time it takes for project creators to set up and distribute projects to annotators. Projects created using the web-based interface can be rapidly distributed to a list of email addresses. Redcoat handles the propagation of documents amongst annotators and automatically scales the annotation workload depending on the number of active annotators. In this paper we discuss these key features and outline Redcoat{'}s system architecture. We also highlight Redcoat{'}s unique benefits over existing annotation tools via a qualitative comparison.",
}
@inproceedings{schmidt-etal-2019-seagle,
    title = "{SEAGLE}: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval",
    author = "Schmidt, Fabian David  and
      Dietsche, Markus  and
      Ponzetto, Simone Paolo  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3034",
    doi = "10.18653/v1/D19-3034",
    pages = "199--204",
    abstract = "We introduce Seagle, a platform for comparative evaluation of semantic text encoding models on information retrieval (IR) tasks. Seagle implements (1) word embedding aggregators, which represent texts as algebraic aggregations of pretrained word embeddings and (2) pretrained semantic encoders, and allows for their comparative evaluation on arbitrary (monolingual and cross-lingual) IR collections. We benchmark Seagle{'}s models on monolingual document retrieval and cross-lingual sentence retrieval. Seagle functionality can be exploited via an easy-to-use web interface and its modular backend (micro-service architecture) can easily be extended with additional semantic search models.",
}
@inproceedings{bolt-etal-2019-stylometry,
    title = "A Stylometry Toolkit for {L}atin Literature",
    author = "Bolt, Thomas J.  and
      Flynt, Jeffrey H.  and
      Chaudhuri, Pramit  and
      Dexter, Joseph P.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3035",
    doi = "10.18653/v1/D19-3035",
    pages = "205--210",
    abstract = "Computational stylometry has become an increasingly important aspect of literary criticism, but many humanists lack the technical expertise or language-specific NLP resources required to exploit computational methods. We demonstrate a stylometry toolkit for analysis of Latin literary texts, which is freely available at www.qcrit.org/stylometry. Our toolkit generates data for a diverse range of literary features and has an intuitive point-and-click interface. The features included have proven effective for multiple literary studies and are calculated using custom heuristics without the need for syntactic parsing. As such, the toolkit models one approach to the user-friendly generation of stylometric data, which could be extended to other premodern and non-English languages underserved by standard NLP resources.",
}
@inproceedings{erera-etal-2019-summarization,
    title = "A Summarization System for Scientific Documents",
    author = "Erera, Shai  and
      Shmueli-Scheuer, Michal  and
      Feigenblat, Guy  and
      Peled Nakash, Ora  and
      Boni, Odellia  and
      Roitman, Haggai  and
      Cohen, Doron  and
      Weiner, Bar  and
      Mass, Yosi  and
      Rivlin, Or  and
      Lev, Guy  and
      Jerbi, Achiya  and
      Herzig, Jonathan  and
      Hou, Yufang  and
      Jochim, Charles  and
      Gleize, Martin  and
      Bonin, Francesca  and
      Bonin, Francesca  and
      Konopnicki, David",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3036",
    doi = "10.18653/v1/D19-3036",
    pages = "211--216",
    abstract = "We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.",
}
@inproceedings{mubarak-etal-2019-system,
    title = "A System for Diacritizing Four Varieties of {A}rabic",
    author = "Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Darwish, Kareem  and
      Eldesouki, Mohamed  and
      Samih, Younes  and
      Sajjad, Hassan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3037",
    doi = "10.18653/v1/D19-3037",
    pages = "217--222",
    abstract = "Short vowels, aka diacritics, are more often omitted when writing different varieties of Arabic including Modern Standard Arabic (MSA), Classical Arabic (CA), and Dialectal Arabic (DA). However, diacritics are required to properly pronounce words, which makes diacritic restoration (a.k.a. diacritization) essential for language learning and text-to-speech applications. In this paper, we present a system for diacritizing MSA, CA, and two varieties of DA, namely Moroccan and Tunisian. The system uses a character level sequence-to-sequence deep learning model that requires no feature engineering and beats all previous SOTA systems for all the Arabic varieties that we test on.",
}
@inproceedings{zhang-etal-2019-tanbih,
    title = "{T}anbih: Get To Know What You Are Reading",
    author = "Zhang, Yifan  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Romeo, Salvatore  and
      An, Jisun  and
      Kwak, Haewoon  and
      Staykovski, Todor  and
      Jaradat, Israa  and
      Karadzhov, Georgi  and
      Baly, Ramy  and
      Darwish, Kareem  and
      Glass, James  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3038",
    doi = "10.18653/v1/D19-3038",
    pages = "223--228",
    abstract = "We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what{'}s behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.",
}
@inproceedings{hagiwara-etal-2019-teaspn,
    title = "{TEASPN}: Framework and Protocol for Integrated Writing Assistance Environments",
    author = "Hagiwara, Masato  and
      Ito, Takumi  and
      Kuribayashi, Tatsuki  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3039",
    doi = "10.18653/v1/D19-3039",
    pages = "229--234",
    abstract = "Language technologies play a key role in assisting people with their writing. Although there has been steady progress in e.g., grammatical error correction (GEC), human writers are yet to benefit from this progress due to the high development cost of integrating with writing software. We propose TEASPN, a protocol and an open-source framework for achieving integrated writing assistance environments. The protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost. As a result, users can enjoy the integrated experience in their favorite writing software. The results from experiments with human participants show that users use a wide range of technologies and rate their writing experience favorably, allowing them to write more fluent text.",
}
@inproceedings{lai-chang-2019-tellmewhy,
    title = "{T}ell{M}e{W}hy: Learning to Explain Corrective Feedback for Second Language Learners",
    author = "Lai, Yi-Huei  and
      Chang, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3040",
    doi = "10.18653/v1/D19-3040",
    pages = "235--240",
    abstract = "We present a writing prototype feedback system, TellMeWhy, to provide explanations of errors in submitted essays. In our approach, the sentence with corrections is analyzed to identify error types and problem words, aimed at customizing explanations based on the context of the error. The method involves learning the relation of errors and problem words, generating common feedback patterns, and extracting grammar patterns, collocations and example sentences. At run-time, a sentence with corrections is classified, and the problem word and template are identified to provide detailed explanations. Preliminary evaluation shows that the method has potential to improve existing commercial writing services.",
}
@inproceedings{zhao-etal-2019-uer,
    title = "{UER}: An Open-Source Toolkit for Pre-training Models",
    author = "Zhao, Zhe  and
      Chen, Hui  and
      Zhang, Jinbin  and
      Zhao, Xin  and
      Liu, Tao  and
      Lu, Wei  and
      Chen, Xi  and
      Deng, Haotang  and
      Ju, Qi  and
      Du, Xiaoyong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3041",
    doi = "10.18653/v1/D19-3041",
    pages = "241--246",
    abstract = "Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training model that remains unexplored. With UER, we have built a model zoo, which contains pre-trained models based on different corpora, encoders, and targets (objectives). With proper pre-trained models, we could achieve new state-of-the-art results on a range of downstream datasets.",
}
@inproceedings{xia-etal-2019-visualizing,
    title = "Visualizing Trends of Key Roles in News Articles",
    author = "Xia, Chen  and
      Zhang, Haoxiang  and
      Moghtader, Jacob  and
      Wu, Allen  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3042",
    doi = "10.18653/v1/D19-3042",
    pages = "247--252",
    abstract = "There are tons of news generated every day reflecting the change of key roles such as people, organizations and political parties. Analyzing the trend of these key roles can help understand the information flow in a more effective way. In this paper, we present a demonstration system that visualizes the news trend of key roles based on natural language processing techniques. Specifically, we apply semantic role labelling to understand relationships between key roles in the news. We also train a dynamic word embedding model to align representations of words in different time periods to measure how the similarities between a key role and news topics change over time. Note: The github link to our demo jupyter notebook and screencast video is https://github.com/kasinxc/Visualizing-Trend-of-Key-Roles-in-News-Articles",
}
@inproceedings{wang-etal-2019-vizseq,
    title = "{V}iz{S}eq: a visual analysis toolkit for text generation tasks",
    author = "Wang, Changhan  and
      Jain, Anirudh  and
      Chen, Danlu  and
      Gu, Jiatao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3043",
    doi = "10.18653/v1/D19-3043",
    pages = "253--258",
    abstract = "Automatic evaluation of text generation tasks (e.g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE. They, however, are abstract numbers and are not perfectly aligned with human assessment. This suggests inspecting detailed examples as a complement to identify system error patterns. In this paper, we present VizSeq, a visual analysis toolkit for instance-level and corpus-level system evaluation on a wide variety of text generation tasks. It supports multimodal sources and multiple text references, providing visualization in Jupyter notebook or a web app interface. It can be used locally or deployed onto public servers for centralized data hosting and benchmarking. It covers most common n-gram based metrics accelerated with multiprocessing, and also provides latest embedding-based metrics such as BERTScore.",
}
@inproceedings{tsarfaty-etal-2019-whats,
    title = "What{'}s Wrong with {H}ebrew {NLP}? And How to Make it Right",
    author = "Tsarfaty, Reut  and
      Sadde, Shoval  and
      Klein, Stav  and
      Seker, Amit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-3044",
    doi = "10.18653/v1/D19-3044",
    pages = "259--264",
    abstract = "For languages with simple morphology such as English, automatic annotation pipelines such as spaCy or Stanford{'}s CoreNLP successfully serve projects in academia and the industry. For many morphologically-rich languages (MRLs), similar pipelines show sub-optimal performance that limits their applicability for text analysis in research and the industry. The sub-optimal performance is mainly due to errors in early morphological disambiguation decisions, that cannot be recovered later on in the pipeline, yielding incoherent annotations on the whole. This paper describes the design and use of the ONLP suite, a joint morpho-syntactic infrastructure for processing Modern Hebrew texts. The joint inference over morphology and syntax substantially limits error propagation, and leads to high accuracy. ONLP provides rich and expressive annotations which already serve diverse academic and commercial needs. Its accompanying demo further serves educational activities, introducing Hebrew NLP intricacies to researchers and non-researchers alike.",
}
@proceedings{emnlp-2019-,
    title = "",
    editor = "Baldwin, Timothy  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
}
@inproceedings{lin-etal-2019-dive,
    title = "Dive into Deep Learning for Natural Language Processing",
    author = "Lin, Haibin  and
      Shi, Xingjian  and
      Lausen, Leonard  and
      Zhang, Aston  and
      He, He  and
      Zha, Sheng  and
      Smola, Alexander",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-2001",
    abstract = "Deep learning has become the dominant approach to NLP problems, especially when applied on large scale corpora. Recent progress on unsupervised pre-training techniques such as BERT, ELMo, GPT-2, and language modeling in general, when applied on large corpora, is shown to be effective in improving a wide variety of downstream tasks. These techniques push the limits of available hardware, requiring specialized frameworks optimized for GPU, ASIC, and distributed cloud-based training.A few complexities pose challenges to scale these models and algorithms effectively. Compared to other areas where deep learning is applied, these NLP models contain a variety of moving parts: text normalization and tokenization, word representation at subword-level and word-level, variable-length models such as RNN and attention, and sequential decoder based on beam search, among others.In this hands-on tutorial, we take a closer look at the challenges from these complexities and see how with proper tooling with Apache MXNet and GluonNLP, we can overcome these challenges and achieve state-of-the-art results for real-world problems. GluonNLP is a powerful new toolkit that combines MXNet{'}s speed, the flexibility of Gluon, and an extensive new library automating the most laborious aspects of deep learning for NLP.",
}
@inproceedings{choudhury-etal-2019-processing,
    title = "Processing and Understanding Mixed Language Data",
    author = "Choudhury, Monojit  and
      Srinivasan, Anirudh  and
      Dandapat, Sandipan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-2002",
    abstract = "Multilingual communities exhibit code-mixing, that is, mixing of two or more socially stable languages in a single conversation, sometimes even in a single utterance. This phenomenon has been widely studied by linguists and interaction scientists in the spoken language of such communities. However, with the prevalence of social media and other informal interactive platforms, code-switching is now also ubiquitously observed in user-generated text. As multilingual communities are more the norm from a global perspective, it becomes essential that code-switched text and speech are adequately handled by language technologies and NUIs.Code-mixing is extremely prevalent in all multilingual societies. Current studies have shown that as much as 20{\%} of user generated content from some geographies, like South Asia, parts of Europe, and Singapore, are code-mixed. Thus, it is very important to handle code-mixed content as a part of NLP systems and applications for these geographies.In the past 5 years, there has been an active interest in computational models for code-mixing with a substantive research outcome in terms of publications, datasets and systems. However, it is not easy to find a single point of access for a complete and coherent overview of the research. This tutorial is expecting to fill this gap and provide new researchers in the area with a foundation in both linguistic and computational aspects of code-mixing. We hope that this then becomes a starting point for those who wish to pursue research, design, development and deployment of code-mixed systems in multilingual societies.",
}
@inproceedings{wen-etal-2019-data,
    title = "Data Collection and End-to-End Learning for Conversational {AI}",
    author = "Wen, Tsung-Hsien  and
      Su, Pei-Hao  and
      Budzianowski, Pawe{\l}  and
      Casanueva, I{\~n}igo  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-2003",
    abstract = "A fundamental long-term goal of conversational AI is to merge two main dialogue system paradigms into a standalone multi-purpose system. Such a system should be capable of conversing about arbitrary topics (Paradigm 1: open-domain dialogue systems), and simultaneously assist humans with completing a wide range of tasks with well-defined semantics such as restaurant search and booking, customer service applications, or ticket bookings (Paradigm 2: task-based dialogue systems).The recent developmental leaps in conversational AI technology are undoubtedly linked to more and more sophisticated deep learning algorithms that capture patterns in increasing amounts of data generated by various data collection mechanisms. The goal of this tutorial is therefore twofold. First, it aims at familiarising the research community with the recent advances in algorithmic design of statistical dialogue systems for both open-domain and task-based dialogue paradigms. The focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. In theory, learning end-to-end from data offers seamless and unprecedented portability of dialogue systems to a wide spectrum of tasks and languages. From a practical point of view, there are still plenty of research challenges and opportunities remaining: in this tutorial we analyse this gap between theory and practice, and introduce the research community with the main advantages as well as with key practical limitations of current end-to-end dialogue learning.The critical requirement of each statistical dialogue system is the data at hand. The system cannot provide assistance for the task without having appropriate task-related data to learn from. Therefore, the second major goal of this tutorial is to provide a comprehensive overview of the current approaches to data collection for dialogue, and analyse the current gaps and challenges with diverse data collection protocols, as well as their relation to and current limitations of data-driven end-to-end dialogue modeling. We will again analyse this relation and limitations both from research and industry perspective, and provide key insights on the application of state-of-the-art methodology into industry-scale conversational AI systems.",
}
@inproceedings{chang-etal-2019-bias,
    title = "Bias and Fairness in Natural Language Processing",
    author = "Chang, Kai-Wei  and
      Prabhakaran, Vinod  and
      Ordonez, Vicente",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-2004",
    abstract = "Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics and diagnostic information from large amounts of training data. Despite the remarkable accuracy of machine learning in various applications, learning algorithms run the risk of relying on societal biases encoded in the training data to make predictions. This often occurs even when gender and ethnicity information is not explicitly provided to the system because learning algorithms are able to discover implicit associations between individuals and their demographic information based on other variables such as names, titles, home addresses, etc. Therefore, machine learning algorithms risk potentially encouraging unfair and discriminatory decision making and raise serious privacy concerns. Without properly quantifying and reducing the reliance on such correlations, broad adoption of these models might have the undesirable effect of magnifying harmful stereotypes or implicit biases that rely on sensitive demographic attributes.In this tutorial, we will review the history of bias and fairness studies in machine learning and language processing and present recent community effort in quantifying and mitigating bias in natural language processing models for a wide spectrum of tasks, including word embeddings, co-reference resolution, machine translation, and vision-and-language tasks. In particular, we will focus on the following topics:+ Definitions of fairness and bias.+ Data, algorithms, and models that propagate and even amplify social bias to NLP applications and metrics to quantify these biases.+ Algorithmic solutions; learning objective; design principles to prevent social bias in NLP systems and their potential drawbacks.The tutorial will bring researchers and practitioners to be aware of this issue, and encourage the research community to propose innovative solutions to promote fairness in NLP.",
}
@inproceedings{mou-etal-2019-discreteness,
    title = "Discreteness in Neural Natural Language Processing",
    author = "Mou, Lili  and
      Zhou, Hao  and
      Li, Lei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-2005",
    abstract = "This tutorial provides a comprehensive guide to the process of discreteness in neural NLP.As a gentle start, we will briefly introduce the background of deep learning based NLP, where we point out the ubiquitous discreteness of natural language and its challenges in neural information processing. Particularly, we will focus on how such discreteness plays a role in the input space, the latent space, and the output space of a neural network. In each part, we will provide examples, discuss machine learning techniques, as well as demonstrate NLP applications.",
}
@inproceedings{vashishth-etal-2019-graph,
    title = "Graph-based Deep Learning in Natural Language Processing",
    author = "Vashishth, Shikhar  and
      Yadati, Naganand  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-2006",
    abstract = "This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.",
}
@inproceedings{glavas-etal-2019-semantic,
    title = "Semantic Specialization of Distributional Word Vectors",
    author = "Glava{\'s}, Goran  and
      Maria Ponti, Edoardo  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-2007",
    abstract = "Distributional word vectors have become an indispensable component of most state-of-art NLP models. As a major artefact of the underlying distributional hypothesis, distributional word vector spaces conflate various paradigmatic and syntagmatic lexico-semantic relations. For example, relations such as synonymy/similarity (e.g., car-automobile) or lexical entailment (e.g., car-vehicle) often cannot be distinguished from antonymy (e.g., black-white), meronymy (e.g., car-wheel) or broader thematic relatedness (e.g., car-driver) based on the distances in the distributional vector space. This inherent property of distributional spaces often harms performance in downstream applications, since different lexico-semantic relations support different classes of NLP applications. For instance, Semantic Similarity provides guidance for Paraphrasing, Dialogue State Tracking, and Text Simplification, Lexical Entailment supports Natural Language Inference and Taxonomy Induction, whereas broader thematic relatedness yields gains for Named Entity Recognition, Parsing, and Text Classification and Retrieval.A plethora of methods have been proposed to emphasize specific lexico-semantic relations in a reshaped (i.e., specialized) vector space. A common solution is to move beyond purely unsupervised word representation learning and include external lexico-semantic knowledge, in a process commonly referred to as semantic specialization. In this tutorial, we provide a thorough overview of specialization methods, covering: 1) joint specialization methods, which augment distributional learning objectives with external linguistic constraints, 2) post-processing retrofitting models, which fine-tune pre-trained distributional vectors to better reflect external linguistic constraints, and 3) the most recently proposed post-specialization methods that generalize the perturbations of the post-processing methods to the whole distributional space. In addition to providing a comprehensive overview of specialization methods, we will introduce the most recent developments, such as (among others): handling asymmetric relations (e.g., hypernymy-hyponymy) in Euclidean and hyperbolic spaces by accounting for vector magnitude as well as for vector distance; cross-lingual transfer of semantic specialization for languages without external lexico-semantic resources; downstream effects of specializing distributional vector spaces; injecting external knowledge into unsupervised pretraining architectures such as ELMo or BERT.",
}
@proceedings{emnlp-2019-2019,
    title = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1000",
}
@inproceedings{lawrence-etal-2019-attending,
    title = "Attending to Future Tokens for Bidirectional Sequence Generation",
    author = "Lawrence, Carolin  and
      Kotnis, Bhushan  and
      Niepert, Mathias",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1001",
    doi = "10.18653/v1/D19-1001",
    pages = "1--10",
    abstract = "Neural sequence generation is typically performed token-by-token and left-to-right. Whenever a token is generated only previously produced tokens are taken into consideration. In contrast, for problems such as sequence classification, bidirectional attention, which takes both past and future tokens into consideration, has been shown to perform much better. We propose to make the sequence generation process bidirectional by employing special placeholder tokens. Treated as a node in a fully connected graph, a placeholder token can take past and future tokens into consideration when generating the actual output token. We verify the effectiveness of our approach experimentally on two conversational tasks where the proposed bidirectional model outperforms competitive baselines by a large margin.",
}
@inproceedings{wiegreffe-pinter-2019-attention,
    title = "Attention is not not Explanation",
    author = "Wiegreffe, Sarah  and
      Pinter, Yuval",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1002",
    doi = "10.18653/v1/D19-1002",
    pages = "11--20",
    abstract = "Attention mechanisms play a central role in NLP systems, especially within recurrent neural network (RNN) models. Recently, there has been increasing interest in whether or not the intermediate representations offered by these modules may be used to explain the reasoning for a model{'}s prediction, and consequently reach insights regarding the model{'}s decision-making process. A recent paper claims that {`}Attention is not Explanation{'} (Jain and Wallace, 2019). We challenge many of the assumptions underlying this work, arguing that such a claim depends on one{'}s definition of explanation, and that testing it needs to take into account all elements of the model. We propose four alternative tests to determine when/whether attention can be used as explanation: a simple uniform-weights baseline; a variance calibration based on multiple random seed runs; a diagnostic framework using frozen weights from pretrained models; and an end-to-end adversarial attention training protocol. Each allows for meaningful interpretation of attention mechanisms in RNN models. We show that even when reliable adversarial distributions can be found, they don{'}t perform well on the simple diagnostic, indicating that prior work does not disprove the usefulness of attention mechanisms for explainability.",
}
@inproceedings{lowell-etal-2019-practical,
    title = "Practical Obstacles to Deploying Active Learning",
    author = "Lowell, David  and
      Lipton, Zachary C.  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1003",
    doi = "10.18653/v1/D19-1003",
    pages = "21--30",
    abstract = "Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL, one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to better performance than would be achieved under independent and identically distributed (i.i.d.) random samples. While AL has shown promise in retrospective evaluations, these studies often ignore practical obstacles to its use. In this paper, we show that while AL may provide benefits when used with specific models and for particular domains, the benefits of current approaches do not generalize reliably across models and tasks. This is problematic because in practice, one does not have the opportunity to explore and compare alternative AL strategies. Moreover, AL couples the training dataset with the model used to guide its acquisition. We find that subsequently training a successor model with an actively-acquired dataset does not consistently outperform training on i.i.d. sampled data. Our findings raise the question of whether the downsides inherent to AL are worth the modest and inconsistent performance gains it tends to afford.",
}
@inproceedings{ben-noach-goldberg-2019-transfer,
    title = "Transfer Learning Between Related Tasks Using Expected Label Proportions",
    author = "Ben Noach, Matan  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1004",
    doi = "10.18653/v1/D19-1004",
    pages = "31--42",
    abstract = "Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We propose a novel application of the XR framework for transfer learning between related tasks, where knowing the labels of task A provides an estimation of the label proportion of task B. We then use a model trained for A to label a large corpus, and use this corpus with an XR loss to train a model for task B. To make the XR framework applicable to large-scale deep-learning setups, we propose a stochastic batched approximation procedure. We demonstrate the approach on the task of Aspect-based Sentiment classification, where we effectively use a sentence-level sentiment predictor to train accurate aspect-based predictor. The method improves upon fully supervised neural system trained on aspect-level data, and is also cumulative with LM-based pretraining, as we demonstrate by improving a BERT-based Aspect-based Sentiment model.",
}
@inproceedings{peters-etal-2019-knowledge,
    title = "Knowledge Enhanced Contextual Word Representations",
    author = "Peters, Matthew E.  and
      Neumann, Mark  and
      Logan, Robert  and
      Schwartz, Roy  and
      Joshi, Vidur  and
      Singh, Sameer  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1005",
    doi = "10.18653/v1/D19-1005",
    pages = "43--54",
    abstract = "Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple knowledge bases (KBs) into large scale models, and thereby enhance their representations with structured, human-curated knowledge. For each KB, we first use an integrated entity linker to retrieve relevant entity embeddings, then update contextual word representations via a form of word-to-entity attention. In contrast to previous approaches, the entity linkers and self-supervised language modeling objective are jointly trained end-to-end in a multitask setting that combines a small amount of entity linking supervision with a large amount of raw text. After integrating WordNet and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert) demonstrates improved perplexity, ability to recall facts as measured in a probing task and downstream performance on relationship extraction, entity typing, and word sense disambiguation. KnowBert{'}s runtime is comparable to BERT{'}s and it scales to large KBs.",
}
@inproceedings{ethayarajh-2019-contextual,
    title = "How Contextual are Contextualized Word Representations? Comparing the Geometry of {BERT}, {ELM}o, and {GPT}-2 Embeddings",
    author = "Ethayarajh, Kawin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1006",
    doi = "10.18653/v1/D19-1006",
    pages = "55--65",
    abstract = "Replacing static word embeddings with contextualized word representations has yielded significant improvements on many NLP tasks. However, just how contextual are the contextualized representations produced by models such as ELMo and BERT? Are there infinitely many context-specific representations for each word, or are words essentially assigned one of a finite number of word-sense representations? For one, we find that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. While representations of the same word in different contexts still have a greater cosine similarity than those of two different words, this self-similarity is much lower in upper layers. This suggests that upper layers of contextualizing models produce more context-specific representations, much like how upper layers of LSTMs produce more task-specific representations. In all layers of ELMo, BERT, and GPT-2, on average, less than 5{\%} of the variance in a word{'}s contextualized representations can be explained by a static embedding for that word, providing some justification for the success of contextualized representations.",
}
@inproceedings{shoemark-etal-2019-room,
    title = "Room to {G}lo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings",
    author = "Shoemark, Philippa  and
      Liza, Farhana Ferdousi  and
      Nguyen, Dong  and
      Hale, Scott  and
      McGillivray, Barbara",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1007",
    doi = "10.18653/v1/D19-1007",
    pages = "66--76",
    abstract = "Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking. We propose a new evaluation framework for semantic change detection and find that (i) using the whole time series is preferable over only comparing between the first and last time points; (ii) independently trained and aligned embeddings perform better than continuously trained embeddings for long time periods; and (iii) that the reference point for comparison matters. We also present an analysis of the changes detected on a large Twitter dataset spanning 5.5 years.",
}
@inproceedings{zhelezniak-etal-2019-correlations,
    title = "Correlations between Word Vector Sets",
    author = "Zhelezniak, Vitalii  and
      Shen, April  and
      Busbridge, Daniel  and
      Savkov, Aleksandar  and
      Hammerla, Nils",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1008",
    doi = "10.18653/v1/D19-1008",
    pages = "77--87",
    abstract = "Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic textual similarity (STS) tasks. In contrast to commonly used geometric approaches, we treat a single word embedding as e.g. 300 observations from a scalar random variable. Using this paradigm, we first illustrate that similarities derived from elementary pooling operations and classic correlation coefficients yield excellent results on standard STS benchmarks, outperforming many recently proposed methods while being much faster and trivial to implement. Next, we demonstrate how to avoid pooling operations altogether and compare sets of word embeddings directly via correlation operators between reproducing kernel Hilbert spaces. Just like cosine similarity is used to compare individual word vectors, we introduce a novel application of the centered kernel alignment (CKA) as a natural generalisation of squared cosine similarity for sets of word vectors. Likewise, CKA is very easy to implement and enjoys very strong empirical results.",
}
@inproceedings{tripodi-navigli-2019-game,
    title = "Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation",
    author = "Tripodi, Rocco  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1009",
    doi = "10.18653/v1/D19-1009",
    pages = "88--99",
    abstract = "Game-theoretic models, thanks to their intrinsic ability to exploit contextual information, have shown to be particularly suited for the Word Sense Disambiguation task. They represent ambiguous words as the players of a non cooperative game and their senses as the strategies that the players can select in order to play the games. The interaction among the players is modeled with a weighted graph and the payoff as an embedding similarity function, that the players try to maximize. The impact of the word and sense embedding representations in the framework has been tested and analyzed extensively: experiments on standard benchmarks show state-of-art performances and different tests hint at the usefulness of using disambiguation to obtain contextualized word representations.",
}
@inproceedings{takanobu-etal-2019-guided,
    title = "Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog",
    author = "Takanobu, Ryuichi  and
      Zhu, Hanlin  and
      Huang, Minlie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1010",
    doi = "10.18653/v1/D19-1010",
    pages = "100--110",
    abstract = "Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals. With the growing needs to handle complex goals across multiple domains, such manually designed reward functions are not affordable to deal with the complexity of real-world tasks. To this end, we propose Guided Dialog Policy Learning, a novel algorithm based on Adversarial Inverse Reinforcement Learning for joint reward estimation and policy optimization in multi-domain task-oriented dialog. The proposed approach estimates the reward signal and infers the user goal in the dialog sessions. The reward estimator evaluates the state-action pairs so that it can guide the dialog policy at each dialog turn. Extensive experiments on a multi-domain dialog dataset show that the dialog policy guided by the learned reward function achieves remarkably higher task success than state-of-the-art baselines.",
}
@inproceedings{yuan-etal-2019-multi,
    title = "Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots",
    author = "Yuan, Chunyuan  and
      Zhou, Wei  and
      Li, Mingming  and
      Lv, Shangwen  and
      Zhu, Fuqing  and
      Han, Jizhong  and
      Hu, Songlin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1011",
    doi = "10.18653/v1/D19-1011",
    pages = "111--120",
    abstract = "Multi-turn retrieval-based conversation is an important task for building intelligent dialogue systems. Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information. Context utterances provide abundant information for extracting more matching features, but it also brings noise signals and unnecessary information. In this paper, we will analyze the side effect of using too many context utterances and propose a multi-hop selector network (MSN) to alleviate the problem. Specifically, MSN firstly utilizes a multi-hop selector to select the relevant utterances as context. Then, the model matches the filtered context with the candidate response and obtains a matching score. Experimental results show that MSN outperforms some state-of-the-art methods on three public multi-turn dialogue datasets.",
}
@inproceedings{lin-etal-2019-moel,
    title = "{M}o{EL}: Mixture of Empathetic Listeners",
    author = "Lin, Zhaojiang  and
      Madotto, Andrea  and
      Shin, Jamin  and
      Xu, Peng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1012",
    doi = "10.18653/v1/D19-1012",
    pages = "121--132",
    abstract = "Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions. However, being empathetic not only requires the ability of generating emotional responses, but more importantly, requires the understanding of user emotions and replying appropriately. In this paper, we propose a novel end-to-end approach for modeling empathy in dialogue systems: Mixture of Empathetic Listeners (MoEL). Our model first captures the user emotions and outputs an emotion distribution. Based on this, MoEL will softly combine the output states of the appropriate Listener(s), which are each optimized to react to certain emotions, and generate an empathetic response. Human evaluations on EMPATHETIC-DIALOGUES dataset confirm that MoEL outperforms multitask training baseline in terms of empathy, relevance, and fluency. Furthermore, the case study on generated responses of different Listeners shows high interpretability of our model.",
}
@inproceedings{qin-etal-2019-entity,
    title = "Entity-Consistent End-to-end Task-Oriented Dialogue System with {KB} Retriever",
    author = "Qin, Libo  and
      Liu, Yijia  and
      Che, Wanxiang  and
      Wen, Haoyang  and
      Li, Yangming  and
      Liu, Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1013",
    doi = "10.18653/v1/D19-1013",
    pages = "133--142",
    abstract = "Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats the KB query as an attention over the entire KB, without the guarantee that the generated entities are consistent with each other. In this paper, we propose a novel framework which queries the KB in two steps to improve the consistency of generated entities. In the first step, inspired by the observation that a response can usually be supported by a single KB row, we introduce a KB retrieval component which explicitly returns the most relevant KB row given a dialogue history. The retrieval result is further used to filter the irrelevant entities in a Seq2Seq response generation model to improve the consistency among the output entities. In the second step, we further perform the attention mechanism to address the most correlated KB column. Two methods are proposed to make the training feasible without labeled retrieval data, which include distant supervision and Gumbel-Softmax technique. Experiments on two publicly available task oriented dialog datasets show the effectiveness of our model by outperforming the baseline systems and producing entity-consistent responses.",
}
@inproceedings{zhou-etal-2019-building,
    title = "Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation",
    author = "Zhou, Mingyang  and
      Arnold, Josh  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1014",
    doi = "10.18653/v1/D19-1014",
    pages = "143--153",
    abstract = "Reinforcement learning (RL) is an effective approach to learn an optimal dialog policy for task-oriented visual dialog systems. A common practice is to apply RL on a neural sequence-to-sequence(seq2seq) framework with the action space being the output vocabulary in the decoder. However, it is difficult to design a reward function that can achieve a balance between learning an effective policy and generating a natural dialog response. This paper proposes a novel framework that alternatively trains a RL policy for image guessing and a supervised seq2seq model to improve dialog generation quality. We evaluate our framework on the GuessWhich task and the framework achieves the state-of-the-art performance in both task completion and dialog quality.",
}
@inproceedings{ghosal-etal-2019-dialoguegcn,
    title = "{D}ialogue{GCN}: A Graph Convolutional Neural Network for Emotion Recognition in Conversation",
    author = "Ghosal, Deepanway  and
      Majumder, Navonil  and
      Poria, Soujanya  and
      Chhaya, Niyati  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1015",
    doi = "10.18653/v1/D19-1015",
    pages = "154--164",
    abstract = "Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.",
}
@inproceedings{zhong-etal-2019-knowledge,
    title = "Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations",
    author = "Zhong, Peixiang  and
      Wang, Di  and
      Miao, Chunyan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1016",
    doi = "10.18653/v1/D19-1016",
    pages = "165--176",
    abstract = "Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.",
}
@inproceedings{yang-etal-2019-interpretable,
    title = "Interpretable Relevant Emotion Ranking with Event-Driven Attention",
    author = "Yang, Yang  and
      Zhou, Deyu  and
      He, Yulan  and
      Zhang, Meng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1017",
    doi = "10.18653/v1/D19-1017",
    pages = "177--187",
    abstract = "Multiple emotions with different intensities are often evoked by events described in documents. Oftentimes, such event information is hidden and needs to be discovered from texts. Unveiling the hidden event information can help to understand how the emotions are evoked and provide explainable results. However, existing studies often ignore the latent event information. In this paper, we proposed a novel interpretable relevant emotion ranking model with the event information incorporated into a deep learning architecture using the event-driven attentions. Moreover, corpus-level event embeddings and document-level event distributions are introduced respectively to consider the global events in corpus and the document-specific events simultaneously. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label approaches. Moreover, interpretable results can be obtained to shed light on the events which trigger certain emotions.",
}
@inproceedings{ni-etal-2019-justifying,
    title = "Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects",
    author = "Ni, Jianmo  and
      Li, Jiacheng  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1018",
    doi = "10.18653/v1/D19-1018",
    pages = "188--197",
    abstract = "Several recent works have considered the problem of generating reviews (or {`}tips{'}) as a form of explanation as to why a recommendation might match a customer{'}s interests. While promising, we demonstrate that existing approaches struggle (in terms of both quality and content) to generate justifications that are relevant to users{'} decision-making process. We seek to introduce new datasets and methods to address the recommendation justification task. In terms of data, we first propose an {`}extractive{'} approach to identify review segments which justify users{'} intentions; this approach is then used to distantly label massive review corpora and construct large-scale personalized recommendation justification datasets. In terms of generation, we are able to design two personalized generation models with this data: (1) a reference-based Seq2Seq model with aspect-planning which can generate justifications covering different aspects, and (2) an aspect-conditional masked language model which can generate diverse justifications based on templates extracted from justification histories. We conduct experiments on two real-world datasets which show that our model is capable of generating convincing and diverse justifications.",
}
@inproceedings{song-etal-2019-using,
    title = "Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning",
    author = "Song, Kaisong  and
      Bing, Lidong  and
      Gao, Wei  and
      Lin, Jun  and
      Zhao, Lujun  and
      Wang, Jiancheng  and
      Sun, Changlong  and
      Liu, Xiaozhong  and
      Zhang, Qiong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1019",
    doi = "10.18653/v1/D19-1019",
    pages = "198--207",
    abstract = "Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satisfied, met and unsatisfied) and sentimental utterance identification (e.g., positive, neutral and negative). In this paper, we conduct a pilot study on the task of service satisfaction analysis (SSA) based on multi-turn CS dialogues. We propose an extensible Context-Assisted Multiple Instance Learning (CAMIL) model to predict the sentiments of all the customer utterances and then aggregate those sentiments into service satisfaction polarity. After that, we propose a novel Context Clue Matching Mechanism (CCMM) to enhance the representations of all customer utterances with their matched context clues, i.e., sentiment and reasoning clues. We construct two CS dialogue datasets from a top E-commerce platform. Extensive experimental results are presented and contrasted against a few previous models to demonstrate the efficacy of our model.",
}
@inproceedings{song-etal-2019-leveraging,
    title = "Leveraging Dependency Forest for Neural Medical Relation Extraction",
    author = "Song, Linfeng  and
      Zhang, Yue  and
      Gildea, Daniel  and
      Yu, Mo  and
      Wang, Zhiguo  and
      Su, Jinsong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1020",
    doi = "10.18653/v1/D19-1020",
    pages = "208--218",
    abstract = "Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees suffer from relatively low accuracies, diminishing their usefulness. We investigate a method to alleviate this problem by utilizing dependency forests. Forests contain more than one possible decisions and therefore have higher recall but more noise compared with 1-best outputs. A graph neural network is used to represent the forests, automatically distinguishing the useful syntactic information from parsing noise. Results on two benchmarks show that our method outperforms the standard tree-based methods, giving the state-of-the-art results in the literature.",
}
@inproceedings{wu-etal-2019-open,
    title = "Open Relation Extraction: Relational Knowledge Transfer from Supervised Data to Unsupervised Data",
    author = "Wu, Ruidong  and
      Yao, Yuan  and
      Han, Xu  and
      Xie, Ruobing  and
      Liu, Zhiyuan  and
      Lin, Fen  and
      Lin, Leyu  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1021",
    doi = "10.18653/v1/D19-1021",
    pages = "219--228",
    abstract = "Open relation extraction (OpenRE) aims to extract relational facts from the open-domain corpus. To this end, it discovers relation patterns between named entities and then clusters those semantically equivalent patterns into a united relation cluster. Most OpenRE methods typically confine themselves to unsupervised paradigms, without taking advantage of existing relational facts in knowledge bases (KBs) and their high-quality labeled instances. To address this issue, we propose Relational Siamese Networks (RSNs) to learn similarity metrics of relations from labeled data of pre-defined relations, and then transfer the relational knowledge to identify novel relations in unlabeled data. Experiment results on two real-world datasets show that our framework can achieve significant improvements as compared with other state-of-the-art methods. Our code is available at https://github.com/thunlp/RSN.",
}
@inproceedings{li-etal-2019-improving,
    title = "Improving Relation Extraction with Knowledge-attention",
    author = "Li, Pengfei  and
      Mao, Kezhi  and
      Yang, Xuefeng  and
      Li, Qi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1022",
    doi = "10.18653/v1/D19-1022",
    pages = "229--239",
    abstract = "While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates prior knowledge from external lexical resources into deep neural networks for relation extraction task. Furthermore, we present three effective ways of integrating knowledge-attention with self-attention to maximize the utilization of both knowledge and data. The proposed relation extraction system is end-to-end and fully attention-based. Experiment results show that the proposed knowledge-attention mechanism has complementary strengths with self-attention, and our integrated models outperform existing CNN, RNN, and self-attention based models. State-of-the-art performance is achieved on TACRED, a complex and large-scale relation extraction dataset.",
}
@inproceedings{wu-etal-2019-jointly,
    title = "Jointly Learning Entity and Relation Representations for Entity Alignment",
    author = "Wu, Yuting  and
      Liu, Xiao  and
      Feng, Yansong  and
      Wang, Zheng  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1023",
    doi = "10.18653/v1/D19-1023",
    pages = "240--249",
    abstract = "Entity alignment is a viable means for integrating heterogeneous knowledge among different knowledge graphs (KGs). Recent developments in the field often take an embedding-based approach to model the structural information of KGs so that entity alignment can be easily performed in the embedding space. However, most existing works do not explicitly utilize useful relation representations to assist in entity alignment, which, as we will show in the paper, is a simple yet effective way for improving entity alignment. This paper presents a novel joint learning framework for entity alignment. At the core of our approach is a Graph Convolutional Network (GCN) based framework for learning both entity and relation representations. Rather than relying on pre-aligned relation seeds to learn relation representations, we first approximate them using entity embeddings learned by the GCN. We then incorporate the relation approximation into entities to iteratively learn better representations for both. Experiments performed on three real-world cross-lingual datasets show that our approach substantially outperforms state-of-the-art entity alignment methods.",
}
@inproceedings{wang-etal-2019-tackling,
    title = "Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion",
    author = "Wang, Zihao  and
      Lai, Kwunping  and
      Li, Piji  and
      Bing, Lidong  and
      Lam, Wai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1024",
    doi = "10.18653/v1/D19-1024",
    pages = "250--260",
    abstract = "For large-scale knowledge graphs (KGs), recent research has been focusing on the large proportion of infrequent relations which have been ignored by previous studies. For example few-shot learning paradigm for relations has been investigated. In this work, we further advocate that handling uncommon entities is inevitable when dealing with infrequent relations. Therefore, we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions. We design a novel model to better extract key information from textual descriptions. Besides, we also develop a novel generative model in our framework to enhance the performance by generating extra triplets during the training stage. Experiments are conducted on two datasets from real-world KGs, and the results show that our framework outperforms previous methods when dealing with infrequent relations and their accompanying uncommon entities.",
}
@inproceedings{cao-etal-2019-low,
    title = "Low-Resource Name Tagging Learned with Weakly Labeled Data",
    author = "Cao, Yixin  and
      Hu, Zikun  and
      Chua, Tat-seng  and
      Liu, Zhiyuan  and
      Ji, Heng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1025",
    doi = "10.18653/v1/D19-1025",
    pages = "261--270",
    abstract = "Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web. In this paper, we propose a novel neural model for name tagging solely based on weakly labeled (WL) data, so that it can be applied in any low-resource settings. To take the best advantage of all WL sentences, we split them into high-quality and noisy portions for two modules, respectively: (1) a classification module focusing on the large portion of noisy data can efficiently and robustly pretrain the tag classifier by capturing textual context semantics; and (2) a costly sequence labeling module focusing on high-quality data utilizes Partial-CRFs with non-entity sampling to achieve global optimum. Two modules are combined via shared parameters. Extensive experiments involving five low-resource languages and fine-grained food domain demonstrate our superior performance (6{\%} and 7.8{\%} F1 gains on average) as well as efficiency.",
}
@inproceedings{yang-etal-2019-learning,
    title = "Learning Dynamic Context Augmentation for Global Entity Linking",
    author = "Yang, Xiyuan  and
      Gu, Xiaotao  and
      Lin, Sheng  and
      Tang, Siliang  and
      Zhuang, Yueting  and
      Wu, Fei  and
      Chen, Zhigang  and
      Hu, Guoping  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1026",
    doi = "10.18653/v1/D19-1026",
    pages = "271--281",
    abstract = "Despite of the recent success of collective entity linking (EL) methods, these {``}global{''} inference methods may yield sub-optimal results when the {``}all-mention coherence{''} assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document. DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plug-and-enhance module. We explore both supervised and reinforcement learning strategies for learning the DCA model. Extensive experiments show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.",
}
@inproceedings{wang-etal-2019-open,
    title = "Open Event Extraction from Online Text using a Generative Adversarial Network",
    author = "Wang, Rui  and
      Zhou, Deyu  and
      He, Yulan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1027",
    doi = "10.18653/v1/D19-1027",
    pages = "282--291",
    abstract = "To extract the structured representations of open-domain events, Bayesian graphical models have made some progress. However, these approaches typically assume that all words in a document are generated from a single event. While this may be true for short text such as tweets, such an assumption does not generally hold for long text such as news articles. Moreover, Bayesian graphical models often rely on Gibbs sampling for parameter inference which may take long time to converge. To address these limitations, we propose an event extraction model based on Generative Adversarial Nets, called Adversarial-neural Event Model (AEM). AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events. A discriminator is used to distinguish documents reconstructed from the latent events and the original documents. A byproduct of the discriminator is that the features generated by the learned discriminator network allow the visualization of the extracted events. Our model has been evaluated on two Twitter datasets and a news article dataset. Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15{\%} is observed in F-measure.",
}
@inproceedings{yan-etal-2019-learning,
    title = "Learning to Bootstrap for Entity Set Expansion",
    author = "Yan, Lingyong  and
      Han, Xianpei  and
      Sun, Le  and
      He, Ben",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1028",
    doi = "10.18653/v1/D19-1028",
    pages = "292--301",
    abstract = "Bootstrapping for Entity Set Expansion (ESE) aims at iteratively acquiring new instances of a specific target category. Traditional bootstrapping methods often suffer from two problems: 1) delayed feedback, i.e., the pattern evaluation relies on both its direct extraction quality and extraction quality in later iterations. 2) sparse supervision, i.e., only few seed entities are used as the supervision. To address the above two problems, we propose a novel bootstrapping method combining the Monte Carlo Tree Search (MCTS) algorithm with a deep similarity network, which can efficiently estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. Experimental results confirm the effectiveness of the proposed method.",
}
@inproceedings{jiang-etal-2019-multi,
    title = "Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text",
    author = "Jiang, Tianwen  and
      Zhao, Tong  and
      Qin, Bing  and
      Liu, Ting  and
      Chawla, Nitesh  and
      Jiang, Meng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1029",
    doi = "10.18653/v1/D19-1029",
    pages = "302--312",
    abstract = "Condition is essential in scientific statement. Without the conditions (e.g., equipment, environment) that were precisely specified, facts (e.g., observations) in the statements may no longer be valid. Existing ScienceIE methods, which aim at extracting factual tuples from scientific text, do not consider the conditions. In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences. The framework has (1) a multi-output module to generate one or multiple tuples and (2) a multi-input module to feed in multiple types of signals as sequences. It improves F1 score relatively by 4.2{\%} on BioNLP2013 and by 6.2{\%} on a new bio-text dataset for tuple extraction.",
}
@inproceedings{subburathinam-etal-2019-cross,
    title = "Cross-lingual Structure Transfer for Relation and Event Extraction",
    author = "Subburathinam, Ananya  and
      Lu, Di  and
      Ji, Heng  and
      May, Jonathan  and
      Chang, Shih-Fu  and
      Sil, Avirup  and
      Voss, Clare",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1030",
    doi = "10.18653/v1/D19-1030",
    pages = "313--325",
    abstract = "The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6{\%} F-score for Relation Extraction, and 63.1{\%} F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.",
}
@inproceedings{chen-etal-2019-uncover,
    title = "Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework",
    author = "Chen, Junfan  and
      Zhang, Richong  and
      Mao, Yongyi  and
      Guo, Hongyu  and
      Xu, Jie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1031",
    doi = "10.18653/v1/D19-1031",
    pages = "326--336",
    abstract = "Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of the prior-art models for such tasks assume that the given text can be noisy, but their corresponding labels are clean. Such unrealistic assumption is contradictory with the fact that the given labels are often noisy as well, thus leading to significant performance degradation of those models on real-world data. To cope with this challenge, we propose a novel label-denoising framework that combines neural network with probabilistic modelling, which naturally takes into account the noisy labels during learning. We empirically demonstrate that our approach significantly improves the current art in uncovering the ground-truth relation labels.",
}
@inproceedings{zheng-etal-2019-doc2edag,
    title = "{D}oc2{EDAG}: An End-to-End Document-level Framework for {C}hinese Financial Event Extraction",
    author = "Zheng, Shun  and
      Cao, Wei  and
      Xu, Wei  and
      Bian, Jiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1032",
    doi = "10.18653/v1/D19-1032",
    pages = "337--346",
    abstract = "Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sentences, and even multiple such event mentions frequently co-exist in the same document. To address these challenges, we propose a novel end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic graph to fulfill the document-level EE (DEE) effectively. Moreover, we reformalize a DEE task with the no-trigger-words design to ease the document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we build a large-scale real-world dataset consisting of Chinese financial announcements with the challenges mentioned above. Extensive experiments with comprehensive analyses illustrate the superiority of Doc2EDAG over state-of-the-art methods. Data and codes can be found at https://github.com/dolphin-zs/Doc2EDAG.",
}
@inproceedings{ding-etal-2019-event,
    title = "Event Detection with Trigger-Aware Lattice Neural Network",
    author = "Ding, Ning  and
      Li, Ziran  and
      Liu, Zhiyuan  and
      Zheng, Haitao  and
      Lin, Zibo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1033",
    doi = "10.18653/v1/D19-1033",
    pages = "347--356",
    abstract = "Event detection (ED) aims to locate trigger words in raw text and then classify them into correct event types. In this task, neural net- work based models became mainstream in re- cent years. However, two problems arise when it comes to languages without natural delim- iters, such as Chinese. First, word-based mod- els severely suffer from the problem of word- trigger mismatch, limiting the performance of the methods. In addition, even if trigger words could be accurately located, the ambi- guity of polysemy of triggers could still af- fect the trigger classification stage. To ad- dress the two issues simultaneously, we pro- pose the Trigger-aware Lattice Neural Net- work (TLNN). (1) The framework dynami- cally incorporates word and character informa- tion so that the trigger-word mismatch issue can be avoided. (2) Moreover, for polysemous characters and words, we model all senses of them with the help of an external linguistic knowledge base, so as to alleviate the prob- lem of ambiguous triggers. Experiments on two benchmark datasets show that our model could effectively tackle the two issues and outperforms previous state-of-the-art methods significantly, giving the best results. The source code of this paper can be obtained from https://github.com/thunlp/TLNN.",
}
@inproceedings{zheng-etal-2019-boundary,
    title = "A Boundary-aware Neural Model for Nested Named Entity Recognition",
    author = "Zheng, Changmeng  and
      Cai, Yi  and
      Xu, Jingyun  and
      Leung, Ho-fung  and
      Xu, Guandong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1034",
    doi = "10.18653/v1/D19-1034",
    pages = "357--366",
    abstract = "In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels. Our model can locate entities precisely by detecting boundaries using sequence labeling models. Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model. We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities. We conduct our experiments on GENIA dataset and the experimental results demonstrate that our model outperforms other state-of-the-art methods.",
}
@inproceedings{zeng-etal-2019-learning,
    title = "Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning",
    author = "Zeng, Xiangrong  and
      He, Shizhu  and
      Zeng, Daojian  and
      Liu, Kang  and
      Liu, Shengping  and
      Zhao, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1035",
    doi = "10.18653/v1/D19-1035",
    pages = "367--377",
    abstract = "The multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn{'}t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the reinforcement learning into a sequence-to-sequence model. The proposed model could generate relational facts freely. Widely conducted experiments on two public datasets demonstrate the efficacy of the proposed method.",
}
@inproceedings{gupta-etal-2019-care,
    title = "{C}a{R}e: Open Knowledge Graph Embeddings",
    author = "Gupta, Swapnil  and
      Kenkre, Sreyash  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1036",
    doi = "10.18653/v1/D19-1036",
    pages = "378--388",
    abstract = "Open Information Extraction (OpenIE) methods are effective at extracting (noun phrase, relation phrase, noun phrase) triples from text, e.g., (Barack Obama, took birth in, Honolulu). Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the construction of Open Knowledge Graphs (OpenKGs). In order to use such OpenKGs in downstream tasks, it is often desirable to learn embeddings of the NPs and RPs present in the graph. Even though several Knowledge Graph (KG) embedding methods have been recently proposed, all of those methods have targeted Ontological KGs, as opposed to OpenKGs. Straightforward application of existing Ontological KG embedding methods to OpenKGs is challenging, as unlike Ontological KGs, OpenKGs are not canonicalized, i.e., a real-world entity may be represented using multiple nodes in the OpenKG, with each node corresponding to a different NP referring to the entity. For example, nodes with labels Barack Obama, Obama, and President Obama may refer to the same real-world entity Barack Obama. Even though canonicalization of OpenKGs has received some attention lately, output of such methods has not been used to improve OpenKG embed- dings. We fill this gap in the paper and propose Canonicalization-infused Representations (CaRe) for OpenKGs. Through extensive experiments, we observe that CaRe enables existing models to adapt to the challenges in OpenKGs and achieve substantial improvements for the link prediction task.",
}
@inproceedings{huang-du-2019-self,
    title = "Self-Attention Enhanced {CNN}s and Collaborative Curriculum Learning for Distantly Supervised Relation Extraction",
    author = "Huang, Yuyun  and
      Du, Jinhua",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1037",
    doi = "10.18653/v1/D19-1037",
    pages = "389--398",
    abstract = "Distance supervision is widely used in relation extraction tasks, particularly when large-scale manual annotations are virtually impossible to conduct. Although Distantly Supervised Relation Extraction (DSRE) benefits from automatic labelling, it suffers from serious mislabelling issues, i.e. some or all of the instances for an entity pair (head and tail entities) do not express the labelled relation. In this paper, we propose a novel model that employs a collaborative curriculum learning framework to reduce the effects of mislabelled data. Specifically, we firstly propose an internal self-attention mechanism between the convolution operations in convolutional neural networks (CNNs) to learn a better sentence representation from the noisy inputs. Then we define two sentence selection models as two relation extractors in order to collaboratively learn and regularise each other under a curriculum scheme to alleviate noisy effects, where the curriculum could be constructed by conflicts or small loss. Finally, experiments are conducted on a widely-used public dataset and the results indicate that the proposed model significantly outperforms baselines including the state-of-the-art in terms of P@N and PR curve metrics, thus evidencing its capability of reducing noisy effects for DSRE.",
}
@inproceedings{ni-florian-2019-neural,
    title = "Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping",
    author = "Ni, Jian  and
      Florian, Radu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1038",
    doi = "10.18653/v1/D19-1038",
    pages = "399--409",
    abstract = "Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications. Since the state-of-the-art RE models require large amounts of manually annotated data and language-specific resources to achieve high accuracy, it is very challenging to transfer an RE model of a resource-rich language to a resource-poor language. In this paper, we propose a new approach for cross-lingual RE model transfer based on bilingual word embedding mapping. It projects word embeddings from a target language to a source language, so that a well-trained source-language neural network RE model can be directly applied to the target language. Experiment results show that the proposed approach achieves very good performance for a number of target languages on both in-house and open datasets, using a small bilingual dictionary with only 1K word pairs.",
}
@inproceedings{deng-sun-2019-leveraging,
    title = "Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction",
    author = "Deng, Xiang  and
      Sun, Huan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1039",
    doi = "10.18653/v1/D19-1039",
    pages = "410--420",
    abstract = "Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). Given two entities, distant supervision exploits sentences that directly mention them for predicting their semantic relation. We refer to this strategy as 1-hop DS, which unfortunately may not work well for long-tail entities with few supporting sentences. In this paper, we introduce a new strategy named 2-hop DS to enhance distantly supervised RE, based on the observation that there exist a large number of relational tables on the Web which contain entity pairs that share common relations. We refer to such entity pairs as anchors for each other, and collect all sentences that mention the anchor entity pairs of a given target entity pair to help relation prediction. We develop a new neural RE method REDS2 in the multi-instance learning paradigm, which adopts a hierarchical model structure to fuse information respectively from 1-hop DS and 2-hop DS. Extensive experimental results on a benchmark dataset show that REDS2 can consistently outperform various baselines across different settings by a substantial margin.",
}
@inproceedings{chen-etal-2019-enteval,
    title = "{E}nt{E}val: A Holistic Evaluation Benchmark for Entity Representations",
    author = "Chen, Mingda  and
      Chu, Zewei  and
      Chen, Yang  and
      Stratos, Karl  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1040",
    doi = "10.18653/v1/D19-1040",
    pages = "421--433",
    abstract = "Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models (Peters et al., 2018) and show that they improve strong baselines on multiple EntEval tasks.",
}
@inproceedings{han-etal-2019-joint,
    title = "Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction",
    author = "Han, Rujun  and
      Ning, Qiang  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1041",
    doi = "10.18653/v1/D19-1041",
    pages = "434--444",
    abstract = "We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the event labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F1 improved by 10{\%} and 6.8{\%} on two benchmark datasets respectively.",
}
@inproceedings{mao-etal-2019-hierarchical,
    title = "Hierarchical Text Classification with Reinforced Label Assignment",
    author = "Mao, Yuning  and
      Tian, Jingjing  and
      Han, Jiawei  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1042",
    doi = "10.18653/v1/D19-1042",
    pages = "445--455",
    abstract = "While existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference. To solve the mismatch between training and inference as well as modeling label dependencies in a more principled way, we formulate HTC as a Markov decision process and propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. The proposed method, HiLAP, explores the hierarchy during both training and inference time in a consistent manner and makes inter-dependent decisions. As a general framework, HiLAP can incorporate different neural encoders as base models for end-to-end training. Experiments on five public datasets and four base models show that HiLAP yields an average improvement of 33.4{\%} in Macro-F1 over flat classifiers and outperforms state-of-the-art HTC methods by a large margin. Data and code can be found at https://github.com/morningmoni/HiLAP.",
}
@inproceedings{du-etal-2019-investigating,
    title = "Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification",
    author = "Du, Chunning  and
      Sun, Haifeng  and
      Wang, Jingyu  and
      Qi, Qi  and
      Liao, Jianxin  and
      Wang, Chun  and
      Ma, Bing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1043",
    doi = "10.18653/v1/D19-1043",
    pages = "456--465",
    abstract = "As an essential component of natural language processing, text classification relies on deep learning in recent years. Various neural networks are designed for text classification on the basis of word embedding. However, polysemy is a fundamental feature of the natural language, which brings challenges to text classification. One polysemic word contains more than one sense, while the word embedding procedure conflates different senses of a polysemic word into a single vector. Extracting the distinct representation for the specific sense could thus lead to fine-grained models with strong generalization ability. It has been demonstrated that multiple senses of a word actually reside in linear superposition within the word embedding so that specific senses can be extracted from the original word embedding. Therefore, we propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses. A novel dynamic routing mechanism named {`}routing-on-hyperplane{'} will select the proper sense for the downstream classification task. Our model is evaluated on 6 different datasets, and the experimental results show that our model is capable of extracting more discriminative semantic features and yields a significant performance gain compared to other baseline methods.",
}
@inproceedings{xiao-etal-2019-label,
    title = "Label-Specific Document Representation for Multi-Label Text Classification",
    author = "Xiao, Lin  and
      Huang, Xin  and
      Chen, Boli  and
      Jing, Liping",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1044",
    doi = "10.18653/v1/D19-1044",
    pages = "466--475",
    abstract = "Multi-label text classification (MLTC) aims to tag most relevant labels for the given document. In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. LSAN takes advantage of label semantic information to determine the semantic connection between labels and document for constructing label-specific document representation. Meanwhile, the self-attention mechanism is adopted to identify the label-specific document representation from document content information. In order to seamlessly integrate the above two parts, an adaptive fusion strategy is proposed, which can effectively output the comprehensive label-specific document representation to build multi-label text classifier. Extensive experimental results demonstrate that LSAN consistently outperforms the state-of-the-art methods on four different datasets, especially on the prediction of low-frequency labels. The code and hyper-parameter settings are released to facilitate other researchers.",
}
@inproceedings{sun-etal-2019-hierarchical,
    title = "Hierarchical Attention Prototypical Networks for Few-Shot Text Classification",
    author = "Sun, Shengli  and
      Sun, Qingfeng  and
      Zhou, Kevin  and
      Lv, Tengchao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1045",
    doi = "10.18653/v1/D19-1045",
    pages = "476--485",
    abstract = "Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available. In this work, we propose a hierarchical attention prototypical networks (HAPN) for few-shot text classification. We design the feature level, word level, and instance level multi cross attention for our model to enhance the expressive ability of semantic space, so it can highlight or weaken the importance of the features, words, and instances separately. We verify the effectiveness of our model on two standard benchmark few-shot text classification datasets{---}FewRel and CSID, and achieve the state-of-the-art performance. The visualization of hierarchical attention layers illustrates that our model can capture more important features, words, and instances. In addition, our attention mechanism increases support set augmentability and accelerates convergence speed in the training stage.",
}
@inproceedings{lai-etal-2019-many,
    title = "Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification",
    author = "Lai, Vivian  and
      Cai, Zheng  and
      Tan, Chenhao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1046",
    doi = "10.18653/v1/D19-1046",
    pages = "486--495",
    abstract = "Feature importance is commonly used to explain machine predictions. While feature importance can be derived from a machine learning model with a variety of methods, the consistency of feature importance via different methods remains understudied. In this work, we systematically compare feature importance from built-in mechanisms in a model such as attention values and post-hoc methods that approximate model behavior such as LIME. Using text classification as a testbed, we find that 1) no matter which method we use, important features from traditional models such as SVM and XGBoost are more similar with each other, than with deep learning models; 2) post-hoc methods tend to generate more similar important features for two models than built-in methods. We further demonstrate how such similarity varies across instances. Notably, important features do not always resemble each other better when two models agree on the predicted label than when they disagree.",
}
@inproceedings{niu-etal-2019-enhancing,
    title = "Enhancing Local Feature Extraction with Global Representation for Neural Text Classification",
    author = "Niu, Guocheng  and
      Xu, Hengru  and
      He, Bolei  and
      Xiao, Xinyan  and
      Wu, Hua  and
      Gao, Sheng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1047",
    doi = "10.18653/v1/D19-1047",
    pages = "496--506",
    abstract = "For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling. This paper proposes a novel Encoder1-Encoder2 architecture, where global information is incorporated into the procedure of local feature extraction from scratch. In particular, Encoder1 serves as a global information provider, while Encoder2 performs as a local feature extractor and is directly fed into the classifier. Meanwhile, two modes are also designed for their interaction. Thanks to the awareness of global information, our method is able to learn better instance specific local features and thus avoids complicated upper operations. Experiments conducted on eight benchmark datasets demonstrate that our proposed architecture promotes local feature driven models by a substantial margin and outperforms the previous best models in the fully-supervised setting.",
}
@inproceedings{ding-gimpel-2019-latent,
    title = "Latent-Variable Generative Models for Data-Efficient Text Classification",
    author = "Ding, Xiaoan  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1048",
    doi = "10.18653/v1/D19-1048",
    pages = "507--517",
    abstract = "Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zero-shot learning (Ng and Jordan,2002; Yogatama et al., 2017; Lewis and Fan,2019). In this paper, we improve generative text classifiers by introducing discrete latent variables into the generative story, and explore several graphical model configurations. We parameterize the distributions using standard neural architectures used in conditional language modeling and perform learning by directly maximizing the log marginal likelihood via gradient-based optimization, which avoids the need to do expectation-maximization. We empirically characterize the performance of our models on six text classification datasets. The choice of where to include the latent variable has a significant impact on performance, with the strongest results obtained when using the latent variable as an auxiliary conditioning variable in the generation of the textual input. This model consistently outperforms both the generative and discriminative classifiers in small-data settings. We analyze our model by finding that the latent variable captures interpretable properties of the data, even with very small training sets.",
}
@inproceedings{anjum-etal-2019-pare,
    title = "{P}a{R}e: A Paper-Reviewer Matching Approach Using a Common Topic Space",
    author = "Anjum, Omer  and
      Gong, Hongyu  and
      Bhat, Suma  and
      Hwu, Wen-Mei  and
      Xiong, JinJun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1049",
    doi = "10.18653/v1/D19-1049",
    pages = "518--528",
    abstract = "Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers. Given the importance of this step, various automated reviewer-paper matching solutions have been proposed to alleviate the burden. Prior approaches including bag-of-words model and probabilistic topic model are less effective to deal with the vocabulary mismatch and partial topic overlap between the submission and reviewer. Our approach, the common topic model, jointly models the topics common to the submission and the reviewer{'}s profile while relying on abstract topic vectors. Experiments and insightful evaluations on two datasets demonstrate that the proposed method achieves consistent improvements compared to the state-of-the-art.",
}
@inproceedings{gauthier-levy-2019-linking,
    title = "Linking artificial and human neural representations of language",
    author = "Gauthier, Jon  and
      Levy, Roger",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1050",
    doi = "10.18653/v1/D19-1050",
    pages = "529--539",
    abstract = "What information from an act of sentence understanding is robustly represented in the human brain? We investigate this question by comparing sentence encoding models on a brain decoding task, where the sentence that an experimental participant has seen must be predicted from the fMRI signal evoked by the sentence. We take a pre-trained BERT architecture as a baseline sentence encoding model and fine-tune it on a variety of natural language understanding (NLU) tasks, asking which lead to improvements in brain-decoding performance. We find that none of the sentence encoding tasks tested yield significant increases in brain decoding performance. Through further task ablations and representational analyses, we find that tasks which produce syntax-light representations yield significant improvements in brain decoding performance. Our results constrain the space of NLU models that could best account for human neural representations of language, but also suggest limits on the possibility of decoding fine-grained syntactic information from fMRI human neuroimaging.",
}
@inproceedings{kryscinski-etal-2019-neural,
    title = "Neural Text Summarization: A Critical Evaluation",
    author = "Kryscinski, Wojciech  and
      Keskar, Nitish Shirish  and
      McCann, Bryan  and
      Xiong, Caiming  and
      Socher, Richard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1051",
    doi = "10.18653/v1/D19-1051",
    pages = "540--551",
    abstract = "Text summarization aims at compressing long documents into a shorter form that conveys the most important parts of the original document. Despite increased interest in the community and notable research effort, progress on benchmark datasets has stagnated. We critically evaluate key ingredients of the current research setup: datasets, evaluation metrics, and models, and highlight three primary shortcomings: 1) automatically collected datasets leave the task underconstrained and may contain noise detrimental to training and evaluation, 2) current evaluation protocol is weakly correlated with human judgment and does not account for important characteristics such as factual correctness, 3) models overfit to layout biases of current datasets and offer limited diversity in their outputs.",
}
@inproceedings{castro-ferreira-etal-2019-neural,
    title = "Neural data-to-text generation: A comparison between pipeline and end-to-end architectures",
    author = "Castro Ferreira, Thiago  and
      van der Lee, Chris  and
      van Miltenburg, Emiel  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1052",
    doi = "10.18653/v1/D19-1052",
    pages = "552--562",
    abstract = "Traditionally, most data-to-text applications have been designed using a modular pipeline architecture, in which non-linguistic input data is converted into natural language through several intermediate transformations. By contrast, recent neural models for data-to-text generation have been proposed as end-to-end approaches, where the non-linguistic input is rendered in natural language with much less explicit intermediate representations in between. This study introduces a systematic comparison between neural pipeline and end-to-end data-to-text approaches for the generation of text from RDF triples. Both architectures were implemented making use of the encoder-decoder Gated-Recurrent Units (GRU) and Transformer, two state-of-the art deep learning methods. Automatic and human evaluations together with a qualitative analysis suggest that having explicit intermediate steps in the generation process results in better texts than the ones generated by end-to-end approaches. Moreover, the pipeline models generalize better to unseen inputs. Data and code are publicly available.",
}
@inproceedings{zhao-etal-2019-moverscore,
    title = "{M}over{S}core: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance",
    author = "Zhao, Wei  and
      Peyrard, Maxime  and
      Liu, Fei  and
      Gao, Yang  and
      Meyer, Christian M.  and
      Eger, Steffen",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1053",
    doi = "10.18653/v1/D19-1053",
    pages = "563--578",
    abstract = "A robust evaluation metric has a profound impact on the development of text generation systems. A desirable metric compares system output against references based on their semantics rather than surface forms. In this paper we investigate strategies to encode system and reference texts to devise a metric that shows a high correlation with human judgment of text quality. We validate our new metric, namely MoverScore, on a number of text generation tasks including summarization, machine translation, image captioning, and data-to-text generation, where the outputs are produced by a variety of neural and non-neural systems. Our findings suggest that metrics combining contextualized representations with a distance measure perform the best. Such metrics also demonstrate strong generalization capability across tasks. For ease-of-use we make our metrics available as web service.",
}
@inproceedings{shen-etal-2019-select,
    title = "Select and Attend: Towards Controllable Content Selection in Text Generation",
    author = "Shen, Xiaoyu  and
      Suzuki, Jun  and
      Inui, Kentaro  and
      Su, Hui  and
      Klakow, Dietrich  and
      Sekine, Satoshi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1054",
    doi = "10.18653/v1/D19-1054",
    pages = "579--590",
    abstract = "Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling content selection from the decoder. The decoupled content selection is human interpretable, whose value can be manually manipulated to control the content of generated text. The model can be trained end-to-end without human annotations by maximizing a lower bound of the marginal likelihood. We further propose an effective way to trade-off between performance and controllability with a single adjustable hyperparameter. In both data-to-text and headline generation tasks, our model achieves promising results, paving the way for controllable content selection in text generation.",
}
@inproceedings{hua-wang-2019-sentence,
    title = "Sentence-Level Content Planning and Style Specification for Neural Text Generation",
    author = "Hua, Xinyu  and
      Wang, Lu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1055",
    doi = "10.18653/v1/D19-1055",
    pages = "591--602",
    abstract = "Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style neural generation models have made impressive progress, yet they often produce outputs that are incoherent and unfaithful to the input. To address these issues, we present an end-to-end trained two-step generation model, where a sentence-level content planner first decides on the keyphrases to cover as well as a desired language style, followed by a surface realization decoder that generates relevant and coherent text. For experiments, we consider three tasks from domains with diverse topics and varying language styles: persuasive argument construction from Reddit, paragraph generation for normal and simple versions of Wikipedia, and abstract generation for scientific articles. Automatic evaluation shows that our system can significantly outperform competitive comparisons. Human judges further rate our system generated text as more fluent and correct, compared to the generations by its variants that do not consider language style.",
}
@inproceedings{daza-frank-2019-translate,
    title = "Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling",
    author = "Daza, Angel  and
      Frank, Anette",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1056",
    doi = "10.18653/v1/D19-1056",
    pages = "603--615",
    abstract = "We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need parallel data during inference time. Our approach can be applied in monolingual, multilingual and cross-lingual settings and is able to produce dependency-based and span-based SRL annotations. We benchmark the labeling performance of our model in different monolingual and multilingual settings using well-known SRL datasets. We then train our model in a cross-lingual setting to generate new SRL labeled data. Finally, we measure the effectiveness of our method by using the generated data to augment the training basis for resource-poor languages and perform manual evaluation to show that it produces high-quality sentences and assigns accurate semantic role annotations. Our proposed architecture offers a flexible method for leveraging SRL data in multiple languages.",
}
@inproceedings{zhang-etal-2019-syntax-enhanced-self,
    title = "Syntax-Enhanced Self-Attention-Based Semantic Role Labeling",
    author = "Zhang, Yue  and
      Wang, Rui  and
      Si, Luo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1057",
    doi = "10.18653/v1/D19-1057",
    pages = "616--626",
    abstract = "As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of en- coding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we con- duct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.",
}
@inproceedings{di-fabio-etal-2019-verbatlas,
    title = "{V}erb{A}tlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling",
    author = "Di Fabio, Andrea  and
      Conia, Simone  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1058",
    doi = "10.18653/v1/D19-1058",
    pages = "627--637",
    abstract = "We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames. The frames define a common, prototypical argument structure while at the same time providing new concept-specific information. In contrast to PropBank, which defines enumerative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to improvements on both the in-domain and out-of-domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org.",
}
@inproceedings{yang-etal-2019-parameter,
    title = "Parameter-free Sentence Embedding via Orthogonal Basis",
    author = "Yang, Ziyi  and
      Zhu, Chenguang  and
      Chen, Weizhu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1059",
    doi = "10.18653/v1/D19-1059",
    pages = "638--648",
    abstract = "We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word{'}s novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.",
}
@inproceedings{chen-etal-2019-evaluation,
    title = "Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations",
    author = "Chen, Mingda  and
      Chu, Zewei  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1060",
    doi = "10.18653/v1/D19-1060",
    pages = "649--662",
    abstract = "Prior work on pretrained sentence embeddings and benchmarks focus on the capabilities of stand-alone sentences. We propose DiscoEval, a test suite of tasks to evaluate whether sentence representations include broader context information. We also propose a variety of training objectives that makes use of natural annotations from Wikipedia to build sentence encoders capable of modeling discourse. We benchmark sentence encoders pretrained with our proposed training objectives, as well as other popular pretrained sentence encoders on DiscoEval and other sentence evaluation tasks. Empirically, we show that these training objectives help to encode different aspects of information in document structures. Moreover, BERT and ELMo demonstrate strong performances over DiscoEval with individual hidden layers showing different characteristics.",
}
@inproceedings{chinnappa-etal-2019-extracting,
    title = "Extracting Possessions from Social Media: Images Complement Language",
    author = "Chinnappa, Dhivya  and
      Murugan, Srikala  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1061",
    doi = "10.18653/v1/D19-1061",
    pages = "663--672",
    abstract = "This paper describes a new dataset and experiments to determine whether authors of tweets possess the objects they tweet about. We work with 5,000 tweets and show that both humans and neural networks benefit from images in addition to text. We also introduce a simple yet effective strategy to incorporate visual information into any neural network beyond weights from pretrained networks. Specifically, we consider the tags identified in an image as an additional textual input, and leverage pretrained word embeddings as usually done with regular text. Experimental results show this novel strategy is beneficial.",
}
@inproceedings{urbanek-etal-2019-learning,
    title = "Learning to Speak and Act in a Fantasy Text Adventure Game",
    author = {Urbanek, Jack  and
      Fan, Angela  and
      Karamcheti, Siddharth  and
      Jain, Saachi  and
      Humeau, Samuel  and
      Dinan, Emily  and
      Rockt{\"a}schel, Tim  and
      Kiela, Douwe  and
      Szlam, Arthur  and
      Weston, Jason},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1062",
    doi = "10.18653/v1/D19-1062",
    pages = "673--683",
    abstract = "We introduce a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. In it, agents can perceive, emote, and act whilst conducting dialogue with other agents. Models and humans can both act as characters within the game. We describe the results of training state-of-the-art generative and retrieval models in this setting. We show that in addition to using past dialogue, these models are able to effectively use the state of the underlying world to condition their predictions. In particular, we show that grounding on the details of the local environment, including location descriptions, and the objects (and their affordances) and characters (and their previous actions) present within it allows better predictions of agent behavior and dialogue. We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.",
}
@inproceedings{nguyen-daume-iii-2019-help,
    title = "Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning",
    author = "Nguyen, Khanh  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1063",
    doi = "10.18653/v1/D19-1063",
    pages = "684--695",
    abstract = "Mobile agents that can leverage help from humans can potentially accomplish more complex tasks than they could entirely on their own. We develop {``}Help, Anna!{''} (HANNA), an interactive photo-realistic simulator in which an agent fulfills object-finding tasks by requesting and interpreting natural language-and-vision assistance. An agent solving tasks in a HANNA environment can leverage simulated human assistants, called ANNA (Automatic Natural Navigation Assistants), which, upon request, provide natural language and visual instructions to direct the agent towards the goals. To address the HANNA problem, we develop a memory-augmented neural agent that hierarchically models multiple levels of decision-making, and an imitation learning algorithm that teaches the agent to avoid repeating past mistakes while simultaneously predicting its own chances of making future progress. Empirically, our approach is able to ask for help more effectively than competitive baselines and, thus, attains higher task success rate on both previously seen and previously unseen environments.",
}
@inproceedings{bordes-etal-2019-incorporating,
    title = "Incorporating Visual Semantics into Sentence Representations within a Grounded Space",
    author = "Bordes, Patrick  and
      Zablocki, Eloi  and
      Soulier, Laure  and
      Piwowarski, Benjamin  and
      Gallinari, Patrick",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1064",
    doi = "10.18653/v1/D19-1064",
    pages = "696--707",
    abstract = "Language grounding is an active field aiming at enriching textual representations with visual information. Generally, textual and visual elements are embedded in the same representation space, which implicitly assumes a one-to-one correspondence between modalities. This hypothesis does not hold when representing words, and becomes problematic when used to learn sentence representations {---} the focus of this paper {---} as a visual scene can be described by a wide variety of sentences. To overcome this limitation, we propose to transfer visual information to textual representations by learning an intermediate representation space: the grounded space. We further propose two new complementary objectives ensuring that (1) sentences associated with the same visual content are close in the grounded space and (2) similarities between related elements are preserved across modalities. We show that this model outperforms the previous state-of-the-art on classification and semantic relatedness tasks.",
}
@inproceedings{forbes-etal-2019-neural,
    title = "Neural Naturalist: Generating Fine-Grained Image Comparisons",
    author = "Forbes, Maxwell  and
      Kaeser-Chen, Christine  and
      Sharma, Piyush  and
      Belongie, Serge",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1065",
    doi = "10.18653/v1/D19-1065",
    pages = "708--717",
    abstract = "We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., {``}heart-shaped face,{''} {``}squat body{''}). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance{---}drawn from a novel stratified sampling approach{---}with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.",
}
@inproceedings{rosales-mendez-etal-2019-fine,
    title = "Fine-Grained Evaluation for Entity Linking",
    author = "Rosales-M{\'e}ndez, Henry  and
      Hogan, Aidan  and
      Poblete, Barbara",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1066",
    doi = "10.18653/v1/D19-1066",
    pages = "718--727",
    abstract = "The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base. While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider. We argue that no one definition of the Entity Linking task fits all, and rather propose a fine-grained categorization of different types of entity mentions and links. We then re-annotate three EL benchmark datasets {--} ACE2004, KORE50, and VoxEL {--} with respect to these categories. We propose a fuzzy recall metric to address the lack of consensus and conclude with fine-grained evaluation results comparing a selection of online EL systems.",
}
@inproceedings{roy-etal-2019-supervising,
    title = "Supervising Unsupervised Open Information Extraction Models",
    author = "Roy, Arpita  and
      Park, Youngja  and
      Lee, Taesung  and
      Pan, Shimei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1067",
    doi = "10.18653/v1/D19-1067",
    pages = "728--737",
    abstract = "We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a small amount of labeled data to improve system performance. It uses the outputs of multiple unsupervised Open IE systems plus a diverse set of lexical and syntactic information such as word embedding, part-of-speech embedding, syntactic role embedding and dependency structure as its input features and produces a sequence of word labels indicating whether the word belongs to a relation, the arguments of the relation or irrelevant. Comparing with existing supervised Open IE systems, our approach leverages the knowledge in existing unsupervised Open IE systems to overcome the problem of insufficient training data. By employing multiple unsupervised Open IE systems, our system learns to combine the strength and avoid the weakness in each individual Open IE system. We have conducted experiments on multiple labeled benchmark data sets. Our evaluation results have demonstrated the superiority of the proposed method over existing supervised and unsupervised models by a significant margin.",
}
@inproceedings{liu-etal-2019-neural-cross,
    title = "Neural Cross-Lingual Event Detection with Minimal Parallel Resources",
    author = "Liu, Jian  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1068",
    doi = "10.18653/v1/D19-1068",
    pages = "738--748",
    abstract = "The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might limit their applicability. In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources. Specifically, to construct a lexical mapping between different languages, we devise a context-dependent translation method; to treat the word order difference problem, we propose a shared syntactic order event detector for multilingual co-training. The efficiency of our method is studied through extensive experiments on two standard datasets. Empirical results indicate that our method is effective in 1) performing cross-lingual transfer concerning different directions and 2) tackling the extremely annotation-poor scenario.",
}
@inproceedings{mesquita-etal-2019-knowledgenet,
    title = "{K}nowledge{N}et: A Benchmark Dataset for Knowledge Base Population",
    author = "Mesquita, Filipe  and
      Cannaviccio, Matteo  and
      Schmidek, Jordan  and
      Mirza, Paramita  and
      Barbosa, Denilson",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1069",
    doi = "10.18653/v1/D19-1069",
    pages = "749--758",
    abstract = "KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction). We discuss five baseline approaches, where the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79{\%} (0.28). However, our best baseline is far from reaching human performance (0.82), indicating our dataset is challenging. The KnowledgeNet dataset and baselines are available at https://github.com/diffbot/knowledge-net",
}
@inproceedings{gupta-durrett-2019-effective,
    title = "Effective Use of Transformer Networks for Entity Tracking",
    author = "Gupta, Aditya  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1070",
    doi = "10.18653/v1/D19-1070",
    pages = "759--769",
    abstract = "Tracking entities in procedural language requires understanding the transformations arising from actions on entities as well as those entities{'} interactions. While self-attention-based pre-trained language encoders like GPT and BERT have been successfully applied across a range of natural language understanding tasks, their ability to handle the nuances of procedural texts is still unknown. In this paper, we explore the use of pre-trained transformer networks for entity tracking tasks in procedural text. First, we test standard lightweight approaches for prediction with pre-trained transformers, and find that these approaches underperforms even simple baselines. We show that much stronger results can be attained by restructuring the input to guide the model to focus on a particular entity. Second, we assess the degree to which the transformer networks capture the process dynamics, investigating such factors as merged entities and oblique entity references. On two different tasks, ingredient detection in recipes and QA over scientific processes, we achieve state-of-the-art results, but our models still largely attend to shallow context clues and do not form complex representations of intermediate process state.",
}
@inproceedings{ren-etal-2019-explicit,
    title = "Explicit Cross-lingual Pre-training for Unsupervised Machine Translation",
    author = "Ren, Shuo  and
      Wu, Yu  and
      Liu, Shujie  and
      Zhou, Ming  and
      Ma, Shuai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1071",
    doi = "10.18653/v1/D19-1071",
    pages = "770--779",
    abstract = "Pre-training has proven to be effective in unsupervised machine translation due to its ability to model deep context information in cross-lingual scenarios. However, the cross-lingual information obtained from shared BPE spaces is inexplicit and limited. In this paper, we propose a novel cross-lingual pre-training method for unsupervised machine translation by incorporating explicit cross-lingual training signals. Specifically, we first calculate cross-lingual n-gram embeddings and infer an n-gram translation table from them. With those n-gram translation pairs, we propose a new pre-training model called Cross-lingual Masked Language Model (CMLM), which randomly chooses source n-grams in the input text stream and predicts their translation candidates at each time step. Experiments show that our method can incorporate beneficial cross-lingual information into pre-trained models. Taking pre-trained CMLM models as the encoder and decoder, we significantly improve the performance of unsupervised machine translation.",
}
@inproceedings{yang-etal-2019-latent,
    title = "Latent Part-of-Speech Sequences for Neural Machine Translation",
    author = "Yang, Xuewen  and
      Liu, Yingru  and
      Xie, Dongliang  and
      Wang, Xin  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1072",
    doi = "10.18653/v1/D19-1072",
    pages = "780--790",
    abstract = "Learning target side syntactic structure has been shown to improve Neural Machine Translation (NMT). However, incorporating syntax through latent variables introduces additional complexity in inference, as the models need to marginalize over the latent syntactic structures. To avoid this, models often resort to greedy search which only allows them to explore a limited portion of the latent space. In this work, we introduce a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics, while allowing for effective and efficient inference over the latent space. LaSyn decouples direct dependence between successive latent variables, which allows its decoder to exhaustively search through the latent syntactic choices, while keeping decoding speed proportional to the size of the latent variable vocabulary. We implement LaSyn by modifying a transformer-based NMT system and design a neural expectation maximization algorithm that we regularize with part-of-speech information as the latent sequences. Evaluations on four different MT tasks show that incorporating target side syntax with LaSyn improves both translation quality, and also provides an opportunity to improve diversity.",
}
@inproceedings{wang-etal-2019-improving-back,
    title = "Improving Back-Translation with Uncertainty-based Confidence Estimation",
    author = "Wang, Shuo  and
      Liu, Yang  and
      Wang, Chao  and
      Luan, Huanbo  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1073",
    doi = "10.18653/v1/D19-1073",
    pages = "791--802",
    abstract = "While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidence of NMT model predictions based on model uncertainty. With word- and sentence-level confidence measures based on uncertainty, it is possible for back-translation to better cope with noise in synthetic bilingual corpora. Experiments on Chinese-English and English-German translation tasks show that uncertainty-based confidence estimation significantly improves the performance of back-translation.",
}
@inproceedings{wang-2019-towards,
    title = "Towards Linear Time Neural Machine Translation with Capsule Networks",
    author = "Wang, Mingxuan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1074",
    doi = "10.18653/v1/D19-1074",
    pages = "803--812",
    abstract = "In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as CapsNMT. CapsNMT uses an aggregation mechanism to map the source sentence into a matrix with pre-determined size, and then applys a deep LSTM network to decode the target sequence from the source representation. Unlike the previous work (CITATION) to store the source sentence with a passive and bottom-up way, the dynamic routing policy encodes the source sentence with an iterative process to decide the credit attribution between nodes from lower and higher layers. CapsNMT has two core properties: it runs in time that is linear in the length of the sequences and provides a more flexible way to aggregate the part-whole information of the source sentence. On WMT14 English-German task and a larger WMT14 English-French task, CapsNMT achieves comparable results with the Transformer system. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for sequence to sequence problems.",
}
@inproceedings{shi-xiao-2019-modeling,
    title = "Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment",
    author = "Shi, Xiaofei  and
      Xiao, Yanghua",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1075",
    doi = "10.18653/v1/D19-1075",
    pages = "813--822",
    abstract = "Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision. To solve this issue, we propose a new embedding-based framework. Through defining dot product-based functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations. We calibrate embeddings of different KGs via a small set of pre-aligned seeds. We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end. Experimental results (especially with the metric \textit{Hits@1}) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance.",
}
@inproceedings{moshtaghi-2019-supervised,
    title = "Supervised and Nonlinear Alignment of Two Embedding Spaces for Dictionary Induction in Low Resourced Languages",
    author = "Moshtaghi, Masud",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1076",
    doi = "10.18653/v1/D19-1076",
    pages = "823--832",
    abstract = "Enabling cross-lingual NLP tasks by leveraging multilingual word embedding has recently attracted much attention. An important motivation is to support lower resourced languages, however, most efforts focus on demonstrating the effectiveness of the techniques using embeddings derived from similar languages to English with large parallel content. In this study, we first describe the general requirements for the success of these techniques and then present a noise tolerant piecewise linear technique to learn a non-linear mapping between two monolingual word embedding vector spaces. We evaluate our approach on inferring bilingual dictionaries. We show that our technique outperforms the state-of-the-art in lower resourced settings with an average of 3.7{\%} improvement of precision @10 across 14 mostly low resourced languages.",
}
@inproceedings{wu-dredze-2019-beto,
    title = "Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of {BERT}",
    author = "Wu, Shijie  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1077",
    doi = "10.18653/v1/D19-1077",
    pages = "833--844",
    abstract = "Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.",
}
@inproceedings{zeng-etal-2019-iterative,
    title = "Iterative Dual Domain Adaptation for Neural Machine Translation",
    author = "Zeng, Jiali  and
      Liu, Yang  and
      Su, Jinsong  and
      Ge, Yubing  and
      Lu, Yaojie  and
      Yin, Yongjing  and
      Luo, Jiebo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1078",
    doi = "10.18653/v1/D19-1078",
    pages = "845--855",
    abstract = "Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the domain-shared translation knowledge, and repeatedly utilizing corpora of different domains can lead to better distillation of domain-shared translation knowledge. To this end, we propose an iterative dual domain adaptation framework for NMT. Specifically, we first pretrain in-domain and out-of-domain NMT models using their own training corpora respectively, and then iteratively perform bidirectional translation knowledge transfer (from in-domain to out-of-domain and then vice versa) based on knowledge distillation until the in-domain NMT model convergences. Furthermore, we extend the proposed framework to the scenario of multiple out-of-domain training corpora, where the above-mentioned transfer is performed sequentially between the in-domain and each out-of-domain NMT models in the ascending order of their domain similarities. Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our framework.",
}
@inproceedings{bi-etal-2019-multi,
    title = "Multi-agent Learning for Neural Machine Translation",
    author = "Bi, Tianchi  and
      Xiong, Hao  and
      He, Zhongjun  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1079",
    doi = "10.18653/v1/D19-1079",
    pages = "856--865",
    abstract = "Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decod- ing from left to right and the other decoding in the opposite direction. In this paper, we extend the training framework to the multi-agent sce- nario by introducing diverse agents in an in- teractive updating process. At training time, each agent learns advanced knowledge from others, and they work together to improve translation quality. Experimental results on NIST Chinese-English, IWSLT 2014 German- English, WMT 2014 English-German and large-scale Chinese-English translation tasks indicate that our approach achieves absolute improvements over the strong baseline sys- tems and shows competitive performance on all tasks.",
}
@inproceedings{kim-etal-2019-pivot,
    title = "Pivot-based Transfer Learning for Neural Machine Translation between Non-{E}nglish Languages",
    author = "Kim, Yunsu  and
      Petrov, Petre  and
      Petrushkov, Pavel  and
      Khadivi, Shahram  and
      Ney, Hermann",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1080",
    doi = "10.18653/v1/D19-1080",
    pages = "866--876",
    abstract = "We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to a significant improvement in source-target translation. We propose three methods to increase the relation among source, pivot, and target languages in the pre-training: 1) step-wise training of a single model for different language pairs, 2) additional adapter component to smoothly connect pre-trained encoder and decoder, and 3) cross-lingual encoder training via autoencoding of the pivot language. Our methods greatly outperform multilingual models up to +2.6{\%} BLEU in WMT 2019 French-German and German-Czech tasks. We show that our improvements are valid also in zero-shot/zero-resource scenarios.",
}
@inproceedings{voita-etal-2019-context,
    title = "Context-Aware Monolingual Repair for Neural Machine Translation",
    author = "Voita, Elena  and
      Sennrich, Rico  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1081",
    doi = "10.18653/v1/D19-1081",
    pages = "877--886",
    abstract = "Modern sentence-level NMT systems often produce plausible translations of isolated sentences. However, when put in context, these translations may end up being inconsistent with each other. We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations. DocRepair performs automatic post-editing on a sequence of sentence-level translations, refining translations of sentences in context of each other. For training, the DocRepair model requires only monolingual document-level data in the target language. It is trained as a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones. The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence. We show that this approach successfully imitates inconsistencies we aim to fix: using contrastive evaluation, we show large improvements in the translation of several contextual phenomena in an English-Russian translation task, as well as improvements in the BLEU score. We also conduct a human evaluation and show a strong preference of the annotators to corrected translations over the baseline ones. Moreover, we analyze which discourse phenomena are hard to capture using monolingual data only.",
}
@inproceedings{hao-etal-2019-multi,
    title = "Multi-Granularity Self-Attention for Neural Machine Translation",
    author = "Hao, Jie  and
      Wang, Xing  and
      Shi, Shuming  and
      Zhang, Jinfeng  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1082",
    doi = "10.18653/v1/D19-1082",
    pages = "887--897",
    abstract = "Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However, prior work on statistical machine translation has shown that extending the basic translation unit from words to phrases has produced substantial improvements, suggesting the possibility of improving NMT performance from explicit modeling of phrases. In this work, we present \textit{multi-granularity self-attention} (Mg-Sa): a neural network that combines multi-head self-attention and phrase modeling. Specifically, we train several attention heads to attend to phrases in either n-gram or syntactic formalisms. Moreover, we exploit interactions among phrases to enhance the strength of structure modeling {--} a commonly-cited weakness of self-attention. Experimental results on WMT14 English-to-German and NIST Chinese-to-English translation tasks show the proposed approach consistently improves performance. Targeted linguistic analysis reveal that Mg-Sa indeed captures useful phrase information at various levels of granularities.",
}
@inproceedings{zhang-etal-2019-improving-deep,
    title = "Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention",
    author = "Zhang, Biao  and
      Titov, Ivan  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1083",
    doi = "10.18653/v1/D19-1083",
    pages = "898--909",
    abstract = "The general trend in NLP is towards increasing model capacity and performance via deeper neural networks. However, simply stacking more layers of the popular Transformer architecture for machine translation results in poor convergence and high computational overhead. Our empirical analysis suggests that convergence is poor due to gradient vanishing caused by the interaction between residual connection and layer normalization. We propose depth-scaled initialization (DS-Init), which decreases parameter variance at the initialization stage, and reduces output variance of residual connections so as to ease gradient back-propagation through normalization layers. To address computational cost, we propose a merged attention sublayer (MAtt) which combines a simplified average-based self-attention sublayer and the encoder-decoder attention sublayer on the decoder side. Results on WMT and IWSLT translation tasks with five translation directions show that deep Transformers with DS-Init and MAtt can substantially outperform their base counterpart in terms of BLEU (+1.1 BLEU on average for 12-layer models), while matching the decoding speed of the baseline model thanks to the efficiency improvements of MAtt. Source code for reproduction will be released soon.",
}
@inproceedings{stengel-eskin-etal-2019-discriminative,
    title = "A Discriminative Neural Model for Cross-Lingual Word Alignment",
    author = "Stengel-Eskin, Elias  and
      Su, Tzu-ray  and
      Post, Matt  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1084",
    doi = "10.18653/v1/D19-1084",
    pages = "910--920",
    abstract = "We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model. In experiments based on a small number of labeled examples (∼1.7K{--}5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11{--}27 F1). We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese. Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.",
}
@inproceedings{wang-etal-2019-one,
    title = "One Model to Learn Both: Zero Pronoun Prediction and Translation",
    author = "Wang, Longyue  and
      Tu, Zhaopeng  and
      Wang, Xing  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1085",
    doi = "10.18653/v1/D19-1085",
    pages = "921--930",
    abstract = "Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but should be recalled in non-pro-drop languages. This discourse phenomenon poses a significant challenge for machine translation (MT) when translating texts from pro-drop to non-pro-drop languages. In this paper, we propose a unified and discourse-aware ZP translation approach for neural MT models. Specifically, we jointly learn to predict and translate ZPs in an end-to-end manner, allowing both components to interact with each other. In addition, we employ hierarchical neural networks to exploit discourse-level context, which is beneficial for ZP prediction and thus translation. Experimental results on both Chinese-English and Japanese-English data show that our approach significantly and accumulatively improves both translation performance and ZP prediction accuracy over not only baseline but also previous works using external ZP prediction models. Extensive analyses confirm that the performance improvement comes from the alleviation of different kinds of errors especially caused by subjective ZPs.",
}
@inproceedings{zheng-etal-2019-dynamic,
    title = "Dynamic Past and Future for Neural Machine Translation",
    author = "Zheng, Zaixiang  and
      Huang, Shujian  and
      Tu, Zhaopeng  and
      Dai, Xin-Yu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1086",
    doi = "10.18653/v1/D19-1086",
    pages = "931--941",
    abstract = "Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated () and untranslated () source contents as recurrent states (CITATION). However, this less interpretable recurrent process hinders its power to model the dynamic updating of and contents during decoding. In this paper, we propose to model the \textit{dynamic principles} by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment. The assignment is learned through a novel variant of routing-by-agreement mechanism (CITATION), namely \textit{Guided Dynamic Routing}, where the translating status at each decoding step \textit{guides} the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling translation to be made from holistic context. Experiments show that our approach achieves substantial improvements over both Rnmt and Transformer by producing more adequate translations. Extensive analysis demonstrates that our method is highly interpretable, which is able to recognize the translated and untranslated contents as expected.",
}
@inproceedings{lei-etal-2019-revisit,
    title = "Revisit Automatic Error Detection for Wrong and Missing Translation {--} A Supervised Approach",
    author = "Lei, Wenqiang  and
      Xu, Weiwen  and
      Aw, Ai Ti  and
      Xiang, Yuanxin  and
      Chua, Tat Seng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1087",
    doi = "10.18653/v1/D19-1087",
    pages = "942--952",
    abstract = "While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.",
}
@inproceedings{he-etal-2019-towards,
    title = "Towards Understanding Neural Machine Translation with Word Importance",
    author = "He, Shilin  and
      Tu, Zhaopeng  and
      Wang, Xing  and
      Wang, Longyue  and
      Lyu, Michael  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1088",
    doi = "10.18653/v1/D19-1088",
    pages = "953--962",
    abstract = "Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory. In this work, we propose to address this gap by focusing on understanding the input-output behavior of NMT models. Specifically, we measure the word importance by attributing the NMT output to every input word through a gradient-based method. We validate the approach on a couple of perturbation operations, language pairs, and model architectures, demonstrating its superiority on identifying input words with higher influence on translation performance. Encouragingly, the calculated importance can serve as indicators of input words that are under-translated by NMT models. Furthermore, our analysis reveals that words of certain syntactic categories have higher importance while the categories vary across language pairs, which can inspire better design principles of NMT architectures for multi-lingual translation.",
}
@inproceedings{tan-etal-2019-multilingual,
    title = "Multilingual Neural Machine Translation with Language Clustering",
    author = "Tan, Xu  and
      Chen, Jiale  and
      He, Di  and
      Xia, Yingce  and
      Qin, Tao  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1089",
    doi = "10.18653/v1/D19-1089",
    pages = "963--973",
    abstract = "Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practical importance due to its advantages in simplifying the training process, reducing online maintenance costs, and enhancing low-resource and zero-shot translation. Given there are thousands of languages in the world and some of them are very different, it is extremely burdensome to handle them all in a single model or use a separate model for each language pair. Therefore, given a fixed resource budget, e.g., the number of models, how to determine which languages should be supported by one model is critical to multilingual NMT, which, unfortunately, has been ignored by previous work. In this work, we develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. We study two methods for language clustering: (1) using prior knowledge, where we cluster languages according to language family, and (2) using language embedding, in which we represent each language by an embedding vector and cluster them in the embedding space. In particular, we obtain the embedding vectors of all the languages by training a universal neural machine translation model. Our experiments on 23 languages show that the first clustering method is simple and easy to understand but leading to suboptimal translation accuracy, while the second method sufficiently captures the relationship among languages well and improves the translation accuracy for almost all the languages over baseline methods.",
}
@inproceedings{czarnowska-etal-2019-dont,
    title = "Don{'}t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction",
    author = "Czarnowska, Paula  and
      Ruder, Sebastian  and
      Grave, Edouard  and
      Cotterell, Ryan  and
      Copestake, Ann",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1090",
    doi = "10.18653/v1/D19-1090",
    pages = "974--983",
    abstract = "Human translators routinely have to translate rare inflections of words {--} due to the Zipfian distribution of words in a language. When translating from Spanish, a good translator would have no problem identifying the proper translation of a statistically rare inflection such as habl{\'a}ramos. Note the lexeme itself, hablar, is relatively common. In this work, we investigate whether state-of-the-art bilingual lexicon inducers are capable of learning this kind of generalization. We introduce 40 morphologically complete dictionaries in 10 languages and evaluate three of the best performing models on the task of translation of less frequent morphological forms. We demonstrate that the performance of state-of-the-art models drops considerably when evaluated on infrequent morphological inflections and then show that adding a simple morphological constraint at training time improves the performance, proving that the bilingual lexicon inducers can benefit from better encoding of morphology.",
}
@inproceedings{anastasopoulos-neubig-2019-pushing,
    title = "Pushing the Limits of Low-Resource Morphological Inflection",
    author = "Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1091",
    doi = "10.18653/v1/D19-1091",
    pages = "984--996",
    abstract = "Recent years have seen exceptional strides in the task of automatic morphological inflection generation. However, for a long tail of languages the necessary resources are hard to come by, and state-of-the-art neural methods that work well under higher resource settings perform poorly in the face of a paucity of data. In response, we propose a battery of improvements that greatly improve performance under such low-resource conditions. First, we present a novel two-step attention architecture for the inflection decoder. In addition, we investigate the effects of cross-lingual transfer from single and multiple languages, as well as monolingual data hallucination. The macro-averaged accuracy of our models outperforms the state-of-the-art by 15 percentage points. Also, we identify the crucial factors for success with cross-lingual transfer for morphological inflection: typological similarity and a common representation across languages.",
}
@inproceedings{zhang-etal-2019-cross,
    title = "Cross-Lingual Dependency Parsing Using Code-Mixed {T}ree{B}ank",
    author = "Zhang, Meishan  and
      Zhang, Yue  and
      Fu, Guohong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1092",
    doi = "10.18653/v1/D19-1092",
    pages = "997--1006",
    abstract = "Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. The basic idea is to map dependency arcs from a source treebank to its target translation according to word alignments. This method, however, can suffer from imperfect alignment between source and target words. To address this problem, we investigate syntactic transfer by code mixing, translating only confident words in a source treebank. Cross-lingual word embeddings are leveraged for transferring syntactic knowledge to the target from the resulting code-mixed treebank. Experiments on University Dependency Treebanks show that code-mixed treebanks are more effective than translated treebanks, giving highly competitive performances among cross-lingual parsing methods.",
}
@inproceedings{liu-etal-2019-hierarchical,
    title = "Hierarchical Pointer Net Parsing",
    author = "Liu, Linlin  and
      Lin, Xiang  and
      Joty, Shafiq  and
      Han, Simeng  and
      Bing, Lidong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1093",
    doi = "10.18653/v1/D19-1093",
    pages = "1007--1017",
    abstract = "Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder of these parsers has a sequential structure, which does not yield the most appropriate inductive bias for deriving tree structures. In this paper, we propose hierarchical pointer network parsers, and apply them to dependency and sentence-level discourse parsing tasks. Our results on standard benchmark datasets demonstrate the effectiveness of our approach, outperforming existing methods and setting a new state-of-the-art.",
}
@inproceedings{cai-lapata-2019-semi,
    title = "Semi-Supervised Semantic Role Labeling with Cross-View Training",
    author = "Cai, Rui  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1094",
    doi = "10.18653/v1/D19-1094",
    pages = "1018--1027",
    abstract = "The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering. Recent approaches rely on high-quality annotations which are costly to obtain, and mostly unavailable in low resource scenarios (e.g., rare languages or domains). Our work aims to reduce the annotation effort involved via semi-supervised learning. We propose an end-to-end SRL model and demonstrate it can effectively leverage unlabeled data under the cross-view training modeling paradigm. Our LSTM-based semantic role labeler is jointly trained with a sentence learner, which performs POS tagging, dependency parsing, and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools. Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.",
}
@inproceedings{bao-etal-2019-low,
    title = "Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations",
    author = "Bao, Zuyi  and
      Huang, Rui  and
      Li, Chen  and
      Zhu, Kenny",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1095",
    doi = "10.18653/v1/D19-1095",
    pages = "1028--1039",
    abstract = "Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-by-word matching. Such requirements and assumptions are infeasible for most languages, especially for languages with large linguistic distances, e.g., English and Chinese. In this work, we propose a Multilingual Language Model with deep semantic Alignment (MLMA) to generate language-independent representations for cross-lingual sequence labeling. Our methods require only monolingual corpora with no bilingual resources at all and take advantage of deep contextualized representations. Experimental results show that our approach achieves new state-of-the-art NER and POS performance across European languages, and is also effective on distant language pairs such as English and Chinese.",
}
@inproceedings{gui-etal-2019-lexicon,
    title = "A Lexicon-Based Graph Neural Network for {C}hinese {NER}",
    author = "Gui, Tao  and
      Zou, Yicheng  and
      Zhang, Qi  and
      Peng, Minlong  and
      Fu, Jinlan  and
      Wei, Zhongyu  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1096",
    doi = "10.18653/v1/D19-1096",
    pages = "1040--1050",
    abstract = "Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success. However, the characteristic of chain structure and the lack of global semantics determine that RNN-based models are vulnerable to word ambiguities. In this work, we try to alleviate this problem by introducing a lexicon-based graph neural network with global semantics, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency. Based on the multiple graph-based interactions among characters, potential words, and the whole-sentence semantics, word ambiguities can be effectively tackled. Experiments on four NER datasets show that the proposed model achieves significant improvements against other baseline models.",
}
@inproceedings{liu-etal-2019-cm,
    title = "{CM}-Net: A Novel Collaborative Memory Network for Spoken Language Understanding",
    author = "Liu, Yijin  and
      Meng, Fandong  and
      Zhang, Jinchao  and
      Zhou, Jie  and
      Chen, Yufeng  and
      Xu, Jinan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1097",
    doi = "10.18653/v1/D19-1097",
    pages = "1051--1060",
    abstract = "Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing models fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance. To address this issue, in this paper we propose a novel Collaborative Memory Network (CM-Net) based on the well-designed block, named CM-block. The CM-block firstly captures slot-specific and intent-specific features from memories in a collaborative manner, and then uses these enriched features to enhance local context representations, based on which the sequential information flow leads to more specific (slot and intent) global utterance representations. Through stacking multiple CM-blocks, our CM-Net is able to alternately perform information exchange among specific memories, local contexts and the global utterance, and thus incrementally enriches each other. We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a self-collected corpus (CAIS). Experimental results show that the CM-Net achieves the state-of-the-art results on the ATIS and SNIPS in most of criteria, and significantly outperforms the baseline models on the CAIS. Additionally, we make the CAIS dataset publicly available for the research community.",
}
@inproceedings{wang-etal-2019-tree,
    title = "Tree Transformer: Integrating Tree Structures into Self-Attention",
    author = "Wang, Yaushian  and
      Lee, Hung-Yi  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1098",
    doi = "10.18653/v1/D19-1098",
    pages = "1061--1070",
    abstract = "Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks. However, it is unclear what the learned attention captures. The attention computed by attention heads seems not to match human intuitions about hierarchical structures. This paper proposes Tree Transformer, which adds an extra constraint to attention heads of the bidirectional Transformer encoder in order to encourage the attention heads to follow tree structures. The tree structures can be automatically induced from raw texts by our proposed {``}Constituent Attention{''} module, which is simply implemented by self-attention between two adjacent words. With the same training procedure identical to BERT, the experiments demonstrate the effectiveness of Tree Transformer in terms of inducing tree structures, better language modeling, and further learning more explainable attention scores.",
}
@inproceedings{lyu-etal-2019-semantic,
    title = "Semantic Role Labeling with Iterative Structure Refinement",
    author = "Lyu, Chunchuan  and
      Cohen, Shay B.  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1099",
    doi = "10.18653/v1/D19-1099",
    pages = "1071--1082",
    abstract = "Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions between individual argument labeling decisions. This contrasts with earlier work and also with the intuition that the labels of individual arguments are strongly interdependent. We model interactions between argument labeling decisions through iterative refinement. Starting with an output produced by a factorized model, we iteratively refine it using a refinement network. Instead of modeling arbitrary interactions among roles and words, we encode prior knowledge about the SRL problem by designing a restricted network architecture capturing non-local interactions. This modeling choice prevents overfitting and results in an effective model, outperforming strong factorized baseline models on all 7 CoNLL-2009 languages, and achieving state-of-the-art results on 5 of them, including English.",
}
@inproceedings{jain-etal-2019-entity,
    title = "Entity Projection via Machine Translation for Cross-Lingual {NER}",
    author = "Jain, Alankar  and
      Paranjape, Bhargavi  and
      Lipton, Zachary C.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1100",
    doi = "10.18653/v1/D19-1100",
    pages = "1083--1092",
    abstract = "Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition. Motivated by this fact, we leverage machine translation to improve annotation-projection approaches to cross-lingual named entity recognition. We propose a system that improves over prior entity-projection methods by: (a) leveraging machine translation systems twice: first for translating sentences and subsequently for translating entities; (b) matching entities based on orthographic and phonetic similarity; and (c) identifying matches based on distributional statistics derived from the dataset. Our approach improves upon current state-of-the-art methods for cross-lingual named entity recognition on 5 diverse languages by an average of 4.1 points. Further, our method achieves state-of-the-art F{\_}1 scores for Armenian, outperforming even a monolingual model trained on Armenian source data.",
}
@inproceedings{simpson-gurevych-2019-bayesian,
    title = "A {B}ayesian Approach for Sequence Tagging with Crowds",
    author = "Simpson, Edwin  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1101",
    doi = "10.18653/v1/D19-1101",
    pages = "1093--1104",
    abstract = "Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data. However, annotators are often unreliable and current aggregation methods cannot capture common types of span annotation error. To address this, we propose a Bayesian method for aggregating sequence tags that reduces errors by modelling sequential dependencies between the annotations as well as the ground-truth labels. By taking a Bayesian approach, we account for uncertainty in the model due to both annotator errors and the lack of data for modelling annotators who complete few tasks. We evaluate our model on crowdsourced data for named entity recognition, information extraction and argument mining, showing that our sequential model outperforms the previous state of the art, and that Bayesian approaches outperform non-Bayesian alternatives. We also find that our approach can reduce crowdsourcing costs through more effective active learning, as it better captures uncertainty in the sequence labels when there are few annotations.",
}
@inproceedings{vania-etal-2019-systematic,
    title = "A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages",
    author = "Vania, Clara  and
      Kementchedjhieva, Yova  and
      S{\o}gaard, Anders  and
      Lopez, Adam",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1102",
    doi = "10.18653/v1/D19-1102",
    pages = "1105--1116",
    abstract = "Parsers are available for only a handful of the world{'}s languages, since they require lots of training data. How far can we get with just a small amount of training data? We systematically compare a set of simple strategies for improving low-resource parsers: data augmentation, which has not been tested before; cross-lingual training; and transliteration. Experimenting on three typologically diverse low-resource languages{---}North S{\'a}mi, Galician, and Kazah{---}We find that (1) when only the low-resource treebank is available, data augmentation is very helpful; (2) when a related high-resource treebank is available, cross-lingual training is helpful and complements data augmentation; and (3) when the high-resource treebank uses a different writing system, transliteration into a shared orthographic spaces is also very helpful.",
}
@inproceedings{meng-etal-2019-target,
    title = "Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing",
    author = "Meng, Tao  and
      Peng, Nanyun  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1103",
    doi = "10.18653/v1/D19-1103",
    pages = "1117--1128",
    abstract = "Prior work on cross-lingual dependency parsing often focuses on capturing the commonalities between source and target languages and overlook the potential to leverage the linguistic properties of the target languages to facilitate the transfer. In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially. Specifically, we explore several types of corpus linguistic statistics and compile them into corpus-statistics constraints to facilitate the inference procedure. We propose new algorithms that adapt two techniques, Lagrangian relaxation and posterior regularization, to conduct inference with corpus-statistics constraints. Experiments show that the Lagrangian relaxation and posterior regularization techniques improve the performances on 15 and 17 out of 19 target languages, respectively. The improvements are especially large for the target languages that have different word order features from the source language.",
}
@inproceedings{lu-etal-2019-look,
    title = "Look-up and Adapt: A One-shot Semantic Parser",
    author = "Lu, Zhichu  and
      Arabshahi, Forough  and
      Labutov, Igor  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1104",
    doi = "10.18653/v1/D19-1104",
    pages = "1129--1139",
    abstract = "Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited {``}supported{''} domain of discourse and fail drastically when faced with an out-of-domain utterance, mainly due to the limitations of their semantic parser. In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch. Our parser maintains a memory consisting of a representative subset of the seen utterances paired with their logical forms. Given an unseen utterance, our parser works by looking up a similar utterance from the memory and adapting its logical form until it fits the unseen utterance. Moreover, we present a data generation strategy for constructing utterance-logical form pairs from different domains. Our results show an improvement of up to 68.8{\%} on one-shot parsing under two different evaluation settings compared to the baselines.",
}
@inproceedings{xiao-etal-2019-similarity,
    title = "Similarity Based Auxiliary Classifier for Named Entity Recognition",
    author = "Xiao, Shiyuan  and
      Ouyang, Yuanxin  and
      Rong, Wenge  and
      Yang, Jianxin  and
      Xiong, Zhang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1105",
    doi = "10.18653/v1/D19-1105",
    pages = "1140--1149",
    abstract = "The segmentation problem is one of the fundamental challenges associated with name entity recognition (NER) tasks that aim to reduce the boundary error when detecting a sequence of entity words. A considerable number of advanced approaches have been proposed and most of them exhibit performance deterioration when entities become longer. Inspired by previous work in which a multi-task strategy is used to solve segmentation problems, we design a similarity based auxiliary classifier (SAC), which can distinguish entity words from non-entity words. Unlike conventional classifiers, SAC uses vectors to indicate tags. Therefore, SAC can calculate the similarities between words and tags, and then compute a weighted sum of the tag vectors, which can be considered a useful feature for NER tasks. Empirical results are used to verify the rationality of the SAC structure and demonstrate the SAC model{'}s potential in performance improvement against our baseline approaches.",
}
@inproceedings{crabbe-etal-2019-variable,
    title = "Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal",
    author = "Crabb{\'e}, Benoit  and
      Fabre, Murielle  and
      Pallier, Christophe",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1106",
    doi = "10.18653/v1/D19-1106",
    pages = "1150--1160",
    abstract = "This paper describes a method of variable beam size inference for Recurrent Neural Network Grammar (rnng) by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. The paper studies the relevance of such methods for speeding up the computations of direct generative parsing for rnng. But it also studies the potential cognitive interpretation of the underlying representations built by the search method (beam activity) through analysis of neuro-imaging signal.",
}
@inproceedings{geva-etal-2019-modeling,
    title = "Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets",
    author = "Geva, Mor  and
      Goldberg, Yoav  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1107",
    doi = "10.18653/v1/D19-1107",
    pages = "1161--1166",
    abstract = "Crowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years. A common crowdsourcing practice is to recruit a small number of high-quality workers, and have them massively generate examples. Having only a few workers generate the majority of examples raises concerns about data diversity, especially when workers freely generate sentences. In this paper, we perform a series of experiments showing these concerns are evident in three recent NLP datasets. We show that model performance improves when training with annotator identifiers as features, and that models are able to recognize the most productive annotators. Moreover, we show that often models do not generalize well to examples from annotators that did not contribute to the training set. Our findings suggest that annotator bias should be monitored during dataset creation, and that test set annotators should be disjoint from training set annotators.",
}
@inproceedings{parvez-etal-2019-robust,
    title = "Robust Text Classifier on Test-Time Budgets",
    author = "Parvez, Md Rizwan  and
      Bolukbasi, Tolga  and
      Chang, Kai-Wei  and
      Saligrama, Venkatesh",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1108",
    doi = "10.18653/v1/D19-1108",
    pages = "1167--1172",
    abstract = "We design a generic framework for learning a robust text classification model that achieves high accuracy under different selection budgets (a.k.a selection rates) at test-time. We take a different approach from existing methods and learn to dynamically filter a large fraction of unimportant words by a low-complexity selector such that any high-complexity state-of-art classifier only needs to process a small fraction of text, relevant for the target task. To this end, we propose a data aggregation method to train the classifier, allowing it to achieve competitive performance on fractured sentences. On four benchmark text classification tasks, we demonstrate that the framework gains consistent speedup with little degradation in accuracy on various selection budgets.",
}
@inproceedings{davison-etal-2019-commonsense,
    title = "Commonsense Knowledge Mining from Pretrained Models",
    author = "Davison, Joe  and
      Feldman, Joshua  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1109",
    doi = "10.18653/v1/D19-1109",
    pages = "1173--1178",
    abstract = "Inferring commonsense knowledge is a key challenge in machine learning. Due to the sparsity of training data, previous work has shown that supervised methods for commonsense knowledge mining underperform when evaluated on novel data. In this work, we develop a method for generating commonsense knowledge using a large, pre-trained bidirectional language model. By transforming relational triples into masked sentences, we can use this model to rank a triple{'}s validity by the estimated pointwise mutual information between the two entities. Since we do not update the weights of the bidirectional model, our approach is not biased by the coverage of any one commonsense knowledge base. Though we do worse on a held-out test set than models explicitly trained on a corresponding training set, our approach outperforms these methods when mining commonsense knowledge from new sources, suggesting that our unsupervised technique generalizes better than current supervised approaches.",
}
@inproceedings{dodge-etal-2019-rnn,
    title = "{RNN} Architecture Learning with Sparse Regularization",
    author = "Dodge, Jesse  and
      Schwartz, Roy  and
      Peng, Hao  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1110",
    doi = "10.18653/v1/D19-1110",
    pages = "1179--1184",
    abstract = "Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient NLP models. Our method applies group lasso to rational RNNs (Peng et al., 2018), a family of models that is closely connected to weighted finite-state automata (WFSAs). We take advantage of rational RNNs{'} natural grouping of the weights, so the group lasso penalty directly removes WFSA states, substantially reducing the number of parameters in the model. Our experiments on a number of sentiment analysis datasets, using both GloVe and BERT embeddings, show that our approach learns neural structures which have fewer parameters without sacrificing performance relative to parameter-rich baselines. Our method also highlights the interpretable properties of rational RNNs. We show that sparsifying such models makes them easier to visualize, and we present models that rely exclusively on as few as three WFSAs after pruning more than 90{\%} of the weights. We publicly release our code.",
}
@inproceedings{dufter-schutze-2019-analytical,
    title = "Analytical Methods for Interpretable Ultradense Word Embeddings",
    author = {Dufter, Philipp  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1111",
    doi = "10.18653/v1/D19-1111",
    pages = "1185--1191",
    abstract = "Word embeddings are useful for a wide variety of tasks, but they lack interpretability. By rotating word spaces, interpretable dimensions can be identified while preserving the information contained in the embeddings without any loss. In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose. In contrast to Densifier, DensRay can be computed in closed form, is hyperparameter-free and thus more robust than Densifier. We evaluate the three methods on lexicon induction and set-based word analogy. In addition we provide qualitative insights as to how interpretable word spaces can be used for removing gender bias from embeddings.",
}
@inproceedings{dou-etal-2019-investigating,
    title = "Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks",
    author = "Dou, Zi-Yi  and
      Yu, Keyi  and
      Anastasopoulos, Antonios",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1112",
    doi = "10.18653/v1/D19-1112",
    pages = "1192--1197",
    abstract = "Learning general representations of text is a fundamental problem for many natural language understanding (NLU) tasks. Previously, researchers have proposed to use language model pre-training and multi-task learning to learn robust representations. However, these methods can achieve sub-optimal performance in low-resource scenarios. Inspired by the recent success of optimization-based meta-learning algorithms, in this paper, we explore the model-agnostic meta-learning algorithm (MAML) and its variants for low-resource NLU tasks. We validate our methods on the GLUE benchmark and show that our proposed models can outperform several strong baselines. We further empirically demonstrate that the learned representations can be adapted to new tasks efficiently and effectively.",
}
@inproceedings{shi-etal-2019-retrofitting,
    title = "Retrofitting Contextualized Word Embeddings with Paraphrases",
    author = "Shi, Weijia  and
      Chen, Muhao  and
      Zhou, Pei  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1113",
    doi = "10.18653/v1/D19-1113",
    pages = "1198--1203",
    abstract = "Contextualized word embeddings, such as ELMo, provide meaningful representations for words and their contexts. They have been shown to have a great impact on downstream applications. However, we observe that the contextualized embeddings of a word might change drastically when its contexts are paraphrased. As these embeddings are over-sensitive to the context, the downstream model may make different predictions when the input sentence is paraphrased. To address this issue, we propose a post-processing approach to retrofit the embedding with paraphrases. Our method learns an orthogonal transformation on the input space of the contextualized word embedding model, which seeks to minimize the variance of word representations on paraphrased contexts. Experiments show that the proposed method significantly improves ELMo on various sentence classification and inference tasks.",
}
@inproceedings{liu-etal-2019-incorporating-contextual,
    title = "Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling",
    author = "Liu, Linqing  and
      Yang, Wei  and
      Rao, Jinfeng  and
      Tang, Raphael  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1114",
    doi = "10.18653/v1/D19-1114",
    pages = "1204--1209",
    abstract = "Semantic similarity modeling is central to many NLP problems such as natural language inference and question answering. Syntactic structures interact closely with semantics in learning compositional representations and alleviating long-range dependency issues. How-ever, such structure priors have not been well exploited in previous work for semantic mod-eling. To examine their effectiveness, we start with the Pairwise Word Interaction Model, one of the best models according to a recent reproducibility study, then introduce components for modeling context and structure using multi-layer BiLSTMs and TreeLSTMs. In addition, we introduce residual connections to the deep convolutional neural network component of the model. Extensive evaluations on eight benchmark datasets show that incorporating structural information contributes to consistent improvements over strong baselines.",
}
@inproceedings{ziegler-etal-2019-neural,
    title = "Neural Linguistic Steganography",
    author = "Ziegler, Zachary  and
      Deng, Yuntian  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1115",
    doi = "10.18653/v1/D19-1115",
    pages = "1210--1215",
    abstract = "Whereas traditional cryptography encrypts a secret message into an unintelligible form, steganography conceals that communication is taking place by encoding a secret message into a cover signal. Language is a particularly pragmatic cover signal due to its benign occurrence and independence from any one medium. Traditionally, linguistic steganography systems encode secret messages in existing text via synonym substitution or word order rearrangements. Advances in neural language models enable previously impractical generation-based techniques. We propose a steganography technique based on arithmetic coding with large-scale neural language models. We find that our approach can generate realistic looking cover sentences as evaluated by humans, while at the same time preserving security by matching the cover message distribution with the language model distribution.",
}
@inproceedings{sun-nenkova-2019-feasibility,
    title = "The Feasibility of Embedding Based Automatic Evaluation for Single Document Summarization",
    author = "Sun, Simeng  and
      Nenkova, Ani",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1116",
    doi = "10.18653/v1/D19-1116",
    pages = "1216--1221",
    abstract = "ROUGE is widely used to automatically evaluate summarization systems. However, ROUGE measures semantic overlap between a system summary and a human reference on word-string level, much at odds with the contemporary treatment of semantic meaning. Here we present a suite of experiments on using distributed representations for evaluating summarizers, both in reference-based and in reference-free setting. Our experimental results show that the max value over each dimension of the summary ELMo word embeddings is a good representation that results in high correlation with human ratings. Averaging the cosine similarity of all encoders we tested yields high correlation with manual scores in reference-free setting. The distributed representations outperform ROUGE in recent corpora for abstractive news summarization but are less good on test data used in past evaluations.",
}
@inproceedings{gui-etal-2019-attention,
    title = "Attention Optimization for Abstractive Document Summarization",
    author = "Gui, Min  and
      Tian, Junfeng  and
      Wang, Rui  and
      Yang, Zhenglu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1117",
    doi = "10.18653/v1/D19-1117",
    pages = "1222--1228",
    abstract = "Attention plays a key role in the improvement of sequence-to-sequence-based document summarization models. To obtain a powerful attention helping with reproducing the most salient information and avoiding repetitions, we augment the vanilla attention model from both local and global aspects. We propose attention refinement unit paired with local variance loss to impose supervision on the attention model at each decoding step, and we also propose a global variance loss to optimize the attention distributions of all decoding steps from the global perspective. The performances on CNN/Daily Mail dataset verify the effectiveness of our methods.",
}
@inproceedings{aralikatte-etal-2019-rewarding,
    title = "Rewarding Coreference Resolvers for Being Consistent with World Knowledge",
    author = "Aralikatte, Rahul  and
      Lent, Heather  and
      Gonzalez, Ana Valeria  and
      Herschcovich, Daniel  and
      Qiu, Chen  and
      Sandholm, Anders  and
      Ringaard, Michael  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1118",
    doi = "10.18653/v1/D19-1118",
    pages = "1229--1235",
    abstract = "Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.",
}
@inproceedings{kiyono-etal-2019-empirical,
    title = "An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction",
    author = "Kiyono, Shun  and
      Suzuki, Jun  and
      Mita, Masato  and
      Mizumoto, Tomoya  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1119",
    doi = "10.18653/v1/D19-1119",
    pages = "1236--1242",
    abstract = "The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set (F0.5=65.0) and the official test set of the BEA-2019 shared task (F0.5=70.2) without making any modifications to the model architecture.",
}
@inproceedings{yang-etal-2019-multilingual,
    title = "A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability",
    author = "Yang, Weiwei  and
      Boyd-Graber, Jordan  and
      Resnik, Philip",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1120",
    doi = "10.18653/v1/D19-1120",
    pages = "1243--1248",
    abstract = "Multilingual topic models (MTMs) learn topics on documents in multiple languages. Past models align topics across languages by implicitly assuming the documents in different languages are highly comparable, often a false assumption. We introduce a new model that does not rely on this assumption, particularly useful in important low-resource language scenarios. Our MTM learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar, outperforming LDA and previous MTMs in classification tasks using documents{'} topic posteriors as features. It also learns coherent topics on documents with low comparability.",
}
@inproceedings{min-zhao-2019-measure,
    title = "Measure Country-Level Socio-Economic Indicators with Streaming News: An Empirical Study",
    author = "Min, Bonan  and
      Zhao, Xiaoxi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1121",
    doi = "10.18653/v1/D19-1121",
    pages = "1249--1254",
    abstract = "Socio-economic conditions are difficult to measure. For example, the U.S. Bureau of Labor Statistics needs to conduct large-scale household surveys regularly to track the unemployment rate, an indicator widely used by economists and policymakers. We argue that events reported in streaming news can be used as {``}micro-sensors{''} for measuring socio-economic conditions. Similar to collecting surveys and then counting answers, it is possible to measure a socio-economic indicator by counting related events. In this paper, we propose Event-Centric Indicator Measure (ECIM), a novel approach to measure socio-economic indicators with events. We empirically demonstrate strong correlation between ECIM values to several representative indicators in socio-economic research.",
}
@inproceedings{azab-etal-2019-towards,
    title = "Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines",
    author = "Azab, Mahmoud  and
      Dadian, Stephane  and
      Nastase, Vivi  and
      An, Larry  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1122",
    doi = "10.18653/v1/D19-1122",
    pages = "1255--1260",
    abstract = "We introduce a new dataset consisting of natural language interactions annotated with medical family histories, obtained during interactions with a genetic counselor and through crowdsourcing, following a questionnaire created by experts in the domain. We describe the data collection process and the annotations performed by medical professionals, including illness and personal attributes (name, age, gender, family relationships) for the patient and their family members. An initial system that performs argument identification and relation extraction shows promising results {--} average F-score of 0.87 on complex sentences on the targeted relations.",
}
@inproceedings{zhu-etal-2019-multi,
    title = "Multi-task Learning for Natural Language Generation in Task-Oriented Dialogue",
    author = "Zhu, Chenguang  and
      Zeng, Michael  and
      Huang, Xuedong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1123",
    doi = "10.18653/v1/D19-1123",
    pages = "1261--1266",
    abstract = "In task-oriented dialogues, Natural Language Generation (NLG) is the final yet crucial step to produce user-facing system utterances. The result of NLG is directly related to the perceived quality and usability of a dialogue system. While most existing systems provide semantically correct responses given goals to present, they struggle to match the variation and fluency in the human language. In this paper, we propose a novel multi-task learning framework, NLG-LM, for natural language generation. In addition to generating high-quality responses conveying the required information, it also explicitly targets for naturalness in generated responses via an unconditioned language model. This can significantly improve the learning of style and variation in human language. Empirical results show that this multi-task learning framework outperforms previous models across multiple datasets. For example, it improves the previous best BLEU score on the E2E-NLG dataset by 2.2{\%}, and on the Laptop dataset by 6.1{\%}.",
}
@inproceedings{zeng-etal-2019-dirichlet,
    title = "{D}irichlet Latent Variable Hierarchical Recurrent Encoder-Decoder in Dialogue Generation",
    author = "Zeng, Min  and
      Wang, Yisen  and
      Luo, Yuan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1124",
    doi = "10.18653/v1/D19-1124",
    pages = "1267--1272",
    abstract = "Variational encoder-decoders have achieved well-recognized performance in the dialogue generation task. Existing works simply assume the Gaussian priors of the latent variable, which are incapable of representing complex latent variables effectively. To address the issues, we propose to use the Dirichlet distribution with flexible structures to characterize the latent variables in place of the traditional Gaussian distribution, called Dirichlet Latent Variable Hierarchical Recurrent Encoder-Decoder model (Dir-VHRED). Based on which, we further find that there is redundancy among the dimensions of latent variable, and the lengths and sentence patterns of the responses can be strongly correlated to each dimension of the latent variable. Therefore, controllable responses can be generated through specifying the value of each dimension of the latent variable. Experimental results on benchmarks show that our proposed Dir-VHRED yields substantial improvements on negative log-likelihood, word-embedding-based and human evaluations.",
}
@inproceedings{tseng-etal-2019-semi,
    title = "Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling",
    author = "Tseng, Bo-Hsiang  and
      Rei, Marek  and
      Budzianowski, Pawe{\l}  and
      Turner, Richard  and
      Byrne, Bill  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1125",
    doi = "10.18653/v1/D19-1125",
    pages = "1273--1278",
    abstract = "Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels. However, collecting these annotations is expensive and time-consuming, holding back development in the area of dialogue modelling. In this paper, we investigate semi-supervised learning methods that are able to reduce the amount of required intermediate labelling. We find that by leveraging un-annotated data instead, the amount of turn-level annotations of dialogue state can be significantly reduced when building a neural dialogue system. Our analysis on the MultiWOZ corpus, covering a range of domains and topics, finds that annotations can be reduced by up to 30{\%} while maintaining equivalent system performance. We also describe and evaluate the first end-to-end dialogue model created for the MultiWOZ corpus.",
}
@inproceedings{shen-etal-2019-progressive,
    title = "A Progressive Model to Enable Continual Learning for Semantic Slot Filling",
    author = "Shen, Yilin  and
      Zeng, Xiangyu  and
      Jin, Hongxia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1126",
    doi = "10.18653/v1/D19-1126",
    pages = "1279--1284",
    abstract = "Semantic slot filling is one of the major tasks in spoken language understanding (SLU). After a slot filling model is trained on precollected data, it is crucial to continually improve the model after deployment to learn users{'} new expressions. As the data amount grows, it becomes infeasible to either store such huge data and repeatedly retrain the model on all data or fine tune the model only on new data without forgetting old expressions. In this paper, we introduce a novel progressive slot filling model, ProgModel. ProgModel consists of a novel context gate that transfers previously learned knowledge to a small size expanded component; and meanwhile enables this new component to be fast trained to learn from new data. As such, ProgModel learns the new knowledge by only using new data at each time and meanwhile preserves the previously learned expressions. Our experiments show that ProgModel needs much less training time and smaller model size to outperform various model fine tuning competitors by up to 4.24{\%} and 3.03{\%} on two benchmark datasets.",
}
@inproceedings{gupta-etal-2019-casa,
    title = "{CASA}-{NLU}: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots",
    author = "Gupta, Arshit  and
      Zhang, Peng  and
      Lalwani, Garima  and
      Diab, Mona",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1127",
    doi = "10.18653/v1/D19-1127",
    pages = "1285--1290",
    abstract = "Natural Language Understanding (NLU) is a core component of dialog systems. It typically involves two tasks - Intent Classification (IC) and Slot Labeling (SL), which are then followed by a dialogue management (DM) component. Such NLU systems cater to utterances in isolation, thus pushing the problem of context management to DM. However, contextual information is critical to the correct prediction of intents in a conversation. Prior work on contextual NLU has been limited in terms of the types of contextual signals used and the understanding of their impact on the model. In this work, we propose a context-aware self-attentive NLU (CASA-NLU) model that uses multiple signals over a variable context window, such as previous intents, slots, dialog acts and utterances, in addition to the current user utterance. CASA-NLU outperforms a recurrent contextual NLU baseline on two conversational datasets, yielding a gain of up to 7{\%} on the IC task. Moreover, a non-contextual variant of CASA-NLU achieves state-of-the-art performance on standard public datasets - SNIPS and ATIS.",
}
@inproceedings{li-etal-2019-sampling,
    title = "Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems",
    author = "Li, Jia  and
      Tao, Chongyang  and
      Wu, Wei  and
      Feng, Yansong  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1128",
    doi = "10.18653/v1/D19-1128",
    pages = "1291--1296",
    abstract = "We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems. Following an idea of dynamically adapting negative examples to matching models in learning, we consider four strategies including minimum sampling, maximum sampling, semi-hard sampling, and decay-hard sampling. Empirical studies on two benchmarks with three matching models indicate that compared with the widely used random sampling strategy, although the first two strategies lead to performance drop, the latter two ones can bring consistent improvement to the performance of all the models on both benchmarks.",
}
@inproceedings{liu-etal-2019-zero,
    title = "Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables",
    author = "Liu, Zihan  and
      Shin, Jamin  and
      Xu, Yan  and
      Winata, Genta Indra  and
      Xu, Peng  and
      Madotto, Andrea  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1129",
    doi = "10.18653/v1/D19-1129",
    pages = "1297--1303",
    abstract = "Despite the surging demands for multilingual task-oriented dialog systems (e.g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios. Hence, we propose a zero-shot adaptation of task-oriented dialogue system to low-resource languages. To tackle this challenge, we first use a set of very few parallel word pairs to refine the aligned cross-lingual word-level representations. We then employ a latent variable model to cope with the variance of similar sentences across different languages, which is induced by imperfect cross-lingual alignments and inherent differences in languages. Finally, the experimental results show that even though we utilize much less external resources, our model achieves better adaptation performance for natural language understanding task (i.e., the intent detection and slot filling) compared to the current state-of-the-art model in the zero-shot scenario.",
}
@inproceedings{shu-etal-2019-modeling,
    title = "Modeling Multi-Action Policy for Task-Oriented Dialogues",
    author = "Shu, Lei  and
      Xu, Hu  and
      Liu, Bing  and
      Molino, Piero",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1130",
    doi = "10.18653/v1/D19-1130",
    pages = "1304--1310",
    abstract = "Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only one DM policy action per turn. This significantly limits the expressive power of the conversational agent and introduces unwanted turns of interactions that may challenge users{'} patience. Longer conversations also lead to more errors and the system needs to be more robust to handle them. In this paper, we compare the performance of several models on the task of predicting multiple acts for each turn. A novel policy model is proposed based on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the limitations of the existing models. Experimental results show that gCAS outperforms other approaches. The datasets and code are available at https://leishu02.github.io/.",
}
@inproceedings{larson-etal-2019-evaluation,
    title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
    author = "Larson, Stefan  and
      Mahendran, Anish  and
      Peper, Joseph J.  and
      Clarke, Christopher  and
      Lee, Andrew  and
      Hill, Parker  and
      Kummerfeld, Jonathan K.  and
      Leach, Kevin  and
      Laurenzano, Michael A.  and
      Tang, Lingjia  and
      Mars, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1131",
    doi = "10.18653/v1/D19-1131",
    pages = "1311--1316",
    abstract = "Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope{---}i.e., queries that do not fall into any of the system{'}s supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.",
}
@inproceedings{niu-bansal-2019-automatically,
    title = "Automatically Learning Data Augmentation Policies for Dialogue Tasks",
    author = "Niu, Tong  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1132",
    doi = "10.18653/v1/D19-1132",
    pages = "1317--1323",
    abstract = "Automatic data augmentation (AutoAugment) (Cubuk et al., 2019) searches for optimal perturbation policies via a controller trained using performance rewards of a sampled policy on the target task, hence reducing data-level model bias. While being a powerful algorithm, their work has focused on computer vision tasks, where it is comparatively easy to apply imperceptible perturbations without changing an image{'}s semantic meaning. In our work, we adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation. We start with a pool of atomic operations that apply subtle semantic-preserving perturbations to the source inputs of a dialogue task (e.g., different POS-tag types of stopword dropout, grammatical errors, and paraphrasing). Next, we allow the controller to learn more complex augmentation policies by searching over the space of the various combinations of these atomic operations. Moreover, we also explore conditioning the controller on the source inputs of the target task, since certain strategies may not apply to inputs that do not contain that strategy{'}s required linguistic features. Empirically, we demonstrate that both our input-agnostic and input-aware controllers discover useful data augmentation policies, and achieve significant improvements over the previous state-of-the-art, including trained on manually-designed policies.",
}
@inproceedings{gao-etal-2019-uniblock,
    title = "uniblock: Scoring and Filtering Corpus with {U}nicode Block Information",
    author = "Gao, Yingbo  and
      Wang, Weiyue  and
      Ney, Hermann",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1133",
    doi = "10.18653/v1/D19-1133",
    pages = "1324--1329",
    abstract = "The preprocessing pipelines in Natural Language Processing usually involve a step of removing sentences consisted of illegal characters. The definition of illegal characters and the specific removal strategy depend on the task, language, domain, etc, which often lead to tiresome and repetitive scripting of rules. In this paper, we introduce a simple statistical method, uniblock, to overcome this problem. For each sentence, uniblock generates a fixed-size feature vector using Unicode block information of the characters. A Gaussian mixture model is then estimated on some clean corpus using variational inference. The learned model can then be used to score sentences and filter corpus. We present experimental results on Sentiment Analysis, Language Modeling and Machine Translation, and show the simplicity and effectiveness of our method.",
}
@inproceedings{taitelbaum-etal-2019-multilingual,
    title = "Multilingual word translation using auxiliary languages",
    author = "Taitelbaum, Hagai  and
      Chechik, Gal  and
      Goldberger, Jacob",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1134",
    doi = "10.18653/v1/D19-1134",
    pages = "1330--1335",
    abstract = "Current multilingual word translation methods are focused on jointly learning mappings from each language to a shared space. The actual translation, however, is still performed as an isolated bilingual task. In this study we propose a multilingual translation procedure that uses all the learned mappings to translate a word from one language to another. For each source word, we first search for the most relevant auxiliary languages. We then use the translations to these languages to form an improved representation of the source word. Finally, this representation is used for the actual translation to the target language. Experiments on a standard multilingual word translation benchmark demonstrate that our model outperforms state of the art results.",
}
@inproceedings{hao-etal-2019-towards,
    title = "Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons",
    author = "Hao, Jie  and
      Wang, Xing  and
      Shi, Shuming  and
      Zhang, Jinfeng  and
      Tu, Zhaopeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1135",
    doi = "10.18653/v1/D19-1135",
    pages = "1336--1341",
    abstract = "Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks RNNs outperforms both individual architectures, while not much is known about why the hybrid models work. With the belief that modeling hierarchical structure is an essential complementary between SANs and RNNs, we propose to further enhance the strength of hybrid models with an advanced variant of RNNs {--} Ordered Neurons LSTM (ON-LSTM), which introduces a syntax-oriented inductive bias to perform tree-like composition. Experimental results on the benchmark machine translation task show that the proposed approach outperforms both individual architectures and a standard hybrid model. Further analyses on targeted linguistic evaluation and logical inference tasks demonstrate that the proposed approach indeed benefits from a better modeling of hierarchical structure.",
}
@inproceedings{thompson-koehn-2019-vecalign,
    title = "{V}ecalign: Improved Sentence Alignment in Linear Time and Space",
    author = "Thompson, Brian  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1136",
    doi = "10.18653/v1/D19-1136",
    pages = "1342--1348",
    abstract = "We introduce Vecalign, a novel bilingual sentence alignment method which is linear in time and space with respect to the number of sentences being aligned and which requires only bilingual sentence embeddings. On a standard German{--}French test set, Vecalign outperforms the previous state-of-the-art method (which has quadratic time complexity and requires a machine translation system) by 5 F1 points. It substantially outperforms the popular Hunalign toolkit at recovering Bible verse alignments in medium- to low-resource language pairs, and it improves downstream MT quality by 1.7 and 1.6 BLEU in Sinhala-English and Nepali-English, respectively, compared to the Hunalign-based Paracrawl pipeline.",
}
@inproceedings{zheng-etal-2019-simpler,
    title = "Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation",
    author = "Zheng, Baigong  and
      Zheng, Renjie  and
      Ma, Mingbo  and
      Huang, Liang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1137",
    doi = "10.18653/v1/D19-1137",
    pages = "1349--1354",
    abstract = "Simultaneous translation is widely useful but remains challenging. Previous work falls into two main categories: (a) fixed-latency policies such as Ma et al. (2019) and (b) adaptive policies such as Gu et al. (2017). The former are simple and effective, but have to aggressively predict future content due to diverging source-target word order; the latter do not anticipate, but suffer from unstable and inefficient training. To combine the merits of both approaches, we propose a simple supervised-learning framework to learn an adaptive policy from oracle READ/WRITE sequences generated from parallel text. At each step, such an oracle sequence chooses to WRITE the next target word if the available source sentence context provides enough information to do so, otherwise READ the next source word. Experiments on German{\textless}={\textgreater}English show that our method, without retraining the underlying NMT model, can learn flexible policies with better BLEU scores and similar latencies compared to previous work.",
}
@inproceedings{keung-etal-2019-adversarial,
    title = "Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and {NER}",
    author = "Keung, Phillip  and
      Lu, Yichao  and
      Bhardwaj, Vikas",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1138",
    doi = "10.18653/v1/D19-1138",
    pages = "1355--1360",
    abstract = "Contextual word embeddings (e.g. GPT, BERT, ELMo, etc.) have demonstrated state-of-the-art performance on various NLP tasks. Recent work with the multilingual version of BERT has shown that the model performs surprisingly well in cross-lingual settings, even when only labeled English data is used to finetune the model. We improve upon multilingual BERT{'}s zero-resource cross-lingual performance via adversarial learning. We report the magnitude of the improvement on the multilingual MLDoc text classification and CoNLL 2002/2003 named entity recognition tasks. Furthermore, we show that language-adversarial training encourages BERT to align the embeddings of English documents and their translations, which may be the cause of the observed performance gains.",
}
@inproceedings{chen-etal-2019-recurrent,
    title = "Recurrent Positional Embedding for Neural Machine Translation",
    author = "Chen, Kehai  and
      Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1139",
    doi = "10.18653/v1/D19-1139",
    pages = "1361--1367",
    abstract = "In the Transformer network architecture, positional embeddings are used to encode order dependencies into the input representation. However, this input representation only involves static order dependencies based on discrete numerical information, that is, are independent of word content. To address this issue, this work proposes a recurrent positional embedding approach based on word vector. In this approach, these recurrent positional embeddings are learned by a recurrent neural network, encoding word content-based order dependencies into the input representation. They are then integrated into the existing multi-head self-attention model as independent heads or part of each head. The experimental results revealed that the proposed approach improved translation performance over that of the state-of-the-art Transformer baseline in WMT{'}14 English-to-German and NIST Chinese-to-English translation tasks.",
}
@inproceedings{tebbifakhr-etal-2019-machine,
    title = "Machine Translation for Machines: the Sentiment Classification Use Case",
    author = "Tebbifakhr, Amirhossein  and
      Bentivogli, Luisa  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1140",
    doi = "10.18653/v1/D19-1140",
    pages = "1368--1374",
    abstract = "We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency ({``}human-oriented{''} quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a {``}machine-oriented{''} criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with {``}machine-oriented{''} translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.",
}
@inproceedings{galle-2019-investigating,
    title = "Investigating the Effectiveness of {BPE}: The Power of Shorter Sequences",
    author = "Gall{\'e}, Matthias",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1141",
    doi = "10.18653/v1/D19-1141",
    pages = "1375--1381",
    abstract = "Byte-Pair Encoding (BPE) is an unsupervised sub-word tokenization technique, commonly used in neural machine translation and other NLP tasks. Its effectiveness makes it a de facto standard, but the reasons for this are not well understood. We link BPE to the broader family of dictionary-based compression algorithms and compare it with other members of this family. Our experiments across datasets, language pairs, translation models, and vocabulary size show that - given a fixed vocabulary size budget - the fewer tokens an algorithm needs to cover the test set, the better the translation (as measured by BLEU).",
}
@inproceedings{thompson-etal-2019-hablex,
    title = "{HABL}ex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation",
    author = "Thompson, Brian  and
      Knowles, Rebecca  and
      Zhang, Xuan  and
      Khayrallah, Huda  and
      Duh, Kevin  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1142",
    doi = "10.18653/v1/D19-1142",
    pages = "1382--1387",
    abstract = "Bilingual lexicons are valuable resources used by professional human translators. While these resources can be easily incorporated in statistical machine translation, it is unclear how to best do so in the neural framework. In this work, we present the HABLex dataset, designed to test methods for bilingual lexicon integration into neural machine translation. Our data consists of human generated alignments of words and phrases in machine translation test sets in three language pairs (Russian-English, Chinese-English, and Korean-English), resulting in clean bilingual lexicons which are well matched to the reference. We also present two simple baselines - constrained decoding and continued training - and an improvement to continued training to address overfitting.",
}
@inproceedings{zhou-etal-2019-handling,
    title = "Handling Syntactic Divergence in Low-resource Machine Translation",
    author = "Zhou, Chunting  and
      Ma, Xuezhe  and
      Hu, Junjie  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1143",
    doi = "10.18653/v1/D19-1143",
    pages = "1388--1394",
    abstract = "Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs. Data augmentation methods such as back-translation make it possible to use monolingual data to help alleviate these issues, but back-translation itself fails in extreme low-resource scenarios, especially for syntactically divergent languages. In this paper, we propose a simple yet effective solution, whereby target-language sentences are re-ordered to match the order of the source and used as an additional source of training-time supervision. Experiments with simulated low-resource Japanese-to-English, and real low-resource Uyghur-to-English scenarios find significant improvements over other semi-supervised alternatives.",
}
@inproceedings{zheng-etal-2019-speculative,
    title = "Speculative Beam Search for Simultaneous Translation",
    author = "Zheng, Renjie  and
      Ma, Mingbo  and
      Zheng, Baigong  and
      Huang, Liang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1144",
    doi = "10.18653/v1/D19-1144",
    pages = "1395--1402",
    abstract = "Beam search is universally used in (full-sentence) machine translation but its application to simultaneous translation remains highly non-trivial, where output words are committed on the fly. In particular, the recently proposed wait-k policy (Ma et al., 2018) is a simple and effective method that (after an initial wait) commits one output word on receiving each input word, making beam search seemingly inapplicable. To address this challenge, we propose a new speculative beam search algorithm that hallucinates several steps into the future in order to reach a more accurate decision by implicitly benefiting from a target language model. This idea makes beam search applicable for the first time to the generation of a single word in each step. Experiments over diverse language pairs show large improvement compared to previous work.",
}
@inproceedings{wang-etal-2019-self-attention,
    title = "Self-Attention with Structural Position Representations",
    author = "Wang, Xing  and
      Tu, Zhaopeng  and
      Wang, Longyue  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1145",
    doi = "10.18653/v1/D19-1145",
    pages = "1403--1409",
    abstract = "Although self-attention networks (SANs) have advanced the state-of-the-art on various NLP tasks, one criticism of SANs is their ability of encoding positions of input words (Shaw et al., 2018). In this work, we propose to augment SANs with structural position representations to model the latent structure of the input sentence, which is complementary to the standard sequential positional representations. Specifically, we use dependency tree to represent the grammatical structure of a sentence, and propose two strategies to encode the positional relationships among words in the dependency tree. Experimental results on NIST Chinese-to-English and WMT14 English-to-German translation tasks show that the proposed approach consistently boosts performance over both the absolute and relative sequential position representations.",
}
@inproceedings{dabre-etal-2019-exploiting,
    title = "Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation",
    author = "Dabre, Raj  and
      Fujita, Atsushi  and
      Chu, Chenhui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1146",
    doi = "10.18653/v1/D19-1146",
    pages = "1410--1416",
    abstract = "This paper highlights the impressive utility of multi-parallel corpora for transfer learning in a one-to-many low-resource neural machine translation (NMT) setting. We report on a systematic comparison of multistage fine-tuning configurations, consisting of (1) pre-training on an external large (209k{--}440k) parallel corpus for English and a helping target language, (2) mixed pre-training or fine-tuning on a mixture of the external and low-resource (18k) target parallel corpora, and (3) pure fine-tuning on the target parallel corpora. Our experiments confirm that multi-parallel corpora are extremely useful despite their scarcity and content-wise redundancy thus exhibiting the true power of multilingualism. Even when the helping target language is not one of the target languages of our concern, our multistage fine-tuning can give 3{--}9 BLEU score gains over a simple one-to-one model.",
}
@inproceedings{dou-etal-2019-unsupervised,
    title = "Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings",
    author = "Dou, Zi-Yi  and
      Hu, Junjie  and
      Anastasopoulos, Antonios  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1147",
    doi = "10.18653/v1/D19-1147",
    pages = "1417--1422",
    abstract = "The recent success of neural machine translation models relies on the availability of high quality, in-domain data. Domain adaptation is required when domain-specific data is scarce or nonexistent. Previous unsupervised domain adaptation strategies include training the model with in-domain copied monolingual or back-translated data. However, these methods use generic representations for text regardless of domain shift, which makes it infeasible for translation models to control outputs conditional on a specific domain. In this work, we propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. Our approach allows the model to assign domain-specific representations to words and output sentences in the desired domain. Our empirical results demonstrate the effectiveness of the proposed strategy, achieving consistent improvements in multiple experimental settings. In addition, we show that combining our method with back translation can further improve the performance of the model.",
}
@inproceedings{jiang-etal-2019-regularization,
    title = "A Regularization-based Framework for Bilingual Grammar Induction",
    author = "Jiang, Yong  and
      Han, Wenjuan  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1148",
    doi = "10.18653/v1/D19-1148",
    pages = "1423--1428",
    abstract = "Grammar induction aims to discover syntactic structures from unannotated sentences. In this paper, we propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language. Unlike previous work on multilingual grammar induction, our approach does not rely on any external resource, such as parallel corpora, word alignments or linguistic phylogenetic trees. We propose three regularization methods that encourage similarity between model parameters, dependency edge scores, and parse trees respectively. We deploy our methods on a state-of-the-art unsupervised discriminative parser and evaluate it on both transfer grammar induction and bilingual grammar induction. Empirical results on multiple languages show that our methods outperform strong baselines.",
}
@inproceedings{tang-etal-2019-encoders,
    title = "Encoders Help You Disambiguate Word Senses in Neural Machine Translation",
    author = "Tang, Gongbo  and
      Sennrich, Rico  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1149",
    doi = "10.18653/v1/D19-1149",
    pages = "1429--1435",
    abstract = "Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of disambiguation. In this paper, we explore the ability of NMT encoders and decoders to disambiguate word senses by evaluating hidden states and investigating the distributions of self-attention. We train a classifier to predict whether a translation is correct given the representation of an ambiguous noun. We find that encoder hidden states outperform word embeddings significantly which indicates that encoders adequately encode relevant information for disambiguation into hidden states. In contrast to encoders, the effect of decoder is different in models with different architectures. Moreover, the attention weights and attention entropy show that self-attention can detect ambiguous nouns and distribute more attention to the context.",
}
@inproceedings{song-park-2019-korean,
    title = "{K}orean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model",
    author = "Song, Hyun-Je  and
      Park, Seong-Bae",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1150",
    doi = "10.18653/v1/D19-1150",
    pages = "1436--1441",
    abstract = "Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging. Thus, a pipeline model of the tasks has been adopted widely by previous studies. However, the model has a problem that it cannot utilize interactions among the tasks. This paper formulates Korean morphological analysis as a combination of the tasks and presents a tied sequence-to-sequence multi-task model for training the two tasks simultaneously without any explicit regularization. The experiments prove the proposed model achieves the state-of-the-art performance.",
}
@inproceedings{alqahtani-etal-2019-efficient,
    title = "Efficient Convolutional Neural Networks for Diacritic Restoration",
    author = "Alqahtani, Sawsan  and
      Mishra, Ajay  and
      Diab, Mona",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1151",
    doi = "10.18653/v1/D19-1151",
    pages = "1442--1448",
    abstract = "Diacritic restoration has gained importance with the growing need for machines to understand written texts. The task is typically modeled as a sequence labeling problem and currently Bidirectional Long Short Term Memory (BiLSTM) models provide state-of-the-art results. Recently, Bai et al. (2018) show the advantages of Temporal Convolutional Neural Networks (TCN) over Recurrent Neural Networks (RNN) for sequence modeling in terms of performance and computational resources. As diacritic restoration benefits from both previous as well as subsequent timesteps, we further apply and evaluate a variant of TCN, Acausal TCN (A-TCN), which incorporates context from both directions (previous and future) rather than strictly incorporating previous context as in the case of TCN. A-TCN yields significant improvement over TCN for diacritization in three different languages: Arabic, Yoruba, and Vietnamese. Furthermore, A-TCN and BiLSTM have comparable performance, making A-TCN an efficient alternative over BiLSTM since convolutions can be trained in parallel. A-TCN is significantly faster than BiLSTM at inference time (270{\%} 334{\%} improvement in the amount of text diacritized per minute).",
}
@inproceedings{murahari-etal-2019-improving,
    title = "Improving Generative Visual Dialog by Answering Diverse Questions",
    author = "Murahari, Vishvak  and
      Chattopadhyay, Prithvijit  and
      Batra, Dhruv  and
      Parikh, Devi  and
      Das, Abhishek",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1152",
    doi = "10.18653/v1/D19-1152",
    pages = "1449--1454",
    abstract = "Prior work on training generative Visual Dialog models with reinforcement learning ((Das et al., ICCV 2017) has explored a Q-Bot-A-Bot image-guessing game and shown that this {`}self-talk{'} approach can lead to improved performance at the downstream dialog-conditioned image-guessing task. However, this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model. We find that this is due in part to repeated interactions between Q-Bot and A-BOT during self-talk, which are not informative with respect to the image. To improve this, we devise a simple auxiliary objective that incentivizes Q-Bot to ask diverse questions, thus reducing repetitions and in turn enabling A-Bot to explore a larger state space during RL i.e. be exposed to more visual concepts to talk about, and varied questions to answer. We evaluate our approach via a host of automatic metrics and human studies, and demonstrate that it leads to better dialog, i.e. dialog that is more diverse (i.e. less repetitive), consistent (i.e. has fewer conflicting exchanges), fluent (i.e., more human-like), and detailed, while still being comparably image-relevant as prior work and ablations.",
}
@inproceedings{do-gaspers-2019-cross,
    title = "Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding",
    author = "Do, Quynh  and
      Gaspers, Judith",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1153",
    doi = "10.18653/v1/D19-1153",
    pages = "1455--1460",
    abstract = "A typical cross-lingual transfer learning approach boosting model performance on a language is to pre-train the model on all available supervised data from another language. However, in large-scale systems this leads to high training times and computational requirements. In addition, characteristic differences between the source and target languages raise a natural question of whether source data selection can improve the knowledge transfer. In this paper, we address this question and propose a simple but effective language model based source-language data selection method for cross-lingual transfer learning in large-scale spoken language understanding. The experimental results show that with data selection i) source data and hence training speed is reduced significantly and ii) model performance is improved.",
}
@inproceedings{huang-etal-2019-multi-head,
    title = "Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations",
    author = "Huang, Po-Yao  and
      Chang, Xiaojun  and
      Hauptmann, Alexander",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1154",
    doi = "10.18653/v1/D19-1154",
    pages = "1461--1467",
    abstract = "With the aim of promoting and understanding the multilingual version of image search, we leverage visual object detection and propose a model with diverse multi-head attention to learn grounded multilingual multimodal representations. Specifically, our model attends to different types of textual semantics in two languages and visual objects for fine-grained alignments between sentences and images. We introduce a new objective function which explicitly encourages attention diversity to learn an improved visual-semantic embedding space. We evaluate our model in the German-Image and English-Image matching tasks on the Multi30K dataset, and in the Semantic Textual Similarity task with the English descriptions of visual content. Results show that our model yields a significant performance gain over other methods in all of the three tasks.",
}
@inproceedings{changpinyo-etal-2019-decoupled,
    title = "Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering",
    author = "Changpinyo, Soravit  and
      Pang, Bo  and
      Sharma, Piyush  and
      Soricut, Radu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1155",
    doi = "10.18653/v1/D19-1155",
    pages = "1468--1474",
    abstract = "Object detection plays an important role in current solutions to vision and language tasks like image captioning and visual question answering. However, popular models like Faster R-CNN rely on a costly process of annotating ground-truths for both the bounding boxes and their corresponding semantic labels, making it less amenable as a primitive task for transfer learning. In this paper, we examine the effect of decoupling box proposal and featurization for down-stream tasks. The key insight is that this allows us to leverage a large amount of labeled annotations that were previously unavailable for standard object detection benchmarks. Empirically, we demonstrate that this leads to effective transfer learning and improved image captioning and visual question answering models, as measured on publicly-available benchmarks.",
}
@inproceedings{jiang-etal-2019-reo,
    title = "{REO}-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning",
    author = "Jiang, Ming  and
      Hu, Junjie  and
      Huang, Qiuyuan  and
      Zhang, Lei  and
      Diesner, Jana  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1156",
    doi = "10.18653/v1/D19-1156",
    pages = "1475--1480",
    abstract = "Popular metrics used for evaluating image captioning systems, such as BLEU and CIDEr, provide a single score to gauge the system{'}s overall effectiveness. This score is often not informative enough to indicate what specific errors are made by a given system. In this study, we present a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems. REO assesses the quality of captions from three perspectives: 1) Relevance to the ground truth, 2) Extraness of the content that is irrelevant to the ground truth, and 3) Omission of the elements in the images and human references. Experiments on three benchmark datasets demonstrate that our method achieves a higher consistency with human judgments and provides more intuitive evaluation results than alternative metrics.",
}
@inproceedings{gao-etal-2019-wslln,
    title = "{WSLLN}:Weakly Supervised Natural Language Localization Networks",
    author = "Gao, Mingfei  and
      Davis, Larry  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1157",
    doi = "10.18653/v1/D19-1157",
    pages = "1481--1487",
    abstract = "We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries. To learn the correspondence between visual segments and texts, most previous methods require temporal coordinates (start and end times) of events for training, which leads to high costs of annotation. WSLLN relieves the annotation burden by training with only video-sentence pairs without accessing to temporal locations of events. With a simple end-to-end structure, WSLLN measures segment-text consistency and conducts segment selection (conditioned on the text) simultaneously. Results from both are merged and optimized as a video-sentence matching problem. Experiments on ActivityNet Captions and DiDeMo demonstrate that WSLLN achieves state-of-the-art performance.",
}
@inproceedings{han-etal-2019-grounding,
    title = "Grounding learning of modifier dynamics: An application to color naming",
    author = "Han, Xudong  and
      Schulz, Philip  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1158",
    doi = "10.18653/v1/D19-1158",
    pages = "1488--1493",
    abstract = "Grounding is crucial for natural language understanding. An important subtask is to understand modified color expressions, such as {``}light blue{''}. We present a model of color modifiers that, compared with previous additive models in RGB space, learns more complex transformations. In addition, we present a model that operates in the HSV color space. We show that certain adjectives are better modeled in that space. To account for all modifiers, we train a hard ensemble model that selects a color space depending on the modifier-color pair. Experimental results show significant and consistent improvements compared to the state-of-the-art baseline model.",
}
@inproceedings{li-etal-2019-robust,
    title = "Robust Navigation with Language Pretraining and Stochastic Sampling",
    author = "Li, Xiujun  and
      Li, Chunyuan  and
      Xia, Qiaolin  and
      Bisk, Yonatan  and
      Celikyilmaz, Asli  and
      Gao, Jianfeng  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1159",
    doi = "10.18653/v1/D19-1159",
    pages = "1494--1499",
    abstract = "Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments. In this paper, we report two simple but highly effective methods to address these challenges and lead to a new state-of-the-art performance. First, we adapt large-scale pretrained language models to learn text representations that generalize better to previously unseen instructions. Second, we propose a stochastic sampling scheme to reduce the considerable gap between the expert actions in training and sampled actions in test, so that the agent can learn to correct its own mistakes during long sequential action decoding. Combining the two techniques, we achieve a new state of the art on the Room-to-Room benchmark with 6{\%} absolute gain over the previous best result (47{\%} -{\textgreater} 53{\%}) on the Success Rate weighted by Path Length metric.",
}
@inproceedings{strzyz-etal-2019-towards,
    title = "Towards Making a Dependency Parser See",
    author = "Strzyz, Michalina  and
      Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1160",
    doi = "10.18653/v1/D19-1160",
    pages = "1500--1506",
    abstract = "We explore whether it is possible to leverage eye-tracking data in an RNN dependency parser (for English) when such information is only available during training - i.e. no aggregated or token-level gaze features are used at inference time. To do so, we train a multitask learning model that parses sentences as sequence labeling and leverages gaze features as auxiliary tasks. Our method also learns to train from disjoint datasets, i.e. it can be used to test whether already collected gaze features are useful to improve the performance on new non-gazed annotated treebanks. Accuracy gains are modest but positive, showing the feasibility of the approach. It can serve as a first step towards architectures that can better leverage eye-tracking data or other complementary information available only for training sentences, possibly leading to improvements in syntactic parsing.",
}
@inproceedings{drozdov-etal-2019-unsupervised-labeled,
    title = "Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders",
    author = "Drozdov, Andrew  and
      Verga, Patrick  and
      Chen, Yi-Pei  and
      Iyyer, Mohit  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1161",
    doi = "10.18653/v1/D19-1161",
    pages = "1507--1512",
    abstract = "Understanding text often requires identifying meaningful constituent spans such as noun phrases and verb phrases. In this work, we show that we can effectively recover these types of labels using the learned phrase vectors from deep inside-outside recursive autoencoders (DIORA). Specifically, we cluster span representations to induce span labels. Additionally, we improve the model{'}s labeling accuracy by integrating latent code learning into the training procedure. We evaluate this approach empirically through unsupervised labeled constituency parsing. Our method outperforms ELMo and BERT on two versions of the Wall Street Journal (WSJ) dataset and is competitive to prior work that requires additional human annotations, improving over a previous state-of-the-art system that depends on ground-truth part-of-speech tags by 5 absolute F1 points (19{\%} relative error reduction).",
}
@inproceedings{davidson-etal-2019-dependency,
    title = "Dependency Parsing for Spoken Dialog Systems",
    author = "Davidson, Sam  and
      Yu, Dian  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1162",
    doi = "10.18653/v1/D19-1162",
    pages = "1513--1519",
    abstract = "Dependency parsing of conversational input can play an important role in language understanding for dialog systems by identifying the relationships between entities extracted from user utterances. Additionally, effective dependency parsing can elucidate differences in language structure and usage for discourse analysis of human-human versus human-machine dialogs. However, models trained on datasets based on news articles and web data do not perform well on spoken human-machine dialog, and currently available annotation schemes do not adapt well to dialog data. Therefore, we propose the Spoken Conversation Universal Dependencies (SCUD) annotation scheme that extends the Universal Dependencies (UD) (Nivre et al., 2016) guidelines to spoken human-machine dialogs. We also provide ConvBank, a conversation dataset between humans and an open-domain conversational dialog system with SCUD annotation. Finally, to demonstrate the utility of the dataset, we train a dependency parser on the ConvBank dataset. We demonstrate that by pre-training a dependency parser on a set of larger public datasets and fine-tuning on ConvBank data, we achieved the best result, 85.05{\%} unlabeled and 77.82{\%} labeled attachment accuracy.",
}
@inproceedings{pasupat-etal-2019-span,
    title = "Span-based Hierarchical Semantic Parsing for Task-Oriented Dialog",
    author = "Pasupat, Panupong  and
      Gupta, Sonal  and
      Mandyam, Karishma  and
      Shah, Rushin  and
      Lewis, Mike  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1163",
    doi = "10.18653/v1/D19-1163",
    pages = "1520--1526",
    abstract = "We propose a semantic parser for parsing compositional utterances into Task Oriented Parse (TOP), a tree representation that has intents and slots as labels of nesting tree nodes. Our parser is span-based: it scores labels of the tree nodes covering each token span independently, but then decodes a valid tree globally. In contrast to previous sequence decoding approaches and other span-based parsers, we (1) improve the training speed by removing the need to run the decoder at training time; and (2) introduce edge scores, which model relations between parent and child labels, to mitigate the independence assumption between node labels and improve accuracy. Our best parser outperforms previous methods on the TOP dataset of mixed-domain task-oriented utterances in both accuracy and training speed.",
}
@inproceedings{yang-etal-2019-enhancing-context,
    title = "Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation",
    author = "Yang, Zhengxin  and
      Zhang, Jinchao  and
      Meng, Fandong  and
      Gu, Shuhao  and
      Feng, Yang  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1164",
    doi = "10.18653/v1/D19-1164",
    pages = "1527--1537",
    abstract = "Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations. The widely used method for document-level translation usually compresses the context information into a representation via hierarchical attention networks. However, this method neither considers the relationship between context words nor distinguishes the roles of context words. To address this problem, we propose a query-guided capsule networks to cluster context information into different perspectives from which the target translation may concern. Experiment results show that our method can significantly outperform strong baselines on multiple data sets of different domains.",
}
@inproceedings{bapna-firat-2019-simple,
    title = "Simple, Scalable Adaptation for Neural Machine Translation",
    author = "Bapna, Ankur  and
      Firat, Orhan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1165",
    doi = "10.18653/v1/D19-1165",
    pages = "1538--1548",
    abstract = "Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maintaining a separate model for each target task. We propose a simple yet efficient approach for adaptation in NMT. Our proposed approach consists of injecting tiny task specific adapter layers into a pre-trained model. These lightweight adapters, with just a small fraction of the original model size, adapt the model to multiple individual tasks simultaneously. We evaluate our approach on two tasks: (i) Domain Adaptation and (ii) Massively Multilingual NMT. Experiments on domain adaptation demonstrate that our proposed approach is on par with full fine-tuning on various domains, dataset sizes and model capacities. On a massively multilingual dataset of 103 languages, our adaptation approach bridges the gap between individual bilingual models and one massively multilingual model for most language pairs, paving the way towards universal machine translation.",
}
@inproceedings{agrawal-carpuat-2019-controlling,
    title = "Controlling Text Complexity in Neural Machine Translation",
    author = "Agrawal, Sweta  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1166",
    doi = "10.18653/v1/D19-1166",
    pages = "1549--1564",
    abstract = "This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence to sequence models that can translate and simplify text jointly. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently.",
}
@inproceedings{kudugunta-etal-2019-investigating,
    title = "Investigating Multilingual {NMT} Representations at Scale",
    author = "Kudugunta, Sneha  and
      Bapna, Ankur  and
      Caswell, Isaac  and
      Firat, Orhan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1167",
    doi = "10.18653/v1/D19-1167",
    pages = "1565--1575",
    abstract = "Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings. However, these black-box representations are poorly understood, and their mode of transfer remains elusive. In this work, we attempt to understand massively multilingual NMT representations (with 103 languages) using Singular Value Canonical Correlation Analysis (SVCCA), a representation similarity framework that allows us to compare representations across different languages, layers and models. Our analysis validates several empirical results and long-standing intuitions, and unveils new observations regarding how representations evolve in a multilingual translation model. We draw three major results from our analysis, with implications on cross-lingual transfer learning: (i) Encoder representations of different languages cluster based on linguistic similarity, (ii) Representations of a source language learned by the encoder are dependent on the target language, and vice-versa, and (iii) Representations of high resource and/or linguistically similar languages are more robust when fine-tuning on an arbitrary language pair, which is critical to determining how much cross-lingual transfer can be expected in a zero or few-shot setting. We further connect our findings with existing empirical observations in multilingual NMT and transfer learning.",
}
@inproceedings{tan-etal-2019-hierarchical,
    title = "Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation",
    author = "Tan, Xin  and
      Zhang, Longyin  and
      Xiong, Deyi  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1168",
    doi = "10.18653/v1/D19-1168",
    pages = "1576--1585",
    abstract = "Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context for translation. In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT). This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence. With this hierarchical architecture, we feedback the extracted global document context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context. In addition, since large-scale in-domain document-level parallel corpora are usually unavailable, we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability. Experimental results on several benchmark corpora show that our proposed model can significantly improve document-level translation performance over several strong NMT baselines.",
}
@inproceedings{cui-etal-2019-cross,
    title = "Cross-Lingual Machine Reading Comprehension",
    author = "Cui, Yiming  and
      Che, Wanxiang  and
      Liu, Ting  and
      Qin, Bing  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1169",
    doi = "10.18653/v1/D19-1169",
    pages = "1586--1595",
    abstract = "Though the community has made great progress on Machine Reading Comprehension (MRC) task, most of the previous works are solving English-based MRC problems, and there are few efforts on other languages mainly due to the lack of large-scale training data.In this paper, we propose Cross-Lingual Machine Reading Comprehension (CLMRC) task for the languages other than English. Firstly, we present several back-translation approaches for CLMRC task which is straightforward to adopt. However, to exactly align the answer into source language is difficult and could introduce additional noise. In this context, we propose a novel model called Dual BERT, which takes advantage of the large-scale training data provided by rich-resource language (such as English) and learn the semantic relations between the passage and question in bilingual context, and then utilize the learned knowledge to improve reading comprehension performance of low-resource language. We conduct experiments on two Chinese machine reading comprehension datasets CMRC 2018 and DRCD. The results show consistent and significant improvements over various state-of-the-art systems by a large margin, which demonstrate the potentials in CLMRC task. Resources available: https://github.com/ymcui/Cross-Lingual-MRC",
}
@inproceedings{hu-etal-2019-multi,
    title = "A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning",
    author = "Hu, Minghao  and
      Peng, Yuxing  and
      Huang, Zhen  and
      Li, Dongsheng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1170",
    doi = "10.18653/v1/D19-1170",
    pages = "1596--1606",
    abstract = "Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings. However, the performance of these models degrades significantly when they are applied to more realistic scenarios, such as answers involve various types, multiple text strings are correct answers, or discrete reasoning abilities are required. In this paper, we introduce the Multi-Type Multi-Span Network (MTMSN), a neural reading comprehension model that combines a multi-type answer predictor designed to support various answer types (e.g., span, count, negation, and arithmetic expression) with a multi-span extraction method for dynamically producing one or multiple text spans. In addition, an arithmetic expression reranking mechanism is proposed to rank expression candidates for further confirming the prediction. Experiments show that our model achieves 79.9 F1 on the DROP hidden test set, creating new state-of-the-art results. Source code (https://github.com/huminghao16/MTMSN) is released to facilitate future work.",
}
@inproceedings{ruckle-etal-2019-neural,
    title = "Neural Duplicate Question Detection without Labeled Training Data",
    author = {R{\"u}ckl{\'e}, Andreas  and
      Moosavi, Nafise Sadat  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1171",
    doi = "10.18653/v1/D19-1171",
    pages = "1607--1617",
    abstract = "Supervised training of neural models to duplicate question detection in community Question Answering (CQA) requires large amounts of labeled question pairs, which can be costly to obtain. To minimize this cost, recent works thus often used alternative methods, e.g., adversarial domain adaptation. In this work, we propose two novel methods{---}weak supervision using the title and body of a question, and the automatic generation of duplicate questions{---}and show that both can achieve improved performances even though they do not require any labeled data. We provide a comparison of popular training strategies and show that our proposed approaches are more effective in many cases because they can utilize larger amounts of data from the CQA forums. Finally, we show that weak supervision with question title and body information is also an effective method to train CQA answer selection models without direct answer supervision.",
}
@inproceedings{xu-etal-2019-asking,
    title = "Asking Clarification Questions in Knowledge-Based Question Answering",
    author = "Xu, Jingjing  and
      Wang, Yuechen  and
      Tang, Duyu  and
      Duan, Nan  and
      Yang, Pengcheng  and
      Zeng, Qi  and
      Zhou, Ming  and
      Sun, Xu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1172",
    doi = "10.18653/v1/D19-1172",
    pages = "1618--1629",
    abstract = "The ability to ask clarification questions is essential for knowledge-based question answering (KBQA) systems, especially for handling ambiguous phenomena. Despite its importance, clarification has not been well explored in current KBQA systems. Further progress requires supervised resources for training and evaluation, and powerful models for clarification-related text understanding and generation. In this paper, we construct a new clarification dataset, CLAQUA, with nearly 40K open-domain examples. The dataset supports three serial tasks: given a question, identify whether clarification is needed; if yes, generate a clarification question; then predict answers base on external user feedback. We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation. Experiments show that the proposed model achieves better performance than strong baselines. The further analysis demonstrates that our dataset brings new challenges and there still remain several unsolved problems, like reasonable automatic evaluation metrics for clarification question generation and powerful models for handling entity sparsity.",
}
@inproceedings{poerner-schutze-2019-multi,
    title = "Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection",
    author = {Poerner, Nina  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1173",
    doi = "10.18653/v1/D19-1173",
    pages = "1630--1641",
    abstract = "We address the problem of Duplicate Question Detection (DQD) in low-resource domain-specific Community Question Answering forums. Our multi-view framework MV-DASE combines an ensemble of sentence encoders via Generalized Canonical Correlation Analysis, using unlabeled data only. In our experiments, the ensemble includes generic and domain-specific averaged word embeddings, domain-finetuned BERT and the Universal Sentence Encoder. We evaluate MV-DASE on the CQADupStack corpus and on additional low-resource Stack Exchange forums. Combining the strengths of different encoders, we significantly outperform BM25, all single-view systems as well as a recent supervised domain-adversarial DQD method.",
}
@inproceedings{parikh-etal-2019-multi,
    title = "Multi-label Categorization of Accounts of Sexism using a Neural Framework",
    author = "Parikh, Pulkit  and
      Abburi, Harika  and
      Badjatiya, Pinkesh  and
      Krishnan, Radhika  and
      Chhaya, Niyati  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1174",
    doi = "10.18653/v1/D19-1174",
    pages = "1642--1652",
    abstract = "Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in utilizing such data to study and counter sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multi-label classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.",
}
@inproceedings{pethe-skiena-2019-trumpiest,
    title = "The Trumpiest Trump? Identifying a Subject{'}s Most Characteristic Tweets",
    author = "Pethe, Charuta  and
      Skiena, Steve",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1175",
    doi = "10.18653/v1/D19-1175",
    pages = "1653--1663",
    abstract = "The sequence of documents produced by any given author varies in style and content, but some documents are more typical or representative of the source than others. We quantify the extent to which a given short text is characteristic of a specific person, using a dataset of tweets from fifteen celebrities. Such analysis is useful for generating excerpts of high-volume Twitter profiles, and understanding how representativeness relates to tweet popularity. We first consider the related task of binary author detection (is x the author of text T?), and report a test accuracy of 90.37{\%} for the best of five approaches to this problem. We then use these models to compute characterization scores among all of an author{'}s texts. A user study shows human evaluators agree with our characterization model for all 15 celebrities in our dataset, each with p-value {\textless} 0.05. We use these classifiers to show surprisingly strong correlations between characterization scores and the popularity of the associated texts. Indeed, we demonstrate a statistically significant correlation between this score and tweet popularity (likes/replies/retweets) for 13 of the 15 celebrities in our study.",
}
@inproceedings{breitfeller-etal-2019-finding,
    title = "Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts",
    author = "Breitfeller, Luke  and
      Ahn, Emily  and
      Jurgens, David  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1176",
    doi = "10.18653/v1/D19-1176",
    pages = "1664--1674",
    abstract = "Microaggressions are subtle, often veiled, manifestations of human biases. These uncivil interactions can have a powerful negative impact on people by marginalizing minorities and disadvantaged groups. The linguistic subtlety of microaggressions in communication has made it difficult for researchers to analyze their exact nature, and to quantify and extract microaggressions automatically. Specifically, the lack of a corpus of real-world microaggressions and objective criteria for annotating them have prevented researchers from addressing these problems at scale. In this paper, we devise a general but nuanced, computationally operationalizable typology of microaggressions based on a small subset of data that we have. We then create two datasets: one with examples of diverse types of microaggressions recollected by their targets, and another with gender-based microaggressions in public conversations on social media. We introduce a new, more objective, criterion for annotation and an active-learning based procedure that increases the likelihood of surfacing posts containing microaggressions. Finally, we analyze the trends that emerge from these new datasets.",
}
@inproceedings{fan-etal-2019-reinforced,
    title = "Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews",
    author = "Fan, Miao  and
      Feng, Chao  and
      Sun, Mingming  and
      Li, Ping",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1177",
    doi = "10.18653/v1/D19-1177",
    pages = "1675--1683",
    abstract = "To automatically assess the helpfulness of a customer review online, conventional approaches generally acquire various linguistic and neural embedding features solely from the textual content of the review itself as the evidence. We, however, find out that a helpful review is largely concerned with the metadata (such as the name, the brand, the category, etc.) of its target product. It leaves us with a challenge of how to choose the correct key-value product metadata to help appraise the helpfulness of free-text reviews more precisely. To address this problem, we propose a novel framework composed of two mutual-benefit modules. Given a product, a selector (agent) learns from both the keys in the product metadata and one of its reviews to take an action that selects the correct value, and a successive predictor (network) makes the free-text review attend to this value to obtain better neural representations for helpfulness assessment. The predictor is directly optimized by SGD with the loss of helpfulness prediction, and the selector could be updated via policy gradient rewarded with the performance of the predictor. We use two real-world datasets from Amazon.com and Yelp.com, respectively, to compare the performance of our framework with other mainstream methods under two application scenarios: helpfulness identification and regression of customer reviews. Extensive results demonstrate that our framework can achieve state-of-the-art performance with substantial improvements.",
}
@inproceedings{andrews-bishop-2019-learning,
    title = "Learning Invariant Representations of Social Media Users",
    author = "Andrews, Nicholas  and
      Bishop, Marcus",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1178",
    doi = "10.18653/v1/D19-1178",
    pages = "1684--1695",
    abstract = "The evolution of social media users{'} behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, naive approaches may fail to generalize to new users or even to future observations of previously known users. In this paper, we propose a novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users{'} invariant features. We fit the model by optimizing a surrogate metric learning objective over a large corpus of unlabeled social media content. Once learned, the mapping may be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. We present a comprehensive evaluation to validate the benefits of the proposed approach using data from Reddit, Twitter, and Wikipedia.",
}
@inproceedings{kang-etal-2019-male,
    title = "(Male, Bachelor) and (Female, {P}h.{D}) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas",
    author = "Kang, Dongyeop  and
      Gangal, Varun  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1179",
    doi = "10.18653/v1/D19-1179",
    pages = "1696--1706",
    abstract = "Stylistic variation in text needs to be studied with different aspects including the writer{'}s personal traits, interpersonal relations, rhetoric, and more. Despite recent attempts on computational modeling of the variation, the lack of parallel corpora of style language makes it difficult to systematically control the stylistic change as well as evaluate such models. We release PASTEL, the parallel and annotated stylistic language dataset, that contains {\textasciitilde}41K parallel sentences (8.3K parallel stories) annotated across different personas. Each persona has different styles in conjunction: gender, age, country, political view, education, ethnic, and time-of-writing. The dataset is collected from human annotators with solid control of input denotation: not only preserving original meaning between text, but promoting stylistic diversity to annotators. We test the dataset on two interesting applications of style language, where PASTEL helps design appropriate experiment and evaluation. First, in predicting a target style (e.g., male or female in gender) given a text, multiple styles of PASTEL make other external style variables controlled (or fixed), which is a more accurate experimental design. Second, a simple supervised model with our parallel text outperforms the unsupervised models using nonparallel text in style transfer. Our dataset is publicly available.",
}
@inproceedings{papalampidi-etal-2019-movie,
    title = "Movie Plot Analysis via Turning Point Identification",
    author = "Papalampidi, Pinelopi  and
      Keller, Frank  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1180",
    doi = "10.18653/v1/D19-1180",
    pages = "1707--1717",
    abstract = "According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e.g., setup, complications, aftermath). We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.",
}
@inproceedings{cao-etal-2019-latent,
    title = "Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention",
    author = "Cao, Lei  and
      Zhang, Huijun  and
      Feng, Ling  and
      Wei, Zihan  and
      Wang, Xin  and
      Li, Ningyun  and
      He, Xiaohao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1181",
    doi = "10.18653/v1/D19-1181",
    pages = "1718--1728",
    abstract = "Despite detection of suicidal ideation on social media has made great progress in recent years, people{'}s implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance. Enlightened by the hidden {``}tree holes{''} phenomenon on microblog, where people at suicide risk tend to disclose their inner real feelings and thoughts to the microblog space whose authors have committed suicide, we explore the use of tree holes to enhance microblog-based suicide risk detection from the following two perspectives. (1) We build suicide-oriented word embeddings based on tree hole contents to strength the sensibility of suicide-related lexicons and context based on tree hole contents. (2) A two-layered attention mechanism is deployed to grasp intermittently changing points from individual{'}s open blog streams, revealing one{'}s inner emotional world more or less. Our experimental results show that with suicide-oriented word embeddings and attention, microblog-based suicide risk detection can achieve over 91{\%} accuracy. A large-scale well-labelled suicide data set is also reported in the paper.",
}
@inproceedings{subramanian-etal-2019-deep,
    title = "Deep Ordinal Regression for Pledge Specificity Prediction",
    author = "Subramanian, Shivashankar  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1182",
    doi = "10.18653/v1/D19-1182",
    pages = "1729--1740",
    abstract = "Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability. At present, there are no publicly available annotated datasets of pledges, and most political analyses rely on manual annotations. In this paper we collate a novel dataset of manifestos from eleven Australian federal election cycles, with over 12,000 sentences annotated with specificity (e.g., rhetorical vs detailed pledge) on a fine-grained scale. We propose deep ordinal regression approaches for specificity prediction, under both supervised and semi-supervised settings, and provide empirical results demonstrating the effectiveness of the proposed techniques over several baseline approaches. We analyze the utility of pledge specificity modeling across a spectrum of policy issues in performing ideology prediction, and further provide qualitative analysis in terms of capturing party-specific issue salience across election cycles.",
}
@inproceedings{shalyminov-etal-2019-data,
    title = "Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks",
    author = "Shalyminov, Igor  and
      Lee, Sungjin  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1183",
    doi = "10.18653/v1/D19-1183",
    pages = "1741--1751",
    abstract = "Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. Data-driven dialogue system development has to be adapted to meet this requirement {---} therefore, reducing the amount of data and annotations necessary for training such systems is a central research problem. In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet), a state-of-the-art approach to goal-oriented dialogue generation which only uses a few example dialogues (i.e. few-shot learning), none of which has to be annotated. We achieve this by performing a 2-stage training. Firstly, we perform unsupervised dialogue representation pre-training on a large source of goal-oriented dialogues in multiple domains, the MetaLWOz corpus. Secondly, at the transfer stage, we train DiKTNet using this representation together with 2 other textual knowledge sources with different levels of generality: ELMo encoder and the main dataset{'}s source domains. Our main dataset is the Stanford Multi-Domain dialogue corpus. We evaluate our model on it in terms of BLEU and Entity F1 scores, and show that our approach significantly and consistently improves upon a series of baseline models as well as over the previous state-of-the-art dialogue generation model, ZSDG. The improvement upon the latter {---} up to 10{\%} in Entity F1 and the average of 3{\%} in BLEU score {---} is achieved using only 10{\%} equivalent of ZSDG{'}s in-domain training data.",
}
@inproceedings{mehri-eskenazi-2019-multi,
    title = "Multi-Granularity Representations of Dialog",
    author = "Mehri, Shikib  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1184",
    doi = "10.18653/v1/D19-1184",
    pages = "1752--1761",
    abstract = "Neural models of dialog rely on generalized latent representations of language. This paper introduces a novel training procedure which explicitly learns multiple representations of language at several levels of granularity. The multi-granularity training algorithm modifies the mechanism by which negative candidate responses are sampled in order to control the granularity of learned latent representations. Strong performance gains are observed on the next utterance retrieval task using both the MultiWOZ dataset and the Ubuntu dialog corpus. Analysis significantly demonstrates that multiple granularities of representation are being learned, and that multi-granularity training facilitates better transfer to downstream tasks.",
}
@inproceedings{wang-etal-2019-real,
    title = "Are You for Real? Detecting Identity Fraud via Dialogue Interactions",
    author = "Wang, Weikang  and
      Zhang, Jiajun  and
      Li, Qian  and
      Zong, Chengqing  and
      Li, Zhifei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1185",
    doi = "10.18653/v1/D19-1185",
    pages = "1762--1771",
    abstract = "Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules. One is the knowledge graph (KG) constructor organizing the personal information for each loan applicant. The other is structured dialogue management that can dynamically generate a series of questions based on the personal KG to ask the applicants and determine their identity states. We also present a heuristic user simulator based on problem analysis to evaluate our method. Experiments have shown that the trainable dialogue system can effectively detect fraudsters, and achieve higher recognition accuracy compared with rule-based systems. Furthermore, our learned dialogue strategies are interpretable and flexible, which can help promote real-world applications.",
}
@inproceedings{zhang-zhang-2019-hierarchy,
    title = "Hierarchy Response Learning for Neural Conversation Generation",
    author = "Zhang, Bo  and
      Zhang, Xiaoming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1186",
    doi = "10.18653/v1/D19-1186",
    pages = "1772--1781",
    abstract = "The neural encoder-decoder models have shown great promise in neural conversation generation. However, they cannot perceive and express the intention effectively, and hence often generate dull and generic responses. Unlike past work that has focused on diversifying the output at word-level or discourse-level with a flat model to alleviate this problem, we propose a hierarchical generation model to capture the different levels of diversity using the conditional variational autoencoders. Specifically, a hierarchical response generation (HRG) framework is proposed to capture the conversation intention in a natural and coherent way. It has two modules, namely, an expression reconstruction model to capture the hierarchical correlation between expression and intention, and an expression attention model to effectively combine the expressions with contents. Finally, the training procedure of HRG is improved by introducing reconstruction loss. Experiment results show that our model can generate the responses with more appropriate content and expression.",
}
@inproceedings{liu-etal-2019-knowledge-aware,
    title = "Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs",
    author = "Liu, Zhibin  and
      Niu, Zheng-Yu  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1187",
    doi = "10.18653/v1/D19-1187",
    pages = "1782--1792",
    abstract = "Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous works. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models.",
}
@inproceedings{cai-etal-2019-adaptive,
    title = "Adaptive Parameterization for Neural Dialogue Generation",
    author = "Cai, Hengyi  and
      Chen, Hongshen  and
      Zhang, Cheng  and
      Song, Yonghao  and
      Zhao, Xiaofang  and
      Yin, Dawei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1188",
    doi = "10.18653/v1/D19-1188",
    pages = "1793--1802",
    abstract = "Neural conversation systems generate responses based on the sequence-to-sequence (SEQ2SEQ) paradigm. Typically, the model is equipped with a single set of learned parameters to generate responses for given input contexts. When confronting diverse conversations, its adaptability is rather limited and the model is hence prone to generate generic responses. In this work, we propose an Adaptive Neural Dialogue generation model, AdaND, which manages various conversations with conversation-specific parameterization. For each conversation, the model generates parameters of the encoder-decoder by referring to the input context. In particular, we propose two adaptive parameterization mechanisms: a context-aware and a topic-aware parameterization mechanism. The context-aware parameterization directly generates the parameters by capturing local semantics of the given context. The topic-aware parameterization enables parameter sharing among conversations with similar topics by first inferring the latent topics of the given context and then generating the parameters with respect to the distributional topics. Extensive experiments conducted on a large-scale real-world conversational dataset show that our model achieves superior performance in terms of both quantitative metrics and human evaluations.",
}
@inproceedings{chen-etal-2019-towards,
    title = "Towards Knowledge-Based Recommender Dialog System",
    author = "Chen, Qibin  and
      Lin, Junyang  and
      Zhang, Yichang  and
      Ding, Ming  and
      Cen, Yukuo  and
      Yang, Hongxia  and
      Tang, Jie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1189",
    doi = "10.18653/v1/D19-1189",
    pages = "1803--1813",
    abstract = "In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System. It integrates the recommender system and the dialog generation system. The dialog generation system can enhance the performance of the recommendation system by introducing information about users{'} preferences, and the recommender system can improve that of the dialog generation system by providing recommendation-aware vocabulary bias. Experimental results demonstrate that our proposed model has significant advantages over the baselines in both the evaluation of dialog generation and recommendation. A series of analyses show that the two systems can bring mutual benefits to each other, and the introduced knowledge contributes to both their performances.",
}
@inproceedings{gao-etal-2019-structuring,
    title = "Structuring Latent Spaces for Stylized Response Generation",
    author = "Gao, Xiang  and
      Zhang, Yizhe  and
      Lee, Sungjin  and
      Galley, Michel  and
      Brockett, Chris  and
      Gao, Jianfeng  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1190",
    doi = "10.18653/v1/D19-1190",
    pages = "1814--1823",
    abstract = "Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate responses that are either less stylized or less context-relevant. We propose StyleFusion, which bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level. We demonstrate this method using dialogues from Reddit data and two sets of sentences with distinct styles (arXiv and Sherlock Holmes novels). Automatic and human evaluation show that, without sacrificing appropriateness, the system generates responses of the targeted style and outperforms competitive baselines.",
}
@inproceedings{pan-etal-2019-improving,
    title = "Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration",
    author = "Pan, Zhufeng  and
      Bai, Kun  and
      Wang, Yan  and
      Zhou, Lianqiang  and
      Liu, Xiaojiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1191",
    doi = "10.18653/v1/D19-1191",
    pages = "1824--1833",
    abstract = "In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a {``}pick-and-combine{''} model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems.",
}
@inproceedings{zhou-etal-2019-unsupervised,
    title = "Unsupervised Context Rewriting for Open Domain Conversation",
    author = "Zhou, Kun  and
      Zhang, Kai  and
      Wu, Yu  and
      Liu, Shujie  and
      Yu, Jingsong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1192",
    doi = "10.18653/v1/D19-1192",
    pages = "1834--1844",
    abstract = "Context modeling has a pivotal role in open domain conversation. Existing works either use heuristic methods or jointly learn context modeling and response generation with an encoder-decoder framework. This paper proposes an explicit context rewriting method, which rewrites the last utterance by considering context history. We leverage pseudo-parallel data and elaborate a context rewriting network, which is built upon the CopyNet with the reinforcement learning method. The rewritten utterance is beneficial to candidate retrieval, explainable context modeling, as well as enabling to employ a single-turn framework to the multi-turn scenario. The empirical results show that our model outperforms baselines in terms of the rewriting quality, the multi-turn response generation, and the end-to-end retrieval-based chatbots.",
}
@inproceedings{gu-etal-2019-dually,
    title = "Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots",
    author = "Gu, Jia-Chen  and
      Ling, Zhen-Hua  and
      Zhu, Xiaodan  and
      Liu, Quan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1193",
    doi = "10.18653/v1/D19-1193",
    pages = "1845--1854",
    abstract = "This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate. Compared with previous persona fusion approach which enhances the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates. Experimental results on PERSONA-CHAT dataset show that the DIM model outperforms its baseline model, i.e., IMN with persona fusion, by a margin of 14.5{\%} and outperforms the present state-of-the-art model by a margin of 27.7{\%} in terms of top-1 accuracy hits@1.",
}
@inproceedings{tuan-etal-2019-dykgchat,
    title = "{D}y{K}g{C}hat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs",
    author = "Tuan, Yi-Lin  and
      Chen, Yun-Nung  and
      Lee, Hung-yi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1194",
    doi = "10.18653/v1/D19-1194",
    pages = "1855--1865",
    abstract = "Data-driven, knowledge-grounded neural conversation models are capable of generating more informative responses. However, these models have not yet demonstrated that they can zero-shot adapt to updated, unseen knowledge graphs. This paper proposes a new task about how to apply dynamic knowledge graphs in neural conversation model and presents a novel TV series conversation corpus (DyKgChat) for the task. Our new task and corpus aids in understanding the influence of dynamic knowledge graphs on responses generation. Also, we propose a preliminary model that selects an output from two networks at each time step: a sequence-to-sequence model (Seq2Seq) and a multi-hop reasoning model, in order to support dynamic knowledge graphs. To benchmark this new task and evaluate the capability of adaptation, we introduce several evaluation metrics and the experiments show that our proposed approach outperforms previous knowledge-grounded conversation models. The proposed corpus and model can motivate the future research directions.",
}
@inproceedings{cai-etal-2019-retrieval,
    title = "Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework",
    author = "Cai, Deng  and
      Wang, Yan  and
      Bi, Wei  and
      Tu, Zhaopeng  and
      Liu, Xiaojiang  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1195",
    doi = "10.18653/v1/D19-1195",
    pages = "1866--1875",
    abstract = "End-to-end sequence generation is a popular technique for developing open domain dialogue systems, though they suffer from the \textit{safe response problem}. Researchers have attempted to tackle this problem by incorporating generative models with the returns of retrieval systems. Recently, a skeleton-then-response framework has been shown promising results for this task. Nevertheless, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator are still challenging. This paper presents a novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator. Extensive experiments demonstrate the effectiveness of our model designs.",
}
@inproceedings{ren-etal-2019-scalable,
    title = "Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation",
    author = "Ren, Liliang  and
      Ni, Jianmo  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1196",
    doi = "10.18653/v1/D19-1196",
    pages = "1876--1885",
    abstract = "Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined slots that need tracking. This issue becomes more severe when it comes to multi-domain dialogues which include larger numbers of slots. In this paper, we investigate how to approach DST using a generation framework without the pre-defined ontology list. Given each turn of user utterance and system response, we directly generate a sequence of belief states by applying a hierarchical encoder-decoder structure. In this way, the computational complexity of our model will be a constant regardless of the number of pre-defined slots. Experiments on both the multi-domain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.",
}
@inproceedings{yang-etal-2019-low,
    title = "Low-Resource Response Generation with Template Prior",
    author = "Yang, Ze  and
      Wu, Wei  and
      Yang, Jian  and
      Xu, Can  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1197",
    doi = "10.18653/v1/D19-1197",
    pages = "1886--1897",
    abstract = "We study open domain response generation with limited message-response pairs. The problem exists in real-world applications but is less explored by the existing work. Since the paired data now is no longer enough to train a neural generation model, we consider leveraging the large scale of unpaired data that are much easier to obtain, and propose response generation with both paired and unpaired data. The generation model is defined by an encoder-decoder architecture with templates as prior, where the templates are estimated from the unpaired data as a neural hidden semi-markov model. By this means, response generation learned from the small paired data can be aided by the semantic and syntactic knowledge in the large unpaired data. To balance the effect of the prior and the input message to response generation, we propose learning the whole generation model with an adversarial approach. Empirical studies on question response generation and sentiment response generation indicate that when only a few pairs are available, our model can significantly outperform several state-of-the-art response generation models in terms of both automatic and human evaluation.",
}
@inproceedings{gao-etal-2019-discrete,
    title = "A Discrete {CVAE} for Response Generation on Short-Text Conversation",
    author = "Gao, Jun  and
      Bi, Wei  and
      Liu, Xiaojiang  and
      Li, Junhui  and
      Zhou, Guodong  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1198",
    doi = "10.18653/v1/D19-1198",
    pages = "1898--1908",
    abstract = "Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder (CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled latent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we propose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and informative responses.",
}
@inproceedings{le-etal-2019-speaking,
    title = "Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations",
    author = "Le, Ran  and
      Hu, Wenpeng  and
      Shang, Mingyue  and
      You, Zhenjun  and
      Bing, Lidong  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1199",
    doi = "10.18653/v1/D19-1199",
    pages = "1909--1919",
    abstract = "Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario. In real multi- party conversations, we can observe who is speaking, but the addressee information is not always explicit. In this paper, we aim to tackle the challenge of identifying all the miss- ing addressees in a conversation session. To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way. We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.",
}
@inproceedings{chang-etal-2019-semi,
    title = "A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation",
    author = "Chang, Jinxin  and
      He, Ruifang  and
      Wang, Longbiao  and
      Zhao, Xiangyu  and
      Yang, Ting  and
      Wang, Ruifang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1200",
    doi = "10.18653/v1/D19-1200",
    pages = "1920--1930",
    abstract = "Neural sequence-to-sequence models for dialog systems suffer from the problem of favoring uninformative and non replier-specific responses due to lack of the global and relevant information guidance. The existing methods model the generation process by leveraging the neural variational network with simple Gaussian. However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response. Therefore, a novel Semi-Supervised Stable Variational Network (SSVN) is proposed to address these issues. We use a unit hypersperical distribution, namely the von Mises-Fisher (vMF), as the latent space of a semi-supervised model, which can obtain the stable KL performance by setting a fixed variance and hence enhance the global information representation. Meanwhile, an unsupervised extractor is introduced to automatically distill the replier-tailored feature which is then injected into a supervised generator to encourage the replier-consistency. Experimental results on two large conversation datasets show that our model outperforms the competitive baseline models significantly, and can generate diverse and replier-specific responses.",
}
@inproceedings{chan-etal-2019-modeling,
    title = "Modeling Personalization in Continuous Space for Response Generation via Augmented {W}asserstein Autoencoders",
    author = "Chan, Zhangming  and
      Li, Juntao  and
      Yang, Xiaopeng  and
      Chen, Xiuying  and
      Hu, Wenpeng  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1201",
    doi = "10.18653/v1/D19-1201",
    pages = "1931--1940",
    abstract = "Variational autoencoders (VAEs) and Wasserstein autoencoders (WAEs) have achieved noticeable progress in open-domain response generation. Through introducing latent variables in continuous space, these models are capable of capturing utterance-level semantics, e.g., topic, syntactic properties, and thus can generate informative and diversified responses. In this work, we improve the WAE for response generation. In addition to the utterance-level information, we also model user-level information in latent continue space. Specifically, we embed user-level and utterance-level information into two multimodal distributions, and combine these two multimodal distributions into a mixed distribution. This mixed distribution will be used as the prior distribution of WAE in our proposed model, named as PersonaWAE. Experimental results on a large-scale real-world dataset confirm the superiority of our model for generating informative and personalized responses, where both automatic and human evaluations outperform state-of-the-art models.",
}
@inproceedings{bak-oh-2019-variational,
    title = "Variational Hierarchical User-based Conversation Model",
    author = "Bak, JinYeong  and
      Oh, Alice",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1202",
    doi = "10.18653/v1/D19-1202",
    pages = "1941--1950",
    abstract = "Generating appropriate conversation responses requires careful modeling of the utterances and speakers together. Some recent approaches to response generation model both the utterances and the speakers, but these approaches tend to generate responses that are overly tailored to the speakers. To overcome this limitation, we propose a new model with a stochastic variable designed to capture the speaker information and deliver it to the conversational context. An important part of this model is the network of speakers in which each speaker is connected to one or more conversational partner, and this network is then used to model the speakers better. To test whether our model generates more appropriate conversation responses, we build a new conversation corpus containing approximately 27,000 speakers and 770,000 conversations. With this corpus, we run experiments of generating conversational responses and compare our model with other state-of-the-art models. By automatic evaluation metrics and human evaluation, we show that our model outperforms other models in generating appropriate responses. An additional advantage of our model is that it generates better responses for various new user scenarios, for example when one of the speakers is a known user in our corpus but the partner is a new user. For replicability, we make available all our code and data.",
}
@inproceedings{kang-etal-2019-recommendation,
    title = "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue",
    author = "Kang, Dongyeop  and
      Balakrishnan, Anusha  and
      Shah, Pararth  and
      Crook, Paul  and
      Boureau, Y-Lan  and
      Weston, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1203",
    doi = "10.18653/v1/D19-1203",
    pages = "1951--1961",
    abstract = "Traditional recommendation systems produce static rather than interactive recommendations invariant to a user{'}s specific requests, clarifications, or current mood, and can suffer from the cold-start problem if their tastes are unknown. These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone{'}s preferences, react to their requests, and recommend more appropriate items. In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal. We leverage the dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. Models are first trained to imitate the behavior of human players without considering the task goal itself (supervised training). We then finetune our models on simulated bot-bot conversations between two paired pre-trained models (bot-play), in order to achieve the dialogue goal. Our experiments show that models finetuned with bot-play learn improved dialogue strategies, reach the dialogue goal more often when paired with a human, and are rated as more consistent by humans compared to models trained without bot-play. The dataset and code are publicly available through the ParlAI framework.",
}
@inproceedings{yu-etal-2019-cosql,
    title = "{C}o{SQL}: A Conversational Text-to-{SQL} Challenge Towards Cross-Domain Natural Language Interfaces to Databases",
    author = "Yu, Tao  and
      Zhang, Rui  and
      Er, Heyang  and
      Li, Suyi  and
      Xue, Eric  and
      Pang, Bo  and
      Lin, Xi Victoria  and
      Tan, Yi Chern  and
      Shi, Tianze  and
      Li, Zihan  and
      Jiang, Youxuan  and
      Yasunaga, Michihiro  and
      Shim, Sungrok  and
      Chen, Tao  and
      Fabbri, Alexander  and
      Li, Zifan  and
      Chen, Luyao  and
      Zhang, Yuwen  and
      Dixit, Shreya  and
      Zhang, Vincent  and
      Xiong, Caiming  and
      Socher, Richard  and
      Lasecki, Walter  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1204",
    doi = "10.18653/v1/D19-1204",
    pages = "1962--1979",
    abstract = "We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. When user questions are answerable by SQL, the expert describes the SQL and execution results to the user, hence maintaining a natural interaction flow. CoSQL introduces new challenges compared to existing task-oriented dialogue datasets: (1) the dialogue states are grounded in SQL, a domain-independent executable representation, instead of domain-specific slot value pairs, and (2) because testing is done on unseen databases, success requires generalizing to new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction. We evaluate a set of strong baselines for each task and show that CoSQL presents significant challenges for future research. The dataset, baselines, and leaderboard will be released at https://yale-lily.github.io/cosql.",
}
@inproceedings{kumar-etal-2019-practical,
    title = "A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection",
    author = "Kumar, Harshit  and
      Agarwal, Arvind  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1205",
    doi = "10.18653/v1/D19-1205",
    pages = "1980--1989",
    abstract = "Dialogue Acts play an important role in conversation modeling. Research has shown the utility of dialogue acts for the response selection task, however, the underlying assumption is that the dialogue acts are readily available, which is impractical, as dialogue acts are rarely available for new conversations. This paper proposes an end-to-end multi-task model for conversation modeling, which is optimized for two tasks, dialogue act prediction and response selection, with the latter being the task of interest. It proposes a novel way of combining the predicted dialogue acts of context and response with the context (previous utterances) and response (follow-up utterance) in a crossway fashion, such that, it achieves at par performance for the response selection task compared to the model that uses actual dialogue acts. Through experiments on two well known datasets, we demonstrate that the multi-task model not only improves the accuracy of the dialogue act prediction task but also improves the MRR for the response selection task. Also, the cross-stitching of dialogue acts of context and response with the context and response is better than using either one of them individually.",
}
@inproceedings{shi-etal-2019-build,
    title = "How to Build User Simulators to Train {RL}-based Dialog Systems",
    author = "Shi, Weiyan  and
      Qian, Kun  and
      Wang, Xuewei  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1206",
    doi = "10.18653/v1/D19-1206",
    pages = "1990--2000",
    abstract = "User simulators are essential for training reinforcement learning (RL) based dialog models. The performance of the simulator directly impacts the RL policy. However, building a good user simulator that models real user behaviors is challenging. We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly. We present implementations of six user simulators trained with different dialog planning and generation methods. We then calculate a set of automatic metrics to evaluate the quality of these simulators both directly and indirectly. We also ask human users to assess the simulators directly and indirectly by rating the simulated dialogs and interacting with the trained systems. This paper presents a comprehensive evaluation framework for user simulator study and provides a better understanding of the pros and cons of different user simulators, as well as their impacts on the trained systems.",
}
@inproceedings{jin-etal-2019-low,
    title = "Low-Rank {HOCA}: Efficient High-Order Cross-Modal Attention for Video Captioning",
    author = "Jin, Tao  and
      Huang, Siyu  and
      Li, Yingming  and
      Zhang, Zhongfei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1207",
    doi = "10.18653/v1/D19-1207",
    pages = "2001--2011",
    abstract = "This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been widely used in video captioning. In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected. Motivated by this, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently. Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications. Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art.",
}
@inproceedings{kim-etal-2019-image,
    title = "Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach",
    author = "Kim, Dong-Jin  and
      Choi, Jinsoo  and
      Oh, Tae-Hyun  and
      Kweon, In So",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1208",
    doi = "10.18653/v1/D19-1208",
    pages = "2012--2023",
    abstract = "Constructing an organized dataset comprised of a large number of images and several captions for each image is a laborious task, which requires vast human effort. On the other hand, collecting a large number of images and sentences separately may be immensely easier. In this paper, we develop a novel data-efficient semi-supervised framework for training an image captioning model. We leverage massive unpaired image and caption data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples via Generative Adversarial Networks to learn the joint distribution of image and caption. To evaluate, we construct scarcely-paired COCO dataset, a modified version of MS COCO caption dataset. The empirical results show the effectiveness of our method compared to several strong baselines, especially when the amount of the paired samples are scarce.",
}
@inproceedings{kang-etal-2019-dual,
    title = "Dual Attention Networks for Visual Reference Resolution in Visual Dialog",
    author = "Kang, Gi-Cheon  and
      Lim, Jaeseo  and
      Zhang, Byoung-Tak",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1209",
    doi = "10.18653/v1/D19-1209",
    pages = "2024--2033",
    abstract = "Visual dialog (VisDial) is a task which requires a dialog agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of questions should be able to capture a temporal context from a dialog history and utilizes visually-grounded information. Visual reference resolution is a problem that addresses these challenges, requiring the agent to resolve ambiguous references in a given question and to find the references in a given image. In this paper, we propose Dual Attention Networks (DAN) for visual reference resolution in VisDial. DAN consists of two kinds of attention modules, REFER and FIND. Specifically, REFER module learns latent relationships between a given question and a dialog history by employing a multi-head attention mechanism. FIND module takes image features and reference-aware representations (i.e., the output of REFER module) as input, and performs visual grounding via bottom-up attention mechanism. We qualitatively and quantitatively evaluate our model on VisDial v1.0 and v0.9 datasets, showing that DAN outperforms the previous state-of-the-art model by a significant margin.",
}
@inproceedings{hessel-etal-2019-unsupervised,
    title = "Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents",
    author = "Hessel, Jack  and
      Lee, Lillian  and
      Mimno, David",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1210",
    doi = "10.18653/v1/D19-1210",
    pages = "2034--2045",
    abstract = "Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present. We present algorithms that discover image-sentence relationships without relying on explicit multimodal annotation in training. We experiment on seven datasets of varying difficulty, ranging from documents consisting of groups of images captioned post hoc by crowdworkers to naturally-occurring user-generated multimodal documents. We find that a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific sentences and specific images within the same document at test time.",
}
@inproceedings{hasan-etal-2019-ur,
    title = "{UR}-{FUNNY}: A Multimodal Language Dataset for Understanding Humor",
    author = "Hasan, Md Kamrul  and
      Rahman, Wasifur  and
      Bagher Zadeh, AmirAli  and
      Zhong, Jianyuan  and
      Tanveer, Md Iftekhar  and
      Morency, Louis-Philippe  and
      Hoque, Mohammed (Ehsan)",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1211",
    doi = "10.18653/v1/D19-1211",
    pages = "2046--2056",
    abstract = "Humor is a unique and creative communicative behavior often displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (visual) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it has been understudied. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research.",
}
@inproceedings{papasarantopoulos-etal-2019-partners,
    title = "Partners in Crime: Multi-view Sequential Inference for Movie Understanding",
    author = "Papasarantopoulos, Nikos  and
      Frermann, Lea  and
      Lapata, Mirella  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1212",
    doi = "10.18653/v1/D19-1212",
    pages = "2057--2067",
    abstract = "Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems. However, for problems requiring inference at the token-level of a sequence (that is, a separate prediction must be made for every time step), it is often the case that single-view systems are used, or that more than one views are fused in a simple manner. We describe an incremental neural architecture paired with a novel training objective for incremental inference. The network operates on multi-view data. We demonstrate the effectiveness of our approach on the problem of predicting perpetrators in crime drama series, for which our model significantly outperforms previous work and strong baselines. Moreover, we introduce two tasks, crime case and speaker type tagging, that contribute to movie understanding and demonstrate the effectiveness of our model on them.",
}
@inproceedings{xiao-etal-2019-guiding,
    title = "Guiding the Flowing of Semantics: Interpretable Video Captioning via {POS} Tag",
    author = "Xiao, Xinyu  and
      Wang, Lingfeng  and
      Fan, Bin  and
      Xiang, Shinming  and
      Pan, Chunhong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1213",
    doi = "10.18653/v1/D19-1213",
    pages = "2068--2077",
    abstract = "In the current video captioning models, the video frames are collected in one network and the semantics are mixed into one feature, which not only increase the difficulty of the caption decoding, but also decrease the interpretability of the captioning models. To address these problems, we propose an Adaptive Semantic Guidance Network (ASGN), which instantiates the whole video semantics to different POS-aware semantics with the supervision of part of speech (POS) tag. In the encoding process, the POS tag activates the related neurons and parses the whole semantic information into corresponding encoded video representations. Furthermore, the potential of the model is stimulated by the POS-aware video features. In the decoding process, the related video features of noun and verb are used as the supervision to construct a new adaptive attention model which can decide whether to attend to the video feature or not. With the explicit improving of the interpretability of the network, the learning process is more transparent and the results are more predictable. Extensive experiments demonstrate the effectiveness of our model when compared with state-of-the-art models.",
}
@inproceedings{qin-etal-2019-stack,
    title = "A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding",
    author = "Qin, Libo  and
      Che, Wanxiang  and
      Li, Yangming  and
      Wen, Haoyang  and
      Liu, Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1214",
    doi = "10.18653/v1/D19-1214",
    pages = "2078--2087",
    abstract = "Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guiding the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.",
}
@inproceedings{deruyttere-etal-2019-talk2car,
    title = "{T}alk2{C}ar: Taking Control of Your Self-Driving Car",
    author = "Deruyttere, Thierry  and
      Vandenhende, Simon  and
      Grujicic, Dusan  and
      Van Gool, Luc  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1215",
    doi = "10.18653/v1/D19-1215",
    pages = "2088--2098",
    abstract = "A long-term goal of artificial intelligence is to have an agent execute commands communicated through natural language. In many cases the commands are grounded in a visual environment shared by the human who gives the command and the agent. Execution of the command then requires mapping the command into the physical visual space, after which the appropriate action can be taken. In this paper we consider the former. Or more specifically, we consider the problem in an autonomous driving setting, where a passenger requests an action that can be associated with an object found in a street scene. Our work presents the Talk2Car dataset, which is the first object referral dataset that contains commands written in natural language for self-driving cars. We provide a detailed comparison with related datasets such as ReferIt, RefCOCO, RefCOCO+, RefCOCOg, Cityscape-Ref and CLEVR-Ref. Additionally, we include a performance analysis using strong state-of-the-art models. The results show that the proposed object referral task is a challenging one for which the models show promising results but still require additional research in natural language processing, computer vision and the intersection of these fields. The dataset can be found on our website: http://macchina-ai.eu/",
}
@inproceedings{zlatkova-etal-2019-fact,
    title = "Fact-Checking Meets Fauxtography: Verifying Claims About Images",
    author = "Zlatkova, Dimitrina  and
      Nakov, Preslav  and
      Koychev, Ivan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1216",
    doi = "10.18653/v1/D19-1216",
    pages = "2099--2108",
    abstract = "The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives. Unfortunately, the number of claims that need to be fact-checked is several orders of magnitude larger than what humans can handle manually. Thus, there has been a lot of research aiming at automating the process. Interestingly, previous work has largely ignored the growing number of claims about images. This is despite the fact that visual imagery is more influential than text and naturally appears alongside fake news. Here we aim at bridging this gap. In particular, we create a new dataset for this problem, and we explore a variety of features modeling the claim, the image, and the relationship between the claim and the image. The evaluation results show sizable improvements over the baseline. We release our dataset, hoping to enable further research on fact-checking claims about images.",
}
@inproceedings{jin-etal-2019-video,
    title = "Video Dialog via Progressive Inference and Cross-Transformer",
    author = "Jin, Weike  and
      Zhao, Zhou  and
      Gu, Mao  and
      Xiao, Jun  and
      Wei, Furu  and
      Zhuang, Yueting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1217",
    doi = "10.18653/v1/D19-1217",
    pages = "2109--2118",
    abstract = "Video dialog is a new and challenging task, which requires the agent to answer questions combining video information with dialog history. And different from single-turn video question answering, the additional dialog history is important for video dialog, which often includes contextual information for the question. Existing visual dialog methods mainly use RNN to encode the dialog history as a single vector representation, which might be rough and straightforward. Some more advanced methods utilize hierarchical structure, attention and memory mechanisms, which still lack an explicit reasoning process. In this paper, we introduce a novel progressive inference mechanism for video dialog, which progressively updates query information based on dialog history and video content until the agent think the information is sufficient and unambiguous. In order to tackle the multi-modal fusion problem, we propose a cross-transformer module, which could learn more fine-grained and comprehensive interactions both inside and between the modalities. And besides answer generation, we also consider question generation, which is more challenging but significant for a complete video dialog system. We evaluate our method on two large-scale datasets, and the extensive experiments show the effectiveness of our method.",
}
@inproceedings{suhr-etal-2019-executing,
    title = "Executing Instructions in Situated Collaborative Interactions",
    author = "Suhr, Alane  and
      Yan, Claudia  and
      Schluger, Jack  and
      Yu, Stanley  and
      Khader, Hadi  and
      Mouallem, Marwa  and
      Zhang, Iris  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1218",
    doi = "10.18653/v1/D19-1218",
    pages = "2119--2130",
    abstract = "We study a collaborative scenario where a user not only instructs a system to complete tasks, but also acts alongside it. This allows the user to adapt to the system abilities by changing their language or deciding to simply accomplish some tasks themselves, and requires the system to effectively recover from errors as the user strategically assigns it new goals. We build a game environment to study this scenario, and learn to map user instructions to system actions. We introduce a learning approach focused on recovery from cascading errors between instructions, and modeling methods to explicitly reason about instructions with multiple goals. We evaluate with a new evaluation protocol using recorded interactions and online games with human users, and observe how users adapt to the system abilities.",
}
@inproceedings{alberti-etal-2019-fusion,
    title = "Fusion of Detected Objects in Text for Visual Question Answering",
    author = "Alberti, Chris  and
      Ling, Jeffrey  and
      Collins, Michael  and
      Reitter, David",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1219",
    doi = "10.18653/v1/D19-1219",
    pages = "2131--2140",
    abstract = "To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language. The {``}Bounding Boxes in Text Transformer{''} (B2T2) also leverages referential information binding words to portions of the image in a single unified architecture. B2T2 is highly effective on the Visual Commonsense Reasoning benchmark, achieving a new state-of-the-art with a 25{\%} relative reduction in error rate compared to published baselines and obtaining the best performance to date on the public leaderboard (as of May 22, 2019). A detailed ablation analysis shows that the early integration of the visual features into the text analysis is key to the effectiveness of the new architecture. A reference implementation of our models is provided.",
}
@inproceedings{jiang-etal-2019-tiger,
    title = "{TIGE}r: Text-to-Image Grounding for Image Caption Evaluation",
    author = "Jiang, Ming  and
      Huang, Qiuyuan  and
      Zhang, Lei  and
      Wang, Xin  and
      Zhang, Pengchuan  and
      Gan, Zhe  and
      Diesner, Jana  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1220",
    doi = "10.18653/v1/D19-1220",
    pages = "2141--2152",
    abstract = "This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems. Popular metrics, such as BLEU and CIDEr, are based solely on text matching between reference captions and machine-generated captions, potentially leading to biased evaluations because references may not fully cover the image content and natural language is inherently ambiguous. Building upon a machine-learned text-image grounding model, TIGEr allows to evaluate caption quality not only based on how well a caption represents image content, but also on how well machine-generated captions match human-generated captions. Our empirical tests show that TIGEr has a higher consistency with human judgments than alternative existing metrics. We also comprehensively assess the metric{'}s effectiveness in caption evaluation by measuring the correlation between human judgments and metric scores.",
}
@inproceedings{wallace-etal-2019-universal,
    title = "Universal Adversarial Triggers for Attacking and Analyzing {NLP}",
    author = "Wallace, Eric  and
      Feng, Shi  and
      Kandpal, Nikhil  and
      Gardner, Matt  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1221",
    doi = "10.18653/v1/D19-1221",
    pages = "2153--2162",
    abstract = "Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94{\%} to 0.55{\%}, 72{\%} of {``}why{''} questions in SQuAD to be answered {``}to kill american people{''}, and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models.",
}
@inproceedings{elsahar-galle-2019-annotate,
    title = "To Annotate or Not? Predicting Performance Drop under Domain Shift",
    author = "Elsahar, Hady  and
      Gall{\'e}, Matthias",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1222",
    doi = "10.18653/v1/D19-1222",
    pages = "2163--2173",
    abstract = "Performance drop due to domain-shift is an endemic problem for NLP models in production. This problem creates an urge to continuously annotate evaluation datasets to measure the expected drop in the model performance which can be prohibitively expensive and slow. In this paper, we study the problem of predicting the performance drop of modern NLP models under domain-shift, in the absence of any target domain labels. We investigate three families of methods ($\mathcal{H}$-divergence, reverse classification accuracy and confidence measures), show how they can be used to predict the performance drop and study their robustness to adversarial domain-shifts. Our results on sentiment classification and sequence labelling show that our method is able to predict performance drops with an error rate as low as 2.15{\%} and 0.89{\%} for sentiment analysis and POS tagging respectively.",
}
@inproceedings{correia-etal-2019-adaptively,
    title = "Adaptively Sparse Transformers",
    author = "Correia, Gon{\c{c}}alo M.  and
      Niculae, Vlad  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1223",
    doi = "10.18653/v1/D19-1223",
    pages = "2174--2184",
    abstract = "Attention mechanisms have become ubiquitous in NLP. Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed attention. The multiple heads learn diverse types of word relationships. However, with standard softmax attention, all attention heads are dense, assigning a non-zero weight to all context words. In this work, we introduce the adaptively sparse Transformer, wherein attention heads have flexible, context-dependent sparsity patterns. This sparsity is accomplished by replacing softmax with alpha-entmax: a differentiable generalization of softmax that allows low-scoring words to receive precisely zero weight. Moreover, we derive a method to automatically learn the alpha parameter {--} which controls the shape and sparsity of alpha-entmax {--} allowing attention heads to choose between focused or spread-out behavior. Our adaptively sparse Transformer improves interpretability and head diversity when compared to softmax Transformers on machine translation datasets. Findings of the quantitative and qualitative analysis of our approach include that heads in different layers learn different sparsity preferences and tend to be more diverse in their attention distributions than softmax Transformers. Furthermore, at no cost in accuracy, sparsity in attention heads helps to uncover different head specializations.",
}
@inproceedings{dodge-etal-2019-show,
    title = "Show Your Work: Improved Reporting of Experimental Results",
    author = "Dodge, Jesse  and
      Gururangan, Suchin  and
      Card, Dallas  and
      Schwartz, Roy  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1224",
    doi = "10.18653/v1/D19-1224",
    pages = "2185--2194",
    abstract = "Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone are insufficient for drawing accurate conclusions about which model performs best. We argue for reporting additional details, especially performance on validation data obtained during model development. We present a novel technique for doing so: expected validation performance of the best-found model as a function of computation budget (i.e., the number of hyperparameter search trials or the overall training time). Using our approach, we find multiple recent model comparisons where authors would have reached a different conclusion if they had used more (or less) computation. Our approach also allows us to estimate the amount of computation required to obtain a given accuracy; applying it to several recently published results yields massive variation across papers, from hours to weeks. We conclude with a set of best practices for reporting experimental results which allow for robust future comparisons, and provide code to allow researchers to use our technique.",
}
@inproceedings{srivatsan-etal-2019-deep,
    title = "A Deep Factorization of Style and Structure in Fonts",
    author = "Srivatsan, Nikita  and
      Barron, Jonathan  and
      Klein, Dan  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1225",
    doi = "10.18653/v1/D19-1225",
    pages = "2195--2205",
    abstract = "We propose a deep factorization model for typographic analysis that disentangles content from style. Specifically, a variational inference procedure factors each training glyph into the combination of a character-specific content embedding and a latent font-specific style variable. The underlying generative model combines these factors through an asymmetric transpose convolutional process to generate the image of the glyph itself. When trained on corpora of fonts, our model learns a manifold over font styles that can be used to analyze or reconstruct new, unseen fonts. On the task of reconstructing missing glyphs from an unknown font given only a small number of observations, our model outperforms both a strong nearest neighbors baseline and a state-of-the-art discriminative model from prior work.",
}
@inproceedings{ponti-etal-2019-cross,
    title = "Cross-lingual Semantic Specialization via Lexical Relation Induction",
    author = "Ponti, Edoardo Maria  and
      Vuli{\'c}, Ivan  and
      Glava{\v{s}}, Goran  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1226",
    doi = "10.18653/v1/D19-1226",
    pages = "2206--2217",
    abstract = "Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent. To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language. Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints. This allows us to specialize any set of distributional vectors in the target language with the refined constraints. We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream tasks in 5 languages: lexical simplification, dialog state tracking, and semantic textual similarity. The gains over the previous state-of-art specialization methods are substantial and consistent across languages. Our results also suggest that the transfer method is effective even for lexically distant source-target language pairs. Finally, as a by-product, our method produces lists of WordNet-style lexical relations in resource-poor languages.",
}
@inproceedings{dankers-etal-2019-modelling,
    title = "Modelling the interplay of metaphor and emotion through multitask learning",
    author = "Dankers, Verna  and
      Rei, Marek  and
      Lewis, Martha  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1227",
    doi = "10.18653/v1/D19-1227",
    pages = "2218--2229",
    abstract = "Metaphors allow us to convey emotion by connecting physical experiences and abstract concepts. The results of previous research in linguistics and psychology suggest that metaphorical phrases tend to be more emotionally evocative than their literal counterparts. In this paper, we investigate the relationship between metaphor and emotion within a computational framework, by proposing the first joint model of these phenomena. We experiment with several multitask learning architectures for this purpose, involving both hard and soft parameter sharing. Our results demonstrate that metaphor identification and emotion prediction mutually benefit from joint learning and our models advance the state of the art in both of these tasks.",
}
@inproceedings{ross-pavlick-2019-well,
    title = "How well do {NLI} models capture verb veridicality?",
    author = "Ross, Alexis  and
      Pavlick, Ellie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1228",
    doi = "10.18653/v1/D19-1228",
    pages = "2230--2240",
    abstract = "In natural language inference (NLI), contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world. We investigate whether a state-of-the-art natural language inference model (BERT) learns to make correct inferences about veridicality in verb-complement constructions. We introduce an NLI dataset for veridicality evaluation consisting of 1,500 sentence pairs, covering 137 unique verbs. We find that both human and model inferences generally follow theoretical patterns, but exhibit a systematic bias towards assuming that verbs are veridical{--}a bias which is amplified in BERT. We further show that, encouragingly, BERT{'}s inferences are sensitive not only to the presence of individual verb types, but also to the syntactic role of the verb, the form of the complement clause (to- vs. that-complements), and negation.",
}
@inproceedings{mccarthy-etal-2019-modeling,
    title = "Modeling Color Terminology Across Thousands of Languages",
    author = "McCarthy, Arya D.  and
      Wu, Winston  and
      Mueller, Aaron  and
      Watson, William  and
      Yarowsky, David",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1229",
    doi = "10.18653/v1/D19-1229",
    pages = "2241--2250",
    abstract = "There is an extensive history of scholarship into what constitutes a {``}basic{''} color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969). This paper employs a set of diverse measures on massively cross-linguistic data to operationalize and critique the Berlin and Kay color term hypotheses. Collectively, the 14 empirically-grounded computational linguistic metrics we design{---}as well as their aggregation{---}correlate strongly with both the Berlin and Kay basic/secondary color term partition (γ = 0.96) and their hypothesized universal acquisition sequence. The measures and result provide further empirical evidence from computational linguistics in support of their claims, as well as additional nuance: they suggest treating the partition as a spectrum instead of a dichotomy.",
}
@inproceedings{shen-etal-2019-negative,
    title = "Negative Focus Detection via Contextual Attention Mechanism",
    author = "Shen, Longxiang  and
      Zou, Bowei  and
      Hong, Yu  and
      Zhou, Guodong  and
      Zhu, Qiaoming  and
      Aw, AiTi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1230",
    doi = "10.18653/v1/D19-1230",
    pages = "2251--2261",
    abstract = "Negation is a universal but complicated linguistic phenomenon, which has received considerable attention from the NLP community over the last decade, since a negated statement often carries both an explicit negative focus and implicit positive meanings. For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context. However, how to capture contextual information for negative focus detection is still an open challenge. To well address this, we come up with an attention-based neural network to model contextual information. In particular, we introduce a framework which consists of a Bidirectional Long Short-Term Memory (BiLSTM) neural network and a Conditional Random Fields (CRF) layer to effectively encode the order information and the long-range context dependency in a sentence. Moreover, we design two types of attention mechanisms, word-level contextual attention and topic-level contextual attention, to take advantage of contextual information across sentences from both the word perspective and the topic perspective, respectively. Experimental results on the SEM{'}12 shared task corpus show that our approach achieves the best performance on negative focus detection, yielding an absolute improvement of 2.11{\%} over the state-of-the-art. This demonstrates the great effectiveness of the two types of contextual attention mechanisms.",
}
@inproceedings{moon-etal-2019-unified,
    title = "A Unified Neural Coherence Model",
    author = "Moon, Han Cheol  and
      Mohiuddin, Tasnim  and
      Joty, Shafiq  and
      Xu, Chi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1231",
    doi = "10.18653/v1/D19-1231",
    pages = "2262--2272",
    abstract = "Recently, neural approaches to coherence modeling have achieved state-of-the-art results in several evaluation tasks. However, we show that most of these models often fail on harder tasks with more realistic application scenarios. In particular, the existing models underperform on tasks that require the model to be sensitive to local contexts such as candidate ranking in conversational dialogue and in machine translation. In this paper, we propose a unified coherence model that incorporates sentence grammar, inter-sentence coherence relations, and global coherence patterns into a common neural framework. With extensive experiments on local and global discrimination tasks, we demonstrate that our proposed model outperforms existing models by a good margin, and establish a new state-of-the-art.",
}
@inproceedings{oh-etal-2019-topic,
    title = "Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information",
    author = "Oh, Byungkook  and
      Seo, Seungmin  and
      Shin, Cheolheon  and
      Jo, Eunju  and
      Lee, Kyong-Ho",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1232",
    doi = "10.18653/v1/D19-1232",
    pages = "2273--2283",
    abstract = "We propose a novel topic-guided coherence modeling (TGCM) for sentence ordering. Our attention based pointer decoder directly utilize sentence vectors in a permutation-invariant manner, without being compressed into a single fixed-length vector as the paragraph representation. Thus, TGCM can improve global dependencies among sentences and preserve relatively informative paragraph-level semantics. Moreover, to predict the next sentence, we capture topic-enhanced sentence-pair interactions between the current predicted sentence and each next-sentence candidate. With the coherent topical context matching, we promote local dependencies that help identify the tight semantic connections for sentence ordering. The experimental results show that TGCM outperforms state-of-the-art models from various perspectives.",
}
@inproceedings{mabona-etal-2019-neural,
    title = "Neural Generative Rhetorical Structure Parsing",
    author = "Mabona, Amandla  and
      Rimell, Laura  and
      Clark, Stephen  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1233",
    doi = "10.18653/v1/D19-1233",
    pages = "2284--2295",
    abstract = "Rhetorical structure trees have been shown to be useful for several document-level tasks including summarization and document classification. Previous approaches to RST parsing have used discriminative models; however, these are less sample efficient than generative models, and RST parsing datasets are typically small. In this paper, we present the first generative model for RST parsing. Our model is a document-level RNN grammar (RNNG) with a bottom-up traversal order. We show that, for our parser{'}s traversal order, previous beam search algorithms for RNNGs have a left-branching bias which is ill-suited for RST parsing.We develop a novel beam search algorithm that keeps track of both structure-and word-generating actions without exhibit-ing this branching bias and results in absolute improvements of 6.8 and 2.9 on unlabelled and labelled F1 over previous algorithms. Overall, our generative model outperforms a discriminative model with the same features by 2.6 F1points and achieves performance comparable to the state-of-the-art, outperforming all published parsers from a recent replication study that do not use additional training data",
}
@inproceedings{badene-etal-2019-weak,
    title = "Weak Supervision for Learning Discourse Structure",
    author = "Badene, Sonia  and
      Thompson, Kate  and
      Lorr{\'e}, Jean-Pierre  and
      Asher, Nicholas",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1234",
    doi = "10.18653/v1/D19-1234",
    pages = "2296--2305",
    abstract = "This paper provides a detailed comparison of a data programming approach with (i) off-the-shelf, state-of-the-art deep learning architectures that optimize their representations (BERT) and (ii) handcrafted-feature approaches previously used in the discourse analysis literature. We compare these approaches on the task of learning discourse structure for multi-party dialogue. The data programming paradigm offered by the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the {``}generative step{''} into probability distributions of the class labels given the data. We show that on our task the generative model outperforms both deep learning architectures as well as more traditional ML approaches when learning discourse structure{---}it even outperforms the combination of deep learning methods and hand-crafted features. We also implement several strategies for {``}decoding{''} our generative model output in order to improve our results. We conclude that weak supervision methods hold great promise as a means for creating and improving data sets for discourse structure.",
}
@inproceedings{huber-carenini-2019-predicting,
    title = "Predicting Discourse Structure using Distant Supervision from Sentiment",
    author = "Huber, Patrick  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1235",
    doi = "10.18653/v1/D19-1235",
    pages = "2306--2316",
    abstract = "Discourse parsing could not yet take full advantage of the neural NLP revolution, mostly due to the lack of annotated datasets. We propose a novel approach that uses distant supervision on an auxiliary task (sentiment classification), to generate abundant data for RST-style discourse structure prediction. Our approach combines a neural variant of multiple-instance learning, using document-level supervision, with an optimal CKY-style tree generation algorithm. In a series of experiments, we train a discourse parser (for only structure prediction) on our automatically generated dataset and compare it with parsers trained on human-annotated corpora (news domain RST-DT and Instructional domain). Results indicate that while our parser does not yet match the performance of a parser trained and tested on the same dataset (intra-domain), it does perform remarkably well on the much more difficult and arguably more useful task of inter-domain discourse structure prediction, where the parser is trained on one domain and tested/applied on another one.",
}
@inproceedings{caragea-etal-2019-myth,
    title = "The Myth of Double-Blind Review Revisited: {ACL} vs. {EMNLP}",
    author = "Caragea, Cornelia  and
      Uban, Ana  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1236",
    doi = "10.18653/v1/D19-1236",
    pages = "2317--2327",
    abstract = "The review and selection process for scientific paper publication is essential for the quality of scholarly publications in a scientific field. The double-blind review system, which enforces author anonymity during the review period, is widely used by prestigious conferences and journals to ensure the integrity of this process. Although the notion of anonymity in the double-blind review has been questioned before, the availability of full text paper collections brings new opportunities for exploring the question: Is the double-blind review process really double-blind? We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper. Specifically, we explore Convolutional Neural Networks trained on various aspects of a paper, e.g., content, style features, and references, to understand the extent to which we can infer the authors of a paper and what aspects contribute the most. Our results show that the authors of a paper can be inferred with accuracy as high as 87{\%} on ACL and 78{\%} on EMNLP for the top 100 most prolific authors.",
}
@inproceedings{liu-etal-2019-uncover,
    title = "Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization",
    author = "Liu, Yingchi  and
      Li, Quanzhi  and
      Cifor, Marika  and
      Liu, Xiaozhong  and
      Zhang, Qiong  and
      Si, Luo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1237",
    doi = "10.18653/v1/D19-1237",
    pages = "2328--2337",
    abstract = "The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the {\#}MeToo and {\#}TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected {\textgreater}10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers{'} characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.",
}
@inproceedings{balashankar-etal-2019-identifying,
    title = "Identifying Predictive Causal Factors from News Streams",
    author = "Balashankar, Ananth  and
      Chakraborty, Sunandan  and
      Fraiberger, Samuel  and
      Subramanian, Lakshminarayanan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1238",
    doi = "10.18653/v1/D19-1238",
    pages = "2338--2348",
    abstract = "We propose a new framework to uncover the relationship between news events and real world phenomena. We present the Predictive Causal Graph (PCG) which allows to detect latent relationships between events mentioned in news streams. This graph is constructed by measuring how the occurrence of a word in the news influences the occurrence of another (set of) word(s) in the future. We show that PCG can be used to extract latent features from news streams, outperforming other graph-based methods in prediction error of 10 stock price time series for 12 months. We then extended PCG to be applicable for longer time windows by allowing time-varying factors, leading to stock price prediction error rates between 1.5{\%} and 5{\%} for about 4 years. We then manually validated PCG, finding that 67{\%} of the causation semantic frame arguments present in the news corpus were directly connected in the PCG, the remaining being connected through a semantically relevant intermediate node.",
}
@inproceedings{mesbah-etal-2019-training,
    title = "Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content",
    author = "Mesbah, Sepideh  and
      Yang, Jie  and
      Sips, Robert-Jan  and
      Valle Torre, Manuel  and
      Lofi, Christoph  and
      Bozzon, Alessandro  and
      Houben, Geert-Jan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1239",
    doi = "10.18653/v1/D19-1239",
    pages = "2349--2359",
    abstract = "Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this paper, we introduce a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset, and subsequently, to automatically generate a large labeled training set from a small set of labeled samples. This allows for efficient social-media ADR detection with low training and re-training costs to adapt to the changes and emergence of informal medical laymen terms. An extensive evaluation performed on Twitter and Reddit data shows that our approach matches the performance of fully-supervised approaches while requiring only 25{\%} of training data.",
}
@inproceedings{mosallanezhad-etal-2019-deep,
    title = "Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference",
    author = "Mosallanezhad, Ahmadreza  and
      Beigi, Ghazaleh  and
      Liu, Huan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1240",
    doi = "10.18653/v1/D19-1240",
    pages = "2360--2369",
    abstract = "User-generated textual data is rich in content and has been used in many user behavioral modeling tasks. However, it could also leak user private-attribute information that they may not want to disclose such as age and location. User{'}s privacy concerns mandate data publishers to protect privacy. One effective way is to anonymize the textual data. In this paper, we study the problem of textual data anonymization and propose a novel Reinforcement Learning-based Text Anonymizor, RLTA, which addresses the problem of private-attribute leakage while preserving the utility of textual data. Our approach first extracts a latent representation of the original text w.r.t. a given task, then leverages deep reinforcement learning to automatically learn an optimal strategy for manipulating text representations w.r.t. the received privacy and utility feedback. Experiments show the effectiveness of this approach in terms of preserving both privacy and utility.",
}
@inproceedings{liu-etal-2019-tree,
    title = "Tree-structured Decoding for Solving Math Word Problems",
    author = "Liu, Qianying  and
      Guan, Wenyv  and
      Li, Sujian  and
      Kawahara, Daisuke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1241",
    doi = "10.18653/v1/D19-1241",
    pages = "2370--2379",
    abstract = "Automatically solving math word problems is an interesting research topic that needs to bridge natural language descriptions and formal math equations. Previous studies introduced end-to-end neural network methods, but these approaches did not efficiently consider an important characteristic of the equation, i.e., an abstract syntax tree. To address this problem, we propose a tree-structured decoding method that generates the abstract syntax tree of the equation in a top-down manner. In addition, our approach can automatically stop during decoding without a redundant stop token. The experimental results show that our method achieves single model state-of-the-art performance on Math23K, which is the largest dataset on this task.",
}
@inproceedings{sun-etal-2019-pullnet,
    title = "{P}ull{N}et: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text",
    author = "Sun, Haitian  and
      Bedrax-Weiss, Tania  and
      Cohen, William",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1242",
    doi = "10.18653/v1/D19-1242",
    pages = "2380--2390",
    abstract = "We consider open-domain question answering (QA) where answers are drawn from either a corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non-trivial (e.g., {``}multi-hop{''}) reasoning. We describe PullNet, an integrated framework for (1) learning what to retrieve and (2) reasoning with this heterogeneous information to find the best answer. PullNet uses an {iterative} process to construct a question-specific subgraph that contains information relevant to the question. In each iteration, a graph convolutional network (graph CNN) is used to identify subgraph nodes that should be expanded using retrieval (or {``}pull{''}) operations on the corpus and/or KB. After the subgraph is complete, another graph CNN is used to extract the answer from the subgraph. This retrieve-and-reason process allows us to answer multi-hop questions using large KBs and corpora. PullNet is weakly supervised, requiring question-answer pairs but not gold inference paths. Experimentally PullNet improves over the prior state-of-the art, and in the setting where a corpus is used with incomplete KB these improvements are often dramatic. PullNet is also often superior to prior systems in a KB-only setting or a text-only setting.",
}
@inproceedings{huang-etal-2019-cosmos,
    title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
    author = "Huang, Lifu  and
      Le Bras, Ronan  and
      Bhagavatula, Chandra  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1243",
    doi = "10.18653/v1/D19-1243",
    pages = "2391--2401",
    abstract = "Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people{'}s everyday narratives, asking such questions as {``}what might be the possible reason of ...?'', or {``}what would have happened if ...'' that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4{\%}) and human performance (94{\%}), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at https://wilburone.github.io/cosmos.",
}
@inproceedings{perez-etal-2019-finding,
    title = "Finding Generalizable Evidence by Learning to Convince {Q}{\&}{A} Models",
    author = "Perez, Ethan  and
      Karamcheti, Siddharth  and
      Fergus, Rob  and
      Weston, Jason  and
      Kiela, Douwe  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1244",
    doi = "10.18653/v1/D19-1244",
    pages = "2402--2411",
    abstract = "We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agents to select the passage sentences that most convince a pretrained QA model of a given answer, if the QA model received those sentences instead of the full passage. Rather than finding evidence that convinces one model alone, we find that agents select evidence that generalizes; agent-chosen evidence increases the plausibility of the supported answer, as judged by other QA models and humans. Given its general nature, this approach improves QA in a robust manner: using agent-selected evidence (i) humans can correctly answer questions with only {\textasciitilde}20{\%} of the full passage and (ii) QA models can generalize to longer passages and harder questions.",
}
@inproceedings{xu-etal-2019-ranking,
    title = "Ranking and Sampling in Open-Domain Question Answering",
    author = "Xu, Yanfu  and
      Lin, Zheng  and
      Liu, Yuanxin  and
      Liu, Rui  and
      Wang, Weiping  and
      Meng, Dan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1245",
    doi = "10.18653/v1/D19-1245",
    pages = "2412--2421",
    abstract = "Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs. Existing approaches always follow the distantly supervised setup where some of the paragraphs are wrong-labeled (noisy), and mainly utilize the paragraph-question relevance to denoise. However, the paragraph-paragraph relevance, which may aggregate the evidence among relevant paragraphs, can also be utilized to discover more useful paragraphs. Moreover, current approaches mainly focus on the positive paragraphs which are known to contain the answer during training. This will affect the generalization ability of the model and make it be disturbed by the similar but irrelevant (distracting) paragraphs during testing. In this paper, we first introduce a ranking model leveraging the paragraph-question and the paragraph-paragraph relevance to compute a confidence score for each paragraph. Furthermore, based on the scores, we design a modified weighted sampling strategy for training to mitigate the influence of the noisy and distracting paragraphs. Experiments on three public datasets (Quasar-T, SearchQA and TriviaQA) show that our model advances the state of the art.",
}
@inproceedings{hayashi-shimbo-2019-non,
    title = "A Non-commutative Bilinear Model for Answering Path Queries in Knowledge Graphs",
    author = "Hayashi, Katsuhiko  and
      Shimbo, Masashi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1246",
    doi = "10.18653/v1/D19-1246",
    pages = "2422--2430",
    abstract = "Bilinear diagonal models for knowledge graph embedding (KGE), such as DistMult and ComplEx, balance expressiveness and computational efficiency by representing relations as diagonal matrices. Although they perform well in predicting atomic relations, composite relations (relation paths) cannot be modeled naturally by the product of relation matrices, as the product of diagonal matrices is commutative and hence invariant with the order of relations. In this paper, we propose a new bilinear KGE model, called BlockHolE, based on block circulant matrices. In BlockHolE, relation matrices can be non-commutative, allowing composite relations to be modeled by matrix product. The model is parameterized in a way that covers a spectrum ranging from diagonal to full relation matrices. A fast computation technique can be developed on the basis of the duality of the Fourier transform of circulant matrices.",
}
@inproceedings{liu-etal-2019-generating,
    title = "Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss",
    author = "Liu, Cao  and
      Liu, Kang  and
      He, Shizhu  and
      Nie, Zaiqing  and
      Zhao, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1247",
    doi = "10.18653/v1/D19-1247",
    pages = "2431--2441",
    abstract = "We tackle the task of question generation over knowledge bases. Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to be definitive. In this paper, we strive toward the above two issues via incorporating diversified contexts and answer-aware loss. Specifically, we propose a neural encoder-decoder model with multi-level copy mechanisms to generate such questions. Furthermore, the answer aware loss is introduced to make generated questions corresponding to more definitive answers. Experiments demonstrate that our model achieves state-of-the-art performance. Meanwhile, such generated question is able to express the given predicate and correspond to a definitive answer.",
}
@inproceedings{shen-etal-2019-multi,
    title = "Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base",
    author = "Shen, Tao  and
      Geng, Xiubo  and
      Qin, Tao  and
      Guo, Daya  and
      Tang, Duyu  and
      Duan, Nan  and
      Long, Guodong  and
      Jiang, Daxin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1248",
    doi = "10.18653/v1/D19-1248",
    pages = "2442--2451",
    abstract = "We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several subtasks and then solve them sequentially, which leads to following issues: 1) errors in earlier subtasks will be propagated and negatively affect downstream ones; and 2) each subtask cannot naturally share supervision signals with others. To tackle these issues, we propose an innovative multi-task learning framework where a pointer-equipped semantic parsing model is designed to resolve coreference in conversations, and naturally empower joint learning with a novel type-aware entity detection model. The proposed framework thus enables shared supervisions and alleviates the effect of error propagation. Experiments on a large-scale conversational question answering dataset containing 1.6M question answering pairs over 12.8M entities show that the proposed framework improves overall F1 score from 67{\%} to 79{\%} compared with previous state-of-the-art work.",
}
@inproceedings{jing-etal-2019-bipar,
    title = "{B}i{P}a{R}: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels",
    author = "Jing, Yimin  and
      Xiong, Deyi  and
      Yan, Zhen",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1249",
    doi = "10.18653/v1/D19-1249",
    pages = "2452--2462",
    abstract = "This paper presents BiPaR, a bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support multilingual and cross-lingual reading comprehension. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written parallelly in two languages. We collect 3,667 bilingual parallel paragraphs from Chinese and English novels, from which we construct 14,668 parallel question-answer pairs via crowdsourced workers following a strict quality control procedure. We analyze BiPaR in depth and find that BiPaR offers good diversification in prefixes of questions, answer types and relationships between questions and passages. We also observe that answering questions of novels requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality, etc. With BiPaR, we build monolingual, multilingual, and cross-lingual MRC baseline models. Even for the relatively simple monolingual MRC on this dataset, experiments show that a strong BERT baseline is over 30 points behind human in terms of both EM and F1 score, indicating that BiPaR provides a challenging testbed for monolingual, multilingual and cross-lingual MRC on novels. The dataset is available at https://multinlp.github.io/BiPaR/.",
}
@inproceedings{petroni-etal-2019-language,
    title = "Language Models as Knowledge Bases?",
    author = {Petroni, Fabio  and
      Rockt{\"a}schel, Tim  and
      Riedel, Sebastian  and
      Lewis, Patrick  and
      Bakhtin, Anton  and
      Wu, Yuxiang  and
      Miller, Alexander},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1250",
    doi = "10.18653/v1/D19-1250",
    pages = "2463--2473",
    abstract = "Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as {``}fill-in-the-blank{''} cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA.",
}
@inproceedings{ran-etal-2019-numnet,
    title = "{N}um{N}et: Machine Reading Comprehension with Numerical Reasoning",
    author = "Ran, Qiu  and
      Lin, Yankai  and
      Li, Peng  and
      Zhou, Jie  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1251",
    doi = "10.18653/v1/D19-1251",
    pages = "2474--2484",
    abstract = "Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human{'}s reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to consider the comparing information and performs numerical reasoning over numbers in the question and passage. Our system achieves an EM-score of 64.56{\%} on the DROP dataset, outperforming all existing machine reading comprehension models by considering the numerical relations among numbers.",
}
@inproceedings{huang-etal-2019-unicoder,
    title = "{U}nicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks",
    author = "Huang, Haoyang  and
      Liang, Yaobo  and
      Duan, Nan  and
      Gong, Ming  and
      Shou, Linjun  and
      Jiang, Daxin  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1252",
    doi = "10.18653/v1/D19-1252",
    pages = "2485--2494",
    abstract = "We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM , three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8{\%} averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5{\%} averaged accuracy improvement (on French and German) is obtained.",
}
@inproceedings{zhang-bansal-2019-addressing,
    title = "Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering",
    author = "Zhang, Shiyue  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1253",
    doi = "10.18653/v1/D19-1253",
    pages = "2495--2509",
    abstract = "Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context. Existing QG models suffer from a {``}semantic drift{''} problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. In this paper, we first propose two semantics-enhanced rewards obtained from downstream question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions. Second, since the traditional evaluation metrics (e.g., BLEU) often fall short in evaluating the quality of generated questions, we propose a QA-based evaluation method which measures the QG model{'}s ability to mimic human annotators in generating QA training data. Experiments show that our method achieves the new state-of-the-art performance w.r.t. traditional metrics, and also performs best on our QA-based evaluation metrics. Further, we investigate how to use our QG model to augment QA datasets and enable semi-supervised QA. We propose two ways to generate synthetic QA pairs: generate new questions from existing articles or collect QA pairs from new articles. We also propose two empirically effective strategies, a data filter and mixing mini-batch training, to properly use the QG-generated data for QA. Experiments show that our method improves over both BiDAF and BERT QA baselines, even without introducing new articles.",
}
@inproceedings{wang-etal-2019-adversarial-domain,
    title = "Adversarial Domain Adaptation for Machine Reading Comprehension",
    author = "Wang, Huazheng  and
      Gan, Zhe  and
      Liu, Xiaodong  and
      Liu, Jingjing  and
      Gao, Jianfeng  and
      Wang, Hongning",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1254",
    doi = "10.18653/v1/D19-1254",
    pages = "2510--2520",
    abstract = "In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. To this end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where ($i$) pseudo questions are first generated for unlabeled passages in the target domain, and then ($ii$) a domain classifier is incorporated into an MRC model to predict which domain a given passage-question pair comes from. The classifier and the passage-question encoder are jointly trained using adversarial learning to enforce domain-invariant representation learning. Comprehensive evaluations demonstrate that our approach ($i$) is generalizable to different MRC models and datasets, ($ii$) can be combined with pre-trained large-scale language models (such as ELMo and BERT), and ($iii$) can be extended to semi-supervised learning.",
}
@inproceedings{bi-etal-2019-incorporating,
    title = "Incorporating External Knowledge into Machine Reading for Generative Question Answering",
    author = "Bi, Bin  and
      Wu, Chen  and
      Yan, Ming  and
      Wang, Wei  and
      Xia, Jiangnan  and
      Li, Chenliang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1255",
    doi = "10.18653/v1/D19-1255",
    pages = "2521--2530",
    abstract = "Commonsense and background knowledge is required for a QA model to answer many nontrivial questions. Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context. In this paper, we propose a new neural model, Knowledge-Enriched Answer Generator (KEAG), which is able to compose a natural answer by exploiting and aggregating evidence from all four information sources available: question, passage, vocabulary and knowledge. During the process of answer generation, KEAG adaptively determines when to utilize symbolic knowledge and which fact from the knowledge is useful. This allows the model to exploit external knowledge that is not explicitly stated in the given text, but that is relevant for generating an answer. The empirical study on public benchmark of answer generation demonstrates that KEAG improves answer quality over models without knowledge and existing knowledge-aware models, confirming its effectiveness in leveraging knowledge.",
}
@inproceedings{pirtoaca-etal-2019-answering,
    title = "Answering questions by learning to rank - Learning to rank by answering questions",
    author = "Pirtoaca, George Sebastian  and
      Rebedea, Traian  and
      Ruseti, Stefan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1256",
    doi = "10.18653/v1/D19-1256",
    pages = "2531--2540",
    abstract = "Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing. The contribution of this article is two-fold. First, it describes a method which can be used to semantically rank documents extracted from Wikipedia or similar natural language corpora. Second, we propose a model employing the semantic ranking that holds the first place in two of the most popular leaderboards for answering multiple-choice questions: ARC Easy and Challenge. To achieve this, we introduce a self-attention based neural network that latently learns to rank documents by their importance related to a given question, whilst optimizing the objective of predicting the correct answer. These documents are considered relevant contexts for the underlying question. We have published the ranked documents so that they can be used off-the-shelf to improve downstream decision models.",
}
@inproceedings{mihaylov-frank-2019-discourse,
    title = "Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension",
    author = "Mihaylov, Todor  and
      Frank, Anette",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1257",
    doi = "10.18653/v1/D19-1257",
    pages = "2541--2552",
    abstract = "In this work, we propose to use linguistic annotations as a basis for a Discourse-Aware Semantic Self-Attention encoder that we employ for reading comprehension on narrative texts. We extract relations between discourse units, events, and their arguments as well as coreferring mentions, using available annotation tools. Our empirical evaluation shows that the investigated structures improve the overall performance (up to +3.4 Rouge-L), especially intra-sentential and cross-sentential discourse relations, sentence-internal semantic role relations, and long-distance coreference relations. We show that dedicating self-attention heads to intra-sentential relations and relations connecting neighboring sentences is beneficial for finding answers to questions in longer contexts. Our findings encourage the use of discourse-semantic annotations to enhance the generalization capacity of self-attention models for reading comprehension.",
}
@inproceedings{nie-etal-2019-revealing,
    title = "Revealing the Importance of Semantic Retrieval for Machine Reading at Scale",
    author = "Nie, Yixin  and
      Wang, Songhe  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1258",
    doi = "10.18653/v1/D19-1258",
    pages = "2553--2566",
    abstract = "Machine Reading at Scale (MRS) is a challenging task in which a system is given an input query and is asked to produce a precise output by {``}reading{''} information from a large knowledge base. The task has gained popularity with its natural combination of information retrieval (IR) and machine comprehension (MC). Advancements in representation learning have led to separated progress in both IR and MC; however, very few studies have examined the relationship and combined design of retrieval and comprehension at different levels of granularity, for development of MRS systems. In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task. The system is evaluated on both fact verification and open-domain multihop QA, achieving state-of-the-art results on the leaderboard test sets of both FEVER and HOTPOTQA. To further demonstrate the importance of semantic retrieval, we present ablation and analysis studies to quantify the contribution of neural retrieval modules at both paragraph-level and sentence-level, and illustrate that intermediate semantic retrieval modules are vital for not only effectively filtering upstream information and thus saving downstream computation, but also for shaping upstream data distribution and providing better data for downstream modeling.",
}
@inproceedings{jin-etal-2019-pubmedqa,
    title = "{P}ub{M}ed{QA}: A Dataset for Biomedical Research Question Answering",
    author = "Jin, Qiao  and
      Dhingra, Bhuwan  and
      Liu, Zhengping  and
      Cohen, William  and
      Lu, Xinghua",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1259",
    doi = "10.18653/v1/D19-1259",
    pages = "2567--2577",
    abstract = "We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1{\%} accuracy, compared to single human performance of 78.0{\%} accuracy and majority-baseline of 55.2{\%} accuracy, leaving much room for improvement. PubMedQA is publicly available at https://pubmedqa.github.io.",
}
@inproceedings{yadav-etal-2019-quick,
    title = "Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering",
    author = "Yadav, Vikas  and
      Bethard, Steven  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1260",
    doi = "10.18653/v1/D19-1260",
    pages = "2578--2589",
    abstract = "We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection can be coupled with any supervised QA model. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2{'}s Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among systems that do not use external resources for training the QA system: 56.82{\%} F1 on ARC (41.24{\%} on Challenge and 64.49{\%} on Easy) and 26.1{\%} EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, e.g., by 5.4{\%} F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.",
}
@inproceedings{qi-etal-2019-answering,
    title = "Answering Complex Open-domain Questions Through Iterative Query Generation",
    author = "Qi, Peng  and
      Lin, Xiaowen  and
      Mehr, Leo  and
      Wang, Zijian  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1261",
    doi = "10.18653/v1/D19-1261",
    pages = "2590--2602",
    abstract = "It is challenging for current one-step retrieve-and-read question answering (QA) systems to answer questions like {``}Which novel by the author of {`}Armada{'} will be adapted as a feature film by Steven Spielberg?{''} because the question seldom contains retrievable clues about the missing entity (here, the author). Answering such a question requires multi-hop reasoning where one must gather information about the missing entity (or facts) to proceed with further reasoning. We present GoldEn (Gold Entity) Retriever, which iterates between reading context and retrieving more supporting documents to answer open-domain multi-hop questions. Instead of using opaque and computationally expensive neural retrieval models, GoldEn Retriever generates natural language search queries given the question and available context, and leverages off-the-shelf information retrieval systems to query for missing entities. This allows GoldEn Retriever to scale up efficiently for open-domain multi-hop reasoning while maintaining interpretability. We evaluate GoldEn Retriever on the recently proposed open-domain multi-hop QA dataset, HotpotQA, and demonstrate that it outperforms the best previously published model despite not using pretrained language models such as BERT.",
}
@inproceedings{chen-etal-2019-nl2psql,
    title = "{NL}2p{SQL}: Generating Pseudo-{SQL} Queries from Under-Specified Natural Language Questions",
    author = "Chen, Fuxiang  and
      Hwang, Seung-won  and
      Choo, Jaegul  and
      Ha, Jung-Woo  and
      Kim, Sunghun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1262",
    doi = "10.18653/v1/D19-1262",
    pages = "2603--2613",
    abstract = "Generating SQL codes from natural language questions (NL2SQL) is an emerging research area. Existing studies have mainly focused on clear scenarios where specified information is fully given to generate a SQL query. However, in developer forums such as Stack Overflow, questions cover more diverse tasks including table manipulation or performance issues, where a table is not specified. The SQL query posted in Stack Overflow, Pseudo-SQL (pSQL), does not usually contain table schemas and is not necessarily executable, is sufficient to guide developers. Here we describe a new NL2pSQL task to generate pSQL codes from natural language questions on under-specified database issues, NL2pSQL. In addition, we define two new metrics suitable for the proposed NL2pSQL task, Canonical-BLEU and SQL-BLEU, instead of the conventional BLEU. With a baseline model using sequence-to-sequence architecture integrated by denoising autoencoder, we confirm the validity of our task. Experiments show that the proposed NL2pSQL approach yields well-formed queries (up to 43{\%} more than a standard Seq2Seq model). Our code and datasets will be publicly released.",
}
@inproceedings{ding-etal-2019-leveraging,
    title = "Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering",
    author = "Ding, Jiwei  and
      Hu, Wei  and
      Xu, Qixin  and
      Qu, Yuzhong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1263",
    doi = "10.18653/v1/D19-1263",
    pages = "2614--2622",
    abstract = "Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results.",
}
@inproceedings{wang-etal-2019-incorporating,
    title = "Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning",
    author = "Wang, Heng  and
      Li, Shuangyin  and
      Pan, Rong  and
      Mao, Mingzhi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1264",
    doi = "10.18653/v1/D19-1264",
    pages = "2623--2631",
    abstract = "Knowledge Graph (KG) reasoning aims at finding reasoning paths for relations, in order to solve the problem of incompleteness in KG. Many previous path-based methods like PRA and DeepPath suffer from lacking memory components, or stuck in training. Therefore, their performances always rely on well-pretraining. In this paper, we present a deep reinforcement learning based model named by AttnPath, which incorporates LSTM and Graph Attention Mechanism as the memory components. We define two metrics, Mean Selection Rate (MSR) and Mean Replacement Rate (MRR), to quantitatively measure how difficult it is to learn the query relations, and take advantages of them to fine-tune the model under the framework of reinforcement learning. Meanwhile, a novel mechanism of reinforcement learning is proposed by forcing an agent to walk forward every step to avoid the agent stalling at the same entity node constantly. Based on this operation, the proposed model not only can get rid of the pretraining process, but also achieves state-of-the-art performance comparing with the other models. We test our model on FB15K-237 and NELL-995 datasets with different tasks. Extensive experiments show that our model is effective and competitive with many current state-of-the-art methods, and also performs well in practice.",
}
@inproceedings{tang-etal-2019-learning,
    title = "Learning to Update Knowledge Graphs by Reading News",
    author = "Tang, Jizhi  and
      Feng, Yansong  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1265",
    doi = "10.18653/v1/D19-1265",
    pages = "2632--2641",
    abstract = "News streams contain rich up-to-date information which can be used to update knowledge graphs (KGs). Most current text-based KG updating methods rely on elaborately designed information extraction (IE) systems and carefully crafted rules, which are often domain-specific and hard to maintain. Besides, such methods often hardly pay enough attention to the implicit information that lies underneath texts. In this paper, we propose a novel neural network method, GUpdater, to tackle these problems. GUpdater is built upon graph neural networks (GNNs) with a text-based attention mechanism to guide the updating message passing through the KG structures. Experiments on a real-world KG updating dataset show that our model can effectively broadcast the news information to the KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets.",
}
@inproceedings{li-cheng-2019-divine,
    title = "{DIVINE}: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning",
    author = "Li, Ruiping  and
      Cheng, Xiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1266",
    doi = "10.18653/v1/D19-1266",
    pages = "2642--2651",
    abstract = "Knowledge graphs (KGs) often suffer from sparseness and incompleteness. Knowledge graph reasoning provides a feasible way to address such problems. Recent studies on knowledge graph reasoning have shown that reinforcement learning (RL) based methods can provide state-of-the-art performance. However, existing RL-based methods require numerous trials for path-finding and rely heavily on meticulous reward engineering to fit specific dataset, which is inefficient and laborious to apply to fast-evolving KGs. To this end, in this paper, we present DIVINE, a novel plug-and-play framework based on generative adversarial imitation learning for enhancing existing RL-based methods. DIVINE guides the path-finding process, and learns reasoning policies and reward functions self-adaptively through imitating the demonstrations automatically sampled from KGs. Experimental results on two benchmark datasets show that our framework improves the performance of existing RL-based methods while eliminating extra reward engineering.",
}
@inproceedings{liu-etal-2019-original,
    title = "Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching",
    author = "Liu, Mingtong  and
      Zhang, Yujie  and
      Xu, Jinan  and
      Chen, Yufeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1267",
    doi = "10.18653/v1/D19-1267",
    pages = "2652--2661",
    abstract = "Sentence matching is a key issue in natural language inference and paraphrase identification. Despite the recent progress on multi-layered neural network with cross sentence attention, one sentence learns attention to the intermediate representations of another sentence, which are propagated from preceding layers and therefore are uncertain and unstable for matching, particularly at the risk of error propagation. In this paper, we present an original semantics-oriented attention and deep fusion network (OSOA-DFN) for sentence matching. Unlike existing models, each attention layer of OSOA-DFN is oriented to the original semantic representation of another sentence, which captures the relevant information from a fixed matching target. The multiple attention layers allow one sentence to repeatedly read the important information of another sentence for better matching. We then additionally design deep fusion to propagate the attention information at each matching layer. At last, we introduce a self-attention mechanism to capture global context to enhance attention-aware representation within each sentence. Experiment results on three sentence matching benchmark datasets SNLI, SciTail and Quora show that OSOA-DFN has the ability to model sentence matching more precisely.",
}
@inproceedings{zhu-etal-2019-representation,
    title = "Representation Learning with Ordered Relation Paths for Knowledge Graph Completion",
    author = "Zhu, Yao  and
      Liu, Hongzhi  and
      Wu, Zhonghai  and
      Song, Yang  and
      Zhang, Tao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1268",
    doi = "10.18653/v1/D19-1268",
    pages = "2662--2671",
    abstract = "Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction. Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning. In addition, these path-based models always ignore nonlinear contributions of path features for link prediction. To solve these problems, we propose a novel KG completion method named OPTransE. Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path. Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction. Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods.",
}
@inproceedings{fu-etal-2019-collaborative,
    title = "Collaborative Policy Learning for Open Knowledge Graph Reasoning",
    author = "Fu, Cong  and
      Chen, Tong  and
      Qu, Meng  and
      Jin, Woojeong  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1269",
    doi = "10.18653/v1/D19-1269",
    pages = "2672--2681",
    abstract = "In recent years, there has been a surge of interests in interpretable graph reasoning methods. However, these models often suffer from limited performance when working on sparse and incomplete graphs, due to the lack of evidential paths that can reach target entities. Here we study open knowledge graph reasoning{---}a task that aims to reason for missing facts over a graph augmented by a background text corpus. A key challenge of the task is to filter out {``}irrelevant{''} facts extracted from corpus, in order to maintain an effective search space during path inference. We propose a novel reinforcement learning framework to train two collaborative agents jointly, i.e., a multi-hop graph reasoner and a fact extractor. The fact extraction agent generates fact triples from corpora to enrich the graph on the fly; while the reasoning agent provides feedback to the fact extractor and guides it towards promoting facts that are helpful for the interpretable reasoning. Experiments on two public datasets demonstrate the effectiveness of the proposed approach.",
}
@inproceedings{du-etal-2019-modeling,
    title = "Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder",
    author = "Du, Li  and
      Ding, Xiao  and
      Liu, Ting  and
      Li, Zhongyang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1270",
    doi = "10.18653/v1/D19-1270",
    pages = "2682--2691",
    abstract = "Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP). Given an observed event, it is trivial for human to infer its intents and effects, while this type of If-Then reasoning still remains challenging for NLP systems. To facilitate this, a If-Then commonsense reasoning dataset Atomic is proposed, together with an RNN-based Seq2Seq model to conduct such reasoning. However, two fundamental problems still need to be addressed: first, the intents of an event may be multiple, while the generations of RNN-based Seq2Seq models are always semantically close; second, external knowledge of the event background may be necessary for understanding events and conducting the If-Then reasoning. To address these issues, we propose a novel context-aware variational autoencoder effectively learning event background information to guide the If-Then reasoning. Experimental results show that our approach improves the accuracy and diversity of inferences compared with state-of-the-art baseline methods.",
}
@inproceedings{liang-etal-2019-asynchronous,
    title = "Asynchronous Deep Interaction Network for Natural Language Inference",
    author = "Liang, Di  and
      Zhang, Fubao  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1271",
    doi = "10.18653/v1/D19-1271",
    pages = "2692--2700",
    abstract = "Natural language inference aims to predict whether a premise sentence can infer another hypothesis sentence. Existing methods typically have framed the reasoning problem as a semantic matching task. The both sentences are encoded and interacted symmetrically and in parallel. However, in the process of reasoning, the role of the two sentences is obviously different, and the sentence pairs for NLI are asymmetrical corpora. In this paper, we propose an asynchronous deep interaction network (ADIN) to complete the task. ADIN is a neural network structure stacked with multiple inference sub-layers, and each sub-layer consists of two local inference modules in an asymmetrical manner. Different from previous methods, this model deconstructs the reasoning process and implements the asynchronous and multi-step reasoning. Experiment results show that ADIN achieves competitive performance and outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail.",
}
@inproceedings{feng-etal-2019-keep,
    title = "Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange",
    author = "Feng, Steven Y.  and
      Li, Aaron W.  and
      Hoey, Jesse",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1272",
    doi = "10.18653/v1/D19-1272",
    pages = "2701--2711",
    abstract = "In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange. This is useful for text data augmentation and the semantic correction of text generated by chatbots and virtual assistants. We introduce a pipeline called SMERTI that combines entity replacement, similarity masking, and text infilling. We measure our pipeline{'}s success by its Semantic Text Exchange Score (STES): the ability to preserve the original text{'}s sentiment and fluency while adjusting semantic content. We propose to use masking (replacement) rate threshold as an adjustable parameter to control the amount of semantic change in the text. Our experiments demonstrate that SMERTI can outperform baseline models on Yelp reviews, Amazon reviews, and news headlines.",
}
@inproceedings{wang-etal-2019-query,
    title = "Query-focused Scenario Construction",
    author = "Wang, Su  and
      Durrett, Greg  and
      Erk, Katrin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1273",
    doi = "10.18653/v1/D19-1273",
    pages = "2712--2722",
    abstract = "The news coverage of events often contains not one but multiple incompatible accounts of what happened. We develop a query-based system that extracts compatible sets of events (scenarios) from such data, formulated as one-class clustering. Our system incrementally evaluates each event{'}s compatibility with already selected events, taking order into account. We use synthetic data consisting of article mixtures for scalable training and evaluate our model on a new human-curated dataset of scenarios about real-world news topics. Stronger neural network models and harder synthetic training settings are both important to achieve high performance, and our final scenario construction system substantially outperforms baselines based on prior work.",
}
@inproceedings{li-etal-2019-semi-supervised-entity,
    title = "Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model",
    author = "Li, Chengjiang  and
      Cao, Yixin  and
      Hou, Lei  and
      Shi, Jiaxin  and
      Li, Juanzi  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1274",
    doi = "10.18653/v1/D19-1274",
    pages = "2723--2732",
    abstract = "Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications. It is challenging due to the heterogeneity of KGs and limited seed alignments. In this paper, we propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG). It can make better use of seed alignments to propagate over the entire graphs with KG-based constraints. Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities. As for the cross-graph model, we extend Graph Attention Network (GAT) with projection constraint to robustly encode graphs, and two KGs share the same GAT to transfer structural knowledge as well as to ignore unimportant neighbors for alignment via attention mechanism. Results on publicly available datasets as well as further analysis demonstrate the effectiveness of KECG. Our codes can be found in https: //github.com/THU-KEG/KECG.",
}
@inproceedings{hewitt-liang-2019-designing,
    title = "Designing and Interpreting Probes with Control Tasks",
    author = "Hewitt, John  and
      Liang, Percy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1275",
    doi = "10.18653/v1/D19-1275",
    pages = "2733--2743",
    abstract = "Probes, supervised models trained to predict properties (like parts-of-speech) from representations (like ELMo), have achieved high accuracy on a range of linguistic tasks. But does this mean that the representations encode linguistic structure or just that the probe has learned the linguistic task? In this paper, we propose control tasks, which associate word types with random outputs, to complement linguistic tasks. By construction, these tasks can only be learned by the probe itself. So a good probe, (one that reflects the representation), should be selective, achieving high linguistic task accuracy and low control task accuracy. The selectivity of a probe puts linguistic task accuracy in context with the probe{'}s capacity to memorize from word types. We construct control tasks for English part-of-speech tagging and dependency edge prediction, and show that popular probes on ELMo representations are not selective. We also find that dropout, commonly used to control probe complexity, is ineffective for improving selectivity of MLPs, but that other forms of regularization are effective. Finally, we find that while probes on the first layer of ELMo yield slightly better part-of-speech tagging accuracy than the second, probes on the second layer are substantially more selective, which raises the question of which layer better represents parts-of-speech.",
}
@inproceedings{li-eisner-2019-specializing,
    title = "Specializing Word Embeddings (for Parsing) by Information Bottleneck",
    author = "Li, Xiang Lisa  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1276",
    doi = "10.18653/v1/D19-1276",
    pages = "2744--2754",
    abstract = "Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic information, resulting in state-of-the-art performance on various tasks. We propose a very fast variational information bottleneck (VIB) method to nonlinearly compress these embeddings, keeping only the information that helps a discriminative parser. We compress each word embedding to either a discrete tag or a continuous vector. In the discrete version, our automatically compressed tags form an alternative tag set: we show experimentally that our tags capture most of the information in traditional POS tag annotations, but our tag sequences can be parsed more accurately at the same level of tag granularity. In the continuous version, we show experimentally that moderately compressing the word embeddings by our method yields a more accurate parser in 8 of 9 languages, unlike simple dimensionality reduction.",
}
@inproceedings{kulmizev-etal-2019-deep,
    title = "Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited",
    author = "Kulmizev, Artur  and
      de Lhoneux, Miryam  and
      Gontrum, Johannes  and
      Fano, Elena  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1277",
    doi = "10.18653/v1/D19-1277",
    pages = "2755--2768",
    abstract = "Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural features but suffer from error propagation, while graph-based parsers benefit from global optimization but have restricted feature scope. In this paper, we show that, even though some details of the picture have changed after the switch to neural networks and continuous representations, the basic trade-off between rich features and global optimization remains essentially the same. Moreover, we show that deep contextualized word embeddings, which allow parsers to pack information about global sentence structure into local feature representations, benefit transition-based parsers more than graph-based parsers, making the two approaches virtually equivalent in terms of both accuracy and error profile. We argue that the reason is that these representations help prevent search errors and thereby allow transition-based parsers to better exploit their inherent strength of making accurate local decisions. We support this explanation by an error analysis of parsing experiments on 13 languages.",
}
@inproceedings{fancellu-etal-2019-semantic,
    title = "Semantic graph parsing with recurrent neural network {DAG} grammars",
    author = "Fancellu, Federico  and
      Gilroy, Sorcha  and
      Lopez, Adam  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1278",
    doi = "10.18653/v1/D19-1278",
    pages = "2769--2778",
    abstract = "Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the *linearized* graphs found in semantic parsing datasets using well-understood sequence models. The cost of this simplicity is that the predicted strings may not be well-formed graphs. We present recurrent neural network DAG grammars, a graph-aware sequence model that generates only well-formed graphs while sidestepping many difficulties in graph prediction. We test our model on the Parallel Meaning Bank{---}a multilingual semantic graphbank. Our approach yields competitive results in English and establishes the first results for German, Italian and Dutch.",
}
@inproceedings{kondratyuk-straka-2019-75,
    title = "75 Languages, 1 Model: Parsing {U}niversal {D}ependencies Universally",
    author = "Kondratyuk, Dan  and
      Straka, Milan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1279",
    doi = "10.18653/v1/D19-1279",
    pages = "2779--2795",
    abstract = "We present UDify, a multilingual multi-task model capable of accurately predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages. By leveraging a multilingual BERT self-attention model pretrained on 104 languages, we found that fine-tuning it on all datasets concatenated together with simple softmax classifiers for each UD task can meet or exceed state-of-the-art UPOS, UFeats, Lemmas, (and especially) UAS, and LAS scores, without requiring any recurrent or language-specific components. We evaluate UDify for multilingual learning, showing that low-resource languages benefit the most from cross-linguistic annotations. We also evaluate for zero-shot learning, with results suggesting that multilingual training provides strong UD predictions even for languages that neither UDify nor BERT have ever been trained on.",
}
@inproceedings{yuan-etal-2019-interactive,
    title = "Interactive Language Learning by Question Answering",
    author = "Yuan, Xingdi  and
      C{\^o}t{\'e}, Marc-Alexandre  and
      Fu, Jie  and
      Lin, Zhouhan  and
      Pal, Chris  and
      Bengio, Yoshua  and
      Trischler, Adam",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1280",
    doi = "10.18653/v1/D19-1280",
    pages = "2796--2813",
    abstract = "Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking component of comprehension. Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring. Thus, models can achieve strong performance through simple word- and phrase-based pattern matching. We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit). In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions. QAit poses questions about the existence, location, and attributes of objects found in the environment. The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.",
}
@inproceedings{khot-etal-2019-whats,
    title = "What{'}s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering",
    author = "Khot, Tushar  and
      Sabharwal, Ashish  and
      Clark, Peter",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1281",
    doi = "10.18653/v1/D19-1281",
    pages = "2814--2828",
    abstract = "Multi-hop textual question answering requires combining information from multiple sentences. We focus on a natural setting where, unlike typical reading comprehension, only partial information is provided with each question. The model must retrieve and use additional knowledge to correctly answer the question. To tackle this challenge, we develop a novel approach that explicitly identifies the knowledge gap between a key span in the provided knowledge and the answer choices. The model, GapQA, learns to fill this gap by determining the relationship between the span and an answer choice, based on retrieved knowledge targeting this gap. We propose jointly training a model to simultaneously fill this knowledge gap and compose it with the provided partial knowledge. On the OpenBookQA dataset, given partial knowledge, explicitly identifying what{'}s missing substantially outperforms previous approaches.",
}
@inproceedings{lin-etal-2019-kagnet,
    title = "{K}ag{N}et: Knowledge-Aware Graph Networks for Commonsense Reasoning",
    author = "Lin, Bill Yuchen  and
      Chen, Xinyue  and
      Chen, Jamin  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1282",
    doi = "10.18653/v1/D19-1282",
    pages = "2829--2839",
    abstract = "Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KagNet, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for Bert-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning.",
}
@inproceedings{lee-etal-2019-learning,
    title = "Learning with Limited Data for Multilingual Reading Comprehension",
    author = "Lee, Kyungjae  and
      Park, Sunghyun  and
      Han, Hojae  and
      Yeo, Jinyoung  and
      Hwang, Seung-won  and
      Lee, Juho",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1283",
    doi = "10.18653/v1/D19-1283",
    pages = "2840--2850",
    abstract = "This paper studies the problem of supporting question answering in a new language with limited training resources. As an extreme scenario, when no such resource exists, one can (1) transfer labels from another language, and (2) generate labels from unlabeled data, using translator and automatic labeling function respectively. However, these approaches inevitably introduce noises to the training data, due to translation or generation errors, which require a judicious use of data with varying confidence. To address this challenge, we propose a weakly-supervised framework that quantifies such noises from automatically generated labels, to deemphasize or fix noisy data in training. On reading comprehension task, we demonstrate the effectiveness of our model on low-resource languages with varying similarity to English, namely, Korean and French.",
}
@inproceedings{min-etal-2019-discrete,
    title = "A Discrete Hard {EM} Approach for Weakly Supervised Question Answering",
    author = "Min, Sewon  and
      Chen, Danqi  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1284",
    doi = "10.18653/v1/D19-1284",
    pages = "2851--2864",
    abstract = "Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible solutions (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2{--}10{\%}, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis.",
}
@inproceedings{pezzelle-fernandez-2019-red,
    title = "Is the Red Square Big? {MAL}e{V}i{C}: Modeling Adjectives Leveraging Visual Contexts",
    author = "Pezzelle, Sandro  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1285",
    doi = "10.18653/v1/D19-1285",
    pages = "2865--2876",
    abstract = "This work aims at modeling how the meaning of gradable adjectives of size ({`}big{'}, {`}small{'}) can be learned from visually-grounded contexts. Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is {`}big{'} or {`}small{'} in a given visual scene. In contrast with the standard computational approach that simplistically treats gradable adjectives as {`}fixed{'} attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context. By means of four main tasks, we show that state-of-the-art models (but not a relatively strong baseline) can learn the function subtending the meaning of size adjectives, though their performance is found to decrease while moving from simple to more complex tasks. Crucially, models fail in developing abstract representations of gradable adjectives that can be used compositionally.",
}
@inproceedings{warstadt-etal-2019-investigating,
    title = "Investigating {BERT}{'}s Knowledge of Language: Five Analysis Methods with {NPI}s",
    author = "Warstadt, Alex  and
      Cao, Yu  and
      Grosu, Ioana  and
      Peng, Wei  and
      Blix, Hagen  and
      Nie, Yining  and
      Alsop, Anna  and
      Bordia, Shikha  and
      Liu, Haokun  and
      Parrish, Alicia  and
      Wang, Sheng-Fu  and
      Phang, Jason  and
      Mohananey, Anhad  and
      Htut, Phu Mon  and
      Jeretic, Paloma  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1286",
    doi = "10.18653/v1/D19-1286",
    pages = "2877--2887",
    abstract = "Though state-of-the-art sentence representation models can perform tasks requiring significant knowledge of grammar, it is an open question how best to evaluate their grammatical knowledge. We explore five experimental methods inspired by prior work evaluating pretrained sentence representation models. We use a single linguistic phenomenon, negative polarity item (NPI) licensing, as a case study for our experiments. NPIs like any are grammatical only if they appear in a licensing environment like negation (Sue doesn{'}t have any cats vs. *Sue has any cats). This phenomenon is challenging because of the variety of NPI licensing environments that exist. We introduce an artificially generated dataset that manipulates key features of NPI licensing for the experiments. We find that BERT has significant knowledge of these features, but its success varies widely across different experimental methods. We conclude that a variety of methods is necessary to reveal all relevant aspects of a model{'}s grammatical knowledge in a given domain.",
}
@inproceedings{an-etal-2019-representation,
    title = "Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study",
    author = "An, Aixiu  and
      Qian, Peng  and
      Wilcox, Ethan  and
      Levy, Roger",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1287",
    doi = "10.18653/v1/D19-1287",
    pages = "2888--2899",
    abstract = "Neural language models have achieved state-of-the-art performances on many NLP tasks, and recently have been shown to learn a number of hierarchically-sensitive syntactic dependencies between individual words. However, equally important for language processing is the ability to combine words into phrasal constituents, and use constituent-level features to drive downstream expectations. Here we investigate neural models{'} ability to represent constituent-level features, using coordinated noun phrases as a case study. We assess whether different neural language models trained on English and French represent phrase-level number and gender features, and use those features to drive downstream expectations. Our results suggest that models use a linear combination of NP constituent number to drive CoordNP/verb number agreement. This behavior is highly regular and even sensitive to local syntactic context, however it differs crucially from observed human behavior. Models have less success with gender agreement. Models trained on large corpora perform best, and there is no obvious advantage for models trained using explicit syntactic supervision.",
}
@inproceedings{ponti-etal-2019-towards,
    title = "Towards Zero-shot Language Modeling",
    author = "Ponti, Edoardo Maria  and
      Vuli{\'c}, Ivan  and
      Cotterell, Ryan  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1288",
    doi = "10.18653/v1/D19-1288",
    pages = "2900--2910",
    abstract = "Can we construct a neural language model which is inductively biased towards learning human language? Motivated by this question, we aim at constructing an informative prior for held-out languages on the task of character-level, open-vocabulary language modelling. We obtain this prior as the posterior over network weights conditioned on the data from a sample of training languages, which is approximated through Laplace{'}s method. Based on a large and diverse sample of languages, the use of our prior outperforms baseline models with an uninformative prior in both zero-shot and few-shot settings, showing that the prior is imbued with universal linguistic knowledge. Moreover, we harness broad language-specific information available for most languages of the world, i.e., features from typological databases, as distant supervision for held-out languages. We explore several language modelling conditioning techniques, including concatenation and meta-networks for parameter generation. They appear beneficial in the few-shot setting, but ineffective in the zero-shot setting. Since the paucity of even plain digital text affects the majority of the world{'}s languages, we hope that these insights will broaden the scope of applications for language technology.",
}
@inproceedings{atkinson-etal-2019-gets,
    title = "What Gets Echoed? Understanding the {``}Pointers{''} in Explanations of Persuasive Arguments",
    author = "Atkinson, David  and
      Srinivasan, Kumar Bhargav  and
      Tan, Chenhao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1289",
    doi = "10.18653/v1/D19-1289",
    pages = "2911--2921",
    abstract = "Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subreddit to build a dataset with 36K naturally occurring explanations of why an argument is persuasive. We propose a novel word-level prediction task to investigate how explanations selectively reuse, or echo, information from what is being explained (henceforth, explanandum). We develop features to capture the properties of a word in the explanandum, and show that our proposed features not only have relatively strong predictive power on the echoing of a word in an explanation, but also enhance neural methods of generating explanations. In particular, while the non-contextual properties of a word itself are more valuable for stopwords, the interaction between the constituent parts of an explanandum is crucial in predicting the echoing of content words. We also find intriguing patterns of a word being echoed. For example, although nouns are generally less likely to be echoed, subjects and objects can, depending on their source, be more likely to be echoed in the explanations.",
}
@inproceedings{ajjour-etal-2019-modeling,
    title = "Modeling Frames in Argumentation",
    author = "Ajjour, Yamen  and
      Alshomary, Milad  and
      Wachsmuth, Henning  and
      Stein, Benno",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1290",
    doi = "10.18653/v1/D19-1290",
    pages = "2922--2932",
    abstract = "In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the {``}right{''} frame(s) to convince the audience to adopt the author{'}s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience{'}s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates{'} topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.",
}
@inproceedings{chakrabarty-etal-2019-ampersand,
    title = "{AMPERSAND}: Argument Mining for {PERS}u{A}sive o{N}line Discussions",
    author = "Chakrabarty, Tuhin  and
      Hidey, Christopher  and
      Muresan, Smaranda  and
      McKeown, Kathy  and
      Hwang, Alyssa",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1291",
    doi = "10.18653/v1/D19-1291",
    pages = "2933--2943",
    abstract = "Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one{'}s argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.",
}
@inproceedings{thorne-etal-2019-evaluating,
    title = "Evaluating adversarial attacks against multiple fact verification systems",
    author = "Thorne, James  and
      Vlachos, Andreas  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1292",
    doi = "10.18653/v1/D19-1292",
    pages = "2944--2953",
    abstract = "Automated fact verification has been progressing owing to advancements in modeling and availability of large datasets. Due to the nature of the task, it is critical to understand the vulnerabilities of these systems against adversarial instances designed to make them predict incorrectly. We introduce two novel scoring metrics, attack potency and system resilience which take into account the correctness of the adversarial instances, an aspect often ignored in adversarial evaluations. We consider six fact verification systems from the recent Fact Extraction and VERification (FEVER) challenge: the four best-scoring ones and two baselines. We evaluate adversarial instances generated by a recently proposed state-of-the-art method, a paraphrasing method, and rule-based attacks devised for fact verification. We find that our rule-based attacks have higher potency, and that while the rankings among the top systems changed, they exhibited higher resilience than the baselines.",
}
@inproceedings{yang-etal-2019-nonsense,
    title = "Nonsense!: Quality Control via Two-Step Reason Selection for Annotating Local Acceptability and Related Attributes in News Editorials",
    author = "Yang, Wonsuk  and
      Yoon, Seungwon  and
      Carpenter, Ada  and
      Park, Jong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1293",
    doi = "10.18653/v1/D19-1293",
    pages = "2954--2963",
    abstract = "Annotation quality control is a critical aspect for building reliable corpora through linguistic annotation. In this study, we present a simple but powerful quality control method using two-step reason selection. We gathered sentential annotations of local acceptance and three related attributes through a crowdsourcing platform. For each attribute, the reason for the choice of the attribute value is selected in a two-step manner. The options given for reason selection were designed to facilitate the detection of a nonsensical reason selection. We assume that a sentential annotation that contains a nonsensical reason is less reliable than the one without such reason. Our method, based solely on this assumption, is found to retain the annotations with satisfactory quality out of the entire annotations mixed with those of low quality.",
}
@inproceedings{jwalapuram-etal-2019-evaluating,
    title = "Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite",
    author = "Jwalapuram, Prathyusha  and
      Joty, Shafiq  and
      Temnikova, Irina  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1294",
    doi = "10.18653/v1/D19-1294",
    pages = "2964--2975",
    abstract = "The ongoing neural revolution in machine translation has made it easier to model larger contexts beyond the sentence-level, which can potentially help resolve some discourse-level ambiguities such as pronominal anaphora, thus enabling better translations. Unfortunately, even when the resulting improvements are seen as substantial by humans, they remain virtually unnoticed by traditional automatic evaluation measures like BLEU, as only a few words end up being affected. Thus, specialized evaluation measures are needed. With this aim in mind, we contribute an extensive, targeted dataset that can be used as a test suite for pronoun translation, covering multiple source languages and different pronoun errors drawn from real system translations, for English. We further propose an evaluation measure to differentiate good and bad pronoun translations. We also conduct a user study to report correlations with human judgments.",
}
@inproceedings{dai-huang-2019-regularization,
    title = "A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing",
    author = "Dai, Zeyu  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1295",
    doi = "10.18653/v1/D19-1295",
    pages = "2976--2987",
    abstract = "We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing. Realizing that external knowledge and linguistic constraints may not always apply in understanding a particular context, we propose a regularization approach that tightly integrates these constraints with contexts for deriving word representations. Meanwhile, it balances attentions over contexts and constraints through adding a regularization term into the objective function. Experiments show that our knowledge regularization approach outperforms all previous systems on the benchmark dataset PDTB for discourse parsing.",
}
@inproceedings{hashimoto-2019-weakly,
    title = "Weakly Supervised Multilingual Causality Extraction from {W}ikipedia",
    author = "Hashimoto, Chikara",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1296",
    doi = "10.18653/v1/D19-1296",
    pages = "2988--2999",
    abstract = "We present a method for extracting causality knowledge from Wikipedia, such as Protectionism -{\textgreater} Trade war, where the cause and effect entities correspond to Wikipedia articles. Such causality knowledge is easy to verify by reading corresponding Wikipedia articles, to translate to multiple languages through Wikidata, and to connect to knowledge bases derived from Wikipedia. Our method exploits Wikipedia article sections that describe causality and the redundancy stemming from the multilinguality of Wikipedia. Experiments showed that our method achieved precision and recall above 98{\%} and 64{\%}, respectively. In particular, it could extract causalities whose cause and effect were written distantly in a Wikipedia article. We have released the code and data for further research.",
}
@inproceedings{li-etal-2019-attribute,
    title = "Attribute-aware Sequence Network for Review Summarization",
    author = "Li, Junjie  and
      Wang, Xuepeng  and
      Yin, Dawei  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1297",
    doi = "10.18653/v1/D19-1297",
    pages = "3000--3010",
    abstract = "Review summarization aims to generate a condensed summary for a review or multiple reviews. Existing review summarization systems mainly generate summary only based on review content and neglect the authors{'} attributes (e.g., gender, age, and occupation). In fact, when summarizing a review, users with different attributes usually pay attention to specific aspects and have their own word-using habits or writing styles. Therefore, we propose an Attribute-aware Sequence Network (ASN) to take the aforementioned users{'} characteristics into account, which includes three modules: an attribute encoder encodes the attribute preferences over the words; an attribute-aware review encoder adopts an attribute-based selective mechanism to select the important information of a review; and an attribute-aware summary decoder incorporates attribute embedding and attribute-specific word-using habits into word prediction. To validate our model, we collect a new dataset TripAtt, comprising 495,440 attribute-review-summary triplets with three kinds of attribute information: gender, age, and travel status. Extensive experiments show that ASN achieves state-of-the-art performance on review summarization in both auto-metric ROUGE and human evaluation.",
}
@inproceedings{xiao-carenini-2019-extractive,
    title = "Extractive Summarization of Long Documents by Combining Global and Local Context",
    author = "Xiao, Wen  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1298",
    doi = "10.18653/v1/D19-1298",
    pages = "3011--3021",
    abstract = "In this paper, we propose a novel neural single-document extractive summarization model for long documents, incorporating both the global context of the whole document and the local context within the current topic. We evaluate the model on two datasets of scientific papers , Pubmed and arXiv, where it outperforms previous work, both extractive and abstractive models, on ROUGE-1, ROUGE-2 and METEOR scores. We also show that, consistently with our goal, the benefits of our method become stronger as we apply it to longer documents. Rather surprisingly, an ablation study indicates that the benefits of our model seem to come exclusively from modeling the local context, even for the longest documents.",
}
@inproceedings{chen-etal-2019-enhancing,
    title = "Enhancing Neural Data-To-Text Generation Models with External Background Knowledge",
    author = "Chen, Shuang  and
      Wang, Jinpeng  and
      Feng, Xiaocheng  and
      Jiang, Feng  and
      Qin, Bing  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1299",
    doi = "10.18653/v1/D19-1299",
    pages = "3022--3032",
    abstract = "Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge. They often assume that writing knowledge can be acquired from the training data alone. However, when people are writing, they not only rely on the data but also consider related knowledge. In this paper, we enhance neural data-to-text models with external knowledge in a simple but effective way to improve the fidelity of generated text. Besides relying on parallel data and text as in previous work, our model attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words. This allows the model to infer relevant facts which are not explicitly stated in the data table from an external knowledge source. Experimental results on twenty-one Wikipedia infobox-to-text datasets show our model, KBAtt, consistently improves a state-of-the-art model on most of the datasets. In addition, to quantify when and why external knowledge is effective, we design a metric, KBGain, which shows a strong correlation with the observed performance boost. This result demonstrates the relevance of external knowledge and sparseness of original data are the main factors affecting system performance.",
}
@inproceedings{luo-etal-2019-reading,
    title = "Reading Like {HER}: Human Reading Inspired Extractive Summarization",
    author = "Luo, Ling  and
      Ao, Xiang  and
      Song, Yan  and
      Pan, Feiyang  and
      Yang, Min  and
      He, Qing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1300",
    doi = "10.18653/v1/D19-1300",
    pages = "3033--3043",
    abstract = "In this work, we re-examine the problem of extractive text summarization for long documents. We observe that the process of extracting summarization of human can be divided into two stages: 1) a rough reading stage to look for sketched information, and 2) a subsequent careful reading stage to select key sentences to form the summary. By simulating such a two-stage process, we propose a novel approach for extractive summarization. We formulate the problem as a contextual-bandit problem and solve it with policy gradient. We adopt a convolutional neural network to encode gist of paragraphs for rough reading, and a decision making policy with an adapted termination mechanism for careful reading. Experiments on the CNN and DailyMail datasets show that our proposed method can provide high-quality summaries with varied length, and significantly outperform the state-of-the-art extractive methods in terms of ROUGE metrics.",
}
@inproceedings{duan-etal-2019-contrastive,
    title = "Contrastive Attention Mechanism for Abstractive Sentence Summarization",
    author = "Duan, Xiangyu  and
      Yu, Hongfei  and
      Yin, Mingming  and
      Zhang, Min  and
      Luo, Weihua  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1301",
    doi = "10.18653/v1/D19-1301",
    pages = "3044--3053",
    abstract = "We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/ Abstractive-Text-Summarization.",
}
@inproceedings{zhu-etal-2019-ncls,
    title = "{NCLS}: Neural Cross-Lingual Summarization",
    author = "Zhu, Junnan  and
      Wang, Qian  and
      Wang, Yining  and
      Zhou, Yu  and
      Zhang, Jiajun  and
      Wang, Shaonan  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1302",
    doi = "10.18653/v1/D19-1302",
    pages = "3054--3064",
    abstract = "Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language. Existing methods simply divide this task into two steps: summarization and translation, leading to the problem of error propagation. To handle that, we present an end-to-end CLS framework, which we refer to as Neural Cross-Lingual Summarization (NCLS), for the first time. Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning. Due to the lack of supervised CLS data, we propose a round-trip translation strategy to acquire two high-quality large-scale CLS datasets based on existing monolingual summarization datasets. Experimental results have shown that our NCLS achieves remarkable improvement over traditional pipeline methods on both English-to-Chinese and Chinese-to-English CLS human-corrected test sets. In addition, NCLS with multi-task learning can further significantly improve the quality of generated summaries. We make our dataset and code publicly available here: http://www.nlpr.ia.ac.cn/cip/dataset.htm.",
}
@inproceedings{xu-etal-2019-clickbait,
    title = "Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning",
    author = "Xu, Peng  and
      Wu, Chien-Sheng  and
      Madotto, Andrea  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1303",
    doi = "10.18653/v1/D19-1303",
    pages = "3065--3075",
    abstract = "Sensational headlines are headlines that capture people{'}s attention and generate reader interest. Conventional abstractive headline generation methods, unlike human writers, do not optimize for maximal reader attention. In this paper, we propose a model that generates sensational headlines without labeled data. We first train a sensationalism scorer by classifying online headlines with many comments ({``}clickbait{''}) against a baseline of headlines generated from a summarization model. The score from the sensationalism scorer is used as the reward for a reinforcement learner. However, maximizing the noisy sensationalism reward will generate unnatural phrases instead of sensational headlines. To effectively leverage this noisy reward, we propose a novel loss function, Auto-tuned Reinforcement Learning (ARL), to dynamically balance reinforcement learning (RL) with maximum likelihood estimation (MLE). Human evaluation shows that 60.8{\%} of samples generated by our model are sensational, which is significantly better than the Pointer-Gen baseline and other RL models.",
}
@inproceedings{wang-etal-2019-concept,
    title = "Concept Pointer Network for Abstractive Summarization",
    author = "Wang, Wenbo  and
      Gao, Yang  and
      Huang, Heyan  and
      Zhou, Yuxiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1304",
    doi = "10.18653/v1/D19-1304",
    pages = "3076--3085",
    abstract = "A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distant-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model{'}s abstractive abilities also supports the quality of the summaries produced within this framework.",
}
@inproceedings{shimorina-gardent-2019-surface,
    title = "Surface Realisation Using Full Delexicalisation",
    author = "Shimorina, Anastasia  and
      Gardent, Claire",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1305",
    doi = "10.18653/v1/D19-1305",
    pages = "3086--3096",
    abstract = "Surface realisation (SR) maps a meaning representation to a sentence and can be viewed as consisting of three subtasks: word ordering, morphological inflection and contraction generation (e.g., clitic attachment in Portuguese or elision in French). We propose a modular approach to surface realisation which models each of these components separately, and evaluate our approach on the 10 languages covered by the SR{'}18 Surface Realisation Shared Task shallow track. We provide a detailed evaluation of how word order, morphological realisation and contractions are handled by the model and an analysis of the differences in word ordering performance across languages.",
}
@inproceedings{jin-etal-2019-imat,
    title = "{IM}a{T}: Unsupervised Text Attribute Transfer via Iterative Matching and Translation",
    author = "Jin, Zhijing  and
      Jin, Di  and
      Mueller, Jonas  and
      Matthews, Nicholas  and
      Santus, Enrico",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1306",
    doi = "10.18653/v1/D19-1306",
    pages = "3097--3109",
    abstract = "Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content. This task remains challenging due to a lack of supervised parallel data. Existing approaches try to explicitly disentangle content and attribute information, but this is difficult and often results in poor content-preservation and ungrammaticality. In contrast, we propose a simpler approach, Iterative Matching and Translation (IMaT), which: (1) constructs a pseudo-parallel corpus by aligning a subset of semantically similar sentences from the source and the target corpora; (2) applies a standard sequence-to-sequence model to learn the attribute transfer; (3) iteratively improves the learned transfer function by refining imperfections in the alignment. In sentiment modification and formality transfer tasks, our method outperforms complex state-of-the-art systems by a large margin. As an auxiliary contribution, we produce a publicly-available test set with human-generated transfer references.",
}
@inproceedings{bohm-etal-2019-better,
    title = "Better Rewards Yield Better Summaries: Learning to Summarise Without References",
    author = {B{\"o}hm, Florian  and
      Gao, Yang  and
      Meyer, Christian M.  and
      Shapira, Ori  and
      Dagan, Ido  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1307",
    doi = "10.18653/v1/D19-1307",
    pages = "3110--3120",
    abstract = "Reinforcement Learning (RL)based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement. To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries. Our reward function only takes the document and system summary as input. Hence, once trained, it can be used to train RL based summarisation systems without using any reference summaries. We show that our learned rewards have significantly higher correlation with human ratings than previous approaches. Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summaries with higher human ratings. The learned reward function and our source code are available at https://github.com/yg211/summary-reward-no-reference.",
}
@inproceedings{cho-etal-2019-mixture,
    title = "Mixture Content Selection for Diverse Sequence Generation",
    author = "Cho, Jaemin  and
      Seo, Minjoon  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1308",
    doi = "10.18653/v1/D19-1308",
    pages = "3121--3131",
    abstract = "Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences. We present a method to explicitly separate diversification from generation using a general plug-and-play module (called SELECTOR) that wraps around and guides an existing encoder-decoder model. The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection. The generation stage uses a standard encoder-decoder model given each selected content from the source sequence. Due to the non-differentiable nature of discrete sampling and the lack of ground truth labels for binary mask, we leverage a proxy for ground truth mask and adopt stochastic hard-EM for training. In question generation (SQuAD) and abstractive summarization (CNN-DM), our method demonstrates significant improvements in accuracy, diversity and training efficiency, including state-of-the-art top-1 accuracy in both datasets, 6{\%} gain in top-5 accuracy, and 3.7 times faster training over a state-of-the-art model. Our code is publicly available at https://github.com/clovaai/FocusSeq2Seq.",
}
@inproceedings{yang-etal-2019-end-end-generative,
    title = "An End-to-End Generative Architecture for Paraphrase Generation",
    author = "Yang, Qian  and
      Huo, Zhouyuan  and
      Shen, Dinghan  and
      Cheng, Yong  and
      Wang, Wenlin  and
      Wang, Guoyin  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1309",
    doi = "10.18653/v1/D19-1309",
    pages = "3132--3142",
    abstract = "Generating high-quality paraphrases is a fundamental yet challenging natural language processing task. Despite the effectiveness of previous work based on generative models, there remain problems with exposure bias in recurrent neural networks, and often a failure to generate realistic sentences. To overcome these challenges, we propose the first end-to-end conditional generative architecture for generating paraphrases via adversarial training, which does not depend on extra linguistic information. Extensive experiments on four public datasets demonstrate the proposed method achieves state-of-the-art results, outperforming previous generative architectures on both automatic metrics (BLEU, METEOR, and TER) and human evaluations.",
}
@inproceedings{gong-etal-2019-table,
    title = "Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)",
    author = "Gong, Heng  and
      Feng, Xiaocheng  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1310",
    doi = "10.18653/v1/D19-1310",
    pages = "3143--3152",
    abstract = "Although Seq2Seq models for table-to-text generation have achieved remarkable progress, modeling table representation in one dimension is inadequate. This is because (1) the table consists of multiple rows and columns, which means that encoding a table should not depend only on one dimensional sequence or set of records and (2) most of the tables are time series data (e.g. NBA game data, stock market data), which means that the description of the current table may be affected by its historical data. To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table{'}s representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively. In addition, we develop a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension{'}s representation. We evaluated our methods on ROTOWIRE, a benchmark dataset of NBA basketball games. Both automatic and human evaluation results demonstrate the effectiveness of our model with improvement of 2.66 in BLEU over the strong baseline and outperformance of state-of-the-art model.",
}
@inproceedings{zheng-etal-2019-subtopic,
    title = "Subtopic-driven Multi-Document Summarization",
    author = "Zheng, Xin  and
      Sun, Aixin  and
      Li, Jing  and
      Muthuswamy, Karthik",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1311",
    doi = "10.18653/v1/D19-1311",
    pages = "3153--3162",
    abstract = "In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper. That is, the underlying topic can be collectively represented by all the documents in the set. Meanwhile, different documents may cover various different subtopics and the same subtopic can be across several documents. Inspired by topic model, the underlying topic of a document set can also be viewed as a collection of different subtopics of different importance. In this paper, we propose a summarization model called STDS. The model generates the underlying topic representation from both document view and subtopic view in parallel. The learning objective is to minimize the distance between the representations learned from the two views. The contextual information is encoded through a hierarchical RNN architecture. Sentence salience is estimated in a hierarchical way with subtopic salience and relative sentence salience, by considering the contextual information. Top ranked sentences are then extracted as a summary. Note that the notion of subtopic enables us to bring in additional information (e.g. comments to news articles) that is helpful for document summarization. Experimental results show that the proposed solution outperforms state-of-the-art methods on benchmark datasets.",
}
@inproceedings{cao-cheung-2019-referring,
    title = "Referring Expression Generation Using Entity Profiles",
    author = "Cao, Meng  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1312",
    doi = "10.18653/v1/D19-1312",
    pages = "3163--3172",
    abstract = "Referring Expression Generation (REG) is the task of generating contextually appropriate references to entities. A limitation of existing REG systems is that they rely on entity-specific supervised training, which means that they cannot handle entities not seen during training. In this study, we address this in two ways. First, we propose task setups in which we specifically test a REG system{'}s ability to generalize to entities not seen during training. Second, we propose a profile-based deep neural network model, ProfileREG, which encodes both the local context and an external profile of the entity to generate reference realizations. Our model generates tokens by learning to choose between generating pronouns, generating from a fixed vocabulary, or copying a word from the profile. We evaluate our model on three different splits of the WebNLG dataset, and show that it outperforms competitive baselines in all settings according to automatic and human evaluations.",
}
@inproceedings{qian-etal-2019-exploring,
    title = "Exploring Diverse Expressions for Paraphrase Generation",
    author = "Qian, Lihua  and
      Qiu, Lin  and
      Zhang, Weinan  and
      Jiang, Xin  and
      Yu, Yong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1313",
    doi = "10.18653/v1/D19-1313",
    pages = "3173--3182",
    abstract = "Paraphrasing plays an important role in various natural language processing (NLP) tasks, such as question answering, information retrieval and sentence simplification. Recently, neural generative models have shown promising results in paraphrase generation. However, prior work mainly focused on single paraphrase generation, while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. Few works have been done to solve diverse paraphrase generation. In this paper, we propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. A reinforcement learning algorithm is applied to train our model. Our experiments on two real-world datasets demonstrate that our model not only gains a significant increase in diversity but also improves generation quality over several state-of-the-art baselines.",
}
@inproceedings{ribeiro-etal-2019-enhancing,
    title = "Enhancing {AMR}-to-Text Generation with Dual Graph Representations",
    author = "Ribeiro, Leonardo F. R.  and
      Gardent, Claire  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1314",
    doi = "10.18653/v1/D19-1314",
    pages = "3183--3194",
    abstract = "Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges. To address this difficulty, we propose a novel graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the AMR graph. The model learns parallel top-down and bottom-up representations of nodes capturing contrasting views of the graph. We also investigate the use of different node message passing strategies, employing different state-of-the-art graph encoders to compute node representations based on incoming and outgoing perspectives. In our experiments, we demonstrate that the dual graph representation leads to improvements in AMR-to-text generation, achieving state-of-the-art results on two AMR datasets",
}
@inproceedings{nishino-etal-2019-keeping,
    title = "Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning",
    author = "Nishino, Toru  and
      Misawa, Shotaro  and
      Kano, Ryuji  and
      Taniguchi, Tomoki  and
      Miura, Yasuhide  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1315",
    doi = "10.18653/v1/D19-1315",
    pages = "3195--3205",
    abstract = "The automated generation of information indicating the characteristics of articles such as headlines, key phrases, summaries and categories helps writers to alleviate their workload. Previous research has tackled these tasks using neural abstractive summarization and classification methods. However, the outputs may be inconsistent if they are generated individually. The purpose of our study is to generate multiple outputs consistently. We introduce a multi-task learning model with a shared encoder and multiple decoders for each task. We propose a novel loss function called hierarchical consistency loss to maintain consistency among the attention weights of the decoders. To evaluate the consistency, we employ a human evaluation. The results show that our model generates more consistent headlines, key phrases and categories. In addition, our model outperforms the baseline model on the ROUGE scores, and generates more adequate and fluent headlines.",
}
@inproceedings{nagata-2019-toward,
    title = "Toward a Task of Feedback Comment Generation for Writing Learning",
    author = "Nagata, Ryo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1316",
    doi = "10.18653/v1/D19-1316",
    pages = "3206--3215",
    abstract = "In this paper, we introduce a novel task called feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. There has been almost no work on this task nor corpus annotated with feedback comments. We have taken the first step by creating learner corpora consisting of approximately 1,900 essays where all preposition errors are manually annotated with feedback comments. We have tested three baseline methods on the dataset, showing that a simple neural retrieval-based method sets a baseline performance with an F-measure of 0.34 to 0.41. Finally, we have looked into the results to explore what modifications we need to make to achieve better performance. We also have explored problems unaddressed in this work",
}
@inproceedings{li-etal-2019-improving-question,
    title = "Improving Question Generation With to the Point Context",
    author = "Li, Jingjing  and
      Gao, Yifan  and
      Bing, Lidong  and
      King, Irwin  and
      Lyu, Michael R.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1317",
    doi = "10.18653/v1/D19-1317",
    pages = "3216--3226",
    abstract = "Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challenge in QG is to identify answer-relevant context words to finish the declarative-to-interrogative sentence transformation. Existing sequence-to-sequence neural models achieve this goal by proximity-based answer position encoding under the intuition that neighboring words of answers are of high possibility to be answer-relevant. However, such intuition may not apply to all cases especially for sentences with complex answer-relevant relations. Consequently, the performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases. To address this issue, we propose a method to jointly model the unstructured sentence and the structured answer-relevant relation (extracted from the sentence in advance) for question generation. Specifically, the structured answer-relevant relation acts as the to the point context and it thus naturally helps keep the generated question to the point, while the unstructured sentence provides the full information. Extensive experiments show that to the point context helps our question generation model achieve significant improvements on several automatic evaluation metrics. Furthermore, our model is capable of generating diverse questions for a sentence which conveys multiple relations of its answer fragment.",
}
@inproceedings{ive-etal-2019-deep,
    title = "Deep Copycat Networks for Text-to-Text Generation",
    author = "Ive, Julia  and
      Madhyastha, Pranava  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1318",
    doi = "10.18653/v1/D19-1318",
    pages = "3227--3236",
    abstract = "Most text-to-text generation tasks, for example text summarisation and text simplification, require copying words from the input to the output. We introduce Copycat, a transformer-based pointer network for such tasks which obtains competitive results in abstractive text summarisation and generates more abstractive summaries. We propose a further extension of this architecture for automatic post-editing, where generation is conditioned over two inputs (source language and machine translation), and the model is capable of deciding where to copy information from. This approach achieves competitive performance when compared to state-of-the-art automated post-editing systems. More importantly, we show that it addresses a well-known limitation of automatic post-editing - overcorrecting translations - and that our novel mechanism for copying source language words improves the results.",
}
@inproceedings{li-tuzhilin-2019-towards,
    title = "Towards Controllable and Personalized Review Generation",
    author = "Li, Pan  and
      Tuzhilin, Alexander",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1319",
    doi = "10.18653/v1/D19-1319",
    pages = "3237--3245",
    abstract = "In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic information. RevGAN utilizes the combination of three novel components, including self-attentive recursive autoencoders, conditional discriminators, and personalized decoders. We test its performance on the several real-world datasets, where our model significantly outperforms state-of-the-art generation models in terms of sentence quality, coherence, personalization, and human evaluations. We also empirically show that the generated reviews could not be easily distinguished from the organically produced reviews and that they follow the same statistical linguistics laws.",
}
@inproceedings{scialom-etal-2019-answers,
    title = "Answers Unite! Unsupervised Metrics for Reinforced Summarization Models",
    author = "Scialom, Thomas  and
      Lamprier, Sylvain  and
      Piwowarski, Benjamin  and
      Staiano, Jacopo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1320",
    doi = "10.18653/v1/D19-1320",
    pages = "3246--3256",
    abstract = "Abstractive summarization approaches based on Reinforcement Learning (RL) have recently been proposed to overcome classical likelihood maximization. RL enables to consider complex, possibly non differentiable, metrics that globally assess the quality and relevance of the generated outputs. ROUGE, the most used summarization metric, is known to suffer from bias towards lexical similarity as well as from sub-optimal accounting for fluency and readability of the generated abstracts. We thus explore and propose alternative evaluation measures: the reported human-evaluation analysis shows that the proposed metrics, based on Question Answering, favorably compare to ROUGE {--} with the additional property of not requiring reference summaries. Training a RL-based model on these metrics leads to improvements (both in terms of human or automated metrics) over current approaches that use ROUGE as reward.",
}
@inproceedings{shao-etal-2019-long,
    title = "Long and Diverse Text Generation with Planning-based Hierarchical Variational Model",
    author = "Shao, Zhihong  and
      Huang, Minlie  and
      Wen, Jiangtao  and
      Xu, Wenfei  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1321",
    doi = "10.18653/v1/D19-1321",
    pages = "3257--3268",
    abstract = "Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.",
}
@inproceedings{sudhakar-etal-2019-transforming,
    title = "{``}Transforming{''} Delete, Retrieve, Generate Approach for Controlled Text Style Transfer",
    author = "Sudhakar, Akhilesh  and
      Upadhyay, Bhargav  and
      Maheswaran, Arjun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1322",
    doi = "10.18653/v1/D19-1322",
    pages = "3269--3279",
    abstract = "Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. In this work we introduce the Generative Style Transformer (GST) - a new approach to rewriting sentences to a target style in the absence of parallel style corpora. GST leverages the power of both, large unsupervised pre-trained language models as well as the Transformer. GST is a part of a larger {`}Delete Retrieve Generate{'} framework, in which we also propose a novel method of deleting style attributes from the source sentence by exploiting the inner workings of the Transformer. Our models outperform state-of-art systems across 5 datasets on sentiment, gender and political slant transfer. We also propose the use of the GLEU metric as an automatic metric of evaluation of style transfer, which we found to compare better with human ratings than the predominantly used BLEU score.",
}
@inproceedings{sharma-etal-2019-entity,
    title = "An Entity-Driven Framework for Abstractive Summarization",
    author = "Sharma, Eva  and
      Huang, Luyang  and
      Hu, Zhe  and
      Wang, Lu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1323",
    doi = "10.18653/v1/D19-1323",
    pages = "3280--3291",
    abstract = "Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for single-document summarization, yet their outputs are often incoherent and unfaithful to the input. In this paper, we introduce SENECA, a novel System for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. Our framework takes a two-step approach: (1) an entity-aware content selection module first identifies salient sentences from the input, then (2) an abstract generation module conducts cross-sentence information compression and abstraction to generate the final summary, which is trained with rewards to promote coherence, conciseness, and clarity. The two components are further connected using reinforcement learning. Automatic evaluation shows that our model significantly outperforms previous state-of-the-art based on ROUGE and our proposed coherence measures on New York Times and CNN/Daily Mail datasets. Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.",
}
@inproceedings{xu-durrett-2019-neural,
    title = "Neural Extractive Text Summarization with Syntactic Compression",
    author = "Xu, Jiacheng  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1324",
    doi = "10.18653/v1/D19-1324",
    pages = "3292--3303",
    abstract = "Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction. In this work, we present a neural model for single-document summarization based on joint extraction and syntactic compression. Our model chooses sentences from the document, identifies possible compressions based on constituency parses, and scores those compressions with a neural model to produce the final summary. For learning, we construct oracle extractive-compressive summaries, then learn both of our components jointly with this supervision. Experimental results on the CNN/Daily Mail and New York Times datasets show that our model achieves strong performance (comparable to state-of-the-art systems) as evaluated by ROUGE. Moreover, our approach outperforms an off-the-shelf compression module, and human and manual evaluation shows that our model{'}s output generally remains grammatical.",
}
@inproceedings{li-etal-2019-domain,
    title = "Domain Adaptive Text Style Transfer",
    author = "Li, Dianqi  and
      Zhang, Yizhe  and
      Gan, Zhe  and
      Cheng, Yu  and
      Brockett, Chris  and
      Dolan, Bill  and
      Sun, Ming-Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1325",
    doi = "10.18653/v1/D19-1325",
    pages = "3304--3313",
    abstract = "Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In this paper, we examine domain adaptation for text style transfer to leverage massively available data from other domains. These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training. To address this challenge, we propose simple yet effective domain adaptive text style transfer models, enabling domain-adaptive information exchange. The proposed models presumably learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information; and (iii) adaptively transfer the styles in a domain-aware manner. We evaluate the proposed models on two style transfer tasks (sentiment and formality) over multiple target domains where only limited non-parallel data is available. Extensive experiments demonstrate the effectiveness of the proposed model compared to the baselines.",
}
@inproceedings{nema-etal-2019-lets,
    title = "Let{'}s Ask Again: Refine Network for Automatic Question Generation",
    author = "Nema, Preksha  and
      Mohankumar, Akash Kumar  and
      Khapra, Mitesh M.  and
      Srinivasan, Balaji Vasan  and
      Ravindran, Balaraman",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1326",
    doi = "10.18653/v1/D19-1326",
    pages = "3314--3323",
    abstract = "In this work, we focus on the task of Automatic Question Generation (AQG) where given a passage and an answer the task is to generate the corresponding question. It is desired that the generated question should be (i) grammatically correct (ii) answerable from the passage and (iii) specific to the given answer. An analysis of existing AQG models shows that they produce questions which do not adhere to one or more of {the above-mentioned qualities}. In particular, the generated questions look like an incomplete draft of the desired question with a clear scope for refinement. {To alleviate this shortcoming}, we propose a method which tries to mimic the human process of generating questions by first creating an initial draft and then refining it. More specifically, we propose Refine Network (RefNet) which contains two decoders. The second decoder uses a dual attention network which pays attention to both (i) the original passage and (ii) the question (initial draft) generated by the first decoder. In effect, it refines the question generated by the first decoder, thereby making it more correct and complete. We evaluate RefNet on three datasets, \textit{viz.}, SQuAD, HOTPOT-QA, and DROP, and show that it outperforms existing state-of-the-art methods by 7-16{\%} on all of these datasets. Lastly, we show that we can improve the quality of the second decoder on specific metrics, such as, fluency and answerability by explicitly rewarding revisions that improve on the corresponding metric during training. The code has been made publicly available .",
}
@inproceedings{jung-etal-2019-earlier,
    title = "Earlier Isn{'}t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization",
    author = "Jung, Taehee  and
      Kang, Dongyeop  and
      Mentch, Lucas  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1327",
    doi = "10.18653/v1/D19-1327",
    pages = "3324--3335",
    abstract = "Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored. Position of sentences in the original text, for example, is a well known bias for news summarization. Following in the spirit of the claim that summarization is a combination of sub-functions, we define three sub-aspects of summarization: position, importance, and diversity and conduct an extensive analysis of the biases of each sub-aspect with respect to the domain of nine different summarization corpora (e.g., news, academic papers, meeting minutes, movie script, books, posts). We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes. Furthermore, our empirical study shows that different types of summarization systems (e.g., neural-based) are composed of different degrees of the sub-aspects. Our study provides useful lessons regarding consideration of underlying sub-aspects when collecting a new summarization dataset or developing a new system.",
}
@inproceedings{kementchedjhieva-etal-2019-lost,
    title = "Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction",
    author = "Kementchedjhieva, Yova  and
      Hartmann, Mareike  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1328",
    doi = "10.18653/v1/D19-1328",
    pages = "3336--3341",
    abstract = "The task of bilingual dictionary induction (BDI) is commonly used for intrinsic evaluation of cross-lingual word embeddings. The largest dataset for BDI was generated automatically, so its quality is dubious. We study the composition and quality of the test sets for five diverse languages from this dataset, with concerning findings: (1) a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and (2) there are pervasive gaps in the gold-standard targets. These issues appear to affect the ranking between cross-lingual embedding systems on individual languages, and the overall degree to which the systems differ in performance. With proper nouns removed from the data, the margin between the top two systems included in the study grows from 3.4{\%} to 17.2{\%}. Manual verification of the predictions, on the other hand, reveals that gaps in the gold standard targets artificially inflate the margin between the two systems on English to Bulgarian BDI from 0.1{\%} to 6.7{\%}. We thus suggest that future research either avoids drawing conclusions from quantitative results on this BDI dataset, or accompanies such evaluation with rigorous error analysis.",
}
@inproceedings{kann-etal-2019-towards,
    title = "Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set",
    author = "Kann, Katharina  and
      Cho, Kyunghyun  and
      Bowman, Samuel R.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1329",
    doi = "10.18653/v1/D19-1329",
    pages = "3342--3349",
    abstract = "Development sets are impractical to obtain for real low-resource languages, since using all available data for training is often more effective. However, development sets are widely used in research papers that purport to deal with low-resource natural language processing (NLP). Here, we aim to answer the following questions: Does using a development set for early stopping in the low-resource setting influence results as compared to a more realistic alternative, where the number of training epochs is tuned on development languages? And does it lead to overestimation or underestimation of performance? We repeat multiple experiments from recent work on neural models for low-resource NLP and compare results for models obtained by training with and without development sets. On average over languages, absolute accuracy differs by up to 1.4{\%}. However, for some languages and tasks, differences are as big as 18.0{\%} accuracy. Our results highlight the importance of realistic experimental setups in the publication of low-resource NLP research results.",
}
@inproceedings{wang-etal-2019-synchronously,
    title = "Synchronously Generating Two Languages with Interactive Decoding",
    author = "Wang, Yining  and
      Zhang, Jiajun  and
      Zhou, Long  and
      Liu, Yuchen  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1330",
    doi = "10.18653/v1/D19-1330",
    pages = "3350--3355",
    abstract = "In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively. Specifically, the generation of one language relies on not only previously generated outputs by itself, but also the outputs predicted in the other language. Experimental results on IWSLT and WMT datasets demonstrate that our method can obtain significant improvements over both conventional Neural Machine Translation (NMT) model and multilingual NMT model.",
}
@inproceedings{stahlberg-byrne-2019-nmt,
    title = "On {NMT} Search Errors and Model Errors: Cat Got Your Tongue?",
    author = "Stahlberg, Felix  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1331",
    doi = "10.18653/v1/D19-1331",
    pages = "3356--3362",
    abstract = "We report on search errors and model errors in neural machine translation (NMT). We present an exact inference procedure for neural sequence models based on a combination of beam search and depth-first search. We use our exact search to find the global best model scores under a Transformer base model for the entire WMT15 English-German test set. Surprisingly, beam search fails to find these global best model scores in most cases, even with a very large beam size of 100. For more than 50{\%} of the sentences, the model in fact assigns its global best score to the empty translation, revealing a massive failure of neural models in properly accounting for adequacy. We show by constraining search with a minimum translation length that at the root of the problem of empty translations lies an inherent bias towards shorter translations. We conclude that vanilla NMT in its current form requires just the right amount of beam search errors, which, from a modelling perspective, is a highly unsatisfactory conclusion indeed, as the model often prefers an empty translation.",
}
@inproceedings{zhou-etal-2019-going,
    title = "{``}Going on a vacation{''} takes longer than {``}Going for a walk{''}: A Study of Temporal Commonsense Understanding",
    author = "Zhou, Ben  and
      Khashabi, Daniel  and
      Ning, Qiang  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1332",
    doi = "10.18653/v1/D19-1332",
    pages = "3363--3369",
    abstract = "Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order. However, this important problem has so far received limited attention. This paper systematically studies this temporal commonsense problem. Specifically, we define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. We find that the best current methods used on MCTACO are still far behind human performance, by about 20{\%}, and discuss several directions for improvement. We hope that the new dataset and our study here can foster more future research on this topic.",
}
@inproceedings{yeh-chen-2019-qainfomax,
    title = "{QAI}nfomax: Learning Robust Question Answering System by Mutual Information Maximization",
    author = "Yeh, Yi-Ting  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1333",
    doi = "10.18653/v1/D19-1333",
    pages = "3370--3375",
    abstract = "Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems truly understand language remains unknown, and existing systems are not good at distinguishing distractor sentences which look related but do not answer the question. To address this problem, we propose QAInfomax as a regularizer in reading comprehension systems by maximizing mutual information among passages, a question, and its answer. QAInfomax helps regularize the model to not simply learn the superficial correlation for answering the questions. The experiments show that our proposed QAInfomax achieves the state-of-the-art performance on the benchmark Adversarial-SQuAD dataset.",
}
@inproceedings{lv-etal-2019-adapting,
    title = "Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations",
    author = "Lv, Xin  and
      Gu, Yuxian  and
      Han, Xu  and
      Hou, Lei  and
      Li, Juanzi  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1334",
    doi = "10.18653/v1/D19-1334",
    pages = "3376--3381",
    abstract = "Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough triples for training, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms state-of-the-art methods in few-shot scenarios. In the future, our codes and datasets will also be available to provide more details.",
}
@inproceedings{trichelair-etal-2019-reasonable,
    title = "How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the {W}inograd Schema Challenge and {SWAG}",
    author = "Trichelair, Paul  and
      Emami, Ali  and
      Trischler, Adam  and
      Suleman, Kaheer  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1335",
    doi = "10.18653/v1/D19-1335",
    pages = "3382--3387",
    abstract = "Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG. The question we ask in this paper is whether improved performance on these benchmarks represents genuine progress towards common-sense-enabled systems. We make case studies of both benchmarks and design protocols that clarify and qualify the results of previous work by analyzing threats to the validity of previous experimental designs. Our protocols account for several properties prevalent in common-sense benchmarks including size limitations, structural regularities, and variable instance difficulty.",
}
@inproceedings{luo-etal-2019-pun,
    title = "Pun-{GAN}: Generative Adversarial Network for Pun Generation",
    author = "Luo, Fuli  and
      Li, Shunyao  and
      Yang, Pengcheng  and
      Li, Lei  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Sun, Xu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1336",
    doi = "10.18653/v1/D19-1336",
    pages = "3388--3393",
    abstract = "In this paper, we focus on the task of generating a pun sentence given a pair of word senses. A major challenge for pun generation is the lack of large-scale pun corpus to guide supervised learning. To remedy this, we propose an adversarial generative network for pun generation (Pun-GAN). It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses. The output of the discriminator is then used as a reward to train the generator via reinforcement learning, encouraging it to produce pun sentences which can support two word senses simultaneously. Experiments show that the proposed Pun-GAN can generate sentences that are more ambiguous and diverse in both automatic and human evaluation.",
}
@inproceedings{zhou-etal-2019-multi,
    title = "Multi-Task Learning with Language Modeling for Question Generation",
    author = "Zhou, Wenjie  and
      Zhang, Minghua  and
      Wu, Yunfang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1337",
    doi = "10.18653/v1/D19-1337",
    pages = "3394--3399",
    abstract = "This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.",
}
@inproceedings{schmidt-etal-2019-autoregressive,
    title = "Autoregressive Text Generation Beyond Feedback Loops",
    author = "Schmidt, Florian  and
      Mandt, Stephan  and
      Hofmann, Thomas",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1338",
    doi = "10.18653/v1/D19-1338",
    pages = "3400--3406",
    abstract = "Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models. However, autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies. In this paper, we combine a latent state space model with a CRF observation model. We argue that such autoregressive observation models form an interesting middle ground that expresses local correlations on the word level but keeps the state evolution non-autoregressive. On unconditional sentence generation we show performance improvements compared to RNN and GAN baselines while avoiding some prototypical failure modes of autoregressive models.",
}
@inproceedings{sheng-etal-2019-woman,
    title = "The Woman Worked as a Babysitter: On Biases in Language Generation",
    author = "Sheng, Emily  and
      Chang, Kai-Wei  and
      Natarajan, Premkumar  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1339",
    doi = "10.18653/v1/D19-1339",
    pages = "3407--3412",
    abstract = "We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce the notion of the regard towards a demographic, use the varying levels of regard towards different demographics as a defining metric for bias in NLG, and analyze the extent to which sentiment scores are a relevant proxy metric for regard. To this end, we collect strategically-generated text from language models and manually annotate the text with both sentiment and regard scores. Additionally, we build an automatic regard classifier through transfer learning, so that we can analyze biases in unseen text. Together, these methods reveal the extent of the biased nature of language model generations. Our analysis provides a study of biases in NLG, bias metrics and correlated human judgments, and empirical evidence on the usefulness of our annotated dataset.",
}
@inproceedings{suntwal-etal-2019-importance,
    title = "On the Importance of Delexicalization for Fact Verification",
    author = "Suntwal, Sandeep  and
      Paul, Mithun  and
      Sharp, Rebecca  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1340",
    doi = "10.18653/v1/D19-1340",
    pages = "3413--3418",
    abstract = "While neural networks produce state-of-the-art performance in many NLP tasks, they generally learn from lexical information, which may transfer poorly between domains. Here, we investigate the importance that a model assigns to various aspects of data while learning and making predictions, specifically, in a recognizing textual entailment (RTE) task. By inspecting the attention weights assigned by the model, we confirm that most of the weights are assigned to noun phrases. To mitigate this dependence on lexicalized information, we experiment with two strategies of masking. First, we replace named entities with their corresponding semantic tags along with a unique identifier to indicate lexical overlap between claim and evidence. Second, we similarly replace other word classes in the sentence (nouns, verbs, adjectives, and adverbs) with their super sense tags (Ciaramita and Johnson, 2003). Our results show that, while performance on the in-domain dataset remains on par with that of the model trained on fully lexicalized data, it improves considerably when tested out of domain. For example, the performance of a state-of-the-art RTE model trained on the masked Fake News Challenge (Pomerleau and Rao, 2017) data and evaluated on Fact Extraction and Verification (Thorne et al., 2018) data improved by over 10{\%} in accuracy score compared to the fully lexicalized model.",
}
@inproceedings{schuster-etal-2019-towards,
    title = "Towards Debiasing Fact Verification Models",
    author = "Schuster, Tal  and
      Shah, Darsh  and
      Yeo, Yun Jie Serene  and
      Roberto Filizzola Ortiz, Daniel  and
      Santus, Enrico  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1341",
    doi = "10.18653/v1/D19-1341",
    pages = "3419--3425",
    abstract = "Fact verification requires validating a claim in the context of evidence. We show, however, that in the popular FEVER dataset this might not necessarily be the case. Claim-only classifiers perform competitively with top evidence-aware models. In this paper, we investigate the cause of this phenomenon, identifying strong cues for predicting labels solely based on the claim, without considering any evidence. We create an evaluation set that avoids those idiosyncrasies. The performance of FEVER-trained models significantly drops when evaluated on this test set. Therefore, we introduce a regularization method which alleviates the effect of bias in the training data, obtaining improvements on the newly created test set. This work is a step towards a more sound evaluation of reasoning capabilities in fact verification models.",
}
@inproceedings{tan-etal-2019-recognizing,
    title = "Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks",
    author = "Tan, Xingwei  and
      Cai, Yi  and
      Zhu, Changxi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1342",
    doi = "10.18653/v1/D19-1342",
    pages = "3426--3431",
    abstract = "Aspect-level sentiment classification, which is a fine-grained sentiment analysis task, has received lots of attention these years. There is a phenomenon that people express both positive and negative sentiments towards an aspect at the same time. Such opinions with conflicting sentiments, however, are ignored by existing studies, which design models based on the absence of them. We argue that the exclusion of conflict opinions is problematic, for the reason that it represents an important style of human thinking {--} dialectic thinking. If a real-world sentiment classification system ignores the existence of conflict opinions when it is designed, it will incorrectly mixed conflict opinions into other sentiment polarity categories in action. Existing models have problems when recognizing conflicting opinions, such as data sparsity. In this paper, we propose a multi-label classification model with dual attention mechanism to address these problems.",
}
@inproceedings{wang-etal-2019-investigating,
    title = "Investigating Dynamic Routing in Tree-Structured {LSTM} for Sentiment Analysis",
    author = "Wang, Jin  and
      Yu, Liang-Chih  and
      Lai, K. Robert  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1343",
    doi = "10.18653/v1/D19-1343",
    pages = "3432--3437",
    abstract = "Deep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis. However, sequential LSTM is a bias model wherein the words in the tail of a sentence are more heavily emphasized than those in the header for building sentence representations. Even tree-LSTM, with useful structural information, could not avoid the bias problem because the root node will be dominant and the nodes in the bottom of the parse tree will be less emphasized even though they may contain salient information. To overcome the bias problem, this study proposes a capsule tree-LSTM model, introducing a dynamic routing algorithm as an aggregation layer to build sentence representation by assigning different weights to nodes according to their contributions to prediction. Experiments on Stanford Sentiment Treebank (SST) for sentiment classification and EmoBank for regression show that the proposed method improved the performance of tree-LSTM and other neural network models. In addition, the deeper the tree structure, the bigger the improvement.",
}
@inproceedings{niu-cai-2019-label,
    title = "A Label Informative Wide {\&} Deep Classifier for Patents and Papers",
    author = "Niu, Muyao  and
      Cai, Jie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1344",
    doi = "10.18653/v1/D19-1344",
    pages = "3438--3443",
    abstract = "In this paper, we provide a simple and effective baseline for classifying both patents and papers to the well-established Cooperative Patent Classification (CPC). We propose a label-informative classifier based on the Wide {\&} Deep structure, where the Wide part encodes string-level similarities between texts and labels, and the Deep part captures semantic-level similarities via non-linear transformations. Our model trains on millions of patents, and transfers to papers by developing distant-supervised training set and domain-specific features. Extensive experiments show that our model achieves comparable performance to the state-of-the-art model used in industry on both patents and papers. The output of this work should facilitate the searching, granting and filing of innovative ideas for patent examiners, attorneys and researchers.",
}
@inproceedings{huang-etal-2019-text,
    title = "Text Level Graph Neural Network for Text Classification",
    author = "Huang, Lianzhe  and
      Ma, Dehong  and
      Li, Sujian  and
      Zhang, Xiaodong  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1345",
    doi = "10.18653/v1/D19-1345",
    pages = "3444--3450",
    abstract = "Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information. However, previous methods based on GNN are mainly faced with the practical problems of fixed corpus level graph structure which don{'}t support online testing and high memory consumption. To tackle the problems, we propose a new GNN based model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. This method removes the burden of dependence between an individual text and entire corpus which support online testing, but still preserve global information. Besides, we build graphs by much smaller windows in the text, which not only extract more local features but also significantly reduce the edge numbers as well as memory consumption. Experiments show that our model outperforms existing models on several text classification datasets even with consuming less memory.",
}
@inproceedings{sabir-etal-2019-semantic,
    title = "Semantic Relatedness Based Re-ranker for Text Spotting",
    author = "Sabir, Ahmed  and
      Moreno, Francesc  and
      Padr{\'o}, Llu{\'\i}s",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1346",
    doi = "10.18653/v1/D19-1346",
    pages = "3451--3457",
    abstract = "Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present a scenario where semantic similarity is not enough, and we devise a neural approach to learn semantic relatedness. The scenario is text spotting in the wild, where a text in an image (e.g. street sign, advertisement or bus destination) must be identified and recognized. Our goal is to improve the performance of vision systems by leveraging semantic information. Our rationale is that the text to be spotted is often related to the image context in which it appears (word pairs such as Delta-airplane, or quarters-parking are not similar, but are clearly related). We show how learning a word-to-word or word-to-sentence relatedness score can improve the performance of text spotting systems up to 2.9 points, outperforming other measures in a benchmark dataset.",
}
@inproceedings{jo-cinarel-2019-delta,
    title = "Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings",
    author = "Jo, Hwiyeol  and
      Cinarel, Ceyda",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1347",
    doi = "10.18653/v1/D19-1347",
    pages = "3458--3463",
    abstract = "We propose a novel and simple method for semi-supervised text classification. The method stems from the hypothesis that a classifier with pretrained word embeddings always outperforms the same classifier with randomly initialized word embeddings, as empirically observed in NLP tasks. Our method first builds two sets of classifiers as a form of model ensemble, and then initializes their word embeddings differently: one using random, the other using pretrained word embeddings. We focus on different predictions between the two classifiers on unlabeled data while following the self-training framework. We also use early-stopping in meta-epoch to improve the performance of our method. Our method, Delta-training, outperforms the self-training and the co-training framework in 4 different text classification datasets, showing robustness against error accumulation.",
}
@inproceedings{soto-yoo-2019-visual,
    title = "Visual Detection with Context for Document Layout Analysis",
    author = "Soto, Carlos  and
      Yoo, Shinjae",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1348",
    doi = "10.18653/v1/D19-1348",
    pages = "3464--3470",
    abstract = "We present 1) a work in progress method to visually segment key regions of scientific articles using an object detection technique augmented with contextual features, and 2) a novel dataset of region-labeled articles. A continuing challenge in scientific literature mining is the difficulty of consistently extracting high-quality text from formatted PDFs. To address this, we adapt the object-detection technique Faster R-CNN for document layout detection, incorporating contextual information that leverages the inherently localized nature of article contents to improve the region detection performance. Due to the limited availability of high-quality region-labels for scientific articles, we also contribute a novel dataset of region annotations, the first version of which covers 9 region classes and 822 article pages. Initial experimental results demonstrate a 23.9{\%} absolute improvement in mean average precision over the baseline model by incorporating contextual features, and a processing speed 14x faster than a text-based technique. Ongoing work on further improvements is also discussed.",
}
@inproceedings{xing-etal-2019-evaluating,
    title = "Evaluating Topic Quality with Posterior Variability",
    author = "Xing, Linzi  and
      Paul, Michael J.  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1349",
    doi = "10.18653/v1/D19-1349",
    pages = "3471--3477",
    abstract = "Probabilistic topic models such as latent Dirichlet allocation (LDA) are popularly used with Bayesian inference methods such as Gibbs sampling to learn posterior distributions over topic model parameters. We derive a novel measure of LDA topic quality using the variability of the posterior distributions. Compared to several existing baselines for automatic topic evaluation, the proposed metric achieves state-of-the-art correlations with human judgments of topic quality in experiments on three corpora. We additionally demonstrate that topic quality estimation can be further improved using a supervised estimator that combines multiple metrics.",
}
@inproceedings{gui-etal-2019-neural,
    title = "Neural Topic Model with Reinforcement Learning",
    author = "Gui, Lin  and
      Leng, Jia  and
      Pergola, Gabriele  and
      Zhou, Yu  and
      Xu, Ruifeng  and
      He, Yulan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1350",
    doi = "10.18653/v1/D19-1350",
    pages = "3478--3483",
    abstract = "In recent years, advances in neural variational inference have achieved many successes in text processing. Examples include neural topic models which are typically built upon variational autoencoder (VAE) with an objective of minimising the error of reconstructing original documents based on the learned latent topic vectors. However, minimising reconstruction errors does not necessarily lead to high quality topics. In this paper, we borrow the idea of reinforcement learning and incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. Furthermore, our proposed model is able to automatically separating background words dynamically from topic words, thus eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. Experimental results on the 20 Newsgroups and the NIPS datasets show superior performance both on perplexity and topic coherence measure compared to state-of-the-art neural topic models.",
}
@inproceedings{sneyd-stevenson-2019-modelling,
    title = "Modelling Stopping Criteria for Search Results using {P}oisson Processes",
    author = "Sneyd, Alison  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1351",
    doi = "10.18653/v1/D19-1351",
    pages = "3484--3489",
    abstract = "Text retrieval systems often return large sets of documents, particularly when applied to large collections. Stopping criteria can reduce the number of these documents that need to be manually evaluated for relevance by predicting when a suitable level of recall has been achieved. In this work, a novel method for determining a stopping criterion is proposed that models the rate at which relevant documents occur using a Poisson process. This method allows a user to specify both a minimum desired level of recall to achieve and a desired probability of having achieved it. We evaluate our method on a public dataset and compare it with previous techniques for determining stopping criteria.",
}
@inproceedings{akkalyoncu-yilmaz-etal-2019-cross,
    title = "Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval",
    author = "Akkalyoncu Yilmaz, Zeynep  and
      Yang, Wei  and
      Zhang, Haotian  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1352",
    doi = "10.18653/v1/D19-1352",
    pages = "3490--3496",
    abstract = "This paper applies BERT to ad hoc document retrieval on news articles, which requires addressing two challenges: relevance judgments in existing test collections are typically provided only at the document level, and documents often exceed the length that BERT was designed to handle. Our solution is to aggregate sentence-level evidence to rank documents. Furthermore, we are able to leverage passage-level relevance judgments fortuitously available in other domains to fine-tune BERT models that are able to capture cross-domain notions of relevance, and can be directly used for ranking news articles. Our simple neural ranking models achieve state-of-the-art effectiveness on three standard test collections.",
}
@inproceedings{lignos-etal-2019-challenges,
    title = "The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval",
    author = "Lignos, Constantine  and
      Cohen, Daniel  and
      Lien, Yen-Chieh  and
      Mehta, Pratik  and
      Croft, W. Bruce  and
      Miller, Scott",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1353",
    doi = "10.18653/v1/D19-1353",
    pages = "3497--3502",
    abstract = "When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT). However, there is no established guidance on how to optimize the resulting MT-IR system. In this paper, we examine the relationship between the performance of MT systems and both neural and term frequency-based IR models to identify how CLIR performance can be best predicted from MT quality. We explore performance at varying amounts of MT training data, byte pair encoding (BPE) merge operations, and across two IR collections and retrieval models. We find that the choice of IR collection can substantially affect the predictive power of MT tuning decisions and evaluation, potentially introducing dissociations between MT-only and overall CLIR performance.",
}
@inproceedings{ethayarajh-2019-rotate,
    title = "Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space",
    author = "Ethayarajh, Kawin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1354",
    doi = "10.18653/v1/D19-1354",
    pages = "3503--3508",
    abstract = "A notable property of word embeddings is that word relationships can exist as linear substructures in the embedding space. For example, {`}gender{'} corresponds to v{\_}woman - v{\_}man and v{\_}queen - v{\_}king. This, in turn, allows word analogies to be solved arithmetically: v{\_}king - v{\_}man + v{\_}woman = v{\_}queen. This property is notable because it suggests that models trained on word embeddings can easily learn such relationships as geometric translations. However, there is no evidence that models exclusively represent relationships in this manner. We document an alternative way in which downstream models might learn these relationships: orthogonal and linear transformations. For example, given a translation vector for {`}gender{'}, we can find an orthogonal matrix R, representing a rotation and reflection, such that R(v{\_}king) = v{\_}queen and R(v{\_}man) = v{\_}woman. Analogical reasoning using orthogonal transformations is almost as accurate as using vector arithmetic; using linear transformations is more accurate than both. Our findings suggest that these transformations can be as good a representation of word relationships as translation vectors.",
}
@inproceedings{huang-etal-2019-glossbert,
    title = "{G}loss{BERT}: {BERT} for Word Sense Disambiguation with Gloss Knowledge",
    author = "Huang, Luyao  and
      Sun, Chi  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1355",
    doi = "10.18653/v1/D19-1355",
    pages = "3509--3514",
    abstract = "Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT based models for WSD. We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task.",
}
@inproceedings{liu-etal-2019-leveraging,
    title = "Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-{SQL}",
    author = "Liu, Haoyan  and
      Fang, Lei  and
      Liu, Qian  and
      Chen, Bei  and
      Lou, Jian-Guang  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1356",
    doi = "10.18653/v1/D19-1356",
    pages = "3515--3520",
    abstract = "One key component in text-to-SQL is to predict the comparison relations between columns and their values. To the best of our knowledge, no existing models explicitly introduce external common knowledge to address this problem, thus their capabilities of predicting comparison relations are limited beyond training data. In this paper, we propose to leverage adjective-noun phrasing knowledge mined from the web to predict the comparison relations in text-to-SQL. Experimental results on both the original and the re-split Spider dataset show that our approach achieves significant improvement over state-of-the-art methods on comparison relation prediction.",
}
@inproceedings{washio-etal-2019-bridging,
    title = "Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling",
    author = "Washio, Koki  and
      Sekine, Satoshi  and
      Kato, Tsuneaki",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1357",
    doi = "10.18653/v1/D19-1357",
    pages = "3521--3527",
    abstract = "Definition modeling includes acquiring word embeddings from dictionary definitions and generating definitions of words. While the meanings of defining words are important in dictionary definitions, it is crucial to capture the lexical semantic relations between defined words and defining words. However, thus far, the utilization of such relations has not been explored for definition modeling. In this paper, we propose definition modeling methods that use lexical semantic relations. To utilize implicit semantic relations in definitions, we use unsupervisedly obtained pattern-based word-pair embeddings that represent semantic relations of word pairs. Experimental results indicate that our methods improve the performance in learning embeddings from definitions, as well as definition generation.",
}
@inproceedings{yoo-etal-2019-dont,
    title = "Don{'}t Just Scratch the Surface: Enhancing Word Representations for {K}orean with Hanja",
    author = "Yoo, Kang Min  and
      Kim, Taeuk  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1358",
    doi = "10.18653/v1/D19-1358",
    pages = "3528--3533",
    abstract = "We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i.e. Hanja). We employ cross-lingual transfer learning in training word representations by leveraging the fact that Hanja is closely related to Chinese. We evaluate the intrinsic quality of representations learned through our approach using the word analogy and similarity tests. In addition, we demonstrate their effectiveness on several downstream tasks, including a novel Korean news headline generation task.",
}
@inproceedings{maru-etal-2019-syntagnet,
    title = "{S}yntag{N}et: Challenging Supervised Word Sense Disambiguation with Lexical-Semantic Combinations",
    author = "Maru, Marco  and
      Scozzafava, Federico  and
      Martelli, Federico  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1359",
    doi = "10.18653/v1/D19-1359",
    pages = "3534--3540",
    abstract = "Current research in knowledge-based Word Sense Disambiguation (WSD) indicates that performances depend heavily on the Lexical Knowledge Base (LKB) employed. This paper introduces SyntagNet, a novel resource consisting of manually disambiguated lexical-semantic combinations. By capturing sense distinctions evoked by syntagmatic relations, SyntagNet enables knowledge-based WSD systems to establish a new state of the art which challenges the hitherto unrivaled performances attained by supervised approaches. To the best of our knowledge, SyntagNet is the first large-scale manually-curated resource of this kind made available to the community (at http://syntagnet.org).",
}
@inproceedings{winata-etal-2019-hierarchical,
    title = "Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition",
    author = "Winata, Genta Indra  and
      Lin, Zhaojiang  and
      Shin, Jamin  and
      Liu, Zihan  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1360",
    doi = "10.18653/v1/D19-1360",
    pages = "3541--3547",
    abstract = "In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant. Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations. On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings. We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots. Finally, we show that combining different subunits are crucial for capturing code-switching entities.",
}
@inproceedings{cui-etal-2019-fine,
    title = "Fine-tune {BERT} with Sparse Self-Attention Mechanism",
    author = "Cui, Baiyun  and
      Li, Yingming  and
      Chen, Ming  and
      Zhang, Zhongfei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1361",
    doi = "10.18653/v1/D19-1361",
    pages = "3548--3553",
    abstract = "In this paper, we develop a novel Sparse Self-Attention Fine-tuning model (referred as SSAF) which integrates sparsity into self-attention mechanism to enhance the fine-tuning performance of BERT. In particular, sparsity is introduced into the self-attention by replacing softmax function with a controllable sparse transformation when fine-tuning with BERT. It enables us to learn a structurally sparse attention distribution, which leads to a more interpretable representation for the whole input. The proposed model is evaluated on sentiment analysis, question answering, and natural language inference tasks. The extensive experimental results across multiple datasets demonstrate its effectiveness and superiority to the baseline methods.",
}
@inproceedings{lange-etal-2019-feature,
    title = "Feature-Dependent Confusion Matrices for Low-Resource {NER} Labeling with Noisy Labels",
    author = "Lange, Lukas  and
      Hedderich, Michael A.  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1362",
    doi = "10.18653/v1/D19-1362",
    pages = "3554--3559",
    abstract = "In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy. Previous works have shown that significant improvements can be reached by injecting information about the confusion between clean and noisy labels in this additional training data into the classifier training. However, for noise estimation, these approaches either do not take the input features (in our case word embeddings) into account, or they need to learn the noise modeling from scratch which can be difficult in a low-resource setting. We propose to cluster the training data using the input features and then compute different confusion matrices for each cluster. To the best of our knowledge, our approach is the first to leverage feature-dependent noise modeling with pre-initialized confusion matrices. We evaluate on low-resource named entity recognition settings in several languages, showing that our methods improve upon other confusion-matrix based methods by up to 9{\%}.",
}
@inproceedings{taitelbaum-etal-2019-multi,
    title = "A Multi-Pairwise Extension of {P}rocrustes Analysis for Multilingual Word Translation",
    author = "Taitelbaum, Hagai  and
      Chechik, Gal  and
      Goldberger, Jacob",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1363",
    doi = "10.18653/v1/D19-1363",
    pages = "3560--3565",
    abstract = "In this paper we present a novel approach to simultaneously representing multiple languages in a common space. Procrustes Analysis (PA) is commonly used to find the optimal orthogonal word mapping in the bilingual case. The proposed Multi Pairwise Procrustes Analysis (MPPA) is a natural extension of the PA algorithm to multilingual word mapping. Unlike previous PA extensions that require a k-way dictionary, this approach requires only pairwise bilingual dictionaries that are much easier to construct.",
}
@inproceedings{tan-etal-2019-domain,
    title = "Out-of-Domain Detection for Low-Resource Text Classification Tasks",
    author = "Tan, Ming  and
      Yu, Yang  and
      Wang, Haoyu  and
      Wang, Dakuo  and
      Potdar, Saloni  and
      Chang, Shiyu  and
      Yu, Mo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1364",
    doi = "10.18653/v1/D19-1364",
    pages = "3566--3572",
    abstract = "Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since in machine learning applications we observe that training data is often insufficient. In this work, we propose an \textit{OOD-resistant Prototypical Network} to tackle this zero-shot OOD detection and few-shot ID classification task. Evaluations on real-world datasets show that the proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task, while maintaining a competitive performance on ID classification task.",
}
@inproceedings{wang-etal-2019-harnessing,
    title = "Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer",
    author = "Wang, Yunli  and
      Wu, Yu  and
      Mou, Lili  and
      Li, Zhoujun  and
      Chao, Wenhan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1365",
    doi = "10.18653/v1/D19-1365",
    pages = "3573--3578",
    abstract = "Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and neural models become a prevailing method in the field. While a rule-based system is still a common preprocessing step for formality style transfer in the neural era, it could introduce noise if we use the rules in a naive way such as data preprocessing. To mitigate this problem, we study how to harness rules into a state-of-the-art neural network that is typically pretrained on massive corpora. We propose three fine-tuning methods in this paper and achieve a new state-of-the-art on benchmark datasets",
}
@inproceedings{lai-etal-2019-multiple,
    title = "Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training",
    author = "Lai, Chih-Te  and
      Hong, Yi-Te  and
      Chen, Hong-You  and
      Lu, Chi-Jen  and
      Lin, Shou-De",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1366",
    doi = "10.18653/v1/D19-1366",
    pages = "3579--3584",
    abstract = "The objective of non-parallel text style transfer, or controllable text generation, is to alter specific attributes (e.g. sentiment, mood, tense, politeness, etc) of a given text while preserving its remaining attributes and content. Generative adversarial network (GAN) is a popular model to ensure the transferred sentences are realistic and have the desired target styles. However, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. In this paper, we propose a new GAN model with a word-level conditional architecture and a two-phase training procedure. By using a style-related condition architecture before generating a word, our model is able to maintain style-unrelated words while changing the others. By separating the training procedure into reconstruction and transfer phases, our model is able to learn a proper text generation process, which further improves the content preservation. We test our model on polarity sentiment transfer and multiple-attribute transfer tasks. The empirical results show that our model achieves comparable evaluation scores in both transfer accuracy and fluency but significantly outperforms other state-of-the-art models in content compatibility on three real-world datasets.",
}
@inproceedings{jiang-etal-2019-improved,
    title = "Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition",
    author = "Jiang, Yufan  and
      Hu, Chi  and
      Xiao, Tong  and
      Zhang, Chunliang  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1367",
    doi = "10.18653/v1/D19-1367",
    pages = "3585--3590",
    abstract = "In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing. In particular, we improve differentiable architecture search by removing the softmax-local constraint. Also, we apply differentiable NAS to named entity recognition (NER). It is the first time that differentiable NAS methods are adopted in NLP tasks other than language modeling. On both the PTB language modeling and CoNLL-2003 English NER data, our method outperforms strong baselines. It achieves a new state-of-the-art on the NER task.",
}
@inproceedings{balkir-etal-2019-using,
    title = "Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets",
    author = "Balkir, Esma  and
      Naslidnyk, Masha  and
      Palfrey, Dave  and
      Mittal, Arpit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1368",
    doi = "10.18653/v1/D19-1368",
    pages = "3591--3596",
    abstract = "Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this paper we use occurrences of entity-relation pairs in the dataset to construct a joint learning model and to increase the quality of sampled negatives during training. We show on three standard datasets that when these two techniques are combined, they give a significant improvement in performance, especially when the batch size and the number of generated negative examples are low relative to the size of the dataset. We then apply our techniques to a dataset containing 2 million entities and demonstrate that our model outperforms the baseline by 2.8{\%} absolute on hits@1.",
}
@inproceedings{wang-2019-single,
    title = "Single Training Dimension Selection for Word Embedding with {PCA}",
    author = "Wang, Yu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1369",
    doi = "10.18653/v1/D19-1369",
    pages = "3597--3602",
    abstract = "In this paper, we present a fast and reliable method based on PCA to select the number of dimensions for word embeddings. First, we train one embedding with a generous upper bound (e.g. 1,000) of dimensions. Then we transform the embeddings using PCA and incrementally remove the lesser dimensions one at a time while recording the embeddings{'} performance on language tasks. Lastly, we select the number of dimensions, balancing model size and accuracy. Experiments using various datasets and language tasks demonstrate that we are able to train about 10 times fewer sets of embeddings while retaining optimal performance. Researchers interested in training the best-performing embeddings for downstream tasks, such as sentiment analysis, question answering and hypernym extraction, as well as those interested in embedding compression should find the method helpful.",
}
@inproceedings{li-etal-2019-surprisingly,
    title = "A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text",
    author = "Li, Bohan  and
      He, Junxian  and
      Neubig, Graham  and
      Berg-Kirkpatrick, Taylor  and
      Yang, Yiming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1370",
    doi = "10.18653/v1/D19-1370",
    pages = "3603--3614",
    abstract = "When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework. In practice, however, VAEs are trained with the evidence lower bound (ELBO) as a surrogate objective to the intractable marginal data likelihood. This approach to training yields unstable results, frequently leading to a disastrous local optimum known as posterior collapse. In this paper, we investigate a simple fix for posterior collapse which yields surprisingly effective results. The combination of two known heuristics, previously considered only in isolation, substantially improves held-out likelihood, reconstruction, and latent representation learning when compared with previous state-of-the-art methods. More interestingly, while our experiments demonstrate superiority on these principle evaluations, our method obtains a worse ELBO. We use these results to argue that the typical surrogate objective for VAEs may not be sufficient or necessarily appropriate for balancing the goals of representation learning and data distribution modeling.",
}
@inproceedings{beltagy-etal-2019-scibert,
    title = "{S}ci{BERT}: A Pretrained Language Model for Scientific Text",
    author = "Beltagy, Iz  and
      Lo, Kyle  and
      Cohan, Arman",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1371",
    doi = "10.18653/v1/D19-1371",
    pages = "3615--3620",
    abstract = "Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks. The code and pretrained models are available at https://github.com/allenai/scibert/.",
}
@inproceedings{weller-seppi-2019-humor,
    title = "Humor Detection: A Transformer Gets the Last Laugh",
    author = "Weller, Orion  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1372",
    doi = "10.18653/v1/D19-1372",
    pages = "3621--3625",
    abstract = "Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humorous. We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using these ratings to determine the level of humor, we then employ a Transformer architecture for its advantages in learning from sentence context. We demonstrate the effectiveness of this approach and show results that are comparable to human performance. We further demonstrate our model{'}s increased capabilities on humor identification problems, such as the previously created datasets for short jokes and puns. These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93.1{\%} for the Puns dataset and 98.6{\%} on the Short Jokes dataset.",
}
@inproceedings{aji-etal-2019-combining,
    title = "Combining Global Sparse Gradients with Local Gradients in Distributed Neural Network Training",
    author = "Aji, Alham Fikri  and
      Heafield, Kenneth  and
      Bogoychev, Nikolay",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1373",
    doi = "10.18653/v1/D19-1373",
    pages = "3626--3631",
    abstract = "One way to reduce network traffic in multi-node data-parallel stochastic gradient descent is to only exchange the largest gradients. However, doing so damages the gradient and degrades the model{'}s performance. Transformer models degrade dramatically while the impact on RNNs is smaller. We restore gradient quality by combining the compressed global gradient with the node{'}s locally computed uncompressed gradient. Neural machine translation experiments show that Transformer convergence is restored while RNNs converge faster. With our method, training on 4 nodes converges up to 1.5x as fast as with uncompressed gradients and scales 3.5x relative to single-node training.",
}
@inproceedings{tsai-etal-2019-small,
    title = "Small and Practical {BERT} Models for Sequence Labeling",
    author = "Tsai, Henry  and
      Riesa, Jason  and
      Johnson, Melvin  and
      Arivazhagan, Naveen  and
      Li, Xin  and
      Archer, Amelia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1374",
    doi = "10.18653/v1/D19-1374",
    pages = "3632--3636",
    abstract = "We propose a practical scheme to train a single multilingual sequence labeling model that yields state of the art results and is small and fast enough to run on a single CPU. Starting from a public multilingual BERT checkpoint, our final model is 6x smaller and 27x faster, and has higher accuracy than a state-of-the-art multilingual baseline. We show that our model especially outperforms on low-resource languages, and works on codemixed input text without being explicitly trained on codemixed examples. We showcase the effectiveness of our method by reporting on part-of-speech tagging and morphological prediction on 70 treebanks and 48 languages.",
}
@inproceedings{zhao-etal-2019-data,
    title = "Data Augmentation with Atomic Templates for Spoken Language Understanding",
    author = "Zhao, Zijian  and
      Zhu, Su  and
      Yu, Kai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1375",
    doi = "10.18653/v1/D19-1375",
    pages = "3637--3643",
    abstract = "Spoken Language Understanding (SLU) converts user utterances into structured semantic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially when domain changes or a new domain comes. In this work, we propose a data augmentation method with atomic templates for SLU, which involves minimum human efforts. The atomic templates produce exemplars for fine-grained constituents of semantic representations. We propose an encoder-decoder model to generate the whole utterance from atomic exemplars. Moreover, the generator could be transferred from source domains to help a new domain which has little data. Experimental results show that our method achieves significant improvements on DSTC 2{\&}3 dataset which is a domain adaptation setting of SLU.",
}
@inproceedings{peng-etal-2019-palm,
    title = "{P}a{LM}: A Hybrid Parser and Language Model",
    author = "Peng, Hao  and
      Schwartz, Roy  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1376",
    doi = "10.18653/v1/D19-1376",
    pages = "3644--3651",
    abstract = "We present PaLM, a hybrid parser and neural language model. Building on an RNN language model, PaLM adds an attention layer over text spans in the left context. An unsupervised constituency parser can be derived from its attention weights, using a greedy decoding algorithm. We evaluate PaLM on language modeling, and empirically show that it outperforms strong baselines. If syntactic annotations are available, the attention component can be trained in a supervised manner, providing syntactically-informed representations of the context, and further improving language modeling performance.",
}
@inproceedings{min-etal-2019-pilot,
    title = "A Pilot Study for {C}hinese {SQL} Semantic Parsing",
    author = "Min, Qingkai  and
      Shi, Yuefeng  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1377",
    doi = "10.18653/v1/D19-1377",
    pages = "3652--3658",
    abstract = "The task of semantic parsing is highly useful for dialogue and question answering systems. Many datasets have been proposed to map natural language text into SQL, among which the recent Spider dataset provides cross-domain samples with multiple tables and complex queries. We build a Spider dataset for Chinese, which is currently a low-resource language in this task area. Interesting research questions arise from the uniqueness of the language, which requires word segmentation, and also from the fact that SQL keywords and columns of DB tables are typically written in English. We compare character- and word-based encoders for a semantic parser, and different embedding schemes. Results show that word-based semantic parser is subject to segmentation errors and cross-lingual word embeddings are useful for text-to-SQL.",
}
@inproceedings{bogin-etal-2019-global,
    title = "Global Reasoning over Database Structures for Text-to-{SQL} Parsing",
    author = "Bogin, Ben  and
      Gardner, Matt  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1378",
    doi = "10.18653/v1/D19-1378",
    pages = "3659--3664",
    abstract = "State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database constants in the new database, due to the local nature of decoding. {\%}since their decisions are based on weak, local information only. In this work, we propose a semantic parser that globally reasons about the structure of the output query to make a more contextually-informed selection of database constants. We use message-passing through a graph neural network to softly select a subset of database constants for the output query, conditioned on the question. Moreover, we train a model to rank queries based on the global alignment of database constants to question words. We apply our techniques to the current state-of-the-art model for Spider, a zero-shot semantic parsing dataset with complex databases, increasing accuracy from 39.4{\%} to 47.4{\%}.",
}
@inproceedings{ouchi-etal-2019-transductive,
    title = "Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis",
    author = "Ouchi, Hiroki  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1379",
    doi = "10.18653/v1/D19-1379",
    pages = "3665--3671",
    abstract = "In transductive learning, an unlabeled test set is used for model training. Although this setting deviates from the common assumption of a completely unseen test set, it is applicable in many real-world scenarios, wherein the texts to be processed are known in advance. However, despite its practical advantages, transductive learning is underexplored in natural language processing. Here we conduct an empirical study of transductive learning for neural models and demonstrate its utility in syntactic and semantic tasks. Specifically, we fine-tune language models (LMs) on an unlabeled test set to obtain test-set-specific word representations. Through extensive experiments, we demonstrate that despite its simplicity, transductive LM fine-tuning consistently improves state-of-the-art neural models in in-domain and out-of-domain settings.",
}
@inproceedings{almarwani-etal-2019-efficient,
    title = "Efficient Sentence Embedding using Discrete Cosine Transform",
    author = "Almarwani, Nada  and
      Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1380",
    doi = "10.18653/v1/D19-1380",
    pages = "3672--3678",
    abstract = "Vector averaging remains one of the most popular sentence embedding methods in spite of its obvious disregard for syntactic structure. While more complex sequential or convolutional networks potentially yield superior classification performance, the improvements in classification accuracy are typically mediocre compared to the simple vector averaging. As an efficient alternative, we propose the use of discrete cosine transform (DCT) to compress word sequences in an order-preserving manner. The lower order DCT coefficients represent the overall feature patterns in sentences, which results in suitable embeddings for tasks that could benefit from syntactic features. Our results in semantic probing tasks demonstrate that DCT embeddings indeed preserve more syntactic information compared with vector averaging. With practically equivalent complexity, the model yields better overall performance in downstream classification tasks that correlate with syntactic features, which illustrates the capacity of DCT to preserve word order information.",
}
@inproceedings{espinosa-etal-2019-search,
    title = "A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection",
    author = "Espinosa, Kurt Junshean  and
      Miwa, Makoto  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1381",
    doi = "10.18653/v1/D19-1381",
    pages = "3679--3686",
    abstract = "We tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search problem on a relation graph of trigger-argument structures. Unlike existing structured prediction tasks such as dependency parsing, the task targets to detect DAG structures, which constitute events, from the relation graph. We define actions to construct events and use all the beams in a beam search to detect all event structures that may be overlapping and nested. The search process constructs events in a bottom-up manner while modelling the global properties for nested and overlapping structures simultaneously using neural networks. We show that the model achieves performance comparable to the state-of-the-art model Turku Event Extraction System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the use of any syntactic and hand-engineered features. Further analyses on the development set show that our model is more computationally efficient while yielding higher F1-score performance.",
}
@inproceedings{yang-etal-2019-paws,
    title = "{PAWS}-{X}: A Cross-lingual Adversarial Dataset for Paraphrase Identification",
    author = "Yang, Yinfei  and
      Zhang, Yuan  and
      Tar, Chris  and
      Baldridge, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1382",
    doi = "10.18653/v1/D19-1382",
    pages = "3687--3692",
    abstract = "Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. We remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. We provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23{\%} over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information.",
}
@inproceedings{cohan-etal-2019-pretrained,
    title = "Pretrained Language Models for Sequential Sentence Classification",
    author = "Cohan, Arman  and
      Beltagy, Iz  and
      King, Daniel  and
      Dalvi, Bhavana  and
      Weld, Dan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1383",
    doi = "10.18653/v1/D19-1383",
    pages = "3693--3699",
    abstract = "As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Random Fields (CRFs) to incorporate dependencies between subsequent labels. In this work, we show that pretrained language models, BERT (Devlin et al., 2018) in particular, can be used for this task to capture contextual dependencies without the need for hierarchical encoding nor a CRF. Specifically, we construct a joint sentence representation that allows BERT Transformer layers to directly utilize contextual information from all words in all sentences. Our approach achieves state-of-the-art results on four datasets, including a new dataset of structured scientific abstracts.",
}
@inproceedings{harding-graesser-etal-2019-emergent,
    title = "Emergent Linguistic Phenomena in Multi-Agent Communication Games",
    author = "Harding Graesser, Laura  and
      Cho, Kyunghyun  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1384",
    doi = "10.18653/v1/D19-1384",
    pages = "3700--3710",
    abstract = "We describe a multi-agent communication framework for examining high-level linguistic phenomena at the community-level. We demonstrate that complex linguistic behavior observed in natural language can be reproduced in this simple setting: i) the outcome of contact between communities is a function of inter- and intra-group connectivity; ii) linguistic contact either converges to the majority protocol, or in balanced cases leads to novel creole languages of lower complexity; and iii) a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed languages. We conclude that at least some of the intricate properties of language evolution need not depend on complex evolved linguistic capabilities, but can emerge from simple social exchanges between perceptually-enabled agents playing communication games.",
}
@inproceedings{wang-potts-2019-talkdown,
    title = "{T}alk{D}own: A Corpus for Condescension Detection in Context",
    author = "Wang, Zijian  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1385",
    doi = "10.18653/v1/D19-1385",
    pages = "3711--3719",
    abstract = "Condescending language use is caustic; it can bring dialogues to an end and bifurcate communities. Thus, systems for condescension detection could have a large positive impact. A challenge here is that condescension is often impossible to detect from isolated utterances, as it depends on the discourse and social context. To address this, we present TalkDown, a new labeled dataset of condescending linguistic acts in context. We show that extending a language-only model with representations of the discourse improves performance, and we motivate techniques for dealing with the low rates of condescension overall. We also use our model to estimate condescension rates in various online communities and relate these differences to differing community norms.",
}
@inproceedings{deutsch-roth-2019-summary,
    title = "Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization",
    author = "Deutsch, Daniel  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1386",
    doi = "10.18653/v1/D19-1386",
    pages = "3720--3729",
    abstract = "A key challenge in topic-focused summarization is determining what information should be included in the summary, a problem known as content selection. In this work, we propose a new method for studying content selection in topic-focused summarization called the summary cloze task. The goal of the summary cloze task is to generate the next sentence of a summary conditioned on the beginning of the summary, a topic, and a reference document(s). The main challenge is deciding what information in the references is relevant to the topic and partial summary and should be included in the summary. Although the cloze task does not address all aspects of the traditional summarization problem, the more narrow scope of the task allows us to collect a large-scale datset of nearly 500k summary cloze instances from Wikipedia. We report experimental results on this new dataset using various extractive models and a two-step abstractive model that first extractively selects a small number of sentences and then abstractively summarizes them. Our results show that the topic and partial summary help the models identify relevant content, but the task remains a significant challenge.",
}
@inproceedings{liu-lapata-2019-text,
    title = "Text Summarization with Pretrained Encoders",
    author = "Liu, Yang  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1387",
    doi = "10.18653/v1/D19-1387",
    pages = "3730--3740",
    abstract = "Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several inter-sentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves state-of-the-art results across the board in both extractive and abstractive settings.",
}
@inproceedings{gao-etal-2019-write,
    title = "How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing",
    author = "Gao, Shen  and
      Chen, Xiuying  and
      Li, Piji  and
      Chan, Zhangming  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1388",
    doi = "10.18653/v1/D19-1388",
    pages = "3741--3751",
    abstract = "Under special circumstances, summaries should conform to a particular style with patterns, such as court judgments and abstracts in academic papers. To this end, the prototype document-summary pairs can be utilized to generate better summaries. There are two main challenges in this task: (1) the model needs to incorporate learned patterns from the prototype, but (2) should avoid copying contents other than the patternized words{---}such as irrelevant facts{---}into the generated summaries. To tackle these challenges, we design a model named Prototype Editing based Summary Generator (PESG). PESG first learns summary patterns and prototype facts by analyzing the correlation between a prototype document and its summary. Prototype facts are then utilized to help extract facts from the input document. Next, an editing generator generates new summary based on the summary pattern or extracted facts. Finally, to address the second challenge, a fact checker is used to estimate mutual information between the input document and generated summary, providing an additional signal for the generator. Extensive experiments conducted on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.",
}
@inproceedings{west-etal-2019-bottlesum,
    title = "{B}ottle{S}um: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle",
    author = "West, Peter  and
      Holtzman, Ari  and
      Buys, Jan  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1389",
    doi = "10.18653/v1/D19-1389",
    pages = "3752--3761",
    abstract = "The principle of the Information Bottleneck (Tishby et al., 1999) produces a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a conditional language modelling objective: given a sentence, our approach seeks a compressed sentence that can best predict the next sentence. Our iterative algorithm under the Information Bottleneck objective searches gradually shorter subsequences of the given sentence while maximizing the probability of the next sentence conditioned on the summary. Using only pretrained language models with no direct supervision, our approach can efficiently perform extractive sentence summarization over a large corpus. Building on our unsupervised extractive summarization, we also present a new approach to self-supervised abstractive summarization, where a transformer-based language model is trained on the output summaries of our unsupervised method. Empirical results demonstrate that our extractive method outperforms other unsupervised models on multiple automatic metrics. In addition, we find that our self-supervised abstractive model outperforms unsupervised baselines (including our own) by human evaluation along multiple attributes.",
}
@inproceedings{shen-etal-2019-improving,
    title = "Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator",
    author = "Shen, Xiaoyu  and
      Zhao, Yang  and
      Su, Hui  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1390",
    doi = "10.18653/v1/D19-1390",
    pages = "3762--3773",
    abstract = "Pointer Generators have been the de facto standard for modern summarization systems. However, this architecture faces two major drawbacks: Firstly, the pointer is limited to copying the exact words while ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment. Secondly, the copy mechanism results in a strong bias towards extractive generations, where most sentences are produced by simply copying from the source text. In this paper, we address these problems by allowing the model to {``}edit{''} pointed tokens instead of always hard copying them. The editing is performed by transforming the pointed word vector into a target space with a learned relation embedding. On three large-scale summarization dataset, we show the model is able to (1) capture more latent alignment relations than exact word matches, (2) improve word alignment accuracy, allowing for better model interpretation and controlling, (3) generate higher-quality summaries validated by both qualitative and quantitative evaluations and (4) bring more abstraction to the generated summaries.",
}
@inproceedings{wang-etal-2019-learning-semantic,
    title = "Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs",
    author = "Wang, Bailin  and
      Titov, Ivan  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1391",
    doi = "10.18653/v1/D19-1391",
    pages = "3774--3785",
    abstract = "Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces a denotation. Weakly-supervised semantic parsers are trained on utterance-denotation pairs treating programs as latent. The task is challenging due to the large search space and spuriousness of programs which may execute to the correct answer but do not generalize to unseen examples. Our goal is to instill an inductive bias in the parser to help it distinguish between spurious and correct programs. We capitalize on the intuition that correct programs would likely respect certain structural constraints were they to be aligned to the question (e.g., program fragments are unlikely to align to overlapping text spans) and propose to model alignments as structured latent variables. In order to make the latent-alignment framework tractable, we decompose the parsing task into (1) predicting a partial {``}abstract program{''} and (2) refining it while modeling structured alignments with differential dynamic programming. We obtain state-of-the-art performance on the WikiTableQuestions and WikiSQL datasets. When compared to a standard attention baseline, we observe that the proposed structured-alignment mechanism is highly beneficial.",
}
@inproceedings{zhang-etal-2019-broad,
    title = "Broad-Coverage Semantic Parsing as Transduction",
    author = "Zhang, Sheng  and
      Ma, Xutai  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1392",
    doi = "10.18653/v1/D19-1392",
    pages = "3786--3798",
    abstract = "We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks {--} AMR, SDP and UCCA {--} demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.",
}
@inproceedings{cai-lam-2019-core,
    title = "Core Semantic First: A Top-down Approach for {AMR} Parsing",
    author = "Cai, Deng  and
      Lam, Wai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1393",
    doi = "10.18653/v1/D19-1393",
    pages = "3799--3809",
    abstract = "We introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it constructs a parse graph incrementally in a top-down fashion. Starting from the root, at each step, a new node and its connections to existing nodes will be jointly predicted. The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details. The \textit{core semantic first} principle emphasizes capturing the main ideas of a sentence, which is of great interest. We evaluate our model on the latest AMR sembank and achieve the state-of-the-art performance in the sense that no heuristic graph re-categorization is adopted. More importantly, the experiments show that our parser is especially good at obtaining the core semantics.",
}
@inproceedings{herzig-berant-2019-dont,
    title = "Don{'}t paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing",
    author = "Herzig, Jonathan  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1394",
    doi = "10.18653/v1/D19-1394",
    pages = "3810--3820",
    abstract = "A major hurdle on the road to conversational interfaces is the difficulty in collecting data that maps language utterances to logical forms. One prominent approach for data collection has been to automatically generate pseudo-language paired with logical forms, and paraphrase the pseudo-language to natural language through crowdsourcing (Wang et al., 2015). However, this data collection procedure often leads to low performance on real data, due to a mismatch between the true distribution of examples and the distribution induced by the data collection procedure. In this paper, we thoroughly analyze two sources of mismatch in this process: the mismatch in logical form distribution and the mismatch in language distribution between the true and induced distributions. We quantify the effects of these mismatches, and propose a new data collection approach that mitigates them. Assuming access to unlabeled utterances from the true distribution, we combine crowdsourcing with a paraphrase model to detect correct logical forms for the unlabeled utterances. On two datasets, our method leads to 70.6 accuracy on average on the true distribution, compared to 51.3 in paraphrasing-based data collection.",
}
@inproceedings{hu-etal-2019-improving,
    title = "Improving Distantly-Supervised Relation Extraction with Joint Label Embedding",
    author = "Hu, Linmei  and
      Zhang, Luhao  and
      Shi, Chuan  and
      Nie, Liqiang  and
      Guan, Weili  and
      Yang, Cheng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1395",
    doi = "10.18653/v1/D19-1395",
    pages = "3821--3829",
    abstract = "Distantly-supervised relation extraction has proven to be effective to find relational facts from texts. However, the existing approaches treat labels as independent and meaningless one-hot vectors, which cause a loss of potential label information for selecting valid instances. In this paper, we propose a novel multi-layer attention-based model to improve relation extraction with joint label embedding. The model makes full use of both structural information from Knowledge Graphs and textual information from entity descriptions to learn label embeddings through gating integration while avoiding the imposed noise with an attention mechanism. Then the learned label embeddings are used as another atten- tion over the instances (whose embeddings are also enhanced with the entity descriptions) for improving relation extraction. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art methods.",
}
@inproceedings{sui-etal-2019-leverage,
    title = "Leverage Lexical Knowledge for {C}hinese Named Entity Recognition via Collaborative Graph Network",
    author = "Sui, Dianbo  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun  and
      Liu, Shengping",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1396",
    doi = "10.18653/v1/D19-1396",
    pages = "3830--3840",
    abstract = "The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model.",
}
@inproceedings{ye-etal-2019-looking,
    title = "Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction",
    author = "Ye, Qinyuan  and
      Liu, Liyuan  and
      Zhang, Maosen  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1397",
    doi = "10.18653/v1/D19-1397",
    pages = "3841--3850",
    abstract = "In recent years there is a surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction (RE). In this paper, we study the problem what limits the performance of DS-trained neural models, conduct thorough analyses, and identify a factor that can influence the performance greatly, shifted label distribution. Specifically, we found this problem commonly exists in real-world DS datasets, and without special handing, typical DS-RE models cannot automatically adapt to this shift, thus achieving deteriorated performance. To further validate our intuition, we develop a simple yet effective adaptation method for DS-trained models, bias adjustment, which updates models learned over the source domain (i.e., DS training set) with a label distribution estimated on the target domain (i.e., test set). Experiments demonstrate that bias adjustment achieves consistent performance gains on DS-trained models, especially on neural models, with an up to 23{\%} relative F1 improvement, which verifies our assumptions. Our code and data can be found at https://github.com/INK-USC/shifted-label-distribution.",
}
@inproceedings{ma-etal-2019-easy,
    title = "Easy First Relation Extraction with Information Redundancy",
    author = "Ma, Shuai  and
      Wang, Gang  and
      Feng, Yansong  and
      Huai, Jinpeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1398",
    doi = "10.18653/v1/D19-1398",
    pages = "3851--3861",
    abstract = "Many existing relation extraction (RE) models make decisions globally using integer linear programming (ILP). However, it is nontrivial to make use of integer linear programming as a blackbox solver for RE. Its cost of time and memory may become unacceptable with the increase of data scale, and redundant information needs to be encoded cautiously for ILP. In this paper, we propose an easy first approach for relation extraction with information redundancies, embedded in the results produced by local sentence level extractors, during which conflict decisions are resolved with domain and uniqueness constraints. Information redundancies are leveraged to support both easy first collective inference for easy decisions in the first stage and ILP for hard decisions in a subsequent stage. Experimental study shows that our approach improves the efficiency and accuracy of RE, and outperforms both ILP and neural network-based methods.",
}
@inproceedings{jie-lu-2019-dependency,
    title = "Dependency-Guided {LSTM}-{CRF} for Named Entity Recognition",
    author = "Jie, Zhanming  and
      Lu, Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1399",
    doi = "10.18653/v1/D19-1399",
    pages = "3862--3872",
    abstract = "Dependency tree structures capture long-distance and syntactic relationships between words in a sentence. The syntactic relations (e.g., nominal subject, object) can potentially infer the existence of certain named entities. In addition, the performance of a named entity recognizer could benefit from the long-distance dependencies between the words in dependency trees. In this work, we propose a simple yet effective dependency-guided LSTM-CRF model to encode the complete dependency trees and capture the above properties for the task of named entity recognition (NER). The data statistics show strong correlations between the entity types and dependency relations. We conduct extensive experiments on several standard datasets and demonstrate the effectiveness of the proposed model in improving NER and achieving state-of-the-art performance. Our analysis reveals that the significant improvements mainly result from the dependency relations and long-distance interactions provided by dependency trees.",
}
@inproceedings{ringel-etal-2019-cross,
    title = "Cross-Cultural Transfer Learning for Text Classification",
    author = "Ringel, Dor  and
      Lavee, Gal  and
      Guy, Ido  and
      Radinsky, Kira",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1400",
    doi = "10.18653/v1/D19-1400",
    pages = "3873--3883",
    abstract = "Large training datasets are required to achieve competitive performance in most natural language tasks. The acquisition process for these datasets is labor intensive, expensive, and time consuming. This process is also prone to human errors. In this work, we show that cross-cultural differences can be harnessed for natural language text classification. We present a transfer-learning framework that leverages widely-available unaligned bilingual corpora for classification tasks, using no task-specific data. Our empirical evaluation on two tasks {--} formality classification and sarcasm detection {--} shows that the cross-cultural difference between German and American English, as manifested in product review text, can be applied to achieve good performance for formality classification, while the difference between Japanese and American English can be applied to achieve good performance for sarcasm detection {--} both without any task-specific labeled data.",
}
@inproceedings{melamud-etal-2019-combining,
    title = "Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification",
    author = "Melamud, Oren  and
      Bornea, Mihaela  and
      Barker, Ken",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1401",
    doi = "10.18653/v1/D19-1401",
    pages = "3884--3893",
    abstract = "Supervised learning models often perform poorly at low-shot tasks, i.e. tasks for which little labeled data is available for training. One prominent approach for improving low-shot learning is to use unsupervised pre-trained neural models. Another approach is to obtain richer supervision by collecting annotator rationales (explanations supporting label annotations). In this work, we combine these two approaches to improve low-shot text classification with two novel methods: a simple bag-of-words embedding approach; and a more complex context-aware method, based on the BERT model. In experiments with two English text classification datasets, we demonstrate substantial performance gains from combining pre-training with rationales. Furthermore, our investigation of a range of train-set sizes reveals that the simple bag-of-words approach is the clear top performer when there are only a few dozen training instances or less, while more complex models, such as BERT or CNN, require more training data to shine.",
}
@inproceedings{kozareva-ravi-2019-proseqo,
    title = "{P}ro{S}eqo: Projection Sequence Networks for On-Device Text Classification",
    author = "Kozareva, Zornitsa  and
      Ravi, Sujith",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1402",
    doi = "10.18653/v1/D19-1402",
    pages = "3894--3903",
    abstract = "We propose a novel on-device sequence model for text classification using recurrent projections. Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings. This results in fast and compact neural networks that can perform on-device inference for complex short and long text classification tasks. We conducted exhaustive evaluation on multiple text classification tasks. Results show that ProSeqo outperformed state-of-the-art neural and on-device approaches for short text classification tasks such as dialog act and intent prediction. To the best of our knowledge, ProSeqo is the first on-device long text classification neural model. It achieved comparable results to previous neural approaches for news article, answers and product categorization, while preserving small memory footprint and maintaining high accuracy.",
}
@inproceedings{geng-etal-2019-induction,
    title = "Induction Networks for Few-Shot Text Classification",
    author = "Geng, Ruiying  and
      Li, Binhua  and
      Li, Yongbin  and
      Zhu, Xiaodan  and
      Jian, Ping  and
      Sun, Jian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1403",
    doi = "10.18653/v1/D19-1403",
    pages = "3904--3913",
    abstract = "Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the sample-wise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that on both datasets, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification.",
}
@inproceedings{yin-etal-2019-benchmarking,
    title = "Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach",
    author = "Yin, Wenpeng  and
      Hay, Jamaal  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1404",
    doi = "10.18653/v1/D19-1404",
    pages = "3914--3923",
    abstract = "Zero-shot text classification (0Shot-TC) is a challenging NLU problem to which little attention has been paid by the research community. 0Shot-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.g., topic, emotion, event, etc.) described by the label. And there are only a few articles studying 0Shot-TC, all focusing only on topical categorization which, we argue, is just the tip of the iceberg in 0Shot-TC. In addition, the chaotic experiments in literature make no uniform comparison, which blurs the progress. This work benchmarks the 0Shot-TC problem by providing unified datasets, standardized evaluations, and state-of-the-art baselines. Our contributions include: i) The datasets we provide facilitate studying 0Shot-TC relative to conceptually different and diverse aspects: the {``}topic{''} aspect includes {``}sports{''} and {``}politics{''} as labels; the {``}emotion{''} aspect includes {``}joy{''} and {``}anger{''}; the {``}situation{''} aspect includes {``}medical assistance{''} and {``}water shortage{''}. ii) We extend the existing evaluation setup (label-partially-unseen) {--} given a dataset, train on some labels, test on all labels {--} to include a more challenging yet realistic evaluation label-fully-unseen 0Shot-TC (Chang et al., 2008), aiming at classifying text snippets without seeing task specific training data at all. iii) We unify the 0Shot-TC of diverse aspects within a textual entailment formulation and study it this way.",
}
@inproceedings{li-etal-2019-logic,
    title = "A Logic-Driven Framework for Consistency of Neural Models",
    author = "Li, Tao  and
      Gupta, Vivek  and
      Mehta, Maitrey  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1405",
    doi = "10.18653/v1/D19-1405",
    pages = "3924--3935",
    abstract = "While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples. In this paper, we formalize such inconsistency as a generalization of prediction error. We propose a learning framework for constraining models using logic rules to regularize them away from inconsistency. Our framework can leverage both labeled and unlabeled examples and is directly compatible with off-the-shelf learning schemes without model redesign. We instantiate our framework on natural language inference, where experiments show that enforcing invariants stated in logic can help make the predictions of neural models both accurate and consistent.",
}
@inproceedings{tikhonov-etal-2019-style,
    title = "Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites",
    author = "Tikhonov, Alexey  and
      Shibaev, Viacheslav  and
      Nagaev, Aleksander  and
      Nugmanova, Aigul  and
      Yamshchikov, Ivan P.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1406",
    doi = "10.18653/v1/D19-1406",
    pages = "3936--3945",
    abstract = "This paper shows that standard assessment methodology for style transfer has several significant problems. First, the standard metrics for style accuracy and semantics preservation vary significantly on different re-runs. Therefore one has to report error margins for the obtained results. Second, starting with certain values of bilingual evaluation understudy (BLEU) between input and output and accuracy of the sentiment transfer the optimization of these two standard metrics diverge from the intuitive goal of the style transfer task. Finally, due to the nature of the task itself, there is a specific dependence between these two metrics that could be easily manipulated. Under these circumstances, we suggest taking BLEU between input and human-written reformulations into consideration for benchmarks. We also propose three new architectures that outperform state of the art in terms of this metric.",
}
@inproceedings{fang-etal-2019-implicit,
    title = "Implicit Deep Latent Variable Models for Text Generation",
    author = "Fang, Le  and
      Li, Chunyuan  and
      Gao, Jianfeng  and
      Dong, Wen  and
      Chen, Changyou",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1407",
    doi = "10.18653/v1/D19-1407",
    pages = "3946--3956",
    abstract = "Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation. One key factor is the exploitation of smooth latent structures to guide the generation. However, the representation power of VAEs is limited due to two reasons: (1) the Gaussian assumption is often made on the variational posteriors; and meanwhile (2) a notorious {``}posterior collapse{''} issue occurs. In this paper, we advocate sample-based representations of variational distributions for natural language, leading to implicit latent features, which can provide flexible representation power compared with Gaussian-based posteriors. We further develop an LVM to directly match the aggregated posterior to the prior. It can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the {``}posterior collapse{''} issue. We demonstrate the effectiveness and versatility of our models in various text generation scenarios, including language modeling, unaligned style transfer, and dialog response generation. The source code to reproduce our experimental results is available on GitHub.",
}
@inproceedings{zhao-ma-2019-text,
    title = "Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach",
    author = "Zhao, Zhenjie  and
      Ma, Xiaojuan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1408",
    doi = "10.18653/v1/D19-1408",
    pages = "3957--3967",
    abstract = "Text emotion distribution learning (EDL) aims to develop models that can predict the intensity values of a sentence across a set of emotion categories. Existing methods based on supervised learning require a large amount of well-labelled training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. In this paper, we propose a meta-learning approach to learn text emotion distributions from a small sample. Specifically, we propose to learn low-rank sentence embeddings by tensor decomposition to capture their contextual semantic similarity, and use K-nearest neighbors (KNNs) of each sentence in the embedding space to generate sample clusters. We then train a meta-learner that can adapt to new data with only a few training samples on the clusters, and further fit the meta-learner on KNNs of a testing sample for EDL. In this way, we effectively augment the learning ability of a model on the small sample. To demonstrate the performance, we compare the proposed approach with state-of-the-art EDL methods on a widely used EDL dataset: SemEval 2007 Task 14 (Strapparava and Mihalcea, 2007). Results show the superiority of our method on small-sample emotion distribution learning.",
}
@inproceedings{garbacea-etal-2019-judge,
    title = "Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation",
    author = "Garbacea, Cristina  and
      Carton, Samuel  and
      Yan, Shiyan  and
      Mei, Qiaozhu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1409",
    doi = "10.18653/v1/D19-1409",
    pages = "3968--3981",
    abstract = "We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compare human-based evaluators with a variety of automated evaluation procedures, including discriminative evaluators that measure how well machine-generated text can be distinguished from human-written text, as well as word overlap metrics that assess how similar the generated text compares to human-written references. We determine to what extent these different evaluators agree on the ranking of a dozen of state-of-the-art generators for online product reviews. We find that human evaluators do not correlate well with discriminative evaluators, leaving a bigger question of whether adversarial accuracy is the correct objective for natural language generation. In general, distinguishing machine-generated text is challenging even for human evaluators, and human decisions correlate better with lexical overlaps. We find lexical diversity an intriguing metric that is indicative of the assessments of different evaluators. A post-experiment survey of participants provides insights into how to evaluate and improve the quality of natural language generation systems.",
}
@inproceedings{reimers-gurevych-2019-sentence,
    title = "Sentence-{BERT}: Sentence Embeddings using {S}iamese {BERT}-Networks",
    author = "Reimers, Nils  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1410",
    doi = "10.18653/v1/D19-1410",
    pages = "3982--3992",
    abstract = "BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations ({\textasciitilde}65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.",
}
@inproceedings{charoenphakdee-etal-2019-learning,
    title = "Learning Only from Relevant Keywords and Unlabeled Documents",
    author = "Charoenphakdee, Nontawat  and
      Lee, Jongyeong  and
      Jin, Yiping  and
      Wanvarie, Dittaya  and
      Sugiyama, Masashi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1411",
    doi = "10.18653/v1/D19-1411",
    pages = "3993--4002",
    abstract = "We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given. Although heuristic methods based on pseudo-labeling have been considered, theoretical understanding of this problem has still been limited. Moreover, previous methods cannot easily incorporate well-developed techniques in supervised text classification. In this paper, we propose a theoretically guaranteed learning framework that is simple to implement and has flexible choices of models, e.g., linear models or neural networks. We demonstrate how to optimize the area under the receiver operating characteristic curve (AUC) effectively and also discuss how to adjust it to optimize other well-known evaluation metrics such as the accuracy and F1-measure. Finally, we show the effectiveness of our framework using benchmark datasets.",
}
@inproceedings{wang-etal-2019-denoising,
    title = "Denoising based Sequence-to-Sequence Pre-training for Text Generation",
    author = "Wang, Liang  and
      Zhao, Wei  and
      Jia, Ruoyu  and
      Li, Sujian  and
      Liu, Jingming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1412",
    doi = "10.18653/v1/D19-1412",
    pages = "4003--4015",
    abstract = "This paper presents a new sequence-to-sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks. Unlike encoder-only (e.g., BERT) or decoder-only (e.g., OpenAI GPT) pre-training approaches, PoDA jointly pre-trains both the encoder and decoder by denoising the noise-corrupted text, and it also has the advantage of keeping the network architecture unchanged in the subsequent fine-tuning stage. Meanwhile, we design a hybrid model of Transformer and pointer-generator networks as the backbone architecture for PoDA. We conduct experiments on two text generation tasks: abstractive summarization, and grammatical error correction. Results on four datasets show that PoDA can improve model performance over strong baselines without using any task-specific techniques and significantly speed up convergence.",
}
@inproceedings{perkins-yang-2019-dialog,
    title = "Dialog Intent Induction with Deep Multi-View Clustering",
    author = "Perkins, Hugh  and
      Yang, Yi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1413",
    doi = "10.18653/v1/D19-1413",
    pages = "4016--4025",
    abstract = "We introduce the dialog intent induction task and present a novel deep multi-view clustering approach to tackle the problem. Dialog intent induction aims at discovering user intents from user query utterances in human-human conversations such as dialogs between customer support agents and customers. Motivated by the intuition that a dialog intent is not only expressed in the user query utterance but also captured in the rest of the dialog, we split a conversation into two independent views and exploit multi-view clustering techniques for inducing the dialog intent. In par- ticular, we propose alternating-view k-means (AV-KMEANS) for joint multi-view represen- tation learning and clustering analysis. The key innovation is that the instance-view representations are updated iteratively by predicting the cluster assignment obtained from the alternative view, so that the multi-view representations of the instances lead to similar cluster assignments. Experiments on two public datasets show that AV-KMEANS can induce better dialog intent clusters than state-of-the-art unsupervised representation learning methods and standard multi-view clustering approaches.",
}
@inproceedings{garg-etal-2019-nearly,
    title = "Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction",
    author = "Garg, Sahil  and
      Galstyan, Aram  and
      Ver Steeg, Greg  and
      Cecchi, Guillermo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1414",
    doi = "10.18653/v1/D19-1414",
    pages = "4026--4036",
    abstract = "Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifi er following the prior work. This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set. We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r.t. state-of-the-art supervised and semi-supervised approaches.",
}
@inproceedings{croce-etal-2019-auditing,
    title = "Auditing Deep Learning processes through Kernel-based Explanatory Models",
    author = "Croce, Danilo  and
      Rossini, Daniele  and
      Basili, Roberto",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1415",
    doi = "10.18653/v1/D19-1415",
    pages = "4037--4046",
    abstract = "While NLP systems become more pervasive, their accountability gains value as a focal point of effort. Epistemological opaqueness of nonlinear learning methods, such as deep learning models, can be a major drawback for their adoptions. In this paper, we discuss the application of Layerwise Relevance Propagation over a linguistically motivated neural architecture, the Kernel-based Deep Architecture, in order to trace back connections between linguistic properties of input instances and system decisions. Such connections then guide the construction of argumentations on network{'}s inferences, i.e., explanations based on real examples, semantically related to the input. We propose here a methodology to evaluate the transparency and coherence of analogy-based explanations modeling an audit stage for the system. Quantitative analysis on two semantic tasks, i.e., question classification and semantic role labeling, show that the explanatory capabilities (native in KDAs) are effective and they pave the way to more complex argumentation methods.",
}
@inproceedings{qian-cheung-2019-enhancing,
    title = "Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation",
    author = "Qian, Dong  and
      Cheung, William K.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1416",
    doi = "10.18653/v1/D19-1416",
    pages = "4047--4057",
    abstract = "While broadly applicable to many natural language processing (NLP) tasks, variational autoencoders (VAEs) are hard to train due to the posterior collapse issue where the latent variable fails to encode the input data effectively. Various approaches have been proposed to alleviate this problem to improve the capability of the VAE. In this paper, we propose to introduce a mutual information (MI) term between the input and its latent variable to regularize the objective of the VAE. Since estimating the MI in the high-dimensional space is intractable, we employ neural networks for the estimation of the MI and provide a training algorithm based on the convex duality approach. Our experimental results on three benchmark datasets demonstrate that the proposed model, compared to the state-of-the-art baselines, exhibits less posterior collapse and has comparable or better performance in language modeling and text generation. We also qualitatively evaluate the inferred latent space and show that the proposed model can generate more reasonable and diverse sentences via linear interpolation in the latent space.",
}
@inproceedings{prabhu-etal-2019-sampling,
    title = "Sampling Bias in Deep Active Classification: An Empirical Study",
    author = "Prabhu, Ameya  and
      Dognin, Charles  and
      Singh, Maneesh",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1417",
    doi = "10.18653/v1/D19-1417",
    pages = "4058--4068",
    abstract = "The exploding cost and time needed for data labeling and model training are bottlenecks for training DNN models on large datasets. Identifying smaller representative data samples with strategies like active learning can help mitigate such bottlenecks. Previous works on active learning in NLP identify the problem of sampling bias in the samples acquired by uncertainty-based querying and develop costly approaches to address it. Using a large empirical study, we demonstrate that active set selection using the posterior entropy of deep models like FastText.zip (FTZ) is robust to sampling biases and to various algorithmic choices (query size and strategies) unlike that suggested by traditional literature. We also show that FTZ based query strategy produces sample sets similar to those from more sophisticated approaches (e.g ensemble networks). Finally, we show the effectiveness of the selected samples by creating tiny high-quality datasets, and utilizing them for fast and cheap training of large models. Based on the above, we propose a simple baseline for deep active text classification that outperforms the state of the art. We expect the presented work to be useful and informative for dataset compression and for problems involving active, semi-supervised or online learning scenarios. Code and models are available at: https://github.com/drimpossible/Sampling-Bias-Active-Learning.",
}
@inproceedings{clark-etal-2019-dont,
    title = "Don{'}t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases",
    author = "Clark, Christopher  and
      Yatskar, Mark  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1418",
    doi = "10.18653/v1/D19-1418",
    pages = "4069--4082",
    abstract = "State-of-the-art models often make use of superficial patterns in the data that do not generalize well to out-of-domain or adversarial settings. For example, textual entailment models often learn that particular key words imply entailment, irrespective of context, and visual question answering models learn to predict prototypical answers, without considering evidence in the image. In this paper, we show that if we have prior knowledge of such biases, we can train a model to be more robust to domain shift. Our method has two stages: we (1) train a naive model that makes predictions exclusively based on dataset biases, and (2) train a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize. Experiments on five datasets with out-of-domain test sets show significantly improved robustness in all settings, including a 12 point gain on a changing priors visual question answering dataset and a 9 point gain on an adversarial question answering test set.",
}
@inproceedings{huang-etal-2019-achieving,
    title = "Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation",
    author = "Huang, Po-Sen  and
      Stanforth, Robert  and
      Welbl, Johannes  and
      Dyer, Chris  and
      Yogatama, Dani  and
      Gowal, Sven  and
      Dvijotham, Krishnamurthy  and
      Kohli, Pushmeet",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1419",
    doi = "10.18653/v1/D19-1419",
    pages = "4083--4093",
    abstract = "Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversarial training and data augmentation to partially mitigate such brittleness, but these are unlikely to find worst-case adversaries due to the complexity of the search space arising from discrete text perturbations. In this work, we approach the problem from the opposite direction: to formally verify a system{'}s robustness against a predefined class of adversarial attacks. We study text classification under synonym replacements or character flip perturbations. We propose modeling these input perturbations as a simplex and then using Interval Bound Propagation {--} a formal model verification method. We modify the conventional log-likelihood training objective to train models that can be efficiently verified, which would otherwise come with exponential search complexity. The resulting models show only little difference in terms of nominal accuracy, but have much improved verified accuracy under perturbations and come with an efficiently computable formal guarantee on worst case adversaries.",
}
@inproceedings{yu-etal-2019-rethinking,
    title = "Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control",
    author = "Yu, Mo  and
      Chang, Shiyu  and
      Zhang, Yang  and
      Jaakkola, Tommi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1420",
    doi = "10.18653/v1/D19-1420",
    pages = "4094--4103",
    abstract = "Selective rationalization has become a common mechanism to ensure that predictive models reveal how they use any available features. The selection may be soft or hard, and identifies a subset of input features relevant for prediction. The setup can be viewed as a co-operate game between the selector (aka rationale generator) and the predictor making use of only the selected features. The co-operative setting may, however, be compromised for two reasons. First, the generator typically has no direct access to the outcome it aims to justify, resulting in poor performance. Second, there{'}s typically no control exerted on the information left outside the selection. We revise the overall co-operative framework to address these challenges. We introduce an introspective model which explicitly predicts and incorporates the outcome into the selection process. Moreover, we explicitly control the rationale complement via an adversary so as not to leave any useful information out of the selection. We show that the two complementary mechanisms maintain both high predictive accuracy and lead to comprehensive rationales.",
}
@inproceedings{labeau-cohen-2019-experimenting,
    title = "Experimenting with Power Divergences for Language Modeling",
    author = "Labeau, Matthieu  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1421",
    doi = "10.18653/v1/D19-1421",
    pages = "4104--4114",
    abstract = "Neural language models are usually trained using Maximum-Likelihood Estimation (MLE). The corresponding objective function for MLE is derived from the Kullback-Leibler (KL) divergence between the empirical probability distribution representing the data and the parametric probability distribution output by the model. However, the word frequency discrepancies in natural language make performance extremely uneven: while the perplexity is usually very low for frequent words, it is especially difficult to predict rare words. In this paper, we experiment with several families (alpha, beta and gamma) of power divergences, generalized from the KL divergence, for learning language models with an objective different than standard MLE. Intuitively, these divergences should affect the way the probability mass is spread during learning, notably by prioritizing performances on high or low-frequency words. In addition, we implement and experiment with various sampling-based objectives, where the computation of the output layer is only done on a small subset of the vocabulary. They are derived as power generalizations of a softmax approximated via Importance Sampling, and Noise Contrastive Estimation, for accelerated learning. Our experiments on the Penn Treebank and Wikitext-2 show that these power divergences can indeed be used to prioritize learning on the frequent or rare words, and lead to general performance improvements in the case of sampling-based learning.",
}
@inproceedings{cui-zhang-2019-hierarchically,
    title = "Hierarchically-Refined Label Attention Network for Sequence Labeling",
    author = "Cui, Leyang  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1422",
    doi = "10.18653/v1/D19-1422",
    pages = "4115--4128",
    abstract = "CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This can be because the simple Markov label transition model of CRF does not give much information gain over strong neural encoding. For better representing label sequences, we investigate a hierarchically-refined label attention network, which explicitly leverages label embeddings and captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. Results on POS tagging, NER and CCG supertagging show that the proposed model not only improves the overall tagging accuracy with similar number of parameters, but also significantly speeds up the training and testing compared to BiLSTM-CRF.",
}
@inproceedings{jia-etal-2019-certified,
    title = "Certified Robustness to Adversarial Word Substitutions",
    author = {Jia, Robin  and
      Raghunathan, Aditi  and
      G{\"o}ksel, Kerem  and
      Liang, Percy},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1423",
    doi = "10.18653/v1/D19-1423",
    pages = "4129--4142",
    abstract = "State-of-the-art NLP models can often be fooled by adversaries that apply seemingly innocuous label-preserving transformations (e.g., paraphrasing) to input text. The number of possible transformations scales exponentially with text length, so data augmentation cannot cover all transformations of an input. This paper considers one exponentially large family of label-preserving transformations, in which every word in the input can be replaced with a similar word. We train the first models that are provably robust to all word substitutions in this family. Our training procedure uses Interval Bound Propagation (IBP) to minimize an upper bound on the worst-case loss that any combination of word substitutions can induce. To evaluate models{'} robustness to these transformations, we measure accuracy on adversarially chosen word substitutions applied to test examples. Our IBP-trained models attain 75{\%} adversarial accuracy on both sentiment analysis on IMDB and natural language inference on SNLI; in comparison, on IMDB, models trained normally and ones trained with data augmentation achieve adversarial accuracy of only 12{\%} and 41{\%}, respectively.",
}
@inproceedings{hao-etal-2019-visualizing,
    title = "Visualizing and Understanding the Effectiveness of {BERT}",
    author = "Hao, Yaru  and
      Dong, Li  and
      Wei, Furu  and
      Xu, Ke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1424",
    doi = "10.18653/v1/D19-1424",
    pages = "4143--4152",
    abstract = "Language model pre-training, such as BERT, has achieved remarkable results in many NLP tasks. However, it is unclear why the pre-training-then-fine-tuning paradigm can improve performance and generalization capability across different tasks. In this paper, we propose to visualize loss landscapes and optimization trajectories of fine-tuning BERT on specific datasets. First, we find that pre-training reaches a good initial point across downstream tasks, which leads to wider optima and easier optimization compared with training from scratch. We also demonstrate that the fine-tuning procedure is robust to overfitting, even though BERT is highly over-parameterized for downstream tasks. Second, the visualization results indicate that fine-tuning BERT tends to generalize better because of the flat and wide optima, and the consistency between the training loss surface and the generalization error surface. Third, the lower layers of BERT are more invariant during fine-tuning, which suggests that the layers that are close to input learn more transferable representations of language.",
}
@inproceedings{kumar-etal-2019-topics,
    title = "Topics to Avoid: Demoting Latent Confounds in Text Classification",
    author = "Kumar, Sachin  and
      Wintner, Shuly  and
      Smith, Noah A.  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1425",
    doi = "10.18653/v1/D19-1425",
    pages = "4153--4163",
    abstract = "Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of \textit{native language identification}. We find that standard text classifiers which perform well on the test set end up learning topical features which are confounds of the prediction task (e.g., if the input text mentions Sweden, the classifier predicts that the author{'}s native language is Swedish). We propose a method that represents the latent topical confounds and a model which {``}unlearns{''} confounding features by predicting both the label of the input text and the confound; but we train the two predictors adversarially in an alternating fashion to learn a text representation that predicts the correct label but is less prone to using information about the confound. We show that this model generalizes better and learns features that are indicative of the writing style rather than the content.",
}
@inproceedings{srivastava-etal-2019-learning,
    title = "Learning to Ask for Conversational Machine Learning",
    author = "Srivastava, Shashank  and
      Labutov, Igor  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1426",
    doi = "10.18653/v1/D19-1426",
    pages = "4164--4174",
    abstract = "Natural language has recently been explored as a new medium of supervision for training machine learning models. Here, we explore learning classification tasks using language in a conversational setting {--} where the automated learner does not simply receive language input from a teacher, but can proactively engage the teacher by asking questions. We present a reinforcement learning framework, where the learner{'}s actions correspond to question types and the reward for asking a question is based on how the teacher{'}s response changes performance of the resulting machine learning model on the learning task. In this framework, learning good question-asking strategies corresponds to asking sequences of questions that maximize the cumulative (discounted) reward, and hence quickly lead to effective classifiers. Empirical analysis across three domains shows that learned question-asking strategies expedite classifier training by asking appropriate questions at different points in the learning process. The approach allows learning classifiers from a blend of strategies, including learning from observations, explanations and clarifications.",
}
@inproceedings{gonen-goldberg-2019-language,
    title = "Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training",
    author = "Gonen, Hila  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1427",
    doi = "10.18653/v1/D19-1427",
    pages = "4175--4185",
    abstract = "We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at least) three reasons: (1) lack of available large-scale code-switched data for training; (2) lack of a replicable evaluation setup that is ASR directed yet isolates language modeling performance from the other intricacies of the ASR system; and (3) the reliance on generative modeling. We tackle these three issues: we propose an ASR-motivated evaluation setup which is decoupled from an ASR system and the choice of vocabulary, and provide an evaluation dataset for English-Spanish code-switching. This setup lends itself to a discriminative training approach, which we demonstrate to work better than generative language modeling. Finally, we explore a variety of training protocols and verify the effectiveness of training with large amounts of monolingual data followed by fine-tuning with small amounts of code-switched data, for both the generative and discriminative cases.",
}
@inproceedings{fan-etal-2019-using,
    title = "Using Local Knowledge Graph Construction to Scale {S}eq2{S}eq Models to Multi-Document Inputs",
    author = "Fan, Angela  and
      Gardent, Claire  and
      Braud, Chlo{\'e}  and
      Bordes, Antoine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1428",
    doi = "10.18653/v1/D19-1428",
    pages = "4186--4196",
    abstract = "Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current approaches extractively select portions of web text as input to Sequence-to-Sequence models using methods such as TF-IDF ranking. We propose constructing a local graph structured knowledge base for each query, which compresses the web search information and reduces redundancy. We show that by linearizing the graph into a structured input sequence, models can encode the graph representations within a standard Sequence-to-Sequence setting. For two generative tasks with very long text input, long-form question answering and multi-document summarization, feeding graph representations as input can achieve better performance than using retrieved text portions.",
}
@inproceedings{yang-etal-2019-fine,
    title = "Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation",
    author = "Yang, Huiyun  and
      Huang, Shujian  and
      Dai, Xin-Yu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1429",
    doi = "10.18653/v1/D19-1429",
    pages = "4197--4206",
    abstract = "In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual target domain samples, which may lead to negative transfer results for certain samples. Besides, an important characteristic of sequence labeling tasks is that different elements within a given sample may also have diverse domain relevance, which requires further consideration. To take the multi-level domain relevance discrepancy into account, in this paper, we propose a fine-grained knowledge fusion model with the domain relevance modeling scheme to control the balance between learning from the target domain data and learning from the source domain model. Experiments on three sequence labeling tasks show that our fine-grained knowledge fusion model outperforms strong baselines and other state-of-the-art sequence labeling domain adaptation methods.",
}
@inproceedings{wu-etal-2019-exploiting,
    title = "Exploiting Monolingual Data at Scale for Neural Machine Translation",
    author = "Wu, Lijun  and
      Wang, Yiren  and
      Xia, Yingce  and
      Qin, Tao  and
      Lai, Jianhuang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1430",
    doi = "10.18653/v1/D19-1430",
    pages = "4207--4216",
    abstract = "While target-side monolingual data has been proven to be very useful to improve neural machine translation (briefly, NMT) through back translation, source-side monolingual data is not well investigated. In this work, we study how to use both the source-side and target-side monolingual data for NMT, and propose an effective strategy leveraging both of them. First, we generate synthetic bitext by translating monolingual data from the two domains into the other domain using the models pretrained on genuine bitext. Next, a model is trained on a noised version of the concatenated synthetic bitext where each source sequence is randomly corrupted. Finally, the model is fine-tuned on the genuine bitext and a clean version of a subset of the synthetic bitext without adding any noise. Our approach achieves state-of-the-art results on WMT16, WMT17, WMT18 English$\leftrightarrow$German translations and WMT19 German$\to$French translations, which demonstrate the effectiveness of our method. We also conduct a comprehensive study on how each part in the pipeline works.",
}
@inproceedings{chen-etal-2019-meta,
    title = "Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs",
    author = "Chen, Mingyang  and
      Zhang, Wen  and
      Zhang, Wei  and
      Chen, Qiang  and
      Chen, Huajun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1431",
    doi = "10.18653/v1/D19-1431",
    pages = "4217--4226",
    abstract = "Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.",
}
@inproceedings{oren-etal-2019-distributionally,
    title = "Distributionally Robust Language Modeling",
    author = "Oren, Yonatan  and
      Sagawa, Shiori  and
      Hashimoto, Tatsunori  and
      Liang, Percy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1432",
    doi = "10.18653/v1/D19-1432",
    pages = "4227--4237",
    abstract = "Language models are generally trained on data spanning a wide range of topics (e.g., news, reviews, fiction), but they might be applied to an a priori unknown target distribution (e.g., restaurant reviews). In this paper, we first show that training on text outside the test distribution can degrade test performance when using standard maximum likelihood (MLE) training. To remedy this without the knowledge of the test distribution, we propose an approach which trains a model that performs well over a wide range of potential test distributions. In particular, we derive a new distributionally robust optimization (DRO) procedure which minimizes the loss of the model over the worst-case mixture of topics with sufficient overlap with the training distribution. Our approach, called topic conditional value at risk (topic CVaR), obtains a 5.5 point perplexity reduction over MLE when the language models are trained on a mixture of Yelp reviews and news and tested only on reviews.",
}
@inproceedings{han-eisenstein-2019-unsupervised,
    title = "Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling",
    author = "Han, Xiaochuang  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1433",
    doi = "10.18653/v1/D19-1433",
    pages = "4238--4248",
    abstract = "Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large corpora of unlabeled text. However, the applicability of this approach is unknown when the target domain varies substantially from the pretraining corpus. We are specifically interested in the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled and pretraining texts. To address this scenario, we propose domain-adaptive fine-tuning, in which the contextualized embeddings are adapted by masked language modeling on text from the target domain. We test this approach on sequence labeling in two challenging domains: Early Modern English and Twitter. Both domains differ substantially from existing pretraining corpora, and domain-adaptive fine-tuning yields substantial improvements over strong BERT baselines, with particularly impressive results on out-of-vocabulary words. We conclude that domain-adaptive fine-tuning offers a simple and effective approach for the unsupervised adaptation of sequence labeling to difficult new domains.",
}
@inproceedings{lalor-etal-2019-learning,
    title = "Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds",
    author = "Lalor, John P.  and
      Wu, Hao  and
      Yu, Hong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1434",
    doi = "10.18653/v1/D19-1434",
    pages = "4249--4259",
    abstract = "Incorporating Item Response Theory (IRT) into NLP tasks can provide valuable information about model performance and behavior. Traditionally, IRT models are learned using human response pattern (RP) data, presenting a significant bottleneck for large data sets like those required for training deep neural networks (DNNs). In this work we propose learning IRT models using RPs generated from artificial crowds of DNN models. We demonstrate the effectiveness of learning IRT models using DNN-generated data through quantitative and qualitative analyses for two NLP tasks. Parameters learned from human and machine RPs for natural language inference and sentiment analysis exhibit medium to large positive correlations. We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods. Finally, we highlight cases where human expectation about item difficulty does not match difficulty as estimated from the machine RPs.",
}
@inproceedings{awasthi-etal-2019-parallel,
    title = "Parallel Iterative Edit Models for Local Sequence Transduction",
    author = "Awasthi, Abhijeet  and
      Sarawagi, Sunita  and
      Goyal, Rasna  and
      Ghosh, Sabyasachi  and
      Piratla, Vihari",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1435",
    doi = "10.18653/v1/D19-1435",
    pages = "4260--4270",
    abstract = "We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). Recent approaches are based on the popular encoder-decoder (ED) model for sequence to sequence learning. The ED model auto-regressively captures full dependency among output tokens but is slow due to sequential decoding. The PIE model does parallel decoding, giving up the advantage of modeling full dependency in the output, yet it achieves accuracy competitive with the ED model for four reasons: 1. predicting edits instead of tokens, 2. labeling sequences instead of generating sequences, 3. iteratively refining predictions to capture dependencies, and 4. factorizing logits over edits and their token argument to harness pre-trained language models like BERT. Experiments on tasks spanning GEC, OCR correction and spell correction demonstrate that the PIE model is an accurate and significantly faster alternative for local sequence transduction.",
}
@inproceedings{ke-etal-2019-araml,
    title = "{ARAML}: A Stable Adversarial Training Framework for Text Generation",
    author = "Ke, Pei  and
      Huang, Fei  and
      Huang, Minlie  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1436",
    doi = "10.18653/v1/D19-1436",
    pages = "4271--4281",
    abstract = "Most of the existing generative adversarial networks (GAN) for text generation suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. To tackle this problem, we propose a novel framework called Adversarial Reward Augmented Maximum Likelihood (ARAML). During adversarial training, the discriminator assigns rewards to samples which are acquired from a stationary distribution near the data rather than the generator{'}s distribution. The generator is optimized with maximum likelihood estimation augmented by the discriminator{'}s rewards instead of policy gradient. Experiments show that our model can outperform state-of-the-art text GANs with a more stable training process.",
}
@inproceedings{ma-etal-2019-flowseq,
    title = "{F}low{S}eq: Non-Autoregressive Conditional Sequence Generation with Generative Flow",
    author = "Ma, Xuezhe  and
      Zhou, Chunting  and
      Li, Xian  and
      Neubig, Graham  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1437",
    doi = "10.18653/v1/D19-1437",
    pages = "4282--4292",
    abstract = "Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length.",
}
@inproceedings{li-etal-2019-compositional,
    title = "Compositional Generalization for Primitive Substitutions",
    author = "Li, Yuanpeng  and
      Zhao, Liang  and
      Wang, Jianyu  and
      Hestness, Joel",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1438",
    doi = "10.18653/v1/D19-1438",
    pages = "4293--4302",
    abstract = "Compositional generalization is a basic mechanism in human language learning, but current neural networks lack such ability. In this paper, we conduct fundamental research for encoding compositionality in neural networks. Conventional methods use a single representation for the input sentence, making it hard to apply prior knowledge of compositionality. In contrast, our approach leverages such knowledge with two representations, one generating attention maps, and the other mapping attended input words to output symbols. We reduce the entropy in each representation to improve generalization. Our experiments demonstrate significant improvements over the conventional methods in five NLP tasks including instruction learning and machine translation. In the SCAN domain, it boosts accuracies from 14.0{\%} to 98.8{\%} in Jump task, and from 92.0{\%} to 99.7{\%} in TurnLeft task. It also beats human performance on a few-shot learning task. We hope the proposed approach can help ease future research towards human-level compositional language learning.",
}
@inproceedings{kocijan-etal-2019-wikicrem,
    title = "{W}iki{CREM}: A Large Unsupervised Corpus for Coreference Resolution",
    author = "Kocijan, Vid  and
      Camburu, Oana-Maria  and
      Cretu, Ana-Maria  and
      Yordanov, Yordan  and
      Blunsom, Phil  and
      Lukasiewicz, Thomas",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1439",
    doi = "10.18653/v1/D19-1439",
    pages = "4303--4312",
    abstract = "Pronoun resolution is a major area of natural language understanding. However, large-scale training sets are still scarce, since manually labelling data is costly. In this work, we introduce WikiCREM (Wikipedia CoREferences Masked) a large-scale, yet accurate dataset of pronoun disambiguation instances. We use a language-model-based approach for pronoun resolution in combination with our WikiCREM dataset. We compare a series of models on a collection of diverse and challenging coreference resolution problems, where we match or outperform previous state-of-the-art approaches on 6 out of 7 datasets, such as GAP, DPR, WNLI, PDP, WinoBias, and WinoGender. We release our model to be used off-the-shelf for solving pronoun disambiguation.",
}
@inproceedings{stepanjans-freitas-2019-identifying,
    title = "Identifying and Explaining Discriminative Attributes",
    author = "Stepanjans, Armins  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1440",
    doi = "10.18653/v1/D19-1440",
    pages = "4313--4322",
    abstract = "Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an explicit word vector representation model (WVM) to support the identification of discriminative attributes. A core contribution of the paper is a quantitative and qualitative comparative analysis of different types of data sources and Knowledge Bases in the construction of explainable and explicit WVMs: (i) knowledge graphs built from dictionary definitions, (ii) entity-attribute-relationships graphs derived from images and (iii) commonsense knowledge graphs. Using a detailed quantitative and qualitative analysis, we demonstrate that these data sources have complementary semantic aspects, supporting the creation of explicit semantic vector spaces. The explicit vector spaces are evaluated using the task of discriminative attribute identification, showing comparable performance to the state-of-the-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability.",
}
@inproceedings{sun-etal-2019-patient,
    title = "Patient Knowledge Distillation for {BERT} Model Compression",
    author = "Sun, Siqi  and
      Cheng, Yu  and
      Gan, Zhe  and
      Liu, Jingjing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1441",
    doi = "10.18653/v1/D19-1441",
    pages = "4323--4332",
    abstract = "Pre-trained language models such as BERT have proven to be highly effective for natural language processing (NLP) tasks. However, the high demand for computing resources in training such models hinders their application in practice. In order to alleviate this resource hunger in large-scale model training, we propose a Patient Knowledge Distillation approach to compress an original large model (teacher) into an equally-effective lightweight shallow network (student). Different from previous knowledge distillation methods, which only use the output from the last layer of the teacher network for distillation, our student model patiently learns from multiple intermediate layers of the teacher model for incremental knowledge extraction, following two strategies: (i) PKD-Last: learning from the last k layers; and (ii) PKD-Skip: learning from every k layers. These two patient distillation schemes enable the exploitation of rich information in the teacher{'}s hidden layers, and encourage the student model to patiently learn from and imitate the teacher through a multi-layer distillation process. Empirically, this translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy.",
}
@inproceedings{wang-wang-2019-neural,
    title = "Neural {G}aussian Copula for Variational Autoencoder",
    author = "Wang, Prince Zizhuang  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1442",
    doi = "10.18653/v1/D19-1442",
    pages = "4333--4343",
    abstract = "Variational language models seek to estimate the posterior of latent variables with an approximated variational posterior. The model often assumes the variational posterior to be factorized even when the true posterior is not. The learned variational posterior under this assumption does not capture the dependency relationships over latent variables. We argue that this would cause a typical training problem called posterior collapse observed in all other variational language models. We propose Gaussian Copula Variational Autoencoder (VAE) to avert this problem. Copula is widely used to model correlation and dependencies of high-dimensional random variables, and therefore it is helpful to maintain the dependency relationships that are lost in VAE. The empirical results show that by modeling the correlation of latent variables explicitly using a neural parametric copula, we can avert this training difficulty while getting competitive results among all other VAE approaches.",
}
@inproceedings{tsai-etal-2019-transformer,
    title = "Transformer Dissection: An Unified Understanding for Transformer{'}s Attention via the Lens of Kernel",
    author = "Tsai, Yao-Hung Hubert  and
      Bai, Shaojie  and
      Yamada, Makoto  and
      Morency, Louis-Philippe  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1443",
    doi = "10.18653/v1/D19-1443",
    pages = "4344--4353",
    abstract = "Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. At the core of the Transformer is the attention mechanism, which concurrently processes all inputs in the streams. In this paper, we present a new formulation of attention via the lens of the kernel. To be more precise, we realize that the attention can be seen as applying kernel smoother over the inputs with the kernel scores being the similarities between inputs. This new formulation gives us a better way to understand individual components of the Transformer{'}s attention, such as the better way to integrate the positional embedding. Another important advantage of our kernel-based formulation is that it paves the way to a larger space of composing Transformer{'}s attention. As an example, we propose a new variant of Transformer{'}s attention which models the input as a product of symmetric kernels. This approach achieves competitive performance to the current state of the art model with less computation. In our experiments, we empirically study different kernel construction strategies on two widely used tasks: neural machine translation and sequence prediction.",
}
@inproceedings{wu-etal-2019-learning-learn,
    title = "Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification",
    author = "Wu, Jiawei  and
      Xiong, Wenhan  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1444",
    doi = "10.18653/v1/D19-1444",
    pages = "4354--4364",
    abstract = "Many tasks in natural language processing can be viewed as multi-label classification problems. However, most of the existing models are trained with the standard cross-entropy loss function and use a fixed prediction policy (e.g., a threshold of 0.5) for all the labels, which completely ignores the complexity and dependencies among different labels. In this paper, we propose a meta-learning method to capture these complex label dependencies. More specifically, our method utilizes a meta-learner to jointly learn the training policies and prediction policies for different labels. The training policies are then used to train the classifier with the cross-entropy loss function, and the prediction policies are further implemented for prediction. Experimental results on fine-grained entity typing and text classification demonstrate that our proposed method can obtain more accurate multi-label classification results.",
}
@inproceedings{kovaleva-etal-2019-revealing,
    title = "Revealing the Dark Secrets of {BERT}",
    author = "Kovaleva, Olga  and
      Romanov, Alexey  and
      Rogers, Anna  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1445",
    doi = "10.18653/v1/D19-1445",
    pages = "4365--4374",
    abstract = "BERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success. In the current work, we focus on the interpretation of self-attention, which is one of the fundamental underlying components of BERT. Using a subset of GLUE tasks and a set of handcrafted features-of-interest, we propose the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT{'}s heads. Our findings suggest that there is a limited set of attention patterns that are repeated across different heads, indicating the overall model overparametrization. While different heads consistently use the same attention patterns, they have varying impact on performance across different tasks. We show that manually disabling attention in certain heads leads to a performance improvement over the regular fine-tuned BERT models.",
}
@inproceedings{wu-etal-2019-machine,
    title = "Machine Translation With Weakly Paired Documents",
    author = "Wu, Lijun  and
      Zhu, Jinhua  and
      He, Di  and
      Gao, Fei  and
      Qin, Tao  and
      Lai, Jianhuang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1446",
    doi = "10.18653/v1/D19-1446",
    pages = "4375--4384",
    abstract = "Neural machine translation, which achieves near human-level performance in some languages, strongly relies on the large amounts of parallel sentences, which hinders its applicability to low-resource language pairs. Recent works explore the possibility of unsupervised machine translation with monolingual data only, leading to much lower accuracy compared with the supervised one. Observing that weakly paired bilingual documents are much easier to collect than bilingual sentences, e.g., from Wikipedia, news websites or books, in this paper, we investigate training translation models with weakly paired bilingual documents. Our approach contains two components. 1) We provide a simple approach to mine implicitly bilingual sentence pairs from document pairs which can then be used as supervised training signals. 2) We leverage the topic consistency of two weakly paired documents and learn the sentence translation model by constraining the word distribution-level alignments. We evaluate our method on weakly paired documents from Wikipedia on six tasks, the widely used WMT16 German$\leftrightarrow$English, WMT13 Spanish$\leftrightarrow$English and WMT16 Romanian$\leftrightarrow$English translation tasks. We obtain 24.1/30.3, 28.1/27.6 and 30.1/27.6 BLEU points separately, outperforming previous results by more than 5 BLEU points in each direction and reducing the gap between unsupervised translation and supervised translation up to 50{\%}.",
}
@inproceedings{lee-etal-2019-countering,
    title = "Countering Language Drift via Visual Grounding",
    author = "Lee, Jason  and
      Cho, Kyunghyun  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1447",
    doi = "10.18653/v1/D19-1447",
    pages = "4385--4395",
    abstract = "Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans. We find that agents that were initially pretrained to produce natural language can also experience detrimental language drift: when a non-linguistic reward is used in a goal-based task, e.g. some scalar success metric, the communication protocol may easily and radically diverge from natural language. We recast translation as a multi-agent communication game and examine auxiliary training constraints for their effectiveness in mitigating language drift. We show that a combination of syntactic (language model likelihood) and semantic (visual grounding) constraints gives the best communication performance, allowing pre-trained agents to retain English syntax while learning to accurately convey the intended meaning.",
}
@inproceedings{voita-etal-2019-bottom,
    title = "The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives",
    author = "Voita, Elena  and
      Sennrich, Rico  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1448",
    doi = "10.18653/v1/D19-1448",
    pages = "4396--4406",
    abstract = "We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We chose the Transformers for our analysis as they have been shown effective with various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and observe that the choice of the objective determines this process. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers.",
}
@inproceedings{vulic-etal-2019-really,
    title = "Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?",
    author = "Vuli{\'c}, Ivan  and
      Glava{\v{s}}, Goran  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1449",
    doi = "10.18653/v1/D19-1449",
    pages = "4407--4418",
    abstract = "Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal. The lack of any supervision makes such approaches conceptually attractive. Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure. The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resource-poor and distant languages. In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings. A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero BLI performance for 87/210 pairs). Even when they succeed, they never surpass the performance of weakly supervised methods (seeded with 500-1,000 translation pairs) using the same self-learning procedure in any BLI setup, and the gaps are often substantial. These findings call for revisiting the main motivations behind fully unsupervised CLWE methods.",
}
@inproceedings{wang-etal-2019-weakly,
    title = "Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings",
    author = "Wang, Haozhou  and
      Henderson, James  and
      Merlo, Paola",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1450",
    doi = "10.18653/v1/D19-1450",
    pages = "4419--4430",
    abstract = "Distributed representations of words which map each word to a continuous vector have proven useful in capturing important linguistic information not only in a single language but also across different languages. Current unsupervised adversarial approaches show that it is possible to build a mapping matrix that aligns two sets of monolingual word embeddings without high quality parallel data, such as a dictionary or a sentence-aligned corpus. However, without an additional step of refinement, the preliminary mapping learnt by these methods is unsatisfactory, leading to poor performance for typologically distant languages. In this paper, we propose a weakly-supervised adversarial training method to overcome this limitation, based on the intuition that mapping across languages is better done at the concept level than at the word level. We propose a concept-based adversarial training method which improves the performance of previous unsupervised adversarial methods for most languages, and especially for typologically distant language pairs.",
}
@inproceedings{yang-etal-2019-aligning,
    title = "Aligning Cross-Lingual Entities with Multi-Aspect Information",
    author = "Yang, Hsiu-Wei  and
      Zou, Yanyan  and
      Shi, Peng  and
      Lu, Wei  and
      Lin, Jimmy  and
      Sun, Xu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1451",
    doi = "10.18653/v1/D19-1451",
    pages = "4431--4441",
    abstract = "Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in target languages. In this work, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other. Specifically, we apply graph convolutional networks (GCNs) to combine multi-aspect information of entities, including topological connections, relations, and attributes of entities, to learn entity embeddings. To exploit the literal descriptions of entities expressed in different languages, we propose two uses of a pretrained multilingual BERT model to bridge cross-lingual gaps. We further propose two strategies to integrate GCN-based and BERT-based modules to boost performance. Extensive experiments on two benchmark datasets demonstrate that our method significantly outperforms existing systems.",
}
@inproceedings{mohtarami-etal-2019-contrastive,
    title = "Contrastive Language Adaptation for Cross-Lingual Stance Detection",
    author = "Mohtarami, Mitra  and
      Glass, James  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1452",
    doi = "10.18653/v1/D19-1452",
    pages = "4442--4452",
    abstract = "We study cross-lingual stance detection, which aims to leverage labeled data in one language to identify the relative perspective (or stance) of a given document with respect to a claim in a different target language. In particular, we introduce a novel contrastive language adaptation approach applied to memory networks, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language. The evaluation results on public benchmark datasets and comparison against current state-of-the-art approaches demonstrate the effectiveness of our approach.",
}
@inproceedings{garg-etal-2019-jointly,
    title = "Jointly Learning to Align and Translate with Transformer Models",
    author = "Garg, Sarthak  and
      Peitz, Stephan  and
      Nallasamy, Udhyakumar  and
      Paulik, Matthias",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1453",
    doi = "10.18653/v1/D19-1453",
    pages = "4453--4462",
    abstract = "The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches. However, on the closely related task of word alignment, traditional statistical word alignment models often remain the go-to solution. In this paper, we present an approach to train a Transformer model to produce both accurate translations and alignments. We extract discrete alignments from the attention probabilities learnt during regular neural machine translation model training and leverage them in a multi-task framework to optimize towards translation and alignment objectives. We demonstrate that our approach produces competitive results compared to GIZA++ trained IBM alignment models without sacrificing translation accuracy and outperforms previous attempts on Transformer model based word alignment. Finally, by incorporating IBM model alignments into our multi-task training, we report significantly better alignment accuracies compared to GIZA++ on three publicly available data sets.",
}
@inproceedings{sap-etal-2019-social,
    title = "Social {IQ}a: Commonsense Reasoning about Social Interactions",
    author = "Sap, Maarten  and
      Rashkin, Hannah  and
      Chen, Derek  and
      Le Bras, Ronan  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1454",
    doi = "10.18653/v1/D19-1454",
    pages = "4463--4473",
    abstract = "We introduce Social IQa, the first large-scale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations (e.g., Q: {``}Jordan wanted to tell Tracy a secret, so Jordan leaned towards Tracy. Why did Jordan do this?{''} A: {``}Make sure no one else could hear{''}). Through crowdsourcing, we collect commonsense questions along with correct and incorrect answers about social interactions, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Empirical results show that our benchmark is challenging for existing question-answering models based on pretrained language models, compared to human performance ({\textgreater}20{\%} gap). Notably, we further establish Social IQa as a resource for transfer learning of commonsense knowledge, achieving state-of-the-art performance on multiple commonsense reasoning tasks (Winograd Schemas, COPA).",
}
@inproceedings{jiang-bansal-2019-self,
    title = "Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning",
    author = "Jiang, Yichen  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1455",
    doi = "10.18653/v1/D19-1455",
    pages = "4474--4484",
    abstract = "Multi-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. The recently proposed HotpotQA (Yang et al., 2018) dataset is comprised of questions embodying four different multi-hop reasoning paradigms (two bridge entity setups, checking multiple properties, and comparing two entities), making it challenging for a single neural network to handle all four. In this work, we present an interpretable, controller-based Self-Assembling Neural Modular Network (Hu et al., 2017, 2018) for multi-hop reasoning, where we design four novel modules (Find, Relocate, Compare, NoOp) to perform unique types of language reasoning. Based on a question, our layout controller RNN dynamically infers a series of reasoning modules to construct the entire network. Empirically, we show that our dynamic, multi-hop modular network achieves significant improvements over the static, single-hop baseline (on both regular and adversarial evaluation). We further demonstrate the interpretability of our model via three analyses. First, the controller can softly decompose the multi-hop question into multiple single-hop sub-questions to promote compositional reasoning behavior of the main network. Second, the controller can predict layouts that conform to the layouts designed by human experts. Finally, the intermediate module can infer the entity that connects two distantly-located supporting facts by addressing the sub-question from the controller.",
}
@inproceedings{geiger-etal-2019-posing,
    title = "Posing Fair Generalization Tasks for Natural Language Inference",
    author = "Geiger, Atticus  and
      Cases, Ignacio  and
      Karttunen, Lauri  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1456",
    doi = "10.18653/v1/D19-1456",
    pages = "4485--4495",
    abstract = "Deep learning models for semantics are generally evaluated using naturalistic corpora. Adversarial testing methods, in which models are evaluated on new examples with known semantic properties, have begun to reveal that good performance at these naturalistic tasks can hide serious shortcomings. However, we should insist that these evaluations be fair {--} that the models are given data sufficient to support the requisite kinds of generalization. In this paper, we define and motivate a formal notion of fairness in this sense. We then apply these ideas to natural language inference by constructing very challenging but provably fair artificial datasets and showing that standard neural models fail to generalize in the required ways; only task-specific models that jointly compose the premise and hypothesis are able to achieve high performance, and even these models do not solve the task perfectly.",
}
@inproceedings{dalvi-etal-2019-everything,
    title = "Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text",
    author = "Dalvi, Bhavana  and
      Tandon, Niket  and
      Bosselut, Antoine  and
      Yih, Wen-tau  and
      Clark, Peter",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1457",
    doi = "10.18653/v1/D19-1457",
    pages = "4496--4505",
    abstract = "Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but *why* some actions need to happen before others. Our approach builds on a prior process comprehension framework for predicting actions{'} effects, to also identify subsequent steps that those effects enable. We present our new model (XPAD) that biases effect predictions towards those that (1) explain more of the actions in the paragraph and (2) are more plausible with respect to background knowledge. We also extend an existing benchmark dataset for procedural text comprehension, ProPara, by adding the new task of explaining actions by predicting their dependencies. We find that XPAD significantly outperforms prior systems on this task, while maintaining the performance on the original task in ProPara. The dataset is available at http://data.allenai.org/propara",
}
@inproceedings{sinha-etal-2019-clutrr,
    title = "{CLUTRR}: A Diagnostic Benchmark for Inductive Reasoning from Text",
    author = "Sinha, Koustuv  and
      Sodhani, Shagun  and
      Dong, Jin  and
      Pineau, Joelle  and
      Hamilton, William L.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1458",
    doi = "10.18653/v1/D19-1458",
    pages = "4506--4515",
    abstract = "The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systematicity of NLU systems. Motivated by the classic work on inductive logic programming, CLUTRR requires that an NLU system infer kinship relations between characters in short stories. Successful performance on this task requires both extracting relationships between entities, as well as inferring the logical rules governing these relationships. CLUTRR allows us to precisely measure a model{'}s ability for systematic generalization by evaluating on held-out combinations of logical rules, and allows us to evaluate a model{'}s robustness by adding curated noise facts. Our empirical results highlight a substantial performance gap between state-of-the-art NLU models (e.g., BERT and MAC) and a graph neural network model that works directly with symbolic inputs{---}with the graph-based model exhibiting both stronger generalization and greater robustness.",
}
@inproceedings{byrne-etal-2019-taskmaster,
    title = "Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset",
    author = "Byrne, Bill  and
      Krishnamoorthi, Karthik  and
      Sankar, Chinnadhurai  and
      Neelakantan, Arvind  and
      Goodrich, Ben  and
      Duckworth, Daniel  and
      Yavuz, Semih  and
      Dubey, Amit  and
      Kim, Kyu-Young  and
      Cedilnik, Andy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1459",
    doi = "10.18653/v1/D19-1459",
    pages = "4516--4525",
    abstract = "A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data. To help satisfy this elementary requirement, we introduce the initial release of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising six domains. Two procedures were used to create this collection, each with unique advantages. The first involves a two-person, spoken {``}Wizard of Oz{''} (WOz) approach in which trained agents and crowdsourced workers interact to complete the task while the second is {``}self-dialog{''} in which crowdsourced workers write the entire dialog themselves. We do not restrict the workers to detailed scripts or to a small knowledge base and hence we observe that our dataset contains more realistic and diverse conversations in comparison to existing datasets. We offer several baseline models including state of the art neural seq2seq architectures with benchmark performance as well as qualitative human evaluations. Dialogs are labeled with API calls and arguments, a simple and cost effective approach which avoids the requirement of complex annotation schema. The layer of abstraction between the dialog model and the service provider API allows for a given model to interact with multiple services that provide similar functionally. Finally, the dataset will evoke interest in written vs. spoken language, discourse patterns, error handling and other linguistic phenomena related to dialog system research, development and design.",
}
@inproceedings{peskov-etal-2019-multi,
    title = "Multi-Domain Goal-Oriented Dialogues ({M}ulti{D}o{GO}): Strategies toward Curating and Annotating Large Scale Dialogue Data",
    author = "Peskov, Denis  and
      Clarke, Nancy  and
      Krone, Jason  and
      Fodor, Brigi  and
      Zhang, Yi  and
      Youssef, Adel  and
      Diab, Mona",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1460",
    doi = "10.18653/v1/D19-1460",
    pages = "4526--4536",
    abstract = "The need for high-quality, large-scale, goal-oriented dialogue datasets continues to grow as virtual assistants become increasingly wide-spread. However, publicly available datasets useful for this area are limited either in their size, linguistic diversity, domain coverage, or annotation granularity. In this paper, we present strategies toward curating and annotating large scale goal oriented dialogue data. We introduce the MultiDoGO dataset to overcome these limitations. With a total of over 81K dialogues harvested across six domains, MultiDoGO is over 8 times the size of MultiWOZ, the other largest comparable dialogue dataset currently available to the public. Over 54K of these harvested conversations are annotated for intent classes and slot labels. We adopt a Wizard-of-Oz approach wherein a crowd-sourced worker (the {``}customer{''}) is paired with a trained annotator (the {``}agent{''}). The data curation process was controlled via biases to ensure a diversity in dialogue flows following variable dialogue policies. We provide distinct class label tags for agents vs. customer utterances, along with applicable slot labels. We also compare and contrast our strategies on annotation granularity, i.e. turn vs. sentence level. Furthermore, we compare and contrast annotations curated by leveraging professional annotators vs the crowd. We believe our strategies for eliciting and annotating such a dialogue dataset scales across modalities and domains and potentially languages in the future. To demonstrate the efficacy of our devised strategies we establish neural baselines for classification on the agent and customer utterances as well as slot labeling for each domain.",
}
@inproceedings{dinan-etal-2019-build,
    title = "Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack",
    author = "Dinan, Emily  and
      Humeau, Samuel  and
      Chintagunta, Bharath  and
      Weston, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1461",
    doi = "10.18653/v1/D19-1461",
    pages = "4537--4546",
    abstract = "The detection of offensive language in the context of a dialogue has become an increasingly important application of natural language processing. The detection of trolls in public forums (Gal{\'a}n-Garc{\'\i}a et al., 2016), and the deployment of chatbots in the public domain (Wolf et al., 2017) are two examples that show the necessity of guarding against adversarially offensive behavior on the part of humans. In this work, we develop a training scheme for a model to become robust to such human attacks by an iterative build it, break it, fix it scheme with humans and models in the loop. In detailed experiments we show this approach is considerably more robust than previous systems. Further, we show that offensive language used within a conversation critically depends on the dialogue context, and cannot be viewed as a single sentence offensive detection task as in most previous work. Our newly collected tasks and methods are all made open source and publicly available.",
}
@inproceedings{quan-etal-2019-gecor,
    title = "{GECOR}: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue",
    author = "Quan, Jun  and
      Xiong, Deyi  and
      Webber, Bonnie  and
      Hu, Changjian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1462",
    doi = "10.18653/v1/D19-1462",
    pages = "4547--4557",
    abstract = "Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues. In this paper, we treat the resolution of ellipsis and co-reference in dialogue as a problem of generating omitted or referred expressions from the dialogue context. We therefore propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model (GECOR) in the context of dialogue. The model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user utterance. A multi-task learning framework is further proposed to integrate the GECOR into an end-to-end task-oriented dialogue. In order to train both the GECOR and the multi-task learning framework, we manually construct a new dataset on the basis of the public dataset CamRest676 with both ellipsis and co-reference annotation. On this dataset, intrinsic evaluations on the resolution of ellipsis and co-reference show that the GECOR model significantly outperforms the sequence-to-sequence (seq2seq) baseline model in terms of EM, BLEU and F1 while extrinsic evaluations on the downstream dialogue task demonstrate that our multi-task learning framework with GECOR achieves a higher success rate of task completion than TSCP, a state-of-the-art end-to-end task-oriented dialogue model.",
}
@inproceedings{lin-etal-2019-task,
    title = "Task-Oriented Conversation Generation Using Heterogeneous Memory Networks",
    author = "Lin, Zehao  and
      Huang, Xinjing  and
      Ji, Feng  and
      Chen, Haiqing  and
      Zhang, Yin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1463",
    doi = "10.18653/v1/D19-1463",
    pages = "4558--4567",
    abstract = "How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.",
}
@inproceedings{zhang-etal-2019-aspect,
    title = "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks",
    author = "Zhang, Chen  and
      Li, Qiuchi  and
      Song, Dawei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1464",
    doi = "10.18653/v1/D19-1464",
    pages = "4568--4578",
    abstract = "Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.",
}
@inproceedings{liao-etal-2019-coupling,
    title = "Coupling Global and Local Context for Unsupervised Aspect Extraction",
    author = "Liao, Ming  and
      Li, Jing  and
      Zhang, Haisong  and
      Wang, Lingzhi  and
      Wu, Xixin  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1465",
    doi = "10.18653/v1/D19-1465",
    pages = "4579--4589",
    abstract = "Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions. To identify aspects, most previous efforts focus on using sequence tagging models trained on human-annotated data. This work studies unsupervised aspect extraction and explores how words appear in global context (on sentence level) and local context (conveyed by neighboring words). We propose a novel neural model, capable of coupling global and local representation to discover aspect words. Experimental results on two benchmarks, laptop and restaurant reviews, show that our model significantly outperforms the state-of-the-art models from previous studies evaluated with varying metrics. Analysis on model output show our ability to learn meaningful and coherent aspect representations. We further investigate how words distribute in global and local context, and find that aspect and non-aspect words do exhibit different context, interpreting our superiority in unsupervised aspect extraction.",
}
@inproceedings{li-etal-2019-transferable,
    title = "Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning",
    author = "Li, Zheng  and
      Li, Xin  and
      Wei, Ying  and
      Bing, Lidong  and
      Zhang, Yu  and
      Yang, Qiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1466",
    doi = "10.18653/v1/D19-1466",
    pages = "4590--4600",
    abstract = "Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem. However, such formulation hinders the effectiveness of supervised methods due to the lack of annotated sequence data in many domains. To address this issue, we firstly explore an unsupervised domain adaptation setting for this task. Prior work can only use common syntactic relations between aspect and opinion words to bridge the domain gaps, which highly relies on external linguistic resources. To resolve it, we propose a novel Selective Adversarial Learning (SAL) method to align the inferred correlation vectors that automatically capture their latent relations. The SAL method can dynamically learn an alignment weight for each word such that more important words can possess higher alignment weights to achieve fine-grained (word-level) adaptation. Empirically, extensive experiments demonstrate the effectiveness of the proposed SAL method.",
}
@inproceedings{hu-etal-2019-constrained,
    title = "{CAN}: Constrained Attention Networks for Multi-Aspect Sentiment Analysis",
    author = "Hu, Mengting  and
      Zhao, Shiwan  and
      Zhang, Li  and
      Cai, Keke  and
      Su, Zhong  and
      Cheng, Renhong  and
      Shen, Xiaowei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1467",
    doi = "10.18653/v1/D19-1467",
    pages = "4601--4610",
    abstract = "Aspect level sentiment classification is a fine-grained sentiment analysis task. To detect the sentiment towards a particular aspect in a sentence, previous studies have developed various attention-based methods for generating aspect-specific sentence representations. However, the attention may inherently introduce noise and downgrade the performance. In this paper, we propose constrained attention networks (CAN), a simple yet effective solution, to regularize the attention for multi-aspect sentiment analysis, which alleviates the drawback of the attention mechanism. Specifically, we introduce orthogonal regularization on multiple aspects and sparse regularization on each single aspect. Experimental results on two public datasets demonstrate the effectiveness of our approach. We further extend our approach to multi-task settings and outperform the state-of-the-art methods.",
}
@inproceedings{karamanolakis-etal-2019-leveraging,
    title = "Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training",
    author = "Karamanolakis, Giannis  and
      Hsu, Daniel  and
      Gravano, Luis",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1468",
    doi = "10.18653/v1/D19-1468",
    pages = "4611--4621",
    abstract = "User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be useful for both users and downstream opinion mining applications. Current supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. And, unfortunately, unsupervised topic models often fail to capture the aspects of interest. In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i.e., weakly positive indicators) for the aspects of interest. First, we show that current weakly supervised approaches fail to leverage the predictive power of seed words for aspect detection. Next, we propose a student-teacher approach that effectively leverages seed words in a bag-of-words classifier (teacher); in turn, we use the teacher to train a second model (student) that is potentially more powerful (e.g., a neural network that uses pre-trained word embeddings). Finally, we show that iterative co-training can be used to cope with noisy seed words, leading to both improved teacher and student models. Our proposed approach consistently outperforms previous weakly supervised approaches (by 14.1 absolute F1 points on average) in six different domains of product reviews and six multilingual datasets of restaurant reviews.",
}
@inproceedings{kruk-etal-2019-integrating,
    title = "Integrating Text and Image: Determining Multimodal Document Intent in {I}nstagram Posts",
    author = "Kruk, Julia  and
      Lubin, Jonah  and
      Sikka, Karan  and
      Lin, Xiao  and
      Jurafsky, Dan  and
      Divakaran, Ajay",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1469",
    doi = "10.18653/v1/D19-1469",
    pages = "4622--4632",
    abstract = "Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image. For example, a caption might evoke an ironic contrast with the image, so neither caption nor image is a mere transcript of the other. Instead they combine{---}via what has been called meaning multiplication (Bateman et al.)- to create a new meaning that has a more complex relation to the literal meanings of text and image. Here we introduce a multimodal dataset of 1299 Instagram posts labeled for three orthogonal taxonomies: the authorial intent behind the image-caption pair, the contextual relationship between the literal meanings of the image and caption, and the semiotic relationship between the signified meanings of the image and caption. We build a baseline deep multimodal classifier to validate the taxonomy, showing that employing both text and image improves intent detection by 9.6 compared to using only the image modality, demonstrating the commonality of non-intersective meaning multiplication. The gain with multimodality is greatest when the image and caption diverge semiotically. Our dataset offers a new resource for the study of the rich meanings that result from pairing text and image.",
}
@inproceedings{zeng-etal-2019-neural,
    title = "Neural Conversation Recommendation with Online Interaction Modeling",
    author = "Zeng, Xingshan  and
      Li, Jing  and
      Wang, Lu  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1470",
    doi = "10.18653/v1/D19-1470",
    pages = "4633--4643",
    abstract = "The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis. It presents a concrete challenge for individuals to better discover and engage in social media discussions. In this paper, we present a novel framework to automatically recommend conversations to users based on their prior conversation behaviors. Built on neural collaborative filtering, our model explores deep semantic features that measure how a user{'}s preferences match an ongoing conversation{'}s context. Furthermore, to identify salient characteristics from interleaving user interactions, our model incorporates graph-structured networks, where both replying relations and temporal features are encoded as conversation context. Experimental results on two large-scale datasets collected from Twitter and Reddit show that our model yields better performance than previous state-of-the-art models, which only utilize lexical features and ignore past user interactions in the conversations.",
}
@inproceedings{wu-etal-2019-different,
    title = "Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection",
    author = "Wu, Lianwei  and
      Rao, Yuan  and
      Jin, Haolin  and
      Nazir, Ambreen  and
      Sun, Ling",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1471",
    doi = "10.18653/v1/D19-1471",
    pages = "4644--4653",
    abstract = "Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focuses on learning shared features among tasks as complementarity features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks. In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection. The selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flows between tasks. Experiments on two public and widely used competition datasets, i.e. RumourEval and PHEME, demonstrate that our proposed method achieves the state-of-the-art performance and boosts the F1-score by more than 0.87{\%}, 1.31{\%}, respectively.",
}
@inproceedings{xie-etal-2019-text,
    title = "Text-based inference of moral sentiment change",
    author = "Xie, Jing Yi  and
      Ferreira Pinto Junior, Renato  and
      Hirst, Graeme  and
      Xu, Yang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1472",
    doi = "10.18653/v1/D19-1472",
    pages = "4654--4663",
    abstract = "We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people{'}s moral perception toward right or wrong, and we build our methodology by exploring moral biases learned from diachronic word embeddings. We demonstrate how a parameter-free model supports inference of historical shifts in moral sentiment toward concepts such as slavery and democracy over centuries at three incremental levels: moral relevance, moral polarity, and fine-grained moral dimensions. We apply this methodology to visualizing moral time courses of individual concepts and analyzing the relations between psycholinguistic variables and rates of moral sentiment change at scale. Our work offers opportunities for applying natural language processing toward characterizing moral sentiment change in society.",
}
@inproceedings{yu-etal-2019-detecting,
    title = "Detecting Causal Language Use in Science Findings",
    author = "Yu, Bei  and
      Li, Yingya  and
      Wang, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1473",
    doi = "10.18653/v1/D19-1473",
    pages = "4664--4674",
    abstract = "Causal interpretation of correlational findings from observational studies has been a major type of misinformation in science communication. Prior studies on identifying inappropriate use of causal language relied on manual content analysis, which is not scalable for examining a large volume of science publications. In this study, we first annotated a corpus of over 3,000 PubMed research conclusion sentences, then developed a BERT-based prediction model that classifies conclusion sentences into {``}no relationship{''}, {``}correlational{''}, {``}conditional causal{''}, and {``}direct causal{''} categories, achieving an accuracy of 0.90 and a macro-F1 of 0.88. We then applied the prediction model to measure the causal language use in the research conclusions of about 38,000 observational studies in PubMed. The prediction result shows that 21.7{\%} studies used direct causal language exclusively in their conclusions, and 32.4{\%} used some direct causal language. We also found that the ratio of causal language use differs among authors from different countries, challenging the notion of a shared consensus on causal language use in the global science community. Our prediction model could also be used to help identify the inappropriate use of causal language in science publications.",
}
@inproceedings{ousidhoum-etal-2019-multilingual,
    title = "Multilingual and Multi-Aspect Hate Speech Analysis",
    author = "Ousidhoum, Nedjma  and
      Lin, Zizheng  and
      Zhang, Hongming  and
      Song, Yangqiu  and
      Yeung, Dit-Yan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1474",
    doi = "10.18653/v1/D19-1474",
    pages = "4675--4684",
    abstract = "Current research on hate speech analysis is typically oriented towards monolingual and single classification tasks. In this paper, we present a new multilingual multi-aspect hate speech analysis dataset and use it to test the current state-of-the-art multilingual multitask learning approaches. We evaluate our dataset in various classification settings, then we discuss how to leverage our annotations in order to improve hate speech detection and classification in general.",
}
@inproceedings{augenstein-etal-2019-multifc,
    title = "{M}ulti{FC}: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims",
    author = "Augenstein, Isabelle  and
      Lioma, Christina  and
      Wang, Dongsheng  and
      Chaves Lima, Lucas  and
      Hansen, Casper  and
      Hansen, Christian  and
      Simonsen, Jakob Grue",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1475",
    doi = "10.18653/v1/D19-1475",
    pages = "4685--4697",
    abstract = "We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-depth analysis of the dataset, highlighting characteristics and challenges. Further, we present results for automatic veracity prediction, both with established baselines and with a novel method for joint ranking of evidence pages and predicting veracity that outperforms all baselines. Significant performance increases are achieved by encoding evidence, and by modelling metadata. Our best-performing model achieves a Macro F1 of 49.2{\%}, showing that this is a challenging testbed for claim veracity prediction.",
}
@inproceedings{xu-etal-2019-deep,
    title = "A Deep Neural Information Fusion Architecture for Textual Network Embeddings",
    author = "Xu, Zenan  and
      Su, Qinliang  and
      Quan, Xiaojun  and
      Zhang, Weijia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1476",
    doi = "10.18653/v1/D19-1476",
    pages = "4698--4706",
    abstract = "Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations. Traditionally, the structural and textual embeddings were learned by models that rarely take the mutual influences between them into account. In this paper, a deep neural architecture is proposed to effectively fuse the two kinds of informations into one representation. The novelties of the proposed architecture are manifested in the aspects of a newly defined objective function, the complementary information fusion method for structural and textual features, and the mutual gate mechanism for textual feature extraction. Experimental results show that the proposed model outperforms the comparing methods on all three datasets.",
}
@inproceedings{del-tredici-etal-2019-shall,
    title = "You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in {NLP}",
    author = "Del Tredici, Marco  and
      Marcheggiani, Diego  and
      Schulte im Walde, Sabine  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1477",
    doi = "10.18653/v1/D19-1477",
    pages = "4707--4717",
    abstract = "Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pay attention to their social connections, but exploit this information in a static way, treating all connections uniformly. This ignores the fact, well known in sociolinguistics, that an individual may be part of several communities which are not equally relevant in all communicative situations. We present a model based on Graph Attention Networks that captures this observation. It dynamically explores the social graph of a user, computes a user representation given the most relevant connections for a target task, and combines it with linguistic information to make a prediction. We apply our model to three different tasks, evaluate it against alternative models, and analyse the results extensively, showing that it significantly outperforms other current methods.",
}
@inproceedings{desai-etal-2019-adaptive,
    title = "Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis",
    author = "Desai, Shrey  and
      Sinno, Barea  and
      Rosenfeld, Alex  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1478",
    doi = "10.18653/v1/D19-1478",
    pages = "4718--4730",
    abstract = "Insightful findings in political science often require researchers to analyze documents of a certain subject or type, yet these documents are usually contained in large corpora that do not distinguish between pertinent and non-pertinent documents. In contrast, we can find corpora that label relevant documents but have limitations (e.g., from a single source or era), preventing their use for political science research. To bridge this gap, we present adaptive ensembling, an unsupervised domain adaptation framework, equipped with a novel text classification model and time-aware training to ensure our methods work well with diachronic corpora. Experiments on an expert-annotated dataset show that our framework outperforms strong benchmarks. Further analysis indicates that our methods are more stable, learn better representations, and extract cleaner corpora for fine-grained analysis.",
}
@inproceedings{huang-carley-2019-hierarchical,
    title = "A Hierarchical Location Prediction Neural Network for {T}witter User Geolocation",
    author = "Huang, Binxuan  and
      Carley, Kathleen",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1480",
    doi = "10.18653/v1/D19-1480",
    pages = "4732--4742",
    abstract = "Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.",
}
@inproceedings{chang-danescu-niculescu-mizil-2019-trouble,
    title = "Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop",
    author = "Chang, Jonathan P.  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1481",
    doi = "10.18653/v1/D19-1481",
    pages = "4743--4754",
    abstract = "Online discussions often derail into toxic exchanges between participants. Recent efforts mostly focused on detecting antisocial behavior after the fact, by analyzing single comments in isolation. To provide more timely notice to human moderators, a system needs to preemptively detect that a conversation is heading towards derailment before it actually turns toxic. This means modeling derailment as an emerging property of a conversation rather than as an isolated utterance-level event. Forecasting emerging conversational properties, however, poses several inherent modeling challenges. First, since conversations are dynamic, a forecasting model needs to capture the flow of the discussion, rather than properties of individual comments. Second, real conversations have an unknown horizon: they can end or derail at any time; thus a practical forecasting model needs to assess the risk in an online fashion, as the conversation develops. In this work we introduce a conversational forecasting model that learns an unsupervised representation of conversational dynamics and exploits it to predict future derailment as the conversation develops. By applying this model to two new diverse datasets of online conversations with labels for antisocial events, we show that it outperforms state-of-the-art systems at forecasting derailment.",
}
@inproceedings{qian-etal-2019-benchmark,
    title = "A Benchmark Dataset for Learning to Intervene in Online Hate Speech",
    author = "Qian, Jing  and
      Bethke, Anna  and
      Liu, Yinyin  and
      Belding, Elizabeth  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1482",
    doi = "10.18653/v1/D19-1482",
    pages = "4755--4764",
    abstract = "Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.",
}
@inproceedings{zhong-etal-2019-detecting,
    title = "Detecting and Reducing Bias in a High Stakes Domain",
    author = "Zhong, Ruiqi  and
      Chen, Yanda  and
      Patton, Desmond  and
      Selous, Charlotte  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1483",
    doi = "10.18653/v1/D19-1483",
    pages = "4765--4775",
    abstract = "Gang-involved youth in cities such as Chicago sometimes post on social media to express their aggression towards rival gangs and previous research has demonstrated that a deep learning approach can predict aggression and loss in posts. To address the possibility of bias in this sensitive application, we developed an approach to systematically interpret the state of the art model. We found, surprisingly, that it frequently bases its predictions on stop words such as {``}a{''} or {``}on{''}, an approach that could harm social media users who have no aggressive intentions. To tackle this bias, domain experts annotated the rationales, highlighting words that explain why a tweet is labeled as {``}aggression{''}. These new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. Our study shows that in high stake scenarios, accuracy alone cannot guarantee a good system and we need new evaluation methods.",
}
@inproceedings{rabinovich-etal-2019-codeswitch,
    title = "{C}ode{S}witch-{R}eddit: Exploration of Written Multilingual Discourse in Online Discussion Forums",
    author = "Rabinovich, Ella  and
      Sultani, Masih  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1484",
    doi = "10.18653/v1/D19-1484",
    pages = "4776--4786",
    abstract = "In contrast to many decades of research on oral code-switching, the study of written multilingual productions has only recently enjoyed a surge of interest. Many open questions remain regarding the sociolinguistic underpinnings of written code-switching, and progress has been limited by a lack of suitable resources. We introduce a novel, large, and diverse dataset of written code-switched productions, curated from topical threads of multiple bilingual communities on the Reddit discussion platform, and explore questions that were mainly addressed in the context of spoken language thus far. We investigate whether findings in oral code-switching concerning content and style, as well as speaker proficiency, are carried over into written code-switching in discussion forums. The released dataset can further facilitate a range of research and practical activities.",
}
@inproceedings{wei-etal-2019-modeling,
    title = "Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity",
    author = "Wei, Penghui  and
      Xu, Nan  and
      Mao, Wenji",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1485",
    doi = "10.18653/v1/D19-1485",
    pages = "4787--4798",
    abstract = "Automatically verifying rumorous information has become an important and challenging task in natural language processing and social media analytics. Previous studies reveal that people{'}s stances towards rumorous messages can provide indicative clues for identifying the veracity of rumors, and thus determining the stances of public reactions is a crucial preceding step for rumor veracity prediction. In this paper, we propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter, which consists of two components. The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network. The top component predicts the rumor veracity by exploiting the temporal dynamics of stance evolution. Experimental results on two benchmark datasets show that our method outperforms previous methods in both rumor stance classification and veracity prediction.",
}
@inproceedings{liu-etal-2019-reconstructing,
    title = "Reconstructing Capsule Networks for Zero-shot Intent Classification",
    author = "Liu, Han  and
      Zhang, Xiaotong  and
      Fan, Lu  and
      Fu, Xuandi  and
      Li, Qimai  and
      Wu, Xiao-Ming  and
      Lam, Albert Y.S.",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1486",
    doi = "10.18653/v1/D19-1486",
    pages = "4799--4809",
    abstract = "Intent classification is an important building block of dialogue systems. With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification. Nevertheless, research on this problem is still in the incipient stage and few methods are available. A recently proposed zero-shot intent classification method, IntentCapsNet, has been shown to achieve state-of-the-art performance. However, it has two unaddressed limitations: (1) it cannot deal with polysemy when extracting semantic capsules; (2) it hardly recognizes the utterances of unseen intents in the generalized zero-shot intent classification setting. To overcome these limitations, we propose to reconstruct capsule networks for zero-shot intent classification. First, we introduce a dimensional attention mechanism to fight against polysemy. Second, we reconstruct the transformation matrices for unseen intents by utilizing abundant latent information of the labeled utterances, which significantly improves the model generalization ability. Experimental results on two task-oriented dialogue datasets in different languages show that our proposed method outperforms IntentCapsNet and other strong baselines.",
}
@inproceedings{bian-etal-2019-domain,
    title = "Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network",
    author = "Bian, Shuqing  and
      Zhao, Wayne Xin  and
      Song, Yang  and
      Zhang, Tao  and
      Wen, Ji-Rong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1487",
    doi = "10.18653/v1/D19-1487",
    pages = "4810--4820",
    abstract = "Person-job fit has been an important task which aims to automatically match job positions with suitable candidates. Previous methods mainly focus on solving the match task in single-domain setting, which may not work well when labeled data is limited. We study the domain adaptation problem for person-job fit. We first propose a deep global match network for capturing the global semantic interactions between two sentences from a job posting and a candidate resume respectively. Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match. Extensive experiment results on a large real-world dataset consisting of six domains have demonstrated the effectiveness of the proposed model, especially when there is not sufficient labeled data.",
}
@inproceedings{linmei-etal-2019-heterogeneous,
    title = "Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification",
    author = "Linmei, Hu  and
      Yang, Tianchi  and
      Shi, Chuan  and
      Ji, Houye  and
      Li, Xiaoli",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1488",
    doi = "10.18653/v1/D19-1488",
    pages = "4821--4830",
    abstract = "Short text classification has found rich and critical applications in news and tweet tagging to help users find relevant information. Due to lack of labeled training data in many practical use cases, there is a pressing need for studying semi-supervised short text classification. Most existing studies focus on long texts and achieve unsatisfactory performance on short texts due to the sparsity and limited labeled data. In this paper, we propose a novel heterogeneous graph neural network based method for semi-supervised short text classification, leveraging full advantage of few labeled data and large unlabeled data through information propagation along the graph. In particular, we first present a flexible HIN (heterogeneous information network) framework for modeling the short texts, which can integrate any type of additional information as well as capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph ATtention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. The attention mechanism can learn the importance of different neighboring nodes as well as the importance of different node (information) types to a current node. Extensive experimental results have demonstrated that our proposed model outperforms state-of-the-art methods across six benchmark datasets significantly.",
}
@inproceedings{redmiles-etal-2019-comparing,
    title = "Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts",
    author = "Redmiles, Elissa  and
      Maszkiewicz, Lisa  and
      Hwang, Emily  and
      Kuchhal, Dhruv  and
      Liu, Everest  and
      Morales, Miraida  and
      Peskov, Denis  and
      Rao, Sudha  and
      Stevens, Rock  and
      Gligori{\'c}, Kristina  and
      Kross, Sean  and
      Mazurek, Michelle  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1489",
    doi = "10.18653/v1/D19-1489",
    pages = "4831--4842",
    abstract = "The readability of a digital text can influence people{'}s ability to learn new things about a range topics from digital resources (e.g., Wikipedia, WebMD). Readability also impacts search rankings, and is used to evaluate the performance of NLP systems. Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts. In this work, we present a comparison of the validity of well-known readability measures and introduce a novel approach, Smart Cloze, which is designed to address shortcomings of existing measures. We compare these approaches across four different corpora: crowdworker-generated stories, Wikipedia articles, security and privacy advice, and health information. On these corpora, we evaluate the convergent and content validity of each measure, and detail tradeoffs in score precision, domain-specificity, and participant burden. These results provide a foundation for more accurate readability measurements and better evaluation of new natural-language-processing systems and tools.",
}
@inproceedings{ma-etal-2019-news2vec,
    title = "{N}ews2vec: News Network Embedding with Subnode Information",
    author = "Ma, Ye  and
      Zong, Lu  and
      Yang, Yikang  and
      Su, Jionglong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1490",
    doi = "10.18653/v1/D19-1490",
    pages = "4843--4852",
    abstract = "With the development of NLP technologies, news can be automatically categorized and labeled according to a variety of characteristics, at the same time be represented as low dimensional embeddings. However, it lacks a systematic approach that effectively integrates the inherited features and inter-textual knowledge of news to represent the collective information with a dense vector. With the aim of filling this gap, the News2vec model is proposed to allow the distributed representation of news taking into account its associated features. To describe the cross-document linkages between news, a network consisting of news and its attributes is constructed. Moreover, the News2vec model treats the news node as a bag of features by developing the Subnode model. Based on the biased random walk and the skip-gram model, each news feature is mapped to a vector, and the news is thus represented as the sum of its features. This approach offers an easy solution to create embeddings for unseen news nodes based on its attributes. To evaluate our model, dimension reduction plots and correlation heat-maps are created to visualize the news vectors, together with the application of two downstream tasks, the stock movement prediction and news recommendation. By comparing with other established text/sentence embedding models, we show that News2vec achieves state-of-the-art performance on these news-related tasks.",
}
@inproceedings{gooding-kochmar-2019-recursive,
    title = "Recursive Context-Aware Lexical Simplification",
    author = "Gooding, Sian  and
      Kochmar, Ekaterina",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1491",
    doi = "10.18653/v1/D19-1491",
    pages = "4853--4863",
    abstract = "This paper presents a novel architecture for recursive context-aware lexical simplification, REC-LS, that is capable of (1) making use of the wider context when detecting the words in need of simplification and suggesting alternatives, and (2) taking previous simplification steps into account. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and outperforms the current state-of-the-art systems in lexical simplification.",
}
@inproceedings{rosenthal-etal-2019-leveraging,
    title = "Leveraging Medical Literature for Section Prediction in Electronic Health Records",
    author = "Rosenthal, Sara  and
      Barker, Ken  and
      Liang, Zhicheng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1492",
    doi = "10.18653/v1/D19-1492",
    pages = "4864--4873",
    abstract = "Electronic Health Records (EHRs) contain both structured content and unstructured (text) content about a patient{'}s medical history. In the unstructured text parts, there are common sections such as Assessment and Plan, Social History, and Medications. These sections help physicians find information easily and can be used by an information retrieval system to return specific information sought by a user. However, it is common that the exact format of sections in a particular EHR does not adhere to known patterns. Therefore, being able to predict sections and headers in EHRs automatically is beneficial to physicians. Prior approaches in EHR section prediction have only used text data from EHRs and have required significant manual annotation. We propose using sections from medical literature (e.g., textbooks, journals, web content) that contain content similar to that found in EHR sections. Our approach uses data from a different kind of source where labels are provided without the need of a time-consuming annotation effort. We use this data to train two models: an RNN and a BERT-based model. We apply the learned models along with source data via transfer learning to predict sections in EHRs. Our results show that medical literature can provide helpful supervision signal for this classification task.",
}
@inproceedings{wu-etal-2019-neural-news,
    title = "Neural News Recommendation with Heterogeneous User Behavior",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      An, Mingxiao  and
      Qi, Tao  and
      Huang, Jianqiang  and
      Huang, Yongfeng  and
      Xie, Xing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1493",
    doi = "10.18653/v1/D19-1493",
    pages = "4874--4883",
    abstract = "News recommendation is important for online news platforms to help users find interested news and alleviate information overload. Existing news recommendation methods usually rely on the news click history to model user interest. However, these methods may suffer from the data sparsity problem, since the news click behaviors of many users in online news platforms are usually very limited. Fortunately, some other kinds of user behaviors such as webpage browsing and search queries can also provide useful clues of users{'} news reading interest. In this paper, we propose a neural news recommendation approach which can exploit heterogeneous user behaviors. Our approach contains two major modules, i.e., news representation and user representation. In the news representation module, we learn representations of news from their titles via CNN networks, and apply attention networks to select important words. In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages. In addition, we use word- and record-level attentions to select informative words and behavior records. Experiments on a real-world dataset validate the effectiveness of our approach.",
}
@inproceedings{wu-etal-2019-reviews,
    title = "Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Qi, Tao  and
      Ge, Suyu  and
      Huang, Yongfeng  and
      Xie, Xing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1494",
    doi = "10.18653/v1/D19-1494",
    pages = "4884--4893",
    abstract = "User and item representation learning is critical for recommendation. Many of existing recommendation methods learn representations of users and items based on their ratings and reviews. However, the user-user and item-item relatedness are usually not considered in these methods, which may be insufficient. In this paper, we propose a neural recommendation approach which can utilize useful information from both review content and user-item graphs. Since reviews and graphs have different characteristics, we propose to use a multi-view learning framework to incorporate them as different views. In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews. In addition, we propose to incorporate a three-level attention network into this view to select important words, sentences and reviews for learning informative user and item representations. In the graph-view, we propose a hierarchical graph neural network to jointly model the user-item, user-user and item-item relatedness by capturing the first- and second-order interactions between users and items in the user-item graph. In addition, we apply attention mechanism to model the importance of these interactions to learn informative user and item representations. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.",
}
@inproceedings{ding-etal-2019-event-representation,
    title = "Event Representation Learning Enhanced with External Commonsense Knowledge",
    author = "Ding, Xiao  and
      Liao, Kuo  and
      Liu, Ting  and
      Li, Zhongyang  and
      Duan, Junwen",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1495",
    doi = "10.18653/v1/D19-1495",
    pages = "4894--4903",
    abstract = "Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78{\%} improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market.",
}
@inproceedings{zhou-etal-2019-learning-discriminate,
    title = "Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification",
    author = "Zhou, Yichao  and
      Jiang, Jyun-Yu  and
      Chang, Kai-Wei  and
      Wang, Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1496",
    doi = "10.18653/v1/D19-1496",
    pages = "4904--4913",
    abstract = "Adversarial attacks against machine learning models have threatened various real-world applications such as spam filtering and sentiment analysis. In this paper, we propose a novel framework, learning to discriminate perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking adversarial attacks for text classification models. To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations. For each potential perturbation, an embedding estimator learns to restore the embedding of the original word based on the context and a replacement token is chosen based on approximate kNN search. DISP can block adversarial attacks for any NLP model without modifying the model structure or training procedure. Extensive experiments on two benchmark datasets demonstrate that DISP significantly outperforms baseline methods in blocking adversarial attacks for text classification. In addition, in-depth analysis shows the robustness of DISP across different situations.",
}
@inproceedings{li-etal-2019-neural-citation,
    title = "A Neural Citation Count Prediction Model based on Peer Review Text",
    author = "Li, Siqing  and
      Zhao, Wayne Xin  and
      Yin, Eddy Jing  and
      Wen, Ji-Rong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1497",
    doi = "10.18653/v1/D19-1497",
    pages = "4914--4924",
    abstract = "Citation count prediction (CCP) has been an important research task for automatically estimating the future impact of a scholarly paper. Previous studies mainly focus on extracting or mining useful features from the paper itself or the associated authors. An important kind of data signals, peer review text, has not been utilized for the CCP task. In this paper, we take the initiative to utilize peer review data for the CCP task with a neural prediction model. Our focus is to learn a comprehensive semantic representation for peer review text for improving the prediction performance. To achieve this goal, we incorporate the abstract-review match mechanism and the cross-review match mechanism to learn deep features from peer review text. We also consider integrating hand-crafted features via a wide component. The deep and wide components jointly make the prediction. Extensive experiments have demonstrated the usefulness of the peer review data and the effectiveness of the proposed model. Our dataset has been released online.",
}
@inproceedings{christopoulou-etal-2019-connecting,
    title = "Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs",
    author = "Christopoulou, Fenia  and
      Miwa, Makoto  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1498",
    doi = "10.18653/v1/D19-1498",
    pages = "4925--4936",
    abstract = "Document-level relation extraction is a complex human process that requires logical inference to extract relationships between named entities in text. Existing approaches use graph-based neural models with words as nodes and edges as relations between them, to encode relations across sentences. These models are node-based, i.e., they form pair representations based solely on the two target node representations. However, entity relations can be better expressed through unique edge representations formed as paths between nodes. We thus propose an edge-oriented graph neural model for document-level relation extraction. The model utilises different types of nodes and edges to create a document-level graph. An inference mechanism on the graph edges enables to learn intra- and inter-sentence relations using multi-instance learning internally. Experiments on two document-level biomedical datasets for chemical-disease and gene-disease associations show the usefulness of the proposed edge-oriented approach.",
}
@inproceedings{shang-etal-2019-semi,
    title = "Semi-supervised Text Style Transfer: Cross Projection in Latent Space",
    author = "Shang, Mingyue  and
      Li, Piji  and
      Fu, Zhenxin  and
      Bing, Lidong  and
      Zhao, Dongyan  and
      Shi, Shuming  and
      Yan, Rui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1499",
    doi = "10.18653/v1/D19-1499",
    pages = "4937--4946",
    abstract = "Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that has long suffered from the shortage of parallel data. In this paper, we first propose a semi-supervised text style transfer model that combines the small-scale parallel data with the large-scale nonparallel data. With these two types of training data, we introduce a projection function between the latent space of different styles and design two constraints to train it. We also introduce two other simple but effective semi-supervised methods to compare with. To evaluate the performance of the proposed methods, we build and release a novel style transfer dataset that alters sentences between the style of ancient Chinese poem and the modern Chinese.",
}
@inproceedings{ravichander-etal-2019-question,
    title = "Question Answering for Privacy Policies: Combining Computational and Legal Perspectives",
    author = "Ravichander, Abhilasha  and
      Black, Alan W  and
      Wilson, Shomir  and
      Norton, Thomas  and
      Sadeh, Norman",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1500",
    doi = "10.18653/v1/D19-1500",
    pages = "4947--4958",
    abstract = "Privacy policies are long and complex documents that are difficult for users to read and understand. Yet, they have legal effects on how user data can be collected, managed and used. Ideally, we would like to empower users to inform themselves about the issues that matter to them, and enable them to selectively explore these issues. We present PrivacyQA, a corpus consisting of 1750 questions about the privacy policies of mobile applications, and over 3500 expert annotations of relevant answers. We observe that a strong neural baseline underperforms human performance by almost 0.3 F1 on PrivacyQA, suggesting considerable room for improvement for future systems. Further, we use this dataset to categorically identify challenges to question answerability, with domain-general implications for any question answering system. The PrivacyQA corpus offers a challenging corpus for question answering, with genuine real world utility.",
}
@inproceedings{chan-etal-2019-stick,
    title = "Stick to the Facts: Learning towards a Fidelity-oriented {E}-Commerce Product Description Generation",
    author = "Chan, Zhangming  and
      Chen, Xiuying  and
      Wang, Yongliang  and
      Li, Juntao  and
      Zhang, Zhiqiang  and
      Gai, Kun  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1501",
    doi = "10.18653/v1/D19-1501",
    pages = "4959--4968",
    abstract = "Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information. However, little attention has been paid to this problem. To bridge this gap we propose a model named Fidelity-oriented Product Description Generator (FPDG). FPDG takes the entity label of each word into account, since the product attribute information is always conveyed by entity words. Specifically, we first propose a Recurrent Neural Network (RNN) decoder based on the Entity-label-guided Long Short-Term Memory (ELSTM) cell, taking both the embedding and the entity label of each word as input. Second, we establish a keyword memory that stores the entity labels as keys and keywords as values, and FPDG will attend to keywords through attending to their entity labels. Experiments conducted a large-scale real-world product description dataset show that our model achieves the state-of-the-art performance in terms of both traditional generation metrics as well as human evaluations. Specifically, FPDG increases the fidelity of the generated descriptions by 25{\%}.",
}
@inproceedings{jin-etal-2019-fine,
    title = "Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks",
    author = "Jin, Hailong  and
      Hou, Lei  and
      Li, Juanzi  and
      Dong, Tiansi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1502",
    doi = "10.18653/v1/D19-1502",
    pages = "4969--4978",
    abstract = "This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities. A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy. Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods.",
}
@inproceedings{du-etal-2019-learning,
    title = "Learning to Infer Entities, Properties and their Relations from Clinical Conversations",
    author = "Du, Nan  and
      Wang, Mingqiu  and
      Tran, Linh  and
      Lee, Gang  and
      Shafran, Izhak",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1503",
    doi = "10.18653/v1/D19-1503",
    pages = "4979--4990",
    abstract = "Recently we proposed the Span Attribute Tagging (SAT) Model to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the challenge of large label space and limited training data using a hierarchical two-stage approach that identifies the span of interest in a tagging step and assigns labels to the span in a classification step. We extend the SAT model to jointly infer not only entities and their properties but also relations between them. Most relation extraction models restrict inferring relations between tokens within a few neighboring sentences, mainly to avoid high computational complexity. In contrast, our proposed Relation-SAT (R-SAT) model is computationally efficient and can infer relations over the entire conversation, spanning an average duration of 10 minutes. We evaluate our model on a corpus of clinical conversations. When the entities are given, the R-SAT outperforms baselines in identifying relations between symptoms and their properties by about 32{\%} (0.82 vs 0.62 F-score) and by about 50{\%} (0.60 vs 0.41 F-score) on medications and their properties. On the more difficult task of jointly inferring entities and relations, the R-SAT model achieves a performance of 0.34 and 0.45 for symptoms and medications respectively, which is significantly better than 0.18 and 0.35 for the baseline model. The contributions of different components of the model are quantified using ablation analysis.",
}
@inproceedings{lee-etal-2019-practical,
    title = "Practical Correlated Topic Modeling and Analysis via the Rectified Anchor Word Algorithm",
    author = "Lee, Moontae  and
      Cho, Sungjun  and
      Bindel, David  and
      Mimno, David",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1504",
    doi = "10.18653/v1/D19-1504",
    pages = "4991--5001",
    abstract = "Despite great scalability on large data and their ability to understand correlations between topics, spectral topic models have not been widely used due to the absence of reliability in real data and lack of practical implementations. This paper aims to solidify the foundations of spectral topic inference and provide a practical implementation for anchor-based topic modeling. Beginning with vocabulary curation, we scrutinize every single inference step with other viable options. We also evaluate our matrix-based approach against popular alternatives including a tensor-based spectral method as well as probabilistic algorithms. Our quantitative and qualitative experiments demonstrate the power of Rectified Anchor Word algorithm in various real datasets, providing a complete guide to practical correlated topic modeling.",
}
@inproceedings{zhang-etal-2019-modeling,
    title = "Modeling the Relationship between User Comments and Edits in Document Revision",
    author = "Zhang, Xuchao  and
      Rajagopal, Dheeraj  and
      Gamon, Michael  and
      Jauhar, Sujay Kumar  and
      Lu, ChangTien",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1505",
    doi = "10.18653/v1/D19-1505",
    pages = "5002--5011",
    abstract = "Management of collaborative documents can be difficult, given the profusion of edits and comments that multiple authors make during a document{'}s evolution. Reliably modeling the relationship between edits and comments is a crucial step towards helping the user keep track of a document in flux. A number of authoring tasks, such as categorizing and summarizing edits, detecting completed to-dos, and visually rearranging comments could benefit from such a contribution. Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring. We begin by collecting a dataset with more than half a million comment-edit pairs based on Wikipedia revision histories. We then propose a hierarchical multi-layer deep neural-network to model the relationship between edits and comments. Our architecture tackles both Comment Ranking and Edit Anchoring tasks by encoding specific edit actions such as additions and deletions, while also accounting for document context. In a number of evaluation settings, our experimental results show that our approach outperforms several strong baselines significantly. We are able to achieve a precision@1 of 71.0{\%} and a precision@3 of 94.4{\%} for Comment Ranking, while we achieve 74.4{\%} accuracy on Edit Anchoring.",
}
@inproceedings{kaliamoorthi-etal-2019-prado,
    title = "{PRADO}: Projection Attention Networks for Document Classification On-Device",
    author = "Kaliamoorthi, Prabhu  and
      Ravi, Sujith  and
      Kozareva, Zornitsa",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1506",
    doi = "10.18653/v1/D19-1506",
    pages = "5012--5021",
    abstract = "Recently, there has been a great interest in the development of small and accurate neural networks that run entirely on devices such as mobile phones, smart watches and IoT. This enables user privacy, consistent user experience and low latency. Although a wide range of applications have been targeted from wake word detection to short text classification, yet there are no on-device networks for long text classification. We propose a novel projection attention neural network PRADO that combines trainable projections with attention and convolutions. We evaluate our approach on multiple large document text classification tasks. Our results show the effectiveness of the trainable projection model in finding semantically similar phrases and reaching high performance while maintaining compact size. Using this approach, we train tiny neural networks just 200 Kilobytes in size that improve over prior CNN and LSTM models and achieve near state of the art performance on multiple long document classification tasks. We also apply our model for transfer learning, show its robustness and ability to further improve the performance in limited data scenarios.",
}
@inproceedings{kim-2019-subword,
    title = "Subword Language Model for Query Auto-Completion",
    author = "Kim, Gyuwan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1507",
    doi = "10.18653/v1/D19-1507",
    pages = "5022--5032",
    abstract = "Current neural query auto-completion (QAC) systems rely on character-level language models, but they slow down when queries are long. We present how to utilize subword language models for the fast and accurate generation of query completion candidates. Representing queries with subwords shorten a decoding length significantly. To deal with issues coming from introducing subword language model, we develop a retrace algorithm and a reranking method by approximate marginalization. As a result, our model achieves up to 2.5 times faster while maintaining a similar quality of generated results compared to the character-level baseline. Also, we propose a new evaluation metric, mean recoverable length (MRL), measuring how many upcoming characters the model could complete correctly. It provides more explicit meaning and eliminates the need for prefix length sampling for existing rank-based metrics. Moreover, we performed a comprehensive analysis with ablation study to figure out the importance of each component.",
}
@inproceedings{lin-etal-2019-enhancing,
    title = "Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph",
    author = "Lin, Xinzhu  and
      He, Xiahui  and
      Chen, Qin  and
      Tou, Huaixiao  and
      Wei, Zhongyu  and
      Chen, Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1508",
    doi = "10.18653/v1/D19-1508",
    pages = "5033--5042",
    abstract = "Symptom diagnosis is a challenging yet profound problem in natural language processing. Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between doctors and patients that contain more rich information are not well studied. In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors. Then, we provide some benchmark models on this dataset to boost the research of dialogue symptom diagnosis. In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build a symptom graph to model the associations between symptoms rather than treating each symptom independently. Experimental results show that both the global attention and symptom graph are effective to boost dialogue symptom diagnosis. In particular, our proposed model achieves the state-of-the-art performance on the constructed dataset.",
}
@inproceedings{qin-etal-2019-counterfactual,
    title = "Counterfactual Story Reasoning and Generation",
    author = "Qin, Lianhui  and
      Bosselut, Antoine  and
      Holtzman, Ari  and
      Bhagavatula, Chandra  and
      Clark, Elizabeth  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1509",
    doi = "10.18653/v1/D19-1509",
    pages = "5043--5053",
    abstract = "Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes. Despite being considered a necessary component of AI-complete systems, few resources have been developed for evaluating counterfactual reasoning in narratives. In this paper, we propose Counterfactual Story Rewriting: given an original story and an intervening counterfactual event, the task is to minimally revise the story to make it compatible with the given counterfactual event. Solving this task will require deep understanding of causal narrative chains and counterfactual invariance, and integration of such story reasoning capabilities into conditional language generation models. We present TIMETRAVEL, a new dataset of 29,849 counterfactual rewritings, each with the original story, a counterfactual event, and human-generated revision of the original story compatible with the counterfactual event. Additionally, we include 81,407 counterfactual {``}branches{''} without a rewritten storyline to support future work on semi- or un-supervised approaches to counterfactual story rewriting. Finally, we evaluate the counterfactual rewriting capacities of several competitive baselines based on pretrained language models, and assess whether common overlap and model-based automatic metrics for text generation correlate well with human scores for counterfactual rewriting.",
}
@inproceedings{malmi-etal-2019-encode,
    title = "Encode, Tag, Realize: High-Precision Text Editing",
    author = "Malmi, Eric  and
      Krause, Sebastian  and
      Rothe, Sascha  and
      Mirylenka, Daniil  and
      Severyn, Aliaksei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1510",
    doi = "10.18653/v1/D19-1510",
    pages = "5054--5065",
    abstract = "We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task. Target texts are reconstructed from the inputs using three main edit operations: keeping a token, deleting it, and adding a phrase before the token. To predict the edit operations, we propose a novel model, which combines a BERT encoder with an autoregressive Transformer decoder. This approach is evaluated on English text on four tasks: sentence fusion, sentence splitting, abstractive summarization, and grammar correction. LaserTagger achieves new state-of-the-art results on three of these tasks, performs comparably to a set of strong seq2seq baselines with a large number of training examples, and outperforms them when the number of examples is limited. Furthermore, we show that at inference time tagging can be more than two orders of magnitude faster than comparable seq2seq models, making it more attractive for running in a live environment.",
}
@inproceedings{wang-etal-2019-answer,
    title = "Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation",
    author = "Wang, Weichao  and
      Feng, Shi  and
      Wang, Daling  and
      Zhang, Yifei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1511",
    doi = "10.18653/v1/D19-1511",
    pages = "5066--5076",
    abstract = "Generating intriguing question is a key step towards building human-like open-domain chatbots. Although some recent works have focused on this task, compared with questions raised by humans, significant gaps remain in maintaining semantic coherence with post, which may result in generating dull or deviated questions. We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation. Thus, we devise two methods to further enhance semantic coherence between post and question under the guidance of answer. First, the coherence score between generated question and answer is used as the reward function in a reinforcement learning framework, to encourage the cases that are consistent with the answer in semantic. Second, we incorporate adversarial training to explicitly control question generation in the direction of question-answer coherence. Extensive experiments show that our two methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions.",
}
@inproceedings{yang-etal-2019-read,
    title = "Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation",
    author = "Yang, Ze  and
      Xu, Can  and
      Wu, Wei  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1512",
    doi = "10.18653/v1/D19-1512",
    pages = "5077--5089",
    abstract = "Automatic news comment generation is beneficial for real applications but has not attracted enough attention from the research community. In this paper, we propose a {``}read-attend-comment{''} procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title. We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm. Experimental results on two public datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.",
}
@inproceedings{tang-etal-2019-topic,
    title = "A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features",
    author = "Tang, Hongyin  and
      Li, Miao  and
      Jin, Beihong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1513",
    doi = "10.18653/v1/D19-1513",
    pages = "5090--5099",
    abstract = "Text generation is among the most fundamental tasks in natural language processing. In this paper, we propose a text generation model that learns semantics and structural features simultaneously. This model captures structural features by a sequential variational autoencoder component and leverages a topic modeling component based on Gaussian distribution to enhance the recognition of text semantics. To make the reconstructed text more coherent to the topics, the model further adapts the encoder of the topic modeling component for a discriminator. The results of experiments over several datasets demonstrate that our model outperforms several states of the art models in terms of text perplexity and topic coherence. Moreover, the latent representations learned by our model is superior to others in a text classification task. Finally, given the input texts, our model can generate meaningful texts which hold similar structures but under different topics.",
}
@inproceedings{tan-bansal-2019-lxmert,
    title = "{LXMERT}: Learning Cross-Modality Encoder Representations from Transformers",
    author = "Tan, Hao  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1514",
    doi = "10.18653/v1/D19-1514",
    pages = "5100--5111",
    abstract = "Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pre-trained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pre-trained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR2, and improve the previous best result by 22{\%} absolute (54{\%} to 76{\%}). Lastly, we demonstrate detailed ablation studies to prove that both our novel model components and pre-training strategies significantly contribute to our strong results. Code and pre-trained models publicly available at: https://github.com/airsplay/lxmert",
}
@inproceedings{liu-hockenmaier-2019-phrase,
    title = "Phrase Grounding by Soft-Label Chain Conditional Random Field",
    author = "Liu, Jiacheng  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1515",
    doi = "10.18653/v1/D19-1515",
    pages = "5112--5122",
    abstract = "The phrase grounding task aims to ground each entity mention in a given caption of an image to a corresponding region in that image. Although there are clear dependencies between how different mentions of the same caption should be grounded, previous structured prediction methods that aim to capture such dependencies need to resort to approximate inference or non-differentiable losses. In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions. In contrast to standard sequence labeling tasks, the phrase grounding task is defined such that there may be multiple correct candidate regions. To address this multiplicity of gold labels, we define so-called Soft-Label Chain CRFs, and present an algorithm that enables convenient end-to-end training. Our method establishes a new state-of-the-art on phrase grounding on the Flickr30k Entities dataset. Analysis shows that our model benefits both from the entity dependencies captured by the CRF and from the soft-label training regime. Our code is available at \url{github.com/liujch1998/SoftLabelCCRF}",
}
@inproceedings{yu-etal-2019-see,
    title = "What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues",
    author = "Yu, Xintong  and
      Zhang, Hongming  and
      Song, Yangqiu  and
      Song, Yan  and
      Zhang, Changshui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1516",
    doi = "10.18653/v1/D19-1516",
    pages = "5123--5132",
    abstract = "Grounding a pronoun to a visual object it refers to requires complex reasoning from various information sources, especially in conversational scenarios. For example, when people in a conversation talk about something all speakers can see, they often directly use pronouns (e.g., it) to refer to it without previous introduction. This fact brings a huge challenge for modern natural language understanding systems, particularly conventional context-based pronoun coreference models. To tackle this challenge, in this paper, we formally define the task of visual-aware pronoun coreference resolution (PCR) and introduce VisPro, a large-scale dialogue PCR dataset, to investigate whether and how the visual information can help resolve pronouns in dialogues. We then propose a novel visual-aware PCR model, VisCoref, for this task and conduct comprehensive experiments and case studies on our dataset. Results demonstrate the importance of the visual information in this PCR case and show the effectiveness of the proposed model.",
}
@inproceedings{wang-etal-2019-youmakeup,
    title = "{Y}ou{M}akeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension",
    author = "Wang, Weiying  and
      Wang, Yongcheng  and
      Chen, Shizhe  and
      Jin, Qin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1517",
    doi = "10.18653/v1/D19-1517",
    pages = "5133--5143",
    abstract = "Multimodal semantic comprehension has attracted increasing research interests recently such as visual question answering and caption generation. However, due to the data limitation, fine-grained semantic comprehension has not been well investigated, which requires to capture semantic details of multimodal contents. In this work, we introduce {``}YouMakeup{''}, a large-scale multimodal instructional video dataset to support fine-grained semantic comprehension research in specific domain. YouMakeup contains 2,800 videos from YouTube, spanning more than 420 hours in total. Each video is annotated with a sequence of natural language descriptions for instructional steps, grounded in temporal video range and spatial facial areas. The annotated steps in a video involve subtle difference in actions, products and regions, which requires fine-grained understanding and reasoning both temporally and spatially. In order to evaluate models{'} ability for fined-grained comprehension, we further propose two groups of tasks including generation tasks and visual question answering from different aspects. We also establish a baseline of step caption generation for future comparison. The dataset will be publicly available at https://github. com/AIM3-RUC/YouMakeup to support research investigation in fine-grained semantic comprehension.",
}
@inproceedings{lu-etal-2019-debug,
    title = "{DEBUG}: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization",
    author = "Lu, Chujie  and
      Chen, Long  and
      Tan, Chilie  and
      Li, Xiaolin  and
      Xiao, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1518",
    doi = "10.18653/v1/D19-1518",
    pages = "5144--5153",
    abstract = "In this paper, we focus on natural language video localization: localizing (ie, grounding) a natural language description in a long and untrimmed video sequence. All currently published models for addressing this problem can be categorized into two types: (i) top-down approach: it does classification and regression for a set of pre-cut video segment candidates; (ii) bottom-up approach: it directly predicts probabilities for each video frame as the temporal boundaries (ie, start and end time point). However, both two approaches suffer several limitations: the former is computation-intensive for densely placed candidates, while the latter has trailed the performance of the top-down counterpart thus far. To this end, we propose a novel dense bottom-up framework: DEnse Bottom-Up Grounding (DEBUG). DEBUG regards all frames falling in the ground truth segment as foreground, and each foreground frame regresses the unique distances from its location to bi-directional ground truth boundaries. Extensive experiments on three challenging benchmarks (TACoS, Charades-STA, and ActivityNet Captions) show that DEBUG is able to match the speed of bottom-up models while surpassing the performance of the state-of-the-art top-down models.",
}
@inproceedings{wang-etal-2019-crossweigh,
    title = "{C}ross{W}eigh: Training Named Entity Tagger from Imperfect Annotations",
    author = "Wang, Zihan  and
      Shang, Jingbo  and
      Liu, Liyuan  and
      Lu, Lihao  and
      Liu, Jiacheng  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1519",
    doi = "10.18653/v1/D19-1519",
    pages = "5154--5163",
    abstract = "Everyone makes mistakes. So do human annotators when curating labels for named entity recognition (NER). Such label mistakes might hurt model training and interfere model comparison. In this study, we dive deep into one of the widely-adopted NER benchmark datasets, CoNLL03 NER. We are able to identify label mistakes in about 5.38{\%} test sentences, which is a significant ratio considering that the state-of-the-art test F1 score is already around 93{\%}. Therefore, we manually correct these label mistakes and form a cleaner test set. Our re-evaluation of popular models on this corrected test set leads to more accurate assessments, compared to those on the original test set. More importantly, we propose a simple yet effective framework, CrossWeigh, to handle label mistakes during NER model training. Specifically, it partitions the training data into several folds and train independent NER models to identify potential mistakes in each fold. Then it adjusts the weights of training data accordingly to train the final NER model. Extensive experiments demonstrate significant improvements of plugging various NER models into our proposed framework on three datasets. All implementations and corrected test set are available at our Github repo https://github.com/ZihanWangKi/CrossWeigh.",
}
@inproceedings{chaudhary-etal-2019-little,
    title = "A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers",
    author = "Chaudhary, Aditi  and
      Xie, Jiateng  and
      Sheikh, Zaid  and
      Neubig, Graham  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1520",
    doi = "10.18653/v1/D19-1520",
    pages = "5164--5174",
    abstract = "Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced languages. However, there are now many proposed solutions to this problem involving either cross-lingual transfer learning, which learns from other highly resourced languages, or active learning, which efficiently selects effective training data based on model predictions. In this paper, we ask the question: given this recent progress, and some amount of human annotation, what is the most effective method for efficiently creating high-quality entity recognizers in under-resourced languages? Based on extensive experimentation using both simulated and real human annotation, we settle on a recipe of starting with a cross-lingual transferred model, then performing targeted annotation of only uncertain entity spans in the target language, minimizing annotator effort. Results demonstrate that cross-lingual transfer is a powerful tool when very little data can be annotated, but an entity-targeted annotation strategy can achieve competitive accuracy quickly, with just one-tenth of training data.",
}
@inproceedings{xiong-etal-2019-open,
    title = "Open Domain Web Keyphrase Extraction Beyond Language Modeling",
    author = "Xiong, Lee  and
      Hu, Chuan  and
      Xiong, Chenyan  and
      Campos, Daniel  and
      Overwijk, Arnold",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1521",
    doi = "10.18653/v1/D19-1521",
    pages = "5175--5184",
    abstract = "This paper studies keyphrase extraction in real-world scenarios where documents are from diverse domains and have variant content quality. We curate and release OpenKP, a large scale open domain keyphrase extraction dataset with near one hundred thousand web documents and expert keyphrase annotations. To handle the variations of domain and content quality, we develop BLING-KPE, a neural keyphrase extraction model that goes beyond language understanding using visual presentations of documents and weak supervision from search queries. Experimental results on OpenKP confirm the effectiveness of BLING-KPE and the contributions of its neural architecture, visual features, and search log weak supervision. Zero-shot evaluations on DUC-2001 demonstrate the improved generalization ability of learning from the open domain data compared to a specific domain.",
}
@inproceedings{balazevic-etal-2019-tucker,
    title = "{T}uck{ER}: Tensor Factorization for Knowledge Graph Completion",
    author = "Balazevic, Ivana  and
      Allen, Carl  and
      Hospedales, Timothy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1522",
    doi = "10.18653/v1/D19-1522",
    pages = "5185--5194",
    abstract = "Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively straightforward but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms previous state-of-the-art models across standard link prediction datasets, acting as a strong baseline for more elaborate models. We show that TuckER is a fully expressive model, derive sufficient bounds on its embedding dimensionalities and demonstrate that several previously introduced linear models can be viewed as special cases of TuckER.",
}
@inproceedings{lertvittayakumjorn-toni-2019-human,
    title = "Human-grounded Evaluations of Explanation Methods for Text Classification",
    author = "Lertvittayakumjorn, Piyawat  and
      Toni, Francesca",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1523",
    doi = "10.18653/v1/D19-1523",
    pages = "5195--5205",
    abstract = "Due to the black-box nature of deep learning models, methods for explaining the models{'} results are crucial to gain trust from humans and support collaboration between AIs and humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2) justifying model predictions, and (3) helping humans investigate uncertain predictions. The results highlight dissimilar qualities of the various explanation methods we consider and show the degree to which these methods could serve for each purpose.",
}
@inproceedings{zhao-etal-2019-context,
    title = "A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature",
    author = "Zhao, He  and
      Luo, Zhunchen  and
      Feng, Chong  and
      Zheng, Anqing  and
      Liu, Xiaopeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1524",
    doi = "10.18653/v1/D19-1524",
    pages = "5206--5215",
    abstract = "We introduce a new task of modeling the role and function for on-line resource citations in scientific literature. By categorizing the on-line resources and analyzing the purpose of resource citations in scientific texts, it can greatly help resource search and recommendation systems to better understand and manage the scientific resources. For this novel task, we are the first to create an annotation scheme, which models the different granularity of information from a hierarchical perspective. And we construct a dataset SciRes, which includes 3,088 manually annotated resource contexts. In this paper, we propose a possible solution by using a multi-task framework to build the scientific resource classifier (SciResCLF) for jointly recognizing the role and function types. Then we use the classification results to help a scientific resource recommendation (SciResREC) task. Experiments show that our model achieves the best results on both the classification task and the recommendation task. The SciRes dataset is released for future research.",
}
@inproceedings{neekhara-etal-2019-adversarial,
    title = "Adversarial Reprogramming of Text Classification Neural Networks",
    author = "Neekhara, Paarth  and
      Hussain, Shehzeen  and
      Dubnov, Shlomo  and
      Koushanfar, Farinaz",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1525",
    doi = "10.18653/v1/D19-1525",
    pages = "5216--5225",
    abstract = "In this work, we develop methods to repurpose text classification neural networks for alternate tasks without modifying the network architecture or parameters. We propose a context based vocabulary remapping method that performs a computationally inexpensive input transformation to reprogram a victim classification model for a new set of sequences. We propose algorithms for training such an input transformation in both white box and black box settings where the adversary may or may not have access to the victim model{'}s architecture and parameters. We demonstrate the application of our model and the vulnerability of neural networks by adversarially repurposing various text-classification models including LSTM, bi-directional LSTM and CNN for alternate classification tasks.",
}
@inproceedings{dong-etal-2019-document,
    title = "Document Hashing with Mixture-Prior Generative Models",
    author = "Dong, Wei  and
      Su, Qinliang  and
      Shen, Dinghan  and
      Chen, Changyou",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1526",
    doi = "10.18653/v1/D19-1526",
    pages = "5226--5235",
    abstract = "Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes. Generative hashing is often used to generate hashing codes in an unsupervised way. However, existing generative hashing methods only considered the use of simple priors, like Gaussian and Bernoulli priors, which limits these methods to further improve their performance. In this paper, two mixture-prior generative models are proposed, under the objective to produce high-quality hashing codes for documents. Specifically, a Gaussian mixture prior is first imposed onto the variational auto-encoder (VAE), followed by a separate step to cast the continuous latent representation of VAE into binary code. To avoid the performance loss caused by the separate casting, a model using a Bernoulli mixture prior is further developed, in which an end-to-end training is admitted by resorting to the straight-through (ST) discrete gradient estimator. Experimental results on several benchmark datasets demonstrate that the proposed methods, especially the one using Bernoulli mixture priors, consistently outperform existing ones by a substantial margin.",
}
@inproceedings{tan-etal-2019-efficient,
    title = "On Efficient Retrieval of Top Similarity Vectors",
    author = "Tan, Shulong  and
      Zhou, Zhixin  and
      Xu, Zhaozhuo  and
      Li, Ping",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1527",
    doi = "10.18653/v1/D19-1527",
    pages = "5236--5246",
    abstract = "Retrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks. In this paper, we demonstrate an efficient method for searching vectors via a typical non-metric matching function: inner product. Our method, which constructs an approximate Inner Product Delaunay Graph (IPDG) for top-1 Maximum Inner Product Search (MIPS), transforms retrieving the most suitable latent vectors into a graph search problem with great benefits of efficiency. Experiments on data representations learned for different machine learning tasks verify the outperforming effectiveness and efficiency of the proposed IPDG.",
}
@inproceedings{zhang-etal-2019-multiplex,
    title = "Multiplex Word Embeddings for Selectional Preference Acquisition",
    author = "Zhang, Hongming  and
      Bai, Jiaxin  and
      Song, Yan  and
      Xu, Kun  and
      Yu, Changlong  and
      Song, Yangqiu  and
      Ng, Wilfred  and
      Yu, Dong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1528",
    doi = "10.18653/v1/D19-1528",
    pages = "5247--5256",
    abstract = "Conventional word embeddings represent words with fixed vectors, which are usually trained based on co-occurrence patterns among words. In doing so, however, the power of such representations is limited, where the same word might be functionalized separately under different syntactic relations. To address this limitation, one solution is to incorporate relational dependencies of different words into their embeddings. Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words. As a result, each word has a center embedding to represent its overall semantics, and several relational embeddings to represent its relational dependencies. Compared to existing models, our model can effectively distinguish words with respect to different relations without introducing unnecessary sparseness. Moreover, to accommodate various relations, we use a small dimension for relational embeddings and our model is able to keep their effectiveness. Experiments on selectional preference acquisition and word similarity demonstrate the effectiveness of the proposed model, and a further study of scalability also proves that our embeddings only need 1/20 of the original embedding size to achieve better performance.",
}
@inproceedings{ma-etal-2019-mulcode,
    title = "{M}ul{C}ode: A Multiplicative Multi-way Model for Compressing Neural Language Model",
    author = "Ma, Yukun  and
      Chen, Patrick H.  and
      Hsieh, Cho-Jui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1529",
    doi = "10.18653/v1/D19-1529",
    pages = "5257--5266",
    abstract = "It is challenging to deploy deep neural nets on memory-constrained devices due to the explosion of numbers of parameters. Especially, the input embedding layer and Softmax layer usually dominate the memory usage in an RNN-based language model. For example, input embedding and Softmax matrices in IWSLT-2014 German-to-English data set account for more than 80{\%} of the total model parameters. To compress these embedding layers, we propose MulCode, a novel multi-way multiplicative neural compressor. MulCode learns an adaptively created matrix and its multiplicative compositions. Together with a prior weighted loss, Multicode is more effective than the state-of-the-art compression methods. On the IWSLT-2014 machine translation data set, MulCode achieved 17 times compression rate for the embedding and Softmax matrices, and when combined with quantization technique, our method can achieve 41.38 times compression rate with very little loss in performance.",
}
@inproceedings{hall-maudslay-etal-2019-name,
    title = "It{'}s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution",
    author = "Hall Maudslay, Rowan  and
      Gonen, Hila  and
      Cotterell, Ryan  and
      Teufel, Simone",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1530",
    doi = "10.18653/v1/D19-1530",
    pages = "5267--5275",
    abstract = "This paper treats gender bias latent in word embeddings. Previous mitigation attempts rely on the operationalisation of gender bias as a projection over a linear subspace. An alternative approach is Counterfactual Data Augmentation (CDA), in which a corpus is duplicated and augmented to remove bias, e.g. by swapping all inherently-gendered words in the copy. We perform an empirical comparison of these approaches on the English Gigaword and Wikipedia, and find that whilst both successfully reduce direct bias and perform well in tasks which quantify embedding quality, CDA variants outperform projection-based methods at the task of drawing non-biased gender analogies by an average of 19{\%} across both corpora. We propose two improvements to CDA: Counterfactual Data Substitution (CDS), a variant of CDA in which potentially biased text is randomly substituted to avoid duplication, and the Names Intervention, a novel name-pairing technique that vastly increases the number of words being treated. CDA/S with the Names Intervention is the only approach which is able to mitigate indirect gender bias: following debiasing, previously biased words are significantly less clustered according to gender (cluster purity is reduced by 49{\%}), thus improving on the state-of-the-art for bias mitigation.",
}
@inproceedings{zhou-etal-2019-examining,
    title = "Examining Gender Bias in Languages with Grammatical Gender",
    author = "Zhou, Pei  and
      Shi, Weijia  and
      Zhao, Jieyu  and
      Huang, Kuan-Hao  and
      Chen, Muhao  and
      Cotterell, Ryan  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1531",
    doi = "10.18653/v1/D19-1531",
    pages = "5276--5284",
    abstract = "Recent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating such bias only in English. These analyses cannot be directly extended to languages that exhibit morphological agreement on gender, such as Spanish and French. In this paper, we propose new metrics for evaluating gender bias in word embeddings of these languages and further demonstrate evidence of gender bias in bilingual embeddings which align these languages with English. Finally, we extend an existing approach to mitigate gender bias in word embedding of these languages under both monolingual and bilingual settings. Experiments on modified Word Embedding Association Test, word similarity, word translation, and word pair translation tasks show that the proposed approaches can effectively reduce the gender bias while preserving the utility of the original embeddings.",
}
@inproceedings{vyas-carpuat-2019-weakly,
    title = "Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation",
    author = "Vyas, Yogarshi  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1532",
    doi = "10.18653/v1/D19-1532",
    pages = "5285--5296",
    abstract = "Words in different languages rarely cover the exact same semantic space. This work characterizes differences in meaning between words across languages using semantic relations that have been used to relate the meaning of English words. However, because of translation ambiguity, semantic relations are not always preserved by translation. We introduce a cross-lingual relation classifier trained only with English examples and a bilingual dictionary. Our classifier relies on a novel attention-based distillation approach to account for translation ambiguity when transferring knowledge from English to cross-lingual settings. On new English-Chinese and English-Hindi test sets, the resulting models largely outperform baselines that more naively rely on bilingual embeddings or dictionaries for cross-lingual transfer, and approach the performance of fully supervised systems on English tasks.",
}
@inproceedings{hadiwinoto-etal-2019-improved,
    title = "Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations",
    author = "Hadiwinoto, Christian  and
      Ng, Hwee Tou  and
      Gan, Wee Chung",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1533",
    doi = "10.18653/v1/D19-1533",
    pages = "5297--5306",
    abstract = "Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis. However, evaluation on word sense disambiguation (WSD) in prior work shows that using contextualized word representations does not outperform the state-of-the-art approach that makes use of non-contextualized word embeddings. In this paper, we explore different strategies of integrating pre-trained contextualized word representations and our best strategy achieves accuracies exceeding the best prior published accuracies by significant margins on multiple benchmark WSD datasets.",
}
@inproceedings{wallace-etal-2019-nlp,
    title = "Do {NLP} Models Know Numbers? Probing Numeracy in Embeddings",
    author = "Wallace, Eric  and
      Wang, Yizhong  and
      Li, Sujian  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1534",
    doi = "10.18653/v1/D19-1534",
    pages = "5307--5315",
    abstract = "The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other tokens{---}they embed them as distributed vectors. Is this enough to capture numeracy? We begin by investigating the numerical reasoning capabilities of a state-of-the-art question answering model on the DROP dataset. We find this model excels on questions that require numerical reasoning, i.e., it already captures numeracy. To understand how this capability emerges, we probe token embedding methods (e.g., BERT, GloVe) on synthetic list maximum, number decoding, and addition tasks. A surprising degree of numeracy is naturally present in standard embeddings. For example, GloVe and word2vec accurately encode magnitude for numbers up to 1,000. Furthermore, character-level embeddings are even more precise{---}ELMo captures numeracy the best for all pre-trained methods{---}but BERT, which uses sub-word units, is less exact.",
}
@inproceedings{liu-etal-2019-split,
    title = "A Split-and-Recombine Approach for Follow-up Query Analysis",
    author = "Liu, Qian  and
      Chen, Bei  and
      Liu, Haoyan  and
      Lou, Jian-Guang  and
      Fang, Lei  and
      Zhou, Bin  and
      Zhang, Dongmei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1535",
    doi = "10.18653/v1/D19-1535",
    pages = "5316--5326",
    abstract = "Context-dependent semantic parsing has proven to be an important yet challenging task. To leverage the advances in context-independent semantic parsing, we propose to perform follow-up query analysis, aiming to restate context-dependent natural language queries with contextual information. To accomplish the task, we propose STAR, a novel approach with a well-designed two-phase process. It is parser-independent and able to handle multifarious follow-up scenarios in different domains. Experiments on the FollowUp dataset show that STAR outperforms the state-of-the-art baseline by a large margin of nearly 8{\%}. The superiority on parsing results verifies the feasibility of follow-up query analysis. We also explore the extensibility of STAR on the SQA dataset, which is very promising.",
}
@inproceedings{zou-lu-2019-text2math,
    title = "{T}ext2{M}ath: End-to-end Parsing Text into Math Expressions",
    author = "Zou, Yanyan  and
      Lu, Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1536",
    doi = "10.18653/v1/D19-1536",
    pages = "5327--5337",
    abstract = "We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach.",
}
@inproceedings{zhang-etal-2019-editing,
    title = "Editing-Based {SQL} Query Generation for Cross-Domain Context-Dependent Questions",
    author = "Zhang, Rui  and
      Yu, Tao  and
      Er, Heyang  and
      Shim, Sungrok  and
      Xue, Eric  and
      Lin, Xi Victoria  and
      Shi, Tianze  and
      Xiong, Caiming  and
      Socher, Richard  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1537",
    doi = "10.18653/v1/D19-1537",
    pages = "5338--5349",
    abstract = "We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at \url{https://github.com/ryanzhumich/sparc_atis_pytorch}.",
}
@inproceedings{he-etal-2019-syntax,
    title = "Syntax-aware Multilingual Semantic Role Labeling",
    author = "He, Shexia  and
      Li, Zuchao  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1538",
    doi = "10.18653/v1/D19-1538",
    pages = "5350--5359",
    abstract = "Recently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation. However, most of these efforts focus on English, while SRL on multiple languages more than English has received relatively little attention so that is kept underdevelopment. Thus this paper intends to fill the gap on multilingual SRL with special focus on the impact of syntax and contextualized word representation. Unlike existing work, we propose a novel method guided by syntactic rule to prune arguments, which enables us to integrate syntax into multilingual SRL model simply and effectively. We present a unified SRL model designed for multiple languages together with the proposed uniform syntax enhancement. Our model achieves new state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. Besides, we pose a discussion on the syntactic role among different languages and verify the effectiveness of deep enhanced representation for multilingual SRL.",
}
@inproceedings{baevski-etal-2019-cloze,
    title = "Cloze-driven Pretraining of Self-attention Networks",
    author = "Baevski, Alexei  and
      Edunov, Sergey  and
      Liu, Yinhan  and
      Zettlemoyer, Luke  and
      Auli, Michael",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1539",
    doi = "10.18653/v1/D19-1539",
    pages = "5360--5369",
    abstract = "We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with BERT. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective.",
}
@inproceedings{rao-etal-2019-bridging,
    title = "Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling",
    author = "Rao, Jinfeng  and
      Liu, Linqing  and
      Tay, Yi  and
      Yang, Wei  and
      Shi, Peng  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1540",
    doi = "10.18653/v1/D19-1540",
    pages = "5370--5381",
    abstract = "A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user{'}s query. On the other hand, many NLP problems, such as question answering and paraphrase identification, can be considered variants of semantic matching, which is to measure the semantic distance between two pieces of short texts. While at a high level both relevance and semantic matching require modeling textual similarity, many existing techniques for one cannot be easily adapted to the other. To bridge this gap, we propose a novel model, HCAN (Hybrid Co-Attention Network), that comprises (1) a hybrid encoder module that includes ConvNet-based and LSTM-based encoders, (2) a relevance matching module that measures soft term matches with importance weighting at multiple granularities, and (3) a semantic matching module with co-attention mechanisms that capture context-aware semantic relatedness. Evaluations on multiple IR and NLP benchmarks demonstrate state-of-the-art effectiveness compared to approaches that do not exploit pretraining on external data. Extensive ablation studies suggest that relevance and semantic matching signals are complementary across many problem settings, regardless of the choice of underlying encoders.",
}
@inproceedings{xia-etal-2019-syntax,
    title = "A Syntax-aware Multi-task Learning Framework for {C}hinese Semantic Role Labeling",
    author = "Xia, Qingrong  and
      Li, Zhenghua  and
      Zhang, Min",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1541",
    doi = "10.18653/v1/D19-1541",
    pages = "5382--5392",
    abstract = "Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL performance on exploiting syntactic knowledge, achieving significant results. Pipeline methods based on automatic syntactic trees and multi-task learning (MTL) approaches using standard syntactic trees are two common research orientations. In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline. Besides, we present a MTL framework that includes the basic SRL module and a dependency parser module. Different from the commonly used hard parameter sharing strategy in MTL, the main idea is to extract implicit syntactic representations from the dependency parser as external inputs for the basic SRL model. Experiments on the benchmarks of Chinese Proposition Bank 1.0 and CoNLL-2009 Chinese datasets show that our proposed framework can effectively improve the performance over the strong baselines. With the external BERT representations, our framework achieves new state-of-the-art 87.54 and 88.5 F1 scores on the two test data of the two benchmarks, respectively. In-depth analysis are conducted to gain more insights on the proposed framework and the effectiveness of syntax.",
}
@inproceedings{arase-tsujii-2019-transfer,
    title = "Transfer Fine-Tuning: A {BERT} Case Study",
    author = "Arase, Yuki  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1542",
    doi = "10.18653/v1/D19-1542",
    pages = "5393--5404",
    abstract = "A semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). It constitutes a set of tasks crucial for research on natural language understanding. Recently, BERT realized a breakthrough in sentence representation learning (Devlin et al., 2019), which is broadly transferable to various NLP tasks. While BERT{'}s performance improves by increasing its model size, the required computational power is an obstacle preventing practical applications from adopting the technology. Herein, we propose to inject phrasal paraphrase relations into BERT in order to generate suitable representations for semantic equivalence assessment instead of increasing the model size. Experiments on standard natural language understanding tasks confirm that our method effectively improves a smaller BERT model while maintaining the model size. The generated model exhibits superior performance compared to a larger BERT model on semantic equivalence assessment tasks. Furthermore, it achieves larger performance gains on tasks with limited training datasets for fine-tuning, which is a property desirable for transfer learning.",
}
@inproceedings{dong-etal-2019-data,
    title = "Data-Anonymous Encoding for Text-to-{SQL} Generation",
    author = "Dong, Zhen  and
      Sun, Shizhao  and
      Liu, Hongzhi  and
      Lou, Jian-Guang  and
      Zhang, Dongmei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1543",
    doi = "10.18653/v1/D19-1543",
    pages = "5405--5414",
    abstract = "On text-to-SQL generation, the input utterance usually contains lots of tokens that are related to column names or cells in the table, called \textit{table-related tokens}. These table-related tokens are troublesome for the downstream neural semantic parser because it brings complex semantics and hinders the sharing across the training examples. However, existing approaches either ignore handling these tokens before the semantic parser or simply use deterministic approaches based on string-match or word embedding similarity. In this work, we propose a more efficient approach to handle table-related tokens before the semantic parser. First, we formulate it as a sequential tagging problem and propose a two-stage anonymization model to learn the semantic relationship between tables and input utterances. Then, we leverage the implicit supervision from SQL queries by policy gradient to guide the training. Experiments demonstrate that our approach consistently improves performances of different neural semantic parsers and significantly outperforms deterministic approaches.",
}
@inproceedings{chen-etal-2019-capturing,
    title = "Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks",
    author = "Chen, Xinchi  and
      Lyu, Chunchuan  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1544",
    doi = "10.18653/v1/D19-1544",
    pages = "5415--5425",
    abstract = "Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model non-local interaction between arguments. We propose a new approach to modeling these interactions while maintaining efficient inference. Specifically, we use Capsule Networks (Sabour et al., 2017): each proposition is encoded as a tuple of \textit{capsules}, one capsule per argument type (i.e. role). These tuples serve as embeddings of entire propositions. In every network layer, the capsules interact with each other and with representations of words in the sentence. Each iteration results in updated proposition embeddings and updated predictions about the SRL structure. Our model substantially outperforms the non-refinement baseline model on all 7 CoNLL-2019 languages and achieves state-of-the-art results on 5 languages (including English) for dependency SRL. We analyze the types of mistakes corrected by the refinement procedure. For example, each role is typically (but not always) filled with at most one argument. Whereas enforcing this approximate constraint is not useful with the modern SRL system, iterative procedure corrects the mistakes by capturing this intuition in a flexible and context-sensitive way.",
}
@inproceedings{iyer-etal-2019-learning,
    title = "Learning Programmatic Idioms for Scalable Semantic Parsing",
    author = "Iyer, Srinivasan  and
      Cheung, Alvin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1545",
    doi = "10.18653/v1/D19-1545",
    pages = "5426--5435",
    abstract = "Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens. In contrast, state of the art (SOTA) semantic parsers still map natural language instructions to source code by building the code syntax tree one node at a time. In this paper, we introduce an iterative method to extract code idioms from large source code corpora by repeatedly collapsing most-frequent depth-2 subtrees of their syntax trees, and train semantic parsers to apply these idioms during decoding. Applying idiom-based decoding on a recent context-dependent semantic parsing task improves the SOTA by 2.2{\%} BLEU score while reducing training time by more than 50{\%}. This improved speed enables us to scale up the model by training on an extended training set that is 5$\times$ larger, to further move up the SOTA by an additional 2.3{\%} BLEU and 0.9{\%} exact match. Finally, idioms also significantly improve accuracy of semantic parsing to SQL on the ATIS-SQL dataset, when training data is limited.",
}
@inproceedings{agashe-etal-2019-juice,
    title = "{J}u{IC}e: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation",
    author = "Agashe, Rajas  and
      Iyer, Srinivasan  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1546",
    doi = "10.18653/v1/D19-1546",
    pages = "5436--5446",
    abstract = "Interactive programming with interleaved code snippet cells and natural language markdown is recently gaining popularity in the form of Jupyter notebooks, which accelerate prototyping and collaboration. To study code generation conditioned on a long context history, we present JuICe, a corpus of 1.5 million examples with a curated test set of 3.7K instances based on online programming assignments. Compared with existing contextual code generation datasets, JuICe provides refined human-curated data, open-domain code, and an order of magnitude more training data. Using JuICe, we train models for two tasks: (1) generation of the API call sequence in a code cell, and (2) full code cell generation, both conditioned on the NL-Code history up to a particular code cell. Experiments using current baseline code generation models show that both context and distant supervision aid in generation, and that the dataset is challenging for current systems.",
}
@inproceedings{yao-etal-2019-model,
    title = "Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-{SQL} Case Study",
    author = "Yao, Ziyu  and
      Su, Yu  and
      Sun, Huan  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1547",
    doi = "10.18653/v1/D19-1547",
    pages = "5447--5458",
    abstract = "As a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results. In this paper, we propose a new, unified formulation of the interactive semantic parsing problem, where the goal is to design a model-based intelligent agent. The agent maintains its own state as the current predicted semantic parse, decides whether and where human intervention is needed, and generates a clarification question in natural language. A key part of the agent is a world model: it takes a percept (either an initial question or subsequent feedback from the user) and transitions to a new state. We then propose a simple yet remarkably effective instantiation of our framework, demonstrated on two text-to-SQL datasets (WikiSQL and Spider) with different state-of-the-art base semantic parsers. Compared to an existing interactive semantic parsing approach that treats the base parser as a black box, our approach solicits less user feedback but yields higher run-time accuracy.",
}
@inproceedings{zhu-etal-2019-modeling,
    title = "Modeling Graph Structure in Transformer for Better {AMR}-to-Text Generation",
    author = "Zhu, Jie  and
      Li, Junhui  and
      Zhu, Muhua  and
      Qian, Longhua  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1548",
    doi = "10.18653/v1/D19-1548",
    pages = "5459--5468",
    abstract = "Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequences. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better model the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e. the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state-of-the-art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.",
}
@inproceedings{huang-carley-2019-syntax,
    title = "Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks",
    author = "Huang, Binxuan  and
      Carley, Kathleen",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1549",
    doi = "10.18653/v1/D19-1549",
    pages = "5469--5477",
    abstract = "Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.",
}
@inproceedings{li-lu-2019-learning,
    title = "Learning Explicit and Implicit Structures for Targeted Sentiment Analysis",
    author = "Li, Hao  and
      Lu, Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1550",
    doi = "10.18653/v1/D19-1550",
    pages = "5478--5488",
    abstract = "Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space. However, the importance of capturing implicit global structural information that resides in the input space is largely unexplored. In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model. Our experimental results show that properly capturing both information is able to lead to better performance than competitive existing approaches. We also conduct extensive experiments to investigate our model{'}s effectiveness and robustness.",
}
@inproceedings{du-etal-2019-capsule,
    title = "Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification",
    author = "Du, Chunning  and
      Sun, Haifeng  and
      Wang, Jingyu  and
      Qi, Qi  and
      Liao, Jianxin  and
      Xu, Tong  and
      Liu, Ming",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1551",
    doi = "10.18653/v1/D19-1551",
    pages = "5489--5498",
    abstract = "Aspect-level sentiment classification is a crucial task for sentiment analysis, which aims to identify the sentiment polarities of specific targets in their context. The main challenge comes from multi-aspect sentences, which express multiple sentiment polarities towards different targets, resulting in overlapped feature representation. However, most existing neural models tend to utilize static pooling operation or attention mechanism to identify sentimental words, which therefore insufficient for dealing with overlapped features. To solve this problem, we propose to utilize capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm. Furthermore, interactive attention mechanism is introduced in the capsule routing procedure to model the semantic relationship between aspect terms and context. The iterative routing also enables encoding sentence from a global perspective. Experimental results on three datasets show that our proposed model achieves state-of-the-art performance.",
}
@inproceedings{zhou-etal-2019-emotion,
    title = "Emotion Detection with Neural Personal Discrimination",
    author = "Zhou, Xiabing  and
      Wang, Zhongqing  and
      Li, Shoushan  and
      Zhou, Guodong  and
      Zhang, Min",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1552",
    doi = "10.18653/v1/D19-1552",
    pages = "5499--5507",
    abstract = "There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. Different from previous researches, we explore the dependence among relevant posts via the authors{'} backgrounds, since the authors with similar backgrounds, e.g., gender, location, tend to express similar emotions. However, such personal attributes are not easy to obtain in most social media websites, and it is hard to capture attributes-aware words to connect similar people. Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions. In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. In this way, social correlationship among different posts can be better addressed. Experimental results show the usefulness of personal attributes, and the effectiveness of our proposed NPD approach in capturing such personal attributes with significant gains over the state-of-the-art models.",
}
@inproceedings{yang-etal-2019-specificity,
    title = "Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification",
    author = "Yang, Pengcheng  and
      Lin, Junyang  and
      Xu, Jingjing  and
      Xie, Jun  and
      Su, Qi  and
      Sun, Xu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1553",
    doi = "10.18653/v1/D19-1553",
    pages = "5508--5517",
    abstract = "The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data. Most previous work follows a two-step process. They first separate the content from the original sentiment, and then directly generate text with the target sentiment only based on the content produced by the first step. However, the second step bears both the target sentiment addition and content reconstruction, thus resulting in a lack of specific information like proper nouns in the generated text. To remedy this, we propose a specificity-driven cascading approach in this work, which can effectively increase the specificity of the generated text and further improve content preservation. In addition, we propose a more reasonable metric to evaluate sentiment modification. The experiments show that our approach outperforms competitive baselines by a large margin, which achieves 11{\%} and 38{\%} relative improvements of the overall metric on the Yelp and Amazon datasets, respectively.",
}
@inproceedings{xu-etal-2019-lexicalat,
    title = "{L}exical{AT}: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification",
    author = "Xu, Jingjing  and
      Zhao, Liang  and
      Yan, Hanqi  and
      Zeng, Qi  and
      Liang, Yun  and
      Sun, Xu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1554",
    doi = "10.18653/v1/D19-1554",
    pages = "5518--5527",
    abstract = "Recent work has shown that current text classification models are fragile and sensitive to simple perturbations. In this work, we propose a novel adversarial training approach, LexicalAT, to improve the robustness of current classification models. The proposed approach consists of a generator and a classifier. The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks. Considering the diversity of attacks, the generator uses a large-scale lexical knowledge base, WordNet, to generate attacking examples by replacing some words in training examples with their synonyms (e.g., sad and unhappy), neighbor words (e.g., fox and wolf), or super-superior words (e.g., chair and armchair). Due to the discrete generation step in the generator, we use policy gradient, a reinforcement learning approach, to train the two modules. Experiments show LexicalAT outperforms strong baselines and reduces test errors on various neural networks, including CNN, RNN, and BERT.",
}
@inproceedings{zhang-singh-2019-leveraging,
    title = "Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery",
    author = "Zhang, Zhe  and
      Singh, Munindar",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1555",
    doi = "10.18653/v1/D19-1555",
    pages = "5528--5538",
    abstract = "Opinionated text often involves attributes such as authorship and location that influence the sentiments expressed for different aspects. We posit that structural and semantic correspondence is both prevalent in opinionated text, especially when associated with attributes, and crucial in accurately revealing its latent aspect and sentiment structure. However, it is not recognized by existing approaches. We propose Trait, an unsupervised probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. To this end, Trait infers and leverages structural and semantic correspondence using a Markov Random Field. We show empirically that by incorporating attributes explicitly Trait significantly outperforms state-of-the-art baselines both by generating attribute profiles that accord with our intuitions, as shown via visualization, and yielding topics of greater semantic cohesion.",
}
@inproceedings{garcia-etal-2019-token,
    title = "From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining",
    author = "Garcia, Alexandre  and
      Colombo, Pierre  and
      d{'}Alch{\'e}-Buc, Florence  and
      Essid, Slim  and
      Clavel, Chlo{\'e}",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1556",
    doi = "10.18653/v1/D19-1556",
    pages = "5539--5548",
    abstract = "The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the development of social network based opinion miners. Unfortunately, gathering reliable data on which a model can be trained is notoriously difficult and existing works rely only on coarsely labeled opinions. In this work we aim at bridging the gap separating fine grained opinion models already developed for written language and coarse grained models developed for spontaneous multimodal opinion mining. We take advantage of the implicit hierarchical structure of opinions to build a joint fine and coarse grained opinion model that exploits different views of the opinion expression. The resulting model shares some properties with attention-based models and is shown to provide competitive results on a recently released multimodal fine grained annotated corpus.",
}
@inproceedings{k-sarma-etal-2019-shallow,
    title = "Shallow Domain Adaptive Embeddings for Sentiment Analysis",
    author = "K Sarma, Prathusha  and
      Liang, Yingyu  and
      Sethares, William",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1557",
    doi = "10.18653/v1/D19-1557",
    pages = "5549--5558",
    abstract = "This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics. A proposed domain adaptation layer learns weights to combine a generic and a domain specific (DS) word embedding into a domain adapted (DA) embedding. The DA word embeddings are then used as inputs to a generic encoder + classifier framework to perform a downstream task such as classification. This adaptation layer is particularly suited to data sets that are modest in size, and which are, therefore, not ideal candidates for (re)training a deep neural network architecture. Results on binary and multi-class classification tasks using popular encoder architectures, including current state-of-the-art methods (with and without the shallow adaptation layer) show the effectiveness of the proposed approach.",
}
@inproceedings{hu-etal-2019-domain-invariant,
    title = "Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification",
    author = "Hu, Mengting  and
      Wu, Yike  and
      Zhao, Shiwan  and
      Guo, Honglei  and
      Cheng, Renhong  and
      Su, Zhong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1558",
    doi = "10.18653/v1/D19-1558",
    pages = "5559--5568",
    abstract = "Cross-domain sentiment classification has drawn much attention in recent years. Most existing approaches focus on learning domain-invariant representations in both the source and target domains, while few of them pay attention to the domain-specific information. Despite the non-transferability of the domain-specific information, simultaneously learning domain-dependent representations can facilitate the learning of domain-invariant representations. In this paper, we focus on aspect-level cross-domain sentiment classification, and propose to distill the domain-invariant sentiment features with the help of an orthogonal domain-dependent task, i.e. aspect detection, which is built on the aspects varying widely in different domains. We conduct extensive experiments on three public datasets and the experimental results demonstrate the effectiveness of our method.",
}
@inproceedings{liang-etal-2019-novel-aspect,
    title = "A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis",
    author = "Liang, Yunlong  and
      Meng, Fandong  and
      Zhang, Jinchao  and
      Xu, Jinan  and
      Chen, Yufeng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1559",
    doi = "10.18653/v1/D19-1559",
    pages = "5569--5580",
    abstract = "Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation. In this paper, we propose a novel Aspect-Guided Deep Transition model, named AGDT, which utilizes the given aspect to guide the sentence encoding from scratch with the specially-designed deep transition architecture. Furthermore, an aspect-oriented objective is designed to enforce AGDT to reconstruct the given aspect with the generated sentence representation. In doing so, our AGDT can accurately generate aspect-specific sentence representation, and thus conduct more accurate sentiment predictions. Experimental results on multiple SemEval datasets demonstrate the effectiveness of our proposed approach, which significantly outperforms the best reported results with the same setting.",
}
@inproceedings{wang-etal-2019-human,
    title = "Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning",
    author = "Wang, Jingjing  and
      Sun, Changlong  and
      Li, Shoushan  and
      Wang, Jiancheng  and
      Si, Luo  and
      Zhang, Min  and
      Liu, Xiaozhong  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1560",
    doi = "10.18653/v1/D19-1560",
    pages = "5581--5590",
    abstract = "Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.",
}
@inproceedings{orbach-etal-2019-dataset,
    title = "A Dataset of General-Purpose Rebuttal",
    author = "Orbach, Matan  and
      Bilu, Yonatan  and
      Gera, Ariel  and
      Kantor, Yoav  and
      Dankin, Lena  and
      Lavee, Tamar  and
      Kotlerman, Lili  and
      Mirkin, Shachar  and
      Jacovi, Michal  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1561",
    doi = "10.18653/v1/D19-1561",
    pages = "5591--5601",
    abstract = "In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.",
}
@inproceedings{amplayo-2019-rethinking,
    title = "Rethinking Attribute Representation and Injection for Sentiment Classification",
    author = "Amplayo, Reinald Kim",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1562",
    doi = "10.18653/v1/D19-1562",
    pages = "5602--5613",
    abstract = "Text attributes, such as user and product information in product reviews, have been used to improve the performance of sentiment classification models. The de facto standard method is to incorporate them as additional biases in the attention mechanism, and more performance gains are achieved by extending the model architecture. In this paper, we show that the above method is the least effective way to represent and inject attributes. To demonstrate this hypothesis, unlike previous models with complicated architectures, we limit our base model to a simple BiLSTM with attention classifier, and instead focus on how and where the attributes should be incorporated in the model. We propose to represent attributes as chunk-wise importance weight matrices and consider four locations in the model (i.e., embedding, encoding, attention, classifier) to inject attributes. Experiments show that our proposed method achieves significant improvements over the standard approach and that attention mechanism is the worst location to inject attributes, contradicting prior work. We also outperform the state-of-the-art despite our use of a simple base model. Finally, we show that these representations transfer well to other tasks. Model implementation and datasets are released here: https://github.com/rktamplayo/CHIM.",
}
@inproceedings{fan-etal-2019-knowledge,
    title = "A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis",
    author = "Fan, Chuang  and
      Yan, Hongyu  and
      Du, Jiachen  and
      Gui, Lin  and
      Bing, Lidong  and
      Yang, Min  and
      Xu, Ruifeng  and
      Mao, Ruibin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1563",
    doi = "10.18653/v1/D19-1563",
    pages = "5614--5624",
    abstract = "Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08{\%} in F-measure.",
}
@inproceedings{toledo-etal-2019-automatic,
    title = "Automatic Argument Quality Assessment - New Datasets and Methods",
    author = "Toledo, Assaf  and
      Gretz, Shai  and
      Cohen-Karlik, Edo  and
      Friedman, Roni  and
      Venezian, Elad  and
      Lahav, Dan  and
      Jacovi, Michal  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1564",
    doi = "10.18653/v1/D19-1564",
    pages = "5625--5635",
    abstract = "We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.",
}
@inproceedings{da-san-martino-etal-2019-fine,
    title = "Fine-Grained Analysis of Propaganda in News Article",
    author = "Da San Martino, Giovanni  and
      Yu, Seunghak  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Petrov, Rostislav  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1565",
    doi = "10.18653/v1/D19-1565",
    pages = "5636--5646",
    abstract = "Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at fragment level with eighteen propaganda techniques and propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.",
}
@inproceedings{chauhan-etal-2019-context,
    title = "Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis",
    author = "Chauhan, Dushyant Singh  and
      Akhtar, Md Shad  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1566",
    doi = "10.18653/v1/D19-1566",
    pages = "5647--5657",
    abstract = "In recent times, multi-modal analysis has been an emerging and highly sought-after field at the intersection of natural language processing, computer vision, and speech processing. The prime objective of such studies is to leverage the diversified information, (e.g., textual, acoustic and visual), for learning a model. The effective interaction among these modalities often leads to a better system in terms of performance. In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis. The proposed model learns the inter-modal interaction among the participating modalities through an auto-encoder mechanism. We employ a context-aware attention module to exploit the correspondence among the neighboring utterances. We evaluate our proposed approach for five standard multi-modal affect analysis datasets. Experimental results suggest the efficacy of the proposed model for both sentiment and emotion analysis over various existing state-of-the-art systems.",
}
@inproceedings{madasu-anvesh-rao-2019-sequential,
    title = "Sequential Learning of Convolutional Features for Effective Text Classification",
    author = "Madasu, Avinash  and
      Anvesh Rao, Vijjini",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1567",
    doi = "10.18653/v1/D19-1567",
    pages = "5658--5667",
    abstract = "Text classification has been one of the major problems in natural language processing. With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this task. However, CNNs which were first proposed for images, face many crucial challenges in the context of text processing, namely in their elementary blocks: convolution filters and max pooling. These challenges have largely been overlooked by the most existing CNN models proposed for text classification. In this paper, we present an experimental study on the fundamental blocks of CNNs in text categorization. Based on this critique, we propose Sequential Convolutional Attentive Recurrent Network (SCARN). The proposed SCARN model utilizes both the advantages of recurrent and convolutional structures efficiently in comparison to previously proposed recurrent convolutional models. We test our model on different text classification datasets across tasks like sentiment analysis and question classification. Extensive experiments establish that SCARN outperforms other recurrent convolutional architectures with significantly less parameters. Furthermore, SCARN achieves better performance compared to equally large various deep CNN and LSTM architectures.",
}
@inproceedings{durmus-etal-2019-role,
    title = "The Role of Pragmatic and Discourse Context in Determining Argument Impact",
    author = "Durmus, Esin  and
      Ladhak, Faisal  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1568",
    doi = "10.18653/v1/D19-1568",
    pages = "5668--5678",
    abstract = "Research in the social sciences and psychology has shown that the persuasiveness of an argument depends not only the language employed, but also on attributes of the source/communicator, the audience, and the appropriateness and strength of the argument{'}s claims given the pragmatic and discourse context of the argument. Among these characteristics of persuasive arguments, prior work in NLP does not explicitly investigate the effect of the pragmatic and discourse context when determining argument quality. This paper presents a new dataset to initiate the study of this aspect of argumentation: it consists of a diverse collection of arguments covering 741 controversial topics and comprising over 47,000 claims. We further propose predictive models that incorporate the pragmatic and discourse context of argumentative claims and show that they outperform models that rely only on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument.",
}
@inproceedings{sun-etal-2019-aspect,
    title = "Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree",
    author = "Sun, Kai  and
      Zhang, Richong  and
      Mensah, Samuel  and
      Mao, Yongyi  and
      Liu, Xudong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1569",
    doi = "10.18653/v1/D19-1569",
    pages = "5679--5688",
    abstract = "We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works typically focus on leveraging the expressive power of neural networks in handling this task, we explore the possibility of integrating dependency trees with neural networks for representation learning. To this end, we present a convolution over a dependency tree (CDT) model which exploits a Bi-directional Long Short Term Memory (Bi-LSTM) to learn representations for features of a sentence, and further enhance the embeddings with a graph convolutional network (GCN) which operates directly on the dependency tree of the sentence. Our approach propagates both contextual and dependency information from opinion words to aspect words, offering discriminative properties for supervision. Experimental results ranks our approach as the new state-of-the-art in aspect-based sentiment classification.",
}
@inproceedings{li-etal-2019-understanding-data,
    title = "Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization",
    author = "Li, Guanlin  and
      Liu, Lemao  and
      Huang, Guoping  and
      Zhu, Conghui  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1570",
    doi = "10.18653/v1/D19-1570",
    pages = "5689--5695",
    abstract = "Many Data Augmentation (DA) methods have been proposed for neural machine translation. Existing works measure the superiority of DA methods in terms of their performance on a specific test set, but we find that some DA methods do not exhibit consistent improvements across translation tasks. Based on the observation, this paper makes an initial attempt to answer a fundamental question: what benefits, which are consistent across different methods and tasks, does DA in general obtain? Inspired by recent theoretic advances in deep learning, the paper understands DA from two perspectives towards the generalization ability of a model: input sensitivity and prediction margin, which are defined independent of specific test set thereby may lead to findings with relatively low variance. Extensive experiments show that relatively consistent benefits across five DA methods and four translation tasks are achieved regarding both perspectives.",
}
@inproceedings{yee-etal-2019-simple,
    title = "Simple and Effective Noisy Channel Modeling for Neural Machine Translation",
    author = "Yee, Kyra  and
      Dauphin, Yann  and
      Auli, Michael",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1571",
    doi = "10.18653/v1/D19-1571",
    pages = "5696--5701",
    abstract = "Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence. This makes decoding decisions based on partial source prefixes even though the full source is available. We pursue an alternative approach based on standard sequence to sequence models which utilize the entire source. These models perform remarkably well as channel models, even though they have neither been trained on, nor designed to factor over incomplete target sentences. Experiments with neural language models trained on billions of words show that noisy channel models can outperform a direct model by up to 3.2 BLEU on WMT{'}17 German-English translation. We evaluate on four language-pairs and our channel models consistently outperform strong alternatives such right-to-left reranking models and ensembles of direct models.",
}
@inproceedings{eisenschlos-etal-2019-multifit,
    title = "{M}ulti{F}i{T}: Efficient Multi-lingual Language Model Fine-tuning",
    author = "Eisenschlos, Julian  and
      Ruder, Sebastian  and
      Czapla, Piotr  and
      Kadras, Marcin  and
      Gugger, Sylvain  and
      Howard, Jeremy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1572",
    doi = "10.18653/v1/D19-1572",
    pages = "5702--5707",
    abstract = "Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of compute, while pretrained cross-lingual models often underperform on low-resource languages. We propose Multi-lingual language model Fine-Tuning (MultiFiT) to enable practitioners to train and fine-tune language models efficiently in their own language. In addition, we propose a zero-shot method using an existing pretrained cross-lingual model. We evaluate our methods on two widely used cross-lingual classification datasets where they outperform models pretrained on orders of magnitude more data and compute. We release all models and code.",
}
@inproceedings{li-etal-2019-hint,
    title = "Hint-Based Training for Non-Autoregressive Machine Translation",
    author = "Li, Zhuohan  and
      Lin, Zi  and
      He, Di  and
      Tian, Fei  and
      Qin, Tao  and
      Wang, Liwei  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1573",
    doi = "10.18653/v1/D19-1573",
    pages = "5708--5713",
    abstract = "Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency. Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time, but could only achieve inferior translation accuracy. In this paper, we proposed a novel approach to leveraging the hints from hidden states and word alignments to help the training of NART models. The results achieve significant improvement over previous NART models for the WMT14 En-De and De-En datasets and are even comparable to a strong LSTM-based ART baseline but one order of magnitude faster in inference.",
}
@inproceedings{fisch-etal-2019-working,
    title = "Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers",
    author = "Fisch, Adam  and
      Guo, Jiang  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1574",
    doi = "10.18653/v1/D19-1574",
    pages = "5714--5720",
    abstract = "This paper explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of our investigation is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing.",
}
@inproceedings{wang-etal-2019-cross,
    title = "Cross-Lingual {BERT} Transformation for Zero-Shot Dependency Parsing",
    author = "Wang, Yuxuan  and
      Che, Wanxiang  and
      Guo, Jiang  and
      Liu, Yijia  and
      Liu, Ting",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1575",
    doi = "10.18653/v1/D19-1575",
    pages = "5721--5727",
    abstract = "This paper investigates the problem of learning cross-lingual representations in a contextual space. We propose Cross-Lingual BERT Transformation (CLBT), a simple and efficient approach to generate cross-lingual contextualized word embeddings based on publicly available pre-trained BERT models (Devlin et al., 2018). In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages. We demonstrate the effectiveness of this approach on zero-shot cross-lingual transfer parsing. Experiments show that our embeddings substantially outperform the previous state-of-the-art that uses static embeddings. We further compare our approach with XLM (Lample and Conneau, 2019), a recently proposed cross-lingual language model trained with massive parallel data, and achieve highly competitive results.",
}
@inproceedings{han-etal-2019-multilingual,
    title = "Multilingual Grammar Induction with Continuous Language Identification",
    author = "Han, Wenjuan  and
      Wang, Ge  and
      Jiang, Yong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1576",
    doi = "10.18653/v1/D19-1576",
    pages = "5728--5733",
    abstract = "The key to multilingual grammar induction is to couple grammar parameters of different languages together by exploiting the similarity between languages. Previous work relies on linguistic phylogenetic knowledge to specify similarity between languages. In this work, we propose a novel universal grammar induction approach that represents language identities with continuous vectors and employs a neural network to predict grammar parameters based on the representation. Without any prior linguistic phylogenetic knowledge, we automatically capture similarity between languages with the vector representations and softly tie the grammar parameters of different languages. In our experiments, we apply our approach to 15 languages across 8 language families and subfamilies in the Universal Dependency Treebank dataset, and we observe substantial performance gain on average over monolingual and multilingual baselines.",
}
@inproceedings{williams-etal-2019-quantifying,
    title = "Quantifying the Semantic Core of Gender Systems",
    author = "Williams, Adina  and
      Blasi, Damian  and
      Wolf-Sonkin, Lawrence  and
      Wallach, Hanna  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1577",
    doi = "10.18653/v1/D19-1577",
    pages = "5734--5739",
    abstract = "Many of the world{'}s languages employ grammatical gender on the lexeme. For instance, in Spanish, house {``}casa{''} is feminine, whereas the word for paper {``}papel{''} is masculine. To a speaker of a genderless language, this categorization seems to exist with neither rhyme nor reason. But, is the association of nouns to gender classes truly arbitrary? In this work, we present the first large-scale investigation of the arbitrariness of gender assignment that uses canonical correlation analysis as a method for correlating the gender of inanimate nouns with their lexical semantic meaning. We find that the gender systems of 18 languages exhibit a significant correlation with an externally grounded definition of lexical semantics.",
}
@inproceedings{prabhakaran-etal-2019-perturbation,
    title = "Perturbation Sensitivity Analysis to Detect Unintended Model Biases",
    author = "Prabhakaran, Vinodkumar  and
      Hutchinson, Ben  and
      Mitchell, Margaret",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1578",
    doi = "10.18653/v1/D19-1578",
    pages = "5740--5745",
    abstract = "Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language. However, most language data reflect the public discourse at the time the data was produced, and hence NLP models are susceptible to learning incidental associations around named referents at a particular point in time, in addition to general linguistic meaning. An NLP system designed to model notions such as sentiment and toxicity should ideally produce scores that are independent of the identity of such entities mentioned in text and their social associations. For example, in a general purpose sentiment analysis system, a phrase such as I hate Katy Perry should be interpreted as having the same sentiment as I hate Taylor Swift. Based on this idea, we propose a generic evaluation framework, Perturbation Sensitivity Analysis, which detects unintended model biases related to named entities, and requires no new annotations or corpora. We demonstrate the utility of this analysis by employing it on two different NLP models {---} a sentiment model and a toxicity model {---} applied on online comments in English language from four different genres.",
}
@inproceedings{chang-mckeown-2019-automatically,
    title = "Automatically Inferring Gender Associations from Language",
    author = "Chang, Serina  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1579",
    doi = "10.18653/v1/D19-1579",
    pages = "5746--5752",
    abstract = "In this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings {--} one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.",
}
@inproceedings{mostafazadeh-davani-etal-2019-reporting,
    title = "Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes",
    author = "Mostafazadeh Davani, Aida  and
      Yeh, Leigh  and
      Atari, Mohammad  and
      Kennedy, Brendan  and
      Portillo Wightman, Gwenyth  and
      Gonzalez, Elaine  and
      Delong, Natalie  and
      Bhatia, Rhea  and
      Mirinjian, Arineh  and
      Ren, Xiang  and
      Dehghani, Morteza",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1580",
    doi = "10.18653/v1/D19-1580",
    pages = "5753--5757",
    abstract = "Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of local news articles, can be used to predict instances of hate crime. We then use the trained model to detect incidents of hate in cities for which the FBI lacks statistics. Lastly, we train models on predicting homicide and kidnapping, compare the predictions to FBI reports, and establish that incidents of hate are indeed under-reported, compared to other types of crimes, in local press.",
}
@inproceedings{saito-etal-2019-minimally,
    title = "Minimally Supervised Learning of Affective Events Using Discourse Relations",
    author = "Saito, Jun  and
      Murawaki, Yugo  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1581",
    doi = "10.18653/v1/D19-1581",
    pages = "5758--5765",
    abstract = "Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective polarity using discourse relations. Our method is simple and only requires a very small seed lexicon and a large raw corpus. Our experiments using Japanese data show that our method learns affective events effectively without manually labeled data. It also improves supervised learning results when labeled data are small.",
}
@inproceedings{yan-etal-2019-event,
    title = "Event Detection with Multi-Order Graph Convolution and Aggregated Attention",
    author = "Yan, Haoran  and
      Jin, Xiaolong  and
      Meng, Xiangbin  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1582",
    doi = "10.18653/v1/D19-1582",
    pages = "5766--5770",
    abstract = "Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related entities than sentence representations. But, existing studies only use first-order syntactic relations (i.e., the arcs) in dependency trees to identify trigger words. For this reason, this paper proposes a new method for event detection, which uses a dependency tree based graph convolution network with aggregative attention to explicitly model and aggregate multi-order syntactic representations in sentences. Experimental comparison with state-of-the-art baselines shows the superiority of the proposed method.",
}
@inproceedings{razniewski-etal-2019-coverage,
    title = "Coverage of Information Extraction from Sentences and Paragraphs",
    author = "Razniewski, Simon  and
      Jain, Nitisha  and
      Mirza, Paramita  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1583",
    doi = "10.18653/v1/D19-1583",
    pages = "5771--5776",
    abstract = "Scalar implicatures are language features that imply the negation of stronger statements, e.g., {``}She was married twice{''} typically implicates that she was not married thrice. In this paper we discuss the importance of scalar implicatures in the context of textual information extraction. We investigate how textual features can be used to predict whether a given text segment mentions all objects standing in a certain relationship with a certain subject. Preliminary results on Wikipedia indicate that this prediction is feasible, and yields informative assessments.",
}
@inproceedings{wang-etal-2019-hmeae,
    title = "{HMEAE}: Hierarchical Modular Event Argument Extraction",
    author = "Wang, Xiaozhi  and
      Wang, Ziqi  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Li, Juanzi  and
      Li, Peng  and
      Sun, Maosong  and
      Zhou, Jie  and
      Ren, Xiang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1584",
    doi = "10.18653/v1/D19-1584",
    pages = "5777--5783",
    abstract = "Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles. In this paper, we propose a Hierarchical Modular Event Argument Extraction (HMEAE) model, to provide effective inductive bias from the concept hierarchy of event argument roles. Specifically, we design a neural module network for each basic unit of the concept hierarchy, and then hierarchically compose relevant unit modules with logical operations into a role-oriented modular network to classify a specific argument role. As many argument roles share the same high-level unit module, their correlation can be utilized to extract specific event arguments better. Experiments on real-world datasets show that HMEAE can effectively leverage useful knowledge from the concept hierarchy and significantly outperform the state-of-the-art baselines. The source code can be obtained from https://github.com/thunlp/HMEAE.",
}
@inproceedings{wadden-etal-2019-entity,
    title = "Entity, Relation, and Event Extraction with Contextualized Span Representations",
    author = "Wadden, David  and
      Wennberg, Ulme  and
      Luan, Yi  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1585",
    doi = "10.18653/v1/D19-1585",
    pages = "5784--5789",
    abstract = "We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (within-sentence) and global (cross-sentence) context. Our framework achieves state-of-the-art results across all tasks, on four datasets from a variety of domains. We perform experiments comparing different techniques to construct span representations. Contextualized embeddings like BERT perform well at capturing relationships among entities in the same or adjacent sentences, while dynamic span graph updates model long-range cross-sentence relationships. For instance, propagating span representations via predicted coreference links can enable the model to disambiguate challenging entity mentions. Our code is publicly available at https://github.com/dwadden/dygiepp and can be easily adapted for new tasks or datasets.",
}
@inproceedings{shi-demberg-2019-next,
    title = "Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains",
    author = "Shi, Wei  and
      Demberg, Vera",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1586",
    doi = "10.18653/v1/D19-1586",
    pages = "5790--5796",
    abstract = "Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. Previous studies have generally focused on extracting better representations of the relational arguments. In order to solve the task, it is however additionally necessary to capture what events are expected to cause or follow each other. Current discourse relation classifiers fall short in this respect. We here show that this shortcoming can be effectively addressed by using the bidirectional encoder representation from transformers (BERT) proposed by Devlin et al. (2019), which were trained on a next-sentence prediction task, and thus encode a representation of likely next sentences. The BERT-based model outperforms the current state of the art in 11-way classification by 8{\%} points on the standard PDTB dataset. Our experiments also demonstrate that the model can be successfully ported to other domains: on the BioDRB dataset, the model outperforms the state of the art system around 15{\%} points.",
}
@inproceedings{kobayashi-etal-2019-split,
    title = "Split or Merge: Which is Better for Unsupervised {RST} Parsing?",
    author = "Kobayashi, Naoki  and
      Hirao, Tsutomu  and
      Nakamura, Kengo  and
      Kamigaito, Hidetaka  and
      Okumura, Manabu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1587",
    doi = "10.18653/v1/D19-1587",
    pages = "5797--5802",
    abstract = "Rhetorical Structure Theory (RST) parsing is crucial for many downstream NLP tasks that require a discourse structure for a text. Most of the previous RST parsers have been based on supervised learning approaches. That is, they require an annotated corpus of sufficient size and quality, and heavily rely on the language and domain dependent corpus. In this paper, we present two language-independent unsupervised RST parsing methods based on dynamic programming. The first one builds the optimal tree in terms of a dissimilarity score function that is defined for splitting a text span into smaller ones. The second builds the optimal tree in terms of a similarity score function that is defined for merging two adjacent spans into a large one. Experimental results on English and German RST treebanks showed that our parser based on span merging achieved the best score, around 0.8 F$_1$ score, which is close to the scores of the previous supervised parsers.",
}
@inproceedings{joshi-etal-2019-bert,
    title = "{BERT} for Coreference Resolution: Baselines and Analysis",
    author = "Joshi, Mandar  and
      Levy, Omer  and
      Zettlemoyer, Luke  and
      Weld, Daniel",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1588",
    doi = "10.18653/v1/D19-1588",
    pages = "5803--5808",
    abstract = "We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication.",
}
@inproceedings{kang-hovy-2019-linguistic,
    title = "Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs",
    author = "Kang, Dongyeop  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1589",
    doi = "10.18653/v1/D19-1589",
    pages = "5809--5815",
    abstract = "Generating a long, coherent text such as a paragraph requires a high-level control of different levels of relations between sentences (e.g., tense, coreference). We call such a logical connection between sentences as a (paragraph) flow. In order to produce a coherent flow of text, we explore two forms of intersentential relations in a paragraph: one is a human-created linguistical relation that forms a structure (e.g., discourse tree) and the other is a relation from latent representation learned from the sentences themselves. Our two proposed models incorporate each form of relations into document-level language models: the former is a supervised model that jointly learns a language model as well as discourse relation prediction, and the latter is an unsupervised model that is hierarchically conditioned by a recurrent neural network (RNN) over the latent information. Our proposed models with both forms of relations outperform the baselines in partially conditioned paragraph generation task. Our codes and data are publicly available.",
}
@inproceedings{kadowaki-etal-2019-event,
    title = "Event Causality Recognition Exploiting Multiple Annotators{'} Judgments and Background Knowledge",
    author = "Kadowaki, Kazuma  and
      Iida, Ryu  and
      Torisawa, Kentaro  and
      Oh, Jong-Hoon  and
      Kloetzer, Julien",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1590",
    doi = "10.18653/v1/D19-1590",
    pages = "5816--5822",
    abstract = "We propose new BERT-based methods for recognizing event causality such as {``}smoke cigarettes{''} {--}{\textgreater} {``}die of lung cancer{''} written in web texts. In our methods, we grasp each annotator{'}s policy by training multiple classifiers, each of which predicts the labels given by a single annotator, and combine the resulting classifiers{'} outputs to predict the final labels determined by majority vote. Furthermore, we investigate the effect of supplying background knowledge to our classifiers. Since BERT models are pre-trained with a large corpus, some sort of background knowledge for event causality may be learned during pre-training. Our experiments with a Japanese dataset suggest that this is actually the case: Performance improved when we pre-trained the BERT models with web texts containing a large number of event causalities instead of Wikipedia articles or randomly sampled web texts. However, this effect was limited. Therefore, we further improved performance by simply adding texts related to an input causality candidate as background knowledge to the input of the BERT models. We believe these findings indicate a promising future research direction.",
}
@inproceedings{xin-etal-2019-part,
    title = "What Part of the Neural Network Does This? Understanding {LSTM}s by Measuring and Dissecting Neurons",
    author = "Xin, Ji  and
      Lin, Jimmy  and
      Yu, Yaoliang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1591",
    doi = "10.18653/v1/D19-1591",
    pages = "5823--5830",
    abstract = "Memory neurons of long short-term memory (LSTM) networks encode and process information in powerful yet mysterious ways. While there has been work to analyze their behavior in carrying low-level information such as linguistic properties, how they directly contribute to label prediction remains unclear. We find inspiration from biologists and study the affinity between individual neurons and labels, propose a novel metric to quantify the sensitivity of neurons to each label, and conduct experiments to show the validity of our proposed metric. We discover that some neurons are trained to specialize on a subset of labels, and while dropping an arbitrary neuron has little effect on the overall accuracy of the model, dropping label-specialized neurons predictably and significantly degrades prediction accuracy on the associated label. We further examine the consistency of neuron-label affinity across different models. These observations provide insight into the inner mechanisms of LSTMs.",
}
@inproceedings{van-schijndel-etal-2019-quantity,
    title = "Quantity doesn{'}t buy quality syntax with neural language models",
    author = "van Schijndel, Marten  and
      Mueller, Aaron  and
      Linzen, Tal",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1592",
    doi = "10.18653/v1/D19-1592",
    pages = "5831--5837",
    abstract = "Recurrent neural networks can learn to predict upcoming words remarkably well on average; in syntactically complex contexts, however, they often assign unexpectedly high probabilities to ungrammatical words. We investigate to what extent these shortcomings can be mitigated by increasing the size of the network and the corpus on which it is trained. We find that gains from increasing network size are minimal beyond a certain point. Likewise, expanding the training corpus yields diminishing returns; we estimate that the training corpus would need to be unrealistically large for the models to match human performance. A comparison to GPT and BERT, Transformer-based models trained on billions of words, reveals that these models perform even more poorly than our LSTMs in some constructions. Our results make the case for more data efficient architectures.",
}
@inproceedings{abdou-etal-2019-higher,
    title = "Higher-order Comparisons of Sentence Encoder Representations",
    author = "Abdou, Mostafa  and
      Kulmizev, Artur  and
      Hill, Felix  and
      Low, Daniel M.  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1593",
    doi = "10.18653/v1/D19-1593",
    pages = "5838--5845",
    abstract = "Representational Similarity Analysis (RSA) is a technique developed by neuroscientists for comparing activity patterns of different measurement modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has several advantages over existing approaches to interpretation of language encoders based on probing or diagnostic classification: namely, it does not require large training samples, is not prone to overfitting, and it enables a more transparent comparison between the representational geometries of different models and modalities. We demonstrate the utility of RSA by establishing a previously unknown correspondence between widely-employed pretrained language encoders and human processing difficulty via eye-tracking data, showcasing its potential in the interpretability toolbox for neural models.",
}
@inproceedings{hale-etal-2019-text,
    title = "Text Genre and Training Data Size in Human-like Parsing",
    author = "Hale, John  and
      Kuncoro, Adhiguna  and
      Hall, Keith  and
      Dyer, Chris  and
      Brennan, Jonathan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1594",
    doi = "10.18653/v1/D19-1594",
    pages = "5846--5852",
    abstract = "Domain-specific training typically makes NLP systems work better. We show that this extends to cognitive modeling as well by relating the states of a neural phrase-structure parser to electrophysiological measures from human participants. These measures were recorded as participants listened to a spoken recitation of the same literary text that was supplied as input to the neural parser. Given more training data, the system derives a better cognitive model {---} but only when the training examples come from the same textual genre. This finding is consistent with the idea that humans adapt syntactic expectations to particular genres during language comprehension (Kaan and Chun, 2018; Branigan and Pickering, 2017).",
}
@inproceedings{derby-etal-2019-feature2vec,
    title = "{F}eature2{V}ec: Distributional semantic modelling of human property knowledge",
    author = "Derby, Steven  and
      Miller, Paul  and
      Devereux, Barry",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1595",
    doi = "10.18653/v1/D19-1595",
    pages = "5853--5859",
    abstract = "Feature norm datasets of human conceptual knowledge, collected in surveys of human volunteers, yield highly interpretable models of word meaning and play an important role in neurolinguistic research on semantic cognition. However, these datasets are limited in size due to practical obstacles associated with exhaustively listing properties for a large number of words. In contrast, the development of distributional modelling techniques and the availability of vast text corpora have allowed researchers to construct effective vector space models of word meaning over large lexicons. However, this comes at the cost of interpretable, human-like information about word meaning. We propose a method for mapping human property knowledge onto a distributional semantic space, which adapts the word2vec architecture to the task of modelling concept features. Our approach gives a measure of concept and feature affinity in a single semantic space, which makes for easy and efficient ranking of candidate human-derived semantic properties for arbitrary words. We compare our model with a previous approach, and show that it performs better on several evaluation tasks. Finally, we discuss how our method could be used to develop efficient sampling techniques to extend existing feature norm datasets in a reliable way.",
}
@inproceedings{ray-etal-2019-sunny,
    title = "Sunny and Dark Outside?! Improving Answer Consistency in {VQA} through Entailed Question Generation",
    author = "Ray, Arijit  and
      Sikka, Karan  and
      Divakaran, Ajay  and
      Lee, Stefan  and
      Burachas, Giedrius",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1596",
    doi = "10.18653/v1/D19-1596",
    pages = "5860--5865",
    abstract = "While models for Visual Question Answering (VQA) have steadily improved over the years, interacting with one quickly reveals that these models lack consistency. For instance, if a model answers {``}red{''} to {``}What color is the balloon?{''}, it might answer {``}no{''} if asked, {``}Is the balloon red?{''}. These responses violate simple notions of entailment and raise questions about how effectively VQA models ground language. In this work, we introduce a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA. For a given observable fact in an image (e.g. the balloon{'}s color), we generate a set of logically consistent question-answer (QA) pairs (e.g. Is the balloon red?) and also collect a human-annotated set of common-sense based consistent QA pairs (e.g. Is the balloon the same color as tomato sauce?). Further, we propose a consistency-improving data augmentation module, a Consistency Teacher Module (CTM). CTM automatically generates entailed (or similar-intent) questions for a source QA pair and fine-tunes the VQA model if the VQA{'}s answer to the entailed question is consistent with the source QA pair. We demonstrate that our CTM-based training improves the consistency of VQA models on the Con-VQA datasets and is a strong baseline for further research.",
}
@inproceedings{huang-etal-2019-geosqa,
    title = "{G}eo{SQA}: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level",
    author = "Huang, Zixian  and
      Shen, Yulin  and
      Li, Xiao  and
      Wei, Yu{'}ang  and
      Cheng, Gong  and
      Zhou, Lin  and
      Dai, Xinyu  and
      Qu, Yuzhong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1597",
    doi = "10.18653/v1/D19-1597",
    pages = "5866--5871",
    abstract = "Scenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a scenario. SQA widely exists in the medical, geography, and legal domains{---}both in practice and in the exams. In this paper, we introduce the GeoSQA dataset. It consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level, where diagrams (e.g., maps, charts) have been manually annotated with natural language descriptions to benefit NLP research. Benchmark results on a variety of state-of-the-art methods for question answering, textual entailment, and reading comprehension demonstrate the unique challenges presented by SQA for future research.",
}
@inproceedings{le-etal-2019-revisiting,
    title = "Revisiting the Evaluation of Theory of Mind through Question Answering",
    author = "Le, Matthew  and
      Boureau, Y-Lan  and
      Nickel, Maximilian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1598",
    doi = "10.18653/v1/D19-1598",
    pages = "5872--5877",
    abstract = "Theory of mind, i.e., the ability to reason about intents and beliefs of agents is an important task in artificial intelligence and central to resolving ambiguous references in natural language dialogue. In this work, we revisit the evaluation of theory of mind through question answering. We show that current evaluation methods are flawed and that existing benchmark tasks can be solved without theory of mind due to dataset biases. Based on prior work, we propose an improved evaluation protocol and dataset in which we explicitly control for data regularities via a careful examination of the answer space. We show that state-of-the-art methods which are successful on existing benchmarks fail to solve theory-of-mind tasks in our proposed approach.",
}
@inproceedings{wang-etal-2019-multi-passage,
    title = "Multi-passage {BERT}: A Globally Normalized {BERT} Model for Open-domain Question Answering",
    author = "Wang, Zhiguo  and
      Ng, Patrick  and
      Ma, Xiaofei  and
      Nallapati, Ramesh  and
      Xiang, Bing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1599",
    doi = "10.18653/v1/D19-1599",
    pages = "5878--5882",
    abstract = "BERT model has been successfully applied to open-domain QA tasks. However, previous work trains BERT by viewing passages corresponding to the same question as independent training instances, which may cause incomparable scores for answers from different passages. To tackle this issue, we propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question, and this change enables our QA model find better answers by utilizing more passages. In addition, we find that splitting articles into passages with the length of 100 words by sliding window improves performance by 4{\%}. By leveraging a passage ranker to select high-quality passages, multi-passage BERT gains additional 2{\%}. Experiments on four standard benchmarks showed that our multi-passage BERT outperforms all state-of-the-art models on all benchmarks. In particular, on the OpenSQuAD dataset, our model gains 21.4{\%} EM and 21.5{\%} F1 over all non-BERT models, and 5.8{\%} EM and 6.5{\%} F1 over BERT-based models.",
}
@inproceedings{cui-etal-2019-span,
    title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension",
    author = "Cui, Yiming  and
      Liu, Ting  and
      Che, Wanxiang  and
      Xiao, Li  and
      Chen, Zhipeng  and
      Ma, Wentao  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1600",
    doi = "10.18653/v1/D19-1600",
    pages = "5883--5889",
    abstract = "Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated on Wikipedia paragraphs by human experts. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. We present several baseline systems as well as anonymous submissions for demonstrating the difficulties in this dataset. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the Chinese machine reading comprehension research. Resources are available: https://github.com/ymcui/cmrc2018",
}
@inproceedings{han-etal-2019-micron,
    title = "{MICRON}: Multigranular Interaction for Contextualizing {R}epresentati{ON} in Non-factoid Question Answering",
    author = "Han, Hojae  and
      Choi, Seungtaek  and
      Park, Haeju  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1601",
    doi = "10.18653/v1/D19-1601",
    pages = "5890--5895",
    abstract = "This paper studies the problem of non-factoid question answering, where the answer may span over multiple sentences. Existing solutions can be categorized into representation- and interaction-focused approaches. We combine their complementary strength, by a hybrid approach allowing multi-granular interactions, but represented at word level, enabling an easy integration with strong word-level signals. Specifically, we propose MICRON: Multigranular Interaction for Contextualizing RepresentatiON, a novel approach which derives contextualized uni-gram representation from n-grams. Our contributions are as follows: First, we enable multi-granular matches between question and answer $n$-grams. Second, by contextualizing word representation with surrounding n-grams, MICRON can naturally utilize word-based signals for query term weighting, known to be effective in information retrieval. We validate MICRON in two public non-factoid question answering datasets: WikiPassageQA and InsuranceQA, showing our model achieves the state of the art among baselines with reported performances on both datasets.",
}
@inproceedings{qiu-etal-2019-machine,
    title = "Machine Reading Comprehension Using Structural Knowledge Graph-aware Network",
    author = "Qiu, Delai  and
      Zhang, Yuanzhe  and
      Feng, Xinwei  and
      Liao, Xiangwen  and
      Jiang, Wenbin  and
      Lyu, Yajuan  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1602",
    doi = "10.18653/v1/D19-1602",
    pages = "5896--5901",
    abstract = "Leveraging external knowledge is an emerging trend in machine comprehension task. Previous work usually utilizes knowledge graphs such as ConceptNet as external knowledge, and extracts triples from them to enhance the initial representation of the machine comprehension context. However, such method cannot capture the structural information in the knowledge graph. To this end, we propose a Structural Knowledge Graph-aware Network(SKG) model, constructing sub-graphs for entities in the machine comprehension context. Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph. Experiments show that SKG achieves state-of-the-art performance on the ReCoRD dataset.",
}
@inproceedings{mueller-etal-2019-answering,
    title = "Answering Conversational Questions on Structured Data without Logical Forms",
    author = "Mueller, Thomas  and
      Piccinno, Francesco  and
      Shaw, Peter  and
      Nicosia, Massimo  and
      Altun, Yasemin",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1603",
    doi = "10.18653/v1/D19-1603",
    pages = "5902--5910",
    abstract = "We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation. We encode tables as graphs using a graph neural network model based on the Transformer architecture. The answers are then selected from the encoded graph using a pointer network. This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references. We demonstrate the validity of this approach with competitive results on the Sequential Question Answering (SQA) task.",
}
@inproceedings{kumar-etal-2019-improving,
    title = "Improving Answer Selection and Answer Triggering using Hard Negatives",
    author = "Kumar, Sawan  and
      Garg, Shweta  and
      Mehta, Kartik  and
      Rasiwasia, Nikhil",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1604",
    doi = "10.18653/v1/D19-1604",
    pages = "5911--5917",
    abstract = "In this paper, we establish the effectiveness of using hard negatives, coupled with a siamese network and a suitable loss function, for the tasks of answer selection and answer triggering. We show that the choice of sampling strategy is key for achieving improved performance on these tasks. Evaluating on recent answer selection datasets - InsuranceQA, SelQA, and an internal QA dataset, we show that using hard negatives with relatively simple model architectures (bag of words and LSTM-CNN) drives significant performance gains. On InsuranceQA, this strategy alone improves over previously reported results by a minimum of 1.6 points in P@1. Using hard negatives with a Transformer encoder provides a further improvement of 2.3 points. Further, we propose to use quadruplet loss for answer triggering, with the aim of producing globally meaningful similarity scores. We show that quadruplet loss function coupled with the selection of hard negatives enables bag-of-words models to improve F1 score by 2.3 points over previous baselines, on SelQA answer triggering dataset. Our results provide key insights into answer selection and answer triggering tasks.",
}
@inproceedings{elgohary-etal-2019-unpack,
    title = "Can You Unpack That? Learning to Rewrite Questions-in-Context",
    author = "Elgohary, Ahmed  and
      Peskov, Denis  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1605",
    doi = "10.18653/v1/D19-1605",
    pages = "5918--5924",
    abstract = "Question answering is an AI-complete problem, but existing datasets lack key elements of language understanding such as coreference and ellipsis resolution. We consider sequential question answering: multiple questions are asked one-by-one in a conversation between a questioner and an answerer. Answering these questions is only possible through understanding the conversation history. We introduce the task of question-in-context rewriting: given the context of a conversation{'}s history, rewrite a context-dependent into a self-contained question with the same answer. We construct, CANARD, a dataset of 40,527 questions based on QuAC (Choi et al., 2018) and train Seq2Seq models for incorporating context into standalone questions.",
}
@inproceedings{dasigi-etal-2019-quoref,
    title = "{Q}uoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning",
    author = "Dasigi, Pradeep  and
      Liu, Nelson F.  and
      Marasovi{\'c}, Ana  and
      Smith, Noah A.  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1606",
    doi = "10.18653/v1/D19-1606",
    pages = "5925--5932",
    abstract = "Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate the ability of models to resolve coreference. We present a new crowdsourced dataset containing more than 24K span-selection questions that require resolving coreference among entities in over 4.7K English paragraphs from Wikipedia. Obtaining questions focused on such phenomena is challenging, because it is hard to avoid lexical cues that shortcut complex reasoning. We deal with this issue by using a strong baseline model as an adversary in the crowdsourcing loop, which helps crowdworkers avoid writing questions with exploitable surface cues. We show that state-of-the-art reading comprehension models perform significantly worse than humans on this benchmark{---}the best model performance is 70.5 F1, while the estimated human performance is 93.4 F1.",
}
@inproceedings{hsu-etal-2019-zero,
    title = "Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model",
    author = "Hsu, Tsung-Yuan  and
      Liu, Chi-Liang  and
      Lee, Hung-yi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1607",
    doi = "10.18653/v1/D19-1607",
    pages = "5933--5940",
    abstract = "Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zero-shot cross-lingual transfer learning on reading comprehension tasks with language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zero-shot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting.",
}
@inproceedings{tafjord-etal-2019-quartz,
    title = "{Q}ua{RT}z: An Open-Domain Dataset of Qualitative Relationship Questions",
    author = "Tafjord, Oyvind  and
      Gardner, Matt  and
      Lin, Kevin  and
      Clark, Peter",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1608",
    doi = "10.18653/v1/D19-1608",
    pages = "5941--5946",
    abstract = "We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., {``}A sunscreen with a higher SPF protects the skin longer.{''}, twinned with 3864 crowdsourced situated questions, e.g., {``}Billy is wearing sunscreen with a lower SPF than Lucy. Who will be best protected from the sun?{''}, plus annotations of the properties being compared. Unlike previous datasets, the general knowledge is textual and not tied to a fixed set of relationships, and tests a system{'}s ability to comprehend and apply textual qualitative knowledge in a novel setting. We find state-of-the-art results are substantially (20{\%}) below human performance, presenting an open challenge to the NLP community.",
}
@inproceedings{andor-etal-2019-giving,
    title = "Giving {BERT} a Calculator: Finding Operations and Arguments with Reading Comprehension",
    author = "Andor, Daniel  and
      He, Luheng  and
      Lee, Kenton  and
      Pitler, Emily",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1609",
    doi = "10.18653/v1/D19-1609",
    pages = "5947--5952",
    abstract = "Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We enable a BERT-based reading comprehension model to perform lightweight numerical reasoning. We augment the model with a predefined set of executable {`}programs{'} which encompass simple arithmetic as well as extraction. Rather than having to learn to manipulate numbers directly, the model can pick a program and execute it. On the recent Discrete Reasoning Over Passages (DROP) dataset, designed to challenge reading comprehension models, we show a 33{\%} absolute improvement by adding shallow programs. The model can learn to predict new operations when appropriate in a math word problem setting (Roy and Roth, 2015) with very few training examples.",
}
@inproceedings{lai-etal-2019-gated,
    title = "A Gated Self-attention Memory Network for Answer Selection",
    author = "Lai, Tuan  and
      Tran, Quan Hung  and
      Bui, Trung  and
      Kihara, Daisuke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1610",
    doi = "10.18653/v1/D19-1610",
    pages = "5953--5959",
    abstract = "Answer selection is an important research problem, with applications in many areas. Previous deep learning based approaches for the task mainly adopt the Compare-Aggregate architecture that performs word-level comparison followed by aggregation. In this work, we take a departure from the popular Compare-Aggregate architecture, and instead, propose a new gated self-attention memory network for the task. Combined with a simple transfer learning technique from a large-scale online corpus, our model outperforms previous methods by a large margin, achieving new state-of-the-art results on two standard answer selection datasets: TrecQA and WikiQA.",
}
@inproceedings{mao-lee-2019-polly,
    title = "Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets",
    author = "Mao, Hong-Ren  and
      Lee, Hung-Yi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1611",
    doi = "10.18653/v1/D19-1611",
    pages = "5960--5968",
    abstract = "Paraphrase generation is an interesting and challenging NLP task which has numerous practical applications. In this paper, we analyze datasets commonly used for paraphrase generation research, and show that simply parroting input sentences surpasses state-of-the-art models in the literature when evaluated on standard metrics. Our findings illustrate that a model could be seemingly adept at generating paraphrases, despite only making trivial changes to the input sentence or even none at all.",
}
@inproceedings{handler-oconnor-2019-query,
    title = "Query-focused Sentence Compression in Linear Time",
    author = "Handler, Abram  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1612",
    doi = "10.18653/v1/D19-1612",
    pages = "5969--5975",
    abstract = "Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11x empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU.",
}
@inproceedings{majumder-etal-2019-generating,
    title = "Generating Personalized Recipes from Historical User Preferences",
    author = "Majumder, Bodhisattwa Prasad  and
      Li, Shuyang  and
      Ni, Jianmo  and
      McAuley, Julian",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1613",
    doi = "10.18653/v1/D19-1613",
    pages = "5976--5982",
    abstract = "Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text instructions aligned with the user{'}s historical preferences. We attend on technique- and recipe-level representations of a user{'}s previously consumed recipes, fusing these {`}user-aware{'} representations in an attention fusion layer to control recipe text generation. Experiments on a new dataset of 180K recipes and 700K interactions show our model{'}s ability to generate plausible and personalized recipes compared to non-personalized baselines.",
}
@inproceedings{qiu-xiong-2019-generating,
    title = "Generating Highly Relevant Questions",
    author = "Qiu, Jiazuo  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1614",
    doi = "10.18653/v1/D19-1614",
    pages = "5983--5987",
    abstract = "The neural seq2seq based question generation (QG) is prone to generating generic and undiversified questions that are poorly relevant to the given passage and target answer. In this paper, we propose two methods to address the issue. (1) By a partial copy mechanism, we prioritize words that are morphologically close to words in the input passage when generating questions; (2) By a QA-based reranker, from the n-best list of question candidates, we select questions that are preferred by both the QA and QG model. Experiments and analyses demonstrate that the proposed two methods substantially improve the relevance of generated questions to passages and answers.",
}
@inproceedings{mao-etal-2019-improving,
    title = "Improving Neural Story Generation by Targeted Common Sense Grounding",
    author = "Mao, Huanru Henry  and
      Majumder, Bodhisattwa Prasad  and
      McAuley, Julian  and
      Cottrell, Garrison",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1615",
    doi = "10.18653/v1/D19-1615",
    pages = "5988--5993",
    abstract = "Stories generated with neural language models have shown promise in grammatical and stylistic consistency. However, the generated stories are still lacking in common sense reasoning, e.g., they often contain sentences deprived of world knowledge. We propose a simple multi-task learning scheme to achieve quantitatively better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. When combined with our two-stage fine-tuning pipeline, our method achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts (Fan et al., 2018) story generation dataset.",
}
@inproceedings{parida-motlicek-2019-abstract,
    title = "Abstract Text Summarization: A Low Resource Challenge",
    author = "Parida, Shantipriya  and
      Motlicek, Petr",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1616",
    doi = "10.18653/v1/D19-1616",
    pages = "5994--5998",
    abstract = "Text summarization is considered as a challenging task in the NLP community. The availability of datasets for the task of multilingual text summarization is rare, and such datasets are difficult to construct. In this work, we build an abstract text summarizer for the German language text using the state-of-the-art {``}Transformer{''} model. We propose an iterative data augmentation approach which uses synthetic data along with the real summarization data for the German language. To generate synthetic data, the Common Crawl (German) dataset is exploited, which covers different domains. The synthetic data is effective for the low resource condition and is particularly helpful for our multilingual scenario where availability of summarizing data is still a challenging issue. The data are also useful in deep learning scenarios where the neural models require a large amount of training data for utilization of its capacity. The obtained summarization performance is measured in terms of ROUGE and BLEU score. We achieve an absolute improvement of +1.5 and +16.0 in ROUGE1 F1 (R1{\_}F1) on the development and test sets, respectively, compared to the system which does not rely on data augmentation.",
}
@inproceedings{hamalainen-alnajjar-2019-generating,
    title = "Generating Modern Poetry Automatically in {F}innish",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1617",
    doi = "10.18653/v1/D19-1617",
    pages = "5999--6004",
    abstract = "We present a novel approach for generating poetry automatically for the morphologically rich Finnish language by using a genetic algorithm. The approach improves the state of the art of the previous Finnish poem generators by introducing a higher degree of freedom in terms of structural creativity. Our approach is evaluated and described within the paradigm of computational creativity, where the fitness functions of the genetic algorithm are assimilated with the notion of aesthetics. The output is considered to be a poem 81.5{\%} of the time by human evaluators.",
}
@inproceedings{xenouleas-etal-2019-sum,
    title = "{SUM}-{QE}: a {BERT}-based Summary Quality Estimation Model",
    author = "Xenouleas, Stratos  and
      Malakasiotis, Prodromos  and
      Apidianaki, Marianna  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1618",
    doi = "10.18653/v1/D19-1618",
    pages = "6005--6011",
    abstract = "We propose SUM-QE, a novel Quality Estimation model for summarization based on BERT. The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references. SUM-QE achieves very high correlations with human ratings, outperforming simpler models addressing these linguistic aspects. Predictions of the SUM-QE model can be used for system development, and to inform users of the quality of automatically produced summaries and other types of generated text.",
}
@inproceedings{du-ji-2019-empirical,
    title = "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation",
    author = "Du, Wanyu  and
      Ji, Yangfeng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1619",
    doi = "10.18653/v1/D19-1619",
    pages = "6012--6018",
    abstract = "Generating paraphrases from given sentences involves decoding words step by step from a large vocabulary. To learn a decoder, supervised learning which maximizes the likelihood of tokens always suffers from the exposure bias. Although both reinforcement learning (RL) and imitation learning (IL) have been widely used to alleviate the bias, the lack of direct comparison leads to only a partial image on their benefits. In this work, we present an empirical study on how RL and IL can help boost the performance of generating paraphrases, with the pointer-generator as a base model. Experiments on the benchmark datasets show that (1) imitation learning is constantly better than reinforcement learning; and (2) the pointer-generator models with imitation learning outperform the state-of-the-art methods with a large margin.",
}
@inproceedings{grenander-etal-2019-countering,
    title = "Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses",
    author = "Grenander, Matt  and
      Dong, Yue  and
      Cheung, Jackie Chi Kit  and
      Louis, Annie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1620",
    doi = "10.18653/v1/D19-1620",
    pages = "6019--6024",
    abstract = "Sentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article. In this paper, we show that recent neural systems excessively exploit this trend, which although powerful for many inputs, is also detrimental when summarizing documents where important content should be extracted from later parts of the article. We propose two techniques to make systems sensitive to the importance of content in different parts of the article. The first technique employs {`}unbiased{'} data; i.e., randomly shuffled sentences of the source document, to pretrain the model. The second technique uses an auxiliary ROUGE-based loss that encourages the model to distribute importance scores throughout a document by mimicking sentence-level ROUGE scores on the training data. We show that these techniques significantly improve the performance of a competitive reinforcement learning based extractive system, with the auxiliary loss being more powerful than pretraining.",
}
@inproceedings{jhamtani-etal-2019-learning,
    title = "Learning Rhyming Constraints using Structured Adversaries",
    author = "Jhamtani, Harsh  and
      Mehta, Sanket Vaibhav  and
      Carbonell, Jaime  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1621",
    doi = "10.18653/v1/D19-1621",
    pages = "6025--6031",
    abstract = "Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry. Much prior work on poetry generation uses manually defined constraints which are satisfied during decoding using either specialized decoding procedures or rejection sampling. The rhyming constraints themselves are typically not learned by the generator. We propose an alternate approach that uses a structured discriminator to learn a poetry generator that directly captures rhyming constraints in a generative adversarial setup. By causing the discriminator to compare poems based only on a learned similarity matrix of pairs of line ending words, the proposed approach is able to successfully learn rhyming patterns in two different English poetry datasets (Sonnet and Limerick) without explicitly being provided with any phonetic information",
}
@inproceedings{zhou-etal-2019-question,
    title = "Question-type Driven Question Generation",
    author = "Zhou, Wenjie  and
      Zhang, Minghua  and
      Wu, Yunfang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1622",
    doi = "10.18653/v1/D19-1622",
    pages = "6032--6037",
    abstract = "Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type $how$ while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.",
}
@inproceedings{li-etal-2019-deep,
    title = "Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization",
    author = "Li, Siyao  and
      Lei, Deren  and
      Qin, Pengda  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1623",
    doi = "10.18653/v1/D19-1623",
    pages = "6038--6044",
    abstract = "Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. However, the conventional reward Rouge-L simply looks for exact n-grams matches between candidates and annotated references, which inevitably makes the generated sentences repetitive and incoherent. In this paper, instead of Rouge-L, we explore the practicability of utilizing the distributional semantics to measure the matching degrees. With distributional semantics, sentence-level evaluation can be obtained, and semantically-correct phrases can also be generated without being limited to the surface form of the reference sentences. Human judgments on Gigaword and CNN/Daily Mail datasets show that our proposed distributional semantics reward (DSR) has distinct superiority in capturing the lexical and compositional diversity of natural language.",
}
@inproceedings{lee-2019-clause,
    title = "Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-{SQL} Generation",
    author = "Lee, Dongjun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1624",
    doi = "10.18653/v1/D19-1624",
    pages = "6045--6051",
    abstract = "Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries over a single table. We focus on the Spider dataset, a complex and cross-domain text-to-SQL task, which includes complex queries over multiple tables. In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address the Spider task. Each of the clause-specific decoders consists of a set of sub-modules, which is defined by the syntax of each clause. Additionally, our model works recursively to support nested queries. When evaluated on the Spider dataset, our approach achieves 4.6{\%} and 9.8{\%} accuracy gain in the test and dev sets, respectively. In addition, we show that our model is significantly more effective at predicting complex and nested queries than previous work.",
}
@inproceedings{mullenbach-etal-2019-nuclear,
    title = "Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects",
    author = "Mullenbach, James  and
      Gordon, Jonathan  and
      Peng, Nanyun  and
      May, Jonathan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1625",
    doi = "10.18653/v1/D19-1625",
    pages = "6052--6058",
    abstract = "How do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine{'}s captain nuclear? These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers. To attack this challenge, we crowdsource a set of human judgments that answer the English-language question {``}Given a whole described by an adjective, does the adjective also describe a given part?{''} We build strong baselines for this task with a classification approach. Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings. This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings. Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects",
}
@inproceedings{shao-etal-2019-aggregating,
    title = "Aggregating Bidirectional Encoder Representations Using {M}atch{LSTM} for Sequence Matching",
    author = "Shao, Bo  and
      Gong, Yeyun  and
      Qi, Weizhen  and
      Duan, Nan  and
      Lin, Xiaola",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1626",
    doi = "10.18653/v1/D19-1626",
    pages = "6059--6063",
    abstract = "In this work, we propose an aggregation method to combine the Bidirectional Encoder Representations from Transformer (BERT) with a MatchLSTM layer for Sequence Matching. Given a sentence pair, we extract the output representations of it from BERT. Then we extend BERT with a MatchLSTM layer to get further interaction of the sentence pair for sequence matching tasks. Taking natural language inference as an example, we split BERT output into two parts, which is from premise sentence and hypothesis sentence. At each position of the hypothesis sentence, both the weighted representation of the premise sentence and the representation of the current token are fed into LSTM. We jointly train the aggregation layer and pre-trained layer for sequence matching. We conduct an experiment on two publicly available datasets, WikiQA and SNLI. Experiments show that our model achieves significantly improvement compared with state-of-the-art methods on both datasets.",
}
@inproceedings{chang-chen-2019-word,
    title = "What Does This Word Mean? Explaining Contextualized Embeddings with Natural Language Definition",
    author = "Chang, Ting-Yun  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1627",
    doi = "10.18653/v1/D19-1627",
    pages = "6064--6070",
    abstract = "Contextualized word embeddings have boosted many NLP tasks compared with traditional static word embeddings. However, the word with a specific sense may have different contextualized embeddings due to its various contexts. To further investigate what contextualized word embeddings capture, this paper analyzes whether they can indicate the corresponding sense definitions and proposes a general framework that is capable of explaining word meanings given contextualized word embeddings for better interpretation. The experiments show that both ELMo and BERT embeddings can be well interpreted via a readable textual form, and the findings may benefit the research community for a better understanding of what the embeddings capture.",
}
@inproceedings{sung-etal-2019-pre,
    title = "Pre-Training {BERT} on Domain Resources for Short Answer Grading",
    author = "Sung, Chul  and
      Dhamecha, Tejas  and
      Saha, Swarnadeep  and
      Ma, Tengfei  and
      Reddy, Vinay  and
      Arora, Rishi",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1628",
    doi = "10.18653/v1/D19-1628",
    pages = "6071--6075",
    abstract = "Pre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data. While there has been a lot of focus on task-specific fine-tuning, there has been limited work on improving the pre-trained representations. In this paper, we explore ways of improving the pre-trained contextual representations for the task of automatic short answer grading, a critical component of intelligent tutoring systems. We show that the pre-trained BERT model can be improved by augmenting data from the domain-specific resources like textbooks. We also present a new approach to use labeled short answering grading data for further enhancement of the language model. Empirical evaluation on multi-domain datasets shows that task-specific fine-tuning on the enhanced pre-trained language model achieves superior performance for short answer grading.",
}
@inproceedings{tandon-etal-2019-wiqa,
    title = "{WIQA}: A dataset for {``}What if...{''} reasoning over procedural text",
    author = "Tandon, Niket  and
      Dalvi, Bhavana  and
      Sakaguchi, Keisuke  and
      Clark, Peter  and
      Bosselut, Antoine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1629",
    doi = "10.18653/v1/D19-1629",
    pages = "6076--6085",
    abstract = "We introduce WIQA, the first large-scale dataset of {``}What if...{''} questions over procedural text. WIQA contains a collection of paragraphs, each annotated with multiple influence graphs describing how one change affects another, and a large (40k) collection of {``}What if...?{''} multiple-choice questions derived from these. For example, given a paragraph about beach erosion, would stormy weather hasten or decelerate erosion? WIQA contains three kinds of questions: perturbations to steps mentioned in the paragraph; external (out-of-paragraph) perturbations requiring commonsense knowledge; and irrelevant (no effect) perturbations. We find that state-of-the-art models achieve 73.8{\%} accuracy, well below the human performance of 96.3{\%}. We analyze the challenges, in particular tracking chains of influences, and present the dataset as an open challenge to the community.",
}
@inproceedings{jiang-de-marneffe-2019-evaluating,
    title = "Evaluating {BERT} for natural language inference: A case study on the {C}ommitment{B}ank",
    author = "Jiang, Nanjiang  and
      de Marneffe, Marie-Catherine",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1630",
    doi = "10.18653/v1/D19-1630",
    pages = "6086--6091",
    abstract = "Natural language inference (NLI) datasets (e.g., MultiNLI) were collected by soliciting hypotheses for a given premise from annotators. Such data collection led to annotation artifacts: systems can identify the premise-hypothesis relationship without observing the premise (e.g., negation in hypothesis being indicative of contradiction). We address this problem by recasting the CommitmentBank for NLI, which contains items involving reasoning over the extent to which a speaker is committed to complements of clause-embedding verbs under entailment-canceling environments (conditional, negation, modal and question). Instead of being constructed to stand in certain relationships with the premise, hypotheses in the recast CommitmentBank are the complements of the clause-embedding verb in each premise, leading to no annotation artifacts in the hypothesis. A state-of-the-art BERT-based model performs well on the CommitmentBank with 85{\%} F1. However analysis of model behavior shows that the BERT models still do not capture the full complexity of pragmatic reasoning, nor encode some of the linguistic generalizations, highlighting room for improvement.",
}
@inproceedings{sharma-etal-2019-incorporating,
    title = "Incorporating Domain Knowledge into Medical {NLI} using Knowledge Graphs",
    author = "Sharma, Soumya  and
      Santra, Bishal  and
      Jana, Abhik  and
      Tokala, Santosh  and
      Ganguly, Niloy  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1631",
    doi = "10.18653/v1/D19-1631",
    pages = "6092--6097",
    abstract = "Recently, biomedical version of embeddings obtained from language models such as BioELMo have shown state-of-the-art results for the textual inference task in the medical domain. In this paper, we explore how to incorporate structured domain knowledge, available in the form of a knowledge graph (UMLS), for the Medical NLI task. Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task (ESIM model). We also experiment with fusing the domain-specific sentiment information for the task. Experiments conducted on MedNLI dataset clearly show that this strategy improves the baseline BioELMo architecture for the Medical NLI task.",
}
@inproceedings{guzman-etal-2019-flores,
    title = "The {FLORES} Evaluation Datasets for Low-Resource Machine Translation: {N}epali{--}{E}nglish and {S}inhala{--}{E}nglish",
    author = "Guzm{\'a}n, Francisco  and
      Chen, Peng-Jen  and
      Ott, Myle  and
      Pino, Juan  and
      Lample, Guillaume  and
      Koehn, Philipp  and
      Chaudhary, Vishrav  and
      Ranzato, Marc{'}Aurelio",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1632",
    doi = "10.18653/v1/D19-1632",
    pages = "6098--6111",
    abstract = "For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on low-resource language pairs because of the lack of freely and publicly available benchmarks. In this work, we introduce the FLORES evaluation datasets for Nepali{--}English and Sinhala{--} English, based on sentences translated from Wikipedia. Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores.",
}
@inproceedings{ghazvininejad-etal-2019-mask,
    title = "Mask-Predict: Parallel Decoding of Conditional Masked Language Models",
    author = "Ghazvininejad, Marjan  and
      Levy, Omer  and
      Liu, Yinhan  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1633",
    doi = "10.18653/v1/D19-1633",
    pages = "6112--6121",
    abstract = "Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster.",
}
@inproceedings{huang-etal-2019-learning-copy,
    title = "Learning to Copy for Automatic Post-Editing",
    author = "Huang, Xuancheng  and
      Liu, Yang  and
      Luan, Huanbo  and
      Xu, Jingfang  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1634",
    doi = "10.18653/v1/D19-1634",
    pages = "6122--6132",
    abstract = "Automatic post-editing (APE), which aims to correct errors in the output of machine translation systems in a post-processing step, is an important task in natural language processing. While recent work has achieved considerable performance gains by using neural networks, how to model the copying mechanism for APE remains a challenge. In this work, we propose a new method for modeling copying for APE. To better identify translation errors, our method learns the representations of source sentences and system outputs in an interactive way. These representations are used to explicitly indicate which words in the system outputs should be copied. Finally, CopyNet (Gu et.al., 2016) can be combined with our method to place the copied words in correct positions in post-edited translations. Experiments on the datasets of the WMT 2016-2017 APE shared tasks show that our approach outperforms all best published results.",
}
@inproceedings{du-etal-2019-exploring,
    title = "Exploring Human Gender Stereotypes with Word Association Test",
    author = "Du, Yupei  and
      Wu, Yuanbin  and
      Lan, Man",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1635",
    doi = "10.18653/v1/D19-1635",
    pages = "6133--6143",
    abstract = "Word embeddings have been widely used to study gender stereotypes in texts. One key problem regarding existing bias scores is to evaluate their validities: do they really reflect true bias levels? For a small set of words (e.g. occupations), we can rely on human annotations or external data. However, for most words, evaluating the correctness of them is still an open problem. In this work, we utilize word association test, which contains rich types of word connections annotated by human participants, to explore how gender stereotypes spread within our minds. Specifically, we use random walk on word association graph to derive bias scores for a large amount of words. Experiments show that these bias scores correlate well with bias in the real world. More importantly, comparing with word-embedding-based bias scores, it provides a different perspective on gender stereotypes in words.",
}
@inproceedings{mishra-etal-2019-modular,
    title = "A Modular Architecture for Unsupervised Sarcasm Generation",
    author = "Mishra, Abhijit  and
      Tater, Tarun  and
      Sankaranarayanan, Karthik",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1636",
    doi = "10.18653/v1/D19-1636",
    pages = "6144--6154",
    abstract = "In this paper, we propose a novel framework for sarcasm generation; the system takes a literal negative opinion as input and translates it into a sarcastic version. Our framework does not require any paired data for training. Sarcasm emanates from context-incongruity which becomes apparent as the sentence unfolds. Our framework introduces incongruity into the literal input version through modules that: (a) filter factual content from the input opinion, (b) retrieve incongruous phrases related to the filtered facts and (c) synthesize sarcastic text from the incongruous filtered and incongruous phrases. The framework employs reinforced neural sequence to sequence learning and information retrieval and is trained only using unlabeled non-sarcastic and sarcastic opinions. Since no labeled dataset exists for such a task, for evaluation, we manually prepare a benchmark dataset containing literal opinions and their sarcastic paraphrases. Qualitative and quantitative performance analyses on the data reveal our system{'}s superiority over baselines built using known unsupervised statistical and neural machine translation and style transfer techniques.",
}
@inproceedings{yang-etal-2019-generating,
    title = "Generating Classical {C}hinese Poems from Vernacular {C}hinese",
    author = "Yang, Zhichao  and
      Cai, Pengshan  and
      Feng, Yansong  and
      Li, Fei  and
      Feng, Weijiang  and
      Chiu, Elena Suet-Ying  and
      Yu, Hong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1637",
    doi = "10.18653/v1/D19-1637",
    pages = "6155--6164",
    abstract = "Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems from vernacular, which allows users to have more control over the semantic of generated poems. We adapt the approach of unsupervised machine translation (UMT) to our task. We use segmentation-based padding and reinforcement learning to address under-translation and over-translation respectively. According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models. Furthermore, we explored guidelines on how to write the input vernacular to generate better poems. Human evaluation showed our approach can generate high-quality poems which are comparable to amateur poems.",
}
@inproceedings{j-kurisinkel-chen-2019-set,
    title = "Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes",
    author = "J Kurisinkel, Litton  and
      Chen, Nancy",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1638",
    doi = "10.18653/v1/D19-1638",
    pages = "6165--6175",
    abstract = "We present set to ordered text, a natural language generation task applied to automatically generating discharge instructions from admission ICD (International Classification of Diseases) codes. This task differs from other natural language generation tasks in the following ways: (1) The input is a set of identifiable entities (ICD codes) where the relations between individual entity are not explicitly specified. (2) The output text is not a narrative description (e.g. news articles) composed from the input. Rather, inferences are made from the input (symptoms specified in ICD codes) to generate the output (instructions). (3) There is an optimal order in which each sentence (instruction) should appear in the output. Unlike most other tasks, neither the input (ICD codes) nor their corresponding symptoms appear in the output, so the ordering of the output instructions needs to be learned in an unsupervised fashion. Based on clinical intuition, we hypothesize that each instruction in the output is mapped to a subset of ICD codes specified in the input. We propose a neural architecture that jointly models (a) subset selection: choosing relevant subsets from a set of input entities; (b) content ordering: learning the order of instructions; and (c) text generation: representing the instructions corresponding to the selected subsets in natural language. In addition, we penalize redundancy during beam search to improve tractability for long text generation. Our model outperforms baseline models in BLEU scores and human evaluation. We plan to extend this work to other tasks such as recipe generation from ingredients.",
}
@inproceedings{barke-etal-2019-constraint,
    title = "Constraint-based Learning of Phonological Processes",
    author = "Barke, Shraddha  and
      Kunkel, Rose  and
      Polikarpova, Nadia  and
      Meinhardt, Eric  and
      Bakovic, Eric  and
      Bergen, Leon",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1639",
    doi = "10.18653/v1/D19-1639",
    pages = "6176--6186",
    abstract = "Phonological processes are context-dependent sound changes in natural languages. We present an unsupervised approach to learning human-readable descriptions of phonological processes from collections of related utterances. Our approach builds upon a technique from the programming languages community called *constraint-based program synthesis*. We contribute a novel encoding of the learning problem into Boolean Satisfiability constraints, which enables both data efficiency and fast inference. We evaluate our system on textbook phonology problems and datasets from the literature, and show that it achieves high accuracy at interactive speeds.",
}
@inproceedings{jiang-etal-2019-detect,
    title = "Detect Camouflaged Spam Content via {S}tone{S}kipping: Graph and Text Joint Embedding for {C}hinese Character Variation Representation",
    author = "Jiang, Zhuoren  and
      Gao, Zhe  and
      He, Guoxiu  and
      Kang, Yangyang  and
      Sun, Changlong  and
      Zhang, Qiong  and
      Si, Luo  and
      Liu, Xiaozhong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1640",
    doi = "10.18653/v1/D19-1640",
    pages = "6187--6196",
    abstract = "The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph. The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global). Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information. Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection.",
}
@inproceedings{lin-ji-2019-attentive,
    title = "An Attentive Fine-Grained Entity Typing Model with Latent Type Representation",
    author = "Lin, Ying  and
      Ji, Heng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1641",
    doi = "10.18653/v1/D19-1641",
    pages = "6197--6202",
    abstract = "We propose a fine-grained entity typing model with a novel attention mechanism and a hybrid type classifier. We advance existing methods in two aspects: feature extraction and type prediction. To capture richer contextual information, we adopt contextualized word representations instead of fixed word embeddings used in previous work. In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts. We also present a hybrid classification method beyond binary relevance to exploit type inter-dependency with latent type representation. Instead of independently predicting each type, we predict a low-dimensional vector that encodes latent type features and reconstruct the type vector from this latent representation. Experiment results on multiple data sets show that our model significantly advances the state-of-the-art on fine-grained entity typing, obtaining up to 6.1{\%} and 5.5{\%} absolute gains in macro averaged F-score and micro averaged F-score respectively.",
}
@inproceedings{ning-etal-2019-improved,
    title = "An Improved Neural Baseline for Temporal Relation Extraction",
    author = "Ning, Qiang  and
      Subramanian, Sanjay  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1642",
    doi = "10.18653/v1/D19-1642",
    pages = "6203--6209",
    abstract = "Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data. Consequently, neural approaches have not been widely used on it, or showed only moderate improvements. This paper proposes a new neural system that achieves about 10{\%} absolute improvement in accuracy over the previous best system (25{\%} error reduction) on two benchmark datasets. The proposed system is trained on the state-of-the-art MATRES dataset and applies contextualized word embeddings, a Siamese encoder of a temporal common sense knowledge base, and global inference via integer linear programming (ILP). We suggest that the new approach could serve as a strong baseline for future research in this area.",
}
@inproceedings{dai-etal-2019-improving,
    title = "Improving Fine-grained Entity Typing with Entity Linking",
    author = "Dai, Hongliang  and
      Du, Donghong  and
      Li, Xin  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1643",
    doi = "10.18653/v1/D19-1643",
    pages = "6210--6215",
    abstract = "Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predictions based on both the context and the information obtained from entity linking results. Experimental results on two commonly used datasets demonstrates the effectiveness of our approach. On both datasets, it achieves more than 5{\%} absolute strict accuracy improvement over the state of the art.",
}
@inproceedings{wang-lu-2019-combining,
    title = "Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities",
    author = "Wang, Bailin  and
      Lu, Wei",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1644",
    doi = "10.18653/v1/D19-1644",
    pages = "6216--6224",
    abstract = "In medical documents, it is possible that an entity of interest not only contains a discontiguous sequence of words but also overlaps with another entity. Entities of such structures are intrinsically hard to recognize due to the large space of possible entity combinations. In this work, we propose a neural two-stage approach to recognizing discontiguous and overlapping entities by decomposing this problem into two subtasks: 1) it first detects all the overlapping spans that either form entities on their own or present as segments of discontiguous entities, based on the representation of segmental hypergraph, 2) next it learns to combine these segments into discontiguous entities with a classifier, which filters out other incorrect combinations of segments. Two neural components are designed for these subtasks respectively and they are learned jointly using a shared encoder for text. Our model achieves the state-of-the-art performance in a standard dataset, even in the absence of external features that previous methods used.",
}
@inproceedings{akimoto-etal-2019-cross,
    title = "Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas",
    author = "Akimoto, Kosuke  and
      Hiraoka, Takuya  and
      Sadamasa, Kunihiko  and
      Niepert, Mathias",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1645",
    doi = "10.18653/v1/D19-1645",
    pages = "6225--6231",
    abstract = "Most existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction method is based on a supervised learning approach and, therefore, may suffer from the lack of sufficient relation labels. In this paper, we propose a novel approach to cross-sentence n-ary relation extraction based on universal schemas. To alleviate the sparsity problem and to leverage inherent decomposability of n-ary relations, we propose to learn relation representations of lower-arity facts that result from decomposing higher-arity facts. The proposed method computes a score of a new n-ary fact by aggregating scores of its decomposed lower-arity facts. We conduct experiments with datasets for ternary relation extraction and empirically show that our method improves the n-ary relation extraction performance compared to previous methods.",
}
@inproceedings{lin-etal-2019-gazetteer,
    title = "Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition",
    author = "Lin, Hongyu  and
      Lu, Yaojie  and
      Han, Xianpei  and
      Sun, Le  and
      Dong, Bin  and
      Jiang, Shanshan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1646",
    doi = "10.18653/v1/D19-1646",
    pages = "6232--6237",
    abstract = "Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck. To alleviate this problem, this paper proposes Gazetteer-Enhanced Attentive Neural Networks, which can enhance region-based NER by learning name knowledge of entity mentions from easily-obtainable gazetteers, rather than only from fully-annotated data. Specially, we first propose an attentive neural network (ANN), which explicitly models the mention-context association and therefore is convenient for integrating externally-learned knowledge. Then we design an auxiliary gazetteer network, which can effectively encode name regularity of mentions only using gazetteers. Finally, the learned gazetteer network is incorporated into ANN for better NER. Experiments show that our ANN can achieve the state-of-the-art performance on ACE2005 named entity recognition benchmark. Besides, incorporating gazetteer network can further improve the performance and significantly reduce the requirement of training data.",
}
@inproceedings{schwab-etal-2019-buster,
    title = "{``}A Buster Keaton of Linguistics{''}: First Automated Approaches for the Extraction of Vossian Antonomasia",
    author = {Schwab, Michel  and
      J{\"a}schke, Robert  and
      Fischer, Frank  and
      Str{\"o}tgen, Jannik},
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1647",
    doi = "10.18653/v1/D19-1647",
    pages = "6238--6243",
    abstract = "Attributing a particular property to a person by naming another person, who is typically wellknown for the respective property, is called a Vossian Antonomasia (VA). This subtpye of metonymy, which overlaps with metaphor, has a specific syntax and is especially frequent in journalistic texts. While identifying Vossian Antonomasia is of particular interest in the study of stylistics, it is also a source of errors in relation and fact extraction as an explicitly mentioned entity occurs only metaphorically and should not be associated with respective contexts. Despite rather simple syntactic variations, the automatic extraction of VA was never addressed as yet since it requires a deeper semantic understanding of mentioned entities and underlying relations. In this paper, we propose a first method for the extraction of VAs that works completely automatically. Our approaches use named entity recognition, distant supervision based on Wikidata, and a bi-directional LSTM for postprocessing. The evaluation on 1.8 million articles of the New York Times corpus shows that our approach significantly outperforms the only existing semi-automatic approach for VA identification by more than 30 percentage points in precision.",
}
@inproceedings{watanabe-etal-2019-multi,
    title = "Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing",
    author = "Watanabe, Taiki  and
      Tamura, Akihiro  and
      Ninomiya, Takashi  and
      Makino, Takuya  and
      Iwakura, Tomoya",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1648",
    doi = "10.18653/v1/D19-1648",
    pages = "6244--6249",
    abstract = "We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical com- pound paraphrase model. Our method en- ables the long short-term memory (LSTM) of the NER model to capture chemical com- pound paraphrases by sharing the parameters of the LSTM and character embeddings be- tween the two models. The experimental re- sults on the BioCreative IV{'}s CHEMDNER task show that our method improves chemi- cal NER and achieves state-of-the-art perfor- mance.",
}
@inproceedings{gao-etal-2019-fewrel,
    title = "{F}ew{R}el 2.0: Towards More Challenging Few-Shot Relation Classification",
    author = "Gao, Tianyu  and
      Han, Xu  and
      Zhu, Hao  and
      Liu, Zhiyuan  and
      Li, Peng  and
      Sun, Maosong  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1649",
    doi = "10.18653/v1/D19-1649",
    pages = "6250--6255",
    abstract = "We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations? To construct FewRel 2.0, we build upon the FewRel dataset by adding a new test set in a quite different domain, and a NOTA relation choice. With the new dataset and extensive experimental analysis, we found (1) that the state-of-the-art few-shot relation classification models struggle on these two aspects, and (2) that the commonly-used techniques for domain adaptation and NOTA detection still cannot handle the two challenges well. Our research calls for more attention and further efforts to these two real-world issues. All details and resources about the dataset and baselines are released at https://github.com/thunlp/fewrel.",
}
@inproceedings{mayhew-etal-2019-ner,
    title = "{ner and pos when nothing is capitalized}",
    author = "Mayhew, Stephen  and
      Tsygankova, Tatiana  and
      Roth, Dan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1650",
    doi = "10.18653/v1/D19-1650",
    pages = "6256--6261",
    abstract = "For those languages which use it, capitalization is an important signal for the fundamental NLP tasks of Named Entity Recognition (NER) and Part of Speech (POS) tagging. In fact, it is such a strong signal that model performance on these tasks drops sharply in common lowercased scenarios, such as noisy web text or machine translation outputs. In this work, we perform a systematic analysis of solutions to this problem, modifying only the casing of the train or test data using lowercasing and truecasing methods. While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text. As shown in our experiments, this result holds across tasks and input representations. Finally, we show that our proposed solution gives an 8{\%} F1 improvement in mention detection on noisy out-of-domain Twitter data.",
}
@inproceedings{bhardwaj-etal-2019-carb,
    title = "{C}a{RB}: A Crowdsourced Benchmark for Open {IE}",
    author = "Bhardwaj, Sangnie  and
      Aggarwal, Samarth  and
      Mausam, Mausam",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1651",
    doi = "10.18653/v1/D19-1651",
    pages = "6262--6267",
    abstract = "Open Information Extraction (Open IE) systems have been traditionally evaluated via manual annotation. Recently, an automated evaluator with a benchmark dataset (OIE2016) was released {--} it scores Open IE systems automatically by matching system predictions with predictions in the benchmark dataset. Unfortunately, our analysis reveals that its data is rather noisy, and the tuple matching in the evaluator has issues, making the results of automated comparisons less trustworthy. We contribute CaRB, an improved dataset and framework for testing Open IE systems. To the best of our knowledge, CaRB is the first crowdsourced Open IE dataset and it also makes substantive changes in the matching code and metrics. NLP experts annotate CaRB{'}s dataset to be more accurate than OIE2016. Moreover, we find that on one pair of Open IE systems, CaRB framework provides contradictory results to OIE2016. Human assessment verifies that CaRB{'}s ranking of the two systems is the accurate ranking. We release the CaRB framework along with its crowdsourced dataset.",
}
@inproceedings{patra-moniz-2019-weakly,
    title = "Weakly Supervised Attention Networks for Entity Recognition",
    author = "Patra, Barun  and
      Moniz, Joel Ruben Antony",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1652",
    doi = "10.18653/v1/D19-1652",
    pages = "6268--6273",
    abstract = "The task of entity recognition has traditionally been modelled as a sequence labelling task. However, this usually requires a large amount of fine-grained data annotated at the token level, which in turn can be expensive and cumbersome to obtain. In this work, we aim to circumvent this requirement of word-level annotated data. To achieve this, we propose a novel architecture for entity recognition from a corpus containing weak binary presence/absence labels, which are relatively easier to obtain. We show that our proposed weakly supervised model, trained solely on a multi-label classification task, performs reasonably well on the task of entity recognition, despite not having access to any token-level ground truth data.",
}
@inproceedings{morio-etal-2019-revealing,
    title = "Revealing and Predicting Online Persuasion Strategy with Elementary Units",
    author = "Morio, Gaku  and
      Egawa, Ryo  and
      Fujita, Katsuhide",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1653",
    doi = "10.18653/v1/D19-1653",
    pages = "6274--6279",
    abstract = "In online arguments, identifying how users construct their arguments to persuade others is important in order to understand a persuasive strategy directly. However, existing research lacks empirical investigations on highly semantic aspects of elementary units (EUs), such as propositions for a persuasive online argument. Therefore, this paper focuses on a pilot study, revealing a persuasion strategy using EUs. Our contributions are as follows: (1) annotating five types of EUs in a persuasive forum, the so-called ChangeMyView, (2) revealing both intuitive and non-intuitive strategic insights for the persuasion by analyzing 4612 annotated EUs, and (3) proposing baseline neural models that identify the EU boundary and type. Our observations imply that EUs definitively characterize online persuasion strategies.",
}
@inproceedings{jiang-etal-2019-challenge,
    title = "A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis",
    author = "Jiang, Qingnan  and
      Chen, Lei  and
      Xu, Ruifeng  and
      Ao, Xiang  and
      Yang, Min",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1654",
    doi = "10.18653/v1/D19-1654",
    pages = "6280--6285",
    abstract = "Aspect-based sentiment analysis (ABSA) has attracted increasing attention recently due to its broad applications. In existing ABSA datasets, most sentences contain only one aspect or multiple aspects with the same sentiment polarity, which makes ABSA task degenerate to sentence-level sentiment analysis. In this paper, we present a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset, in which each sentence contains at least two different aspects with different sentiment polarities. The release of this dataset would push forward the research in this field. In addition, we propose simple yet effective CapsNet and CapsNet-BERT models which combine the strengths of recent NLP advances. Experiments on our new dataset show that the proposed model significantly outperforms the state-of-the-art baseline methods",
}
@inproceedings{wang-etal-2019-learning-noisy,
    title = "Learning with Noisy Labels for Sentence-level Sentiment Classification",
    author = "Wang, Hao  and
      Liu, Bing  and
      Li, Chaozhuo  and
      Yang, Yan  and
      Li, Tianrui",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1655",
    doi = "10.18653/v1/D19-1655",
    pages = "6286--6292",
    abstract = "Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training. NetAb consists of two convolutional neural networks, one with a noise transition layer for dealing with the input noisy labels and the other for predicting {`}clean{'} labels. We train the two networks using their respective loss functions in a mutual reinforcement manner. Experimental results demonstrate the effectiveness of the proposed model.",
}
@inproceedings{liu-etal-2019-dens,
    title = "{DENS}: A Dataset for Multi-class Emotion Analysis",
    author = "Liu, Chen  and
      Osama, Muhammad  and
      De Andrade, Anderson",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1656",
    doi = "10.18653/v1/D19-1656",
    pages = "6293--6298",
    abstract = "We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both classic literature available on Project Gutenberg and modern online narratives avail- able on Wattpad, annotated using Amazon Mechanical Turk. A number of statistics and baseline benchmarks are provided for the dataset. Of the tested techniques, we find that the fine-tuning of a pre-trained BERT model achieves the best results, with an average micro-F1 score of 60.4{\%}. Our results show that the dataset provides a novel opportunity in emotion analysis that requires moving beyond existing sentence-level techniques.",
}
@inproceedings{li-caragea-2019-multi,
    title = "Multi-Task Stance Detection with Sentiment and Stance Lexicons",
    author = "Li, Yingjie  and
      Caragea, Cornelia",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1657",
    doi = "10.18653/v1/D19-1657",
    pages = "6299--6305",
    abstract = "Stance detection aims to detect whether the opinion holder is in support of or against a given target. Recent works show improvements in stance detection by using either the attention mechanism or sentiment information. In this paper, we propose a multi-task framework that incorporates target-specific attention mechanism and at the same time takes sentiment classification as an auxiliary task. Moreover, we used a sentiment lexicon and constructed a stance lexicon to provide guidance for the attention layer. Experimental results show that the proposed model significantly outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset.",
}
@inproceedings{dong-de-melo-2019-robust,
    title = "A Robust Self-Learning Framework for Cross-Lingual Text Classification",
    author = "Dong, Xin  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1658",
    doi = "10.18653/v1/D19-1658",
    pages = "6306--6310",
    abstract = "Based on massive amounts of data, recent pretrained contextual representation models have made significant strides in advancing a number of different English NLP tasks. However, for other languages, relevant training data may be lacking, while state-of-the-art deep learning methods are known to be data-hungry. In this paper, we present an elegantly simple robust self-learning framework to include unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. We leverage a multilingual model{'}s own predictions on unlabeled non-English data in order to obtain additional information that can be used during further fine-tuning. Compared with original multilingual models and other cross-lingual classification models, we observe significant gains in effectiveness on document and sentiment classification for a range of diverse languages.",
}
@inproceedings{kruengkrai-2019-learning,
    title = "Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora",
    author = "Kruengkrai, Canasai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1659",
    doi = "10.18653/v1/D19-1659",
    pages = "6311--6316",
    abstract = "Flipping sentiment while preserving sentence meaning is challenging because parallel sentences with the same content but different sentiment polarities are not always available for model learning. We introduce a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose a model that learns to minimize the sentiment and content losses in a fully end-to-end manner. Our model is simple and offers well-balanced results across two domains: Yelp restaurant and Amazon product reviews.",
}
@inproceedings{miyazaki-etal-2019-label,
    title = "Label Embedding using Hierarchical Structure of Labels for {T}witter Classification",
    author = "Miyazaki, Taro  and
      Makino, Kiminobu  and
      Takei, Yuka  and
      Okamoto, Hiroki  and
      Goto, Jun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1660",
    doi = "10.18653/v1/D19-1660",
    pages = "6317--6322",
    abstract = "Twitter is used for various applications such as disaster monitoring and news material gathering. In these applications, each Tweet is classified into pre-defined classes. These classes have a semantic relationship with each other and can be classified into a hierarchical structure, which is regarded as important information. Label texts of pre-defined classes themselves also include important clues for classification. Therefore, we propose a method that can consider the hierarchical structure of labels and label texts themselves. We conducted evaluation over the Text REtrieval Conference (TREC) 2018 Incident Streams (IS) track dataset, and we found that our method outperformed the methods of the conference participants.",
}
@inproceedings{hurtado-bodell-etal-2019-interpretable,
    title = "Interpretable Word Embeddings via Informative Priors",
    author = "Hurtado Bodell, Miriam  and
      Arvidsson, Martin  and
      Magnusson, M{\aa}ns",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1661",
    doi = "10.18653/v1/D19-1661",
    pages = "6323--6329",
    abstract = "Word embeddings have demonstrated strong performance on NLP tasks. However, lack of interpretability and the unsupervised nature of word embeddings have limited their use within computational social science and digital humanities. We propose the use of informative priors to create interpretable and domain-informed dimensions for probabilistic word embeddings. Experimental results show that sensible priors can capture latent semantic concepts better than or on-par with the current state of the art, while retaining the simplicity and generalizability of using priors.",
}
@inproceedings{barrett-etal-2019-adversarial,
    title = "Adversarial Removal of Demographic Attributes Revisited",
    author = "Barrett, Maria  and
      Kementchedjhieva, Yova  and
      Elazar, Yanai  and
      Elliott, Desmond  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1662",
    doi = "10.18653/v1/D19-1662",
    pages = "6330--6335",
    abstract = "Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on. We revisit their experiments and conduct a series of follow-up experiments showing that, in fact, the diagnostic classifier generalizes poorly to both new in-domain samples and new domains, indicating that it relies on correlations specific to their particular data sample. We further show that a diagnostic classifier trained on the biased baseline neural network also does not generalize to new samples. In other words, the biases detected in Elazar and Goldberg (2018) seem restricted to their particular data sample, and would therefore not bias the decisions of the model on new samples, whether in-domain or out-of-domain. In light of this, we discuss better methodologies for detecting bias in our models.",
}
@inproceedings{patro-etal-2019-deep,
    title = "A deep-learning framework to detect sarcasm targets",
    author = "Patro, Jasabanta  and
      Bansal, Srijan  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1663",
    doi = "10.18653/v1/D19-1663",
    pages = "6336--6342",
    abstract = "In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts. Identification of sarcasm targets can help in many core natural language processing tasks such as aspect based sentiment analysis, opinion mining etc. To begin with, we perform an empirical study of the socio-linguistic features and identify those that are statistically significant in indicating sarcasm targets (p-values in the range(0.05,0.001)). Finally, we present a deep-learning framework augmented with socio-linguistic features to detect sarcasm targets in sarcastic book-snippets and tweets.We achieve a huge improvement in the performance in terms of exact match and dice scores compared to the current state-of-the-art baseline.",
}
@inproceedings{fan-etal-2019-plain,
    title = "In Plain Sight: Media Bias Through the Lens of Factual Reporting",
    author = "Fan, Lisa  and
      White, Marshall  and
      Sharma, Eva  and
      Su, Ruisi  and
      Choubey, Prafulla Kumar  and
      Huang, Ruihong  and
      Wang, Lu",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1664",
    doi = "10.18653/v1/D19-1664",
    pages = "6343--6349",
    abstract = "The increasing prevalence of political bias in news media calls for greater public awareness of it, as well as robust methods for its detection. While prior work in NLP has primarily focused on the lexical bias captured by linguistic attributes such as word choice and syntax, other types of bias stem from the actual content selected for inclusion in the text. In this work, we investigate the effects of informational bias: factual content that can nevertheless be deployed to sway reader opinion. We first produce a new dataset, BASIL, of 300 news articles annotated with 1,727 bias spans and find evidence that informational bias appears in news articles more frequently than lexical bias. We further study our annotations to observe how informational bias surfaces in news articles by different media outlets. Lastly, a baseline model for informational bias prediction is presented by fine-tuning BERT on our labeled data, indicating the challenges of the task and future directions.",
}
@inproceedings{ferreira-vlachos-2019-incorporating,
    title = "Incorporating Label Dependencies in Multilabel Stance Detection",
    author = "Ferreira, William  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1665",
    doi = "10.18653/v1/D19-1665",
    pages = "6350--6354",
    abstract = "Stance detection in social media is a well-studied task in a variety of domains. Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are mutually exclusive, and typically positive, negative or neutral. In this paper, we address versions of the task in which an utterance can have multiple labels, thus corresponding to multilabel classification. We propose a method that explicitly incorporates label dependencies in the training objective and compare it against a variety of baselines, as well as a reduction of multilabel to multiclass learning. In experiments with three datasets, we find that our proposed method improves upon all baselines on two out of three datasets. We also show that the reduction of multilabel to multiclass classification can be very competitive, especially in cases where the output consists of a small number of labels and one can enumerate over all label combinations.",
}
@inproceedings{merullo-etal-2019-investigating,
    title = "Investigating Sports Commentator Bias within a Large Corpus of {A}merican Football Broadcasts",
    author = "Merullo, Jack  and
      Yeh, Luke  and
      Handler, Abram  and
      Grissom II, Alvin  and
      O{'}Connor, Brendan  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1666",
    doi = "10.18653/v1/D19-1666",
    pages = "6355--6361",
    abstract = "Sports broadcasters inject drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. Prior studies based on small datasets and manual coding show that such theatrics evince commentator bias in sports broadcasts. To examine this phenomenon, we assemble FOOTBALL, which contains 1,455 broadcast transcripts from American football games across six decades that are automatically annotated with 250K player mentions and linked with racial metadata. We identify major confounding factors for researchers examining racial bias in FOOTBALL, and perform a computational analysis that supports conclusions from prior social science studies.",
}
@inproceedings{chen-etal-2019-charge,
    title = "Charge-Based Prison Term Prediction with Deep Gating Network",
    author = "Chen, Huajie  and
      Cai, Deng  and
      Dai, Wei  and
      Dai, Zehui  and
      Ding, Yadong",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1667",
    doi = "10.18653/v1/D19-1667",
    pages = "6362--6367",
    abstract = "Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction. While existing work merely predicts the total prison term, in reality a defendant is often charged with multiple crimes. In this paper, we argue that charge-based prison term prediction (CPTP) not only better fits realistic needs, but also makes the total prison term prediction more accurate and interpretable. We collect the first large-scale structured data for CPTP and evaluate several competitive baselines. Based on the observation that fine-grained feature selection is the key to achieving good performance, we propose the Deep Gating Network (DGN) for charge-specific feature selection and aggregation. Experiments show that DGN achieves the state-of-the-art performance.",
}
@inproceedings{assael-etal-2019-restoring,
    title = "Restoring ancient text using deep learning: a case study on {G}reek epigraphy",
    author = "Assael, Yannis  and
      Sommerschield, Thea  and
      Prag, Jonathan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1668",
    doi = "10.18653/v1/D19-1668",
    pages = "6368--6375",
    abstract = "Ancient History relies on disciplines such as Epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, {``}inscriptions{''}, are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents Pythia, the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks. Its architecture is carefully designed to handle long-term context information, and deal efficiently with missing or corrupted character and word representations. To train it, we wrote a non-trivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML. On PHI-ML, Pythia{'}s predictions achieve a 30.1{\%} character error rate, compared to the 57.3{\%} of human epigraphists. Moreover, in 73.5{\%} of cases the ground-truth sequence was among the Top-20 hypotheses of Pythia, which effectively demonstrates the impact of this assistive method on the field of digital epigraphy, and sets the state-of-the-art in ancient text restoration.",
}
@inproceedings{zhao-etal-2019-embedding,
    title = "Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition",
    author = "Zhao, Zhenjie  and
      Cattle, Andrew  and
      Papalexakis, Evangelos  and
      Ma, Xiaojuan",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1669",
    doi = "10.18653/v1/D19-1669",
    pages = "6376--6381",
    abstract = "We propose a novel tensor embedding method that can effectively extract lexical features for humor recognition. Specifically, we use word-word co-occurrence to encode the contextual content of documents, and then decompose the tensor to get corresponding vector representations. We show that this simple method can capture features of lexical humor effectively for continuous humor recognition. In particular, we achieve a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B (Potash et al., 2017) but without the need for any external training corpus. In addition, we further show that this approach is also beneficial for small sample humor recognition tasks through a semi-supervised label propagation procedure, which achieves about 0.7 accuracy on the 16000 One-Liners (Mihalcea and Strapparava, 2005) and Pun of the Day (Yang et al., 2015) humour classification datasets using only 10{\%} of known labels.",
}
@inproceedings{wei-zou-2019-eda,
    title = "{EDA}: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks",
    author = "Wei, Jason  and
      Zou, Kai",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1670",
    doi = "10.18653/v1/D19-1670",
    pages = "6382--6388",
    abstract = "We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50{\%} of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.",
}
@inproceedings{wu-etal-2019-neural-news-recommendation,
    title = "Neural News Recommendation with Multi-Head Self-Attention",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Ge, Suyu  and
      Qi, Tao  and
      Huang, Yongfeng  and
      Xie, Xing",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1671",
    doi = "10.18653/v1/D19-1671",
    pages = "6389--6394",
    abstract = "News recommendation can help users find interested news and alleviate information overload. Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. In this paper, we propose a neural news recommendation approach with multi-head self-attention (NRMS). The core of our approach is a news encoder and a user encoder. In the news encoder, we use multi-head self-attentions to learn news representations from news titles by modeling the interactions between words. In the user encoder, we learn representations of users from their browsed news and use multi-head self-attention to capture the relatedness between the news. Besides, we apply additive attention to learn more informative news and user representations by selecting important words and news. Experiments on a real-world dataset validate the effectiveness and efficiency of our approach.",
}
@inproceedings{huang-etal-2019-matters,
    title = "What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis",
    author = "Huang, Xiaolei  and
      May, Jonathan  and
      Peng, Nanyun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1672",
    doi = "10.18653/v1/D19-1672",
    pages = "6395--6401",
    abstract = "Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further explore how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embedding. Our results shed light on future research for improving cross-lingual NER.",
}
@inproceedings{zhang-etal-2019-telling,
    title = "Telling the Whole Story: A Manually Annotated {C}hinese Dataset for the Analysis of Humor in Jokes",
    author = "Zhang, Dongyu  and
      Zhang, Heting  and
      Liu, Xikai  and
      Lin, Hongfei  and
      Xia, Feng",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1673",
    doi = "10.18653/v1/D19-1673",
    pages = "6402--6407",
    abstract = "Humor plays important role in human communication, which makes it important problem for natural language processing. Prior work on the analysis of humor focuses on whether text is humorous or not, or the degree of funniness, but this is insufficient to explain why it is funny. We therefore create a dataset on humor with 9,123 manually annotated jokes in Chinese. We propose a novel annotation scheme to give scenarios of how humor arises in text. Specifically, our annotations of linguistic humor not only contain the degree of funniness, like previous work, but they also contain key words that trigger humor as well as character relationship, scene, and humor categories. We report reasonable agreement between annota-tors. We also conduct an analysis and exploration of the dataset. To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor. We also contribute with a scarce and valuable dataset, which we will release publicly.",
}
@inproceedings{nishino-etal-2019-generating,
    title = "Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints",
    author = "Nishino, Masaaki  and
      Takase, Sho  and
      Hirao, Tsutomu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1674",
    doi = "10.18653/v1/D19-1674",
    pages = "6408--6412",
    abstract = "An anagram is a sentence or a phrase that is made by permutating the characters of an input sentence or a phrase. For example, {``}Trims cash{''} is an anagram of {``}Christmas{''}. Existing automatic anagram generation methods can find possible combinations of words form an anagram. However, they do not pay much attention to the naturalness of the generated anagrams. In this paper, we show that simple depth-first search can yield natural anagrams when it is combined with modern neural language models. Human evaluation results show that the proposed method can generate significantly more natural anagrams than baseline methods.",
}
@inproceedings{popat-etal-2019-stancy,
    title = "{STANCY}: Stance Classification Based on Consistency Cues",
    author = "Popat, Kashyap  and
      Mukherjee, Subhabrata  and
      Yates, Andrew  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1675",
    doi = "10.18653/v1/D19-1675",
    pages = "6413--6418",
    abstract = "Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users{'} perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.",
}
@inproceedings{khalil-etal-2019-cross,
    title = "Cross-lingual intent classification in a low resource industrial setting",
    author = "Khalil, Talaat  and
      Kie{\l}czewski, Kornel  and
      Chouliaras, Georgios Christos  and
      Keldibek, Amina  and
      Versteegh, Maarten",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1676",
    doi = "10.18653/v1/D19-1676",
    pages = "6419--6424",
    abstract = "This paper explores different approaches to multilingual intent classification in a low resource setting. Recent advances in multilingual text representations promise cross-lingual transfer for classifiers. We investigate the potential for this transfer in an applied industrial setting and compare to multilingual classification using machine translated text. Our results show that while the recently developed methods show promise, practical application calls for a combination of techniques for useful results.",
}
@inproceedings{park-etal-2019-softregex,
    title = "{S}oft{R}egex: Generating Regex from Natural Language Descriptions using Softened Regex Equivalence",
    author = "Park, Jun-U  and
      Ko, Sang-Ki  and
      Cognetta, Marco  and
      Han, Yo-Sub",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1677",
    doi = "10.18653/v1/D19-1677",
    pages = "6425--6431",
    abstract = "We continue the study of generating se-mantically correct regular expressions from natural language descriptions (NL). The current state-of-the-art model SemRegex produces regular expressions from NLs by rewarding the reinforced learning based on the semantic (rather than syntactic) equivalence between two regular expressions. Since the regular expression equivalence problem is PSPACE-complete, we introduce the EQ{\_}Reg model for computing the simi-larity of two regular expressions using deep neural networks. Our EQ{\_}Reg mod-el essentially softens the equivalence of two regular expressions when used as a reward function. We then propose a new regex generation model, SoftRegex, us-ing the EQ{\_}Reg model, and empirically demonstrate that SoftRegex substantially reduces the training time (by a factor of at least 3.6) and produces state-of-the-art results on three benchmark datasets.",
}
@inproceedings{khadanga-etal-2019-using,
    title = "Using Clinical Notes with Time Series Data for {ICU} Management",
    author = "Khadanga, Swaraj  and
      Aggarwal, Karan  and
      Joty, Shafiq  and
      Srivastava, Jaideep",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1678",
    doi = "10.18653/v1/D19-1678",
    pages = "6432--6437",
    abstract = "Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital{'}s resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model.",
}
@inproceedings{renduchintala-etal-2019-spelling,
    title = "Spelling-Aware Construction of Macaronic Texts for Teaching Foreign-Language Vocabulary",
    author = "Renduchintala, Adithya  and
      Koehn, Philipp  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1679",
    doi = "10.18653/v1/D19-1679",
    pages = "6438--6443",
    abstract = "We present a machine foreign-language teacher that modifies text in a student{'}s native language (L1) by replacing some word tokens with glosses in a foreign language (L2), in such a way that the student can acquire L2 vocabulary simply by reading the resulting macaronic text. The machine teacher uses no supervised data from human students. Instead, to guide the machine teacher{'}s choice of which words to replace, we equip a cloze language model with a training procedure that can incrementally learn representations for novel words, and use this model as a proxy for the word guessing and learning ability of real human students. We use Mechanical Turk to evaluate two variants of the student model: (i) one that generates a representation for a novel word using only surrounding context and (ii) an extension that also uses the spelling of the novel word.",
}
@inproceedings{min-etal-2019-towards,
    title = "Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature",
    author = "Min, Bonan  and
      Chan, Yee Seng  and
      Qiu, Haoling  and
      Fasching, Joshua",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1680",
    doi = "10.18653/v1/D19-1680",
    pages = "6444--6448",
    abstract = "Solving long-lasting problems such as food insecurity requires a comprehensive understanding of interventions applied by governments and international humanitarian assistance organizations, and their results and consequences. Towards achieving this grand goal, a crucial first step is to extract past interventions and when and where they have been applied, from hundreds of thousands of reports automatically. In this paper, we developed a corpus annotated with interventions to foster research, and developed an information extraction system for extracting interventions and their location and time from text. We demonstrate early, very encouraging results on extracting interventions.",
}
@inproceedings{paz-argaman-tsarfaty-2019-run,
    title = "{RUN} through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation",
    author = "Paz-Argaman, Tzuf  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1681",
    doi = "10.18653/v1/D19-1681",
    pages = "6449--6455",
    abstract = "Following navigation instructions in natural language (NL) requires a composition of language, action, and knowledge of the environment. Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map. Previous work on map-based NL navigation relied on small artificial worlds with a fixed set of entities known in advance. Here we introduce the Realistic Urban Navigation (RUN) task, aimed at interpreting NL navigation instructions based on a real, dense, urban map. Using Amazon Mechanical Turk, we collected a dataset of 2515 instructions aligned with actual routes over three regions of Manhattan. We then empirically study which aspects of a neural architecture are important for the RUN success, and empirically show that entity abstraction, attention over words and worlds, and a constantly updating world-state, significantly contribute to task accuracy.",
}
@inproceedings{tan-etal-2019-context,
    title = "Context-Aware Conversation Thread Detection in Multi-Party Chat",
    author = "Tan, Ming  and
      Wang, Dakuo  and
      Gao, Yupeng  and
      Wang, Haoyu  and
      Potdar, Saloni  and
      Guo, Xiaoxiao  and
      Chang, Shiyu  and
      Yu, Mo",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D19-1682",
    doi = "10.18653/v1/D19-1682",
    pages = "6456--6461",
    abstract = "In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs. In this work, we propose a novel Context-Aware Thread Detection (CATD) model that automatically disentangles these conversation threads. We evaluate our model on four real-world datasets and demonstrate an overall im-provement in thread detection accuracy over state-of-the-art benchmarks.",
}
@proceedings{nsurl-2019-international,
    title = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    editor = "Freihat, Abed Alhakim  and
      Abbas, Mourad",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.0",
}
@inproceedings{seelawi-etal-2019-nsurl,
    title = "{NSURL}-2019 Task 8: Semantic Question Similarity in {A}rabic",
    author = "Seelawi, Haitham  and
      Mustafa, Ahmad  and
      Al-Bataineh, Hesham  and
      Farhan, Wael  and
      Al-Natsheh, Hussein T.",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.1",
    pages = "1--8",
}
@inproceedings{taghizadeh-etal-2019-nsurl,
    title = "{NSURL}-2019 Task 7: Named Entity Recognition for {F}arsi",
    author = "Taghizadeh, Nasrin  and
      Borhanifard, Zeinab  and
      Pour, Melika Golestani  and
      Farhoodi, Mojgan  and
      Mahmoudi, Maryam  and
      Azimzadeh, Masoumeh  and
      Faili, Hesham",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.2",
    pages = "9--15",
}
@inproceedings{modupe-etal-2019-yoruba,
    title = "{Y}or{\`u}b{\'a} Gender Recognition from Speech using Attention-based {B}i{LSTM}",
    author = "Modupe, Ibukunola Abosede  and
      Sefara, Tshephisho Joseph  and
      Sunday, Ojo",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.3",
    pages = "16--22",
}
@inproceedings{mohseni-tebbifakhr-2019-morphobert,
    title = "{M}orpho{BERT}: a {P}ersian {NER} System with {BERT} and Morphological Analysis",
    author = "Mohseni, Mahdi  and
      Tebbifakhr, Amirhossein",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.4",
    pages = "23--30",
}
@inproceedings{sharifi-etal-2019-atynegar,
    title = "{A}ty{N}egar at {NSURL}-2019 Task 8: Semantic Question Similarity in {A}rabic",
    author = "Sharifi, Atieh  and
      Hassanpoor, Hossein  and
      Maduyieh, Najmeh Zare",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.5",
    pages = "31--36",
}
@inproceedings{taher-etal-2019-beheshti,
    title = "Beheshti-{NER}: {P}ersian named entity recognition Using {BERT}",
    author = "Taher, Ehsan  and
      Hoseini, Seyed Abbas  and
      Shamsfard, Mehrnoush",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.6",
    pages = "37--42",
}
@inproceedings{joukhadar-etal-2019-arabic,
    title = "{A}rabic Dialogue Act Recognition for Textual Chatbot Systems",
    author = "Joukhadar, Alaa  and
      Saghergy, Huda  and
      Kweider, Leen  and
      Ghneim, Nada",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.7",
    pages = "43--49",
}
@inproceedings{fadel-etal-2019-tha3aroon,
    title = "Tha3aroon at {NSURL}-2019 Task 8: Semantic Question Similarity in {A}rabic",
    author = "Fadel, Ali  and
      Tuffaha, Ibraheem  and
      Al-Ayyoub, Mahmoud",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.8",
    pages = "50--58",
}
@inproceedings{yemmene-besacier-2019-motivations,
    title = "Motivations, challenges, and perspectives for the development of an Automatic Speech Recognition System for the under-resourced {N}giemboon Language",
    author = "Yemmene, Patrice  and
      Besacier, Laurent",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.9",
    pages = "59--67",
}
@inproceedings{m-2019-nitk,
    title = "{NITK}-{IT}{\_}{NLP}@{NSURL}2019: Transfer Learning based {POS} Tagger for Under Resourced {B}hojpuri and {M}agahi Language",
    author = "M, Anand Kumar",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.10",
    pages = "68--72",
}
@inproceedings{proisl-etal-2019-illiterati,
    title = "{T}he{\_}{I}lliterati: Part-of-Speech Tagging for {M}agahi and {B}hojpuri without even knowing the alphabet",
    author = "Proisl, Thomas  and
      Uhrig, Peter  and
      Blombach, Andreas  and
      Dykes, Natalie  and
      Heinrich, Philipp  and
      Kabashi, Besim  and
      Mammarella, Sefora",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.11",
    pages = "73--79",
}
@inproceedings{lichouri-etal-2019-st,
    title = "{ST} {NSURL} 2019 Shared Task: Semantic Question Similarity in {A}rabic",
    author = "Lichouri, Mohamed  and
      Abbas, Mourad  and
      Benaziz, Besma  and
      Freihat, Abed Alhakim",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.12",
    pages = "80--84",
}
@inproceedings{oo-etal-2019-statistical,
    title = "Statistical Machine Translation between {M}yanmar ({B}urmese) and Dawei ({T}avoyan)",
    author = "Oo, Thazin Myint  and
      Thu, Ye Kyaw  and
      Soe, Khin Mar  and
      Supnithi, Thepchai",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.13",
    pages = "85--93",
}
@inproceedings{wai-etal-2019-string,
    title = "String Similarity Measures for {M}yanmar Language ({B}urmese)",
    author = "Wai, Khaing Hsu  and
      Thu, Ye Kyaw  and
      Thant, Hnin Aye  and
      Moe, Swe Zin  and
      Supnithi, Thepchai",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.14",
    pages = "94--102",
}
@inproceedings{zaidi-2019-inferential,
    title = "An Inferential Phonological Connectionist Approach to the perception of Assimilated-{E}nglish Connected Speech",
    author = "Zaidi, Hiba",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.15",
    pages = "103--106",
}
@inproceedings{zafarian-asghari-2019-improving,
    title = "Improving {NER} Models by exploiting Named Entity Gazetteer as External Knowledge",
    author = "Zafarian, Atefeh  and
      Asghari, Habibollah",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.16",
    pages = "107--111",
}
@inproceedings{al-theiabat-al-sadi-2019-inception,
    title = "The Inception Team at {NSURL}-2019 Task 8: Semantic Question Similarity in {A}rabic",
    author = "Al-Theiabat, Hana  and
      Al-Sadi, Aisha",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.17",
    pages = "112--117",
}
@inproceedings{olamma-etal-2019-hidden,
    title = "Hidden {M}arkov-based Part-of-Speech Tagger for {I}gbo Resource-Scarce {A}frican Language",
    author = "Olamma, Ihenaetu  and
      Kingsley, Michael  and
      Ojo, Sunday",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.18",
    pages = "118--123",
}
@inproceedings{okediya-etal-2019-building,
    title = "Building Ontologyfor {Y}or{\`u}b{\'a} Language",
    author = "Okediya, Theresa  and
      Afolabi, Ibukun  and
      Iheanetu, Olamma  and
      Ojo, Sunday",
    booktitle = "Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers",
    month = "11--12 " # sep,
    year = "2019",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.nsurl-1.19",
    pages = "124--130",
}
@proceedings{lilt-2019--2,
    title = "",
    booktitle = "Linguistic Issues in Language Technology, Volume 18, 2019 - Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    month = jul,
    year = "2019",
    publisher = "CSLI Publications",
}
@inproceedings{pardeshi-etal-2019-exploiting,
    title = "Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    author = "Pardeshi, Prashant  and
      Butler, Alistair  and
      Horn, Stephen  and
      Yoshimoto, Kei  and
      Nagasaki, Iku",
    booktitle = "Linguistic Issues in Language Technology, Volume 18, 2019 - Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    month = jul,
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-18.1",
}
@inproceedings{li-etal-2019-building,
    title = "Building a {C}hinese {AMR} Bank with Concept and Relation Alignments",
    author = "Li, Bin  and
      Wen, Yuan  and
      Song, Li  and
      Qu, Weiguang  and
      Xue, Nianwen",
    booktitle = "Linguistic Issues in Language Technology, Volume 18, 2019 - Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    month = jul,
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-18.2",
    abstract = "Abstract Meaning Representation (AMR) is a meaning representation framework in which the meaning of a full sentence is represented as a single-rooted, acyclic, directed graph. In this article, we describe an on-going project to build a Chinese AMR (CAMR) corpus, which currently includes 10,149 sentences from the newsgroup and weblog portion of the Chinese TreeBank (CTB). We describe the annotation specifications for the CAMR corpus, which follow the annotation principles of English AMR but make adaptations where needed to accommodate the linguistic facts of Chinese. The CAMR specifications also include a systematic treatment of sentence-internal discourse relations. One significant change we have made to the AMR annotation methodology is the inclusion of the alignment between word tokens in the sentence and the concepts/relations in the CAMR annotation to make it easier for automatic parsers to model the correspondence between a sentence and its meaning representation. We develop an annotation tool for CAMR, and the inter-agreement as measured by the Smatch score between the two annotators is 0.83, indicating reliable annotation. We also present some quantitative analysis of the CAMR corpus. 46.71{\%} of the AMRs of the sentences are non-tree graphs. Moreover, the AMR of 88.95{\%} of the sentences has concepts inferred from the context of the sentence but do not correspond to a specific word.",
}
@inproceedings{kishimoto-pardeshi-2019-parsed,
    title = "Parsed Corpus as a Source for Testing Generalizations in {J}apanese Syntax",
    author = "Kishimoto, Hideki  and
      Pardeshi, Prashant",
    booktitle = "Linguistic Issues in Language Technology, Volume 18, 2019 - Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    month = jul,
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-18.3",
    abstract = "In this paper, we discuss constituent ordering generalizations in Japanese. Japanese has SOV as its basic order, but a significant range of argument order variations brought about by {`}scrambling{'} is permitted. Although scrambling does not induce much in the way of semantic effects, it is conceivable that marked orders are derived from the unmarked order under some pragmatic or other motivations. The difference in the effect of basic and derived order is not reflected in native speaker{'}s grammaticality judgments, but we suggest that the intuition about the ordering of arguments may be attested in corpus data. By using the Keyaki treebank (a proper subset of which is NINJAL Parsed Corpus of Modern Japanese (NPCMJ)), it is shown that the naturallyoccurring corpus data confirm that marked orderings of arguments are less frequent than their unmarked ordering counterparts. We suggest some possible motivations lying behind the argument order variations.",
}
@inproceedings{kubota-kubota-2019-probing,
    title = "Probing the nature of an island constraint with a parsed corpus",
    author = "Kubota, Yusuke  and
      Kubota, Ai",
    booktitle = "Linguistic Issues in Language Technology, Volume 18, 2019 - Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    month = jul,
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-18.4",
    abstract = "This paper presents a case study of the use of the NINJAL Parsed Corpus of Modern Japanese (NPCMJ) for syntactic research. NPCMJ is the first phrase structure-based treebank for Japanese that is specifically designed for application in linguistic (in addition to NLP) research. After discussing some basic methodological issues pertaining to the use of treebanks for theoretical linguistics research, we introduce our case study on the status of the Coordinate Structure Constraint (CSC) in Japanese, showing that NPCMJ enables us to easily retrieve examples that support one of the key claims of Kubota and Lee (2015): that the CSC should be viewed as a pragmatic, rather than a syntactic constraint. The corpus-based study we conducted moreover revealed a previously unnoticed tendency that was highly relevant for further clarifying the principles governing the empirical data in question. We conclude the paper by briefly discussing some further methodological issues brought up by our case study pertaining to the relationship between linguistic research and corpus development.",
}
@inproceedings{pintzuk-2019-adding,
    title = "Adding linguistic information to parsed corpora",
    author = "Pintzuk, Susan",
    booktitle = "Linguistic Issues in Language Technology, Volume 18, 2019 - Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    month = jul,
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-18.5",
    abstract = "No matter how comprehensively corpus builders design their annotation schemes, users frequently find that information is missing that they need for their research. In this methodological paper I describe and illustrate five methods of adding linguistic information to corpora that have been morphosyntactically annotated (=parsed) in the style of Penn treebanks. Some of these methods involve manual operations; some are executed by CorpusSearch functions; some require a combination of manual and automated procedures. Which method is used depends almost entirely on the type of information to be added and the goals of the user. Of course, the main goal, regardless of method, is to record within the corpus additional information that can be used for analysis and also retained through further searches and data processing.",
}
@inproceedings{wallis-etal-2019-exploiting,
    title = "Exploiting parsed corpora in grammar teaching",
    author = "Wallis, Sean  and
      Cushing, Ian  and
      Aarts, Bas",
    booktitle = "Linguistic Issues in Language Technology, Volume 18, 2019 - Exploiting Parsed Corpora: Applications in Research, Pedagogy, and Processing",
    month = jul,
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-18.6",
    abstract = "The principal barrier to the uptake of technologies in schools is not technological, but social and political. Teachers must be convinced of the pedagogical benefits of a particular curriculum before they will agree to learn the means to teach it. The teaching of formal grammar to first language students in schools is no exception to this rule. Over the last three decades, most schools in England have been legally required to teach grammatical subject knowledge, i.e. linguistic knowledge of grammar terms and structure, to children age five and upwards as part of the national curriculum in English. A mandatory set of curriculum specifications for England and Wales was published in 2014, and elsewhere similar requirements were imposed. However, few current English school teachers were taught grammar themselves, and the dominant view has long been in favour of {`}real books{'} rather than the teaching of a formal grammar. English grammar teaching thus faces multiple challenges: to convince teachers of the value of grammar in their own teaching, to teach the teachers the knowledge they need, and to develop relevant resources to use in the classroom. Alongside subject knowledge, teachers need pedagogical knowledge {--} how to teach grammar effectively and how to integrate this teaching into other kinds of language learning. The paper introduces the Englicious1 web platform for schools, and summarises its development and impact since publication. Englicious draws data from the fully-parsed British Component of the International Corpus of English, ICE-GB. The corpus offers plentiful examples of genuine natural language, speech and writing, with context and potentially audio playback. However, corpus examples may be ageinappropriate or over-complex, and without grammar training, teachers are insufficiently equipped to use them. In the absence of grammatical knowledge among teachers, it is insufficient simply to give teachers and children access to a corpus. Whereas so-called {`}classroom concordancing{'} approaches offer access to tools and encourage bottom-up learning, Englicious approaches the question of grammar teaching in a concept-driven, top-down way. It contains a modular series of professional development resources, lessons and exercises focused on each concept in turn, in which corpus examples are used extensively. Teachers must be able to discuss with a class why, for instance, work is a noun in a particular sentence, rather than merely report that it is. The paper describes the development of Englicious from secondary to primary, and outlines some of the practical challenges facing the design of this type of teaching resource. A key question, the {`}selection problem{'}, concerns how tools parameterise the selection of relevant examples for teaching purposes. Finally we discuss curricula for teaching teachers and the evaluation of the effectiveness of the intervention.",
}
@proceedings{lilt-2019-,
    title = "",
    booktitle = "Linguistic Issues in Language Technology, Volume 17, 2019",
    year = "2019",
    publisher = "CSLI Publications",
}
@inproceedings{vaidya-palmer-2019-syntactic,
    title = "Syntactic composition and selectional preferences in {H}indi Light Verb Constructions",
    author = "Vaidya, Ashwini  and
      Palmer, Martha",
    booktitle = "Linguistic Issues in Language Technology, Volume 17, 2019",
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-17.1",
    abstract = "Previous work on light verb constructions (e.g. chorii kar {`}theft do; steal{'}) in Hindi describes their syntactic formation via co-predication (Ahmed et al., 2012, Butt, 2014). This implies that both noun and light verb contribute their arguments, and these overlapping argument structures must be composed in the syntax. In this paper, we present a co-predication analysis using Tree-Adjoining Grammar, which models syntactic composition and semantic selectional preferences without transformations (deletion or argument identification). The analysis has two key components (i) an underspecified category for the nominal and (ii) combinatorial constraints on the noun and light verb to specify selectional preferences. The former has the advantage of syntactic composition without argument identification and the latter prevents over-generalization, while recognizing the semantic contribution of both predicates. This work additionally accounts for the agreement facts for the Hindi LVC.",
}
@inproceedings{lowe-2019-argument,
    title = "Argument alternations in complex predicates: an {LFG}+glue perspective",
    author = "Lowe, John J.",
    booktitle = "Linguistic Issues in Language Technology, Volume 17, 2019",
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-17.2",
    abstract = "Vaidya et al. (2019) discuss argument alternations in Hindi complex predicates, and propose an analysis within an LTAG framework, comparing this with an LFG analysis of complex predicates. In this paper I clarify the inadequacies in existing LFG analyses of complex predicates, and show how the LFG+glue approach proposed by Lowe (2015) can both address these inadequacies and provide a relatively simple treatment of the phenomena discussed by Vaidya et al. (2019).",
}
@inproceedings{muller-2019-complex,
    title = "Complex predicates: Structure, potential structure and underspecification",
    author = {M{\"u}ller, Stephan},
    booktitle = "Linguistic Issues in Language Technology, Volume 17, 2019",
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-17.3",
    abstract = "This paper compares a recent TAG-based analysis of complex predicates in Hindi/Urdu with its HPSG analog. It points out that TAG combines actual structure while HPSG (and Categorial Grammar and other valence-based frameworks) specify valence of lexical items and hence potential structure. This makes it possible to have light verbs decide which arguments of embedded heads get realized, somthing that is not possible in TAG. TAG has to retreat to disjunctions instead. While this allows straight-forward analyses of active/passive alternations based on the light verb in valence-based frameworks, such an option does not exist for TAG and it has to be assumed that preverbs come with different sets of arguments.",
}
@inproceedings{butt-2019-complex,
    title = "Complex Predicates and Multidimensionality in Grammar",
    author = "Butt, Miriam",
    booktitle = "Linguistic Issues in Language Technology, Volume 17, 2019",
    year = "2019",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2019.lilt-17.4",
    abstract = "This paper contributes to the on-going discussion of how best to analyze and handle complex predicate formations, commenting in particular on the properties of Hindi N-V complex predicates as set out by Vaidya et al. (2019). I highlight features of existing LFG analyses and focus in particular on the modular architecture of LFG, its attendant multidimensional lexicon and the analytic consequences which follow from this. I point out where the previously existing LFG proposals have been misunderstood as viewed from the lens of theories such as LTAG and HPSG, which assume a very different architectural set-up and provide a comparative discussion of the issues.",
}
@proceedings{jep-taln-recital-2019-actes-de-la-sur-le-traitement,
    title = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL {\textbackslash}{\&} IC)",
    editor = "Morin, Emmanuel  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-tia.0",
}
@inproceedings{lanza-daille-2019-terminology,
    title = "Terminology systematization for Cybersecurity domain in {I}talian Language",
    author = "Lanza, Claudia  and
      Daille, B{\'e}atrice",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL {\textbackslash}{\&} IC)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-tia.1",
    pages = "7--18",
    abstract = "This paper aims at presenting the first steps to improve the quality of the first draft of an Italian thesaurus for Cybersecurity terminology that has been realized for a specific project activity in collaboration with CybersecurityLab at Informatics and Telematics Institute (IIT) of the National Council of Research (CNR) in Italy. In particular, the paper will focus, first, on the terminological knowledge base built to retrieve the most representative candidate terms of Cybersecurity domain in Italian language, giving examples of the main gold standard repositories that have been used to build this semantic tool. Attention will be then given to the methodology and software employed to configure a system of NLP rules to get the desired semantic results and to proceed with the enhancement of the candidate terms selection which are meant to be inserted in the controlled vocabulary.",
}
@inproceedings{randriatsitohaina-hamon-2019-identification,
    title = "Identification des cat{\'e}gories de relations aliment-m{\'e}dicament (Identification of categories of food-drug relations)",
    author = "Randriatsitohaina, Tsanta  and
      Hamon, Thierry",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL {\textbackslash}{\&} IC)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-tia.2",
    pages = "19--30",
    abstract = "Les interactions aliment-m{\'e}dicament se produisent lorsque des aliments et des m{\'e}dicaments pris ensembles provoquent un effet inattendu. Leur reconnaissance automatique dans les textes peut {\^e}tre consid{\'e}r{\'e}e comme une t{\^a}che d{'}extraction de relation {\`a} l{'}aide de m{\'e}thodes de classification. Toutefois, {\'e}tant donn{\'e} que ces interactions sont d{\'e}crites de mani{\`e}re tr{\`e}s fine, nous sommes confront{\'e}s au manque de donn{\'e}es et au manque d{'}exemples par type de relation. Pour r{\'e}soudre ce probl{\`e}me, nous proposons une approche efficace pour regrouper des relations partageant une repr{\'e}sentation similaire en groupes et r{\'e}duire le manque d{'}exemples. Notre approche am{\'e}liore les performances de la classification des FDI. Enfin, nous contrastons une m{\'e}thode de regroupement intuitive bas{\'e}e sur la d{\'e}finition des types de relation et un apprentissage non supervis{\'e} bas{\'e} sur les instances de chaque type de relation.",
    language = "French",
}
@inproceedings{mirisaee-etal-2019-terminology,
    title = "Terminology-based Text Embedding for Computing Document Similarities on Technical Content",
    author = "Mirisaee, Hamid  and
      Gaussier, Eric  and
      Lagnier, Cedric  and
      Guerraz, Agnes",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL {\textbackslash}{\&} IC)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-tia.3",
    pages = "31--42",
    abstract = "We propose in this paper a new, hybrid document embedding approach in order to address the problem of document similarities with respect to the technical content. To do so, we employ a state-of-the-art graph techniques to first extract the keyphrases (composite keywords) of documents and, then, use them to score the sentences. Using the ranked sentences, we propose two approaches to embed documents and show their performances with respect to two baselines. With domain expert annotations, we illustrate that the proposed methods can find more relevant documents and outperform the baselines up to 27{\%} in terms of NDCG.",
}
@inproceedings{bebeshina-clairet-despres-2019-exploiter,
    title = "Exploiter un r{\'e}seau lexico-s{\'e}mantique pour la construction d{'}ontologie (Lexical Semantic Network Use for Ontology Building )",
    author = "Bebeshina-Clairet, Nadia  and
      Despr{\`e}s, Sylvie",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL {\textbackslash}{\&} IC)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-tia.4",
    pages = "43--54",
    abstract = "Dans le pr{\'e}sent article nous nous int{\'e}ressons {\`a} l{'}exploitation des ressources de connaissance lexicos{\'e}mantiques dans le cadre de la construction et de l{'}enrichissement d{'}ontologie. En effet, la construction d{'}ontologie est souvent men{\'e}e de mani{\`e}re descendante o{\`u} ses concepts de haut niveau sont d{\'e}finis pour ensuite {\^e}tre sp{\'e}cifi{\'e}s sur la base d{'}un consensus entre les experts humains. Nous explorons une technique d{'}utilisation des r{\'e}seaux lexico-s{\'e}mantiques (RLS) multilingue et monolingue pour enrichir une ontologie existante. Cette technique vise {\`a} r{\'e}duire l{'}effort humain n{\'e}cessaire {\`a} la localisation ou {\`a} l{'}enrichissement d{'}une ontologie.",
    language = "French",
}
@inproceedings{kageura-chen-2019-entropic,
    title = "Entropic characterisation of termino-conceptual structure : A preliminary study",
    author = "Kageura, Kyo  and
      Chen, Long-Huei",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL {\textbackslash}{\&} IC)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-tia.5",
    pages = "55--68",
    abstract = "Terms represent concepts, which consist of conceptual characteristics. In actual concept-term formation, which is done by researchers, the process is in reverse: conceptual elements/characteristics are consolidated to form concepts, which are represented by terms. As concepts do not exist on the fly, what we may call termino-conceptual system provides scaffolding in this process. Terminologists, both in practice and in research, do not only collect and list terms but also analyse, describe and define terms and systematise terminologies. To carry out these tasks, terminologists must refer to conceptual systems, to the extent that they contribute to systematising terminologies; terminologists thus also deal with the sphere of termino-conceptual system. In this paper, we consolidate the status of termino-conceptual sphere and propose a way to characterise the structure of termino-conceptual system by using entropy. The entropic characterisation of English terminologies of six domain, i.e. agriculture, botany, chemistry, computer science, physics and psychology are presented.",
}
@inproceedings{brin-henry-etal-2019-tempo,
    title = "{T}em{PO} : towards a conceptualisation of pathology in speech and language therapy",
    author = "Brin-Henry, Fr{\'e}d{\'e}rique  and
      Costa, Rute  and
      Despr{\`e}s, Sylvie",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL {\textbackslash}{\&} IC)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-tia.6",
    pages = "69--81",
    abstract = "",
}
@proceedings{jep-taln-recital-2019-actes-de-la,
    title = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    editor = "Morin, Emmanuel  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.0",
}
@inproceedings{regnault-2019-adaptation,
    title = "Adaptation d{'}une m{\'e}tagrammaire du fran{\c{c}}ais contemporain au fran{\c{c}}ais m{\'e}di{\'e}val (Adapting an existing metagrammar for Contemporary {F}rench to Medieval {F}rench)",
    author = "Regnault, Mathilde",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.1",
    pages = "459--472",
    abstract = "Le fran{\c{c}}ais m{\'e}di{\'e}val se caract{\'e}rise par une importante variabilit{\'e} langagi{\`e}re. Nous cherchons {\`a} {\'e}tendre un corpus d{'}ancien fran{\c{c}}ais annot{\'e} en syntaxe de d{\'e}pendance avec de nouveaux textes de cette p{\'e}riode et de moyen fran{\c{c}}ais. Pour cela, nous voulons adapter des outils existants et non entra{\^\i}ner un parser avec des donn{\'e}es annot{\'e}es. Dans cet article, nous pr{\'e}sentons un {\'e}tat de l{'}art pour ce projet et notre d{\'e}marche : adapter FRMG (French Metagrammar) {\`a} des {\'e}tats de langue ant{\'e}rieurs.",
    language = "French",
}
@inproceedings{bouhandi-2019-apport,
    title = "Apport des termes complexes pour enrichir l{'}analyse distributionnelle en domaine sp{\'e}cialis{\'e} (Multi-words terms impact in improving domain-specific distributed representations )",
    author = "Bouhandi, Merieme",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.2",
    pages = "473--486",
    abstract = "L{'}essor et les performances des mod{\`e}les de s{\'e}mantique distributionnelle sont principalement dus {\`a} l{'}accroissement de la quantit{\'e} de donn{\'e}es textuelles disponibles ainsi qu{'}{\`a} la g{\'e}n{\'e}ralisation des m{\'e}thodes neuronales pour la construction de ces mod{\`e}les. La qualit{\'e} des repr{\'e}sentations distribu{\'e}es est souvent corr{\'e}l{\'e}e {\`a} la quantit{\'e} de donn{\'e}es disponibles et les corpus sp{\'e}cialis{\'e}s, g{\'e}n{\'e}ralement d{'}une taille modeste, se trouvent de ce fait p{\'e}nalis{\'e}s. Alors que la plupart des mod{\`e}les de s{\'e}mantique distributionnelle traitent de mots isol{\'e}s, nous partons de l{'}hypoth{\`e}se que l{'}exploitation des termes, notamment complexes, est essentielle notamment en langue de sp{\'e}cialit{\'e} car ils sont porteurs d{'}une dimension s{\'e}mantique suppl{\'e}mentaire. Ainsi, nous {\'e}valuons une m{\'e}thode de g{\'e}n{\'e}ralisation des contextes distributionnels par un m{\'e}canisme d{'}inclusion lexicale reposant sur les termes complexes. Nos diff{\'e}rentes repr{\'e}sentations distributionnelles sont ensuite confront{\'e}es {\`a} une t{\^a}che d{'}extraction de concepts m{\'e}dicaux {\`a} partir des rapports m{\'e}dicaux propos{\'e}e par l{'}{\'e}dition 2010 du challenge i2b2.",
    language = "French",
}
@inproceedings{lopez-espejel-2019-automatic,
    title = "Automatic summarization of medical conversations, a review",
    author = "L{\'o}pez Espejel, Jessica",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.3",
    pages = "487--498",
    abstract = "ion et pour l{'}analyse du dialogue. Nous d{\'e}crivons aussi les utilisation du Traitement Automatique des Langues dans le domaine m{\'e}dical. A BSTRACT Conversational analysis plays an important role in the development of simulation devices for the training of health professionals (doctors, nurses). Our goal is to develop an original automatic synthesis method for medical conversations between a patient and a healthcare professional, based on recent advances in summarization using convolutional and recurrent neural networks. The proposed method must be adapted to the specific problems related to the synthesis of dialogues. This article presents a review of the different methods for extractive and abstractive summarization, and for dialogue analysis. We also describe the use of Natural Language Processing in the medical field.",
}
@inproceedings{oberle-2019-detection,
    title = "D{\'e}tection automatique de cha{\^\i}nes de cor{\'e}f{\'e}rence pour le fran{\c{c}}ais {\'e}crit: r{\`e}gles et ressources adapt{\'e}es au rep{\'e}rage de ph{\'e}nom{\`e}nes linguistiques sp{\'e}cifiques (Automatic coreference resolution for written {F}rench : rules and resources for specific linguistic phenomena)",
    author = "Oberle, Bruno",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.4",
    pages = "499--512",
    abstract = "Nous pr{\'e}sentons un syst{\`e}me end-to-end de d{\'e}tection automatique des cha{\^\i}nes de cor{\'e}f{\'e}rence, {\`a} base de r{\`e}gles, pour le fran{\c{c}}ais {\'e}crit. Ce syst{\`e}me insiste sur la prise en compte de ph{\'e}nom{\`e}nes linguistiques n{\'e}glig{\'e}s par d{'}autres syst{\`e}mes. Nous avons {\'e}labor{\'e} des ressources lexicales pour la r{\'e}solution des anaphores infid{\`e}les (Mon chat... Cet animal...), notamment lorsqu{'}elles incluent une entit{\'e} nomm{\'e}e (La Seine... Ce fleuve...). Nous utilisons {\'e}galement des r{\`e}gles pour le rep{\'e}rage de mentions de groupes (Pierre et Paul) et d{'}anaphores z{\'e}ros (Pierre boit et {\o} fume), ainsi que des r{\`e}gles pour la d{\'e}tection des pronoms de premi{\`e}re et deuxi{\`e}me personnes dans les citations (Paul a dit : {``}Je suis {\'e}tudiant.{''}). L{'}article pr{\'e}sente l{'}{\'e}laboration des ressources et r{\`e}gles utilis{\'e}es pour la gestion de ces ph{\'e}nom{\`e}nes sp{\'e}cifiques, avant de d{\'e}crire le syst{\`e}me dans son ensemble, et notamment les diff{\'e}rentes phases de la r{\'e}solution de la cor{\'e}f{\'e}rence.",
    language = "French",
}
@inproceedings{gallina-2019-etat,
    title = "Etat de l{'}art des m{\'e}thodes d{'}apprentissage profond pour l{'}extraction automatique de termes-cl{\'e}s (State of the art of deep learning methods for automatic keyphrase extraction )",
    author = "Gallina, Ygor",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.5",
    pages = "513--524",
    abstract = "Les termes-cl{\'e}s facilitent la recherche de documents dans de larges collections de donn{\'e}es. Le co{\^u}t d{'}annotation de document en termes-cl{\'e}s tr{\`e}s {\'e}lev{\'e}, c{'}est pourquoi les chercheurs s{'}int{\'e}ressent {\`a} cette probl{\'e}matique. Dans cet article nous pr{\'e}sentons un {\'e}tat de l{'}art sur l{'}extraction automatique de termes-cl{\'e}s en nous int{\'e}ressant particuli{\`e}rement aux mod{\`e}les d{'}apprentissage profond. En effet, la r{\'e}cente publication d{'}un demi-million de documents annot{\'e}s {\`a} permis le d{\'e}veloppement de mod{\`e}les neuronaux profonds.",
    language = "French",
}
@inproceedings{kelodjoue-2019-extraction,
    title = "Extraction d{'}opinions pour l{'}analyse multicrit{\`e}re {\`a} partir de corpus oraux transcrits : {\'E}tat de l{'}art (Opinion extraction for multi-criteria analysis of transcribed oral corpora)",
    author = "Kelodjoue, Emmanuelle",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.6",
    pages = "525--540",
    abstract = "Cet article pr{\'e}sente une revue de la litt{\'e}rature dans les domaines de l{'}analyse de sentiments et du r{\'e}sum{\'e} automatique appliqu{\'e}s aux corpus oraux transcrits. Peu de travaux ont {\'e}t{\'e} r{\'e}alis{\'e}s dans ces deux domaines avec des corpus oraux transcrits. C{'}est pourquoi nous pr{\'e}sentons ci-dessous les tendances g{\'e}n{\'e}rales dans ces deux domaines et nous nous concentrons ensuite sur les syst{\`e}mes d{\'e}velopp{\'e}s pour le traitement de ce type de donn{\'e}es. Les m{\'e}thodes supervis{\'e}es pour l{'}analyse de sentiments et les m{\'e}thodes extractives pour le r{\'e}sum{\'e} automatique sont actuellement dominantes dans le d{\'e}veloppement des syst{\`e}mes automatiques pour le traitement des corpus oraux transcrits. Le pr{\'e}sent article est une vue d{'}ensemble de l{'}{\'e}tat de l{'}art en analyse de sentiments et du r{\'e}sum{\'e} automatique appliqu{\'e}s {\`a} des corpus oraux transcrits pour une aide {\`a} l{'}analyse des verbatim.",
    language = "French",
}
@inproceedings{schaub-vaudapiviz-2019-les,
    title = "Les syst{\`e}mes de dialogue orient{\'e}s-but : {\'e}tat de l{'}art et perspectives d{'}am{\'e}lioration (Goal-oriented dialog systems : a recent overview and research prospects )",
    author = "Schaub, L{\'e}on-Paul  and
      Vaudapiviz, Cyndel",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.7",
    pages = "541--562",
    abstract = "La gestion et la s{\'e}lection des informations pertinentes pour un tour de parole donn{\'e} restent un probl{\`e}me pour les syst{\`e}mes de dialogue {\`a} domaine ouvert. Pour ces derniers, les interactions possibles entre un utilisateur et un agent sont a priori infinies et ind{\'e}finies. La possibilit{\'e} d{'}une r{\'e}ponse erron{\'e}e de l{'}agent {\`a} l{'}utilisateur demeure donc {\'e}lev{\'e}e. Pour les syst{\`e}mes orient{\'e}s-but, le probl{\`e}me est consid{\'e}r{\'e} comme r{\'e}solu, mais d{'}apr{\`e}s notre exp{\'e}rience aucun syst{\`e}me ne montre une robustesse remarquable lorsqu{'}il est {\'e}valu{\'e} en situation r{\'e}elle. Dans cet article, nous dressons un {\'e}tat de l{'}art des m{\'e}thodes d{'}apprentissage de l{'}agent et des diff{\'e}rents mod{\`e}les d{'}agent conversationnel. Selon nous, l{'}une des pistes d{'}am{\'e}lioration de l{'}agent r{\'e}side dans sa m{\'e}moire, car cette derni{\`e}re (souvent repr{\'e}sent{\'e}e par le triplet : tour de parole courant, historique du dialogue et base de connaissances) n{'}est pas encore mod{\'e}lis{\'e}e avec assez de pr{\'e}cision. En dotant l{'}agent d{'}un mod{\`e}le de m{\'e}moire d{'}inspiration cognitive, nous pensons pouvoir augmenter les performances d{'}un syst{\`e}me de dialogue orient{\'e}-but en situation r{\'e}elle, par l{'}emploi d{'}algorithmes d{'}apprentissage automatique avec une approche antagoniste en support d{'}un nouveau mod{\`e}le de m{\'e}moire pour l{'}agent.",
    language = "French",
}
@inproceedings{veron-2019-lifelong,
    title = "Lifelong learning et syst{\`e}mes de dialogue : d{\'e}finition et perspectives (Lifelong learning and dialogue system : definition and discussion )",
    author = "Veron, Mathilde",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.8",
    pages = "563--576",
    abstract = "Le but de cet article est de d{\'e}finir comment le Lifelong Learning (LL) pourrait {\^e}tre appliqu{\'e} aux syst{\`e}mes de dialogue orient{\'e}s t{\^a}che. Un syst{\`e}me de dialogue devrait {\^e}tre en mesure d{'}apprendre de nouvelles connaissances, apr{\`e}s avoir {\'e}t{\'e} d{\'e}ploy{\'e}, et ceci de mani{\`e}re continue gr{\^a}ce {\`a} ses interactions avec l{'}utilisateur. Nous identifions ainsi deux aspects s{'}appliquant {\`a} un tel syst{\`e}me : l{'}am{\'e}lioration de ses capacit{\'e}s conversationnelles, et l{'}enrichissement de sa base de connaissances. Nous appliquons ces id{\'e}es {\`a} un chatbot d{\'e}velopp{\'e} dans le cadre du projet LIHLITH. Nous montrons ainsi qu{'}un tel syst{\`e}me doit {\^e}tre capable (1) de d{\'e}tecter la pr{\'e}sence d{'}une situation inconnue (2) de d{\'e}cider quand et comment interagir avec l{'}utilisateur afin d{'}extraire de nouvelles connaissances et (3) de s{'}adapter {\`a} ces nouvelles connaissances, tout en consid{\'e}rant la fiabilit{\'e} de celles-ci.",
    language = "French",
}
@inproceedings{scholivet-2019-methodes,
    title = "M{\'e}thodes de repr{\'e}sentation de la langue pour l{'}analyse syntaxique multilingue (Language representation methods for multilingual syntactic parsing )",
    author = "Scholivet, Manon",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.9",
    pages = "577--590",
    abstract = "L{'}existence de mod{\`e}les universels pour d{\'e}crire la syntaxe des langues a longtemps {\'e}t{\'e} d{\'e}battue. L{'}apparition de ressources comme le World Atlas of Language Structures et les corpus des Universal Dependencies rend possible l{'}{\'e}tude d{'}une grammaire universelle pour l{'}analyse syntaxique en d{\'e}pendances. Notre travail se concentre sur l{'}{\'e}tude de diff{\'e}rentes repr{\'e}sentations des langues dans des syst{\`e}mes multilingues appris sur des corpus arbor{\'e}s de 37 langues. Nos tests d{'}analyse syntaxique montrent que repr{\'e}senter la langue dont est issu chaque mot permet d{'}obtenir de meilleurs r{\'e}sultats qu{'}en cas d{'}un apprentissage sur une simple concat{\'e}nation des langues. En revanche, l{'}utilisation d{'}un vecteur pour repr{\'e}senter la langue ne permet pas une am{\'e}lioration {\'e}vidente des r{\'e}sultats dans le cas d{'}une langue n{'}ayant pas du tout de donn{\'e}es d{'}apprentissage.",
    language = "French",
}
@inproceedings{terzic-2019-parsing,
    title = "Parsing des textes journalistiques en serbe {\`a} l{'}aide du logiciel Talismane (Parsing of newspaper texts in {S}erbian using Talismane)",
    author = "Terzic, Dusica",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.10",
    pages = "591--604",
    abstract = "Cet article pr{\'e}sente la cr{\'e}ation d{'}un treebank journalistique serbe, ParCoJour. Il est compos{\'e} de 30K tokens et dot{\'e} de trois couches d{'}annotation : {\'e}tiquetage morphosyntaxique, lemmatisation et annotation syntaxique. Une fois construit, ParCoJour a {\'e}t{\'e} utilis{\'e} dans trois exp{\'e}riences afin d{'}{\'e}valuer l{'}impact du domaine textuel sur le parsing du serbe en comparant les performances de Talismane, un syst{\`e}me par apprentissage automatique, sur deux types de corpus, journalistique et litt{\'e}raire : 1) parsing du corpus journalistique avec un mod{\`e}le entra{\^\i}n{\'e} sur le corpus journalistique ; 2) parsing du corpus journalistique avec un mod{\`e}le entra{\^\i}n{\'e} sur le corpus litt{\'e}raire ; 3) parsing du corpus litt{\'e}raire avec un mod{\`e}le entra{\^\i}n{\'e} sur le corpus journalistique. Les r{\'e}sultats sont compar{\'e}s {\`a} ceux o{\`u} les deux corpus relevaient du domaine litt{\'e}raire. Le changement de domaine textuel dans la deuxi{\`e}me et la troisi{\`e}me exp{\'e}rience entra{\^\i}ne une baisse des performances, mais les r{\'e}sultats de parsing restent satisfaisants.",
    language = "French",
}
@inproceedings{bellato-2019-vers,
    title = "Vers la traduction automatique d{'}adverbiaux temporels du fran{\c{c}}ais vers la langue des signes fran{\c{c}}aise (Towards the automatic translation of temporal adverbials from {F}rench to {F}rench sign language)",
    author = "Bellato, Sandra",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume III : RECITAL",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-recital.11",
    pages = "605--616",
    abstract = "Nous pr{\'e}sentons ici de premiers travaux abordant la question de r{\`e}gles de passage entre deux formalismes d{\'e}crivant la s{\'e}mantique d{'}adverbiaux temporels respectivement pour le fran{\c{c}}ais et pour la Langue des Signes Fran{\c{c}}aise (LSF). Ces travaux prennent place dans une vis{\'e}e de traduction automatique d{'}une langue vers l{'}autre. Nous nous appuyons sur un corpus rassemblant 95 adverbiaux temporels du fran{\c{c}}ais traduits par trois locuteurs de la LSF.",
    language = "French",
}
@proceedings{jep-taln-recital-2019-actes,
    title = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    editor = "Morin, Emmanuel  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.0",
}
@inproceedings{montariol-allauzen-2019-apprentissage,
    title = "Apprentissage de plongements de mots dynamiques avec r{\'e}gularisation de la d{\'e}rive (Learning dynamic word embeddings with drift regularisation)",
    author = "Montariol, Syrielle  and
      Allauzen, Alexandre",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.1",
    pages = "13--26",
    abstract = "L{'}usage, le sens et la connotation des mots peuvent changer au cours du temps. Les plongements lexicaux diachroniques permettent de mod{\'e}liser ces changements de mani{\`e}re non supervis{\'e}e. Dans cet article nous {\'e}tudions l{'}impact de plusieurs fonctions de co{\^u}t sur l{'}apprentissage de plongements dynamiques, en comparant les comportements de variantes du mod{\`e}le Dynamic Bernoulli Embeddings. Les plongements dynamiques sont estim{\'e}s sur deux corpus couvrant les m{\^e}mes deux d{\'e}cennies, le New York Times Annotated Corpus en anglais et une s{\'e}lection d{'}articles du journal Le Monde en fran{\c{c}}ais, ce qui nous permet de mettre en place un processus d{'}analyse bilingue de l{'}{\'e}volution de l{'}usage des mots.",
    language = "French",
}
@inproceedings{connes-dugue-2019-apprentissage,
    title = "Apprentissage de plongements lexicaux par une approche r{\'e}seaux complexes (Complex networks based word embeddings)",
    author = "Connes, Victor  and
      Dugu{\'e}, Nicolas",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.2",
    pages = "27--38",
    abstract = {La litt{\'e}rature des r{\'e}seaux complexes a montr{\'e} la pertinence de l{'}{\'e}tude de la langue sous forme de r{\'e}seau pour diff{\'e}rentes applications : d{\'e}sambigu{\"\i}sation, r{\'e}sum{\'e} automatique, classification des langues, etc. Cette m{\^e}me litt{\'e}rature a d{\'e}montr{\'e} que les r{\'e}seaux de co-occurrences de mots poss{\`e}dent une structure de communaut{\'e}s latente. Nous formulons l{'}hypoth{\`e}se que cette structuration du r{\'e}seau sous forme de communaut{\'e}s est utile pour travailler sur la s{\'e}mantique d{'}une langue et introduisons donc dans cet article une m{\'e}thode d{'}apprentissage de plongements originale bas{\'e}e sur cette hypoth{\`e}se. Cette hypoth{\`e}se est coh{\'e}rente avec la proximit{\'e} qui existe entre la d{\'e}tection de communaut{\'e}s sur un r{\'e}seau de co-occurrences et la factorisation d{'}une matrice de co-occurrences, m{\'e}thode couramment utilis{\'e}e pour l{'}apprentissage de plongements lexicaux. Nous d{\'e}crivons notre m{\'e}thode structur{\'e}e en trois {\'e}tapes : construction et pr{\'e}-traitement du r{\'e}seau, d{\'e}tection de la structure de communaut{\'e}s, construction des plongements de mots {\`a} partir de cette structure. Apr{\`e}s avoir d{\'e}crit cette nouvelle m{\'e}thodologie, nous montrons la pertinence de notre approche avec des premiers r{\'e}sultats d{'}{\'e}valuation sur les t{\^a}ches de cat{\'e}gorisation et de similarit{\'e}. Enfin, nous discutons des perspectives importantes d{'}un tel mod{\`e}le issu des r{\'e}seaux complexes : les dimensions du mod{\`e}le (les communaut{\'e}s) semblent interpr{\'e}tables, l{'}apprentissage est rapide, la construction d{'}un nouveau plongement est presque instantan{\'e}e, et il est envisageable d{'}en exp{\'e}rimenter une version incr{\'e}mentale pour travailler sur des corpus textuels temporels.},
    language = "French",
}
@inproceedings{tanguy-etal-2019-comparaison,
    title = "Comparaison qualitative et extrins{\`e}que d{'}analyseurs syntaxiques du fran{\c{c}}ais : confrontation de mod{\`e}les distributionnels sur un corpus sp{\'e}cialis{\'e} (Extrinsic evaluation of {F}rench dependency parsers on a specialised corpus : comparison of distributional thesauri )",
    author = "Tanguy, Ludovic  and
      Brunet, Pauline  and
      Ferret, Olivier",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.3",
    pages = "39--54",
    abstract = "Nous pr{\'e}sentons une {\'e}tude visant {\`a} comparer 11 diff{\'e}rents analyseurs en d{\'e}pendances du fran{\c{c}}ais sur un corpus sp{\'e}cialis{\'e} (constitu{\'e} des archives des articles de la conf{\'e}rence TALN). En l{'}absence de gold standard, nous utilisons chacune des sorties de ces analyseurs pour construire des th{\'e}saurus distributionnels en utilisant une m{\'e}thode {\`a} base de fr{\'e}quence. Nous comparons ces 11 th{\'e}saurus afin de proposer un premier aper{\c{c}}u de l{'}impact du choix d{'}un analyseur par rapport {\`a} un autre.",
    language = "French",
}
@inproceedings{vial-etal-2019-compression,
    title = {Compression de vocabulaire de sens gr{\^a}ce aux relations s{\'e}mantiques pour la d{\'e}sambigu{\"\i}sation lexicale (Sense Vocabulary Compression through Semantic Knowledge for Word Sense Disambiguation)},
    author = {Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin  and
      Schwab, Didier},
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.4",
    pages = "55--70",
    abstract = {En D{\'e}sambigu{\"\i}sation Lexicale (DL), les syst{\`e}mes supervis{\'e}s dominent largement les campagnes d{'}{\'e}valuation. La performance et la couverture de ces syst{\`e}mes sont cependant rapidement limit{\'e}s par la faible quantit{\'e} de corpus annot{\'e}s en sens disponibles. Dans cet article, nous pr{\'e}sentons deux nouvelles m{\'e}thodes qui visent {\`a} r{\'e}soudre ce probl{\`e}me en exploitant les relations s{\'e}mantiques entre les sens tels que la synonymie, l{'}hyperonymie et l{'}hyponymie, afin de compresser le vocabulaire de sens de WordNet, et ainsi r{\'e}duire le nombre d{'}{\'e}tiquettes diff{\'e}rentes n{\'e}cessaires pour pouvoir d{\'e}sambigu{\"\i}ser tous les mots de la base lexicale. Nos m{\'e}thodes permettent de r{\'e}duire consid{\'e}rablement la taille des mod{\`e}les de DL neuronaux, avec l{'}avantage d{'}am{\'e}liorer leur couverture sans donn{\'e}es suppl{\'e}mentaires, et sans impacter leur pr{\'e}cision. En plus de nos m{\'e}thodes, nous pr{\'e}sentons un syst{\`e}me de DL qui tire parti des r{\'e}cents travaux sur les repr{\'e}sentations vectorielles de mots contextualis{\'e}es, afin d{'}obtenir des r{\'e}sultats qui surpassent largement l{'}{\'e}tat de l{'}art sur toutes les t{\^a}ches d{'}{\'e}valuation de la DL.},
    language = "French",
}
@inproceedings{grabar-etal-2019-corpus,
    title = "Corpus annot{\'e} de cas cliniques en fran{\c{c}}ais (Annotated corpus with clinical cases in {F}rench)",
    author = "Grabar, Natalia  and
      Grouin, Cyril  and
      Hamon, Thierry  and
      Claveau, Vincent",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.5",
    pages = "71--84",
    abstract = "Les corpus textuels sont utiles pour diverses applications de traitement automatique des langues (TAL) en fournissant les donn{\'e}es n{\'e}cessaires pour leur cr{\'e}ation, adaptation ou {\'e}valuation. Cependant, dans certains domaines comme le domaine m{\'e}dical, l{'}acc{\`e}s aux donn{\'e}es est rendu compliqu{\'e}, voire impossible, pour des raisons de confidentialit{\'e} et d{'}{\'e}thique. Il existe n{\'e}anmoins de r{\'e}els besoins en corpus cliniques pour l{'}enseignement et la recherche. Pour r{\'e}pondre {\`a} ce d{\'e}fi, nous pr{\'e}sentons dans cet article le corpus CAS contenant des cas cliniques de patients, r{\'e}els ou fictifs, que nous avons compil{\'e}s. Ces cas cliniques en fran{\c{c}}ais couvrent plusieurs sp{\'e}cialit{\'e}s m{\'e}dicales et focalisent donc sur diff{\'e}rentes situations cliniques. Actuellement, le corpus contient 4 300 cas (environ 1,5M d{'}occurrences de mots). Il est accompagn{\'e} d{'}informations (discussions des cas cliniques, mots-cl{\'e}s, etc.) et d{'}annotations que nous avons effectu{\'e}es au regard des besoins de la recherche en TAL dans ce domaine. Nous pr{\'e}sentons {\'e}galement les r{\'e}sultats de premi{\`e}res exp{\'e}riences de recherche et d{'}extraction d{'}information qui ont {\'e}t{\'e} effectu{\'e}es avec ce corpus annot{\'e}. Ces exp{\'e}riences peuvent fournir une baseline {\`a} d{'}autres chercheurs souhaitant travailler avec les donn{\'e}es.",
    language = "French",
}
@inproceedings{caubriere-etal-2019-curriculum,
    title = "Curriculum d{'}apprentissage : reconnaissance d{'}entit{\'e}s nomm{\'e}es pour l{'}extraction de concepts s{\'e}mantiques (Curriculum learning : named entity recognition for semantic concept extraction)",
    author = "Caubri{\`e}re, Antoine  and
      Tomashenko, Natalia  and
      Est{\`e}ve, Yannick  and
      Laurent, Antoine  and
      Morin, Emmanuel",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.6",
    pages = "85--98",
    abstract = "Dans cet article, nous pr{\'e}sentons une approche de bout en bout d{'}extraction de concepts s{\'e}mantiques de la parole. En particulier, nous mettons en avant l{'}apport d{'}une cha{\^\i}ne d{'}apprentissage successif pilot{\'e}e par une strat{\'e}gie de curriculum d{'}apprentissage. Dans la cha{\^\i}ne d{'}apprentissage mise en place, nous exploitons des donn{\'e}es fran{\c{c}}aises annot{\'e}es en entit{\'e}s nomm{\'e}es que nous supposons {\^e}tre des concepts plus g{\'e}n{\'e}riques que les concepts s{\'e}mantiques li{\'e}s {\`a} une application informatique sp{\'e}cifique. Dans cette {\'e}tude, il s{'}agit d{'}extraire des concepts s{\'e}mantiques dans le cadre de la t{\^a}che MEDIA. Pour renforcer le syst{\`e}me propos{\'e}, nous exploitons aussi des strat{\'e}gies d{'}augmentation de donn{\'e}es, un mod{\`e}le de langage 5-gramme, ainsi qu{'}un mode {\'e}toile aidant le syst{\`e}me {\`a} se concentrer sur les concepts et leurs valeurs lors de l{'}apprentissage. Les r{\'e}sultats montrent un int{\'e}r{\^e}t {\`a} l{'}utilisation des donn{\'e}es d{'}entit{\'e}s nomm{\'e}es, permettant un gain relatif allant jusqu{'}{\`a} 6,5 {\%}.",
    language = "French",
}
@inproceedings{hamza-bernhard-2019-detection,
    title = "D{\'e}tection des ellipses dans des corpus de sous-titres en anglais (Ellipsis Detection in {E}nglish Subtitles Corpora )",
    author = "Hamza, Anissa  and
      Bernhard, Delphine",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.7",
    pages = "99--112",
    abstract = "Cet article pr{\'e}sente une m{\'e}thodologie de d{\'e}tection des ellipses en anglais qui repose sur des patrons combinant des informations sur les tokens, leur {\'e}tiquette morphosyntaxique et leur lemme. Les patrons sont {\'e}valu{\'e}s sur deux corpus de sous-titres. Ces travaux constituent une {\'e}tape pr{\'e}alable {\`a} une {\'e}tude contrastive et multi-genres de l{'}ellipse.",
    language = "French",
}
@inproceedings{van-de-cruys-2019-la,
    title = "La g{\'e}n{\'e}ration automatique de po{\'e}sie en fran{\c{c}}ais (Automatic Poetry Generation in {F}rench)",
    author = "Van de Cruys, Tim",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.8",
    pages = "113--126",
    abstract = "La g{\'e}n{\'e}ration automatique de po{\'e}sie est une t{\^a}che ardue pour un syst{\`e}me informatique. Pour qu{'}un po{\`e}me ait du sens, il est important de prendre en compte {\`a} la fois des aspects linguistiques et litt{\'e}raires. Ces derni{\`e}res ann{\'e}es, un certain nombre d{'}approches fructueuses sont apparues, capables de mod{\'e}liser de mani{\`e}re ad{\'e}quate divers aspects du langage naturel. En particulier, les mod{\`e}les de langue bas{\'e}s sur les r{\'e}seaux de neurones ont am{\'e}lior{\'e} l{'}{\'e}tat de l{'}art par rapport {\`a} la mod{\'e}lisation pr{\'e}dictive de langage, tandis que les topic models sont capables de capturer une certaine coh{\'e}rence th{\'e}matique. Dans cet article, on explorera comment ces approches peuvent {\^e}tre adapt{\'e}es et combin{\'e}es afin de mod{\'e}liser les aspects linguistiques et litt{\'e}raires n{\'e}cessaires pour la g{\'e}n{\'e}ration de po{\'e}sie. Le syst{\`e}me est exclusivement entra{\^\i}n{\'e} sur des textes g{\'e}n{\'e}riques, et sa sortie est contrainte afin de conf{\'e}rer un caract{\`e}re po{\'e}tique au vers g{\'e}n{\'e}r{\'e}. Le cadre pr{\'e}sent{\'e} est appliqu{\'e} {\`a} la g{\'e}n{\'e}ration de po{\`e}mes en fran{\c{c}}ais, et {\'e}valu{\'e} {\`a} l{'}aide d{'}une {\'e}valuation humaine.",
    language = "French",
}
@inproceedings{dinarelli-grobol-2019-modeles,
    title = "Mod{\`e}les neuronaux hybrides pour la mod{\'e}lisation de s{\'e}quences : le meilleur de trois mondes ()",
    author = {Dinarelli, Marco  and
      Grobol, Lo{\"\i}c},
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.9",
    pages = "127--142",
    abstract = "Nous proposons une architecture neuronale avec les caract{\'e}ristiques principales des mod{\`e}les neuronaux de ces derni{\`e}res ann{\'e}es : les r{\'e}seaux neuronaux r{\'e}currents bidirectionnels, les mod{\`e}les encodeur-d{\'e}codeur, et le mod{\`e}le Transformer. Nous {\'e}valuons nos mod{\`e}les sur trois t{\^a}ches d{'}{\'e}tiquetage de s{\'e}quence, avec des r{\'e}sultats aux environs de l{'}{\'e}tat de l{'}art et souvent meilleurs, montrant ainsi l{'}int{\'e}r{\^e}t de cette architecture hybride pour ce type de t{\^a}ches.",
    language = "French",
}
@inproceedings{todirascu-etal-2019-polylexfle,
    title = "{P}olylex{FLE} : une base de donn{\'e}es d{'}expressions polylexicales pour le {FLE} ({P}olylex{FLE} : a database of multiword expressions for {F}rench {L}2 language learning)",
    author = "Todirascu, Amalia  and
      Cargill, Marion  and
      Francois, Thomas",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume I : Articles longs",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-long.10",
    pages = "143--156",
    abstract = "Nous pr{\'e}sentons la base PolylexFLE, contenant 4295 expressions polylexicales. Elle est integr{\'e}e dans une plateforme d{'}apprentissage du FLE, SimpleApprenant, destin{\'e}e {\`a} l{'}apprentissage des expressions polylexicales verbales (idiomatiques, collocations ou expressions fig{\'e}es). Afin de proposer des exercices adapt{\'e}s au niveau du Cadre europ{\'e}en de r{\'e}f{\'e}rence pour les langues (CECR), nous avons utilis{\'e} une proc{\'e}dure mixte (manuelle et automatique) pour annoter 1098 expressions selon les niveaux de comp{\'e}tence du CECR. L{'}article se concentre sur la proc{\'e}dure automatique qui identifie, dans un premier temps, les expressions de la base PolylexFLE dans un corpus {\`a} l{'}aide d{'}un syst{\`e}me {\`a} base d{'}expressions r{\'e}guli{\`e}res. Dans un second temps, leur distribution au sein de corpus, annot{\'e} selon l{'}{\'e}chelle du CECR, est estim{\'e}e et transform{\'e}e en un niveau CECR unique.",
    language = "French",
}
@proceedings{jep-taln-recital-2019-actes-de-la-sur,
    title = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : D{\'e}monstrations",
    editor = "Morin, Emmanuel  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-demo.0",
}
@inproceedings{schwab-etal-2019-apporter,
    title = "Apporter des connaissances s{\'e}mantiques {\`a} un jeu de pictogrammes destin{\'e} {\`a} des personnes en situation de handicap : Un ensemble de liens entre {P}rinceton {W}ord{N}et et Arasaac, Arasaac-{WN} (Giving semantic knowledge to a set of pictograms for people with disabilities : a set of links between {W}ord{N}et and Arasaac, Arasaac-{WN} )",
    author = {Schwab, Didier  and
      Trial, Pauline  and
      C{\'e}line, Vaschalde  and
      Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin},
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : D{\'e}monstrations",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-demo.1",
    pages = "619--622",
    abstract = "Cet article pr{\'e}sente une ressource qui fait le lien entre WordNet et Arasaac, la plus grande base de pictogrammes librement disponible. Cette ressource est particuli{\`e}rement int{\'e}ressante pour la cr{\'e}ation d{'}applications visant l{'}aide aux personnes en situation de handicap cognitif.",
    language = "French",
}
@inproceedings{dubuisson-duplessis-etal-2019-cameli,
    title = "Cameli@ : analyses automatiques d{'}e-mails pour am{\'e}liorer la relation client (Cameli@ : automatic e-mail analysis to improve the customer relationship )",
    author = "Dubuisson Duplessis, Guillaume  and
      Kerroua, Sofiane  and
      Kuznik, Ludivine  and
      Gu{\'e}net, Anne-Laure",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : D{\'e}monstrations",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-demo.2",
    pages = "623--626",
    abstract = "Cette d{\'e}monstration pr{\'e}sente un syst{\`e}me actuellement en production d{'}analyses automatiques d{'}emails en fran{\c{c}}ais incluant des analyses th{\'e}matiques, des analyses de l{'}opinion, des t{\^a}ches d{'}extraction d{'}information et une t{\^a}che de pseudo-anonymisation.",
    language = "French",
}
@inproceedings{schmitt-etal-2019-demonstrateur,
    title = "D{\'e}monstrateur en-ligne du projet {ANR} {PARSEME}-{FR} sur les expressions polylexicales (On-line demonstrator of the {PARSEME}-{FR} project on multiword expressions)",
    author = "Schmitt, Marine  and
      Moreau, Elise  and
      Constant, Mathieu  and
      Savary, Agata",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : D{\'e}monstrations",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-demo.3",
    pages = "627--630",
    abstract = "Nous pr{\'e}sentons le d{\'e}monstrateur en-ligne du projet ANR PARSEME-FR d{\'e}di{\'e} aux expressions polylexicales. Il inclut diff{\'e}rents outils d{'}identification de telles expressions et un outil d{'}exploration des ressources linguistiques de ce projet.",
    language = "French",
}
@inproceedings{hamon-etal-2019-sylnews,
    title = "{S}yl{N}ews, un agr{\'e}filter multilingue ({S}yl{N}ews, a multilingual aggrefilter)",
    author = "Hamon, Olivier  and
      Espasa, K{\'e}vin  and
      Quispe, Sara",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : D{\'e}monstrations",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-demo.4",
    pages = "631--634",
    abstract = "Depuis plusieurs ann{\'e}es, Syllabs int{\`e}gre de nombreux composants au sein d{'}un agr{\'e}filter, utilisant des technologies d{'}extraction d{'}information d{\'e}velopp{\'e}es en interne et dans un contexte multilingue. Originellement con{\c{c}}u pour agr{\'e}ger des contenus issus de la presse, SylNews peut {\^e}tre utilis{\'e} {\`a} des fins de veille, pour explorer des contenus, ou pour identifier d{'}une mani{\`e}re plus globale les sujets chauds de l{'}ensemble ou d{'}une partie des contenus stock{\'e}s.",
    language = "French",
}
@inproceedings{calapodescu-etal-2019-sentiment,
    title = "{``}Sentiment Aware Map{''} : exploration cartographique de points d{'}int{\'e}r{\^e}t via l{'}analyse de sentiments au niveau des aspects ()",
    author = {Calapodescu, Ioan  and
      Brun, Caroline  and
      Nikoulina, Vassilina  and
      A{\"\i}t-Mokhtar, Salah},
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : D{\'e}monstrations",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-demo.5",
    pages = "635--638",
    abstract = "",
    language = "French",
}
@inproceedings{arnold-etal-2019-interpretation,
    title = "Interpr{\'e}tation et visualisation contextuelle de {NOTAM}s (messages aux navigants a{\'e}riens) ()",
    author = "Arnold, Alexandre  and
      Dupont, G{\'e}rard  and
      Kobus, Catherine  and
      Lancelot, Fran{\c{c}}ois  and
      Narayan, Pooja",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume IV : D{\'e}monstrations",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-demo.6",
    pages = "639--643",
    abstract = "Dans cet article, nous pr{\'e}sentons une d{\'e}monstration de visualisation de l{'}information extraite automatiquement de la partie textuelle des NOTAMs. Dans le domaine a{\'e}ronautique, les NOTAMs sont des messages publi{\'e}s par les agences gouvernementales de contr{\^o}le de la navigation a{\'e}rienne. Nous d{\'e}taillons la construction du jeu de donn{\'e}es, les exp{\'e}riences d{'}extraction d{'}information par apprentissage profond (approche et r{\'e}sultats), ainsi que le lien avec la visualisation contextuelle sur des cartes d{'}a{\'e}roports.",
    language = "French",
}
@proceedings{jep-taln-recital-2019-actes-de-la-sur-le,
    title = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    editor = "Morin, Emmanuel  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.0",
}
@inproceedings{grabar-etal-2019-recherche,
    title = "Recherche et extraction d{'}information dans des cas cliniques. Pr{\'e}sentation de la campagne d{'}{\'e}valuation {DEFT} 2019 (Information Retrieval and Information Extraction from Clinical Cases)",
    author = "Grabar, Natalia  and
      Grouin, Cyril  and
      Hamon, Thierry  and
      Claveau, Vincent",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.1",
    pages = "7--16",
    abstract = "Cet article pr{\'e}sente la campagne d{'}{\'e}valuation DEFT 2019 sur l{'}analyse de textes cliniques r{\'e}dig{\'e}s en fran{\c{c}}ais. Le corpus se compose de cas cliniques publi{\'e}s et discut{\'e}s dans des articles scientifiques, et index{\'e}s par des mots-cl{\'e}s. Nous proposons trois t{\^a}ches ind{\'e}pendantes : l{'}indexation des cas cliniques et discussions, {\'e}valu{\'e}e prioritairement par la MAP (mean average precision), l{'}appariement entre cas cliniques et discussions, {\'e}valu{\'e} au moyen d{'}une pr{\'e}cision, et l{'}extraction d{'}information parmi quatre cat{\'e}gories ({\^a}ge, genre, origine de la consultation, issue), {\'e}valu{\'e}e en termes de rappel, pr{\'e}cision et F-mesure. Nous pr{\'e}sentons les r{\'e}sultats obtenus par les participants sur chaque t{\^a}che.",
    language = "French",
}
@inproceedings{suignard-etal-2019-participation,
    title = "Participation d{'}{EDF} {R}{\&}{D} {\`a} {DEFT} 2019 : des vecteurs et des r{\`e}gles ! ({EDF} {R}{\&}{D} submission to {DEFT} 2019 )",
    author = "Suignard, Philippe  and
      Bothua, Meryl  and
      Benamar, Alexandra",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.2",
    pages = "17--28",
    abstract = "Ce papier d{\'e}crit la participation d{'}EDF R{\&}D {\`a} la campagne d{'}{\'e}valuation DEFT 2019. Notre {\'e}quipe a particip{\'e} aux trois t{\^a}ch{\'e}s propos{\'e}es : Indexation de cas cliniques (T{\^a}che T1) ; D{\'e}tection de similarit{\'e} entre des cas cliniques et des discussions (T{\^a}che T2) ; Extraction d{'}information dans des cas cliniques (T{\^a}che 3). Nous avons utilis{\'e} des m{\'e}thodes symboliques et/ou num{\'e}riques en fonction de ces t{\^a}ches. Aucune donn{\'e}e suppl{\'e}mentaire, autre que les donn{\'e}es d{'}apprentissage, n{'}a {\'e}t{\'e} utilis{\'e}e. Notre {\'e}quipe obtient des r{\'e}sultats satisfaisants sur l{'}ensemble des taches et se classe premi{\`e}re sur la tache 2. Les m{\'e}thodes propos{\'e}es sont facilement transposables {\`a} d{'}autres t{\^a}ches d{'}indexation et de d{\'e}tection de similarit{\'e} qui peuvent int{\'e}resser plusieurs entit{\'e}s du groupe EDF.",
    language = "French",
}
@inproceedings{hilbey-etal-2019-participation,
    title = "Participation de l{'}{\'e}quipe {LAI} {\`a} {DEFT} 2019 (Participation of team {LAI} in the {DEFT} 2019 challenge )",
    author = "Hilbey, Jacques  and
      Del{\'e}ger, Louise  and
      Tannier, Xavier",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.3",
    pages = "29--34",
    abstract = "Nous pr{\'e}sentons dans cet article les m{\'e}thodes con{\c{c}}ues et les r{\'e}sultats obtenus lors de notre participation {\`a} la t{\^a}che 3 de la campagne d{'}{\'e}valuation DEFT 2019. Nous avons utilis{\'e} des approches simples {\`a} base de r{\`e}gles ou d{'}apprentissage automatique, et si nos r{\'e}sultats sont tr{\`e}s bons sur les informations simples {\`a} extraire comme l{'}{\^a}ge et le sexe du patient, ils restent mitig{\'e}s sur les t{\^a}ches plus difficiles.",
    language = "French",
}
@inproceedings{mensonides-etal-2019-defi,
    title = "{D{\'E}}fi Fouille de Textes 2019 : indexation par extraction et appariement textuel ({DEFT} 2019 : extraction-based document indexing and textual document similarity matching )",
    author = "Mensonides, Jean-Christophe  and
      Jean, Pierre-Antoine  and
      Tchechmedjiev, Andon  and
      Harispe, S{\'e}bastien",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.4",
    pages = "35--48",
    abstract = "Cet article pr{\'e}sente la contribution de l{'}{\'e}quipe du Laboratoire de G{\'e}nie Informatique et d{'}Ing{\'e}nierie de Production (LGI2P) d{'}IMT Mines Al{\`e}s au D{\'E}fi Fouille de Textes (DEFT) 2019. Il d{\'e}taille en particulier deux approches propos{\'e}es pour les t{\^a}ches li{\'e}es {\`a} (1) l{'}indexation et {\`a} (2) la similarit{\'e} de documents. Ces m{\'e}thodes reposent sur des techniques robustes et {\'e}prouv{\'e}es du domaine de la Recherche d{'}Information et du Traitement Automatique du Langage Naturel, qui ont {\'e}t{\'e} adapt{\'e}es {\`a} la nature sp{\'e}cifique du corpus (biom{\'e}dical/clinique) et coupl{\'e}es {\`a} des m{\'e}canismes d{\'e}velopp{\'e}s pour r{\'e}pondre aux sp{\'e}cificit{\'e}s des t{\^a}ches trait{\'e}es. Pour la t{\^a}che 1, nous proposons une m{\'e}thode d{'}indexation par extraction appliqu{\'e}e sur une version normalis{\'e}e du corpus (MAP de 0,48 {\`a} l{'}{\'e}valuation) ; les sp{\'e}cificit{\'e}s de la phase de normalisation seront en particulier d{\'e}taill{\'e}es. Pour la t{\^a}che 2, au-del{\`a} de la pr{\'e}sentation de l{'}approche propos{\'e}e bas{\'e}e sur l{'}{\'e}valuation de similarit{\'e}s sur des repr{\'e}sentations de documents (score de 0,91 {\`a} l{'}{\'e}valuation), nous proposons une {\'e}tude comparative de l{'}impact des choix de la distance et de la mani{\`e}re de repr{\'e}senter les textes sur la performance de l{'}approche.",
    language = "French",
}
@inproceedings{buscaldi-etal-2019-indexation,
    title = "Indexation et appariements de documents cliniques pour le Deft 2019 (Indexing and pairing texts of the medical domain )",
    author = {Buscaldi, Davide  and
      Ghoul, Dhaou  and
      Le Roux, Joseph  and
      Lejeune, Ga{\"e}l},
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.5",
    pages = "49--56",
    abstract = "Dans cet article, nous pr{\'e}sentons nos m{\'e}thodes pour les t{\^a}ches d{'}indexation et d{'}appariements du D{\'e}fi Fouile de Textes (Deft) 2019. Pour la ta{\^c}he d{'}indexation nous avons test{\'e} deux m{\'e}thodes, une fond{\'e}e sur l{'}appariemetn pr{\'e}alable des documents du jeu de tset avec les documents du jeu d{'}entra{\^\i}nement et une autre m{\'e}thode fond{\'e}e sur l{'}annotation terminologique. Ces m{\'e}thodes ont malheureusement offert des r{\'e}sultats assez faible. Pour la t{\^a}che d{'}appariement, nous avons d{\'e}vellop{\'e} une m{\'e}thode sans apprentissage fond{\'e}e sur des similarit{\'e}s de cha{\^\i}nes de caract{\`e}res ainsi qu{'}une m{\'e}thode exploitant des r{\'e}seaux siamois. L{\`a} encore les r{\'e}sultats ont {\'e}t{\'e} plut{\^o}t d{\'e}cevant m{\^e}me si la m{\'e}thode non supervis{\'e}e atteint un score plut{\^o}t honorable pour une m{\'e}thode non-supervis{\'e}e : 62{\%} .",
    language = "French",
}
@inproceedings{bouhandi-etal-2019-deft,
    title = "{D}e{FT} 2019 : Auto-encodeurs, Gradient Boosting et combinaisons de mod{\`e}les pour l{'}identification automatique de mots-cl{\'e}s. Participation de l{'}{\'e}quipe {TALN} du {LS}2{N} (Autoencoders, gradient boosting and ensemble systems for automatic keyphrase assignment : The {LS}2{N} team participation{'}s in the 2019 edition of {D}e{FT})",
    author = "Bouhandi, M{\'e}ri{\`e}me  and
      Boudin, Florian  and
      Gallina, Ygor",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.6",
    pages = "57--66",
    abstract = "Nous pr{\'e}sentons dans cet article la participation de l{'}{\'e}quipe TALN du LS2N {\`a} la t{\^a}che d{'}indexation de cas cliniques (t{\^a}che 1). Nous proposons deux syst{\`e}mes permettant d{'}identifier, dans la liste de mots-cl{\'e}s fournie, les mots-cl{\'e}s correspondant {\`a} un couple cas clinique/discussion, ainsi qu{'}un classifieur entra{\^\i}n{\'e} sur la combinaison des sorties des deux syst{\`e}mes. Nous pr{\'e}senterons dans le d{\'e}tail les descripteurs utilis{\'e}s pour repr{\'e}senter les mots-cl{\'e}s ainsi que leur impact sur nos syst{\`e}mes de classification.",
    language = "French",
}
@inproceedings{maudet-etal-2019-qwant,
    title = "Qwant Research @{DEFT} 2019 : appariement de documents et extraction d{'}informations {\`a} partir de cas cliniques (Document matching and information retrieval using clinical cases)",
    author = "Maudet, Estelle  and
      Cattan, Oralie  and
      de Seyssel, Maureen  and
      Servan, Christophe",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.7",
    pages = "67--80",
    abstract = "Dans ce papier, nous pr{\'e}sentons la participation de Qwant Research aux t{\^a}ches 2 et 3 de l{'}{\'e}dition 2019 du d{\'e}fi fouille de textes (DEFT) portant sur l{'}analyse de documents cliniques r{\'e}dig{\'e}s en fran{\c{c}}ais. La t{\^a}che 2 est une t{\^a}che de similarit{\'e} s{\'e}mantique qui demande d{'}apparier cas cliniques et discussions m{\'e}dicales. Pour r{\'e}soudre cette t{\^a}che, nous proposons une approche reposant sur des mod{\`e}les de langue et {\'e}valuons l{'}impact de diff{\'e}rents pr{\'e}-traitements et de diff{\'e}rentes techniques d{'}appariement sur les r{\'e}sultats. Pour la t{\^a}che 3, nous avons d{\'e}velopp{\'e} un syst{\`e}me d{'}extraction d{'}information qui produit des r{\'e}sultats encourageants en termes de pr{\'e}cision. Nous avons exp{\'e}riment{\'e} deux approches diff{\'e}rentes, l{'}une se fondant exclusivement sur l{'}utilisation de r{\'e}seaux de neurones pour traiter la t{\^a}che, l{'}autre reposant sur l{'}exploitation des informations linguistiques issues d{'}une analyse syntaxique.",
    language = "French",
}
@inproceedings{sileo-etal-2019-aprentissage,
    title = "Aprentissage non-supervis{\'e} pour l{'}appariement et l{'}{\'e}tiquetage de cas cliniques en fran{\c{c}}ais - {DEFT}2019 (Unsupervised learning for matching and labelling of french clincal cases - {DEFT}2019 )",
    author = "Sileo, Damien  and
      Van de Cruys, Tim  and
      Muller, Philippe  and
      Pradel, Camille",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.8",
    pages = "81--90",
    abstract = "Nous pr{\'e}sentons le syst{\`e}me utilis{\'e} par l{'}{\'e}quipe Synapse/IRIT dans la comp{\'e}tition DEFT2019 portant sur deux t{\^a}ches li{\'e}es {\`a} des cas cliniques r{\'e}dig{\'e}s en fran{\c{c}}ais : l{'}une d{'}appariement entre des cas cliniques et des discussions, l{'}autre d{'}extraction de mots-clefs. Une des particularit{\'e} est l{'}emploi d{'}apprentissage non-supervis{\'e} sur les deux t{\^a}ches, sur un corpus construit sp{\'e}cifiquement pour le domaine m{\'e}dical en fran{\c{c}}ais",
    language = "French",
}
@inproceedings{drame-etal-2019-indexation,
    title = "Indexation et appariement de documents cliniques avec le mod{\`e}le vectoriel (Indexing and matching clinical documents using the vector space model)",
    author = "Dram{\'e}, Khadim  and
      Diop, Ibrahima  and
      Faty, Lamine  and
      Ndoye, Birame",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. D{\'e}fi Fouille de Textes (atelier TALN-RECITAL)",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-deft.9",
    pages = "91--97",
    abstract = "Dans ce papier, nous pr{\'e}sentons les m{\'e}thodes que nous avons d{\'e}velopp{\'e}es pour participer aux t{\^a}ches 1 et 2 de l{'}{\'e}dition 2019 du d{\'e}fi fouille de textes (DEFT 2019). Pour la premi{\`e}re t{\^a}che, qui s{'}int{\'e}resse {\`a} l{'}indexation de cas cliniques, une m{\'e}thode utilisant la pond{\'e}ration TF-IDF (term frequency {--} inverse document frequency) a {\'e}t{\'e} propos{\'e}e. Quant {\`a} la seconde t{\^a}che, la m{\'e}thode propos{\'e}e repose sur le mod{\`e}le vectoriel pour apparier des discussions aux cas cliniques correspondants ; pour cela, le cosinus est utilis{\'e} comme mesure de similarit{\'e}. L{'}indexation s{\'e}mantique latente (latent semantic indexing {--} LSI) est {\'e}galement exp{\'e}riment{\'e}e pour {\'e}tendre cette m{\'e}thode. Pour chaque m{\'e}thode, diff{\'e}rentes configurations ont {\'e}t{\'e} test{\'e}es et {\'e}valu{\'e}es sur les donn{\'e}es de test du DEFT 2019.",
    language = "French",
}
@proceedings{jep-taln-recital-2019-actes-de,
    title = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    editor = "Morin, Emmanuel  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.0",
}
@inproceedings{thompson-etal-2019-analyse,
    title = "Analyse faiblement supervis{\'e}e de conversation en actes de dialogue (Weakly supervised dialog act analysis)",
    author = "Thompson, Catherine  and
      Asher, Nicholas  and
      Muller, Philippe  and
      Auguste, J{\'e}r{\'e}my",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.1",
    pages = "159--166",
    abstract = "Nous nous int{\'e}ressons ici {\`a} l{'}analyse de conversation par chat dans un contexte orient{\'e}-t{\^a}che avec un conseiller technique s{'}adressant {\`a} un client, o{\`u} l{'}objectif est d{'}{\'e}tiqueter les {\'e}nonc{\'e}s en actes de dialogue, pour alimenter des analyses des conversations en aval. Nous proposons une m{\'e}thode l{\'e}g{\`e}rement supervis{\'e}e {\`a} partir d{'}heuristiques simples, de quelques annotations de d{\'e}veloppement, et une m{\'e}thode d{'}ensemble sur ces r{\`e}gles qui sert {\`a} annoter automatiquement un corpus plus large de fa{\c{c}}on bruit{\'e}e qui peut servir d{'}entrainement {\`a} un mod{\`e}le supervis{\'e}. Nous comparons cette approche {\`a} une approche supervis{\'e}e classique et montrons qu{'}elle atteint des r{\'e}sultats tr{\`e}s proches, {\`a} un co{\^u}t moindre et tout en {\'e}tant plus facile {\`a} adapter {\`a} de nouvelles donn{\'e}es.",
    language = "French",
}
@inproceedings{mdhaffar-etal-2019-apport,
    title = "Apport de l{'}adaptation automatique des mod{\`e}les de langage pour la reconnaissance de la parole: {\'e}valuation qualitative extrins{\`e}que dans un contexte de traitement de cours magistraux (Contribution of automatic adaptation of language models for speech recognition : extrinsic qualitative evaluation in a context of educational courses)",
    author = "Mdhaffar, Salima  and
      Est{\`e}ve, Yannick  and
      Hernandez, Nicolas  and
      Laurent, Antoine  and
      Quiniou, Solen",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.2",
    pages = "167--174",
    abstract = "Malgr{\'e} les faiblesses connues de cette m{\'e}trique, les performances de diff{\'e}rents syst{\`e}mes de reconnaissance automatique de la parole sont g{\'e}n{\'e}ralement compar{\'e}es {\`a} l{'}aide du taux d{'}erreur sur les mots. Les transcriptions automatiques de ces syst{\`e}mes sont de plus en plus exploitables et utilis{\'e}es dans des syst{\`e}mes complexes de traitement automatique du langage naturel, par exemple pour la traduction automatique, l{'}indexation, la recherche documentaire... Des {\'e}tudes r{\'e}centes ont propos{\'e} des m{\'e}triques permettant de comparer la qualit{\'e} des transcriptions automatiques de diff{\'e}rents syst{\`e}mes en fonction de la t{\^a}che vis{\'e}e. Dans cette {\'e}tude nous souhaitons mesurer, qualitativement, l{'}apport de l{'}adaptation automatique des mod{\`e}les de langage au domaine vis{\'e} par un cours magistral. Les transcriptions du discours de l{'}enseignant peuvent servir de support {\`a} la navigation dans le document vid{\'e}o du cours magistral ou permettre l{'}enrichissement de son contenu p{\'e}dagogique. C{'}est {\`a}-travers le prisme de ces deux t{\^a}ches que nous {\'e}valuons l{'}apport de l{'}adaptation du mod{\`e}le de langage. Les exp{\'e}riences ont {\'e}t{\'e} men{\'e}es sur un corpus de cours magistraux et montrent combien le taux d{'}erreur sur les mots est une m{\'e}trique insuffisante qui masque les apports effectifs de l{'}adaptation des mod{\`e}les de langage.",
    language = "French",
}
@inproceedings{badene-etal-2019-apprentissage,
    title = "Apprentissage faiblement supervis{\'e} de la structure discursive (Learning discourse structure using weak supervision )",
    author = "Badene, Sonia  and
      Thompson, Catherine  and
      Asher, Nicholas  and
      Lorr{\'e}, Jean-Pierre",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.3",
    pages = "175--184",
    abstract = "L{'}av{\`e}nement des techniques d{'}apprentissage automatique profond a fait na{\^\i}tre un besoin {\'e}norme de donn{\'e}es d{'}entra{\^\i}nement. De telles donn{\'e}es d{'}entra{\^\i}nement sont extr{\^e}mement co{\^u}teuses {\`a} cr{\'e}er, surtout lorsqu{'}une expertise dans le domaine est requise. L{'}une de ces t{\^a}ches est l{'}apprentissage de la structure s{\'e}mantique du discours, t{\^a}che tr{\`e}s complexe avec des structures r{\'e}cursives avec des donn{\'e}es {\'e}parses, mais qui est essentielle pour extraire des informations s{\'e}mantiques profondes du texte. Nous d{\'e}crivons nos exp{\'e}rimentations sur l{'}attachement des unit{\'e}s discursives pour former une structure, en utilisant le paradigme du data programming dans lequel peu ou pas d{'}annotations sont utilis{\'e}es pour construire un ensemble de donn{\'e}es d{'}entra{\^\i}nement {``}bruit{\'e}{''}. Le corpus de dialogues utilis{\'e} illustre des contraintes {\`a} la fois linguistiques et non-linguistiques int{\'e}ressantes qui doivent {\^e}tre apprises. Nous nous concentrons sur la structure des r{\`e}gles utilis{\'e}es pour construire un mod{\`e}le g{\'e}n{\'e}ratif et montrons la comp{\'e}titivit{\'e} de notre approche par rapport {\`a} l{'}apprentissage supervis{\'e} classique.",
    language = "French",
}
@inproceedings{bechet-etal-2019-calor,
    title = "{CALOR}-{QUEST} : un corpus d{'}entra{\^\i}nement et d{'}{\'e}valuation pour la compr{\'e}hension automatique de textes (Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document)",
    author = "Bechet, Frederic  and
      Aloui, Cindy  and
      Charlet, Delphine  and
      Damnati, Geraldine  and
      Heinecke, Johannes  and
      Nasr, Alexis  and
      Herledan, Frederic",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.4",
    pages = "185--194",
    abstract = "La compr{\'e}hension automatique de texte est une t{\^a}che faisant partie de la famille des syst{\`e}mes de Question/R{\'e}ponse o{\`u} les questions ne sont pas {\`a} port{\'e}e g{\'e}n{\'e}rale mais sont li{\'e}es {\`a} un document particulier. R{\'e}cemment de tr{\`e}s grand corpus (SQuAD, MS MARCO) contenant des triplets (document, question, r{\'e}ponse) ont {\'e}t{\'e} mis {\`a} la disposition de la communaut{\'e} scientifique afin de d{\'e}velopper des m{\'e}thodes supervis{\'e}es {\`a} base de r{\'e}seaux de neurones profonds en obtenant des r{\'e}sultats prometteurs. Ces m{\'e}thodes sont cependant tr{\`e}s gourmandes en donn{\'e}es d{'}apprentissage, donn{\'e}es qui n{'}existent pour le moment que pour la langue anglaise. Le but de cette {\'e}tude est de permettre le d{\'e}veloppement de telles ressources pour d{'}autres langues {\`a} moindre co{\^u}t en proposant une m{\'e}thode g{\'e}n{\'e}rant de mani{\`e}re semi-automatique des questions {\`a} partir d{'}une analyse s{\'e}mantique d{'}un grand corpus. La collecte de questions naturelle est r{\'e}duite {\`a} un ensemble de validation/test. L{'}application de cette m{\'e}thode sur le corpus CALOR-Frame a permis de d{\'e}velopper la ressource CALOR-QUEST pr{\'e}sent{\'e}e dans cet article.",
    language = "French",
}
@inproceedings{eshkol-taravella-etal-2019-chunker,
    title = "Chunker diff{\'e}rents types de discours oraux : d{\'e}fis pour l{'}apprentissage automatique (Chunking different spoken speech types : challenges for machine learning)",
    author = "Eshkol-Taravella, Iris  and
      Maarouf, Mariame  and
      Skrovec, Marie  and
      Badin, Flora",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.5",
    pages = "195--204",
    abstract = "Le travail d{\'e}crit le d{\'e}veloppement d{'}un chunker pour l{'}oral par apprentissage supervis{\'e} avec les CRFs, {\`a} partir d{'}un corpus de r{\'e}f{\'e}rence de petite taille et compos{\'e} de productions de nature diff{\'e}rente : monologue pr{\'e}par{\'e} vs discussion spontan{\'e}e. La m{\'e}thodologie respecte les sp{\'e}cificit{\'e}s des donn{\'e}es trait{\'e}es. L{'}apprentissage tient compte des r{\'e}sultats propos{\'e}s par diff{\'e}rents {\'e}tiqueteurs morpho-syntaxiques disponibles sans correction manuelle de leurs r{\'e}sultats. Les exp{\'e}riences montrent que le genre de discours (monologue vs discussion), la nature de discours (spontan{\'e} vs pr{\'e}par{\'e}) et la taille du corpus peuvent influencer les r{\'e}sultats de l{'}apprentissage, ce qui confirme que la nature des donn{\'e}es trait{\'e}es est {\`a} prendre en consid{\'e}ration dans l{'}interpr{\'e}tation des r{\'e}sultats.",
    language = "French",
}
@inproceedings{zhai-etal-2019-classification,
    title = "Classification automatique des proc{\'e}d{\'e}s de traduction (Automatic Classification of Translation Processes)",
    author = "Zhai, Yuming  and
      Illouz, Gabriel  and
      Vilnat, Anne",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.6",
    pages = "205--214",
    abstract = "En vue de distinguer la traduction litt{\'e}rale des autres proc{\'e}d{\'e}s de traduction, des traducteurs et linguistes ont propos{\'e} plusieurs typologies pour caract{\'e}riser les diff{\'e}rents proc{\'e}d{\'e}s de traduction, tels que l{'}{\'e}quivalence idiomatique, la g{\'e}n{\'e}ralisation, la particularisation, la modulation s{\'e}mantique, etc. En revanche, les techniques d{'}extraction de paraphrases {\`a} partir de corpus parall{\`e}les bilingues n{'}ont pas exploit{\'e} ces informations. Dans ce travail, nous proposons une classification automatique des proc{\'e}d{\'e}s de traduction en nous basant sur des exemples annot{\'e}s manuellement dans un corpus parall{\`e}le (anglais-fran{\c{c}}ais) de TED Talks. M{\^e}me si le jeu de donn{\'e}es est petit, les r{\'e}sultats exp{\'e}rimentaux sont encourageants, et les exp{\'e}riences montrent la direction {\`a} suivre dans les futurs travaux.",
    language = "French",
}
@inproceedings{wisniewski-2019-combien,
    title = "Combien d{'}exemples de tests sont-ils n{\'e}cessaires {\`a} une {\'e}valuation fiable ? Quelques observations sur l{'}{\'e}valuation de l{'}analyse morphosyntaxique du fran{\c{c}}ais. (Some observations on the evaluation of {P}o{S} taggers)",
    author = "Wisniewski, Guillaume",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.7",
    pages = "215--222",
    abstract = "L{'}objectif de ce travail est de pr{\'e}senter plusieurs observations, sur l{'}{\'e}valuation des analyseurs morphosyntaxique en fran{\c{c}}ais, visant {\`a} remettre en cause le cadre habituel de l{'}apprentissage statistique dans lequel les ensembles de test et d{'}apprentissage sont fix{\'e}s arbitrairement et ind{\'e}pendemment du mod{\`e}le consid{\'e}r{\'e}. Nous montrons qu{'}il est possible de consid{\'e}rer des ensembles de test plus petits que ceux g{\'e}n{\'e}ralement utilis{\'e}s sans cons{\'e}quences sur la qualit{\'e} de l{'}{\'e}valuation. Les exemples ainsi « {\'e}conomis{\'e}s » peuvent {\^e}tre utilis{\'e}s en apprentissage pour am{\'e}liorer les performances des syst{\`e}mes notamment dans des t{\^a}ches d{'}adaptation au domaine.",
    language = "French",
}
@inproceedings{randriatsitohaina-hamon-2019-de,
    title = "De l{'}extraction des interactions m{\'e}dicament-m{\'e}dicament vers les interactions aliment-m{\'e}dicament {\`a} partir de textes biom{\'e}dicaux: Adaptation de domaine (From the extraction of drug-drug interactions to the food-drug interactions in biomedical texts : domain adaptation)",
    author = "Randriatsitohaina, Tsanta  and
      Hamon, Thierry",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.8",
    pages = "223--232",
    abstract = "Les interactions aliments-m{\'e}dicaments (FDI) se produisent lorsque des aliments et des m{\'e}dicaments sont pris simultan{\'e}ment et provoquent un effet inattendu. Nous consid{\'e}rons l{'}extraction de ces interactions dans les textes comme une t{\^a}che d{'}extraction de relation pouvant {\^e}tre r{\'e}solue par des m{\'e}thodes de classification. Toutefois, {\'e}tant donn{\'e} que ces interactions sont d{\'e}crites de mani{\`e}re tr{\`e}s fine, nous sommes confront{\'e}s au manque de donn{\'e}es et au manque d{'}exemples par type de relation. Pour r{\'e}soudre ce probl{\`e}me, nous proposons d{'}appliquer une adaptation de domaine {\`a} partir des interactions m{\'e}dicament-m{\'e}dicament (DDI) qui est une t{\^a}che similaire, afin d{'}{\'e}tablir une correspondance entre les types de relations et d{'}{\'e}tiqueter les instances FDI selon les types DDI. Notre approche confirme une coh{\'e}rence entre les 2 domaines et fournit une base pour la sp{\'e}cification des relations et la pr{\'e}-annotation de nouvelles donn{\'e}es. Les performances des mod{\`e}les de classification appuie {\'e}galement l{'}efficacit{\'e} de l{'}adaptation de domaine sur notre t{\^a}che.",
    language = "French",
}
@inproceedings{namer-etal-2019-demonette2,
    title = "Demonette2 - Une base de donn{\'e}es d{\'e}rivationnelle du fran{\c{c}}ais {\`a} grande {\'e}chelle : premiers r{\'e}sultats (Demonette2 {--} A large scale derivational database for {F}rench: first results)",
    author = "Namer, Fiammetta  and
      Barque, Lucie  and
      Bonami, Olivier  and
      Haas, Pauline  and
      Hathout, Nabil  and
      Tribout, Delphine",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.9",
    pages = "233--244",
    abstract = "Cet article pr{\'e}sente la conception et le d{\'e}veloppement de Demonette2, une base de donn{\'e}es d{\'e}rivationnelle {\`a} grande {\'e}chelle du fran{\c{c}}ais, d{\'e}velopp{\'e}e dans le cadre du projet ANR D{\'e}monext (ANR-17-CE23-0005). L{'}article d{\'e}crit les objectifs du projet, la structure de la base et expose les premiers r{\'e}sultats du projet, en mettant l{'}accent sur un enjeu crucial : la question du codage s{\'e}mantique des entr{\'e}es et des relations.",
    language = "French",
}
@inproceedings{bigeard-grabar-2019-detecter,
    title = "D{\'e}tecter la non-adh{\'e}rence m{\'e}dicamenteuse dans les forums de discussion avec les m{\'e}thodes de recherche d{'}information (Detect drug non-compliance in {I}nternet fora using Information Retrieval methods )",
    author = "Bigeard, Elise  and
      Grabar, Natalia",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.10",
    pages = "245--254",
    abstract = "Les m{\'e}thodes de recherche d{'}information permettent d{'}explorer les donn{\'e}es textuelles. Nous les exploitons pour la d{\'e}tection de messages avec la non-adh{\'e}rence m{\'e}dicamenteuse dans les forums de discussion. La non-adh{\'e}rence m{\'e}dicamenteuse correspond aux cas lorsqu{'}un patient ne respecte pas les indications de son m{\'e}decin et modifie les prises de m{\'e}dicaments (augmente ou diminue les doses, par exemple). Le moteur de recherche exploit{\'e} montre 0,9 de pr{\'e}cision sur les 10 premiers r{\'e}sultats avec un corpus {\'e}quilibr{\'e}, et 0,4 avec un corpus respectant la distribution naturelle des messages, qui est tr{\`e}s d{\'e}s{\'e}quilibr{\'e}e en d{\'e}faveur de la cat{\'e}gorie recherch{\'e}e. La pr{\'e}cision diminue avec l{'}augmentation du nombre de r{\'e}sultats consid{\'e}r{\'e}s alors que le rappel augmente. Nous exploitons {\'e}galement le moteur de recherche sur de nouvelles donn{\'e}es et avec des types pr{\'e}cis de non-adh{\'e}rence.",
    language = "French",
}
@inproceedings{cardon-grabar-2019-detection,
    title = "D{\'e}tection automatique de phrases parall{\`e}les dans un corpus biom{\'e}dical comparable technique / simplifi{\'e} (Automatic detection of parallel sentences in comparable biomedical corpora)",
    author = "Cardon, Remi  and
      Grabar, Natalia",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.11",
    pages = "255--264",
    abstract = "Les phrases parall{\`e}les contiennent des informations identiques ou tr{\`e}s proches s{\'e}mantiquement et offrent des indications importantes sur le fonctionnement de la langue. Lorsque les phrases sont diff{\'e}renci{\'e}es par leur registre (comme expert vs. non-expert), elles peuvent {\^e}tre exploit{\'e}es pour la simplification automatique de textes. Le but de la simplification automatique est d{'}am{\'e}liorer la compr{\'e}hension de textes. Par exemple, dans le domaine biom{\'e}dical, la simplification peut permettre aux patients de mieux comprendre les textes relatifs {\`a} leur sant{\'e}. Il existe cependant tr{\`e}s peu de ressources pour la simplification en fran{\c{c}}ais. Nous proposons donc d{'}exploiter des corpus comparables, diff{\'e}renci{\'e}s par leur technicit{\'e}, pour y d{\'e}tecter des phrases parall{\`e}les et les aligner. Les donn{\'e}es de r{\'e}f{\'e}rence sont cr{\'e}{\'e}es manuellement et montrent un accord inter-annotateur de 0,76. Nous exp{\'e}rimentons sur des donn{\'e}es {\'e}quilibr{\'e}es et d{\'e}s{\'e}quilibr{\'e}es. La F-mesure sur les donn{\'e}es {\'e}quilibr{\'e}es atteint jusqu{'}{\`a} 0,94. Sur les donn{\'e}es d{\'e}s{\'e}quilibr{\'e}es, les r{\'e}sultats sont plus faibles (jusqu{'}{\`a} 0,92 de F-mesure) mais restent comp{\'e}titifs lorsque les mod{\`e}les sont entra{\^\i}n{\'e}s sur les donn{\'e}es {\'e}quilibr{\'e}es.",
    language = "French",
}
@inproceedings{sagot-2019-developpement,
    title = "D{\'e}veloppement d{'}un lexique morphologique et syntaxique de l{'}ancien fran{\c{c}}ais (Development of a morphological and syntactic lexicon of {O}ld {F}rench)",
    author = "Sagot, Beno{\^\i}t",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.12",
    pages = "265--274",
    abstract = "Nous d{\'e}crivons dans cet article notre travail de d{\'e}veloppement d{'}un lexique morphologique et syntaxique {\`a} grande {\'e}chelle de l{'}ancien fran{\c{c}}ais pour le traitement automatique des langues. Nous nous sommes appuy{\'e}s sur des ressources dictionnairiques et lexicales dans lesquelles l{'}extraction d{'}informations structur{\'e}es et exploitables a n{\'e}cessit{\'e} des d{\'e}veloppements sp{\'e}cifiques. De plus, la mise en correspondance d{'}informations provenant de ces diff{\'e}rentes sources a soulev{\'e} des difficult{\'e}s. Nous donnons quelques indications quantitatives sur le lexique obtenu, et discutons de sa fiabilit{\'e} dans sa version actuelle et des perspectives d{'}am{\'e}lioration permises par l{'}existence d{'}une premi{\`e}re version, notamment au travers de l{'}analyse automatique de donn{\'e}es textuelles.",
    language = "French",
}
@inproceedings{bardet-etal-2019-etude,
    title = "{\'E}tude de l{'}apprentissage par transfert de syst{\`e}mes de traduction automatique neuronaux (Study on transfer learning in neural machine translation )",
    author = {Bardet, Adrien  and
      Bougares, Fethi  and
      Barrault, Lo{\"\i}c},
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.13",
    pages = "275--284",
    abstract = "L{'}apprentissage par transfert est une solution au probl{\`e}me de l{'}apprentissage de syst{\`e}mes de traduction automatique neuronaux pour des paires de langues peu dot{\'e}es. Dans cet article, nous proposons une analyse de cette m{\'e}thode. Nous souhaitons {\'e}valuer l{'}impact de la quantit{\'e} de donn{\'e}es et celui de la proximit{\'e} des langues impliqu{\'e}es pour obtenir le meilleur transfert possible. Nous prenons en compte ces deux param{\`e}tres non seulement pour une t{\^a}che de traduction {``}classique{''} mais {\'e}galement lorsque les corpus de donn{\'e}es font d{\'e}faut. Enfin, il s{'}agit de proposer une approche o{\`u} volume de donn{\'e}es et proximit{\'e} des langues sont combin{\'e}es afin de ne plus avoir {\`a} trancher entre ces deux {\'e}l{\'e}ments.",
    language = "French",
}
@inproceedings{perquin-etal-2019-evaluation,
    title = "{\'E}valuation objective de plongements pour la synth{\`e}se de parole guid{\'e}e par r{\'e}seaux de neurones (Objective evaluation of embeddings for speech synthesis guided by neural networks)",
    author = "Perquin, Antoine  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Lolive, Damien  and
      Amsaleg, Laurent",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.14",
    pages = "285--292",
    abstract = "L{'}{\'e}valuation de plongements issus de r{\'e}seaux de neurones est un proc{\'e}d{\'e} complexe. La qualit{\'e} des plongements est li{\'e}e {\`a} la t{\^a}che sp{\'e}cifique pour laquelle ils ont {\'e}t{\'e} entra{\^\i}n{\'e}s et l{'}{\'e}valuation de cette t{\^a}che peut {\^e}tre un proc{\'e}d{\'e} long et on{\'e}reux s{'}il y a besoin d{'}annotateurs humains. Il peut donc {\^e}tre pr{\'e}f{\'e}rable d{'}estimer leur qualit{\'e} gr{\^a}ce {\`a} des mesures objectives rapides et reproductibles sur des t{\^a}ches annexes. Cet article propose une m{\'e}thode g{\'e}n{\'e}rique pour estimer la qualit{\'e} d{'}un plongement. Appliqu{\'e}e {\`a} la synth{\`e}se de parole par s{\'e}lection d{'}unit{\'e}s guid{\'e}e par r{\'e}seaux de neurones, cette m{\'e}thode permet de comparer deux syst{\`e}mes distincts.",
    language = "French",
}
@inproceedings{meftah-etal-2019-exploration,
    title = "Exploration de l{'}apprentissage par transfert pour l{'}analyse de textes des r{\'e}seaux sociaux (Exploring neural transfer learning for social media text analysis )",
    author = "Meftah, Sara  and
      Semmar, Nasredine  and
      Tamaazousti, Youssef  and
      Essafi, Hassane  and
      Sadat, Fatiha",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.15",
    pages = "293--302",
    abstract = "L{'}apprentissage par transfert repr{\'e}sente la capacit{\'e} qu{'}un mod{\`e}le neuronal entra{\^\i}n{\'e} sur une t{\^a}che {\`a} g{\'e}n{\'e}raliser suffisamment et correctement pour produire des r{\'e}sultats pertinents sur une autre t{\^a}che proche mais diff{\'e}rente. Nous pr{\'e}sentons dans cet article une approche fond{\'e}e sur l{'}apprentissage par transfert pour construire automatiquement des outils d{'}analyse de textes des r{\'e}seaux sociaux en exploitant les similarit{\'e}s entre les textes d{'}une langue bien dot{\'e}e (forme standard d{'}une langue) et les textes d{'}une langue peu dot{\'e}e (langue utilis{\'e}e en r{\'e}seaux sociaux). Nous avons exp{\'e}riment{\'e} notre approche sur plusieurs langues ainsi que sur trois t{\^a}ches d{'}annotation linguistique ({\'e}tiquetage morpho-syntaxique, annotation en parties du discours et reconnaissance d{'}entit{\'e}s nomm{\'e}es). Les r{\'e}sultats obtenus sont tr{\`e}s satisfaisants et montrent l{'}int{\'e}r{\^e}t de l{'}apprentissage par transfert pour tirer profit des mod{\`e}les neuronaux profonds sans la contrainte d{'}avoir {\`a} disposition une quantit{\'e} de donn{\'e}es importante n{\'e}cessaire pour avoir une performance acceptable.",
    language = "French",
}
@inproceedings{montariol-etal-2019-exploring,
    title = "Exploring sentence informativeness",
    author = "Montariol, Syrielle  and
      Gar{\'\i} Soler, Aina  and
      Allauzen, Alexandre",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.16",
    pages = "303--312",
    abstract = "This study is a preliminary exploration of the concept of informativeness {--}how much information a sentence gives about a word it contains{--} and its potential benefits to building quality word representations from scarce data. We propose several sentence-level classifiers to predict informativeness, and we perform a manual annotation on a set of sentences. We conclude that these two measures correspond to different notions of informativeness. However, our experiments show that using the classifiers{'} predictions to train word embeddings has an impact on embedding quality.",
}
@inproceedings{laleye-etal-2019-hybridation,
    title = "Hybridation d{'}un agent conversationnel avec des plongements lexicaux pour la formation au diagnostic m{\'e}dical (Hybridization of a conversational agent with word embeddings for medical diagnostic training)",
    author = {Laleye, Fr{\'e}jus A. A.  and
      de Chalendar, Ga{\"e}l  and
      Brouquet, Antoine  and
      Blani{\'e}, Antonia  and
      Benhamou, Dan},
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.17",
    pages = "313--322",
    abstract = "Dans le contexte m{\'e}dical, un patient ou m{\'e}decin virtuel dialoguant permet de former les apprenants au diagnostic m{\'e}dical via la simulation de mani{\`e}re autonome. Dans ce travail, nous avons exploit{\'e} les propri{\'e}t{\'e}s s{\'e}mantiques captur{\'e}es par les repr{\'e}sentations distribu{\'e}es de mots pour la recherche de questions similaires dans le syst{\`e}me de dialogues d{'}un agent conversationnel m{\'e}dical. Deux syst{\`e}mes de dialogues ont {\'e}t{\'e} cr{\'e}{\'e}s et {\'e}valu{\'e}s sur des jeux de donn{\'e}es collect{\'e}es lors des tests avec les apprenants. Le premier syst{\`e}me fond{\'e} sur la correspondance de r{\`e}gles de dialogue cr{\'e}{\'e}es {\`a} la main pr{\'e}sente une performance globale de 92{\%} comme taux de r{\'e}ponses coh{\'e}rentes sur le cas clinique {\'e}tudi{\'e} tandis que le second syst{\`e}me qui combine les r{\`e}gles de dialogue et la similarit{\'e} s{\'e}mantique r{\'e}alise une performance de 97{\%} de r{\'e}ponses coh{\'e}rentes en r{\'e}duisant de 7{\%} les erreurs de compr{\'e}hension par rapport au syst{\`e}me de correspondance de r{\`e}gles.",
    language = "French",
}
@inproceedings{bebeshina-clairet-lafourcade-2019-inference,
    title = "Inf{\'e}rence des relations s{\'e}mantiques dans un r{\'e}seau lexico-s{\'e}mantique multilingue (Inferring semantic relations in a multilingual lexical semantic network)",
    author = "Bebeshina-Clairet, Nadia  and
      Lafourcade, Mathieu",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.18",
    pages = "323--332",
    abstract = "Les m{\'e}thodes endog{\`e}nes se trouvent au coeur de la construction des ressources de connaissance telles que les r{\'e}seaux lexico-s{\'e}mantiques. Dans le cadre de l{'}exp{\'e}rience d{\'e}crite dans le pr{\'e}sent article, nous nous focalisons sur les m{\'e}thodes d{'}inf{\'e}rence des relations. Nous consid{\'e}rons, en particulier, les cas d{'}inf{\'e}rence des relations s{\'e}mantiques et des raffinements de sens. Les diff{\'e}rents m{\'e}canismes d{'}inf{\'e}rence des relations s{\'e}mantiques y compris dans le contexte de polys{\'e}mie de termes ont {\'e}t{\'e} d{\'e}crits par Zarrouk (2015) pour le contexte monolingue. {\`A} notre connaissance, il n{'}existe pas de travaux concernant l{'}inf{\'e}rence des relations s{\'e}mantiques et des raffinements dans le contexte d{'}am{\'e}lioration d{'}une ressource multilingue.",
    language = "French",
}
@inproceedings{antoine-etal-2019-ma,
    title = "Ma copie adore le v{\'e}lo : analyse des besoins r{\'e}els en correction orthographique sur un corpus de dict{\'e}es d{'}enfants (A corpus analysis to define the needs of dyslexic children in terms of spelling correction)",
    author = "Antoine, Jean-Yves  and
      Crochetet, Marion  and
      Arbizu, Celine  and
      Lopez, Emmanuelle  and
      Pouplin, Samuel",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.19",
    pages = "333--342",
    abstract = "Cet article pr{\'e}sente la constitution d{'}un corpus de textes produits, sur des donn{\'e}es lors de dict{\'e}es, par des enfants paralys{\'e}s c{\'e}r{\'e}braux (PC) ou dysorthographiques, son annotation en termes d{'}erreurs orthographiques, et enfin son analyse quantitative. Cette analyse de corpus a pour objectif de d{\'e}finir des besoins r{\'e}els en mati{\`e}re de correction orthographique, et ce pour les personnes souffrant de troubles du langage {\'e}crit comme pour le grand public. Notre {\'e}tude sugg{\`e}re que les correcteurs orthographiques ne r{\'e}pondent que partiellement {\`a} ces besoins.",
    language = "French",
}
@inproceedings{seminck-etal-2019-modeles,
    title = "Mod{\`e}les de langue appliqu{\'e}s aux sch{\'e}mas {W}inograd fran{\c{c}}ais (Language Models applied to {F}rench {W}inograd Schemas)",
    author = "Seminck, Olga  and
      Segonne, Vincent  and
      Amsili, Pascal",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.20",
    pages = "343--350",
    abstract = "Les sch{\'e}mas Winograd sont des probl{\`e}mes de r{\'e}solution d{'}anaphores con{\c{c}}us pour n{\'e}cessiter un raisonnement sur des connaissances du monde. Par construction, ils sont insensibles {\`a} des statistiques simples (co-occurrences en corpus). Pourtant, aujourd{'}hui, les syst{\`e}mes {\'e}tat de l{'}art pour l{'}anglais se basent sur des mod{\`e}les de langue pour r{\'e}soudre les sch{\'e}mas (Trinh {\&} Le, 2018). Nous pr{\'e}sentons dans cet article une {\'e}tude visant {\`a} tester des mod{\`e}les similaires sur les sch{\'e}mas en fran{\c{c}}ais. Cela nous conduit {\`a} revenir sur les m{\'e}triques d{'}{\'e}valuation utilis{\'e}es dans la communaut{\'e} pour les sch{\'e}mas Winograd. Les performances que nous obtenons, surtout compar{\'e}es {\`a} celles de Amsili {\&} Seminck (2017b), sugg{\`e}rent que l{'}approche par mod{\`e}le de langue des sch{\'e}mas Winograd reste limit{\'e}e, sans doute en partie {\`a} cause du fait que les mod{\`e}les de langue encodent tr{\`e}s difficilement le genre de raisonnement n{\'e}cessaire {\`a} la r{\'e}solution des sch{\'e}mas Winograd.",
    language = "French",
}
@inproceedings{chiril-etal-2019-multilingual,
    title = "Multilingual and Multitarget Hate Speech Detection in Tweets",
    author = "Chiril, Patricia  and
      Benamara Zitoune, Farah  and
      Moriceau, V{\'e}ronique  and
      Coulomb-Gully, Marl{\`e}ne  and
      Kumar, Abhishek",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.21",
    pages = "351--360",
    abstract = "Social media networks have become a space where users are free to relate their opinions and sentiments which may lead to a large spreading of hatred or abusive messages which have to be moderated. This paper proposes a supervised approach to hate speech detection from a multilingual perspective. We focus in particular on hateful messages towards two different targets (immigrants and women) in English tweets, as well as sexist messages in both English and French. Several models have been developed ranging from feature-engineering approaches to neural ones. Our experiments show very encouraging results on both languages.",
}
@inproceedings{eshkol-taravella-jung-kang-2019-observation,
    title = "Observation de l{'}exp{\'e}rience client dans les restaurants (Mapping Reviewers{'} Experience in Restaurants)",
    author = "Eshkol-Taravella, Iris  and
      Jung Kang, Hyun",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.22",
    pages = "361--370",
    abstract = "Ces derni{\`e}res ann{\'e}es, les recherches sur la fouille d{'}opinions ou l{'}analyse des sentiments sont men{\'e}es activement dans le domaine du Traitement Automatique des Langues (TAL). De nombreuses {\'e}tudes scientifiques portent sur l{'}extraction automatique des opinions positives ou n{\'e}gatives et de leurs cibles. Ce travail propose d{'}identifier automatiquement une {\'e}valuation, exprim{\'e}e explicitement ou implicitement par des internautes dans le corpus d{'}avis tir{\'e} du Web. Six cat{\'e}gories d{'}{\'e}valuation sont propos{\'e}es : opinion positive, opinion n{\'e}gative, opinion mixte, intention, suggestion et description. La m{\'e}thode utilis{\'e}e est fond{\'e}e sur l{'}apprentissage supervis{\'e} qui tient compte des caract{\'e}ristiques linguistiques de chaque cat{\'e}gorie retenue. L{'}une des difficult{\'e}s que nous avons rencontr{\'e}e concerne le d{\'e}s{\'e}quilibre entre les classes d{'}{\'e}valuation cr{\'e}{\'e}es, cependant, cet obstacle a pu {\^e}tre surmont{\'e} dans l{'}apprentissage gr{\^a}ce aux strat{\'e}gies de sur-{\'e}chantillonnage et aux strat{\'e}gies algorithmiques.",
    language = "French",
}
@inproceedings{kevers-etal-2019-outiller,
    title = "Outiller une langue peu dot{\'e}e gr{\^a}ce au {TALN} : l{'}exemple du corse et de la {BDLC} (Tooling up a less-resourced language with {NLP} : the example of {C}orsican and {BDLC})",
    author = "Kevers, Laurent  and
      Gu{\'e}niot, Florian  and
      Ghjacumina Tognotti, Aurelia  and
      Retali-Medori, Stella",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.23",
    pages = "371--380",
    abstract = "Nos recherches sur la langue corse nous am{\`e}nent naturellement {\`a} envisager l{'}utilisation d{'}outils pour le traitement automatique du langage. Apr{\`e}s une br{\`e}ve introduction sur le corse et sur le projet qui constitue notre cadre de travail, nous proposons un {\'e}tat des lieux concernant l{'}application du TAL aux langues peu dot{\'e}es, dont le corse. Nous d{\'e}finissons ensuite les actions qui peuvent {\^e}tre entreprises, ainsi que la mani{\`e}re dont elles peuvent s{'}int{\'e}grer dans le cadre de notre projet, afin de progresser vers la constitution de ressources et la construction d{'}outils pour le TAL corse.",
    language = "French",
}
@inproceedings{barhoumi-etal-2019-plongements,
    title = "Plongements lexicaux sp{\'e}cifiques {\`a} la langue arabe : application {\`a} l{'}analyse d{'}opinions ({A}rabic-specific embedddings : application in Sentiment Analysis)",
    author = "Barhoumi, Amira  and
      Camelin, Nathalie  and
      Aloulou, Chafik  and
      Est{\`e}ve, Yannick  and
      Hadrich Belguith, Lamia",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.24",
    pages = "381--390",
    abstract = "Nous nous int{\'e}ressons, dans cet article, {\`a} la t{\^a}che d{'}analyse d{'}opinions en arabe. Nous {\'e}tudions la sp{\'e}cificit{\'e} de la langue arabe pour la d{\'e}tection de polarit{\'e}. Nous nous focalisons ici sur les caract{\'e}ristiques d{'}agglutination et de richesse morphologique de cette langue. Nous avons particuli{\`e}rement {\'e}tudi{\'e} diff{\'e}rentes repr{\'e}sentations d{'}unit{\'e} lexicale : token, lemme et light stemme. Nous avons construit et test{\'e} des espaces continus de ces diff{\'e}rentes repr{\'e}sentations lexicales. Nous avons mesur{\'e} l{'}apport de tels types de representations vectorielles dans notre cadre sp{\'e}cifique. Les performances du r{\'e}seau CNN montrent un gain significatif de 2{\%} par rapport {\`a} l{'}{\'e}tat de l{'}art.",
    language = "French",
}
@inproceedings{mathlouthi-bouzid-ben-othmane-zribi-2019-q,
    title = "{Q}-learning pour la r{\'e}solution des anaphores pronominales en langue arabe ({Q}-learning for pronominal anaphora resolution in {A}rabic texts)",
    author = "Mathlouthi Bouzid, Saoussen  and
      Ben Othmane Zribi, Chiraz",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.25",
    pages = "391--398",
    abstract = "La r{\'e}solution d{'}anaphores est une t{\^a}che fondamentale pour la plupart des applications du TALN. Cette t{\^a}che reste un probl{\`e}me difficile qui n{\'e}cessite plusieurs sources de connaissances et des techniques d{'}apprentissage efficaces, notamment pour la langue arabe. Cet article pr{\'e}sente une nouvelle approche de r{\'e}solution d{'}anaphores pronominales dans les textes arabes en se basant sur une m{\'e}thode d{'}Apprentissage par Renforcement AR qui utilise l{'}algorithme Q-learning. Le processus de r{\'e}solution comporte une {\'e}tape d{'}identification des pronoms et des ant{\'e}c{\'e}dents candidats et une autre de r{\'e}solution. L{'}algorithme Q-learning permet d{'}apprendre dans un environnement dynamique et incertain. Il cherche {\`a} optimiser pour chaque pronom anaphorique, une s{\'e}quence de choix de crit{\`e}res pour {\'e}valuer les ant{\'e}c{\'e}dents et s{\'e}lectionner le meilleur. Le syst{\`e}me de r{\'e}solution est {\'e}valu{\'e} sur des textes litt{\'e}raires, des textes journalistiques et des manuels techniques. Le taux de pr{\'e}cision atteint jusqu{'}{\`a} 77,14{\%}.",
    language = "French",
}
@inproceedings{bourgeade-muller-2019-representation,
    title = "Repr{\'e}sentation s{\'e}mantique distributionnelle et alignement de conversations par chat (Distributional semantic representation and alignment of online chat conversations )",
    author = "Bourgeade, Tom  and
      Muller, Philippe",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.26",
    pages = "399--408",
    abstract = {Les mesures de similarit{\'e} textuelle ont une place importante en TAL, du fait de leurs nombreuses applications, en recherche d{'}information et en classification notamment. En revanche, le dialogue fait moins l{'}objet d{'}attention sur cette question. Nous nous int{\'e}ressons ici {\`a} la production d{'}une similarit{\'e} dans le contexte d{'}un corpus de conversations par chat {\`a} l{'}aide de m{\'e}thodes non-supervis{\'e}es, exploitant {\`a} diff{\'e}rents niveaux la notion de s{\'e}mantique distributionnelle, sous forme d{'}embeddings. Dans un m{\^e}me temps, pour enrichir la mesure, et permettre une meilleure interpr{\'e}tation des r{\'e}sultats, nous {\'e}tablissons des alignements explicites des tours de parole dans les conversations, en exploitant la distance de Wasserstein, qui permet de prendre en compte leur dimension structurelle. Enfin, nous {\'e}valuons notre approche {\`a} l{'}aide d{'}une t{\^a}che externe sur la petite partie annot{\'e}e du corpus, et observons qu{'}elle donne de meilleurs r{\'e}sultats qu{'}une variante plus na{\"\i}ve {\`a} base de moyennes.},
    language = "French",
}
@inproceedings{gliosca-amsili-2019-resolution,
    title = "R{\'e}solution des cor{\'e}f{\'e}rences neuronale : une approche bas{\'e}e sur les t{\^e}tes (Neural coreference resolution : a head-based approach)",
    author = "Gliosca, Quentin  and
      Amsili, Pascal",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.27",
    pages = "409--416",
    abstract = "L{'}av{\`e}nement des approches neuronales de bout en bout a entra{\^\i}n{\'e} une rupture dans la fa{\c{c}}on dont {\'e}tait jusqu{'}{\`a} pr{\'e}sent envisag{\'e}e et impl{\'e}ment{\'e}e la t{\^a}che de r{\'e}solution des cor{\'e}f{\'e}rences. Nous pensons que cette rupture impose de remettre en question la conception des mentions en termes de syntagmes maximaux, au moins pour certaines applications dont nous donnons deux exemples. Dans cette perspective, nous proposons une nouvelle formulation de la t{\^a}che, bas{\'e}e sur les t{\^e}tes, accompagn{\'e}e d{'}une adaptation du mod{\`e}le de Lee et al. (2017) qui l{'}impl{\'e}mente.",
    language = "French",
}
@inproceedings{hazem-etal-2019-reutilisation,
    title = "R{\'e}utilisation de Textes dans les Manuscrits Anciens (Text Reuse in Ancient Manuscripts)",
    author = "Hazem, Amir  and
      Daille, B{\'e}atrice  and
      Stutzmann, Dominique  and
      Currie, Jacob  and
      Jacquin, Christine",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.28",
    pages = "417--426",
    abstract = "Nous nous int{\'e}ressons dans cet article {\`a} la probl{\'e}matique de r{\'e}utilisation de textes dans les livres liturgiques du Moyen {\^A}ge. Plus particuli{\`e}rement, nous {\'e}tudions les variations textuelles de la pri{\`e}re Obsecro Te souvent pr{\'e}sente dans les livres d{'}heures. L{'}observation manuelle de 772 copies de l{'}Obsecro Te a montr{\'e} l{'}existence de plus de 21 000 variantes textuelles. Dans le but de pouvoir les extraire automatiquement et les cat{\'e}goriser, nous proposons dans un premier temps une classification lexico-s{\'e}mantique au niveau n-grammes de mots pour ensuite rendre compte des performances de plusieurs approches {\'e}tat-de-l{'}art d{'}appariement automatique de variantes textuelles de l{'}Obsecro Te.",
    language = "French",
}
@inproceedings{miletic-etal-2019-transformation,
    title = "Transformation d{'}annotations en parties du discours et lemmes vers le format {U}niversal {D}ependencies : {\'e}tude de cas pour l{'}alsacien et l{'}occitan (Converting {POS}-tag and Lemma Annotations into the {U}niversal {D}ependencies Format : A Case Study on {A}lsatian and {O}ccitan )",
    author = "Mileti{\'c}, Aleksandra  and
      Bernhard, Delphine  and
      Bras, Myriam  and
      Ligozat, Anne-Laure  and
      Vergez-Couret, Marianne",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.29",
    pages = "427--436",
    abstract = "Cet article pr{\'e}sente un retour d{'}exp{\'e}rience sur la transformation de corpus annot{\'e}s pour l{'}alsacien et l{'}occitan vers le format CONLL-U d{\'e}fini dans le projet Universal Dependencies. Il met en particulier l{'}accent sur divers points de vigilance {\`a} prendre en compte, concernant la tok{\'e}nisation et la d{\'e}finition des cat{\'e}gories pour l{'}annotation.",
    language = "French",
}
@inproceedings{dupont-2019-un,
    title = "Un corpus libre, {\'e}volutif et versionn{\'e} en entit{\'e}s nomm{\'e}es du Fran{\c{c}}ais (A free, evolving and versioned french named entity recognition corpus)",
    author = "Dupont, Yoann",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.30",
    pages = "437--446",
    abstract = "Les corpus annot{\'e}s sont des ressources difficiles {\`a} cr{\'e}er en raison du grand effort humain qu{'}elles impliquent. Une fois rendues disponibles, elles sont difficilement modifiables et tendent {\`a} ne pas {\'e}voluer pas dans le temps. Dans cet article, nous pr{\'e}sentons un corpus annot{\'e} pour la reconnaissance des entit{\'e}s nomm{\'e}es libre et {\'e}volutif en utilisant les textes d{'}articles Wikinews fran{\c{c}}ais de 2016 {\`a} 2018, pour un total de 1191 articles annot{\'e}s. Nous d{\'e}crivons succinctement le guide d{'}annotation avant de situer notre corpus par rapport {\`a} d{'}autres corpus d{\'e}j{\`a} existants. Nous donnerons {\'e}galement un accord intra-annotateur afin de donner un indice de stabilit{\'e} des annotations ainsi que le processus global pour poursuivre les travaux d{'}enrichissement du corpus.",
    language = "French",
}
@inproceedings{studzinski-perotto-etal-2019-une,
    title = "Une approche hybride pour la segmentation automatique de documents juridiques (A hybrid approach for automatic text segmentation)",
    author = "Studzinski Perotto, Filipo  and
      Taleb, Fadila  and
      Trupin, Eric  and
      Saidali, Youssouf  and
      Holzem, Maryvonne  and
      Labiche, Jacques  and
      Vercouter, Laurent",
    booktitle = "Actes de la Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts",
    month = "7",
    year = "2019",
    address = "Toulouse, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2019.jeptalnrecital-court.31",
    pages = "447--456",
    abstract = "Cet article 1 propose une approche hybride pour la segmentation de documents bas{\'e}e sur l{'}agr{\'e}gation de diff{\'e}rentes solutions. Divers algorithmes de segmentation peuvent {\^e}tre utilis{\'e}s dans le syst{\`e}me, ce qui permet la combinaison de strat{\'e}gies multiples (sp{\'e}cifiques au domaine, supervis{\'e}es et nonsupervis{\'e}es). Un ensemble de documents {\'e}tiquet{\'e}s, segment{\'e}s au pr{\'e}alable et repr{\'e}sentatif du domaine cibl{\'e}, doit {\^e}tre fourni pour {\^e}tre utilis{\'e} comme ensemble d{'}entra{\^\i}nement pour l{'}apprentissage des m{\'e}thodes supervis{\'e}es, et aussi comme ensemble de test pour l{'}{\'e}valuation de la performance de chaque m{\'e}thode, ce qui d{\'e}terminera leur poids lors de la phase d{'}agr{\'e}gation. L{'}approche propos{\'e}e pr{\'e}sente de bonnes performances dans un sc{\'e}nario exp{\'e}rimental issu d{'}un corpus extrait du domaine juridique.",
    language = "French",
}
@proceedings{gwc-2019-global,
    title = "Proceedings of the 10th Global Wordnet Conference",
    editor = "Vossen, Piek  and
      Fellbaum, Christiane",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.0",
}
@inproceedings{veres-2019-making,
    title = "Making Sense of schema.org with {W}ord{N}et",
    author = "Veres, Csaba",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.1",
    pages = "1--9",
    abstract = "The schema.org initiative was designed to introduce machine readable metadata into the World Wide Web. This paper investigates conceptual biases in the schema through a mapping exercise between schema.org types and WordNet synsets. We create a mapping ontology which establishes the relationship between schema metadata types and the corresponding everyday concepts. This in turn can be used to enhance metadata annotation to include a more complete description of knowledge on the Web of data.",
}
@inproceedings{mititelu-mitrofan-2019-leaving,
    title = "Leaving No Stone Unturned When Identifying and Classifying Verbal Multiword Expressions in the {R}omanian {W}ordnet",
    author = "Mititelu, Verginica  and
      Mitrofan, Maria",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.2",
    pages = "10--15",
    abstract = "We present here the enhancement of the Romanian wordnet with a new type of information, very useful in language processing, namely types of verbal multi-word expressions. All verb literals made of two or more words are attached a label specific to the type of verbal multi-word expression they correspond to. These labels were created in the PARSEME Cost Action and were used in the version 1.1 of the shared task they organized. The results of this annotation are compared to those obtained in the annotation of a Romanian news corpus with the same labels. Given the alignment of the Romanian wordnet to the Princeton WordNet, this type of annotation can be further used for drawing comparisons between equivalent verbal literals in various languages, provided that such information is annotated in the wordnets of the respective languages and their wordnets are aligned to Princeton WordNet, and thus to the Romanian wordnet.",
}
@inproceedings{loukachevitch-parkhomenko-2019-thesaurus,
    title = "Thesaurus Verification Based on Distributional Similarities",
    author = "Loukachevitch, Natalia  and
      Parkhomenko, Ekaterina",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.3",
    pages = "16--23",
    abstract = "In this paper we consider an approach to verification of large lexical-semantic resources as WordNet. The method of verification procedure is based on the analysis of discrepancies of corpus-based and thesaurus-based word similarities. We calculated such word similarities on the basis of a Russian news collection and Russian wordnet (RuWordNet). We applied the procedure to more than 30 thousand words and found some serious errors in word sense description, including incorrect or absent relations or missed main senses of ambiguous words.",
}
@inproceedings{huber-hinrichs-2019-including,
    title = "Including {S}wiss Standard {G}erman in {G}erma{N}et",
    author = "Huber, Eva  and
      Hinrichs, Erhard",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.4",
    pages = "24--32",
    abstract = "GermaNet (Henrich and Hinrichs, 2010; Hamp and Feldweg, 1997) is a comprehensive wordnet of Standard German spoken in the Federal Republic of Germany. The GermaNet team aims at modelling the basic vocabulary of the language. German is an official language or a minority language in many countries. It is an official language in Austria, Germany and Switzerland, each with its own codified standard variety (Auer, 2014, p. 21), and also in Belgium, Liechtenstein, and Luxemburg. German is recognized as a minority language in thirteen additional countries, including Brasil, Italy, Poland, and Russia. However, the different standard varieties of German are currently not represented in GermaNet. With this project, we make a start on changing this by including one variety, namely Swiss Standard German, into GermaNet. This shall give a more inclusive perspective on the German language. We will argue that Swiss Standard German words, Helvetisms, are best included into the already existing wordnet GermaNet, rather than creating them as a separate wordnet.",
}
@inproceedings{nielsen-2019-danish,
    title = "{D}anish in {W}ikidata lexemes",
    author = "Nielsen, Finn {\AA}rup",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.5",
    pages = "33--38",
    abstract = "Wikidata introduced support for lexicographic data in 2018. Here we describe the lexicographic part of Wikidata as well as experiences with setting up lexemes for the Danish language. We note various possible annotations for lexemes as well as discuss various choices made.",
}
@inproceedings{azerkovich-2019-using,
    title = "Using Thesaurus Data to Improve Coreference Resolution for {R}ussian",
    author = "Azerkovich, Ilya",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.6",
    pages = "39--45",
    abstract = "Semantic information about entities, specifically, how close in meaning two mentions are to each other, can become very useful for the task of co-reference resolution. One of the most well-researched and widely used forms of presenting this information are measures of semantic similarity and semantic relatedness. These metrics are often computed, relying upon the structure of a thesaurus, but it is also possible to use alternative resources. One such source is Wikipedia, which possesses the category structure similar to that of a thesaurus. In this work we describe an attempt to use semantic relatedness measures, calculated on thesaurus and Wikipedia data, to improve the quality of a co-reference resolution system for Russian language. The results show that this is a viable solution and that combining the two sources yields the most gain in quality.",
}
@inproceedings{batita-zrigui-2019-extended,
    title = "The Extended {A}rabic {W}ord{N}et: a Case Study and an Evaluation using a Word Sense Disambiguation System",
    author = "Batita, Mohamed Ali  and
      Zrigui, Mounir",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.7",
    pages = "46--53",
    abstract = "Arabic WordNet (AWN) represents one of the best-known lexical resources for the Arabic language. However, it contains various issues that affect its use in different Natural Language Processing (NLP) applications. Due to resources deficiency, the update of Arabic WordNet requires much effort. There have only been only two updates it was first published in 2006. The most significant of those being in 2013, which represented a significant development in the usability and coverage of Arabic WordNet. This paper provides a study case on the updates of the Arabic WordNet and the development of its contents. More precisely, we present the new content in terms of relations that have been added to the extended version of Arabic WordNet. We also validate and evaluate its contents at different levels. We use its different versions in a Word Sense Disambiguation system. Finally, we compare the results and evaluate them. Results show that newly added semantic relations can improve the performance of a Word Sense Disambiguation system.",
}
@inproceedings{dimitrova-stefanova-2019-hidden,
    title = "On Hidden Semantic Relations between Nouns in {W}ord{N}et",
    author = "Dimitrova, Tsvetana  and
      Stefanova, Valentina",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.8",
    pages = "54--63",
    abstract = "The paper presents an effort on transferability of noun{--}verb and noun{--}adjective derivative and semantic relations to noun-noun relations. The approach relies on information from semantic classes and existing inter-POS derivative and (morpho)semantic relations between noun and verb, and noun and adjective synsets. We have added semantic relations between nouns in WordNet that are indirectly linked via verbs and adjectives. Observations on the combination between the relations and semantic classes of nouns they link, may facilitate further efforts in assigning semantic properties to nouns pointing to their abilities to participate in predicate-argument structures.",
}
@inproceedings{loukachevitch-gerasimova-2019-linking,
    title = "Linking {R}ussian {W}ordnet {R}u{W}ord{N}et to {W}ord{N}et",
    author = "Loukachevitch, Natalia  and
      Gerasimova, Anastasia",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.9",
    pages = "64--71",
    abstract = "In this paper we consider the linking procedure of Russian wordnet (RuWordNet) to Wordnet. The specificity of the procedure in our case is based on the fact that a lot of bilingual (Russian and English) lexical data have been gathered in another Russian thesaurus RuThes, which has a different structure than WordNet. Previously, RuThes has been semi-automatically transformed into RuWordNet, having the WordNet-like structure. Now, the RuThes English data are utilized to establish matching from the RuWordNet synsets to the WordNet synsets.",
}
@inproceedings{oliveira-rademaker-2019-fast,
    title = "Fast developing of a Natural Language Interface for a {P}ortuguese {W}ord{N}et: Leveraging on Sentence Embeddings",
    author = "Oliveira, Hugo Gon{\c{c}}alo  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.10",
    pages = "72--78",
    abstract = "We describe how a natural language interface can be developed for a wordnet with a small set of handcrafted templates, leveraging on sentence embeddings. The proposed approach does not use rules for parsing natural language queries but experiments showed that the embeddings model is tolerant enough for correctly predicting relation types that do not match known patterns exactly. It was tested with OpenWordNet-PT, for which this method may provide an alternative interface, with benefits also on the curation process.",
}
@inproceedings{bernardy-maskharashvili-2019-two,
    title = "Two experiments for embedding {W}ordnet hierarchy into vector spaces",
    author = "Bernardy, Jean-Philippe  and
      Maskharashvili, Aleksandre",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.11",
    pages = "79--84",
    abstract = "In this paper, we investigate mapping of the WORDNET hyponymy relation to feature vectors. Our aim is to model lexical knowledge in such a way that it can be used as input in generic machine-learning models, such as phrase entailment predictors. We propose two models. The first one leverages an existing mapping of words to feature vectors (fastText), and attempts to classify such vectors as within or outside of each class. The second model is fully supervised, using solely WORDNET as a ground truth. It maps each concept to an interval or a disjunction thereof. The first model approaches but not quite attain state of the art performance. The second model can achieve near-perfect accuracy.",
}
@inproceedings{sommerauer-etal-2019-towards,
    title = "Towards interpretable, data-derived distributional meaning representations for reasoning: A dataset of properties and concepts",
    author = "Sommerauer, Pia  and
      Fokkens, Antske  and
      Vossen, Piek",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.12",
    pages = "85--98",
    abstract = "This paper proposes a framework for investigating which types of semantic properties are represented by distributional data. The core of our framework consists of relations between concepts and properties. We provide hypotheses on which properties are reflected in distributional data or not based on the type of relation. We outline strategies for creating a dataset of positive and negative examples for various semantic properties, which cannot easily be separated on the basis of general similarity (e.g. fly: seagull, penguin). This way, a distributional model can only distinguish between positive and negative examples through evidence for a target property. Once completed, this dataset can be used to test our hypotheses and work towards data-derived interpretable representations.",
}
@inproceedings{hajnicz-bartosiak-2019-connections,
    title = "Connections between the semantic layer of Walenty valency dictionary and {P}l{W}ord{N}et",
    author = "Hajnicz, Elzbieta  and
      Bartosiak, Tomasz",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.13",
    pages = "99--107",
    abstract = "In this paper we discuss how Walenty is using PLWORDNET to represent semantic information. We decided to use PLWORDNET lexical units and synsets to describe both the predicate meaning and the semantic fields of its arguments. The original design decision required some further refinement caused by the structure of PLWORDNET and complex relations between arguments.",
}
@inproceedings{vial-etal-2019-sense,
    title = "Sense Vocabulary Compression through the Semantic Knowledge of {W}ord{N}et for Neural Word Sense Disambiguation",
    author = {Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin  and
      Schwab, Didier},
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.14",
    pages = "108--117",
    abstract = "In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database. We propose two different methods that greatly reduce the size of neural WSD models, with the benefit of improving their coverage without additional training data, and without impacting their precision. In addition to our methods, we present a WSD system which relies on pre-trained BERT word vectors in order to achieve results that significantly outperforms the state of the art on all WSD evaluation tasks.",
}
@inproceedings{rutkowski-etal-2019-estimating,
    title = "Estimating senses with sets of lexically related words for {P}olish word sense disambiguation",
    author = "Rutkowski, Szymon  and
      Rychlik, Piotr  and
      Mykowiecka, Agnieszka",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.15",
    pages = "118--124",
    abstract = "We propose a new algorithm for word sense disambiguation, exploiting data from a WordNet with many types of lexical relations, such as plWordNet for Polish. In this method, sense probabilities in context are approximated with a language model. To estimate the likelihood of a sense appearing amidst the word sequence, the token being disambiguated is substituted with words related lexically to the given sense or words appearing in its WordNet gloss. We test this approach on a set of sense-annotated Polish sentences with a number of neural language models. Our best setup achieves the accuracy score of 55.12{\%} (72.02{\%} when first senses are excluded), up from 51.77{\%} of an existing PageRank-based method. While not exceeding the first (often meaning most frequent) sense baseline in the standard case, this encourages further research on combining WordNet data with neural models.",
}
@inproceedings{pedersen-etal-2019-merging,
    title = "Merging {D}an{N}et with {P}rinceton {W}ordnet",
    author = "Pedersen, Bolette Sandford  and
      Nimb, Sanni  and
      Olsen, Ida R{\o}rmann  and
      Olsen, Sussi",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.16",
    pages = "125--134",
    abstract = "In this paper we describe the merge of the Danish wordnet, DanNet, with Princeton Wordnet applying a two-step approach. We first link from the English Princeton core to Danish (5,000 base concepts) and then proceed to linking the rest of the Danish vocabulary to English, thus going from Danish to English. Since the Danish wordnet is built bottom-up from Danish lexica and corpora, all taxonomies are monolingually based and thus not necessarily directly compatible with the coverage and structure of the Princeton WordNet. This fact proves to pose some challenges to the linking procedure since a considerable number of the links cannot be realised via the preferred cross-language synonym link which implies a more or less precise correlation between the two concepts. Instead, a subpart of the links are realised through near synonym or hyponymy links to compensate for the fact that no precise translation can be found in the target resource. The tool WordnetLoom is currently used for manual linking but procedures for a more automatic procedure in future is discussed. We conclude that the two resources actually differ from each other quite more than expected, both vocabulary and structure-wise.",
}
@inproceedings{sarmah-etal-2019-development,
    title = "Development of {A}ssamese Rule based Stemmer using {W}ord{N}et",
    author = "Sarmah, Jumi  and
      Sarma, Shikhar Kumar  and
      Barman, Anup Kumar",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.17",
    pages = "135--139",
    abstract = "Stemming is a technique that reduces any inflected word to its root form. Assamese is a morphologically rich, scheduled Indian language. There are various forms of suffixes applied to a word in various contexts. Such inflected words if normalized will help improve the performance of various Natural Language Processing applications. This paper basically tries to develop a Look-up and rule-based suffix stripping approach for the Assamese language using WordNet. The authors prepare the dictionary with the root words extracted from Assamese WordNet and Named Entities. Appropriate stemming rules for the inflected nouns, verbs have been set to the rule engine and later tested the stemmed output with the morphological root words of Assamese WordNet and Named Entities by computing hamming distance. This developed stemmer for the Assamese language achieves accuracy of 85{\%}. Also, the authors reported the IR system{'}s performance on applying the Assamese stemmer and proved its efficiency by retrieving sense oriented results based on the fired query. Thus, Morphological Analyzer will embark the research wing for developing various Assamese NLP applications.",
}
@inproceedings{klubicka-etal-2019-synthetic,
    title = "Synthetic, yet natural: Properties of {W}ord{N}et random walk corpora and the impact of rare words on embedding performance",
    author = "Klubi{\v{c}}ka, Filip  and
      Maldonado, Alfredo  and
      Mahalunkar, Abhijit  and
      Kelleher, John",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.18",
    pages = "140--150",
    abstract = "Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find that the distributions in the psuedo-corpora exhibit properties found in natural corpora, such as Zipf{'}s and Heaps{'} law, and also observe that the proportion of rare words in a pseudo-corpus affects the performance of its embeddings on word similarity.",
}
@inproceedings{tseng-hsieh-2019-augmenting,
    title = "Augmenting {C}hinese {W}ord{N}et semantic relations with contextualized embeddings",
    author = "Tseng, Yu-Hsiang  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.19",
    pages = "151--159",
    abstract = "Constructing semantic relations in WordNet has been a labour-intensive task, especially in a dynamic and fast-changing language environment. Combined with recent advancements of contextualized embeddings, this paper proposes the concept of morphology-guided sense vectors, which can be used to semi-automatically augment semantic relations in Chinese Wordnet (CWN). This paper (1) built sense vectors with pre-trained contextualized embedding models; (2) demonstrated the sense vectors computed were consistent with the sense distinctions made in CWN; and (3) predicted the potential semantically-related sense pairs with high accuracy by sense vectors model.",
}
@inproceedings{veres-2019-visualising,
    title = "Visualising {W}ord{N}et Embeddings: some preliminary results",
    author = "Veres, Csaba",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.20",
    pages = "160--165",
    abstract = "AutoExtend is a method for learning unambiguous vector embeddings for word senses. We visualise these word embeddings with t-SNE, which further compresses the vectors to the x,y plane. We show that the t-SNE co-ordinates can be used to reveal interesting semantic relations between word senses, and propose a new method that uses the simple x,y coordinates to compute semantic similarity. This can be used to propose new links and alterations to existing ones in WordNet. We plan to add this approach to the existing toolbox of methods in an attempt to understand learned semantic relations in word embeddings.",
}
@inproceedings{slaughter-etal-2019-making,
    title = "The Making of {C}optic {W}ordnet",
    author = {Slaughter, Laura  and
      Costa, Luis Morgado Da  and
      Miyagawa, So  and
      B{\"u}chler, Marco  and
      Zeldes, Amir  and
      Behlmer, Heike},
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.21",
    pages = "166--175",
    abstract = "With the increasing availability of wordnets for ancient languages, such as Ancient Greek and Latin, gaps remain in the coverage of less studied languages of antiquity. This paper reports on the construction and evaluation of a new wordnet for Coptic, the language of Late Roman, Byzantine and Early Islamic Egypt in the first millenium CE. We present our approach to constructing the wordnet which uses multilingual Coptic dictionaries and wordnets for five different languages. We further discuss the results of this effort and outline our on-going/future work.",
}
@inproceedings{irimia-etal-2019-evaluating,
    title = "Evaluating the {W}ordnet and {C}o{R}o{L}a-based Word Embedding Vectors for {R}omanian as Resources in the Task of Microworlds Lexicon Expansion",
    author = "Irimia, Elena  and
      Mitrofan, Maria  and
      Mititelu, Verginica",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.22",
    pages = "176--184",
    abstract = "Within a larger frame of facilitating human-robot interaction, we present here the creation of a core vocabulary to be learned by a robot. It is extracted from two tokenised and lemmatized scenarios pertaining to two imagined microworlds in which the robot is supposed to play an assistive role. We also evaluate two resources for their utility for expanding this vocabulary so as to better cope with the robot{'}s communication needs. The language under study is Romanian and the resources used are the Romanian wordnet and word embedding vectors extracted from the large representative corpus of contemporary Romanian, CoRoLa. The evaluation is made for two situations: one in which the words are not semantically disambiguated before expanding the lexicon, and another one in which they are disambiguated with senses from the Romanian wordnet. The appropriateness of each resource is discussed.",
}
@inproceedings{kanzaki-isahara-2019-towards,
    title = "Towards linking synonymous expressions of compound verbs to {J}apanese {W}ord{N}et",
    author = "Kanzaki, Kyoko  and
      Isahara, Hitoshi",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.23",
    pages = "185--190",
    abstract = "This paper describes our project on Japanese compound verbs. Japanese {``}Verb (adnominal form) + Verb{''} compounds, which are treated as single verbs, frequently appear in daily communication. They are not sufficiently registered in Japanese dictionaries or thesauri. We are now compiling a list of the synonymous expressions of compound verbs in {``}compound verb lexicon{''} built by the National Institute of Japanese Language and Linguistics. We extracted synonymous words and phrases of compound verbs from five hundred million Japanese web corpora. As a result, synonymous expressions of 1800 compound verbs were obtained automatically among 2700 in the {``}compound verb lexicon{''}. From our data, we observed that some compound verbs represent not only motion but also additional nuances such as an emotional one. In order to reflect the abundant meanings that compound verbs own, we will try to think of a link of synonymous expressions to Japanese wordnet. Concretely, in the case of synonymous phrases, we try to link adverbial expressions which are a part of phrases to the adverbial synset in Japanese wordnet.",
}
@inproceedings{griesel-etal-2019-thinking,
    title = "Thinking globally, acting locally {--} Progress in the {A}frican {W}ordnet Project",
    author = "Griesel, Marissa  and
      Bosch, Sonja  and
      Mojapelo, Mampaka Lydia",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.24",
    pages = "191--196",
    abstract = "The African Wordnet Project (AWN) includes all nine indigenous South African languages, namely isiZulu, isiXhosa, Setswana, Sesotho sa Leboa, Tshivenda, Siswati, Sesotho, isiNdebele and Xitsonga. The AWN currently includes 61 000 synsets as well as definitions and usage examples for a large part of the synsets. The project recently received extended funding from the South African Centre for Digital Language Resources (SADiLaR) and aims to update all aspects of the current resource, including the seed list used for new development, software tools used and mapping the AWN to the latest version of PWN 3.1. As with any resource development project, it is essential to also include phases of focused quality assurance and updating of the basis on which the resource is built. The African languages remain under-resourced. This paper describes progress made in the development of the AWN as well as recent technical improvements.",
}
@inproceedings{alvez-etal-2019-commonsense,
    title = "Commonsense Reasoning Using {W}ord{N}et and {SUMO}: a Detailed Analysis",
    author = "{\'A}lvez, Javier  and
      Gonzalez-Dios, Itziar  and
      Rigau, German",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.25",
    pages = "197--205",
    abstract = "We describe a detailed analysis of a sample of large benchmark of commonsense reasoning problems that has been automatically obtained from WordNet, SUMO and their mapping. The objective is to provide a better assessment of the quality of both the benchmark and the involved knowledge resources for advanced commonsense reasoning tasks. By means of this analysis, we are able to detect some knowledge misalignments, mapping errors and lack of knowledge and resources. Our final objective is the extraction of some guidelines towards a better exploitation of this commonsense knowledge framework by the improvement of the included resources.",
}
@inproceedings{sio-costa-2019-building,
    title = "Building the {C}antonese {W}ordnet",
    author = "Sio, Joanna Ut-Seong  and
      Costa, Luis Morgado Da",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.26",
    pages = "206--215",
    abstract = "This paper reports on the development of the Cantonese Wordnet, a new wordnet project based on Hong Kong Cantonese. It is built using the expansion approach, leveraging on the existing Chinese Open Wordnet, and the Princeton Wordnet{'}s semantic hierarchy. The main goal of our project was to produce a high quality, human-curated resource {--} and this paper reports on the initial efforts and steady progress of our building method. It is our belief that the lexical data made available by this wordnet, including Jyutping romanization, will be useful for a variety of future uses, including many language processing tasks and linguistic research on Cantonese and its interactions with other Chinese dialects.",
}
@inproceedings{kobylinski-wasiluk-2019-deep,
    title = "Deep Learning in Event Detection in {P}olish",
    author = "Kobyli{\'n}ski, {\L}ukasz  and
      Wasiluk, Micha{\l}",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.27",
    pages = "216--221",
    abstract = "Event detection is an important NLP task that has been only recently tackled in the context of Polish, mostly due to lack of language resources. The available annotated corpora are still relatively small and supervised learning approaches are limited by the size of training datasets. Event detection tools are very much needed, as they can be used to annotate more language resources automatically and to improve the accuracy of other NLP tasks, which rely on the detection of events, such as question answering or machine translation. In this paper we present a deep learning based approach to this task, which proved to capture the knowledge contained in the training data most effectively and outperform previously proposed methods. We show a direct comparison to previously published results, using the same data and experimental setup.",
}
@inproceedings{gonzalez-dios-2019-textual,
    title = "Textual genre based approach to use {W}ord{N}et in language-for-specific-purpose classroom as dictionary",
    author = "Gonzalez-Dios, Itziar",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.28",
    pages = "222--227",
    abstract = "When teaching language for specific purposes (LSP) linguistic resources are needed to help students understand and write specialised texts. As building a lexical resource is costly, we explore the use of wordnets to represent the terms that can be found in particular textual domains. In order to gather the terms to be included in wordnets, we propose a textual genre approach, that leads us to introduce a new relation term used in to link all the possible terms/synsets that can appear in a text to the synset of the textual genre. This way, students can use wordnet as dictionary or thesaurus when writing specialised texts. We explain our approach by means of the logbooks and terms in Basque. A side effect of this works is also enriching the wordnets with new variants and synsets.",
}
@inproceedings{kafe-2019-fitting,
    title = "Fitting Semantic Relations to Word Embeddings",
    author = "Kafe, Eric",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.29",
    pages = "228--237",
    abstract = "We fit WordNet relations to word embeddings, using 3CosAvg and LRCos, two set-based methods for analogy resolution, and introduce 3CosWeight, a new, weighted variant of 3CosAvg. We test the performance of the resulting semantic vectors in lexicographic semantics tests, and show that none of the tested classifiers can learn symmetric relations like synonymy and antonymy, since the source and target words of these relations are the same set. By contrast, with the asymmetric relations (hyperonymy / hyponymy and meronymy), both 3CosAvg and LRCos clearly outperform the baseline in all cases, while 3CosWeight attained the best scores with hyponymy and meronymy, suggesting that this new method could provide a useful alternative to previous approaches.",
}
@inproceedings{batsuren-etal-2019-building,
    title = "Building the {M}ongolian {W}ord{N}et",
    author = "Batsuren, Khuyagbaatar  and
      Ganbold, Amarsanaa  and
      Chagnaa, Altangerel  and
      Giunchiglia, Fausto",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.30",
    pages = "238--244",
    abstract = "This paper presents the Mongolian Wordnet (MOW), and a general methodology of how to construct it from various sources e.g. lexical resources and expert translations. As of today, the MOW contains 23,665 synsets, 26,875 words, 2,979 glosses, and 213 examples. The manual evaluation of the resource1 estimated its quality at 96.4{\%}.",
}
@inproceedings{mccrae-etal-2019-english,
    title = "{E}nglish {W}ord{N}et 2019 {--} An Open-Source {W}ord{N}et for {E}nglish",
    author = "McCrae, John P.  and
      Rademaker, Alexandre  and
      Bond, Francis  and
      Rudnicka, Ewa  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.31",
    pages = "245--252",
    abstract = "We describe the release of a new wordnet for English based on the Princeton WordNet, but now developed under an open-source model. In particular, this version of WordNet, which we call English WordNet 2019, which has been developed by multiple people around the world through GitHub, fixes many errors in previous wordnets for English. We give some details of the changes that have been made in this version and give some perspectives about likely future changes that will be made as this project continues to evolve.",
}
@inproceedings{branco-etal-2019-assessing,
    title = "Assessing Wordnets with {W}ord{N}et Embeddings",
    author = "Branco, Ruben  and
      Rodrigues, Jo{\~a}o  and
      Saedi, Chakaveh  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.32",
    pages = "253--259",
    abstract = "An effective conversion method was proposed in the literature to obtain a lexical semantic space from a lexical semantic graph, thus permitting to obtain WordNet embeddings from WordNets. In this paper, we propose the exploitation of this conversion methodology as the basis for the comparative assessment of WordNets: given two WordNets, their relative quality in terms of capturing the lexical semantics of a given language, can be assessed by (i) converting each WordNet into the corresponding semantic space (i.e. into WordNet embeddings), (ii) evaluating the resulting WordNet embeddings under the typical semantic similarity prediction task used to evaluate word embeddings in general; and (iii) comparing the performance in that task of the two word embeddings, extracted from the two WordNets. A better performance in that evaluation task results from the word embeddings that are better at capturing the semantic similarity of words, which, in turn, result from the WordNet that is of higher quality at capturing the semantics of words.",
}
@inproceedings{kashyap-etal-2019-spoken,
    title = "Spoken {W}ord{N}et",
    author = "Kashyap, Kishore  and
      Sarma, Shikhar Kr  and
      Sweta, Kumari",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.33",
    pages = "260--263",
    abstract = "WordNets have been used in a wide variety of applications, including in design and development of intelligent and human assisting systems. Although WordNet was initially developed as an online lexical database, (Miller, 1995 and Fellbaum, 1998) later developments have inspired using WordNet database as resources in NLP applications, Language Technology developments, and as sources of structured learned materials. This paper proposes, conceptualizes, designs, and develops a voice enabled information retrieval system, facilitating WordNet knowledge presentation in a spoken format, based on a spoken query. In practice, the work converts the WordNet resource into a structured voiced based knowledge extraction system, where a spoken query is processed in a pipeline, and then extracting the relevant WordNet resources, structuring through another process pipeline, and then presented in spoken format. Thus the system facilitates a speech interface to the existing WordNet and we named the system as {``}Spoken WordNet{''}. The system interacts with two interfaces, one designed and developed for Web, and the other as an App interface for smartphone. This is also a kind of restructuring the WordNet as a friendly version for visually challenged users. User can input query string in the form of spoken English sentence or word. Jaccard Similarity is calculated between the input sentence and the synset definitions. The one with highest similarity score is taken as the synset of interest among multiple available synsets. User is also prompted to choose a contextual synset, in case of ambiguities.",
}
@inproceedings{declerck-siegel-2019-ontolex,
    title = "{O}nto{L}ex as a possible Bridge between {W}ord{N}ets and full lexical Descriptions",
    author = "Declerck, Thierry  and
      Siegel, Melanie",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.34",
    pages = "264--271",
    abstract = "In this paper we describe our current work on representing a recently created German lexical semantics resource in OntoLex-Lemon and in conformance with WordNet specifications. Besides presenting the representation effort, we show the utilization of OntoLex-Lemon to bridge from WordNet-like resources to full lexical descriptions and extend the coverage of WordNets to other types of lexical data, such as decomposition results, exemplified for German data, and inflectional phenomena, here outlined for English data.",
}
@inproceedings{im-2019-semi,
    title = "Semi-automatic Annotation of Event Structure, Argument Structure, and Opposition Structure to {W}ord{N}et by Using Event Structure Frame",
    author = "Im, Seohyun",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.35",
    pages = "272--279",
    abstract = "In this paper, we present semi-automatic annotation of the Event Structure Frames to synsets of English verbs in WordNet. The Event Structure Frame is a sub-eventual structure frame which combines event structure (lexical aspect) with argument structure represented by semantic roles and opposition structure which represents the presupposed and entailed sub-events of a matrix event. Our annotation work is done semi-automatically by GESL-based automatic annotation and manual error-correction. GESL is an automatic annotation tool of the Event Structure Frame to verbs in a sentence. We apply GESL to the example sentence given for each synset of a verb in WordNet. We expect that our work will make WordNet much more useful for any NLP and its applications which require lexical semantic information of English verbs.",
}
@inproceedings{stoyanova-leseva-2019-enhancing,
    title = "Enhancing Conceptual Description through Resource Linking and Exploration of Semantic Relations",
    author = "Stoyanova, Ivelina  and
      Leseva, Svetlozara",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.36",
    pages = "280--289",
    abstract = "The paper presents current efforts towards linking two large lexical semantic resources {--} WordNet and FrameNet {--} to the end of their mutual enrichment and the facilitation of the access, extraction and analysis of various types of semantic and syntactic information. In the second part of the paper, we go on to examine the relation of inheritance and other semantic relations as represented in WordNet and FrameNet and how they correspond to each other when the resources are aligned. We discuss the implications with respect to the enhancement of the two resources through the definition of new relations and the detailisation of conceptual frames.",
}
@inproceedings{simov-etal-2019-aligning,
    title = "Aligning the {B}ulgarian {BTB} {W}ord{N}et with the {B}ulgarian {W}ikipedia",
    author = "Simov, Kiril  and
      Osenova, Petya  and
      Laskova, Laska  and
      Radev, Ivajlo  and
      Kancheva, Zara",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.37",
    pages = "290--297",
    abstract = "The paper reports on an ongoing work that manually maps the Bulgarian WordNet BTB-WN with Bulgarian Wikipedia. The preparatory work of extracting the Wikipedia articles and provisionally relating them to the WordNet lemmas was done automatically. The manual work includes checking of the corresponding senses in both resources as well as the missing ones. The main cases of mapping are considered. The first experiments of mapping about 1000 synsets show the establishment of more than 78 {\%} of exact correspondences and nearly 15 {\%} of new synsets.",
}
@inproceedings{arican-etal-2019-english,
    title = "{E}nglish-{T}urkish Parallel Semantic Annotation of {P}enn-{T}reebank",
    author = {Ar{\i}can, Bilge Nas  and
      Bakay, {\"O}zge  and
      Avar, Beg{\"u}m  and
      Y{\i}ld{\i}z, Olcay Taner  and
      Ergelen, {\"O}zlem},
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.38",
    pages = "298--306",
    abstract = "This paper reports our efforts in constructing a sense-labeled English-Turkish parallel corpus using the traditional method of manual tagging. We tagged a pre-built parallel treebank which was translated from the Penn Treebank corpus. This approach allowed us to generate a resource combining syntactic and semantic information. We provide statistics about the corpus itself as well as information regarding its development process.",
}
@inproceedings{bakay-etal-2019-comparing,
    title = "Comparing Sense Categorization Between {E}nglish {P}rop{B}ank and {E}nglish {W}ord{N}et",
    author = {Bakay, {\"O}zge  and
      Avar, Beg{\"u}m  and
      Y{\i}ld{\i}z, Olcay Taner},
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.39",
    pages = "307--314",
    abstract = "Given the fact that verbs play a crucial role in language comprehension, this paper presents a study which compares the verb senses in English PropBank with the ones in English WordNet through manual tagging. After analyzing 1554 senses in 1453 distinct verbs, we have found out that while the majority of the senses in PropBank have their one-to-one correspondents in WordNet, a substantial amount of them are differentiated. Furthermore, by analysing the differences between our manually-tagged and an automatically-tagged resource, we claim that manual tagging can help provide better results in sense annotation.",
}
@inproceedings{lualdi-etal-2019-building,
    title = "Building {ASLN}et, A {W}ordnet for {A}merican {S}ign {L}anguage",
    author = "Lualdi, Colin  and
      Hudson, Jack  and
      Buchholz, Noah  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.40",
    pages = "315--322",
    abstract = "We discuss the creation of ASLNet by aligning the Princeton WordNet (PWN) with SignStudy, an online database of American Sign Language (ASL) signs. This alignment will have many immediate benefits for first and second-sign language learners as well as ASL researchers by highlighting semantic relations among signs. We begin to address the interesting theoretical question of to what extent the wordnet-style organization of the English lexicon (and those of wordnets in other spoken languages) is applicable to ASL, and whether ASL requires positing additional, language or modality-specific relations among signs. Significantly, the mapping of SignStudy and PWN provides a bridge between ASL and the worldwide wordnet community, which comprises speakers of dozens of languages working in academic and language technology settings.",
}
@inproceedings{naskret-2019-collaborative,
    title = "A collaborative system for building and maintaining wordnets.",
    author = "Naskr{\k{e}}t, Tomasz",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.41",
    pages = "323--328",
    abstract = "A collaborative system for wordnet construction and maintenance is presented. Its key modules include WordnetLoom editor, Wordnet Tracker and JavaScript Graph. They offer a number of functionalities that allow solving problems on every stage of building, editing and aligning wordnets by teams of lexicographers working in parallel. The experience collected in recent years has allowed us to refine applications and add new modules to provide the best user experience in a reliable and easily maintainable way.",
}
@inproceedings{jastrzab-kwiatkowski-2019-enriching,
    title = "Enriching Keywords Database {U}sing{W}ordnets {--} a Case Study",
    author = "Jastrz{\k{a}}b, Tomasz  and
      Kwiatkowski, Grzegorz",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.42",
    pages = "329--335",
    abstract = "In the paper, we study the case of building a keywords database related to the Polish Classification of Activities (PKD 2007). The database enables automatic classification of the companies to the industry branches. The classification is performed based on the company{'}s activity description. We present the initial design of the keywords database and the ways in which wordnets were used to enrich it. Finally, we present the preliminary statistical evaluation of the produced resource.",
}
@inproceedings{kocon-janz-2019-propagation,
    title = "Propagation of emotions, arousal and polarity in {W}ord{N}et using Heterogeneous Structured Synset Embeddings",
    author = "Koco{\'n}, Jan  and
      Janz, Arkadiusz",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.43",
    pages = "336--341",
    abstract = "In this paper we present a novel method for emotive propagation in a wordnet based on a large emotive seed. We introduce a sense-level emotive lexicon annotated with polarity, arousal and emotions. The data were annotated as a part of a large study involving over 20,000 participants. A total of 30,000 lexical units in Polish WordNet were described with metadata, each unit received about 50 annotations concerning polarity, arousal and 8 basic emotions, marked on a multilevel scale. We present a preliminary approach to propagating emotive metadata to unlabeled lexical units based on the distribution of manual annotations using logistic regression and description of mixed synset embeddings based on our Heterogeneous Structured Synset Embeddings.",
}
@inproceedings{bond-etal-2019-testing,
    title = "Testing {Z}ipf{'}s meaning-frequency law with wordnets as sense inventories",
    author = "Bond, Francis  and
      Janz, Arkadiusz  and
      Maziarz, Marek  and
      Rudnicka, Ewa",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.44",
    pages = "342--352",
    abstract = "According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, French, Polish, Japanese, Indonesian and Chinese. We have proved that the law works pretty well for all of these languages if we take - as Zipf did - mean values of meaning count and averaged ranks. On the other hand, the law disastrously fails in predicting the number of senses for a single lemma. We have also provided the evidence that slope coefficients of Zipfian log-log linear model may vary from language to language.",
}
@inproceedings{dziob-etal-2019-plwordnet,
    title = "pl{W}ord{N}et 4.1 - a Linguistically Motivated, Corpus-based Bilingual Resource",
    author = "Dziob, Agnieszka  and
      Piasecki, Maciej  and
      Rudnicka, Ewa",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.45",
    pages = "353--362",
    abstract = "The paper presents the latest release of the Polish WordNet, namely plWordNet 4.1. The most significant developments since 3.0 version include new relations for nouns and verbs, mapping semantic role-relations from the valency lexicon Walenty onto the plWordNet structure and sense-level inter-lingual mapping. Several statistics are presented in order to illustrate the development and contemporary state of the wordnet.",
}
@inproceedings{bond-etal-2019-comparison,
    title = "A Comparison of Sense-level Sentiment Scores",
    author = "Bond, Francis  and
      Janz, Arkadiusz  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.46",
    pages = "363--372",
    abstract = "In this paper, we compare a variety of sense-tagged sentiment resources, including SentiWordNet, ML-Senticon, plWordNet emo and the NTU Multilingual Corpus. The goal is to investigate the quality of the resources and see how well the sentiment polarity annotation maps across languages.",
}
@inproceedings{de-paiva-rademaker-2019-portuguese,
    title = "{P}ortuguese Manners of Speaking",
    author = "de Paiva, Valeria  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.47",
    pages = "373--377",
    abstract = "Lexical resources need to be as complete as possible. Very little work seems to have been done on adverbs, the smallest part of speech class in Princeton WordNet counting the number of synsets. Amongst adverbs, manner adverbs ending in {`}-ly{'} seem the easiest to work with, as their meaning is almost the same as the one of the associated adjective. This phenomenon seems to be parallel in English and Portuguese, where these manner adverbs finish in the suffix {`}-mente{'}. We use this correspondence to improve the coverage of adverbs in the lexical resource OpenWordNet-PT, a wordnet for Portuguese.",
}
@inproceedings{rademaker-etal-2019-completing,
    title = "Completing the {P}rinceton Annotated Gloss Corpus Project",
    author = "Rademaker, Alexandre  and
      Cuconato, Bruno  and
      Cid, Alessandra  and
      Tessarollo, Alexandre  and
      Andrade, Henrique",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.48",
    pages = "378--386",
    abstract = "In the Princeton WordNet Gloss Corpus, the word forms from the definitions ({``}glosses{''}) in WordNet{'}s synsets are manually linked to the context-appropriate sense in the WordNet. The glosses then become a sense-disambiguated corpus annotated against WordNet version 3.0. The result is also called a semantic concordance, which can be seen as both a lexicon (WordNet extension) and an annotated corpus. In this work we motivate and present the initial steps to complete the annotation of all open-class words in this corpus. Finally, we introduce a freely-available annotation interface built as an Emacs extension, and evaluate a preliminary annotation effort.",
}
@inproceedings{bond-bond-2019-geonames,
    title = "{G}eo{N}ames {W}ordnet (geown): extracting wordnets from {G}eo{N}ames",
    author = "Bond, Francis  and
      Bond, Arthur",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.49",
    pages = "387--393",
    abstract = "This paper introduces a new multilingual lexicon of geographical place names. The names are based on (and linked to) the GeoNames collection. Each location is treated as a new synset, which is linked by instance{\_}hypernym to a small set of supertypes. These supertypes are linked to the collaborative interlingual index, based on mappings from GeoDomainWordnet. If a location is already in the interlingual index, then it is also linked to the entry, using mappings from the Geo-Wordnet. Finally, if GeoNames places the location in a larger location, this is linked using the mero{\_}location link. Wordnets can be built for any language in GeoNames, we give results for those wordnets in the Open Multilingual Wordnet. We discuss how it is mapped and the characteristics of the extracted wordnets.",
}
@inproceedings{lohk-etal-2019-new,
    title = "New Polysemy Structures in Wordnets Induced by Vertical Polysemy",
    author = "Lohk, Ahti  and
      Orav, Heili  and
      Vare, Kadri  and
      Bond, Francis  and
      Vaik, Rasmus",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.50",
    pages = "394--403",
    abstract = "This paper aims to study auto-hyponymy and auto-troponymy relations (or vertical polysemy) in 11 wordnets uploaded into the new Open Multilingual Wordnet (OMW) webpage. We investigate how vertical polysemy forms polysemy structures (or sense clusters) in semantic hierarchies of the wordnets. Our main results and discoveries are new polysemy structures that have not previously been associated with vertical polysemy, along with some inconsistencies of semantic relations analysis in the studied wordnets, which should not be there. In the case study, we turn attention to polysemy structures in the Estonian Wordnet (version 2.2.0), analyzing them and giving the lexicographers comments. In addition, we describe the detection algorithm of polysemy structures and an overview of the state of polysemy structures in 11 wordnets.",
}
@inproceedings{kanojia-etal-2019-utilizing-wordnets,
    title = "Utilizing Wordnets for Cognate Detection among {I}ndian Languages",
    author = "Kanojia, Diptesh  and
      Patel, Kevin  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak  and
      Haffari, Gholemreza",
    booktitle = "Proceedings of the 10th Global Wordnet Conference",
    month = jul,
    year = "2019",
    address = "Wroclaw, Poland",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2019.gwc-1.51",
    pages = "404--412",
    abstract = "Automatic Cognate Detection (ACD) is a challenging task which has been utilized to help NLP applications like Machine Translation, Information Retrieval and Computational Phylogenetics. Unidentified cognate pairs can pose a challenge to these applications and result in a degradation of performance. In this paper, we detect cognate word pairs among ten Indian languages with Hindi and use deep learning methodologies to predict whether a word pair is cognate or not. We identify IndoWordnet as a potential resource to detect cognate word pairs based on orthographic similarity-based methods and train neural network models using the data obtained from it. We identify parallel corpora as another potential resource and perform the same experiments for them. We also validate the contribution of Wordnets through further experimentation and report improved performance of up to 26{\%}. We discuss the nuances of cognate detection among closely related Indian languages and release the lists of detected cognates as a dataset. We also observe the behaviour of, to an extent, unrelated Indian language pairs and release the lists of detected cognates among them as well.",
}
@proceedings{ccnlg-2019-creativity,
    title = "Proceedings of the 4th Workshop on Computational Creativity in Language Generation",
    editor = "Burtenshaw, Benjamin  and
      Manjavacas, Enrique",
    month = "29 " # oct # "--3 " # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.ccnlg-1.0",
}
@inproceedings{ammanabrolu-etal-2019-toward,
    title = "Toward Automated Quest Generation in Text-Adventure Games",
    author = "Ammanabrolu, Prithviraj  and
      Broniec, William  and
      Mueller, Alex  and
      Paul, Jeremy  and
      Riedl, Mark",
    booktitle = "Proceedings of the 4th Workshop on Computational Creativity in Language Generation",
    month = "29 " # oct # "--3 " # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.ccnlg-1.1",
    pages = "1--12",
}
@inproceedings{yuan-etal-2019-efficient,
    title = "Efficient text generation of user-defined topic using generative adversarial networks",
    author = "Yuan, Chenhan  and
      Huang, Yi-Chin  and
      Tsai, Cheng-Hung",
    booktitle = "Proceedings of the 4th Workshop on Computational Creativity in Language Generation",
    month = "29 " # oct # "--3 " # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.ccnlg-1.2",
    pages = "13--21",
}
@inproceedings{santhanam-shaikh-2019-emotional,
    title = "Emotional Neural Language Generation Grounded in Situational Contexts",
    author = "Santhanam, Sashank  and
      Shaikh, Samira",
    booktitle = "Proceedings of the 4th Workshop on Computational Creativity in Language Generation",
    month = "29 " # oct # "--3 " # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.ccnlg-1.3",
    pages = "22--27",
}
@inproceedings{naskar-etal-2019-text,
    title = "Text Embellishment using Attention Based Encoder-Decoder Model",
    author = "Naskar, Subhajit  and
      Saha, Soumya  and
      Mukherjee, Sreeparna",
    booktitle = "Proceedings of the 4th Workshop on Computational Creativity in Language Generation",
    month = "29 " # oct # "--3 " # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.ccnlg-1.4",
    pages = "28--38",
}
@inproceedings{ono-etal-2019-advertising,
    title = "Advertising Plot Generation System Based on Comprehensive Narrative Analysis of Advertisement Videos",
    author = "Ono, Juumpei  and
      Sasaki, Atsushi  and
      Ogata, Takashi",
    booktitle = "Proceedings of the 4th Workshop on Computational Creativity in Language Generation",
    month = "29 " # oct # "--3 " # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.ccnlg-1.5",
    pages = "39--46",
}
@inproceedings{saberi-lee-2019-noun,
    title = "Noun Generation for Nominalization in Academic Writing",
    author = "Saberi, Dariush  and
      Lee, John",
    booktitle = "Proceedings of the 4th Workshop on Computational Creativity in Language Generation",
    month = "29 " # oct # "--3 " # nov,
    year = "2019",
    address = "Tokyo, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2019.ccnlg-1.6",
    pages = "47--51",
}
@proceedings{paclic-2018-pacific-asia-language,
    title = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    editor = "Politzer-Ahles, Stephen  and
      Hsu, Yu-Yin  and
      Huang, Chu-Ren  and
      Yao, Yao",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3000",
}
@inproceedings{nakazawa-etal-2018-overview,
    title = "Overview of the 5th Workshop on {A}sian Translation",
    author = "Nakazawa, Toshiaki  and
      Sudoh, Katsuhito  and
      Higashiyama, Shohei  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Mino, Hideya  and
      Goto, Isao  and
      Pa, Win Pa  and
      Kunchukuttan, Anoop  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3001",
}
@inproceedings{kocmi-etal-2018-cuni,
    title = "{CUNI} {NMT} System for {WAT} 2018 Translation Tasks",
    author = "Kocmi, Tom  and
      Parida, Shantipriya  and
      Bojar, Ond?ej",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3002",
}
@inproceedings{dabre-etal-2018-nicts,
    title = "{NICT}{'}s Participation in {WAT} 2018: Approaches Using Multilingualism and Recurrently Stacked Layers",
    author = "Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Fujita, Atsushi  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3003",
}
@inproceedings{li-etal-2018-srcb,
    title = "{SRCB} Neural Machine Translation Systems in {WAT} 2018",
    author = "Li, Yihan  and
      Liu, Boyan  and
      Tong, Yixuan  and
      Jiang, Shanshan  and
      Dong, Bin",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3004",
}
@inproceedings{ehara-2018-smt,
    title = "{SMT} reranked {NMT} (2)",
    author = "Ehara, Terumasa",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3005",
}
@inproceedings{wang-etal-2018-english,
    title = "{E}nglish-{M}yanmar {NMT} and {SMT} with Pre-ordering: {NICT}{'}s Machine Translation Systems at {WAT}-2018",
    author = "Wang, Rui  and
      Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3006",
}
@inproceedings{marie-etal-2018-combination,
    title = "Combination of Statistical and Neural Machine Translation for {M}yanmar-{E}nglish",
    author = "Marie, Benjamin  and
      Fujita, Atsushi  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3007",
}
@inproceedings{zhang-etal-2018-tmu,
    title = "{TMU} {J}apanese-{C}hinese Unsupervised {NMT} System for {WAT} 2018 Translation Task",
    author = "Zhang, Longtu  and
      Zhao, Yuting  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3008",
}
@inproceedings{thida-etal-2018-statistical,
    title = "Statistical Machine Translation Using 5-grams Word Segmentation in Decoding",
    author = "Thida, Aye  and
      Nway Han, Nway  and
      Oo, Sheinn Thawtar",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3009",
}
@inproceedings{philip-etal-2018-cvit,
    title = "{CVIT-MT} Systems for {WAT}-2018",
    author = "Philip, Jerin  and
      Namboodiri, Vinay P.  and
      Jawahar, C.V.",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3010",
}
@inproceedings{ojha-etal-2018-rgnlp,
    title = "The {RGNLP} Machine Translation Systems for {WAT} 2018",
    author = "Ojha, Atul Kr.  and
      Chowdhury, Koel Dutta  and
      Liu, Chao-Hong  and
      Saxena, Karan",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3011",
}
@inproceedings{sen-etal-2018-iitp,
    title = "{IITP}-{MT} at {WAT}2018: Transformer-based Multilingual Indic-{E}nglish Neural Machine Translation System",
    author = "Sen, Sukanta  and
      Gupta, Kamal Kumar  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3012",
}
@inproceedings{banerjee-etal-2018-multilingual,
    title = "Multilingual {I}ndian Language Translation System at {WAT} 2018: Many-to-one Phrase-based {SMT}",
    author = "Banerjee, Tamali  and
      Kunchukuttan, Anoop  and
      Bhattacharya, Pushpak",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3013",
}
@inproceedings{matsumura-etal-2018-tmu,
    title = "{TMU} {J}apanese-{E}nglish Neural Machine Translation System using Generative Adversarial Network for {WAT} 2018",
    author = "Matsumura, Yukio  and
      Katsumata, Satoru  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3014",
}
@inproceedings{kawara-etal-2018-osaka,
    title = "{O}saka {U}niversity {MT} Systems for {WAT} 2018: Rewarding, Preordering, and Domain Adaptation",
    author = "Kawara, Yuki  and
      Takebayashi, Yuto  and
      Chu, Chenhui  and
      Arase, Yuki",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3015",
}
@inproceedings{sin-etal-2018-ucsynlp,
    title = "{UCSYNLP}-Lab Machine Translation Systems for {WAT} 2018",
    author = "Sin, Yi Mon Shwe  and
      Oo, Thazin Myint  and
      Mo, Hsu Myat  and
      Pa, Win Pa  and
      Soe, Khim Mar  and
      Thu, Ye Kyaw",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3016",
}
@inproceedings{wang-etal-2018-xmu,
    title = "{XMU} Neural Machine Translation Systems for {WAT}2018 {M}yanmar-{E}nglish Translation Task",
    author = "Wang, Boli  and
      Hu, Jinming  and
      Chen, Yidong  and
      Shi, Xiaodong",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-3017",
}
@proceedings{paclic-2018-pacific-asia,
    title = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing",
    editor = "Politzer-Ahles, Stephen  and
      Hsu, Yu-Yin  and
      Huang, Chu-Ren  and
      Yao, Yao",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-2000",
}
@inproceedings{dong-etal-2018-non,
    title = "How do non-tastes taste? A corpus-based study on {C}hinese people{'}s perception of spicy and numbing food",
    author = "Dong, Sicong  and
      Zhong, Yin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-2001",
}
@inproceedings{fukutome-harada-2018-flavor,
    title = "Flavor Wheel Terminology and Challenges in Translation {--} Focusing on {E}nglish and {J}apanese Vocabulary for Wine, Sake and Soy sauce {--}",
    author = "Fukutome, Nami  and
      Harada, Yasunari",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-2002",
}
@inproceedings{kim-etal-2018-conceptual,
    title = "Conceptual Metaphorical Extensions of {`}Eat{'} and {`}Drink{'} in {E}nglish and {K}orean: A Usage-based Approach",
    author = "Kim, Jong-Bok  and
      Kim, Jungsoo  and
      Sim, Rok",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-2003",
}
@inproceedings{klyueva-etal-2018-food,
    title = "Food-Related Sentiment Analysis for {C}antonese",
    author = "Klyueva, Natalia  and
      Long, Yunfei  and
      Huang, Chu-Ren  and
      Lu, Qin",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-2004",
}
@inproceedings{lim-2018-corpus,
    title = "A corpus-based study of braised dishes in {C}hinese-{E}nglish menus",
    author = "Lim, Lily",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-2005",
}
@inproceedings{zhong-huang-2018-pleasing,
    title = "Pleasing to the Mouth of Pleasant Personality: A corpus-based study of conceptualization of desserts in online {C}hinese food reviews",
    author = "Zhong, Yin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 25th Joint Workshop on Linguistics and Language Processing",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-2006",
}
@proceedings{paclic-2018-pacific,
    title = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    editor = "Politzer-Ahles, Stephen  and
      Hsu, Yu-Yin  and
      Huang, Chu-Ren  and
      Yao, Yao",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1000",
}
@inproceedings{abe-etal-2018-multi,
    title = "Multi-dialect Neural Machine Translation and Dialectometry",
    author = "Abe, Kaori  and
      Matsubayashi, Yuichiroh  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1001",
}
@inproceedings{angle-etal-2018-automated,
    title = "Automated Error Correction and Validation for {POS} Tagging of {H}indi",
    author = "Angle, Sachi  and
      Mishra, Pruthwik  and
      Sharma, Dipti Mishra",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1002",
}
@inproceedings{asahara-2018-reading,
    title = "Between Reading Time and Clause Boundaries in {J}apanese - Wrap-up Effect in a Head-Final Language",
    author = "Asahara, Masayuki",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1003",
}
@inproceedings{ashihara-etal-2018-contextualized,
    title = "Contextualized Word Representations for Multi-Sense Embedding",
    author = "Ashihara, Kazuki  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki  and
      Uchida, Satoru",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1004",
}
@inproceedings{bai-etal-2018-domain,
    title = "Domain Adaptation for Sentiment Analysis using Keywords in the Target Domain as the Learning Weight",
    author = "Bai, Jing  and
      Shinnou, Hiroyuki  and
      Komiya, Kanako",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1005",
}
@inproceedings{bakari-etal-2018-towards,
    title = "Towards an Automatic Text Comprehension for the {A}rabic Question-Answering: Semantic and Logical Representation of Texts",
    author = "Bakari, Wided  and
      Bellot, Patrice  and
      Neji, Mahmoud",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1006",
}
@inproceedings{battu-etal-2018-predicting,
    title = "Predicting the Genre and Rating of a Movie Based on its Synopsis",
    author = "Battu, Varshit  and
      Batchu, Vishal  and
      Gangula, Rama Rohit Reddy  and
      Dakannagari, Mohana Murali Krishna Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1007",
}
@inproceedings{brosas-etal-2018-classifying,
    title = "Classifying and Extracting Data from {F}acebook Posts for Online Persona Identification",
    author = "Brosas, Hazel  and
      Lim, Eugene  and
      Sevilla, Danica  and
      Silva, Denise  and
      Ong, Ethel",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1008",
}
@inproceedings{chen-chung-2018-investigating,
    title = "Investigating the {E}nglish {ADJECTIVE} {OF} Construction in Academic Writing",
    author = "Chen, Liyin  and
      Chung, Siaw-Fong",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1009",
}
@inproceedings{chen-etal-2018-detecting,
    title = "Detecting Free Translation in Parallel Corpora from Attention Scores",
    author = "Chen, Qi  and
      Kwong, Oi Yee  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1010",
}
@inproceedings{chen-etal-2018-effects,
    title = "Effects of Stimulus Duration and Vowel Quality in Tone Perception by {E}nglish Musicians and Non-musicians",
    author = "Chen, Si  and
      Zhu, Yiqing  and
      Wayland, Ratree  and
      Yang, Yike",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1011",
}
@inproceedings{chen-etal-2018-changing,
    title = "Changing against tone merging trends in community? The case of {C}. {Y}. Leung",
    author = "Chen, Ziqi  and
      Yao, Yao  and
      Yu, Alan C. L.",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1012",
}
@inproceedings{choi-etal-2018-automatic,
    title = "Automatic Evaluation of {E}nglish-to-{K}orean and {K}orean-to-{E}nglish Neural Machine Translation Systems by Linguistic Test Points",
    author = "Choi, Sung-Kwon  and
      Choi, Gyu-Hyeun  and
      Kim, Youngkil",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1013",
}
@inproceedings{chui-etal-2018-corpus,
    title = "A Corpus Study of Linguistic-Cultural Conceptualization of {FEAR} in {C}hinese and {R}ussian",
    author = "Chui, Kawai  and
      Yeh, Hsiang-Lin  and
      Tsai, Jie-Li",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1014",
}
@inproceedings{dai-etal-2018-improving,
    title = "Improving Scientific Relation Classification with Task Specific Supersense",
    author = "Dai, Qin  and
      Inoue, Naoya  and
      Reisert, Paul  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1015",
}
@inproceedings{danda-etal-2018-many,
    title = "Too Many Questions? What Can We Do? : Multiple Question Span Detection",
    author = "Danda, Prathyusha  and
      Srivastava, Brij Mohan Lal  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1016",
}
@inproceedings{dhakras-shrivastava-2018-bowler,
    title = "{B}o{WL}er: A neural approach to extractive text summarization",
    author = "Dhakras, Pranav  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1017",
}
@inproceedings{doan-etal-2018-effectiveness,
    title = "Effectiveness of Character Language Model for {V}ietnamese Named Entity Recognition",
    author = "Doan, Xuan-Dung  and
      Dang, Trung-Thanh  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1018",
}
@inproceedings{fkih-omri-2018-fca,
    title = "{FCA}{\_}{R}etrieval: A Multi-operator Algorithm for Information Retrieval from Binary Concept Lattice",
    author = "Fkih, Fethi  and
      Omri, Mohamed Nazih",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1019",
}
@inproceedings{gao-lee-2018-exclamative,
    title = "Exclamative Sentences in Emotion Expressions in {M}andarin {C}hinese: A Corpus-based Approach",
    author = "Gao, Xuefeng  and
      Lee, Sophia Yat Mei",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1020",
}
@inproceedings{gundapu-mamidi-2018-word,
    title = "Word Level Language Identification in {E}nglish {T}elugu Code Mixed Data",
    author = "Gundapu, Sunil  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1021",
}
@inproceedings{hien-spring-2018-effect,
    title = "The Effect of {L}2 Onset on {L}2 and {L}3 learning: The Case of Native Speakers of Burkinabe languages",
    author = "Hien, Alain  and
      Spring, Ryan",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1022",
}
@inproceedings{hsiao-chung-2018-arguing,
    title = "Are They Arguing or not: A Corpus-based Study",
    author = "Hsiao, Min-Chun  and
      Chung, Siaw-Fong",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1023",
}
@inproceedings{hsu-german-2018-prosodic,
    title = "Prosodic Organization and Focus Realization in {T}aiwan {M}andarin",
    author = "Hsu, Yu-Yin  and
      German, James",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1024",
}
@inproceedings{igamberdiev-shin-2018-metaphor,
    title = "Metaphor Identification with Paragraph and Word Vectorization: An Attention-Based Neural Approach",
    author = "Igamberdiev, Timour  and
      Shin, Hyopil",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1025",
}
@inproceedings{iwai-etal-2018-annotating,
    title = "Annotating a Driving Experience Corpus with Behavior and Subjectivity",
    author = "Iwai, Ritsuko  and
      Kawahara, Daisuke  and
      Kumada, Takatsune  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1026",
}
@inproceedings{jiang-etal-2018-semantic,
    title = "A Semantic Study of Coordinate Compounds in {C}hinese",
    author = "Jiang, Yingdi  and
      Yu, Yonglian  and
      Liu, Zhiying",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1027",
}
@inproceedings{kameswari-mamidi-2018-political,
    title = "Political Discourse Analysis : A Case Study of 2014 {A}ndhra {P}radesh State Assembly Election of Interpersonal Speech Choices",
    author = "Kameswari, Lalitha  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1028",
}
@inproceedings{kato-etal-2018-annotation,
    title = "Annotation of {`}Word List by Semantic Principles{'} Labels for the {B}alanced {C}orpus of {C}ontemporary {W}ritten {J}apanese",
    author = "Kato, Sachi  and
      Asahara, Masayuki  and
      Yamazaki, Makoto",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1029",
}
@inproceedings{kato-matsubara-2018-model,
    title = "Model-Theoretic Incremental Interpretation Based on {D}iscourse {R}epresentation {T}heory",
    author = "Kato, Yoshihide  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1030",
}
@inproceedings{niina-shimada-2018-trivia,
    title = "Trivia Score and Ranking Estimation Using Support Vector Regression and {R}ank{N}et",
    author = "Niina, Kazuya  and
      Shimada, Kazutaka",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1031",
}
@inproceedings{khoo-lin-2018-grammatical,
    title = "Grammatical Variations between {S}ingapore, Mainland {C}hina, and {T}aiwan {M}andarin: A Pilot Study of Aspect Marking",
    author = "Khoo, Yong Kang  and
      Lin, Jingxia",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1032",
}
@inproceedings{kitagawa-komachi-2018-long,
    title = "Long Short-Term Memory for {J}apanese Word Segmentation",
    author = "Kitagawa, Yoshiaki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1033",
}
@inproceedings{kiyono-etal-2018-reducing,
    title = "Reducing Odd Generation from Neural Headline Generation",
    author = "Kiyono, Shun  and
      Takase, Sho  and
      Suzuki, Jun  and
      Okazaki, Naoaki  and
      Inui, Kentaro  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1034",
}
@inproceedings{kodaira-komachi-2018-rule,
    title = "The Rule of Three: Abstractive Text Summarization in Three Bullet Points",
    author = "Kodaira, Tomonori  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1035",
}
@inproceedings{kwong-2018-non,
    title = "The Non-deictic Use of Demonstratives in Conversations and Interpreted Speeches in Contemporary {H}ong {K}ong {C}antonese",
    author = "Kwong, Oi Yee",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1036",
}
@inproceedings{lau-etal-2018-questions,
    title = "Questions as a Pre-event, Pivot Event and Post-event of Emotions",
    author = "Lau, Helena Yan Ping  and
      Lee, Sophia Yat Mei  and
      Wang, Zhongqing",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1037",
}
@inproceedings{le-sadat-2018-improving,
    title = "Improving the neural network-based machine transliteration for low-resourced language pair",
    author = "Le, Ngoc Tan  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1038",
}
@inproceedings{lee-wong-2018-epistemic,
    title = "Epistemic Indefinites and Reportative Indefinites in {C}antonese",
    author = "Lee, Tommy Tsz-Ming  and
      Wong, Hok-Yuen",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1039",
}
@inproceedings{lee-lee-2018-new,
    title = "A New Angle on {L}2 Texts: A Statistical Approach to Translation Universals",
    author = "Lee, Younghee Cheri  and
      Lee, Yong-Hun",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1040",
}
@inproceedings{li-etal-2018-perceptual,
    title = "Perceptual evaluation of {M}andarin tone sandhi production by {C}antonese speakers before and after perceptual training",
    author = "Li, Bei  and
      Yang, Yike  and
      Chen, Si",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1041",
}
@inproceedings{li-etal-2018-chinese,
    title = "{C}hinese Spelling Check based on Neural Machine Translation",
    author = "Li, Chiao-Wen  and
      Chen, Jhih-Jie  and
      Chang, Jason",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1042",
}
@inproceedings{liang-etal-2018-research,
    title = "Research on Entity Relation Extraction for Military Field",
    author = "Liang, Chen  and
      Zan, Hongying  and
      Liu, Yajun  and
      Wu, Yunfang",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1043",
}
@inproceedings{lin-hsu-2018-whether,
    title = "Whether and How {M}andarin Sandhied Tone 3 and Underlying Tone 2 differ in Terms of Vowel Quality?",
    author = "Lin, Yu-Jung  and
      Hsu, Yu-Yin",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1044",
}
@inproceedings{liu-etal-2018-demn,
    title = "{DEMN}: Distilled-Exposition Enhanced Matching Network for Story Comprehension",
    author = "Liu, Chunhua  and
      Zhang, Haiou  and
      Jiang, Shan  and
      Yu, Dong",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1045",
}
@inproceedings{liu-etal-2018-automatic,
    title = "Automatic Error Correction on {J}apanese Functional Expressions Using Character-based Neural Machine Translation",
    author = "Liu, Jun  and
      Cheng, Fei  and
      Wang, Yiran  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1046",
}
@inproceedings{liu-2018-minimalist,
    title = "Minimalist Parsing of Heavy {NP} Shift",
    author = "Liu, Lei",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1047",
}
@inproceedings{liu-ning-2018-factors,
    title = "Factors Affecting Accent of New and Similar Vowels in {H}ong {K}ong {C}antonese Pronounced by {U}rdu Speakers from Secondary School",
    author = "Liu, Yi  and
      Ning, Jinghong",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1048",
}
@inproceedings{lu-etal-2018-deep,
    title = "Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis",
    author = "Lu, Yujie  and
      Ni, Boyi  and
      Ji, Qijin  and
      Sakamoto, Kotaro  and
      Shibuki, Hideyuki  and
      Mori, Tatsunori",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1049",
}
@inproceedings{luo-etal-2018-study,
    title = "A Study on the {K}orean and {C}hinese Pronunciation of {C}hinese Characters and Learning {K}orean as a Second Language",
    author = "Luo, Xiao  and
      Yang, Yike  and
      Sun, Jing",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1050",
}
@inproceedings{maruyama-yamamoto-2018-lexical,
    title = "Lexical Substitution is Practical for Rare Word Simplification",
    author = "Maruyama, Takumi  and
      Yamamoto, Kazuhide",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1051",
}
@inproceedings{matsuno-etal-2018-reduction,
    title = "Reduction of Parameter Redundancy in Biaffine Classifiers with Symmetric and Circulant Weight Matrices",
    author = "Matsuno, Tomoki  and
      Hayashi, Katsuhiko  and
      Ishihara, Takahiro  and
      Manabe, Hitoshi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1052",
}
@inproceedings{mishra-etal-2018-equgener,
    title = "{E}qu{G}ener: A Reasoning Network for Word Problem Solving by Generating Arithmetic Equations",
    author = "Mishra, Pruthwik  and
      Kurisinkel, Litton J  and
      Sharma, Dipti Misra  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1053",
}
@inproceedings{miyazaki-komachi-2018-japanese,
    title = "{J}apanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention",
    author = "Miyazaki, Ryosuke  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1054",
}
@inproceedings{nguyen-hoang-etal-2018-genre,
    title = "Genre-Oriented Web Content Extraction with Deep Convolutional Neural Networks and Statistical Methods",
    author = "Nguyen-Hoang, Bao-Dai  and
      Pham-Hong, Bao-Tran  and
      Jin, Yiping  and
      Le, Phu T. V.",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1055",
}
@inproceedings{nguyen-son-etal-2018-identifying,
    title = "Identifying Computer-Translated Paragraphs using Coherence Features",
    author = "Nguyen-Son, Hoang-Quoc  and
      Nguyen, Huy H.  and
      Tieu, Ngoc-Dung T.  and
      Yamagishi, Junichi  and
      Echizen, Isao",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1056",
}
@inproceedings{nishiyama-etal-2018-dataset,
    title = "Dataset Construction Method for Word Reading Disambiguation",
    author = "Nishiyama, Koki  and
      Yamamoto, Kazuhide  and
      Nakajima, Hideharu",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1057",
}
@inproceedings{okamura-etal-2018-improving,
    title = "Improving {J}apanese semantic-role-labeling performance with transfer learning as case for limited resources of tagged corpora on aggregated language",
    author = "Okamura, Takuya  and
      Takeuchi, Koichi  and
      Ishihara, Yasuhiro  and
      Taguchi, Masahiro  and
      Inada, Yoshihiko  and
      Iizuka, Masaya  and
      Abo, Tatsuhiko  and
      Ueda, Hitoshi",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1058",
}
@inproceedings{oota-etal-2018-affect,
    title = "Affect in Tweets using Experts Model",
    author = "Oota, Subba Reddy  and
      Avvaru, Adithya  and
      Marreddy, Mounika Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1059",
}
@inproceedings{oya-2018-utilization,
    title = "Utilization of Dependency Type per Sentence to Identify Differences among Genres of {E}nglish Texts",
    author = "Oya, Masanori",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1060",
}
@inproceedings{panchendrarajan-amaresan-2018-bidirectional,
    title = "Bidirectional {LSTM}-{CRF} for Named Entity Recognition",
    author = "Panchendrarajan, Rrubaa  and
      Amaresan, Aravindh",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1061",
}
@inproceedings{peng-etal-2018-smartwrite,
    title = "{S}mart{W}rite: Extracting {C}hinese Lexical Grammar Patterns Using Dependency Parsing",
    author = "Peng, Cheng-Cyuan  and
      Yang, Ching-Yu  and
      Chen, Jhih-Jie  and
      Chang, Jason",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1062",
}
@inproceedings{pham-pham-2018-building,
    title = "Building a Spoonerism Detection System for {V}ietnamese",
    author = "Pham, Thai-Hoang  and
      Pham, Xuan-Khoai",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1063",
}
@inproceedings{rachman-etal-2018-semantic,
    title = "Semantic Role Labeling in Conversational Chat using Deep Bi-Directional Long Short-Term Memory Networks with Attention Mechanism",
    author = "Rachman, Valdi  and
      Mahendra, Rahmad  and
      Wicaksono, Alfan Farizki  and
      Meydiarso, Ahmad Rizqi  and
      Ikhwantri, Fariz",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1064",
}
@inproceedings{rong-etal-2018-topic,
    title = "Topic-comment constructions in {L}1-{C}hinese learners{'} {E}nglish",
    author = "Rong, Yicheng  and
      Chang, Hui  and
      Cheung, Candice Chi-Hang",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1065",
}
@inproceedings{sango-etal-2018-effectiveness,
    title = "Effectiveness of Domain Adaptation in {J}apanese Predicate-Argument Structure Analysis",
    author = "Sango, Mizuki  and
      Nishikawa, Hitoshi  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1066",
}
@inproceedings{shen-etal-2018-customized,
    title = "Customized Attention Mechanism for Relation Classification",
    author = "Shen, Shirong  and
      Wen, Yang  and
      Zhou, Lijuan  and
      Zan, Hongyin",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1067",
}
@inproceedings{shinnou-etal-2018-domain,
    title = "Domain Adaptation Using a Combination of Multiple Embeddings for Sentiment Analysis",
    author = "Shinnou, Hiroyuki  and
      Zhao, Xinyu  and
      Komiya, Kanako",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1068",
}
@inproceedings{siriwittayakorn-2018-expect,
    title = "Expect the Unexpected? The Processing of Possibility Hedges in Medical Diagnoses and Medical Advice",
    author = "Siriwittayakorn, Teeranoot",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1069",
}
@inproceedings{song-etal-2018-feature,
    title = "Feature Attention Network: Interpretable Depression Detection from Social Media",
    author = "Song, Hoyun  and
      You, Jinseon  and
      Chung, Jin-Woo  and
      Park, Jong C.",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1070",
}
@inproceedings{spala-etal-2018-comparison,
    title = "A Comparison Study of Human Evaluated Automated Highlighting Systems",
    author = "Spala, Sasha  and
      Dernoncourt, Franck  and
      Chang, Walter  and
      Dockhorn, Carl",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1071",
}
@inproceedings{suzuki-etal-2018-fine,
    title = "Fine-tuning for Named Entity Recognition Using Part-of-Speech Tagging",
    author = "Suzuki, Masaya  and
      Komiya, Kanako  and
      Sasaki, Minoru  and
      Shinnou, Hiroyuki",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1072",
}
@inproceedings{tabajunda-2018-linguistic,
    title = "Linguistic Schoolscape as Public Communication: A Study of Announcements and Signages in De La Salle University {--} Dasmari{\~n}as",
    author = "Tabajunda, Dastin",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1073",
}
@inproceedings{tagami-etal-2018-suspicious,
    title = "Suspicious News Detection Using Micro Blog Text",
    author = "Tagami, Tsubasa  and
      Ouchi, Hiroki  and
      Asano, Hiroki  and
      Hanawa, Kazuaki  and
      Uchiyama, Kaori  and
      Suzuki, Kaito  and
      Inui, Kentaro  and
      Komiya, Atsushi  and
      Fujimura, Atsuo  and
      Yamashita, Ryo  and
      Yanai, Hitofumi  and
      Machino, Akinori",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1074",
}
@inproceedings{takehisa-2018-case,
    title = "The Case for Thematically Underspecified External Arguments",
    author = "Takehisa, Tomokazu",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1075",
}
@inproceedings{tang-2018-syntax,
    title = "The syntax of the final particle k",
    author = "Tang, Qing",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1076",
}
@inproceedings{te-etal-2018-using,
    title = "Using Social Media Posts as Knowledge Resource for Generating Life Stories",
    author = "Te, Robee Khyra Mae J.  and
      Lam, Janica Mae M.  and
      Ong, Ethel",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1077",
}
@inproceedings{tsai-lai-2018-functions,
    title = "The Functions of Must-constructions in Spoken Corpus: A Constructionist Perspective",
    author = "Tsai, Tsi-Chuen  and
      Lai, Huei-Ling",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1078",
}
@inproceedings{tsay-myers-2018-communicative,
    title = "Communicative Efficiency in Child {M}andarin",
    author = "Tsay, Jane  and
      Myers, James",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1079",
}
@inproceedings{tummalapalli-mamidi-2018-syllables,
    title = "Syllables for Sentence Classification in Morphologically Rich Languages",
    author = "Tummalapalli, Madhuri  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1080",
}
@inproceedings{vo-yamamoto-2018-vietsentilex,
    title = "{V}iet{S}enti{L}ex: a sentiment dictionary that considers the polarity of ambiguous sentiment words",
    author = "Vo, Huynh Quoc Viet  and
      Yamamoto, Kazuhide",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1081",
}
@inproceedings{wan-fang-2018-examination,
    title = "A Re-examination of Syntactic Complexity by Investigating the Internal Structure Variations of Adverbial Clauses across Speech and Writing",
    author = "Wan, Mingyu  and
      Fang, Alex Chengyu",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1082",
}
@inproceedings{wang-2018-sketching,
    title = "Sketching a {C}hinese writer{'}s vocabulary profile in {E}nglish: the case of {H}a {J}in",
    author = "Wang, Vincent",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1083",
}
@inproceedings{wong-etal-2018-facilitating,
    title = "Facilitating and Blocking Conditions of Haplology: A comparative study of {H}ong {K}ong {C}antonese and {T}aiwan {M}andarin",
    author = "Wong, Sam Yin  and
      Chen, I-Hsuan  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1084",
}
@inproceedings{wu-2018-evidentiality,
    title = "Evidentiality, Alethic Modality and Temporal Modifiers: The Case of Benl{\'a}i vs. Yu{\'a}nl{\'a}i in {M}andarin {C}hinese",
    author = "Wu, Jiun-Shiung",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1085",
}
@inproceedings{wu-2018-lexical,
    title = "Lexical Constraints on the Acquisition of {E}nglish Split Intransitivity-An Experimental Investigation of {C}hinese-speaking {L}2 Learners",
    author = "Wu, Lili",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1086",
}
@inproceedings{xia-xun-2018-distributed,
    title = "Distributed Representation of {C}hinese Collocation",
    author = "Xia, Bo  and
      Xun, Endong",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1087",
}
@inproceedings{xiong-nuo-2018-attention,
    title = "Attention-based {BLSTM}-{CRF} Architecture for {M}ongolian Named Entity Recognition",
    author = "Xiong, Yuzhu  and
      Nuo, Minghua",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1088",
}
@inproceedings{yamashiro-etal-2018-neural,
    title = "Neural {J}apanese Zero Anaphora Resolution using Smoothed Large-scale Case Frames with Word Embedding",
    author = "Yamashiro, Souta  and
      Nishikawa, Hitoshi  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1089",
}
@inproceedings{yang-etal-2018-development,
    title = "Development of Perceptual Training Software for Realizing High Variability Training Paradigm and Self Adaptive Training Paradigm",
    author = "Yang, Ruining  and
      Nanjo, Hiroaki  and
      Dantsuji, Masatake",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1090",
}
@inproceedings{yang-etal-2018-semantic,
    title = "Semantic Transparency of Radicals in {C}hinese Characters: An Ontological Perspective",
    author = "Yang, Yike  and
      Huang, Chu-Ren  and
      Dong, Sicong  and
      Chen, Si",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1091",
}
@inproceedings{yang-2018-orthographic,
    title = "Orthographic Awareness and Phonological Awareness of Late {C}hinese-{E}nglish Bilinguals: Evidence from Word-Picture Interference Tasks",
    author = "Yang, Yumeng",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1092",
}
@inproceedings{yip-2018-communicating,
    title = "Communicating Social Support in Online Self-help Groups for Anxiety and Depression: A Qualitative Discourse Analysis",
    author = "Yip, Jesse Wai Chi",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1093",
}
@inproceedings{yoon-kim-2018-scrambling,
    title = "A-Scrambling and Subjecthood in {K}orean: An Experimental Approach",
    author = "Yoon, James Hye-Suk  and
      Kim, Ji-Hye",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1094",
}
@inproceedings{zhang-2018-comparison,
    title = "A Comparison of Tone Normalization Methods for Language Variation Research",
    author = "Zhang, Jingwei",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1095",
}
@inproceedings{zhao-lepage-2018-context,
    title = "Context Encoder for Analogies on Strings",
    author = "Zhao, Tianjing  and
      Lepage, Yves",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1096",
}
@inproceedings{zheng-2018-corpus,
    title = "A Corpus-based Multidimensional Analysis of Linguistic Features of Truth and Deception",
    author = "Zheng, Fanghua",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1097",
}
@inproceedings{zhou-etal-2018-automatic,
    title = "Automatic Identification of Indicators of Compromise using Neural-Based Sequence Labelling",
    author = "Zhou, Shengping  and
      Long, Zi  and
      Tan, Lianzhi  and
      Guo, Hao",
    booktitle = "Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation",
    month = "1{--}3 " # dec,
    year = "2018",
    address = "Hong Kong",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/Y18-1098",
}
@proceedings{ws-2018-nlp-computer,
    title = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    editor = "Pil{\'a}n, Ildik{\'o}  and
      Volodina, Elena  and
      Alfter, David  and
      Borin, Lars",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7100",
}
@inproceedings{antonsen-argese-2018-using,
    title = "Using authentic texts for grammar exercises for a minority language",
    author = "Antonsen, Lene  and
      Argese, Chiara",
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7101",
    pages = "1--9",
}
@inproceedings{boyd-2018-normalization,
    title = "Normalization in Context: Inter-Annotator Agreement for Meaning-Based Target Hypothesis Annotation",
    author = "Boyd, Adriane",
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7102",
    pages = "10--22",
}
@inproceedings{hamed-zesch-2018-role,
    title = "The Role of Diacritics in Increasing the Difficulty of {A}rabic Lexical Recognition Tests",
    author = "Hamed, Osama  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7103",
    pages = "23--31",
}
@inproceedings{kempfert-kohn-2018-automatic,
    title = "An Automatic Error Tagger for {G}erman",
    author = {Kempfert, Inga  and
      K{\"o}hn, Christine},
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7104",
    pages = "32--40",
}
@inproceedings{lange-ljunglof-2018-demonstrating,
    title = "Demonstrating the {MUSTE} Language Learning Environment",
    author = {Lange, Herbert  and
      Ljungl{\"o}f, Peter},
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7105",
    pages = "41--46",
}
@inproceedings{megyesi-etal-2018-learner,
    title = "Learner Corpus Anonymization in the Age of {GDPR}: Insights from the Creation of a Learner Corpus of {S}wedish",
    author = {Megyesi, Be{\'a}ta  and
      Granstedt, Lena  and
      Johansson, Sofia  and
      Prentice, Julia  and
      Ros{\'e}n, Dan  and
      Schenstr{\"o}m, Carl-Johan  and
      Sundberg, Gunl{\"o}g  and
      Wir{\'e}n, Mats  and
      Volodina, Elena},
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7106",
    pages = "47--56",
}
@inproceedings{mieskes-pado-2018-work,
    title = "Work Smart - Reducing Effort in Short-Answer Grading",
    author = "Mieskes, Margot  and
      Pad{\'o}, Ulrike",
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7107",
    pages = "57--68",
}
@inproceedings{schneider-graen-2018-nlp,
    title = "{NLP} Corpus Observatory {--} Looking for Constellations in Parallel Corpora to Improve Learners{'} Collocational Skills",
    author = {Schneider, Gerold  and
      Gra{\"e}n, Johannes},
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7108",
    pages = "69--78",
}
@inproceedings{weiss-etal-2018-linguistically,
    title = "A Linguistically-Informed Search Engine to Identifiy Reading Material for Functional Illiteracy Classes",
    author = "Weiss, Zarah  and
      Dittrich, Sabrina  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7109",
    pages = "79--90",
}
@inproceedings{ziai-etal-2018-feedback,
    title = "Feedback Strategies for Form and Meaning in a Real-life Language Tutoring System",
    author = "Ziai, Ramon  and
      Rudzewitz, Bjoern  and
      De Kuthy, Kordula  and
      Nuxoll, Florian  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 7th workshop on {NLP} for Computer Assisted Language Learning",
    month = nov,
    year = "2018",
    address = "Stockholm, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W18-7110",
    pages = "91--98",
}
@proceedings{ws-2018-automatic,
    title = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
    editor = {J{\"o}nsson, Arne  and
      Rennes, Evelina  and
      Saggion, Horacio  and
      Stajner, Sanja  and
      Yaneva, Victoria},
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-7000",
}
@inproceedings{francois-2018-interface,
    title = "The Interface Between Readability and Automatic Text Simplification",
    author = "Fran{\c{c}}ois, Thomas",
    booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-7001",
    doi = "10.18653/v1/W18-7001",
    pages = "1--2",
}
@inproceedings{grabar-cardon-2018-clear,
    title = "{CLEAR} {--} Simple Corpus for Medical {F}rench",
    author = "Grabar, Natalia  and
      Cardon, R{\'e}mi",
    booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-7002",
    doi = "10.18653/v1/W18-7002",
    pages = "3--9",
}
@inproceedings{grabar-etal-2018-study,
    title = "Study of Readability of Health Documents with Eye-tracking Approaches",
    author = "Grabar, Natalia  and
      Farce, Emmanuel  and
      Sparrow, Laurent",
    booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-7003",
    doi = "10.18653/v1/W18-7003",
    pages = "10--20",
}
@inproceedings{hmida-etal-2018-assisted,
    title = "Assisted Lexical Simplification for {F}rench Native Children with Reading Difficulties",
    author = "Hmida, Firas  and
      Billami, Mokhtar B.  and
      Fran{\c{c}}ois, Thomas  and
      Gala, N{\'u}ria",
    booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-7004",
    doi = "10.18653/v1/W18-7004",
    pages = "21--28",
}
@inproceedings{martin-etal-2018-reference,
    title = "Reference-less Quality Estimation of Text Simplification Systems",
    author = "Martin, Louis  and
      Humeau, Samuel  and
      Mazar{\'e}, Pierre-Emmanuel  and
      de La Clergerie, {\'E}ric  and
      Bordes, Antoine  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-7005",
    doi = "10.18653/v1/W18-7005",
    pages = "29--38",
}
@inproceedings{stajner-popovic-2018-improving,
    title = "Improving Machine Translation of {E}nglish Relative Clauses with Automatic Text Simplification",
    author = "{\v{S}}tajner, Sanja  and
      Popovi{\'c}, Maja",
    booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-7006",
    doi = "10.18653/v1/W18-7006",
    pages = "39--48",
}
@proceedings{ws-2018-nlg,
    title = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    editor = "Foster, Mary Ellen  and
      Buschmeier, Hendrik  and
      Gkatzia, Dimitra",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6900",
}
@inproceedings{willemsen-etal-2018-context,
    title = "Context-sensitive Natural Language Generation for robot-assisted second language tutoring",
    author = "Willemsen, Bram  and
      de Wit, Jan  and
      Krahmer, Emiel  and
      de Haas, Mirjam  and
      Vogt, Paul",
    booktitle = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6901",
    doi = "10.18653/v1/W18-6901",
    pages = "1--7",
    abstract = "This paper describes the L2TOR intelligent tutoring system (ITS), focusing primarily on its output generation module. The L2TOR ITS is developed for the purpose of investigating the efficacy of robot-assisted second language tutoring in early childhood. We explain the process of generating contextually-relevant utterances, such as task-specific feedback messages, and discuss challenges regarding multimodality and multilingualism for situated natural language generation from a robot tutoring perspective.",
}
@inproceedings{belakova-gkatzia-2018-learning,
    title = "Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction",
    author = "Belakova, Jekaterina  and
      Gkatzia, Dimitra",
    booktitle = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6902",
    doi = "10.18653/v1/W18-6902",
    pages = "8--11",
    abstract = "One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user{'}s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction.",
}
@inproceedings{ritschel-andre-2018-shaping,
    title = "Shaping a social robot{'}s humor with Natural Language Generation and socially-aware reinforcement learning",
    author = "Ritschel, Hannes  and
      Andr{\'e}, Elisabeth",
    booktitle = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6903",
    doi = "10.18653/v1/W18-6903",
    pages = "12--16",
    abstract = "Humor is an important aspect in human interaction to regulate conversations, increase interpersonal attraction and trust. For social robots, humor is one aspect to make interactions more natural, enjoyable, and to increase credibility and acceptance. In combination with appropriate non-verbal behavior, natural language generation offers the ability to create content on-the-fly. This work outlines the building-blocks for providing an individual, multimodal interaction experience by shaping the robot{'}s humor with the help of Natural Language Generation and Reinforcement Learning based on human social signals.",
}
@inproceedings{pomarlan-etal-2018-sensors,
    title = "From sensors to sense: Integrated heterogeneous ontologies for Natural Language Generation",
    author = "Pomarlan, Mihai  and
      Porzel, Robert  and
      Bateman, John  and
      Malaka, Rainer",
    booktitle = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6904",
    doi = "10.18653/v1/W18-6904",
    pages = "17--21",
    abstract = "We propose the combination of a robotics ontology (KnowRob) with a linguistically motivated one (GUM) under the upper ontology DUL. We use the DUL Event, Situation, Description pattern to formalize reasoning techniques to convert between a robot{'}s beliefstate and its linguistic utterances. We plan to employ these techniques to equip robots with a reason-aloud ability, through which they can explain their actions as they perform them, in natural language, at a level of granularity appropriate to the user, their query and the context at hand.",
}
@inproceedings{cass-etal-2018-farewell,
    title = "A farewell to arms: Non-verbal communication for non-humanoid robots",
    author = "Cass, Aaron G.  and
      Striegnitz, Kristina  and
      Webb, Nick",
    booktitle = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6905",
    doi = "10.18653/v1/W18-6905",
    pages = "22--26",
    abstract = "Human-robot interactions situated in a dynamic environment create a unique mix of challenges for conversational systems. We argue that, on the one hand, NLG can contribute to addressing these challenges and that, on the other hand, they pose interesting research problems for NLG. To illustrate our position we describe our research on non-humanoid robots using non-verbal signals to support communication.",
}
@inproceedings{zarriess-schlangen-2018-data,
    title = "Being data-driven is not enough: Revisiting interactive instruction giving as a challenge for {NLG}",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the Workshop on {NLG} for Human{--}Robot Interaction",
    month = nov,
    year = "2018",
    address = "Tilburg, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6906",
    doi = "10.18653/v1/W18-6906",
    pages = "27--31",
    abstract = "Modeling traditional NLG tasks with data-driven techniques has been a major focus of research in NLG in the past decade. We argue that existing modeling techniques are mostly tailored to textual data and are not sufficient to make NLG technology meet the requirements of agents which target fluid interaction and collaboration in the real world. We revisit interactive instruction giving as a challenge for datadriven NLG and, based on insights from previous GIVE challenges, propose that instruction giving should be addressed in a setting that involves visual grounding and spoken language. These basic design decisions will require NLG frameworks that are capable of monitoring their environment as well as timing and revising their verbal output. We believe that these are core capabilities for making NLG technology transferrable to interactive systems.",
}
@proceedings{ws-2018-intelligent,
    title = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    editor = {Alonso, Jose M.  and
      Catala, Alejandro  and
      Theune, Mari{\"e}t},
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6700",
}
@inproceedings{wubben-2018-applications,
    title = "Applications of {NLG} in practical conversational {AI} settings",
    author = "Wubben, Sander",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6701",
    doi = "10.18653/v1/W18-6701",
    pages = "1--2",
}
@inproceedings{su-etal-2018-generating,
    title = "Generating Description for Sequential Images with Local-Object Attention Conditioned on Global Semantic Context",
    author = "Su, Jing  and
      Lin, Chenghua  and
      Zhou, Mian  and
      Dai, Qingyun  and
      Lv, Haoyu",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6702",
    doi = "10.18653/v1/W18-6702",
    pages = "3--8",
}
@inproceedings{riou-etal-2018-automation,
    title = "Automation and Optimisation of Humor Trait Generation in a Vocal Dialogue System",
    author = "Riou, Matthieu  and
      Huet, St{\'e}phane  and
      Jabaian, Bassam  and
      Lef{\`e}vre, Fabrice",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6703",
    doi = "10.18653/v1/W18-6703",
    pages = "9--14",
}
@inproceedings{matsumoto-etal-2018-textual,
    title = "Textual Entailment based Question Generation",
    author = "Matsumoto, Takaaki  and
      Hasegawa, Kimihiro  and
      Yamakawa, Yukari  and
      Mitamura, Teruko",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6704",
    doi = "10.18653/v1/W18-6704",
    pages = "15--19",
}
@inproceedings{gopalakrishna-pillai-etal-2018-trouble,
    title = "Trouble on the Road: Finding Reasons for Commuter Stress from Tweets",
    author = "Gopalakrishna Pillai, Reshmi  and
      Thelwall, Mike  and
      Orasan, Constantin",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6705",
    doi = "10.18653/v1/W18-6705",
    pages = "20--25",
}
@inproceedings{lee-etal-2018-assisted,
    title = "Assisted Nominalization for Academic {E}nglish Writing",
    author = "Lee, John  and
      Saberi, Dariush  and
      Lam, Marvin  and
      Webster, Jonathan",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6706",
    doi = "10.18653/v1/W18-6706",
    pages = "26--30",
}
@inproceedings{kim-etal-2018-two,
    title = "Two-Step Training and Mixed Encoding-Decoding for Implementing a Generative Chatbot with a Small Dialogue Corpus",
    author = "Kim, Jintae  and
      Lee, Hyeon-Gu  and
      Kim, Harksoo  and
      Lee, Yeonsoo  and
      Kim, Young-Gil",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6707",
    doi = "10.18653/v1/W18-6707",
    pages = "31--35",
}
@inproceedings{catala-etal-2018-supporting,
    title = "Supporting Content Design with an Eye Tracker: The Case of Weather-based Recommendations",
    author = "Catala, Alejandro  and
      Alonso, Jose M.  and
      Bugarin, Alberto",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6708",
    doi = "10.18653/v1/W18-6708",
    pages = "36--41",
}
@inproceedings{sedoc-etal-2018-chateval,
    title = "{C}hat{E}val: A Tool for the Systematic Evaluation of Chatbots",
    author = "Sedoc, Jo{\~a}o  and
      Ippolito, Daphne  and
      Kirubarajan, Arun  and
      Thirani, Jai  and
      Ungar, Lyle  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6709",
    doi = "10.18653/v1/W18-6709",
    pages = "42--44",
}
@inproceedings{anselma-etal-2018-checkyourmeal,
    title = "{C}heck{Y}our{M}eal!: diet management with {NLG}",
    author = "Anselma, Luca  and
      Donetti, Simone  and
      Mazzei, Alessandro  and
      Pirone, Andrea",
    booktitle = "Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2{IS}{\&}{NLG})",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6710",
    doi = "10.18653/v1/W18-6710",
    pages = "45--47",
}
@proceedings{ws-2018-creativity,
    title = "Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2018)",
    editor = "Gon{\c{c}}alo Oliveira, Hugo  and
      Burtenshaw, Ben  and
      Herv{\'a}s, Raquel",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6600",
}
@inproceedings{burtenshaw-2018-brief,
    title = "A Brief Introduction to Natural Language Generation within Computational Creativity",
    author = "Burtenshaw, Ben",
    booktitle = "Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2018)",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6601",
    doi = "10.18653/v1/W18-6601",
    pages = "2--4",
}
@inproceedings{ferreira-goncalo-oliveira-2018-seeking,
    title = "Seeking the Ideal Narrative Model for Computer-Generated Narratives",
    author = "Ferreira, Mariana  and
      Gon{\c{c}}alo Oliveira, Hugo",
    booktitle = "Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2018)",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6602",
    doi = "10.18653/v1/W18-6602",
    pages = "5--10",
}
@inproceedings{berov-standvoss-2018-discourse,
    title = "Discourse Embellishment Using a Deep Encoder-Decoder Network",
    author = "Berov, Leonid  and
      Standvoss, Kai",
    booktitle = "Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2018)",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6603",
    doi = "10.18653/v1/W18-6603",
    pages = "11--16",
}
@inproceedings{goncalo-oliveira-rodrigues-2018-exploring,
    title = "Exploring Lexical-Semantic Knowledge in the Generation of Novel Riddles in {P}ortuguese",
    author = "Gon{\c{c}}alo Oliveira, Hugo  and
      Rodrigues, Ricardo",
    booktitle = "Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2018)",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6604",
    doi = "10.18653/v1/W18-6604",
    pages = "17--25",
}
@inproceedings{doust-gervas-2018-content,
    title = "Content Determination for Chess as a Source for Suspenseful Narratives",
    author = "Doust, Richard  and
      Gerv{\'a}s, Pablo",
    booktitle = "Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2018)",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6605",
    doi = "10.18653/v1/W18-6605",
    pages = "26--33",
}
@inproceedings{tapscott-etal-2018-generating,
    title = "Generating Stories Using Role-playing Games and Simulated Human-like Conversations",
    author = "Tapscott, Alan  and
      Le{\'o}n, Carlos  and
      Gerv{\'a}s, Pablo",
    booktitle = "Proceedings of the 3rd Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2018)",
    month = nov,
    year = "2018",
    address = "Tilburg, the Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6606",
    doi = "10.18653/v1/W18-6606",
    pages = "34--42",
}
@proceedings{ws-2018-international-natural-language,
    title = "Proceedings of the 11th International Conference on Natural Language Generation",
    editor = "Krahmer, Emiel  and
      Gatt, Albert  and
      Goudbeek, Martijn",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6500",
}
@inproceedings{marcheggiani-perez-beltrachini-2018-deep,
    title = "Deep Graph Convolutional Encoders for Structured Data to Text Generation",
    author = "Marcheggiani, Diego  and
      Perez-Beltrachini, Laura",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6501",
    doi = "10.18653/v1/W18-6501",
    pages = "1--9",
    abstract = "Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.",
}
@inproceedings{wang-etal-2018-describing,
    title = "Describing a Knowledge Base",
    author = "Wang, Qingyun  and
      Pan, Xiaoman  and
      Huang, Lifu  and
      Zhang, Boliang  and
      Jiang, Zhiying  and
      Ji, Heng  and
      Knight, Kevin",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6502",
    doi = "10.18653/v1/W18-6502",
    pages = "10--21",
    abstract = "We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) \textit{slot-aware attention} to capture the association between a slot type and its corresponding slot value; and (ii) a new \textit{table position self-attention} to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a \textit{KB reconstruction} based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8{\%} - 72.6{\%} F-score.",
}
@inproceedings{deriu-cieliebak-2018-syntactic,
    title = "Syntactic Manipulation for Generating more Diverse and Interesting Texts",
    author = "Deriu, Jan Milan  and
      Cieliebak, Mark",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6503",
    doi = "10.18653/v1/W18-6503",
    pages = "22--34",
    abstract = "Natural Language Generation plays an important role in the domain of dialogue systems as it determines how users perceive the system. Recently, deep-learning based systems have been proposed to tackle this task, as they generalize better and require less amounts of manual effort to implement them for new domains. However, deep learning systems usually adapt a very homogeneous sounding writing style which expresses little variation. In this work, we present our system for Natural Language Generation where we control various aspects of the surface realization in order to increase the lexical variability of the utterances, such that they sound more diverse and interesting. For this, we use a Semantically Controlled Long Short-term Memory Network (SC-LSTM), and apply its specialized cell to control various syntactic features of the generated texts. We present an in-depth human evaluation where we show the effects of these surface manipulation on the perception of potential users.",
}
@inproceedings{van-der-lee-etal-2018-automated,
    title = "Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods",
    author = "van der Lee, Chris  and
      Krahmer, Emiel  and
      Wubben, Sander",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6504",
    doi = "10.18653/v1/W18-6504",
    pages = "35--45",
    abstract = "The current study investigated novel techniques and methods for trainable approaches to data-to-text generation. Neural Machine Translation was explored for the conversion from data to text as well as the addition of extra templatization steps of the data input and text output in the conversion process. Evaluation using BLEU did not find the Neural Machine Translation technique to perform any better compared to rule-based or Statistical Machine Translation, and the templatization method seemed to perform similarly or sometimes worse compared to direct data-to-text conversion. However, the human evaluation metrics indicated that Neural Machine Translation yielded the highest quality output and that the templatization method was able to increase text quality in multiple situations.",
}
@inproceedings{gehrmann-etal-2018-end,
    title = "End-to-End Content and Plan Selection for Data-to-Text Generation",
    author = "Gehrmann, Sebastian  and
      Dai, Falcon  and
      Elder, Henry  and
      Rush, Alexander",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6505",
    doi = "10.18653/v1/W18-6505",
    pages = "46--56",
    abstract = "Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training. An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation.",
}
@inproceedings{chen-etal-2018-simplenlg,
    title = "{S}imple{NLG}-{ZH}: a Linguistic Realisation Engine for {M}andarin",
    author = "Chen, Guanyi  and
      van Deemter, Kees  and
      Lin, Chenghua",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6506",
    doi = "10.18653/v1/W18-6506",
    pages = "57--66",
    abstract = "We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG (Gatt and Reiter, 2009). We explain the core grammar (morphology and syntax) and the lexicon of SimpleNLG-ZH, which is very different from English and other languages for which SimpleNLG engines have been built. The system was evaluated by regenerating expressions from a body of test sentences and a corpus of human-authored expressions. Human evaluation was conducted to estimate the quality of regenerated sentences.",
}
@inproceedings{cascallar-fuentes-etal-2018-adapting,
    title = "Adapting {S}imple{NLG} to {G}alician language",
    author = "Cascallar-Fuentes, Andrea  and
      Ramos-Soto, Alejandro  and
      Bugar{\'\i}n Diz, Alberto",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6507",
    doi = "10.18653/v1/W18-6507",
    pages = "67--72",
    abstract = "In this paper, we describe SimpleNLG-GL, an adaptation of the linguistic realisation SimpleNLG library for the Galician language. This implementation is derived from SimpleNLG-ES, the English-Spanish version of this library. It has been tested using a battery of examples which covers the most common rules for Galician.",
}
@inproceedings{de-jong-theune-2018-going,
    title = "Going {D}utch: Creating {S}imple{NLG}-{NL}",
    author = {de Jong, Ruud  and
      Theune, Mari{\"e}t},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6508",
    doi = "10.18653/v1/W18-6508",
    pages = "73--78",
    abstract = "This paper presents SimpleNLG-NL, an adaptation of the SimpleNLG surface realisation engine for the Dutch language. It describes a novel method for determining and testing the grammatical constructions to be implemented, using target sentences sampled from a treebank.",
}
@inproceedings{chen-etal-2018-learning,
    title = "Learning to Flip the Bias of News Headlines",
    author = "Chen, Wei-Fan  and
      Wachsmuth, Henning  and
      Al-Khatib, Khalid  and
      Stein, Benno",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6509",
    doi = "10.18653/v1/W18-6509",
    pages = "79--88",
    abstract = "This paper introduces the task of {``}flipping{''} the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias. To study this task, we create a corpus with bias-labeled articles from \textit{all-sides.com}. As a first step, we analyze the corpus and discuss intrinsic characteristics of bias. They point to the main challenges of bias flipping, which in turn lead to a specific setting in the generation process. The paper in hand narrows down the general bias flipping task to focus on bias flipping for news article \textit{headlines}. A manual annotation of headlines from each side reveals that they are self-informative in general and often convey bias. We apply an autoencoder incorporating information from an article{'}s content to learn how to automatically flip the bias. From 200 generated headlines, 73 are classified as understandable by annotators, and 83 maintain the topic while having opposite bias. Insights from our analysis shed light on how to solve the main challenges of bias flipping.",
}
@inproceedings{fikri-etal-2018-stylistically,
    title = "Stylistically User-Specific Generation",
    author = "Fikri, Abdurrisyad  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6510",
    doi = "10.18653/v1/W18-6510",
    pages = "89--98",
    abstract = "Recent neural models for response generation show good results in terms of general responses. In real conversations, however, depending on the speaker/responder, similar utterances should require different responses. In this study, we attempt to consider individual user{'}s information in adjusting the notable sequence-to-sequence (seq2seq) model for more diverse, user-specific responses. We assume that we need user-specific features to adjust the response and we argue that some selected representative words from the users are suitable for this task. Furthermore, we prove that even for unseen or unknown users, our model can provide more diverse and interesting responses, while maintaining correlation with input utterances. Experimental results with human evaluation show that our model can generate more interesting responses than the popular seq2seqmodel and achieve higher relevance with input utterances than our baseline.",
}
@inproceedings{chiyah-garcia-etal-2018-explainable,
    title = "Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models",
    author = "Chiyah Garcia, Francisco Javier  and
      Robb, David A.  and
      Liu, Xingkun  and
      Laskov, Atanas  and
      Patron, Pedro  and
      Hastie, Helen",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6511",
    doi = "10.18653/v1/W18-6511",
    pages = "99--108",
    abstract = "As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert {`}speak aloud{'} and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.",
}
@inproceedings{groves-etal-2018-treat,
    title = "Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts",
    author = "Groves, Isabel  and
      Tian, Ye  and
      Douratsos, Ioannis",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6512",
    doi = "10.18653/v1/W18-6512",
    pages = "109--118",
    abstract = "The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability. We draw inspiration from second language (L2) assessment and extract a set of linguistic features to predict human judgments of sentence naturalness. Our experiment using a small dataset showed that the feature-based approach yields promising results, with the added potential of providing interpretability into the source of the problems.",
}
@inproceedings{loyola-etal-2018-content,
    title = "Content Aware Source Code Change Description Generation",
    author = "Loyola, Pablo  and
      Marrese-Taylor, Edison  and
      Balazs, Jorge  and
      Matsuo, Yutaka  and
      Satoh, Fumiko",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6513",
    doi = "10.18653/v1/W18-6513",
    pages = "119--128",
    abstract = "We propose to study the generation of descriptions from source code changes by integrating the messages included on code commits and the intra-code documentation inside the source in the form of docstrings. Our hypothesis is that although both types of descriptions are not directly aligned in semantic terms {---}one explaining a change and the other the actual functionality of the code being modified{---} there could be certain common ground that is useful for the generation. To this end, we propose an architecture that uses the source code-docstring relationship to guide the description generation. We discuss the results of the approach comparing against a baseline based on a sequence-to-sequence model, using standard automatic natural language generation metrics as well as with a human study, thus offering a comprehensive view of the feasibility of the approach.",
}
@inproceedings{agarwal-etal-2018-improving,
    title = "Improving Context Modelling in Multimodal Dialogue Generation",
    author = "Agarwal, Shubham  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Konstas, Ioannis  and
      Rieser, Verena",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6514",
    doi = "10.18653/v1/W18-6514",
    pages = "129--134",
    abstract = "In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system. Our work is based on the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017) in the fashion domain. We introduce a multimodal extension to the Hierarchical Recurrent Encoder-Decoder (HRED) model and show that this extension outperforms strong baselines in terms of text-based similarity metrics. We also showcase the shortcomings of current vision and language models by performing an error analysis on our system{'}s output.",
}
@inproceedings{aoki-etal-2018-generating,
    title = "Generating Market Comments Referring to External Resources",
    author = "Aoki, Tatsuya  and
      Miyazawa, Akira  and
      Ishigaki, Tatsuya  and
      Goshima, Keiichi  and
      Aoki, Kasumi  and
      Kobayashi, Ichiro  and
      Takamura, Hiroya  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6515",
    doi = "10.18653/v1/W18-6515",
    pages = "135--139",
    abstract = "Comments on a stock market often include the reason or cause of changes in stock prices, such as {``}Nikkei turns lower as yen{'}s rise hits exporters.{''} Generating such informative sentences requires capturing the relationship between different resources, including a target stock price. In this paper, we propose a model for automatically generating such informative market comments that refer to external resources. We evaluated our model through an automatic metric in terms of BLEU and human evaluation done by an expert in finance. The results show that our model outperforms the existing model both in BLEU scores and human judgment.",
}
@inproceedings{belz-etal-2018-spatialvoc2k,
    title = "{S}patial{VOC}2{K}: A Multilingual Dataset of Images with Annotations and Features for Spatial Relations between Objects",
    author = "Belz, Anja  and
      Muscat, Adrian  and
      Anguill, Pierre  and
      Sow, Mouhamadou  and
      Vincent, Ga{\'e}tan  and
      Zinessabah, Yassine",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6516",
    doi = "10.18653/v1/W18-6516",
    pages = "140--145",
    abstract = "We present SpatialVOC2K, the first multilingual image dataset with spatial relation annotations and object features for image-to-text generation, built using 2,026 images from the PASCAL VOC2008 dataset. The dataset incorporates (i) the labelled object bounding boxes from VOC2008, (ii) geometrical, language and depth features for each object, and (iii) for each pair of objects in both orders, (a) the single best preposition and (b) the set of possible prepositions in the given language that describe the spatial relationship between the two objects. Compared to previous versions of the dataset, we have roughly doubled the size for French, and completely reannotated as well as increased the size of the English portion, providing single best prepositions for English for the first time. Furthermore, we have added explicit 3D depth features for objects. We are releasing our dataset for free reuse, along with evaluation tools to enable comparative evaluation.",
}
@inproceedings{birmingham-etal-2018-adding,
    title = "Adding the Third Dimension to Spatial Relation Detection in 2{D} Images",
    author = "Birmingham, Brandon  and
      Muscat, Adrian  and
      Belz, Anja",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6517",
    doi = "10.18653/v1/W18-6517",
    pages = "146--151",
    abstract = "Detection of spatial relations between objects in images is currently a popular subject in image description research. A range of different language and geometric object features have been used in this context, but methods have not so far used explicit information about the third dimension (depth), except when manually added to annotations. The lack of such information hampers detection of spatial relations that are inherently 3D. In this paper, we use a fully automatic method for creating a depth map of an image and derive several different object-level depth features from it which we add to an existing feature set to test the effect on spatial relation detection. We show that performance increases are obtained from adding depth features in all scenarios tested.",
}
@inproceedings{chali-baghaee-2018-automatic,
    title = "Automatic Opinion Question Generation",
    author = "Chali, Yllias  and
      Baghaee, Tina",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6518",
    doi = "10.18653/v1/W18-6518",
    pages = "152--158",
    abstract = "We study the problem of opinion question generation from sentences with the help of community-based question answering systems. For this purpose, we use a sequence to sequence attentional model, and we adopt coverage mechanism to prevent sentences from repeating themselves. Experimental results on the Amazon question/answer dataset show an improvement in automatic evaluation metrics as well as human evaluations from the state-of-the-art question generation systems.",
}
@inproceedings{chen-etal-2018-modelling,
    title = "Modelling Pro-drop with the Rational Speech Acts Model",
    author = "Chen, Guanyi  and
      van Deemter, Kees  and
      Lin, Chenghua",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6519",
    doi = "10.18653/v1/W18-6519",
    pages = "159--164",
    abstract = "We extend the classic Referring Expressions Generation task by considering zero pronouns in {``}pro-drop{''} languages such as Chinese, modelling their use by means of the Bayesian Rational Speech Acts model (Frank and Goodman, 2012). By assuming that highly salient referents are most likely to be referred to by zero pronouns (i.e., pro-drop is more likely for salient referents than the less salient ones), the model offers an attractive explanation of a phenomenon not previously addressed probabilistically.",
}
@inproceedings{choi-etal-2018-self,
    title = "Self-Learning Architecture for Natural Language Generation",
    author = "Choi, Hyungtak  and
      K.M., Siddarth  and
      Yang, Haehun  and
      Jeon, Heesik  and
      Hwang, Inchul  and
      Kim, Jihie",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6520",
    doi = "10.18653/v1/W18-6520",
    pages = "165--170",
    abstract = "In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants. Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive. We examine three different models based on our proposed architecture - Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain - to reduce the human labor required for template generation. We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture. In both automatic and human evaluation, the self-learning architecture outperforms previous works trained with a fully human-labeled dataset. This is promising for commercial conversational assistant solutions.",
}
@inproceedings{castro-ferreira-etal-2018-enriching,
    title = "Enriching the {W}eb{NLG} corpus",
    author = "Castro Ferreira, Thiago  and
      Moussallem, Diego  and
      Krahmer, Emiel  and
      Wubben, Sander",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6521",
    doi = "10.18653/v1/W18-6521",
    pages = "171--176",
    abstract = "This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a,b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation. We also produce a silver-standard German translation of the corpus to enable the exploitation of NLG approaches to other languages than English. The enriched corpus is publicly available.",
}
@inproceedings{forrest-etal-2018-towards,
    title = "Towards making {NLG} a voice for interpretable Machine Learning",
    author = "Forrest, James  and
      Sripada, Somayajulu  and
      Pang, Wei  and
      Coghill, George",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6522",
    doi = "10.18653/v1/W18-6522",
    pages = "177--182",
    abstract = "This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning framework called LIME. Our study shows that self-reported rating of NLG explanation was higher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clear-cut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations.",
}
@inproceedings{gatti-etal-2018-template,
    title = "Template-based multilingual football reports generation using {W}ikidata as a knowledge base",
    author = {Gatti, Lorenzo  and
      van der Lee, Chris  and
      Theune, Mari{\"e}t},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6523",
    doi = "10.18653/v1/W18-6523",
    pages = "183--188",
    abstract = "This paper presents a new version of a football reports generation system called PASS. The original version generated Dutch text and relied on a limited hand-crafted knowledge base. We describe how, in a short amount of time, we extended PASS to produce English texts, exploiting machine translation and Wikidata as a large-scale source of multilingual knowledge.",
}
@inproceedings{xing-fernandez-2018-automatic,
    title = "Automatic Evaluation of Neural Personality-based Chatbots",
    author = "Xing, Yujie  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6524",
    doi = "10.18653/v1/W18-6524",
    pages = "189--194",
    abstract = "Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging. In this paper, we focus on sequence-to-sequence models for open-domain dialogue response generation and propose a new method to evaluate the extent to which such models are able to generate responses that reflect different personality traits.",
}
@inproceedings{hamalainen-2018-poem,
    title = "Poem Machine - a Co-creative {NLG} Web Application for Poem Writing",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6525",
    doi = "10.18653/v1/W18-6525",
    pages = "195--196",
    abstract = "We present Poem Machine, an interactive online tool for co-authoring Finnish poetry with a computationally creative agent. Poem Machine can produce poetry of its own and assist the user in authoring poems. The main target group for the system is primary school children, and its use as a part of teaching is currently under study.",
}
@inproceedings{iwama-kano-2018-japanese,
    title = "{J}apanese Advertising Slogan Generator using Case Frame and Word Vector",
    author = "Iwama, Kango  and
      Kano, Yoshinobu",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6526",
    doi = "10.18653/v1/W18-6526",
    pages = "197--198",
    abstract = "There has been many works published for automatic sentence generation of a variety of domains. However, there would be still no single method available at present that can generate sentences for all of domains. Each domain will require a suitable generation method. We focus on automatic generation of Japanese advertisement slogans in this paper. We use our advertisement slogan database, case frame information, and word vector information. We employed our system to apply for a copy competition for human copywriters, where our advertisement slogan was left as a finalist. Our system could be regarded as the world first system that generates slogans in a practical level, as an advertising agency already employs our system in their business.",
}
@inproceedings{mille-etal-2018-underspecified,
    title = "Underspecified {U}niversal {D}ependency Structures as Inputs for Multilingual Surface Realisation",
    author = "Mille, Simon  and
      Belz, Anja  and
      Bohnet, Bernd  and
      Wanner, Leo",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6527",
    doi = "10.18653/v1/W18-6527",
    pages = "199--209",
    abstract = "In this paper, we present the datasets used in the Shallow and Deep Tracks of the First Multilingual Surface Realisation Shared Task (SR{'}18). For the Shallow Track, data in ten languages has been released: Arabic, Czech, Dutch, English, Finnish, French, Italian, Portuguese, Russian and Spanish. For the Deep Track, data in three languages is made available: English, French and Spanish. We describe in detail how the datasets were derived from the Universal Dependencies V2.0, and report on an evaluation of the Deep Track input quality. In addition, we examine the motivation for, and likely usefulness of, deriving NLG inputs from annotations in resources originally developed for Natural Language Understanding (NLU), and assess whether the resulting inputs supply enough information of the right kind for the final stage in the NLG process.",
}
@inproceedings{fu-white-2018-lstm,
    title = "{LSTM} Hypertagging",
    author = "Fu, Reid  and
      White, Michael",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6528",
    doi = "10.18653/v1/W18-6528",
    pages = "210--220",
    abstract = "Hypertagging, or supertagging for surface realization, is the process of assigning lexical categories to nodes in an input semantic graph. Previous work has shown that hypertagging significantly increases realization speed and quality by reducing the search space of the realizer. Building on recent work using LSTMs to improve accuracy on supertagging for parsing, we develop an LSTM hypertagging method for OpenCCG, an open source NLP toolkit for CCG. Our results show significant improvements in both hypertagging accuracy and downstream realization performance.",
}
@inproceedings{jagfeld-etal-2018-sequence,
    title = "Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity",
    author = "Jagfeld, Glorianna  and
      Jenne, Sabrina  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6529",
    doi = "10.18653/v1/W18-6529",
    pages = "221--232",
    abstract = "We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs. On the datasets of two recent generation challenges, our models achieve comparable or better automatic evaluation results than the best challenge submissions. Subsequent detailed statistical and human analyses shed light on the differences between the two input representations and the diversity of the generated texts. In a controlled experiment with synthetic training data generated from templates, we demonstrate the ability of neural models to learn novel combinations of the templates and thereby generalize beyond the linguistic structures they were trained on.",
}
@inproceedings{camargo-de-souza-etal-2018-generating,
    title = "Generating {E}-Commerce Product Titles and Predicting their Quality",
    author = "Camargo de Souza, Jos{\'e} G.  and
      Kozielski, Michael  and
      Mathur, Prashant  and
      Chang, Ernie  and
      Guerini, Marco  and
      Negri, Matteo  and
      Turchi, Marco  and
      Matusov, Evgeny",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6530",
    doi = "10.18653/v1/W18-6530",
    pages = "233--243",
    abstract = "E-commerce platforms present products using titles that summarize product information. These titles cannot be created by hand, therefore an algorithmic solution is required. The task of automatically generating these titles given noisy user provided titles is one way to achieve the goal. The setting requires the generation process to be fast and the generated title to be both human-readable and concise. Furthermore, we need to understand if such generated titles are usable. As such, we propose approaches that (i) automatically generate product titles, (ii) predict their quality. Our approach scales to millions of products and both automatic and human evaluations performed on real-world data indicate our approaches are effective and applicable to existing e-commerce scenarios.",
}
@inproceedings{anselma-mazzei-2018-designing,
    title = "Designing and testing the messages produced by a virtual dietitian",
    author = "Anselma, Luca  and
      Mazzei, Alessandro",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6531",
    doi = "10.18653/v1/W18-6531",
    pages = "244--253",
    abstract = "This paper presents a project about the automatic generation of persuasive messages in the context of the diet management. In the first part of the paper we introduce the basic mechanisms related to data interpretation and content selection for a numerical data-to-text generation architecture. In the second part of the paper we discuss a number of factors influencing the design of the messages. In particular, we consider the design of the aggregation procedure. Finally, we present the results of a human-based evaluation concerning this design factor.",
}
@inproceedings{qader-etal-2018-generation,
    title = "Generation of Company descriptions using concept-to-text and text-to-text deep models: dataset collection and systems evaluation",
    author = "Qader, Raheel  and
      Jneid, Khoder  and
      Portet, Fran{\c{c}}ois  and
      Labb{\'e}, Cyril",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6532",
    doi = "10.18653/v1/W18-6532",
    pages = "254--263",
    abstract = "In this paper we study the performance of several state-of-the-art sequence-to-sequence models applied to generation of short company descriptions. The models are evaluated on a newly created and publicly available company dataset that has been collected from Wikipedia. The dataset consists of around 51K company descriptions that can be used for both concept-to-text and text-to-text generation tasks. Automatic metrics and human evaluation scores computed on the generated company descriptions show promising results despite the difficulty of the task as the dataset (like most available datasets) has not been originally designed for machine learning. In addition, we perform correlation analysis between automatic metrics and human evaluations and show that certain automatic metrics are more correlated to human judgments.",
}
@inproceedings{parde-nielsen-2018-automatically,
    title = "Automatically Generating Questions about Novel Metaphors in Literature",
    author = "Parde, Natalie  and
      Nielsen, Rodney",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6533",
    doi = "10.18653/v1/W18-6533",
    pages = "264--273",
    abstract = "The automatic generation of stimulating questions is crucial to the development of intelligent cognitive exercise applications. We developed an approach that generates appropriate \textit{Questioning the Author} queries based on novel metaphors in diverse syntactic relations in literature. We show that the generated questions are comparable to human-generated questions in terms of naturalness, sensibility, and depth, and score slightly higher than human-generated questions in terms of clarity. We also show that questions generated about novel metaphors are rated as cognitively deeper than questions generated about non- or conventional metaphors, providing evidence that metaphor novelty can be leveraged to promote cognitive exercise.",
}
@inproceedings{alnajjar-hamalainen-2018-master,
    title = "A Master-Apprentice Approach to Automatic Creation of Culturally Satirical Movie Titles",
    author = {Alnajjar, Khalid  and
      H{\"a}m{\"a}l{\"a}inen, Mika},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6534",
    doi = "10.18653/v1/W18-6534",
    pages = "274--283",
    abstract = "Satire has played a role in indirectly expressing critique towards an authority or a person from time immemorial. We present an autonomously creative master-apprentice approach consisting of a genetic algorithm and an NMT model to produce humorous and culturally apt satire out of movie titles automatically. Furthermore, we evaluate the approach in terms of its creativity and its output. We provide a solid definition for creativity to maximize the objectiveness of the evaluation.",
}
@inproceedings{reed-etal-2018-neural,
    title = "Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?",
    author = "Reed, Lena  and
      Oraby, Shereen  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6535",
    doi = "10.18653/v1/W18-6535",
    pages = "284--295",
    abstract = "Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. For example a recommendation may consist of an explicitly evaluative utterance e.g. \textit{Chanpen Thai is the best option}, along with content related by the justification discourse relation, e.g. \textit{It has great food and service}, that combines multiple propositions into a single phrase. While neural generation methods integrate sentence planning and surface realization in one end-to-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training. We systematically create large training corpora that exhibit particular sentence planning operations and then test neural models to see what they learn. We compare models without explicit latent variables for sentence planning with ones that provide explicit supervision during training. We show that only the models with additional supervision can reproduce sentence planning and discourse operations and generalize to situations unseen in training.",
}
@inproceedings{harrison-walker-2018-neural,
    title = "Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features",
    author = "Harrison, Vrindavan  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6536",
    doi = "10.18653/v1/W18-6536",
    pages = "296--306",
    abstract = "Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder{--}Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu{\_}4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that the added features improve the quality of the generated questions.",
}
@inproceedings{amidei-etal-2018-evaluation,
    title = "Evaluation methodologies in Automatic Question Generation 2013-2018",
    author = "Amidei, Jacopo  and
      Piwek, Paul  and
      Willis, Alistair",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6537",
    doi = "10.18653/v1/W18-6537",
    pages = "307--317",
    abstract = "In the last few years Automatic Question Generation (AQG) has attracted increasing interest. In this paper we survey the evaluation methodologies used in AQG. Based on a sample of 37 papers, our research shows that the systems{'} development has not been accompanied by similar developments in the methodologies used for the systems{'} evaluation. Indeed, in the papers we examine here, we find a wide variety of both intrinsic and extrinsic evaluation methodologies. Such diverse evaluation practices make it difficult to reliably compare the quality of different generation systems. Our study suggests that, given the rapidly increasing level of research in the area, a common framework is urgently needed to compare the performance of AQG systems and NLG systems more generally.",
}
@inproceedings{syed-etal-2018-task,
    title = "Task Proposal: The {TL};{DR} Challenge",
    author = {Syed, Shahbaz  and
      V{\"o}lske, Michael  and
      Potthast, Martin  and
      Lipka, Nedim  and
      Stein, Benno  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6538",
    doi = "10.18653/v1/W18-6538",
    pages = "318--321",
    abstract = "The TL;DR challenge fosters research in abstractive summarization of informal text, the largest and fastest-growing source of textual data on the web, which has been overlooked by summarization research so far. The challenge owes its name to the frequent practice of social media users to supplement long posts with a {``}TL;DR{''}{---}for {``}too long; didn{'}t read{''}{---}followed by a short summary as a courtesy to those who would otherwise reply with the exact same abbreviation to indicate they did not care to read a post for its apparent length. Posts featuring TL;DR summaries form an excellent ground truth for summarization, and by tapping into this resource for the first time, we have mined millions of training examples from social media, opening the door to all kinds of generative models.",
}
@inproceedings{dusek-etal-2018-findings,
    title = "Findings of the {E}2{E} {NLG} Challenge",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Novikova, Jekaterina  and
      Rieser, Verena",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6539",
    doi = "10.18653/v1/W18-6539",
    pages = "322--328",
    abstract = "This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-end generation systems are promising since they reduce the need for data annotation. However, they are currently limited to small, delexicalised datasets. The E2E NLG shared task aims to assess whether these novel approaches can generate better-quality output by learning from a dataset containing higher lexical richness, syntactic complexity and diverse discourse phenomena. We compare 62 systems submitted by 17 institutions, covering a wide range of approaches, including machine learning architectures {--} with the majority implementing sequence-to-sequence models (seq2seq) {--} as well as systems based on grammatical rules and templates.",
}
@inproceedings{mendez-etal-2018-adapting,
    title = "Adapting Descriptions of People to the Point of View of a Moving Observer",
    author = "M{\'e}ndez, Gonzalo  and
      Herv{\'a}s, Raquel  and
      Gerv{\'a}s, Pablo  and
      de la Rosa, Ricardo  and
      Ruiz, Daniel",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6540",
    doi = "10.18653/v1/W18-6540",
    pages = "329--338",
    abstract = "This paper addresses the task of generating descriptions of people for an observer that is moving within a scene. As the observer moves, the descriptions of the people around him also change. A referring expression generation algorithm adapted to this task needs to continuously monitor the changes in the field of view of the observer, his relative position to the people being described, and the relative position of these people to any landmarks around them, and to take these changes into account in the referring expressions generated. This task presents two advantages: many of the mechanisms already available for static contexts may be applied with small adaptations, and it introduces the concept of changing conditions into the task of referring expression generation. In this paper we describe the design of an algorithm that takes these aspects into account in order to create descriptions of people within a 3D virtual environment. The evaluation of this algorithm has shown that, by changing the descriptions in real time according to the observers point of view, they are able to identify the described person quickly and effectively.",
}
@inproceedings{ngonga-ngomo-etal-2018-bengal,
    title = "{BENGAL}: An Automatic Benchmark Generator for Entity Recognition and Linking",
    author = {Ngonga Ngomo, Axel-Cyrille  and
      R{\"o}der, Michael  and
      Moussallem, Diego  and
      Usbeck, Ricardo  and
      Speck, Ren{\'e}},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6541",
    doi = "10.18653/v1/W18-6541",
    pages = "339--349",
    abstract = "The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present Bengal, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on benchmarks in English generated by Bengal and on 16 benchmarks created manually. We show that our approach can be ported easily across languages by presenting results achieved by 4 tools on both Brazilian Portuguese and Spanish. Overall, our results suggest that our automatic benchmark generation approach can create varied benchmarks that have characteristics similar to those of existing benchmarks. Our approach is open-source. Our experimental results are available at \url{http://faturl.com/bengalexpinlg} and the code at \url{https://github.com/dice-group/BENGAL}.",
}
@inproceedings{shvets-etal-2018-sentence,
    title = "Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem",
    author = "Shvets, Alexander  and
      Mille, Simon  and
      Wanner, Leo",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6542",
    doi = "10.18653/v1/W18-6542",
    pages = "350--359",
    abstract = "An increasing amount of research tackles the challenge of text generation from abstract ontological or semantic structures, which are in their very nature potentially large connected graphs. These graphs must be {``}packaged{''} into sentence-wise subgraphs. We interpret the problem of sentence packaging as a community detection problem with post optimization. Experiments on the texts of the VerbNet/FrameNet structure annotated-Penn Treebank, which have been converted into graphs by a coreference merge using Stanford CoreNLP, show a high F1-score of 0.738.",
}
@inproceedings{shimorina-gardent-2018-handling,
    title = "Handling Rare Items in Data-to-Text Generation",
    author = "Shimorina, Anastasia  and
      Gardent, Claire",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6543",
    doi = "10.18653/v1/W18-6543",
    pages = "360--370",
    abstract = "Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism. We investigate the relative impact of these two methods on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that rare items strongly impact performance; (ii) that combining delexicalisation and copying yields the strongest improvement; (iii) that copying underperforms for rare and unseen items and (iv) that the impact of these two mechanisms greatly varies depending on how the dataset is constructed and on how it is split into train, dev and test.",
}
@inproceedings{thomson-etal-2018-comprehension,
    title = "Comprehension Driven Document Planning in Natural Language Generation Systems",
    author = "Thomson, Craig  and
      Reiter, Ehud  and
      Sripada, Somayajulu",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6544",
    doi = "10.18653/v1/W18-6544",
    pages = "371--380",
    abstract = "This paper proposes an approach to NLG system design which focuses on generating output text which can be more easily processed by the reader. Ways in which cognitive theory might be combined with existing NLG techniques are discussed and two simple experiments in content ordering are presented.",
}
@inproceedings{zhang-etal-2018-adapting,
    title = "Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study",
    author = "Zhang, Jianmin  and
      Tan, Jiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6545",
    doi = "10.18653/v1/W18-6545",
    pages = "381--390",
    abstract = "Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied to multi-document summarization (MDS). In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS. We propose an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task. Our approach only makes use of a small number of multi-document summaries for fine tuning. Experimental results on two benchmark DUC datasets demonstrate that our approach can outperform a variety of baseline neural models.",
}
@inproceedings{howcroft-etal-2018-toward,
    title = "Toward {B}ayesian Synchronous Tree Substitution Grammars for Sentence Planning",
    author = "Howcroft, David M.  and
      Klakow, Dietrich  and
      Demberg, Vera",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6546",
    doi = "10.18653/v1/W18-6546",
    pages = "391--396",
    abstract = "Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets.",
}
@inproceedings{ilinykh-etal-2018-task,
    title = "The Task Matters: Comparing Image Captioning and Task-Based Dialogical Image Description",
    author = "Ilinykh, Nikolai  and
      Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6547",
    doi = "10.18653/v1/W18-6547",
    pages = "397--402",
    abstract = "Image captioning models are typically trained on data that is collected from people who are asked to describe an image, without being given any further task context. As we argue here, this context independence is likely to cause problems for transferring to task settings in which image description is bound by task demands. We demonstrate that careful design of data collection is required to obtain image descriptions which are contextually bounded to a particular meta-level task. As a task, we use MeetUp!, a text-based communication game where two players have the goal of finding each other in a visual environment. To reach this goal, the players need to describe images representing their current location. We analyse a dataset from this domain and show that the nature of image descriptions found in MeetUp! is diverse, dynamic and rich with phenomena that are not present in descriptions obtained through a simple image captioning task, which we ran for comparison.",
}
@inproceedings{kuptavanich-etal-2018-generating,
    title = "Generating Summaries of Sets of Consumer Products: Learning from Experiments",
    author = "Kuptavanich, Kittipitch  and
      Reiter, Ehud  and
      Van Deemter, Kees  and
      Siddharthan, Advaith",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6548",
    doi = "10.18653/v1/W18-6548",
    pages = "403--407",
    abstract = "We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset. When a set of consumer products is large and varied, it can be difficult for a consumer to understand how the products in the set differ; consequently, it can be challenging to choose the most suitable product from the set. To assist consumers, we generated high-level summaries of product sets. Two generation algorithms are presented, discussed, and evaluated with human users. Our evaluation results suggest a positive contribution to consumers{'} understanding of the domain.",
}
@inproceedings{manome-etal-2018-neural,
    title = "Neural sentence generation from formal semantics",
    author = "Manome, Kana  and
      Yoshikawa, Masashi  and
      Yanaka, Hitomi  and
      Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Mineshima, Koji  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6549",
    doi = "10.18653/v1/W18-6549",
    pages = "408--414",
    abstract = "Sequence-to-sequence models have shown strong performance in a wide range of NLP tasks, yet their applications to sentence generation from logical representations are underdeveloped. In this paper, we present a sequence-to-sequence model for generating sentences from logical meaning representations based on event semantics. We use a semantic parsing system based on Combinatory Categorial Grammar (CCG) to obtain data annotated with logical formulas. We augment our sequence-to-sequence model with masking for predicates to constrain output sentences. We also propose a novel evaluation method for generation using Recognizing Textual Entailment (RTE). Combining parsing and generation, we test whether or not the output sentence entails the original text and vice versa. Experiments showed that our model outperformed a baseline with respect to both BLEU scores and accuracies in RTE.",
}
@inproceedings{van-miltenburg-etal-2018-talking,
    title = "Talking about other people: an endless range of possibilities",
    author = "van Miltenburg, Emiel  and
      Elliott, Desmond  and
      Vossen, Piek",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6550",
    doi = "10.18653/v1/W18-6550",
    pages = "415--420",
    abstract = "Image description datasets, such as Flickr30K and MS COCO, show a high degree of variation in the ways that crowd-workers talk about the world. Although this gives us a rich and diverse collection of data to work with, it also introduces uncertainty about how the world should be described. This paper shows the extent of this uncertainty in the PEOPLE-domain. We present a taxonomy of different ways to talk about other people. This taxonomy serves as a reference point to think about how other people should be described, and can be used to classify and compute statistics about labels applied to people.",
}
@inproceedings{ramos-soto-etal-2018-meteorologists,
    title = "Meteorologists and Students: A resource for language grounding of geographical descriptors",
    author = "Ramos-Soto, Alejandro  and
      Reiter, Ehud  and
      van Deemter, Kees  and
      Alonso, Jose  and
      Gatt, Albert",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6551",
    doi = "10.18653/v1/W18-6551",
    pages = "421--425",
    abstract = "We present a data resource which can be useful for research purposes on language grounding tasks in the context of geographical referring expression generation. The resource is composed of two data sets that encompass 25 different geographical descriptors and a set of associated graphical representations, drawn as polygons on a map by two groups of human subjects: teenage students and expert meteorologists.",
}
@inproceedings{sharma-etal-2018-cyclegen,
    title = "{C}yclegen: Cyclic consistency based product review generator from attributes",
    author = "Sharma, Vasu  and
      Sharma, Harsh  and
      Bishnu, Ankita  and
      Patel, Labhesh",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6552",
    doi = "10.18653/v1/W18-6552",
    pages = "426--430",
    abstract = "In this paper we present an automatic review generator system which can generate personalized reviews based on the user identity, product identity and designated rating the user wishes to allot to the review. We combine this with a sentiment analysis system which performs the complimentary task of assigning ratings to reviews based purely on the textual content of the review. We introduce an additional loss term to ensure cyclic consistency of the sentiment rating of the generated review with the conditioning rating used to generate the review. The introduction of this new loss term constraints the generation space while forcing it to generate reviews adhering better to the requested rating. The use of {`}soft{'} generation and cyclic consistency allows us to train our model in an end to end fashion. We demonstrate the working of our model on product reviews from Amazon dataset.",
}
@inproceedings{song-etal-2018-neural,
    title = "Neural Transition-based Syntactic Linearization",
    author = "Song, Linfeng  and
      Zhang, Yue  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6553",
    doi = "10.18653/v1/W18-6553",
    pages = "431--440",
    abstract = "The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentence along with its syntactic tree, have shown state-of-the-art performance. Recent work shows that a multilayer LSTM language model outperforms competitive statistical syntactic linearization systems without using syntax. In this paper, we study neural syntactic linearization, building a transition-based syntactic linearizer leveraging a feed forward neural network, observing significantly better results compared to LSTM language models on this task.",
}
@inproceedings{juraska-walker-2018-characterizing,
    title = "Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs",
    author = "Juraska, Juraj  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6554",
    doi = "10.18653/v1/W18-6554",
    pages = "441--450",
    abstract = "One of the biggest challenges of end-to-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied. Here we take a large corpus of 50K crowd-sourced utterances in the restaurant domain and develop text analysis methods that systematically characterize types of sentences in the training data. We then automatically label the training data to allow us to conduct two kinds of experiments with a neural generator. First, we test the effect of training the system with different stylistic partitions and quantify the effect of smaller, but more stylistically controlled training data. Second, we propose a method of labeling the style variants during training, and show that we can modify the style of the generated utterances using our stylistic labels. We contrast and compare these methods that can be used with any existing large corpus, showing how they vary in terms of semantic quality and stylistic control.",
}
@inproceedings{agarwal-etal-2018-char2char,
    title = "Char2char Generation with Reranking for the {E}2{E} {NLG} Challenge",
    author = "Agarwal, Shubham  and
      Dymetman, Marc  and
      Gaussier, {\'E}ric",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6555",
    doi = "10.18653/v1/W18-6555",
    pages = "451--456",
    abstract = "This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing \textit{delexicalization} (relexicalization) steps at the word-level to handle rare words. By contrast, we train a simple character level seq2seq model, which requires no pre/post-processing (delexicalization, tokenization or even lowercasing), with surprisingly good results. For further improvement, we explore two re-ranking approaches for scoring candidates. We also introduce a synthetic dataset creation procedure, which opens up a new way of creating artificial datasets for Natural Language Generation.",
}
@inproceedings{elder-etal-2018-e2e,
    title = "{E}2{E} {NLG} Challenge Submission: Towards Controllable Generation of Diverse Natural Language",
    author = "Elder, Henry  and
      Gehrmann, Sebastian  and
      O{'}Connor, Alexander  and
      Liu, Qun",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6556",
    doi = "10.18653/v1/W18-6556",
    pages = "457--462",
    abstract = "In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.",
}
@inproceedings{puzikov-gurevych-2018-e2e,
    title = "{E}2{E} {NLG} Challenge: Neural Models vs. Templates",
    author = "Puzikov, Yevgeniy  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6557",
    doi = "10.18653/v1/W18-6557",
    pages = "463--471",
    abstract = "E2E NLG Challenge is a shared task on generating restaurant descriptions from sets of key-value pairs. This paper describes the results of our participation in the challenge. We develop a simple, yet effective neural encoder-decoder model which produces fluent restaurant descriptions and outperforms a strong baseline. We further analyze the data provided by the organizers and conclude that the task can also be approached with a template-based model developed in just a few hours.",
}
@inproceedings{smiley-etal-2018-e2e,
    title = "The {E}2{E} {NLG} Challenge: A Tale of Two Systems",
    author = "Smiley, Charese  and
      Davoodi, Elnaz  and
      Song, Dezhao  and
      Schilder, Frank",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6558",
    doi = "10.18653/v1/W18-6558",
    pages = "472--477",
    abstract = "This paper presents the two systems we entered into the 2017 E2E NLG Challenge: TemplGen, a templated-based system and SeqGen, a neural network-based system. Through the automatic evaluation, SeqGen achieved competitive results compared to the template-based approach and to other participating systems as well. In addition to the automatic evaluation, in this paper we present and discuss the human evaluation results of our two systems.",
}
@inproceedings{funke-etal-2018-interactive,
    title = "Interactive health insight miner: an adaptive, semantic-based approach",
    author = {Funke, Isabel  and
      Helaoui, Rim  and
      H{\"a}rm{\"a}, Aki},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6559",
    doi = "10.18653/v1/W18-6559",
    pages = "478--479",
    abstract = "E-health applications aim to support the user in adopting healthy habits. An important feature is to provide insights into the user{'}s lifestyle. To actively engage the user in the insight mining process, we propose an ontology-based framework with a Controlled Natural Language interface, which enables the user to ask for specific insights and to customize personal information.",
}
@inproceedings{madsack-etal-2018-multi,
    title = "Multi-Language Surface Realisation as {REST} {API} based {NLG} Microservice",
    author = {Madsack, Andreas  and
      Heininger, Johanna  and
      Davaasambuu, Nyamsuren  and
      Voronik, Vitaliia  and
      K{\"a}ufl, Michael  and
      Wei{\ss}graeber, Robert},
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6560",
    doi = "10.18653/v1/W18-6560",
    pages = "480--481",
    abstract = "We present a readily available API that solves the morphology component for surface realizers in 10 languages (e.g., English, German and Finnish) for any topic and is available as REST API. This can be used to add morphology to any kind of NLG application (e.g., a multi-language chatbot), without requiring computational linguistic knowledge by the integrator.",
}
@inproceedings{li-etal-2018-statistical,
    title = "Statistical {NLG} for Generating the Content and Form of Referring Expressions",
    author = "Li, Xiao  and
      van Deemter, Kees  and
      Lin, Chenghua",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6561",
    doi = "10.18653/v1/W18-6561",
    pages = "482--491",
    abstract = "This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully. The model does not only select attributes and values for referring to a target referent, but also performs Linguistic Realisation, generating an actual Noun Phrase. Our evaluations suggest that the attribute selection aspect of the algorithm exceeds classic REG algorithms, while the Noun Phrases generated are as similar to those in a previously developed corpus as were Noun Phrases produced by a new set of human speakers.",
}
@inproceedings{gatt-etal-2018-specificity,
    title = "Specificity measures and reference",
    author = "Gatt, Albert  and
      Mar{\'\i}n, Nicol{\'a}s  and
      Rivas-Gervilla, Gustavo  and
      S{\'a}nchez, Daniel",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6562",
    doi = "10.18653/v1/W18-6562",
    pages = "492--502",
    abstract = "In this paper we study empirically the validity of measures of referential success for referring expressions involving gradual properties. More specifically, we study the ability of several measures of referential success to predict the success of a user in choosing the right object, given a referring expression. Experimental results indicate that certain fuzzy measures of success are able to predict human accuracy in reference resolution. Such measures are therefore suitable for the estimation of the success or otherwise of a referring expression produced by a generation algorithm, especially in case the properties in a domain cannot be assumed to have crisp denotations.",
}
@inproceedings{zarriess-schlangen-2018-decoding,
    title = "Decoding Strategies for Neural Referring Expression Generation",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6563",
    doi = "10.18653/v1/W18-6563",
    pages = "503--512",
    abstract = "RNN-based sequence generation is now widely used in NLP and NLG (natural language generation). Most work focusses on how to train RNNs, even though also decoding is not necessarily straightforward: previous work on neural MT found seq2seq models to radically prefer short candidates, and has proposed a number of beam search heuristics to deal with this. In this work, we assess decoding strategies for referring expression generation with neural models. Here, expression length is crucial: output should neither contain too much or too little information, in order to be pragmatically adequate. We find that most beam search heuristics developed for MT do not generalize well to referring expression generation (REG), and do not generally outperform greedy decoding. We observe that beam search heuristics for termination seem to override the model{'}s knowledge of what a good stopping point is. Therefore, we also explore a recent approach called trainable decoding, which uses a small network to modify the RNN{'}s hidden state for better decoding results. We find this approach to consistently outperform greedy decoding for REG.",
}
@proceedings{ws-2018-machine-translation-shared,
    title = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Specia, Lucia  and
      Turchi, Marco  and
      Verspoor, Karin",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6400",
}
@inproceedings{bojar-etal-2018-findings,
    title = "Findings of the 2018 Conference on Machine Translation ({WMT}18)",
    author = "Bojar, Ond{\v{r}}ej  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6401",
    doi = "10.18653/v1/W18-6401",
    pages = "272--303",
    abstract = "This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test sets to probe specific aspects of translation.",
}
@inproceedings{barrault-etal-2018-findings,
    title = "Findings of the Third Shared Task on Multimodal Machine Translation",
    author = {Barrault, Lo{\"\i}c  and
      Bougares, Fethi  and
      Specia, Lucia  and
      Lala, Chiraag  and
      Elliott, Desmond  and
      Frank, Stella},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6402",
    doi = "10.18653/v1/W18-6402",
    pages = "304--323",
    abstract = "We present the results from the third shared task on multimodal machine translation. In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech. The image can be used in addition to (or instead of) the source sentence. This year the task was extended with a third target language (Czech) and a new test set. In addition, a variant of this task was introduced with its own test set where the source sentence is given in multiple languages: English, French and German, and participating systems are required to generate a translation in Czech. Seven teams submitted 45 different systems to the two variants of the task. Compared to last year, the performance of the multimodal submissions improved, but text-only systems remain competitive.",
}
@inproceedings{neves-etal-2018-findings,
    title = "Findings of the {WMT} 2018 Biomedical Translation Shared Task: Evaluation on {M}edline test sets",
    author = "Neves, Mariana  and
      Jimeno Yepes, Antonio  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Grozea, Cristian  and
      Siu, Amy  and
      Kittner, Madeleine  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6403",
    doi = "10.18653/v1/W18-6403",
    pages = "324--339",
    abstract = "Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e.g. research results presented in scientific publications. In this paper, we provide an overview of the Biomedical Translation shared task in the Workshop on Machine Translation (WMT) 2018, which specifically examined the performance of machine translation systems for biomedical texts. This year, we provided test sets of scientific publications from two sources (EDP and Medline) and for six language pairs (English with each of Chinese, French, German, Portuguese, Romanian and Spanish). We describe the development of the various test sets, the submissions that we received and the evaluations that we carried out. We obtained a total of 39 runs from six teams and some of this year{'}s BLEU scores were somewhat higher that last year{'}s, especially for teams that made use of biomedical resources or state-of-the-art MT algorithms (e.g. Transformer). Finally, our manual evaluation scored automatic translations higher than the reference translations for German and Spanish.",
}
@inproceedings{bei-etal-2018-empirical,
    title = "An Empirical Study of Machine Translation for the Shared Task of {WMT}18",
    author = "Bei, Chao  and
      Zong, Hao  and
      Wang, Yiming  and
      Fan, Baoyong  and
      Li, Shiqi  and
      Yuan, Conghu",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6404",
    doi = "10.18653/v1/W18-6404",
    pages = "340--344",
    abstract = "This paper describes the Global Tone Communication Co., Ltd.{'}s submission of the WMT18 shared news translation task. We participated in the English-to-Chinese direction and get the best BLEU (43.8) scores among all the participants. The submitted system focus on data clearing and techniques to build a competitive model for this task. Unlike other participants, the submitted system are mainly relied on the data filtering to obtain the best BLEU score. We do data filtering not only for provided sentences but also for the back translated sentences. The techniques we apply for data filtering include filtering by rules, language models and translation models. We also conduct several experiments to validate the effectiveness of training techniques. According to our experiments, the Annealing Adam optimizing function and ensemble decoding are the most effective techniques for the model training.",
}
@inproceedings{bicici-2018-robust,
    title = "Robust parfda Statistical Machine Translation Results",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6405",
    doi = "10.18653/v1/W18-6405",
    pages = "345--354",
    abstract = "We build parallel feature decay algorithms (parfda) Moses statistical machine translation (SMT) models for language pairs in the translation task. parfda obtains results close to the top constrained phrase-based SMT with an average of 2.252 BLEU points difference on WMT 2017 datasets using significantly less computation for building SMT systems than that would be spent using all available corpora. We obtain BLEU upper bounds based on target coverage to identify which systems used additional data. We use PRO for tuning to decrease fluctuations in the results and postprocess translation outputs to decrease translation errors due to the casing of words. F1 scores on the key phrases of the English to Turkish testsuite that we prepared reveal that parfda achieves 2nd best results. Truecasing translations before scoring obtained the best results overall.",
}
@inproceedings{casas-etal-2018-talp,
    title = "The {TALP}-{UPC} Machine Translation Systems for {WMT}18 News Shared Translation Task",
    author = "Casas, Noe  and
      Escolano, Carlos  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6406",
    doi = "10.18653/v1/W18-6406",
    pages = "355--360",
    abstract = "In this article we describe the TALP-UPC research group participation in the WMT18 news shared translation task for Finnish-English and Estonian-English within the multi-lingual subtrack. All of our primary submissions implement an attention-based Neural Machine Translation architecture. Given that Finnish and Estonian belong to the same language family and are similar, we use as training data the combination of the datasets of both language pairs to paliate the data scarceness of each individual pair. We also report the translation quality of systems trained on individual language pair data to serve as baseline and comparison reference.",
}
@inproceedings{del-etal-2018-phrase,
    title = "Phrase-based Unsupervised Machine Translation with Compositional Phrase Embeddings",
    author = {Del, Maksym  and
      T{\"a}ttar, Andre  and
      Fishel, Mark},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6407",
    doi = "10.18653/v1/W18-6407",
    pages = "361--367",
    abstract = "This paper describes the University of Tartu{'}s submission to the unsupervised machine translation track of WMT18 news translation shared task. We build several baseline translation systems for both directions of the English-Estonian language pair using monolingual data only; the systems belong to the phrase-based unsupervised machine translation paradigm where we experimented with phrase lengths of up to 3. As a main contribution, we performed a set of standalone experiments with compositional phrase embeddings as a substitute for phrases as individual vocabulary entries. Results show that reasonable n-gram vectors can be obtained by simply summing up individual word vectors which retains or improves the performance of phrase-based unsupervised machine tranlation systems while avoiding limitations of atomic phrase vectors.",
}
@inproceedings{deng-etal-2018-alibabas,
    title = "{A}libaba{'}s Neural Machine Translation Systems for {WMT}18",
    author = "Deng, Yongchao  and
      Cheng, Shanbo  and
      Lu, Jun  and
      Song, Kai  and
      Wang, Jingang  and
      Wu, Shenglan  and
      Yao, Liang  and
      Zhang, Guchun  and
      Zhang, Haibo  and
      Zhang, Pei  and
      Zhu, Changfeng  and
      Chen, Boxing",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6408",
    doi = "10.18653/v1/W18-6408",
    pages = "368--376",
    abstract = "This paper describes the submission systems of Alibaba for WMT18 shared news translation task. We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese. Our systems are based on Google{'}s Transformer model architecture, into which we integrated the most recent features from the academic research. We also employed most techniques that have been proven effective during the past WMT years, such as BPE, back translation, data selection, model ensembling and reranking, at industrial scale. For some morphologically-rich languages, we also incorporated linguistic knowledge into our neural network. For the translation tasks in which we have participated, our resulting systems achieved the best case sensitive BLEU score in all 5 directions. Notably, our English → Russian system outperformed the second reranked system by 5 BLEU score.",
}
@inproceedings{graca-etal-2018-rwth,
    title = "The {RWTH} {A}achen {U}niversity {E}nglish-{G}erman and {G}erman-{E}nglish Unsupervised Neural Machine Translation Systems for {WMT} 2018",
    author = "Gra{\c{c}}a, Miguel  and
      Kim, Yunsu  and
      Schamper, Julian  and
      Geng, Jiahui  and
      Ney, Hermann",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6409",
    doi = "10.18653/v1/W18-6409",
    pages = "377--385",
    abstract = "This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer.",
}
@inproceedings{gronroos-etal-2018-cognate,
    title = "Cognate-aware morphological segmentation for multilingual neural translation",
    author = {Gr{\"o}nroos, Stig-Arne  and
      Virpioja, Sami  and
      Kurimo, Mikko},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6410",
    doi = "10.18653/v1/W18-6410",
    pages = "386--393",
    abstract = "This article describes the Aalto University entry to the WMT18 News Translation Shared Task. We participate in the multilingual subtrack with a system trained under the constrained condition to translate from English to both Finnish and Estonian. The system is based on the Transformer model. We focus on improving the consistency of morphological segmentation for words that are similar orthographically, semantically, and distributionally; such words include etymological cognates, loan words, and proper names. For this, we introduce Cognate Morfessor, a multilingual variant of the Morfessor method. We show that our approach improves the translation quality particularly for Estonian, which has less resources for training the translation model.",
}
@inproceedings{gwinnup-etal-2018-afrl,
    title = "The {AFRL} {WMT}18 Systems: Ensembling, Continuation and Combination",
    author = "Gwinnup, Jeremy  and
      Anderson, Tim  and
      Erdmann, Grant  and
      Young, Katherine",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6411",
    doi = "10.18653/v1/W18-6411",
    pages = "394--398",
    abstract = "This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT18 evaluation campaign. This year, we examined the developments and additions to popular neural machine translation toolkits and measure improvements in performance on the Russian{--}English language pair.",
}
@inproceedings{haddow-etal-2018-university,
    title = "The {U}niversity of {E}dinburgh{'}s Submissions to the {WMT}18 News Translation Task",
    author = "Haddow, Barry  and
      Bogoychev, Nikolay  and
      Emelin, Denis  and
      Germann, Ulrich  and
      Grundkiewicz, Roman  and
      Heafield, Kenneth  and
      Miceli Barone, Antonio Valerio  and
      Sennrich, Rico",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6412",
    doi = "10.18653/v1/W18-6412",
    pages = "399--409",
    abstract = "The University of Edinburgh made submissions to all 14 language pairs in the news translation task, with strong performances in most pairs. We introduce new RNN-variant, mixed RNN/Transformer ensembles, data selection and weighting, and extensions to back-translation.",
}
@inproceedings{hu-etal-2018-tencentfmrd,
    title = "{T}encent{F}m{RD} Neural Machine Translation for {WMT}18",
    author = "Hu, Bojie  and
      Han, Ambyer  and
      Huang, Shen",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6413",
    doi = "10.18653/v1/W18-6413",
    pages = "410--417",
    abstract = "This paper describes the Neural Machine Translation (NMT) system of TencentFmRD for Chinese↔English news translation tasks of WMT 2018. Our systems are neural machine translation systems trained with our original system TenTrans. TenTrans is an improved NMT system based on Transformer self-attention mechanism. In addition to the basic settings of Transformer training, TenTrans uses multi-model fusion techniques, multiple features reranking, different segmentation models and joint learning. Finally, we adopt some data selection strategies to fine-tune the trained system and achieve a stable performance improvement. Our Chinese→English system achieved the second best BLEU scores and fourth best cased BLEU scores among all WMT18 submitted systems.",
}
@inproceedings{iranzo-sanchez-etal-2018-mllp,
    title = "The {MLLP}-{UPV} {G}erman-{E}nglish Machine Translation System for {WMT}18",
    author = "Iranzo-S{\'a}nchez, Javier  and
      Baquero-Arnal, Pau  and
      Garc{\'e}s D{\'\i}az-Mun{\'\i}o, Gon{\c{c}}al V.  and
      Mart{\'\i}nez-Villaronga, Adri{\`a}  and
      Civera, Jorge  and
      Juan, Alfons",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6414",
    doi = "10.18653/v1/W18-6414",
    pages = "418--424",
    abstract = "This paper describes the statistical machine translation system built by the MLLP research group of Universitat Polit{\`e}cnica de Val{\`e}ncia for the German→English news translation shared task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We used an ensemble of Transformer architecture{--}based neural machine translation systems. To train our system under {``}constrained{''} conditions, we filtered the provided parallel data with a scoring technique using character-based language models, and we added parallel data based on synthetic source sentences generated from the provided monolingual corpora.",
}
@inproceedings{junczys-dowmunt-2018-microsofts,
    title = "{M}icrosoft{'}s Submission to the {WMT}2018 News Translation Task: How {I} Learned to Stop Worrying and Love the Data",
    author = "Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6415",
    doi = "10.18653/v1/W18-6415",
    pages = "425--430",
    abstract = "This paper describes the Microsoft submission to the WMT2018 news translation shared task. We participated in one language direction {--} English-German. Our system follows current best-practice and combines state-of-the-art models with new data filtering (dual conditional cross-entropy filtering) and sentence weighting methods. We trained fairly standard Transformer-big models with an updated version of Edinburgh{'}s training scheme for WMT2017 and experimented with different filtering schemes for Paracrawl. According to automatic metrics (BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point margin over the next strongest system. Based on human evaluation we ranked first among constrained systems. We believe this is mostly caused by our data filtering/weighting regime.",
}
@inproceedings{kocmi-etal-2018-cuni-submissions,
    title = "{CUNI} Submissions in {WMT}18",
    author = "Kocmi, Tom  and
      Sudarikov, Roman  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6416",
    doi = "10.18653/v1/W18-6416",
    pages = "431--437",
    abstract = "We participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the low-resource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method. We first train a {``}parent model{''} for the high-resource language pair followed by adaptation on the related low-resource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data. Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.",
}
@inproceedings{koehn-etal-2018-jhu,
    title = "The {JHU} Machine Translation Systems for {WMT} 2018",
    author = "Koehn, Philipp  and
      Duh, Kevin  and
      Thompson, Brian",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6417",
    doi = "10.18653/v1/W18-6417",
    pages = "438--444",
    abstract = "We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German{--}English, English{--} German, and Russian{--}English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years.",
}
@inproceedings{mahata-etal-2018-jucbnmt,
    title = "{JUCBNMT} at {WMT}2018 News Translation Task: Character Based Neural Machine Translation of {F}innish to {E}nglish",
    author = "Mahata, Sainik Kumar  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6418",
    doi = "10.18653/v1/W18-6418",
    pages = "445--448",
    abstract = "In the current work, we present a description of the system submitted to WMT 2018 News Translation Shared task. The system was created to translate news text from Finnish to English. The system used a Character Based Neural Machine Translation model to accomplish the given task. The current paper documents the preprocessing steps, the description of the submitted system and the results produced using the same. Our system garnered a BLEU score of 12.9.",
}
@inproceedings{marie-etal-2018-nicts,
    title = "{NICT}{'}s Neural and Statistical Machine Translation Systems for the {WMT}18 News Translation Task",
    author = "Marie, Benjamin  and
      Wang, Rui  and
      Fujita, Atsushi  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6419",
    doi = "10.18653/v1/W18-6419",
    pages = "449--455",
    abstract = "This paper presents the NICT{'}s participation to the WMT18 shared news translation task. We participated in the eight translation directions of four language pairs: Estonian-English, Finnish-English, Turkish-English and Chinese-English. For each translation direction, we prepared state-of-the-art statistical (SMT) and neural (NMT) machine translation systems. Our NMT systems were trained with the transformer architecture using the provided parallel data enlarged with a large quantity of back-translated monolingual data that we generated with a new incremental training framework. Our primary submissions to the task are the result of a simple combination of our SMT and NMT systems. Our systems are ranked first for the Estonian-English and Finnish-English language pairs (constraint) according to BLEU-cased.",
}
@inproceedings{molchanov-2018-promt,
    title = "{PROMT} Systems for {WMT} 2018 Shared Translation Task",
    author = "Molchanov, Alexander",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6420",
    doi = "10.18653/v1/W18-6420",
    pages = "456--460",
    abstract = "This paper describes the PROMT submissions for the WMT 2018 Shared News Translation Task. This year we participated only in the English-Russian language pair. We built two primary neural networks-based systems: 1) a pure Marian-based neural system and 2) a hybrid system which incorporates OpenNMT-based neural post-editing component into our RBMT engine. We also submitted pure rule-based translation (RBMT) for contrast. We show competitive results with both primary submissions which significantly outperform the RBMT baseline.",
}
@inproceedings{morishita-etal-2018-ntts,
    title = "{NTT}{'}s Neural Machine Translation Systems for {WMT} 2018",
    author = "Morishita, Makoto  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6421",
    doi = "10.18653/v1/W18-6421",
    pages = "461--466",
    abstract = "This paper describes NTT{'}s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.",
}
@inproceedings{pham-etal-2018-karlsruhe,
    title = "The Karlsruhe Institute of Technology Systems for the News Translation Task in {WMT} 2018",
    author = "Pham, Ngoc-Quan  and
      Niehues, Jan  and
      Waibel, Alexander",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6422",
    doi = "10.18653/v1/W18-6422",
    pages = "467--472",
    abstract = "We present our experiments in the scope of the news translation task in WMT 2018, in directions: English→German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points.",
}
@inproceedings{pinnis-etal-2018-tildes,
    title = "Tilde{'}s Machine Translation Systems for {WMT} 2018",
    author = "Pinnis, M{\=a}rcis  and
      Rikters, Mat{\=\i}ss  and
      Kri{\v{s}}lauks, Rihards",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6423",
    doi = "10.18653/v1/W18-6423",
    pages = "473--481",
    abstract = "The paper describes the development process of the Tilde{'}s NMT systems that were submitted for the WMT 2018 shared task on news translation. We describe the data filtering and pre-processing workflows, the NMT system training architectures, and automatic evaluation results. For the WMT 2018 shared task, we submitted seven systems (both constrained and unconstrained) for English-Estonian and Estonian-English translation directions. The submitted systems were trained using Transformer models.",
}
@inproceedings{popel-2018-cuni,
    title = "{CUNI} Transformer Neural {MT} System for {WMT}18",
    author = "Popel, Martin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6424",
    doi = "10.18653/v1/W18-6424",
    pages = "482--487",
    abstract = "We describe our NMT system submitted to the WMT2018 shared task in news translation. Our system is based on the Transformer model (Vaswani et al., 2017). We use an improved technique of backtranslation, where we iterate the process of translating monolingual data in one direction and training an NMT model for the opposite direction using synthetic parallel data. We apply a simple but effective filtering of the synthetic data. We pre-process the input sentences using coreference resolution in order to disambiguate the gender of pro-dropped personal pronouns. Finally, we apply two simple post-processing substitutions on the translated output. Our system is significantly (p {\textless} 0.05) better than all other English-Czech and Czech-English systems in WMT2018.",
}
@inproceedings{raganato-etal-2018-university,
    title = "The {U}niversity of {H}elsinki submissions to the {WMT}18 news task",
    author = {Raganato, Alessandro  and
      Scherrer, Yves  and
      Nieminen, Tommi  and
      Hurskainen, Arvi  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6425",
    doi = "10.18653/v1/W18-6425",
    pages = "488--495",
    abstract = "This paper describes the University of Helsinki{'}s submissions to the WMT18 shared news translation task for English-Finnish and English-Estonian, in both directions. This year, our main submissions employ a novel neural architecture, the Transformer, using the open-source OpenNMT framework. Our experiments couple domain labeling and fine tuned multilingual models with shared vocabularies between the source and target language, using the provided parallel data of the shared task and additional back-translations. Finally, we compare, for the English-to-Finnish case, the effectiveness of different machine translation architectures, starting from a rule-based approach to our best neural model, analyzing the output and highlighting future research.",
}
@inproceedings{schamper-etal-2018-rwth,
    title = "The {RWTH} {A}achen {U}niversity Supervised Machine Translation Systems for {WMT} 2018",
    author = "Schamper, Julian  and
      Rosendahl, Jan  and
      Bahar, Parnia  and
      Kim, Yunsu  and
      Nix, Arne  and
      Ney, Hermann",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6426",
    doi = "10.18653/v1/W18-6426",
    pages = "496--503",
    abstract = "This paper describes the statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the German→English task where we to all automatic scored first with respect metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8{\%} BLEU over our last year{'}s submission and by 4.8{\%} BLEU over the winning system of the 2017 German→English task. In English→Turkish task, we show 3.6{\%} BLEU improvement over the last year{'}s winning system. We further report results on the Chinese→English task where we improve 2.2{\%} BLEU on average over our baseline systems but stay behind the 2018 winning systems.",
}
@inproceedings{stahlberg-etal-2018-university,
    title = "The {U}niversity of {C}ambridge{'}s Machine Translation Systems for {WMT}18",
    author = "Stahlberg, Felix  and
      de Gispert, Adri{\`a}  and
      Byrne, Bill",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6427",
    doi = "10.18653/v1/W18-6427",
    pages = "504--512",
    abstract = "The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation. We compare recurrent, convolutional, and self-attention-based neural models on German-English, English-German, and Chinese-English. Our final system combines all neural models together with a phrase-based SMT system in an MBR-based scheme. We report small but consistent gains on top of strong Transformer ensembles.",
}
@inproceedings{stojanovski-etal-2018-lmu,
    title = "The {LMU} {M}unich Unsupervised Machine Translation Systems",
    author = "Stojanovski, Dario  and
      Hangya, Viktor  and
      Huck, Matthias  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6428",
    doi = "10.18653/v1/W18-6428",
    pages = "513--521",
    abstract = "We describe LMU Munich{'}s unsupervised machine translation systems for English↔German translation. These systems were used to participate in the WMT18 news translation shared task and more specifically, for the unsupervised learning sub-track. The systems are trained on English and German monolingual data only and exploit and combine previously proposed techniques such as using word-by-word translated data based on bilingual word embeddings, denoising and on-the-fly backtranslation.",
}
@inproceedings{wang-etal-2018-tencent,
    title = "Tencent Neural Machine Translation Systems for {WMT}18",
    author = "Wang, Mingxuan  and
      Gong, Li  and
      Zhu, Wenhuan  and
      Xie, Jun  and
      Bian, Chao",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6429",
    doi = "10.18653/v1/W18-6429",
    pages = "522--527",
    abstract = "We participated in the WMT 2018 shared news translation task on English↔Chinese language pair. Our systems are based on attentional sequence-to-sequence models with some form of recursion and self-attention. Some data augmentation methods are also introduced to improve the translation performance. The best translation result is obtained with ensemble and reranking techniques. Our Chinese→English system achieved the highest cased BLEU score among all 16 submitted systems, and our English→Chinese system ranked the third out of 18 submitted systems.",
}
@inproceedings{wang-etal-2018-niutrans,
    title = "The {N}iu{T}rans Machine Translation System for {WMT}18",
    author = "Wang, Qiang  and
      Li, Bei  and
      Liu, Jiqiang  and
      Jiang, Bojian  and
      Zhang, Zheyang  and
      Li, Yinqiao  and
      Lin, Ye  and
      Xiao, Tong  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6430",
    doi = "10.18653/v1/W18-6430",
    pages = "528--534",
    abstract = "This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks. Our baseline systems are based on the Transformer architecture. We further improve the translation performance 2.4-2.6 BLEU points from four aspects, including architectural improvements, diverse ensemble decoding, reranking, and post-processing. Among constrained submissions, we rank 2nd out of 16 submitted systems on Chinese → English task and 3rd out of 16 on English → Chinese task, respectively.",
}
@inproceedings{xu-carpuat-2018-university,
    title = "The {U}niversity of {M}aryland{'}s {C}hinese-{E}nglish Neural Machine Translation Systems at {WMT}18",
    author = "Xu, Weijia  and
      Carpuat, Marine",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6431",
    doi = "10.18653/v1/W18-6431",
    pages = "535--540",
    abstract = "This paper describes the University of Maryland{'}s submission to the WMT 2018 Chinese↔English news translation tasks. Our systems are BPE-based self-attentional Transformer networks with parallel and backtranslated monolingual training data. Using ensembling and reranking, we improve over the Transformer baseline by +1.4 BLEU for Chinese→English and +3.97 BLEU for English→Chinese on \textit{newstest2017}. Our best systems reach BLEU scores of 24.4 for Chinese→English and 39.0 for English→Chinese on \textit{newstest2018}.",
}
@inproceedings{bojar-etal-2018-evald,
    title = "{E}val{D} Reference-Less Discourse Evaluation for {WMT}18",
    author = "Bojar, Ond{\v{r}}ej  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}  and
      Rysov{\'a}, Kate{\v{r}}ina  and
      Rysov{\'a}, Magdal{\'e}na",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6432",
    doi = "10.18653/v1/W18-6432",
    pages = "541--545",
    abstract = "We present the results of automatic evaluation of discourse in machine translation (MT) outputs using the EVALD tool. EVALD was originally designed and trained to assess the quality of \textit{human} writing, for native speakers and foreign-language learners. MT has seen a tremendous leap in translation quality at the level of sentences and it is thus interesting to see if the human-level evaluation is becoming relevant.",
}
@inproceedings{burlot-etal-2018-wmt18,
    title = "The {WMT}{'}18 Morpheval test suites for {E}nglish-{C}zech, {E}nglish-{G}erman, {E}nglish-{F}innish and {T}urkish-{E}nglish",
    author = {Burlot, Franck  and
      Scherrer, Yves  and
      Ravishankar, Vinit  and
      Bojar, Ond{\v{r}}ej  and
      Gr{\"o}nroos, Stig-Arne  and
      Koponen, Maarit  and
      Nieminen, Tommi  and
      Yvon, Fran{\c{c}}ois},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6433",
    doi = "10.18653/v1/W18-6433",
    pages = "546--560",
    abstract = "Progress in the quality of machine translation output calls for new automatic evaluation procedures and metrics. In this paper, we extend the Morpheval protocol introduced by Burlot and Yvon (2017) for the English-to-Czech and English-to-Latvian translation directions to three additional language pairs, and report its use to analyze the results of WMT 2018{'}s participants for these language pairs. Considering additional, typologically varied source and target languages also enables us to draw some generalizations regarding this morphology-oriented evaluation procedure.",
}
@inproceedings{cinkova-bojar-2018-testsuite,
    title = "Testsuite on {C}zech{--}{E}nglish Grammatical Contrasts",
    author = "Cinkov{\'a}, Silvie  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6434",
    doi = "10.18653/v1/W18-6434",
    pages = "561--569",
    abstract = "We present a pilot study of machine translation of selected grammatical contrasts between Czech and English in WMT18 News Translation Task. For each phenomenon, we run a dedicated test which checks if the candidate translation expresses the phenomenon as expected or not. The proposed type of analysis is not an evaluation in the strict sense because the phenomenon can be correctly translated in various ways and we anticipate only one. What is nevertheless interesting are the differences between various MT systems and the single reference translation in their general tendency in handling the given phenomenon.",
}
@inproceedings{guillou-etal-2018-pronoun,
    title = "A Pronoun Test Suite Evaluation of the {E}nglish{--}{G}erman {MT} Systems at {WMT} 2018",
    author = "Guillou, Liane  and
      Hardmeier, Christian  and
      Lapshinova-Koltunski, Ekaterina  and
      Lo{\'a}iciga, Sharid",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6435",
    doi = "10.18653/v1/W18-6435",
    pages = "570--577",
    abstract = "We evaluate the output of 16 English-to-German MT systems with respect to the translation of pronouns in the context of the WMT 2018 competition. We work with a test suite specifically designed to assess system quality in various fine-grained categories known to be problematic. The main evaluation scores come from a semi-automatic process, combining automatic reference matching with extensive manual annotation of uncertain cases. We find that current NMT systems are good at translating pronouns with intra-sentential reference, but the inter-sentential cases remain difficult. NMT systems are also good at the translation of event pronouns, unlike systems from the phrase-based SMT paradigm. No single system performs best at translating all types of anaphoric pronouns, suggesting unexplained random effects influencing the translation of pronouns with NMT.",
}
@inproceedings{macketanz-etal-2018-fine,
    title = "Fine-grained evaluation of {G}erman-{E}nglish Machine Translation based on a Test Suite",
    author = "Macketanz, Vivien  and
      Avramidis, Eleftherios  and
      Burchardt, Aljoscha  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6436",
    doi = "10.18653/v1/W18-6436",
    pages = "578--587",
    abstract = "We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite. The test suite has been devised manually by a team of language professionals in order to cover a broad variety of linguistic phenomena that MT often fails to translate properly. It contains 5,000 test sentences covering 106 linguistic phenomena in 14 categories, with an increased focus on verb tenses, aspects and moods. The MT outputs are evaluated in a semi-automatic way through regular expressions that focus only on the part of the sentence that is relevant to each phenomenon. Through our analysis, we are able to compare systems based on their performance on these categories. Additionally, we reveal strengths and weaknesses of particular systems and we identify grammatical phenomena where the overall performance of MT is relatively low.",
}
@inproceedings{rios-etal-2018-word,
    title = "The Word Sense Disambiguation Test Suite at {WMT}18",
    author = {Rios, Annette  and
      M{\"u}ller, Mathias  and
      Sennrich, Rico},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6437",
    doi = "10.18653/v1/W18-6437",
    pages = "588--596",
    abstract = "We present a task to measure an MT system{'}s capability to translate ambiguous words with their correct sense according to the given context. The task is based on the German{--}English Word Sense Disambiguation (WSD) test set ContraWSD (Rios Gonzales et al., 2017), but it has been filtered to reduce noise, and the evaluation has been adapted to assess MT output directly rather than scoring existing translations. We evaluate all German{--}English submissions to the WMT{'}18 shared translation task, plus a number of submissions from previous years, and find that performance on the task has markedly improved compared to the 2016 WMT submissions (81{\%}→93{\%} accuracy on the WSD task). We also find that the unsupervised submissions to the task have a low WSD capability, and predominantly translate ambiguous source words with the same sense.",
}
@inproceedings{caglayan-etal-2018-lium,
    title = "{LIUM}-{CVC} Submissions for {WMT}18 Multimodal Translation Task",
    author = {Caglayan, Ozan  and
      Bardet, Adrien  and
      Bougares, Fethi  and
      Barrault, Lo{\"\i}c  and
      Wang, Kai  and
      Masana, Marc  and
      Herranz, Luis  and
      van de Weijer, Joost},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6438",
    doi = "10.18653/v1/W18-6438",
    pages = "597--602",
    abstract = "This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previous multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR.",
}
@inproceedings{gronroos-etal-2018-memad,
    title = "The {M}e{MAD} Submission to the {WMT}18 Multimodal Translation Task",
    author = {Gr{\"o}nroos, Stig-Arne  and
      Huet, Benoit  and
      Kurimo, Mikko  and
      Laaksonen, Jorma  and
      Merialdo, Bernard  and
      Pham, Phu  and
      Sj{\"o}berg, Mats  and
      Sulubacak, Umut  and
      Tiedemann, J{\"o}rg  and
      Troncy, Raphael  and
      V{\'a}zquez, Ra{\'u}l},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6439",
    doi = "10.18653/v1/W18-6439",
    pages = "603--611",
    abstract = "This paper describes the MeMAD project entry to the WMT Multimodal Machine Translation Shared Task. We propose adapting the Transformer neural machine translation (NMT) architecture to a multi-modal setting. In this paper, we also describe the preliminary experiments with text-only translation systems leading us up to this choice. We have the top scoring system for both English-to-German and English-to-French, according to the automatic metrics for flickr18. Our experiments show that the effect of the visual features in our system is small. Our largest gains come from the quality of the underlying text-only NMT system. We find that appropriate use of additional data is effective.",
}
@inproceedings{gwinnup-etal-2018-afrl-ohio,
    title = "The {AFRL}-{O}hio {S}tate {WMT}18 Multimodal System: Combining Visual with Traditional",
    author = "Gwinnup, Jeremy  and
      Sandvick, Joshua  and
      Hutt, Michael  and
      Erdmann, Grant  and
      Duselis, John  and
      Davis, James",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6440",
    doi = "10.18653/v1/W18-6440",
    pages = "612--615",
    abstract = "AFRL-Ohio State extends its usage of visual domain-driven machine translation for use as a peer with traditional machine translation systems. As a peer, it is enveloped into a system combination of neural and statistical MT systems to present a composite translation.",
}
@inproceedings{helcl-etal-2018-cuni,
    title = "{CUNI} System for the {WMT}18 Multimodal Translation Task",
    author = "Helcl, Jind{\v{r}}ich  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Vari{\v{s}}, Du{\v{s}}an",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6441",
    doi = "10.18653/v1/W18-6441",
    pages = "616--623",
    abstract = "We present our submission to the WMT18 Multimodal Translation Task. The main feature of our submission is applying a self-attentive network instead of a recurrent neural network. We evaluate two methods of incorporating the visual features in the model: first, we include the image representation as another input to the network; second, we train the model to predict the visual features and use it as an auxiliary objective. For our submission, we acquired both textual and multimodal additional data. Both of the proposed methods yield significant improvements over recurrent networks and self-attentive textual baselines.",
}
@inproceedings{lala-etal-2018-sheffield,
    title = "{S}heffield Submissions for {WMT}18 Multimodal Translation Shared Task",
    author = "Lala, Chiraag  and
      Madhyastha, Pranava Swaroop  and
      Scarton, Carolina  and
      Specia, Lucia",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6442",
    doi = "10.18653/v1/W18-6442",
    pages = "624--631",
    abstract = "This paper describes the University of Sheffield{'}s submissions to the WMT18 Multimodal Machine Translation shared task. We participated in both tasks 1 and 1b. For task 1, we build on a standard sequence to sequence attention-based neural machine translation system (NMT) and investigate the utility of multimodal re-ranking approaches. More specifically, n-best translation candidates from this system are re-ranked using novel multimodal cross-lingual word sense disambiguation models. For task 1b, we explore three approaches: (i) re-ranking based on cross-lingual word sense disambiguation (as for task 1), (ii) re-ranking based on consensus of NMT n-best lists from German-Czech, French-Czech and English-Czech systems, and (iii) data augmentation by generating English source data through machine translation from French to English and from German to English followed by hypothesis selection using a multimodal-reranker.",
}
@inproceedings{zheng-etal-2018-ensemble,
    title = "Ensemble Sequence Level Training for Multimodal {MT}: {OSU}-{B}aidu {WMT}18 Multimodal Machine Translation System Report",
    author = "Zheng, Renjie  and
      Yang, Yilin  and
      Ma, Mingbo  and
      Huang, Liang",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6443",
    doi = "10.18653/v1/W18-6443",
    pages = "632--636",
    abstract = "This paper describes multimodal machine translation systems developed jointly by Oregon State University and Baidu Research for WMT 2018 Shared Task on multimodal translation. In this paper, we introduce a simple approach to incorporate image information by feeding image features to the decoder side. We also explore different sequence level training methods including scheduled sampling and reinforcement learning which lead to substantial improvements. Our systems ensemble several models using different architectures and training methods and achieve the best performance for three subtasks: En-De and En-Cs in task 1 and (En+De+Fr)-Cs task 1B.",
}
@inproceedings{duma-menzel-2018-translation,
    title = "Translation of Biomedical Documents with Focus on {S}panish-{E}nglish",
    author = "Duma, Mirela-Stefania  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6444",
    doi = "10.18653/v1/W18-6444",
    pages = "637--643",
    abstract = "For the WMT 2018 shared task of translating documents pertaining to the Biomedical domain, we developed a scoring formula that uses an unsophisticated and effective method of weighting term frequencies and was integrated in a data selection pipeline. The method was applied on five language pairs and it performed best on Portuguese-English, where a BLEU score of 41.84 placed it third out of seven runs submitted by three institutions. In this paper, we describe our method and results with a special focus on Spanish-English where we compare it against a state-of-the-art method. Our contribution to the task lies in introducing a fast, unsupervised method for selecting domain-specific data for training models which obtain good results using only 10{\%} of the general domain data.",
}
@inproceedings{grozea-2018-ensemble,
    title = "Ensemble of Translators with Automatic Selection of the Best Translation {--} the submission of {FOKUS} to the {WMT} 18 biomedical translation task {--}",
    author = "Grozea, Cristian",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6445",
    doi = "10.18653/v1/W18-6445",
    pages = "644--647",
    abstract = "This paper describes the system of Fraunhofer FOKUS for the WMT 2018 biomedical translation task. Our approach, described here, was to automatically select the most promising translation from a set of candidates produced with NMT (Transformer) models. We selected the highest fidelity translation of each sentence by using a dictionary, stemming and a set of heuristics. Our method is simple, can use any machine translators, and requires no further training in addition to that already employed to build the NMT models. The downside is that the score did not increase over the best in ensemble, but was quite close to it (difference about 0.5 BLEU).",
}
@inproceedings{huck-etal-2018-lmu,
    title = "{LMU} {M}unich{'}s Neural Machine Translation Systems at {WMT} 2018",
    author = "Huck, Matthias  and
      Stojanovski, Dario  and
      Hangya, Viktor  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6446",
    doi = "10.18653/v1/W18-6446",
    pages = "648--654",
    abstract = "We present the LMU Munich machine translation systems for the English{--}German language pair. We have built neural machine translation systems for both translation directions (English→German and German→English) and for two different domains (the biomedical domain and the news domain). The systems were used for our participation in the WMT18 biomedical translation task and in the shared task on machine translation of news. The main focus of our recent system development efforts has been on achieving improvements in the biomedical domain over last year{'}s strong biomedical translation engine for English→German (Huck et al., 2017a). Considerable progress has been made in the latter task, which we report on in this paper.",
}
@inproceedings{khan-etal-2018-hunter,
    title = "Hunter {NMT} System for {WMT}18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation",
    author = "Khan, Abdul  and
      Panda, Subhadarshi  and
      Xu, Jia  and
      Flokas, Lampros",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6447",
    doi = "10.18653/v1/W18-6447",
    pages = "655--661",
    abstract = "This paper describes the submission of Hunter Neural Machine Translation (NMT) to the WMT{'}18 Biomedical translation task from English to French. The discrepancy between training and test data distribution brings a challenge to translate text in new domains. Beyond the previous work of combining in-domain with out-of-domain models, we found accuracy and efficiency gain in combining different in-domain models. We conduct extensive experiments on NMT with \textit{transfer learning}. We train on different in-domain Biomedical datasets one after another. That means parameters of the previous training serve as the initialization of the next one. Together with a pre-trained out-of-domain News model, we enhanced translation quality with 3.73 BLEU points over the baseline. Furthermore, we applied ensemble learning on training models of intermediate epochs and achieved an improvement of 4.02 BLEU points over the baseline. Overall, our system is 11.29 BLEU points above the best system of last year on the EDP 2017 test set.",
}
@inproceedings{soares-becker-2018-ufrgs,
    title = "{UFRGS} Participation on the {WMT} Biomedical Translation Shared Task",
    author = "Soares, Felipe  and
      Becker, Karin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6448",
    doi = "10.18653/v1/W18-6448",
    pages = "662--666",
    abstract = "This paper describes the machine translation systems developed by the Universidade Federal do Rio Grande do Sul (UFRGS) team for the biomedical translation shared task. Our systems are based on statistical machine translation and neural machine translation, using the Moses and OpenNMT toolkits, respectively. We participated in four translation directions for the English/Spanish and English/Portuguese language pairs. To create our training data, we concatenated several parallel corpora, both from in-domain and out-of-domain sources, as well as terminological resources from UMLS. Our systems achieved the best BLEU scores according to the official shared task evaluation.",
}
@inproceedings{tubay-costa-jussa-2018-neural,
    title = "Neural Machine Translation with the Transformer and Multi-Source {R}omance Languages for the Biomedical {WMT} 2018 task",
    author = "Tubay, Brian  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6449",
    doi = "10.18653/v1/W18-6449",
    pages = "667--670",
    abstract = "The Transformer architecture has become the state-of-the-art in Machine Translation. This model, which relies on attention-based mechanisms, has outperformed previous neural machine translation architectures in several tasks. In this system description paper, we report details of training neural machine translation with multi-source Romance languages with the Transformer model and in the evaluation frame of the biomedical WMT 2018 task. Using multi-source languages from the same family allows improvements of over 6 BLEU points.",
}
@inproceedings{ma-etal-2018-results,
    title = "Results of the {WMT}18 Metrics Shared Task: Both characters and embeddings achieve good performance",
    author = "Ma, Qingsong  and
      Bojar, Ond{\v{r}}ej  and
      Graham, Yvette",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6450",
    doi = "10.18653/v1/W18-6450",
    pages = "671--688",
    abstract = "This paper presents the results of the WMT18 Metrics Shared Task. We asked participants of this task to score the outputs of the MT systems involved in the WMT18 News Translation Task with automatic metrics. We collected scores of 10 metrics and 8 research groups. In addition to that, we computed scores of 8 standard metrics (BLEU, SentBLEU, chrF, NIST, WER, PER, TER and CDER) as baselines. The collected scores were evaluated in terms of system-level correlation (how well each metric{'}s scores correlate with WMT18 official manual ranking of systems) and in terms of segment-level correlation (how often a metric agrees with humans in judging the quality of a particular sentence relative to alternate outputs). This year, we employ a single kind of manual evaluation: direct assessment (DA).",
}
@inproceedings{specia-etal-2018-findings,
    title = "Findings of the {WMT} 2018 Shared Task on Quality Estimation",
    author = "Specia, Lucia  and
      Blain, Fr{\'e}d{\'e}ric  and
      Logacheva, Varvara  and
      F. Astudillo, Ram{\'o}n  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6451",
    doi = "10.18653/v1/W18-6451",
    pages = "689--709",
    abstract = "We report the results of the WMT18 shared task on Quality Estimation, i.e. the task of predicting the quality of the output of machine translation systems at various granularity levels: word, phrase, sentence and document. This year we include four language pairs, three text domains, and translations produced by both statistical and neural machine translation systems. Participating teams from ten institutions submitted a variety of systems to different task variants and language pairs.",
}
@inproceedings{chatterjee-etal-2018-findings,
    title = "Findings of the {WMT} 2018 Shared Task on Automatic Post-Editing",
    author = "Chatterjee, Rajen  and
      Negri, Matteo  and
      Rubino, Raphael  and
      Turchi, Marco",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6452",
    doi = "10.18653/v1/W18-6452",
    pages = "710--725",
    abstract = "We present the results from the fourth round of the WMT shared task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a {``}black-box{''} machine translation system by learning from human corrections. Keeping the same general evaluation setting of the three previous rounds, this year we focused on one language pair (English-German) and on domain-specific data (Information Technology), with MT outputs produced by two different paradigms: phrase-based (PBSMT) and neural (NMT). Five teams submitted respectively 11 runs for the PBSMT subtask and 10 runs for the NMT subtask. In the former subtask, characterized by original translations of lower quality, top results achieved impressive improvements, up to -6.24 TER and +9.53 BLEU points over the baseline {``}\textit{do-nothing}{''} system. The NMT subtask proved to be more challenging due to the higher quality of the original translations and the availability of less training data. In this case, top results show smaller improvements up to -0.38 TER and +0.8 BLEU points.",
}
@inproceedings{koehn-etal-2018-findings,
    title = "Findings of the {WMT} 2018 Shared Task on Parallel Corpus Filtering",
    author = "Koehn, Philipp  and
      Khayrallah, Huda  and
      Heafield, Kenneth  and
      Forcada, Mikel L.",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6453",
    doi = "10.18653/v1/W18-6453",
    pages = "726--739",
    abstract = "We posed the shared task of assigning sentence-level quality scores for a very noisy corpus of sentence pairs crawled from the web, with the goal of sub-selecting 1{\%} and 10{\%} of high-quality data to be used to train machine translation systems. Seventeen participants from companies, national research labs, and universities participated in this task.",
}
@inproceedings{guo-etal-2018-meteor,
    title = "{M}eteor++: Incorporating Copy Knowledge into Machine Translation Evaluation",
    author = "Guo, Yinuo  and
      Ruan, Chong  and
      Hu, Junfeng",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6454",
    doi = "10.18653/v1/W18-6454",
    pages = "740--745",
    abstract = "In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call \textbf{copy knowledge}. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT{'}2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric \textbf{Meteor++}. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets.",
}
@inproceedings{panja-naskar-2018-iter,
    title = "{ITER}: Improving Translation Edit Rate through Optimizable Edit Costs",
    author = "Panja, Joybrata  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6455",
    doi = "10.18653/v1/W18-6455",
    pages = "746--750",
    abstract = "The paper presents our participation in the WMT 2018 Metrics Shared Task. We propose an improved version of Translation Edit/Error Rate (TER). In addition to including the basic edit operations in TER, namely - insertion, deletion, substitution and shift, our metric also allows stem matching, optimizable edit costs and better normalization so as to correlate better with human judgement scores. The proposed metric shows much higher correlation with human judgments than TER.",
}
@inproceedings{shimanaka-etal-2018-ruse,
    title = "{RUSE}: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation",
    author = "Shimanaka, Hiroki  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6456",
    doi = "10.18653/v1/W18-6456",
    pages = "751--758",
    abstract = "We introduce the RUSE metric for the WMT18 metrics shared task. Sentence embeddings can capture global information that cannot be captured by local features based on character or word N-grams. Although training sentence embeddings using small-scale translation datasets with manual evaluation is difficult, sentence embeddings trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. We use a multi-layer perceptron regressor based on three types of sentence embeddings. The experimental results of the WMT16 and WMT17 datasets show that the RUSE metric achieves a state-of-the-art performance in both segment- and system-level metrics tasks with embedding features only.",
}
@inproceedings{basu-etal-2018-keep,
    title = "Keep It or Not: Word Level Quality Estimation for Post-Editing",
    author = "Basu, Prasenjit  and
      Pal, Santanu  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6457",
    doi = "10.18653/v1/W18-6457",
    pages = "759--764",
    abstract = "The paper presents our participation in the WMT 2018 shared task on word level quality estimation (QE) of machine translated (MT) text, i.e., to predict whether a word in MT output for a given source context is correctly translated and hence should be retained in the post-edited translation (PE), or not. To perform the QE task, we measure the similarity of the source context of the target MT word with the context for which the word is retained in PE in the training data. This is achieved in two different ways, using \textit{Bag-of-Words} (\textit{BoW}) model and \textit{Document-to-Vector} (\textit{Doc2Vec}) model. In the \textit{BoW} model, we compute the cosine similarity while in the \textit{Doc2Vec} model we consider the Doc2Vec similarity. By applying the Kneedle algorithm on the F1mult vs. similarity score plot, we derive the threshold based on which OK/BAD decisions are taken for the MT words. Experimental results revealed that the Doc2Vec model performs better than the BoW model on the word level QE task.",
}
@inproceedings{bicici-2018-rtm,
    title = "{RTM} results for Predicting Translation Performance",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6458",
    doi = "10.18653/v1/W18-6458",
    pages = "765--769",
    abstract = "With improved prediction combination using weights based on their training performance and stacking and multilayer perceptrons to build deeper prediction models, RTMs become the 3rd system in general at the sentence-level prediction of translation scores and achieve the lowest RMSE in English to German NMT QET results. For the document-level task, we compare document-level RTM models with sentence-level RTM models obtained with the concatenation of document sentences and obtain similar results.",
}
@inproceedings{choudhary-etal-2018-neural,
    title = "Neural Machine Translation for {E}nglish-{T}amil",
    author = "Choudhary, Himanshu  and
      Pathak, Aditya Kumar  and
      Saha, Rajiv Ratan  and
      Kumaraguru, Ponnurangam",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6459",
    doi = "10.18653/v1/W18-6459",
    pages = "770--775",
    abstract = "A huge amount of valuable resources is available on the web in English, which are often translated into local languages to facilitate knowledge sharing among local people who are not much familiar with English. However, translating such content manually is very tedious, costly, and time-consuming process. To this end, machine translation is an efficient approach to translate text without any human involvement. Neural machine translation (NMT) is one of the most recent and effective translation technique amongst all existing machine translation systems. In this paper, we apply NMT for English-Tamil language pair. We propose a novel neural machine translation technique using word-embedding along with Byte-Pair-Encoding (BPE) to develop an efficient translation system that overcomes the OOV (Out Of Vocabulary) problem for languages which do not have much translations available online. We use the BLEU score for evaluating the system performance. Experimental results confirm that our proposed MIDAS translator (8.33 BLEU score) outperforms Google translator (3.75 BLEU score).",
}
@inproceedings{duma-menzel-2018-benefit,
    title = "The Benefit of Pseudo-Reference Translations in Quality Estimation of {MT} Output",
    author = "Duma, Melania  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6460",
    doi = "10.18653/v1/W18-6460",
    pages = "776--781",
    abstract = "In this paper, a novel approach to Quality Estimation is introduced, which extends the method in (Duma and Menzel, 2017) by also considering pseudo-reference translations as data sources to the tree and sequence kernels used before. Two variants of the system were submitted to the sentence level WMT18 Quality Estimation Task for the English-German language pair. They have been ranked 4th and 6th out of 13 systems in the SMT track, while in the NMT track ranks 4 and 5 out of 11 submissions have been reached.",
}
@inproceedings{etchegoyhen-etal-2018-supervised,
    title = "Supervised and Unsupervised Minimalist Quality Estimators: Vicomtech{'}s Participation in the {WMT} 2018 Quality Estimation Task",
    author = "Etchegoyhen, Thierry  and
      Mart{\'\i}nez Garcia, Eva  and
      Azpeitia, Andoni",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6461",
    doi = "10.18653/v1/W18-6461",
    pages = "782--787",
    abstract = "We describe Vicomtech{'}s participation in the WMT 2018 shared task on quality estimation, for which we submitted minimalist quality estimators. The core of our approach is based on two simple features: lexical translation overlaps and language model cross-entropy scores. These features are exploited in two system variants: uMQE is an unsupervised system, where the final quality score is obtained by averaging individual feature scores; sMQE is a supervised variant, where the final score is estimated by a Support Vector Regressor trained on the available annotated datasets. The main goal of our minimalist approach to quality estimation is to provide reliable estimators that require minimal deployment effort, few resources, and, in the case of uMQE, do not depend on costly data annotation or post-editing. Our approach was applied to all language pairs in sentence quality estimation, obtaining competitive results across the board.",
}
@inproceedings{hu-etal-2018-contextual,
    title = "Contextual Encoding for Translation Quality Estimation",
    author = "Hu, Junjie  and
      Chang, Wei-Cheng  and
      Wu, Yuexin  and
      Neubig, Graham",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6462",
    doi = "10.18653/v1/W18-6462",
    pages = "788--793",
    abstract = "The task of word-level quality estimation (QE) consists of taking a source sentence and machine-generated translation, and predicting which words in the output are correct and which are wrong. In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach. The first part uses an embedding layer to represent words and their part-of-speech tags in both languages. The second part leverages a one-dimensional convolution layer to integrate local context information for each target word. The third part applies a stack of feed-forward and recurrent neural networks to further encode the global context in the sentence before making the predictions. This model was submitted as the CMU entry to the WMT2018 shared task on QE, and achieves strong results, ranking first in three of the six tracks.",
}
@inproceedings{ive-etal-2018-sheffield,
    title = "{S}heffield Submissions for the {WMT}18 Quality Estimation Shared Task",
    author = "Ive, Julia  and
      Scarton, Carolina  and
      Blain, Fr{\'e}d{\'e}ric  and
      Specia, Lucia",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6463",
    doi = "10.18653/v1/W18-6463",
    pages = "794--800",
    abstract = "In this paper we present the University of Sheffield submissions for the WMT18 Quality Estimation shared task. We discuss our submissions to all four sub-tasks, where ours is the only team to participate in all language pairs and variations (37 combinations). Our systems show competitive results and outperform the baseline in nearly all cases.",
}
@inproceedings{sanchez-martinez-etal-2018-ualacant,
    title = "{UA}lacant machine translation quality estimation at {WMT} 2018: a simple approach using phrase tables and feed-forward neural networks",
    author = "S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Espl{\`a}-Gomis, Miquel  and
      Forcada, Mikel L.",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6464",
    doi = "10.18653/v1/W18-6464",
    pages = "801--808",
    abstract = "We describe the Universitat d{'}Alacant submissions to the word- and sentence-level machine translation (MT) quality estimation (QE) shared task at WMT 2018. Our approach to word-level MT QE builds on previous work to mark the words in the machine-translated sentence as \textit{OK} or \textit{BAD}, and is extended to determine if a word or sequence of words need to be inserted in the gap after each word. Our sentence-level submission simply uses the edit operations predicted by the word-level approach to approximate TER. The method presented ranked first in the sub-task of identifying insertions in gaps for three out of the six datasets, and second in the rest of them.",
}
@inproceedings{wang-etal-2018-alibaba,
    title = "{A}libaba Submission for {WMT}18 Quality Estimation Task",
    author = "Wang, Jiayi  and
      Fan, Kai  and
      Li, Bo  and
      Zhou, Fengming  and
      Chen, Boxing  and
      Shi, Yangbin  and
      Si, Luo",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6465",
    doi = "10.18653/v1/W18-6465",
    pages = "809--815",
    abstract = "The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations. This paper presents the QE Brain system, which proposes the neural Bilingual Expert model as a feature extractor based on conditional target language model with a bidirectional transformer and then processes the semantic representations of source and the translation output with a Bi-LSTM predictive model for automatic quality estimation. The system has been applied to the sentence-level scoring and ranking tasks as well as the word-level tasks for finding errors for each word in translations. An extensive set of experimental results have shown that our system outperformed the best results in WMT 2017 Quality Estimation tasks and obtained top results in WMT 2018.",
}
@inproceedings{yankovskaya-etal-2018-quality,
    title = "Quality Estimation with Force-Decoded Attention and Cross-lingual Embeddings",
    author = {Yankovskaya, Elizaveta  and
      T{\"a}ttar, Andre  and
      Fishel, Mark},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6466",
    doi = "10.18653/v1/W18-6466",
    pages = "816--821",
    abstract = "This paper describes the submissions of the team from the University of Tartu for the sentence-level Quality Estimation shared task of WMT18. The proposed models use features based on attention weights of a neural machine translation system and cross-lingual phrase embeddings as input features of a regression model. Two of the proposed models require only a neural machine translation system with an attention mechanism with no additional resources. Results show that combining neural networks and baseline features leads to significant improvements over the baseline features alone.",
}
@inproceedings{junczys-dowmunt-grundkiewicz-2018-ms,
    title = "{MS}-{UE}din Submission to the {WMT}2018 {APE} Shared Task: Dual-Source Transformer for Automatic Post-Editing",
    author = "Junczys-Dowmunt, Marcin  and
      Grundkiewicz, Roman",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6467",
    doi = "10.18653/v1/W18-6467",
    pages = "822--826",
    abstract = "This paper describes the Microsoft and University of Edinburgh submission to the Automatic Post-editing shared task at WMT2018. Based on training data and systems from the WMT2017 shared task, we re-implement our own models from the last shared task and introduce improvements based on extensive parameter sharing. Next we experiment with our implementation of dual-source transformer models and data selection for the IT domain. Our submissions decisively wins the SMT post-editing sub-task establishing the new state-of-the-art and is a very close second (or equal, 16.46 vs 16.50 TER) in the NMT sub-task. Based on the rather weak results in the NMT sub-task, we hypothesize that neural-on-neural APE might not be actually useful.",
}
@inproceedings{pal-etal-2018-transformer,
    title = "A Transformer-Based Multi-Source Automatic Post-Editing System",
    author = {Pal, Santanu  and
      Herbig, Nico  and
      Kr{\"u}ger, Antonio  and
      van Genabith, Josef},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6468",
    doi = "10.18653/v1/W18-6468",
    pages = "827--835",
    abstract = "This paper presents our English{--}German Automatic Post-Editing (APE) system submitted to the APE Task organized at WMT 2018 (Chatterjee et al., 2018). The proposed model is an extension of the transformer architecture: two separate self-attention-based encoders encode the machine translation output (mt) and the source (src), followed by a joint encoder that attends over a combination of these two encoded sequences (encsrc and encmt) for generating the post-edited sentence. We compare this multi-source architecture (i.e, {src, mt} → pe) to a monolingual transformer (i.e., mt → pe) model and an ensemble combining the multi-source {src, mt} → pe and single-source mt → pe models. For both the PBSMT and the NMT task, the ensemble yields the best results, followed by the multi-source model and last the single-source approach. Our best model, the ensemble, achieves a BLEU score of 66.16 and 74.22 for the PBSMT and NMT task, respectively.",
}
@inproceedings{pylypenko-rubino-2018-dfki,
    title = "{DFKI}-{MLT} System Description for the {WMT}18 Automatic Post-editing Task",
    author = "Pylypenko, Daria  and
      Rubino, Raphael",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6469",
    doi = "10.18653/v1/W18-6469",
    pages = "836--839",
    abstract = "This paper presents the Automatic Post-editing (APE) systems submitted by the DFKI-MLT group to the WMT{'}18 APE shared task. Three monolingual neural sequence-to-sequence APE systems were trained using target-language data only: one using an attentional recurrent neural network architecture and two using the attention-only (\textit{transformer}) architecture. The training data was composed of machine translated (MT) output used as source to the APE model aligned with their manually post-edited version or reference translation as target. We made use of the provided training sets only and trained APE models applicable to phrase-based and neural MT outputs. Results show better performances reached by the attention-only model over the recurrent one, significant improvement over the baseline when post-editing phrase-based MT output but degradation when applied to neural MT output.",
}
@inproceedings{shin-lee-2018-multi,
    title = "Multi-encoder Transformer Network for Automatic Post-Editing",
    author = "Shin, Jaehun  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6470",
    doi = "10.18653/v1/W18-6470",
    pages = "840--845",
    abstract = "This paper describes the POSTECH{'}s submission to the WMT 2018 shared task on Automatic Post-Editing (APE). We propose a new neural end-to-end post-editing model based on the transformer network. We modified the encoder-decoder attention to reflect the relation between the machine translation output, the source and the post-edited translation in APE problem. Experiments on WMT17 English-German APE data set show an improvement in both TER and BLEU score over the best result of WMT17 APE shared task. Our primary submission achieves -4.52 TER and +6.81 BLEU score on PBSMT task and -0.13 TER and +0.40 BLEU score for NMT task compare to the baseline.",
}
@inproceedings{tebbifakhr-etal-2018-multi,
    title = "Multi-source transformer with combined losses for automatic post editing",
    author = "Tebbifakhr, Amirhossein  and
      Agrawal, Ruchit  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6471",
    doi = "10.18653/v1/W18-6471",
    pages = "846--852",
    abstract = "Recent approaches to the Automatic Post-editing (APE) of Machine Translation (MT) have shown that best results are obtained by neural multi-source models that correct the raw MT output by also considering information from the corresponding source sentence. To this aim, we present for the first time a neural multi-source APE model based on the Transformer architecture. Moreover, we employ sequence-level loss functions in order to avoid exposure bias during training and to be consistent with the automatic evaluation metrics used for the task. These are the main features of our submissions to the WMT 2018 APE shared task, where we participated both in the PBSMT subtask (i.e. the correction of MT outputs from a phrase-based system) and in the NMT subtask (i.e. the correction of neural outputs). In the first subtask, our system improves over the baseline up to -5.3 TER and +8.23 BLEU points ranking second out of 11 submitted runs. In the second one, characterized by the higher quality of the initial translations, we report lower but statistically significant gains (up to -0.38 TER and +0.8 BLEU), ranking first out of 10 submissions.",
}
@inproceedings{ash-etal-2018-speechmatics,
    title = "The Speechmatics Parallel Corpus Filtering System for {WMT}18",
    author = "Ash, Tom  and
      Francis, Remi  and
      Williams, Will",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6472",
    doi = "10.18653/v1/W18-6472",
    pages = "853--859",
    abstract = "Our entry to the parallel corpus filtering task uses a two-step strategy. The first step uses a series of pragmatic hard {`}rules{'} to remove the worst example sentences. This first step reduces the effective corpus size down from the initial 1 billion to 160 million tokens. The second step uses four different heuristics weighted to produce a score that is then used for further filtering down to 100 or 10 million tokens. Our final system produces competitive results without requiring excessive fine tuning to the exact task or language pair. The first step in isolation provides a very fast filter that gives most of the gains of the final system.",
}
@inproceedings{azpeitia-etal-2018-stacc,
    title = "{STACC}, {OOV} Density and N-gram Saturation: Vicomtech{'}s Participation in the {WMT} 2018 Shared Task on Parallel Corpus Filtering",
    author = "Azpeitia, Andoni  and
      Etchegoyhen, Thierry  and
      Mart{\'\i}nez Garcia, Eva",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6473",
    doi = "10.18653/v1/W18-6473",
    pages = "860--866",
    abstract = "We describe Vicomtech{'}s participation in the WMT 2018 Shared Task on parallel corpus filtering. We aimed to evaluate a simple approach to the task, which can efficiently process large volumes of data and can be easily deployed for new datasets in different language pairs and domains. We based our approach on STACC, an efficient and portable method for parallel sentence identification in comparable corpora. To address the specifics of the corpus filtering task, which features significant volumes of noisy data, the core method was expanded with a penalty based on the amount of unknown words in sentence pairs. Additionally, we experimented with a complementary data saturation method based on source sentence n-grams, with the goal of demoting parallel sentence pairs that do not contribute significant amounts of yet unobserved n-grams. Our approach requires no prior training and is highly efficient on the type of large datasets featured in the corpus filtering task. We achieved competitive results with this simple and portable method, ranking in the top half among competing systems overall.",
}
@inproceedings{barbu-barbu-mititelu-2018-hybrid,
    title = "A hybrid pipeline of rules and machine learning to filter web-crawled parallel corpora",
    author = "Barbu, Eduard  and
      Barbu Mititelu, Verginica",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6474",
    doi = "10.18653/v1/W18-6474",
    pages = "867--871",
    abstract = "A hybrid pipeline comprising rules and machine learning is used to filter a noisy web English-German parallel corpus for the Parallel Corpus Filtering task. The core of the pipeline is a module based on the logistic regression algorithm that returns the probability that a translation unit is accepted. The training set for the logistic regression is created by automatic annotation. The quality of the automatic annotation is estimated by manually labeling the training set.",
}
@inproceedings{erdmann-gwinnup-2018-coverage,
    title = "Coverage and Cynicism: The {AFRL} Submission to the {WMT} 2018 Parallel Corpus Filtering Task",
    author = "Erdmann, Grant  and
      Gwinnup, Jeremy",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6475",
    doi = "10.18653/v1/W18-6475",
    pages = "872--876",
    abstract = "The WMT 2018 Parallel Corpus Filtering Task aims to test various methods of filtering a noisy parallel corpus, to make it useful for training machine translation systems. We describe the AFRL submissions, including their preprocessing methods and quality metrics. Numerical results indicate relative benefits of different options and show where our methods are competitive.",
}
@inproceedings{fomicheva-gonzalez-rubio-2018-maje,
    title = "{MAJE} Submission to the {WMT}2018 Shared Task on Parallel Corpus Filtering",
    author = "Fomicheva, Marina  and
      Gonz{\'a}lez-Rubio, Jes{\'u}s",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6476",
    doi = "10.18653/v1/W18-6476",
    pages = "877--881",
    abstract = "This paper describes the participation of Webinterpret in the shared task on parallel corpus filtering at the Third Conference on Machine Translation (WMT 2018). The paper describes the main characteristics of our approach and discusses the results obtained on the data sets published for the shared task.",
}
@inproceedings{hangya-fraser-2018-unsupervised,
    title = "An Unsupervised System for Parallel Corpus Filtering",
    author = "Hangya, Viktor  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6477",
    doi = "10.18653/v1/W18-6477",
    pages = "882--887",
    abstract = "In this paper we describe LMU Munich{'}s submission for the \textit{WMT 2018 Parallel Corpus Filtering} shared task which addresses the problem of cleaning noisy parallel corpora. The task of mining and cleaning parallel sentences is important for improving the quality of machine translation systems, especially for low-resource languages. We tackle this problem in a fully unsupervised fashion relying on bilingual word embeddings created without any bilingual signal. After pre-filtering noisy data we rank sentence pairs by calculating bilingual sentence-level similarities and then remove redundant data by employing monolingual similarity as well. Our unsupervised system achieved good performance during the official evaluation of the shared task, scoring only a few BLEU points behind the best systems, while not requiring any parallel training data.",
}
@inproceedings{junczys-dowmunt-2018-dual,
    title = "Dual Conditional Cross-Entropy Filtering of Noisy Parallel Corpora",
    author = "Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6478",
    doi = "10.18653/v1/W18-6478",
    pages = "888--895",
    abstract = "In this work we introduce dual conditional cross-entropy filtering for noisy parallel data. For each sentence pair of the noisy parallel corpus we compute cross-entropy scores according to two inverse translation models trained on clean data. We penalize divergent cross-entropies and weigh the penalty by the cross-entropy average of both models. Sorting or thresholding according to these scores results in better subsets of parallel data. We achieve higher BLEU scores with models trained on parallel data filtered only from Paracrawl than with models trained on clean WMT data. We further evaluate our method in the context of the WMT2018 shared task on parallel corpus filtering and achieve the overall highest ranking scores of the shared task, scoring top in three out of four subtasks.",
}
@inproceedings{khayrallah-etal-2018-jhu,
    title = "The {JHU} Parallel Corpus Filtering Systems for {WMT} 2018",
    author = "Khayrallah, Huda  and
      Xu, Hainan  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6479",
    doi = "10.18653/v1/W18-6479",
    pages = "896--899",
    abstract = "This work describes our submission to the WMT18 Parallel Corpus Filtering shared task. We use a slightly modified version of the Zipporah Corpus Filtering toolkit (Xu and Koehn, 2017), which computes an adequacy score and a fluency score on a sentence pair, and use a weighted sum of the scores as the selection criteria. This work differs from Zipporah in that we experiment with using the noisy corpus to be filtered to compute the combination weights, and thus avoids generating synthetic data as in standard Zipporah.",
}
@inproceedings{littell-etal-2018-measuring,
    title = "Measuring sentence parallelism using Mahalanobis distances: The {NRC} unsupervised submissions to the {WMT}18 Parallel Corpus Filtering shared task",
    author = "Littell, Patrick  and
      Larkin, Samuel  and
      Stewart, Darlene  and
      Simard, Michel  and
      Goutte, Cyril  and
      Lo, Chi-kiu",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6480",
    doi = "10.18653/v1/W18-6480",
    pages = "900--907",
    abstract = "The WMT18 shared task on parallel corpus filtering (Koehn et al., 2018b) challenged teams to score sentence pairs from a large high-recall, low-precision web-scraped parallel corpus (Koehn et al., 2018a). Participants could use existing sample corpora (e.g. past WMT data) as a supervisory signal to learn what a {``}clean{''} corpus looks like. However, in lower-resource situations it often happens that the target corpus of the language is the \textit{only} sample of parallel text in that language. We therefore made several unsupervised entries, setting ourselves an additional constraint that we not utilize the additional clean parallel corpora. One such entry fairly consistently scored in the top ten systems in the 100M-word conditions, and for one task{---}translating the European Medicines Agency corpus (Tiedemann, 2009){---}scored among the best systems even in the 10M-word conditions.",
}
@inproceedings{lo-etal-2018-accurate,
    title = "Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The {NRC} supervised submissions to the Parallel Corpus Filtering task",
    author = "Lo, Chi-kiu  and
      Simard, Michel  and
      Stewart, Darlene  and
      Larkin, Samuel  and
      Goutte, Cyril  and
      Littell, Patrick",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6481",
    doi = "10.18653/v1/W18-6481",
    pages = "908--916",
    abstract = "We present our semantic textual similarity approach in filtering a noisy web crawled parallel corpus using YiSi{---}a novel semantic machine translation evaluation metric. The systems mainly based on this supervised approach perform well in the WMT18 Parallel Corpus Filtering shared task (4th place in 100-million-word evaluation, 8th place in 10-million-word evaluation, and 6th place overall, out of 48 submissions). In fact, our best performing system{---}NRC-yisi-bicov is one of the only four submissions ranked top 10 in both evaluations. Our submitted systems also include some initial filtering steps for scaling down the size of the test corpus and a final redundancy removal step for better semantic and token coverage of the filtered corpus. In this paper, we also describe our unsuccessful attempt in automatically synthesizing a noisy parallel development corpus for tuning the weights to combine different parallelism and fluency features.",
}
@inproceedings{lu-etal-2018-alibaba,
    title = "{A}libaba Submission to the {WMT}18 Parallel Corpus Filtering Task",
    author = "Lu, Jun  and
      Lv, Xiaoyu  and
      Shi, Yangbin  and
      Chen, Boxing",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6482",
    doi = "10.18653/v1/W18-6481",
    pages = "917--922",
    abstract = "This paper describes the Alibaba Machine Translation Group submissions to the WMT 2018 Shared Task on Parallel Corpus Filtering. While evaluating the quality of the parallel corpus, the three characteristics of the corpus are investigated, i.e. 1) the bilingual/translation quality, 2) the monolingual quality and 3) the corpus diversity. Both rule-based and model-based methods are adapted to score the parallel sentence pairs. The final parallel corpus filtering system is reliable, easy to build and adapt to other language pairs.",
}
@inproceedings{paetzold-2018-utfpr-wmt,
    title = "{UTFPR} at {WMT} 2018: Minimalistic Supervised Corpora Filtering for Machine Translation",
    author = "Paetzold, Gustavo",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6483",
    doi = "10.18653/v1/W18-6483",
    pages = "923--927",
    abstract = "We present the UTFPR systems at the WMT 2018 parallel corpus filtering task. Our supervised approach discerns between good and bad translations by training classic binary classification models over an artificially produced binary classification dataset derived from a high-quality translation set, and a minimalistic set of 6 semantic distance features that rely only on easy-to-gather resources. We rank translations by their probability for the {``}good{''} label. Our results show that logistic regression pairs best with our approach, yielding more consistent results throughout the different settings evaluated.",
}
@inproceedings{papavassiliou-etal-2018-ilsp,
    title = "The {ILSP}/{ARC} submission to the {WMT} 2018 Parallel Corpus Filtering Shared Task",
    author = "Papavassiliou, Vassilis  and
      Sofianopoulos, Sokratis  and
      Prokopidis, Prokopis  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6484",
    doi = "10.18653/v1/W18-6484",
    pages = "928--933",
    abstract = "This paper describes the submission of the Institute for Language and Speech Processing/Athena Research and Innovation Center (ILSP/ARC) for the WMT 2018 Parallel Corpus Filtering shared task. We explore several properties of sentences and sentence pairs that our system explored in the context of the task with the purpose of clustering sentence pairs according to their appropriateness in training MT systems. We also discuss alternative methods for ranking the sentence pairs of the most appropriate clusters with the aim of generating the two datasets (of 10 and 100 million words as required in the task) that were evaluated. By summarizing the results of several experiments that were carried out by the organizers during the evaluation phase, our submission achieved an average BLEU score of 26.41, even though it does not make use of any language-specific resources like bilingual lexica, monolingual corpora, or MT output, while the average score of the best participant system was 27.91.",
}
@inproceedings{pham-etal-2018-systran,
    title = "{SYSTRAN} Participation to the {WMT}2018 Shared Task on Parallel Corpus Filtering",
    author = "Pham, MinhQuang  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6485",
    doi = "10.18653/v1/W18-6485",
    pages = "934--938",
    abstract = "This paper describes the participation of SYSTRAN to the shared task on parallel corpus filtering at the Third Conference on Machine Translation (WMT 2018). We participate for the first time using a neural sentence similarity classifier which aims at predicting the relatedness of sentence pairs in a multilingual context. The paper describes the main characteristics of our approach and discusses the results obtained on the data sets published for the shared task.",
}
@inproceedings{pinnis-2018-tildes,
    title = "Tilde{'}s Parallel Corpus Filtering Methods for {WMT} 2018",
    author = "Pinnis, M{\=a}rcis",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6486",
    doi = "10.18653/v1/W18-6486",
    pages = "939--945",
    abstract = "The paper describes parallel corpus filtering methods that allow reducing noise of noisy {``}parallel{''} corpora from a level where the corpora are not usable for neural machine translation training (i.e., the resulting systems fail to achieve reasonable translation quality; well below 10 BLEU points) up to a level where the trained systems show decent (over 20 BLEU points on a 10 million word dataset and up to 30 BLEU points on a 100 million word dataset). The paper also documents Tilde{'}s submissions to the WMT 2018 shared task on parallel corpus filtering.",
}
@inproceedings{rossenbach-etal-2018-rwth,
    title = "The {RWTH} {A}achen {U}niversity Filtering System for the {WMT} 2018 Parallel Corpus Filtering Task",
    author = "Rossenbach, Nick  and
      Rosendahl, Jan  and
      Kim, Yunsu  and
      Gra{\c{c}}a, Miguel  and
      Gokrani, Aman  and
      Ney, Hermann",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6487",
    doi = "10.18653/v1/W18-6487",
    pages = "946--954",
    abstract = "This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2{\%} BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8{\%} BLEU.",
}
@inproceedings{sanchez-cartagena-etal-2018-prompsits,
    title = "Prompsit{'}s submission to {WMT} 2018 Parallel Corpus Filtering shared task",
    author = "S{\'a}nchez-Cartagena, V{\'\i}ctor M.  and
      Ba{\~n}{\'o}n, Marta  and
      Ortiz-Rojas, Sergio  and
      Ram{\'\i}rez, Gema",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6488",
    doi = "10.18653/v1/W18-6488",
    pages = "955--962",
    abstract = "This paper describes Prompsit Language Engineering{'}s submissions to the WMT 2018 parallel corpus filtering shared task. Our four submissions were based on an automatic classifier for identifying pairs of sentences that are mutual translations. A set of hand-crafted hard rules for discarding sentences with evident flaws were applied before the classifier. We explored different strategies for achieving a training corpus with diverse vocabulary and fluent sentences: language model scoring, an active-learning-inspired data selection algorithm and n-gram saturation. Our submissions were very competitive in comparison with other participants on the 100 million word training corpus.",
}
@inproceedings{wang-etal-2018-nicts,
    title = "{NICT}{'}s Corpus Filtering Systems for the {WMT}18 Parallel Corpus Filtering Task",
    author = "Wang, Rui  and
      Marie, Benjamin  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6489",
    doi = "10.18653/v1/W18-6489",
    pages = "963--967",
    abstract = "This paper presents the NICT{'}s participation in the WMT18 shared parallel corpus filtering task. The organizers provided 1 billion words German-English corpus crawled from the web as part of the Paracrawl project. This corpus is too noisy to build an acceptable neural machine translation (NMT) system. Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data. Finally, we sampled 100 million and 10 million words and built corresponding NMT systems. Empirical results show that our NMT systems trained on sampled data achieve promising performance.",
}
@proceedings{ws-2018-machine-translation,
    title = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Fishel, Mark  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Post, Matt  and
      Specia, Lucia  and
      Turchi, Marco  and
      Verspoor, Karin",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6300",
}
@inproceedings{ott-etal-2018-scaling,
    title = "Scaling Neural Machine Translation",
    author = "Ott, Myle  and
      Edunov, Sergey  and
      Grangier, David  and
      Auli, Michael",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6301",
    doi = "10.18653/v1/W18-6301",
    pages = "1--9",
    abstract = "Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8-GPU machine with careful tuning and implementation. On WMT{'}14 English-German translation, we match the accuracy of Vaswani et al. (2017) in under 5 hours when training on 8 GPUs and we obtain a new state of the art of 29.3 BLEU after training for 85 minutes on 128 GPUs. We further improve these results to 29.8 BLEU by training on the much larger Paracrawl dataset. On the WMT{'}14 English-French task, we obtain a state-of-the-art BLEU of 43.2 in 8.5 hours on 128 GPUs.",
}
@inproceedings{nikolov-etal-2018-character,
    title = "Character-level {C}hinese-{E}nglish Translation through {ASCII} Encoding",
    author = "Nikolov, Nikola I.  and
      Hu, Yuhuang  and
      Tan, Mi Xue  and
      Hahnloser, Richard H.R.",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6302",
    doi = "10.18653/v1/W18-6302",
    pages = "10--16",
    abstract = "Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European languages. We use the Wubi encoding scheme, which preserves the original shape and semantic information of the characters, while also being reversible. We show promising results from training Wubi-based models on the character- and subword-level with recurrent as well as convolutional models.",
}
@inproceedings{zhang-komachi-2018-neural,
    title = "Neural Machine Translation of Logographic Language Using Sub-character Level Information",
    author = "Zhang, Longtu  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6303",
    doi = "10.18653/v1/W18-6303",
    pages = "17--25",
    abstract = "Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units. However, important differences between languages with logographic and alphabetic writing systems have long been overlooked. This study focuses on these differences and uses a simple approach to improve the performance of NMT systems utilizing decomposed sub-character level information for logographic languages. Our results indicate that our approach not only improves the translation capabilities of NMT systems between Chinese and English, but also further improves NMT systems between Chinese and Japanese, because it utilizes the shared information brought by similar sub-character units.",
}
@inproceedings{tang-etal-2018-analysis,
    title = "An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation",
    author = "Tang, Gongbo  and
      Sennrich, Rico  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6304",
    doi = "10.18653/v1/W18-6304",
    pages = "26--35",
    abstract = "Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation. In this paper, we focus on analyzing encoder-decoder attention mechanisms, in the case of word sense disambiguation (WSD) in NMT models. We hypothesize that attention mechanisms pay more attention to context tokens when translating ambiguous words. We explore the attention distribution patterns when translating ambiguous nouns. Counterintuitively, we find that attention mechanisms are likely to distribute more attention to the ambiguous noun itself rather than context tokens, in comparison to other nouns. We conclude that attention is not the main mechanism used by NMT models to incorporate contextual information for WSD. The experimental results suggest that NMT models learn to encode contextual information necessary for WSD in the encoder hidden states. For the attention mechanism in Transformer models, we reveal that the first few layers gradually learn to {``}align{''} source and target tokens and the last few layers learn to extract features from the related but unaligned context tokens.",
}
@inproceedings{sostaric-etal-2018-discourse,
    title = "Discourse-Related Language Contrasts in {E}nglish-{C}roatian Human and Machine Translation",
    author = "{\v{S}}o{\v{s}}tari{\'c}, Margita  and
      Hardmeier, Christian  and
      Stymne, Sara",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6305",
    doi = "10.18653/v1/W18-6305",
    pages = "36--48",
    abstract = "We present an analysis of a number of coreference phenomena in English-Croatian human and machine translations. The aim is to shed light on the differences in the way these structurally different languages make use of discourse information and provide insights for discourse-aware machine translation system development. The phenomena are automatically identified in parallel data using annotation produced by parsers and word alignment tools, enabling us to pinpoint patterns of interest in both languages. We make the analysis more fine-grained by including three corpora pertaining to three different registers. In a second step, we create a test set with the challenging linguistic constructions and use it to evaluate the performance of three MT systems. We show that both SMT and NMT systems struggle with handling these discourse phenomena, even though NMT tends to perform somewhat better than SMT. By providing an overview of patterns frequently occurring in actual language use, as well as by pointing out the weaknesses of current MT systems that commonly mistranslate them, we hope to contribute to the effort of resolving the issue of discourse phenomena in MT applications.",
}
@inproceedings{stojanovski-fraser-2018-coreference,
    title = "Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments",
    author = "Stojanovski, Dario  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6306",
    doi = "10.18653/v1/W18-6306",
    pages = "49--60",
    abstract = "Cross-sentence context can provide valuable information in Machine Translation and is critical for translation of anaphoric pronouns and for providing consistent translations. In this paper, we devise simple oracle experiments targeting coreference and coherence. Oracles are an easy way to evaluate the effect of different discourse-level phenomena in NMT using BLEU and eliminate the necessity to manually define challenge sets for this purpose. We propose two context-aware NMT models and compare them against models working on a concatenation of consecutive sentences. Concatenation models perform better, but are computationally expensive. We show that NMT models taking advantage of context oracle signals can achieve considerable gains in BLEU, of up to 7.02 BLEU for coreference and 1.89 BLEU for coherence on subtitles translation. Access to strong signals allows us to make clear comparisons between context-aware models.",
}
@inproceedings{muller-etal-2018-large,
    title = "A Large-Scale Test Set for the Evaluation of Context-Aware Pronoun Translation in Neural Machine Translation",
    author = {M{\"u}ller, Mathias  and
      Rios, Annette  and
      Voita, Elena  and
      Sennrich, Rico},
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6307",
    doi = "10.18653/v1/W18-6307",
    pages = "61--72",
    abstract = "The translation of pronouns presents a special challenge to machine translation to this day, since it often requires context outside the current sentence. Recent work on models that have access to information across sentence boundaries has seen only moderate improvements in terms of automatic evaluation metrics such as BLEU. However, metrics that quantify the overall translation quality are ill-equipped to measure gains from additional context. We argue that a different kind of evaluation is needed to assess how well models translate inter-sentential phenomena such as pronouns. This paper therefore presents a test suite of contrastive translations focused specifically on the translation of pronouns. Furthermore, we perform experiments with several context-aware models. We show that, while gains in BLEU are moderate for those systems, they outperform baselines by a large margin in terms of accuracy on our contrastive test set. Our experiments also show the effectiveness of parameter tying for multi-encoder architectures.",
}
@inproceedings{pappas-etal-2018-beyond,
    title = "Beyond Weight Tying: Learning Joint Input-Output Embeddings for Neural Machine Translation",
    author = "Pappas, Nikolaos  and
      Miculicich, Lesly  and
      Henderson, James",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6308",
    doi = "10.18653/v1/W18-6308",
    pages = "73--83",
    abstract = "Tying the weights of the target word embeddings with the target word classifiers of neural machine translation models leads to faster training and often to better translation quality. Given the success of this parameter sharing, we investigate other forms of sharing in between no sharing and hard equality of parameters. In particular, we propose a \textit{structure-aware} output layer which captures the semantic structure of the output space of words within a joint input-output embedding. The model is a generalized form of \textit{weight tying} which shares parameters but allows learning a more flexible relationship with input word embeddings and allows the effective capacity of the output layer to be controlled. In addition, the model shares weights across output classifiers and translation contexts which allows it to better leverage prior knowledge about them. Our evaluation on English-to-Finnish and English-to-German datasets shows the effectiveness of the method against strong encoder-decoder baselines trained with or without \textit{weight tying}.",
}
@inproceedings{lu-etal-2018-neural,
    title = "A neural interlingua for multilingual machine translation",
    author = "Lu, Yichao  and
      Keung, Phillip  and
      Ladhak, Faisal  and
      Bhardwaj, Vikas  and
      Zhang, Shaonan  and
      Sun, Jason",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6309",
    doi = "10.18653/v1/W18-6309",
    pages = "84--92",
    abstract = "We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture. We demonstrate that our model learns a language-independent representation by performing direct zero-shot translation (without using pivot translation), and by using the source sentence embeddings to create an English Yelp review classifier that, through the mediation of the neural interlingua, can also classify French and German reviews. Furthermore, we show that, despite using a smaller number of parameters than a pairwise collection of bilingual NMT models, our approach produces comparable BLEU scores for each language pair in WMT15.",
}
@inproceedings{herold-etal-2018-improving,
    title = "Improving Neural Language Models with Weight Norm Initialization and Regularization",
    author = "Herold, Christian  and
      Gao, Yingbo  and
      Ney, Hermann",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6310",
    doi = "10.18653/v1/W18-6310",
    pages = "93--100",
    abstract = "Embedding and projection matrices are commonly used in neural language models (NLM) as well as in other sequence processing networks that operate on large vocabularies. We examine such matrices in fine-tuned language models and observe that a NLM learns word vectors whose norms are related to the word frequencies. We show that by initializing the weight norms with scaled log word counts, together with other techniques, lower perplexities can be obtained in early epochs of training. We also introduce a weight norm regularization loss term, whose hyperparameters are tuned via a grid search. With this method, we are able to significantly improve perplexities on two word-level language modeling tasks (without dynamic evaluation): from 54.44 to 53.16 on Penn Treebank (PTB) and from 61.45 to 60.13 on WikiText-2 (WT2).",
}
@inproceedings{maruf-etal-2018-contextual,
    title = "Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations",
    author = "Maruf, Sameen  and
      Martins, Andr{\'e} F. T.  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6311",
    doi = "10.18653/v1/W18-6311",
    pages = "101--112",
    abstract = "Recent works in neural machine translation have begun to explore document translation. However, translating online multi-speaker conversations is still an open problem. In this work, we propose the task of translating Bilingual Multi-Speaker Conversations, and explore neural architectures which exploit both source and target-side conversation histories for this task. To initiate an evaluation for this task, we introduce datasets extracted from Europarl v7 and OpenSubtitles2016. Our experiments on four language-pairs confirm the significance of leveraging conversation history, both in terms of BLEU and manual evaluation.",
}
@inproceedings{toral-etal-2018-attaining,
    title = "Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation",
    author = "Toral, Antonio  and
      Castilho, Sheila  and
      Hu, Ke  and
      Way, Andy",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6312",
    doi = "10.18653/v1/W18-6312",
    pages = "113--123",
    abstract = "We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.",
}
@inproceedings{thompson-etal-2018-freezing,
    title = "Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation",
    author = "Thompson, Brian  and
      Khayrallah, Huda  and
      Anastasopoulos, Antonios  and
      McCarthy, Arya D.  and
      Duh, Kevin  and
      Marvin, Rebecca  and
      McNamee, Paul  and
      Gwinnup, Jeremy  and
      Anderson, Tim  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6313",
    doi = "10.18653/v1/W18-6313",
    pages = "124--132",
    abstract = "To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component{'}s contribution to, and capacity for, domain adaptation. We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed. We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.",
}
@inproceedings{wang-etal-2018-denoising,
    title = "Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection",
    author = "Wang, Wei  and
      Watanabe, Taro  and
      Hughes, Macduff  and
      Nakagawa, Tetsuji  and
      Chelba, Ciprian",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6314",
    doi = "10.18653/v1/W18-6314",
    pages = "133--143",
    abstract = "Measuring domain relevance of data and identifying or selecting well-fit domain data for machine translation (MT) is a well-studied topic, but denoising is not yet. Denoising is concerned with a different type of data quality and tries to reduce the negative impact of data noise on MT training, in particular, neural MT (NMT) training. This paper generalizes methods for measuring and selecting data for domain MT and applies them to denoising NMT training. The proposed approach uses trusted data and a denoising curriculum realized by online data selection. Intrinsic and extrinsic evaluations of the approach show its significant effectiveness for NMT to train on data with severe noise.",
}
@inproceedings{burlot-yvon-2018-using,
    title = "Using Monolingual Data in Neural Machine Translation: a Systematic Study",
    author = "Burlot, Franck  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6315",
    doi = "10.18653/v1/W18-6315",
    pages = "144--155",
    abstract = "Neural Machine Translation (MT) has radically changed the way systems are developed. A major difference with the previous generation (Phrase-Based MT) is the way monolingual target data, which often abounds, is used in these two paradigms. While Phrase-Based MT can seamlessly integrate very large language models trained on billions of sentences, the best option for Neural MT developers seems to be the generation of artificial parallel data through back-translation - a technique that fails to fully take advantage of existing datasets. In this paper, we conduct a systematic study of back-translation, comparing alternative uses of monolingual data, as well as multiple data generation procedures. Our findings confirm that back-translation is very effective and give new explanations as to why this is the case. We also introduce new data simulation techniques that are almost as effective, yet much cheaper to implement.",
}
@inproceedings{lakew-etal-2018-neural,
    title = "Neural Machine Translation into Language Varieties",
    author = "Lakew, Surafel Melaku  and
      Erofeeva, Aliia  and
      Federico, Marcello",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6316",
    doi = "10.18653/v1/W18-6316",
    pages = "156--164",
    abstract = "Both research and commercial machine translation have so far neglected the importance of properly handling the spelling, lexical and grammar divergences occurring among language varieties. Notable cases are standard national varieties such as Brazilian and European Portuguese, and Canadian and European French, which popular online machine translation services are not keeping distinct. We show that an evident side effect of modeling such varieties as unique classes is the generation of inconsistent translations. In this work, we investigate the problem of training neural machine translation from English to specific pairs of language varieties, assuming both labeled and unlabeled parallel texts, and low-resource conditions. We report experiments from English to two pairs of dialects, European-Brazilian Portuguese and European-Canadian French, and two pairs of standardized varieties, Croatian-Serbian and Indonesian-Malay. We show significant BLEU score improvements over baseline systems when translation into similar languages is learned as a multilingual task with shared representations.",
}
@inproceedings{guo-etal-2018-effective,
    title = "Effective Parallel Corpus Mining using Bilingual Sentence Embeddings",
    author = "Guo, Mandy  and
      Shen, Qinlan  and
      Yang, Yinfei  and
      Ge, Heming  and
      Cer, Daniel  and
      Hernandez Abrego, Gustavo  and
      Stevens, Keith  and
      Constant, Noah  and
      Sung, Yun-Hsuan  and
      Strope, Brian  and
      Kurzweil, Ray",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6317",
    doi = "10.18653/v1/W18-6317",
    pages = "165--176",
    abstract = "This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings. Our embedding models are trained to produce similar representations exclusively for bilingual sentence pairs that are translations of each other. This is achieved using a novel training method that introduces hard negatives consisting of sentences that are not translations but have some degree of semantic similarity. The quality of the resulting embeddings are evaluated on parallel corpus reconstruction and by assessing machine translation systems trained on gold vs. mined sentence pairs. We find that the sentence embeddings can be used to reconstruct the United Nations Parallel Corpus (Ziemski et al., 2016) at the sentence-level with a precision of 48.9{\%} for en-fr and 54.9{\%} for en-es. When adapted to document-level matching, we achieve a parallel document matching accuracy that is comparable to the significantly more computationally intensive approach of Uszkoreit et al. (2010). Using reconstructed parallel data, we are able to train NMT models that perform nearly as well as models trained on the original data (within 1-2 BLEU).",
}
@inproceedings{alkhouli-etal-2018-alignment,
    title = "On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation",
    author = "Alkhouli, Tamer  and
      Bretschner, Gabriel  and
      Ney, Hermann",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6318",
    doi = "10.18653/v1/W18-6318",
    pages = "177--185",
    abstract = "This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by augmenting an additional alignment head to the multi-head source-to-target attention component. This is used to compute sharper attention weights. We describe how to use the alignment head to achieve competitive performance. To study the effect of adding the alignment head, we simulate a dictionary-guided translation task, where the user wants to guide translation using pre-defined dictionary entries. Using the proposed approach, we achieve up to 3.8{\%} BLEU improvement when using the dictionary, in comparison to 2.4{\%} BLEU in the baseline case. We also propose alignment pruning to speed up decoding in alignment-based neural machine translation (ANMT), which speeds up translation by a factor of 1.8 without loss in translation performance. We carry out experiments on the shared WMT 2016 English→Romanian news task and the BOLT Chinese→English discussion forum task.",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    doi = "10.18653/v1/W18-6319",
    pages = "186--191",
    abstract = "The field of machine translation faces an under-recognized problem because of inconsistency in the reporting of scores from its dominant metric. Although people refer to {``}the{''} BLEU score, BLEU is in fact a parameterized metric whose values can vary wildly with changes to these parameters. These parameters are often not reported or are hard to find, and consequently, BLEU scores between papers cannot be directly compared. I quantify this variation, finding differences as high as 1.8 between commonly used configurations. The main culprit is different tokenization and normalization schemes applied to the reference. Pointing to the success of the parsing community, I suggest machine translation researchers settle upon the BLEU scheme used by the annual Conference on Machine Translation (WMT), which does not allow for user-supplied reference processing, and provide a new tool, SACREBLEU, to facilitate this.",
}
@inproceedings{forcada-etal-2018-exploring,
    title = "Exploring gap filling as a cheaper alternative to reading comprehension questionnaires when evaluating machine translation for gisting",
    author = "Forcada, Mikel L.  and
      Scarton, Carolina  and
      Specia, Lucia  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6320",
    doi = "10.18653/v1/W18-6320",
    pages = "192--203",
    abstract = "A popular application of machine translation (MT) is \textit{gisting}: MT is consumed \textit{as is} to make sense of text in a foreign language. Evaluation of the usefulness of MT for gisting is surprisingly uncommon. The classical method uses \textit{reading comprehension questionnaires} (RCQ), in which informants are asked to answer professionally-written questions in their language about a foreign text that has been machine-translated into their language. Recently, \textit{gap-filling} (GF), a form of \textit{cloze} testing, has been proposed as a cheaper alternative to RCQ. In GF, certain words are removed from reference translations and readers are asked to fill the gaps left using the machine-translated text as a hint. This paper reports, for the first time, a comparative evaluation, using both RCQ and GF, of translations from multiple MT systems for the same foreign texts, and a systematic study on the effect of variables such as gap density, gap-selection strategies, and document context in GF. The main findings of the study are: (a) both RCQ and GF clearly identify MT to be useful; (b) global RCQ and GF rankings for the MT systems are mostly in agreement; (c) GF scores vary very widely across informants, making comparisons among MT systems hard, and (d) unlike RCQ, which is framed around documents, GF evaluation can be framed at the sentence level. These findings support the use of GF as a cheaper alternative to RCQ.",
}
@inproceedings{stahlberg-etal-2018-simple,
    title = "Simple Fusion: Return of the Language Model",
    author = "Stahlberg, Felix  and
      Cross, James  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6321",
    doi = "10.18653/v1/W18-6321",
    pages = "204--211",
    abstract = "Neural Machine Translation (NMT) typically leverages monolingual data in training through backtranslation. We investigate an alternative simple method to use monolingual data for NMT training: We combine the scores of a pre-trained and fixed language model (LM) with the scores of a translation model (TM) while the TM is trained from scratch. To achieve that, we train the translation model to predict the residual probability of the training data added to the prediction of the LM. This enables the TM to focus its capacity on modeling the source sentence since it can rely on the LM for fluency. We show that our method outperforms previous approaches to integrate LMs into NMT while the architecture is simpler as it does not require gating networks to balance TM and LM. We observe gains of between +0.24 and +2.36 BLEU on all four test sets (English-Turkish, Turkish-English, Estonian-English, Xhosa-English) on top of ensembles without LM. We compare our method with alternative ways to utilize monolingual data such as backtranslation, shallow fusion, and cold fusion.",
}
@inproceedings{murray-chiang-2018-correcting,
    title = "Correcting Length Bias in Neural Machine Translation",
    author = "Murray, Kenton  and
      Chiang, David",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6322",
    doi = "10.18653/v1/W18-6322",
    pages = "212--223",
    abstract = "We study two problems in neural machine translation (NMT). First, in beam search, whereas a wider beam should in principle help translation, it often hurts NMT. Second, NMT has a tendency to produce translations that are too short. Here, we argue that these problems are closely related and both rooted in label bias. We show that correcting the brevity problem almost eliminates the beam problem; we compare some commonly-used methods for doing this, finding that a simple per-word reward works well; and we introduce a simple and quick way to tune this reward using the perceptron algorithm.",
}
@inproceedings{silva-etal-2018-extracting,
    title = "Extracting In-domain Training Corpora for Neural Machine Translation Using Data Selection Methods",
    author = "Silva, Catarina Cruz  and
      Liu, Chao-Hong  and
      Poncelas, Alberto  and
      Way, Andy",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6323",
    doi = "10.18653/v1/W18-6323",
    pages = "224--231",
    abstract = "Data selection is a process used in selecting a subset of parallel data for the training of machine translation (MT) systems, so that 1) resources for training might be reduced, 2) trained models could perform better than those trained with the whole corpus, and/or 3) trained models are more tailored to specific domains. It has been shown that for statistical MT (SMT), the use of data selection helps improve the MT performance significantly. In this study, we reviewed three data selection approaches for MT, namely Term Frequency{--} Inverse Document Frequency, Cross-Entropy Difference and Feature Decay Algorithm, and conducted experiments on Neural Machine Translation (NMT) with the selected data using the three approaches. The results showed that for NMT systems, using data selection also improved the performance, though the gain is not as much as for SMT systems.",
}
@inproceedings{zhou-etal-2018-massively,
    title = "Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation",
    author = "Zhou, Zhong  and
      Sperber, Matthias  and
      Waibel, Alexander",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6324",
    doi = "10.18653/v1/W18-6324",
    pages = "232--243",
    abstract = "We work on translation from rich-resource languages to low-resource languages. The main challenges we identify are the lack of low-resource language data, effective methods for cross-lingual transfer, and the variable-binding problem that is common in neural systems. We build a translation system that addresses these challenges using eight European language families as our test ground. Firstly, we add the source and the target family labels and study intra-family and inter-family influences for effective cross-lingual transfer. We achieve an improvement of +9.9 in BLEU score for English-Swedish translation using eight families compared to the single-family multi-source multi-target baseline. Moreover, we find that training on two neighboring families closest to the low-resource language is often enough. Secondly, we construct an ablation study and find that reasonably good results can be achieved even with considerably less target data. Thirdly, we address the variable-binding problem by building an order-preserving named entity translation model. We obtain 60.6{\%} accuracy in qualitative evaluation where our translations are akin to human translations in a preliminary study.",
}
@inproceedings{kocmi-bojar-2018-trivial,
    title = "Trivial Transfer Learning for Low-Resource Neural Machine Translation",
    author = "Kocmi, Tom  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6325",
    doi = "10.18653/v1/W18-6325",
    pages = "244--252",
    abstract = "Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a {``}parent{''} model for a high-resource language pair and then continue the training on a low-resource pair only by replacing the training corpus. This {``}child{''} model performs significantly better than the baseline trained for low-resource pair only. We are the first to show this for targeting different languages, and we observe the improvements even for unrelated languages with different alphabets.",
}
@inproceedings{libovicky-etal-2018-input,
    title = "Input Combination Strategies for Multi-Source Transformer Decoder",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Helcl, Jind{\v{r}}ich  and
      Mare{\v{c}}ek, David",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6326",
    doi = "10.18653/v1/W18-6326",
    pages = "253--260",
    abstract = "In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four different input combination strategies for the encoder-decoder attention: serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of multimodal translation and translation with multiple source languages. The experiments show that the models are able to use multiple sources and improve over single source baselines.",
}
@inproceedings{sachan-neubig-2018-parameter,
    title = "Parameter Sharing Methods for Multilingual Self-Attentional Translation Models",
    author = "Sachan, Devendra  and
      Neubig, Graham",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6327",
    doi = "10.18653/v1/W18-6327",
    pages = "261--271",
    abstract = "In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even leading to performance gains over bilingually trained models. However, these improvements are not uniform; often multilingual parameter sharing results in a decrease in accuracy due to translation models not being able to accommodate different languages in their limited parameter space. In this work, we examine parameter sharing techniques that strike a happy medium between full sharing and individual training, specifically focusing on the self-attentional \textit{Transformer} model. We find that the full parameter sharing approach leads to increases in BLEU scores mainly when the target languages are from a similar language family. However, even in the case where target languages are from different families where full parameter sharing leads to a noticeable drop in BLEU scores, our proposed methods for partial sharing of parameters can lead to substantial improvements in translation accuracy.",
}
@proceedings{ws-2018-approaches-subjectivity,
    title = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      Hoste, Veronique  and
      Klinger, Roman",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6200",
}
@inproceedings{riloff-2018-identifying,
    title = "Identifying Affective Events and the Reasons for their Polarity",
    author = "Riloff, Ellen",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6201",
    doi = "10.18653/v1/W18-6201",
    pages = "1",
    abstract = "Many events have a positive or negative impact on our lives (e.g., {``}I bought a house{''} is typically good news, but {''}My house burned down{''} is bad news). Recognizing events that have affective polarity is essential for narrative text understanding, conversational dialogue, and applications such as summarization and sarcasm detection. We will discuss our recent work on identifying affective events and categorizing them based on the underlying reasons for their affective polarity. First, we will describe a weakly supervised learning method to induce a large set of affective events from a text corpus by optimizing for semantic consistency. Second, we will present models to classify affective events based on Human Need Categories, which often explain people{'}s motivations and desires. Our best results use a co-training model that consists of event expression and event context classifiers and exploits both labeled and unlabeled texts. We will conclude with a discussion of interesting directions for future work in this area.",
}
@inproceedings{ilic-etal-2018-deep,
    title = "Deep contextualized word representations for detecting sarcasm and irony",
    author = "Ili{\'c}, Suzana  and
      Marrese-Taylor, Edison  and
      Balazs, Jorge  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6202",
    doi = "10.18653/v1/W18-6202",
    pages = "2--7",
    abstract = "Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.",
}
@inproceedings{sun-etal-2018-implicit,
    title = "Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings",
    author = "Sun, Yuqi  and
      Shi, Haoyue  and
      Hu, Junfeng",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6203",
    doi = "10.18653/v1/W18-6203",
    pages = "8--13",
    abstract = "In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors. Shi et al. (2016) show that this kind of \textit{pseudo multi-senses} can be eliminated by linear transformations. In this paper, we show that \textit{pseudo multi-senses} may come from a uniform and meaningful phenomenon such as subjective and sentimental usage, though they are seemingly redundant. In this paper, we present an unsupervised algorithm to find a linear transformation which can minimize the transformed distance of a group of sense pairs. The major shrinking direction of this transformation is found to be related with subjective shift. Therefore, we can not only eliminate \textit{pseudo multi-senses} in multisense embeddings, but also identify these subjective senses and tag the subjective and sentimental usage of words in the corpus automatically.",
}
@inproceedings{saroufim-etal-2018-language,
    title = "Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings",
    author = "Saroufim, Carl  and
      Almatarky, Akram  and
      Abdel Hady, Mohammad",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6204",
    doi = "10.18653/v1/W18-6204",
    pages = "14--23",
    abstract = "Data annotation is a critical step to train a text model but it is tedious, expensive and time-consuming. We present a language independent method to train a sentiment polarity model with limited amount of manually-labeled data. Word embeddings such as Word2Vec are efficient at incorporating semantic and syntactic properties of words, yielding good results for document classification. However, these embeddings might map words with opposite polarities, to vectors close to each other. We train Sentiment Specific Word Embeddings (SSWE) on top of an unsupervised Word2Vec model, using either Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) on data auto-labeled as {``}Positive{''} or {``}Negative{''}. For this task, we rely on the universality of emojis and emoticons to auto-label a large number of French tweets using a small set of positive and negative emojis and emoticons. Finally, we apply a transfer learning approach to refine the network weights with a small-size manually-labeled training data set. Experiments are conducted to evaluate the performance of this approach on French sentiment classification using benchmark data sets from SemEval 2016 competition. We were able to achieve a performance improvement by using SSWE over Word2Vec. We also used a graph-based approach for label propagation to auto-generate a sentiment lexicon.",
}
@inproceedings{ohman-etal-2018-creating,
    title = "Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation",
    author = {{\"O}hman, Emily  and
      Kajava, Kaisla  and
      Tiedemann, J{\"o}rg  and
      Honkela, Timo},
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6205",
    doi = "10.18653/v1/W18-6205",
    pages = "24--30",
    abstract = "This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, \textit{Sentimentator}, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.",
}
@inproceedings{klinger-etal-2018-iest,
    title = "{IEST}: {WASSA}-2018 Implicit Emotions Shared Task",
    author = "Klinger, Roman  and
      De Clercq, Orph{\'e}e  and
      Mohammad, Saif  and
      Balahur, Alexandra",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6206",
    doi = "10.18653/v1/W18-6206",
    pages = "31--42",
    abstract = "Past shared tasks on emotions use data with both overt expressions of emotions (\textit{I am so happy to see you!}) as well as subtle expressions where the emotions have to be inferred, for instance from event descriptions. Further, most datasets do not focus on the cause or the stimulus of the emotion. Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Based on this intention, we call this the Implicit Emotion Shared Task (IEST) because the systems have to infer the emotion mostly from the context. Every tweet has an occurrence of an explicit emotion word that is masked. The tweets are collected in a manner such that they are likely to include a description of the cause of the emotion {--} the stimulus. Altogether, 30 teams submitted results which range from macro F1 scores of 21 {\%} to 71 {\%}. The baseline (Max-Ent bag of words and bigrams) obtains an F1 score of 60 {\%} which was available to the participants during the development phase. A study with human annotators suggests that automatic methods outperform human predictions, possibly by honing into subtle textual clues not used by humans. Corpora, resources, and results are available at the shared task website at \url{http://implicitemotions.wassa2018.com}.",
}
@inproceedings{rozental-etal-2018-amobee,
    title = "{A}mobee at {IEST} 2018: Transfer Learning from Language Models",
    author = "Rozental, Alon  and
      Fleischer, Daniel  and
      Kelrich, Zohar",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6207",
    doi = "10.18653/v1/W18-6207",
    pages = "43--49",
    abstract = "This paper describes the system developed at Amobee for the WASSA 2018 implicit emotions shared task (IEST). The goal of this task was to predict the emotion expressed by missing words in tweets without an explicit mention of those words. We developed an ensemble system consisting of language models together with LSTM-based networks containing a CNN attention mechanism. Our approach represents a novel use of language models{---}specifically trained on a large Twitter dataset{---}to predict and classify emotions. Our system reached 1st place with a macro F1 score of 0.7145.",
}
@inproceedings{balazs-etal-2018-iiidyt,
    title = "{IIIDYT} at {IEST} 2018: Implicit Emotion Classification With Deep Contextualized Word Representations",
    author = "Balazs, Jorge  and
      Marrese-Taylor, Edison  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6208",
    doi = "10.18653/v1/W18-6208",
    pages = "50--56",
    abstract = "In this paper we describe our system designed for the WASSA 2018 Implicit Emotion Shared Task (IEST), which obtained 2nd place out of 30 teams with a test macro F1 score of 0.710. The system is composed of a single pre-trained ELMo layer for encoding words, a Bidirectional Long-Short Memory Network BiLSTM for enriching word representations with context, a max-pooling operation for creating sentence representations from them, and a Dense Layer for projecting the sentence representations into label space. Our official submission was obtained by ensembling 6 of these models initialized with different random seeds. The code for replicating this paper is available at \url{https://github.com/jabalazs/implicit_emotion}.",
}
@inproceedings{chronopoulou-etal-2018-ntua,
    title = "{NTUA}-{SLP} at {IEST} 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification",
    author = "Chronopoulou, Alexandra  and
      Margatina, Aikaterini  and
      Baziotis, Christos  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6209",
    doi = "10.18653/v1/W18-6209",
    pages = "57--64",
    abstract = "In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from which a certain word has been removed, we are asked to predict the emotion of the missing word. In this work, we experiment with neural Transfer Learning (TL) methods. Our models are based on LSTM networks, augmented with a self-attention mechanism. We use the weights of various pretrained models, for initializing specific layers of our networks. We leverage a big collection of unlabeled Twitter messages, for pretraining word2vec word embeddings and a set of diverse language models. Moreover, we utilize a sentiment analysis dataset for pretraining a model, which encodes emotion related information. The submitted model consists of an ensemble of the aforementioned TL models. Our team ranked 3rd out of 30 participants, achieving an F1 score of 0.703.",
}
@inproceedings{lukes-sogaard-2018-sentiment,
    title = "Sentiment analysis under temporal shift",
    author = "Lukes, Jan  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6210",
    doi = "10.18653/v1/W18-6210",
    pages = "65--71",
    abstract = "Sentiment analysis models often rely on training data that is several years old. In this paper, we show that lexical features change polarity over time, leading to degrading performance. This effect is particularly strong in sparse models relying only on highly predictive features. Using predictive feature selection, we are able to significantly improve the accuracy of such models over time.",
}
@inproceedings{sekulic-etal-2018-just,
    title = "Not Just Depressed: Bipolar Disorder Prediction on {R}eddit",
    author = "Sekulic, Ivan  and
      Gjurkovi{\'c}, Matej  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6211",
    doi = "10.18653/v1/W18-6211",
    pages = "72--78",
    abstract = "Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide. We present a preliminary study on bipolar disorder prediction from user-generated text on Reddit, which relies on users{'} self-reported labels. Our benchmark classifiers for bipolar disorder prediction outperform the baselines and reach accuracy and F1-scores of above 86{\%}. Feature analysis shows interesting differences in language use between users with bipolar disorders and the control group, including differences in the use of emotion-expressive words.",
}
@inproceedings{bhatia-p-2018-topic,
    title = "Topic-Specific Sentiment Analysis Can Help Identify Political Ideology",
    author = "Bhatia, Sumit  and
      P, Deepak",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6212",
    doi = "10.18653/v1/W18-6212",
    pages = "79--84",
    abstract = "Ideological leanings of an individual can often be gauged by the sentiment one expresses about different issues. We propose a simple framework that represents a political ideology as a distribution of sentiment polarities towards a set of topics. This representation can then be used to detect ideological leanings of documents (speeches, news articles, etc.) based on the sentiments expressed towards different topics. Experiments performed using a widely used dataset show the promise of our proposed approach that achieves comparable performance to other methods despite being much simpler and more interpretable.",
}
@inproceedings{alkorta-etal-2018-saying,
    title = "Saying no but meaning yes: negation and sentiment analysis in {B}asque",
    author = "Alkorta, Jon  and
      Gojenola, Koldo  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6213",
    doi = "10.18653/v1/W18-6213",
    pages = "85--90",
    abstract = "In this work, we have analyzed the effects of negation on the semantic orientation in Basque. The analysis shows that negation markers can strengthen, weaken or have no effect on sentiment orientation of a word or a group of words. Using the Constraint Grammar formalism, we have designed and evaluated a set of linguistic rules to formalize these three phenomena. The results show that two phenomena, strengthening and no change, have been identified accurately and the third one, weakening, with acceptable results.",
}
@inproceedings{xiang-etal-2018-leveraging,
    title = "Leveraging Writing Systems Change for Deep Learning Based {C}hinese Emotion Analysis",
    author = "Xiang, Rong  and
      Long, Yunfei  and
      Lu, Qin  and
      Xiong, Dan  and
      Chen, I-Hsuan",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6214",
    doi = "10.18653/v1/W18-6214",
    pages = "91--96",
    abstract = "Social media text written in Chinese communities contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and some minor text using Latin letters, an alphabet-based writing system. This phenomenon is called writing systems changes (WSCs). Past studies have shown that WSCs can be used to express emotions, particularly where the social and political environment is more conservative. However, because WSCs can break the syntax of the major text, it poses more challenges in Natural Language Processing (NLP) tasks like emotion classification. In this work, we present a novel deep learning based method to include WSCs as an effective feature for emotion analysis. The method first identifies all WSCs points. Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model. Emotions in the minor text are further highlighted through an attention mechanism before emotion classification. Performance evaluation shows that incorporating WSCs features using deep learning models can improve performance measured by F1-scores compared to the state-of-the-art model.",
}
@inproceedings{byrkjeland-etal-2018-ternary,
    title = "Ternary {T}witter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings",
    author = {Byrkjeland, Mats  and
      G{\o}rvell de Lichtenberg, Frederik  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6215",
    doi = "10.18653/v1/W18-6215",
    pages = "97--106",
    abstract = "The paper proposes the Ternary Sentiment Embedding Model, a new model for creating sentiment embeddings based on the Hybrid Ranking Model of Tang et al. (2016), but trained on ternary-labeled data instead of binary-labeled, utilizing sentiment embeddings from datasets made with different distant supervision methods. The model is used as part of a complete Twitter Sentiment Analysis system and empirically compared to existing systems, showing that it outperforms Hybrid Ranking and that the quality of the distant-supervised dataset has a great impact on the quality of the produced sentiment embeddings.",
}
@inproceedings{daudert-buitelaar-2018-linking,
    title = "Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification",
    author = "Daudert, Tobias  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6216",
    doi = "10.18653/v1/W18-6216",
    pages = "107--115",
    abstract = "Social media{'}s popularity in society and research is gaining momentum and simultaneously increasing the importance of short textual content such as microblogs. Microblogs are affected by many factors including the news media, therefore, we exploit sentiments conveyed from news to detect and classify sentiment in microblogs. Given that texts can deal with the same entity but might not be vastly related when it comes to sentiment, it becomes necessary to introduce further measures ensuring the relatedness of texts while leveraging the contained sentiments. This paper describes ongoing research introducing distributional semantics to improve the exploitation of news-contained sentiment to enhance microblog sentiment classification.",
}
@inproceedings{brun-nikoulina-2018-aspect,
    title = "Aspect Based Sentiment Analysis into the Wild",
    author = "Brun, Caroline  and
      Nikoulina, Vassilina",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6217",
    doi = "10.18653/v1/W18-6217",
    pages = "116--122",
    abstract = "In this paper, we test state-of-the-art Aspect Based Sentiment Analysis (ABSA) systems trained on a widely used dataset on actual data. We created a new manually annotated dataset of user generated data from the same domain as the training dataset, but from other sources and analyse the differences between the new and the standard ABSA dataset. We then analyse the results in performance of different versions of the same system on both datasets. We also propose light adaptation methods to increase system robustness.",
}
@inproceedings{markov-etal-2018-role,
    title = "The Role of Emotions in Native Language Identification",
    author = "Markov, Ilia  and
      Nastase, Vivi  and
      Strapparava, Carlo  and
      Sidorov, Grigori",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6218",
    doi = "10.18653/v1/W18-6218",
    pages = "123--129",
    abstract = "We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language. To check the role of emotions in native language identification (NLI), we model emotion information through polarity and emotion load features, and use document representations using these features to classify the native language of the author. The results indicate that emotion is relevant for NLI, even for high proficiency levels and across topics.",
}
@inproceedings{ambartsoumian-popowich-2018-self,
    title = "Self-Attention: A Better Building Block for Sentiment Analysis Neural Network Classifiers",
    author = "Ambartsoumian, Artaches  and
      Popowich, Fred",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6219",
    doi = "10.18653/v1/W18-6219",
    pages = "130--139",
    abstract = "Sentiment Analysis has seen much progress in the past two decades. For the past few years, neural network approaches, primarily RNNs and CNNs, have been the most successful for this task. Recently, a new category of neural networks, self-attention networks (SANs), have been created which utilizes the attention mechanism as the basic building block. Self-attention networks have been shown to be effective for sequence modeling tasks, while having no recurrence or convolutions. In this work we explore the effectiveness of the SANs for sentiment analysis. We demonstrate that SANs are superior in performance to their RNN and CNN counterparts by comparing their classification accuracy on six datasets as well as their model characteristics such as training speed and memory consumption. Finally, we explore the effects of various SAN modifications such as multi-head attention as well as two methods of incorporating sequence position information into SANs.",
}
@inproceedings{long-etal-2018-dual,
    title = "Dual Memory Network Model for Biased Product Review Classification",
    author = "Long, Yunfei  and
      Ma, Mingyu  and
      Lu, Qin  and
      Xiang, Rong  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6220",
    doi = "10.18653/v1/W18-6220",
    pages = "140--148",
    abstract = "In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6{\%}, 1.2{\%}, and 0.9{\%}, respectively. The improvements are also deemed very significant measured by \textit{p-values}.",
}
@inproceedings{cuba-gyllensten-sahlgren-2018-measuring,
    title = "Measuring Issue Ownership using Word Embeddings",
    author = "Cuba Gyllensten, Amaru  and
      Sahlgren, Magnus",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6221",
    doi = "10.18653/v1/W18-6221",
    pages = "149--155",
    abstract = "Sentiment and topic analysis are common methods used for social media monitoring. Essentially, these methods answers questions such as, {``}what is being talked about, regarding \textit{X}{''}, and {``}what do people feel, regarding \textit{X}{''}. In this paper, we investigate another venue for social media monitoring, namely \textit{issue ownership} and \textit{agenda setting}, which are concepts from political science that have been used to explain voter choice and electoral outcomes. We argue that issue alignment and agenda setting can be seen as a kind of semantic source similarity of the kind {``}how similar is source \textit{A} to issue owner \textit{P}, when talking about issue \textit{X}{''}, and as such can be measured using word/document embedding techniques. We present work in progress towards measuring that kind of conditioned similarity, and introduce a new notion of similarity for predictive embeddings. We then test this method by measuring the similarity between politically aligned media and political parties, conditioned on bloc-specific issues.",
}
@inproceedings{kaljahi-foster-2018-sentiment,
    title = "Sentiment Expression Boundaries in Sentiment Polarity Classification",
    author = "Kaljahi, Rasoul  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6222",
    doi = "10.18653/v1/W18-6222",
    pages = "156--166",
    abstract = "We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis. We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification. Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.",
}
@inproceedings{sawhney-etal-2018-exploring,
    title = "Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning",
    author = "Sawhney, Ramit  and
      Manchanda, Prachi  and
      Mathur, Puneet  and
      Shah, Rajiv  and
      Singh, Raj",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6223",
    doi = "10.18653/v1/W18-6223",
    pages = "167--175",
    abstract = "The increasing suicide rates amongst youth and its high correlation with suicidal ideation expression on social media warrants a deeper investigation into models for the detection of suicidal intent in text such as tweets to enable prevention. However, the complexity of the natural language constructs makes this task very challenging. Deep Learning architectures such as LSTMs, CNNs, and RNNs show promise in sentence level classification problems. This work investigates the ability of deep learning architectures to build an accurate and robust model for suicidal ideation detection and compares their performance with standard baselines in text classification problems. The experimental results reveal the merit in C-LSTM based models as compared to other deep learning and machine learning based classification models for suicidal ideation detection.",
}
@inproceedings{paetzold-2018-utfpr,
    title = "{UTFPR} at {IEST} 2018: Exploring Character-to-Word Composition for Emotion Analysis",
    author = "Paetzold, Gustavo",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6224",
    doi = "10.18653/v1/W18-6224",
    pages = "176--181",
    abstract = "We introduce the UTFPR system for the Implicit Emotions Shared Task of 2018: A compositional character-to-word recurrent neural network that does not exploit heavy and/or hard-to-obtain resources. We find that our approach can outperform multiple baselines, and offers an elegant and effective solution to the problem of orthographic variance in tweets.",
}
@inproceedings{naderalvojoud-etal-2018-humir,
    title = "{HUMIR} at {IEST}-2018: Lexicon-Sensitive and Left-Right Context-Sensitive {B}i{LSTM} for Implicit Emotion Recognition",
    author = "Naderalvojoud, Behzad  and
      Ucan, Alaettin  and
      Akcapinar Sezer, Ebru",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6225",
    doi = "10.18653/v1/W18-6225",
    pages = "182--188",
    abstract = "This paper describes the approaches used in HUMIR system for the WASSA-2018 shared task on the implicit emotion recognition. The objective of this task is to predict the emotion expressed by the target word that has been excluded from the given tweet. We suppose this task as a word sense disambiguation in which the target word is considered as a synthetic word that can express 6 emotions depending on the context. To predict the correct emotion, we propose a deep neural network model that uses two BiLSTM networks to represent the contexts in the left and right sides of the target word. The BiLSTM outputs achieved from the left and right contexts are considered as context-sensitive features. These features are used in a feed-forward neural network to predict the target word emotion. Besides this approach, we also combine the BiLSTM model with lexicon-based and emotion-based features. Finally, we employ all models in the final system using Bagging ensemble method. We achieved macro F-measure value of 68.8 on the official test set and ranked sixth out of 30 participants.",
}
@inproceedings{zhou-wu-2018-nlp,
    title = "{NLP} at {IEST} 2018: {B}i{LSTM}-Attention and {LSTM}-Attention via Soft Voting in Emotion Classification",
    author = "Zhou, Qimin  and
      Wu, Hao",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6226",
    doi = "10.18653/v1/W18-6226",
    pages = "189--194",
    abstract = "This paper describes our method that competed at WASSA2018 \textit{Implicit Emotion Shared Task}. The goal of this task is to classify the emotions of excluded words in tweets into six different classes: sad, joy, disgust, surprise, anger and fear. For this, we examine a BiLSTM architecture with attention mechanism (BiLSTM-Attention) and a LSTM architecture with attention mechanism (LSTM-Attention), and try different dropout rates based on these two models. We then exploit an ensemble of these methods to give the final prediction which improves the model performance significantly compared with the baseline model. The proposed method achieves 7th position out of 30 teams and outperforms the baseline method by 12.5{\%} in terms of \textit{macro F1}.",
}
@inproceedings{plaza-del-arco-etal-2018-sinai-iest,
    title = "{SINAI} at {IEST} 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification",
    author = "Plaza-del-Arco, Flor Miriam  and
      Mart{\'\i}nez-C{\'a}mara, Eugenio  and
      Martin, Maite  and
      Ure{\~n}a- L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6227",
    doi = "10.18653/v1/W18-6227",
    pages = "195--200",
    abstract = "In this paper, we describe our participation in WASSA 2018 Implicit Emotion Shared Task (IEST 2018). We claim that the use of emotional external knowledge may enhance the performance and the capacity of generalization of an emotion classification system based on neural networks. Accordingly, we submitted four deep learning systems grounded in a sequence encoding layer. They mainly differ in the feature vector space and the recurrent neural network used in the sequence encoding layer. The official results show that the systems that used emotional external knowledge have a higher capacity of generalization, hence our claim holds.",
}
@inproceedings{liu-2018-emonlp-iest,
    title = "{E}mo{NLP} at {IEST} 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets",
    author = "Liu, Man",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6228",
    doi = "10.18653/v1/W18-6228",
    pages = "201--204",
    abstract = "This paper describes our system submitted to IEST 2018, a shared task (Klinger et al., 2018) to predict the emotion types. Six emotion types are involved: anger, joy, fear, surprise, disgust and sad. We perform three different approaches: feed forward neural network (FFNN), convolutional BLSTM (ConBLSTM) and Gradient Boosting Regression Tree Method (GBM). Word embeddings used in convolutional BLSTM are pre-trained on 470 million tweets which are filtered using the emotional words and emojis. In addition, broad sets of features (i.e. syntactic features, lexicon features, cluster features) are adopted to train GBM and FFNN. The three approaches are finally ensembled by the weighted average of predicted probabilities of each emotion label.",
}
@inproceedings{wang-2018-hgsgnlp,
    title = "{HGSGNLP} at {IEST} 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets",
    author = "Wang, Wenting",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6229",
    doi = "10.18653/v1/W18-6229",
    pages = "205--210",
    abstract = "This paper describes our system designed for the WASSA-2018 Implicit Emotion Shared Task (IEST). The task is to predict the emotion category expressed in a tweet by removing the terms \textit{angry}, \textit{afraid}, \textit{happy}, \textit{sad}, \textit{surprised}, \textit{disgusted} and their synonyms. Our final submission is an ensemble of one supervised learning model and three deep neural network based models, where each model approaches the problem from essentially different directions. Our system achieves the macro F1 score of 65.8{\%}, which is a 5.9{\%} performance improvement over the baseline and is ranked 12 out of 30 participating teams.",
}
@inproceedings{senarath-thayasivam-2018-datasearch,
    title = "{D}ata{SEARCH} at {IEST} 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning",
    author = "Senarath, Yasas  and
      Thayasivam, Uthayasanker",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6230",
    doi = "10.18653/v1/W18-6230",
    pages = "211--216",
    abstract = "This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks. The system described in this paper is composed of a sequential combination of Long Short-Term Memory and Convolutional Neural Network for feature extraction and Feedforward Neural Network for classification. In this paper, we successfully show that features extracted using multiple pre-trained embeddings can be used to improve the overall performance of the system with Emoji being one of the significant features. The evaluations show that our approach outperforms the baseline system by more than 8{\%} without using any external corpus or lexicon. This approach is ranked 8th in Implicit Emotion Shared Task (IEST) at WASSA-2018.",
}
@inproceedings{pecar-etal-2018-nl,
    title = "{NL}-{FIIT} at {IEST}-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing",
    author = "Pecar, Samuel  and
      Farkas, Michal  and
      Simko, Marian  and
      Lacko, Peter  and
      Bielikova, Maria",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6231",
    doi = "10.18653/v1/W18-6231",
    pages = "217--223",
    abstract = "In this paper, we present neural models submitted to Shared Task on Implicit Emotion Recognition, organized as part of WASSA 2018. We propose a Bi-LSTM architecture with regularization through dropout and Gaussian noise. Our models use three different embedding layers: GloVe word embeddings trained on Twitter dataset, ELMo embeddings and also sentence embeddings. We see preprocessing as one of the most important parts of the task. We focused on handling emojis, emoticons, hashtags, and also various shortened word forms. In some cases, we proposed to remove some parts of the text, as they do not affect emotion of the original sentence. We also experimented with other modifications like category weights for learning and stacking multiple layers. Our model achieved a macro average F1 score of 65.55{\%}, significantly outperforming the baseline model produced by a simple logistic regression.",
}
@inproceedings{priban-martinek-2018-uwb,
    title = "{UWB} at {IEST} 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network",
    author = "P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Mart{\'\i}nek, Ji{\v{r}}{\'\i}",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6232",
    doi = "10.18653/v1/W18-6232",
    pages = "224--230",
    abstract = "This paper describes our system created for the WASSA 2018 Implicit Emotion Shared Task. The goal of this task is to predict the emotion of a given tweet, from which a certain emotion word is removed. The removed word can be \textit{sad}, \textit{happy}, \textit{disgusted}, \textit{angry}, \textit{afraid} or a synonym of one of them. Our proposed system is based on deep-learning methods. We use Bidirectional Long Short-Term Memory (BiLSTM) with word embeddings as an input. Pre-trained DeepMoji model and pre-trained emoji2vec emoji embeddings are also used as additional inputs. Our System achieves 0.657 macro F1 score and our rank is 13th out of 30.",
}
@inproceedings{rissola-etal-2018-usi,
    title = "{USI}-{IR} at {IEST} 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection",
    author = "R{\'\i}ssola, Esteban  and
      Giachanou, Anastasia  and
      Crestani, Fabio",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6233",
    doi = "10.18653/v1/W18-6233",
    pages = "231--234",
    abstract = "This paper describes the participation of USI-IR in WASSA 2018 Implicit Emotion Shared Task. We propose a relevance feedback approach employing a sequential model (biLSTM) and word embeddings derived from a large collection of tweets. To this end, we assume that the top-\textit{k} predictions produce at a first classification step are correct (based on the model accuracy) and use them as new examples to re-train the network.",
}
@inproceedings{proisl-etal-2018-emotiklue,
    title = "{E}moti{KLUE} at {IEST} 2018: Topic-Informed Classification of Implicit Emotions",
    author = "Proisl, Thomas  and
      Heinrich, Philipp  and
      Kabashi, Besim  and
      Evert, Stefan",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6234",
    doi = "10.18653/v1/W18-6234",
    pages = "235--242",
    abstract = "EmotiKLUE is a submission to the Implicit Emotion Shared Task. It is a deep learning system that combines independent representations of the left and right contexts of the emotion word with the topic distribution of an LDA topic model. EmotiKLUE achieves a macro average \textit{F₁}score of 67.13{\%}, significantly outperforming the baseline produced by a simple ML classifier. Further enhancements after the evaluation period lead to an improved \textit{F₁}score of 68.10{\%}.",
}
@inproceedings{gratian-haid-2018-braint,
    title = "{B}rain{T} at {IEST} 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion Classification",
    author = "Gratian, Vachagan  and
      Haid, Marina",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6235",
    doi = "10.18653/v1/W18-6235",
    pages = "243--247",
    abstract = "We present \textit{BrainT}, a multi-class, averaged perceptron tested on implicit emotion prediction of tweets. We show that the dataset is linearly separable and explore ways in fine-tuning the baseline classifier. Our results indicate that the bag-of-words features benefit the model moderately and prediction can be improved with bigrams, trigrams, \textit{skip-one}-tetragrams and POS-tags. Furthermore, we find preprocessing of the n-grams, including stemming, lowercasing, stopword filtering, emoji and emoticon conversion generally not useful. The model is trained on an annotated corpus of 153,383 tweets and predictions on the test data were submitted to the WASSA-2018 Implicit Emotion Shared Task. BrainT attained a Macro F-score of 0.63.",
}
@inproceedings{witon-etal-2018-disney,
    title = "Disney at {IEST} 2018: Predicting Emotions using an Ensemble",
    author = "Witon, Wojciech  and
      Colombo, Pierre  and
      Modi, Ashutosh  and
      Kapadia, Mubbasir",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6236",
    doi = "10.18653/v1/W18-6236",
    pages = "248--253",
    abstract = "This paper describes our participating system in the WASSA 2018 shared task on emotion prediction. The task focuses on implicit emotion prediction in a tweet. In this task, keywords corresponding to the six emotion labels used (anger, fear, disgust, joy, sad, and surprise) have been removed from the tweet text, making emotion prediction implicit and the task challenging. We propose a model based on an ensemble of classifiers for prediction. Each classifier uses a sequence of Convolutional Neural Network (CNN) architecture blocks and uses ELMo (Embeddings from Language Model) as an input. Our system achieves a 66.2{\%} F1 score on the test set. The best performing system in the shared task has reported a 71.4{\%} F1 score.",
}
@inproceedings{rathnayaka-etal-2018-sentylic,
    title = "Sentylic at {IEST} 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection",
    author = "Rathnayaka, Prabod  and
      Abeysinghe, Supun  and
      Samarajeewa, Chamod  and
      Manchanayake, Isura  and
      Walpola, Malaka",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6237",
    doi = "10.18653/v1/W18-6237",
    pages = "254--259",
    abstract = "In this paper, we present the system we have used for the Implicit WASSA 2018 Implicit Emotion Shared Task. The task is to predict the emotion of a tweet of which the explicit mentions of emotion terms have been removed. The idea is to come up with a model which has the ability to implicitly identify the emotion expressed given the context words. We have used a Gated Recurrent Neural Network (GRU) and a Capsule Network based model for the task. Pre-trained word embeddings have been utilized to incorporate contextual knowledge about words into the model. GRU layer learns latent representations using the input word embeddings. Subsequent Capsule Network layer learns high-level features from that hidden representation. The proposed model managed to achieve a macro-F1 score of 0.692.",
}
@inproceedings{salaka-etal-2018-fast,
    title = "Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning",
    author = "Salaka, Viraj  and
      Warushavithana, Menuka  and
      de Silva, Nisansa  and
      Perera, Amal Shehan  and
      Ratnayaka, Gathika  and
      Rupasinghe, Thejan",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6238",
    doi = "10.18653/v1/W18-6238",
    pages = "260--265",
    abstract = "This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain. The added complexity of the language used in law, and the inability of the existing systems to accurately predict the sentiments of words in law are the main motivations behind this study. This is a transfer learning approach which can be used for other domain adaptation tasks as well. The proposed methodology achieves an improvement of over 6{\%} compared to the source model{'}s accuracy in the legal domain.",
}
@inproceedings{gopalakrishna-pillai-etal-2018-makes,
    title = "What Makes You Stressed? Finding Reasons From Tweets",
    author = "Gopalakrishna Pillai, Reshmi  and
      Thelwall, Mike  and
      Orasan, Constantin",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6239",
    doi = "10.18653/v1/W18-6239",
    pages = "266--272",
    abstract = "Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals. This paper introduces a novel framework for finding reasons for stress from tweets, analyzing multiple categories for the first time. Three word-vector based methods are evaluated on collections of tweets about politics or airlines and are found to be more accurate than standard machine learning algorithms.",
}
@inproceedings{mazoure-etal-2018-emojigan,
    title = "{E}moji{GAN}: learning emojis distributions with a generative model",
    author = "Mazoure, Bogdan  and
      Doan, Thang  and
      Ray, Saibal",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6240",
    doi = "10.18653/v1/W18-6240",
    pages = "273--279",
    abstract = "Generative models have recently experienced a surge in popularity due to the development of more efficient training algorithms and increasing computational power. Models such as adversarial generative networks (GANs) have been successfully used in various areas such as computer vision, medical imaging, style transfer and natural language generation. Adversarial nets were recently shown to yield results in the image-to-text task, where given a set of images, one has to provide their corresponding text description. In this paper, we take a similar approach and propose a image-to-emoji architecture, which is trained on data from social networks and can be used to score a given picture using ideograms. We show empirical results of our algorithm on data obtained from the most influential Instagram accounts.",
}
@inproceedings{abercrombie-batista-navarro-2018-identifying,
    title = "Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions",
    author = "Abercrombie, Gavin  and
      Batista-Navarro, Riza Theresa",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6241",
    doi = "10.18653/v1/W18-6241",
    pages = "280--285",
    abstract = "Analysis of the topics mentioned and opinions expressed in parliamentary debate motions{--}or proposals{--}is difficult for human readers, but necessary for understanding and automatic processing of the content of the subsequent speeches. We present a dataset of debate motions with pre-existing {`}\textit{policy}{'} labels, and investigate the utility of these labels for simultaneous topic and opinion polarity analysis. For topic detection, we apply one-versus-the-rest supervised topic classification, finding that good performance is achieved in predicting the \textit{policy} topics, and that textual features derived from the debate titles associated with the motions are particularly indicative of motion topic. We then examine whether the output could also be used to determine the positions taken by proposers towards the different policies by investigating how well humans agree in interpreting the opinion polarities of the motions. Finding very high levels of agreement, we conclude that the \textit{policies} used can be reliable labels for use in these tasks, and that successful topic detection can therefore provide opinion analysis of the motions {`}for free{'}.",
}
@inproceedings{van-den-beukel-aroyo-2018-homonym,
    title = "Homonym Detection For Humor Recognition In Short Text",
    author = "van den Beukel, Sven  and
      Aroyo, Lora",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6242",
    doi = "10.18653/v1/W18-6242",
    pages = "286--291",
    abstract = "In this paper, automatic homophone- and homograph detection are suggested as new useful features for humor recognition systems. The system combines style-features from previous studies on humor recognition in short text with ambiguity-based features. The performance of two potentially useful homograph detection methods is evaluated using crowdsourced annotations as ground truth. Adding homophones and homographs as features to the classifier results in a small but significant improvement over the style-features alone. For the task of humor recognition, recall appears to be a more important quality measure than precision. Although the system was designed for humor recognition in oneliners, it also performs well at the classification of longer humorous texts.",
}
@inproceedings{xu-etal-2018-emo2vec,
    title = "{E}mo2{V}ec: Learning Generalized Emotion Representation by Multi-task Training",
    author = "Xu, Peng  and
      Madotto, Andrea  and
      Wu, Chien-Sheng  and
      Park, Ji Ho  and
      Fung, Pascale",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6243",
    doi = "10.18653/v1/W18-6243",
    pages = "292--298",
    abstract = "In this paper, we propose Emo2Vec which encodes emotional semantics into vectors. We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection, and personality recognition. Our evaluation of Emo2Vec shows that it outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora. When concatenated with GloVe, Emo2Vec achieves competitive performances to state-of-the-art results on several tasks using a simple logistic regression classifier.",
}
@inproceedings{meisheri-khadilkar-2018-learning,
    title = "Learning representations for sentiment classification using Multi-task framework",
    author = "Meisheri, Hardik  and
      Khadilkar, Harshad",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6244",
    doi = "10.18653/v1/W18-6244",
    pages = "299--308",
    abstract = "Most of the existing state of the art sentiment classification techniques involve the use of pre-trained embeddings. This paper postulates a generalized representation that collates training on multiple datasets using a Multi-task learning framework. We incorporate publicly available, pre-trained embeddings with Bidirectional LSTM{'}s to develop the multi-task model. We validate the representations on an independent test Irony dataset that can contain several sentiments within each sample, with an arbitrary distribution. Our experiments show a significant improvement in results as compared to the available baselines for individual datasets on which independent models are trained. Results also suggest superior performance of the representations generated over Irony dataset.",
}
@inproceedings{sun-etal-2018-super,
    title = "Super Characters: A Conversion from Sentiment Classification to Image Classification",
    author = "Sun, Baohua  and
      Yang, Lin  and
      Dong, Patrick  and
      Zhang, Wenhan  and
      Dong, Jason  and
      Young, Charles",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6245",
    doi = "10.18653/v1/W18-6245",
    pages = "309--315",
    abstract = "We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text features are extracted automatically from the generated Super Characters images, hence there is no need of any explicit step of embedding the words or characters into numerical vector representations. Experimental results on large social media corpus show that the Super Characters method consistently outperforms other methods for sentiment classification and topic classification tasks on ten large social media datasets of millions of contents in four different languages, including Chinese, Japanese, Korean and English.",
}
@inproceedings{rethmeier-etal-2018-learning,
    title = "Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task {CNN}s",
    author = {Rethmeier, Nils  and
      H{\"u}bner, Marc  and
      Hennig, Leonhard},
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6246",
    doi = "10.18653/v1/W18-6246",
    pages = "316--321",
    abstract = "Comments on web news contain controversies that manifest as inter-group agreement-conflicts. Tracking such \textit{rapidly evolving controversy} could ease conflict resolution or journalist-user interaction. However, this presupposes controversy online-prediction that scales to diverse domains using incidental supervision signals instead of manual labeling. To more deeply interpret comment-controversy model decisions we frame prediction as binary classification and evaluate baselines and multi-task CNNs that use an auxiliary news-genre-encoder. Finally, we use ablation and interpretability methods to determine the impacts of topic, discourse and sentiment indicators, contextual vs. global word influence, as well as genre-keywords vs. per-genre-controversy keywords {--} to find that the models learn plausible controversy features using only incidentally supervised signals.",
}
@inproceedings{muralidhar-etal-2018-words,
    title = "Words Worth: Verbal Content and Hirability Impressions in {Y}ou{T}ube Video Resumes",
    author = "Muralidhar, Skanda  and
      Nguyen, Laurent  and
      Gatica-Perez, Daniel",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6247",
    doi = "10.18653/v1/W18-6247",
    pages = "322--327",
    abstract = "Automatic hirability prediction from video resumes is gaining increasing attention in both psychology and computing. Most existing works have investigated hirability from the perspective of nonverbal behavior, with verbal content receiving little interest. In this study, we leverage the advances in deep-learning based text representation techniques (like word embedding) in natural language processing to investigate the relationship between verbal content and perceived hirability ratings. To this end, we use 292 conversational video resumes from YouTube, develop a computational framework to automatically extract various representations of verbal content, and evaluate them in a regression task. We obtain a best performance of R{\mbox{$^2$}} = 0.23 using GloVe, and R{\mbox{$^2$}} = 0.22 using Word2Vec representations for manual and automatically transcribed texts respectively. Our inference results indicate the feasibility of using deep learning based verbal content representation in inferring hirability scores from online conversational video resumes.",
}
@inproceedings{hilte-etal-2018-predicting,
    title = "Predicting Adolescents{'} Educational Track from Chat Messages on {D}utch Social Media",
    author = "Hilte, Lisa  and
      Daelemans, Walter  and
      Vandekerckhove, Reinhild",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6248",
    doi = "10.18653/v1/W18-6248",
    pages = "328--334",
    abstract = "We aim to predict Flemish adolescents{'} educational track based on their Dutch social media writing. We distinguish between the three main types of Belgian secondary education: General (theory-oriented), Vocational (practice-oriented), and Technical Secondary Education (hybrid). The best results are obtained with a Naive Bayes model, i.e. an F-score of 0.68 (std. dev. 0.05) in 10-fold cross-validation experiments on the training data and an F-score of 0.60 on unseen data. Many of the most informative features are character n-grams containing specific occurrences of chatspeak phenomena such as emoticons. While the detection of the most theory- and practice-oriented educational tracks seems to be a relatively easy task, the hybrid Technical level appears to be much harder to capture based on online writing style, as expected.",
}
@inproceedings{guellil-etal-2018-arabizi,
    title = "{A}rabizi sentiment analysis based on transliteration and automatic corpus annotation",
    author = "Guellil, Imane  and
      Adeel, Ahsan  and
      Azouaou, Faical  and
      Benali, Fodil  and
      Hachani, Ala-eddine  and
      Hussain, Amir",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6249",
    doi = "10.18653/v1/W18-6249",
    pages = "335--341",
    abstract = "Arabizi is a form of writing Arabic text which relies on Latin letters, numerals and punctuation rather than Arabic letters. In the literature, the difficulties associated with Arabizi sentiment analysis have been underestimated, principally due to the complexity of Arabizi. In this paper, we present an approach to automatically classify sentiments of Arabizi messages into positives or negatives. In the proposed approach, Arabizi messages are first transliterated into Arabic. Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus. For corpus validation, shallow machine learning algorithms such as Support Vectors Machine (SVM) and Naive Bays (NB) are used. Simulations results demonstrate the outperformance of NB algorithm over all others. The highest achieved F1-score is up to 78{\%} and 76{\%} for manually and automatically transliterated dataset respectively. Ongoing work is aimed at improving the transliterator module and annotated sentiment dataset.",
}
@inproceedings{alhuzali-etal-2018-ubc,
    title = "{UBC}-{NLP} at {IEST} 2018: Learning Implicit Emotion With an Ensemble of Language Models",
    author = "Alhuzali, Hassan  and
      Elaraby, Mohamed  and
      Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6250",
    doi = "10.18653/v1/W18-6250",
    pages = "342--347",
    abstract = "We describe UBC-NLP contribution to IEST-2018, focused at learning implicit emotion in Twitter data. Among the 30 participating teams, our system ranked the 4th (with 69.3{\%} \textit{F}-score). Post competition, we were able to score slightly higher than the 3rd ranking system (reaching 70.7{\%}). Our system is trained on top of a pre-trained language model (LM), fine-tuned on the data provided by the task organizers. Our best results are acquired by an average of an ensemble of language models. We also offer an analysis of system performance and the impact of training data size on the task. For example, we show that training our best model for only one epoch with {\textless} 40{\%} of the data enables better performance than the baseline reported by Klinger et al. (2018) for the task.",
}
@proceedings{ws-2018-2018-emnlp-w,
    title = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6100",
}
@inproceedings{shoemark-etal-2018-inducing,
    title = "Inducing a lexicon of sociolinguistic variables from code-mixed text",
    author = "Shoemark, Philippa  and
      Kirby, James  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6101",
    doi = "10.18653/v1/W18-6101",
    pages = "1--6",
    abstract = "Sociolinguistics is often concerned with how variants of a linguistic item (e.g., \textit{nothing} vs. \textit{nothin{'}}) are used by different groups or in different situations. We introduce the task of inducing lexical variables from code-mixed text: that is, identifying equivalence pairs such as (\textit{football}, \textit{fitba}) along with their linguistic code (\textit{football}→British, \textit{fitba}→Scottish). We adapt a framework for identifying gender-biased word pairs to this new task, and present results on three different pairs of English dialects, using tweets as the code-mixed text. Our system achieves precision of over 70{\%} for two of these three datasets, and produces useful results even without extensive parameter tuning. Our success in adapting this framework from gender to language variety suggests that it could be used to discover other types of analogous pairs as well.",
}
@inproceedings{miyazaki-etal-2018-twitter,
    title = "{T}witter Geolocation using Knowledge-Based Methods",
    author = "Miyazaki, Taro  and
      Rahimi, Afshin  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6102",
    doi = "10.18653/v1/W18-6102",
    pages = "7--16",
    abstract = "Automatic geolocation of microblog posts from their text content is particularly difficult because many location-indicative terms are rare terms, notably entity names such as locations, people or local organisations. Their low frequency means that key terms observed in testing are often unseen in training, such that standard classifiers are unable to learn weights for them. We propose a method for reasoning over such terms using a knowledge base, through exploiting their relations with other entities. Our technique uses a graph embedding over the knowledge base, which we couple with a text representation to learn a geolocation classifier, trained end-to-end. We show that our method improves over purely text-based methods, which we ascribe to more robust treatment of low-count and out-of-vocabulary entities.",
}
@inproceedings{harrigian-2018-geocoding,
    title = "Geocoding Without Geotags: A Text-based Approach for reddit",
    author = "Harrigian, Keith",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6103",
    doi = "10.18653/v1/W18-6103",
    pages = "17--27",
    abstract = "In this paper, we introduce the first geolocation inference approach for reddit, a social media platform where user pseudonymity has thus far made supervised demographic inference difficult to implement and validate. In particular, we design a text-based heuristic schema to generate ground truth location labels for reddit users in the absence of explicitly geotagged data. After evaluating the accuracy of our labeling procedure, we train and test several geolocation inference models across our reddit data set and three benchmark Twitter geolocation data sets. Ultimately, we show that geolocation models trained and applied on the same domain substantially outperform models attempting to transfer training data across domains, even more so on reddit where platform-specific interest-group metadata can be used to improve inferences.",
}
@inproceedings{rameshkumar-etal-2018-assigning,
    title = "Assigning people to tasks identified in email: The {EPA} dataset for addressee tagging for detected task intent",
    author = "Rameshkumar, Revanth  and
      Bailey, Peter  and
      Jha, Abhishek  and
      Quirk, Chris",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6104",
    doi = "10.18653/v1/W18-6104",
    pages = "28--32",
    abstract = {We describe the Enron People Assignment (EPA) dataset, in which tasks that are described in emails are associated with the person(s) responsible for carrying out these tasks. We identify tasks and the responsible people in the Enron email dataset. We define evaluation methods for this challenge and report scores for our model and na{\"\i}ve baselines. The resulting model enables a user experience operating within a commercial email service: given a person and a task, it determines if the person should be notified of the task.},
}
@inproceedings{zheng-etal-2018-correct,
    title = "How do you correct run-on sentences it{'}s not as easy as it seems",
    author = "Zheng, Junchao  and
      Napoles, Courtney  and
      Tetreault, Joel  and
      Omelianchuk, Kostiantyn",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6105",
    doi = "10.18653/v1/W18-6105",
    pages = "33--38",
    abstract = "Run-on sentences are common grammatical mistakes but little research has tackled this problem to date. This work introduces two machine learning models to correct run-on sentences that outperform leading methods for related tasks, punctuation restoration and whole-sentence grammatical error correction. Due to the limited annotated data for this error, we experiment with artificially generating training data from clean newswire text. Our findings suggest artificial training data is viable for this task. We discuss implications for correcting run-ons and other types of mistakes that have low coverage in error-annotated corpora.",
}
@inproceedings{nagata-etal-2018-pos,
    title = "A {POS} Tagging Model Adapted to Learner {E}nglish",
    author = "Nagata, Ryo  and
      Mizumoto, Tomoya  and
      Kikuchi, Yuta  and
      Kawasaki, Yoshifumi  and
      Funakoshi, Kotaro",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6106",
    doi = "10.18653/v1/W18-6106",
    pages = "39--48",
    abstract = "There has been very limited work on the adaptation of Part-Of-Speech (POS) tagging to learner English despite the fact that POS tagging is widely used in related tasks. In this paper, we explore how we can adapt POS tagging to learner English efficiently and effectively. Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this. Considering the previous findings and the discussion, we introduce the design of our model based on bidirectional Long Short-Term Memory. In addition, we describe how to adapt it to a wide variety of native languages (potentially, hundreds of them). In the evaluation section, we empirically show that it is effective for POS tagging in learner English, achieving an accuracy of 0.964, which significantly outperforms the state-of-the-art POS-tagger. We further investigate the tagging results in detail, revealing which part of the model design does or does not improve the performance.",
}
@inproceedings{mandal-nanmaran-2018-normalization,
    title = "Normalization of Transliterated Words in Code-Mixed Data Using {S}eq2{S}eq Model {\&} {L}evenshtein Distance",
    author = "Mandal, Soumil  and
      Nanmaran, Karthick",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6107",
    doi = "10.18653/v1/W18-6107",
    pages = "49--53",
    abstract = "Building tools for code-mixed data is rapidly gaining popularity in the NLP research community as such data is exponentially rising on social media. Working with code-mixed data contains several challenges, especially due to grammatical inconsistencies and spelling variations in addition to all the previous known challenges for social media scenarios. In this article, we present a novel architecture focusing on normalizing phonetic typing variations, which is commonly seen in code-mixed data. One of the main features of our architecture is that in addition to normalizing, it can also be utilized for back-transliteration and word identification in some cases. Our model achieved an accuracy of 90.27{\%} on the test data.",
}
@inproceedings{malykh-etal-2018-robust,
    title = "Robust Word Vectors: Context-Informed Embeddings for Noisy Texts",
    author = "Malykh, Valentin  and
      Logacheva, Varvara  and
      Khakhulin, Taras",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6108",
    doi = "10.18653/v1/W18-6108",
    pages = "54--63",
    abstract = "We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos, which are common in almost any user-generated content, and hinder automatic text processing. Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages. We present the results on a number of Natural Language Processing (NLP) tasks and languages for the variety of related architectures and show that proposed architecture is typo-proof.",
}
@inproceedings{sjoblom-etal-2018-paraphrase,
    title = "Paraphrase Detection on Noisy Subtitles in Six Languages",
    author = {Sj{\"o}blom, Eetu  and
      Creutz, Mathias  and
      Aulamo, Mikko},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6109",
    doi = "10.18653/v1/W18-6109",
    pages = "64--73",
    abstract = "We perform automatic paraphrase detection on subtitle data from the Opusparcus corpus comprising six European languages: German, English, Finnish, French, Russian, and Swedish. We train two types of supervised sentence embedding models: a word-averaging (WA) model and a gated recurrent averaging network (GRAN) model. We find out that GRAN outperforms WA and is more robust to noisy training data. Better results are obtained with more and noisier data than less and cleaner data. Additionally, we experiment on other datasets, without reaching the same level of performance, because of domain mismatch between training and test data.",
}
@inproceedings{fu-barrio-2018-distantly,
    title = "Distantly Supervised Attribute Detection from Reviews",
    author = "Fu, Lisheng  and
      Barrio, Pablo",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6110",
    doi = "10.18653/v1/W18-6110",
    pages = "74--78",
    abstract = "This work aims to detect specific attributes of a place (e.g., if it has a romantic atmosphere, or if it offers outdoor seating) from its user reviews via distant supervision: without direct annotation of the review text, we use the crowdsourced attribute labels of the place as labels of the review text. We then use review-level attention to pay more attention to those reviews related to the attributes. The experimental results show that our attention-based model predicts attributes for places from reviews with over 98{\%} accuracy. The attention weights assigned to each review provide explanation of capturing relevant reviews.",
}
@inproceedings{boyd-2018-using,
    title = "Using {W}ikipedia Edits in Low Resource Grammatical Error Correction",
    author = "Boyd, Adriane",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6111",
    doi = "10.18653/v1/W18-6111",
    pages = "79--84",
    abstract = "We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5{\%}.",
}
@inproceedings{kurniawan-louvan-2018-empirical,
    title = "Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in {I}ndonesian Conversational Texts",
    author = "Kurniawan, Kemal  and
      Louvan, Samuel",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6112",
    doi = "10.18653/v1/W18-6112",
    pages = "85--92",
    abstract = "Despite the long history of named-entity recognition (NER) task in the natural language processing community, previous work rarely studied the task on conversational texts. Such texts are challenging because they contain a lot of word variations which increase the number of out-of-vocabulary (OOV) words. The high number of OOV words poses a difficulty for word-based neural models. Meanwhile, there is plenty of evidence to the effectiveness of character-based neural models in mitigating this OOV problem. We report an empirical evaluation of neural sequence labeling models with character embedding to tackle NER task in Indonesian conversational texts. Our experiments show that (1) character models outperform word embedding-only models by up to 4 F1 points, (2) character models perform better in OOV cases with an improvement of as high as 15 F1 points, and (3) character models are robust against a very high OOV rate.",
}
@inproceedings{skianis-etal-2018-orthogonal,
    title = "Orthogonal Matching Pursuit for Text Classification",
    author = "Skianis, Konstantinos  and
      Tziortziotis, Nikolaos  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6113",
    doi = "10.18653/v1/W18-6113",
    pages = "93--103",
    abstract = "In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low sparsity. In this paper, we apply a greedy variable selection algorithm, called Orthogonal Matching Pursuit, for the text classification task. We also extend standard group OMP by introducing overlapping Group OMP to handle overlapping groups of features. Empirical analysis verifies that both OMP and overlapping GOMP constitute powerful regularizers, able to produce effective and very sparse models. Code and data are available online.",
}
@inproceedings{kreek-apostolova-2018-training,
    title = "Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical Data",
    author = "Kreek, R. Andrew  and
      Apostolova, Emilia",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6114",
    doi = "10.18653/v1/W18-6114",
    pages = "104--109",
    abstract = "Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the question of how well performance metrics computed on noisy, historical data reflect the performance on the intended future machine learning model input. The results demonstrate the utility of dirty training datasets used to build prediction models for cleaner (and different) prediction inputs.",
}
@inproceedings{yirmibesoglu-eryigit-2018-detecting,
    title = "Detecting Code-Switching between {T}urkish-{E}nglish Language Pair",
    author = {Yirmibe{\c{s}}o{\u{g}}lu, Zeynep  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6115",
    doi = "10.18653/v1/W18-6115",
    pages = "110--115",
    abstract = "Code-switching (usage of different languages within a single conversation context in an alternative manner) is a highly increasing phenomenon in social media and colloquial usage which poses different challenges for natural language processing. This paper introduces the first study for the detection of Turkish-English code-switching and also a small test data collected from social media in order to smooth the way for further studies. The proposed system using character level n-grams and conditional random fields (CRFs) obtains 95.6{\%} micro-averaged F1-score on the introduced test data set.",
}
@inproceedings{mandal-singh-2018-language,
    title = "Language Identification in Code-Mixed Data using Multichannel Neural Networks and Context Capture",
    author = "Mandal, Soumil  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6116",
    doi = "10.18653/v1/W18-6116",
    pages = "116--120",
    abstract = "An accurate language identification tool is an absolute necessity for building complex NLP systems to be used on code-mixed data. Lot of work has been recently done on the same, but there{'}s still room for improvement. Inspired from the recent advancements in neural network architectures for computer vision tasks, we have implemented multichannel neural networks combining CNN and LSTM for word level language identification of code-mixed data. Combining this with a Bi-LSTM-CRF context capture module, accuracies of 93.28{\%} and 93.32{\%} is achieved on our two testing sets.",
}
@inproceedings{benotti-etal-2018-modeling,
    title = "Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues",
    author = "Benotti, Luciana  and
      Bhaskaran, Jayadev  and
      Kjartansson, Sigtryggur  and
      Lang, David",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6117",
    doi = "10.18653/v1/W18-6117",
    pages = "121--131",
    abstract = "In this paper we investigate the task of modeling how long it would take a student to respond to a tutor question during a tutoring dialogue. Solving such a task has applications in educational settings such as intelligent tutoring systems, as well as in platforms that help busy human tutors to keep students engaged. Knowing how long it would normally take a student to respond to different types of questions could help tutors optimize their own time while answering multiple dialogues concurrently, as well as deciding when to prompt a student again. We study this problem using data from a service that offers tutor support for math, chemistry and physics through an instant messaging platform. We create a dataset of 240K questions. We explore several strong baselines for this task and compare them with human performance.",
}
@inproceedings{nisioi-etal-2018-content,
    title = "Content Extraction and Lexical Analysis from Customer-Agent Interactions",
    author = "Nisioi, Sergiu  and
      Bucur, Anca  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6118",
    doi = "10.18653/v1/W18-6118",
    pages = "132--136",
    abstract = "In this paper, we provide a lexical comparative analysis of the vocabulary used by customers and agents in an Enterprise Resource Planning (ERP) environment and a potential solution to clean the data and extract relevant content for NLP. As a result, we demonstrate that the actual vocabulary for the language that prevails in the ERP conversations is highly divergent from the standardized dictionary and further different from general language usage as extracted from the Common Crawl corpus. Moreover, in specific business communication circumstances, where it is expected to observe a high usage of standardized language, code switching and non-standard expression are predominant, emphasizing once more the discrepancy between the day-to-day use of language and the standardized one.",
}
@inproceedings{xu-etal-2018-preferred,
    title = "Preferred Answer Selection in {S}tack {O}verflow: Better Text Representations ... and Metadata, Metadata, Metadata",
    author = "Xu, Steven  and
      Bennett, Andrew  and
      Hoogeveen, Doris  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6119",
    doi = "10.18653/v1/W18-6119",
    pages = "137--147",
    abstract = "Community question answering (cQA) forums provide a rich source of data for facilitating non-factoid question answering over many technical domains. Given this, there is considerable interest in answer retrieval from these kinds of forums. However this is a difficult task as the structure of these forums is very rich, and both metadata and text features are important for successful retrieval. While there has recently been a lot of work on solving this problem using deep learning models applied to question/answer text, this work has not looked at how to make use of the rich metadata available in cQA forums. We propose an attention-based model which achieves state-of-the-art results for text-based answer selection alone, and by making use of complementary meta-data, achieves a substantially higher result over two reference datasets novel to this work.",
}
@inproceedings{kim-etal-2018-word,
    title = "Word-like character n-gram embedding",
    author = "Kim, Geewook  and
      Fukui, Kazuki  and
      Shimodaira, Hidetoshi",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6120",
    doi = "10.18653/v1/W18-6120",
    pages = "148--152",
    abstract = "We propose a new word embedding method called \textit{word-like character} n\textit{-gram embedding}, which learns distributed representations of words by embedding word-like character n-grams. Our method is an extension of recently proposed \textit{segmentation-free word embedding}, which directly embeds frequent character n-grams from a raw corpus. However, its n-gram vocabulary tends to contain too many non-word n-grams. We solved this problem by introducing an idea of \textit{expected word frequency}. Compared to the previously proposed methods, our method can embed more words, along with the words that are not included in a given basic word dictionary. Since our method does not rely on word segmentation with rich word dictionaries, it is especially effective when the text in the corpus is in unsegmented language and contains many neologisms and informal words (e.g., Chinese SNS dataset). Our experimental results on Sina Weibo (a Chinese microblog service) and Twitter show that the proposed method can embed more words and improve the performance of downstream tasks.",
}
@inproceedings{makino-etal-2018-classification,
    title = "Classification of Tweets about Reported Events using Neural Networks",
    author = "Makino, Kiminobu  and
      Takei, Yuka  and
      Miyazaki, Taro  and
      Goto, Jun",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6121",
    doi = "10.18653/v1/W18-6121",
    pages = "153--163",
    abstract = "We developed a system that automatically extracts {``}Event-describing Tweets{''} which include incidents or accidents information for creating news reports. Event-describing Tweets can be classified into {``}Reported-event Tweets{''} and {``}New-information Tweets.{''} Reported-event Tweets cite news agencies or user generated content sites, and New-information Tweets are other Event-describing Tweets. A system is needed to classify them so that creators of factual TV programs can use them in their productions. Proposing this Tweet classification task is one of the contributions of this paper, because no prior papers have used the same task even though program creators and other events information collectors have to do it to extract required information from social networking sites. To classify Tweets in this task, this paper proposes a method to input and concatenate character and word sequences in Japanese Tweets by using convolutional neural networks. This proposed method is another contribution of this paper. For comparison, character or word input methods and other neural networks are also used. Results show that a system using the proposed method and architectures can classify Tweets with an F1 score of 88 {\%}.",
}
@inproceedings{balachandran-etal-2018-learning,
    title = "Learning to Define Terms in the Software Domain",
    author = "Balachandran, Vidhisha  and
      Rajagopal, Dheeraj  and
      Kanjirathinkal, Rose Catherine  and
      Cohen, William",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6122",
    doi = "10.18653/v1/W18-6122",
    pages = "164--172",
    abstract = "One way to test a person{'}s knowledge of a domain is to ask them to define domain-specific terms. Here, we investigate the task of automatically generating definitions of technical terms by reading text from the technical domain. Specifically, we learn definitions of software entities from a large corpus built from the user forum Stack Overflow. To model definitions, we train a language model and incorporate additional domain-specific information like word co-occurrence, and ontological category information. Our approach improves previous baselines by 2 BLEU points for the definition generation task. Our experiments also show the additional challenges associated with the task and the short-comings of language-model based architectures for definition generation.",
}
@inproceedings{iter-etal-2018-frameit,
    title = "{F}rame{I}t: Ontology Discovery for Noisy User-Generated Text",
    author = "Iter, Dan  and
      Halevy, Alon  and
      Tan, Wang-Chiew",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6123",
    doi = "10.18653/v1/W18-6123",
    pages = "173--183",
    abstract = "A common need of NLP applications is to extract structured data from text corpora in order to perform analytics or trigger an appropriate action. The ontology defining the structure is typically application dependent and in many cases it is not known a priori. We describe the FrameIt System that provides a workflow for (1) quickly discovering an ontology to model a text corpus and (2) learning an SRL model that extracts the instances of the ontology from sentences in the corpus. FrameIt exploits data that is obtained in the ontology discovery phase as weak supervision data to bootstrap the SRL model and then enables the user to refine the model with active learning. We present empirical results and qualitative analysis of the performance of FrameIt on three corpora of noisy user-generated text.",
}
@inproceedings{benton-dredze-2018-using,
    title = "Using Author Embeddings to Improve Tweet Stance Classification",
    author = "Benton, Adrian  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6124",
    doi = "10.18653/v1/W18-6124",
    pages = "184--194",
    abstract = "Many social media classification tasks analyze the content of a message, but do not consider the context of the message. For example, in tweet stance classification {--} where a tweet is categorized according to a viewpoint it espouses {--} the expressed viewpoint depends on latent beliefs held by the user. In this paper we investigate whether incorporating knowledge about the author can improve tweet stance classification. Furthermore, since author information and embeddings are often unavailable for labeled training examples, we propose a semi-supervised pretraining method to predict user embeddings. Although the neural stance classifiers we learn are often outperformed by a baseline SVM, author embedding pre-training yields improvements over a non-pre-trained neural network on four out of five domains in the SemEval 2016 6A tweet stance classification task. In a tweet gun control stance classification dataset, improvements from pre-training are only apparent when training data is limited.",
}
@inproceedings{enghoff-etal-2018-low,
    title = "Low-resource named entity recognition via multi-source projection: Not quite there yet?",
    author = "Enghoff, Jan Vium  and
      Harrison, S{\o}ren  and
      Agi{\'c}, {\v{Z}}eljko",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6125",
    doi = "10.18653/v1/W18-6125",
    pages = "195--201",
    abstract = "Projecting linguistic annotations through word alignments is one of the most prevalent approaches to cross-lingual transfer learning. Conventional wisdom suggests that annotation projection {``}just works{''} regardless of the task at hand. We carefully consider multi-source projection for named entity recognition. Our experiment with 17 languages shows that to detect named entities in true low-resource languages, annotation projection may not be the right way to move forward. On a more positive note, we also uncover the conditions that do favor named entity projection from multiple sources. We argue these are infeasible under noisy low-resource constraints.",
}
@inproceedings{fu-etal-2018-case,
    title = "A Case Study on Learning a Unified Encoder of Relations",
    author = "Fu, Lisheng  and
      Min, Bonan  and
      Nguyen, Thien Huu  and
      Grishman, Ralph",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6126",
    doi = "10.18653/v1/W18-6126",
    pages = "202--207",
    abstract = "Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema. An individual corpus is often small, and the models may often be biased or overfitted to the corpus. We hypothesize that we can learn a better representation by combining multiple relation datasets. We attempt to use a shared encoder to learn the unified feature representation and to augment it with regularization by adversarial training. The additional corpora feeding the encoder can help to learn a better feature representation layer even though the relation schemas are different. We use ACE05 and ERE datasets as our case study for experiments. The multi-task model obtains significant improvement on both datasets.",
}
@inproceedings{wood-doughty-etal-2018-convolutions,
    title = "Convolutions Are All You Need (For Classifying Character Sequences)",
    author = "Wood-Doughty, Zach  and
      Andrews, Nicholas  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6127",
    doi = "10.18653/v1/W18-6127",
    pages = "208--213",
    abstract = "While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.",
}
@inproceedings{inuzuka-etal-2018-step,
    title = "Step or Not: Discriminator for The Real Instructions in User-generated Recipes",
    author = "Inuzuka, Shintaro  and
      Ito, Takahiko  and
      Harashima, Jun",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6128",
    doi = "10.18653/v1/W18-6128",
    pages = "214",
    abstract = "In a recipe sharing service, users publish recipe instructions in the form of a series of steps. However, some of the {``}steps{''} are not actually part of the cooking process. Specifically, advertisements of recipes themselves (e.g., {``}introduced on TV{''}) and comments (e.g., {``}Thanks for many messages{''}) may often be included in the step section of the recipe, like the recipe author{'}s communication tool. However, such \textit{fake} steps can cause problems when using recipe search indexing or when being spoken by devices such as smart speakers. As presented in this talk, we have constructed a discriminator that distinguishes between such a fake step and the step actually used for cooking. This project includes, but is not limited to, the creation of annotation data by classifying and analyzing recipe steps and the construction of identification models. Our models use only text information to identify the step. In our test, machine learning models achieved higher accuracy than rule-based methods that use manually chosen clue words.",
}
@inproceedings{hanson-simenstad-2018-combining,
    title = "Combining Human and Machine Transcriptions on the Zooniverse Platform",
    author = "Hanson, Daniel  and
      Simenstad, Andrea",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6129",
    doi = "10.18653/v1/W18-6129",
    pages = "215--216",
    abstract = "Transcribing handwritten documents to create fully searchable texts is an essential part of the archival process. Traditional text recognition methods, such as optical character recognition (OCR), do not work on handwritten documents due to their frequent noisiness and OCR{'}s need for individually segmented letters. Crowdsourcing and improved machine models are two modern methods for transcribing handwritten documents.",
}
@proceedings{ws-2018-universal,
    title = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    editor = "de Marneffe, Marie-Catherine  and
      Lynn, Teresa  and
      Schuster, Sebastian",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6000",
}
@inproceedings{alzetta-etal-2018-assessing,
    title = "Assessing the Impact of Incremental Error Detection and Correction. A Case Study on the {I}talian {U}niversal {D}ependency Treebank",
    author = "Alzetta, Chiara  and
      Dell{'}Orletta, Felice  and
      Montemagni, Simonetta  and
      Simi, Maria  and
      Venturi, Giulia",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6001",
    doi = "10.18653/v1/W18-6001",
    pages = "1--7",
    abstract = "Detection and correction of errors and inconsistencies in {``}gold treebanks{''} are becoming more and more central topics of corpus annotation. The paper illustrates a new incremental method for enhancing treebanks, with particular emphasis on the extension of error patterns across different textual genres and registers. Impact and role of corrections have been assessed in a dependency parsing experiment carried out with four different parsers, whose results are promising. For both evaluation datasets, the performance of parsers increases, in terms of the standard LAS and UAS measures and of a more focused measure taking into account only relations involved in error patterns, and at the level of individual dependencies.",
}
@inproceedings{berdicevskis-etal-2018-using,
    title = "Using {U}niversal {D}ependencies in cross-linguistic complexity research",
    author = {Berdicevskis, Aleksandrs  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Ehret, Katharina  and
      von Prince, Kilu  and
      Ross, Daniel  and
      Thompson, Bill  and
      Yan, Chunxiao  and
      Demberg, Vera  and
      Lupyan, Gary  and
      Rama, Taraka  and
      Bentz, Christian},
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6002",
    doi = "10.18653/v1/W18-6002",
    pages = "8--17",
    abstract = "We evaluate corpus-based measures of linguistic complexity obtained using Universal Dependencies (UD) treebanks. We propose a method of estimating robustness of the complexity values obtained using a given measure and a given treebank. The results indicate that measures of syntactic complexity might be on average less robust than those of morphological complexity. We also estimate the validity of complexity measures by comparing the results for very similar languages and checking for unexpected differences. We show that some of those differences that arise can be diminished by using parallel treebanks and, more importantly from the practical point of view, by harmonizing the language-specific solutions in the UD annotation.",
}
@inproceedings{bouma-etal-2018-expletives,
    title = "Expletives in {U}niversal {D}ependency Treebanks",
    author = "Bouma, Gosse  and
      Hajic, Jan  and
      Haug, Dag  and
      Nivre, Joakim  and
      Solberg, Per Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6003",
    doi = "10.18653/v1/W18-6003",
    pages = "18--26",
    abstract = "Although treebanks annotated according to the guidelines of Universal Dependencies (UD) now exist for many languages, the goal of annotating the same phenomena in a cross-linguistically consistent fashion is not always met. In this paper, we investigate one phenomenon where we believe such consistency is lacking, namely expletive elements. Such elements occupy a position that is structurally associated with a core argument (or sometimes an oblique dependent), yet are non-referential and semantically void. Many UD treebanks identify at least some elements as expletive, but the range of phenomena differs between treebanks, even for closely related languages, and sometimes even for different treebanks for the same language. In this paper, we present criteria for identifying expletives that are applicable across languages and compatible with the goals of UD, give an overview of expletives as found in current UD treebanks, and present recommendations for the annotation of expletives so that more consistent annotation can be achieved in future releases.",
}
@inproceedings{cecchini-etal-2018-challenges,
    title = "Challenges in Converting the Index {T}homisticus Treebank into {U}niversal {D}ependencies",
    author = "Cecchini, Flavio Massimiliano  and
      Passarotti, Marco  and
      Marongiu, Paola  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6004",
    doi = "10.18653/v1/W18-6004",
    pages = "27--36",
    abstract = "This paper describes the changes applied to the original process used to convert the \textit{Index Thomisticus} Treebank, a corpus including texts in Medieval Latin by Thomas Aquinas, into the annotation style of Universal Dependencies. The changes are made both to harmonise the Universal Dependencies version of the \textit{Index Thomisticus} Treebank with the two other available Latin treebanks and to fix errors and inconsistencies resulting from the original process. The paper details the treatment of different issues in PoS tagging, lemmatisation and assignment of dependency relations. Finally, it assesses the quality of the new conversion process by providing an evaluation against a gold standard.",
}
@inproceedings{dobrovoljc-martinc-2018-er,
    title = "Er ... well, it matters, right? On the role of data representations in spoken language dependency parsing",
    author = "Dobrovoljc, Kaja  and
      Martinc, Matej",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6005",
    doi = "10.18653/v1/W18-6005",
    pages = "37--46",
    abstract = "Despite the significant improvement of data-driven dependency parsing systems in recent years, they still achieve a considerably lower performance in parsing spoken language data in comparison to written data. On the example of Spoken Slovenian Treebank, the first spoken data treebank using the UD annotation scheme, we investigate which speech-specific phenomena undermine parsing performance, through a series of training data and treebank modification experiments using two distinct state-of-the-art parsing systems. Our results show that utterance segmentation is the most prominent cause of low parsing performance, both in parsing raw and pre-segmented transcriptions. In addition to shorter utterances, both parsers perform better on normalized transcriptions including basic markers of prosody and excluding disfluencies, discourse markers and fillers. On the other hand, the effects of written training data addition and speech-specific dependency representations largely depend on the parsing system selected.",
}
@inproceedings{droganova-etal-2018-mind,
    title = "Mind the Gap: Data Enrichment in Dependency Parsing of Elliptical Constructions",
    author = "Droganova, Kira  and
      Ginter, Filip  and
      Kanerva, Jenna  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6006",
    doi = "10.18653/v1/W18-6006",
    pages = "47--54",
    abstract = "In this paper, we focus on parsing rare and non-trivial constructions, in particular ellipsis. We report on several experiments in enrichment of training data for this specific construction, evaluated on five languages: Czech, English, Finnish, Russian and Slovak. These data enrichment methods draw upon self-training and tri-training, combined with a stratified sampling method mimicking the structural complexity of the original treebank. In addition, using these same methods, we also demonstrate small improvements over the CoNLL-17 parsing shared task winning system for four of the five languages, not only restricted to the elliptical constructions.",
}
@inproceedings{dyer-2018-integration,
    title = "Integration complexity and the order of cosisters",
    author = "Dyer, William",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6007",
    doi = "10.18653/v1/W18-6007",
    pages = "55--65",
    abstract = "The cost of integrating dependent constituents to their heads is thought to involve the distance between dependent and head and the complexity of the integration (Gibson, 1998). The former has been convincingly addressed by Dependency Distance Minimization (DDM) (cf. Liu et al., 2017). The current study addresses the latter by proposing a novel theory of integration complexity derived from the entropy of the probability distribution of a dependent{'}s heads. An analysis of Universal Dependency corpora provides empirical evidence regarding the preferred order of isomorphic cosisters{---}sister constituents of the same syntactic form on the same side of their head{---}such as the adjectives in \textit{pretty blue fish}. Integration complexity, alongside DDM, allows for a general theory of constituent order based on integration cost.",
}
@inproceedings{gerdes-etal-2018-sud,
    title = "{SUD} or Surface-Syntactic {U}niversal {D}ependencies: An annotation scheme near-isomorphic to {UD}",
    author = "Gerdes, Kim  and
      Guillaume, Bruno  and
      Kahane, Sylvain  and
      Perrier, Guy",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6008",
    doi = "10.18653/v1/W18-6008",
    pages = "66--74",
    abstract = "This article proposes a surface-syntactic annotation scheme called SUD that is near-isomorphic to the Universal Dependencies (UD) annotation scheme while following distributional criteria for defining the dependency tree structure and the naming of the syntactic functions. Rule-based graph transformation grammars allow for a bi-directional transformation of UD into SUD. The back-and-forth transformation can serve as an error-mining tool to assure the intra-language and inter-language coherence of the UD treebanks.",
}
@inproceedings{kanayama-etal-2018-coordinate,
    title = "Coordinate Structures in {U}niversal {D}ependencies for Head-final Languages",
    author = "Kanayama, Hiroshi  and
      Han, Na-Rae  and
      Asahara, Masayuki  and
      Hwang, Jena D.  and
      Miyao, Yusuke  and
      Choi, Jinho D.  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6009",
    doi = "10.18653/v1/W18-6009",
    pages = "75--84",
    abstract = "This paper discusses the representation of coordinate structures in the Universal Dependencies framework for two head-final languages, Japanese and Korean. UD applies a strict principle that makes the head of coordination the left-most conjunct. However, the guideline may produce syntactic trees which are difficult to accept in head-final languages. This paper describes the status in the current Japanese and Korean corpora and proposes alternative designs suitable for these languages.",
}
@inproceedings{lacroix-2018-investigating,
    title = "Investigating {NP}-Chunking with {U}niversal {D}ependencies for {E}nglish",
    author = "Lacroix, Oph{\'e}lie",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6010",
    doi = "10.18653/v1/W18-6010",
    pages = "85--90",
    abstract = "Chunking is a pre-processing task generally dedicated to improving constituency parsing. In this paper, we want to show that universal dependency (UD) parsing can also leverage the information provided by the task of chunking even though annotated chunks are not provided with universal dependency trees. In particular, we introduce the possibility of deducing noun-phrase (NP) chunks from universal dependencies, focusing on English as a first example. We then demonstrate how the task of NP-chunking can benefit PoS-tagging in a multi-task learning setting {--} comparing two different strategies {--} and how it can be used as a feature for dependency parsing in order to learn enriched models.",
}
@inproceedings{mccarthy-etal-2018-marrying,
    title = "Marrying {U}niversal {D}ependencies and {U}niversal {M}orphology",
    author = "McCarthy, Arya D.  and
      Silfverberg, Miikka  and
      Cotterell, Ryan  and
      Hulden, Mans  and
      Yarowsky, David",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6011",
    doi = "10.18653/v1/W18-6011",
    pages = "91--101",
    abstract = "The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages{---}UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project{'}s annotations could be used to validate the other{'}s. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13{\%} recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.",
}
@inproceedings{nivre-etal-2018-enhancing,
    title = "Enhancing {U}niversal {D}ependency Treebanks: A Case Study",
    author = "Nivre, Joakim  and
      Marongiu, Paola  and
      Ginter, Filip  and
      Kanerva, Jenna  and
      Montemagni, Simonetta  and
      Schuster, Sebastian  and
      Simi, Maria",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6012",
    doi = "10.18653/v1/W18-6012",
    pages = "102--107",
    abstract = "We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies. We apply a rule-based system developed for English and a data-driven system trained on Finnish to Swedish and Italian. We find that both systems are accurate enough to bootstrap enhanced dependencies in existing UD treebanks. In the case of Italian, results are even on par with those of a prototype language-specific system.",
}
@inproceedings{noh-etal-2018-enhancing,
    title = "Enhancing {U}niversal {D}ependencies for {K}orean",
    author = "Noh, Youngbin  and
      Han, Jiyoon  and
      Oh, Tae Hwan  and
      Kim, Hansaem",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6013",
    doi = "10.18653/v1/W18-6013",
    pages = "108--116",
    abstract = "In this paper, for the purpose of enhancing Universal Dependencies for the Korean language, we propose a modified method for mapping Korean Part-of-Speech(POS) tagset in relation to Universal Part-of-Speech (UPOS) tagset in order to enhance the Universal Dependencies for the Korean Language. Previous studies suggest that UPOS reflects several issues that influence dependency annotation by using the POS of Korean predicates, particularly the distinctiveness in using verb, adjective, and copula.",
}
@inproceedings{omura-asahara-2018-ud,
    title = "{UD}-{J}apanese {BCCWJ}: {U}niversal {D}ependencies Annotation for the {B}alanced {C}orpus of {C}ontemporary {W}ritten {J}apanese",
    author = "Omura, Mai  and
      Asahara, Masayuki",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6014",
    doi = "10.18653/v1/W18-6014",
    pages = "117--125",
    abstract = "In this paper, we describe a corpus UD Japanese-BCCWJ that was created by converting the Balanced Corpus of Contemporary Written Japanese (BCCWJ), a Japanese language corpus, to adhere to the UD annotation schema. The BCCWJ already assigns dependency information at the level of the bunsetsu (a Japanese syntactic unit comparable to the phrase). We developed a program to convert the BCCWJ to UD based on this dependency structure, and this corpus is the result of completely automatic conversion using the program. UD Japanese-BCCWJ is the largest-scale UD Japanese corpus and the second-largest of all UD corpora, including 1,980 documents, 57,109 sentences, and 1,273k words across six distinct domains.",
}
@inproceedings{partanen-etal-2018-first,
    title = "The First {K}omi-{Z}yrian {U}niversal {D}ependencies Treebanks",
    author = "Partanen, Niko  and
      Blokland, Rogier  and
      Lim, KyungTae  and
      Poibeau, Thierry  and
      Rie{\ss}ler, Michael",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6015",
    doi = "10.18653/v1/W18-6015",
    pages = "126--132",
    abstract = "Two Komi-Zyrian treebanks were included in the Universal Dependencies 2.2 release. This article contextualizes the treebanks, discusses the process through which they were created, and outlines the future plans and timeline for the next improvements. Special attention is paid to the possibilities of using UD in the documentation and description of endangered languages.",
}
@inproceedings{sade-etal-2018-hebrew,
    title = "The {H}ebrew {U}niversal {D}ependency Treebank: Past Present and Future",
    author = "Sade, Shoval  and
      Seker, Amit  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6016",
    doi = "10.18653/v1/W18-6016",
    pages = "133--143",
    abstract = "The Hebrew treebank (HTB), consisting of 6221 morpho-syntactically annotated newspaper sentences, has been the only resource for training and validating statistical parsers and taggers for Hebrew, for almost two decades now. During these decades, the HTB has gone through a trajectory of automatic and semi-automatic conversions, until arriving at its UDv2 form. In this work we manually validate the UDv2 version of the HTB, and, according to our findings, we apply scheme changes that bring the UD HTB to the same theoretical grounds as the rest of UD. Our experimental parsing results with UDv2New confirm that improving the coherence and internal consistency of the UD HTB indeed leads to improved parsing performance. At the same time, our analysis demonstrates that there is more to be done at the point of intersection of UD with other linguistic processing layers, in particular, at the points where UD interfaces external morphological and lexical resources.",
}
@inproceedings{tyers-etal-2018-multi,
    title = "Multi-source synthetic treebank creation for improved cross-lingual dependency parsing",
    author = "Tyers, Francis  and
      Sheyanova, Mariya  and
      Martynova, Aleksandra  and
      Stepachev, Pavel  and
      Vinogorodskiy, Konstantin",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6017",
    doi = "10.18653/v1/W18-6017",
    pages = "144--150",
    abstract = "This paper describes a method of creating synthetic treebanks for cross-lingual dependency parsing using a combination of machine translation (including pivot translation), annotation projection and the spanning tree algorithm. Sentences are first automatically translated from a lesser-resourced language to a number of related highly-resourced languages, parsed and then the annotations are projected back to the lesser-resourced language, leading to multiple trees for each sentence from the lesser-resourced language. The final treebank is created by merging the possible trees into a graph and running the spanning tree algorithm to vote for the best tree for each sentence. We present experiments aimed at parsing Faroese using a combination of Danish, Swedish and Norwegian. In a similar experimental setup to the CoNLL 2018 shared task on dependency parsing we report state-of-the-art results on dependency parsing for Faroese using an off-the-shelf parser.",
}
@inproceedings{vasquez-etal-2018-toward,
    title = "Toward {U}niversal {D}ependencies for {S}hipibo-Konibo",
    author = "Vasquez, Alonso  and
      Ego Aguirre, Renzo  and
      Angulo, Candy  and
      Miller, John  and
      Villanueva, Claudia  and
      Agi{\'c}, {\v{Z}}eljko  and
      Zariquiey, Roberto  and
      Oncevay, Arturo",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6018",
    doi = "10.18653/v1/W18-6018",
    pages = "151--161",
    abstract = "We present an initial version of the Universal Dependencies (UD) treebank for Shipibo-Konibo, the first South American, Amazonian, Panoan and Peruvian language with a resource built under UD. We describe the linguistic aspects of how the tagset was defined and the treebank was annotated; in addition we present our specific treatment of linguistic units called \textit{clitics}. Although the treebank is still under development, it allowed us to perform a typological comparison against Spanish, the predominant language in Peru, and dependency syntax parsing experiments in both monolingual and cross-lingual approaches.",
}
@inproceedings{vilares-gomez-rodriguez-2018-transition-based,
    title = "Transition-based Parsing with Lighter Feed-Forward Networks",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6019",
    doi = "10.18653/v1/W18-6019",
    pages = "162--172",
    abstract = "We explore whether it is possible to build lighter parsers, that are statistically equivalent to their corresponding standard version, for a wide set of languages showing different structures and morphologies. As testbed, we use the Universal Dependencies and transition-based dependency parsers trained on feed-forward networks. For these, most existing research assumes \textit{de facto standard} embedded features and relies on pre-computation tricks to obtain speed-ups. We explore how these features and their size can be reduced and whether this translates into speed-ups with a negligible impact on accuracy. The experiments show that \textit{grand-daughter} features can be removed for the majority of treebanks without a significant (negative or positive) LAS difference. They also show how the size of the embeddings can be notably reduced.",
}
@inproceedings{wroblewska-2018-extended,
    title = "Extended and Enhanced {P}olish Dependency Bank in {U}niversal {D}ependencies Format",
    author = "Wr{\'o}blewska, Alina",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6020",
    doi = "10.18653/v1/W18-6020",
    pages = "173--182",
    abstract = "The paper presents the largest Polish Dependency Bank in Universal Dependencies format {--} PDBUD {--} with 22K trees and 352K tokens. PDBUD builds on its previous version, i.e. the Polish UD treebank (PL-SZ), and contains all 8K PL-SZ trees. The PL-SZ trees are checked and possibly corrected in the current edition of PDBUD. Further 14K trees are automatically converted from a new version of Polish Dependency Bank. The PDBUD trees are expanded with the enhanced edges encoding the shared dependents and the shared governors of the coordinated conjuncts and with the semantic roles of some dependents. The conducted evaluation experiments show that PDBUD is large enough for training a high-quality graph-based dependency parser for Polish.",
}
@inproceedings{yu-etal-2018-approximate,
    title = "Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning",
    author = "Yu, Xiang  and
      Vu, Ngoc Thang  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6021",
    doi = "10.18653/v1/W18-6021",
    pages = "183--191",
    abstract = "We present a general approach with reinforcement learning (RL) to approximate dynamic oracles for transition systems where exact dynamic oracles are difficult to derive. We treat oracle parsing as a reinforcement learning problem, design the reward function inspired by the classical dynamic oracle, and use Deep Q-Learning (DQN) techniques to train the oracle with gold trees as features. The combination of a priori knowledge and data-driven methods enables an efficient dynamic oracle, which improves the parser performance over static oracles in several transition systems.",
}
@inproceedings{zeldes-abrams-2018-coptic,
    title = "The {C}optic {U}niversal {D}ependency Treebank",
    author = "Zeldes, Amir  and
      Abrams, Mitchell",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies ({UDW} 2018)",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6022",
    doi = "10.18653/v1/W18-6022",
    pages = "192--201",
    abstract = "This paper presents the Coptic Universal Dependency Treebank, the first dependency treebank within the Egyptian subfamily of the Afro-Asiatic languages. We discuss the composition of the corpus, challenges in adapting the UD annotation scheme to existing conventions for annotating Coptic, and evaluate inter-annotator agreement on UD annotation for the language. Some specific constructions are taken as a starting point for discussing several more general UD annotation guidelines, in particular for appositions, ambiguous passivization, incorporation and object-doubling.",
}
@proceedings{ws-2018-2018-emnlp-smm4h,
    title = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    editor = "Gonzalez-Hernandez, Graciela  and
      Weissenbacher, Davy  and
      Sarker, Abeed  and
      Paul, Michael",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5900",
}
@inproceedings{roller-etal-2018-football,
    title = "Football and Beer - a Social Media Analysis on {T}witter in Context of the {FIFA} Football World Cup 2018",
    author = "Roller, Roland  and
      Thomas, Philippe  and
      Schmeier, Sven",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5901",
    doi = "10.18653/v1/W18-5901",
    pages = "1--4",
    abstract = "In many societies alcohol is a legal and common recreational substance and socially accepted. Alcohol consumption often comes along with social events as it helps people to increase their sociability and to overcome their inhibitions. On the other hand we know that increased alcohol consumption can lead to serious health issues, such as cancer, cardiovascular diseases and diseases of the digestive system, to mention a few. This work examines alcohol consumption during the FIFA Football World Cup 2018, particularly the usage of alcohol related information on Twitter. For this we analyse the tweeting behaviour and show that the tournament strongly increases the interest in beer. Furthermore we show that countries who had to leave the tournament at early stage might have done something good to their fans as the interest in beer decreased again.",
}
@inproceedings{skeppstedt-etal-2018-stance,
    title = "Stance-Taking in Topics Extracted from Vaccine-Related Tweets and Discussion Forum Posts",
    author = "Skeppstedt, Maria  and
      Stede, Manfred  and
      Kerren, Andreas",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5902",
    doi = "10.18653/v1/W18-5902",
    pages = "5--8",
    abstract = "The occurrence of stance-taking towards vaccination was measured in documents extracted by topic modelling from two different corpora, one discussion forum corpus and one tweet corpus. For some of the topics extracted, their most closely associated documents contained a proportion of vaccine stance-taking texts that exceeded the corpus average by a large margin. These extracted document sets would, therefore, form a useful resource in a process for computer-assisted analysis of argumentation on the subject of vaccination.",
}
@inproceedings{pirina-coltekin-2018-identifying,
    title = "Identifying Depression on {R}eddit: The Effect of Training Data",
    author = {Pirina, Inna  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5903",
    doi = "10.18653/v1/W18-5903",
    pages = "9--12",
    abstract = "This paper presents a set of classification experiments for identifying depression in posts gathered from social media platforms. In addition to the data gathered previously by other researchers, we collect additional data from the social media platform Reddit. Our experiments show promising results for identifying depression from social media texts. More importantly, however, we show that the choice of corpora is crucial in identifying depression and can lead to misleading conclusions in case of poor choice of data.",
}
@inproceedings{weissenbacher-etal-2018-overview,
    title = "Overview of the Third Social Media Mining for Health ({SMM}4{H}) Shared Tasks at {EMNLP} 2018",
    author = "Weissenbacher, Davy  and
      Sarker, Abeed  and
      Paul, Michael J.  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5904",
    doi = "10.18653/v1/W18-5904",
    pages = "13--16",
    abstract = "The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets. The third execution of the SMM4H shared tasks, co-hosted with EMNLP-2018, comprised of four subtasks. These subtasks involve annotated user posts from Twitter (tweets) and focus on the (i) automatic classification of tweets mentioning a drug name, (ii) automatic classification of tweets containing reports of first-person medication intake, (iii) automatic classification of tweets presenting self-reports of adverse drug reaction (ADR) detection, and (iv) automatic classification of vaccine behavior mentions in tweets. A total of 14 teams participated and 78 system runs were submitted (23 for task 1, 20 for task 2, 18 for task 3, 17 for task 4).",
}
@inproceedings{flekova-etal-2018-changes,
    title = "Changes in Psycholinguistic Attributes of Social Media Users Before, During, and After Self-Reported Influenza Symptoms",
    author = "Flekova, Lucie  and
      Lampos, Vasileios  and
      Cox, Ingemar",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5905",
    doi = "10.18653/v1/W18-5905",
    pages = "17--21",
    abstract = "Previous research has linked psychological and social variables to physical health. At the same time, psychological and social variables have been successfully predicted from the language used by individuals in social media. In this paper, we conduct an initial exploratory study linking these two areas. Using the social media platform of Twitter, we identify users self-reporting symptoms that are descriptive of influenza-like illness (ILI). We analyze the tweets of those users in the periods before, during, and after the reported symptoms, exploring emotional, cognitive, and structural components of language. We observe a post-ILI increase in social activity and cognitive processes, possibly supporting previous offline findings linking more active social activities and stronger cognitive coping skills to a better immune status.",
}
@inproceedings{bobicev-sokolova-2018-thumbs,
    title = "Thumbs Up and Down: Sentiment Analysis of Medical Online Forums",
    author = "Bobicev, Victoria  and
      Sokolova, Marina",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5906",
    doi = "10.18653/v1/W18-5906",
    pages = "22--26",
    abstract = "In the current study, we apply multi-class and multi-label sentence classification to sentiment analysis of online medical forums. We aim to identify major health issues discussed in online social media and the types of sentiments those issues evoke. We use ontology of personal health information for Information Extraction and apply Machine Learning methods in automated recognition of the expressed sentiments.",
}
@inproceedings{mathur-etal-2018-identification,
    title = "Identification of Emergency Blood Donation Request on {T}witter",
    author = "Mathur, Puneet  and
      Ayyar, Meghna  and
      Chopra, Sahil  and
      Shahid, Simra  and
      Mehnaz, Laiba  and
      Shah, Rajiv",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5907",
    doi = "10.18653/v1/W18-5907",
    pages = "27--31",
    abstract = "Social media-based text mining in healthcare has received special attention in recent times due to the enhanced accessibility of social media sites like Twitter. The increasing trend of spreading important information in distress can help patients reach out to prospective blood donors in a time bound manner. However such manual efforts are mostly inefficient due to the limited network of a user. In a novel step to solve this problem, we present an annotated Emergency Blood Donation Request (EBDR) dataset to classify tweets referring to the necessity of urgent blood donation requirement. Additionally, we also present an automated feature-based SVM classification technique that can help selective EBDR tweets reach relevant personals as well as medical authorities. Our experiments also present a quantitative evidence that linguistic along with handcrafted heuristics can act as the most representative set of signals this task with an accuracy of 97.89{\%}.",
}
@inproceedings{onishi-etal-2018-dealing,
    title = "Dealing with Medication Non-Adherence Expressions in {T}witter",
    author = "Onishi, Takeshi  and
      Weissenbacher, Davy  and
      Klein, Ari  and
      O{'}Connor, Karen  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5908",
    doi = "10.18653/v1/W18-5908",
    pages = "32--33",
    abstract = "Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.",
}
@inproceedings{wu-etal-2018-detecting,
    title = "Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Liu, Junxin  and
      Wu, Sixing  and
      Huang, Yongfeng  and
      Xie, Xing",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5909",
    doi = "10.18653/v1/W18-5909",
    pages = "34--37",
    abstract = "This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions. In our system we propose a neural approach with hierarchical tweet representation and multi-head self-attention (HTR-MSA) for both tasks. Our system achieved the first place in both the first and third shared tasks of SMM4H with an F-score of 91.83{\%} and 52.20{\%} respectively.",
}
@inproceedings{xherija-2018-classification,
    title = "Classification of Medication-Related Tweets Using Stacked Bidirectional {LSTM}s with Context-Aware Attention",
    author = "Xherija, Orest",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5910",
    doi = "10.18653/v1/W18-5910",
    pages = "38--42",
    abstract = "This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available.",
}
@inproceedings{joshi-etal-2018-shot,
    title = "Shot Or Not: Comparison of {NLP} Approaches for Vaccination Behaviour Detection",
    author = "Joshi, Aditya  and
      Dai, Xiang  and
      Karimi, Sarvnaz  and
      Sparks, Ross  and
      Paris, C{\'e}cile  and
      MacIntyre, C Raina",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5911",
    doi = "10.18653/v1/W18-5911",
    pages = "43--47",
    abstract = "Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine. We present our submission for vaccination behaviour detection shared task at the SMM4H workshop. Our findings are based on three prevalent text classification approaches: rule-based, statistical and deep learning-based. Our final submissions are: (1) an ensemble of statistical classifiers with task-specific features derived using lexicons, language processing tools and word embeddings; and, (2) a LSTM classifier with pre-trained language models.",
}
@inproceedings{nikhil-mundra-2018-neural,
    title = "Neural {D}rug{N}et",
    author = "Nikhil, Nishant  and
      Mundra, Shivansh",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5912",
    doi = "10.18653/v1/W18-5912",
    pages = "48--49",
    abstract = "In this paper, we describe the system submitted for the shared task on Social Media Mining for Health Applications by the team Light. Previous works demonstrate that LSTMs have achieved remarkable performance in natural language processing tasks. We deploy an ensemble of two LSTM models. The first one is a pretrained language model appended with a classifier and takes words as input, while the second one is a LSTM model with an attention unit over it which takes character tri-gram as input. We call the ensemble of these two models: Neural-DrugNet. Our system ranks 2nd in the second shared task: Automatic classification of posts describing medication intake.",
}
@inproceedings{minard-etal-2018-irisa,
    title = "{IRISA} at {SMM}4{H} 2018: Neural Network and Bagging for Tweet Classification",
    author = "Minard, Anne-Lyse  and
      Raymond, Christian  and
      Claveau, Vincent",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5913",
    doi = "10.18653/v1/W18-5913",
    pages = "50--51",
    abstract = "This paper describes the systems developed by IRISA to participate to the four tasks of the SMM4H 2018 challenge. For these tweet classification tasks, we adopt a common approach based on recurrent neural networks (BiLSTM). Our main contributions are the use of certain features, the use of Bagging in order to deal with unbalanced datasets, and on the automatic selection of difficult examples. These techniques allow us to reach 91.4, 46.5, 47.8, 85.0 as F1-scores for Tasks 1 to 4.",
}
@inproceedings{coltekin-rama-2018-drug,
    title = "Drug-Use Identification from Tweets with Word and Character N-Grams",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Rama, Taraka},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5914",
    doi = "10.18653/v1/W18-5914",
    pages = "52--53",
    abstract = "This paper describes our systems in social media mining for health applications (SMM4H) shared task. We participated in all four tracks of the shared task using linear models with a combination of character and word n-gram features. We did not use any external data or domain specific information. The resulting systems achieved above-average scores among other participating systems, with F1-scores of 91.22, 46.8, 42.4, and 85.53 on tasks 1, 2, 3, and 4 respectively.",
}
@inproceedings{aroyehun-gelbukh-2018-automatic,
    title = "Automatic Identification of Drugs and Adverse Drug Reaction Related Tweets",
    author = "Aroyehun, Segun Taofeek  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5915",
    doi = "10.18653/v1/W18-5915",
    pages = "54--55",
    abstract = "We describe our submissions to the Third Social Media Mining for Health Applications Shared Task. We participated in two tasks (tasks 1 and 3). For both tasks, we experimented with a traditional machine learning model (Naive Bayes Support Vector Machine (NBSVM)), deep learning models (Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM)), and the combination of deep learning model with SVM. We observed that the NBSVM reaches superior performance on both tasks on our development split of the training data sets. Official result for task 1 based on the blind evaluation data shows that the predictions of the NBSVM achieved our team{'}s best F-score of 0.910 which is above the average score received by all submissions to the task. On task 3, the combination of of BiLSTM and SVM gives our best F-score for the positive class of 0.394.",
}
@inproceedings{ellendorff-etal-2018-uzh,
    title = "{UZH}@{SMM}4{H}: System Descriptions",
    author = "Ellendorff, Tilia  and
      Cornelius, Joseph  and
      Gordon, Heath  and
      Colic, Nicola  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5916",
    doi = "10.18653/v1/W18-5916",
    pages = "56--60",
    abstract = {Our team at the University of Z{\"u}rich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task. We experimented with different approaches for text classification, namely traditional feature-based classifiers (Logistic Regression and Support Vector Machines), shallow neural networks, RCNNs, and CNNs. This system description paper provides details regarding the different system architectures and the achieved results.},
}
@inproceedings{tokala-etal-2018-deep,
    title = "Deep Learning for Social Media Health Text Classification",
    author = "Tokala, Santosh  and
      Gambhir, Vaibhav  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5917",
    doi = "10.18653/v1/W18-5917",
    pages = "61--64",
    abstract = "This paper describes the systems developed for 1st and 2nd tasks of the 3rd Social Media Mining for Health Applications Shared Task at EMNLP 2018. The first task focuses on automatic detection of posts mentioning a drug name or dietary supplement, a binary classification. The second task is about distinguishing the tweets that present personal medication intake, possible medication intake and non-intake. We performed extensive experiments with various classifiers like Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDT) and deep learning architectures such as Long Short-Term Memory Networks (LSTM), jointed Convolutional Neural Networks (CNN) and LSTM architecture, and attention based LSTM architecture both at word and character level. We have also explored using various pre-trained embeddings like Global Vectors for Word Representation (GloVe), Word2Vec and task-specific embeddings learned using CNN-LSTM and LSTMs.",
}
@inproceedings{bobicev-etal-2018-using,
    title = "Using {PPM} for Health Related Text Detection",
    author = "Bobicev, Victoria  and
      Lazu, Victoria  and
      Istrati, Daniela",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5918",
    doi = "10.18653/v1/W18-5918",
    pages = "65--66",
    abstract = "This paper describes the participation of the LILU team in SMM4H challenge on social media mining for health related events description such as drug intakes or vaccinations.",
}
@inproceedings{saha-etal-2018-leveraging,
    title = "Leveraging Web Based Evidence Gathering for Drug Information Identification from Tweets",
    author = "Saha, Rupsa  and
      Naskar, Abir  and
      Dasgupta, Tirthankar  and
      Dey, Lipika",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5919",
    doi = "10.18653/v1/W18-5919",
    pages = "67--69",
    abstract = "In this paper, we have explored web-based evidence gathering and different linguistic features to automatically extract drug names from tweets and further classify such tweets into Adverse Drug Events or not. We have evaluated our proposed models with the dataset as released by the SMM4H workshop shared Task-1 and Task-3 respectively. Our evaluation results shows that the proposed model achieved good results, with Precision, Recall and F-scores of 78.5{\%}, 88{\%} and 82.9{\%} respectively for Task1 and 33.2{\%}, 54.7{\%} and 41.3{\%} for Task3.",
}
@inproceedings{bagherzadeh-etal-2018-clac,
    title = "{CL}a{C} at {SMM}4{H} Task 1, 2, and 4",
    author = "Bagherzadeh, Parsa  and
      Sheikh, Nadia  and
      Bergler, Sabine",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5920",
    doi = "10.18653/v1/W18-5920",
    pages = "70--73",
    abstract = "CLaC Labs participated in Tasks 1, 2, and 4 using the same base architecture for all tasks with various parameter variations. This was our first exploration of this data and the SMM4H Tasks, thus a unified system was useful to compare the behavior of our architecture over the different datasets and how they interact with different linguistic features.",
}
@proceedings{ws-2018-research,
    title = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    editor = "Kuebler, Sandra  and
      Nicolai, Garrett",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5800",
}
@inproceedings{magri-2018-efficient,
    title = "Efficient Computation of Implicational Universals in Constraint-Based Phonology Through the Hyperplane Separation Theorem",
    author = "Magri, Giorgio",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5801",
    doi = "10.18653/v1/W18-5801",
    pages = "1--10",
    abstract = "This paper focuses on the most basic \textit{implicational universals} in phonological theory, called \textit{T-orders} after Anttila and Andrus (2006). It develops necessary and sufficient constraint characterizations of T-orders within \textit{Harmonic Grammar} and \textit{Optimality Theory}. These conditions rest on the rich convex geometry underlying these frameworks. They are phonologically intuitive and have significant algorithmic implications.",
}
@inproceedings{hussain-etal-2018-lexical,
    title = "Lexical Networks in !{X}ung",
    author = "Hussain, Syed-Amad  and
      Elsner, Micha  and
      Miller, Amanda",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5802",
    doi = "10.18653/v1/W18-5802",
    pages = "11--20",
    abstract = "We investigate the lexical network properties of the large phoneme inventory Southern African language Mangetti Dune !Xung as it compares to English and other commonly-studied languages. Lexical networks are graphs in which nodes (words) are linked to their minimal pairs; global properties of these networks are believed to mediate lexical access in the minds of speakers. We show that the network properties of !Xung are within the range found in previously-studied languages. By simulating data ({''}pseudolexicons{''}) with varying levels of phonotactic structure, we find that the lexical network properties of !Xung diverge from previously-studied languages when fewer phonotactic constraints are retained. We conclude that lexical network properties are representative of an underlying cognitive structure which is necessary for efficient word retrieval and that the phonotactics of !Xung may be shaped by a selective pressure which preserves network properties within this cognitively useful range.",
}
@inproceedings{kirkedal-2018-acoustic,
    title = "Acoustic Word Disambiguation with Phonogical Features in {D}anish {ASR}",
    author = "Kirkedal, Andreas S{\o}eborg",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5803",
    doi = "10.18653/v1/W18-5803",
    pages = "21--31",
    abstract = "Phonological features can indicate word class and we can use word class information to disambiguate both homophones and homographs in automatic speech recognition (ASR). We show Danish st{\o}d can be predicted from speech and used to improve ASR. We discover which acoustic features contain the signal of st{\o}d, how to use these features to predict st{\o}d and how we can make use of st{\o}d and st{\o}dpredictive acoustic features to improve overall ASR accuracy and decoding speed. In the process, we discover acoustic features that are novel to the phonetic characterisation of st{\o}d.",
}
@inproceedings{godard-etal-2018-adaptor,
    title = "{A}daptor {G}rammars for the Linguist: Word Segmentation Experiments for Very Low-Resource Languages",
    author = "Godard, Pierre  and
      Besacier, Laurent  and
      Yvon, Fran{\c{c}}ois  and
      Adda-Decker, Martine  and
      Adda, Gilles  and
      Maynard, H{\'e}l{\`e}ne  and
      Rialland, Annie",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5804",
    doi = "10.18653/v1/W18-5804",
    pages = "32--42",
    abstract = "Computational Language Documentation attempts to make the most recent research in speech and language technologies available to linguists working on language preservation and documentation. In this paper, we pursue two main goals along these lines. The first is to improve upon a strong baseline for the unsupervised word discovery task on two very low-resource Bantu languages, taking advantage of the expertise of linguists on these particular languages. The second consists in exploring the Adaptor Grammar framework as a decision and prediction tool for linguists studying a new language. We experiment 162 grammar configurations for each language and show that using Adaptor Grammars for word segmentation enables us to test hypotheses about a language. Specializing a generic grammar with language specific knowledge leads to great improvements for the word discovery task, ultimately achieving a leap of about 30{\%} token F-score from the results of a strong baseline.",
}
@inproceedings{nicolai-etal-2018-string,
    title = "String Transduction with Target Language Models and Insertion Handling",
    author = "Nicolai, Garrett  and
      Najafi, Saeed  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5805",
    doi = "10.18653/v1/W18-5805",
    pages = "43--53",
    abstract = "Many character-level tasks can be framed as sequence-to-sequence transduction, where the target is a word from a natural language. We show that leveraging target language models derived from unannotated target corpora, combined with a precise alignment of the training data, yields state-of-the art results on cognate projection, inflection generation, and phoneme-to-grapheme conversion.",
}
@inproceedings{erdmann-habash-2018-complementary,
    title = "Complementary Strategies for Low Resourced Morphological Modeling",
    author = "Erdmann, Alexander  and
      Habash, Nizar",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5806",
    doi = "10.18653/v1/W18-5806",
    pages = "54--65",
    abstract = "Morphologically rich languages are challenging for natural language processing tasks due to data sparsity. This can be addressed either by introducing out-of-context morphological knowledge, or by developing machine learning architectures that specifically target data sparsity and/or morphological information. We find these approaches to complement each other in a morphological paradigm modeling task in Modern Standard Arabic, which, in addition to being morphologically complex, features ubiquitous ambiguity, exacerbating sparsity with noise. Given a small number of out-of-context rules describing closed class morphology, we combine them with word embeddings leveraging subword strings and noise reduction techniques. The combination outperforms both approaches individually by about 20{\%} absolute. While morphological resources already exist for Modern Standard Arabic, our results inform how comparable resources might be constructed for non-standard dialects or any morphologically rich, low resourced language, given scarcity of time and funding.",
}
@inproceedings{dolatian-heinz-2018-modeling,
    title = "Modeling Reduplication with 2-way Finite-State Transducers",
    author = "Dolatian, Hossep  and
      Heinz, Jeffrey",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5807",
    doi = "10.18653/v1/W18-5807",
    pages = "66--77",
    abstract = "This article describes a novel approach to the computational modeling of reduplication. Reduplication is a well-studied linguistic phenomenon. However, it is often treated as a stumbling block within finite-state treatments of morphology. Most finite-state implementations of computational morphology cannot adequately capture the productivity of unbounded copying in reduplication, nor can they adequately capture bounded copying. We show that an understudied type of finite-state machines, two-way finite-state transducers (2-way FSTs), captures virtually all reduplicative processes, including total reduplication. 2-way FSTs can model reduplicative typology in a way which is convenient, easy to design and debug in practice, and linguistically-motivated. By virtue of being finite-state, 2-way FSTs are likewise incorporable into existing finite-state systems and programs. A small but representative typology of reduplicative processes is described in this article, alongside their corresponding 2-way FST models.",
}
@inproceedings{eskander-etal-2018-automatically,
    title = "Automatically Tailoring Unsupervised Morphological Segmentation to the Language",
    author = "Eskander, Ramy  and
      Rambow, Owen  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5808",
    doi = "10.18653/v1/W18-5808",
    pages = "78--83",
    abstract = "Morphological segmentation is beneficial for several natural language processing tasks dealing with large vocabularies. Unsupervised methods for morphological segmentation are essential for handling a diverse set of languages, including low-resource languages. Eskander et al. (2016) introduced a Language Independent Morphological Segmenter (LIMS) using Adaptor Grammars (AG) based on the best-on-average performing AG configuration. However, while LIMS worked best on average and outperforms other state-of-the-art unsupervised morphological segmentation approaches, it did not provide the optimal AG configuration for five out of the six languages. We propose two language-independent classifiers that enable the selection of the optimal or nearly-optimal configuration for the morphological segmentation of unseen languages.",
}
@inproceedings{yamashita-etal-2018-comparison,
    title = "A Comparison of Entity Matching Methods between {E}nglish and {J}apanese Katakana",
    author = "Yamashita, Michiharu  and
      Awashima, Hideki  and
      Oiwa, Hidekazu",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5809",
    doi = "10.18653/v1/W18-5809",
    pages = "84--92",
    abstract = "Japanese Katakana is one component of the Japanese writing system and is used to express English terms, loanwords, and onomatopoeia in Japanese characters based on the phonemes. The main purpose of this research is to find the best entity matching methods between English and Katakana. We built two research questions to clarify which types of entity matching systems works better than others. The first question is what transliteration should be used for conversion. We need to transliterate English or Katakana terms into the same form in order to compute the string similarity. We consider five conversions that transliterate English to Katakana directly, Katakana to English directly, English to Katakana via phoneme, Katakana to English via phoneme, and both English and Katakana to phoneme. The second question is what should be used for the similarity measure at entity matching. To investigate the problem, we choose six methods, which are Overlap Coefficient, Cosine, Jaccard, Jaro-Winkler, Levenshtein, and the similarity of the phoneme probability predicted by RNN. Our results show that 1) matching using phonemes and conversion of Katakana to English works better than other methods, and 2) the similarity of phonemes outperforms other methods while other similarity score is changed depending on data and models.",
}
@inproceedings{prickett-etal-2018-seq2seq,
    title = "{S}eq2{S}eq Models with Dropout can Learn Generalizable Reduplication",
    author = "Prickett, Brandon  and
      Traylor, Aaron  and
      Pater, Joe",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5810",
    doi = "10.18653/v1/W18-5810",
    pages = "93--100",
    abstract = "Natural language reduplication can pose a challenge to neural models of language, and has been argued to require variables (Marcus et al., 1999). Sequence-to-sequence neural networks have been shown to perform well at a number of other morphological tasks (Cotterell et al., 2016), and produce results that highly correlate with human behavior (Kirov, 2017; Kirov {\&} Cotterell, 2018) but do not include any explicit variables in their architecture. We find that they can learn a reduplicative pattern that generalizes to novel segments if they are trained with dropout (Srivastava et al., 2014). We argue that this matches the scope of generalization observed in human reduplication.",
}
@inproceedings{zeldes-2018-characterwise,
    title = "A Characterwise Windowed Approach to {H}ebrew Morphological Segmentation",
    author = "Zeldes, Amir",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5811",
    doi = "10.18653/v1/W18-5811",
    pages = "101--110",
    abstract = "This paper presents a novel approach to the segmentation of orthographic word forms in contemporary Hebrew, focusing purely on splitting without carrying out morphological analysis or disambiguation. Casting the analysis task as character-wise binary classification and using adjacent character and word-based lexicon-lookup features, this approach achieves over 98{\%} accuracy on the benchmark SPMRL shared task data for Hebrew, and 97{\%} accuracy on a new out of domain Wikipedia dataset, an improvement of {\mbox{$\approx$}}4{\%} and 5{\%} over previous state of the art performance.",
}
@inproceedings{sofroniev-coltekin-2018-phonetic,
    title = "Phonetic Vector Representations for Sound Sequence Alignment",
    author = {Sofroniev, Pavel  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5812",
    doi = "10.18653/v1/W18-5812",
    pages = "111--116",
    abstract = "This study explores a number of data-driven vector representations of the IPA-encoded sound segments for the purpose of sound sequence alignment. We test the alternative representations based on the alignment accuracy in the context of computational historical linguistics. We show that the data-driven methods consistently do better than linguistically-motivated articulatory-acoustic features. The similarity scores obtained using the data-driven representations in a monolingual context, however, performs worse than the state-of-the-art distance (or similarity) scoring methods proposed in earlier studies of computational historical linguistics. We also show that adapting representations to the task at hand improves the results, yielding alignment accuracy comparable to the state of the art methods.",
}
@inproceedings{tikhonov-yamshchikov-2018-sounds,
    title = "Sounds Wilde. Phonetically Extended Embeddings for Author-Stylized Poetry Generation",
    author = "Tikhonov, Aleksey  and
      Yamshchikov, Ivan P.",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5813",
    doi = "10.18653/v1/W18-5813",
    pages = "117--124",
    abstract = "This paper addresses author-stylized text generation. Using a version of a language model with extended phonetic and semantic embeddings for poetry generation we show that phonetics has comparable contribution to the overall model performance as the information on the target author. Phonetic information is shown to be important for English and Russian language. Humans tend to attribute machine generated texts to the target author.",
}
@inproceedings{pierrehumbert-granell-2018-hapax,
    title = "On Hapax Legomena and Morphological Productivity",
    author = "Pierrehumbert, Janet  and
      Granell, Ramon",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5814",
    doi = "10.18653/v1/W18-5814",
    pages = "125--130",
    abstract = "Quantifying and predicting morphological productivity is a long-standing challenge in corpus linguistics and psycholinguistics. The same challenge reappears in natural language processing in the context of handling words that were not seen in the training set (out-of-vocabulary, or OOV, words). Prior research showed that a good indicator of the productivity of a morpheme is the number of words involving it that occur exactly once (the \textit{hapax legomena}). A technical connection was adduced between this result and Good-Turing smoothing, which assigns probability mass to unseen events on the basis of the simplifying assumption that word frequencies are stationary. In a large-scale study of 133 affixes in Wikipedia, we develop evidence that success in fact depends on tapping the frequency range in which the assumptions of Good-Turing are violated.",
}
@inproceedings{cardenas-zeman-2018-morphological,
    title = "A Morphological Analyzer for {S}hipibo-Konibo",
    author = "Cardenas, Ronald  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5815",
    doi = "10.18653/v1/W18-5815",
    pages = "131--139",
    abstract = "We present a fairly complete morphological analyzer for Shipibo-Konibo, a low-resourced native language spoken in the Amazonian region of Peru. We resort to the robustness of finite-state systems in order to model the complex morphosyntax of the language. Evaluation over raw corpora shows promising coverage of grammatical phenomena, limited only by the scarce lexicon. We make this tool freely available so as to aid the production of annotated corpora and impulse further research in native languages of Peru.",
}
@inproceedings{taji-etal-2018-arabic,
    title = "An {A}rabic Morphological Analyzer and Generator with Copious Features",
    author = "Taji, Dima  and
      Khalifa, Salam  and
      Obeid, Ossama  and
      Eryani, Fadhl  and
      Habash, Nizar",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5816",
    doi = "10.18653/v1/W18-5816",
    pages = "140--150",
    abstract = "We introduce CALIMA-Star, a very rich Arabic morphological analyzer and generator that provides functional and form-based morphological features as well as built-in tokenization, phonological representation, lexical rationality and much more. This tool includes a fast engine that can be easily integrated into other systems, as well as an easy-to-use API and a web interface. CALIMA-Star also supports morphological reinflection. We evaluate CALIMA-Star against four commonly used analyzers for Arabic in terms of speed and morphological content.",
}
@inproceedings{graf-mayer-2018-sanskrit,
    title = "{S}anskrit n-Retroflexion is Input-Output Tier-Based Strictly Local",
    author = "Graf, Thomas  and
      Mayer, Connor",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5817",
    doi = "10.18653/v1/W18-5817",
    pages = "151--160",
    abstract = "Sanskrit /n/-retroflexion is one of the most complex segmental processes in phonology. While it is still star-free, it does not fit in any of the subregular classes that are commonly entertained in the literature. We show that when construed as a phonotactic dependency, the process fits into a class we call \textit{input-output tier-based strictly local} (IO-TSL), a natural extension of the familiar class TSL. IO-TSL increases the power of TSL{'}s tier projection function by making it an input-output strictly local transduction. Assuming that /n/-retroflexion represents the upper bound on the complexity of segmental phonology, this shows that all of segmental phonology can be captured by combining the intuitive notion of tiers with the independently motivated machinery of strictly local mappings.",
}
@inproceedings{wiemerslage-etal-2018-phonological,
    title = "Phonological Features for Morphological Inflection",
    author = "Wiemerslage, Adam  and
      Silfverberg, Miikka  and
      Hulden, Mans",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5818",
    doi = "10.18653/v1/W18-5818",
    pages = "161--166",
    abstract = "Modeling morphological inflection is an important task in Natural Language Processing. In contrast to earlier work that has largely used orthographic representations, we experiment with this task in a phonetic character space, representing inputs as either IPA segments or bundles of phonological distinctive features. We show that both of these inputs, somewhat counterintuitively, achieve similar accuracies on morphological inflection, slightly lower than orthographic models. We conclude that providing detailed phonological representations is largely redundant when compared to IPA segments, and that articulatory distinctions relevant for word inflection are already latently present in the distributional properties of many graphemic writing systems.",
}
@inproceedings{ermolaeva-2018-extracting,
    title = "Extracting Morphophonology from Small Corpora",
    author = "Ermolaeva, Marina",
    booktitle = "Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5819",
    doi = "10.18653/v1/W18-5819",
    pages = "167--175",
    abstract = "Probabilistic approaches have proven themselves well in learning phonological structure. In contrast, theoretical linguistics usually works with deterministic generalizations. The goal of this paper is to explore possible interactions between information-theoretic methods and deterministic linguistic knowledge and to examine some ways in which both can be used in tandem to extract phonological and morphophonological patterns from a small annotated dataset. Local and nonlocal processes in Mishar Tatar (Turkic/Kipchak) are examined as a case study.",
}
@proceedings{ws-2018-2018-emnlp,
    title = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    editor = "Chuklin, Aleksandr  and
      Dalton, Jeff  and
      Kiseleva, Julia  and
      Borisov, Alexey  and
      Burtsev, Mikhail",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5700",
}
@inproceedings{shalyminov-etal-2018-neural,
    title = "Neural Response Ranking for Social Conversation: A Data-Efficient Approach",
    author = "Shalyminov, Igor  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5701",
    doi = "10.18653/v1/W18-5701",
    pages = "1--8",
    abstract = "The overall objective of {`}social{'} dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart from raw dialogue data, user-provided ratings are the most common signal used to train such systems to produce engaging responses. In this paper we show that social dialogue systems can be trained effectively from raw unannotated data. Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting {`}good{'} system responses to user utterances, i.e. responses which are likely to lead to long and engaging conversations. We show that (1) our neural ranker consistently outperforms several strong baselines when trained to optimise for user ratings; (2) when trained on larger amounts of data and only using conversation length as the objective, the ranker performs better than the one trained using ratings {--} ultimately reaching a Precision@1 of 0.87. This advance will make data collection for social conversational agents simpler and less expensive in the future.",
}
@inproceedings{kristianto-etal-2018-autonomous,
    title = "Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning",
    author = "Kristianto, Giovanni Yoko  and
      Zhang, Huiwen  and
      Tong, Bin  and
      Iwayama, Makoto  and
      Kobayashi, Yoshiyuki",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5702",
    doi = "10.18653/v1/W18-5702",
    pages = "9--16",
    abstract = "Solving composites tasks, which consist of several inherent sub-tasks, remains a challenge in the research area of dialogue. Current studies have tackled this issue by manually decomposing the composite tasks into several sub-domains. However, much human effort is inevitable. This paper proposes a dialogue framework that autonomously models meaningful sub-domains and learns the policy over them. Our experiments show that our framework outperforms the baseline without subdomains by 11{\%} in terms of success rate, and is competitive with that with manually defined sub-domains.",
}
@inproceedings{galitsky-ilvovsky-2018-building,
    title = "Building Dialogue Structure from Discourse Tree of a Question",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5703",
    doi = "10.18653/v1/W18-5703",
    pages = "17--23",
    abstract = "In this section we propose a reasoning-based approach to a dialogue management for a customer support chat bot. To build a dialogue scenario, we analyze the discourse tree (DT) of an initial query of a customer support dialogue that is frequently complex and multi-sentence. We then enforce rhetorical agreement between DT of the initial query and that of the answers, requests and responses. The chat bot finds answers, which are not only relevant by topic but also suitable for a given step of a conversation and match the question by style, communication means, experience level and other domain-independent attributes. We evaluate a performance of proposed algorithm in car repair domain and observe a 5 to 10{\%} improvement for single and three-step dialogues respectively, in comparison with baseline approaches to dialogue management.",
}
@inproceedings{guerini-etal-2018-methodology,
    title = "A Methodology for Evaluating Interaction Strategies of Task-Oriented Conversational Agents",
    author = "Guerini, Marco  and
      Falcone, Sara  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5704",
    doi = "10.18653/v1/W18-5704",
    pages = "24--32",
    abstract = "In task-oriented conversational agents, more attention has been usually devoted to assessing task effectiveness, rather than to \textit{how} the task is achieved. However, conversational agents are moving towards more complex and human-like interaction capabilities (e.g. the ability to use a formal/informal register, to show an empathetic behavior), for which standard evaluation methodologies may not suffice. In this paper, we provide a novel methodology to assess - in a completely controlled way - the impact on the quality of experience of agent{'}s interaction strategies. The methodology is based on a within subject design, where two slightly different transcripts of the same interaction with a conversational agent are presented to the user. Through a series of pilot experiments we prove that this methodology allows fast and cheap experimentation/evaluation, focusing on aspects that are overlooked by current methods.",
}
@inproceedings{aissa-etal-2018-reinforcement,
    title = "A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems",
    author = "Aissa, Wafa  and
      Soulier, Laure  and
      Denoyer, Ludovic",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5705",
    doi = "10.18653/v1/W18-5705",
    pages = "33--39",
    abstract = "Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback as a reward in the learning process. Experiments are carried out on two TREC datasets. We outline the effectiveness of our approach.",
}
@inproceedings{limsopatham-etal-2018-research,
    title = "Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper",
    author = "Limsopatham, Nut  and
      Rokhlenko, Oleg  and
      Carmel, David",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5706",
    doi = "10.18653/v1/W18-5706",
    pages = "40--45",
    abstract = "Recent advances in automatic speech recognition lead toward enabling a voice conversation between a human user and an intelligent virtual assistant. This provides a potential foundation for developing artificial personal shoppers for e-commerce websites, such as Alibaba, Amazon, and eBay. Personal shoppers are valuable to the on-line shops as they enhance user engagement and trust by promptly dealing with customers{'} questions and concerns. Developing an artificial personal shopper requires the agent to leverage knowledge about the customer and products, while interacting with the customer in a human-like conversation. In this position paper, we motivate and describe \textit{the artificial personal shopper task}, and then address a research agenda for this task by adapting and advancing existing information retrieval and natural language processing technologies.",
}
@inproceedings{saito-2018-curriculum,
    title = "Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management",
    author = "Saito, Atsushi",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5707",
    doi = "10.18653/v1/W18-5707",
    pages = "46--51",
    abstract = "Learning from sparse and delayed reward is a central issue in reinforcement learning. In this paper, to tackle reward sparseness problem of task oriented dialogue management, we propose a curriculum based approach on the number of slots of user goals. This curriculum makes it possible to learn dialogue management for sets of user goals with large number of slots. We also propose a dialogue policy based on progressive neural networks whose modules with parameters are appended with previous parameters fixed as the curriculum proceeds, and this policy improves performances over the one with single set of parameters.",
}
@inproceedings{du-black-2018-data,
    title = "Data Augmentation for Neural Online Chats Response Selection",
    author = "Du, Wenchao  and
      Black, Alan",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5708",
    doi = "10.18653/v1/W18-5708",
    pages = "52--58",
    abstract = "Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models. We investigate two data augmentation proxies, permutation and flipping, for neural dialog response selection task on various models over multiple datasets, including both Chinese and English languages. Different from standard data augmentation techniques, our method combines the original and synthesized data for prediction. Empirical results show that our approach can gain 1 to 3 recall-at-1 points over baseline models in both full-scale and small-scale settings.",
}
@inproceedings{agarwal-etal-2018-knowledge,
    title = "A Knowledge-Grounded Multimodal Search-Based Conversational Agent",
    author = "Agarwal, Shubham  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Konstas, Ioannis  and
      Rieser, Verena",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5709",
    doi = "10.18653/v1/W18-5709",
    pages = "59--66",
    abstract = "Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB).",
}
@inproceedings{petrovski-etal-2018-embedding,
    title = "Embedding Individual Table Columns for Resilient {SQL} Chatbots",
    author = "Petrovski, Bojan  and
      Aguado, Ignacio  and
      Hossmann, Andreea  and
      Baeriswyl, Michael  and
      Musat, Claudiu",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5710",
    doi = "10.18653/v1/W18-5710",
    pages = "67--73",
    abstract = "Most of the world{'}s data is stored in relational databases. Accessing these requires specialized knowledge of the Structured Query Language (SQL), putting them out of the reach of many people. A recent research thread in Natural Language Processing (NLP) aims to alleviate this problem by automatically translating natural language questions into SQL queries. While the proposed solutions are a great start, they lack robustness and do not easily generalize: the methods require high quality descriptions of the database table columns, and the most widely used training dataset, WikiSQL, is heavily biased towards using those descriptions as part of the questions. In this work, we propose solutions to both problems: we entirely eliminate the need for column descriptions, by relying solely on their contents, and we augment the WikiSQL dataset by paraphrasing column names to reduce bias. We show that the accuracy of existing methods drops when trained on our augmented, column-agnostic dataset, and that our own method reaches state of the art accuracy, while relying on column contents only.",
}
@inproceedings{louvan-magnini-2018-exploring,
    title = "Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding",
    author = "Louvan, Samuel  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5711",
    doi = "10.18653/v1/W18-5711",
    pages = "74--80",
    abstract = "Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system. Most approaches for this task rely solely on the domain-specific datasets for training. We propose a joint model of slot filling and Named Entity Recognition (NER) in a multi-task learning (MTL) setup. Our experiments on three slot filling datasets show that using NER as an auxiliary task improves slot filling performance and achieve competitive performance compared with state-of-the-art. In particular, NER is effective when supervised at the lower layer of the model. For low-resource scenarios, we found that MTL is effective for one dataset.",
}
@inproceedings{jiang-de-rijke-2018-sequence,
    title = "Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots",
    author = "Jiang, Shaojie  and
      de Rijke, Maarten",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5712",
    doi = "10.18653/v1/W18-5712",
    pages = "81--86",
    abstract = "Diversity is a long-studied topic in information retrieval that usually refers to the requirement that retrieved results should be non-repetitive and cover different aspects. In a conversational setting, an additional dimension of diversity matters: an engaging response generation system should be able to output responses that are diverse and interesting. Sequence-to-sequence (Seq2Seq) models have been shown to be very effective for response generation. However, dialogue responses generated by Seq2Seq models tend to have low diversity. In this paper, we review known sources and existing approaches to this low-diversity problem. We also identify a source of low diversity that has been little studied so far, namely model over-confidence. We sketch several directions for tackling model over-confidence and, hence, the low-diversity problem, including confidence penalties and label smoothing.",
}
@inproceedings{weston-etal-2018-retrieve,
    title = "Retrieve and Refine: Improved Sequence Generation Models For Dialogue",
    author = "Weston, Jason  and
      Dinan, Emily  and
      Miller, Alexander",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5713",
    doi = "10.18653/v1/W18-5713",
    pages = "87--92",
    abstract = "Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted to the given retrieval set leading to erroneous replies that cannot be tuned to the specific context. In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it {--} the final sequence generator treating the retrieval as additional context. We show on the recent ConvAI2 challenge task our approach produces responses superior to both standard retrieval and generation models in human evaluations.",
}
@proceedings{ws-2018-international-health,
    title = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    editor = "Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Rinaldi, Fabio",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5600",
}
@inproceedings{bell-etal-2018-detecting,
    title = "Detecting Diabetes Risk from Social Media Activity",
    author = "Bell, Dane  and
      Laparra, Egoitz  and
      Kousik, Aditya  and
      Ishihara, Terron  and
      Surdeanu, Mihai  and
      Kobourov, Stephen",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5601",
    doi = "10.18653/v1/W18-5601",
    pages = "1--11",
    abstract = "This work explores the detection of individuals{'} risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep learning architecture with several contributions: following previous observations that language use differs by gender, it captures and uses gender information through domain adaptation; it captures recency of posts under the hypothesis that more recent posts are more representative of an individual{'}s current risk status; and, lastly, it demonstrates that in this scenario where activity factors are sparsely represented in the data, a bag-of-word neural network model using custom dictionaries of food and activity words performs better than other neural sequence models. Our best model, which incorporates all these contributions, achieves a risk-detection F1 of 41.9, considerably higher than the baseline rate (36.9).",
}
@inproceedings{nguyen-etal-2018-treatment,
    title = "Treatment Side Effect Prediction from Online User-generated Content",
    author = "Nguyen, Van Hoang  and
      Sugiyama, Kazunari  and
      Kan, Min-Yen  and
      Halder, Kishaloy",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5602",
    doi = "10.18653/v1/W18-5602",
    pages = "12--21",
    abstract = "With Health 2.0, patients and caregivers increasingly seek information regarding possible drug side effects during their medical treatments in online health communities. These are helpful platforms for non-professional medical opinions, yet pose risk of being unreliable in quality and insufficient in quantity to cover the wide range of potential drug reactions. Existing approaches which analyze such user-generated content in online forums heavily rely on feature engineering of both documents and users, and often overlook the relationships between posts within a common discussion thread. Inspired by recent advancements, we propose a neural architecture that models the textual content of user-generated documents and user experiences in online communities to predict side effects during treatment. Experimental results show that our proposed architecture outperforms baseline models.",
}
@inproceedings{suster-etal-2018-revisiting,
    title = "Revisiting neural relation classification in clinical notes with external information",
    author = "{\v{S}}uster, Simon  and
      Sushil, Madhumita  and
      Daelemans, Walter",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5603",
    doi = "10.18653/v1/W18-5603",
    pages = "22--28",
    abstract = "Recently, segment convolutional neural networks have been proposed for end-to-end relation extraction in the clinical domain, achieving results comparable to or outperforming the approaches with heavy manual feature engineering. In this paper, we analyze the errors made by the neural classifier based on confusion matrices, and then investigate three simple extensions to overcome its limitations. We find that including ontological association between drugs and problems, and data-induced association between medical concepts does not reliably improve the performance, but that large gains are obtained by the incorporation of semantic classes to capture relation triggers.",
}
@inproceedings{kaur-molla-2018-supervised,
    title = "Supervised Machine Learning for Extractive Query Based Summarisation of Biomedical Data",
    author = "Kaur, Mandeep  and
      Moll{\'a}, Diego",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5604",
    doi = "10.18653/v1/W18-5604",
    pages = "29--37",
    abstract = "The automation of text summarisation of biomedical publications is a pressing need due to the plethora of information available online. This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries. In particular, we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge. We tackled the problem of annotating sentences for training classification systems and show that a simple annotation approach outperforms regression-based summarisation.",
}
@inproceedings{zhai-etal-2018-comparing,
    title = "Comparing {CNN} and {LSTM} character-level embeddings in {B}i{LSTM}-{CRF} models for chemical and disease named entity recognition",
    author = "Zhai, Zenan  and
      Nguyen, Dat Quoc  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5605",
    doi = "10.18653/v1/W18-5605",
    pages = "38--43",
    abstract = "We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks. Empirical results over the BioCreative V CDR corpus show that the use of either type of character-level word embeddings in conjunction with the BiLSTM-CRF models leads to comparable state-of-the-art performance. However, the models using CNN-based character-level word embeddings have a computational performance advantage, increasing training time over word-based models by 25{\%} while the LSTM-based character-level word embeddings more than double the required training time.",
}
@inproceedings{rojas-barahona-etal-2018-deep,
    title = "Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy",
    author = "Rojas-Barahona, Lina M.  and
      Tseng, Bo-Hsiang  and
      Dai, Yinpei  and
      Mansfield, Clare  and
      Ramadan, Osman  and
      Ultes, Stefan  and
      Crawford, Michael  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5606",
    doi = "10.18653/v1/W18-5606",
    pages = "44--54",
    abstract = "In recent years, we have seen deep learning and distributed representations of words and sentences make impact on a number of natural language processing tasks, such as similarity, entailment and sentiment analysis. Here we introduce a new task: understanding of mental health concepts derived from Cognitive Behavioural Therapy (CBT). We define a mental health ontology based on the CBT principles, annotate a large corpus where this phenomena is exhibited and perform understanding using deep learning and distributed representations. Our results show that the performance of deep learning models combined with word embeddings or sentence embeddings significantly outperform non-deep-learning models in this difficult task. This understanding module will be an essential component of a statistical dialogue system delivering therapy.",
}
@inproceedings{galvan-etal-2018-investigating,
    title = "Investigating the Challenges of Temporal Relation Extraction from Clinical Text",
    author = "Galvan, Diana  and
      Okazaki, Naoaki  and
      Matsuda, Koji  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5607",
    doi = "10.18653/v1/W18-5607",
    pages = "55--64",
    abstract = "Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain. The complexity of temporal representation in language is evident as results of the 2016 Clinical TempEval challenge indicate: the current state-of-the-art systems perform well in solving mention-identification tasks of event and time expressions but poorly in temporal relation extraction, showing a gap of around 0.25 point below human performance. We explore to adapt the tree-based LSTM-RNN model proposed by Miwa and Bansal (2016) to temporal relation extraction from clinical text, obtaining a five point improvement over the best 2016 Clinical TempEval system and two points over the state-of-the-art. We deliver a deep analysis of the results and discuss the next step towards human-like temporal reasoning.",
}
@inproceedings{kajiyama-etal-2018-de,
    title = "De-identifying Free Text of {J}apanese Dummy Electronic Health Records",
    author = "Kajiyama, Kohei  and
      Horiguchi, Hiromasa  and
      Okumura, Takashi  and
      Morita, Mizuki  and
      Kano, Yoshinobu",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5608",
    doi = "10.18653/v1/W18-5608",
    pages = "65--70",
    abstract = "A new law was established in Japan to promote utilization of EHRs for research and developments, while de-identification is required to use EHRs. However, studies of automatic de-identification in the healthcare domain is not active for Japanese language, no de-identification tool available in practical performance for Japanese medical domains, as far as we know. Previous work shows that rule-based methods are still effective, while deep learning methods are reported to be better recently. In order to implement and evaluate a de-identification tool in a practical level, we implemented three methods, rule-based, CRF, and LSTM. We prepared three datasets of pseudo EHRs with de-identification tags manually annotated. These datasets are derived from shared task data to compare with previous work, and our new data to increase training data. Our result shows that our LSTM-based method is better and robust, which leads to our future work that plans to apply our system to actual de-identification tasks in hospitals.",
}
@inproceedings{herrmannova-etal-2018-unsupervised,
    title = "Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study",
    author = "Herrmannova, Drahomira  and
      Young, Steven  and
      Patton, Robert  and
      Stahl, Christopher  and
      Kleinstreuer, Nicole  and
      Wolfe, Mary",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5609",
    doi = "10.18653/v1/W18-5609",
    pages = "71--82",
    abstract = "Identifying and extracting data elements such as study descriptors in publication full texts is a critical yet manual and labor-intensive step required in a number of tasks. In this paper we address the question of identifying data elements in an unsupervised manner. Specifically, provided a set of criteria describing specific study parameters, such as species, route of administration, and dosing regimen, we develop an unsupervised approach to identify text segments (sentences) relevant to the criteria. A binary classifier trained to identify publications that met the criteria performs better when trained on the candidate sentences than when trained on sentences randomly picked from the text, supporting the intuition that our method is able to accurately identify study descriptors.",
}
@inproceedings{cardon-grabar-2018-identification,
    title = "Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers",
    author = "Cardon, R{\'e}mi  and
      Grabar, Natalia",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5610",
    doi = "10.18653/v1/W18-5610",
    pages = "83--93",
    abstract = "Parallel aligned sentences provide useful information for different NLP applications. Yet, this kind of data is seldom available, especially for languages other than English. We propose to exploit comparable corpora in French which are distinguished by their registers (specialized and simplified versions) to detect and align parallel sentences. These corpora are related to the biomedical area. Our purpose is to state whether a given pair of specialized and simplified sentences is to be aligned or not. Manually created reference data show 0.76 inter-annotator agreement. We exploit a set of features and several automatic classifiers. The automatic alignment reaches up to 0.93 Precision, Recall and F-measure. In order to better evaluate the method, it is applied to data in English from the \textit{SemEval} STS competitions. The same features and models are applied in monolingual and cross-lingual contexts, in which they show up to 0.90 and 0.73 F-measure, respectively.",
}
@inproceedings{moen-etal-2018-evaluation,
    title = "Evaluation of a Prototype System that Automatically Assigns Subject Headings to Nursing Narratives Using Recurrent Neural Network",
    author = {Moen, Hans  and
      Hakala, Kai  and
      Peltonen, Laura-Maria  and
      Suhonen, Henry  and
      Loukasm{\"a}ki, Petri  and
      Salakoski, Tapio  and
      Ginter, Filip  and
      Salanter{\"a}, Sanna},
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5611",
    doi = "10.18653/v1/W18-5611",
    pages = "94--100",
    abstract = "We present our initial evaluation of a prototype system designed to assist nurses in assigning subject headings to nursing narratives {--} written in the context of documenting patient care in hospitals. Currently nurses may need to memorize several hundred subject headings from standardized nursing terminologies when structuring and assigning the right section/subject headings to their text. Our aim is to allow nurses to write in a narrative manner without having to plan and structure the text with respect to sections and subject headings, instead the system should assist with the assignment of subject headings and restructuring afterwards. We hypothesize that this could reduce the time and effort needed for nursing documentation in hospitals. A central component of the system is a text classification model based on a long short-term memory (LSTM) recurrent neural network architecture, trained on a large data set of nursing notes. A simple Web-based interface has been implemented for user interaction. To evaluate the system, three nurses write a set of artificial nursing shift notes in a fully unstructured narrative manner, without planning for or consider the use of sections and subject headings. These are then fed to the system which assigns subject headings to each sentence and then groups them into paragraphs. Manual evaluation is conducted by a group of nurses. The results show that about 70{\%} of the sentences are assigned to correct subject headings. The nurses believe that such a system can be of great help in making nursing documentation in hospitals easier and less time consuming. Finally, various measures and approaches for improving the system are discussed.",
}
@inproceedings{banisakher-etal-2018-automatically,
    title = "Automatically Detecting the Position and Type of Psychiatric Evaluation Report Sections",
    author = "Banisakher, Deya  and
      Rishe, Naphtali  and
      Finlayson, Mark A.",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5612",
    doi = "10.18653/v1/W18-5612",
    pages = "101--110",
    abstract = "Psychiatric evaluation reports represent a rich and still mostly-untapped source of information for developing systems for automatic diagnosis and treatment of mental health problems. These reports contain free-text structured within sections using a convention of headings. We present a model for automatically detecting the position and type of different psychiatric evaluation report sections. We developed this model using a corpus of 150 sample reports that we gathered from the Web, and used sentences as a processing unit while section headings were used as labels of section type. From these labels we generated a unified hierarchy of labels of section types, and then learned n-gram models of the language found in each section. To model conventions for section order, we integrated these n-gram models with a Hierarchical Hidden Markov Model (HHMM) representing the probabilities of observed section orders found in the corpus, and then used this HHMM n-gram model in a decoding framework to infer the most likely section boundaries and section types for documents with their section labels removed. We evaluated our model over two tasks, namely, identifying section boundaries and identifying section types and orders. Our model significantly outperformed baselines for each task with an F1 of 0.88 for identifying section types, and a 0.26 WindowDiff (Wd) and 0.20 and (Pk) scores, respectively, for identifying section boundaries.",
}
@inproceedings{rama-etal-2018-iterative,
    title = "Iterative development of family history annotation guidelines using a synthetic corpus of clinical text",
    author = "Rama, Taraka  and
      Brekke, P{\aa}l  and
      Nytr{\o}, {\O}ystein  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5613",
    doi = "10.18653/v1/W18-5613",
    pages = "111--121",
    abstract = "In this article, we describe the development of annotation guidelines for family history information in Norwegian clinical text. We make use of incrementally developed synthetic clinical text describing patients{'} family history relating to cases of cardiac disease and present a general methodology which integrates the synthetically produced clinical statements and guideline development. We analyze inter-annotator agreement based on the developed guidelines and present results from experiments aimed at evaluating the validity and applicability of the annotated corpus using machine learning techniques. The resulting annotated corpus contains 477 sentences and 6030 tokens. Both the annotation guidelines and the annotated corpus are made freely available and as such constitutes the first publicly available resource of Norwegian clinical text.",
}
@inproceedings{grabar-etal-2018-cas,
    title = "{CAS}: {F}rench Corpus with Clinical Cases",
    author = "Grabar, Natalia  and
      Claveau, Vincent  and
      Dalloux, Cl{\'e}ment",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5614",
    doi = "10.18653/v1/W18-5614",
    pages = "122--128",
    abstract = "Textual corpora are extremely important for various NLP applications as they provide information necessary for creating, setting and testing these applications and the corresponding tools. They are also crucial for designing reliable methods and reproducible results. Yet, in some areas, such as the medical area, due to confidentiality or to ethical reasons, it is complicated and even impossible to access textual data representative of those produced in these areas. We propose the CAS corpus built with clinical cases, such as they are reported in the published scientific literature in French. We describe this corpus, currently containing over 397,000 word occurrences, and the existing linguistic and semantic annotations.",
}
@inproceedings{holderness-etal-2018-analysis,
    title = "Analysis of Risk Factor Domains in Psychosis Patient Health Records",
    author = "Holderness, Eben  and
      Miller, Nicholas  and
      Bolton, Kirsten  and
      Cawkwell, Philip  and
      Meteer, Marie  and
      Pustejovsky, James  and
      Hua-Hall, Mei",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5615",
    doi = "10.18653/v1/W18-5615",
    pages = "129--138",
    abstract = "Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.",
}
@inproceedings{girardi-etal-2018-patient,
    title = "Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks",
    author = "Girardi, Ivan  and
      Ji, Pengfei  and
      Nguyen, An-phi  and
      Hollenstein, Nora  and
      Ivankay, Adam  and
      Kuhn, Lorenz  and
      Marchiori, Chiara  and
      Zhang, Ce",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5616",
    doi = "10.18653/v1/W18-5616",
    pages = "139--148",
    abstract = "We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best \textit{point of care} and \textit{time to treat}. We use an attention-based convolutional neural network architecture trained on 600,000 doctor notes in German. We compare two approaches, one that uses the full text of the medical notes and one that uses only a selected list of medical entities extracted from the text. These approaches achieve 79{\%} and 66{\%} precision, respectively, but on a confidence threshold of 0.6, precision increases to 85{\%} and 75{\%}, respectively. In addition, a method to detect \textit{warning symptoms} is implemented to render the classification task transparent from a medical perspective. The method is based on the learning of attention scores and a method of automatic validation using the same data.",
}
@inproceedings{legrand-etal-2018-syntax,
    title = "Syntax-based Transfer Learning for the Task of Biomedical Relation Extraction",
    author = {Legrand, Jo{\"e}l  and
      Toussaint, Yannick  and
      Ra{\"\i}ssi, Chedy  and
      Coulet, Adrien},
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5617",
    doi = "10.18653/v1/W18-5617",
    pages = "149--159",
    abstract = "Transfer learning (TL) proposes to enhance machine learning performance on a problem, by reusing labeled data originally designed for a related problem. In particular, domain adaptation consists, for a specific task, in reusing training data developed for the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because those usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains. In this paper, we experiment with TL for the task of Relation Extraction (RE) from biomedical texts, using the TreeLSTM model. We empirically show the impact of TreeLSTM alone and with domain adaptation by obtaining better performances than the state of the art on two biomedical RE tasks and equal performances for two others, for which few annotated data are available. Furthermore, we propose an analysis of the role that syntactic features may play in TL for RE.",
}
@inproceedings{sheikhshabbafghi-etal-2018-domain,
    title = "In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition",
    author = "Sheikhshabbafghi, Golnar  and
      Birol, Inanc  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5618",
    doi = "10.18653/v1/W18-5618",
    pages = "160--164",
    abstract = "Rapidly expanding volume of publications in the biomedical domain makes it increasingly difficult for a timely evaluation of the latest literature. That, along with a push for automated evaluation of clinical reports, present opportunities for effective natural language processing methods. In this study we target the problem of named entity recognition, where texts are processed to annotate terms that are relevant for biomedical studies. Terms of interest in the domain include gene and protein names, and cell lines and types. Here we report on a pipeline built on Embeddings from Language Models (ELMo) and a deep learning package for natural language processing (AllenNLP). We trained context-aware token embeddings on a dataset of biomedical papers using ELMo, and incorporated these embeddings in the LSTM-CRF model used by AllenNLP for named entity recognition. We show these representations improve named entity recognition for different types of biomedical named entities. We also achieve a new state of the art in gene mention detection on the BioCreative II gene mention shared task.",
}
@inproceedings{lin-etal-2018-self,
    title = "Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction",
    author = "Lin, Chen  and
      Miller, Timothy  and
      Dligach, Dmitriy  and
      Amiri, Hadi  and
      Bethard, Steven  and
      Savova, Guergana",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5619",
    doi = "10.18653/v1/W18-5619",
    pages = "165--176",
    abstract = "Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance. We propose to build a recurrent neural network with multiple semantically heterogeneous embeddings within a self-training framework. Our framework makes use of labeled, unlabeled, and social media data, operates on basic features, and is scalable and generalizable. With this method, we establish the state-of-the-art result for both in- and cross-domain for a clinical temporal relation extraction task.",
}
@inproceedings{jeblee-hirst-2018-listwise,
    title = "Listwise temporal ordering of events in clinical notes",
    author = "Jeblee, Serena  and
      Hirst, Graeme",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5620",
    doi = "10.18653/v1/W18-5620",
    pages = "177--182",
    abstract = "We present metrics for listwise temporal ordering of events in clinical notes, as well as a baseline listwise temporal ranking model that generates a timeline of events that can be used in downstream medical natural language processing tasks.",
}
@inproceedings{viani-etal-2018-time,
    title = "Time Expressions in Mental Health Records for Symptom Onset Extraction",
    author = "Viani, Natalia  and
      Yin, Lucia  and
      Kam, Joyce  and
      Alawi, Ayunni  and
      Bittar, Andr{\'e}  and
      Dutta, Rina  and
      Patel, Rashmi  and
      Stewart, Robert  and
      Velupillai, Sumithra",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5621",
    doi = "10.18653/v1/W18-5621",
    pages = "183--192",
    abstract = "For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain.",
}
@inproceedings{tourille-etal-2018-evaluation,
    title = "Evaluation of a Sequence Tagging Tool for Biomedical Texts",
    author = "Tourille, Julien  and
      Doutreligne, Matthieu  and
      Ferret, Olivier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Paris, Nicolas  and
      Tannier, Xavier",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5622",
    doi = "10.18653/v1/W18-5622",
    pages = "193--203",
    abstract = "Many applications in biomedical natural language processing rely on sequence tagging as an initial step to perform more complex analysis. To support text analysis in the biomedical domain, we introduce Yet Another SEquence Tagger (YASET), an open-source multi purpose sequence tagger that implements state-of-the-art deep learning algorithms for sequence tagging. Herein, we evaluate YASET on part-of-speech tagging and named entity recognition in a variety of text genres including articles from the biomedical literature in English and clinical narratives in French. To further characterize performance, we report distributions over 30 runs and different sizes of training datasets. YASET provides state-of-the-art performance on the CoNLL 2003 NER dataset (F1=0.87), MEDPOST corpus (F1=0.97), MERLoT corpus (F1=0.99) and NCBI disease corpus (F1=0.81). We believe that YASET is a versatile and efficient tool that can be used for sequence tagging in biomedical and clinical texts.",
}
@inproceedings{zhang-etal-2018-learning-summarize,
    title = "Learning to Summarize Radiology Findings",
    author = "Zhang, Yuhao  and
      Ding, Daisy Yi  and
      Qian, Tianpei  and
      Manning, Christopher D.  and
      Langlotz, Curtis P.",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5623",
    doi = "10.18653/v1/W18-5623",
    pages = "204--213",
    abstract = "The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians. However, the process of generating impressions by summarizing findings is time-consuming for radiologists and prone to errors. We propose to automate the generation of radiology impressions with neural sequence-to-sequence learning. We further propose a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process. On a large dataset of radiology reports collected from actual hospital studies, our model outperforms existing non-neural and neural baselines under the ROUGE metrics. In a blind experiment, a board-certified radiologist indicated that 67{\%} of sampled system summaries are at least as good as the corresponding human-written summaries, suggesting significant clinical validity. To our knowledge our work represents the first attempt in this direction.",
}
@proceedings{ws-2018-fact,
    title = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    editor = "Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5500",
}
@inproceedings{thorne-etal-2018-fact,
    title = "The Fact Extraction and {VER}ification ({FEVER}) Shared Task",
    author = "Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5501",
    doi = "10.18653/v1/W18-5501",
    pages = "1--9",
    abstract = "We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of 64.21{\%}. In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.",
}
@inproceedings{torabi-asr-taboada-2018-data,
    title = "The Data Challenge in Misinformation Detection: Source Reputation vs. Content Veracity",
    author = "Torabi Asr, Fatemeh  and
      Taboada, Maite",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5502",
    doi = "10.18653/v1/W18-5502",
    pages = "10--15",
    abstract = "Misinformation detection at the level of full news articles is a text classification problem. Reliably labeled data in this domain is rare. Previous work relied on news articles collected from so-called {``}reputable{''} and {``}suspicious{''} websites and labeled accordingly. We leverage fact-checking websites to collect individually-labeled news articles with regard to the veracity of their content and use this data to test the cross-domain generalization of a classifier trained on bigger text collections but labeled according to source reputation. Our results suggest that reputation-based classification is not sufficient for predicting the veracity level of the majority of news articles, and that the system performance on different test datasets depends on topic distribution. Therefore collecting well-balanced and carefully-assessed training data is a priority for developing robust misinformation detection systems.",
}
@inproceedings{dumitrache-etal-2018-crowdsourcing,
    title = "Crowdsourcing Semantic Label Propagation in Relation Classification",
    author = "Dumitrache, Anca  and
      Aroyo, Lora  and
      Welty, Chris",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5503",
    doi = "10.18653/v1/W18-5503",
    pages = "16--21",
    abstract = "Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels. Most progress in relation extraction and classification has been made with crowdsourced corrections to distant-supervised labels, and there is evidence that indicates still more would be better. In this paper, we explore the problem of propagating human annotation signals gathered for open-domain relation classification through the CrowdTruth methodology for crowdsourcing, that captures ambiguity in annotations by measuring inter-annotator disagreement. Our approach propagates annotations to sentences that are similar in a low dimensional embedding space, expanding the number of labels by two orders of magnitude. Our experiments show significant improvement in a sentence-level multi-class relation classifier.",
}
@inproceedings{gupta-etal-2018-retrieve,
    title = "Retrieve and Re-rank: A Simple and Effective {IR} Approach to Simple Question Answering over Knowledge Graphs",
    author = "Gupta, Vishal  and
      Chinnakotla, Manoj  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5504",
    doi = "10.18653/v1/W18-5504",
    pages = "22--27",
    abstract = "SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80{\%} which sets a new state-of-the-art on the SimpleQuestions dataset.",
}
@inproceedings{kevin-etal-2018-information,
    title = "Information Nutrition Labels: A Plugin for Online News Evaluation",
    author = {Kevin, Vincentius  and
      H{\"o}gden, Birte  and
      Schwenger, Claudia  and
      {\c{S}}ahan, Ali  and
      Madan, Neelu  and
      Aggarwal, Piush  and
      Bangaru, Anusha  and
      Muradov, Farid  and
      Aker, Ahmet},
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5505",
    doi = "10.18653/v1/W18-5505",
    pages = "28--33",
    abstract = "In this paper we present a browser plugin \textit{NewsScan} that assists online news readers in evaluating the quality of online content they read by providing \textit{information nutrition labels} for online news articles. In analogy to groceries, where nutrition labels help consumers make choices that they consider best for themselves, information nutrition labels tag online news articles with data that help readers judge the articles they engage with. This paper discusses the choice of the labels, their implementation and visualization.",
}
@inproceedings{taniguchi-etal-2018-joint,
    title = "Joint Modeling for Query Expansion and Information Extraction with Reinforcement Learning",
    author = "Taniguchi, Motoki  and
      Miura, Yasuhide  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5506",
    doi = "10.18653/v1/W18-5506",
    pages = "34--39",
    abstract = "Information extraction about an event can be improved by incorporating external evidence. In this study, we propose a joint model for pseudo-relevance feedback based query expansion and information extraction with reinforcement learning. Our model generates an event-specific query to effectively retrieve documents relevant to the event. We demonstrate that our model is comparable or has better performance than the previous model in two publicly available datasets. Furthermore, we analyzed the influences of the retrieval effectiveness in our model on the extraction performance.",
}
@inproceedings{conforti-etal-2018-towards,
    title = "Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles",
    author = "Conforti, Costanza  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5507",
    doi = "10.18653/v1/W18-5507",
    pages = "40--49",
    abstract = "In this paper, we propose to adapt the four-staged pipeline proposed by Zubiaga et al. (2018) for the Rumor Verification task to the problem of Fake News Detection. We show that the recently released FNC-1 corpus covers two of its steps, namely the \textit{Tracking} and the \textit{Stance Detection} task. We identify asymmetry in length in the input to be a key characteristic of the latter step, when adapted to the framework of Fake News Detection, and propose to handle it as a specific type of \textit{Cross-Level Stance Detection}. Inspired by theories from the field of Journalism Studies, we implement and test two architectures to successfully model the internal structure of an article and its interactions with a claim.",
}
@inproceedings{esteves-etal-2018-belittling,
    title = "Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web",
    author = "Esteves, Diego  and
      Reddy, Aniketh Janardhan  and
      Chawla, Piyush  and
      Lehmann, Jens",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5508",
    doi = "10.18653/v1/W18-5508",
    pages = "50--59",
    abstract = "With the growth of the internet, the number of \textit{fake-news} online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-making process to bullying and violence episodes. Therefore, fact-checking algorithms became a valuable asset. To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source. However, most of the widely used Web indicators have either been shutdown to the public (e.g., Google PageRank) or are not free for use (Alexa Rank). Further existing databases are short-manually curated lists of online sources, which do not scale. Finally, most of the research on the topic is theoretical-based or explore confidential data in a restricted simulation environment. In this paper we explore current research, highlight the challenges and propose solutions to tackle the problem of classifying websites into a credibility scale. The proposed model automatically extracts source reputation cues and computes a credibility factor, providing valuable insights which can help in belittling dubious and confirming trustful unknown websites. Experimental results outperform state of the art in the 2-classes and 5-classes setting.",
}
@inproceedings{naderi-hirst-2018-automated,
    title = "Automated Fact-Checking of Claims in Argumentative Parliamentary Debates",
    author = "Naderi, Nona  and
      Hirst, Graeme",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5509",
    doi = "10.18653/v1/W18-5509",
    pages = "60--65",
    abstract = "We present an automated approach to distinguish true, false, stretch, and dodge statements in questions and answers in the Canadian Parliament. We leverage the truthfulness annotations of a U.S. fact-checking corpus by training a neural net model and incorporating the prediction probabilities into our models. We find that in concert with other linguistic features, these probabilities can improve the multi-class classification results. We further show that dodge statements can be detected with an F1 measure as high as 82.57{\%} in binary classification settings.",
}
@inproceedings{ghanem-etal-2018-stance,
    title = "Stance Detection in Fake News A Combined Feature Representation",
    author = "Ghanem, Bilal  and
      Rosso, Paolo  and
      Rangel, Francisco",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5510",
    doi = "10.18653/v1/W18-5510",
    pages = "66--71",
    abstract = "With the uncontrolled increasing of fake news and rumors over the Web, different approaches have been proposed to address the problem. In this paper, we present an approach that combines lexical, word embeddings and n-gram features to detect the stance in fake news. Our approach has been tested on the Fake News Challenge (FNC-1) dataset. Given a news title-article pair, the FNC-1 task aims at determining the relevance of the article and the title. Our proposed approach has achieved an accurate result (59.6 {\%} Macro F1) that is close to the state-of-the-art result with 0.013 difference using a simple feature representation. Furthermore, we have investigated the importance of different lexicons in the detection of the classification labels.",
}
@inproceedings{obamuyide-vlachos-2018-zero,
    title = "Zero-shot Relation Classification as Textual Entailment",
    author = "Obamuyide, Abiola  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5511",
    doi = "10.18653/v1/W18-5511",
    pages = "72--78",
    abstract = "We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classification by exploiting relation descriptions, (ii) utilize existing textual entailment models, and (iii) leverage readily available textual entailment datasets, to enhance the performance of relation classification systems. Our experiments show that the proposed approach achieves 20.16{\%} and 61.32{\%} in F1 zero-shot classification performance on two datasets, which further improved to 22.80{\%} and 64.78{\%} respectively with the use of conditional encoding.",
}
@inproceedings{kim-etal-2018-teaching,
    title = "Teaching Syntax by Adversarial Distraction",
    author = "Kim, Juho  and
      Malon, Christopher  and
      Kadav, Asim",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5512",
    doi = "10.18653/v1/W18-5512",
    pages = "79--84",
    abstract = "Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different grammar and word order change the desired classification. We introduce several datasets based on synthetic transformations of natural entailment examples in SNLI or FEVER, to teach aspects of grammar and word order. We show that without retraining, popular entailment models are unaware that these syntactic differences change meaning. With retraining, some but not all popular entailment models can learn to compare the syntax properly.",
}
@inproceedings{alhindi-etal-2018-evidence,
    title = "Where is Your Evidence: Improving Fact-checking by Justification Modeling",
    author = "Alhindi, Tariq  and
      Petridis, Savvas  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5513",
    doi = "10.18653/v1/W18-5513",
    pages = "85--90",
    abstract = "Fact-checking is a journalistic practice that compares a claim made publicly against trusted sources of facts. Wang (2017) introduced a large dataset of validated claims from the POLITIFACT.com website (LIAR dataset), enabling the development of machine learning approaches for fact-checking. However, approaches based on this dataset have focused primarily on modeling the claim and speaker-related metadata, without considering the evidence used by humans in labeling the claims. We extend the LIAR dataset by automatically extracting the justification from the fact-checking article used by humans to label a given claim. We show that modeling the extracted justification in conjunction with the claim (and metadata) provides a significant improvement regardless of the machine learning model used (feature-based or deep learning) both in a binary classification task (true, false) and in a six-way classification task (pants on fire, false, mostly false, half true, mostly true, true).",
}
@inproceedings{loureiro-jorge-2018-affordance,
    title = "Affordance Extraction and Inference based on Semantic Role Labeling",
    author = "Loureiro, Daniel  and
      Jorge, Al{\'\i}pio",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5514",
    doi = "10.18653/v1/W18-5514",
    pages = "91--96",
    abstract = "Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of {`}coffee{'} and {`}tea{'} make them similar, or how they could be related to {`}shop{'}. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.",
}
@inproceedings{yoneda-etal-2018-ucl,
    title = "{UCL} Machine Reading Group: Four Factor Framework For Fact Finding ({H}exa{F})",
    author = "Yoneda, Takuma  and
      Mitchell, Jeff  and
      Welbl, Johannes  and
      Stenetorp, Pontus  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5515",
    doi = "10.18653/v1/W18-5515",
    pages = "97--102",
    abstract = "In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62.52{\%} on the provisional test set (without additional human evaluation), and 65.41{\%} on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.",
}
@inproceedings{hanselowski-etal-2018-ukp,
    title = "{UKP}-Athene: Multi-Sentence Textual Entailment for Claim Verification",
    author = "Hanselowski, Andreas  and
      Zhang, Hao  and
      Li, Zile  and
      Sorokin, Daniil  and
      Schiller, Benjamin  and
      Schulz, Claudia  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5516",
    doi = "10.18653/v1/W18-5516",
    pages = "103--108",
    abstract = "The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).",
}
@inproceedings{malon-2018-team,
    title = "Team Papelo: Transformer Networks at {FEVER}",
    author = "Malon, Christopher",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5517",
    doi = "10.18653/v1/W18-5517",
    pages = "109--113",
    abstract = "We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language modeling, to classify a broad set of potential evidence. The precision of the entailment classifier allows us to enhance recall by considering every statement from several articles to decide upon each claim. We include not only the articles best matching the claim text by TFIDF score, but read additional articles whose titles match named entities and capitalized expressions occurring in the claim text. The entailment module evaluates potential evidence one statement at a time, together with the title of the page the evidence came from (providing a hint about possible pronoun antecedents). In preliminary evaluation, the system achieves .5736 FEVER score, .6108 label accuracy, and .6485 evidence F1 on the FEVER shared task test set.",
}
@inproceedings{kowollik-aker-2018-uni,
    title = "Uni-{DUE} Student Team: Tackling fact checking through decomposable attention neural network",
    author = "Kowollik, Jan  and
      Aker, Ahmet",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5518",
    doi = "10.18653/v1/W18-5518",
    pages = "114--118",
    abstract = "In this paper we present our system for the FEVER Challenge. The task of this challenge is to verify claims by extracting information from Wikipedia. Our system has two parts. In the first part it performs a search for candidate sentences by treating the claims as query. In the second part it filters out noise from these candidates and uses the remaining ones to decide whether they support or refute or entail not enough information to verify the claim. We show that this system achieves a FEVER score of 0.3927 on the FEVER shared task development data set which is a 25.5{\%} improvement over the baseline score.",
}
@inproceedings{nooralahzadeh-ovrelid-2018-sirius,
    title = "{SIRIUS}-{LTG}: An Entity Linking Approach to Fact Extraction and Verification",
    author = "Nooralahzadeh, Farhad  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5519",
    doi = "10.18653/v1/W18-5519",
    pages = "119--123",
    abstract = "This article presents the SIRIUS-LTG system for the Fact Extraction and VERification (FEVER) Shared Task. It consists of three components: 1) \textit{Wikipedia Page Retrieval}: First we extract the entities in the claim, then we find potential Wikipedia URI candidates for each of the entities using a SPARQL query over DBpedia 2) \textit{Sentence selection}: We investigate various techniques i.e. Smooth Inverse Frequency (SIF), Word Mover{'}s Distance (WMD), Soft-Cosine Similarity, Cosine similarity with unigram Term Frequency Inverse Document Frequency (TF-IDF) to rank sentences by their similarity to the claim. 3) \textit{Textual Entailment}: We compare three models for the task of claim classification. We apply a Decomposable Attention (DA) model (Parikh et al., 2016), a Decomposed Graph Entailment (DGE) model (Khot et al., 2018) and a Gradient-Boosted Decision Trees (TalosTree) model (Sean et al., 2017) for this task. The experiments show that the pipeline with simple Cosine Similarity using TFIDF in sentence selection along with DA model as labelling model achieves the best results on the development set (F1 evidence: 32.17, label accuracy: 59.61 and FEVER score: 0.3778). Furthermore, it obtains 30.19, 48.87 and 36.55 in terms of F1 evidence, label accuracy and FEVER score, respectively, on the test set. Our system ranks 15th among 23 participants in the shared task prior to any human-evaluation of the evidence.",
}
@inproceedings{taniguchi-etal-2018-integrating,
    title = "Integrating Entity Linking and Evidence Ranking for Fact Extraction and Verification",
    author = "Taniguchi, Motoki  and
      Taniguchi, Tomoki  and
      Takahashi, Takumi  and
      Miura, Yasuhide  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5520",
    doi = "10.18653/v1/W18-5520",
    pages = "124--126",
    abstract = "We describe here our system and results on the FEVER shared task. We prepared a pipeline system which composes of a document selection, a sentence retrieval, and a recognizing textual entailment (RTE) components. A simple entity linking approach with text match is used as the document selection component, this component identifies relevant documents for a given claim by using mentioned entities as clues. The sentence retrieval component selects relevant sentences as candidate evidence from the documents based on TF-IDF. Finally, the RTE component selects evidence sentences by ranking the sentences and classifies the claim simultaneously. The experimental results show that our system achieved the FEVER score of 0.4016 and outperformed the official baseline system.",
}
@inproceedings{chakrabarty-etal-2018-robust,
    title = "Robust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification.",
    author = "Chakrabarty, Tuhin  and
      Alhindi, Tariq  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5521",
    doi = "10.18653/v1/W18-5521",
    pages = "127--131",
    abstract = "This paper presents the ColumbiaNLP submission for the FEVER Workshop Shared Task. Our system is an end-to-end pipeline that extracts factual evidence from Wikipedia and infers a decision about the truthfulness of the claim based on the extracted evidence. Our pipeline achieves significant improvement over the baseline for all the components (Document Retrieval, Sentence Selection and Textual Entailment) both on the development set and the test set. Our team finished 6th out of 24 teams on the leader-board based on the preliminary results with a FEVER score of 49.06 on the blind test set compared to 27.45 of the baseline system.",
}
@inproceedings{reddy-etal-2018-defactonlp,
    title = "{D}e{F}acto{NLP}: Fact Verification using Entity Recognition, {TFIDF} Vector Comparison and Decomposable Attention",
    author = "Reddy, Aniketh Janardhan  and
      Rocha, Gil  and
      Esteves, Diego",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5522",
    doi = "10.18653/v1/W18-5522",
    pages = "132--137",
    abstract = "In this paper, we describe DeFactoNLP, the system we designed for the FEVER 2018 Shared Task. The aim of this task was to conceive a system that can not only automatically assess the veracity of a claim but also retrieve evidence supporting this assessment from Wikipedia. In our approach, the Wikipedia documents whose Term Frequency-Inverse Document Frequency (TFIDF) vectors are most similar to the vector of the claim and those documents whose names are similar to those of the named entities (NEs) mentioned in the claim are identified as the documents which might contain evidence. The sentences in these documents are then supplied to a textual entailment recognition module. This module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information to assess the veracity of the claim. Various features computed using these probabilities are finally used by a Random Forest classifier to determine the overall truthfulness of the claim. The sentences which support this classification are returned as evidence. Our approach achieved a 0.4277 evidence F1-score, a 0.5136 label accuracy and a 0.3833 FEVER score.",
}
@inproceedings{li-etal-2018-end,
    title = "An End-to-End Multi-task Learning Model for Fact Checking",
    author = "Li, Sizhen  and
      Zhao, Shuai  and
      Cheng, Bo  and
      Yang, Hao",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5523",
    doi = "10.18653/v1/W18-5523",
    pages = "138--144",
    abstract = "With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. Here we decompose this problem into two parts: an entity linking task (retrieving relative Wikipedia pages) and recognizing textual entailment between the claim and selected pages. In this paper, we present an end-to-end multi-task learning with bi-direction attention (EMBA) model to classify the claim as {``}supports{''}, {``}refutes{''} or {``}not enough info{''} with respect to the pages retrieved and detect sentences as evidence at the same time. We conduct experiments on the FEVER (Fact Extraction and VERification) paper test dataset and shared task test dataset, a new public dataset for verification against textual sources. Experimental results show that our method achieves comparable performance compared with the baseline system.",
}
@inproceedings{otto-2018-team,
    title = "Team {GESIS} Cologne: An all in all sentence-based approach for {FEVER}",
    author = "Otto, Wolfgang",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5524",
    doi = "10.18653/v1/W18-5524",
    pages = "145--149",
    abstract = "In this system description of our pipeline to participate at the Fever Shared Task, we describe our sentence-based approach. Throughout all steps of our pipeline, we regarded single sentences as our processing unit. In our IR-Component, we searched in the set of all possible Wikipedia introduction sentences without limiting sentences to a fixed number of relevant documents. In the entailment module, we judged every sentence separately and combined the result of the classifier for the top 5 sentences with the help of an ensemble classifier to make a judgment whether the truth of a statement can be derived from the given claim.",
}
@inproceedings{hidey-diab-2018-team,
    title = "Team {SWEEP}er: Joint Sentence Extraction and Fact Checking with Pointer Networks",
    author = "Hidey, Christopher  and
      Diab, Mona",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5525",
    doi = "10.18653/v1/W18-5525",
    pages = "150--155",
    abstract = "Many tasks such as question answering and reading comprehension rely on information extracted from unreliable sources. These systems would thus benefit from knowing whether a statement from an unreliable source is correct. We present experiments on the FEVER (Fact Extraction and VERification) task, a shared task that involves selecting sentences from Wikipedia and predicting whether a claim is supported by those sentences, refuted, or there is not enough information. Fact checking is a task that benefits from not only asserting or disputing the veracity of a claim but also finding evidence for that position. As these tasks are dependent on each other, an ideal model would consider the veracity of the claim when finding evidence and also find only the evidence that is relevant. We thus jointly model sentence extraction and verification on the FEVER shared task. Among all participants, we ranked 5th on the blind test set (prior to any additional human evaluation of the evidence).",
}
@inproceedings{luken-etal-2018-qed,
    title = "{QED}: A fact verification system for the {FEVER} shared task",
    author = "Luken, Jackson  and
      Jiang, Nanjiang  and
      de Marneffe, Marie-Catherine",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5526",
    doi = "10.18653/v1/W18-5526",
    pages = "156--160",
    abstract = "This paper describes our system submission to the 2018 Fact Extraction and VERification (FEVER) shared task. The system uses a heuristics-based approach for evidence extraction and a modified version of the inference model by Parikh et al. (2016) for classification. Our process is broken down into three modules: potentially relevant documents are gathered based on key phrases in the claim, then any possible evidence sentences inside those documents are extracted, and finally our classifier discards any evidence deemed irrelevant and uses the remaining to classify the claim{'}s veracity. Our system beats the shared task baseline by 12{\%} and is successful at finding correct evidence (evidence retrieval F1 of 62.5{\%} on the development set).",
}
@inproceedings{padia-etal-2018-team,
    title = "Team {UMBC}-{FEVER} : Claim verification using Semantic Lexical Resources",
    author = "Padia, Ankur  and
      Ferraro, Francis  and
      Finin, Tim",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5527",
    doi = "10.18653/v1/W18-5527",
    pages = "161--165",
    abstract = "We describe our system used in the 2018 FEVER shared task. The system employed a frame-based information retrieval approach to select Wikipedia sentences providing evidence and used a two-layer multilayer perceptron to classify a claim as correct or not. Our submission achieved a score of 0.3966 on the Evidence F1 metric with accuracy of 44.79{\%}, and FEVER score of 0.2628 F1 points.",
}
@inproceedings{paul-etal-2018-mostly,
    title = "A mostly unlexicalized model for recognizing textual entailment",
    author = "Paul, Mithun  and
      Sharp, Rebecca  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the First Workshop on Fact Extraction and {VER}ification ({FEVER})",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5528",
    doi = "10.18653/v1/W18-5528",
    pages = "166--171",
    abstract = "Many approaches to automatically recognizing entailment relations have employed classifiers over hand engineered lexicalized features, or deep learning models that implicitly capture lexicalization through word embeddings. This reliance on lexicalization may complicate the adaptation of these tools between domains. For example, such a system trained in the news domain may learn that a sentence like {``}Palestinians recognize Texas as part of Mexico{''} tends to be unsupported, but this fact (and its corresponding lexicalized cues) have no value in, say, a scientific domain. To mitigate this dependence on lexicalized information, in this paper we propose a model that reads two sentences, from any given domain, to determine entailment without using lexicalized features. Instead our model relies on features that are either unlexicalized or are domain independent such as proportion of negated verbs, antonyms, or noun overlap. In its current implementation, this model does not perform well on the FEVER dataset, due to two reasons. First, for the information retrieval portion of the task we used the baseline system provided, since this was not the aim of our project. Second, this is work in progress and we still are in the process of identifying more features and gradually increasing the accuracy of our model. In the end, we hope to build a generic end-to-end classifier, which can be used in a domain outside the one in which it was trained, with no or minimal re-training.",
}
@proceedings{ws-2018-2018,
    title = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    editor = "Linzen, Tal  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5400",
}
@inproceedings{kerinec-etal-2018-deep,
    title = "When does deep multi-task learning work for loosely related document classification tasks?",
    author = "Kerinec, Emma  and
      Braud, Chlo{\'e}  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5401",
    doi = "10.18653/v1/W18-5401",
    pages = "1--8",
    abstract = "This work aims to contribute to our understanding of \textit{when} multi-task learning through parameter sharing in deep neural networks leads to improvements over single-task learning. We focus on the setting of learning from \textit{loosely related} tasks, for which no theoretical guarantees exist. We therefore approach the question empirically, studying which properties of datasets and single-task learning characteristics correlate with improvements from multi-task learning. We are the first to study this in a text classification setting and across more than 500 different task pairs.",
}
@inproceedings{elloumi-etal-2018-analyzing,
    title = "Analyzing Learned Representations of a Deep {ASR} Performance Prediction Model",
    author = "Elloumi, Zied  and
      Besacier, Laurent  and
      Galibert, Olivier  and
      Lecouteux, Benjamin",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5402",
    doi = "10.18653/v1/W18-5402",
    pages = "9--15",
    abstract = "This paper addresses a relatively new task: prediction of ASR performance on unseen broadcast programs. In a previous paper, we presented an ASR performance prediction system using CNNs that encode both text (ASR transcript) and speech, in order to predict word error rate. This work is dedicated to the analysis of speech signal embeddings and text embeddings learnt by the CNN while training our prediction model. We try to better understand which information is captured by the deep model and its relation with different conditioning factors. It is shown that hidden layers convey a clear signal about speech style, accent and broadcast type. We then try to leverage these 3 types of information at training time through multi-task learning. Our experiments show that this allows to train slightly more efficient ASR performance prediction systems that - in addition - simultaneously tag the analyzed utterances according to their speech style, accent and broadcast program origin.",
}
@inproceedings{croce-etal-2018-explaining,
    title = "Explaining non-linear Classifier Decisions within Kernel-based Deep Architectures",
    author = "Croce, Danilo  and
      Rossini, Daniele  and
      Basili, Roberto",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5403",
    doi = "10.18653/v1/W18-5403",
    pages = "16--24",
    abstract = "Nonlinear methods such as deep neural networks achieve state-of-the-art performances in several semantic NLP tasks. However epistemologically transparent decisions are not provided as for the limited interpretability of the underlying acquired neural models. In neural-based semantic inference tasks epistemological transparency corresponds to the ability of tracing back causal connections between the linguistic properties of a input instance and the produced classification output. In this paper, we propose the use of a methodology, called \textit{Layerwise Relevance Propagation}, over linguistically motivated neural architectures, namely \textit{Kernel-based Deep Architectures} (KDA), to guide argumentations and explanation inferences. In such a way, each decision provided by a KDA can be linked to real examples, linguistically related to the input instance: these can be used to motivate the network output. Quantitative analysis shows that richer explanations about the semantic and syntagmatic structures of the examples characterize more convincing arguments in two tasks, i.e. question classification and semantic role labeling.",
}
@inproceedings{sogaard-etal-2018-nightmare,
    title = "Nightmare at test time: How punctuation prevents parsers from generalizing",
    author = "S{\o}gaard, Anders  and
      de Lhoneux, Miryam  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5404",
    doi = "10.18653/v1/W18-5404",
    pages = "25--29",
    abstract = "Punctuation is a strong indicator of syntactic structure, and parsers trained on text with punctuation often rely heavily on this signal. Punctuation is a diversion, however, since human language processing does not rely on punctuation to the same extent, and in informal texts, we therefore often leave out punctuation. We also use punctuation ungrammatically for emphatic or creative purposes, or simply by mistake. We show that (a) dependency parsers are sensitive to \textit{both} absence of punctuation and to alternative uses; (b) neural parsers tend to be more sensitive than vintage parsers; (c) training neural parsers \textit{without} punctuation outperforms all out-of-the-box parsers across all scenarios where punctuation departs from standard punctuation. Our main experiments are on synthetically corrupted data to study the effect of punctuation in isolation and avoid potential confounds, but we also show effects on out-of-domain data.",
}
@inproceedings{spinks-moens-2018-evaluating,
    title = "Evaluating Textual Representations through Image Generation",
    author = "Spinks, Graham  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5405",
    doi = "10.18653/v1/W18-5405",
    pages = "30--39",
    abstract = "We present a methodology for determining the quality of textual representations through the ability to generate images from them. Continuous representations of textual input are ubiquitous in modern Natural Language Processing techniques either at the core of machine learning algorithms or as the by-product at any given layer of a neural network. While current techniques to evaluate such representations focus on their performance on particular tasks, they don{'}t provide a clear understanding of the level of informational detail that is stored within them, especially their ability to represent spatial information. The central premise of this paper is that visual inspection or analysis is the most convenient method to quickly and accurately determine information content. Through the use of text-to-image neural networks, we propose a new technique to compare the quality of textual representations by visualizing their information content. The method is illustrated on a medical dataset where the correct representation of spatial information and shorthands are of particular importance. For four different well-known textual representations, we show with a quantitative analysis that some representations are consistently able to deliver higher quality visualizations of the information content. Additionally, we show that the quantitative analysis technique correlates with the judgment of a human expert evaluator in terms of alignment.",
}
@inproceedings{camacho-collados-pilehvar-2018-role,
    title = "On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis",
    author = "Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5406",
    doi = "10.18653/v1/W18-5406",
    pages = "40--46",
    abstract = "Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system, with potential impact in its final performance. Despite its importance, text preprocessing has not received much attention in the deep learning literature. In this paper we investigate the impact of simple text preprocessing decisions (particularly tokenizing, lemmatizing, lowercasing and multiword grouping) on the performance of a standard neural text classifier. We perform an extensive evaluation on standard benchmarks from text categorization and sentiment analysis. While our experiments show that a simple tokenization of input text is generally adequate, they also highlight significant degrees of variability across preprocessing techniques. This reveals the importance of paying attention to this usually-overlooked step in the pipeline, particularly when comparing different models. Finally, our evaluation provides insights into the best preprocessing practices for training word embeddings.",
}
@inproceedings{bastings-etal-2018-jump,
    title = "Jump to better conclusions: {SCAN} both left and right",
    author = "Bastings, Jasmijn  and
      Baroni, Marco  and
      Weston, Jason  and
      Cho, Kyunghyun  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5407",
    doi = "10.18653/v1/W18-5407",
    pages = "47--55",
    abstract = "Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for. To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS. We show that models that do well on SCAN do not necessarily do well on NACS, and that NACS exhibits properties more closely aligned with realistic use-cases for sequence-to-sequence models.",
}
@inproceedings{jacovi-etal-2018-understanding,
    title = "Understanding Convolutional Neural Networks for Text Classification",
    author = "Jacovi, Alon  and
      Sar Shalom, Oren  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5408",
    doi = "10.18653/v1/W18-5408",
    pages = "56--65",
    abstract = "We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the networks process and classify text. We examine common hypotheses to this problem: that filters, accompanied by global max-pooling, serve as ngram detectors. We show that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important ngrams from the rest. Finally, we show practical use cases derived from our findings in the form of model interpretability (explaining a trained model by deriving a concrete identity for each filter, bridging the gap between visualization tools in vision tasks and NLP) and prediction interpretability (explaining predictions).",
}
@inproceedings{ronning-etal-2018-linguistic,
    title = "Linguistic representations in multi-task neural networks for ellipsis resolution",
    author = "R{\o}nning, Ola  and
      Hardt, Daniel  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5409",
    doi = "10.18653/v1/W18-5409",
    pages = "66--73",
    abstract = "Sluicing resolution is the task of identifying the antecedent to a question ellipsis. Antecedents are often sentential constituents, and previous work has therefore relied on syntactic parsing, together with complex linguistic features. A recent model instead used partial parsing as an auxiliary task in sequential neural network architectures to inject syntactic information. We explore the linguistic information being brought to bear by such networks, both by defining subsets of the data exhibiting relevant linguistic characteristics, and by examining the internal representations of the network. Both perspectives provide evidence for substantial linguistic knowledge being deployed by the neural networks.",
}
@inproceedings{kiyono-etal-2018-unsupervised,
    title = "Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models",
    author = "Kiyono, Shun  and
      Takase, Sho  and
      Suzuki, Jun  and
      Okazaki, Naoaki  and
      Inui, Kentaro  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5410",
    doi = "10.18653/v1/W18-5410",
    pages = "74--81",
    abstract = "Developing a method for understanding the inner workings of black-box neural methods is an important research endeavor. Conventionally, many studies have used an attention matrix to interpret how Encoder-Decoder-based models translate a given source sentence to the corresponding target sentence. However, recent studies have empirically revealed that an attention matrix is not optimal for token-wise translation analyses. We propose a method that explicitly models the token-wise alignment between the source and target sequences to provide a better analysis. Experiments show that our method can acquire token-wise alignments that are superior to those of an attention mechanism.",
}
@inproceedings{sushil-etal-2018-rule,
    title = "Rule induction for global explanation of trained models",
    author = "Sushil, Madhumita  and
      {\v{S}}uster, Simon  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5411",
    doi = "10.18653/v1/W18-5411",
    pages = "82--97",
    abstract = "Understanding the behavior of a trained network and finding explanations for its outputs is important for improving the network{'}s performance and generalization ability, and for ensuring trust in automated systems. Several approaches have previously been proposed to identify and visualize the most important features by analyzing a trained network. However, the relations between different features and classes are lost in most cases. We propose a technique to induce sets of if-then-else rules that capture these relations to globally explain the predictions of a network. We first calculate the importance of the features in the trained network. We then weigh the original inputs with these feature importance scores, simplify the transformed input space, and finally fit a rule induction model to explain the model predictions. We find that the output rule-sets can explain the predictions of a neural network trained for 4-class text classification from the 20 newsgroups dataset to a macro-averaged F-score of 0.80. We make the code available at \url{https://github.com/clips/interpret_with_rules}.",
}
@inproceedings{ravfogel-etal-2018-lstm,
    title = "Can {LSTM} Learn to Capture Agreement? The Case of {B}asque",
    author = "Ravfogel, Shauli  and
      Goldberg, Yoav  and
      Tyers, Francis",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5412",
    doi = "10.18653/v1/W18-5412",
    pages = "98--107",
    abstract = "Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical phenomena can they acquire? We focus on the task of agreement prediction in Basque, as a case study for a task that requires implicit understanding of sentence structure and the acquisition of a complex but consistent morphological system. Analyzing experimental results from two syntactic prediction tasks {--} verb number prediction and suffix recovery {--} we find that sequential models perform worse on agreement prediction in Basque than one might expect on the basis of a previous agreement prediction work in English. Tentative findings based on diagnostic classifiers suggest the network makes use of local heuristics as a proxy for the hierarchical structure of the sentence. We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.",
}
@inproceedings{loula-etal-2018-rearranging,
    title = "Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks",
    author = "Loula, Jo{\~a}o  and
      Baroni, Marco  and
      Lake, Brenden",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5413",
    doi = "10.18653/v1/W18-5413",
    pages = "108--114",
    abstract = "Systematic compositionality is the ability to recombine meaningful units with regular and predictable outcomes, and it{'}s seen as key to the human capacity for generalization in language. Recent work (Lake and Baroni, 2018) has studied systematic compositionality in modern seq2seq models using generalization to novel navigation instructions in a grounded environment as a probing tool. Lake and Baroni{'}s main experiment required the models to quickly bootstrap the meaning of new words. We extend this framework here to settings where the model needs only to recombine well-trained functional words (such as {``}\textit{around}{''} and {``}\textit{right}{''}) in novel contexts. Our findings confirm and strengthen the earlier ones: seq2seq models can be impressively good at generalizing to novel combinations of previously-seen input, but only when they receive extensive training on the specific pattern to be generalized (e.g., generalizing from many examples of {``}X \textit{around right}{''} to {``}\textit{jump around right}{''}), while failing when generalization requires novel application of compositional rules (e.g., inferring the meaning of {``}\textit{around right}{''} from those of {``}\textit{right}{''} and {``}\textit{around}{''}).",
}
@inproceedings{sennhauser-berwick-2018-evaluating,
    title = "Evaluating the Ability of {LSTM}s to Learn Context-Free Grammars",
    author = "Sennhauser, Luzi  and
      Berwick, Robert",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5414",
    doi = "10.18653/v1/W18-5414",
    pages = "115--124",
    abstract = "While long short-term memory (LSTM) neural net architectures are designed to capture sequence information, human language is generally composed of hierarchical structures. This raises the question as to whether LSTMs can learn hierarchical structures. We explore this question with a well-formed bracket prediction task using two types of brackets modeled by an LSTM. Demonstrating that such a system is learnable by an LSTM is the first step in demonstrating that the entire class of CFLs is also learnable. We observe that the model requires exponential memory in terms of the number of characters and embedded depth, where a sub-linear memory should suffice. Still, the model does more than memorize the training input. It learns how to distinguish between relevant and irrelevant information. On the other hand, we also observe that the model does not generalize well. We conclude that LSTMs do not learn the relevant underlying context-free rules, suggesting the good overall performance is attained rather by an efficient way of evaluating nuisance variables. LSTMs are a way to quickly reach good results for many natural language tasks, but to understand and generate natural language one has to investigate other concepts that can make more direct use of natural language{'}s structural nature.",
}
@inproceedings{pryzant-etal-2018-interpretable,
    title = "Interpretable Neural Architectures for Attributing an Ad{'}s Performance to its Writing Style",
    author = "Pryzant, Reid  and
      Basu, Sugato  and
      Sone, Kazoo",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5415",
    doi = "10.18653/v1/W18-5415",
    pages = "125--135",
    abstract = "How much does {``}free shipping!{''} help an advertisement{'}s ability to persuade? This paper presents two methods for \textit{performance attribution}: finding the degree to which an outcome can be attributed to parts of a text while controlling for potential confounders. Both algorithms are based on interpreting the behaviors and parameters of trained neural networks. One method uses a CNN to encode the text, an adversarial objective function to control for confounders, and projects its weights onto its activations to interpret the importance of each phrase towards each output class. The other method leverages residualization to control for confounds and performs interpretation by aggregating over learned word vectors. We demonstrate these algorithms{'} efficacy on 118,000 internet search advertisements and outcomes, finding language indicative of high and low click through rate (CTR) regardless of who the ad is by or what it is for. Our results suggest the proposed algorithms are high performance and data efficient, able to glean actionable insights from fewer than 10,000 data points. We find that quick, easy, and authoritative language is associated with success, while lackluster embellishment is related to failure. These findings agree with the advertising industry{'}s emperical wisdom, automatically revealing insights which previously required manual A/B testing to discover.",
}
@inproceedings{wallace-etal-2018-interpreting,
    title = "Interpreting Neural Networks with Nearest Neighbors",
    author = "Wallace, Eric  and
      Feng, Shi  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5416",
    doi = "10.18653/v1/W18-5416",
    pages = "136--144",
    abstract = "Local model interpretation methods explain individual predictions by assigning an importance value to each input feature. This value is often determined by measuring the change in confidence when a feature is removed. However, the confidence of neural networks is not a robust measure of model uncertainty. This issue makes reliably judging the importance of the input features difficult. We address this by changing the test-time behavior of neural networks using Deep k-Nearest Neighbors. Without harming text classification accuracy, this algorithm provides a more robust uncertainty metric which we use to generate feature importance values. The resulting interpretations better align with human perception than baseline methods. Finally, we use our interpretation method to analyze model predictions on dataset annotation artifacts.",
}
@inproceedings{kementchedjhieva-lopez-2018-indicatements,
    title = "{`}Indicatements{'} that character language models learn {E}nglish morpho-syntactic units and regularities",
    author = "Kementchedjhieva, Yova  and
      Lopez, Adam",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5417",
    doi = "10.18653/v1/W18-5417",
    pages = "145--153",
    abstract = "Character language models have access to surface morphological patterns, but it is not clear whether or \textit{how} they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction.",
}
@inproceedings{gupta-schutze-2018-lisa,
    title = "{LISA}: Explaining Recurrent Neural Network Judgments via Layer-w{I}se Semantic Accumulation and Example to Pattern Transformation",
    author = {Gupta, Pankaj  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5418",
    doi = "10.18653/v1/W18-5418",
    pages = "154--164",
    abstract = "Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks. Despite their success, it still remains a challenge to understand their hidden behavior. In this work, we analyze and interpret the cumulative nature of RNN via a proposed technique named as \textit{Layer-wIse-Semantic-Accumulation} (LISA) for explaining decisions and detecting the most likely (i.e., saliency) patterns that the network relies on while decision making. We demonstrate (1) \textit{LISA}: {``}How an RNN accumulates or builds semantics during its sequential processing for a given text example and expected response{''} (2) \textit{Example2pattern}: {``}How the saliency patterns look like for each category in the data according to the network in decision making{''}. We analyse the sensitiveness of RNNs about different inputs to check the increase or decrease in prediction scores and further extract the saliency patterns learned by the network. We employ two relation classification datasets: SemEval 10 Task 8 and TAC KBP Slot Filling to explain RNN predictions via the \textit{LISA} and \textit{example2pattern}.",
}
@inproceedings{hupkes-etal-2018-analysing,
    title = "Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue",
    author = "Hupkes, Dieuwke  and
      Bouwmeester, Sanne  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5419",
    doi = "10.18653/v1/W18-5419",
    pages = "165--174",
    abstract = "We investigate how encoder-decoder models trained on a synthetic dataset of task-oriented dialogues process disfluencies, such as hesitations and self-corrections. We find that, contrary to earlier results, disfluencies have very little impact on the task success of seq-to-seq models with attention. Using visualisations and diagnostic classifiers, we analyse the representations that are incrementally built by the model, and discover that models develop little to no awareness of the structure of disfluencies. However, adding disfluencies to the data appears to help the model create clearer representations overall, as evidenced by the attention patterns the different models exhibit.",
}
@inproceedings{stahlberg-etal-2018-operation,
    title = "An Operation Sequence Model for Explainable Neural Machine Translation",
    author = "Stahlberg, Felix  and
      Saunders, Danielle  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5420",
    doi = "10.18653/v1/W18-5420",
    pages = "175--186",
    abstract = "We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the target sentence by monotonically walking through the source sentence. Word reordering is modeled by operations which allow setting markers in the target sentence and move a target-side write head between those markers. In contrast to many modern neural models, our system emits explicit word alignment information which is often crucial to practical machine translation as it improves explainability. Our technique can outperform a plain text system in terms of BLEU score under the recent Transformer architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU difference on Spanish-English.",
}
@inproceedings{krug-stober-2018-introspection,
    title = "Introspection for convolutional automatic speech recognition",
    author = "Krug, Andreas  and
      Stober, Sebastian",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5421",
    doi = "10.18653/v1/W18-5421",
    pages = "187--199",
    abstract = "Artificial Neural Networks (ANNs) have experienced great success in the past few years. The increasing complexity of these models leads to less understanding about their decision processes. Therefore, introspection techniques have been proposed, mostly for images as input data. Patterns or relevant regions in images can be intuitively interpreted by a human observer. This is not the case for more complex data like speech recordings. In this work, we investigate the application of common introspection techniques from computer vision to an Automatic Speech Recognition (ASR) task. To this end, we use a model similar to image classification, which predicts letters from spectrograms. We show difficulties in applying image introspection to ASR. To tackle these problems, we propose normalized averaging of aligned inputs (NAvAI): a data-driven method to reveal learned patterns for prediction of specific classes. Our method integrates information from many data examples through local introspection techniques for Convolutional Neural Networks (CNNs). We demonstrate that our method provides better interpretability of letter-specific patterns than existing methods.",
}
@inproceedings{trifonov-etal-2018-learning,
    title = "Learning and Evaluating Sparse Interpretable Sentence Embeddings",
    author = "Trifonov, Valentin  and
      Ganea, Octavian-Eugen  and
      Potapenko, Anna  and
      Hofmann, Thomas",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5422",
    doi = "10.18653/v1/W18-5422",
    pages = "200--210",
    abstract = "Previous research on word embeddings has shown that sparse representations, which can be either learned on top of existing dense embeddings or obtained through model constraints during training time, have the benefit of increased interpretability properties: to some degree, each dimension can be understood by a human and associated with a recognizable feature in the data. In this paper, we transfer this idea to sentence embeddings and explore several approaches to obtain a sparse representation. We further introduce a novel, quantitative and automated evaluation metric for sentence embedding interpretability, based on topic coherence methods. We observe an increase in interpretability compared to dense models, on a dataset of movie dialogs and on the scene descriptions from the MS COCO dataset.",
}
@inproceedings{wilcox-etal-2018-rnn,
    title = "What do {RNN} Language Models Learn about Filler{--}Gap Dependencies?",
    author = "Wilcox, Ethan  and
      Levy, Roger  and
      Morita, Takashi  and
      Futrell, Richard",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5423",
    doi = "10.18653/v1/W18-5423",
    pages = "211--221",
    abstract = "RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn. Here we investigate whether state-of-the-art RNN language models represent long-distance \textbf{filler{--}gap dependencies} and constraints on them. Examining RNN behavior on experimentally controlled sentences designed to expose filler{--}gap dependencies, we show that RNNs can represent the relationship in multiple syntactic positions and over large spans of text. Furthermore, we show that RNNs learn a subset of the known restrictions on filler{--}gap dependencies, known as \textbf{island constraints}: RNNs show evidence for wh-islands, adjunct islands, and complex NP islands. These studies demonstrates that state-of-the-art RNN models are able to learn and generalize about empty syntactic positions.",
}
@inproceedings{jumelet-hupkes-2018-language,
    title = "Do Language Models Understand Anything? On the Ability of {LSTM}s to Understand Negative Polarity Items",
    author = "Jumelet, Jaap  and
      Hupkes, Dieuwke",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5424",
    doi = "10.18653/v1/W18-5424",
    pages = "222--231",
    abstract = "In this paper, we attempt to link the inner workings of a neural language model to linguistic theory, focusing on a complex phenomenon well discussed in formal linguistics: (negative) polarity items. We briefly discuss the leading hypotheses about the licensing contexts that allow negative polarity items and evaluate to what extent a neural language model has the ability to correctly process a subset of such constructions. We show that the model finds a relation between the licensing context and the negative polarity item and appears to be aware of the \textit{scope} of this context, which we extract from a parse tree of the sentence. With this research, we hope to pave the way for other studies linking formal linguistics to deep learning.",
}
@inproceedings{skachkova-etal-2018-closing,
    title = "Closing Brackets with Recurrent Neural Networks",
    author = "Skachkova, Natalia  and
      Trost, Thomas  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5425",
    doi = "10.18653/v1/W18-5425",
    pages = "232--239",
    abstract = "Many natural and formal languages contain words or symbols that require a matching counterpart for making an expression well-formed. The combination of opening and closing brackets is a typical example of such a construction. Due to their commonness, the ability to follow such rules is important for language modeling. Currently, recurrent neural networks (RNNs) are extensively used for this task. We investigate whether they are capable of learning the rules of opening and closing brackets by applying them to synthetic Dyck languages that consist of different types of brackets. We provide an analysis of the statistical properties of these languages as a baseline and show strengths and limits of Elman-RNNs, GRUs and LSTMs in experiments on random samples of these languages. In terms of perplexity and prediction accuracy, the RNNs get close to the theoretical baseline in most cases.",
}
@inproceedings{giulianelli-etal-2018-hood,
    title = "Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information",
    author = "Giulianelli, Mario  and
      Harding, Jack  and
      Mohnert, Florian  and
      Hupkes, Dieuwke  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5426",
    doi = "10.18653/v1/W18-5426",
    pages = "240--248",
    abstract = "How do neural language models keep track of number agreement between subject and verb? We show that {`}diagnostic classifiers{'}, trained to predict number from the internal states of a language model, provide a detailed understanding of how, when, and where this information is represented. Moreover, they give us insight into when and where number information is corrupted in cases where the language model ends up making agreement errors. To demonstrate the causal role played by the representations we find, we then use agreement information to influence the course of the LSTM during the processing of difficult sentences. Results from such an intervention reveal a large increase in the language model{'}s accuracy. Together, these results show that diagnostic classifiers give us an unrivalled detailed look into the representation of linguistic information in neural models, and demonstrate that this knowledge can be used to improve their performance.",
}
@inproceedings{tutek-snajder-2018-iterative,
    title = "Iterative Recursive Attention Model for Interpretable Sequence Classification",
    author = "Tutek, Martin  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5427",
    doi = "10.18653/v1/W18-5427",
    pages = "249--257",
    abstract = "Natural language processing has greatly benefited from the introduction of the attention mechanism. However, standard attention models are of limited interpretability for tasks that involve a series of inference steps. We describe an iterative recursive attention model, which constructs incremental representations of input data through reusing results of previously computed queries. We train our model on sentiment classification datasets and demonstrate its capacity to identify and combine different aspects of the input in an easily interpretable manner, while obtaining performance close to the state of the art.",
}
@inproceedings{hiebert-etal-2018-interpreting,
    title = "Interpreting Word-Level Hidden State Behaviour of Character-Level {LSTM} Language Models",
    author = "Hiebert, Avery  and
      Peterson, Cole  and
      Fyshe, Alona  and
      Mehta, Nishant",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5428",
    doi = "10.18653/v1/W18-5428",
    pages = "258--266",
    abstract = "While Long Short-Term Memory networks (LSTMs) and other forms of recurrent neural network have been successfully applied to language modeling on a character level, the hidden state dynamics of these models can be difficult to interpret. We investigate the hidden states of such a model by using the HDBSCAN clustering algorithm to identify points in the text at which the hidden state is similar. Focusing on whitespace characters prior to the beginning of a word reveals interpretable clusters that offer insight into how the LSTM may combine contextual and character-level information to identify parts of speech. We also introduce a method for deriving word vectors from the hidden state representation in order to investigate the word-level knowledge of the model. These word vectors encode meaningful semantic information even for words that appear only once in the training text.",
}
@inproceedings{letarte-etal-2018-importance,
    title = "Importance of Self-Attention for Sentiment Analysis",
    author = {Letarte, Ga{\"e}l  and
      Paradis, Fr{\'e}d{\'e}rik  and
      Gigu{\`e}re, Philippe  and
      Laviolette, Fran{\c{c}}ois},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5429",
    doi = "10.18653/v1/W18-5429",
    pages = "267--275",
    abstract = "Despite their superior performance, deep learning models often lack interpretability. In this paper, we explore the modeling of insightful relations between words, in order to understand and enhance predictions. To this effect, we propose the Self-Attention Network (SANet), a flexible and interpretable architecture for text classification. Experiments indicate that gains obtained by self-attention is task-dependent. For instance, experiments on sentiment analysis tasks showed an improvement of around 2{\%} when using self-attention compared to a baseline without attention, while topic classification showed no gain. Interpretability brought forward by our architecture highlighted the importance of neighboring word interactions to extract sentiment.",
}
@inproceedings{sommerauer-fokkens-2018-firearms,
    title = "Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell",
    author = "Sommerauer, Pia  and
      Fokkens, Antske",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5430",
    doi = "10.18653/v1/W18-5430",
    pages = "276--286",
    abstract = "This paper presents an approach for investigating the nature of semantic information captured by word embeddings. We propose a method that extends an existing human-elicited semantic property dataset with gold negative examples using crowd judgments. Our experimental approach tests the ability of supervised classifiers to identify semantic features in word embedding vectors and compares this to a feature-identification method based on full vector cosine similarity. The idea behind this method is that properties identified by classifiers, but not through full vector comparison are captured by embeddings. Properties that cannot be identified by either method are not. Our results provide an initial indication that semantic properties relevant for the way entities interact (e.g. dangerous) are captured, while perceptual information (e.g. colors) is not represented. We conclude that, though preliminary, these results show that our method is suitable for identifying which properties are captured by embeddings.",
}
@inproceedings{raganato-tiedemann-2018-analysis,
    title = "An Analysis of Encoder Representations in Transformer-Based Machine Translation",
    author = {Raganato, Alessandro  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5431",
    doi = "10.18653/v1/W18-5431",
    pages = "287--297",
    abstract = "The attention mechanism is a successful technique in modern NLP, especially in tasks like machine translation. The recently proposed network architecture of the \textit{Transformer} is based entirely on attention mechanisms and achieves new state of the art results in neural machine translation, outperforming other sequence-to-sequence models. However, so far not much is known about the internal properties of the model and the representations it learns to achieve that performance. To study this question, we investigate the information that is learned by the attention mechanism in Transformer models with different translation quality. We assess the representations of the encoder by extracting dependency relations based on self-attention weights, we perform four probing tasks to study the amount of syntactic and semantic captured information and we also test attention in a transfer learning scenario. Our analysis sheds light on the relative strengths and weaknesses of the various encoder representations. We observe that specific attention heads mark syntactic dependency relations and we can also confirm that lower layers tend to learn more about syntax while higher layers tend to encode more semantics.",
}
@inproceedings{wei-etal-2018-evaluating,
    title = "Evaluating Grammaticality in Seq2seq Models with a Broad Coverage {HPSG} Grammar: A Case Study on Machine Translation",
    author = "Wei, Johnny  and
      Pham, Khiem  and
      O{'}Connor, Brendan  and
      Dillon, Brian",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5432",
    doi = "10.18653/v1/W18-5432",
    pages = "298--305",
    abstract = "Sequence to sequence (seq2seq) models are often employed in settings where the target output is natural language. However, the syntactic properties of the language generated from these models are not well understood. We explore whether such output belongs to a formal and realistic grammar, by employing the English Resource Grammar (ERG), a broad coverage, linguistically precise HPSG-based grammar of English. From a French to English parallel corpus, we analyze the parseability and grammatical constructions occurring in output from a seq2seq translation model. Over 93{\%} of the model translations are parseable, suggesting that it learns to generate conforming to a grammar. The model has trouble learning the distribution of rarer syntactic rules, and we pinpoint several constructions that differentiate translations between the references and our model.",
}
@inproceedings{hao-etal-2018-context,
    title = "Context-Free Transductions with Neural Stacks",
    author = "Hao, Yiding  and
      Merrill, William  and
      Angluin, Dana  and
      Frank, Robert  and
      Amsel, Noah  and
      Benz, Andrew  and
      Mendelsohn, Simon",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5433",
    doi = "10.18653/v1/W18-5433",
    pages = "306--315",
    abstract = "This paper analyzes the behavior of stack-augmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, context-free language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.",
}
@inproceedings{harbecke-etal-2018-learning,
    title = "Learning Explanations from Language Data",
    author = "Harbecke, David  and
      Schwarzenberg, Robert  and
      Alt, Christoph",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5434",
    doi = "10.18653/v1/W18-5434",
    pages = "316--318",
    abstract = "PatternAttribution is a recent method, introduced in the vision domain, that explains classifications of deep neural networks. We demonstrate that it also generates meaningful interpretations in the language domain.",
}
@inproceedings{rychalska-etal-2018-much,
    title = "How much should you ask? On the question structure in {QA} systems.",
    author = "Rychalska, Barbara  and
      Basaj, Dominika  and
      Wr{\'o}blewska, Anna  and
      Biecek, Przemyslaw",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5435",
    doi = "10.18653/v1/W18-5435",
    pages = "319--321",
    abstract = "Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner. However, users are still used to query-like systems where they type in keywords to search for answer. In this study we validate which parts of questions are essential for obtaining valid answer. In order to conclude that, we take advantage of LIME - a framework that explains prediction by local approximation. We find that grammar and natural language is disregarded by QA. State-of-the-art model can answer properly even if {'}asked{'} only with a few words with high coefficients calculated with LIME. According to our knowledge, it is the first time that QA model is being explained by LIME.",
}
@inproceedings{rychalska-etal-2018-care,
    title = "Does it care what you asked? Understanding Importance of Verbs in Deep Learning {QA} System",
    author = "Rychalska, Barbara  and
      Basaj, Dominika  and
      Wr{\'o}blewska, Anna  and
      Biecek, Przemyslaw",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5436",
    doi = "10.18653/v1/W18-5436",
    pages = "322--324",
    abstract = "In this paper we present the results of an investigation of the importance of verbs in a deep learning QA system trained on SQuAD dataset. We show that main verbs in questions carry little influence on the decisions made by the system - in over 90{\%} of researched cases swapping verbs for their antonyms did not change system decision. We track this phenomenon down to the insides of the net, analyzing the mechanism of self-attention and values contained in hidden layers of RNN. Finally, we recognize the characteristics of the SQuAD dataset as the source of the problem. Our work refers to the recently popular topic of adversarial examples in NLP, combined with investigating deep net structure.",
}
@inproceedings{poerner-etal-2018-interpretable,
    title = "Interpretable Textual Neuron Representations for {NLP}",
    author = {Poerner, Nina  and
      Roth, Benjamin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5437",
    doi = "10.18653/v1/W18-5437",
    pages = "325--327",
    abstract = "Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs. We propose and evaluate ways of transferring this technology to NLP. Our results suggest that gradient ascent with a gumbel softmax layer produces n-gram representations that outperform naive corpus search in terms of target neuron activation. The representations highlight differences in syntax awareness between the language and visual models of the Imaginet architecture.",
}
@inproceedings{saphra-lopez-2018-language,
    title = "Language Models Learn {POS} First",
    author = "Saphra, Naomi  and
      Lopez, Adam",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5438",
    doi = "10.18653/v1/W18-5438",
    pages = "328--330",
    abstract = "A glut of recent research shows that language models capture linguistic structure. Such work answers the question of whether a model represents linguistic structure. But how and when are these structures acquired? Rather than treating the training process itself as a black box, we investigate how representations of linguistic structure are learned over time. In particular, we demonstrate that different aspects of linguistic structure are learned at different rates, with part of speech tagging acquired early and global topic information learned continuously.",
}
@inproceedings{garneau-etal-2018-predicting,
    title = "Predicting and interpreting embeddings for out of vocabulary words in downstream tasks",
    author = "Garneau, Nicolas  and
      Leboeuf, Jean-Samuel  and
      Lamontagne, Luc",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5439",
    doi = "10.18653/v1/W18-5439",
    pages = "331--333",
    abstract = "We propose a novel way to handle out of vocabulary (OOV) words in downstream natural language processing (NLP) tasks. We implement a network that predicts useful embeddings for OOV words based on their morphology and on the context in which they appear. Our model also incorporates an attention mechanism indicating the focus allocated to the left context words, the right context words or the word{'}s characters, hence making the prediction more interpretable. The model is a {``}drop-in{''} module that is jointly trained with the downstream task{'}s neural network, thus producing embeddings specialized for the task at hand. When the task is mostly syntactical, we observe that our model aims most of its attention on surface form characters. On the other hand, for tasks more semantical, the network allocates more attention to the surrounding words. In all our tests, the module helps the network to achieve better performances in comparison to the use of simple random embeddings.",
}
@inproceedings{bacon-regier-2018-probing,
    title = "Probing sentence embeddings for structure-dependent tense",
    author = "Bacon, Geoff  and
      Regier, Terry",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5440",
    doi = "10.18653/v1/W18-5440",
    pages = "334--336",
    abstract = "Learning universal sentence representations which accurately model sentential semantic content is a current goal of natural language processing research. A prominent and successful approach is to train recurrent neural networks (RNNs) to encode sentences into fixed length vectors. Many core linguistic phenomena that one would like to model in universal sentence representations depend on syntactic structure. Despite the fact that RNNs do not have explicit syntactic structural representations, there is some evidence that RNNs can approximate such structure-dependent phenomena under certain conditions, in addition to their widespread success in practical tasks. In this work, we assess RNNs{'} ability to learn the structure-dependent phenomenon of main clause tense.",
}
@inproceedings{poliak-etal-2018-collecting-diverse,
    title = "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
    author = "Poliak, Adam  and
      Haldar, Aparajita  and
      Rudinger, Rachel  and
      Hu, J. Edward  and
      Pavlick, Ellie  and
      White, Aaron Steven  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5441",
    doi = "10.18653/v1/W18-5441",
    pages = "337--340",
    abstract = "We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at \url{http://www.decomp.net/}, and will grow over time as additional resources are recast and added from novel sources.",
}
@inproceedings{jang-etal-2018-interpretable,
    title = "Interpretable Word Embedding Contextualization",
    author = "Jang, Kyoung-Rok  and
      Myaeng, Sung-Hyon  and
      Kim, Sang-Bum",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5442",
    doi = "10.18653/v1/W18-5442",
    pages = "341--343",
    abstract = "In this paper, we propose a method of calibrating a word embedding, so that the semantic it conveys becomes more relevant to the context. Our method is novel because the output shows clearly which senses that were originally presented in a target word embedding become stronger or weaker. This is possible by utilizing the technique of using sparse coding to recover senses that comprises a word embedding.",
}
@inproceedings{verwimp-etal-2018-state,
    title = "State Gradients for {RNN} Memory Analysis",
    author = "Verwimp, Lyan  and
      Van hamme, Hugo  and
      Renkens, Vincent  and
      Wambacq, Patrick",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5443",
    doi = "10.18653/v1/W18-5443",
    pages = "344--346",
    abstract = "We present a framework for analyzing what the state in RNNs remembers from its input embeddings. We compute the gradients of the states with respect to the input embeddings and decompose the gradient matrix with Singular Value Decomposition to analyze which directions in the embedding space are best transferred to the hidden state space, characterized by the largest singular values. We apply our approach to LSTM language models and investigate to what extent and for how long certain classes of words are remembered on average for a certain corpus. Additionally, the extent to which a specific property or relationship is remembered by the RNN can be tracked by comparing a vector characterizing that property with the direction(s) in embedding space that are best preserved in hidden state space.",
}
@inproceedings{marecek-rosa-2018-extracting,
    title = "Extracting Syntactic Trees from Transformer Encoder Self-Attentions",
    author = "Mare{\v{c}}ek, David  and
      Rosa, Rudolf",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5444",
    doi = "10.18653/v1/W18-5444",
    pages = "347--349",
    abstract = "This is a work in progress about extracting the sentence tree structures from the encoder{'}s self-attention weights, when translating into another language using the Transformer neural network architecture. We visualize the structures and discuss their characteristics with respect to the existing syntactic theories and annotations.",
}
@inproceedings{lippincott-2018-portable,
    title = "Portable, layer-wise task performance monitoring for {NLP} models",
    author = "Lippincott, Tom",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5445",
    doi = "10.18653/v1/W18-5445",
    pages = "350--352",
    abstract = "There is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from general observations (e.g. RNNs can represent unbounded dependencies in a sequence) to specific arguments about linguistic phenomena (early layers encode lexical information, deeper layers syntactic). The recent ascendancy of DNNs is fueling efforts in the NLP community to explore these claims. Previous work has tended to focus on easily-accessible representations like word or sentence embeddings, with deeper structure requiring more ad hoc methods to extract and examine. In this work, we introduce Vivisect, a toolkit that aims at a general solution for broad and fine-grained monitoring in the major DNN frameworks, with minimal change to research patterns.",
}
@inproceedings{wang-etal-2018-glue,
    title = "{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
    author = "Wang, Alex  and
      Singh, Amanpreet  and
      Michael, Julian  and
      Hill, Felix  and
      Levy, Omer  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5446",
    doi = "10.18653/v1/W18-5446",
    pages = "353--355",
    abstract = "Human ability to understand language is \textit{general, flexible, and robust}. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions.",
}
@inproceedings{vania-lopez-2018-explicitly,
    title = "Explicitly modeling case improves neural dependency parsing",
    author = "Vania, Clara  and
      Lopez, Adam",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5447",
    doi = "10.18653/v1/W18-5447",
    pages = "356--358",
    abstract = "Neural dependency parsing models that compose word representations from characters can presumably exploit morphosyntax when making attachment decisions. How much do they know about morphology? We investigate how well they handle morphological case, which is important for parsing. Our experiments on Czech, German and Russian suggest that adding explicit morphological case{---}either oracle or predicted{---}improves neural dependency parsing, indicating that the learned representations in these models do not fully encode the morphological knowledge that they need, and can still benefit from targeted forms of explicit linguistic modeling.",
}
@inproceedings{zhang-bowman-2018-language,
    title = "Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis",
    author = "Zhang, Kelly  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5448",
    doi = "10.18653/v1/W18-5448",
    pages = "359--361",
    abstract = "Recently, researchers have found that deep LSTMs trained on tasks like machine translation learn substantial syntactic and semantic information about their input sentences, including part-of-speech. These findings begin to shed light on why pretrained representations, like ELMo and CoVe, are so beneficial for neural language understanding models. We still, though, do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn. With this in mind, we compare four objectives{---}language modeling, translation, skip-thought, and autoencoding{---}on their ability to induce syntactic and part-of-speech information, holding constant the quantity and genre of the training data, as well as the LSTM architecture.",
}
@inproceedings{derby-etal-2018-representation,
    title = "Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model",
    author = "Derby, Steven  and
      Miller, Paul  and
      Murphy, Brian  and
      Devereux, Barry",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5449",
    doi = "10.18653/v1/W18-5449",
    pages = "362--364",
    abstract = "Performance in language modelling has been significantly improved by training recurrent neural networks on large corpora. This progress has come at the cost of interpretability and an understanding of how these architectures function, making principled development of better language models more difficult. We look inside a state-of-the-art neural language model to analyse how this model represents high-level lexico-semantic information. In particular, we investigate how the model represents words by extracting activation patterns where they occur in the text, and compare these representations directly to human semantic knowledge.",
}
@inproceedings{peters-etal-2018-interpretable,
    title = "Interpretable Structure Induction via Sparse Attention",
    author = "Peters, Ben  and
      Niculae, Vlad  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5450",
    doi = "10.18653/v1/W18-5450",
    pages = "365--367",
    abstract = "Neural network methods are experiencing wide adoption in NLP, thanks to their empirical performance on many tasks. Modern neural architectures go way beyond simple feedforward and recurrent models: they are complex pipelines that perform soft, differentiable computation instead of discrete logic. The price of such soft computing is the introduction of dense dependencies, which make it hard to disentangle the patterns that trigger a prediction. Our recent work on sparse and structured latent computation presents a promising avenue for enhancing interpretability of such neural pipelines. Through this extended abstract, we aim to discuss and explore the potential and impact of our methods.",
}
@inproceedings{strobelt-etal-2018-debugging,
    title = "Debugging Sequence-to-Sequence Models with {S}eq2{S}eq-Vis",
    author = "Strobelt, Hendrik  and
      Gehrmann, Sebastian  and
      Behrisch, Michael  and
      Perer, Adam  and
      Pfister, Hanspeter  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5451",
    doi = "10.18653/v1/W18-5451",
    pages = "368--370",
    abstract = "Neural attention-based sequence-to-sequence models (seq2seq) (Sutskever et al., 2014; Bahdanau et al., 2014) have proven to be accurate and robust for many sequence prediction tasks. They have become the standard approach for automatic translation of text, at the cost of increased model complexity and uncertainty. End-to-end trained neural models act as a black box, which makes it difficult to examine model decisions and attribute errors to a specific part of a model. The highly connected and high-dimensional internal representations pose a challenge for analysis and visualization tools. The development of methods to understand seq2seq predictions is crucial for systems in production settings, as mistakes involving language are often very apparent to human readers. For instance, a widely publicized incident resulted from a translation system mistakenly translating {``}good morning{''} into {``}attack them{''} leading to a wrongful arrest (Hern, 2017).",
}
@inproceedings{htut-etal-2018-grammar-induction,
    title = "Grammar Induction with Neural Language Models: An Unusual Replication",
    author = "Htut, Phu Mon  and
      Cho, Kyunghyun  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5452",
    doi = "10.18653/v1/W18-5452",
    pages = "371--373",
    abstract = "Grammar induction is the task of learning syntactic structure without the expert-labeled treebanks (Charniak and Carroll, 1992; Klein and Manning, 2002). Recent work on latent tree learning offers a new family of approaches to this problem by inducing syntactic structure using the supervision from a downstream NLP task (Yogatama et al., 2017; Maillard et al., 2017; Choi et al., 2018). In a recent paper published at ICLR, Shen et al. (2018) introduce such a model and report near state-of-the-art results on the target task of language modeling, and the first strong latent tree learning result on constituency parsing. During the analysis of this model, we discover issues that make the original results hard to trust, including tuning and even training on what is effectively the test set. Here, we analyze the model under different configurations to understand what it learns and to identify the conditions under which it succeeds. We find that this model represents the first empirical success for neural network latent tree learning, and that neural language modeling warrants further study as a setting for grammar induction.",
}
@inproceedings{dhar-bisazza-2018-syntactic,
    title = "Does Syntactic Knowledge in Multilingual Language Models Transfer Across Languages?",
    author = "Dhar, Prajit  and
      Bisazza, Arianna",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5453",
    doi = "10.18653/v1/W18-5453",
    pages = "374--377",
    abstract = "Recent work has shown that neural models can be successfully trained on multiple languages simultaneously. We investigate whether such models learn to share and exploit common syntactic knowledge among the languages on which they are trained. This extended abstract presents our preliminary results.",
}
@inproceedings{burns-etal-2018-exploiting,
    title = "Exploiting Attention to Reveal Shortcomings in Memory Models",
    author = "Burns, Kaylee  and
      Nematzadeh, Aida  and
      Grant, Erin  and
      Gopnik, Alison  and
      Griffiths, Tom",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5454",
    doi = "10.18653/v1/W18-5454",
    pages = "378--380",
    abstract = "The decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity. Practical use of machine learning models, especially for question and answering applications, demands a system that is interpretable. We analyze the attention of a memory network model to reconcile contradictory performance on a challenging question-answering dataset that is inspired by theory-of-mind experiments. We equate success on questions to task classification, which explains not only test-time failures but also how well the model generalizes to new training conditions.",
}
@inproceedings{madhyastha-etal-2018-end,
    title = "End-to-end Image Captioning Exploits Distributional Similarity in Multimodal Space",
    author = "Madhyastha, Pranava Swaroop  and
      Wang, Josiah  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5455",
    doi = "10.18653/v1/W18-5455",
    pages = "381--383",
    abstract = "We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn {`}distributional similarity{'} in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space. To validate our hypothesis, we focus on the {`}image{'} side of image captioning, and vary the input image representation but keep the RNN text generation model of a CNN-RNN constant. Our analysis indicates that image captioning models (i) are capable of separating structure from noisy input representations; (ii) experience virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space; (iii) cluster images with similar visual and linguistic information together. Our experiments all point to one fact: that our distributional similarity hypothesis holds. We conclude that, regardless of the image representation, image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.",
}
@inproceedings{paperno-2018-limitations,
    title = "Limitations in learning an interpreted language with recurrent models",
    author = "Paperno, Denis",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5456",
    doi = "10.18653/v1/W18-5456",
    pages = "384--386",
    abstract = "In this submission I report work in progress on learning simplified interpreted languages by means of recurrent models. The data is constructed to reflect core properties of natural language as modeled in formal syntax and semantics. Preliminary results suggest that LSTM networks do generalise to compositional interpretation, albeit only in the most favorable learning setting.",
}
@proceedings{ws-2018-bioasq,
    title = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    editor = "Kakadiaris, Ioannis A.  and
      Paliouras, George  and
      Krithara, Anastasia",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5300",
}
@inproceedings{nentidis-etal-2018-results,
    title = "Results of the sixth edition of the {B}io{ASQ} Challenge",
    author = "Nentidis, Anastasios  and
      Krithara, Anastasia  and
      Bougiatiotis, Konstantinos  and
      Paliouras, Georgios  and
      Kakadiaris, Ioannis",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5301",
    doi = "10.18653/v1/W18-5301",
    pages = "1--10",
    abstract = "This paper presents the results of the sixth edition of the BioASQ challenge. The BioASQ challenge aims at the promotion of systems and methodologies through the organization of a challenge on two tasks: semantic indexing and question answering. In total, 26 teams with more than 90 systems participated in this year{'}s challenge. As in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the-art systems are continuously improving, pushing the frontier of research.",
}
@inproceedings{eckert-neves-2018-semantic,
    title = "Semantic role labeling tools for biomedical question answering: a study of selected tools on the {B}io{ASQ} datasets",
    author = "Eckert, Fabian  and
      Neves, Mariana",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5302",
    doi = "10.18653/v1/W18-5302",
    pages = "11--21",
    abstract = "Question answering (QA) systems usually rely on advanced natural language processing components to precisely understand the questions and extract the answers. Semantic role labeling (SRL) is known to boost performance for QA, but its use for biomedical texts has not yet been fully studied. We analyzed the performance of three SRL tools (BioKIT, BIOSMILE and PathLSTM) on 1776 questions from the BioASQ challenge. We compared the systems regarding the coverage of the questions and snippets, as well as based on pre-defined criteria, such as easiness of installation, supported formats and usability. Finally, we integrated two of the tools in a simple QA system to further evaluate their performance over the official BioASQ test sets.",
}
@inproceedings{molla-2018-macquarie,
    title = "{M}acquarie {U}niversity at {B}io{ASQ} 6b: Deep learning and deep reinforcement learning for query-based summarisation",
    author = "Moll{\'a}, Diego",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5303",
    doi = "10.18653/v1/W18-5303",
    pages = "22--29",
    abstract = "This paper describes Macquarie University{'}s contribution to the BioASQ Challenge (BioASQ 6b, Phase B). We focused on the extraction of the ideal answers, and the task was approached as an instance of query-based multi-document summarisation. In particular, this paper focuses on the experiments related to the deep learning and reinforcement learning approaches used in the submitted runs. The best run used a deep learning model under a regression-based framework. The deep learning architecture used features derived from the output of LSTM chains on word embeddings, plus features based on similarity with the query, and sentence position. The reinforcement learning approach was a proof-of-concept prototype that trained a global policy using REINFORCE. The global policy was implemented as a neural network that used tf.idf features encoding the candidate sentence, question, and context.",
}
@inproceedings{brokos-etal-2018-aueb,
    title = "{AUEB} at {B}io{ASQ} 6: Document and Snippet Retrieval",
    author = "Brokos, George  and
      Liosis, Polyvios  and
      McDonald, Ryan  and
      Pappas, Dimitris  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5304",
    doi = "10.18653/v1/W18-5304",
    pages = "30--39",
    abstract = "We present AUEB{'}s submissions to the BioASQ 6 document and snippet retrieval tasks (parts of Task 6b, Phase A). Our models use novel extensions to deep learning architectures that operate solely over the text of the query and candidate document/snippets. Our systems scored at the top or near the top for all batches of the challenge, highlighting the effectiveness of deep learning for these tasks.",
}
@inproceedings{rosso-mateus-etal-2018-mindlab,
    title = "{M}ind{L}ab Neural Network Approach at {B}io{ASQ} 6{B}",
    author = "Rosso-Mateus, Andr{\'e}s  and
      Gonz{\'a}lez, Fabio A.  and
      Montes-y-G{\'o}mez, Manuel",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5305",
    doi = "10.18653/v1/W18-5305",
    pages = "40--46",
    abstract = "Biomedical Question Answering is concerned with the development of methods and systems that automatically find answers to natural language posed questions. In this work, we describe the system used in the BioASQ Challenge task 6b for document retrieval and snippet retrieval (with particular emphasis in this subtask). The proposed model makes use of semantic similarity patterns that are evaluated and measured by a convolutional neural network architecture. Subsequently, the snippet ranking performance is improved with a pseudo-relevance feedback approach in a later step. Based on the preliminary results, we reached the second position in snippet retrieval sub-task.",
}
@inproceedings{jin-etal-2018-attentionmesh,
    title = "{A}ttention{M}e{SH}: Simple, Effective and Interpretable Automatic {M}e{SH} Indexer",
    author = "Jin, Qiao  and
      Dhingra, Bhuwan  and
      Cohen, William  and
      Lu, Xinghua",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5306",
    doi = "10.18653/v1/W18-5306",
    pages = "47--56",
    abstract = "There are millions of articles in PubMed database. To facilitate information retrieval, curators in the National Library of Medicine (NLM) assign a set of Medical Subject Headings (MeSH) to each article. MeSH is a hierarchically-organized vocabulary, containing about 28K different concepts, covering the fields from clinical medicine to information sciences. Several automatic MeSH indexing models have been developed to improve the time-consuming and financially expensive manual annotation, including the NLM official tool {--} Medical Text Indexer, and the winner of BioASQ Task5a challenge {--} DeepMeSH. However, these models are complex and not interpretable. We propose a novel end-to-end model, AttentionMeSH, which utilizes deep learning and attention mechanism to index MeSH terms to biomedical text. The attention mechanism enables the model to associate textual evidence with annotations, thus providing interpretability at the word level. The model also uses a novel masking mechanism to enhance accuracy and speed. In the final week of BioASQ Chanllenge Task6a, we ranked 2nd by average MiF using an on-construction model. After the contest, we achieve close to state-of-the-art MiF performance of ∼ 0.684 using our final model. Human evaluations show AttentionMeSH also provides high level of interpretability, retrieving about 90{\%} of all expert-labeled relevant words given an MeSH-article pair at 20 output.",
}
@inproceedings{li-etal-2018-extraction,
    title = "Extraction Meets Abstraction: Ideal Answer Generation for Biomedical Questions",
    author = "Li, Yutong  and
      Gekakis, Nicholas  and
      Wu, Qiuze  and
      Li, Boyue  and
      Chandu, Khyathi  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5307",
    doi = "10.18653/v1/W18-5307",
    pages = "57--65",
    abstract = "The growing number of biomedical publications is a challenge for human researchers, who invest considerable effort to search for relevant documents and pinpointed answers. Biomedical Question Answering can automatically generate answers for a user{'}s topic or question, significantly reducing the effort required to locate the most relevant information in a large document corpus. Extractive summarization techniques, which concatenate the most relevant text units drawn from multiple documents, perform well on automatic evaluation metrics like ROUGE, but score poorly on human readability, due to the presence of redundant text and grammatical errors in the answer. This work moves toward abstractive summarization, which attempts to distill and present the meaning of the original text in a more coherent way. We incorporate a sentence fusion approach, based on Integer Linear Programming, along with three novel approaches for sentence ordering, in an attempt to improve the human readability of ideal answers. Using an open framework for configuration space exploration (BOOM), we tested over 2000 unique system configurations in order to identify the best-performing combinations for the sixth edition of Phase B of the BioASQ challenge.",
}
@inproceedings{bhandwaldar-zadrozny-2018-uncc,
    title = "{UNCC} {QA}: Biomedical Question Answering system",
    author = "Bhandwaldar, Abhishek  and
      Zadrozny, Wlodek",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5308",
    doi = "10.18653/v1/W18-5308",
    pages = "66--71",
    abstract = "In this paper, we detail our submission to the BioASQ competition{'}s Biomedical Semantic Question and Answering task. Our system uses extractive summarization techniques to generate answers and has scored highest ROUGE-2 and Rogue-SU4 in all test batch sets. Our contributions are named-entity based method for answering factoid and list questions, and an extractive summarization techniques for building paragraph-sized summaries, based on lexical chains. Our system got highest ROUGE-2 and ROUGE-SU4 scores for ideal-type answers in all test batch sets. We also discuss the limitations of the described system, such lack of the evaluation on other criteria (e.g. manual). Also, for factoid- and list -type question our system got low accuracy (which suggests that our algorithm needs to improve in the ranking of entities).",
}
@inproceedings{kamath-etal-2018-adaption,
    title = "An Adaption of {BIOASQ} Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.",
    author = "Kamath, Sanjay  and
      Grau, Brigitte  and
      Ma, Yue",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5309",
    doi = "10.18653/v1/W18-5309",
    pages = "72--78",
    abstract = "BIOASQ Task B Phase B challenge focuses on extracting answers from snippets for a given question. The dataset provided by the organizers contains answers, but not all their variants. Henceforth a manual annotation was performed to extract all forms of correct answers. This article shows the impact of using all occurrences of correct answers for training on the evaluation scores which are improved significantly.",
}
@inproceedings{naresh-kumar-etal-2018-ontology,
    title = "Ontology-Based Retrieval {\&} Neural Approaches for {B}io{ASQ} Ideal Answer Generation",
    author = "Naresh Kumar, Ashwin  and
      Kesavamoorthy, Harini  and
      Das, Madhura  and
      Kalwad, Pramati  and
      Chandu, Khyathi  and
      Mitamura, Teruko  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5310",
    doi = "10.18653/v1/W18-5310",
    pages = "79--89",
    abstract = "The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine. Biomedical Question Answering systems automatically identify the most relevant documents and pinpointed answers, given an information need expressed as a natural language question. Generating a non-redundant, human-readable summary that satisfies the information need of a given biomedical question is the focus of the Ideal Answer Generation task, part of the BioASQ challenge. This paper presents a system for ideal answer generation (using ontology-based retrieval and a neural learning-to-rank approach, combined with extractive and abstractive summarization techniques) which achieved the highest ROUGE score of 0.659 on the BioASQ 5b batch 2 test.",
}
@proceedings{ws-2018-argument,
    title = "Proceedings of the 5th Workshop on Argument Mining",
    editor = "Slonim, Noam  and
      Aharonov, Ranit",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5200",
}
@inproceedings{galassi-etal-2018-argumentative,
    title = "Argumentative Link Prediction using Residual Networks and Multi-Objective Learning",
    author = "Galassi, Andrea  and
      Lippi, Marco  and
      Torroni, Paolo",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5201",
    doi = "10.18653/v1/W18-5201",
    pages = "1--10",
    abstract = "We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.",
}
@inproceedings{morio-fujita-2018-end,
    title = "End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture",
    author = "Morio, Gaku  and
      Fujita, Katsuhide",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5202",
    doi = "10.18653/v1/W18-5202",
    pages = "11--21",
    abstract = "Argument Mining (AM) is a relatively recent discipline, which concentrates on extracting claims or premises from discourses, and inferring their structures. However, many existing works do not consider micro-level AM studies on discussion threads sufficiently. In this paper, we tackle AM for discussion threads. Our main contributions are follows: (1) A novel combination scheme focusing on micro-level inner- and inter- post schemes for a discussion thread. (2) Annotation of large-scale civic discussion threads with the scheme. (3) \textit{Parallel constrained pointer architecture} (PCPA), a novel end-to-end technique to discriminate sentence types, inner-post relations, and inter-post interactions simultaneously. The experimental results demonstrate that our proposed model shows better accuracy in terms of relations extraction, in comparison to existing state-of-the-art models.",
}
@inproceedings{lauscher-etal-2018-arguminsci,
    title = "{A}rgumin{S}ci: A Tool for Analyzing Argumentation and Rhetorical Aspects in Scientific Writing",
    author = "Lauscher, Anne  and
      Glava{\v{s}}, Goran  and
      Eckert, Kai",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5203",
    doi = "10.18653/v1/W18-5203",
    pages = "22--28",
    abstract = "Argumentation is arguably one of the central features of scientific language. We present \textit{ArguminSci}, an easy-to-use tool that analyzes argumentation and other rhetorical aspects of scientific writing, which we collectively dub \textit{scitorics}. The main aspect we focus on is the fine-grained argumentative analysis of scientific text through identification of argument components. The functionality of \textit{ArguminSci} is accessible via three interfaces: as a command line tool, via a RESTful application programming interface, and as a web application.",
}
@inproceedings{mayer-etal-2018-evidence,
    title = "Evidence Type Classification in Randomized Controlled Trials",
    author = "Mayer, Tobias  and
      Cabrio, Elena  and
      Villata, Serena",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5204",
    doi = "10.18653/v1/W18-5204",
    pages = "29--34",
    abstract = "Randomized Controlled Trials (RCT) are a common type of experimental studies in the medical domain for evidence-based decision making. The ability to automatically extract the \textit{arguments} proposed therein can be of valuable support for clinicians and practitioners in their daily evidence-based decision making activities. Given the peculiarity of the medical domain and the required level of detail, standard approaches to argument component detection in \textit{argument(ation) mining} are not fine-grained enough to support such activities. In this paper, we introduce a new sub-task of the argument component identification task: \textit{evidence type classification}. To address it, we propose a supervised approach and we test it on a set of RCT abstracts on different medical topics.",
}
@inproceedings{passon-etal-2018-predicting,
    title = "Predicting the Usefulness of {A}mazon Reviews Using Off-The-Shelf Argumentation Mining",
    author = "Passon, Marco  and
      Lippi, Marco  and
      Serra, Giuseppe  and
      Tasso, Carlo",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5205",
    doi = "10.18653/v1/W18-5205",
    pages = "35--39",
    abstract = "Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.",
}
@inproceedings{lauscher-etal-2018-argument,
    title = "An Argument-Annotated Corpus of Scientific Publications",
    author = "Lauscher, Anne  and
      Glava{\v{s}}, Goran  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5206",
    doi = "10.18653/v1/W18-5206",
    pages = "40--46",
    abstract = "Argumentation is an essential feature of scientific language. We present an annotation study resulting in a corpus of scientific publications annotated with argumentative components and relations. The argumentative annotations have been added to the existing Dr. Inventor Corpus, already annotated for four other rhetorical aspects. We analyze the annotated argumentative structures and investigate the relations between argumentation and other rhetorical aspects of scientific writing, such as discourse roles and citation contexts.",
}
@inproceedings{torsi-morante-2018-annotating,
    title = "Annotating Claims in the Vaccination Debate",
    author = "Torsi, Benedetta  and
      Morante, Roser",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5207",
    doi = "10.18653/v1/W18-5207",
    pages = "47--56",
    abstract = "In this paper we present annotation experiments with three different annotation schemes for the identification of argument components in texts related to the vaccination debate. Identifying claims about vaccinations made by participants in the debate is of great societal interest, as the decision to vaccinate or not has impact in public health and safety. Since most corpora that have been annotated with argumentation information contain texts that belong to a specific genre and have a well defined argumentation structure, we needed to adjust the annotation schemes to our corpus, which contains heterogeneous texts from the Web. We started with a complex annotation scheme that had to be simplified due to low IAA. In our final experiment, which focused on annotating claims, annotators reached 57.3{\%} IAA.",
}
@inproceedings{lugini-litman-2018-argument,
    title = "Argument Component Classification for Classroom Discussions",
    author = "Lugini, Luca  and
      Litman, Diane",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5208",
    doi = "10.18653/v1/W18-5208",
    pages = "57--67",
    abstract = "This paper focuses on argument component classification for transcribed spoken classroom discussions, with the goal of automatically classifying student utterances into claims, evidence, and warrants. We show that an existing method for argument component classification developed for another educationally-oriented domain performs poorly on our dataset. We then show that feature sets from prior work on argument mining for student essays and online dialogues can be used to improve performance considerably. We also provide a comparison between convolutional neural networks and recurrent neural networks when trained under different conditions to classify argument components in classroom discussions. While neural network models are not always able to outperform a logistic regression model, we were able to gain some useful insights: convolutional networks are more robust than recurrent networks both at the character and at the word level, and specificity information can help boost performance in multi-task training.",
}
@inproceedings{walker-etal-2018-evidence,
    title = "Evidence Types, Credibility Factors, and Patterns or Soft Rules for Weighing Conflicting Evidence: Argument Mining in the Context of Legal Rules Governing Evidence Assessment",
    author = "Walker, Vern R.  and
      Foerster, Dina  and
      Ponce, Julia Monica  and
      Rosen, Matthew",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5209",
    doi = "10.18653/v1/W18-5209",
    pages = "68--78",
    abstract = "This paper reports on the results of an empirical study of adjudicatory decisions about veterans{'} claims for disability benefits in the United States. It develops a typology of kinds of relevant evidence (argument premises) employed in cases, and it identifies factors that the tribunal considers when assessing the credibility or trustworthiness of individual items of evidence. It also reports on patterns or {``}soft rules{''} that the tribunal uses to comparatively weigh the probative value of conflicting evidence. These evidence types, credibility factors, and comparison patterns are developed to be inter-operable with legal rules governing the evidence assessment process in the U.S. This approach should be transferable to other legal and non-legal domains.",
}
@inproceedings{reisert-etal-2018-feasible,
    title = "Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates",
    author = "Reisert, Paul  and
      Inoue, Naoya  and
      Kuribayashi, Tatsuki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5210",
    doi = "10.18653/v1/W18-5210",
    pages = "79--89",
    abstract = "Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e.g. attack-support relations, stance, explicit premise/claim). This paper goes a step further by addressing the task of automatically identifying reasoning patterns of arguments using predefined templates, which is called \textit{argument template (AT) instantiation}. The contributions of this work are three-fold. First, we develop a simple, yet expressive set of easily annotatable ATs that can represent a majority of writer{'}s reasoning for texts with diverse policy topics while maintaining the computational feasibility of the task. Second, we create a small, but highly reliable annotated corpus of instantiated ATs on top of reliably annotated support and attack relations and conduct an annotation study. Third, we formulate the task of AT instantiation as structured prediction constrained by a feasible set of templates. Our evaluation demonstrates that we can annotate ATs with a reasonably high inter-annotator agreement, and the use of template-constrained inference is useful for instantiating ATs with only partial reasoning comprehension clues.",
}
@inproceedings{botschen-etal-2018-frame,
    title = "Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning",
    author = "Botschen, Teresa  and
      Sorokin, Daniil  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5211",
    doi = "10.18653/v1/W18-5211",
    pages = "90--96",
    abstract = "Common-sense argumentative reasoning is a challenging task that requires holistic understanding of the argumentation where external knowledge about the world is hypothesized to play a key role. We explore the idea of using event knowledge about prototypical situations from FrameNet and fact knowledge about concrete entities from Wikidata to solve the task. We find that both resources can contribute to an improvement over the non-enriched approach and point out two persisting challenges: first, integration of many annotations of the same type, and second, fusion of complementary annotations. After our explorations, we question the key role of external world knowledge with respect to the argumentative reasoning task and rather point towards a logic-based analysis of the chain of reasoning.",
}
@inproceedings{gu-etal-2018-incorporating,
    title = "Incorporating Topic Aspects for Online Comment Convincingness Evaluation",
    author = "Gu, Yunfan  and
      Wei, Zhongyu  and
      Xu, Maoran  and
      Fu, Hao  and
      Liu, Yang  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5212",
    doi = "10.18653/v1/W18-5212",
    pages = "97--104",
    abstract = "In this paper, we propose to incorporate topic aspects information for online comments convincingness evaluation. Our model makes use of graph convolutional network to utilize implicit topic information within a discussion thread to assist the evaluation of convincingness of each single comment. In order to test the effectiveness of our proposed model, we annotate topic information on top of a public dataset for argument convincingness evaluation. Experimental results show that topic information is able to improve the performance for convincingness evaluation. We also make a move to detect topic aspects automatically.",
}
@inproceedings{green-2018-proposed,
    title = "Proposed Method for Annotation of Scientific Arguments in Terms of Semantic Relations and Argument Schemes",
    author = "Green, Nancy",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5213",
    doi = "10.18653/v1/W18-5213",
    pages = "105--110",
    abstract = "This paper presents a proposed method for annotation of scientific arguments in biological/biomedical journal articles. Semantic entities and relations are used to represent the propositional content of arguments in instances of argument schemes. We describe an experiment in which we encoded the arguments in a journal article to identify issues in this approach. Our catalogue of argument schemes and a copy of the annotated article are now publically available.",
}
@inproceedings{naderi-hirst-2018-using,
    title = "Using context to identify the language of face-saving",
    author = "Naderi, Nona  and
      Hirst, Graeme",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5214",
    doi = "10.18653/v1/W18-5214",
    pages = "111--120",
    abstract = "We created a corpus of utterances that attempt to save face from parliamentary debates and use it to automatically analyze the language of reputation defence. Our proposed model that incorporates information regarding threats to reputation can predict reputation defence language with high confidence. Further experiments and evaluations on different datasets show that the model is able to generalize to new utterances and can predict the language of reputation defence in a new dataset.",
}
@inproceedings{le-etal-2018-dave,
    title = "Dave the debater: a retrieval-based and generative argumentative dialogue agent",
    author = "Le, Dieu Thu  and
      Nguyen, Cam-Tu  and
      Nguyen, Kim Anh",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5215",
    doi = "10.18653/v1/W18-5215",
    pages = "121--130",
    abstract = "In this paper, we explore the problem of developing an argumentative dialogue agent that can be able to discuss with human users on controversial topics. We describe two systems that use retrieval-based and generative models to make argumentative responses to the users. The experiments show promising results although they have been trained on a small dataset.",
}
@inproceedings{eger-etal-2018-pd3,
    title = "{PD}3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection",
    author = {Eger, Steffen  and
      R{\"u}ckl{\'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5216",
    doi = "10.18653/v1/W18-5216",
    pages = "131--143",
    abstract = "We consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging. We combine direct transfer using bilingual embeddings with annotation projection, which projects labels across unlabeled parallel data. We do so by either merging respective source and target language datasets or alternatively by using multi-task learning. Our combination strategy considerably improves upon both direct transfer and projection with few available parallel sentences, the most realistic scenario for many low-resource target languages.",
}
@inproceedings{rocha-etal-2018-cross,
    title = "Cross-Lingual Argumentative Relation Identification: from {E}nglish to {P}ortuguese",
    author = "Rocha, Gil  and
      Stab, Christian  and
      Lopes Cardoso, Henrique  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5217",
    doi = "10.18653/v1/W18-5217",
    pages = "144--154",
    abstract = "Argument mining aims to detect and identify argument structures from textual resources. In this paper, we aim to address the task of argumentative relation identification, a subtask of argument mining, for which several approaches have been recently proposed in a monolingual setting. To overcome the lack of annotated resources in less-resourced languages, we present the first attempt to address this subtask in a cross-lingual setting. We compare two standard strategies for cross-language learning, namely: projection and direct-transfer. Experimental results show that by using unsupervised language adaptation the proposed approaches perform at a competitive level when compared with fully-supervised in-language learning settings.",
}
@inproceedings{skeppstedt-etal-2018-less,
    title = "More or less controlled elicitation of argumentative text: Enlarging a microtext corpus via crowdsourcing",
    author = "Skeppstedt, Maria  and
      Peldszus, Andreas  and
      Stede, Manfred",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5218",
    doi = "10.18653/v1/W18-5218",
    pages = "155--163",
    abstract = "We present an extension of an annotated corpus of short argumentative texts that had originally been built in a controlled text production experiment. Our extension more than doubles the size of the corpus by means of crowdsourcing. We report on the setup of this experiment and on the consequences that crowdsourcing had for assembling the data, and in particular for annotation. We labeled the argumentative structure by marking claims, premises, and relations between them, following the scheme used in the original corpus, but had to make a few modifications in response to interesting phenomena in the data. Finally, we report on an experiment with the automatic prediction of this argumentation structure: We first replicated the approach of an earlier study on the original corpus, and compare the performance to various settings involving the extension.",
}
@proceedings{ws-2018-abusive,
    title = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    editor = "Fi{\v{s}}er, Darja  and
      Huang, Ruihong  and
      Prabhakaran, Vinodkumar  and
      Voigt, Rob  and
      Waseem, Zeerak  and
      Wernimont, Jacqueline",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5100",
}
@inproceedings{mishra-etal-2018-neural,
    title = "Neural Character-based Composition Models for Abuse Detection",
    author = "Mishra, Pushkar  and
      Yannakoudakis, Helen  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5101",
    doi = "10.18653/v1/W18-5101",
    pages = "1--10",
    abstract = "The advent of social media in recent years has fed into some highly undesirable phenomena such as proliferation of offensive language, hate speech, sexist remarks, etc. on the Internet. In light of this, there have been several efforts to automate the detection and moderation of such abusive content. However, deliberate obfuscation of words by users to evade detection poses a serious challenge to the effectiveness of these efforts. The current state of the art approaches to abusive language detection, based on recurrent neural networks, do not explicitly address this problem and resort to a generic OOV (out of vocabulary) embedding for unseen words. However, in using a single embedding for all unseen words we lose the ability to distinguish between obfuscated and non-obfuscated or rare words. In this paper, we address this problem by designing a model that can compose embeddings for unseen words. We experimentally demonstrate that our approach significantly advances the current state of the art in abuse detection on datasets from two different domains, namely Twitter and Wikipedia talk page.",
}
@inproceedings{de-gibert-etal-2018-hate,
    title = "Hate Speech Dataset from a White Supremacy Forum",
    author = "de Gibert, Ona  and
      Perez, Naiara  and
      Garc{\'\i}a-Pablos, Aitor  and
      Cuadros, Montse",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5102",
    doi = "10.18653/v1/W18-5102",
    pages = "11--20",
    abstract = "Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic. Due to the massive rise of user-generated web content on social media, the amount of hate speech is also steadily increasing. Over the past years, interest in online hate speech detection and, particularly, the automation of this task has continuously grown, along with the societal impact of the phenomenon. This paper describes a hate speech dataset composed of thousands of sentences manually labelled as containing hate speech or not. The sentences have been extracted from Stormfront, a white supremacist forum. A custom annotation tool has been developed to carry out the manual labelling task which, among other things, allows the annotators to choose whether to read the context of a sentence before labelling it. The paper also provides a thoughtful qualitative and quantitative study of the resulting dataset and several baseline experiments with different classification models. The dataset is publicly available.",
}
@inproceedings{gunasekara-nejadgholi-2018-review,
    title = "A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content",
    author = "Gunasekara, Isuru  and
      Nejadgholi, Isar",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5103",
    doi = "10.18653/v1/W18-5103",
    pages = "21--25",
    abstract = "Language toxicity identification presents a gray area in the ethical debate surrounding freedom of speech and censorship. Today{'}s social media landscape is littered with unfiltered content that can be anywhere from slightly abusive to hate inducing. In response, we focused on training a multi-label classifier to detect both the type and level of toxicity in online content. This content is typically colloquial and conversational in style. Its classification therefore requires huge amounts of annotated data due to its variability and inconsistency. We compare standard methods of text classification in this task. A conventional one-vs-rest SVM classifier with character and word level frequency-based representation of text reaches 0.9763 ROC AUC score. We demonstrated that leveraging more advanced technologies such as word embeddings, recurrent neural networks, attention mechanism, stacking of classifiers and semi-supervised training can improve the ROC AUC score of classification to 0.9862. We suggest that in order to choose the right model one has to consider the accuracy of models as well as inference complexity based on the application.",
}
@inproceedings{kshirsagar-etal-2018-predictive,
    title = "Predictive Embeddings for Hate Speech Detection on {T}witter",
    author = "Kshirsagar, Rohan  and
      Cukuvac, Tyrus  and
      McKeown, Kathy  and
      McGregor, Susan",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5104",
    doi = "10.18653/v1/W18-5104",
    pages = "26--32",
    abstract = "We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.",
}
@inproceedings{van-aken-etal-2018-challenges,
    title = "Challenges for Toxic Comment Classification: An In-Depth Error Analysis",
    author = {van Aken, Betty  and
      Risch, Julian  and
      Krestel, Ralf  and
      L{\"o}ser, Alexander},
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5105",
    doi = "10.18653/v1/W18-5105",
    pages = "33--42",
    abstract = "Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task{'}s challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of-the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.",
}
@inproceedings{singh-etal-2018-aggression,
    title = "Aggression Detection on Social Media Text Using Deep Neural Networks",
    author = "Singh, Vinay  and
      Varshney, Aman  and
      Akhtar, Syed Sarfaraz  and
      Vijay, Deepanshu  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5106",
    doi = "10.18653/v1/W18-5106",
    pages = "43--50",
    abstract = "In the past few years, bully and aggressive posts on social media have grown significantly, causing serious consequences for victims/users of all demographics. Majority of the work in this field has been done for English only. In this paper, we introduce a deep learning based classification system for Facebook posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users. Our work focuses on text from users majorly in the Indian Subcontinent. The dataset that we used for our models is provided by \textbf{TRAC-1}in their shared task. Our classification model assigns each Facebook post/comment to one of the three predefined categories: {``}Overtly Aggressive{''}, {``}Covertly Aggressive{''} and {``}Non-Aggressive{''}. We experimented with 6 classification models and our CNN model on a 10 K-fold cross-validation gave the best result with the prediction accuracy of 73.2{\%}.",
}
@inproceedings{sprugnoli-etal-2018-creating,
    title = "Creating a {W}hats{A}pp Dataset to Study Pre-teen Cyberbullying",
    author = "Sprugnoli, Rachele  and
      Menini, Stefano  and
      Tonelli, Sara  and
      Oncini, Filippo  and
      Piras, Enrico",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5107",
    doi = "10.18653/v1/W18-5107",
    pages = "51--59",
    abstract = "Although WhatsApp is used by teenagers as one major channel of cyberbullying, such interactions remain invisible due to the app privacy policies that do not allow ex-post data collection. Indeed, most of the information on these phenomena rely on surveys regarding self-reported data. In order to overcome this limitation, we describe in this paper the activities that led to the creation of a WhatsApp dataset to study cyberbullying among Italian students aged 12-13. We present not only the collected chats with annotations about user role and type of offense, but also the living lab created in a collaboration between researchers and schools to monitor and analyse cyberbullying. Finally, we discuss some open issues, dealing with ethical, operational and epistemic aspects.",
}
@inproceedings{svec-etal-2018-improving,
    title = "Improving Moderation of Online Discussions via Interpretable Neural Models",
    author = "{\v{S}}vec, Andrej  and
      Pikuliak, Mat{\'u}{\v{s}}  and
      {\v{S}}imko, Mari{\'a}n  and
      Bielikov{\'a}, M{\'a}ria",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5108",
    doi = "10.18653/v1/W18-5108",
    pages = "60--65",
    abstract = "Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappropriate comments for moderators to see. Second, we highlight inappropriate parts within these comments to make the moderation faster. We evaluated our method on data from a major Slovak news discussion platform.",
}
@inproceedings{caines-etal-2018-aggressive,
    title = "Aggressive language in an online hacking forum",
    author = "Caines, Andrew  and
      Pastrana, Sergio  and
      Hutchings, Alice  and
      Buttery, Paula",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5109",
    doi = "10.18653/v1/W18-5109",
    pages = "66--74",
    abstract = "We probe the heterogeneity in levels of abusive language in different sections of the Internet, using an annotated corpus of Wikipedia page edit comments to train a binary classifier for abuse detection. Our test data come from the CrimeBB Corpus of hacking-related forum posts and we find that (a) forum interactions are rarely abusive, (b) the abusive language which does exist tends to be relatively mild compared to that found in the Wikipedia comments domain, and tends to involve aggressive posturing rather than hate speech or threats of violence. We observe that the purpose of conversations in online forums tend to be more constructive and informative than those in Wikipedia page edit comments which are geared more towards adversarial interactions, and that this may explain the lower levels of abuse found in our forum data than in Wikipedia comments. Further work remains to be done to compare these results with other inter-domain classification experiments, and to understand the impact of aggressive language in forum conversations.",
}
@inproceedings{fehn-unsvag-gamback-2018-effects,
    title = "The Effects of User Features on {T}witter Hate Speech Detection",
    author = {Fehn Unsv{\aa}g, Elise  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5110",
    doi = "10.18653/v1/W18-5110",
    pages = "75--85",
    abstract = "The paper investigates the potential effects user features have on hate speech classification. A quantitative analysis of Twitter data was conducted to better understand user characteristics, but no correlations were found between hateful text and the characteristics of the users who had posted it. However, experiments with a hate speech classifier based on datasets from three different languages showed that combining certain user features with textual features gave slight improvements of classification performance. While the incorporation of user features resulted in varying impact on performance for the different datasets used, user network-related features provided the most consistent improvements.",
}
@inproceedings{wang-2018-interpreting,
    title = "Interpreting Neural Network Hate Speech Classifiers",
    author = "Wang, Cindy",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5111",
    doi = "10.18653/v1/W18-5111",
    pages = "86--92",
    abstract = "Deep neural networks have been applied to hate speech detection with apparent success, but they have limited practical applicability without transparency into the predictions they make. In this paper, we perform several experiments to visualize and understand a state-of-the-art neural network classifier for hate speech (Zhang et al., 2018). We adapt techniques from computer vision to visualize sensitive regions of the input stimuli and identify the features learned by individual neurons. We also introduce a method to discover the keywords that are most predictive of hate speech. Our analyses explain the aspects of neural networks that work well and point out areas for further improvement.",
}
@inproceedings{magu-luo-2018-determining,
    title = "Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks",
    author = "Magu, Rijul  and
      Luo, Jiebo",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5112",
    doi = "10.18653/v1/W18-5112",
    pages = "93--100",
    abstract = "While analysis of online explicit abusive language detection has lately seen an ever-increasing focus, implicit abuse detection remains a largely unexplored space. We carry out a study on a subcategory of implicit hate: euphemistic hate speech. We propose a method to assist in identifying unknown euphemisms (or code words) given a set of hateful tweets containing a known code word. Our approach leverages word embeddings and network analysis (through centrality measures and community detection) in a manner that can be generalized to identify euphemisms across contexts- not just hate speech.",
}
@inproceedings{lee-etal-2018-comparative,
    title = "Comparative Studies of Detecting Abusive Language on {T}witter",
    author = "Lee, Younghun  and
      Yoon, Seunghyun  and
      Jung, Kyomin",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5113",
    doi = "10.18653/v1/W18-5113",
    pages = "101--106",
    abstract = "The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, \textit{Hate and Abusive Speech on Twitter}, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on \textit{Hate and Abusive Speech on Twitter}, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.",
}
@inproceedings{sharifirad-etal-2018-boosting,
    title = "Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs",
    author = "Sharifirad, Sima  and
      Jafarpour, Borna  and
      Matwin, Stan",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5114",
    doi = "10.18653/v1/W18-5114",
    pages = "107--114",
    abstract = "Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of the training dataset. Insufficient and imbalanced datasets will lead to poor performance. An interesting solution to poor datasets is to take advantage of the world knowledge in the form of knowledge graphs to improve our training data. In this paper, we use ConceptNet and Wikidata to improve sexist tweet classification by two methods (1) text augmentation and (2) text generation. In our text generation approach, we generate new tweets by replacing words using data acquired from ConceptNet relations in order to increase the size of our training set, this method is very helpful with frustratingly small datasets, preserves the label and increases diversity. In our text augmentation approach, the number of tweets remains the same but their words are augmented (concatenation) with words extracted from their ConceptNet relations and their description extracted from Wikidata. In our text augmentation approach, the number of tweets in each class remains the same but the range of each tweet increases. Our experiments show that our approach improves sexist tweet classification significantly in our entire machine learning models. Our approach can be readily applied to any other small dataset size like hate speech or abusive language and text classification problem using any machine learning model.",
}
@inproceedings{sahlgren-etal-2018-learning,
    title = "Learning Representations for Detecting Abusive Language",
    author = "Sahlgren, Magnus  and
      Isbister, Tim  and
      Olsson, Fredrik",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5115",
    doi = "10.18653/v1/W18-5115",
    pages = "115--123",
    abstract = "This paper discusses the question whether it is possible to learn a generic representation that is useful for detecting various types of abusive language. The approach is inspired by recent advances in transfer learning and word embeddings, and we learn representations from two different datasets containing various degrees of abusive language. We compare the learned representation with two standard approaches; one based on lexica, and one based on data-specific $n$-grams. Our experiments show that learned representations \textit{do} contain useful information that can be used to improve detection performance when training data is limited.",
}
@inproceedings{ljubesic-etal-2018-datasets,
    title = "Datasets of {S}lovene and {C}roatian Moderated News Comments",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Erjavec, Toma{\v{z}}  and
      Fi{\v{s}}er, Darja",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5116",
    doi = "10.18653/v1/W18-5116",
    pages = "124--131",
    abstract = "This paper presents two large newly constructed datasets of moderated news comments from two highly popular online news portals in the respective countries: the Slovene RTV MCC and the Croatian 24sata. The datasets are analyzed by performing manual annotation of the types of the content which have been deleted by moderators and by investigating deletion trends among users and threads. Next, initial experiments on automatically detecting the deleted content in the datasets are presented. Both datasets are published in encrypted form, to enable others to perform experiments on detecting content to be deleted without revealing potentially inappropriate content. Finally, the baseline classification models trained on the non-encrypted datasets are disseminated as well to enable real-world use.",
}
@inproceedings{karan-snajder-2018-cross,
    title = "Cross-Domain Detection of Abusive Language Online",
    author = "Karan, Mladen  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5117",
    doi = "10.18653/v1/W18-5117",
    pages = "132--137",
    abstract = "We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types. To this end, we compare the cross-domain performance of simple classification models on nine different datasets, finding that the models fail to generalize to out-domain datasets and that having at least some in-domain data is important. We also show that using the frustratingly simple domain adaptation (Daume III, 2007) in most cases improves the results over in-domain training, especially when used to augment a smaller dataset with a larger one.",
}
@inproceedings{mathur-etal-2018-offend,
    title = "Did you offend me? Classification of Offensive Tweets in {H}inglish Language",
    author = "Mathur, Puneet  and
      Sawhney, Ramit  and
      Ayyar, Meghna  and
      Shah, Rajiv",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5118",
    doi = "10.18653/v1/W18-5118",
    pages = "138--148",
    abstract = "The use of code-switched languages (\textit{e.g.}, Hinglish, which is derived by the blending of Hindi with the English language) is getting much popular on Twitter due to their ease of communication in native languages. However, spelling variations and absence of grammar rules introduce ambiguity and make it difficult to understand the text automatically. This paper presents the Multi-Input Multi-Channel Transfer Learning based model (MIMCT) to detect offensive (hate speech or abusive) Hinglish tweets from the proposed Hinglish Offensive Tweet (HOT) dataset using transfer learning coupled with multiple feature inputs. Specifically, it takes multiple primary word embedding along with secondary extracted features as inputs to train a multi-channel CNN-LSTM architecture that has been pre-trained on English tweets through transfer learning. The proposed MIMCT model outperforms the baseline supervised classification models, transfer learning based CNN and LSTM models to establish itself as the state of the art in the unexplored domain of Hinglish offensive text classification.",
}
@inproceedings{wu-etal-2018-decipherment,
    title = "Decipherment for Adversarial Offensive Language Detection",
    author = "Wu, Zhelun  and
      Kambhatla, Nishant  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5119",
    doi = "10.18653/v1/W18-5119",
    pages = "149--159",
    abstract = "Automated filters are commonly used by online services to stop users from sending age-inappropriate, bullying messages, or asking others to expose personal information. Previous work has focused on rules or classifiers to detect and filter offensive messages, but these are vulnerable to cleverly disguised plaintext and unseen expressions especially in an adversarial setting where the users can repeatedly try to bypass the filter. In this paper, we model the disguised messages as if they are produced by encrypting the original message using an invented cipher. We apply automatic decipherment techniques to decode the disguised malicious text, which can be then filtered using rules or classifiers. We provide experimental results on three different datasets and show that decipherment is an effective tool for this task.",
}
@inproceedings{castelle-2018-linguistic,
    title = "The Linguistic Ideologies of Deep Abusive Language Classification",
    author = "Castelle, Michael",
    booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5120",
    doi = "10.18653/v1/W18-5120",
    pages = "160--170",
    abstract = "This paper brings together theories from sociolinguistics and linguistic anthropology to critically evaluate the so-called {``}language ideologies{''} {---} the set of beliefs and ways of speaking about language{---}in the practices of abusive language classification in modern machine learning-based NLP. This argument is made at both a conceptual and empirical level, as we review approaches to abusive language from different fields, and use two neural network methods to analyze three datasets developed for abusive language classification tasks (drawn from Wikipedia, Facebook, and StackOverflow). By evaluating and comparing these results, we argue for the importance of incorporating theories of pragmatics and metapragmatics into both the design of classification tasks as well as in ML architectures.",
}
@proceedings{ws-2018-sigdial,
    title = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    editor = "Komatani, Kazunori  and
      Litman, Diane  and
      Yu, Kai  and
      Papangelis, Alex  and
      Cavedon, Lawrence  and
      Nakano, Mikio",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5000",
}
@inproceedings{zhao-eskenazi-2018-zero,
    title = "Zero-Shot Dialog Generation with Cross-Domain Latent Actions",
    author = "Zhao, Tiancheng  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5001",
    doi = "10.18653/v1/W18-5001",
    pages = "1--10",
    abstract = "This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimum data. ZSDG requires an end-to-end generative dialog system to generalize to a new domain for which only a domain description is provided and no training dialogs are available. Then a novel learning framework, Action Matching, is proposed. This algorithm can learn a cross-domain embedding space that models the semantics of dialog responses which in turn, enables a neural dialog generation model to generalize to new domains. We evaluate our methods on two datasets, a new synthetic dialog dataset, and an existing human-human multi-domain dialog dataset. Experimental results show that our method is able to achieve superior performance in learning dialog models that can rapidly adapt their behavior to new domains and suggests promising future research.",
}
@inproceedings{pragst-ultes-2018-changing,
    title = "Changing the Level of Directness in Dialogue using Dialogue Vector Models and Recurrent Neural Networks",
    author = "Pragst, Louisa  and
      Ultes, Stefan",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5002",
    doi = "10.18653/v1/W18-5002",
    pages = "11--19",
    abstract = "In cooperative dialogues, identifying the intent of ones conversation partner and acting accordingly is of great importance. While this endeavour is facilitated by phrasing intentions as directly as possible, we can observe in human-human communication that a number of factors such as cultural norms and politeness may result in expressing one{'}s intent indirectly. Therefore, in human-computer communication we have to anticipate the possibility of users being indirect and be prepared to interpret their actual meaning. Furthermore, a dialogue system should be able to conform to human expectations by adjusting the degree of directness it uses to improve the user experience. To reach those goals, we propose an approach to differentiate between direct and indirect utterances and find utterances of the opposite characteristic that express the same intent. In this endeavour, we employ dialogue vector models and recurrent neural networks.",
}
@inproceedings{hu-etal-2018-modeling,
    title = "Modeling Linguistic and Personality Adaptation for Natural Language Generation",
    author = "Hu, Zhichao  and
      Fox Tree, Jean  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5003",
    doi = "10.18653/v1/W18-5003",
    pages = "20--31",
    abstract = "Previous work has shown that conversants adapt to many aspects of their partners{'} language. Other work has shown that while every person is unique, they often share general patterns of behavior. Theories of personality aim to explain these shared patterns, and studies have shown that many linguistic cues are correlated with personality traits. We propose an adaptation measure for adaptive natural language generation for dialogs that integrates the predictions of both personality theories and adaptation theories, that can be applied as a dialog unfolds, on a turn by turn basis. We show that our measure meets criteria for validity, and that adaptation varies according to corpora and task, speaker, and the set of features used to model it. We also produce fine-grained models according to the dialog segmentation or the speaker, and demonstrate the decaying trend of adaptation.",
}
@inproceedings{inaba-takahashi-2018-estimating,
    title = "Estimating User Interest from Open-Domain Dialogue",
    author = "Inaba, Michimasa  and
      Takahashi, Kenichi",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5004",
    doi = "10.18653/v1/W18-5004",
    pages = "32--40",
    abstract = "Dialogue personalization is an important issue in the field of open-domain chat-oriented dialogue systems. If these systems could consider their users{'} interests, user engagement and satisfaction would be greatly improved. This paper proposes a neural network-based method for estimating users{'} interests from their utterances in chat dialogues to personalize dialogue systems{'} responses. We introduce a method for effectively extracting topics and user interests from utterances and also propose a pre-training approach that increases learning efficiency. Our experimental results indicate that the proposed model can estimate user{'}s interest more accurately than baseline approaches.",
}
@inproceedings{sinclair-etal-2018-ability,
    title = "Does Ability Affect Alignment in Second Language Tutorial Dialogue?",
    author = "Sinclair, Arabella  and
      Lopez, Adam  and
      Lucas, C. G.  and
      Gasevic, Dragan",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5005",
    doi = "10.18653/v1/W18-5005",
    pages = "41--50",
    abstract = "The role of alignment between interlocutors in second language learning is different to that in fluent conversational dialogue. Learners gain linguistic skill through increased alignment, yet the extent to which they can align will be constrained by their ability. Tutors may use alignment to teach and encourage the student, yet still must push the student and correct their errors, decreasing alignment. To understand how learner ability interacts with alignment, we measure the influence of ability on lexical priming, an indicator of alignment. We find that lexical priming in learner-tutor dialogues differs from that in conversational and task-based dialogues, and we find evidence that alignment increases with ability and with word complexity.",
}
@inproceedings{gilmartin-etal-2018-just,
    title = "Just Talking - Modelling Casual Conversation",
    author = "Gilmartin, Emer  and
      Saam, Christian  and
      Vogel, Carl  and
      Campbell, Nick  and
      Wade, Vincent",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5006",
    doi = "10.18653/v1/W18-5006",
    pages = "51--59",
    abstract = "Casual conversation has become a focus for artificial dialogue applications. Such talk is ubiquitous and its structure differs from that found in the task-based interactions which have been the focus of dialogue system design for many years. It is unlikely that such conversations can be modelled as an extension of task-based talk. We review theories of casual conversation, report on our studies of the structure of casual dialogue, and outline challenges we see for the development of spoken dialog systems capable of carrying on casual friendly conversation in addition to performing well-defined tasks.",
}
@inproceedings{kreyssig-etal-2018-neural,
    title = "Neural User Simulation for Corpus-based Policy Optimisation of Spoken Dialogue Systems",
    author = "Kreyssig, Florian  and
      Casanueva, I{\~n}igo  and
      Budzianowski, Pawe{\l}  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5007",
    doi = "10.18653/v1/W18-5007",
    pages = "60--69",
    abstract = "User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Cross-model evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.",
}
@inproceedings{sakai-etal-2018-introduction,
    title = "Introduction method for argumentative dialogue using paired question-answering interchange about personality",
    author = "Sakai, Kazuki  and
      Higashinaka, Ryuichiro  and
      Yoshikawa, Yuichiro  and
      Ishiguro, Hiroshi  and
      Tomita, Junji",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5008",
    doi = "10.18653/v1/W18-5008",
    pages = "70--79",
    abstract = "To provide a better discussion experience in current argumentative dialogue systems, it is necessary for the user to feel motivated to participate, even if the system already responds appropriately. In this paper, we propose a method that can smoothly introduce argumentative dialogue by inserting an initial discourse, consisting of question-answer pairs concerning personality. The system can induce interest of the users prior to agreement or disagreement during the main discourse. By disclosing their interests, the users will feel familiarity and motivation to further engage in the argumentative dialogue and understand the system{'}s intent. To verify the effectiveness of a question-answer dialogue inserted before the argument, a subjective experiment was conducted using a text chat interface. The results suggest that inserting the question-answer dialogue enhances familiarity and naturalness. Notably, the results suggest that women more than men regard the dialogue as more natural and the argument as deepened, following an exchange concerning personality.",
}
@inproceedings{ramanarayanan-pugh-2018-automatic,
    title = "Automatic Token and Turn Level Language Identification for Code-Switched Text Dialog: An Analysis Across Language Pairs and Corpora",
    author = "Ramanarayanan, Vikram  and
      Pugh, Robert",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5009",
    doi = "10.18653/v1/W18-5009",
    pages = "80--88",
    abstract = "We examine the efficacy of various feature{--}learner combinations for language identification in different types of text-based code-switched interactions {--} human-human dialog, human-machine dialog as well as monolog {--} at both the token and turn levels. In order to examine the generalization of such methods across language pairs and datasets, we analyze 10 different datasets of code-switched text. We extract a variety of character- and word-based text features and pass them into multiple learners, including conditional random fields, logistic regressors and recurrent neural networks. We further examine the efficacy of novel character-level embedding and GloVe features in improving performance and observe that our best-performing text system significantly outperforms a majority vote baseline across language pairs and datasets.",
}
@inproceedings{perera-etal-2018-situated,
    title = "A Situated Dialogue System for Learning Structural Concepts in Blocks World",
    author = "Perera, Ian  and
      Allen, James  and
      Teng, Choh Man  and
      Galescu, Lucian",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5010",
    doi = "10.18653/v1/W18-5010",
    pages = "89--98",
    abstract = "We present a modular, end-to-end dialogue system for a situated agent to address a multimodal, natural language dialogue task in which the agent learns complex representations of block structure classes through assertions, demonstrations, and questioning. The concept to learn is provided to the user through a set of positive and negative visual examples, from which the user determines the underlying constraints to be provided to the system in natural language. The system in turn asks questions about demonstrated examples and simulates new examples to check its knowledge and verify the user{'}s description is complete. We find that this task is non-trivial for users and generates natural language that is varied yet understood by our deep language understanding architecture.",
}
@inproceedings{gervits-scheutz-2018-pardon,
    title = "Pardon the Interruption: Managing Turn-Taking through Overlap Resolution in Embodied Artificial Agents",
    author = "Gervits, Felix  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5011",
    doi = "10.18653/v1/W18-5011",
    pages = "99--109",
    abstract = "Speech overlap is a common phenomenon in natural conversation and in task-oriented interactions. As human-robot interaction (HRI) becomes more sophisticated, the need to effectively manage turn-taking and resolve overlap becomes more important. In this paper, we introduce a computational model for speech overlap resolution in embodied artificial agents. The model identifies when overlap has occurred and uses timing information, dialogue history, and the agent{'}s goals to generate context-appropriate behavior. We implement this model in a Nao robot using the DIARC cognitive robotic architecture. The model is evaluated on a corpus of task-oriented human dialogue, and we find that the robot can replicate many of the most common overlap resolution behaviors found in the human data.",
}
@inproceedings{lukin-etal-2018-consequences,
    title = "Consequences and Factors of Stylistic Differences in Human-Robot Dialogue",
    author = "Lukin, Stephanie  and
      Pollard, Kimberly  and
      Bonial, Claire  and
      Marge, Matthew  and
      Henry, Cassidy  and
      Artstein, Ron  and
      Traum, David  and
      Voss, Clare",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5012",
    doi = "10.18653/v1/W18-5012",
    pages = "110--118",
    abstract = "This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue. Differences in verbosity and structure (i.e., single-intent vs. multi-intent instructions) arose naturally without restrictions or prior guidance on how users should speak with the robot. Different styles were found to produce different rates of miscommunication, and correlations were found between style differences and individual user variation, trust, and interaction experience with the robot. Understanding potential consequences and factors that influence style can inform design of dialogue systems that are robust to natural variation from human users.",
}
@inproceedings{das-pon-barry-2018-turn,
    title = "Turn-Taking Strategies for Human-Robot Peer-Learning Dialogue",
    author = "Das, Ranjini  and
      Pon-Barry, Heather",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5013",
    doi = "10.18653/v1/W18-5013",
    pages = "119--129",
    abstract = "In this paper, we apply the contribution model of grounding to a corpus of human-human peer-mentoring dialogues. From this analysis, we propose effective turn-taking strategies for human-robot interaction with a teachable robot. Specifically, we focus on (1) how robots can encourage humans to present and (2) how robots can signal that they are going to begin a new presentation. We evaluate the strategies against a corpus of human-robot dialogues and offer three guidelines for teachable robots to follow to achieve more human-like collaborative dialogue.",
}
@inproceedings{plane-etal-2018-predicting,
    title = "Predicting Perceived Age: Both Language Ability and Appearance are Important",
    author = "Plane, Sarah  and
      Marvasti, Ariel  and
      Egan, Tyler  and
      Kennington, Casey",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5014",
    doi = "10.18653/v1/W18-5014",
    pages = "130--139",
    abstract = "When interacting with robots in a situated spoken dialogue setting, human dialogue partners tend to assign anthropomorphic and social characteristics to those robots. In this paper, we explore the age and educational level that human dialogue partners assign to three different robotic systems, including an un-embodied spoken dialogue system. We found that how a robot speaks is as important to human perceptions as the way the robot looks. Using the data from our experiment, we derived prosodic, emotional, and linguistic features from the participants to train and evaluate a classifier that predicts perceived intelligence, age, and education level.",
}
@inproceedings{zhang-etal-2018-multimodal,
    title = "Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog",
    author = "Zhang, Jiaping  and
      Zhao, Tiancheng  and
      Yu, Zhou",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5015",
    doi = "10.18653/v1/W18-5015",
    pages = "140--150",
    abstract = "Creating an intelligent conversational system that understands vision and language is one of the ultimate goals in Artificial Intelligence (AI) (Winograd, 1972). Extensive research has focused on vision-to-language generation, however, limited research has touched on combining these two modalities in a goal-driven dialog context. We propose a multimodal hierarchical reinforcement learning framework that dynamically integrates vision and language for task-oriented visual dialog. The framework jointly learns the multimodal dialog state representation and the hierarchical dialog policy to improve both dialog task success and efficiency. We also propose a new technique, state adaptation, to integrate context awareness in the dialog state representation. We evaluate the proposed framework and the state adaptation technique in an image guessing game and achieve promising results.",
}
@inproceedings{patki-howard-2018-language,
    title = "Language-Guided Adaptive Perception for Efficient Grounded Communication with Robotic Manipulators in Cluttered Environments",
    author = "Patki, Siddharth  and
      Howard, Thomas",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5016",
    doi = "10.18653/v1/W18-5016",
    pages = "151--160",
    abstract = "The utility of collaborative manipulators for shared tasks is highly dependent on the speed and accuracy of communication between the human and the robot. The run-time of recently developed probabilistic inference models for situated symbol grounding of natural language instructions depends on the complexity of the representation of the environment in which they reason. As we move towards more complex bi-directional interactions, tasks, and environments, we need intelligent perception models that can selectively infer precise pose, semantics, and affordances of the objects when inferring exhaustively detailed world models is inefficient and prohibits real-time interaction with these robots. In this paper we propose a model of language and perception for the problem of adapting the configuration of the robot perception pipeline for tasks where constructing exhaustively detailed models of the environment is inefficient and inconsequential for symbol grounding. We present experimental results from a synthetic corpus of natural language instructions for robot manipulation in example environments. The results demonstrate that by adapting perception we get significant gains in terms of run-time for perception and situated symbol grounding of the language instructions without a loss in the accuracy of the latter.",
}
@inproceedings{lubis-etal-2018-unsupervised,
    title = "Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System",
    author = "Lubis, Nurul  and
      Sakti, Sakriani  and
      Yoshino, Koichiro  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5017",
    doi = "10.18653/v1/W18-5017",
    pages = "161--170",
    abstract = "Positive emotion elicitation seeks to improve user{'}s emotional state through dialogue system interaction, where a chat-based scenario is layered with an implicit goal to address user{'}s emotional needs. Standard neural dialogue system approaches still fall short in this situation as they tend to generate only short, generic responses. Learning from expert actions is critical, as these potentially differ from standard dialogue acts. In this paper, we propose using a hierarchical neural network for response generation that is conditioned on 1) expert{'}s action, 2) dialogue context, and 3) user emotion, encoded from user input. We construct a corpus of interactions between a counselor and 30 participants following a negative emotional exposure to learn expert actions and responses in a positive emotion elicitation scenario. Instead of relying on the expensive, labor intensive, and often ambiguous human annotations, we unsupervisedly cluster the expert{'}s responses and use the resulting labels to train the network. Our experiments and evaluation show that the proposed approach yields lower perplexity and generates a larger variety of responses.",
}
@inproceedings{engonopoulos-etal-2018-discovering,
    title = "Discovering User Groups for Natural Language Generation",
    author = "Engonopoulos, Nikos  and
      Teichmann, Christoph  and
      Koller, Alexander",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5018",
    doi = "10.18653/v1/W18-5018",
    pages = "171--179",
    abstract = "We present a model which predicts how individual users of a dialog system understand and produce utterances based on user groups. In contrast to previous work, these user groups are not specified beforehand, but learned in training. We evaluate on two referring expression (RE) generation tasks; our experiments show that our model can identify user groups and learn how to most effectively talk to them, and can dynamically assign unseen users to the correct groups as they interact with the system.",
}
@inproceedings{oraby-etal-2018-controlling,
    title = "Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators",
    author = "Oraby, Shereen  and
      Reed, Lena  and
      Tandon, Shubhangi  and
      T.S., Sharath  and
      Lukin, Stephanie  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5019",
    doi = "10.18653/v1/W18-5019",
    pages = "180--190",
    abstract = "Natural language generators for task-oriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable for generators to control the style of an utterance. To date, work on task-oriented neural generation has primarily focused on semantic fidelity rather than achieving stylistic goals, while work on style has been done in contexts where it is difficult to measure content preservation. Here we present three different sequence-to-sequence models and carefully test how well they disentangle content and style. We use a statistical generator, Personage, to synthesize a new corpus of over 88,000 restaurant domain utterances whose style varies according to models of personality, giving us total control over both the semantic content and the stylistic variation in the training data. We then vary the amount of explicit stylistic supervision given to the three models. We show that our most explicit model can simultaneously achieve high fidelity to both semantic and stylistic goals: this model adds a context vector of 36 stylistic parameters as input to the hidden state of the encoder at each time step, showing the benefits of explicit stylistic supervision, even when the amount of training data is large.",
}
@inproceedings{mangrulkar-etal-2018-context,
    title = "A Context-aware Convolutional Natural Language Generation model for Dialogue Systems",
    author = "Mangrulkar, Sourab  and
      Shrivastava, Suhani  and
      Thenkanidiyoor, Veena  and
      Aroor Dinesh, Dileep",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5020",
    doi = "10.18653/v1/W18-5020",
    pages = "191--200",
    abstract = "Natural language generation (NLG) is an important component in spoken dialog systems (SDSs). A model for NLG involves sequence to sequence learning. State-of-the-art NLG models are built using recurrent neural network (RNN) based sequence to sequence models (Ond{\v{r}}ej Du{\v{s}}ek and Filip Jur{\v{c}}{\'\i}{\v{c}}ek, 2016a). Convolutional sequence to sequence based models have been used in the domain of machine translation but their application as Natural Language Generators in dialogue systems is still unexplored. In this work, we propose a novel approach to NLG using convolutional neural network (CNN) based sequence to sequence learning. CNN-based approach allows to build a hierarchical model which encapsulates dependencies between words via shorter path unlike RNNs. In contrast to recurrent models, convolutional approach allows for efficient utilization of computational resources by parallelizing computations over all elements, and eases the learning process by applying constant number of nonlinearities. We also propose to use CNN-based reranker for obtaining responses having semantic correspondence with input dialogue acts. The proposed model is capable of entrainment. Studies using a standard dataset shows the effectiveness of the proposed CNN-based approach to NLG.",
}
@inproceedings{zhao-kawahara-2018-unified,
    title = "A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System",
    author = "Zhao, Tianyu  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5021",
    doi = "10.18653/v1/W18-5021",
    pages = "201--208",
    abstract = "In spoken dialog systems (SDSs), dialog act (DA) segmentation and recognition provide essential information for response generation. A majority of previous works assumed ground-truth segmentation of DA units, which is not available from automatic speech recognition (ASR) in SDS. We propose a unified architecture based on neural networks, which consists of a sequence tagger for segmentation and a classifier for recognition. The DA recognition model is based on hierarchical neural networks to incorporate the context of preceding sentences. We investigate sharing some layers of the two components so that they can be trained jointly and learn generalized features from both tasks. An evaluation on the Switchboard Dialog Act (SwDA) corpus shows that the jointly-trained models outperform independently-trained models, single-step models, and other reported results in DA segmentation, recognition, and joint tasks.",
}
@inproceedings{xie-etal-2018-cost,
    title = "Cost-Sensitive Active Learning for Dialogue State Tracking",
    author = "Xie, Kaige  and
      Chang, Cheng  and
      Ren, Liliang  and
      Chen, Lu  and
      Yu, Kai",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5022",
    doi = "10.18653/v1/W18-5022",
    pages = "209--213",
    abstract = "Dialogue state tracking (DST), when formulated as a supervised learning problem, relies on labelled data. Since dialogue state annotation usually requires labelling all turns of a single dialogue and utilizing context information, it is very expensive to annotate all available unlabelled data. In this paper, a novel cost-sensitive active learning framework is proposed based on a set of new dialogue-level query strategies. This is the first attempt to apply active learning for dialogue state tracking. Experiments on DSTC2 show that active learning with mixed data query strategies can effectively achieve the same DST performance with significantly less data annotation compared to traditional training approaches.",
}
@inproceedings{lai-tetreault-2018-discourse,
    title = "Discourse Coherence in the Wild: A Dataset, Evaluation and Methods",
    author = "Lai, Alice  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5023",
    doi = "10.18653/v1/W18-5023",
    pages = "214--223",
    abstract = "To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.",
}
@inproceedings{masumura-etal-2018-neural,
    title = "Neural Dialogue Context Online End-of-Turn Detection",
    author = "Masumura, Ryo  and
      Tanaka, Tomohiro  and
      Ando, Atsushi  and
      Ishii, Ryo  and
      Higashinaka, Ryuichiro  and
      Aono, Yushi",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5024",
    doi = "10.18653/v1/W18-5024",
    pages = "224--228",
    abstract = "This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker{'}s utterances and collocutor{'}s utterances. The proposed method combines multiple time-asynchronous long short-term memory recurrent neural networks, which can capture speaker{'}s and collocutor{'}s multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce speaker{'}s acoustic sequential features and collocutor{'}s linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the speaker{'}s utterances and collocutor{'}s utterances into consideration.",
}
@inproceedings{papangelis-etal-2018-spoken,
    title = "Spoken Dialogue for Information Navigation",
    author = "Papangelis, Alexandros  and
      Papadakos, Panagiotis  and
      Stylianou, Yannis  and
      Tzitzikas, Yannis",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5025",
    doi = "10.18653/v1/W18-5025",
    pages = "229--234",
    abstract = "Aiming to expand the current research paradigm for training conversational AI agents that can address real-world challenges, we take a step away from traditional slot-filling goal-oriented spoken dialogue systems (SDS) and model the dialogue in a way that allows users to be more expressive in describing their needs. The goal is to help users make informed decisions rather than being fed matching items. To this end, we describe the Linked-Data SDS (LD-SDS), a system that exploits semantic knowledge bases that connect to linked data, and supports complex constraints and preferences. We describe the required changes in language understanding and state tracking, and the need for mined features, and we report the promising results (in terms of semantic errors, effort, etc) of a preliminary evaluation after training two statistical dialogue managers in various conditions.",
}
@inproceedings{kageyama-etal-2018-improving,
    title = "Improving User Impression in Spoken Dialog System with Gradual Speech Form Control",
    author = "Kageyama, Yukiko  and
      Chiba, Yuya  and
      Nose, Takashi  and
      Ito, Akinori",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5026",
    doi = "10.18653/v1/W18-5026",
    pages = "235--240",
    abstract = "This paper examines a method to improve the user impression of a spoken dialog system by introducing a mechanism that gradually changes form of utterances every time the user uses the system. In some languages, including Japanese, the form of utterances changes corresponding to social relationship between the talker and the listener. Thus, this mechanism can be effective to express the system{'}s intention to make social distance to the user closer; however, an actual effect of this method is not investigated enough when introduced to the dialog system. In this paper, we conduct dialog experiments and show that controlling the form of system utterances can improve the users{'} impression.",
}
@inproceedings{abu-ali-etal-2018-bilingual,
    title = "A Bilingual Interactive Human Avatar Dialogue System",
    author = "Abu Ali, Dana  and
      Ahmad, Muaz  and
      Al Hassan, Hayat  and
      Dozsa, Paula  and
      Hu, Ming  and
      Varias, Jose  and
      Habash, Nizar",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5027",
    doi = "10.18653/v1/W18-5027",
    pages = "241--244",
    abstract = "This demonstration paper presents a bilingual (Arabic-English) interactive human avatar dialogue system. The system is named TOIA (time-offset interaction application), as it simulates face-to-face conversations between humans using digital human avatars recorded in the past. TOIA is a conversational agent, similar to a chat bot, except that it is based on an actual human being and can be used to preserve and tell stories. The system is designed to allow anybody, simply using a laptop, to create an avatar of themselves, thus facilitating cross-cultural and cross-generational sharing of narratives to wider audiences. The system currently supports monolingual and cross-lingual dialogues in Arabic and English, but can be extended to other languages.",
}
@inproceedings{lee-etal-2018-dialcrowd,
    title = "{D}ial{C}rowd: A toolkit for easy dialog system assessment",
    author = "Lee, Kyusong  and
      Zhao, Tiancheng  and
      Black, Alan W.  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5028",
    doi = "10.18653/v1/W18-5028",
    pages = "245--248",
    abstract = "When creating a dialog system, developers need to test each version to ensure that it is performing correctly. Recently the trend has been to test on large datasets or to ask many users to try out a system. Crowdsourcing has solved the issue of finding users, but it presents new challenges such as how to use a crowdsourcing platform and what type of test is appropriate. DialCrowd has been designed to make system assessment easier and to ensure the quality of the result. This paper describes DialCrowd, what specific needs it fulfills and how it works. It then relates a test of DialCrowd by a group of dialog system developer.",
}
@inproceedings{pautler-etal-2018-leveraging,
    title = "Leveraging Multimodal Dialog Technology for the Design of Automated and Interactive Student Agents for Teacher Training",
    author = "Pautler, David  and
      Ramanarayanan, Vikram  and
      Cofino, Kirby  and
      Lange, Patrick  and
      Suendermann-Oeft, David",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5029",
    doi = "10.18653/v1/W18-5029",
    pages = "249--252",
    abstract = "We present a paradigm for interactive teacher training that leverages multimodal dialog technology to puppeteer custom-designed embodied conversational agents (ECAs) in student roles. We used the open-source multimodal dialog system HALEF to implement a small-group classroom math discussion involving Venn diagrams where a human teacher candidate has to interact with two student ECAs whose actions are controlled by the dialog system. Such an automated paradigm has the potential to be extended and scaled to a wide range of interactive simulation scenarios in education, medicine, and business where group interaction training is essential.",
}
@inproceedings{ravichander-black-2018-empirical,
    title = "An Empirical Study of Self-Disclosure in Spoken Dialogue Systems",
    author = "Ravichander, Abhilasha  and
      Black, Alan W.",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5030",
    doi = "10.18653/v1/W18-5030",
    pages = "253--263",
    abstract = "Self-disclosure is a key social strategy employed in conversation to build relations and increase conversational depth. It has been heavily studied in psychology and linguistic literature, particularly for its ability to induce self-disclosure from the recipient, a phenomena known as reciprocity. However, we know little about how self-disclosure manifests in conversation with automated dialog systems, especially as any self-disclosure on the part of a dialog system is patently disingenuous. In this work, we run a large-scale quantitative analysis on the effect of self-disclosure by analyzing interactions between real-world users and a spoken dialog system in the context of social conversation. We find that indicators of reciprocity occur even in human-machine dialog, with far-reaching implications for chatbots in a variety of domains including education, negotiation and social dialog.",
}
@inproceedings{higashinaka-etal-2018-role,
    title = "Role play-based question-answering by real users for building chatbots with consistent personalities",
    author = "Higashinaka, Ryuichiro  and
      Mizukami, Masahiro  and
      Kawabata, Hidetoshi  and
      Yamaguchi, Emi  and
      Adachi, Noritake  and
      Tomita, Junji",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5031",
    doi = "10.18653/v1/W18-5031",
    pages = "264--272",
    abstract = "Having consistent personalities is important for chatbots if we want them to be believable. Typically, many question-answer pairs are prepared by hand for achieving consistent responses; however, the creation of such pairs is costly. In this study, our goal is to collect a large number of question-answer pairs for a particular character by using role play-based question-answering in which multiple users play the roles of certain characters and respond to questions by online users. Focusing on two famous characters, we conducted a large-scale experiment to collect question-answer pairs by using real users. We evaluated the effectiveness of role play-based question-answering and found that, by using our proposed method, the collected pairs lead to good-quality chatbots that exhibit consistent personalities.",
}
@inproceedings{ultes-etal-2018-addressing,
    title = "Addressing Objects and Their Relations: The Conversational Entity Dialogue Model",
    author = "Ultes, Stefan  and
      Budzianowski, Pawe{\l}  and
      Casanueva, I{\~n}igo  and
      Rojas-Barahona, Lina M.  and
      Tseng, Bo-Hsiang  and
      Wu, Yen-Chen  and
      Young, Steve  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5032",
    doi = "10.18653/v1/W18-5032",
    pages = "273--283",
    abstract = "Statistical spoken dialogue systems usually rely on a single- or multi-domain dialogue model that is restricted in its capabilities of modelling complex dialogue structures, e.g., relations. In this work, we propose a novel dialogue model that is centred around entities and is able to model relations as well as multiple entities of the same type. We demonstrate in a prototype implementation benefits of relation modelling on the dialogue level and show that a trained policy using these relations outperforms the multi-domain baseline. Furthermore, we show that by modelling the relations on the dialogue level, the system is capable of processing relations present in the user input and even learns to address them in the system response.",
}
@inproceedings{manuvinakurike-etal-2018-conversational,
    title = "Conversational Image Editing: Incremental Intent Identification in a New Dialogue Task",
    author = "Manuvinakurike, Ramesh  and
      Bui, Trung  and
      Chang, Walter  and
      Georgila, Kallirroi",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5033",
    doi = "10.18653/v1/W18-5033",
    pages = "284--295",
    abstract = "We present {``}conversational image editing{''}, a novel real-world application domain combining dialogue, visual information, and the use of computer vision. We discuss the importance of dialogue incrementality in this task, and build various models for incremental intent identification based on deep learning and traditional classification algorithms. We show how our model based on convolutional neural networks outperforms models based on random forests, long short term memory networks, and conditional random fields. By training embeddings based on image-related dialogue corpora, we outperform pre-trained out-of-the-box embeddings, for intention identification tasks. Our experiments also provide evidence that incremental intent processing may be more efficient for the user and could save time in accomplishing tasks.",
}
@inproceedings{bernard-2018-fine,
    title = "Fine-Grained Discourse Structures in Continuation Semantics",
    author = "Bernard, Timoth{\'e}e",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5034",
    doi = "10.18653/v1/W18-5034",
    pages = "296--305",
    abstract = "In this work, we are interested in the computation of logical representations of discourse. We argue that all discourse connectives are anaphors obeying different sets of constraints and show how this view allows one to account for the semantically parenthetical use of attitude verbs and verbs of report (e.g., think, say) and for sequences of conjunctions (A CONJ{\_}1 B CONJ{\_}2 C). We implement this proposal in event semantics using de Groote (2006){'}s dynamic framework.",
}
@inproceedings{dasgupta-etal-2018-automatic-extraction,
    title = "Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks",
    author = "Dasgupta, Tirthankar  and
      Saha, Rupsa  and
      Dey, Lipika  and
      Naskar, Abir",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5035",
    doi = "10.18653/v1/W18-5035",
    pages = "306--316",
    abstract = "In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text. These relations can be expressed in arbitrarily complex ways. The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence. The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes. We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model. We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bi-directional LSTM model enhanced with an additional linguistic layer performs better. We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community.",
}
@inproceedings{guerini-etal-2018-toward,
    title = "Toward zero-shot Entity Recognition in Task-oriented Conversational Agents",
    author = "Guerini, Marco  and
      Magnolini, Simone  and
      Balaraman, Vevake  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5036",
    doi = "10.18653/v1/W18-5036",
    pages = "317--326",
    abstract = "We present a domain portable zero-shot learning approach for entity recognition in task-oriented conversational agents, which does not assume any annotated sentences at training time. Rather, we derive a neural model of the entity names based only on available gazetteers, and then apply the model to recognize new entities in the context of user utterances. In order to evaluate our working hypothesis we focus on nominal entities that are largely used in e-commerce to name products. Through a set of experiments in two languages (English and Italian) and three different domains (furniture, food, clothing), we show that the neural gazetteer-based approach outperforms several competitive baselines, with minimal requirements of linguistic features.",
}
@inproceedings{bourgonje-stede-2018-identifying,
    title = "Identifying Explicit Discourse Connectives in {G}erman",
    author = "Bourgonje, Peter  and
      Stede, Manfred",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5037",
    doi = "10.18653/v1/W18-5037",
    pages = "327--331",
    abstract = "We are working on an end-to-end Shallow Discourse Parsing system for German and in this paper focus on the first subtask: the identification of explicit connectives. Starting with the feature set from an English system and a Random Forest classifier, we evaluate our approach on a (relatively small) German annotated corpus, the Potsdam Commentary Corpus. We introduce new features and experiment with including additional training data obtained through annotation projection and achieve an f-score of 83.89.",
}
@inproceedings{casanueva-etal-2018-feudal-dialogue,
    title = "Feudal Dialogue Management with Jointly Learned Feature Extractors",
    author = "Casanueva, I{\~n}igo  and
      Budzianowski, Pawe{\l}  and
      Ultes, Stefan  and
      Kreyssig, Florian  and
      Tseng, Bo-Hsiang  and
      Wu, Yen-chen  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5038",
    doi = "10.18653/v1/W18-5038",
    pages = "332--337",
    abstract = "Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains. Recently, Feudal Dialogue Management (FDM), has shown to increase the scalability to large domains by decomposing the dialogue management decision into two steps, making use of the domain ontology to abstract the dialogue state in each step. In order to abstract the state space, however, previous work on FDM relies on handcrafted feature functions. In this work, we show that these feature functions can be learned jointly with the policy model while obtaining similar performance, even outperforming the handcrafted features in several environments and domains.",
}
@inproceedings{tseng-etal-2018-variational,
    title = "Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems",
    author = "Tseng, Bo-Hsiang  and
      Kreyssig, Florian  and
      Budzianowski, Pawe{\l}  and
      Casanueva, I{\~n}igo  and
      Wu, Yen-Chen  and
      Ultes, Stefan  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5039",
    doi = "10.18653/v1/W18-5039",
    pages = "338--343",
    abstract = "Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using conditional variational auto-encoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.",
}
@inproceedings{nishida-nakayama-2018-coherence,
    title = "Coherence Modeling Improves Implicit Discourse Relation Recognition",
    author = "Nishida, Noriki  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5040",
    doi = "10.18653/v1/W18-5040",
    pages = "344--349",
    abstract = "The research described in this paper examines how to learn linguistic knowledge associated with discourse relations from unlabeled corpora. We introduce an unsupervised learning method on text coherence that could produce numerical representations that improve implicit discourse relation recognition in a semi-supervised manner. We also empirically examine two variants of coherence modeling: order-oriented and topic-oriented negative sampling, showing that, of the two, topic-oriented negative sampling tends to be more effective.",
}
@inproceedings{liu-lane-2018-adversarial,
    title = "Adversarial Learning of Task-Oriented Neural Dialog Models",
    author = "Liu, Bing  and
      Lane, Ian",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5041",
    doi = "10.18653/v1/W18-5041",
    pages = "350--359",
    abstract = "In this work, we propose an adversarial learning method for reward estimation in reinforcement learning (RL) based task-oriented dialog models. Most of the current RL based task-oriented dialog systems require the access to a reward signal from either user feedback or user ratings. Such user ratings, however, may not always be consistent or available in practice. Furthermore, online dialog policy learning with RL typically requires a large number of queries to users, suffering from sample efficiency problem. To address these challenges, we propose an adversarial learning method to learn dialog rewards directly from dialog samples. Such rewards are further used to optimize the dialog policy with policy gradient based RL. In the evaluation in a restaurant search domain, we show that the proposed adversarial dialog learning method achieves advanced dialog success rate comparing to strong baseline methods. We further discuss the covariate shift problem in online adversarial dialog learning and show how we can address that with partial access to user feedback.",
}
@inproceedings{das-etal-2018-constructing,
    title = "Constructing a Lexicon of {E}nglish Discourse Connectives",
    author = "Das, Debopam  and
      Scheffler, Tatjana  and
      Bourgonje, Peter  and
      Stede, Manfred",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5042",
    doi = "10.18653/v1/W18-5042",
    pages = "360--365",
    abstract = "We present a new lexicon of English discourse connectives called DiMLex-Eng, built by merging information from two annotated corpora and an additional list of relation signals from the literature. The format follows the German connective lexicon DiMLex, which provides a cross-linguistically applicable XML schema. DiMLex-Eng contains 149 English connectives, and gives information on syntactic categories, discourse semantics and non-connective uses (if any). We report on the development steps and discuss design decisions encountered in the lexicon expansion phase. The resource is freely available for use in studies of discourse structure and computational applications.",
}
@inproceedings{homma-etal-2018-maximizing,
    title = "Maximizing {SLU} Performance with Minimal Training Data Using Hybrid {RNN} Plus Rule-based Approach",
    author = "Homma, Takeshi  and
      Arantes, Adriano S.  and
      Gonzalez Diaz, Maria Teresa  and
      Togami, Masahito",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5043",
    doi = "10.18653/v1/W18-5043",
    pages = "366--370",
    abstract = "Spoken language understanding (SLU) by using recurrent neural networks (RNN) achieves good performances for large training data sets, but collecting large training datasets is a challenge, especially for new voice applications. Therefore, the purpose of this study is to maximize SLU performances, especially for small training data sets. To this aim, we propose a novel CRF-based dialog act selector which chooses suitable dialog acts from outputs of RNN SLU and rule-based SLU. We evaluate the selector by using DSTC2 corpus when RNN SLU is trained by less than 1,000 training sentences. The evaluation demonstrates the selector achieves Micro F1 better than both RNN and rule-based SLUs. In addition, it shows the selector achieves better Macro F1 than RNN SLU and the same Macro F1 as rule-based SLU. Thus, we confirmed our method offers advantages in SLU performances for small training data sets.",
}
@inproceedings{chiba-etal-2018-analysis,
    title = "An Analysis of the Effect of Emotional Speech Synthesis on Non-Task-Oriented Dialogue System",
    author = "Chiba, Yuya  and
      Nose, Takashi  and
      Kase, Taketo  and
      Yamanaka, Mai  and
      Ito, Akinori",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5044",
    doi = "10.18653/v1/W18-5044",
    pages = "371--375",
    abstract = "This paper explores the effect of emotional speech synthesis on a spoken dialogue system when the dialogue is non-task-oriented. Although the use of emotional speech responses have been shown to be effective in a limited domain, e.g., scenario-based and counseling dialogue, the effect is still not clear in the non-task-oriented dialogue such as voice chatting. For this purpose, we constructed a simple dialogue system with example- and rule-based dialogue management. In the system, two types of emotion labeling with emotion estimation are adopted, i.e., system-driven and user-cooperative emotion labeling. We conducted a dialogue experiment where subjects evaluate the subjective quality of the system and the dialogue from the multiple aspects such as richness of the dialogue and impression of the agent. We then analyze and discuss the results and show the advantage of using appropriate emotions for the expressive speech responses in the non-task-oriented system.",
}
@inproceedings{rastogi-etal-2018-multi,
    title = "Multi-task Learning for Joint Language Understanding and Dialogue State Tracking",
    author = "Rastogi, Abhinav  and
      Gupta, Raghav  and
      Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5045",
    doi = "10.18653/v1/W18-5045",
    pages = "376--384",
    abstract = "This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.",
}
@inproceedings{rahimi-litman-2018-weighting,
    title = "Weighting Model Based on Group Dynamics to Measure Convergence in Multi-party Dialogue",
    author = "Rahimi, Zahra  and
      Litman, Diane",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5046",
    doi = "10.18653/v1/W18-5046",
    pages = "385--390",
    abstract = "This paper proposes a new weighting method for extending a dyad-level measure of convergence to multi-party dialogues by considering group dynamics instead of simply averaging. Experiments indicate the usefulness of the proposed weighted measure and also show that in general a proper weighting of the dyad-level measures performs better than non-weighted averaging in multiple tasks.",
}
@inproceedings{zhu-yu-2018-concept,
    title = "Concept Transfer Learning for Adaptive Language Understanding",
    author = "Zhu, Su  and
      Yu, Kai",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5047",
    doi = "10.18653/v1/W18-5047",
    pages = "391--399",
    abstract = "Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated. To address this issue, in this paper, a novel concept transfer learning approach is proposed. Here, substructures within literal concept definition are investigated to reveal the relationship between concepts. A hierarchical semantic representation for concepts is proposed, where a semantic slot is represented as a composition of \textit{atomic concepts}. Based on this new hierarchical representation, transfer learning approaches are developed for adaptive LU. The approaches are applied to two tasks: value set mismatch and domain adaptation, and evaluated on two LU benchmarks: ATIS and DSTC 2{\&}3. Thorough empirical studies validate both the efficiency and effectiveness of the proposed method. In particular, we achieve state-of-the-art performance (F₁-score 96.08{\%}) on ATIS by only using lexicon features.",
}
@inproceedings{galescu-etal-2018-cogent,
    title = "{C}ogent: A Generic Dialogue System Shell Based on a Collaborative Problem Solving Model",
    author = "Galescu, Lucian  and
      Teng, Choh Man  and
      Allen, James  and
      Perera, Ian",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5048",
    doi = "10.18653/v1/W18-5048",
    pages = "400--409",
    abstract = "The bulk of current research in dialogue systems is focused on fairly simple task models, primarily state-based. Progress on developing dialogue systems for more complex tasks has been limited by the lack generic toolkits to build from. In this paper we report on our development from the ground up of a new dialogue model based on collaborative problem solving. We implemented the model in a dialogue system shell (Cogent) that al-lows developers to plug in problem-solving agents to create dialogue systems in new domains. The Cogent shell has now been used by several independent teams of researchers to develop dialogue systems in different domains, with varied lexicons and interaction style, each with their own problem-solving back-end. We believe this to be the first practical demonstration of the feasibility of a CPS-based dialogue system shell.",
}
@inproceedings{biyani-etal-2018-identifying,
    title = "Identifying Domain Independent Update Intents in Task Based Dialogs",
    author = "Biyani, Prakhar  and
      Akkaya, Cem  and
      Tsioutsiouliklis, Kostas",
    booktitle = "Proceedings of the 19th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-5049",
    doi = "10.18653/v1/W18-5049",
    pages = "410--419",
    abstract = "One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs. Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance. However, in certain cases, DA{'}s fail to capture the actual update intent of the user. In this paper, we describe such cases and propose a new type of semantic class for user intents. This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair. We define five types of UI{'}s, which are independent of the domain of the conversation. We build a multi-class classification model using LSTM{'}s to identify the type of UI in user utterances in the Restaurant and Shopping domains. Experimental results show that our models achieve strong classification performance in terms of F-1 score.",
}
@proceedings{ws-2018-joint-linguistic,
    title = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    editor = "Savary, Agata  and
      Ramisch, Carlos  and
      Hwang, Jena D.  and
      Schneider, Nathan  and
      Andresen, Melanie  and
      Pradhan, Sameer  and
      Petruck, Miriam R. L.",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4900",
}
@inproceedings{levin-2018-annotation,
    title = "Annotation Schemes for Surface Construction Labeling",
    author = "Levin, Lori",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4901",
    pages = "1",
    abstract = "In this talk I will describe the interaction of linguistics and language technologies in Surface Construction Labeling (SCL) from the perspective of corpus annotation tasks such as definiteness, modality, and causality. Linguistically, following Construction Grammar, SCL recognizes that meaning may be carried by morphemes, words, or arbitrary constellations of morpho-lexical elements. SCL is like Shallow Semantic Parsing in that it does not attempt a full compositional analysis of meaning, but rather identifies only the main elements of a semantic frame, where the frames may be invoked by constructions as well as lexical items. Computationally, SCL is different from tasks such as information extraction in that it deals only with meanings that are expressed in a conventional, grammaticalized way and does not address inferred meanings. I review the work of Dunietz (2018) on the labeling of causal frames including causal connectives and cause and effect arguments. I will describe how to design an annotation scheme for SCL, including isolating basic units of form and meaning and building a {``}constructicon{''}. I will conclude with remarks about the nature of universal categories and universal meaning representations in language technologies. This talk describes joint work with Jaime Carbonell, Jesse Dunietz, Nathan Schneider, and Miriam Petruck.",
}
@inproceedings{przepiorkowski-patejuk-2018-lexical,
    title = "From {L}exical {F}unctional {G}rammar to Enhanced {U}niversal {D}ependencies",
    author = "Przepi{\'o}rkowski, Adam  and
      Patejuk, Agnieszka",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4902",
    pages = "2--4",
    abstract = "This is a summary of an invited talk.",
}
@inproceedings{schneider-2018-leaving,
    title = "Leaving no token behind: comprehensive (and delicious) annotation of {MWE}s and supersenses",
    author = "Schneider, Nathan",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4903",
    pages = "5",
    abstract = {I will describe an unorthodox approach to lexical semantic annotation that prioritizes corpus coverage, democratizing analysis of a wide range of expression types. I argue that a lexicon-free lexical semantics{---}defined in terms of units and supersense tags{---}is an appetizing direction for NLP, as it is robust, cost-effective, easily understood, not too language-specific, and can serve as a foundation for richer semantic structure. Linguistic delicacies from the STREUSLE and DiMSUM corpora, which have been multiword- and supersense-annotated, attest to the veritable sm{\"o}rg{\aa}sbord of noncanonical constructions in English, including various flavors of prepositions, MWEs, and other curiosities. Bio: Nathan Schneider is an annotation schemer and computational modeler for natural language. As Assistant Professor of Linguistics and Computer Science at Georgetown University, he looks for synergies between practical language technologies and the scientific study of language. He specializes in broad-coverage semantic analysis: designing linguistic meaning representations, annotating them in corpora, and automating them with statistical natural language processing techniques. A central focus in this research is the nexus between grammar and lexicon as manifested in multiword expressions and adpositions/case markers. He has inhabited UC Berkeley (BA in Computer Science and Linguistics), Carnegie Mellon University (Ph.D. in Language Technologies), and the University of Edinburgh (postdoc). Now a Hoya and leader of NERT, he continues to play with data and algorithms for linguistic meaning.},
}
@inproceedings{bhattasali-etal-2018-processing,
    title = "Processing {MWE}s: Neurocognitive Bases of Verbal {MWE}s and Lexical Cohesiveness within {MWE}s",
    author = "Bhattasali, Shohini  and
      Fabre, Murielle  and
      Hale, John",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4904",
    pages = "6--17",
    abstract = "Multiword expressions have posed a challenge in the past for computational linguistics since they comprise a heterogeneous family of word clusters and are difficult to detect in natural language data. In this paper, we present a fMRI study based on language comprehension to provide neuroimaging evidence for processing MWEs. We investigate whether different MWEs have distinct neural bases, e.g. if verbal MWEs involve separate brain areas from non-verbal MWEs and if MWEs with varying levels of cohesiveness activate dissociable brain regions. Our study contributes neuroimaging evidence illustrating that different MWEs elicit spatially distinct patterns of activation. We also adapt an association measure, usually used to detect MWEs, as a cognitively plausible metric for language processing.",
}
@inproceedings{gron-etal-2018-interplay,
    title = "The Interplay of Form and Meaning in Complex Medical Terms: Evidence from a Clinical Corpus",
    author = {Gr{\"o}n, Leonie  and
      Bertels, Ann  and
      Heylen, Kris},
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4905",
    pages = "18--29",
    abstract = "We conduct a corpus study to investigate the structure of multi-word expressions (MWEs) in the clinical domain. Based on an existing medical taxonomy, we develop an annotation scheme and label a sample of MWEs from a Dutch corpus with semantic and grammatical features. The analysis of the annotated data shows that the formal structure of clinical MWEs correlates with their conceptual properties. The insights gained from this study could inform the design of Natural Language Processing (NLP) systems for clinical writing, but also for other specialized genres.",
}
@inproceedings{danlos-2018-discourse,
    title = "Discourse and Lexicons: Lexemes, {MWE}s, Grammatical Constructions and Compositional Word Combinations to Signal Discourse Relations",
    author = "Danlos, Laurence",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4906",
    pages = "30--40",
    abstract = "Lexicons generally record a list of lexemes or non-compositional multiword expressions. We propose to build lexicons for compositional word combinations, namely {``}secondary discourse connectives{''}. Secondary discourse connectives play the same function as {``}primary discourse connectives{''} but the latter are either lexemes or non-compositional multiword expressions. The paper defines primary and secondary connectives, and explains why it is possible to build a lexicon for the compositional ones and how it could be organized. It also puts forward the utility of such a lexicon in discourse annotation and parsing. Finally, it opens the discussion on the constructions that signal a discourse relation between two spans of text.",
}
@inproceedings{colson-2018-chinese,
    title = "From {C}hinese Word Segmentation to Extraction of Constructions: Two Sides of the Same Algorithmic Coin",
    author = "Colson, Jean-Pierre",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4907",
    pages = "41--50",
    abstract = "This paper presents the results of two experiments carried out within the framework of computational construction grammar. Starting from the constructionist point of view that there are just constructions in language, including lexical ones, we tested the validity of a clustering algorithm that was primarily designed for MWE extraction, the cpr-score (Colson, 2017), on Chinese word segmentation. Our results indicate a striking recall rate of 75 percent without any special adaptation to Chinese or to the lexicon, which confirms that there is some similarity between extracting MWEs and CWS. Our second experiment also suggests that the same methodology might be used for extracting more schematic or abstract constructions, thereby providing evidence for the statistical foundation of construction grammar.",
}
@inproceedings{markantonatou-etal-2018-fixed,
    title = "Fixed Similes: Measuring aspects of the relation between {MWE} idiomatic semantics and syntactic flexibility",
    author = "Markantonatou, Stella  and
      Kouris, Panagiotis  and
      Maistros, Yanis",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4908",
    pages = "51--61",
    abstract = "We shed light on aspects of the relation between the semantics and the syntactic flexibility of multiword expressions by investigating fixed adjective similes (FS), a predicative multiword expression class not studied in this respect before. We find that only a subset of the syntactic structures observed in the data are related with idiomaticity. We identify and measure two aspects of idiomaticity, one of which seems to allow for predictions about FS syntactic flexibility. Our research draws on a resource developed with the semantic and detailed syntactic annotation of web-retrieved Modern Greek material, indicating frequency of use of the individual similes.",
}
@inproceedings{hatty-schulte-im-walde-2018-fine,
    title = "Fine-Grained Termhood Prediction for {G}erman Compound Terms Using Neural Networks",
    author = {H{\"a}tty, Anna  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4909",
    pages = "62--73",
    abstract = "Automatic term identification and investigating the understandability of terms in a specialized domain are often treated as two separate lines of research. We propose a combined approach for this matter, by defining fine-grained classes of termhood and framing a classification task. The classes reflect tiers of a term{'}s association to a domain. The new setup is applied to German closed compounds as term candidates in the domain of cooking. For the prediction of the classes, we compare several neural network architectures and also take salient information about the compounds{'} components into account. We show that applying a similar class distinction to the compounds{'} components and propagating this information within the network improves the compound class prediction results.",
}
@inproceedings{laoudi-etal-2018-towards,
    title = "Towards a Computational Lexicon for {M}oroccan {D}arija: Words, Idioms, and Constructions",
    author = "Laoudi, Jamal  and
      Bonial, Claire  and
      Donatelli, Lucia  and
      Tratz, Stephen  and
      Voss, Clare",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4910",
    pages = "74--85",
    abstract = "In this paper, we explore the challenges of building a computational lexicon for Moroccan Darija (MD), an Arabic dialect spoken by over 32 million people worldwide but which only recently has begun appearing frequently in written form in social media. We raise the question of what belongs in such a lexicon and start by describing our work building traditional word-level lexicon entries with their English translations. We then discuss challenges in translating idiomatic MD text that led to creating multi-word expression lexicon entries whose meanings could not be fully derived from the individual words. Finally, we provide a preliminary exploration of constructions to be considered for inclusion in an MD constructicon by translating examples of English constructions and examining their MD counterparts.",
}
@inproceedings{inurrieta-etal-2018-verbal,
    title = "Verbal Multiword Expressions in {B}asque Corpora",
    author = "I{\~n}urrieta, Uxoa  and
      Aduriz, Itziar  and
      Estarrona, Ainara  and
      Gonzalez-Dios, Itziar  and
      Gurrutxaga, Antton  and
      Urizar, Ruben  and
      Alegria, I{\~n}aki",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4911",
    pages = "86--95",
    abstract = "This paper presents a Basque corpus where Verbal Multiword Expressions (VMWEs) were annotated following universal guidelines. Information on the annotation is given, and some ideas for discussion upon the guidelines are also proposed. The corpus is useful not only for NLP-related research, but also to draw conclusions on Basque phraseology in comparison with other languages.",
}
@inproceedings{donatelli-etal-2018-annotation,
    title = "Annotation of Tense and Aspect Semantics for Sentential {AMR}",
    author = "Donatelli, Lucia  and
      Regan, Michael  and
      Croft, William  and
      Schneider, Nathan",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4912",
    pages = "96--108",
    abstract = "Although English grammar encodes a number of semantic contrasts with tense and aspect marking, these semantics are currently ignored by Abstract Meaning Representation (AMR) annotations. This paper extends sentence-level AMR to include a coarse-grained treatment of tense and aspect semantics. The proposed framework augments the representation of finite predications to include a four-way temporal distinction (event time before, up to, at, or after speech time) and several aspectual distinctions (including static vs. dynamic, habitual vs. episodic, and telic vs. atelic). This will enable AMR to be used for NLP tasks and applications that require sophisticated reasoning about time and event structure.",
}
@inproceedings{westpfahl-gorisch-2018-syntax,
    title = "A Syntax-Based Scheme for the Annotation and Segmentation of {G}erman Spoken Language Interactions",
    author = "Westpfahl, Swantje  and
      Gorisch, Jan",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4913",
    pages = "109--120",
    abstract = "Unlike corpora of written language where segmentation can mainly be derived from orthographic punctuation marks, the basis for segmenting spoken language corpora is not predetermined by the primary data, but rather has to be established by the corpus compilers. This impedes consistent querying and visualization of such data. Several ways of segmenting have been proposed, some of which are based on syntax. In this study, we developed and evaluated annotation and segmentation guidelines in reference to the topological field model for German. We can show that these guidelines are used consistently across annotators. We also investigated the influence of various interactional settings with a rather simple measure, the word-count per segment and unit-type. We observed that the word count and the distribution of each unit type differ in varying interactional settings and that our developed segmentation and annotation guidelines are used consistently across annotators. In conclusion, our syntax-based segmentations reflect interactional properties that are intrinsic to the social interactions that participants are involved in. This can be used for further analysis of social interaction and opens the possibility for automatic segmentation of transcripts.",
}
@inproceedings{kohn-kohn-2018-annotated,
    title = "An Annotated Corpus of Picture Stories Retold by Language Learners",
    author = {K{\"o}hn, Christine  and
      K{\"o}hn, Arne},
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4914",
    pages = "121--132",
    abstract = "Corpora with language learner writing usually consist of essays, which are difficult to annotate reliably and to process automatically due to the high degree of freedom and the nature of learner language. We develop a task which mildly constrains learner utterances to facilitate consistent annotation and reliable automatic processing but at the same time does not prime learners with textual information. In this task, learners retell a comic strip. We present the resulting task-based corpus of stories written by learners of German. We designed the corpus to be able to serve multiple purposes: The corpus was manually annotated, including target hypotheses and syntactic structures. We achieve a very high inter-annotator agreement: κ = 0.765 for the annotation of minimal target hypotheses and κ = 0.507 for the extended target hypotheses. We attribute this to the design of our task and the annotation guidelines, which are based on those for the Falko corpus (Reznicek et al., 2012).",
}
@inproceedings{stowe-etal-2018-developing,
    title = "Developing and Evaluating Annotation Procedures for {T}witter Data during Hazard Events",
    author = "Stowe, Kevin  and
      Palmer, Martha  and
      Anderson, Jennings  and
      Kogan, Marina  and
      Palen, Leysia  and
      Anderson, Kenneth M.  and
      Morss, Rebecca  and
      Demuth, Julie  and
      Lazrus, Heather",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4915",
    pages = "133--143",
    abstract = "When a hazard such as a hurricane threatens, people are forced to make a wide variety of decisions, and the information they receive and produce can influence their own and others{'} actions. As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions. This work aims to improve understanding of natural disasters through social media and provide an annotation scheme to identify themes in user{'}s social media behavior and facilitate efforts in supervised machine learning. To that end, this work has three contributions: (1) the creation of an annotation scheme to consistently identify hazard-related themes in Twitter, (2) an overview of agreement rates and difficulties in identifying annotation categories, and (3) a public release of both the dataset and guidelines developed from this scheme.",
}
@inproceedings{oinam-etal-2018-treebank,
    title = "A Treebank for the Healthcare Domain",
    author = "Oinam, Nganthoibi  and
      Mishra, Diwakar  and
      Patel, Pinal  and
      Choudhary, Narayan  and
      Desai, Hitesh",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4916",
    pages = "144--155",
    abstract = "This paper presents a treebank for the healthcare domain developed at ezDI. The treebank is created from a wide array of clinical health record documents across hospitals. The data has been de-identified and annotated for constituent syntactic structure. The treebank contains a total of 52053 sentences that have been sampled for subdomains as well as linguistic variations. The paper outlines the sampling process followed to ensure a better domain representation in the corpus, the annotation process and challenges, and corpus statistics. The Penn Treebank tagset and guidelines were largely followed, but there were many syntactic contexts that warranted adaptation of the guidelines. The treebank created was used to re-train the Berkeley parser and the Stanford parser. These parsers were also trained with the GENIA treebank for comparative quality assessment. Our treebank yielded great-er accuracy on both parsers. Berkeley parser performed better on our treebank with an average F1 measure of 91 across 5-folds. This was a significant jump from the out-of-the-box F1 score of 70 on Berkeley parser{'}s default grammar.",
}
@inproceedings{cao-etal-2018-rst,
    title = "The {RST} {S}panish-{C}hinese Treebank",
    author = "Cao, Shuyuan  and
      da Cunha, Iria  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4917",
    pages = "156--166",
    abstract = "Discourse analysis is necessary for different tasks of Natural Language Processing (NLP). As two of the most spoken languages in the world, discourse analysis between Spanish and Chinese is important for NLP research. This paper aims to present the first open Spanish-Chinese parallel corpus annotated with discourse information, whose theoretical framework is based on the Rhetorical Structure Theory (RST). We have evaluated and harmonized each annotation part to obtain a high annotated-quality corpus. The corpus is already available to the public.",
}
@inproceedings{peng-zeldes-2018-roads,
    title = "All Roads Lead to {UD}: Converting {S}tanford and {P}enn Parses to {E}nglish {U}niversal {D}ependencies with Multilayer Annotations",
    author = "Peng, Siyao  and
      Zeldes, Amir",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4918",
    pages = "167--177",
    abstract = "We describe and evaluate different approaches to the conversion of gold standard corpus data from Stanford Typed Dependencies (SD) and Penn-style constituent trees to the latest English Universal Dependencies representation (UD 2.2). Our results indicate that pure SD to UD conversion is highly accurate across multiple genres, resulting in around 1.5{\%} errors, but can be improved further to fewer than 0.5{\%} errors given access to annotations beyond the pure syntax tree, such as entity types and coreference resolution, which are necessary for correct generation of several UD relations. We show that constituent-based conversion using CoreNLP (with automatic NER) performs substantially worse in all genres, including when using gold constituent trees, primarily due to underspecification of phrasal grammatical functions.",
}
@inproceedings{haagsma-etal-2018-side,
    title = "The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses",
    author = "Haagsma, Hessel  and
      Nissim, Malvina  and
      Bos, Johan",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4919",
    pages = "178--184",
    abstract = "Disambiguation of potentially idiomatic expressions involves determining the sense of a potentially idiomatic expression in a given context, e.g. determining that make hay in {`}Investment banks made hay while takeovers shone.{'} is used in a figurative sense. This enables automatic interpretation of idiomatic expressions, which is important for applications like machine translation and sentiment analysis. In this work, we present an unsupervised approach for English that makes use of literalisations of idiom senses to improve disambiguation, which is based on the lexical cohesion graph-based method by Sporleder and Li (2009). Experimental results show that, while literalisation carries novel information, its performance falls short of that of state-of-the-art unsupervised methods.",
}
@inproceedings{hakimi-parizi-cook-2018-character,
    title = "Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?",
    author = "Hakimi Parizi, Ali  and
      Cook, Paul",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4920",
    pages = "185--192",
    abstract = "In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs.",
}
@inproceedings{walsh-etal-2018-constructing,
    title = "Constructing an Annotated Corpus of Verbal {MWE}s for {E}nglish",
    author = "Walsh, Abigail  and
      Bonial, Claire  and
      Geeraert, Kristina  and
      McCrae, John P.  and
      Schneider, Nathan  and
      Somers, Clarissa",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4921",
    pages = "193--200",
    abstract = "This paper describes the construction and annotation of a corpus of verbal MWEs for English, as part of the PARSEME Shared Task 1.1 on automatic identification of verbal MWEs. The criteria for corpus selection, the categories of MWEs used, and the training process are discussed, along with the particular issues that led to revisions in edition 1.1 of the annotation guidelines. Finally, an overview of the characteristics of the final annotated corpus is presented, as well as some discussion on inter-annotator agreement.",
}
@inproceedings{matsumoto-etal-2018-cooperating,
    title = "Cooperating Tools for {MWE} Lexicon Management and Corpus Annotation",
    author = "Matsumoto, Yuji  and
      Kato, Akihiko  and
      Shindo, Hiroyuki  and
      Morita, Toshio",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4922",
    pages = "201--206",
    abstract = "We present tools for lexicon and corpus management that offer cooperating functionality in corpus annotation. The former, named Cradle, stores a set of words and expressions where multi-word expressions are defined with their own part-of-speech information and internal syntactic structures. The latter, named ChaKi, manages text corpora with part-of-speech (POS) and syntactic dependency structure annotations. Those two tools cooperate so that the words and multi-word expressions stored in Cradle are directly referred to by ChaKi in conducting corpus annotation, and the words and expressions annotated in ChaKi can be output as a list of lexical entities that are to be stored in Cradle.",
}
@inproceedings{fort-etal-2018-fingers,
    title = "{``}Fingers in the Nose{''}: Evaluating Speakers{'} Identification of Multi-Word Expressions Using a Slightly Gamified Crowdsourcing Platform",
    author = {Fort, Kar{\"e}n  and
      Guillaume, Bruno  and
      Constant, Matthieu  and
      Lef{\`e}bvre, Nicolas  and
      Pilatte, Yann-Alan},
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4923",
    pages = "207--213",
    abstract = "This article presents the results we obtained in crowdsourcing French speakers{'} intuition concerning multi-work expressions (MWEs). We developed a slightly gamified crowdsourcing platform, part of which is designed to test users{'} ability to identify MWEs with no prior training. The participants perform relatively well at the task, with a recall reaching 65{\%} for MWEs that do not behave as function words.",
}
@inproceedings{boukaram-etal-2018-improving,
    title = "Improving Domain Independent Question Parsing with Synthetic Treebanks",
    author = "Boukaram, Halim-Antoine  and
      Habash, Nizar  and
      Ziadee, Micheline  and
      Sakr, Majd",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4924",
    pages = "214--221",
    abstract = "Automatic syntactic parsing for question constructions is a challenging task due to the paucity of training examples in most treebanks. The near absence of question constructions is due to the dominance of the news domain in treebanking efforts. In this paper, we compare two synthetic low-cost question treebank creation methods with a conventional manual high-cost annotation method in the context of three domains (news questions, political talk shows, and chatbots) for Modern Standard Arabic, a language with relatively low resources and rich morphology. Our results show that synthetic methods can be effective at significantly reducing parsing errors for a target domain without having to invest large resources on manual annotation; and the combination of manual and synthetic methods is our best domain-independent performer.",
}
@inproceedings{ramisch-etal-2018-edition,
    title = "Edition 1.1 of the {PARSEME} Shared Task on Automatic Identification of Verbal Multiword Expressions",
    author = {Ramisch, Carlos  and
      Cordeiro, Silvio Ricardo  and
      Savary, Agata  and
      Vincze, Veronika  and
      Barbu Mititelu, Verginica  and
      Bhatia, Archna  and
      Buljan, Maja  and
      Candito, Marie  and
      Gantar, Polona  and
      Giouli, Voula  and
      G{\"u}ng{\"o}r, Tunga  and
      Hawwari, Abdelati  and
      I{\~n}urrieta, Uxoa  and
      Kovalevskait{\.e}, Jolanta  and
      Krek, Simon  and
      Lichte, Timm  and
      Liebeskind, Chaya  and
      Monti, Johanna  and
      Parra Escart{\'\i}n, Carla  and
      QasemiZadeh, Behrang  and
      Ramisch, Renata  and
      Schneider, Nathan  and
      Stoyanova, Ivelina  and
      Vaidya, Ashwini  and
      Walsh, Abigail},
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4925",
    pages = "222--240",
    abstract = "This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year{'}s shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for most languages, corpora for some new languages, and new evaluation settings. Corpora were created for 20 languages, which are also briefly discussed. We report organizational principles behind the shared task and the evaluation metrics employed for ranking. The 17 participating systems, their methods and obtained results are also presented and analysed.",
}
@inproceedings{moreau-etal-2018-crf,
    title = "{CRF}-Seq and {CRF}-{D}ep{T}ree at {PARSEME} Shared Task 2018: Detecting Verbal {MWE}s using Sequential and Dependency-Based Approaches",
    author = "Moreau, Erwan  and
      Alsulaimani, Ashjan  and
      Maldonado, Alfredo  and
      Vogel, Carl",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4926",
    pages = "241--247",
    abstract = "This paper describes two systems for detecting Verbal Multiword Expressions (VMWEs) which both competed in the closed track at the PARSEME VMWE Shared Task 2018. CRF-DepTree-categs implements an approach based on the dependency tree, intended to exploit the syntactic and semantic relations between tokens; CRF-Seq-nocategs implements a robust sequential method which requires only lemmas and morphosyntactic tags. Both systems ranked in the top half of the ranking, the latter ranking second for token-based evaluation. The code for both systems is published under the GNU General Public License version 3.0 and is available at \url{http://github.com/erwanm/adapt-vmwe18}.",
}
@inproceedings{berk-etal-2018-deep,
    title = "Deep-{BGT} at {PARSEME} Shared Task 2018: Bidirectional {LSTM}-{CRF} Model for Verbal Multiword Expression Identification",
    author = {Berk, G{\"o}zde  and
      Erden, Berna  and
      G{\"u}ng{\"o}r, Tunga},
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4927",
    pages = "248--253",
    abstract = "This paper describes the Deep-BGT system that participated to the PARSEME shared task 2018 on automatic identification of verbal multiword expressions (VMWEs). Our system is language-independent and uses the bidirectional Long Short-Term Memory model with a Conditional Random Field layer on top (bidirectional LSTM-CRF). To the best of our knowledge, this paper is the first one that employs the bidirectional LSTM-CRF model for VMWE identification. Furthermore, the gappy 1-level tagging scheme is used for discontiguity and overlaps. Our system was evaluated on 10 languages in the open track and it was ranked the second in terms of the general ranking metric.",
}
@inproceedings{boros-burtica-2018-gbd,
    title = "{GBD}-{NER} at {PARSEME} Shared Task 2018: Multi-Word Expression Detection Using Bidirectional Long-Short-Term Memory Networks and Graph-Based Decoding",
    author = "Boros, Tiberiu  and
      Burtica, Ruxandra",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4928",
    pages = "254--260",
    abstract = "This paper addresses the issue of multi-word expression (MWE) detection by employing a new decoding strategy inspired after graph-based parsing. We show that this architecture achieves state-of-the-art results with minimum feature-engineering, just by relying on lexicalized and morphological attributes. We validate our approach in a multilingual setting, using standard MWE corpora supplied in the PARSEME Shared Task.",
}
@inproceedings{ehren-etal-2018-mumpitz,
    title = "{M}umpitz at {PARSEME} Shared Task 2018: A Bidirectional {LSTM} for the Identification of Verbal Multiword Expressions",
    author = "Ehren, Rafael  and
      Lichte, Timm  and
      Samih, Younes",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4929",
    pages = "261--267",
    abstract = "In this paper, we describe Mumpitz, the system we submitted to the PARSEME Shared task on automatic identification of verbal multiword expressions (VMWEs). Mumpitz consists of a Bidirectional Recurrent Neural Network (BRNN) with Long Short-Term Memory (LSTM) units and a heuristic that leverages the dependency information provided in the PARSEME corpus data to differentiate VMWEs in a sentence. We submitted results for seven languages in the closed track of the task and for one language in the open track. For the open track we used the same system, but with pretrained instead of randomly initialized word embeddings to improve the system performance.",
}
@inproceedings{stodden-etal-2018-trapacc,
    title = "{TRAPACC} and {TRAPACCS} at {PARSEME} Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions",
    author = "Stodden, Regina  and
      QasemiZadeh, Behrang  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4930",
    pages = "268--274",
    abstract = "We describe the TRAPACC system and its variant TRAPACCS that participated in the closed track of the PARSEME Shared Task 2018 on labeling verbal multiword expressions (VMWEs). TRAPACC is a modified arc-standard transition system based on Constant and Nivre{'}s (2016) model of joint syntactic and lexical analysis in which the oracle is approximated using a classifier. For TRAPACC, the classifier consists of a data-independent dimension reduction and a convolutional neural network (CNN) for learning and labelling transitions. TRAPACCS extends TRAPACC by replacing the softmax layer of the CNN with a support vector machine (SVM). We report the results obtained for 19 languages, for 8 of which our system yields the best results compared to other participating systems in the closed-track of the shared task.",
}
@inproceedings{waszczuk-2018-traversal,
    title = "{TRAVERSAL} at {PARSEME} Shared Task 2018: Identification of Verbal Multiword Expressions Using a Discriminative Tree-Structured Model",
    author = "Waszczuk, Jakub",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4931",
    pages = "275--282",
    abstract = "This paper describes a system submitted to the closed track of the PARSEME shared task (edition 1.1) on automatic identification of verbal multiword expressions (VMWEs). The system represents VMWE identification as a labeling task where one of two labels (MWE or not-MWE) must be predicted for each node in the dependency tree based on local context, including adjacent nodes and their labels. The system relies on multiclass logistic regression to determine the globally optimal labeling of a tree. The system ranked 1st in the general cross-lingual ranking of the closed track systems, according to both official evaluation measures: MWE-based F1 and token-based F1.",
}
@inproceedings{pasquer-etal-2018-varide,
    title = "{V}ar{IDE} at {PARSEME} Shared Task 2018: Are Variants Really as Alike as Two Peas in a Pod?",
    author = "Pasquer, Caroline  and
      Ramisch, Carlos  and
      Savary, Agata  and
      Antoine, Jean-Yves",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4932",
    pages = "283--289",
    abstract = "We describe the VarIDE system (standing for Variant IDEntification) which participated in the edition 1.1 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). Our system focuses on the task of VMWE variant identification by using morphosyntactic information in the training data to predict if candidates extracted from the test corpus could be idiomatic, thanks to a naive Bayes classifier. We report results for 19 languages.",
}
@inproceedings{zampieri-etal-2018-veyn,
    title = "{V}eyn at {PARSEME} Shared Task 2018: Recurrent Neural Networks for {VMWE} Identification",
    author = "Zampieri, Nicolas  and
      Scholivet, Manon  and
      Ramisch, Carlos  and
      Favre, Benoit",
    booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4933",
    pages = "290--296",
    abstract = "This paper describes the Veyn system, submitted to the closed track of the PARSEME Shared Task 2018 on automatic identification of verbal multiword expressions (VMWEs). Veyn is based on a sequence tagger using recurrent neural networks. We represent VMWEs using a variant of the begin-inside-outside encoding scheme combined with the VMWE category tag. In addition to the system description, we present development experiments to determine the best tagging scheme. Veyn is freely available, covers 19 languages, and was ranked ninth (MWE-based) and eight (Token-based) among 13 submissions, considering macro-averaged F1 across languages.",
}
@proceedings{ws-2018-modeling-polysynthetic,
    title = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    editor = "Klavans, Judith L.",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4800",
}
@inproceedings{klavans-2018-computational,
    title = "Computational Challenges for Polysynthetic Languages",
    author = "Klavans, Judith L.",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4801",
    pages = "1--11",
    abstract = "Given advances in computational linguistic analysis of complex languages using Machine Learning as well as standard Finite State Transducers, coupled with recent efforts in language revitalization, the time was right to organize a first workshop to bring together experts in language technology and linguists on the one hand with language practitioners and revitalization experts on the other. This one-day meeting provides a promising forum to discuss new research on polysynthetic languages in combination with the needs of linguistic communities where such languages are written and spoken.",
}
@inproceedings{moeller-etal-2018-neural,
    title = "A Neural Morphological Analyzer for {A}rapaho Verbs Learned from a Finite State Transducer",
    author = "Moeller, Sarah  and
      Kazeminejad, Ghazaleh  and
      Cowell, Andrew  and
      Hulden, Mans",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4802",
    pages = "12--20",
    abstract = "We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68{\%} (unambiguous verbs) and 92.90{\%} (all verbs).",
}
@inproceedings{littell-2018-finite,
    title = "Finite-state morphology for Kwak{'}wala: A phonological approach",
    author = "Littell, Patrick",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4803",
    pages = "21--30",
    abstract = "This paper presents the phonological layer of a Kwak{'}wala finite-state morphological transducer, using the phonological hypotheses of Lincoln and Rath (1986) and the lenient composition operation of Karttunen (1998) to mediate the complicated relationship between underlying and surface forms. The resulting system decomposes the wide variety of surface forms in such a way that the morphological layer can be specified using unique and largely concatenative morphemes.",
}
@inproceedings{andriyanets-tyers-2018-prototype,
    title = "A prototype finite-state morphological analyser for {C}hukchi",
    author = "Andriyanets, Vasilisa  and
      Tyers, Francis",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4804",
    pages = "31--40",
    abstract = "In this article we describe the application of finite-state transducers to the morphological and phonological systems of Chukchi, a polysynthetic language spoken in the north of the Russian Federation. The language exhibits progressive and regressive vowel harmony, productive incorporation and extensive circumfixing. To implement the analyser we use the well-known Helsinki Finite-State Toolkit (HFST). The resulting model covers the majority of the morphological and phonological processes. A brief evaluation carried out on publically-available corpora shows that the coverage of the transducer is between and 53{\%} and 76{\%}. An error evaluation of 100 tokens randomly selected from the corpus, which were not covered by the analyser shows that most of the morphological processes are covered and that the majority of errors are caused by a limited stem lexicon.",
}
@inproceedings{lessard-etal-2018-natural,
    title = "Natural Language Generation for Polysynthetic Languages: Language Teaching and Learning Software for {K}anyen{'}k{\'e}ha ({M}ohawk)",
    author = "Lessard, Greg  and
      Brinklow, Nathan  and
      Levison, Michael",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4805",
    pages = "41--52",
    abstract = "Kanyen{'}k{\'e}ha (in English, Mohawk) is an Iroquoian language spoken primarily in Eastern Canada (Ontario, Qu{\'e}bec). Classified as endangered, it has only a small number of speakers and very few younger native speakers. Consequently, teachers and courses, teaching materials and software are urgently needed. In the case of software, the polysynthetic nature of Kanyen{'}k{\'e}ha means that the number of possible combinations grows exponentially and soon surpasses attempts to capture variant forms by hand. It is in this context that we describe an attempt to produce language teaching materials based on a generative approach. A natural language generation environment (ivi/Vinci) embedded in a web environment (VinciLingua) makes it possible to produce, by rule, variant forms of indefinite complexity. These may be used as models to explore, or as materials to which learners respond. Generated materials may take the form of written text, oral utterances, or images; responses may be typed on a keyboard, gestural (using a mouse) or, to a limited extent, oral. The software also provides complex orthographic, morphological and syntactic analysis of learner productions. We describe the trajectory of development of materials for a suite of four courses on Kanyen{'}k{\'e}ha, the first of which will be taught in the fall of 2018.",
}
@inproceedings{kazantseva-etal-2018-kawennon,
    title = "{K}awenn{\'o}n:nis: the Wordmaker for {K}anyen{'}k{\'e}ha",
    author = "Kazantseva, Anna  and
      Maracle, Owennatekha Brian  and
      Maracle, Ronkwe{'}tiy{\'o}hstha Josiah  and
      Pine, Aidan",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4806",
    pages = "53--64",
    abstract = "In this paper we describe preliminary work on Kawenn{\'o}n:nis, a verb conjugator for Kanyen{'}k{\'e}ha (Ohsweken dialect). The project is the result of a collaboration between Onkwawenna Kentyohkwa Kanyen{'}k{\'e}ha immersion school and the Canadian National Research Council{'}s Indigenous Language Technology lab. The purpose of Kawenn{\'o}n:nis is to build on the educational successes of the Onkwawenna Kentyohkwa school and develop a tool that assists students in learning how to conjugate verbs in Kanyen{'}k{\'e}ha; a skill that is essential to mastering the language. Kawenn{\'o}n:nis is implemented with both web and mobile front-ends that communicate with an application programming interface that in turn communicates with a symbolic language model implemented as a finite state transducer. Eventually, it will serve as a foundation for several other applications for both Kanyen{'}k{\'e}ha and other Iroquoian languages.",
}
@inproceedings{micher-2018-using,
    title = "Using the {N}unavut Hansard Data for Experiments in Morphological Analysis and Machine Translation",
    author = "Micher, Jeffrey",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4807",
    pages = "65--72",
    abstract = "Inuktitut is a polysynthetic language spoken in Northern Canada and is one of the official languages of the Canadian territory of Nunavut. As such, the Nunavut Legislature publishes all of its proceedings in parallel English and Inuktitut. Several parallel English-Inuktitut corpora from these proceedings have been created from these data and are publically available. The corpus used for current experiments is described. Morphological processing of one of these corpora was carried out and details about the processing are provided. Then, the processed corpus was used in morphological analysis and machine translation (MT) experiments. The morphological analysis experiments aimed to improve the coverage of morphological processing of the corpus, and compare an additional experimental condition to previously published results. The machine translation experiments made use of the additional morphologically analyzed word types in a statistical machine translation system designed to translate to and from Inuktitut morphemes. Results are reported and next steps are defined.",
}
@inproceedings{mager-etal-2018-lost,
    title = "Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages",
    author = "Mager, Manuel  and
      Mager, Elisabeth  and
      Medina-Urrea, Alfonso  and
      Meza Ruiz, Ivan Vladimir  and
      Kann, Katharina",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4808",
    pages = "73--83",
    abstract = "Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available. Thus, translation performance is far from the state of the art for high-resource and more intensively studied language pairs. To shed light on the phenomena which hamper automatic translation to and from polysynthetic languages, we study translations from three low-resource, polysynthetic languages (Nahuatl, Wixarika and Yorem Nokki) into Spanish and vice versa. Doing so, we find that in a morpheme-to-morpheme alignment an important amount of information contained in polysynthetic morphemes has no Spanish counterpart, and its translation is often omitted. We further conduct a qualitative analysis and, thus, identify morpheme types that are commonly hard to align or ignored in the translation process.",
}
@inproceedings{moeller-hulden-2018-automatic,
    title = "Automatic Glossing in a Low-Resource Setting for Language Documentation",
    author = "Moeller, Sarah  and
      Hulden, Mans",
    booktitle = "Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4809",
    pages = "84--93",
    abstract = "Morphological analysis of morphologically rich and low-resource languages is important to both descriptive linguistics and natural language processing. Field documentary efforts usually procure analyzed data in cooperation with native speakers who are capable of providing some level of linguistic information. Manually annotating such data is very expensive and the traditional process is arguably too slow in the face of language endangerment and loss. We report on a case study of learning to automatically gloss a Nakh-Daghestanian language, Lezgi, from a very small amount of seed data. We compare a conditional random field based sequence labeler and a neural encoder-decoder model and show that a nearly 0.9 F1-score on labeled accuracy of morphemes can be achieved with 3,000 words of transcribed oral text. Errors are mostly limited to morphemes with high allomorphy. These results are potentially useful for developing rapid annotation and fieldwork tools to support documentation of morphologically rich, endangered languages.",
}
@proceedings{ws-2018-joint-acl,
    title = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    editor = "Bunt, Harry",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4700",
}
@inproceedings{manuvirakurike-etal-2018-dialedit,
    title = "{D}ial{E}dit: Annotations for Spoken Conversational Image Editing",
    author = "Manuvirakurike, Ramesh  and
      Brixey, Jacqueline  and
      Bui, Trung  and
      Chang, Walter  and
      Artstein, Ron  and
      Georgila, Kallirroi",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4701",
    pages = "1--9",
}
@inproceedings{araki-etal-2018-interoperable,
    title = "Interoperable Annotation of Events and Event Relations across Domains",
    author = "Araki, Jun  and
      Mulaffer, Lamana  and
      Pandian, Arun  and
      Yamakawa, Yukari  and
      Oflazer, Kemal  and
      Mitamura, Teruko",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4702",
    pages = "10--20",
}
@inproceedings{bunt-etal-2018-downward,
    title = "Downward Compatible Revision of Dialogue Annotation",
    author = {Bunt, Harry  and
      Gilmartin, Emer  and
      Keizer, Simon  and
      Pelachaud, Catherine  and
      Petukhova, Volha  and
      Pr{\'e}vot, Laurent  and
      Theune, Mari{\"e}t},
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4703",
    pages = "21--34",
}
@inproceedings{lee-etal-2018-revision,
    title = "The Revision of {ISO}-Space,Focused on the Movement Link",
    author = "Lee, Kiyong  and
      Pustejovsky, James  and
      Bunt, Harry",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4704",
    pages = "35--44",
}
@inproceedings{gilmartin-vogel-2018-chat,
    title = "Chat,Chunk and Topic in Casual Conversation",
    author = "Gilmartin, Emer  and
      Vogel, Carl",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4705",
    pages = "45--52",
}
@inproceedings{zymla-2018-annotation,
    title = "Annotation of the Syntax/Semantics interface as a Bridge between Deep Linguistic Parsing and {T}ime{ML}",
    author = "Zymla, Mark-Matthias",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4706",
    pages = "53--59",
}
@inproceedings{manuvirakurike-etal-2018-dialogue,
    title = "A Dialogue Annotation Scheme for Weight Management Chat using the Trans-Theoretical Model of Health Behavior Change",
    author = "Manuvirakurike, Ramesh  and
      Bharawadj, Sumanth  and
      Georgila, Kallirroi",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4707",
    pages = "60--68",
}
@inproceedings{hao-etal-2018-annotating,
    title = "Annotating Measurable Quantitative Informationin Language: for an {ISO} Standard",
    author = "Hao, Tianyong  and
      Wang, Haotai  and
      Cao, Xinyu  and
      Lee, Kiyong",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4708",
    pages = "69--75",
}
@inproceedings{woods-fernando-2018-improving,
    title = "Improving String Processing for Temporal Relations",
    author = "Woods, David  and
      Fernando, Tim",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4709",
    pages = "76--86",
}
@inproceedings{prasad-etal-2018-discourse,
    title = "Discourse Annotation in the {PDTB}: The Next Generation",
    author = "Prasad, Rashmi  and
      Webber, Bonnie  and
      Lee, Alan",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4710",
    pages = "87--97",
}
@inproceedings{karkada-etal-2018-towards,
    title = "Towards Understanding End-of-trip Instructions in a Taxi Ride Scenario",
    author = "Karkada, Deepthi  and
      Manuvirakurike, Ramesh  and
      Georgila, Kallirroi",
    booktitle = "Proceedings 14th Joint {ACL} - {ISO} Workshop on Interoperable Semantic Annotation",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4711",
    pages = "98--107",
}
@proceedings{ws-2018-linguistic-complexity,
    title = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    editor = "Becerra-Bonache, Leonor  and
      Jim{\'e}nez-L{\'o}pez, M. Dolores  and
      Mart{\'\i}n-Vide, Carlos  and
      Torrens-Urrutia, Adri{\`a}",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4600",
}
@inproceedings{becerra-bonache-etal-2018-gold,
    title = "A Gold Standard to Measure Relative Linguistic Complexity with a Grounded Language Learning Model",
    author = "Becerra-Bonache, Leonor  and
      Christiansen, Henning  and
      Jim{\'e}nez-L{\'o}pez, M. Dolores",
    booktitle = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4601",
    pages = "1--9",
    abstract = "This paper focuses on linguistic complexity from a relative perspective. It presents a grounded language learning system that can be used to study linguistic complexity from a developmental point of view and introduces a tool for generating a gold standard in order to evaluate the performance of the learning system. In general, researchers agree that it is more feasible to approach complexity from an objective or theory-oriented viewpoint than from a subjective or user-related point of view. Studies that have adopted a relative complexity approach have showed some preferences for L2 learners. In this paper, we try to show that computational models of the process of language acquisition may be an important tool to consider children and the process of first language acquisition as suitable candidates for evaluating the complexity of languages.",
}
@inproceedings{branco-2018-computational,
    title = "Computational Complexity of Natural Languages: A Reasoned Overview",
    author = "Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4602",
    pages = "10--19",
    abstract = "There has been an upsurge of research interest in natural language complexity. As this interest will benefit from being informed by established contributions in this area, this paper presents a reasoned overview of central results concerning the computational complexity of natural language parsing. This overview also seeks to help to understand why, contrary to recent and widespread assumptions, it is by no means sufficient that an agent handles sequences of items under a pattern $a^n b^n$ or under a pattern $a^n b^m c^n d^m$ to ascertain ipso facto that this is the result of at least an underlying context-free grammar or an underlying context-sensitive grammar, respectively. In addition, it seeks to help to understand why it is also not sufficient that an agent handles sequences of items under a pattern $a^n b^n$ for it to be deemed as having a cognitive capacity of higher computational complexity.",
}
@inproceedings{chersoni-etal-2018-modeling,
    title = "Modeling Violations of Selectional Restrictions with Distributional Semantics",
    author = "Chersoni, Emmanuele  and
      Torrens Urrutia, Adri{\`a}  and
      Blache, Philippe  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4603",
    pages = "20--29",
    abstract = "Distributional Semantic Models have been successfully used for modeling selectional preferences in a variety of scenarios, since distributional similarity naturally provides an estimate of the degree to which an argument satisfies the requirement of a given predicate. However, we argue that the performance of such models on rare verb-argument combinations has received relatively little attention: it is not clear whether they are able to distinguish the combinations that are simply atypical, or implausible, from the semantically anomalous ones, and in particular, they have never been tested on the task of modeling their differences in processing complexity. In this paper, we compare two different models of thematic fit by testing their ability of identifying violations of selectional restrictions in two datasets from the experimental studies.",
}
@inproceedings{gutierrez-vasques-mijangos-2018-comparing,
    title = "Comparing morphological complexity of {S}panish, {O}tomi and {N}ahuatl",
    author = "Gutierrez-Vasques, Ximena  and
      Mijangos, Victor",
    booktitle = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4604",
    pages = "30--37",
    abstract = "We use two small parallel corpora for comparing the morphological complexity of Spanish, Otomi and Nahuatl. These are languages that belong to different linguistic families, the latter are low-resourced. We take into account two quantitative criteria, on one hand the distribution of types over tokens in a corpus, on the other, perplexity and entropy as indicators of word structure predictability. We show that a language can be complex in terms of how many different morphological word forms can produce, however, it may be less complex in terms of predictability of its internal structure of words.",
}
@inproceedings{jain-etal-2018-uniform,
    title = "{U}niform {I}nformation {D}ensity Effects on Syntactic Choice in {H}indi",
    author = "Jain, Ayush  and
      Singh, Vishal  and
      Ranjan, Sidharth  and
      Rajkumar, Rajakrishnan  and
      Agarwal, Sumeet",
    booktitle = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4605",
    pages = "38--48",
    abstract = "According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 2010), speakers tend to distribute information density across the signal uniformly while producing language. The prior works cited above studied syntactic reduction in language production at particular choice points in a sentence. In contrast, we use a variant of the above UID hypothesis in order to investigate the extent to which word order choices in Hindi are influenced by the drive to minimize the variance of information across entire sentences. To this end, we propose multiple lexical and syntactic measures (at both word and constituent levels) to capture the uniform spread of information across a sentence. Subsequently, we incorporate these measures in machine learning models aimed to distinguish between a naturally occurring corpus sentence and its grammatical variants (expressing the same idea). Our results indicate that our UID measures are not a significant factor in predicting the corpus sentence in the presence of lexical surprisal, a competing control predictor. Finally, in the light of other recent works, we conclude with a discussion of reasons for UID not being suitable for a theory of word order.",
}
@inproceedings{pilan-volodina-2018-investigating,
    title = "Investigating the importance of linguistic complexity features across different datasets related to language learning",
    author = "Pil{\'a}n, Ildik{\'o}  and
      Volodina, Elena",
    booktitle = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4606",
    pages = "49--58",
    abstract = "We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consideration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pinpoint fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions.",
}
@inproceedings{torrens-urrutia-2018-approach,
    title = "An Approach to Measuring Complexity with a Fuzzy Grammar {\&} Degrees of Grammaticality",
    author = "Torrens Urrutia, Adri{\`a}",
    booktitle = "Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New-Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4607",
    pages = "59--67",
    abstract = "This paper presents an approach to evaluate complexity of a given natural language input by means of a Fuzzy Grammar with some fuzzy logic formulations. Usually, the approaches in linguistics has described a natural language grammar by means of discrete terms. However, a grammar can be explained in terms of degrees by following the concepts of linguistic gradience {\&} fuzziness. Understanding a grammar as a fuzzy or gradient object allows us to establish degrees of grammaticality for every linguistic input. This shall be meaningful for linguistic complexity considering that the less grammatical an input is the more complex its processing will be. In this regard, the degree of complexity of a linguistic input (which is a linguistic representation of a natural language expression) depends on the chosen grammar. The bases of the fuzzy grammar are shown here. Some of these are described by Fuzzy Type Theory. The linguistic inputs are characterized by constraints through a Property Grammar.",
}
@proceedings{ws-2018-joint,
    title = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    editor = "Alex, Beatrice  and
      Degaetano-Ortlieb, Stefania  and
      Feldman, Anna  and
      Kazantseva, Anna  and
      Reiter, Nils  and
      Szpakowicz, Stan",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4500",
}
@inproceedings{orlikowski-etal-2018-learning,
    title = "Learning Diachronic Analogies to Analyze Concept Change",
    author = "Orlikowski, Matthias  and
      Hartung, Matthias  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4501",
    pages = "1--11",
    abstract = "We propose to study the evolution of concepts by learning to complete diachronic analogies between lists of terms which relate to the same concept at different points in time. We present a number of models based on operations on word embedddings that correspond to different assumptions about the characteristics of diachronic analogies and change in concept vocabularies. These are tested in a quantitative evaluation for nine different concepts on a corpus of Dutch newspapers from the 1950s and 1980s. We show that a model which treats the concept terms as analogous and learns weights to compensate for diachronic changes (weighted linear combination) is able to more accurately predict the missing term than a learned transformation and two baselines for most of the evaluated concepts. We also find that all models tend to be coherent in relation to the represented concept, but less discriminative in regard to other concepts. Additionally, we evaluate the effect of aligning the time-specific embedding spaces using orthogonal Procrustes, finding varying effects on performance, depending on the model, concept and evaluation metric. For the weighted linear combination, however, results improve with alignment in a majority of cases. All related code is released publicly.",
}
@inproceedings{feder-etal-2018-linked,
    title = "A Linked {C}optic Dictionary Online",
    author = "Feder, Frank  and
      Kupreyev, Maxim  and
      Manning, Emma  and
      Schroeder, Caroline T.  and
      Zeldes, Amir",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4502",
    pages = "12--21",
    abstract = "We describe a new project publishing a freely available online dictionary for Coptic. The dictionary encompasses comprehensive cross-referencing mechanisms, including linking entries to an online scanned edition of Crum{'}s Coptic Dictionary, internal cross-references and etymological information, translated searchable definitions in English, French and German, and linked corpus data which provides frequencies and corpus look-up for headwords and multiword expressions. Headwords are available for linking in external projects using a REST API. We describe the challenges in encoding our dictionary using TEI XML and implementing linking mechanisms to construct a Web interface querying frequency information, which draw on NLP tools to recognize inflected forms in context. We evaluate our dictionary{'}s coverage using digital corpora of Coptic available online.",
}
@inproceedings{degaetano-ortlieb-teich-2018-using,
    title = "Using relative entropy for detection and analysis of periods of diachronic linguistic change",
    author = "Degaetano-Ortlieb, Stefania  and
      Teich, Elke",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4503",
    pages = "22--33",
    abstract = "We present a data-driven approach to detect periods of linguistic change and the lexical and grammatical features contributing to change. We focus on the development of scientific English in the late modern period. Our approach is based on relative entropy (Kullback-Leibler Divergence) comparing temporally adjacent periods and sliding over the time line from past to present. Using a diachronic corpus of scientific publications of the Royal Society of London, we show how periods of change reflect the interplay between lexis and grammar, where periods of lexical expansion are typically followed by periods of grammatical consolidation resulting in a balance between expressivity and communicative efficiency. Our method is generic and can be applied to other data sets, languages and time ranges.",
}
@inproceedings{van-cranenburgh-2018-cliche,
    title = "Cliche Expressions in Literary and Genre Novels",
    author = "van Cranenburgh, Andreas",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4504",
    pages = "34--43",
    abstract = "Should writers {``}avoid clich{\'e}s like the plague{''}? Clich{\'e}s are said to be a prominent characteristic of {``}low brow{''} literature, and conversely, a negative marker of {``}high brow{''} literature. Clich{\'e}s may concern the storyline, the characters, or the style of writing. We focus on clich{\'e} expressions, ready-made stock phrases which can be taken as a sign of uncreative writing. We present a corpus study in which we examine to what extent clich{\'e} expressions can be attested in a corpus of various kinds of contemporary fiction, based on a large, curated lexicon of clich{\'e} expressions. The results show to what extent the negative view on clich{\'e}s is supported by data: we find a significant negative correlation of -0.48 between clich{\'e} density and literary ratings of texts. We also investigate interactions with genre and characterize the language of clich{\'e}s with several basic textual features. Code used for this paper is available at \url{https://github.com/andreasvc/litcliches/}",
}
@inproceedings{baumann-etal-2018-analysis,
    title = "Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry",
    author = "Baumann, Timo  and
      Hussein, Hussein  and
      Meyer-Sickendiek, Burkhard",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4505",
    pages = "44--49",
    abstract = "We show how to classify the phrasing of readout poems with the help of machine learning algorithms that use manually engineered features or automatically learn representations. We investigate modern and postmodern poems from the webpage lyrikline, and focus on two exemplary rhythmical patterns in order to detect the rhythmic phrasing: The Parlando and the Variable Foot. These rhythmical patterns have been compared by using two important theoretical works: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both, we focus on a combination of four different features: The grouping structure, the metrical structure, the time-span-variation, and the prolongation in order to detect the rhythmic phrasing in the two rhythmical types. We use manually engineered features based on text-speech alignment and parsing for classification. We also train a neural network to learn its own representation based on text, speech and audio during pauses. The neural network outperforms manual feature engineering, reaching an f-measure of 0.85.",
}
@inproceedings{arnold-tilton-2018-cross,
    title = "Cross-Discourse and Multilingual Exploration of Textual Corpora with the {D}ual{N}eighbors Algorithm",
    author = "Arnold, Taylor  and
      Tilton, Lauren",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4506",
    pages = "50--59",
    abstract = "Word choice is dependent on the cultural context of writers and their subjects. Different words are used to describe similar actions, objects, and features based on factors such as class, race, gender, geography and political affinity. Exploratory techniques based on locating and counting words may, therefore, lead to conclusions that reinforce culturally inflected boundaries. We offer a new method, the DualNeighbors algorithm, for linking thematically similar documents both within and across discursive and linguistic barriers to reveal cross-cultural connections. Qualitative and quantitative evaluations of this technique are shown as applied to two cultural datasets of interest to researchers across the humanities and social sciences. An open-source implementation of the DualNeighbors algorithm is provided to assist in its application.",
}
@inproceedings{underwood-2018-historical,
    title = "The Historical Significance of Textual Distances",
    author = "Underwood, Ted",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4507",
    pages = "60--69",
    abstract = "Measuring similarity is a basic task in information retrieval, and now often a building-block for more complex arguments about cultural change. But do measures of textual similarity and distance really correspond to evidence about cultural proximity and differentiation? To explore that question empirically, this paper compares textual and social measures of the similarities between genres of English-language fiction. Existing measures of textual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that strive to anchor textual measurement in a social context.",
}
@inproceedings{do-dinh-etal-2018-one,
    title = "One Size Fits All? A simple {LSTM} for non-literal token and construction-level classification",
    author = "Do Dinh, Erik-L{\^a}n  and
      Eger, Steffen  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4508",
    pages = "70--80",
    abstract = "In this paper, we tackle four different tasks of non-literal language classification: token and construction level metaphor detection, classification of idiomatic use of infinitive-verb compounds, and classification of non-literal particle verbs. One of the tasks operates on the token level, while the three other tasks classify constructions such as {``}hot topic{''} or {``}stehen lassen{''} ({``}to allow sth. to stand{''} vs. {``}to abandon so.{''}). The two metaphor detection tasks are in English, while the two non-literal language detection tasks are in German. We propose a simple context-encoding LSTM model and show that it outperforms the state-of-the-art on two tasks. Additionally, we experiment with different embeddings for the token level metaphor detection task and find that 1) their performance varies according to the genre, and 2) word2vec embeddings perform best on 3 out of 4 genres, despite being one of the simplest tested model. In summary, we present a large-scale analysis of a neural model for non-literal language classification (i) at different granularities, (ii) in different languages, (iii) over different non-literal language phenomena.",
}
@inproceedings{haider-kuhn-2018-supervised,
    title = "Supervised Rhyme Detection with {S}iamese Recurrent Networks",
    author = "Haider, Thomas  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4509",
    pages = "81--86",
    abstract = "We present the first supervised approach to rhyme detection with Siamese Recurrent Networks (SRN) that offer near perfect performance (97{\%} accuracy) with a single model on rhyme pairs for German, English and French, allowing future large scale analyses. SRNs learn a similarity metric on variable length character sequences that can be used as judgement on the distance of imperfect rhyme pairs and for binary classification. For training, we construct a diachronically balanced rhyme goldstandard of New High German (NHG) poetry. For further testing, we sample a second collection of NHG poetry and set of contemporary Hip-Hop lyrics, annotated for rhyme and assonance. We train several high-performing SRN models and evaluate them qualitatively on selected sonnetts.",
}
@inproceedings{hamalainen-etal-2018-normalizing,
    title = "Normalizing Early {E}nglish Letters to Present-day {E}nglish Spelling",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      S{\"a}ily, Tanja  and
      Rueter, Jack  and
      Tiedemann, J{\"o}rg  and
      M{\"a}kel{\"a}, Eetu},
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4510",
    pages = "87--96",
    abstract = "This paper presents multiple methods for normalizing the most deviant and infrequent historical spellings in a corpus consisting of personal correspondence from the 15th to the 19th century. The methods include machine translation (neural and statistical), edit distance and rule-based FST. Different normalization methods are compared and evaluated. All of the methods have their own strengths in word normalization. This calls for finding ways of combining the results from these methods to leverage their individual strengths.",
}
@inproceedings{lam-etal-2018-power,
    title = "Power Networks: A Novel Neural Architecture to Predict Power Relations",
    author = "Lam, Michelle  and
      Xu, Catherina  and
      Prabhakaran, Vinodkumar",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4511",
    pages = "97--102",
    abstract = "Can language analysis reveal the underlying social power relations that exist between participants of an interaction? Prior work within NLP has shown promise in this area, but the performance of automatically predicting power relations using NLP analysis of social interactions remains wanting. In this paper, we present a novel neural architecture that captures manifestations of power within individual emails which are then aggregated in an order-preserving way in order to infer the direction of power between pairs of participants in an email thread. We obtain an accuracy of 80.4{\%}, a 10.1{\%} improvement over state-of-the-art methods, in this task. We further apply our model to the task of predicting power relations between individuals based on the entire set of messages exchanged between them; here also, our model significantly outperforms the 70.0{\%} accuracy using prior state-of-the-art techniques, obtaining an accuracy of 83.0{\%}.",
}
@inproceedings{makarov-2018-automated,
    title = "Automated Acquisition of Patterns for Coding Political Event Data: Two Case Studies",
    author = "Makarov, Peter",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4512",
    pages = "103--112",
    abstract = "We present a simple approach to the generation and labeling of extraction patterns for coding political event data, an important task in computational social science. We use weak supervision to identify pattern candidates and learn distributed representations for them. Given seed extraction patterns from existing pattern dictionaries, we use label propagation to label pattern candidates. We present two case studies. i) We derive patterns of acceptable quality for a number of international relations {\&} conflicts categories using pattern candidates of O{'}Connor et al (2013). ii) We derive patterns for coding protest events that outperform an established set of Tabari / Petrarch hand-crafted patterns.",
}
@inproceedings{moritz-etal-2018-method,
    title = "A Method for Human-Interpretable Paraphrasticality Prediction",
    author = {Moritz, Maria  and
      Hellrich, Johannes  and
      B{\"u}chel, Sven},
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4513",
    pages = "113--118",
    abstract = "The detection of reused text is important in a wide range of disciplines. However, even as research in the field of plagiarism detection is constantly improving, heavily modified or paraphrased text is still challenging for current methodologies. For historical texts, these problems are even more severe, since text sources were often subject to stronger and more frequent modifications. Despite the need for tools to automate text criticism, e.g., tracing modifications in historical text, algorithmic support is still limited. While current techniques can tell if and how frequently a text has been modified, very little work has been done on determining the degree and kind of paraphrastic modification{---}despite such information being of substantial interest to scholars. We present a human-interpretable, feature-based method to measure paraphrastic modification. Evaluating our technique on three data sets, we find that our approach performs competitive to text similarity scores borrowed from machine translation evaluation, being much harder to interpret.",
}
@inproceedings{pilan-volodina-2018-exploring,
    title = "Exploring word embeddings and phonological similarity for the unsupervised correction of language learner errors",
    author = "Pil{\'a}n, Ildik{\'o}  and
      Volodina, Elena",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4514",
    pages = "119--128",
    abstract = "The presence of misspellings and other errors or non-standard word forms poses a considerable challenge for NLP systems. Although several supervised approaches have been proposed previously to normalize these, annotated training data is scarce for many languages. We investigate, therefore, an unsupervised method where correction candidates for Swedish language learners{'} errors are retrieved from word embeddings. Furthermore, we compare the usefulness of combining cosine similarity with orthographic and phonological similarity based on a neural grapheme-to-phoneme conversion system we train for this purpose. Although combinations of similarity measures have been explored for finding error correction candidates, it remains unclear how these measures relate to each other and how much they contribute individually to identifying the correct alternative. We experiment with different combinations of these and find that integrating phonological information is especially useful when the majority of learner errors are related to misspellings, but less so when errors are of a variety of types including, e.g. grammatical errors.",
}
@inproceedings{roesiger-etal-2018-towards,
    title = "Towards Coreference for Literary Text: Analyzing Domain-Specific Phenomena",
    author = "Roesiger, Ina  and
      Schulz, Sarah  and
      Reiter, Nils",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4515",
    pages = "129--138",
    abstract = "Coreference resolution is the task of grouping together references to the same discourse entity. Resolving coreference in literary texts could benefit a number of Digital Humanities (DH) tasks, such as analyzing the depiction of characters and/or their relations. Domain-dependent training data has shown to improve coreference resolution for many domains, e.g. the biomedical domain, as its properties differ significantly from news text or dialogue, on which automatic systems are typically trained. Literary texts could also benefit from corpora annotated with coreference. We therefore analyze the specific properties of coreference-related phenomena on a number of texts and give directions for the adaptation of annotation guidelines. As some of the adaptations have profound impact, we also present a new annotation tool for coreference, with a focus on enabling annotation of long texts with many discourse entities.",
}
@inproceedings{schmidt-burghardt-2018-evaluation,
    title = "An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of {G}otthold {E}phraim {L}essing",
    author = "Schmidt, Thomas  and
      Burghardt, Manuel",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4516",
    pages = "139--149",
    abstract = "We present results from a project in the research area of sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing{'}s plays manually annotated with sentiment information. We explore the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants or stop words elimination to explore the influence of these parameters and find best practices for our domain of application. The best performing configuration accomplishes an accuracy of 70{\%}. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research.",
}
@inproceedings{touileb-etal-2018-automatic,
    title = "Automatic identification of unknown names with specific roles",
    author = "Touileb, Samia  and
      Pedersen, Truls  and
      Sj{\o}vaag, Helle",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4517",
    pages = "150--158",
    abstract = "Automatically identifying persons in a particular role within a large corpus can be a difficult task, especially if you don{'}t know who you are actually looking for. Resources compiling names of persons can be available, but no exhaustive lists exist. However, such lists usually contain known names that are {``}visible{''} in the national public sphere, and tend to ignore the marginal and international ones. In this article we propose a method for automatically generating suggestions of names found in a corpus of Norwegian news articles, and which {``}naturally{''} belong to a given initial list of members, and that were not known (compiled in a list) beforehand. The approach is based, in part, on the assumption that surface level syntactic features reveal parts of the underlying semantic content and can help uncover the structure of the language.",
}
@inproceedings{opitz-etal-2018-induction,
    title = "Induction of a Large-Scale Knowledge Graph from the {R}egesta {I}mperii",
    author = "Opitz, Juri  and
      Born, Leo  and
      Nastase, Vivi",
    booktitle = "Proceedings of the Second Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4518",
    pages = "159--168",
    abstract = "We induce and visualize a Knowledge Graph over the Regesta Imperii (RI), an important large-scale resource for medieval history research. The RI comprise more than 150,000 digitized abstracts of medieval charters issued by the Roman-German kings and popes distributed over many European locations and a time span of more than 700 years. Our goal is to provide a resource for historians to visualize and query the RI, possibly aiding medieval history research. The resulting medieval graph and visualization tools are shared publicly.",
}
@proceedings{ws-2018-trolling,
    title = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    editor = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Zampieri, Marcos  and
      Malmasi, Shervin",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4400",
}
@inproceedings{kumar-etal-2018-benchmarking,
    title = "Benchmarking Aggression Identification in Social Media",
    author = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4401",
    pages = "1--11",
    abstract = "In this paper, we present the report and findings of the Shared Task on Aggression Identification organised as part of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COLING 2018. The task was to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and Non-aggressive texts. For this task, the participants were provided with a dataset of 15,000 aggression-annotated Facebook Posts and Comments each in Hindi (in both Roman and Devanagari script) and English for training and validation. For testing, two different sets - one from Facebook and another from a different social media - were provided. A total of 130 teams registered to participate in the task, 30 teams submitted their test runs, and finally 20 teams also sent their system description paper which are included in the TRAC workshop proceedings. The best system obtained a weighted F-score of 0.64 for both Hindi and English on the Facebook test sets, while the best scores on the surprise set were 0.60 and 0.50 for English and Hindi respectively. The results presented in this report depict how challenging the task is. The positive response from the community and the great levels of participation in the first edition of this shared task also highlights the interest in this topic.",
}
@inproceedings{safi-samghabadi-etal-2018-ritual,
    title = "{R}i{TUAL}-{UH} at {TRAC} 2018 Shared Task: Aggression Identification",
    author = "Safi Samghabadi, Niloofar  and
      Mave, Deepthi  and
      Kar, Sudipta  and
      Solorio, Thamar",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4402",
    pages = "12--18",
    abstract = "This paper presents our system for {``}TRAC 2018 Shared Task on Aggression Identification{''}. Our best systems for the English dataset use a combination of lexical and semantic features. However, for Hindi data using only lexical features gave us the best results. We obtained weighted F1-measures of 0.5921 for the English Facebook task (ranked 12th), 0.5663 for the English Social Media task (ranked 6th), 0.6292 for the Hindi Facebook task (ranked 1st), and 0.4853 for the Hindi Social Media task (ranked 2nd).",
}
@inproceedings{ramiandrisoa-mothe-2018-irit,
    title = "{IRIT} at {TRAC} 2018",
    author = "Ramiandrisoa, Faneva  and
      Mothe, Josiane",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4403",
    pages = "19--27",
    abstract = "This paper describes the participation of the IRIT team to the TRAC 2018 shared task on Aggression Identification and more precisely to the shared task in English language. The three following methods have been used: a) a combination of machine learning techniques that relies on a set of features and document/text vectorization, b) Convolutional Neural Network (CNN) and c) a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Best results were obtained when using the method (a) on the English test data from Facebook which ranked our method sixteenth out of thirty teams, and the method (c) on the English test data from other social media, where we obtained the fifteenth rank out of thirty.",
}
@inproceedings{raiyani-etal-2018-fully,
    title = "Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over {F}acebook and {T}witter",
    author = "Raiyani, Kashyap  and
      Gon{\c{c}}alves, Teresa  and
      Quaresma, Paulo  and
      Nogueira, Vitor Beires",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4404",
    pages = "28--41",
    abstract = "Paper presents the different methodologies developed {\&} tested and discusses their results, with the goal of identifying the best possible method for the aggression identification problem in social media.",
}
@inproceedings{huang-etal-2018-cyberbullying,
    title = "Cyberbullying Intervention Based on Convolutional Neural Networks",
    author = "Huang, Qianjia  and
      Inkpen, Diana  and
      Zhang, Jianhong  and
      Van Bruwaene, David",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4405",
    pages = "42--51",
    abstract = "This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contributions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools. Second, we present a mechanism that provides individuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text, but also provides a system-level flagging/intervention in a situation related to cyberbullying.",
}
@inproceedings{nikhil-etal-2018-lstms,
    title = "{LSTM}s with Attention for Aggression Detection",
    author = "Nikhil, Nishant  and
      Pahwa, Ramit  and
      Nirala, Mehul Kumar  and
      Khilnani, Rohan",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4406",
    pages = "52--57",
    abstract = "In this paper, we describe the system submitted for the shared task on Aggression Identification in Facebook posts and comments by the team Nishnik. Previous works demonstrate that LSTMs have achieved remarkable performance in natural language processing tasks. We deploy an LSTM model with an attention unit over it. Our system ranks 6th and 4th in the Hindi subtask for Facebook comments and subtask for generalized social media data respectively. And it ranks 17th and 10th in the corresponding English subtasks.",
}
@inproceedings{kumar-etal-2018-trac,
    title = "{TRAC}-1 Shared Task on Aggression Identification: {IIT}({ISM})@{COLING}{'}18",
    author = "Kumar, Ritesh  and
      Bhanodai, Guggilla  and
      Pamula, Rajendra  and
      Chennuru, Maheshwar Reddy",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4407",
    pages = "58--65",
    abstract = "This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards TRAC-1 Shared Task on Aggression Identification in Social Media for COLING 2018. In this paper we label aggression identification into three categories: Overtly Aggressive, Covertly Aggressive and Non-aggressive. We train a model to differentiate between these categories and then analyze the results in order to better understand how we can distinguish between them. We participated in two different tasks named as English (Facebook) task and English (Social Media) task. For English (Facebook) task System 05 was our best run (i.e. 0.3572) above the Random Baseline (i.e. 0.3535). For English (Social Media) task our system 02 got the value (i.e. 0.1960) below the Random Bseline (i.e. 0.3477). For all of our runs we used Long Short-Term Memory model. Overall, our performance is not satisfactory. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.",
}
@inproceedings{roy-etal-2018-ensemble,
    title = "An Ensemble Approach for Aggression Identification in {E}nglish and {H}indi Text",
    author = "Roy, Arjun  and
      Kapil, Prashant  and
      Basak, Kingshuk  and
      Ekbal, Asif",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4408",
    pages = "66--73",
    abstract = "This paper describes our system submitted in the shared task at COLING 2018 TRAC-1: Aggression Identification. The objective of this task was to predict online aggression spread through online textual post or comment. The dataset was released in two languages, English and Hindi. We submitted a single system for Hindi and a single system for English. Both the systems are based on an ensemble architecture where the individual models are based on Convoluted Neural Network and Support Vector Machine. Evaluation shows promising results for both the languages.The total submission for English was 30 and Hindi was 15. Our system on English facebook and social media obtained F1 score of 0.5151 and 0.5099 respectively where Hindi facebook and social media obtained F1 score of 0.5599 and 0.3790 respectively.",
}
@inproceedings{galery-etal-2018-aggression,
    title = "Aggression Identification and Multi Lingual Word Embeddings",
    author = "Galery, Thiago  and
      Charitos, Efstathios  and
      Tian, Ye",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4409",
    pages = "74--79",
    abstract = "The system presented here took part in the 2018 Trolling, Aggression and Cyberbullying shared task (Forest and Trees team) and uses a Gated Recurrent Neural Network architecture (Cho et al., 2014) in an attempt to assess whether combining pre-trained English and Hindi fastText (Mikolov et al., 2018) word embeddings as a representation of the sequence input would improve classification performance. The motivation for this comes from the fact that the shared task data for English contained many Hindi tokens and therefore some users might be doing code-switching: the alternation between two or more languages in communication. To test this hypothesis, we also aligned Hindi and English vectors using pre-computed SVD matrices that pulls representations from different languages into a common space (Smith et al., 2017). Two conditions were tested: (i) one with standard pre-trained fastText word embeddings where each Hindi word is treated as an OOV token, and (ii) another where word embeddings for Hindi and English are loaded in a common vector space, so Hindi tokens can be assigned a meaningful representation. We submitted the second (i.e., multilingual) system and obtained the scores of 0.531 weighted F1 for the EN-FB dataset and 0.438 weighted F1 for the EN-TW dataset.",
}
@inproceedings{maitra-sarkhel-2018-k,
    title = "A K-Competitive Autoencoder for Aggression Detection in Social Media Text",
    author = "Maitra, Promita  and
      Sarkhel, Ritesh",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4410",
    pages = "80--89",
    abstract = "We present an approach to detect aggression from social media text in this work. A winner-takes-all autoencoder, called Emoti-KATE is proposed for this purpose. Using a log-normalized, weighted word-count vector at input dimensions, the autoencoder simulates a competition between neurons in the hidden layer to minimize the reconstruction loss between the input and final output layers. We have evaluated the performance of our system on the datasets provided by the organizers of TRAC workshop, 2018. Using the encoding generated by Emoti-KATE, a 3-way classification is performed for every social media text in the dataset. Each data point is classified as {`}Overtly Aggressive{'}, {`}Covertly Aggressive{'} or {`}Non-aggressive{'}. Results show that our (team name: PMRS) proposed method is able to achieve promising results on some of these datasets. In this paper, we have described the effects of introducing an winner-takes-all autoencoder for the task of aggression detection, reported its performance on four different datasets, analyzed some of its limitations and how to improve its performance in future works.",
}
@inproceedings{aroyehun-gelbukh-2018-aggression,
    title = "Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling",
    author = "Aroyehun, Segun Taofeek  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4411",
    pages = "90--97",
    abstract = "With the advent of the read-write web which facilitates social interactions in online spaces, the rise of anti-social behaviour in online spaces has attracted the attention of researchers. In this paper, we address the challenge of automatically identifying aggression in social media posts. Our team, saroyehun, participated in the English track of the Aggression Detection in Social Media Shared Task. On this task, we investigate the efficacy of deep neural network models of varying complexity. Our results reveal that deep neural network models require more data points to do better than an NBSVM linear baseline based on character n-grams. Our improved deep neural network models were trained on augmented data and pseudo labeled examples. Our LSTM classifier receives a weighted macro-F1 score of 0.6425 to rank first overall on the Facebook subtask of the shared task. On the social media sub-task, our CNN-LSTM model records a weighted macro-F1 score of 0.5920 to place third overall.",
}
@inproceedings{srivastava-etal-2018-identifying,
    title = "Identifying Aggression and Toxicity in Comments using Capsule Network",
    author = "Srivastava, Saurabh  and
      Khurana, Prerna  and
      Tewari, Vartika",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4412",
    pages = "98--105",
    abstract = "Aggression and related activities like trolling, hate speech etc. involve toxic comments in various forms. These are common scenarios in today{'}s time and websites react by shutting down their comment sections. To tackle this, an algorithmic solution is preferred to human moderation which is slow and expensive. In this paper, we propose a single model capsule network with focal loss to achieve this task which is suitable for production environment. Our model achieves competitive results over other strong baseline methods, which show its effectiveness and that focal loss exhibits significant improvement in such cases where class imbalance is a regular issue. Additionally, we show that the problem of extensive data preprocessing, data augmentation can be tackled by capsule networks implicitly. We achieve an overall ROC AUC of 98.46 on Kaggle-toxic comment dataset and show that it beats other architectures by a good margin. As comments tend to be written in more than one language, and transliteration is a common problem, we further show that our model handles this effectively by applying our model on TRAC shared task dataset which contains comments in code-mixed Hindi-English.",
}
@inproceedings{sharma-etal-2018-degree,
    title = "Degree based Classification of Harmful Speech using {T}witter Data",
    author = "Sharma, Sanjana  and
      Agrawal, Saksham  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4413",
    pages = "106--112",
    abstract = "Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence. This serves as a preliminary work to lay down foundation on defining different classes of harmful speech and subsequent work will be done in making it{'}s automatic detection more robust and efficient.",
}
@inproceedings{orasan-2018-aggressive,
    title = "Aggressive Language Identification Using Word Embeddings and Sentiment Features",
    author = "Or{\u{a}}san, Constantin",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4414",
    pages = "113--119",
    abstract = "This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods.",
}
@inproceedings{madisetty-sankar-desarkar-2018-aggression,
    title = "Aggression Detection in Social Media using Deep Neural Networks",
    author = "Madisetty, Sreekanth  and
      Sankar Desarkar, Maunendra",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4415",
    pages = "120--127",
    abstract = "With the rise of user-generated content in social media coupled with almost non-existent moderation in many such systems, aggressive contents have been observed to rise in such forums. In this paper, we work on the problem of aggression detection in social media. Aggression can sometimes be expressed directly or overtly or it can be hidden or covert in the text. On the other hand, most of the content in social media is non-aggressive in nature. We propose an ensemble based system to classify an input post to into one of three classes, namely, Overtly Aggressive, Covertly Aggressive, and Non-aggressive. Our approach uses three deep learning methods, namely, Convolutional Neural Networks (CNN) with five layers (input, convolution, pooling, hidden, and output), Long Short Term Memory networks (LSTM), and Bi-directional Long Short Term Memory networks (Bi-LSTM). A majority voting based ensemble method is used to combine these classifiers (CNN, LSTM, and Bi-LSTM). We trained our method on Facebook comments dataset and tested on Facebook comments (in-domain) and other social media posts (cross-domain). Our system achieves the F1-score (weighted) of 0.604 for Facebook posts and 0.508 for social media posts.",
}
@inproceedings{fortuna-etal-2018-merging,
    title = "Merging Datasets for Aggressive Text Identification",
    author = "Fortuna, Paula  and
      Ferreira, Jos{\'e}  and
      Pires, Luiz  and
      Routar, Guilherme  and
      Nunes, S{\'e}rgio",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4416",
    pages = "128--139",
    abstract = "This paper presents the approach of the team {``}groutar{''} to the shared task on Aggression Identification, considering the test sets in English, both from Facebook and general Social Media. This experiment aims to test the effect of merging new datasets in the performance of classification models. We followed a standard machine learning approach with training, validation, and testing phases, and considered features such as part-of-speech, frequencies of insults, punctuation, sentiment, and capitalization. In terms of algorithms, we experimented with Boosted Logistic Regression, Multi-Layer Perceptron, Parallel Random Forest and eXtreme Gradient Boosting. One question appearing was how to merge datasets using different classification systems (e.g. aggression vs. toxicity). Other issue concerns the possibility to generalize models and apply them to data from different social networks. Regarding these, we merged two datasets, and the results showed that training with similar data is an advantage in the classification of social networks data. However, adding data from different platforms, allowed slightly better results in both Facebook and Social Media, indicating that more generalized models can be an advantage.",
}
@inproceedings{arroyo-fernandez-etal-2018-cyberbullying,
    title = "Cyberbullying Detection Task: the {EBSI}-{LIA}-{UNAM} System ({ELU}) at {COLING}{'}18 {TRAC}-1",
    author = "Arroyo-Fern{\'a}ndez, Ignacio  and
      Forest, Dominic  and
      Torres-Moreno, Juan-Manuel  and
      Carrasco-Ruiz, Mauricio  and
      Legeleux, Thomas  and
      Joannette, Karen",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4417",
    pages = "140--149",
    abstract = "The phenomenon of cyberbullying has growing in worrying proportions with the development of social networks. Forums and chat rooms are spaces where serious damage can now be done to others, while the tools for avoiding on-line spills are still limited. This study aims to assess the ability that both classical and state-of-the-art vector space modeling methods provide to well known learning machines to identify aggression levels in social network cyberbullying (i.e. social network posts manually labeled as Overtly Aggressive, Covertly Aggressive and Non-aggressive). To this end, an exploratory stage was performed first in order to find relevant settings to test, i.e. by using training and development samples, we trained multiple learning machines using multiple vector space modeling methods and discarded the less informative configurations. Finally, we selected the two best settings and their voting combination to form three competing systems. These systems were submitted to the competition of the TRACK-1 task of the Workshop on Trolling, Aggression and Cyberbullying. Our voting combination system resulted second place in predicting Aggression levels on a test set of untagged social network posts.",
}
@inproceedings{risch-krestel-2018-aggression,
    title = "Aggression Identification Using Deep Learning and Data Augmentation",
    author = "Risch, Julian  and
      Krestel, Ralf",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4418",
    pages = "150--158",
    abstract = "Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and {---} at an extreme level {---} is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the costly manual moderation of online discussions. Further, the automation allows unprecedented analyses of discussion datasets that contain millions of posts. This system description paper presents our submission to the First Shared Task on Aggression Identification. We propose to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000. Thereby, we introduce linguistic variety into the dataset. As a consequence of the larger amount of training data, we are able to train a special deep neural net, which generalizes especially well to unseen data. To further boost the performance, we combine this neural net with three logistic regression classifiers trained on character and word n-grams, and hand-picked syntactic features. This ensemble is more robust than the individual single models. Our team named {``}Julian{''} achieves an F1-score of 60{\%} on both English datasets, 63{\%} on the Hindi Facebook dataset, and 38{\%} on the Hindi Twitter dataset.",
}
@inproceedings{husseini-orabi-etal-2018-cyber,
    title = "Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text",
    author = "Husseini Orabi, Ahmed  and
      Husseini Orabi, Mahmoud  and
      Huang, Qianjia  and
      Inkpen, Diana  and
      Van Bruwaene, David",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4419",
    pages = "159--165",
    abstract = "In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL). We use CSC-MTL to improve the performance of cyber-aggression detection from text. Our approach provides a robust shared feature representation for multi-task learning by detecting contrasts and similarities among polarity and neutral classes. We participated in the cyber-aggression shared task under the team name uOttawa. We report 59.74{\%} F1 performance for the Facebook test set and 56.9{\%} for the Twitter test set, for detecting aggression from text.",
}
@inproceedings{risch-krestel-2018-delete,
    title = "Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom",
    author = "Risch, Julian  and
      Krestel, Ralf",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4420",
    pages = "166--176",
    abstract = "Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics. Today, moderators ensure respectful and informative discussions by deleting not only insults, defamation, and hate speech, but also unverifiable facts. This process has to be transparent and comprehensive in order to keep the community engaged. Further, news providers have to make sure to not give the impression of censorship or dissemination of fake news. Yet manual moderation is very expensive and becomes more and more unfeasible with the increasing amount of comments. Hence, we propose a semi-automatic, holistic approach, which includes comment features but also their context, such as information about users and articles. For evaluation, we present experiments on a novel corpus of 3 million news comments annotated by a team of professional moderators.",
}
@inproceedings{tommasel-etal-2018-textual,
    title = "Textual Aggression Detection through Deep Learning",
    author = "Tommasel, Antonela  and
      Rodriguez, Juan Manuel  and
      Godoy, Daniela",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4421",
    pages = "177--187",
    abstract = "Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the widespread of social media networks, bullying, once limited to particular places, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour that aims at harming other individuals, and involves rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the dangerous consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), it is crucial to understand how cyberbullying occurs to prevent it from escalating. Given the massive information overload on the Web, there is an imperious need to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos{'}s approach for detecting aggressive content in multiple social media sites. The approach is based on combining Support Vector Machines and Recurrent Neural Network models for analysing a wide-range of character, word, word embeddings, sentiment and irony features. Results confirmed the difficulty of the task (particularly for detecting covert aggressions), showing the limitations of traditionally used features.",
}
@inproceedings{golem-etal-2018-combining,
    title = "Combining Shallow and Deep Learning for Aggressive Text Detection",
    author = "Golem, Viktor  and
      Karan, Mladen  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4422",
    pages = "188--198",
    abstract = "We describe the participation of team TakeLab in the aggression detection shared task at the TRAC1 workshop for English. Aggression manifests in a variety of ways. Unlike some forms of aggression that are impossible to prevent in day-to-day life, aggressive speech abounding on social networks could in principle be prevented or at least reduced by simply disabling users that post aggressively worded messages. The first step in achieving this is to detect such messages. The task, however, is far from being trivial, as what is considered as aggressive speech can be quite subjective, and the task is further complicated by the noisy nature of user-generated text on social networks. Our system learns to distinguish between open aggression, covert aggression, and non-aggression in social media texts. We tried different machine learning approaches, including traditional (shallow) machine learning models, deep learning models, and a combination of both. We achieved respectable results, ranking 4th and 8th out of 31 submissions on the Facebook and Twitter test sets, respectively.",
}
@inproceedings{modha-etal-2018-filtering,
    title = "Filtering Aggression from the Multilingual Social Media Feed",
    author = "Modha, Sandip  and
      Majumder, Prasenjit  and
      Mandl, Thomas",
    booktitle = "Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying ({TRAC}-2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4423",
    pages = "199--207",
    abstract = "This paper describes the participation of team DA-LD-Hildesheim from the Information Retrieval Lab(IRLAB) at DA-IICT Gandhinagar, India in collaboration with the University of Hildesheim, Germany and LDRP-ITR, Gandhinagar, India in a shared task on Aggression Identification workshop in COLING 2018. The objective of the shared task is to identify the level of aggression from the User-Generated contents within Social media written in English, Devnagiri Hindi and Romanized Hindi. Aggression levels are categorized into three predefined classes namely: {`}Overtly Aggressive{`}, {`}Covertly Aggressive{`} and {`}Non-aggressive{`}. The participating teams are required to develop a multi-class classifier which classifies User-generated content into these pre-defined classes. Instead of relying on a bag-of-words model, we have used pre-trained vectors for word embedding. We have performed experiments with standard machine learning classifiers. In addition, we have developed various deep learning models for the multi-class classification problem. Using the validation data, we found that validation accuracy of our deep learning models outperform all standard machine learning classifiers and voting based ensemble techniques and results on test data support these findings. We have also found that hyper-parameters of the deep neural network are the keys to improve the results.",
}
@proceedings{ws-2018-events,
    title = "Proceedings of the Workshop Events and Stories in the News 2018",
    editor = "Caselli, Tommaso  and
      Miller, Ben  and
      van Erp, Marieke  and
      Vossen, Piek  and
      Palmer, Martha  and
      Hovy, Eduard  and
      Mitamura, Teruko  and
      Caswell, David  and
      Brown, Susan W.  and
      Bonial, Claire",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4300",
}
@inproceedings{pustejovsky-krishnaswamy-2018-every,
    title = "Every Object Tells a Story",
    author = "Pustejovsky, James  and
      Krishnaswamy, Nikhil",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4301",
    pages = "1--6",
    abstract = "Most work within the computational event modeling community has tended to focus on the interpretation and ordering of events that are associated with verbs and event nominals in linguistic expressions. What is often overlooked in the construction of a global interpretation of a narrative is the role contributed by the objects participating in these structures, and the latent events and activities conventionally associated with them. Recently, the analysis of visual images has also enriched the scope of how events can be identified, by anchoring both linguistic expressions and ontological labels to segments, subregions, and properties of images. By semantically grounding event descriptions in their visualization, the importance of object-based attributes becomes more apparent. In this position paper, we look at the narrative structure of objects: that is, how objects reference events through their intrinsic attributes, such as affordances, purposes, and functions. We argue that, not only do objects encode conventionalized events, but that when they are composed within specific habitats, the ensemble can be viewed as modeling coherent event sequences, thereby enriching the global interpretation of the evolving narrative being constructed.",
}
@inproceedings{croft-etal-2018-rich,
    title = "A Rich Annotation Scheme for Mental Events",
    author = "Croft, William  and
      Pe{\v{s}}kov{\'a}, Pavl{\'\i}na  and
      Regan, Michael  and
      Lee, Sook-kyung",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4302",
    pages = "7--17",
    abstract = "We present a rich annotation scheme for the structure of mental events. Mental events are those in which the verb describes a mental state or process, usually oriented towards an external situation. While physical events have been described in detail and there are numerous studies of their semantic analysis and annotation, mental events are less thoroughly studied. The annotation scheme proposed here is based on decompositional analyses in the semantic and typological linguistic literature. The scheme was applied to the news corpus from the 2016 Events workshop, and error analysis of the test annotation provides suggestions for refinement and clarification of the annotation scheme.",
}
@inproceedings{miller-2018-cross,
    title = "Cross-Document Narrative Alignment of Environmental News: A Position Paper on the Challenge of Using Event Chains to Proxy Narrative Features",
    author = "Miller, Ben",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4303",
    pages = "18--24",
    abstract = "Cross-document event chain co-referencing in corpora of news articles would achieve increased precision and generalizability from a method that consistently recognizes narrative, discursive, and phenomenological features such as tense, mood, tone, canonicity and breach, person, hermeneutic composability, speed, and time. Current models that capture primarily linguistic data such as entities, times, and relations or causal relationships may only incidentally capture narrative framing features of events. That limits efforts at narrative and event chain segmentation, among other predicate tasks for narrative search and narrative-based reasoning. It further limits research on audience engagement with journalism about complex subjects. This position paper explores the above proposition with respect to narrative theory and ongoing research on segmenting event chains into narrative units. Our own work in progress approaches this task using event segmentation, word embeddings, and variable length pattern matching in a corpus of 2,000 articles describing environmental events. Our position is that narrative features may or may not be implicitly captured by current methods explicitly focused on events as linguistic phenomena, that they are not explicitly captured, and that further research is required.",
}
@inproceedings{yarlott-etal-2018-identifying,
    title = "Identifying the Discourse Function of News Article Paragraphs",
    author = "Yarlott, W. Victor  and
      Cornelio, Cristina  and
      Gao, Tian  and
      Finlayson, Mark",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4304",
    pages = "25--33",
    abstract = "Discourse structure is a key aspect of all forms of text, providing valuable information both to humans and machines. We applied the hierarchical theory of news discourse developed by van Dijk to examine how paragraphs operate as units of discourse structure within news articles{---}what we refer to here as document-level discourse. This document-level discourse provides a characterization of the content of each paragraph that describes its relation to the events presented in the article (such as main events, backgrounds, and consequences) as well as to other components of the story (such as commentary and evaluation). The purpose of a news discourse section is of great utility to story understanding as it affects both the importance and temporal order of items introduced in the text{---}therefore, if we know the news discourse purpose for different sections, we should be able to better rank events for their importance and better construct timelines. We test two hypotheses: first, that people can reliably annotate news articles with van Dijk{'}s theory; second, that we can reliably predict these labels using machine learning. We show that people have a high degree of agreement with each other when annotating the theory (F1 {\textgreater} 0.8, Cohen{'}s kappa {\textgreater} 0.6), demonstrating that it can be both learned and reliably applied by human annotators. Additionally, we demonstrate first steps toward machine learning of the theory, achieving a performance of F1 = 0.54, which is 65{\%} of human performance. Moreover, we have generated a gold-standard, adjudicated corpus of 50 documents for document-level discourse annotation based on the ACE Phase 2 corpus.",
}
@inproceedings{yuan-yu-2018-evaluation,
    title = "An Evaluation of Information Extraction Tools for Identifying Health Claims in News Headlines",
    author = "Yuan, Shi  and
      Yu, Bei",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4305",
    pages = "34--43",
    abstract = "This study evaluates the performance of four information extraction tools (extractors) on identifying health claims in health news headlines. A health claim is defined as a triplet: IV (what is being manipulated), DV (what is being measured) and their relation. Tools that can identify health claims provide the foundation for evaluating the accuracy of these claims against authoritative resources. The evaluation result shows that 26{\%} headlines do not in-clude health claims, and all extractors face difficulty separating them from the rest. For those with health claims, OPENIE-5.0 performed the best with F-measure at 0.6 level for ex-tracting {``}IV-relation-DV{''}. However, the characteristic linguistic structures in health news headlines, such as incomplete sentences and non-verb relations, pose particular challenge to existing tools.",
}
@inproceedings{caselli-inel-2018-crowdsourcing,
    title = "Crowdsourcing {S}tory{L}ines: Harnessing the Crowd for Causal Relation Annotation",
    author = "Caselli, Tommaso  and
      Inel, Oana",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4306",
    pages = "44--54",
    abstract = "This paper describes a crowdsourcing experiment on the annotation of plot-like structures in English news articles. CrowdThruth methodology and metrics have been applied to select valid annotations from the crowd. We further run an in-depth analysis of the annotated data by comparing them with available expert data. Our results show a valuable use of crowdsourcing annotations for such complex semantic tasks, and suggest a new annotation approach which combine crowd and experts.",
}
@inproceedings{reale-etal-2018-spot,
    title = "Can You Spot the Semantic Predicate in this Video?",
    author = "Reale, Christopher  and
      Bonial, Claire  and
      Kwon, Heesung  and
      Voss, Clare",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4307",
    pages = "55--60",
    abstract = "We propose a method to improve human activity recognition in video by leveraging semantic information about the target activities from an expert-defined linguistic resource, VerbNet. Our hypothesis is that activities that share similar event semantics, as defined by the semantic predicates of VerbNet, will be more likely to share some visual components. We use a deep convolutional neural network approach as a baseline and incorporate linguistic information from VerbNet through multi-task learning. We present results of experiments showing the added information has negligible impact on recognition performance. We discuss how this may be because the lexical semantic information defined by VerbNet is generally not visually salient given the video processing approach used here, and how we may handle this in future approaches.",
}
@inproceedings{dai-etal-2018-fine,
    title = "Fine-grained Structure-based News Genre Categorization",
    author = "Dai, Zeyu  and
      Taneja, Himanshu  and
      Huang, Ruihong",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4308",
    pages = "61--67",
    abstract = "Journalists usually organize and present the contents of a news article following a well-defined structure. In this work, we propose a new task to categorize news articles based on their content presentation structures, which is beneficial for various NLP applications. We first define a small set of news elements considering their functions (e.g., \textit{introducing the main story or event, catching the reader{'}s attention} and \textit{providing details}) in a news story and their writing style (\textit{narrative} or \textit{expository}), and then formally define four commonly used news article structures based on their selections and organizations of news elements. We create an annotated dataset for structure-based news genre identification, and finally, we build a predictive model to assess the feasibility of this classification task using structure indicative features.",
}
@inproceedings{piskorski-etal-2018-training,
    title = "On Training Classifiers for Linking Event Templates",
    author = "Piskorski, Jakub  and
      {\v{S}}ari{\'c}, Fredi  and
      Zavarella, Vanni  and
      Atkinson, Martin",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4309",
    pages = "68--78",
    abstract = "The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7{\%} (92.9{\%}) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6{\%} (GOLD) and 97{\%} (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events.",
}
@inproceedings{sorgente-etal-2018-hei,
    title = "{HEI}: Hunter Events Interface A platform based on services for the detection and reasoning about events",
    author = "Sorgente, Antonio  and
      Calabrese, Antonio  and
      Coda, Gianluca  and
      Vanacore, Paolo  and
      Mele, Francesco",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4310",
    pages = "79--88",
    abstract = "In this paper we present the definition and implementation of the Hunter Events Interface (HEI) System. The HEI System is a system for events annotation and temporal reasoning in Natural Language Texts and media, mainly oriented to texts of historical and cultural contents available on the Web. In this work we assume that events are defined through various components: actions, participants, locations, and occurrence intervals. The HEI system, through independent services, locates (annotates) the various components, and successively associates them to a specific event. The objective of this work is to build a system integrating services for the identification of events, the discovery of their connections, and the evaluation of their consistency. We believe this interface is useful to develop applications that use the notion of story, to integrate data of digital cultural archives, and to build systems of fruition in the same field. The HEI system has been partially developed within the TrasTest project",
}
@proceedings{ws-2018-natural-language,
    title = "Proceedings of the First Workshop on Natural Language Processing for {I}nternet Freedom",
    editor = "Brew, Chris  and
      Feldman, Anna  and
      Leberknight, Chris",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4200",
}
@inproceedings{knockel-etal-2018-effect,
    title = "The effect of information controls on developers in {C}hina: An analysis of censorship in {C}hinese open source projects",
    author = "Knockel, Jeffrey  and
      Crete-Nishihata, Masashi  and
      Ruan, Lotus",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for {I}nternet Freedom",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4201",
    pages = "1--11",
    abstract = "Censorship of Internet content in China is understood to operate through a system of intermediary liability whereby service providers are liable for the content on their platforms. Previous work studying censorship has found huge variability in the implementation of censorship across different products even within the same industry segment. In this work we explore the extent to which these censorship features are present in the open source projects of individual developers in China by collecting their blacklists and comparing their similarity. We collect files from a popular online code repository, extract lists of strings, and then classify whether each is a Chinese blacklist. Overall, we found over 1,000 Chinese blacklists comprising over 200,000 unique keywords, representing the largest dataset of Chinese blacklisted keywords to date. We found very little keyword overlap between lists, raising questions as to their origins, as the lists seem too large to have been individually curated, yet the lack of overlap suggests that they have no common source.",
}
@inproceedings{ng-etal-2018-linguistic,
    title = "Linguistic Characteristics of Censorable Language on {S}ina{W}eibo",
    author = "Ng, Kei Yin  and
      Feldman, Anna  and
      Peng, Jing  and
      Leberknight, Chris",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for {I}nternet Freedom",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4202",
    pages = "12--22",
    abstract = "This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability.",
}
@inproceedings{ji-knight-2018-creative,
    title = "Creative Language Encoding under Censorship",
    author = "Ji, Heng  and
      Knight, Kevin",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for {I}nternet Freedom",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4203",
    pages = "23--33",
    abstract = "People often create obfuscated language for online communication to avoid Internet censorship, share sensitive information, express strong sentiment or emotion, plan for secret actions, trade illegal products, or simply hold interesting conversations. In this position paper we systematically categorize human-created obfuscated language on various levels, investigate their basic mechanisms, give an overview on automated techniques needed to simulate human encoding. These encoders have potential to frustrate and evade, co-evolve with dynamic human or automated decoders, and produce interesting and adoptable code words. We also summarize remaining challenges for future research on the interaction between Natural Language Processing (NLP) and encryption, and leveraging NLP techniques for encoding and decoding.",
}
@proceedings{ws-2018-international-language,
    title = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    editor = "Sinha, Manjira  and
      Dasgupta, Tirthankar",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4100",
}
@inproceedings{bernardy-etal-2018-compositional,
    title = "A Compositional {B}ayesian Semantics for Natural Language",
    author = "Bernardy, Jean-Philippe  and
      Blanck, Rasmus  and
      Chatzikyriakidis, Stergios  and
      Lappin, Shalom",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4101",
    pages = "1--10",
    abstract = "We propose a compositional Bayesian semantics that interprets declarative sentences in a natural language by assigning them probability conditions. These are conditional probabilities that estimate the likelihood that a competent speaker would endorse an assertion, given certain hypotheses. Our semantics is implemented in a functional programming language. It estimates the marginal probability of a sentence through Markov Chain Monte Carlo (MCMC) sampling of objects in vector space models satisfying specified hypotheses. We apply our semantics to examples with several predicates and generalised quantifiers, including higher-order quantifiers. It captures the vagueness of predication (both gradable and non-gradable), without positing a precise boundary for classifier application. We present a basic account of semantic learning based on our semantic system. We compare our proposal to other current theories of probabilistic semantics, and we show that it offers several important advantages over these accounts.",
}
@inproceedings{wolohan-etal-2018-detecting,
    title = "Detecting Linguistic Traces of Depression in Topic-Restricted Text: Attending to Self-Stigmatized Depression with {NLP}",
    author = "Wolohan, JT  and
      Hiraga, Misato  and
      Mukherjee, Atreyee  and
      Sayyed, Zeeshan Ali  and
      Millard, Matthew",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4102",
    pages = "11--21",
    abstract = "Natural language processing researchers have proven the ability of machine learning approaches to detect depression-related cues from language; however, to date, these efforts have primarily assumed it was acceptable to leave depression-related texts in the data. Our concerns with this are twofold: first, that the models may be overfitting on depression-related signals, which may not be present in all depressed users (only those who talk about depression on social media); and second, that these models would under-perform for users who are sensitive to the public stigma of depression. This study demonstrates the validity to those concerns. We construct a novel corpus of texts from 12,106 Reddit users and perform lexical and predictive analyses under two conditions: one where all text produced by the users is included and one where the depression data is withheld. We find significant differences in the language used by depressed users under the two conditions as well as a difference in the ability of machine learning algorithms to correctly detect depression. However, despite the lexical differences and reduced classification performance{--}each of which suggests that users may be able to fool algorithms by avoiding direct discussion of depression{--}a still respectable overall performance suggests lexical models are reasonably robust and well suited for a role in a diagnostic or monitoring capacity.",
}
@inproceedings{issa-2018-opennmt,
    title = "An {O}pen{NMT} Model to {A}rabic Broken Plurals",
    author = "Issa, Elsayed",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4103",
    pages = "22--30",
    abstract = "Arabic Broken Plurals show an interesting phenomenon in Arabic morphology as they are formed by shifting the consonants of the syllables into different syllable patterns, and subsequently, the pattern of the word changes. The present paper, therefore, attempts to look at Arabic broken plurals from the perspective of neural networks by implementing an OpenNMT experiment to better understand and interpret the behavior of these plurals, especially when it comes to L2 acquisition. The results show that the model is successful in predicting the Arabic template. However, it fails to predict certain consonants such as the emphatics and the gutturals. This reinforces the fact that these consonants or sounds are the most difficult for L2 learners to acquire.",
}
@inproceedings{karuna-etal-2018-enhancing,
    title = "Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers",
    author = {Karuna, Prakruthi  and
      Purohit, Hemant  and
      Uzuner, {\"O}zlem  and
      Jajodia, Sushil  and
      Ganesan, Rajesh},
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4104",
    pages = "31--40",
    abstract = "Ever increasing ransomware attacks and thefts of intellectual property demand cybersecurity solutions to protect critical documents. One emerging solution is to place fake text documents in the repository of critical documents for deceiving and catching cyber attackers. We can generate fake text documents by obscuring the salient information in legit text documents. However, the obscuring process can result in linguistic inconsistencies, such as broken co-references and illogical flow of ideas across the sentences, which can discern the fake document and render it unbelievable. In this paper, we propose a novel method to generate believable fake text documents by automatically improving the linguistic consistency of computer-generated fake text. Our method focuses on enhancing syntactic cohesion and semantic coherence across discourse segments. We conduct experiments with human subjects to evaluate the effect of believability improvements in distinguishing legit texts from fake texts. Results show that the probability to distinguish legit texts from believable fake texts is consistently lower than from fake texts that have not been improved in believability. This indicates the effectiveness of our method in generating believable fake text.",
}
@inproceedings{opitz-frank-2018-addressing,
    title = "Addressing the {W}inograd Schema Challenge as a Sequence Ranking Task",
    author = "Opitz, Juri  and
      Frank, Anette",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4105",
    pages = "41--52",
    abstract = "The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.",
}
@inproceedings{collard-2018-finite,
    title = "Finite State Reasoning for Presupposition Satisfaction",
    author = "Collard, Jacob",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4106",
    pages = "53--62",
    abstract = "Sentences with presuppositions are often treated as uninterpretable or unvalued (neither true nor false) if their presuppositions are not satisfied. However, there is an open question as to how this satisfaction is calculated. In some cases, determining whether a presupposition is satisfied is not a trivial task (or even a decidable one), yet native speakers are able to quickly and confidently identify instances of presupposition failure. I propose that this can be accounted for with a form of possible world semantics that encapsulates some reasoning abilities, but is limited in its computational power, thus circumventing the need to solve computationally difficult problems. This can be modeled using a variant of the framework of finite state semantics proposed by Rooth (2017). A few modifications to this system are necessary, including its extension into a three-valued logic to account for presupposition. Within this framework, the logic necessary to calculate presupposition satisfaction is readily available, but there is no risk of needing exceptional computational power. This correctly predicts that certain presuppositions will not be calculated intuitively, while others can be easily evaluated.",
}
@inproceedings{jessiman-etal-2018-language,
    title = "Language-Based Automatic Assessment of Cognitive and Communicative Functions Related to {P}arkinson{'}s Disease",
    author = "Jessiman, Lesley  and
      Murray, Gabriel  and
      Braley, McKenzie",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4107",
    pages = "63--74",
    abstract = "We explore the use of natural language processing and machine learning for detecting evidence of Parkinson{'}s disease from transcribed speech of subjects who are describing everyday tasks. Experiments reveal the difficulty of treating this as a binary classification task, and a multi-class approach yields superior results. We also show that these models can be used to predict cognitive abilities across all subjects.",
}
@inproceedings{kurdi-2018-spontaneous,
    title = "Can spontaneous spoken language disfluencies help describe syntactic dependencies? An empirical study",
    author = "Kurdi, M. Zakaria",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4108",
    pages = "75--84",
    abstract = "This paper explores the correlations between key syntactic dependencies and the occurrence of simple spoken language disfluencies such as filled pauses and incomplete words. The working hypothesis here is that interruptions caused by these phenomena are more likely to happen between weakly connected words from a syntactic point of view than between strongly connected ones. The obtained results show significant patterns with the regard to key syntactic phenomena, like confirming the positive correlation between the frequency of disfluencies and multiples measures of syntactic complexity. In addition, they show that there is a stronger relationship between the verb and its subject than with its object, which confirms the idea of a hierarchical incrementality. Also, this work uncovered an interesting role played by a verb particle as a syntactic delimiter of some verb complements. Finally, the interruptions by disfluencies patterns show that verbs have a more privileged relationship with their preposition compared to the object Noun Phrase (NP).",
}
@inproceedings{arias-trejo-etal-2018-word,
    title = "Word-word Relations in Dementia and Typical Aging",
    author = "Arias-Trejo, Natalia  and
      Minto-Garc{\'\i}a, Aline  and
      Luna-Umanzor, Diana I.  and
      R{\'\i}os-Ponce, Alma E.  and
      Mariana, Balderas-Pliego  and
      Bel-Enguix, Gemma",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4109",
    pages = "85--93",
    abstract = "Older adults tend to suffer a decline in some of their cognitive capabilities, being language one of least affected processes. Word association norms (WAN) also known as free word associations reflect word-word relations, the participant reads or hears a word and is asked to write or say the first word that comes to mind. Free word associations show how the organization of semantic memory remains almost unchanged with age. We have performed a WAN task with very small samples of older adults with Alzheimer{'}s disease (AD), vascular dementia (VaD) and mixed dementia (MxD), and also with a control group of typical aging adults, matched by age, sex and education. All of them are native speakers of Mexican Spanish. The results show, as expected, that Alzheimer disease has a very important impact in lexical retrieval, unlike vascular and mixed dementia. This suggests that linguistic tests elaborated from WAN can be also used for detecting AD at early stages.",
}
@inproceedings{kumar-bora-2018-part,
    title = "Part-of-Speech Annotation of {E}nglish-{A}ssamese code-mixed texts: Two Approaches",
    author = "Kumar, Ritesh  and
      Bora, Manas Jyoti",
    booktitle = "Proceedings of the First International Workshop on Language Cognition and Computational Models",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4110",
    pages = "94--103",
    abstract = "In this paper, we discuss the development of a part-of-speech tagger for English-Assamese code-mixed texts. We provide a comparison of 2 approaches to annotating code-mixed data {--} a) annotation of the texts from the two languages using monolingual resources from each language and b) annotation of the text through a different resource created specifically for code-mixed data. We present a comparative study of the efforts required in each approach and the final performance of the system. Based on this, we argue that it might be a better approach to develop new technologies using code-mixed data instead of monolingual, {`}clean{'} data, especially for those languages where we do not have significant tools and technologies available till now.",
}
@proceedings{ws-2018-semantic,
    title = "Proceedings of the Third Workshop on Semantic Deep Learning",
    editor = "Anke, Luis Espinosa  and
      Gromann, Dagmar  and
      Declerck, Thierry",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4000",
}
@inproceedings{gupta-etal-2018-replicated,
    title = "Replicated {S}iamese {LSTM} in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts",
    author = {Gupta, Pankaj  and
      Andrassy, Bernt  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4001",
    pages = "1--11",
    abstract = "The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22{\%} and 7{\%} gain (Accuracy@10) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.",
}
@inproceedings{yoon-etal-2018-word,
    title = "Word-Embedding based Content Features for Automated Oral Proficiency Scoring",
    author = "Yoon, Su-Youn  and
      Loukina, Anastassia  and
      Lee, Chong Min  and
      Mulholland, Matthew  and
      Wang, Xinhao  and
      Choi, Ikkyu",
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4002",
    pages = "12--22",
    abstract = "In this study, we develop content features for an automated scoring system of non-native English speakers{'} spontaneous speech. The features calculate the lexical similarity between the question text and the ASR word hypothesis of the spoken response, based on traditional word vector models or word embeddings. The proposed features do not require any sample training responses for each question, and this is a strong advantage since collecting question-specific data is an expensive task, and sometimes even impossible due to concerns about question exposure. We explore the impact of these new features on the automated scoring of two different question types: (a) providing opinions on familiar topics and (b) answering a question about a stimulus material. The proposed features showed statistically significant correlations with the oral proficiency scores, and the combination of new features with the speech-driven features achieved a small but significant further improvement for the latter question type. Further analyses suggested that the new features were effective in assigning more accurate scores for responses with serious content issues.",
}
@inproceedings{nieto-pina-johansson-2018-automatically,
    title = "Automatically Linking Lexical Resources with Word Sense Embedding Models",
    author = "Nieto-Pi{\~n}a, Luis  and
      Johansson, Richard",
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4003",
    pages = "23--29",
    abstract = "Automatically learnt word sense embeddings are developed as an attempt to refine the capabilities of coarse word embeddings. The word sense representations obtained this way are, however, sensitive to underlying corpora and parameterizations, and they might be difficult to relate to formally defined word senses. We propose to tackle this problem by devising a mechanism to establish links between word sense embeddings and lexical resources created by experts. We evaluate the applicability of these links in a task to retrieve instances of word sense unlisted in the lexicon.",
}
@inproceedings{ezeani-etal-2018-transferred,
    title = "Transferred Embeddings for {I}gbo Similarity, Analogy, and Diacritic Restoration Tasks",
    author = "Ezeani, Ignatius  and
      Onyenwe, Ikechukwu  and
      Hepple, Mark",
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4004",
    pages = "30--38",
    abstract = "Existing NLP models are mostly trained with data from well-resourced languages. Most minority languages face the challenge of lack of resources - data and technologies - for NLP research. Building these resources from scratch for each minority language will be very expensive, time-consuming and amount largely to unnecessarily re-inventing the wheel. In this paper, we applied transfer learning techniques to create Igbo word embeddings from a variety of existing English trained embeddings. Transfer learning methods were also used to build standard datasets for Igbo word similarity and analogy tasks for intrinsic evaluation of embeddings. These projected embeddings were also applied to diacritic restoration task. Our results indicate that the projected models not only outperform the trained ones on the semantic-based tasks of analogy, word-similarity, and odd-word identifying, but they also achieve enhanced performance on the diacritic restoration with learned diacritic embeddings.",
}
@inproceedings{parupalli-etal-2018-towards-enhancing,
    title = "Towards Enhancing Lexical Resource and Using Sense-annotations of {O}nto{S}ense{N}et for Sentiment Analysis",
    author = "Parupalli, Sreekavitha  and
      Anvesh Rao, Vijjini  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4005",
    pages = "39--44",
    abstract = "This paper illustrates the interface of the tool we developed for crowd sourcing and we explain the annotation procedure in detail. Our tool is named as {`}పారుపల్లి పదజాలం{'} (Parupalli Padajaalam) which means web of words by Parupalli. The aim of this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu resource. Recent works have shown the importance of word-level annotations on sentiment analysis. With this as basis, we aim to analyze the importance of sense-annotations obtained from OntoSenseNet in performing the task of sentiment analysis. We explain the features extracted from OntoSenseNet (Telugu). Furthermore we compute and explain the adverbial class distribution of verbs in OntoSenseNet. This task is known to aid in disambiguating word-senses which helps in enhancing the performance of word-sense disambiguation (WSD) task(s).",
}
@inproceedings{schockaert-2018-knowledge,
    title = "Knowledge Representation with Conceptual Spaces",
    author = "Schockaert, Steven",
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4006",
    pages = "45",
    abstract = "Entity embeddings are vector space representations of a given domain of interest. They are typically learned from text corpora (possibly in combination with any available structured knowledge), based on the intuition that similar entities should be represented by similar vectors. The usefulness of such entity embeddings largely stems from the fact that they implicitly encode a rich amount of knowledge about the considered domain, beyond mere similarity. In an embedding of movies, for instance, we may expect all movies from a given genre to be located in some low-dimensional manifold. This is particularly useful in supervised learning settings, where it may e.g. allow neural movie recommenders to base predictions on the genre of a movie, without that genre having to be specified explicitly for each movie, or without even the need to specify that the genre of a movie is a property that may have predictive value for the considered task. In unsupervised settings, however, such implicitly encoded knowledge cannot be leveraged. Conceptual spaces, as proposed by Grdenfors, are similar to entity embeddings, but provide more structure. In conceptual spaces, among others, dimensions are interpretable and grouped into facets, and properties and concepts are explicitly modelled as (vague) regions. Thanks to this additional structure, conceptual spaces can be used as a knowledge representation framework, which can also be effectively exploited in unsupervised settings. Given a conceptual space of movies, for instance, we are able to answer queries that ask about similarity w.r.t. a particular facet (e.g. movies which are cinematographically similar to Jurassic Park), that refer to a given feature (e.g. movies which are scarier than Jurassic Park but otherwise similar), or that refer to particular properties or concepts (e.g. thriller from the 1990s with a dinosaur theme). Compared to standard entity embeddings, however, conceptual spaces are more challenging to learn in a purely data-driven fashion. In this talk, I will give an overview of some approaches for learning such representations that have recently been developed within the context of the FLEXILOG project.",
}
@inproceedings{christodoulopoulos-2018-knowledge,
    title = "Knowledge Representation and Extraction at Scale",
    author = "Christodoulopoulos, Christos",
    booktitle = "Proceedings of the Third Workshop on Semantic Deep Learning",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-4007",
    pages = "46",
    abstract = "These days, most general knowledge question-answering systems rely on large-scale knowledge bases comprising billions of facts about millions of entities. Having a structured source of semantic knowledge means that we can answer questions involving single static facts (e.g. {``}Who was the 8th president of the US?{''}) or dynamically generated ones (e.g. {``}How old is Donald Trump?{''}). More importantly, we can answer questions involving multiple inference steps ({``}Is the queen older than the president of the US?{''}). In this talk, I{'}m going to be discussing some of the unique challenges that are involved with building and maintaining a consistent knowledge base for Alexa, extending it with new facts and using it to serve answers in multiple languages. I will focus on three recent projects from our group. First, a way of measuring the completeness of a knowledge base, that is based on usage patterns. The definition of the usage of the KB is done in terms of the relation distribution of entities seen in question-answer logs. Instead of directly estimating the relation distribution of individual entities, it is generalized to the {``}class signature{''} of each entity. For example, users ask for baseball players{'} height, age, and batting average, so a knowledge base is complete (with respect to baseball players) if every entity has facts for those three relations. Second, an investigation into fact extraction from unstructured text. I will present a method for creating distant (weak) supervision labels for training a large-scale relation extraction system. I will also discuss the effectiveness of neural network approaches by decoupling the model architecture from the feature design of a state-of-the-art neural network system. Surprisingly, a much simpler classifier trained on similar features performs on par with the highly complex neural network system (at 75x reduction to the training time), suggesting that the features are a bigger contributor to the final performance. Finally, I will present the Fact Extraction and VERification (FEVER) dataset and challenge. The dataset comprises more than 185,000 human-generated claims extracted from Wikipedia pages. False claims were generated by mutating true claims in a variety of ways, some of which were meaningaltering. During the verification step, annotators were required to label a claim for its validity and also supply full-sentence textual evidence from (potentially multiple) Wikipedia articles for the label. With FEVER, we aim to help create a new generation of transparent and interprable knowledge extraction systems.",
}
@proceedings{ws-2018-nlp-similar,
    title = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    editor = {Zampieri, Marcos  and
      Nakov, Preslav  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Malmasi, Shervin  and
      Ali, Ahmed},
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3900",
}
@inproceedings{zampieri-etal-2018-language,
    title = "Language Identification and Morphosyntactic Tagging: The Second {V}ar{D}ial Evaluation Campaign",
    author = {Zampieri, Marcos  and
      Malmasi, Shervin  and
      Nakov, Preslav  and
      Ali, Ahmed  and
      Shon, Suwon  and
      Glass, James  and
      Scherrer, Yves  and
      Samard{\v{z}}i{\'c}, Tanja  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      van der Lee, Chris  and
      Grondelaers, Stefan  and
      Oostdijk, Nelleke  and
      Speelman, Dirk  and
      van den Bosch, Antal  and
      Kumar, Ritesh  and
      Lahiri, Bornini  and
      Jain, Mayank},
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3901",
    pages = "1--17",
    abstract = "We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth edition of the VarDial workshop, collocated with COLING{'}2018. This year, the campaign included five shared tasks, including two task re-runs {--} Arabic Dialect Identification (ADI) and German Dialect Identification (GDI) {--}, and three new tasks {--} Morphosyntactic Tagging of Tweets (MTT), Discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). A total of 24 teams submitted runs across the five shared tasks, and contributed 22 system description papers, which were included in the VarDial workshop proceedings and are referred to in this report.",
}
@inproceedings{lusetti-etal-2018-encoder,
    title = "Encoder-Decoder Methods for Text Normalization",
    author = {Lusetti, Massimo  and
      Ruzsics, Tatyana  and
      G{\"o}hring, Anne  and
      Samard{\v{z}}i{\'c}, Tanja  and
      Stark, Elisabeth},
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3902",
    pages = "18--28",
    abstract = "Text normalization is the task of mapping non-canonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. It is an up-stream task necessary to enable the subsequent direct employment of standard natural language processing tools and indispensable for languages such as Swiss German, with strong regional variation and no written standard. Text normalization has been addressed with a variety of methods, most successfully with character-level statistical machine translation (CSMT). In the meantime, machine translation has changed and the new methods, known as neural encoder-decoder (ED) models, resulted in remarkable improvements. Text normalization, however, has not yet followed. A number of neural methods have been tried, but CSMT remains the state-of-the-art. In this work, we normalize Swiss German WhatsApp messages using the ED framework. We exploit the flexibility of this framework, which allows us to learn from the same training data in different ways. In particular, we modify the decoding stage of a plain ED model to include target-side language models operating at different levels of granularity: characters and words. Our systematic comparison shows that our approach results in an improvement over the CSMT state-of-the-art.",
}
@inproceedings{castro-etal-2018-high,
    title = "A High Coverage Method for Automatic False {F}riends Detection for {S}panish and {P}ortuguese",
    author = "Castro, Santiago  and
      Bonanata, Jairo  and
      Ros{\'a}, Aiala",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3903",
    pages = "29--36",
    abstract = "False friends are words in two languages that look or sound similar, but have different meanings. They are a common source of confusion among language learners. Methods to detect them automatically do exist, however they make use of large aligned bilingual corpora, which are hard to find and expensive to build, or encounter problems dealing with infrequent words. In this work we propose a high coverage method that uses word vector representations to build a false friends classifier for any pair of languages, which we apply to the particular case of Spanish and Portuguese. The required resources are a large corpus for each language and a small bilingual lexicon for the pair.",
}
@inproceedings{silfverberg-drobac-2018-sub,
    title = "Sub-label dependencies for Neural Morphological Tagging {--} The Joint Submission of {U}niversity of {C}olorado and {U}niversity of {H}elsinki for {V}ar{D}ial 2018",
    author = "Silfverberg, Miikka  and
      Drobac, Senka",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3904",
    pages = "37--45",
    abstract = "This paper presents the submission of the UH{\&}CU team (Joint University of Colorado and University of Helsinki team) for the VarDial 2018 shared task on morphosyntactic tagging of Croatian, Slovenian and Serbian tweets. Our system is a bidirectional LSTM tagger which emits tags as character sequences using an LSTM generator in order to be able to handle unknown tags and combinations of several tags for one token which occur in the shared task data sets. To the best of our knowledge, using an LSTM generator is a novel approach. The system delivers sizable improvements of more than 6{\%}-points over a baseline trigram tagger. Overall, the performance of our system is quite even for all three languages.",
}
@inproceedings{steimel-2018-part,
    title = "Part of Speech Tagging in {L}uyia: A {B}antu Macrolanguage",
    author = "Steimel, Kenneth",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3905",
    pages = "46--54",
    abstract = "Luyia is a macrolanguage in central Kenya. The Luyia languages, like other Bantu languages, have a complex morphological system. This system can be leveraged to aid in part of speech tagging. Bag-of-characters taggers trained on a source Luyia language can be applied directly to another Luyia language with some degree of success. In addition, mixing data from the target language with data from the source language does produce more accurate predictive models compared to models trained on just the target language data when the training set size is small. However, for both of these tagging tasks, models involving the more distantly related language, Tiriki, are better at predicting part of speech tags for Wanga data. The models incorporating Bukusu data are not as successful despite the closer relationship between Bukusu and Wanga. Overlapping vocabulary between the Wanga and Tiriki corpora as well as a bias towards open class words help Tiriki outperform Bukusu.",
}
@inproceedings{coltekin-etal-2018-tubingen,
    title = {{T}{\"u}bingen-{O}slo Team at the {V}ar{D}ial 2018 Evaluation Campaign: An Analysis of N-gram Features in Language Variety Identification},
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Rama, Taraka  and
      Blaschke, Verena},
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3906",
    pages = "55--65",
    abstract = "This paper describes our systems for the VarDial 2018 evaluation campaign. We participated in all language identification tasks, namely, Arabic dialect identification (ADI), German dialect identification (GDI), discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). In all of the tasks, we only used textual transcripts (not using audio features for ADI). We submitted system runs based on support vector machine classifiers (SVMs) with bag of character and word n-grams as features, and gated bidirectional recurrent neural networks (RNNs) using units of characters and words. Our SVM models outperformed our RNN models in all tasks, obtaining the first place on the DFS task, third place on the ADI task, and second place on others according to the official rankings. As well as describing the models we used in the shared task participation, we present an analysis of the n-gram features used by the SVM models in each task, and also report additional results (that were run after the official competition deadline) on the GDI surprise dialect track.",
}
@inproceedings{jauhiainen-etal-2018-iterative,
    title = "Iterative Language Model Adaptation for {I}ndo-{A}ryan Language Identification",
    author = "Jauhiainen, Tommi  and
      Jauhiainen, Heidi  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3907",
    pages = "66--75",
    abstract = "This paper presents the experiments and results obtained by the SUKI team in the Indo-Aryan Language Identification shared task of the VarDial 2018 Evaluation Campaign. The shared task was an open one, but we did not use any corpora other than what was distributed by the organizers. A total of eight teams provided results for this shared task. Our submission using a HeLI-method based language identifier with iterative language model adaptation obtained the best results in the shared task with a macro F1-score of 0.958.",
}
@inproceedings{baldwin-2018-language,
    title = "Language and the Shifting Sands of Domain, Space and Time (Invited Talk)",
    author = "Baldwin, Timothy",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3908",
    pages = "76",
    abstract = "In this talk, I will first present recent work on domain debiasing in the context of language identification, then discuss a new line of work on language variety analysis in the form of dialect map generation. Finally, I will reflect on the interplay between time and space on language variation, and speculate on how these can be captured in a single model.",
}
@inproceedings{butnaru-ionescu-2018-unibuckernel-reloaded,
    title = "{U}nibuc{K}ernel Reloaded: First Place in {A}rabic Dialect Identification for the Second Year in a Row",
    author = "Butnaru, Andrei  and
      Ionescu, Radu Tudor",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3909",
    pages = "77--87",
    abstract = "We present a machine learning approach that ranked on the first place in the Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial Evaluation Campaign. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech or phonetic transcripts, we also use a kernel based on dialectal embeddings generated from audio recordings by the organizers. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Preliminary experiments indicate that KRR provides better classification results. Our approach is shallow and simple, but the empirical results obtained in the 2018 ADI Closed Shared Task prove that it achieves the best performance. Furthermore, our top macro-F1 score (58.92{\%}) is significantly better than the second best score (57.59{\%}) in the 2018 ADI Shared Task, according to the statistical significance test performed by the organizers. Nevertheless, we obtain even better post-competition results (a macro-F1 score of 62.28{\%}) using the audio embeddings released by the organizers after the competition. With a very similar approach (that did not include phonetic features), we also ranked first in the ADI Closed Shared Tasks of the 2017 VarDial Evaluation Campaign, surpassing the second best method by 4.62{\%}. We therefore conclude that our multiple kernel learning method is the best approach to date for Arabic dialect identification.",
}
@inproceedings{van-miltenburg-etal-2018-varying,
    title = "Varying image description tasks: spoken versus written descriptions",
    author = "van Miltenburg, Emiel  and
      Koolen, Ruud  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3910",
    pages = "88--100",
    abstract = "Automatic image description systems are commonly trained and evaluated on written image descriptions. At the same time, these systems are often used to provide spoken descriptions (e.g. for visually impaired users) through apps like TapTapSee or Seeing AI. This is not a problem, as long as spoken and written descriptions are very similar. However, linguistic research suggests that spoken language often differs from written language. These differences are not regular, and vary from context to context. Therefore, this paper investigates whether there are differences between written and spoken image descriptions, even if they are elicited through similar tasks. We compare descriptions produced in two languages (English and Dutch), and in both languages observe substantial differences between spoken and written descriptions. Future research should see if users prefer the spoken over the written style and, if so, aim to emulate spoken descriptions.",
}
@inproceedings{mocialov-etal-2018-transfer,
    title = "Transfer Learning for {B}ritish {S}ign {L}anguage Modelling",
    author = "Mocialov, Boris  and
      Hastie, Helen  and
      Turner, Graham",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3911",
    pages = "101--110",
    abstract = "Automatic speech recognition and spoken dialogue systems have made great advances through the use of deep machine learning methods. This is partly due to greater computing power but also through the large amount of data available in common languages, such as English. Conversely, research in minority languages, including sign languages, is hampered by the severe lack of data. This has led to work on transfer learning methods, whereby a model developed for one language is reused as the starting point for a model on a second language, which is less resourced. In this paper, we examine two transfer learning techniques of fine-tuning and layer substitution for language modelling of British Sign Language. Our results show improvement in perplexity when using transfer learning with standard stacked LSTM models, trained initially using a large corpus for standard English from the Penn Treebank corpus.",
}
@inproceedings{barreiro-mota-2018-paraphrastic,
    title = "Paraphrastic Variance between {E}uropean and {B}razilian {P}ortuguese",
    author = "Barreiro, Anabela  and
      Mota, Cristina",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3912",
    pages = "111--121",
    abstract = "This paper presents a methodology to extract a paraphrase database for the European and Brazilian varieties of Portuguese, and discusses a set of paraphrastic categories of multiwords and phrasal units, such as the compounds {``}toda a gente{''} versus {``}todo o mundo{''} {`}everybody{'} or the gerundive constructions [estar a + V-Inf] versus [ficar + V-Ger] (e.g., {``}estive a observar{''} | {``}fiquei observando{''} {`}I was observing{'}), which are extremely relevant to high quality paraphrasing. The variants were manually aligned in the e-PACT corpus, using the CLUE-Aligner tool. The methodology, inspired in the Logos Model, focuses on a semantico-syntactic analysis of each paraphrastic unit and constitutes a subset of the Gold-CLUE-Paraphrases. The construction of a larger dataset of paraphrastic contrasts among the distinct varieties of the Portuguese language is indispensable for variety adaptation, i.e., for dealing with the cultural, linguistic and stylistic differences between them, making it possible to convert texts (semi-)automatically from one variety into another, a key function in paraphrasing systems. This topic represents an interesting new line of research with valuable applications in language learning, language generation, question-answering, summarization, and machine translation, among others. The paraphrastic units are the first resource of its kind for Portuguese to become available to the scientific community for research purposes.",
}
@inproceedings{ali-2018-character,
    title = "Character Level Convolutional Neural Network for {A}rabic Dialect Identification",
    author = "Ali, Mohamed",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3913",
    pages = "122--127",
    abstract = "This submission is for the description paper for our system in the ADI shared task.",
}
@inproceedings{michon-etal-2018-neural,
    title = "Neural Network Architectures for {A}rabic Dialect Identification",
    author = "Michon, Elise  and
      Pham, Minh Quang  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3914",
    pages = "128--136",
    abstract = "SYSTRAN competes this year for the first time to the DSL shared task, in the Arabic Dialect Identification subtask. We participate by training several Neural Network models showing that we can obtain competitive results despite the limited amount of training data available for learning. We report our experiments and detail the network architecture and parameters of our 3 runs: our best performing system consists in a Multi-Input CNN that learns separate embeddings for lexical, phonetic and acoustic input features (F1: 0.5289); we also built a CNN-biLSTM network aimed at capturing both spatial and sequential features directly from speech spectrograms (F1: 0.3894 at submission time, F1: 0.4235 with later found parameters); and finally a system relying on binary CNN-biLSTMs (F1: 0.4339).",
}
@inproceedings{jauhiainen-etal-2018-heli,
    title = "{H}e{LI}-based Experiments in Discriminating Between {D}utch and {F}lemish Subtitles",
    author = "Jauhiainen, Tommi  and
      Jauhiainen, Heidi  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3915",
    pages = "137--144",
    abstract = "This paper presents the experiments and results obtained by the SUKI team in the Discriminating between Dutch and Flemish in Subtitles shared task of the VarDial 2018 Evaluation Campaign. Our best submission was ranked 8th, obtaining macro F1-score of 0.61. Our best results were produced by a language identifier implementing the HeLI method without any modifications. We describe, in addition to the best method we used, some of the experiments we did with unsupervised clustering.",
}
@inproceedings{pichel-campos-etal-2018-measuring,
    title = "Measuring language distance among historical varieties using perplexity. Application to {E}uropean {P}ortuguese.",
    author = "Pichel Campos, Jose Ramom  and
      Gamallo, Pablo  and
      Alegria, I{\~n}aki",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3916",
    pages = "145--155",
    abstract = "The objective of this work is to quantify, with a simple and robust measure, the distance between historical varieties of a language. The measure will be inferred from text corpora corresponding to historical periods. Different approaches have been proposed for similar aims: Language Identification, Phylogenetics, Historical Linguistics or Dialectology. In our approach, we used a perplexity-based measure to calculate language distance between all the historical periods of a specific language: European Portuguese. Perplexity has also proven to be a robust metric to calculate distance between languages. However, this measure has not been tested yet to identify diachronic periods within the historical evolution of a specific language. For this purpose, a historical Portuguese corpus has been constructed from different open sources containing texts with close original spelling. The results of our experiments show that Portuguese keeps an important degree of homogeneity over time. We anticipate this metric to be a starting point to be applied to other languages.",
}
@inproceedings{ljubesic-2018-comparing,
    title = "Comparing {CRF} and {LSTM} performance on the task of morphosyntactic tagging of non-standard varieties of {S}outh {S}lavic languages",
    author = "Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3917",
    pages = "156--163",
    abstract = "This paper presents two systems taking part in the Morphosyntactic Tagging of Tweets shared task on Slovene, Croatian and Serbian data, organized inside the VarDial Evaluation Campaign. While one system relies on the traditional method for sequence labeling (conditional random fields), the other relies on its neural alternative (bidirectional long short-term memory). We investigate the similarities and differences of these two approaches, showing that both methods yield very good and quite similar results, with the neural model outperforming the traditional one more as the level of non-standardness of the text increases. Through an error analysis we show that the neural system is better at long-range dependencies, while the traditional system excels and slightly outperforms the neural system at the local ones. We present in the paper new state-of-the-art results in morphosyntactic annotation of non-standard text for Slovene, Croatian and Serbian.",
}
@inproceedings{barbaresi-2018-computationally,
    title = "Computationally efficient discrimination between language varieties with large feature vectors and regularized classifiers",
    author = "Barbaresi, Adrien",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3918",
    pages = "164--171",
    abstract = "The present contribution revolves around efficient approaches to language classification which have been field-tested in the Vardial evaluation campaign. The methods used in several language identification tasks comprising different language types are presented and their results are discussed, giving insights on real-world application of regularization, linear classifiers and corresponding linguistic features. The use of a specially adapted Ridge classifier proved useful in 2 tasks out of 3. The overall approach (XAC) has slightly outperformed most of the other systems on the DFS task (Dutch and Flemish) and on the ILI task (Indo-Aryan languages), while its comparative performance was poorer in on the GDI task (Swiss German dialects).",
}
@inproceedings{ali-2018-character-level,
    title = "Character Level Convolutional Neural Network for {G}erman Dialect Identification",
    author = "Ali, Mohamed",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3919",
    pages = "172--177",
    abstract = "This submission is a description paper for our system in GDI shared task",
}
@inproceedings{ciobanu-etal-2018-discriminating,
    title = "Discriminating between {I}ndo-{A}ryan Languages Using {SVM} Ensembles",
    author = "Ciobanu, Alina Maria  and
      Zampieri, Marcos  and
      Malmasi, Shervin  and
      Pal, Santanu  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3920",
    pages = "178--184",
    abstract = "In this paper we present a system based on SVM ensembles trained on characters and words to discriminate between five similar languages of the Indo-Aryan family: Hindi, Braj Bhasha, Awadhi, Bhojpuri, and Magahi. The system competed in the Indo-Aryan Language Identification (ILI) shared task organized within the VarDial Evaluation Campaign 2018. Our best entry in the competition, named ILIdentification, scored 88.95{\%} F1 score and it was ranked 3rd out of 8 teams.",
}
@inproceedings{gupta-etal-2018-iit,
    title = "{IIT} ({BHU}) System for {I}ndo-{A}ryan Language Identification ({ILI}) at {V}ar{D}ial 2018",
    author = "Gupta, Divyanshu  and
      Dhakad, Gourav  and
      Gupta, Jayprakash  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3921",
    pages = "185--190",
    abstract = "Text language Identification is a Natural Language Processing task of identifying and recognizing a given language out of many different languages from a piece of text. This paper describes our submission to the ILI 2018 shared-task, which includes the identification of 5 closely related Indo-Aryan languages. We developed a word-level LSTM(Long Short-term Memory) model, a specific type of Recurrent Neural Network model, for this task. Given a sentence, our model embeds each word of the sentence and convert into its trainable word embedding, feeds them into our LSTM network and finally predict the language. We obtained an F1 macro score of 0.836, ranking 5th in the task.",
}
@inproceedings{kreutz-daelemans-2018-exploring,
    title = "Exploring Classifier Combinations for Language Variety Identification",
    author = "Kreutz, Tim  and
      Daelemans, Walter",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3922",
    pages = "191--198",
    abstract = "This paper describes CLiPS{'}s submissions for the Discriminating between Dutch and Flemish in Subtitles (DFS) shared task at VarDial 2018. We explore different ways to combine classifiers trained on different feature groups. Our best system uses two Linear SVM classifiers; one trained on lexical features (word n-grams) and one trained on syntactic features (PoS n-grams). The final prediction for a document to be in Flemish Dutch or Netherlandic Dutch is made by the classifier that outputs the highest probability for one of the two labels. This confidence vote approach outperforms a meta-classifier on the development data and on the test data.",
}
@inproceedings{van-halteren-oostdijk-2018-identification,
    title = "Identification of Differences between {D}utch Language Varieties with the {V}ar{D}ial2018 {D}utch-{F}lemish Subtitle Data",
    author = "van Halteren, Hans  and
      Oostdijk, Nelleke",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3923",
    pages = "199--209",
    abstract = "With the goal of discovering differences between Belgian and Netherlandic Dutch, we participated as Team Taurus in the Dutch-Flemish Subtitles task of VarDial2018. We used a rather simple marker-based method, but a wide range of features, including lexical, lexico-syntactic and syntactic ones, and achieved a second position in the ranking. Inspection of highly distin-guishing features did point towards differences between the two language varieties, but because of the nature of the experimental data, we have to treat our observations as very tentative and in need of further investigation.",
}
@inproceedings{naser-hanani-2018-birzeit,
    title = "Birzeit {A}rabic Dialect Identification System for the 2018 {V}ar{D}ial Challenge",
    author = "Naser, Rabee  and
      Hanani, Abualsoud",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3924",
    pages = "210--217",
    abstract = "This paper describes our Automatic Dialect Recognition (ADI) system for the VarDial 2018 challenge, with the goal of distinguishing four major Arabic dialects, as well as Modern Standard Arabic (MSA). The training and development ADI VarDial 2018 data consists of 16,157 utterances, their words transcription, their phonetic transcriptions obtained with four non-Arabic phoneme recognizers and acoustic embedding data. Our overall system is a combination of four different systems. One system uses the words transcriptions and tries to recognize the speaker dialect by modeling the sequence of words for each dialect. Another system tries to recognize the dialect by modeling the phones sequence produced by non-Arabic phone recognizers, whereas, the other two systems use GMM trained on the acoustic features for recognizing the dialect. The best performance was achieved by the fused system which combines four systems together, with F1 micro of 68.77{\%}.",
}
@inproceedings{benites-etal-2018-twist,
    title = "Twist Bytes - {G}erman Dialect Identification with Data Mining Optimization",
    author = {Benites, Fernando  and
      Grubenmann, Ralf  and
      von D{\"a}niken, Pius  and
      von Gr{\"u}nigen, Dirk  and
      Deriu, Jan  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3925",
    pages = "218--227",
    abstract = "We describe our approaches used in the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2018. The goal was to identify to which out of four dialects spoken in German speaking part of Switzerland a sentence belonged to. We adopted two different meta classifier approaches and used some data mining insights to improve the preprocessing and the meta classifier parameters. Especially, we focused on using different feature extraction methods and how to combine them, since they influenced very differently the performance of the system. Our system achieved second place out of 8 teams, with a macro averaged F-1 of 64.6{\%}.",
}
@inproceedings{du-wang-2018-stevendu2018s,
    title = "{STEVENDU}2018{'}s system in {V}ar{D}ial 2018: Discriminating between {D}utch and {F}lemish in Subtitles",
    author = "Du, Steven  and
      Wang, Yuan Yuan",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3926",
    pages = "228--234",
    abstract = "This paper introduces the submitted system for team STEVENDU2018 during VarDial 2018 Discriminating between Dutch and Flemish in Subtitles(DFS). Post evaluation analyses are also presented, the results obtained indicate that it is a challenging task to discriminate Dutch and Flemish.",
}
@inproceedings{meftah-etal-2018-using,
    title = "Using Neural Transfer Learning for Morpho-syntactic Tagging of {S}outh-{S}lavic Languages Tweets",
    author = "Meftah, Sara  and
      Semmar, Nasredine  and
      Sadat, Fatiha  and
      Raaijmakers, Stephan",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3927",
    pages = "235--243",
    abstract = "In this paper, we describe a morpho-syntactic tagger of tweets, an important component of the CEA List DeepLIMA tool which is a multilingual text analysis platform based on deep learning. This tagger is built for the Morpho-syntactic Tagging of Tweets (MTT) Shared task of the 2018 VarDial Evaluation Campaign. The MTT task focuses on morpho-syntactic annotation of non-canonical Twitter varieties of three South-Slavic languages: Slovene, Croatian and Serbian. We propose to use a neural network model trained in an end-to-end manner for the three languages without any need for task or domain specific features engineering. The proposed approach combines both character and word level representations. Considering the lack of annotated data in the social media domain for South-Slavic languages, we have also implemented a cross-domain Transfer Learning (TL) approach to exploit any available related out-of-domain annotated data.",
}
@inproceedings{kroon-etal-2018-simple,
    title = "When Simple n-gram Models Outperform Syntactic Approaches: Discriminating between {D}utch and {F}lemish",
    author = "Kroon, Martin  and
      Medvedeva, Masha  and
      Plank, Barbara",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3928",
    pages = "244--253",
    abstract = "In this paper we present the results of our participation in the Discriminating between Dutch and Flemish in Subtitles VarDial 2018 shared task. We try techniques proven to work well for discriminating between language varieties as well as explore the potential of using syntactic features, i.e. hierarchical syntactic subtrees. We experiment with different combinations of features. Discriminating between these two languages turned out to be a very hard task, not only for a machine: human performance is only around 0.51 F1 score; our best system is still a simple Naive Bayes model with word unigrams and bigrams. The system achieved an F1 score (macro) of 0.62, which ranked us 4th in the shared task.",
}
@inproceedings{jauhiainen-etal-2018-heli-based,
    title = "{H}e{LI}-based Experiments in {S}wiss {G}erman Dialect Identification",
    author = "Jauhiainen, Tommi  and
      Jauhiainen, Heidi  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3929",
    pages = "254--262",
    abstract = "In this paper we present the experiments and results by the SUKI team in the German Dialect Identification shared task of the VarDial 2018 Evaluation Campaign. Our submission using HeLI with adaptive language models obtained the best results in the shared task with a macro F1-score of 0.686, which is clearly higher than the other submitted results. Without some form of unsupervised adaptation on the test set, it might not be possible to reach as high an F1-score with the level of domain difference between the datasets of the shared task. We describe the methods used in detail, as well as some additional experiments carried out during the shared task.",
}
@inproceedings{elaraby-abdul-mageed-2018-deep,
    title = "Deep Models for {A}rabic Dialect Identification on Benchmarked Data",
    author = "Elaraby, Mohamed  and
      Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3930",
    pages = "263--274",
    abstract = "The Arabic Online Commentary (AOC) (Zaidan and Callison-Burch, 2011) is a large-scale repos-itory of Arabic dialects with manual labels for4varieties of the language. Existing dialect iden-tification models exploiting the dataset pre-date the recent boost deep learning brought to NLPand hence the data are not benchmarked for use with deep learning, nor is it clear how much neural networks can help tease the categories in the data apart. We treat these two limitations:We (1) benchmark the data, and (2) empirically test6different deep learning methods on thetask, comparing peformance to several classical machine learning models under different condi-tions (i.e., both binary and multi-way classification). Our experimental results show that variantsof (attention-based) bidirectional recurrent neural networks achieve best accuracy (acc) on thetask, significantly outperforming all competitive baselines. On blind test data, our models reach87.65{\%}acc on the binary task (MSA vs. dialects),87.4{\%}acc on the 3-way dialect task (Egyptianvs. Gulf vs. Levantine), and82.45{\%}acc on the 4-way variants task (MSA vs. Egyptian vs. Gulfvs. Levantine). We release our benchmark for future work on the dataset",
}
@inproceedings{costa-jussa-etal-2018-neural,
    title = "A Neural Approach to Language Variety Translation",
    author = "Costa-juss{\`a}, Marta R.  and
      Zampieri, Marcos  and
      Pal, Santanu",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3931",
    pages = "275--282",
    abstract = "In this paper we present the first neural-based machine translation system trained to translate between standard national varieties of the same language. We take the pair Brazilian - European Portuguese as an example and compare the performance of this method to a phrase-based statistical machine translation system. We report a performance improvement of 0.9 BLEU points in translating from European to Brazilian Portuguese and 0.2 BLEU points when translating in the opposite direction. We also carried out a human evaluation experiment with native speakers of Brazilian Portuguese which indicates that humans prefer the output produced by the neural-based system in comparison to the statistical system.",
}
@inproceedings{ali-2018-character-level-convolutional,
    title = "Character Level Convolutional Neural Network for {I}ndo-{A}ryan Language Identification",
    author = "Ali, Mohamed",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3932",
    pages = "283--287",
    abstract = "This submission is a description paper for our system in ILI shared task",
}
@inproceedings{ciobanu-etal-2018-german,
    title = "{G}erman Dialect Identification Using Classifier Ensembles",
    author = "Ciobanu, Alina Maria  and
      Malmasi, Shervin  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the Fifth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial 2018)",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3933",
    pages = "288--294",
    abstract = "In this paper we present the GDI classification entry to the second German Dialect Identification (GDI) shared task organized within the scope of the VarDial Evaluation Campaign 2018. We present a system based on SVM classifier ensembles trained on characters and words. The system was trained on a collection of speech transcripts of five Swiss-German dialects provided by the organizers. The transcripts included in the dataset contained speakers from Basel, Bern, Lucerne, and Zurich. Our entry in the challenge reached 62.03{\%} F1 score and was ranked third out of eight teams.",
}
@proceedings{ws-2018-linguistic,
    title = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    editor = "Machonis, Peter  and
      Barreiro, Anabela  and
      Kocijan, Kristina  and
      Silberztein, Max",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3800",
}
@inproceedings{liberman-2018-corpus,
    title = "Corpus Phonetics: Past, Present, and Future",
    author = "Liberman, Mark",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3801",
    pages = "1",
    abstract = "Invited talk",
}
@inproceedings{silberztein-2018-using,
    title = "Using Linguistic Resources to Evaluate the Quality of Annotated Corpora",
    author = "Silberztein, Max",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3802",
    pages = "2--11",
    abstract = "Statistical and neural-network-based methods that compute their results by comparing a given text to be analyzed with a reference corpus assume that the reference corpus is complete and reliable enough. In this article, I conduct several experiments on an extract of the Open American National Corpus to verify this assumption.",
}
@inproceedings{zhang-moldovan-2018-rule,
    title = "Rule-based vs. Neural Net Approaches to Semantic Textual Similarity",
    author = "Zhang, Linrui  and
      Moldovan, Dan",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3803",
    pages = "12--17",
    abstract = "This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper, we present an end-to-end attention-based Bi-LSTM neural network system that solely takes word-level features, without expensive feature engineering work or the usage of external resources. By comparing its performance with traditional rule-based systems against SemEval-2012 benchmark, we make an assessment on the limitations and strengths of neural net systems to rule-based systems on Semantic Textual Similarity.",
}
@inproceedings{machonis-2018-linguistic,
    title = "Linguistic Resources for Phrasal Verb Identification",
    author = "Machonis, Peter",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3804",
    pages = "18--27",
    abstract = "This paper shows how a Lexicon-Grammar dictionary of English phrasal verbs (PV) can be transformed into an electronic dictionary, and with the help of multiple grammars, dictionaries, and filters within the linguistic development environment, NooJ, how to accurately identify PV in large corpora. The NooJ program is an alternative to statistical methods commonly used in NLP: all PV are listed in a dictionary and then located by means of a PV grammar in both continuous and discontinuous format. Results are then refined with a series of dictionaries, disambiguating grammars, and other linguistics recourses. The main advantage of such a program is that all PV can be identified in any corpus. The only drawback is that PV not listed in the dictionary (e.g., archaic forms, recent neologisms) are not identified; however, new PV can easily be added to the electronic dictionary, which is freely available to all.",
}
@inproceedings{kocijan-etal-2018-designing,
    title = "Designing a {C}roatian Aspectual Derivatives Dictionary: Preliminary Stages",
    author = "Kocijan, Kristina  and
      {\v{S}}ojat, Kre{\v{s}}imir  and
      Poljak, Dario",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3805",
    pages = "28--37",
    abstract = "The paper focusses on derivationally connected verbs in Croatian, i.e. on verbs that share the same lexical morpheme and are derived from other verbs via prefixation, suffixation and/or stem alternations. As in other Slavic languages with rich derivational morphology, each verb is marked for aspect, either perfective or imperfective. Some verbs, mostly of foreign origin, are marked as bi-aspectual verbs. The main objective of this paper is to detect and to describe major derivational processes and affixes used in the derivation of aspectually connected verbs with NooJ. Annotated chains are exported into a format adequate for web database system and further used to enhance the aspectual and derivational information for each verb.",
}
@inproceedings{boudhina-fehri-2018-rule,
    title = "A Rule-Based System for Disambiguating {F}rench Locative Verbs and Their Translation into {A}rabic",
    author = "Boudhina, Safa  and
      Fehri, H{\'e}la",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3806",
    pages = "38--46",
    abstract = "This paper presents a rule-based system for disambiguating frensh locative verbs and their translation to Arabic language. The disambiguation phase is based on the use of the French Verb dictionary (LVF) of Dubois and Dubois Charlier as a linguistic resource, from which a base of disambiguation rules is extracted. The extracted rules thus take the form of transducers which will be subsequently applied to texts. The translation phase consists in translating the disambiguated locative verbs returned by the disambiguation phase. The translation takes into account the verb{'}s tense used as well as the inflected form of the verb. This phase is based on bilingual dictionaries that contain the different French locative verbs and their translation into the Arabic language. The experimentation and the evaluation are done in the linguistic platform NooJ. The obtained results are satisfactory.",
}
@inproceedings{rodrigo-etal-2018-pedagogical,
    title = "A Pedagogical Application of {N}oo{J} in Language Teaching: The Adjective in {S}panish and {I}talian",
    author = "Rodrigo, Andrea  and
      Monteleone, Mario  and
      Reyes, Silvia",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3807",
    pages = "47--56",
    abstract = "In this paper, a pedagogical application of NooJ to the teaching and learning of Spanish as a foreign language is presented, which is directed to a specific addressee: learners whose mother tongue is Italian. The category {`}adjective{'} has been chosen on account of its lower frequency of occurrence in texts written in Spanish, and particularly in the Argentine Rioplatense variety, and with the aim of developing strategies to increase its use. In addition, the features that the adjective shares with other grammatical categories render it extremely productive and provide elements that enrich the learners{'} proficiency. The reference corpus contains the front pages of the Argentinian newspaper Clar{\'\i}n related to an emblematic historical moment, whose starting point is 24 March 1976, when a military coup began, and covers a thirty year period until 24 March 2006. It can be seen how the term desaparecido emerges with all its cultural and social charge, providing a context which allows an approach to Rioplatense Spanish from a more comprehensive perspective. Finally, a pedagogical proposal accounting for the application of the NooJ platform in language teaching is included.",
}
@inproceedings{dorr-voss-2018-stylus,
    title = "{STYLUS}: A Resource for Systematically Derived Language Usage",
    author = "Dorr, Bonnie  and
      Voss, Clare",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3808",
    pages = "57--64",
    abstract = "We describe a resource derived through extraction of a set of argument realizations from an existing lexical-conceptual structure (LCS) Verb Database of 500 verb classes (containing a total of 9525 verb entries) to include information about realization of arguments for a range of different verb classes. We demonstrate that our extended resource, called STYLUS (SysTematicallY Derived Language USe), enables systematic derivation of regular patterns of language usage without requiring manual annotation. We posit that both spatially oriented applications such as robot navigation and more general applications such as narrative generation require a layered representation scheme where a set of primitives (often grounded in space/motion such as GO) is coupled with a representation of constraints at the syntax-semantics interface. We demonstrate that the resulting resource covers three cases of lexico-semantic operations applicable to both language understanding and language generation.",
}
@inproceedings{gezmu-etal-2018-contemporary,
    title = "Contemporary {A}mharic Corpus: Automatically Morpho-Syntactically Tagged {A}mharic Corpus",
    author = {Gezmu, Andargachew Mekonnen  and
      Seyoum, Binyam Ephrem  and
      Gasser, Michael  and
      N{\"u}rnberger, Andreas},
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3809",
    pages = "65--70",
    abstract = "We introduced the contemporary Amharic corpus, which is automatically tagged for morpho-syntactic information. Texts are collected from 25,199 documents from different domains and about 24 million orthographic words are tokenized. Since it is partly a web corpus, we made some automatic spelling error correction. We have also modified the existing morphological analyzer, HornMorpho, to use it for the automatic tagging.",
}
@inproceedings{malireddy-etal-2018-gold,
    title = "Gold Corpus for Telegraphic Summarization",
    author = "Malireddy, Chanakya  and
      Somisetty, Srivenkata N M  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3810",
    pages = "71--77",
    abstract = "Most extractive summarization techniques operate by ranking all the source sentences and then select the top ranked sentences as the summary. Such methods are known to produce good summaries, especially when applied to news articles and scientific texts. However, they don{'}t fare so well when applied to texts such as fictional narratives, which don{'}t have a single central or recurrent theme. This is because usually the information or plot of the story is spread across several sentences. In this paper, we discuss a different summarization technique called Telegraphic Summarization. Here, we don{'}t select whole sentences, rather pick short segments of text spread across sentences, as the summary. We have tailored a set of guidelines to create such summaries and, using the same, annotate a gold corpus of 200 English short stories.",
}
@inproceedings{abera-h-mariam-2018-design,
    title = "Design of a {T}igrinya Language Speech Corpus for Speech Recognition",
    author = "Abera, Hafte  and
      H/Mariam, Sebsibe",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3811",
    pages = "78--82",
    abstract = "In this paper, we describe the first Tigrinya Languages speech corpora designed and development for speech recognition purposes. Tigrinya, often written as Tigrigna (ትግርኛ) /tɪˈɡrinjə/ belongs to the Semitic branch of the Afro-Asiatic languages where it shows the characteristic features of a Semitic language. It is spoken by ethnic Tigray-Tigrigna people in the Horn of Africa. The paper outlines different corpus designing process analysis of related work on speech corpora creation for different languages. The authors provide also procedures that were used for the creation of Tigrinya speech recognition corpus which is the under-resourced language. One hundred and thirty speakers, native to Tigrinya language, were recorded for training and test dataset set. Each speaker read 100 texts, which consisted of syllabically rich and balanced sentences. Ten thousand sets of sentences were used to prompt sheets. These sentences contained all of the contextual syllables and phones.",
}
@inproceedings{teferra-abate-etal-2018-parallel,
    title = "Parallel Corpora for bi-Directional Statistical Machine Translation for Seven {E}thiopian Language Pairs",
    author = "Teferra Abate, Solomon  and
      Melese, Michael  and
      Yifiru Tachbelie, Martha  and
      Meshesha, Million  and
      Atinafu, Solomon  and
      Mulugeta, Wondwossen  and
      Assabie, Yaregal  and
      Abera, Hafte  and
      Ephrem, Binyam  and
      Abebe, Tewodros  and
      Tsegaye, Wondimagegnhue  and
      Lemma, Amanuel  and
      Andargie, Tsegaye  and
      Shifaw, Seifedin",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3812",
    pages = "83--90",
    abstract = "In this paper, we describe the development of parallel corpora for Ethiopian Languages: Amharic, Tigrigna, Afan-Oromo, Wolaytta and Geez. To check the usability of all the corpora we conducted baseline bi-directional statistical machine translation (SMT) experiments for seven language pairs. The performance of the bi-directional SMT systems shows that all the corpora can be used for further investigations. We have also shown that the morphological complexity of the Ethio-Semitic languages has a negative impact on the performance of the SMT especially when they are target languages. Based on the results we obtained, we are currently working towards handling the morphological complexities to improve the performance of statistical machine translation among the Ethiopian languages.",
}
@inproceedings{sikos-pado-2018-using,
    title = "Using Embeddings to Compare {F}rame{N}et Frames Across Languages",
    author = "Sikos, Jennifer  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3813",
    pages = "91--101",
    abstract = "Much interest in Frame Semantics is fueled by the substantial extent of its applicability across languages. At the same time, lexicographic studies have found that the applicability of individual frames can be diminished by cross-lingual divergences regarding polysemy, syntactic valency, and lexicalization. Due to the large effort involved in manual investigations, there are so far no broad-coverage resources with {``}problematic{''} frames for any language pair. Our study investigates to what extent multilingual vector representations of frames learned from manually annotated corpora can address this need by serving as a wide coverage source for such divergences. We present a case study for the language pair English {---} German using the FrameNet and SALSA corpora and find that inferences can be made about cross-lingual frame applicability using a vector space model.",
}
@inproceedings{zhai-etal-2018-construction,
    title = "Construction of a Multilingual Corpus Annotated with Translation Relations",
    author = "Zhai, Yuming  and
      Max, Aur{\'e}lien  and
      Vilnat, Anne",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3814",
    pages = "102--111",
    abstract = "Translation relations, which distinguish literal translation from other translation techniques, constitute an important subject of study for human translators (Chuquet and Paillard, 1989). However, automatic processing techniques based on interlingual relations, such as machine translation or paraphrase generation exploiting translational equivalence, have not exploited these relations explicitly until now. In this work, we present a categorisation of translation relations and annotate them in a parallel multilingual (English, French, Chinese) corpus of oral presentations, the TED Talks. Our long term objective will be to automatically detect these relations in order to integrate them as important characteristics for the search of monolingual segments in relation of equivalence (paraphrases) or of entailment. The annotated corpus resulting from our work will be made available to the community.",
}
@inproceedings{boitet-etal-2018-towards,
    title = "Towards an Automatic Classification of Illustrative Examples in a Large {J}apanese-{F}rench Dictionary Obtained by {OCR}",
    author = "Boitet, Christian  and
      Mangeot, Mathieu  and
      Tomokiyo, Mutsuko",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3815",
    pages = "112--121",
    abstract = "We work on improving the Cesselin, a large and open source Japanese-French bilingual dictionary digitalized by OCR, available on the web, and contributively improvable online. Labelling its examples (about 226000) would significantly enhance their usefulness for language learners. Examples are proverbs, idiomatic constructions, normal usage examples, and, for nouns, phrases containing a quantifier. Proverbs are easy to spot, but not examples of other types. To find a method for automatically or at least semi-automatically annotating them, we have studied many entries, and hypothesized that the degree of lexical similarity between results of MT into a third language might give good cues. To confirm that hypothesis, we sampled 500 examples and used Google Translate to translate into English their Japanese expressions and their French translations. The hypothesis holds well, in particular for distinguishing examples of normal usage from idiomatic examples. Finally, we propose a detailed annotation procedure and discuss its future automatization.",
}
@inproceedings{barreiro-batista-2018-contractions,
    title = "{C}ontractions: To Align or Not to Align, That Is the Question",
    author = "Barreiro, Anabela  and
      Batista, Fernando",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3816",
    pages = "122--130",
    abstract = "This paper performs a detailed analysis on the alignment of Portuguese contractions, based on a previously aligned bilingual corpus. The alignment task was performed manually in a subset of the English-Portuguese CLUE4Translation Alignment Collection. The initial parallel corpus was pre-processed and, a decision was made as to whether the contraction should be maintained or decomposed in the alignment. Decomposition was required in the cases in which the two words that have been concatenated, i.e., the preposition and the determiner or pronoun, go in two separate translation alignment pairs (e.g., [no seio de] [a Uni{\~a}o Europeia] | [within] [the European Union]). Most contractions required decomposition in contexts where they are positioned at the end of a multiword unit. On the other hand, contractions tend to be maintained when they occur in the beginning or in the middle of the multiword unit, i.e., in the frozen part of the multiword (e.g., [no que diz respeito a] | [with regard to] or [al{\'e}m disso] [in addition]. A correct alignment of multiwords and phrasal units containing contractions is instrumental for machine translation, paraphrasing, and variety adaptation.",
}
@inproceedings{dhar-etal-2018-enabling,
    title = "Enabling Code-Mixed Translation: Parallel Corpus Creation and {MT} Augmentation Approach",
    author = "Dhar, Mrinal  and
      Kumar, Vaibhav  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3817",
    pages = "131--140",
    abstract = "Code-mixing, use of two or more languages in a single sentence, is ubiquitous; generated by multi-lingual speakers across the world. The phenomenon presents itself prominently in social media discourse. Consequently, there is a growing need for translating code-mixed hybrid language into standard languages. However, due to the lack of gold parallel data, existing machine translation systems fail to properly translate code-mixed text. In an effort to initiate the task of machine translation of code-mixed content, we present a newly created parallel corpus of code-mixed English-Hindi and English. We selected previously available English-Hindi code-mixed data as a starting point for the creation of our parallel corpus. We then chose 4 human translators, fluent in both English and Hindi, for translating the 6088 code-mixed English-Hindi sentences to English. With the help of the created parallel corpus, we analyzed the structure of English-Hindi code-mixed data and present a technique to augment run-of-the-mill machine translation (MT) approaches that can help achieve superior translations without the need for specially designed translation systems. We present an augmentation pipeline for existing MT approaches, like Phrase Based MT (Moses) and Neural MT, to improve the translation of code-mixed text. The augmentation pipeline is presented as a pre-processing step and can be plugged with any existing MT system, which we demonstrate by improving translations done by systems like Moses, Google Neural Machine Translation System (NMTS) and Bing Translator for English-Hindi code-mixed content.",
}
@proceedings{ws-2018-natural,
    title = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    editor = "Tseng, Yuen-Hsien  and
      Chen, Hsin-Hsi  and
      Ng, Vincent  and
      Komachi, Mamoru",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3700",
}
@inproceedings{desai-etal-2018-generating,
    title = "Generating Questions for Reading Comprehension using Coherence Relations",
    author = "Desai, Takshak  and
      Dakle, Parag  and
      Moldovan, Dan",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3701",
    doi = "10.18653/v1/W18-3701",
    pages = "1--10",
    abstract = "In this paper, we have proposed a technique for generating complex reading comprehension questions from a discourse that are more useful than factual ones derived from assertions. Our system produces a set of general-level questions using coherence relations and a set of well-defined syntactic transformations on the input text. Generated questions evaluate comprehension abilities like a comprehensive analysis of the text and its structure, correct identification of the author{'}s intent, a thorough evaluation of stated arguments; and a deduction of the high-level semantic relations that hold between text spans. Experiments performed on the RST-DT corpus allow us to conclude that our system possesses a strong aptitude for generating intricate questions. These questions are capable of effectively assessing a student{'}s interpretation of the text.",
}
@inproceedings{lin-2018-syntactic,
    title = "Syntactic and Lexical Approaches to Reading Comprehension",
    author = "Lin, Henry",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3702",
    doi = "10.18653/v1/W18-3702",
    pages = "11--19",
    abstract = "Among the challenges of teaching reading comprehension in K {--} 12 are identifying the portions of a text that are difficult for a student, comprehending major critical ideas, and understanding context-dependent polysemous words. We present a simple, unsupervised but robust and accurate syntactic method for achieving the first objective and a modified hierarchical lexical method for the second objective. Focusing on pinpointing troublesome sentences instead of the overall readability and on concepts central to a reading, we believe these methods will greatly facilitate efforts to help students improve reading skills",
}
@inproceedings{saddiki-etal-2018-feature,
    title = "Feature Optimization for Predicting Readability of {A}rabic {L}1 and {L}2",
    author = "Saddiki, Hind  and
      Habash, Nizar  and
      Cavalli-Sforza, Violetta  and
      Al Khalil, Muhamed",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3703",
    doi = "10.18653/v1/W18-3703",
    pages = "20--29",
    abstract = "Advances in automatic readability assessment can impact the way people consume information in a number of domains. Arabic, being a low-resource and morphologically complex language, presents numerous challenges to the task of automatic readability assessment. In this paper, we present the largest and most in-depth computational readability study for Arabic to date. We study a large set of features with varying depths, from shallow words to syntactic trees, for both L1 and L2 readability tasks. Our best L1 readability accuracy result is 94.8{\%} (75{\%} error reduction from a commonly used baseline). The comparable results for L2 are 72.4{\%} (45{\%} error reduction). We also demonstrate the added value of leveraging L1 features for L2 readability prediction.",
}
@inproceedings{maharjan-rus-2018-tutorial,
    title = "A Tutorial {M}arkov Analysis of Effective Human Tutorial Sessions",
    author = "Maharjan, Nabin  and
      Rus, Vasile",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3704",
    doi = "10.18653/v1/W18-3704",
    pages = "30--34",
    abstract = "This paper investigates what differentiates effective tutorial sessions from less effective sessions. Towards this end, we characterize and explore human tutors{'} actions in tutorial dialogue sessions by mapping the tutor-tutee interactions, which are streams of dialogue utterances, into streams of actions, based on the language-as-action theory. Next, we use human expert judgment measures, evidence of learning (EL) and evidence of soundness (ES), to identify effective and ineffective sessions. We perform sub-sequence pattern mining to identify sub-sequences of dialogue modes that discriminate good sessions from bad sessions. We finally use the results of sub-sequence analysis method to generate a tutorial Markov process for effective tutorial sessions.",
}
@inproceedings{mathias-bhattacharyya-2018-thank,
    title = "Thank {``}Goodness{''}! A Way to Measure Style in Student Essays",
    author = "Mathias, Sandeep  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3705",
    doi = "10.18653/v1/W18-3705",
    pages = "35--41",
    abstract = "Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, like language modeling and also a state-of-the-art deep learning system. We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.",
}
@inproceedings{rao-etal-2018-overview,
    title = "Overview of {NLPTEA}-2018 Share Task {C}hinese Grammatical Error Diagnosis",
    author = "Rao, Gaoqi  and
      Gong, Qi  and
      Zhang, Baolin  and
      Xun, Endong",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3706",
    doi = "10.18653/v1/W18-3706",
    pages = "42--51",
    abstract = "This paper presents the NLPTEA 2018 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 20 teams registered for this shared task, 13 teams developed the system and submitted a total of 32 runs. Progress in system performances was obviously, reaching F1 of 36.12{\%} in position level and 25.27{\%} in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers.",
}
@inproceedings{fu-etal-2018-chinese,
    title = "{C}hinese Grammatical Error Diagnosis using Statistical and Prior Knowledge driven Features with Probabilistic Ensemble Enhancement",
    author = "Fu, Ruiji  and
      Pei, Zhengqi  and
      Gong, Jiefu  and
      Song, Wei  and
      Teng, Dechuan  and
      Che, Wanxiang  and
      Wang, Shijin  and
      Hu, Guoping  and
      Liu, Ting",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3707",
    doi = "10.18653/v1/W18-3707",
    pages = "52--59",
    abstract = "This paper describes our system at NLPTEA-2018 Task {\#}1: Chinese Grammatical Error Diagnosis. Grammatical Error Diagnosis is one of the most challenging NLP tasks, which is to locate grammar errors and tell error types. Our system is built on the model of bidirectional Long Short-Term Memory with a conditional random field layer (BiLSTM-CRF) but integrates with several new features. First, richer features are considered in the BiLSTM-CRF model; second, a probabilistic ensemble approach is adopted; third, Template Matcher are used during a post-processing to bring in human knowledge. In official evaluation, our system obtains the highest F1 scores at identifying error types and locating error positions, the second highest F1 score at sentence level error detection. We also recommend error corrections for specific error types and achieve the best F1 performance among all participants.",
}
@inproceedings{li-etal-2018-hybrid,
    title = "A Hybrid System for {C}hinese Grammatical Error Diagnosis and Correction",
    author = "Li, Chen  and
      Zhou, Junpei  and
      Bao, Zuyi  and
      Liu, Hengyou  and
      Xu, Guangwei  and
      Li, Linlin",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3708",
    doi = "10.18653/v1/W18-3708",
    pages = "60--69",
    abstract = "This paper introduces the DM{\_}NLP team{'}s system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners. This task aims at not only detecting four types of grammatical errors including redundant words (R), missing words (M), bad word selection (S) and disordered words (W), but also recommending corrections for errors of M and S types. We proposed a hybrid system including four models for this task with two stages: the detection stage and the correction stage. In the detection stage, we first used a BiLSTM-CRF model to tag potential errors by sequence labeling, along with some handcraft features. Then we designed three Grammatical Error Correction (GEC) models to generate corrections, which could help to tune the detection result. In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types. Our system reached the highest precision in the correction subtask, which was the most challenging part of this shared task, and got top 3 on F1 scores for position detection of errors.",
}
@inproceedings{hu-etal-2018-ling,
    title = "Ling@{CASS} Solution to the {NLP}-{TEA} {CGED} Shared Task 2018",
    author = "Hu, Qinan  and
      Zhang, Yongwei  and
      Liu, Fang  and
      Gu, Yueguo",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3709",
    doi = "10.18653/v1/W18-3709",
    pages = "70--76",
    abstract = "In this study, we employ the sequence to sequence learning to model the task of grammar error correction. The system takes potentially erroneous sentences as inputs, and outputs correct sentences. To breakthrough the bottlenecks of very limited size of manually labeled data, we adopt a semi-supervised approach. Specifically, we adapt correct sentences written by native Chinese speakers to generate pseudo grammatical errors made by learners of Chinese as a second language. We use the pseudo data to pre-train the model, and the CGED data to fine-tune it. Being aware of the significance of precision in a grammar error correction system in real scenarios, we use ensembles to boost precision. When using inputs as simple as Chinese characters, the ensembled system achieves a precision at 86.56{\%} in the detection of erroneous sentences, and a precision at 51.53{\%} in the correction of errors of Selection and Missing types.",
}
@inproceedings{li-qi-2018-chinese,
    title = "{C}hinese Grammatical Error Diagnosis Based on Policy Gradient {LSTM} Model",
    author = "Li, Changliang  and
      Qi, Ji",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3710",
    doi = "10.18653/v1/W18-3710",
    pages = "77--82",
    abstract = "Chinese Grammatical Error Diagnosis (CGED) is a natural language processing task for the NLPTEA2018 workshop held during ACL2018. The goal of this task is to diagnose Chinese sentences containing four kinds of grammatical errors through the model and find out the sentence errors. Chinese grammatical error diagnosis system is a very important tool, which can help Chinese learners automatically diagnose grammatical errors in many scenarios. However, due to the limitations of the Chinese language{'}s own characteristics and datasets, the traditional model faces the problem of extreme imbalances in the positive and negative samples and the disappearance of gradients. In this paper, we propose a sequence labeling method based on the Policy Gradient LSTM model and apply it to this task to solve the above problems. The results show that our model can achieve higher precision scores in the case of lower False positive rate (FPR) and it is convenient to optimize the model on-line.",
}
@inproceedings{fudholi-suominen-2018-importance,
    title = "The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid",
    author = "Fudholi, Dzikri  and
      Suominen, Hanna",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3711",
    doi = "10.18653/v1/W18-3711",
    pages = "83--87",
    abstract = "Verbal communication {---} and pronunciation as its part {---} is a core skill that can be developed through guided learning. An artificial intelligence system can take a role in these guided learning approaches as an enabler of an application for pronunciation learning with a recommender system to guide language learners through exercises and feedback system to correct their pronunciation. In this paper, we report on a user study on language learners{'} perceived usefulness of the application. 16 international students who spoke non-native English and lived in Australia participated. 13 of them said they need to improve their pronunciation skills in English because of their foreign accent. The feedback system with features for pronunciation scoring, speech replay, and giving a pronunciation example was deemed essential by most of the respondents. In contrast, a clear dichotomy between the recommender system perceived as useful or useless existed; the system had features to prompt new common words or old poorly-scored words. These results can be used to target research and development from information retrieval and reinforcement learning for better and better recommendations to speech recognition and speech analytics for accent acquisition.",
}
@inproceedings{bakharia-2018-selecting,
    title = "Selecting {NLP} Techniques to Evaluate Learning Design Objectives in Collaborative Multi-perspective Elaboration Activities",
    author = "Bakharia, Aneesha",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3712",
    doi = "10.18653/v1/W18-3712",
    pages = "88--92",
    abstract = "PerspectivesX is a multi-perspective elaboration tool designed to encourage learner submission and curation across a range of collaborative learning activities. In this paper, it is shown that the learning design objectives of collaborative learning activities can be evaluated using NLP techniques, but that careful analysis of learner impact and pedagogical intent are required in order to select appropriate techniques. In particular, this paper focuses on the NLP techniques required to deliver an instructor dashboard, personalized learner feedback and content recommendation within multi-perspective elaboration activities. Key NLP techniques considered for inclusion include summarization, topic modeling, paraphrase detection and diversified content recommendation.",
}
@inproceedings{dasgupta-etal-2018-augmenting,
    title = "Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring",
    author = "Dasgupta, Tirthankar  and
      Naskar, Abir  and
      Dey, Lipika  and
      Saha, Rupsa",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3713",
    doi = "10.18653/v1/W18-3713",
    pages = "93--102",
    abstract = "In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task. The novelty of the work lies in the fact that instead of considering only the word and sentence representation of a text, we try to augment the different complex linguistic, cognitive and psycological features associated within a text document along with a hierarchical convolution recurrent neural network framework. Our preliminary investigation shows that incorporation of such qualitative feature vectors along with standard word/sentence embeddings can give us better understanding about improving the overall evaluation of the input essays.",
}
@inproceedings{le-etal-2018-joint,
    title = "Joint learning of frequency and word embeddings for multilingual readability assessment",
    author = "Le, Dieu-Thu  and
      Nguyen, Cam-Tu  and
      Wang, Xiaoliang",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3714",
    doi = "10.18653/v1/W18-3714",
    pages = "103--107",
    abstract = "This paper describes two models that employ word frequency embeddings to deal with the problem of readability assessment in multiple languages. The task is to determine the difficulty level of a given document, i.e., how hard it is for a reader to fully comprehend the text. The proposed models show how frequency information can be integrated to improve the readability assessment. The experimental results testing on both English and Chinese datasets show that the proposed models improve the results notably when comparing to those using only traditional word embeddings.",
}
@inproceedings{lange-ljunglof-2018-mulle,
    title = "{MULLE}: A grammar-based {L}atin language learning tool to supplement the classroom setting",
    author = {Lange, Herbert  and
      Ljungl{\"o}f, Peter},
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3715",
    doi = "10.18653/v1/W18-3715",
    pages = "108--112",
    abstract = "MULLE is a tool for language learning that focuses on teaching Latin as a foreign language. It is aimed for easy integration into the traditional classroom setting and syllabus, which makes it distinct from other language learning tools that provide standalone learning experience. It uses grammar-based lessons and embraces methods of gamification to improve the learner motivation. The main type of exercise provided by our application is to practice translation, but it is also possible to shift the focus to vocabulary or morphology training.",
}
@inproceedings{bel-enguix-etal-2018-textual,
    title = "Textual Features Indicative of Writing Proficiency in Elementary School {S}panish Documents",
    author = "Bel-Enguix, Gemma  and
      Due{\~n}as Ch{\'a}vez, Diana  and
      Curiel D{\'\i}az, Arturo",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3716",
    doi = "10.18653/v1/W18-3716",
    pages = "113--118",
    abstract = "Childhood acquisition of written language is not straightforward. Writing skills evolve differently depending on external factors, such as the conditions in which children practice their productions and the quality of their instructors{'} guidance. This can be challenging in low-income areas, where schools may struggle to ensure ideal acquisition conditions. Developing computational tools to support the learning process may counterweight negative environmental influences; however, few work exists on the use of information technologies to improve childhood literacy. This work centers around the computational study of Spanish word and syllable structure in documents written by 2nd and 3rd year elementary school students. The studied texts were compared against a corpus of short stories aimed at the same age group, so as to observe whether the children tend to produce similar written patterns as the ones they are expected to interpret at their literacy level. The obtained results show some significant differences between the two kinds of texts, pointing towards possible strategies for the implementation of new education software in support of written language acquisition.",
}
@inproceedings{kotani-yoshimi-2018-assessment,
    title = "Assessment of an Index for Measuring Pronunciation Difficulty",
    author = "Kotani, Katsunori  and
      Yoshimi, Takehiko",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3717",
    doi = "10.18653/v1/W18-3717",
    pages = "119--124",
    abstract = "This study assesses an index for measur-ing the pronunciation difficulty of sen-tences (henceforth, pronounceability) based on the normalized edit distance from a reference sentence to a transcrip-tion of learners{'} pronunciation. Pro-nounceability should be examined when language teachers use a computer-assisted language learning system for pronunciation learning to maintain the motivation of learners. However, unlike the evaluation of learners{'} pronunciation performance, previous research did not focus on pronounceability not only for English but also for Asian languages. This study found that the normalized edit distance was reliable but not valid. The lack of validity appeared to be because of an English test used for determining the proficiency of learners.",
}
@inproceedings{wu-shih-2018-short,
    title = "A Short Answer Grading System in {C}hinese by Support Vector Approach",
    author = "Wu, Shih-Hung  and
      Shih, Wen-Feng",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3718",
    doi = "10.18653/v1/W18-3718",
    pages = "125--129",
    abstract = "In this paper, we report a short answer grading system in Chinese. We build a system based on standard machine learning approaches and test it with translated corpus from two publicly available corpus in English. The experiment results show similar results on two different corpus as in English.",
}
@inproceedings{kwong-2018-fidelity,
    title = "From Fidelity to Fluency: Natural Language Processing for Translator Training",
    author = "Kwong, Oi Yee",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3719",
    doi = "10.18653/v1/W18-3719",
    pages = "130--134",
    abstract = "This study explores the use of natural language processing techniques to enhance bilingual lexical access beyond simple equivalents, to enable translators to navigate along a wider cross-lingual lexical space and more examples showing different translation strategies, which is essential for them to learn to produce not only faithful but also fluent translations.",
}
@inproceedings{chandrasekaran-kan-2018-countering,
    title = "Countering Position Bias in Instructor Interventions in {MOOC} Discussion Forums",
    author = "Chandrasekaran, Muthu Kumar  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3720",
    doi = "10.18653/v1/W18-3720",
    pages = "135--142",
    abstract = "We systematically confirm that instructors are strongly influenced by the user interface presentation of Massive Online Open Course (MOOC) discussion forums. In a large scale dataset, we conclusively show that instructor interventions exhibit strong position bias, as measured by the position where the thread appeared on the user interface at the time of intervention. We measure and remove this bias, enabling unbiased statistical modelling and evaluation. We show that our de-biased classifier improves predicting interventions over the state-of-the-art on courses with sufficient number of interventions by 8.2{\%} in F1 and 24.4{\%} in recall on average.",
}
@inproceedings{shiokawa-etal-2018-measuring,
    title = "Measuring Beginner Friendliness of {J}apanese Web Pages explaining Academic Concepts by Integrating Neural Image Feature and Text Features",
    author = "Shiokawa, Hayato  and
      Kawaguchi, Kota  and
      Han, Bingcai  and
      Utsuro, Takehito  and
      Kawada, Yasuhide  and
      Yoshioka, Masaharu  and
      Kando, Noriko",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3721",
    doi = "10.18653/v1/W18-3721",
    pages = "143--151",
    abstract = "Search engine is an important tool of modern academic study, but the results are lack of measurement of beginner friendliness. In order to improve the efficiency of using search engine for academic study, it is necessary to invent a technique of measuring the beginner friendliness of a Web page explaining academic concepts and to build an automatic measurement system. This paper studies how to integrate heterogeneous features such as a neural image feature generated from the image of the Web page by a variant of CNN (convolutional neural network) as well as text features extracted from the body text of the HTML file of the Web page. Integration is performed through the framework of the SVM classifier learning. Evaluation results show that heterogeneous features perform better than each individual type of features.",
}
@inproceedings{marrese-taylor-etal-2018-learning,
    title = "Learning to Automatically Generate Fill-In-The-Blank Quizzes",
    author = "Marrese-Taylor, Edison  and
      Nakajima, Ai  and
      Matsuo, Yutaka  and
      Yuichi, Ono",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3722",
    doi = "10.18653/v1/W18-3722",
    pages = "152--156",
    abstract = "In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for each. We present an empirical study based on data obtained from a language learning platform showing that both of our proposed settings offer promising results.",
}
@inproceedings{tseng-etal-2018-multilingual,
    title = "Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration",
    author = "Tseng, Yuen-Hsien  and
      Lee, Lung-Hao  and
      Chien, Yu-Ta  and
      Chang, Chun-Yen  and
      Li, Tsung-Yen",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3723",
    doi = "10.18653/v1/W18-3723",
    pages = "157--164",
    abstract = "Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.",
}
@inproceedings{zhou-etal-2018-chinese,
    title = "{C}hinese Grammatical Error Diagnosis Based on {CRF} and {LSTM}-{CRF} model",
    author = "Zhou, Yujie  and
      Shao, Yinan  and
      Zhou, Yong",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3724",
    doi = "10.18653/v1/W18-3724",
    pages = "165--171",
    abstract = "When learning Chinese as a foreign language, the learners may have some grammatical errors due to negative migration of their native languages. However, few grammar checking applications have been developed to support the learners. The goal of this paper is to develop a tool to automatically diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W) in Chinese sentences written by those foreign learners. In this paper, a conventional linear CRF model with specific feature engineering and a LSTM-CRF model are used to solve the CGED (Chinese Grammatical Error Diagnosis) task. We make some improvement on both models and the submitted results have better performance on false positive rate and accuracy than the average of all runs from CGED2018 for all three evaluation levels.",
}
@inproceedings{zhao-etal-2018-contextualized,
    title = "Contextualized Character Representation for {C}hinese Grammatical Error Diagnosis",
    author = "Zhao, Jianbo  and
      Li, Si  and
      Lin, Zhiqing",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3725",
    doi = "10.18653/v1/W18-3725",
    pages = "172--179",
    abstract = "Nowadays, more and more people are learning Chinese as their second language. Establishing an automatic diagnosis system for Chinese grammatical error has become an important challenge. In this paper, we propose a Chinese grammatical error diagnosis (CGED) model with contextualized character representation. Compared to the traditional model using LSTM (Long-Short Term Memory), our model have better performance and there is no need to add too many artificial features.",
}
@inproceedings{zhang-etal-2018-cmmc,
    title = "{CMMC}-{BDRC} Solution to the {NLP}-{TEA}-2018 {C}hinese Grammatical Error Diagnosis Task",
    author = "Zhang, Yongwei  and
      Hu, Qinan  and
      Liu, Fang  and
      Gu, Yueguo",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3726",
    doi = "10.18653/v1/W18-3726",
    pages = "180--187",
    abstract = "Chinese grammatical error diagnosis is an important natural language processing (NLP) task, which is also an important application using artificial intelligence technology in language education. This paper introduces a system developed by the Chinese Multilingual {\&} Multimodal Corpus and Big Data Research Center for the NLP-TEA shared task, named Chinese Grammar Error Diagnosis (CGED). This system regards diagnosing errors task as a sequence tagging problem, while takes correction task as a text classification problem. Finally, in the 12 teams, this system gets the highest F1 score in the detection task and the second highest F1 score in mean in the identification task, position task and the correction task.",
}
@inproceedings{liu-etal-2018-detecting,
    title = "Detecting Simultaneously {C}hinese Grammar Errors Based on a {B}i{LSTM}-{CRF} Model",
    author = "Liu, Yajun  and
      Zan, Hongying  and
      Zhong, Mengjie  and
      Ma, Hongchao",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3727",
    doi = "10.18653/v1/W18-3727",
    pages = "188--193",
    abstract = "In the process of learning and using Chinese, many learners of Chinese as foreign language(CFL) may have grammar errors due to negative migration of their native languages. This paper introduces our system that can simultaneously diagnose four types of grammatical errors including redundant (R), missing (M), selection (S), disorder (W) in NLPTEA-5 shared task. We proposed a Bidirectional LSTM CRF neural network (BiLSTM-CRF) that combines BiLSTM and CRF without hand-craft features for Chinese Grammatical Error Diagnosis (CGED). Evaluation includes three levels, which are detection level, identification level and position level. At the detection level and identification level, our system got the third recall scores, and achieved good F1 values.",
}
@inproceedings{wang-shih-2018-hybrid,
    title = "A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for {C}hinese Grammatical Error Detection",
    author = "Wang, Yiyi  and
      Shih, Chilin",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3728",
    doi = "10.18653/v1/W18-3728",
    pages = "194--198",
    abstract = "This paper presents a method of combining Conditional Random Fields (CRFs) model with a post-processing layer using Google n-grams statistical information tailored to detect word selection and word order errors made by learners of Chinese as Foreign Language (CFL). We describe the architecture of the model and its performance in the shared task of the ACL 2018 Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA). This hybrid approach yields comparably high false positive rate (FPR = 0.1274) and precision (Pd= 0.7519; Pi= 0.6311), but low recall (Rd = 0.3035; Ri = 0.1696 ) in grammatical error detection and identification tasks. Additional statistical information and linguistic rules can be added to enhance the model performance in the future.",
}
@inproceedings{wu-etal-2018-cyut,
    title = "{CYUT}-{III} Team {C}hinese Grammatical Error Diagnosis System Report in {NLPTEA}-2018 {CGED} Shared Task",
    author = "Wu, Shih-Hung  and
      Wang, Jun-Wei  and
      Chen, Liang-Pu  and
      Yang, Ping-Che",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3729",
    doi = "10.18653/v1/W18-3729",
    pages = "199--202",
    abstract = "This paper reports how we build a Chinese Grammatical Error Diagnosis system in the NLPTEA-2018 CGED shared task. In 2018, we sent three runs with three different approaches. The first one is a pattern-based approach by frequent error pattern matching. The second one is a sequential labelling approach by conditional random fields (CRF). The third one is a rewriting approach by sequence to sequence (seq2seq) model. The three approaches have different properties that aim to optimize different performance metrics and the formal run results show the differences as we expected.",
}
@inproceedings{lin-chen-2018-detecting,
    title = "Detecting Grammatical Errors in the {NTOU} {CGED} System by Identifying Frequent Subsentences",
    author = "Lin, Chuan-Jie  and
      Chen, Shao-Heng",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3730",
    doi = "10.18653/v1/W18-3730",
    pages = "203--206",
    abstract = "The main goal of Chinese grammatical error diagnosis task is to detect word er-rors in the sentences written by Chinese-learning students. Our previous system would generate error-corrected sentences as candidates and their sentence likeli-hood were measured based on a large scale Chinese n-gram dataset. This year we further tried to identify long frequent-ly-seen subsentences and label them as correct in order to avoid propose too many error candidates. Two new methods for suggesting missing and selection er-rors were also tested.",
}
@proceedings{ws-2018-multilingual,
    title = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    editor = "Mille, Simon  and
      Belz, Anja  and
      Bohnet, Bernd  and
      Pitler, Emily  and
      Wanner, Leo",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3600",
}
@inproceedings{mille-etal-2018-first,
    title = "The First Multilingual Surface Realisation Shared Task ({SR}{'}18): Overview and Evaluation Results",
    author = "Mille, Simon  and
      Belz, Anja  and
      Bohnet, Bernd  and
      Graham, Yvette  and
      Pitler, Emily  and
      Wanner, Leo",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3601",
    doi = "10.18653/v1/W18-3601",
    pages = "1--12",
    abstract = "We report results from the SR{'}18 Shared Task, a new multilingual surface realisation task organised as part of the ACL{'}18 Workshop on Multilingual Surface Realisation. As in its English-only predecessor task SR{'}11, the shared task comprised two tracks with different levels of complexity: (a) a shallow track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (b) a deep track where additionally, functional words and morphological information were removed. The shallow track was offered in ten, and the deep track in three languages. Systems were evaluated (a) automatically, using a range of intrinsic metrics, and (b) by human judges in terms of readability and meaning similarity. This report presents the evaluation results, along with descriptions of the SR{'}18 tracks, data and evaluation methods. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.",
}
@inproceedings{puzikov-gurevych-2018-binlin,
    title = "{B}in{L}in: A Simple Method of Dependency Tree Linearization",
    author = "Puzikov, Yevgeniy  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3602",
    doi = "10.18653/v1/W18-3602",
    pages = "13--28",
    abstract = "Surface Realization Shared Task 2018 is a workshop on generating sentences from lemmatized sets of dependency triples. This paper describes the results of our participation in the challenge. We develop a data-driven pipeline system which first orders the lemmas and then conjugates the words to finish the surface realization process. Our contribution is a novel sequential method of ordering lemmas, which, despite its simplicity, achieves promising results. We demonstrate the effectiveness of the proposed approach, describe its limitations and outline ways to improve it.",
}
@inproceedings{singh-etal-2018-iit,
    title = "{IIT} ({BHU}) Varanasi at {MSR}-{SRST} 2018: A Language Model Based Approach for Natural Language Generation",
    author = "Singh, Shreyansh  and
      Sharma, Ayush  and
      Chawla, Avi  and
      Singh, A.K.",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3603",
    doi = "10.18653/v1/W18-3603",
    pages = "29--34",
    abstract = "This paper describes our submission system for the Shallow Track of Surface Realization Shared Task 2018 (SRST{'}18). The task was to convert genuine UD structures, from which word order information had been removed and the tokens had been lemmatized, into their correct sentential form. We divide the problem statement into two parts, word reinflection and correct word order prediction. For the first sub-problem, we use a Long Short Term Memory based Encoder-Decoder approach. For the second sub-problem, we present a Language Model (LM) based approach. We apply two different sub-approaches in the LM Based approach and the combined result of these two approaches is considered as the final output of the system.",
}
@inproceedings{castro-ferreira-etal-2018-surface,
    title = "Surface Realization Shared Task 2018 ({SR}18): The {T}ilburg {U}niversity Approach",
    author = "Castro Ferreira, Thiago  and
      Wubben, Sander  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3604",
    doi = "10.18653/v1/W18-3604",
    pages = "35--38",
    abstract = "This study describes the approach developed by the Tilburg University team to the shallow task of the Multilingual Surface Realization Shared Task 2018 (SR18). Based on (Castro Ferreira et al., 2017), the approach works by first preprocessing an input dependency tree into an ordered linearized string, which is then realized using a statistical machine translation model. Our approach shows promising results, with BLEU scores above 50 for 5 different languages (English, French, Italian, Portuguese and Spanish) and above 35 for the Dutch language.",
}
@inproceedings{king-white-2018-osu,
    title = "The {OSU} Realizer for {SRST} {`}18: Neural Sequence-to-Sequence Inflection and Incremental Locality-Based Linearization",
    author = "King, David  and
      White, Michael",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3605",
    doi = "10.18653/v1/W18-3605",
    pages = "39--48",
    abstract = "Surface realization is a nontrivial task as it involves taking structured data and producing grammatically and semantically correct utterances. Many competing grammar-based and statistical models for realization still struggle with relatively simple sentences. For our submission to the 2018 Surface Realization Shared Task, we tackle the shallow task by first generating inflected wordforms with a neural sequence-to-sequence model before incrementally linearizing them. For linearization, we use a global linear model trained using early update that makes use of features that take into account the dependency structure and dependency locality. Using this pipeline sufficed to produce surprisingly strong results in the shared task. In future work, we intend to pursue joint approaches to linearization and morphological inflection and incorporating a neural language model into the linearization choices.",
}
@inproceedings{elder-hokamp-2018-generating,
    title = "Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models",
    author = "Elder, Henry  and
      Hokamp, Chris",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3606",
    doi = "10.18653/v1/W18-3606",
    pages = "49--53",
    abstract = "This work presents state of the art results in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to training high-performing models, and solve this issue by generating large amounts of synthetic training data. We also propose preprocessing techniques which make the structure contained in the input features more accessible to sequence models. Our models were ranked first on all evaluation metrics in the English portion of the 2018 Surface Realization shared task.",
}
@inproceedings{madsack-etal-2018-ax-semantics,
    title = "{AX} Semantics{'} Submission to the Surface Realization Shared Task 2018",
    author = {Madsack, Andreas  and
      Heininger, Johanna  and
      Davaasambuu, Nyamsuren  and
      Voronik, Vitaliia  and
      K{\"a}ufl, Michael  and
      Wei{\ss}graeber, Robert},
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3607",
    doi = "10.18653/v1/W18-3607",
    pages = "54--57",
    abstract = "In this paper we describe our system and experimental results on the development set of the Surface Realisation Shared Task. Our system is an entry for the Shallow-Task, with two different models based on deep-learning implementations for building the sentence combined with a rule-based morphology component.",
}
@inproceedings{sobrevilla-cabezudo-pardo-2018-nilc,
    title = "{NILC}-{SWORNEMO} at the Surface Realization Shared Task: Exploring Syntax-Based Word Ordering using Neural Models",
    author = "Sobrevilla Cabezudo, Marco Antonio  and
      Pardo, Thiago",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3608",
    doi = "10.18653/v1/W18-3608",
    pages = "58--64",
    abstract = "This paper describes the submission by the NILC Computational Linguistics research group of the University of S{\~a}o Paulo/Brazil to the Track 1 of the Surface Realization Shared Task (SRST Track 1). We present a neural-based method that works at the syntactic level to order the words (which we refer by NILC-SWORNEMO, standing for {``}Syntax-based Word ORdering using NEural MOdels{''}). Additionally, we apply a bottom-up approach to build the sentence and, using language-specific lexicons, we produce the proper word form of each lemma in the sentence. The results obtained by our method outperformed the average of the results for English, Portuguese and Spanish in the track.",
}
@inproceedings{basile-mazzei-2018-dipinfo,
    title = "The {D}ip{I}nfo-{U}ni{T}o system for {SRST} 2018",
    author = "Basile, Valerio  and
      Mazzei, Alessandro",
    booktitle = "Proceedings of the First Workshop on Multilingual Surface Realisation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3609",
    doi = "10.18653/v1/W18-3609",
    pages = "65--71",
    abstract = "This paper describes the system developed by the DipInfo-UniTo team to participate to the shallow track of the Surface Realization Shared Task 2018. The system employs two separate neural networks with different architectures to predict the word ordering and the morphological inflection independently from each other. The UniTO realizer is language independent, and its simple architecture allowed it to be scored in the central part of the final ranking of the shared task.",
}
@proceedings{ws-2018-international-natural,
    title = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3500",
}
@inproceedings{dorantes-etal-2018-sociolinguistic,
    title = "Sociolinguistic Corpus of {W}hats{A}pp Chats in {S}panish among College Students",
    author = "Dorantes, Alejandro  and
      Sierra, Gerardo  and
      Donohue P{\'e}rez, Tlauhlia Yam{\'\i}n  and
      Bel-Enguix, Gemma  and
      Jasso Rosales, M{\'o}nica",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3501",
    doi = "10.18653/v1/W18-3501",
    pages = "1--6",
    abstract = "This work presents the Sociolinguistic Corpus of WhatsApp Chats in Spanish among College Students, a corpus of raw data for general use. Its purpose is to offer data for the study of of language and interactions via Instant Messaging (IM) among bachelors. Our paper consists of an overview of both the corpus{'}s content and demographic metadata. Furthermore, it presents the current research being conducted with it {---}namely parenthetical expressions, orality traits, and code-switching. This work also includes a brief outline of similar corpora and recent studies in the field of IM.",
}
@inproceedings{castro-etal-2018-crowd,
    title = "A Crowd-Annotated {S}panish Corpus for Humor Analysis",
    author = "Castro, Santiago  and
      Chiruzzo, Luis  and
      Ros{\'a}, Aiala  and
      Garat, Diego  and
      Moncecchi, Guillermo",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3502",
    doi = "10.18653/v1/W18-3502",
    pages = "7--11",
    abstract = "Computational Humor involves several tasks, such as humor recognition, humor generation, and humor scoring, for which it is useful to have human-curated data. In this work we present a corpus of 27,000 tweets written in Spanish and crowd-annotated by their humor value and funniness score, with about four annotations per tweet, tagged by 1,300 people over the Internet. It is equally divided between tweets coming from humorous and non-humorous accounts. The inter-annotator agreement Krippendorff{'}s alpha value is 0.5710. The dataset is available for general usage and can serve as a basis for humor detection and as a first step to tackle subjectivity.",
}
@inproceedings{singh-etal-2018-twitter,
    title = "A {T}witter Corpus for {H}indi-{E}nglish Code Mixed {POS} Tagging",
    author = "Singh, Kushagra  and
      Sen, Indira  and
      Kumaraguru, Ponnurangam",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3503",
    doi = "10.18653/v1/W18-3503",
    pages = "12--17",
    abstract = "Code-mixing is a linguistic phenomenon where multiple languages are used in the same occurrence that is increasingly common in multilingual societies. Code-mixed content on social media is also on the rise, prompting the need for tools to automatically understand such content. Automatic Parts-of-Speech (POS) tagging is an essential step in any Natural Language Processing (NLP) pipeline, but there is a lack of annotated data to train such models. In this work, we present a unique language tagged and POS-tagged dataset of code-mixed English-Hindi tweets related to five incidents in India that led to a lot of Twitter activity. Our dataset is unique in two dimensions: (i) it is larger than previous annotated datasets and (ii) it closely resembles typical real-world tweets. Additionally, we present a POS tagging model that is trained on this dataset to provide an example of how this dataset can be used. The model also shows the efficacy of our dataset in enabling the creation of code-mixed social media POS taggers.",
}
@inproceedings{mathur-etal-2018-detecting,
    title = "Detecting Offensive Tweets in {H}indi-{E}nglish Code-Switched Language",
    author = "Mathur, Puneet  and
      Shah, Rajiv  and
      Sawhney, Ramit  and
      Mahata, Debanjan",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3504",
    doi = "10.18653/v1/W18-3504",
    pages = "18--26",
    abstract = "The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort to ease communication. The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script. The paper introduces a novel tweet dataset, titled Hindi-English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: non-offensive, abusive and hate-speech. Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English followed by retraining on Hinglish tweets.",
}
@inproceedings{hsu-ku-2018-socialnlp,
    title = "{S}ocial{NLP} 2018 {E}motion{X} Challenge Overview: Recognizing Emotions in Dialogues",
    author = "Hsu, Chao-Chun  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3505",
    doi = "10.18653/v1/W18-3505",
    pages = "27--31",
    abstract = "This paper describes an overview of the Dialogue Emotion Recognition Challenge, EmotionX, at the Sixth SocialNLP Workshop, which recognizes the emotion of each utterance in dialogues. This challenge offers the EmotionLines dataset as the experimental materials. The EmotionLines dataset contains conversations from Friends TV show transcripts (Friends) and real chatting logs (EmotionPush), where every dialogue utterance is labeled with emotions. Organizers provide baseline results. 18 teams registered in this challenge and 5 of them submitted their results successfully. The best team achieves the unweighted accuracy 62.48 and 62.5 on EmotionPush and Friends, respectively. In this paper we present the task definition, test collection, the evaluation results of the groups that participated in this challenge, and their approach.",
}
@inproceedings{luo-etal-2018-emotionx,
    title = "{E}motion{X}-{DLC}: Self-Attentive {B}i{LSTM} for Detecting Sequential Emotions in Dialogues",
    author = "Luo, Linkai  and
      Yang, Haiqin  and
      Chin, Francis Y. L.",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3506",
    doi = "10.18653/v1/W18-3506",
    pages = "32--36",
    abstract = "In this paper, we propose a self-attentive bidirectional long short-term memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX challenge. The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification. Building on top of BiLSTM, the self-attentive network can model the contextual dependencies between utterances which are helpful for classifying the ambiguous emotions. We achieve 59.6 and 55.0 unweighted accuracy scores in the Friends and the EmotionPush test sets, respectively.",
}
@inproceedings{khosla-2018-emotionx,
    title = "{E}motion{X}-{AR}: {CNN}-{DCNN} autoencoder based Emotion Classifier",
    author = "Khosla, Sopan",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3507",
    doi = "10.18653/v1/W18-3507",
    pages = "37--44",
    abstract = "In this paper, we model emotions in EmotionLines dataset using a convolutional-deconvolutional autoencoder (CNN-DCNN) framework. We show that adding a joint reconstruction loss improves performance. Quantitative evaluation with jointly trained network, augmented with linguistic features, reports best accuracies for emotion prediction; namely joy, sadness, anger, and neutral emotion in text.",
}
@inproceedings{albalooshi-etal-2018-emotionx,
    title = "{E}motion{X}-{S}mart{D}ubai{\_}{NLP}: Detecting User Emotions In Social Media Text",
    author = "AlBalooshi, Hessa  and
      Rahmanian, Shahram  and
      Venkatesh Kumar, Rahul",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3508",
    doi = "10.18653/v1/W18-3508",
    pages = "45--49",
    abstract = "This paper describes the working note on {``}EmotionX{''} shared task. It is hosted by SocialNLP 2018. The objective of this task is to detect the emotions, based on each speaker{'}s utterances that are in English. Taking this as multiclass text classification problem, we have experimented to develop a model to classify the target class. The primary challenge in this task is to detect the emotions in short messages, communicated through social media. This paper describes the participation of SmartDubai{\_}NLP team in EmotionX shared task and our investigation to detect the emotions from utterance using Neural networks and Natural language understanding.",
}
@inproceedings{saxena-etal-2018-emotionx,
    title = "{E}motion{X}-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling",
    author = "Saxena, Rohit  and
      Bhat, Savita  and
      Pedanekar, Niranjan",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3509",
    doi = "10.18653/v1/W18-3509",
    pages = "50--55",
    abstract = "This paper presents our system submitted to the EmotionX challenge. It is an emotion detection task on dialogues in the EmotionLines dataset. We formulate this as a hierarchical network where network learns data representation at both utterance level and dialogue level. Our model is inspired by Hierarchical Attention network (HAN) and uses pre-trained word embeddings as features. We formulate emotion detection in dialogues as a sequence labeling problem to capture the dependencies among labels. We report the performance accuracy for four emotions (anger, joy, neutral and sadness). The model achieved unweighted accuracy of 55.38{\%} on Friends test dataset and 56.73{\%} on EmotionPush test dataset. We report an improvement of 22.51{\%} in Friends dataset and 36.04{\%} in EmotionPush dataset over baseline results.",
}
@inproceedings{torres-2018-emotionx,
    title = "{E}motion{X}-{JTML}: Detecting emotions with Attention",
    author = "Torres, Johnny",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3510",
    doi = "10.18653/v1/W18-3510",
    pages = "56--60",
    abstract = "This paper addresses the problem of automatic recognition of emotions in conversational text datasets for the EmotionX challenge. Emotion is a human characteristic expressed through several modalities (e.g., auditory, visual, tactile). Trying to detect emotions only from the text becomes a difficult task even for humans. This paper evaluates several neural architectures based on Attention Models, which allow extracting relevant parts of the context within a conversation to identify the emotion associated with each utterance. Empirical results in the validation datasets demonstrate the effectiveness of the approach compared to the reference models for some instances, and other cases show better results with simpler models.",
}
@inproceedings{parupalli-etal-2018-towards,
    title = "Towards Automation of Sense-type Identification of Verbs in {O}nto{S}ense{N}et",
    author = "Parupalli, Sreekavitha  and
      Anvesh Rao, Vijjini  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3511",
    doi = "10.18653/v1/W18-3511",
    pages = "61--66",
    abstract = "In this paper, we discuss the enrichment of a manually developed resource, OntoSenseNet for Telugu. OntoSenseNet is a sense annotated resource that marks each verb of Telugu with a primary and a secondary sense. The area of research is relatively recent but has a large scope of development. We provide an introductory work to enrich the OntoSenseNet to promote further research in Telugu. Classifiers are adopted to learn the sense relevant features of the words in the resource and also to automate the tagging of sense-types for verbs. We perform a comparative analysis of different classifiers applied on OntoSenseNet. The results of the experiment prove that automated enrichment of the resource is effective using SVM classifiers and Adaboost ensemble.",
}
@inproceedings{stowe-etal-2018-improving,
    title = "Improving Classification of {T}witter Behavior During Hurricane Events",
    author = "Stowe, Kevin  and
      Anderson, Jennings  and
      Palmer, Martha  and
      Palen, Leysia  and
      Anderson, Ken",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3512",
    doi = "10.18653/v1/W18-3512",
    pages = "67--75",
    abstract = "A large amount of social media data is generated during natural disasters, and identifying the relevant portions of this data is critical for researchers attempting to understand human behavior, the effects of information sources, and preparatory actions undertaken during these events. In order to classify human behavior during hazard events, we employ machine learning for two tasks: identifying hurricane related tweets and classifying user evacuation behavior during hurricanes. We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.",
}
@inproceedings{bilbao-jayo-almeida-2018-political,
    title = "Political discourse classification in social networks using context sensitive convolutional neural networks",
    author = "Bilbao-Jayo, Aritz  and
      Almeida, Aitor",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3513",
    doi = "10.18653/v1/W18-3513",
    pages = "76--85",
    abstract = "In this study we propose a new approach to analyse the political discourse in on-line social networks such as Twitter. To do so, we have built a discourse classifier using Convolutional Neural Networks. Our model has been trained using election manifestos annotated manually by political scientists following the Regional Manifestos Project (RMP) methodology. In total, it has been trained with more than 88,000 sentences extracted from more that 100 annotated manifestos. Our approach takes into account the context of the phrase in order to classify it, like what was previously said and the political affiliation of the transmitter. To improve the classification results we have used a simplified political message taxonomy developed within the Electronic Regional Manifestos Project (E-RMP). Using this taxonomy, we have validated our approach analysing the Twitter activity of the main Spanish political parties during 2015 and 2016 Spanish general election and providing a study of their discourse.",
}
@proceedings{ws-2018-deep,
    title = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    editor = "Haffari, Reza  and
      Cherry, Colin  and
      Foster, George  and
      Khadivi, Shahram  and
      Salehi, Bahar",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3400",
}
@inproceedings{kann-etal-2018-character,
    title = "Character-level Supervision for Low-resource {POS} Tagging",
    author = "Kann, Katharina  and
      Bjerva, Johannes  and
      Augenstein, Isabelle  and
      Plank, Barbara  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3401",
    doi = "10.18653/v1/W18-3401",
    pages = "1--11",
    abstract = "Neural part-of-speech (POS) taggers are known to not perform well with little training data. As a step towards overcoming this problem, we present an architecture for learning more robust neural POS taggers by jointly training a hierarchical, recurrent model and a recurrent character-based sequence-to-sequence network supervised using an auxiliary objective. This way, we introduce stronger character-level supervision into the model, which enables better generalization to unseen words and provides regularization, making our encoding less prone to overfitting. We experiment with three auxiliary tasks: lemmatization, character-based word autoencoding, and character-based random string autoencoding. Experiments with minimal amounts of labeled data on 34 languages show that our new architecture outperforms a single-task baseline and, surprisingly, that, on average, raw text autoencoding can be as beneficial for low-resource POS tagging as using lemma information. Our neural POS tagger closes the gap to a state-of-the-art POS tagger (MarMoT) for low-resource scenarios by 43{\%}, even outperforming it on languages with templatic morphology, e.g., Arabic, Hebrew, and Turkish, by some margin.",
}
@inproceedings{hedderich-klakow-2018-training,
    title = "Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data",
    author = "Hedderich, Michael A.  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3402",
    doi = "10.18653/v1/W18-3402",
    pages = "12--18",
    abstract = "Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier{'}s performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35{\%} by using additional, noisy data and handling the noise.",
}
@inproceedings{bollmann-etal-2018-multi,
    title = "Multi-task learning for historical text normalization: Size matters",
    author = "Bollmann, Marcel  and
      S{\o}gaard, Anders  and
      Bingel, Joachim",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3403",
    doi = "10.18653/v1/W18-3403",
    pages = "19--24",
    abstract = "Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models. Previous work has been limited to datasets from a specific language and a specific historical period, and it is not clear whether results generalize. It therefore remains an open problem, when historical text normalization benefits from multi-task learning. We explore the benefits of multi-task learning across 10 different datasets, representing different languages and periods. Our main finding{---}contrary to what has been observed for other NLP tasks{---}is that multi-task learning mainly works when target task data is very scarce.",
}
@inproceedings{dudy-bedrick-2018-compositional,
    title = "Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication",
    author = "Dudy, Shiran  and
      Bedrick, Steven",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3404",
    doi = "10.18653/v1/W18-3404",
    pages = "25--32",
    abstract = "Icon-based communication systems are widely used in the field of Augmentative and Alternative Communication. Typically, icon-based systems have lagged behind word- and character-based systems in terms of predictive typing functionality, due to the challenges inherent to training icon-based language models. We propose a method for synthesizing training data for use in icon-based language models, and explore two different modeling strategies. We propose a method to generate language models for corpus-less symbol-set.",
}
@inproceedings{dutta-chowdhury-etal-2018-multimodal,
    title = "Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data",
    author = "Dutta Chowdhury, Koel  and
      Hasanuzzaman, Mohammed  and
      Liu, Qun",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3405",
    doi = "10.18653/v1/W18-3405",
    pages = "33--42",
    abstract = "In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data. A three-way parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-the-art MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features.",
}
@inproceedings{ikhwantri-etal-2018-multi,
    title = "Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus",
    author = "Ikhwantri, Fariz  and
      Louvan, Samuel  and
      Kurniawan, Kemal  and
      Abisena, Bagas  and
      Rachman, Valdi  and
      Wicaksono, Alfan Farizki  and
      Mahendra, Rahmad",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3406",
    doi = "10.18653/v1/W18-3406",
    pages = "43--50",
    abstract = "Most Semantic Role Labeling (SRL) approaches are supervised methods which require a significant amount of annotated corpus, and the annotation requires linguistic expertise. In this paper, we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition (ER) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL. We evaluate our approach on Indonesian conversational dataset. Our experiments show that multi-task active learning can outperform single-task active learning method and standard multi-task learning. According to our results, active learning is more efficient by using 12{\%} less of training data compared to passive learning in both single-task and multi-task setting. We also introduce a new dataset for SRL in Indonesian conversational domain to encourage further research in this area.",
}
@inproceedings{kameswara-sarma-etal-2018-domain,
    title = "Domain Adapted Word Embeddings for Improved Sentiment Classification",
    author = "Kameswara Sarma, Prathusha  and
      Liang, Yingyu  and
      Sethares, Bill",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3407",
    doi = "10.18653/v1/W18-3407",
    pages = "51--59",
    abstract = "Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a method to combine the breadth of generic embeddings with the specificity of domain specific embeddings. The resulting embeddings, called Domain Adapted (DA) word embeddings, are formed by first aligning corresponding word vectors using Canonical Correlation Analysis (CCA) or the related nonlinear Kernel CCA (KCCA) and then combining them via convex optimization. Results from evaluation on sentiment classification tasks show that the DA embeddings substantially outperform both generic, DS embeddings when used as input features to standard or state-of-the-art sentence encoding algorithms for classification.",
}
@inproceedings{komiya-shinnou-2018-investigating,
    title = "Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus",
    author = "Komiya, Kanako  and
      Shinnou, Hiroyuki",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3408",
    doi = "10.18653/v1/W18-3408",
    pages = "60--67",
    abstract = "Fine-tuning is a popular method to achieve better performance when only a small target corpus is available. However, it requires tuning of a number of metaparameters and thus it might carry risk of adverse effect when inappropriate metaparameters are used. Therefore, we investigate effective parameters for fine-tuning when only a small target corpus is available. In the current study, we target at improving Japanese word embeddings created from a huge corpus. First, we demonstrate that even the word embeddings created from the huge corpus are affected by domain shift. After that, we investigate effective parameters for fine-tuning of the word embeddings using a small target corpus. We used perplexity of a language model obtained from a Long Short-Term Memory network to assess the word embeddings input into the network. The experiments revealed that fine-tuning sometimes give adverse effect when only a small target corpus is used and batch size is the most important parameter for fine-tuning. In addition, we confirmed that effect of fine-tuning is higher when size of a target corpus was larger.",
}
@inproceedings{liu-etal-2018-semi,
    title = "Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification",
    author = "Liu, Mingkuan  and
      Wen, Musen  and
      Kopru, Selcuk  and
      Liu, Xianjing  and
      Lu, Alan",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3409",
    doi = "10.18653/v1/W18-3409",
    pages = "68--76",
    abstract = "The lack of high-quality labeled training data has been one of the critical challenges facing many industrial machine learning tasks. To tackle this challenge, in this paper, we propose a semi-supervised learning method to utilize unlabeled data and user feedback signals to improve the performance of ML models. The method employs a primary model Main and an auxiliary evaluation model Eval, where Main and Eval models are trained iteratively by automatically generating labeled data from unlabeled data and/or users{'} feedback signals. The proposed approach is applied to different text classification tasks. We report results on both the publicly available Yahoo! Answers dataset and our e-commerce product classification dataset. The experimental results show that the proposed method reduces the classification error rate by 4{\%} and up to 15{\%} across various experimental setups and datasets. A detailed comparison with other semi-supervised learning approaches is also presented later in the paper. The results from various text classification tasks demonstrate that our method outperforms those developed in previous related studies.",
}
@inproceedings{miceli-barone-2018-low,
    title = "Low-rank passthrough neural networks",
    author = "Miceli Barone, Antonio Valerio",
    booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3410",
    doi = "10.18653/v1/W18-3410",
    pages = "77--86",
    abstract = "Various common deep learning architectures, such as LSTMs, GRUs, Resnets and Highway Networks, employ state passthrough connections that support training with high feed-forward depth or recurrence over many time steps. These {``}Passthrough Networks{''} architectures also enable the decoupling of the network state size from the number of parameters of the network, a possibility has been studied by Sak et al. (2014) with their low-rank parametrization of the LSTM. In this work we extend this line of research, proposing effective, low-rank and low-rank plus diagonal matrix parametrizations for Passthrough Networks which exploit this decoupling property, reducing the data complexity and memory requirements of the network while preserving its memory capacity. This is particularly beneficial in low-resource settings as it supports expressive models with a compact parametrization less susceptible to overfitting. We present competitive experimental results on several tasks, including language modeling and a near state of the art result on sequential randomly-permuted MNIST classification, a hard task on natural data.",
}
@proceedings{ws-2018-grand,
    title = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    editor = "Zadeh, Amir  and
      Liang, Paul Pu  and
      Morency, Louis-Philippe  and
      Poria, Soujanya  and
      Cambria, Erik  and
      Scherer, Stefan",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3300",
}
@inproceedings{blanchard-etal-2018-getting,
    title = "Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities",
    author = "Blanchard, Nathaniel  and
      Moreira, Daniel  and
      Bharati, Aparna  and
      Scheirer, Walter",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3301",
    doi = "10.18653/v1/W18-3301",
    pages = "1--10",
    abstract = "In the last decade, video blogs (vlogs) have become an extremely popular method through which people express sentiment. The ubiquitousness of these videos has increased the importance of multimodal fusion models, which incorporate video and audio features with traditional text features for automatic sentiment detection. Multimodal fusion offers a unique opportunity to build models that learn from the full depth of expression available to human viewers. In the detection of sentiment in these videos, acoustic and video features provide clarity to otherwise ambiguous transcripts. In this paper, we present a multimodal fusion model that exclusively uses high-level video and audio features to analyze spoken sentences for sentiment. We discard traditional transcription features in order to minimize human intervention and to maximize the deployability of our model on at-scale real-world data. We select high-level features for our model that have been successful in non-affect domains in order to test their generalizability in the sentiment detection domain. We train and test our model on the newly released CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset, obtaining an F1 score of 0.8049 on the validation set and an F1 score of 0.6325 on the held-out challenge test set.",
}
@inproceedings{williams-etal-2018-recognizing,
    title = "Recognizing Emotions in Video Using Multimodal {DNN} Feature Fusion",
    author = "Williams, Jennifer  and
      Kleinegesse, Steven  and
      Comanescu, Ramona  and
      Radu, Oana",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3302",
    doi = "10.18653/v1/W18-3302",
    pages = "11--19",
    abstract = "We present our system description of input-level multimodal fusion of audio, video, and text for recognition of emotions and their intensities for the 2018 First Grand Challenge on Computational Modeling of Human Multimodal Language. Our proposed approach is based on input-level feature fusion with sequence learning from Bidirectional Long-Short Term Memory (BLSTM) deep neural networks (DNNs). We show that our fusion approach outperforms unimodal predictors. Our system performs 6-way simultaneous classification and regression, allowing for overlapping emotion labels in a video segment. This leads to an overall binary accuracy of 90{\%}, overall 4-class accuracy of 89.2{\%} and an overall mean-absolute-error (MAE) of 0.12. Our work shows that an early fusion technique can effectively predict the presence of multi-label emotions as well as their coarse-grained intensities. The presented multimodal approach creates a simple and robust baseline on this new Grand Challenge dataset. Furthermore, we provide a detailed analysis of emotion intensity distributions as output from our DNN, as well as a related discussion concerning the inherent difficulty of this task.",
}
@inproceedings{sahay-etal-2018-multimodal,
    title = "Multimodal Relational Tensor Network for Sentiment and Emotion Classification",
    author = "Sahay, Saurav  and
      Kumar, Shachi H  and
      Xia, Rui  and
      Huang, Jonathan  and
      Nachman, Lama",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3303",
    doi = "10.18653/v1/W18-3303",
    pages = "20--27",
    abstract = "Understanding Affect from video segments has brought researchers from the language, audio and video domains together. Most of the current multimodal research in this area deals with various techniques to fuse the modalities, and mostly treat the segments of a video independently. Motivated by the work of (Zadeh et al., 2017) and (Poria et al., 2017), we present our architecture, Relational Tensor Network, where we use the inter-modal interactions within a segment (intra-segment) and also consider the sequence of segments in a video to model the inter-segment inter-modal interactions. We also generate rich representations of text and audio modalities by leveraging richer audio and linguistic context alongwith fusing fine-grained knowledge based polarity scores from text. We present the results of our model on CMU-MOSEI dataset and show that our model outperforms many baselines and state of the art methods for sentiment classification and emotion recognition.",
}
@inproceedings{choi-etal-2018-convolutional,
    title = "Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data",
    author = "Choi, Woo Yong  and
      Song, Kyu Ye  and
      Lee, Chan Woo",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3304",
    doi = "10.18653/v1/W18-3304",
    pages = "28--34",
    abstract = "Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multimodal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset.",
}
@inproceedings{sheikh-etal-2018-sentiment,
    title = "Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities",
    author = "Sheikh, Imran  and
      Dumpala, Sri Harsha  and
      Chakraborty, Rupayan  and
      Kopparapu, Sunil Kumar",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3305",
    doi = "10.18653/v1/W18-3305",
    pages = "35--39",
    abstract = "Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views. This work focuses on improving the representations of these views by performing a deep canonical correlation analysis with the representations of the better performing manual transcription view. Enhanced representations of the imperfect views can be obtained even in absence of the perfect views and give an improved performance during test conditions. Evaluations on the CMU-MOSI and CMU-MOSEI datasets demonstrate the effectiveness of the proposed approach.",
}
@inproceedings{tian-etal-2018-polarity,
    title = "Polarity and Intensity: the Two Aspects of Sentiment Analysis",
    author = "Tian, Leimin  and
      Lai, Catherine  and
      Moore, Johanna",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3306",
    doi = "10.18653/v1/W18-3306",
    pages = "40--47",
    abstract = "Current multimodal sentiment analysis frames sentiment score prediction as a general Machine Learning task. However, what the sentiment score actually represents has often been overlooked. As a measurement of opinions and affective states, a sentiment score generally consists of two aspects: polarity and intensity. We decompose sentiment scores into these two aspects and study how they are conveyed through individual modalities and combined multimodal models in a naturalistic monologue setting. In particular, we build unimodal and multimodal multi-task learning models with sentiment score prediction as the main task and polarity and/or intensity classification as the auxiliary tasks. Our experiments show that sentiment analysis benefits from multi-task learning, and individual modalities differ when conveying the polarity and intensity aspects of sentiment.",
}
@inproceedings{tits-etal-2018-asr,
    title = "{ASR}-based Features for Emotion Recognition: A Transfer Learning Approach",
    author = "Tits, No{\'e}  and
      El Haddad, Kevin  and
      Dutoit, Thierry",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3307",
    doi = "10.18653/v1/W18-3307",
    pages = "48--52",
    abstract = "During the last decade, the applications of signal processing have drastically improved with deep learning. However areas of affecting computing such as emotional speech synthesis or emotion recognition from spoken language remains challenging. In this paper, we investigate the use of a neural Automatic Speech Recognition (ASR) as a feature extractor for emotion recognition. We show that these features outperform the eGeMAPS feature set to predict the valence and arousal emotional dimensions, which means that the audio-to-text mapping learned by the ASR system contains information related to the emotional dimensions in spontaneous speech. We also examine the relationship between first layers (closer to speech) and last layers (closer to text) of the ASR and valence/arousal.",
}
@inproceedings{pham-etal-2018-seq2seq2sentiment,
    title = "{S}eq2{S}eq2{S}entiment: Multimodal Sequence to Sequence Models for Sentiment Analysis",
    author = "Pham, Hai  and
      Manzini, Thomas  and
      Liang, Paul Pu  and
      Pocz{\'o}s, Barnab{\'a}s",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3308",
    doi = "10.18653/v1/W18-3308",
    pages = "53--63",
    abstract = "Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a Seq2Seq Modality Translation Model and a Hierarchical Seq2Seq Modality Translation Model. We also explore multiple different variations on the multimodal inputs and outputs of these seq2seq models. Our experiments on multimodal sentiment analysis using the CMU-MOSI dataset indicate that our methods learn informative multimodal representations that outperform the baselines and achieve improved performance on multimodal sentiment analysis, specifically in the Bimodal case where our model is able to improve F1 Score by twelve points. We also discuss future directions for multimodal Seq2Seq methods.",
}
@inproceedings{williams-etal-2018-dnn,
    title = "{DNN} Multimodal Fusion Techniques for Predicting Video Sentiment",
    author = "Williams, Jennifer  and
      Comanescu, Ramona  and
      Radu, Oana  and
      Tian, Leimin",
    booktitle = "Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-{HML})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3309",
    doi = "10.18653/v1/W18-3309",
    pages = "64--72",
    abstract = "We present our work on sentiment prediction using the benchmark MOSI dataset from the CMU-MultimodalDataSDK. Previous work on multimodal sentiment analysis have been focused on input-level feature fusion or decision-level fusion for multimodal fusion. Here, we propose an intermediate-level feature fusion, which merges weights from each modality (audio, video, and text) during training with subsequent additional training. Moreover, we tested principle component analysis (PCA) for feature selection. We found that applying PCA increases unimodal performance, and multimodal fusion outperforms unimodal models. Our experiments show that our proposed intermediate-level feature fusion outperforms other fusion techniques, and it achieves the best performance with an overall binary accuracy of 74.0{\%} on video+text modalities. Our work also improves feature selection for unimodal sentiment analysis, while proposing a novel and effective multimodal fusion architecture for this task.",
}
@proceedings{ws-2018-approaches,
    title = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    editor = "Aguilar, Gustavo  and
      AlGhamdi, Fahad  and
      Soto, Victor  and
      Solorio, Thamar  and
      Diab, Mona  and
      Hirschberg, Julia",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3200",
}
@inproceedings{soto-hirschberg-2018-joint,
    title = "Joint Part-of-Speech and Language {ID} Tagging for Code-Switched Data",
    author = "Soto, Victor  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3201",
    doi = "10.18653/v1/W18-3201",
    pages = "1--10",
    abstract = "Code-switching is the fluent alternation between two or more languages in conversation between bilinguals. Large populations of speakers code-switch during communication, but little effort has been made to develop tools for code-switching, including part-of-speech taggers. In this paper, we propose an approach to POS tagging of code-switched English-Spanish data based on recurrent neural networks. We test our model on known monolingual benchmarks to demonstrate that our neural POS tagging model is on par with state-of-the-art methods. We next test our code-switched methods on the Miami Bangor corpus of English Spanish conversation, focusing on two types of experiments: POS tagging alone, for which we achieve 96.34{\%} accuracy, and joint part-of-speech and language ID tagging, which achieves similar POS tagging accuracy (96.39{\%}) and very high language ID accuracy (98.78{\%}). Finally, we show that our proposed models outperform other state-of-the-art code-switched taggers.",
}
@inproceedings{sivasankaran-etal-2018-phone,
    title = "Phone Merging For Code-Switched Speech Recognition",
    author = "Sivasankaran, Sunit  and
      Srivastava, Brij Mohan Lal  and
      Sitaram, Sunayana  and
      Bali, Kalika  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3202",
    doi = "10.18653/v1/W18-3202",
    pages = "11--19",
    abstract = "Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of code-switched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2{\%} WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.",
}
@inproceedings{adouane-etal-2018-improving,
    title = "Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in {A}lgerian texts",
    author = "Adouane, Wafia  and
      Bernardy, Jean-Philippe  and
      Dobnik, Simon",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3203",
    doi = "10.18653/v1/W18-3203",
    pages = "20--28",
    abstract = "We explore the effect of injecting background knowledge to different deep neural network (DNN) configurations in order to mitigate the problem of the scarcity of annotated data when applying these models on datasets of low-resourced languages. The background knowledge is encoded in the form of lexicons and pre-trained sub-word embeddings. The DNN models are evaluated on the task of detecting code-switching and borrowing points in non-standardised user-generated Algerian texts. Overall results show that DNNs benefit from adding background knowledge. However, the gain varies between models and categories. The proposed DNN architectures are generic and could be applied to other low-resourced languages.",
}
@inproceedings{chandu-etal-2018-code,
    title = "Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques",
    author = {Chandu, Khyathi  and
      Loginova, Ekaterina  and
      Gupta, Vishal  and
      van Genabith, Josef  and
      Neumann, G{\"u}nter  and
      Chinnakotla, Manoj  and
      Nyberg, Eric  and
      Black, Alan W.},
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3204",
    doi = "10.18653/v1/W18-3204",
    pages = "29--38",
    abstract = "Code-Mixing (CM) is the phenomenon of alternating between two or more languages which is prevalent in bi- and multi-lingual communities. Most NLP applications today are still designed with the assumption of a single interaction language and are most likely to break given a CM utterance with multiple languages mixed at a morphological, phrase or sentence level. For example, popular commercial search engines do not yet fully understand the intents expressed in CM queries. As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian). We share the details of our data collection process, techniques which were used to avoid inducing lexical bias amongst the crowd workers and other CM specific linguistic properties of the dataset. Our final dataset, which is available freely for research purposes, has 1,694 Hinglish, 2,848 Tamlish and 1,391 Tenglish factoid questions and their answers. We discuss the techniques used by the participants for the first edition of this ongoing challenge.",
}
@inproceedings{gupta-etal-2018-transliteration,
    title = "Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base",
    author = "Gupta, Vishal  and
      Chinnakotla, Manoj  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3205",
    doi = "10.18653/v1/W18-3205",
    pages = "39--50",
    abstract = "Humans can learn multiple languages. If they know a fact in one language, they can answer a question in another language they understand. They can also answer Code-mix (CM) questions: questions which contain both languages. This behavior is attributed to the unique learning ability of humans. Our task aims to study if machines can achieve this. We demonstrate how effectively a machine can answer CM questions. In this work, we adopt a two phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. We show experiments on the SimpleQuestions dataset. Our network is trained only on English questions provided in this dataset and noisy Hindi translations of these questions and can answer English-Hindi CM questions effectively without the need of translation into English. Back-transliterated CM questions outperform their lexical and sentence level translated counterparts by 5{\%} {\&} 35{\%} in accuracy respectively, highlighting the efficacy of our approach in a resource constrained setting.",
}
@inproceedings{mave-etal-2018-language,
    title = "Language Identification and Analysis of Code-Switched Social Media Text",
    author = "Mave, Deepthi  and
      Maharjan, Suraj  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3206",
    doi = "10.18653/v1/W18-3206",
    pages = "51--61",
    abstract = "In this paper, we detail our work on comparing different word-level language identification systems for code-switched Hindi-English data and a standard Spanish-English dataset. In this regard, we build a new code-switched dataset for Hindi-English. To understand the code-switching patterns in these language pairs, we investigate different code-switching metrics. We find that the CRF model outperforms the neural network based models by a margin of 2-5 percentage points for Spanish-English and 3-5 percentage points for Hindi-English.",
}
@inproceedings{winata-etal-2018-code,
    title = "Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning",
    author = "Winata, Genta Indra  and
      Madotto, Andrea  and
      Wu, Chien-Sheng  and
      Fung, Pascale",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3207",
    doi = "10.18653/v1/W18-3207",
    pages = "62--67",
    abstract = "Lack of text data has been the major issue on code-switching language modeling. In this paper, we introduce multi-task learning based language model which shares syntax representation of languages to leverage linguistic information and tackle the low resource data issue. Our model jointly learns both language modeling and Part-of-Speech tagging on code-switched utterances. In this way, the model is able to identify the location of code-switching points and improves the prediction of next word. Our approach outperforms standard LSTM based language model, with an improvement of 9.7{\%} and 7.4{\%} in perplexity on SEAME Phase I and Phase II dataset respectively.",
}
@inproceedings{bullock-etal-2018-predicting,
    title = "Predicting the presence of a Matrix Language in code-switching",
    author = "Bullock, Barbara  and
      Guzm{\'a}n, Wally  and
      Serigos, Jacqueline  and
      Sharath, Vivek  and
      Toribio, Almeida Jacqueline",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3208",
    doi = "10.18653/v1/W18-3208",
    pages = "68--75",
    abstract = "One language is often assumed to be dominant in code-switching but this assumption has not been empirically tested. We operationalize the matrix language (ML) at the level of the sentence, using three common definitions from linguistics. We test whether these converge and then model this convergence via a set of metrics that together quantify the nature of C-S. We conduct our experiment on four Spanish-English corpora. Our results demonstrate that our model can separate some corpora according to whether they have a dominant ML or not but that the corpora span a range of mixing types that cannot be sorted neatly into an insertional vs. alternational dichotomy.",
}
@inproceedings{rallabandi-etal-2018-automatic,
    title = "Automatic Detection of Code-switching Style from Acoustics",
    author = "Rallabandi, SaiKrishna  and
      Sitaram, Sunayana  and
      Black, Alan W",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3209",
    doi = "10.18653/v1/W18-3209",
    pages = "76--81",
    abstract = "Multilingual speakers switch between languages in an non-trivial fashion displaying inter sentential, intra sentential, and congruent lexicalization based transitions. While monolingual ASR systems may be capable of recognizing a few words from a foreign language, they are usually not robust enough to handle these varied styles of code-switching. There is also a lack of large code-switched speech corpora capturing all these styles making it difficult to build code-switched speech recognition systems. We hypothesize that it may be useful for an ASR system to be able to first detect the switching style of a particular utterance from acoustics, and then use specialized language models or other adaptation techniques for decoding the speech. In this paper, we look at the first problem of detecting code-switching style from acoustics. We classify code-switched Spanish-English and Hindi-English corpora using two metrics and show that features extracted from acoustics alone can distinguish between different kinds of code-switching in these language pairs.",
}
@inproceedings{bawa-etal-2018-accommodation,
    title = "Accommodation of Conversational Code-Choice",
    author = "Bawa, Anshul  and
      Choudhury, Monojit  and
      Bali, Kalika",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3210",
    doi = "10.18653/v1/W18-3210",
    pages = "82--91",
    abstract = "Bilingual speakers often freely mix languages. However, in such bilingual conversations, are the language choices of the speakers coordinated? How much does one speaker{'}s choice of language affect other speakers? In this paper, we formulate code-choice as a linguistic style, and show that speakers are indeed sensitive to and accommodating of each other{'}s code-choice. We find that the saliency or markedness of a language in context directly affects the degree of accommodation observed. More importantly, we discover that accommodation of code-choices persists over several conversational turns. We also propose an alternative interpretation of conversational accommodation as a retrieval problem, and show that the differences in accommodation characteristics of code-choices are based on their markedness in context.",
}
@inproceedings{chandu-etal-2018-language,
    title = "Language Informed Modeling of Code-Switched Text",
    author = "Chandu, Khyathi  and
      Manzini, Thomas  and
      Singh, Sumeet  and
      Black, Alan W.",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3211",
    doi = "10.18653/v1/W18-3211",
    pages = "92--97",
    abstract = "Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities. CS texts have a complex interplay between languages and occur in informal contexts that make them harder to collect and construct NLP tools for. We approach this problem through Language Modeling (LM) on a new Hindi-English mixed corpus containing 59,189 unique sentences collected from blogging websites. We implement and discuss different Language Models derived from a multi-layered LSTM architecture. We hypothesize that encoding language information strengthens a language model by helping to learn code-switching points. We show that our highest performing model achieves a test perplexity of 19.52 on the CS corpus that we collected and processed. On this data we demonstrate that our performance is an improvement over AWD-LSTM LM (a recent state of the art on monolingual English).",
}
@inproceedings{attia-etal-2018-ghht,
    title = "{GHHT} at {CALCS} 2018: Named Entity Recognition for Dialectal {A}rabic Using Neural Networks",
    author = "Attia, Mohammed  and
      Samih, Younes  and
      Maier, Wolfgang",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3212",
    doi = "10.18653/v1/W18-3212",
    pages = "98--102",
    abstract = "This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic. We build a a Deep Neural Network that combines word and character-based representations in convolutional and recurrent networks with a CRF layer. The model is augmented with stacked layers of enriched information such pre-trained embeddings, Brown clusters and named entity gazetteers. Our system is ranked second among those participating in the shared task achieving an FB1 average of 70.09{\%}.",
}
@inproceedings{jain-etal-2018-simple,
    title = "Simple Features for Strong Performance on Named Entity Recognition in Code-Switched {T}witter Data",
    author = "Jain, Devanshu  and
      Kustikova, Maria  and
      Darbari, Mayank  and
      Gupta, Rishabh  and
      Mayhew, Stephen",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3213",
    doi = "10.18653/v1/W18-3213",
    pages = "103--109",
    abstract = "In this work, we address the problem of Named Entity Recognition (NER) in code-switched tweets as a part of the Workshop on Computational Approaches to Linguistic Code-switching (CALCS) at ACL{'}18. Code-switching is the phenomenon where a speaker switches between two languages or variants of the same language within or across utterances, known as intra-sentential or inter-sentential code-switching, respectively. Processing such data is challenging using state of the art methods since such technology is generally geared towards processing monolingual text. In this paper we explored ways to use language identification and translation to recognize named entities in such data, however, utilizing simple features (sans multi-lingual features) with Conditional Random Field (CRF) classifier achieved the best results. Our experiments were mainly aimed at the (ENG-SPA) English-Spanish dataset but we submitted a language-independent version of our system to the (MSA-EGY) Arabic-Egyptian dataset as well and achieved good results.",
}
@inproceedings{winata-etal-2018-bilingual,
    title = "Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition",
    author = "Winata, Genta Indra  and
      Wu, Chien-Sheng  and
      Madotto, Andrea  and
      Fung, Pascale",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3214",
    doi = "10.18653/v1/W18-3214",
    pages = "110--114",
    abstract = "We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data. Our model uses bilingual character representation and transfer learning to address out-of-vocabulary words. In order to mitigate data noise, we propose to use token replacement and normalization. In the 3rd Workshop on Computational Approaches to Linguistic Code-Switching Shared Task, we achieved second place with 62.76{\%} harmonic mean F1-score for English-Spanish language pair without using any gazetteer and knowledge-based information.",
}
@inproceedings{sikdar-etal-2018-named,
    title = "Named Entity Recognition on Code-Switched Data Using Conditional Random Fields",
    author = {Sikdar, Utpal Kumar  and
      Barik, Biswanath  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3215",
    doi = "10.18653/v1/W18-3215",
    pages = "115--119",
    abstract = "Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories. Identification of named entities in code-mixed social media texts is a more difficult and challenging task as the contexts are short, ambiguous and often noisy. This work proposes a Conditional Random Fields based named entity recognition system to identify proper names in code-switched data and classify them into nine categories. The system ranked fifth among nine participant systems and achieved a 59.25{\%} F1-score.",
}
@inproceedings{janke-etal-2018-university,
    title = "The {U}niversity of {T}exas System Submission for the Code-Switching Workshop Shared Task 2018",
    author = "Janke, Florian  and
      Li, Tongrui  and
      Rinc{\'o}n, Eric  and
      Guzm{\'a}n, Gualberto  and
      Bullock, Barbara  and
      Toribio, Almeida Jacqueline",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3216",
    doi = "10.18653/v1/W18-3216",
    pages = "120--125",
    abstract = "This paper describes the system for the Named Entity Recognition Shared Task of the Third Workshop on Computational Approaches to Linguistic Code-Switching (CALCS) submitted by the Bilingual Annotations Tasks (BATs) research group of the University of Texas. Our system uses several features to train a Conditional Random Field (CRF) model for classifying input words as Named Entities (NEs) using the Inside-Outside-Beginning (IOB) tagging scheme. We participated in the Modern Standard Arabic-Egyptian Arabic (MSA-EGY) and English-Spanish (ENG-SPA) tasks, achieving weighted average F-scores of 65.62 and 54.16 respectively. We also describe the performance of a deep neural network (NN) trained on a subset of the CRF features, which did not surpass CRF performance.",
}
@inproceedings{geetha-etal-2018-tackling,
    title = "Tackling Code-Switched {NER}: Participation of {CMU}",
    author = "Geetha, Parvathy  and
      Chandu, Khyathi  and
      Black, Alan W",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3217",
    doi = "10.18653/v1/W18-3217",
    pages = "126--131",
    abstract = "Named Entity Recognition plays a major role in several downstream applications in NLP. Though this task has been heavily studied in formal monolingual texts and also noisy texts like Twitter data, it is still an emerging task in code-switched (CS) content on social media. This paper describes our participation in the shared task of NER on code-switched data for Spanglish (Spanish + English) and Arabish (Arabic + English). In this paper we describe models that intuitively developed from the data for the shared task Named Entity Recognition on Code-switched Data. Owing to the sparse and non-linear relationships between words in Twitter data, we explored neural architectures that are capable of non-linearities fairly well. In specific, we trained character level models and word level models based on Bidirectional LSTMs (Bi-LSTMs) to perform sequential tagging. We train multiple models to identify nominal mentions and subsequently use this information to predict the labels of named entity in a sequence. Our best model is a character level model along with word level pre-trained multilingual embeddings that gave an F-score of 56.72 in Spanglish and a word level model that gave an F-score of 65.02 in Arabish on the test data.",
}
@inproceedings{claeser-etal-2018-multilingual,
    title = "Multilingual Named Entity Recognition on {S}panish-{E}nglish Code-switched Tweets using Support Vector Machines",
    author = "Claeser, Daniel  and
      Kent, Samantha  and
      Felske, Dennis",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3218",
    doi = "10.18653/v1/W18-3218",
    pages = "132--137",
    abstract = "This paper describes our system submission for the ACL 2018 shared task on named entity recognition (NER) in code-switched Twitter data. Our best result (F1 = 53.65) was obtained using a Support Vector Machine (SVM) with 14 features combined with rule-based post processing.",
}
@inproceedings{aguilar-etal-2018-named,
    title = "Named Entity Recognition on Code-Switched Data: Overview of the {CALCS} 2018 Shared Task",
    author = "Aguilar, Gustavo  and
      AlGhamdi, Fahad  and
      Soto, Victor  and
      Diab, Mona  and
      Hirschberg, Julia  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3219",
    doi = "10.18653/v1/W18-3219",
    pages = "138--147",
    abstract = "In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76{\%} and 71.61{\%} for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.",
}
@inproceedings{trivedi-etal-2018-iit,
    title = "{IIT} ({BHU}) Submission for the {ACL} Shared Task on Named Entity Recognition on Code-switched Data",
    author = "Trivedi, Shashwat  and
      Rangwani, Harsh  and
      Kumar Singh, Anil",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3220",
    doi = "10.18653/v1/W18-3220",
    pages = "148--153",
    abstract = "This paper describes the best performing system for the shared task on Named Entity Recognition (NER) on code-switched data for the language pair Spanish-English (ENG-SPA). We introduce a gated neural architecture for the NER task. Our final model achieves an F1 score of 63.76{\%}, outperforming the baseline by 10{\%}.",
}
@inproceedings{wang-etal-2018-code,
    title = "Code-Switched Named Entity Recognition with Embedding Attention",
    author = "Wang, Changhan  and
      Cho, Kyunghyun  and
      Kiela, Douwe",
    booktitle = "Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3221",
    doi = "10.18653/v1/W18-3221",
    pages = "154--158",
    abstract = "We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data. Our system ranked first place for MS Arabic-Egyptian named entity recognition and third place for English-Spanish.",
}
@proceedings{ws-2018-economics,
    title = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    editor = "Hahn, Udo  and
      Hoste, V{\'e}ronique  and
      Tsai, Ming-Feng",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3100",
}
@inproceedings{jacobs-etal-2018-economic,
    title = "Economic Event Detection in Company-Specific News Text",
    author = "Jacobs, Gilles  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3101",
    doi = "10.18653/v1/W18-3101",
    pages = "1--10",
    abstract = "This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups",
}
@inproceedings{tabari-etal-2018-causality,
    title = "Causality Analysis of {T}witter Sentiments and Stock Market Returns",
    author = "Tabari, Narges  and
      Biswas, Piyusha  and
      Praneeth, Bhanu  and
      Seyeditabari, Armin  and
      Hadzikadic, Mirsad  and
      Zadrozny, Wlodek",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3102",
    doi = "10.18653/v1/W18-3102",
    pages = "11--19",
    abstract = "Sentiment analysis is the process of identifying the opinion expressed in text. Recently, it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. In this paper, we use a public dataset of labeled tweets that has been labeled by Amazon Mechanical Turk and then we propose a baseline classification model. Then, by using Granger causality of both sentiment datasets with the different stocks, we shows that there is causality between social media and stock market returns (in both directions) for many stocks. Finally, We evaluate this causality analysis by showing that in the event of a specific news on certain dates, there are evidences of trending the same news on Twitter for that stock.",
}
@inproceedings{handschke-etal-2018-corpus,
    title = "A Corpus of Corporate Annual and Social Responsibility Reports: 280 Million Tokens of Balanced Organizational Writing",
    author = {H{\"a}ndschke, Sebastian G.M.  and
      Buechel, Sven  and
      Goldenstein, Jan  and
      Poschmann, Philipp  and
      Duan, Tinghui  and
      Walgenbach, Peter  and
      Hahn, Udo},
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3103",
    doi = "10.18653/v1/W18-3103",
    pages = "20--31",
    abstract = "We introduce JOCo, a novel text corpus for NLP analytics in the field of economics, business and management. This corpus is composed of corporate annual and social responsibility reports of the top 30 US, UK and German companies in the major (DJIA, FTSE 100, DAX), middle-sized (S{\&}P 500, FTSE 250, MDAX) and technology (NASDAQ, FTSE AIM 100, TECDAX) stock indices, respectively. Altogether, this adds up to 5,000 reports from 270 companies headquartered in three of the world{'}s most important economies. The corpus spans a time frame from 2000 up to 2015 and contains, in total, 282M tokens. We also feature JOCo in a small-scale experiment to demonstrate its potential for NLP-fueled studies in economics, business and management research.",
}
@inproceedings{theil-etal-2018-word,
    title = "Word Embeddings-Based Uncertainty Detection in Financial Disclosures",
    author = "Theil, Christoph Kilian  and
      {\v{S}}tajner, Sanja  and
      Stuckenschmidt, Heiner",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3104",
    doi = "10.18653/v1/W18-3104",
    pages = "32--37",
    abstract = "In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures. Leveraging general-domain and domain-specific word embedding models, we automatically expand an existing dictionary of uncertainty triggers. We furthermore examine how an expert filtering affects the quality of such an expansion. We show that the dictionary expansions significantly improve regressions on stock return volatility. Lastly, we prove that the expansions significantly boost the automatic detection of uncertain sentences.",
}
@inproceedings{lai-etal-2018-simple,
    title = "A Simple End-to-End Question Answering Model for Product Information",
    author = "Lai, Tuan  and
      Bui, Trung  and
      Li, Sheng  and
      Lipka, Nedim",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3105",
    doi = "10.18653/v1/W18-3105",
    pages = "38--43",
    abstract = "When evaluating a potential product purchase, customers may have many questions in mind. They want to get adequate information to determine whether the product of interest is worth their money. In this paper we present a simple deep learning model for answering questions regarding product facts and specifications. Given a question and a product specification, the model outputs a score indicating their relevance. To train and evaluate our proposed model, we collected a dataset of 7,119 questions that are related to 153 different products. Experimental results demonstrate that {--}despite its simplicity{--} the performance of our model is shown to be comparable to a more complex state-of-the-art baseline.",
}
@inproceedings{mansar-ferradans-2018-sentence,
    title = "Sentence Classification for Investment Rules Detection",
    author = "Mansar, Youness  and
      Ferradans, Sira",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3106",
    doi = "10.18653/v1/W18-3106",
    pages = "44--48",
    abstract = "In the last years, compliance requirements for the banking sector have greatly augmented, making the current compliance processes difficult to maintain. Any process that allows to accelerate the identification and implementation of compliance requirements can help address this issues. The contributions of the paper are twofold: we propose a new NLP task that is the investment rule detection, and a group of methods identify them. We show that the proposed methods are highly performing and fast, thus can be deployed in production.",
}
@inproceedings{daudert-etal-2018-leveraging,
    title = "Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain",
    author = "Daudert, Tobias  and
      Buitelaar, Paul  and
      Negi, Sapna",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3107",
    doi = "10.18653/v1/W18-3107",
    pages = "49--54",
    abstract = "With the rising popularity of social media in the society and in research, analysing texts short in length, such as microblogs, becomes an increasingly important task. As a medium of communication, microblogs carry peoples sentiments and express them to the public. Given that sentiments are driven by multiple factors including the news media, the question arises if the sentiment expressed in news and the news article themselves can be leveraged to detect and classify sentiment in microblogs. Prior research has highlighted the impact of sentiments and opinions on the market dynamics, making the financial domain a prime case study for this approach. Therefore, this paper describes ongoing research dealing with the exploitation of news contained sentiment to improve microblog sentiment classification in a financial context.",
}
@inproceedings{gaillat-etal-2018-implicit,
    title = "Implicit and Explicit Aspect Extraction in Financial Microblogs",
    author = "Gaillat, Thomas  and
      Stearns, Bernardo  and
      Sridhar, Gopal  and
      McDermott, Ross  and
      Zarrouk, Manel  and
      Davis, Brian",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3108",
    doi = "10.18653/v1/W18-3108",
    pages = "55--61",
    abstract = "This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.",
}
@inproceedings{balashankar-etal-2018-unsupervised,
    title = "Unsupervised Word Influencer Networks from News Streams",
    author = "Balashankar, Ananth  and
      Chakraborty, Sunandan  and
      Subramanian, Lakshminarayanan",
    booktitle = "Proceedings of the First Workshop on Economics and Natural Language Processing",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3109",
    doi = "10.18653/v1/W18-3109",
    pages = "62--68",
    abstract = "In this paper, we propose a new unsupervised learning framework to use news events for predicting trends in stock prices. We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams. Using the temporal occurrence of words, WIN measures how the appearance of one word in a news stream influences the emergence of another set of words in the future. The latent word-word influencer relationships in WIN are the building blocks for causal reasoning and predictive modeling. We demonstrate the efficacy of WIN by using it for unsupervised extraction of latent features for stock price prediction and obtain 2 orders lower prediction error compared to a similar causal graph based method. WIN discovered influencer links from seemingly unrelated words from topics like politics to finance. WIN also validated 67{\%} of the causal evidence found manually in the text through a direct edge and the rest 33{\%} through a path of length 2.",
}
@proceedings{ws-2018-representation,
    title = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    editor = "Augenstein, Isabelle  and
      Cao, Kris  and
      He, He  and
      Hill, Felix  and
      Gella, Spandana  and
      Kiros, Jamie  and
      Mei, Hongyuan  and
      Misra, Dipendra",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3000",
}
@inproceedings{altszyler-etal-2018-corpus,
    title = "Corpus Specificity in {LSA} and Word2vec: The Role of Out-of-Domain Documents",
    author = "Altszyler, Edgar  and
      Sigman, Mariano  and
      Fern{\'a}ndez Slezak, Diego",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3001",
    doi = "10.18653/v1/W18-3001",
    pages = "1--10",
    abstract = "Despite the popularity of word embeddings, the precise way by which they acquire semantic relations between words remain unclear. In the present article, we investigate whether LSA and word2vec capacity to identify relevant semantic relations increases with corpus size. One intuitive hypothesis is that the capacity to identify relevant associations should increase as the amount of data increases. However, if corpus size grows in topics which are not specific to the domain of interest, signal to noise ratio may weaken. Here we investigate the effect of corpus specificity and size in word-embeddings, and for this, we study two ways for progressive elimination of documents: the elimination of random documents vs. the elimination of documents unrelated to a specific task. We show that word2vec can take advantage of all the documents, obtaining its best performance when it is trained with the whole corpus. On the contrary, the specialization (removal of out-of-domain documents) of the training corpus, accompanied by a decrease of dimensionality, can increase LSA word-representation quality while speeding up the processing time. From a cognitive-modeling point of view, we point out that LSA{'}s word-knowledge acquisitions may not be efficiently exploiting higher-order co-occurrences and global relations, whereas word2vec does.",
}
@inproceedings{gao-etal-2018-hierarchical,
    title = "Hierarchical Convolutional Attention Networks for Text Classification",
    author = "Gao, Shang  and
      Ramanathan, Arvind  and
      Tourassi, Georgia",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3002",
    doi = "10.18653/v1/W18-3002",
    pages = "11--23",
    abstract = "Recent work in machine translation has demonstrated that self-attention mechanisms can be used in place of recurrent neural networks to increase training speed without sacrificing model accuracy. We propose combining this approach with the benefits of convolutional filters and a hierarchical structure to create a document classification model that is both highly accurate and fast to train {--} we name our method Hierarchical Convolutional Attention Networks. We demonstrate the effectiveness of this architecture by surpassing the accuracy of the current state-of-the-art on several classification tasks while being twice as fast to train.",
}
@inproceedings{jo-choi-2018-extrofitting,
    title = "{E}xtrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons",
    author = "Jo, Hwiyeol  and
      Choi, Stanley Jungkyu",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3003",
    doi = "10.18653/v1/W18-3003",
    pages = "24--29",
    abstract = "We propose post-processing method for enriching not only word representation but also its vector space using semantic lexicons, which we call extrofitting. The method consists of 3 steps as follows: (i) Expanding 1 or more dimension(s) on all the word vectors, filling with their representative value. (ii) Transferring semantic knowledge by averaging each representative values of synonyms and filling them in the expanded dimension(s). These two steps make representations of the synonyms close together. (iii) Projecting the vector space using Linear Discriminant Analysis, which eliminates the expanded dimension(s) with semantic knowledge. When experimenting with GloVe, we find that our method outperforms Faruqui{'}s retrofitting on some of word similarity task. We also report further analysis on our method in respect to word vector dimensions, vocabulary size as well as other well-known pretrained word vectors (e.g., Word2Vec, Fasttext).",
}
@inproceedings{moirangthem-lee-2018-chat,
    title = "Chat Discrimination for Intelligent Conversational Agents with a Hybrid {CNN}-{LMTGRU} Network",
    author = "Moirangthem, Dennis Singh  and
      Lee, Minho",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3004",
    doi = "10.18653/v1/W18-3004",
    pages = "30--40",
    abstract = "Recently, intelligent dialog systems and smart assistants have attracted the attention of many, and development of novel dialogue agents have become a research challenge. Intelligent agents that can handle both domain-specific task-oriented and open-domain chit-chat dialogs are one of the major requirements in the current systems. In order to address this issue and to realize such smart hybrid dialogue systems, we develop a model to discriminate user utterance between task-oriented and chit-chat conversations. We introduce a hybrid of convolutional neural network (CNN) and a lateral multiple timescale gated recurrent units (LMTGRU) that can represent multiple temporal scale dependencies for the discrimination task. With the help of the combined slow and fast units of the LMTGRU, our model effectively determines whether a user will have a chit-chat conversation or a task-specific conversation with the system. We also show that the LMTGRU structure helps the model to perform well on longer text inputs. We address the lack of dataset by constructing a dataset using Twitter and Maluuba Frames data. The results of the experiments demonstrate that the proposed hybrid network outperforms the conventional models on the chat discrimination task as well as performed comparable to the baselines on various benchmark datasets.",
}
@inproceedings{salama-etal-2018-text,
    title = "Text Completion using Context-Integrated Dependency Parsing",
    author = {Salama, Amr Rekaby  and
      Ala{\c{c}}am, {\"O}zge  and
      Menzel, Wolfgang},
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3005",
    doi = "10.18653/v1/W18-3005",
    pages = "41--49",
    abstract = "Incomplete linguistic input, i.e. due to a noisy environment, is one of the challenges that a successful communication system has to deal with. In this paper, we study text completion with a data set composed of sentences with gaps where a successful completion cannot be achieved through a uni-modal (language-based) approach. We present a solution based on a context-integrating dependency parser incorporating an additional non-linguistic modality. An incompleteness in one channel is compensated by information from another one and the parser learns the association between the two modalities from a multiple level knowledge representation. We examined several model variations by adjusting the degree of influence of different modalities in the decision making on possible filler words and their exact reference to a non-linguistic context element. Our model is able to fill the gap with 95.4{\%} word and 95.2{\%} exact reference accuracy hence the successful prediction can be achieved not only on the word level (such as mug) but also with respect to the correct identification of its context reference (such as mug 2 among several mug instances).",
}
@inproceedings{li-etal-2018-quantum,
    title = "Quantum-Inspired Complex Word Embedding",
    author = "Li, Qiuchi  and
      Uprety, Sagar  and
      Wang, Benyou  and
      Song, Dawei",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3006",
    doi = "10.18653/v1/W18-3006",
    pages = "50--57",
    abstract = "A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words. For example, existing approaches in word embeddings will assign high probabilities to the words {``}Penguin{''} and {``}Fly{''} if they frequently co-occur, but it fails to capture the fact that they occur in an opposite sense - Penguins do not fly. We hypothesize that humans do not associate a single polarity or sentiment to each word. The word contributes to the overall polarity of a combination of words depending upon which other words it is combined with. This is analogous to the behavior of microscopic particles which exist in all possible states at the same time and interfere with each other to give rise to new states depending upon their relative phases. We make use of the Hilbert Space representation of such particles in Quantum Mechanics where we subscribe a relative phase to each word, which is a complex number, and investigate two such quantum inspired models to derive the meaning of a combination of words. The proposed models achieve better performances than state-of-the-art non-quantum models on binary sentence classification tasks.",
}
@inproceedings{nishida-etal-2018-natural,
    title = "Natural Language Inference with Definition Embedding Considering Context On the Fly",
    author = "Nishida, Kosuke  and
      Nishida, Kyosuke  and
      Asano, Hisako  and
      Tomita, Junji",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3007",
    doi = "10.18653/v1/W18-3007",
    pages = "58--63",
    abstract = "Natural language inference (NLI) is one of the most important tasks in NLP. In this study, we propose a novel method using word dictionaries, which are pairs of a word and its definition, as external knowledge. Our neural definition embedding mechanism encodes input sentences with the definitions of each word of the sentences on the fly. It can encode the definition of words considering the context of input sentences by using an attention mechanism. We evaluated our method using WordNet as a dictionary and confirmed that our method performed better than baseline models when using the full or a subset of 100d GloVe as word embeddings.",
}
@inproceedings{pivovarova-yangarber-2018-comparison,
    title = "Comparison of Representations of Named Entities for Document Classification",
    author = "Pivovarova, Lidia  and
      Yangarber, Roman",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3008",
    doi = "10.18653/v1/W18-3008",
    pages = "64--68",
    abstract = "We explore representations for multi-word names in text classification tasks, on Reuters (RCV1) topic and sector classification. We find that: the best way to treat names is to split them into tokens and use each token as a separate feature; NEs have more impact on sector classification than topic classification; replacing NEs with entity types is not an effective strategy; representing tokens by different embeddings for proper names vs. common nouns does not improve results. We highlight the improvements over state-of-the-art results that our CNN models yield.",
}
@inproceedings{tang-etal-2018-speeding,
    title = "Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding",
    author = "Tang, Shuai  and
      Jin, Hailin  and
      Fang, Chen  and
      Wang, Zhaowen  and
      de Sa, Virginia",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3009",
    doi = "10.18653/v1/W18-3009",
    pages = "69--78",
    abstract = "We propose an asymmetric encoder-decoder structure, which keeps an RNN as the encoder and has a CNN as the decoder, and the model only explores the subsequent context information as the supervision. The asymmetry in both model architecture and training pair reduces a large amount of the training time. The contribution of our work is summarized as 1. We design experiments to show that an autoregressive decoder or an RNN decoder is not necessary for the encoder-decoder type of models in terms of learning sentence representations, and based on our results, we present 2 findings. 2. The two interesting findings lead to our final model design, which has an RNN encoder and a CNN decoder, and it learns to encode the current sentence and decode the subsequent contiguous words all at once. 3. With a suite of techniques, our model performs good on downstream tasks and can be trained efficiently on a large unlabelled corpus.",
}
@inproceedings{levi-2018-connecting,
    title = "Connecting Supervised and Unsupervised Sentence Embeddings",
    author = "Levi, Gil",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3010",
    doi = "10.18653/v1/W18-3010",
    pages = "79--83",
    abstract = "Representing sentences as numerical vectors while capturing their semantic context is an important and useful intermediate step in natural language processing. Representations that are both general and discriminative can serve as a tool for tackling various NLP tasks. While common sentence representation methods are unsupervised in nature, recently, an approach for learning universal sentence representation in a supervised setting was presented in (Conneau et al.,2017). We argue that although promising results were obtained, an improvement can be reached by adding various unsupervised constraints that are motivated by auto-encoders and by language models. We show that by adding such constraints, superior sentence embeddings can be achieved. We compare our method with the original implementation and show improvements in several tasks.",
}
@inproceedings{chen-sheng-2018-hybrid,
    title = "A Hybrid Learning Scheme for {C}hinese Word Embedding",
    author = "Chen, Wenfan  and
      Sheng, Weiguo",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3011",
    doi = "10.18653/v1/W18-3011",
    pages = "84--90",
    abstract = "To improve word embedding, subword information has been widely employed in state-of-the-art methods. These methods can be classified to either compositional or predictive models. In this paper, we propose a hybrid learning scheme, which integrates compositional and predictive model for word embedding. Such a scheme can take advantage of both models, thus effectively learning word embedding. The proposed scheme has been applied to learn word representation on Chinese. Our results show that the proposed scheme can significantly improve the performance of word embedding in terms of analogical reasoning and is robust to the size of training data.",
}
@inproceedings{ethayarajh-2018-unsupervised,
    title = "Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline",
    author = "Ethayarajh, Kawin",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3012",
    doi = "10.18653/v1/W18-3012",
    pages = "91--100",
    abstract = "Using a random walk model of text generation, Arora et al. (2017) proposed a strong baseline for computing sentence embeddings: take a weighted average of word embeddings and modify with SVD. This simple method even outperforms far more complex approaches such as LSTMs on textual similarity tasks. In this paper, we first show that word vector length has a confounding effect on the probability of a sentence being generated in Arora et al.{'}s model. We propose a random walk model that is robust to this confound, where the probability of word generation is inversely related to the angular distance between the word and sentence embeddings. Our approach beats Arora et al.{'}s by up to 44.4{\%} on textual similarity tasks and is competitive with state-of-the-art methods. Unlike Arora et al.{'}s method, ours requires no hyperparameter tuning, which means it can be used when there is no labelled data.",
}
@inproceedings{yaghoobzadeh-etal-2018-evaluating,
    title = "Evaluating Word Embeddings in Multi-label Classification Using Fine-Grained Name Typing",
    author = {Yaghoobzadeh, Yadollah  and
      Kann, Katharina  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3013",
    doi = "10.18653/v1/W18-3013",
    pages = "101--106",
    abstract = "Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy and completeness of these properties in embeddings. This requires fine-grained analysis of embedding subspaces. Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding. The task we use is fine-grained name typing: given a large corpus, find all types that a name can refer to based on the name embedding. Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes, and allow the direct evaluation of embeddings without confounding factors like sentence context.",
}
@inproceedings{wang-etal-2018-exploiting,
    title = "Exploiting Common Characters in {C}hinese and {J}apanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization",
    author = "Wang, Jilei  and
      Luo, Shiying  and
      Shi, Weiyan  and
      Dai, Tao  and
      Xia, Shu-Tao",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3015",
    doi = "10.18653/v1/W18-3015",
    pages = "113--121",
    abstract = "Learning vector space representation of words (i.e., word embeddings) has recently attracted wide research interests, and has been extended to cross-lingual scenario. Currently most cross-lingual word embedding learning models are based on sentence alignment, which inevitably introduces much noise. In this paper, we show in Chinese and Japanese, the acquisition of semantic relation among words can benefit from the large number of common characters shared by both languages; inspired by this unique feature, we design a method named CJC targeting to generate cross-lingual context of words. We combine CJC with GloVe based on matrix factorization, and then propose an integrated model named CJ-Glo. Taking two sentence-aligned models and CJ-BOC (also exploits common characters but is based on CBOW) as baseline algorithms, we compare them with CJ-Glo on a series of NLP tasks including cross-lingual synonym, word analogy and sentence alignment. The result indicates CJ-Glo achieves the best performance among these methods, and is more stable in cross-lingual tasks; moreover, compared with CJ-BOC, CJ-Glo is less sensitive to the alteration of parameters.",
}
@inproceedings{saedi-etal-2018-wordnet,
    title = "{W}ord{N}et Embeddings",
    author = "Saedi, Chakaveh  and
      Branco, Ant{\'o}nio  and
      Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Silva, Jo{\~a}o",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3016",
    doi = "10.18653/v1/W18-3016",
    pages = "122--131",
    abstract = "Semantic networks and semantic spaces have been two prominent approaches to represent lexical semantics. While a unified account of the lexical meaning relies on one being able to convert between these representations, in both directions, the conversion direction from semantic networks into semantic spaces started to attract more attention recently. In this paper we present a methodology for this conversion and assess it with a case study. When it is applied over WordNet, the performance of the resulting embeddings in a mainstream semantic similarity task is very good, substantially superior to the performance of word embeddings based on very large collections of texts like word2vec.",
}
@inproceedings{wu-wang-2018-knowledge,
    title = "Knowledge Graph Embedding with Numeric Attributes of Entities",
    author = "Wu, Yanrong  and
      Wang, Zhichun",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3017",
    doi = "10.18653/v1/W18-3017",
    pages = "132--136",
    abstract = "Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion task. The previous embedding approaches only model entities and their relations, ignoring a large number of entities{'} numeric attributes in KGs. In this paper, we propose a new KG embedding model which jointly model entity relations and numeric attributes. Our approach combines an attribute embedding model with a translation-based structure embedding model, which learns the embeddings of entities, relations, and attributes simultaneously. Experiments of link prediction on YAGO and Freebase show that the performance is effectively improved by adding entities{'} numeric attributes in the embedding model.",
}
@inproceedings{vulic-2018-injecting,
    title = "Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation",
    author = "Vuli{\'c}, Ivan",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3018",
    doi = "10.18653/v1/W18-3018",
    pages = "137--143",
    abstract = "Word vector space specialisation models offer a portable, light-weight approach to fine-tuning arbitrary distributional vector spaces to discern between synonymy and antonymy. Their effectiveness is drawn from external linguistic constraints that specify the exact lexical relation between words. In this work, we show that a careful selection of the external constraints can steer and improve the specialisation. By simply selecting appropriate constraints, we report state-of-the-art results on a suite of tasks with well-defined benchmarks where modeling lexical contrast is crucial: 1) true semantic similarity, with highest reported scores on SimLex-999 and SimVerb-3500 to date; 2) detecting antonyms; and 3) distinguishing antonyms from synonyms.",
}
@inproceedings{ustun-etal-2018-characters,
    title = "Characters or Morphemes: How to Represent Words?",
    author = {{\"U}st{\"u}n, Ahmet  and
      Kurfal{\i}, Murathan  and
      Can, Burcu},
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3019",
    doi = "10.18653/v1/W18-3019",
    pages = "144--153",
    abstract = "In this paper, we investigate the effects of using subword information in representation learning. We argue that using syntactic subword units effects the quality of the word representations positively. We introduce a morpheme-based model and compare it against to word-based, character-based, and character n-gram level models. Our model takes a list of candidate segmentations of a word and learns the representation of the word based on different segmentations that are weighted by an attention mechanism. We performed experiments on Turkish as a morphologically rich language and English with a comparably poorer morphology. The results show that morpheme-based models are better at learning word representations of morphologically complex languages compared to character-based and character n-gram level models since the morphemes help to incorporate more syntactic knowledge in learning, that makes morpheme-based models better at syntactic tasks.",
}
@inproceedings{jacob-etal-2018-learning,
    title = "Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences",
    author = "Jacob, Athul Paul  and
      Lin, Zhouhan  and
      Sordoni, Alessandro  and
      Bengio, Yoshua",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3020",
    doi = "10.18653/v1/W18-3020",
    pages = "154--158",
    abstract = "We propose a hierarchical model for sequential data that learns a tree on-the-fly, i.e. while reading the sequence. In the model, a recurrent network adapts its structure and reuses recurrent weights in a recursive manner. This creates adaptive skip-connections that ease the learning of long-term dependencies. The tree structure can either be inferred without supervision through reinforcement learning, or learned in a supervised manner. We provide preliminary experiments in a novel Math Expression Evaluation (MEE) task, which is created to have a hierarchical tree structure that can be used to study the effectiveness of our model. Additionally, we test our model in a well-known propositional logic and language modelling tasks. Experimental results have shown the potential of our approach.",
}
@inproceedings{hartmann-sogaard-2018-limitations,
    title = "Limitations of Cross-Lingual Learning from Image Search",
    author = "Hartmann, Mareike  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3021",
    doi = "10.18653/v1/W18-3021",
    pages = "159--163",
    abstract = "Cross-lingual representation learning is an important step in making NLP scale to all the world{'}s languages. Previous work on bilingual lexicon induction suggests that it is possible to learn cross-lingual representations of words based on similarities between images associated with these words. However, that work focused (almost exclusively) on the translation of nouns only. Here, we investigate whether the meaning of other parts-of-speech (POS), in particular adjectives and verbs, can be learned in the same way. Our experiments across five language pairs indicate that previous work does not scale to the problem of learning cross-lingual representations beyond simple nouns.",
}
@inproceedings{yang-etal-2018-learning,
    title = "Learning Semantic Textual Similarity from Conversations",
    author = "Yang, Yinfei  and
      Yuan, Steve  and
      Cer, Daniel  and
      Kong, Sheng-yi  and
      Constant, Noah  and
      Pilar, Petr  and
      Ge, Heming  and
      Sung, Yun-Hsuan  and
      Strope, Brian  and
      Kurzweil, Ray",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3022",
    doi = "10.18653/v1/W18-3022",
    pages = "164--174",
    abstract = "We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational responses. The resulting sentence embeddings perform well on the Semantic Textual Similarity (STS) Benchmark and SemEval 2017{'}s Community Question Answering (CQA) question similarity subtask. Performance is further improved by introducing multitask training, combining conversational response prediction and natural language inference. Extensive experiments show the proposed model achieves the best performance among all neural models on the STS Benchmark and is competitive with the state-of-the-art feature engineered and mixed systems for both tasks.",
}
@inproceedings{yu-etal-2018-multilingual,
    title = "Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification",
    author = "Yu, Katherine  and
      Li, Haoran  and
      Oguz, Barlas",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3023",
    doi = "10.18653/v1/W18-3023",
    pages = "175--179",
    abstract = "In this paper we continue experiments where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings. In this framework we introduce a simple method of adding a loss to the learning objective which penalizes distance between representations of bilingually aligned sentences. We evaluate cross-lingual transfer using two approaches, cross-lingual similarity search on an aligned corpus (Europarl) and cross-lingual document classification on a recently published benchmark Reuters corpus, and we find the similarity loss significantly improves performance on both. Furthermore, we notice that while our Reuters results are very competitive, our English results are not as competitive, showing room for improvement in the current cross-lingual state-of-the-art. Our results are based on a set of 6 European languages.",
}
@inproceedings{liu-etal-2018-lstms,
    title = "{LSTM}s Exploit Linguistic Attributes of Data",
    author = "Liu, Nelson F.  and
      Levy, Omer  and
      Schwartz, Roy  and
      Tan, Chenhao  and
      Smith, Noah A.",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3024",
    doi = "10.18653/v1/W18-3024",
    pages = "180--186",
    abstract = "While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM{'}s ability to learn a nonlinguistic task: recalling elements from its input. We find that models trained on natural language data are able to recall tokens from much longer sequences than models trained on non-language sequential data. Furthermore, we show that the LSTM learns to solve the memorization task by explicitly using a subset of its neurons to count timesteps in the input. We hypothesize that the patterns and structure in natural language data enable LSTMs to learn by providing approximate ways of reducing loss, but understanding the effect of different training data on the learnability of LSTMs remains an open question.",
}
@inproceedings{broscheit-2018-learning,
    title = "Learning Distributional Token Representations from Visual Features",
    author = "Broscheit, Samuel",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3025",
    doi = "10.18653/v1/W18-3025",
    pages = "187--194",
    abstract = "In this study, we compare token representations constructed from visual features (i.e., pixels) with standard lookup-based embeddings. Our goal is to gain insight about the challenges of encoding a text representation from low-level features, e.g. from characters or pixels. We focus on Chinese, which{---}as a logographic language{---}has properties that make a representation via visual features challenging and interesting. To train and evaluate different models for the token representation, we chose the task of character-based neural machine translation (NMT) from Chinese to English. We found that a token representation computed only from visual features can achieve competitive results to lookup embeddings. However, we also show different strengths and weaknesses in the models{'} performance in a part-of-speech tagging task and also a semantic similarity task. In summary, we show that it is possible to achieve a \textit{text representation} only from pixels. We hope that this is a useful stepping stone for future studies that exclusively rely on visual input, or aim at exploiting visual features of written language.",
}
@inproceedings{newman-griffis-etal-2018-jointly,
    title = "Jointly Embedding Entities and Text with Distant Supervision",
    author = "Newman-Griffis, Denis  and
      Lai, Albert M  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3026",
    doi = "10.18653/v1/W18-3026",
    pages = "195--206",
    abstract = "Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.",
}
@inproceedings{daza-frank-2018-sequence,
    title = "A Sequence-to-Sequence Model for Semantic Role Labeling",
    author = "Daza, Angel  and
      Frank, Anette",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3027",
    doi = "10.18653/v1/W18-3027",
    pages = "207--216",
    abstract = "We explore a novel approach for Semantic Role Labeling (SRL) by casting it as a sequence-to-sequence process. We employ an attention-based model enriched with a copying mechanism to ensure faithful regeneration of the input sequence, while enabling interleaved generation of argument role labels. We apply this model in a monolingual setting, performing PropBank SRL on English language data. The constrained sequence generation set-up enforced with the copying mechanism allows us to analyze the performance and special properties of the model on manually labeled data and benchmarking against state-of-the-art sequence labeling models. We show that our model is able to solve the SRL argument labeling task on English data, yet further structural decoding constraints will need to be added to make the model truly competitive. Our work represents the first step towards more advanced, generative SRL labeling setups.",
}
@inproceedings{ljubesic-etal-2018-predicting,
    title = "Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Fi{\v{s}}er, Darja  and
      Peti-Stanti{\'c}, Anita",
    booktitle = "Proceedings of The Third Workshop on Representation Learning for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-3028",
    doi = "10.18653/v1/W18-3028",
    pages = "217--222",
    abstract = "The notions of concreteness and imageability, traditionally important in psycholinguistics, are gaining significance in semantic-oriented natural language processing tasks. In this paper we investigate the predictability of these two concepts via supervised learning, using word embeddings as explanatory variables. We perform predictions both within and across languages by exploiting collections of cross-lingual embeddings aligned to a single vector space. We show that the notions of concreteness and imageability are highly predictable both within and across languages, with a moderate loss of up to 20{\%} in correlation when predicting across languages. We further show that the cross-lingual transfer via word embeddings is more efficient than the simple transfer via bilingual dictionaries.",
}
@proceedings{ws-2018-relevance,
    title = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    editor = "Dinu, Georgiana  and
      Ballesteros, Miguel  and
      Sil, Avirup  and
      Bowman, Sam  and
      Hamza, Wael  and
      Sogaard, Anders  and
      Naseem, Tahira  and
      Goldberg, Yoav",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2900",
}
@inproceedings{edmiston-stratos-2018-compositional,
    title = "Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments",
    author = "Edmiston, Daniel  and
      Stratos, Karl",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2901",
    doi = "10.18653/v1/W18-2901",
    pages = "1--5",
    abstract = "This work introduces a novel, linguistically motivated architecture for composing morphemes to derive word embeddings. The principal novelty in the work is to treat stems as vectors and affixes as functions over vectors. In this way, our model{'}s architecture more closely resembles the compositionality of morphemes in natural language. Such a model stands in opposition to models which treat morphemes uniformly, making no distinction between stem and affix. We run this new architecture on a dependency parsing task in Korean{---}a language rich in derivational morphology{---}and compare it against a lexical baseline,along with other sub-word architectures. StAffNet, the name of our architecture, shows competitive performance with the state-of-the-art on this task.",
}
@inproceedings{currey-heafield-2018-unsupervised,
    title = "Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation",
    author = "Currey, Anna  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2902",
    doi = "10.18653/v1/W18-2902",
    pages = "6--12",
    abstract = "Incorporating source syntactic information into neural machine translation (NMT) has recently proven successful (Eriguchi et al., 2016; Luong et al., 2016). However, this is generally done using an outside parser to syntactically annotate the training data, making this technique difficult to use for languages or domains for which a reliable parser is not available. In this paper, we introduce an unsupervised tree-to-sequence (tree2seq) model for neural machine translation; this model is able to induce an unsupervised hierarchical structure on the source sentence based on the downstream task of neural machine translation. We adapt the Gumbel tree-LSTM of Choi et al. (2018) to NMT in order to create the encoder. We evaluate our model against sequential and supervised parsing baselines on three low- and medium-resource language pairs. For low-resource cases, the unsupervised tree2seq encoder significantly outperforms the baselines; no improvements are seen for medium-resource translation.",
}
@inproceedings{maillard-clark-2018-latent,
    title = "Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing",
    author = "Maillard, Jean  and
      Clark, Stephen",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2903",
    doi = "10.18653/v1/W18-2903",
    pages = "13--18",
    abstract = "Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.",
}
@inproceedings{strubell-mccallum-2018-syntax,
    title = "Syntax Helps {ELM}o Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for {SRL}?",
    author = "Strubell, Emma  and
      McCallum, Andrew",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2904",
    doi = "10.18653/v1/W18-2904",
    pages = "19--27",
    abstract = "Do unsupervised methods for learning rich, contextualized token representations obviate the need for explicit modeling of linguistic structure in neural network models for semantic role labeling (SRL)? We address this question by incorporating the massively successful ELMo embeddings (Peters et al., 2018) into LISA (Strubell and McCallum, 2018), a strong, linguistically-informed neural network architecture for SRL. In experiments on the CoNLL-2005 shared task we find that though ELMo out-performs typical word embeddings, beginning to close the gap in F1 between LISA with predicted and gold syntactic parses, syntactically-informed models still out-perform syntax-free models when both use ELMo, especially on out-of-domain data. Our results suggest that linguistic structures are indeed still relevant in this golden age of deep learning for NLP.",
}
@inproceedings{karpinska-etal-2018-subcharacter,
    title = "Subcharacter Information in {J}apanese Embeddings: When Is It Worth It?",
    author = "Karpinska, Marzena  and
      Li, Bofang  and
      Rogers, Anna  and
      Drozd, Aleksandr",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2905",
    doi = "10.18653/v1/W18-2905",
    pages = "28--37",
    abstract = "Languages with logographic writing systems present a difficulty for traditional character-level models. Leveraging the subcharacter information was recently shown to be beneficial for a number of intrinsic and extrinsic tasks in Chinese. We examine whether the same strategies could be applied for Japanese, and contribute a new analogy dataset for this language.",
}
@inproceedings{kanayama-etal-2018-neural,
    title = "A neural parser as a direct classifier for head-final languages",
    author = "Kanayama, Hiroshi  and
      Muraoka, Masayasu  and
      Kohita, Ryosuke",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2906",
    doi = "10.18653/v1/W18-2906",
    pages = "38--46",
    abstract = "This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese. Unlike the transition- and graph-based algorithms in most state-of-the-art parsers, our parser directly selects the head word of a dependent from a limited number of candidates. This method drastically simplifies the model so that we can easily interpret the output of the neural model. Moreover, by exploiting grammatical knowledge to restrict possible modification types, we can control the output of the parser to reduce specific errors without adding annotated corpora. The neural parser performed well both on conventional Japanese corpora and the Japanese version of Universal Dependency corpus, and the advantages of distributed representations were observed in the comparison with the non-neural conventional model.",
}
@inproceedings{nooralahzadeh-ovrelid-2018-syntactic,
    title = "Syntactic Dependency Representations in Neural Relation Classification",
    author = "Nooralahzadeh, Farhad  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2907",
    doi = "10.18653/v1/W18-2907",
    pages = "47--53",
    abstract = "We investigate the use of different syntactic dependency representations in a neural relation classification task and compare the CoNLL, Stanford Basic and Universal Dependencies schemes. We further compare with a syntax-agnostic approach and perform an error analysis in order to gain a better understanding of the results.",
}
@proceedings{ws-2018-eight,
    title = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    editor = "Idiart, Marco  and
      Lenci, Alessandro  and
      Poibeau, Thierry  and
      Villavicencio, Aline",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2800",
}
@inproceedings{antonio-rodrigues-etal-2018-predicting,
    title = "Predicting Brain Activation with {W}ord{N}et Embeddings",
    author = "Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Branco, Ruben  and
      Silva, Jo{\~a}o  and
      Saedi, Chakaveh  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2801",
    doi = "10.18653/v1/W18-2801",
    pages = "1--5",
    abstract = "The task of taking a semantic representation of a noun and predicting the brain activity triggered by it in terms of fMRI spatial patterns was pioneered by Mitchell et al. 2008. That seminal work used word co-occurrence features to represent the meaning of the nouns. Even though the task does not impose any specific type of semantic representation, the vast majority of subsequent approaches resort to feature-based models or to semantic spaces (aka word embeddings). We address this task, with competitive results, by using instead a semantic network to encode lexical semantics, thus providing further evidence for the cognitive plausibility of this approach to model lexical meaning.",
}
@inproceedings{yung-demberg-2018-speakers,
    title = "Do Speakers Produce Discourse Connectives Rationally?",
    author = "Yung, Frances  and
      Demberg, Vera",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2802",
    doi = "10.18653/v1/W18-2802",
    pages = "6--16",
    abstract = "A number of different discourse connectives can be used to mark the same discourse relation, but it is unclear what factors affect connective choice. One recent account is the Rational Speech Acts theory, which predicts that speakers try to maximize the informativeness of an utterance such that the listener can interpret the intended meaning correctly. Existing prior work uses referential language games to test the rational account of speakers{'} production of concrete meanings, such as identification of objects within a picture. Building on the same paradigm, we design a novel Discourse Continuation Game to investigate speakers{'} production of abstract discourse relations. Experimental results reveal that speakers significantly prefer a more informative connective, in line with predictions of the RSA model.",
}
@inproceedings{calvillo-crocker-2018-language,
    title = "Language Production Dynamics with Recurrent Neural Networks",
    author = "Calvillo, Jes{\'u}s  and
      Crocker, Matthew",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2803",
    doi = "10.18653/v1/W18-2803",
    pages = "17--26",
    abstract = "We present an analysis of the internal mechanism of the recurrent neural model of sentence production presented by Calvillo et al. (2016). The results show clear patterns of computation related to each layer in the network allowing to infer an algorithmic account, where the semantics activates the semantically related words, then each word generated at each time step activates syntactic and semantic constraints on possible continuations, while the recurrence preserves information through time. We propose that such insights could generalize to other models with similar architecture, including some used in computational linguistics for language modeling, machine translation and image caption generation.",
}
@inproceedings{zhu-etal-2018-multi,
    title = "Multi-glance Reading Model for Text Understanding",
    author = "Zhu, Pengcheng  and
      Yang, Yujiu  and
      Gao, Wenqiang  and
      Liu, Yi",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2804",
    doi = "10.18653/v1/W18-2804",
    pages = "27--35",
    abstract = "In recent years, a variety of recurrent neural networks have been proposed, e.g LSTM. However, existing models only read the text once, it cannot describe the situation of repeated reading in reading comprehension. In fact, when reading or analyzing a text, we may read the text several times rather than once if we couldn{'}t well understand it. So, how to model this kind of the reading behavior? To address the issue, we propose a multi-glance mechanism (MGM) for modeling the habit of reading behavior. In the proposed framework, the actual reading process can be fully simulated, and then the obtained information can be consistent with the task. Based on the multi-glance mechanism, we design two types of recurrent neural network models for repeated reading: Glance Cell Model (GCM) and Glance Gate Model (GGM). Visualization analysis of the GCM and the GGM demonstrates the effectiveness of multi-glance mechanisms. Experiments results on the large-scale datasets show that the proposed methods can achieve better performance.",
}
@inproceedings{asahara-etal-2018-predicting,
    title = "Predicting {J}apanese Word Order in Double Object Constructions",
    author = "Asahara, Masayuki  and
      Nambu, Satoshi  and
      Sano, Shin-Ichiro",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2805",
    doi = "10.18653/v1/W18-2805",
    pages = "36--40",
    abstract = "This paper presents a statistical model to predict Japanese word order in the double object constructions. We employed a Bayesian linear mixed model with manually annotated predicate-argument structure data. The findings from the refined corpus analysis confirmed the effects of information status of an NP as {`}givennew ordering{'} in addition to the effects of {`}long-before-short{'} as a tendency of the general Japanese word order.",
}
@inproceedings{mcgregor-lim-2018-affordances,
    title = "Affordances in Grounded Language Learning",
    author = "McGregor, Stephen  and
      Lim, KyungTae",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2806",
    doi = "10.18653/v1/W18-2806",
    pages = "41--46",
    abstract = "We present a novel methodology involving mappings between different modes of semantic representation. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner{'}s direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning.",
}
@inproceedings{kluth-schultheis-2018-rating,
    title = "Rating Distributions and {B}ayesian Inference: Enhancing Cognitive Models of Spatial Language Use",
    author = "Kluth, Thomas  and
      Schultheis, Holger",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2807",
    doi = "10.18653/v1/W18-2807",
    pages = "47--55",
    abstract = "We present two methods that improve the assessment of cognitive models. The first method is applicable to models computing average acceptability ratings. For these models, we propose an extension that simulates a full rating distribution (instead of average ratings) and allows generating individual ratings. Our second method enables Bayesian inference for models generating individual data. To this end, we propose to use the cross-match test (Rosenbaum, 2005) as a likelihood function. We exemplarily present both methods using cognitive models from the domain of spatial language use. For spatial language use, determining linguistic acceptability judgments of a spatial preposition for a depicted spatial relation is assumed to be a crucial process (Logan and Sadler, 1996). Existing models of this process compute an average acceptability rating. We extend the models and {--} based on existing data {--} show that the extended models allow extracting more information from the empirical data and yield more readily interpretable information about model successes and failures. Applying Bayesian inference, we find that model performance relies less on mechanisms of capturing geometrical aspects than on mapping the captured geometry to a rating interval.",
}
@inproceedings{li-etal-2018-role,
    title = "The Role of Syntax During Pronoun Resolution: Evidence from f{MRI}",
    author = "Li, Jixing  and
      Fabre, Murielle  and
      Luh, Wen-Ming  and
      Hale, John",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2808",
    doi = "10.18653/v1/W18-2808",
    pages = "56--64",
    abstract = "The current study examined the role of syntactic structure during pronoun resolution. We correlated complexity measures derived by the syntax-sensitive Hobbs algorithm and a neural network model for pronoun resolution with brain activity of participants listening to an audiobook during fMRI recording. Compared to the neural network model, the Hobbs algorithm is associated with larger clusters of brain activation in a network including the left Broca{'}s area.",
}
@inproceedings{stanojevic-stabler-2018-sound,
    title = "A Sound and Complete Left-Corner Parsing for {M}inimalist {G}rammars",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Stabler, Edward",
    booktitle = "Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing",
    month = jul,
    year = "2018",
    address = "Melbourne",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2809",
    doi = "10.18653/v1/W18-2809",
    pages = "65--74",
    abstract = "This paper presents a left-corner parser for minimalist grammars. The relation between the parser and the grammar is transparent in the sense that there is a very simple 1-1 correspondence between derivations and parses. Like left-corner context-free parsers, left-corner minimalist parsers can be non-terminating when the grammar has empty left corners, so an easily computed left-corner oracle is defined to restrict the search.",
}
@proceedings{ws-2018-neural,
    title = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    editor = "Birch, Alexandra  and
      Finch, Andrew  and
      Luong, Thang  and
      Neubig, Graham  and
      Oda, Yusuke",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2700",
}
@inproceedings{birch-etal-2018-findings,
    title = "Findings of the Second Workshop on Neural Machine Translation and Generation",
    author = "Birch, Alexandra  and
      Finch, Andrew  and
      Luong, Minh-Thang  and
      Neubig, Graham  and
      Oda, Yusuke",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2701",
    doi = "10.18653/v1/W18-2701",
    pages = "1--10",
    abstract = "This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop{'}s shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.",
}
@inproceedings{jauregi-unanue-etal-2018-shared,
    title = "A Shared Attention Mechanism for Interpretation of Neural Automatic Post-Editing Systems",
    author = "Jauregi Unanue, Inigo  and
      Zare Borzeshi, Ehsan  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2702",
    doi = "10.18653/v1/W18-2702",
    pages = "11--17",
    abstract = "Automatic post-editing (APE) systems aim to correct the systematic errors made by machine translators. In this paper, we propose a neural APE system that encodes the source (src) and machine translated (mt) sentences with two separate encoders, but leverages a shared attention mechanism to better understand how the two inputs contribute to the generation of the post-edited (pe) sentences. Our empirical observations have showed that when the mt is incorrect, the attention shifts weight toward tokens in the src sentence to properly edit the incorrect translation. The model has been trained and evaluated on the official data from the WMT16 and WMT17 APE IT domain English-German shared tasks. Additionally, we have used the extra 500K artificial data provided by the shared task. Our system has been able to reproduce the accuracies of systems trained with the same data, while at the same time providing better interpretability.",
}
@inproceedings{hoang-etal-2018-iterative,
    title = "Iterative Back-Translation for Neural Machine Translation",
    author = "Hoang, Vu Cong Duy  and
      Koehn, Philipp  and
      Haffari, Gholamreza  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2703",
    doi = "10.18653/v1/W18-2703",
    pages = "18--24",
    abstract = "We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 German↔English tasks.",
}
@inproceedings{bisk-tran-2018-inducing,
    title = "Inducing Grammars with and for Neural Machine Translation",
    author = "Bisk, Yonatan  and
      Tran, Ke",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2704",
    doi = "10.18653/v1/W18-2704",
    pages = "25--35",
    abstract = "Machine translation systems require semantic knowledge and grammatical understanding. Neural machine translation (NMT) systems often assume this information is captured by an attention mechanism and a decoder that ensures fluency. Recent work has shown that incorporating explicit syntax alleviates the burden of modeling both types of knowledge. However, requiring parses is expensive and does not explore the question of what syntax a model needs during translation. To address both of these issues we introduce a model that simultaneously translates while inducing dependency trees. In this way, we leverage the benefits of structure while investigating what syntax NMT must induce to maximize performance. We show that our dependency trees are 1. language pair dependent and 2. improve translation quality.",
}
@inproceedings{khayrallah-etal-2018-regularized,
    title = "Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation",
    author = "Khayrallah, Huda  and
      Thompson, Brian  and
      Duh, Kevin  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2705",
    doi = "10.18653/v1/W18-2705",
    pages = "36--44",
    abstract = "Supervised domain adaptation{---}where a large generic corpus and a smaller in-domain corpus are both available for training{---}is a challenge for neural machine translation (NMT). Standard practice is to train a generic model and use it to initialize a second model, then continue training the second model on in-domain data to produce an in-domain model. We add an auxiliary term to the training objective during continued training that minimizes the cross entropy between the in-domain model{'}s output word distribution and that of the out-of-domain model to prevent the model{'}s output from differing too much from the original out-of-domain model. We perform experiments on EMEA (descriptions of medicines) and TED (rehearsed presentations), initialized from a general domain (WMT) model. Our method shows improvements over standard continued training by up to 1.5 BLEU.",
}
@inproceedings{fan-etal-2018-controllable,
    title = "Controllable Abstractive Summarization",
    author = "Fan, Angela  and
      Grangier, David  and
      Auli, Michael",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2706",
    doi = "10.18653/v1/W18-2706",
    pages = "45--54",
    abstract = "Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, our system can produce high quality summaries that follow user preferences. Without user input, we set the control variables automatically {--} on the full text CNN-Dailymail dataset, we outperform state of the art abstractive systems (both in terms of F1-ROUGE1 40.38 vs. 39.53 F1-ROUGE and human evaluation.",
}
@inproceedings{imamura-etal-2018-enhancement,
    title = "Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation",
    author = "Imamura, Kenji  and
      Fujita, Atsushi  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2707",
    doi = "10.18653/v1/W18-2707",
    pages = "55--63",
    abstract = "A large-scale parallel corpus is required to train encoder-decoder neural machine translation. The method of using synthetic parallel texts, in which target monolingual corpora are automatically translated into source sentences, is effective in improving the decoder, but is unreliable for enhancing the encoder. In this paper, we propose a method that enhances the encoder and attention using target monolingual corpora by generating multiple source sentences via sampling. By using multiple source sentences, diversity close to that of humans is achieved. Our experimental results show that the translation quality is improved by increasing the number of synthetic source sentences for each given target sentence, and quality close to that using a manually created parallel corpus was achieved.",
}
@inproceedings{kothur-etal-2018-document,
    title = "Document-Level Adaptation for Neural Machine Translation",
    author = "Kothur, Sachith Sri Ram  and
      Knowles, Rebecca  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2708",
    doi = "10.18653/v1/W18-2708",
    pages = "64--73",
    abstract = "It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator{'}s corrections within the document itself. We focus on adaptation within a single document {--} appropriate for an interactive translation scenario where a model adapts to a human translator{'}s input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3{\%} novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2{\%} novel word translation accuracy.",
}
@inproceedings{khayrallah-koehn-2018-impact,
    title = "On the Impact of Various Types of Noise on Neural Machine Translation",
    author = "Khayrallah, Huda  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2709",
    doi = "10.18653/v1/W18-2709",
    pages = "74--83",
    abstract = "We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.",
}
@inproceedings{niu-etal-2018-bi,
    title = "Bi-Directional Neural Machine Translation with Synthetic Parallel Data",
    author = "Niu, Xing  and
      Denkowski, Michael  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2710",
    doi = "10.18653/v1/W18-2710",
    pages = "84--91",
    abstract = "Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.",
}
@inproceedings{nishimura-etal-2018-multi,
    title = "Multi-Source Neural Machine Translation with Missing Data",
    author = "Nishimura, Yuta  and
      Sudoh, Katsuhito  and
      Neubig, Graham  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2711",
    doi = "10.18653/v1/W18-2711",
    pages = "92--99",
    abstract = "Multi-source translation is an approach to exploit multiple inputs (e.g. in two different languages) to increase translation accuracy. In this paper, we examine approaches for multi-source neural machine translation (NMT) using an incomplete multilingual corpus in which some translations are missing. In practice, many multilingual corpora are not complete due to the difficulty to provide translations in all of the relevant languages (for example, in TED talks, most English talks only have subtitles for a small portion of the languages that TED supports). Existing studies on multi-source translation did not explicitly handle such situations. This study focuses on the use of incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts and examines a very simple implementation where missing source translations are replaced by a special symbol {\textless}NULL{\textgreater}. These methods allow us to use incomplete corpora both at training time and test time. In experiments with real incomplete multilingual corpora of TED Talks, the multi-source NMT with the {\textless}NULL{\textgreater} tokens achieved higher translation accuracies measured by BLEU than those by any one-to-one NMT systems.",
}
@inproceedings{pham-etal-2018-towards,
    title = "Towards one-shot learning for rare-word translation with external experts",
    author = "Pham, Ngoc-Quan  and
      Niehues, Jan  and
      Waibel, Alexander",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2712",
    doi = "10.18653/v1/W18-2712",
    pages = "100--109",
    abstract = "Neural machine translation (NMT) has significantly improved the quality of automatic translation models. One of the main challenges in current systems is the translation of rare words. We present a generic approach to address this weakness by having external models annotate the training data as Experts, and control the model-expert interaction with a pointer network and reinforcement learning. Our experiments using phrase-based models to simulate Experts to complement neural machine translation models show that the model can be trained to copy the annotations into the output consistently. We demonstrate the benefit of our proposed framework in outof domain translation scenarios with only lexical resources, improving more than 1.0 BLEU point in both translation directions English-Spanish and German-English.",
}
@inproceedings{imamura-sumita-2018-nict,
    title = "{NICT} Self-Training Approach to Neural Machine Translation at {NMT}-2018",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2713",
    doi = "10.18653/v1/W18-2713",
    pages = "110--115",
    abstract = "This paper describes the NICT neural machine translation system submitted at the NMT-2018 shared task. A characteristic of our approach is the introduction of self-training. Since our self-training does not change the model structure, it does not influence the efficiency of translation, such as the translation speed. The experimental results showed that the translation quality improved not only in the sequence-to-sequence (seq-to-seq) models but also in the transformer models.",
}
@inproceedings{hoang-etal-2018-fast,
    title = "Fast Neural Machine Translation Implementation",
    author = "Hoang, Hieu  and
      Dwojak, Tomasz  and
      Krislauks, Rihards  and
      Torregrosa, Daniel  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2714",
    doi = "10.18653/v1/W18-2714",
    pages = "116--121",
    abstract = "This paper describes the submissions to the efficiency track for GPUs at the Workshop for Neural Machine Translation and Generation by members of the University of Edinburgh, Adam Mickiewicz University, Tilde and University of Alicante. We focus on efficient implementation of the recurrent deep-learning model as implemented in Amun, the fast inference engine for neural machine translation. We improve the performance with an efficient mini-batching algorithm, and by fusing the softmax operation with the k-best extraction algorithm. Submissions using Amun were first, second and third fastest in the GPU efficiency track.",
}
@inproceedings{senellart-etal-2018-opennmt,
    title = "{O}pen{NMT} System Description for {WNMT} 2018: 800 words/sec on a single-core {CPU}",
    author = "Senellart, Jean  and
      Zhang, Dakun  and
      Wang, Bo  and
      Klein, Guillaume  and
      Ramatchandirin, Jean-Pierre  and
      Crego, Josep  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2715",
    doi = "10.18653/v1/W18-2715",
    pages = "122--128",
    abstract = "We present a system description of the OpenNMT Neural Machine Translation entry for the WNMT 2018 evaluation. In this work, we developed a heavily optimized NMT inference model targeting a high-performance CPU system. The final system uses a combination of four techniques, all of them lead to significant speed-ups in combination: (a) sequence distillation, (b) architecture modifications, (c) precomputation, particularly of vocabulary, and (d) CPU targeted quantization. This work achieves the fastest performance of the shared task, and led to the development of new features that have been integrated to OpenNMT and available to the community.",
}
@inproceedings{junczys-dowmunt-etal-2018-marian-cost,
    title = "{M}arian: Cost-effective High-Quality Neural Machine Translation in {C}++",
    author = "Junczys-Dowmunt, Marcin  and
      Heafield, Kenneth  and
      Hoang, Hieu  and
      Grundkiewicz, Roman  and
      Aue, Anthony",
    booktitle = "Proceedings of the 2nd Workshop on Neural Machine Translation and Generation",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2716",
    doi = "10.18653/v1/W18-2716",
    pages = "129--135",
    abstract = "This paper describes the submissions of the {``}Marian{''} team to the WNMT 2018 shared task. We investigate combinations of teacher-student training, low-precision matrix products, auto-tuning and other methods to optimize the Transformer model on GPU and CPU. By further integrating these methods with the new averaging attention networks, a recently introduced faster Transformer variant, we create a number of high-quality, high-performance models on the GPU and CPU, dominating the Pareto frontier for this shared task.",
}
@proceedings{ws-2018-machine,
    title = "Proceedings of the Workshop on Machine Reading for Question Answering",
    editor = "Choi, Eunsol  and
      Seo, Minjoon  and
      Chen, Danqi  and
      Jia, Robin  and
      Berant, Jonathan",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2600",
}
@inproceedings{gong-bowman-2018-ruminating,
    title = "Ruminating Reader: Reasoning with Gated Multi-hop Attention",
    author = "Gong, Yichen  and
      Bowman, Samuel",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2601",
    doi = "10.18653/v1/W18-2601",
    pages = "1--11",
    abstract = "To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BiDAF). We propose novel layer structures that construct a query aware context vector representation and fuse encoding representation with intermediate representation on top of BiDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BiDAF baseline by 2.1 F1 score and 2.7 EM score. Our analysis shows that different hops of the attention have different responsibilities in selecting answers.",
}
@inproceedings{rondeau-hazen-2018-systematic,
    title = "Systematic Error Analysis of the {S}tanford Question Answering Dataset",
    author = "Rondeau, Marc-Antoine  and
      Hazen, T. J.",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2602",
    doi = "10.18653/v1/W18-2602",
    pages = "12--20",
    abstract = "We analyzed the outputs of multiple question answering (QA) models applied to the Stanford Question Answering Dataset (SQuAD) to identify the core challenges for QA systems on this data set. Through an iterative process, challenging aspects were hypothesized through qualitative analysis of the common error cases. A classifier was then constructed to predict whether SQuAD test examples were likely to be difficult for systems to answer based on features associated with the hypothesized aspects. The classifier{'}s performance was used to accept or reject each aspect as an indicator of difficulty. With this approach, we ensured that our hypotheses were systematically tested and not simply accepted based on our pre-existing biases. Our explanations are not accepted based on human evaluation of individual examples. This process also enabled us to identify the primary QA strategy learned by the models, i.e., systems determined the acceptable answer type for a question and then selected the acceptable answer span of that type containing the highest density of words present in the question within its local vicinity in the passage.",
}
@inproceedings{yu-etal-2018-multi,
    title = "A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension",
    author = "Yu, Seunghak  and
      Indurthi, Sathish Reddy  and
      Back, Seohyun  and
      Lee, Haejun",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2603",
    doi = "10.18653/v1/W18-2603",
    pages = "21--30",
    abstract = "Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.",
}
@inproceedings{ren-etal-2018-tackling,
    title = "Tackling Adversarial Examples in {QA} via Answer Sentence Selection",
    author = "Ren, Yuanhang  and
      Du, Ye  and
      Wang, Di",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2604",
    doi = "10.18653/v1/W18-2604",
    pages = "31--36",
    abstract = "Question answering systems deteriorate dramatically in the presence of adversarial sentences in articles. According to Jia and Liang (2017), the single BiDAF system (Seo et al., 2016) only achieves an F1 score of 4.8 on the ADDANY adversarial dataset. In this paper, we present a method to tackle this problem via answer sentence selection. Given a paragraph of an article and a corresponding query, instead of directly feeding the whole paragraph to the single BiDAF system, a sentence that most likely contains the answer to the query is first selected, which is done via a deep neural network based on TreeLSTM (Tai et al., 2015). Experiments on ADDANY adversarial dataset validate the effectiveness of our method. The F1 score has been improved to 52.3.",
}
@inproceedings{he-etal-2018-dureader,
    title = "{D}u{R}eader: a {C}hinese Machine Reading Comprehension Dataset from Real-world Applications",
    author = "He, Wei  and
      Liu, Kai  and
      Liu, Jing  and
      Lyu, Yajuan  and
      Zhao, Shiqi  and
      Xiao, Xinyan  and
      Liu, Yuan  and
      Wang, Yizhong  and
      Wu, Hua  and
      She, Qiaoqiao  and
      Liu, Xuan  and
      Wu, Tian  and
      Wang, Haifeng",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2605",
    doi = "10.18653/v1/W18-2605",
    pages = "37--46",
    abstract = "This paper introduces DuReader, a new large-scale, open-domain Chinese machine reading comprehension (MRC) dataset, designed to address real-world MRC. DuReader has three advantages over previous MRC datasets: (1) data sources: questions and documents are based on Baidu Search and Baidu Zhidao; answers are manually generated. (2) question types: it provides rich annotations for more question types, especially yes-no and opinion questions, that leaves more opportunity for the research community. (3) scale: it contains 200K questions, 420K answers and 1M documents; it is the largest Chinese MRC dataset so far. Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements. To help the community make these improvements, both DuReader and baseline systems have been posted online. We also organize a shared competition to encourage the exploration of more models. Since the release of the task, there are significant improvements over the baselines.",
}
@inproceedings{franke-etal-2018-robust,
    title = "Robust and Scalable Differentiable Neural Computer for Question Answering",
    author = {Franke, J{\"o}rg  and
      Niehues, Jan  and
      Waibel, Alex},
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2606",
    doi = "10.18653/v1/W18-2606",
    pages = "47--59",
    abstract = "Deep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem solver which can be used in a wide range of tasks. But in reality, it is hard to apply this model to new tasks. We analyze the DNC and identify possible improvements within the application of question answering. This motivates a more robust and scalable DNC (rsDNC). The objective precondition is to keep the general character of this model intact while making its application more reliable and speeding up its required training time. The rsDNC is distinguished by a more robust training, a slim memory unit and a bidirectional architecture. We not only achieve new state-of-the-art performance on the bAbI task, but also minimize the performance variance between different initializations. Furthermore, we demonstrate the simplified applicability of the rsDNC to new tasks with passable results on the CNN RC task without adaptions.",
}
@inproceedings{boratko-etal-2018-systematic,
    title = "A Systematic Classification of Knowledge, Reasoning, and Context within the {ARC} Dataset",
    author = "Boratko, Michael  and
      Padigela, Harshit  and
      Mikkilineni, Divyendra  and
      Yuvraj, Pritish  and
      Das, Rajarshi  and
      McCallum, Andrew  and
      Chang, Maria  and
      Fokoue-Nkoutche, Achille  and
      Kapanipathi, Pavan  and
      Mattei, Nicholas  and
      Musa, Ryan  and
      Talamadupula, Kartik  and
      Witbrock, Michael",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2607",
    doi = "10.18653/v1/W18-2607",
    pages = "60--70",
    abstract = "The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into easy and challenge sets. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the challenge set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.",
}
@inproceedings{shvartzshanider-etal-2018-recipe,
    title = "{RECIPE}: Applying Open Domain Question Answering to Privacy Policies",
    author = "Shvartzshanider, Yan  and
      Balashankar, Ananth  and
      Wies, Thomas  and
      Subramanian, Lakshminarayanan",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2608",
    doi = "10.18653/v1/W18-2608",
    pages = "71--77",
    abstract = "We describe our experiences in using an open domain question answering model (Chen et al., 2017) to evaluate an out-of-domain QA task of assisting in analyzing privacy policies of companies. Specifically, Relevant CI Parameters Extractor (RECIPE) seeks to answer questions posed by the theory of contextual integrity (CI) regarding the information flows described in the privacy statements. These questions have a simple syntactic structure and the answers are factoids or descriptive in nature. The model achieved an F1 score of 72.33, but we noticed that combining the results of this model with a neural dependency parser based approach yields a significantly higher F1 score of 92.35 compared to manual annotations. This indicates that future work which in-corporates signals from parsing like NLP tasks more explicitly can generalize better on out-of-domain tasks.",
}
@inproceedings{subramanian-etal-2018-neural,
    title = "Neural Models for Key Phrase Extraction and Question Generation",
    author = "Subramanian, Sandeep  and
      Wang, Tong  and
      Yuan, Xingdi  and
      Zhang, Saizheng  and
      Trischler, Adam  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2609",
    doi = "10.18653/v1/W18-2609",
    pages = "78--88",
    abstract = "We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.",
}
@inproceedings{wadhwa-etal-2018-comparative,
    title = "Comparative Analysis of Neural {QA} models on {SQ}u{AD}",
    author = "Wadhwa, Soumya  and
      Chandu, Khyathi  and
      Nyberg, Eric",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2610",
    doi = "10.18653/v1/W18-2610",
    pages = "89--97",
    abstract = "The task of Question Answering has gained prominence in the past few decades for testing the ability of machines to understand natural language. Large datasets for Machine Reading have led to the development of neural models that cater to deeper language understanding compared to information retrieval tasks. Different components in these neural architectures are intended to tackle different challenges. As a first step towards achieving generalization across multiple domains, we attempt to understand and compare the peculiarities of existing end-to-end neural models on the Stanford Question Answering Dataset (SQuAD) by performing quantitative as well as qualitative analysis of the results attained by each of them. We observed that prediction errors reflect certain model-specific biases, which we further discuss in this paper.",
}
@inproceedings{yang-etal-2018-adaptations,
    title = "Adaptations of {ROUGE} and {BLEU} to Better Evaluate Machine Reading Comprehension Task",
    author = "Yang, An  and
      Liu, Kai  and
      Liu, Jing  and
      Lyu, Yajuan  and
      Li, Sujian",
    booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2611",
    doi = "10.18653/v1/W18-2611",
    pages = "98--104",
    abstract = "Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between candidate and reference answers, such as ROUGE and BLEU. However, bias may appear when these metrics are used for specific question types, especially questions inquiring yes-no opinions and entity lists. In this paper, we make adaptations on the metrics to better correlate $n$-gram overlap with the human judgment for answers to these two question types. Statistical analysis proves the effectiveness of our approach. Our adaptations may provide positive guidance for the development of real-scene MRC systems.",
}
@proceedings{ws-2018-nlp,
    title = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    editor = "Park, Eunjeong L.  and
      Hagiwara, Masato  and
      Milajevs, Dmitrijs  and
      Tan, Liling",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2500",
}
@inproceedings{gardner-etal-2018-allennlp,
    title = "{A}llen{NLP}: A Deep Semantic Natural Language Processing Platform",
    author = "Gardner, Matt  and
      Grus, Joel  and
      Neumann, Mark  and
      Tafjord, Oyvind  and
      Dasigi, Pradeep  and
      Liu, Nelson F.  and
      Peters, Matthew  and
      Schmitz, Michael  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2501",
    doi = "10.18653/v1/W18-2501",
    pages = "1--6",
    abstract = "Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research. However, many research codebases bury high-level parameters under implementation details, are challenging to run and debug, and are difficult enough to extend that they are more likely to be rewritten. This paper describes AllenNLP, a library for applying deep learning methods to NLP research that addresses these issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions. AllenNLP has already increased the rate of research experimentation and the sharing of NLP components at the Allen Institute for Artificial Intelligence, and we are working to have the same impact across the field.",
}
@inproceedings{nothman-etal-2018-stop,
    title = "Stop Word Lists in Free Open-source Software Packages",
    author = "Nothman, Joel  and
      Qin, Hanmin  and
      Yurchak, Roman",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2502",
    doi = "10.18653/v1/W18-2502",
    pages = "7--12",
    abstract = "Open-source software packages for language processing often include stop word lists. Users may apply them without awareness of their surprising omissions (e.g. {``}hasn{'}t{''} but not {``}hadn{'}t{''}) and inclusions ({``}computer{''}), or their incompatibility with a particular tokenizer. Motivated by issues raised about the Scikit-learn stop list, we investigate variation among and consistency within 52 popular English-language stop lists, and propose strategies for mitigating these issues.",
}
@inproceedings{hu-etal-2018-texar,
    title = "{T}exar: A Modularized, Versatile, and Extensible Toolbox for Text Generation",
    author = "Hu, Zhiting  and
      Yang, Zichao  and
      Zhao, Tiancheng  and
      Shi, Haoran  and
      He, Junxian  and
      Wang, Di  and
      Ma, Xuezhe  and
      Liu, Zhengzhong  and
      Liang, Xiaodan  and
      Qin, Lianhui  and
      Chaplot, Devendra Singh  and
      Tan, Bowen  and
      Yu, Xingjiang  and
      Xing, Eric",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2503",
    doi = "10.18653/v1/W18-2503",
    pages = "13--22",
    abstract = "We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks. Different from many existing toolkits that are specialized for specific applications (e.g., neural machine translation), Texar is designed to be highly flexible and versatile. This is achieved by abstracting the common patterns underlying the diverse tasks and methodologies, creating a library of highly reusable modules and functionalities, and enabling arbitrary model architectures and various algorithmic paradigms. The features make Texar particularly suitable for technique sharing and generalization across different text generation applications. The toolkit emphasizes heavily on extensibility and modularized system design, so that components can be freely plugged in or swapped out. We conduct extensive experiments and case studies to demonstrate the use and advantage of the toolkit.",
}
@inproceedings{gildea-etal-2018-acl,
    title = "The {ACL} {A}nthology: Current State and Future Directions",
    author = "Gildea, Daniel  and
      Kan, Min-Yen  and
      Madnani, Nitin  and
      Teichmann, Christoph  and
      Villalba, Mart{\'\i}n",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2504",
    doi = "10.18653/v1/W18-2504",
    pages = "23--28",
    abstract = "The Association of Computational Linguistic{'}s Anthology is the open source archive, and the main source for computational linguistics and natural language processing{'}s scientific literature. The ACL Anthology is currently maintained exclusively by community volunteers and has to be available and up-to-date at all times. We first discuss the current, open source approach used to achieve this, and then discuss how the planned use of Docker images will improve the Anthology{'}s long-term stability. This change will make it easier for researchers to utilize Anthology data for experimentation. We believe the ACL community can directly benefit from the extension-friendly architecture of the Anthology. We end by issuing an open challenge of reviewer matching we encourage the community to rally towards.",
}
@inproceedings{agirre-etal-2018-risk,
    title = "The risk of sub-optimal use of Open Source {NLP} Software: {UKB} is inadvertently state-of-the-art in knowledge-based {WSD}",
    author = "Agirre, Eneko  and
      L{\'o}pez de Lacalle, Oier  and
      Soroa, Aitor",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2505",
    doi = "10.18653/v1/W18-2505",
    pages = "29--33",
    abstract = "UKB is an open source collection of programs for performing, among other tasks, Knowledge-Based Word Sense Disambiguation (WSD). Since it was released in 2009 it has been often used out-of-the-box in sub-optimal settings. We show that nine years later it is the state-of-the-art on knowledge-based WSD. This case shows the pitfalls of releasing open source NLP software without optimal default settings and precise instructions for reproducibility.",
}
@inproceedings{pressel-etal-2018-baseline,
    title = "{B}aseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting {NLP}",
    author = "Pressel, Daniel  and
      Ray Choudhury, Sagnik  and
      Lester, Brian  and
      Zhao, Yanjie  and
      Barta, Matt",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2506",
    doi = "10.18653/v1/W18-2506",
    pages = "34--40",
    abstract = "We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP. The library provides easily extensible abstractions and implementations for data loading, model development, training and export of deep learning architectures. It also provides implementations for simple, high-performance, deep learning models for various NLP tasks, against which newly developed models can be compared. Deep learning experiments are hard to reproduce, Baseline provides functionalities to track them. The goal is to allow a researcher to focus on model development, delegating the repetitive tasks to the library.",
}
@inproceedings{kuchaiev-etal-2018-openseq2seq,
    title = "{O}pen{S}eq2{S}eq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models",
    author = "Kuchaiev, Oleksii  and
      Ginsburg, Boris  and
      Gitman, Igor  and
      Lavrukhin, Vitaly  and
      Case, Carl  and
      Micikevicius, Paulius",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2507",
    doi = "10.18653/v1/W18-2507",
    pages = "41--46",
    abstract = "We present OpenSeq2Seq {--} an open-source toolkit for training sequence-to-sequence models. The main goal of our toolkit is to allow researchers to most effectively explore different sequence-to-sequence architectures. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq provides building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.",
}
@inproceedings{germann-etal-2018-integrating,
    title = "Integrating Multiple {NLP} Technologies into an Open-source Platform for Multilingual Media Monitoring",
    author = "Germann, Ulrich  and
      Liepins, Ren{\=a}rs  and
      Gosko, Didzis  and
      Barzdins, Guntis",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2508",
    doi = "10.18653/v1/W18-2508",
    pages = "47--51",
    abstract = "The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. It assembles numerous state-of-the-art NLP technologies into a fully automated media ingestion pipeline that can record live broadcasts, detect and transcribe spoken content, translate from several languages (original text or transcribed speech) into English, recognize Named Entities, detect topics, cluster and summarize documents across language barriers, and extract and store factual claims in these news items. This paper describes the intended use cases and discusses the system design decisions that allowed us to integrate state-of-the-art NLP modules into an effective workflow with comparatively little effort.",
}
@inproceedings{rush-2018-annotated,
    title = "The Annotated Transformer",
    author = "Rush, Alexander",
    booktitle = "Proceedings of Workshop for {NLP} Open Source Software ({NLP}-{OSS})",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2509",
    doi = "10.18653/v1/W18-2509",
    pages = "52--60",
    abstract = "A major goal of open-source NLP is to quickly and accurately reproduce the results of new work, in a manner that the community can easily use and modify. While most papers publish enough detail for replication, it still may be difficult to achieve good results in practice. This paper presents a worked exercise of paper reproduction with the goal of implementing the results of the recent Transformer model. The replication exercise aims at simple code structure that follows closely with the original work, while achieving an efficient usable system.",
}
@proceedings{ws-2018-named,
    title = "Proceedings of the Seventh Named Entities Workshop",
    editor = "Chen, Nancy  and
      Banchs, Rafael E.  and
      Duan, Xiangyu  and
      Zhang, Min  and
      Li, Haizhou",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2400",
}
@inproceedings{andrew-2018-automatic,
    title = "Automatic Extraction of Entities and Relation from Legal Documents",
    author = "Andrew, Judith Jeyafreeda",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2401",
    doi = "10.18653/v1/W18-2401",
    pages = "1--8",
    abstract = "In recent years, the journalists and computer sciences speak to each other to identify useful technologies which would help them in extracting useful information. This is called {``}computational Journalism{''}. In this paper, we present a method that will enable the journalists to automatically identifies and annotates entities such as names of people, organizations, role and functions of people in legal documents; the relationship between these entities are also explored. The system uses a combination of both statistical and rule based technique. The statistical method used is Conditional Random Fields and for the rule based technique, document and language specific regular expressions are used.",
}
@inproceedings{lee-choi-2018-connecting,
    title = "Connecting Distant Entities with Induction through Conditional Random Fields for Named Entity Recognition: Precursor-Induced {CRF}",
    author = "Lee, Wangjin  and
      Choi, Jinwook",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2402",
    doi = "10.18653/v1/W18-2402",
    pages = "9--13",
    abstract = "This paper presents a method of designing specific high-order dependency factor on the linear chain conditional random fields (CRFs) for named entity recognition (NER). Named entities tend to be separated from each other by multiple outside tokens in a text, and thus the first-order CRF, as well as the second-order CRF, may innately lose transition information between distant named entities. The proposed design uses outside label in NER as a transmission medium of precedent entity information on the CRF. Then, empirical results apparently demonstrate that it is possible to exploit long-distance label dependency in the original first-order linear chain CRF structure upon NER while reducing computational loss rather than in the second-order CRF.",
}
@inproceedings{inan-dikenelli-2018-sequence,
    title = "A Sequence Learning Method for Domain-Specific Entity Linking",
    author = "Inan, Emrah  and
      Dikenelli, Oguz",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2403",
    doi = "10.18653/v1/W18-2403",
    pages = "14--21",
    abstract = "Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. Although graph-based approaches are shown to achieve remarkable results, they are computationally expensive for general datasets. Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. In this paper, we address these problems by introducing a two-fold neural model. First, we match easy mention-entity pairs and using the domain information of this pair to filter candidate entities of closer mentions. Second, we resolve more ambiguous pairs using bidirectional Long Short-Term Memory and CRF models for the entity disambiguation. Our proposed system outperforms state-of-the-art systems on the generated domain-specific evaluation dataset.",
}
@inproceedings{wu-etal-2018-attention,
    title = "Attention-based Semantic Priming for Slot-filling",
    author = "Wu, Jiewen  and
      Banchs, Rafael E.  and
      D{'}Haro, Luis Fernando  and
      Krishnaswamy, Pavitra  and
      Chen, Nancy",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2404",
    doi = "10.18653/v1/W18-2404",
    pages = "22--26",
    abstract = "The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena. We propose that an attention-based RNN architecture can be used to simulate semantic priming for sequence labelling. Specifically, we employ pre-trained word embeddings to characterize the semantic relationship between utterances and labels. We validate the approach using varying sizes of the ATIS and MEDIA datasets, and show up to 1.4-1.9{\%} improvement in F1 score. The developed framework can enable more explainable and generalizable spoken language understanding systems.",
}
@inproceedings{singh-etal-2018-named,
    title = "Named Entity Recognition for {H}indi-{E}nglish Code-Mixed Social Media Text",
    author = "Singh, Vinay  and
      Vijay, Deepanshu  and
      Akhtar, Syed Sarfaraz  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2405",
    doi = "10.18653/v1/W18-2405",
    pages = "27--35",
    abstract = "Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a sub-task of Information Extraction. The challenge of NER for tweets lie in the insufficient information available in a tweet. There has been a significant amount of work done related to entity extraction, but only for resource rich languages and domains such as newswire. Entity extraction is, in general, a challenging task for such an informal text, and code-mixed text further complicates the process with it{'}s unstructured and incomplete information. We propose experiments with different machine learning classification algorithms with word, character and lexical features. The algorithms we experimented with are Decision tree, Long Short-Term Memory (LSTM), and Conditional Random Field (CRF). In this paper, we present a corpus for NER in Hindi-English Code-Mixed along with extensive experiments on our machine learning models which achieved the best f1-score of 0.95 with both CRF and LSTM.",
}
@inproceedings{hardmeier-etal-2018-forms,
    title = "Forms of Anaphoric Reference to Organisational Named Entities: Hoping to widen appeal, they diversified",
    author = "Hardmeier, Christian  and
      Bevacqua, Luca  and
      Lo{\'a}iciga, Sharid  and
      Rohde, Hannah",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2406",
    doi = "10.18653/v1/W18-2406",
    pages = "36--40",
    abstract = "Proper names of organisations are a special case of collective nouns. Their meaning can be conceptualised as a collective unit or as a plurality of persons, allowing for different morphological marking of coreferent anaphoric pronouns. This paper explores the variability of references to organisation names with 1) a corpus analysis and 2) two crowd-sourced story continuation experiments. The first shows that the preference for singular vs. plural conceptualisation is dependent on the level of formality of a text. In the second, we observe a strong preference for the plural they otherwise typical of informal speech. Using edited corpus data instead of constructed sentences as stimuli reduces this preference.",
}
@inproceedings{li-etal-2018-named,
    title = "Named-Entity Tagging and Domain adaptation for Better Customized Translation",
    author = "Li, Zhongwei  and
      Wang, Xuancong  and
      Aw, Ai Ti  and
      Chng, Eng Siong  and
      Li, Haizhou",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2407",
    doi = "10.18653/v1/W18-2407",
    pages = "41--46",
    abstract = "Customized translation need pay spe-cial attention to the target domain ter-minology especially the named-entities for the domain. Adding linguistic features to neural machine translation (NMT) has been shown to benefit translation in many studies. In this paper, we further demonstrate that adding named-entity (NE) feature with named-entity recognition (NER) into the source language produces better translation with NMT. Our experiments show that by just including the different NE classes and boundary tags, we can increase the BLEU score by around 1 to 2 points using the standard test sets from WMT2017. We also show that adding NE tags using NER and applying in-domain adaptation can be combined to further improve customized machine translation.",
}
@inproceedings{chen-etal-2018-news,
    title = "{NEWS} 2018 Whitepaper",
    author = "Chen, Nancy  and
      Duan, Xiangyu  and
      Zhang, Min  and
      Banchs, Rafael E.  and
      Li, Haizhou",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2408",
    doi = "10.18653/v1/W18-2408",
    pages = "47--54",
    abstract = "Transliteration is defined as phonetic translation of names across languages. Transliteration of Named Entities (NEs) is necessary in many applications, such as machine translation, corpus alignment, cross-language IR, information extraction and automatic lexicon acquisition. All such systems call for high-performance transliteration, which is the focus of shared task in the NEWS 2018 workshop. The objective of the shared task is to promote machine transliteration research by providing a common benchmarking platform for the community to evaluate the state-of-the-art technologies.",
}
@inproceedings{chen-etal-2018-report,
    title = "Report of {NEWS} 2018 Named Entity Transliteration Shared Task",
    author = "Chen, Nancy  and
      Banchs, Rafael E.  and
      Zhang, Min  and
      Duan, Xiangyu  and
      Li, Haizhou",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2409",
    doi = "10.18653/v1/W18-2409",
    pages = "55--73",
    abstract = "This report presents the results from the Named Entity Transliteration Shared Task conducted as part of The Seventh Named Entities Workshop (NEWS 2018) held at ACL 2018 in Melbourne, Australia. Similar to previous editions of NEWS, the Shared Task featured 19 tasks on proper name transliteration, including 13 different languages and two different Japanese scripts. A total of 6 teams from 8 different institutions participated in the evaluation, submitting 424 runs, involving different transliteration methodologies. Four performance metrics were used to report the evaluation results. The NEWS shared task on machine transliteration has successfully achieved its objectives by providing a common ground for the research community to conduct comparative evaluations of state-of-the-art technologies that will benefit the future research and development in this area.",
}
@inproceedings{singhania-etal-2018-statistical,
    title = "Statistical Machine Transliteration Baselines for {NEWS} 2018",
    author = "Singhania, Snigdha  and
      Nguyen, Minh  and
      Ngo, Gia H.  and
      Chen, Nancy",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2410",
    doi = "10.18653/v1/W18-2410",
    pages = "74--78",
    abstract = "This paper reports the results of our trans-literation experiments conducted on NEWS 2018 Shared Task dataset. We focus on creating the baseline systems trained using two open-source, statistical transliteration tools, namely Sequitur and Moses. We discuss the pre-processing steps performed on this dataset for both the systems. We also provide a re-ranking system which uses top hypotheses from Sequitur and Moses to create a consolidated list of transliterations. The results obtained from each of these models can be used to present a good starting point for the participating teams.",
}
@inproceedings{kundu-etal-2018-deep,
    title = "A Deep Learning Based Approach to Transliteration",
    author = "Kundu, Soumyadeep  and
      Paul, Sayantan  and
      Pal, Santanu",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2411",
    doi = "10.18653/v1/W18-2411",
    pages = "79--83",
    abstract = "In this paper, we propose different architectures for language independent machine transliteration which is extremely important for natural language processing (NLP) applications. Though a number of statistical models for transliteration have already been proposed in the past few decades, we proposed some neural network based deep learning architectures for the transliteration of named entities. Our transliteration systems adapt two different neural machine translation (NMT) frameworks: recurrent neural network and convolutional sequence to sequence based NMT. It is shown that our method provides quite satisfactory results when it comes to multi lingual machine transliteration. Our submitted runs are an ensemble of different transliteration systems for all the language pairs. In the NEWS 2018 Shared Task on Transliteration, our method achieves top performance for the En{--}Pe and Pe{--}En language pairs and comparable results for other cases.",
}
@inproceedings{najafi-etal-2018-comparison,
    title = "Comparison of Assorted Models for Transliteration",
    author = "Najafi, Saeed  and
      Hauer, Bradley  and
      Riyadh, Rashed Rubby  and
      Yu, Leyuan  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2412",
    doi = "10.18653/v1/W18-2412",
    pages = "84--88",
    abstract = "We report the results of our experiments in the context of the NEWS 2018 Shared Task on Transliteration. We focus on the comparison of several diverse systems, including three neural MT models. A combination of discriminative, generative, and neural models obtains the best results on the development sets. We also put forward ideas for improving the shared task.",
}
@inproceedings{grundkiewicz-heafield-2018-neural,
    title = "Neural Machine Translation Techniques for Named Entity Transliteration",
    author = "Grundkiewicz, Roman  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2413",
    doi = "10.18653/v1/W18-2413",
    pages = "89--94",
    abstract = "Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models. To build a strong transliteration system, we apply well-established techniques from NMT, such as dropout regularization, model ensembling, rescoring with right-to-left models, and back-translation. Our submission to the NEWS 2018 Shared Task on Named Entity Transliteration ranked first in several tracks.",
}
@inproceedings{le-sadat-2018-low,
    title = "Low-Resource Machine Transliteration Using Recurrent Neural Networks of {A}sian Languages",
    author = "Le, Ngoc Tan  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2414",
    doi = "10.18653/v1/W18-2414",
    pages = "95--100",
    abstract = "Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. We participated in the NEWS 2018 shared task for the English-Vietnamese transliteration task.",
}
@proceedings{ws-2018-bionlp,
    title = "Proceedings of the {B}io{NLP} 2018 workshop",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2300",
}
@inproceedings{newman-griffis-zirikly-2018-embedding,
    title = "Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility",
    author = "Newman-Griffis, Denis  and
      Zirikly, Ayah",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2301",
    doi = "10.18653/v1/W18-2301",
    pages = "1--11",
    abstract = "Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research. We present the first analysis of automatically extracting descriptions of patient mobility, using a recently-developed dataset of free text electronic health records. We frame the task as a named entity recognition (NER) problem, and investigate the applicability of NER techniques to mobility extraction. As text corpora focused on patient functioning are scarce, we explore domain adaptation of word embeddings for use in a recurrent neural network NER system. We find that embeddings trained on a small in-domain corpus perform nearly as well as those learned from large out-of-domain corpora, and that domain adaptation techniques yield additional improvements in both precision and recall. Our analysis identifies several significant challenges in extracting descriptions of patient mobility, including the length and complexity of annotated entities and high linguistic variability in mobility descriptions.",
}
@inproceedings{jeblee-etal-2018-multi,
    title = "Multi-task learning for interpretable cause of death classification using key phrase prediction",
    author = "Jeblee, Serena  and
      Gomes, Mireille  and
      Hirst, Graeme",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2302",
    doi = "10.18653/v1/W18-2302",
    pages = "12--17",
    abstract = "We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features.",
}
@inproceedings{chokwijitkul-etal-2018-identifying,
    title = "Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach",
    author = "Chokwijitkul, Thanat  and
      Nguyen, Anthony  and
      Hassanzadeh, Hamed  and
      Perez, Siegfried",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2303",
    doi = "10.18653/v1/W18-2303",
    pages = "18--27",
    abstract = "Automatic identification of heart disease risk factors in clinical narratives can expedite disease progression modelling and support clinical decisions. Existing practical solutions for cardiovascular risk detection are mostly hybrid systems entailing the integration of knowledge-driven and data-driven methods, relying on dictionaries, rules and machine learning methods that require a substantial amount of human effort. This paper proposes a comparative analysis on the applicability of deep learning, a re-emerged data-driven technique, in the context of clinical text classification. Various deep learning architectures were devised and evaluated for extracting heart disease risk factors from clinical documents. The data provided for the 2014 i2b2/UTHealth shared task focusing on identifying risk factors for heart disease was used for system development and evaluation. Results have shown that a relatively simple deep learning model can achieve a high micro-averaged F-measure of 0.9081, which is comparable to the best systems from the shared task. This is highly encouraging given the simplicity of the deep learning approach compared to the heavily feature-engineered hybrid approaches that were required to achieve state-of-the-art performances.",
}
@inproceedings{saputra-etal-2018-keyphrases,
    title = "Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks",
    author = "Saputra, Ilham Fathy  and
      Mahendra, Rahmad  and
      Wicaksono, Alfan Farizki",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2304",
    doi = "10.18653/v1/W18-2304",
    pages = "28--34",
    abstract = "We propose keyphrases extraction technique to extract important terms from the healthcare user-generated contents. We employ deep learning architecture, i.e. Long Short-Term Memory, and leverage word embeddings, medical concepts from a knowledge base, and linguistic components as our features. The proposed model achieves 61.37{\%} F-1 score. Experimental results indicate that our proposed approach outperforms the baseline methods, i.e. RAKE and CRF, on the task of extracting keyphrases from Indonesian health forum posts.",
}
@inproceedings{seva-etal-2018-identifying,
    title = "Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning",
    author = "{\v{S}}eva, Jurica  and
      Wackerbauer, Martin  and
      Leser, Ulf",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2305",
    doi = "10.18653/v1/W18-2305",
    pages = "35--46",
    abstract = "We present a machine learning pipeline that identifies key sentences in abstracts of oncological articles to aid evidence-based medicine. This problem is characterized by the lack of gold standard datasets, data imbalance and thematic differences between available silver standard corpora. Additionally, available training and target data differs with regard to their domain (professional summaries vs. sentences in abstracts). This makes supervised machine learning inapplicable. We propose the use of two semi-supervised machine learning approaches: To mitigate difficulties arising from heterogeneous data sources, overcome data imbalance and create reliable training data we propose using transductive learning from positive and unlabelled data (PU Learning). For obtaining a realistic classification model, we propose the use of abstracts summarised in relevant sentences as unlabelled examples through Self-Training. The best model achieves 84{\%} accuracy and 0.84 F1 score on our dataset",
}
@inproceedings{wang-etal-2018-ontology,
    title = "Ontology alignment in the biomedical domain using entity definitions and context",
    author = "Wang, Lucy  and
      Bhagavatula, Chandra  and
      Neumann, Mark  and
      Lo, Kyle  and
      Wilhelm, Chris  and
      Ammar, Waleed",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2306",
    doi = "10.18653/v1/W18-2306",
    pages = "47--55",
    abstract = "Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.",
}
@inproceedings{galea-etal-2018-sub,
    title = "Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization",
    author = "Galea, Dieter  and
      Laponogov, Ivan  and
      Veselkov, Kirill",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2307",
    doi = "10.18653/v1/W18-2307",
    pages = "56--66",
    abstract = "Word2vec embeddings are limited to computing vectors for in-vocabulary terms and do not take into account sub-word information. Character-based representations, such as fastText, mitigate such limitations. We optimize and compare these representations for the biomedical domain. fastText was found to consistently outperform word2vec in named entity recognition tasks for entities such as chemicals and genes. This is likely due to gained information from computed out-of-vocabulary term vectors, as well as the word compositionality of such entities. Contrastingly, performance varied on intrinsic datasets. Optimal hyper-parameters were intrinsic dataset-dependent, likely due to differences in term types distributions. This indicates embeddings should be chosen based on the task at hand. We therefore provide a number of optimized hyper-parameter sets and pre-trained word2vec and fastText models, available on \url{https://github.com/dterg/bionlp-embed}.",
}
@inproceedings{jin-szolovits-2018-pico,
    title = "{PICO} Element Detection in Medical Text via Long Short-Term Memory Neural Networks",
    author = "Jin, Di  and
      Szolovits, Peter",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2308",
    doi = "10.18653/v1/W18-2308",
    pages = "67--75",
    abstract = "Successful evidence-based medicine (EBM) applications rely on answering clinical questions by analyzing large medical literature databases. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we present a Long Short-Term Memory (LSTM) neural network based model to automatically detect PICO elements. By jointly classifying subsequent sentences in the given text, we achieve state-of-the-art results on PICO element classification compared to several strong baseline models. We also make our curated data public as a benchmarking dataset so that the community can benefit from it.",
}
@inproceedings{wang-etal-2018-coding,
    title = "Coding Structures and Actions with the {COSTA} Scheme in Medical Conversations",
    author = "Wang, Nan  and
      Song, Yan  and
      Xia, Fei",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2309",
    doi = "10.18653/v1/W18-2309",
    pages = "76--86",
    abstract = "This paper describes the COSTA scheme for coding structures and actions in conversation. Informed by Conversation Analysis, the scheme introduces an innovative method for marking multi-layer structural organization of conversation and a structure-informed taxonomy of actions. In addition, we create a corpus of naturally occurring medical conversations, containing 318 video-recorded and manually transcribed pediatric consultations. Based on the annotated corpus, we investigate 1) treatment decision-making process in medical conversations, and 2) effects of physician-caregiver communication behaviors on antibiotic over-prescribing. Although the COSTA annotation scheme is developed based on data from the task-specific domain of pediatric consultations, it can be easily extended to apply to more general domains and other languages.",
}
@inproceedings{pfeiffer-etal-2018-neural,
    title = "A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval",
    author = {Pfeiffer, Jonas  and
      Broscheit, Samuel  and
      Gemulla, Rainer  and
      G{\"o}schl, Mathias},
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2310",
    doi = "10.18653/v1/W18-2310",
    pages = "87--97",
    abstract = "In this study, we investigate learning-to-rank and query refinement approaches for information retrieval in the pharmacogenomic domain. The goal is to improve the information retrieval process of biomedical curators, who manually build knowledge bases for personalized medicine. We study how to exploit the relationships between genes, variants, drugs, diseases and outcomes as features for document ranking and query refinement. For a supervised approach, we are faced with a small amount of annotated data and a large amount of unannotated data. Therefore, we explore ways to use a neural document auto-encoder in a semi-supervised approach. We show that a combination of established algorithms, feature-engineering and a neural auto-encoder model yield promising results in this setting.",
}
@inproceedings{bjorne-salakoski-2018-biomedical,
    title = "Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing",
    author = {Bj{\"o}rne, Jari  and
      Salakoski, Tapio},
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2311",
    doi = "10.18653/v1/W18-2311",
    pages = "98--108",
    abstract = "Event and relation extraction are central tasks in biomedical text mining. Where relation extraction concerns the detection of semantic connections between pairs of entities, event extraction expands this concept with the addition of trigger words, multiple arguments and nested events, in order to more accurately model the diversity of natural language. In this work we develop a convolutional neural network that can be used for both event and relation extraction. We use a linear representation of the input text, where information is encoded with various vector space embeddings. Most notably, we encode the parse graph into this linear space using dependency path embeddings. We integrate our neural network into the open source Turku Event Extraction System (TEES) framework. Using this system, our machine learning model can be easily applied to a large set of corpora from e.g. the BioNLP, DDI Extraction and BioCreative shared tasks. We evaluate our system on 12 different event, relation and NER corpora, showing good generalizability to many tasks and achieving improved performance on several corpora.",
}
@inproceedings{sharma-etal-2018-bioama,
    title = "{B}io{AMA}: Towards an End to End {B}io{M}edical Question Answering System",
    author = "Sharma, Vasu  and
      Kulkarni, Nitish  and
      Pranavi, Srividya  and
      Bayomi, Gabriel  and
      Nyberg, Eric  and
      Mitamura, Teruko",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2312",
    doi = "10.18653/v1/W18-2312",
    pages = "109--117",
    abstract = "In this paper, we present a novel Biomedical Question Answering system, BioAMA: {``}Biomedical Ask Me Anything{''} on task 5b of the annual BioASQ challenge. In this work, we focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and well-formed {`}ideal{'} answers. For summary-type questions, we combine effective IR-based techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7{\%} improvement over the previous best model). Additionally, we propose a novel NLI-based framework to answer the yes/no questions. To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings. Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised or unsupervised techniques.",
}
@inproceedings{das-etal-2018-phrase2vecglm,
    title = "{P}hrase2{V}ec{GLM}: Neural generalized language model{--}based semantic tagging for complex query reformulation in medical {IR}",
    author = "Das, Manirupa  and
      Fosler-Lussier, Eric  and
      Lin, Simon  and
      Moosavinasab, Soheil  and
      Chen, David  and
      Rust, Steve  and
      Huang, Yungui  and
      Ramnath, Rajiv",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2313",
    doi = "10.18653/v1/W18-2313",
    pages = "118--128",
    abstract = "In this work, we develop a novel, completely unsupervised, neural language model-based document ranking approach to semantic tagging of documents, using the document to be tagged as a query into the GLM to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text. For this we extend the word embedding-based general language model due to Ganguly et al 2015, to employ phrasal embeddings, and use the semantic tags thus obtained for downstream query expansion, both directly and in feedback loop settings. Our method, evaluated using the TREC 2016 clinical decision support challenge dataset, shows statistically significant improvement not only over various baselines that use standard MeSH terms and UMLS concepts for query expansion, but also over baselines using human expert{--}assigned concept tags for the queries, run on top of a standard Okapi BM25{--}based document retrieval system.",
}
@inproceedings{nguyen-verspoor-2018-convolutional,
    title = "Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings",
    author = "Nguyen, Dat Quoc  and
      Verspoor, Karin",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2314",
    doi = "10.18653/v1/W18-2314",
    pages = "129--136",
    abstract = "We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model. We experiment with two common neural architectures, CNN and LSTM, to learn word vector representations from character embeddings. Through a task on the BioCreative-V CDR corpus, extracting relationships between chemicals and diseases, we show that models exploiting the character-based word representations improve on models that do not use this information, obtaining state-of-the-art result relative to previous neural approaches.",
}
@inproceedings{liu-etal-2018-domain,
    title = "Domain Adaptation for Disease Phrase Matching with Adversarial Networks",
    author = "Liu, Miaofeng  and
      Han, Jialong  and
      Zhang, Haisong  and
      Song, Yan",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2315",
    doi = "10.18653/v1/W18-2315",
    pages = "137--141",
    abstract = "With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems. Disease phrase matching, i.e., deciding whether two given disease phrases interpret each other, is a basic but crucial preprocessing step for the above tasks. Being capable of relieving the scarceness of annotations, domain adaptation is generally considered useful in medical systems. However, efforts on applying it to phrase matching remain limited. This paper presents a domain-adaptive matching network for disease phrases. Our network achieves domain adaptation by adversarial training, i.e., preferring features indicating whether the two phrases match, rather than which domain they come from. Experiments suggest that our model has the best performance among the very few non-adaptive or adaptive methods that can benefit from out-of-domain annotations.",
}
@inproceedings{salimi-rozovskaya-2018-predicting,
    title = "Predicting Discharge Disposition Using Patient Complaint Notes in Electronic Medical Records",
    author = "Salimi, Mohamad  and
      Rozovskaya, Alla",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2316",
    doi = "10.18653/v1/W18-2316",
    pages = "142--146",
    abstract = "Overcrowding in emergency rooms is a major challenge faced by hospitals across the United States. Overcrowding can result in longer wait times, which, in turn, has been shown to adversely affect patient satisfaction, clinical outcomes, and procedure reimbursements. This paper presents research that aims to automatically predict discharge disposition of patients who received medical treatment in an emergency department. We make use of a corpus that consists of notes containing patient complaints, diagnosis information, and disposition, entered by health care providers. We use this corpus to develop a model that uses the complaint and diagnosis information to predict patient disposition. We show that the proposed model substantially outperforms the baseline of predicting the most common disposition type. The long-term goal of this research is to build a model that can be implemented as a real-time service in an application to predict disposition as patients arrive.",
}
@inproceedings{wang-meng-2018-bacteria,
    title = "Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model",
    author = "Wang, Qiuyue  and
      Meng, Xiaofeng",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2317",
    doi = "10.18653/v1/W18-2317",
    pages = "147--150",
    abstract = "Automatic recognition of biomedical entities in text is the crucial initial step in biomedical text mining. In this pa-per, we investigate employing modern neural network models for recognizing biomedical entities. To compensate for the small amount of training data in biomedical domain, we propose to integrate dictionaries into the neural model. Our experiments on BB3 data sets demonstrate that state-of-the-art neural network model is promising in recognizing biomedical entities even with very little training data. When integrated with dictionaries, its performance could be greatly improved, achieving the competitive performance compared with the best dictionary-based system on the entities with specific terminology, and much higher performance on the entities with more general terminology.",
}
@inproceedings{yeganova-etal-2018-singlecite,
    title = "{S}ingle{C}ite: Towards an improved Single Citation Search in {P}ub{M}ed",
    author = "Yeganova, Lana  and
      Comeau, Donald C  and
      Kim, Won  and
      Wilbur, W John  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2318",
    doi = "10.18653/v1/W18-2318",
    pages = "151--155",
    abstract = "A search that is targeted at finding a specific document in databases is called a Single Citation search. Single citation searches are particularly important for scholarly databases, such as PubMed, because users are frequently searching for a specific publication. In this work we describe SingleCite, a single citation matching system designed to facilitate user{'}s search for a specific document. We report on the progress that has been achieved towards building that functionality.",
}
@inproceedings{zhao-etal-2018-framework,
    title = "A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity",
    author = "Zhao, Mengnan  and
      Masino, Aaron J.  and
      Yang, Christopher C.",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2319",
    doi = "10.18653/v1/W18-2319",
    pages = "156--160",
    abstract = "We investigate the quality of task specific word embeddings created with relatively small, targeted corpora. We present a comprehensive evaluation framework including both intrinsic and extrinsic evaluation that can be expanded to named entities beyond drug name. Intrinsic evaluation results tell that drug name embeddings created with a domain specific document corpus outperformed the previously published versions that derived from a very large general text corpus. Extrinsic evaluation uses word embedding for the task of drug name recognition with Bi-LSTM model and the results demonstrate the advantage of using domain-specific word embeddings as the only input feature for drug name recognition with F1-score achieving 0.91. This work suggests that it may be advantageous to derive domain specific embeddings for certain tasks even when the domain specific corpus is of limited size.",
}
@inproceedings{kim-etal-2018-mesh,
    title = "{M}e{SH}-based dataset for measuring the relevance of text retrieval",
    author = "Kim, Won Gyu  and
      Yeganova, Lana  and
      Comeau, Donald  and
      Wilbur, W John  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2320",
    doi = "10.18653/v1/W18-2320",
    pages = "161--165",
    abstract = "Creating simulated search environments has been of a significant interest in infor-mation retrieval, in both general and bio-medical search domains. Existing collec-tions include modest number of queries and are constructed by manually evaluat-ing retrieval results. In this work we pro-pose leveraging MeSH term assignments for creating synthetic test beds. We select a suitable subset of MeSH terms as queries, and utilize MeSH term assignments as pseudo-relevance rankings for retrieval evaluation. Using well studied retrieval functions, we show that their performance on the proposed data is consistent with similar findings in previous work. We further use the proposed retrieval evaluation framework to better understand how to combine heterogeneous sources of textual information.",
}
@inproceedings{zhou-etal-2018-crf,
    title = "{CRF}-{LSTM} Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs",
    author = "Zhou, Kaiyin  and
      Zhang, Sheng  and
      Meng, Xiangyu  and
      Luo, Qi  and
      Wang, Yuxing  and
      Ding, Ke  and
      Feng, Yukun  and
      Chen, Mo  and
      Cohen, Kevin  and
      Xia, Jingbo",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2321",
    doi = "10.18653/v1/W18-2321",
    pages = "166--171",
    abstract = "Sequence labeling of biomedical entities, e.g., side effects or phenotypes, was a long-term task in BioNLP and MedNLP communities. Thanks to effects made among these communities, adverse reaction NER has developed dramatically in recent years. As an illuminative application, to achieve knowledge discovery via the combination of the text mining result and bioinformatics idea shed lights on the pharmacological mechanism research.",
}
@inproceedings{nagata-etal-2018-prediction,
    title = "Prediction Models for Risk of Type-2 Diabetes Using Health Claims",
    author = "Nagata, Masatoshi  and
      Takai, Kohichi  and
      Yasuda, Keiji  and
      Heracleous, Panikos  and
      Yoneyama, Akio",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2322",
    doi = "10.18653/v1/W18-2322",
    pages = "172--176",
    abstract = "This study focuses on highly accurate prediction of the onset of type-2 diabetes. We investigated whether prediction accuracy can be improved by utilizing lab test data obtained from health checkups and incorporating health claim text data such as medically diagnosed diseases with ICD10 codes and pharmacy information. In a previous study, prediction accuracy was increased slightly by adding diagnosis disease name and independent variables such as prescription medicine. Therefore, in the current study we explored more suitable models for prediction by using state-of-the-art techniques such as XGBoost and long short-term memory (LSTM) based on recurrent neural networks. In the current study, text data was vectorized using word2vec, and the prediction model was compared with logistic regression. The results obtained confirmed that onset of type-2 diabetes can be predicted with a high degree of accuracy when the XGBoost model is used.",
}
@inproceedings{wang-etal-2018-learning-better,
    title = "On Learning Better Embeddings from {C}hinese Clinical Records: Study on Combining In-Domain and Out-Domain Data",
    author = "Wang, Yaqiang  and
      Chen, Yunhui  and
      Shu, Hongping  and
      Jiang, Yongguang",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2323",
    doi = "10.18653/v1/W18-2323",
    pages = "177--182",
    abstract = "High quality word embeddings are of great significance to advance applications of biomedical natural language processing. In recent years, a surge of interest on how to learn good embeddings and evaluate embedding quality based on English medical text has become increasing evident, however a limited number of studies based on Chinese medical text, particularly Chinese clinical records, were performed. Herein, we proposed a novel approach of improving the quality of learned embeddings using out-domain data as a supplementary in the case of limited Chinese clinical records. Moreover, the embedding quality evaluation method was conducted based on Medical Conceptual Similarity Property. The experimental results revealed that selecting good training samples was necessary, and collecting right amount of out-domain data and trading off between the quality of embeddings and the training time consumption were essential factors for better embeddings.",
}
@inproceedings{trieu-etal-2018-investigating,
    title = "Investigating Domain-Specific Information for Neural Coreference Resolution on Biomedical Texts",
    author = "Trieu, Hai-Long  and
      Nguyen, Nhung T. H.  and
      Miwa, Makoto  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2324",
    doi = "10.18653/v1/W18-2324",
    pages = "183--188",
    abstract = "Existing biomedical coreference resolution systems depend on features and/or rules based on syntactic parsers. In this paper, we investigate the utility of the state-of-the-art general domain neural coreference resolution system on biomedical texts. The system is an end-to-end system without depending on any syntactic parsers. We also investigate the domain specific features to enhance the system for biomedical texts. Experimental results on the BioNLP Protein Coreference dataset and the CRAFT corpus show that, with no parser information, the adapted system compared favorably with the systems that depend on parser information on these datasets, achieving 51.23{\%} on the BioNLP dataset and 36.33{\%} on the CRAFT corpus in F1 score. In-domain embeddings and domain-specific features helped improve the performance on the BioNLP dataset, but they did not on the CRAFT corpus.",
}
@inproceedings{rosenthal-faulkner-2018-toward,
    title = "Toward Cross-Domain Engagement Analysis in Medical Notes",
    author = "Rosenthal, Sara  and
      Faulkner, Adam",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-2325",
    doi = "10.18653/v1/W18-2325",
    pages = "189--193",
    abstract = "We present a novel annotation task evaluating a patient{'}s engagement with their health care regimen. The concept of engagement supplements the traditional concept of adherence with a focus on the patient{'}s affect, lifestyle choices, and health goal status. We describe an engagement annotation task across two patient note domains: traditional clinical notes and a novel domain, care manager notes, where we find engagement to be more common. The annotation task resulted in a kappa of .53, suggesting strong annotator intuitions regarding engagement-bearing language. In addition, we report the results of a series of preliminary engagement classification experiments using domain adaptation.",
}
@proceedings{ws-2018-amta-2018-technologies,
    title = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    editor = "Liu, Chao-Hong",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2200",
}
@inproceedings{ortega-pillaipakkamnatt-2018-using,
    title = "Using Morphemes from Agglutinative Languages like {Q}uechua and {F}innish to Aid in Low-Resource Translation",
    author = "Ortega, John  and
      Pillaipakkamnatt, Krishnan",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2201",
    pages = "1--11",
}
@inproceedings{dowling-etal-2018-smt,
    title = "{SMT} versus {NMT}: Preliminary comparisons for {I}rish",
    author = "Dowling, Meghan  and
      Lynn, Teresa  and
      Poncelas, Alberto  and
      Way, Andy",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2202",
    pages = "12--20",
}
@inproceedings{lai-etal-2018-tibetan,
    title = "{T}ibetan-{C}hinese Neural Machine Translation based on Syllable Segmentation",
    author = "Lai, Wen  and
      Zhao, Xiaobing  and
      Bao, Wei",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2203",
    pages = "21--29",
}
@inproceedings{oco-roxas-2018-survey,
    title = "A Survey of Machine Translation Work in the {P}hilippines: From 1998 to 2018",
    author = "Oco, Nathaniel  and
      Roxas, Rachel",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2204",
    pages = "30--36",
}
@inproceedings{skorokhodov-etal-2018-semi,
    title = "Semi-Supervised Neural Machine Translation with Language Models",
    author = "Skorokhodov, Ivan  and
      Rykachevskiy, Anton  and
      Emelyanenko, Dmitry  and
      Slotin, Sergey  and
      Ponkratov, Anton",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2205",
    pages = "37--44",
}
@inproceedings{gusev-oboturov-2018-system,
    title = "System Description of Supervised and Unsupervised Neural Machine Translation Approaches from {``}{NL} Processing{''} Team at {D}eep{H}ack.{B}abel Task",
    author = "Gusev, Ilya  and
      Oboturov, Artem",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2206",
    pages = "45--52",
}
@inproceedings{cherivirala-etal-2018-apertiums,
    title = "Apertium{'}s Web Toolchain for Low-Resource Language Technology",
    author = "Cherivirala, Sushain  and
      Chiplunkar, Shardul  and
      Washington, Jonathan  and
      Unhammer, Kevin",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Technologies for {MT} of Low Resource Languages ({L}o{R}es{MT} 2018)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2207",
    pages = "53--62",
}
@proceedings{ws-2018-amta-2018,
    title = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    editor = "Astudillo, Ram{\'o}n  and
      Gra{\c{c}}a, Jo{\~a}o  and
      Martins, Andr{\'e}",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2100",
}
@inproceedings{ueffing-2018-automatic,
    title = "Automatic Post-Editing and Machine Translation Quality Estimation at e{B}ay",
    author = "Ueffing, Nicola",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2101",
    pages = "1--34",
}
@inproceedings{knowles-koehn-2018-lightweight,
    title = "Lightweight Word-Level Confidence Estimation for Neural Interactive Translation Prediction",
    author = "Knowles, Rebecca  and
      Koehn, Philipp",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2102",
    pages = "35--40",
}
@inproceedings{graca-2018-unbabel,
    title = "{U}nbabel: How to combine {AI} with the crowd to scale professional-quality translation",
    author = "Gra{\c{c}}a, Jo{\~a}o",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2103",
    pages = "41--85",
}
@inproceedings{khalilov-2018-machine,
    title = "Machine translation at Booking.com: what{'}s next?",
    author = "Khalilov, Maxim",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2104",
    pages = "86--143",
}
@inproceedings{junczys-dowmunt-2018-experiencing,
    title = "Are we experiencing the Golden Age of Automatic Post-Editing?",
    author = "Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2105",
    pages = "144--206",
}
@inproceedings{federico-2018-challenges,
    title = "Challenges in Adaptive Neural Machine Translation",
    author = "Federico, Marcello",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2106",
    pages = "207--242",
}
@inproceedings{avramidis-etal-2018-fine,
    title = "Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite",
    author = "Avramidis, Eleftherios  and
      Macketanz, Vivien  and
      Lommel, Arle  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2107",
    pages = "243--248",
}
@inproceedings{knowles-etal-2018-comparison,
    title = "A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair",
    author = "Knowles, Rebecca  and
      Ortega, John  and
      Koehn, Philipp",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2108",
    pages = "249--255",
}
@proceedings{ws-2018-amta,
    title = "Proceedings of the {AMTA} 2018 Workshop on The Role of Authoritative Standards in the {MT} Environment",
    editor = "DeCamp, Jennifer",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2000",
}
@inproceedings{decamp-2018-language,
    title = "Language Codes",
    author = "DeCamp, Jennifer",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on The Role of Authoritative Standards in the {MT} Environment",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2001",
    pages = "1--24",
}
@inproceedings{wright-2018-termbase,
    title = "Termbase Exchange ({TBX})",
    author = "Wright, Sue",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on The Role of Authoritative Standards in the {MT} Environment",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2002",
    pages = "25--47",
}
@inproceedings{filip-2018-xliff,
    title = "{XLIFF} 2",
    author = "Filip, David",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on The Role of Authoritative Standards in the {MT} Environment",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2003",
    pages = "48--55",
}
@inproceedings{rivers-2018-translation,
    title = "Translation Quality Standards",
    author = "Rivers, Bill",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on The Role of Authoritative Standards in the {MT} Environment",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2004",
    pages = "56--68",
}
@inproceedings{lommel-2018-translation,
    title = "Translation Quality Metrics",
    author = "Lommel, Arle",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on The Role of Authoritative Standards in the {MT} Environment",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2005",
    pages = "69--94",
}
@inproceedings{melby-2018-translation,
    title = "Translation {API} Cases and Classes ({TAPICC})",
    author = "Melby, Alan",
    booktitle = "Proceedings of the {AMTA} 2018 Workshop on The Role of Authoritative Standards in the {MT} Environment",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-2006",
    pages = "95--112",
}
@proceedings{ws-2018-association-machine,
    title = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    editor = "Campbell, Janice  and
      Yanishevsky, Alex  and
      Doyon, Jennifer  and
      Jones, Doug",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1900",
}
@inproceedings{hughes-2018-keynote,
    title = "{K}eynote: Machine Translation Beyond the Sentence",
    author = "Hughes, Macduff",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1901",
}
@inproceedings{rubino-2018-keynote,
    title = "{K}eynote: Setting up a Machine Translation Program for {IARPA}",
    author = "Rubino, Carl",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1902",
}
@inproceedings{poor-2018-keynote,
    title = "{K}eynote: Use more Machine Translation and Keep Your Customers Happy",
    author = "Poor, Glen",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1903",
}
@inproceedings{decamp-2018-technology,
    title = "Technology Showcase and Presentations",
    author = "DeCamp, Jennifer",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1904",
    pages = "1--4",
}
@inproceedings{lommel-2018-augmented,
    title = "Augmented Translation: A New Approach to Combining Human and Machine Capabilities",
    author = "Lommel, Arle",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1905",
    pages = "5--12",
}
@inproceedings{saillard-2018-training,
    title = "Training, feedback and productivity measurement with {NMT} and Adaptive {MT}",
    author = "Saillard, Jean-Luc",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1906",
    pages = "13--27",
}
@inproceedings{depalma-2018-collision,
    title = "The Collision of Quality and Technology with Reality",
    author = "DePalma, Don",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1907",
    pages = "28--34",
}
@inproceedings{ramirez-sanchez-campbell-2018-language,
    title = "Same-language machine translation for local flavours/flavors",
    author = "Ram{\'\i}rez-S{\'a}nchez, Gema  and
      Campbell, Janice",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1908",
    pages = "35--53",
}
@inproceedings{soldini-2018-thinking,
    title = "Thinking of Going Neural? Factors Honda {R}{\&}{D} {A}mericas is Considering before Making the Switch",
    author = "Soldini, Phil",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1909",
    pages = "54--71",
}
@inproceedings{pinnis-kalnins-2018-developing,
    title = "Developing a Neural Machine Translation Service for the 2017-2018 {E}uropean {U}nion Presidency",
    author = "Pinnis, M{\=a}rcis  and
      Kalnins, Rihards",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1910",
    pages = "72--83",
}
@inproceedings{yanishevsky-2018-neural,
    title = "Neural Won! Now What?",
    author = "Yanishevsky, Alex",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1911",
    pages = "84--112",
}
@inproceedings{sanchez-2018-mt,
    title = "{MT} for L10n: How we build and evaluate {MT} systems at e{B}ay",
    author = "S{\'a}nchez, Jose",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1912",
    pages = "113--149",
}
@inproceedings{ma-2018-vmware,
    title = "{VM}ware {MT} Tiered Model",
    author = "Ma, Lynn",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1913",
    pages = "150--165",
}
@inproceedings{munteanu-gispert-2018-turning,
    title = "Turning {NMT} Research into Commercial Products",
    author = "Munteanu, Dragos  and
      Gispert, Adri{\`a}",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1914",
    pages = "166--193",
}
@inproceedings{lam-2018-beyond,
    title = "Beyond Quality, Considerations for an {MT} solution",
    author = "Lam, Quinn",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1915",
    pages = "194--199",
}
@inproceedings{lafferty-2018-towards,
    title = "Towards Less Post-Editing",
    author = "Lafferty, Bill",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1916",
    pages = "200--222",
}
@inproceedings{sloto-etal-2018-leveraging,
    title = "Leveraging Data Resources for Cross-Linguistic Information Retrieval Using Statistical Machine Translation",
    author = "Sloto, Steve  and
      Clifton, Ann  and
      Hanneman, Greg  and
      Porter, Patrick  and
      Gates, Donna  and
      Hildebrand, Almut  and
      Kumar, Anish",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1917",
    pages = "223--233",
}
@inproceedings{decamp-2018-impact,
    title = "The Impact of Advances in Neural and Statistical {MT} on the Translation Workforce",
    author = "DeCamp, Jennifer",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1918",
    pages = "234--243",
}
@inproceedings{boukhvalov-hogg-2018-pemt,
    title = "{PEMT} for the Public Sector - Evolution of a Solution",
    author = "Boukhvalov, Konstantine  and
      Hogg, Sandy",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1919",
    pages = "244--274",
}
@inproceedings{miller-etal-2018-embedding,
    title = "Embedding Register-Aware {MT} into the {CAT} Workflow",
    author = "Miller, Corey  and
      Silverman, Danielle  and
      Jurica, Vanesa  and
      Richerson, Elizabeth  and
      Morris, Rodney  and
      Mallard, Elisabeth",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1920",
    pages = "275--282",
}
@inproceedings{klavans-etal-2018-challenges,
    title = "Challenges in Speech Recognition and Translation of High-Value Low-Density Polysynthetic Languages",
    author = "Klavans, Judith  and
      Morgan, John  and
      LaRocca, Stephen  and
      Micher, Jeffrey  and
      Voss, Clare",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1921",
    pages = "283--293",
}
@inproceedings{tzoukermann-miller-2018-evaluating,
    title = "Evaluating Automatic Speech Recognition in Translation",
    author = "Tzoukermann, Evelyne  and
      Miller, Corey",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1922",
    pages = "294--302",
}
@inproceedings{meermeier-etal-2018-portable,
    title = "Portable Speech-to-Speech Translation on an Android Smartphone: The {MFLTS} System",
    author = "Meermeier, Ralf  and
      Colbath, Sean  and
      Lillie, Martha",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1923",
    pages = "303--308",
}
@inproceedings{munteanu-tsou-2018-tutorial,
    title = "{T}utorial: De-mystifying Neural {MT}",
    author = "Munteanu, Dragos  and
      Tsou, Ling",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1924",
}
@inproceedings{lommel-melby-2018-tutorial,
    title = "{T}utorial: {MQM}-{DQF}: A Good Marriage (Translation Quality for the 21st Century)",
    author = "Lommel, Arle  and
      Melby, Alan",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1925",
}
@inproceedings{picinini-etal-2018-tutorial,
    title = "{T}utorial: Corpora Quality Management for {MT} - Practices and Roles",
    author = "Picinini, Silvio  and
      Smith, Pete  and
      Ueffing, Nicola",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 2: User Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1926",
}
@proceedings{ws-2018-association,
    title = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    editor = "Cherry, Colin  and
      Neubig, Graham",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1800",
}
@inproceedings{bisazza-2018-keynote,
    title = "{K}eynote: Unveiling the Linguistic Weaknesses of Neural {MT}",
    author = "Bisazza, Arianna",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1801",
}
@inproceedings{jehl-riezler-2018-document,
    title = "Document-Level Information as Side Constraints for Improved Neural Patent Translation",
    author = "Jehl, Laura  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1802",
    pages = "1--12",
}
@inproceedings{martindale-carpuat-2018-fluency,
    title = "Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect {MT}",
    author = "Martindale, Marianna  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1803",
    pages = "13--25",
}
@inproceedings{chatterjee-etal-2018-combining,
    title = "Combining Quality Estimation and Automatic Post-editing to Enhance Machine Translation output",
    author = "Chatterjee, Rajen  and
      Negri, Matteo  and
      Turchi, Marco  and
      Blain, Fr{\'e}d{\'e}ric  and
      Specia, Lucia",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1804",
    pages = "26--38",
}
@inproceedings{conforti-etal-2018-neural,
    title = "Neural Morphological Tagging of Lemma Sequences for Machine Translation",
    author = "Conforti, Costanza  and
      Huck, Matthias  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1805",
    pages = "39--53",
}
@inproceedings{liu-kirchhoff-2018-context,
    title = "Context Models for {OOV} Word Translation in Low-Resource Languages",
    author = "Liu, Angli  and
      Kirchhoff, Katrin",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1806",
    pages = "54--67",
}
@inproceedings{heigold-etal-2018-robust,
    title = "How Robust Are Character-Based Word Embeddings in Tagging and {MT} Against Wrod Scramlbing or Randdm Nouse?",
    author = {Heigold, Georg  and
      Varanasi, Stalin  and
      Neumann, G{\"u}nter  and
      van Genabith, Josef},
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1807",
    pages = "68--80",
}
@inproceedings{lohar-etal-2018-balancing,
    title = "Balancing Translation Quality and Sentiment Preservation (Non-archival Extended Abstract)",
    author = "Lohar, Pintu  and
      Afli, Haithem  and
      Way, Andy",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1808",
    pages = "81--88",
}
@inproceedings{wong-lee-2018-register,
    title = "Register-sensitive Translation: a Case Study of {M}andarin and {C}antonese (Non-archival Extended Abstract)",
    author = "Wong, Tak-sum  and
      Lee, John",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1809",
    pages = "89--96",
}
@inproceedings{ataman-federico-2018-evaluation,
    title = "An Evaluation of Two Vocabulary Reduction Methods for Neural Machine Translation",
    author = "Ataman, Duygu  and
      Federico, Marcello",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1810",
    pages = "97--110",
}
@inproceedings{marie-fujita-2018-smorgasbord,
    title = "A Smorgasbord of Features to Combine Phrase-Based and Neural Machine Translation",
    author = "Marie, Benjamin  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1811",
    pages = "111--124",
}
@inproceedings{marvin-koehn-2018-exploring,
    title = "Exploring Word Sense Disambiguation Abilities of Neural Machine Translation Systems (Non-archival Extended Abstract)",
    author = "Marvin, Rebecca  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1812",
    pages = "125--131",
}
@inproceedings{shearing-etal-2018-improving,
    title = "Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)",
    author = "Shearing, Steven  and
      Kirov, Christo  and
      Khayrallah, Huda  and
      Yarowsky, David",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1813",
    pages = "132--139",
}
@inproceedings{schamoni-etal-2018-dataset,
    title = "A Dataset and Reranking Method for Multimodal {MT} of User-Generated Image Captions",
    author = "Schamoni, Shigehiko  and
      Hitschler, Julian  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1814",
    pages = "140--153",
}
@inproceedings{siahbani-etal-2018-simultaneous,
    title = "Simultaneous Translation using Optimized Segmentation",
    author = "Siahbani, Maryam  and
      Shavarani, Hassan  and
      Alinejad, Ashkan  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1815",
    pages = "154--167",
}
@inproceedings{helcl-etal-2018-neural,
    title = "Neural Monkey: The Current State and Beyond",
    author = "Helcl, Jind{\v{r}}ich  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Kocmi, Tom  and
      Musil, Tom{\'a}{\v{s}}  and
      C{\'\i}fka, Ond{\v{r}}ej  and
      Vari{\v{s}}, Du{\v{s}}an  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1816",
    pages = "168--176",
}
@inproceedings{klein-etal-2018-opennmt,
    title = "{O}pen{NMT}: Neural Machine Translation Toolkit",
    author = "Klein, Guillaume  and
      Kim, Yoon  and
      Deng, Yuntian  and
      Nguyen, Vincent  and
      Senellart, Jean  and
      Rush, Alexander",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1817",
    pages = "177--184",
}
@inproceedings{neubig-etal-2018-xnmt,
    title = "{XNMT}: The e{X}tensible Neural Machine Translation Toolkit",
    author = "Neubig, Graham  and
      Sperber, Matthias  and
      Wang, Xinyi  and
      Felix, Matthieu  and
      Matthews, Austin  and
      Padmanabhan, Sarguna  and
      Qi, Ye  and
      Sachan, Devendra  and
      Arthur, Philip  and
      Godard, Pierre  and
      Hewitt, John  and
      Riad, Rachid  and
      Wang, Liming",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1818",
    pages = "185--192",
}
@inproceedings{vaswani-etal-2018-tensor2tensor,
    title = "{T}ensor2{T}ensor for Neural Machine Translation",
    author = "Vaswani, Ashish  and
      Bengio, Samy  and
      Brevdo, Eugene  and
      Chollet, Francois  and
      Gomez, Aidan  and
      Gouws, Stephan  and
      Jones, Llion  and
      Kaiser, {\L}ukasz  and
      Kalchbrenner, Nal  and
      Parmar, Niki  and
      Sepassi, Ryan  and
      Shazeer, Noam  and
      Uszkoreit, Jakob",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1819",
    pages = "193--199",
}
@inproceedings{hieber-etal-2018-sockeye,
    title = "The Sockeye Neural Machine Translation Toolkit at {AMTA} 2018",
    author = "Hieber, Felix  and
      Domhan, Tobias  and
      Denkowski, Michael  and
      Vilar, David  and
      Sokolov, Artem  and
      Clifton, Ann  and
      Post, Matt",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1820",
    pages = "200--207",
}
@inproceedings{stahlberg-etal-2018-versatile,
    title = "Why not be Versatile? Applications of the {SGNMT} Decoder for Machine Translation",
    author = "Stahlberg, Felix  and
      Saunders, Danielle  and
      Iglesias, Gonzalo  and
      Byrne, Bill",
    booktitle = "Proceedings of the 13th Conference of the Association for Machine Translation in the {A}mericas (Volume 1: Research Track)",
    month = mar,
    year = "2018",
    address = "Boston, MA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/W18-1821",
    pages = "208--216",
}
@proceedings{ws-2018-graph,
    title = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    editor = "Glava{\v{s}}, Goran  and
      Somasundaran, Swapna  and
      Riedl, Martin  and
      Hovy, Eduard",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1700",
    doi = "10.18653/v1/W18-17",
}
@inproceedings{luo-etal-2018-scientific,
    title = "Scientific Discovery as Link Prediction in Influence and Citation Graphs",
    author = "Luo, Fan  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Hahn-Powell, Gus  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1701",
    doi = "10.18653/v1/W18-1701",
    pages = "1--6",
    abstract = "We introduce a machine learning approach for the identification of {``}white spaces{''} in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as {``}CTCF activates FOXA1{''}, which were automatically extracted using open-domain machine reading. We model this prediction task using graph-based features extracted from the above influence graph, as well as from a citation graph that captures scientific communities. We evaluated the proposed approach through backtesting. Although the data is heavily unbalanced (50 times more negative examples than positives), our approach predicts which influence links will be discovered in the {``}near future{''} with a F1 score of 27 points, and a mean average precision of 68{\%}.",
}
@inproceedings{zhang-etal-2018-efficient,
    title = "Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph",
    author = "Zhang, Zheng  and
      Zweigenbaum, Pierre  and
      Yin, Ruiqing",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1702",
    doi = "10.18653/v1/W18-1702",
    pages = "7--11",
    abstract = "Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sentences, extract word pairs and define edge weights, but also supports user-customized functions. By using parallelization techniques, it can generate a large word co-occurrence network of the whole English Wikipedia data within hours. And thanks to its nodes-edges-weight three-level progressive calculation design, rebuilding networks with different configurations is even faster as it does not need to start all over again. This tool also works with other graph libraries such as igraph, NetworkX and graph-tool as a front end providing data to boost network generation speed.",
}
@inproceedings{jansen-2018-multi,
    title = "Multi-hop Inference for Sentence-level {T}ext{G}raphs: How Challenging is Meaningfully Combining Information for Science Question Answering?",
    author = "Jansen, Peter",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1703",
    doi = "10.18653/v1/W18-1703",
    pages = "12--17",
    abstract = "Question Answering for complex questions is often modelled as a graph construction or traversal task, where a solver must build or traverse a graph of facts that answer and explain a given question. This {``}multi-hop{''} inference has been shown to be extremely challenging, with few models able to aggregate more than two facts before being overwhelmed by {``}semantic drift{''}, or the tendency for long chains of facts to quickly drift off topic. This is a major barrier to current inference models, as even elementary science questions require an average of 4 to 6 facts to answer and explain. In this work we empirically characterize the difficulty of building or traversing a graph of sentences connected by lexical overlap, by evaluating chance sentence aggregation quality through 9,784 manually-annotated judgements across knowledge graphs built from three free-text corpora (including study guides and Simple Wikipedia). We demonstrate semantic drift tends to be high and aggregation quality low, at between 0.04 and 3, and highlight scenarios that maximize the likelihood of meaningfully combining information.",
}
@inproceedings{linhares-pontes-etal-2018-multi,
    title = "Multi-Sentence Compression with Word Vertex-Labeled Graphs and Integer Linear Programming",
    author = "Linhares Pontes, Elvys  and
      Huet, St{\'e}phane  and
      Gouveia da Silva, Thiago  and
      Linhares, Andr{\'e}a Carneiro  and
      Torres-Moreno, Juan-Manuel",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1704",
    doi = "10.18653/v1/W18-1704",
    pages = "18--27",
    abstract = "Multi-Sentence Compression (MSC) aims to generate a short sentence with key information from a cluster of closely related sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes a new Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, and novel 3-gram scores to generate more informative sentences while maintaining their grammaticality. Our system is of good quality and outperforms the state-of-the-art for evaluations led on news dataset. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. Additional tests, which take advantage of the fact that the length of compressions can be modulated, still improve ROUGE scores with shorter output sentences.",
}
@inproceedings{pham-chen-2018-large,
    title = "Large-scale spectral clustering using diffusion coordinates on landmark-based bipartite graphs",
    author = "Pham, Khiem  and
      Chen, Guangliang",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1705",
    doi = "10.18653/v1/W18-1705",
    pages = "28--37",
    abstract = "Spectral clustering has received a lot of attention due to its ability to separate nonconvex, non-intersecting manifolds, but its high computational complexity has significantly limited its applicability. Motivated by the document-term co-clustering framework by Dhillon (2001), we propose a landmark-based scalable spectral clustering approach in which we first use the selected landmark set and the given data to form a bipartite graph and then run a diffusion process on it to obtain a family of diffusion coordinates for clustering. We show that our proposed algorithm can be implemented based on very efficient operations on the affinity matrix between the given data and selected landmarks, thus capable of handling large data. Finally, we demonstrate the excellent performance of our method by comparing with the state-of-the-art scalable algorithms on several benchmark data sets.",
}
@inproceedings{chang-etal-2018-efficient,
    title = "Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings",
    author = "Chang, Haw-Shiuan  and
      Agrawal, Amol  and
      Ganesh, Ananya  and
      Desai, Anirudha  and
      Mathur, Vinayak  and
      Hough, Alfred  and
      McCallum, Andrew",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1706",
    doi = "10.18653/v1/W18-1706",
    pages = "38--48",
    abstract = "Word sense induction (WSI), which addresses polysemy by unsupervised discovery of multiple word senses, resolves ambiguities for downstream NLP tasks and also makes word representations more interpretable. This paper proposes an accurate and efficient graph-based method for WSI that builds a global non-negative vector embedding basis (which are interpretable like topics) and clusters the basis indexes in the ego network of each polysemous word. By adopting distributional inclusion vector embeddings as our basis formation model, we avoid the expensive step of nearest neighbor search that plagues other graph-based methods without sacrificing the quality of sense clusters. Experiments on three datasets show that our proposed method produces similar or better sense clusters and embeddings compared with previous state-of-the-art methods while being significantly more efficient.",
}
@inproceedings{skianis-etal-2018-fusing,
    title = "Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification",
    author = "Skianis, Konstantinos  and
      Malliaros, Fragkiskos  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1707",
    doi = "10.18653/v1/W18-1707",
    pages = "49--58",
    abstract = "Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words(GoW) model in which each document is represented by a graph that encodes relationships between the different terms. Based on this formulation, the importance of a term is determined by weighting the corresponding node in the document, collection and label graphs, using node centrality criteria. We also introduce novel graph-based weighting schemes by enriching graphs with word-embedding similarities, in order to reward or penalize semantic relationships. Our methods produce more discriminative feature weights for text categorization, outperforming existing frequency-based criteria.",
}
@inproceedings{dhingra-etal-2018-embedding,
    title = "Embedding Text in Hyperbolic Spaces",
    author = "Dhingra, Bhuwan  and
      Shallue, Christopher  and
      Norouzi, Mohammad  and
      Dai, Andrew  and
      Dahl, George",
    booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1708",
    doi = "10.18653/v1/W18-1708",
    pages = "59--69",
    abstract = "Natural language text exhibits hierarchical structure in a variety of respects. Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data. Recent work by Nickel and Kiela (2017) proposed using hyperbolic instead of Euclidean embedding spaces to represent hierarchical data and demonstrated encouraging results when embedding graphs. In this work, we extend their method with a re-parameterization technique that allows us to learn hyperbolic embeddings of arbitrarily parameterized objects. We apply this framework to learn word and sentence embeddings in hyperbolic space in an unsupervised manner from text corpora. The resulting embeddings seem to encode certain intuitive notions of hierarchy, such as word-context frequency and phrase constituency. However, the implicit continuous hierarchy in the learned hyperbolic space makes interrogating the model{'}s learned hierarchies more difficult than for models that learn explicit edges between items. The learned hyperbolic embeddings show improvements over Euclidean embeddings in some {--} but not all {--} downstream tasks, suggesting that hierarchical organization is more useful for some tasks than others.",
}
@proceedings{ws-2018-stylistic,
    title = "Proceedings of the Second Workshop on Stylistic Variation",
    editor = "Brooke, Julian  and
      Flekova, Lucie  and
      Koppel, Moshe  and
      Solorio, Thamar",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1600",
    doi = "10.18653/v1/W18-16",
}
@inproceedings{degaetano-ortlieb-2018-stylistic,
    title = "Stylistic variation over 200 years of court proceedings according to gender and social class",
    author = "Degaetano-Ortlieb, Stefania",
    booktitle = "Proceedings of the Second Workshop on Stylistic Variation",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1601",
    doi = "10.18653/v1/W18-1601",
    pages = "1--10",
    abstract = "We present an approach to detect stylistic variation across social variables (here: gender and social class), considering also diachronic change in language use. For detection of stylistic variation, we use relative entropy, measuring the difference between probability distributions at different linguistic levels (here: lexis and grammar). In addition, by relative entropy, we can determine which linguistic units are related to stylistic variation.",
}
@inproceedings{balusu-etal-2018-stylistic,
    title = "Stylistic Variation in Social Media Part-of-Speech Tagging",
    author = "Balusu, Murali Raghu Babu  and
      Merghani, Taha  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the Second Workshop on Stylistic Variation",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1602",
    doi = "10.18653/v1/W18-1602",
    pages = "11--19",
    abstract = "Social media features substantial stylistic variation, raising new challenges for syntactic analysis of online writing. However, this variation is often aligned with author attributes such as age, gender, and geography, as well as more readily-available social network metadata. In this paper, we report new evidence on the link between language and social networks in the task of part-of-speech tagging. We find that tagger error rates are correlated with network structure, with high accuracy in some parts of the network, and lower accuracy elsewhere. As a result, tagger accuracy depends on training from a balanced sample of the network, rather than training on texts from a narrow subcommunity. We also describe our attempts to add robustness to stylistic variation, by building a mixture-of-experts model in which each expert is associated with a region of the social network. While prior work found that similar approaches yield performance improvements in sentiment analysis and entity linking, we were unable to obtain performance improvements in part-of-speech tagging, despite strong evidence for the link between part-of-speech error rates and social network structure.",
}
@inproceedings{hu-etal-2018-detecting,
    title = "Detecting Syntactic Features of Translated {C}hinese",
    author = {Hu, Hai  and
      Li, Wen  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the Second Workshop on Stylistic Variation",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1603",
    doi = "10.18653/v1/W18-1603",
    pages = "20--28",
    abstract = "We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features. Using Support Vector Machines (SVMs) as classifier on a genre-balanced corpus in translation studies of Chinese, we find that constituent parse trees and dependency triples as features without lexical information perform very well on the task, with an F-measure above 90{\%}, close to the results of lexical n-gram features, without the risk of learning topic information rather than translation features. Thus, we claim syntactic features alone can accurately distinguish translated from original Chinese. Translated Chinese exhibits an increased use of determiners, subject position pronouns, NP + {``}的{''} as NP modifiers, multiple NPs or VPs conjoined by ''、'', among other structures. We also interpret the syntactic features with reference to previous translation studies in Chinese, particularly the usage of pronouns.",
}
@inproceedings{potash-etal-2018-evaluating,
    title = "Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting",
    author = "Potash, Peter  and
      Romanov, Alexey  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the Second Workshop on Stylistic Variation",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1604",
    doi = "10.18653/v1/W18-1604",
    pages = "29--38",
    abstract = "Language generation tasks that seek to mimic human ability to use language creatively are difficult to evaluate, since one must consider creativity, style, and other non-trivial aspects of the generated text. The goal of this paper is to develop evaluations methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions for this task. Ghostwriting must produce text that is similar in style to the emulated artist, yet distinct in content. We develop a novel evaluation methodology that addresses several complementary aspects of this task, and illustrate how such evaluation can be used to meaning fully analyze system performance. We provide a corpus of lyrics for 13 rap artists, annotated for stylistic similarity, which allows us to assess the feasibility of manual evaluation for generated verse.",
}
@inproceedings{rangel-etal-2018-cross,
    title = "Cross-corpus Native Language Identification via Statistical Embedding",
    author = "Rangel, Francisco  and
      Rosso, Paolo  and
      Brooke, Julian  and
      Uitdenbogerd, Alexandra",
    booktitle = "Proceedings of the Second Workshop on Stylistic Variation",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1605",
    doi = "10.18653/v1/W18-1605",
    pages = "39--43",
    abstract = "In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus. The motivation behind this study is to investigate native language identification in the Australian academic scenario where a majority of students come from China, Indonesia, and Arabic-speaking nations. We have proposed a statistical embedding representation reporting a significant improvement over common single-layer approaches of the state of the art, identifying Chinese, Arabic, and Indonesian in a cross-corpus scenario. The proposed approach was shown to be competitive even when the data is scarce and imbalanced.",
}
@proceedings{ws-2018-storytelling,
    title = "Proceedings of the First Workshop on Storytelling",
    editor = "Mitchell, Margaret  and
      Huang, Ting-Hao {`}Kenneth{'}  and
      Ferraro, Francis  and
      Misra, Ishan",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1500",
    doi = "10.18653/v1/W18-15",
}
@inproceedings{kasunic-kaufman-2018-learning,
    title = "Learning to Listen: Critically Considering the Role of {AI} in Human Storytelling and Character Creation",
    author = "Kasunic, Anna  and
      Kaufman, Geoff",
    booktitle = "Proceedings of the First Workshop on Storytelling",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1501",
    doi = "10.18653/v1/W18-1501",
    pages = "1--13",
    abstract = "In this opinion piece, we argue that there is a need for alternative design directions to complement existing AI efforts in narrative and character generation and algorithm development. To make our argument, we a) outline the predominant roles and goals of AI research in storytelling; b) present existing discourse on the benefits and harms of narratives; and c) highlight the pain points in character creation revealed by semi-structured interviews we conducted with 14 individuals deeply involved in some form of character creation. We conclude by proffering several specific design avenues that we believe can seed fruitful research collaborations. In our vision, AI collaborates with humans during creative processes and narrative generation, helps amplify voices and perspectives that are currently marginalized or misrepresented, and engenders experiences of narrative that support spectatorship and listening roles.",
}
@inproceedings{roemmele-gordon-2018-linguistic,
    title = "Linguistic Features of Helpfulness in Automated Support for Creative Writing",
    author = "Roemmele, Melissa  and
      Gordon, Andrew",
    booktitle = "Proceedings of the First Workshop on Storytelling",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1502",
    doi = "10.18653/v1/W18-1502",
    pages = "14--19",
    abstract = "We examine an emerging NLP application that supports creative writing by automatically suggesting continuing sentences in a story. The application tracks users{'} modifications to generated sentences, which can be used to quantify their {``}helpfulness{''} in advancing the story. We explore the task of predicting helpfulness based on automatically detected linguistic features of the suggestions. We illustrate this analysis on a set of user interactions with the application using an initial selection of features relevant to story generation.",
}
@inproceedings{lukin-etal-2018-pipeline,
    title = "A Pipeline for Creative Visual Storytelling",
    author = "Lukin, Stephanie  and
      Hobbs, Reginald  and
      Voss, Clare",
    booktitle = "Proceedings of the First Workshop on Storytelling",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1503",
    doi = "10.18653/v1/W18-1503",
    pages = "20--32",
    abstract = "Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and cross-disciplinary approaches in natural language processing, generation, and computer vision. We define a computational creative visual storytelling as one with the ability to alter the telling of a story along three aspects: to speak about different environments, to produce variations based on narrative goals, and to adapt the narrative to the audience. These aspects of creative storytelling and their effect on the narrative have yet to be explored in visual storytelling. This paper presents a pipeline of task-modules, Object Identification, Single-Image Inferencing, and Multi-Image Narration, that serve as a preliminary design for building a creative visual storyteller. We have piloted this design for a sequence of images in an annotation task. We present and analyze the collected corpus and describe plans towards automation.",
}
@inproceedings{gillick-bamman-2018-telling,
    title = "Telling Stories with Soundtracks: An Empirical Analysis of Music in Film",
    author = "Gillick, Jon  and
      Bamman, David",
    booktitle = "Proceedings of the First Workshop on Storytelling",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1504",
    doi = "10.18653/v1/W18-1504",
    pages = "33--42",
    abstract = "Soundtracks play an important role in carrying the story of a film. In this work, we collect a corpus of movies and television shows matched with subtitles and soundtracks and analyze the relationship between story, song, and audience reception. We look at the content of a film through the lens of its latent topics and at the content of a song through descriptors of its musical attributes. In two experiments, we find first that individual topics are strongly associated with musical attributes, and second, that musical attributes of soundtracks are predictive of film ratings, even after controlling for topic and genre.",
}
@inproceedings{peng-etal-2018-towards,
    title = "Towards Controllable Story Generation",
    author = "Peng, Nanyun  and
      Ghazvininejad, Marjan  and
      May, Jonathan  and
      Knight, Kevin",
    booktitle = "Proceedings of the First Workshop on Storytelling",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1505",
    doi = "10.18653/v1/W18-1505",
    pages = "43--49",
    abstract = "We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation.",
}
@inproceedings{roemmele-gordon-2018-encoder,
    title = "An Encoder-decoder Approach to Predicting Causal Relations in Stories",
    author = "Roemmele, Melissa  and
      Gordon, Andrew",
    booktitle = "Proceedings of the First Workshop on Storytelling",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1506",
    doi = "10.18653/v1/W18-1506",
    pages = "50--59",
    abstract = "We address the task of predicting causally related events in stories according to a standard evaluation framework, the Choice of Plausible Alternatives (COPA). We present a neural encoder-decoder model that learns to predict relations between adjacent sequences in stories as a means of modeling causality. We explore this approach using different methods for extracting and representing sequence pairs as well as different model architectures. We also compare the impact of different training datasets on our model. In particular, we demonstrate the usefulness of a corpus not previously applied to COPA, the ROCStories corpus. While not state-of-the-art, our results establish a new reference point for systems evaluated on COPA, and one that is particularly informative for future neural-based approaches.",
}
@inproceedings{tozzo-etal-2018-neural,
    title = "Neural Event Extraction from Movies Description",
    author = "Tozzo, Alex  and
      Jovanovi{\'c}, Dejan  and
      Amer, Mohamed",
    booktitle = "Proceedings of the First Workshop on Storytelling",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1507",
    doi = "10.18653/v1/W18-1507",
    pages = "60--66",
    abstract = "We present a novel approach for event extraction and abstraction from movie descriptions. Our event frame consists of {``}who{''}, {``}did what{''} {``}to whom{''}, {``}where{''}, and {``}when{''}. We formulate our problem using a recurrent neural network, enhanced with structural features extracted from syntactic parser, and trained using curriculum learning by progressively increasing the difficulty of the sentences. Our model serves as an intermediate step towards question answering systems, visual storytelling, and story completion tasks. We evaluate our approach on MovieQA dataset.",
}
@proceedings{ws-2018-international-spatial,
    title = "Proceedings of the First International Workshop on Spatial Language Understanding",
    editor = "Kordjamshidi, Parisa  and
      Bhatia, Archna  and
      Pustejovsky, James  and
      Moens, Marie-Francine",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1400",
    doi = "10.18653/v1/W18-14",
}
@inproceedings{dobnik-etal-2018-exploring,
    title = "Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models",
    author = "Dobnik, Simon  and
      Ghanimifard, Mehdi  and
      Kelleher, John",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1401",
    doi = "10.18653/v1/W18-1401",
    pages = "1--11",
    abstract = "The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of models of spatial lexical semantics for human-robot dialogue systems. Furthermore, they also provide an insight into the kinds of the semantic knowledge captured by neural language models trained on spatial descriptions, which has implications for image captioning systems.",
}
@inproceedings{perera-etal-2018-building,
    title = "Building and Learning Structures in a Situated Blocks World Through Deep Language Understanding",
    author = "Perera, Ian  and
      Allen, James  and
      Teng, Choh Man  and
      Galescu, Lucian",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1402",
    doi = "10.18653/v1/W18-1402",
    pages = "12--20",
    abstract = "We demonstrate a system for understanding natural language utterances for structure description and placement in a situated blocks world context. By relying on a rich, domain-specific adaptation of a generic ontology and a logical form structure produced by a semantic parser, we obviate the need for an intermediate, domain-specific representation and can produce a reasoner that grounds and reasons over concepts and constraints with real-valued data. This linguistic base enables more flexibility in interpreting natural language expressions invoking intrinsic concepts and features of structures and space. We demonstrate some of the capabilities of a system grounded in deep language understanding and present initial results in a structure learning task.",
}
@inproceedings{platonov-schubert-2018-computational,
    title = "Computational Models for Spatial Prepositions",
    author = "Platonov, Georgiy  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1403",
    doi = "10.18653/v1/W18-1403",
    pages = "21--30",
    abstract = "Developing computational models of spatial prepositions (such as on, in, above, etc.) is crucial for such tasks as human-machine collaboration, story understanding, and 3D model generation from descriptions. However, these prepositions are notoriously vague and ambiguous, with meanings depending on the types, shapes and sizes of entities in the argument positions, the physical and task context, and other factors. As a result truth value judgments for prepositional relations are often uncertain and variable. In this paper we treat the modeling task as calling for assignment of probabilities to such relations as a function of multiple factors, where such probabilities can be viewed as estimates of whether humans would judge the relations to hold in given circumstances. We implemented our models in a 3D blocks world and a room world in a computer graphics setting, and found that true/false judgments based on these models do not differ much more from human judgments that the latter differ from one another. However, what really matters pragmatically is not the accuracy of truth value judgments but whether, for instance, the computer models suffice for identifying objects described in terms of prepositional relations, (e.g., {``}the box to the left of the table{''}, where there are multiple boxes). For such tasks, our models achieved accuracies above 90{\%} for most relations.",
}
@inproceedings{dorr-olsen-2018-lexical,
    title = "Lexical Conceptual Structure of Literal and Metaphorical Spatial Language: A Case Study of {``}Push{''}",
    author = "Dorr, Bonnie  and
      Olsen, Mari",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1404",
    doi = "10.18653/v1/W18-1404",
    pages = "31--40",
    abstract = "Prior methodologies for understanding spatial language have treated literal expressions such as {``}Mary pushed the car over the edge{''} differently from metaphorical extensions such as {``}Mary{'}s job pushed her over the edge{''}. We demonstrate a methodology for standardizing literal and metaphorical meanings, by building on work in Lexical Conceptual Structure (LCS), a general-purpose representational component used in machine translation. We argue that spatial predicates naturally extend into other fields (e.g., circumstantial or temporal), and that LCS provides both a framework for distinguishing spatial from non-spatial, and a system for finding metaphorical meaning extensions. We start with MetaNet (MN), a large repository of conceptual metaphors, condensing 197 spatial entries into sixteen top-level categories of motion frames. Using naturally occurring instances of English push , and expansions of MN frames, we demonstrate that literal and metaphorical extensions exhibit patterns predicted and represented by the LCS model.",
}
@inproceedings{petruck-ellsworth-2018-representing,
    title = "Representing Spatial Relations in {F}rame{N}et",
    author = "Petruck, Miriam R. L.  and
      Ellsworth, Michael J.",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1405",
    doi = "10.18653/v1/W18-1405",
    pages = "41--45",
    abstract = "While humans use natural language to express spatial relations between and across entities in the world with great facility, natural language systems have a facility that depends on that human facility. This position paper presents approach to representing spatial relations in language, and advocates its adoption for representing the meaning of spatial language. This work shows the importance of axis-orientation systems for capturing the complexity of spatial relations, which FrameNet encodes with semantic types.",
}
@inproceedings{baldridge-etal-2018-points,
    title = "Points, Paths, and Playscapes: Large-scale Spatial Language Understanding Tasks Set in the Real World",
    author = "Baldridge, Jason  and
      Bedrax-Weiss, Tania  and
      Luong, Daphne  and
      Narayanan, Srini  and
      Pang, Bo  and
      Pereira, Fernando  and
      Soricut, Radu  and
      Tseng, Michael  and
      Zhang, Yuan",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1406",
    doi = "10.18653/v1/W18-1406",
    pages = "46--52",
    abstract = "Spatial language understanding is important for practical applications and as a building block for better abstract language understanding. Much progress has been made through work on understanding spatial relations and values in images and texts as well as on giving and following navigation instructions in restricted domains. We argue that the next big advances in spatial language understanding can be best supported by creating large-scale datasets that focus on points and paths based in the real world, and then extending these to create online, persistent playscapes that mix human and bot players, where the bot players must learn, evolve, and survive according to their depth of understanding of scenes, navigation, and interactions.",
}
@inproceedings{manzoor-kordjamshidi-2018-anaphora,
    title = "Anaphora Resolution for Improving Spatial Relation Extraction from Text",
    author = "Manzoor, Umar  and
      Kordjamshidi, Parisa",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1407",
    doi = "10.18653/v1/W18-1407",
    pages = "53--62",
    abstract = "Spatial relation extraction from generic text is a challenging problem due to the ambiguity of the prepositions spatial meaning as well as the nesting structure of the spatial descriptions. In this work, we highlight the difficulties that the anaphora can make in the extraction of spatial relations. We use external multi-modal (here visual) resources to find the most probable candidates for resolving the anaphoras that refer to the landmarks of the spatial relations. We then use global inference to decide jointly on resolving the anaphora and extraction of the spatial relations. Our preliminary results show that resolving anaphora improves the state-of-the-art results on spatial relation extraction.",
}
@inproceedings{dorr-voss-2018-case,
    title = "The Case for Systematically Derived Spatial Language Usage",
    author = "Dorr, Bonnie  and
      Voss, Clare",
    booktitle = "Proceedings of the First International Workshop on Spatial Language Understanding",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1408",
    doi = "10.18653/v1/W18-1408",
    pages = "63--70",
    abstract = "This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexical-semantic resources. Furthermore, even with access to such resources, effective solutions to many application areas such as robot navigation and narrative generation also require additional knowledge at the syntax-semantics interface to cover the wide range of spatial expressions observed and available to natural language speakers. We ground our insights in, and present our extensions to, an existing lexico-semantic resource, covering 500 semantic classes of verbs, of which 219 fall within a spatial subset. We demonstrate that these extensions enable systematic derivation of regular patterns of spatial language without requiring manual annotation.",
}
@proceedings{ws-2018-semantics,
    title = "Proceedings of the Workshop on Computational Semantics beyond Events and Roles",
    editor = "Blanco, Eduardo  and
      Morante, Roser",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1300",
    doi = "10.18653/v1/W18-13",
}
@inproceedings{ulinski-etal-2018-using,
    title = "Using Hedge Detection to Improve Committed Belief Tagging",
    author = "Ulinski, Morgan  and
      Benjamin, Seth  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Workshop on Computational Semantics beyond Events and Roles",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1301",
    doi = "10.18653/v1/W18-1301",
    pages = "1--5",
    abstract = "We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief.",
}
@inproceedings{zerva-ananiadou-2018-paths,
    title = "Paths for uncertainty: Exploring the intricacies of uncertainty identification for news",
    author = "Zerva, Chrysoula  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the Workshop on Computational Semantics beyond Events and Roles",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1302",
    doi = "10.18653/v1/W18-1302",
    pages = "6--20",
    abstract = "Currently, news articles are produced, shared and consumed at an extremely rapid rate. Although their quantity is increasing, at the same time, their quality and trustworthiness is becoming fuzzier. Hence, it is important not only to automate information extraction but also to quantify the certainty of this information. Automated identification of certainty has been studied both in the scientific and newswire domains, but performance is considerably higher in tasks focusing on scientific text. We compare the differences in the definition and expression of uncertainty between a scientific domain, i.e., biomedicine, and newswire. We delve into the different aspects that affect the certainty of an extracted event in a news article and examine whether they can be easily identified by techniques already validated in the biomedical domain. Finally, we present a comparison of the syntactic and lexical differences between the the expression of certainty in the biomedical and newswire domains, using two annotated corpora.",
}
@inproceedings{parde-nielsen-2018-detecting,
    title = "Detecting Sarcasm is Extremely Easy ;-)",
    author = "Parde, Natalie  and
      Nielsen, Rodney",
    booktitle = "Proceedings of the Workshop on Computational Semantics beyond Events and Roles",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1303",
    doi = "10.18653/v1/W18-1303",
    pages = "21--26",
    abstract = "Detecting sarcasm in text is a particularly challenging problem in computational semantics, and its solution may vary across different types of text. We analyze the performance of a domain-general sarcasm detection system on datasets from two very different domains: Twitter, and Amazon product reviews. We categorize the errors that we identify with each, and make recommendations for addressing these issues in NLP systems in the future.",
}
@inproceedings{kalouli-crouch-2018-gkr,
    title = "{GKR}: the Graphical Knowledge Representation for semantic parsing",
    author = "Kalouli, Aikaterini-Lida  and
      Crouch, Richard",
    booktitle = "Proceedings of the Workshop on Computational Semantics beyond Events and Roles",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1304",
    doi = "10.18653/v1/W18-1304",
    pages = "27--37",
    abstract = "This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and its existential commitments. Our representation is a layered graph with each sub-graph holding different kinds of information, including one sub-graph for concepts and one for contexts. Our first evaluation of the system shows an F-score of 85{\%} in accurately representing sentences as semantic graphs.",
}
@inproceedings{habernal-2018-computational,
    title = "Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks",
    author = "Habernal, Ivan",
    booktitle = "Proceedings of the Workshop on Computational Semantics beyond Events and Roles",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1305",
    doi = "10.18653/v1/W18-1305",
    pages = "38",
    abstract = "The classical view on argumentation, such that arguments are logical structures consisting of different distinguishable parts and that parties exchange arguments in a rational way, is prevalent in textbooks but nonexistent in the real world. Instead, argumentation is a multifaceted communication tool built upon humans{'} capabilities to easily use common sense, emotions, and social context. As humans, we are pretty good at it. Computational Argumentation tries to tackle these phenomena but has a long and not so easy way to go. In this talk, I would like to shed a light on several recent attempts to deal with argumentation computationally, such as addressing argument quality, understanding argument reasoning, dealing with fallacies, and how should we never ever argue online.",
}
@proceedings{ws-2018-subword,
    title = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    editor = {Faruqui, Manaal  and
      Sch{\"u}tze, Hinrich  and
      Trancoso, Isabel  and
      Tsvetkov, Yulia  and
      Yaghoobzadeh, Yadollah},
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1200",
    doi = "10.18653/v1/W18-12",
}
@inproceedings{shapiro-duh-2018-morphological,
    title = "Morphological Word Embeddings for {A}rabic Neural Machine Translation in Low-Resource Settings",
    author = "Shapiro, Pamela  and
      Duh, Kevin",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1201",
    doi = "10.18653/v1/W18-1201",
    pages = "1--11",
    abstract = "Neural machine translation has achieved impressive results in the last few years, but its success has been limited to settings with large amounts of parallel data. One way to improve NMT for lower-resource settings is to initialize a word-based NMT model with pretrained word embeddings. However, rare words still suffer from lower quality word embeddings when trained with standard word-level objectives. We introduce word embeddings that utilize morphological resources, and compare to purely unsupervised alternatives. We work with Arabic, a morphologically rich language with available linguistic resources, and perform Ar-to-En MT experiments on a small corpus of TED subtitles. We find that word embeddings utilizing subword information consistently outperform standard word embeddings on a word similarity task and as initialization of the source word embeddings in a low-resource NMT system.",
}
@inproceedings{el-kishky-etal-2018-entropy,
    title = "Entropy-Based Subword Mining with an Application to Word Embeddings",
    author = "El-Kishky, Ahmed  and
      Xu, Frank  and
      Zhang, Aston  and
      Macke, Stephen  and
      Han, Jiawei",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1202",
    doi = "10.18653/v1/W18-1202",
    pages = "12--21",
    abstract = "Recent literature has shown a wide variety of benefits to mapping traditional one-hot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have long-tail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropy-based subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.",
}
@inproceedings{adouane-etal-2018-comparison,
    title = "A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts",
    author = "Adouane, Wafia  and
      Dobnik, Simon  and
      Bernardy, Jean-Philippe  and
      Semmar, Nasredine",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1203",
    doi = "10.18653/v1/W18-1203",
    pages = "22--31",
    abstract = "This paper seeks to examine the effect of including background knowledge in the form of character pre-trained neural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited resources. As a test, we explore the task of language identification in mixed-language short non-edited texts with an under-resourced language, namely the case of Algerian Arabic for which both labelled and unlabelled data are limited. We compare the performance of two traditional machine learning methods and a deep neural networks (DNNs) model. The results show that overall DNNs perform better on labelled data for the majority categories and struggle with the minority ones. While the effect of the untokenised and unlabelled data encoded as LM differs for each category, bootstrapping, however, improves the performance of all systems and all categories. These methods are language independent and could be generalised to other under-resourced languages for which a small labelled data and a larger unlabelled data are available.",
}
@inproceedings{papay-etal-2018-addressing,
    title = "Addressing Low-Resource Scenarios with Character-aware Embeddings",
    author = "Papay, Sean  and
      Pad{\'o}, Sebastian  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1204",
    doi = "10.18653/v1/W18-1204",
    pages = "32--37",
    abstract = "Most modern approaches to computing word embeddings assume the availability of text corpora with billions of words. In this paper, we explore a setup where only corpora with millions of words are available, and many words in any new text are out of vocabulary. This setup is both of practical interests {--} modeling the situation for specific domains and low-resource languages {--} and of psycholinguistic interest, since it corresponds much more closely to the actual experiences and challenges of human language learning and use. We compare standard skip-gram word embeddings with character-based embeddings on word relatedness prediction. Skip-grams excel on large corpora, while character-based embeddings do well on small corpora generally and rare and complex words specifically. The models can be combined easily.",
}
@inproceedings{li-etal-2018-subword,
    title = "Subword-level Composition Functions for Learning Word Embeddings",
    author = "Li, Bofang  and
      Drozd, Aleksandr  and
      Liu, Tao  and
      Du, Xiaoyong",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1205",
    doi = "10.18653/v1/W18-1205",
    pages = "38--48",
    abstract = "Subword-level information is crucial for capturing the meaning and morphology of words, especially for out-of-vocabulary entries. We propose CNN- and RNN-based subword-level composition functions for learning word embeddings, and systematically compare them with popular word-level and subword-level models (Skip-Gram and FastText). Additionally, we propose a hybrid training scheme in which a pure subword-level model is trained jointly with a conventional word-level embedding model based on lookup-tables. This increases the fitness of all types of subword-level word embeddings; the word-level embeddings can be discarded after training, leaving only compact subword-level representation with much smaller data volume. We evaluate these embeddings on a set of intrinsic and extrinsic tasks, showing that subword-level models have advantage on tasks related to morphology and datasets with high OOV rate, and can be combined with other types of embeddings.",
}
@inproceedings{liu-etal-2018-discovering,
    title = "Discovering Phonesthemes with Sparse Regularization",
    author = "Liu, Nelson F.  and
      Levow, Gina-Anne  and
      Smith, Noah A.",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1206",
    doi = "10.18653/v1/W18-1206",
    pages = "49--54",
    abstract = "We introduce a simple method for extracting non-arbitrary form-meaning representations from a collection of semantic vectors. We treat the problem as one of feature selection for a model trained to predict word vectors from subword features. We apply this model to the problem of automatically discovering phonesthemes, which are submorphemic sound clusters that appear in words with similar meaning. Many of our model-predicted phonesthemes overlap with those proposed in the linguistics literature, and we validate our approach with human judgments.",
}
@inproceedings{banerjee-bhattacharyya-2018-meaningless,
    title = "Meaningless yet meaningful: Morphology grounded subword-level {NMT}",
    author = "Banerjee, Tamali  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1207",
    doi = "10.18653/v1/W18-1207",
    pages = "55--60",
    abstract = "We explore the use of two independent subsystems Byte Pair Encoding (BPE) and Morfessor as basic units for subword-level neural machine translation (NMT). We show that, for linguistically distant language-pairs Morfessor-based segmentation algorithm produces significantly better quality translation than BPE. However, for close language-pairs BPE-based subword-NMT may translate better than Morfessor-based subword-NMT. We propose a combined approach of these two segmentation algorithms Morfessor-BPE (M-BPE) which outperforms these two baseline systems in terms of BLEU score. Our results are supported by experiments on three language-pairs: English-Hindi, Bengali-Hindi and English-Bengali.",
}
@inproceedings{ganu-p-2018-fast,
    title = "Fast Query Expansion on an Accounting Corpus using Sub-Word Embeddings",
    author = "Ganu, Hrishikesh  and
      P., Viswa Datha",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1208",
    doi = "10.18653/v1/W18-1208",
    pages = "61--65",
    abstract = "We present early results from a system under development which uses sub-word embeddings for query expansion in presence of mis-spelled words and other aberrations. We work for a company which creates accounting software and the end goal is to improve customer experience when they search for help on our {``}Customer Care{''} portal. Our customers use colloquial language, non-standard acronyms and sometimes mis-spell words when they use our Search portal or interact over other channels. However, our Knowledge Base has curated content which leverages technical terms and is in language which is quite formal. This results in the answer not being retrieved even though the answer might actually be present in the documentation (as assessed by a human). We address this problem by creating equivalence classes of words with similar meanings (with the additional property that the mappings to these equivalence classes are robust to mis-spellings) using sub-word embeddings and then use them to fine tune an Elasticsearch index to improve recall. We demonstrate through an end-end system that using sub-word embeddings leads to a significant lift in correct answers retrieved for an accounting corpus available in the public domain.",
}
@inproceedings{salle-villavicencio-2018-incorporating,
    title = "Incorporating Subword Information into Matrix Factorization Word Embeddings",
    author = "Salle, Alexandre  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1209",
    doi = "10.18653/v1/W18-1209",
    pages = "66--71",
    abstract = "The positive effect of adding subword information to word embeddings has been demonstrated for predictive models. In this paper we investigate whether similar benefits can also be derived from incorporating subwords into counting models. We evaluate the impact of different types of subwords (n-grams and unsupervised morphemes), with results confirming the importance of subword information in learning representations of rare and out-of-vocabulary words.",
}
@inproceedings{dudy-etal-2018-multi,
    title = "A Multi-Context Character Prediction Model for a Brain-Computer Interface",
    author = "Dudy, Shiran  and
      Xu, Shaobin  and
      Bedrick, Steven  and
      Smith, David",
    booktitle = "Proceedings of the Second Workshop on Subword/Character {LE}vel Models",
    month = jun,
    year = "2018",
    address = "New Orleans",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1210",
    doi = "10.18653/v1/W18-1210",
    pages = "72--77",
    abstract = "Brain-computer interfaces and other augmentative and alternative communication devices introduce language-modeing challenges distinct from other character-entry methods. In particular, the acquired signal of the EEG (electroencephalogram) signal is noisier, which, in turn, makes the user intent harder to decipher. In order to adapt to this condition, we propose to maintain ambiguous history for every time step, and to employ, apart from the character language model, word information to produce a more robust prediction system. We present preliminary results that compare this proposed Online-Context Language Model (OCLM) to current algorithms that are used in this type of setting. Evaluation on both perplexity and predictive accuracy demonstrates promising results when dealing with ambiguous histories in order to provide to the front end a distribution of the next character the user might type.",
}
@proceedings{ws-2018-modeling,
    title = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    editor = "Nissim, Malvina  and
      Patti, Viviana  and
      Plank, Barbara  and
      Wagner, Claudia",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1100",
    doi = "10.18653/v1/W18-11",
}
@inproceedings{ahuja-etal-2018-makes,
    title = "What makes us laugh? Investigations into Automatic Humor Classification",
    author = "Ahuja, Vikram  and
      Bali, Taradheesh  and
      Singh, Navjyoti",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1101",
    doi = "10.18653/v1/W18-1101",
    pages = "1--9",
    abstract = "Most scholarly works in the field of computational detection of humour derive their inspiration from the incongruity theory. Incongruity is an indispensable facet in drawing a line between humorous and non-humorous occurrences but is immensely inadequate in shedding light on what actually made the particular occurrence a funny one. Classical theories like Script-based Semantic Theory of Humour and General Verbal Theory of Humour try and achieve this feat to an adequate extent. In this paper we adhere to a more holistic approach towards classification of humour based on these classical theories with a few improvements and revisions. Through experiments based on our linear approach and performed on large data-sets of jokes, we are able to demonstrate the adaptability and show componentizability of our model, and that a host of classification techniques can be used to overcome the challenging problem of distinguishing between various categories and sub-categories of jokes.",
}
@inproceedings{chan-fyshe-2018-social,
    title = "Social and Emotional Correlates of Capitalization on {T}witter",
    author = "Chan, Sophia  and
      Fyshe, Alona",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1102",
    doi = "10.18653/v1/W18-1102",
    pages = "10--15",
    abstract = "Social media text is replete with unusual capitalization patterns. We posit that capitalizing a token like THIS performs two expressive functions: it marks a person socially, and marks certain parts of an utterance as more salient than others. Focusing on gender and sentiment, we illustrate using a corpus of tweets that capitalization appears in more negative than positive contexts, and is used more by females compared to males. Yet we find that both genders use capitalization in a similar way when expressing sentiment.",
}
@inproceedings{mora-lavid-lopez-2018-building,
    title = "Building an annotated dataset of app store reviews with Appraisal features in {E}nglish and {S}panish",
    author = "Mora, Natalia  and
      Lavid-L{\'o}pez, Julia",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1103",
    doi = "10.18653/v1/W18-1103",
    pages = "16--24",
    abstract = "This paper describes the creation and annotation of a dataset consisting of 250 English and Spanish app store reviews from Google{'}s Play Store with Appraisal features. This is one of the most influential linguistic frameworks for the analysis of evaluation and opinion in discourse due to its insightful descriptive features. However, it has not been extensively applied in NLP in spite of its potential for the classification of the subjective content of these reviews. We describe the dataset, the annotation scheme and guidelines, the agreement studies, the annotation results and their impact on the characterisation of this genre.",
}
@inproceedings{alhuzali-etal-2018-enabling,
    title = "Enabling Deep Learning of Emotion With First-Person Seed Expressions",
    author = "Alhuzali, Hassan  and
      Abdul-Mageed, Muhammad  and
      Ungar, Lyle",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1104",
    doi = "10.18653/v1/W18-1104",
    pages = "25--35",
    abstract = "The computational treatment of emotion in natural language text remains relatively limited, and Arabic is no exception. This is partly due to lack of labeled data. In this work, we describe and manually validate a method for the automatic acquisition of emotion labeled data and introduce a newly developed data set for Modern Standard and Dialectal Arabic emotion detection focused at Robert Plutchik{'}s 8 basic emotion types. Using a hybrid supervision method that exploits first person emotion seeds, we show how we can acquire promising results with a deep gated recurrent neural network. Our best model reaches 70{\%} \textit{F}-score, significantly (i.e., 11{\%}, $p < 0.05$) outperforming a competitive baseline. Applying our method and data on an external dataset of 4 emotions released around the same time we finalized our work, we acquire 7{\%} absolute gain in $F$-score over a linear SVM classifier trained on gold data, thus validating our approach.",
}
@inproceedings{bohra-etal-2018-dataset,
    title = "A Dataset of {H}indi-{E}nglish Code-Mixed Social Media Text for Hate Speech Detection",
    author = "Bohra, Aditya  and
      Vijay, Deepanshu  and
      Singh, Vinay  and
      Akhtar, Syed Sarfaraz  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1105",
    doi = "10.18653/v1/W18-1105",
    pages = "36--41",
    abstract = "Hate speech detection in social media texts is an important Natural language Processing task, which has several crucial applications like sentiment analysis, investigating cyberbullying and examining socio-political controversies. While relevant research has been done independently on code-mixed social media texts and hate speech detection, our work is the first attempt in detecting hate speech in Hindi-English code-mixed social media text. In this paper, we analyze the problem of hate speech detection in code-mixed texts and present a Hindi-English code-mixed dataset consisting of tweets posted online on Twitter. The tweets are annotated with the language at word level and the class they belong to (Hate Speech or Normal Speech). We also propose a supervised classification system for detecting hate speech in the text using various character level, word level, and lexicon based features.",
}
@inproceedings{hovy-2018-social,
    title = "The Social and the Neural Network: How to Make Natural Language Processing about People again",
    author = "Hovy, Dirk",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1106",
    doi = "10.18653/v1/W18-1106",
    pages = "42--49",
    abstract = "Over the years, natural language processing has increasingly focused on tasks that can be solved by statistical models, but ignored the social aspects of language. These limitations are in large part due to historically available data and the limitations of the models, but have narrowed our focus and biased the tools demographically. However, with the increased availability of data sets including socio-demographic information and more expressive (neural) models, we have the opportunity to address both issues. I argue that this combination can broaden the focus of NLP to solve a whole new range of tasks, enable us to generate novel linguistic insights, and provide fairer tools for everyone.",
}
@inproceedings{lippincott-carrell-2018-observational,
    title = "Observational Comparison of Geo-tagged and Randomly-drawn Tweets",
    author = "Lippincott, Tom  and
      Carrell, Annabelle",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1107",
    doi = "10.18653/v1/W18-1107",
    pages = "50--55",
    abstract = "Twitter is a ubiquitous source of micro-blog social media data, providing the academic, industrial, and public sectors real-time access to actionable information. A particularly attractive property of some tweets is *geo-tagging*, where a user account has opted-in to attaching their current location to each message. Unfortunately (from a researcher{'}s perspective) only a fraction of Twitter accounts agree to this, and these accounts are likely to have systematic diffences with the general population. This work is an exploratory study of these differences across the full range of Twitter content, and complements previous studies that focus on the English-language subset. Additionally, we compare methods for querying users by self-identified properties, finding that the constrained semantics of the {``}description{''} field provides cleaner, higher-volume results than more complex regular expressions.",
}
@inproceedings{wood-doughty-etal-2018-johns,
    title = "{J}ohns {H}opkins or johnny-hopkins: Classifying Individuals versus Organizations on {T}witter",
    author = "Wood-Doughty, Zach  and
      Mahajan, Praateek  and
      Dredze, Mark",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1108",
    doi = "10.18653/v1/W18-1108",
    pages = "56--61",
    abstract = "Twitter user accounts include a range of different user types. While many individuals use Twitter, organizations also have Twitter accounts. Identifying opinions and trends from Twitter requires the accurate differentiation of these two groups. Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets. We present a method that relies solely on the account profile, allowing for the classification of individuals versus organizations based on a single tweet. Our method obtains accuracies comparable to methods that rely on much more information by leveraging two improvements: a character-based Convolutional Neural Network, and an automatically derived labeled corpus an order of magnitude larger than the previously available dataset. We make both the dataset and the resulting tool available.",
}
@inproceedings{mencarini-2018-potential,
    title = "The Potential of the Computational Linguistic Analysis of Social Media for Population Studies",
    author = "Mencarini, Letizia",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1109",
    doi = "10.18653/v1/W18-1109",
    pages = "62--68",
    abstract = "The paper provides an outline of the scope for synergy between computational linguistic analysis and population stud-ies. It first reviews where population studies stand in terms of using social media data. Demographers are entering the realm of big data in force. But, this paper argues, population studies have much to gain from computational linguis-tic analysis, especially in terms of ex-plaining the drivers behind population processes. The paper gives two examples of how the method can be applied, and concludes with a fundamental caveat. Yes, computational linguistic analysis provides a possible key for integrating micro theory into any demographic analysis of social media data. But results may be of little value in as much as knowledge about fundamental sample characteristics are unknown.",
}
@inproceedings{durmus-cardie-2018-understanding,
    title = "Understanding the Effect of Gender and Stance in Opinion Expression in Debates on {``}Abortion{''}",
    author = "Durmus, Esin  and
      Cardie, Claire",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1110",
    doi = "10.18653/v1/W18-1110",
    pages = "69--75",
    abstract = "In this paper, we focus on understanding linguistic differences across groups with different self-identified gender and stance in expressing opinions about ABORTION. We provide a new dataset consisting of users{'} gender, stance on ABORTION as well as the debates in ABORTION drawn from debate.org. We use the gender and stance information to identify significant linguistic differences across individuals with different gender and stance. We show the importance of considering the stance information along with the gender since we observe significant linguistic differences across individuals with different stance even within the same gender group.",
}
@inproceedings{chhaya-etal-2018-frustrated,
    title = "Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email",
    author = "Chhaya, Niyati  and
      Chawla, Kushal  and
      Goyal, Tanya  and
      Chanda, Projjal  and
      Singh, Jaya",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1111",
    doi = "10.18653/v1/W18-1111",
    pages = "76--86",
    abstract = "Email conversations are the primary mode of communication in enterprises. The email content expresses an individual{'}s needs, requirements and intentions. Affective information in the email text can be used to get an insight into the sender{'}s mood or emotion. We present a novel approach to model human frustration in text. We identify linguistic features that influence human perception of frustration and model it as a supervised learning task. The paper provides a detailed comparison across traditional regression and word distribution-based models. We report a mean-squared error (MSE) of 0.018 against human-annotated frustration for the best performing model. The approach establishes the importance of affect features in frustration prediction for email data. We further evaluate the efficacy of the proposed feature set and model in predicting other tone or affects in text, namely formality and politeness; results demonstrate a comparable performance against the state-of-the-art baselines.",
}
@inproceedings{gjurkovic-snajder-2018-reddit,
    title = "{R}eddit: A Gold Mine for Personality Prediction",
    author = "Gjurkovi{\'c}, Matej  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1112",
    doi = "10.18653/v1/W18-1112",
    pages = "87--97",
    abstract = "Automated personality prediction from social media is gaining increasing attention in natural language processing and social sciences communities. However, due to high labeling costs and privacy issues, the few publicly available datasets are of limited size and low topic diversity. We address this problem by introducing a large-scale dataset derived from Reddit, a source so far overlooked for personality prediction. The dataset is labeled with Myers-Briggs Type Indicators (MBTI) and comes with a rich set of features for more than 9k users. We carry out a preliminary feature analysis, revealing marked differences between the MBTI dimensions and poles. Furthermore, we use the dataset to train and evaluate benchmark personality prediction models, achieving macro F1-scores between 67{\%} and 82{\%} on the individual dimensions and 82{\%} accuracy for exact or one-off accurate type prediction. These results are encouraging and comparable with the reliability of standardized tests.",
}
@inproceedings{plank-2018-predicting,
    title = "Predicting Authorship and Author Traits from Keystroke Dynamics",
    author = "Plank, Barbara",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1113",
    doi = "10.18653/v1/W18-1113",
    pages = "98--104",
    abstract = "Written text transmits a good deal of nonverbal information related to the author{'}s identity and social factors, such as age, gender and personality. However, it is less known to what extent behavioral biometric traces transmit such information. We use typist data to study the predictiveness of authorship, and present first experiments on predicting both age and gender from keystroke dynamics. Our results show that the model based on keystroke features, while being two orders of magnitude smaller, leads to significantly higher accuracies for authorship than the text-based system. For user attribute prediction, the best approach is to combine the two, suggesting that extralinguistic factors are disclosed to a larger degree in written text, while author identity is better transmitted in typing behavior.",
}
@inproceedings{wood-doughty-etal-2018-predicting,
    title = "Predicting {T}witter User Demographics from Names Alone",
    author = "Wood-Doughty, Zach  and
      Andrews, Nicholas  and
      Marvin, Rebecca  and
      Dredze, Mark",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1114",
    doi = "10.18653/v1/W18-1114",
    pages = "105--111",
    abstract = "Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends. These tools often require hundreds of user-authored messages for each user, which may be prohibitive to obtain when analyzing millions of users. We explore character-level neural models that learn a representation of a user{'}s name and screen name to predict gender and ethnicity, allowing for demographic inference with minimal data. We release trained models1 which may enable new demographic analyses that would otherwise require enormous amounts of data collection",
}
@inproceedings{tighe-cheng-2018-modeling,
    title = "Modeling Personality Traits of {F}ilipino {T}witter Users",
    author = "Tighe, Edward  and
      Cheng, Charibeth",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1115",
    doi = "10.18653/v1/W18-1115",
    pages = "112--122",
    abstract = "Recent studies in the field of text-based personality recognition experiment with different languages, feature extraction techniques, and machine learning algorithms to create better and more accurate models; however, little focus is placed on exploring the language use of a group of individuals defined by nationality. Individuals of the same nationality share certain practices and communicate certain ideas that can become embedded into their natural language. Many nationals are also not limited to speaking just one language, such as how Filipinos speak Filipino and English, the two national languages of the Philippines. The addition of several regional/indigenous languages, along with the commonness of code-switching, allow for a Filipino to have a rich vocabulary. This presents an opportunity to create a text-based personality model based on how Filipinos speak, regardless of the language they use. To do so, data was collected from 250 Filipino Twitter users. Different combinations of data processing techniques were experimented upon to create personality models for each of the Big Five. The results for both regression and classification show that Conscientiousness is consistently the easiest trait to model, followed by Extraversion. Classification models for Agreeableness and Neuroticism had subpar performances, but performed better than those of Openness. An analysis on personality trait score representation showed that classifying extreme outliers generally produce better results for all traits except for Neuroticism and Openness.",
}
@inproceedings{vilares-gomez-rodriguez-2018-grounding,
    title = "Grounding the Semantics of Part-of-Day Nouns Worldwide using {T}witter",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1116",
    doi = "10.18653/v1/W18-1116",
    pages = "123--128",
    abstract = "The usage of part-of-day nouns, such as {`}night{'}, and their time-specific greetings ({`}good night{'}), varies across languages and cultures. We show the possibilities that Twitter offers for studying the semantics of these terms and its variability between countries. We mine a worldwide sample of multilingual tweets with temporal greetings, and study how their frequencies vary in relation with local time. The results provide insights into the semantics of these temporal expressions and the cultural and sociological factors influencing their usage.",
}
@proceedings{ws-2018-generalization,
    title = "Proceedings of the Workshop on Generalization in the Age of Deep Learning",
    editor = "Bisk, Yonatan  and
      Levy, Omer  and
      Yatskar, Mark",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1000",
    doi = "10.18653/v1/W18-10",
}
@inproceedings{wadhwa-etal-2018-towards,
    title = "Towards Inference-Oriented Reading Comprehension: {P}arallel{QA}",
    author = "Wadhwa, Soumya  and
      Embar, Varsha  and
      Grabmair, Matthias  and
      Nyberg, Eric",
    booktitle = "Proceedings of the Workshop on Generalization in the Age of Deep Learning",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1001",
    doi = "10.18653/v1/W18-1001",
    pages = "1--7",
    abstract = "In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.",
}
@inproceedings{jastrzebski-etal-2018-commonsense,
    title = "Commonsense mining as knowledge base completion? A study on the impact of novelty",
    author = "Jastrz{\k{e}}bski, Stanislaw  and
      Bahdanau, Dzmitry  and
      Hosseini, Seyedarian  and
      Noukhovitch, Michael  and
      Bengio, Yoshua  and
      Cheung, Jackie",
    booktitle = "Proceedings of the Workshop on Generalization in the Age of Deep Learning",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1002",
    doi = "10.18653/v1/W18-1002",
    pages = "8--16",
    abstract = "Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method that outperforms the previous state of the art on predicting more novel triples.",
}
@inproceedings{kuhnle-copestake-2018-deep,
    title = "Deep learning evaluation using deep linguistic processing",
    author = "Kuhnle, Alexander  and
      Copestake, Ann",
    booktitle = "Proceedings of the Workshop on Generalization in the Age of Deep Learning",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1003",
    doi = "10.18653/v1/W18-1003",
    pages = "17--23",
    abstract = "We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing {`}deep{'} linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value on a static and monolithic dataset.",
}
@inproceedings{weber-etal-2018-fine,
    title = "The Fine Line between Linguistic Generalization and Failure in {S}eq2{S}eq-Attention Models",
    author = "Weber, Noah  and
      Shekhar, Leena  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of the Workshop on Generalization in the Age of Deep Learning",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1004",
    doi = "10.18653/v1/W18-1004",
    pages = "24--27",
    abstract = "Seq2Seq based neural architectures have become the go-to architecture to apply to sequence to sequence language tasks. Despite their excellent performance on these tasks, recent work has noted that these models typically do not fully capture the linguistic structure required to generalize beyond the dense sections of the data distribution (Ettinger et al., 2017), and as such, are likely to fail on examples from the tail end of the distribution (such as inputs that are noisy (Belinkov and Bisk, 2018), or of different length (Bentivogli et al., 2016)). In this paper we look at a model{'}s ability to generalize on a simple symbol rewriting task with a clearly defined structure. We find that the model{'}s ability to generalize this structure beyond the training distribution depends greatly on the chosen random seed, even when performance on the test set remains the same. This finding suggests that model{'}s ability to capture generalizable structure is highly sensitive, and more so, this sensitivity may not be apparent when evaluating the model on standard test sets.",
}
@inproceedings{mitchell-etal-2018-extrapolation,
    title = "Extrapolation in {NLP}",
    author = "Mitchell, Jeff  and
      Stenetorp, Pontus  and
      Minervini, Pasquale  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the Workshop on Generalization in the Age of Deep Learning",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-1005",
    doi = "10.18653/v1/W18-1005",
    pages = "28--33",
    abstract = "We argue that extrapolation to unseen data will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We show that this is true for two popular models: the Decomposable Attention Model and word2vec.",
}
@proceedings{ws-2018-figurative,
    title = "Proceedings of the Workshop on Figurative Language Processing",
    editor = "Beigman Klebanov, Beata  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0900",
    doi = "10.18653/v1/W18-09",
}
@inproceedings{gero-chilton-2018-challenges,
    title = "Challenges in Finding Metaphorical Connections",
    author = "Gero, Katy  and
      Chilton, Lydia",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0901",
    doi = "10.18653/v1/W18-0901",
    pages = "1--6",
    abstract = "Poetry is known for its novel expression using figurative language. We introduce a writing task that contains the essential challenges of generating meaningful figurative language and can be evaluated. We investigate how to find metaphorical connections between abstract themes and concrete domains by asking people to write four-line poems on a given metaphor, such as {``}death is a rose{''} or {``}anger is wood{''}. We find that only 21{\%} of poems successfully make a metaphorical connection. We present five alternate ways people respond to the prompt and release our dataset of 100 categorized poems. We suggest opportunities for computational approaches.",
}
@inproceedings{skalicky-crossley-2018-linguistic,
    title = "Linguistic Features of Sarcasm and Metaphor Production Quality",
    author = "Skalicky, Stephen  and
      Crossley, Scott",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0902",
    doi = "10.18653/v1/W18-0902",
    pages = "7--16",
    abstract = "Using linguistic features to detect figurative language has provided a deeper in-sight into figurative language. The purpose of this study is to assess whether linguistic features can help explain differences in quality of figurative language. In this study a large corpus of metaphors and sarcastic responses are collected from human subjects and rated for figurative language quality based on theoretical components of metaphor, sarcasm, and creativity. Using natural language processing tools, specific linguistic features related to lexical sophistication and semantic cohesion were used to predict the human ratings of figurative language quality. Results demonstrate linguistic features were able to predict small amounts of variance in metaphor and sarcasm production quality.",
}
@inproceedings{stowe-palmer-2018-leveraging,
    title = "Leveraging Syntactic Constructions for Metaphor Identification",
    author = "Stowe, Kevin  and
      Palmer, Martha",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0903",
    doi = "10.18653/v1/W18-0903",
    pages = "17--26",
    abstract = "Identification of metaphoric language in text is critical for generating effective semantic representations for natural language understanding. Computational approaches to metaphor identification have largely relied on heuristic based models or feature-based machine learning, using hand-crafted lexical resources coupled with basic syntactic information. However, recent work has shown the predictive power of syntactic constructions in determining metaphoric source and target domains (Sullivan 2013). Our work intends to explore syntactic constructions and their relation to metaphoric language. We undertake a corpus-based analysis of predicate-argument constructions and their metaphoric properties, and attempt to effectively represent syntactic constructions as features for metaphor processing, both in identifying source and target domains and in distinguishing metaphoric words from non-metaphoric.",
}
@inproceedings{mykowiecka-etal-2018-literal,
    title = "Literal, Metphorical or Both? Detecting Metaphoricity in Isolated Adjective-Noun Phrases",
    author = "Mykowiecka, Agnieszka  and
      Marciniak, Malgorzata  and
      Wawer, Aleksander",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0904",
    doi = "10.18653/v1/W18-0904",
    pages = "27--33",
    abstract = "The paper addresses the classification of isolated Polish adjective-noun phrases according to their metaphoricity. We tested neural networks to predict if a phrase has a literal or metaphorical sense or can have both senses depending on usage. The input to the neural network consists of word embeddings, but we also tested the impact of information about the domain of the adjective and about the abstractness of the noun. We applied our solution to English data available on the Internet and compared it to results published in papers. We found that the solution based on word embeddings only can achieve results comparable with complex solutions requiring additional information.",
}
@inproceedings{flor-beigman-klebanov-2018-catching,
    title = "Catching Idiomatic Expressions in {EFL} Essays",
    author = "Flor, Michael  and
      Beigman Klebanov, Beata",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0905",
    doi = "10.18653/v1/W18-0905",
    pages = "34--44",
    abstract = "This paper presents an exploratory study on large-scale detection of idiomatic expressions in essays written by non-native speakers of English. We describe a computational search procedure for automatic detection of idiom-candidate phrases in essay texts. The study used a corpus of essays written during a standardized examination of English language proficiency. Automatically-flagged candidate expressions were manually annotated for idiomaticity. The study found that idioms are widely used in EFL essays. The study also showed that a search algorithm that accommodates the syntactic and lexical exibility of idioms can increase the recall of idiom instances by 30{\%}, but it also increases the amount of false positives.",
}
@inproceedings{bizzoni-lappin-2018-predicting,
    title = "Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks",
    author = "Bizzoni, Yuri  and
      Lappin, Shalom",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0906",
    doi = "10.18653/v1/W18-0906",
    pages = "45--55",
    abstract = "We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sentences, with each set containing one reference metaphorical sentence, and four ranked candidate paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase acceptability. It reaches an encouraging 75{\%} accuracy on the binary classification task, and high Pearson (.75) and Spearman (.68) correlations on the gradient judgment prediction task.",
}
@inproceedings{leong-etal-2018-report,
    title = "A Report on the 2018 {VUA} Metaphor Detection Shared Task",
    author = "Leong, Chee Wee (Ben)  and
      Beigman Klebanov, Beata  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0907",
    doi = "10.18653/v1/W18-0907",
    pages = "56--66",
    abstract = "As the community working on computational approaches to figurative language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.",
}
@inproceedings{pramanick-etal-2018-lstm,
    title = "An {LSTM}-{CRF} Based Approach to Token-Level Metaphor Detection",
    author = "Pramanick, Malay  and
      Gupta, Ashim  and
      Mitra, Pabitra",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0908",
    doi = "10.18653/v1/W18-0908",
    pages = "67--75",
    abstract = "Automatic processing of figurative languages is gaining popularity in NLP community for their ubiquitous nature and increasing volume. In this era of web 2.0, automatic analysis of sarcasm and metaphors is important for their extensive usage. Metaphors are a part of figurative language that compares different concepts, often on a cognitive level. Many approaches have been proposed for automatic detection of metaphors, even using sequential models or neural networks. In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF. We used fewer features, as compared to the previous state-of-the-art sequential model. On experimentation with VUAMC, our method obtained an F-score of 0.674.",
}
@inproceedings{pramanick-mitra-2018-unsupervised,
    title = "Unsupervised Detection of Metaphorical Adjective-Noun Pairs",
    author = "Pramanick, Malay  and
      Mitra, Pabitra",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0909",
    doi = "10.18653/v1/W18-0909",
    pages = "76--80",
    abstract = "Metaphor is a popular figure of speech. Popularity of metaphors calls for their automatic identification and interpretation. Most of the unsupervised methods directed at detection of metaphors use some hand-coded knowledge. We propose an unsupervised framework for metaphor detection that does not require any hand-coded knowledge. We applied clustering on features derived from Adjective-Noun pairs for classifying them into two disjoint classes. We experimented with adjective-noun pairs of a popular dataset annotated for metaphors and obtained an accuracy of 72.87{\%} with k-means clustering algorithm.",
}
@inproceedings{zayed-etal-2018-phrase,
    title = "Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning",
    author = "Zayed, Omnia  and
      McCrae, John Philip  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0910",
    doi = "10.18653/v1/W18-0910",
    pages = "81--90",
    abstract = "Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.",
}
@inproceedings{bizzoni-ghanimifard-2018-bigrams,
    title = "Bigrams and {B}i{LSTM}s Two Neural Networks for Sequential Metaphor Detection",
    author = "Bizzoni, Yuri  and
      Ghanimifard, Mehdi",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0911",
    doi = "10.18653/v1/W18-0911",
    pages = "91--101",
    abstract = "We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.",
}
@inproceedings{rosen-2018-computationally,
    title = "Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues",
    author = "Rosen, Zachary",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0912",
    doi = "10.18653/v1/W18-0912",
    pages = "102--109",
    abstract = "The current study seeks to implement a deep learning classification algorithm using argument-structure level representation of metaphoric constructions, for the identification of source domain mappings in metaphoric utterances. It thus builds on previous work in computational metaphor interpretation (Mohler et al. 2014; Shutova 2010; Bollegala {\&} Shutova 2013; Hong 2016; Su et al. 2017) while implementing a theoretical framework based off of work in the interface of metaphor and construction grammar (Sullivan 2006, 2007, 2013). The results indicate that it is possible to achieve an accuracy of approximately 80.4{\%} using the proposed method, combining construction grammatical features with a simple deep learning NN. I attribute this increase in accuracy to the use of constructional cues, extracted from the raw text of metaphoric instances.",
}
@inproceedings{wu-etal-2018-neural,
    title = "Neural Metaphor Detecting with {CNN}-{LSTM} Model",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Chen, Yubo  and
      Wu, Sixing  and
      Yuan, Zhigang  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0913",
    doi = "10.18653/v1/W18-0913",
    pages = "110--114",
    abstract = "Metaphors are figurative languages widely used in daily life and literatures. It{'}s an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06{\%} F-score in the all POS testing subtask and 67.15{\%} in the verbs testing subtask.",
}
@inproceedings{swarnkar-singh-2018-di,
    title = "Di-{LSTM} Contrast : A Deep Neural Network for Metaphor Detection",
    author = "Swarnkar, Krishnkant  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0914",
    doi = "10.18653/v1/W18-0914",
    pages = "115--120",
    abstract = "The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.",
}
@inproceedings{mosolova-etal-2018-conditional,
    title = "Conditional Random Fields for Metaphor Detection",
    author = "Mosolova, Anna  and
      Bondarenko, Ivan  and
      Fomin, Vadim",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0915",
    doi = "10.18653/v1/W18-0915",
    pages = "121--123",
    abstract = "We present an algorithm for detecting metaphor in sentences which was used in Shared Task on Metaphor Detection by First Workshop on Figurative Language Processing. The algorithm is based on different features and Conditional Random Fields.",
}
@inproceedings{mykowiecka-etal-2018-detecting,
    title = "Detecting Figurative Word Occurrences Using Recurrent Neural Networks",
    author = "Mykowiecka, Agnieszka  and
      Wawer, Aleksander  and
      Marciniak, Malgorzata",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0916",
    doi = "10.18653/v1/W18-0916",
    pages = "124--127",
    abstract = "The paper addresses detection of figurative usage of words in English text. The chosen method was to use neural nets fed by pretrained word embeddings. The obtained results show that simple solutions, based on words embeddings only, are comparable to complex solutions, using many sources of information which are not available for languages less-studied than English.",
}
@inproceedings{skurniak-etal-2018-multi,
    title = "Multi-Module Recurrent Neural Networks with Transfer Learning",
    author = "Skurniak, Filip  and
      Janicka, Maria  and
      Wawer, Aleksander",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0917",
    doi = "10.18653/v1/W18-0917",
    pages = "128--132",
    abstract = "This paper describes multiple solutions designed and tested for the problem of word-level metaphor detection. The proposed systems are all based on variants of recurrent neural network architectures. Specifically, we explore multiple sources of information: pre-trained word embeddings (Glove), a dictionary of language concreteness and a transfer learning scenario based on the states of an encoder network from neural network machine translation system. One of the architectures is based on combining all three systems: (1) Neural CRF (Conditional Random Fields), trained directly on the metaphor data set; (2) Neural Machine Translation encoder of a transfer learning scenario; (3) a neural network used to predict final labels, trained directly on the metaphor data set. Our results vary between test sets: Neural CRF standalone is the best one on submission data, while combined system scores the highest on a test subset randomly selected from training data.",
}
@inproceedings{stemle-onysko-2018-using,
    title = "Using Language Learner Data for Metaphor Detection",
    author = "Stemle, Egon  and
      Onysko, Alexander",
    booktitle = "Proceedings of the Workshop on Figurative Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0918",
    doi = "10.18653/v1/W18-0918",
    pages = "133--138",
    abstract = "This article describes the system that participated in the shared task on metaphor detection on the Vrije University Amsterdam Metaphor Corpus (VUA). The ST was part of the workshop on processing figurative language at the 16th annual conference of the North American Chapter of the Association for Computational Linguistics (NAACL2018). The system combines a small assertion of trending techniques, which implement matured methods from NLP and ML; in particular, the system uses word embeddings from standard corpora and from corpora representing different proficiency levels of language learners in a LSTM BiRNN architecture. The system is available under the APLv2 open-source license.",
}
@proceedings{ws-2018-acl,
    title = "Proceedings of the Second {ACL} Workshop on Ethics in Natural Language Processing",
    editor = "Alfano, Mark  and
      Hovy, Dirk  and
      Mitchell, Margaret  and
      Strube, Michael",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0800",
    doi = "10.18653/v1/W18-08",
}
@inproceedings{schmaltz-2018-utility,
    title = "On the Utility of Lay Summaries and {AI} Safety Disclosures: Toward Robust, Open Research Oversight",
    author = "Schmaltz, Allen",
    booktitle = "Proceedings of the Second {ACL} Workshop on Ethics in Natural Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0801",
    doi = "10.18653/v1/W18-0801",
    pages = "1--6",
    abstract = "In this position paper, we propose that the community consider encouraging researchers to include two riders, a {``}Lay Summary{''} and an {``}AI Safety Disclosure{''}, as part of future NLP papers published in ACL forums that present user-facing systems. The goal is to encourage researchers{--}via a relatively non-intrusive mechanism{--}to consider the societal implications of technologies carrying (un)known and/or (un)knowable long-term risks, to highlight failure cases, and to provide a mechanism by which the general public (and scientists in other disciplines) can more readily engage in the discussion in an informed manner. This simple proposal requires minimal additional up-front costs for researchers; the lay summary, at least, has significant precedence in the medical literature and other areas of science; and the proposal is aimed to supplement, rather than replace, existing approaches for encouraging researchers to consider the ethical implications of their work, such as those of the Collaborative Institutional Training Initiative (CITI) Program and institutional review boards (IRBs).",
}
@inproceedings{cercas-curry-rieser-2018-metoo,
    title = "{\#}{M}e{T}oo {A}lexa: How Conversational Systems Respond to Sexual Harassment",
    author = "Cercas Curry, Amanda  and
      Rieser, Verena",
    booktitle = "Proceedings of the Second {ACL} Workshop on Ethics in Natural Language Processing",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0802",
    doi = "10.18653/v1/W18-0802",
    pages = "7--14",
    abstract = "Conversational AI systems, such as Amazon{'}s Alexa, are rapidly developing from purely transactional systems to social chatbots, which can respond to a wide variety of user requests. In this article, we establish how current state-of-the-art conversational systems react to inappropriate requests, such as bullying and sexual harassment on the part of the user, by collecting and analysing the novel {\#}MeTooAlexa corpus. Our results show that commercial systems mainly avoid answering, while rule-based chatbots show a variety of behaviours and often deflect. Data-driven systems, on the other hand, are often non-coherent, but also run the risk of being interpreted as flirtatious and sometimes react with counter-aggression. This includes our own system, trained on {``}clean{''} data, which suggests that inappropriate system behaviour is not caused by data bias.",
}
@proceedings{ws-2018-models,
    title = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    editor = "Poesio, Massimo  and
      Ng, Vincent  and
      Ogrodniczuk, Maciej",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0700",
    doi = "10.18653/v1/W18-07",
}
@inproceedings{aktas-etal-2018-anaphora,
    title = "Anaphora Resolution for {T}witter Conversations: An Exploratory Study",
    author = "Akta{\c{s}}, Berfin  and
      Scheffler, Tatjana  and
      Stede, Manfred",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0701",
    doi = "10.18653/v1/W18-0701",
    pages = "1--10",
    abstract = "We present a corpus study of pronominal anaphora on Twitter conversations. After outlining the specific features of this genre, with respect to reference resolution, we explain the construction of our corpus and the annotation steps. From this we derive a list of phenomena that need to be considered when performing anaphora resolution on this type of data. Finally, we test the performance of an off-the-shelf resolution system, and provide some qualitative error analysis.",
}
@inproceedings{poesio-etal-2018-anaphora,
    title = "Anaphora Resolution with the {ARRAU} Corpus",
    author = "Poesio, Massimo  and
      Grishina, Yulia  and
      Kolhatkar, Varada  and
      Moosavi, Nafise  and
      Roesiger, Ina  and
      Roussel, Adam  and
      Simonjetz, Fabian  and
      Uma, Alexandra  and
      Uryupina, Olga  and
      Yu, Juntao  and
      Zinsmeister, Heike",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0702",
    doi = "10.18653/v1/W18-0702",
    pages = "11--22",
    abstract = "The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including non-referring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Task{--}identity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena.",
}
@inproceedings{roesiger-2018-rule,
    title = "Rule- and Learning-based Methods for Bridging Resolution in the {ARRAU} Corpus",
    author = "Roesiger, Ina",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0703",
    doi = "10.18653/v1/W18-0703",
    pages = "23--33",
    abstract = "We present two systems for bridging resolution, which we submitted to the CRAC shared task on bridging anaphora resolution in the ARRAU corpus (track 2): a rule-based approach following Hou et al. 2014 and a learning-based approach. The re-implementation of Hou et al. 2014 achieves very poor performance when being applied to ARRAU. We found that the reasons for this lie in the different bridging annotations: whereas the rule-based system suggests many referential bridging pairs, ARRAU contains mostly lexical bridging. We describe the differences between these two types of bridging and adapt the rule-based approach to be able to handle lexical bridging. The modified rule-based approach achieves reasonable performance on all (sub)-tasks and outperforms a simple learning-based approach.",
}
@inproceedings{zeldes-2018-predictive,
    title = "A Predictive Model for Notional Anaphora in {E}nglish",
    author = "Zeldes, Amir",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0704",
    doi = "10.18653/v1/W18-0704",
    pages = "34--43",
    abstract = "Notional anaphors are pronouns which disagree with their antecedents{'} grammatical categories for notional reasons, such as plural to singular agreement in: {``}the government ... they{''}. Since such cases are rare and conflict with evidence from strictly agreeing cases ({``}the government ... it{''}), they present a substantial challenge to both coreference resolution and referring expression generation. Using the OntoNotes corpus, this paper takes an ensemble approach to predicting English notional anaphora in context on the basis of the largest empirical data to date. In addition to state of the art prediction accuracy, the results suggest that theoretical approaches positing a plural construal at the antecedent{'}s utterance are insufficient, and that circumstances at the anaphor{'}s utterance location, as well as global factors such as genre, have a strong effect on the choice of referring expression.",
}
@inproceedings{roesiger-etal-2018-integrating,
    title = "Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution",
    author = {Roesiger, Ina  and
      K{\"o}per, Maximilian  and
      Nguyen, Kim Anh  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0705",
    doi = "10.18653/v1/W18-0705",
    pages = "44--49",
    abstract = "Cases of coreference and bridging resolution often require knowledge about semantic relations between anaphors and antecedents. We suggest state-of-the-art neural-network classifiers trained on relation benchmarks to predict and integrate likelihoods for relations. Two experiments with representations differing in noise and complexity improve our bridging but not our coreference resolver.",
}
@inproceedings{pagel-roesiger-2018-towards,
    title = "Towards Bridging Resolution in {G}erman: Data Analysis and Rule-based Experiments",
    author = "Pagel, Janis  and
      Roesiger, Ina",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0706",
    doi = "10.18653/v1/W18-0706",
    pages = "50--60",
    abstract = "Bridging resolution is the task of recognising bridging anaphors and linking them to their antecedents. While there is some work on bridging resolution for English, there is only little work for German. We present two datasets which contain bridging annotations, namely DIRNDL and GRAIN, and compare the performance of a rule-based system with a simple baseline approach on these two corpora. The performance for full bridging resolution ranges between an F1 score of 13.6{\%} for DIRNDL and 11.8{\%} for GRAIN. An analysis using oracle lists suggests that the system could, to a certain extent, benefit from ranking and re-ranking antecedent candidates. Furthermore, we investigate the importance of single features and show that the features used in our work seem promising for future bridging resolution approaches.",
}
@inproceedings{roussel-2018-detecting,
    title = "Detecting and Resolving Shell Nouns in {G}erman",
    author = "Roussel, Adam",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0707",
    doi = "10.18653/v1/W18-0707",
    pages = "61--67",
    abstract = "This paper describes the design and evaluation of a system for the automatic detection and resolution of shell nouns in German. Shell nouns are general nouns, such as fact, question, or problem, whose full interpretation relies on a content phrase located elsewhere in a text, which these nouns simultaneously serve to characterize and encapsulate. To accomplish this, the system uses a series of lexico-syntactic patterns in order to extract shell noun candidates and their content in parallel. Each pattern has its own classifier, which makes the final decision as to whether or not a link is to be established and the shell noun resolved. Overall, about 26.2{\%} of the annotated shell noun instances were correctly identified by the system, and of these cases, about 72.5{\%} are assigned the correct content phrase. Though it remains difficult to identify shell noun instances reliably (recall is accordingly low in this regard), this system usually assigns the right content to correctly classified cases. cases.",
}
@inproceedings{nedoluzhko-etal-2018-paws,
    title = "{PAWS}: A Multi-lingual Parallel Treebank with Anaphoric Relations",
    author = "Nedoluzhko, Anna  and
      Nov{\'a}k, Michal  and
      Ogrodniczuk, Maciej",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0708",
    doi = "10.18653/v1/W18-0708",
    pages = "68--76",
    abstract = "We present PAWS, a multi-lingual parallel treebank with coreference annotation. It consists of English texts from the Wall Street Journal translated into Czech, Russian and Polish. In addition, the texts are syntactically parsed and word-aligned. PAWS is based on PCEDT 2.0 and continues the tradition of multilingual treebanks with coreference annotation. The paper focuses on the coreference annotation in PAWS and its language-specific differences. PAWS offers linguistic material that can be further leveraged in cross-lingual studies, especially on coreference.",
}
@inproceedings{novak-2018-fine,
    title = "A Fine-grained Large-scale Analysis of Coreference Projection",
    author = "Nov{\'a}k, Michal",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0709",
    doi = "10.18653/v1/W18-0709",
    pages = "77--86",
    abstract = "We perform a fine-grained large-scale analysis of coreference projection. By projecting gold coreference from Czech to English and vice versa on Prague Czech-English Dependency Treebank 2.0 Coref, we set an upper bound of a proposed projection approach for these two languages. We undertake a detailed thorough analysis that combines the analysis of projection{'}s subtasks with analysis of performance on individual mention types. The findings are accompanied with examples from the corpus.",
}
@inproceedings{li-etal-2018-modeling,
    title = "Modeling Brain Activity Associated with Pronoun Resolution in {E}nglish and {C}hinese",
    author = "Li, Jixing  and
      Fabre, Murielle  and
      Luh, Wen-Ming  and
      Hale, John",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0710",
    doi = "10.18653/v1/W18-0710",
    pages = "87--96",
    abstract = "Typological differences between English and Chinese suggest stronger reliance on salience of the antecedent during pronoun resolution in Chinese. We examined this hypothesis by correlating a difficulty measure of pronoun resolution derived by the activation-based ACT-R model with the brain activity of English and Chinese participants listening to a same audiobook during fMRI recording. The ACT-R model predicts higher overall difficulty for English speakers, which is supported at the brain level in left Broca{'}s area. More generally, it confirms that computational modeling approach is able to dissociate different dimensions that are involved in the complex process of pronoun resolution in the brain.",
}
@inproceedings{loaiciga-etal-2018-event,
    title = "Event versus entity co-reference: Effects of context and form of referring expression",
    author = "Lo{\'a}iciga, Sharid  and
      Bevacqua, Luca  and
      Rohde, Hannah  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0711",
    doi = "10.18653/v1/W18-0711",
    pages = "97--103",
    abstract = "Anaphora resolution systems require both an enumeration of possible candidate antecedents and an identification process of the antecedent. This paper focuses on (i) the impact of the form of referring expression on entity-vs-event preferences and (ii) how properties of the passage interact with referential form. Two crowd-sourced story-continuation experiments were conducted, using constructed and naturally-occurring passages, to see how participants interpret \textit{It} and \textit{This} pronouns following a context sentence that makes available event and entity referents. Our participants show a strong, but not categorical, bias to use \textit{This} to refer to events and \textit{It} to refer to entities. However, these preferences vary with passage characteristics such as verb class (a proxy in our constructed examples for the number of explicit and implicit entities) and more subtle author intentions regarding subsequent re-mention (the original event-vs-entity re-mention of our corpus items).",
}
@proceedings{ws-2018-linguistics,
    title = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    editor = "Loveys, Kate  and
      Niederhoffer, Kate  and
      Prud{'}hommeaux, Emily  and
      Resnik, Rebecca  and
      Resnik, Philip",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0600",
    doi = "10.18653/v1/W18-06",
}
@inproceedings{arseniev-koehler-etal-2018-type,
    title = "What type of happiness are you looking for? - A closer look at detecting mental health from language",
    author = "Arseniev-Koehler, Alina  and
      Mozgai, Sharon  and
      Scherer, Stefan",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0601",
    doi = "10.18653/v1/W18-0601",
    pages = "1--12",
    abstract = "Computational models to detect mental illnesses from text and speech could enhance our understanding of mental health while offering opportunities for early detection and intervention. However, these models are often disconnected from the lived experience of depression and the larger diagnostic debates in mental health. This article investigates these disconnects, primarily focusing on the labels used to diagnose depression, how these labels are computationally represented, and the performance metrics used to evaluate computational models. We also consider how medical instruments used to measure depression, such as the Patient Health Questionnaire (PHQ), contribute to these disconnects. To illustrate our points, we incorporate mixed-methods analyses of 698 interviews on emotional health, which are coupled with self-report PHQ screens for depression. We propose possible strategies to bridge these gaps between modern psychiatric understandings of depression, lay experience of depression, and computational representation.",
}
@inproceedings{morales-etal-2018-linguistically,
    title = "A Linguistically-Informed Fusion Approach for Multimodal Depression Detection",
    author = "Morales, Michelle  and
      Scherer, Stefan  and
      Levitan, Rivka",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0602",
    doi = "10.18653/v1/W18-0602",
    pages = "13--24",
    abstract = "Automated depression detection is inherently a multimodal problem. Therefore, it is critical that researchers investigate fusion techniques for multimodal design. This paper presents the first-ever comprehensive study of fusion techniques for depression detection. In addition, we present novel linguistically-motivated fusion techniques, which we find outperform existing approaches.",
}
@inproceedings{shing-etal-2018-expert,
    title = "Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings",
    author = "Shing, Han-Chin  and
      Nair, Suraj  and
      Zirikly, Ayah  and
      Friedenberg, Meir  and
      Daum{\'e} III, Hal  and
      Resnik, Philip",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0603",
    doi = "10.18653/v1/W18-0603",
    pages = "25--36",
    abstract = "We report on the creation of a dataset for studying assessment of suicide risk via online postings in Reddit. Evaluation of risk-level annotations by experts yields what is, to our knowledge, the first demonstration of reliability in risk assessment by clinicians based on social media postings. We also introduce and demonstrate the value of a new, detailed rubric for assessing suicide risk, compare crowdsourced with expert performance, and present baseline predictive modeling experiments using the new dataset, which will be made available to researchers through the American Association of Suicidology.",
}
@inproceedings{lynn-etal-2018-clpsych,
    title = "{CLP}sych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays",
    author = "Lynn, Veronica  and
      Goodman, Alissa  and
      Niederhoffer, Kate  and
      Loveys, Kate  and
      Resnik, Philip  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0604",
    doi = "10.18653/v1/W18-0604",
    pages = "37--46",
    abstract = "We describe the shared task for the CLPsych 2018 workshop, which focused on predicting current and future psychological health from an essay authored in childhood. Language-based predictions of a person{'}s current health have the potential to supplement traditional psychological assessment such as questionnaires, improving intake risk measurement and monitoring. Predictions of future psychological health can aid with both early detection and the development of preventative care. Research into the mental health trajectory of people, beginning from their childhood, has thus far been an area of little work within the NLP community. This shared task represents one of the first attempts to evaluate the use of early language to predict future health; this has the potential to support a wide variety of clinical health care tasks, from early assessment of lifetime risk for mental health problems, to optimal timing for targeted interventions aimed at both prevention and treatment.",
}
@inproceedings{iserman-etal-2018-approach,
    title = "An Approach to the {CLP}sych 2018 Shared Task Using Top-Down Text Representation and Simple Bottom-Up Model Selection",
    author = "Iserman, Micah  and
      Ireland, Molly  and
      Littlefield, Andrew  and
      Davis, Tyler  and
      Maliepaard, Sage",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0605",
    doi = "10.18653/v1/W18-0605",
    pages = "47--56",
    abstract = "The Computational Linguistics and Clinical Psychology (CLPsych) 2018 Shared Task asked teams to predict cross-sectional indices of anxiety and distress, and longitudinal indices of psychological distress from a subsample of the National Child Development Study, started in the United Kingdom in 1958. Teams aimed to predict mental health outcomes from essays written by 11-year-olds about what they believed their lives would be like at age 25. In the hopes of producing results that could be easily disseminated and applied, we used largely theory-based dictionaries to process the texts, and a simple data-driven approach to model selection. This approach yielded only modest results in terms of out-of-sample accuracy, but most of the category-level findings are interpretable and consistent with existing literature on psychological distress, anxiety, and depression.",
}
@inproceedings{altszyler-etal-2018-using,
    title = "Using contextual information for automatic triage of posts in a peer-support forum",
    author = "Altszyler, Edgar  and
      Berenstein, Ariel J.  and
      Milne, David  and
      Calvo, Rafael A.  and
      Fernandez Slezak, Diego",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0606",
    doi = "10.18653/v1/W18-0606",
    pages = "57--68",
    abstract = "Mental health forums are online spaces where people can share their experiences anonymously and get peer support. These forums, require the supervision of moderators to provide support in delicate cases, such as posts expressing suicide ideation. The large increase in the number of forum users makes the task of the moderators unmanageable without the help of automatic triage systems. In the present paper, we present a Machine Learning approach for the triage of posts. Most approaches in the literature focus on the content of the posts, but only a few authors take advantage of features extracted from the context in which they appear. Our approach consists of the development and implementation of a large variety of new features from both, the content and the context of posts, such as previous messages, interaction with other users and author{'}s history. Our method has competed in the CLPsych 2017 Shared Task, obtaining the first place for several of the subtasks. Moreover, we also found that models that take advantage of post context improve significantly its performance in the detection of flagged posts (posts that require moderators attention), as well as those that focus on post content outperforms in the detection of most urgent events.",
}
@inproceedings{ive-etal-2018-hierarchical,
    title = "Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health",
    author = "Ive, Julia  and
      Gkotsis, George  and
      Dutta, Rina  and
      Stewart, Robert  and
      Velupillai, Sumithra",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0607",
    doi = "10.18653/v1/W18-0607",
    pages = "69--77",
    abstract = "Mental health problems represent a major public health challenge. Automated analysis of text related to mental health is aimed to help medical decision-making, public health policies and to improve health care. Such analysis may involve text classification. Traditionally, automated classification has been performed mainly using machine learning methods involving costly feature engineering. Recently, the performance of those methods has been dramatically improved by neural methods. However, mainly Convolutional neural networks (CNNs) have been explored. In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health. We show that this architecture improves overall classification results as compared to previously reported results on the same data. Benefitting from the attention mechanism, it can also efficiently select text elements crucial for classification decisions, which can also be used for in-depth analysis.",
}
@inproceedings{loveys-etal-2018-cross,
    title = "Cross-cultural differences in language markers of depression online",
    author = "Loveys, Kate  and
      Torrez, Jonathan  and
      Fine, Alex  and
      Moriarty, Glen  and
      Coppersmith, Glen",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0608",
    doi = "10.18653/v1/W18-0608",
    pages = "78--87",
    abstract = "Depression is a global mental health condition that affects all cultures. Despite this, the way depression is expressed varies by culture. Uptake of machine learning technology for diagnosing mental health conditions means that increasingly more depression classifiers are created from online language data. Yet, culture is rarely considered as a factor affecting online language in this literature. This study explores cultural differences in online language data of users with depression. Written language data from 1,593 users with self-reported depression from the online peer support community 7 Cups of Tea was analyzed using the Linguistic Inquiry and Word Count (LIWC), topic modeling, data visualization, and other techniques. We compared the language of users identifying as White, Black or African American, Hispanic or Latino, and Asian or Pacific Islander. Exploratory analyses revealed cross-cultural differences in depression expression in online language data, particularly in relation to emotion expression, cognition, and functioning. The results have important implications for avoiding depression misclassification from machine-driven assessments when used in a clinical setting, and for avoiding inadvertent cultural biases in this line of research more broadly.",
}
@inproceedings{husseini-orabi-etal-2018-deep,
    title = "Deep Learning for Depression Detection of {T}witter Users",
    author = "Husseini Orabi, Ahmed  and
      Buddhitha, Prasadith  and
      Husseini Orabi, Mahmoud  and
      Inkpen, Diana",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0609",
    doi = "10.18653/v1/W18-0609",
    pages = "88--97",
    abstract = "Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders. In recent years, this research area has started to evolve with the continuous increase in popularity of social media platforms that became an integral part of people{'}s life. This close relationship between social media platforms and their users has made these platforms to reflect the users{'} personal life with different limitations. In such an environment, researchers are presented with a wealth of information regarding one{'}s life. In addition to the level of complexity in identifying mental illnesses through social media platforms, adopting supervised machine learning approaches such as deep neural networks have not been widely accepted due to the difficulties in obtaining sufficient amounts of annotated training data. Due to these reasons, we try to identify the most effective deep neural network architecture among a few of selected architectures that were successfully used in natural language processing tasks. The chosen architectures are used to detect users with signs of mental illnesses (depression in our case) given limited unstructured text data extracted from the Twitter social media platform.",
}
@inproceedings{guntuku-etal-2018-current,
    title = "Current and Future Psychological Health Prediction using Language and Socio-Demographics of Children for the {CLP}ysch 2018 Shared Task",
    author = "Guntuku, Sharath Chandra  and
      Giorgi, Salvatore  and
      Ungar, Lyle",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0610",
    doi = "10.18653/v1/W18-0610",
    pages = "98--106",
    abstract = "This article is a system description and report on the submission of a team from the University of Pennsylvania in the {'}CLPsych 2018{'} shared task. The goal of the shared task was to use childhood language as a marker for both current and future psychological health over individual lifetimes. Our system employs multiple textual features derived from the essays written and individuals{'} socio-demographic variables at the age of 11. We considered several word clustering approaches, and explore the use of linear regression based on different feature sets. Our approach showed best results for predicting distress at the age of 42 and for predicting current anxiety on Disattenuated Pearson Correlation, and ranked fourth in the future health prediction task. In addition to the subtasks presented, we attempted to provide insight into mental health aspects at different ages. Our findings indicate that misspellings, words with illegible letters and increased use of personal pronouns are correlated with poor mental health at age 11, while descriptions about future physical activity, family and friends are correlated with good mental health.",
}
@inproceedings{rios-etal-2018-predicting,
    title = "Predicting Psychological Health from Childhood Essays with Convolutional Neural Networks for the {CLP}sych 2018 Shared Task (Team {UKNLP})",
    author = "Rios, Anthony  and
      Tran, Tung  and
      Kavuluru, Ramakanth",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0611",
    doi = "10.18653/v1/W18-0611",
    pages = "107--112",
    abstract = "This paper describes the systems we developed for tasks A and B of the 2018 CLPsych shared task. The first task (task A) focuses on predicting behavioral health scores at age 11 using childhood essays. The second task (task B) asks participants to predict future psychological distress at ages 23, 33, 42, and 50 using the age 11 essays. We propose two convolutional neural network based methods that map each task to a regression problem. Among seven teams we ranked third on task A with disattenuated Pearson correlation (DPC) score of 0.5587. Likewise, we ranked third on task B with an average DPC score of 0.3062.",
}
@inproceedings{simchon-gilead-2018-psychologically,
    title = "A Psychologically Informed Approach to {CLP}sych Shared Task 2018",
    author = "Simchon, Almog  and
      Gilead, Michael",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0612",
    doi = "10.18653/v1/W18-0612",
    pages = "113--118",
    abstract = "This paper describes our approach to the CLPsych 2018 Shared Task, in which we attempted to predict cross-sectional psychological health at age 11 and future psychological distress based on childhood essays. We attempted several modeling approaches and observed best cross-validated prediction accuracy with relatively simple models based on psychological theory. The models provided reasonable predictions in most outcomes. Notably, our model was especially successful in predicting out-of-sample psychological distress (across people and across time) at age 50.",
}
@inproceedings{zaporojets-etal-2018-predicting,
    title = "Predicting Psychological Health from Childhood Essays. The {UG}ent-{IDL}ab {CLP}sych 2018 Shared Task System.",
    author = "Zaporojets, Klim  and
      Sterckx, Lucas  and
      Deleu, Johannes  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0613",
    doi = "10.18653/v1/W18-0613",
    pages = "119--125",
    abstract = "This paper describes the IDLab system submitted to Task A of the CLPsych 2018 shared task. The goal of this task is predicting psychological health of children based on language used in hand-written essays and socio-demographic control variables. Our entry uses word- and character-based features as well as lexicon-based features and features derived from the essays such as the quality of the language. We apply linear models, gradient boosting as well as neural-network based regressors (feed-forward, CNNs and RNNs) to predict scores. We then make ensembles of our best performing models using a weighted average.",
}
@inproceedings{radford-etal-2018-adult,
    title = "Can adult mental health be predicted by childhood future-self narratives? Insights from the {CLP}sych 2018 Shared Task",
    author = "Radford, Kylie  and
      Lavrencic, Louise  and
      Peters, Ruth  and
      Kiely, Kim  and
      Hachey, Ben  and
      Nowson, Scott  and
      Radford, Will",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0614",
    doi = "10.18653/v1/W18-0614",
    pages = "126--135",
    abstract = "The CLPsych 2018 Shared Task B explores how childhood essays can predict psychological distress throughout the author{'}s life. Our main aim was to build tools to help our psychologists understand the data, propose features and interpret predictions. We submitted two linear regression models: ModelA uses simple demographic and word-count features, while ModelB uses linguistic, entity, typographic, expert-gazetteer, and readability features. Our models perform best at younger prediction ages, with our best unofficial score at 23 of 0.426 disattenuated Pearson correlation. This task is challenging and although predictive performance is limited, we propose that tight integration of expertise across computational linguistics and clinical psychology is a productive direction.",
}
@inproceedings{iter-etal-2018-automatic,
    title = "Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia",
    author = "Iter, Dan  and
      Yoon, Jong  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0615",
    doi = "10.18653/v1/W18-0615",
    pages = "136--146",
    abstract = "Schizophrenia is a mental disorder which afflicts an estimated 0.7{\%} of adults world wide. It affects many areas of mental function, often evident from incoherent speech. Diagnosing schizophrenia relies on subjective judgments resulting in disagreements even among trained clinicians. Recent studies have proposed the use of natural language processing for diagnosis by drawing on automatically-extracted linguistic features like discourse coherence and lexicon. Here, we present the first benchmark comparison of previously proposed coherence models for detecting symptoms of schizophrenia and evaluate their performance on a new dataset of recorded interviews between subjects and clinicians. We also present two alternative coherence metrics based on modern sentence embedding techniques that outperform the previous methods on our dataset. Lastly, we propose a novel computational model for reference incoherence based on ambiguous pronoun usage and show that it is a highly predictive feature on our data. While the number of subjects is limited in this pilot study, our results suggest new directions for diagnosing common symptoms of schizophrenia.",
}
@inproceedings{parish-morris-etal-2018-oral,
    title = "Oral-Motor and Lexical Diversity During Naturalistic Conversations in Adults with Autism Spectrum Disorder",
    author = "Parish-Morris, Julia  and
      Sariyanidi, Evangelos  and
      Zampella, Casey  and
      Bartley, G. Keith  and
      Ferguson, Emily  and
      Pallathra, Ashley A.  and
      Bateman, Leila  and
      Plate, Samantha  and
      Cola, Meredith  and
      Pandey, Juhi  and
      Brodkin, Edward S.  and
      Schultz, Robert T.  and
      Tun{\c{c}}, Birkan",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0616",
    doi = "10.18653/v1/W18-0616",
    pages = "147--157",
    abstract = "Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social communication and the presence of restricted, repetitive patterns of behaviors and interests. Prior research suggests that restricted patterns of behavior in ASD may be cross-domain phenomena that are evident in a variety of modalities. Computational studies of language in ASD provide support for the existence of an underlying dimension of restriction that emerges during a conversation. Similar evidence exists for restricted patterns of facial movement. Using tools from computational linguistics, computer vision, and information theory, this study tests whether cognitive-motor restriction can be detected across multiple behavioral domains in adults with ASD during a naturalistic conversation. Our methods identify restricted behavioral patterns, as measured by entropy in word use and mouth movement. Results suggest that adults with ASD produce significantly less diverse mouth movements and words than neurotypical adults, with an increased reliance on repeated patterns in both domains. The diversity values of the two domains are not significantly correlated, suggesting that they provide complementary information.",
}
@inproceedings{litvinova-etal-2018-dynamics,
    title = "Dynamics of an idiostyle of a {R}ussian suicidal blogger",
    author = "Litvinova, Tatiana  and
      Litvinova, Olga  and
      Seredin, Pavel",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0617",
    doi = "10.18653/v1/W18-0617",
    pages = "158--167",
    abstract = "Over 800000 people die of suicide each year. It is es-timated that by the year 2020, this figure will have in-creased to 1.5 million. It is considered to be one of the major causes of mortality during adolescence. Thus there is a growing need for methods of identifying su-icidal individuals. Language analysis is known to be a valuable psychodiagnostic tool, however the material for such an analysis is not easy to obtain. Currently as the Internet communications are developing, there is an opportunity to study texts of suicidal individuals. Such an analysis can provide a useful insight into the peculiarities of suicidal thinking, which can be used to further develop methods for diagnosing the risk of suicidal behavior. The paper analyzes the dynamics of a number of linguistic parameters of an idiostyle of a Russian-language blogger who died by suicide. For the first time such an analysis has been conducted using the material of Russian online texts. For text processing, the LIWC program is used. A correlation analysis was performed to identify the relationship between LIWC variables and number of days prior to suicide. Data visualization, as well as comparison with the results of related studies was performed.",
}
@inproceedings{macavaney-etal-2018-rsdd,
    title = "{RSDD}-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses",
    author = "MacAvaney, Sean  and
      Desmet, Bart  and
      Cohan, Arman  and
      Soldaini, Luca  and
      Yates, Andrew  and
      Zirikly, Ayah  and
      Goharian, Nazli",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0618",
    doi = "10.18653/v1/W18-0618",
    pages = "168--173",
    abstract = "Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one{'}s mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.",
}
@inproceedings{zamani-etal-2018-predicting,
    title = "Predicting Human Trustfulness from {F}acebook Language",
    author = "Zamani, Mohammadzaman  and
      Buffone, Anneke  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0619",
    doi = "10.18653/v1/W18-0619",
    pages = "174--181",
    abstract = "Trustfulness {---} one{'}s general tendency to have confidence in unknown people or situations {---} predicts many important real-world outcomes such as mental health and likelihood to cooperate with others such as clinicians. While data-driven measures of interpersonal trust have previously been introduced, here, we develop the first language-based assessment of the personality trait of trustfulness by fitting one{'}s language to an accepted questionnaire-based trust score. Further, using trustfulness as a type of case study, we explore the role of questionnaire size as well as word count in developing language-based predictive models of users{'} psychological traits. We find that leveraging a longer questionnaire can yield greater test set accuracy, while, for training, we find it beneficial to include users who took smaller questionnaires which offers more observations for training. Similarly, after noting a decrease in individual prediction error as word count increased, we found a word count-weighted training scheme was helpful when there were very few users in the first place.",
}
@inproceedings{ireland-iserman-2018-within,
    title = "Within and Between-Person Differences in Language Used Across Anxiety Support and Neutral {R}eddit Communities",
    author = "Ireland, Molly  and
      Iserman, Micah",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0620",
    doi = "10.18653/v1/W18-0620",
    pages = "182--193",
    abstract = "Although many studies have distinguished between the social media language use of people who do and do not have a mental health condition, within-person context-sensitive comparisons (for example, analyzing individuals{'} language use when seeking support or discussing neutral topics) are less common. Two dictionary-based analyses of Reddit communities compared (1) anxious individuals{'} comments in anxiety support communities (e.g., /r/PanicParty) with the same users{'} comments in neutral communities (e.g., /r/todayilearned), and, (2) within popular neutral communities, comments by members of anxiety subreddits with comments by other users. Each comparison yielded theory-consistent effects as well as unexpected results that suggest novel hypotheses to be tested in the future. Results have relevance for improving researchers{'} and practitioners{'} ability to unobtrusively assess anxiety symptoms in conversations that are not explicitly about mental health.",
}
@inproceedings{soldaini-etal-2018-helping,
    title = "Helping or Hurting? Predicting Changes in Users{'} Risk of Self-Harm Through Online Community Interactions",
    author = "Soldaini, Luca  and
      Walsh, Timothy  and
      Cohan, Arman  and
      Han, Julien  and
      Goharian, Nazli",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic",
    month = jun,
    year = "2018",
    address = "New Orleans, LA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0621",
    doi = "10.18653/v1/W18-0621",
    pages = "194--203",
    abstract = "In recent years, online communities have formed around suicide and self-harm prevention. While these communities offer support in moment of crisis, they can also normalize harmful behavior, discourage professional treatment, and instigate suicidal ideation. In this work, we focus on how interaction with others in such a community affects the mental state of users who are seeking support. We first build a dataset of conversation threads between users in a distressed state and community members offering support. We then show how to construct a classifier to predict whether distressed users are helped or harmed by the interactions in the thread, and we achieve a macro-F1 score of up to 0.69.",
}
@proceedings{ws-2018-innovative,
    title = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    editor = "Tetreault, Joel  and
      Burstein, Jill  and
      Kochmar, Ekaterina  and
      Leacock, Claudia  and
      Yannakoudakis, Helen",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0500",
    doi = "10.18653/v1/W18-05",
}
@inproceedings{loukina-etal-2018-using,
    title = "Using exemplar responses for training and evaluating automated speech scoring systems",
    author = "Loukina, Anastassia  and
      Zechner, Klaus  and
      Bruno, James  and
      Beigman Klebanov, Beata",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0501",
    doi = "10.18653/v1/W18-0501",
    pages = "1--12",
    abstract = "Automated scoring engines are usually trained and evaluated against human scores and compared to the benchmark of human-human agreement. In this paper we compare the performance of an automated speech scoring engine using two corpora: a corpus of almost 700,000 randomly sampled spoken responses with scores assigned by one or two raters during operational scoring, and a corpus of 16,500 exemplar responses with scores reviewed by multiple expert raters. We show that the choice of corpus used for model evaluation has a major effect on estimates of system performance with r varying between 0.64 and 0.80. Surprisingly, this is not the case for the choice of corpus for model training: when the training corpus is sufficiently large, the systems trained on different corpora showed almost identical performance when evaluated on the same corpus. We show that this effect is consistent across several learning algorithms. We conclude that evaluating the model on a corpus of exemplar responses if one is available provides additional evidence about system validity; at the same time, investing effort into creating a corpus of exemplar responses for model training is unlikely to lead to a substantial gain in model performance.",
}
@inproceedings{jin-etal-2018-using,
    title = "Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System",
    author = "Jin, Lifeng  and
      King, David  and
      Hussein, Amad  and
      White, Michael  and
      Danforth, Douglas",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0502",
    doi = "10.18653/v1/W18-0502",
    pages = "13--23",
    abstract = "When interpreting questions in a virtual patient dialogue system one must inevitably tackle the challenge of a long tail of relatively infrequently asked questions. To make progress on this challenge, we investigate the use of paraphrasing for data augmentation and neural memory-based classification, finding that the two methods work best in combination. In particular, we find that the neural memory-based approach not only outperforms a straight CNN classifier on low frequency questions, but also takes better advantage of the augmented data created by paraphrasing, together yielding a nearly 10{\%} absolute improvement in accuracy on the least frequently asked questions.",
}
@inproceedings{bingel-etal-2018-predicting,
    title = "Predicting misreadings from gaze in children with reading difficulties",
    author = "Bingel, Joachim  and
      Barrett, Maria  and
      Klerke, Sigrid",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0503",
    doi = "10.18653/v1/W18-0503",
    pages = "24--34",
    abstract = "We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching. Our approach employs several linguistic and gaze-based features to inform an ensemble of different classifiers, including multi-task learning models that let us transfer knowledge about individual readers to attain better predictions. Notably, the data we use in this work stems from noisy readings in the wild, outside of controlled lab conditions. Our experiments show that despite the noise and despite the small fraction of misreadings, gaze data improves the performance more than any other feature group and our models achieve good performance. We further show that gaze patterns for misread words do not fully generalize across readers, but that we can transfer some knowledge between readers using multitask learning at least in some cases. Applications of our models include partial automation of reading assessment as well as personalized text simplification.",
}
@inproceedings{chinkina-etal-2018-automatic,
    title = "Automatic Input Enrichment for Selecting Reading Material: An Online Study with {E}nglish Teachers",
    author = "Chinkina, Maria  and
      Oswal, Ankita  and
      Meurers, Detmar",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0504",
    doi = "10.18653/v1/W18-0504",
    pages = "35--44",
    abstract = "Input material at the appropriate level is crucial for language acquisition. Automating the search for such material can systematically and efficiently support teachers in their pedagogical practice. This is the goal of the computational linguistic task of automatic input enrichment (Chinkina {\&} Meurers, 2016): It analyzes and re-ranks a collection of texts in order to prioritize those containing target linguistic forms. In the online study described in the paper, we collected 240 responses from English teachers in order to investigate whether they preferred automatic input enrichment over web search when selecting reading material for class. Participants demonstrated a general preference for the material provided by an automatic input enrichment system. It was also rated significantly higher than the texts retrieved by a standard web search engine with regard to the representation of linguistic forms and equivalent with regard to the relevance of the content to the topic. We discuss the implications of the results for language teaching and consider the potential strands of future research.",
}
@inproceedings{nadeem-ostendorf-2018-estimating,
    title = "Estimating Linguistic Complexity for Science Texts",
    author = "Nadeem, Farah  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0505",
    doi = "10.18653/v1/W18-0505",
    pages = "45--55",
    abstract = "Evaluation of text difficulty is important both for downstream tasks like text simplification, and for supporting educators in classrooms. Existing work on automated text complexity analysis uses linear models with engineered knowledge-driven features as inputs. While this offers interpretability, these models have lower accuracy for shorter texts. Traditional readability metrics have the additional drawback of not generalizing to informational texts such as science. We propose a neural approach, training on science and other informational texts, to mitigate both problems. Our results show that neural methods outperform knowledge-based linear models for short texts, and have the capacity to generalize to genres not present in the training data.",
}
@inproceedings{settles-etal-2018-second,
    title = "Second Language Acquisition Modeling",
    author = "Settles, Burr  and
      Brust, Chris  and
      Gustafson, Erin  and
      Hagiwara, Masato  and
      Madnani, Nitin",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0506",
    doi = "10.18653/v1/W18-0506",
    pages = "56--65",
    abstract = "We present the task of \textit{second language acquisition (SLA) modeling}. Given a history of errors made by learners of a second language, the task is to predict errors that they are likely to make at arbitrary points in the future. We describe a large corpus of more than 7M words produced by more than 6k learners of English, Spanish, and French using Duolingo, a popular online language-learning app. Then we report on the results of a shared task challenge aimed studying the SLA task via this corpus, which attracted 15 teams and synthesized work from various fields including cognitive science, linguistics, and machine learning.",
}
@inproceedings{yimam-etal-2018-report,
    title = "A Report on the Complex Word Identification Shared Task 2018",
    author = {Yimam, Seid Muhie  and
      Biemann, Chris  and
      Malmasi, Shervin  and
      Paetzold, Gustavo  and
      Specia, Lucia  and
      {\v{S}}tajner, Sanja  and
      Tack, Ana{\"\i}s  and
      Zampieri, Marcos},
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0507",
    doi = "10.18653/v1/W18-0507",
    pages = "66--78",
    abstract = "We report the findings of the second Complex Word Identification (CWI) shared task organized as part of the BEA workshop co-located with NAACL-HLT{'}2018. The second CWI shared task featured multilingual and multi-genre datasets divided into four tracks: English monolingual, German monolingual, Spanish monolingual, and a multilingual track with a French test set, and two tasks: binary classification and probabilistic classification. A total of 12 teams submitted their results in different task/track combinations and 11 of them wrote system description papers that are referred to in this report and appear in the BEA workshop proceedings.",
}
@inproceedings{alfter-volodina-2018-towards,
    title = "Towards Single Word Lexical Complexity Prediction",
    author = "Alfter, David  and
      Volodina, Elena",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0508",
    doi = "10.18653/v1/W18-0508",
    pages = "79--88",
    abstract = "In this paper we present work-in-progress where we investigate the usefulness of previously created word lists to the task of single-word lexical complexity analysis and prediction of the complexity level for learners of Swedish as a second language. The word lists used map each word to a single CEFR level, and the task consists of predicting CEFR levels for unseen words. In contrast to previous work on word-level lexical complexity, we experiment with topics as additional features and show that linking words to topics significantly increases accuracy of classification.",
}
@inproceedings{holz-etal-2018-coast,
    title = "{COAST} - Customizable Online Syllable Enhancement in Texts. A flexible framework for automatically enhancing reading materials",
    author = "Holz, Heiko  and
      Weiss, Zarah  and
      Brehm, Oliver  and
      Meurers, Detmar",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0509",
    doi = "10.18653/v1/W18-0509",
    pages = "89--100",
    abstract = "This paper presents COAST, a web-based application to easily and automatically enhance syllable structure, word stress, and spacing in texts, that was designed in close collaboration with learning therapists to ensure its practical relevance. Such syllable-enhanced texts are commonly used in learning therapy or private tuition to promote the recognition of syllables in order to improve reading and writing skills. In a state of the art solutions for automatic syllable enhancement, we put special emphasis on syllable stress and support specific marking of the primary syllable stress in words. Core features of our tool are i) a highly customizable text enhancement and template functionality, and ii) a novel crowd-sourcing mechanism that we employ to address the issue of data sparsity in language resources. We successfully tested COAST with real-life practitioners in a series of user tests validating the concept of our framework.",
}
@inproceedings{king-dickinson-2018-annotating,
    title = "Annotating picture description task responses for content analysis",
    author = "King, Levi  and
      Dickinson, Markus",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0510",
    doi = "10.18653/v1/W18-0510",
    pages = "101--109",
    abstract = "Given that all users of a language can be creative in their language usage, the overarching goal of this work is to investigate issues of variability and acceptability in written text, for both non-native speakers (NNSs) and native speakers (NSs). We control for meaning by collecting a dataset of picture description task (PDT) responses from a number of NSs and NNSs, and we define and annotate a handful of features pertaining to form and meaning, to capture the multi-dimensional ways in which responses can vary and can be acceptable. By examining the decisions made in this corpus development, we highlight the questions facing anyone working with learner language properties like variability, acceptability and native-likeness. We find reliable inter-annotator agreement, though disagreements point to difficult areas for establishing a link between form and meaning.",
}
@inproceedings{lugini-etal-2018-annotating,
    title = "Annotating Student Talk in Text-based Classroom Discussions",
    author = "Lugini, Luca  and
      Litman, Diane  and
      Godley, Amanda  and
      Olshefski, Christopher",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0511",
    doi = "10.18653/v1/W18-0511",
    pages = "110--116",
    abstract = "Classroom discussions in English Language Arts have a positive effect on students{'} reading, writing and reasoning skills. Although prior work has largely focused on teacher talk and student-teacher interactions, we focus on three theoretically-motivated aspects of high-quality student talk: argumentation, specificity, and knowledge domain. We introduce an annotation scheme, then show that the scheme can be used to produce reliable annotations and that the annotations are predictive of discussion quality. We also highlight opportunities provided by our scheme for education and natural language processing research.",
}
@inproceedings{ramanarayanan-lamar-2018-toward,
    title = "Toward Automatically Measuring Learner Ability from Human-Machine Dialog Interactions using Novel Psychometric Models",
    author = "Ramanarayanan, Vikram  and
      LaMar, Michelle",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0512",
    doi = "10.18653/v1/W18-0512",
    pages = "117--126",
    abstract = "While dialog systems have been widely deployed for computer-assisted language learning (CALL) and formative assessment systems in recent years, relatively limited work has been done with respect to the psychometrics and validity of these technologies in evaluating and providing feedback regarding student learning and conversational ability. This paper formulates a Markov decision process based measurement model, and applies it to text chat data collected from crowdsourced native and non-native English language speakers interacting with an automated dialog agent. We investigate how well the model measures speaker conversational ability, and find that it effectively captures the differences in how native and non-native speakers of English accomplish the dialog task. Such models could have important implications for CALL systems of the future that effectively combine dialog management with measurement of learner conversational ability in real-time.",
}
@inproceedings{rudzewitz-etal-2018-generating,
    title = "Generating Feedback for {E}nglish Foreign Language Exercises",
    author = {Rudzewitz, Bj{\"o}rn  and
      Ziai, Ramon  and
      De Kuthy, Kordula  and
      M{\"o}ller, Verena  and
      Nuxoll, Florian  and
      Meurers, Detmar},
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0513",
    doi = "10.18653/v1/W18-0513",
    pages = "127--136",
    abstract = "While immediate feedback on learner language is often discussed in the Second Language Acquisition literature (e.g., Mackey 2006), few systems used in real-life educational settings provide helpful, metalinguistic feedback to learners. In this paper, we present a novel approach leveraging task information to generate the expected range of well-formed and ill-formed variability in learner answers along with the required diagnosis and feedback. We combine this offline generation approach with an online component that matches the actual student answers against the pre-computed hypotheses. The results obtained for a set of 33 thousand answers of 7th grade German high school students learning English show that the approach successfully covers frequent answer patterns. At the same time, paraphrases and content errors require a more flexible alignment approach, for which we are planning to complement the method with the CoMiC approach successfully used for the analysis of reading comprehension answers (Meurers et al., 2011).",
}
@inproceedings{tack-etal-2018-nt2lex,
    title = "{NT}2{L}ex: A {CEFR}-Graded Lexical Resource for {D}utch as a Foreign Language Linked to Open {D}utch {W}ord{N}et",
    author = {Tack, Ana{\"\i}s  and
      Fran{\c{c}}ois, Thomas  and
      Desmet, Piet  and
      Fairon, C{\'e}drick},
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0514",
    doi = "10.18653/v1/W18-0514",
    pages = "137--146",
    abstract = "In this paper, we introduce NT2Lex, a novel lexical resource for Dutch as a foreign language (NT2) which includes frequency distributions of 17,743 words and expressions attested in expert-written textbook texts and readers graded along the scale of the Common European Framework of Reference (CEFR). In essence, the lexicon informs us about what kind of vocabulary should be understood when reading Dutch as a non-native reader at a particular proficiency level. The main novelty of the resource with respect to the previously developed CEFR-graded lexicons concerns the introduction of corpus-based evidence for L2 word sense complexity through the linkage to Open Dutch WordNet (Postma et al., 2016). The resource thus contains, on top of the lemmatised and part-of-speech tagged lexical entries, a total of 11,999 unique word senses and 8,934 distinct synsets.",
}
@inproceedings{vajjala-rama-2018-experiments,
    title = "Experiments with Universal {CEFR} Classification",
    author = "Vajjala, Sowmya  and
      Rama, Taraka",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0515",
    doi = "10.18653/v1/W18-0515",
    pages = "147--153",
    abstract = "The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels. While the description of CEFR guidelines is generic across languages, the development of automated proficiency classification systems for different languages follow different approaches. In this paper, we explore universal CEFR classification using domain-specific and domain-agnostic, theory-guided as well as data-driven features. We report the results of our preliminary experiments in monolingual, cross-lingual, and multilingual classification with three languages: German, Czech, and Italian. Our results show that both monolingual and multilingual models achieve similar performance, and cross-lingual classification yields lower, but comparable results to monolingual classification.",
}
@inproceedings{jiang-etal-2018-chengyu,
    title = "Chengyu Cloze Test",
    author = "Jiang, Zhiying  and
      Zhang, Boliang  and
      Huang, Lifu  and
      Ji, Heng",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0516",
    doi = "10.18653/v1/W18-0516",
    pages = "154--158",
    abstract = "We present a neural recommendation model for Chengyu, which is a special type of Chinese idiom. Given a query, which is a sentence with an empty slot where the Chengyu is taken out, our model will recommend the best Chengyu candidate that best fits the slot context. The main challenge lies in that the literal meaning of a Chengyu is usually very different from it{'}s figurative meaning. We propose a new neural approach to leverage the definition of each Chengyu and incorporate it as background knowledge. Experiments on both Chengyu cloze test and coherence checking in college entrance exams show that our system achieves 89.5{\%} accuracy on cloze test and outperforms human subjects who attended competitive universities in China. We will make all of our data sets and resources publicly available as a new benchmark for research purposes.",
}
@inproceedings{aburaed-saggion-2018-lastus,
    title = "{L}a{STUS}/{TALN} at Complex Word Identification ({CWI}) 2018 Shared Task",
    author = "AbuRa{'}ed, Ahmed  and
      Saggion, Horacio",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0517",
    doi = "10.18653/v1/W18-0517",
    pages = "159--165",
    abstract = "This paper presents the participation of the LaSTUS/TALN team in the Complex Word Identification (CWI) Shared Task 2018 in the English monolingual track . The purpose of the task was to determine if a word in a given sentence can be judged as complex or not by a certain target audience. For the English track, task organizers provided a training and a development datasets of 27,299 and 3,328 words respectively together with the sentence in which each word occurs. The words were judged as complex or not by 20 human evaluators; ten of whom are natives. We submitted two systems: one system modeled each word to evaluate as a numeric vector populated with a set of lexical, semantic and contextual features while the other system relies on a word embedding representation and a distance metric. We trained two separate classifiers to automatically decide if each word is complex or not. We submitted six runs, two for each of the three subsets of the English monolingual CWI track.",
}
@inproceedings{bingel-bjerva-2018-cross,
    title = "Cross-lingual complex word identification with multitask learning",
    author = "Bingel, Joachim  and
      Bjerva, Johannes",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0518",
    doi = "10.18653/v1/W18-0518",
    pages = "166--174",
    abstract = "We approach the 2018 Shared Task on Complex Word Identification by leveraging a cross-lingual multitask learning approach. Our method is highly language agnostic, as evidenced by the ability of our system to generalize across languages, including languages for which we have no training data. In the shared task, this is the case for French, for which our system achieves the best performance. We further provide a qualitative and quantitative analysis of which words pose problems for our system.",
}
@inproceedings{butnaru-ionescu-2018-unibuckernel,
    title = "{U}nibuc{K}ernel: A kernel-based learning method for complex word identification",
    author = "Butnaru, Andrei  and
      Ionescu, Radu Tudor",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0519",
    doi = "10.18653/v1/W18-0519",
    pages = "175--183",
    abstract = "In this paper, we present a kernel-based learning approach for the 2018 Complex Word Identification (CWI) Shared Task. Our approach is based on combining multiple low-level features, such as character n-grams, with high-level semantic features that are either automatically learned using word embeddings or extracted from a lexical knowledge base, namely WordNet. After feature extraction, we employ a kernel method for the learning phase. The feature matrix is first transformed into a normalized kernel matrix. For the binary classification task (simple versus complex), we employ Support Vector Machines. For the regression task, in which we have to predict the complexity level of a word (a word is more complex if it is labeled as complex by more annotators), we employ v-Support Vector Regression. We applied our approach only on the three English data sets containing documents from Wikipedia, WikiNews and News domains. Our best result during the competition was the third place on the English Wikipedia data set. However, in this paper, we also report better post-competition results.",
}
@inproceedings{gooding-kochmar-2018-camb,
    title = "{CAMB} at {CWI} Shared Task 2018: Complex Word Identification with Ensemble-Based Voting",
    author = "Gooding, Sian  and
      Kochmar, Ekaterina",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0520",
    doi = "10.18653/v1/W18-0520",
    pages = "184--194",
    abstract = "This paper presents the winning systems we submitted to the Complex Word Identification Shared Task 2018. We describe our best performing systems{'} implementations and discuss our key findings from this research. Our best-performing systems achieve an F1 score of 0.8792 on the NEWS, 0.8430 on the WIKINEWS and 0.8115 on the WIKIPEDIA test sets in the monolingual English binary classification track, and a mean absolute error of 0.0558 on the NEWS, 0.0674 on the WIKINEWS and 0.0739 on the WIKIPEDIA test sets in the probabilistic track.",
}
@inproceedings{kajiwara-komachi-2018-complex,
    title = "Complex Word Identification Based on Frequency in a Learner Corpus",
    author = "Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0521",
    doi = "10.18653/v1/W18-0521",
    pages = "195--199",
    abstract = "We introduce the TMU systems for the Complex Word Identification (CWI) Shared Task 2018. TMU systems use random forest classifiers and regressors whose features are the number of characters, the number of words, and the frequency of target words in various corpora. Our simple systems performed best on 5 tracks out of 12 tracks. Our ablation analysis revealed the usefulness of a learner corpus for CWI task.",
}
@inproceedings{wani-etal-2018-whole,
    title = "The Whole is Greater than the Sum of its Parts: Towards the Effectiveness of Voting Ensemble Classifiers for Complex Word Identification",
    author = "Wani, Nikhil  and
      Mathias, Sandeep  and
      Gajjam, Jayashree Aanand  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0522",
    doi = "10.18653/v1/W18-0522",
    pages = "200--205",
    abstract = "In this paper, we present an effective system using voting ensemble classifiers to detect contextually complex words for non-native English speakers. To make the final decision, we channel a set of eight calibrated classifiers based on lexical, size and vocabulary features and train our model with annotated datasets collected from a mixture of native and non-native speakers. Thereafter, we test our system on three datasets namely News, WikiNews, and Wikipedia and report competitive results with an F1-Score ranging between 0.777 to 0.855 for each of the datasets. Our system outperforms multiple other models and falls within 0.042 to 0.026 percent of the best-performing model{'}s score in the shared task.",
}
@inproceedings{klerke-etal-2018-grotoco,
    title = "Grotoco@{SLAM}: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models",
    author = "Klerke, Sigrid  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Plank, Barbara",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0523",
    doi = "10.18653/v1/W18-0523",
    pages = "206--211",
    abstract = "We present our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We focus on evaluating a range of features for the task, including user-derived measures, while examining how far we can get with a simple linear classifier. Our analysis reveals that errors differ per exercise format, which motivates our final and best-performing system: a task-wise (per exercise-format) model.",
}
@inproceedings{nayak-rao-2018-context,
    title = "Context Based Approach for Second Language Acquisition",
    author = "Nayak, Nihal V.  and
      Rao, Arjun R.",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0524",
    doi = "10.18653/v1/W18-0524",
    pages = "212--216",
    abstract = "SLAM 2018 focuses on predicting a student{'}s mistake while using the Duolingo application. In this paper, we describe the system we developed for this shared task. Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks - English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en). We conduct an ablation study with several features during the development of this system and discover that context based features plays a major role in language acquisition modeling. Our model beats Duolingo{'}s baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812). Our work makes a case for providing favourable textual context for students while learning second language.",
}
@inproceedings{osika-etal-2018-second,
    title = "Second Language Acquisition Modeling: An Ensemble Approach",
    author = "Osika, Anton  and
      Nilsson, Susanna  and
      Sydorchuk, Andrii  and
      Sahin, Faruk  and
      Huss, Anders",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0525",
    doi = "10.18653/v1/W18-0525",
    pages = "217--222",
    abstract = "Accurate prediction of students{'} knowledge is a fundamental building block of personalized learning systems. Here, we propose an ensemble model to predict student knowledge gaps. Applying our approach to student trace data from the online educational platform Duolingo we achieved highest score on all three datasets in the 2018 Shared Task on Second Language Acquisition Modeling. We describe our model and discuss relevance of the task compared to how it would be setup in a production environment for personalized education.",
}
@inproceedings{rich-etal-2018-modeling,
    title = "Modeling Second-Language Learning from a Psychological Perspective",
    author = "Rich, Alexander  and
      Osborn Popp, Pamela  and
      Halpern, David  and
      Rothe, Anselm  and
      Gureckis, Todd",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0526",
    doi = "10.18653/v1/W18-0526",
    pages = "223--230",
    abstract = "Psychological research on learning and memory has tended to emphasize small-scale laboratory studies. However, large datasets of people using educational software provide opportunities to explore these issues from a new perspective. In this paper we describe our approach to the Duolingo Second Language Acquisition Modeling (SLAM) competition which was run in early 2018. We used a well-known class of algorithms (gradient boosted decision trees), with features partially informed by theories from the psychological literature. After detailing our modeling approach and a number of supplementary simulations, we reflect on the degree to which psychological theory aided the model, and the potential for cognitive science and predictive modeling competitions to gain from each other.",
}
@inproceedings{tomoschuk-lovelett-2018-memory,
    title = "A Memory-Sensitive Classification Model of Errors in Early Second Language Learning",
    author = "Tomoschuk, Brendan  and
      Lovelett, Jarrett",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0527",
    doi = "10.18653/v1/W18-0527",
    pages = "231--239",
    abstract = "In this paper, we explore a variety of linguistic and cognitive features to better understand second language acquisition in early users of the language learning app Duolingo. With these features, we trained a random forest classifier to predict errors in early learners of French, Spanish, and English. Of particular note was our finding that mean and variance in error for each user and token can be a memory efficient replacement for their respective dummy-encoded categorical variables. At test, these models improved over the baseline model with AUROC values of 0.803 for English, 0.823 for French, and 0.829 for Spanish.",
}
@inproceedings{afrin-litman-2018-annotation,
    title = "Annotation and Classification of Sentence-level Revision Improvement",
    author = "Afrin, Tazin  and
      Litman, Diane",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0528",
    doi = "10.18653/v1/W18-0528",
    pages = "240--246",
    abstract = "Studies of writing revisions rarely focus on revision quality. To address this issue, we introduce a corpus of between-draft revisions of student argumentative essays, annotated as to whether each revision improves essay quality. We demonstrate a potential usage of our annotations by developing a machine learning model to predict revision improvement. With the goal of expanding training data, we also extract revisions from a dataset edited by expert proofreaders. Our results indicate that blending expert and non-expert revisions increases model performance, with expert data particularly important for predicting low-quality revisions.",
}
@inproceedings{bryant-briscoe-2018-language,
    title = "Language Model Based Grammatical Error Correction without Annotated Training Data",
    author = "Bryant, Christopher  and
      Briscoe, Ted",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0529",
    doi = "10.18653/v1/W18-0529",
    pages = "247--253",
    abstract = "Since the end of the CoNLL-2014 shared task on grammatical error correction (GEC), research into language model (LM) based approaches to GEC has largely stagnated. In this paper, we re-examine LMs in GEC and show that it is entirely possible to build a simple system that not only requires minimal annotated data (∼1000 sentences), but is also fairly competitive with several state-of-the-art systems. This approach should be of particular interest for languages where very little annotated training data exists, although we also hope to use it as a baseline to motivate future research.",
}
@inproceedings{flor-riordan-2018-semantic,
    title = "A Semantic Role-based Approach to Open-Domain Automatic Question Generation",
    author = "Flor, Michael  and
      Riordan, Brian",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0530",
    doi = "10.18653/v1/W18-0530",
    pages = "254--263",
    abstract = "We present a novel rule-based system for automatic generation of factual questions from sentences, using semantic role labeling (SRL) as the main form of text analysis. The system is capable of generating both wh-questions and yes/no questions from the same semantic analysis. We present an extensive evaluation of the system and compare it to a recent neural network architecture for question generation. The SRL-based system outperforms the neural system in both average quality and variety of generated questions.",
}
@inproceedings{gao-etal-2018-automated,
    title = "Automated Content Analysis: A Case Study of Computer Science Student Summaries",
    author = "Gao, Yanjun  and
      Davies, Patricia M.  and
      Passonneau, Rebecca J.",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0531",
    doi = "10.18653/v1/W18-0531",
    pages = "264--272",
    abstract = "Technology is transforming Higher Education learning and teaching. This paper reports on a project to examine how and why automated content analysis could be used to assess precis writing by university students. We examine the case of one hundred and twenty-two summaries written by computer science freshmen. The texts, which had been hand scored using a teacher-designed rubric, were autoscored using the Natural Language Processing software, PyrEval. Pearson{'}s correlation coefficient and Spearman rank correlation were used to analyze the relationship between the teacher score and the PyrEval score for each summary. Three content models automatically constructed by PyrEval from different sets of human reference summaries led to consistent correlations, showing that the approach is reliable. Also observed was that, in cases where the focus of student assessment centers on formative feedback, categorizing the PyrEval scores by examining the average and standard deviations could lead to novel interpretations of their relationships. It is suggested that this project has implications for the ways in which automated content analysis could be used to help university students improve their summarization skills.",
}
@inproceedings{kulkarni-boyer-2018-toward,
    title = "Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models",
    author = "Kulkarni, Mayank  and
      Boyer, Kristy",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0532",
    doi = "10.18653/v1/W18-0532",
    pages = "273--283",
    abstract = "There has been an increase in popularity of data-driven question answering systems given their recent success. This pa-per explores the possibility of building a tutorial question answering system for Java programming from data sampled from a community-based question answering forum. This paper reports on the creation of a dataset that could support building such a tutorial question answering system and discusses the methodology to create the 106,386 question strong dataset. We investigate how retrieval-based and generative models perform on the given dataset. The work also investigates the usefulness of using hybrid approaches such as combining retrieval-based and generative models. The results indicate that building data-driven tutorial systems using community-based question answering forums holds significant promise.",
}
@inproceedings{liang-etal-2018-distractor,
    title = "Distractor Generation for Multiple Choice Questions Using Learning to Rank",
    author = "Liang, Chen  and
      Yang, Xiao  and
      Dave, Neisarg  and
      Wham, Drew  and
      Pursel, Bart  and
      Giles, C. Lee",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0533",
    doi = "10.18653/v1/W18-0533",
    pages = "284--290",
    abstract = "We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions. Our proposed models can learn to select distractors that resemble those in actual exam questions, which is different from most existing unsupervised ontology-based and similarity-based methods. We empirically study feature-based and neural net (NN) based ranking models with experiments on the recently released SciQ dataset and our MCQL dataset. Experimental results show that feature-based ensemble learning methods (random forest and LambdaMART) outperform both the NN-based method and unsupervised baselines. These two datasets can also be used as benchmarks for distractor generation.",
}
@inproceedings{del-rio-gayo-etal-2018-portuguese,
    title = "A {P}ortuguese Native Language Identification Dataset",
    author = "del R{\'\i}o Gayo, Iria  and
      Zampieri, Marcos  and
      Malmasi, Shervin",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0534",
    doi = "10.18653/v1/W18-0534",
    pages = "291--296",
    abstract = "In this paper we present NLI-PT, the first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author{'}s first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency parses. NLI-PT can be used not only in NLI but also in research on several topics in the field of Second Language Acquisition and educational NLP. We discuss possible applications of this dataset and present the results obtained for the first lexical baseline system for Portuguese NLI.",
}
@inproceedings{vajjala-lucic-2018-onestopenglish,
    title = "{O}ne{S}top{E}nglish corpus: A new corpus for automatic readability assessment and text simplification",
    author = "Vajjala, Sowmya  and
      Lu{\v{c}}i{\'c}, Ivana",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0535",
    doi = "10.18653/v1/W18-0535",
    pages = "297--304",
    abstract = "This paper describes the collection and compilation of the OneStopEnglish corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification. The corpus consists of 189 texts, each in three versions (567 in total). The corpus is now freely available under a CC by-SA 4.0 license and we hope that it would foster further research on the topics of readability assessment and text simplification.",
}
@inproceedings{zhang-etal-2018-effect,
    title = "The Effect of Adding Authorship Knowledge in Automated Text Scoring",
    author = "Zhang, Meng  and
      Chen, Xie  and
      Cummins, Ronan  and
      Andersen, {\O}istein E.  and
      Briscoe, Ted",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0536",
    doi = "10.18653/v1/W18-0536",
    pages = "305--314",
    abstract = "Some language exams have multiple writing tasks. When a learner writes multiple texts in a language exam, it is not surprising that the quality of these texts tends to be similar, and the existing automated text scoring (ATS) systems do not explicitly model this similarity. In this paper, we suggest that it could be useful to include the other texts written by this learner in the same exam as extra references in an ATS system. We propose various approaches of fusing information from multiple tasks and pass this authorship knowledge into our ATS model on six different datasets. We show that this can positively affect the model performance at a global level.",
}
@inproceedings{alfter-pilan-2018-sb,
    title = "{SB}@{GU} at the Complex Word Identification 2018 Shared Task",
    author = "Alfter, David  and
      Pil{\'a}n, Ildik{\'o}",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0537",
    doi = "10.18653/v1/W18-0537",
    pages = "315--321",
    abstract = "In this paper, we describe our experiments for the Shared Task on Complex Word Identification (CWI) 2018 (Yimam et al., 2018), hosted by the 13th Workshop on Innovative Use of NLP for Building Educational Applications (BEA) at NAACL 2018. Our system for English builds on previous work for Swedish concerning the classification of words into proficiency levels. We investigate different features for English and compare their usefulness using feature selection methods. For the German, Spanish and French data we use simple systems based on character n-gram models and show that sometimes simple models achieve comparable results to fully feature-engineered systems.",
}
@inproceedings{aroyehun-etal-2018-complex,
    title = "Complex Word Identification: Convolutional Neural Network vs. Feature Engineering",
    author = "Aroyehun, Segun Taofeek  and
      Angel, Jason  and
      P{\'e}rez Alvarez, Daniel Alejandro  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0538",
    doi = "10.18653/v1/W18-0538",
    pages = "322--327",
    abstract = "We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other languages from the perspective of non-native speakers. Our goal is to compare two approaches: feature engineering and a deep neural network. Both approaches achieved comparable performance on the English test set. We demonstrated the flexibility of the deep-learning approach by using the same deep neural network setup in the Spanish track. Our systems achieved competitive results: all our systems were within 0.01 of the system with the best macro-F1 score on the test sets except on Wikipedia test set, on which our best system is 0.04 below the best macro-F1 score.",
}
@inproceedings{de-hertog-tack-2018-deep,
    title = "Deep Learning Architecture for Complex Word Identification",
    author = {De Hertog, Dirk  and
      Tack, Ana{\"\i}s},
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0539",
    doi = "10.18653/v1/W18-0539",
    pages = "328--334",
    abstract = "We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional information of the item itself, morphological structure, psychological measures, corpus-counts and topical information. We constructed a deep learning architecture that combines those features and apply it to the probabilistic and binary classification task for all English sets and Spanish. We achieved reasonable performance on all sets with best performances seen on the probabilistic task, particularly on the English news set (MAE 0.054 and F1-score of 0.872). An analysis of the results shows that reasonable performance can be achieved with a single architecture without any domain-specific tweaking of the parameter settings and that distributional features capture almost all of the information also found in hand-crafted features.",
}
@inproceedings{hartmann-dos-santos-2018-nilc,
    title = "{NILC} at {CWI} 2018: Exploring Feature Engineering and Feature Learning",
    author = "Hartmann, Nathan  and
      dos Santos, Leandro Borges",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0540",
    doi = "10.18653/v1/W18-0540",
    pages = "335--340",
    abstract = "This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature engineering method using lexical, n-gram and psycholinguistic features, (ii) a shallow neural network method using only word embeddings, and (iii) a Long Short-Term Memory (LSTM) language model, which is pre-trained on a large text corpus to produce a contextualized word vector. The feature engineering method obtained our best results for the classification task and the LSTM model achieved the best results for the probabilistic classification task. Our results show that deep neural networks are able to perform as well as traditional machine learning methods using manually engineered features for the task of complex word identification in English.",
}
@inproceedings{popovic-2018-complex,
    title = "Complex Word Identification Using Character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0541",
    doi = "10.18653/v1/W18-0541",
    pages = "341--348",
    abstract = "This paper investigates the use of character n-gram frequencies for identifying complex words in English, German and Spanish texts. The approach is based on the assumption that complex words are likely to contain different character sequences than simple words. The multinomial Naive Bayes classifier was used with n-grams of different lengths as features, and the best results were obtained for the combination of 2-grams and 4-grams. This variant was submitted to the Complex Word Identification Shared Task 2018 for all texts and achieved F-scores between 70{\%} and 83{\%}. The system was ranked in the middle range for all English texts, as third of fourteen submissions for German, and as tenth of seventeen submissions for Spanish. The method is not very convenient for the cross-language task, achieving only 59{\%} on the French text.",
}
@inproceedings{bestgen-2018-predicting,
    title = "Predicting Second Language Learner Successes and Mistakes by Means of Conjunctive Features",
    author = "Bestgen, Yves",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0542",
    doi = "10.18653/v1/W18-0542",
    pages = "349--355",
    abstract = "This paper describes the system developed by the Centre for English Corpus Linguistics for the 2018 Duolingo SLAM challenge. It aimed at predicting the successes and mistakes of second language learners on each of the words that compose the exercises they answered. Its main characteristic is to include conjunctive features, built by combining word ngrams with metadata about the user and the exercise. It achieved a relatively good performance, ranking fifth out of 15 systems. Complementary analyses carried out to gauge the contribution of the different sets of features to the performance confirmed the usefulness of the conjunctive features for the SLAM task.",
}
@inproceedings{chen-etal-2018-feature,
    title = "Feature Engineering for Second Language Acquisition Modeling",
    author = "Chen, Guanliang  and
      Hauff, Claudia  and
      Houben, Geert-Jan",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0543",
    doi = "10.18653/v1/W18-0543",
    pages = "356--364",
    abstract = "Knowledge tracing serves as a keystone in delivering personalized education. However, few works attempted to model students{'} knowledge state in the setting of Second Language Acquisition. The Duolingo Shared Task on Second Language Acquisition Modeling provides students{'} trace data that we extensively analyze and engineer features from for the task of predicting whether a student will correctly solve a vocabulary exercise. Our analyses of students{'} learning traces reveal that factors like exercise format and engagement impact their exercise performance to a large extent. Overall, we extracted 23 different features as input to a Gradient Tree Boosting framework, which resulted in an AUC score of between 0.80 and 0.82 on the official test set.",
}
@inproceedings{kaneko-etal-2018-tmu,
    title = "{TMU} System for {SLAM}-2018",
    author = "Kaneko, Masahiro  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0544",
    doi = "10.18653/v1/W18-0544",
    pages = "365--369",
    abstract = "We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018). To model learner error patterns, it is necessary to maintain a considerable amount of information regarding the type of exercises learners have been learning in the past and the manner in which they answered them. Tracking an enormous learner{'}s learning history and their correct and mistaken answers is essential to predict the learner{'}s future mistakes. Therefore, we propose a model which tracks the learner{'}s learning history efficiently. Our systems ranked fourth in the English and Spanish subtasks, and fifth in the French subtask.",
}
@inproceedings{vie-2018-deep,
    title = "Deep Factorization Machines for Knowledge Tracing",
    author = "Vie, Jill-J{\^e}nn",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0545",
    doi = "10.18653/v1/W18-0545",
    pages = "370--373",
    abstract = "This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to beat the logistic regression baseline (AUC 0.774) but not the top performing model (AUC 0.861) and reveals interesting strategies to build upon item response theory models.",
}
@inproceedings{xu-etal-2018-cluf,
    title = "{CLUF}: a Neural Model for Second Language Acquisition Modeling",
    author = "Xu, Shuyao  and
      Chen, Jin  and
      Qin, Long",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0546",
    doi = "10.18653/v1/W18-0546",
    pages = "374--380",
    abstract = "Second Language Acquisition Modeling is the task to predict whether a second language learner would respond correctly in future exercises based on their learning history. In this paper, we propose a neural network based system to utilize rich contextual, linguistic and user information. Our neural model consists of a Context encoder, a Linguistic feature encoder, a User information encoder and a Format information encoder (CLUF). Furthermore, a decoder is introduced to combine such encoded features and make final predictions. Our system ranked in first place in the English track and second place in the Spanish and French track with an AUROC score of 0.861, 0.835 and 0.854 respectively.",
}
@inproceedings{yuan-2018-neural,
    title = "Neural sequence modelling for learner error prediction",
    author = "Yuan, Zheng",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0547",
    doi = "10.18653/v1/W18-0547",
    pages = "381--388",
    abstract = "This paper describes our use of two recurrent neural network sequence models: sequence labelling and sequence-to-sequence models, for the prediction of future learner errors in our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We show that these two models capture complementary information as combining them improves performance. Furthermore, the same network architecture and group of features can be used directly to build competitive prediction models in all three language tracks, demonstrating that our approach generalises well across languages.",
}
@inproceedings{ha-yaneva-2018-automatic,
    title = "Automatic Distractor Suggestion for Multiple-Choice Tests Using Concept Embeddings and Information Retrieval",
    author = "Ha, Le An  and
      Yaneva, Victoria",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0548",
    doi = "10.18653/v1/W18-0548",
    pages = "389--398",
    abstract = "Developing plausible distractors (wrong answer options) when writing multiple-choice questions has been described as one of the most challenging and time-consuming parts of the item-writing process. In this paper we propose a fully automatic method for generating distractor suggestions for multiple-choice questions used in high-stakes medical exams. The system uses a question stem and the correct answer as an input and produces a list of suggested distractors ranked based on their similarity to the stem and the correct answer. To do this we use a novel approach of combining concept embeddings with information retrieval methods. We frame the evaluation as a prediction task where we aim to {``}predict{''} the human-produced distractors used in large sets of medical questions, i.e. if a distractor generated by our system is good enough it is likely to feature among the list of distractors produced by the human item-writers. The results reveal that combining concept embeddings with information retrieval approaches significantly improves the generation of plausible distractors and enables us to match around 1 in 5 of the human-produced distractors. The approach proposed in this paper is generalisable to all scenarios where the distractors refer to concepts.",
}
@inproceedings{zhang-litman-2018-co,
    title = "Co-Attention Based Neural Network for Source-Dependent Essay Scoring",
    author = "Zhang, Haoran  and
      Litman, Diane",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0549",
    doi = "10.18653/v1/W18-0549",
    pages = "399--409",
    abstract = "This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.",
}
@inproceedings{horbach-etal-2018-cross,
    title = "Cross-Lingual Content Scoring",
    author = "Horbach, Andrea  and
      Stennmanns, Sebastian  and
      Zesch, Torsten",
    booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0550",
    doi = "10.18653/v1/W18-0550",
    pages = "410--419",
    abstract = "We investigate the feasibility of cross-lingual content scoring, a scenario where training and test data in an automatic scoring task are from two different languages. Cross-lingual scoring can contribute to educational equality by allowing answers in multiple languages. Training a model in one language and applying it to another language might also help to overcome data sparsity issues by re-using trained models from other languages. As there is no suitable dataset available for this new task, we create a comparable bi-lingual corpus by extending the English ASAP dataset with German answers. Our experiments with cross-lingual scoring based on machine-translating either training or test data show a considerable drop in scoring quality.",
}
@proceedings{ws-2018-society,
    title = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    editor = "Jarosz, Gaja  and
      O{'}Connor, Brendan  and
      Pater, Joe",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0300",
    doi = "10.7275/R5GF0RQW",
}
@inproceedings{enguehard-etal-2018-statistical,
    title = "Statistical Learning Theory and Linguistic Typology: a Learnability Perspective on {OT}{'}s Strict Domination",
    author = "Enguehard, {\'E}mile  and
      Flemming, Edward  and
      Magri, Giorgio",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0301",
    doi = "10.7275/R5BP010Z",
    pages = "1--11",
}
@inproceedings{goodkind-etal-2018-detecting,
    title = "Detecting Language Impairments in Autism: A Computational Analysis of Semi-structured Conversations with Vector Semantics",
    author = "Goodkind, Adam  and
      Lee, Michelle  and
      Martin, Gary E.  and
      Losh, Molly  and
      Bicknell, Klinton",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0302",
    doi = "10.7275/R56W988P",
    pages = "12--22",
}
@inproceedings{graf-2018-grammar,
    title = "Grammar Size and Quantitative Restrictions on Movement",
    author = "Graf, Thomas",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0303",
    doi = "10.7275/R5348HJ8",
    pages = "23--33",
}
@inproceedings{hou-smith-2018-modeling,
    title = "Modeling the Decline in {E}nglish Passivization",
    author = "Hou, Liwen  and
      Smith, David",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0304",
    doi = "10.7275/R5ZC812C",
    pages = "34--43",
}
@inproceedings{kodner-2018-syntactic,
    title = "Syntactic Category Learning as Iterative Prototype-Driven Clustering",
    author = "Kodner, Jordan",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0305",
    doi = "10.7275/R5TQ5ZQ4",
    pages = "44--54",
}
@inproceedings{abney-2018-bidirectional,
    title = "A bidirectional mapping between {E}nglish and {CNF}-based reasoners",
    author = "Abney, Steven",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0306",
    doi = "10.7275/R5PZ571N",
    pages = "55--63",
}
@inproceedings{aksenova-deshmukh-2018-formal,
    title = "Formal Restrictions On Multiple Tiers",
    author = {Aks{\"e}nova, Al{\"e}na  and
      Deshmukh, Sanket},
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0307",
    doi = "10.7275/R5K64G8S",
    pages = "64--73",
}
@inproceedings{bhattasali-etal-2018-differentiating,
    title = "Differentiating Phrase Structure Parsing and Memory Retrieval in the Brain",
    author = "Bhattasali, Shohini  and
      Hale, John  and
      Pallier, Christophe  and
      Brennan, Jonathan  and
      Luh, Wen-Ming  and
      Spreng, R. Nathan",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0308",
    doi = "10.7275/R5FF3QJ2",
    pages = "74--80",
}
@inproceedings{dunn-2018-modeling,
    title = "Modeling the Complexity and Descriptive Adequacy of Construction Grammars",
    author = "Dunn, Jonathan",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0309",
    doi = "10.7275/R59P2ZTB",
    pages = "81--90",
}
@inproceedings{lamont-2018-decomposing,
    title = "Decomposing phonological transformations in serial derivations",
    author = "Lamont, Andrew",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0310",
    doi = "10.7275/R55X273D",
    pages = "91--101",
}
@inproceedings{mccoy-frank-2018-phonologically,
    title = "Phonologically Informed Edit Distance Algorithms for Word Alignment with Low-Resource Languages",
    author = "McCoy, Richard T.  and
      Frank, Robert",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0311",
    doi = "10.7275/R5251GC0",
    pages = "102--112",
}
@inproceedings{moreton-2018-conditions,
    title = "Conditions on abruptness in a gradient-ascent Maximum Entropy learner",
    author = "Moreton, Elliott",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0312",
    doi = "10.7275/R5XG9PBX",
    pages = "113--124",
}
@inproceedings{schrimpf-2018-using,
    title = "Using Rhetorical Topics for Automatic Summarization",
    author = "Schrimpf, Natalie M.",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0313",
    doi = "10.7275/R5SQ8XM6",
    pages = "125--135",
}
@inproceedings{silfverberg-etal-2018-sound,
    title = "Sound Analogies with Phoneme Embeddings",
    author = "Silfverberg, Miikka P.  and
      Mao, Lingshuang  and
      Hulden, Mans",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0314",
    doi = "10.7275/R5NZ85VD",
    pages = "136--144",
}
@inproceedings{strother-garcia-2018-imdlawn,
    title = "Imdlawn Tashlhiyt {B}erber Syllabification is Quantifier-Free",
    author = "Strother-Garcia, Kristina",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0315",
    doi = "10.7275/R5J67F4D",
    pages = "145--153",
}
@inproceedings{vu-2018-towards,
    title = "Towards a Formal Description of {NPI}-licensing Patterns",
    author = "Vu, Mai Ha",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0316",
    doi = "10.7275/R5DF6PDP",
    pages = "154--163",
}
@inproceedings{yang-etal-2018-organization,
    title = "The Organization of Lexicons: a Cross-Linguistic Analysis of Monosyllabic Words",
    author = "Yang, Shiying  and
      Sanker, Chelsea  and
      Priva, Uriel Cohen",
    booktitle = "Proceedings of the Society for Computation in Linguistics ({SC}i{L}) 2018",
    year = "2018",
    url = "https://www.aclweb.org/anthology/W18-0317",
    doi = "10.7275/R58P5XPZ",
    pages = "164--173",
}
@proceedings{ws-2018-international,
    title = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    editor = "Pirinen, Tommi A.  and
      Rie{\ss}ler, Michael  and
      Rueter, Jack  and
      Trosterud, Trond  and
      Tyers, Francis M.",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0200",
    doi = "10.18653/v1/W18-02",
}
@inproceedings{partanen-etal-2018-dependency,
    title = "Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations",
    author = "Partanen, Niko  and
      Lim, Kyungtae  and
      Rie{\ss}ler, Michael  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0201",
    doi = "10.18653/v1/W18-0201",
    pages = "1--17",
}
@inproceedings{nyrkko-2018-building,
    title = "Building a {F}innish {SOM}-based ontology concept tagger and harvester",
    author = {Nyrkk{\"o}, Seppo},
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0202",
    doi = "10.18653/v1/W18-0202",
    pages = "18--25",
}
@inproceedings{arkhangelskiy-georgieva-2018-sound,
    title = "Sound-aligned corpus of {U}dmurt dialectal texts",
    author = "Arkhangelskiy, Timofey  and
      Georgieva, Ekaterina",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0203",
    doi = "10.18653/v1/W18-0203",
    pages = "26--38",
}
@inproceedings{ferenczi-etal-2018-automatic,
    title = "Automatic Generation of {W}iktionary Entries for Finno-Ugric Minority Languages",
    author = "Ferenczi, Zsanett  and
      Mittelholcz, Iv{\'a}n  and
      Simon, Eszter",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0204",
    doi = "10.18653/v1/W18-0204",
    pages = "39--50",
}
@inproceedings{hamalainen-rueter-2018-development,
    title = "Development of an Open Source Natural Language Generation Tool for {F}innish",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Rueter, Jack},
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0205",
    doi = "10.18653/v1/W18-0205",
    pages = "51--58",
}
@inproceedings{koskenniemi-2018-guessing,
    title = "Guessing lexicon entries using finite-state methods",
    author = "Koskenniemi, Kimmo",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0206",
    doi = "10.18653/v1/W18-0206",
    pages = "59--75",
}
@inproceedings{bjerva-augenstein-2018-tracking,
    title = "Tracking Typological Traits of Uralic Languages in Distributed Language Representations",
    author = "Bjerva, Johannes  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0207",
    doi = "10.18653/v1/W18-0207",
    pages = "76--86",
}
@inproceedings{leinonen-etal-2018-new,
    title = "New Baseline in Automatic Speech Recognition for {N}orthern {S}{\'a}mi",
    author = "Leinonen, Juho  and
      Smit, Peter  and
      Virpioja, Sami  and
      Kurimo, Mikko",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0208",
    doi = "10.18653/v1/W18-0208",
    pages = "87--97",
}
@inproceedings{silfverberg-hulden-2018-initial,
    title = "Initial Experiments in Data-Driven Morphological Analysis for {F}innish",
    author = "Silfverberg, Miikka  and
      Hulden, Mans",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0209",
    doi = "10.18653/v1/W18-0209",
    pages = "98--105",
}
@inproceedings{rueter-tyers-2018-towards,
    title = "Towards an open-source universal-dependency treebank for {E}rzya",
    author = "Rueter, Jack  and
      Tyers, Francis",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0210",
    doi = "10.18653/v1/W18-0210",
    pages = "106--118",
}
@inproceedings{fejes-2018-utilization,
    title = "Utilization of {N}ganasan digital resources: a statistical approach to vowel harmony",
    author = "Fejes, L{\'a}szl{\'o}",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0211",
    doi = "10.18653/v1/W18-0211",
    pages = "119--138",
}
@inproceedings{kaalep-2018-parallel,
    title = "Parallel Forms in {E}stonian Finite State Morphology",
    author = "Kaalep, Heiki-Jaan",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0212",
    doi = "10.18653/v1/W18-0212",
    pages = "139--153",
}
@inproceedings{wilbur-2018-extracting,
    title = "Extracting inflectional class assignment in Pite Saami: Nouns, verbs and those pesky adjectives",
    author = "Wilbur, Joshua",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0213",
    doi = "10.18653/v1/W18-0213",
    pages = "154--168",
}
@inproceedings{voutilainen-palolahti-2018-analysing,
    title = "Analysing {F}innish with word lists: the {DDI} approach to morphology revisited",
    author = "Voutilainen, Atro  and
      Palolahti, Maria",
    booktitle = "Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages",
    month = jan,
    year = "2018",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0214",
    doi = "10.18653/v1/W18-0214",
    pages = "169--179",
}
@proceedings{ws-2018-cognitive,
    title = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    editor = "Sayeed, Asad  and
      Jacobs, Cassandra  and
      Linzen, Tal  and
      van Schijndel, Marten",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0100",
    doi = "10.18653/v1/W18-01",
}
@inproceedings{jaffe-etal-2018-coreference,
    title = "Coreference and Focus in Reading Times",
    author = "Jaffe, Evan  and
      Shain, Cory  and
      Schuler, William",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0101",
    doi = "10.18653/v1/W18-0101",
    pages = "1--9",
}
@inproceedings{goodkind-bicknell-2018-predictive,
    title = "Predictive power of word surprisal for reading times is a linear function of language model quality",
    author = "Goodkind, Adam  and
      Bicknell, Klinton",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0102",
    doi = "10.18653/v1/W18-0102",
    pages = "10--18",
}
@inproceedings{cho-etal-2018-dynamic,
    title = "Dynamic encoding of structural uncertainty in gradient symbols",
    author = "Cho, Pyeong Whan  and
      Goldrick, Matthew  and
      Lewis, Richard L.  and
      Smolensky, Paul",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0103",
    doi = "10.18653/v1/W18-0103",
    pages = "19--28",
}
@inproceedings{gwilliams-etal-2018-phonological,
    title = "Phonological (un)certainty weights lexical activation",
    author = "Gwilliams, Laura  and
      Poeppel, David  and
      Marantz, Alec  and
      Linzen, Tal",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0104",
    doi = "10.18653/v1/W18-0104",
    pages = "29--34",
}
@inproceedings{miscevic-etal-2018-predicting,
    title = "Predicting and Explaining Human Semantic Search in a Cognitive Model",
    author = "Miscevic, Filip  and
      Nematzadeh, Aida  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0105",
    doi = "10.18653/v1/W18-0105",
    pages = "35--45",
}
@inproceedings{matusevych-etal-2018-modeling,
    title = "Modeling bilingual word associations as connected monolingual networks",
    author = "Matusevych, Yevgen  and
      Kalantari Dehaghi, Amir Ardalan  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0106",
    doi = "10.18653/v1/W18-0106",
    pages = "46--56",
}
@inproceedings{abnar-etal-2018-experiential,
    title = "Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity",
    author = "Abnar, Samira  and
      Ahmed, Rasyan  and
      Mijnheer, Max  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0107",
    doi = "10.18653/v1/W18-0107",
    pages = "57--66",
}
@inproceedings{savinelli-etal-2018-exactly,
    title = "Exactly two things to learn from modeling scope ambiguity resolution: Developmental continuity and numeral semantics",
    author = "Savinelli, K.J.  and
      Scontras, Greg  and
      Pearl, Lisa",
    booktitle = "Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2018)",
    month = jan,
    year = "2018",
    address = "Salt Lake City, Utah",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-0108",
    doi = "10.18653/v1/W18-0108",
    pages = "67--75",
}
@proceedings{alta-2018-australasian,
    title = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    editor = "Kim, Sunghwan Mac  and
      Zhang, Xiuzhen (Jenny)",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1000",
}
@inproceedings{hoang-etal-2018-improved,
    title = "Improved Neural Machine Translation using Side Information",
    author = "Hoang, Cong Duy Vu  and
      Haffari, Gholamreza  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1001",
    pages = "6--16",
    abstract = "In this work, we investigate whether side information is helpful in neural machine translation (NMT). We study various kinds of side information, including topical information, personal trait, then propose different ways of incorporating them into the existing NMT models. Our experimental results show the benefits of side information in improving the NMT models.",
}
@inproceedings{tsuge-ishihara-2018-text,
    title = "Text-dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden {M}arkov Model ({HMM}) and {G}aussian Mixture Model",
    author = "Tsuge, Satoru  and
      Ishihara, Shunichi",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1002",
    pages = "17--25",
    abstract = "Among the more typical forensic voice comparison (FVC) approaches, the acoustic-phonetic statistical approach is suitable for text-dependent FVC, but it does not fully exploit available time-varying information of speech in its modelling. The automatic approach, on the other hand, essentially deals with text-independent cases, which means temporal information is not explicitly incorporated in the modelling. Text-dependent likelihood ratio (LR)-based FVC studies, in particular those that adopt the automatic approach, are few. This preliminary LR-based FVC study compares two statistical models, the Hidden Markov Model (HMM) and the Gaussian Mixture Model (GMM), for the calculation of forensic LRs using the same speech data. FVC experiments were carried out using different lengths of Japanese short words under a forensically realistic, but challenging condition: only two speech tokens for model training and LR estimation. Log-likelihood-ratio cost (Cllr) was used as the assessment metric. The study demonstrates that the HMM system constantly outperforms the GMM system in terms of average Cllr values. However, words longer than three mora are needed if the advantage of the HMM is to become evident. With a seven-mora word, for example, the HMM outperformed the GMM by a Cllr value of 0.073.",
}
@inproceedings{solano-etal-2018-development,
    title = "Development of Natural Language Processing Tools for {C}ook {I}slands {M}{\=a}ori",
    author = "Solano, Rolando Coto  and
      Nicholas, Sally Akevai  and
      Wray, Samantha",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1003",
    pages = "26--33",
    abstract = "This paper presents three ongoing projects for NLP in Cook Islands Maori: Untrained Forced Alignment (approx. 9{\%} error when detecting the center of words), speech-to-text (37{\%} WER in the best trained models) and POS tagging (92{\%} accuracy for the best performing model). Included as part of these projects are new resources filling in a gap in Australasian languages, including gold standard POS-tagged written corpora, transcribed speech corpora, time-aligned corpora down to the level of phonemes. These are part of efforts to accelerate the documentation of Cook Islands Maori and to increase its vitality amongst its users.",
}
@inproceedings{de-vine-etal-2018-unsupervised,
    title = "Unsupervised Mining of Analogical Frames by Constraint Satisfaction",
    author = "De Vine, Lance  and
      Geva, Shlomo  and
      Bruza, Peter",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1004",
    pages = "34--43",
    abstract = "It has been demonstrated that vector-based representations of words trained on large text corpora encode linguistic regularities that may be exploited via the use of vector space arithmetic. This capability has been extensively explored and is generally measured via tasks which involve the automated completion of linguistic proportional analogies. The question remains, however, as to what extent it is possible to induce relations from word embeddings in a principled and systematic way, without the provision of exemplars or seed terms. In this paper we propose an extensible and efficient framework for inducing relations via the use of constraint satisfaction. The method is efficient, unsupervised and can be customized in various ways. We provide both quantitative and qualitative analysis of the results.",
}
@inproceedings{hossain-schwitter-2018-specifying,
    title = "Specifying Conceptual Models Using Restricted Natural Language",
    author = "Hossain, Bayzid Ashik  and
      Schwitter, Rolf",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1005",
    pages = "44--52",
    abstract = "The key activity to design an information system is conceptual modelling which brings out and describes the general knowledge that is required to build a system. In this paper we propose a novel approach to conceptual modelling where the domain experts will be able to specify and construct a model using a restricted form of natural language. A restricted natural language is a subset of a natural language that has well-defined computational properties and therefore can be translated unambiguously into a formal notation. We will argue that a restricted natural language is suitable for writing precise and consistent specifications that lead to executable conceptual models. Using a restricted natural language will allow the domain experts to describe a scenario in the terminology of the application domain without the need to formally encode this scenario. The resulting textual specification can then be automatically translated into the language of the desired conceptual modelling framework.",
}
@inproceedings{holt-chisholm-2018-extracting,
    title = "Extracting structured data from invoices",
    author = "Holt, Xavier  and
      Chisholm, Andrew",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1006",
    pages = "53--59",
    abstract = "Business documents encode a wealth of information in a format tailored to human consumption {--} i.e. aesthetically disbursed natural language text, graphics and tables. We address the task of extracting key fields (e.g. the amount due on an invoice) from a wide-variety of potentially unseen document formats. In contrast to traditional template driven extraction systems, we introduce a content-driven machine-learning approach which is both robust to noise and generalises to unseen document formats. In a comparison of our approach with alternative invoice extraction systems, we observe an absolute accuracy gain of 20{\textbackslash}{\%} across compared fields, and a 25{\textbackslash}{\%}{--}94{\textbackslash}{\%} reduction in extraction latency.",
}
@inproceedings{he-etal-2018-exploring,
    title = "Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation",
    author = "He, Xuanli  and
      Tran, Quan  and
      Havard, William  and
      Besacier, Laurent  and
      Zukerman, Ingrid  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1007",
    pages = "61--65",
    abstract = "In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i.e. human transcriptions, instead of Automatic Speech Recognition (ASR){'}s transcriptions. In spoken dialog systems, however, the agent would only have access to noisy ASR transcriptions, which may further suffer performance degradation due to domain shift. In this paper, we explore the effectiveness of using both acoustic and textual signals, either oracle or ASR transcriptions, and investigate speaker domain adaptation for DA classification. Our multimodal model proves to be superior to the unimodal models, particularly when the oracle transcriptions are not available. We also propose an effective method for speaker domain adaptation, which achieves competitive results.",
}
@inproceedings{poostchi-piccardi-2018-cluster,
    title = "Cluster Labeling by Word Embeddings and {W}ord{N}et's Hypernymy",
    author = "Poostchi, Hanieh  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1008",
    pages = "66--70",
    abstract = "Cluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection. Once assigned, the labels can play an important role in applications such as navigation, search and document classification. However, finding appropriately descriptive labels is still a challenging task. In this paper, we propose various approaches for assigning labels to word clusters by leveraging word embeddings and the synonymity and hypernymy relations in the WordNet lexical ontology. Experiments carried out using the WebAP document dataset have shown that one of the approaches stand out in the comparison and is capable of selecting labels that are reasonably aligned with those chosen by a pool of four human annotators.",
}
@inproceedings{nandakumar-etal-2018-comparative,
    title = "A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions",
    author = "Nandakumar, Navnita  and
      Salehi, Bahar  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1009",
    pages = "71--76",
    abstract = "In this paper, we perform a comparative evaluation of off-the-shelf embedding models over the task of compositionality prediction of multiword expressions(``MWEs''). Our experimental results suggest that character- and document-level models capture knowledge of MWE compositionality and are effective in modelling varying levels of compositionality, with the advantage over word-level models that they do not require token-level identification of MWEs in the training corpus.",
}
@inproceedings{mathur-etal-2018-towards,
    title = "Towards Efficient Machine Translation Evaluation by Modelling Annotators",
    author = "Mathur, Nitika  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1010",
    pages = "77--82",
    abstract = "Accurate evaluation of translation has long been a difficult, yet important problem. Current evaluations use direct assessment (DA), based on crowd sourcing judgements from a large pool of workers, along with quality control checks, and a robust method for combining redundant judgements. In this paper we show that the quality control mechanism is overly conservative, which increases the time and expense of the evaluation. We propose a model that does not rely on a pre-processing step to filter workers and takes into account varying annotator reliabilities. Our model effectively weights each worker's scores based on the inferred precision of the worker, and is much more reliable than the mean of either the raw scores or the standardised scores. We also show that DA does not deliver on the promise of longitudinal evaluation, and propose redesigning the structure of the annotation tasks that can solve this problem.",
}
@inproceedings{molla-seneviratne-2018-overview,
    title = "Overview of the 2018 {ALTA} Shared Task: Classifying Patent Applications",
    author = "Moll{\'a}, Diego  and
      Seneviratne, Dilesha",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1011",
    pages = "84--88",
    abstract = "We present an overview of the 2018 ALTA shared task. This is the 9th of the series of shared tasks organised by ALTA since 2010. The task was to classify Australian patent classifications following the sections defined by the International Patient Classification (IPC), using data made available by IP Australia. We introduce the task, describe the data and present the results of the participating teams. Some of the participating teams outperformed state of the art.",
}
@inproceedings{benites-etal-2018-classifying,
    title = "Classifying Patent Applications with Ensemble Methods",
    author = "Benites, Fernando  and
      Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1012",
    pages = "89--92",
    abstract = "We present methods for the automatic classification of patent applications using an annotated dataset provided by the organizers of the ALTA 2018 shared task - Classifying Patent Applications. The goal of the task is to use computational methods to categorize patent applications according to a coarse-grained taxonomy of eight classes based on the International Patent Classification (IPC). We tested a variety of approaches for this task and the best results, 0.778 micro-averaged F1-Score, were achieved by SVM ensembles using a combination of words and characters as features. Our team, BMZ, was ranked first among 14 teams in the competition.",
}
@inproceedings{hepburn-2018-universal,
    title = "Universal Language Model Fine-tuning for Patent Classification",
    author = "Hepburn, Jason",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2018",
    month = dec,
    year = "2018",
    address = "Dunedin, New Zealand",
    url = "https://www.aclweb.org/anthology/U18-1013",
    pages = "93--96",
    abstract = "This paper describes the methods used for the 2018 ALTA Shared Task. The task this year was to automatically classify Australian patents into their main International Patent Classification section. Our final submission used a Support Vector Machine (SVM) and Universal Language Model with Fine-tuning (ULMFiT). Our system achieved the best results in the student category.",
}
@proceedings{semeval-2018-joint,
    title = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    editor = "Nissim, Malvina  and
      Berant, Jonathan  and
      Lenci, Alessandro",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2000",
    doi = "10.18653/v1/S18-2",
}
@inproceedings{kenyon-dean-etal-2018-resolving,
    title = "Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization",
    author = "Kenyon-Dean, Kian  and
      Cheung, Jackie Chi Kit  and
      Precup, Doina",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2001",
    doi = "10.18653/v1/S18-2001",
    pages = "1--10",
    abstract = "We present an approach to event coreference resolution by developing a general framework for clustering that uses supervised representation learning. We propose a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function. These terms encourage the model to create embeddings of event mentions that are amenable to clustering. We then use agglomerative clustering on these embeddings to build event coreference chains. For both within- and cross-document coreference on the ECB+ corpus, our model obtains better results than models that require significantly more pre-annotated information. This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.",
}
@inproceedings{hong-etal-2018-learning,
    title = "Learning distributed event representations with a multi-task approach",
    author = "Hong, Xudong  and
      Sayeed, Asad  and
      Demberg, Vera",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2002",
    doi = "10.18653/v1/S18-2002",
    pages = "11--21",
    abstract = "Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.",
}
@inproceedings{schulte-im-walde-etal-2018-assessing,
    title = "Assessing Meaning Components in {G}erman Complex Verbs: A Collection of Source-Target Domains and Directionality",
    author = {Schulte im Walde, Sabine  and
      K{\"o}per, Maximilian  and
      Springorum, Sylvia},
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2003",
    doi = "10.18653/v1/S18-2003",
    pages = "22--32",
    abstract = "This paper presents a collection to assess meaning components in German complex verbs, which frequently undergo meaning shifts. We use a novel strategy to obtain source and target domain characterisations via sentence generation rather than sentence annotation. A selection of arrows adds spatial directional information to the generated contexts. We provide a broad qualitative description of the dataset, and a series of standard classification experiments verifies the quantitative reliability of the presented resource. The setup for collecting the meaning components is applicable also to other languages, regarding complex verbs as well as other language-specific targets that involve meaning shifts.",
}
@inproceedings{samardzhiev-etal-2018-learning,
    title = "Learning Neural Word Salience Scores",
    author = "Samardzhiev, Krasen  and
      Gargett, Andrew  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2004",
    doi = "10.18653/v1/S18-2004",
    pages = "33--42",
    abstract = "Measuring the salience of a word is an essential step in numerous NLP tasks. Heuristic approaches such as tfidf have been used so far to estimate the salience of words. We propose \textit{Neural Word Salience} (NWS) scores, unlike heuristics, are learnt from a corpus. Specifically, we learn word salience scores such that, using pre-trained word embeddings as the input, can accurately predict the words that appear in a sentence, given the words that appear in the sentences preceding or succeeding that sentence. Experimental results on sentence similarity prediction show that the learnt word salience scores perform comparably or better than some of the state-of-the-art approaches for representing sentences on benchmark datasets for sentence similarity, while using only a fraction of the training and prediction times required by prior methods. Moreover, our NWS scores positively correlate with psycholinguistic measures such as concreteness, and imageability implying a close connection to the salience as perceived by humans.",
}
@inproceedings{kiritchenko-mohammad-2018-examining,
    title = "Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems",
    author = "Kiritchenko, Svetlana  and
      Mohammad, Saif",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2005",
    doi = "10.18653/v1/S18-2005",
    pages = "43--53",
    abstract = "Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 {`}Affect in Tweets{'}. We find that several of the systems show statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available.",
}
@inproceedings{beschke-menzel-2018-graph,
    title = "Graph Algebraic {C}ombinatory {C}ategorial {G}rammar",
    author = "Beschke, Sebastian  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2006",
    doi = "10.18653/v1/S18-2006",
    pages = "54--64",
    abstract = "This paper describes CCG/AMR, a novel grammar for semantic parsing of Abstract Meaning Representations. CCG/AMR equips Combinatory Categorial Grammar derivations with graph semantics by assigning each CCG combinator an interpretation in terms of a graph algebra. We provide an algorithm that induces a CCG/AMR from a corpus and show that it creates a compact lexicon with low ambiguity and achieves a robust coverage of 78{\%} of the examined sentences under ideal conditions. We also identify several phenomena that affect any approach relying either on CCG or graph algebraic approaches for AMR parsing. This includes differences of representation between CCG and AMR, as well as non-compositional constructions that are not expressible through a monotonous construction process. To our knowledge, this paper provides the first analysis of these corpus issues.",
}
@inproceedings{sorokin-gurevych-2018-mixing,
    title = "Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories",
    author = "Sorokin, Daniil  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2007",
    doi = "10.18653/v1/S18-2007",
    pages = "65--75",
    abstract = "The first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity. We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data. Our approach outperforms the previous state-of-the-art system on this data, resulting in an average 8{\%} improvement of the final score. We further demonstrate that our model delivers a strong performance across different entity categories.",
}
@inproceedings{naumann-etal-2018-quantitative,
    title = "Quantitative Semantic Variation in the Contexts of Concrete and Abstract Words",
    author = "Naumann, Daniela  and
      Frassinelli, Diego  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2008",
    doi = "10.18653/v1/S18-2008",
    pages = "76--85",
    abstract = "Across disciplines, researchers are eager to gain insight into empirical features of abstract vs. concrete concepts. In this work, we provide a detailed characterisation of the distributional nature of abstract and concrete words across 16,620 English nouns, verbs and adjectives. Specifically, we investigate the following questions: (1) What is the distribution of concreteness in the contexts of concrete and abstract target words? (2) What are the differences between concrete and abstract words in terms of contextual semantic diversity? (3) How does the entropy of concrete and abstract word contexts differ? Overall, our studies show consistent differences in the distributional representation of concrete and abstract words, thus challenging existing theories of cognition and providing a more fine-grained description of their nature.",
}
@inproceedings{badaro-etal-2018-emowordnet,
    title = "{E}mo{W}ord{N}et: Automatic Expansion of Emotion Lexicon Using {E}nglish {W}ord{N}et",
    author = "Badaro, Gilbert  and
      Jundi, Hussein  and
      Hajj, Hazem  and
      El-Hajj, Wassim",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2009",
    doi = "10.18653/v1/S18-2009",
    pages = "86--93",
    abstract = "Nowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On the other hand, politicians, companies, and businesses are interested in analyzing automatically people{'}s opinions and emotions. In the last decade, a lot of efforts has been put into extracting sentiment polarity from texts. Recently, the focus has expanded to also cover emotion recognition from texts. In this work, we expand an existing emotion lexicon, DepecheMood, by leveraging semantic knowledge from English WordNet (EWN). We create an expanded lexicon, EmoWordNet, consisting of 67K terms aligned with EWN, almost 1.8 times the size of DepecheMood. We also evaluate EmoWordNet in an emotion recognition task using SemEval 2007 news headlines dataset and we achieve an improvement compared to the use of DepecheMood. EmoWordNet is publicly available to speed up research in the field on \url{http://oma-project.com}.",
}
@inproceedings{bakarov-etal-2018-limitations,
    title = "The Limitations of Cross-language Word Embeddings Evaluation",
    author = "Bakarov, Amir  and
      Suvorov, Roman  and
      Sochenkov, Ilya",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2010",
    doi = "10.18653/v1/S18-2010",
    pages = "94--100",
    abstract = "The aim of this work is to explore the possible limitations of existing methods of cross-language word embeddings evaluation, addressing the lack of correlation between intrinsic and extrinsic cross-language evaluation methods. To prove this hypothesis, we construct English-Russian datasets for extrinsic and intrinsic evaluation tasks and compare performances of 5 different cross-language models on them. The results say that the scores even on different intrinsic benchmarks do not correlate to each other. We can conclude that the use of human references as ground truth for cross-language word embeddings is not proper unless one does not understand how do native speakers process semantics in their cognition.",
}
@inproceedings{barbieri-camacho-collados-2018-gender,
    title = "How Gender and Skin Tone Modifiers Affect Emoji Semantics in {T}witter",
    author = "Barbieri, Francesco  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2011",
    doi = "10.18653/v1/S18-2011",
    pages = "101--106",
    abstract = "In this paper we analyze the use of emojis in social media with respect to gender and skin tone. By gathering a dataset of over twenty two million tweets from United States some findings are clearly highlighted after performing a simple frequency-based analysis. Moreover, we carry out a semantic analysis on the usage of emojis and their modifiers (e.g. gender and skin tone) by embedding all words, emojis and modifiers into the same vector space. Our analyses reveal that some stereotypes related to the skin color and gender seem to be reflected on the use of these modifiers. For example, emojis representing hand gestures are more widely utilized with lighter skin tones, and the usage across skin tones differs significantly. At the same time, the vector corresponding to the male modifier tends to be semantically close to emojis related to business or technology, whereas their female counterparts appear closer to emojis about love or makeup.",
}
@inproceedings{choi-etal-2018-element,
    title = "Element-wise Bilinear Interaction for Sentence Matching",
    author = "Choi, Jihun  and
      Kim, Taeuk  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2012",
    doi = "10.18653/v1/S18-2012",
    pages = "107--112",
    abstract = "When we build a neural network model predicting the relationship between two sentences, the most general and intuitive approach is to use a Siamese architecture, where the sentence vectors obtained from a shared encoder is given as input to a classifier. For the classifier to work effectively, it is important to extract appropriate features from the two vectors and feed them as input. There exist several previous works that suggest heuristic-based function for matching sentence vectors, however it cannot be said that the heuristics tailored for a specific task generalize to other tasks. In this work, we propose a new matching function, ElBiS, that learns to model element-wise interaction between two vectors. From experiments, we empirically demonstrate that the proposed ElBiS matching function outperforms the concatenation-based or heuristic-based matching functions on natural language inference and paraphrase identification, while maintaining the fused representation compact.",
}
@inproceedings{crouch-kalouli-2018-named,
    title = "Named Graphs for Semantic Representation",
    author = "Crouch, Richard  and
      Kalouli, Aikaterini-Lida",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2013",
    doi = "10.18653/v1/S18-2013",
    pages = "113--118",
    abstract = "A position paper arguing that purely graphical representations for natural language semantics lack a fundamental degree of expressiveness, and cannot deal with even basic Boolean operations like negation or disjunction. Moving from graphs to named graphs leads to representations that stand some chance of having sufficient expressive power. Named $\mathcal{FL}_0$ graphs are of particular interest.",
}
@inproceedings{dligach-miller-2018-learning,
    title = "Learning Patient Representations from Text",
    author = "Dligach, Dmitriy  and
      Miller, Timothy",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2014",
    doi = "10.18653/v1/S18-2014",
    pages = "119--123",
    abstract = "Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping. Phenotyping has numerous applications such as outcome prediction, clinical trial recruitment, and retrospective studies. Supervised machine learning for phenotyping typically relies on sparse patient representations such as bag-of-words. We consider an alternative that involves learning patient representations. We develop a neural network model for learning patient representations and show that the learned representations are general enough to obtain state-of-the-art performance on a standard comorbidity detection task.",
}
@inproceedings{hu-moss-2018-polarity,
    title = "Polarity Computations in Flexible Categorial Grammar",
    author = "Hu, Hai  and
      Moss, Larry",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2015",
    doi = "10.18653/v1/S18-2015",
    pages = "124--129",
    abstract = "This paper shows how to take parse trees in CCG and algorithmically find the polarities of all the constituents. Our work uses the well-known polarization principle corresponding to function application, and we have extended this with principles for type raising and composition. We provide an algorithm, extending the polarity marking algorithm of van Benthem. We discuss how our system works in practice, taking input from the C{\&}C parser.",
}
@inproceedings{kallmeyer-etal-2018-coarse,
    title = "Coarse Lexical Frame Acquisition at the Syntax{--}Semantics Interface Using a Latent-Variable {PCFG} Model",
    author = "Kallmeyer, Laura  and
      QasemiZadeh, Behrang  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2016",
    doi = "10.18653/v1/S18-2016",
    pages = "130--141",
    abstract = "We present a method for unsupervised lexical frame acquisition at the syntax{--}semantics interface. Given a set of input strings derived from dependency parses, our method generates a set of clusters that resemble lexical frame structures. Our work is motivated not only by its practical applications (e.g., to build, or expand the coverage of lexical frame databases), but also to gain linguistic insight into frame structures with respect to lexical distributions in relation to grammatical structures. We model our task using a hierarchical Bayesian network and employ tools and methods from latent variable probabilistic context free grammars (L-PCFGs) for statistical inference and parameter fitting, for which we propose a new split and merge procedure. We show that our model outperforms several baselines on a portion of the Wall Street Journal sentences that we have newly annotated for evaluation purposes.",
}
@inproceedings{mei-etal-2018-halo,
    title = "{H}alo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction",
    author = "Mei, Hongyuan  and
      Zhang, Sheng  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2017",
    doi = "10.18653/v1/S18-2017",
    pages = "142--147",
    abstract = "Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called \textit{Halo}, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings.",
}
@inproceedings{ning-etal-2018-exploiting,
    title = "Exploiting Partially Annotated Data in Temporal Relation Extraction",
    author = "Ning, Qiang  and
      Yu, Zhongzhi  and
      Fan, Chuchu  and
      Roth, Dan",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2018",
    doi = "10.18653/v1/S18-2018",
    pages = "148--153",
    abstract = "Annotating temporal relations (TempRel) between events described in natural language is known to be labor intensive, partly because the total number of TempRels is quadratic in the number of events. As a result, only a small number of documents are typically annotated, limiting the coverage of various lexical/semantic phenomena. In order to improve existing approaches, one possibility is to make use of the readily available, partially annotated data (P as in partial) that cover more documents. However, missing annotations in P are known to hurt, rather than help, existing systems. This work is a case study in exploring various usages of P for TempRel extraction. Results show that despite missing annotations, P is still a useful supervision signal for this task within a constrained bootstrapping learning framework. The system described in this system is publicly available.",
}
@inproceedings{pierrejean-tanguy-2018-predicting,
    title = "Predicting Word Embeddings Variability",
    author = "Pierrejean, B{\'e}n{\'e}dicte  and
      Tanguy, Ludovic",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2019",
    doi = "10.18653/v1/S18-2019",
    pages = "154--159",
    abstract = "Neural word embeddings models (such as those built with word2vec) are known to have stability problems: when retraining a model with the exact same hyperparameters, words neighborhoods may change. We propose a method to estimate such variation, based on the overlap of neighbors of a given word in two models trained with identical hyperparameters. We show that this inherent variation is not negligible, and that it does not affect every word in the same way. We examine the influence of several features that are intrinsic to a word, corpus or embedding model and provide a methodology that can predict the variability (and as such, reliability) of a word representation in a semantic vector space.",
}
@inproceedings{vu-shwartz-2018-integrating,
    title = "Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment",
    author = "Vu, Tu  and
      Shwartz, Vered",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2020",
    doi = "10.18653/v1/S18-2020",
    pages = "160--166",
    abstract = "Supervised distributional methods are applied successfully in lexical entailment, but recent work questioned whether these methods actually learn a relation between two words. Specifically, Levy et al. (2015) claimed that linear classifiers learn only separate properties of each word. We suggest a cheap and easy way to boost the performance of these methods by integrating multiplicative features into commonly used representations. We provide an extensive evaluation with different classifiers and evaluation setups, and suggest a suitable evaluation setup for the task, eliminating biases existing in previous ones.",
}
@inproceedings{yadav-etal-2018-deep,
    title = "Deep Affix Features Improve Neural Named Entity Recognizers",
    author = "Yadav, Vikas  and
      Sharp, Rebecca  and
      Bethard, Steven",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2021",
    doi = "10.18653/v1/S18-2021",
    pages = "167--172",
    abstract = "We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word. We apply this approach to multilingual and multi-domain NER and show that it achieves state of the art results on the CoNLL 2002 Spanish and Dutch and CoNLL 2003 German NER datasets, consistently achieving 1.5-2.3 percent over the state of the art without relying on any dictionary features. Additionally, we show improvement on SemEval 2013 task 9.1 DrugNER, achieving state of the art results on the MedLine dataset and the second best results overall (-1.3{\%} from state of the art). We also establish a new benchmark on the I2B2 2010 Clinical NER dataset with 84.70 F-score.",
}
@inproceedings{zhang-etal-2018-fine,
    title = "Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds",
    author = "Zhang, Sheng  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2022",
    doi = "10.18653/v1/S18-2022",
    pages = "173--179",
    abstract = "Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context {--} both document and sentence level information {--} than prior work. We find that additional context improves performance, with further improvements gained by utilizing adaptive classification thresholds. Experiments show that our approach without reliance on hand-crafted features achieves the state-of-the-art results on three benchmark datasets.",
}
@inproceedings{poliak-etal-2018-hypothesis,
    title = "Hypothesis Only Baselines in Natural Language Inference",
    author = "Poliak, Adam  and
      Naradowsky, Jason  and
      Haldar, Aparajita  and
      Rudinger, Rachel  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2023",
    doi = "10.18653/v1/S18-2023",
    pages = "180--191",
    abstract = "We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.",
}
@inproceedings{sagarkar-etal-2018-quality,
    title = "Quality Signals in Generated Stories",
    author = "Sagarkar, Manasvi  and
      Wieting, John  and
      Tu, Lifu  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2024",
    doi = "10.18653/v1/S18-2024",
    pages = "192--202",
    abstract = "We study the problem of measuring the quality of automatically-generated stories. We focus on the setting in which a few sentences of a story are provided and the task is to generate the next sentence ({``}continuation{''}) in the story. We seek to identify what makes a story continuation interesting, relevant, and have high overall quality. We crowdsource annotations along these three criteria for the outputs of story continuation systems, design features, and train models to predict the annotations. Our trained scorer can be used as a rich feature function for story generation, a reward function for systems that use reinforcement learning to learn to generate stories, and as a partial evaluation metric for story generation.",
}
@inproceedings{yin-roth-2018-term,
    title = "Term Definitions Help Hypernymy Detection",
    author = "Yin, Wenpeng  and
      Roth, Dan",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2025",
    doi = "10.18653/v1/S18-2025",
    pages = "203--213",
    abstract = "Existing methods of hypernymy detection mainly rely on statistics over a big corpus, either mining some co-occurring patterns like {``}animals such as cats{''} or embedding words of interest into context-aware vectors. These approaches are therefore limited by the availability of a large enough corpus that can cover all terms of interest and provide sufficient contextual information to represent their meaning. In this work, we propose a new paradigm, HyperDef, for hypernymy detection {--} expressing word meaning by encoding word definitions, along with context driven representation. This has two main benefits: (i) Definitional sentences express (sense-specific) corpus-independent meanings of words, hence definition-driven approaches enable strong generalization {--} once trained, the model is expected to work well in open-domain testbeds; (ii) Global context from a large corpus and definitions provide complementary information for words. Consequently, our model, HyperDef, once trained on task-agnostic data, gets state-of-the-art results in multiple benchmarks",
}
@inproceedings{wojatzki-etal-2018-agree,
    title = "Agree or Disagree: Predicting Judgments on Nuanced Assertions",
    author = "Wojatzki, Michael  and
      Zesch, Torsten  and
      Mohammad, Saif  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2026",
    doi = "10.18653/v1/S18-2026",
    pages = "214--224",
    abstract = "Being able to predict whether people agree or disagree with an assertion (i.e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view. We formalize this as two NLP tasks: predicting judgments of (i) individuals and (ii) groups based on the text of the assertion and previous judgments. We evaluate a wide range of approaches on a crowdsourced data set containing over 100,000 judgments on over 2,000 assertions. We find that predicting individual judgments is a hard task with our best results only slightly exceeding a majority baseline, but that judgments of groups can be more reliably predicted using a Siamese neural network, which outperforms all other approaches by a wide margin.",
}
@inproceedings{mousselly-sergieh-etal-2018-multimodal,
    title = "A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning",
    author = "Mousselly-Sergieh, Hatem  and
      Botschen, Teresa  and
      Gurevych, Iryna  and
      Roth, Stefan",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2027",
    doi = "10.18653/v1/S18-2027",
    pages = "225--234",
    abstract = "Current methods for knowledge graph (KG) representation learning focus solely on the structure of the KG and do not exploit any kind of external information, such as visual and linguistic information corresponding to the KG entities. In this paper, we propose a multimodal translation-based approach that defines the energy of a KG triple as the sum of sub-energy functions that leverage both multimodal (visual and linguistic) and structural KG representations. Next, a ranking-based loss is minimized using a simple neural network architecture. Moreover, we introduce a new large-scale dataset for multimodal KG representation learning. We compared the performance of our approach to other baselines on two standard tasks, namely knowledge graph completion and triple classification, using our as well as the WN9-IMG dataset. The results demonstrate that our approach outperforms all baselines on both tasks and datasets.",
}
@inproceedings{allen-teng-2018-putting,
    title = "Putting Semantics into Semantic Roles",
    author = "Allen, James  and
      Teng, Choh Man",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2028",
    doi = "10.18653/v1/S18-2028",
    pages = "235--244",
    abstract = "While there have been many proposals for theories of semantic roles over the years, these models are mostly justified by intuition and the only evaluation methods have been inter-annotator agreement. We explore three different ideas for providing more rigorous theories of semantic roles. These ideas give rise to more objective criteria for designing role sets, and lend themselves to some experimental evaluation. We illustrate the discussion by examining the semantic roles in TRIPS.",
}
@inproceedings{basile-etal-2018-measuring,
    title = "Measuring Frame Instance Relatedness",
    author = "Basile, Valerio  and
      Lopez Condori, Roque  and
      Cabrio, Elena",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2029",
    doi = "10.18653/v1/S18-2029",
    pages = "245--254",
    abstract = "Frame semantics is a well-established framework to represent the meaning of natural language in computational terms. In this work, we aim to propose a quantitative measure of relatedness between pairs of frame instances. We test our method on a dataset of sentence pairs, highlighting the correlation between our metric and human judgments of semantic similarity. Furthermore, we propose an application of our measure for clustering frame instances to extract prototypical knowledge from natural language.",
}
@inproceedings{cui-etal-2018-solving,
    title = "Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition",
    author = "Cui, Xia  and
      Kojaku, Sadamori  and
      Masuda, Naoki  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2030",
    doi = "10.18653/v1/S18-2030",
    pages = "255--264",
    abstract = "Feature sparseness is a problem common to cross-domain and short-text classification tasks. To overcome this feature sparseness problem, we propose a novel method based on graph decomposition to find candidate features for expanding feature vectors. Specifically, we first create a feature-relatedness graph, which is subsequently decomposed into core-periphery (CP) pairs and use the peripheries as the expansion candidates of the cores. We expand both training and test instances using the computed related features and use them to train a text classifier. We observe that prioritising features that are common to both training and test instances as cores during the CP decomposition to further improve the accuracy of text classification. We evaluate the proposed CP-decomposition-based feature expansion method on benchmark datasets for cross-domain sentiment classification and short-text classification. Our experimental results show that the proposed method consistently outperforms all baselines on short-text classification tasks, and perform competitively with pivot-based cross-domain sentiment classification methods.",
}
@inproceedings{mahabal-etal-2018-robust,
    title = "Robust Handling of Polysemy via Sparse Representations",
    author = "Mahabal, Abhijit  and
      Roth, Dan  and
      Mittal, Sid",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2031",
    doi = "10.18653/v1/S18-2031",
    pages = "265--275",
    abstract = "Words are polysemous and multi-faceted, with many shades of meanings. We suggest that sparse distributed representations are more suitable than other, commonly used, (dense) representations to express these multiple facets, and present Category Builder, a working system that, as we show, makes use of sparse representations to support multi-faceted lexical representations. We argue that the set expansion task is well suited to study these meaning distinctions since a word may belong to multiple sets with a different reason for membership in each. We therefore exhibit the performance of Category Builder on this task, while showing that our representation captures at the same time analogy problems such as {``}the Ganga of Egypt{''} or {``}the Voldemort of Tolkien{''}. Category Builder is shown to be a more expressive lexical representation and to outperform dense representations such as Word2Vec in some analogy classes despite being shown only two of the three input terms.",
}
@inproceedings{tran-cheng-2018-multiplicative,
    title = "Multiplicative Tree-Structured Long Short-Term Memory Networks for Semantic Representations",
    author = "Tran, Nam Khanh  and
      Cheng, Weiwei",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-2032",
    doi = "10.18653/v1/S18-2032",
    pages = "276--286",
    abstract = "Tree-structured LSTMs have shown advantages in learning semantic representations by exploiting syntactic information. Most existing methods model tree structures by bottom-up combinations of constituent nodes using the same shared compositional function and often making use of input word information only. The inability to capture the richness of compositionality makes these models lack expressive power. In this paper, we propose multiplicative tree-structured LSTMs to tackle this problem. Our model makes use of not only word information but also relation information between words. It is more expressive, as different combination functions can be used for each child node. In addition to syntactic trees, we also investigate the use of Abstract Meaning Representation in tree-structured models, in order to incorporate both syntactic and semantic information from the sentence. Experimental results on common NLP tasks show the proposed models lead to better sentence representation and AMR brings benefits in complex tasks.",
}
@proceedings{semeval-2018-international,
    title = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1000",
    doi = "10.18653/v1/S18-1",
}
@inproceedings{mohammad-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 1: Affect in Tweets",
    author = "Mohammad, Saif  and
      Bravo-Marquez, Felipe  and
      Salameh, Mohammad  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1001",
    doi = "10.18653/v1/S18-1001",
    pages = "1--17",
    abstract = "We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.",
}
@inproceedings{duppada-etal-2018-seernet,
    title = "{S}eer{N}et at {S}em{E}val-2018 Task 1: Domain Adaptation for Affect in Tweets",
    author = "Duppada, Venkatesh  and
      Jain, Royal  and
      Hiray, Sushant",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1002",
    doi = "10.18653/v1/S18-1002",
    pages = "18--23",
    abstract = "The paper describes the best performing system for the SemEval-2018 Affect in Tweets(English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal classification valence is classified into 7 different classes ranging from -3 to 3 whereas emotion is classified into 4 different classes 0 to 3 separately for each emotion namely anger, fear, joy and sadness. The regression sub-tasks estimate the intensity of valence and each emotion. The system performs domain adaptation of 4 different models and creates an ensemble to give the final prediction. The proposed system achieved 1stposition out of 75 teams which participated in the fore-mentioned sub-tasks. We outperform the baseline model by margins ranging from 49.2{\%} to 76.4 {\%}, thus, pushing the state-of-the-art significantly.",
}
@inproceedings{barbieri-etal-2018-semeval,
    title = "{S}em{E}val 2018 Task 2: Multilingual Emoji Prediction",
    author = "Barbieri, Francesco  and
      Camacho-Collados, Jose  and
      Ronzano, Francesco  and
      Espinosa-Anke, Luis  and
      Ballesteros, Miguel  and
      Basile, Valerio  and
      Patti, Viviana  and
      Saggion, Horacio",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1003",
    doi = "10.18653/v1/S18-1003",
    pages = "24--33",
    abstract = "This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018. Given the text of a tweet, the task consists of predicting the most likely emoji to be used along such tweet. Two subtasks were proposed, one for English and one for Spanish, and participants were allowed to submit a system run to one or both subtasks. In total, 49 teams participated to the English subtask and 22 teams submitted a system run to the Spanish subtask. Evaluation was carried out emoji-wise, and the final ranking was based on macro F-Score. Data and further information about this task can be found at \url{https://competitions.codalab.org/competitions/17344}.",
}
@inproceedings{coltekin-rama-2018-tubingen,
    title = {{T}{\"u}bingen-{O}slo at {S}em{E}val-2018 Task 2: {SVM}s perform better than {RNN}s in Emoji Prediction},
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Rama, Taraka},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1004",
    doi = "10.18653/v1/S18-1004",
    pages = "34--38",
    abstract = "This paper describes our participation in the SemEval-2018 task Multilingual Emoji Prediction. We participated in both English and Spanish subtasks, experimenting with support vector machines (SVMs) and recurrent neural networks. Our SVM classifier obtained the top rank in both subtasks with macro-averaged F1-measures of 35.99{\%} for English and 22.36{\%} for Spanish data sets. Similar to a few earlier attempts, the results with neural networks were not on par with linear SVMs.",
}
@inproceedings{van-hee-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 3: Irony Detection in {E}nglish Tweets",
    author = "Van Hee, Cynthia  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1005",
    doi = "10.18653/v1/S18-1005",
    pages = "39--50",
    abstract = "This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. {\#}irony, {\#}sarcasm, {\#}not) and were subsequently manually annotated to minimise the amount of noise in the corpus. Prior to distributing the data, hashtags that were used to collect the tweets were removed from the corpus. For both tasks, a training corpus of 3,834 tweets was provided, as well as a test set containing 784 tweets. Our shared tasks received submissions from 43 teams for the binary classification Task A and from 31 teams for the multiclass Task B. The highest classification scores obtained for both subtasks are respectively F1= 0.71 and F1= 0.51 and demonstrate that fine-grained irony classification is much more challenging than binary irony detection.",
}
@inproceedings{wu-etal-2018-thu,
    title = "{THU}{\_}{NGN} at {S}em{E}val-2018 Task 3: Tweet Irony Detection with Densely connected {LSTM} and Multi-task Learning",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Wu, Sixing  and
      Liu, Junxin  and
      Yuan, Zhigang  and
      Huang, Yongfeng",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1006",
    doi = "10.18653/v1/S18-1006",
    pages = "51--56",
    abstract = "Detecting irony is an important task to mine fine-grained information from social web messages. Therefore, the Semeval-2018 task 3 is aimed to detect the ironic tweets (subtask A) and their ironic types (subtask B). In order to address this task, we propose a system based on a densely connected LSTM network with multi-task learning strategy. In our dense LSTM model, each layer will take all outputs from previous layers as input. The last LSTM layer will output the hidden representations of texts, and they will be used in three classification task. In addition, we incorporate several types of features to improve the model performance. Our model achieved an F-score of 70.54 (ranked 2/43) in the subtask A and 49.47 (ranked 3/29) in the subtask B. The experimental results validate the effectiveness of our system.",
}
@inproceedings{choi-chen-2018-semeval,
    title = "{S}em{E}val 2018 Task 4: Character Identification on Multiparty Dialogues",
    author = "Choi, Jinho D.  and
      Chen, Henry Y.",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1007",
    doi = "10.18653/v1/S18-1007",
    pages = "57--64",
    abstract = "Character identification is a task of entity linking that finds the global entity of each personal mention in multiparty dialogue. For this task, the first two seasons of the popular TV show Friends are annotated, comprising a total of 448 dialogues, 15,709 mentions, and 401 entities. The personal mentions are detected from nominals referring to certain characters in the show, and the entities are collected from the list of all characters in those two seasons of the show. This task is challenging because it requires the identification of characters that are mentioned but may not be active during the conversation. Among 90+ participants, four of them submitted their system outputs and showed strengths in different aspects about the task. Thorough analyses of the distributed datasets, system outputs, and comparative studies are also provided. To facilitate the momentum, we create an open-source project for this task and publicly release a larger and cleaner dataset, hoping to support researchers for more enhanced modeling.",
}
@inproceedings{aina-etal-2018-amore,
    title = "{AMORE}-{UPF} at {S}em{E}val-2018 Task 4: {B}i{LSTM} with Entity Library",
    author = "Aina, Laura  and
      Silberer, Carina  and
      Sorodoc, Ionut-Teodor  and
      Westera, Matthijs  and
      Boleda, Gemma",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1008",
    doi = "10.18653/v1/S18-1008",
    pages = "65--69",
    abstract = "This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues. It is a simple, standard model with one key innovation, an entity library. Our results show that this innovation greatly facilitates the identification of infrequent characters. Because of the generic nature of our model, this finding is potentially relevant to any task that requires the effective learning from sparse or imbalanced data.",
}
@inproceedings{postma-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 5: Counting Events and Participants in the Long Tail",
    author = "Postma, Marten  and
      Ilievski, Filip  and
      Vossen, Piek",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1009",
    doi = "10.18653/v1/S18-1009",
    pages = "70--80",
    abstract = "This paper discusses SemEval-2018 Task 5: a referential quantification task of counting events and participants in local, long-tail news documents with high ambiguity. The complexity of this task challenges systems to establish the meaning, reference and identity across documents. The task consists of three subtasks and spans across three domains. We detail the design of this referential quantification task, describe the participating systems, and present additional analysis to gain deeper insight into their performance.",
}
@inproceedings{mirza-etal-2018-koi,
    title = "{KOI} at {S}em{E}val-2018 Task 5: Building Knowledge Graph of Incidents",
    author = "Mirza, Paramita  and
      Darari, Fariz  and
      Mahendra, Rahmad",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1010",
    doi = "10.18653/v1/S18-1010",
    pages = "81--87",
    abstract = "We present KOI (Knowledge of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events. KOI-KG can then be used to efficiently answer questions such {``}How many killing incidents happened in 2017 that involve Sean?{''} The required steps in building the KG include: (i) document preprocessing involving word sense disambiguation, named-entity recognition, temporal expression recognition and normalization, and semantic role labeling; (ii) incidental event extraction and coreference resolution via document clustering; and (iii) KG construction and population.",
}
@inproceedings{laparra-etal-2018-semeval,
    title = "{S}em{E}val 2018 Task 6: Parsing Time Normalizations",
    author = "Laparra, Egoitz  and
      Xu, Dongfang  and
      Elsayed, Ahmed  and
      Bethard, Steven  and
      Palmer, Martha",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1011",
    doi = "10.18653/v1/S18-1011",
    pages = "88--96",
    abstract = "This paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018. The aim of the task is to parse time expressions into the compositional semantic graphs of the Semantically Compositional Annotation of Time Expressions (SCATE) schema, which allows the representation of a wider variety of time expressions than previous approaches. Two tracks were included, one to evaluate the parsing of individual components of the produced graphs, in a classic information extraction way, and another one to evaluate the quality of the time intervals resulting from the interpretation of those graphs. Though 40 participants registered for the task, only one team submitted output, achieving 0.55 F1 in Track 1 (parsing) and 0.70 F1 in Track 2 (intervals).",
}
@inproceedings{olex-etal-2018-chrono,
    title = "{C}hrono at {S}em{E}val-2018 Task 6: A System for Normalizing Temporal Expressions",
    author = "Olex, Amy  and
      Maffey, Luke  and
      Morgan, Nicholas  and
      McInnes, Bridget",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1012",
    doi = "10.18653/v1/S18-1012",
    pages = "97--101",
    abstract = "Temporal information extraction is a challenging task. Here we describe Chrono, a hybrid rule-based and machine learning system that identifies temporal expressions in text and normalizes them into the SCATE schema. After minor parsing logic adjustments, Chrono has emerged as the top performing system for SemEval 2018 Task 6: Parsing Time Normalizations.",
}
@inproceedings{dragoni-2018-neurosent,
    title = "{NEUROSENT}-{PDI} at {S}em{E}val-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text",
    author = "Dragoni, Mauro",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1013",
    doi = "10.18653/v1/S18-1013",
    pages = "102--108",
    abstract = "This paper describes the NeuroSent system that participated in SemEval 2018 Task 1. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.",
}
@inproceedings{karasalo-etal-2018-foi,
    title = "{FOI} {DSS} at {S}em{E}val-2018 Task 1: Combining {LSTM} States, Embeddings, and Lexical Features for Affect Analysis",
    author = "Karasalo, Maja  and
      Nilsson, Mattias  and
      Rosell, Magnus  and
      Wickenberg Bolin, Ulrika",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1014",
    doi = "10.18653/v1/S18-1014",
    pages = "109--115",
    abstract = "This paper describes the system used and results obtained for team FOI DSS at SemEval-2018 Task 1: Affect In Tweets. The team participated in all English language subtasks, with a method utilizing transfer learning from LSTM nets trained on large sentiment datasets combined with embeddings and lexical features. For four out of five subtasks, the system performed in the range of 92-95{\%} of the winning systems, in terms of the competition metrics. Analysis of the results suggests that improved pre-processing and addition of more lexical features may further elevate performance.",
}
@inproceedings{zhang-etal-2018-nlpzzx,
    title = "{NLPZZX} at {S}em{E}val-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination",
    author = "Zhang, Zhengxin  and
      Zhou, Qimin  and
      Wu, Hao",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1015",
    doi = "10.18653/v1/S18-1015",
    pages = "116--122",
    abstract = "In this paper, we put forward a system that competed at SemEval-2018 Task 1: {``}Affect in Tweets{''}. Our system uses a simple yet effective ensemble method which combines several neural network components. We participate in two subtasks for English tweets: EI-reg and V-reg. For two subtasks, different combinations of neural components are examined. For EI-reg, our system achieves an accuracy of 0.727 in Pearson Correlation Coefficient (all instances) and an accuracy of 0.555 in Pearson Correlation Coefficient (0.5-1). For V-reg, the achieved accuracy scores are respectively 0.835 and 0.670",
}
@inproceedings{de-bruyne-etal-2018-lt3,
    title = "{LT}3 at {S}em{E}val-2018 Task 1: A classifier chain to detect emotions in tweets",
    author = "De Bruyne, Luna  and
      De Clercq, Orph{\'e}e  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1016",
    doi = "10.18653/v1/S18-1016",
    pages = "123--127",
    abstract = "This paper presents an emotion classification system for English tweets, submitted for the SemEval shared task on Affect in Tweets, subtask 5: Detecting Emotions. The system combines lexicon, n-gram, style, syntactic and semantic features. For this multi-class multi-label problem, we created a classifier chain. This is an ensemble of eleven binary classifiers, one for each possible emotion category, where each model gets the predictions of the preceding models as additional features. The predicted labels are combined to get a multi-label representation of the predictions. Our system was ranked eleventh among thirty five participating teams, with a Jaccard accuracy of 52.0{\%} and macro- and micro-average F1-scores of 49.3{\%} and 64.0{\%}, respectively.",
}
@inproceedings{plaza-del-arco-etal-2018-sinai,
    title = "{SINAI} at {S}em{E}val-2018 Task 1: Emotion Recognition in Tweets",
    author = "Plaza-del-Arco, Flor Miriam  and
      Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Martin, Maite  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1017",
    doi = "10.18653/v1/S18-1017",
    pages = "128--132",
    abstract = "Emotion classification is a new task that combines several disciplines including Artificial Intelligence and Psychology, although Natural Language Processing is perhaps the most challenging area. In this paper, we describe our participation in SemEval-2018 Task1: Affect in Tweets. In particular, we have participated in EI-oc, EI-reg and E-c subtasks for English and Spanish languages.",
}
@inproceedings{priban-etal-2018-uwb,
    title = "{UWB} at {S}em{E}val-2018 Task 1: Emotion Intensity Detection in Tweets",
    author = "P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Hercig, Tom{\'a}{\v{s}}  and
      Lenc, Ladislav",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1018",
    doi = "10.18653/v1/S18-1018",
    pages = "133--140",
    abstract = "This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.",
}
@inproceedings{kim-etal-2018-attnconvnet,
    title = "{A}ttn{C}onvnet at {S}em{E}val-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification",
    author = "Kim, Yanghoon  and
      Lee, Hwanhee  and
      Jung, Kyomin",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1019",
    doi = "10.18653/v1/S18-1019",
    pages = "141--145",
    abstract = "In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human{'}s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.",
}
@inproceedings{graff-etal-2018-ingeotec,
    title = "{INGEOTEC} at {S}em{E}val-2018 Task 1: {E}vo{MSA} and μ{TC} for Sentiment Analysis",
    author = "Graff, Mario  and
      Miranda-Jim{\'e}nez, Sabino  and
      Tellez, Eric S.  and
      Moctezuma, Daniela",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1020",
    doi = "10.18653/v1/S18-1020",
    pages = "146--150",
    abstract = "This paper describes our participation in Affective Tweets task for emotional intensity and sentiment intensity subtasks for English, Spanish, and Arabic languages. We used two approaches, μTC and EvoMSA. The first one is a generic text categorization and regression system; and the second one, a two-stage architecture for Sentiment Analysis. Both approaches are multilingual and domain independent.",
}
@inproceedings{daval-frerot-etal-2018-epita,
    title = "Epita at {S}em{E}val-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach",
    author = "Daval-Frerot, Guillaume  and
      Bouchekif, Abdesselam  and
      Moreau, Anatole",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1021",
    doi = "10.18653/v1/S18-1021",
    pages = "151--155",
    abstract = "In this paper we present our system for detecting valence task. The major issue was to apply a state-of-the-art system despite the small dataset provided: the system would quickly overfit. The main idea of our proposal is to use transfer learning, which allows to avoid learning from scratch. Indeed, we start to train a first model to predict if a tweet is positive, negative or neutral. For this we use an external dataset which is larger and similar to the target dataset. Then, the pre-trained model is re-used as the starting point to train a new model that classifies a tweet into one of the seven various levels of sentiment intensity. Our system, trained using transfer learning, achieves 0.776 and 0.763 respectively for Pearson correlation coefficient and weighted quadratic kappa metrics on the subtask evaluation dataset.",
}
@inproceedings{aono-himeno-2018-kde,
    title = "{KDE}-{AFFECT} at {S}em{E}val-2018 Task 1: Estimation of Affects in Tweet by Using Convolutional Neural Network for n-gram",
    author = "Aono, Masaki  and
      Himeno, Shinnosuke",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1022",
    doi = "10.18653/v1/S18-1022",
    pages = "156--161",
    abstract = "This paper describes our approach to SemEval-2018 Task1: Estimation of Affects in Tweet for 1a and 2a. Our team KDE-AFFECT employs several methods including one-dimensional Convolutional Neural Network for $n$-grams, together with word embedding and other preprocessing such as vocabulary unification and Emoji conversion into four emotional words.",
}
@inproceedings{ezen-can-can-2018-rnn,
    title = "{RNN} for Affects at {S}em{E}val-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem",
    author = "Ezen-Can, Aysu  and
      Can, Ethem F.",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1023",
    doi = "10.18653/v1/S18-1023",
    pages = "162--166",
    abstract = "Written communication lacks the multimodal features such as posture, gesture and gaze that make it easy to model affective states. Especially in social media such as Twitter, due to the space constraints, the sources of information that can be mined are even more limited due to character limitations. These limitations constitute a challenge for understanding short social media posts. In this paper, we present an approach that utilizes multiple binary classifiers that represent different affective categories to model Twitter posts (e.g., tweets). We train domain-independent recurrent neural network models without any outside information such as affect lexicons. We then use these domain independent binary ranking models to evaluate the applicability of such deep learning models on the affect identification task. This approach allows different model architectures and parameter settings for each affect category instead of building one single multi-label classifier. The contributions of this paper are two-folds: we show that modeling tweets with a small training set is possible with the use of RNNs and we also prove that formulating affect identification as a binary classification task is highly effective.",
}
@inproceedings{mulki-etal-2018-tw,
    title = "Tw-{S}t{AR} at {S}em{E}val-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification",
    author = "Mulki, Hala  and
      Bechikh Ali, Chedi  and
      Haddad, Hatem  and
      Babao{\u{g}}lu, Ismail",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1024",
    doi = "10.18653/v1/S18-1024",
    pages = "167--171",
    abstract = "In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 {``}Affect in Tweets{''}, subtask E-c {``}Detecting Emotions (multi-label classification){''}. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets{'} features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.",
}
@inproceedings{kravchenko-pivovarova-2018-dl,
    title = "{DL} Team at {S}em{E}val-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings",
    author = "Kravchenko, Dmitry  and
      Pivovarova, Lidia",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1025",
    doi = "10.18653/v1/S18-1025",
    pages = "172--176",
    abstract = "The paper describes our approach for SemEval-2018 Task 1: Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter affect detection task to determine which features produce the most informative representation of a sentence. We demonstrate that general-purpose word embeddings produces more informative sentence representation than lexicon features. However, combining lexicon features with embeddings yields higher performance than embeddings alone.",
}
@inproceedings{turcu-etal-2018-emointens,
    title = "{E}mo{I}ntens Tracker at {S}em{E}val-2018 Task 1: Emotional Intensity Levels in {\#}Tweets",
    author = "Turcu, Ramona-Andreea  and
      Amarandei, Sandra Maria  and
      Flescan-Lovin-Arseni, Iuliana-Alexandra  and
      Gifu, Daniela  and
      Trandabat, Diana",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1026",
    doi = "10.18653/v1/S18-1026",
    pages = "177--180",
    abstract = "The „Affect in Tweets{''} task is centered on emotions categorization and evaluation matrix using multi-language tweets (English and Spanish). In this research, SemEval Affect dataset was preprocessed, categorized, and evaluated accordingly (precision, recall, and accuracy). The system described in this paper is based on the implementation of supervised machine learning (Naive Bayes, KNN and SVM), deep learning (NN Tensor Flow model), and decision trees algorithms.",
}
@inproceedings{husseini-orabi-etal-2018-uottawa,
    title = "u{O}ttawa at {S}em{E}val-2018 Task 1: Self-Attentive Hybrid {GRU}-Based Network",
    author = "Husseini Orabi, Ahmed  and
      Husseini Orabi, Mahmoud  and
      Inkpen, Diana  and
      Van Bruwaene, David",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1027",
    doi = "10.18653/v1/S18-1027",
    pages = "181--185",
    abstract = "We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task.",
}
@inproceedings{wu-etal-2018-thu-ngn,
    title = "{THU}{\_}{NGN} at {S}em{E}val-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention {CNN}-{LSTM}",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Liu, Junxin  and
      Yuan, Zhigang  and
      Wu, Sixing  and
      Huang, Yongfeng",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1028",
    doi = "10.18653/v1/S18-1028",
    pages = "186--192",
    abstract = "Traditional sentiment analysis approaches mainly focus on classifying the sentiment polarities or emotion categories of texts. However, they can{'}t exploit the sentiment intensity information. Therefore, the SemEval-2018 Task 1 is aimed to automatically determine the intensity of emotions or sentiment of tweets to mine fine-grained sentiment information. In order to address this task, we propose a system based on an attention CNN-LSTM model. In our model, LSTM is used to extract the long-term contextual information from texts. We apply attention techniques to selecting this information. A CNN layer with different size of kernels is used to extract local features. The dense layers take the pooled CNN feature maps and predict the intensity scores. Our system reaches average Pearson correlation score of 0.722 (ranked 12/48) in emotion intensity regression task, and 0.810 in valence regression task (ranked 15/38). It indicates that our system can be further extended.",
}
@inproceedings{jabreel-moreno-2018-eitaka,
    title = "{E}i{TAKA} at {S}em{E}val-2018 Task 1: An Ensemble of N-Channels {C}onv{N}et and {XG}boost Regressors for Emotion Analysis of Tweets",
    author = "Jabreel, Mohammed  and
      Moreno, Antonio",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1029",
    doi = "10.18653/v1/S18-1029",
    pages = "193--199",
    abstract = "This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost regressor based on a set of embedding and lexicons based features. Our system was evaluated on the testing sets of the tasks outperforming all other approaches for the Arabic version of valence intensity regression task and valence ordinal classification task.",
}
@inproceedings{ahmad-etal-2018-centement,
    title = "{CENTEMENT} at {S}em{E}val-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities",
    author = "Ahmad, Tariq  and
      Ramsay, Allan  and
      Ahmed, Hanady",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1030",
    doi = "10.18653/v1/S18-1030",
    pages = "200--204",
    abstract = "In this paper we present our contribution to SemEval-2018, a classifier for classifying multi-label emotions of Arabic and English tweets. We attempted {``}Affect in Tweets{''}, specifically Task E-c: Detecting Emotions (multi-label classification). Our method is based on preprocessing the tweets and creating word vectors combined with a self correction step to remove noise. We also make use of emotion specific thresholds. The final submission was selected upon the best performance achieved, selected when using a range of thresholds. Our system was evaluated on the Arabic and English datasets provided for the task by the competition organisers, where it ranked 2nd for the Arabic dataset (out of 14 entries) and 12th for the English dataset (out of 35 entries).",
}
@inproceedings{wang-zhou-2018-yuan,
    title = "Yuan at {S}em{E}val-2018 Task 1: Tweets Emotion Intensity Prediction using Ensemble Recurrent Neural Network",
    author = "Wang, Min  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1031",
    doi = "10.18653/v1/S18-1031",
    pages = "205--209",
    abstract = "We perform the LSTM and BiLSTM model for the emotion intensity prediction. We only join the third subtask in Task 1:Affect in Tweets. Our system rank 6th among all the teams.",
}
@inproceedings{abdou-etal-2018-affecthor,
    title = "{A}ffec{T}hor at {S}em{E}val-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets",
    author = "Abdou, Mostafa  and
      Kulmizev, Artur  and
      Gin{\'e}s i Ametll{\'e}, Joan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1032",
    doi = "10.18653/v1/S18-1032",
    pages = "210--217",
    abstract = "In this paper we describe our submission to SemEval-2018 Task 1: Affects in Tweets. The model which we present is an ensemble of various neural architectures and gradient boosted trees, and employs three different types of vectorial tweet representations. Furthermore, our system is language-independent and ranked first in 5 out of the 12 subtasks in which we participated, while achieving competitive results in the remaining ones. Comparatively remarkable performance is observed on both the Arabic and Spanish languages.",
}
@inproceedings{rozental-fleischer-2018-amobee,
    title = "{A}mobee at {S}em{E}val-2018 Task 1: {GRU} Neural Network with a {CNN} Attention Mechanism for Sentiment Classification",
    author = "Rozental, Alon  and
      Fleischer, Daniel",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1033",
    doi = "10.18653/v1/S18-1033",
    pages = "218--225",
    abstract = "This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks. Our system consists of three parts: training task-specific word embeddings, training a model consisting of gated-recurrent-units (GRU) with a convolution neural network (CNN) attention mechanism and training stacking-based ensembles for each of the sub-tasks. Our algorithm reached the 3rd and 1st places in the valence ordinal classification sub-tasks in English and Spanish, respectively.",
}
@inproceedings{gao-chen-2018-deepsa2018,
    title = "deep{SA}2018 at {S}em{E}val-2018 Task 1: Multi-task Learning of Different Label for Affect in Tweets",
    author = "Gao, Zi-Yuan  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1034",
    doi = "10.18653/v1/S18-1034",
    pages = "226--230",
    abstract = "This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are five classification models in the proposed multi-task learning approach. These classification models are trained sequentially to learn different features for different classification tasks. In addition to the data released for SemEval-2018, we use datasets from previous SemEvals during system construction. Our Pearson correlation score is 0.638 on the official SemEval-2018 Task 1 test set.",
}
@inproceedings{xu-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models",
    author = "Xu, Huimin  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1035",
    doi = "10.18653/v1/S18-1035",
    pages = "231--235",
    abstract = "This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.",
}
@inproceedings{badaro-etal-2018-ema,
    title = "{EMA} at {S}em{E}val-2018 Task 1: Emotion Mining for {A}rabic",
    author = "Badaro, Gilbert  and
      El Jundi, Obeida  and
      Khaddaj, Alaa  and
      Maarouf, Alaa  and
      Kain, Raslan  and
      Hajj, Hazem  and
      El-Hajj, Wassim",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1036",
    doi = "10.18653/v1/S18-1036",
    pages = "236--244",
    abstract = "While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks. For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming. Moreover, several features were evaluated along with different classification and regression techniques. For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique. EMA achieved the 1st place in subtask 5, and 3rd place in subtasks 1 and 3.",
}
@inproceedings{baziotis-etal-2018-ntua,
    title = "{NTUA}-{SLP} at {S}em{E}val-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive {RNN}s and Transfer Learning",
    author = "Baziotis, Christos  and
      Nikolaos, Athanasiou  and
      Chronopoulou, Alexandra  and
      Kolovou, Athanasia  and
      Paraskevopoulos, Georgios  and
      Ellinas, Nikolaos  and
      Narayanan, Shrikanth  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1037",
    doi = "10.18653/v1/S18-1037",
    pages = "245--255",
    abstract = "In this paper we present deep-learning models that submitted to the SemEval-2018 Task 1 competition: {``}Affect in Tweets{''}. We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a multi-layer self attention mechanism. The attention mechanism improves the model performance and allows us to identify salient words in tweets, as well as gain insight into the models making them more interpretable. Our model utilizes a set of word2vec word embeddings trained on a large collection of 550 million Twitter messages, augmented by a set of word affective features. Due to the limited amount of task-specific training data, we opted for a transfer learning approach by pretraining the Bi-LSTMs on the dataset of Semeval 2017, Task 4A. The proposed approach ranked 1st in Subtask E {``}Multi-Label Emotion Classification{''}, 2nd in Subtask A {``}Emotion Intensity Regression{''} and achieved competitive results in other subtasks.",
}
@inproceedings{gupta-yang-2018-crystalfeel,
    title = "{C}rystal{F}eel at {S}em{E}val-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons",
    author = "Gupta, Raj Kumar  and
      Yang, Yinping",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1038",
    doi = "10.18653/v1/S18-1038",
    pages = "256--263",
    abstract = "While sentiment and emotion analysis has received a considerable amount of research attention, the notion of understanding and detecting the intensity of emotions is relatively less explored. This paper describes a system developed for predicting emotion intensity in tweets. Given a Twitter message, CrystalFeel uses features derived from parts-of-speech, n-grams, word embedding, and multiple affective lexicons including Opinion Lexicon, SentiStrength, AFFIN, NRC Emotion {\&} Hash Emotion, and our in-house developed EI Lexicons to predict the degree of the intensity associated with fear, anger, sadness, and joy in the tweet. We found that including the affective lexicons-based features allowed the system to obtain strong prediction performance, while revealing interesting emotion word-level and message-level associations. On gold test data, CrystalFeel obtained Pearson correlations of 0.717 on average emotion intensity and of 0.816 on sentiment intensity.",
}
@inproceedings{park-etal-2018-plusemo2vec,
    title = "{P}lus{E}mo2{V}ec at {S}em{E}val-2018 Task 1: Exploiting emotion knowledge from emoji and {\#}hashtags",
    author = "Park, Ji Ho  and
      Xu, Peng  and
      Fung, Pascale",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1039",
    doi = "10.18653/v1/S18-1039",
    pages = "264--272",
    abstract = "This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both sentence and word level representations of emotion inside texts through large distantly labeled corpora with emojis and hashtags. We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR) and logistic regression to solve the competition tasks. Our system is placed among the Top3 for all subtasks we participated.",
}
@inproceedings{zhang-etal-2018-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2018 Task 1: {B}i{LSTM} with Attention based Sentiment Analysis for Affect in Tweets",
    author = "Zhang, You  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1040",
    doi = "10.18653/v1/S18-1040",
    pages = "273--278",
    abstract = "We implemented the sentiment system in all five subtasks for English and Spanish. All subtasks involve emotion or sentiment intensity prediction (regression and ordinal classification) and emotions determining (multi-labels classification). The useful BiLSTM (Bidirectional Long-Short Term Memory) model with attention mechanism was mainly applied for our system. We use BiLSTM in order to get word information extracted from both directions. The attention mechanism was used to find the contribution of each word for improving the scores. Furthermore, based on BiLSTMATT (BiLSTM with attention mechanism) a few deep-learning algorithms were employed for different subtasks. For regression and ordinal classification tasks we used domain adaptation and ensemble learning methods to leverage base model. While a single base model was used for multi-labels task.",
}
@inproceedings{kuijper-etal-2018-ug18,
    title = "{UG}18 at {S}em{E}val-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in {S}panish",
    author = "Kuijper, Marloes  and
      van Lenthe, Mike  and
      van Noord, Rik",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1041",
    doi = "10.18653/v1/S18-1041",
    pages = "279--285",
    abstract = "The present study describes our submission to SemEval 2018 Task 1: Affect in Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to automatically generate additional training data by (i) translating training data from other languages and (ii) applying a semi-supervised learning method. We find strong support for both approaches, with those models outperforming our regular models in all subtasks. However, creating a stepwise ensemble of different models as opposed to simply averaging did not result in an increase in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc) in the four Spanish subtasks we participated in.",
}
@inproceedings{li-etal-2018-isclab,
    title = "{ISCLAB} at {S}em{E}val-2018 Task 1: {UIR}-Miner for Affect in Tweets",
    author = "Li, Meng  and
      Dong, Zhenyuan  and
      Fan, Zhihao  and
      Meng, Kongming  and
      Cao, Jinghua  and
      Ding, Guanqi  and
      Liu, Yuhan  and
      Shan, Jiawei  and
      Li, Binyang",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1042",
    doi = "10.18653/v1/S18-1042",
    pages = "286--290",
    abstract = "This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity prediction of emotion and sentiment, LSTM network module to solve multi-label classification, and the hierarchical attention network module for solving emotion and sentiment classification problem. According to the metrics of SemEval 2018, our system gets the final scores of 0.636, 0.531, 0.731, 0.708, and 0.408 on 5 subtasks, respectively.",
}
@inproceedings{meisheri-dey-2018-tcs,
    title = "{TCS} Research at {S}em{E}val-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture",
    author = "Meisheri, Hardik  and
      Dey, Lipika",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1043",
    doi = "10.18653/v1/S18-1043",
    pages = "291--299",
    abstract = "This paper presents system description of our submission to the SemEval-2018 task-1: Affect in tweets for the English language. We combine three different features generated using deep learning models and traditional methods in support vector machines to create a unified ensemble system. A robust representation of a tweet is learned using a multi-attention based architecture which uses a mixture of different pre-trained embeddings. In addition to this analysis of different features is also presented. Our system ranked 2nd, 5th, and 7th in different subtasks among 75 teams.",
}
@inproceedings{kim-lee-2018-dmcb,
    title = "{DMCB} at {S}em{E}val-2018 Task 1: Transfer Learning of Sentiment Classification Using Group {LSTM} for Emotion Intensity prediction",
    author = "Kim, Youngmin  and
      Lee, Hyunju",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1044",
    doi = "10.18653/v1/S18-1044",
    pages = "300--304",
    abstract = "This paper describes a system attended in the SemEval-2018 Task 1 {``}Affect in tweets{''} that predicts emotional intensities. We use Group LSTM with an attention model and transfer learning with sentiment classification data as a source data (SemEval 2017 Task 4a). A transfer model structure consists of a source domain and a target domain. Additionally, we try a new dropout that is applied to LSTMs in the Group LSTM. Our system ranked 8th at the subtask 1a (emotion intensity regression). We also show various results with different architectures in the source, target and transfer models.",
}
@inproceedings{naderi-etal-2018-deepminer,
    title = "{D}eep{M}iner at {S}em{E}val-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning",
    author = "Naderi, Habibeh  and
      Haji Soleimani, Behrouz  and
      Mohammad, Saif  and
      Kiritchenko, Svetlana  and
      Matwin, Stan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1045",
    doi = "10.18653/v1/S18-1045",
    pages = "305--312",
    abstract = "In this paper, we propose a regression system to infer the emotion intensity of a tweet. We develop a multi-aspect feature learning mechanism to capture the most discriminative semantic features of a tweet as well as the emotion information conveyed by each word in it. We combine six types of feature groups: (1) a tweet representation learned by an LSTM deep neural network on the training data, (2) a tweet representation learned by an LSTM network on a large corpus of tweets that contain emotion words (a distant supervision corpus), (3) word embeddings trained on the distant supervision corpus and averaged over all words in a tweet, (4) word and character n-grams, (5) features derived from various sentiment and emotion lexicons, and (6) other hand-crafted features. As part of the word embedding training, we also learn the distributed representations of multi-word expressions (MWEs) and negated forms of words. An SVR regressor is then trained over the full set of features. We evaluate the effectiveness of our ensemble feature sets on the SemEval-2018 Task 1 datasets and achieve a Pearson correlation of 72{\%} on the task of tweet emotion intensity prediction.",
}
@inproceedings{chi-etal-2018-zewen,
    title = "Zewen at {S}em{E}val-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets",
    author = "Chi, Zewen  and
      Huang, Heyan  and
      Chen, Jiangui  and
      Wu, Hao  and
      Wei, Ran",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1046",
    doi = "10.18653/v1/S18-1046",
    pages = "313--318",
    abstract = "This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emo-tions need to be quantified into a real valued score in [0, 1]. We propose an en-semble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTM-CNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 17th among 48 systems in EI-reg and 19th among 38 systems in V-reg.",
}
@inproceedings{unnithan-etal-2018-amrita,
    title = "Amrita{\_}student at {S}em{E}val-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets",
    author = "Unnithan, Nidhin A  and
      K., Shalini  and
      Ganesh H. B., Barathi  and
      Kumar M, Anand  and
      K. P., Soman",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1047",
    doi = "10.18653/v1/S18-1047",
    pages = "319--323",
    abstract = "In this paper we did an analysis of {``}Affects in Tweets{''} which was one of the task conducted by semeval 2018. Task was to build a model which is able to do regression and classification of different emotions from the given tweets data set. We developed a base model for all the subtasks using distributed representation (Doc2Vec) and applied machine learning techniques for classification and regression. Distributed representation is an unsupervised algorithm which is capable of learning fixed length feature representation from variable length texts. Machine learning techniques used for regression is {'}Linear Regression{'} while {'}Random Forest Tree{'} is used for classification purpose. Empirical results obtained for all the subtasks by our model are shown in this paper.",
}
@inproceedings{s-etal-2018-ssn,
    title = "{SSN} {MLRG}1 at {S}em{E}val-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection",
    author = "S, Angel Deborah  and
      S, Rajalakshmi  and
      Rajendram, S Milton  and
      T T, Mirnalinee",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1048",
    doi = "10.18653/v1/S18-1048",
    pages = "324--328",
    abstract = "The system developed by the SSN MLRG1 team for Semeval-2018 task 1 on affect in tweets uses rule based feature selection and one-hot encoding to generate the input feature vector. Multilayer Perceptron was used to build the model for emotion intensity ordinal classification, sentiment analysis ordinal classification and emotion classfication subtasks. Support Vector Machine was used to build the model for emotion intensity regression and sentiment intensity regression subtasks.",
}
@inproceedings{j-r-etal-2018-cennlp,
    title = "{CENNLP} at {S}em{E}val-2018 Task 1: Constrained Vector Space Model in Affects in Tweets",
    author = "J R, Naveen  and
      Ganesh H. B., Barathi  and
      Kumar M, Anand  and
      K P, Soman",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1049",
    doi = "10.18653/v1/S18-1049",
    pages = "329--333",
    abstract = "This paper discusses on task 1, {``}Affect in Tweets{''} sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based on the provided tweet data and also measure the intensity of these emotions for subsequent subtasks. Our approach in these task was to come up with a model on count based representation and use machine learning techniques for regression and classification related tasks. In this work, we use a simple bag of words technique for supervised text classification model as to compare, that even with some advance distributed representation models we can still achieve significant accuracy. Further, fine tuning on various parameters for the bag of word, representation model we acquired better scores over various other baseline models (Vinayan et al.) participated in the sharedtask.",
}
@inproceedings{george-etal-2018-teamcen,
    title = "{T}eam{CEN} at {S}em{E}val-2018 Task 1: Global Vectors Representation in Emotion Detection",
    author = "George, Anon  and
      Ganesh H. B., Barathi  and
      Kumar M, Anand  and
      K P, Soman",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1050",
    doi = "10.18653/v1/S18-1050",
    pages = "334--338",
    abstract = "Emotions are a way of expressing human sentiments. In the modern era, social media is a platform where we convey our emotions. These emotions can be joy, anger, sadness and fear. Understanding the emotions from the written sentences is an interesting part in knowing about the writer. In the amount of digital language shared through social media, a considerable amount of data reflects the sentiment or emotion towards some product, person and organization. Since these texts are from users with diverse social aspects, these texts can be used to enrich the application related to the business intelligence. More than the sentiment, identification of intensity of the sentiment will enrich the performance of the end application. In this paper we experimented the intensity prediction as a text classification problem that evaluates the distributed representation text using aggregated sum and dimensionality reduction of the glove vectors of the words present in the respective texts .",
}
@inproceedings{kotakonda-etal-2018-iit,
    title = "{IIT} {D}elhi at {S}em{E}val-2018 Task 1 : Emotion Intensity Prediction",
    author = "Kotakonda, Bhaskar  and
      Gowda, Prashanth  and
      Lall, Brejesh",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1051",
    doi = "10.18653/v1/S18-1051",
    pages = "339--344",
    abstract = "This paper discusses the experiments performed for predicting the emotion intensity in tweets using a generalized supervised learning approach. We extract 3 kind of features from each of the tweets - one denoting the sentiment and emotion metrics obtained from different sentiment lexicons, one denoting the semantic representation of the word using dense representations like Glove, Word2vec and finally the syntactic information through POS N-grams, Word clusters, etc. We provide a comparative analysis of the significance of each of these features individually and in combination tested over standard regressors avaliable in scikit-learn. We apply an ensemble of these models to choose the best combination over cross validation.",
}
@inproceedings{du-nie-2018-mutux,
    title = "Mutux at {S}em{E}val-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection",
    author = "Du, Pan  and
      Nie, Jian-Yun",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1052",
    doi = "10.18653/v1/S18-1052",
    pages = "345--349",
    abstract = "This paper describes MuTuX, our system that is designed for task 1-5a, emotion classification analysis of tweets on SemEval2018. The system aims at exploring the potential of context information of terms for emotion analysis. A Recurrent Neural Network is adopted to capture the context information of terms in tweets. Only term features and the sequential relations are used in our system. The results submitted ranks 16th out of 35 systems on the task of emotion detection in English-language tweets.",
}
@inproceedings{abdullah-shaikh-2018-teamuncc,
    title = "{T}eam{UNCC} at {S}em{E}val-2018 Task 1: Emotion Detection in {E}nglish and {A}rabic Tweets using Deep Learning",
    author = "Abdullah, Malak  and
      Shaikh, Samira",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1053",
    doi = "10.18653/v1/S18-1053",
    pages = "350--357",
    abstract = "Task 1 in the International Workshop SemEval 2018, Affect in Tweets, introduces five subtasks (El-reg, El-oc, V-reg, V-oc, and E-c) to detect the intensity of emotions in English, Arabic, and Spanish tweets. This paper describes TeamUNCC{'}s system to detect emotions in English and Arabic tweets. Our approach is novel in that we present the same architecture for all the five subtasks in both English and Arabic. The main input to the system is a combination of word2vec and doc2vec embeddings and a set of psycholinguistic features (e.g. from AffectTweets Weka-package). We apply a fully connected neural network architecture and obtain performance results that show substantial improvements in Spearman correlation scores over the baseline models provided by Task 1 organizers, (ranging from 0.03 to 0.23). TeamUNCC{'}s system ranks third in subtask El-oc and fourth in other subtasks for Arabic tweets.",
}
@inproceedings{elango-uppal-2018-riddl,
    title = "{RIDDL} at {S}em{E}val-2018 Task 1: Rage Intensity Detection with Deep Learning",
    author = "Elango, Venkatesh  and
      Uppal, Karan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1054",
    doi = "10.18653/v1/S18-1054",
    pages = "358--363",
    abstract = "We present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity of sentiment, and valence for tweets. For modeling, though we use a traditional LSTM network, we combine our model with several state-of-the-art techniques to improve its performance in a low-resource setting. For example, we use an encoder-decoder network to initialize the LSTM weights. Without any task specific optimization we achieve competitive results (macro-average Pearson correlation coefficient 0.696) in the El-reg task. In this paper, we describe our development strategy in detail along with an exposition of our results.",
}
@inproceedings{nagoudi-2018-arb,
    title = "{ARB}-{SEN} at {S}em{E}val-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in {A}rabic Tweets",
    author = "Nagoudi, El Moatez Billah",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1055",
    doi = "10.18653/v1/S18-1055",
    pages = "364--368",
    abstract = "This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN. This system is designed for the International Workshop on Semantic Evaluation 2018 (SemEval-2018), Task1: Affect in Tweets. ARB-SEN proposes two supervised models to estimate the sentiment intensity in Arabic tweets. Both models use a set of features including sentiment lexicon, negation, word embedding and emotion symbols features. Our system combines these features to assist the sentiment analysis task. ARB-SEN system achieves a correlation score of 0.720, ranking 6th among all participants in the valence intensity regression (V-reg) for the Arabic sub-task organized within the SemEval 2018 evaluation campaign.",
}
@inproceedings{gee-wang-2018-psyml,
    title = "psy{ML} at {S}em{E}val-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis",
    author = "Gee, Grace  and
      Wang, Eugene",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1056",
    doi = "10.18653/v1/S18-1056",
    pages = "369--376",
    abstract = "In this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM with attention mechanism. We perform transfer learning by first pre-training the LSTM networks on sentiment data before concatenating the penultimate layers of these networks into a single vector as input to new dense layers. For the E-c subtask, we utilize a novel approach to train models for correlated emotion classes. Our system performs 4/48, 3/39, 8/38, 4/37, 4/35 on all English subtasks EI-reg, EI-oc, V-reg, V-oc, E-c of SemEval 2018 Task 1: Affect in Tweets.",
}
@inproceedings{narwekar-girju-2018-uiuc,
    title = "{UIUC} at {S}em{E}val-2018 Task 1: Recognizing Affect with Ensemble Models",
    author = "Narwekar, Abhishek Avinash  and
      Girju, Roxana",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1057",
    doi = "10.18653/v1/S18-1057",
    pages = "377--384",
    abstract = "Our submission to the SemEval-2018 Task1: Affect in Tweets shared task competition is a supervised learning model relying on standard lexicon features coupled with word embedding features. We used an ensemble of diverse models, including random forests, gradient boosted trees, and linear models, corrected for training-development set mismatch. We submitted the system{'}s output for subtasks 1 (emotion intensity prediction), 2 (emotion ordinal classification), 3 (valence intensity regression) and 4 (valence ordinal classification), for English tweets. We placed 25th, 19th, 24th and 15th in the four subtasks respectively. The baseline considered was an SVM (Support Vector Machines) model with linear kernel on the lexicon and embedding based features. Our system{'}s final performance measured in Pearson correlation scores outperformed the baseline by a margin of 2.2{\%} to 14.6{\%} across all tasks.",
}
@inproceedings{nyegaard-signori-etal-2018-ku,
    title = "{KU}-{MTL} at {S}em{E}val-2018 Task 1: Multi-task Identification of Affect in Tweets",
    author = "Nyegaard-Signori, Thomas  and
      Helms, Casper Veistrup  and
      Bjerva, Johannes  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1058",
    doi = "10.18653/v1/S18-1058",
    pages = "385--389",
    abstract = "We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464.",
}
@inproceedings{liu-2018-emonlp,
    title = "{E}mo{NLP} at {S}em{E}val-2018 Task 2: {E}nglish Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional {LSTM}",
    author = "Liu, Man",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1059",
    doi = "10.18653/v1/S18-1059",
    pages = "390--394",
    abstract = "This paper describes our system used in the English Emoji Prediction Task 2 at the SemEval-2018. Our system is based on two supervised machine learning algorithms: Gradient Boosting Regression Tree Method (GBM) and Bidirectional Long Short-term Memory Network (BLSTM). Besides the common features, we extract various lexicon and syntactic features from external resources. After comparing the results of two algorithms, GBM is chosen for the final evaluation.",
}
@inproceedings{wang-pedersen-2018-umdsub,
    title = "{UMDS}ub at {S}em{E}val-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding",
    author = "Wang, Zhenduo  and
      Pedersen, Ted",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1060",
    doi = "10.18653/v1/S18-1060",
    pages = "395--399",
    abstract = "This paper describes the UMDSub system that participated in Task 2 of SemEval-2018. We developed a system that predicts an emoji given the raw text in a English tweet. The system is a Multi-channel Convolutional Neural Network based on subword embeddings for the representation of tweets. This model improves on character or word based methods by about 2{\%}. Our system placed 21st of 48 participating systems in the official evaluation.",
}
@inproceedings{beaulieu-asamoah-owusu-2018-umduluth,
    title = "{UMD}uluth-{CS}8761 at {S}em{E}val-2018 Task 2: Emojis: Too many Choices?",
    author = "Beaulieu, Jonathan  and
      Asamoah Owusu, Dennis",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1061",
    doi = "10.18653/v1/S18-1061",
    pages = "400--404",
    abstract = "In this paper, we present our system for assigning an emoji to a tweet based on the text. Each tweet was originally posted with an emoji which the task providers removed. Our task was to decide out of 20 emojis, which originally came with the tweet. Two datasets were provided - one in English and the other in Spanish. We treated the task as a standard classification task with the emojis as our classes and the tweets as our documents. Our best performing system used a Bag of Words model with a Linear Support Vector Machine as its{'} classifier. We achieved a macro F1 score of 32.73{\%} for the English data and 17.98{\%} for the Spanish data.",
}
@inproceedings{alexa-etal-2018-dabblers,
    title = "The Dabblers at {S}em{E}val-2018 Task 2: Multilingual Emoji Prediction",
    author = "Alexa, Larisa  and
      Loren{\textcommabelow{t}}, Alina  and
      G{\^\i}fu, Daniela  and
      Trandab{\u{a}}{\textcommabelow{t}}, Diana",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1062",
    doi = "10.18653/v1/S18-1062",
    pages = "405--409",
    abstract = "The {``}Multilingual Emoji Prediction{''} task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).",
}
@inproceedings{wu-etal-2018-thu-ngn-semeval,
    title = "{THU}{\_}{NGN} at {S}em{E}val-2018 Task 2: Residual {CNN}-{LSTM} Network with Attention for {E}nglish Emoji Prediction",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Wu, Sixing  and
      Yuan, Zhigang  and
      Liu, Junxin  and
      Huang, Yongfeng",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1063",
    doi = "10.18653/v1/S18-1063",
    pages = "410--414",
    abstract = "Emojis are widely used by social media and social network users when posting their messages. It is important to study the relationships between messages and emojis. Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i.e., predicting which emojis are evoked by text-based tweets. We propose a residual CNN-LSTM with attention (\textbf{RCLA}) model for this task. Our model combines CNN and LSTM layers to capture both local and long-range contextual information for tweet representation. In addition, attention mechanism is used to select important components. Besides, residual connection is applied to CNN layers to facilitate the training of neural networks. We also incorporated additional features such as POS tags and sentiment features extracted from lexicons. Our model achieved 30.25{\%} macro-averaged F-score in the first subtask (i.e., emoji prediction in English), ranking 7th out of 48 participants.",
}
@inproceedings{ribeiro-silva-2018-teaminf,
    title = "{\#}{T}eam{INF} at {S}em{E}val-2018 Task 2: Emoji Prediction in Tweets",
    author = "Ribeiro, Alison  and
      Silva, N{\'a}dia",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1064",
    doi = "10.18653/v1/S18-1064",
    pages = "415--418",
    abstract = "In this paper, we describe a methodology to predict emoji in tweets. Our approach is based on the classic bag-of-words model in conjunction with word embeddings. The used classification algorithm was Logistic Regression. This architecture was used and evaluated in the context of the SemEval 2018 challenge (task 2, subtask 1).",
}
@inproceedings{xie-song-2018-eica,
    title = "{EICA} Team at {S}em{E}val-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction",
    author = "Xie, Yufei  and
      Song, Qingqing",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1065",
    doi = "10.18653/v1/S18-1065",
    pages = "419--422",
    abstract = "The advent of social media has brought along a novel way of communication where meaning is composed by combining short text messages and visual enhancements, the so-called emojis. We describe our system for participating in SemEval-2018 Task 2 on Multilingual Emoji Prediction. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important types turned out to be the metadata feature. In subtask 1: Emoji Prediction in English, our primary submission obtain a MAP of 16.45, Precision of 31.557, Recall of 16.771 and Accuracy of 30.992.",
}
@inproceedings{chen-etal-2018-emojiit,
    title = "{E}moji{I}t at {S}em{E}val-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words",
    author = "Chen, Shiyun  and
      Wang, Maoquan  and
      He, Liang",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1066",
    doi = "10.18653/v1/S18-1066",
    pages = "423--427",
    abstract = "This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurrent neural network which has achieved satisfactory performs in Natural Language processing. Considering the text comes from social media, it contains many discrepant abbreviations and online terms, we also combine word-level and character-level word vector embedding to better handling the words not appear in the vocabulary. Our single model1 achieved 29.50{\%} Macro F-score in test data and ranks 9th among 48 teams.",
}
@inproceedings{chen-etal-2018-peperomia,
    title = "Peperomia at {S}em{E}val-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction",
    author = "Chen, Jing  and
      Yang, Dechuan  and
      Li, Xilian  and
      Chen, Wei  and
      Wang, Tengjiao",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1067",
    doi = "10.18653/v1/S18-1067",
    pages = "428--432",
    abstract = "This paper describes our participation in SemEval 2018 Task 2: Multilingual Emoji Prediction, in which participants are asked to predict a tweet{'}s most associated emoji from 20 emojis. Instead of regarding it as a 20-class classification problem we regard it as a text similarity problem. We propose a vector similarity based approach for this task. First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji{'}s embedding is evaluated. The most similar emoji is chosen as the predicted label. Experimental results show that our approach performs comparably with the classification approach and shows its advantage in classifying emojis with similar semantic meaning.",
}
@inproceedings{lu-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 2: Leverage Traditional {NLP} Features and Neural Networks Methods to Address {T}witter Emoji Prediction Task",
    author = "Lu, Xingwu  and
      Mao, Xin  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1068",
    doi = "10.18653/v1/S18-1068",
    pages = "433--437",
    abstract = "This paper describes our submissions to Task 2 in SemEval 2018, i.e., Multilingual Emoji Prediction. We first investigate several traditional Natural Language Processing (NLP) features, and then design several deep learning models. For subtask 1: Emoji Prediction in English, we combine two different methods to represent tweet, i.e., supervised model using traditional features and deep learning model. For subtask 2: Emoji Prediction in Spanish, we only use deep learning model.",
}
@inproceedings{baziotis-etal-2018-ntua-slp,
    title = "{NTUA}-{SLP} at {S}em{E}val-2018 Task 2: Predicting Emojis using {RNN}s with Context-aware Attention",
    author = "Baziotis, Christos  and
      Nikolaos, Athanasiou  and
      Kolovou, Athanasia  and
      Paraskevopoulos, Georgios  and
      Ellinas, Nikolaos  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1069",
    doi = "10.18653/v1/S18-1069",
    pages = "438--444",
    abstract = "In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 {``}Multilingual Emoji Prediction{''}. We participated in subtask A, in which we are called to predict the most likely associated emoji in English tweets. The proposed architecture relies on a Long Short-Term Memory network, augmented with an attention mechanism, that conditions the weight of each word, on a {``}context vector{''} which is taken as the aggregation of a tweet{'}s meaning. Moreover, we initialize the embedding layer of our model, with word2vec word embeddings, pretrained on a dataset of 550 million English tweets. Finally, our model does not rely on hand-crafted features or lexicons and is trained end-to-end with back-propagation. We ranked 2nd out of 48 teams.",
}
@inproceedings{coster-etal-2018-hatching,
    title = "Hatching Chick at {S}em{E}val-2018 Task 2: Multilingual Emoji Prediction",
    author = {Coster, Jo{\"e}l  and
      van Dalen, Reinder Gerard  and
      Stierman, Nathalie Adri{\"e}nne Jacqueline},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1070",
    doi = "10.18653/v1/S18-1070",
    pages = "445--448",
    abstract = "As part of a SemEval 2018 shared task an attempt was made to build a system capable of predicting the occurence of a language{'}s most frequently used emoji in Tweets. Specifically, models for English and Spanish data were created and trained on 500.000 and 100.000 tweets respectively. In order to create these models, first a logistic regressor, a sequential LSTM, a random forest regressor and a SVM were tested. The latter was found to perform best and therefore optimized individually for both languages. During developmet f1-scores of 61 and 82 were obtained for English and Spanish data respectively, in comparison, f1-scores on the official evaluation data were 21 and 18. The significant decrease in performance during evaluation might be explained by overfitting during development and might therefore have partially be prevented by using cross-validation. Over all, emoji which occur in a very specific context such as a Christmas tree were found to be most predictable.",
}
@inproceedings{zhou-etal-2018-epution,
    title = "{EPUTION} at {S}em{E}val-2018 Task 2: Emoji Prediction with User Adaption",
    author = "Zhou, Liyuan  and
      Xu, Qiongkai  and
      Suominen, Hanna  and
      Gedeon, Tom",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1071",
    doi = "10.18653/v1/S18-1071",
    pages = "449--453",
    abstract = "This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media {---} more precisely, Twitter {---} with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users{'} tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.",
}
@inproceedings{groot-etal-2018-pickleteam,
    title = "{P}ickle{T}eam! at {S}em{E}val-2018 Task 2: {E}nglish and {S}panish Emoji Prediction from Tweets",
    author = "Groot, Daphne  and
      Kruizinga, R{\'e}mon  and
      Veldthuis, Hennie  and
      de Wit, Simon  and
      Haagsma, Hessel",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1072",
    doi = "10.18653/v1/S18-1072",
    pages = "454--458",
    abstract = "We present a system for emoji prediction on English and Spanish tweets, prepared for the SemEval-2018 task on Multilingual Emoji Prediction. We compared the performance of an SVM, LSTM and an ensemble of these two. We found the SVM performed best on our development set with an accuracy of 61.3{\%} for English and 83{\%} for Spanish. The features used for the SVM are lowercased word n-grams in the range of 1 to 20, tokenised by a TweetTokenizer and stripped of stop words. On the test set, our model achieved an accuracy of 34{\%} on English, with a slightly lower score of 29.7{\%} accuracy on Spanish.",
}
@inproceedings{wang-etal-2018-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2018 Task 2: Multi-ensemble {B}i-{GRU} Model with Attention Mechanism for Multilingual Emoji Prediction",
    author = "Wang, Nan  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1073",
    doi = "10.18653/v1/S18-1073",
    pages = "459--465",
    abstract = "This paper describes our approach to SemEval-2018 Task 2, which aims to predict the most likely associated emoji, given a tweet in English or Spanish. We normalized text-based tweets during pre-processing, following which we utilized a bi-directional gated recurrent unit with an attention mechanism to build our base model. Multi-models with or without class weights were trained for the ensemble methods. We boosted models without class weights, and only strong boost classifiers were identified. In our system, not only was a boosting method used, but we also took advantage of the voting ensemble method to enhance our final system result. Our method demonstrated an obvious improvement of approximately 3{\%} of the macro F1 score in English and 2{\%} in Spanish.",
}
@inproceedings{effrosynidis-etal-2018-duth,
    title = "{DUTH} at {S}em{E}val-2018 Task 2: Emoji Prediction in Tweets",
    author = "Effrosynidis, Dimitrios  and
      Peikos, Georgios  and
      Symeonidis, Symeon  and
      Arampatzis, Avi",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1074",
    doi = "10.18653/v1/S18-1074",
    pages = "466--469",
    abstract = "This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team. First, we employed a combination of pre-processing techniques to reduce the noise of tweets and produce a number of features. Then, we built several N-grams, to represent the combination of word and emojis. Finally, we trained our system with a tuned LinearSVC classifier. Our approach in the leaderboard ranked 18th amongst 48 teams.",
}
@inproceedings{basile-lino-2018-tajjeb,
    title = "{TAJJEB} at {S}em{E}val-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction",
    author = "Basile, Angelo  and
      Lino, Kenny W.",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1075",
    doi = "10.18653/v1/S18-1075",
    pages = "470--476",
    abstract = "Emojis are widely used on social media andunderstanding their meaning is important forboth practical purposes (e.g. opinion mining,sentiment detection) and theoretical purposes(e.g. how different L1 speakers use them, dothey have some syntax?); this paper presents aset of experiments that aim to predict a singleemoji from a tweet. We built different mod-els and we found that the test results are verydifferent from the validation results.",
}
@inproceedings{zanzotto-santilli-2018-syntnn,
    title = "{S}ynt{NN} at {S}em{E}val-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer Perceptrons",
    author = "Zanzotto, Fabio Massimo  and
      Santilli, Andrea",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1076",
    doi = "10.18653/v1/S18-1076",
    pages = "477--481",
    abstract = "In this paper, we present SyntNN as a way to include traditional syntactic models in multilayer neural networks used in the task of Semeval Task 2 of emoji prediction. The model builds on the distributed tree embedder also known as distributed tree kernel. Initial results are extremely encouraging but additional analysis is needed to overcome the problem of overfitting.",
}
@inproceedings{jin-pedersen-2018-duluth,
    title = "{D}uluth {UROP} at {S}em{E}val-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling",
    author = "Jin, Shuning  and
      Pedersen, Ted",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1077",
    doi = "10.18653/v1/S18-1077",
    pages = "482--485",
    abstract = "This paper describes the Duluth UROP systems that participated in SemEval{--}2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of ensembles made up of classifiers using Naive Bayes, Logistic Regression, and Random Forests. We used unigram and bigram features and tried to offset the skewness of the data through the use of oversampling. Our task evaluation results place us 19th of 48 systems in the English evaluation, and 5th of 21 in the Spanish. After the evaluation we realized that some simple changes to our pre-processing could significantly improve our results. After making these changes we attained results that would have placed us sixth in the English evaluation, and second in the Spanish.",
}
@inproceedings{j-r-etal-2018-cennlp-semeval,
    title = "{CENNLP} at {S}em{E}val-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction Representation",
    author = "J R, Naveen  and
      V, Hariharan  and
      Ganesh H. B., Barathi  and
      Kumar M, Anand  and
      K P, Soman",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1078",
    doi = "10.18653/v1/S18-1078",
    pages = "486--490",
    abstract = "Emoji is one of the {``}fastest growing language {''} in pop-culture, especially in social media and it is very unlikely for its usage to decrease. These are generally used to bring an extra level of meaning to the texts, posted on social media platforms. Providing such an added info, gives more insights to the plain text, arising to hidden interpretation within the text. This paper explains our analysis on Task 2, {''} Multilingual Emoji Prediction{''} sharedtask conducted by Semeval-2018. In the task, a predicted emoji based on a piece of Twitter text are labelled under 20 different classes (most commonly used emojis) where these classes are learnt and further predicted are made for unseen Twitter text. In this work, we have experimented and analysed emojis predicted based on Twitter text, as a classification problem where the entailing emoji is considered as a label for every individual text data. We have implemented this using distributed representation of text through fastText. Also, we have made an effort to demonstrate how fastText framework can be useful in case of emoji prediction. This task is divide into two subtask, they are based on dataset presented in two different languages English and Spanish.",
}
@inproceedings{gerber-shardlow-2018-manchester,
    title = "{M}anchester Metropolitan at {S}em{E}val-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets",
    author = "Gerber, Luciano  and
      Shardlow, Matthew",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1079",
    doi = "10.18653/v1/S18-1079",
    pages = "491--496",
    abstract = "We present our submission to the Semeval 2018 task on emoji prediction. We used a random forest, with an ensemble of bag-of-words, sentiment and psycholinguistic features. Although we performed well on the trial dataset (attaining a macro f-score of 63.185 for English and 81.381 for Spanish), our approach did not perform as well on the test data. We describe our features and classi cation protocol, as well as initial experiments, concluding with a discussion of the discrepancy between our trial and test results.",
}
@inproceedings{kopev-etal-2018-tweety,
    title = "Tweety at {S}em{E}val-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine",
    author = "Kopev, Daniel  and
      Atanasov, Atanas  and
      Zlatkova, Dimitrina  and
      Hardalov, Momchil  and
      Koychev, Ivan  and
      Nikolova, Ivelina  and
      Angelova, Galia",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1080",
    doi = "10.18653/v1/S18-1080",
    pages = "497--501",
    abstract = "We present the system built for SemEval-2018 Task 2 on Emoji Prediction. Although Twitter messages are very short we managed to design a wide variety of features: textual, semantic, sentiment, emotion-, and color-related ones. We investigated different methods of text preprocessing including replacing text emojis with respective tokens and splitting hashtags to capture more meaning. To represent text we used word n-grams and word embeddings. We experimented with a wide range of classifiers and our best results were achieved using a SVM-based classifier and a Hierarchical Attention Neural Network.",
}
@inproceedings{guibon-etal-2018-lis,
    title = "{LIS} at {S}em{E}val-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction",
    author = {Guibon, Ga{\"e}l  and
      Ochs, Magalie  and
      Bellot, Patrice},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1081",
    doi = "10.18653/v1/S18-1081",
    pages = "502--506",
    abstract = "In this paper we present the system submitted to the SemEval2018 task2 : Multilingual Emoji Prediction. Our system approaches both languages as being equal by first; considering word embeddings associated to automatically computed features of different types, then by applying bagging algorithm RandomForest to predict the emoji of a tweet.",
}
@inproceedings{swanberg-etal-2018-alanis,
    title = "{ALANIS} at {S}em{E}val-2018 Task 3: A Feature Engineering Approach to Irony Detection in {E}nglish Tweets",
    author = "Swanberg, Kevin  and
      Mirza, Madiha  and
      Pedersen, Ted  and
      Wang, Zhenduo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1082",
    doi = "10.18653/v1/S18-1082",
    pages = "507--511",
    abstract = "This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.",
}
@inproceedings{dragoni-2018-neurosent-pdi,
    title = "{NEUROSENT}-{PDI} at {S}em{E}val-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model",
    author = "Dragoni, Mauro",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1083",
    doi = "10.18653/v1/S18-1083",
    pages = "512--519",
    abstract = "This paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.",
}
@inproceedings{hercig-2018-uwb,
    title = "{UWB} at {S}em{E}val-2018 Task 3: Irony detection in {E}nglish tweets",
    author = "Hercig, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1084",
    doi = "10.18653/v1/S18-1084",
    pages = "520--524",
    abstract = "This paper describes our system created for the SemEval-2018 Task 3: Irony detection in English tweets. Our strongly constrained system uses only the provided training data without any additional external resources. Our system is based on Maximum Entropy classifier and various features using parse tree, POS tags, and morphological features. Even without additional lexicons and word embeddings we achieved fourth place in Subtask A and seventh in Subtask B in terms of accuracy.",
}
@inproceedings{vu-etal-2018-nihrio,
    title = "{NIHRIO} at {S}em{E}val-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in {T}witter",
    author = "Vu, Thanh  and
      Nguyen, Dat Quoc  and
      Vu, Xuan-Son  and
      Nguyen, Dai Quoc  and
      Catt, Michael  and
      Trenell, Michael",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1085",
    doi = "10.18653/v1/S18-1085",
    pages = "525--530",
    abstract = "This paper describes our NIHRIO system for SemEval-2018 Task 3 {``}Irony detection in English tweets.{''} We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features. Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at least fourth using the accuracy metric and sixth using the F1 metric. Our code is available at: \url{https://github.com/NIHRIO/IronyDetectionInTwitter}",
}
@inproceedings{ghanem-etal-2018-ldr,
    title = "{LDR} at {S}em{E}val-2018 Task 3: A Low Dimensional Text Representation for Irony Detection",
    author = "Ghanem, Bilal  and
      Rangel, Francisco  and
      Rosso, Paolo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1086",
    doi = "10.18653/v1/S18-1086",
    pages = "531--536",
    abstract = "In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection. We have approached the task with our low dimensionality representation method (LDR), which exploits low dimensional features extracted from text on the basis of the occurrence probability of the words depending on each class. Our intuition is that words in ironic texts have different probability of occurrence than in non-ironic ones. Our approach obtained acceptable results in both subtasks A and B. We have performed an error analysis that shows the difference on correct and incorrect classified tweets.",
}
@inproceedings{marrese-taylor-etal-2018-iiidyt,
    title = "{IIIDYT} at {S}em{E}val-2018 Task 3: Irony detection in {E}nglish tweets",
    author = "Marrese-Taylor, Edison  and
      Ilic, Suzana  and
      Balazs, Jorge  and
      Prendinger, Helmut  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1087",
    doi = "10.18653/v1/S18-1087",
    pages = "537--540",
    abstract = "In this paper we introduce our system for the task of Irony detection in English tweets, a part of SemEval 2018. We propose representation learning approach that relies on a multi-layered bidirectional LSTM, without using external features that provide additional semantic information. Although our model is able to outperform the baseline in the validation set, our results show limited generalization power over the test set. Given the limited size of the dataset, we think the usage of more pre-training schemes would greatly improve the obtained results.",
}
@inproceedings{mikhalkova-etal-2018-punfields,
    title = "{P}un{F}ields at {S}em{E}val-2018 Task 3: Detecting Irony by Tools of Humor Analysis",
    author = "Mikhalkova, Elena  and
      Karyakin, Yuri  and
      Voronov, Alexander  and
      Grigoriev, Dmitry  and
      Leoznov, Artem",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1088",
    doi = "10.18653/v1/S18-1088",
    pages = "541--545",
    abstract = "The paper describes our search for a universal algorithm of detecting intentional lexical ambiguity in different forms of creative language. At SemEval-2018 Task 3, we used PunFields, the system of automatic analysis of English puns that we introduced at SemEval-2017, to detect irony in tweets. Preliminary tests showed that it can reach the score of F1=0.596. However, at the competition, its result was F1=0.549.",
}
@inproceedings{cho-etal-2018-hashcount,
    title = "{H}ash{C}ount at {S}em{E}val-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection",
    author = "Cho, Won Ik  and
      Kang, Woo Hyun  and
      Kim, Nam Soo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1089",
    doi = "10.18653/v1/S18-1089",
    pages = "546--552",
    abstract = "This paper proposes a novel feature extraction process for SemEval task 3: Irony detection in English tweets. The proposed system incorporates a concatenative featurization of tweet and hashtags, which helps distinguishing between the irony-related and the other components. The system embeds tweets into a vector sequence with widely used pretrained word vectors, partially using a character embedding for the words that are out of vocabulary. Identification was performed with BiLSTM and CNN classifiers, achieving F1 score of 0.5939 (23/42) and 0.3925 (10/28) each for the binary and the multi-class case, respectively. The reliability of the proposed scheme was verified by analyzing the Gold test data, which demonstrates how hashtags can be taken into account when identifying various types of irony.",
}
@inproceedings{rohanian-etal-2018-wlv,
    title = "{WLV} at {S}em{E}val-2018 Task 3: Dissecting Tweets in Search of Irony",
    author = "Rohanian, Omid  and
      Taslimipoor, Shiva  and
      Evans, Richard  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1090",
    doi = "10.18653/v1/S18-1090",
    pages = "553--559",
    abstract = "This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content.",
}
@inproceedings{san-2018-random,
    title = "Random Decision Syntax Trees at {S}em{E}val-2018 Task 3: {LSTM}s and Sentiment Scores for Irony Detection",
    author = "San, Aidan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1091",
    doi = "10.18653/v1/S18-1091",
    pages = "560--564",
    abstract = "We propose a Long Short Term Memory Neural Network model for irony detection in tweets in this paper. Our model is trained using word embeddings and emoji embeddings. We show that adding sentiment scores to our model improves the F1 score of our baseline LSTM by approximately .012, and therefore show that high-level features can be used to improve word embeddings in certain Natural Language Processing applications. Our model ranks 24/43 for binary classification and 5/31 for multiclass classification. We make our model easily accessible to the research community.",
}
@inproceedings{gonzalez-etal-2018-elirf,
    title = "{EL}i{RF}-{UPV} at {S}em{E}val-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets",
    author = "Gonz{\'a}lez, Jos{\'e}-{\'A}ngel  and
      Hurtado, Llu{\'\i}s-F.  and
      Pla, Ferran",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1092",
    doi = "10.18653/v1/S18-1092",
    pages = "565--569",
    abstract = "This paper describes the participation of ELiRF-UPV team at tasks 1 and 3 of Semeval-2018. We present a deep learning based system that assembles Convolutional Neural Networks and Long Short-Term Memory neural networks. This system has been used with slight modifications for the two tasks addressed both for English and Spanish. Finally, the results obtained in the competition are reported and discussed.",
}
@inproceedings{ghosh-veale-2018-ironymagnet,
    title = "{I}rony{M}agnet at {S}em{E}val-2018 Task 3: A {S}iamese network for Irony detection in Social media",
    author = "Ghosh, Aniruddha  and
      Veale, Tony",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1093",
    doi = "10.18653/v1/S18-1093",
    pages = "570--575",
    abstract = "This paper describes our system, entitled IronyMagnet, for the 3rd Task of the SemEval 2018 workshop, {``}Irony Detection in English Tweets{''}. In Task 1, irony classification task has been considered as a binary classification task. Now for the first time, finer categories of irony are considered as part of a shared task. In task 2, three types of irony are considered; {``}Irony by contrast{''} - ironic instances where evaluative expression portrays inverse polarity (positive, negative) of the literal proposition; {``}Situational irony{''} - ironic instances where output of a situation do not comply with its expectation; {``}Other verbal irony{''} - instances where ironic intent does not rely on polarity contrast or unexpected outcome. We proposed a Siamese neural network for irony detection, which is consisted of two subnetworks, each containing a long short term memory layer(LSTM) and an embedding layer initialized with vectors from Glove word embedding 1 . The system achieved a f-score of 0.72, and 0.50 in task 1, and task 2 respectively.",
}
@inproceedings{sherif-etal-2018-ctsys,
    title = "{CTS}ys at {S}em{E}val-2018 Task 3: Irony in Tweets",
    author = "Sherif, Myan  and
      Mamdouh, Sherine  and
      Ghazi, Wegdan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1094",
    doi = "10.18653/v1/S18-1094",
    pages = "576--580",
    abstract = "The objective of this paper is to provide a description for a system built as our participation in SemEval-2018 Task 3 on Irony detection in English tweets. This system classifies a tweet as either ironic or non-ironic through a supervised learning approach. Our approach is to implement three feature models, and to then improve the performance of the supervised learning classification of tweets by combining many data features and using a voting system on four different classifiers. We describe the process of pre-processing data, extracting features, and running different types of classifiers against our feature set. In the competition, our system achieved an F1-score of 0.4675, ranking 35th in subtask A, and an F1-score score of 0.3014 ranking 22th in subtask B.",
}
@inproceedings{ahmed-etal-2018-irony,
    title = "Irony Detector at {S}em{E}val-2018 Task 3: Irony Detection in {E}nglish Tweets using Word Graph",
    author = "Ahmed, Usman  and
      Zafar, Lubna  and
      Qayyum, Faiza  and
      Arshad Islam, Muhammad",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1095",
    doi = "10.18653/v1/S18-1095",
    pages = "581--586",
    abstract = "This paper describes the Irony detection system that participates in SemEval-2018 Task 3: Irony detection in English tweets. The system participated in the subtasks A and B. This paper discusses the results of our system in the development, evaluation and post evaluation. Each class in the dataset is represented as directed unweighted graphs. Then, the comparison is carried out with each class graph which results in a vector. This vector is used as features by machine learning algorithm. The model is evaluated on a hold on strategy. The organizers randomly split 80{\%} (3,833 instances) training set (provided to the participant in training their system) and testing set 20{\%}(958 instances). The test set is reserved to evaluate the performance of participants systems. During the evaluation, our system ranked 23 in the Coda Lab result of the subtask A (binary class problem). The binary class system achieves accuracy 0.6135, precision 0.5091, recall 0.7170 and F measure 0.5955. The subtask B (multi-class problem) system is ranked 22 in Coda Lab results. The multiclass model achieves the accuracy 0.4158, precision 0.4055, recall 0.3526 and f measure 0.3101.",
}
@inproceedings{dearden-baron-2018-lancaster,
    title = "{L}ancaster at {S}em{E}val-2018 Task 3: Investigating Ironic Features in {E}nglish Tweets",
    author = "Dearden, Edward  and
      Baron, Alistair",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1096",
    doi = "10.18653/v1/S18-1096",
    pages = "587--593",
    abstract = "This paper describes the system we submitted to SemEval-2018 Task 3. The aim of the system is to distinguish between irony and non-irony in English tweets. We create a targeted feature set and analyse how different features are useful in the task of irony detection, achieving an F1-score of 0.5914. The analysis of individual features provides insight that may be useful in future attempts at detecting irony in tweets.",
}
@inproceedings{hernandez-farias-etal-2018-inaoe,
    title = "{INAOE}-{UPV} at {S}em{E}val-2018 Task 3: An Ensemble Approach for Irony Detection in {T}witter",
    author = "Hern{\'a}ndez Far{\'\i}as, Delia Iraz{\'u}  and
      S{\'a}nchez-Vega, Fernando  and
      Montes-y-G{\'o}mez, Manuel  and
      Rosso, Paolo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1097",
    doi = "10.18653/v1/S18-1097",
    pages = "594--599",
    abstract = "This paper describes an ensemble approach to the SemEval-2018 Task 3. The proposed method is composed of two renowned methods in text classification together with a novel approach for capturing ironic content by exploiting a tailored lexicon for irony detection. We experimented with different ensemble settings. The obtained results show that our method has a good performance for detecting the presence of ironic content in Twitter.",
}
@inproceedings{yin-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods",
    author = "Yin, Zhenghang  and
      Wang, Feixiang  and
      Lan, Man  and
      Wang, Wenting",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1098",
    doi = "10.18653/v1/S18-1098",
    pages = "600--606",
    abstract = "The paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask B is a fine-grained classification task including four classes. To address them, we explored supervised machine learning method alone and in combination with neural networks.",
}
@inproceedings{durlich-2018-kluenicorn,
    title = "{KLUE}nicorn at {S}em{E}val-2018 Task 3: A Naive Approach to Irony Detection",
    author = {D{\"u}rlich, Luise},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1099",
    doi = "10.18653/v1/S18-1099",
    pages = "607--612",
    abstract = "This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on {``}Irony detection in English tweets{''}. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information.",
}
@inproceedings{baziotis-etal-2018-ntua-slp-semeval,
    title = "{NTUA}-{SLP} at {S}em{E}val-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive {RNN}s",
    author = "Baziotis, Christos  and
      Nikolaos, Athanasiou  and
      Papalampidi, Pinelopi  and
      Kolovou, Athanasia  and
      Paraskevopoulos, Georgios  and
      Ellinas, Nikolaos  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1100",
    doi = "10.18653/v1/S18-1100",
    pages = "613--621",
    abstract = "In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 {``}Irony detection in English tweets{''}. We design and ensemble two independent models, based on recurrent neural networks (Bi-LSTM), which operate at the word and character level, in order to capture both the semantic and syntactic information in tweets. Our models are augmented with a self-attention mechanism, in order to identify the most informative words. The embedding layer of our word-level model is initialized with word2vec word embeddings, pretrained on a collection of 550 million English tweets. We did not utilize any handcrafted features, lexicons or external datasets as prior information and our models are trained end-to-end using back propagation on constrained data. Furthermore, we provide visualizations of tweets with annotations for the salient tokens of the attention layer that can help to interpret the inner workings of the proposed models. We ranked 2nd out of 42 teams in Subtask A and 2nd out of 31 teams in Subtask B. However, post-task-completion enhancements of our models achieve state-of-the-art results ranking 1st for both subtasks.",
}
@inproceedings{peng-etal-2018-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2018 Task 3: Ensemble Neural Network Models for Irony Detection on {T}witter",
    author = "Peng, Bo  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1101",
    doi = "10.18653/v1/S18-1101",
    pages = "622--627",
    abstract = "This paper describe the system we proposed to participate the first year of Irony detection in English tweets competition. Previous works demonstrate that LSTMs models have achieved remarkable performance in natural language processing; besides, combining multiple classification from various individual classifiers in general is more powerful than a single classification. In order to obtain more precision classification of irony detection, our system trained several individual neural network classifiers and combined their results according to the ensemble-learning algorithm.",
}
@inproceedings{nikhil-mayank-srivastava-2018-binarizer,
    title = "Binarizer at {S}em{E}val-2018 Task 3: Parsing dependency and deep learning for irony detection",
    author = "Nikhil, Nishant  and
      Mayank Srivastava, Muktabh",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1102",
    doi = "10.18653/v1/S18-1102",
    pages = "628--632",
    abstract = "In this paper, we describe the system submitted for the SemEval 2018 Task 3 (Irony detection in English tweets) Subtask A by the team Binarizer. Irony detection is a key task for many natural language processing works. Our method treats ironical tweets to consist of smaller parts containing different emotions. We break down tweets into separate phrases using a dependency parser. We then embed those phrases using an LSTM-based neural network model which is pre-trained to predict emoticons for tweets. Finally, we train a fully-connected network to achieve classification.",
}
@inproceedings{s-etal-2018-ssn-mlrg1,
    title = "{SSN} {MLRG}1 at {S}em{E}val-2018 Task 3: Irony Detection in {E}nglish Tweets Using {M}ulti{L}ayer Perceptron",
    author = "S, Rajalakshmi  and
      S, Angel Deborah  and
      Rajendram, S Milton  and
      T T, Mirnalinee",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1103",
    doi = "10.18653/v1/S18-1103",
    pages = "633--637",
    abstract = "Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analysis. This paper describes the work on identifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classification subtask, which classifies the given text into one of the four class labels.",
}
@inproceedings{rangwani-etal-2018-nlprl,
    title = "{NLPRL}-{IITBHU} at {S}em{E}val-2018 Task 3: Combining Linguistic Features and Emoji pre-trained {CNN} for Irony Detection in Tweets",
    author = "Rangwani, Harsh  and
      Kulshreshtha, Devang  and
      Kumar Singh, Anil",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1104",
    doi = "10.18653/v1/S18-1104",
    pages = "638--642",
    abstract = "This paper describes our participation in SemEval 2018 Task 3 on Irony Detection in Tweets. We combine linguistic features with pre-trained activations of a neural network. The CNN is trained on the emoji prediction task. We combine the two feature sets and feed them into an XGBoost Classifier for classification. Subtask-A involves classification of tweets into ironic and non-ironic instances whereas Subtask-B involves classification of the tweet into - non-ironic, verbal irony, situational irony or other verbal irony. It is observed that combining features from these two different feature spaces improves our system results. We leverage the SMOTE algorithm to handle the problem of class imbalance in Subtask-B. Our final model achieves an F1-score of 0.65 and 0.47 on Subtask-A and Subtask-B respectively. Our system ranks 4th on both tasks respectively, outperforming the baseline by 6{\%} on Subtask-A and 14{\%} on Subtask-B.",
}
@inproceedings{hernandez-farias-etal-2018-valento,
    title = "{V}alen{TO} at {S}em{E}val-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in {E}nglish Tweets",
    author = "Hern{\'a}ndez Far{\'\i}as, Delia Iraz{\'u}  and
      Patti, Viviana  and
      Rosso, Paolo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1105",
    doi = "10.18653/v1/S18-1105",
    pages = "643--648",
    abstract = "In this paper we describe the system used by the ValenTO team in the shared task on Irony Detection in English Tweets at SemEval 2018. The system takes as starting point emotIDM, an irony detection model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. We experimented with different settings, by exploiting different classifiers and features, and participated both to the binary irony detection task and to the task devoted to distinguish among different types of irony. We report on the results obtained by our system both in a constrained setting and unconstrained setting, where we explored the impact of using additional data in the training phase, such as corpora annotated for the presence of irony or sarcasm from the state of the art. Overall, the performance of our system seems to validate the important role that affective information has for identifying ironic content in Twitter.",
}
@inproceedings{pamungkas-patti-2018-nondicevosulserio,
    title = "{\#}{N}on{D}icevo{S}ul{S}erio at {S}em{E}val-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in {E}nglish Tweets",
    author = "Pamungkas, Endang Wahyu  and
      Patti, Viviana",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1106",
    doi = "10.18653/v1/S18-1106",
    pages = "649--654",
    abstract = "This paper describes the participation of the {\#}NonDicevoSulSerio team at SemEval2018-Task3, which focused on Irony Detection in English Tweets and was articulated in two tasks addressing the identification of irony at different levels of granularity. We participated in both tasks proposed: Task A is a classical binary classification task to determine whether a tweet is ironic or not, while Task B is a multiclass classification task devoted to distinguish different types of irony, where systems have to predict one out of four labels describing verbal irony by clash, other verbal irony, situational irony, and non-irony. We addressed both tasks by proposing a model built upon a well-engineered features set involving both syntactic and lexical features, and a wide range of affective-based features, covering different facets of sentiment and emotions. The use of new features for taking advantage of the affective information conveyed by emojis has been analyzed. On this line, we also tried to exploit the possible incongruity between sentiment expressed in the text and in the emojis included in a tweet. We used a Support Vector Machine classifier, and obtained promising results. We also carried on experiments in an unconstrained setting.",
}
@inproceedings{park-etal-2018-knu,
    title = "{KNU} {CI} System at {S}em{E}val-2018 Task4: Character Identification by Solving Sequence-Labeling Problem",
    author = "Park, Cheoneum  and
      Song, Heejun  and
      Lee, Changki",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1107",
    doi = "10.18653/v1/S18-1107",
    pages = "655--659",
    abstract = "Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder{--}decoder model. The in-put document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder{--}decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder{--}decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00{\%} and a label accuracy of 85.10{\%} at the scene-level.",
}
@inproceedings{vossen-2018-newsreader,
    title = "{N}ews{R}eader at {S}em{E}val-2018 Task 5: Counting events by reasoning over event-centric-knowledge-graphs",
    author = "Vossen, Piek",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1108",
    doi = "10.18653/v1/S18-1108",
    pages = "660--666",
    abstract = "In this paper, we describe the participation of the NewsReader system in the SemEval-2018 Task 5 on Counting Events and Participants in the Long Tail. NewsReader is a generic unsupervised text processing system that detects events with participants, time and place to generate Event Centric Knowledge Graphs (ECKGs). We minimally adapted these ECKGs to establish a baseline performance for the task. We first use the ECKGs to establish which documents report on the same incident and what event mentions are coreferential. Next, we aggregate ECKGs across coreferential mentions and use the aggregated knowledge to answer the questions of the task. Our participation tests the quality of NewsReader to create ECKGs, as well as the potential of ECKGs to establish event identity and reason over the result to answer the task queries.",
}
@inproceedings{abreu-oliveira-2018-feup,
    title = "{FEUP} at {S}em{E}val-2018 Task 5: An Experimental Study of a Question Answering System",
    author = "Abreu, Carla  and
      Oliveira, Eug{\'e}nio",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1109",
    doi = "10.18653/v1/S18-1109",
    pages = "667--673",
    abstract = "We present the approach developed at the Faculty of Engineering of the University of Porto to participate in SemEval-2018 Task 5: Counting Events and Participants within Highly Ambiguous Data covering a very long tail.The work described here presents the experimental system developed to extract entities from news articles for the sake of Question Answering. We propose a supervised learning approach to enable the recognition of two different types of entities: Locations and Participants. We also discuss the use of distance-based algorithms (using Levenshtein distance and Q-grams) for the detection of documents{'} closeness based on the entities extracted. For the experiments, we also used a multi-agent system that improved the performance.",
}
@inproceedings{liu-etal-2018-nai,
    title = "{NAI}-{SEA} at {S}em{E}val-2018 Task 5: An Event Search System",
    author = "Liu, Yingchi  and
      Li, Quanzhi  and
      Si, Luo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1110",
    doi = "10.18653/v1/S18-1110",
    pages = "674--678",
    abstract = "In this paper, we describe Alibaba{'}s participating system in the semEval-2018 Task5: Counting Events and Participants in the Long Tail. We designed and implemented a pipeline system that consists of components to extract question properties and document features, document event category classifications, document retrieval and document clustering. To retrieve the majority of the relevant documents, we carefully designed our system to extract key information from each question and document pair. After retrieval, we perform further document clustering to count the number of events. The task contains 3 subtasks, on which we achieved F1 score of 78.33, 50.52, 63.59 , respectively, for document level retrieval. Our system ranks first in all the three subtasks on document level retrieval, and it also ranks first in incident-level evaluation by RSME measure in subtask 3.",
}
@inproceedings{gabor-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers",
    author = {G{\'a}bor, Kata  and
      Buscaldi, Davide  and
      Schumann, Anne-Kathrin  and
      QasemiZadeh, Behrang  and
      Zargayouna, Ha{\"\i}fa  and
      Charnois, Thierry},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1111",
    doi = "10.18653/v1/S18-1111",
    pages = "679--688",
    abstract = "This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.",
}
@inproceedings{rotsztejn-etal-2018-eth,
    title = "{ETH}-{DS}3{L}ab at {S}em{E}val-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction",
    author = "Rotsztejn, Jonathan  and
      Hollenstein, Nora  and
      Zhang, Ce",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1112",
    doi = "10.18653/v1/S18-1112",
    pages = "689--696",
    abstract = "Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing. In this paper, we present a system for relation classification and extraction based on an ensemble of convolutional and recurrent neural networks that ranked first in 3 out of the 4 Subtasks at SemEval 2018 Task 7. We provide detailed explanations and grounds for the design choices behind the most relevant features and analyze their importance.",
}
@inproceedings{phandi-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 8: Semantic Extraction from {C}ybersec{U}rity {RE}ports using Natural Language Processing ({S}ecure{NLP})",
    author = "Phandi, Peter  and
      Silva, Amila  and
      Lu, Wei",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1113",
    doi = "10.18653/v1/S18-1113",
    pages = "697--706",
    abstract = "This paper describes the SemEval 2018 shared task on semantic extraction from cybersecurity reports, which is introduced for the first time as a shared task on SemEval. This task comprises four SubTasks done incrementally to predict the characteristics of a specific malware using cybersecurity reports. To the best of our knowledge, we introduce the world{'}s largest publicly available dataset of annotated malware reports in this task. This task received in total 18 submissions from 9 participating teams.",
}
@inproceedings{ma-etal-2018-dm,
    title = "{DM}{\_}{NLP} at {S}em{E}val-2018 Task 8: neural sequence labeling with linguistic features",
    author = "Ma, Chunping  and
      Zheng, Huafei  and
      Xie, Pengjun  and
      Li, Chen  and
      Li, Linlin  and
      Si, Luo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1114",
    doi = "10.18653/v1/S18-1114",
    pages = "707--711",
    abstract = "This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM{\_}NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful for inferring malware actions and capabilities, and SubTask 2 predicts token labels ({``}Action{''}, {``}Entity{''}, {``}Modifier{''} and {``}Others{''}) for a given malware-related sentence. Since we leverage results of Subtask 2 directly to infer the result of Subtask 1, the paper focus on the system solving Subtask 2. By taking Subtask 2 as a sequence labeling task, our system relies on a recurrent neural network named BiLSTM-CNN-CRF with rich linguistic features, such as POS tags, dependency parsing labels, chunking labels, NER labels, Brown clustering. Our system achieved the highest F1 score in both token level and phrase level.",
}
@inproceedings{camacho-collados-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 9: Hypernym Discovery",
    author = "Camacho-Collados, Jose  and
      Delli Bovi, Claudio  and
      Espinosa-Anke, Luis  and
      Oramas, Sergio  and
      Pasini, Tommaso  and
      Santus, Enrico  and
      Shwartz, Vered  and
      Navigli, Roberto  and
      Saggion, Horacio",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1115",
    doi = "10.18653/v1/S18-1115",
    pages = "712--724",
    abstract = "This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classification task, taking a pair of candidate words as input. Instead, our reformulated task is defined as follows: given an input term, retrieve (or discover) its suitable hypernyms from a target corpus. We proposed five different subtasks covering three languages (English, Spanish, and Italian), and two specific domains of knowledge in English (Medical and Music). Participants were allowed to compete in any or all of the subtasks. Overall, a total of 11 teams participated, with a total of 39 different systems submitted through all subtasks. Data, results and further information about the task can be found at \url{https://competitions.codalab.org/competitions/17119}.",
}
@inproceedings{bernier-colborne-barriere-2018-crim,
    title = "{CRIM} at {S}em{E}val-2018 Task 9: A Hybrid Approach to Hypernym Discovery",
    author = "Bernier-Colborne, Gabriel  and
      Barri{\`e}re, Caroline",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1116",
    doi = "10.18653/v1/S18-1116",
    pages = "725--731",
    abstract = "This report describes the system developed by the CRIM team for the hypernym discovery task at SemEval 2018. This system exploits a combination of supervised projection learning and unsupervised pattern-based hypernym discovery. It was ranked first on the 3 sub-tasks for which we submitted results.",
}
@inproceedings{krebs-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 10: Capturing Discriminative Attributes",
    author = "Krebs, Alicia  and
      Lenci, Alessandro  and
      Paperno, Denis",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1117",
    doi = "10.18653/v1/S18-1117",
    pages = "732--740",
    abstract = "This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Participants were asked to identify whether an attribute could help discriminate between two concepts. For example, a successful system should determine that {`}urine{'} is a discriminating feature in the word pair {`}kidney{'}, {`}bone{'}. The aim of the task is to better evaluate the capabilities of state of the art semantic models, beyond pure semantic similarity. The task attracted submissions from 21 teams, and the best system achieved a 0.75 F1 score.",
}
@inproceedings{lai-etal-2018-sunnynlp,
    title = "{SUNNYNLP} at {S}em{E}val-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding Features",
    author = "Lai, Sunny  and
      Leung, Kwong Sak  and
      Leung, Yee",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1118",
    doi = "10.18653/v1/S18-1118",
    pages = "741--746",
    abstract = "We present SUNNYNLP, our system for solving SemEval 2018 Task 10: {``}Capturing Discriminative Attributes{''}. Our Support-Vector-Machine(SVM)-based system combines features extracted from pre-trained embeddings and statistical information from Is-A taxonomy to detect semantic difference of concepts pairs. Our system is demonstrated to be effective in detecting semantic difference and is ranked 1st in the competition in terms of F1 measure. The open source of our code is coined SUNNYNLP.",
}
@inproceedings{ostermann-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 11: Machine Comprehension Using Commonsense Knowledge",
    author = "Ostermann, Simon  and
      Roth, Michael  and
      Modi, Ashutosh  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1119",
    doi = "10.18653/v1/S18-1119",
    pages = "747--757",
    abstract = "This report summarizes the results of the SemEval 2018 task on machine comprehension using commonsense knowledge. For this machine comprehension task, we created a new corpus, MCScript. It contains a high number of questions that require commonsense knowledge for finding the correct answer. 11 teams from 4 different countries participated in this shared task, most of them used neural approaches. The best performing system achieves an accuracy of 83.95{\%}, outperforming the baselines by a large margin, but still far from the human upper bound, which was found to be at 98{\%}.",
}
@inproceedings{wang-etal-2018-yuanfudao,
    title = "Yuanfudao at {S}em{E}val-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension",
    author = "Wang, Liang  and
      Sun, Meng  and
      Zhao, Wei  and
      Shen, Kewei  and
      Liu, Jingming",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1120",
    doi = "10.18653/v1/S18-1120",
    pages = "758--762",
    abstract = "This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment the input with relation embedding from the graph of general knowledge ConceptNet. As a result, our system achieves state-of-the-art performance with 83.95{\%} accuracy on the official test data. Code is publicly available at \url{https://github.com/intfloat/commonsense-rc}.",
}
@inproceedings{habernal-etal-2018-semeval,
    title = "{S}em{E}val-2018 Task 12: The Argument Reasoning Comprehension Task",
    author = "Habernal, Ivan  and
      Wachsmuth, Henning  and
      Gurevych, Iryna  and
      Stein, Benno",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1121",
    doi = "10.18653/v1/S18-1121",
    pages = "763--772",
    abstract = "A natural language argument is composed of a claim as well as reasons given as premises for the claim. The warrant explaining the reasoning is usually left implicit, as it is clear from the context and common sense. This makes a comprehension of arguments easy for humans but hard for machines. This paper summarizes the first shared task on argument reasoning comprehension. Given a premise and a claim along with some topic information, the goal was to automatically identify the correct warrant among two candidates that are plausible and lexically close, but in fact imply opposite claims. We describe the dataset with 1970 instances that we built for the task, and we outline the 21 computational approaches that participated, most of which used neural networks. The results reveal the complexity of the task, with many approaches hardly improving over the random accuracy of about 0.5. Still, the best observed accuracy (0.712) underlines the principle feasibility of identifying warrants. Our analysis indicates that an inclusion of external knowledge is key to reasoning comprehension.",
}
@inproceedings{choi-lee-2018-gist,
    title = "{GIST} at {S}em{E}val-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task",
    author = "Choi, HongSeok  and
      Lee, Hyunju",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1122",
    doi = "10.18653/v1/S18-1122",
    pages = "773--777",
    abstract = "This paper describes our GIST team system that participated in SemEval-2018 Argument Reasoning Comprehension task (Task 12). Here, we address two challenging factors: unstated common senses and two lexically close warrants that lead to contradicting claims. A key idea for our system is full use of transfer learning from the Natural Language Inference (NLI) task to this task. We used Enhanced Sequential Inference Model (ESIM) to learn the NLI dataset. We describe how to use ESIM for transfer learning to choose correct warrant through a proposed system. We show comparable results through ablation experiments. Our system ranked 1st among 22 systems, outperforming all the systems more than 10{\%}.",
}
@inproceedings{renslow-neumann-2018-lightrel,
    title = "{L}ight{R}el at {S}em{E}val-2018 Task 7: Lightweight and Fast Relation Classification",
    author = {Renslow, Tyler  and
      Neumann, G{\"u}nter},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1123",
    doi = "10.18653/v1/S18-1123",
    pages = "778--782",
    abstract = "We present LightRel, a lightweight and fast relation classifier. Our goal is to develop a high baseline for different relation extraction tasks. By defining only very few data-internal, word-level features and external knowledge sources in the form of word clusters and word embeddings, we train a fast and simple linear classifier",
}
@inproceedings{dhyani-2018-ohiostate,
    title = "{O}hio{S}tate at {S}em{E}val-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers Using Piecewise Convolutional Neural Networks",
    author = "Dhyani, Dushyanta",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1124",
    doi = "10.18653/v1/S18-1124",
    pages = "783--787",
    abstract = "We describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task. Our simple piecewise convolution neural encoder performs decently in an end to end manner. A simple inter-task data augmentation significantly boosts the performance of the model. Our best-performing systems stood 8th out of 20 teams on the classification task on noisy data and 12th out of 28 teams on the classification task on clean data.",
}
@inproceedings{luan-etal-2018-uwnlp,
    title = "The {UWNLP} system at {S}em{E}val-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings",
    author = "Luan, Yi  and
      Ostendorf, Mari  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1125",
    doi = "10.18653/v1/S18-1125",
    pages = "788--792",
    abstract = "This paper describes our submission for SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers. Our model is based on the end-to-end relation extraction model of (Miwa and Bansal, 2016) with several enhancements such as character-level encoding attention mechanism on selecting pretrained concept candidate embeddings. Our official submission ranked the second in relation classification task (Subtask 1.1 and Subtask 2 Senerio 2), and the first in the relation extraction task (Subtask 2 Scenario 1).",
}
@inproceedings{suarez-paniagua-etal-2018-uc3m,
    title = "{UC}3{M}-{NII} Team at {S}em{E}val-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network",
    author = "Su{\'a}rez-Paniagua, V{\'\i}ctor  and
      Segura-Bedmar, Isabel  and
      Aizawa, Akiko",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1126",
    doi = "10.18653/v1/S18-1126",
    pages = "793--797",
    abstract = "This paper reports our participation for SemEval-2018 Task 7 on extraction and classification of relationships between entities in scientific papers. Our approach is based on the use of a Convolutional Neural Network (CNN) trained on350 abstract with manually annotated entities and relations. Our hypothesis is that this deep learning model can be applied to extract and classify relations between entities for scientific papers at the same time. We use the Part-of-Speech and the distances to the target entities as part of the embedding for each word and we blind all the entities by marker names. In addition, we use sampling techniques to overcome the imbalance issues of this dataset. Our architecture obtained an F1-score of 35.4{\%} for the relation extraction task and 18.5{\%} for the relation classification task with a basic configuration of the one step CNN.",
}
@inproceedings{jin-etal-2018-mit,
    title = "{MIT}-{MEDG} at {S}em{E}val-2018 Task 7: Semantic Relation Classification via Convolution Neural Network",
    author = "Jin, Di  and
      Dernoncourt, Franck  and
      Sergeeva, Elena  and
      McDermott, Matthew  and
      Chauhan, Geeticka",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1127",
    doi = "10.18653/v1/S18-1127",
    pages = "798--804",
    abstract = "SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types. We tested 3 classes of models: Linear classifiers, Long Short-Term Memory (LSTM) models, and Convolutional Neural Network (CNN) models. Ultimately, the CNN model class proved most performant, so we specialized to this model for our final submissions. We improved performance beyond a vanilla CNN by including a variant of negative sampling, using custom word embeddings learned over a corpus of ACL articles, training over corpora of both tasks 1.1 and 1.2, using reversed feature, using part of context words beyond the entity pairs and using ensemble methods to improve our final predictions. We also tested attention based pooling, up-sampling, and data augmentation, but none improved performance. Our model achieved rank 6 out of 28 (macro-averaged F1-score: 72.7) in subtask 1.1, and rank 4 out of 20 (macro F1: 80.6) in subtask 1.2.",
}
@inproceedings{nooralahzadeh-etal-2018-sirius,
    title = "{SIRIUS}-{LTG}-{U}i{O} at {S}em{E}val-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers",
    author = "Nooralahzadeh, Farhad  and
      {\O}vrelid, Lilja  and
      L{\o}nning, Jan Tore",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1128",
    doi = "10.18653/v1/S18-1128",
    pages = "805--810",
    abstract = "This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between two entities, then we introduce a convolutional neural network (CNN) which takes the shortest dependency path embeddings as input and performs relation classification with differing objectives for each subtask of the shared task. This approach achieved overall F1 scores of 76.7 and 83.2 for relation classification on clean and noisy data, respectively. Furthermore, for combined relation extraction and classification on clean data, it obtained F1 scores of 37.4 and 33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared task.",
}
@inproceedings{yin-etal-2018-ircms,
    title = "{IRCMS} at {S}em{E}val-2018 Task 7 : Evaluating a basic {CNN} Method and Traditional Pipeline Method for Relation Classification",
    author = "Yin, Zhongbo  and
      Luo, Zhunchen  and
      Luo, Wei  and
      Bin, Mao  and
      Tian, Changhai  and
      Ye, Yuming  and
      Wu, Shuai",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1129",
    doi = "10.18653/v1/S18-1129",
    pages = "811--815",
    abstract = "This paper presents our participation for sub-task1 (1.1 and 1.2) in SemEval 2018 task 7: Semantic Relation Extraction and Classification in Scientific Papers (G{\'a}bor et al., 2018). We experimented on this task with two methods: CNN method and traditional pipeline method. We use the context between two entities (included) as input information for both methods, which extremely reduce the noise effect. For the CNN method, we construct a simple convolution neural network to automatically learn features from raw texts without any manual processing. Moreover, we use the softmax function to classify the entity pair into a specific relation category. For the traditional pipeline method, we use the Hackabout method as a representation which is described in section3.5. The CNN method{'}s result is much better than traditional pipeline method (49.1{\%} vs. 42.3{\%} and 71.1{\%} vs. 54.6{\%} ).",
}
@inproceedings{neves-etal-2018-bf3r,
    title = "{B}f3{R} at {S}em{E}val-2018 Task 7: Evaluating Two Relation Extraction Tools for Finding Semantic Relations in Biomedical Abstracts",
    author = {Neves, Mariana  and
      Butzke, Daniel  and
      Sch{\"o}nfelder, Gilbert  and
      Grune, Barbara},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1130",
    doi = "10.18653/v1/S18-1130",
    pages = "816--820",
    abstract = "Automatic extraction of semantic relations from text can support finding relevant information from scientific publications. We describe our participation in Task 7 of SemEval-2018 for which we experimented with two relations extraction tools - jSRE and TEES - for the extraction and classification of six relation types. The results we obtained with TEES were significantly superior than those with jSRE (33.4{\%} vs. 30.09{\%} and 20.3{\%} vs. 16{\%}). Additionally, we utilized the model trained with TEES for extracting semantic relations from biomedical abstracts, for which we present a preliminary evaluation.",
}
@inproceedings{sysoev-mayorov-2018-texterra,
    title = "Texterra at {S}em{E}val-2018 Task 7: Exploiting Syntactic Information for Relation Extraction and Classification in Scientific Papers",
    author = "Sysoev, Andrey  and
      Mayorov, Vladimir",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1131",
    doi = "10.18653/v1/S18-1131",
    pages = "821--825",
    abstract = "In this work we evaluate applicability of entity pair models and neural network architectures for relation extraction and classification in scientific papers at SemEval-2018. We carry out experiments with representing entity pairs through sentence tokens and through shortest path in dependency tree, comparing approaches based on convolutional and recurrent neural networks. With convolutional network applied to shortest path in dependency tree we managed to be ranked eighth in subtask 1.1 ({``}clean data{''}), ninth in 1.2 ({``}noisy data{''}). Similar model applied to separate parts of the shortest path was mounted to ninth (extraction track) and seventh (classification track) positions in subtask 2 ranking.",
}
@inproceedings{keiper-etal-2018-unima,
    title = "{U}ni{M}a at {S}em{E}val-2018 Task 7: Semantic Relation Extraction and Classification from Scientific Publications",
    author = "Keiper, Thorsten  and
      Lyu, Zhonghao  and
      Pooladzadeh, Sara  and
      Xu, Yuan  and
      Zhang, Jingyi  and
      Lauscher, Anne  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1132",
    doi = "10.18653/v1/S18-1132",
    pages = "826--830",
    abstract = "Large repositories of scientific literature call for the development of robust methods to extract information from scholarly papers. This problem is addressed by the SemEval 2018 Task 7 on extracting and classifying relations found within scientific publications. In this paper, we present a feature-based and a deep learning-based approach to the task and discuss the results of the system runs that we submitted for evaluation.",
}
@inproceedings{macavaney-etal-2018-gu,
    title = "{GU} {IRLAB} at {S}em{E}val-2018 Task 7: Tree-{LSTM}s for Scientific Relation Classification",
    author = "MacAvaney, Sean  and
      Soldaini, Luca  and
      Cohan, Arman  and
      Goharian, Nazli",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1133",
    doi = "10.18653/v1/S18-1133",
    pages = "831--835",
    abstract = "SemEval 2018 Task 7 focuses on relation extraction and classification in scientific literature. In this work, we present our tree-based LSTM network for this shared task. Our approach placed 9th (of 28) for subtask 1.1 (relation classification), and 5th (of 20) for subtask 1.2 (relation classification with noisy entities). We also provide an ablation study of features included as input to the network.",
}
@inproceedings{hettinger-etal-2018-claire,
    title = "{C}lai{RE} at {S}em{E}val-2018 Task 7: Classification of Relations using Embeddings",
    author = "Hettinger, Lena  and
      Dallmann, Alexander  and
      Zehe, Albin  and
      Niebler, Thomas  and
      Hotho, Andreas",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1134",
    doi = "10.18653/v1/S18-1134",
    pages = "836--841",
    abstract = "In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). We compare two models for classification, a C-LSTM which utilizes only word embeddings and an SVM that also takes handcrafted features into account. To adapt to the domain of science we train word embeddings on scientific papers collected from arXiv.org. The hand-crafted features consist of lexical features to model the semantic relations as well as the entities between which the relation holds. Classification of Relations using Embeddings (ClaiRE) achieved an F1 score of 74.89{\%} for the first subtask and 78.39{\%} for the second.",
}
@inproceedings{gluhak-etal-2018-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts",
    author = "Gluhak, Martin  and
      di Buono, Maria Pia  and
      Akkasi, Abbas  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1135",
    doi = "10.18653/v1/S18-1135",
    pages = "842--847",
    abstract = "We describe two systems for semantic relation classification with which we participated in the SemEval 2018 Task 7, subtask 1 on semantic relation classification: an SVM model and a CNN model. Both models combine dense pretrained word2vec features and hancrafted sparse features. For training the models, we combine the two datasets provided for the subtasks in order to balance the under-represented classes. The SVM model performed better than CNN, achieving a F1-macro score of 69.98{\%} on subtask 1.1 and 75.69{\%} on subtask 1.2. The system ranked 7th on among 28 submissions on subtask 1.1 and 7th among 20 submissions on subtask 1.2.",
}
@inproceedings{dragoni-2018-neurosent-pdi-semeval,
    title = "{NEUROSENT}-{PDI} at {S}em{E}val-2018 Task 7: Discovering Textual Relations With a Neural Network Model",
    author = "Dragoni, Mauro",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1136",
    doi = "10.18653/v1/S18-1136",
    pages = "848--852",
    abstract = "Discovering semantic relations within textual documents is a timely topic worthy of investigation. Natural language processing strategies are generally used for linking chunks of text in order to extract information that can be exploited by semantic search engines for performing complex queries. The scientific domain is an interesting area where these techniques can be applied. In this paper, we describe a system based on neural networks applied to the SemEval 2018 Task 7. The system relies on the use of word embeddings for composing the vectorial representation of text chunks. Such representations are used for feeding a neural network aims to learn the structure of paths connecting chunks associated with a specific relation. Preliminary results demonstrated the suitability of the proposed approach encouraging the investigation of this research direction.",
}
@inproceedings{mahendran-etal-2018-scirel,
    title = "{S}ci{REL} at {S}em{E}val-2018 Task 7: A System for Semantic Relation Extraction and Classification",
    author = "Mahendran, Darshini  and
      Brahmana, Chathurika  and
      McInnes, Bridget",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1137",
    doi = "10.18653/v1/S18-1137",
    pages = "853--857",
    abstract = "This paper describes our system, SciREL (Scientific abstract RELation extraction system), developed for the SemEval 2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers. We present a feature-vector based system to extract explicit semantic relation and classify them. Our system is trained in the ACL corpus (BIrd et al., 2008) that contains annotated abstracts given by the task organizers. When an abstract with annotated entities is given as the input into our system, it extracts the semantic relations through a set of defined features and classifies them into one of the given six categories of relations through feature engineering and a learned model. For the best combination of features, our system SciREL obtained an F-measure of 20.03 on the official test corpus which includes 150 abstracts in the relation classification Subtask 1.1. In this paper, we provide an in-depth error analysis of our results to prevent duplication of research efforts in the development of future systems",
}
@inproceedings{barik-etal-2018-ntnu,
    title = "{NTNU} at {S}em{E}val-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers",
    author = {Barik, Biswanath  and
      Sikdar, Utpal Kumar  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1138",
    doi = "10.18653/v1/S18-1138",
    pages = "858--862",
    abstract = "The paper presents NTNU{'}s contribution to SemEval-2018 Task 7 on relation identification and classification. The class weights and parameters of five alternative supervised classifiers were optimized through grid search and cross-validation. The outputs of the classifiers were combined through voting for the final prediction. A wide variety of features were explored, with the most informative identified by feature selection. The best setting achieved F1 scores of 47.4{\%} and 66.0{\%} in the relation classification subtasks 1.1 and 1.2. For relation identification and classification in subtask 2, it achieved F1 scores of 33.9{\%} and 17.0{\%},",
}
@inproceedings{pratap-etal-2018-talla,
    title = "Talla at {S}em{E}val-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks",
    author = "Pratap, Bhanu  and
      Shank, Daniel  and
      Ositelu, Oladipo  and
      Galbraith, Byron",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1139",
    doi = "10.18653/v1/S18-1139",
    pages = "863--867",
    abstract = "This paper describes our approach to SemEval-2018 Task 7 {--} given an entity-tagged text from the ACL Anthology corpus, identify and classify pairs of entities that have one of six possible semantic relationships. Our model consists of a convolutional neural network leveraging pre-trained word embeddings, unlabeled ACL-abstracts, and multiple window sizes to automatically learn useful features from entity-tagged sentences. We also experiment with a hybrid loss function, a combination of cross-entropy loss and ranking loss, to boost the separation in classification scores. Lastly, we include WordNet-based features to further improve the performance of our model. Our best model achieves an F1(macro) score of 74.2 and 84.8 on subtasks 1.1 and 1.2, respectively.",
}
@inproceedings{r-etal-2018-teamdl,
    title = "{T}eam{DL} at {S}em{E}val-2018 Task 8: Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random Fields",
    author = "R, Manikandan  and
      Madgula, Krishna  and
      Saha, Snehanshu",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1140",
    doi = "10.18653/v1/S18-1140",
    pages = "868--873",
    abstract = "In this work we present our participation to SemEval-2018 Task 8 subtasks 1 {\&} 2 respectively. We developed Convolution Neural Network system for malware sentence classification (subtask 1) and Conditional Random Fields system for malware token label prediction (subtask 2). We experimented with couple of word embedding strategies, feature sets and achieved competitive performance across the two subtasks. For subtask 1 We experimented with two category of word embeddings namely native embeddings and task specific embedding using Word2vec and Glove algorithms. 1. Native Embeddings: All words including the unknown ones that are randomly initialized use embeddings from original Word2vec/Glove models. 2. Task specific : The embeddings are generated by training Word2vec/Glove algorithms on sentences from MalwareTextDB We found that glove outperforms rest of embeddings for subtask 1. For subtask 2, we used N-grams of size 6, previous, next tokens and labels, features giving disjunctions of words anywhere in the left or right, word shape features, word lemma of current, previous and next words, word-tag pair features, POS tags, prefix and suffixes.",
}
@inproceedings{fu-etal-2018-hccl,
    title = "{HCCL} at {S}em{E}val-2018 Task 8: An End-to-End System for Sequence Labeling from Cybersecurity Reports",
    author = "Fu, Mingming  and
      Zhao, Xuemin  and
      Yan, Yonghong",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1141",
    doi = "10.18653/v1/S18-1141",
    pages = "874--877",
    abstract = "This paper describes HCCL team systems that participated in SemEval 2018 Task 8: SecureNLP (Semantic Extraction from cybersecurity reports using NLP). To solve the problem, our team applied a neural network architecture that benefits from both word and character level representaions automatically, by using combination of Bi-directional LSTM, CNN and CRF (Ma and Hovy, 2016). Our system is truly end-to-end, requiring no feature engineering or data preprocessing, and we ranked 4th in the subtask 1, 7th in the subtask2 and 3rd in the SubTask2-relaxed.",
}
@inproceedings{padia-etal-2018-umbc,
    title = "{UMBC} at {S}em{E}val-2018 Task 8: Understanding Text about Malware",
    author = "Padia, Ankur  and
      Roy, Arpita  and
      Satyapanich, Taneeya  and
      Ferraro, Francis  and
      Pan, Shimei  and
      Park, Youngja  and
      Joshi, Anupam  and
      Finin, Tim",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1142",
    doi = "10.18653/v1/S18-1142",
    pages = "878--884",
    abstract = "We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieve F1 score of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at \url{http://bit.ly/cyber2vec}.",
}
@inproceedings{loyola-etal-2018-villani,
    title = "Villani at {S}em{E}val-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning",
    author = "Loyola, Pablo  and
      Gajananan, Kugamoorthy  and
      Watanabe, Yuji  and
      Satoh, Fumiko",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1143",
    doi = "10.18653/v1/S18-1143",
    pages = "885--889",
    abstract = "In this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports. The goal is to explore if natural language processing methods can provide relevant and actionable knowledge to contribute to better understand malicious behavior. Our method consists of an attention-based Bi-LSTM which achieved competitive performance of 0.57 for the Subtask 1. In the due process we also present ablation studies across multiple embeddings and their level of representation and also report the strategies we used to mitigate the extreme imbalance between classes.",
}
@inproceedings{sikdar-etal-2018-flytxt,
    title = {{F}lytxt{\_}{NTNU} at {S}em{E}val-2018 Task 8: Identifying and Classifying Malware Text Using Conditional Random Fields and Na{\"\i}ve {B}ayes Classifiers},
    author = {Sikdar, Utpal Kumar  and
      Barik, Biswanath  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1144",
    doi = "10.18653/v1/S18-1144",
    pages = "890--893",
    abstract = "Cybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware sentences given a document (subTask1 of SemEval 2018, Task 8), as well as to classifying malware tokens in the sentences (subTask2). The approach achieved good results, ranking second of twelve participants for both subtasks, with F-scores of 57{\%} for subTask1 and 28{\%} for subTask2.",
}
@inproceedings{brew-2018-digital,
    title = "Digital Operatives at {S}em{E}val-2018 Task 8: Using dependency features for malware {NLP}",
    author = "Brew, Chris",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1145",
    doi = "10.18653/v1/S18-1145",
    pages = "894--897",
    abstract = "The four sub-tasks of SecureNLP build towards a capability for quickly highlighting critical information from malware reports, such as the specific actions taken by a malware sample. Digital Operatives (DO) submitted to sub-tasks 1 and 2, using standard text analysis technology (text classification for sub-task 1, and a CRF for sub-task 2). Performance is broadly competitive with other submitted systems on sub-task 1 and weak on sub-task 2. The annotation guidelines for the intermediate sub-tasks create a linkage to the final task, which is both an annotation challenge and a potentially useful feature of the task. The methods that DO chose do not attempt to make use of this linkage, which may be a missed opportunity. This motivates a post-hoc error analysis. It appears that the annotation task is very hard, and that in some cases both deep conceptual knowledge and substantial surrounding context are needed in order to correctly classify sentences.",
}
@inproceedings{onofrei-etal-2018-apollo,
    title = "{A}pollo at {S}em{E}val-2018 Task 9: Detecting Hypernymy Relations Using Syntactic Dependencies",
    author = "Onofrei, Mihaela  and
      Hulub, Ionu{\textcommabelow{t}}  and
      Trandab{\u{a}}{\textcommabelow{t}}, Diana  and
      G{\^\i}fu, Daniela",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1146",
    doi = "10.18653/v1/S18-1146",
    pages = "898--902",
    abstract = "This paper presents the participation of Apollo{'}s team in the SemEval-2018 Task 9 {``}Hypernym Discovery{''}, Subtask 1: {``}General-Purpose Hypernym Discovery{''}, which tries to produce a ranked list of hypernyms for a specific term. We propose a novel approach for automatic extraction of hypernymy relations from a corpus by using dependency patterns. We estimated that the application of these patterns leads to a higher score than using the traditional lexical patterns.",
}
@inproceedings{zhang-etal-2018-sjtu,
    title = "{SJTU}-{NLP} at {S}em{E}val-2018 Task 9: Neural Hypernym Discovery with Term Embeddings",
    author = "Zhang, Zhuosheng  and
      Li, Jiangtong  and
      Zhao, Hai  and
      Tang, Bingjie",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1147",
    doi = "10.18653/v1/S18-1147",
    pages = "903--908",
    abstract = "This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary. We introduce a neural network architecture for the concerned task and empirically study various neural network models to build the representations in latent space for words and phrases. The evaluated models include convolutional neural network, long-short term memory network, gated recurrent unit and recurrent convolutional neural network. We also explore different embedding methods, including word embedding and sense embedding for better performance.",
}
@inproceedings{qiu-etal-2018-nlp,
    title = "{NLP}{\_}{HZ} at {S}em{E}val-2018 Task 9: a Nearest Neighbor Approach",
    author = "Qiu, Wei  and
      Chen, Mosha  and
      Li, Linlin  and
      Si, Luo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1148",
    doi = "10.18653/v1/S18-1148",
    pages = "909--913",
    abstract = "Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus. This paper proposes a simple but effective method for the discovery of hypernym sets based on word embedding, which can be used to measure the contextual similarities between words. Given a test hyponym word, we get its hypernym lists by computing the similarities between the hyponym word and words in the training data, and fill the test word{'}s hypernym lists with the hypernym list in the training set of the nearest similarity distance to the test word. In SemEval 2018 task9, our results, achieve 1st on Spanish, 2nd on Italian, 6th on English in the metric of MAP.",
}
@inproceedings{hassan-etal-2018-umduluth,
    title = "{UMD}uluth-{CS}8761 at {S}em{E}val-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings",
    author = "Hassan, Arshia Zernab  and
      Vallabhajosyula, Manikya Swathi  and
      Pedersen, Ted",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1149",
    doi = "10.18653/v1/S18-1149",
    pages = "914--918",
    abstract = "Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on co-occurrence frequencies of word pairs, Hearst Patterns based on regular expressions, and word embeddings created from the UMBC corpus. Our system Babbage participated in Subtask 1A for English and placed 6th of 19 systems when identifying concept hypernyms, and 12th of 18 systems for entity hypernyms.",
}
@inproceedings{issa-alaa-aldine-etal-2018-expr,
    title = "{EXPR} at {S}em{E}val-2018 Task 9: A Combined Approach for Hypernym Discovery",
    author = "Issa Alaa Aldine, Ahmad  and
      Harzallah, Mounira  and
      Berio, Giuseppe  and
      B{\'e}chet, Nicolas  and
      Faour, Ahmad",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1150",
    doi = "10.18653/v1/S18-1150",
    pages = "919--923",
    abstract = "In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on a corpus to extract candidate hypernyms and represent their dependency paths as a feature vector. The feature vector is concatenated with a feature vector obtained using Wikipedia pre-trained term embedding model. The concatenated feature vector fits a supervised machine learning method to learn a classifier model. This model is able to classify new candidate hypernyms as hypernym or not. Our system performs well to discover new hypernyms not defined in gold hypernyms.",
}
@inproceedings{maldonado-klubicka-2018-adapt,
    title = "{ADAPT} at {S}em{E}val-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora",
    author = "Maldonado, Alfredo  and
      Klubi{\v{c}}ka, Filip",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1151",
    doi = "10.18653/v1/S18-1151",
    pages = "924--927",
    abstract = "This paper describes a simple but competitive unsupervised system for hypernym discovery. The system uses skip-gram word embeddings with negative sampling, trained on specialised corpora. Candidate hypernyms for an input word are predicted based based on cosine similarity scores. Two sets of word embedding models were trained separately on two specialised corpora: a medical corpus and a music industry corpus. Our system scored highest in the medical domain among the competing unsupervised systems but performed poorly on the music industry domain. Our system does not depend on any external data other than raw specialised corpora.",
}
@inproceedings{berend-etal-2018-300,
    title = "300-sparsans at {S}em{E}val-2018 Task 9: Hypernymy as interaction of sparse attributes",
    author = {Berend, G{\'a}bor  and
      Makrai, M{\'a}rton  and
      F{\"o}ldi{\'a}k, P{\'e}ter},
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1152",
    doi = "10.18653/v1/S18-1152",
    pages = "928--934",
    abstract = "This paper describes 300-sparsians{'}s participation in SemEval-2018 Task 9: Hypernym Discovery, with a system based on sparse coding and a formal concept hierarchy obtained from word embeddings. Our system took first place in subtasks (1B) Italian (all and entities), (1C) Spanish entities, and (2B) music entities.",
}
@inproceedings{brychcin-etal-2018-uwb,
    title = "{UWB} at {S}em{E}val-2018 Task 10: Capturing Discriminative Attributes from Word Distributions",
    author = "Brychc{\'\i}n, Tom{\'a}{\v{s}}  and
      Hercig, Tom{\'a}{\v{s}}  and
      Steinberger, Josef  and
      Konkol, Michal",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1153",
    doi = "10.18653/v1/S18-1153",
    pages = "935--939",
    abstract = "We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1{\%} and is ranked {\#}4 among 26 submitted systems.",
}
@inproceedings{sommerauer-etal-2018-meaning,
    title = "Meaning{\_}space at {S}em{E}val-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings",
    author = "Sommerauer, Pia  and
      Fokkens, Antske  and
      Vossen, Piek",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1154",
    doi = "10.18653/v1/S18-1154",
    pages = "940--946",
    abstract = "This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploiting explicitly encoded knowledge about concepts in WordNet and information encoded in distributional semantic vectors. Rather than aiming for high performance, we explore which kind of semantic knowledge is best captured by different methods. The results indicate that WordNet glosses on different levels of the hierarchy capture many attributes relevant for this task. In combination with exploiting word embedding similarities, this source of information yielded our best results. Our best performing system ranked 5th out of 13 final ranks. Our analysis yields insights into the different kinds of attributes represented by different sources of knowledge.",
}
@inproceedings{attia-etal-2018-ghh,
    title = "{GHH} at {S}em{E}val-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics",
    author = "Attia, Mohammed  and
      Samih, Younes  and
      Faruqui, Manaal  and
      Maier, Wolfgang",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1155",
    doi = "10.18653/v1/S18-1155",
    pages = "947--952",
    abstract = "This paper describes our system submission to the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Given two concepts and an attribute, the task is to determine whether the attribute is semantically related to one concept and not the other. In this work we assume that discriminative attributes can be detected by discovering the association (or lack of association) between a pair of words. The hypothesis we test in this contribution is whether the semantic difference between two pairs of concepts can be treated in terms of measuring the distance between words in a vector space, or can simply be obtained as a by-product of word co-occurrence counts.",
}
@inproceedings{gamallo-2018-citiusnlp,
    title = "{C}itius{NLP} at {S}em{E}val-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes",
    author = "Gamallo, Pablo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1156",
    doi = "10.18653/v1/S18-1156",
    pages = "953--957",
    abstract = "This article describes the unsupervised strategy submitted by the CitiusNLP team to the SemEval 2018 Task 10, a task which consists of predict whether a word is a discriminative attribute between two other words. Our strategy relies on the correspondence between discriminative attributes and relevant contexts of a word. More precisely, the method uses transparent distributional models to extract salient contexts of words which are identified as discriminative attributes. The system performance reaches about 70{\%} accuracy when it is applied on the development dataset, but its accuracy goes down (63{\%}) on the official test dataset.",
}
@inproceedings{wu-etal-2018-thu-ngn-semeval-2018,
    title = "{THU}{\_}{NGN} at {S}em{E}val-2018 Task 10: Capturing Discriminative Attributes with {MLP}-{CNN} model",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Wu, Sixing  and
      Yuan, Zhigang  and
      Huang, Yongfeng",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1157",
    doi = "10.18653/v1/S18-1157",
    pages = "958--962",
    abstract = "Existing semantic models are capable of identifying the semantic similarity of words. However, it{'}s hard for these models to discriminate between a word and another similar word. Thus, the aim of SemEval-2018 Task 10 is to predict whether a word is a discriminative attribute between two concepts. In this task, we apply a multilayer perceptron (MLP)-convolutional neural network (CNN) model to identify whether an attribute is discriminative. The CNNs are used to extract low-level features from the inputs. The MLP takes both the flatten CNN maps and inputs to predict the labels. The evaluation F-score of our system on the test set is 0.629 (ranked 15th), which indicates that our system still needs to be improved. However, the behaviours of our system in our experiments provide useful information, which can help to improve the collective understanding of this novel task.",
}
@inproceedings{dumitru-etal-2018-alb,
    title = "{ALB} at {S}em{E}val-2018 Task 10: A System for Capturing Discriminative Attributes",
    author = "Dumitru, Bogdan  and
      Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1158",
    doi = "10.18653/v1/S18-1158",
    pages = "963--967",
    abstract = "Semantic difference detection attempts to capture whether a word is a discriminative attribute between two other words. For example, the discriminative feature red characterizes the first word from the (apple, banana) pair, but not the second. Modeling semantic difference is essential for language understanding systems, as it provides useful information for identifying particular aspects of word senses. This paper describes our system implementation (the ALB system of the NLP@Unibuc team) for the 10th task of the SemEval 2018 workshop, {``}Capturing Discriminative Attributes{''}. We propose a method for semantic difference detection that uses an SVM classifier with features based on co-occurrence counts and shallow semantic parsing, achieving 0.63 F1 score in the competition.",
}
@inproceedings{gonzalez-etal-2018-elirf-upv,
    title = "{EL}i{RF}-{UPV} at {S}em{E}val-2018 Task 10: Capturing Discriminative Attributes with Knowledge Graphs and {W}ikipedia",
    author = "Gonz{\'a}lez, Jos{\'e}-{\'A}ngel  and
      Hurtado, Llu{\'\i}s-F.  and
      Segarra, Encarna  and
      Pla, Ferran",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1159",
    doi = "10.18653/v1/S18-1159",
    pages = "968--971",
    abstract = "This paper describes the participation of ELiRF-UPV team at task 10, Capturing Discriminative Attributes, of SemEval-2018. Our best approach consists of using ConceptNet, Wikipedia and NumberBatch embeddings in order to stablish relationships between concepts and attributes. Furthermore, this system achieves competitive results in the official evaluation.",
}
@inproceedings{taslimipoor-etal-2018-wolves,
    title = "Wolves at {S}em{E}val-2018 Task 10: Semantic Discrimination based on Knowledge and Association",
    author = "Taslimipoor, Shiva  and
      Rohanian, Omid  and
      Ha, Le An  and
      Corpas Pastor, Gloria  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1160",
    doi = "10.18653/v1/S18-1160",
    pages = "972--976",
    abstract = "This paper describes the system submitted to SemEval 2018 shared task 10 {`}Capturing Dicriminative Attributes{'}. We use a combination of knowledge-based and co-occurrence features to capture the semantic difference between two words in relation to an attribute. We define scores based on association measures, ngram counts, word similarity, and ConceptNet relations. The system is ranked 4th (joint) on the official leaderboard of the task.",
}
@inproceedings{arroyo-fernandez-etal-2018-unam,
    title = "{UNAM} at {S}em{E}val-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones",
    author = "Arroyo-Fern{\'a}ndez, Ignacio  and
      Meza, Ivan  and
      M{\'e}ndez-Cruz, Carlos-Francisco",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1161",
    doi = "10.18653/v1/S18-1161",
    pages = "977--984",
    abstract = "In this paper we report an unsupervised method aimed to identify whether an attribute is discriminative for two words (which are treated as concepts, in our particular case). To this end, we use geometrically inspired vector operations underlying unsupervised decision functions. These decision functions operate on state-of-the-art neural word embeddings of the attribute and the concepts. The main idea can be described as follows: if attribute $q$ discriminates concept $a$ from concept $b$, then $q$ is excluded from the feature set shared by these two concepts: the intersection. That is, the membership $q\in (a\cap b)$ does not hold. As $a,b,q$ are represented with neural word embeddings, we tested vector operations allowing us to measure membership, i.e. fuzzy set operations (t-norm, for fuzzy intersection, and t-conorm, for fuzzy union) and the similarity between $q$ and the convex cone described by $a$ and $b$.",
}
@inproceedings{speer-lowry-duda-2018-luminoso,
    title = "{L}uminoso at {S}em{E}val-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge",
    author = "Speer, Robyn  and
      Lowry-Duda, Joanna",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1162",
    doi = "10.18653/v1/S18-1162",
    pages = "985--989",
    abstract = "Luminoso participated in the SemEval 2018 task on {``}Capturing Discriminative Attributes{''} with a system based on ConceptNet, an open knowledge graph focused on general knowledge. In this paper, we describe how we trained a linear classifier on a small number of semantically-informed features to achieve an F1 score of 0.7368 on the task, close to the task{'}s high score of 0.75.",
}
@inproceedings{santus-etal-2018-bomji,
    title = "{B}om{J}i at {S}em{E}val-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes",
    author = "Santus, Enrico  and
      Biemann, Chris  and
      Chersoni, Emmanuele",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1163",
    doi = "10.18653/v1/S18-1163",
    pages = "990--994",
    abstract = "This paper describes BomJi, a supervised system for capturing discriminative attributes in word pairs (e.g. yellow as discriminative for banana over watermelon). The system relies on an XGB classifier trained on carefully engineered graph-, pattern- and word embedding-based features. It participated in the SemEval-2018 Task 10 on Capturing Discriminative Attributes, achieving an F1 score of 0.73 and ranking 2nd out of 26 participant systems.",
}
@inproceedings{grishin-2018-igevorse,
    title = "Igevorse at {S}em{E}val-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes",
    author = "Grishin, Maxim",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1164",
    doi = "10.18653/v1/S18-1164",
    pages = "995--998",
    abstract = "This paper presents a comparison of several approaches for capturing discriminative attributes and considers an impact of concatenation of several word embeddings of different nature on the classification performance. A similarity-based method is proposed and compared with classical machine learning approaches. It is shown that this method outperforms others on all the considered word vector models and there is a performance increase when concatenated datasets are used.",
}
@inproceedings{zhou-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection",
    author = "Zhou, Yunxiao  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1165",
    doi = "10.18653/v1/S18-1165",
    pages = "999--1002",
    abstract = "This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.",
}
@inproceedings{vinayan-etal-2018-amritanlp,
    title = "{A}mrita{NLP} at {S}em{E}val-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.",
    author = "Vinayan, Vivek  and
      Kumar M, Anand  and
      K P, Soman",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1166",
    doi = "10.18653/v1/S18-1166",
    pages = "1003--1007",
    abstract = "The {``}Capturing Discriminative Attributes{''} sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 {\&} 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result).",
}
@inproceedings{kulmizev-etal-2018-discriminator,
    title = "Discriminator at {S}em{E}val-2018 Task 10: Minimally Supervised Discrimination",
    author = "Kulmizev, Artur  and
      Abdou, Mostafa  and
      Ravishankar, Vinit  and
      Nissim, Malvina",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1167",
    doi = "10.18653/v1/S18-1167",
    pages = "1008--1012",
    abstract = "We participated to the SemEval-2018 shared task on capturing discriminative attributes (Task 10) with a simple system that ranked 8th amongst the 26 teams that took part in the evaluation. Our final score was 0.67, which is competitive with the winning score of 0.75, particularly given that our system is a zero-shot system that requires no training and minimal parameter optimisation. In addition to describing the submitted system, and discussing the implications of the relative success of such a system on this task, we also report on other, more complex models we experimented with.",
}
@inproceedings{king-etal-2018-unbnlp,
    title = "{UNBNLP} at {S}em{E}val-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes",
    author = "King, Milton  and
      Hakimi Parizi, Ali  and
      Cook, Paul",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1168",
    doi = "10.18653/v1/S18-1168",
    pages = "1013--1016",
    abstract = "In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.",
}
@inproceedings{mao-etal-2018-abdn,
    title = "{ABDN} at {S}em{E}val-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and {W}ord{N}et",
    author = "Mao, Rui  and
      Chen, Guanyi  and
      Li, Ruizhe  and
      Lin, Chenghua",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1169",
    doi = "10.18653/v1/S18-1169",
    pages = "1017--1021",
    abstract = "This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word{'}s similarity with each of the two semantically similar words, based on an extended word embedding method and WordNet. Instead of computing the similarities between the attribute and semantically similar words by using standard word embeddings, we propose a novel method that combines word and context embeddings which can better measure similarities. Our model is simple and effective, which achieves an average F1 score of 0.62 on the test set.",
}
@inproceedings{zhang-carpuat-2018-umd,
    title = "{UMD} at {S}em{E}val-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?",
    author = "Zhang, Alexander  and
      Carpuat, Marine",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1170",
    doi = "10.18653/v1/S18-1170",
    pages = "1022--1026",
    abstract = "We describe the University of Maryland{'}s submission to SemEval-018 Task 10, {``}Capturing Discriminative Attributes{''}: given word triples (w1, w2, d), the goal is to determine whether d is a discriminating attribute belonging to w1 but not w2. Our study aims to determine whether word embeddings can address this challenging task. Our submission casts this problem as supervised binary classification using only word embedding features. Using a gaussian SVM model trained only on validation data results in an F-score of 60{\%}. We also show that cosine similarity features are more effective, both in unsupervised systems (F-score of 65{\%}) and supervised systems (F-score of 67{\%}).",
}
@inproceedings{shiue-etal-2018-ntu,
    title = "{NTU} {NLP} Lab System at {S}em{E}val-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge",
    author = "Shiue, Yow-Ting  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1171",
    doi = "10.18653/v1/S18-1171",
    pages = "1027--1033",
    abstract = "This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73{\%} F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data.",
}
@inproceedings{gonzalez-etal-2018-elirf-upv-semeval,
    title = "{EL}i{RF}-{UPV} at {S}em{E}val-2018 Task 11: Machine Comprehension using Commonsense Knowledge",
    author = "Gonz{\'a}lez, Jos{\'e}-{\'A}ngel  and
      Hurtado, Llu{\'\i}s-F.  and
      Segarra, Encarna  and
      Pla, Ferran",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1172",
    doi = "10.18653/v1/S18-1172",
    pages = "1034--1037",
    abstract = "This paper describes the participation of ELiRF-UPV team at task 11, Machine Comprehension using Commonsense Knowledge, of SemEval-2018. Our approach is based on the use of word embeddings, NumberBatch Embeddings, and a Deep Learning architecture to find the best answer for the multiple-choice questions based on the narrative text. The results obtained are in line with those obtained by the other participants and they encourage us to continue working on this problem.",
}
@inproceedings{liu-etal-2018-ynu,
    title = "{YNU}{\_}{AI}1799 at {S}em{E}val-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble",
    author = "Liu, Qingxun  and
      Yao, Hongdou  and
      Zhou, Xaobing  and
      Xie, Ge",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1173",
    doi = "10.18653/v1/S18-1173",
    pages = "1038--1042",
    abstract = "In this paper, we describe a machine reading comprehension system that participated in SemEval-2018 Task 11: Machine Comprehension using commonsense knowledge. In this work, we train a series of neural network models such as multi-LSTM, BiLSTM, multi- BiLSTM-CNN and attention-based BiLSTM, etc. On top of some sub models, there are two kinds of word embedding: (a) general word embedding generated from unsupervised neural language model; and (b) position embedding generated from general word embedding. Finally, we make a hard vote on the predictions of these models and achieve relatively good result. The proposed approach achieves 8th place in Task 11 with the accuracy of 0.7213.",
}
@inproceedings{ding-zhou-2018-ynu,
    title = "{YNU}{\_}{D}eep at {S}em{E}val-2018 Task 11: An Ensemble of Attention-based {B}i{LSTM} Models for Machine Comprehension",
    author = "Ding, Peng  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1174",
    doi = "10.18653/v1/S18-1174",
    pages = "1043--1047",
    abstract = "We firstly use GloVe to learn the distributed representations automatically from the instance, question and answer triples. Then an attentionbased Bidirectional LSTM (BiLSTM) model is used to encode the triples. We also perform a simple ensemble method to improve the effectiveness of our model. The system we developed obtains an encouraging result on this task. It achieves the accuracy 0.7472 on the test set. We rank 5th according to the official ranking.",
}
@inproceedings{sheng-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task",
    author = "Sheng, Yixuan  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1175",
    doi = "10.18653/v1/S18-1175",
    pages = "1048--1052",
    abstract = "This paper describes the system we submitted to the Task 11 in SemEval 2018, i.e., Machine Comprehension using Commonsense Knowledge. Given a passage and some questions that each have two candidate answers, this task requires the participate system to select out one answer meet the meaning of original text or commonsense knowledge from the candidate answers. For this task, we use a deep learning method to obtain final predict answer by calculating relevance of choices representations and question-aware document representation.",
}
@inproceedings{jiang-sun-2018-csreader,
    title = "{CSR}eader at {S}em{E}val-2018 Task 11: Multiple Choice Question Answering as Textual Entailment",
    author = "Jiang, Zhengping  and
      Sun, Qi",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1176",
    doi = "10.18653/v1/S18-1176",
    pages = "1053--1057",
    abstract = "In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective. Since some of the knowledge required is not explicitly mentioned in the text, we try to exploit commonsense knowledge by using pretrained word embeddings during contextual embeddings and by dynamically generating a weighted representation of related script knowledge. In the model two kinds of prediction structure are ensembled, and the final accuracy of our system is 10 percent higher than the naiive baseline.",
}
@inproceedings{yuan-etal-2018-ynu,
    title = "{YNU}-{HPCC} at {S}emeval-2018 Task 11: Using an Attention-based {CNN}-{LSTM} for Machine Comprehension using Commonsense Knowledge",
    author = "Yuan, Hang  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1177",
    doi = "10.18653/v1/S18-1177",
    pages = "1058--1062",
    abstract = "This shared task is a typical question answering task. Compared with the normal question and answer system, it needs to give the answer to the question based on the text provided. The essence of the problem is actually reading comprehension. Typically, there are several questions for each text that correspond to it. And for each question, there are two candidate answers (and only one of them is correct). To solve this problem, the usual approach is to use convolutional neural networks (CNN) and recurrent neural network (RNN) or their improved models (such as long short-term memory (LSTM)). In this paper, an attention-based CNN-LSTM model is proposed for this task. By adding an attention mechanism and combining the two models, this experimental result has been significantly improved.",
}
@inproceedings{xia-2018-jiangnan,
    title = "Jiangnan at {S}em{E}val-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task",
    author = "Xia, Jiangnan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1178",
    doi = "10.18653/v1/S18-1178",
    pages = "1063--1067",
    abstract = "This paper describes our submission for the International Workshop on Semantic Evaluation (SemEval-2018) shared task 11{--} Machine Comprehension using Commonsense Knowledge (Ostermann et al., 2018b). We use a deep neural network model to choose the correct answer from the candidate answers pair when the document and question are given. The interactions between document, question and answers are modeled by attention mechanism and a variety of manual features are used to improve model performance. We also use CoVe (McCann et al., 2017) as an external source of knowledge which is not mentioned in the document. As a result, our system achieves 80.91{\%} accuracy on the test data, which is on the third place of the leaderboard.",
}
@inproceedings{reznikova-derczynski-2018-iucm,
    title = "{IUCM} at {S}em{E}val-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source",
    author = "Reznikova, Sofia  and
      Derczynski, Leon",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1179",
    doi = "10.18653/v1/S18-1179",
    pages = "1068--1072",
    abstract = "This paper describes the IUCM entry at SemEval-2018 Task 11, on machine comprehension using commonsense knowledge. First, clustering and topic modeling are used to divide given texts into topics. Then, during the answering phase, other texts of the same topic are retrieved and used as commonsense knowledge. Finally, the answer is selected. While clustering itself shows good results, finding an answer proves to be more challenging. This paper reports the results of system evaluation and suggests potential improvements.",
}
@inproceedings{li-zhou-2018-lyb3b,
    title = "Lyb3b at {S}em{E}val-2018 Task 11: Machine Comprehension Task using Deep Learning Models",
    author = "Li, Yongbin  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1180",
    doi = "10.18653/v1/S18-1180",
    pages = "1073--1077",
    abstract = "Machine Comprehension of text is a typical Natural Language Processing task which remains an elusive challenge. This paper is to solve the task 11 of SemEval-2018, Machine Comprehension using Commonsense Knowledge task. We use deep learning model to solve the problem. We build distributed word embedding of text, question and answering respectively instead of manually extracting features by linguistic tools. Meanwhile, we use a series of frameworks such as CNN model, LSTM model, LSTM with attention model and biLSTM with attention model for processing word vector. Experiments demonstrate the superior performance of biLSTM with attention framework compared to other models. We also delete high frequency words and combine word vector and data augmentation methods, achieved a certain effect. The approach we proposed rank 6th in official results, with accuracy rate of 0.7437 in test dataset.",
}
@inproceedings{merkhofer-etal-2018-mitre,
    title = "{MITRE} at {S}em{E}val-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge",
    author = "Merkhofer, Elizabeth  and
      Henderson, John  and
      Bloom, David  and
      Strickhart, Laura  and
      Zarrella, Guido",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1181",
    doi = "10.18653/v1/S18-1181",
    pages = "1078--1082",
    abstract = "This paper describes MITRE{'}s participation in SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Logistic regression ties the systems together into an ensemble submitted for evaluation. The resulting system answers reading comprehension questions with 82.27{\%} accuracy.",
}
@inproceedings{kim-etal-2018-snu,
    title = "{SNU}{\_}{IDS} at {S}em{E}val-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension",
    author = "Kim, Taeuk  and
      Choi, Jihun  and
      Lee, Sang-goo",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1182",
    doi = "10.18653/v1/S18-1182",
    pages = "1083--1088",
    abstract = "We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic built on a set of given sentences (a claim, reason, and warrant) is plausible or not. The model utilizes contextualized word vectors pre-trained on large machine translation (MT) datasets as a form of transfer learning, which can help to mitigate the lack of training data. Quantitative analysis shows that simply leveraging LSTMs trained on MT datasets outperforms several baselines and non-transferred models, achieving accuracies of about 70{\%} on the development set and about 60{\%} on the test set.",
}
@inproceedings{liu-etal-2018-itnlp,
    title = "{ITNLP}-{ARC} at {S}em{E}val-2018 Task 12: Argument Reasoning Comprehension with Attention",
    author = "Liu, Wenjie  and
      Sun, Chengjie  and
      Lin, Lei  and
      Liu, Bingquan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1183",
    doi = "10.18653/v1/S18-1183",
    pages = "1089--1093",
    abstract = "Reasoning is a very important topic and has many important applications in the field of natural language processing. Semantic Evaluation (SemEval) 2018 Task 12 {``}The Argument Reasoning Comprehension{''} committed to research natural language reasoning. In this task, we proposed a novel argument reasoning comprehension system, ITNLP-ARC, which use Neural Networks technology to solve this problem. In our system, the LSTM model is involved to encode both the premise sentences and the warrant sentences. The attention model is used to merge the two premise sentence vectors. Through comparing the similarity between the attention vector and each of the two warrant vectors, we choose the one with higher similarity as our system{'}s final answer.",
}
@inproceedings{tian-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task",
    author = "Tian, Junfeng  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1184",
    doi = "10.18653/v1/S18-1184",
    pages = "1094--1098",
    abstract = "This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, we use their surrounds (i.e., claim, reason, debate context) as another attention vectors to get contextual representations, which work as final clues to select the correct warrant. Our model achieves 60.4{\%} accuracy and ranks 3rd among 22 participating systems.",
}
@inproceedings{niven-kao-2018-nlitrans,
    title = "{NLIT}rans at {S}em{E}val-2018 Task 12: Transfer of Semantic Knowledge for Argument Comprehension",
    author = "Niven, Timothy  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1185",
    doi = "10.18653/v1/S18-1185",
    pages = "1099--1103",
    abstract = "The Argument Reasoning Comprehension Task is a difficult challenge requiring significant language understanding and complex reasoning over world knowledge. We focus on transfer of a sentence encoder to bootstrap more complicated architectures given the small size of the dataset. Our best model uses a pre-trained BiLSTM to encode input sentences, learns task-specific features for the argument and warrants, then performs independent argument-warrant matching. This model achieves mean test set accuracy of 61.31{\%}. Encoder transfer yields a significant gain to our best model over random initialization. Sharing parameters for independent warrant evaluation provides regularization and effectively doubles the size of the dataset. We demonstrate that regularization comes from ignoring statistical correlations between warrant positions. We also report an experiment with our best model that only matches warrants to reasons, ignoring claims. Performance is still competitive, suggesting that our model is not necessarily learning the intended task.",
}
@inproceedings{zhao-etal-2018-blcu,
    title = "{BLCU}{\_}{NLP} at {S}em{E}val-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention",
    author = "Zhao, Meiqian  and
      Liu, Chunhua  and
      Liu, Lu  and
      Zhao, Yan  and
      Yu, Dong",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1186",
    doi = "10.18653/v1/S18-1186",
    pages = "1104--1108",
    abstract = "To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind. In this paper, we propose a hierarchical attention model to identify the right warrant which explains why the reason stands for the claim. Our model focuses not only on the similar part between warrants and other information but also on the contradictory part between two opposing warrants. In addition, we use the ensemble method for different models. Our model achieves an accuracy of 61{\%}, ranking second in this task. Experimental results demonstrate that our model is effective to make correct choices.",
}
@inproceedings{liao-etal-2018-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2018 Task 12: The Argument Reasoning Comprehension Task Using a Bi-directional {LSTM} with Attention Model",
    author = "Liao, Quanlei  and
      Yang, Xutao  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1187",
    doi = "10.18653/v1/S18-1187",
    pages = "1109--1113",
    abstract = "An argument is divided into two parts, the claim and the reason. To obtain a clearer conclusion, some additional explanation is required. In this task, the explanations are called warrants. This paper introduces a bi-directional long short term memory (Bi-LSTM) with an attention model to select a correct warrant from two to explain an argument. We address this question as a question-answering system. For each warrant, the model produces a probability that it is correct. Finally, the system chooses the highest correct probability as the answer. Ensemble learning is used to enhance the performance of the model. Among all of the participants, we ranked 15th on the test results.",
}
@inproceedings{liebeck-etal-2018-hhu,
    title = "{HHU} at {S}em{E}val-2018 Task 12: Analyzing an Ensemble-based Deep Learning Approach for the Argument Mining Task of Choosing the Correct Warrant",
    author = "Liebeck, Matthias  and
      Funke, Andreas  and
      Conrad, Stefan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1188",
    doi = "10.18653/v1/S18-1188",
    pages = "1114--1119",
    abstract = "This paper describes our participation in the SemEval-2018 Task 12 Argument Reasoning Comprehension Task which calls to develop systems that, given a reason and a claim, predict the correct warrant from two opposing options. We decided to use a deep learning architecture and combined 623 models with different hyperparameters into an ensemble. Our extensive analysis of our architecture and ensemble reveals that the decision to use an ensemble was suboptimal. Additionally, we benchmark a support vector machine as a baseline. Furthermore, we experimented with an alternative data split and achieved more stable results.",
}
@inproceedings{ding-zhou-2018-ynu-deep,
    title = "{YNU} Deep at {S}em{E}val-2018 Task 12: A {B}i{LSTM} Model with Neural Attention for Argument Reasoning Comprehension",
    author = "Ding, Peng  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1189",
    doi = "10.18653/v1/S18-1189",
    pages = "1120--1123",
    abstract = "This paper describes the system submitted to SemEval-2018 Task 12 (The Argument Reasoning Comprehension Task). Enabling a computer to understand a text so that it can answer comprehension questions is still a challenging goal of NLP. We propose a Bidirectional LSTM (BiLSTM) model that reads two sentences separated by a delimiter to determine which warrant is correct. We extend this model with a neural attention mechanism that encourages the model to make reasoning over the given claims and reasons. Officially released results show that our system ranks 6th among 22 submissions to this task.",
}
@inproceedings{joshi-etal-2018-unimelb,
    title = "{U}ni{M}elb at {S}em{E}val-2018 Task 12: Generative Implication using {LSTM}s, {S}iamese Networks and Semantic Representations with Synonym Fuzzing",
    author = "Joshi, Anirudh  and
      Baldwin, Tim  and
      Sinnott, Richard O.  and
      Paris, Cecile",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1190",
    doi = "10.18653/v1/S18-1190",
    pages = "1124--1128",
    abstract = "This paper describes a warrant classification system for SemEval 2018 Task 12, that attempts to learn semantic representations of reasons, claims and warrants. The system consists of 3 stacked LSTMs: one for the reason, one for the claim, and one shared Siamese Network for the 2 candidate warrants. Our main contribution is to force the embeddings into a shared feature space using vector operations, semantic similarity classification, Siamese networks, and multi-task learning. In doing so, we learn a form of generative implication, in encoding implication interrelationships between reasons, claims, and the associated correct and incorrect warrants. We augment the limited data in the task further by utilizing WordNet synonym {``}fuzzing{''}. When applied to SemEval 2018 Task 12, our system performs well on the development data, and officially ranked 8th among 21 teams.",
}
@inproceedings{sui-etal-2018-joker,
    title = "Joker at {S}em{E}val-2018 Task 12: The Argument Reasoning Comprehension with Neural Attention",
    author = "Sui, Guobin  and
      Chao, Wenhan  and
      Luo, Zhunchen",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1191",
    doi = "10.18653/v1/S18-1191",
    pages = "1129--1132",
    abstract = "This paper describes a classification system that participated in the SemEval-2018 Task 12: The Argument Reasoning Comprehension Task. Briefly the task can be described as that a natural language {``}argument{''} is what we have, with reason, claim, and correct and incorrect warrants, and we need to choose the correct warrant. In order to make fully understand of the semantic information of the sentences, we proposed a neural network architecture with attention mechanism to achieve this goal. Besides we try to introduce keywords into the model to improve accuracy. Finally the proposed system achieved 5th place among 22 participating systems",
}
@inproceedings{brassard-etal-2018-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors",
    author = "Brassard, Ana  and
      Kuculo, Tin  and
      Boltu{\v{z}}i{\'c}, Filip  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1192",
    doi = "10.18653/v1/S18-1192",
    pages = "1133--1136",
    abstract = "This paper describes our system for the SemEval-2018 Task 12: Argument Reasoning Comprehension Task. We utilize skip-thought vectors, sentence-level distributional vectors inspired by the popular word embeddings and the skip-gram model. We encode preprocessed sentences from the dataset into vectors, then perform a binary supervised classification of the warrant that justifies the use of the reason as support for the claim. We explore a few variations of the model, reaching 54.1{\%} accuracy on the test set, which placed us 16th out of 22 teams participating in the task.",
}
@inproceedings{li-zhou-2018-lyb3b-semeval,
    title = "Lyb3b at {S}em{E}val-2018 Task 12: Ensemble-based Deep Learning Models for Argument Reasoning Comprehension Task",
    author = "Li, Yongbin  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1193",
    doi = "10.18653/v1/S18-1193",
    pages = "1137--1141",
    abstract = "Reasoning is a crucial part of natural language argumentation. In order to comprehend an argument, we have to reconstruct and analyze its reasoning. In this task, given a natural language argument with a reason and a claim, the goal is to choose the correct implicit reasoning from two options, in order to form a reasonable structure of (Reason, Warrant, Claim). Our approach is to build distributed word embedding of reason, warrant and claim respectively, meanwhile, we use a series of frameworks such as CNN model, LSTM model, GRU with attention model and biLSTM with attention model for processing word vector. Finally, ensemble mechanism is used to integrate the results of each framework to improve the final accuracy. Experiments demonstrate superior performance of ensemble mechanism compared to each separate framework. We are the 11th in official results, the final model can reach a 0.568 accuracy rate on the test dataset.",
}
@inproceedings{chen-etal-2018-transrw,
    title = "{TRANSRW} at {S}em{E}val-2018 Task 12: Transforming Semantic Representations for Argument Reasoning Comprehension",
    author = "Chen, Zhimin  and
      Song, Wei  and
      Liu, Lizhen",
    booktitle = "Proceedings of The 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S18-1194",
    doi = "10.18653/v1/S18-1194",
    pages = "1142--1145",
    abstract = "This paper describes our system in SemEval-2018 task 12: Argument Reasoning Comprehension. The task is to select the correct warrant that explains reasoning of a particular argument consisting of a claim and a reason. The main idea of our methods is based on the assumption that the semantic composition of the reason and the warrant should be close to the semantic representation of the corresponding claim. We propose two neural network models. The first one considers two warrant candidates simultaneously, while the second one processes each candidate separately and then chooses the best one. We also incorporate sentiment polarity by assuming that there are kinds of sentiment associations between the reason, the warrant and the claim. The experiments show that the first framework is more effective and sentiment polarity is useful.",
}
@article{tacl-2018-transactions,
    title = "Transactions of the Association for Computational Linguistics, Volume 6",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1000",
}
@article{frermann-etal-2018-whodunnit,
    title = "Whodunnit? Crime Drama as a Case for Natural Language Understanding",
    author = "Frermann, Lea  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1001",
    doi = "10.1162/tacl_a_00001",
    pages = "1--15",
    abstract = "In this paper we argue that crime drama exemplified in television programs such as CSI: Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it. We propose to treat crime drama as a new inference task, capitalizing on the fact that each episode poses the same basic question (i.e., who committed the crime) and naturally provides the answer when the perpetrator is revealed. We develop a new dataset based on CSI episodes, formalize perpetrator identification as a sequence labeling problem, and develop an LSTM-based model which learns from multi-modal data. Experimental results show that an incremental inference strategy is key to making accurate guesses as well as learning from representations fusing textual, visual, and acoustic input.",
}
@article{angelidis-lapata-2018-multiple,
    title = "Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis",
    author = "Angelidis, Stefanos  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1002",
    doi = "10.1162/tacl_a_00002",
    pages = "17--31",
    abstract = "We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SpoT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate superior performance against multiple baselines, whereas a judgement elicitation study shows that EDU-level opinion extraction produces more informative summaries than sentence-based alternatives.",
}
@article{cotterell-schutze-2018-joint,
    title = "Joint Semantic Synthesis and Morphological Analysis of the Derived Word",
    author = {Cotterell, Ryan  and
      Sch{\"u}tze, Hinrich},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1003",
    doi = "10.1162/tacl_a_00003",
    pages = "33--48",
    abstract = "Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+able+ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word{'}s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3{\%} and 5{\%}. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist{'}s notion of morphological productivity.",
}
@article{janner-etal-2018-representation,
    title = "Representation Learning for Grounded Spatial Reasoning",
    author = "Janner, Michael  and
      Narasimhan, Karthik  and
      Barzilay, Regina",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1004",
    doi = "10.1162/tacl_a_00004",
    pages = "49--61",
    abstract = "The interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an agent can act and receive rewards. The proposed model learns a representation of the world steered by instruction text. This design allows for precise alignment of local neighborhoods with corresponding verbalizations, while also handling global references in the instructions. We train our model with reinforcement learning using a variant of generalized value iteration. The model outperforms state-of-the-art approaches on several metrics, yielding a 45{\%} reduction in goal localization error.",
}
@article{liu-lapata-2018-learning,
    title = "Learning Structured Text Representations",
    author = "Liu, Yang  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1005",
    doi = "10.1162/tacl_a_00005",
    pages = "63--75",
    abstract = "In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias (Cheng et al., 2016; Kim et al., 2017), we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluations across different tasks and datasets show that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.",
}
@article{reimers-etal-2018-event,
    title = "Event Time Extraction with a Decision Tree of Neural Classifiers",
    author = "Reimers, Nils  and
      Dehghani, Nazanin  and
      Gurevych, Iryna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1006",
    doi = "10.1162/tacl_a_00006",
    pages = "77--89",
    abstract = "Extracting the information from text when an event happened is challenging. Documents do not only report on current events, but also on past events as well as on future events. Often, the relevant time information for an event is scattered across the document. In this paper we present a novel method to automatically anchor events in time. To our knowledge it is the first approach that takes temporal information from the complete document into account. We created a decision tree that applies neural network based classifiers at its nodes. We use this tree to incrementally infer, in a stepwise manner, at which time frame an event happened. We evaluate the approach on the TimeBank-EventTime Corpus (Reimers et al., 2016) achieving an accuracy of 42.0{\%} compared to an inter-annotator agreement (IAA) of 56.7{\%}. For events that span over a single day we observe an accuracy improvement of 33.1 points compared to the state-of-the-art CAEVO system (Chambers et al., 2014). Without retraining, we apply this model to the SemEval-2015 Task 4 on automatic timeline generation and achieve an improvement of 4.01 points F1-score compared to the state-of-the-art. Our code is publically available.",
}
@article{somasundaran-etal-2018-towards,
    title = "Towards Evaluating Narrative Quality In Student Writing",
    author = "Somasundaran, Swapna  and
      Flor, Michael  and
      Chodorow, Martin  and
      Molloy, Hillary  and
      Gyawali, Binod  and
      McCulla, Laura",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1007",
    doi = "10.1162/tacl_a_00007",
    pages = "91--106",
    abstract = "This work lays the foundation for automated assessments of narrative quality in student writing. We first manually score essays for narrative-relevant traits and sub-traits, and measure inter-annotator agreement. We then explore linguistic features that are indicative of good narrative writing and use them to build an automated scoring system. Experiments show that our features are more effective in scoring specific aspects of narrative quality than a state-of-the-art feature set.",
}
@article{antoniak-mimno-2018-evaluating,
    title = "Evaluating the Stability of Embedding-based Word Similarities",
    author = "Antoniak, Maria  and
      Mimno, David",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1008",
    doi = "10.1162/tacl_a_00008",
    pages = "107--119",
    abstract = "Word embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if they are sensitive to inconsequential variations in the source documents. We find that nearest-neighbor distances are highly sensitive to small changes in the training corpus for a variety of algorithms. For all methods, including specific documents in the training set can result in substantial variations. We show that these effects are more prominent for smaller training corpora. We recommend that users never rely on single embedding models for distance calculations, but rather average over multiple bootstrap samples, especially for small corpora.",
}
@article{zayats-ostendorf-2018-conversation,
    title = "Conversation Modeling on {R}eddit Using a Graph-Structured {LSTM}",
    author = "Zayats, Victoria  and
      Ostendorf, Mari",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1009",
    doi = "10.1162/tacl_a_00009",
    pages = "121--132",
    abstract = "This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM (long-short term memory) which represents both hierarchical and temporal conversation structure. In experiments with a task of predicting popularity of comments in Reddit discussions, the proposed model outperforms a node-independent architecture for different sets of input features. Analyses show a benefit to the model over the full course of the discussion, improving detection in both early and late stages. Further, the use of language cues with the bidirectional tree state updates helps with identifying controversial comments.",
}
@article{collell-moens-2018-learning,
    title = "Learning Representations Specialized in Spatial Knowledge: Leveraging Language and Vision",
    author = "Collell, Guillem  and
      Moens, Marie-Francine",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1010",
    doi = "10.1162/tacl_a_00010",
    pages = "133--144",
    abstract = "Spatial understanding is crucial in many real-world problems, yet little progress has been made towards building representations that capture spatial knowledge. Here, we move one step forward in this direction and learn such representations by leveraging a task consisting in predicting continuous 2D spatial arrangements of objects given object-relationship-object instances (e.g., {``}cat under chair{''}) and a simple neural network model that learns the task from annotated images. We show that the model succeeds in this task and, furthermore, that it is capable of predicting correct spatial arrangements for unseen objects if either CNN features or word embeddings of the objects are provided. The differences between visual and linguistic features are discussed. Next, to evaluate the spatial representations learned in the previous task, we introduce a task and a dataset consisting in a set of crowdsourced human ratings of spatial similarity for object pairs. We find that both CNN (convolutional neural network) features and word embeddings predict human judgments of similarity well and that these vectors can be further specialized in spatial knowledge if we update them when training the model that predicts spatial arrangements of objects. Overall, this paper paves the way towards building distributed spatial representations, contributing to the understanding of spatial expressions in language.",
}
@article{zheng-etal-2018-modeling,
    title = "Modeling Past and Future for Neural Machine Translation",
    author = "Zheng, Zaixiang  and
      Zhou, Hao  and
      Huang, Shujian  and
      Mou, Lili  and
      Dai, Xinyu  and
      Chen, Jiajun  and
      Tu, Zhaopeng",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1011",
    doi = "10.1162/tacl_a_00011",
    pages = "145--157",
    abstract = "Existing neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel mechanism that separates the source information into two parts: translated Past contents and untranslated Future contents, which are modeled by two additional recurrent layers. The Past and Future contents are fed to both the attention model and the decoder states, which provides Neural Machine Translation (NMT) systems with the knowledge of translated and untranslated contents. Experimental results show that the proposed approach significantly improves the performance in Chinese-English, German-English, and English-German translation tasks. Specifically, the proposed model outperforms the conventional coverage model in terms of both the translation quality and the alignment error rate.",
}
@article{roy-roth-2018-mapping,
    title = "Mapping to Declarative Knowledge for Word Problem Solving",
    author = "Roy, Subhro  and
      Roth, Dan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1012",
    doi = "10.1162/tacl_a_00012",
    pages = "159--172",
    abstract = "Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, supporting interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.",
}
@article{long-etal-2018-video,
    title = "Video Captioning with Multi-Faceted Attention",
    author = "Long, Xiang  and
      Gan, Chuang  and
      de Melo, Gerard",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1013",
    doi = "10.1162/tacl_a_00013",
    pages = "173--184",
    abstract = "Video captioning has attracted an increasing amount of interest, due in part to its potential for improved accessibility and information retrieval. While existing methods rely on different kinds of visual features and model architectures, they do not make full use of pertinent semantic cues. We present a unified and extensible framework to jointly leverage multiple sorts of visual features and semantic attributes. Our novel architecture builds on LSTMs with two multi-faceted attention layers. These first learn to automatically select the most salient visual features or semantic attributes, and then yield overall representations for the input and output of the sentence generation component via custom feature scaling operations. Experimental results on the challenging MSVD and MSR-VTT datasets show that our framework outperforms previous work and performs robustly even in the presence of added noise to the features and attributes.",
}
@article{aldarmaki-etal-2018-unsupervised,
    title = "Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings",
    author = "Aldarmaki, Hanan  and
      Mohan, Mahesh  and
      Diab, Mona",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1014",
    doi = "10.1162/tacl_a_00014",
    pages = "185--196",
    abstract = "Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. We show empirically that the performance of bilingual correspondents that are learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.",
}
@article{sedghi-sabharwal-2018-knowledge,
    title = "Knowledge Completion for Generics using Guided Tensor Factorization",
    author = "Sedghi, Hanie  and
      Sabharwal, Ashish",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1015",
    doi = "10.1162/tacl_a_00015",
    pages = "197--210",
    abstract = "Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as {``}all trees produce oxygen{''} or {``}some animals live in forests{''}, we consider the problem of inferring additional such facts at a precision similar to that of the starting KB. Such KBs capture general knowledge about the world, and are crucial for various applications such as question answering. Different from commonly studied named entity KBs such as Freebase, generics KBs involve quantification, have more complex underlying regularities, tend to be more incomplete, and violate the commonly used locally closed world assumption (LCWA). We show that existing KB completion methods struggle with this new task, and present the first approach that is successful. Our results demonstrate that external information, such as relation schemas and entity taxonomies, if used appropriately, can be a surprisingly powerful tool in this setting. First, our simple yet effective knowledge guided tensor factorization approach achieves state-of-the-art results on two generics KBs (80{\%} precise) for science, doubling their size at 74{\%}{--}86{\%} precision. Second, our novel taxonomy guided, submodular, active learning method for collecting annotations about rare entities (e.g., oriole, a bird) is 6x more effective at inferring further new facts about them than multiple active learning baselines.",
}
@article{jin-etal-2018-unsupervised,
    title = "Unsupervised Grammar Induction with Depth-bounded {PCFG}",
    author = "Jin, Lifeng  and
      Doshi-Velez, Finale  and
      Miller, Timothy  and
      Schuler, William  and
      Schwartz, Lane",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1016",
    doi = "10.1162/tacl_a_00016",
    pages = "211--224",
    abstract = "There has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.",
}
@article{kiperwasser-ballesteros-2018-scheduled,
    title = "Scheduled Multi-Task Learning: From Syntax to Translation",
    author = "Kiperwasser, Eliyahu  and
      Ballesteros, Miguel",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1017",
    doi = "10.1162/tacl_a_00017",
    pages = "225--240",
    abstract = "Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner. We propose a framework in which our model begins learning syntax and translation interleaved, gradually putting more focus on translation. Using this approach, we achieve considerable improvements in terms of BLEU score on relatively large parallel corpus (WMT14 English to German) and a low-resource (WIT German to English) setup.",
}
@article{crane-2018-questionable,
    title = "Questionable Answers in Question Answering Research: Reproducibility and Variability of Published Results",
    author = "Crane, Matt",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1018",
    doi = "10.1162/tacl_a_00018",
    pages = "241--252",
    abstract = "{``}Based on theoretical reasoning it has been suggested that the reliability of findings published in the scientific literature decreases with the popularity of a research field{''} (Pfeiffer and Hoffmann, 2009). As we know, deep learning is very popular and the ability to reproduce results is an important part of science. There is growing concern within the deep learning community about the reproducibility of results that are presented. In this paper we present a number of controllable, yet unreported, effects that can substantially change the effectiveness of a sample model, and thusly the reproducibility of those results. Through these environmental effects we show that the commonly held belief that distribution of source code is all that is needed for reproducibility is not enough. Source code without a reproducible environment does not mean anything at all. In addition the range of results produced from these effects can be larger than the majority of incremental improvement reported.",
}
@article{williams-etal-2018-latent,
    title = "Do latent tree learning models identify meaningful structure in sentences?",
    author = "Williams, Adina  and
      Drozdov, Andrew  and
      Bowman, Samuel R.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1019",
    doi = "10.1162/tacl_a_00019",
    pages = "253--267",
    abstract = "Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without exposure to ground-truth parse trees at training time. Surprisingly, these models often perform better at sentence understanding tasks than models that use parse trees from conventional parsers. This paper aims to investigate what these latent tree learning models learn. We replicate two such models in a shared codebase and find that (i) only one of these models outperforms conventional tree-structured models on sentence classification, (ii) its parsing strategies are not especially consistent across random restarts, (iii) the parses it produces tend to be shallower than standard Penn Treebank (PTB) parses, and (iv) they do not resemble those of PTB or any other semantic or syntactic formalism that the authors are aware of.",
}
@article{mudinas-etal-2018-bootstrap,
    title = "Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora",
    author = "Mudinas, Andrius  and
      Zhang, Dell  and
      Levene, Mark",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1020",
    doi = "10.1162/tacl_a_00020",
    pages = "269--285",
    abstract = "There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words ({``}seeds{''}). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, a nd t hen u ses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier v ia supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.",
}
@article{welbl-etal-2018-constructing,
    title = "Constructing Datasets for Multi-hop Reading Comprehension Across Documents",
    author = "Welbl, Johannes  and
      Stenetorp, Pontus  and
      Riedel, Sebastian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1021",
    doi = "10.1162/tacl_a_00021",
    pages = "287--302",
    abstract = "Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently no resources exist to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence {---} effectively performing multihop, alias multi-step, inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information; and providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 54.5{\%} on an annotated test set, compared to human performance at 85.0{\%}, leaving ample room for improvement.",
}
@article{kunchukuttan-etal-2018-leveraging,
    title = "Leveraging Orthographic Similarity for Multilingual Neural Transliteration",
    author = "Kunchukuttan, Anoop  and
      Khapra, Mitesh  and
      Singh, Gurneet  and
      Bhattacharyya, Pushpak",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1022",
    doi = "10.1162/tacl_a_00022",
    pages = "303--316",
    abstract = "We address the task of joint training of transliteration models for multiple language pairs (multilingual transliteration). This is an instance of multitask learning, where individual tasks (language pairs) benefit from sharing knowledge with related tasks. We focus on transliteration involving related tasks i.e., languages sharing writing systems and phonetic properties (orthographically similar languages). We propose a modified neural encoder-decoder model that maximizes parameter sharing across language pairs in order to effectively leverage orthographic similarity. We show that multilingual transliteration significantly outperforms bilingual transliteration in different scenarios (average increase of 58{\%} across a variety of languages we experimented with). We also show that multilingual transliteration models can generalize well to languages/language pairs not encountered during training and hence perform well on the zeroshot transliteration task. We show that further improvements can be achieved by using phonetic feature input.",
}
@article{kocisky-etal-2018-narrativeqa,
    title = "The {N}arrative{QA} Reading Comprehension Challenge",
    author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}}  and
      Schwarz, Jonathan  and
      Blunsom, Phil  and
      Dyer, Chris  and
      Hermann, Karl Moritz  and
      Melis, G{\'a}bor  and
      Grefenstette, Edward",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1023",
    doi = "10.1162/tacl_a_00023",
    pages = "317--328",
    abstract = "Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.",
}
@article{rabinovich-etal-2018-native,
    title = "Native Language Cognate Effects on Second Language Lexical Choice",
    author = "Rabinovich, Ella  and
      Tsvetkov, Yulia  and
      Wintner, Shuly",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1024",
    doi = "10.1162/tacl_a_00024",
    pages = "329--342",
    abstract = "We present a computational analysis of cognate effects on the spontaneous linguistic productions of advanced non-native speakers. Introducing a large corpus of highly competent non-native English speakers, and using a set of carefully selected lexical items, we show that the lexical choices of non-natives are affected by cognates in their native language. This effect is so powerful that we are able to reconstruct the phylogenetic language tree of the Indo-European language family solely from the frequencies of specific lexical items in the English of authors with various native languages. We quantitatively analyze non-native lexical choice, highlighting cognate facilitation as one of the important phenomena shaping the language of non-native speakers.",
}
@article{laparra-etal-2018-characters,
    title = "From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations",
    author = "Laparra, Egoitz  and
      Xu, Dongfang  and
      Bethard, Steven",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1025",
    doi = "10.1162/tacl_a_00025",
    pages = "343--356",
    abstract = "This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.",
}
@article{simpson-gurevych-2018-finding,
    title = "Finding Convincing Arguments Using Scalable {B}ayesian Preference Learning",
    author = "Simpson, Edwin  and
      Gurevych, Iryna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1026",
    doi = "10.1162/tacl_a_00026",
    pages = "357--371",
    abstract = "We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.",
}
@article{niu-bansal-2018-polite,
    title = "Polite Dialogue Generation Without Parallel Data",
    author = "Niu, Tong  and
      Bansal, Mohit",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1027",
    doi = "10.1162/tacl_a_00027",
    pages = "373--389",
    abstract = "Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contextually-relevant, as well as paralinguistically accurate. Moreover, parallel datasets for regular-to-stylistic pairs are usually unavailable. We present three weakly-supervised models that can generate diverse, polite (or rude) dialogue responses without parallel data. Our late fusion model (Fusion) merges the decoder of an encoder-attention-decoder dialogue model with a language model trained on stand-alone polite utterances. Our label-finetuning (LFT) model prepends to each source sequence a politeness-score scaled label (predicted by our state-of-the-art politeness classifier) during training, and at test time is able to generate polite, neutral, and rude responses by simply scaling the label embedding by the corresponding score. Our reinforcement learning model (Polite-RL) encourages politeness generation by assigning rewards proportional to the politeness classifier score of the sampled response. We also present two retrievalbased, polite dialogue model baselines. Human evaluation validates that while the Fusion and the retrieval-based models achieve politeness with poorer context-relevance, the LFT and Polite-RL models can produce significantly more polite responses without sacrificing dialogue quality.",
}
@article{jurgens-etal-2018-measuring,
    title = "Measuring the Evolution of a Scientific Field through Citation Frames",
    author = "Jurgens, David  and
      Kumar, Srijan  and
      Hoover, Raine  and
      McFarland, Dan  and
      Jurafsky, Dan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1028",
    doi = "10.1162/tacl_a_00028",
    pages = "391--406",
    abstract = "Citations have long been used to characterize the state of a scientific field and to identify influential works. However, writers use citations for different purposes, and this varied purpose influences uptake by future scholars. Unfortunately, our understanding of how scholars use and frame citations has been limited to small-scale manual citation analysis of individual papers. We perform the largest behavioral study of citations to date, analyzing how scientific works frame their contributions through different types of citations and how this framing affects the field as a whole. We introduce a new dataset of nearly 2,000 citations annotated for their function, and use it to develop a state-of-the-art classifier and label the papers of an entire field: Natural Language Processing. We then show how differences in framing affect scientific uptake and reveal the evolution of the publication venues and the field as a whole. We demonstrate that authors are sensitive to discourse structure and publication venue when citing, and that how a paper frames its work through citations is predictive of the citation count it will receive. Finally, we use changes in citation framing to show that the field of NLP is undergoing a significant increase in consensus.",
}
@article{tu-etal-2018-learning,
    title = "Learning to Remember Translation History with a Continuous Cache",
    author = "Tu, Zhaopeng  and
      Liu, Yang  and
      Shi, Shuming  and
      Zhang, Tong",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1029",
    doi = "10.1162/tacl_a_00029",
    pages = "407--420",
    abstract = "Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.",
}
@article{shao-etal-2018-universal,
    title = "Universal Word Segmentation: Implementation and Interpretation",
    author = "Shao, Yan  and
      Hardmeier, Christian  and
      Nivre, Joakim",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1030",
    doi = "10.1162/tacl_a_00033",
    pages = "421--435",
    abstract = "Word segmentation is a low-level NLP task that is non-trivial for a considerable number of languages. In this paper, we present a sequence tagging framework and apply it to word segmentation for a wide range of languages with different writing systems and typological characteristics. Additionally, we investigate the correlations between various typological factors and word segmentation accuracy. The experimental results indicate that segmentation accuracy is positively related to word boundary markers and negatively to the number of unique non-segmental terms. Based on the analysis, we design a small set of language-specific settings and extensively evaluate the segmentation system on the Universal Dependencies datasets. Our model obtains state-of-the-art accuracies on all the UD languages. It performs substantially better on languages that are non-trivial to segment, such as Chinese, Japanese, Arabic and Hebrew, when compared to previous work.",
}
@article{guu-etal-2018-generating,
    title = "Generating Sentences by Editing Prototypes",
    author = "Guu, Kelvin  and
      Hashimoto, Tatsunori B.  and
      Oren, Yonatan  and
      Liang, Percy",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1031",
    doi = "10.1162/tacl_a_00030",
    pages = "437--450",
    abstract = "We propose a new generative language model for sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional language models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies.",
}
@article{gerz-etal-2018-language,
    title = "Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction",
    author = "Gerz, Daniela  and
      Vuli{\'c}, Ivan  and
      Ponti, Edoardo  and
      Naradowsky, Jason  and
      Reichart, Roi  and
      Korhonen, Anna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1032",
    doi = "10.1162/tacl_a_00032",
    pages = "451--465",
    abstract = "Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available.",
}
@article{prabhakaran-etal-2018-detecting,
    title = "Detecting Institutional Dialog Acts in Police Traffic Stops",
    author = "Prabhakaran, Vinodkumar  and
      Griffiths, Camilla  and
      Su, Hang  and
      Verma, Prateek  and
      Morgan, Nelson  and
      Eberhardt, Jennifer L.  and
      Jurafsky, Dan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1033",
    doi = "10.1162/tacl_a_00031",
    pages = "467--481",
    abstract = "We apply computational dialog methods to police body-worn camera footage to model conversations between police officers and community members in traffic stops. Relying on the theory of institutional talk, we develop a labeling scheme for police speech during traffic stops, and a tagger to detect institutional dialog acts (Reasons, Searches, Offering Help) from transcribed text at the turn (78{\%} F-score) and stop (89{\%} F-score) level. We then develop speech recognition and segmentation algorithms to detect these acts at the stop level from raw camera audio (81{\%} F-score, with even higher accuracy for crucial acts like conveying the reason for the stop). We demonstrate that the dialog structures produced by our tagger could reveal whether officers follow law enforcement norms like introducing themselves, explaining the reason for the stop, and asking permission for searches. This work may therefore inform and aid efforts to ensure the procedural justice of police-community interactions.",
}
@article{arora-etal-2018-linear,
    title = "Linear Algebraic Structure of Word Senses, with Applications to Polysemy",
    author = "Arora, Sanjeev  and
      Li, Yuanzhi  and
      Liang, Yingyu  and
      Ma, Tengyu  and
      Risteski, Andrej",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1034",
    doi = "10.1162/tacl_a_00034",
    pages = "483--495",
    abstract = "Word embeddings are ubiquitous in NLP and information retrieval, but it is unclear what they represent when the word is polysemous. Here it is shown that multiple word senses reside in linear superposition within the word embedding and simple sparse coding can recover vectors that approximately capture the senses. The success of our approach, which applies to several embedding methods, is mathematically explained using a variant of the random walk on discourses model (Arora et al., 2016). A novel aspect of our technique is that each extracted word sense is accompanied by one of about 2000 {``}discourse atoms{''} that gives a succinct description of which other words co-occur with that word sense. Discourse atoms can be of independent interest, and make the method potentially more useful. Empirical tests are used to verify and support the theory.",
}
@article{jaech-ostendorf-2018-low,
    title = "Low-Rank {RNN} Adaptation for Context-Aware Language Modeling",
    author = "Jaech, Aaron  and
      Ostendorf, Mari",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1035",
    doi = "10.1162/tacl_a_00035",
    pages = "497--510",
    abstract = "A context-aware language model uses location, user and/or domain metadata (context) to adapt its predictions. In neural language models, context information is typically represented as an embedding and it is given to the RNN as an additional input, which has been shown to be useful in many applications. We introduce a more powerful mechanism for using context to adapt an RNN by letting the context vector control a low-rank transformation of the recurrent layer weight matrix. Experiments show that allowing a greater fraction of the model parameters to be adjusted has benefits in terms of perplexity and classification for several different types of context.",
}
@article{buckman-neubig-2018-neural,
    title = "Neural Lattice Language Models",
    author = "Buckman, Jacob  and
      Neubig, Graham",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1036",
    doi = "10.1162/tacl_a_00036",
    pages = "529--541",
    abstract = "In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language models. These models construct a lattice of possible paths through a sentence and marginalize across this lattice to calculate sequence probabilities or optimize parameters. This approach allows us to seamlessly incorporate linguistic intuitions {---} including polysemy and the existence of multiword lexical items {---} into our language model. Experiments on multiple language modeling tasks show that English neural lattice language models that utilize polysemous embeddings are able to improve perplexity by 9.95{\%} relative to a word-level baseline, and that a Chinese model that handles multi-character tokens is able to improve perplexity by 20.94{\%} relative to a character-level baseline.",
}
@article{khani-etal-2018-planning,
    title = "Planning, Inference and Pragmatics in Sequential Language Games",
    author = "Khani, Fereshte  and
      Goodman, Noah D.  and
      Liang, Percy",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1037",
    doi = "10.1162/tacl_a_00037",
    pages = "543--555",
    abstract = "We study sequential language games in which two players, each with private information, communicate to achieve a common goal. In such games, a successful player must (i) infer the partner{'}s private information from the partner{'}s messages, (ii) generate messages that are most likely to help with the goal, and (iii) reason pragmatically about the partner{'}s strategy. We propose a model that captures all three characteristics and demonstrate their importance in capturing human behavior on a new goal-oriented dataset we collected using crowdsourcing.",
}
@article{zhang-etal-2018-probabilistic,
    title = "Probabilistic Verb Selection for Data-to-Text Generation",
    author = "Zhang, Dell  and
      Yuan, Jiahao  and
      Wang, Xiaoling  and
      Foster, Adam",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1038",
    doi = "10.1162/tacl_a_00038",
    pages = "511--527",
    abstract = "In data-to-text Natural Language Generation (NLG) systems, computers need to find the right words to describe phenomena seen in the data. This paper focuses on the problem of choosing appropriate verbs to express the direction and magnitude of a percentage change (e.g., in stock prices). Rather than simply using the same verbs again and again, we present a principled data-driven approach to this problem based on Shannon{'}s noisy-channel model so as to bring variation and naturalness into the generated text. Our experiments on three large-scale real-world news corpora demonstrate that the proposed probabilistic model can be learned to accurately imitate human authors{'} pattern of usage around verbs, outperforming the state-of-the-art method significantly.",
}
@article{chen-etal-2018-adversarial,
    title = "Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification",
    author = "Chen, Xilun  and
      Sun, Yu  and
      Athiwaratkun, Ben  and
      Cardie, Claire  and
      Weinberger, Kilian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1039",
    doi = "10.1162/tacl_a_00039",
    pages = "557--570",
    abstract = "In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN1) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exist. ADAN has two discriminative branches: a sentiment classifier and an adversarial language discriminator. Both branches take input from a shared feature extractor to learn hidden representations that are simultaneously indicative for the classification task and invariant across languages. Experiments on Chinese and Arabic sentiment classification demonstrate that ADAN significantly outperforms state-of-the-art systems.",
}
@article{paun-etal-2018-comparing,
    title = "Comparing {B}ayesian Models of Annotation",
    author = "Paun, Silviu  and
      Carpenter, Bob  and
      Chamberlain, Jon  and
      Hovy, Dirk  and
      Kruschwitz, Udo  and
      Poesio, Massimo",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1040",
    doi = "10.1162/tacl_a_00040",
    pages = "571--585",
    abstract = "The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used for 1, and coefficients of agreement for 2 and 3. Lately, model-based analysis of corpus annotations have proven better at all three tasks. But there has been relatively little work comparing them on the same datasets. This paper aims to fill this gap by analyzing six models of annotation, covering different approaches to annotator ability, item difficulty, and parameter pooling (tying) across annotators and items. We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators. We conclude with guidelines for model selection, application, and implementation.",
}
@article{bender-friedman-2018-data,
    title = "Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science",
    author = "Bender, Emily M.  and
      Friedman, Batya",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1041",
    doi = "10.1162/tacl_a_00041",
    pages = "587--604",
    abstract = "In this paper, we propose data statements as a design solution and professional practice for natural language processing technologists, in both research and development. Through the adoption and widespread use of data statements, the field can begin to address critical scientific and ethical issues that result from the use of data from certain populations in the development of technology for other populations. We present a form that data statements can take and explore the implications of adopting them as part of regular practice. We argue that data statements will help alleviate issues related to exclusion and bias in language technology, lead to better precision in claims about how natural language processing research can generalize and thus better engineering results, protect companies from public embarrassment, and ultimately lead to language technology that meets its users in their own preferred linguistic style and furthermore does not misrepresent them to others.",
}
@article{webster-etal-2018-mind,
    title = "Mind the {GAP}: A Balanced Corpus of Gendered Ambiguous Pronouns",
    author = "Webster, Kellie  and
      Recasens, Marta  and
      Axelrod, Vera  and
      Baldridge, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1042",
    doi = "10.1162/tacl_a_00240",
    pages = "605--617",
    abstract = "Coreference resolution is an important task for natural language understanding, and the resolution of ambiguous pronouns a longstanding challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in sufficient volume or diversity to accurately indicate the practical utility of models. Furthermore, we find gender bias in existing corpora and systems favoring masculine entities. To address this, we present and release GAP, a gender-balanced labeled corpus of 8,908 ambiguous pronoun{--}name pairs sampled to provide diverse coverage of challenges posed by real-world text. We explore a range of baselines that demonstrate the complexity of the challenge, the best achieving just 66.9{\%} F1. We show that syntactic structure and continuous neural models provide promising, complementary cues for approaching the challenge.",
}
@article{van-noord-etal-2018-exploring,
    title = "Exploring Neural Methods for Parsing Discourse Representation Structures",
    author = "van Noord, Rik  and
      Abzianidze, Lasha  and
      Toral, Antonio  and
      Bos, Johan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1043",
    doi = "10.1162/tacl_a_00241",
    pages = "619--633",
    abstract = "Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn indices increases parser performance. Adding silver training data boosts performance even further.",
}
@article{pu-etal-2018-integrating,
    title = "Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation",
    author = "Pu, Xiao  and
      Pappas, Nikolaos  and
      Henderson, James  and
      Popescu-Belis, Andrei",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1044",
    doi = "10.1162/tacl_a_00242",
    pages = "635--649",
    abstract = "This paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words. We first introduce three adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant processes, and random walks, which are then applied to large word contexts represented in a low-rank space and evaluated on SemEval shared-task data. We then learn word vectors jointly with sense vectors defined by our best WSD method, within a state-of-the-art NMT system. We show that the concatenation of these vectors, and the use of a sense selection mechanism based on the weighted average of sense vectors, outperforms several baselines including sense-aware ones. This is demonstrated by translation on five language pairs. The improvements are more than 1 BLEU point over strong NMT baselines, +4{\%} accuracy over all ambiguous nouns and verbs, or +20{\%} when scored manually over several challenging words.",
}
@article{kirov-cotterell-2018-recurrent,
    title = "Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate",
    author = "Kirov, Christo  and
      Cotterell, Ryan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1045",
    doi = "10.1162/tacl_a_00247",
    pages = "651--665",
    abstract = "Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and McClelland famously introduced a neural architecture that learned to transduce English verb stems to their past tense forms. Shortly thereafter in 1988, Pinker and Prince presented a comprehensive rebuttal of many of Rumelhart and McClelland{'}s claims. Much of the force of their attack centered on the empirical inadequacy of the Rumelhart and McClelland model. Today, however, that model is severely outmoded. We show that the Encoder-Decoder network architectures used in modern NLP systems obviate most of Pinker and Prince{'}s criticisms without requiring any simplification of the past tense mapping problem. We suggest that the empirical performance of modern networks warrants a reexamination of their utility in linguistic and cognitive modeling.",
}
@article{wang-eisner-2018-surface,
    title = "Surface Statistics of an Unknown Language Indicate How to Parse It",
    author = "Wang, Dingquan  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1046",
    doi = "10.1162/tacl_a_00248",
    pages = "667--685",
    abstract = "We introduce a novel framework for delexicalized dependency parsing in a new language. We show that useful features of the target language can be extracted automatically from an unparsed corpus, which consists only of gold part-of-speech (POS) sequences. Providing these features to our neural parser enables it to parse sequences like those in the corpus. Strikingly, our system has no supervision in the target language. Rather, it is a multilingual system that is trained end-to-end on a variety of other languages, so it learns a feature extractor that works well. We show experimentally across multiple languages: (1) Features computed from the unparsed corpus improve parsing accuracy. (2) Including thousands of synthetic languages in the training yields further improvement. (3) Despite being computed from unparsed corpora, our learned task-specific features beat previous work{'}s interpretable typological features that require parsed corpora or expert categorization of the language. Our best method improved attachment scores on held-out test languages by an average of 5.6 percentage points over past work that does not inspect the unparsed data (McDonald et al., 2011), and by 20.7 points over past {``}grammar induction{''} work that does not use training languages (Naseem et al., 2010).",
}
@article{yin-schutze-2018-attentive,
    title = "Attentive Convolution: Equipping {CNN}s with {RNN}-style Attention Mechanisms",
    author = {Yin, Wenpeng  and
      Sch{\"u}tze, Hinrich},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1047",
    doi = "10.1162/tacl_a_00249",
    pages = "687--702",
    abstract = "In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i.e., it is applied to pooling) rather than as attentive convolution (i.e., it is integrated into convolution). Convolution is the differentiator of CNNs in that it can powerfully model the higher-level representation of a word by taking into account its local fixed-size context in the input text tx. In this work, we propose an attentive convolution network, ATTCONV. It extends the context scope of the convolution operation, deriving higher-level features for a word not only from local context, but also from information extracted from nonlocal context by the attention mechanism commonly used in RNNs. This nonlocal context can come (i) from parts of the input text tx that are distant or (ii) from extra (i.e., external) contexts ty. Experiments on sentence modeling with zero-context (sentiment analysis), single-context (textual entailment) and multiple-context (claim verification) demonstrate the effectiveness of ATTCONV in sentence representation learning with the incorporation of context. In particular, attentive convolution outperforms attentive pooling and is a strong competitor to popular attentive RNNs.1",
}
@article{hosseini-etal-2018-learning,
    title = "Learning Typed Entailment Graphs with Global Soft Constraints",
    author = "Hosseini, Mohammad Javad  and
      Chambers, Nathanael  and
      Reddy, Siva  and
      Holt, Xavier R.  and
      Cohen, Shay B.  and
      Johnson, Mark  and
      Steedman, Mark",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "6",
    year = "2018",
    url = "https://www.aclweb.org/anthology/Q18-1048",
    doi = "10.1162/tacl_a_00250",
    pages = "703--717",
    abstract = "This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.",
}
@proceedings{acl-2018-association-linguistics-tutorial,
    title = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    editor = "Artzi, Yoav  and
      Eisenstein, Jacob",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5000",
}
@inproceedings{bender-2018-100,
    title = "100 Things You Always Wanted to Know about Semantics {\&} Pragmatics But Were Afraid to Ask",
    author = "Bender, Emily M.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5001",
    doi = "10.18653/v1/P18-5001",
    pages = "1",
    abstract = "Meaning is a fundamental concept in Natural Language Processing (NLP), given its aim to build systems that mean what they say to you, and understand what you say to them. In order for NLP to scale beyond partial, task-specific solutions, it must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this tutorial is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that{'}s accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics. The tutorial content is based on a manuscript in progress I am co-authoring with Prof. Alex Lascarides of the University of Edinburgh.",
}
@inproceedings{gao-etal-2018-neural,
    title = "Neural Approaches to Conversational {AI}",
    author = "Gao, Jianfeng  and
      Galley, Michel  and
      Li, Lihong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5002",
    doi = "10.18653/v1/P18-5002",
    pages = "2--7",
    abstract = "This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.",
}
@inproceedings{aziz-schulz-2018-variational,
    title = "Variational Inference and Deep Generative Models",
    author = "Aziz, Wilker  and
      Schulz, Philip",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5003",
    doi = "10.18653/v1/P18-5003",
    pages = "8--9",
    abstract = "NLP has seen a surge in neural network models in recent years. These models provide state-of-the-art performance on many supervised tasks. Unsupervised and semi-supervised learning has only been addressed scarcely, however. Deep generative models (DGMs) make it possible to integrate neural networks with probabilistic graphical models. Using DGMs one can easily design latent variable models that account for missing observations and thereby enable unsupervised and semi-supervised learning with neural networks. The method of choice for training these models is variational inference. This tutorial offers a general introduction to variational inference followed by a thorough and example-driven discussion of how to use variational methods for training DGMs. It provides both the mathematical background necessary for deriving the learning algorithms as well as practical implementation guidelines. Importantly, the tutorial will cover models with continuous and discrete variables. We provide practical coding exercises implemented in IPython notebooks as well as short notes on the more intricate mathematical details that the audience can use as a reference after the tutorial. We expect that with these additional materials the tutorial will have a long-lasting impact on the community.",
}
@inproceedings{anderson-etal-2018-connecting,
    title = "Connecting Language and Vision to Actions",
    author = "Anderson, Peter  and
      Das, Abhishek  and
      Wu, Qi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5004",
    doi = "10.18653/v1/P18-5004",
    pages = "10--14",
    abstract = "A long-term goal of AI research is to build intelligent agents that can see the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and act in a physical or embodied environment. To this end, recent advances at the intersection of language and vision have made incredible progress {--} from being able to generate natural language descriptions of images/videos, to answering questions about them, to even holding free-form conversations about visual content! However, while these agents can passively describe images or answer (a sequence of) questions about them, they cannot act in the world (what if I cannot answer a question from my current view, or I am asked to move or manipulate something?). Thus, the challenge now is to extend this progress in language and vision to embodied agents that take actions and actively interact with their visual environments. To reduce the entry barrier for new researchers, this tutorial will provide an overview of the growing number of multimodal tasks and datasets that combine textual and visual understanding. We will comprehensively review existing state-of-the-art approaches to selected tasks such as image captioning, visual question answering (VQA) and visual dialog, presenting the key architectural building blocks (such as co-attention) and novel algorithms (such as cooperative/adversarial games) used to train models for these tasks. We will then discuss some of the current and upcoming challenges of combining language, vision and actions, and introduce some recently-released interactive 3D simulation environments designed for this purpose.",
}
@inproceedings{kordoni-2018-beyond,
    title = "Beyond Multiword Expressions: Processing Idioms and Metaphors",
    author = "Kordoni, Valia",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5005",
    doi = "10.18653/v1/P18-5005",
    pages = "15--16",
    abstract = "Idioms and metaphors are characteristic to all areas of human activity and to all types of discourse. Their processing is a rapidly growing area in NLP, since they have become a big challenge for NLP systems. Their omnipresence in language has been established in a number of corpus studies and the role they play in human reasoning has also been confirmed in psychological experiments. This makes idioms and metaphors an important research area for computational and cognitive linguistics, and their automatic identification and interpretation indispensable for any semantics-oriented NLP application. This tutorial aims to provide attendees with a clear notion of the linguistic characteristics of idioms and metaphors, computational models of idioms and metaphors using state-of-the-art NLP techniques, their relevance for the intersection of deep learning and natural language processing, what methods and resources are available to support their use, and what more could be done in the future. Our target audience are researchers and practitioners in machine learning, parsing (syntactic and semantic) and language technology, not necessarily experts in idioms and metaphors, who are interested in tasks that involve or could benefit from considering idioms and metaphors as a pervasive phenomenon in human language and communication.",
}
@inproceedings{gardner-etal-2018-neural,
    title = "Neural Semantic Parsing",
    author = "Gardner, Matt  and
      Dasigi, Pradeep  and
      Iyer, Srinivasan  and
      Suhr, Alane  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5006",
    doi = "10.18653/v1/P18-5006",
    pages = "17--18",
    abstract = "Semantic parsing, the study of translating natural language utterances into machine-executable programs, is a well-established research area and has applications in question answering, instruction following, voice assistants, and code generation. In the last two years, the models used for semantic parsing have changed dramatically with the introduction of neural encoder-decoder methods that allow us to rethink many of the previous assumptions underlying semantic parsing. We aim to inform those already interested in semantic parsing research of these new developments in the field, as well as introduce the topic as an exciting research area to those who are unfamiliar with it. Current approaches for neural semantic parsing share several similarities with neural machine translation, but the key difference between the two fields is that semantic parsing translates natural language into a formal language, while machine translation translates it into a different natural language. The formal language used in semantic parsing allows for constrained decoding, where the model is constrained to only produce outputs that are valid formal statements. We will describe the various approaches researchers have taken to do this. We will also discuss the choice of formal languages used by semantic parsers, and describe why much recent work has chosen to use standard programming languages instead of more linguistically-motivated representations. We will then describe a particularly challenging setting for semantic parsing, where there is additional context or interaction that the parser must take into account when translating natural language to formal language, and give an overview of recent work in this direction. Finally, we will introduce some tools available in AllenNLP for doing semantic parsing research.",
}
@inproceedings{wang-etal-2018-deep,
    title = "Deep Reinforcement Learning for {NLP}",
    author = "Wang, William Yang  and
      Li, Jiwei  and
      He, Xiaodong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5007",
    doi = "10.18653/v1/P18-5007",
    pages = "19--21",
    abstract = "Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems. However, since language is often discrete and the space for all sentences is infinite, there are many challenges for formulating reinforcement learning problems of NLP tasks. In this tutorial, we provide a gentle introduction to the foundation of deep reinforcement learning, as well as some practical DRL solutions in NLP. We describe recent advances in designing deep reinforcement learning for NLP, with a special focus on generation, dialogue, and information extraction. Finally, we discuss why they succeed, and when they may fail, aiming at providing some practical advice about deep reinforcement learning for solving real-world NLP problems.",
}
@inproceedings{sil-etal-2018-multi,
    title = "Multi-lingual Entity Discovery and Linking",
    author = "Sil, Avi  and
      Ji, Heng  and
      Roth, Dan  and
      Cucerzan, Silviu-Petru",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-5008",
    doi = "10.18653/v1/P18-5008",
    pages = "22--29",
    abstract = "The primary goals of this tutorial are to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. We will start by discussing the traditional EL techniques and metrics and address questions relevant to the adequacy of these to across domains and languages. We will then present more recent approaches such as Neural EL, discuss the basic building blocks of a state-of-the-art neural EL system and analyze some of the current results on English EL. We will then proceed to Cross-lingual EL and discuss methods that work across languages. In particular, we will discuss and compare multiple methods that make use of multi-lingual word embeddings. We will also present EL methods that work for both name tagging and linking in very low resource languages. Finally, we will discuss the uses of cross-lingual EL in a variety of applications like search engines and commercial product selling applications. Also, contrary to the 2014 EL tutorial, we will also focus on Entity Discovery which is an essential component of EL.",
}
@proceedings{acl-2018-acl-2018,
    title = "Proceedings of {ACL} 2018, System Demonstrations",
    editor = "Liu, Fei  and
      Solorio, Thamar",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4000",
}
@inproceedings{lin-etal-2018-platforms,
    title = "Platforms for Non-speakers Annotating Names in Any Language",
    author = "Lin, Ying  and
      Costello, Cash  and
      Zhang, Boliang  and
      Lu, Di  and
      Ji, Heng  and
      Mayfield, James  and
      McNamee, Paul",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4001",
    doi = "10.18653/v1/P18-4001",
    pages = "1--6",
    abstract = "We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language. These platforms provided high-quality {'}{`}silver standard{''} annotations for low-resource language name taggers (Zhang et al., 2017) that achieved state-of-the-art performance on two surprise languages (Oromo and Tigrinya) at LoreHLT20171 and ten languages at TAC-KBP EDL2017 (Ji et al., 2017). We discuss strengths and limitations and compare other methods of creating silver- and gold-standard annotations using native speakers. We will make our tools publicly available for research use.",
}
@inproceedings{white-etal-2018-novelperspective,
    title = "{N}ovel{P}erspective: Identifying Point of View Characters",
    author = "White, Lyndon  and
      Togneri, Roberto  and
      Liu, Wei  and
      Bennamoun, Mohammed",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4002",
    doi = "10.18653/v1/P18-4002",
    pages = "7--12",
    abstract = "We present NovelPerspective: a tool to allow consumers to subset their digital literature, based on point of view (POV) character. Many novels have multiple main characters each with their own storyline running in parallel. A well-known example is George R. R. Martin{'}s novel: {``}A Game of Thrones{''}, and others from that series. Our tool detects the main character that each section is from the POV of, and allows the user to generate a new ebook with only those sections. This gives consumers new options in how they consume their media; allowing them to pursue the storylines sequentially, or skip chapters about characters they find boring. We present two heuristic-based baselines, and two machine learning based methods for the detection of the main character.",
}
@inproceedings{hermjakob-etal-2018-box,
    title = "Out-of-the-box Universal {R}omanization Tool uroman",
    author = "Hermjakob, Ulf  and
      May, Jonathan  and
      Knight, Kevin",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4003",
    doi = "10.18653/v1/P18-4003",
    pages = "13--18",
    abstract = "We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation. The tool relies on Unicode data and other tables, and handles nearly all character sets, including some that are quite obscure such as Tibetan and Tifinagh. uroman converts digital numbers in various scripts to Western Arabic numerals. Romanization enables the application of string-similarity metrics to texts from different scripts without the need and complexity of an intermediate phonetic representation. The tool is freely and publicly available as a Perl script suitable for inclusion in data processing pipelines and as an interactive demo web page.",
}
@inproceedings{ravenscroft-etal-2018-harrigt,
    title = "{H}arri{GT}: A Tool for Linking News to Science",
    author = "Ravenscroft, James  and
      Clare, Amanda  and
      Liakata, Maria",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4004",
    doi = "10.18653/v1/P18-4004",
    pages = "19--24",
    abstract = "Being able to reliably link scientific works to the newspaper articles that discuss them could provide a breakthrough in the way we rationalise and measure the impact of science on our society. Linking these articles is challenging because the language used in the two domains is very different, and the gathering of online resources to align the two is a substantial information retrieval endeavour. We present HarriGT, a semi-automated tool for building corpora of news articles linked to the scientific papers that they discuss. Our aim is to facilitate future development of information-retrieval tools for newspaper/scientific work citation linking. HarriGT retrieves newspaper articles from an archive containing 17 years of UK web content. It also integrates with 3 large external citation networks, leveraging named entity extraction, and document classification to surface relevant examples of scientific literature to the user. We also provide a tuned candidate ranking algorithm to highlight potential links between scientific papers and newspaper articles to the user, in order of likelihood. HarriGT is provided as an open source tool (\url{http://harrigt.xyz}).",
}
@inproceedings{weissenborn-etal-2018-jack,
    title = "Jack the Reader {--} A Machine Reading Framework",
    author = {Weissenborn, Dirk  and
      Minervini, Pasquale  and
      Augenstein, Isabelle  and
      Welbl, Johannes  and
      Rockt{\"a}schel, Tim  and
      Bo{\v{s}}njak, Matko  and
      Mitchell, Jeff  and
      Demeester, Thomas  and
      Dettmers, Tim  and
      Stenetorp, Pontus  and
      Riedel, Sebastian},
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4005",
    doi = "10.18653/v1/P18-4005",
    pages = "25--30",
    abstract = "Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. To that end, we present Jack the Reader (JACK), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating new datasets and applying them on a growing set of implemented baseline models. JACK is currently supporting (but not limited to) three tasks: Question Answering, Natural Language Inference, and Link Prediction. It is developed with the aim of increasing research efficiency and code reuse.",
}
@inproceedings{yang-etal-2018-yedda,
    title = "{YEDDA}: A Lightweight Collaborative Text Span Annotation Tool",
    author = "Yang, Jie  and
      Zhang, Yue  and
      Li, Linwei  and
      Li, Xingxuan",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4006",
    doi = "10.18653/v1/P18-4006",
    pages = "31--36",
    abstract = "In this paper, we introduce Yedda, a lightweight but efficient and comprehensive open-source tool for text span annotation. Yedda provides a systematic solution for text span annotation, ranging from collaborative user annotation to administrator evaluation and analysis. It overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys, which are configurable with custom labels. Yedda also gives intelligent recommendations by learning the up-to-date annotated text. An administrator client is developed to evaluate annotation quality of multiple annotators and generate detailed comparison report for each annotator pair. Experiments show that the proposed system can reduce the annotation time by half compared with existing annotation tools. And the annotation time can be further compressed by 16.47{\%} through intelligent recommendation.",
}
@inproceedings{han-etal-2018-nextgen,
    title = "{N}ext{G}en {AML}: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation",
    author = "Han, Jingguang  and
      Barman, Utsab  and
      Hayes, Jeremiah  and
      Du, Jinhua  and
      Burgin, Edward  and
      Wan, Dadong",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4007",
    doi = "10.18653/v1/P18-4007",
    pages = "37--42",
    abstract = "Most of the current anti money laundering (AML) systems, using handcrafted rules, are heavily reliant on existing structured databases, which are not capable of effectively and efficiently identifying hidden and complex ML activities, especially those with dynamic and time-varying characteristics, resulting in a high percentage of false positives. Therefore, analysts are engaged for further investigation which significantly increases human capital cost and processing time. To alleviate these issues, this paper presents a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language processing (NLP) technologies in a distributed and scalable manner to augment AML monitoring and investigation. The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and tweets) to provide additional evidence to human investigators for final decision-making. Each NLP module is evaluated on a task-specific data set, and the overall experiments are performed on synthetic and real-world datasets. Feedback from AML practitioners suggests that our system can reduce approximately 30{\%} time and cost compared to their previous manual approaches of AML investigation.",
}
@inproceedings{puttkammer-etal-2018-nlp,
    title = "{NLP} Web Services for Resource-Scarce Languages",
    author = "Puttkammer, Martin  and
      Eiselen, Roald  and
      Hocking, Justin  and
      Koen, Frederik",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4008",
    doi = "10.18653/v1/P18-4008",
    pages = "43--49",
    abstract = "In this paper, we present a project where existing text-based core technologies were ported to Java-based web services from various architectures. These technologies were developed over a period of eight years through various government funded projects for 10 resource-scarce languages spoken in South Africa. We describe the API and a simple web front-end capable of completing various predefined tasks.",
}
@inproceedings{yang-etal-2018-dcfee,
    title = "{DCFEE}: A Document-level {C}hinese Financial Event Extraction System based on Automatically Labeled Training Data",
    author = "Yang, Hang  and
      Chen, Yubo  and
      Liu, Kang  and
      Xiao, Yang  and
      Zhao, Jun",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4009",
    doi = "10.18653/v1/P18-4009",
    pages = "50--55",
    abstract = "We present an event extraction framework to detect event mentions and extract events from the document-level financial news. Up to now, methods based on supervised learning paradigm gain the highest performance in public datasets (such as ACE2005, KBP2015). These methods heavily depend on the manually labeled training data. However, in particular areas, such as financial, medical and judicial domains, there is no enough labeled data due to the high cost of data labeling process. Moreover, most of the current methods focus on extracting events from one sentence, but an event is usually expressed by multiple sentences in one document. To solve these problems, we propose a Document-level Chinese Financial Event Extraction (DCFEE) system which can automatically generate a large scaled labeled data and extract events from the whole document. Experimental results demonstrate the effectiveness of it",
}
@inproceedings{liu-etal-2018-sentence,
    title = "Sentence Suggestion of {J}apanese Functional Expressions for {C}hinese-speaking Learners",
    author = "Liu, Jun  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4010",
    doi = "10.18653/v1/P18-4010",
    pages = "56--61",
    abstract = "We present a computer-assisted learning system, Jastudy, which is particularly designed for Chinese-speaking learners of Japanese as a second language (JSL) to learn Japanese functional expressions with suggestion of appropriate example sentences. The system automatically recognizes Japanese functional expressions using a free Japanese morphological analyzer MeCab, which is retrained on a new Conditional Random Fields (CRF) model. In order to select appropriate example sentences, we apply a pairwise-based machine learning tool, Support Vector Machine for Ranking (SVMrank) to estimate the complexity of the example sentences using Japanese{--}Chinese homographs as an important feature. In addition, we cluster the example sentences that contain Japanese functional expressions with two or more meanings and usages, based on part-of-speech, conjugation forms of verbs and semantic attributes, using the K-means clustering algorithm in Scikit-Learn. Experimental results demonstrate the effectiveness of our approach.",
}
@inproceedings{hermjakob-etal-2018-translating,
    title = "Translating a Language You Don{'}t Know In the {C}hinese Room",
    author = "Hermjakob, Ulf  and
      May, Jonathan  and
      Pust, Michael  and
      Knight, Kevin",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4011",
    doi = "10.18653/v1/P18-4011",
    pages = "62--67",
    abstract = "In a corruption of John Searle{'}s famous AI thought experiment, the Chinese Room (Searle, 1980), we twist its original intent by enabling humans to translate text, e.g. from Uyghur to English, even if they don{'}t have any prior knowledge of the source language. Our enabling tool, which we call the Chinese Room, is equipped with the same resources made available to a machine translation engine. We find that our superior language model and world knowledge allows us to create perfectly fluent and nearly adequate translations, with human expertise required only for the target language. The Chinese Room tool can be used to rapidly create small corpora of parallel data when bilingual translators are not readily available, in particular for low-resource languages.",
}
@inproceedings{hartung-etal-2018-santo,
    title = "{SANTO}: A Web-based Annotation Tool for Ontology-driven Slot Filling",
    author = "Hartung, Matthias  and
      ter Horst, Hendrik  and
      Grimm, Frank  and
      Diekmann, Tim  and
      Klinger, Roman  and
      Cimiano, Philipp",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4012",
    doi = "10.18653/v1/P18-4012",
    pages = "68--73",
    abstract = "Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult. Being optimized for relation extraction at sentence level, many annotation tools lack in facilitating the annotation of relational structures that are widely spread across the text. This leads to non-intuitive and cumbersome visualizations, making the annotation process unnecessarily time-consuming. We propose SANTO, an easy-to-use, domain-adaptive annotation tool specialized for complex slot filling tasks which may involve problems of cardinality and referential grounding. The web-based architecture enables fast and clearly structured annotation for multiple users in parallel. Relational structures are formulated as templates following the conceptualization of an underlying ontology. Further, import and export procedures of standard formats enable interoperability with external sources and tools.",
}
@inproceedings{yang-zhang-2018-ncrf,
    title = "{NCRF}++: An Open-source Neural Sequence Labeling Toolkit",
    author = "Yang, Jie  and
      Zhang, Yue",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4013",
    doi = "10.18653/v1/P18-4013",
    pages = "74--79",
    abstract = "This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch \url{http://pytorch.org/}, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.",
}
@inproceedings{mayhew-roth-2018-talen,
    title = "{TALEN}: Tool for Annotation of Low-resource {EN}tities",
    author = "Mayhew, Stephen  and
      Roth, Dan",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4014",
    doi = "10.18653/v1/P18-4014",
    pages = "80--86",
    abstract = "We present a new web-based interface, TALEN, designed for named entity annotation in low-resource settings where the annotators do not speak the language. To address this non-traditional scenario, TALEN includes such features as in-place lexicon integration, TF-IDF token statistics, Internet search, and entity propagation, all implemented so as to make this difficult task efficient and frictionless. We conduct a small user study to compare against a popular annotation tool, showing that TALEN achieves higher precision and recall against ground-truth annotations, and that users strongly prefer it over the alternative. TALEN is available at: \url{github.com/CogComp/talen}.",
}
@inproceedings{shen-etal-2018-web,
    title = "A Web-scale system for scientific knowledge exploration",
    author = "Shen, Zhihong  and
      Ma, Hao  and
      Wang, Kuansan",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4015",
    doi = "10.18653/v1/P18-4015",
    pages = "87--92",
    abstract = "To enable efficient exploration of Web-scale scientific knowledge, it is necessary to organize scientific publications into a hierarchical concept structure. In this work, we present a large-scale system to (1) identify hundreds of thousands of scientific concepts, (2) tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and (3) build a six-level concept hierarchy with a subsumption-based model. The system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.",
}
@inproceedings{lukin-etal-2018-scoutbot,
    title = "{S}cout{B}ot: A Dialogue System for Collaborative Navigation",
    author = "Lukin, Stephanie M.  and
      Gervits, Felix  and
      Hayes, Cory J.  and
      Moolchandani, Pooja  and
      Leuski, Anton  and
      Rogers III, John G.  and
      Sanchez Amaro, Carlos  and
      Marge, Matthew  and
      Voss, Clare R.  and
      Traum, David",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4016",
    doi = "10.18653/v1/P18-4016",
    pages = "93--98",
    abstract = "ScoutBot is a dialogue interface to physical and simulated robots that supports collaborative exploration of environments. The demonstration will allow users to issue unconstrained spoken language commands to ScoutBot. ScoutBot will prompt for clarification if the user{'}s instruction needs additional input. It is trained on human-robot dialogue collected from Wizard-of-Oz experiments, where robot responses were initiated by a human wizard in previous interactions. The demonstration will show a simulated ground robot (Clearpath Jackal) in a simulated environment supported by ROS (Robot Operating System).",
}
@inproceedings{germann-etal-2018-summa,
    title = "The {SUMMA} Platform: A Scalable Infrastructure for Multi-lingual Multi-media Monitoring",
    author = "Germann, Ulrich  and
      Liepins, Ren{\=a}rs  and
      Barzdins, Guntis  and
      Gosko, Didzis  and
      Miranda, Sebasti{\~a}o  and
      Nogueira, David",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4017",
    doi = "10.18653/v1/P18-4017",
    pages = "99--104",
    abstract = "The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes. The Platform offers a fully automated media ingestion pipeline capable of recording live broadcasts, detection and transcription of spoken content, translation of all text (original or transcribed) into English, recognition and linking of Named Entities, topic detection, clustering and cross-lingual multi-document summarization of related media items, and last but not least, extraction and storage of factual claims in these news items. Browser-based graphical user interfaces provide humans with aggregated information as well as structured access to individual news items stored in the Platform{'}s database. This paper describes the intended use cases and provides an overview over the system{'}s implementation.",
}
@inproceedings{shen-etal-2018-cruise,
    title = "{CRUISE}: Cold-Start New Skill Development via Iterative Utterance Generation",
    author = "Shen, Yilin  and
      Ray, Avik  and
      Patel, Abhishek  and
      Jin, Hongxia",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4018",
    doi = "10.18653/v1/P18-4018",
    pages = "105--110",
    abstract = "We present a system, CRUISE, that guides ordinary software developers to build a high quality natural language understanding (NLU) engine from scratch. This is the fundamental step of building a new skill in personal assistants. Unlike existing solutions that require either developers or crowdsourcing to manually generate and annotate a large number of utterances, we design a hybrid rule-based and data-driven approach with the capability to iteratively generate more and more utterances. Our system only requires light human workload to iteratively prune incorrect utterances. CRUISE outputs a well trained NLU engine and a large scale annotated utterance corpus that third parties can use to develop their custom skills. Using both benchmark dataset and custom datasets we collected in real-world settings, we validate the high quality of CRUISE generated utterances via both competitive NLU performance and human evaluation. We also show the largely reduced human workload in terms of both cognitive load and human pruning time consumption.",
}
@inproceedings{christodoulides-2018-praaline,
    title = "{P}raaline: An Open-Source System for Managing, Annotating, Visualising and Analysing Speech Corpora",
    author = "Christodoulides, George",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4019",
    doi = "10.18653/v1/P18-4019",
    pages = "111--115",
    abstract = "In this system demonstration we present the latest developments of Praaline, an open-source software system for constituting and managing, manually and automatically annotating, visualising and analysing spoken language and multimodal corpora. We review the system{'}s functionality and design architecture, present current use cases and directions for future development.",
}
@inproceedings{junczys-dowmunt-etal-2018-marian,
    title = "{M}arian: Fast Neural Machine Translation in {C}++",
    author = "Junczys-Dowmunt, Marcin  and
      Grundkiewicz, Roman  and
      Dwojak, Tomasz  and
      Hoang, Hieu  and
      Heafield, Kenneth  and
      Neckermann, Tom  and
      Seide, Frank  and
      Germann, Ulrich  and
      Aji, Alham Fikri  and
      Bogoychev, Nikolay  and
      Martins, Andr{\'e} F. T.  and
      Birch, Alexandra",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4020",
    doi = "10.18653/v1/P18-4020",
    pages = "116--121",
    abstract = "We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs. Marian is written entirely in C++. We describe the design of the encoder-decoder framework and demonstrate that a research-friendly toolkit can achieve high training and translation speed.",
}
@inproceedings{burtsev-etal-2018-deeppavlov,
    title = "{D}eep{P}avlov: Open-Source Library for Dialogue Systems",
    author = "Burtsev, Mikhail  and
      Seliverstov, Alexander  and
      Airapetyan, Rafael  and
      Arkhipov, Mikhail  and
      Baymurzina, Dilyara  and
      Bushkov, Nickolay  and
      Gureenkova, Olga  and
      Khakhulin, Taras  and
      Kuratov, Yuri  and
      Kuznetsov, Denis  and
      Litinsky, Alexey  and
      Logacheva, Varvara  and
      Lymar, Alexey  and
      Malykh, Valentin  and
      Petrov, Maxim  and
      Polulyakh, Vadim  and
      Pugachev, Leonid  and
      Sorokin, Alexey  and
      Vikhreva, Maria  and
      Zaynutdinov, Marat",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4021",
    doi = "10.18653/v1/P18-4021",
    pages = "122--127",
    abstract = "Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of feature-rich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chit-chat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.",
}
@inproceedings{zeyer-etal-2018-returnn,
    title = "{RETURNN} as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition",
    author = "Zeyer, Albert  and
      Alkhouli, Tamer  and
      Ney, Hermann",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4022",
    doi = "10.18653/v1/P18-4022",
    pages = "128--133",
    abstract = "We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1{\%} BLEU improvement absolute and it allows to train deeper recurrent encoder networks. Promising preliminary results on max. expected BLEU training are presented. We are able to train state-of-the-art models for translation and end-to-end models for speech recognition and show results on WMT 2017 and Switchboard. The flexibility of RETURNN allows a fast research feedback loop to experiment with alternative architectures, and its generality allows to use it on a wide range of applications.",
}
@inproceedings{romeo-etal-2018-flexible,
    title = "A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines",
    author = "Romeo, Salvatore  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4023",
    doi = "10.18653/v1/P18-4023",
    pages = "134--139",
    abstract = "Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution. Community Question Answering is a research area that benefits from deep linguistic analysis to improve the experience of the community of forum users. In this paper, we present a UIMA framework to distribute the computation of cQA tasks over computer clusters such that traditional systems can scale to large datasets and deliver fast processing.",
}
@inproceedings{huang-etal-2018-moon,
    title = "Moon {IME}: Neural-based {C}hinese {P}inyin Aided Input Method with Customizable Association",
    author = "Huang, Yafang  and
      Li, Zuchao  and
      Zhang, Zhuosheng  and
      Zhao, Hai",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-4024",
    doi = "10.18653/v1/P18-4024",
    pages = "140--145",
    abstract = "Chinese pinyin input method engine (IME) lets user conveniently input Chinese into a computer by typing pinyin through the common keyboard. In addition to offering high conversion quality, modern pinyin IME is supposed to aid user input with extended association function. However, existing solutions for such functions are roughly based on oversimplified matching algorithms at word-level, whose resulting products provide limited extension associated with user inputs. This work presents the Moon IME, a pinyin IME that integrates the attention-based neural machine translation (NMT) model and Information Retrieval (IR) to offer amusive and customizable association ability. The released IME is implemented on Windows via text services framework.",
}
@proceedings{acl-2018-acl,
    title = "Proceedings of {ACL} 2018, Student Research Workshop",
    editor = "Shwartz, Vered  and
      Tabassum, Jeniya  and
      Voigt, Rob  and
      Che, Wanxiang  and
      de Marneffe, Marie-Catherine  and
      Nissim, Malvina",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3000",
}
@inproceedings{pecar-2018-towards,
    title = "Towards Opinion Summarization of Customer Reviews",
    author = "Pecar, Samuel",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3001",
    doi = "10.18653/v1/P18-3001",
    pages = "1--8",
    abstract = "In recent years, the number of texts has grown rapidly. For example, most review-based portals, like Yelp or Amazon, contain thousands of user-generated reviews. It is impossible for any human reader to process even the most relevant of these documents. The most promising tool to solve this task is a text summarization. Most existing approaches, however, work on small, homogeneous, English datasets, and do not account to multi-linguality, opinion shift, and domain effects. In this paper, we introduce our research plan to use neural networks on user-generated travel reviews to generate summaries that take into account shifting opinions over time. We outline future directions in summarization to address all of these issues. By resolving the existing problems, we will make it easier for users of review-sites to make more informed decisions.",
}
@inproceedings{fernandez-downey-2018-sampling,
    title = "Sampling Informative Training Data for {RNN} Language Models",
    author = "Fernandez, Jared  and
      Downey, Doug",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3002",
    doi = "10.18653/v1/P18-3002",
    pages = "9--13",
    abstract = "We propose an unsupervised importance sampling approach to selecting training data for recurrent neural network (RNNs) language models. To increase the information content of the training set, our approach preferentially samples high perplexity sentences, as determined by an easily queryable n-gram language model. We experimentally evaluate the heldout perplexity of models trained with our various importance sampling distributions. We show that language models trained on data sampled using our proposed approach outperform models trained over randomly sampled subsets of both the Billion Word (Chelba et al., 2014 Wikitext-103 benchmark corpora (Merity et al., 2016).",
}
@inproceedings{sharif-etal-2018-learning,
    title = "Learning-based Composite Metrics for Improved Caption Evaluation",
    author = "Sharif, Naeha  and
      White, Lyndon  and
      Bennamoun, Mohammed  and
      Ali Shah, Syed Afaq",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3003",
    doi = "10.18653/v1/P18-3003",
    pages = "14--20",
    abstract = "The evaluation of image caption quality is a challenging task, which requires the assessment of two main aspects in a caption: adequacy and fluency. These quality aspects can be judged using a combination of several linguistic features. However, most of the current image captioning metrics focus only on specific linguistic facets, such as the lexical or semantic, and fail to meet a satisfactory level of correlation with human judgements at the sentence-level. We propose a learning-based framework to incorporate the scores of a set of lexical and semantic metrics as features, to capture the adequacy and fluency of captions at different linguistic levels. Our experimental results demonstrate that composite metrics draw upon the strengths of stand-alone measures to yield improved correlation and accuracy.",
}
@inproceedings{kawara-etal-2018-recursive,
    title = "Recursive Neural Network Based Preordering for {E}nglish-to-{J}apanese Machine Translation",
    author = "Kawara, Yuki  and
      Chu, Chenhui  and
      Arase, Yuki",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3004",
    doi = "10.18653/v1/P18-3004",
    pages = "21--27",
    abstract = "The word order between source and target languages significantly influences the translation quality. Preordering can effectively address this problem. Previous preordering methods require a manual feature design, making language dependent design difficult. In this paper, we propose a preordering method with recursive neural networks that learn features from raw inputs. Experiments show the proposed method is comparable to the state-of-the-art method but without a manual feature design.",
}
@inproceedings{singh-2018-pushing,
    title = "Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information",
    author = "Singh, Sonit",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3005",
    doi = "10.18653/v1/P18-3005",
    pages = "28--36",
    abstract = "Recently, there has been increasing interest in the intersection of computer vision and natural language processing. Researchers have studied several interesting tasks, including generating text descriptions from images and videos and language embedding of images. More recent work has further extended the scope of this area to combine videos and language, learning to solve non-visual tasks using visual cues, visual question answering, and visual dialog. Despite a large body of research on the intersection of vision-language technology, its adaption to the medical domain is not fully explored. To address this research gap, we aim to develop machine learning models that can reason jointly on medical images and clinical text for advanced search, retrieval, annotation and description of medical images.",
}
@inproceedings{dai-2018-recognizing,
    title = "Recognizing Complex Entity Mentions: A Review and Future Directions",
    author = "Dai, Xiang",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3006",
    doi = "10.18653/v1/P18-3006",
    pages = "37--44",
    abstract = "Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other. However, in practice, there are many domains, such as the biomedical domain, in which there are nested, overlapping, and discontinuous entity mentions. These complex mentions cannot be directly recognized by conventional sequence tagging models because they may break the assumptions based on which sequence tagging techniques are built. We review the existing methods which are revised to tackle complex entity mentions and categorize them as tokenlevel and sentence-level approaches. We then identify the research gap, and discuss some directions that we are exploring.",
}
@inproceedings{thilakaratne-etal-2018-automatic,
    title = "Automatic Detection of Cross-Disciplinary Knowledge Associations",
    author = "Thilakaratne, Menasha  and
      Falkner, Katrina  and
      Atapattu, Thushari",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3007",
    doi = "10.18653/v1/P18-3007",
    pages = "45--51",
    abstract = "Detecting interesting, cross-disciplinary knowledge associations hidden in scientific publications can greatly assist scientists to formulate and validate scientifically sensible novel research hypotheses. This will also introduce new areas of research that can be successfully linked with their research discipline. Currently, this process is mostly performed manually by exploring the scientific publications, requiring a substantial amount of time and effort. Due to the exponential growth of scientific literature, it has become almost impossible for a scientist to keep track of all research advances. As a result, scientists tend to deal with fragments of the literature according to their specialisation. Consequently, important and hidden associations among these fragmented knowledge that can be linked to produce significant scientific discoveries remain unnoticed. This doctoral work aims to develop a novel knowledge discovery approach that suggests most promising research pathways by analysing the existing scientific literature.",
}
@inproceedings{singh-etal-2018-language,
    title = "Language Identification and Named Entity Recognition in {H}inglish Code Mixed Tweets",
    author = "Singh, Kushagra  and
      Sen, Indira  and
      Kumaraguru, Ponnurangam",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3008",
    doi = "10.18653/v1/P18-3008",
    pages = "52--58",
    abstract = "While growing code-mixed content on Online Social Networks(OSN) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging. To meet this challenge, a family of tools for analyzing code-mixed data such as language identifiers, parts-of-speech (POS) taggers, chunkers have been developed. Named Entity Recognition (NER) is an important text analysis task which is not only informative by itself, but is also needed for downstream NLP tasks such as semantic role labeling. In this work, we present an exploration of automatic NER of code-mixed data. We compare our method with existing off-the-shelf NER tools for social media content,and find that our systems outperforms the best baseline by 33.18 {\%} (F1 score).",
}
@inproceedings{bladier-etal-2018-german,
    title = "{G}erman and {F}rench Neural Supertagging Experiments for {LTAG} Parsing",
    author = "Bladier, Tatiana  and
      van Cranenburgh, Andreas  and
      Samih, Younes  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3009",
    doi = "10.18653/v1/P18-3009",
    pages = "59--66",
    abstract = "We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars. We use a supertagging approach combined with deep learning. We show the challenges of extracting LTAG supertags from the French Treebank, introduce the use of left- and right-sister-adjunction, present a neural architecture for the supertagger, and report experiments of n-best supertagging for French and German.",
}
@inproceedings{vanmassenhove-way-2018-supernmt,
    title = "{S}uper{NMT}: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags",
    author = "Vanmassenhove, Eva  and
      Way, Andy",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3010",
    doi = "10.18653/v1/P18-3010",
    pages = "67--73",
    abstract = "In this paper we incorporate semantic supersensetags and syntactic supertag features into EN{--}FR and EN{--}DE factored NMT systems. In experiments on various test sets, we observe that such features (and particularly when combined) help the NMT model training to converge faster and improve the model quality according to the BLEU scores.",
}
@inproceedings{dohare-etal-2018-unsupervised,
    title = "Unsupervised Semantic Abstractive Summarization",
    author = "Dohare, Shibhansh  and
      Gupta, Vivek  and
      Karnick, Harish",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3011",
    doi = "10.18653/v1/P18-3011",
    pages = "74--83",
    abstract = "Automatic abstractive summary generation remains a significant open problem for natural language processing. In this work, we develop a novel pipeline for Semantic Abstractive Summarization (SAS). SAS, as introduced by Liu et. al. (2015) first generates an AMR graph of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7{\%} F1 score in node prediction.",
}
@inproceedings{sankhavara-2018-biomedical,
    title = "Biomedical Document Retrieval for Clinical Decision Support System",
    author = "Sankhavara, Jainisha",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3012",
    doi = "10.18653/v1/P18-3012",
    pages = "84--90",
    abstract = "The availability of huge amount of biomedical literature have opened up new possibilities to apply Information Retrieval and NLP for mining documents from them. In this work, we are focusing on biomedical document retrieval from literature for clinical decision support systems. We compare statistical and NLP based approaches of query reformulation for biomedical document retrieval. Also, we have modeled the biomedical document retrieval as a learning to rank problem. We report initial results for statistical and NLP based query reformulation approaches and learning to rank approach with future direction of research.",
}
@inproceedings{sawhney-etal-2018-computational,
    title = "A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets",
    author = "Sawhney, Ramit  and
      Manchanda, Prachi  and
      Singh, Raj  and
      Aggarwal, Swati",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3013",
    doi = "10.18653/v1/P18-3013",
    pages = "91--98",
    abstract = "Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide. This paper proposes a novel supervised approach for detecting suicidal ideation in content on Twitter. A set of features is proposed for training both linear and ensemble classifiers over a dataset of manually annotated tweets. The performance of the proposed methodology is compared against four baselines that utilize varying approaches to validate its utility. The results are finally summarized by reflecting on the effect of the inclusion of the proposed features one by one for suicidal ideation detection.",
}
@inproceedings{parupalli-etal-2018-bcsat,
    title = "{BCSAT} : A Benchmark Corpus for Sentiment Analysis in {T}elugu Using Word-level Annotations",
    author = "Parupalli, Sreekavitha  and
      Anvesh Rao, Vijjini  and
      Mamidi, Radhika",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3014",
    doi = "10.18653/v1/P18-3014",
    pages = "99--104",
    abstract = "The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using word-level sentiment annotations. From OntoSenseNet, we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus.",
}
@inproceedings{arumae-liu-2018-reinforced,
    title = "Reinforced Extractive Summarization with Question-Focused Rewards",
    author = "Arumae, Kristjan  and
      Liu, Fei",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3015",
    doi = "10.18653/v1/P18-3015",
    pages = "105--111",
    abstract = "We investigate a new training paradigm for extractive summarization. Traditionally, human abstracts are used to derive goldstandard labels for extraction units. However, the labels are often inaccurate, because human abstracts and source documents cannot be easily aligned at the word level. In this paper we convert human abstracts to a set of Cloze-style comprehension questions. System summaries are encouraged to preserve salient source content useful for answering questions and share common words with the abstracts. We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries. Our experiments show that the proposed method is effective. It surpasses state-of-the-art systems on the standard summarization dataset.",
}
@inproceedings{katsumata-etal-2018-graph,
    title = "Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models",
    author = "Katsumata, Satoru  and
      Matsumura, Yukio  and
      Yamagishi, Hayahide  and
      Komachi, Mamoru",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3016",
    doi = "10.18653/v1/P18-3016",
    pages = "112--119",
    abstract = "Encoder-decoder models typically only employ words that are frequently used in the training corpus because of the computational costs and/or to exclude noisy words. However, this vocabulary set may still include words that interfere with learning in encoder-decoder models. This paper proposes a method for selecting more suitable words for learning encoders by utilizing not only frequency, but also co-occurrence information, which we capture using the HITS algorithm. The proposed method is applied to two tasks: machine translation and grammatical error correction. For Japanese-to-English translation, this method achieved a BLEU score that was 0.56 points more than that of a baseline. It also outperformed the baseline method for English grammatical error correction, with an F-measure that was 1.48 points higher.",
}
@inproceedings{bhatnagar-etal-2018-exploring,
    title = "Exploring Chunk Based Templates for Generating a subset of {E}nglish Text",
    author = "Bhatnagar, Nikhilesh  and
      Shrivastava, Manish  and
      Mamidi, Radhika",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3017",
    doi = "10.18653/v1/P18-3017",
    pages = "120--126",
    abstract = "Natural Language Generation (NLG) is a research task which addresses the automatic generation of natural language text representative of an input non-linguistic collection of knowledge. In this paper, we address the task of the generation of grammatical sentences in an isolated context given a partial bag-of-words which the generated sentence must contain. We view the task as a search problem (a problem of choice) involving combinations of smaller chunk based templates extracted from a training corpus to construct a complete sentence. To achieve that, we propose a fitness function which we use in conjunction with an evolutionary algorithm as the search procedure to arrive at a potentially grammatical sentence (modeled by the fitness score) which satisfies the input constraints.",
}
@inproceedings{wallace-boyd-graber-2018-trick,
    title = "Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions",
    author = "Wallace, Eric  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3018",
    doi = "10.18653/v1/P18-3018",
    pages = "127--133",
    abstract = "Modern question answering systems have been touted as approaching human performance. However, existing question answering datasets are imperfect tests. Questions are written with humans in mind, not computers, and often do not properly expose model limitations. To address this, we develop an adversarial writing setting, where humans interact with trained models and try to break them. This annotation process yields a challenge set, which despite being easy for trivia players to answer, systematically stumps automated question answering systems. Diagnosing model errors on the evaluation data provides actionable insights to explore in developing robust and generalizable question answering systems.",
}
@inproceedings{a-2018-alignment,
    title = "Alignment Analysis of Sequential Segmentation of Lexicons to Improve Automatic Cognate Detection",
    author = "A, Pranav",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3019",
    doi = "10.18653/v1/P18-3019",
    pages = "134--140",
    abstract = "Ranking functions in information retrieval are often used in search engines to extract the relevant answers to the query. This paper makes use of this notion of information retrieval and applies onto the problem domain of cognate detection. The main contributions of this paper are: (1) positional tokenization, which incorporates the sequential notion; (2) graphical error modelling, which calculates the morphological shifts. The current research work only distinguishes whether a pair of words are cognates or not. However, we also study if we could predict a possible cognate from the given input. Our study shows that language modelling based retrieval functions with positional tokenization and error modelling tend to give better results than competing baselines.",
}
@inproceedings{kezar-2018-mixed,
    title = "Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories",
    author = "Kezar, Lee",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3020",
    doi = "10.18653/v1/P18-3020",
    pages = "141--145",
    abstract = "Conversational agents, having the goal of natural language generation, must rely on language models which can integrate emotion into their responses. Recent projects outline models which can produce emotional sentences, but unlike human language, they tend to be restricted to one affective category out of a few. To my knowledge, none allow for the intentional coexistence of multiple emotions on the word or sentence level. Building on prior research which allows for variation in the intensity of a singular emotion, this research proposal outlines an LSTM (Long Short-Term Memory) language model which allows for variation in multiple emotions simultaneously.",
}
@inproceedings{etoori-etal-2018-automatic,
    title = "Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning",
    author = "Etoori, Pravallika  and
      Chinnakotla, Manoj  and
      Mamidi, Radhika",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3021",
    doi = "10.18653/v1/P18-3021",
    pages = "146--152",
    abstract = "Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resource-scarce and do not have such parallel data due to low volume of queries and non-existence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resource-scarce languages. We propose a sequence-to-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.",
}
@inproceedings{khullar-etal-2018-automatic,
    title = "Automatic Question Generation using Relative Pronouns and Adverbs",
    author = "Khullar, Payal  and
      Rachna, Konigari  and
      Hase, Mukul  and
      Shrivastava, Manish",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-3022",
    doi = "10.18653/v1/P18-3022",
    pages = "153--158",
    abstract = "This paper presents a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. Our system is syntax-based, runs on dependency parse information of a single-sentence input, and achieves high accuracy in terms of syntactic correctness, semantic adequacy, fluency and uniqueness. One of the key advantages of our system, in comparison with other rule-based approaches, is that we nearly eliminate the chances of getting a wrong wh-word in the generated question, by fetching the requisite wh-word from the input sentence itself. Depending upon the input, we generate both factoid and descriptive type questions. To the best of our information, the exploitation of wh-pronouns and wh-adverbs to generate questions is novel in the Automatic Question Generation task.",
}
@proceedings{acl-2018-association-linguistics,
    title = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2000",
}
@inproceedings{wolf-etal-2018-continuous,
    title = "Continuous Learning in a Hierarchical Multiscale Neural Network",
    author = "Wolf, Thomas  and
      Chaumond, Julien  and
      Delangue, Clement",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2001",
    doi = "10.18653/v1/P18-2001",
    pages = "1--7",
    abstract = "We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework. We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion. We use elastic weights consolidation as a higher-level to prevent catastrophic forgetting in our continuous learning framework.",
}
@inproceedings{salle-villavicencio-2018-restricted,
    title = "Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality",
    author = "Salle, Alexandre  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2002",
    doi = "10.18653/v1/P18-2002",
    pages = "8--13",
    abstract = "Increasing the capacity of recurrent neural networks (RNN) usually involves augmenting the size of the hidden layer, with significant increase of computational cost. Recurrent neural tensor networks (RNTN) increase capacity using distinct hidden layer weights for each word, but with greater costs in memory usage. In this paper, we introduce restricted recurrent neural tensor networks (r-RNTN) which reserve distinct hidden layer weights for frequent vocabulary words while sharing a single set of weights for infrequent words. Perplexity evaluations show that for fixed hidden layer sizes, r-RNTNs improve language model performance over RNNs using only a small fraction of the parameters of unrestricted RNTNs. These results hold for r-RNTNs using Gated Recurrent Units and Long Short-Term Memory.",
}
@inproceedings{blevins-etal-2018-deep,
    title = "Deep {RNN}s Encode Soft Hierarchical Syntax",
    author = "Blevins, Terra  and
      Levy, Omer  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2003",
    doi = "10.18653/v1/P18-2003",
    pages = "14--19",
    abstract = "We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision. We consider four syntax tasks at different depths of the parse tree; for each word, we predict its part of speech as well as the first (parent), second (grandparent) and third level (great-grandparent) constituent labels that appear above it. These predictions are made from representations produced at different depths in networks that are pretrained with one of four objectives: dependency parsing, semantic role labeling, machine translation, or language modeling. In every case, we find a correspondence between network depth and syntactic depth, suggesting that a soft syntactic hierarchy emerges. This effect is robust across all conditions, indicating that the models encode significant amounts of syntax even in the absence of an explicit syntactic training supervision.",
}
@inproceedings{ali-renals-2018-word,
    title = "Word Error Rate Estimation for Speech Recognition: e-{WER}",
    author = "Ali, Ahmed  and
      Renals, Steve",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2004",
    doi = "10.18653/v1/P18-2004",
    pages = "20--24",
    abstract = "Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we propose a novel approach to estimate WER, or e-WER, which does not require a gold-standard transcription of the test set. Our e-WER framework uses a comprehensive set of features: ASR recognised text, character recognition results to complement recognition output, and internal decoder features. We report results for the two features; black-box and glass-box using unseen 24 Arabic broadcast programs. Our system achieves 16.9{\%} WER root mean squared error (RMSE) across 1,400 sentences. The estimated overall WER e-WER was 25.3{\%} for the three hours test set, while the actual WER was 28.5{\%}.",
}
@inproceedings{li-etal-2018-towards,
    title = "Towards Robust and Privacy-preserving Text Representations",
    author = "Li, Yitong  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2005",
    doi = "10.18653/v1/P18-2005",
    pages = "25--30",
    abstract = "Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes. Consequently, the authorship of training and evaluation corpora can have unforeseen impacts, including differing model performance for different user groups, as well as privacy implications. In this paper, we propose an approach to explicitly obscure important author characteristics at training time, such that representations learned are invariant to these attributes. Evaluating on two tasks, we show that this leads to increased privacy in the learned representations, as well as more robust models to varying evaluation conditions, including out-of-domain corpora.",
}
@inproceedings{ebrahimi-etal-2018-hotflip,
    title = "{H}ot{F}lip: White-Box Adversarial Examples for Text Classification",
    author = "Ebrahimi, Javid  and
      Rao, Anyi  and
      Lowd, Daniel  and
      Dou, Dejing",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2006",
    doi = "10.18653/v1/P18-2006",
    pages = "31--36",
    abstract = "We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.",
}
@inproceedings{k-sarma-etal-2018-domain,
    title = "Domain Adapted Word Embeddings for Improved Sentiment Classification",
    author = "K Sarma, Prathusha  and
      Liang, Yingyu  and
      Sethares, Bill",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2007",
    doi = "10.18653/v1/P18-2007",
    pages = "37--42",
    abstract = "Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a method to combine the breadth of generic embeddings with the specificity of domain specific embeddings. The resulting embeddings, called Domain Adapted (DA) word embeddings, are formed by aligning corresponding word vectors using Canonical Correlation Analysis (CCA) or the related nonlinear Kernel CCA. Evaluation results on sentiment classification tasks show that the DA embeddings substantially outperform both generic, DS embeddings when used as input features to standard or state-of-the-art sentence encoding algorithms for classification.",
}
@inproceedings{duong-etal-2018-active,
    title = "Active learning for deep semantic parsing",
    author = "Duong, Long  and
      Afshar, Hadi  and
      Estival, Dominique  and
      Pink, Glen  and
      Cohen, Philip  and
      Johnson, Mark",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2008",
    doi = "10.18653/v1/P18-2008",
    pages = "43--48",
    abstract = "Semantic parsing requires training data that is expensive and slow to collect. We apply active learning to both traditional and {``}overnight{''} data collection approaches. We show that it is possible to obtain good training hyperparameters from seed data which is only a small fraction of the full dataset. We show that uncertainty sampling based on least confidence score is competitive in traditional data collection but not applicable for overnight collection. We propose several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.",
}
@inproceedings{ein-dor-etal-2018-learning,
    title = "Learning Thematic Similarity Metric from Article Sections Using Triplet Networks",
    author = "Ein Dor, Liat  and
      Mass, Yosi  and
      Halfon, Alon  and
      Venezian, Elad  and
      Shnayderman, Ilya  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2009",
    doi = "10.18653/v1/P18-2009",
    pages = "49--54",
    abstract = "In this paper we suggest to leverage the partition of articles into sections, in order to learn thematic similarity metric between sentences. We assume that a sentence is thematically closer to sentences within its section than to sentences from other sections. Based on this assumption, we use Wikipedia articles to automatically create a large dataset of weakly labeled sentence triplets, composed of a pivot sentence, one sentence from the same section and one from another section. We train a triplet network to embed sentences from the same section closer. To test the performance of the learned embeddings, we create and release a sentence clustering benchmark. We show that the triplet network learns useful thematic metrics, that significantly outperform state-of-the-art semantic similarity methods and multipurpose embeddings on the task of thematic clustering of sentences. We also show that the learned embeddings perform well on the task of sentence semantic similarity prediction.",
}
@inproceedings{ustalov-etal-2018-unsupervised,
    title = "Unsupervised Semantic Frame Induction using Triclustering",
    author = "Ustalov, Dmitry  and
      Panchenko, Alexander  and
      Kutuzov, Andrey  and
      Biemann, Chris  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2010",
    doi = "10.18653/v1/P18-2010",
    pages = "55--62",
    abstract = "We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach, Triframes, shows state-of-the art results on this task on a FrameNet-derived dataset and performing on par with competitive methods on a verb class clustering task.",
}
@inproceedings{patil-etal-2018-identification,
    title = "Identification of Alias Links among Participants in Narratives",
    author = "Patil, Sangameshwar  and
      Pawar, Sachin  and
      Hingmire, Swapnil  and
      Palshikar, Girish  and
      Varma, Vasudeva  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2011",
    doi = "10.18653/v1/P18-2011",
    pages = "63--68",
    abstract = "Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications. This task becomes challenging because these participants are often referred to using multiple aliases. In this paper, we propose an approach based on linguistic knowledge for identification of aliases mentioned using proper nouns, pronouns or noun phrases with common noun headword. We use Markov Logic Network (MLN) to encode the linguistic knowledge for identification of aliases. We evaluate on four diverse history narratives of varying complexity. Our approach performs better than the state-of-the-art approach as well as a combination of standard named entity recognition and coreference resolution techniques.",
}
@inproceedings{zukov-gregoric-etal-2018-named,
    title = "Named Entity Recognition With Parallel Recurrent Neural Networks",
    author = "{\v{Z}}ukov-Gregori{\v{c}}, Andrej  and
      Bachrach, Yoram  and
      Coope, Sam",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2012",
    doi = "10.18653/v1/P18-2012",
    pages = "69--74",
    abstract = "We present a new architecture for named entity recognition. Our model employs multiple independent bidirectional LSTM units across the same input and promotes diversity among them by employing an inter-model regularization term. By distributing computation across multiple smaller LSTMs we find a significant reduction in the total number of parameters. We find our architecture achieves state-of-the-art performance on the CoNLL 2003 NER dataset.",
}
@inproceedings{jain-etal-2018-type,
    title = "Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision",
    author = "Jain, Prachi  and
      Kumar, Pankaj  and
      {Mausam}  and
      Chakrabarti, Soumen",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2013",
    doi = "10.18653/v1/P18-2013",
    pages = "75--80",
    abstract = "State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings. We observe that when they fail, they often make entity predictions that are incompatible with the type required by the relation. In response, we enhance each base factorization with two type-compatibility terms between entity-relation pairs, and combine the signals in a novel manner. Without explicit supervision from a type catalog, our proposed modification obtains up to 7{\%} MRR gains over base models, and new state-of-the-art results on several datasets. Further analysis reveals that our models better represent the latent types of entities and their embeddings also predict supervised types better than the embeddings fitted by baseline models.",
}
@inproceedings{christopoulou-etal-2018-walk,
    title = "A Walk-based Model on Entity Graphs for Relation Extraction",
    author = "Christopoulou, Fenia  and
      Miwa, Makoto  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2014",
    doi = "10.18653/v1/P18-2014",
    pages = "81--88",
    abstract = "We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All the entities in a sentence are placed as nodes in a fully-connected graph structure. The edges are represented with position-aware contexts around the entity pairs. In order to consider different relation paths between two entities, we construct up to $l$-length walks between each pair. The resulting walks are merged and iteratively used to update the edge representations into longer walks representations. We show that the model achieves performance comparable to the state-of-the-art systems on the ACE 2005 dataset without using any external tools.",
}
@inproceedings{phi-etal-2018-ranking,
    title = "Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction",
    author = "Phi, Van-Thuy  and
      Santoso, Joan  and
      Shimbo, Masashi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2015",
    doi = "10.18653/v1/P18-2015",
    pages = "89--95",
    abstract = "This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction. We first point out that these tasks are related. Then, inspired by ranking relation instances and patterns computed by the HITS algorithm, and selecting cluster centroids using the K-means, LSA, or NMF method, we propose methods for selecting the initial seeds from an existing resource, or reducing the level of noise in the distantly labeled data. Experiments show that our proposed methods achieve a better performance than the baseline systems in both tasks.",
}
@inproceedings{xu-etal-2018-automatic,
    title = "Automatic Extraction of Commonsense {L}ocated{N}ear Knowledge",
    author = "Xu, Frank F.  and
      Lin, Bill Yuchen  and
      Zhu, Kenny",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2016",
    doi = "10.18653/v1/P18-2016",
    pages = "96--101",
    abstract = "LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life. In this paper, we study how to automatically extract such relationship through a sentence-level relation classifier and aggregating the scores of entity pairs from a large corpus. Also, we release two benchmark datasets for evaluation and future research.",
}
@inproceedings{zhang-etal-2018-neural,
    title = "Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering",
    author = "Zhang, Rui  and
      Nogueira dos Santos, C{\'\i}cero  and
      Yasunaga, Michihiro  and
      Xiang, Bing  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2017",
    doi = "10.18653/v1/P18-2017",
    pages = "102--107",
    abstract = "Coreference resolution aims to identify in a text all mentions that refer to the same real world entity. The state-of-the-art end-to-end neural coreference model considers all text spans in a document as potential mentions and learns to link an antecedent for each possible mention. In this paper, we propose to improve the end-to-end coreference resolution system by (1) using a biaffine attention model to get antecedent scores for each possible mention, and (2) jointly optimizing the mention detection accuracy and mention clustering accuracy given the mention cluster labels. Our model achieves the state-of-the-art performance on the CoNLL-2012 shared task English test set.",
}
@inproceedings{mrksic-vulic-2018-fully,
    title = "Fully Statistical Neural Belief Tracking",
    author = "Mrk{\v{s}}i{\'c}, Nikola  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2018",
    doi = "10.18653/v1/P18-2018",
    pages = "108--113",
    abstract = "This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models.",
}
@inproceedings{pezzelle-etal-2018-guessed,
    title = "Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers",
    author = "Pezzelle, Sandro  and
      Steinert-Threlkeld, Shane  and
      Bernardi, Raffaella  and
      Szymanik, Jakub",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2019",
    doi = "10.18653/v1/P18-2019",
    pages = "114--119",
    abstract = "We study the role of linguistic context in predicting quantifiers ({`}few{'}, {`}all{'}). We collect crowdsourced data from human participants and test various models in a local (single-sentence) and a global context (multi-sentence) condition. Models significantly out-perform humans in the former setting and are only slightly better in the latter. While human performance improves with more linguistic context (especially on proportional quantifiers), model performance suffers. Models are very effective in exploiting lexical and morpho-syntactic patterns; humans are better at genuinely understanding the meaning of the (global) context.",
}
@inproceedings{riedl-pado-2018-named,
    title = "A Named Entity Recognition Shootout for {G}erman",
    author = "Riedl, Martin  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2020",
    doi = "10.18653/v1/P18-2020",
    pages = "120--125",
    abstract = "We ask how to practically build a model for German named entity recognition (NER) that performs at the state of the art for both contemporary and historical texts, i.e., a big-data and a small-data scenario. The two best-performing model families are pitted against each other (linear-chain CRFs and BiLSTM) to observe the trade-off between expressiveness and data requirements. BiLSTM outperforms the CRF when large datasets are available and performs inferior for the smallest dataset. BiLSTMs profit substantially from transfer learning, which enables them to be trained on multiple corpora, resulting in a new state-of-the-art model for German NER on two contemporary German corpora (CoNLL 2003 and GermEval 2014) and two historic corpora.",
}
@inproceedings{meyers-etal-2018-dataset,
    title = "A dataset for identifying actionable feedback in collaborative software development",
    author = "Meyers, Benjamin S.  and
      Munaiah, Nuthan  and
      Prud{'}hommeaux, Emily  and
      Meneely, Andrew  and
      Wolff, Josephine  and
      Ovesdotter Alm, Cecilia  and
      Murukannaiah, Pradeep",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2021",
    doi = "10.18653/v1/P18-2021",
    pages = "126--131",
    abstract = "Software developers and testers have long struggled with how to elicit proactive responses from their coworkers when reviewing code for security vulnerabilities and errors. For a code review to be successful, it must not only identify potential problems but also elicit an active response from the colleague responsible for modifying the code. To understand the factors that contribute to this outcome, we analyze a novel dataset of more than one million code reviews for the Google Chromium project, from which we extract linguistic features of feedback that elicited responsive actions from coworkers. Using a manually-labeled subset of reviewer comments, we trained a highly accurate classifier to identify acted-upon comments (AUC = 0.85). Our results demonstrate the utility of our dataset, the feasibility of using NLP for this new task, and the potential of NLP to improve our understanding of how communications between colleagues can be authored to elicit positive, proactive responses.",
}
@inproceedings{vaidyanathan-etal-2018-snag,
    title = "{SNAG}: Spoken Narratives and Gaze Dataset",
    author = "Vaidyanathan, Preethi  and
      Prud{'}hommeaux, Emily T.  and
      Pelz, Jeff B.  and
      Alm, Cecilia O.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2022",
    doi = "10.18653/v1/P18-2022",
    pages = "132--137",
    abstract = "Humans rely on multiple sensory modalities when examining and reasoning over images. In this paper, we describe a new multimodal dataset that consists of gaze measurements and spoken descriptions collected in parallel during an image inspection task. The task was performed by multiple participants on 100 general-domain images showing everyday objects and activities. We demonstrate the usefulness of the dataset by applying an existing visual-linguistic data fusion framework in order to label important image regions with appropriate linguistic labels.",
}
@inproceedings{li-etal-2018-analogical,
    title = "Analogical Reasoning on {C}hinese Morphological and Semantic Relations",
    author = "Li, Shen  and
      Zhao, Zhe  and
      Hu, Renfen  and
      Li, Wensi  and
      Liu, Tao  and
      Du, Xiaoyong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2023",
    doi = "10.18653/v1/P18-2023",
    pages = "138--143",
    abstract = "Analogical reasoning is effective in capturing linguistic regularities. This paper proposes an analogical reasoning task on Chinese. After delving into Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28 explicit semantic relations. A big and balanced dataset CA8 is then built for this task, including 17813 questions. Furthermore, we systematically explore the influences of vector representations, context features, and corpora on analogical reasoning. With the experiments, CA8 is proved to be a reliable benchmark for evaluating Chinese word embeddings.",
}
@inproceedings{zhang-etal-2018-construction,
    title = "Construction of a {C}hinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions",
    author = "Zhang, Dongyu  and
      Lin, Hongfei  and
      Yang, Liang  and
      Zhang, Shaowu  and
      Xu, Bo",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2024",
    doi = "10.18653/v1/P18-2024",
    pages = "144--150",
    abstract = "Metaphors are frequently used to convey emotions. However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions. Furthermore, most studies focus on English, and few in other languages, particularly Sino-Tibetan languages such as Chinese, for emotion analysis from metaphorical texts, although there are likely to be many differences in emotional expressions of metaphorical usages across different languages. We therefore construct a significant new corpus on metaphor, with 5,605 manually annotated sentences in Chinese. We present an annotation scheme that contains annotations of linguistic metaphors, emotional categories (joy, anger, sadness, fear, love, disgust and surprise), and intensity. The annotation agreement analyses for multiple annotators are described. We also use the corpus to explore and analyze the emotionality of metaphors. To the best of our knowledge, this is the first relatively large metaphor corpus with an annotation of emotions in Chinese.",
}
@inproceedings{qin-etal-2018-automatic,
    title = "Automatic Article Commenting: the Task and Dataset",
    author = "Qin, Lianhui  and
      Liu, Lemao  and
      Bi, Wei  and
      Wang, Yan  and
      Liu, Xiaojiang  and
      Hu, Zhiting  and
      Zhao, Hai  and
      Shi, Shuming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2025",
    doi = "10.18653/v1/P18-2025",
    pages = "151--156",
    abstract = "Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent chatbots, etc. This paper proposes the new task of automatic article commenting, and introduces a large-scale Chinese dataset with millions of real comments and a human-annotated subset characterizing the comments{'} varying quality. Incorporating the human bias of comment quality, we further develop automatic metrics that generalize a broad set of popular reference-based metrics and exhibit greatly improved correlations with human evaluations.",
}
@inproceedings{belyy-etal-2018-improved,
    title = "Improved Evaluation Framework for Complex Plagiarism Detection",
    author = "Belyy, Anton  and
      Dubova, Marina  and
      Nekrasov, Dmitry",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2026",
    doi = "10.18653/v1/P18-2026",
    pages = "157--162",
    abstract = "Plagiarism is a major issue in science and education. Complex plagiarism, such as plagiarism of ideas, is hard to detect, and therefore it is especially important to track improvement of methods correctly. In this paper, we study the performance of plagdet, the main measure for plagiarim detection, on manually paraphrased datasets (such as PAN Summary). We reveal its fallibility under certain conditions and propose an evaluation framework with normalization of inner terms, which is resilient to the dataset imbalance. We conclude with the experimental justification of the proposed measure. The implementation of the new framework is made publicly available as a Github repository.",
}
@inproceedings{lin-etal-2018-global,
    title = "Global Encoding for Abstractive Summarization",
    author = "Lin, Junyang  and
      Sun, Xu  and
      Ma, Shuming  and
      Su, Qi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2027",
    doi = "10.18653/v1/P18-2027",
    pages = "163--169",
    abstract = "In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context. It consists of a convolutional gated unit to perform global encoding to improve the representations of the source-side information. Evaluations on the LCSTS and the English Gigaword both demonstrate that our model outperforms the baseline models, and the analysis shows that our model is capable of generating summary of higher quality and reducing repetition.",
}
@inproceedings{zhao-etal-2018-language,
    title = "A Language Model based Evaluator for Sentence Compression",
    author = "Zhao, Yang  and
      Luo, Zhiyuan  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2028",
    doi = "10.18653/v1/P18-2028",
    pages = "170--175",
    abstract = "We herein present a language-model-based evaluator for deletion-based sentence compression and view this task as a series of deletion-and-evaluation operations using the evaluator. More specifically, the evaluator is a syntactic neural language model that is first built by learning the syntactic and structural collocation among words. Subsequently, a series of trial-and-error deletion operations are conducted on the source sentences via a reinforcement learning framework to obtain the best target compression. An empirical study shows that the proposed model can effectively generate more readable compression, comparable or superior to several strong baselines. Furthermore, we introduce a 200-sentence test set for a large-scale dataset, setting a new baseline for the future research.",
}
@inproceedings{glenski-etal-2018-identifying,
    title = "Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources",
    author = "Glenski, Maria  and
      Weninger, Tim  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2029",
    doi = "10.18653/v1/P18-2029",
    pages = "176--181",
    abstract = "In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to better understand how users react to trusted and deceptive news sources across two popular, and very different, social media platforms. To that end, (1) we develop a model to classify user reactions into one of nine types, such as answer, elaboration, and question, etc, and (2) we measure the speed and the type of reaction for trusted and deceptive news sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there are significant differences in the speed and the type of reactions between trusted and deceptive news sources on Twitter, but far smaller differences on Reddit.",
}
@inproceedings{subramanian-etal-2018-content,
    title = "Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model",
    author = "Subramanian, Shivashankar  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2030",
    doi = "10.18653/v1/P18-2030",
    pages = "182--188",
    abstract = "Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition {---} commonly measured by its signature count {---} based on its textual content has utility for policymakers as well as those posting the petition. In this work, we model this task using CNN regression with an auxiliary ordinal regression objective. We demonstrate the effectiveness of our proposed approach using UK and US government petition datasets.",
}
@inproceedings{nogueira-dos-santos-etal-2018-fighting,
    title = "Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer",
    author = "Nogueira dos Santos, Cicero  and
      Melnyk, Igor  and
      Padhi, Inkit",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2031",
    doi = "10.18653/v1/P18-2031",
    pages = "189--194",
    abstract = "We introduce a new approach to tackle the problem of offensive language in online social media. Our approach uses unsupervised text style transfer to translate offensive sentences into non-offensive ones. We propose a new method for training encoder-decoders using non-parallel data that combines a collaborative classifier, attention and the cycle consistency loss. Experimental results on data from Twitter and Reddit show that our method outperforms a state-of-the-art text style transfer system in two out of three quantitative metrics and produces reliable non-offensive transferred sentences.",
}
@inproceedings{jaidka-etal-2018-diachronic,
    title = "Diachronic degradation of language models: Insights from social media",
    author = "Jaidka, Kokil  and
      Chhaya, Niyati  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2032",
    doi = "10.18653/v1/P18-2032",
    pages = "195--200",
    abstract = "Natural languages change over time because they evolve to the needs of their users and the socio-technological environment. This study investigates the diachronic accuracy of pre-trained language models for downstream tasks in machine learning and user profiling. It asks the question: given that the social media platform and its users remain the same, how is language changing over time? How can these differences be used to track the changes in the affect around a particular topic? To our knowledge, this is the first study to show that it is possible to measure diachronic semantic drifts within social media and within the span of a few years.",
}
@inproceedings{wei-etal-2018-task,
    title = "Task-oriented Dialogue System for Automatic Diagnosis",
    author = "Wei, Zhongyu  and
      Liu, Qianlong  and
      Peng, Baolin  and
      Tou, Huaixiao  and
      Chen, Ting  and
      Huang, Xuanjing  and
      Wong, Kam-fai  and
      Dai, Xiangying",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2033",
    doi = "10.18653/v1/P18-2033",
    pages = "201--207",
    abstract = "In this paper, we make a move to build a dialogue system for automatic diagnosis. We first build a dataset collected from an online medical forum by extracting symptoms from both patients{'} self-reports and conversational data between patients and doctors. Then we propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. Experimental results on our dataset show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and our dialogue system is able to collect these symptoms automatically and make a better diagnosis.",
}
@inproceedings{qiu-etal-2018-transfer,
    title = "Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in {E}-commerce",
    author = "Qiu, Minghui  and
      Yang, Liu  and
      Ji, Feng  and
      Zhou, Wei  and
      Huang, Jun  and
      Chen, Haiqing  and
      Croft, Bruce  and
      Lin, Wei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2034",
    doi = "10.18653/v1/P18-2034",
    pages = "208--213",
    abstract = "Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist and observed a significant improvement over the existing online model.",
}
@inproceedings{singla-etal-2018-multi,
    title = "A Multi-task Approach to Learning Multilingual Representations",
    author = "Singla, Karan  and
      Can, Dogan  and
      Narayanan, Shrikanth",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2035",
    doi = "10.18653/v1/P18-2035",
    pages = "214--220",
    abstract = "We present a novel multi-task modeling approach to learning multilingual distributed representations of text. Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model. Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone. Our model shows competitive performance in a standard cross-lingual document classification task. We also show the effectiveness of our method in a limited resource scenario.",
}
@inproceedings{nakashole-flauger-2018-characterizing,
    title = "Characterizing Departures from Linearity in Word Translation",
    author = "Nakashole, Ndapa  and
      Flauger, Raphael",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2036",
    doi = "10.18653/v1/P18-2036",
    pages = "221--227",
    abstract = "We investigate the behavior of maps learned by machine translation methods. The maps translate words by projecting between word embedding spaces of different languages. We locally approximate these maps using linear maps, and find that they vary across the word embedding space. This demonstrates that the underlying maps are non-linear. Importantly, we show that the locally linear maps vary by an amount that is tightly correlated with the distance between the neighborhoods on which they are trained. Our results can be used to test non-linear methods, and to drive the design of more accurate maps for word translation.",
}
@inproceedings{schwenk-2018-filtering,
    title = "Filtering and Mining Parallel Data in a Joint Multilingual Space",
    author = "Schwenk, Holger",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2037",
    doi = "10.18653/v1/P18-2037",
    pages = "228--234",
    abstract = "We learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large news collections. We are able to improve a competitive baseline on the WMT{'}14 English to German task by 0.3 BLEU by filtering out 25{\%} of the training data. The same approach is used to mine additional bitexts for the WMT{'}14 system and to obtain competitive results on the BUCC shared task to identify parallel sentences in comparable corpora. The approach is generic, it can be applied to many language pairs and it is independent of the architecture of the machine translation system.",
}
@inproceedings{ye-ling-2018-hybrid,
    title = "Hybrid semi-{M}arkov {CRF} for Neural Sequence Labeling",
    author = "Ye, Zhixiu  and
      Ling, Zhen-Hua",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2038",
    doi = "10.18653/v1/P18-2038",
    pages = "235--240",
    abstract = "This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing. Based on conventional conditional random fields (CRFs), SCRFs have been designed for the tasks of assigning labels to segments by extracting features from and describing transitions between segments instead of words. In this paper, we improve the existing SCRF methods by employing word-level and segment-level information simultaneously. First, word-level labels are utilized to derive the segment scores in SCRFs. Second, a CRF output layer and an SCRF output layer are integrated into a unified neural network and trained jointly. Experimental results on CoNLL 2003 named entity recognition (NER) shared task show that our model achieves state-of-the-art performance when no external knowledge is used.",
}
@inproceedings{seyler-etal-2018-study,
    title = "A Study of the Importance of External Knowledge in the Named Entity Recognition Task",
    author = "Seyler, Dominic  and
      Dembelova, Tatiana  and
      Del Corro, Luciano  and
      Hoffart, Johannes  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2039",
    doi = "10.18653/v1/P18-2039",
    pages = "241--246",
    abstract = "In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER). We present a novel modular framework that divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources, such as a knowledge-base, a list of names, or document-specific semantic annotations. Further, we show the effects on performance when incrementally adding deeper knowledge and discuss effectiveness/efficiency trade-offs.",
}
@inproceedings{krasnashchok-jouili-2018-improving,
    title = "Improving Topic Quality by Promoting Named Entities in Topic Modeling",
    author = "Krasnashchok, Katsiaryna  and
      Jouili, Salim",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2040",
    doi = "10.18653/v1/P18-2040",
    pages = "247--253",
    abstract = "News related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper we use named entities as domain-specific terms for news-centric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity.",
}
@inproceedings{chalkidis-etal-2018-obligation,
    title = "Obligation and Prohibition Extraction Using Hierarchical {RNN}s",
    author = "Chalkidis, Ilias  and
      Androutsopoulos, Ion  and
      Michos, Achilleas",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2041",
    doi = "10.18653/v1/P18-2041",
    pages = "254--259",
    abstract = "We consider the task of detecting contractual obligations and prohibitions. We show that a self-attention mechanism improves the performance of a BILSTM classifier, the previous state of the art for this task, by allowing it to focus on indicative tokens. We also introduce a hierarchical BILSTM, which converts each sentence to an embedding, and processes the sentence embeddings to classify each sentence. Apart from being faster to train, the hierarchical BILSTM outperforms the flat one, even when the latter considers surrounding sentences, because the hierarchical model has a broader discourse view.",
}
@inproceedings{wang-etal-2018-paper,
    title = "Paper Abstract Writing through Editing Mechanism",
    author = "Wang, Qingyun  and
      Zhou, Zhihao  and
      Huang, Lifu  and
      Whitehead, Spencer  and
      Zhang, Boliang  and
      Ji, Heng  and
      Knight, Kevin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2042",
    doi = "10.18653/v1/P18-2042",
    pages = "260--265",
    abstract = "We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design a novel Writing-editing Network that can attend to both the title and the previously generated abstract drafts and then iteratively revise and polish the abstract. With two series of Turing tests, where the human judges are asked to distinguish the system-generated abstracts from human-written ones, our system passes Turing tests by junior domain experts at a rate up to 30{\%} and by non-expert at a rate up to 80{\%}.",
}
@inproceedings{gadetsky-etal-2018-conditional,
    title = "Conditional Generators of Words Definitions",
    author = "Gadetsky, Artyom  and
      Yakubovskiy, Ilya  and
      Vetrov, Dmitry",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2043",
    doi = "10.18653/v1/P18-2043",
    pages = "266--271",
    abstract = "We explore recently introduced definition modeling technique that provided the tool for evaluation of different distributed vector representations of words through modeling dictionary definitions of words. In this work, we study the problem of word ambiguities in definition modeling and propose a possible solution by employing latent variable modeling and soft attention mechanisms. Our quantitative and qualitative evaluation and analysis of the model shows that taking into account words{'} ambiguity and polysemy leads to performance improvement.",
}
@inproceedings{chaturvedi-etal-2018-cnn,
    title = "{CNN} for Text-Based Multiple Choice Question Answering",
    author = "Chaturvedi, Akshay  and
      Pandit, Onkar  and
      Garain, Utpal",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2044",
    doi = "10.18653/v1/P18-2044",
    pages = "272--277",
    abstract = "The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.",
}
@inproceedings{liu-etal-2018-narrative,
    title = "Narrative Modeling with Memory Chains and Semantic Supervision",
    author = "Liu, Fei  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2045",
    doi = "10.18653/v1/P18-2045",
    pages = "278--284",
    abstract = "Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art.",
}
@inproceedings{uva-etal-2018-injecting,
    title = "Injecting Relational Structural Representation in Neural Networks for Question Similarity",
    author = "Uva, Antonio  and
      Bonadiman, Daniele  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2046",
    doi = "10.18653/v1/P18-2046",
    pages = "285--291",
    abstract = "Effectively using full syntactic parsing information in Neural Networks (NNs) for solving relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning a model with Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs using our approach can learn more accurate models, especially after fine tuning on GS.",
}
@inproceedings{li-etal-2018-simple,
    title = "A Simple and Effective Approach to Coverage-Aware Neural Machine Translation",
    author = "Li, Yanyang  and
      Xiao, Tong  and
      Li, Yinqiao  and
      Wang, Qiang  and
      Xu, Changming  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2047",
    doi = "10.18653/v1/P18-2047",
    pages = "292--297",
    abstract = "We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT). Unlike the popular length normalization and coverage models, our model does not require training nor reranking the limited n-best outputs. Moreover, it is robust to large beam sizes, which is not well studied in previous work. On the Chinese-English and English-German translation tasks, our approach yields +0.4 1.5 BLEU improvements over the state-of-the-art baselines.",
}
@inproceedings{wang-etal-2018-dynamic,
    title = "Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation",
    author = "Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2048",
    doi = "10.18653/v1/P18-2048",
    pages = "298--304",
    abstract = "Traditional Neural machine translation (NMT) involves a fixed training procedure where each sentence is sampled once during each epoch. In reality, some sentences are well-learned during the initial few epochs; however, using this approach, the well-learned sentences would continue to be trained along with those sentences that were not well learned for 10-30 epochs, which results in a wastage of time. Here, we propose an efficient method to dynamically sample the sentences in order to accelerate the NMT training. In this approach, a weight is assigned to each sentence based on the measured difference between the training costs of two iterations. Further, in each epoch, a certain percentage of sentences are dynamically sampled according to their weights. Empirical results based on the NIST Chinese-to-English and the WMT English-to-German tasks show that the proposed method can significantly accelerate the NMT training and improve the NMT performance.",
}
@inproceedings{ataman-federico-2018-compositional,
    title = "Compositional Representation of Morphologically-Rich Input for Neural Machine Translation",
    author = "Ataman, Duygu  and
      Federico, Marcello",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2049",
    doi = "10.18653/v1/P18-2049",
    pages = "305--311",
    abstract = "Neural machine translation (NMT) models are typically trained with fixed-size input and output vocabularies, which creates an important bottleneck on their accuracy and generalization capability. As a solution, various studies proposed segmenting words into sub-word units and performing translation at the sub-lexical level. However, statistical word segmentation methods have recently shown to be prone to morphological errors, which can lead to inaccurate translations. In this paper, we propose to overcome this problem by replacing the source-language embedding layer of NMT with a bi-directional recurrent neural network that generates compositional representations of the input at any desired level of granularity. We test our approach in a low-resource setting with five languages from different morphological typologies, and under different composition assumptions. By training NMT to compose word representations from character n-grams, our approach consistently outperforms (from 1.71 to 2.48 BLEU points) NMT learning embeddings of statistically generated sub-word units.",
}
@inproceedings{michel-neubig-2018-extreme,
    title = "Extreme Adaptation for Personalized Neural Machine Translation",
    author = "Michel, Paul  and
      Neubig, Graham",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2050",
    doi = "10.18653/v1/P18-2050",
    pages = "312--318",
    abstract = "Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform translation, but this is not captured well by standard one-size-fits-all models. In this paper, we propose a simple and parameter-efficient adaptation technique that only requires adapting the bias of the output softmax to each particular user of the MT system, either directly or through a factored approximation. Experiments on TED talks in three languages demonstrate improvements in translation accuracy, and better reflection of speaker traits in the target text.",
}
@inproceedings{saunders-etal-2018-multi,
    title = "Multi-representation ensembles and delayed {SGD} updates improve syntax-based {NMT}",
    author = "Saunders, Danielle  and
      Stahlberg, Felix  and
      de Gispert, Adri{\`a}  and
      Byrne, Bill",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2051",
    doi = "10.18653/v1/P18-2051",
    pages = "319--325",
    abstract = "We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We formulate beam search over such ensembles using WFSTs, and describe a delayed SGD update training procedure that is especially effective for long representations like linearized syntax. Our approach gives state-of-the-art performance on a difficult Japanese-English task.",
}
@inproceedings{petrushkov-etal-2018-learning,
    title = "Learning from Chunk-based Feedback in Neural Machine Translation",
    author = "Petrushkov, Pavel  and
      Khadivi, Shahram  and
      Matusov, Evgeny",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2052",
    doi = "10.18653/v1/P18-2052",
    pages = "326--331",
    abstract = "We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation. We propose a simple and effective way of utilizing such feedback in NMT training. We demonstrate how the common machine translation problem of domain mismatch between training and deployment can be reduced solely based on chunk-level user feedback. We conduct a series of simulation experiments to test the effectiveness of the proposed method. Our results show that chunk-level feedback outperforms sentence based feedback by up to 2.61{\%} BLEU absolute.",
}
@inproceedings{ma-etal-2018-bag,
    title = "Bag-of-Words as Target for Neural Machine Translation",
    author = "Ma, Shuming  and
      Sun, Xu  and
      Wang, Yizhong  and
      Lin, Junyang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2053",
    doi = "10.18653/v1/P18-2053",
    pages = "332--338",
    abstract = "A sentence can be translated into more than one correct sentences. However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage. Since most of the correct translations for one sentence share the similar bag-of-words, it is possible to distinguish the correct translations from the incorrect ones by the bag-of-words. In this paper, we propose an approach that uses both the sentences and the bag-of-words as targets in the training stage, in order to encourage the model to generate the potentially correct sentences that are not appeared in the training set. We evaluate our model on a Chinese-English translation dataset, and experiments show our model outperforms the strong baselines by the BLEU score of 4.55.",
}
@inproceedings{shu-nakayama-2018-improving,
    title = "Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation",
    author = "Shu, Raphael  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2054",
    doi = "10.18653/v1/P18-2054",
    pages = "339--344",
    abstract = "To achieve high translation performance, neural machine translation models usually rely on the beam search algorithm for decoding sentences. The beam search finds good candidate translations by considering multiple hypotheses of translations simultaneously. However, as the algorithm produces hypotheses in a monotonic left-to-right order, a hypothesis can not be revisited once it is discarded. We found such monotonicity forces the algorithm to sacrifice some good decoding paths. To mitigate this problem, we relax the monotonic constraint of the beam search by maintaining all found hypotheses in a single priority queue and using a universal score function for hypothesis selection. The proposed algorithm allows discarded hypotheses to be recovered in a later step. Despite its simplicity, we show that the proposed decoding algorithm enhances the quality of selected hypotheses and improve the translations even for high-performance models in English-Japanese translation task.",
}
@inproceedings{king-cook-2018-leveraging,
    title = "Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of {E}nglish verb-noun combinations",
    author = "King, Milton  and
      Cook, Paul",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2055",
    doi = "10.18653/v1/P18-2055",
    pages = "345--350",
    abstract = "Verb-noun combinations (VNCs) - e.g., blow the whistle, hit the roof, and see stars - are a common type of English idiom that are ambiguous with literal usages. In this paper we propose and evaluate models for classifying VNC usages as idiomatic or literal, based on a variety of approaches to forming distributed representations. Our results show that a model based on averaging word embeddings performs on par with, or better than, a previously-proposed approach based on skip-thoughts. Idiomatic usages of VNCs are known to exhibit lexico-syntactic fixedness. We further incorporate this information into our models, demonstrating that this rich linguistic knowledge is complementary to the information carried by distributed representations.",
}
@inproceedings{ferret-2018-using,
    title = "Using pseudo-senses for improving the extraction of synonyms from word embeddings",
    author = "Ferret, Olivier",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2056",
    doi = "10.18653/v1/P18-2056",
    pages = "351--357",
    abstract = "The methods proposed recently for specializing word embeddings according to a particular perspective generally rely on external knowledge. In this article, we propose Pseudofit, a new method for specializing word embeddings according to semantic similarity without any external knowledge. Pseudofit exploits the notion of pseudo-sense for building several representations for each word and uses these representations for making the initial embeddings more generic. We illustrate the interest of Pseudofit for acquiring synonyms and study several variants of Pseudofit according to this perspective.",
}
@inproceedings{roller-etal-2018-hearst,
    title = "Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora",
    author = "Roller, Stephen  and
      Kiela, Douwe  and
      Nickel, Maximilian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2057",
    doi = "10.18653/v1/P18-2057",
    pages = "358--363",
    abstract = "Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.",
}
@inproceedings{he-etal-2018-jointly,
    title = "Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling",
    author = "He, Luheng  and
      Lee, Kenton  and
      Levy, Omer  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2058",
    doi = "10.18653/v1/P18-2058",
    pages = "364--369",
    abstract = "Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates.",
}
@inproceedings{malaviya-etal-2018-sparse,
    title = "Sparse and Constrained Attention for Neural Machine Translation",
    author = "Malaviya, Chaitanya  and
      Ferreira, Pedro  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2059",
    doi = "10.18653/v1/P18-2059",
    pages = "370--376",
    abstract = "In neural machine translation, words are sometimes dropped from the source or generated repeatedly in the translation. We explore novel strategies to address the coverage problem that change only the attention transformation. Our approach allocates fertilities to source words, used to bound the attention each word can receive. We experiment with various sparse and constrained attention transformations and propose a new one, constrained sparsemax, shown to be differentiable and sparse. Empirical evaluation is provided in three languages pairs.",
}
@inproceedings{wang-etal-2018-neural-hidden,
    title = "Neural Hidden {M}arkov Model for Machine Translation",
    author = "Wang, Weiyue  and
      Zhu, Derui  and
      Alkhouli, Tamer  and
      Gan, Zixuan  and
      Ney, Hermann",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2060",
    doi = "10.18653/v1/P18-2060",
    pages = "377--382",
    abstract = "Attention-based neural machine translation (NMT) models selectively focus on specific source positions to produce a translation, which brings significant improvements over pure encoder-decoder sequence-to-sequence models. This work investigates NMT while replacing the attention component. We study a neural hidden Markov model (HMM) consisting of neural network-based alignment and lexicon models, which are trained jointly using the forward-backward algorithm. We show that the attention component can be effectively replaced by the neural network alignment model and the neural HMM approach is able to provide comparable performance with the state-of-the-art attention-based models on the WMT 2017 German↔English and Chinese→English translation tasks.",
}
@inproceedings{van-der-goot-etal-2018-bleaching,
    title = "Bleaching Text: Abstract Features for Cross-lingual Gender Prediction",
    author = "van der Goot, Rob  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Matroos, Ian  and
      Nissim, Malvina  and
      Plank, Barbara",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2061",
    doi = "10.18653/v1/P18-2061",
    pages = "383--389",
    abstract = "Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform dependent. Cross-lingual embeddings circumvent some of these limitations, but capture gender-specific style less. We propose an alternative: bleaching text, i.e., transforming lexical strings into more abstract features. This study provides evidence that such features allow for better transfer across languages. Moreover, we present a first study on the ability of humans to perform cross-lingual gender prediction. We find that human predictive power proves similar to that of our bleached models, and both perform better than lexical models.",
}
@inproceedings{riley-gildea-2018-orthographic,
    title = "Orthographic Features for Bilingual Lexicon Induction",
    author = "Riley, Parker  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2062",
    doi = "10.18653/v1/P18-2062",
    pages = "390--394",
    abstract = "Recent embedding-based methods in bilingual lexicon induction show good results, but do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages. This work extends embedding-based methods to incorporate these features, resulting in significant accuracy gains for related languages.",
}
@inproceedings{kundu-etal-2018-neural,
    title = "Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking",
    author = "Kundu, Gourab  and
      Sil, Avi  and
      Florian, Radu  and
      Hamza, Wael",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2063",
    doi = "10.18653/v1/P18-2063",
    pages = "395--400",
    abstract = "We propose an entity-centric neural crosslingual coreference model that builds on multi-lingual embeddings and language independent features. We perform both intrinsic and extrinsic evaluations of our model. In the intrinsic evaluation, we show that our model, when trained on English and tested on Chinese and Spanish, achieves competitive results to the models trained directly on Chinese and Spanish respectively. In the extrinsic evaluation, we show that our English model helps achieve superior entity linking accuracy on Chinese and Spanish test sets than the top 2015 TAC system without using any annotated data from Chinese or Spanish.",
}
@inproceedings{murthy-etal-2018-judicious,
    title = "Judicious Selection of Training Data in Assisting Language for Multilingual Neural {NER}",
    author = "Murthy, Rudra  and
      Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2064",
    doi = "10.18653/v1/P18-2064",
    pages = "401--406",
    abstract = "Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other assisting languages. We show that the divergence in the tag distributions of the common named entities between the primary and assisting languages can reduce the effectiveness of multilingual learning. To alleviate this problem, we propose a metric based on symmetric KL divergence to filter out the highly divergent training instances in the assisting language. We empirically show that our data selection strategy improves NER performance in many languages, including those with very limited training data.",
}
@inproceedings{cui-etal-2018-neural,
    title = "Neural Open Information Extraction",
    author = "Cui, Lei  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2065",
    doi = "10.18653/v1/P18-2065",
    pages = "407--413",
    abstract = "Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.",
}
@inproceedings{zhao-etal-2018-document,
    title = "Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention",
    author = "Zhao, Yue  and
      Jin, Xiaolong  and
      Wang, Yuanzhuo  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2066",
    doi = "10.18653/v1/P18-2066",
    pages = "414--419",
    abstract = "Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods.",
}
@inproceedings{wu-etal-2018-learning,
    title = "Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots",
    author = "Wu, Yu  and
      Wu, Wei  and
      Li, Zhoujun  and
      Zhou, Ming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2067",
    doi = "10.18653/v1/P18-2067",
    pages = "420--425",
    abstract = "We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge the matching degree of unlabeled pairs, and then performs learning with both the weak signals and the unlabeled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method.",
}
@inproceedings{zhao-feng-2018-improving,
    title = "Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention",
    author = "Zhao, Lin  and
      Feng, Zhe",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2068",
    doi = "10.18653/v1/P18-2068",
    pages = "426--431",
    abstract = "We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken dialogue data. This model predicts slot values by jointly learning to copy a word which may be out-of-vocabulary (OOV) from an input utterance through a pointer network, or generate a word within the vocabulary through an attentional Seq2Seq model. Experimental results show the effectiveness of our slot filling model, especially at addressing the OOV problem. Additionally, we integrate the proposed model into a spoken language understanding system and achieve the state-of-the-art performance on the benchmark data.",
}
@inproceedings{ramadan-etal-2018-large,
    title = "Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing",
    author = "Ramadan, Osman  and
      Budzianowski, Pawe{\l}  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2069",
    doi = "10.18653/v1/P18-2069",
    pages = "432--437",
    abstract = "Robust dialogue belief tracking is a key component in maintaining good quality dialogue systems. The tasks that dialogue systems are trying to solve are becoming increasingly complex, requiring scalability to multi-domain, semantically rich dialogues. However, most current approaches have difficulty scaling up with domains because of the dependency of the model parameters on the dialogue ontology. In this paper, a novel approach is introduced that fully utilizes semantic similarity between dialogue utterances and the ontology terms, allowing the information to be shared across domains. The evaluation is performed on a recently collected multi-domain dialogues dataset, one order of magnitude larger than currently available corpora. Our model demonstrates great capability in handling multi-domain dialogues, simultaneously outperforming existing state-of-the-art models in single-domain dialogue tracking tasks.",
}
@inproceedings{jia-etal-2018-modeling,
    title = "Modeling discourse cohesion for discourse parsing via memory network",
    author = "Jia, Yanyan  and
      Ye, Yuan  and
      Feng, Yansong  and
      Lai, Yuxuan  and
      Yan, Rui  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2070",
    doi = "10.18653/v1/P18-2070",
    pages = "438--443",
    abstract = "Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance. Most existing approaches design sophisticated features or exploit various off-the-shelf tools, but achieve little success. In this paper, we propose a new transition-based discourse parser that makes use of memory networks to take discourse cohesion into account. The automatically captured discourse cohesion benefits discourse parsing, especially for long span scenarios. Experiments on the RST discourse treebank show that our method outperforms traditional featured based methods, and the memory based discourse cohesion can improve the overall parsing performance significantly.",
}
@inproceedings{yang-li-2018-scidtb,
    title = "{S}ci{DTB}: Discourse Dependency {T}ree{B}ank for Scientific Abstracts",
    author = "Yang, An  and
      Li, Sujian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2071",
    doi = "10.18653/v1/P18-2071",
    pages = "444--449",
    abstract = "Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.",
}
@inproceedings{johnson-etal-2018-predicting,
    title = "Predicting accuracy on large datasets from smaller pilot data",
    author = "Johnson, Mark  and
      Anderson, Peter  and
      Dras, Mark  and
      Steedman, Mark",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2072",
    doi = "10.18653/v1/P18-2072",
    pages = "450--455",
    abstract = "Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from models trained on a small pilot training dataset. We model how accuracy varies as a function of training size on subsets of the pilot data, and use that model to predict how much training data would be required to achieve the desired accuracy. We introduce a new performance extrapolation task to evaluate how well different extrapolations predict accuracy on larger training sets. We show that details of hyperparameter optimisation and the extrapolation models can have dramatic effects in a document classification task. We believe this is an important first step in developing methods for estimating the resources required to meet specific engineering performance targets.",
}
@inproceedings{bernardy-etal-2018-influence,
    title = "The Influence of Context on Sentence Acceptability Judgements",
    author = "Bernardy, Jean-Philippe  and
      Lappin, Shalom  and
      Lau, Jey Han",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2073",
    doi = "10.18653/v1/P18-2073",
    pages = "456--461",
    abstract = "We investigate the influence that document context exerts on human acceptability judgements for English sentences, via two sets of experiments. The first compares ratings for sentences presented on their own with ratings for the same set of sentences given in their document contexts. The second assesses the accuracy with which two types of neural models {---} one that incorporates context during training and one that does not {---} predict these judgements. Our results indicate that: (1) context improves acceptability ratings for ill-formed sentences, but also reduces them for well-formed sentences; and (2) context helps unsupervised systems to model acceptability.",
}
@inproceedings{collell-moens-2018-neural,
    title = "Do Neural Network Cross-Modal Mappings Really Bridge Modalities?",
    author = "Collell, Guillem  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2074",
    doi = "10.18653/v1/P18-2074",
    pages = "462--468",
    abstract = "Feed-forward networks are widely used in cross-modal applications to bridge modalities by mapping distributed vectors of one modality to the other, or to a shared space. The predicted vectors are then used to perform e.g., retrieval or labeling. Thus, the success of the whole system relies on the ability of the mapping to make the neighborhood structure (i.e., the pairwise similarities) of the predicted vectors akin to that of the target vectors. However, whether this is achieved has not been investigated yet. Here, we propose a new similarity measure and two ad hoc experiments to shed light on this issue. In three cross-modal benchmarks we learn a large number of language-to-vision and vision-to-language neural network mappings (up to five layers) using a rich diversity of image and text features and loss functions. Our results reveal that, surprisingly, the neighborhood structure of the predicted vectors consistently resembles more that of the input vectors than that of the target vectors. In a second experiment, we further show that untrained nets do not significantly disrupt the neighborhood (i.e., semantic) structure of the input vectors.",
}
@inproceedings{fried-klein-2018-policy,
    title = "Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing",
    author = "Fried, Daniel  and
      Klein, Dan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2075",
    doi = "10.18653/v1/P18-2075",
    pages = "469--476",
    abstract = "Dynamic oracles provide strong supervision for training constituency parsers with exploration, but must be custom defined for a given parser{'}s transition system. We explore using a policy gradient method as a parser-agnostic alternative. In addition to directly optimizing for a tree-level metric such as F1, policy gradient has the potential to reduce exposure bias by allowing exploration during training; moreover, it does not require a dynamic oracle for supervision. On four constituency parsers in three languages, the method substantially outperforms static oracle likelihood training in almost all settings. For parsers where a dynamic oracle is available (including a novel oracle which we define for the transition system of Dyer et al., 2016), policy gradient typically recaptures a substantial fraction of the performance gain afforded by the dynamic oracle.",
}
@inproceedings{hong-huang-2018-linear,
    title = "Linear-time Constituency Parsing with {RNN}s and Dynamic Programming",
    author = "Hong, Juneki  and
      Huang, Liang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2076",
    doi = "10.18653/v1/P18-2076",
    pages = "477--483",
    abstract = "Recently, span-based constituency parsing has achieved competitive accuracies with extremely simple models by using bidirectional RNNs to model {``}spans{''}. However, the minimal span parser of Stern et al. (2017a) which holds the current state of the art accuracy is a chart parser running in cubic time, $O(n^3)$, which is too slow for longer sentences and for applications beyond sentence boundaries such as end-to-end discourse parsing and joint sentence boundary detection and parsing. We propose a linear-time constituency parser with RNNs and dynamic programming using graph-structured stack and beam search, which runs in time $O(n b^2)$ where $b$ is the beam size. We further speed this up to $O(n b log b)$ by integrating cube pruning. Compared with chart parsing baselines, this linear-time parser is substantially faster for long sentences on the Penn Treebank and orders of magnitude faster for discourse parsing, and achieves the highest F1 accuracy on the Penn Treebank among single model end-to-end systems.",
}
@inproceedings{dozat-manning-2018-simpler,
    title = "Simpler but More Accurate Semantic Dependency Parsing",
    author = "Dozat, Timothy  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2077",
    doi = "10.18653/v1/P18-2077",
    pages = "484--490",
    abstract = "While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6{\%} labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9{\%} labeled F1.",
}
@inproceedings{ding-etal-2018-simplified,
    title = "Simplified Abugidas",
    author = "Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2078",
    doi = "10.18653/v1/P18-2078",
    pages = "491--495",
    abstract = "An abugida is a writing system where the consonant letters represent syllables with a default vowel and other vowels are denoted by diacritics. We investigate the feasibility of recovering the original text written in an abugida after omitting subordinate diacritics and merging consonant letters with similar phonetic values. This is crucial for developing more efficient input methods by reducing the complexity in abugidas. Four abugidas in the southern Brahmic family, i.e., Thai, Burmese, Khmer, and Lao, were studied using a newswire 20,000-sentence dataset. We compared the recovery performance of a support vector machine and an LSTM-based recurrent neural network, finding that the abugida graphemes could be recovered with 94{\%} - 97{\%} accuracy at the top-1 level and 98{\%} - 99{\%} at the top-4 level, even after omitting most diacritics (10 - 30 types) and merging the remaining 30 - 50 characters into 21 graphemes.",
}
@inproceedings{yang-etal-2018-automatic,
    title = "Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network",
    author = "Yang, Pengcheng  and
      Sun, Xu  and
      Li, Wei  and
      Ma, Shuming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2079",
    doi = "10.18653/v1/P18-2079",
    pages = "496--502",
    abstract = "As more and more academic papers are being submitted to conferences and journals, evaluating all these papers by professionals is time-consuming and can cause inequality due to the personal factors of the reviewers. In this paper, in order to assist professionals in evaluating academic papers, we propose a novel task: automatic academic paper rating (AAPR), which automatically determine whether to accept academic papers. We build a new dataset for this task and propose a novel modularized hierarchical convolutional neural network to achieve automatic academic paper rating. Evaluation results show that the proposed model outperforms the baselines by a large margin. The dataset and code are available at \url{https://github.com/lancopku/AAPR}",
}
@inproceedings{cozma-etal-2018-automated,
    title = "Automated essay scoring with string kernels and word embeddings",
    author = "Cozma, M{\u{a}}d{\u{a}}lina  and
      Butnaru, Andrei  and
      Ionescu, Radu Tudor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2080",
    doi = "10.18653/v1/P18-2080",
    pages = "503--509",
    abstract = "In this work, we present an approach based on combining string kernels and word embeddings for automatic essay scoring. String kernels capture the similarity among strings based on counting common character n-grams, which are a low-level yet powerful type of feature, demonstrating state-of-the-art results in various text classification tasks such as Arabic dialect identification or native language identification. To our best knowledge, we are the first to apply string kernels to automatically score essays. We are also the first to combine them with a high-level semantic feature representation, namely the bag-of-super-word-embeddings. We report the best performance on the Automated Student Assessment Prize data set, in both in-domain and cross-domain settings, surpassing recent state-of-the-art deep learning approaches.",
}
@inproceedings{kornilova-etal-2018-party,
    title = "Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction",
    author = "Kornilova, Anastassia  and
      Argyle, Daniel  and
      Eidelman, Vladimir",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2081",
    doi = "10.18653/v1/P18-2081",
    pages = "510--515",
    abstract = "Predicting how Congressional legislators will vote is important for understanding their past and future behavior. However, previous work on roll-call prediction has been limited to single session settings, thus not allowing for generalization across sessions. In this paper, we show that text alone is insufficient for modeling voting outcomes in new contexts, as session changes lead to changes in the underlying data generation process. We propose a novel neural method for encoding documents alongside additional metadata, achieving an average of a 4{\%} boost in accuracy over the previous state-of-the-art.",
}
@inproceedings{hida-etal-2018-dynamic,
    title = "Dynamic and Static Topic Model for Analyzing Time-Series Document Collections",
    author = "Hida, Rem  and
      Takeishi, Naoya  and
      Yairi, Takehisa  and
      Hori, Koichi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2082",
    doi = "10.18653/v1/P18-2082",
    pages = "516--520",
    abstract = "For extracting meaningful topics from texts, their structures should be considered properly. In this paper, we aim to analyze structured time-series documents such as a collection of news articles and a series of scientific papers, wherein topics evolve along time depending on multiple topics in the past and are also related to each other at each time. To this end, we propose a dynamic and static topic model, which simultaneously considers the dynamic structures of the temporal topic evolution and the static structures of the topic hierarchy at each time. We show the results of experiments on collections of scientific papers, in which the proposed method outperformed conventional models. Moreover, we show an example of extracted topic structures, which we found helpful for analyzing research activities.",
}
@inproceedings{huang-2018-phrasectm,
    title = "{P}hrase{CTM}: Correlated Topic Modeling on Phrases within {M}arkov Random Fields",
    author = "Huang, Weijing",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2083",
    doi = "10.18653/v1/P18-2083",
    pages = "521--526",
    abstract = "Recent emerged phrase-level topic models are able to provide topics of phrases, which are easy to read for humans. But these models are lack of the ability to capture the correlation structure among the discovered numerous topics. We propose a novel topic model PhraseCTM and a two-stage method to find out the correlated topics at phrase level. In the first stage, we train PhraseCTM, which models the generation of words and phrases simultaneously by linking the phrases and component words within Markov Random Fields when they are semantically coherent. In the second stage, we generate the correlation of topics from PhraseCTM. We evaluate our method by a quantitative experiment and a human study, showing the correlated topic modeling on phrases is a good and practical way to interpret the underlying themes of a corpus.",
}
@inproceedings{torki-2018-document,
    title = "A Document Descriptor using Covariance of Word Vectors",
    author = "Torki, Marwan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2084",
    doi = "10.18653/v1/P18-2084",
    pages = "527--532",
    abstract = "In this paper, we address the problem of finding a novel document descriptor based on the covariance matrix of the word vectors of a document. Our descriptor has a fixed length, which makes it easy to use in many supervised and unsupervised applications. We tested our novel descriptor in different tasks including supervised and unsupervised settings. Our evaluation shows that our document covariance descriptor fits different tasks with competitive performance against state-of-the-art methods.",
}
@inproceedings{li-lu-2018-learning,
    title = "Learning with Structured Representations for Negation Scope Extraction",
    author = "Li, Hao  and
      Lu, Wei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2085",
    doi = "10.18653/v1/P18-2085",
    pages = "533--539",
    abstract = "We report an empirical study on the task of negation scope extraction given the negation cue. Our key observation is that certain useful information such as features related to negation cue, long-distance dependencies as well as some latent structural information can be exploited for such a task. We design approaches based on conditional random fields (CRF), semi-Markov CRF, as well as latent-variable CRF models to capture such information. Extensive experiments on several standard datasets demonstrate that our approaches are able to achieve better results than existing approaches reported in the literature.",
}
@inproceedings{yin-etal-2018-end,
    title = "End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions",
    author = {Yin, Wenpeng  and
      Roth, Dan  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2086",
    doi = "10.18653/v1/P18-2086",
    pages = "540--545",
    abstract = "This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem. The premises and hypotheses in SciTail were generated with no awareness of each other, and did not specifically aim at the entailment task. This makes it more challenging than other entailment data sets and more directly useful to the end-task {--} question answering. We propose DEISTE (deep explorations of inter-sentence interactions for textual entailment) for this entailment task. Given word-to-word interactions between the premise-hypothesis pair (P, H), DEISTE consists of: (i) a parameter-dynamic convolution to make important words in P and H play a dominant role in learnt representations; and (ii) a position-aware attentive convolution to encode the representation and position information of the aligned word pairs. Experiments show that DEISTE gets {\mbox{$\approx$}}5{\%} improvement over prior state of the art and that the pretrained DEISTE on SciTail generalizes well on RTE-5.",
}
@inproceedings{cai-etal-2018-sense,
    title = "Sense-Aware Neural Models for Pun Location in Texts",
    author = "Cai, Yitao  and
      Li, Yin  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2087",
    doi = "10.18653/v1/P18-2087",
    pages = "546--551",
    abstract = "A homographic pun is a form of wordplay in which one signifier (usually a word) suggests two or more meanings by exploiting polysemy for an intended humorous or rhetorical effect. In this paper, we focus on the task of pun location, which aims to identify the pun word in a given short text. We propose a sense-aware neural model to address this challenging task. Our model first obtains several WSD results for the text, and then leverages a bidirectional LSTM network to model each sequence of word senses. The outputs at each time step for different LSTM networks are then concatenated for prediction. Evaluation results on the benchmark SemEval 2017 dataset demonstrate the efficacy of our proposed model.",
}
@inproceedings{santus-etal-2018-rank,
    title = "A Rank-Based Similarity Metric for Word Embeddings",
    author = "Santus, Enrico  and
      Wang, Hongmin  and
      Chersoni, Emmanuele  and
      Zhang, Yue",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2088",
    doi = "10.18653/v1/P18-2088",
    pages = "552--557",
    abstract = "Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation benchmark for these representations, with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity estimation and outperforms it in the recently-introduced and challenging task of outlier detection, thus suggesting that rank-based measures can improve clustering quality.",
}
@inproceedings{erdmann-etal-2018-addressing,
    title = "Addressing Noise in Multidialectal Word Embeddings",
    author = "Erdmann, Alexander  and
      Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2089",
    doi = "10.18653/v1/P18-2089",
    pages = "558--565",
    abstract = "Word embeddings are crucial to many natural language processing tasks. The quality of embeddings relies on large non-noisy corpora. Arabic dialects lack large corpora and are noisy, being linguistically disparate with no standardized spelling. We make three contributions to address this noise. First, we describe simple but effective adaptations to word embedding tools to maximize the informative content leveraged in each training sentence. Second, we analyze methods for representing disparate dialects in one embedding space, either by mapping individual dialects into a shared space or learning a joint model of all dialects. Finally, we evaluate via dictionary induction, showing that two metrics not typically reported in the task enable us to analyze our contributions{'} effects on low and high frequency words. In addition to boosting performance between 2-53{\%}, we specifically improve on noisy, low frequency forms without compromising accuracy on high frequency forms.",
}
@inproceedings{zhang-zweigenbaum-2018-gneg,
    title = "{GNEG}: Graph-Based Negative Sampling for word2vec",
    author = "Zhang, Zheng  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2090",
    doi = "10.18653/v1/P18-2090",
    pages = "566--571",
    abstract = "Negative sampling is an important component in word2vec for distributed word representation learning. We hypothesize that taking into account global, corpus-level information and generating a different noise distribution for each target word better satisfies the requirements of negative examples for each training word than the original frequency-based distribution. In this purpose we pre-compute word co-occurrence statistics from the corpus and apply to it network algorithms such as random walk. We test this hypothesis through a set of experiments whose results show that our approach boosts the word analogy task by about 5{\%} and improves the performance on word similarity tasks by about 1{\%} compared to the skip-gram negative sampling baseline.",
}
@inproceedings{akama-etal-2018-unsupervised,
    title = "Unsupervised Learning of Style-sensitive Word Vectors",
    author = "Akama, Reina  and
      Watanabe, Kento  and
      Yokoi, Sho  and
      Kobayashi, Sosuke  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2091",
    doi = "10.18653/v1/P18-2091",
    pages = "572--578",
    abstract = "This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) embedding model (Mikolov et al., 2013b) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.",
}
@inproceedings{he-etal-2018-exploiting,
    title = "Exploiting Document Knowledge for Aspect-level Sentiment Classification",
    author = "He, Ruidan  and
      Lee, Wee Sun  and
      Ng, Hwee Tou  and
      Dahlmeier, Daniel",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2092",
    doi = "10.18653/v1/P18-2092",
    pages = "579--585",
    abstract = "Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document-level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015, and 2016, and we show that attention-based LSTM benefits from document-level knowledge in multiple ways.",
}
@inproceedings{liu-etal-2018-modeling,
    title = "Modeling Sentiment Association in Discourse for Humor Recognition",
    author = "Liu, Lizhen  and
      Zhang, Donghai  and
      Song, Wei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2093",
    doi = "10.18653/v1/P18-2093",
    pages = "586--591",
    abstract = "Humor is one of the most attractive parts in human communication. However, automatically recognizing humor in text is challenging due to the complex characteristics of humor. This paper proposes to model sentiment association between discourse units to indicate how the punchline breaks the expectation of the setup. We found that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. On the perspective of using sentiment related features, sentiment association in discourse is more useful than counting the number of emotional words.",
}
@inproceedings{xu-etal-2018-double,
    title = "Double Embeddings and {CNN}-based Sequence Labeling for Aspect Extraction",
    author = "Xu, Hu  and
      Liu, Bing  and
      Shu, Lei  and
      Yu, Philip S.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2094",
    doi = "10.18653/v1/P18-2094",
    pages = "592--598",
    abstract = "One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.",
}
@inproceedings{shnarch-etal-2018-will,
    title = "Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining",
    author = "Shnarch, Eyal  and
      Alzate, Carlos  and
      Dankin, Lena  and
      Gleize, Martin  and
      Hou, Yufang  and
      Choshen, Leshem  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2095",
    doi = "10.18653/v1/P18-2095",
    pages = "599--605",
    abstract = "The process of obtaining high quality labeled data for natural language understanding tasks is often slow, error-prone, complicated and expensive. With the vast usage of neural networks, this issue becomes more notorious since these networks require a large amount of labeled data to produce satisfactory results. We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks. Experiments in the context of topic-dependent evidence detection with two forms of weak labeled data show the advantages of the blending scheme. In addition, we provide a manually annotated data set for the task of topic-dependent evidence detection. We believe that blending weak and strong labeled data is a general notion that may be applicable to many language understanding tasks, and can especially assist researchers who wish to train a network but have a small amount of high quality labeled data for their task of interest.",
}
@inproceedings{kampman-etal-2018-investigating,
    title = "Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction",
    author = "Kampman, Onno  and
      J. Barezi, Elham  and
      Bertero, Dario  and
      Fung, Pascale",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2096",
    doi = "10.18653/v1/P18-2096",
    pages = "606--611",
    abstract = "We propose a tri-modal architecture to predict Big Five personality trait scores from video clips with different channels for audio, text, and video data. For each channel, stacked Convolutional Neural Networks are employed. The channels are fused both on decision-level and by concatenating their respective fully connected layers. It is shown that a multimodal fusion approach outperforms each single modality channel, with an improvement of 9.4{\%} over the best individual modality (video). Full backpropagation is also shown to be better than a linear combination of modalities, meaning complex interactions between modalities can be leveraged to build better models. Furthermore, we can see the prediction relevance of each modality for each trait. The described model can be used to increase the emotional intelligence of virtual agents.",
}
@inproceedings{suzuki-etal-2018-empirical,
    title = "An Empirical Study of Building a Strong Baseline for Constituency Parsing",
    author = "Suzuki, Jun  and
      Takase, Sho  and
      Kamigaito, Hidetaka  and
      Morishita, Makoto  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2097",
    doi = "10.18653/v1/P18-2097",
    pages = "612--618",
    abstract = "This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers{'} performance (almost) without requiring any explicit task-specific knowledge or architecture of constituent parsing.",
}
@inproceedings{stymne-etal-2018-parser,
    title = "Parser Training with Heterogeneous Treebanks",
    author = "Stymne, Sara  and
      de Lhoneux, Miryam  and
      Smith, Aaron  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2098",
    doi = "10.18653/v1/P18-2098",
    pages = "619--625",
    abstract = "How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question. We start by investigating previously suggested, but little evaluated, strategies for exploiting multiple treebanks based on concatenating training sets, with or without fine-tuning. We go on to propose a new method based on treebank embeddings. We perform experiments for several languages and show that in many cases fine-tuning and treebank embeddings lead to substantial improvements over single treebanks or concatenation, with average gains of 2.0{--}3.5 LAS points. We argue that treebank embeddings should be preferred due to their conceptual simplicity, flexibility and extensibility.",
}
@inproceedings{grunewald-etal-2018-generalized,
    title = "Generalized chart constraints for efficient {PCFG} and {TAG} parsing",
    author = {Gr{\"u}newald, Stefan  and
      Henning, Sophie  and
      Koller, Alexander},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2099",
    doi = "10.18653/v1/P18-2099",
    pages = "626--631",
    abstract = "Chart constraints, which specify at which string positions a constituent may begin or end, have been shown to speed up chart parsers for PCFGs. We generalize chart constraints to more expressive grammar formalisms and describe a neural tagger which predicts chart constraints at very high precision. Our constraints accelerate both PCFG and TAG parsing, and combine effectively with other pruning techniques (coarse-to-fine and supertagging) for an overall speedup of two orders of magnitude, while improving accuracy.",
}
@inproceedings{zhu-etal-2018-exploring,
    title = "Exploring Semantic Properties of Sentence Embeddings",
    author = "Zhu, Xunjie  and
      Li, Tingfeng  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2100",
    doi = "10.18653/v1/P18-2100",
    pages = "632--637",
    abstract = "Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In this paper, we assess to what extent prominent sentence embedding methods exhibit select semantic properties. We propose a framework that generate triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their sentence embeddings.",
}
@inproceedings{rei-etal-2018-scoring,
    title = "Scoring Lexical Entailment with a Supervised Directional Similarity Network",
    author = "Rei, Marek  and
      Gerz, Daniela  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2101",
    doi = "10.18653/v1/P18-2101",
    pages = "638--643",
    abstract = "We present the Supervised Directional Similarity Network, a novel neural architecture for learning task-specific transformation functions on top of general-purpose word embeddings. Relying on only a limited amount of supervision from task-specific scores on a subset of the vocabulary, our architecture is able to generalise and transform a general-purpose distributional vector space to model the relation of lexical entailment. Experiments show excellent performance on scoring graded lexical entailment, raising the state-of-the-art on the HyperLex dataset by approximately 25{\%}.",
}
@inproceedings{yang-etal-2018-extracting,
    title = "Extracting Commonsense Properties from Embeddings with Limited Human Guidance",
    author = "Yang, Yiben  and
      Birnbaum, Larry  and
      Wang, Ji-Ping  and
      Downey, Doug",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2102",
    doi = "10.18653/v1/P18-2102",
    pages = "644--649",
    abstract = "Intelligent systems require common sense, but automatically extracting this knowledge from text can be difficult. We propose and assess methods for extracting one type of commonsense knowledge, object-property comparisons, from pre-trained embeddings. In experiments, we show that our approach exceeds the accuracy of previous work but requires substantially less hand-annotated knowledge. Further, we show that an active learning approach that synthesizes common-sense queries can boost accuracy.",
}
@inproceedings{glockner-etal-2018-breaking,
    title = "Breaking {NLI} Systems with Sentences that Require Simple Lexical Inferences",
    author = "Glockner, Max  and
      Shwartz, Vered  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2103",
    doi = "10.18653/v1/P18-2103",
    pages = "650--655",
    abstract = "We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences.",
}
@inproceedings{zaremoodi-etal-2018-adaptive,
    title = "Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation",
    author = "Zaremoodi, Poorya  and
      Buntine, Wray  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2104",
    doi = "10.18653/v1/P18-2104",
    pages = "656--661",
    abstract = "Neural Machine Translation (NMT) is notorious for its need for large amounts of bilingual data. An effective approach to compensate for this requirement is Multi-Task Learning (MTL) to leverage different linguistic resources as a source of inductive bias. Current MTL architectures are based on the Seq2Seq transduction, and (partially) share different components of the models among the tasks. However, this MTL approach often suffers from task interference and is not able to fully capture commonalities among subsets of tasks. We address this issue by extending the recurrent units with multiple {``}blocks{''} along with a trainable {``}routing network{''}. The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. Empirical evaluation of two low-resource translation tasks, English to Vietnamese and Farsi, show +1 BLEU score improvements compared to strong baselines.",
}
@inproceedings{stewart-etal-2018-automatic,
    title = "Automatic Estimation of Simultaneous Interpreter Performance",
    author = "Stewart, Craig  and
      Vogler, Nikolai  and
      Hu, Junjie  and
      Boyd-Graber, Jordan  and
      Neubig, Graham",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2105",
    doi = "10.18653/v1/P18-2105",
    pages = "662--666",
    abstract = "Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.",
}
@inproceedings{mulcaire-etal-2018-polyglot,
    title = "Polyglot Semantic Role Labeling",
    author = "Mulcaire, Phoebe  and
      Swayamdipta, Swabha  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2106",
    doi = "10.18653/v1/P18-2106",
    pages = "667--672",
    abstract = "Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with a new approach where we combine resources from different languages in the CoNLL 2009 shared task to build a single polyglot semantic dependency parser. Notwithstanding the absence of parallel data, and the dissimilarity in annotations between languages, our approach results in improvement in parsing performance on several languages over a monolingual baseline. Analysis of the polyglot models{'} performance provides a new understanding of the similarities and differences between languages in the shared task.",
}
@inproceedings{zou-lu-2018-learning,
    title = "Learning Cross-lingual Distributed Logical Representations for Semantic Parsing",
    author = "Zou, Yanyan  and
      Lu, Wei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2107",
    doi = "10.18653/v1/P18-2107",
    pages = "673--679",
    abstract = "With the development of several multilingual datasets used for semantic parsing, recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup. However, how to improve the performance of a monolingual semantic parser for a specific language by leveraging data annotated in different languages remains a research question that is under-explored. In this work, we present a study to show how learning distributed representations of the logical forms from data annotated in different languages can be used for improving the performance of a monolingual semantic parser. We extend two existing monolingual semantic parsers to incorporate such cross-lingual distributed logical representations as features. Experiments show that our proposed approach is able to yield improved semantic parsing results on the standard multilingual GeoQuery dataset.",
}
@inproceedings{asada-etal-2018-enhancing,
    title = "Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information",
    author = "Asada, Masaki  and
      Miwa, Makoto  and
      Sasaki, Yutaka",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2108",
    doi = "10.18653/v1/P18-2108",
    pages = "680--685",
    abstract = "We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks. In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.",
}
@inproceedings{agarwal-etal-2018-dianed,
    title = "dia{NED}: Time-Aware Named Entity Disambiguation for Diachronic Corpora",
    author = {Agarwal, Prabal  and
      Str{\"o}tgen, Jannik  and
      del Corro, Luciano  and
      Hoffart, Johannes  and
      Weikum, Gerhard},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2109",
    doi = "10.18653/v1/P18-2109",
    pages = "686--693",
    abstract = "Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval. However, NED quality drops when inputs span long time periods like in archives or historic corpora. This paper presents the first time-aware method for NED that resolves ambiguities even when mention contexts give only few cues. The method is based on computing temporal signatures for entities and comparing these to the temporal contexts of input mentions. Our experiments show superior quality on a newly created diachronic corpus.",
}
@inproceedings{huang-paul-2018-examining,
    title = "Examining Temporality in Document Classification",
    author = "Huang, Xiaolei  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2110",
    doi = "10.18653/v1/P18-2110",
    pages = "694--699",
    abstract = "Many corpora span broad periods of time. Language processing models trained during one time period may not work well in future time periods, and the best model may depend on specific times of year (e.g., people might describe hotels differently in reviews during the winter versus the summer). This study investigates how document classifiers trained on documents from certain time intervals perform on documents from other time intervals, considering both seasonal intervals (intervals that repeat across years, e.g., winter) and non-seasonal intervals (e.g., specific years). We show experimentally that classification performance varies over time, and that performance can be improved by using a standard domain adaptation approach to adjust for changes in time.",
}
@inproceedings{jaech-ostendorf-2018-personalized,
    title = "Personalized Language Model for Query Auto-Completion",
    author = "Jaech, Aaron  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2111",
    doi = "10.18653/v1/P18-2111",
    pages = "700--705",
    abstract = "Query auto-completion is a search engine feature whereby the system suggests completed queries as the user types. Recently, the use of a recurrent neural network language model was suggested as a method of generating query completions. We show how an adaptable language model can be used to generate personalized completions and how the model can use online updating to make predictions for users not seen during training. The personalized predictions are significantly better than a baseline that uses no user information.",
}
@inproceedings{ni-mcauley-2018-personalized,
    title = "Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations",
    author = "Ni, Jianmo  and
      McAuley, Julian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2112",
    doi = "10.18653/v1/P18-2112",
    pages = "706--711",
    abstract = "In this paper, we focus on the problem of building assistive systems that can help users to write reviews. We cast this problem using an encoder-decoder framework that generates personalized reviews by expanding short phrases (e.g. review summaries, product titles) provided as input to the system. We incorporate aspect-level information via an aspect encoder that learns aspect-aware user and item representations. An attention fusion layer is applied to control generation by attending on the outputs of multiple encoders. Experimental results show that our model successfully learns representations capable of generating coherent and diverse reviews. In addition, the learned aspect-aware representations discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences.",
}
@inproceedings{scarton-specia-2018-learning,
    title = "Learning Simplifications for Specific Target Audiences",
    author = "Scarton, Carolina  and
      Specia, Lucia",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2113",
    doi = "10.18653/v1/P18-2113",
    pages = "712--718",
    abstract = "Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text. Most recent work is based on sequence-to-sequence neural models similar to those used for machine translation (MT). Different from MT, TS data comprises more elaborate transformations, such as sentence splitting. It can also contain multiple simplifications of the same original text targeting different audiences, such as school grade levels. We explore these two features of TS to build models tailored for specific grade levels. Our approach uses a standard sequence-to-sequence architecture where the original sequence is annotated with information about the target audience and/or the (predicted) type of simplification operation. We show that it outperforms state-of-the-art TS approaches (up to 3 and 12 BLEU and SARI points, respectively), including when training data for the specific complex-simple combination of grade levels is not available, i.e. zero-shot learning.",
}
@inproceedings{aharoni-goldberg-2018-split,
    title = "Split and Rephrase: Better Evaluation and Stronger Baselines",
    author = "Aharoni, Roee  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2114",
    doi = "10.18653/v1/P18-2114",
    pages = "719--724",
    abstract = "Splitting and rephrasing a complex sentence into several shorter sentences that convey the same meaning is a challenging problem in NLP. We show that while vanilla seq2seq models can reach high scores on the proposed benchmark (Narayan et al., 2017), they suffer from memorization of the training set which contains more than 89{\%} of the unique simple sentences from the validation and test sets. To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8.68 BLEU and fostering further progress on the task.",
}
@inproceedings{ma-etal-2018-autoencoder,
    title = "Autoencoder as Assistant Supervisor: Improving Text Representation for {C}hinese Social Media Text Summarization",
    author = "Ma, Shuming  and
      Sun, Xu  and
      Lin, Junyang  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2115",
    doi = "10.18653/v1/P18-2115",
    pages = "725--731",
    abstract = "Most of the current abstractive text summarization models are based on the sequence-to-sequence model (Seq2Seq). The source content of social media is long and noisy, so it is difficult for Seq2Seq to learn an accurate semantic representation. Compared with the source content, the annotated summary is short and well written. Moreover, it shares the same meaning as the source content. In this work, we supervise the learning of the representation of the source content with that of the summary. In implementation, we regard a summary autoencoder as an assistant supervisor of Seq2Seq. Following previous work, we evaluate our model on a popular Chinese social media dataset. Experimental results show that our model achieves the state-of-the-art performances on the benchmark dataset.",
}
@inproceedings{levy-etal-2018-long,
    title = "Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum",
    author = "Levy, Omer  and
      Lee, Kenton  and
      FitzGerald, Nicholas  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2116",
    doi = "10.18653/v1/P18-2116",
    pages = "732--739",
    abstract = "LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. We present an alternative view to explain the success of LSTMs: the gates themselves are versatile recurrent models that provide more representational power than previously appreciated. We do this by decoupling the LSTM{'}s gates from the embedded simple RNN, producing a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. Ablations on a range of problems demonstrate that the gating mechanism alone performs as well as an LSTM in most settings, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.",
}
@inproceedings{weiss-etal-2018-practical,
    title = "On the Practical Computational Power of Finite Precision {RNN}s for Language Recognition",
    author = "Weiss, Gail  and
      Goldberg, Yoav  and
      Yahav, Eran",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2117",
    doi = "10.18653/v1/P18-2117",
    pages = "740--745",
    abstract = "While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time. We consider the case of RNNs with finite precision whose computation time is linear in the input length. Under these limitations, we show that different RNN variants have different computational power. In particular, we show that the LSTM and the Elman-RNN with ReLU activation are strictly stronger than the RNN with a squashing activation and the GRU. This is achieved because LSTMs and ReLU-RNNs can easily implement counting behavior. We show empirically that the LSTM does indeed learn to effectively use the counting mechanism.",
}
@inproceedings{wang-etal-2018-co,
    title = "A Co-Matching Model for Multi-choice Reading Comprehension",
    author = "Wang, Shuohang  and
      Yu, Mo  and
      Jiang, Jing  and
      Chang, Shiyu",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2118",
    doi = "10.18653/v1/P18-2118",
    pages = "746--751",
    abstract = "Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.",
}
@inproceedings{sharma-etal-2018-tackling,
    title = "Tackling the Story Ending Biases in The Story Cloze Test",
    author = "Sharma, Rishi  and
      Allen, James  and
      Bakhshandeh, Omid  and
      Mostafazadeh, Nasrin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2119",
    doi = "10.18653/v1/P18-2119",
    pages = "752--757",
    abstract = "The Story Cloze Test (SCT) is a recent framework for evaluating story comprehension and script learning. There have been a variety of models tackling the SCT so far. Although the original goal behind the SCT was to require systems to perform deep language understanding and commonsense reasoning for successful narrative understanding, some recent models could perform significantly better than the initial baselines by leveraging human-authorship biases discovered in the SCT dataset. In order to shed some light on this issue, we have performed various data analysis and analyzed a variety of top performing models presented for this task. Given the statistics we have aggregated, we have designed a new crowdsourcing scheme that creates a new SCT dataset, which overcomes some of the biases. We benchmark a few models on the new dataset and show that the top-performing model on the original SCT dataset fails to keep up its performance. Our findings further signify the importance of benchmarking NLP systems on various evolving test sets.",
}
@inproceedings{lei-etal-2018-multi,
    title = "A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification",
    author = "Lei, Zeyang  and
      Yang, Yujiu  and
      Yang, Min  and
      Liu, Yi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2120",
    doi = "10.18653/v1/P18-2120",
    pages = "758--763",
    abstract = "Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation sub-spaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.",
}
@inproceedings{shimizu-etal-2018-pretraining,
    title = "Pretraining Sentiment Classifiers with Unlabeled Dialog Data",
    author = "Shimizu, Toru  and
      Shimizu, Nobuyuki  and
      Kobayashi, Hayato",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2121",
    doi = "10.18653/v1/P18-2121",
    pages = "764--770",
    abstract = "The huge cost of creating labeled training data is a common problem for supervised learning tasks such as sentiment classification. Recent studies showed that pretraining with unlabeled data via a language model can improve the performance of classification models. In this paper, we take the concept a step further by using a conditional language model, instead of a language model. Specifically, we address a sentiment classification task for a tweet analysis service as a case study and propose a pretraining strategy with unlabeled dialog data (tweet-reply pairs) via an encoder-decoder model. Experimental results show that our strategy can improve the performance of sentiment classifiers and outperform several state-of-the-art strategies including language model pretraining.",
}
@inproceedings{huang-etal-2018-disambiguating,
    title = "Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection",
    author = "Huang, Hen-Hsen  and
      Chen, Chiao-Chen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2122",
    doi = "10.18653/v1/P18-2122",
    pages = "771--777",
    abstract = "The reliability of self-labeled data is an important issue when the data are regarded as ground-truth for training and testing learning-based models. This paper addresses the issue of false-alarm hashtags in the self-labeled data for irony detection. We analyze the ambiguity of hashtag usages and propose a novel neural network-based model, which incorporates linguistic information from different aspects, to disambiguate the usage of three hashtags that are widely used to collect the training data for irony detection. Furthermore, we apply our model to prune the self-labeled training data. Experimental results show that the irony detection model trained on the less but cleaner training instances outperforms the models trained on all data.",
}
@inproceedings{xu-etal-2018-cross,
    title = "Cross-Target Stance Classification with Self-Attention Networks",
    author = "Xu, Chang  and
      Paris, C{\'e}cile  and
      Nepal, Surya  and
      Sparks, Ross",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2123",
    doi = "10.18653/v1/P18-2123",
    pages = "778--783",
    abstract = "In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target. We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios.",
}
@inproceedings{rajpurkar-etal-2018-know,
    title = "Know What You Don{'}t Know: Unanswerable Questions for {SQ}u{AD}",
    author = "Rajpurkar, Pranav  and
      Jia, Robin  and
      Liang, Percy",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2124",
    doi = "10.18653/v1/P18-2124",
    pages = "784--789",
    abstract = "Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuADRUn, a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuADRUn, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuADRUn is a challenging natural language understanding task for existing models: a strong neural system that gets 86{\%} F1 on SQuAD achieves only 66{\%} F1 on SQuADRUn. We release SQuADRUn to the community as the successor to SQuAD.",
}
@inproceedings{winn-muresan-2018-lighter,
    title = "{`}Lighter{'} Can Still Be Dark: Modeling Comparative Color Descriptions",
    author = "Winn, Olivia  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-2125",
    doi = "10.18653/v1/P18-2125",
    pages = "790--795",
    abstract = "We propose a novel paradigm of grounding comparative adjectives within the realm of color descriptions. Given a reference RGB color and a comparative term (e.g., lighter, darker), our model learns to ground the comparative as a direction in the RGB space such that the colors along the vector, rooted at the reference color, satisfy the comparison. Our model generates grounded representations of comparative adjectives with an average accuracy of 0.65 cosine similarity to the desired direction of change. These vectors approach colors with Delta-E scores of under 7 compared to the target colors, indicating the differences are very small with respect to human perception. Our approach makes use of a newly created dataset for this task derived from existing labeled color data.",
}
@proceedings{acl-2018-association,
    title = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1000",
}
@inproceedings{athiwaratkun-etal-2018-probabilistic,
    title = "Probabilistic {F}ast{T}ext for Multi-Sense Word Embeddings",
    author = "Athiwaratkun, Ben  and
      Wilson, Andrew  and
      Anandkumar, Anima",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1001",
    doi = "10.18653/v1/P18-1001",
    pages = "1--11",
    abstract = "We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation allows the model to share the {``}strength{''} across sub-word structures (e.g. Latin roots), producing accurate representations of rare, misspelt, or even unseen words. Moreover, each component of the mixture can capture a different word sense. Probabilistic FastText outperforms both FastText, which has no probabilistic model, and dictionary-level probabilistic embeddings, which do not incorporate subword structures, on several word-similarity benchmarks, including English RareWord and foreign language datasets. We also achieve state-of-art performance on benchmarks that measure ability to discern different meanings. Thus, our model is the first to achieve best of both the worlds: multi-sense representations while having enriched semantics on rare words.",
}
@inproceedings{khodak-etal-2018-la,
    title = "A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors",
    author = "Khodak, Mikhail  and
      Saunshi, Nikunj  and
      Liang, Yingyu  and
      Ma, Tengyu  and
      Stewart, Brandon  and
      Arora, Sanjeev",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1002",
    doi = "10.18653/v1/P18-1002",
    pages = "12--22",
    abstract = "Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.",
}
@inproceedings{jameel-etal-2018-unsupervised,
    title = "Unsupervised Learning of Distributional Relation Vectors",
    author = "Jameel, Shoaib  and
      Bouraoui, Zied  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1003",
    doi = "10.18653/v1/P18-1003",
    pages = "23--33",
    abstract = "Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that co-occurrence statistics can be used to capture rich information about the relationships between different words, existing approaches for modeling such relationships are based on manipulating pre-trained word vectors. In this paper, we introduce a novel method which directly learns relation vectors from co-occurrence statistics. To this end, we first introduce a variant of GloVe, in which there is an explicit connection between word vectors and PMI weighted co-occurrence vectors. We then show how relation vectors can be naturally embedded into the resulting vector space.",
}
@inproceedings{glavas-vulic-2018-explicit,
    title = "Explicit Retrofitting of Distributional Word Vectors",
    author = "Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1004",
    doi = "10.18653/v1/P18-1004",
    pages = "34--45",
    abstract = "Semantic specialization of distributional word vectors, referred to as retrofitting, is a process of fine-tuning word vectors using external lexical knowledge in order to better embed some semantic relation. Existing retrofitting models integrate linguistic constraints directly into learning objectives and, consequently, specialize only the vectors of words from the constraints. In this work, in contrast, we transform external lexico-semantic relations into training examples which we use to learn an explicit retrofitting model (ER). The ER model allows us to learn a global specialization function and specialize the vectors of words unobserved in the training data as well. We report large gains over original distributional vector spaces in (1) intrinsic word similarity evaluation and on (2) two downstream tasks − lexical simplification and dialog state tracking. Finally, we also successfully specialize vector spaces of new languages (i.e., unseen in the training data) by coupling ER with shared multilingual distributional vector spaces.",
}
@inproceedings{yang-etal-2018-unsupervised,
    title = "Unsupervised Neural Machine Translation with Weight Sharing",
    author = "Yang, Zhen  and
      Chen, Wei  and
      Wang, Feng  and
      Xu, Bo",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1005",
    doi = "10.18653/v1/P18-1005",
    pages = "46--55",
    abstract = "Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.",
}
@inproceedings{ren-etal-2018-triangular,
    title = "Triangular Architecture for Rare Language Translation",
    author = "Ren, Shuo  and
      Chen, Wenhu  and
      Liu, Shujie  and
      Li, Mu  and
      Zhou, Ming  and
      Ma, Shuai",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1006",
    doi = "10.18653/v1/P18-1006",
    pages = "56--65",
    abstract = "Neural Machine Translation (NMT) performs poor on the low-resource language pair (X,Z), especially when Z is a rare language. By introducing another rich language Y, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data (Y,Z) (may be small) and (X,Y) (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, Z is taken as the intermediate latent variable, and translation models of Z are jointly optimized with an unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of (X,Y). Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods.",
}
@inproceedings{kudo-2018-subword,
    title = "Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates",
    author = "Kudo, Taku",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1007",
    doi = "10.18653/v1/P18-1007",
    pages = "66--75",
    abstract = "Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings.",
}
@inproceedings{chen-etal-2018-best,
    title = "The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation",
    author = "Chen, Mia Xu  and
      Firat, Orhan  and
      Bapna, Ankur  and
      Johnson, Melvin  and
      Macherey, Wolfgang  and
      Foster, George  and
      Jones, Llion  and
      Schuster, Mike  and
      Shazeer, Noam  and
      Parmar, Niki  and
      Vaswani, Ashish  and
      Uszkoreit, Jakob  and
      Kaiser, Lukasz  and
      Chen, Zhifeng  and
      Wu, Yonghui  and
      Hughes, Macduff",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1008",
    doi = "10.18653/v1/P18-1008",
    pages = "76--86",
    abstract = "The past year has witnessed rapid advances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolutional seq2seq model, which was then out-performed by the more recent Transformer model. Each of these new approaches consists of a fundamental architecture accompanied by a set of modeling and training techniques that are in principle applicable to other seq2seq architectures. In this paper, we tease apart the new architectures and their accompanying techniques in two ways. First, we identify several key modeling and training techniques, and apply them to the RNN architecture, yielding a new RNMT+ model that outperforms all of the three fundamental architectures on the benchmark WMT{'}14 English to French and English to German tasks. Second, we analyze the properties of each fundamental seq2seq architecture and devise new hybrid architectures intended to combine their strengths. Our hybrid models obtain further improvements, outperforming the RNMT+ model on both benchmark datasets.",
}
@inproceedings{choi-etal-2018-ultra,
    title = "Ultra-Fine Entity Typing",
    author = "Choi, Eunsol  and
      Levy, Omer  and
      Choi, Yejin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1009",
    doi = "10.18653/v1/P18-1009",
    pages = "87--96",
    abstract = "We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in. We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks. We present a model that can predict ultra-fine types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking. Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets.",
}
@inproceedings{murty-etal-2018-hierarchical,
    title = "Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking",
    author = "Murty, Shikhar  and
      Verga, Patrick  and
      Vilnis, Luke  and
      Radovanovic, Irena  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1010",
    doi = "10.18653/v1/P18-1010",
    pages = "97--109",
    abstract = "Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: \textit{MedMentions}, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and \textit{TypeNet}, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.",
}
@inproceedings{ding-etal-2018-improving,
    title = "Improving Knowledge Graph Embedding Using Simple Constraints",
    author = "Ding, Boyang  and
      Wang, Quan  and
      Wang, Bin  and
      Guo, Li",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1011",
    doi = "10.18653/v1/P18-1011",
    pages = "110--121",
    abstract = "Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available at \url{https://github.com/iieir-km/ComplEx-NNE_AER}.",
}
@inproceedings{chandrahas-etal-2018-towards,
    title = "Towards Understanding the Geometry of Knowledge Graph Embeddings",
    author = "{Chandrahas}  and
      Sharma, Aditya  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1012",
    doi = "10.18653/v1/P18-1012",
    pages = "122--131",
    abstract = "Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embedding methods. These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space. Despite this popularity and effectiveness of KG embeddings in various tasks (e.g., link prediction), geometric understanding of such embeddings (i.e., arrangement of entity and relation vectors in vector space) is unexplored {--} we fill this gap in the paper. We initiate a study to analyze the geometry of KG embeddings and correlate it with task performance and other hyperparameters. To the best of our knowledge, this is the first study of its kind. Through extensive experiments on real-world datasets, we discover several insights. For example, we find that there are sharp differences between the geometry of embeddings learnt by different classes of KG embeddings methods. We hope that this initial study will inspire other follow-up research on this important but unexplored problem.",
}
@inproceedings{hsu-etal-2018-unified,
    title = "A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss",
    author = "Hsu, Wan-Ting  and
      Lin, Chieh-Kai  and
      Lee, Ming-Ying  and
      Min, Kerui  and
      Tang, Jing  and
      Sun, Min",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1013",
    doi = "10.18653/v1/P18-1013",
    pages = "132--141",
    abstract = "We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph. In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated. Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions. By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.",
}
@inproceedings{jadhav-rajan-2018-extractive,
    title = "Extractive Summarization with {SWAP}-{NET}: Sentences and Words from Alternating Pointer Networks",
    author = "Jadhav, Aishwarya  and
      Rajan, Vaibhav",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1014",
    doi = "10.18653/v1/P18-1014",
    pages = "142--151",
    abstract = "We present a new neural sequence-to-sequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP-NET that models the interaction of key words and salient sentences using a new two-level pointer network based architecture. SWAP-NET identifies both salient sentences and key words in an input document, and then combines them to form the extractive summary. Experiments on large scale benchmark corpora demonstrate the efficacy of SWAP-NET that outperforms state-of-the-art extractive summarizers.",
}
@inproceedings{cao-etal-2018-retrieve,
    title = "Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization",
    author = "Cao, Ziqiang  and
      Li, Wenjie  and
      Li, Sujian  and
      Wei, Furu",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1015",
    doi = "10.18653/v1/P18-1015",
    pages = "152--161",
    abstract = "Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular IR platform to Retrieve proper summaries as candidate templates. Then, we extend the seq2seq framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Experiments show that, in terms of informativeness, our model significantly outperforms the state-of-the-art methods, and even soft templates themselves demonstrate high competitiveness. In addition, the import of high-quality external summaries improves the stability and readability of generated summaries.",
}
@inproceedings{sulem-etal-2018-simple,
    title = "Simple and Effective Text Simplification Using Semantic and Neural Methods",
    author = "Sulem, Elior  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1016",
    doi = "10.18653/v1/P18-1016",
    pages = "162--173",
    abstract = "Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.",
}
@inproceedings{mohammad-2018-obtaining,
    title = "Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 {E}nglish Words",
    author = "Mohammad, Saif",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1017",
    doi = "10.18653/v1/P18-1017",
    pages = "174--184",
    abstract = "Words play a central role in language and thought. Factor analysis studies have shown that the primary dimensions of meaning are valence, arousal, and dominance (VAD). We present the NRC VAD Lexicon, which has human ratings of valence, arousal, and dominance for more than 20,000 English words. We use Best{--}Worst Scaling to obtain fine-grained scores and address issues of annotation consistency that plague traditional rating scale methods of annotation. We show that the ratings obtained are vastly more reliable than those in existing lexicons. We also show that there exist statistically significant differences in the shared understanding of valence, arousal, and dominance across demographic variables such as age, gender, and personality.",
}
@inproceedings{schneider-etal-2018-comprehensive,
    title = "Comprehensive Supersense Disambiguation of {E}nglish Prepositions and Possessives",
    author = "Schneider, Nathan  and
      Hwang, Jena D.  and
      Srikumar, Vivek  and
      Prange, Jakob  and
      Blodgett, Austin  and
      Moeller, Sarah R.  and
      Stern, Aviram  and
      Bitan, Adi  and
      Abend, Omri",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1018",
    doi = "10.18653/v1/P18-1018",
    pages = "185--196",
    abstract = "Semantic relations are often signaled with prepositional or possessive marking{---}but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations are comprehensive with respect to types and tokens of these markers; use broadly applicable supersense classes rather than fine-grained dictionary definitions; unite prepositions and possessives under the same class inventory; and distinguish between a marker{'}s lexical contribution and the role it marks in the context of a predicate or scene. Strong interannotator agreement rates, as well as encouraging disambiguation results with established supervised methods, speak to the viability of the scheme and task.",
}
@inproceedings{nye-etal-2018-corpus,
    title = "A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature",
    author = "Nye, Benjamin  and
      Li, Junyi Jessy  and
      Patel, Roma  and
      Yang, Yinfei  and
      Marshall, Iain  and
      Nenkova, Ani  and
      Wallace, Byron",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1019",
    doi = "10.18653/v1/P18-1019",
    pages = "197--207",
    abstract = "We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the {`}PICO{'} elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.",
}
@inproceedings{sakaguchi-van-durme-2018-efficient,
    title = "Efficient Online Scalar Annotation with Bounded Support",
    author = "Sakaguchi, Keisuke  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1020",
    doi = "10.18653/v1/P18-1020",
    pages = "208--218",
    abstract = "We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation.",
}
@inproceedings{hua-wang-2018-neural,
    title = "Neural Argument Generation Augmented with Externally Retrieved Evidence",
    author = "Hua, Xinyu  and
      Wang, Lu",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1021",
    doi = "10.18653/v1/P18-1021",
    pages = "219--230",
    abstract = "High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments.",
}
@inproceedings{potthast-etal-2018-stylometric,
    title = "A Stylometric Inquiry into Hyperpartisan and Fake News",
    author = "Potthast, Martin  and
      Kiesel, Johannes  and
      Reinartz, Kevin  and
      Bevendorff, Janek  and
      Stein, Benno",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1022",
    doi = "10.18653/v1/P18-1022",
    pages = "231--240",
    abstract = "We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news. A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97{\%} of the 299 fake news articles identified are also hyperpartisan. We show how a style analysis can distinguish hyperpartisan news from the mainstream (F1 = 0.78), and satire from both (F1 = 0.81). But stylometry is no silver bullet as style-based fake news detection does not work (F1 = 0.46). We further reveal that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream. This result is robust: it has been confirmed by three different modeling approaches, one of which employs Unmasking in a novel way. Applications of our results include partisanship detection and pre-screening for semi-automatic fake news detection.",
}
@inproceedings{wachsmuth-etal-2018-retrieval,
    title = "Retrieval of the Best Counterargument without Prior Topic Knowledge",
    author = "Wachsmuth, Henning  and
      Syed, Shahbaz  and
      Stein, Benno",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1023",
    doi = "10.18653/v1/P18-1023",
    pages = "241--251",
    abstract = "Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments{'} premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60{\%} accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.",
}
@inproceedings{trivedi-etal-2018-linknbed,
    title = "{L}ink{NB}ed: Multi-Graph Representation Learning with Entity Linkage",
    author = "Trivedi, Rakshit  and
      Sisman, Bunyamin  and
      Dong, Xin Luna  and
      Faloutsos, Christos  and
      Ma, Jun  and
      Zha, Hongyuan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1024",
    doi = "10.18653/v1/P18-1024",
    pages = "252--262",
    abstract = "Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs and eventually construct a unified graph is a crucial next step for the success of knowledge-based inference for many downstream applications. To this end, we propose LinkNBed, a deep relational learning framework that learns entity and relationship representations across multiple graphs. We identify entity linkage across graphs as a vital component to achieve our goal. We design a novel objective that leverage entity linkage and build an efficient multi-task training procedure. Experiments on link prediction and entity linkage demonstrate substantial improvements over the state-of-the-art relational learning approaches.",
}
@inproceedings{vilnis-etal-2018-probabilistic,
    title = "Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures",
    author = "Vilnis, Luke  and
      Li, Xiang  and
      Murty, Shikhar  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1025",
    doi = "10.18653/v1/P18-1025",
    pages = "263--272",
    abstract = "Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE), are a natural way to model transitive relational data (e.g. entailment graphs). However, OE learns a deterministic knowledge base, limiting expressiveness of queries and the ability to use uncertainty for both prediction and learning (e.g. learning from expectations). Probabilistic extensions of OE have provided the ability to somewhat calibrate these denotational probabilities while retaining the consistency and inductive bias of ordered models, but lack the ability to model the negative correlations found in real-world knowledge. In this work we show that a broad class of models that assign probability measures to OE can never capture negative correlation, which motivates our construction of a novel box lattice and accompanying probability measure to capture anti-correlation and even disjoint concepts, while still providing the benefits of probabilistic modeling, such as the ability to perform rich joint and conditional queries over arbitrary sets of concepts, and both learning from and predicting calibrated uncertainty. We show improvements over previous approaches in modeling the Flickr and WordNet entailment graphs, and investigate the power of the model.",
}
@inproceedings{beck-etal-2018-graph,
    title = "Graph-to-Sequence Learning using Gated Graph Neural Networks",
    author = "Beck, Daniel  and
      Haffari, Gholamreza  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1026",
    doi = "10.18653/v1/P18-1026",
    pages = "273--283",
    abstract = "Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on graph-to-sequence obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results shows that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.",
}
@inproceedings{khandelwal-etal-2018-sharp,
    title = "Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context",
    author = "Khandelwal, Urvashi  and
      He, He  and
      Qi, Peng  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1027",
    doi = "10.18653/v1/P18-1027",
    pages = "284--294",
    abstract = "We know very little about how neural language models (LM) use prior linguistic context. In this paper, we investigate the role of context in an LSTM LM, through ablation studies. Specifically, we analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped. On two standard datasets, Penn Treebank and WikiText-2, we find that the model is capable of using about 200 tokens of context on average, but sharply distinguishes nearby context (recent 50 tokens) from the distant history. The model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context (beyond 50 tokens), suggesting the distant past is modeled only as a rough semantic field or topic. We further find that the neural caching model (Grave et al., 2017b) especially helps the LSTM to copy words from within this distant context. Overall, our analysis not only provides a better understanding of how neural LMs use their context, but also sheds light on recent success from cache-based models.",
}
@inproceedings{schwartz-etal-2018-bridging,
    title = "Bridging {CNN}s, {RNN}s, and Weighted Finite-State Machines",
    author = "Schwartz, Roy  and
      Thomson, Sam  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1028",
    doi = "10.18653/v1/P18-1028",
    pages = "295--305",
    abstract = "Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with weighted finite-state automata (WFSAs) to learn a soft version of traditional surface patterns. We show that SoPa is an extension of a one-layer CNN, and that such CNNs are equivalent to a restricted version of SoPa, and accordingly, to a restricted form of WFSA. Empirically, on three text classification tasks, SoPa is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline, and is particularly useful in small data settings.",
}
@inproceedings{srivastava-etal-2018-zero,
    title = "Zero-shot Learning of Classifiers from Natural Language Quantification",
    author = "Srivastava, Shashank  and
      Labutov, Igor  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1029",
    doi = "10.18653/v1/P18-1029",
    pages = "306--316",
    abstract = "Humans can efficiently learn new concepts using language. We present a framework through which a set of explanations of a concept can be used to learn a classifier without access to any labeled examples. We use semantic parsing to map explanations to probabilistic assertions grounded in latent class labels and observed attributes of unlabeled data, and leverage the differential semantics of linguistic quantifiers (e.g., {`}usually{'} vs {`}always{'}) to drive model training. Experiments on three domains show that the learned classifiers outperform previous approaches for learning with limited data, and are comparable with fully supervised classifiers trained from a small number of labeled examples.",
}
@inproceedings{zhang-etal-2018-sentence,
    title = "Sentence-State {LSTM} for Text Representation",
    author = "Zhang, Yue  and
      Liu, Qi  and
      Song, Linfeng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1030",
    doi = "10.18653/v1/P18-1030",
    pages = "317--327",
    abstract = "Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.",
}
@inproceedings{howard-ruder-2018-universal,
    title = "Universal Language Model Fine-tuning for Text Classification",
    author = "Howard, Jeremy  and
      Ruder, Sebastian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1031",
    doi = "10.18653/v1/P18-1031",
    pages = "328--339",
    abstract = "Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24{\%} on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code.",
}
@inproceedings{poerner-etal-2018-evaluating,
    title = "Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement",
    author = {Poerner, Nina  and
      Sch{\"u}tze, Hinrich  and
      Roth, Benjamin},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1032",
    doi = "10.18653/v1/P18-1032",
    pages = "340--350",
    abstract = "The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.",
}
@inproceedings{finegan-dollak-etal-2018-improving,
    title = "Improving Text-to-{SQL} Evaluation Methodology",
    author = "Finegan-Dollak, Catherine  and
      Kummerfeld, Jonathan K.  and
      Zhang, Li  and
      Ramanathan, Karthik  and
      Sadasivam, Sesh  and
      Zhang, Rui  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1033",
    doi = "10.18653/v1/P18-1033",
    pages = "351--360",
    abstract = "To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.",
}
@inproceedings{sun-etal-2018-semantic,
    title = "Semantic Parsing with Syntax- and Table-Aware {SQL} Generation",
    author = "Sun, Yibo  and
      Tang, Duyu  and
      Duan, Nan  and
      Ji, Jianshu  and
      Cao, Guihong  and
      Feng, Xiaocheng  and
      Qin, Bing  and
      Liu, Ting  and
      Zhou, Ming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1034",
    doi = "10.18653/v1/P18-1034",
    pages = "361--372",
    abstract = "We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question- SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0{\%} to 74.4{\%}.",
}
@inproceedings{hershcovich-etal-2018-multitask,
    title = "Multitask Parsing Across Semantic Representations",
    author = "Hershcovich, Daniel  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1035",
    doi = "10.18653/v1/P18-1035",
    pages = "373--385",
    abstract = "The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks. We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks. Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.",
}
@inproceedings{sahin-steedman-2018-character,
    title = "Character-Level Models versus Morphology in Semantic Role Labeling",
    author = {{\c{S}}ahin, G{\"o}zde G{\"u}l  and
      Steedman, Mark},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1036",
    doi = "10.18653/v1/P18-1036",
    pages = "386--396",
    abstract = "Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill for high-level semantic analysis tasks, such as semantic role labeling (SRL). In this work, we train various types of SRL models that use word, character and morphology level information and analyze how performance of characters compare to words and morphology for several languages. We conduct an in-depth error analysis for each morphological typology and analyze the strengths and limitations of character-level models that relate to out-of-domain data, training data size, long range dependencies and model complexity. Our exhaustive analyses shed light on important characteristics of character-level models and their semantic capability.",
}
@inproceedings{lyu-titov-2018-amr,
    title = "{AMR} Parsing as Graph Prediction with Latent Alignment",
    author = "Lyu, Chunchuan  and
      Titov, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1037",
    doi = "10.18653/v1/P18-1037",
    pages = "397--407",
    abstract = "Abstract meaning representations (AMRs) are broad-coverage sentence-level semantic representations. AMRs represent sentences as rooted labeled directed acyclic graphs. AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences. We introduce a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. As exact inference requires marginalizing over alignments and is infeasible, we use the variational autoencoding framework and a continuous relaxation of the discrete alignments. We show that joint modeling is preferable to using a pipeline of align and parse. The parser achieves the best reported results on the standard benchmark (74.4{\%} on LDC2016E25).",
}
@inproceedings{chen-etal-2018-accurate,
    title = "Accurate {SHRG}-Based Semantic Parsing",
    author = "Chen, Yufei  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1038",
    doi = "10.18653/v1/P18-1038",
    pages = "408--418",
    abstract = "We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process. Our parser achieves an accuracy of 90.35 for EDS (89.51 for DMRS) in terms of elementary dependency match, which is a 4.87 (5.45) point improvement over the best existing data-driven model, indicating, in our view, the importance of linguistically-informed derivation for data-driven semantic parsing. This accuracy is equivalent to that of English Resource Grammar guided models, suggesting that (recurrent) neural network models are able to effectively learn deep linguistic knowledge from annotations.",
}
@inproceedings{huang-etal-2018-using,
    title = "Using Intermediate Representations to Solve Math Word Problems",
    author = "Huang, Danqing  and
      Yao, Jin-Ge  and
      Lin, Chin-Yew  and
      Zhou, Qingyu  and
      Yin, Jian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1039",
    doi = "10.18653/v1/P18-1039",
    pages = "419--428",
    abstract = "To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to solve the problem. The gap between natural language and equations makes it difficult for a learned model to generalize from limited data. In this work we present an intermediate meaning representation scheme that tries to reduce this gap. We use a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then execute them to obtain the final answers. Since the intermediate forms are latent, we propose an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Our experiments show using intermediate forms outperforms directly predicting equations.",
}
@inproceedings{liu-etal-2018-discourse,
    title = "Discourse Representation Structure Parsing",
    author = "Liu, Jiangming  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1040",
    doi = "10.18653/v1/P18-1040",
    pages = "429--439",
    abstract = "We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages: basic DRS structure prediction, condition prediction (i.e., predicates and relations), and referent prediction (i.e., variables). Experimental results on the Groningen Meaning Bank (GMB) show that our model outperforms competitive baselines by a wide margin.",
}
@inproceedings{shen-etal-2018-baseline,
    title = "Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms",
    author = "Shen, Dinghan  and
      Wang, Guoyin  and
      Wang, Wenlin  and
      Min, Martin Renqiang  and
      Su, Qinliang  and
      Zhang, Yizhe  and
      Li, Chunyuan  and
      Henao, Ricardo  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1041",
    doi = "10.18653/v1/P18-1041",
    pages = "440--450",
    abstract = "Many deep learning architectures have been proposed to model the \textit{compositionality} in text sequences, requiring substantial number of parameters and expensive computations. However, there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions. In this paper, we conduct a point-by-point comparative study between Simple Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling operations, relative to word-embedding-based RNN/CNN models. Surprisingly, SWEMs exhibit comparable or even superior performance in the majority of cases considered. Based upon this understanding, we propose two additional pooling strategies over learned word embeddings: (\textit{i}) a max-pooling operation for improved interpretability; and (\textit{ii}) a hierarchical pooling operation, which preserves spatial ($n$-gram) information within text sequences. We present experiments on 17 datasets encompassing three tasks: (\textit{i}) (long) document classification; (\textit{ii}) text sequence matching; and (\textit{iii}) short text tasks, including classification and tagging.",
}
@inproceedings{wieting-gimpel-2018-paranmt,
    title = "{P}ara{NMT}-50{M}: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations",
    author = "Wieting, John  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1042",
    doi = "10.18653/v1/P18-1042",
    pages = "451--462",
    abstract = "We describe ParaNMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the non-English side of a large parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M can be a valuable resource for paraphrase generation and can provide a rich source of semantic knowledge to improve downstream natural language understanding tasks. To show its utility, we use ParaNMT-50M to train paraphrastic sentence embeddings that outperform all supervised systems on every SemEval semantic textual similarity competition, in addition to showing how it can be used for paraphrase generation.",
}
@inproceedings{rashkin-etal-2018-event2mind,
    title = "{E}vent2{M}ind: Commonsense Inference on Events, Intents, and Reactions",
    author = "Rashkin, Hannah  and
      Sap, Maarten  and
      Allaway, Emily  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1043",
    doi = "10.18653/v1/P18-1043",
    pages = "463--473",
    abstract = "We investigate a new commonsense inference task: given an event described in a short free-form text ({``}X drinks coffee in the morning{''}), a system reasons about the likely intents ({``}X wants to stay awake{''}) and reactions ({``}X feels alert{''}) of the event{'}s participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people{'}s intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.",
}
@inproceedings{kurita-etal-2018-neural,
    title = "Neural Adversarial Training for Semi-supervised {J}apanese Predicate-argument Structure Analysis",
    author = "Kurita, Shuhei  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1044",
    doi = "10.18653/v1/P18-1044",
    pages = "474--484",
    abstract = "Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora. In this paper, we propose a novel Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. In our experiments, our model outperforms existing state-of-the-art models for Japanese PAS analysis.",
}
@inproceedings{choubey-huang-2018-improving,
    title = "Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures",
    author = "Choubey, Prafulla Kumar  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1045",
    doi = "10.18653/v1/P18-1045",
    pages = "485--495",
    abstract = "This paper proposes a novel approach for event coreference resolution that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation. We explicitly model correlations between the main event chains of a document with topic transition sentences, inter-coreference chain correlations, event mention distributional characteristics and sub-event structure, and use them with scores obtained from a local coreference relation classifier for jointly resolving multiple event chains in a document. Our experiments across KBP 2016 and 2017 datasets suggest that each of the structures contribute to improving event coreference resolution performance.",
}
@inproceedings{qin-etal-2018-dsgan,
    title = "{DSGAN}: Generative Adversarial Training for Distant Supervision Relation Extraction",
    author = "Qin, Pengda  and
      Xu, Weiran  and
      Wang, William Yang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1046",
    doi = "10.18653/v1/P18-1046",
    pages = "496--505",
    abstract = "Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive samples in sentence level. In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentence-level true-positive generator. Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator. The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline. We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification. The experimental results show that the proposed strategy significantly improves the performance of distant supervision relation extraction comparing to state-of-the-art systems.",
}
@inproceedings{zeng-etal-2018-extracting,
    title = "Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism",
    author = "Zeng, Xiangrong  and
      Zeng, Daojian  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1047",
    doi = "10.18653/v1/P18-1047",
    pages = "506--514",
    abstract = "The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to extract relational triplets precisely. In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes. We adopt two different strategies in decoding process: employing only one united decoder or applying multiple separated decoders. We test our models in two public datasets and our model outperform the baseline method significantly.",
}
@inproceedings{hong-etal-2018-self,
    title = "Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection",
    author = "Hong, Yu  and
      Zhou, Wenxuan  and
      Zhang, Jingli  and
      Zhou, Guodong  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1048",
    doi = "10.18653/v1/P18-1048",
    pages = "515--526",
    abstract = "Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes. Detailed experiments on the ACE 2005 and TAC-KBP 2015 corpora show that our proposed method is highly effective and adaptable.",
}
@inproceedings{meng-rumshisky-2018-context,
    title = "Context-Aware Neural Model for Temporal Information Extraction",
    author = "Meng, Yuanliang  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1049",
    doi = "10.18653/v1/P18-1049",
    pages = "527--536",
    abstract = "We propose a context-aware neural network model for temporal information extraction. This model has a uniform architecture for event-event, event-timex and timex-timex pairs. A Global Context Layer (GCL), inspired by Neural Turing Machine (NTM), stores processed temporal relations in narrative order, and retrieves them for use when relevant entities come in. Relations are then classified in context. The GCL model has long-term memory and attention mechanisms to resolve irregular long-distance dependencies that regular RNNs such as LSTM cannot recognize. It does not require any new input features, while outperforming the existing models in literature. To our knowledge it is also the first model to use NTM-like architecture to process the information from global context in discourse-scale natural text processing. We are going to release the source code in the future.",
}
@inproceedings{yao-huang-2018-temporal,
    title = "Temporal Event Knowledge Acquisition via Identifying Narratives",
    author = "Yao, Wenlin  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1050",
    doi = "10.18653/v1/P18-1050",
    pages = "537--547",
    abstract = "Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal {``}before/after{''} event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperforms several recent neural network models on the narrative cloze task.",
}
@inproceedings{vanni-etal-2018-textual,
    title = "Textual Deconvolution Saliency ({TDS}) : a deep tool box for linguistic analysis",
    author = "Vanni, Laurent  and
      Ducoffe, Melanie  and
      Aguilar, Carlos  and
      Precioso, Frederic  and
      Mayaffre, Damon",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1051",
    doi = "10.18653/v1/P18-1051",
    pages = "548--557",
    abstract = "In this paper, we propose a new strategy, called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification. We extend Deconvolution Networks to text in order to present a new perspective on text analysis to the linguistic community. We empirically demonstrated the efficiency of our Text Deconvolution Saliency on corpora from three different languages: English, French, and Latin. For every tested dataset, our Text Deconvolution Saliency automatically encodes complex linguistic patterns based on co-occurrences and possibly on grammatical and syntax analysis.",
}
@inproceedings{joty-etal-2018-coherence,
    title = "Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach",
    author = "Joty, Shafiq  and
      Mohiuddin, Muhammad Tasnim  and
      Tien Nguyen, Dat",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1052",
    doi = "10.18653/v1/P18-1052",
    pages = "558--568",
    abstract = "We propose a novel coherence model for written asynchronous conversations (e.g., forums, emails), and show its applications in coherence assessment and thread reconstruction tasks. We conduct our research in two steps. First, we propose improvements to the recently proposed neural entity grid model by lexicalizing its entity transitions. Then, we extend the model to asynchronous conversations by incorporating the underlying conversational structure in the entity grid representation and feature computation. Our model achieves state of the art results on standard coherence assessment tasks in monologue and conversations outperforming existing models. We also demonstrate its effectiveness in reconstructing thread structures.",
}
@inproceedings{yin-etal-2018-deep,
    title = "Deep Reinforcement Learning for {C}hinese Zero Pronoun Resolution",
    author = "Yin, Qingyu  and
      Zhang, Yu  and
      Zhang, Wei-Nan  and
      Liu, Ting  and
      Wang, William Yang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1053",
    doi = "10.18653/v1/P18-1053",
    pages = "569--578",
    abstract = "Recent neural network models for Chinese zero pronoun resolution gain great performance by capturing semantic information for zero pronouns and candidate antecedents, but tend to be short-sighted, operating solely by making local decisions. They typically predict coreference links between the zero pronoun and one single candidate antecedent at a time while ignoring their influence on future decisions. Ideally, modeling useful information of preceding potential antecedents is crucial for classifying later zero pronoun-candidate antecedent pairs, a need which leads traditional models of zero pronoun resolution to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to deal with the task. With the help of the reinforcement learning agent, our system learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. Experimental results on OntoNotes 5.0 show that our approach substantially outperforms the state-of-the-art methods under three experimental settings.",
}
@inproceedings{shibata-kurohashi-2018-entity,
    title = "Entity-Centric Joint Modeling of {J}apanese Coreference Resolution and Predicate Argument Structure Analysis",
    author = "Shibata, Tomohide  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1054",
    doi = "10.18653/v1/P18-1054",
    pages = "579--589",
    abstract = "Predicate argument structure analysis is a task of identifying structured events. To improve this field, we need to identify a salient entity, which cannot be identified without performing coreference resolution and predicate argument structure analysis simultaneously. This paper presents an entity-centric joint model for Japanese coreference resolution and predicate argument structure analysis. Each entity is assigned an embedding, and when the result of both analyses refers to an entity, the entity embedding is updated. The analyses take the entity embedding into consideration to access the global information of entities. Our experimental results demonstrate the proposed method can improve the performance of the inter-sentential zero anaphora resolution drastically, which is a notoriously difficult task in predicate argument structure analysis.",
}
@inproceedings{torr-2018-constraining,
    title = "Constraining {MG}bank: Agreement, {L}-Selection and Supertagging in {M}inimalist {G}rammars",
    author = "Torr, John",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1055",
    doi = "10.18653/v1/P18-1055",
    pages = "590--600",
    abstract = "This paper reports on two strategies that have been implemented for improving the efficiency and precision of wide-coverage Minimalist Grammar (MG) parsing. The first extends the formalism presented in Torr and Stabler (2016) with a mechanism for enforcing fine-grained selectional restrictions and agreements. The second is a method for factoring computationally costly null heads out from bottom-up MG parsing; this has the additional benefit of rendering the formalism fully compatible for the first time with highly efficient Markovian supertaggers. These techniques aided in the task of generating MGbank, the first wide-coverage corpus of Minimalist Grammar derivation trees.",
}
@inproceedings{xu-etal-2018-much,
    title = "Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power",
    author = "Xu, Yang  and
      Cole, Jeremy  and
      Reitter, David",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1056",
    doi = "10.18653/v1/P18-1056",
    pages = "601--610",
    abstract = "Linguistic alignment between dialogue partners has been claimed to be affected by their relative social power. A common finding has been that interlocutors of higher power tend to receive more alignment than those of lower power. However, these studies overlook some low-level linguistic features that can also affect alignment, which casts doubts on these findings. This work characterizes the effect of power on alignment with logistic regression models in two datasets, finding that the effect vanishes or is reversed after controlling for low-level features such as utterance length. Thus, linguistic alignment is explained better by low-level features than by social power. We argue that a wider range of factors, especially cognitive factors, need to be taken into account for future studies on observational data when social factors of language use are in question.",
}
@inproceedings{fabbri-etal-2018-tutorialbank,
    title = "{T}utorial{B}ank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation",
    author = "Fabbri, Alexander  and
      Li, Irene  and
      Trairatvorakul, Prawat  and
      He, Yijiao  and
      Ting, Weitai  and
      Tung, Robert  and
      Westerfield, Caitlin  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1057",
    doi = "10.18653/v1/P18-1057",
    pages = "611--620",
    abstract = "The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly sift through multiple sources to find valuable, relevant information. To address this situation, we introduce TutorialBank, a new, publicly available dataset which aims to facilitate NLP education and research. We have manually collected and categorized over 5,600 resources on NLP as well as the related fields of Artificial Intelligence (AI), Machine Learning (ML) and Information Retrieval (IR). Our dataset is notably the largest manually-picked corpus of resources intended for NLP education which does not include only academic papers. Additionally, we have created both a search engine and a command-line tool for the resources and have annotated the corpus to include lists of research topics, relevant resources for each topic, prerequisite relations among topics, relevant sub-parts of individual resources, among other annotations. We are releasing the dataset and present several avenues for further research.",
}
@inproceedings{carlile-etal-2018-give,
    title = "Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays",
    author = "Carlile, Winston  and
      Gurrapadi, Nishant  and
      Ke, Zixuan  and
      Ng, Vincent",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1058",
    doi = "10.18653/v1/P18-1058",
    pages = "621--631",
    abstract = "While argument persuasiveness is one of the most important dimensions of argumentative essay quality, it is relatively little studied in automated essay scoring research. Progress on scoring argument persuasiveness is hindered in part by the scarcity of annotated corpora. We present the first corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument{'}s persuasiveness. This corpus could trigger the development of novel computational models concerning argument persuasiveness that provide useful feedback to students on why their arguments are (un)persuasive in addition to how persuasive they are.",
}
@inproceedings{choshen-abend-2018-inherent,
    title = "Inherent Biases in Reference-based Evaluation for Grammatical Error Correction",
    author = "Choshen, Leshem  and
      Abend, Omri",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1059",
    doi = "10.18653/v1/P18-1059",
    pages = "632--642",
    abstract = "The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality (henceforth, low coverage bias or LCB). This paper shows that overcoming LCB in Grammatical Error Correction (GEC) evaluation cannot be attained by re-scaling or by increasing the number of references in any feasible range, contrary to previous suggestions. This is due to the long-tailed distribution of valid corrections for a sentence. Concretely, we show that LCB incentivizes GEC systems to avoid correcting even when they can generate a valid correction. Consequently, existing systems obtain comparable or superior performance compared to humans, by making few but targeted changes to the input. Similar effects on Text Simplification further support our claims.",
}
@inproceedings{chaganty-etal-2018-price,
    title = "The price of debiasing automatic metrics in natural language evalaution",
    author = "Chaganty, Arun  and
      Mussmann, Stephen  and
      Liang, Percy",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1060",
    doi = "10.18653/v1/P18-1060",
    pages = "643--653",
    abstract = "For evaluating generation systems, automatic metrics such as BLEU cost nothing to run but have been shown to correlate poorly with human judgment, leading to systematic bias against certain model improvements. On the other hand, averaging human judgments, the unbiased gold standard, is often too expensive. In this paper, we use control variates to combine automatic metrics with human evaluation to obtain an unbiased estimator with lower cost than human evaluation alone. In practice, however, we obtain only a 7-13{\%} cost reduction on evaluating summarization and open-response question answering systems. We then prove that our estimator is optimal: there is no unbiased estimator with lower cost. Our theory further highlights the two fundamental bottlenecks{---}the automatic metric and the prompt shown to human evaluators{---}both of which need to be improved to obtain greater cost savings.",
}
@inproceedings{zhou-etal-2018-neural,
    title = "Neural Document Summarization by Jointly Learning to Score and Select Sentences",
    author = "Zhou, Qingyu  and
      Yang, Nan  and
      Wei, Furu  and
      Huang, Shaohan  and
      Zhou, Ming  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1061",
    doi = "10.18653/v1/P18-1061",
    pages = "654--663",
    abstract = "Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. It first reads the document sentences with a hierarchical encoder to obtain the representation of sentences. Then it builds the output summary by extracting sentences one by one. Different from previous methods, our approach integrates the selection strategy into the scoring model, which directly predicts the relative importance given previously selected sentences. Experiments on the CNN/Daily Mail dataset show that the proposed framework significantly outperforms the state-of-the-art extractive summarization models.",
}
@inproceedings{shang-etal-2018-unsupervised,
    title = "Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization",
    author = "Shang, Guokan  and
      Ding, Wensi  and
      Zhang, Zekun  and
      Tixier, Antoine  and
      Meladianos, Polykarpos  and
      Vazirgiannis, Michalis  and
      Lorr{\'e}, Jean-Pierre",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1062",
    doi = "10.18653/v1/P18-1062",
    pages = "664--674",
    abstract = "We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations. Our work combines the strengths of multiple recent approaches while addressing their weaknesses. Moreover, we leverage recent advances in word embeddings and graph degeneracy applied to NLP to take exterior semantic knowledge into account, and to design custom diversity and informativeness measures. Experiments on the AMI and ICSI corpus show that our system improves on the state-of-the-art. Code and data are publicly available, and our system can be interactively tested.",
}
@inproceedings{chen-bansal-2018-fast,
    title = "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting",
    author = "Chen, Yen-Chun  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1063",
    doi = "10.18653/v1/P18-1063",
    pages = "675--686",
    abstract = "Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the non-differentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. Moreover, by first operating at the sentence-level and then the word-level, we enable parallel decoding of our neural generative model that results in substantially faster (10-20x) inference speed as well as 4x faster training convergence than previous long-paragraph encoder-decoder models. We also demonstrate the generalization of our model on the test-only DUC-2002 dataset, where we achieve higher scores than a state-of-the-art model.",
}
@inproceedings{guo-etal-2018-soft,
    title = "Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation",
    author = "Guo, Han  and
      Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1064",
    doi = "10.18653/v1/P18-1064",
    pages = "687--697",
    abstract = "An accurate abstractive summary of a document should contain all its salient information and should be logically entailed by the input document. We improve these important aspects of abstractive summarization via multi-task learning with the auxiliary tasks of question generation and entailment generation, where the former teaches the summarization model how to look for salient questioning-worthy details, and the latter teaches the model how to rewrite a summary which is a directed-logical subset of the input document. We also propose novel multi-task architectures with high-level (semantic) layer-specific sharing across multiple encoder and decoder layers of the three tasks, as well as soft-sharing mechanisms (and show performance ablations and analysis examples of each contribution). Overall, we achieve statistically significant improvements over the state-of-the-art on both the CNN/DailyMail and Gigaword datasets, as well as on the DUC-2002 transfer setup. We also present several quantitative and qualitative analysis studies of our model{'}s learned saliency and entailment skills.",
}
@inproceedings{ocampo-diaz-ng-2018-modeling,
    title = "Modeling and Prediction of Online Product Review Helpfulness: A Survey",
    author = "Ocampo Diaz, Gerardo  and
      Ng, Vincent",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1065",
    doi = "10.18653/v1/P18-1065",
    pages = "698--708",
    abstract = "As the amount of free-form user-generated reviews in e-commerce websites continues to increase, there is an increasing need for automatic mechanisms that sift through the vast amounts of user reviews and identify quality content. Review helpfulness modeling is a task which studies the mechanisms that affect review helpfulness and attempts to accurately predict it. This paper provides an overview of the most relevant work in helpfulness prediction and understanding in the past decade, discusses the insights gained from said work, and provides guidelines for future research.",
}
@inproceedings{lin-etal-2018-mining,
    title = "Mining Cross-Cultural Differences and Similarities in Social Media",
    author = "Lin, Bill Yuchen  and
      Xu, Frank F.  and
      Zhu, Kenny  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1066",
    doi = "10.18653/v1/P18-1066",
    pages = "709--719",
    abstract = "Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media. For instance, people of distinct cultures often hold different opinions on a single named entity. Also, understanding slang terms across languages requires knowledge of cross-cultural similarities. In this paper, we study the problem of computing such cross-cultural differences and similarities. We present a lightweight yet effective approach, and evaluate it on two novel tasks: 1) mining cross-cultural differences of named entities and 2) finding similar terms for slang across languages. Experimental results show that our framework substantially outperforms a number of baseline methods on both tasks. The framework could be useful for machine translation applications and research in computational social science.",
}
@inproceedings{johnson-goldwasser-2018-classification,
    title = "Classification of Moral Foundations in Microblog Political Discourse",
    author = "Johnson, Kristen  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1067",
    doi = "10.18653/v1/P18-1067",
    pages = "720--730",
    abstract = "Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology. Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues. The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.",
}
@inproceedings{dong-lapata-2018-coarse,
    title = "Coarse-to-Fine Decoding for Neural Semantic Parsing",
    author = "Dong, Li  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1068",
    doi = "10.18653/v1/P18-1068",
    pages = "731--742",
    abstract = "Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.",
}
@inproceedings{dong-etal-2018-confidence,
    title = "Confidence Modeling for Neural Semantic Parsing",
    author = "Dong, Li  and
      Quirk, Chris  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1069",
    doi = "10.18653/v1/P18-1069",
    pages = "743--753",
    abstract = "In this work we focus on confidence modeling for neural semantic parsers which are built upon sequence-to-sequence models. We outline three major causes of uncertainty, and design various metrics to quantify these factors. These metrics are then used to estimate confidence scores that indicate whether model predictions are likely to be correct. Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying on attention scores.",
}
@inproceedings{yin-etal-2018-structvae,
    title = "{S}truct{VAE}: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing",
    author = "Yin, Pengcheng  and
      Zhou, Chunting  and
      He, Junxian  and
      Neubig, Graham",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1070",
    doi = "10.18653/v1/P18-1070",
    pages = "754--765",
    abstract = "Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.",
}
@inproceedings{chen-etal-2018-sequence,
    title = "Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing",
    author = "Chen, Bo  and
      Sun, Le  and
      Han, Xianpei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1071",
    doi = "10.18653/v1/P18-1071",
    pages = "766--777",
    abstract = "This paper proposes a neural semantic parsing approach {--} Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.",
}
@inproceedings{sogaard-etal-2018-limitations,
    title = "On the Limitations of Unsupervised Bilingual Dictionary Induction",
    author = "S{\o}gaard, Anders  and
      Ruder, Sebastian  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1072",
    doi = "10.18653/v1/P18-1072",
    pages = "778--788",
    abstract = "Unsupervised machine translation - i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora - seems impossible, but nevertheless, Lample et al. (2017) recently proposed a fully unsupervised machine translation (MT) model. The model relies heavily on an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction (Conneau et al., 2017), which we examine here. Our results identify the limitations of current unsupervised MT: unsupervised bilingual dictionary induction performs much worse on morphologically rich languages that are not dependent marking, when monolingual corpora from different domains or different embedding algorithms are used. We show that a simple trick, exploiting a weak supervision signal from identical words, enables more robust induction and establish a near-perfect correlation between unsupervised bilingual dictionary induction performance and a previously unexplored graph similarity metric.",
}
@inproceedings{artetxe-etal-2018-robust,
    title = "A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1073",
    doi = "10.18653/v1/P18-1073",
    pages = "789--798",
    abstract = "Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios. This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution. Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems. Our implementation is released as an open source project at \url{https://github.com/artetxem/vecmap}.",
}
@inproceedings{lin-etal-2018-multi,
    title = "A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling",
    author = "Lin, Ying  and
      Yang, Shengqi  and
      Stoyanov, Veselin  and
      Ji, Heng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1074",
    doi = "10.18653/v1/P18-1074",
    pages = "799--809",
    abstract = "We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. In this new architecture, we combine various transfer models using two layers of parameter sharing. On the first layer, we construct the basis of the architecture to provide universal word representation and feature extraction capability for all models. On the second level, we adopt different parameter sharing strategies for different transfer schemes. This architecture proves to be particularly effective for low-resource settings, when there are less than 200 training sentences for the target task. Using Name Tagging as a target task, our approach achieved 4.3{\%}-50.5{\%} absolute F-score gains compared to the mono-lingual single-task baseline model.",
}
@inproceedings{hangya-etal-2018-two,
    title = "Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable",
    author = {Hangya, Viktor  and
      Braune, Fabienne  and
      Fraser, Alexander  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1075",
    doi = "10.18653/v1/P18-1075",
    pages = "810--820",
    abstract = "Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here. We make two contributions. First, we test a delightfully simple method for domain adaptation of bilingual word embeddings. We evaluate these embeddings on two bilingual tasks involving different domains: cross-lingual twitter sentiment classification and medical bilingual lexicon induction. Second, we tailor a broadly applicable semi-supervised classification method from computer vision to these tasks. We show that this method also helps in low-resource setups. Using both methods together we achieve large improvements over our baselines, by using only additional unlabeled data.",
}
@inproceedings{mihaylov-frank-2018-knowledgeable,
    title = "Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge",
    author = "Mihaylov, Todor  and
      Frank, Anette",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1076",
    doi = "10.18653/v1/P18-1076",
    pages = "821--832",
    abstract = "We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting. Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this knowledge with the context representation before inferring the answer. This allows the model to attract and imply knowledge from an external knowledge source that is not explicitly stated in the text, but that is relevant for inferring the answer. Our model improves results over a very strong baseline on a hard Common Nouns dataset, making it a strong competitor of much more complex models. By including knowledge explicitly, our model can also provide evidence about the background knowledge used in the RC process.",
}
@inproceedings{labutov-etal-2018-multi,
    title = "Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds",
    author = "Labutov, Igor  and
      Yang, Bishan  and
      Prakash, Anusha  and
      Azaria, Amos",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1077",
    doi = "10.18653/v1/P18-1077",
    pages = "833--844",
    abstract = "Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.",
}
@inproceedings{clark-gardner-2018-simple,
    title = "Simple and Effective Multi-Paragraph Reading Comprehension",
    author = "Clark, Christopher  and
      Gardner, Matt",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1078",
    doi = "10.18653/v1/P18-1078",
    pages = "845--855",
    abstract = "We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively applying these models to each paragraph independently often results in them being distracted by irrelevant text. We show that it is possible to significantly improve performance by using a modified training scheme that teaches the model to ignore non-answer containing paragraphs. Our method involves sampling multiple paragraphs from each document, and using an objective function that requires the model to produce globally correct output. We additionally identify and improve upon a number of other design decisions that arise when working with document-level data. Experiments on TriviaQA and SQuAD shows our method advances the state of the art, including a 10 point gain on TriviaQA.",
}
@inproceedings{ribeiro-etal-2018-semantically,
    title = "Semantically Equivalent Adversarial Rules for Debugging {NLP} models",
    author = "Ribeiro, Marco Tulio  and
      Singh, Sameer  and
      Guestrin, Carlos",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1079",
    doi = "10.18653/v1/P18-1079",
    pages = "856--865",
    abstract = "Complex machine learning models for NLP are often brittle, making different predictions for input instances that are extremely similar semantically. To automatically detect this behavior for individual instances, we present semantically equivalent adversaries (SEAs) {--} semantic-preserving perturbations that induce changes in the model{'}s predictions. We generalize these adversaries into semantically equivalent adversarial rules (SEARs) {--} simple, universal replacement rules that induce adversaries on many instances. We demonstrate the usefulness and flexibility of SEAs and SEARs by detecting bugs in black-box state-of-the-art models for three domains: machine comprehension, visual question-answering, and sentiment analysis. Via user studies, we demonstrate that we generate high-quality local adversaries for more instances than humans, and that SEARs induce four times as many mistakes as the bugs discovered by human experts. SEARs are also actionable: retraining models using data augmentation significantly reduces bugs, while maintaining accuracy.",
}
@inproceedings{prabhumoye-etal-2018-style,
    title = "Style Transfer Through Back-Translation",
    author = "Prabhumoye, Shrimai  and
      Tsvetkov, Yulia  and
      Salakhutdinov, Ruslan  and
      Black, Alan W",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1080",
    doi = "10.18653/v1/P18-1080",
    pages = "866--876",
    abstract = "Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context. This paper introduces a new method for automatic style transfer. We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties. Then adversarial generation techniques are used to make the output match the desired style. We evaluate this technique on three different style transformations: sentiment, gender and political slant. Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.",
}
@inproceedings{bhowmik-de-melo-2018-generating,
    title = "Generating Fine-Grained Open Vocabulary Entity Type Descriptions",
    author = "Bhowmik, Rajarshi  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1081",
    doi = "10.18653/v1/P18-1081",
    pages = "877--888",
    abstract = "While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graphs entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines.",
}
@inproceedings{fan-etal-2018-hierarchical,
    title = "Hierarchical Neural Story Generation",
    author = "Fan, Angela  and
      Lewis, Mike  and
      Dauphin, Yann",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1082",
    doi = "10.18653/v1/P18-1082",
    pages = "889--898",
    abstract = "We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.",
}
@inproceedings{wang-etal-2018-metrics,
    title = "No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling",
    author = "Wang, Xin  and
      Chen, Wenhu  and
      Wang, Yuan-Fang  and
      Wang, William Yang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1083",
    doi = "10.18653/v1/P18-1083",
    pages = "899--909",
    abstract = "Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem. Different from captions, stories have more expressive language styles and contain many imaginary concepts that do not appear in the images. Thus it poses challenges to behavioral cloning algorithms. Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost. Therefore, we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function. Though automatic evaluation indicates slight performance boost over state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation shows that our approach achieves significant improvement in generating more human-like stories than SOTA systems.",
}
@inproceedings{rotman-etal-2018-bridging,
    title = "Bridging Languages through Images with Deep Partial Canonical Correlation Analysis",
    author = "Rotman, Guy  and
      Vuli{\'c}, Ivan  and
      Reichart, Roi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1084",
    doi = "10.18653/v1/P18-1084",
    pages = "910--921",
    abstract = "We present a deep neural network that leverages images to improve bilingual text embeddings. Relying on bilingual image tags and descriptions, our approach conditions text embedding induction on the shared visual information for both languages, producing highly correlated bilingual embeddings. In particular, we propose a novel model based on Partial Canonical Correlation Analysis (PCCA). While the original PCCA finds linear projections of two views in order to maximize their canonical correlation conditioned on a shared third variable, we introduce a non-linear Deep PCCA (DPCCA) model, and develop a new stochastic iterative algorithm for its optimization. We evaluate PCCA and DPCCA on multilingual word similarity and cross-lingual image description retrieval. Our models outperform a large variety of previous methods, despite not having access to any visual signal during test time inference.",
}
@inproceedings{kiros-etal-2018-illustrative,
    title = "Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search",
    author = "Kiros, Jamie  and
      Chan, William  and
      Hinton, Geoffrey",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1085",
    doi = "10.18653/v1/P18-1085",
    pages = "922--933",
    abstract = "We introduce Picturebook, a large-scale lookup operation to ground language via {`}snapshots{'} of our physical world accessed through image search. For each word in a vocabulary, we extract the top-$k$ images from Google image search and feed the images through a convolutional network to extract a word embedding. We introduce a multimodal gating function to fuse our Picturebook embeddings with other word representations. We also introduce Inverse Picturebook, a mechanism to map a Picturebook embedding back into words. We experiment and report results across a wide range of tasks: word similarity, natural language inference, semantic relatedness, sentiment/topic classification, image-sentence ranking and machine translation. We also show that gate activations corresponding to Picturebook embeddings are highly correlated to human judgments of concreteness ratings.",
}
@inproceedings{gao-etal-2018-action,
    title = "What Action Causes This? Towards Naive Physical Action-Effect Prediction",
    author = "Gao, Qiaozi  and
      Yang, Shaohua  and
      Chai, Joyce  and
      Vanderwende, Lucy",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1086",
    doi = "10.18653/v1/P18-1086",
    pages = "934--945",
    abstract = "Despite recent advances in knowledge representation, automated reasoning, and machine learning, artificial agents still lack the ability to understand basic action-effect relations regarding the physical world, for example, the action of cutting a cucumber most likely leads to the state where the cucumber is broken apart into smaller pieces. If artificial agents (e.g., robots) ever become our partners in joint tasks, it is critical to empower them with such action-effect understanding so that they can reason about the state of the world and plan for actions. Towards this goal, this paper introduces a new task on naive physical action-effect prediction, which addresses the relations between concrete actions (expressed in the form of verb-noun pairs) and their effects on the state of the physical world as depicted by images. We collected a dataset for this task and developed an approach that harnesses web image data through distant supervision to facilitate learning for action-effect prediction. Our empirical results have shown that web data can be used to complement a small number of seed examples (e.g., three examples for each action) for model learning. This opens up possibilities for agents to learn physical action-effect relations for tasks at hand through communication with humans with a few examples.",
}
@inproceedings{li-etal-2018-transformation,
    title = "Transformation Networks for Target-Oriented Sentiment Classification",
    author = "Li, Xin  and
      Bing, Lidong  and
      Lam, Wai  and
      Shi, Bei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1087",
    doi = "10.18653/v1/P18-1087",
    pages = "946--956",
    abstract = "Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence. RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance. After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model that achieves new state-of-the-art results on a few benchmarks. Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer. Between the two layers, we propose a component which first generates target-specific representations of words in the sentence, and then incorporates a mechanism for preserving the original contextual information from the RNN layer.",
}
@inproceedings{wang-etal-2018-target,
    title = "Target-Sensitive Memory Networks for Aspect Sentiment Classification",
    author = "Wang, Shuai  and
      Mazumder, Sahisnu  and
      Liu, Bing  and
      Zhou, Mianwei  and
      Chang, Yi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1088",
    doi = "10.18653/v1/P18-1088",
    pages = "957--967",
    abstract = "Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect/target and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Memory networks (MNs) have been used for this task recently and have achieved state-of-the-art results. In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target. However, we found an important problem with the current MNs in performing the ASC task. Simply improving the attention mechanism will not solve it. The problem is referred to as target-sensitive sentiment, which means that the sentiment polarity of the (detected) context is dependent on the given target and it cannot be inferred from the context alone. To tackle this problem, we propose the target-sensitive memory networks (TMNs). Several alternative techniques are designed for the implementation of TMNs and their effectiveness is experimentally evaluated.",
}
@inproceedings{sharma-etal-2018-identifying,
    title = "Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification",
    author = "Sharma, Raksha  and
      Bhattacharyya, Pushpak  and
      Dandapat, Sandipan  and
      Bhatt, Himanshu Sharad",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1089",
    doi = "10.18653/v1/P18-1089",
    pages = "968--978",
    abstract = "Getting manually labeled data in each domain is always an expensive and a time consuming task. Cross-domain sentiment analysis has emerged as a demanding concept where a labeled source domain facilitates a sentiment classifier for an unlabeled target domain. However, polarity orientation (positive or negative) and the significance of a word to express an opinion often differ from one domain to another domain. Owing to these differences, cross-domain sentiment classification is still a challenging task. In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification. We present a novel approach based on χ2 test and cosine-similarity between context vector of words to identify polarity preserving significant words across domains. Furthermore, we show that a weighted ensemble of the classifiers enhances the cross-domain classification performance.",
}
@inproceedings{xu-etal-2018-unpaired,
    title = "Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach",
    author = "Xu, Jingjing  and
      Sun, Xu  and
      Zeng, Qi  and
      Zhang, Xiaodong  and
      Ren, Xuancheng  and
      Wang, Houfeng  and
      Li, Wenjie",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1090",
    doi = "10.18653/v1/P18-1090",
    pages = "979--988",
    abstract = "The goal of sentiment-to-sentiment {``}translation{''} is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.",
}
@inproceedings{pan-etal-2018-discourse,
    title = "Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference",
    author = "Pan, Boyuan  and
      Yang, Yazheng  and
      Zhao, Zhou  and
      Zhuang, Yueting  and
      Cai, Deng  and
      He, Xiaofei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1091",
    doi = "10.18653/v1/P18-1091",
    pages = "989--999",
    abstract = "Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is one of the most important problems in natural language processing. It requires to infer the logical relationship between two given sentences. While current approaches mostly focus on the interaction architectures of the sentences, in this paper, we propose to transfer knowledge from some important discourse markers to augment the quality of the NLI model. We observe that people usually use some discourse markers such as {``}so{''} or {``}but{''} to represent the logical relationship between two sentences. These words potentially have deep connections with the meanings of the sentences, thus can be utilized to help improve the representations of them. Moreover, we use reinforcement learning to optimize a new objective function with a reward defined by the property of the NLI datasets to make full use of the labels information. Experiments show that our method achieves the state-of-the-art performance on several large-scale datasets.",
}
@inproceedings{pavez-etal-2018-working,
    title = "Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module",
    author = "Pavez, Juan  and
      Allende, H{\'e}ctor  and
      Allende-Cid, H{\'e}ctor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1092",
    doi = "10.18653/v1/P18-1092",
    pages = "1000--1009",
    abstract = "During the last years, there has been a lot of interest in achieving some kind of complex reasoning using deep neural networks. To do that, models like Memory Networks (MemNNs) have combined external memory storages and attention mechanisms. These architectures, however, lack of more complex reasoning mechanisms that could allow, for instance, relational reasoning. Relation Networks (RNs), on the other hand, have shown outstanding results in relational reasoning tasks. Unfortunately, their computational cost grows quadratically with the number of memories, something prohibitive for larger problems. To solve these issues, we introduce the Working Memory Network, a MemNN architecture with a novel working memory storage and reasoning module. Our model retains the relational reasoning abilities of the RN while reducing its computational complexity from quadratic to linear. We tested our model on the text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than 0.5{\%}. Moreover, a simple ensemble of two of our models solves all 20 tasks in the joint version of the benchmark.",
}
@inproceedings{tay-etal-2018-reasoning,
    title = "Reasoning with Sarcasm by Reading In-Between",
    author = "Tay, Yi  and
      Luu, Anh Tuan  and
      Hui, Siu Cheung  and
      Su, Jian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1093",
    doi = "10.18653/v1/P18-1093",
    pages = "1010--1020",
    abstract = "Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.",
}
@inproceedings{bose-etal-2018-adversarial,
    title = "Adversarial Contrastive Estimation",
    author = "Bose, Avishek Joey  and
      Ling, Huan  and
      Cao, Yanshuai",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1094",
    doi = "10.18653/v1/P18-1094",
    pages = "1021--1032",
    abstract = "Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.",
}
@inproceedings{lin-etal-2018-adaptive,
    title = "Adaptive Scaling for Sparse Detection in Information Extraction",
    author = "Lin, Hongyu  and
      Lu, Yaojie  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1095",
    doi = "10.18653/v1/P18-1095",
    pages = "1033--1043",
    abstract = "This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes. These characteristics often result in deficient performance of neural network based detection models. In this paper, we propose \textit{adaptive scaling}, an algorithm which can handle the positive sparsity problem and directly optimize over F-measure via dynamic cost-sensitive learning. To this end, we borrow the idea of marginal utility from economics and propose a theoretical framework for instance importance measuring without introducing any additional hyper-parameters. Experiments show that our algorithm leads to a more effective and stable training of neural network based detection models.",
}
@inproceedings{ruder-plank-2018-strong,
    title = "Strong Baselines for Neural Semi-Supervised Learning under Domain Shift",
    author = "Ruder, Sebastian  and
      Plank, Barbara",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1096",
    doi = "10.18653/v1/P18-1096",
    pages = "1044--1054",
    abstract = "Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks for part-of-speech tagging and sentiment analysis are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state-of-the-art for NLP. Hence classic approaches constitute an important and strong baseline.",
}
@inproceedings{ge-etal-2018-fluency,
    title = "Fluency Boost Learning and Inference for Neural Grammatical Error Correction",
    author = "Ge, Tao  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1097",
    doi = "10.18653/v1/P18-1097",
    pages = "1055--1065",
    abstract = "Most of the neural sequence-to-sequence (seq2seq) models for grammatical error correction (GEC) have two limitations: (1) a seq2seq model may not be well generalized with only limited error-corrected data; (2) a seq2seq model may fail to completely correct a sentence with multiple errors through normal seq2seq inference. We attempt to address these limitations by proposing a fluency boost learning and inference mechanism. Fluency boosting learning generates fluency-boost sentence pairs during training, enabling the error correction model to learn how to improve a sentence{'}s fluency from more instances, while fluency boosting inference allows the model to correct a sentence incrementally with multiple inference steps until the sentence{'}s fluency stops increasing. Experiments show our approaches improve the performance of seq2seq models for GEC, achieving state-of-the-art results on both CoNLL-2014 and JFLEG benchmark datasets.",
}
@inproceedings{xie-xing-2018-neural,
    title = "A Neural Architecture for Automated {ICD} Coding",
    author = "Xie, Pengtao  and
      Xing, Eric",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1098",
    doi = "10.18653/v1/P18-1098",
    pages = "1066--1076",
    abstract = "The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases. Medical coding {--} which assigns a subset of ICD codes to a patient visit {--} is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error prone. In this paper, we build a neural architecture for automated coding. It takes the diagnosis descriptions (DDs) of a patient as inputs and selects the most relevant ICD codes. This architecture contains four major ingredients: (1) tree-of-sequences LSTM encoding of code descriptions (CDs), (2) adversarial learning for reconciling the different writing styles of DDs and CDs, (3) isotonic constraints for incorporating the importance order among the assigned codes, and (4) attentional matching for performing many-to-one and one-to-many mappings from DDs to CDs. We demonstrate the effectiveness of the proposed methods on a clinical datasets with 59K patient visits.",
}
@inproceedings{alam-etal-2018-domain,
    title = "Domain Adaptation with Adversarial Training and Graph Embeddings",
    author = "Alam, Firoj  and
      Joty, Shafiq  and
      Imran, Muhammad",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1099",
    doi = "10.18653/v1/P18-1099",
    pages = "1077--1087",
    abstract = "The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.",
}
@inproceedings{jin-etal-2018-tdnn,
    title = "{TDNN}: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring",
    author = "Jin, Cancan  and
      He, Ben  and
      Hui, Kai  and
      Sun, Le",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1100",
    doi = "10.18653/v1/P18-1100",
    pages = "1088--1097",
    abstract = "Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for non-target prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.",
}
@inproceedings{zhao-etal-2018-unsupervised,
    title = "Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation",
    author = "Zhao, Tiancheng  and
      Lee, Kyusong  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1101",
    doi = "10.18653/v1/P18-1101",
    pages = "1098--1107",
    abstract = "The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.",
}
@inproceedings{zhang-etal-2018-learning,
    title = "Learning to Control the Specificity in Neural Response Generation",
    author = "Zhang, Ruqing  and
      Guo, Jiafeng  and
      Fan, Yixing  and
      Lan, Yanyan  and
      Xu, Jun  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1102",
    doi = "10.18653/v1/P18-1102",
    pages = "1108--1117",
    abstract = "In conversation, a general response (e.g., {``}I don{'}t know{''}) could correspond to a large variety of input utterances. Previous generative conversational models usually employ a single model to learn the relationship between different utterance-response pairs, thus tend to favor general and trivial responses which appear frequently. To address this problem, we propose a novel controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. Specifically, we introduce an explicit specificity control variable into a sequence-to-sequence model, which interacts with the usage representation of words through a Gaussian Kernel layer, to guide the model to generate responses at different specificity levels. We describe two ways to acquire distant labels for the specificity control variable in learning. Empirical studies show that our model can significantly outperform the state-of-the-art response generation models under both automatic and human evaluations.",
}
@inproceedings{zhou-etal-2018-multi,
    title = "Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network",
    author = "Zhou, Xiangyang  and
      Li, Lu  and
      Dong, Daxiang  and
      Liu, Yi  and
      Chen, Ying  and
      Zhao, Wayne Xin  and
      Yu, Dianhai  and
      Wu, Hua",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1103",
    doi = "10.18653/v1/P18-1103",
    pages = "1118--1127",
    abstract = "Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.",
}
@inproceedings{zhou-wang-2018-mojitalk,
    title = "{M}oji{T}alk: Generating Emotional Responses at Scale",
    author = "Zhou, Xianda  and
      Wang, William Yang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1104",
    doi = "10.18653/v1/P18-1104",
    pages = "1128--1137",
    abstract = "Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels. Additionally, explicitly controlling the emotion and sentiment of generated text is also difficult. In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis. We collect a large corpus of Twitter conversations that include emojis in the response and assume the emojis convey the underlying emotions of the sentence. We investigate several conditional variational autoencoders training on these conversations, which allow us to use emojis to control the emotion of the generated text. Experimentally, we show in our quantitative and qualitative analyses that the proposed models can successfully generate high-quality abstractive conversation responses in accordance with designated emotions.",
}
@inproceedings{kobayashi-tanaka-ishii-2018-taylors,
    title = "{T}aylor{'}s law for Human Linguistic Sequences",
    author = "Kobayashi, Tatsuru  and
      Tanaka-Ishii, Kumiko",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1105",
    doi = "10.18653/v1/P18-1105",
    pages = "1138--1148",
    abstract = "Taylor{'}s law describes the fluctuation characteristics underlying a system in which the variance of an event within a time span grows by a power law with respect to the mean. Although Taylor{'}s law has been applied in many natural and social systems, its application for language has been scarce. This article describes a new way to quantify Taylor{'}s law in natural language and conducts Taylor analysis of over 1100 texts across 14 languages. We found that the Taylor exponents of natural language written texts exhibit almost the same value. The exponent was also compared for other language-related data, such as the child-directed speech, music, and programming languages. The results show how the Taylor exponent serves to quantify the fundamental structural complexity underlying linguistic time series. The article also shows the applicability of these findings in evaluating language models.",
}
@inproceedings{kodner-cerezo-falco-2018-framework,
    title = "A Framework for Representing Language Acquisition in a Population Setting",
    author = "Kodner, Jordan  and
      Cerezo Falco, Christopher",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1106",
    doi = "10.18653/v1/P18-1106",
    pages = "1149--1159",
    abstract = "Language variation and change are driven both by individuals{'} internal cognitive processes and by the social structures through which language propagates. A wide range of computational frameworks have been proposed to connect these drivers. We compare the strengths and weaknesses of existing approaches and propose a new analytic framework which combines previous network models{'} ability to capture realistic social structure with practically and more elegant computational properties. The framework privileges the process of language acquisition and embeds learners in a social network but is modular so that population structure can be combined with different acquisition models. We demonstrate two applications for the framework: a test of practical concerns that arise when modeling acquisition in a population setting and an application of the framework to recent work on phonological mergers in progress.",
}
@inproceedings{born-sarkar-2018-prefix,
    title = "Prefix Lexicalization of Synchronous {CFG}s using Synchronous {TAG}",
    author = "Born, Logan  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1107",
    doi = "10.18653/v1/P18-1107",
    pages = "1160--1170",
    abstract = "We show that an epsilon-free, chain-free synchronous context-free grammar (SCFG) can be converted into a weakly equivalent synchronous tree-adjoining grammar (STAG) which is prefix lexicalized. This transformation at most doubles the grammar{'}s rank and cubes its size, but we show that in practice the size increase is only quadratic. Our results extend Greibach normal form from CFGs to SCFGs and prove new formal properties about SCFG, a formalism with many applications in natural language processing.",
}
@inproceedings{shen-etal-2018-straight,
    title = "Straight to the Tree: Constituency Parsing with Neural Syntactic Distance",
    author = "Shen, Yikang  and
      Lin, Zhouhan  and
      Jacob, Athul Paul  and
      Sordoni, Alessandro  and
      Courville, Aaron  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1108",
    doi = "10.18653/v1/P18-1108",
    pages = "1171--1180",
    abstract = "In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for each split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin.",
}
@inproceedings{zhao-etal-2018-gaussian,
    title = "{G}aussian Mixture Latent Vector Grammars",
    author = "Zhao, Yanpeng  and
      Zhang, Liwen  and
      Tu, Kewei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1109",
    doi = "10.18653/v1/P18-1109",
    pages = "1181--1189",
    abstract = "We introduce Latent Vector Grammars (LVeGs), a new framework that extends latent variable grammars such that each nonterminal symbol is associated with a continuous vector space representing the set of (infinitely many) subtypes of the nonterminal. We show that previous models such as latent variable grammars and compositional vector grammars can be interpreted as special cases of LVeGs. We then present Gaussian Mixture LVeGs (GM-LVeGs), a new special case of LVeGs that uses Gaussian mixtures to formulate the weights of production rules over subtypes of nonterminals. A major advantage of using Gaussian mixtures is that the partition function and the expectations of subtype rules can be computed using an extension of the inside-outside algorithm, which enables efficient inference and learning. We apply GM-LVeGs to part-of-speech tagging and constituency parsing and show that GM-LVeGs can achieve competitive accuracies.",
}
@inproceedings{joshi-etal-2018-extending,
    title = "Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples",
    author = "Joshi, Vidur  and
      Peters, Matthew  and
      Hopkins, Mark",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1110",
    doi = "10.18653/v1/P18-1110",
    pages = "1190--1199",
    abstract = "We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train a parser on the Wall Street Journal alone that achieves over 90{\%} F1 on the Brown corpus. For more syntactically distant domains, we provide a simple way to adapt a parser using only dozens of partial annotations. For instance, we increase the percentage of error-free geometry-domain parses in a held-out set from 45{\%} to 73{\%} using approximately five dozen training examples. In the process, we demonstrate a new state-of-the-art single model result on the Wall Street Journal test set of 94.3{\%}. This is an absolute increase of 1.7{\%} over the previous state-of-the-art of 92.6{\%}.",
}
@inproceedings{shwartz-dagan-2018-paraphrase,
    title = "Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations",
    author = "Shwartz, Vered  and
      Dagan, Ido",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1111",
    doi = "10.18653/v1/P18-1111",
    pages = "1200--1211",
    abstract = "Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.",
}
@inproceedings{tkachenko-etal-2018-searching,
    title = "Searching for the {X}-Factor: Exploring Corpus Subjectivity for Word Embeddings",
    author = "Tkachenko, Maksim  and
      Chia, Chong Cher  and
      Lauw, Hady",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1112",
    doi = "10.18653/v1/P18-1112",
    pages = "1212--1221",
    abstract = "We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.",
}
@inproceedings{mao-etal-2018-word,
    title = "Word Embedding and {W}ord{N}et Based Metaphor Identification and Interpretation",
    author = "Mao, Rui  and
      Lin, Chenghua  and
      Guerin, Frank",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1113",
    doi = "10.18653/v1/P18-1113",
    pages = "1222--1231",
    abstract = "Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.",
}
@inproceedings{xu-etal-2018-incorporating,
    title = "Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings",
    author = "Xu, Yang  and
      Liu, Jiawei  and
      Yang, Wei  and
      Huang, Liusheng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1114",
    doi = "10.18653/v1/P18-1114",
    pages = "1232--1242",
    abstract = "Traditional word embedding approaches learn semantic information at word level while ignoring the meaningful internal structures of words like morphemes. Furthermore, existing morphology-based models directly incorporate morphemes to train word embeddings, but still neglect the latent meanings of morphemes. In this paper, we explore to employ the latent meanings of morphological compositions of words to train and enhance word embeddings. Based on this purpose, we propose three Latent Meaning Models (LMMs), named LMM-A, LMM-S and LMM-M respectively, which adopt different strategies to incorporate the latent meanings of morphemes during the training process. Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models. The results demonstrate that our models outperform the baselines on five word similarity datasets. On Wordsim-353 and RG-65 datasets, our models nearly achieve 5{\%} and 7{\%} gains over the classic CBOW model, respectively. For the syntactic analogy and text classification tasks, our models also surpass all the baselines including a morphology-based model.",
}
@inproceedings{schulz-etal-2018-stochastic,
    title = "A Stochastic Decoder for Neural Machine Translation",
    author = "Schulz, Philip  and
      Aziz, Wilker  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1115",
    doi = "10.18653/v1/P18-1115",
    pages = "1243--1252",
    abstract = "The process of translation is ambiguous, in that there are typically many valid translations for a given sentence. This gives rise to significant variation in parallel corpora, however, most current models of machine translation do not account for this variation, instead treating the problem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to account for local lexical and syntactic variation in parallel corpora. We provide an in-depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on several different language pairs demonstrate that the model consistently improves over strong baselines.",
}
@inproceedings{ma-etal-2018-forest,
    title = "Forest-Based Neural Machine Translation",
    author = "Ma, Chunpeng  and
      Tamura, Akihiro  and
      Utiyama, Masao  and
      Zhao, Tiejun  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1116",
    doi = "10.18653/v1/P18-1116",
    pages = "1253--1263",
    abstract = "Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-sequence NMT model). The BLEU score of the proposed method is higher than that of the sequence-to-sequence NMT, tree-based NMT, and forest-based SMT systems.",
}
@inproceedings{voita-etal-2018-context,
    title = "Context-Aware Neural Machine Translation Learns Anaphora Resolution",
    author = "Voita, Elena  and
      Serdyukov, Pavel  and
      Sennrich, Rico  and
      Titov, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1117",
    doi = "10.18653/v1/P18-1117",
    pages = "1264--1274",
    abstract = "Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).",
}
@inproceedings{maruf-haffari-2018-document,
    title = "Document Context Neural Machine Translation with Memory Networks",
    author = "Maruf, Sameen  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1118",
    doi = "10.18653/v1/P18-1118",
    pages = "1275--1284",
    abstract = "We present a document-level neural machine translation model which takes both source and target document context into account using memory networks. We model the problem as a structured prediction problem with interdependencies among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model end-to-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.",
}
@inproceedings{gritta-etal-2018-melbourne,
    title = "Which {M}elbourne? Augmenting Geocoding with Maps",
    author = "Gritta, Milan  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1119",
    doi = "10.18653/v1/P18-1119",
    pages = "1285--1296",
    abstract = "The purpose of text geolocation is to associate geographic information contained in a document with a set (or sets) of coordinates, either implicitly by using linguistic features and/or explicitly by using geographic metadata combined with heuristics. We introduce a geocoder (location mention disambiguator) that achieves state-of-the-art (SOTA) results on three diverse datasets by exploiting the implicit lexical clues. Moreover, we propose a new method for systematic encoding of geographic metadata to generate two distinct views of the same text. To that end, we introduce the Map Vector (MapVec), a sparse representation obtained by plotting prior geographic probabilities, derived from population figures, on a World Map. We then integrate the implicit (language) and explicit (map) features to significantly improve a range of metrics. We also introduce an open-source dataset for geoparsing of news events covering global disease outbreaks and epidemics to help future evaluation in geoparsing.",
}
@inproceedings{jiang-riloff-2018-learning,
    title = "Learning Prototypical Goal Activities for Locations",
    author = "Jiang, Tianyu  and
      Riloff, Ellen",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1120",
    doi = "10.18653/v1/P18-1120",
    pages = "1297--1307",
    abstract = "People go to different places to engage in activities that reflect their goals. For example, people go to restaurants to eat, libraries to study, and churches to pray. We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). Our research aims to learn goal-acts for specific locations using a text corpus and semi-supervised learning. First, we extract activities and locations that co-occur in goal-oriented syntactic patterns. Next, we create an activity profile matrix and apply a semi-supervised label propagation algorithm to iteratively revise the activity strengths for different locations using a small set of labeled data. We show that this approach outperforms several baseline methods when judged against goal-acts identified by human annotators.",
}
@inproceedings{li-etal-2018-guess,
    title = "Guess Me if You Can: Acronym Disambiguation for Enterprises",
    author = "Li, Yang  and
      Zhao, Bo  and
      Fuxman, Ariel  and
      Tao, Fangbo",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1121",
    doi = "10.18653/v1/P18-1121",
    pages = "1308--1317",
    abstract = "Acronyms are abbreviations formed from the initial components of words or phrases. In enterprises, people often use acronyms to make communications more efficient. However, acronyms could be difficult to understand for people who are not familiar with the subject matter (new employees, etc.), thereby affecting productivity. To alleviate such troubles, we study how to automatically resolve the true meanings of acronyms in a given context. Acronym disambiguation for enterprises is challenging for several reasons. First, acronyms may be highly ambiguous since an acronym used in the enterprise could have multiple internal and external meanings. Second, there are usually no comprehensive knowledge bases such as Wikipedia available in enterprises. Finally, the system should be generic to work for any enterprise. In this work we propose an end-to-end framework to tackle all these challenges. The framework takes the enterprise corpus as input and produces a high-quality acronym disambiguation system as output. Our disambiguation models are trained via distant supervised learning, without requiring any manually labeled training examples. Therefore, our proposed framework can be deployed to any enterprise to support high-quality acronym disambiguation. Experimental results on real world data justified the effectiveness of our system.",
}
@inproceedings{ning-etal-2018-multi,
    title = "A Multi-Axis Annotation Scheme for Event Temporal Relations",
    author = "Ning, Qiang  and
      Wu, Hao  and
      Roth, Dan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1122",
    doi = "10.18653/v1/P18-1122",
    pages = "1318--1328",
    abstract = "Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition. This paper proposes a new multi-axis modeling to better capture the temporal structure of events. In addition, we identify that event end-points are a major source of confusion in annotation, so we also propose to annotate TempRels based on start-points only. A pilot expert annotation effort using the proposed scheme shows significant improvement in IAA from the conventional 60{'}s to 80{'}s (Cohen{'}s Kappa). This better-defined annotation scheme further enables the use of crowdsourcing to alleviate the labor intensity for each annotator. We hope that this work can foster more interesting studies towards event understanding.",
}
@inproceedings{pandey-etal-2018-exemplar,
    title = "Exemplar Encoder-Decoder for Neural Conversation Generation",
    author = "Pandey, Gaurav  and
      Contractor, Danish  and
      Kumar, Vineet  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1123",
    doi = "10.18653/v1/P18-1123",
    pages = "1329--1338",
    abstract = "In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize \textit{similar} examples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model and the corresponding responses are used by our decoder to generate the ground truth response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. As a result, our model learns to assign higher similarity scores to those retrieved contexts whose responses are crucial for generating the final response. We present detailed experiments on two large data sets and we find that our method out-performs state of the art sequence to sequence generative models on several recently proposed evaluation metrics.",
}
@inproceedings{gur-etal-2018-dialsql,
    title = "{D}ial{SQL}: Dialogue Based Structured Query Generation",
    author = "Gur, Izzeddin  and
      Yavuz, Semih  and
      Su, Yu  and
      Yan, Xifeng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1124",
    doi = "10.18653/v1/P18-1124",
    pages = "1339--1349",
    abstract = "The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to structured queries. However, further improvement of the existing approaches turns out to be quite challenging. Rather than solely relying on algorithmic innovations, in this work, we introduce DialSQL, a dialogue-based structured query generation framework that leverages human intelligence to boost the performance of existing algorithms via user interaction. DialSQL is capable of identifying potential errors in a generated SQL query and asking users for validation via simple multi-choice questions. User feedback is then leveraged to revise the query. We design a generic simulator to bootstrap synthetic training dialogues and evaluate the performance of DialSQL on the WikiSQL dataset. Using SQLNet as a black box query generation tool, DialSQL improves its performance from 61.3{\%} to 69.0{\%} using only 2.4 validation questions per dialogue.",
}
@inproceedings{zhang-etal-2018-conversations,
    title = "Conversations Gone Awry: Detecting Early Signs of Conversational Failure",
    author = "Zhang, Justine  and
      Chang, Jonathan  and
      Danescu-Niculescu-Mizil, Cristian  and
      Dixon, Lucas  and
      Hua, Yiqing  and
      Taraborelli, Dario  and
      Thain, Nithum",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1125",
    doi = "10.18653/v1/P18-1125",
    pages = "1350--1361",
    abstract = "One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very start of a conversation whether it will get out of hand. As opposed to detecting undesirable behavior after the fact, this task aims to enable early, actionable prediction at a time when the conversation might still be salvaged. To this end, we develop a framework for capturing pragmatic devices{---}such as politeness strategies and rhetorical prompts{---}used to start a conversation, and analyze their relation to its future trajectory. Applying this framework in a controlled setting, we demonstrate the feasibility of detecting early warning signs of antisocial behavior in online discussions.",
}
@inproceedings{cifka-bojar-2018-bleu,
    title = "Are {BLEU} and Meaning Representation in Opposition?",
    author = "C{\'\i}fka, Ond{\v{r}}ej  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1126",
    doi = "10.18653/v1/P18-1126",
    pages = "1362--1371",
    abstract = "One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted. We propose several variations of the attentive NMT architecture bringing this meeting point back. Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks.",
}
@inproceedings{choshen-abend-2018-automatic,
    title = "Automatic Metric Validation for Grammatical Error Correction",
    author = "Choshen, Leshem  and
      Abend, Omri",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1127",
    doi = "10.18653/v1/P18-1127",
    pages = "1372--1382",
    abstract = "Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings. However, such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement. We propose MAEGE, an automatic methodology for GEC metric validation, that overcomes many of the difficulties in the existing methodology. Experiments with MAEGE shed a new light on metric quality, showing for example that the standard $M^2$ metric fares poorly on corpus-level ranking. Moreover, we use MAEGE to perform a detailed analysis of metric behavior, showing that some types of valid edits are consistently penalized by existing metrics.",
}
@inproceedings{dror-etal-2018-hitchhikers,
    title = "The Hitchhiker{'}s Guide to Testing Statistical Significance in Natural Language Processing",
    author = "Dror, Rotem  and
      Baumer, Gili  and
      Shlomov, Segev  and
      Reichart, Roi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1128",
    doi = "10.18653/v1/P18-1128",
    pages = "1383--1392",
    abstract = "Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/ theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research. Based on this discussion we propose a simple practical protocol for statistical significance test selection in NLP setups and accompany this protocol with a brief survey of the most relevant tests. We then survey recent empirical papers published in ACL and TACL during 2017 and show that while our community assigns great value to experimental results, statistical significance testing is often ignored or misused. We conclude with a brief discussion of open issues that should be properly addressed so that this important tool can be applied. in NLP research in a statistically sound manner.",
}
@inproceedings{liu-etal-2018-distilling,
    title = "Distilling Knowledge for Search-based Structured Prediction",
    author = "Liu, Yijia  and
      Che, Wanxiang  and
      Zhao, Huaipeng  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1129",
    doi = "10.18653/v1/P18-1129",
    pages = "1393--1402",
    abstract = "Many natural language processing tasks can be modeled into structured prediction and solved as a search problem. In this paper, we distill an ensemble of multiple models trained with different initialization into a single model. In addition to learning to match the ensemble{'}s probability output on the reference states, we also use the ensemble to explore the search space and learn from the encountered states in the exploration. Experimental results on two typical search-based structured prediction tasks {--} transition-based dependency parsing and neural machine translation show that distillation can effectively improve the single model{'}s performance and the final model achieves improvements of 1.32 in LAS and 2.65 in BLEU score on these two tasks respectively over strong baselines and it outperforms the greedy structured prediction models in previous literatures.",
}
@inproceedings{ma-etal-2018-stack,
    title = "Stack-Pointer Networks for Dependency Parsing",
    author = "Ma, Xuezhe  and
      Hu, Zecong  and
      Liu, Jingzhou  and
      Peng, Nanyun  and
      Neubig, Graham  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1130",
    doi = "10.18653/v1/P18-1130",
    pages = "1403--1414",
    abstract = "We introduce a novel architecture for dependency parsing: stack-pointer networks (StackPtr). Combining pointer networks (Vinyals et al., 2015) with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the status of the depth-first search and the pointer networks select one child for the word at the top of the stack at each step. The StackPtr parser benefits from the information of whole sentence and all previously derived subtree structures, and removes the left-to-right restriction in classical transition-based parsers. Yet the number of steps for building any (non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm with $O(n^2)$ time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-the-art performances on 21 of them",
}
@inproceedings{blodgett-etal-2018-twitter,
    title = "{T}witter {U}niversal {D}ependency Parsing for {A}frican-{A}merican and Mainstream {A}merican {E}nglish",
    author = "Blodgett, Su Lin  and
      Wei, Johnny  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1131",
    doi = "10.18653/v1/P18-1131",
    pages = "1415--1425",
    abstract = "Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools. We broaden English dependency parsing to handle social media English, particularly social media African-American English (AAE), by developing and annotating a new dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework. We describe our standards for handling Twitter- and AAE-specific features and evaluate a variety of cross-domain strategies for improving parsing with no, or very little, in-domain labeled data, including a new data synthesis approach. We analyze these methods{'} impact on performance disparities between AAE and Mainstream American English tweets, and assess parsing accuracy for specific AAE lexical and syntactic features. Our annotated data and a parsing model are available at: \url{http://slanglab.cs.umass.edu/TwitterAAE/}.",
}
@inproceedings{kuncoro-etal-2018-lstms,
    title = "{LSTM}s Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better",
    author = "Kuncoro, Adhiguna  and
      Dyer, Chris  and
      Hale, John  and
      Yogatama, Dani  and
      Clark, Stephen  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1132",
    doi = "10.18653/v1/P18-1132",
    pages = "1426--1436",
    abstract = "Language exhibits hierarchical structure, but recent work using a subject-verb agreement diagnostic argued that state-of-the-art language models, LSTMs, fail to learn long-range syntax sensitive dependencies. Using the same diagnostic, we show that, in fact, LSTMs do succeed in learning such dependencies{---}provided they have enough capacity. We then explore whether models that have access to explicit syntactic information learn agreement more effectively, and how the way in which this structural information is incorporated into the model impacts performance. We find that the mere presence of syntactic information does not improve accuracy, but when model architecture is determined by syntax, number agreement is improved. Further, we find that the choice of how syntactic structure is built affects how well number agreement is learned: top-down construction outperforms left-corner and bottom-up variants in capturing non-local structural dependencies.",
}
@inproceedings{lei-etal-2018-sequicity,
    title = "{S}equicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures",
    author = "Lei, Wenqiang  and
      Jin, Xisen  and
      Kan, Min-Yen  and
      Ren, Zhaochun  and
      He, Xiangnan  and
      Yin, Dawei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1133",
    doi = "10.18653/v1/P18-1133",
    pages = "1437--1447",
    abstract = "Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.",
}
@inproceedings{xu-hu-2018-end,
    title = "An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking",
    author = "Xu, Puyang  and
      Hu, Qi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1134",
    doi = "10.18653/v1/P18-1134",
    pages = "1448--1457",
    abstract = "We highlight a practical yet rarely discussed problem in dialogue state tracking (DST), namely handling unknown slot values. Previous approaches generally assume predefined candidate lists and thus are not designed to output unknown values, especially when the spoken language understanding (SLU) module is absent as in many end-to-end (E2E) systems. We describe in this paper an E2E architecture based on the pointer network (PtrNet) that can effectively extract unknown slot values while still obtains state-of-the-art accuracy on the standard DSTC2 benchmark. We also provide extensive empirical evidence to show that tracking unknown values can be challenging and our approach can bring significant improvement with the help of an effective feature dropout technique.",
}
@inproceedings{zhong-etal-2018-global,
    title = "Global-Locally Self-Attentive Encoder for Dialogue State Tracking",
    author = "Zhong, Victor  and
      Xiong, Caiming  and
      Socher, Richard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1135",
    doi = "10.18653/v1/P18-1135",
    pages = "1458--1467",
    abstract = "Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to shares parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states. GLAD obtains 88.3{\%} joint goal accuracy and 96.4{\%} request accuracy on the WoZ state tracking task, outperforming prior work by 3.9{\%} and 4.8{\%}. On the DSTC2 task, our model obtains 74.7{\%} joint goal accuracy and 97.3{\%} request accuracy, outperforming prior work by 1.3{\%} and 0.8{\%}",
}
@inproceedings{madotto-etal-2018-mem2seq,
    title = "{M}em2{S}eq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems",
    author = "Madotto, Andrea  and
      Wu, Chien-Sheng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1136",
    doi = "10.18653/v1/P18-1136",
    pages = "1468--1478",
    abstract = "End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating knowledge bases. In this paper, we propose a novel yet simple end-to-end differentiable model called memory-to-sequence (Mem2Seq) to address this issue. Mem2Seq is the first neural generative model that combines the multi-hop attention over memories with the idea of pointer network. We empirically show how Mem2Seq controls each generation step, and how its multi-hop attention mechanism helps in learning correlations between memories. In addition, our model is quite general without complicated task-specific designs. As a result, we show that Mem2Seq can be trained faster and attain the state-of-the-art performance on three different task-oriented dialog datasets.",
}
@inproceedings{zhang-etal-2018-tailored,
    title = "Tailored Sequence to Sequence Models to Different Conversation Scenarios",
    author = "Zhang, Hainan  and
      Lan, Yanyan  and
      Guo, Jiafeng  and
      Xu, Jun  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1137",
    doi = "10.18653/v1/P18-1137",
    pages = "1479--1488",
    abstract = "Sequence to sequence (Seq2Seq) models have been widely used for response generation in the area of conversation. However, the requirements for different conversation scenarios are distinct. For example, customer service requires the generated responses to be specific and accurate, while chatbot prefers diverse responses so as to attract different users. The current Seq2Seq model fails to meet these diverse requirements, by using a general average likelihood as the optimization criteria. As a result, it usually generates safe and commonplace responses, such as {`}I don{'}t know{'}. In this paper, we propose two tailored optimization criteria for Seq2Seq to different conversation scenarios, i.e., the maximum generated likelihood for specific-requirement scenario, and the conditional value-at-risk for diverse-requirement scenario. Experimental results on the Ubuntu dialogue corpus (Ubuntu service scenario) and Chinese Weibo dataset (social chatbot scenario) show that our proposed models not only satisfies diverse requirements for different scenarios, but also yields better performances against traditional Seq2Seq models in terms of both metric-based and human evaluations.",
}
@inproceedings{liu-etal-2018-knowledge,
    title = "Knowledge Diffusion for Neural Dialogue Generation",
    author = "Liu, Shuman  and
      Chen, Hongshen  and
      Ren, Zhaochun  and
      Feng, Yang  and
      Liu, Qun  and
      Yin, Dawei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1138",
    doi = "10.18653/v1/P18-1138",
    pages = "1489--1498",
    abstract = "End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation. This method can not only match the relevant facts for the input utterance but diffuse them to similar entities. With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base. Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats. The experiment results also show that our model outperforms competitive baseline models significantly.",
}
@inproceedings{ke-etal-2018-generating,
    title = "Generating Informative Responses with Controlled Sentence Function",
    author = "Ke, Pei  and
      Guan, Jian  and
      Huang, Minlie  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1139",
    doi = "10.18653/v1/P18-1139",
    pages = "1499--1508",
    abstract = "Sentence function is a significant factor to achieve the purpose of the speaker, which, however, has not been touched in large-scale conversation generation so far. In this paper, we present a model to generate informative responses with controlled sentence function. Our model utilizes a continuous latent variable to capture various word patterns that realize the expected sentence function, and introduces a type controller to deal with the compatibility of controlling sentence function and generating informative content. Conditioned on the latent variable, the type controller determines the type (i.e., function-related, topic, and ordinary word) of a word to be generated at each decoding position. Experiments show that our model outperforms state-of-the-art baselines, and it has the ability to generate responses with both controlled sentence function and informative content.",
}
@inproceedings{shi-yu-2018-sentiment,
    title = "Sentiment Adaptive End-to-End Dialog Systems",
    author = "Shi, Weiyan  and
      Yu, Zhou",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1140",
    doi = "10.18653/v1/P18-1140",
    pages = "1509--1519",
    abstract = "End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating. However, current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. We incorporated user sentiment information in both supervised and reinforcement learning settings. In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task. This work is the first attempt to incorporate multimodal user information in the adaptive end-to-end dialog system training framework and attained state-of-the-art performance.",
}
@inproceedings{dufter-etal-2018-embedding,
    title = "Embedding Learning Through Multilingual Concept Induction",
    author = {Dufter, Philipp  and
      Zhao, Mengjie  and
      Schmitt, Martin  and
      Fraser, Alexander  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1141",
    doi = "10.18653/v1/P18-1141",
    pages = "1520--1530",
    abstract = "We present a new method for estimating vector space representations of words: embedding learning by concept induction. We test this method on a highly parallel corpus and learn semantic representations of words in 1259 different languages in a single common space. An extensive experimental evaluation on crosslingual word similarity and sentiment analysis indicates that concept-based multilingual embedding learning performs better than previous approaches.",
}
@inproceedings{ponti-etal-2018-isomorphic,
    title = "Isomorphic Transfer of Syntactic Structures in Cross-Lingual {NLP}",
    author = "Ponti, Edoardo Maria  and
      Reichart, Roi  and
      Korhonen, Anna  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1142",
    doi = "10.18653/v1/P18-1142",
    pages = "1531--1542",
    abstract = "The transfer or share of knowledge between languages is a potential solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, demonstrating the importance of syntactic structure compatibility for boosting cross-lingual transfer in NLP.",
}
@inproceedings{pratapa-etal-2018-language,
    title = "Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data",
    author = "Pratapa, Adithya  and
      Bhat, Gayatri  and
      Choudhury, Monojit  and
      Sitaram, Sunayana  and
      Dandapat, Sandipan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1143",
    doi = "10.18653/v1/P18-1143",
    pages = "1543--1553",
    abstract = "Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language. We present a computational technique for creation of grammatically valid artificial CM data based on the Equivalence Constraint Theory. We show that when training examples are sampled appropriately from this synthetic data and presented in certain order (aka training curriculum) along with monolingual and real CM data, it can significantly reduce the perplexity of an RNN-based language model. We also show that randomly generated CM data does not help in decreasing the perplexity of the LMs.",
}
@inproceedings{zhang-yang-2018-chinese,
    title = "{C}hinese {NER} Using Lattice {LSTM}",
    author = "Zhang, Yue  and
      Yang, Jie",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1144",
    doi = "10.18653/v1/P18-1144",
    pages = "1554--1564",
    abstract = "We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.",
}
@inproceedings{lin-etal-2018-nugget,
    title = "Nugget Proposal Networks for {C}hinese Event Detection",
    author = "Lin, Hongyu  and
      Lu, Yaojie  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1145",
    doi = "10.18653/v1/P18-1145",
    pages = "1565--1574",
    abstract = "Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese. In this paper, we propose Nugget Proposal Networks (NPNs), which can solve the word-trigger mismatch problem by directly proposing entire trigger nuggets centered at each character regardless of word boundaries. Specifically, NPNs perform event detection in a character-wise paradigm, where a hybrid representation for each character is first learned to capture both structural and semantic information from both characters and words. Then based on learned representations, trigger nuggets are proposed and categorized by exploiting character compositional structures of Chinese event triggers. Experiments on both ACE2005 and TAC KBP 2017 datasets show that NPNs significantly outperform the state-of-the-art methods.",
}
@inproceedings{nimishakavi-etal-2018-higher,
    title = "Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation",
    author = "Nimishakavi, Madhav  and
      Gupta, Manish  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1146",
    doi = "10.18653/v1/P18-1146",
    pages = "1575--1584",
    abstract = "Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused only on binary RSI, i.e., inducing only the subject and object type signatures per relation. However, in practice, many relations are high-order, i.e., they have more than two arguments and inducing type signatures of all arguments is necessary. For example, in the sports domain, inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is more informative than inducing just win(WinningPlayer, OpponentPlayer). We refer to this problem as Higher-order Relation Schema Induction (HRSI). In this paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a novel framework for the HRSI problem. To the best of our knowledge, this is the first attempt at inducing higher-order relation schemata from unlabeled text. Using the experimental analysis on three real world datasets we show how TFBA helps in dealing with sparsity and induce higher-order schemata.",
}
@inproceedings{glass-gliozzo-2018-discovering,
    title = "Discovering Implicit Knowledge with Unary Relations",
    author = "Glass, Michael  and
      Gliozzo, Alfio",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1147",
    doi = "10.18653/v1/P18-1147",
    pages = "1585--1594",
    abstract = "State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence. However, the vast majority of relations are either implicit or not sententially localized. This is a major problem for Knowledge Base Population, severely limiting recall. In this paper we propose a new methodology to identify relations between two entities, consisting of detecting a very large number of unary relations, and using them to infer missing entities. We describe a deep learning architecture able to learn thousands of such relations very efficiently by using a common deep learning based representation. Our approach largely outperforms state of the art relation extraction technology on a newly introduced web scale knowledge base population benchmark, that we release to the research community.",
}
@inproceedings{le-titov-2018-improving,
    title = "Improving Entity Linking by Modeling Latent Relations between Mentions",
    author = "Le, Phong  and
      Titov, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1148",
    doi = "10.18653/v1/P18-1148",
    pages = "1595--1604",
    abstract = "Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base. Entity linking systems often exploit relations between textual mentions in a document (e.g., coreference) to decide if the linking decisions are compatible. Unlike previous approaches, which relied on supervised systems or heuristics to predict these relations, we treat relations as latent variables in our neural entity-linking model. We induce the relations without any supervision while optimizing the entity-linking system in an end-to-end fashion. Our multi-relational model achieves the best reported scores on the standard benchmark (AIDA-CoNLL) and substantially outperforms its relation-agnostic version. Its training also converges much faster, suggesting that the injected structural bias helps to explain regularities in the training data.",
}
@inproceedings{vashishth-etal-2018-dating,
    title = "Dating Documents using Graph Convolution Networks",
    author = "Vashishth, Shikhar  and
      Dasgupta, Shib Sankar  and
      Ray, Swayambhu Nath  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1149",
    doi = "10.18653/v1/P18-1149",
    pages = "1605--1615",
    abstract = "Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web. Document Dating is a challenging problem which requires inference over the temporal structure of the document. Prior document dating systems have largely relied on handcrafted features while ignoring such document-internal structures. In this paper, we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way. To the best of our knowledge, this is the first application of deep learning for the problem of document dating. Through extensive experiments on real-world datasets, we find that NeuralDater significantly outperforms state-of-the-art baseline by 19{\%} absolute (45{\%} relative) accuracy points.",
}
@inproceedings{song-etal-2018-graph,
    title = "A Graph-to-Sequence Model for {AMR}-to-Text Generation",
    author = "Song, Linfeng  and
      Zhang, Yue  and
      Wang, Zhiguo  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1150",
    doi = "10.18653/v1/P18-1150",
    pages = "1616--1626",
    abstract = "The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus facing challenges with large-graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.",
}
@inproceedings{trisedya-etal-2018-gtr,
    title = "{GTR}-{LSTM}: A Triple Encoder for Sentence Generation from {RDF} Data",
    author = "Trisedya, Bayu Distiawan  and
      Qi, Jianzhong  and
      Zhang, Rui  and
      Wang, Wei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1151",
    doi = "10.18653/v1/P18-1151",
    pages = "1627--1637",
    abstract = "A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object. The RDF triple representation offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework. To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder. The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples. Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6{\%}, 6.0{\%}, and 16.4{\%} in three common metrics BLEU, METEOR, and TER, respectively.",
}
@inproceedings{holtzman-etal-2018-learning,
    title = "Learning to Write with Cooperative Discriminators",
    author = "Holtzman, Ari  and
      Buys, Jan  and
      Forbes, Maxwell  and
      Bosselut, Antoine  and
      Golub, David  and
      Choi, Yejin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1152",
    doi = "10.18653/v1/P18-1152",
    pages = "1638--1649",
    abstract = "Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that can guide a base RNN generator towards more globally coherent generations. More concretely, discriminators each specialize in a different principle of communication, such as Grice{'}s maxims, and are collectively combined with the base RNN generator through a composite decoding objective. Human evaluation demonstrates that text generated by our model is preferred over that of baselines by a large margin, significantly enhancing the overall coherence, style, and information of the generations.",
}
@inproceedings{yu-etal-2018-neural,
    title = "A Neural Approach to Pun Generation",
    author = "Yu, Zhiwei  and
      Tan, Jiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1153",
    doi = "10.18653/v1/P18-1153",
    pages = "1650--1660",
    abstract = "Automatic pun generation is an interesting and challenging text generation task. Previous efforts rely on templates or laboriously manually annotated pun datasets, which heavily constrains the quality and diversity of generated puns. Since sequence-to-sequence models provide an effective technique for text generation, it is promising to investigate these models on the pun generation task. In this paper, we propose neural network models for homographic pun generation, and they can generate puns without requiring any pun data for training. We first train a conditional neural language model from a general text corpus, and then generate puns from the language model with an elaborately designed decoding algorithm. Automatic and human evaluations show that our models are able to generate homographic puns of good readability and quality.",
}
@inproceedings{jhamtani-etal-2018-learning,
    title = "Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data",
    author = "Jhamtani, Harsh  and
      Gangal, Varun  and
      Hovy, Eduard  and
      Neubig, Graham  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1154",
    doi = "10.18653/v1/P18-1154",
    pages = "1661--1671",
    abstract = "This paper examines the problem of generating natural language descriptions of chess games. We introduce a new large-scale chess commentary dataset and propose methods to generate commentary for individual moves in a chess game. The introduced dataset consists of more than 298K chess move-commentary pairs across 11K chess games. We highlight how this task poses unique research challenges in natural language generation: the data contain a large variety of styles of commentary and frequently depend on pragmatic context. We benchmark various baselines and propose an end-to-end trainable neural model which takes into account multiple pragmatic aspects of the game state that may be commented upon to describe a given chess move. Through a human study on predictions for a subset of the data which deals with direct move descriptions, we observe that outputs from our models are rated similar to ground truth commentary texts in terms of correctness and fluency.",
}
@inproceedings{dai-etal-2018-credit,
    title = "From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction",
    author = "Dai, Zihang  and
      Xie, Qizhe  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1155",
    doi = "10.18653/v1/P18-1155",
    pages = "1672--1682",
    abstract = "In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction.",
}
@inproceedings{saha-etal-2018-duorc,
    title = "{D}uo{RC}: Towards Complex Language Understanding with Paraphrased Reading Comprehension",
    author = "Saha, Amrita  and
      Aralikatte, Rahul  and
      Khapra, Mitesh M.  and
      Sankaranarayanan, Karthik",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1156",
    doi = "10.18653/v1/P18-1156",
    pages = "1683--1693",
    abstract = "We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie - one from Wikipedia and the other from IMDb - written by two different authors. We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize answers from the other version. This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version. Further, since the two versions have different levels of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating external background knowledge. Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences. Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset, even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42{\%} on DuoRC v/s 86{\%} on SQuAD dataset). This opens up several interesting research avenues wherein DuoRC could complement other RC datasets to explore novel neural approaches for studying language understanding.",
}
@inproceedings{liu-etal-2018-stochastic,
    title = "Stochastic Answer Networks for Machine Reading Comprehension",
    author = "Liu, Xiaodong  and
      Shen, Yelong  and
      Duh, Kevin  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1157",
    doi = "10.18653/v1/P18-1157",
    pages = "1694--1704",
    abstract = "We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training. We show that this simple trick improves robustness and achieves results competitive to the state-of-the-art on the Stanford Question Answering Dataset (SQuAD), the Adversarial SQuAD, and the Microsoft MAchine Reading COmprehension Dataset (MS MARCO).",
}
@inproceedings{wang-etal-2018-multi-granularity,
    title = "Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering",
    author = "Wang, Wei  and
      Yan, Ming  and
      Wu, Chen",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1158",
    doi = "10.18653/v1/P18-1158",
    pages = "1705--1714",
    abstract = "This paper describes a novel hierarchical attention network for reading comprehension style question answering, which aims to answer questions for a given narrative paragraph. In the proposed method, attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph. Specifically, it first encode the question and paragraph with fine-grained language embeddings, to better capture the respective representations at semantic level. Then it proposes a multi-granularity fusion approach to fully fuse information from both global and attended representations. Finally, it introduces a hierarchical attention network to focuses on the answer span progressively with multi-level soft-alignment. Extensive experiments on the large-scale SQuAD, TriviaQA dataset validate the effectiveness of the proposed method. At the time of writing the paper, our model achieves state-of-the-art on the both SQuAD and TriviaQA Wiki leaderboard as well as two adversarial SQuAD datasets.",
}
@inproceedings{wang-etal-2018-joint,
    title = "Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension",
    author = "Wang, Zhen  and
      Liu, Jiachen  and
      Xiao, Xinyan  and
      Lyu, Yajuan  and
      Wu, Tian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1159",
    doi = "10.18653/v1/P18-1159",
    pages = "1715--1724",
    abstract = "While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates. This problem can be even worse in open-domain scenarios, where candidates from multiple passages should be combined to answer a single question. In this paper, we formulate reading comprehension as an extract-then-select two-stage procedure. We first extract answer candidates from passages, then select the final answer by combining information from all the candidates. Furthermore, we regard candidate extraction as a latent variable and train the two-stage process jointly with reinforcement learning. As a result, our approach has improved the state-of-the-art performance significantly on two challenging open-domain reading comprehension datasets. Further analysis demonstrates the effectiveness of our model components, especially the information fusion of all the candidates and the joint training of the extract-then-select procedure.",
}
@inproceedings{min-etal-2018-efficient,
    title = "Efficient and Robust Question Answering from Minimal Context over Documents",
    author = "Min, Sewon  and
      Zhong, Victor  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1160",
    doi = "10.18653/v1/P18-1160",
    pages = "1725--1735",
    abstract = "Neural models for question answering (QA) over documents have achieved significant performance improvements. Although effective, these models do not scale to large corpora due to their complex modeling of interactions between the document and the question. Moreover, recent work has shown that such models are sensitive to adversarial inputs. In this paper, we study the minimal context required to answer the question, and find that most questions in existing datasets can be answered with a small set of sentences. Inspired by this observation, we propose a simple sentence selector to select the minimal set of sentences to feed into the QA model. Our overall system achieves significant reductions in training (up to 15 times) and inference times (up to 13 times), with accuracy comparable to or better than the state-of-the-art on SQuAD, NewsQA, TriviaQA and SQuAD-Open. Furthermore, our experimental results and analyses show that our approach is more robust to adversarial inputs.",
}
@inproceedings{lin-etal-2018-denoising,
    title = "Denoising Distantly Supervised Open-Domain Question Answering",
    author = "Lin, Yankai  and
      Ji, Haozhe  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1161",
    doi = "10.18653/v1/P18-1161",
    pages = "1736--1745",
    abstract = "Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text. Existing DS-QA models usually retrieve related paragraphs from a large-scale corpus and apply reading comprehension technique to extract answers from the most relevant paragraph. They ignore the rich information contained in other paragraphs. Moreover, distant supervision data inevitably accompanies with the wrong labeling problem, and these noisy data will substantially degrade the performance of DS-QA. To address these issues, we propose a novel DS-QA model which employs a paragraph selector to filter out those noisy paragraphs and a paragraph reader to extract the correct answer from those denoised paragraphs. Experimental results on real-world datasets show that our model can capture useful information from noisy data and achieve significant improvements on DS-QA as compared to all baselines.",
}
@inproceedings{wu-etal-2018-question,
    title = "Question Condensing Networks for Answer Selection in Community Question Answering",
    author = "Wu, Wei  and
      Sun, Xu  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1162",
    doi = "10.18653/v1/P18-1162",
    pages = "1746--1755",
    abstract = "Answer selection is an important subtask of community question answering (CQA). In a real-world CQA forum, a question is often represented as two parts: a subject that summarizes the main points of the question, and a body that elaborates on the subject in detail. Previous researches on answer selection usually ignored the difference between these two parts and concatenated them as the question representation. In this paper, we propose the Question Condensing Networks (QCN) to make use of the subject-body relationship of community questions. In our model, the question subject is the primary part of the question representation, and the question body information is aggregated based on similarity and disparity with the question subject. Experimental results show that QCN outperforms all existing models on two CQA datasets.",
}
@inproceedings{cheng-etal-2018-towards,
    title = "Towards Robust Neural Machine Translation",
    author = "Cheng, Yong  and
      Tu, Zhaopeng  and
      Meng, Fandong  and
      Zhai, Junjie  and
      Liu, Yang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1163",
    doi = "10.18653/v1/P18-1163",
    pages = "1756--1766",
    abstract = "Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models. In this paper, we propose to improve the robustness of NMT models with adversarial stability training. The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.",
}
@inproceedings{kuang-etal-2018-attention,
    title = "Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings",
    author = "Kuang, Shaohui  and
      Li, Junhui  and
      Branco, Ant{\'o}nio  and
      Luo, Weihua  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1164",
    doi = "10.18653/v1/P18-1164",
    pages = "1767--1776",
    abstract = "In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase. Differently from statistical machine translation, the associations between source words and their possible target counterparts are not explicitly stored. Source and target words are at the two ends of a long information processing procedure, mediated by hidden states at both the source encoding and the target decoding phases. This makes it possible that a source word is incorrectly translated into a target word that is not any of its admissible equivalent counterparts in the target language. In this paper, we seek to somewhat shorten the distance between source and target words in that procedure, and thus strengthen their association, by means of a method we term bridging source and target word embeddings. We experiment with three strategies: (1) a source-side bridging model, where source word embeddings are moved one step closer to the output target sequence; (2) a target-side bridging model, which explores the more relevant source word embeddings for the prediction of the target sequence; and (3) a direct bridging model, which directly connects source and target word embeddings seeking to minimize errors in the translation of ones by the others. Experiments and analysis presented in this paper demonstrate that the proposed bridging models are able to significantly improve quality of both sentence translation, in general, and alignment and translation of individual source words with target words, in particular.",
}
@inproceedings{kreutzer-etal-2018-reliability,
    title = "Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning",
    author = "Kreutzer, Julia  and
      Uyheng, Joshua  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1165",
    doi = "10.18653/v1/P18-1165",
    pages = "1777--1788",
    abstract = "We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator, and the effect of the quality of reward estimates on the overall RL task. Our analysis of cardinal (5-point ratings) and ordinal (pairwise preferences) feedback shows that their intra- and inter-annotator α-agreement is comparable. Best reliability is obtained for standardized cardinal feedback, and cardinal feedback is also easiest to learn and generalize from. Finally, improvements of over 1 BLEU can be obtained by integrating a regression-based reward estimator trained on cardinal feedback for 800 translations into RL for NMT. This shows that RL is possible even from small amounts of fairly reliable human feedback, pointing to a great potential for applications at larger scale.",
}
@inproceedings{zhang-etal-2018-accelerating,
    title = "Accelerating Neural Transformer via an Average Attention Network",
    author = "Zhang, Biao  and
      Xiong, Deyi  and
      Su, Jinsong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1166",
    doi = "10.18653/v1/P18-1166",
    pages = "1789--1798",
    abstract = "With parallelizable attention networks, the neural Transformer is very fast to train. However, due to the auto-regressive architecture and self-attention in the decoder, the decoding procedure becomes slow. To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer. The average attention network consists of two layers, with an average layer that models dependencies on previous positions and a gating layer that is stacked over the average layer to enhance the expressiveness of the proposed attention network. We apply this network on the decoder part of the neural Transformer to replace the original target-side self-attention model. With masking tricks and dynamic programming, our model enables the neural Transformer to decode sentences over four times faster than its original version with almost no loss in training time and translation performance. We conduct a series of experiments on WMT17 translation tasks, where on 6 different language pairs, we obtain robust and consistent speed-ups in decoding.",
}
@inproceedings{domhan-2018-much,
    title = "How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures",
    author = "Domhan, Tobias",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1167",
    doi = "10.18653/v1/P18-1167",
    pages = "1799--1808",
    abstract = "With recent advances in network architectures for Neural Machine Translation (NMT) recurrent models have effectively been replaced by either convolutional or self-attentional approaches, such as in the Transformer. While the main innovation of the Transformer architecture is its use of self-attentional layers, there are several other aspects, such as attention with multiple heads and the use of many attention layers, that distinguish the model from previous baselines. In this work we take a fine-grained look at the different architectures for NMT. We introduce an Architecture Definition Language (ADL) allowing for a flexible combination of common building blocks. Making use of this language we show in experiments that one can bring recurrent and convolutional models very close to the Transformer performance by borrowing concepts from the Transformer architecture, but not using self-attention. Additionally, we find that self-attention is much more important on the encoder side than on the decoder side, where it can be replaced by a RNN or CNN without a loss in performance in most settings. Surprisingly, even a model without any target side self-attention performs well.",
}
@inproceedings{goldman-etal-2018-weakly,
    title = "Weakly Supervised Semantic Parsing with Abstract Examples",
    author = "Goldman, Omer  and
      Latcinnik, Veronica  and
      Nave, Ehud  and
      Globerson, Amir  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1168",
    doi = "10.18653/v1/P18-1168",
    pages = "1809--1819",
    abstract = "Training semantic parsers from weak supervision (denotations) rather than strong supervision (programs) complicates training in two ways. First, a large search space of potential programs needs to be explored at training time to find a correct program. Second, spurious programs that accidentally lead to a correct denotation add noise to training. In this work we propose that in closed worlds with clear semantic types, one can substantially alleviate these problems by utilizing an abstract representation, where tokens in both the language utterance and program are lifted to an abstract form. We show that these abstractions can be defined with a handful of lexical rules and that they result in sharing between different examples that alleviates the difficulties in training. To test our approach, we develop the first semantic parser for CNLVR, a challenging visual reasoning dataset, where the search space is large and overcoming spuriousness is critical, because denotations are either TRUE or FALSE, and thus random programs are likely to lead to a correct denotation. Our method substantially improves performance, and reaches 82.5{\%} accuracy, a 14.7{\%} absolute accuracy improvement compared to the best reported accuracy so far.",
}
@inproceedings{lawrence-riezler-2018-improving,
    title = "Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback",
    author = "Lawrence, Carolin  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1169",
    doi = "10.18653/v1/P18-1169",
    pages = "1820--1830",
    abstract = "Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies in a proper reweighting of the estimator so as to avoid known degeneracies in counterfactual learning, while still being applicable to stochastic gradient optimization. To conduct experiments with human users, we devise an easy-to-use interface to collect human feedback on semantic parses. Our work is the first to show that semantic parsers can be improved significantly by counterfactual learning from logged human feedback data.",
}
@inproceedings{groschwitz-etal-2018-amr,
    title = "{AMR} dependency parsing with a typed semantic algebra",
    author = "Groschwitz, Jonas  and
      Lindemann, Matthias  and
      Fowlie, Meaghan  and
      Johnson, Mark  and
      Koller, Alexander",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1170",
    doi = "10.18653/v1/P18-1170",
    pages = "1831--1841",
    abstract = "We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.",
}
@inproceedings{peng-etal-2018-sequence,
    title = "Sequence-to-sequence Models for Cache Transition Systems",
    author = "Peng, Xiaochang  and
      Song, Linfeng  and
      Gildea, Daniel  and
      Satta, Giorgio",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1171",
    doi = "10.18653/v1/P18-1171",
    pages = "1842--1852",
    abstract = "In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs. We transform the sequence to graph mapping problem to a word sequence to transition action sequence problem using a special transition system called a cache transition system. To address the sparsity issue of neural AMR parsing, we feed feature embeddings from the transition state to provide relevant local information for each decoder state. We present a monotonic hard attention model for the transition framework to handle the strictly left-to-right alignment between each transition state and the current buffer input focus. We evaluate our neural transition model on the AMR parsing task, and our parser outperforms other sequence-to-sequence approaches and achieves competitive results in comparison with the best-performing models.",
}
@inproceedings{xin-etal-2018-batch,
    title = "Batch {IS} {NOT} Heavy: Learning Word Representations From All Samples",
    author = "Xin, Xin  and
      Yuan, Fajie  and
      He, Xiangnan  and
      Jose, Joemon M.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1172",
    doi = "10.18653/v1/P18-1172",
    pages = "1853--1862",
    abstract = "Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations. However, it is known that sampling methods are biased especially when the sampling distribution deviates from the true data distribution. Besides, SGD suffers from dramatic fluctuation due to the one-sample learning scheme. In this work, we propose AllVec that uses batch gradient learning to generate word representations from all training samples. Remarkably, the time complexity of AllVec remains at the same level as SGD, being determined by the number of positive samples rather than all samples. We evaluate AllVec on several benchmark tasks. Experiments show that AllVec outperforms sampling-based SGD methods with comparable efficiency, especially for small training corpora.",
}
@inproceedings{peng-etal-2018-backpropagating,
    title = "Backpropagating through Structured Argmax using a {SPIGOT}",
    author = "Peng, Hao  and
      Thomson, Sam  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1173",
    doi = "10.18653/v1/P18-1173",
    pages = "1863--1873",
    abstract = "We introduce structured projection of intermediate gradients (SPIGOT), a new method for backpropagating through neural networks that include hard-decision structured predictions (e.g., parsing) in intermediate layers. SPIGOT requires no marginal inference, unlike structured attention networks and reinforcement learning-inspired solutions. Like so-called straight-through estimators, SPIGOT defines gradient-like quantities associated with intermediate nondifferentiable operations, allowing backpropagation before and after them; SPIGOT{'}s proxy aims to ensure that, after a parameter update, the intermediate structure will remain well-formed. We experiment on two structured NLP pipelines: syntactic-then-semantic dependency parsing, and semantic parsing followed by sentiment classification. We show that training with SPIGOT leads to a larger improvement on the downstream task than a modularly-trained pipeline, the straight-through estimator, and structured attention, reaching a new state of the art on semantic dependency parsing.",
}
@inproceedings{liu-etal-2018-learning-actively,
    title = "Learning How to Actively Learn: A Deep Imitation Learning Approach",
    author = "Liu, Ming  and
      Buntine, Wray  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1174",
    doi = "10.18653/v1/P18-1174",
    pages = "1874--1883",
    abstract = "Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method that learns an AL {``}policy{''} using {``}imitation learning{''} (IL). Our IL-based approach makes use of an efficient and effective {``}algorithmic expert{''}, which provides the policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using heuristics and reinforcement learning.",
}
@inproceedings{hancock-etal-2018-training,
    title = "Training Classifiers with Natural Language Explanations",
    author = "Hancock, Braden  and
      Varma, Paroma  and
      Wang, Stephanie  and
      Bringmann, Martin  and
      Liang, Percy  and
      R{\'e}, Christopher",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1175",
    doi = "10.18653/v1/P18-1175",
    pages = "1884--1895",
    abstract = "Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification). In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier. On three relation extraction tasks, we find that users are able to train classifiers with comparable F1 scores from 5-100 faster by providing explanations instead of just labels. Furthermore, given the inherent imperfection of labeling functions, we find that a simple rule-based semantic parser suffices.",
}
@inproceedings{mudrakarta-etal-2018-model,
    title = "Did the Model Understand the Question?",
    author = "Mudrakarta, Pramod Kaushik  and
      Taly, Ankur  and
      Sundararajan, Mukund  and
      Dhamdhere, Kedar",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1176",
    doi = "10.18653/v1/P18-1176",
    pages = "1896--1906",
    abstract = "We analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text. Using the notion of {``}attribution{''} (word importance), we find that these deep networks often ignore important question terms. Leveraging such behavior, we perturb questions to craft a variety of adversarial examples. Our strongest attacks drop the accuracy of a visual question answering model from 61.1{\%} to 19{\%}, and that of a tabular question answering model from 33.5{\%} to 3.3{\%}. Additionally, we show how attributions can strengthen attacks proposed by Jia and Liang (2017) on paragraph comprehension models. Our results demonstrate that attributions can augment standard measures of accuracy and empower investigation of model performance. When a model is accurate but for the wrong reasons, attributions can surface erroneous logic in the model that indicates inadequacies in the test data.",
}
@inproceedings{du-cardie-2018-harvesting,
    title = "Harvesting Paragraph-level Question-Answer Pairs from {W}ikipedia",
    author = "Du, Xinya  and
      Cardie, Claire",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1177",
    doi = "10.18653/v1/P18-1177",
    pages = "1907--1917",
    abstract = "We study the task of generating from Wikipedia articles question-answer pairs that cover content beyond a single sentence. We propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism. As compared to models that only take into account sentence-level information (Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the linguistic knowledge introduced by the coreference representation aids question generation significantly, producing models that outperform the current state-of-the-art. We apply our system (composed of an answer span extraction system and the passage-level QG system) to the 10,000 top ranking Wikipedia articles and create a corpus of over one million question-answer pairs. We provide qualitative analysis for the this large-scale generated corpus from Wikipedia.",
}
@inproceedings{wang-etal-2018-multi-passage,
    title = "Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification",
    author = "Wang, Yizhong  and
      Liu, Kai  and
      Liu, Jing  and
      He, Wei  and
      Lyu, Yajuan  and
      Wu, Hua  and
      Li, Sujian  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1178",
    doi = "10.18653/v1/P18-1178",
    pages = "1918--1927",
    abstract = "Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.",
}
@inproceedings{ye-etal-2018-language,
    title = "Language Generation via {DAG} Transduction",
    author = "Ye, Yajie  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1179",
    doi = "10.18653/v1/P18-1179",
    pages = "1928--1937",
    abstract = "A DAG automaton is a formal device for manipulating graphs. By augmenting a DAG automaton with transduction rules, a DAG transducer has potential applications in fundamental NLP tasks. In this paper, we propose a novel DAG transducer to perform graph-to-program transformation. The target structure of our transducer is a program licensed by a declarative programming language rather than linguistic structures. By executing such a program, we can easily get a surface string. Our transducer is designed especially for natural language generation (NLG) from type-logical semantic graphs. Taking Elementary Dependency Structures, a format of English Resource Semantics, as input, our NLG system achieves a BLEU-4 score of 68.07. This remarkable result demonstrates the feasibility of applying a DAG transducer to resolve NLG, as well as the effectiveness of our design.",
}
@inproceedings{deutsch-etal-2018-distributional,
    title = "A Distributional and Orthographic Aggregation Model for {E}nglish Derivational Morphology",
    author = "Deutsch, Daniel  and
      Hewitt, John  and
      Roth, Dan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1180",
    doi = "10.18653/v1/P18-1180",
    pages = "1938--1947",
    abstract = "Modeling derivational morphology to generate words with particular semantics is useful in many text generation tasks, such as machine translation or abstractive question answering. In this work, we tackle the task of derived word generation. That is, we attempt to generate the word {``}runner{''} for {``}someone who runs.{''} We identify two key problems in generating derived words from root words and transformations. We contribute a novel aggregation model of derived word generation that learns derivational transformations both as orthographic functions using sequence-to-sequence models and as functions in distributional word embedding space. The model then learns to choose between the hypothesis of each system. We also present two ways of incorporating corpus information into derived word generation.",
}
@inproceedings{lau-etal-2018-deep,
    title = "Deep-speare: A joint neural model of poetic language, meter and rhyme",
    author = "Lau, Jey Han  and
      Cohn, Trevor  and
      Baldwin, Timothy  and
      Brooke, Julian  and
      Hammond, Adam",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1181",
    doi = "10.18653/v1/P18-1181",
    pages = "1948--1958",
    abstract = "In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgements. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems. Expert evaluation, however, reveals that a vanilla language model captures meter implicitly, and that machine-generated poems still underperform in terms of readability and emotion. Our research shows the importance expert evaluation for poetry generation, and that future research should look beyond rhyme/meter and focus on poetic language.",
}
@inproceedings{castro-ferreira-etal-2018-neuralreg,
    title = "{N}eural{REG}: An end-to-end approach to referring expression generation",
    author = "Castro Ferreira, Thiago  and
      Moussallem, Diego  and
      K{\'a}d{\'a}r, {\'A}kos  and
      Wubben, Sander  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1182",
    doi = "10.18653/v1/P18-1182",
    pages = "1959--1969",
    abstract = "Traditionally, Referring Expression Generation (REG) models first decide on the form and then on the content of references to discourse entities in text, typically relying on features such as salience and grammatical function. In this paper, we present a new approach (NeuralREG), relying on deep neural networks, which makes decisions about form and content in one go without explicit feature extraction. Using a delexicalized version of the WebNLG corpus, we show that the neural model substantially improves over two strong baselines.",
}
@inproceedings{xu-cohen-2018-stock,
    title = "Stock Movement Prediction from Tweets and Historical Prices",
    author = "Xu, Yumo  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1183",
    doi = "10.18653/v1/P18-1183",
    pages = "1970--1979",
    abstract = "Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or topic modeling, our model introduces recurrent, continuous latent variables for a better treatment of stochasticity, and uses neural variational inference to address the intractable posterior inference. We also provide a hybrid objective with temporal auxiliary to flexibly capture predictive dependencies. We demonstrate the state-of-the-art performance of our proposed model on a new stock movement prediction dataset which we collected.",
}
@inproceedings{ma-etal-2018-rumor,
    title = "Rumor Detection on {T}witter with Tree-structured Recursive Neural Networks",
    author = "Ma, Jing  and
      Gao, Wei  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1184",
    doi = "10.18653/v1/P18-1184",
    pages = "1980--1989",
    abstract = "Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.",
}
@inproceedings{lu-etal-2018-visual,
    title = "Visual Attention Model for Name Tagging in Multimodal Social Media",
    author = "Lu, Di  and
      Neves, Leonardo  and
      Carvalho, Vitor  and
      Zhang, Ning  and
      Ji, Heng",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1185",
    doi = "10.18653/v1/P18-1185",
    pages = "1990--1999",
    abstract = "Everyday billions of multimodal posts containing both images and text are shared in social media sites such as Snapchat, Twitter or Instagram. This combination of image and text in a single message allows for more creative and expressive forms of communication, and has become increasingly common in such sites. This new paradigm brings new challenges for natural language understanding, as the textual component tends to be shorter, more informal, and often is only understood if combined with the visual context. In this paper, we explore the task of name tagging in multimodal social media posts. We start by creating two new multimodal datasets: the first based on Twitter posts and the second based on Snapchat captions (exclusively submitted to public and crowd-sourced stories). We then propose a novel model architecture based on Visual Attention that not only provides deeper visual understanding on the decisions of the model, but also significantly outperforms other state-of-the-art baseline methods for this task.",
}
@inproceedings{moon-etal-2018-multimodal,
    title = "Multimodal Named Entity Disambiguation for Noisy Social Media Posts",
    author = "Moon, Seungwhan  and
      Neves, Leonardo  and
      Carvalho, Vitor",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1186",
    doi = "10.18653/v1/P18-1186",
    pages = "2000--2008",
    abstract = "We introduce the new Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with accompanying images. Social media posts bring significant challenges for disambiguation tasks because 1) ambiguity not only comes from polysemous entities, but also from inconsistent or incomplete notations, 2) very limited context is provided with surrounding words, and 3) there are many emerging entities often unseen during training. To this end, we build a new dataset called SnapCaptionsKB, a collection of Snapchat image captions submitted to public and crowd-sourced stories, with named entity mentions fully annotated and linked to entities in an external knowledge base. We then build a deep zeroshot multimodal network for MNED that 1) extracts contexts from both text and image, and 2) predicts correct entity in the knowledge graph embeddings space, allowing for zeroshot disambiguation of entities unseen in training set as well. The proposed model significantly outperforms the state-of-the-art text-only NED models, showing efficacy and potentials of the MNED task.",
}
@inproceedings{rahimi-etal-2018-semi,
    title = "Semi-supervised User Geolocation via Graph Convolutional Networks",
    author = "Rahimi, Afshin  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1187",
    doi = "10.18653/v1/P18-1187",
    pages = "2009--2019",
    abstract = "Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state-of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.",
}
@inproceedings{narayan-etal-2018-document,
    title = "Document Modeling with External Attention for Sentence Extraction",
    author = "Narayan, Shashi  and
      Cardenas, Ronald  and
      Papasarantopoulos, Nikos  and
      Cohen, Shay B.  and
      Lapata, Mirella  and
      Yu, Jiangsheng  and
      Chang, Yi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1188",
    doi = "10.18653/v1/P18-1188",
    pages = "2020--2030",
    abstract = "Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop a framework composed of a hierarchical document encoder and an attention-based extractor with attention over external information. We evaluate our model on extractive document summarization (where the external information is image captions and the title of the document) and answer selection (where the external information is a question). We show that our model consistently outperforms strong baselines, in terms of both informativeness and fluency (for CNN document summarization) and achieves state-of-the-art results for answer selection on WikiQA and NewsQA.",
}
@inproceedings{card-etal-2018-neural,
    title = "Neural Models for Documents with Metadata",
    author = "Card, Dallas  and
      Tan, Chenhao  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1189",
    doi = "10.18653/v1/P18-1189",
    pages = "2031--2040",
    abstract = "Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customization typically requires derivation of a custom inference algorithm. In this paper, we build on recent advances in variational inference methods and propose a general neural framework, based on topic models, to enable flexible incorporation of metadata and allow for rapid exploration of alternative models. Our approach achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity. Finally, we demonstrate the potential of our framework through an exploration of a corpus of articles about US immigration.",
}
@inproceedings{shen-etal-2018-nash,
    title = "{NASH}: Toward End-to-End Neural Architecture for Generative Semantic Hashing",
    author = "Shen, Dinghan  and
      Su, Qinliang  and
      Chapfuwa, Paidamoyo  and
      Wang, Wenlin  and
      Wang, Guoyin  and
      Henao, Ricardo  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1190",
    doi = "10.18653/v1/P18-1190",
    pages = "2041--2050",
    abstract = "Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handled \textit{ad-hoc}. In this paper, we present an \textit{end-to-end} Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as \textit{Bernoulli} latent variables. A neural variational inference framework is proposed for training, where gradients are directly backpropagated through the discrete latent variable to optimize the hash function. We also draw the connections between proposed method and \textit{rate-distortion theory}, which provides a theoretical foundation for the effectiveness of our framework. Experimental results on three public datasets demonstrate that our method significantly outperforms several state-of-the-art models on both \textit{unsupervised} and \textit{supervised} scenarios.",
}
@inproceedings{fitzgerald-etal-2018-large,
    title = "Large-Scale {QA}-{SRL} Parsing",
    author = "FitzGerald, Nicholas  and
      Michael, Julian  and
      He, Luheng  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1191",
    doi = "10.18653/v1/P18-1191",
    pages = "2051--2060",
    abstract = "We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6{\%} and span-level accuracy of 77.6{\%} (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.",
}
@inproceedings{he-etal-2018-syntax,
    title = "Syntax for Semantic Role Labeling, To Be, Or Not To Be",
    author = "He, Shexia  and
      Li, Zuchao  and
      Zhao, Hai  and
      Bai, Hongxiao",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1192",
    doi = "10.18653/v1/P18-1192",
    pages = "2061--2071",
    abstract = "Semantic role labeling (SRL) is dedicated to recognizing the predicate-argument structure of a sentence. Previous studies have shown syntactic information has a remarkable contribution to SRL performance. However, such perception was challenged by a few recent neural SRL models which give impressive performance without a syntactic backbone. This paper intends to quantify the importance of syntactic information to dependency SRL in deep learning framework. We propose an enhanced argument labeling model companying with an extended korder argument pruning algorithm for effectively exploiting syntactic information. Our model achieves state-of-the-art results on the CoNLL-2008, 2009 benchmarks for both English and Chinese, showing the quantitative significance of syntax to neural SRL together with a thorough empirical survey over existing models.",
}
@inproceedings{suhr-artzi-2018-situated,
    title = "Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation",
    author = "Suhr, Alane  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1193",
    doi = "10.18653/v1/P18-1193",
    pages = "2072--2082",
    abstract = "We propose a learning approach for mapping context-dependent sequential instructions to actions. We address the problem of discourse and state dependencies with an attention-based model that considers both the history of the interaction and the state of the world. To train from start and goal states without access to demonstrations, we propose SESTRA, a learning algorithm that takes advantage of single-step reward observations and immediate expected reward maximization. We evaluate on the SCONE domains, and show absolute accuracy improvements of 9.8{\%}-25.3{\%} across the domains over approaches that use high-level logical representations.",
}
@inproceedings{luo-etal-2018-marrying,
    title = "Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding",
    author = "Luo, Bingfeng  and
      Feng, Yansong  and
      Wang, Zheng  and
      Huang, Songfang  and
      Yan, Rui  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1194",
    doi = "10.18653/v1/P18-1194",
    pages = "2083--2093",
    abstract = "The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: {``}Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?{''}. In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.",
}
@inproceedings{elbayad-etal-2018-token,
    title = "Token-level and sequence-level loss smoothing for {RNN} language models",
    author = "Elbayad, Maha  and
      Besacier, Laurent  and
      Verbeek, Jakob",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1195",
    doi = "10.18653/v1/P18-1195",
    pages = "2094--2103",
    abstract = "Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. Second, it suffers from {'}exposure bias{'}: during training tokens are predicted given ground-truth sequences, while at test time prediction is conditioned on generated output sequences. To overcome these limitations we build upon the recent reward augmented maximum likelihood approach that encourages the model to predict sentences that are close to the ground truth according to a given performance metric. We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach. Our experiments on two different tasks, image captioning and machine translation, show that token-level and sequence-level loss smoothing are complementary, and significantly improve results.",
}
@inproceedings{spithourakis-riedel-2018-numeracy,
    title = "Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers",
    author = "Spithourakis, Georgios  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1196",
    doi = "10.18653/v1/P18-1196",
    pages = "2104--2115",
    abstract = "Numeracy is the ability to understand and work with numbers. It is a necessary skill for composing and understanding documents in clinical, scientific, and other technical domains. In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composition, and propose a novel neural architecture that uses a continuous probability density function to model numerals from an open vocabulary. Our evaluation on clinical and scientific datasets shows that using hierarchical models to distinguish numerals from words improves a perplexity metric on the subset of numerals by 2 and 4 orders of magnitude, respectively, over non-hierarchical models. A combination of strategies can further improve perplexity. Our continuous probability density function model reduces mean absolute percentage errors by 18{\%} and 54{\%} in comparison to the second best strategy for each dataset, respectively.",
}
@inproceedings{gupta-zhang-2018-attend,
    title = "To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness",
    author = "Gupta, Amulya  and
      Zhang, Zhu",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1197",
    doi = "10.18653/v1/P18-1197",
    pages = "2116--2125",
    abstract = "With the recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT), attention mechanisms have become increasingly popular. The purpose of this paper is two-fold; firstly, we propose a novel attention model on Tree Long Short-Term Memory Networks (Tree-LSTMs), a tree-structured generalization of standard LSTM. Secondly, we study the interaction between attention and syntactic structures, by experimenting with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTMs, and Dependency Tree-LSTMs. Our models are evaluated on two semantic relatedness tasks: semantic relatedness scoring for sentence pairs (SemEval 2012, Task 6 and SemEval 2014, Task 1) and paraphrase detection for question pairs (Quora, 2017).",
}
@inproceedings{conneau-etal-2018-cram,
    title = "What you can cram into a single {\$}{\&}!{\#}* vector: Probing sentence embeddings for linguistic properties",
    author = {Conneau, Alexis  and
      Kruszewski, German  and
      Lample, Guillaume  and
      Barrault, Lo{\"\i}c  and
      Baroni, Marco},
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1198",
    doi = "10.18653/v1/P18-1198",
    pages = "2126--2136",
    abstract = "Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. {``}Downstream{''} tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.",
}
@inproceedings{qin-etal-2018-robust,
    title = "Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning",
    author = "Qin, Pengda  and
      Xu, Weiran  and
      Wang, William Yang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1199",
    doi = "10.18653/v1/P18-1199",
    pages = "2137--2147",
    abstract = "Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost{---}The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution{---}We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.",
}
@inproceedings{takahashi-etal-2018-interpretable,
    title = "Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder",
    author = "Takahashi, Ryo  and
      Tian, Ran  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1200",
    doi = "10.18653/v1/P18-1200",
    pages = "2148--2159",
    abstract = "Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity vectors. However, relations reside on low dimension sub-manifolds in the parameter space of arbitrary matrices {--} for one reason, composition of two relations M1, M2 may match a third M3 (e.g. composition of relations currency{\_}of{\_}country and country{\_}of{\_}film usually matches currency{\_}of{\_}film{\_}budget), which imposes compositional constraints to be satisfied by the parameters (i.e. M1*M2=M3). In this paper we investigate a dimension reduction technique by training relations jointly with an autoencoder, which is expected to better capture compositional constraints. We achieve state-of-the-art on Knowledge Base Completion tasks with strongly improved Mean Rank, and show that joint training with an autoencoder leads to interpretable sparse codings of relations, helps discovering compositional constraints and benefits from compositional training. Our source code is released at \url{github.com/tianran/glimvec}.",
}
@inproceedings{huang-etal-2018-zero,
    title = "Zero-Shot Transfer Learning for Event Extraction",
    author = "Huang, Lifu  and
      Ji, Heng  and
      Cho, Kyunghyun  and
      Dagan, Ido  and
      Riedel, Sebastian  and
      Voss, Clare",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1201",
    doi = "10.18653/v1/P18-1201",
    pages = "2160--2170",
    abstract = "Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus cannot be applied to new event types without extra annotation effort. We take a fresh look at event extraction and model it as a generic grounding problem: mapping each event mention to a specific type in a target event ontology. We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space. Based on this new framework, we can select, for each event mention, the event type which is semantically closest in this space as its type. By leveraging manual annotations available for a small set of existing event types, our framework can be applied to new unseen event types without additional manual annotations. When tested on 23 unseen event types, our zero-shot framework, without manual annotations, achieved performance comparable to a supervised model trained from 3,000 sentences annotated with 500 event mentions.",
}
@inproceedings{wang-pan-2018-recursive,
    title = "Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction",
    author = "Wang, Wenya  and
      Pan, Sinno Jialin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1202",
    doi = "10.18653/v1/P18-1202",
    pages = "2171--2181",
    abstract = "Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization. Supervised learning methods have proven to be effective for this task. However, in many domains, the lack of labeled data hinders the learning of a precise extraction model. In this case, unsupervised domain adaptation methods are desired to transfer knowledge from the source domain to any unlabeled target domain. In this paper, we develop a novel recursive neural network that could reduce domain shift effectively in word level through syntactic relations. We treat these relations as invariant {``}pivot information{''} across domains to build structural correspondences and generate an auxiliary task to predict the relation between any two adjacent words in the dependency tree. In the end, we demonstrate state-of-the-art results on three benchmark datasets.",
}
@inproceedings{peng-etal-2018-deep,
    title = "{D}eep {D}yna-{Q}: Integrating Planning for Task-Completion Dialogue Policy Learning",
    author = "Peng, Baolin  and
      Li, Xiujun  and
      Gao, Jianfeng  and
      Liu, Jingjing  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1203",
    doi = "10.18653/v1/P18-1203",
    pages = "2182--2192",
    abstract = "Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usually lacks the language complexity of human interlocutors and the biases in its design may tend to degrade the agent. To address these issues, we present Deep Dyna-Q, which to our knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning. We incorporate into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience. During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience. The effectiveness of our approach is demonstrated on a movie-ticket booking task in both simulated and human-in-the-loop settings.",
}
@inproceedings{wang-etal-2018-learning,
    title = "Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders",
    author = "Wang, Yansen  and
      Liu, Chenyi  and
      Huang, Minlie  and
      Nie, Liqiang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1204",
    doi = "10.18653/v1/P18-1204",
    pages = "2193--2203",
    abstract = "Asking good questions in open-domain conversational systems is quite significant but rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of interrogatives, topic words, and ordinary words. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (soft typed decoder and hard typed decoder) in which a type distribution over the three types is estimated and the type distribution is used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.",
}
@inproceedings{zhang-etal-2018-personalizing,
    title = "Personalizing Dialogue Agents: {I} have a dog, do you have pets too?",
    author = "Zhang, Saizheng  and
      Dinan, Emily  and
      Urbanek, Jack  and
      Szlam, Arthur  and
      Kiela, Douwe  and
      Weston, Jason",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1205",
    doi = "10.18653/v1/P18-1205",
    pages = "2204--2213",
    abstract = "Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i)condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.",
}
@inproceedings{kim-etal-2018-efficient,
    title = "Efficient Large-Scale Neural Domain Classification with Personalized Attention",
    author = "Kim, Young-Bum  and
      Kim, Dongchan  and
      Kumar, Anjishnu  and
      Sarikaya, Ruhi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1206",
    doi = "10.18653/v1/P18-1206",
    pages = "2214--2224",
    abstract = "In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (IPDAs). This scenario is observed in mainstream IPDAs in industry that allow third parties to develop thousands of new domains to augment built-in first party domains to rapidly increase domain coverage and overall IPDA capabilities. We propose a scalable neural model architecture with a shared encoder, a novel attention mechanism that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. Our architecture is designed to efficiently accommodate incremental domain additions achieving two orders of magnitude speed up compared to full model retraining. We consider the practical constraints of real-time production systems, and design to minimize memory footprint and runtime latency. We demonstrate that incorporating personalization significantly improves domain classification accuracy in a setting with thousands of overlapping domains.",
}
@inproceedings{gu-etal-2018-multimodal,
    title = "Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment",
    author = "Gu, Yue  and
      Yang, Kangning  and
      Fu, Shiyu  and
      Chen, Shuhong  and
      Li, Xinyu  and
      Marsic, Ivan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1207",
    doi = "10.18653/v1/P18-1207",
    pages = "2225--2235",
    abstract = "Multimodal affective computing, learning to recognize and interpret human affect and subjective information from multiple data sources, is still a challenge because: (i) it is hard to extract informative features to represent human affects from heterogeneous inputs; (ii) current fusion strategies only fuse different modalities at abstract levels, ignoring time-dependent interactions between modalities. Addressing such issues, we introduce a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data. Our introduced model outperforms state-of-the-art approaches on published datasets, and we demonstrate that our model is able to visualize and interpret synchronized attention over modalities.",
}
@inproceedings{bagher-zadeh-etal-2018-multimodal,
    title = "Multimodal Language Analysis in the Wild: {CMU}-{MOSEI} Dataset and Interpretable Dynamic Fusion Graph",
    author = "Bagher Zadeh, AmirAli  and
      Liang, Paul Pu  and
      Poria, Soujanya  and
      Cambria, Erik  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1208",
    doi = "10.18653/v1/P18-1208",
    pages = "2236--2246",
    abstract = "Analyzing human multimodal language is an emerging area of research in NLP. Intrinsically this language is multimodal (heterogeneous), sequential and asynchronous; it consists of the language (words), visual (expressions) and acoustic (paralinguistic) modalities all in the form of asynchronous coordinated sequences. From a resource perspective, there is a genuine need for large scale datasets that allow for in-depth studies of this form of language. In this paper we introduce CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI), the largest dataset of sentiment analysis and emotion recognition to date. Using data from CMU-MOSEI and a novel multimodal fusion technique called the Dynamic Fusion Graph (DFG), we conduct experimentation to exploit how modalities interact with each other in human multimodal language. Unlike previously proposed fusion techniques, DFG is highly interpretable and achieves competative performance when compared to the previous state of the art.",
}
@inproceedings{liu-etal-2018-efficient,
    title = "Efficient Low-rank Multimodal Fusion With Modality-Specific Factors",
    author = "Liu, Zhun  and
      Shen, Ying  and
      Lakshminarasimhan, Varun Bharadhwaj  and
      Liang, Paul Pu  and
      Bagher Zadeh, AmirAli  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1209",
    doi = "10.18653/v1/P18-1209",
    pages = "2247--2256",
    abstract = "Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exploited the expressiveness of tensors for multimodal representation. However, these methods often suffer from exponential increase in dimensions and in computational complexity introduced by transformation of input into tensor. In this paper, we propose the Low-rank Multimodal Fusion method, which performs multimodal fusion using low-rank tensors to improve efficiency. We evaluate our model on three different tasks: multimodal sentiment analysis, speaker trait analysis, and emotion recognition. Our model achieves competitive results on all these tasks while drastically reducing computational complexity. Additional experiments also show that our model can perform robustly for a wide range of low-rank settings, and is indeed much more efficient in both training and inference compared to other methods that utilize tensor representations.",
}
@inproceedings{rohde-etal-2018-discourse,
    title = "Discourse Coherence: Concurrent Explicit and Implicit Relations",
    author = "Rohde, Hannah  and
      Johnson, Alexander  and
      Schneider, Nathan  and
      Webber, Bonnie",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1210",
    doi = "10.18653/v1/P18-1210",
    pages = "2257--2267",
    abstract = "Theories of discourse coherence posit relations between discourse segments as a key feature of coherent text. Our prior work suggests that multiple discourse relations can be simultaneously operative between two segments for reasons not predicted by the literature. Here we test how this joint presence can lead participants to endorse seemingly divergent conjunctions (e.g., BUT and SO) to express the link they see between two segments. These apparent divergences are not symptomatic of participant naivety or bias, but arise reliably from the concurrent availability of multiple relations between segments {--} some available through explicit signals and some via inference. We believe that these new results can both inform future progress in theoretical work on discourse coherence and lead to higher levels of performance in discourse parsing.",
}
@inproceedings{srivastava-jojic-2018-spatial,
    title = "A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text",
    author = "Srivastava, Shashank  and
      Jojic, Nebojsa",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1211",
    doi = "10.18653/v1/P18-1211",
    pages = "2268--2277",
    abstract = "We present a generative probabilistic model of documents as sequences of sentences, and show that inference in it can lead to extraction of long-range latent discourse structure from a collection of documents. The approach is based on embedding sequences of sentences from longer texts into a 2- or 3-D spatial grids, in which one or two coordinates model smooth topic transitions, while the third captures the sequential nature of the modeled text. A significant advantage of our approach is that the learned models are naturally visualizable and interpretable, as semantic similarity and sequential structure are modeled along orthogonal directions in the grid. We show that the method is effective in capturing discourse structures in narrative text across multiple genres, including biographies, stories, and newswire reports. In particular, our method outperforms or is competitive with state-of-the-art generative approaches on tasks such as predicting the outcome of a story, and sentence ordering.",
}
@inproceedings{ning-etal-2018-joint,
    title = "Joint Reasoning for Temporal and Causal Relations",
    author = "Ning, Qiang  and
      Feng, Zhili  and
      Wu, Hao  and
      Roth, Dan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1212",
    doi = "10.18653/v1/P18-1212",
    pages = "2278--2288",
    abstract = "Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must occur earlier than its effect, temporal and causal relations are closely related and one relation often dictates the value of the other. However, limited attention has been paid to studying these two relations jointly. This paper presents a joint inference framework for them using constrained conditional models (CCMs). Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints that are inherent in the nature of time and causality. We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.",
}
@inproceedings{rashkin-etal-2018-modeling,
    title = "Modeling Naive Psychology of Characters in Simple Commonsense Stories",
    author = "Rashkin, Hannah  and
      Bosselut, Antoine  and
      Sap, Maarten  and
      Knight, Kevin  and
      Choi, Yejin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1213",
    doi = "10.18653/v1/P18-1213",
    pages = "2289--2299",
    abstract = "Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people{'}s mental states {---} a capability that is trivial for humans but remarkably hard for machines. To facilitate research addressing this challenge, we introduce a new annotation framework to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions. Our work presents a new large-scale dataset with rich low-level annotations and establishes baseline performance on several new tasks, suggesting avenues for future research.",
}
@inproceedings{li-etal-2018-deep,
    title = "A Deep Relevance Model for Zero-Shot Document Filtering",
    author = "Li, Chenliang  and
      Zhou, Wei  and
      Ji, Feng  and
      Duan, Yu  and
      Chen, Haiqing",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1214",
    doi = "10.18653/v1/P18-1214",
    pages = "2300--2310",
    abstract = "In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand. As a fundamental task, information filtering therefore becomes a critical necessity. In this paper, we propose a novel deep relevance model for zero-shot document filtering, named DAZER. DAZER estimates the relevance between a document and a category by taking a small set of seed words relevant to the category. With pre-trained word embeddings from a large external corpus, DAZER is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space. The relevance signals are extracted through a gated convolutional process. The gate mechanism controls which convolution filters output the relevance signals in a category dependent manner. Experiments on two document collections of two different tasks (i.e., topic categorization and sentiment analysis) demonstrate that DAZER significantly outperforms the existing alternative solutions, including the state-of-the-art deep relevance ranking models.",
}
@inproceedings{wang-2018-disconnected,
    title = "Disconnected Recurrent Neural Networks for Text Categorization",
    author = "Wang, Baoxin",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1215",
    doi = "10.18653/v1/P18-1215",
    pages = "2311--2320",
    abstract = "Recurrent neural network (RNN) has achieved remarkable performance in text categorization. RNN can model the entire sequence and capture long-term dependencies, but it does not do well in extracting key patterns. In contrast, convolutional neural network (CNN) is good at extracting local and position-invariant features. In this paper, we present a novel model named disconnected recurrent neural network (DRNN), which incorporates position-invariance into RNN. By limiting the distance of information flow in RNN, the hidden state at each time step is restricted to represent words near the current position. The proposed model makes great improvements over RNN and CNN models and achieves the best performance on several benchmark datasets for text categorization.",
}
@inproceedings{wang-etal-2018-joint-embedding,
    title = "Joint Embedding of Words and Labels for Text Classification",
    author = "Wang, Guoyin  and
      Li, Chunyuan  and
      Wang, Wenlin  and
      Zhang, Yizhe  and
      Shen, Dinghan  and
      Zhang, Xinyuan  and
      Henao, Ricardo  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1216",
    doi = "10.18653/v1/P18-1216",
    pages = "2321--2331",
    abstract = "Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors. We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels. The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones. Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information, in addition to input text sequences. Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.",
}
@inproceedings{peng-etal-2018-neural,
    title = "Neural Sparse Topical Coding",
    author = "Peng, Min  and
      Xie, Qianqian  and
      Zhang, Yanchun  and
      Wang, Hua  and
      Zhang, Xiuzhen  and
      Huang, Jimin  and
      Tian, Gang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1217",
    doi = "10.18653/v1/P18-1217",
    pages = "2332--2340",
    abstract = "Topic models with sparsity enhancement have been proven to be effective at learning discriminative and coherent latent topics of short texts, which is critical to many scientific and engineering applications. However, the extensions of these models require carefully tailored graphical models and re-deduced inference algorithms, limiting their variations and applications. We propose a novel sparsity-enhanced topic model, Neural Sparse Topical Coding (NSTC) base on a sparsity-enhanced topic model called Sparse Topical Coding (STC). It focuses on replacing the complex inference process with the back propagation, which makes the model easy to explore extensions. Moreover, the external semantic information of words in word embeddings is incorporated to improve the representation of short texts. To illustrate the flexibility offered by the neural network based framework, we present three extensions base on NSTC without re-deduced inference algorithms. Experiments on Web Snippet and 20Newsgroups datasets demonstrate that our models outperform existing methods.",
}
@inproceedings{gong-etal-2018-document,
    title = "Document Similarity for Texts of Varying Lengths via Hidden Topics",
    author = "Gong, Hongyu  and
      Sakakini, Tarek  and
      Bhat, Suma  and
      Xiong, JinJun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1218",
    doi = "10.18653/v1/P18-1218",
    pages = "2341--2351",
    abstract = "Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of the incorporation of domain knowledge to text matching.",
}
@inproceedings{mathias-etal-2018-eyes,
    title = "Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour",
    author = "Mathias, Sandeep  and
      Kanojia, Diptesh  and
      Patel, Kevin  and
      Agrawal, Samarth  and
      Mishra, Abhijit  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1219",
    doi = "10.18653/v1/P18-1219",
    pages = "2352--2362",
    abstract = "Predicting a reader{'}s rating of text quality is a challenging task that involves estimating different subjective aspects of the text, like structure, clarity, etc. Such subjective aspects are better handled using cognitive information. One such source of cognitive information is gaze behaviour. In this paper, we show that gaze behaviour does indeed help in effectively predicting the rating of text quality. To do this, we first we model text quality as a function of three properties - organization, coherence and cohesion. Then, we demonstrate how capturing gaze behaviour helps in predicting each of these properties, and hence the overall quality, by reporting improvements obtained by adding gaze features to traditional textual features for score prediction. We also hypothesize that if a reader has fully understood the text, the corresponding gaze behaviour would give a better indication of the assigned rating, as opposed to partial understanding. Our experiments validate this hypothesis by showing greater agreement between the given rating and the predicted rating when the reader has a full understanding of the text.",
}
@inproceedings{dong-smith-2018-multi,
    title = "Multi-Input Attention for Unsupervised {OCR} Correction",
    author = "Dong, Rui  and
      Smith, David",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1220",
    doi = "10.18653/v1/P18-1220",
    pages = "2363--2372",
    abstract = "We propose a novel approach to OCR post-correction that exploits repeated texts in large corpora both as a source of noisy target outputs for unsupervised training and as a source of evidence when decoding. A sequence-to-sequence model with attention is applied for single-input correction, and a new decoder with multi-input attention averaging is developed to search for consensus among multiple sequences. We design two ways of training the correction model without human annotation, either training to match noisily observed textual variants or bootstrapping from a uniform error model. On two corpora of historical newspapers and books, we show that these unsupervised techniques cut the character and word error rates nearly in half on single inputs and, with the addition of multi-input decoding, can rival supervised methods.",
}
@inproceedings{parvez-etal-2018-building,
    title = "Building Language Models for Text with Named Entities",
    author = "Parvez, Md Rizwan  and
      Chakraborty, Saikat  and
      Ray, Baishakhi  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1221",
    doi = "10.18653/v1/P18-1221",
    pages = "2373--2383",
    abstract = "Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2{\%} better perplexity in recipe generation and 22.06{\%} on code generation than state-of-the-art language models.",
}
@inproceedings{han-etal-2018-hyperdoc2vec,
    title = "hyperdoc2vec: Distributed Representations of Hypertext Documents",
    author = "Han, Jialong  and
      Song, Yan  and
      Zhao, Wayne Xin  and
      Shi, Shuming  and
      Zhang, Haisong",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1222",
    doi = "10.18653/v1/P18-1222",
    pages = "2384--2394",
    abstract = "Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life. Although being effective on plain documents, conventional text embedding methods suffer from information loss if directly adapted to hyper-documents. In this paper, we propose a general embedding approach for hyper-documents, namely, hyperdoc2vec, along with four criteria characterizing necessary information that hyper-document embedding models should preserve. Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks, i.e., paper classification and citation recommendation, in the academic paper domain. Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t. the four criteria.",
}
@inproceedings{liu-etal-2018-entity,
    title = "Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval",
    author = "Liu, Zhenghao  and
      Xiong, Chenyan  and
      Sun, Maosong  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1223",
    doi = "10.18653/v1/P18-1223",
    pages = "2395--2405",
    abstract = "This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems. EDRM represents queries and documents by their words and entity annotations. The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks. The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval. Our experiments on a commercial search log demonstrate the effectiveness of EDRM. Our analyses reveal that knowledge graph semantics significantly improve the generalization ability of neural ranking models.",
}
@inproceedings{chen-etal-2018-neural-natural,
    title = "Neural Natural Language Inference Models Enhanced with External Knowledge",
    author = "Chen, Qian  and
      Zhu, Xiaodan  and
      Ling, Zhen-Hua  and
      Inkpen, Diana  and
      Wei, Si",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1224",
    doi = "10.18653/v1/P18-1224",
    pages = "2406--2417",
    abstract = "Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.",
}
@inproceedings{kang-etal-2018-adventure,
    title = "{A}dv{E}ntu{R}e: Adversarial Training for Textual Entailment with Knowledge-Guided Examples",
    author = "Kang, Dongyeop  and
      Khot, Tushar  and
      Sabharwal, Ashish  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1225",
    doi = "10.18653/v1/P18-1225",
    pages = "2418--2428",
    abstract = "We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it. First, we propose knowledge-guided adversarial example generators for incorporating large lexical resources in entailment models via only a handful of rule templates. Second, to make the entailment model{---}a discriminator{---}more robust, we propose the first GAN-style approach for training it using a natural language example generator that iteratively adjusts to the discriminator{'}s weaknesses. We demonstrate effectiveness using two entailment datasets, where the proposed methods increase accuracy by 4.7{\%} on SciTail and by 2.8{\%} on a 1{\%} sub-sample of SNLI. Notably, even a single hand-written rule, negate, improves the accuracy of negation examples in SNLI by 6.1{\%}.",
}
@inproceedings{park-etal-2018-subword,
    title = "Subword-level Word Vector Representations for {K}orean",
    author = "Park, Sungjoon  and
      Byun, Jeongmin  and
      Baek, Sion  and
      Cho, Yongseok  and
      Oh, Alice",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1226",
    doi = "10.18653/v1/P18-1226",
    pages = "2429--2438",
    abstract = "Research on distributed word representations is focused on widely-used languages such as English. Although the same methods can be used for other languages, language-specific knowledge can enhance the accuracy and richness of word vector representations. In this paper, we look at improving distributed word representations for Korean using knowledge about the unique linguistic structure of Korean. Specifically, we decompose Korean words into the jamo-level, beyond the character-level, allowing a systematic use of subword information. To evaluate the vectors, we develop Korean test sets for word similarity and analogy and make them publicly available. The results show that our simple method outperforms word2vec and character-level Skip-Grams on semantic and syntactic similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis.",
}
@inproceedings{jin-etal-2018-incorporating,
    title = "Incorporating {C}hinese Characters of Words for Lexical Sememe Prediction",
    author = "Jin, Huiming  and
      Zhu, Hao  and
      Liu, Zhiyuan  and
      Xie, Ruobing  and
      Sun, Maosong  and
      Lin, Fen  and
      Lin, Leyu",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1227",
    doi = "10.18653/v1/P18-1227",
    pages = "2439--2449",
    abstract = "Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words. To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words. We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words.",
}
@inproceedings{an-etal-2018-semaxis,
    title = "{S}em{A}xis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment",
    author = "An, Jisun  and
      Kwak, Haewoon  and
      Ahn, Yong-Yeol",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1228",
    doi = "10.18653/v1/P18-1228",
    pages = "2450--2461",
    abstract = "Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SemAxis, a simple yet powerful framework to characterize word semantics using many semantic axes in word-vector spaces beyond sentiment. We demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SemAxis outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.",
}
@inproceedings{mao-etal-2018-end,
    title = "End-to-End Reinforcement Learning for Automatic Taxonomy Induction",
    author = "Mao, Yuning  and
      Ren, Xiang  and
      Shen, Jiaming  and
      Gu, Xiaotao  and
      Han, Jiawei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1229",
    doi = "10.18653/v1/P18-1229",
    pages = "2462--2472",
    abstract = "We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (\textit{i.e.},, detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine \textit{which} term to select and \textit{where} to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6{\%} on ancestor F1.",
}
@inproceedings{luo-etal-2018-incorporating,
    title = "Incorporating Glosses into Neural Word Sense Disambiguation",
    author = "Luo, Fuli  and
      Liu, Tianyu  and
      Xia, Qiaolin  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1230",
    doi = "10.18653/v1/P18-1230",
    pages = "2473--2482",
    abstract = "Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods. However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions). In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge. Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word. GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods. We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information. The experimental results show that our model outperforms the state-of-the-art systems on several English all-words WSD datasets.",
}
@inproceedings{barnes-etal-2018-bilingual,
    title = "Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages",
    author = "Barnes, Jeremy  and
      Klinger, Roman  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1231",
    doi = "10.18653/v1/P18-1231",
    pages = "2483--2493",
    abstract = "Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches. However, they either require large amounts of parallel data or do not sufficiently capture sentiment information. We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. This model only requires a small bilingual lexicon, a source-language corpus annotated for sentiment, and monolingual word embeddings for each language. We perform experiments on three language combinations (Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment classification and find that our model significantly outperforms state-of-the-art methods on four out of six experimental setups, as well as capturing complementary information to machine translation. Our analysis of the resulting embedding space provides evidence that it represents sentiment information in the resource-poor target language without any annotated data in that language.",
}
@inproceedings{shi-etal-2018-learning-domain,
    title = "Learning Domain-Sensitive and Sentiment-Aware Word Embeddings",
    author = "Shi, Bei  and
      Fu, Zihao  and
      Bing, Lidong  and
      Lam, Wai",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1232",
    doi = "10.18653/v1/P18-1232",
    pages = "2494--2504",
    abstract = "Word embeddings have been widely used in sentiment classification because of their efficacy for semantic representations of words. Given reviews from different domains, some existing methods for word embeddings exploit sentiment information, but they cannot produce domain-sensitive embeddings. On the other hand, some other existing methods can generate domain-sensitive word embeddings, but they cannot distinguish words with similar contexts but opposite sentiment polarity. We propose a new method for learning domain-sensitive and sentiment-aware embeddings that simultaneously capture the information of sentiment semantics and domain sensitivity of individual words. Our method can automatically determine and produce domain-common embeddings and domain-specific embeddings. The differentiation of domain-common and domain-specific words enables the advantage of data augmentation of common semantics from multiple domains and capture the varied semantics of specific words from different domains at the same time. Experimental results show that our model provides an effective way to learn domain-sensitive and sentiment-aware word embeddings which benefit sentiment classification at both sentence level and lexicon term level.",
}
@inproceedings{peng-etal-2018-cross,
    title = "Cross-Domain Sentiment Classification with Target Domain Specific Information",
    author = "Peng, Minlong  and
      Zhang, Qi  and
      Jiang, Yu-gang  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1233",
    doi = "10.18653/v1/P18-1233",
    pages = "2505--2513",
    abstract = "The task of adopting a model with good performance to a target domain that is different from the source domain used for training has received considerable attention in sentiment analysis. Most existing approaches mainly focus on learning representations that are domain-invariant in both the source and target domains. Few of them pay attention to domain-specific information, which should also be informative. In this work, we propose a method to simultaneously extract domain specific and invariant representations and train a classifier on each of the representation, respectively. And we introduce a few target domain labeled data for learning domain-specific information. To effectively utilize the target domain labeled data, we train the domain invariant representation based classifier with both the source and target domain labeled data and train the domain-specific representation based classifier with only the target domain labeled data. These two classifiers then boost each other in a co-training style. Extensive sentiment analysis experiments demonstrated that the proposed method could achieve better performance than state-of-the-art methods.",
}
@inproceedings{xue-li-2018-aspect,
    title = "Aspect Based Sentiment Analysis with Gated Convolutional Networks",
    author = "Xue, Wei  and
      Li, Tao",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1234",
    doi = "10.18653/v1/P18-1234",
    pages = "2514--2523",
    abstract = "Aspect based sentiment analysis (ABSA) can provide more detailed information than general sentiment analysis, because it aims to predict the sentiment polarities of the given aspects or entities in text. We summarize previous approaches into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). Most previous approaches employ long short-term memory and attention mechanisms to predict the sentiment polarity of the concerned targets, which are often complicated and need more training time. We propose a model based on convolutional neural networks and gating mechanisms, which is more accurate and efficient. First, the novel Gated Tanh-ReLU Units can selectively output the sentiment features according to the given aspect or entity. The architecture is much simpler than attention layer used in the existing models. Second, the computations of our model could be easily parallelized during training, because convolutional layers do not have time dependency as in LSTM layers, and gating units also work independently. The experiments on SemEval datasets demonstrate the efficiency and effectiveness of our models.",
}
@inproceedings{dong-de-melo-2018-helping,
    title = "A Helping Hand: Transfer Learning for Deep Sentiment Analysis",
    author = "Dong, Xin  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1235",
    doi = "10.18653/v1/P18-1235",
    pages = "2524--2534",
    abstract = "Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains. In this work, we present an approach to feed generic cues into the training process of such networks, leading to better generalization abilities given limited training data. We propose to induce sentiment embeddings via supervision on extrinsic data, which are then fed into the model via a dedicated memory-based component. We observe significant gains in effectiveness on a range of different datasets in seven different languages.",
}
@inproceedings{amplayo-etal-2018-cold,
    title = "Cold-Start Aware User and Product Attention for Sentiment Classification",
    author = "Amplayo, Reinald Kim  and
      Kim, Jihyeok  and
      Sung, Sua  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1236",
    doi = "10.18653/v1/P18-1236",
    pages = "2535--2544",
    abstract = "The use of user/product information in sentiment analysis is important, especially for cold-start users/products, whose number of reviews are very limited. However, current models do not deal with the cold-start problem which is typical in review websites. In this paper, we present Hybrid Contextualized Sentiment Classifier (HCSC), which contains two modules: (1) a fast word encoder that returns word vectors embedded with short and long range dependency features; and (2) Cold-Start Aware Attention (CSAA), an attention mechanism that considers the existence of cold-start problem when attentively pooling the encoded word vectors. HCSC introduces shared vectors that are constructed from similar users/products, and are used when the original distinct vectors do not have sufficient information (i.e. cold-start). This is decided by a frequency-guided selective gate vector. Our experiments show that in terms of RMSE, HCSC performs significantly better when compared with on famous datasets, despite having less complexity, and thus can be trained much faster. More importantly, our model performs significantly better than previous models when the training data is sparse and has cold-start problems.",
}
@inproceedings{al-khatib-etal-2018-modeling,
    title = "Modeling Deliberative Argumentation Strategies on {W}ikipedia",
    author = "Al-Khatib, Khalid  and
      Wachsmuth, Henning  and
      Lang, Kevin  and
      Herpel, Jakob  and
      Hagen, Matthias  and
      Stein, Benno",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1237",
    doi = "10.18653/v1/P18-1237",
    pages = "2545--2555",
    abstract = "This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally. Our ultimate goal is to predict the best next deliberative move of each participant. In this paper, we present a model for deliberative discussions and we illustrate its operationalization. Previous models have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. In contrast, we derive our model statistically from several types of metadata that can be used for move description. Applied to six million discussions from Wikipedia talk pages, our approach results in a model with 13 categories along three dimensions: discourse acts, argumentative relations, and frames. On this basis, we automatically generate a corpus with about 200,000 turns, labeled for the 13 categories. We then operationalize the model with three supervised classifiers and provide evidence that the proposed categories can be predicted.",
}
@inproceedings{sharma-etal-2018-conceptual,
    title = "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning",
    author = "Sharma, Piyush  and
      Ding, Nan  and
      Goodman, Sebastian  and
      Soricut, Radu",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1238",
    doi = "10.18653/v1/P18-1238",
    pages = "2556--2565",
    abstract = "We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset.",
}
@inproceedings{hewitt-etal-2018-learning,
    title = "Learning Translations via Images with a Massively Multilingual Image Dataset",
    author = "Hewitt, John  and
      Ippolito, Daphne  and
      Callahan, Brendan  and
      Kriz, Reno  and
      Wijaya, Derry Tanti  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1239",
    doi = "10.18653/v1/P18-1239",
    pages = "2566--2576",
    abstract = "We conduct the most comprehensive study to date into translating words via images. To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents. Past datasets have been limited to only a few high-resource languages and unrealistically easy translation settings. In contrast, we have collected by far the largest available dataset for this task, with images for approximately 10,000 words in each of 100 languages. We run experiments on a dozen high resource languages and 20 low resources languages, demonstrating the effect of word concreteness and part-of-speech on translation quality. {\%}We find that while image features work best for concrete nouns, they are sometimes effective on other parts of speech. To improve image-based translation, we introduce a novel method of predicting word concreteness from images, which improves on a previous state-of-the-art unsupervised technique. This allows us to predict when image-based translation may be effective, enabling consistent improvements to a state-of-the-art text-based word translation system. Our code and the Massively Multilingual Image Dataset (MMID) are available at \url{http://multilingual-images.org/}.",
}
@inproceedings{jing-etal-2018-automatic,
    title = "On the Automatic Generation of Medical Imaging Reports",
    author = "Jing, Baoyu  and
      Xie, Pengtao  and
      Xing, Eric",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1240",
    doi = "10.18653/v1/P18-1240",
    pages = "2577--2586",
    abstract = "Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-writing can be error-prone for unexperienced physicians, and time-consuming and tedious for experienced physicians. To address these issues, we study the automatic generation of medical imaging reports. This task presents several challenges. First, a complete report contains multiple heterogeneous forms of information, including findings and tags. Second, abnormal regions in medical images are difficult to identify. Third, the reports are typically long, containing multiple sentences. To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the prediction of tags and the generation of paragraphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs. We demonstrate the effectiveness of the proposed methods on two publicly available dataset.",
}
@inproceedings{chen-etal-2018-attacking,
    title = "Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning",
    author = "Chen, Hongge  and
      Zhang, Huan  and
      Chen, Pin-Yu  and
      Yi, Jinfeng  and
      Hsieh, Cho-Jui",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1241",
    doi = "10.18653/v1/P18-1241",
    pages = "2587--2597",
    abstract = "Visual language grounding is widely studied in modern neural image captioning systems, which typically adopts an encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for language caption generation. To study the robustness of language grounding to adversarial perturbations in machine vision and perception, we propose Show-and-Fool, a novel algorithm for crafting adversarial examples in neural image captioning. The proposed algorithm provides two evaluation approaches, which check if we can mislead neural image captioning systems to output some randomly chosen captions or keywords. Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems. Consequently, our approach leads to new robustness implications of neural image captioning and novel insights in visual language grounding.",
}
@inproceedings{goyal-etal-2018-think,
    title = "Think Visually: Question Answering through Virtual Imagery",
    author = "Goyal, Ankit  and
      Wang, Jian  and
      Deng, Jia",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1242",
    doi = "10.18653/v1/P18-1242",
    pages = "2598--2608",
    abstract = "In this paper, we study the problem of geometric reasoning (a form of visual reasoning) in the context of question-answering. We introduce Dynamic Spatial Memory Network (DSMN), a new deep network architecture that specializes in answering questions that admit latent visual representations, and learns to generate and reason over such representations. Further, we propose two synthetic benchmarks, FloorPlanQA and ShapeIntersection, to evaluate the geometric reasoning capability of QA systems. Experimental results validate the effectiveness of our proposed DSMN for visual thinking tasks.",
}
@inproceedings{zhang-etal-2018-interactive,
    title = "Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game",
    author = "Zhang, Haichao  and
      Yu, Haonan  and
      Xu, Wei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1243",
    doi = "10.18653/v1/P18-1243",
    pages = "2609--2619",
    abstract = "Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training data, and is hardly adaptive to new scenarios or flexible for acquiring new knowledge without inefficient retraining or catastrophic forgetting. We highlight the perspective that conversational interaction serves as a natural interface both for language learning and for novel knowledge acquisition and propose a joint imitation and reinforcement approach for grounded language learning through an interactive conversational game. The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. Results compared with other methods verified the effectiveness of the proposed approach.",
}
@inproceedings{seki-etal-2018-purely,
    title = "A Purely End-to-End System for Multi-speaker Speech Recognition",
    author = "Seki, Hiroshi  and
      Hori, Takaaki  and
      Watanabe, Shinji  and
      Le Roux, Jonathan  and
      Hershey, John R.",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1244",
    doi = "10.18653/v1/P18-1244",
    pages = "2620--2630",
    abstract = "Recently, there has been growing interest in multi-speaker speech recognition, where the utterances of multiple speakers are recognized from their mixture. Promising techniques have been proposed for this task, but earlier works have required additional training data such as isolated source signals or senone alignments for effective learning. In this paper, we propose a new sequence-to-sequence framework to directly decode multiple label sequences from a single speech sequence by unifying source separation and speech recognition functions in an end-to-end manner. We further propose a new objective function to improve the contrast between the hidden vectors to avoid generating similar hypotheses. Experimental results show that the model is directly able to learn a mapping from a speech mixture to multiple label sequences, achieving 83.1{\%} relative improvement compared to a model trained without the proposed objective. Interestingly, the results are comparable to those produced by previous end-to-end works featuring explicit separation and recognition modules.",
}
@inproceedings{wolf-sonkin-etal-2018-structured,
    title = "A Structured Variational Autoencoder for Contextual Morphological Inflection",
    author = "Wolf-Sonkin, Lawrence  and
      Naradowsky, Jason  and
      Mielke, Sabrina J.  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1245",
    doi = "10.18653/v1/P18-1245",
    pages = "2631--2641",
    abstract = "Statistical morphological inflectors are typically trained on fully supervised, type-level data. One remaining open research question is the following: How can we effectively exploit raw, token-level data to improve their performance? To this end, we introduce a novel generative latent-variable model for the semi-supervised learning of inflection generation. To enable posterior inference over the latent variables, we derive an efficient variational inference procedure based on the wake-sleep algorithm. We experiment on 23 languages, using the Universal Dependencies corpora in a simulated low-resource setting, and find improvements of over 10{\%} absolute accuracy in some cases.",
}
@inproceedings{bohnet-etal-2018-morphosyntactic,
    title = "Morphosyntactic Tagging with a Meta-{B}i{LSTM} Model over Context Sensitive Token Encodings",
    author = "Bohnet, Bernd  and
      McDonald, Ryan  and
      Sim{\~o}es, Gon{\c{c}}alo  and
      Andor, Daniel  and
      Pitler, Emily  and
      Maynez, Joshua",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1246",
    doi = "10.18653/v1/P18-1246",
    pages = "2642--2652",
    abstract = "The rise of neural networks, and particularly recurrent neural networks, has produced significant advances in part-of-speech tagging accuracy. One characteristic common among these models is the presence of rich initial word encodings. These encodings typically are composed of a recurrent character-based representation with dynamically and pre-trained word embeddings. However, these encodings do not consider a context wider than a single word and it is only through subsequent recurrent layers that word or sub-word information interacts. In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations. In particular we show that optimal results are obtained by integrating these context sensitive representations through synchronized training with a meta-model that learns to combine their states.",
}
@inproceedings{malaviya-etal-2018-neural,
    title = "Neural Factor Graph Models for Cross-lingual Morphological Tagging",
    author = "Malaviya, Chaitanya  and
      Gormley, Matthew R.  and
      Neubig, Graham",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1247",
    doi = "10.18653/v1/P18-1247",
    pages = "2653--2663",
    abstract = "Morphological analysis involves predicting the syntactic traits of a word (e.g. POS: Noun, Case: Acc, Gender: Fem). Previous work in morphological tagging improves performance for low-resource languages (LRLs) through cross-lingual training with a high-resource language (HRL) from the same family, but is limited by the strict, often false, assumption that tag sets exactly overlap between the HRL and LRL. In this paper we propose a method for cross-lingual morphological tagging that aims to improve information sharing between languages by relaxing this assumption. The proposed model uses factorial conditional random fields with neural network potentials, making it possible to (1) utilize the expressive power of neural network representations to smooth over superficial differences in the surface forms, (2) model pairwise and transitive relationships between tags, and (3) accurately generate tag sets that are unseen or rare in the training data. Experiments on four languages from the Universal Dependencies Treebank demonstrate superior tagging accuracies over existing cross-lingual approaches.",
}
@inproceedings{gomez-rodriguez-etal-2018-global,
    title = "Global Transition-based Non-projective Dependency Parsing",
    author = "G{\'o}mez-Rodr{\'\i}guez, Carlos  and
      Shi, Tianze  and
      Lee, Lillian",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1248",
    doi = "10.18653/v1/P18-1248",
    pages = "2664--2675",
    abstract = "Shi, Huang, and Lee (2017a) obtained state-of-the-art results for English and Chinese dependency parsing by combining dynamic-programming implementations of transition-based dependency parsers with a minimal set of bidirectional LSTM features. However, their results were limited to projective parsing. In this paper, we extend their approach to support non-projectivity by providing the first practical implementation of the MH₄ algorithm, an $O(n^4)$ mildly nonprojective dynamic-programming parser with very high coverage on non-projective treebanks. To make MH₄ compatible with minimal transition-based feature sets, we introduce a transition-based interpretation of it in which parser items are mapped to sequences of transitions. We thus obtain the first implementation of global decoding for non-projective transition-based parsing, and demonstrate empirically that it is effective than its projective counterpart in parsing a number of highly non-projective languages.",
}
@inproceedings{kitaev-klein-2018-constituency,
    title = "Constituency Parsing with a Self-Attentive Encoder",
    author = "Kitaev, Nikita  and
      Klein, Dan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1249",
    doi = "10.18653/v1/P18-1249",
    pages = "2676--2686",
    abstract = "We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-of-the-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.",
}
@inproceedings{chen-etal-2018-pre,
    title = "Pre- and In-Parsing Models for Neural Empty Category Detection",
    author = "Chen, Yufei  and
      Zhao, Yuanyuan  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1250",
    doi = "10.18653/v1/P18-1250",
    pages = "2687--2696",
    abstract = "Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.",
}
@inproceedings{argueta-chiang-2018-composing,
    title = "Composing Finite State Transducers on {GPU}s",
    author = "Argueta, Arturo  and
      Chiang, David",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1251",
    doi = "10.18653/v1/P18-1251",
    pages = "2697--2705",
    abstract = "Weighted finite state transducers (FSTs) are frequently used in language processing to handle tasks such as part-of-speech tagging and speech recognition. There has been previous work using multiple CPU cores to accelerate finite state algorithms, but limited attention has been given to parallel graphics processing unit (GPU) implementations. In this paper, we introduce the first (to our knowledge) GPU implementation of the FST composition operation, and we also discuss the optimizations used to achieve the best performance on this architecture. We show that our approach obtains speedups of up to 6 times over our serial implementation and 4.5 times over OpenFST.",
}
@inproceedings{jiang-etal-2018-supervised,
    title = "Supervised Treebank Conversion: Data and Approaches",
    author = "Jiang, Xinzhou  and
      Li, Zhenghua  and
      Zhang, Bo  and
      Zhang, Min  and
      Li, Sheng  and
      Si, Luo",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1252",
    doi = "10.18653/v1/P18-1252",
    pages = "2706--2716",
    abstract = "Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance. However, previous work mainly focuses on unsupervised treebank conversion and has made little progress due to the lack of manually labeled data where each sentence has two syntactic trees complying with two different guidelines at the same time, referred as bi-tree aligned data. In this work, we for the first time propose the task of supervised treebank conversion. First, we manually construct a bi-tree aligned dataset containing over ten thousand sentences. Then, we propose two simple yet effective conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser. Experimental results show that 1) the two conversion approaches achieve comparable conversion accuracy, and 2) treebank conversion is superior to the widely used multi-task learning framework in multi-treebank exploitation and leads to significantly higher parsing accuracy.",
}
@inproceedings{lu-etal-2018-object,
    title = "Object-oriented Neural Programming ({OONP}) for Document Understanding",
    author = "Lu, Zhengdong  and
      Liu, Xianggen  and
      Cui, Haotian  and
      Yan, Yukun  and
      Zheng, Daqi",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1253",
    doi = "10.18653/v1/P18-1253",
    pages = "2717--2726",
    abstract = "We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and builds and updates an intermediate ontology during the process to summarize its partial understanding of the text. OONP supports a big variety of forms (both symbolic and differentiable) for representing the state and the document, and a rich family of operations to compose the representation. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL), reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.",
}
@inproceedings{hale-etal-2018-finding,
    title = "Finding syntax in human encephalography with beam search",
    author = "Hale, John  and
      Dyer, Chris  and
      Kuncoro, Adhiguna  and
      Brennan, Jonathan",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1254",
    doi = "10.18653/v1/P18-1254",
    pages = "2727--2736",
    abstract = "Recurrent neural network grammars (RNNGs) are generative models of (tree , string ) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count. When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak. By contrast, a non-syntactic neural language model yields no reliable effects. Model comparisons attribute the early peak to syntactic composition within the RNNG. This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.",
}
@inproceedings{rao-daume-iii-2018-learning,
    title = "Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information",
    author = "Rao, Sudha  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1255",
    doi = "10.18653/v1/P18-1255",
    pages = "2737--2746",
    abstract = "Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of 77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines.",
}
@inproceedings{cianflone-etal-2018-lets,
    title = "Let{'}s do it {``}again{''}: A First Computational Approach to Detecting Adverbial Presupposition Triggers",
    author = "Cianflone, Andre  and
      Feng, Yulan  and
      Kabbara, Jad  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P18-1256",
    doi = "10.18653/v1/P18-1256",
    pages = "2747--2755",
    abstract = "We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.",
}
@proceedings{rocling-ijclclp-2018-linguistics,
    title = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    editor = "Lee, Chi-Chun (Jeremy)  and
      Yang, Cheng-Zen  and
      Chien, Jen-Tzung",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1000",
}
@inproceedings{chou-etal-2018-ji,
    title = "基於數字文本相關之語者驗證系統的研究與實作 (Study and Implementation on Digit-related Speaker Verification) [In {C}hinese]",
    author = "Chou, Chung-Hung  and
      Jang, Jyh-Shing Roger  and
      Hsiao, Shan-Wen",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1001",
    pages = "1--15",
}
@inproceedings{rajaratnam-etal-2018-isolated,
    title = "Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition",
    author = "Rajaratnam, Krishan  and
      Shah, Kunal  and
      Kalita, Jugal",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1002",
    pages = "16--30",
}
@inproceedings{su-etal-2018-shi,
    title = "使用性別資訊於語者驗證系統之研究與實作 (A study and implementation on Speaker Verification System using Gender Information) [In {C}hinese]",
    author = "Su, Yu-Jui  and
      Jang, Jyh-Shing Roger  and
      Chan, Po-Cheng",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1003",
    pages = "31--45",
}
@inproceedings{chen-etal-2018-hui,
    title = "會議語音辨識使用語者資訊之語言模型調適技術 (On the Use of Speaker-Aware Language Model Adaptation Techniques for Meeting Speech Recognition ) [In {C}hinese]",
    author = "Chen, Ying-wen  and
      Lo, Tien-hong  and
      Chang, Hsiu-jui  and
      Chao, Wei-Cheng  and
      Chen, Berlin",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1004",
    pages = "46--60",
}
@inproceedings{lee-wang-2018-fan,
    title = "繁體中文依存句法剖析器 (Traditional {C}hinese Dependency Parser) [In {C}hinese]",
    author = "Lee, Yen-Hsuan  and
      Wang, Yih-Ru",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1005",
    pages = "61--75",
}
@inproceedings{wu-etal-2018-shi,
    title = "是非題之支持證據檢索 (Supporting Evidence Retrieval for Answering Yes/No Questions) [In {C}hinese]",
    author = "Wu, Meng-Tse  and
      Lin, Yi-Chung  and
      Su, Keh-Yih",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1006",
    pages = "76--77",
}
@inproceedings{lo-chen-2018-tan,
    title = "探討聲學模型的合併技術與半監督鑑別式訓練於會議語音辨識之研究 (Investigating acoustic model combination and semi-supervised discriminative training for meeting speech recognition) [In {C}hinese]",
    author = "Lo, Tien-Hong  and
      Chen, Berlin",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1007",
    pages = "78--80",
}
@inproceedings{chen-etal-2018-ji,
    title = "基於基因演算法的組合式多文件摘要方法 (An Ensemble Approach for Multi-document Summarization using Genetic Algorithms) [In {C}hinese]",
    author = "Chen, Chun-Chang  and
      Chung, Yu-Hang  and
      Yang, Cheng-Zen  and
      Fan, Jhih-Sheng  and
      Lee, Chao-Yuan",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1008",
    pages = "81--95",
}
@inproceedings{huang-etal-2018-wavenet,
    title = "{W}ave{N}et 聲碼器及其於語音轉換之應用 ({W}ave{N}et Vocoder and its Applications in Voice Conversion) [In {C}hinese]",
    author = "Huang, Wen-Chin  and
      Lo, Chen-Chou  and
      Hwang, Hsin-Te  and
      Tsao, Yu  and
      Wang, Hsin-Min",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1009",
    pages = "96--110",
}
@inproceedings{chao-etal-2018-tan,
    title = "探討鑑別式訓練聲學模型之類神經網路架構及優化方法的改進 (Discriminative Training of Acoustic Models Leveraging Improved Neural Network Architecture and Optimization Method) [In {C}hinese]",
    author = "Chao, Wei-Cheng  and
      Chang, Hsiu-Jui  and
      Lo, Tien-Hong  and
      Chen, Berlin",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1010",
    pages = "111--113",
}
@inproceedings{lai-wang-2018-shi,
    title = "使用長短期記憶類神經網路建構中文語音辨識器之研究 (A study on {M}andarin speech recognition using Long Short-Term Memory neural network) [In {C}hinese]",
    author = "Lai, Chien-hung  and
      Wang, Yih-Ru",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1011",
    pages = "114--115",
}
@inproceedings{tseng-etal-2018-tan,
    title = "探索結合快速文本及卷積神經網路於可讀性模型之建立 (Exploring Combination of {F}ast{T}ext and Convolutional Neural Networks for Building Readability Models) [In {C}hinese]",
    author = "Tseng, Hou-Chiang  and
      Chen, Berlin  and
      Sung, Yao-Ting",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1012",
    pages = "116--125",
}
@inproceedings{do-2018-weather,
    title = "Weather Forecast Voice System",
    author = "Do, Dinh Thanh",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1013",
    pages = "126--139",
}
@inproceedings{luo-etal-2018-wei,
    title = "未登錄詞之向量表示法模型於中文機器閱讀理解之應用 (An {OOV} Word Embedding Framework for {C}hinese Machine Reading Comprehension) [In {C}hinese]",
    author = "Luo, Shang-Bao  and
      Lee, Ching-Hsien  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1014",
    pages = "140--141",
}
@inproceedings{huang-liu-2018-zhi,
    title = "智慧手機客語拼音輸入法之研發-以臺灣海陸腔為例 (Research and Implementation of {H}akka {P}inyin Input Method for Mobile Cell - An Example of {T}aiwan {H}io{L}iuk Accent) [In {C}hinese]",
    author = "Huang, Feng-Long  and
      Liu, Ming-Chan",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1015",
    pages = "142--156",
}
@inproceedings{chou-wang-2018-yi,
    title = "以深層類神經網路標記中文階層式多標籤語意概念 (Hierarchical Multi-Label {C}hinese Word Semantic Labeling using Deep Neural Network ) [In {C}hinese]",
    author = "Chou, Wei-Chieh  and
      Wang, Yih-Ru",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1016",
    pages = "157--157",
}
@inproceedings{chen-etal-2018-lena,
    title = "{LENA} computerized automatic analysis of speech development from birth to three",
    author = "Chen, Li-Mei  and
      Oller, D. Kimbrough  and
      Lee, Chia-Cheng  and
      Liu, Chin-Ting Jimbo",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1017",
    pages = "158--168",
}
@inproceedings{daiya-singh-2018-using,
    title = "Using Statistical and Semantic Models for Multi-Document Summarization",
    author = "Daiya, Divyanshu  and
      Singh, Anukarsh",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1018",
    pages = "169--183",
}
@inproceedings{tsai-etal-2018-tai,
    title = "台語古詩朗誦系統{A} {T}aiwanese Text-to-Speech System for Ancient Poems[In {C}hinese]",
    author = "Tsai, Yu-Lin  and
      Huang, Chao-Hsiang  and
      Lin, Chuan-Jie",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1019",
    pages = "184--198",
}
@inproceedings{gong-liu-2018-semantic,
    title = "On the Semantic Relations and Functional Properties of Noun-Noun Compounds in {M}andarin",
    author = "Gong, Shu-Ping  and
      Liu, Chih-Hung",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1020",
    pages = "199--213",
}
@inproceedings{wang-etal-2018-lstm,
    title = "An {LSTM} Approach to Short Text Sentiment Classification with Word Embeddings",
    author = "Wang, Jenq-Haur  and
      Liu, Ting-Wei  and
      Luo, Xiong  and
      Wang, Long",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1021",
    pages = "214--223",
}
@inproceedings{chang-etal-2018-ai,
    title = "{AI} Clerk: 會賣東西的機器人 ({AI} Clerk: Shopping Assistant) [In {C}hinese]",
    author = "Chang, Ru-Yng  and
      Pan, Huan-Yi  and
      Lin, Bo-Lin  and
      Chen, Wei-Lun  and
      Hsieh, Jia En  and
      Huang, Wen-Yu  and
      Li, Lu-Hsuan",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1022",
    pages = "224--235",
}
@inproceedings{yeh-chen-2018-jie,
    title = "結合卷積神經網路與遞迴神經網路於推文極性分類 (Combining Convolutional Neural Network and Recurrent Neural Network for Tweet Polarity Classification) [In {C}hinese]",
    author = "Yeh, Chih-Ting  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1023",
    pages = "236--245",
}
@inproceedings{wang-2018-fu,
    title = "輔助建立混合性系統之自然語言處理系統深度評估平台 {---} 以評估依存關係分析器為例 (The Platform providing {NLP} System Deep Comparative Evaluation and Auxiliary Information for Hybrid {NLP} System Building: Trial on Dependency Parser Evaluation) [In {C}hinese]",
    author = "Wang, Yi-siang",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1024",
    pages = "246--255",
}
@inproceedings{shiue-etal-2018-smart,
    title = "Smart vs. Solid Solutions in Computational {L}inguistics{---}{M}achine Translation or Information Retrieval",
    author = "Shiue, Su-Mei  and
      Huang, Lang-Jyi  and
      Tsai, Wei-Ho  and
      Chen, Yen-Lin",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1025",
    pages = "256--265",
}
@inproceedings{shiue-etal-2018-four,
    title = "On Four Metaheuristic Applications to Speech {E}nhancement{---}{I}mplementing Optimization Algorithms with {MATLAB} R2018a",
    author = "Shiue, Su-Mei  and
      Huang, Lang-Jyi  and
      Tsai, Wei-Ho  and
      Chen, Yen-Lin",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1026",
    pages = "266--275",
}
@inproceedings{chen-etal-2018-jie,
    title = "節能知識問答機器人 (Energy Saving Knowledge Chatbot) [In {C}hinese]",
    author = "Chen, Jhih-Jie  and
      Chang, Shih-Ying  and
      Chiu, Tsu-Jin  and
      Tsai, Ming-Chiao  and
      Chang, Jason S.",
    booktitle = "Proceedings of the 30th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2018)",
    month = oct,
    year = "2018",
    address = "Hsinchu, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O18-1027",
    pages = "276--285",
}
@proceedings{naacl-2018-2018-north-american-chapter-association-linguistics,
    title = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    editor = "Bansal, Mohit  and
      Passonneau, Rebecca",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-6000",
    doi = "10.18653/v1/N18-6",
}
@inproceedings{neubig-allamanis-2018-modelling,
    title = "Modelling Natural Language, Programs, and their Intersection",
    author = "Neubig, Graham  and
      Allamanis, Miltiadis",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-6001",
    doi = "10.18653/v1/N18-6001",
    pages = "1--3",
    abstract = "As computers and information grow a more integral part of our world, it is becoming more and more important for humans to be able to interact with their computers in complex ways. One way to do so is by programming, but the ability to understand and generate programming languages is a highly specialized skill. As a result, in the past several years there has been an increasing research interest in methods that focus on the intersection of programming and natural language, allowing users to use natural language to interact with computers in the complex ways that programs allow us to do. In this tutorial, we will focus on machine learning models of programs and natural language focused on making this goal a reality. First, we will discuss the similarities and differences between programming and natural language. Then we will discuss methods that have been designed to cover a variety of tasks in this field, including automatic explanation of programs in natural language (code-to-language), automatic generation of programs from natural language specifications (language-to-code), modeling the natural language elements of source code, and analysis of communication in collaborative programming communities. The tutorial will be aimed at NLP researchers and practitioners, aiming to describe the interesting opportunities that models at the intersection of natural and programming languages provide, and also how their techniques could provide benefit to the practice of software engineering as a whole.",
}
@inproceedings{gardent-narayan-2018-deep,
    title = "Deep Learning Approaches to Text Production",
    author = "Gardent, Claire  and
      Narayan, Shashi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-6002",
    doi = "10.18653/v1/N18-6002",
    pages = "4--9",
    abstract = "Text production is a key component of many NLP applications. In data-driven approaches, it is used for instance, to generate dialogue turns from dialogue moves, to verbalise the content of Knowledge bases or to generate natural English sentences from rich linguistic representations, such as dependency trees or Abstract Meaning Representations. In text-driven methods on the other hand, text production is at work in sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, text summarisation and end-to-end dialogue systems. Following the success of encoder-decoder models in modeling sequence-rewriting tasks such as machine translation, deep learning models have successfully been applied to the various text production tasks. In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural models for text production. Each text production task raises a slightly different communication goal (e.g, how to take the dialogue context into account when producing a dialogue turn; how to detect and merge relevant information when summarising a text; or how to produce a well-formed text that correctly capture the information contained in some input data in the case of data-to-text generation). We will outline the constraints specific to each subtasks and examine how the existing neural models account for them.",
}
@inproceedings{ren-etal-2018-scalable,
    title = "Scalable Construction and Reasoning of Massive Knowledge Bases",
    author = "Ren, Xiang  and
      Peng, Nanyun  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-6003",
    doi = "10.18653/v1/N18-6003",
    pages = "10--16",
    abstract = "In today{'}s information-based society, there is abundant knowledge out there carried in the form of natural language texts (e.g., news articles, social media posts, scientific publications), which spans across various domains (e.g., corporate documents, advertisements, legal acts, medical reports), which grows at an astonishing rate. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. How to turn such massive and unstructured text data into structured, actionable knowledge, and furthermore, how to teach machines learn to reason and complete the extracted knowledge is a grand challenge to the research community. Traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In the first part of the tutorial, we introduce how to extract structured facts (i.e., entities and their relations for types of interest) from text corpora to construct knowledge bases, with a focus on methods that are weakly-supervised and domain-independent for timely knowledge base construction across various application domains. In the second part, we introduce how to leverage other knowledge, such as the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct low-resource information extraction. In the third part, we describe recent advances in knowledge base reasoning. We start with the gentle introduction to the literature, focusing on path-based and embedding based methods. We then describe DeepPath, a recent attempt of using deep reinforcement learning to combine the best of both worlds for knowledge base reasoning.",
}
@inproceedings{camacho-collados-etal-2018-interplay,
    title = "The interplay between lexical resources and Natural Language Processing",
    author = "Camacho-Collados, Jose  and
      Espinosa Anke, Luis  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-6004",
    doi = "10.18653/v1/N18-6004",
    pages = "17--23",
    abstract = "Incorporating linguistic, world and common sense knowledge into AI/NLP systems is currently an important research area, with several open problems and challenges. At the same time, processing and storing this knowledge in lexical resources is not a straightforward task. We propose to address these complementary goals from two methodological perspectives: the use of NLP methods to help the process of constructing and enriching lexical resources and the use of lexical resources for improving NLP applications. This tutorial may be useful for two main types of audience: those working on language resources who are interested in becoming acquainted with automatic NLP techniques, with the end goal of speeding and/or easing up the process of resource curation; and on the other hand, researchers in NLP who would like to benefit from the knowledge of lexical resources to improve their systems and models.",
}
@inproceedings{tsvetkov-etal-2018-socially,
    title = "Socially Responsible {NLP}",
    author = "Tsvetkov, Yulia  and
      Prabhakaran, Vinodkumar  and
      Voigt, Rob",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-6005",
    doi = "10.18653/v1/N18-6005",
    pages = "24--26",
    abstract = "As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This tutorial will provide an overview of real-world applications of language technologies and the potential ethical implications associated with them. We will discuss philosophical foundations of ethical research along with state of the art techniques. Through this tutorial, we intend to provide the NLP researcher with an overview of tools to ensure that the data, algorithms, and models that they build are socially responsible. These tools will include a checklist of common pitfalls that one should avoid (e.g., demographic bias in data collection), as well as methods to adequately mitigate these issues (e.g., adjusting sampling rates or de-biasing through regularization). The tutorial is based on a new course on Ethics and NLP developed at Carnegie Mellon University.",
}
@inproceedings{su-etal-2018-deep,
    title = "Deep Learning for Conversational {AI}",
    author = "Su, Pei-Hao  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Casanueva, I{\~n}igo  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-6006",
    doi = "10.18653/v1/N18-6006",
    pages = "27--32",
    abstract = "Spoken Dialogue Systems (SDS) have great commercial potential as they promise to revolutionise the way in which humans interact with machines. The advent of deep learning led to substantial developments in this area of NLP research, and the goal of this tutorial is to familiarise the research community with the recent advances in what some call the most difficult problem in NLP. From a research perspective, the design of spoken dialogue systems provides a number of significant challenges, as these systems depend on: a) solving several difficult NLP and decision-making tasks; and b) combining these into a functional dialogue system pipeline. A key long-term goal of dialogue system research is to enable open-domain systems that can converse about arbitrary topics and assist humans with completing a wide range of tasks. Furthermore, such systems need to autonomously learn on-line to improve their performance and recover from errors using both signals from their environment and from implicit and explicit user feedback. While the design of such systems has traditionally been modular, domain and language-specific, advances in deep learning have alleviated many of the design problems. The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale conversational AI systems. We plan to introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems, which includes three key components: 1) Language Understanding; 2) Dialogue Management; and 3) Language Generation. The differences between goal-oriented dialogue systems and chat-bot style conversational agents will be explained in order to show the motivation behind the design of both, with the main focus on the pipeline SDS framework. For each key component, we will define the research problem, provide a brief literature review and introduce the current state-of-the-art approaches. Complementary resources (e.g. available datasets and toolkits) will also be discussed. Finally, future work, outstanding challenges, and current industry practices will be presented. All of the presented material will be made available online for future reference.",
}
@proceedings{naacl-2018-2018-north-american-chapter-association,
    title = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    editor = "Liu, Yang  and
      Paek, Tim  and
      Patwardhan, Manasi",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5000",
    doi = "10.18653/v1/N18-5",
}
@inproceedings{schreiber-etal-2018-nlp,
    title = "{NLP} Lean Programming Framework: Developing {NLP} Applications More Effectively",
    author = {Schreiber, Marc  and
      Kraft, Bodo  and
      Z{\"u}ndorf, Albert},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5001",
    doi = "10.18653/v1/N18-5001",
    pages = "1--5",
    abstract = "This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom Natural Language Processing (NLP) models and pipelines by utilizing common software development build systems. This approach allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. Additionally, NLPf provides an annotation tool which improves the annotation process significantly by providing a well-designed GUI and sophisticated way of using input devices. Due to NLPf{'}s properties developers and domain experts are able to build domain-specific NLP application more effectively. Project page: \url{https://gitlab.com/schrieveslaach/NLPf} Video Tutorial: \url{https://www.youtube.com/watch?v=44UJspVebTA} (Demonstration starts at 11:40 min) This paper is related to: - Interfaces and resources to support linguistic annotation - Software architectures and reusable components - Software tools for evaluation or error analysis",
}
@inproceedings{tu-etal-2018-pay,
    title = "Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures",
    author = "Tu, Zhucheng  and
      Li, Mengping  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5002",
    doi = "10.18653/v1/N18-5002",
    pages = "6--10",
    abstract = "We demonstrate the serverless deployment of neural networks for model inferencing in NLP applications using Amazon{'}s Lambda service for feedforward evaluation and DynamoDB for storing word embeddings. Our architecture realizes a pay-per-request pricing model, requiring zero ongoing costs for maintaining server instances. All virtual machine management is handled behind the scenes by the cloud provider without any direct developer intervention. We describe a number of techniques that allow efficient use of serverless resources, and evaluations confirm that our design is both scalable and inexpensive.",
}
@inproceedings{finley-etal-2018-automated,
    title = "An automated medical scribe for documenting clinical encounters",
    author = "Finley, Gregory  and
      Edwards, Erik  and
      Robinson, Amanda  and
      Brenndoerfer, Michael  and
      Sadoughi, Najmeh  and
      Fone, James  and
      Axtmann, Nico  and
      Miller, Mark  and
      Suendermann-Oeft, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5003",
    doi = "10.18653/v1/N18-5003",
    pages = "11--15",
    abstract = "A medical scribe is a clinical professional who charts patient{--}physician encounters in real time, relieving physicians of most of their administrative burden and substantially increasing productivity and job satisfaction. We present a complete implementation of an automated medical scribe. Our system can serve either as a scalable, standardized, and economical alternative to human scribes; or as an assistive tool for them, providing a first draft of a report along with a convenient means to modify it. This solution is, to our knowledge, the first automated scribe ever presented and relies upon multiple speech and language technologies, including speaker diarization, medical speech recognition, knowledge extraction, and natural language generation.",
}
@inproceedings{singh-etal-2018-cl,
    title = "{CL} Scholar: The {ACL} {A}nthology Knowledge Graph Miner",
    author = "Singh, Mayank  and
      Dogga, Pradeep  and
      Patro, Sohan  and
      Barnwal, Dhiraj  and
      Dutt, Ritam  and
      Haldar, Rajarshi  and
      Goyal, Pawan  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5004",
    doi = "10.18653/v1/N18-5004",
    pages = "16--20",
    abstract = "We present CL Scholar, the ACL Anthology knowledge graph miner to facilitate high-quality search and exploration of current research progress in the computational linguistics community. In contrast to previous works, periodically crawling, indexing and processing of new incoming articles is completely automated in the current system. CL Scholar utilizes both textual and network information for knowledge graph construction. As an additional novel initiative, CL Scholar supports more than 1200 scholarly natural language queries along with standard keyword-based search on constructed knowledge graph. It answers binary, statistical and list based natural language queries. The current system is deployed at \url{http://cnerg.iitkgp.ac.in/aclakg}. We also provide REST API support along with bulk download facility. Our code and data are available at \url{https://github.com/CLScholar}.",
}
@inproceedings{stab-etal-2018-argumentext,
    title = "{A}rgumen{T}ext: Searching for Arguments in Heterogeneous Sources",
    author = "Stab, Christian  and
      Daxenberger, Johannes  and
      Stahlhut, Chris  and
      Miller, Tristan  and
      Schiller, Benjamin  and
      Tauchmann, Christopher  and
      Eger, Steffen  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5005",
    doi = "10.18653/v1/N18-5005",
    pages = "21--25",
    abstract = "Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89{\%} of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.",
}
@inproceedings{jaradat-etal-2018-claimrank,
    title = "{C}laim{R}ank: Detecting Check-Worthy Claims in {A}rabic and {E}nglish",
    author = "Jaradat, Israa  and
      Gencheva, Pepa  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      M{\`a}rquez, Llu{\'\i}s  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5006",
    doi = "10.18653/v1/N18-5006",
    pages = "26--30",
    abstract = "We present ClaimRank, an online system for detecting check-worthy claims. While originally trained on political debates, the system can work for any kind of text, e.g., interviews or just regular news articles. Its aim is to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should consider first. ClaimRank supports both Arabic and English, it is trained on actual annotations from nine reputable fact-checking organizations (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post), and thus it can mimic the claim selection strategies for each and any of them, as well as for the union of them all.",
}
@inproceedings{ruder-etal-2018-360deg,
    title = "360{\mbox{$^\circ$}} Stance Detection",
    author = "Ruder, Sebastian  and
      Glover, John  and
      Mehrabani, Afshin  and
      Ghaffari, Parsa",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5007",
    doi = "10.18653/v1/N18-5007",
    pages = "31--35",
    abstract = "The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360{\mbox{$^\circ$}} Stance Detection, a tool that aggregates news with multiple perspectives on a topic. It presents them on a spectrum ranging from support to opposition, enabling the user to base their opinion on multiple pieces of diverse evidence.",
}
@inproceedings{schneider-etal-2018-debugsl,
    title = "{D}ebug{SL}: An Interactive Tool for Debugging Sentiment Lexicons",
    author = "Schneider, Andrew  and
      Male, John  and
      Bhogadhi, Saroja  and
      Dragut, Eduard",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5008",
    doi = "10.18653/v1/N18-5008",
    pages = "36--40",
    abstract = "We introduce DebugSL, a visual (Web) debugging tool for sentiment lexicons (SLs). Its core component implements our algorithms for the automatic detection of polarity inconsistencies in SLs. An inconsistency is a set of words and/or word-senses whose polarity assignments cannot all be simultaneously satisfied. DebugSL finds inconsistencies of small sizes in SLs and has a rich user interface which helps users in the correction process. The project source code is available at \url{https://github.com/atschneid/DebugSL} A screencast of DebugSL can be viewed at \url{https://cis.temple.edu/~edragut/DebugSL.webm}",
}
@inproceedings{zhang-etal-2018-elisa,
    title = "{ELISA}-{EDL}: A Cross-lingual Entity Extraction, Linking and Localization System",
    author = "Zhang, Boliang  and
      Lin, Ying  and
      Pan, Xiaoman  and
      Lu, Di  and
      May, Jonathan  and
      Knight, Kevin  and
      Ji, Heng",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5009",
    doi = "10.18653/v1/N18-5009",
    pages = "41--45",
    abstract = "We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap. We make all of our data sets, resources and system training and testing APIs publicly available for research purpose.",
}
@inproceedings{maheshwari-etal-2018-entity,
    title = "Entity Resolution and Location Disambiguation in the {A}ncient {H}indu Temples Domain using Web Data",
    author = "Maheshwari, Ayush  and
      Kumar, Vishwajeet  and
      Ramakrishnan, Ganesh  and
      Nath, J. Saketha",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5010",
    doi = "10.18653/v1/N18-5010",
    pages = "46--50",
    abstract = "We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. Scarce, unstructured information poses a challenge to Entity Resolution(ER) and snippet ranking. Additionally, because the same set of entities may be associated with multiple locations, Location Disambiguation(LD) is a problem. The mentions and descriptions of temples exist in the order of hundreds of thousands, with such data generated by various users in various forms such as text (Wikipedia pages), videos (YouTube videos), blogs, etc. We demonstrate an integrated approach using a combination of grammar rules for parsing and unsupervised (clustering) algorithms to resolve entity and locations with high confidence. A demo of our system is accessible at \url{tinyurl.com/templedemos}. Our system is open source and available on GitHub.",
}
@inproceedings{gokcen-etal-2018-madly,
    title = "Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It{'}s Hard for Computers",
    author = "Gokcen, Ajda  and
      Hill, Ethan  and
      White, Michael",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5011",
    doi = "10.18653/v1/N18-5011",
    pages = "51--55",
    abstract = "Madly Ambiguous is an open source, online game aimed at teaching audiences of all ages about structural ambiguity and why it{'}s hard for computers. After a brief introduction to structural ambiguity, users are challenged to complete a sentence in a way that tricks the computer into guessing an incorrect interpretation. Behind the scenes are two different NLP-based methods for classifying the user{'}s input, one representative of classic rule-based approaches to disambiguation and the other representative of recent neural network approaches. Qualitative feedback from the system{'}s use in online, classroom, and science museum settings indicates that it is engaging and successful in conveying the intended take home messages. A demo of Madly Ambiguous can be played at \url{http://madlyambiguous.osu.edu}.",
}
@inproceedings{vu-etal-2018-vncorenlp,
    title = "{V}n{C}ore{NLP}: A {V}ietnamese Natural Language Processing Toolkit",
    author = "Vu, Thanh  and
      Nguyen, Dat Quoc  and
      Nguyen, Dai Quoc  and
      Dras, Mark  and
      Johnson, Mark",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5012",
    doi = "10.18653/v1/N18-5012",
    pages = "56--60",
    abstract = "We present an easy-to-use and fast toolkit, namely VnCoreNLP{---}a Java NLP annotation pipeline for Vietnamese. Our VnCoreNLP supports key natural language processing (NLP) tasks including word segmentation, part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing, and obtains state-of-the-art (SOTA) results for these tasks. We release VnCoreNLP to provide rich linguistic annotations to facilitate research work on Vietnamese NLP. Our VnCoreNLP is open-source and available at: \url{https://github.com/vncorenlp/VnCoreNLP}",
}
@inproceedings{liang-etal-2018-cnns,
    title = "{CNN}s for {NLP} in the Browser: Client-Side Deployment and Visualization Opportunities",
    author = "Liang, Yiyun  and
      Tu, Zhucheng  and
      Huang, Laetitia  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5013",
    doi = "10.18653/v1/N18-5013",
    pages = "61--65",
    abstract = "We demonstrate a JavaScript implementation of a convolutional neural network that performs feedforward inference completely in the browser. Such a deployment means that models can run completely on the client, on a wide range of devices, without making backend server requests. This design is useful for applications with stringent latency requirements or low connectivity. Our evaluations show the feasibility of JavaScript as a deployment target. Furthermore, an in-browser implementation enables seamless integration with the JavaScript ecosystem for information visualization, providing opportunities to visually inspect neural networks and better understand their inner workings.",
}
@inproceedings{spinks-moens-2018-generating,
    title = "Generating Continuous Representations of Medical Texts",
    author = "Spinks, Graham  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5014",
    doi = "10.18653/v1/N18-5014",
    pages = "66--70",
    abstract = "We present an architecture that generates medical texts while learning an informative, continuous representation with discriminative features. During training the input to the system is a dataset of captions for medical X-Rays. The acquired continuous representations are of particular interest for use in many machine learning techniques where the discrete and high-dimensional nature of textual input is an obstacle. We use an Adversarially Regularized Autoencoder to create realistic text in both an unconditional and conditional setting. We show that this technique is applicable to medical texts which often contain syntactic and domain-specific shorthands. A quantitative evaluation shows that we achieve a lower model perplexity than a traditional LSTM generator.",
}
@inproceedings{steele-specia-2018-vis,
    title = "Vis-Eval Metric Viewer: A Visualisation Tool for Inspecting and Evaluating Metric Scores of Machine Translation Output",
    author = "Steele, David  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5015",
    doi = "10.18653/v1/N18-5015",
    pages = "71--75",
    abstract = "Machine Translation systems are usually evaluated and compared using automated evaluation metrics such as BLEU and METEOR to score the generated translations against human translations. However, the interaction with the output from the metrics is relatively limited and results are commonly a single score along with a few additional statistics. Whilst this may be enough for system comparison it does not provide much useful feedback or a means for inspecting translations and their respective scores. VisEval Metric Viewer VEMV is a tool designed to provide visualisation of multiple evaluation scores so they can be easily interpreted by a user. VEMV takes in the source, reference, and hypothesis files as parameters, and scores the hypotheses using several popular evaluation metrics simultaneously. Scores are produced at both the sentence and dataset level and results are written locally to a series of HTML files that can be viewed on a web browser. The individual scored sentences can easily be inspected using powerful search and selection functions and results can be visualised with graphical representations of the scores and distributions.",
}
@inproceedings{deleris-etal-2018-know,
    title = "Know Who Your {F}riends Are: Understanding Social Connections from Unstructured Text",
    author = "Deleris, L{\'e}a  and
      Bonin, Francesca  and
      Daly, Elizabeth  and
      Deparis, St{\'e}phane  and
      Hou, Yufang  and
      Jochim, Charles  and
      Lassoued, Yassine  and
      Levacher, Killian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5016",
    doi = "10.18653/v1/N18-5016",
    pages = "76--80",
    abstract = "Having an understanding of interpersonal relationships is helpful in many contexts. Our system seeks to assist humans with that task, using textual information (e.g., case notes, speech transcripts, posts, books) as input. Specifically, our system first extracts qualitative and quantitative information elements (which we call signals) about interactions among persons, aggregates those to provide a condensed view of relationships and then enables users to explore all facets of the resulting social (multi-)graph through a visual interface.",
}
@inproceedings{liu-etal-2018-riskfinder,
    title = "{R}isk{F}inder: A Sentence-level Risk Detector for Financial Reports",
    author = "Liu, Yu-Wen  and
      Liu, Liang-Chih  and
      Wang, Chuan-Ju  and
      Tsai, Ming-Feng",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5017",
    doi = "10.18653/v1/N18-5017",
    pages = "81--85",
    abstract = "This paper presents a web-based information system, RiskFinder, for facilitating the analyses of soft and hard information in financial reports. In particular, the system broadens the analyses from the word level to sentence level, which makes the system useful for practitioner communities and unprecedented among financial academics. The proposed system has four main components: 1) a Form 10-K risk-sentiment dataset, consisting of a set of risk-labeled financial sentences and pre-trained sentence embeddings; 2) metadata, including basic information on each company that published the Form 10-K financial report as well as several relevant financial measures; 3) an interface that highlights risk-related sentences in the financial reports based on the latest sentence embedding techniques; 4) a visualization of financial time-series data for a corresponding company. This paper also conducts some case studies to showcase that the system can be of great help in capturing valuable insight within large amounts of textual information. The system is now online available at \url{https://cfda.csie.org/RiskFinder/}.",
}
@inproceedings{kim-etal-2018-smilee,
    title = "{SMILEE}: Symmetric Multi-modal Interactions with Language-gesture Enabled ({AI}) Embodiment",
    author = "Kim, Sujeong  and
      Salter, David  and
      DeLuccia, Luke  and
      Son, Kilho  and
      Amer, Mohamed R.  and
      Tamrakar, Amir",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5018",
    doi = "10.18653/v1/N18-5018",
    pages = "86--90",
    abstract = "We demonstrate an intelligent conversational agent system designed for advancing human-machine collaborative tasks. The agent is able to interpret a user{'}s communicative intent from both their verbal utterances and non-verbal behaviors, such as gestures. The agent is also itself able to communicate both with natural language and gestures, through its embodiment as an avatar thus facilitating natural symmetric multi-modal interactions. We demonstrate two intelligent agents with specialized skills in the Blocks World as use-cases of our system.",
}
@inproceedings{deleris-etal-2018-decision,
    title = "Decision Conversations Decoded",
    author = "Deleris, L{\'e}a  and
      Ganguly, Debasis  and
      Levacher, Killian  and
      Stephenson, Martin  and
      Bonin, Francesca",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5019",
    doi = "10.18653/v1/N18-5019",
    pages = "91--95",
    abstract = "We describe the vision and current version of a Natural Language Processing system aimed at group decision making facilitation. Borrowing from the scientific field of Decision Analysis, its essential role is to identify alternatives and criteria associated with a given decision, to keep track of who proposed them and of the expressed sentiment towards them. Based on this information, the system can help identify agreement and dissent or recommend an alternative. Overall, it seeks to help a group reach a decision in a natural yet auditable fashion.",
}
@inproceedings{fang-etal-2018-sounding,
    title = "Sounding Board: A User-Centric and Content-Driven Social Chatbot",
    author = "Fang, Hao  and
      Cheng, Hao  and
      Sap, Maarten  and
      Clark, Elizabeth  and
      Holtzman, Ari  and
      Choi, Yejin  and
      Smith, Noah A.  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-5020",
    doi = "10.18653/v1/N18-5020",
    pages = "96--100",
    abstract = "We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design. We also share insights gained from large-scale online logs based on 160,000 conversations with real-world users.",
}
@proceedings{naacl-2018-2018-north-american-chapter,
    title = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    editor = "Cordeiro, Silvio Ricardo  and
      Oraby, Shereen  and
      Pavalanathan, Umashanthi  and
      Rim, Kyeongmin",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4000",
    doi = "10.18653/v1/N18-4",
}
@inproceedings{shin-doyle-2018-alignment,
    title = "Alignment, Acceptance, and Rejection of Group Identities in Online Political Discourse",
    author = "Shin, Hagyeong  and
      Doyle, Gabriel",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4001",
    doi = "10.18653/v1/N18-4001",
    pages = "1--8",
    abstract = "Conversation is a joint social process, with participants cooperating to exchange information. This process is helped along through linguistic alignment: participants{'} adoption of each other{'}s word use. This alignment is robust, appearing many settings, and is nearly always positive. We create an alignment model for examining alignment in Twitter conversations across antagonistic groups. This model finds that some word categories, specifically pronouns used to establish group identity and common ground, are negatively aligned. This negative alignment is observed despite other categories, which are less related to the group dynamics, showing the standard positive alignment. This suggests that alignment is strongly biased toward cooperative alignment, but that different linguistic features can show substantially different behaviors.",
}
@inproceedings{aedmaa-etal-2018-combining,
    title = "Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of {E}stonian Particle Verbs",
    author = {Aedmaa, Eleri  and
      K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4002",
    doi = "10.18653/v1/N18-4002",
    pages = "9--16",
    abstract = "This paper presents two novel datasets and a random-forest classifier to automatically predict literal vs. non-literal language usage for a highly frequent type of multi-word expression in a low-resource language, i.e., Estonian. We demonstrate the value of language-specific indicators induced from theoretical linguistic research, which outperform a high majority baseline when combined with language-independent features of non-literal language (such as abstractness).",
}
@inproceedings{seyffarth-2018-verb,
    title = "Verb Alternations and Their Impact on Frame Induction",
    author = "Seyffarth, Esther",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4003",
    doi = "10.18653/v1/N18-4003",
    pages = "17--24",
    abstract = "Frame induction is the automatic creation of frame-semantic resources similar to FrameNet or PropBank, which map lexical units of a language to frame representations of each lexical unit{'}s semantics. For verbs, these representations usually include a specification of their argument slots and of the selectional restrictions that apply to each slot. Verbs that participate in diathesis alternations have different syntactic realizations whose semantics are closely related, but not identical. We discuss the influence that such alternations have on frame induction, compare several possible frame structures for verbs in the causative alternation, and propose a systematic analysis of alternating verbs that encodes their similarities as well as their differences.",
}
@inproceedings{emami-etal-2018-generalized,
    title = "A Generalized Knowledge Hunting Framework for the {W}inograd Schema Challenge",
    author = "Emami, Ali  and
      Trischler, Adam  and
      Suleman, Kaheer  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4004",
    doi = "10.18653/v1/N18-4004",
    pages = "25--31",
    abstract = "We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.",
}
@inproceedings{pierrejean-tanguy-2018-towards,
    title = "Towards Qualitative Word Embeddings Evaluation: Measuring Neighbors Variation",
    author = "Pierrejean, B{\'e}n{\'e}dicte  and
      Tanguy, Ludovic",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4005",
    doi = "10.18653/v1/N18-4005",
    pages = "32--39",
    abstract = "We propose a method to study the variation lying between different word embeddings models trained with different parameters. We explore the variation between models trained with only one varying parameter by observing the distributional neighbors variation and show how changing only one parameter can have a massive impact on a given semantic space. We show that the variation is not affecting all words of the semantic space equally. Variation is influenced by parameters such as setting a parameter to its minimum or maximum value but it also depends on the corpus intrinsic features such as the frequency of a word. We identify semantic classes of words remaining stable across the models trained and specific words having high variation.",
}
@inproceedings{macavaney-zeldes-2018-deeper,
    title = "A Deeper Look into Dependency-Based Word Embeddings",
    author = "MacAvaney, Sean  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4006",
    doi = "10.18653/v1/N18-4006",
    pages = "40--45",
    abstract = "We investigate the effect of various dependency-based word embeddings on distinguishing between functional and domain similarity, word similarity rankings, and two downstream tasks in English. Variations include word embeddings trained using context windows from Stanford and Universal dependencies at several levels of enhancement (ranging from unlabeled, to Enhanced++ dependencies). Results are compared to basic linear contexts and evaluated on several datasets. We found that embeddings trained with Universal and Stanford dependency contexts excel at different tasks, and that enhanced dependencies often improve performance.",
}
@inproceedings{kameswara-sarma-2018-learning,
    title = "Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets",
    author = "Kameswara Sarma, Prathusha",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4007",
    doi = "10.18653/v1/N18-4007",
    pages = "46--53",
    abstract = "This research proposal describes two algorithms that are aimed at learning word embeddings for data sparse and sentiment rich data sets. The goal is to use word embeddings adapted for domain specific data sets in downstream applications such as sentiment classification. The first approach learns word embeddings in a supervised fashion via SWESA (Supervised Word Embeddings for Sentiment Analysis), an algorithm for sentiment analysis on data sets that are of modest size. SWESA leverages document labels to jointly learn polarity-aware word embeddings and a classifier to classify unseen documents. In the second approach domain adapted (DA) word embeddings are learned by exploiting the specificity of domain specific data sets and the breadth of generic word embeddings. The new embeddings are formed by aligning corresponding word vectors using Canonical Correlation Analysis (CCA) or the related nonlinear Kernel CCA. Experimental results on binary sentiment classification tasks using both approaches for standard data sets are presented.",
}
@inproceedings{ezeani-etal-2018-igbo,
    title = "{I}gbo Diacritic Restoration using Embedding Models",
    author = "Ezeani, Ignatius  and
      Hepple, Mark  and
      Onyenwe, Ikechukwu  and
      Chioma, Enemouh",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4008",
    doi = "10.18653/v1/N18-4008",
    pages = "54--60",
    abstract = "Igbo is a low-resource language spoken by approximately 30 million people worldwide. It is the native language of the Igbo people of south-eastern Nigeria. In Igbo language, diacritics - orthographic and tonal - play a huge role in the distinguishing the meaning and pronunciation of words. Omitting diacritics in texts often leads to lexical ambiguity. Diacritic restoration is a pre-processing task that replaces missing diacritics on words from which they have been removed. In this work, we applied embedding models to the diacritic restoration task and compared their performances to those of n-gram models. Although word embedding models have been successfully applied to various NLP tasks, it has not been used, to our knowledge, for diacritic restoration. Two classes of word embeddings models were used: those projected from the English embedding space; and those trained with Igbo bible corpus ({\mbox{$\approx$}} 1m). Our best result, 82.49{\%}, is an improvement on the baseline n-gram models.",
}
@inproceedings{lavrentovich-2018-using,
    title = "Using Classifier Features to Determine Language Transfer on Morphemes",
    author = "Lavrentovich, Alexandra",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4009",
    doi = "10.18653/v1/N18-4009",
    pages = "61--66",
    abstract = "The aim of this thesis is to perform a Native Language Identification (NLI) task where we identify an English learner{'}s native language background based only on the learner{'}s English writing samples. We focus on the use of English grammatical morphemes across four proficiency levels. The outcome of the computational task is connected to a position in second language acquisition research that holds all learners acquire English grammatical morphemes in the same order, regardless of native language background. We use the NLI task as a tool to uncover cross-linguistic influence on the developmental trajectory of morphemes. We perform a cross-corpus evaluation across proficiency levels to increase the reliability and validity of the linguistic features that predict the native language background. We include native English data to determine the different morpheme patterns used by native versus non-native English speakers. Furthermore, we conduct a human NLI task to determine the type and magnitude of language transfer cues used by human raters versus the classifier.",
}
@inproceedings{liu-lane-2018-end,
    title = "End-to-End Learning of Task-Oriented Dialogs",
    author = "Liu, Bing  and
      Lane, Ian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4010",
    doi = "10.18653/v1/N18-4010",
    pages = "67--73",
    abstract = "In this thesis proposal, we address the limitations of conventional pipeline design of task-oriented dialog systems and propose end-to-end learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learning-from-simulation approaches to improve the online interactive learning efficiency.",
}
@inproceedings{acharya-etal-2018-towards,
    title = "Towards Generating Personalized Hospitalization Summaries",
    author = "Acharya, Sabita  and
      Di Eugenio, Barbara  and
      Boyd, Andrew  and
      Cameron, Richard  and
      Dunn Lopez, Karen  and
      Martyn-Nemeth, Pamela  and
      Dickens, Carolyn  and
      Ardati, Amer",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4011",
    doi = "10.18653/v1/N18-4011",
    pages = "74--82",
    abstract = "Most of the health documents, including patient education materials and discharge notes, are usually flooded with medical jargons and contain a lot of generic information about the health issue. In addition, patients are only provided with the doctor{'}s perspective of what happened to them in the hospital while the care procedure performed by nurses during their entire hospital stay is nowhere included. The main focus of this research is to generate personalized hospital-stay summaries for patients by combining information from physician discharge notes and nursing plan of care. It uses a metric to identify medical concepts that are Complex, extracts definitions for the concept from three external knowledge sources, and provides the simplest definition to the patient. It also takes various features of the patient into account, like their concerns and strengths, ability to understand basic health information, level of engagement in taking care of their health, and familiarity with the health issue and personalizes the content of the summaries accordingly. Our evaluation showed that the summaries contain 80{\%} of the medical concepts that are considered as being important by both doctor and nurses. Three patient advisors (i.e. individuals who are trained in understanding patient experience extensively) verified the usability of our summaries and mentioned that they would like to get such summaries when they are discharged from hospital.",
}
@inproceedings{deng-tam-2018-read,
    title = "Read and Comprehend by Gated-Attention Reader with More Belief",
    author = "Deng, Haohui  and
      Tam, Yik-Cheung",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4012",
    doi = "10.18653/v1/N18-4012",
    pages = "83--91",
    abstract = "Gated-Attention (GA) Reader has been effective for reading comprehension. GA Reader makes two assumptions: (1) a uni-directional attention that uses an input query to gate token encodings of a document; (2) encoding at the cloze position of an input query is considered for answer prediction. In this paper, we propose Collaborative Gating (CG) and Self-Belief Aggregation (SBA) to address the above assumptions respectively. In CG, we first use an input document to gate token encodings of an input query so that the influence of irrelevant query tokens may be reduced. Then the filtered query is used to gate token encodings of an document in a collaborative fashion. In SBA, we conjecture that query tokens other than the cloze token may be informative for answer prediction. We apply self-attention to link the cloze token with other tokens in a query so that the importance of query tokens with respect to the cloze position are weighted. Then their evidences are weighted, propagated and aggregated for better reading comprehension. Experiments show that our approaches advance the state-of-theart results in CNN, Daily Mail, and Who Did What public test sets.",
}
@inproceedings{nangia-bowman-2018-listops,
    title = "{L}ist{O}ps: A Diagnostic Dataset for Latent Tree Learning",
    author = "Nangia, Nikita  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4013",
    doi = "10.18653/v1/N18-4013",
    pages = "92--99",
    abstract = "Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while they perform well on tasks like sentence classification, they do not learn grammars that conform to any plausible semantic or syntactic formalism (Williams et al., 2018a). Studying the parsing ability of such models in natural language can be challenging due to the inherent complexities of natural language, like having several valid parses for a single sentence. In this paper we introduce ListOps, a toy dataset created to study the parsing ability of latent tree models. ListOps sequences are in the style of prefix arithmetic. The dataset is designed to have a single correct parsing strategy that a system needs to learn to succeed at the task. We show that the current leading latent tree models are unable to learn to parse and succeed at ListOps. These models achieve accuracies worse than purely sequential RNNs.",
}
@inproceedings{kurosawa-etal-2018-japanese,
    title = "{J}apanese Predicate Conjugation for Neural Machine Translation",
    author = "Kurosawa, Michiki  and
      Matsumura, Yukio  and
      Yamagishi, Hayahide  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4014",
    doi = "10.18653/v1/N18-4014",
    pages = "100--105",
    abstract = "Neural machine translation (NMT) has a drawback in that can generate only high-frequency words owing to the computational costs of the softmax function in the output layer. In Japanese-English NMT, Japanese predicate conjugation causes an increase in vocabulary size. For example, one verb can have as many as 19 surface varieties. In this research, we focus on predicate conjugation for compressing the vocabulary size in Japanese. The vocabulary list is filled with the various forms of verbs. We propose methods using predicate conjugation information without discarding linguistic information. The proposed methods can generate low-frequency words and deal with unknown words. Two methods were considered to introduce conjugation information: the first considers it as a token (conjugation token) and the second considers it as an embedded vector (conjugation feature). The results using these methods demonstrate that the vocabulary size can be compressed by approximately 86.1{\%} (Tanaka corpus) and the NMT models can output the words not in the training data set. Furthermore, BLEU scores improved by 0.91 points in Japanese-to-English translation, and 0.32 points in English-to-Japanese translation with ASPEC.",
}
@inproceedings{shimanaka-etal-2018-metric,
    title = "Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations",
    author = "Shimanaka, Hiroki  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4015",
    doi = "10.18653/v1/N18-4015",
    pages = "106--111",
    abstract = "Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Al-though it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.",
}
@inproceedings{ramesh-sankaranarayanan-2018-neural,
    title = "Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora",
    author = "Ramesh, Sree Harsha  and
      Sankaranarayanan, Krishna Prasad",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4016",
    doi = "10.18653/v1/N18-4016",
    pages = "112--119",
    abstract = "Resources for the non-English languages are scarce and this paper addresses this problem in the context of machine translation, by automatically extracting parallel sentence pairs from the multilingual articles available on the Internet. In this paper, we have used an end-to-end Siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia. Subsequently, we have showed that using the harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs: English{--}Hindi and English{--}Tamil, when compared to training exclusively on the limited bilingual corpora collected for these language pairs.",
}
@inproceedings{htut-etal-2018-training,
    title = "Training a Ranking Function for Open-Domain Question Answering",
    author = "Htut, Phu Mon  and
      Bowman, Samuel  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4017",
    doi = "10.18653/v1/N18-4017",
    pages = "120--127",
    abstract = "In recent years, there have been amazing advances in deep learning methods for machine reading. In machine reading, the machine reader has to extract the answer from the given ground truth paragraph. Recently, the state-of-the-art machine reading models achieve human level performance in SQuAD which is a reading comprehension-style question answering (QA) task. The success of machine reading has inspired researchers to combine Information Retrieval with machine reading to tackle open-domain QA. However, these systems perform poorly compared to reading comprehension-style QA because it is difficult to retrieve the pieces of paragraphs that contain the answer to the question. In this study, we propose two neural network rankers that assign scores to different passages based on their likelihood of containing the answer to a given question. Additionally, we analyze the relative importance of semantic similarity and word level relevance matching in open-domain QA.",
}
@inproceedings{vijay-etal-2018-corpus,
    title = "Corpus Creation and Emotion Prediction for {H}indi-{E}nglish Code-Mixed Social Media Text",
    author = "Vijay, Deepanshu  and
      Bohra, Aditya  and
      Singh, Vinay  and
      Akhtar, Syed Sarfaraz  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4018",
    doi = "10.18653/v1/N18-4018",
    pages = "128--135",
    abstract = "Emotion Prediction is a Natural Language Processing (NLP) task dealing with detection and classification of emotions in various monolingual and bilingual texts. While some work has been done on code-mixed social media text and in emotion prediction separately, our work is the first attempt which aims at identifying the emotion associated with Hindi-English code-mixed social media text. In this paper, we analyze the problem of emotion identification in code-mixed content and present a Hindi-English code-mixed corpus extracted from twitter and annotated with the associated emotion. For every tweet in the dataset, we annotate the source language of all the words present, and also the causal language of the expressed emotion. Finally, we propose a supervised classification system which uses various machine learning techniques for detecting the emotion associated with the text using a variety of character level, word level, and lexicon based features.",
}
@inproceedings{tornblad-etal-2018-sensing,
    title = "{S}ensing and Learning Human Annotators Engaged in Narrative Sensemaking",
    author = "Tornblad, McKenna  and
      Lapresi, Luke  and
      Homan, Christopher  and
      Ptucha, Raymond  and
      Ovesdotter Alm, Cecilia",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4019",
    doi = "10.18653/v1/N18-4019",
    pages = "136--143",
    abstract = "While labor issues and quality assurance in crowdwork are increasingly studied, how annotators make sense of texts and how they are personally impacted by doing so are not. We study these questions via a narrative-sorting annotation task, where carefully selected (by sequentiality, topic, emotional content, and length) collections of tweets serve as examples of everyday storytelling. As readers process these narratives, we measure their facial expressions, galvanic skin response, and self-reported reactions. From the perspective of annotator well-being, a reassuring outcome was that the sorting task did not cause a measurable stress response, however readers reacted to humor. In terms of sensemaking, readers were more confident when sorting sequential, target-topical, and highly emotional tweets. As crowdsourcing becomes more common, this research sheds light onto the perceptive capabilities and emotional impact of human readers.",
}
@inproceedings{jindal-2018-generating,
    title = "Generating Image Captions in {A}rabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks",
    author = "Jindal, Vasu",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-4020",
    doi = "10.18653/v1/N18-4020",
    pages = "144--151",
    abstract = "Image caption generation has gathered widespread interest in the artificial intelligence community. Automatic generation of an image description requires both computer vision and natural language processing techniques. While, there has been advanced research in the English caption generation, research on generating Arabic descriptions of an image is extremely limited. Semitic languages like Arabic are heavily influenced by root-words. We leverage this critical dependency of Arabic to generate captions of an image directly in Arabic using root-word based Recurrent Neural Network and Deep Neural Networks. Experimental results on dataset from various Middle Eastern newspaper websites allow us to report the first BLEU score for direct Arabic caption generation. We also compare the results of our approach with BLEU score captions generated in English and translated in Arabic. Experimental results confirm that generating image captions using root-words directly in Arabic significantly outperforms the English-Arabic translated captions using state-of-the-art methods.",
}
@proceedings{naacl-2018-2018-north-american,
    title = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    editor = "Bangalore, Srinivas  and
      Chu-Carroll, Jennifer  and
      Li, Yunyao",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3000",
    doi = "10.18653/v1/N18-3",
}
@inproceedings{amoia-etal-2018-scalable,
    title = "Scalable Wide and Deep Learning for Computer Assisted Coding",
    author = "Amoia, Marilisa  and
      Diehl, Frank  and
      Gimenez, Jesus  and
      Pinto, Joel  and
      Schumann, Raphael  and
      Stemmer, Fabian  and
      Vozila, Paul  and
      Zhang, Yi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3001",
    doi = "10.18653/v1/N18-3001",
    pages = "1--7",
    abstract = "In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. As a first step towards reimbursement healthcare institutions need to associate ICD-10 billing codes to these documents. This is done by trained clinical coders who may use a computer assisted solution for shortlisting of codes. In this work, we present our work to build a machine learning based scalable system for predicting ICD-10 codes from electronic medical records. We address data imbalance issues by implementing two system architectures using convolutional neural networks and logistic regression models. We illustrate the pros and cons of those system designs and show that the best performance can be achieved by leveraging the advantages of both using a system combination approach.",
}
@inproceedings{shah-etal-2018-neural,
    title = "Neural Network based Extreme Classification and Similarity Models for Product Matching",
    author = "Shah, Kashif  and
      Kopru, Selcuk  and
      Ruvini, Jean-David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3002",
    doi = "10.18653/v1/N18-3002",
    pages = "8--15",
    abstract = "Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5{\%} in term of accuracy and are capable of extremely efficient training and inference.",
}
@inproceedings{kim-etal-2018-scalable,
    title = "A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding",
    author = "Kim, Young-Bum  and
      Kim, Dongchan  and
      Kim, Joo-Kyung  and
      Sarikaya, Ruhi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3003",
    doi = "10.18653/v1/N18-3003",
    pages = "16--24",
    abstract = "Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale. In this paper, we propose a set of efficient and scalable shortlisting-reranking neural models for effective large-scale domain classification for IPDAs. The shortlisting stage focuses on efficiently trimming all domains down to a list of k-best candidate domains, and the reranking stage performs a list-wise reranking of the initial k-best domains with additional contextual information. We show the effectiveness of our approach with extensive experiments on 1,500 IPDA domains.",
}
@inproceedings{gangadharaiah-etal-2018-need,
    title = "What we need to learn if we want to do and not just talk",
    author = "Gangadharaiah, Rashmi  and
      Narayanaswamy, Balakrishnan  and
      Elkan, Charles",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3004",
    doi = "10.18653/v1/N18-3004",
    pages = "25--32",
    abstract = "In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates. Our paper makes the first attempt at evaluating state of the art models on a large real world task with human users. We show that methods that achieve state of the art performance on synthetic datasets, perform poorly in real world dialog tasks. We propose a hybrid model, where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. The hybrid model on the customer support data achieves a 78{\%} relative improvement in fluency, and a 200{\%} improvement in accuracy of external calls.",
}
@inproceedings{kang-etal-2018-data,
    title = "Data Collection for Dialogue System: A Startup Perspective",
    author = "Kang, Yiping  and
      Zhang, Yunqi  and
      Kummerfeld, Jonathan K.  and
      Tang, Lingjia  and
      Mars, Jason",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3005",
    doi = "10.18653/v1/N18-3005",
    pages = "33--40",
    abstract = "Industrial dialogue systems such as Apple Siri and Google Now rely on large scale diverse and robust training data to enable their sophisticated conversation capability. Crowdsourcing provides a scalable and inexpensive way of data collection but collecting high quality data efficiently requires thoughtful orchestration of the crowdsourcing jobs. Prior study of this topic have focused on tasks only in the academia settings with limited scope or only provide intrinsic dataset analysis, lacking indication on how it affects the trained model performance. In this paper, we present a study of crowdsourcing methods for a user intent classification task in our deployed dialogue system. Our task requires classification of 47 possible user intents and contains many intent pairs with subtle differences. We consider different crowdsourcing job types and job prompts and analyze quantitatively the quality of the collected data and the downstream model performance on a test set of real user queries from production logs. Our observation provides insights into designing efficient crowdsourcing jobs and provide recommendations for future dialogue system data collection process.",
}
@inproceedings{shah-etal-2018-bootstrapping,
    title = "Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning",
    author = {Shah, Pararth  and
      Hakkani-T{\"u}r, Dilek  and
      Liu, Bing  and
      T{\"u}r, Gokhan},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3006",
    doi = "10.18653/v1/N18-3006",
    pages = "41--51",
    abstract = "End-to-end neural models show great promise towards building conversational agents that are trained from data and on-line experience using supervised and reinforcement learning. However, these models require a large corpus of dialogues to learn effectively. For goal-oriented dialogues, such datasets are expensive to collect and annotate, since each task involves a separate schema and database of entities. Further, the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows, which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents. In this paper, we study a recently proposed approach for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing to generate fully-annotated dialogues with diverse and natural utterances. We discuss the advantages of this approach for industry applications of conversational agents, wherein an agent can be rapidly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users of the system.",
}
@inproceedings{ueffing-etal-2018-quality,
    title = "Quality Estimation for Automatically Generated Titles of e{C}ommerce Browse Pages",
    author = "Ueffing, Nicola  and
      C. de Souza, Jos{\'e} G.  and
      Leusch, Gregor",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3007",
    doi = "10.18653/v1/N18-3007",
    pages = "52--59",
    abstract = "At eBay, we are automatically generating a large amount of natural language titles for eCommerce browse pages using machine translation (MT) technology. While automatic approaches can generate millions of titles very fast, they are prone to errors. We therefore develop quality estimation (QE) methods which can automatically detect titles with low quality in order to prevent them from going live. In this paper, we present different approaches: The first one is a Random Forest (RF) model that explores hand-crafted, robust features, which are a mix of established features commonly used in Machine Translation Quality Estimation (MTQE) and new features developed specifically for our task. The second model is based on Siamese Networks (SNs) which embed the metadata input sequence and the generated title in the same space and do not require hand-crafted features at all. We thoroughly evaluate and compare those approaches on in-house data. While the RF models are competitive for scenarios with smaller amounts of training data and somewhat more robust, they are clearly outperformed by the SN models when the amount of training data is larger.",
}
@inproceedings{yoon-etal-2018-atypical,
    title = "Atypical Inputs in Educational Applications",
    author = "Yoon, Su-Youn  and
      Cahill, Aoife  and
      Loukina, Anastassia  and
      Zechner, Klaus  and
      Riordan, Brian  and
      Madnani, Nitin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3008",
    doi = "10.18653/v1/N18-3008",
    pages = "60--67",
    abstract = "In large-scale educational assessments, the use of automated scoring has recently become quite common. While the majority of student responses can be processed and scored without difficulty, there are a small number of responses that have atypical characteristics that make it difficult for an automated scoring system to assign a correct score. We describe a pipeline that detects and processes these kinds of responses at run-time. We present the most frequent kinds of what are called non-scorable responses along with effective filtering models based on various NLP and speech processing technologies. We give an overview of two operational automated scoring systems {---}one for essay scoring and one for speech scoring{---} and describe the filtering models they use. Finally, we present an evaluation and analysis of filtering models used for spoken responses in an assessment of language proficiency.",
}
@inproceedings{mitcheltree-etal-2018-using,
    title = "Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles",
    author = "Mitcheltree, Christopher  and
      Wharton, Skyler  and
      Saluja, Avneesh",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3009",
    doi = "10.18653/v1/N18-3009",
    pages = "68--75",
    abstract = "Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A $k$-means baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.",
}
@inproceedings{chiticariu-etal-2018-systemt,
    title = "{S}ystem{T}: Declarative Text Understanding for Enterprise",
    author = "Chiticariu, Laura  and
      Danilevsky, Marina  and
      Li, Yunyao  and
      Reiss, Frederick  and
      Zhu, Huaiyu",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3010",
    doi = "10.18653/v1/N18-3010",
    pages = "76--83",
    abstract = "The rise of enterprise applications over unstructured and semi-structured documents poses new challenges to text understanding systems across multiple dimensions. We present SystemT, a declarative text understanding system that addresses these challenges and has been deployed in a wide range of enterprise applications. We highlight the design considerations and decisions behind SystemT in addressing the needs of the enterprise setting. We also summarize the impact of SystemT on business and education.",
}
@inproceedings{ammar-etal-2018-construction,
    title = "Construction of the Literature Graph in Semantic Scholar",
    author = "Ammar, Waleed  and
      Groeneveld, Dirk  and
      Bhagavatula, Chandra  and
      Beltagy, Iz  and
      Crawford, Miles  and
      Downey, Doug  and
      Dunkelberger, Jason  and
      Elgohary, Ahmed  and
      Feldman, Sergey  and
      Ha, Vu  and
      Kinney, Rodney  and
      Kohlmeier, Sebastian  and
      Lo, Kyle  and
      Murray, Tyler  and
      Ooi, Hsu-Han  and
      Peters, Matthew  and
      Power, Joanna  and
      Skjonsberg, Sam  and
      Wang, Lucy  and
      Wilhelm, Chris  and
      Yuan, Zheng  and
      van Zuylen, Madeleine  and
      Etzioni, Oren",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3011",
    doi = "10.18653/v1/N18-3011",
    pages = "84--91",
    abstract = "We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting literature graph consists of more than 280M nodes, representing papers, authors, entities and various interactions between them (e.g., authorships, citations, entity mentions). We reduce literature graph construction into familiar NLP tasks (e.g., entity extraction and linking), point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task. The methods described in this paper are used to enable semantic features in \url{www.semanticscholar.org}.",
}
@inproceedings{kreutzer-etal-2018-neural,
    title = "Can Neural Machine Translation be Improved with User Feedback?",
    author = "Kreutzer, Julia  and
      Khadivi, Shahram  and
      Matusov, Evgeny  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3012",
    doi = "10.18653/v1/N18-3012",
    pages = "92--105",
    abstract = "We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform. Previous work has been confined to simulation experiments, whereas in this paper we work with real logged feedback for offline bandit learning of NMT parameters. We conduct a thorough analysis of the available explicit user judgments{---}five-star ratings of translation quality{---}and show that they are not reliable enough to yield significant improvements in bandit learning. In contrast, we successfully utilize implicit task-based feedback collected in a cross-lingual search task to improve task-specific and machine translation quality metrics.",
}
@inproceedings{iglesias-etal-2018-accelerating,
    title = "Accelerating {NMT} Batched Beam Decoding with {LMBR} Posteriors for Deployment",
    author = "Iglesias, Gonzalo  and
      Tambellini, William  and
      De Gispert, Adri{\`a}  and
      Hasler, Eva  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3013",
    doi = "10.18653/v1/N18-3013",
    pages = "106--113",
    abstract = "We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with Transformers. We also discuss acceleration strategies for deployment, and the effect of the beam size and batching on memory and speed.",
}
@inproceedings{quinn-ballesteros-2018-pieces,
    title = "Pieces of Eight: 8-bit Neural Machine Translation",
    author = "Quinn, Jerry  and
      Ballesteros, Miguel",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3014",
    doi = "10.18653/v1/N18-3014",
    pages = "114--120",
    abstract = "Neural machine translation has achieved levels of fluency and adequacy that would have been surprising a short time ago. Output quality is extremely relevant for industry purposes, however it is equally important to produce results in the shortest time possible, mainly for latency-sensitive applications and to control cloud hosting costs. In this paper we show the effectiveness of translating with 8-bit quantization for models that have been trained using 32-bit floating point values. Results show that 8-bit translation makes a non-negligible impact in terms of speed with no degradation in accuracy and adequacy.",
}
@inproceedings{finley-etal-2018-dictations,
    title = "From dictations to clinical reports using machine translation",
    author = "Finley, Gregory  and
      Salloum, Wael  and
      Sadoughi, Najmeh  and
      Edwards, Erik  and
      Robinson, Amanda  and
      Axtmann, Nico  and
      Brenndoerfer, Michael  and
      Miller, Mark  and
      Suendermann-Oeft, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3015",
    doi = "10.18653/v1/N18-3015",
    pages = "121--128",
    abstract = "A typical workflow to document clinical encounters entails dictating a summary, running speech recognition, and post-processing the resulting text into a formatted letter. Post-processing entails a host of transformations including punctuation restoration, truecasing, marking sections and headers, converting dates and numerical expressions, parsing lists, etc. In conventional implementations, most of these tasks are accomplished by individual modules. We introduce a novel holistic approach to post-processing that relies on machine callytranslation. We show how this technique outperforms an alternative conventional system{---}even learning to correct speech recognition errors during post-processing{---}while being much simpler to maintain.",
}
@inproceedings{pivovarova-etal-2018-benchmarks,
    title = "Benchmarks and models for entity-oriented polarity detection",
    author = "Pivovarova, Lidia  and
      Klami, Arto  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3016",
    doi = "10.18653/v1/N18-3016",
    pages = "129--136",
    abstract = "We address the problem of determining entity-oriented polarity in business news. This can be viewed as classifying the polarity of the sentiment expressed toward a given mention of a company in a news article. We present a complete, end-to-end approach to the problem. We introduce a new dataset of over 17,000 manually labeled documents, which is substantially larger than any currently available resources. We propose a benchmark solution based on convolutional neural networks for classifying entity-oriented polarity. Although our dataset is much larger than those currently available, it is small on the scale of datasets commonly used for training robust neural network models. To compensate for this, we use transfer learning{---}pre-train the model on a much larger dataset, annotated for a related but different classification task, in order to learn a good representation for business text, and then fine-tune it on the smaller polarity dataset.",
}
@inproceedings{gaspers-etal-2018-selecting,
    title = "Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System",
    author = "Gaspers, Judith  and
      Karanasou, Penny  and
      Chatterjee, Rajen",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3017",
    doi = "10.18653/v1/N18-3017",
    pages = "137--144",
    abstract = "This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in order to keep utterances that improve NLU performance and language-specific post-processing methods are investigated. These methods are tested in a large-scale NLU task with translating around 10 millions training utterances from English to German. The results show a large improvement for using MT data over a grammar-based and over an in-house data collection baseline, while reducing the manual effort greatly. Both filtering and post-processing approaches improve results further.",
}
@inproceedings{goyal-etal-2018-fast,
    title = "Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents",
    author = "Goyal, Anuj Kumar  and
      Metallinou, Angeliki  and
      Matsoukas, Spyros",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3018",
    doi = "10.18653/v1/N18-3018",
    pages = "145--152",
    abstract = "Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models for new natural language domains is a time and data intensive process. We propose efficient deep neural network architectures that maximally re-use available resources through transfer learning. Our methods are applied for expanding the understanding capabilities of a popular commercial agent and are evaluated on hundreds of new domains, designed by internal or external developers. We demonstrate that our proposed methods significantly increase accuracy in low resource settings and enable rapid development of accurate models with less data.",
}
@inproceedings{jha-etal-2018-bag,
    title = "Bag of Experts Architectures for Model Reuse in Conversational Language Understanding",
    author = "Jha, Rahul  and
      Marin, Alex  and
      Shivaprasad, Suvamsh  and
      Zitouni, Imed",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3019",
    doi = "10.18653/v1/N18-3019",
    pages = "153--161",
    abstract = "Slot tagging, the task of detecting entities in input user utterances, is a key component of natural language understanding systems for personal digital assistants. Since each new domain requires a different set of slots, the annotation costs for labeling data for training slot tagging models increases rapidly as the number of domains grow. To tackle this, we describe Bag of Experts (BoE) architectures for model reuse for both LSTM and CRF based models. Extensive experimentation over a dataset of 10 domains drawn from data relevant to our commercial personal digital assistant shows that our BoE models outperform the baseline models with a statistically significant average margin of 5.06{\%} in absolute F1-score when training with 2000 instances per domain, and achieve an even higher improvement of 12.16{\%} when only 25{\%} of the training data is used.",
}
@inproceedings{mathur-etal-2018-multi,
    title = "Multi-lingual neural title generation for e-Commerce browse pages",
    author = "Mathur, Prashant  and
      Ueffing, Nicola  and
      Leusch, Gregor",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3020",
    doi = "10.18653/v1/N18-3020",
    pages = "162--169",
    abstract = "To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-resource as well as low-resource languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.",
}
@inproceedings{niraula-etal-2018-novel,
    title = "A Novel Approach to Part Name Discovery in Noisy Text",
    author = "Niraula, Nobal Bikram  and
      Whyatt, Daniel  and
      Kao, Anne",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3021",
    doi = "10.18653/v1/N18-3021",
    pages = "170--176",
    abstract = "As a specialized example of information extraction, part name extraction is an area that presents unique challenges. Part names are typically multi-word terms longer than two words. There is little consistency in how terms are described in noisy free text, with variations spawned by typos, ad hoc abbreviations, acronyms, and incomplete names. This makes search and analyses of parts in these data extremely challenging. In this paper, we present our algorithm, PANDA (Part Name Discovery Analytics), based on a unique method that exploits statistical, linguistic and machine learning techniques to discover part names in noisy text such as that in manufacturing quality documentation, supply chain management records, service communication logs, and maintenance reports. Experiments show that PANDA is scalable and outperforms existing techniques significantly.",
}
@inproceedings{kollar-etal-2018-alexa,
    title = "The {A}lexa Meaning Representation Language",
    author = "Kollar, Thomas  and
      Berry, Danielle  and
      Stuart, Lauren  and
      Owczarzak, Karolina  and
      Chung, Tagyoung  and
      Mathias, Lambert  and
      Kayser, Michael  and
      Snow, Bradford  and
      Matsoukas, Spyros",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3022",
    doi = "10.18653/v1/N18-3022",
    pages = "177--184",
    abstract = "This paper introduces a meaning representation for spoken language understanding. The Alexa meaning representation language (AMRL), unlike previous approaches, which factor spoken utterances into domains, provides a common representation for how people communicate in spoken language. AMRL is a rooted graph, links to a large-scale ontology, supports cross-domain queries, fine-grained types, complex utterances and composition. A spoken language dataset has been collected for Alexa, which contains ∼20k examples across eight domains. A version of this meaning representation was released to developers at a trade show in 2016.",
}
@inproceedings{mehrabani-etal-2018-practical,
    title = "Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems",
    author = "Mehrabani, Mahnoosh  and
      Thomson, David  and
      Stern, Benjamin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3023",
    doi = "10.18653/v1/N18-3023",
    pages = "185--192",
    abstract = "Spoken Language Understanding (SLU), which extracts semantic information from speech, is not flawless, specially in practical applications. The reliability of the output of an SLU system can be evaluated using a semantic confidence measure. Confidence measures are a solution to improve the quality of spoken dialogue systems, by rejecting low-confidence SLU results. In this study we discuss real-world applications of confidence scoring in a customer service scenario. We build confidence models for three major types of dialogue states that are considered as different domains: how may I help you, number capture, and confirmation. Practical challenges to train domain-dependent confidence models, including data limitations, are discussed, and it is shown that feature engineering plays an important role to improve performance. We explore a wide variety of predictor features based on speech recognition, intent classification, and high-level domain knowledge, and find the combined feature set with the best rejection performance for each application.",
}
@inproceedings{ambroselli-etal-2018-prediction,
    title = "Prediction for the Newsroom: Which Articles Will Get the Most Comments?",
    author = "Ambroselli, Carl  and
      Risch, Julian  and
      Krestel, Ralf  and
      Loos, Andreas",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3024",
    doi = "10.18653/v1/N18-3024",
    pages = "193--199",
    abstract = "The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80{\%} in comparison to state-of-the-art approaches.",
}
@inproceedings{hopkinson-etal-2018-demand,
    title = "Demand-Weighted Completeness Prediction for a Knowledge Base",
    author = "Hopkinson, Andrew  and
      Gurdasani, Amit  and
      Palfrey, Dave  and
      Mittal, Arpit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3025",
    doi = "10.18653/v1/N18-3025",
    pages = "200--207",
    abstract = "In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure Demand-Weighted Completeness, and show that a simple neural network model performs well at this prediction task.",
}
@inproceedings{fiorini-lu-2018-personalized,
    title = "Personalized neural language models for real-world query auto completion",
    author = "Fiorini, Nicolas  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3026",
    doi = "10.18653/v1/N18-3026",
    pages = "208--215",
    abstract = "Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user types each character, predicting the user{'}s intent using various signals {--} one of the most common being popularity. Recently, deep learning approaches have been proposed for the QAC task, to specifically address the main limitation of previous popularity-based methods: the inability to predict unseen queries. In this work we improve previous methods based on neural language modeling, with the goal of building an end-to-end system. We particularly focus on using real-world data by integrating user information for personalized suggestions when possible. We also make use of time information and study how to increase diversity in the suggestions while studying the impact on scalability. Our empirical results demonstrate a marked improvement on two separate datasets over previous best methods in both accuracy and scalability, making a step towards neural query auto-completion in production search engines.",
}
@inproceedings{elsafty-etal-2018-document,
    title = "Document-based Recommender System for Job Postings using Dense Representations",
    author = "Elsafty, Ahmed  and
      Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans - Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-3027",
    doi = "10.18653/v1/N18-3027",
    pages = "216--224",
    abstract = "Job boards and professional social networks heavily use recommender systems in order to better support users in exploring job advertisements. Detecting the similarity between job advertisements is important for job recommendation systems as it allows, for example, the application of item-to-item based recommendations. In this work, we research the usage of dense vector representations to enhance a large-scale job recommendation system and to rank German job advertisements regarding their similarity. We follow a two-folded evaluation scheme: (1) we exploit historic user interactions to automatically create a dataset of similar jobs that enables an offline evaluation. (2) In addition, we conduct an online A/B test and evaluate the best performing method on our platform reaching more than 1 million users. We achieve the best results by combining job titles with full-text job descriptions. In particular, this method builds dense document representation using words of the titles to weigh the importance of words of the full-text description. In the online evaluation, this approach allows us to increase the click-through rate on job recommendations for active users by 8.0{\%}.",
}
@proceedings{naacl-2018-2018-north,
    title = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2000",
    doi = "10.18653/v1/N18-2",
}
@inproceedings{hou-2018-enhanced,
    title = "Enhanced Word Representations for Bridging Anaphora Resolution",
    author = "Hou, Yufang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2001",
    doi = "10.18653/v1/N18-2001",
    pages = "1--7",
    abstract = "Most current models of word representations (e.g., GloVe) have successfully captured fine-grained semantics. However, semantic similarity exhibited in these word embeddings is not suitable for resolving bridging anaphora, which requires the knowledge of associative similarity (i.e., relatedness) instead of semantic similarity information between synonyms or hypernyms. We create word embeddings (embeddings{\_}PP) to capture such relatedness by exploring the syntactic structure of noun phrases. We demonstrate that using embeddings {\_}PP alone achieves around 30{\%} of accuracy for bridging anaphora resolution on the ISNotes corpus. Furthermore, we achieve a substantial gain over the state-of-the-art system (Hou et al., 2013b) for bridging antecedent selection.",
}
@inproceedings{rudinger-etal-2018-gender,
    title = "Gender Bias in Coreference Resolution",
    author = "Rudinger, Rachel  and
      Naradowsky, Jason  and
      Leonard, Brian  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2002",
    doi = "10.18653/v1/N18-2002",
    pages = "8--14",
    abstract = "We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these {``}Winogender schemas,{''} we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.",
}
@inproceedings{zhao-etal-2018-gender,
    title = "Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods",
    author = "Zhao, Jieyu  and
      Wang, Tianlu  and
      Yatskar, Mark  and
      Ordonez, Vicente  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2003",
    doi = "10.18653/v1/N18-2003",
    pages = "15--20",
    abstract = "In this paper, we introduce a new benchmark for co-reference resolution focused on gender bias, WinoBias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing datasets.",
}
@inproceedings{baly-etal-2018-integrating,
    title = "Integrating Stance Detection and Fact Checking in a Unified Corpus",
    author = "Baly, Ramy  and
      Mohtarami, Mitra  and
      Glass, James  and
      M{\`a}rquez, Llu{\'\i}s  and
      Moschitti, Alessandro  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2004",
    doi = "10.18653/v1/N18-2004",
    pages = "21--27",
    abstract = "A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim{'}s factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (rationales) from the documents. Yet, this setup is not directly supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. In this paper, we support the interdependencies between these tasks as annotations in the same corpus. We implement this setup on an Arabic fact checking corpus, the first of its kind.",
}
@inproceedings{rajendran-etal-2018-something,
    title = "Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset",
    author = "Rajendran, Pavithra  and
      Bollegala, Danushka  and
      Parsons, Simon",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2005",
    doi = "10.18653/v1/N18-2005",
    pages = "28--34",
    abstract = "Online reviews have become a popular portal among customers making decisions about purchasing products. A number of corpora of reviews have been widely investigated in NLP in general, and, in particular, in argument mining. This is a subset of NLP that deals with extracting arguments and the relations among them from user-based content. A major problem faced by argument mining research is the lack of human-annotated data. In this paper, we investigate the use of weakly supervised and semi-supervised methods for automatically annotating data, and thus providing large annotated datasets. We do this by building on previous work that explores the classification of opinions present in reviews based whether the stance is expressed explicitly or implicitly. In the work described here, we automatically annotate stance as implicit or explicit and our results show that the datasets we generate, although noisy, can be used to learn better models for implicit/explicit opinion classification.",
}
@inproceedings{schulz-etal-2018-multi,
    title = "Multi-Task Learning for Argumentation Mining in Low-Resource Settings",
    author = "Schulz, Claudia  and
      Eger, Steffen  and
      Daxenberger, Johannes  and
      Kahse, Tobias  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2006",
    doi = "10.18653/v1/N18-2006",
    pages = "35--41",
    abstract = "We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.",
}
@inproceedings{dhingra-etal-2018-neural,
    title = "Neural Models for Reasoning over Multiple Mentions Using Coreference",
    author = "Dhingra, Bhuwan  and
      Jin, Qiao  and
      Yang, Zhilin  and
      Cohen, William  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2007",
    doi = "10.18653/v1/N18-2007",
    pages = "42--48",
    abstract = "Many problems in NLP require aggregating information from multiple mentions of the same entity which may be far apart in the text. Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks. We present a recurrent layer which is instead biased towards coreferent dependencies. The layer uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster. Incorporating this layer into a state-of-the-art reading comprehension model improves performance on three datasets {--} Wikihop, LAMBADA and the bAbi AI tasks {--} with large gains when training data is scarce.",
}
@inproceedings{huang-etal-2018-automatic,
    title = "Automatic Dialogue Generation with Expressed Emotions",
    author = {Huang, Chenyang  and
      Za{\"\i}ane, Osmar  and
      Trabelsi, Amine  and
      Dziri, Nouha},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2008",
    doi = "10.18653/v1/N18-2008",
    pages = "49--54",
    abstract = "Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself. They learn from collections of past responses and generate one based on a given utterance without considering, speech act, desired style or emotion to be expressed. In this research, we address the problem of forcing the dialogue generation to express emotion. We present three models that either concatenate the desired emotion with the source input during the learning, or push the emotion in the decoder. The results, evaluated with an emotion tagger, are encouraging with all three models, but present better outcome and promise with our model that adds the emotion vector in the decoder.",
}
@inproceedings{li-etal-2018-guiding,
    title = "Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network",
    author = "Li, Chenliang  and
      Xu, Weiran  and
      Li, Si  and
      Gao, Sheng",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2009",
    doi = "10.18653/v1/N18-2009",
    pages = "55--60",
    abstract = "Neural network models, based on the attentional encoder-decoder model, have good capability in abstractive text summarization. However, these models are hard to be controlled in the process of generation, which leads to a lack of key information. We propose a guiding generation model that combines the extractive method and the abstractive method. Firstly, we obtain keywords from the text by a extractive model. Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation. In addition, we use a prediction-guide mechanism, which can obtain the long-term value for future decoding, to further guide the summary generation. We evaluate our model on the CNN/Daily Mail dataset. The experimental results show that our model leads to significant improvements.",
}
@inproceedings{su-etal-2018-natural,
    title = "Natural Language Generation by Hierarchical Decoding with Linguistic Patterns",
    author = "Su, Shang-Yu  and
      Lo, Kai-Ling  and
      Yeh, Yi-Ting  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2010",
    doi = "10.18653/v1/N18-2010",
    pages = "61--66",
    abstract = "Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains a encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple cross entropy loss training criterion. However, the simple encoder-decoder architecture usually suffers from generating complex and long sentences, because the decoder has to learn all grammar and diction knowledge. This paper introduces a hierarchical decoding NLG model based on linguistic patterns in different levels, and shows that the proposed method outperforms the traditional one with a smaller model size. Furthermore, the design of the hierarchical decoding is flexible and easily-extendible in various NLG systems.",
}
@inproceedings{ghazvininejad-etal-2018-neural,
    title = "Neural Poetry Translation",
    author = "Ghazvininejad, Marjan  and
      Choi, Yejin  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2011",
    doi = "10.18653/v1/N18-2011",
    pages = "67--71",
    abstract = "We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source text to an English poem. Human evaluation of the translations ranks the quality as acceptable 78.2{\%} of the time.",
}
@inproceedings{novikova-etal-2018-rankme,
    title = "{R}ank{ME}: Reliable Human Ratings for Natural Language Generation",
    author = "Novikova, Jekaterina  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Rieser, Verena",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2012",
    doi = "10.18653/v1/N18-2012",
    pages = "72--78",
    abstract = "Human evaluation for natural language generation (NLG) often suffers from inconsistent user ratings. While previous research tends to attribute this problem to individual user preferences, we show that the quality of human judgements can also be improved by experimental design. We present a novel rank-based magnitude estimation method (RankME), which combines the use of continuous scales and relative assessments. We show that RankME significantly improves the reliability and consistency of human ratings compared to traditional evaluation methods. In addition, we show that it is possible to evaluate NLG systems according to multiple, distinct criteria, which is important for error analysis. Finally, we demonstrate that RankME, in combination with Bayesian estimation of system quality, is a cost-effective alternative for ranking multiple NLG systems.",
}
@inproceedings{vu-etal-2018-sentence,
    title = "Sentence Simplification with Memory-Augmented Neural Networks",
    author = "Vu, Tu  and
      Hu, Baotian  and
      Munkhdalai, Tsendsuren  and
      Yu, Hong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2013",
    doi = "10.18653/v1/N18-2013",
    pages = "79--85",
    abstract = "Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.",
}
@inproceedings{beigman-klebanov-etal-2018-corpus,
    title = "A Corpus of Non-Native Written {E}nglish Annotated for Metaphor",
    author = "Beigman Klebanov, Beata  and
      Leong, Chee Wee (Ben)  and
      Flor, Michael",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2014",
    doi = "10.18653/v1/N18-2014",
    pages = "86--91",
    abstract = "We present a corpus of 240 argumentative essays written by non-native speakers of English annotated for metaphor. The corpus is made publicly available. We provide benchmark performance of state-of-the-art systems on this new corpus, and explore the relationship between writing proficiency and metaphor use.",
}
@inproceedings{srinivasan-etal-2018-simple,
    title = "A Simple and Effective Approach to the Story Cloze Test",
    author = "Srinivasan, Siddarth  and
      Arora, Richa  and
      Riedl, Mark",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2015",
    doi = "10.18653/v1/N18-2015",
    pages = "92--96",
    abstract = "In the Story Cloze Test, a system is presented with a 4-sentence prompt to a story, and must determine which one of two potential endings is the {`}right{'} ending to the story. Previous work has shown that ignoring the training set and training a model on the validation set can achieve high accuracy on this task due to stylistic differences between the story endings in the training set and validation and test sets. Following this approach, we present a simpler fully-neural approach to the Story Cloze Test using skip-thought embeddings of the stories in a feed-forward network that achieves close to state-of-the-art performance on this task without any feature engineering. We also find that considering just the last sentence of the prompt instead of the whole prompt yields higher accuracy with our approach.",
}
@inproceedings{kulkarni-etal-2018-annotated,
    title = "An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols",
    author = "Kulkarni, Chaitanya  and
      Xu, Wei  and
      Ritter, Alan  and
      Machiraju, Raghu",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2016",
    doi = "10.18653/v1/N18-2016",
    pages = "97--106",
    abstract = "We describe an effort to annotate a corpus of natural language instructions consisting of 622 wet lab protocols to facilitate automatic or semi-automatic conversion of protocols into a machine-readable format and benefit biological research. Experimental results demonstrate the utility of our corpus for developing machine learning approaches to shallow semantic parsing of instructional texts. We make our annotated Wet Lab Protocol Corpus available to the research community.",
}
@inproceedings{gururangan-etal-2018-annotation,
    title = "Annotation Artifacts in Natural Language Inference Data",
    author = "Gururangan, Suchin  and
      Swayamdipta, Swabha  and
      Levy, Omer  and
      Schwartz, Roy  and
      Bowman, Samuel  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2017",
    doi = "10.18653/v1/N18-2017",
    pages = "107--112",
    abstract = "Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67{\%} of SNLI (Bowman et. al, 2015) and 53{\%} of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem.",
}
@inproceedings{chen-soo-2018-humor,
    title = "Humor Recognition Using Deep Learning",
    author = "Chen, Peng-Yu  and
      Soo, Von-Wun",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2018",
    doi = "10.18653/v1/N18-2018",
    pages = "113--117",
    abstract = "Humor is an essential but most fascinating element in personal communication. How to build computational models to discover the structures of humor, recognize humor and even generate humor remains a challenge and there have been yet few attempts on it. In this paper, we construct and collect four datasets with distinct joke types in both English and Chinese and conduct learning experiments on humor recognition. We implement a Convolutional Neural Network (CNN) with extensive filter size, number and Highway Networks to increase the depth of networks. Results show that our model outperforms in recognition of different types of humor with benchmarks collected in both English and Chinese languages on accuracy, precision, and recall in comparison to previous works.",
}
@inproceedings{qian-etal-2018-leveraging,
    title = "Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection",
    author = "Qian, Jing  and
      ElSherief, Mai  and
      Belding, Elizabeth  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2019",
    doi = "10.18653/v1/N18-2019",
    pages = "118--123",
    abstract = "Hate speech detection is a critical, yet challenging problem in Natural Language Processing (NLP). Despite the existence of numerous studies dedicated to the development of NLP hate speech detection approaches, the accuracy is still poor. The central problem is that social media posts are short and noisy, and most existing hate speech detection solutions take each post as an isolated input instance, which is likely to yield high false positive and negative rates. In this paper, we radically improve automated hate speech detection by presenting a novel model that leverages intra-user and inter-user representation learning for robust hate speech detection on Twitter. In addition to the target Tweet, we collect and analyze the user{'}s historical posts to model intra-user Tweet representations. To suppress the noise in a single Tweet, we also model the similar Tweets posted by all other users with reinforced inter-user representation learning techniques. Experimentally, we show that leveraging these two representations can significantly improve the f-score of a strong bidirectional LSTM baseline model by 10.1{\%}.",
}
@inproceedings{choshen-abend-2018-reference,
    title = "Reference-less Measure of Faithfulness for Grammatical Error Correction",
    author = "Choshen, Leshem  and
      Abend, Omri",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2020",
    doi = "10.18653/v1/N18-2020",
    pages = "124--129",
    abstract = "We propose USim, a semantic measure for Grammatical Error Correction (that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output{'}s grammaticality. USim operates by comparing the semantic symbolic structure of the source and the correction, without relying on manually-curated references. Our experiments establish the validity of USim, by showing that the semantic structures can be consistently applied to ungrammatical text, that valid corrections obtain a high USim similarity score to the source, and that invalid corrections obtain a lower score.",
}
@inproceedings{rooshenas-etal-2018-training,
    title = "Training Structured Prediction Energy Networks with Indirect Supervision",
    author = "Rooshenas, Amirmohammad  and
      Kamath, Aishwarya  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2021",
    doi = "10.18653/v1/N18-2021",
    pages = "130--135",
    abstract = "This paper introduces rank-based training of structured prediction energy networks (SPENs). Our method samples from output structures using gradient descent and minimizes the ranking violation of the sampled structures with respect to a scalar scoring function defined with domain knowledge. We have successfully trained SPEN for citation field extraction without any labeled data instances, where the only source of supervision is a simple human-written scoring function. Such scoring functions are often easy to provide; the SPEN then furnishes an efficient structured prediction inference procedure.",
}
@inproceedings{stewart-etal-2018-si,
    title = "Si {O} No, Que Penses? {C}atalonian Independence and Linguistic Identity on Social Media",
    author = "Stewart, Ian  and
      Pinter, Yuval  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2022",
    doi = "10.18653/v1/N18-2022",
    pages = "136--141",
    abstract = "Political identity is often manifested in language variation, but the relationship between the two is still relatively unexplored from a quantitative perspective. This study examines the use of Catalan, a language local to the semi-autonomous region of Catalonia in Spain, on Twitter in discourse related to the 2017 independence referendum. We corroborate prior findings that pro-independence tweets are more likely to include the local language than anti-independence tweets. We also find that Catalan is used more often in referendum-related discourse than in other contexts, contrary to prior findings on language variation. This suggests a strong role for the Catalan language in the expression of Catalonian political identity.",
}
@inproceedings{vilares-gomez-rodriguez-2018-transition,
    title = "A Transition-Based Algorithm for Unrestricted {AMR} Parsing",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2023",
    doi = "10.18653/v1/N18-2023",
    pages = "142--149",
    abstract = "Non-projective parsing can be useful to handle cycles and reentrancy in AMR graphs. We explore this idea and introduce a greedy left-to-right non-projective transition-based parser. At each parsing configuration, an oracle decides whether to create a concept or whether to connect a pair of existing concepts. The algorithm handles reentrancy and arbitrary cycles natively, i.e. within the transition system itself. The model is evaluated on the LDC2015E86 corpus, obtaining results close to the state of the art, including a Smatch of 64{\%}, and showing good behavior on reentrant edges.",
}
@inproceedings{koper-schulte-im-walde-2018-analogies,
    title = "Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2024",
    doi = "10.18653/v1/N18-2024",
    pages = "150--156",
    abstract = "We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs. In contrast to corpus-based studies, a novel dataset demonstrates that {``}regular{''} shifts represent the smallest class. Classification experiments relying on a standard similarity model successfully distinguish between four types of shifts, with verb classes boosting the performance, and affective features for abstractness, emotion and sentiment representing the most salient indicators.",
}
@inproceedings{lan-xu-2018-character,
    title = "Character-Based Neural Networks for Sentence Pair Modeling",
    author = "Lan, Wuwei  and
      Xu, Wei",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2025",
    doi = "10.18653/v1/N18-2025",
    pages = "157--163",
    abstract = "Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural models for these tasks rely on pretrained word embedding and compose sentence-level semantics in varied ways; however, few works have attempted to verify whether we really need pretrained embeddings in these tasks. In this paper, we study how effective subword-level (character and character n-gram) representations are in sentence pair modeling. Though it is well-known that subword models are effective in tasks with single sentence input, including language modeling and machine translation, they have not been systematically studied in sentence pair modeling tasks where the semantic and string similarities between texts matter. Our experiments show that subword models without any pretrained word embedding can achieve new state-of-the-art results on two social media datasets and competitive results on news data for paraphrase identification.",
}
@inproceedings{vempala-etal-2018-determining,
    title = "Determining Event Durations: Models and Error Analysis",
    author = "Vempala, Alakananda  and
      Blanco, Eduardo  and
      Palmer, Alexis",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2026",
    doi = "10.18653/v1/N18-2026",
    pages = "164--168",
    abstract = "This paper presents models to predict event durations. We introduce aspectual features that capture deeper linguistic information than previous work, and experiment with neural networks. Our analysis shows that tense, aspect and temporal structure of the clause provide useful clues, and that an LSTM ensemble captures relevant context around the event.",
}
@inproceedings{schlechtweg-etal-2018-diachronic,
    title = "Diachronic Usage Relatedness ({DUR}el): A Framework for the Annotation of Lexical Semantic Change",
    author = "Schlechtweg, Dominik  and
      Schulte im Walde, Sabine  and
      Eckmann, Stefanie",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2027",
    doi = "10.18653/v1/N18-2027",
    pages = "169--174",
    abstract = "We propose a framework that extends synchronic polysemy annotation to diachronic changes in lexical meaning, to counteract the lack of resources for evaluating computational models of lexical semantic change. Our framework exploits an intuitive notion of semantic relatedness, and distinguishes between innovative and reductive meaning changes with high inter-annotator agreement. The resulting test set for German comprises ratings from five annotators for the relatedness of 1,320 use pairs across 22 target words.",
}
@inproceedings{song-etal-2018-directional,
    title = "Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings",
    author = "Song, Yan  and
      Shi, Shuming  and
      Li, Jing  and
      Zhang, Haisong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2028",
    doi = "10.18653/v1/N18-2028",
    pages = "175--180",
    abstract = "In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction. In doing so, a direction vector is introduced for each word, whose embedding is thus learned by not only word co-occurrence patterns in its context, but also the directions of its contextual words. Theoretical and empirical studies on complexity illustrate that our model can be trained as efficient as the original skip-gram model, when compared to other extensions of the skip-gram model. Experimental results show that our model outperforms others on different datasets in semantic (word similarity measurement) and syntactic (part-of-speech tagging) evaluations, respectively.",
}
@inproceedings{glavas-vulic-2018-discriminating,
    title = "Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model",
    author = "Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2029",
    doi = "10.18653/v1/N18-2029",
    pages = "181--187",
    abstract = "We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs. STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages. We also show that, if coupled with a bilingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data.",
}
@inproceedings{braune-etal-2018-evaluating,
    title = "Evaluating bilingual word embeddings on the long tail",
    author = "Braune, Fabienne  and
      Hangya, Viktor  and
      Eder, Tobias  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2030",
    doi = "10.18653/v1/N18-2030",
    pages = "188--193",
    abstract = "Bilingual word embeddings are useful for bilingual lexicon induction, the task of mining translations of given words. Many studies have shown that bilingual word embeddings perform well for bilingual lexicon induction but they focused on frequent words in general domains. For many applications, bilingual lexicon induction of rare and domain-specific words is of critical importance. Therefore, we design a new task to evaluate bilingual word embeddings on rare words in different domains. We show that state-of-the-art approaches fail on this task and present simple new techniques to improve bilingual word embeddings for mining rare words. We release new gold standard datasets and code to stimulate research on this task.",
}
@inproceedings{coates-bollegala-2018-frustratingly,
    title = "Frustratingly Easy Meta-Embedding {--} Computing Meta-Embeddings by Averaging Source Word Embeddings",
    author = "Coates, Joshua  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2031",
    doi = "10.18653/v1/N18-2031",
    pages = "194--198",
    abstract = "Creating accurate meta-embeddings from pre-trained source embeddings has received attention lately. Methods based on global and locally-linear transformation and concatenation have shown to produce accurate meta-embeddings. In this paper, we show that the arithmetic mean of two distinct word embedding sets yields a performant meta-embedding that is comparable or better than more complex meta-embedding learning methods. The result seems counter-intuitive given that vector spaces in different source embeddings are not comparable and cannot be simply averaged. We give insight into why averaging can still produce accurate meta-embedding despite the incomparability of the source vector spaces.",
}
@inproceedings{nguyen-etal-2018-introducing,
    title = "Introducing Two {V}ietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness",
    author = "Nguyen, Kim Anh  and
      Schulte im Walde, Sabine  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2032",
    doi = "10.18653/v1/N18-2032",
    pages = "199--205",
    abstract = "We present two novel datasets for the low-resource language Vietnamese to assess models of semantic similarity: ViCon comprises pairs of synonyms and antonyms across word classes, thus offering data to distinguish between similarity and dissimilarity. ViSim-400 provides degrees of similarity across five semantic relations, as rated by human judges. The two datasets are verified through standard co-occurrence and neural network models, showing results comparable to the respective English datasets.",
}
@inproceedings{buljan-etal-2018-lexical,
    title = "Lexical Substitution for Evaluating Compositional Distributional Models",
    author = "Buljan, Maja  and
      Pad{\'o}, Sebastian  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2033",
    doi = "10.18653/v1/N18-2033",
    pages = "206--211",
    abstract = "Compositional Distributional Semantic Models (CDSMs) model the meaning of phrases and sentences in vector space. They have been predominantly evaluated on limited, artificial tasks such as semantic sentence similarity on hand-constructed datasets. This paper argues for lexical substitution (LexSub) as a means to evaluate CDSMs. LexSub is a more natural task, enables us to evaluate meaning composition at the level of individual words, and provides a common ground to compare CDSMs with dedicated LexSub models. We create a LexSub dataset for CDSM evaluation from a corpus with manual {``}all-words{''} LexSub annotation. Our experiments indicate that the Practical Lexical Function CDSM outperforms simple component-wise CDSMs and performs on par with the context2vec LexSub model using the same context.",
}
@inproceedings{dingwall-potts-2018-mittens,
    title = "{M}ittens: an Extension of {G}lo{V}e for Learning Domain-Specialized Representations",
    author = "Dingwall, Nicholas  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2034",
    doi = "10.18653/v1/N18-2034",
    pages = "212--217",
    abstract = "We present a simple extension of the GloVe representation learning model that begins with general-purpose representations and updates them based on data from a specialized domain. We show that the resulting representations can lead to faster learning and better results on a variety of tasks.",
}
@inproceedings{shwartz-waterson-2018-olive,
    title = "Olive Oil is Made \textit{of} Olives, Baby Oil is Made \textit{for} Babies: Interpreting Noun Compounds Using Paraphrases in a Neural Model",
    author = "Shwartz, Vered  and
      Waterson, Chris",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2035",
    doi = "10.18653/v1/N18-2035",
    pages = "218--224",
    abstract = "Automatic interpretation of the relation between the constituents of a noun compound, e.g. olive oil (source) and baby oil (purpose) is an important task for many NLP applications. Recent approaches are typically based on either noun-compound representations or paraphrases. While the former has initially shown promising results, recent work suggests that the success stems from memorizing single prototypical words for each relation. We explore a neural paraphrasing approach that demonstrates superior performance when such memorization is not possible.",
}
@inproceedings{kashefi-etal-2018-semantic,
    title = "Semantic Pleonasm Detection",
    author = "Kashefi, Omid  and
      Lucas, Andrew T.  and
      Hwa, Rebecca",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2036",
    doi = "10.18653/v1/N18-2036",
    pages = "225--230",
    abstract = "Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.",
}
@inproceedings{paula-etal-2018-similarity,
    title = "Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks",
    author = "Paula, Felipe  and
      Wilkens, Rodrigo  and
      Idiart, Marco  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2037",
    doi = "10.18653/v1/N18-2037",
    pages = "231--235",
    abstract = "Semantic Verbal Fluency tests have been used in the detection of certain clinical conditions, like Dementia. In particular, given a sequence of semantically related words, a large number of switches from one semantic class to another has been linked to clinical conditions. In this work, we investigate three similarity measures for automatically identifying switches in semantic chains: semantic similarity from a manually constructed resource, and word association strength and semantic relatedness, both calculated from corpora. This information is used for building classifiers to distinguish healthy controls from clinical cases with early stages of Alzheimer{'}s Disease and Mild Cognitive Deficits. The overall results indicate that for clinical conditions the classifiers that use these similarity measures outperform those that use a gold standard taxonomy.",
}
@inproceedings{ronning-etal-2018-sluice,
    title = "Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees",
    author = "R{\o}nning, Ola  and
      Hardt, Daniel  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2038",
    doi = "10.18653/v1/N18-2038",
    pages = "236--241",
    abstract = "Sluice resolution in English is the problem of finding antecedents of \textit{wh}-fronted ellipses. Previous work has relied on hand-crafted features over syntax trees that scale poorly to other languages and domains; in particular, to dialogue, which is one of the most interesting applications of sluice resolution. Syntactic information is arguably important for sluice resolution, but we show that multi-task learning with partial parsing as auxiliary tasks effectively closes the gap and buys us an additional 9{\%} error reduction over previous work. Since we are not directly relying on features from partial parsers, our system is more robust to domain shifts, giving a 26{\%} error reduction on embedded sluices in dialogue.",
}
@inproceedings{schluter-2018-word,
    title = "The Word Analogy Testing Caveat",
    author = "Schluter, Natalie",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2039",
    doi = "10.18653/v1/N18-2039",
    pages = "242--246",
    abstract = "There are some important problems in the evaluation of word embeddings using standard word analogy tests. In particular, in virtue of the assumptions made by systems generating the embeddings, these remain tests over randomness. We show that even supposing there were such word analogy regularities that should be detected in the word embeddings obtained via unsupervised means, standard word analogy test implementation practices provide distorted or contrived results. We raise concerns regarding the use of Principal Component Analysis to 2 or 3 dimensions as a provision of visual evidence for the existence of word analogy relations in embeddings. Finally, we propose some solutions to these problems.",
}
@inproceedings{wang-etal-2018-transition,
    title = "Transition-Based {C}hinese {AMR} Parsing",
    author = "Wang, Chuan  and
      Li, Bin  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2040",
    doi = "10.18653/v1/N18-2040",
    pages = "247--252",
    abstract = "This paper presents the first AMR parser built on the Chinese AMR bank. By applying a transition-based AMR parsing framework to Chinese, we first investigate how well the transitions first designed for English AMR parsing generalize to Chinese and provide a comparative analysis between the transitions for English and Chinese. We then perform a detailed error analysis to identify the major challenges in Chinese AMR parsing that we hope will inform future research in this area.",
}
@inproceedings{kumar-etal-2018-knowledge,
    title = "Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis",
    author = "Kumar, Abhishek  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2041",
    doi = "10.18653/v1/N18-2041",
    pages = "253--258",
    abstract = "We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis. The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment prediction. It uses the Knowledge Graph Embedding generated using the WordNet. We build our model by combining the two-layered attention network with the supervised model based on Support Vector Regression using a Multilayer Perceptron network for sentiment analysis. We evaluate our model on the benchmark dataset of SemEval 2017 Task 5. Experimental results show that the proposed model surpasses the top system of SemEval 2017 Task 5. The model performs significantly better by improving the state-of-the-art system at SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.",
}
@inproceedings{maharjan-etal-2018-letting,
    title = "Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books",
    author = "Maharjan, Suraj  and
      Kar, Sudipta  and
      Montes, Manuel  and
      Gonz{\'a}lez, Fabio A.  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2042",
    doi = "10.18653/v1/N18-2042",
    pages = "259--265",
    abstract = "Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow. An author{'}s dexterity in the use of these emotions captivates readers and makes it difficult for them to put the book down. In this paper, we model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books. We obtained the best weighted F1-score of 69{\%} for predicting books{'} success in a multitask setting (simultaneously predicting success and genre of books).",
}
@inproceedings{hazarika-etal-2018-modeling,
    title = "Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis",
    author = "Hazarika, Devamanyu  and
      Poria, Soujanya  and
      Vij, Prateek  and
      Krishnamurthy, Gangeshwar  and
      Cambria, Erik  and
      Zimmermann, Roger",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2043",
    doi = "10.18653/v1/N18-2043",
    pages = "266--270",
    abstract = "Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.",
}
@inproceedings{yadav-etal-2018-multi,
    title = "Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment",
    author = "Yadav, Shweta  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak  and
      Sheth, Amit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2044",
    doi = "10.18653/v1/N18-2044",
    pages = "271--277",
    abstract = "In recent past, social media has emerged as an active platform in the context of healthcare and medicine. In this paper, we present a study where medical user{'}s opinions on health-related issues are analyzed to capture the medical sentiment at a blog level. The medical sentiments can be studied in various facets such as medical condition, treatment, and medication that characterize the overall health status of the user. Considering these facets, we treat analysis of this information as a multi-task classification problem. In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog. Our evaluation shows promising results for our target tasks.",
}
@inproceedings{liu-etal-2018-recurrent,
    title = "Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis",
    author = "Liu, Fei  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2045",
    doi = "10.18653/v1/N18-2045",
    pages = "278--283",
    abstract = "While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) {---} extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects {---} remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external {``}memory chains{''} with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those using external knowledge bases.",
}
@inproceedings{grundkiewicz-junczys-dowmunt-2018-near,
    title = "Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation",
    author = "Grundkiewicz, Roman  and
      Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2046",
    doi = "10.18653/v1/N18-2046",
    pages = "284--290",
    abstract = "We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.",
}
@inproceedings{mohammed-etal-2018-strong,
    title = "Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks",
    author = "Mohammed, Salman  and
      Shi, Peng  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2047",
    doi = "10.18653/v1/N18-2047",
    pages = "291--296",
    abstract = "We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the popular SimpleQuestions dataset, we find that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.",
}
@inproceedings{weise-levitan-2018-looking,
    title = "Looking for Structure in Lexical and Acoustic-Prosodic Entrainment Behaviors",
    author = "Weise, Andreas  and
      Levitan, Rivka",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2048",
    doi = "10.18653/v1/N18-2048",
    pages = "297--302",
    abstract = "Entrainment has been shown to occur for various linguistic features individually. Motivated by cognitive theories regarding linguistic entrainment, we analyze speakers{'} overall entrainment behaviors and search for an underlying structure. We consider various measures of both acoustic-prosodic and lexical entrainment, measuring the latter with a novel application of two previously introduced methods in addition to a standard high-frequency word measure. We present a negative result of our search, finding no meaningful correlations, clusters, or principal components in various entrainment measures, and discuss practical and theoretical implications.",
}
@inproceedings{wang-etal-2018-modeling,
    title = "Modeling Semantic Plausibility by Injecting World Knowledge",
    author = "Wang, Su  and
      Durrett, Greg  and
      Erk, Katrin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2049",
    doi = "10.18653/v1/N18-2049",
    pages = "303--308",
    abstract = "Distributional data tells us that a man can swallow candy, but not that a man can swallow a paintball, since this is never attested. However both are physically plausible events. This paper introduces the task of semantic plausibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as man swallow paintball. Simple models based on distributional representations perform poorly on this task, despite doing well on selection preference, but injecting manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausibility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.",
}
@inproceedings{wang-etal-2018-bi,
    title = "A Bi-Model Based {RNN} Semantic Frame Parsing Model for Intent Detection and Slot Filling",
    author = "Wang, Yu  and
      Shen, Yilin  and
      Jin, Hongxia",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2050",
    doi = "10.18653/v1/N18-2050",
    pages = "309--314",
    abstract = "Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or {``}encoder-decoder{''} models), and generate the intents and semantic tags either using separate models. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. None of the approaches consider the cross-impact between the intent detection task and the slot filling task. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-art result on the benchmark ATIS data, with about 0.5{\%} intent accuracy improvement and 0.9 {\%} slot filling improvement.",
}
@inproceedings{plachouras-etal-2018-comparison,
    title = "A Comparison of Two Paraphrase Models for Taxonomy Augmentation",
    author = "Plachouras, Vassilis  and
      Petroni, Fabio  and
      Nugent, Timothy  and
      Leidner, Jochen L.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2051",
    doi = "10.18653/v1/N18-2051",
    pages = "315--320",
    abstract = "Taxonomies are often used to look up the concepts they contain in text documents (for instance, to classify a document). The more comprehensive the taxonomy, the higher recall the application has that uses the taxonomy. In this paper, we explore automatic taxonomy augmentation with paraphrases. We compare two state-of-the-art paraphrase models based on Moses, a statistical Machine Translation system, and a sequence-to-sequence neural network, trained on a paraphrase datasets with respect to their abilities to add novel nodes to an existing taxonomy from the risk domain. We conduct component-based and task-based evaluations. Our results show that paraphrasing is a viable method to enrich a taxonomy with more terms, and that Moses consistently outperforms the sequence-to-sequence neural model. To the best of our knowledge, this is the first approach to augment taxonomies with paraphrases.",
}
@inproceedings{hatty-schulte-im-walde-2018-laypeople,
    title = "A Laypeople Study on Terminology Identification across Domains and Task Definitions",
    author = {H{\"a}tty, Anna  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2052",
    doi = "10.18653/v1/N18-2052",
    pages = "321--326",
    abstract = "This paper introduces a new dataset of term annotation. Given that even experts vary significantly in their understanding of termhood, and that term identification is mostly performed as a binary task, we offer a novel perspective to explore the common, natural understanding of what constitutes a term: Laypeople annotate single-word and multi-word terms, across four domains and across four task definitions. Analyses based on inter-annotator agreement offer insights into differences in term specificity, term granularity and subtermhood.",
}
@inproceedings{nguyen-etal-2018-novel,
    title = "A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network",
    author = "Nguyen, Dai Quoc  and
      Nguyen, Tu Dinh  and
      Nguyen, Dat Quoc  and
      Phung, Dinh",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2053",
    doi = "10.18653/v1/N18-2053",
    pages = "327--333",
    abstract = "In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237.",
}
@inproceedings{wu-tsai-2018-cross,
    title = "Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding",
    author = "Wu, Chun-Kai  and
      Tsai, Richard Tzong-Han",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2054",
    doi = "10.18653/v1/N18-2054",
    pages = "334--339",
    abstract = "Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62{\%}. Tested 30 times, our system achieved an average improvement of 2.76{\%} over the current best system (26.86{\%} over baseline), a statistically significant result.",
}
@inproceedings{choubey-etal-2018-identifying,
    title = "Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations",
    author = "Choubey, Prafulla Kumar  and
      Raju, Kaushik  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2055",
    doi = "10.18653/v1/N18-2055",
    pages = "340--345",
    abstract = "Identifying the most dominant and central event of a document, which governs and connects other foreground and background events in the document, is useful for many applications, such as text summarization, storyline generation and text segmentation. We observed that the central event of a document usually has many coreferential event mentions that are scattered throughout the document for enabling a smooth transition of subtopics. Our empirical experiments, using gold event coreference relations, have shown that the central event of a document can be well identified by mining properties of event coreference chains. But the performance drops when switching to system predicted event coreference relations. In addition, we found that the central event can be more accurately identified by further considering the number of sub-events as well as the realis status of an event.",
}
@inproceedings{zhao-etal-2018-improve,
    title = "Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding",
    author = "Zhao, Huasha  and
      Yang, Yi  and
      Zhang, Qiong  and
      Si, Luo",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2056",
    doi = "10.18653/v1/N18-2056",
    pages = "346--351",
    abstract = "Entity recognition is a widely benchmarked task in natural language processing due to its massive applications. The state-of-the-art solution applies a neural architecture named BiLSTM-CRF to model the language sequences. In this paper, we propose an entity recognition system that improves this neural architecture with two novel techniques. The first technique is Multi-Task Data Selection, which ensures the consistency of data distribution and labeling guidelines between source and target datasets. The other one is constrained decoding using knowledge base. The decoder of the model operates at the document level, and leverages global and external information sources to further improve performance. Extensive experiments have been conducted to show the advantages of each technique. Our system achieves state-of-the-art results on the English entity recognition task in KBP 2017 official evaluation, and it also yields very strong results in other languages.",
}
@inproceedings{nagesh-surdeanu-2018-keep,
    title = "Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift",
    author = "Nagesh, Ajay  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2057",
    doi = "10.18653/v1/N18-2057",
    pages = "352--358",
    abstract = "We propose a novel approach to semi-supervised learning for information extraction that uses ladder networks (Rasmus et al., 2015). In particular, we focus on the task of named entity classification, defined as identifying the correct label (e.g., person or organization name) of an entity mention in a given context. Our approach is simple, efficient and has the benefit of being robust to semantic drift, a dominant problem in most semi-supervised learning systems. We empirically demonstrate the superior performance of our system compared to the state-of-the-art on two standard datasets for named entity classification. We obtain between 62{\%} and 200{\%} improvement over the state-of-art baseline on these two datasets.",
}
@inproceedings{ferguson-etal-2018-semi,
    title = "Semi-Supervised Event Extraction with Paraphrase Clusters",
    author = "Ferguson, James  and
      Lockard, Colin  and
      Weld, Daniel  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2058",
    doi = "10.18653/v1/N18-2058",
    pages = "359--364",
    abstract = "Supervised event extraction systems are limited in their accuracy due to the lack of available training data. We present a method for self-training event extraction systems by bootstrapping additional training data. This is done by taking advantage of the occurrence of multiple mentions of the same event instances across newswire articles from multiple sources. If our system can make a high-confidence extraction of some mentions in such a cluster, it can then acquire diverse training examples by adding the other mentions as well. Our experiments show significant performance improvements on multiple event extractors over ACE 2005 and TAC-KBP 2015 datasets.",
}
@inproceedings{wen-etal-2018-structure,
    title = "Structure Regularized Neural Network for Entity Relation Classification for {C}hinese Literature Text",
    author = "Wen, Ji  and
      Sun, Xu  and
      Ren, Xuancheng  and
      Su, Qi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2059",
    doi = "10.18653/v1/N18-2059",
    pages = "365--370",
    abstract = "Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency tree, which has the benefits of reducing the complexity of the whole model. Experimental results show that the proposed method significantly improves the F1 score by 10.3, and outperforms the state-of-the-art approaches on Chinese literature text.",
}
@inproceedings{patel-etal-2018-syntactic,
    title = "Syntactic Patterns Improve Information Extraction for Medical Search",
    author = "Patel, Roma  and
      Yang, Yinfei  and
      Marshall, Iain  and
      Nenkova, Ani  and
      Wallace, Byron",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2060",
    doi = "10.18653/v1/N18-2060",
    pages = "371--377",
    abstract = "Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both neural and linear) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited and of the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.",
}
@inproceedings{espinosa-anke-schockaert-2018-syntactically,
    title = "Syntactically Aware Neural Architectures for Definition Extraction",
    author = "Espinosa-Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2061",
    doi = "10.18653/v1/N18-2061",
    pages = "378--385",
    abstract = "Automatically identifying definitional knowledge in text corpora (Definition Extraction or DE) is an important task with direct applications in, among others, Automatic Glossary Generation, Taxonomy Learning, Question Answering and Semantic Search. It is generally cast as a binary classification problem between definitional and non-definitional sentences. In this paper we present a set of neural architectures combining Convolutional and Recurrent Neural Networks, which are further enriched by incorporating linguistic information via syntactic dependencies. Our experimental results in the task of sentence classification, on two benchmarking DE datasets (one generic, one domain-specific), show that these models obtain consistent state of the art results. Furthermore, we demonstrate that models trained on clean Wikipedia-like definitions can successfully be applied to more noisy domain-specific corpora.",
}
@inproceedings{fernandez-gonzalez-gomez-rodriguez-2018-dynamic-oracle,
    title = "A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2062",
    doi = "10.18653/v1/N18-2062",
    pages = "386--392",
    abstract = "We propose an efficient dynamic oracle for training the 2-Planar transition-based parser, a linear-time parser with over 99{\%} coverage on non-projective syntactic corpora. This novel approach outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the Swap transition, which can handle unrestricted non-projectivity.",
}
@inproceedings{rama-etal-2018-automatic,
    title = "Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?",
    author = {Rama, Taraka  and
      List, Johann-Mattis  and
      Wahle, Johannes  and
      J{\"a}ger, Gerhard},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2063",
    doi = "10.18653/v1/N18-2063",
    pages = "393--400",
    abstract = "We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family{'}s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.",
}
@inproceedings{wisniewski-etal-2018-automatically,
    title = "Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles",
    author = "Wisniewski, Guillaume  and
      Lacroix, Oph{\'e}lie  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2064",
    doi = "10.18653/v1/N18-2064",
    pages = "401--406",
    abstract = "This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance. Instead of associating each sentence in the training set with a single gold reference we propose to consider a set of references encoding alternative syntactic representations. Training a parser with a dynamic oracle will then automatically select among all alternatives the reference that will be predicted with the highest accuracy. Experiments on the UD corpora show the validity of this approach.",
}
@inproceedings{yoshikawa-etal-2018-consistent,
    title = "Consistent {CCG} Parsing over Multiple Sentences for Improved Logical Reasoning",
    author = "Yoshikawa, Masashi  and
      Mineshima, Koji  and
      Noji, Hiroshi  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2065",
    doi = "10.18653/v1/N18-2065",
    pages = "407--412",
    abstract = "In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize the similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.",
}
@inproceedings{aufrant-etal-2018-exploiting,
    title = "Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2066",
    doi = "10.18653/v1/N18-2066",
    pages = "413--419",
    abstract = "Because the most common transition systems are projective, training a transition-based dependency parser often implies to either ignore or rewrite the non-projective training examples, which has an adverse impact on accuracy. In this work, we propose a simple modification of dynamic oracles, which enables the use of non-projective data when training projective parsers. Evaluation on 73 treebanks shows that our method achieves significant gains (+2 to +7 UAS for the most non-projective languages) and consistently outperforms traditional projectivization and pseudo-projectivization approaches.",
}
@inproceedings{shi-etal-2018-improving,
    title = "Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers",
    author = "Shi, Tianze  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos  and
      Lee, Lillian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2067",
    doi = "10.18653/v1/N18-2067",
    pages = "420--425",
    abstract = "We generalize Cohen, G{\'o}mez-Rodr{\'\i}guez, and Satta{'}s (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. This includes novel parsers with better coverage than Cohen et al. (2011), and even a variant that reduces time complexity to $O(n^6)$, improving over the known bounds in exact inference for non-projective transition-based parsing. We hope that this piece of theoretical work inspires design of novel transition systems with better coverage and better run-time guarantees.",
}
@inproceedings{pasquer-etal-2018-towards,
    title = "Towards a Variability Measure for Multiword Expressions",
    author = "Pasquer, Caroline  and
      Savary, Agata  and
      Antoine, Jean-Yves  and
      Ramisch, Carlos",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2068",
    doi = "10.18653/v1/N18-2068",
    pages = "426--432",
    abstract = "One of the most outstanding properties of multiword expressions (MWEs), especially verbal ones (VMWEs), important both in theoretical models and applications, is their idiosyncratic variability. Some MWEs are always continuous, while some others admit certain types of insertions. Components of some MWEs are rarely or never modified, while some others admit either specific or unrestricted modification. This unpredictable variability profile of MWEs hinders modeling and processing them as {``}words-with-spaces{''} on the one hand, and as regular syntactic structures on the other hand. Since variability of MWEs is a matter of scale rather than a binary property, we propose a 2-dimensional language-independent measure of variability dedicated to verbal MWEs based on syntactic and discontinuity-related clues. We assess its relevance with respect to a linguistic benchmark and its utility for the tasks of VMWE classification and variant identification on a French corpus.",
}
@inproceedings{madhyastha-etal-2018-defoiling,
    title = "Defoiling Foiled Image Captions",
    author = "Madhyastha, Pranava Swaroop  and
      Wang, Josiah  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2069",
    doi = "10.18653/v1/N18-2069",
    pages = "433--438",
    abstract = "We address the task of detecting foiled image captions, i.e. identifying whether a caption contains a word that has been deliberately replaced by a semantically similar word, thus rendering it inaccurate with respect to the image being described. Solving this problem should in principle require a fine-grained understanding of images to detect subtle perturbations in captions. In such contexts, encoding sufficiently descriptive image information becomes a key challenge. In this paper, we demonstrate that it is possible to solve this task using simple, interpretable yet powerful representations based on explicit object information over multilayer perceptron models. Our models achieve state-of-the-art performance on a recently published dataset, with scores exceeding those achieved by humans on the task. We also measure the upper-bound performance of our models using gold standard annotations. Our study and analysis reveals that the simpler model performs well even without image information, suggesting that the dataset contains strong linguistic bias.",
}
@inproceedings{cohn-gordon-etal-2018-pragmatically,
    title = "Pragmatically Informative Image Captioning with Character-Level Inference",
    author = "Cohn-Gordon, Reuben  and
      Goodman, Noah  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2070",
    doi = "10.18653/v1/N18-2070",
    pages = "439--443",
    abstract = "We combine a neural image captioner with a Rational Speech Acts (RSA) model to make a system that is pragmatically informative: its objective is to produce captions that are not merely true but also distinguish their inputs from similar images. Previous attempts to combine RSA with neural image captioning require an inference which normalizes over the entire set of possible utterances. This poses a serious problem of efficiency, previously solved by sampling a small subset of possible utterances. We instead solve this problem by implementing a version of RSA which operates at the level of characters ({``}a{''}, {``}b{''}, {``}c{''}, ...) during the unrolling of the caption. We find that the utterance-level effect of referential captions can be obtained with only character-level decisions. Finally, we introduce an automatic method for testing the performance of pragmatic speaker models, and show that our model outperforms a non-pragmatic baseline as well as a word-level RSA captioner.",
}
@inproceedings{tan-bansal-2018-object,
    title = "Object Ordering with Bidirectional Matchings for Visual Reasoning",
    author = "Tan, Hao  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2071",
    doi = "10.18653/v1/N18-2071",
    pages = "444--451",
    abstract = "Visual reasoning with compositional natural language instructions, e.g., based on the newly-released Cornell Natural Language Visual Reasoning (NLVR) dataset, is a challenging task, where the model needs to have the ability to create an accurate mapping between the diverse phrases and the several objects placed in complex arrangements in the image. Further, this mapping needs to be processed to answer the question in the statement given the ordering and relationship of the objects across three similar images. In this paper, we propose a novel end-to-end neural model for the NLVR task, where we first use joint bidirectional attention to build a two-way conditioning between the visual information and the language phrases. Next, we use an RL-based pointer network to sort and process the varying number of unordered objects (so as to match the order of the statement phrases) in each of the three images and then pool over the three decisions. Our model achieves strong improvements (of 4-6{\%} absolute) over the state-of-the-art on both the structured representation and raw image versions of the dataset.",
}
@inproceedings{kobayashi-2018-contextual,
    title = "Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations",
    author = "Kobayashi, Sosuke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2072",
    doi = "10.18653/v1/N18-2072",
    pages = "452--457",
    abstract = "We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions. Words predicted according to a context are numerous but appropriate for the augmentation of the original words. Furthermore, we retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility. Through the experiments for six various different text classification tasks, we demonstrate that the proposed method improves classifiers based on the convolutional or recurrent neural networks.",
}
@inproceedings{sasaki-etal-2018-cross,
    title = "Cross-Lingual Learning-to-Rank with Shared Representations",
    author = "Sasaki, Shota  and
      Sun, Shuo  and
      Schamoni, Shigehiko  and
      Duh, Kevin  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2073",
    doi = "10.18653/v1/N18-2073",
    pages = "458--463",
    abstract = "Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.",
}
@inproceedings{shaw-etal-2018-self,
    title = "Self-Attention with Relative Position Representations",
    author = "Shaw, Peter  and
      Uszkoreit, Jakob  and
      Vaswani, Ashish",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2074",
    doi = "10.18653/v1/N18-2074",
    pages = "464--468",
    abstract = "Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. (2017) achieves state-of-the-art results for machine translation. In contrast to recurrent and convolutional neural networks, it does not explicitly model relative or absolute position information in its structure. Instead, it requires adding representations of absolute positions to its inputs. In this work we present an alternative approach, extending the self-attention mechanism to efficiently consider representations of the relative positions, or distances between sequence elements. On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations, respectively. Notably, we observe that combining relative and absolute position representations yields no further improvement in translation quality. We describe an efficient implementation of our method and cast it as an instance of relation-aware self-attention mechanisms that can generalize to arbitrary graph-labeled inputs.",
}
@inproceedings{koshorek-etal-2018-text,
    title = "Text Segmentation as a Supervised Learning Task",
    author = "Koshorek, Omri  and
      Cohen, Adir  and
      Mor, Noam  and
      Rotman, Michael  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2075",
    doi = "10.18653/v1/N18-2075",
    pages = "469--473",
    abstract = "Text segmentation, the task of dividing a document into contiguous segments based on its semantic structure, is a longstanding challenge in language understanding. Previous work on text segmentation focused on unsupervised methods such as clustering or graph search, due to the paucity in labeled data. In this work, we formulate text segmentation as a supervised learning problem, and present a large new dataset for text segmentation that is automatically extracted and labeled from Wikipedia. Moreover, we develop a segmentation model based on this dataset and show that it generalizes well to unseen natural text.",
}
@inproceedings{li-etal-2018-whats,
    title = "What{'}s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training",
    author = "Li, Yitong  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2076",
    doi = "10.18653/v1/N18-2076",
    pages = "474--479",
    abstract = "Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (\textit{in domain}) and dissimilar (\textit{out of domain}) instances to those seen in training. This requires learning an underlying task, while not learning irrelevant signals and biases specific to individual domains. We propose a novel method to optimise both in- and out-of-domain accuracy based on joint learning of a structured neural model with domain-specific and domain-general components, coupled with adversarial training for domain. Evaluating on multi-domain language identification and multi-domain sentiment analysis, we show substantial improvements over standard domain adaptation techniques, and domain-adversarial training.",
}
@inproceedings{apidianaki-etal-2018-automated,
    title = "Automated Paraphrase Lattice Creation for {H}y{TER} Machine Translation Evaluation",
    author = "Apidianaki, Marianna  and
      Wisniewski, Guillaume  and
      Cocos, Anne  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2077",
    doi = "10.18653/v1/N18-2077",
    pages = "480--485",
    abstract = "We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions. The original HyTER metric relied on hand-crafted paraphrase networks which restricted its applicability to new data. We test, for the first time, HyTER with automatically built paraphrase lattices. We show that although the metric obtains good results on small and carefully curated data with both manually and automatically selected substitutes, it achieves medium performance on much larger and noisier datasets, demonstrating the limits of the metric for tuning and evaluation of current MT systems.",
}
@inproceedings{marcheggiani-etal-2018-exploiting,
    title = "Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks",
    author = "Marcheggiani, Diego  and
      Bastings, Jasmijn  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2078",
    doi = "10.18653/v1/N18-2078",
    pages = "486--492",
    abstract = "Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English{--}German language pair.",
}
@inproceedings{dalvi-etal-2018-incremental,
    title = "Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation",
    author = "Dalvi, Fahim  and
      Durrani, Nadir  and
      Sajjad, Hassan  and
      Vogel, Stephan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2079",
    doi = "10.18653/v1/N18-2079",
    pages = "493--499",
    abstract = "We address the problem of simultaneous translation by modifying the Neural MT decoder to operate with dynamically built encoder and attention. We propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. Our agent outperforms previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova, 2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to Neural MT training to better match the incremental decoding framework.",
}
@inproceedings{vilar-2018-learning,
    title = "Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models",
    author = "Vilar, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2080",
    doi = "10.18653/v1/N18-2080",
    pages = "500--505",
    abstract = "In this paper we explore the use of Learning Hidden Unit Contribution for the task of neural machine translation. The method was initially proposed in the context of speech recognition for adapting a general system to the specific acoustic characteristics of each speaker. Similar in spirit, in a machine translation framework we want to adapt a general system to a specific domain. We show that the proposed method achieves improvements of up to 2.6 BLEU points over a general system, and up to 6 BLEU points if the initial system has only been trained on out-of-domain data, a situation which may easily happen in practice. The good performance together with its short training time and small memory footprint make it a very attractive solution for domain adaptation.",
}
@inproceedings{hasler-etal-2018-neural,
    title = "Neural Machine Translation Decoding with Terminology Constraints",
    author = "Hasler, Eva  and
      de Gispert, Adri{\`a}  and
      Iglesias, Gonzalo  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2081",
    doi = "10.18653/v1/N18-2081",
    pages = "506--512",
    abstract = "Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem. We describe our approach to constrained neural decoding based on finite-state machines and multi-stack decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints.",
}
@inproceedings{poliak-etal-2018-evaluation,
    title = "On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference",
    author = "Poliak, Adam  and
      Belinkov, Yonatan  and
      Glass, James  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2082",
    doi = "10.18653/v1/N18-2082",
    pages = "513--523",
    abstract = "We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use these representations as features to train a natural language inference (NLI) classifier based on datasets recast from existing semantic annotations. In applying this process to a representative NMT system, we find its encoder appears most suited to supporting inferences at the syntax-semantics interface, as compared to anaphora resolution requiring world knowledge. We conclude with a discussion on the merits and potential deficiencies of the existing process, and how it may be improved and extended as a broader framework for evaluating semantic coverage",
}
@inproceedings{pourdamghani-etal-2018-using,
    title = "Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation",
    author = "Pourdamghani, Nima  and
      Ghazvininejad, Marjan  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2083",
    doi = "10.18653/v1/N18-2083",
    pages = "524--528",
    abstract = "We present a method for improving word alignments using word similarities. This method is based on encouraging common alignment links between semantically similar words. We use word vectors trained on monolingual data to estimate similarity. Our experiments on translating fifteen languages into English show consistent BLEU score improvements across the languages.",
}
@inproceedings{qi-etal-2018-pre,
    title = "When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?",
    author = "Qi, Ye  and
      Sachan, Devendra  and
      Felix, Matthieu  and
      Padmanabhan, Sarguna  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2084",
    doi = "10.18653/v1/N18-2084",
    pages = "529--535",
    abstract = "The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases {--} providing gains of up to 20 BLEU points in the most favorable setting.",
}
@inproceedings{cotterell-etal-2018-languages,
    title = "Are All Languages Equally Hard to Language-Model?",
    author = "Cotterell, Ryan  and
      Mielke, Sabrina J.  and
      Eisner, Jason  and
      Roark, Brian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2085",
    doi = "10.18653/v1/N18-2085",
    pages = "536--541",
    abstract = "For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of language models, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both n-gram and LSTM language models. We show complex inflectional morphology to be a cause of performance differences among languages.",
}
@inproceedings{chen-hulden-2018-computational,
    title = "The Computational Complexity of Distinctive Feature Minimization in Phonology",
    author = "Chen, Hubie  and
      Hulden, Mans",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2086",
    doi = "10.18653/v1/N18-2086",
    pages = "542--547",
    abstract = "We analyze the complexity of the problem of determining whether a set of phonemes forms a natural class and, if so, that of finding the minimal feature specification for the class. A standard assumption in phonology is that finding a minimal feature specification is an automatic part of acquisition and generalization. We find that the natural class decision problem is tractable (i.e. is in P), while the minimization problem is not; the decision version of the problem which determines whether a natural class can be defined with $k$ features or less is NP-complete. We also show that, empirically, a greedy algorithm for finding minimal feature specifications will sometimes fail, and thus cannot be assumed to be the basis for human performance in solving the problem.",
}
@inproceedings{cotterell-etal-2018-unsupervised,
    title = "Unsupervised Disambiguation of Syncretism in Inflected Lexicons",
    author = "Cotterell, Ryan  and
      Kirov, Christo  and
      Mielke, Sabrina J.  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2087",
    doi = "10.18653/v1/N18-2087",
    pages = "548--553",
    abstract = "Lexical ambiguity makes it difficult to compute useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token{'}s context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages.",
}
@inproceedings{salant-berant-2018-contextualized,
    title = "Contextualized Word Representations for Reading Comprehension",
    author = "Salant, Shimi  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2088",
    doi = "10.18653/v1/N18-2088",
    pages = "554--559",
    abstract = "Reading a document and extracting an answer to a question about its content has attracted substantial attention recently. While most work has focused on the interaction between the question and the document, in this work we evaluate the importance of context when the question and document are processed independently. We take a standard neural architecture for this task, and show that by providing rich contextualized word representations from a large pre-trained language model as well as allowing the model to choose between context-dependent and context-independent word representations, we can obtain dramatic improvements and reach performance comparable to state-of-the-art on the competitive SQuAD dataset.",
}
@inproceedings{michael-etal-2018-crowdsourcing,
    title = "Crowdsourcing Question-Answer Meaning Representations",
    author = "Michael, Julian  and
      Stanovsky, Gabriel  and
      He, Luheng  and
      Dagan, Ido  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2089",
    doi = "10.18653/v1/N18-2089",
    pages = "560--568",
    abstract = "We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs. We develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions. A qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, and QA-SRL) along with many previously under-resourced ones, including implicit arguments and relations. We also report baseline models for question generation and answering, and summarize a recent approach for using QAMR labels to improve an Open IE system. These results suggest the freely available QAMR data and annotation scheme should support significant future work.",
}
@inproceedings{song-etal-2018-leveraging,
    title = "Leveraging Context Information for Natural Question Generation",
    author = "Song, Linfeng  and
      Wang, Zhiguo  and
      Hamza, Wael  and
      Zhang, Yue  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2090",
    doi = "10.18653/v1/N18-2090",
    pages = "569--574",
    abstract = "The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.",
}
@inproceedings{wang-bansal-2018-robust,
    title = "Robust Machine Comprehension Models via Adversarial Training",
    author = "Wang, Yicheng  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2091",
    doi = "10.18653/v1/N18-2091",
    pages = "575--581",
    abstract = "It is shown that many published models for the Stanford Question Answering Dataset (Rajpurkar et al., 2016) lack robustness, suffering an over 50{\%} decrease in F1 score during adversarial evaluation based on the AddSent (Jia and Liang, 2017) algorithm. It has also been shown that retraining models on data generated by AddSent has limited effect on their robustness. We propose a novel alternative adversary-generation algorithm, AddSentDiverse, that significantly increases the variance within the adversarial training data by providing effective examples that punish the model for making certain superficial assumptions. Further, in order to improve robustness to AddSent{'}s semantic perturbations (e.g., antonyms), we jointly improve the model{'}s semantic-relationship learning capabilities in addition to our AddSentDiverse-based adversarial training data augmentation. With these additions, we show that we can make a state-of-the-art model significantly more robust, achieving a 36.5{\%} increase in F1 score under many different types of adversarial evaluation while maintaining performance on the regular SQuAD task.",
}
@inproceedings{dhingra-etal-2018-simple,
    title = "Simple and Effective Semi-Supervised Question Answering",
    author = "Dhingra, Bhuwan  and
      Danish, Danish  and
      Rajagopal, Dheeraj",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2092",
    doi = "10.18653/v1/N18-2092",
    pages = "582--587",
    abstract = "Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora. However, large domain specific annotated corpora are limited and expensive to construct. In this work, we envision a system where the end user specifies a set of base documents and only a few labelled examples. Our system exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further fine-tunes the model on the labeled examples. We evaluate our proposed system across three diverse datasets from different domains, and find it to be highly effective with very little labeled data. We attain more than 50{\%} F1 score on SQuAD and TriviaQA with less than a thousand labelled examples. We are also releasing a set of 3.2M cloze-style questions for practitioners to use while building QA systems.",
}
@inproceedings{yu-etal-2018-typesql,
    title = "{T}ype{SQL}: Knowledge-Based Type-Aware Neural Text-to-{SQL} Generation",
    author = "Yu, Tao  and
      Li, Zifan  and
      Zhang, Zilin  and
      Zhang, Rui  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2093",
    doi = "10.18653/v1/N18-2093",
    pages = "588--594",
    abstract = "Interacting with relational databases through natural language helps users with any background easily query and analyze a vast amount of data. This requires a system that understands users{'} questions and converts them to SQL queries automatically. In this paper, we present a novel approach TypeSQL which formats the problem as a slot filling task in a more reasonable way. In addition, TypeSQL utilizes type information to better understand rare entities and numbers in the questions. We experiment this idea on the WikiSQL dataset and outperform the prior art by 6{\%} in much shorter time. We also show that accessing the content of databases can significantly improve the performance when users{'} queries are not well-formed. TypeSQL can reach 82.6{\%} accuracy, a 17.5{\%} absolute improvement compared to the previous content-sensitive model.",
}
@inproceedings{jaech-etal-2018-community,
    title = "Community Member Retrieval on Social Media Using Textual Information",
    author = "Jaech, Aaron  and
      Hathi, Shobhit  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2094",
    doi = "10.18653/v1/N18-2094",
    pages = "595--601",
    abstract = "This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive labeled examples defines the community. The solution introduces an unsupervised proxy task for learning user embeddings: user re-identification. Experiments with 16 different communities show that the resulting embeddings are more effective for community membership identification than common unsupervised representations.",
}
@inproceedings{chen-etal-2018-cross,
    title = "Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators",
    author = "Chen, Cen  and
      Yang, Yinfei  and
      Zhou, Jun  and
      Li, Xiaolong  and
      Bao, Forrest Sheng",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2095",
    doi = "10.18653/v1/N18-2095",
    pages = "602--607",
    abstract = "With the growing amount of reviews in e-commerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingly to consumers. Recent studies on review helpfulness require plenty of labeled samples for each domain/category of interests. However, such an approach based on close-world assumption is not always practical, especially for domains with limited reviews or the {``}out-of-vocabulary{''} problem. Therefore, we propose a convolutional neural network (CNN) based model which leverages both word-level and character-based representations. To transfer knowledge between domains, we further extend our model to jointly model different domains with auxiliary domain discriminators. On the Amazon product review dataset, our approach significantly outperforms the state of the art in terms of both accuracy and cross-domain robustness.",
}
@inproceedings{volkova-etal-2018-predicting,
    title = "Predicting Foreign Language Usage from {E}nglish-Only Social Media Posts",
    author = "Volkova, Svitlana  and
      Ranshous, Stephen  and
      Phillips, Lawrence",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2096",
    doi = "10.18653/v1/N18-2096",
    pages = "608--614",
    abstract = "Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English. We rely on this corpus to build predictive models to infer non-English languages that users speak exclusively from their English tweets. Unlike native language identification task, we rely on large amounts of informal social media communications rather than ESL essays. We contrast the predictive power of the state-of-the-art machine learning models trained on lexical, syntactic, and stylistic signals with neural network models learned from word, character and byte representations extracted from English only tweets. We report that content, style and syntax are the most predictive of non-English languages that users speak on Twitter. Neural network models learned from byte representations of user content combined with transfer learning yield the best performance. Finally, by analyzing cross-lingual transfer {--} the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 12 languages in social media.",
}
@inproceedings{cohan-etal-2018-discourse,
    title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
    author = "Cohan, Arman  and
      Dernoncourt, Franck  and
      Kim, Doo Soon  and
      Bui, Trung  and
      Kim, Seokhwan  and
      Chang, Walter  and
      Goharian, Nazli",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2097",
    doi = "10.18653/v1/N18-2097",
    pages = "615--621",
    abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
}
@inproceedings{jain-etal-2018-mixed,
    title = "A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization",
    author = "Jain, Parag  and
      Laha, Anirban  and
      Sankaranarayanan, Karthik  and
      Nema, Preksha  and
      Khapra, Mitesh M.  and
      Shetty, Shreyas",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2098",
    doi = "10.18653/v1/N18-2098",
    pages = "622--627",
    abstract = "Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the form of tables as they are prevalent across a wide variety of domains. We formulate the standard table summarization problem, which deals with tables conforming to a single predefined schema. To this end, we propose a mixed hierarchical attention based encoder-decoder model which is able to leverage the structure in addition to the content of the tables. Our experiments on the publicly available weathergov dataset show around 18 BLEU (around 30{\%}) improvement over the current state-of-the-art.",
}
@inproceedings{jiang-etal-2018-effective,
    title = "Effective Crowdsourcing for a New Type of Summarization Task",
    author = "Jiang, Youxuan  and
      Finegan-Dollak, Catherine  and
      Kummerfeld, Jonathan K.  and
      Lasecki, Walter",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2099",
    doi = "10.18653/v1/N18-2099",
    pages = "628--633",
    abstract = "Most summarization research focuses on summarizing the entire given text, but in practice readers are often interested in only one aspect of the document or conversation. We propose targeted summarization as an umbrella category for summarization tasks that intentionally consider only parts of the input data. This covers query-based summarization, update summarization, and a new task we propose where the goal is to summarize a particular aspect of a document. However, collecting data for this new task is hard because directly asking annotators (e.g., crowd workers) to write summaries leads to data with low accuracy when there are a large number of facts to include. We introduce a novel crowdsourcing workflow, Pin-Refine, that allows us to collect high-quality summaries for our task, a necessary step for the development of automatic systems.",
}
@inproceedings{mahata-etal-2018-key2vec,
    title = "{K}ey2{V}ec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings",
    author = "Mahata, Debanjan  and
      Kuriakose, John  and
      Shah, Rajiv Ratn  and
      Zimmermann, Roger",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2100",
    doi = "10.18653/v1/N18-2100",
    pages = "634--639",
    abstract = "Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.",
}
@inproceedings{kaffee-etal-2018-learning,
    title = "Learning to Generate {W}ikipedia Summaries for Underserved Languages from {W}ikidata",
    author = "Kaffee, Lucie-Aim{\'e}e  and
      Elsahar, Hady  and
      Vougiouklis, Pavlos  and
      Gravier, Christophe  and
      Laforest, Fr{\'e}d{\'e}rique  and
      Hare, Jonathon  and
      Simperl, Elena",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2101",
    doi = "10.18653/v1/N18-2101",
    pages = "640--645",
    abstract = "While Wikipedia exists in 287 languages, its content is unevenly distributed among them. In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata. To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples. We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.",
}
@inproceedings{pasunuru-bansal-2018-multi,
    title = "Multi-Reward Reinforced Summarization with Saliency and Entailment",
    author = "Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2102",
    doi = "10.18653/v1/N18-2102",
    pages = "646--653",
    abstract = "Abstractive text summarization is the task of compressing and rewriting a long document into a short summary while maintaining saliency, directed logical entailment, and non-redundancy. In this work, we address these three important aspects of a good summary via a reinforcement learning approach with two novel reward functions: ROUGESal and Entail, on top of a coverage-based baseline. The ROUGESal reward modifies the ROUGE metric by up-weighting the salient phrases/words detected via a keyphrase classifier. The Entail reward gives high (length-normalized) scores to logically-entailed summaries using an entailment classifier. Further, we show superior performance improvement when these rewards are combined with traditional metric (ROUGE) based rewards, via our novel and effective multi-reward approach of optimizing multiple rewards simultaneously in alternate mini-batches. Our method achieves the new state-of-the-art results on CNN/Daily Mail dataset as well as strong improvements in a test-only transfer setup on DUC-2002.",
}
@inproceedings{peyrard-gurevych-2018-objective,
    title = "Objective Function Learning to Match Human Judgements for Optimization-Based Summarization",
    author = "Peyrard, Maxime  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2103",
    doi = "10.18653/v1/N18-2103",
    pages = "654--660",
    abstract = "Supervised summarization systems usually rely on supervision at the sentence or n-gram level provided by automatic metrics like ROUGE, which act as noisy proxies for human judgments. In this work, we learn a summary-level scoring function $\theta$ including human judgments as supervision and automatically generated data as regularization. We extract summaries with a genetic algorithm using $\theta$ as a fitness function. We observe strong and promising performances across datasets in both automatic and manual evaluation.",
}
@inproceedings{honda-etal-2018-pruning,
    title = "Pruning Basic Elements for Better Automatic Evaluation of Summaries",
    author = "Honda, Ukyo  and
      Hirao, Tsutomu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2104",
    doi = "10.18653/v1/N18-2104",
    pages = "661--666",
    abstract = "We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task.",
}
@inproceedings{boudin-2018-unsupervised,
    title = "Unsupervised Keyphrase Extraction with Multipartite Graphs",
    author = "Boudin, Florian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2105",
    doi = "10.18653/v1/N18-2105",
    pages = "667--672",
    abstract = "We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutually reinforcing relationship to improve candidate ranking. We further introduce a novel mechanism to incorporate keyphrase selection preferences into the model. Experiments conducted on three widely used datasets show significant improvements over state-of-the-art graph-based models.",
}
@inproceedings{chaturvedi-etal-2018-heard,
    title = "Where Have {I} Heard This Story Before? Identifying Narrative Similarity in Movie Remakes",
    author = "Chaturvedi, Snigdha  and
      Srivastava, Shashank  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2106",
    doi = "10.18653/v1/N18-2106",
    pages = "673--678",
    abstract = "People can identify correspondences between narratives in everyday life. For example, an analogy with the Cinderella story may be made in describing the unexpected success of an underdog in seemingly different stories. We present a new task and dataset for story understanding: identifying instances of similar narratives from a collection of narrative texts. We present an initial approach for this problem, which finds correspondences between narratives in terms of plot events, and resemblances between characters and their social relationships. Our approach yields an 8{\%} absolute improvement in performance over a competitive information-retrieval baseline on a novel dataset of plot summaries of 577 movie remakes from Wikipedia.",
}
@inproceedings{barbieri-etal-2018-multimodal,
    title = "Multimodal Emoji Prediction",
    author = "Barbieri, Francesco  and
      Ballesteros, Miguel  and
      Ronzano, Francesco  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2107",
    doi = "10.18653/v1/N18-2107",
    pages = "679--686",
    abstract = "Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instagram posts are composed of pictures together with texts which sometimes include emojis. We show that these emojis can be predicted by using the text, but also using the picture. Our main finding is that incorporating the two synergistic modalities, in a combined model, improves accuracy in an emoji prediction task. This result demonstrates that these two modalities (text and images) encode different information on the use of emojis and therefore can complement each other.",
}
@inproceedings{lee-etal-2018-higher,
    title = "Higher-Order Coreference Resolution with Coarse-to-Fine Inference",
    author = "Lee, Kenton  and
      He, Luheng  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2108",
    doi = "10.18653/v1/N18-2108",
    pages = "687--692",
    abstract = "We introduce a fully-differentiable approximation to higher-order inference for coreference resolution. Our approach uses the antecedent distribution from a span-ranking architecture as an attention mechanism to iteratively refine span representations. This enables the model to softly consider multiple hops in the predicted clusters. To alleviate the computational cost of this iterative process, we introduce a coarse-to-fine approach that incorporates a less accurate but more efficient bilinear factor, enabling more aggressive pruning without hurting accuracy. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the English OntoNotes benchmark, while being far more computationally efficient.",
}
@inproceedings{fernandez-gonzalez-gomez-rodriguez-2018-non,
    title = "Non-Projective Dependency Parsing with Non-Local Transitions",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2109",
    doi = "10.18653/v1/N18-2109",
    pages = "693--700",
    abstract = "We present a novel transition system, based on the Covington non-projective parser, introducing non-local transitions that can directly create arcs involving nodes to the left of the current focus positions. This avoids the need for long sequences of No-Arcs transitions to create long-distance arcs, thus alleviating error propagation. The resulting parser outperforms the original version and achieves the best accuracy on the Stanford Dependencies conversion of the Penn Treebank among greedy transition-based parsers.",
}
@inproceedings{karlekar-etal-2018-detecting,
    title = "Detecting Linguistic Characteristics of {A}lzheimer{'}s Dementia by Interpreting Neural Models",
    author = "Karlekar, Sweta  and
      Niu, Tong  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2110",
    doi = "10.18653/v1/N18-2110",
    pages = "701--707",
    abstract = "Alzheimer{'}s disease (AD) is an irreversible and progressive brain disease that can be stopped or slowed down with medical treatment. Language changes serve as a sign that a patient{'}s cognitive functions have been impacted, potentially leading to early diagnosis. In this work, we use NLP techniques to classify and analyze the linguistic characteristics of AD patients using the DementiaBank dataset. We apply three neural models based on CNNs, LSTM-RNNs, and their combination, to distinguish between language samples from AD and control patients. We achieve a new independent benchmark accuracy for the AD classification task. More importantly, we next interpret what these neural models have learned about the linguistic characteristics of AD patients, via analysis based on activation clustering and first-derivative saliency techniques. We then perform novel automatic pattern discovery inside activation clusters, and consolidate AD patients{'} distinctive grammar patterns. Additionally, we show that first derivative saliency can not only rediscover previous language patterns of AD patients, but also shed light on the limitations of neural models. Lastly, we also include analysis of gender-separated AD data.",
}
@inproceedings{louis-sutton-2018-deep,
    title = "Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts",
    author = "Louis, Annie  and
      Sutton, Charles",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2111",
    doi = "10.18653/v1/N18-2111",
    pages = "708--713",
    abstract = "An essential aspect to understanding narratives is to grasp the interaction between characters in a story and the actions they take. We examine whether computational models can capture this interaction, when both character attributes and actions are expressed as complex natural language descriptions. We propose role-playing games as a testbed for this problem, and introduce a large corpus of game transcripts collected from online discussion forums. Using neural language models which combine character and action descriptions from these stories, we show that we can learn the latent ties. Action sequences are better predicted when the character performing the action is also taken into account, and vice versa for character attributes.",
}
@inproceedings{casanueva-etal-2018-feudal,
    title = "Feudal Reinforcement Learning for Dialogue Management in Large Domains",
    author = "Casanueva, I{\~n}igo  and
      Budzianowski, Pawe{\l}  and
      Su, Pei-Hao  and
      Ultes, Stefan  and
      Rojas-Barahona, Lina M.  and
      Tseng, Bo-Hsiang  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2112",
    doi = "10.18653/v1/N18-2112",
    pages = "714--719",
    abstract = "Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisation. Traditional RL algorithms, however, fail to scale to large domains due to the curse of dimensionality. We propose a novel Dialogue Management architecture, based on Feudal RL, which decomposes the decision into two steps; a first step where a master policy selects a subset of primitive actions, and a second step where a primitive action is chosen from the selected subset. The structural information included in the domain ontology is used to abstract the dialogue state space, taking the decisions at each step using different parts of the abstracted state. This, combined with an information sharing mechanism between slots, increases the scalability to large domains. We show that an implementation of this approach, based on Deep-Q Networks, significantly outperforms previous state of the art in several dialogue domains and environments, without the need of any additional reward signal.",
}
@inproceedings{robertson-goldwater-2018-evaluating,
    title = "Evaluating Historical Text Normalization Systems: How Well Do They Generalize?",
    author = "Robertson, Alexander  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2113",
    doi = "10.18653/v1/N18-2113",
    pages = "720--725",
    abstract = {We highlight several issues in the evaluation of historical text normalization systems that make it hard to tell how well these systems would actually work in practice{---}i.e., for new datasets or languages; in comparison to more na{\"\i}ve systems; or as a preprocessing step for downstream NLP tools. We illustrate these issues and exemplify our proposed evaluation practices by comparing two neural models against a na{\"\i}ve baseline system. We show that the neural models generalize well to unseen words in tests on five languages; nevertheless, they provide no clear benefit over the na{\"\i}ve baseline for downstream POS tagging of an English historical collection. We conclude that future work should include more rigorous evaluation, including both intrinsic and extrinsic measures where possible.},
}
@inproceedings{xiao-etal-2018-gated,
    title = "Gated Multi-Task Network for Text Classification",
    author = "Xiao, Liqiang  and
      Zhang, Honglun  and
      Chen, Wenqing",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2114",
    doi = "10.18653/v1/N18-2114",
    pages = "726--731",
    abstract = "Multi-task learning with Convolutional Neural Network (CNN) has shown great success in many Natural Language Processing (NLP) tasks. This success can be largely attributed to the feature sharing by fusing some layers among tasks. However, most existing approaches just fully or proportionally share the features without distinguishing the helpfulness of them. By that the network would be confused by the helpless even harmful features, generating undesired interference between tasks. In this paper, we introduce gate mechanism into multi-task CNN and propose a new Gated Sharing Unit, which can filter the feature flows between tasks and greatly reduce the interference. Experiments on 9 text classification datasets shows that our approach can learn selection rules automatically and gain a great improvement over strong baselines.",
}
@inproceedings{huang-etal-2018-natural,
    title = "Natural Language to Structured Query Generation via Meta-Learning",
    author = "Huang, Po-Sen  and
      Wang, Chenglong  and
      Singh, Rishabh  and
      Yih, Wen-tau  and
      He, Xiaodong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2115",
    doi = "10.18653/v1/N18-2115",
    pages = "732--738",
    abstract = "In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function. When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1{\%}{--}5.4{\%} absolute accuracy gains over the non-meta-learning counterparts.",
}
@inproceedings{chen-gimpel-2018-smaller,
    title = "Smaller Text Classifiers with Discriminative Cluster Embeddings",
    author = "Chen, Mingda  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2116",
    doi = "10.18653/v1/N18-2116",
    pages = "739--745",
    abstract = "Word embedding parameters often dominate overall model sizes in neural methods for natural language processing. We reduce deployed model sizes of text classifiers by learning a hard word clustering in an end-to-end manner. We use the Gumbel-Softmax distribution to maximize over the latent clustering while minimizing the task loss. We propose variations that selectively assign additional parameters to words, which further improves accuracy while still remaining parameter-efficient.",
}
@inproceedings{al-hanai-etal-2018-role,
    title = "Role-specific Language Models for Processing Recorded Neuropsychological Exams",
    author = "Al Hanai, Tuka  and
      Au, Rhoda  and
      Glass, James",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2117",
    doi = "10.18653/v1/N18-2117",
    pages = "746--752",
    abstract = "Neuropsychological examinations are an important screening tool for the presence of cognitive conditions (e.g. Alzheimer{'}s, Parkinson{'}s Disease), and require a trained tester to conduct the exam through spoken interactions with the subject. While audio is relatively easy to record, it remains a challenge to automatically diarize (who spoke when?), decode (what did they say?), and assess a subject{'}s cognitive health. This paper demonstrates a method to determine the cognitive health (impaired or not) of 92 subjects, from audio that was diarized using an automatic speech recognition system trained on TED talks and on the structured language used by testers and subjects. Using leave-one-out cross validation and logistic regression modeling we show that even with noisily decoded data (81{\%} WER) we can still perform accurate enough diarization (0.02{\%} confusion rate) to determine the cognitive state of a subject (0.76 AUC).",
}
@inproceedings{goo-etal-2018-slot,
    title = "Slot-Gated Modeling for Joint Slot Filling and Intent Prediction",
    author = "Goo, Chih-Wen  and
      Gao, Guang  and
      Hsu, Yun-Kai  and
      Huo, Chih-Li  and
      Chen, Tsung-Chieh  and
      Hsu, Keng-Wei  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2118",
    doi = "10.18653/v1/N18-2118",
    pages = "753--757",
    abstract = "Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2{\%} and 1.9{\%} relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively",
}
@inproceedings{gella-keller-2018-evaluation,
    title = "An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data",
    author = "Gella, Spandana  and
      Keller, Frank",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2119",
    doi = "10.18653/v1/N18-2119",
    pages = "758--763",
    abstract = "Recent research in language and vision has developed models for predicting and disambiguating verbs from images. Here, we ask whether the predictions made by such models correspond to human intuitions about visual verbs. We show that the image regions a verb prediction model identifies as salient for a given verb correlate with the regions fixated by human observers performing a verb classification task.",
}
@inproceedings{manjunatha-etal-2018-learning,
    title = "Learning to Color from Language",
    author = "Manjunatha, Varun  and
      Iyyer, Mohit  and
      Boyd-Graber, Jordan  and
      Davis, Larry",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2120",
    doi = "10.18653/v1/N18-2120",
    pages = "764--769",
    abstract = "Automatic colorization is the process of adding color to greyscale images. We condition this process on language, allowing end users to manipulate a colorized image by feeding in different captions. We present two different architectures for language-conditioned colorization, both of which produce more accurate and plausible colorizations than a language-agnostic version. Furthermore, we demonstrate through crowdsourced experiments that we can dramatically alter colorizations simply by manipulating descriptive color words in captions.",
}
@inproceedings{chandrasekaran-etal-2018-punny,
    title = "Punny Captions: Witty Wordplay in Image Descriptions",
    author = "Chandrasekaran, Arjun  and
      Parikh, Devi  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2121",
    doi = "10.18653/v1/N18-2121",
    pages = "770--775",
    abstract = "Wit is a form of rich interaction that is often grounded in a specific situation (e.g., a comment in response to an event). In this work, we attempt to build computational models that can produce witty descriptions for a given image. Inspired by a cognitive account of humor appreciation, we employ linguistic wordplay, specifically puns, in image descriptions. We develop two approaches which involve retrieving witty descriptions for a given image from a large corpus of sentences, or generating them via an encoder-decoder neural network architecture. We compare our approach against meaningful baseline approaches via human studies and show substantial improvements. Moreover, in a Turing test style evaluation, people find the image descriptions generated by our model to be slightly wittier than human-written witty descriptions when the human is subject to similar constraints as the model regarding word usage and style.",
}
@inproceedings{dharmaretnam-fyshe-2018-emergence,
    title = "The Emergence of Semantics in Neural Network Representations of Visual Information",
    author = "Dharmaretnam, Dhanush  and
      Fyshe, Alona",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2122",
    doi = "10.18653/v1/N18-2122",
    pages = "776--780",
    abstract = "Word vector models learn about semantics through corpora. Convolutional Neural Networks (CNNs) can learn about semantics through images. At the most abstract level, some of the information in these models must be shared, as they model the same real-world phenomena. Here we employ techniques previously used to detect semantic representations in the human brain to detect semantic representations in CNNs. We show the accumulation of semantic information in the layers of the CNN, and discover that, for misclassified images, the correct class can be recovered in intermediate layers of a CNN.",
}
@inproceedings{cirik-etal-2018-visual,
    title = "Visual Referring Expression Recognition: What Do Systems Actually Learn?",
    author = "Cirik, Volkan  and
      Morency, Louis-Philippe  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2123",
    doi = "10.18653/v1/N18-2123",
    pages = "781--787",
    abstract = "We present an empirical analysis of state-of-the-art systems for referring expression recognition {--} the task of identifying the object in an image referred to by a natural language expression {--} with the goal of gaining insight into how these systems reason about language and vision. Surprisingly, we find strong evidence that even sophisticated and linguistically-motivated models for this task may ignore linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. For example, we show that a system trained and tested on the input image without the input referring expression can achieve a precision of 71.2{\%} in top-2 predictions. Furthermore, a system that predicts only the object category given the input can achieve a precision of 84.2{\%} in top-2 predictions. These surprisingly positive results for what should be deficient prediction scenarios suggest that careful analysis of what our models are learning {--} and further, how our data is constructed {--} is critical as we seek to make substantive progress on grounded language tasks.",
}
@inproceedings{rahgooy-etal-2018-visually,
    title = "Visually Guided Spatial Relation Extraction from Text",
    author = "Rahgooy, Taher  and
      Manzoor, Umar  and
      Kordjamshidi, Parisa",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2124",
    doi = "10.18653/v1/N18-2124",
    pages = "788--794",
    abstract = "Extraction of spatial relations from sentences with complex/nesting relationships is very challenging as often needs resolving inherent semantic ambiguities. We seek help from visual modality to fill the information gap in the text modality and resolve spatial semantic ambiguities. We use various recent vision and language datasets and techniques to train inter-modality alignment models, visual relationship classifiers and propose a novel global inference model to integrate these components into our structured output prediction model for spatial role and relation extraction. Our global inference model enables us to utilize the visual and geometric relationships between objects and improves the state-of-art results of spatial information extraction from text.",
}
@inproceedings{wang-etal-2018-watch,
    title = "Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning",
    author = "Wang, Xin  and
      Wang, Yuan-Fang  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-2125",
    doi = "10.18653/v1/N18-2125",
    pages = "795--801",
    abstract = "A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.",
}
@proceedings{naacl-2018-2018,
    title = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1000",
    doi = "10.18653/v1/N18-1",
}
@inproceedings{wang-etal-2018-label-aware,
    title = "Label-Aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition",
    author = "Wang, Zhenghui  and
      Qu, Yanru  and
      Chen, Liheng  and
      Shen, Jian  and
      Zhang, Weinan  and
      Zhang, Shaodian  and
      Gao, Yimei  and
      Gu, Gen  and
      Chen, Ken  and
      Yu, Yong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1001",
    doi = "10.18653/v1/N18-1001",
    pages = "1--15",
    abstract = "We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.",
}
@inproceedings{xu-barbosa-2018-neural,
    title = "Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss",
    author = "Xu, Peng  and
      Barbosa, Denilson",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1002",
    doi = "10.18653/v1/N18-1002",
    pages = "16--25",
    abstract = "The task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these issues do so with heuristics or with the help of hand-crafted features. Instead, we propose an end-to-end solution with a neural network model that uses a variant of cross-entropy loss function to handle out-of-context labels, and hierarchical loss normalization to cope with overly-specific ones. Also, previous work solve FETC a multi-label classification followed by ad-hoc post-processing. In contrast, our solution is more elegant: we use public word embeddings to train a single-label that jointly learns representations for entity mentions and their context. We show experimentally that our approach is robust against noise and consistently outperforms the state-of-the-art on established benchmarks for the task.",
}
@inproceedings{gupta-etal-2018-joint,
    title = "Joint Bootstrapping Machines for High Confidence Relation Extraction",
    author = {Gupta, Pankaj  and
      Roth, Benjamin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1003",
    doi = "10.18653/v1/N18-1003",
    pages = "26--36",
    abstract = "Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminated. We introduce BREX, a new bootstrapping method that protects against such contamination by highly effective confidence assessment. This is achieved by using entity and template seeds jointly (as opposed to just one as in previous work), by expanding entities and templates in parallel and in a mutually constraining fashion in each iteration and by introducing higherquality similarity measures for templates. Experimental results show that BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the art for four relationships.",
}
@inproceedings{cotterell-eisner-2018-deep,
    title = "A Deep Generative Model of Vowel Formant Typology",
    author = "Cotterell, Ryan  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1004",
    doi = "10.18653/v1/N18-1004",
    pages = "37--46",
    abstract = "What makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, we tackle the problem of vowel system typology, i.e., we propose a generative probability model of which vowels a language contains. In contrast to previous work, we work directly with the acoustic information{---}the first two formant values{---}rather than modeling discrete sets of symbols from the international phonetic alphabet. We develop a novel generative probability model and report results on over 200 languages.",
}
@inproceedings{kann-etal-2018-fortification,
    title = "Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages",
    author = {Kann, Katharina  and
      Mager Hois, Jesus Manuel  and
      Meza-Ruiz, Ivan Vladimir  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1005",
    doi = "10.18653/v1/N18-1005",
    pages = "47--57",
    abstract = "Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approaches{---}one with, one without need for external unlabeled resources{---}, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75{\%}. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research.",
}
@inproceedings{passban-etal-2018-improving,
    title = "Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation",
    author = "Passban, Peyman  and
      Liu, Qun  and
      Way, Andy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1006",
    doi = "10.18653/v1/N18-1006",
    pages = "58--68",
    abstract = "Recently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches. However, its performance drops considerably in the presence of morphologically rich languages (MRLs). Neural engines usually fail to tackle the large vocabulary and high out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable to exploit existing word-based models to translate this set of languages. In this paper, we propose an extension to the state-of-the-art model of Chung et al. (2016), which works at the character level and boosts the decoder with target-side morphological information. In our architecture, an additional morphology table is plugged into the model. Each time the decoder samples from a target vocabulary, the table sends auxiliary signals from the most relevant affixes in order to enrich the decoder{'}s current state and constrain it to provide better predictions. We evaluated our model to translate English into German, Russian, and Turkish as three MRLs and observed significant improvements.",
}
@inproceedings{tran-etal-2018-parsing,
    title = "Parsing Speech: a Neural Approach to Integrating Lexical and Acoustic-Prosodic Information",
    author = "Tran, Trang  and
      Toshniwal, Shubham  and
      Bansal, Mohit  and
      Gimpel, Kevin  and
      Livescu, Karen  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1007",
    doi = "10.18653/v1/N18-1007",
    pages = "69--81",
    abstract = "In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text and prosodic features. We find that different types of acoustic-prosodic features are individually helpful, and together give statistically significant improvements in parse and disfluency detection F1 scores over a strong text-only baseline. For this study with known sentence boundaries, error analyses show that the main benefit of acoustic-prosodic features is in sentences with disfluencies, attachment decisions are most improved, and transcription errors obscure gains from prosody.",
}
@inproceedings{anastasopoulos-chiang-2018-tied,
    title = "Tied Multitask Learning for Neural Speech Translation",
    author = "Anastasopoulos, Antonios  and
      Chiang, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1008",
    doi = "10.18653/v1/N18-1008",
    pages = "82--91",
    abstract = "We explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions. First, we introduce a model where the second task decoder receives information from the decoder of the first task, since higher-level intermediate representations should provide useful information. Second, we apply regularization that encourages transitivity and invertibility. We show that the application of these notions on jointly trained models improves performance on the tasks of low-resource speech transcription and translation. It also leads to better performance when using attention information for word discovery over unsegmented input.",
}
@inproceedings{gillick-bamman-2018-please,
    title = "Please Clap: Modeling Applause in Campaign Speeches",
    author = "Gillick, Jon  and
      Bamman, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1009",
    doi = "10.18653/v1/N18-1009",
    pages = "92--102",
    abstract = "This work examines the rhetorical techniques that speakers employ during political campaigns. We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause. In contrast to existing datasets, we tackle the challenge of working with transcripts that derive from uncorrected closed captioning, using associated audio recordings to automatically extract and align labels for instances of audience applause. In prediction experiments, we find that lexical features carry the most information, but that a variety of features are predictive, including prosody, long-term contextual dependencies, and theoretically motivated features designed to capture rhetorical techniques.",
}
@inproceedings{jo-etal-2018-attentive,
    title = "Attentive Interaction Model: Modeling Changes in View in Argumentation",
    author = "Jo, Yohan  and
      Poddar, Shivani  and
      Jeon, Byungsoo  and
      Shen, Qinlan  and
      Ros{\'e}, Carolyn  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1010",
    doi = "10.18653/v1/N18-1010",
    pages = "103--116",
    abstract = "We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder{'}s (OH{'}s) reasoning and a challenger{'}s argument, with the goal of predicting if the argument successfully changes the OH{'}s view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH{'}s reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH{'}s reasoning and that of the challenger{'}s argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH{'}s change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones.",
}
@inproceedings{ziai-meurers-2018-automatic,
    title = "Automatic Focus Annotation: Bringing Formal Pragmatics Alive in Analyzing the Information Structure of Authentic Data",
    author = "Ziai, Ramon  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1011",
    doi = "10.18653/v1/N18-1011",
    pages = "117--128",
    abstract = "Analyzing language in context, both from a theoretical and from a computational perspective, is receiving increased interest. Complementing the research in linguistics on discourse and information structure, in computational linguistics identifying discourse concepts was also shown to improve the performance of certain applications, for example, Short Answer Assessment systems (Ziai and Meurers, 2014). Building on the research that established detailed annotation guidelines for manual annotation of information structural concepts for written (Dipper et al., 2007; Ziai and Meurers, 2014) and spoken language data (Calhoun et al., 2010), this paper presents the first approach automating the analysis of focus in authentic written data. Our classification approach combines a range of lexical, syntactic, and semantic features to achieve an accuracy of 78.1{\%} for identifying focus.",
}
@inproceedings{rao-tetreault-2018-dear,
    title = "Dear Sir or Madam, May {I} Introduce the {GYAFC} Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer",
    author = "Rao, Sudha  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1012",
    doi = "10.18653/v1/N18-1012",
    pages = "129--140",
    abstract = "Style transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus for a particular stylistic transfer (formality) and show that techniques from the machine translation community can serve as strong baselines for future work. We also discuss challenges of using automatic metrics.",
}
@inproceedings{dai-huang-2018-improving,
    title = "Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph",
    author = "Dai, Zeyu  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1013",
    doi = "10.18653/v1/N18-1013",
    pages = "141--151",
    abstract = "We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.",
}
@inproceedings{juraska-etal-2018-deep,
    title = "A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation",
    author = "Juraska, Juraj  and
      Karagiannis, Panagiotis  and
      Bowden, Kevin  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1014",
    doi = "10.18653/v1/N18-1014",
    pages = "152--162",
    abstract = "Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.",
}
@inproceedings{watanabe-etal-2018-melody,
    title = "A Melody-Conditioned Lyrics Language Model",
    author = "Watanabe, Kento  and
      Matsubayashi, Yuichiroh  and
      Fukayama, Satoru  and
      Goto, Masataka  and
      Inui, Kentaro  and
      Nakano, Tomoyasu",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1015",
    doi = "10.18653/v1/N18-1015",
    pages = "163--172",
    abstract = "This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody. Previously proposed models for lyrics generation suffer from the inability of capturing the relationship between lyrics and melody partly due to the unavailability of lyrics-melody aligned data. In this study, we first propose a new practical method for creating a large collection of lyrics-melody aligned data and then create a collection of 1,000 lyrics-melody pairs augmented with precise syllable-note alignments and word/sentence/paragraph boundaries. We then provide a quantitative analysis of the correlation between word/sentence/paragraph boundaries in lyrics and melodies. We then propose an RNN-based lyrics language model conditioned on a featurized melody. Experimental results show that the proposed model generates fluent lyrics while maintaining the compatibility between boundaries of lyrics and melody structures.",
}
@inproceedings{bosselut-etal-2018-discourse,
    title = "Discourse-Aware Neural Rewards for Coherent Text Generation",
    author = "Bosselut, Antoine  and
      Celikyilmaz, Asli  and
      He, Xiaodong  and
      Gao, Jianfeng  and
      Huang, Po-Sen  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1016",
    doi = "10.18653/v1/N18-1016",
    pages = "173--184",
    abstract = "In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text. In particular, we propose to learn neural rewards to model cross-sentence ordering as a means to approximate desired discourse structure. Empirical results demonstrate that a generator trained with the learned reward produces more coherent and less repetitive text than models trained with cross-entropy or with reinforcement learning with commonly used scores as rewards.",
}
@inproceedings{fu-feng-2018-natural,
    title = "Natural Answer Generation with Heterogeneous Memory",
    author = "Fu, Yao  and
      Feng, Yansong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1017",
    doi = "10.18653/v1/N18-1017",
    pages = "185--195",
    abstract = "Memory augmented encoder-decoder framework has achieved promising progress for natural language generation tasks. Such frameworks enable a decoder to retrieve from a memory during generation. However, less research has been done to take care of the memory contents from different sources, which are often of heterogeneous formats. In this work, we propose a novel attention mechanism to encourage the decoder to actively interact with the memory by taking its heterogeneity into account. Our solution attends across the generated history and memory to explicitly avoid repetition, and introduce related knowledge to enrich our generated sentences. Experiments on the answer sentence generation task show that our method can effectively explore heterogeneous memory to produce readable and meaningful answer sentences while maintaining high coverage for given answer information.",
}
@inproceedings{ma-etal-2018-query,
    title = "Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation",
    author = "Ma, Shuming  and
      Sun, Xu  and
      Li, Wei  and
      Li, Sujian  and
      Li, Wenjie  and
      Ren, Xuancheng",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1018",
    doi = "10.18653/v1/N18-1018",
    pages = "196--206",
    abstract = "Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantically improper. In this work, we introduce a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our proposed model generates the words by querying distributed word representations (i.e. neural word embeddings), hoping to capturing the meaning of the according words. Following previous work, we evaluate our model on two paraphrase-oriented tasks, namely text simplification and short text abstractive summarization. Experimental results show that our model outperforms the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a Chinese summarization dataset. Moreover, our model achieves state-of-the-art performances on these three benchmark datasets.",
}
@inproceedings{kriz-etal-2018-simplification,
    title = "Simplification Using Paraphrases and Context-Based Lexical Substitution",
    author = "Kriz, Reno  and
      Miltsakaki, Eleni  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1019",
    doi = "10.18653/v1/N18-1019",
    pages = "207--217",
    abstract = "Lexical simplification involves identifying complex words or phrases that need to be simplified, and recommending simpler meaning-preserving substitutes that can be more easily understood. We propose a complex word identification (CWI) model that exploits both lexical and contextual features, and a simplification mechanism which relies on a word-embedding lexical substitution model to replace the detected complex words with simpler paraphrases. We compare our CWI and lexical simplification models to several baselines, and evaluate the performance of our simplification system against human judgments. The results show that our models are able to detect complex words with higher accuracy than other commonly used methods, and propose good simplification substitutes in context. They also highlight the limited contribution of context features for CWI, which nonetheless improve simplification compared to context-unaware models.",
}
@inproceedings{elsahar-etal-2018-zero,
    title = "Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types",
    author = "Elsahar, Hady  and
      Gravier, Christophe  and
      Laforest, Frederique",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1020",
    doi = "10.18653/v1/N18-1020",
    pages = "218--228",
    abstract = "We present a neural model for question generation from knowledge graphs triples in a {``}Zero-shot{''} setup, that is generating questions for predicate, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in a encoder-decoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model outperforms state-of-the-art on this task.",
}
@inproceedings{amorim-etal-2018-automated,
    title = "Automated Essay Scoring in the Presence of Biased Ratings",
    author = "Amorim, Evelin  and
      Can{\c{c}}ado, Marcia  and
      Veloso, Adriano",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1021",
    doi = "10.18653/v1/N18-1021",
    pages = "229--237",
    abstract = "Studies in Social Sciences have revealed that when people evaluate someone else, their evaluations often reflect their biases. As a result, rater bias may introduce highly subjective factors that make their evaluations inaccurate. This may affect automated essay scoring models in many ways, as these models are typically designed to model (potentially biased) essay raters. While there is sizeable literature on rater effects in general settings, it remains unknown how rater bias affects automated essay scoring. To this end, we present a new annotated corpus containing essays and their respective scores. Different from existing corpora, our corpus also contains comments provided by the raters in order to ground their scores. We present features to quantify rater bias based on their comments, and we found that rater bias plays an important role in automated essay scoring. We investigated the extent to which rater bias affects models based on hand-crafted features. Finally, we propose to rectify the training set by removing essays associated with potentially biased scores while learning the scoring model.",
}
@inproceedings{bhagavatula-etal-2018-content,
    title = "Content-Based Citation Recommendation",
    author = "Bhagavatula, Chandra  and
      Feldman, Sergey  and
      Power, Russell  and
      Ammar, Waleed",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1022",
    doi = "10.18653/v1/N18-1022",
    pages = "238--251",
    abstract = "We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18{\%} in F1@20 and over 22{\%} in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it favors self-citations which are less useful in a citation recommendation setup. We release an online portal for citation recommendation based on our method, (URL: \url{http://bit.ly/citeDemo}) and a new dataset OpenCorpus of 7 million research articles to facilitate future research on this task.",
}
@inproceedings{khashabi-etal-2018-looking,
    title = "Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences",
    author = "Khashabi, Daniel  and
      Chaturvedi, Snigdha  and
      Roth, Michael  and
      Upadhyay, Shyam  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1023",
    doi = "10.18653/v1/N18-1023",
    pages = "252--262",
    abstract = "We present a reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains 6,500+ questions for 1000+ paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 88.1{\%}. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.",
}
@inproceedings{farag-etal-2018-neural,
    title = "Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input",
    author = "Farag, Youmna  and
      Yannakoudakis, Helen  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1024",
    doi = "10.18653/v1/N18-1024",
    pages = "263--271",
    abstract = "We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.",
}
@inproceedings{grangier-auli-2018-quickedit,
    title = "{Q}uick{E}dit: Editing Text {\&} Translations by Crossing Words Out",
    author = "Grangier, David  and
      Auli, Michael",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1025",
    doi = "10.18653/v1/N18-1025",
    pages = "272--282",
    abstract = "We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates the initial sentence by avoiding marked words. The approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with change markers. Our model is trained on translation bitext by simulating post-edits. We demonstrate the advantage of our approach for translation post-editing through simulated post-edits. We also evaluate our model for paraphrasing through a user study.",
}
@inproceedings{sen-etal-2018-tempo,
    title = "Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction",
    author = "Sen, Procheta  and
      Ganguly, Debasis  and
      Jones, Gareth",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1026",
    doi = "10.18653/v1/N18-1026",
    pages = "283--292",
    abstract = "Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on task extraction has focused on identifying tasks within a single session, where the notion of a session is defined by a fixed length time window. By contrast, in this work we seek to identify tasks that span across multiple sessions. To identify tasks, we conduct a global analysis of a query log in its entirety without restricting analysis to individual temporal windows. To capture inherent task semantics, we represent queries as vectors in an abstract space. We learn the embedding of query words in this space by leveraging the temporal and lexical contexts of queries. Embedded query vectors are then clustered into tasks. Experiments demonstrate that task extraction effectiveness is improved significantly with our proposed method of query vector embedding in comparison to existing approaches that make use of documents retrieved from a collection to estimate semantic similarities between queries.",
}
@inproceedings{rei-sogaard-2018-zero,
    title = "Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens",
    author = "Rei, Marek  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1027",
    doi = "10.18653/v1/N18-1027",
    pages = "293--302",
    abstract = "Can attention- or gradient-based visualization techniques be used to infer token-level labels for binary sequence tagging problems, using networks trained only on sentence-level labels? We construct a neural network architecture based on soft attention, train it as a binary sentence classifier and evaluate against token-level annotation on four different datasets. Inferring token labels from a network provides a method for quantitatively evaluating what the model is learning, along with generating useful feedback in assistance systems. Our results indicate that attention-based methods are able to predict token-level labels more accurately, compared to gradient-based methods, sometimes even rivaling the supervised oracle network.",
}
@inproceedings{stathopoulos-etal-2018-variable,
    title = "Variable Typing: Assigning Meaning to Variables in Mathematical Text",
    author = "Stathopoulos, Yiannos  and
      Baker, Simon  and
      Rei, Marek  and
      Teufel, Simone",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1028",
    doi = "10.18653/v1/N18-1028",
    pages = "303--312",
    abstract = "Information about the meaning of mathematical variables in text is useful in NLP/IR tasks such as symbol disambiguation, topic modeling and mathematical information retrieval (MIR). We introduce \textit{variable typing}, the task of assigning one \textit{mathematical type} (multi-word technical terms referring to mathematical concepts) to each variable in a sentence of mathematical text. As part of this work, we also introduce a new annotated data set composed of 33,524 data points extracted from scientific documents published on arXiv. Our intrinsic evaluation demonstrates that our data set is sufficient to successfully train and evaluate current classifiers from three different model architectures. The best performing model is evaluated on an extrinsic task: MIR, by producing a \textit{typed formula index}. Our results show that the best performing MIR models make use of our typed index, compared to a formula index only containing raw symbols, thereby demonstrating the usefulness of variable typing.",
}
@inproceedings{k-m-etal-2018-learning,
    title = "Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing",
    author = "K M, Annervaz  and
      Basu Roy Chowdhury, Somnath  and
      Dukkipati, Ambedkar",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1029",
    doi = "10.18653/v1/N18-1029",
    pages = "313--322",
    abstract = "Machine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge using a Bayesian setup, but these learning models do not have the ability to access any organized world knowledge on demand. In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks. Our aim is to develop a deep learning model that can extract relevant prior support facts from knowledge graphs depending on the task using attention mechanism. We introduce a convolution-based model for learning representations of knowledge graph entity and relation clusters in order to reduce the attention space. We show that the proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. Using this method we show significant improvement in performance for text classification with 20Newsgroups (News20) {\&} DBPedia datasets, and natural language inference with Stanford Natural Language Inference (SNLI) dataset. We also demonstrate that a deep learning model can be trained with substantially less amount of labeled training data, when it has access to organized world knowledge in the form of a knowledge base.",
}
@inproceedings{cocos-etal-2018-comparing,
    title = "Comparing Constraints for Taxonomic Organization",
    author = "Cocos, Anne  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1030",
    doi = "10.18653/v1/N18-1030",
    pages = "323--333",
    abstract = "Building a taxonomy from the ground up involves several sub-tasks: selecting terms to include, predicting semantic relations between terms, and selecting a subset of relational instances to keep, given constraints on the taxonomy graph. Methods for this final step {--} taxonomic organization {--} vary both in terms of the constraints they impose, and whether they enable discovery of synonymous terms. It is hard to isolate the impact of these factors on the quality of the resulting taxonomy because organization methods are rarely compared directly. In this paper, we present a head-to-head comparison of six taxonomic organization algorithms that vary with respect to their structural and transitivity constraints, and treatment of synonymy. We find that while transitive algorithms out-perform their non-transitive counterparts, the top-performing transitive algorithm is prohibitively slow for taxonomies with as few as 50 entities. We propose a simple modification to a non-transitive optimum branching algorithm to explicitly incorporate synonymy, resulting in a method that is substantially faster than the best transitive algorithm while giving complementary performance.",
}
@inproceedings{nguyen-chiang-2018-improving,
    title = "Improving Lexical Choice in Neural Machine Translation",
    author = "Nguyen, Toan  and
      Chiang, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1031",
    doi = "10.18653/v1/N18-1031",
    pages = "334--343",
    abstract = "We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to fix the norms of both vectors to a constant value. Second, we integrate a simple lexical module which is jointly trained with the rest of the model. We evaluate our approaches on eight language pairs with data sizes ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU, surpassing phrase-based translation in nearly all settings.",
}
@inproceedings{gu-etal-2018-universal,
    title = "Universal Neural Machine Translation for Extremely Low Resource Languages",
    author = "Gu, Jiatao  and
      Hassan, Hany  and
      Devlin, Jacob  and
      Li, Victor O.K.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1032",
    doi = "10.18653/v1/N18-1032",
    pages = "344--354",
    abstract = "In this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language. The lexical part is shared through a Universal Lexical Representation to support multi-lingual word-level sharing. The sentence-level sharing is represented by a model of experts from all source languages that share the source encoders with all other languages. This enables the low-resource language to utilize the lexical and sentence representations of the higher resource languages. Our approach is able to achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences, compared to the 18 BLEU of strong baseline system which uses multi-lingual training and back-translation. Furthermore, we show that the proposed approach can achieve almost 20 BLEU on the same dataset through fine-tuning a pre-trained multi-lingual system in a zero-shot setting.",
}
@inproceedings{edunov-etal-2018-classical,
    title = "Classical Structured Prediction Losses for Sequence to Sequence Learning",
    author = "Edunov, Sergey  and
      Ott, Myle  and
      Auli, Michael  and
      Grangier, David  and
      Ranzato, Marc{'}Aurelio",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1033",
    doi = "10.18653/v1/N18-1033",
    pages = "355--364",
    abstract = "There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT{'}14 German-English translation as well as Gigaword abstractive summarization. On the large WMT{'}14 English-French task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art.",
}
@inproceedings{benton-dredze-2018-deep,
    title = "Deep {D}irichlet Multinomial Regression",
    author = "Benton, Adrian  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1034",
    doi = "10.18653/v1/N18-1034",
    pages = "365--374",
    abstract = "Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features {--} a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR{'}s model fit is less sensitive to training parameters than DMR.",
}
@inproceedings{zeng-etal-2018-microblog,
    title = "Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse",
    author = "Zeng, Xingshan  and
      Li, Jing  and
      Wang, Lu  and
      Beauchamp, Nicholas  and
      Shugars, Sarah  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1035",
    doi = "10.18653/v1/N18-1035",
    pages = "375--385",
    abstract = "Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommendation, we exploit both the conversational context, and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics. Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.",
}
@inproceedings{habernal-etal-2018-name,
    title = "Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation",
    author = "Habernal, Ivan  and
      Wachsmuth, Henning  and
      Gurevych, Iryna  and
      Stein, Benno",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1036",
    doi = "10.18653/v1/N18-1036",
    pages = "386--396",
    abstract = "Arguing without committing a fallacy is one of the main requirements of an ideal debate. But even when debating rules are strictly enforced and fallacious arguments punished, arguers often lapse into attacking the opponent by an ad hominem argument. As existing research lacks solid empirical investigation of the typology of ad hominem arguments as well as their potential causes, this paper fills this gap by (1) performing several large-scale annotation studies, (2) experimenting with various neural architectures and validating our working hypotheses, such as controversy or reasonableness, and (3) providing linguistic insights into triggers of ad hominem using explainable neural network architectures.",
}
@inproceedings{wang-etal-2018-scene,
    title = "Scene Graph Parsing as Dependency Parsing",
    author = "Wang, Yu-Siang  and
      Liu, Chenxi  and
      Zeng, Xiaohui  and
      Yuille, Alan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1037",
    doi = "10.18653/v1/N18-1037",
    pages = "397--407",
    abstract = "In this paper, we study the problem of parsing structured knowledge graphs from textual descriptions. In particular, we consider the scene graph representation that considers objects together with their attributes and relations: this representation has been proved useful across a variety of vision and language applications. We begin by introducing an alternative but equivalent edge-centric view of scene graphs that connect to dependency parses. Together with a careful redesign of label and action space, we combine the two-stage pipeline used in prior work (generic dependency parsing followed by simple post-processing) into one, enabling end-to-end training. The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49.67{\%} to ground truth graphs on our evaluation set, surpassing best previous approaches by 5{\%}. We further demonstrate the effectiveness of our learned parser on image retrieval applications.",
}
@inproceedings{kiela-etal-2018-learning,
    title = "Learning Visually Grounded Sentence Representations",
    author = "Kiela, Douwe  and
      Conneau, Alexis  and
      Jabri, Allan  and
      Nickel, Maximilian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1038",
    doi = "10.18653/v1/N18-1038",
    pages = "408--418",
    abstract = "We investigate grounded sentence representations, where we train a sentence encoder to predict the image features of a given caption{---}i.e., we try to {``}imagine{''} how a sentence would be depicted visually{---}and use the resultant features as sentence representations. We examine the quality of the learned representations on a variety of standard sentence representation quality benchmarks, showing improved performance for grounded models over non-grounded ones. In addition, we thoroughly analyze the extent to which grounding contributes to improved performance, and show that the system also learns improved word embeddings.",
}
@inproceedings{pezzelle-etal-2018-comparatives,
    title = "Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision",
    author = "Pezzelle, Sandro  and
      Sorodoc, Ionut-Teodor  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1039",
    doi = "10.18653/v1/N18-1039",
    pages = "419--430",
    abstract = "The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.",
}
@inproceedings{chao-etal-2018-negative,
    title = "Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets",
    author = "Chao, Wei-Lun  and
      Hu, Hexiang  and
      Sha, Fei",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1040",
    doi = "10.18653/v1/N18-1040",
    pages = "431--441",
    abstract = "Visual question answering (Visual QA) has attracted a lot of attention lately, seen essentially as a form of (visual) Turing test that artificial intelligence should strive to achieve. In this paper, we study a crucial component of this task: how can we design good datasets for the task? We focus on the design of multiple-choice based datasets where the learner has to select the right answer from a set of candidate ones including the target (i.e., the correct one) and the decoys (i.e., the incorrect ones). Through careful analysis of the results attained by state-of-the-art learning models and human annotators on existing datasets, we show that the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or both while still doing well on the task. Inspired by this, we propose automatic procedures to remedy such design deficiencies. We apply the procedures to re-construct decoy answers for two popular Visual QA datasets as well as to create a new Visual QA dataset from the Visual Genome project, resulting in the largest dataset for this task. Extensive empirical studies show that the design deficiencies have been alleviated in the remedied datasets and the performance on them is likely a more faithful indicator of the difference among learning models. The datasets are released and publicly available via \url{http://www.teds.usc.edu/website_vqa/}.",
}
@inproceedings{issa-etal-2018-abstract,
    title = "{A}bstract {M}eaning {R}epresentation for Paraphrase Detection",
    author = "Issa, Fuad  and
      Damonte, Marco  and
      Cohen, Shay B.  and
      Yan, Xiaohui  and
      Chang, Yi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1041",
    doi = "10.18653/v1/N18-1041",
    pages = "442--452",
    abstract = {Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that na{\"\i}ve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6{\%} for accuracy and 90.0{\%} for F$_1$ measure.},
}
@inproceedings{petroni-etal-2018-attr2vec,
    title = "attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines",
    author = "Petroni, Fabio  and
      Plachouras, Vassilis  and
      Nugent, Timothy  and
      Leidner, Jochen L.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1042",
    doi = "10.18653/v1/N18-1042",
    pages = "453--462",
    abstract = "The widespread use of word embeddings is associated with the recent successes of many natural language processing (NLP) systems. The key approach of popular models such as word2vec and GloVe is to learn dense vector representations from the context of words. More recently, other approaches have been proposed that incorporate different types of contextual information, including topics, dependency relations, n-grams, and sentiment. However, these models typically integrate only limited additional contextual information, and often in ad hoc ways. In this work, we introduce attr2vec, a novel framework for jointly learning embeddings for words and contextual attributes based on factorization machines. We perform experiments with different types of contextual information. Our experimental results on a text classification task demonstrate that using attr2vec to jointly learn embeddings for words and Part-of-Speech (POS) tags improves results compared to learning the embeddings independently. Moreover, we use attr2vec to train dependency-based embeddings and we show that they exhibit higher similarity between functionally related words compared to traditional approaches.",
}
@inproceedings{jana-goyal-2018-network-embedding,
    title = "Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation?",
    author = "Jana, Abhik  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1043",
    doi = "10.18653/v1/N18-1043",
    pages = "463--473",
    abstract = "Distributed representations of words learned from text have proved to be successful in various natural language processing tasks in recent times. While some methods represent words as vectors computed from text using predictive model (Word2vec) or dense count based model (GloVe), others attempt to represent these in a distributional thesaurus network structure where the neighborhood of a word is a set of words having adequate context overlap. Being motivated by recent surge of research in network embedding techniques (DeepWalk, LINE, node2vec etc.), we turn a distributional thesaurus network into dense word vectors and investigate the usefulness of distributional thesaurus embedding in improving overall word representation. This is the first attempt where we show that combining the proposed word representation obtained by distributional thesaurus embedding with the state-of-the-art word representations helps in improving the performance by a significant margin when evaluated against NLP tasks like word similarity and relatedness, synonym detection, analogy detection. Additionally, we show that even without using any handcrafted lexical resources we can come up with representations having comparable performance in the word similarity and relatedness tasks compared to the representations where a lexical resource has been used.",
}
@inproceedings{rosenfeld-erk-2018-deep,
    title = "Deep Neural Models of Semantic Shift",
    author = "Rosenfeld, Alex  and
      Erk, Katrin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1044",
    doi = "10.18653/v1/N18-1044",
    pages = "474--484",
    abstract = "Diachronic distributional models track changes in word use over time. In this paper, we propose a deep neural network diachronic distributional model. Instead of modeling lexical change via a time series as is done in previous work, we represent time as a continuous variable and model a word{'}s usage as a function of time. Additionally, we have also created a novel synthetic task which measures a model{'}s ability to capture the semantic trajectory. This evaluation quantitatively measures how well a model captures the semantic trajectory of a word over time. Finally, we explore how well the derivatives of our model can be used to measure the speed of lexical change.",
}
@inproceedings{chang-etal-2018-distributional,
    title = "Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection",
    author = "Chang, Haw-Shiuan  and
      Wang, Ziyun  and
      Vilnis, Luke  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1045",
    doi = "10.18653/v1/N18-1045",
    pages = "485--495",
    abstract = "Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limits the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces distributional inclusion vector embedding (DIVE), a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings which preserve the inclusion property of word contexts. In experimental evaluations more comprehensive than any previous literature of which we are aware{---}evaluating on 11 datasets using multiple existing as well as newly proposed scoring functions{---}we find that our method provides up to double the precision of previous unsupervised methods, and the highest average performance, using a much more compact word representation, and yielding many new state-of-the-art results.",
}
@inproceedings{chinnappa-blanco-2018-mining,
    title = "Mining Possessions: Existence, Type and Temporal Anchors",
    author = "Chinnappa, Dhivya  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1046",
    doi = "10.18653/v1/N18-1046",
    pages = "496--505",
    abstract = "This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between possessor and possessee. We present new annotations for this task, and experimental results using both traditional classifiers and neural networks. Results show that the three subtasks (predicting possession existence, possession type and temporal anchors) can be automated.",
}
@inproceedings{ishihara-etal-2018-neural,
    title = "Neural Tensor Networks with Diagonal Slice Matrices",
    author = "Ishihara, Takahiro  and
      Hayashi, Katsuhiko  and
      Manabe, Hitoshi  and
      Shimbo, Masashi  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1047",
    doi = "10.18653/v1/N18-1047",
    pages = "506--515",
    abstract = "Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time. We address these issues by applying eigendecomposition to each slice matrix of a tensor to reduce its number of paramters. First, we evaluate our proposed NTN models on knowledge graph completion. Second, we extend the models to recursive NTNs (RNTNs) and evaluate them on logical reasoning tasks. These experiments show that our proposed models learn better and faster than the original (R)NTNs.",
}
@inproceedings{vulic-etal-2018-post,
    title = "Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources",
    author = "Vuli{\'c}, Ivan  and
      Glava{\v{s}}, Goran  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1048",
    doi = "10.18653/v1/N18-1048",
    pages = "516--527",
    abstract = "Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. By design, these post-processing methods only update the vectors of words occurring in external lexicons, leaving the representations of all unseen words intact. In this paper, we show that constraint-driven vector space specialisation can be extended to unseen words. We propose a novel post-specialisation method that: a) preserves the useful linguistic knowledge for seen words; while b) propagating this external signal to unseen words in order to improve their vector representations as well. Our post-specialisation approach explicits a non-linear specialisation function in the form of a deep neural network by learning to predict specialised vectors from their original distributional counterparts. The learned function is then used to specialise vectors of unseen words. This approach, applicable to any post-processing model, yields considerable gains over the initial specialisation models both in intrinsic word similarity tasks, and in two downstream tasks: dialogue state tracking and lexical text simplification. The positive effects persist across three languages, demonstrating the importance of specialising the full vocabulary of distributional word vector spaces.",
}
@inproceedings{pagliardini-etal-2018-unsupervised,
    title = "Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features",
    author = "Pagliardini, Matteo  and
      Gupta, Prakhar  and
      Jaggi, Martin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1049",
    doi = "10.18653/v1/N18-1049",
    pages = "528--540",
    abstract = "The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.",
}
@inproceedings{liu-etal-2018-learning-domain,
    title = "Learning Domain Representation for Multi-Domain Sentiment Classification",
    author = "Liu, Qi  and
      Zhang, Yue  and
      Liu, Jiangming",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1050",
    doi = "10.18653/v1/N18-1050",
    pages = "541--550",
    abstract = "Training data for sentiment analysis are abundant in multiple domains, yet scarce for other domains. It is useful to leveraging data available for all existing domains to enhance performance on different domains. We investigate this problem by learning domain-specific representations of input sentences using neural network. In particular, a descriptor vector is learned for representing each domain, which is used to map adversarially trained domain-general Bi-LSTM input representations into domain-specific representations. Based on this model, we further expand the input representation with exemplary domain knowledge, collected by attending over a memory network of domain training data. Results show that our model outperforms existing methods on multi-domain sentiment analysis significantly, giving the best accuracies on two different benchmarks.",
}
@inproceedings{duan-etal-2018-learning-sentence,
    title = "Learning Sentence Representations over Tree Structures for Target-Dependent Classification",
    author = "Duan, Junwen  and
      Ding, Xiao  and
      Liu, Ting",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1051",
    doi = "10.18653/v1/N18-1051",
    pages = "551--560",
    abstract = "Target-dependent classification tasks, such as aspect-level sentiment analysis, perform fine-grained classifications towards specific targets. Semantic compositions over tree structures are promising for such tasks, as they can potentially capture long-distance interactions between targets and their contexts. However, previous work that operates on tree structures resorts to syntactic parsers or Treebank annotations, which are either subject to noise in informal texts or highly expensive to obtain. To address above issues, we propose a reinforcement learning based approach, which automatically induces target-specific sentence representations over tree structures. The underlying model is a RNN encoder-decoder that explores possible binary tree structures and a reward mechanism that encourages structures that improve performances on downstream tasks. We evaluate our approach on two benchmark tasks: firm-specific cumulative abnormal return prediction (based on formal news texts) and aspect-level sentiment analysis (based on informal social media texts). Experimental results show that our model gives superior performances compared to previous work that operates on parsed trees. Moreover, our approach gives some intuitions on how target-specific sentence representations can be achieved from its word constituents.",
}
@inproceedings{zhou-etal-2018-relevant,
    title = "Relevant Emotion Ranking from Text Constrained with Emotion Relationships",
    author = "Zhou, Deyu  and
      Yang, Yang  and
      He, Yulan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1052",
    doi = "10.18653/v1/N18-1052",
    pages = "561--571",
    abstract = "Text might contain or invoke multiple emotions with varying intensities. As such, emotion detection, to predict multiple emotions associated with a given text, can be cast into a multi-label classification problem. We would like to go one step further so that a ranked list of relevant emotions are generated where top ranked emotions are more intensely associated with text compared to lower ranked emotions, whereas the rankings of irrelevant emotions are not important. A novel framework of relevant emotion ranking is proposed to tackle the problem. In the framework, the objective loss function is designed elaborately so that both emotion prediction and rankings of only relevant emotions can be achieved. Moreover, we observe that some emotions co-occur more often while other emotions rarely co-exist. Such information is incorporated into the framework as constraints to improve the accuracy of emotion detection. Experimental results on two real-world corpora show that the proposed framework can effectively deal with emotion detection and performs remarkably better than the state-of-the-art emotion detection approaches and multi-label learning methods.",
}
@inproceedings{akhtar-etal-2018-solving,
    title = "Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality",
    author = "Akhtar, Md Shad  and
      Sawant, Palaash  and
      Sen, Sukanta  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1053",
    doi = "10.18653/v1/N18-1053",
    pages = "572--582",
    abstract = "Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great challenge in creating efficient word representation model for solving the underlying problem. The problem is more intensified in resource-poor scenario due to the absence of sufficient amount of corpus. In this work we propose to minimize the effect of data sparsity by leveraging bilingual word embeddings learned through a parallel corpus. We train and evaluate Long Short Term Memory (LSTM) based architecture for aspect level sentiment classification. The neural network architecture is further assisted by the hand-crafted features for the prediction. We show the efficacy of the proposed model against state-of-the-art methods in two experimental setups i.e. multi-lingual and cross-lingual.",
}
@inproceedings{marasovic-frank-2018-srl4orl,
    title = "{SRL}4{ORL}: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling",
    author = "Marasovi{\'c}, Ana  and
      Frank, Anette",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1054",
    doi = "10.18653/v1/N18-1054",
    pages = "583--594",
    abstract = "For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question {``}Who expressed what kind of sentiment towards what?{''}. Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model, which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis we determine what works and what might be done to make further improvements for ORL.",
}
@inproceedings{junczys-dowmunt-etal-2018-approaching,
    title = "Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task",
    author = "Junczys-Dowmunt, Marcin  and
      Grundkiewicz, Roman  and
      Guha, Shubha  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1055",
    doi = "10.18653/v1/N18-1055",
    pages = "595--606",
    abstract = "Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10{\%} M{\mbox{$^2$}} on the CoNLL-2014 benchmark and 5.9{\%} on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2{\%} on the CoNLL-2014 benchmark and by 4{\%} on JFLEG.",
}
@inproceedings{upadhyay-etal-2018-robust,
    title = "Robust Cross-Lingual Hypernymy Detection Using Dependency Context",
    author = "Upadhyay, Shyam  and
      Vyas, Yogarshi  and
      Carpuat, Marine  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1056",
    doi = "10.18653/v1/N18-1056",
    pages = "607--618",
    abstract = "Cross-lingual Hypernymy Detection involves determining if a word in one language ({``}fruit{''}) is a hypernym of a word in another language ({``}pomme{''} i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BiSparse-Dep, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BiSparse-Dep can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages {--} Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.",
}
@inproceedings{xie-etal-2018-noising,
    title = "Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction",
    author = "Xie, Ziang  and
      Genthial, Guillaume  and
      Xie, Stanley  and
      Ng, Andrew  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1057",
    doi = "10.18653/v1/N18-1057",
    pages = "619--628",
    abstract = "Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizing parallel data by noising a clean monolingual corpus. While most previous approaches introduce perturbations using features computed from local context windows, we instead develop error generation processes using a neural sequence transduction model trained to translate clean examples to their noisy counterparts. Given a corpus of clean examples, we propose beam search noising procedures to synthesize additional noisy examples that human evaluators were nearly unable to discriminate from nonsynthesized examples. Surprisingly, when trained on additional data synthesized using our best-performing noising scheme, our model approaches the same performance as when trained on additional nonsynthesized data.",
}
@inproceedings{sachan-xing-2018-self,
    title = "Self-Training for Jointly Learning to Ask and Answer Questions",
    author = "Sachan, Mrinmaya  and
      Xing, Eric",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1058",
    doi = "10.18653/v1/N18-1058",
    pages = "629--640",
    abstract = "Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many domains. To alleviate these issues, we propose a self-training method for jointly learning to ask as well as answer questions, leveraging unlabeled text along with labeled question answer pairs for learning. We evaluate our approach on four benchmark datasets: SQUAD, MS MARCO, WikiQA and TrecQA, and show significant improvements over a number of established baselines on both question answering and question generation tasks. We also achieved new state-of-the-art results on two competitive answer sentence selection tasks: WikiQA and TrecQA.",
}
@inproceedings{talmor-berant-2018-web,
    title = "The Web as a Knowledge-Base for Answering Complex Questions",
    author = "Talmor, Alon  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1059",
    doi = "10.18653/v1/N18-1059",
    pages = "641--651",
    abstract = "Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, ComplexWebQuestions, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.",
}
@inproceedings{liang-etal-2018-meaning,
    title = "A Meaning-Based Statistical {E}nglish Math Word Problem Solver",
    author = "Liang, Chao-Chun  and
      Wong, Yu-Shiang  and
      Lin, Yi-Chung  and
      Su, Keh-Yih",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1060",
    doi = "10.18653/v1/N18-1060",
    pages = "652--662",
    abstract = "We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.",
}
@inproceedings{kamila-etal-2018-fine,
    title = "Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates",
    author = "Kamila, Sabyasachi  and
      Hasanuzzaman, Mohammed  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak  and
      Way, Andy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1061",
    doi = "10.18653/v1/N18-1061",
    pages = "663--674",
    abstract = "Temporal orientation refers to an individual{'}s tendency to connect to the psychological concepts of past, present or future, and it affects personality, motivation, emotion, decision making and stress coping processes. The study of the social media users{'} psycho-demographic attributes from the perspective of human temporal orientation can be of utmost interest and importance to the business and administrative decision makers as it can provide an extra precious information for them to make informed decisions. In this paper, we propose a very first study to demonstrate the association between the sentiment view of the temporal orientation of the users and their different psycho-demographic attributes by analyzing their tweets. We first create a temporal orientation classifier in a minimally supervised way which classifies each tweet of the users in one of the three temporal categories, namely past, present, and future. A deep Bi-directional Long Short Term Memory (BLSTM) is used for the tweet classification task. Our tweet classifier achieves an accuracy of 78.27{\%} when tested on a manually created test set. We then determine the users{'} overall temporal orientation based on their tweets on the social media. The sentiment is added to the tweets at the fine-grained level where each temporal tweet is given a sentiment with either of the positive, negative or neutral. Our experiment reveals that depending upon the sentiment view of temporal orientation, a user{'}s attributes vary. We finally measure the correlation between the users{'} sentiment view of temporal orientation and their different psycho-demographic factors using regression.",
}
@inproceedings{torabi-asr-etal-2018-querying,
    title = "Querying Word Embeddings for Similarity and Relatedness",
    author = "Torabi Asr, Fatemeh  and
      Zinkov, Robert  and
      Jones, Michael",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1062",
    doi = "10.18653/v1/N18-1062",
    pages = "675--684",
    abstract = "Word embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness norming data. Skipgram generates a set of word and context embeddings, the latter typically discarded after training. We demonstrate the usefulness of context embeddings in predicting asymmetric association between words from a recently published dataset of production norms (Jouravlev {\&} McRae, 2016). Our findings suggest that humans respond with words closer to the cue within the context embedding space (rather than the word embedding space), when asked to generate thematically related words.",
}
@inproceedings{sulem-etal-2018-semantic,
    title = "Semantic Structural Evaluation for Text Simplification",
    author = "Sulem, Elior  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1063",
    doi = "10.18653/v1/N18-1063",
    pages = "685--696",
    abstract = "Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. SAMSA provides a reference-less automatic evaluation procedure, avoiding the problems that reference-based methods face due to the vast space of valid simplifications for a given sentence. Our human evaluation experiments show both SAMSA{'}s substantial correlation with human judgments, as well as the deficiency of existing reference-based measures in evaluating structural simplification.",
}
@inproceedings{amplayo-etal-2018-entity,
    title = "Entity Commonsense Representation for Neural Abstractive Summarization",
    author = "Amplayo, Reinald Kim  and
      Lim, Seonjae  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1064",
    doi = "10.18653/v1/N18-1064",
    pages = "697--707",
    abstract = "A major proportion of a text summary includes important entities found in the original text. These entities build up the topic of the summary. Moreover, they hold commonsense information once they are linked to a knowledge base. Based on these observations, this paper investigates the usage of linked entities to guide the decoder of a neural text summarizer to generate concise and better summaries. To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary. Current available ELS{'}s are still not sufficiently effective, possibly introducing unresolved ambiguities and irrelevant entities. We resolve the imperfections of the ELS by (a) encoding entities with selective disambiguation, and (b) pooling entity vectors using firm attention. By applying E2T to a simple sequenceto-sequence model with attention mechanism as base model, we see significant improvements of the performance in the Gigaword (sentence to title) and CNN (long document to multi-sentence highlights) summarization datasets by at least 2 ROUGE points.",
}
@inproceedings{grusky-etal-2018-newsroom,
    title = "{N}ewsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies",
    author = "Grusky, Max  and
      Naaman, Mor  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1065",
    doi = "10.18653/v1/N18-1065",
    pages = "708--719",
    abstract = "We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates. We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges. The dataset is available online at summari.es.",
}
@inproceedings{richardson-etal-2018-polyglot,
    title = "Polyglot Semantic Parsing in {API}s",
    author = "Richardson, Kyle  and
      Berant, Jonathan  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1066",
    doi = "10.18653/v1/N18-1066",
    pages = "720--730",
    abstract = "Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particular, we focus on translating text to code signature representations using the software component datasets of Richardson and Kuhn (2017b,a). The advantage of such models is that they can be used for parsing a wide variety of input natural languages and output programming languages, or mixed input languages, using a single unified model. To facilitate modeling of this type, we develop a novel graph-based decoding framework that achieves state-of-the-art performance on the above datasets, and apply this method to two other benchmark SP tasks.",
}
@inproceedings{rudinger-etal-2018-neural-models,
    title = "Neural Models of Factuality",
    author = "Rudinger, Rachel  and
      White, Aaron Steven  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1067",
    doi = "10.18653/v1/N18-1067",
    pages = "731--744",
    abstract = "We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. We report model results on this extended factuality dataset as well.",
}
@inproceedings{an-etal-2018-accurate,
    title = "Accurate Text-Enhanced Knowledge Graph Representation Learning",
    author = "An, Bo  and
      Chen, Bo  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1068",
    doi = "10.18653/v1/N18-1068",
    pages = "745--755",
    abstract = "Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in different triples with the same representation, without considering the ambiguity of relations and entities. To appropriately handle the semantic variety of entities/relations in distinct triples, we propose an accurate text-enhanced knowledge graph representation learning method, which can represent a relation/entity with different representations in different triples by exploiting additional textual information. Specifically, our method enhances representations by exploiting the entity descriptions and triple-specific relation mention. And a mutual attention mechanism between relation mention and entity description is proposed to learn more accurate textual representations for further improving knowledge graph representation. Experimental results show that our method achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation models.",
}
@inproceedings{yanaka-etal-2018-acquisition,
    title = "Acquisition of Phrase Correspondences Using Natural Deduction Proofs",
    author = "Yanaka, Hitomi  and
      Mineshima, Koji  and
      Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1069",
    doi = "10.18653/v1/N18-1069",
    pages = "756--766",
    abstract = "How to identify, extract, and use phrasal knowledge is a crucial problem for the task of Recognizing Textual Entailment (RTE). To solve this problem, we propose a method for detecting paraphrases via natural deduction proofs of semantic relations between sentence pairs. Our solution relies on a graph reformulation of partial variable unifications and an algorithm that induces subgraph alignments between meaning representations. Experiments show that our method can automatically detect various paraphrases that are absent from existing paraphrase databases. In addition, the detection of paraphrases using proof information improves the accuracy of RTE tasks.",
}
@inproceedings{mohtarami-etal-2018-automatic,
    title = "Automatic Stance Detection Using End-to-End Memory Networks",
    author = "Mohtarami, Mitra  and
      Baly, Ramy  and
      Glass, James  and
      Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1070",
    doi = "10.18653/v1/N18-1070",
    pages = "767--776",
    abstract = "We present an effective end-to-end memory network model that jointly (i) predicts whether a given document can be considered as relevant evidence for a given claim, and (ii) extracts snippets of evidence that can be used to reason about the factuality of the target claim. Our model combines the advantages of convolutional and recurrent neural networks as part of a memory network. We further introduce a similarity matrix at the inference level of the memory network in order to extract snippets of evidence for input claims more accurately. Our experiments on a public benchmark dataset, FakeNewsChallenge, demonstrate the effectiveness of our approach.",
}
@inproceedings{yang-etal-2018-collective,
    title = "Collective Entity Disambiguation with Structured Gradient Tree Boosting",
    author = "Yang, Yi  and
      Irsoy, Ozan  and
      Rahman, Kazi Shefaet",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1071",
    doi = "10.18653/v1/N18-1071",
    pages = "777--786",
    abstract = "We present a gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many top-performing natural language processing systems. Surprisingly, most works limit the use of gradient tree boosting as a tool for regular classification or regression problems, despite the structured nature of language. To the best of our knowledge, our work is the first one that employs the structured gradient tree boosting (SGTB) algorithm for collective entity disambiguation. By defining global features over previous disambiguation decisions and jointly modeling them with local features, our system is able to produce globally optimized entity assignments for mentions in a document. Exact inference is prohibitively expensive for our globally normalized model. To solve this problem, we propose Bidirectional Beam Search with Gold path (BiBSG), an approximate inference algorithm that is a variant of the standard beam search algorithm. BiBSG makes use of global information from both past and future to perform better local search. Experiments on standard benchmark datasets show that SGTB significantly improves upon published results. Specifically, SGTB outperforms the previous state-of-the-art neural system by near 1{\%} absolute accuracy on the popular AIDA-CoNLL dataset.",
}
@inproceedings{kolyvakis-etal-2018-deepalignment,
    title = "{D}eep{A}lignment: Unsupervised Ontology Matching with Refined Word Vectors",
    author = "Kolyvakis, Prodromos  and
      Kalousis, Alexandros  and
      Kiritsis, Dimitris",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1072",
    doi = "10.18653/v1/N18-1072",
    pages = "787--798",
    abstract = "Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such {``}parallel{''} ontologies raise the need for a process that will establish alignments between their entities in order to unify and extend the existing knowledge. In this work, we present a novel entity alignment method which we dub DeepAlignment. DeepAlignment refines pre-trained word vectors aiming at deriving ontological entity descriptions which are tailored to the ontology matching task. The absence of explicit information relevant to the ontology matching task during the refinement process makes DeepAlignment completely unsupervised. We empirically evaluate our method using standard ontology matching benchmarks. We present significant performance improvements over the current state-of-the-art, demonstrating the advantages that representation learning techniques bring to ontology matching.",
}
@inproceedings{gao-etal-2018-efficient,
    title = "Efficient Sequence Learning with Group Recurrent Networks",
    author = "Gao, Fei  and
      Wu, Lijun  and
      Zhao, Li  and
      Qin, Tao  and
      Cheng, Xueqi  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1073",
    doi = "10.18653/v1/N18-1073",
    pages = "799--808",
    abstract = "Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation, speech recognition and so on. One of the key factors to these successes is big models. However, training such big models usually takes days or even weeks of time even if using tens of GPU cards. In this paper, we propose an efficient architecture to improve the efficiency of such RNN model training, which adopts the group strategy for recurrent layers, while exploiting the representation rearrangement strategy between layers as well as time steps. To demonstrate the advantages of our models, we conduct experiments on several datasets and tasks. The results show that our architecture achieves comparable or better accuracy comparing with baselines, with a much smaller number of parameters and at a much lower computational cost.",
}
@inproceedings{thorne-etal-2018-fever,
    title = "{FEVER}: a Large-scale Dataset for Fact Extraction and {VER}ification",
    author = "Thorne, James  and
      Vlachos, Andreas  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1074",
    doi = "10.18653/v1/N18-1074",
    pages = "809--819",
    abstract = "In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo by annotators achieving 0.6841 in Fleiss kappa. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment. To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles. The best accuracy we achieve on labeling a claim accompanied by the correct evidence is 31.87{\%}, while if we ignore the evidence we achieve 50.91{\%}. Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.",
}
@inproceedings{su-etal-2018-global,
    title = "Global Relation Embedding for Relation Extraction",
    author = {Su, Yu  and
      Liu, Honglei  and
      Yavuz, Semih  and
      G{\"u}r, Izzeddin  and
      Sun, Huan  and
      Yan, Xifeng},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1075",
    doi = "10.18653/v1/N18-1075",
    pages = "820--830",
    abstract = "We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9{\%} to 89.3{\%}.",
}
@inproceedings{cheng-erk-2018-implicit,
    title = "Implicit Argument Prediction with Event Knowledge",
    author = "Cheng, Pengxiang  and
      Erk, Katrin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1076",
    doi = "10.18653/v1/N18-1076",
    pages = "831--840",
    abstract = "Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated on very small datasets. We propose to train models for implicit argument prediction on a simple cloze task, for which data can be generated automatically at scale. This allows us to use a neural model, which draws on narrative coherence and entity salience for predictions. We show that our model has superior performance on both synthetic and natural data.",
}
@inproceedings{ning-etal-2018-improving,
    title = "Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource",
    author = "Ning, Qiang  and
      Wu, Hao  and
      Peng, Haoruo  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1077",
    doi = "10.18653/v1/N18-1077",
    pages = "841--851",
    abstract = "Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource {--} a probabilistic knowledge base acquired in the news domain {--} by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987{--}2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available.",
}
@inproceedings{moon-etal-2018-multimodal-named,
    title = "Multimodal Named Entity Recognition for Short Social Media Posts",
    author = "Moon, Seungwhan  and
      Neves, Leonardo  and
      Carvalho, Vitor",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1078",
    doi = "10.18653/v1/N18-1078",
    pages = "852--860",
    abstract = "We introduce a new task called Multimodal Named Entity Recognition (MNER) for noisy user-generated data such as tweets or Snapchat captions, which comprise short text with accompanying images. These social media posts often come in inconsistent or incomplete syntax and lexical notations with very limited surrounding textual contexts, bringing significant challenges for NER. To this end, we create a new dataset for MNER called SnapCaptions (Snapchat image-caption pairs submitted to public and crowd-sourced stories with fully annotated named entities). We then build upon the state-of-the-art Bi-LSTM word/character based NER models with 1) a deep image network which incorporates relevant visual context to augment textual information, and 2) a generic \textit{modality-attention} module which learns to attenuate irrelevant modalities while amplifying the most informative ones to extract contexts from, adaptive to each sample and token. The proposed MNER model with modality attention significantly outperforms the state-of-the-art text-only NER models by successfully leveraging provided visual contexts, opening up potential applications of MNER on myriads of social media platforms.",
}
@inproceedings{katiyar-cardie-2018-nested,
    title = "Nested Named Entity Recognition Revisited",
    author = "Katiyar, Arzoo  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1079",
    doi = "10.18653/v1/N18-1079",
    pages = "861--871",
    abstract = "We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-the-art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention.",
}
@inproceedings{verga-etal-2018-simultaneously,
    title = "Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction",
    author = "Verga, Patrick  and
      Strubell, Emma  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1080",
    doi = "10.18653/v1/N18-1080",
    pages = "872--884",
    abstract = "Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computation for each mention pair, and ignores relationships expressed across sentence boundaries. These problems are exacerbated by the document- (rather than sentence-) level annotation common in biological text. In response, we propose a model which simultaneously predicts relationships between all mention pairs in a document. We form pairwise predictions over entire paper abstracts using an efficient self-attention encoder. All-pairs mention scores allow us to perform multi-instance learning by aggregating over mentions to form entity pair representations. We further adapt to settings without mention-level annotation by jointly training to predict named entities and adding a corpus of weakly labeled data. In experiments on two Biocreative benchmark datasets, we achieve state of the art performance on the Biocreative V Chemical Disease Relation dataset for models without external KB resources. We also introduce a new dataset an order of magnitude larger than existing human-annotated biological information extraction datasets and more accurate than distantly supervised alternatives.",
}
@inproceedings{stanovsky-etal-2018-supervised,
    title = "Supervised Open Information Extraction",
    author = "Stanovsky, Gabriel  and
      Michael, Julian  and
      Zettlemoyer, Luke  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1081",
    doi = "10.18653/v1/N18-1081",
    pages = "885--895",
    abstract = "We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model outperforms the existing state-of-the-art Open IE systems on benchmark datasets.",
}
@inproceedings{gong-etal-2018-embedding,
    title = "Embedding Syntax and Semantics of Prepositions via Tensor Decomposition",
    author = "Gong, Hongyu  and
      Bhat, Suma  and
      Viswanath, Pramod",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1082",
    doi = "10.18653/v1/N18-1082",
    pages = "896--906",
    abstract = "Prepositions are among the most frequent words in English and play complex roles in the syntax and semantics of sentences. Not surprisingly, they pose well-known difficulties in automatic processing of sentences (prepositional attachment ambiguities and idiosyncratic uses in phrases). Existing methods on preposition representation treat prepositions no different from content words (e.g., word2vec and GloVe). In addition, recent studies aiming at solving prepositional attachment and preposition selection problems depend heavily on external linguistic resources and use dataset-specific word representations. In this paper we use \textit{word-triple} counts (one of the triples being a preposition) to capture a preposition{'}s interaction with its attachment and complement. We then derive preposition embeddings via tensor decomposition on a large unlabeled corpus. We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing phrasal verbs. Furthermore, our preposition embeddings are used as simple features in two challenging downstream tasks: preposition selection and prepositional attachment disambiguation. We achieve results comparable to or better than the state-of-the-art on multiple standardized datasets.",
}
@inproceedings{bjerva-augenstein-2018-phonology,
    title = "From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings",
    author = "Bjerva, Johannes  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1083",
    doi = "10.18653/v1/N18-1083",
    pages = "907--916",
    abstract = "A core part of linguistic typology is the classification of languages according to linguistic properties, such as those detailed in the World Atlas of Language Structure (WALS). Doing this manually is prohibitively time-consuming, which is in part evidenced by the fact that only 100 out of over 7,000 languages spoken in the world are fully covered in WALS. We learn distributed language representations, which can be used to predict typological properties on a massively multilingual scale. Additionally, quantitative and qualitative analyses of these language embeddings can tell us how language similarities are encoded in NLP models for tasks at different typological levels. The representations are learned in an unsupervised manner alongside tasks at three typological levels: phonology (grapheme-to-phoneme prediction, and phoneme reconstruction), morphology (morphological inflection), and syntax (part-of-speech tagging). We consider more than 800 languages and find significant differences in the language representations encoded, depending on the target task. For instance, although Norwegian Bokm{\aa}l and Danish are typologically close to one another, they are phonologically distant, which is reflected in their language embeddings growing relatively distant in a phonological task. We are also able to predict typological features in WALS with high accuracies, even for unseen language families.",
}
@inproceedings{keith-etal-2018-monte,
    title = "{M}onte {C}arlo Syntax Marginals for Exploring and Using Dependency Parses",
    author = "Keith, Katherine  and
      Blodgett, Su Lin  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1084",
    doi = "10.18653/v1/N18-1084",
    pages = "917--928",
    abstract = "Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions. However, ambiguity is inherent to natural language syntax, and communicating such ambiguity is important for error analysis and better-informed downstream applications. In this work, we propose a transition sampling algorithm to sample from the full joint distribution of parse trees defined by a transition-based parsing model, and demonstrate the use of the samples in probabilistic dependency analysis. First, we define the new task of dependency path prediction, inferring syntactic substructures over part of a sentence, and provide the first analysis of performance on this task. Second, we demonstrate the usefulness of our Monte Carlo syntax marginal method for parser error analysis and calibration. Finally, we use this method to propagate parse uncertainty to two downstream information extraction applications: identifying persons killed by police and semantic role assignment.",
}
@inproceedings{lin-eisner-2018-neural,
    title = "Neural Particle Smoothing for Sampling from Conditional Sequence Models",
    author = "Lin, Chu-Cheng  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1085",
    doi = "10.18653/v1/N18-1085",
    pages = "929--941",
    abstract = "We introduce neural particle smoothing, a sequential Monte Carlo method for sampling annotations of an input string from a given probability model. In contrast to conventional particle filtering algorithms, we train a proposal distribution that looks ahead to the end of the input string by means of a right-to-left LSTM. We demonstrate that this innovation can improve the quality of the sample. To motivate our formal choices, we explain how neural transduction models and our sampler can be viewed as low-dimensional but nonlinear approximations to working with HMMs over very large state spaces.",
}
@inproceedings{buys-blunsom-2018-neural,
    title = "Neural Syntactic Generative Models with Exact Marginalization",
    author = "Buys, Jan  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1086",
    doi = "10.18653/v1/N18-1086",
    pages = "942--952",
    abstract = "We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporting batched training and enabling online computation of next word probabilities. For supervised dependency parsing, our model achieves a state-of-the-art result among generative approaches. We also report empirical results on unsupervised syntactic models and their role in language modeling. We find that our model formulation of latent dependencies with exact marginalization do not lead to better intrinsic language modeling performance than vanilla RNNs, and that parsing accuracy is not correlated with language modeling perplexity in stack-based models.",
}
@inproceedings{zalmout-etal-2018-noise,
    title = "Noise-Robust Morphological Disambiguation for Dialectal {A}rabic",
    author = "Zalmout, Nasser  and
      Erdmann, Alexander  and
      Habash, Nizar",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1087",
    doi = "10.18653/v1/N18-1087",
    pages = "953--964",
    abstract = "User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is characterized with morpho-syntactic and phonetic variations. These issues increase sparsity in NLP models and reduce accuracy. We present a neural morphological tagging and disambiguation model for Egyptian Arabic, with various extensions to handle noisy and inconsistent content. Our models achieve about 5{\%} relative error reduction (1.1{\%} absolute improvement) for full morphological analysis, and around 22{\%} relative error reduction (1.8{\%} absolute improvement) for part-of-speech tagging, over a state-of-the-art baseline.",
}
@inproceedings{liu-etal-2018-parsing,
    title = "Parsing Tweets into {U}niversal {D}ependencies",
    author = "Liu, Yijia  and
      Zhu, Yi  and
      Che, Wanxiang  and
      Qin, Bing  and
      Schneider, Nathan  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1088",
    doi = "10.18653/v1/N18-1088",
    pages = "965--975",
    abstract = "We study the problem of analyzing tweets with universal dependencies (UD). We extend the UD guidelines to cover special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. Using the extended guidelines, we create a new tweet treebank for English (Tweebank v2) that is four times larger than the (unlabeled) Tweebank v1 introduced by Kong et al. (2014). We characterize the disagreements between our annotators and show that it is challenging to deliver consistent annotation due to ambiguity in understanding and explaining tweets. Nonetheless, using the new treebank, we build a pipeline system to parse raw tweets into UD. To overcome the annotation noise without sacrificing computational efficiency, we propose a new method to distill an ensemble of 20 transition-based parsers into a single one. Our parser achieves an improvement of 2.2 in LAS over the un-ensembled baseline and outperforms parsers that are state-of-the-art on other treebanks in both accuracy and speed.",
}
@inproceedings{yasunaga-etal-2018-robust,
    title = "Robust Multilingual Part-of-Speech Tagging via Adversarial Training",
    author = "Yasunaga, Michihiro  and
      Kasai, Jungo  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1089",
    doi = "10.18653/v1/N18-1089",
    pages = "976--986",
    abstract = "Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. Yet, the specific effects of the robustness obtained from AT are still unclear in the context of natural language processing. In this paper, we propose and analyze a neural POS tagging model that exploits AT. In our experiments on the Penn Treebank WSJ corpus and the Universal Dependencies (UD) dataset (27 languages), we find that AT not only improves the overall tagging accuracy, but also 1) prevents over-fitting well in low resource languages and 2) boosts tagging accuracy for rare / unseen words. We also demonstrate that 3) the improved tagging performance by AT contributes to the downstream task of dependency parsing, and that 4) AT helps the model to learn cleaner word representations. 5) The proposed AT model is generally effective in different sequence labeling tasks. These positive results motivate further use of AT for natural language tasks.",
}
@inproceedings{bhat-etal-2018-universal,
    title = "{U}niversal {D}ependency Parsing for {H}indi-{E}nglish Code-Switching",
    author = "Bhat, Irshad  and
      Bhat, Riyaz A.  and
      Shrivastava, Manish  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1090",
    doi = "10.18653/v1/N18-1090",
    pages = "987--998",
    abstract = "Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this paper, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages the part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Results show that our neural stacking parser is 1.5{\%} LAS points better than the augmented parsing model and 3.8{\%} LAS points better than the one which uses first-best normalization and/or back-transliteration.",
}
@inproceedings{gaddy-etal-2018-whats,
    title = "What{'}s Going On in Neural Constituency Parsers? An Analysis",
    author = "Gaddy, David  and
      Stern, Mitchell  and
      Klein, Dan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1091",
    doi = "10.18653/v1/N18-1091",
    pages = "999--1010",
    abstract = "A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity. The goal of this work is to analyze the extent to which information provided directly by the model structure in classical systems is still being captured by neural methods. To this end, we propose a high-performance neural model (92.08 F1 on PTB) that is representative of recent work and perform a series of investigative experiments. We find that our model implicitly learns to encode much of the same information that was explicitly provided by grammars and lexicons in the past, indicating that this scaffolding can largely be subsumed by powerful general-purpose neural machinery.",
}
@inproceedings{rios-etal-2018-deep,
    title = "Deep Generative Model for Joint Alignment and Word Representation",
    author = "Rios, Miguel  and
      Aziz, Wilker  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1092",
    doi = "10.18653/v1/N18-1092",
    pages = "1011--1023",
    abstract = "This work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributional context and jointly learn how to embed and align with a deep generative model. Our EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. Besides, it embeds words as posterior probability densities, rather than point estimates, which allows us to compare words in context using a measure of overlap between distributions (e.g. KL divergence). We investigate our model{'}s performance on a range of lexical semantics tasks achieving competitive results on several standard benchmarks including natural language inference, paraphrasing, and text similarity.",
}
@inproceedings{jiang-etal-2018-learning,
    title = "Learning Word Embeddings for Low-Resource Languages by {PU} Learning",
    author = "Jiang, Chao  and
      Yu, Hsiang-Fu  and
      Hsieh, Cho-Jui  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1093",
    doi = "10.18653/v1/N18-1093",
    pages = "1024--1034",
    abstract = "Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages.",
}
@inproceedings{durmus-cardie-2018-exploring,
    title = "Exploring the Role of Prior Beliefs for Argument Persuasion",
    author = "Durmus, Esin  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1094",
    doi = "10.18653/v1/N18-1094",
    pages = "1035--1045",
    abstract = "Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one{'}s stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader-level factors: political and religious ideology. We find that prior beliefs affected by these reader-level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion.",
}
@inproceedings{wiegand-etal-2018-inducing,
    title = "Inducing a Lexicon of Abusive Words {--} a Feature-Based Approach",
    author = "Wiegand, Michael  and
      Ruppenhofer, Josef  and
      Schmidt, Anna  and
      Greenberg, Clayton",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1095",
    doi = "10.18653/v1/N18-1095",
    pages = "1046--1056",
    abstract = "We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the cross-domain detection of abusive microposts.",
}
@inproceedings{prabhakaran-etal-2018-author,
    title = "Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions",
    author = "Prabhakaran, Vinodkumar  and
      Ganeshkumar, Premkumar  and
      Rambow, Owen",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1096",
    doi = "10.18653/v1/N18-1096",
    pages = "1057--1068",
    abstract = "Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interactions. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do {---} an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.",
}
@inproceedings{nguyen-2018-comparing,
    title = "Comparing Automatic and Human Evaluation of Local Explanations for Text Classification",
    author = "Nguyen, Dong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1097",
    doi = "10.18653/v1/N18-1097",
    pages = "1069--1078",
    abstract = "Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable. Recently, a variety of approaches have been proposed for generating local explanations. While robust evaluations are needed to drive further progress, so far it is unclear which evaluation approaches are suitable. This paper is a first step towards more robust evaluations of local explanations. We evaluate a variety of local explanation approaches using automatic measures based on word deletion. Furthermore, we show that an evaluation using a crowdsourcing experiment correlates moderately with these automatic measures and that a variety of other factors also impact the human judgements.",
}
@inproceedings{gupta-etal-2018-deep-temporal,
    title = "Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time",
    author = {Gupta, Pankaj  and
      Rajaram, Subburam  and
      Sch{\"u}tze, Hinrich  and
      Andrassy, Bernt},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1098",
    doi = "10.18653/v1/N18-1098",
    pages = "1079--1089",
    abstract = "Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.",
}
@inproceedings{hao-etal-2018-lessons,
    title = "Lessons from the {B}ible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation",
    author = "Hao, Shudong  and
      Boyd-Graber, Jordan  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1099",
    doi = "10.18653/v1/N18-1099",
    pages = "1090--1100",
    abstract = "Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evaluate the quality of multilingual topics. We introduce a new intrinsic evaluation of multilingual topic models that correlates well with human judgments of multilingual topic coherence as well as performance in downstream applications. Importantly, we also study evaluation for low-resource languages. Because standard metrics fail to accurately measure topic quality when robust external resources are unavailable, we propose an adaptation model that improves the accuracy and reliability of these metrics in low-resource settings.",
}
@inproceedings{mullenbach-etal-2018-explainable,
    title = "Explainable Prediction of Medical Codes from Clinical Text",
    author = "Mullenbach, James  and
      Wiegreffe, Sarah  and
      Duke, Jon  and
      Sun, Jimeng  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1100",
    doi = "10.18653/v1/N18-1100",
    pages = "1101--1111",
    abstract = "Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignment.",
}
@inproceedings{williams-etal-2018-broad,
    title = "A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference",
    author = "Williams, Adina  and
      Nangia, Nikita  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1101",
    doi = "10.18653/v1/N18-1101",
    pages = "1112--1122",
    abstract = "This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement.",
}
@inproceedings{washio-kato-2018-filling,
    title = "Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations",
    author = "Washio, Koki  and
      Kato, Tsuneaki",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1102",
    doi = "10.18653/v1/N18-1102",
    pages = "1123--1133",
    abstract = "Recognizing lexical semantic relations between word pairs is an important task for many applications of natural language processing. One of the mainstream approaches to this task is to exploit the lexico-syntactic paths connecting two target words, which reflect the semantic relations of word pairs. However, this method requires that the considered words co-occur in a sentence. This requirement is hardly satisfied because of Zipf{'}s law, which states that most content words occur very rarely. In this paper, we propose novel methods with a neural model of P(path|w1,w2) to solve this problem. Our proposed model of P (path|w1, w2 ) can be learned in an unsupervised manner and can generalize the co-occurrences of word pairs and dependency paths. This model can be used to augment the path data of word pairs that do not co-occur in the corpus, and extract features capturing relational information from word pairs. Our experimental results demonstrate that our methods improve on previous neural approaches based on dependency paths and successfully solve the focused problem.",
}
@inproceedings{vulic-mrksic-2018-specialising,
    title = "Specialising Word Vectors for Lexical Entailment",
    author = "Vuli{\'c}, Ivan  and
      Mrk{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1103",
    doi = "10.18653/v1/N18-1103",
    pages = "1134--1145",
    abstract = "We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model.",
}
@inproceedings{damonte-cohen-2018-cross,
    title = "Cross-Lingual {A}bstract {M}eaning {R}epresentation Parsing",
    author = "Damonte, Marco  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1104",
    doi = "10.18653/v1/N18-1104",
    pages = "1146--1155",
    abstract = "Abstract Meaning Representation (AMR) research has mostly focused on English. We show that it is possible to use AMR annotations for English as a semantic representation for sentences written in other languages. We exploit an AMR parser for English and parallel corpora to learn AMR parsers for Italian, Spanish, German and Chinese. Qualitative analysis show that the new parsers overcome structural differences between the languages. We further propose a method to evaluate the parsers that does not require gold standard data in the target languages. This method highly correlates with the gold standard evaluation, obtaining a Pearson correlation coefficient of 0.95.",
}
@inproceedings{schuster-etal-2018-sentences,
    title = "Sentences with Gapping: Parsing and Reconstructing Elided Predicates",
    author = "Schuster, Sebastian  and
      Nivre, Joakim  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1105",
    doi = "10.18653/v1/N18-1105",
    pages = "1156--1168",
    abstract = "Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments. Surface syntax representations of such sentences are often produced poorly by parsers, and even if correct, not well suited to downstream natural language understanding tasks such as relation extraction that are typically designed to extract information from sentences with canonical clause structure. In this paper, we present two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. We find that both methods can reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. We further demonstrate that one of our methods can be applied to other languages based on a case study on Swedish.",
}
@inproceedings{szubert-etal-2018-structured,
    title = "A Structured Syntax-Semantics Interface for {E}nglish-{AMR} Alignment",
    author = "Szubert, Ida  and
      Lopez, Adam  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1106",
    doi = "10.18653/v1/N18-1106",
    pages = "1169--1180",
    abstract = "Abstract Meaning Representation (AMR) annotations are often assumed to closely mirror dependency syntax, but AMR explicitly does not require this, and the assumption has never been tested. To test it, we devise an expressive framework to align AMR graphs to dependency graphs, which we use to annotate 200 AMRs. Our annotation explains how 97{\%} of AMR edges are evoked by words or syntax. Previously existing AMR alignment frameworks did not allow for mapping AMR onto syntax, and as a consequence they explained at most 23{\%}. While we find that there are indeed many cases where AMR annotations closely mirror syntax, there are also pervasive differences. We use our annotations to test a baseline AMR-to-syntax aligner, finding that this task is more difficult than AMR-to-string alignment; and to pinpoint errors in an AMR parser. We make our data and code freely available for further research on AMR parsing and generation, and the relationship of AMR to syntax.",
}
@inproceedings{kasai-etal-2018-end,
    title = "End-to-End Graph-Based {TAG} Parsing with Neural Networks",
    author = "Kasai, Jungo  and
      Frank, Robert  and
      Xu, Pauli  and
      Merrill, William  and
      Rambow, Owen",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1107",
    doi = "10.18653/v1/N18-1107",
    pages = "1181--1194",
    abstract = "We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously reported best results by more than 2.2 LAS and UAS points. The graph-based parsing architecture allows for global inference and rich feature representations for TAG parsing, alleviating the fundamental trade-off between transition-based and graph-based parsing systems. We also demonstrate that the proposed parser achieves state-of-the-art performance in the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and Unbounded Dependency Recovery. This provides further support for the claim that TAG is a viable formalism for problems that require rich structural analysis of sentences.",
}
@inproceedings{gulordava-etal-2018-colorless,
    title = "Colorless Green Recurrent Networks Dream Hierarchically",
    author = "Gulordava, Kristina  and
      Bojanowski, Piotr  and
      Grave, Edouard  and
      Linzen, Tal  and
      Baroni, Marco",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1108",
    doi = "10.18653/v1/N18-1108",
    pages = "1195--1205",
    abstract = "Recurrent neural networks (RNNs) achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues ({``}The colorless green ideas I ate with the chair sleep furiously{''}), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence.",
}
@inproceedings{yu-etal-2018-diverse,
    title = "Diverse Few-Shot Text Classification with Multiple Metrics",
    author = "Yu, Mo  and
      Guo, Xiaoxiao  and
      Yi, Jinfeng  and
      Chang, Shiyu  and
      Potdar, Saloni  and
      Cheng, Yu  and
      Tesauro, Gerald  and
      Wang, Haoyu  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1109",
    doi = "10.18653/v1/N18-1109",
    pages = "1206--1215",
    abstract = "We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more realistic setting, where tasks are diverse. However, it imposes tremendous difficulties to existing state-of-the-art metric-based algorithms since a single metric is insufficient to capture complex task variations in natural language domain. To alleviate the problem, we propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. Extensive quantitative evaluations on real-world sentiment analysis and dialog intent classification datasets demonstrate that the proposed method performs favorably against state-of-the-art few shot learning algorithms in terms of predictive accuracy. We make our code and data available for further study.",
}
@inproceedings{lopez-monroy-etal-2018-early,
    title = "Early Text Classification Using Multi-Resolution Concept Representations",
    author = "L{\'o}pez-Monroy, Adrian Pastor  and
      Gonz{\'a}lez, Fabio A.  and
      Montes, Manuel  and
      Escalante, Hugo Jair  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1110",
    doi = "10.18653/v1/N18-1110",
    pages = "1216--1225",
    abstract = "The intensive use of e-communications in everyday life has given rise to new threats and risks. When the vulnerable asset is the user, detecting these potential attacks before they cause serious damages is extremely important. This paper proposes a novel document representation to improve the early detection of risks in social media sources. The goal is to effectively identify the potential risk using as few text as possible and with as much anticipation as possible. Accordingly, we devise a Multi-Resolution Representation (MulR), which allows us to generate multiple {``}views{''} of the analyzed text. These views capture different semantic meanings for words and documents at different levels of detail, which is very useful in early scenarios to model the variable amounts of evidence. Intuitively, the representation captures better the content of short documents (very early stages) in low resolutions, whereas large documents (medium/large stages) are better modeled with higher resolutions. We evaluate the proposed ideas in two different tasks where anticipation is critical: sexual predator detection and depression detection. The experimental evaluation for these early tasks revealed that the proposed approach outperforms previous methodologies by a considerable margin.",
}
@inproceedings{chen-cardie-2018-multinomial,
    title = "Multinomial Adversarial Networks for Multi-Domain Text Classification",
    author = "Chen, Xilun  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1111",
    doi = "10.18653/v1/N18-1111",
    pages = "1226--1240",
    abstract = "Many text classification tasks are known to be highly domain-dependent. Unfortunately, the availability of training data can vary drastically across domains. Worse still, for some domains there may not be any annotated data at all. In this work, we propose a multinomial adversarial network (MAN) to tackle this real-world problem of multi-domain text classification (MDTC) in which labeled data may exist for multiple domains, but in insufficient amounts to train effective classifiers for one or more of the domains. We provide theoretical justifications for the MAN framework, proving that different instances of MANs are essentially minimizers of various f-divergence metrics (Ali and Silvey, 1966) among multiple probability distributions. MANs are thus a theoretically sound generalization of traditional adversarial networks that discriminate over two distributions. More specifically, for the MDTC task, MAN learns features that are invariant across multiple domains by resorting to its ability to reduce the divergence among the feature distributions of each domain. We present experimental results showing that MANs significantly outperform the prior art on the MDTC task. We also show that MANs achieve state-of-the-art performance for domains with no labeled data.",
}
@inproceedings{ziser-reichart-2018-pivot,
    title = "Pivot Based Language Modeling for Improved Neural Domain Adaptation",
    author = "Ziser, Yftah  and
      Reichart, Roi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1112",
    doi = "10.18653/v1/N18-1112",
    pages = "1241--1251",
    abstract = "Representation learning with pivot-based methods and with Neural Networks (NNs) have lead to significant progress in domain adaptation for Natural Language Processing. However, most previous work that follows these approaches does not explicitly exploit the structure of the input text, and its output is most often a single representation vector for the entire text. In this paper we present the Pivot Based Language Model (PBLM), a representation learning model that marries together pivot-based and NN modeling in a structure aware manner. Particularly, our model processes the information in the text with a sequential NN (LSTM) and its output consists of a representation vector for every input word. Unlike most previous representation learning models in domain adaptation, PBLM can naturally feed structure aware text classifiers such as LSTM and CNN. We experiment with the task of cross-domain sentiment classification on 20 domain pairs and show substantial improvements over strong baselines.",
}
@inproceedings{wu-etal-2018-reinforced,
    title = "Reinforced Co-Training",
    author = "Wu, Jiawei  and
      Li, Lei  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1113",
    doi = "10.18653/v1/N18-1113",
    pages = "1252--1262",
    abstract = "Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled data and select samples based on prediction confidence to augment the training. However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space. In this paper, we propose a novel method, Reinforced Co-Training, to select high-quality unlabeled samples to better co-train on. More specifically, our approach uses Q-learning to learn a data selection policy with a small labeled dataset, and then exploits this policy to train the co-training classifiers automatically. Experimental results on clickbait detection and generic text classification tasks demonstrate that our proposed method can obtain more accurate text classification results.",
}
@inproceedings{huang-etal-2018-tensor,
    title = "Tensor Product Generation Networks for Deep {NLP} Modeling",
    author = "Huang, Qiuyuan  and
      Smolensky, Paul  and
      He, Xiaodong  and
      Deng, Li  and
      Wu, Dapeng",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1114",
    doi = "10.18653/v1/N18-1114",
    pages = "1263--1273",
    abstract = "We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks. A network architecture {---} the Tensor Product Generation Network (TPGN) {---} is proposed which is capable in principle of carrying out TPR computation, but which uses unconstrained deep learning to design its internal representations. Instantiated in a model for image-caption generation, TPGN outperforms LSTM baselines when evaluated on the COCO dataset. The TPR-capable structure enables interpretation of internal representations and operations, which prove to contain considerable grammatical content. Our caption-generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation.",
}
@inproceedings{tran-etal-2018-context,
    title = "The Context-Dependent Additive Recurrent Neural Net",
    author = "Tran, Quan Hung  and
      Lai, Tuan  and
      Haffari, Gholamreza  and
      Zukerman, Ingrid  and
      Bui, Trung  and
      Bui, Hung",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1115",
    doi = "10.18653/v1/N18-1115",
    pages = "1274--1283",
    abstract = "Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). Here, instead of relying solely on the information presented in the text, the learning agents have access to a strong external signal given to assist the learning process. In this paper, we propose a novel family of Recurrent Neural Network unit: the Context-dependent Additive Recurrent Neural Network (CARNN) that is designed specifically to address this type of problem. The experimental results on public datasets in the dialog problem (Babi dialog Task 6 and Frame), contextual language model (Switchboard and Penn Tree Bank) and question answering (Trec QA) show that our novel CARNN-based architectures outperform previous methods.",
}
@inproceedings{chen-etal-2018-combining,
    title = "Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention",
    author = "Chen, Huadong  and
      Huang, Shujian  and
      Chiang, David  and
      Dai, Xinyu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1116",
    doi = "10.18653/v1/N18-1116",
    pages = "1284--1293",
    abstract = "Natural language sentences, being hierarchical, can be represented at different levels of granularity, like words, subwords, or characters. But most neural machine translation systems require the sentence to be represented as a sequence at a single level of granularity. It can be difficult to determine which granularity is better for a particular translation task. In this paper, we improve the model by incorporating multiple levels of granularity. Specifically, we propose (1) an encoder with character attention which augments the (sub)word-level representation with character-level information; (2) a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively. Experiments on three translation tasks demonstrate that our proposed models outperform the standard word-based model, the subword-based model, and a strong character-based model.",
}
@inproceedings{shen-etal-2018-dense,
    title = "Dense Information Flow for Neural Machine Translation",
    author = "Shen, Yanyao  and
      Tan, Xu  and
      He, Di  and
      Qin, Tao  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1117",
    doi = "10.18653/v1/N18-1117",
    pages = "1294--1303",
    abstract = "Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework. From the optimization perspective, residual connections are adopted to improve learning performance for both encoder and decoder in most of these deep architectures, and advanced attention connections are applied as well. Inspired by the success of the DenseNet model in computer vision problems, in this paper, we propose a densely connected NMT architecture (DenseNMT) that is able to train more efficiently for NMT. The proposed DenseNMT not only allows dense connection in creating new features for both encoder and decoder, but also uses the dense attention structure to improve attention quality. Our experiments on multiple datasets show that DenseNMT structure is more competitive and efficient.",
}
@inproceedings{bawden-etal-2018-evaluating,
    title = "Evaluating Discourse Phenomena in Neural Machine Translation",
    author = "Bawden, Rachel  and
      Sennrich, Rico  and
      Birch, Alexandra  and
      Haddow, Barry",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1118",
    doi = "10.18653/v1/N18-1118",
    pages = "1304--1313",
    abstract = "For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models{'} ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50{\%} accuracy on our coreference test set and 53.5{\%} for coherence/cohesion (compared to a non-contextual baseline of 50{\%}). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5{\%} for coreference and 57{\%} for coherence/cohesion), highlighting the importance of target-side context.",
}
@inproceedings{post-vilar-2018-fast,
    title = "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation",
    author = "Post, Matt  and
      Vilar, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1119",
    doi = "10.18653/v1/N18-1119",
    pages = "1314--1324",
    abstract = "The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithm{'}s remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye.",
}
@inproceedings{zhang-etal-2018-guiding,
    title = "Guiding Neural Machine Translation with Retrieved Translation Pieces",
    author = "Zhang, Jingyi  and
      Utiyama, Masao  and
      Sumita, Eiichro  and
      Neubig, Graham  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1120",
    doi = "10.18653/v1/N18-1120",
    pages = "1325--1335",
    abstract = "One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect n-grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call {``}translation pieces{''}. We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation.",
}
@inproceedings{liu-etal-2018-handling,
    title = "Handling Homographs in Neural Machine Translation",
    author = "Liu, Frederick  and
      Lu, Han  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1121",
    doi = "10.18653/v1/N18-1121",
    pages = "1336--1345",
    abstract = "Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs.",
}
@inproceedings{yang-etal-2018-improving,
    title = "Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets",
    author = "Yang, Zhen  and
      Chen, Wei  and
      Wang, Feng  and
      Xu, Bo",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1122",
    doi = "10.18653/v1/N18-1122",
    pages = "1346--1355",
    abstract = "This paper proposes an approach for applying GANs to NMT. We build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generator and a discriminator. The generator aims to generate sentences which are hard to be discriminated from human-translated sentences ( i.e., the golden target sentences); And the discriminator makes efforts to discriminate the machine-generated sentences from human-translated ones. The two sub models play a mini-max game and achieve the win-win situation when they reach a Nash Equilibrium. Additionally, the static sentence-level BLEU is utilized as the reinforced objective for the generator, which biases the generation towards high BLEU points. During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator. Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.",
}
@inproceedings{zaremoodi-haffari-2018-neural,
    title = "Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach",
    author = "Zaremoodi, Poorya  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1123",
    doi = "10.18653/v1/N18-1123",
    pages = "1356--1365",
    abstract = "Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. This is particularly inconvenient for language pairs for which enough parallel text is not available. In this paper, we use monolingual linguistic resources in the source side to address this challenging problem based on a multi-task learning approach. More specifically, we scaffold the machine translation task on auxiliary tasks including semantic parsing, syntactic parsing, and named-entity recognition. This effectively injects semantic and/or syntactic knowledge into the translation model, which would otherwise require a large amount of training bitext to learn from. We empirically analyze and show the effectiveness of our multitask learning approach on three translation tasks: English-to-French, English-to-Farsi, and English-to-Vietnamese.",
}
@inproceedings{miculicich-werlen-etal-2018-self,
    title = "Self-Attentive Residual Decoder for Neural Machine Translation",
    author = "Miculicich Werlen, Lesly  and
      Pappas, Nikolaos  and
      Ram, Dhananjay  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1124",
    doi = "10.18653/v1/N18-1124",
    pages = "1366--1379",
    abstract = "Neural sequence-to-sequence networks with attention have achieved remarkable performance for machine translation. One of the reasons for their effectiveness is their ability to capture relevant source-side contextual information at each time-step prediction through an attention mechanism. However, the target-side context is solely based on the sequence model which, in practice, is prone to a recency bias and lacks the ability to capture effectively non-sequential dependencies among words. To address this limitation, we propose a target-side-attentive residual recurrent network for decoding, where attention over previous words contributes directly to the prediction of the next word. The residual learning facilitates the flow of information from the distant past and is able to emphasize any of the previously translated words, hence it gains access to a wider context. The proposed model outperforms a neural MT baseline as well as a memory and self-attention network on three language pairs. The analysis of the attention learned by the decoder confirms that it emphasizes a wider context, and that it captures syntactic-like structures.",
}
@inproceedings{li-etal-2018-target,
    title = "Target Foresight Based Attention for Neural Machine Translation",
    author = "Li, Xintong  and
      Liu, Lemao  and
      Tu, Zhaopeng  and
      Shi, Shuming  and
      Meng, Max",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1125",
    doi = "10.18653/v1/N18-1125",
    pages = "1380--1390",
    abstract = "In neural machine translation, an attention model is used to identify the aligned source words for a target word (target foresight word) in order to select translation context, but it does not make use of any information of this target foresight word at all. Previous work proposed an approach to improve the attention model by explicitly accessing this target foresight word and demonstrated the substantial gains in alignment task. However, this approach is useless in machine translation task on which the target foresight word is unavailable. In this paper, we propose a new attention model enhanced by the implicit information of target foresight word oriented to both alignment and translation tasks. Empirical experiments on Chinese-to-English and Japanese-to-English datasets show that the proposed attention model delivers significant improvements in terms of both alignment error rate and BLEU.",
}
@inproceedings{bergmanis-goldwater-2018-context,
    title = "Context Sensitive Neural Lemmatization with {L}ematus",
    author = "Bergmanis, Toms  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1126",
    doi = "10.18653/v1/N18-1126",
    pages = "1391--1400",
    abstract = "The main motivation for developing contextsensitive lemmatizers is to improve performance on unseen and ambiguous words. Yet previous systems have not carefully evaluated whether the use of context actually helps in these cases. We introduce Lematus, a lemmatizer based on a standard encoder-decoder architecture, which incorporates character-level sentence context. We evaluate its lemmatization accuracy across 20 languages in both a full data setting and a lower-resource setting with 10k training examples in each language. In both settings, we show that including context significantly improves results against a context-free version of the model. Context helps more for ambiguous words than for unseen words, though the latter has a greater effect on overall performance differences between languages. We also compare to three previous context-sensitive lemmatization systems, which all use pre-extracted edit trees as well as hand-selected features and/or additional sources of information such as tagged training data. Without using any of these, our context-sensitive model outperforms the best competitor system (Lemming) in the fulldata setting, and performs on par in the lowerresource setting.",
}
@inproceedings{aguilar-etal-2018-modeling,
    title = "Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media",
    author = "Aguilar, Gustavo  and
      L{\'o}pez-Monroy, Adrian Pastor  and
      Gonz{\'a}lez, Fabio  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1127",
    doi = "10.18653/v1/N18-1127",
    pages = "1401--1412",
    abstract = "Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45{\%} and 3.69{\%}, establishing a more suitable approach for social media environments.",
}
@inproceedings{assylbekov-takhanov-2018-reusing,
    title = "Reusing Weights in Subword-Aware Neural Language Models",
    author = "Assylbekov, Zhenisbek  and
      Takhanov, Rustem",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1128",
    doi = "10.18653/v1/N18-1128",
    pages = "1413--1423",
    abstract = "We propose several ways of reusing subword embeddings and other weights in subword-aware neural language models. The proposed techniques do not benefit a competitive character-aware model, but some of them improve the performance of syllable- and morpheme-aware models while showing significant reductions in model sizes. We discover a simple hands-on principle: in a multi-layer input embedding model, layers should be tied consecutively bottom-up if reused at output. Our best morpheme-aware model with properly reused weights beats the competitive word-level model by a large margin across multiple languages and has 20{\%}-87{\%} fewer parameters.",
}
@inproceedings{kulkarni-wang-2018-simple,
    title = "Simple Models for Word Formation in Slang",
    author = "Kulkarni, Vivek  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1129",
    doi = "10.18653/v1/N18-1129",
    pages = "1424--1434",
    abstract = "We propose the first generative models for three types of extra-grammatical word formation phenomena abounding in slang: Blends, Clippings, and Reduplicatives. Adopting a data-driven approach coupled with linguistic knowledge, we propose simple models with state of the art performance on human annotated gold standard datasets. Overall, our models reveal insights into the generative processes of word formation in slang {--} insights which are increasingly relevant in the context of the rising prevalence of slang and non-standard varieties on the Internet",
}
@inproceedings{matthews-etal-2018-using,
    title = "Using Morphological Knowledge in Open-Vocabulary Neural Language Models",
    author = "Matthews, Austin  and
      Neubig, Graham  and
      Dyer, Chris",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1130",
    doi = "10.18653/v1/N18-1130",
    pages = "1435--1445",
    abstract = {Languages with productive morphology pose problems for language models that generate words from a fixed vocabulary. Although character-based models allow any possible word type to be generated, they are linguistically na{\"\i}ve: they must discover that words exist and are delimited by spaces{---}basic linguistic facts that are built in to the structure of word-based models. We introduce an open-vocabulary language model that incorporates more sophisticated linguistic knowledge by predicting words using a mixture of three generative processes: (1) by generating words as a sequence of characters, (2) by directly generating full word forms, and (3) by generating words as a sequence of morphemes that are combined using a hand-written morphological analyzer. Experiments on Finnish, Turkish, and Russian show that our model outperforms character sequence models and other strong baselines on intrinsic and extrinsic measures. Furthermore, we show that our model learns to exploit morphological knowledge encoded in the analyzer, and, as a byproduct, it can perform effective unsupervised morphological disambiguation.},
}
@inproceedings{ju-etal-2018-neural,
    title = "A Neural Layered Model for Nested Named Entity Recognition",
    author = "Ju, Meizhi  and
      Miwa, Makoto  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1131",
    doi = "10.18653/v1/N18-1131",
    pages = "1446--1459",
    abstract = "Entity mentions embedded in longer entity mentions are referred to as nested entities. Most named entity recognition (NER) systems deal only with the flat entities and ignore the inner nested ones, which fails to capture finer-grained semantic information in underlying texts. To address this issue, we propose a novel neural model to identify nested entities by dynamically stacking flat NER layers. Each flat NER layer is based on the state-of-the-art flat NER model that captures sequential context representation with bidirectional Long Short-Term Memory (LSTM) layer and feeds it to the cascaded CRF layer. Our model merges the output of the LSTM layer in the current flat NER layer to build new representation for detected entities and subsequently feeds them into the next flat NER layer. This allows our model to extract outer entities by taking full advantage of information encoded in their corresponding inner entities, in an inside-to-outside way. Our model dynamically stacks the flat NER layers until no outer entities are extracted. Extensive evaluation shows that our dynamic model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7{\%} and 72.2{\%} on GENIA and ACE2005 datasets, respectively, in terms of F-score.",
}
@inproceedings{ghaeini-etal-2018-dr,
    title = "{DR}-{B}i{LSTM}: Dependent Reading Bidirectional {LSTM} for Natural Language Inference",
    author = "Ghaeini, Reza  and
      Hasan, Sadid A.  and
      Datla, Vivek  and
      Liu, Joey  and
      Lee, Kathy  and
      Qadir, Ashequl  and
      Ling, Yuan  and
      Prakash, Aaditya  and
      Fern, Xiaoli  and
      Farri, Oladimeji",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1132",
    doi = "10.18653/v1/N18-1132",
    pages = "1460--1469",
    abstract = "We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI dataset.",
}
@inproceedings{cai-wang-2018-kbgan,
    title = "{KBGAN}: Adversarial Learning for Knowledge Graph Embeddings",
    author = "Cai, Liwei  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1133",
    doi = "10.18653/v1/N18-1133",
    pages = "1470--1480",
    abstract = "We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a nontrivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TRANSE and TRANSD, each with assistance from one of the two probability-based models, DISTMULT and COMPLEX. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.",
}
@inproceedings{botschen-etal-2018-multimodal,
    title = "Multimodal Frame Identification with Multilingual Evaluation",
    author = "Botschen, Teresa  and
      Gurevych, Iryna  and
      Klie, Jan-Christoph  and
      Mousselly-Sergieh, Hatem  and
      Roth, Stefan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1134",
    doi = "10.18653/v1/N18-1134",
    pages = "1481--1491",
    abstract = "An essential step in FrameNet Semantic Role Labeling is the Frame Identification (FrameId) task, which aims at disambiguating a situation around a predicate. Whilst current FrameId methods rely on textual representations only, we hypothesize that FrameId can profit from a richer understanding of the situational context. Such contextual information can be obtained from common sense knowledge, which is more present in images than in text. In this paper, we extend a state-of-the-art FrameId system in order to effectively leverage multimodal representations. We conduct a comprehensive evaluation on the English FrameNet and its German counterpart SALSA. Our analysis shows that for the German data, textual representations are still competitive with multimodal ones. However on the English data, our multimodal FrameId approach outperforms its unimodal counterpart, setting a new state of the art. Its benefits are particularly apparent in dealing with ambiguous and rare instances, the main source of errors of current systems. For research purposes, we release (a) the implementation of our system, (b) our evaluation splits for SALSA 2.0, and (c) the embeddings for synsets and IMAGINED words.",
}
@inproceedings{peng-etal-2018-learning,
    title = "Learning Joint Semantic Parsers from Disjoint Data",
    author = "Peng, Hao  and
      Thomson, Sam  and
      Swayamdipta, Swabha  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1135",
    doi = "10.18653/v1/N18-1135",
    pages = "1492--1502",
    abstract = "We present a new approach to learning a semantic parser from multiple datasets, even when the target semantic formalisms are drastically different and the underlying corpora do not overlap. We handle such {``}disjoint{''} data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.",
}
@inproceedings{vyas-etal-2018-identifying,
    title = "Identifying Semantic Divergences in Parallel Text without Annotations",
    author = "Vyas, Yogarshi  and
      Niu, Xing  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1136",
    doi = "10.18653/v1/N18-1136",
    pages = "1503--1515",
    abstract = "Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.",
}
@inproceedings{perez-beltrachini-lapata-2018-bootstrapping,
    title = "Bootstrapping Generators from Noisy Data",
    author = "Perez-Beltrachini, Laura  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1137",
    doi = "10.18653/v1/N18-1137",
    pages = "1516--1527",
    abstract = "A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.",
}
@inproceedings{zhang-etal-2018-shaped,
    title = "{SHAPED}: Shared-Private Encoder-Decoder for Text Style Adaptation",
    author = "Zhang, Ye  and
      Ding, Nan  and
      Soricut, Radu",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1138",
    doi = "10.18653/v1/N18-1138",
    pages = "1528--1538",
    abstract = "Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal. When the training signal contains a variety of writing styles, such models may end up learning an {`}average{'} style that is directly influenced by the training data make-up and cannot be controlled by the needs of an application. We describe a family of model architectures capable of capturing both generic language characteristics via shared model parameters, as well as particular style characteristics via private model parameters. Such models are able to generate language according to a specific learned style, while still taking advantage of their power to model generic language phenomena. Furthermore, we describe an extension that uses a mixture of output distributions from all learned styles to perform on-the-fly style adaptation based on the textual input alone. Experimentally, we find that the proposed models consistently outperform models that encapsulate single-style or average-style language generation capabilities.",
}
@inproceedings{nema-etal-2018-generating,
    title = "Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization",
    author = "Nema, Preksha  and
      Shetty, Shreyas  and
      Jain, Parag  and
      Laha, Anirban  and
      Sankaranarayanan, Karthik  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1139",
    doi = "10.18653/v1/N18-1139",
    pages = "1539--1550",
    abstract = "In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values (such as Indian, actor, director, etc). One simple choice is to treat the table as a sequence of fields and values and then use a standard seq2seq model for this task. However, such a model is too generic and does not exploit task specific characteristics. For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level). Further, a human would continue attending to a field for a few timesteps till all the information from that field has been rendered and then never return back to this field (because there is nothing left to say about it). To capture this behavior we use (i) a fused bifocal attention mechanism which exploits and combines this micro and macro level information and (ii) a gated orthogonalization mechanism which tries to ensure that a field is remembered for a few time steps and then forgotten. We experiment with a recently released dataset which contains fact tables about people and their corresponding one line biographical descriptions in English. In addition, we also introduce two similar datasets for French and German. Our experiments show that the proposed model gives 21{\%} relative improvement over a recently proposed state of the art method and 10{\%} relative improvement over basic seq2seq models. The code and the datasets developed as a part of this work are publicly available on \url{https://github.com/PrekshaNema25/StructuredData_To_Descriptions}",
}
@inproceedings{suster-daelemans-2018-clicr,
    title = "{C}li{CR}: a Dataset of Clinical Case Reports for Machine Reading Comprehension",
    author = "{\v{S}}uster, Simon  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1140",
    doi = "10.18653/v1/N18-1140",
    pages = "1551--1563",
    abstract = "We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines and state-of-the-art neural readers to the dataset, and observe a considerable gap in performance (20{\%} F1) between the best human and machine readers. We analyze the skills required for successful answering and show how reader performance varies depending on the applicable skills. We find that inferences using domain knowledge and object tracking are the most frequently required skills, and that recognizing omitted information and spatio-temporal reasoning are the most difficult for the machines.",
}
@inproceedings{tang-etal-2018-learning,
    title = "Learning to Collaborate for Question Answering and Asking",
    author = "Tang, Duyu  and
      Duan, Nan  and
      Yan, Zhao  and
      Zhang, Zhirui  and
      Sun, Yibo  and
      Liu, Shujie  and
      Lv, Yuanhua  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1141",
    doi = "10.18653/v1/N18-1141",
    pages = "1564--1574",
    abstract = "Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in literature. In this paper, we give a systematic study that seeks to leverage the connection to improve both QA and QG. We present a training algorithm that generalizes both Generative Adversarial Network (GAN) and Generative Domain-Adaptive Nets (GDAN) under the question answering scenario. The two key ideas are improving the QG model with QA through incorporating additional QA-specific signal as the loss function, and improving the QA model with QG through adding artificially generated training instances. We conduct experiments on both document based and knowledge based question answering tasks. We have two main findings. Firstly, the performance of a QG model (e.g in terms of BLEU score) could be easily improved by a QA model via policy gradient. Secondly, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Learning when to regard generated questions as positive instances could bring performance boost.",
}
@inproceedings{yoon-etal-2018-learning,
    title = "Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering",
    author = "Yoon, Seunghyun  and
      Shin, Joongbo  and
      Jung, Kyomin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1142",
    doi = "10.18653/v1/N18-1142",
    pages = "1575--1584",
    abstract = "In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is encoded to a vector representation from an word-level to a chunk-level to effectively capture the entire meaning. In particular, by adapting the hierarchical structure, our model shows very small performance degradations in longer text comprehension while other state-of-the-art recurrent neural network models suffer from it. Additionally, the latent topic clustering module extracts semantic information from target samples. This clustering module is useful for any text related tasks by allowing each data sample to find its nearest topic cluster, thus helping the neural network model analyze the entire data. We evaluate our models on the Ubuntu Dialogue Corpus and consumer electronic domain question answering dataset, which is related to Samsung products. The proposed model shows state-of-the-art results for ranking question-answer pairs.",
}
@inproceedings{chung-etal-2018-supervised,
    title = "Supervised and Unsupervised Transfer Learning for Question Answering",
    author = "Chung, Yu-An  and
      Lee, Hung-Yi  and
      Glass, James",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1143",
    doi = "10.18653/v1/N18-1143",
    pages = "1585--1594",
    abstract = "Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models. The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7{\%}. Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.",
}
@inproceedings{dalvi-etal-2018-tracking,
    title = "Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension",
    author = "Dalvi, Bhavana  and
      Huang, Lifu  and
      Tandon, Niket  and
      Yih, Wen-tau  and
      Clark, Peter",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1144",
    doi = "10.18653/v1/N18-1144",
    pages = "1595--1604",
    abstract = "We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints). The end-task, tracking the location and existence of entities through the text, is challenging because the causal effects of actions are often implicit and need to be inferred. We find that previous models that have worked well on synthetic data achieve only mediocre performance on ProPara, and introduce two new neural models that exploit alternative mechanisms for state prediction, in particular using LSTM input encoding and span prediction. The new models improve accuracy by up to 19{\%}. We are releasing the ProPara dataset and our models to the community.",
}
@inproceedings{jin-etal-2018-combining,
    title = "Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation",
    author = "Jin, Mingmin  and
      Luo, Xin  and
      Zhu, Huiling  and
      Zhuo, Hankz Hankui",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1145",
    doi = "10.18653/v1/N18-1145",
    pages = "1605--1614",
    abstract = "With the rise of e-commerce, people are accustomed to writing their reviews after receiving the goods. These comments are so important that a bad review can have a direct impact on others buying. Besides, the abundant information within user reviews is very useful for extracting user preferences and item properties. In this paper, we investigate the approach to effectively utilize review information for recommender systems. The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding. In the experiments on popular review dataset Amazon , our LTMF model outperforms previous proposed HFT model and ConvMF model in rating prediction. Furthermore, LTMF shows the better ability on making topic clustering than traditional topic model based method, which implies integrating the information from deep learning and topic modeling is a meaningful approach to make a better understanding of reviews.",
}
@inproceedings{pryzant-etal-2018-deconfounded,
    title = "Deconfounded Lexicon Induction for Interpretable Social Science",
    author = "Pryzant, Reid  and
      Shen, Kelly  and
      Jurafsky, Dan  and
      Wagner, Stefan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1146",
    doi = "10.18653/v1/N18-1146",
    pages = "1615--1625",
    abstract = "NLP algorithms are increasingly used in computational social science to take linguistic observations and predict outcomes like human preferences or actions. Making these social models transparent and interpretable often requires identifying features in the input that predict outcomes while also controlling for potential confounds. We formalize this need as a new task: inducing a lexicon that is predictive of a set of target variables yet uncorrelated to a set of confounding variables. We introduce two deep learning algorithms for the task. The first uses a bifurcated architecture to separate the explanatory power of the text and confounds. The second uses an adversarial discriminator to force confound-invariant text encodings. Both elicit lexicons from learned weights and attentional scores. We use them to induce lexicons that are predictive of timely responses to consumer complaints (controlling for product), enrollment from course descriptions (controlling for subject), and sales from product descriptions (controlling for seller). In each domain our algorithms pick words that are associated with \textit{narrative persuasion}; more predictive and less confound-related than those of standard feature weighting and lexicon induction techniques like regression and log odds.",
}
@inproceedings{chambers-etal-2018-detecting,
    title = "Detecting Denial-of-Service Attacks from Social Media Text: Applying {NLP} to Computer Security",
    author = "Chambers, Nathanael  and
      Fry, Ben  and
      McMasters, James",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1147",
    doi = "10.18653/v1/N18-1147",
    pages = "1626--1635",
    abstract = "This paper describes a novel application of NLP models to detect denial of service attacks using only social media as evidence. Individual networks are often slow in reporting attacks, so a detection system from public data could better assist a response to a broad attack across multiple services. We explore NLP methods to use social media as an indirect measure of network service status. We describe two learning frameworks for this task: a feed-forward neural network and a partially labeled LDA model. Both models outperform previous work by significant margins (20{\%} F1 score). We further show that the topic-based model enables the first fine-grained analysis of how the public reacts to ongoing network attacks, discovering multiple {``}stages{''} of observation. This is the first model that both detects network attacks (with best performance) and provides an analysis of when and how the public interprets service outages. We describe the models, present experiments on the largest twitter DDoS corpus to date, and conclude with an analysis of public reactions based on the learned model{'}s output.",
}
@inproceedings{card-smith-2018-importance,
    title = "The Importance of Calibration for Estimating Proportions from Annotations",
    author = "Card, Dallas  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1148",
    doi = "10.18653/v1/N18-1148",
    pages = "1636--1646",
    abstract = "Estimating label proportions in a target corpus is a type of measurement that is useful for answering certain types of social-scientific questions. While past work has described a number of relevant approaches, nearly all are based on an assumption which we argue is invalid for many problems, particularly when dealing with human annotations. In this paper, we identify and differentiate between two relevant data generating scenarios (intrinsic vs. extrinsic labels), introduce a simple but novel method which emphasizes the importance of calibration, and then analyze and experimentally validate the appropriateness of various methods for each of the two scenarios.",
}
@inproceedings{kang-etal-2018-dataset,
    title = "A Dataset of Peer Reviews ({P}eer{R}ead): Collection, Insights and {NLP} Applications",
    author = "Kang, Dongyeop  and
      Ammar, Waleed  and
      Dalvi, Bhavana  and
      van Zuylen, Madeleine  and
      Kohlmeier, Sebastian  and
      Hovy, Eduard  and
      Schwartz, Roy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1149",
    doi = "10.18653/v1/N18-1149",
    pages = "1647--1661",
    abstract = "Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead v1),1 providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers. We describe the data collection process and report interesting observed phenomena in the peer reviews. We also propose two novel NLP tasks based on this dataset and provide simple baseline models. In the first task, we show that simple models can predict whether a paper is accepted with up to 21{\%} error reduction compared to the majority baseline. In the second task, we predict the numerical scores of review aspects and show that simple models can outperform the mean baseline for aspects with high variance such as {`}originality{'} and {`}impact{'}.",
}
@inproceedings{celikyilmaz-etal-2018-deep,
    title = "Deep Communicating Agents for Abstractive Summarization",
    author = "Celikyilmaz, Asli  and
      Bosselut, Antoine  and
      He, Xiaodong  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1150",
    doi = "10.18653/v1/N18-1150",
    pages = "1662--1675",
    abstract = "We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.",
}
@inproceedings{zhang-etal-2018-encoding,
    title = "Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts",
    author = "Zhang, Yingyi  and
      Li, Jing  and
      Song, Yan  and
      Zhang, Chengzhi",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1151",
    doi = "10.18653/v1/N18-1151",
    pages = "1676--1686",
    abstract = "Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. Considering that conversations are formed by reposting and replying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase identification. In this paper, we present a neural keyphrase extraction framework for microblog posts that takes their conversation context into account, where four types of neural encoders, namely, averaged embedding, RNN, attention, and memory networks, are proposed to represent the conversation context. Experimental results on Twitter and Weibo datasets show that our framework with such encoders outperforms state-of-the-art approaches.",
}
@inproceedings{zopf-2018-estimating,
    title = "Estimating Summary Quality with Pairwise Preferences",
    author = "Zopf, Markus",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1152",
    doi = "10.18653/v1/N18-1152",
    pages = "1687--1696",
    abstract = "Automatic evaluation systems in the field of automatic summarization have been relying on the availability of gold standard summaries for over ten years. Gold standard summaries are expensive to obtain and often require the availability of domain experts to achieve high quality. In this paper, we propose an alternative evaluation approach based on pairwise preferences of sentences. In comparison to gold standard summaries, they are simpler and cheaper to obtain. In our experiments, we show that humans are able to provide useful feedback in the form of pairwise preferences. The new framework performs better than the three most popular versions of ROUGE with less expensive human input. We also show that our framework can reuse already available evaluation data and achieve even better results.",
}
@inproceedings{krishna-srinivasan-2018-generating,
    title = "Generating Topic-Oriented Summaries Using Neural Attention",
    author = "Krishna, Kundan  and
      Srinivasan, Balaji Vasan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1153",
    doi = "10.18653/v1/N18-1153",
    pages = "1697--1705",
    abstract = "Summarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics and a single summary cannot do justice to all the topics. Further, the interests of readers can vary and the notion of importance can change across them. Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. In this paper, we propose an attention based RNN framework to generate multiple summaries of a single document tuned to different topics of interest. Our method outperforms existing baselines and our results suggest that the attention of generative networks can be successfully biased to look at sentences relevant to a topic and effectively used to generate topic-tuned summaries.",
}
@inproceedings{chen-etal-2018-generative,
    title = "Generative Bridging Network for Neural Sequence Prediction",
    author = "Chen, Wenhu  and
      Li, Guanlin  and
      Ren, Shuo  and
      Liu, Shujie  and
      Zhang, Zhirui  and
      Li, Mu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1154",
    doi = "10.18653/v1/N18-1154",
    pages = "1706--1715",
    abstract = "In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE directly maximizing the conditional likelihood, the bridge extends the point-wise ground truth to a bridge distribution conditioned on it, and the generator is optimized to minimize their KL-divergence. Three different GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to penalize confidence, enhance language smoothness and relieve learning burden. Experiments conducted on two recognized sequence prediction tasks (machine translation and abstractive text summarization) show that our proposed GBNs can yield significant improvements over strong baselines. Furthermore, by analyzing samples drawn from different bridges, expected influences on the generator are verified.",
}
@inproceedings{kamigaito-etal-2018-higher,
    title = "Higher-Order Syntactic Attention Network for Longer Sentence Compression",
    author = "Kamigaito, Hidetaka  and
      Hayashi, Katsuhiko  and
      Hirao, Tsutomu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1155",
    doi = "10.18653/v1/N18-1155",
    pages = "1716--1726",
    abstract = "A sentence compression method using LSTM can generate fluent compressed sentences. However, the performance of this method is significantly degraded when compressing longer sentences since it does not explicitly handle syntactic features. To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states. Furthermore, to avoid the influence of incorrect parse results, we trained HiSAN by maximizing jointly the probability of a correct output with the attention distribution. Experimental results on Google sentence compression dataset showed that our method achieved the best performance on F1 as well as ROUGE-1,2 and L scores, 83.2, 82.9, 75.8 and 82.7, respectively. In human evaluation, our methods also outperformed baseline methods in both readability and informativeness.",
}
@inproceedings{zhou-etal-2018-neural-storyline,
    title = "Neural Storyline Extraction Model for Storyline Generation from News Articles",
    author = "Zhou, Deyu  and
      Guo, Linsen  and
      He, Yulan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1156",
    doi = "10.18653/v1/N18-1156",
    pages = "1727--1736",
    abstract = "Storyline generation aims to extract events described on news articles under a certain news topic and reveal how those events evolve over time. Most approaches to storyline generation first train supervised models to extract events from news articles published in different time periods and then link relevant extracted events into coherent stories. They are domain dependent and cannot deal with unseen event types. To tackle this problem, approaches based on probabilistic graphic models jointly model the generations of events and storylines without the use of annotated data. However, the parameter inference procedure is too complex and models often require long time to converge. In this paper, we propose a novel neural network based approach to extract structured representations and evolution patterns of storylines without using annotated data. In this model, title and main body of a news article are assumed to share the similar storyline distribution. Moreover, similar documents described in neighboring time periods are assumed to share similar storyline distributions. Based on these assumptions, structured representations and evolution patterns of storylines can be extracted. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms state-of-the-art approaches for storyline generation on both accuracy and efficiency.",
}
@inproceedings{sakaue-etal-2018-provable,
    title = "Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function",
    author = "Sakaue, Shinsaku  and
      Hirao, Tsutomu  and
      Nishino, Masaaki  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1157",
    doi = "10.18653/v1/N18-1157",
    pages = "1737--1746",
    abstract = "Submodular maximization with the greedy algorithm has been studied as an effective approach to extractive summarization. This approach is known to have three advantages: its applicability to many useful submodular objective functions, the efficiency of the greedy algorithm, and the provable performance guarantee. However, when it comes to compressive summarization, we are currently missing a counterpart of the extractive method based on submodularity. In this paper, we propose a fast greedy method for compressive summarization. Our method is applicable to any monotone submodular objective function, including many functions well-suited for document summarization. We provide an approximation guarantee of our greedy algorithm. Experiments show that our method is about 100 to 400 times faster than an existing method based on integer-linear-programming (ILP) formulations and that our method empirically achieves more than 95{\%}-approximation.",
}
@inproceedings{narayan-etal-2018-ranking,
    title = "Ranking Sentences for Extractive Summarization with Reinforcement Learning",
    author = "Narayan, Shashi  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1158",
    doi = "10.18653/v1/N18-1158",
    pages = "1747--1759",
    abstract = "Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.",
}
@inproceedings{handler-oconnor-2018-relational,
    title = "Relational Summarization for Corpus Analysis",
    author = "Handler, Abram  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1159",
    doi = "10.18653/v1/N18-1159",
    pages = "1760--1769",
    abstract = "This work introduces a new problem, relational summarization, in which the goal is to generate a natural language summary of the relationship between two lexical items in a corpus, without reference to a knowledge base. Motivated by the needs of novel user interfaces, we define the task and give examples of its application. We also present a new query-focused method for finding natural language sentences which express relationships. Our method allows for summarization of more than two times more query pairs than baseline relation extractors, while returning measurably more readable output. Finally, to help guide future work, we analyze the challenges of relational summarization using both a news and a social media corpus.",
}
@inproceedings{gorinski-lapata-2018-whats,
    title = "What{'}s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis",
    author = "Gorinski, Philip John  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1160",
    doi = "10.18653/v1/N18-1160",
    pages = "1770--1781",
    abstract = "This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation. Overviews are natural language texts that give a first impression of a movie, describing aspects such as its genre, plot, mood, or artistic style. We create a dataset that consists of movie scripts, attribute-value pairs for the movies{'} aspects, as well as overviews, which we extract from an online database. We present a novel end-to-end model for overview generation, consisting of a multi-label encoder for identifying screenplay attributes, and an LSTM decoder to generate natural language sentences conditioned on the identified attributes. Automatic and human evaluation show that the encoder is able to reliably assign good labels for the movie{'}s attributes, and the overviews provide descriptions of the movie{'}s content which are informative and faithful.",
}
@inproceedings{zopf-etal-2018-scores,
    title = "Which Scores to Predict in Sentence Regression for Text Summarization?",
    author = {Zopf, Markus  and
      Loza Menc{\'\i}a, Eneldo  and
      F{\"u}rnkranz, Johannes},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1161",
    doi = "10.18653/v1/N18-1161",
    pages = "1782--1791",
    abstract = "The task of automatic text summarization is to generate a short text that summarizes the most important information in a given set of documents. Sentence regression is an emerging branch in automatic text summarizations. Its key idea is to estimate the importance of information via learned utility scores for individual sentences. These scores are then used for selecting sentences from the source documents, typically according to a greedy selection strategy. Recently proposed state-of-the-art models learn to predict ROUGE recall scores of individual sentences, which seems reasonable since the final summaries are evaluated according to ROUGE recall. In this paper, we show in extensive experiments that following this intuition leads to suboptimal results and that learning to predict ROUGE precision scores leads to better results. The crucial difference is to aim not at covering as much information as possible but at wasting as little space as possible in every greedy step.",
}
@inproceedings{park-etal-2018-hierarchical,
    title = "A Hierarchical Latent Structure for Variational Conversation Modeling",
    author = "Park, Yookoon  and
      Cho, Jaemin  and
      Kim, Gunhee",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1162",
    doi = "10.18653/v1/N18-1162",
    pages = "1792--1801",
    abstract = "Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.",
}
@inproceedings{sandbank-etal-2018-detecting,
    title = "Detecting Egregious Conversations between Customers and Virtual Agents",
    author = "Sandbank, Tommy  and
      Shmueli-Scheuer, Michal  and
      Herzig, Jonathan  and
      Konopnicki, David  and
      Richards, John  and
      Piorkowski, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1163",
    doi = "10.18653/v1/N18-1163",
    pages = "1802--1811",
    abstract = "Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20{\%} over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.",
}
@inproceedings{jiang-etal-2018-learning-disentangle,
    title = "Learning to Disentangle Interleaved Conversational Threads with a {S}iamese Hierarchical Network and Similarity Ranking",
    author = "Jiang, Jyun-Yu  and
      Chen, Francine  and
      Chen, Yan-Ying  and
      Wang, Wei",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1164",
    doi = "10.18653/v1/N18-1164",
    pages = "1812--1822",
    abstract = "An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. In this paper, we propose to leverage representation learning for conversation disentanglement. A Siamese hierarchical convolutional neural network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. With the estimated similarity scores, our algorithm for conversation identification by similarity ranking (CISIR) then derives conversations based on high-confidence message pairs and pairwise redundancy. Experiments were conducted with four publicly available datasets of conversations from Reddit and IRC channels. The experimental results show that our approach significantly outperforms comparative baselines in both pairwise similarity estimation and conversation disentanglement.",
}
@inproceedings{chen-etal-2018-variational-knowledge,
    title = "Variational Knowledge Graph Reasoning",
    author = "Chen, Wenhu  and
      Xiong, Wenhan  and
      Yan, Xifeng  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1165",
    doi = "10.18653/v1/N18-1165",
    pages = "1823--1832",
    abstract = "Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving predicting the relation of a given entity pair. We frame this prediction problem as an inference problem in a probabilistic graphical model and aim at resolving it from a variational inference perspective. In order to model the relation between the query entity pair, we assume that there exists an underlying latent variable (paths connecting two nodes) in the KG, which carries the equivalent semantics of their relations. However, due to the intractability of connections in large KGs, we propose to use variation inference to maximize the evidence lower bound. More specifically, our framework (Diva) is composed of three modules, i.e. a posterior approximator, a prior (path finder), and a likelihood (path reasoner). By using variational inference, we are able to incorporate them closely into a unified architecture and jointly optimize them to perform KG reasoning. With active interactions among these sub-modules, Diva is better at handling noise and coping with more complex reasoning scenarios. In order to evaluate our method, we conduct the experiment of the link prediction task on multiple datasets and achieve state-of-the-art performances on both datasets.",
}
@inproceedings{cheng-miyao-2018-inducing,
    title = "Inducing Temporal Relations from Time Anchor Annotation",
    author = "Cheng, Fei  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1166",
    doi = "10.18653/v1/N18-1166",
    pages = "1833--1843",
    abstract = "Recognizing temporal relations among events and time expressions has been an essential but challenging task in natural language processing. Conventional annotation of judging temporal relations puts a heavy load on annotators. In reality, the existing annotated corpora include annotations on only {``}salient{''} event pairs, or on pairs in a fixed window of sentences. In this paper, we propose a new approach to obtain temporal relations from absolute time value (a.k.a. time anchors), which is suitable for texts containing rich temporal information such as news articles. We start from time anchors for events and time expressions, and temporal relation annotations are induced automatically by computing relative order of two time anchors. This proposal shows several advantages over the current methods for temporal relation annotation: it requires less annotation effort, can induce inter-sentence relations easily, and increases informativeness of temporal relations. We compare the empirical statistics and automatic recognition results with our data against a previous temporal relation corpus. We also reveal that our data contributes to a significant improvement of the downstream time anchor prediction task, demonstrating 14.1 point increase in overall accuracy.",
}
@inproceedings{radhakrishnan-etal-2018-elden,
    title = "{ELDEN}: Improved Entity Linking Using Densified Knowledge Graphs",
    author = "Radhakrishnan, Priya  and
      Talukdar, Partha  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1167",
    doi = "10.18653/v1/N18-1167",
    pages = "1844--1853",
    abstract = "Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system{'}s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms state-of-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN{'}s approach is simple, yet effective. We have made ELDEN{'}s code and data publicly available.",
}
@inproceedings{ye-etal-2018-interpretable,
    title = "Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions",
    author = "Ye, Hai  and
      Jiang, Xin  and
      Luo, Zhunchen  and
      Chao, Wenhan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1168",
    doi = "10.18653/v1/N18-1168",
    pages = "1854--1864",
    abstract = "In this paper, we propose to study the problem of court view generation from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequence-to-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.",
}
@inproceedings{li-etal-2018-delete,
    title = "Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer",
    author = "Li, Juncen  and
      Jia, Robin  and
      He, He  and
      Liang, Percy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1169",
    doi = "10.18653/v1/N18-1169",
    pages = "1865--1874",
    abstract = "We consider the task of text attribute transfer: transforming a sentence to alter a specific attribute (e.g., sentiment) while preserving its attribute-independent content (e.g., {``}screen is just the right size{''} to {``}screen is too small{''}). Our training data includes only sentences labeled with their attribute (e.g., positive and negative), but not pairs of sentences that only differ in the attributes, so we must learn to disentangle attributes from attribute-independent content in an unsupervised way. Previous work using adversarial methods has struggled to produce high-quality outputs. In this paper, we propose simpler methods motivated by the observation that text attributes are often marked by distinctive phrases (e.g., {``}too small{''}). Our strongest method extracts content words by deleting phrases associated with the sentence{'}s original attribute value, retrieves new phrases associated with the target attribute, and uses a neural model to fluently combine these into a final output. Based on human evaluation, our best method generates grammatical and appropriate responses on 22{\%} more inputs than the best previous system, averaged over three attribute transfer datasets: altering sentiment of reviews on Yelp, altering sentiment of reviews on Amazon, and altering image captions to be more romantic or humorous.",
}
@inproceedings{iyyer-etal-2018-adversarial,
    title = "Adversarial Example Generation with Syntactically Controlled Paraphrase Networks",
    author = "Iyyer, Mohit  and
      Wieting, John  and
      Gimpel, Kevin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1170",
    doi = "10.18653/v1/N18-1170",
    pages = "1875--1885",
    abstract = "We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) {``}fool{''} pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data.",
}
@inproceedings{kenyon-dean-etal-2018-sentiment,
    title = "Sentiment Analysis: It{'}s Complicated!",
    author = "Kenyon-Dean, Kian  and
      Ahmed, Eisha  and
      Fujimoto, Scott  and
      Georges-Filteau, Jeremy  and
      Glasz, Christopher  and
      Kaur, Barleen  and
      Lalande, Auguste  and
      Bhanderi, Shruti  and
      Belfer, Robert  and
      Kanagasabai, Nirmal  and
      Sarrazingendron, Roman  and
      Verma, Rohit  and
      Ruths, Derek",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1171",
    doi = "10.18653/v1/N18-1171",
    pages = "1886--1895",
    abstract = "Sentiment analysis is used as a proxy to measure human emotion, where the objective is to categorize text according to some predefined notion of sentiment. Sentiment analysis datasets are typically constructed with gold-standard sentiment labels, assigned based on the results of manual annotations. When working with such annotations, it is common for dataset constructors to discard {``}noisy{''} or {``}controversial{''} data where there is significant disagreement on the proper label. In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30{\%} of the originally annotated data. We argue that the removal of such data is a problematic trend because, when performing real-time sentiment classification of short-text, an automated system cannot know a priori which samples would fall into this category of disputed sentiment. We therefore propose the notion of a {``}complicated{''} class of sentiment to categorize such text, and argue that its inclusion in the short-text sentiment analysis framework will improve the quality of automated sentiment analysis systems as they are implemented in real-world settings. We motivate this argument by building and analyzing a new publicly available TSA dataset of over 7,000 tweets annotated with 5x coverage, named MTSA. Our analysis of classifier performance over our dataset offers insights into sentiment analysis dataset and model design, how current techniques would perform in the real world, and how researchers should handle difficult data.",
}
@inproceedings{augenstein-etal-2018-multi,
    title = "Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces",
    author = "Augenstein, Isabelle  and
      Ruder, Sebastian  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1172",
    doi = "10.18653/v1/N18-1172",
    pages = "1896--1906",
    abstract = "We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis.",
}
@inproceedings{buechel-hahn-2018-word,
    title = "Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem",
    author = "Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1173",
    doi = "10.18653/v1/N18-1173",
    pages = "1907--1918",
    abstract = "Predicting the emotional value of lexical items is a well-known problem in sentiment analysis. While research has focused on polarity for quite a long time, meanwhile this early focus has been shifted to more expressive emotion representation models (such as Basic Emotions or Valence-Arousal-Dominance). This change resulted in a proliferation of heterogeneous formats and, in parallel, often small-sized, non-interoperable resources (lexicons and corpus annotations). In particular, the limitations in size hampered the application of deep learning methods in this area because they typically require large amounts of input data. We here present a solution to get around this language data bottleneck by rephrasing word emotion induction as a multi-task learning problem. In this approach, the prediction of each independent emotion dimension is considered as an individual task and hidden layers are shared between these dimensions. We investigate whether multi-task learning is more advantageous than single-task learning for emotion prediction by comparing our model against a wide range of alternative emotion and polarity induction methods featuring 9 typologically diverse languages and a total of 15 conditions. Our model turns out to outperform each one of them. Against all odds, the proposed deep learning approach yields the largest gain on the smallest data sets, merely composed of one thousand samples.",
}
@inproceedings{ding-riloff-2018-human,
    title = "Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data",
    author = "Ding, Haibo  and
      Riloff, Ellen",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1174",
    doi = "10.18653/v1/N18-1174",
    pages = "1919--1929",
    abstract = "We often talk about events that impact us positively or negatively. For example {``}I got a job{''} is good news, but {``}I lost my job{''} is bad news. When we discuss an event, we not only understand its affective polarity but also the reason why the event is beneficial or detrimental. For example, getting or losing a job has affective polarity primarily because it impacts us financially. Our work aims to categorize affective events based upon human need categories that often explain people{'}s motivations and desires: PHYSIOLOGICAL, HEALTH, LEISURE, SOCIAL, FINANCIAL, COGNITION, and FREEDOM. We create classification models based on event expressions as well as models that use contexts surrounding event mentions. We also design a co-training model that learns from unlabeled data by simultaneously training event expression and event context classifiers in an iterative learning process. Our results show that co-training performs well, producing substantially better results than the individual classifiers.",
}
@inproceedings{habernal-etal-2018-argument,
    title = "The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants",
    author = "Habernal, Ivan  and
      Wachsmuth, Henning  and
      Gurevych, Iryna  and
      Stein, Benno",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1175",
    doi = "10.18653/v1/N18-1175",
    pages = "1930--1940",
    abstract = "Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.",
}
@inproceedings{levitan-etal-2018-linguistic,
    title = "Linguistic Cues to Deception and Perceived Deception in Interview Dialogues",
    author = "Levitan, Sarah Ita  and
      Maredia, Angel  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1176",
    doi = "10.18653/v1/N18-1176",
    pages = "1941--1950",
    abstract = "We explore deception detection in interview dialogues. We analyze a set of linguistic features in both truthful and deceptive responses to interview questions. We also study the perception of deception, identifying characteristics of statements that are perceived as truthful or deceptive by interviewers. Our analysis show significant differences between truthful and deceptive question responses, as well as variations in deception patterns across gender and native language. This analysis motivated our selection of features for machine learning experiments aimed at classifying globally deceptive speech. Our best classification performance is 72.74{\%} F1-Score (about 17{\%} better than human performance), which is achieved using a combination of linguistic features and individual traits.",
}
@inproceedings{fried-etal-2018-unified,
    title = "Unified Pragmatic Models for Generating and Following Instructions",
    author = "Fried, Daniel  and
      Andreas, Jacob  and
      Klein, Dan",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1177",
    doi = "10.18653/v1/N18-1177",
    pages = "1951--1963",
    abstract = "We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about why speakers produce certain instructions, and about how listeners will react upon hearing them. Like previous pragmatic models, we use learned base listener and speaker models to build a pragmatic speaker that uses the base listener to simulate the interpretation of candidate descriptions, and a pragmatic listener that reasons counterfactually about alternative descriptions. We extend these models to tasks with sequential structure. Evaluation of language generation and interpretation shows that pragmatic inference improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at producing instructions correctly interpreted by humans) in diverse settings.",
}
@inproceedings{subramanian-etal-2018-hierarchical,
    title = "Hierarchical Structured Model for Fine-to-Coarse Manifesto Text Analysis",
    author = "Subramanian, Shivashankar  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1178",
    doi = "10.18653/v1/N18-1178",
    pages = "1964--1974",
    abstract = "Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party{'}s fine-grained position on a particular issue, as well as for coarse-grained positioning of a party on the left{--}right spectrum. In this paper we propose a two-stage model for automatically performing both levels of analysis over manifestos. In the first step we employ a hierarchical multi-task structured deep model to predict fine- and coarse-grained positions, and in the second step we perform post-hoc calibration of coarse-grained positions using probabilistic soft logic. We empirically show that the proposed model outperforms state-of-art approaches at both granularities using manifestos from twelve countries, written in ten different languages.",
}
@inproceedings{sanchez-etal-2018-behavior,
    title = "Behavior Analysis of {NLI} Models: Uncovering the Influence of Three Factors on Robustness",
    author = "Sanchez, Ivan  and
      Mitchell, Jeff  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1179",
    doi = "10.18653/v1/N18-1179",
    pages = "1975--1985",
    abstract = "Natural Language Inference is a challenging task that has received substantial attention, and state-of-the-art models now achieve impressive test set performance in the form of accuracy scores. Here, we go beyond this single evaluation metric to examine robustness to semantically-valid alterations to the input data. We identify three factors - insensitivity, polarity and unseen pairs - and compare their impact on three SNLI models under a variety of conditions. Our results demonstrate a number of strengths and weaknesses in the models{'} ability to generalise to new in-domain instances. In particular, while strong performance is possible on unseen hypernyms, unseen antonyms are more challenging for all the models. More generally, the models suffer from an insensitivity to certain small but semantically significant alterations, and are also often influenced by simple statistical correlations between words and training labels. Overall, we show that evaluations of NLI models can benefit from studying the influence of factors intrinsic to the models or found in the dataset used.",
}
@inproceedings{berzak-etal-2018-assessing,
    title = "Assessing Language Proficiency from Eye Movements in Reading",
    author = "Berzak, Yevgeni  and
      Katz, Boris  and
      Levy, Roger",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1180",
    doi = "10.18653/v1/N18-1180",
    pages = "1986--1996",
    abstract = "We present a novel approach for determining learners{'} second language proficiency which utilizes behavioral traces of eye movements during reading. Our approach provides stand-alone eyetracking based English proficiency scores which reflect the extent to which the learner{'}s gaze patterns in reading are similar to those of native English speakers. We show that our scores correlate strongly with standardized English proficiency tests. We also demonstrate that gaze information can be used to accurately predict the outcomes of such tests. Our approach yields the strongest performance when the test taker is presented with a suite of sentences for which we have eyetracking data from other readers. However, it remains effective even using eyetracking with sentences for which eye movement data have not been previously collected. By deriving proficiency as an automatic byproduct of eye movements during ordinary reading, our approach offers a potentially valuable new tool for second language proficiency assessment. More broadly, our results open the door to future methods for inferring reader characteristics from the behavioral traces of reading.",
}
@inproceedings{zhan-levy-2018-comparing,
    title = "Comparing Theories of Speaker Choice Using a Model of Classifier Production in {M}andarin {C}hinese",
    author = "Zhan, Meilin  and
      Levy, Roger",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1181",
    doi = "10.18653/v1/N18-1181",
    pages = "1997--2005",
    abstract = "Speakers often have more than one way to express the same meaning. What general principles govern speaker choice in the face of optionality when near semantically invariant alternation exists? Studies have shown that optional reduction in language is sensitive to contextual predictability, such that more predictable a linguistic unit is, the more likely it is to get reduced. Yet it is unclear whether these cases of speaker choice are driven by audience design versus toward facilitating production. Here we argue that for a different optionality phenomenon, namely classifier choice in Mandarin Chinese, Uniform Information Density and at least one plausible variant of availability-based production make opposite predictions regarding the relationship between the predictability of the upcoming material and speaker choices. In a corpus analysis of Mandarin Chinese, we show that the distribution of speaker choices supports the availability-based production account and not the Uniform Information Density.",
}
@inproceedings{amiri-etal-2018-spotting,
    title = "Spotting Spurious Data with Neural Networks",
    author = "Amiri, Hadi  and
      Miller, Timothy  and
      Savova, Guergana",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1182",
    doi = "10.18653/v1/N18-1182",
    pages = "2006--2016",
    abstract = "Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings. In this paper, we present effective approaches inspired by queueing theory and psychology of learning to automatically identify spurious instances in datasets. Our approaches discriminate instances based on their {``}difficulty to learn,{''} determined by a downstream learner. Our methods can be applied to any dataset assuming the existence of a neural network model for the target task of the dataset. Our best approach outperforms competing state-of-the-art baselines and has a MAP of 0.85 and 0.22 in identifying spurious instances in synthetic and carefully-crowdsourced real-world datasets respectively.",
}
@inproceedings{cole-reitter-2018-timing,
    title = "The Timing of Lexical Memory Retrievals in Language Production",
    author = "Cole, Jeremy  and
      Reitter, David",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1183",
    doi = "10.18653/v1/N18-1183",
    pages = "2017--2027",
    abstract = "This paper explores the time course of lexical memory retrieval by modeling fluent language production. The duration of retrievals is predicted using the ACT-R cognitive architecture. In a large-scale observational study of a spoken corpus, we find that language production at a time point preceding a word is sped up or slowed down depending on activation of that word. This computational analysis has consequences for the theoretical model of language production. The results point to interference between lexical and phonological stages as well as a quantifiable buffer for lexical information that opens up the possibility of non-sequential retrievals.",
}
@inproceedings{barrett-etal-2018-unsupervised,
    title = "Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing",
    author = "Barrett, Maria  and
      Gonz{\'a}lez-Gardu{\~n}o, Ana Valeria  and
      Frermann, Lea  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1184",
    doi = "10.18653/v1/N18-1184",
    pages = "2028--2038",
    abstract = "When learning POS taggers and syntactic chunkers for low-resource languages, different resources may be available, and often all we have is a small tag dictionary, motivating type-constrained unsupervised induction. Even small dictionaries can improve the performance of unsupervised induction algorithms. This paper shows that performance can be further improved by including data that is readily available or can be easily obtained for most languages, i.e., eye-tracking, speech, or keystroke logs (or any combination thereof). We project information from all these data sources into shared spaces, in which the union of words is represented. For English unsupervised POS induction, the additional information, which is not required at test time, leads to an average error reduction on Ontonotes domains of 1.5{\%} over systems augmented with state-of-the-art word embeddings. On Penn Treebank the best model achieves 5.4{\%} error reduction over a word embeddings baseline. We also achieve significant improvements for syntactic chunk induction. Our analysis shows that improvements are even bigger when the available tag dictionaries are smaller.",
}
@inproceedings{ma-etal-2018-challenging,
    title = "Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog",
    author = "Ma, Kaixin  and
      Jurczyk, Tomasz  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1185",
    doi = "10.18653/v1/N18-1185",
    pages = "2039--2048",
    abstract = "This paper presents a new corpus and a robust deep learning architecture for a task in reading comprehension, passage completion, on multiparty dialog. Given a dialog in text and a passage containing factual descriptions about the dialog where mentions of the characters are replaced by blanks, the task is to fill the blanks with the most appropriate character names that reflect the contexts in the dialog. Since there is no dataset that challenges the task of passage completion in this genre, we create a corpus by selecting transcripts from a TV show that comprise 1,681 dialogs, generating passages for each dialog through crowdsourcing, and annotating mentions of characters in both the dialog and the passages. Given this dataset, we build a deep neural model that integrates rich feature extraction from convolutional neural networks into sequence modeling in recurrent neural networks, optimized by utterance and dialog level attentions. Our model outperforms the previous state-of-the-art model on this task in a different genre using bidirectional LSTM, showing a 13.0+{\%} improvement for longer dialogs. Our analysis shows the effectiveness of the attention mechanisms and suggests a direction to machine comprehension on multiparty dialog.",
}
@inproceedings{wu-etal-2018-dialog,
    title = "Dialog Generation Using Multi-Turn Reasoning Neural Networks",
    author = "Wu, Xianchao  and
      Mart{\'\i}nez, Ander  and
      Klyen, Momo",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1186",
    doi = "10.18653/v1/N18-1186",
    pages = "2049--2059",
    abstract = "In this paper, we propose a generalizable dialog generation approach that adapts multi-turn reasoning, one recent advancement in the field of document comprehension, to generate responses ({``}answers{''}) by taking current conversation session context as a {``}document{''} and current query as a {``}question{''}. The major idea is to represent a conversation session into memories upon which attention-based memory reading mechanism can be performed multiple times, so that (1) user{'}s query is properly extended by contextual clues and (2) optimal responses are step-by-step generated. Considering that the speakers of one conversation are not limited to be one, we separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. Namely, we utilize the queries{'} memory, the responses{'} memory, and their unified memory, following the time sequence of the conversation session. Experiments on Japanese 10-sentence (5-round) conversation modeling show impressive results on how multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines.",
}
@inproceedings{liu-etal-2018-dialogue,
    title = "Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems",
    author = {Liu, Bing  and
      T{\"u}r, Gokhan  and
      Hakkani-T{\"u}r, Dilek  and
      Shah, Pararth  and
      Heck, Larry},
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1187",
    doi = "10.18653/v1/N18-1187",
    pages = "2060--2069",
    abstract = "In this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions. Popular methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. Efficiency of such learning method may suffer from the mismatch of dialogue state distribution between offline training and online interactive learning stages. To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback. We design a neural network based task-oriented dialogue agent that can be optimized end-to-end with the proposed learning method. Experimental results show that our end-to-end dialogue agent can learn effectively from the mistake it makes via imitation learning from user teaching. Applying reinforcement learning with user feedback after the imitation learning stage further improves the agent{'}s capability in successfully completing a task.",
}
@inproceedings{xu-etal-2018-lsdscc,
    title = "{LSDSCC}: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics",
    author = "Xu, Zhen  and
      Jiang, Nan  and
      Liu, Bingquan  and
      Rong, Wenge  and
      Wu, Bowen  and
      Wang, Baoxun  and
      Wang, Zhuoran  and
      Wang, Xiaolong",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1188",
    doi = "10.18653/v1/N18-1188",
    pages = "2070--2080",
    abstract = "It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.",
}
@inproceedings{rios-kavuluru-2018-emr,
    title = "{EMR} Coding with Semi-Parametric Multi-Head Matching Networks",
    author = "Rios, Anthony  and
      Kavuluru, Ramakanth",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1189",
    doi = "10.18653/v1/N18-1189",
    pages = "2081--2091",
    abstract = "Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient{'}s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. In this paper, we present a new neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. Our evaluations are conducted using a well known de-identified EMR dataset (MIMIC) with a variety of multi-label performance measures.",
}
@inproceedings{wendlandt-etal-2018-factors,
    title = "Factors Influencing the Surprising Instability of Word Embeddings",
    author = "Wendlandt, Laura  and
      Kummerfeld, Jonathan K.  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1190",
    doi = "10.18653/v1/N18-1190",
    pages = "2092--2102",
    abstract = "Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this paper, we consider one aspect of embedding spaces, namely their stability. We show that even relatively high frequency words (100-200 occurrences) are often unstable. We provide empirical evidence for how various factors contribute to the stability of word embeddings, and we analyze the effects of stability on downstream tasks.",
}
@inproceedings{liu-zhang-2018-mining,
    title = "Mining Evidences for Concept Stock Recommendation",
    author = "Liu, Qi  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1191",
    doi = "10.18653/v1/N18-1191",
    pages = "2103--2112",
    abstract = "We investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to mine evidences from large scale textual data, which contain valuable market information. In particular, distributed word similarities trained over large scale raw texts are taken as a basis of relevance measuring, and deep reinforcement learning is leveraged to learn a strategy of topic expansion, given a small amount of manually labeled data from financial analysts. Results on two Chinese stock market datasets show that our method outperforms a strong baseline using information retrieval techniques.",
}
@inproceedings{liu-etal-2018-binarized,
    title = "Binarized {LSTM} Language Model",
    author = "Liu, Xuan  and
      Cao, Di  and
      Yu, Kai",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1192",
    doi = "10.18653/v1/N18-1192",
    pages = "2113--2121",
    abstract = "Long short-term memory (LSTM) language model (LM) has been widely investigated for automatic speech recognition (ASR) and natural language processing (NLP). Although excellent performance is obtained for large vocabulary tasks, tremendous memory consumption prohibits the use of LSTM LM in low-resource devices. The memory consumption mainly comes from the word embedding layer. In this paper, a novel binarized LSTM LM is proposed to address the problem. Words are encoded into binary vectors and other LSTM parameters are further binarized to achieve high memory compression. This is the first effort to investigate binary LSTM for large vocabulary LM. Experiments on both English and Chinese LM and ASR tasks showed that can achieve a compression ratio of 11.3 without any loss of LM and ASR performances and a compression ratio of 31.6 with acceptable minor performance degradation.",
}
@inproceedings{hazarika-etal-2018-conversational,
    title = "Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos",
    author = "Hazarika, Devamanyu  and
      Poria, Soujanya  and
      Zadeh, Amir  and
      Cambria, Erik  and
      Morency, Louis-Philippe  and
      Zimmermann, Roger",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1193",
    doi = "10.18653/v1/N18-1193",
    pages = "2122--2132",
    abstract = "Emotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, we address recognizing utterance-level emotions in dyadic conversational videos. We propose a deep neural framework, termed Conversational Memory Network (CMN), which leverages contextual information from the conversation history. In particular, CMN uses multimodal approach comprising audio, visual and textual features with gated recurrent units to model past utterances of each speaker into memories. These memories are then merged using attention-based hops to capture inter-speaker dependencies. Experiments show a significant improvement of 3 − 4{\%} in accuracy over the state of the art.",
}
@inproceedings{su-etal-2018-time,
    title = "How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues",
    author = "Su, Shang-Yu  and
      Yuan, Pei-Chieh  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1194",
    doi = "10.18653/v1/N18-1194",
    pages = "2133--2142",
    abstract = "Spoken language understanding (SLU) is an essential component in conversational systems. Most SLU components treats each utterance independently, and then the following components aggregate the multi-turn information in the separate phases. In order to avoid error propagation and effectively utilize contexts, prior work leveraged history for contextual SLU. However, most previous models only paid attention to the related content in history utterances, ignoring their temporal information. In the dialogues, it is intuitive that the most recent utterances are more important than the least recent ones, in other words, time-aware attention should be in a decaying manner. Therefore, this paper designs and investigates various types of time-decay attention on the sentence-level and speaker-level, and further proposes a flexible universal time-decay attention mechanism. The experiments on the benchmark Dialogue State Tracking Challenge (DSTC4) dataset show that the proposed time-decay attention mechanisms significantly improve the state-of-the-art model for contextual understanding performance.",
}
@inproceedings{loukina-etal-2018-towards,
    title = "Towards Understanding Text Factors in Oral Reading",
    author = "Loukina, Anastassia  and
      Liceralde, Van Rynald T.  and
      Beigman Klebanov, Beata",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1195",
    doi = "10.18653/v1/N18-1195",
    pages = "2143--2154",
    abstract = "Using a case study, we show that variation in oral reading rate across passages for professional narrators is consistent across readers and much of it can be explained using features of the texts being read. While text complexity is a poor predictor of the reading rate, a substantial share of variability can be explained by timing and story-based factors with performance reaching r=0.75 for unseen passages and narrator.",
}
@inproceedings{monroe-etal-2018-generating,
    title = "Generating Bilingual Pragmatic Color References",
    author = "Monroe, Will  and
      Hu, Jennifer  and
      Jong, Andrew  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1196",
    doi = "10.18653/v1/N18-1196",
    pages = "2155--2165",
    abstract = "Contextual influences on language often exhibit substantial cross-lingual regularities; for example, we are more verbose in situations that require finer distinctions. However, these regularities are sometimes obscured by semantic and syntactic differences. Using a newly-collected dataset of color reference games in Mandarin Chinese (which we release to the public), we confirm that a variety of constructions display the same sensitivity to contextual difficulty in Chinese and English. We then show that a neural speaker agent trained on bilingual data with a simple multitask learning approach displays more human-like patterns of context dependence and is more pragmatically informative than its monolingual Chinese counterpart. Moreover, this is not at the expense of language-specific semantic understanding: the resulting speaker model learns the different basic color term systems of English and Chinese (with noteworthy cross-lingual influences), and it can identify synonyms between the two languages using vector analogy operations on its output layer, despite having no exposure to parallel data.",
}
@inproceedings{andreas-etal-2018-learning,
    title = "Learning with Latent Language",
    author = "Andreas, Jacob  and
      Klein, Dan  and
      Levine, Sergey",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1197",
    doi = "10.18653/v1/N18-1197",
    pages = "2166--2179",
    abstract = "The named concepts and compositional operators present in natural language provide a rich source of information about the abstractions humans use to navigate the world. Can this linguistic background knowledge improve the generality and efficiency of learned classifiers and control policies? This paper aims to show that using the space of natural language strings as a parameter space is an effective way to capture natural task structure. In a pretraining phase, we learn a language interpretation model that transforms inputs (e.g. images) into outputs (e.g. labels) given natural language descriptions. To learn a new concept (e.g. a classifier), we search directly in the space of descriptions to minimize the interpreter{'}s loss on training examples. Crucially, our models do not require language data to learn these concepts: language is used only in pretraining to impose structure on subsequent learning. Results on image classification, text editing, and reinforcement learning show that, in all settings, models with a linguistic parameterization outperform those without.",
}
@inproceedings{wang-etal-2018-object,
    title = "Object Counts! Bringing Explicit Detections Back into Image Captioning",
    author = "Wang, Josiah  and
      Madhyastha, Pranava Swaroop  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1198",
    doi = "10.18653/v1/N18-1198",
    pages = "2180--2193",
    abstract = "The use of explicit object detectors as an intermediate step to image captioning {--} which used to constitute an essential stage in early work {--} is often bypassed in the currently dominant end-to-end approaches, where the language model is conditioned directly on a mid-level image embedding. We argue that explicit detections provide rich semantic information, and can thus be used as an interpretable representation to better understand why end-to-end image captioning systems work well. We provide an in-depth analysis of end-to-end image captioning by exploring a variety of cues that can be derived from such object detections. Our study reveals that end-to-end image captioning systems rely on matching image representations to generate captions, and that encoding the frequency, size and position of objects are complementary and all play a role in forming a good image representation. It also reveals that different object categories contribute in different ways towards image captioning.",
}
@inproceedings{hessel-etal-2018-quantifying,
    title = "Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets",
    author = "Hessel, Jack  and
      Mimno, David  and
      Lee, Lillian",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1199",
    doi = "10.18653/v1/N18-1199",
    pages = "2194--2205",
    abstract = "Multimodal machine learning algorithms aim to learn visual-textual correspondences. Previous work suggests that concepts with concrete visual manifestations may be easier to learn than concepts with abstract ones. We give an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. We apply the approach in four settings, ranging from image captions to images/text scraped from historical books. In addition to enabling explorations of concepts in multimodal datasets, our concreteness scores predict the capacity of machine learning algorithms to learn textual/visual relationships. We find that 1) concrete concepts are indeed easier to learn; 2) the large number of algorithms we consider have similar failure cases; 3) the precise positive relationship between concreteness and performance varies between datasets. We conclude with recommendations for using concreteness scores to facilitate future multimodal research.",
}
@inproceedings{azab-etal-2018-speaker,
    title = "Speaker Naming in Movies",
    author = "Azab, Mahmoud  and
      Wang, Mingzhe  and
      Smith, Max  and
      Kojima, Noriyuki  and
      Deng, Jia  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1200",
    doi = "10.18653/v1/N18-1200",
    pages = "2206--2216",
    abstract = "We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. To evaluate the performance of our model, we introduce a new dataset consisting of six episodes of the Big Bang Theory TV show and eighteen full movies covering different genres. Our experiments show that our multimodal model significantly outperforms several competitive baselines on the average weighted F-score metric. To demonstrate the effectiveness of our framework, we design an end-to-end memory network model that leverages our speaker naming model and achieves state-of-the-art results on the subtitles task of the MovieQA 2017 Challenge.",
}
@inproceedings{rajani-mooney-2018-stacking,
    title = "Stacking with Auxiliary Features for Visual Question Answering",
    author = "Rajani, Nazneen Fatema  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1201",
    doi = "10.18653/v1/N18-1201",
    pages = "2217--2226",
    abstract = "Visual Question Answering (VQA) is a well-known and challenging task that requires systems to jointly reason about natural language and vision. Deep learning models in various forms have been the standard for solving VQA. However, some of these VQA models are better at certain types of image-question pairs than other models. Ensembling VQA models intelligently to leverage their diverse expertise is, therefore, advantageous. Stacking With Auxiliary Features (SWAF) is an intelligent ensembling technique which learns to combine the results of multiple models using features of the current problem as context. We propose four categories of auxiliary features for ensembling for VQA. Three out of the four categories of features can be inferred from an image-question pair and do not require querying the component models. The fourth category of auxiliary features uses model-specific explanations. In this paper, we describe how we use these various categories of auxiliary features to improve performance for VQA. Using SWAF to effectively ensemble three recent systems, we obtain a new state-of-the-art. Our work also highlights the advantages of explainable AI models.",
}
@inproceedings{peters-etal-2018-deep,
    title = "Deep Contextualized Word Representations",
    author = "Peters, Matthew  and
      Neumann, Mark  and
      Iyyer, Mohit  and
      Gardner, Matt  and
      Clark, Christopher  and
      Lee, Kenton  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1202",
    doi = "10.18653/v1/N18-1202",
    pages = "2227--2237",
    abstract = "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.",
}
@inproceedings{suhr-etal-2018-learning,
    title = "Learning to Map Context-Dependent Sentences to Executable Formal Queries",
    author = "Suhr, Alane  and
      Iyer, Srinivasan  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1203",
    doi = "10.18653/v1/N18-1203",
    pages = "2238--2249",
    abstract = "We propose a context-dependent model to map utterances within an interaction to executable formal queries. To incorporate interaction history, the model maintains an interaction-level encoder that updates after each turn, and can copy sub-sequences of previously predicted queries during generation. Our approach combines implicit and explicit modeling of references between utterances. We evaluate our model on the ATIS flight planning interactions, and demonstrate the benefits of modeling context and explicit references.",
}
@inproceedings{clark-etal-2018-neural,
    title = "Neural Text Generation in Stories Using Entity Representations as Context",
    author = "Clark, Elizabeth  and
      Ji, Yangfeng  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1204",
    doi = "10.18653/v1/N18-1204",
    pages = "2250--2260",
    abstract = "We introduce an approach to neural text generation that explicitly represents entities mentioned in the text. Entity representations are vectors that are updated as the text proceeds; they are designed specifically for narrative text like fiction or news stories. Our experiments demonstrate that modeling entities offers a benefit in two automatic evaluations: mention generation (in which a model chooses which entity to mention next and which words to use in the mention) and selection between a correct next sentence and a distractor from later in the same story. We also conduct a human evaluation on automatically generated text in story contexts; this study supports our emphasis on entities and suggests directions for further research.",
}
@inproceedings{chen-etal-2018-recurrent,
    title = "Recurrent Neural Networks as Weighted Language Recognizers",
    author = "Chen, Yining  and
      Gilroy, Sorcha  and
      Maletti, Andreas  and
      May, Jonathan  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1205",
    doi = "10.18653/v1/N18-1205",
    pages = "2261--2271",
    abstract = "We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax, which are commonly used in natural language processing applications. We show that most problems for such RNNs are undecidable, including consistency, equivalence, minimization, and the determination of the highest-weighted string. However, for consistent RNNs the last problem becomes decidable, although the solution length can surpass all computable bounds. If additionally the string is limited to polynomial length, the problem becomes NP-complete. In summary, this shows that approximations and heuristic algorithms are necessary in practical applications of those RNNs.",
}
@proceedings{lrec-2018-international,
    title = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Hasida, Koiti  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios  and
      Tokunaga, Takenobu",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1000",
}
@inproceedings{kocabiyikoglu-etal-2018-augmenting,
    title = "Augmenting Librispeech with {F}rench Translations: A Multimodal Corpus for Direct Speech Translation Evaluation",
    author = "Kocabiyikoglu, Ali Can  and
      Besacier, Laurent  and
      Kraif, Olivier",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1001",
}
@inproceedings{etchegoyhen-etal-2018-evaluating,
    title = "Evaluating Domain Adaptation for Machine Translation Across Scenarios",
    author = "Etchegoyhen, Thierry  and
      Fern{\'a}ndez Torn{\'e}, Anna  and
      Azpeitia, Andoni  and
      Mart{\'\i}nez Garcia, Eva  and
      Matamala, Anna",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1002",
}
@inproceedings{hadiwinoto-ng-2018-upping,
    title = "Upping the Ante: Towards a Better Benchmark for {C}hinese-to-{E}nglish Machine Translation",
    author = "Hadiwinoto, Christian  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1003",
}
@inproceedings{negri-etal-2018-escape,
    title = "{ESCAPE}: a Large-scale Synthetic Corpus for Automatic Post-Editing",
    author = "Negri, Matteo  and
      Turchi, Marco  and
      Chatterjee, Rajen  and
      Bertoldi, Nicola",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1004",
}
@inproceedings{shao-etal-2018-evaluating,
    title = "Evaluating Machine Translation Performance on {C}hinese Idioms with a Blacklist Method",
    author = "Shao, Yutong  and
      Sennrich, Rico  and
      Webber, Bonnie  and
      Fancellu, Federico",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1005",
}
@inproceedings{jana-goyal-2018-network,
    title = "Network Features Based Co-hyponymy Detection",
    author = "Jana, Abhik  and
      Goyal, Pawan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1006",
}
@inproceedings{novak-novak-2018-cross,
    title = "Cross-Lingual Generation and Evaluation of a Wide-Coverage Lexical Semantic Resource",
    author = "Nov{\'a}k, Attila  and
      Nov{\'a}k, Borb{\'a}la",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1007",
}
@inproceedings{mikolov-etal-2018-advances,
    title = "Advances in Pre-Training Distributed Word Representations",
    author = "Mikolov, Tomas  and
      Grave, Edouard  and
      Bojanowski, Piotr  and
      Puhrsch, Christian  and
      Joulin, Armand",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1008",
}
@inproceedings{brown-etal-2018-integrating,
    title = "Integrating {G}enerative {L}exicon Event Structures into {V}erb{N}et",
    author = "Brown, Susan Windisch  and
      Pustejovsky, James  and
      Zaenen, Annie  and
      Palmer, Martha",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1009",
}
@inproceedings{kulahcioglu-de-melo-2018-fontlex,
    title = "{F}ont{L}ex: A Typographical Lexicon based on Affective Associations",
    author = "Kulahcioglu, Tugba  and
      de Melo, Gerard",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1010",
}
@inproceedings{hellwig-etal-2018-multi,
    title = "Multi-layer Annotation of the Rigveda",
    author = "Hellwig, Oliver  and
      Hettrich, Heinrich  and
      Modi, Ashutosh  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1011",
}
@inproceedings{futrell-etal-2018-natural,
    title = "The Natural Stories Corpus",
    author = "Futrell, Richard  and
      Gibson, Edward  and
      Tily, Harry J.  and
      Blank, Idan  and
      Vishnevetsky, Anastasia  and
      Piantadosi, Steven  and
      Fedorenko, Evelina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1012",
}
@inproceedings{hahm-etal-2018-semi,
    title = "Semi-automatic {K}orean {F}rame{N}et Annotation over {KAIST} Treebank",
    author = "Hahm, Younggyun  and
      Kim, Jiseong  and
      Kwon, Sunggoo  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1013",
}
@inproceedings{damnati-etal-2018-handling,
    title = "Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text",
    author = "Damnati, G{\'e}raldine  and
      Auguste, Jeremy  and
      Nasr, Alexis  and
      Charlet, Delphine  and
      Heinecke, Johannes  and
      B{\'e}chet, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1014",
}
@inproceedings{darwish-etal-2018-multi,
    title = "Multi-Dialect {A}rabic {POS} Tagging: A {CRF} Approach",
    author = "Darwish, Kareem  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Eldesouki, Mohamed  and
      Samih, Younes  and
      Alharbi, Randah  and
      Attia, Mohammed  and
      Magdy, Walid  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1015",
}
@inproceedings{kafle-huenerfauth-2018-corpus,
    title = "A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts",
    author = "Kafle, Sushant  and
      Huenerfauth, Matt",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1016",
}
@inproceedings{traum-etal-2018-dialogue,
    title = "Dialogue Structure Annotation for Multi-Floor Interaction",
    author = "Traum, David  and
      Henry, Cassidy  and
      Lukin, Stephanie  and
      Artstein, Ron  and
      Gervits, Felix  and
      Pollard, Kimberly  and
      Bonial, Claire  and
      Lei, Su  and
      Voss, Clare  and
      Marge, Matthew  and
      Hayes, Cory  and
      Hill, Susan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1017",
}
@inproceedings{kraus-etal-2018-effects,
    title = "Effects of Gender Stereotypes on Trust and Likability in Spoken Human-Robot Interaction",
    author = "Kraus, Matthias  and
      Kraus, Johannes  and
      Baumann, Martin  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1018",
}
@inproceedings{kontogiorgos-etal-2018-multimodal,
    title = "A Multimodal Corpus for Mutual Gaze and Joint Attention in Multiparty Situated Interaction",
    author = "Kontogiorgos, Dimosthenis  and
      Avramova, Vanya  and
      Alexanderson, Simon  and
      Jonell, Patrik  and
      Oertel, Catharine  and
      Beskow, Jonas  and
      Skantze, Gabriel  and
      Gustafson, Joakim",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1019",
}
@inproceedings{elmadany-etal-2018-improving,
    title = "Improving Dialogue Act Classification for Spontaneous {A}rabic Speech and Instant Messages at Utterance Level",
    author = "Elmadany, AbdelRahim  and
      Abdou, Sherif  and
      Gheith, Mervat",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1020",
}
@inproceedings{kamocki-etal-2018-data,
    title = "Data Management Plan ({DMP}) for Language Data under the New General Da-ta Protection Regulation ({GDPR})",
    author = "Kamocki, Pawel  and
      Mapelli, Val{\'e}rie  and
      Choukri, Khalid",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1021",
}
@inproceedings{branco-2018-depleting,
    title = "We Are Depleting Our Research Subject as We Are Investigating It: In Language Technology, more Replication and Diversity Are Needed",
    author = "Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1022",
}
@inproceedings{trippel-zinn-2018-lessons,
    title = "Lessons Learned: On the Challenges of Migrating a Research Data Repository from a Research Institution to a University Library.",
    author = "Trippel, Thorsten  and
      Zinn, Claus",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1023",
}
@inproceedings{cieri-etal-2018-introducing,
    title = "Introducing {NIEUW}: Novel Incentives and Workflows for Eliciting Linguistic Data",
    author = "Cieri, Christopher  and
      Fiumara, James  and
      Liberman, Mark  and
      Callison-Burch, Chris  and
      Wright, Jonathan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1024",
}
@inproceedings{cohen-etal-2018-three,
    title = "Three Dimensions of Reproducibility in Natural Language Processing",
    author = "Cohen, K. Bretonnel  and
      Xia, Jingbo  and
      Zweigenbaum, Pierre  and
      Callahan, Tiffany  and
      Hargraves, Orin  and
      Goss, Foster  and
      Ide, Nancy  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Grouin, Cyril  and
      Hunter, Lawrence E.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1025",
}
@inproceedings{orizu-he-2018-content,
    title = "Content-Based Conflict of Interest Detection on {W}ikipedia",
    author = "Orizu, Udochukwu  and
      He, Yulan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1026",
}
@inproceedings{mohammad-2018-word,
    title = "Word Affect Intensities",
    author = "Mohammad, Saif",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1027",
}
@inproceedings{buechel-hahn-2018-representation,
    title = "Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons",
    author = "Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1028",
}
@inproceedings{vlachostergiou-etal-2018-unfolding,
    title = "Unfolding the External Behavior and Inner Affective State of Teammates through Ensemble Learning: Experimental Evidence from a Dyadic Team Corpus",
    author = "Vlachostergiou, Aggeliki  and
      Dennison, Mark  and
      Neubauer, Catherine  and
      Scherer, Stefan  and
      Khooshabeh, Peter  and
      Harrison, Andre",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1029",
}
@inproceedings{mohammad-kiritchenko-2018-understanding,
    title = "Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories",
    author = "Mohammad, Saif  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1030",
}
@inproceedings{min-etal-2018-ace,
    title = "When {ACE} met {KBP}: End-to-End Evaluation of Knowledge Base Population with Component-level Annotation",
    author = "Min, Bonan  and
      Freedman, Marjorie  and
      Bock, Roger  and
      Weischedel, Ralph",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1031",
}
@inproceedings{christodoulopoulos-mittal-2018-simple,
    title = "Simple Large-scale Relation Extraction from Unstructured Text",
    author = "Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1032",
}
@inproceedings{alsuhaibani-bollegala-2018-joint,
    title = "Joint Learning of Sense and Word Embeddings",
    author = "Alsuhaibani, Mohammed  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1033",
}
@inproceedings{gromann-declerck-2018-comparing,
    title = "Comparing Pretrained Multilingual Word Embeddings on an Ontology Alignment Task",
    author = "Gromann, Dagmar  and
      Declerck, Thierry",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1034",
}
@inproceedings{popescu-etal-2018-large,
    title = "A Large Resource of Patterns for Verbal Paraphrases",
    author = "Popescu, Octavian  and
      Vo, Ngoc Phuoc An  and
      Sheinin, Vadim",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1035",
}
@inproceedings{xu-etal-2018-building,
    title = "Building Parallel Monolingual {G}an {C}hinese Dialects Corpus",
    author = "Xu, Fan  and
      Wang, Mingwen  and
      Li, Maoxi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1036",
}
@inproceedings{mirkin-etal-2018-recorded,
    title = "A Recorded Debating Dataset",
    author = "Mirkin, Shachar  and
      Jacovi, Michal  and
      Lavee, Tamar  and
      Kuo, Hong-Kwang  and
      Thomas, Samuel  and
      Sager, Leslie  and
      Kotlerman, Lili  and
      Venezian, Elad  and
      Slonim, Noam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1037",
}
@inproceedings{sugisaki-etal-2018-building,
    title = "Building a Corpus from Handwritten Picture Postcards: Transcription, Annotation and Part-of-Speech Tagging",
    author = "Sugisaki, Kyoko  and
      Wiedmer, Nicolas  and
      Hausendorf, Heiko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1038",
}
@inproceedings{garcia-salido-etal-2018-lexical,
    title = "A Lexical Tool for Academic Writing in {S}panish based on Expert and Novice Corpora",
    author = "Garc{\'\i}a Salido, Marcos  and
      Garc{\'\i}a, Marcos  and
      Villayandre-Llamazares, Milka  and
      Alonso-Ramos, Margarita",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1039",
}
@inproceedings{brasoveanu-etal-2018-framing,
    title = "Framing Named Entity Linking Error Types",
    author = "Bra{\c{s}}oveanu, Adrian  and
      Rizzo, Giuseppe  and
      Kuntschik, Philipp  and
      Weichselbraun, Albert  and
      Nixon, Lyndon J.B.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1040",
}
@inproceedings{roberts-etal-2018-framenet,
    title = "A {F}rame{N}et for Cancer Information in Clinical Narratives: Schema and Annotation",
    author = "Roberts, Kirk  and
      Si, Yuqi  and
      Gandhi, Anshul  and
      Bernstam, Elmer",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1041",
}
@inproceedings{shardlow-etal-2018-new,
    title = "A New Corpus to Support Text Mining for the Curation of Metabolites in the {C}h{EBI} Database",
    author = "Shardlow, Matthew  and
      Nguyen, Nhung  and
      Owen, Gareth  and
      O{'}Donovan, Claire  and
      Leach, Andrew  and
      McNaught, John  and
      Turner, Steve  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1042",
}
@inproceedings{neveol-etal-2018-parallel,
    title = "Parallel Corpora for the Biomedical Domain",
    author = "N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Jimeno Yepes, Antonio  and
      Neves, Mariana  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1043",
}
@inproceedings{zlabinger-etal-2018-medical,
    title = "Medical Entity Corpus with {PICO} elements and Sentiment Analysis",
    author = "Zlabinger, Markus  and
      Andersson, Linda  and
      Hanbury, Allan  and
      Andersson, Michael  and
      Quasnik, Vanessa  and
      Brassey, Jon",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1044",
}
@inproceedings{hazem-daille-2018-word,
    title = "Word Embedding Approach for Synonym Extraction of Multi-Word Terms",
    author = "Hazem, Amir  and
      Daille, B{\'e}atrice",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1045",
}
@inproceedings{roberts-egg-2018-large,
    title = "A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality",
    author = "Roberts, Will  and
      Egg, Markus",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1046",
}
@inproceedings{semmar-2018-hybrid,
    title = "A Hybrid Approach for Automatic Extraction of Bilingual Multiword Expressions from Parallel Corpora",
    author = "Semmar, Nasredine",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1047",
}
@inproceedings{agrawal-etal-2018-beating,
    title = "No more beating about the bush : A Step towards Idiom Handling for {I}ndian Language {NLP}",
    author = "Agrawal, Ruchit  and
      Chenthil Kumar, Vighnesh  and
      Muralidharan, Vigneshwaran  and
      Sharma, Dipti",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1048",
}
@inproceedings{kamila-etal-2018-sentence,
    title = "Sentence Level Temporality Detection using an Implicit Time-sensed Resource",
    author = "Kamila, Sabyasachi  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1049",
}
@inproceedings{sakaguchi-etal-2018-comprehensive,
    title = "Comprehensive Annotation of Various Types of Temporal Information on the Time Axis",
    author = "Sakaguchi, Tomohiro  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1050",
}
@inproceedings{caselli-morante-2018-systems,
    title = "Systems{'} Agreements and Disagreements in Temporal Processing: An Extensive Error Analysis of the {T}emp{E}val-3 Task",
    author = "Caselli, Tommaso  and
      Morante, Roser",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1051",
}
@inproceedings{vempala-blanco-2018-annotating,
    title = "Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies",
    author = "Vempala, Alakananda  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1052",
}
@inproceedings{de-hertog-desmet-2018-contextualized,
    title = "Contextualized Usage-Based Material Selection",
    author = "De Hertog, Dirk  and
      Desmet, Piet",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1053",
}
@inproceedings{yoo-kim-2018-cbfc,
    title = "{CBFC}: a parallel {L}2 speech corpus for {K}orean and {F}rench learners",
    author = "Yoo, Hiyon  and
      Kim, Inyoung",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1054",
}
@inproceedings{wilkens-etal-2018-sw4all,
    title = "{SW}4{ALL}: a {CEFR} Classified and Aligned Corpus for Language Learning",
    author = "Wilkens, Rodrigo  and
      Zilio, Leonardo  and
      Fairon, C{\'e}drick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1055",
}
@inproceedings{quiniou-daille-2018-towards,
    title = "Towards a Diagnosis of Textual Difficulties for Children with Dyslexia",
    author = "Quiniou, Solen  and
      Daille, B{\'e}atrice",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1056",
}
@inproceedings{marimon-etal-2018-coreference,
    title = "Coreference Resolution in {F}ree{L}ing 4.0",
    author = "Marimon, Montserrat  and
      Padr{\'o}, Llu{\'\i}s  and
      Turmo, Jordi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1057",
}
@inproceedings{rosiger-2018-bashi,
    title = "{BASHI}: A Corpus of {W}all {S}treet {J}ournal Articles Annotated with Bridging Links",
    author = {R{\"o}siger, Ina},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1058",
}
@inproceedings{oberle-2018-sacr,
    title = "{SACR}: A Drag-and-Drop Based Tool for Coreference Annotation",
    author = "Oberle, Bruno",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1059",
}
@inproceedings{niton-etal-2018-deep,
    title = "Deep Neural Networks for Coreference Resolution for {P}olish",
    author = "Nito{\'n}, Bart{\l}omiej  and
      Morawiecki, Pawe{\l}  and
      Ogrodniczuk, Maciej",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1060",
}
@inproceedings{vincze-etal-2018-szegedkoref,
    title = "{S}zeged{K}oref: A {H}ungarian Coreference Corpus",
    author = "Vincze, Veronika  and
      Heged{\H{u}}s, Kl{\'a}ra  and
      Sliz-Nagy, Alex  and
      Farkas, Rich{\'a}rd",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1061",
}
@inproceedings{ahmad-chang-2018-corpus,
    title = "A Corpus to Learn Refer-to-as Relations for Nominals",
    author = "Ahmad, Wasi  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1062",
}
@inproceedings{plu-etal-2018-sanaphor,
    title = "{S}anaphor++: Combining Deep Neural Networks with Semantics for Coreference Resolution",
    author = {Plu, Julien  and
      Prokofyev, Roman  and
      Tonon, Alberto  and
      Cudr{\'e}-Mauroux, Philippe  and
      Difallah, Djellel Eddine  and
      Troncy, Rapha{\"e}l  and
      Rizzo, Giuseppe},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1063",
}
@inproceedings{grobol-etal-2018-ancor,
    title = "{ANCOR}-{AS}: Enriching the {ANCOR} Corpus with Syntactic Annotations",
    author = {Grobol, Lo{\"\i}c  and
      Tellier, Isabelle  and
      de la Clergerie, {\'E}ric  and
      Dinarelli, Marco  and
      Landragin, Fr{\'e}d{\'e}ric},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1064",
}
@inproceedings{lapshinova-koltunski-etal-2018-parcorfull,
    title = "{P}ar{C}or{F}ull: a Parallel Corpus Annotated with Full Coreference",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Hardmeier, Christian  and
      Krielke, Pauline",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1065",
}
@inproceedings{ziolko-etal-2018-application,
    title = "An Application for Building a {P}olish Telephone Speech Corpus",
    author = "Zi{\'o}{\l}ko, Bartosz  and
      {\.Z}elasko, Piotr  and
      Gawlik, Ireneusz  and
      P{\k{e}}dzim{\k{a}}{\.z}, Tomasz  and
      Jadczyk, Tomasz",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1066",
}
@inproceedings{takamichi-saruwatari-2018-cpjd,
    title = "{CPJD} Corpus: Crowdsourced Parallel Speech Corpus of {J}apanese Dialects",
    author = "Takamichi, Shinnosuke  and
      Saruwatari, Hiroshi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1067",
}
@inproceedings{yancey-lepage-2018-korean,
    title = "{K}orean {L}2 Vocabulary Prediction: Can a Large Annotated Corpus be Used to Train Better Models for Predicting Unknown Words?",
    author = "Yancey, Kevin  and
      Lepage, Yves",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1068",
}
@inproceedings{granet-etal-2018-crowdsourcing,
    title = "Crowdsourcing-based Annotation of the Accounting Registers of the {I}talian Comedy",
    author = "Granet, Adeline  and
      Hervy, Benjamin  and
      Roman-Jimenez, Geoffrey  and
      Hachicha, Marouane  and
      Morin, Emmanuel  and
      Mouch{\`e}re, Harold  and
      Quiniou, Solen  and
      Raschia, Guillaume  and
      Rubellin, Fran{\c{c}}oise  and
      Viard-Gaudin, Christian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1069",
}
@inproceedings{lefakis-etal-2018-feidegger,
    title = "{FEIDEGGER}: A Multi-modal Corpus of Fashion Images and Descriptions in {G}erman",
    author = "Lefakis, Leonidas  and
      Akbik, Alan  and
      Vollgraf, Roland",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1070",
}
@inproceedings{millour-fort-2018-toward,
    title = "Toward a Lightweight Solution for Less-resourced Languages: Creating a {POS} Tagger for {A}lsatian Using Voluntary Crowdsourcing",
    author = {Millour, Alice  and
      Fort, Kar{\"e}n},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1071",
}
@inproceedings{katsuta-yamamoto-2018-crowdsourced,
    title = "Crowdsourced Corpus of Sentence Simplification with Core Vocabulary",
    author = "Katsuta, Akihiro  and
      Yamamoto, Kazuhide",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1072",
}
@inproceedings{hendrickx-etal-2018-multilingual,
    title = "A Multilingual Wikified Data Set of Educational Material",
    author = "Hendrickx, Iris  and
      Takoulidou, Eirini  and
      Naskos, Thanasis  and
      Kermanidis, Katia Lida  and
      Sosoni, Vilelmini  and
      de Vos, Hugo  and
      Stasimioti, Maria  and
      van Zaanen, Menno  and
      Georgakopoulou, Panayota  and
      Kordoni, Valia  and
      Popovic, Maja  and
      Egg, Markus  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1073",
}
@inproceedings{ganbold-etal-2018-using,
    title = "Using Crowd Agreement for {W}ordnet Localization",
    author = "Ganbold, Amarsanaa  and
      Chagnaa, Altangerel  and
      Bella, G{\'a}bor",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1074",
}
@inproceedings{sosoni-etal-2018-translation,
    title = "Translation Crowdsourcing: Creating a Multilingual Corpus of Online Educational Content",
    author = "Sosoni, Vilelmini  and
      Kermanidis, Katia Lida  and
      Stasimioti, Maria  and
      Naskos, Thanasis  and
      Takoulidou, Eirini  and
      van Zaanen, Menno  and
      Castilho, Sheila  and
      Georgakopoulou, Panayota  and
      Kordoni, Valia  and
      Egg, Markus",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1075",
}
@inproceedings{ehara-2018-building,
    title = "Building an {E}nglish Vocabulary Knowledge Dataset of {J}apanese {E}nglish-as-a-Second-Language Learners Using Crowdsourcing",
    author = "Ehara, Yo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1076",
}
@inproceedings{zhang-moldovan-2018-chinese,
    title = "{C}hinese Relation Classification using Long Short Term Memory Networks",
    author = "Zhang, Linrui  and
      Moldovan, Dan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1077",
}
@inproceedings{li-etal-2018-uir,
    title = "The {UIR} Uncertainty Corpus for {C}hinese: Annotating {C}hinese Microblog Corpus for Uncertainty Identification from Social Media",
    author = "Li, Binyang  and
      Xiang, Jun  and
      Chen, Le  and
      Han, Xu  and
      Yu, Xiaoyan  and
      Xu, Ruifeng  and
      Wang, Tengjiao  and
      Wong, Kam-fai",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1078",
}
@inproceedings{ge-etal-2018-eventwiki,
    title = "{E}vent{W}iki: A Knowledge Base of Major Events",
    author = "Ge, Tao  and
      Cui, Lei  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1079",
}
@inproceedings{koroleva-paroubek-2018-annotating,
    title = "Annotating Spin in Biomedical Scientific Publications : the case of Random Controlled Trials ({RCT}s)",
    author = "Koroleva, Anna  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1080",
}
@inproceedings{matsumoto-etal-2018-visualization,
    title = "Visualization of the occurrence trend of infectious diseases using {T}witter",
    author = "Matsumoto, Ryusei  and
      Yoshida, Minoru  and
      Matsumoto, Kazuyuki  and
      Matsuda, Hironobu  and
      Kita, Kenji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1081",
}
@inproceedings{martinc-pollak-2018-reusable,
    title = "Reusable workflows for gender prediction",
    author = "Martinc, Matej  and
      Pollak, Senja",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1082",
}
@inproceedings{hoenen-schenk-2018-knowing,
    title = "Knowing the Author by the Company His Words Keep",
    author = "Hoenen, Armin  and
      Schenk, Niko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1083",
}
@inproceedings{zielinski-mutschke-2018-towards,
    title = "Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications",
    author = "Zielinski, Andrea  and
      Mutschke, Peter",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1084",
}
@inproceedings{strotgen-etal-2018-krauts,
    title = "{KRAUTS}: A {G}erman Temporally Annotated News Corpus",
    author = {Str{\"o}tgen, Jannik  and
      Minard, Anne-Lyse  and
      Lange, Lukas  and
      Speranza, Manuela  and
      Magnini, Bernardo},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1085",
}
@inproceedings{khashabi-etal-2018-cogcompnlp,
    title = "{C}og{C}omp{NLP}: Your {S}wiss Army Knife for {NLP}",
    author = "Khashabi, Daniel  and
      Sammons, Mark  and
      Zhou, Ben  and
      Redman, Tom  and
      Christodoulopoulos, Christos  and
      Srikumar, Vivek  and
      Rizzolo, Nicholas  and
      Ratinov, Lev  and
      Luo, Guanheng  and
      Do, Quang  and
      Tsai, Chen-Tse  and
      Roy, Subhro  and
      Mayhew, Stephen  and
      Feng, Zhili  and
      Wieting, John  and
      Yu, Xiaodong  and
      Song, Yangqiu  and
      Gupta, Shashank  and
      Upadhyay, Shyam  and
      Arivazhagan, Naveen  and
      Ning, Qiang  and
      Ling, Shaoshi  and
      Roth, Dan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1086",
}
@inproceedings{nehring-sasaki-2018-framework,
    title = "A Framework for the Needs of Different Types of Users in Multilingual Semantic Enrichment",
    author = "Nehring, Jan  and
      Sasaki, Felix",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1087",
}
@inproceedings{bartolini-etal-2018-lrec,
    title = "The {LREC} Workshops Map",
    author = "Bartolini, Roberto  and
      Goggi, Sara  and
      Monachini, Monica  and
      Pardelli, Gabriella",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1088",
}
@inproceedings{gartner-etal-2018-preserving,
    title = "Preserving Workflow Reproducibility: The {R}e{P}lay-{DH} Client as a Tool for Process Documentation",
    author = {G{\"a}rtner, Markus  and
      Hahn, Uli  and
      Hermann, Sibylle},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1089",
}
@inproceedings{chiarcos-schenk-2018-acoli,
    title = "The {AC}o{L}i {C}o{NLL} Libraries: Beyond Tab-Separated Values",
    author = "Chiarcos, Christian  and
      Schenk, Niko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1090",
}
@inproceedings{indig-etal-2018-whats,
    title = "What{'}s Wrong, Python? {--} A Visual Differ and Graph Library for {NLP} in Python",
    author = "Indig, Bal{\'a}zs  and
      Simonyi, Andr{\'a}s  and
      Ligeti-Nagy, No{\'e}mi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1091",
}
@inproceedings{wang-etal-2018-scholargraph,
    title = "{S}cholar{G}raph:a {C}hinese Knowledge Graph of {C}hinese Scholars",
    author = "Wang, Shuo  and
      Hao, Zehui  and
      Meng, Xiaofeng  and
      Wang, Qiuyue",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1092",
}
@inproceedings{faralli-etal-2018-enriching,
    title = "Enriching Frame Representations with Distributionally Induced Senses",
    author = "Faralli, Stefano  and
      Panchenko, Alexander  and
      Biemann, Chris  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1093",
}
@inproceedings{declerck-etal-2018-integrated,
    title = "An Integrated Formal Representation for Terminological and Lexical Data included in Classification Schemes",
    author = "Declerck, Thierry  and
      Egorova, Kseniya  and
      Schnur, Eileen",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1094",
}
@inproceedings{panunzi-etal-2018-one,
    title = "One event, many representations. Mapping action concepts through visual features.",
    author = "Panunzi, Alessandro  and
      Gregori, Lorenzo  and
      Ravelli, Andrea Amelio",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1095",
}
@inproceedings{wan-2018-tel,
    title = "{T}el(s)-Telle(s)-Signs: Highly Accurate Automatic Crosslingual Hypernym Discovery",
    author = "Wan, Ada",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1096",
}
@inproceedings{wiegand-etal-2018-disambiguation,
    title = "Disambiguation of Verbal Shifters",
    author = "Wiegand, Michael  and
      Loda, Sylvette  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1097",
}
@inproceedings{huangfu-surdeanu-2018-bootstrapping,
    title = "Bootstrapping Polar-Opposite Emotion Dimensions from Online Reviews",
    author = "Huangfu, Luwen  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1098",
}
@inproceedings{rajendran-etal-2018-sentiment,
    title = "Sentiment-Stance-Specificity ({SSS}) Dataset: Identifying Support-based Entailment among Opinions.",
    author = "Rajendran, Pavithra  and
      Bollegala, Danushka  and
      Parsons, Simon",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1099",
}
@inproceedings{gangula-mamidi-2018-resource,
    title = "Resource Creation Towards Automated Sentiment Analysis in {T}elugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction",
    author = "Gangula, Rama Rohit Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1100",
}
@inproceedings{attia-etal-2018-multilingual,
    title = "Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks",
    author = "Attia, Mohammed  and
      Samih, Younes  and
      Elkahky, Ali  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1101",
}
@inproceedings{khodak-etal-2018-large,
    title = "A Large Self-Annotated Corpus for Sarcasm",
    author = "Khodak, Mikhail  and
      Saunshi, Nikunj  and
      Vodrahalli, Kiran",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1102",
}
@inproceedings{asai-etal-2018-happydb,
    title = "{H}appy{DB}: A Corpus of 100,000 Crowdsourced Happy Moments",
    author = "Asai, Akari  and
      Evensen, Sara  and
      Golshan, Behzad  and
      Halevy, Alon  and
      Li, Vivian  and
      Lopatenko, Andrei  and
      Stepanov, Daniela  and
      Suhara, Yoshihiko  and
      Tan, Wang-Chiew  and
      Xu, Yinzhan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1103",
}
@inproceedings{barnes-etal-2018-multibooked,
    title = "{M}ulti{B}ooked: A Corpus of {B}asque and {C}atalan Hotel Reviews Annotated for Aspect-level Sentiment Classification",
    author = "Barnes, Jeremy  and
      Badia, Toni  and
      Lambert, Patrik",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1104",
}
@inproceedings{santos-etal-2018-blogset,
    title = "{B}log{S}et-{BR}: A {B}razilian {P}ortuguese Blog Corpus",
    author = "Santos, Henrique  and
      Woloszyn, Vinicius  and
      Vieira, Renata",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1105",
}
@inproceedings{proisl-2018-someweta,
    title = "{S}o{M}e{W}e{T}a: A Part-of-Speech Tagger for {G}erman Social Media and Web Texts",
    author = "Proisl, Thomas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1106",
}
@inproceedings{mendels-etal-2018-collecting,
    title = "Collecting Code-Switched Data from Social Media",
    author = "Mendels, Gideon  and
      Soto, Victor  and
      Jaech, Aaron  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1107",
}
@inproceedings{donato-paggio-2018-classifying,
    title = "Classifying the Informative Behaviour of Emoji in Microblogs",
    author = "Donato, Giulia  and
      Paggio, Patrizia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1108",
}
@inproceedings{van-der-goot-etal-2018-taxonomy,
    title = "A Taxonomy for In-depth Evaluation of Normalization for User Generated Content",
    author = "van der Goot, Rob  and
      van Noord, Rik  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1109",
}
@inproceedings{sharma-strzalkowski-2018-gaining,
    title = "Gaining and Losing Influence in Online Conversation",
    author = "Sharma, Arun  and
      Strzalkowski, Tomek",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1110",
}
@inproceedings{zaghouani-charfi-2018-arap,
    title = "Arap-Tweet: A Large Multi-Dialect {T}witter Corpus for Gender, Age and Language Variety Identification",
    author = "Zaghouani, Wajdi  and
      Charfi, Anis",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1111",
}
@inproceedings{okinina-etal-2018-transc,
    title = "Transc{\&}Anno: A Graphical Tool for the Transcription and On-the-Fly Annotation of Handwritten Documents",
    author = "Okinina, Nadezda  and
      Nicolas, Lionel  and
      Lyding, Verena",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1112",
}
@inproceedings{nastase-hitschler-2018-correction,
    title = "Correction of {OCR} Word Segmentation Errors in Articles from the {ACL} Collection through Neural Machine Translation Methods",
    author = "Nastase, Vivi  and
      Hitschler, Julian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1113",
}
@inproceedings{hoenen-2018-manuscripts,
    title = "From Manuscripts to Archetypes through Iterative Clustering",
    author = "Hoenen, Armin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1114",
}
@inproceedings{yamauchi-etal-2018-building,
    title = "Building A Handwritten Cuneiform Character Imageset",
    author = "Yamauchi, Kenji  and
      Yamamoto, Hajime  and
      Mori, Wakaha",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1115",
}
@inproceedings{goodman-etal-2018-pdf,
    title = "{PDF}-to-Text Reanalysis for Linguistic Data Mining",
    author = "Goodman, Michael Wayne  and
      Georgi, Ryan  and
      Xia, Fei",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1116",
}
@inproceedings{jonell-etal-2018-crowdsourced,
    title = "Crowdsourced Multimodal Corpora Collection Tool",
    author = "Jonell, Patrik  and
      Oertel, Catharine  and
      Kontogiorgos, Dimosthenis  and
      Beskow, Jonas  and
      Gustafson, Joakim",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1117",
}
@inproceedings{miehle-etal-2018-expert,
    title = "Expert Evaluation of a Spoken Dialogue System in a Clinical Operating Room",
    author = "Miehle, Juliana  and
      Gerstenlauer, Nadine  and
      Ostler, Daniel  and
      Feu{\ss}ner, Hubertus  and
      Minker, Wolfgang  and
      Ultes, Stefan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1118",
}
@inproceedings{shirai-fukuoka-2018-jaist,
    title = "{JAIST} Annotated Corpus of Free Conversation",
    author = "Shirai, Kiyoaki  and
      Fukuoka, Tomotaka",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1119",
}
@inproceedings{petukhova-etal-2018-metalogue,
    title = "The Metalogue Debate Trainee Corpus: Data Collection and Annotations",
    author = "Petukhova, Volha  and
      Malchanau, Andrei  and
      Oualil, Youssef  and
      Klakow, Dietrich  and
      Luz, Saturnino  and
      Haider, Fasih  and
      Campbell, Nick  and
      Koryzis, Dimitris  and
      Spiliotopoulos, Dimitris  and
      Albert, Pierre  and
      Linz, Nicklas  and
      Alexandersson, Jan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1120",
}
@inproceedings{malchanau-etal-2018-towards,
    title = "Towards Continuous Dialogue Corpus Creation: writing to corpus and generating from it",
    author = "Malchanau, Andrei  and
      Petukhova, Volha  and
      Bunt, Harry",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1121",
}
@inproceedings{liesenfeld-2018-mycancor,
    title = "{MYC}an{C}or: A Video Corpus of spoken {M}alaysian {C}antonese",
    author = "Liesenfeld, Andreas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1122",
}
@inproceedings{shore-etal-2018-kth,
    title = "{KTH} Tangrams: A Dataset for Research on Alignment and Conceptual Pacts in Task-Oriented Dialogue",
    author = "Shore, Todd  and
      Androulakaki, Theofronia  and
      Skantze, Gabriel",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1123",
}
@inproceedings{pragst-etal-2018-vector,
    title = "On the Vector Representation of Utterances in Dialogue Context",
    author = "Pragst, Louisa  and
      Rach, Niklas  and
      Minker, Wolfgang  and
      Ultes, Stefan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1124",
}
@inproceedings{garcia-sardina-etal-2018-es,
    title = "{ES}-Port: a Spontaneous Spoken Human-Human Technical Support Corpus for Dialogue Research in {S}panish",
    author = "Garc{\'\i}a-Sardi{\~n}a, Laura  and
      Serras, Manex  and
      del Pozo, Arantza",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1125",
}
@inproceedings{dermouche-pelachaud-2018-analysis,
    title = "From analysis to modeling of engagement as sequences of multimodal behaviors",
    author = "Dermouche, Soumia  and
      Pelachaud, Catherine",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1126",
}
@inproceedings{barbaresi-2018-corpus,
    title = "A corpus of {G}erman political speeches from the 21st century",
    author = "Barbaresi, Adrien",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1127",
}
@inproceedings{frank-ivanovic-2018-building,
    title = "Building Literary Corpora for Computational Literary Analysis - A Prototype to Bridge the Gap between {CL} and {DH}",
    author = "Frank, Andrew  and
      Ivanovic, Christine",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1128",
}
@inproceedings{mcnew-etal-2018-towards,
    title = "Towards faithfully visualizing global linguistic diversity",
    author = "McNew, Garland  and
      Derungs, Curdin  and
      Moran, Steven",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1129",
}
@inproceedings{blatte-blessing-2018-germaparl,
    title = "The {G}erma{P}arl Corpus of Parliamentary Protocols",
    author = {Bl{\"a}tte, Andreas  and
      Blessing, Andre},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1130",
}
@inproceedings{ek-etal-2018-identifying,
    title = "Identifying Speakers and Addressees in Dialogues Extracted from Literary Fiction",
    author = {Ek, Adam  and
      Wir{\'e}n, Mats  and
      {\"O}stling, Robert  and
      N. Bj{\"o}rkenstam, Kristina  and
      Grigonyt{\.e}, Gintar{\.e}  and
      Gustafson Capkov{\'a}, Sofia},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1131",
}
@inproceedings{chen-ma-2018-word,
    title = "Word Embedding Evaluation Datasets and {W}ikipedia Title Embedding for {C}hinese",
    author = "Chen, Chi-Yen  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1132",
}
@inproceedings{karima-smaili-2018-automatic,
    title = "An Automatic Learning of an {A}lgerian Dialect Lexicon by using Multilingual Word Embeddings",
    author = {Karima, Abidi  and
      Sma{\"\i}li, Kamel},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1133",
}
@inproceedings{broad-etal-2018-candidate,
    title = "Candidate Ranking for Maintenance of an Online Dictionary",
    author = "Broad, Claire  and
      Langone, Helen  and
      Brizan, David Guy",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1134",
}
@inproceedings{sharoff-2018-language,
    title = "Language adaptation experiments via cross-lingual embeddings for related languages",
    author = "Sharoff, Serge",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1135",
}
@inproceedings{uresova-etal-2018-tools,
    title = "Tools for Building an Interlinked Synonym Lexicon Network",
    author = "Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}ov{\'a}, Eva  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1136",
}
@inproceedings{halpern-2018-large,
    title = "Very Large-Scale Lexical Resources to Enhance {C}hinese and {J}apanese Machine Translation",
    author = "Halpern, Jack",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1137",
}
@inproceedings{hamalainen-etal-2018-combining,
    title = "Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages",
    author = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Tarvainen, Liisa Lotta  and
      Rueter, Jack},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1138",
}
@inproceedings{yang-etal-2018-transfer,
    title = "Transfer of Frames from {E}nglish {F}rame{N}et to Construct {C}hinese {F}rame{N}et: A Bilingual Corpus-Based Approach",
    author = "Yang, Tsung-Han  and
      Huang, Hen-Hsen  and
      Yen, An-Zi  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1139",
}
@inproceedings{durlich-francois-2018-efllex,
    title = "{EFLL}ex: A Graded Lexical Resource for Learners of {E}nglish as a Foreign Language",
    author = {D{\"u}rlich, Luise  and
      Fran{\c{c}}ois, Thomas},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1140",
}
@inproceedings{jauregi-unanue-etal-2018-english,
    title = "{E}nglish-{B}asque Statistical and Neural Machine Translation",
    author = "Jauregi Unanue, Inigo  and
      Garmendia Arratibel, Lierni  and
      Zare Borzeshi, Ehsan  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1141",
}
@inproceedings{macketanz-etal-2018-tq,
    title = "{TQ}-{A}uto{T}est {--} An Automated Test Suite for (Machine) Translation Quality",
    author = "Macketanz, Vivien  and
      Ai, Renlong  and
      Burchardt, Aljoscha  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1142",
}
@inproceedings{zhao-etal-2018-exploiting,
    title = "Exploiting Pre-Ordering for Neural Machine Translation",
    author = "Zhao, Yang  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1143",
}
@inproceedings{choi-etal-2018-improving,
    title = "Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages",
    author = "Choi, Gyu-Hyeon  and
      Shin, Jong-Hun  and
      Kim, Young-Kil",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1144",
}
@inproceedings{dou-etal-2018-dynamic,
    title = "Dynamic Oracle for Neural Machine Translation in Decoding Phase",
    author = "Dou, Zi-Yi  and
      Zhou, Hao  and
      Huang, Shu-Jian  and
      Dai, Xin-Yu  and
      Chen, Jia-Jun",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1145",
}
@inproceedings{li-etal-2018-one,
    title = "One Sentence One Model for Neural Machine Translation",
    author = "Li, Xiaoqing  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1146",
}
@inproceedings{inoue-etal-2018-parallel,
    title = "A Parallel Corpus of {A}rabic-{J}apanese News Articles",
    author = "Inoue, Go  and
      Habash, Nizar  and
      Matsumoto, Yuji  and
      Aoyama, Hiroyuki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1147",
}
@inproceedings{fadaee-etal-2018-examining,
    title = "Examining the Tip of the Iceberg: A Data Set for Idiom Translation",
    author = "Fadaee, Marzieh  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1148",
}
@inproceedings{arcan-etal-2018-automatic,
    title = "Automatic Enrichment of Terminological Resources: the {IATE} {RDF} Example",
    author = "Arcan, Mihael  and
      Montiel-Ponsoda, Elena  and
      McCrae, John P.  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1149",
}
@inproceedings{wu-yarowsky-2018-comparative,
    title = "A Comparative Study of Extremely Low-Resource Transliteration of the World{'}s Languages",
    author = "Wu, Winston  and
      Yarowsky, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1150",
}
@inproceedings{kumar-etal-2018-translating,
    title = "Translating Web Search Queries into Natural Language Questions",
    author = "Kumar, Adarsh  and
      Dandapat, Sandipan  and
      Chordia, Sushil",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1151",
}
@inproceedings{sakaizawa-komachi-2018-construction,
    title = "Construction of a {J}apanese Word Similarity Dataset",
    author = "Sakaizawa, Yuya  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1152",
}
@inproceedings{majewska-etal-2018-acquiring,
    title = "Acquiring Verb Classes Through Bottom-Up Semantic Verb Clustering",
    author = "Majewska, Olga  and
      McCarthy, Diana  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1153",
}
@inproceedings{shi-etal-2018-constructing,
    title = "Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense",
    author = "Shi, Haoyue  and
      Wang, Xihao  and
      Sun, Yuqi  and
      Hu, Junfeng",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1154",
}
@inproceedings{haider-2018-urdu,
    title = "{U}rdu Word Embeddings",
    author = "Haider, Samar",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1155",
}
@inproceedings{hasegawa-etal-2018-social,
    title = "Social Image Tags as a Source of Word Embeddings: A Task-oriented Evaluation",
    author = "Hasegawa, Mika  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1156",
}
@inproceedings{anchieta-pardo-2018-towards,
    title = "Towards {AMR}-{BR}: A {S}em{B}ank for {B}razilian {P}ortuguese Language",
    author = "Anchi{\^e}ta, Rafael  and
      Pardo, Thiago",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1157",
}
@inproceedings{piao-etal-2018-towards,
    title = "Towards a {W}elsh Semantic Annotation System",
    author = "Piao, Scott  and
      Rayson, Paul  and
      Knight, Dawn  and
      Watkins, Gareth",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1158",
}
@inproceedings{marzinotto-etal-2018-semantic,
    title = "Semantic Frame Parsing for Information Extraction : the {CALOR} corpus",
    author = "Marzinotto, Gabriel  and
      Auguste, Jeremy  and
      Bechet, Frederic  and
      Damnati, Geraldine  and
      Nasr, Alexis",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1159",
}
@inproceedings{ahrens-etal-2018-using,
    title = "Using a Corpus of {E}nglish and {C}hinese Political Speeches for Metaphor Analysis",
    author = "Ahrens, Kathleen  and
      Zeng, Huiheng  and
      Wong, Shun-han Rebekah",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1160",
}
@inproceedings{sequeira-etal-2018-multi,
    title = "A Multi- versus a Single-classifier Approach for the Identification of Modality in the {P}ortuguese Language",
    author = "Sequeira, Jo{\~a}o  and
      Gon{\c{c}}alves, Teresa  and
      Quaresma, Paulo  and
      Mendes, Am{\'a}lia  and
      Hendrickx, Iris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1161",
}
@inproceedings{suzuki-etal-2018-words,
    title = "All-words Word Sense Disambiguation Using Concept Embeddings",
    author = "Suzuki, Rui  and
      Komiya, Kanako  and
      Asahara, Masayuki  and
      Sasaki, Minoru  and
      Shinnou, Hiroyuki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1162",
}
@inproceedings{melacci-etal-2018-enhancing,
    title = "Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources",
    author = "Melacci, Stefano  and
      Globo, Achille  and
      Rigutini, Leonardo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1163",
}
@inproceedings{ustalov-etal-2018-unsupervised-word,
    title = "An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages",
    author = "Ustalov, Dmitry  and
      Teslenko, Denis  and
      Panchenko, Alexander  and
      Chernoskutov, Mikhail  and
      Biemann, Chris  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1164",
}
@inproceedings{han-etal-2018-unsupervised,
    title = "Unsupervised {K}orean Word Sense Disambiguation using {C}ore{N}et",
    author = "Han, Kijong  and
      Nam, Sangha  and
      Kim, Jiseong  and
      Hahm, Younggyun  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1165",
}
@inproceedings{vial-etal-2018-ufsac,
    title = "{UFSAC}: Unification of Sense Annotated Corpora and Tools",
    author = {Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin  and
      Schwab, Didier},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1166",
}
@inproceedings{remus-biemann-2018-retrofitting,
    title = "Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities",
    author = "Remus, Steffen  and
      Biemann, Chris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1167",
}
@inproceedings{uslu-etal-2018-fastsense,
    title = "{F}ast{S}ense: An Efficient Word Sense Disambiguation Classifier",
    author = "Uslu, Tolga  and
      Mehler, Alexander  and
      Baumartz, Daniel  and
      Hemati, Wahed",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1168",
}
@inproceedings{forbes-etal-2018-text,
    title = "Text Annotation Graphs: Annotating Complex Natural Language Phenomena",
    author = "Forbes, Angus  and
      Lee, Kristine  and
      Hahn-Powell, Gus  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1169",
}
@inproceedings{reimerink-leon-arauz-2018-manzanilla,
    title = "{M}anzanilla: An Image Annotation Tool for {TKB} Building",
    author = "Reimerink, Arianne  and
      Le{\'o}n-Ara{\'u}z, Pilar",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1170",
}
@inproceedings{fam-lepage-2018-tools,
    title = "Tools for The Production of Analogical Grids and a Resource of N-gram Analogical Grids in 11 Languages",
    author = "Fam, Rashel  and
      Lepage, Yves",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1171",
}
@inproceedings{navarretta-2018-automatic,
    title = "The Automatic Annotation of the Semiotic Type of Hand Gestures in {O}bama{'} s Humorous Speeches",
    author = "Navarretta, Costanza",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1172",
}
@inproceedings{alghamdi-diab-2018-wasa,
    title = "{WASA}: A Web Application for Sequence Annotation",
    author = "AlGhamdi, Fahad  and
      Diab, Mona",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1173",
}
@inproceedings{yamazaki-etal-2018-annotation,
    title = "Annotation and Quantitative Analysis of Speaker Information in Novel Conversation Sentences in {J}apanese",
    author = "Yamazaki, Makoto  and
      Miyazaki, Yumi  and
      Kashino, Wakako",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1174",
}
@inproceedings{shindo-etal-2018-pdfanno,
    title = "{PDFA}nno: a Web-based Linguistic Annotation Tool for {PDF} Documents",
    author = "Shindo, Hiroyuki  and
      Munesada, Yohei  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1175",
}
@inproceedings{gartner-kuhn-2018-lightweight,
    title = "A Lightweight Modeling Middleware for Corpus Processing",
    author = {G{\"a}rtner, Markus  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1176",
}
@inproceedings{nazarenko-etal-2018-annotation,
    title = "An Annotation Language for Semantic Search of Legal Sources",
    author = "Nazarenko, Adeline  and
      Levy, Fran{\c{c}}ois  and
      Wyner, Adam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1177",
}
@inproceedings{van-son-etal-2018-resource,
    title = "Resource Interoperability for Sustainable Benchmarking: The Case of Events",
    author = "van Son, Chantal  and
      Inel, Oana  and
      Morante, Roser  and
      Aroyo, Lora  and
      Vossen, Piek",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1178",
}
@inproceedings{mohtaj-etal-2018-parsivar,
    title = "{P}arsivar: A Language Processing Toolkit for {P}ersian",
    author = "Mohtaj, Salar  and
      Roshanfekr, Behnam  and
      Zafarian, Atefeh  and
      Asghari, Habibollah",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1179",
}
@inproceedings{moreau-vogel-2018-multilingual,
    title = "Multilingual Word Segmentation: Training Many Language-Specific Tokenizers Smoothly Thanks to the {U}niversal {D}ependencies Corpus",
    author = "Moreau, Erwan  and
      Vogel, Carl",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1180",
}
@inproceedings{mubarak-2018-build,
    title = "Build Fast and Accurate Lemmatization for {A}rabic",
    author = "Mubarak, Hamdy",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1181",
}
@inproceedings{pryzant-etal-2018-jesc,
    title = "{JESC}: {J}apanese-{E}nglish Subtitle Corpus",
    author = "Pryzant, Reid  and
      Chung, Youngjoo  and
      Jurafsky, Dan  and
      Britz, Denny",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1182",
}
@inproceedings{ramos-etal-2018-building,
    title = "Building a Corpus for Personality-dependent Natural Language Understanding and Generation",
    author = "Ramos, Ricelli  and
      Neto, Georges  and
      Silva, Barbara  and
      Monteiro, Danielle  and
      Paraboni, Ivandr{\'e}  and
      Dias, Rafael",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1183",
}
@inproceedings{schraagen-etal-2018-linguistic,
    title = "Linguistic and Sociolinguistic Annotation of 17th Century {D}utch Letters",
    author = "Schraagen, Marijn  and
      Dietz, Feike  and
      van Koppen, Marjo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1184",
}
@inproceedings{maruyama-yamamoto-2018-simplified,
    title = "Simplified Corpus with Core Vocabulary",
    author = "Maruyama, Takumi  and
      Yamamoto, Kazuhide",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1185",
}
@inproceedings{huang-wu-2018-pragmatic,
    title = "A Pragmatic Approach for Classical {C}hinese Word Segmentation",
    author = "Huang, Shilei  and
      Wu, Jiangqin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1186",
}
@inproceedings{mathias-bhattacharyya-2018-asap,
    title = "{ASAP}++: Enriching the {ASAP} Automated Essay Grading Dataset with Essay Attribute Scores",
    author = "Mathias, Sandeep  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1187",
}
@inproceedings{sabeti-etal-2018-mirastext,
    title = "{M}iras{T}ext: An Automatically Generated Text Corpus for {P}ersian",
    author = "Sabeti, Behnam  and
      Abedi Firouzjaee, Hossein  and
      Janalizadeh Choobbasti, Ali  and
      Mortazavi Najafabadi, S.H.E.  and
      Vaheb, Amir",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1188",
}
@inproceedings{barbu-mititelu-etal-2018-reference,
    title = "The Reference Corpus of the Contemporary {R}omanian Language ({C}o{R}o{L}a)",
    author = "Barbu Mititelu, Verginica  and
      Tufi{\textcommabelow{s}}, Dan  and
      Irimia, Elena",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1189",
}
@inproceedings{preum-etal-2018-corpus,
    title = "A Corpus of Drug Usage Guidelines Annotated with Type of Advice",
    author = "Preum, Sarah Masud  and
      Parvez, Md. Rizwan  and
      Chang, Kai-Wei  and
      Stankovic, John",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1190",
}
@inproceedings{mitrofan-tufis-2018-bioro,
    title = "{B}io{R}o: The Biomedical Corpus for the {R}omanian Language",
    author = "Mitrofan, Maria  and
      Tufi{\c{s}}, Dan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1191",
}
@inproceedings{wood-etal-2018-comparison,
    title = "A Comparison Of Emotion Annotation Schemes And A New Annotated Data Set",
    author = "Wood, Ian  and
      McCrae, John P.  and
      Andryushechkin, Vladimir  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1192",
}
@inproceedings{khandelwal-etal-2018-humor,
    title = "Humor Detection in {E}nglish-{H}indi Code-Mixed Social Media Content : Corpus and Baseline System",
    author = "Khandelwal, Ankush  and
      Swami, Sahil  and
      Akhtar, Syed S.  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1193",
}
@inproceedings{yoshino-etal-2018-dialogue,
    title = "Dialogue Scenario Collection of Persuasive Dialogue with Emotional Expressions via Crowdsourcing",
    author = "Yoshino, Koichiro  and
      Ishikawa, Yoko  and
      Mizukami, Masahiro  and
      Suzuki, Yu  and
      Sakti, Sakriani  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1194",
}
@inproceedings{eskander-2018-sentiarabic,
    title = "{S}enti{A}rabic: A Sentiment Analyzer for {S}tandard {A}rabic",
    author = "Eskander, Ramy",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1195",
}
@inproceedings{fedotov-etal-2018-contextual,
    title = "Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition",
    author = "Fedotov, Dmitrii  and
      Ivanko, Denis  and
      Sidorov, Maxim  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1196",
}
@inproceedings{mohammad-kiritchenko-2018-wikiart,
    title = "{W}iki{A}rt Emotions: An Annotated Dataset of Emotions Evoked by Art",
    author = "Mohammad, Saif  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1197",
}
@inproceedings{rodrigues-etal-2018-arabic,
    title = "{A}rabic Data Science Toolkit: An {API} for {A}rabic Language Feature Extraction",
    author = "Rodrigues, Paul  and
      Novak, Valerie  and
      Rytting, C. Anton  and
      Yelle, Julie  and
      Boutz, Jennifer",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1198",
}
@inproceedings{tafreshi-diab-2018-sentence,
    title = "Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus",
    author = "Tafreshi, Shabnam  and
      Diab, Mona",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1199",
}
@inproceedings{kokkinakis-etal-2018-swedish,
    title = "A {S}wedish Cookie-Theft Corpus",
    author = "Kokkinakis, Dimitrios  and
      Lundholm Fors, Kristina  and
      Fraser, Kathleen  and
      Nordlund, Arto",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1200",
}
@inproceedings{lohr-etal-2018-sharing,
    title = "Sharing Copies of Synthetic Clinical Corpora without Physical Distribution {---} A Case Study to Get Around {IPR}s and Privacy Constraints Featuring the {G}erman {JSYNCC} Corpus",
    author = "Lohr, Christina  and
      Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1201",
}
@inproceedings{eckart-de-castilho-etal-2018-legal,
    title = "A Legal Perspective on Training Models for Natural Language Processing",
    author = "Eckart de Castilho, Richard  and
      Dore, Giulia  and
      Margoni, Thomas  and
      Labropoulou, Penny  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1202",
}
@inproceedings{del-gratta-etal-2018-lremap,
    title = "{LREM}ap, a Song of Resources and Evaluation",
    author = "Del Gratta, Riccardo  and
      Goggi, Sara  and
      Pardelli, Gabriella  and
      Calzolari, Nicoletta",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1203",
}
@inproceedings{van-den-heuvel-etal-2018-metadata,
    title = "Metadata Collection Records for Language Resources",
    author = "van den Heuvel, Henk  and
      Komen, Erwin  and
      Oostdijk, Nelleke",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1204",
}
@inproceedings{piperidis-etal-2018-managing,
    title = "Managing Public Sector Data for Multilingual Applications Development",
    author = "Piperidis, Stelios  and
      Labropoulou, Penny  and
      Deligiannis, Miltos  and
      Giagkou, Maria",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1205",
}
@inproceedings{hinrichs-etal-2018-bridging,
    title = "Bridging the {LAPPS} {G}rid and {CLARIN}",
    author = "Hinrichs, Erhard  and
      Ide, Nancy  and
      Pustejovsky, James  and
      Haji{\v{c}}, Jan  and
      Hinrichs, Marie  and
      Elahi, Mohammad Fazleh  and
      Suderman, Keith  and
      Verhagen, Marc  and
      Rim, Kyeongmin  and
      Stra{\v{n}}{\'a}k, Pavel  and
      Mi{\v{s}}utka, Jozef",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1206",
}
@inproceedings{hsieh-etal-2018-fluid,
    title = "Fluid Annotation: A Granularity-aware Annotation Tool for {C}hinese Word Fluidity",
    author = "Hsieh, Shu-Kai  and
      Tseng, Yu-Hsiang  and
      Lee, Chih-Yao  and
      Chiang, Chiung-Yu",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1207",
}
@inproceedings{varadi-etal-2018-e,
    title = "{E}-magyar {--} A Digital Language Processing System",
    author = "V{\'a}radi, Tam{\'a}s  and
      Simon, Eszter  and
      Sass, B{\'a}lint  and
      Mittelholcz, Iv{\'a}n  and
      Nov{\'a}k, Attila  and
      Indig, Bal{\'a}zs  and
      Farkas, Rich{\'a}rd  and
      Vincze, Veronika",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1208",
}
@inproceedings{niekler-etal-2018-ilcm,
    title = "{ILCM} - A Virtual Research Infrastructure for Large-Scale Qualitative Data",
    author = "Niekler, Andreas  and
      Bleier, Arnim  and
      Kahmann, Christian  and
      Posch, Lisa  and
      Wiedemann, Gregor  and
      Erdogan, Kenan  and
      Heyer, Gerhard  and
      Strohmaier, Markus",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1209",
}
@inproceedings{fiser-etal-2018-clarins,
    title = "{CLARIN}{'}s Key Resource Families",
    author = "Fi{\v{s}}er, Darja  and
      Lenardi{\v{c}}, Jakob  and
      Erjavec, Toma{\v{z}}",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1210",
}
@inproceedings{sales-etal-2018-indra,
    title = "{I}ndra: A Word Embedding and Semantic Relatedness Server",
    author = "Sales, Juliano Efson  and
      Souza, Leonardo  and
      Barzegar, Siamak  and
      Davis, Brian  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1211",
}
@inproceedings{abrami-mehler-2018-uima,
    title = "A {UIMA} Database Interface for Managing {NLP}-related Text Annotations",
    author = "Abrami, Giuseppe  and
      Mehler, Alexander",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1212",
}
@inproceedings{losch-etal-2018-european,
    title = "{E}uropean Language Resource Coordination: Collecting Language Resources for Public Sector Multilingual Information Management",
    author = {L{\"o}sch, Andrea  and
      Mapelli, Val{\'e}rie  and
      Piperidis, Stelios  and
      Vasi{\c{l}}jevs, Andrejs  and
      Smal, Lilli  and
      Declerck, Thierry  and
      Schnur, Eileen  and
      Choukri, Khalid  and
      van Genabith, Josef},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1213",
}
@inproceedings{pinnis-etal-2018-tilde,
    title = "Tilde {MT} Platform for Developing Client Specific {MT} Solutions",
    author = "Pinnis, M{\=a}rcis  and
      Vasi{\c{l}}jevs, Andrejs  and
      Kalni{\c{n}}{\v{s}}, Rihards  and
      Rozis, Roberts  and
      Skadi{\c{n}}{\v{s}}, Raivis  and
      {\v{S}}ics, Valters",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1214",
}
@inproceedings{gorman-etal-2018-improving,
    title = "Improving homograph disambiguation with supervised machine learning",
    author = "Gorman, Kyle  and
      Mazovetskiy, Gleb  and
      Nikolaev, Vitaly",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1215",
}
@inproceedings{chua-etal-2018-text,
    title = "Text Normalization Infrastructure that Scales to Hundreds of Language Varieties",
    author = "Chua, Mason  and
      van Esch, Daan  and
      Coccaro, Noah  and
      Cho, Eunjoon  and
      Bhandari, Sujeet  and
      Jia, Libin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1216",
}
@inproceedings{nastase-etal-2018-demodify,
    title = "{D}e{M}odify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion",
    author = "Nastase, Vivi  and
      Fritz, Devon  and
      Frank, Anette",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1217",
}
@inproceedings{creutz-2018-open,
    title = "Open Subtitles Paraphrase Corpus for Six Languages",
    author = "Creutz, Mathias",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1218",
}
@inproceedings{batanovic-etal-2018-fine,
    title = "Fine-grained Semantic Textual Similarity for {S}erbian",
    author = "Batanovi{\'c}, Vuk  and
      Cvetanovi{\'c}, Milo{\v{s}}  and
      Nikoli{\'c}, Bo{\v{s}}ko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1219",
}
@inproceedings{arase-tsujii-2018-spade,
    title = "{SPADE}: Evaluation Dataset for Monolingual Phrase Alignment",
    author = "Arase, Yuki  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1220",
}
@inproceedings{kovatchev-etal-2018-etpc,
    title = "{ETPC} - A Paraphrase Identification Corpus Annotated with Extended Paraphrase Typology and Negation",
    author = "Kovatchev, Venelin  and
      Mart{\'\i}, M. Ant{\`o}nia  and
      Salam{\'o}, Maria",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1221",
}
@inproceedings{schulder-etal-2018-introducing,
    title = "Introducing a Lexicon of Verbal Polarity Shifters for {E}nglish",
    author = {Schulder, Marc  and
      Wiegand, Michael  and
      Ruppenhofer, Josef  and
      K{\"o}ser, Stephanie},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1222",
}
@inproceedings{nakamura-kawahara-2018-jfckb,
    title = "{JFCKB}: {J}apanese Feature Change Knowledge Base",
    author = "Nakamura, Tetsuaki  and
      Kawahara, Daisuke",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1223",
}
@inproceedings{wojatzki-etal-2018-quantifying,
    title = "Quantifying Qualitative Data for Understanding Controversial Issues",
    author = "Wojatzki, Michael  and
      Mohammad, Saif  and
      Zesch, Torsten  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1224",
}
@inproceedings{gambino-etal-2018-distribution,
    title = "Distribution of Emotional Reactions to News Articles in {T}witter",
    author = "Gambino, Omar Ju{\'a}rez  and
      Calvo, Hiram  and
      Garc{\'\i}a-Mendoza, Consuelo-Varinia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1225",
}
@inproceedings{kumar-etal-2018-aggression,
    title = "Aggression-annotated Corpus of {H}indi-{E}nglish Code-mixed Data",
    author = "Kumar, Ritesh  and
      Reganti, Aishwarya N.  and
      Bhatia, Akshit  and
      Maheshwari, Tushar",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1226",
}
@inproceedings{uresova-etal-2018-creating,
    title = "Creating a Verb Synonym Lexicon Based on a Parallel Corpus",
    author = "Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}ov{\'a}, Eva  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1227",
}
@inproceedings{nooralahzadeh-etal-2018-evaluation,
    title = "Evaluation of Domain-specific Word Embeddings using Knowledge Resources",
    author = "Nooralahzadeh, Farhad  and
      {\O}vrelid, Lilja  and
      L{\o}nning, Jan Tore",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1228",
}
@inproceedings{liebeskind-etal-2018-automatic,
    title = "Automatic Thesaurus Construction for {M}odern {H}ebrew",
    author = "Liebeskind, Chaya  and
      Dagan, Ido  and
      Schler, Jonathan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1229",
}
@inproceedings{kim-etal-2018-automatic,
    title = "Automatic {W}ordnet Mapping: from {C}ore{N}et to {P}rinceton {W}ord{N}et",
    author = "Kim, Jiseong  and
      Hahm, Younggyun  and
      Kwon, Sunggoo  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1230",
}
@inproceedings{ogorman-etal-2018-new,
    title = "The New {P}ropbank: Aligning {P}ropbank with {AMR} through {POS} Unification",
    author = "O{'}Gorman, Tim  and
      Pradhan, Sameer  and
      Palmer, Martha  and
      Bonn, Julia  and
      Conger, Katie  and
      Gung, James",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1231",
}
@inproceedings{sloos-etal-2018-boarnsterhim,
    title = "The Boarnsterhim Corpus: A Bilingual {F}risian-{D}utch Panel and Trend Study",
    author = "Sloos, Marjoleine  and
      Drenth, Eduard  and
      Heeringa, Wilbert",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1232",
}
@inproceedings{djegdjiga-etal-2018-french,
    title = "The {F}rench-{A}lgerian Code-Switching Triggered audio corpus ({FACST})",
    author = "Djegdjiga, Amazouz  and
      Adda-Decker, Martine  and
      Lamel, Lori",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1233",
}
@inproceedings{goldman-etal-2018-strategies,
    title = "Strategies and Challenges for Crowdsourcing Regional Dialect Perception Data for {S}wiss {G}erman and {S}wiss {F}rench",
    author = "Goldman, Jean-Philippe  and
      Clematide, Simon  and
      Avanzi, Mathieu  and
      Tandler, Raphael",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1234",
}
@inproceedings{pandey-etal-2018-phonetically,
    title = "Phonetically Balanced Code-Mixed Speech Corpus for {H}indi-{E}nglish Automatic Speech Recognition",
    author = "Pandey, Ayushi  and
      Srivastava, Brij Mohan Lal  and
      Kumar, Rohit  and
      Nellore, Bhanu Teja  and
      Teja, Kasi Sai  and
      Gangashetty, Suryakanth V.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1235",
}
@inproceedings{liu-etal-2018-chinese,
    title = "{C}hinese-{P}ortuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts",
    author = "Liu, Siyou  and
      Wang, Longyue  and
      Liu, Chao-Hong",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1236",
}
@inproceedings{ulinski-etal-2018-evaluating,
    title = "Evaluating the {W}ords{E}ye Text-to-Scene System: Imaginative and Realistic Sentences",
    author = "Ulinski, Morgan  and
      Coyne, Bob  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1237",
}
@inproceedings{broux-etal-2018-computer,
    title = "Computer-assisted Speaker Diarization: How to Evaluate Human Corrections",
    author = "Broux, Pierre-Alexandre  and
      Doukhan, David  and
      Petitrenaud, Simon  and
      Meignier, Sylvain  and
      Carrive, Jean",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1238",
}
@inproceedings{tsuchiya-2018-performance,
    title = "Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment",
    author = "Tsuchiya, Masatoshi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1239",
}
@inproceedings{svoboda-beliga-2018-evaluation,
    title = "Evaluation of {C}roatian Word Embeddings",
    author = "Svoboda, Luk{\'a}{\v{s}}  and
      Beliga, Slobodan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1240",
}
@inproceedings{bayomi-lawless-2018-c,
    title = "{C}-{HTS}: A Concept-based Hierarchical Text Segmentation approach",
    author = "Bayomi, Mostafa  and
      Lawless, S{\'e}amus",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1241",
}
@inproceedings{blodgett-schneider-2018-semantic,
    title = "Semantic Supersenses for {E}nglish Possessives",
    author = "Blodgett, Austin  and
      Schneider, Nathan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1242",
}
@inproceedings{parde-nielsen-2018-corpus,
    title = "A Corpus of Metaphor Novelty Scores for Syntactically-Related Word Pairs",
    author = "Parde, Natalie  and
      Nielsen, Rodney",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1243",
}
@inproceedings{panchenko-etal-2018-improving,
    title = "Improving Hypernymy Extraction with Distributional Semantic Classes",
    author = "Panchenko, Alexander  and
      Ustalov, Dmitry  and
      Faralli, Stefano  and
      Ponzetto, Simone P.  and
      Biemann, Chris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1244",
}
@inproceedings{getman-etal-2018-laying,
    title = "Laying the Groundwork for Knowledge Base Population: Nine Years of Linguistic Resources for {TAC} {KBP}",
    author = "Getman, Jeremy  and
      Ellis, Joe  and
      Strassel, Stephanie  and
      Song, Zhiyi  and
      Tracey, Jennifer",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1245",
}
@inproceedings{mandya-etal-2018-dataset,
    title = "A Dataset for Inter-Sentence Relation Extraction using Distant Supervision",
    author = "Mandya, Angrosh  and
      Bollegala, Danushka  and
      Coenen, Frans  and
      Atkinson, Katie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1246",
}
@inproceedings{naplava-etal-2018-diacritics,
    title = "Diacritics Restoration Using Neural Networks",
    author = "N{\'a}plava, Jakub  and
      Straka, Milan  and
      Stra{\v{n}}{\'a}k, Pavel  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1247",
}
@inproceedings{ion-etal-2018-ensemble,
    title = "Ensemble {R}omanian Dependency Parsing with Neural Networks",
    author = "Ion, Radu  and
      Irimia, Elena  and
      Barbu Mititelu, Verginica",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1248",
}
@inproceedings{baird-etal-2018-classifying,
    title = "Classifying Sluice Occurrences in Dialogue",
    author = "Baird, Austin  and
      Hamza, Anissa  and
      Hardt, Daniel",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1249",
}
@inproceedings{araki-etal-2018-collection,
    title = "Collection of Multimodal Dialog Data and Analysis of the Result of Annotation of Users{'} Interest Level",
    author = "Araki, Masahiro  and
      Tomimasu, Sayaka  and
      Nakano, Mikio  and
      Komatani, Kazunori  and
      Okada, Shogo  and
      Fujie, Shinya  and
      Sugiyama, Hiroaki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1250",
}
@inproceedings{lotz-etal-2018-recognizing,
    title = "Recognizing Behavioral Factors while Driving: A Real-World Multimodal Corpus to Monitor the Driver{'}s Affective State",
    author = "Lotz, Alicia  and
      Ihme, Klas  and
      Charnoz, Audrey  and
      Maroudis, Pantelis  and
      Dmitriev, Ivan  and
      Wendemuth, Andreas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1251",
}
@inproceedings{hsu-etal-2018-emotionlines,
    title = "{E}motion{L}ines: An Emotion Corpus of Multi-Party Conversations",
    author = "Hsu, Chao-Chun  and
      Chen, Sheng-Yeh  and
      Kuo, Chuan-Chun  and
      Huang, Ting-Hao  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1252",
}
@inproceedings{schabus-skowron-2018-academic,
    title = "Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website",
    author = "Schabus, Dietmar  and
      Skowron, Marcin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1253",
}
@inproceedings{funk-etal-2018-community,
    title = "Community-Driven Crowdsourcing: Data Collection with Local Developers",
    author = "Funk, Christina  and
      Tseng, Michael  and
      Rajakumar, Ravindran  and
      Ha, Linne",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1254",
}
@inproceedings{wibawa-etal-2018-building,
    title = "Building Open {J}avanese and {S}undanese Corpora for Multilingual Text-to-Speech",
    author = "Wibawa, Jaka Aris Eko  and
      Sarin, Supheakmungkol  and
      Li, Chenfang  and
      Pipatsrisawat, Knot  and
      Sodimana, Keshan  and
      Kjartansson, Oddur  and
      Gutkin, Alexander  and
      Jansche, Martin  and
      Ha, Linne",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1255",
}
@inproceedings{hartmann-etal-2018-integrated,
    title = "An Integrated Representation of Linguistic and Social Functions of Code-Switching",
    author = "Hartmann, Silvana  and
      Choudhury, Monojit  and
      Bali, Kalika",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1256",
}
@inproceedings{park-cardie-2018-corpus,
    title = "A Corpus of e{R}ulemaking User Comments for Measuring Evaluability of Arguments",
    author = "Park, Joonsuk  and
      Cardie, Claire",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1257",
}
@inproceedings{musi-etal-2018-multi,
    title = "A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations",
    author = "Musi, Elena  and
      Stede, Manfred  and
      Kriese, Leonard  and
      Muresan, Smaranda  and
      Rocci, Andrea",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1258",
}
@inproceedings{hajicova-mirovsky-2018-discourse,
    title = "Discourse Coherence Through the Lens of an Annotated Text Corpus: A Case Study",
    author = "Haji{\v{c}}ov{\'a}, Eva  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1259",
}
@inproceedings{malmi-etal-2018-automatic,
    title = "Automatic Prediction of Discourse Connectives",
    author = "Malmi, Eric  and
      Pighin, Daniele  and
      Krause, Sebastian  and
      Kozhevnikov, Mikhail",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1260",
}
@inproceedings{tennage-etal-2018-handling,
    title = "Handling Rare Word Problem using Synthetic Training Data for {S}inhala and {T}amil Neural Machine Translation",
    author = "Tennage, Pasindu  and
      Sandaruwan, Prabath  and
      Thilakarathne, Malith  and
      Herath, Achini  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1261",
}
@inproceedings{marsico-etal-2018-bdproto,
    title = "{BDPROTO}: A Database of Phonological Inventories from Ancient and Reconstructed Languages",
    author = "Marsico, Egidio  and
      Flavier, Sebastien  and
      Verkerk, Annemarie  and
      Moran, Steven",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1262",
}
@inproceedings{wu-etal-2018-creating,
    title = "Creating a Translation Matrix of the {B}ible{'}s Names Across 591 Languages",
    author = "Wu, Winston  and
      Vyas, Nidhi  and
      Yarowsky, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1263",
}
@inproceedings{reddy-etal-2018-building,
    title = "Building a Word Segmenter for {S}anskrit Overnight",
    author = "Reddy, Vikas  and
      Krishna, Amrith  and
      Sharma, Vishnu  and
      Gupta, Prateek  and
      M R, Vineeth  and
      Goyal, Pawan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1264",
}
@inproceedings{griffitt-etal-2018-simple,
    title = "Simple Semantic Annotation and Situation Frames: Two Approaches to Basic Text Understanding in {LORELEI}",
    author = "Griffitt, Kira  and
      Tracey, Jennifer  and
      Bies, Ann  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1265",
}
@inproceedings{bonial-etal-2018-abstract,
    title = "{A}bstract {M}eaning {R}epresentation of Constructions: The More We Include, the Better the Representation",
    author = "Bonial, Claire  and
      Badarau, Bianca  and
      Griffitt, Kira  and
      Hermjakob, Ulf  and
      Knight, Kevin  and
      O{'}Gorman, Tim  and
      Palmer, Martha  and
      Schneider, Nathan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1266",
}
@inproceedings{van-noord-etal-2018-evaluating,
    title = "Evaluating Scoped Meaning Representations",
    author = "van Noord, Rik  and
      Abzianidze, Lasha  and
      Haagsma, Hessel  and
      Bos, Johan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1267",
}
@inproceedings{pasini-etal-2018-huge,
    title = "Huge Automatically Extracted Training-Sets for Multilingual Word {S}ense{D}isambiguation",
    author = "Pasini, Tommaso  and
      Elia, Francesco  and
      Navigli, Roberto",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1268",
}
@inproceedings{conneau-kiela-2018-senteval,
    title = "{S}ent{E}val: An Evaluation Toolkit for Universal Sentence Representations",
    author = "Conneau, Alexis  and
      Kiela, Douwe",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1269",
}
@inproceedings{neale-2018-survey,
    title = "A Survey on Automatically-Constructed {W}ord{N}ets and their Evaluation: Lexical and Word Embedding-based Approaches",
    author = "Neale, Steven",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1270",
}
@inproceedings{metaxas-etal-2018-linguistically,
    title = "Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition",
    author = "Metaxas, Dimitris  and
      Dilsizian, Mark  and
      Neidle, Carol",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1271",
}
@inproceedings{chen-etal-2018-conduct,
    title = "{CONDUCT}: An Expressive Conducting Gesture Dataset for Sound Control",
    author = "Chen, Lei  and
      Gibet, Sylvie  and
      Marteau, Camille",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1272",
}
@inproceedings{batra-etal-2018-neural,
    title = "Neural Caption Generation for News Images",
    author = "Batra, Vishwash  and
      He, Yulan  and
      Vogiatzis, George",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1273",
}
@inproceedings{kar-etal-2018-mpst,
    title = "{MPST}: A Corpus of Movie Plot Synopses with Tags",
    author = "Kar, Sudipta  and
      Maharjan, Suraj  and
      L{\'o}pez-Monroy, A. Pastor  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1274",
}
@inproceedings{lison-etal-2018-opensubtitles2018,
    title = "{O}pen{S}ubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora",
    author = {Lison, Pierre  and
      Tiedemann, J{\"o}rg  and
      Kouylekov, Milen},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1275",
}
@inproceedings{ren-etal-2018-building,
    title = "Building an Ellipsis-aware {C}hinese Dependency Treebank for Web Text",
    author = "Ren, Xuancheng  and
      Sun, Xu  and
      Wen, Ji  and
      Wei, Bingzhen  and
      Zhan, Weidong  and
      Zhang, Zhiyuan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1276",
}
@inproceedings{goutte-etal-2018-eurogames16,
    title = "{E}uro{G}ames16: Evaluating Change Detection in Online Conversation",
    author = "Goutte, Cyril  and
      Wang, Yunli  and
      Liao, Fangming  and
      Zanussi, Zachary  and
      Larkin, Samuel  and
      Grinberg, Yuri",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1277",
}
@inproceedings{gupta-etal-2018-deep,
    title = "A Deep Neural Network based Approach for Entity Extraction in Code-Mixed {I}ndian Social Media Text",
    author = "Gupta, Deepak  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1278",
}
@inproceedings{sanguinetti-etal-2018-postwita,
    title = "{P}o{STWITA}-{UD}: an {I}talian {T}witter Treebank in {U}niversal {D}ependencies",
    author = "Sanguinetti, Manuela  and
      Bosco, Cristina  and
      Lavelli, Alberto  and
      Mazzei, Alessandro  and
      Antonelli, Oronzo  and
      Tamburini, Fabio",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1279",
}
@inproceedings{doudagiri-etal-2018-annotating,
    title = "Annotating If the Authors of a Tweet are Located at the Locations They Tweet About",
    author = "Doudagiri, Vivek  and
      Vempala, Alakananda  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1280",
}
@inproceedings{kisler-schiel-2018-mocca,
    title = "{MOCCA}: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation",
    author = "Kisler, Thomas  and
      Schiel, Florian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1281",
}
@inproceedings{bunt-etal-2018-towards,
    title = "Towards an {ISO} Standard for the Annotation of Quantification",
    author = "Bunt, Harry  and
      Pustejovsky, James  and
      Lee, Kiyong",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1282",
}
@inproceedings{banski-etal-2018-lightweight,
    title = "Lightweight Grammatical Annotation in the {TEI}: New Perspectives",
    author = "Ba{\'n}ski, Piotr  and
      Haaf, Susanne  and
      Mueller, Martin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1283",
}
@inproceedings{rigouts-terryn-etal-2018-gold,
    title = "A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents",
    author = "Rigouts Terryn, Ayla  and
      Hoste, V{\'e}ronique  and
      Lefever, Els",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1284",
}
@inproceedings{zinn-etal-2018-handling,
    title = "Handling Big Data and Sensitive Data Using {EUDAT}{'}s Generic Execution Framework and the {W}eb{L}icht Workflow Engine.",
    author = "Zinn, Claus  and
      Qui, Wei  and
      Hinrichs, Marie  and
      Dima, Emanuel  and
      Chernov, Alexandr",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1285",
}
@inproceedings{panchenko-etal-2018-building,
    title = "Building a Web-Scale Dependency-Parsed Corpus from {C}ommon{C}rawl",
    author = "Panchenko, Alexander  and
      Ruppert, Eugen  and
      Faralli, Stefano  and
      Ponzetto, Simone P.  and
      Biemann, Chris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1286",
}
@inproceedings{asahara-etal-2018-universal,
    title = "{U}niversal {D}ependencies Version 2 for {J}apanese",
    author = "Asahara, Masayuki  and
      Kanayama, Hiroshi  and
      Tanaka, Takaaki  and
      Miyao, Yusuke  and
      Uematsu, Sumire  and
      Mori, Shinsuke  and
      Matsumoto, Yuji  and
      Omura, Mai  and
      Murawaki, Yugo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1287",
}
@inproceedings{das-stede-2018-developing,
    title = "Developing the {B}angla {RST} {D}iscourse {T}reebank",
    author = "Das, Debopam  and
      Stede, Manfred",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1288",
}
@inproceedings{wolinski-etal-2018-new,
    title = "A New Version of the Sk{\l}adnica Treebank of {P}olish Harmonised with the Walenty Valency Dictionary",
    author = "Woli{\'n}ski, Marcin  and
      Hajnicz, El{\.z}bieta  and
      Bartosiak, Tomasz",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1289",
}
@inproceedings{droganova-etal-2018-parse,
    title = "Parse Me if You Can: Artificial Treebanks for Parsing Experiments on Elliptical Constructions",
    author = "Droganova, Kira  and
      Zeman, Daniel  and
      Kanerva, Jenna  and
      Ginter, Filip",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1290",
}
@inproceedings{lango-etal-2018-semi,
    title = "Semi-Automatic Construction of Word-Formation Networks (for {P}olish and {S}panish)",
    author = "Lango, Mateusz  and
      {\v{S}}ev{\v{c}}{\'\i}kov{\'a}, Magda  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1291",
}
@inproceedings{sagot-2018-multilingual,
    title = "A multilingual collection of {C}o{NLL}-{U}-compatible morphological lexicons",
    author = "Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1292",
}
@inproceedings{kirov-etal-2018-unimorph,
    title = "{U}ni{M}orph 2.0: {U}niversal {M}orphology",
    author = {Kirov, Christo  and
      Cotterell, Ryan  and
      Sylak-Glassman, John  and
      Walther, G{\'e}raldine  and
      Vylomova, Ekaterina  and
      Xia, Patrick  and
      Faruqui, Manaal  and
      Mielke, Sabrina J.  and
      McCarthy, Arya  and
      K{\"u}bler, Sandra  and
      Yarowsky, David  and
      Eisner, Jason  and
      Hulden, Mans},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1293",
}
@inproceedings{lovick-etal-2018-computational,
    title = "A Computational Architecture for the Morphology of {U}pper {T}anana",
    author = "Lovick, Olga  and
      Cox, Christopher  and
      Silfverberg, Miikka  and
      Arppe, Antti  and
      Hulden, Mans",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1294",
}
@inproceedings{zelasko-2018-expanding,
    title = "Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?",
    author = "{\.Z}elasko, Piotr",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1295",
}
@inproceedings{farber-etal-2018-high,
    title = "A High-Quality Gold Standard for Citation-based Tasks",
    author = {F{\"a}rber, Michael  and
      Thiemann, Alexander  and
      Jatowt, Adam},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1296",
}
@inproceedings{mariani-etal-2018-measuring,
    title = "Measuring Innovation in Speech and Language Processing Publications.",
    author = "Mariani, Joseph  and
      Francopoulo, Gil  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1297",
}
@inproceedings{ferres-etal-2018-pdfdigest,
    title = "{PDF}digest: an Adaptable Layout-Aware {PDF}-to-{XML} Textual Content Extractor for Scientific Articles",
    author = "Ferr{\'e}s, Daniel  and
      Saggion, Horacio  and
      Ronzano, Francesco  and
      Bravo, {\`A}lex",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1298",
}
@inproceedings{kergosien-etal-2018-automatic,
    title = "Automatic Identification of Research Fields in Scientific Papers",
    author = {Kergosien, Eric  and
      Farvardin, Amin  and
      Teisseire, Maguelonne  and
      Bessagnet, Marie-No{\"e}lle  and
      Sch{\"o}pfel, Joachim  and
      Chaudiron, St{\'e}phane  and
      Jacquemin, Bernard  and
      Lacayrelle, Annig  and
      Roche, Mathieu  and
      Sallaberry, Christian  and
      Tonneau, Jean Philippe},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1299",
}
@inproceedings{kibrik-fedorova-2018-portrait,
    title = "A «Portrait» Approach to Multichannel Discourse",
    author = "Kibrik, Andrej  and
      Fedorova, Olga",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1300",
}
@inproceedings{zeyrek-etal-2018-multilingual,
    title = "Multilingual Extension of {PDTB}-Style Annotation: The Case of {TED} Multilingual Discourse Bank",
    author = "Zeyrek, Deniz  and
      Mendes, Am{\'a}lia  and
      Kurfal{\i}, Murathan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1301",
}
@inproceedings{chu-etal-2018-building,
    title = "Building a Macro {C}hinese {D}iscourse {T}reebank",
    author = "Chu, Xiaomin  and
      Jiang, Feng  and
      Xu, Sheng  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1302",
}
@inproceedings{hiippala-orekhova-2018-enhancing,
    title = "Enhancing the {AI}2 Diagrams Dataset Using {R}hetorical {S}tructure {T}heory",
    author = "Hiippala, Tuomo  and
      Orekhova, Serafina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1303",
}
@inproceedings{de-kuthy-etal-2018-qud,
    title = "{QUD}-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation",
    author = "De Kuthy, Kordula  and
      Reiter, Nils  and
      Riester, Arndt",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1304",
}
@inproceedings{lopes-etal-2018-spot,
    title = "The Spot the Difference corpus: a multi-modal corpus of spontaneous task oriented spoken interactions",
    author = "Lopes, Jos{\'e}  and
      Hemmingsson, Nils  and
      {\AA}strand, Oliver",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1305",
}
@inproceedings{cianflone-kosseim-2018-attention,
    title = "Attention for Implicit Discourse Relation Recognition",
    author = "Cianflone, Andre  and
      Kosseim, Leila",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1306",
}
@inproceedings{bothe-etal-2018-context,
    title = "A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks",
    author = "Bothe, Chandrakant  and
      Weber, Cornelius  and
      Magg, Sven  and
      Wermter, Stefan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1307",
}
@inproceedings{helfrich-etal-2018-treeannotator,
    title = "{T}ree{A}nnotator: Versatile Visual Annotation of Hierarchical Text Relations",
    author = {Helfrich, Philipp  and
      Rieb, Elias  and
      Abrami, Giuseppe  and
      L{\"u}cking, Andy  and
      Mehler, Alexander},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1308",
}
@inproceedings{gilmartin-etal-2018-chats,
    title = "Chats and Chunks: Annotation and Analysis of Multiparty Long Casual Conversations",
    author = "Gilmartin, Emer  and
      Vogel, Carl  and
      Campbell, Nick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1309",
}
@inproceedings{tanguy-etal-2018-extending,
    title = "Extending the gold standard for a lexical substitution task: is it worth it?",
    author = "Tanguy, Ludovic  and
      Fabre, C{\'e}cile  and
      Rivi{\`e}re, Laura",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1310",
}
@inproceedings{moritz-steding-2018-lexical,
    title = "Lexical and Semantic Features for Cross-lingual Text Reuse Classification: an Experiment in {E}nglish and {L}atin Paraphrases",
    author = "Moritz, Maria  and
      Steding, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1311",
}
@inproceedings{yuan-sharoff-2018-investigating,
    title = "Investigating the Influence of Bilingual {MWU} on Trainee Translation Quality",
    author = "Yuan, Yu  and
      Sharoff, Serge",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1312",
}
@inproceedings{ferenczi-etal-2018-evaluation,
    title = "Evaluation of Dictionary Creating Methods for Finno-Ugric Minority Languages",
    author = "Ferenczi, Zsanett  and
      Mittelholcz, Iv{\'a}n  and
      Simon, Eszter  and
      V{\'a}radi, Tam{\'a}s",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1313",
}
@inproceedings{laaridh-etal-2018-dysarthric,
    title = "Dysarthric speech evaluation: automatic and perceptual approaches",
    author = "Laaridh, Imed  and
      Meunier, Christine  and
      Fredouille, Corinne",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1314",
}
@inproceedings{braunger-etal-2018-towards,
    title = "Towards an Automatic Assessment of Crowdsourced Data for {NLU}",
    author = "Braunger, Patricia  and
      Maier, Wolfgang  and
      Wessling, Jan  and
      Schmidt, Maria",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1315",
}
@inproceedings{yeo-etal-2018-visual,
    title = "Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning",
    author = "Yeo, Jinyoung  and
      Lee, Gyeongbok  and
      Wang, Gengyu  and
      Choi, Seungtaek  and
      Cho, Hyunsouk  and
      Kim Amplayo, Reinald  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1316",
}
@inproceedings{klubicka-etal-2018-worth,
    title = "Is it worth it? Budget-related evaluation metrics for model selection",
    author = "Klubi{\v{c}}ka, Filip  and
      Salton, Giancarlo D.  and
      Kelleher, John D.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1317",
}
@inproceedings{huber-etal-2018-automated,
    title = "Automated Evaluation of Out-of-Context Errors",
    author = "Huber, Patrick  and
      Niehues, Jan  and
      Waibel, Alex",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1318",
}
@inproceedings{galibert-etal-2018-matics,
    title = "Matics Software Suite: New Tools for Evaluation and Data Exploration",
    author = "Galibert, Olivier  and
      Bernard, Guillaume  and
      Delaborde, Agnes  and
      Lecadre, Sabrina  and
      Kahn, Juliette",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1319",
}
@inproceedings{abdou-etal-2018-mgad,
    title = "{MGAD}: Multilingual Generation of Analogy Datasets",
    author = "Abdou, Mostafa  and
      Kulmizev, Artur  and
      Ravishankar, Vinit",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1320",
}
@inproceedings{faralli-etal-2018-misa,
    title = "{MI}s{A}: Multilingual {``}{I}s{A}{''} Extraction from Corpora",
    author = "Faralli, Stefano  and
      Lefever, Els  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1321",
}
@inproceedings{perez-miguel-etal-2018-biomedical,
    title = "Biomedical term normalization of {EHR}s with {UMLS}",
    author = "Perez-Miguel, Naiara  and
      Cuadros, Montse  and
      Rigau, German",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1322",
}
@inproceedings{lechelle-langlais-2018-revisiting,
    title = "Revisiting the Task of Scoring Open {IE} Relations",
    author = "L{\'e}chelle, William  and
      Langlais, Philippe",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1323",
}
@inproceedings{sarkar-etal-2018-supervised,
    title = "A supervised approach to taxonomy extraction using word embeddings",
    author = "Sarkar, Rajdeep  and
      McCrae, John P.  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1324",
}
@inproceedings{zhang-sun-2018-chinese,
    title = "A {C}hinese Dataset with Negative Full Forms for General Abbreviation Prediction",
    author = "Zhang, Yi  and
      Sun, Xu",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1325",
}
@inproceedings{lim-etal-2018-korean,
    title = "{K}orean {T}ime{B}ank Including Relative Temporal Information",
    author = "Lim, Chae-Gyun  and
      Jeong, Young-Seob  and
      Choi, Ho-Jin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1326",
}
@inproceedings{ide-etal-2018-mining,
    title = "Mining Biomedical Publications With The {LAPPS} {G}rid",
    author = "Ide, Nancy  and
      Suderman, Keith  and
      Kim, Jin-Dong",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1327",
}
@inproceedings{sankepally-oard-2018-initial,
    title = "An Initial Test Collection for Ranked Retrieval of {SMS} Conversations",
    author = "Sankepally, Rashmi  and
      Oard, Douglas W.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1328",
}
@inproceedings{camelin-etal-2018-frnewslink,
    title = "{F}r{N}ews{L}ink : a corpus linking {TV} Broadcast News Segments and Press Articles",
    author = "Camelin, Nathalie  and
      Damnati, G{\'e}raldine  and
      Bouchekif, Abdessalam  and
      Landeau, Anais  and
      Charlet, Delphine  and
      Est{\`e}ve, Yannick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1329",
}
@inproceedings{hernandez-hazem-2018-pyrata,
    title = "{P}y{RATA}, Python Rule-based fe{A}ture s{T}ructure Analysis",
    author = "Hernandez, Nicolas  and
      Hazem, Amir",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1330",
}
@inproceedings{zajic-etal-2018-towards,
    title = "Towards Processing of the Oral History Interviews and Related Printed Documents",
    author = {Zaj{\'\i}c, Zbyn{\v{e}}k  and
      Skorkovsk{\'a}, Lucie  and
      Neduchal, Petr  and
      Ircing, Pavel  and
      Psutka, Josef V.  and
      Hr{\'u}z, Marek  and
      Pra{\v{z}}{\'a}k, Ale{\v{s}}  and
      Soutner, Daniel  and
      {\v{S}}vec, Jan  and
      Bure{\v{s}}, Luk{\'a}{\v{s}}  and
      M{\"u}ller, Lud{\v{e}}k},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1331",
}
@inproceedings{hoenen-2018-multi,
    title = "Multi Modal Distance - An Approach to Stemma Generation With Weighting",
    author = "Hoenen, Armin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1332",
}
@inproceedings{han-schlangen-2018-corpus,
    title = "A Corpus of Natural Multimodal Spatial Scene Descriptions",
    author = "Han, Ting  and
      Schlangen, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1333",
}
@inproceedings{ito-etal-2018-effects,
    title = "The Effects of Unimodal Representation Choices on Multimodal Learning",
    author = "Ito, Fernando Tadao  and
      de Medeiros Caseli, Helena  and
      Moreira, Jander",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1334",
}
@inproceedings{krishnaswamy-pustejovsky-2018-evaluation,
    title = "An Evaluation Framework for Multimodal Interaction",
    author = "Krishnaswamy, Nikhil  and
      Pustejovsky, James",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1335",
}
@inproceedings{abdelali-etal-2018-waw,
    title = "The {WAW} Corpus: The First Corpus of Interpreted Speeches and their Translations for {E}nglish and {A}rabic",
    author = "Abdelali, Ahmed  and
      Temnikova, Irina  and
      Hedaya, Samy  and
      Vogel, Stephan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1336",
}
@inproceedings{wroblewska-2018-polish,
    title = "{P}olish Corpus of Annotated Descriptions of Images",
    author = "Wr{\'o}blewska, Alina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1337",
}
@inproceedings{gross-etal-2018-action,
    title = "Action Verb Corpus",
    author = "Gross, Stephanie  and
      Hirschmanner, Matthias  and
      Krenn, Brigitte  and
      Neubarth, Friedrich  and
      Zillich, Michael",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1338",
}
@inproceedings{fayet-etal-2018-emo,
    title = "{EMO}{\&}{LY} ({EMO}tion and {A}noma{LY}) : A new corpus for anomaly detection in an audiovisual stream with emotional context.",
    author = "Fayet, C{\'e}dric  and
      Delhay, Arnaud  and
      Lolive, Damien  and
      Marteau, Pierre-Fran{\c{c}}ois",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1339",
}
@inproceedings{curtis-etal-2018-development,
    title = "Development of an Annotated Multimodal Dataset for the Investigation of Classification and Summarisation of Presentations using High-Level Paralinguistic Features",
    author = "Curtis, Keith  and
      Campbell, Nick  and
      Jones, Gareth",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1340",
}
@inproceedings{nguyen-2018-bktreebank,
    title = "{BKT}reebank: Building a {V}ietnamese Dependency Treebank",
    author = "Nguyen, Kiem-Hieu",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1341",
}
@inproceedings{schneider-furbacher-2018-gecotagger,
    title = "{G}e{C}o{T}agger: Annotation of {G}erman Verb Complements with Conditional Random Fields",
    author = {Schneider, Roman  and
      F{\"u}rbacher, Monica},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1342",
}
@inproceedings{bladier-etal-2018-aet,
    title = "{AET}: Web-based Adjective Exploration Tool for {G}erman",
    author = "Bladier, Tatiana  and
      Seyffarth, Esther  and
      Hellwig, Oliver  and
      Petersen, Wiebke",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1343",
}
@inproceedings{akbik-vollgraf-2018-zap,
    title = "{ZAP}: An Open-Source Multilingual Annotation Projection Framework",
    author = "Akbik, Alan  and
      Vollgraf, Roland",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1344",
}
@inproceedings{javed-etal-2018-palmyra,
    title = "{P}almyra: A Platform Independent Dependency Annotation Tool for Morphologically Rich Languages",
    author = "Javed, Talha  and
      Habash, Nizar  and
      Taji, Dima",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1345",
}
@inproceedings{tratz-phan-2018-web,
    title = "A Web-based System for Crowd-in-the-Loop Dependency Treebanking",
    author = "Tratz, Stephen  and
      Phan, Nhien",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1346",
}
@inproceedings{chun-etal-2018-building,
    title = "Building {U}niversal {D}ependency Treebanks in {K}orean",
    author = "Chun, Jayeol  and
      Han, Na-Rae  and
      Hwang, Jena D.  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1347",
}
@inproceedings{falenska-etal-2018-moving,
    title = "Moving {TIGER} beyond Sentence-Level",
    author = "Falenska, Agnieszka  and
      Eckart, Kerstin  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1348",
}
@inproceedings{chiruzzo-wonsever-2018-spanish,
    title = "{S}panish {HPSG} Treebank based on the {A}n{C}ora Corpus",
    author = "Chiruzzo, Luis  and
      Wonsever, Dina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1349",
}
@inproceedings{seyoum-etal-2018-universal,
    title = "{U}niversal {D}ependencies for {A}mharic",
    author = "Seyoum, Binyam Ephrem  and
      Miyao, Yusuke  and
      Mekonnen, Baye Yimam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1350",
}
@inproceedings{arps-petitjean-2018-parser,
    title = "A Parser for {LTAG} and Frame Semantics",
    author = "Arps, David  and
      Petitjean, Simon",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1351",
}
@inproceedings{lim-etal-2018-multilingual,
    title = "Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and {K}omi-{Z}yrian",
    author = "Lim, KyungTae  and
      Partanen, Niko  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1352",
}
@inproceedings{gutkin-etal-2018-fonbund,
    title = "{F}on{B}und: A Library for Combining Cross-lingual Phonological Segment Data",
    author = "Gutkin, Alexander  and
      Jansche, Martin  and
      Merkulova, Tatiana",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1353",
}
@inproceedings{de-silva-etal-2018-voice,
    title = "Voice Builder: A Tool for Building Text-To-Speech Voices",
    author = "De Silva, Pasindu  and
      Wattanavekin, Theeraphol  and
      Hao, Tang  and
      Pipatsrisawat, Knot",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1354",
}
@inproceedings{takaoka-etal-2018-sudachi,
    title = "{S}udachi: a {J}apanese Tokenizer for Business",
    author = "Takaoka, Kazuma  and
      Hisamoto, Sorami  and
      Kawahara, Noriko  and
      Sakamoto, Miho  and
      Uchida, Yoshitaka  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1355",
}
@inproceedings{tanaka-etal-2018-chemical,
    title = "Chemical Compounds Knowledge Visualization with Natural Language Processing and Linked Data",
    author = "Tanaka, Kazunari  and
      Iwakura, Tomoya  and
      Koyanagi, Yusuke  and
      Ikeda, Noriko  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1356",
}
@inproceedings{cao-gete-2018-using,
    title = "Using Discourse Information for Education with a {S}panish-{C}hinese Parallel Corpus",
    author = "Cao, Shuyuan  and
      Gete, Harritxu",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1357",
}
@inproceedings{berkling-2018-2nd,
    title = "A 2nd Longitudinal Corpus for Children{'}s Writing with Enhanced Output for Specific Spelling Patterns",
    author = "Berkling, Kay",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1358",
}
@inproceedings{yamaguchi-etal-2018-development,
    title = "Development of a Mobile Observation Support System for Students: {F}ish{W}atchr Mini",
    author = "Yamaguchi, Masaya  and
      Kitamura, Masanori  and
      Yanagida, Naomi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1359",
}
@inproceedings{odijk-etal-2018-anncor,
    title = "The {A}nn{C}or {CHILDES} Treebank",
    author = "Odijk, Jan  and
      Dimitriadis, Alexis  and
      van der Klis, Martijn  and
      van Koppen, Marjo  and
      Otten, Meie  and
      van der Veen, Remco",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1360",
}
@inproceedings{cao-etal-2018-babycloud,
    title = "{B}aby{C}loud, a Technological Platform for Parents and Researchers",
    author = "Cao, Xu{\^a}n-Nga  and
      Dakhlia, Cyrille  and
      Del Carmen, Patricia  and
      Jaouani, Mohamed-Amine  and
      Ould-Arbi, Malik  and
      Dupoux, Emmanuel",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1361",
}
@inproceedings{minami-etal-2018-infant,
    title = "Infant Word Comprehension-to-Production Index Applied to Investigation of Noun Learning Predominance Using Cross-lingual {CDI} database",
    author = "Minami, Yasuhiro  and
      Kobayashi, Tessei  and
      Okumura, Yuko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1362",
}
@inproceedings{lee-etal-2018-building,
    title = "Building a {TOCFL} Learner Corpus for {C}hinese Grammatical Error Diagnosis",
    author = "Lee, Lung-Hao  and
      Tseng, Yuen-Hsien  and
      Chang, Li-Ping",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1363",
}
@inproceedings{goldman-schwab-2018-miaparle,
    title = "{MIAPARLE}: Online training for the discrimination of stress contrasts",
    author = "Goldman, Jean-Philippe  and
      Schwab, Sandra",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1364",
}
@inproceedings{zesch-horbach-2018-escrito,
    title = "{ESCRITO} - An {NLP}-Enhanced Educational Scoring Toolkit",
    author = "Zesch, Torsten  and
      Horbach, Andrea",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1365",
}
@inproceedings{al-khalil-etal-2018-leveled,
    title = "A Leveled Reading Corpus of {M}odern {S}tandard {A}rabic",
    author = "Al Khalil, Muhamed  and
      Saddiki, Hind  and
      Habash, Nizar  and
      Alfalasi, Latifa",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1366",
}
@inproceedings{agerri-etal-2018-developing,
    title = "Developing New Linguistic Resources and Tools for the {G}alician Language",
    author = "Agerri, Rodrigo  and
      G{\'o}mez Guinovart, Xavier  and
      Rigau, German  and
      Solla Portela, Miguel Anxo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1367",
}
@inproceedings{lachler-etal-2018-modeling,
    title = "Modeling {N}orthern {H}aida Verb Morphology",
    author = "Lachler, Jordan  and
      Antonsen, Lene  and
      Trosterud, Trond  and
      Moshagen, Sjur  and
      Arppe, Antti",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1368",
}
@inproceedings{richter-etal-2018-low,
    title = "Low-resource Post Processing of Noisy {OCR} Output for Historical Corpus Digitisation",
    author = "Richter, Caitlin  and
      Wickes, Matthew  and
      Beser, Deniz  and
      Marcus, Mitch",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1369",
}
@inproceedings{hedeland-etal-2018-introducing,
    title = "Introducing the {CLARIN} Knowledge Centre for Linguistic Diversity and Language Documentation",
    author = "Hedeland, Hanna  and
      Lehmberg, Timm  and
      Rau, Felix  and
      Salffner, Sophie  and
      Seyfeddinipur, Mandana  and
      Witt, Andreas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1370",
}
@inproceedings{galuscakova-neuzilova-2018-low,
    title = "Low Resource Methods for Medieval Document Sections Analysis",
    author = "Galu{\v{s}}{\v{c}}{\'a}kov{\'a}, Petra  and
      Neu{\v{z}}ilov{\'a}, Lucie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1371",
}
@inproceedings{grubenmann-etal-2018-sb,
    title = "{SB}-{CH}: A {S}wiss {G}erman Corpus with Sentiment Annotations",
    author = {Grubenmann, Ralf  and
      Tuggener, Don  and
      von D{\"a}niken, Pius  and
      Deriu, Jan  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1372",
}
@inproceedings{senuma-aizawa-2018-universal,
    title = "{U}niversal {D}ependencies for {A}inu",
    author = "Senuma, Hajime  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1373",
}
@inproceedings{cassidy-etal-2018-signbank,
    title = "{S}ignbank: Software to Support Web Based Dictionaries of Sign Language",
    author = "Cassidy, Steve  and
      Crasborn, Onno  and
      Nieminen, Henri  and
      Stoop, Wessel  and
      Hulsbosch, Micha  and
      Even, Susan  and
      Komen, Erwin  and
      Johnston, Trevor",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1374",
}
@inproceedings{ito-etal-2018-j,
    title = "{J}-{M}e{D}ic: A {J}apanese Disease Name Dictionary based on Real Clinical Usage",
    author = "Ito, Kaoru  and
      Nagai, Hiroyuki  and
      Okahisa, Taro  and
      Wakamiya, Shoko  and
      Iwao, Tomohide  and
      Aramaki, Eiji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1375",
}
@inproceedings{kanzaki-isahara-2018-building,
    title = "Building a List of Synonymous Words and Phrases of {J}apanese Compound Verbs",
    author = "Kanzaki, Kyoko  and
      Isahara, Hitoshi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1376",
}
@inproceedings{leon-arauz-reimerink-2018-evaluating,
    title = "Evaluating {E}co{L}exi{CAT}: a Terminology-Enhanced {CAT} Tool",
    author = "Le{\'o}n-Ara{\'u}z, Pilar  and
      Reimerink, Arianne",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1377",
}
@inproceedings{pedersen-etal-2018-danish,
    title = "A {D}anish {F}rame{N}et Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier",
    author = "Pedersen, Bolette  and
      Nimb, Sanni  and
      S{\o}gaard, Anders  and
      Hartmann, Mareike  and
      Olsen, Sussi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1378",
}
@inproceedings{jochim-etal-2018-slide,
    title = "{SLIDE} - a Sentiment Lexicon of Common Idioms",
    author = "Jochim, Charles  and
      Bonin, Francesca  and
      Bar-Haim, Roy  and
      Slonim, Noam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1379",
}
@inproceedings{bin-zia-etal-2018-pronouncur,
    title = "{P}ronounc{UR}: An {U}rdu Pronunciation Lexicon Generator",
    author = "Bin Zia, Haris  and
      Ali Raza, Agha  and
      Athar, Awais",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1380",
}
@inproceedings{mykowiecka-etal-2018-simlex,
    title = "{S}im{L}ex-999 for {P}olish",
    author = "Mykowiecka, Agnieszka  and
      Marciniak, Ma{\l}gorzata  and
      Rychlik, Piotr",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1381",
}
@inproceedings{rodrigues-branco-2018-finely,
    title = "Finely Tuned, 2 Billion Token Based Word Embeddings for {P}ortuguese",
    author = "Rodrigues, Jo{\~a}o  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1382",
}
@inproceedings{ziad-etal-2018-teanga,
    title = "{T}eanga: A Linked Data based platform for Natural Language Processing",
    author = "Ziad, Housam  and
      McCrae, John P.  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1383",
}
@inproceedings{rehm-etal-2018-automatic,
    title = "Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena",
    author = "Rehm, Georg  and
      Moreno-Schneider, Julian  and
      Bourgonje, Peter",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1384",
}
@inproceedings{witte-sateli-2018-lodexporter,
    title = "The {LOD}e{X}porter: Flexible Generation of Linked Open Data Triples from {NLP} Frameworks for Automatic Knowledge Base Construction",
    author = "Witte, Ren{\'e}  and
      Sateli, Bahar",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1385",
}
@inproceedings{klimek-etal-2018-lido,
    title = "{L}i{D}o {RDF}: From a Relational Database to a Linked Data Graph of Linguistic Terms and Bibliographic Data",
    author = {Klimek, Bettina  and
      Sch{\"a}dlich, Robert  and
      Kr{\"o}ger, Dustin  and
      Knese, Edwin  and
      El{\ss}mann, Benedikt},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1386",
}
@inproceedings{chiarcos-etal-2018-towards,
    title = "Towards a Linked Open Data Edition of {S}umerian Corpora",
    author = "Chiarcos, Christian  and
      Pag{\'e}-Perron, {\'E}milie  and
      Khait, Ilya  and
      Schenk, Niko  and
      Reckling, Lucas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1387",
}
@inproceedings{tufis-cristea-2018-birds,
    title = "A Bird{'}s-eye View of Language Processing Projects at the {R}omanian Academy",
    author = "Tufi{\textcommabelow{s}}, Dan  and
      Cristea, Dan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1388",
}
@inproceedings{schmitz-etal-2018-pmki,
    title = "{PMKI}: an {E}uropean Commission action for the interoperability, maintainability and sustainability of Language Resources",
    author = "Schmitz, Peter  and
      Francesconi, Enrico  and
      Hajlaoui, Najeh  and
      Batouche, Brahim",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1389",
}
@inproceedings{meurer-2018-abkhaz,
    title = "The {A}bkhaz National Corpus",
    author = "Meurer, Paul",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1390",
}
@inproceedings{vasiljevs-etal-2018-collecting,
    title = "Collecting Language Resources from Public Administrations in the Nordic and Baltic Countries",
    author = "Vasi{\c{l}}jevs, Andrejs  and
      Kalni{\c{n}}{\v{s}}, Rihards  and
      Rozis, Roberts  and
      B{\=e}rzi{\c{n}}{\v{s}}, Aivars",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1391",
}
@inproceedings{moussallem-etal-2018-lidioms,
    title = "{LI}dioms: A Multilingual Linked Idioms Data Set",
    author = "Moussallem, Diego  and
      Sherif, Mohamed Ahmed  and
      Esteves, Diego  and
      Zampieri, Marcos  and
      Ngonga Ngomo, Axel-Cyrille",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1392",
}
@inproceedings{matsuyoshi-etal-2018-annotating,
    title = "Annotating Modality Expressions and Event Factuality for a {J}apanese Chess Commentary Corpus",
    author = "Matsuyoshi, Suguru  and
      Kameko, Hirotaka  and
      Murawaki, Yugo  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1393",
}
@inproceedings{jiang-etal-2018-annotating,
    title = "Annotating {C}hinese Light Verb Constructions according to {PARSEME} guidelines",
    author = "Jiang, Menghan  and
      Klyueva, Natalia  and
      Xu, Hongzhi  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1394",
}
@inproceedings{krstev-etal-2018-using,
    title = "Using {E}nglish Baits to Catch {S}erbian Multi-Word Terminology",
    author = "Krstev, Cvetana  and
      {\v{S}}andrih, Branislava  and
      Stankovi{\'c}, Ranka  and
      Mladenovi{\'c}, Miljana",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1395",
}
@inproceedings{kato-etal-2018-construction,
    title = "Construction of Large-scale {E}nglish Verbal Multiword Expression Annotated Corpus",
    author = "Kato, Akihiko  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1396",
}
@inproceedings{inurrieta-etal-2018-konbitzul,
    title = "{K}onbitzul: an {MWE}-specific database for {S}panish-{B}asque",
    author = "I{\~n}urrieta, Uxoa  and
      Aduriz, Itziar  and
      D{\'\i}az de Ilarraza, Arantza  and
      Labaka, Gorka  and
      Sarasola, Kepa",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1397",
}
@inproceedings{sales-etal-2018-multilingual,
    title = "A Multilingual Test Collection for the Semantic Search of Entity Categories",
    author = "Sales, Juliano Efson  and
      Barzegar, Siamak  and
      Franco, Wellington  and
      Bermeitinger, Bernhard  and
      Cunha, Tiago  and
      Davis, Brian  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1398",
}
@inproceedings{cabezas-garcia-leon-arauz-2018-towards,
    title = "Towards the Inference of Semantic Relations in Complex Nominals: a Pilot Study",
    author = "Cabezas-Garc{\'\i}a, Melania  and
      Le{\'o}n-Ara{\'u}z, Pilar",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1399",
}
@inproceedings{rodriguez-fernandez-etal-2018-generation,
    title = "Generation of a {S}panish Artificial Collocation Error Corpus",
    author = "Rodr{\'\i}guez-Fern{\'a}ndez, Sara  and
      Carlini, Roberto  and
      Wanner, Leo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1400",
}
@inproceedings{varis-klyueva-2018-improving,
    title = "Improving a Neural-based Tagger for Multiword Expressions Identification",
    author = "Vari{\v{s}}, Du{\v{s}}an  and
      Klyueva, Natalia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1401",
}
@inproceedings{aharodnik-etal-2018-designing,
    title = "Designing a {R}ussian Idiom-Annotated Corpus",
    author = "Aharodnik, Katsiaryna  and
      Feldman, Anna  and
      Peng, Jing",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1402",
}
@inproceedings{horsmann-zesch-2018-deeptc,
    title = "{D}eep{TC} {--} An Extension of {DKP}ro Text Classification for Fostering Reproducibility of Deep Learning Experiments",
    author = "Horsmann, Tobias  and
      Zesch, Torsten",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1403",
}
@inproceedings{zimmerman-etal-2018-improving,
    title = "Improving Hate Speech Detection with Deep Learning Ensembles",
    author = "Zimmerman, Steven  and
      Kruschwitz, Udo  and
      Fox, Chris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1404",
}
@inproceedings{cuba-gyllensten-sahlgren-2018-distributional,
    title = "Distributional Term Set Expansion",
    author = "Cuba Gyllensten, Amaru  and
      Sahlgren, Magnus",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1405",
}
@inproceedings{bel-pocostales-2018-domain,
    title = "Can Domain Adaptation be Handled as Analogies?",
    author = "Bel, N{\'u}ria  and
      Pocostales, Joel",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1406",
}
@inproceedings{hsieh-etal-2018-author,
    title = "Author Profiling from {F}acebook Corpora",
    author = "Hsieh, Fernando  and
      Dias, Rafael  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1407",
}
@inproceedings{ein-dor-etal-2018-semantic,
    title = "Semantic Relatedness of {W}ikipedia Concepts {--} Benchmark Data and a Working Solution",
    author = "Ein Dor, Liat  and
      Halfon, Alon  and
      Kantor, Yoav  and
      Levy, Ran  and
      Mass, Yosi  and
      Rinott, Ruty  and
      Shnarch, Eyal  and
      Slonim, Noam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1408",
}
@inproceedings{boumber-etal-2018-experiments,
    title = "Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution",
    author = "Boumber, Dainis  and
      Zhang, Yifan  and
      Mukherjee, Arjun",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1409",
}
@inproceedings{nguyen-etal-2018-fast,
    title = "A Fast and Accurate {V}ietnamese Word Segmenter",
    author = "Nguyen, Dat Quoc  and
      Nguyen, Dai Quoc  and
      Vu, Thanh  and
      Dras, Mark  and
      Johnson, Mark",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1410",
}
@inproceedings{tyers-etal-2018-finite,
    title = "Finite-state morphological analysis for {G}agauz",
    author = {Tyers, Francis  and
      Bayatli, Sevilay  and
      Karanfil, G{\"u}ll{\"u}  and
      G{\"o}k{\i}rmak, Memduh  and
      Tyers, Francis M.},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1411",
}
@inproceedings{kabashi-proisl-2018-albanian,
    title = "{A}lbanian Part-of-Speech Tagging: Gold Standard and Evaluation",
    author = "Kabashi, Besim  and
      Proisl, Thomas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1412",
}
@inproceedings{s-bhattacharyya-2018-morphology,
    title = "Morphology Injection for {E}nglish-{M}alayalam Statistical Machine Translation",
    author = "S, Sreelekha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1413",
}
@inproceedings{attia-etal-2018-morpho,
    title = "The Morpho-syntactic Annotation of {A}nimacy for a Dependency Parser",
    author = "Attia, Mohammed  and
      Nikolaev, Vitaly  and
      Elkahky, Ali",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1414",
}
@inproceedings{obeid-etal-2018-madari,
    title = "{MADAR}i: A Web Interface for Joint {A}rabic Morphological Annotation and Spelling Correction",
    author = "Obeid, Ossama  and
      Khalifa, Salam  and
      Habash, Nizar  and
      Bouamor, Houda  and
      Zaghouani, Wajdi  and
      Oflazer, Kemal",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1415",
}
@inproceedings{chen-schwartz-2018-morphological,
    title = "A Morphological Analyzer for {S}t. {L}awrence {I}sland / {C}entral {S}iberian {Y}upik",
    author = "Chen, Emily  and
      Schwartz, Lane",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1416",
}
@inproceedings{chiarcos-etal-2018-universal,
    title = "Universal Morphologies for the Caucasus region",
    author = {Chiarcos, Christian  and
      Donandt, Kathrin  and
      Ionov, Maxim  and
      Rind-Pawlowski, Monika  and
      Sargsian, Hasmik  and
      Wichers Schreur, Jesse  and
      Abromeit, Frank  and
      F{\"a}th, Christian},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1417",
}
@inproceedings{phan-matsumoto-2018-emtc,
    title = "{EMTC}: Multilabel Corpus in Movie Domain for Emotion Analysis in Conversational Text",
    author = "Phan, Duc-Anh  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1418",
}
@inproceedings{pecore-villaneau-2018-complex,
    title = "Complex and Precise Movie and Book Annotations in {F}rench Language for Aspect Based Sentiment Analysis",
    author = "Pecore, Stefania  and
      Villaneau, Jeanne",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1419",
}
@inproceedings{moreno-ortiz-perez-hernandez-2018-lingmotif,
    title = "{L}ingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis",
    author = "Moreno-Ortiz, Antonio  and
      P{\'e}rez-Hern{\'a}ndez, Chantal",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1420",
}
@inproceedings{wang-tahara-2018-japanese,
    title = "A {J}apanese Corpus for Analyzing Customer Loyalty Information",
    author = "Wang, Yiou  and
      Tahara, Takuji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1421",
}
@inproceedings{sluyter-gathje-etal-2018-footweets,
    title = "{F}oo{T}weets: A Bilingual Parallel Corpus of World Cup Tweets",
    author = {Sluyter-G{\"a}thje, Henny  and
      Lohar, Pintu  and
      Afli, Haithem  and
      Way, Andy},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1422",
}
@inproceedings{gaillat-etal-2018-ssix,
    title = "The {SSIX} Corpora: Three Gold Standard Corpora for Sentiment Analysis in {E}nglish, {S}panish and {G}erman Financial Microblogs",
    author = "Gaillat, Thomas  and
      Zarrouk, Manel  and
      Freitas, Andr{\'e}  and
      Davis, Brian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1423",
}
@inproceedings{joshi-etal-2018-sarcasm,
    title = "Sarcasm Target Identification: Dataset and An Introductory Approach",
    author = "Joshi, Aditya  and
      Goel, Pranav  and
      Bhattacharyya, Pushpak  and
      Carman, Mark",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1424",
}
@inproceedings{chathuranga-etal-2018-annotating,
    title = "Annotating Opinions and Opinion Targets in Student Course Feedback",
    author = "Chathuranga, Janaka  and
      Ediriweera, Shanika  and
      Hasantha, Ravindu  and
      Munasinghe, Pranidhith  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1425",
}
@inproceedings{rouces-etal-2018-generating,
    title = "Generating a Gold Standard for a {S}wedish Sentiment Lexicon",
    author = "Rouces, Jacobo  and
      Tahmasebi, Nina  and
      Borin, Lars  and
      R{\o}dven Eide, Stian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1426",
}
@inproceedings{tulkens-etal-2018-wordkit,
    title = "{W}ord{K}it: a Python Package for Orthographic and Phonological Featurization",
    author = "Tulkens, St{\'e}phan  and
      Sandra, Dominiek  and
      Daelemans, Walter",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1427",
}
@inproceedings{lukes-etal-2018-pronunciation,
    title = "Pronunciation Variants and {ASR} of Colloquial Speech: A Case Study on {C}zech",
    author = "Luke{\v{s}}, David  and
      Kop{\v{r}}ivov{\'a}, Marie  and
      Komrskov{\'a}, Zuzana  and
      Poukarov{\'a}, Petra",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1428",
}
@inproceedings{mortensen-etal-2018-epitran,
    title = "{E}pitran: Precision {G}2{P} for Many Languages",
    author = "Mortensen, David R.  and
      Dalmia, Siddharth  and
      Littell, Patrick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1429",
}
@inproceedings{kalouli-etal-2018-multilingual,
    title = "A Multilingual Approach to Question Classification",
    author = "Kalouli, Aikaterini-Lida  and
      Kaiser, Katharina  and
      Hautli-Janisz, Annette  and
      Kaiser, Georg A.  and
      Butt, Miriam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1430",
}
@inproceedings{cui-etal-2018-dataset,
    title = "Dataset for the First Evaluation on {C}hinese Machine Reading Comprehension",
    author = "Cui, Yiming  and
      Liu, Ting  and
      Chen, Zhipeng  and
      Ma, Wentao  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1431",
}
@inproceedings{hazem-etal-2018-multi,
    title = "A Multi-Domain Framework for Textual Similarity. A Case Study on Question-to-Question and Question-Answering Similarity Tasks",
    author = "Hazem, Amir  and
      El Amal Boussaha, Basma  and
      Hernandez, Nicolas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1432",
}
@inproceedings{jansen-etal-2018-worldtree,
    title = "{W}orld{T}ree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference",
    author = "Jansen, Peter  and
      Wainwright, Elizabeth  and
      Marmorstein, Steven  and
      Morrison, Clayton",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1433",
}
@inproceedings{fukunaga-etal-2018-analysis,
    title = "Analysis of Implicit Conditions in Database Search Dialogues",
    author = "Fukunaga, Shun-ya  and
      Nishikawa, Hitoshi  and
      Tokunaga, Takenobu  and
      Yokono, Hikaru  and
      Takahashi, Tetsuro",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1434",
}
@inproceedings{van-waterschoot-etal-2018-information,
    title = "An Information-Providing Closed-Domain Human-Agent Interaction Corpus",
    author = "van Waterschoot, Jelte  and
      Dubuisson Duplessis, Guillaume  and
      Gatti, Lorenzo  and
      Bruijnes, Merijn  and
      Heylen, Dirk",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1435",
}
@inproceedings{yokota-nakayama-2018-augmenting,
    title = "Augmenting Image Question Answering Dataset by Exploiting Image Captions",
    author = "Yokota, Masashi  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1436",
}
@inproceedings{lee-etal-2018-semi,
    title = "Semi-supervised Training Data Generation for Multilingual Question Answering",
    author = "Lee, Kyungjae  and
      Yoon, Kyoungho  and
      Park, Sunghyun  and
      Hwang, Seung-won",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1437",
}
@inproceedings{dulceanu-etal-2018-photoshopquia,
    title = "{P}hotoshop{Q}ui{A}: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering",
    author = "Dulceanu, Andrei  and
      Le Dinh, Thang  and
      Chang, Walter  and
      Bui, Trung  and
      Kim, Doo Soon  and
      Vu, Manh Chien  and
      Kim, Seokhwan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1438",
}
@inproceedings{pappas-etal-2018-bioread,
    title = "{B}io{R}ead: A New Dataset for Biomedical Reading Comprehension",
    author = "Pappas, Dimitris  and
      Androutsopoulos, Ion  and
      Papageorgiou, Haris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1439",
}
@inproceedings{gupta-etal-2018-mmqa,
    title = "{MMQA}: A Multi-domain Multi-lingual Question-Answering Framework for {E}nglish and {H}indi",
    author = "Gupta, Deepak  and
      Kumari, Surabhi  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1440",
}
@inproceedings{green-larasati-2018-first,
    title = "The First 100 Days: A Corpus Of Political Agendas on {T}witter",
    author = "Green, Nathan  and
      Larasati, Septina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1441",
}
@inproceedings{yadav-etal-2018-medical,
    title = "Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System",
    author = "Yadav, Shweta  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1442",
}
@inproceedings{sanguinetti-etal-2018-italian,
    title = "An {I}talian {T}witter Corpus of Hate Speech against Immigrants",
    author = "Sanguinetti, Manuela  and
      Poletto, Fabio  and
      Bosco, Cristina  and
      Patti, Viviana  and
      Stranisci, Marco",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1443",
}
@inproceedings{di-tommaso-etal-2018-large,
    title = "A Large Multilingual and Multi-domain Dataset for Recommender Systems",
    author = "Di Tommaso, Giorgia  and
      Faralli, Stefano  and
      Velardi, Paola",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1444",
}
@inproceedings{voigt-etal-2018-rtgender,
    title = "{R}t{G}ender: A Corpus for Studying Differential Responses to Gender",
    author = "Voigt, Rob  and
      Jurgens, David  and
      Prabhakaran, Vinodkumar  and
      Jurafsky, Dan  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1445",
}
@inproceedings{meftah-semmar-2018-neural,
    title = "A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts",
    author = "Meftah, Sara  and
      Semmar, Nasredine",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1446",
}
@inproceedings{fredriksen-etal-2018-utilizing,
    title = "Utilizing Large {T}witter Corpora to Create Sentiment Lexica",
    author = {Fredriksen, Valerij  and
      Jahren, Brage  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1447",
}
@inproceedings{fernandez-gallardo-weiss-2018-nautilus,
    title = "The Nautilus Speaker Characterization Corpus: Speech Recordings and Labels of Speaker Characteristics and Voice Descriptions",
    author = "Fern{\'a}ndez Gallardo, Laura  and
      Weiss, Benjamin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1448",
}
@inproceedings{schiel-zitzelsberger-2018-evaluation,
    title = "Evaluation of Automatic Formant Trackers",
    author = "Schiel, Florian  and
      Zitzelsberger, Thomas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1449",
}
@inproceedings{smidl-etal-2018-design,
    title = "Design and Development of Speech Corpora for Air Traffic Control Training",
    author = "{\v{S}}m{\'\i}dl, Lubo{\v{s}}  and
      {\v{S}}vec, Jan  and
      Tihelka, Daniel  and
      Matou{\v{s}}ek, Jind{\v{r}}ich  and
      Romportl, Jan  and
      Ircing, Pavel",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1450",
}
@inproceedings{van-der-westhuizen-niesler-2018-first,
    title = "A First {S}outh {A}frican Corpus of Multilingual Code-switched Soap Opera Speech",
    author = "van der Westhuizen, Ewald  and
      Niesler, Thomas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1451",
}
@inproceedings{poerner-schiel-2018-web,
    title = "A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings",
    author = "Poerner, Nina  and
      Schiel, Florian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1452",
}
@inproceedings{delpech-etal-2018-real,
    title = "A Real-life, {F}rench-accented Corpus of Air Traffic Control Communications",
    author = "Delpech, Estelle  and
      Laignelet, Marion  and
      Pimm, Christophe  and
      Raynal, C{\'e}line  and
      Trzos, Michal  and
      Arnold, Alexandre  and
      Pronto, Dominique",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1453",
}
@inproceedings{salimbajevs-2018-creating,
    title = "Creating {L}ithuanian and {L}atvian Speech Corpora from Inaccurately Annotated Web Data",
    author = "Salimbajevs, Askars",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1454",
}
@inproceedings{sitaram-etal-2018-discovering,
    title = "Discovering Canonical {I}ndian {E}nglish Accents: A Crowdsourcing-based Approach",
    author = "Sitaram, Sunayana  and
      Manjunath, Varun  and
      Bharadwaj, Varun  and
      Choudhury, Monojit  and
      Bali, Kalika  and
      Tjalve, Michael",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1455",
}
@inproceedings{ohsuga-etal-2018-extending,
    title = "Extending Search System based on Interactive Visualization for Speech Corpora",
    author = "Ohsuga, Tomoko  and
      Ishimoto, Yuichi  and
      Kajiyama, Tomoko  and
      Kozawa, Shunsuke  and
      Uchimoto, Kiyotaka  and
      Itahashi, Shuichi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1456",
}
@inproceedings{schweitzer-etal-2018-german,
    title = "{G}erman Radio Interviews: The {GRAIN} Release of the {SFB}732 Silver Standard Collection",
    author = {Schweitzer, Katrin  and
      Eckart, Kerstin  and
      G{\"a}rtner, Markus  and
      Falenska, Agnieszka  and
      Riester, Arndt  and
      R{\"o}siger, Ina  and
      Schweitzer, Antje  and
      Stehwien, Sabrina  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1457",
}
@inproceedings{mieskes-stiegelmayr-2018-preparing,
    title = "Preparing Data from Psychotherapy for Natural Language Processing",
    author = "Mieskes, Margot  and
      Stiegelmayr, Andreas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1458",
}
@inproceedings{vaheb-etal-2018-mirasvoice,
    title = "{M}iras{V}oice: A bilingual ({E}nglish-{P}ersian) speech corpus",
    author = "Vaheb, Amir  and
      Janalizadeh Choobbasti, Ali  and
      Mortazavi Najafabadi, S.H.E.  and
      Safavi, Saeid  and
      Sabeti, Behnam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1459",
}
@inproceedings{pareti-lando-2018-dialog,
    title = "Dialog Intent Structure: A Hierarchical Schema of Linked Dialog Acts",
    author = "Pareti, Silvia  and
      Lando, Tatiana",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1460",
}
@inproceedings{nakamura-kawahara-2018-jdcfc,
    title = "{JDCFC}: A {J}apanese Dialogue Corpus with Feature Changes",
    author = "Nakamura, Tetsuaki  and
      Kawahara, Daisuke",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1461",
}
@inproceedings{yoshino-etal-2018-japanese,
    title = "{J}apanese Dialogue Corpus of Information Navigation and Attentive Listening Annotated with Extended {ISO}-24617-2 Dialogue Act Tags",
    author = "Yoshino, Koichiro  and
      Tanaka, Hiroki  and
      Sugiyama, Kyoshiro  and
      Kondo, Makoto  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1462",
}
@inproceedings{artstein-etal-2018-niki,
    title = "The Niki and Julie Corpus: Collaborative Multimodal Dialogues between Humans, Robots, and Virtual Agents",
    author = "Artstein, Ron  and
      Boberg, Jill  and
      Gainer, Alesia  and
      Gratch, Jonathan  and
      Johnson, Emmanuel  and
      Leuski, Anton  and
      Lucas, Gale  and
      Traum, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1463",
}
@inproceedings{wang-etal-2018-constructing,
    title = "Constructing a {C}hinese Medical Conversation Corpus Annotated with Conversational Structures and Actions",
    author = "Wang, Nan  and
      Song, Yan  and
      Xia, Fei",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1464",
}
@inproceedings{ishii-etal-2018-predicting,
    title = "Predicting Nods by using Dialogue Acts in Dialogue",
    author = "Ishii, Ryo  and
      Higashinaka, Ryuichiro  and
      Tomita, Junji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1465",
}
@inproceedings{koutsombogera-vogel-2018-modeling,
    title = "Modeling Collaborative Multimodal Behavior in Group Dialogues: The {MULTISIMO} Corpus",
    author = "Koutsombogera, Maria  and
      Vogel, Carl",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1466",
}
@inproceedings{ochs-etal-2018-semi,
    title = "A Semi-autonomous System for Creating a Human-Machine Interaction Corpus in Virtual Reality: Application to the {ACORFORM}ed System for Training Doctors to Break Bad News",
    author = "Ochs, Magalie  and
      Blache, Philippe  and
      de Montcheuil, Gr{\'e}goire  and
      Pergandi, Jean-Marie  and
      Saubesty, Jorane  and
      Francon, Daniel  and
      Mestre, Daniel",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1467",
}
@inproceedings{novitasari-etal-2018-construction,
    title = "Construction of {E}nglish-{F}rench Multimodal Affective Conversational Corpus from {TV} Dramas",
    author = "Novitasari, Sashi  and
      Do, Quoc Truong  and
      Sakti, Sakriani  and
      Lestari, Dessi  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1468",
}
@inproceedings{sheinin-etal-2018-quest,
    title = "{QUEST}: A Natural Language Interface to Relational Databases",
    author = "Sheinin, Vadim  and
      Khorashani, Elahe  and
      Yeo, Hangu  and
      Xu, Kun  and
      Vo, Ngoc Phuoc An  and
      Popescu, Octavian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1469",
}
@inproceedings{verwimp-etal-2018-tf,
    title = "{TF}-{LM}: {T}ensor{F}low-based Language Modeling Toolkit",
    author = "Verwimp, Lyan  and
      Van hamme, Hugo  and
      Wambacq, Patrick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1470",
}
@inproceedings{park-shin-2018-grapheme,
    title = "Grapheme-level Awareness in Word Embeddings for Morphologically Rich Languages",
    author = "Park, Suzi  and
      Shin, Hyopil",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1471",
}
@inproceedings{schmirler-etal-2018-building,
    title = "Building a Constraint Grammar Parser for {P}lains {C}ree Verbs and Arguments",
    author = "Schmirler, Katherine  and
      Arppe, Antti  and
      Trosterud, Trond  and
      Antonsen, Lene",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1472",
}
@inproceedings{heinzerling-strube-2018-bpemb,
    title = "{BPE}mb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages",
    author = "Heinzerling, Benjamin  and
      Strube, Michael",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1473",
}
@inproceedings{rocha-paraboni-2018-reference,
    title = "Reference production in human-computer interaction: Issues for Corpus-based Referring Expression Generation",
    author = "Rocha, Danillo  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1474",
}
@inproceedings{lan-paraboni-2018-definite,
    title = "Definite Description Lexical Choice: taking Speaker{'}s Personality into account",
    author = "Lan, Alex  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1475",
}
@inproceedings{mariotti-paraboni-2018-referring,
    title = "Referring Expression Generation in time-constrained communication",
    author = "Mariotti, Andr{\'e}  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1476",
}
@inproceedings{laokulrat-etal-2018-incorporating,
    title = "Incorporating Semantic Attention in Video Description Generation",
    author = "Laokulrat, Natsuda  and
      Okazaki, Naoaki  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1477",
}
@inproceedings{lareau-etal-2018-gendr,
    title = "{G}en{DR}: A Generic Deep Realizer with Complex Lexicalization",
    author = "Lareau, Fran{\c{c}}ois  and
      Lambrey, Florie  and
      Dubinskaite, Ieva  and
      Galarreta-Piquette, Daniel  and
      Nejat, Maryam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1478",
}
@inproceedings{stajner-nisioi-2018-detailed,
    title = "A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification",
    author = "{\v{S}}tajner, Sanja  and
      Nisioi, Sergiu",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1479",
}
@inproceedings{vossen-etal-2018-dont,
    title = "Don{'}t Annotate, but Validate: a Data-to-Text Method for Capturing Event Data",
    author = "Vossen, Piek  and
      Ilievski, Filip  and
      Postma, Marten  and
      Segers, Roxane",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1480",
}
@inproceedings{moussallem-etal-2018-rdf2pt,
    title = "{RDF}2{PT}: Generating {B}razilian {P}ortuguese Texts from {RDF} Data",
    author = "Moussallem, Diego  and
      Ferreira, Thiago  and
      Zampieri, Marcos  and
      Cavalcanti, Maria Claudia  and
      Xex{\'e}o, Geraldo  and
      Neves, Mariana  and
      Ngonga Ngomo, Axel-Cyrille",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1481",
}
@inproceedings{berlingerio-bonin-2018-towards,
    title = "Towards a music-language mapping",
    author = "Berlingerio, Michele  and
      Bonin, Francesca",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1482",
}
@inproceedings{isard-etal-2018-cycling,
    title = "Up-cycling Data for Natural Language Generation",
    author = "Isard, Amy  and
      Oberlander, Jon  and
      Grover, Claire",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1483",
}
@inproceedings{le-fokkens-2018-neural,
    title = "Neural Models of Selectional Preferences for Implicit Semantic Role Labeling",
    author = "Le, Minh  and
      Fokkens, Antske",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1484",
}
@inproceedings{barbaresi-etal-2018-database,
    title = "A database of {G}erman definitory contexts from selected web sources",
    author = "Barbaresi, Adrien  and
      Lemnitzer, Lothar  and
      Geyken, Alexander",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1485",
}
@inproceedings{migueles-abraira-etal-2018-annotating,
    title = "Annotating {A}bstract {M}eaning {R}epresentations for {S}panish",
    author = "Migueles-Abraira, Noelia  and
      Agerri, Rodrigo  and
      Diaz de Ilarraza, Arantza",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1486",
}
@inproceedings{lhomme-etal-2018-browsing,
    title = "Browsing the Terminological Structure of a Specialized Domain: A Method Based on Lexical Functions and their Classification",
    author = "L{'}Homme, Marie-Claude  and
      Robichaud, Beno{\^\i}t  and
      Pr{\'e}vil, Nathalie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1487",
}
@inproceedings{sayeed-etal-2018-rollenwechsel,
    title = "Rollenwechsel-{E}nglish: a large-scale semantic role corpus",
    author = "Sayeed, Asad  and
      Shkadzko, Pavel  and
      Demberg, Vera",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1488",
}
@inproceedings{ponkiya-etal-2018-towards,
    title = "Towards a Standardized Dataset for Noun Compound Interpretation",
    author = "Ponkiya, Girishkumar  and
      Patel, Kevin  and
      Bhattacharyya, Pushpak  and
      Palshikar, Girish K",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1489",
}
@inproceedings{zhang-xue-2018-structured,
    title = "Structured Interpretation of Temporal Relations",
    author = "Zhang, Yuchen  and
      Xue, Nianwen",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1490",
}
@inproceedings{lin-etal-2018-nl2bash,
    title = "{NL}2{B}ash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System",
    author = "Lin, Xi Victoria  and
      Wang, Chenglong  and
      Zettlemoyer, Luke  and
      Ernst, Michael D.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1491",
}
@inproceedings{welch-etal-2018-world,
    title = "World Knowledge for {A}bstract {M}eaning {R}epresentation Parsing",
    author = "Welch, Charles  and
      Kummerfeld, Jonathan K.  and
      Feng, Song  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1492",
}
@inproceedings{gref-etal-2018-improved,
    title = "Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research",
    author = {Gref, Michael  and
      K{\"o}hler, Joachim  and
      Leh, Almut},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1493",
}
@inproceedings{ma-etal-2018-sound,
    title = "Sound Signal Processing with {S}eq2{T}ree Network",
    author = "Ma, Weicheng  and
      Cao, Kai  and
      Ni, Zhaoheng  and
      Chin, Peter  and
      Li, Xiang",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1494",
}
@inproceedings{nikulasdottir-etal-2018-open,
    title = "Open {ASR} for {I}celandic: Resources and a Baseline System",
    author = {Nikul{\'a}sd{\'o}ttir, Anna Bj{\"o}rk  and
      Helgad{\'o}ttir, Inga R{\'u}n  and
      P{\'e}tursson, Matth{\'\i}as  and
      Gu{\dh}nason, J{\'o}n},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1495",
}
@inproceedings{meng-etal-2018-towards,
    title = "Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models",
    author = "Meng, Zhao  and
      Mou, Lili  and
      Jin, Zhi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1496",
}
@inproceedings{he-etal-2018-discriminating,
    title = "Discriminating between Similar Languages on Imbalanced Conversational Texts",
    author = "He, Junqing  and
      Huang, Xian  and
      Zhao, Xuemin  and
      Zhang, Yan  and
      Yan, Yonghong",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1497",
}
@inproceedings{stadtschnitzer-schmidt-2018-data,
    title = "Data-Driven Pronunciation Modeling of {S}wiss {G}erman Dialectal Speech for Automatic Speech Recognition",
    author = "Stadtschnitzer, Michael  and
      Schmidt, Christoph",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1498",
}
@inproceedings{simonnet-etal-2018-simulating,
    title = "Simulating {ASR} errors for training {SLU} systems",
    author = "Simonnet, Edwin  and
      Ghannay, Sahar  and
      Camelin, Nathalie  and
      Est{\`e}ve, Yannick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1499",
}
@inproceedings{tomashenko-esteve-2018-evaluation,
    title = "Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models",
    author = "Tomashenko, Natalia  and
      Est{\`e}ve, Yannick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1500",
}
@inproceedings{steiner-le-maguer-2018-creating,
    title = "Creating New Language and Voice Components for the Updated {M}ary{TTS} Text-to-Speech Synthesis Platform",
    author = "Steiner, Ingmar  and
      Le Maguer, S{\'e}bastien",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1501",
}
@inproceedings{hayakawa-etal-2018-speech,
    title = "Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task",
    author = "Hayakawa, Akira  and
      Vogel, Carl  and
      Luz, Saturnino  and
      Campbell, Nick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1502",
}
@inproceedings{tauchmann-etal-2018-beyond,
    title = "Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data",
    author = "Tauchmann, Christopher  and
      Arnold, Thomas  and
      Hanselowski, Andreas  and
      Meyer, Christian M.  and
      Mieskes, Margot",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1503",
}
@inproceedings{linhares-pontes-etal-2018-new,
    title = "A New Annotated {P}ortuguese/{S}panish Corpus for the Multi-Sentence Compression Task",
    author = "Linhares Pontes, Elvys  and
      Torres-Moreno, Juan-Manuel  and
      Huet, St{\'e}phane  and
      Linhares, Andr{\'e}a Carneiro",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1504",
}
@inproceedings{p-v-s-etal-2018-live,
    title = "Live Blog Corpus for Summarization",
    author = "P.V.S., Avinesh  and
      Peyrard, Maxime  and
      Meyer, Christian M.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1505",
}
@inproceedings{nguyen-etal-2018-tsix,
    title = "{TS}ix: A Human-involved-creation Dataset for Tweet Summarization",
    author = "Nguyen, Minh-Tien  and
      Lai, Dac Viet  and
      Nguyen, Huy-Tien  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1506",
}
@inproceedings{jhaveri-etal-2018-workbench,
    title = "A Workbench for Rapid Generation of Cross-Lingual Summaries",
    author = "Jhaveri, Nisarg  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1507",
}
@inproceedings{yamamura-shimada-2018-annotation,
    title = "Annotation and Analysis of Extractive Summaries for the {K}yutech Corpus",
    author = "Yamamura, Takashi  and
      Shimada, Kazutaka",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1508",
}
@inproceedings{dernoncourt-etal-2018-repository,
    title = "A Repository of Corpora for Summarization",
    author = "Dernoncourt, Franck  and
      Ghassemi, Mohammad  and
      Chang, Walter",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1509",
}
@inproceedings{zopf-2018-auto,
    title = "Auto-h{MDS}: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus",
    author = "Zopf, Markus",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1510",
}
@inproceedings{gao-etal-2018-pyreval,
    title = "{P}yr{E}val: An Automated Method for Summary Content Analysis",
    author = "Gao, Yanjun  and
      Warner, Andrew  and
      Passonneau, Rebecca",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1511",
}
@inproceedings{ostermann-etal-2018-mapping,
    title = "Mapping Texts to Scripts: An Entailment Study",
    author = "Ostermann, Simon  and
      Seitz, Hannah  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1512",
}
@inproceedings{rodrigues-etal-2018-semantic,
    title = "Semantic Equivalence Detection: Are Interrogatives Harder than Declaratives?",
    author = "Rodrigues, Jo{\~a}o  and
      Saedi, Chakaveh  and
      Branco, Ant{\'o}nio  and
      Silva, Jo{\~a}o",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1513",
}
@inproceedings{uchida-etal-2018-cefr,
    title = "{CEFR}-based Lexical Simplification Dataset",
    author = "Uchida, Satoru  and
      Takada, Shohei  and
      Arase, Yuki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1514",
}
@inproceedings{de-jong-etal-2018-clarin,
    title = "{CLARIN}: Towards {FAIR} and Responsible Data Science Using Language Resources",
    author = "de Jong, Franciska  and
      Maegaard, Bente  and
      De Smedt, Koenraad  and
      Fi{\v{s}}er, Darja  and
      Van Uytvanck, Dieter",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1515",
}
@inproceedings{cieri-etal-2018-solved,
    title = "From {`}Solved Problems{'} to New Challenges: A Report on {LDC} Activities",
    author = "Cieri, Christopher  and
      Liberman, Mark  and
      Strassel, Stephanie  and
      DiPersio, Denise  and
      Wright, Jonathan  and
      Mazzucchi, Andrea",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1516",
}
@inproceedings{mapelli-etal-2018-new,
    title = "New directions in {ELRA} activities",
    author = "Mapelli, Val{\'e}rie  and
      Arranz, Victoria  and
      Mazo, H{\'e}l{\`e}ne  and
      Kamocki, Pawel  and
      Popescu, Vladimir",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1517",
}
@inproceedings{lin-etal-2018-framework,
    title = "A Framework for Multi-Language Service Design with the Language Grid",
    author = "Lin, Donghui  and
      Murakami, Yohei  and
      Ishida, Toru",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1518",
}
@inproceedings{rehm-hegele-2018-language,
    title = "Language Technology for Multilingual {E}urope: An Analysis of a Large-Scale Survey regarding Challenges, Demands, Gaps and Needs",
    author = "Rehm, Georg  and
      Hegele, Stefanie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1519",
}
@inproceedings{li-etal-2018-annotating,
    title = "Annotating High-Level Structures of Short Stories and Personal Anecdotes",
    author = "Li, Boyang  and
      Cardier, Beth  and
      Wang, Tong  and
      Metze, Florian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1520",
}
@inproceedings{lefever-etal-2018-discovering,
    title = "Discovering the Language of Wine Reviews: A Text Mining Account",
    author = "Lefever, Els  and
      Hendrickx, Iris  and
      Croijmans, Ilja  and
      van den Bosch, Antal  and
      Majid, Asifa",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1521",
}
@inproceedings{bond-matthews-2018-toward,
    title = "Toward An Epic Epigraph Graph",
    author = "Bond, Francis  and
      Matthews, Graham",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1522",
}
@inproceedings{proisl-etal-2018-delta,
    title = "Delta vs. N-Gram Tracing: Evaluating the Robustness of Authorship Attribution Methods",
    author = {Proisl, Thomas  and
      Evert, Stefan  and
      Jannidis, Fotis  and
      Sch{\"o}ch, Christof  and
      Konle, Leonard  and
      Pielstr{\"o}m, Steffen},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1523",
}
@inproceedings{newell-etal-2018-attribution,
    title = "An Attribution Relations Corpus for Political News",
    author = "Newell, Edward  and
      Margolin, Drew  and
      Ruths, Derek",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1524",
}
@inproceedings{gatt-etal-2018-face2text,
    title = "{F}ace2{T}ext: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions",
    author = "Gatt, Albert  and
      Tanti, Marc  and
      Muscat, Adrian  and
      Paggio, Patrizia  and
      Farrugia, Reuben A  and
      Borg, Claudia  and
      Camilleri, Kenneth P  and
      Rosner, Michael  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1525",
}
@inproceedings{habernal-etal-2018-adapting,
    title = "Adapting Serious Game for Fallacious Argumentation to {G}erman: Pitfalls, Insights, and Best Practices",
    author = "Habernal, Ivan  and
      Pauli, Patrick  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1526",
}
@inproceedings{goldman-etal-2018-crowdsourcing,
    title = "Crowdsourcing Regional Variation Data and Automatic Geolocalisation of Speakers of {E}uropean {F}rench",
    author = {Goldman, Jean-Philippe  and
      Scherrer, Yves  and
      Glikman, Julie  and
      Avanzi, Mathieu  and
      Benzitoun, Christophe  and
      Boula de Mare{\"u}il, Philippe},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1527",
}
@inproceedings{behnke-etal-2018-improving,
    title = "Improving Machine Translation of Educational Content via Crowdsourcing",
    author = "Behnke, Maximiliana  and
      Miceli Barone, Antonio Valerio  and
      Sennrich, Rico  and
      Sosoni, Vilelmini  and
      Naskos, Thanasis  and
      Takoulidou, Eirini  and
      Stasimioti, Maria  and
      van Zaanen, Menno  and
      Castilho, Sheila  and
      Gaspari, Federico  and
      Georgakopoulou, Panayota  and
      Kordoni, Valia  and
      Egg, Markus  and
      Kermanidis, Katia Lida",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1528",
}
@inproceedings{sharp-etal-2018-grounding,
    title = "Grounding Gradable Adjectives through Crowdsourcing",
    author = "Sharp, Rebecca  and
      Paul, Mithun  and
      Nagesh, Ajay  and
      Bell, Dane  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1529",
}
@inproceedings{adams-etal-2018-evaluation,
    title = "Evaluation Phonemic Transcription of Low-Resource Tonal Languages for Language Documentation",
    author = "Adams, Oliver  and
      Cohn, Trevor  and
      Neubig, Graham  and
      Cruz, Hilaria  and
      Bird, Steven  and
      Michaud, Alexis",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1530",
}
@inproceedings{godard-etal-2018-low,
    title = "A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments",
    author = "Godard, Pierre  and
      Adda, Gilles  and
      Adda-Decker, Martine  and
      Benjumea, Juan  and
      Besacier, Laurent  and
      Cooper-Leavitt, Jamison  and
      Kouarata, Guy-Noel  and
      Lamel, Lori  and
      Maynard, H{\'e}l{\`e}ne  and
      Mueller, Markus  and
      Rialland, Annie  and
      Stueker, Sebastian  and
      Yvon, Fran{\c{c}}ois  and
      Zanon-Boito, Marcely",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1531",
}
@inproceedings{brixey-etal-2018-chahta,
    title = "Chahta Anumpa: A multimodal corpus of the {C}hoctaw Language",
    author = "Brixey, Jacqueline  and
      Pincus, Eli  and
      Artstein, Ron",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1532",
}
@inproceedings{hamlaoui-etal-2018-bulbasaa,
    title = "{BULB}asaa: A Bilingual Basaa-{F}rench Speech Corpus for the Evaluation of Language Documentation Tools",
    author = {Hamlaoui, Fatima  and
      Makasso, Emmanuel-Moselly  and
      M{\"u}ller, Markus  and
      Engelmann, Jonas  and
      Adda, Gilles  and
      Waibel, Alex  and
      St{\"u}ker, Sebastian},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1533",
}
@inproceedings{jokinen-2018-researching,
    title = "Researching Less-Resourced Languages {--} the {D}igi{S}ami Corpus",
    author = "Jokinen, Kristiina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1534",
}
@inproceedings{bouamor-etal-2018-madar,
    title = "The {MADAR} {A}rabic Dialect Corpus and Lexicon",
    author = "Bouamor, Houda  and
      Habash, Nizar  and
      Salameh, Mohammad  and
      Zaghouani, Wajdi  and
      Rambow, Owen  and
      Abdulrahim, Dana  and
      Obeid, Ossama  and
      Khalifa, Salam  and
      Eryani, Fadhl  and
      Erdmann, Alexander  and
      Oflazer, Kemal",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1535",
}
@inproceedings{nasution-etal-2018-designing,
    title = "Designing a Collaborative Process to Create Bilingual Dictionaries of {I}ndonesian Ethnic Languages",
    author = "Nasution, Arbi Haza  and
      Murakami, Yohei  and
      Ishida, Toru",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1536",
}
@inproceedings{newell-cheung-2018-constructing,
    title = "Constructing a Lexicon of Relational Nouns",
    author = "Newell, Edward  and
      Cheung, Jackie C.K.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1537",
}
@inproceedings{wu-yarowsky-2018-creating,
    title = "Creating Large-Scale Multilingual Cognate Tables",
    author = "Wu, Winston  and
      Yarowsky, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1538",
}
@inproceedings{drouin-etal-2018-lexical,
    title = "Lexical Profiling of Environmental Corpora",
    author = "Drouin, Patrick  and
      L{'}Homme, Marie-Claude  and
      Robichaud, Beno{\^\i}t",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1539",
}
@inproceedings{klang-nugues-2018-linking,
    title = "Linking, Searching, and Visualizing Entities in {W}ikipedia",
    author = "Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1540",
}
@inproceedings{lin-etal-2018-learning,
    title = "Learning to Map Natural Language Statements into Knowledge Base Representations for Knowledge Base Construction",
    author = "Lin, Chin-Ho  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1541",
}
@inproceedings{silva-etal-2018-building,
    title = "Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition",
    author = "Silva, Vivian  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1542",
}
@inproceedings{ferre-etal-2018-combining,
    title = "Combining rule-based and embedding-based approaches to normalize textual entities with an ontology",
    author = "Ferr{\'e}, Arnaud  and
      Del{\'e}ger, Louise  and
      Zweigenbaum, Pierre  and
      N{\'e}dellec, Claire",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1543",
}
@inproceedings{elsahar-etal-2018-rex,
    title = "{T}-{RE}x: A Large Scale Alignment of Natural Language with Knowledge Base Triples",
    author = "Elsahar, Hady  and
      Vougiouklis, Pavlos  and
      Remaci, Arslen  and
      Gravier, Christophe  and
      Hare, Jonathon  and
      Laforest, Frederique  and
      Simperl, Elena",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1544",
}
@inproceedings{imamura-sumita-2018-multilingual,
    title = "Multilingual Parallel Corpus for Global Communication Plan",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1545",
}
@inproceedings{soares-etal-2018-large,
    title = "A Large Parallel Corpus of Full-Text Scientific Articles",
    author = "Soares, Felipe  and
      Moreira, Viviane  and
      Becker, Karin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1546",
}
@inproceedings{liu-etal-2018-negpar,
    title = "{N}eg{P}ar: A parallel corpus annotated for negation",
    author = "Liu, Qianchu  and
      Fancellu, Federico  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1547",
}
@inproceedings{kunchukuttan-etal-2018-iit,
    title = "The {IIT} {B}ombay {E}nglish-{H}indi Parallel Corpus",
    author = "Kunchukuttan, Anoop  and
      Mehta, Pratik  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1548",
}
@inproceedings{karimi-etal-2018-extracting,
    title = "Extracting an {E}nglish-{P}ersian Parallel Corpus from Comparable Corpora",
    author = "Karimi, Akbar  and
      Ansari, Ebrahim  and
      Sadeghi Bigham, Bahram",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1549",
}
@inproceedings{grave-etal-2018-learning,
    title = "Learning Word Vectors for 157 Languages",
    author = "Grave, Edouard  and
      Bojanowski, Piotr  and
      Gupta, Prakhar  and
      Joulin, Armand  and
      Mikolov, Tomas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1550",
}
@inproceedings{straka-etal-2018-sumeczech,
    title = "{S}ume{C}zech: Large {C}zech News-Based Summarization Dataset",
    author = "Straka, Milan  and
      Mediankin, Nikita  and
      Kocmi, Tom  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Hude{\v{c}}ek, Vojt{\v{e}}ch  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1551",
}
@inproceedings{klaussner-vogel-2018-diachronic,
    title = "A Diachronic Corpus for Literary Style Analysis",
    author = "Klaussner, Carmen  and
      Vogel, Carl",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1552",
}
@inproceedings{scarton-etal-2018-text,
    title = "Text Simplification from Professionally Produced Corpora",
    author = "Scarton, Carolina  and
      Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1553",
}
@inproceedings{visser-etal-2018-intertextual,
    title = "Intertextual Correspondence for Integrating Corpora",
    author = "Visser, Jacky  and
      Duthie, Rory  and
      Lawrence, John  and
      Reed, Chris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1554",
}
@inproceedings{seminck-amsili-2018-gold,
    title = "A Gold Anaphora Annotation Layer on an Eye Movement Corpus",
    author = "Seminck, Olga  and
      Amsili, Pascal",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1555",
}
@inproceedings{asao-etal-2018-annotating,
    title = "Annotating Zero Anaphora for Question Answering",
    author = "Asao, Yoshihiko  and
      Iida, Ryu  and
      Torisawa, Kentaro",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1556",
}
@inproceedings{agerri-etal-2018-building,
    title = "Building Named Entity Recognition Taggers via Parallel Corpora",
    author = "Agerri, Rodrigo  and
      Chung, Yiling  and
      Aldabe, Itziar  and
      Aranberri, Nora  and
      Labaka, Gorka  and
      Rigau, German",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1557",
}
@inproceedings{song-etal-2018-cross,
    title = "Cross-Document, Cross-Language Event Coreference Annotation Using Event Hoppers",
    author = "Song, Zhiyi  and
      Bies, Ann  and
      Mott, Justin  and
      Li, Xuansong  and
      Strassel, Stephanie  and
      Caruso, Christopher",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1558",
}
@inproceedings{ghosal-etal-2018-tap,
    title = "{TAP}-{DLND} 1.0 : A Corpus for Document Level Novelty Detection",
    author = "Ghosal, Tirthankar  and
      Salam, Amitra  and
      Tiwari, Swati  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1559",
}
@inproceedings{schwenk-li-2018-corpus,
    title = "A Corpus for Multilingual Document Classification in Eight Languages",
    author = "Schwenk, Holger  and
      Li, Xian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1560",
}
@inproceedings{herrmannova-etal-2018-analyzing,
    title = "Analyzing Citation-Distance Networks for Evaluating Publication Impact",
    author = "Herrmannova, Drahomira  and
      Knoth, Petr  and
      Patton, Robert",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1561",
}
@inproceedings{godea-nielsen-2018-annotating,
    title = "Annotating Educational Questions for Student Response Analysis",
    author = "Godea, Andreea  and
      Nielsen, Rodney",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1562",
}
@inproceedings{kim-choi-2018-incorporating,
    title = "Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision",
    author = "Kim, Eun-kyung  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1563",
}
@inproceedings{ostermann-etal-2018-mcscript,
    title = "{MCS}cript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge",
    author = "Ostermann, Simon  and
      Modi, Ashutosh  and
      Roth, Michael  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1564",
}
@inproceedings{mi-etal-2018-neural,
    title = "A Neural Network Based Model for Loanword Identification in {U}yghur",
    author = "Mi, Chenggang  and
      Yang, Yating  and
      Wang, Lei  and
      Zhou, Xi  and
      Jiang, Tonghai",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1565",
}
@inproceedings{jiang-etal-2018-revisiting,
    title = "Revisiting Distant Supervision for Relation Extraction",
    author = "Jiang, Tingsong  and
      Liu, Jing  and
      Lin, Chin-Yew  and
      Sui, Zhifang",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1566",
}
@inproceedings{staron-etal-2018-incorporating,
    title = "Incorporating Contextual Information for Language-Independent, Dynamic Disambiguation Tasks",
    author = {Staron, Tobias  and
      Ala{\c{c}}am, {\"O}zge  and
      Menzel, Wolfgang},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1567",
}
@inproceedings{geiger-braschler-2018-overcoming,
    title = "Overcoming the Long Tail Problem: A Case Study on {CO}2-Footprint Estimation of Recipes using Information Retrieval",
    author = "Geiger, Melanie  and
      Braschler, Martin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1568",
}
@inproceedings{yatsu-araki-2018-comparison,
    title = "Comparison of Pun Detection Methods Using {J}apanese Pun Corpus",
    author = "Yatsu, Motoki  and
      Araki, Kenji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1569",
}
@inproceedings{mukuze-etal-2018-vision,
    title = "A vision-grounded dataset for predicting typical locations for verbs",
    author = "Mukuze, Nelson  and
      Rohrbach, Anna  and
      Demberg, Vera  and
      Schiele, Bernt",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1570",
}
@inproceedings{sato-heffernan-2018-creating,
    title = "Creating dialect sub-corpora by clustering: a case in {J}apanese for an adaptive method",
    author = "Sato, Yo  and
      Heffernan, Kevin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1571",
}
@inproceedings{van-hout-etal-2018-fast,
    title = "A Fast and Flexible Webinterface for Dialect Research in the Low Countries",
    author = "van Hout, Roeland  and
      van der Sijs, Nicoline  and
      Komen, Erwin  and
      van den Heuvel, Henk",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1572",
}
@inproceedings{el-haj-etal-2018-arabic,
    title = "{A}rabic Dialect Identification in the Context of Bivalency and Code-Switching",
    author = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Aboelezz, Mariam",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1573",
}
@inproceedings{habash-etal-2018-unified,
    title = "Unified Guidelines and Resources for {A}rabic Dialect Orthography",
    author = "Habash, Nizar  and
      Eryani, Fadhl  and
      Khalifa, Salam  and
      Rambow, Owen  and
      Abdulrahim, Dana  and
      Erdmann, Alexander  and
      Faraj, Reem  and
      Zaghouani, Wajdi  and
      Bouamor, Houda  and
      Zalmout, Nasser  and
      Hassan, Sara  and
      Al-Shargi, Faisal  and
      Alkhereyf, Sakhar  and
      Abdulkareem, Basma  and
      Eskander, Ramy  and
      Salameh, Mohammad  and
      Saddiki, Hind",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1574",
}
@inproceedings{saadane-etal-2018-automatic,
    title = "Automatic Identification of Maghreb Dialects Using a Dictionary-Based Approach",
    author = "Sa{\^a}dane, Houda  and
      Seffih, Hosni  and
      Fluhr, Christian  and
      Choukri, Khalid  and
      Semmar, Nasredine",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1575",
}
@inproceedings{abu-kwaik-etal-2018-shami,
    title = "{S}hami: A Corpus of {L}evantine {A}rabic Dialects",
    author = "Abu Kwaik, Kathrein  and
      Saad, Motaz  and
      Chatzikyriakidis, Stergios  and
      Dobnik, Simon",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1576",
}
@inproceedings{abdul-mageed-etal-2018-tweet,
    title = "You Tweet What You Speak: A City-Level Dataset of {A}rabic Dialects",
    author = "Abdul-Mageed, Muhammad  and
      Alhuzali, Hassan  and
      Elaraby, Mohamed",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1577",
}
@inproceedings{wan-2018-visualizing,
    title = "Visualizing the {``}Dictionary of Regionalisms of {F}rance{''} ({DRF})",
    author = "Wan, Ada",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1578",
}
@inproceedings{alsarsour-etal-2018-dart,
    title = "{DART}: A Large Dataset of Dialectal {A}rabic Tweets",
    author = "Alsarsour, Israa  and
      Mohamed, Esraa  and
      Suwaileh, Reem  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1579",
}
@inproceedings{wray-2018-classification,
    title = "Classification of Closely Related Sub-dialects of {A}rabic Using Support-Vector Machines",
    author = "Wray, Samantha",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1580",
}
@inproceedings{wiedemann-heyer-2018-page,
    title = "Page Stream Segmentation with Convolutional Neural Nets Combining Textual and Visual Features",
    author = "Wiedemann, Gregor  and
      Heyer, Gerhard",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1581",
}
@inproceedings{norman-etal-2018-automating,
    title = "Automating Document Discovery in the Systematic Review Process: How to Use Chaff to Extract Wheat",
    author = "Norman, Christopher  and
      Leeflang, Mariska  and
      Zweigenbaum, Pierre  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1582",
}
@inproceedings{ahmia-etal-2018-two,
    title = "Two Multilingual Corpora Extracted from the Tenders Electronic Daily for Machine Learning and Machine Translation Applications.",
    author = "Ahmia, Oussama  and
      B{\'e}chet, Nicolas  and
      Marteau, Pierre-Fran{\c{c}}ois",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1583",
}
@inproceedings{belohlavek-etal-2018-using,
    title = "Using Adversarial Examples in Natural Language Processing",
    author = "B{\v{e}}lohl{\'a}vek, Petr  and
      Pl{\'a}tek, Ond{\v{r}}ej  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Straka, Milan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1584",
}
@inproceedings{mojica-de-la-vega-ng-2018-modeling,
    title = "Modeling Trolling in Social Media Conversations",
    author = "Mojica de la Vega, Luis Gerardo  and
      Ng, Vincent",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1585",
}
@inproceedings{schumann-martinez-alonso-2018-automatic,
    title = "Automatic Annotation of Semantic Term Types in the Complete {ACL} {A}nthology Reference Corpus",
    author = "Schumann, Anne-Kathrin  and
      Mart{\'\i}nez Alonso, H{\'e}ctor",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1586",
}
@inproceedings{andruszkiewicz-hazan-2018-annotated,
    title = "Annotated Corpus of Scientific Conference{'}s Homepages for Information Extraction",
    author = "Andruszkiewicz, Piotr  and
      Hazan, Rafa{\l}",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1587",
}
@inproceedings{yu-ng-2018-improving,
    title = "Improving Unsupervised Keyphrase Extraction using Background Knowledge",
    author = "Yu, Yang  and
      Ng, Vincent",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1588",
}
@inproceedings{gleim-etal-2018-wikidragon,
    title = "{W}iki{D}ragon: A {J}ava Framework For Diachronic Content And Network Analysis Of {M}edia{W}ikis",
    author = {Gleim, R{\"u}diger  and
      Mehler, Alexander  and
      Y. Song, Sung},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1589",
}
@inproceedings{fokkens-etal-2018-studying,
    title = "Studying Muslim Stereotyping through Microportrait Extraction",
    author = "Fokkens, Antske  and
      Ruigrok, Nel  and
      Beukeboom, Camiel  and
      Sarah, Gagestein  and
      van Atteveldt, Wouter",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1590",
}
@inproceedings{perez-rosas-etal-2018-analyzing,
    title = "Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Sun, Xuetong  and
      Li, Christy  and
      Wang, Yuchen  and
      Resnicow, Kenneth  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1591",
}
@inproceedings{katerenchuk-etal-2018-interpersonal,
    title = "Interpersonal Relationship Labels for the {CALLHOME} Corpus",
    author = "Katerenchuk, Denys  and
      Brizan, David Guy  and
      Rosenberg, Andrew",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1592",
}
@inproceedings{higuchi-etal-2018-text,
    title = "Text Mining for History: first steps on building a large dataset",
    author = "Higuchi, Suemi  and
      Freitas, Cl{\'a}udia  and
      Cuconato, Bruno  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1593",
}
@inproceedings{goeuriot-etal-2018-building,
    title = "Building Evaluation Datasets for Cultural Microblog Retrieval",
    author = "Goeuriot, Lorraine  and
      Mothe, Josiane  and
      Mulhem, Philippe  and
      SanJuan, Eric",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1594",
}
@inproceedings{rikters-etal-2018-training,
    title = "Training and Adapting Multilingual {NMT} for Less-resourced and Morphologically Rich Languages",
    author = "Rikters, Mat{\=\i}ss  and
      Pinnis, M{\=a}rcis  and
      Kri{\v{s}}lauks, Rihards",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1595",
}
@inproceedings{yuan-etal-2018-cross,
    title = "Cross-lingual Terminology Extraction for Translation Quality Estimation",
    author = "Yuan, Yu  and
      Gao, Yuze  and
      Zhang, Yue  and
      Sharoff, Serge",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1596",
}
@inproceedings{honnet-etal-2018-machine,
    title = "Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing {S}wiss {G}erman",
    author = "Honnet, Pierre-Edouard  and
      Popescu-Belis, Andrei  and
      Musat, Claudiu  and
      Baeriswyl, Michael",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1597",
}
@inproceedings{farhath-etal-2018-improving,
    title = "Improving domain-specific {SMT} for low-resourced languages using data from different domains",
    author = "Farhath, Fathima  and
      Theivendiram, Pranavan  and
      Ranathunga, Surangika  and
      Jayasena, Sanath  and
      Dias, Gihan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1598",
}
@inproceedings{papavassiliou-etal-2018-discovering,
    title = "Discovering Parallel Language Resources for Training {MT} Engines",
    author = "Papavassiliou, Vassilis  and
      Prokopidis, Prokopis  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1599",
}
@inproceedings{van-brussel-etal-2018-fine,
    title = "A fine-grained error analysis of {NMT}, {SMT} and {RBMT} output for {E}nglish-to-{D}utch",
    author = "Van Brussel, Laura  and
      Tezcan, Arda  and
      Macken, Lieve",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1600",
}
@inproceedings{hamed-etal-2018-collection,
    title = "Collection and Analysis of Code-switch {E}gyptian {A}rabic-{E}nglish Speech Corpus",
    author = "Hamed, Injy  and
      Elmahdy, Mohamed  and
      Abdennadher, Slim",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1601",
}
@inproceedings{lala-specia-2018-multimodal,
    title = "Multimodal Lexical Translation",
    author = "Lala, Chiraag  and
      Specia, Lucia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1602",
}
@inproceedings{lacruz-etal-2018-literality,
    title = "Literality and cognitive effort: {J}apanese and {S}panish",
    author = "Lacruz, Isabel  and
      Carl, Michael  and
      Yamada, Masaru",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1603",
}
@inproceedings{van-der-wees-etal-2018-evaluation,
    title = "Evaluation of Machine Translation Performance Across Multiple Genres and Languages",
    author = "van der Wees, Marlies  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1604",
}
@inproceedings{zweigenbaum-etal-2018-multilingual,
    title = "A Multilingual Dataset for Evaluating Parallel Sentence Extraction from Comparable Corpora",
    author = "Zweigenbaum, Pierre  and
      Sharoff, Serge  and
      Rapp, Reinhard",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1605",
}
@inproceedings{makazhanov-etal-2018-manual,
    title = "Manual vs Automatic Bitext Extraction",
    author = "Makazhanov, Aibek  and
      Myrzakhmetov, Bagdat  and
      Assylbekov, Zhenisbek",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1606",
}
@inproceedings{khalifa-etal-2018-morphologically,
    title = "A Morphologically Annotated Corpus of Emirati {A}rabic",
    author = "Khalifa, Salam  and
      Habash, Nizar  and
      Eryani, Fadhl  and
      Obeid, Ossama  and
      Abdulrahim, Dana  and
      Al Kaabi, Meera",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1607",
}
@inproceedings{more-etal-2018-conll,
    title = "{C}o{NLL}-{UL}: Universal Morphological Lattices for {U}niversal {D}ependency Parsing",
    author = {More, Amir  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Habash, Nizar  and
      Sagot, Beno{\^\i}t  and
      Seddah, Djam{\'e}  and
      Taji, Dima  and
      Tsarfaty, Reut},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1608",
}
@inproceedings{kieras-wolinski-2018-manually,
    title = "Manually Annotated Corpus of {P}olish Texts Published between 1830 and 1918",
    author = "Kiera{\'s}, Witold  and
      Woli{\'n}ski, Marcin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1609",
}
@inproceedings{marzi-etal-2018-evaluating,
    title = "Evaluating Inflectional Complexity Crosslinguistically: a Processing Perspective",
    author = "Marzi, Claudia  and
      Ferro, Marcello  and
      Nahli, Ouafae  and
      Belik, Patrizia  and
      Bompolas, Stavros  and
      Pirrelli, Vito",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1610",
}
@inproceedings{littell-etal-2018-parser,
    title = "Parser combinators for {T}igrinya and {O}romo morphology",
    author = "Littell, Patrick  and
      McCoy, Tom  and
      Han, Na-Rae  and
      Rijhwani, Shruti  and
      Sheikh, Zaid  and
      Mortensen, David  and
      Mitamura, Teruko  and
      Levin, Lori",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1611",
}
@inproceedings{wu-yarowsky-2018-massively,
    title = "Massively Translingual Compound Analysis and Translation Discovery",
    author = "Wu, Winston  and
      Yarowsky, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1612",
}
@inproceedings{steiner-ruppenhofer-2018-building,
    title = "Building a Morphological Treebank for {G}erman from a Linguistic Database",
    author = "Steiner, Petra  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1613",
}
@inproceedings{agic-schluter-2018-baselines,
    title = "Baselines and Test Data for Cross-Lingual Inference",
    author = "Agi{\'c}, {\v{Z}}eljko  and
      Schluter, Natalie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1614",
}
@inproceedings{stajner-etal-2018-cats,
    title = "{CATS}: A Tool for Customized Alignment of Text Simplification Corpora",
    author = "{\v{S}}tajner, Sanja  and
      Franco-Salvador, Marc  and
      Rosso, Paolo  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1615",
}
@inproceedings{ha-etal-2018-kit,
    title = "{KIT}-Multi: A Translation-Oriented Multilingual Embedding Corpus",
    author = "Ha, Thanh-Le  and
      Niehues, Jan  and
      Sperber, Matthias  and
      Pham, Ngoc Quan  and
      Waibel, Alexander",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1616",
}
@inproceedings{sliwa-etal-2018-multi,
    title = "Multi-lingual Argumentative Corpora in {E}nglish, {T}urkish, {G}reek, {A}lbanian, {C}roatian, {S}erbian, {M}acedonian, {B}ulgarian, {R}omanian and {A}rabic",
    author = "Sliwa, Alfred  and
      Ma, Yuan  and
      Liu, Ruishen  and
      Borad, Niravkumar  and
      Ziyaei, Seyedeh  and
      Ghobadi, Mina  and
      Sabbah, Firas  and
      Aker, Ahmet",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1617",
}
@inproceedings{barzegar-etal-2018-semr,
    title = "{S}em{R}-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages",
    author = "Barzegar, Siamak  and
      Davis, Brian  and
      Zarrouk, Manel  and
      Handschuh, Siegfried  and
      Freitas, Andre",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1618",
}
@inproceedings{bernhard-etal-2018-corpora,
    title = "Corpora with Part-of-Speech Annotations for Three Regional Languages of {F}rance: {A}lsatian, {O}ccitan and {P}icard",
    author = "Bernhard, Delphine  and
      Ligozat, Anne-Laure  and
      Martin, Fanny  and
      Bras, Myriam  and
      Magistry, Pierre  and
      Vergez-Couret, Marianne  and
      Steibl{\'e}, Lucie  and
      Erhart, Pascale  and
      Hathout, Nabil  and
      Huck, Dominique  and
      Rey, Christophe  and
      Reyn{\'e}s, Philippe  and
      Rosset, Sophie  and
      Sibille, Jean  and
      Lavergne, Thomas",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1619",
}
@inproceedings{alharbi-etal-2018-part,
    title = "Part-of-Speech Tagging for {A}rabic {G}ulf Dialect Using {B}i-{LSTM}",
    author = "Alharbi, Randah  and
      Magdy, Walid  and
      Darwish, Kareem  and
      AbdelAli, Ahmed  and
      Mubarak, Hamdy",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1620",
}
@inproceedings{alosaimy-atwell-2018-web,
    title = "Web-based Annotation Tool for Inflectional Language Resources",
    author = "Alosaimy, Abdulrahman  and
      Atwell, Eric",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1621",
}
@inproceedings{barteld-etal-2018-hints,
    title = "{H}i{NTS}: A Tagset for {M}iddle {L}ow {G}erman",
    author = {Barteld, Fabian  and
      Ihden, Sarah  and
      Dreessen, Katharina  and
      Schr{\"o}der, Ingrid},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1622",
}
@inproceedings{neale-etal-2018-leveraging,
    title = "Leveraging Lexical Resources and Constraint Grammar for Rule-Based Part-of-Speech Tagging in {W}elsh",
    author = "Neale, Steven  and
      Donnelly, Kevin  and
      Watkins, Gareth  and
      Knight, Dawn",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1623",
}
@inproceedings{thayaparan-etal-2018-graph,
    title = "Graph Based Semi-Supervised Learning Approach for {T}amil {POS} tagging",
    author = "Thayaparan, Mokanarangan  and
      Ranathunga, Surangika  and
      Thayasivam, Uthayasanker",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1624",
}
@inproceedings{miehle-etal-2018-causes,
    title = "What Causes the Differences in Communication Styles? A Multicultural Study on Directness and Elaborateness",
    author = "Miehle, Juliana  and
      Minker, Wolfgang  and
      Ultes, Stefan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1625",
}
@inproceedings{jonell-etal-2018-farmi,
    title = "{FARMI}: A {F}r{A}mework for Recording Multi-Modal Interactions",
    author = "Jonell, Patrik  and
      Bystedt, Mattias  and
      Fallgren, Per  and
      Kontogiorgos, Dimosthenis  and
      Lopes, Jos{\'e}  and
      Malisz, Zofia  and
      Mascarenhas, Samuel  and
      Oertel, Catharine  and
      Raveh, Eran  and
      Shore, Todd",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1626",
}
@inproceedings{sakai-etal-2018-creating,
    title = "Creating Large-Scale Argumentation Structures for Dialogue Systems",
    author = "Sakai, Kazuki  and
      Inago, Akari  and
      Higashinaka, Ryuichiro  and
      Yoshikawa, Yuichiro  and
      Ishiguro, Hiroshi  and
      Tomita, Junji",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1627",
}
@inproceedings{walker-etal-2018-exploring,
    title = "Exploring Conversational Language Generation for Rich Content about Hotels",
    author = "Walker, Marilyn  and
      Smither, Albry  and
      Oraby, Shereen  and
      Harrison, Vrindavan  and
      Shemtov, Hadar",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1628",
}
@inproceedings{fillwock-traum-2018-identification,
    title = "Identification of Personal Information Shared in Chat-Oriented Dialogue",
    author = "Fillwock, Sarah  and
      Traum, David",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1629",
}
@inproceedings{ngo-etal-2018-vietnamese,
    title = "A {V}ietnamese Dialog Act Corpus Based on {ISO} 24617-2 standard",
    author = "Ngo, Thi-Lan  and
      Khac Linh, Pham  and
      Takeda, Hideaki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1630",
}
@inproceedings{guntakandla-nielsen-2018-annotating,
    title = "Annotating Reflections for Health Behavior Change Therapy",
    author = "Guntakandla, Nishitha  and
      Nielsen, Rodney",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1631",
}
@inproceedings{alsaif-etal-2018-annotating,
    title = "Annotating Attribution Relations in {A}rabic",
    author = "Alsaif, Amal  and
      Alyahya, Tasniem  and
      Alotaibi, Madawi  and
      Almuzaini, Huda  and
      Algahtani, Abeer",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1632",
}
@inproceedings{gilmartin-etal-2018-adele,
    title = "The {ADELE} Corpus of Dyadic Social Text Conversations:Dialog Act Annotation with {ISO} 24617-2",
    author = "Gilmartin, Emer  and
      Saam, Christian  and
      Spillane, Brendan  and
      O{'}Reilly, Maria  and
      Su, Ketong  and
      Calvo, Arturo  and
      Cerrato, Loredana  and
      Levacher, Killian  and
      Campbell, Nick  and
      Wade, Vincent",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1633",
}
@inproceedings{zeyrek-kurfali-2018-assessment,
    title = "An Assessment of Explicit Inter- and Intra-sentential Discourse Connectives in {T}urkish Discourse Bank",
    author = "Zeyrek, Deniz  and
      Kurfal{\i}, Murathan",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1634",
}
@inproceedings{burga-etal-2018-compilation,
    title = "Compilation of Corpora for the Study of the Information Structure{--}Prosody Interface",
    author = "Burga, Alicia  and
      Dom{\'\i}nguez, M{\'o}nica  and
      Farr{\'u}s, Mireia  and
      Wanner, Leo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1635",
}
@inproceedings{sakaida-etal-2018-preliminary,
    title = "Preliminary Analysis of Embodied Interactions between Science Communicators and Visitors Based on a Multimodal Corpus of {J}apanese Conversations in a Science Museum",
    author = "Sakaida, Rui  and
      Makino, Ryosaku  and
      Bono, Mayumi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1636",
}
@inproceedings{kishimoto-etal-2018-improving,
    title = "Improving Crowdsourcing-Based Annotation of {J}apanese Discourse Relations",
    author = "Kishimoto, Yudai  and
      Sawada, Shinnosuke  and
      Murawaki, Yugo  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1637",
}
@inproceedings{mirzaei-safari-2018-persian,
    title = "{P}ersian Discourse Treebank and coreference corpus",
    author = "Mirzaei, Azadeh  and
      Safari, Pegah",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1638",
}
@inproceedings{meng-etal-2018-automatic,
    title = "Automatic Labeling of Problem-Solving Dialogues for Computational Microgenetic Learning Analytics",
    author = "Meng, Yuanliang  and
      Rumshisky, Anna  and
      Sullivan, Florence",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1639",
}
@inproceedings{teruel-etal-2018-increasing,
    title = "Increasing Argument Annotation Reproducibility by Using Inter-annotator Agreement to Improve Guidelines",
    author = "Teruel, Milagro  and
      Cardellino, Cristian  and
      Cardellino, Fernando  and
      Alonso Alemany, Laura  and
      Villata, Serena",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1640",
}
@inproceedings{horbach-pinkal-2018-semi,
    title = "Semi-Supervised Clustering for Short Answer Scoring",
    author = "Horbach, Andrea  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1641",
}
@inproceedings{cao-etal-2018-analyzing,
    title = "Analyzing Vocabulary Commonality Index Using Large-scaled Database of Child Language Development",
    author = "Cao, Yan  and
      Minami, Yasuhiro  and
      Okumura, Yuko  and
      Kobayashi, Tessei",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1642",
}
@inproceedings{tavosanis-cominetti-2018-icon,
    title = "The {IC}o{N} Corpus of Academic Written {I}talian ({L}1 and {L}2)",
    author = "Tavosanis, Mirko  and
      Cominetti, Federica",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1643",
}
@inproceedings{katinskaia-etal-2018-revita,
    title = "{R}evita: a Language-learning Platform at the Intersection of {ITS} and {CALL}",
    author = "Katinskaia, Anisia  and
      Nouri, Javad  and
      Yangarber, Roman",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1644",
}
@inproceedings{braun-zahner-2018-distribution,
    title = "The Distribution and Prosodic Realization of Verb Forms in {G}erman Infant-Directed Speech",
    author = "Braun, Bettina  and
      Zahner, Katharina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1645",
}
@inproceedings{moran-etal-2018-cross,
    title = "Cross-linguistically Small World Networks are Ubiquitous in Child-directed Speech",
    author = "Moran, Steven  and
      Pajovi{\'c}, Danica  and
      Stoll, Sabine",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1646",
}
@inproceedings{li-lee-2018-l1,
    title = "{L}1-{L}2 Parallel Treebank of Learner {C}hinese: Overused and Underused Syntactic Structures",
    author = "Li, Keying  and
      Lee, John",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1647",
}
@inproceedings{dargis-etal-2018-use,
    title = "The Use of Text Alignment in Semi-Automatic Error Analysis: Use Case in the Development of the Corpus of the {L}atvian Language Learners",
    author = "Dar{\c{g}}is, Roberts  and
      Auzi{\c{n}}a, Ilze  and
      Lev{\=a}ne-Petrova, Krist{\=\i}ne",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1648",
}
@inproceedings{del-rio-mendes-2018-error,
    title = "Error annotation in a Learner Corpus of {P}ortuguese",
    author = "del R{\'\i}o, Iria  and
      Mendes, Am{\'a}lia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1649",
}
@inproceedings{zilio-etal-2018-sla,
    title = "An {SLA} Corpus Annotated with Pedagogically Relevant Grammatical Structures",
    author = "Zilio, Leonardo  and
      Wilkens, Rodrigo  and
      Fairon, C{\'e}drick",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1650",
}
@inproceedings{gezmu-etal-2018-portable,
    title = "Portable Spelling Corrector for a Less-Resourced Language: {A}mharic",
    author = {Gezmu, Andargachew Mekonnen  and
      N{\"u}rnberger, Andreas  and
      Seyoum, Binyam Ephrem},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1651",
}
@inproceedings{boula-de-mareuil-etal-2018-speaking,
    title = "A Speaking Atlas of the Regional Languages of {F}rance",
    author = {Boula de Mare{\"u}il, Philippe  and
      Rilliard, Albert  and
      Vernier, Fr{\'e}d{\'e}ric},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1652",
}
@inproceedings{maheshwari-etal-2018-towards,
    title = "Towards Language Technology for Mi{'}kmaq",
    author = "Maheshwari, Anant  and
      Bouscarrat, L{\'e}o  and
      Cook, Paul",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1653",
}
@inproceedings{steible-bernhard-2018-pronunciation,
    title = "Pronunciation Dictionaries for the {A}lsatian Dialects to Analyze Spelling and Phonetic Variation",
    author = "Steibl{\'e}, Lucie  and
      Bernhard, Delphine",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1654",
}
@inproceedings{mercado-gonzales-etal-2018-chanot,
    title = "{C}h{A}not: An Intelligent Annotation Tool for Indigenous and Highly Agglutinative Languages in {P}eru",
    author = "Mercado-Gonzales, Rodolfo  and
      Pereira-Noriega, Jos{\'e}  and
      Sobrevilla, Marco  and
      Oncevay, Arturo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1655",
}
@inproceedings{soria-etal-2018-dldp,
    title = "The {DLDP} Survey on Digital Use and Usability of {EU} Regional and Minority Languages",
    author = "Soria, Claudia  and
      Quochi, Valeria  and
      Russo, Irene",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1656",
}
@inproceedings{jimerson-prudhommeaux-2018-asr,
    title = "{ASR} for Documenting Acutely Under-Resourced Indigenous Languages",
    author = "Jimerson, Robbie  and
      Prud{'}hommeaux, Emily",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1657",
}
@inproceedings{brum-volpe-nunes-2018-building,
    title = "Building a Sentiment Corpus of Tweets in {B}razilian {P}ortuguese",
    author = "Brum, Henrico  and
      Volpe Nunes, Maria das Gra{\c{c}}as",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1658",
}
@inproceedings{abercrombie-batista-navarro-2018-aye,
    title = "{`}Aye{'} or {`}No{'}? Speech-level Sentiment Analysis of Hansard {UK} Parliamentary Debate Transcripts",
    author = "Abercrombie, Gavin  and
      Batista-Navarro, Riza",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1659",
}
@inproceedings{chamberlain-etal-2018-scalable,
    title = "Scalable Visualisation of Sentiment and Stance",
    author = "Chamberlain, Jon  and
      Kruschwitz, Udo  and
      Hoeber, Orland",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1660",
}
@inproceedings{velldal-etal-2018-norec,
    title = "{N}o{R}e{C}: The {N}orwegian Review Corpus",
    author = "Velldal, Erik  and
      {\O}vrelid, Lilja  and
      Bergem, Eivind Alexander  and
      Stadsnes, Cathrine  and
      Touileb, Samia  and
      J{\o}rgensen, Fredrik",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1661",
}
@inproceedings{rouces-etal-2018-sensaldo,
    title = "{S}en{SALDO}: Creating a Sentiment Lexicon for {S}wedish",
    author = "Rouces, Jacobo  and
      Tahmasebi, Nina  and
      Borin, Lars  and
      R{\o}dven Eide, Stian",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1662",
}
@inproceedings{penaloza-etal-2018-corpus,
    title = "Corpus Building and Evaluation of Aspect-based Opinion Summaries from Tweets in {S}panish",
    author = "Pe{\~n}aloza, Daniel  and
      L{\'o}pez, Rodrigo  and
      Tenorio, Juanjos{\'e}  and
      G{\'o}mez, H{\'e}ctor  and
      Oncevay-Marcos, Arturo  and
      Sobrevilla Cabezudo, Marco A.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1663",
}
@inproceedings{cignarella-etal-2018-application,
    title = "Application and Analysis of a Multi-layered Scheme for Irony on the {I}talian {T}witter Corpus {TWITTIR{\`O}}",
    author = "Cignarella, Alessandra Teresa  and
      Bosco, Cristina  and
      Patti, Viviana  and
      Lai, Mirko",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1664",
}
@inproceedings{kocon-etal-2018-classifier,
    title = "Classifier-based Polarity Propagation in a {W}ord{N}et",
    author = "Koco{\'n}, Jan  and
      Janz, Arkadiusz  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1665",
}
@inproceedings{ebling-etal-2018-smile,
    title = "{SMILE} {S}wiss {G}erman Sign Language Dataset",
    author = {Ebling, Sarah  and
      Camg{\"o}z, Necati Cihan  and
      Boyes Braem, Penny  and
      Tissi, Katja  and
      Sidler-Miserez, Sandra  and
      Stoll, Stephanie  and
      Hadfield, Simon  and
      Haug, Tobias  and
      Bowden, Richard  and
      Tornay, Sandrine  and
      Razavi, Marzieh  and
      Magimai-Doss, Mathew},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1666",
}
@inproceedings{kimmelman-etal-2018-ipsl,
    title = "{IPSL}: A Database of Iconicity Patterns in Sign Languages. Creation and Use",
    author = "Kimmelman, Vadim  and
      Klezovich, Anna  and
      Moroz, George",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1667",
}
@inproceedings{yu-etal-2018-sign,
    title = "Sign Languages and the Online World Online Dictionaries {\&} Lexicostatistics",
    author = "Yu, Shi  and
      Geraci, Carlo  and
      Abner, Natasha",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1668",
}
@inproceedings{filhol-hadjadj-2018-elicitation,
    title = "Elicitation protocol and material for a corpus of long prepared monologues in Sign Language",
    author = "Filhol, Michael  and
      Hadjadj, Mohamed Nassime",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1669",
}
@inproceedings{brock-nakadai-2018-deep,
    title = "Deep {JSLC}: A Multimodal Corpus Collection for Data-driven Generation of {J}apanese {S}ign {L}anguage Expressions",
    author = "Brock, Heike  and
      Nakadai, Kazuhiro",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1670",
}
@inproceedings{hadjadj-etal-2018-modeling,
    title = "Modeling {F}rench {S}ign {L}anguage: a proposal for a semantically compositional system",
    author = "Hadjadj, Mohamed Nassime  and
      Filhol, Michael  and
      Braffort, Annelies",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1671",
}
@inproceedings{koiso-etal-2018-construction,
    title = "Construction of the Corpus of Everyday {J}apanese Conversation: An Interim Report",
    author = "Koiso, Hanae  and
      Den, Yasuharu  and
      Iseki, Yuriko  and
      Kashino, Wakako  and
      Kawabata, Yoshiko  and
      Nishikawa, Ken{'}ya  and
      Tanaka, Yayoi  and
      Usuda, Yasuyuki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1672",
}
@inproceedings{astesano-etal-2018-carcinologic,
    title = "Carcinologic Speech Severity Index Project: A Database of Speech Disorder Productions to Assess Quality of Life Related to Speech After Cancer",
    author = "Ast{\'e}sano, Corine  and
      Balaguer, Mathieu  and
      Farinas, J{\'e}r{\^o}me  and
      Fredouille, Corinne  and
      Gaillard, Pascal  and
      Ghio, Alain  and
      Laaridh, Imed  and
      Lalain, Muriel  and
      Lepage, Beno{\^\i}t  and
      Mauclair, Julie  and
      Nocaudie, Olivier  and
      Pinquier, Julien  and
      Pont, Oriol  and
      Pouchoulin, Gilles  and
      Puech, Mich{\`e}le  and
      Robert, Dani{\`e}le  and
      Sicard, Etienne  and
      Woisard, Virginie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1673",
}
@inproceedings{rialland-etal-2018-parallel,
    title = "Parallel Corpora in {M}boshi ({B}antu {C}25, {C}ongo-{B}razzaville)",
    author = {Rialland, Annie  and
      Adda-Decker, Martine  and
      Kouarata, Guy-No{\"e}l  and
      Adda, Gilles  and
      Besacier, Laurent  and
      Lamel, Lori  and
      Gauthier, Elodie  and
      Godard, Pierre  and
      Cooper-Leavitt, Jamison},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1674",
}
@inproceedings{khan-etal-2018-multimodal,
    title = "A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks",
    author = "Khan, Arif  and
      Steiner, Ingmar  and
      Sugano, Yusuke  and
      Bulling, Andreas  and
      Macdonald, Ross",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1675",
}
@inproceedings{cai-etal-2018-statistical,
    title = "Statistical Analysis of Missing Translation in Simultaneous Interpretation Using A Large-scale Bilingual Speech Corpus",
    author = "Cai, Zhongxi  and
      Ryu, Koichiro  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1676",
}
@inproceedings{sini-etal-2018-synpaflex,
    title = "{S}yn{P}a{F}lex-Corpus: An Expressive {F}rench Audiobooks Corpus dedicated to expressive speech synthesis.",
    author = {Sini, Aghilas  and
      Lolive, Damien  and
      Vidal, Ga{\"e}lle  and
      Tahon, Marie  and
      Delais-Roussarie, {\'E}lisabeth},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1677",
}
@inproceedings{pezik-2018-increasing,
    title = "Increasing the Accessibility of Time-Aligned Speech Corpora with Spokes Mix",
    author = "P{\k{e}}zik, Piotr",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1678",
}
@inproceedings{fougeron-etal-2018-monpage,
    title = "The {M}on{P}a{G}e{\_}{HA} Database for the Documentation of Spoken {F}rench Throughout Adulthood",
    author = "Fougeron, C{\'e}cile  and
      Delvaux, V{\'e}ronique  and
      M{\'e}nard, Lucie  and
      Laganaro, Marina",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1679",
}
@inproceedings{fallgren-etal-2018-bringing,
    title = "Bringing Order to Chaos: A Non-Sequential Approach for Browsing Large Sets of Found Audio Data",
    author = "Fallgren, Per  and
      Malisz, Zofia  and
      Edlund, Jens",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1680",
}
@inproceedings{herms-etal-2018-coloss,
    title = "{C}o{L}o{SS}: Cognitive Load Corpus with Speech and Performance Data from a Symbol-Digit Dual-Task",
    author = {Herms, Robert  and
      Wirzberger, Maria  and
      Eibl, Maximilian  and
      Rey, G{\"u}nter Daniel},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1681",
}
@inproceedings{tracey-strassel-2018-vast,
    title = "{VAST}: A Corpus of Video Annotation for Speech Technologies",
    author = "Tracey, Jennifer  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1682",
}
@inproceedings{manuvinakurike-etal-2018-edit,
    title = "Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing",
    author = "Manuvinakurike, Ramesh  and
      Brixey, Jacqueline  and
      Bui, Trung  and
      Chang, Walter  and
      Kim, Doo Soon  and
      Artstein, Ron  and
      Georgila, Kallirroi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1683",
}
@inproceedings{feltracco-etal-2018-enriching,
    title = "Enriching a Lexicon of Discourse Connectives with Corpus-based Data",
    author = "Feltracco, Anna  and
      Jezek, Elisabetta  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1684",
}
@inproceedings{scarton-etal-2018-simpa,
    title = "{S}im{PA}: A Sentence-Level Simplification Corpus for the Public Administration Domain",
    author = "Scarton, Carolina  and
      Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1685",
}
@inproceedings{wagner-filho-etal-2018-brwac,
    title = "The br{W}a{C} Corpus: A New Open Resource for {B}razilian {P}ortuguese",
    author = "Wagner Filho, Jorge A.  and
      Wilkens, Rodrigo  and
      Idiart, Marco  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1686",
}
@inproceedings{kral-lenc-2018-czech,
    title = "{C}zech Text Document Corpus v 2.0",
    author = "Kr{\'a}l, Pavel  and
      Lenc, Ladislav",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1687",
}
@inproceedings{muller-etal-2018-corpora,
    title = "Corpora of Typical Sentences",
    author = {M{\"u}ller, Lydia  and
      Quasthoff, Uwe  and
      Sumalvico, Maciej},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1688",
}
@inproceedings{kupietz-etal-2018-german,
    title = "The {G}erman Reference Corpus {D}e{R}e{K}o: New Developments {--} New Opportunities",
    author = {Kupietz, Marc  and
      L{\"u}ngen, Harald  and
      Kamocki, Pawe{\l}  and
      Witt, Andreas},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1689",
}
@inproceedings{steingrimsson-etal-2018-risamalheild,
    title = "{R}isam{\'a}lheild: A Very Large {I}celandic Text Corpus",
    author = {Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Helgad{\'o}ttir, Sigr{\'u}n  and
      R{\"o}gnvaldsson, Eir{\'\i}kur  and
      Barkarson, Starka{\dh}ur  and
      Gu{\dh}nason, J{\'o}n},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1690",
}
@inproceedings{vezzani-etal-2018-trimed,
    title = "{T}ri{MED}: A Multilingual Terminological Database",
    author = "Vezzani, Federica  and
      Di Nunzio, Giorgio Maria  and
      Henrot, Genevi{\`e}ve",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1691",
}
@inproceedings{bosch-etal-2018-preparation,
    title = "Preparation and Usage of {X}hosa Lexicographical Data for a Multilingual, Federated Environment",
    author = "Bosch, Sonja  and
      Eckart, Thomas  and
      Klimek, Bettina  and
      Goldhahn, Dirk  and
      Quasthoff, Uwe",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1692",
}
@inproceedings{mendes-etal-2018-lexicon,
    title = "A Lexicon of Discourse Markers for {P}ortuguese {--} {LDM}-{PT}",
    author = "Mendes, Am{\'a}lia  and
      del Rio, Iria  and
      Stede, Manfred  and
      Dombek, Felix",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1693",
}
@inproceedings{khan-etal-2018-one,
    title = "One Language to rule them all: modelling Morphological Patterns in a Large Scale {I}talian Lexicon with {SWRL}",
    author = "Khan, Fahad  and
      Bellandi, Andrea  and
      Frontini, Francesca  and
      Monachini, Monica",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1694",
}
@inproceedings{de-melo-2018-metaphor,
    title = "Metaphor Suggestions based on a Semantic Metaphor Repository",
    author = "de Melo, Gerard",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1695",
}
@inproceedings{wawer-sarzynska-2018-linguistic,
    title = "The Linguistic Category Model in {P}olish ({LCM}-{PL})",
    author = "Wawer, Aleksander  and
      Sarzy{\'n}ska, Justyna",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1696",
}
@inproceedings{maguino-valencia-etal-2018-wordnet,
    title = "{W}ord{N}et-Shp: Towards the Building of a Lexical Database for a {P}eruvian Minority Language",
    author = "Magui{\~n}o-Valencia, Diego  and
      Oncevay-Marcos, Arturo  and
      Sobrevilla Cabezudo, Marco A.",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1697",
}
@inproceedings{fonseca-etal-2018-retrieving,
    title = "Retrieving Information from the {F}rench Lexical Network in {RDF}/{OWL} Format",
    author = "Fonseca, Alexsandro  and
      Sadat, Fatiha  and
      Lareau, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1698",
}
@inproceedings{ghaddar-langlais-2018-transforming,
    title = "Transforming {W}ikipedia into a Large-Scale Fine-Grained Entity Type Corpus",
    author = "Ghaddar, Abbas  and
      Langlais, Philippe",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1699",
}
@inproceedings{abudukelimu-etal-2018-error,
    title = "Error Analysis of {U}yghur Name Tagging: Language-specific Techniques and Remaining Challenges",
    author = "Abudukelimu, Halidanmu  and
      Abulizi, Abudoukelimu  and
      Zhang, Boliang  and
      Pan, Xiaoman  and
      Lu, Di  and
      Ji, Heng  and
      Liu, Yang",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1700",
}
@inproceedings{poostchi-etal-2018-bilstm,
    title = "{B}i{LSTM}-{CRF} for {P}ersian Named-Entity Recognition {A}rman{P}erso{NERC}orpus: the First Entity-Annotated {P}ersian Dataset",
    author = "Poostchi, Hanieh  and
      Zare Borzeshi, Ehsan  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1701",
}
@inproceedings{kang-park-2018-data,
    title = "Data Anonymization for Requirements Quality Analysis: a Reproducible Automatic Error Detection Task",
    author = "Kang, Juyeon  and
      Park, Jungyeul",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1702",
}
@inproceedings{schiersch-etal-2018-german,
    title = "A {G}erman Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events",
    author = "Schiersch, Martin  and
      Mironova, Veselina  and
      Schmitt, Maximilian  and
      Thomas, Philippe  and
      Gabryszak, Aleksandra  and
      Hennig, Leonhard",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1703",
}
@inproceedings{schon-etal-2018-corpus,
    title = "A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products",
    author = {Sch{\"o}n, Saskia  and
      Mironova, Veselina  and
      Gabryszak, Aleksandra  and
      Hennig, Leonhard},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1704",
}
@inproceedings{pirovani-oliveira-2018-portuguese,
    title = "{P}ortuguese Named Entity Recognition using Conditional Random Fields and Local Grammars",
    author = "Pirovani, Juliana  and
      Oliveira, Elias",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1705",
}
@inproceedings{lu-etal-2018-cner,
    title = "{M}-{CNER}: A Corpus for {C}hinese Named Entity Recognition in Multi-Domains",
    author = "Lu, Qi  and
      Yang, YaoSheng  and
      Li, Zhenghua  and
      Chen, Wenliang  and
      Zhang, Min",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1706",
}
@inproceedings{bowden-etal-2018-slugnerds,
    title = "{S}lug{NERDS}: A Named Entity Recognition Tool for Open Domain Dialogue Systems",
    author = "Bowden, Kevin  and
      Wu, Jiaqi  and
      Oraby, Shereen  and
      Misra, Amita  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1707",
}
@inproceedings{lee-etal-2018-transfer,
    title = "Transfer Learning for Named-Entity Recognition with Neural Networks",
    author = "Lee, Ji Young  and
      Dernoncourt, Franck  and
      Szolovits, Peter",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1708",
}
@inproceedings{mikulova-bejcek-2018-forfun,
    title = "{F}or{F}un 1.0: {P}rague Database of Forms and Functions {--} An Invaluable Resource for Linguistic Research",
    author = "Mikulov{\'a}, Marie  and
      Bej{\v{c}}ek, Eduard",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1709",
}
@inproceedings{ovrelid-etal-2018-lia,
    title = "The {LIA} Treebank of Spoken {N}orwegian Dialects",
    author = "{\O}vrelid, Lilja  and
      K{\aa}sen, Andre  and
      Hagen, Kristin  and
      N{\o}klestad, Anders  and
      Solberg, Per Erik  and
      Johannessen, Janne Bondi",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1710",
}
@inproceedings{wisniewski-2018-errator,
    title = "{E}rrator: a Tool to Help Detect Annotation Errors in the {U}niversal {D}ependencies Project",
    author = "Wisniewski, Guillaume",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1711",
}
@inproceedings{bhardwaj-etal-2018-sandhikosh,
    title = "{S}andhi{K}osh: A Benchmark Corpus for Evaluating {S}anskrit Sandhi Tools",
    author = "Bhardwaj, Shubham  and
      Gantayat, Neelamadhav  and
      Chaturvedi, Nikhil  and
      Garg, Rahul  and
      Agarwal, Sumeet",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1712",
}
@inproceedings{kriz-hladka-2018-czech,
    title = "{C}zech Legal Text Treebank 2.0",
    author = "Kr{\'\i}{\v{z}}, Vincent  and
      Hladk{\'a}, Barbora",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1713",
}
@inproceedings{gruzitis-etal-2018-creation,
    title = "Creation of a Balanced State-of-the-Art Multilayer Corpus for {NLU}",
    author = "Gruzitis, Normunds  and
      Pretkalnina, Lauma  and
      Saulite, Baiba  and
      Rituma, Laura  and
      Nespore-Berzkalne, Gunta  and
      Znotins, Arturs  and
      Paikens, Peteris",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1714",
}
@inproceedings{duan-schuler-2018-test,
    title = "Test Sets for {C}hinese Nonlocal Dependency Parsing",
    author = "Duan, Manjuan  and
      Schuler, William",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1715",
}
@inproceedings{delecraz-etal-2018-adding,
    title = "Adding Syntactic Annotations to Flickr30k Entities Corpus for Multimodal Ambiguous Prepositional-Phrase Attachment Resolution",
    author = "Delecraz, Sebastien  and
      Nasr, Alexis  and
      Bechet, Frederic  and
      Favre, Benoit",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1716",
}
@inproceedings{chiarcos-etal-2018-analyzing,
    title = "Analyzing {M}iddle {H}igh {G}erman Syntax with {RDF} and {SPARQL}",
    author = {Chiarcos, Christian  and
      Kosmehl, Benjamin  and
      F{\"a}th, Christian  and
      Sukhareva, Maria},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1717",
}
@inproceedings{seddah-etal-2018-cheating,
    title = "Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer",
    author = "Seddah, Djam{\'e}  and
      de la Clergerie, Eric  and
      Sagot, Beno{\^\i}t  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Candito, Marie",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1718",
}
@inproceedings{alzetta-etal-2018-universal,
    title = "{U}niversal {D}ependencies and Quantitative Typological Trends. A Case Study on Word Order",
    author = "Alzetta, Chiara  and
      Dell{'}Orletta, Felice  and
      Montemagni, Simonetta  and
      Venturi, Giulia",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1719",
}
@inproceedings{washio-kato-2018-undersampling,
    title = "Undersampling Improves Hypernymy Prototypicality Learning",
    author = "Washio, Koki  and
      Kato, Tsuneaki",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1720",
}
@inproceedings{dimitrova-etal-2018-interoperability,
    title = "Interoperability of Language-related Information: Mapping the {BLL} Thesaurus to Lexvo and Glottolog",
    author = {Dimitrova, Vanya  and
      F{\"a}th, Christian  and
      Chiarcos, Christian  and
      Renner-Westermann, Heike  and
      Abromeit, Frank},
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1721",
}
@inproceedings{branco-etal-2018-browsing,
    title = "Browsing and Supporting Pluricentric Global {W}ordnet, or just your {W}ordnet of Interest",
    author = "Branco, Ant{\'o}nio  and
      Branco, Ruben  and
      Saedi, Chakaveh  and
      Silva, Jo{\~a}o",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1722",
}
@inproceedings{alvez-etal-2018-cross,
    title = "Cross-checking {W}ord{N}et and {SUMO} Using Meronymy",
    author = "{\'A}lvez, Javier  and
      Gonzalez-Dios, Itziar  and
      Rigau, German",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1723",
}
@inproceedings{ma-shih-2018-extended,
    title = "Extended {H}ow{N}et 2.0 {--} An Entity-Relation Common-Sense Representation Model",
    author = "Ma, Wei-Yun  and
      Shih, Yueh-Yin",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1724",
}
@inproceedings{segers-etal-2018-circumstantial,
    title = "The Circumstantial Event Ontology ({CEO}) and {ECB}+/{CEO}: an Ontology and Corpus for Implicit Causal Relations between Events",
    author = "Segers, Roxane  and
      Caselli, Tommaso  and
      Vossen, Piek",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1725",
}
@inproceedings{el-haj-etal-2018-profiling,
    title = "Profiling Medical Journal Articles Using a Gene Ontology Semantic Tagger",
    author = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Piao, Scott  and
      Knight, Jo",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1726",
}
@inproceedings{gervits-scheutz-2018-towards,
    title = "Towards a Conversation-Analytic Taxonomy of Speech Overlap",
    author = "Gervits, Felix  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1727",
}
@inproceedings{kanojia-etal-2018-indian,
    title = "{I}ndian Language Wordnets and their Linkages with {P}rinceton {W}ord{N}et",
    author = "Kanojia, Diptesh  and
      Patel, Kevin  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L18-1728",
}
@proceedings{conll-2018-conll-sigmorphon,
    title = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    editor = "Hulden, Mans  and
      Cotterell, Ryan",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3000",
}
@inproceedings{cotterell-etal-2018-conll,
    title = "The {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    author = "Cotterell, Ryan  and
      Kirov, Christo  and
      Sylak-Glassman, John  and
      Walther, G{\'e}raldine  and
      Vylomova, Ekaterina  and
      McCarthy, Arya D.  and
      Kann, Katharina  and
      Mielke, Sabrina J.  and
      Nicolai, Garrett  and
      Silfverberg, Miikka  and
      Yarowsky, David  and
      Eisner, Jason  and
      Hulden, Mans",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3001",
    doi = "10.18653/v1/K18-3001",
    pages = "1--27",
}
@inproceedings{agirrezabal-2018-ku,
    title = "{KU}-{CST} at {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: a Tridirectional Model",
    author = "Agirrezabal, Manex",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3002",
    doi = "10.18653/v1/K18-3002",
    pages = "28--32",
}
@inproceedings{fam-lepage-2018-ips,
    title = "{IPS}-{WASEDA} system at {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task on morphological inflection",
    author = "Fam, Rashel  and
      Lepage, Yves",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3003",
    doi = "10.18653/v1/K18-3003",
    pages = "33--42",
}
@inproceedings{madsack-etal-2018-ax,
    title = "{AX} Semantics{'} Submission to the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task",
    author = "Madsack, Andreas  and
      Cavallo, Alessia  and
      Heininger, Johanna  and
      Wei{\ss}graeber, Robert",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3004",
    doi = "10.18653/v1/K18-3004",
    pages = "43--47",
}
@inproceedings{jain-singh-2018-experiments,
    title = "Experiments on Morphological Reinflection: {C}o{NLL}-2018 Shared Task",
    author = "Jain, Rishabh  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3005",
    doi = "10.18653/v1/K18-3005",
    pages = "48--57",
}
@inproceedings{kann-etal-2018-nyu,
    title = "The {NYU} System for the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task on Universal Morphological Reinflection",
    author = "Kann, Katharina  and
      Lauly, Stanislas  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3006",
    doi = "10.18653/v1/K18-3006",
    pages = "58--63",
}
@inproceedings{dumitrescu-boros-2018-attention,
    title = "Attention-free encoder decoder for morphological processing",
    author = "Dumitrescu, Stefan Daniel  and
      Boros, Tiberiu",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3007",
    doi = "10.18653/v1/K18-3007",
    pages = "64--68",
}
@inproceedings{makarov-clematide-2018-uzh,
    title = "{UZH} at {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task on Universal Morphological Reinflection",
    author = "Makarov, Peter  and
      Clematide, Simon",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3008",
    doi = "10.18653/v1/K18-3008",
    pages = "69--75",
}
@inproceedings{schroder-etal-2018-finding,
    title = {Finding the way from {\"a} to a: Sub-character morphological inflection for the {SIGMORPHON} 2018 shared task},
    author = {Schr{\"o}der, Fynn  and
      Kamlot, Marcel  and
      Billing, Gregor  and
      K{\"o}hn, Arne},
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3009",
    doi = "10.18653/v1/K18-3009",
    pages = "76--85",
}
@inproceedings{liu-etal-2018-morphological,
    title = "Morphological Reinflection in Context: {CU} Boulder{'}s Submission to {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task",
    author = "Liu, Ling  and
      Subbiah, Ilamvazhuthy  and
      Wiemerslage, Adam  and
      Lilley, Jonathan  and
      Moeller, Sarah",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3010",
    doi = "10.18653/v1/K18-3010",
    pages = "86--92",
}
@inproceedings{kementchedjhieva-etal-2018-copenhagen,
    title = "Copenhagen at {C}o{NLL}{--}{SIGMORPHON} 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding",
    author = "Kementchedjhieva, Yova  and
      Bjerva, Johannes  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3011",
    doi = "10.18653/v1/K18-3011",
    pages = "93--98",
}
@inproceedings{sorokin-2018-gain,
    title = "What can we gain from language models for morphological inflection?",
    author = "Sorokin, Alexey",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3012",
    doi = "10.18653/v1/K18-3012",
    pages = "99--104",
}
@inproceedings{sharma-etal-2018-iit,
    title = "{IIT}({BHU}){--}{IIITH} at {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task on Universal Morphological Reinflection",
    author = "Sharma, Abhishek  and
      Katrapati, Ganesh  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3013",
    doi = "10.18653/v1/K18-3013",
    pages = "105--111",
}
@inproceedings{rama-coltekin-2018-tubingen,
    title = {{T}{\"u}bingen-{O}slo system at {SIGMORPHON} shared task on morphological inflection. A multi-tasking multilingual sequence to sequence model.},
    author = {Rama, Taraka  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3014",
    doi = "10.18653/v1/K18-3014",
    pages = "112--115",
}
@inproceedings{najafi-etal-2018-combining,
    title = "Combining Neural and Non-Neural Methods for Low-Resource Morphological Reinflection",
    author = "Najafi, Saeed  and
      Hauer, Bradley  and
      Riyadh, Rashed Rubby  and
      Yu, Leyuan  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3015",
    doi = "10.18653/v1/K18-3015",
    pages = "116--120",
}
@inproceedings{acs-2018-bme,
    title = "{BME}-{HAS} System for {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    author = "{\'A}cs, Judit",
    booktitle = "Proceedings of the {C}o{NLL}{--}{SIGMORPHON} 2018 Shared Task: Universal Morphological Reinflection",
    month = oct,
    year = "2018",
    address = "Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-3016",
    doi = "10.18653/v1/K18-3016",
    pages = "121--126",
}
@proceedings{conll-2018-conll,
    title = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    editor = "Zeman, Daniel  and
      Haji{\v{c}}, Jan",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2000",
}
@inproceedings{zeman-etal-2018-conll,
    title = "{C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to {U}niversal {D}ependencies",
    author = "Zeman, Daniel  and
      Haji{\v{c}}, Jan  and
      Popel, Martin  and
      Potthast, Martin  and
      Straka, Milan  and
      Ginter, Filip  and
      Nivre, Joakim  and
      Petrov, Slav",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2001",
    doi = "10.18653/v1/K18-2001",
    pages = "1--21",
    abstract = "Every year, the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2018, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on test input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. This shared task constitutes a 2nd edition{---}the first one took place in 2017 (Zeman et al., 2017); the main metric from 2017 has been kept, allowing for easy comparison, also in 2018, and two new main metrics have been used. New datasets added to the Universal Dependencies collection between mid-2017 and the spring of 2018 have contributed to increased difficulty of the task this year. In this overview paper, we define the task and the updated evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.",
}
@inproceedings{fares-etal-2018-2018,
    title = "The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of {E}nglish {U}niversal {D}ependency Parsers",
    author = {Fares, Murhaf  and
      Oepen, Stephan  and
      {\O}vrelid, Lilja  and
      Bj{\"o}rne, Jari  and
      Johansson, Richard},
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2002",
    doi = "10.18653/v1/K18-2002",
    pages = "22--33",
    abstract = "We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018). We review the basic task setup, downstream applications involved, and end-to-end results for seventeen participating teams. Based on in-depth quantitative and qualitative analysis, we correlate intrinsic evaluation results at different layers of morph-syntactic analysis with observed downstream behavior.",
}
@inproceedings{duthoo-mesnard-2018-cea,
    title = "{CEA} {LIST}: Processing Low-Resource Languages for {C}o{NLL} 2018",
    author = "Duthoo, Elie  and
      Mesnard, Olivier",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2003",
    doi = "10.18653/v1/K18-2003",
    pages = "34--44",
    abstract = "In this paper, we describe the system used for our first participation at the CoNLL 2018 shared task. The submitted system largely reused the state of the art parser from CoNLL 2017 (\url{https://github.com/tdozat/Parser-v2}). We enhanced this system for morphological features predictions, and we used all available resources to provide accurate models for low-resource languages. We ranked 5th of 27 participants in MLAS for building morphology aware dependency trees, 2nd for morphological features only, and 3rd for tagging (UPOS) and parsing (LAS) low-resource languages.",
}
@inproceedings{rybak-wroblewska-2018-semi,
    title = "Semi-Supervised Neural System for Tagging, Parsing and Lematization",
    author = "Rybak, Piotr  and
      Wr{\'o}blewska, Alina",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2004",
    doi = "10.18653/v1/K18-2004",
    pages = "45--54",
    abstract = "This paper describes the ICS PAS system which took part in CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. The system consists of jointly trained tagger, lemmatizer, and dependency parser which are based on features extracted by a biLSTM network. The system uses both fully connected and dilated convolutional neural architectures. The novelty of our approach is the use of an additional loss function, which reduces the number of cycles in the predicted dependency graphs, and the use of self-training to increase the system performance. The proposed system, i.e. ICS PAS (Warszawa), ranked 3th/4th in the official evaluation obtaining the following overall results: 73.02 (LAS), 60.25 (MLAS) and 64.44 (BLEX).",
}
@inproceedings{che-etal-2018-towards,
    title = "Towards Better {UD} Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation",
    author = "Che, Wanxiang  and
      Liu, Yijia  and
      Wang, Yuxuan  and
      Zheng, Bo  and
      Liu, Ting",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2005",
    doi = "10.18653/v1/K18-2005",
    pages = "55--64",
    abstract = "This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford{'}s winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84{\%}) and outperformed the other systems by a large margin.",
}
@inproceedings{li-etal-2018-joint-learning,
    title = "Joint Learning of {POS} and Dependencies for Multilingual {U}niversal {D}ependency Parsing",
    author = "Li, Zuchao  and
      He, Shexia  and
      Zhang, Zhuosheng  and
      Zhao, Hai",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2006",
    doi = "10.18653/v1/K18-2006",
    pages = "65--73",
    abstract = "This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system predicts the part-of-speech tag and dependency tree jointly. For the basic tasks, including tokenization, lemmatization and morphology prediction, we employ the official baseline model (UDPipe). To train the low-resource languages, we adopt a sampling method based on other richresource languages. Our system achieves a macro-average of 68.31{\%} LAS F1 score, with an improvement of 2.51{\%} compared with the UDPipe.",
}
@inproceedings{wu-etal-2018-multilingual,
    title = "Multilingual {U}niversal {D}ependency Parsing from Raw Text with Low-Resource Language Enhancement",
    author = "Wu, Yingting  and
      Zhao, Hai  and
      Tong, Jia-Jun",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2007",
    doi = "10.18653/v1/K18-2007",
    pages = "74--80",
    abstract = "This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Given the annotated gold standard data in CoNLL-U format, we train the tokenizer, tagger and parser separately for each treebank based on an open source pipeline tool UDPipe. Our system reads the plain texts for input, performs the pre-processing steps (tokenization, lemmas, morphology) and finally outputs the syntactic dependencies. For the low-resource languages with no training data, we use cross-lingual techniques to build models with some close languages instead. In the official evaluation, our system achieves the macro-averaged scores of 65.61{\%}, 52.26{\%}, 55.71{\%} for LAS, MLAS and BLEX respectively.",
}
@inproceedings{nguyen-verspoor-2018-improved,
    title = "An Improved Neural Network Model for Joint {POS} Tagging and Dependency Parsing",
    author = "Nguyen, Dat Quoc  and
      Verspoor, Karin",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2008",
    doi = "10.18653/v1/K18-2008",
    pages = "81--91",
    abstract = "We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51{\%} and 92.87{\%}, respectively, producing 1.5+{\%} absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97{\%}. Furthermore, experimental results on parsing 61 {``}big{''} Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8{\%} higher average POS tagging score and 3.6{\%} higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: \url{https://github.com/datquocnguyen/jPTDP}",
}
@inproceedings{wan-etal-2018-ibm,
    title = "{IBM} Research at the {C}o{NLL} 2018 Shared Task on Multilingual Parsing",
    author = "Wan, Hui  and
      Naseem, Tahira  and
      Lee, Young-Suk  and
      Castelli, Vittorio  and
      Ballesteros, Miguel",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2009",
    doi = "10.18653/v1/K18-2009",
    pages = "92--102",
    abstract = "This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall.",
}
@inproceedings{hershcovich-etal-2018-universal,
    title = "{U}niversal {D}ependency Parsing with a General Transition-Based {DAG} Parser",
    author = "Hershcovich, Daniel  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2010",
    doi = "10.18653/v1/K18-2010",
    pages = "103--112",
    abstract = "This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning.",
}
@inproceedings{smith-etal-2018-82,
    title = "82 Treebanks, 34 Models: {U}niversal {D}ependency Parsing with Multi-Treebank Models",
    author = "Smith, Aaron  and
      Bohnet, Bernd  and
      de Lhoneux, Miryam  and
      Nivre, Joakim  and
      Shao, Yan  and
      Stymne, Sara",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2011",
    doi = "10.18653/v1/K18-2011",
    pages = "113--123",
    abstract = "We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing. Our system is a pipeline consisting of three components: the first performs joint word and sentence segmentation; the second predicts part-of-speech tags and morphological features; the third predicts dependency trees from words and tags. Instead of training a single parsing model for each treebank, we trained models with multiple treebanks for one language or closely related languages, greatly reducing the number of models. On the official test run, we ranked 7th of 27 teams for the LAS and MLAS metrics. Our system obtained the best scores overall for word segmentation, universal POS tagging, and morphological features.",
}
@inproceedings{kirnap-etal-2018-tree,
    title = "Tree-Stack {LSTM} in Transition Based Dependency Parsing",
    author = {K{\i}rnap, {\"O}mer  and
      Dayan{\i}k, Erenay  and
      Yuret, Deniz},
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2012",
    doi = "10.18653/v1/K18-2012",
    pages = "124--132",
    abstract = "We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack{'}s σ-LSTM, buffer{'}s β-LSTM, actions{'} LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the {``}KParse{''} team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages.",
}
@inproceedings{kanerva-etal-2018-turku,
    title = "{T}urku Neural Parser Pipeline: An End-to-End System for the {C}o{NLL} 2018 Shared Task",
    author = "Kanerva, Jenna  and
      Ginter, Filip  and
      Miekka, Niko  and
      Leino, Akseli  and
      Salakoski, Tapio",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2013",
    doi = "10.18653/v1/K18-2013",
    pages = "133--142",
    abstract = "In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. Compared to the last year, this year the shared task includes two new main metrics to measure the morphological tagging and lemmatization accuracies in addition to syntactic trees. Basing our motivation into these new metrics, we developed an end-to-end parsing pipeline especially focusing on developing a novel and state-of-the-art component for lemmatization. Our system reached the highest aggregate ranking on three main metrics out of 26 teams by achieving 1st place on metric involving lemmatization, and 2nd on both morphological tagging and parsing.",
}
@inproceedings{lim-etal-2018-sex,
    title = "{SE}x {B}i{ST}: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations",
    author = "Lim, KyungTae  and
      Park, Cheoneum  and
      Lee, Changki  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2014",
    doi = "10.18653/v1/K18-2014",
    pages = "143--152",
    abstract = "We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies). The main characteristic of our work is the encoding of three different modes of contextual information for parsing: (i) Treebank feature representations, (ii) Multilingual word representations, (iii) ELMo representations obtained via unsupervised learning from external resources. Our parser performed well in the official end-to-end evaluation (73.02 LAS {--} 4th/26 teams, and 78.72 UAS {--} 2nd/26); remarkably, we achieved the best UAS scores on all the English corpora by applying the three suggested feature representations. Finally, we were also ranked 1st at the optional event extraction task, part of the 2018 Extrinsic Parser Evaluation campaign.",
}
@inproceedings{bhat-etal-2018-slt,
    title = "The {SLT}-Interactions Parsing System at the {C}o{NLL} 2018 Shared Task",
    author = "Bhat, Riyaz A.  and
      Bhat, Irshad  and
      Bangalore, Srinivas",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2015",
    doi = "10.18653/v1/K18-2015",
    pages = "153--159",
    abstract = "This paper describes our system (SLT-Interactions) for the CoNLL 2018 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system performs three main tasks: word segmentation (only for few treebanks), POS tagging and parsing. While segmentation is learned separately, we use neural stacking for joint learning of POS tagging and parsing tasks. For all the tasks, we employ simple neural network architectures that rely on long short-term memory (LSTM) networks for learning task-dependent features. At the basis of our parser, we use an arc-standard algorithm with Swap action for general non-projective parsing. Additionally, we use neural stacking as a knowledge transfer mechanism for cross-domain parsing of low resource domains. Our system shows substantial gains against the UDPipe baseline, with an average improvement of 4.18{\%} in LAS across all languages. Overall, we are placed at the 12th position on the official test sets.",
}
@inproceedings{qi-etal-2018-universal,
    title = "{U}niversal {D}ependency Parsing from Scratch",
    author = "Qi, Peng  and
      Dozat, Timothy  and
      Zhang, Yuhao  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2016",
    doi = "10.18653/v1/K18-2016",
    pages = "160--170",
    abstract = "This paper describes Stanford{'}s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation, to POS tagging and dependency parsing. Our single system submission achieved very competitive performance on big treebanks. Moreover, after fixing an unfortunate bug, our corrected system would have placed the 2nd, 1st, and 3rd on the official evaluation metrics LAS, MLAS, and BLEX, and would have outperformed all submission systems on low-resource treebank categories on all metrics by a large margin. We further show the effectiveness of different model components through extensive ablation studies.",
}
@inproceedings{boros-etal-2018-nlp,
    title = "{NLP}-Cube: End-to-End Raw Text Processing With Neural Networks",
    author = "Boros, Tiberiu  and
      Dumitrescu, Stefan Daniel  and
      Burtica, Ruxandra",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2017",
    doi = "10.18653/v1/K18-2017",
    pages = "171--179",
    abstract = "We introduce NLP-Cube: an end-to-end Natural Language Processing framework, evaluated in CoNLL{'}s {``}Multilingual Parsing from Raw Text to Universal Dependencies 2018{''} Shared Task. It performs sentence splitting, tokenization, compound word expansion, lemmatization, tagging and parsing. Based entirely on recurrent neural networks, written in Python, this ready-to-use open source system is freely available on GitHub. For each task we describe and discuss its specific network architecture, closing with an overview on the results obtained in the competition.",
}
@inproceedings{arakelyan-etal-2018-towards,
    title = "Towards {J}oint{UD}: Part-of-speech Tagging and Lemmatization using Recurrent Neural Networks",
    author = "Arakelyan, Gor  and
      Hambardzumyan, Karen  and
      Khachatrian, Hrant",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2018",
    doi = "10.18653/v1/K18-2018",
    pages = "180--186",
    abstract = "This paper describes our submission to CoNLL UD Shared Task 2018. We have extended an LSTM-based neural network designed for sequence tagging to additionally generate character-level sequences. The network was jointly trained to produce lemmas, part-of-speech tags and morphological features. Sentence segmentation, tokenization and dependency parsing were handled by UDPipe 1.2 baseline. The results demonstrate the viability of the proposed multitask architecture, although its performance still remains far from state-of-the-art.",
}
@inproceedings{rosa-marecek-2018-cuni,
    title = "{CUNI} x-ling: Parsing Under-Resourced Languages in {C}o{NLL} 2018 {UD} Shared Task",
    author = "Rosa, Rudolf  and
      Mare{\v{c}}ek, David",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2019",
    doi = "10.18653/v1/K18-2019",
    pages = "187--196",
    abstract = "This is a system description paper for the CUNI x-ling submission to the CoNLL 2018 UD Shared Task. We focused on parsing under-resourced languages, with no or little training data available. We employed a wide range of approaches, including simple word-based treebank translation, combination of delexicalized parsers, and exploitation of available morphological dictionaries, with a dedicated setup tailored to each of the languages. In the official evaluation, our submission was identified as the clear winner of the Low-resource languages category.",
}
@inproceedings{straka-2018-udpipe,
    title = "{UDP}ipe 2.0 Prototype at {C}o{NLL} 2018 {UD} Shared Task",
    author = "Straka, Milan",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2020",
    doi = "10.18653/v1/K18-2020",
    pages = "197--207",
    abstract = "UDPipe is a trainable pipeline which performs sentence segmentation, tokenization, POS tagging, lemmatization and dependency parsing. We present a prototype for UDPipe 2.0 and evaluate it in the CoNLL 2018 UD Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, which employs three metrics for submission ranking. Out of 26 participants, the prototype placed first in the MLAS ranking, third in the LAS ranking and third in the BLEX ranking. In extrinsic parser evaluation EPE 2018, the system ranked first in the overall score.",
}
@inproceedings{seker-etal-2018-universal,
    title = "Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the {ONLP} Lab Submission to the {C}o{NLL} 2018 Shared Task",
    author = "Seker, Amit  and
      More, Amir  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2021",
    doi = "10.18653/v1/K18-2021",
    pages = "208--215",
    abstract = "We present the contribution of the ONLP lab at the Open University of Israel to the UD shared task on multilingual parsing from raw text to Universal Dependencies. Our contribution is based on a transition-based parser called {`}yap {--} yet another parser{'}, which includes a standalone morphological model, a standalone dependency model, and a joint morphosyntactic model. In the task we used \textit{yap}{`}s standalone dependency parser to parse input morphologically disambiguated by UDPipe, and obtained the official score of 58.35 LAS. In our follow up investigation we use yap to show how the incorporation of morphological and lexical resources may improve the performance of end-to-end raw-to-dependencies parsing in the case of a \textit{morphologically-rich} and \textit{low-resource} language, Modern Hebrew. Our results on Hebrew underscore the importance of CoNLL-UL, a UD-compatible standard for accessing external lexical resources, for enhancing end-to-end UD parsing, in particular for morphologically rich and low-resource languages. We thus encourage the community to create, convert, or make available more such lexica in future tasks.",
}
@inproceedings{onder-etal-2018-sparse,
    title = "{SP}arse: {K}o{\c{c}} {U}niversity Graph-Based Parsing System for the {C}o{NLL} 2018 Shared Task",
    author = {{\"O}nder, Berkay  and
      G{\"u}meli, Can  and
      Yuret, Deniz},
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2022",
    doi = "10.18653/v1/K18-2022",
    pages = "216--222",
    abstract = "We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48{\%} LAS, 78.63{\%} MLAS, 78.69{\%} BLEX and 81.76{\%} CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78{\%} LAS, 59.10{\%} MLAS, 61.38{\%} BLEX and 61.72{\%} CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.",
}
@inproceedings{jawahar-etal-2018-elmolex,
    title = "{ELM}o{L}ex: Connecting {ELM}o and Lexicon Features for Dependency Parsing",
    author = "Jawahar, Ganesh  and
      Muller, Benjamin  and
      Fethi, Amal  and
      Martin, Louis  and
      Villemonte de la Clergerie, {\'E}ric  and
      Sagot, Beno{\^\i}t  and
      Seddah, Djam{\'e}",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2023",
    doi = "10.18653/v1/K18-2023",
    pages = "223--237",
    abstract = "In this paper, we present the details of the neural dependency parser and the neural tagger submitted by our team {`}ParisNLP{'} to the CoNLL 2018 Shared Task on parsing from raw text to Universal Dependencies. We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set. Henceforth, we call our system {`}ELMoLex{'}. In addition to incorporating character embeddings, ELMoLex benefits from pre-trained word vectors, ELMo and morphosyntactic features (whenever available) to correctly handle rare or unknown words which are prevalent in languages with complex morphology. ELMoLex ranked 11th by Labeled Attachment Score metric (70.64{\%}), Morphology-aware LAS metric (55.74{\%}) and ranked 9th by Bilexical dependency metric (60.70{\%}).",
}
@inproceedings{ozates-etal-2018-morphology,
    title = "A Morphology-Based Representation Model for {LSTM}-Based Dependency Parsing of Agglutinative Languages",
    author = {{\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l  and
      {\"O}zg{\"u}r, Arzucan  and
      G{\"u}ng{\"o}r, Tunga  and
      {\"O}zt{\"u}rk, Balk{\i}z},
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2024",
    doi = "10.18653/v1/K18-2024",
    pages = "238--247",
    abstract = "We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.",
}
@inproceedings{ji-etal-2018-antnlp,
    title = "{A}nt{NLP} at {C}o{NLL} 2018 Shared Task: A Graph-Based Parser for {U}niversal {D}ependency Parsing",
    author = "Ji, Tao  and
      Liu, Yufang  and
      Wang, Yijun  and
      Wu, Yuanbin  and
      Lan, Man",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2025",
    doi = "10.18653/v1/K18-2025",
    pages = "248--255",
    abstract = "We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26).",
}
@inproceedings{chen-etal-2018-simple,
    title = "A Simple yet Effective Joint Training Method for Cross-Lingual {U}niversal {D}ependency Parsing",
    author = "Chen, Danlu  and
      Lin, Mengxiao  and
      Hu, Zhifeng  and
      Qiu, Xipeng",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-2026",
    doi = "10.18653/v1/K18-2026",
    pages = "256--263",
    abstract = "This paper describes Fudan{'}s submission to CoNLL 2018{'}s shared task Universal Dependency Parsing. We jointly train models when two languages are similar according to linguistic typology and then ensemble the models using a simple re-parse algorithm. We outperform the baseline method by 4.4{\%} (2.1{\%}) on average on development (test) set in CoNLL 2018 UD Shared Task.",
}
@proceedings{conll-2018-natural,
    title = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    editor = "Korhonen, Anna  and
      Titov, Ivan",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1000",
}
@inproceedings{thai-etal-2018-embedded,
    title = "Embedded-State Latent Conditional Random Fields for Sequence Labeling",
    author = "Thai, Dung  and
      Ramesh, Sree Harsha  and
      Murty, Shikhar  and
      Vilnis, Luke  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1001",
    doi = "10.18653/v1/K18-1001",
    pages = "1--10",
    abstract = "Complex textual information extraction tasks are often posed as sequence labeling or \textit{shallow parsing}, where fields are extracted using local labels made consistent through probabilistic inference in a graphical model with constrained transitions. Recently, it has become common to locally parametrize these models using rich features extracted by recurrent neural networks (such as LSTM), while enforcing consistent outputs through a simple linear-chain model, representing Markovian dependencies between successive labels. However, the simple graphical model structure belies the often complex non-local constraints between output labels. For example, many fields, such as a first name, can only occur a fixed number of times, or in the presence of other fields. While RNNs have provided increasingly powerful context-aware local features for sequence tagging, they have yet to be integrated with a global graphical model of similar expressivity in the output distribution. Our model goes beyond the linear chain CRF to incorporate multiple hidden states per output label, but parametrizes them parsimoniously with low-rank log-potential scoring matrices, effectively learning an embedding space for hidden states. This augmented latent space of inference variables complements the rich feature representation of the RNN, and allows exact global inference obeying complex, learned non-local output constraints. We experiment with several datasets and show that the model outperforms baseline CRF+RNN models when global output constraints are necessary at inference-time, and explore the interpretable latent structure.",
}
@inproceedings{fu-etal-2018-continuous,
    title = "Continuous Word Embedding Fusion via Spectral Decomposition",
    author = "Fu, Tianfan  and
      Zhang, Cheng  and
      Mandt, Stephan",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1002",
    doi = "10.18653/v1/K18-1002",
    pages = "11--20",
    abstract = "Word embeddings have become a mainstream tool in statistical natural language processing. Practitioners often use pre-trained word vectors, which were trained on large generic text corpora, and which are readily available on the web. However, pre-trained word vectors oftentimes lack important words from specific domains. It is therefore often desirable to extend the vocabulary and embed new words into a set of pre-trained word vectors. In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings. We build on the established view of word embeddings as matrix factorizations to present a spectral algorithm for this task. Experiments on several domain-specific corpora with specialized vocabularies demonstrate that our method is able to embed the new words efficiently into the original embedding space. Compared to competing methods, our method is faster, parameter-free, and deterministic.",
}
@inproceedings{tran-nguyen-2018-dual,
    title = "Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems",
    author = "Tran, Van-Khanh  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1003",
    doi = "10.18653/v1/K18-1003",
    pages = "21--30",
    abstract = "Recent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data. However, a modest training data may harm such models{'} performance. Thus, how to build a generator that can utilize as much of knowledge from a low-resource setting data is a crucial issue in NLG. This paper presents a variational neural-based generation model to tackle the NLG problem of having limited labeled dataset, in which we integrate a variational inference into an encoder-decoder generator and introduce a novel auxiliary auto-encoding with an effective training procedure. Experiments showed that the proposed methods not only outperform the previous models when having sufficient training dataset but also demonstrate strong ability to work acceptably well when the training data is scarce.",
}
@inproceedings{nguyen-etal-2018-trio,
    title = "A Trio Neural Model for Dynamic Entity Relatedness Ranking",
    author = "Nguyen, Tu  and
      Tran, Tuan  and
      Nejdl, Wolfgang",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1004",
    doi = "10.18653/v1/K18-1004",
    pages = "31--41",
    abstract = "Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in a static setting and unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity relations are very dynamic over time. In this work, we propose a neural network-based approach that leverages public attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.",
}
@inproceedings{ebrahimi-etal-2018-unified,
    title = "A Unified Neural Network Model for Geolocating {T}witter Users",
    author = "Ebrahimi, Mohammad  and
      ShafieiBavani, Elaheh  and
      Wong, Raymond  and
      Chen, Fang",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1005",
    doi = "10.18653/v1/K18-1005",
    pages = "42--53",
    abstract = "Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation. However, many users do not share their exact geographical coordinates due to reasons such as privacy concerns. The lack of explicit location information has motivated a growing body of research in recent years looking at different automatic ways of determining the user{'}s primary location. In this paper, we propose a unified user geolocation method which relies on a fusion of neural networks. Our joint model incorporates different types of available information including tweet text, user network, and metadata to predict users{'} locations. Moreover, we utilize a bidirectional LSTM network augmented with an attention mechanism to identify the most location indicative words in textual content of tweets. The experiments demonstrate that our approach achieves state-of-the-art performance over two Twitter benchmark geolocation datasets. We also conduct an ablation study to evaluate the contribution of each type of information in user geolocation performance.",
}
@inproceedings{kuznetsov-gurevych-2018-corpus,
    title = "Corpus-Driven Thematic Hierarchy Induction",
    author = "Kuznetsov, Ilia  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1006",
    doi = "10.18653/v1/K18-1006",
    pages = "54--64",
    abstract = "Thematic role hierarchy is a widely used linguistic tool to describe interactions between semantic roles and their syntactic realizations. Despite decades of dedicated research and numerous thematic hierarchy suggestions in the literature, this concept has not been used in NLP so far due to incompatibility and limited scope of existing hierarchies. We introduce an empirical framework for thematic hierarchy induction and evaluate several role ranking strategies on English and German full-text corpus data. We hypothesize that global thematic hierarchy induction is feasible, that a hierarchy can be induced from just fractions of training data and that resulting hierarchies apply cross-lingually. We evaluate these assumptions empirically.",
}
@inproceedings{minervini-riedel-2018-adversarially,
    title = "Adversarially Regularising Neural {NLI} Models to Integrate Logical Background Knowledge",
    author = "Minervini, Pasquale  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1007",
    doi = "10.18653/v1/K18-1007",
    pages = "65--74",
    abstract = "Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation. In NLP, however, most example generation strategies produce input text by using known, pre-specified semantic transformations, requiring significant manual effort and in-depth understanding of the problem and domain. In this paper, we investigate the problem of automatically generating adversarial examples that violate a set of given First-Order Logic constraints in Natural Language Inference (NLI). We reduce the problem of identifying such adversarial examples to a combinatorial optimisation problem, by maximising a quantity measuring the degree of violation of such constraints and by using a language model for generating linguistically-plausible examples. Furthermore, we propose a method for adversarially regularising neural NLI models for incorporating background knowledge. Our results show that, while the proposed method does not always improve results on the SNLI and MultiNLI datasets, it significantly and consistently increases the predictive accuracy on adversarially-crafted datasets {--} up to a 79.6{\%} relative improvement {--} while drastically reducing the number of background knowledge violations. Furthermore, we show that adversarial examples transfer among model architectures, and that the proposed adversarial training procedure improves the robustness of NLI models to adversarial examples.",
}
@inproceedings{tulkens-etal-2018-strings,
    title = "From Strings to Other Things: Linking the Neighborhood and Transposition Effects in Word Reading",
    author = "Tulkens, St{\'e}phan  and
      Sandra, Dominiek  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1008",
    doi = "10.18653/v1/K18-1008",
    pages = "75--85",
    abstract = "We investigate the relation between the transposition and deletion effects in word reading, i.e., the finding that readers can successfully read {``}SLAT{''} as {``}SALT{''}, or {``}WRK{''} as {``}WORK{''}, and the neighborhood effect. In particular, we investigate whether lexical orthographic neighborhoods take into account transposition and deletion in determining neighbors. If this is the case, it is more likely that the neighborhood effect takes place early during processing, and does not solely rely on similarity of internal representations. We introduce a new neighborhood measure, rd20, which can be used to quantify neighborhood effects over arbitrary feature spaces. We calculate the rd20 over large sets of words in three languages using various feature sets and show that feature sets that do not allow for transposition or deletion explain more variance in Reaction Time (RT) measurements. We also show that the rd20 can be calculated using the hidden state representations of an Multi-Layer Perceptron, and show that these explain less variance than the raw features. We conclude that the neighborhood effect is unlikely to have a perceptual basis, but is more likely to be the result of items co-activating after recognition. All code is available at: \url{www.github.com/clips/conll2018}",
}
@inproceedings{zhang-etal-2018-global,
    title = "Global Attention for Name Tagging",
    author = "Zhang, Boliang  and
      Whitehead, Spencer  and
      Huang, Lifu  and
      Ji, Heng",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1009",
    doi = "10.18653/v1/K18-1009",
    pages = "86--96",
    abstract = "Many name tagging approaches use local contextual information with much success, but can fail when the local context is ambiguous or limited. We present a new framework to improve name tagging by utilizing local, document-level, and corpus-level contextual information. For each word, we retrieve document-level context from other sentences within the same document and corpus-level context from sentences in other documents. We propose a model that learns to incorporate document-level and corpus-level contextual information alongside local contextual information via document-level and corpus-level attentions, which dynamically weight their respective contextual information and determines the influence of this information through gating mechanisms. Experiments on benchmark datasets show the effectiveness of our approach, which achieves state-of-the-art results for Dutch, German, and Spanish on the CoNLL-2002 and CoNLL-2003 datasets. We will make our code and pre-trained models publicly available for research purposes.",
}
@inproceedings{elbayad-etal-2018-pervasive,
    title = "Pervasive Attention: 2{D} Convolutional Neural Networks for Sequence-to-Sequence Prediction",
    author = "Elbayad, Maha  and
      Besacier, Laurent  and
      Verbeek, Jakob",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1010",
    doi = "10.18653/v1/K18-1010",
    pages = "97--107",
    abstract = "Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.",
}
@inproceedings{blohm-etal-2018-comparing,
    title = "Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension",
    author = "Blohm, Matthias  and
      Jagfeld, Glorianna  and
      Sood, Ekta  and
      Yu, Xiang  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1011",
    doi = "10.18653/v1/K18-1011",
    pages = "108--118",
    abstract = "We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset. To investigate the limitations of our model as well as the behavioral difference between convolutional and recurrent neural networks, we generate adversarial examples to confuse the model and compare to human performance. Furthermore, we assess the generalizability of our model by analyzing its differences to human inference, drawing upon insights from cognitive science.",
}
@inproceedings{gupta-etal-2018-uncovering,
    title = "Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering",
    author = "Gupta, Deepak  and
      Lenka, Pabitra  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1012",
    doi = "10.18653/v1/K18-1012",
    pages = "119--130",
    abstract = "Existing research on question answering (QA) and comprehension reading (RC) are mainly focused on the resource-rich language like English. In recent times, the rapid growth of multi-lingual web content has posed several challenges to the existing QA systems. Code-mixing is one such challenge that makes the task more complex. In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA). For evaluation, we manually create the code-mixed questions for Hindi-English language pair. In order to show the effectiveness of our neural network based CMQA technique, we utilize two benchmark datasets, SQuAD and MMQA. Experiments show that our proposed model achieves encouraging performance on CMQG and CMQA.",
}
@inproceedings{jung-etal-2018-learning,
    title = "Learning to Embed Semantic Correspondence for Natural Language Understanding",
    author = "Jung, Sangkeun  and
      Lee, Jinsik  and
      Kim, Jiwon",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1013",
    doi = "10.18653/v1/K18-1013",
    pages = "131--140",
    abstract = "While learning embedding models has yielded fruitful results in several NLP subfields, most notably Word2Vec, embedding correspondence has relatively not been well explored especially in the context of natural language understanding (NLU), a task that typically extracts structured semantic knowledge from a text. A NLU embedding model can facilitate analyzing and understanding relationships between unstructured texts and their corresponding structured semantic knowledge, essential for both researchers and practitioners of NLU. Toward this end, we propose a framework that learns to embed semantic correspondence between text and its extracted semantic knowledge, called semantic frame. One key contributed technique is semantic frame reconstruction used to derive a one-to-one mapping between embedded vectors and their corresponding semantic frames. Embedding into semantically meaningful vectors and computing their distances in vector space provides a simple, but effective way to measure semantic similarities. With the proposed framework, we demonstrate three key areas where the embedding model can be effective: visualization, semantic search and re-ranking.",
}
@inproceedings{saito-etal-2018-commonsense,
    title = "Commonsense Knowledge Base Completion and Generation",
    author = "Saito, Itsumi  and
      Nishida, Kyosuke  and
      Asano, Hisako  and
      Tomita, Junji",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1014",
    doi = "10.18653/v1/K18-1014",
    pages = "141--150",
    abstract = "This study focuses on acquisition of commonsense knowledge. A previous study proposed a commonsense knowledge base completion (CKB completion) method that predicts a confidence score of for triplet-style knowledge for improving the coverage of CKBs. To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation. Experimental results show that the joint learning method improved completion accuracy and the generation model created reasonable knowledge. Our generation model could also be used to augment data and improve the accuracy of completion.",
}
@inproceedings{peris-casacuberta-2018-active,
    title = "Active Learning for Interactive Neural Machine Translation of Data Streams",
    author = "Peris, {\'A}lvaro  and
      Casacuberta, Francisco",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1015",
    doi = "10.18653/v1/K18-1015",
    pages = "151--160",
    abstract = "We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent. The user will interactively translate those samples. Once validated, these data is useful for adapting the neural machine translation model. We propose two novel methods for selecting the samples to be validated. We exploit the information from the attention mechanism of a neural machine translation system. Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system. Moreover, it enables to balance the human effort required for achieving a certain translation quality. Moreover, our neural system outperforms classical approaches by a large margin.",
}
@inproceedings{abbet-etal-2018-churn,
    title = "Churn Intent Detection in Multilingual Chatbot Conversations and Social Media",
    author = "Abbet, Christian  and
      M{'}hamdi, Meryem  and
      Giannakopoulos, Athanasios  and
      West, Robert  and
      Hossmann, Andreea  and
      Baeriswyl, Michael  and
      Musat, Claudiu",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1016",
    doi = "10.18653/v1/K18-1016",
    pages = "161--170",
    abstract = "We propose a new method to detect when users express the intent to leave a service, also known as churn. While previous work focuses solely on social media, we show that this intent can be detected in chatbot conversations. As companies increasingly rely on chatbots they need an overview of potentially churny users. To this end, we crowdsource and publish a dataset of churn intent expressions in chatbot interactions in German and English. We show that classifiers trained on social media data can detect the same intent in the context of chatbots. We introduce a classification architecture that outperforms existing work on churn intent detection in social media. Moreover, we show that, using bilingual word embeddings, a system trained on combined English and German data outperforms monolingual approaches. As the only existing dataset is in English, we crowdsource and publish a novel dataset of German tweets. We thus underline the universal aspect of the problem, as examples of churn intent in English help us identify churn in German tweets and chatbot conversations.",
}
@inproceedings{barrena-etal-2018-learning,
    title = "Learning Text Representations for 500{K} Classification Tasks on Named Entity Disambiguation",
    author = "Barrena, Ander  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1017",
    doi = "10.18653/v1/K18-1017",
    pages = "171--180",
    abstract = "Named Entity Disambiguation algorithms typically learn a single model for all target entities. In this paper we present a word expert model and train separate deep learning models for each target entity string, yielding 500K classification tasks. This gives us the opportunity to benchmark popular text representation alternatives on this massive dataset. In order to face scarce training data we propose a simple data-augmentation technique and transfer-learning. We show that bag-of-word-embeddings are better than LSTMs for tasks with scarce training data, while the situation is reversed when having larger amounts. Transferring a LSTM which is learned on all datasets is the most effective context representation option for the word experts in all frequency bands. The experiments show that our system trained on out-of-domain Wikipedia data surpass comparable NED systems which have been trained on in-domain training data.",
}
@inproceedings{li-etal-2018-hierarchical,
    title = "Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis",
    author = "Li, Lishuang  and
      Liu, Yang  and
      Zhou, AnQiao",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1018",
    doi = "10.18653/v1/K18-1018",
    pages = "181--189",
    abstract = "Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words. In addition, most existing methods ignore the position information of the aspect when encoding the sentence. In this paper, we argue that the position-aware representations are beneficial to this task. Therefore, we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words. The experimental results on SemEval 2014 dataset show that our approach outperforms the state-of-the-art methods.",
}
@inproceedings{zhang-etal-2018-bidirectional,
    title = "Bidirectional Generative Adversarial Networks for Neural Machine Translation",
    author = "Zhang, Zhirui  and
      Liu, Shujie  and
      Li, Mu  and
      Zhou, Ming  and
      Chen, Enhong",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1019",
    doi = "10.18653/v1/K18-1019",
    pages = "190--199",
    abstract = "Generative Adversarial Network (GAN) has been proposed to tackle the exposure bias problem of Neural Machine Translation (NMT). However, the discriminator typically results in the instability of the GAN training due to the inadequate training problem: the search space is so huge that sampled translations are not sufficient for discriminator training. To address this issue and stabilize the GAN training, in this paper, we propose a novel Bidirectional Generative Adversarial Network for Neural Machine Translation (BGAN-NMT), which aims to introduce a generator model to act as the discriminator, whereby the discriminator naturally considers the entire translation space so that the inadequate training problem can be alleviated. To satisfy this property, generator and discriminator are both designed to model the joint probability of sentence pairs, with the difference that, the generator decomposes the joint probability with a source language model and a source-to-target translation model, while the discriminator is formulated as a target language model and a target-to-source translation model. To further leverage the symmetry of them, an auxiliary GAN is introduced and adopts generator and discriminator models of original one as its own discriminator and generator respectively. Two GANs are alternately trained to update the parameters. Experiment results on German-English and Chinese-English translation tasks demonstrate that our method not only stabilizes GAN training but also achieves significant improvements over baseline systems.",
}
@inproceedings{shoshan-radinsky-2018-latent,
    title = "Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?",
    author = "Shoshan, Eylon  and
      Radinsky, Kira",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1020",
    doi = "10.18653/v1/K18-1020",
    pages = "200--210",
    abstract = "Named-entity Recognition (NER) is an important task in the NLP field , and is widely used to solve many challenges. However, in many scenarios, not all of the entities are explicitly mentioned in the text. Sometimes they could be inferred from the context or from other indicative words. Consider the following sentence: {``}CMA can easily hydrolyze into free acetic acid.{''} Although water is not mentioned explicitly, one can infer that H2O is an entity involved in the process. In this work, we present the problem of Latent Entities Extraction (LEE). We present several methods for determining whether entities are discussed in a text, even though, potentially, they are not explicitly written. Specifically, we design a neural model that handles extraction of multiple entities jointly. We show that our model, along with multi-task learning approach and a novel task grouping algorithm, reaches high performance in identifying latent entities. Our experiments are conducted on a large biological dataset from the biochemical field. The dataset contains text descriptions of biological processes, and for each process, all of the involved entities in the process are labeled, including implicitly mentioned ones. We believe LEE is a task that will significantly improve many NER and subsequent applications and improve text understanding and inference.",
}
@inproceedings{kementchedjhieva-etal-2018-generalizing,
    title = "Generalizing {P}rocrustes Analysis for Better Bilingual Dictionary Induction",
    author = "Kementchedjhieva, Yova  and
      Ruder, Sebastian  and
      Cotterell, Ryan  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1021",
    doi = "10.18653/v1/K18-1021",
    pages = "211--220",
    abstract = "Most recent approaches to bilingual dictionary induction find a linear alignment between the word vector spaces of two languages. We show that projecting the two languages onto a third, latent space, rather than directly onto each other, while equivalent in terms of expressivity, makes it easier to learn approximate alignments. Our modified approach also allows for supporting languages to be included in the alignment process, to obtain an even better performance in low resource settings.",
}
@inproceedings{bennani-smires-etal-2018-simple,
    title = "Simple Unsupervised Keyphrase Extraction using Sentence Embeddings",
    author = "Bennani-Smires, Kamil  and
      Musat, Claudiu  and
      Hossmann, Andreea  and
      Baeriswyl, Michael  and
      Jaggi, Martin",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1022",
    doi = "10.18653/v1/K18-1022",
    pages = "221--229",
    abstract = "Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and generalizes very poorly outside the domain of the training data. At the same time, unsupervised systems have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Addressing these drawbacks, in this paper, we tackle keyphrase extraction from single documents with EmbedRank: a novel unsupervised method, that leverages sentence embeddings. EmbedRank achieves higher F-scores than graph-based state of the art systems on standard datasets and is suitable for real-time processing of large amounts of Web data. With EmbedRank, we also explicitly increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrases{'} semantic overlap leads to no gains in F-score, our high diversity selection is preferred by humans.",
}
@inproceedings{martschat-markert-2018-temporally,
    title = "A Temporally Sensitive Submodularity Framework for Timeline Summarization",
    author = "Martschat, Sebastian  and
      Markert, Katja",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1023",
    doi = "10.18653/v1/K18-1023",
    pages = "230--240",
    abstract = "Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summarization (MDS) in the importance of date selection, interdependencies between summaries of different dates and by having very short summaries compared to the number of corpus documents. However, we show that MDS optimization models using submodular functions can be adapted to yield well-performing TLS models by designing objective functions and constraints that model the temporal dimension inherent in TLS. Importantly, these adaptations retain the elegance and advantages of the original MDS models (clear separation of features and inference, performance guarantees and scalability, little need for supervision) that current TLS-specific models lack.",
}
@inproceedings{yi-etal-2018-chinese,
    title = "{C}hinese Poetry Generation with a Salient-Clue Mechanism",
    author = "Yi, Xiaoyuan  and
      Li, Ruoyu  and
      Sun, Maosong",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1024",
    doi = "10.18653/v1/K18-1024",
    pages = "241--250",
    abstract = "As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines.",
}
@inproceedings{beard-etal-2018-multi,
    title = "Multi-Modal Sequence Fusion via Recursive Attention for Emotion Recognition",
    author = "Beard, Rory  and
      Das, Ritwik  and
      Ng, Raymond W. M.  and
      Gopalakrishnan, P. G. Keerthana  and
      Eerens, Luka  and
      Swietojanski, Pawel  and
      Miksik, Ondrej",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1025",
    doi = "10.18653/v1/K18-1025",
    pages = "251--259",
    abstract = "Natural human communication is nuanced and inherently multi-modal. Humans possess specialised sensoria for processing vocal, visual, and linguistic, and para-linguistic information, but form an intricately fused percept of the multi-modal data stream to provide a holistic representation. Analysis of emotional content in face-to-face communication is a cognitive task to which humans are particularly attuned, given its sociological importance, and poses a difficult challenge for machine emulation due to the subtlety and expressive variability of cross-modal cues. Inspired by the empirical success of recent so-called End-To-End Memory Networks and related works, we propose an approach based on recursive multi-attention with a shared external memory updated over multiple gated iterations of analysis. We evaluate our model across several large multi-modal datasets and show that global contextualised memory with gated memory update can effectively achieve emotion recognition.",
}
@inproceedings{derby-etal-2018-using,
    title = "Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge",
    author = "Derby, Steven  and
      Miller, Paul  and
      Murphy, Brian  and
      Devereux, Barry",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1026",
    doi = "10.18653/v1/K18-1026",
    pages = "260--270",
    abstract = "Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms. However, the dimensions of dense embedding spaces are not designed to resemble human semantic knowledge. Moreover, embeddings are often built from a single source of information (typically text data), even though neurocognitive research suggests that semantics is deeply linked to both language and perception. In this paper, we combine multimodal information from both text and image-based representations derived from state-of-the-art distributional models to produce sparse, interpretable vectors using Joint Non-Negative Sparse Embedding. Through in-depth analyses comparing these sparse models to human-derived behavioural and neuroimaging data, we demonstrate their ability to predict interpretable linguistic descriptions of human ground-truth semantic knowledge.",
}
@inproceedings{rama-2018-similarity,
    title = "Similarity Dependent {C}hinese Restaurant Process for Cognate Identification in Multilingual Wordlists",
    author = "Rama, Taraka",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1027",
    doi = "10.18653/v1/K18-1027",
    pages = "271--281",
    abstract = "We present and evaluate two similarity dependent Chinese Restaurant Process (sd-CRP) algorithms at the task of automated cognate detection. The sd-CRP clustering algorithms do not require any predefined threshold for detecting cognate sets in a multilingual word list. We evaluate the performance of the algorithms on six language families (more than 750 languages) and find that both the sd-CRP variants performs as well as InfoMap and better than UPGMA at the task of inferring cognate clusters. The algorithms presented in this paper are family agnostic and can be applied to any linguistically under-studied language family.",
}
@inproceedings{artetxe-etal-2018-uncovering,
    title = "Uncovering Divergent Linguistic Information in Word Embeddings with Lessons for Intrinsic and Extrinsic Evaluation",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Lopez-Gazpio, I{\~n}igo  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1028",
    doi = "10.18653/v1/K18-1028",
    pages = "282--291",
    abstract = "Following the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent and often mutually incompatible aspects like semantics/syntax and similarity/relatedness. In this paper, we show that each embedding model captures more information than directly apparent. A linear transformation that adjusts the similarity order of the model without any external resource can tailor it to achieve better results in those aspects, providing a new perspective on how embeddings encode divergent linguistic information. In addition, we explore the relation between intrinsic and extrinsic evaluation, as the effect of our transformations in downstream tasks is higher for unsupervised systems than for supervised ones.",
}
@inproceedings{shen-etal-2018-comparing,
    title = "Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games",
    author = "Shen, Judy Hanwen  and
      Hofer, Matthias  and
      Felbo, Bjarke  and
      Levy, Roger",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1029",
    doi = "10.18653/v1/K18-1029",
    pages = "292--301",
    abstract = "Simple reference games are of central theoretical and empirical importance in the study of situated language use. Although language provides rich, compositional truth-conditional semantics to facilitate reference, speakers and listeners may sometimes lack the overall lexical and cognitive resources to guarantee successful reference through these means alone. However, language also has rich associational structures that can serve as a further resource for achieving successful reference. Here we investigate this use of associational information in a setting where only associational information is available: a simplified version of the popular game Codenames. Using optimal experiment design techniques, we compare a range of models varying in the type of associative information deployed and in level of pragmatic sophistication against human behavior. In this setting we find that listeners{'} behavior reflects direct bigram collocational associations more strongly than word-embedding or semantic knowledge graph-based associations and that there is little evidence for pragmatically sophisticated behavior on the part of either speakers or listeners. More generally, we demonstrate the effective use of simple tasks to derive insights into the nature of complex linguistic phenomena.",
}
@inproceedings{barrett-etal-2018-sequence,
    title = "Sequence Classification with Human Attention",
    author = "Barrett, Maria  and
      Bingel, Joachim  and
      Hollenstein, Nora  and
      Rei, Marek  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1030",
    doi = "10.18653/v1/K18-1030",
    pages = "302--312",
    abstract = "Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP. Specifically, we use estimated human attention derived from eye-tracking corpora to regularize attention functions in recurrent neural networks. We show substantial improvements across a range of tasks, including sentiment analysis, grammatical error detection, and detection of abusive language.",
}
@inproceedings{kann-etal-2018-sentence,
    title = "Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!",
    author = "Kann, Katharina  and
      Rothe, Sascha  and
      Filippova, Katja",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1031",
    doi = "10.18653/v1/K18-1031",
    pages = "313--323",
    abstract = "Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency evaluation of natural language generation output at the sentence level. We further introduce WPSLOR, a novel WordPiece-based version, which harnesses a more compact language model. Even though word-overlap metrics like ROUGE are computed with the help of hand-written references, our referenceless methods obtain a significantly higher correlation with human fluency scores on a benchmark dataset of compressed sentences. Finally, we present ROUGE-LM, a reference-based metric which is a natural extension of WPSLOR to the case of available references. We show that ROUGE-LM yields a significantly higher correlation with human judgments than all baseline metrics, including WPSLOR on its own.",
}
@inproceedings{demeester-etal-2018-predefined,
    title = "Predefined Sparseness in Recurrent Sequence Models",
    author = "Demeester, Thomas  and
      Deleu, Johannes  and
      Godin, Fr{\'e}deric  and
      Develder, Chris",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1032",
    doi = "10.18653/v1/K18-1032",
    pages = "324--333",
    abstract = "Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models. However, sparseness is typically induced starting from a dense model, and thus this advantage does not hold during training. We propose techniques to enforce sparseness upfront in recurrent sequence models for NLP applications, to also benefit training. First, in language modeling, we show how to increase hidden state sizes in recurrent layers without increasing the number of parameters, leading to more expressive models. Second, for sequence labeling, we show that word embeddings with predefined sparseness lead to similar performance as dense embeddings, at a fraction of the number of trainable parameters.",
}
@inproceedings{liu-etal-2018-learning,
    title = "Learning to Actively Learn Neural Machine Translation",
    author = "Liu, Ming  and
      Buntine, Wray  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1033",
    doi = "10.18653/v1/K18-1033",
    pages = "334--344",
    abstract = "Traditional active learning (AL) methods for machine translation (MT) rely on heuristics. However, these heuristics are limited when the characteristics of the MT problem change due to e.g. the language pair or the amount of the initial bitext. In this paper, we present a framework to learn sentence selection strategies for neural MT. We train the AL query strategy using a high-resource language-pair based on AL simulations, and then transfer it to the low-resource language-pair of interest. The learned query strategy capitalizes on the shared characteristics between the language pairs to make an effective use of the AL budget. Our experiments on three language-pairs confirms that our method is more effective than strong heuristic-based methods in various conditions, including cold-start and warm-start as well as small and extremely small data conditions.",
}
@inproceedings{krishna-etal-2018-upcycle,
    title = "Upcycle Your {OCR}: Reusing {OCR}s for Post-{OCR} Text Correction in {R}omanised {S}anskrit",
    author = "Krishna, Amrith  and
      Majumder, Bodhisattwa P.  and
      Bhat, Rajesh  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1034",
    doi = "10.18653/v1/K18-1034",
    pages = "345--355",
    abstract = "We propose a post-OCR text correction approach for digitising texts in Romanised Sanskrit. Owing to the lack of resources our approach uses OCR models trained for other languages written in Roman. Currently, there exists no dataset available for Romanised Sanskrit OCR. So, we bootstrap a dataset of 430 images, scanned in two different settings and their corresponding ground truth. For training, we synthetically generate training images for both the settings. We find that the use of copying mechanism (Gu et al., 2016) yields a percentage increase of 7.69 in Character Recognition Rate (CRR) than the current state of the art model in solving monotone sequence-to-sequence tasks (Schnober et al., 2016). We find that our system is robust in combating OCR-prone errors, as it obtains a CRR of 87.01{\%} from an OCR output with CRR of 35.76{\%} for one of the dataset settings. A human judgement survey performed on the models shows that our proposed model results in predictions which are faster to comprehend and faster to improve for a human than the other systems.",
}
@inproceedings{cheng-lapata-2018-weakly,
    title = "Weakly-Supervised Neural Semantic Parsing with a Generative Ranker",
    author = "Cheng, Jianpeng  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1035",
    doi = "10.18653/v1/K18-1035",
    pages = "356--367",
    abstract = "Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. The parser generates candidate tree-structured logical forms from utterances using clues of denotations. These candidates are then ranked based on two criterion: their likelihood of executing to the correct denotation, and their agreement with the utterance semantics. We present a scheduled training procedure to balance the contribution of the two objectives. Furthermore, we propose to use a neurally encoded lexicon to inject prior domain knowledge to the model. Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range.",
}
@inproceedings{tkachenko-sirts-2018-modeling,
    title = "Modeling Composite Labels for Neural Morphological Tagging",
    author = "Tkachenko, Alexander  and
      Sirts, Kairit",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1036",
    doi = "10.18653/v1/K18-1036",
    pages = "368--379",
    abstract = "Neural morphological tagging has been regarded as an extension to POS tagging task, treating each morphological tag as a monolithic label and ignoring its internal structure. We propose to view morphological tags as composite labels and explicitly model their internal structure in a neural sequence tagger. For this, we explore three different neural architectures and compare their performance with both CRF and simple neural multiclass baselines. We evaluate our models on 49 languages and show that the neural architecture that models the morphological labels as sequences of morphological category values performs significantly better than both baselines establishing state-of-the-art results in morphological tagging for most languages.",
}
@inproceedings{collins-etal-2018-evolutionary,
    title = "Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks",
    author = "Collins, Edward  and
      Rozanov, Nikolai  and
      Zhang, Bingbing",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1037",
    doi = "10.18653/v1/K18-1037",
    pages = "380--391",
    abstract = "Classification tasks are usually analysed and improved through new model architectures or hyperparameter optimisation but the underlying properties of datasets are discovered on an ad-hoc basis as errors occur. However, understanding the properties of the data is crucial in perfecting models. In this paper we analyse exactly which characteristics of a dataset best determine how difficult that dataset is for the task of text classification. We then propose an intuitive measure of difficulty for text classification datasets which is simple and fast to calculate. We empirically prove that this measure generalises to unseen data by comparing it to state-of-the-art datasets and results. This measure can be used to analyse the precise source of errors in a dataset and allows fast estimation of how difficult a dataset is to learn. We searched for this measure by training 12 classical and neural network based models on 78 real-world datasets, then use a genetic algorithm to discover the best measure of difficulty. Our difficulty-calculating code and datasets are publicly available.",
}
@inproceedings{merlo-ackermann-2018-vectorial,
    title = "Vectorial Semantic Spaces Do Not Encode Human Judgments of Intervention Similarity",
    author = "Merlo, Paola  and
      Ackermann, Francesco",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1038",
    doi = "10.18653/v1/K18-1038",
    pages = "392--401",
    abstract = "Despite their practical success and impressive performances, neural-network-based and distributed semantics techniques have often been criticized as they remain fundamentally opaque and difficult to interpret. In a vein similar to recent pieces of work investigating the linguistic abilities of these representations, we study another core, defining property of language: the property of long-distance dependencies. Human languages exhibit the ability to interpret discontinuous elements distant from each other in the string as if they were adjacent. This ability is blocked if a similar, but extraneous, element intervenes between the discontinuous components. We present results that show, under exhaustive and precise conditions, that one kind of word embeddings and the similarity spaces they define do not encode the properties of intervention similarity in long-distance dependencies, and that therefore they fail to represent this core linguistic notion.",
}
@inproceedings{kadar-etal-2018-lessons,
    title = "Lessons Learned in Multilingual Grounded Language Learning",
    author = "K{\'a}d{\'a}r, {\'A}kos  and
      Elliott, Desmond  and
      C{\^o}t{\'e}, Marc-Alexandre  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1039",
    doi = "10.18653/v1/K18-1039",
    pages = "402--412",
    abstract = "Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, and that low-resource languages benefit from training with higher-resource languages. We demonstrate that a multilingual model can be trained equally well on either translations or comparable sentence pairs, and that annotating the same set of images in multiple language enables further improvements via an additional caption-caption ranking objective.",
}
@inproceedings{fevry-phang-2018-unsupervised,
    title = "Unsupervised Sentence Compression using Denoising Auto-Encoders",
    author = "F{\'e}vry, Thibault  and
      Phang, Jason",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1040",
    doi = "10.18653/v1/K18-1040",
    pages = "413--422",
    abstract = "In sentence compression, the task of shortening sentences while retaining the original meaning, models tend to be trained on large corpora containing pairs of verbose and compressed sentences. To remove the need for paired corpora, we emulate a summarization task and add noise to extend sentences and train a denoising auto-encoder to recover the original, constructing an end-to-end training regime without the need for any examples of compressed sentences. We conduct a human evaluation of our model on a standard text summarization dataset and show that it performs comparably to a supervised baseline based on grammatical correctness and retention of meaning. Despite being exposed to no target data, our unsupervised models learn to generate imperfect but reasonably readable sentence summaries. Although we underperform supervised models based on ROUGE scores, our models are competitive with a supervised baseline based on human evaluation for grammatical correctness and retention of meaning.",
}
@inproceedings{elekes-etal-2018-resources,
    title = "Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data",
    author = {Elekes, {\'A}bel  and
      Englhardt, Adrian  and
      Sch{\"a}ler, Martin  and
      B{\"o}hm, Klemens},
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1041",
    doi = "10.18653/v1/K18-1041",
    pages = "423--432",
    abstract = "Word embeddings are powerful tools that facilitate better analysis of natural language. However, their quality highly depends on the resource used for training. There are various approaches relying on n-gram corpora, such as the Google n-gram corpus. However, n-gram corpora only offer a small window into the full text {--} 5 words for the Google corpus at best. This gives way to the concern whether the extracted word semantics are of high quality. In this paper, we address this concern with two contributions. First, we provide a resource containing 120 word-embedding models {--} one of the largest collection of embedding models. Furthermore, the resource contains the n-gramed versions of all used corpora, as well as our scripts used for corpus generation, model generation and evaluation. Second, we define a set of meaningful experiments allowing to evaluate the aforementioned quality differences. We conduct these experiments using our resource to show its usage and significance. The evaluation results confirm that one generally can expect high quality for n-grams with n {\textgreater} 3.",
}
@inproceedings{aghaebrahimian-2018-linguistically,
    title = "Linguistically-Based Deep Unstructured Question Answering",
    author = "Aghaebrahimian, Ahmad",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1042",
    doi = "10.18653/v1/K18-1042",
    pages = "433--443",
    abstract = "In this paper, we propose a new linguistically-based approach to answering non-factoid open-domain questions from unstructured data. First, we elaborate on an architecture for textual encoding based on which we introduce a deep end-to-end neural model. This architecture benefits from a bilateral attention mechanism which helps the model to focus on a question and the answer sentence at the same time for phrasal answer extraction. Second, we feed the output of a constituency parser into the model directly and integrate linguistic constituents into the network to help it concentrate on chunks of an answer rather than on its single words for generating more natural output. By optimizing this architecture, we managed to obtain near-to-human-performance results and competitive to a state-of-the-art system on SQuAD and MS-MARCO datasets respectively.",
}
@inproceedings{li-etal-2018-dimsim,
    title = "{DIMSIM}: An Accurate {C}hinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding",
    author = "Li, Min  and
      Danilevsky, Marina  and
      Noeman, Sara  and
      Li, Yunyao",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1043",
    doi = "10.18653/v1/K18-1043",
    pages = "444--453",
    abstract = "Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks. However, existing approaches are designed mainly for Indo-European languages and fail to capture the unique properties of Chinese pronunciation. In this paper, we propose a high dimensional encoded phonetic similarity algorithm for Chinese, DIMSIM. The encodings are learned from annotated data to separately map initial and final phonemes into n-dimensional coordinates. Pinyin phonetic similarities are then calculated by aggregating the similarities of initial, final and tone. DIMSIM demonstrates a 7.5X improvement on mean reciprocal rank over the state-of-the-art phonetic similarity approaches.",
}
@inproceedings{el-baff-etal-2018-challenge,
    title = "Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus",
    author = "El Baff, Roxanne  and
      Wachsmuth, Henning  and
      Al-Khatib, Khalid  and
      Stein, Benno",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1044",
    doi = "10.18653/v1/K18-1044",
    pages = "454--464",
    abstract = "News editorials are said to shape public opinion, which makes them a powerful tool and an important source of political argumentation. However, rarely do editorials change anyone{'}s stance on an issue completely, nor do they tend to argue explicitly (but rather follow a subtle rhetorical strategy). So, what does argumentation quality mean for editorials then? We develop the notion that an effective editorial challenges readers with opposing stance, and at the same time empowers the arguing skills of readers that share the editorial{'}s stance {---} or even challenges both sides. To study argumentation quality based on this notion, we introduce a new corpus with 1000 editorials from the New York Times, annotated for their perceived effect along with the annotators{'} political orientations. Analyzing the corpus, we find that annotators with different orientation disagree on the effect significantly. While only 1{\%} of all editorials changed anyone{'}s stance, more than 5{\%} meet our notion. We conclude that our corpus serves as a suitable resource for studying the argumentation quality of news editorials.",
}
@inproceedings{cohen-widdows-2018-bringing,
    title = "Bringing Order to Neural Word Embeddings with Embeddings Augmented by Random Permutations ({EARP})",
    author = "Cohen, Trevor  and
      Widdows, Dominic",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1045",
    doi = "10.18653/v1/K18-1045",
    pages = "465--475",
    abstract = "Word order is clearly a vital part of human language, but it has been used comparatively lightly in distributional vector models. This paper presents a new method for incorporating word order information into word vector embedding models by combining the benefits of permutation-based order encoding with the more recent method of skip-gram with negative sampling. The new method introduced here is called Embeddings Augmented by Random Permutations (EARP). It operates by applying permutations to the coordinates of context vector representations during the process of training. Results show an 8{\%} improvement in accuracy on the challenging Bigger Analogy Test Set, and smaller but consistent improvements on other analogy reference sets. These findings demonstrate the importance of order-based information in analogical retrieval tasks, and the utility of random permutations as a means to augment neural embeddings.",
}
@inproceedings{nie-etal-2018-aggregated,
    title = "Aggregated Semantic Matching for Short Text Entity Linking",
    author = "Nie, Feng  and
      Zhou, Shuyan  and
      Liu, Jing  and
      Wang, Jinpeng  and
      Lin, Chin-Yew  and
      Pan, Rong",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1046",
    doi = "10.18653/v1/K18-1046",
    pages = "476--485",
    abstract = "The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base. Entity linking in long text has been well studied in previous work. However, short text entity linking is more challenging since the text are noisy and less coherent. To better utilize the local information provided in short texts, we propose a novel neural network framework, Aggregated Semantic Matching (ASM), in which two different aspects of semantic information between the local context and the candidate entity are captured via representation-based and interaction-based neural semantic matching models, and then two matching signals work jointly for disambiguation with a rank aggregation mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on public tweet datasets.",
}
@inproceedings{niu-bansal-2018-adversarial,
    title = "Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models",
    author = "Niu, Tong  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1047",
    doi = "10.18653/v1/K18-1047",
    pages = "486--496",
    abstract = "We present two categories of model-agnostic adversarial strategies that reveal the weaknesses of several generative, task-oriented dialogue models: Should-Not-Change strategies that evaluate over-sensitivity to small and semantics-preserving edits, as well as Should-Change strategies that test if a model is over-stable against subtle yet semantics-changing modifications. We next perform adversarial training with each strategy, employing a max-margin approach for negative generative examples. This not only makes the target dialogue model more robust to the adversarial inputs, but also helps it perform significantly better on the original inputs. Moreover, training on all strategies combined achieves further improvements, achieving a new state-of-the-art performance on the original task (also verified via human evaluation). In addition to adversarial training, we also address the robustness task at the model-level, by feeding it subword units as both inputs and outputs, and show that the resulting model is equally competitive, requires only 1/4 of the original vocabulary size, and is robust to one of the adversarial strategies (to which the original model is vulnerable) even without adversarial training.",
}
@inproceedings{chaudhuri-etal-2018-improving,
    title = "Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge",
    author = "Chaudhuri, Debanjan  and
      Kristiadi, Agustinus  and
      Lehmann, Jens  and
      Fischer, Asja",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1048",
    doi = "10.18653/v1/K18-1048",
    pages = "497--507",
    abstract = "Building systems that can communicate with humans is a core problem in Artificial Intelligence. This work proposes a novel neural network architecture for response selection in an end-to-end multi-turn conversational dialogue setting. The architecture applies context level attention and incorporates additional external knowledge provided by descriptions of domain-specific words. It uses a bi-directional Gated Recurrent Unit (GRU) for encoding context and responses and learns to attend over the context words given the latent response representation and vice versa. In addition, it incorporates external domain specific information using another GRU for encoding the domain keyword descriptions. This allows better representation of domain-specific keywords in responses and hence improves the overall performance. Experimental results show that our model outperforms all other state-of-the-art methods for response selection in multi-turn conversations.",
}
@inproceedings{chung-etal-2018-lifted,
    title = "The Lifted Matrix-Space Model for Semantic Composition",
    author = "Chung, WooJin  and
      Wang, Sheng-Fu  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1049",
    doi = "10.18653/v1/K18-1049",
    pages = "508--518",
    abstract = "Tree-structured neural network architectures for sentence encoding draw inspiration from the approach to semantic composition generally seen in formal linguistics, and have shown empirical improvements over comparable sequence models by doing so. Moreover, adding multiplicative interaction terms to the composition functions in these models can yield significant further improvements. However, existing compositional approaches that adopt such a powerful composition function scale poorly, with parameter counts exploding as model dimension or vocabulary size grows. We introduce the Lifted Matrix-Space model, which uses a global transformation to map vector word embeddings to matrices, which can then be composed via an operation based on matrix-matrix multiplication. Its composition function effectively transmits a larger number of activations across layers with relatively few model parameters. We evaluate our model on the Stanford NLI corpus, the Multi-Genre NLI corpus, and the Stanford Sentiment Treebank and find that it consistently outperforms TreeLSTM (Tai et al., 2015), the previous best known composition function for tree-structured models.",
}
@inproceedings{kolitsas-etal-2018-end,
    title = "End-to-End Neural Entity Linking",
    author = "Kolitsas, Nikolaos  and
      Ganea, Octavian-Eugen  and
      Hofmann, Thomas",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1050",
    doi = "10.18653/v1/K18-1050",
    pages = "519--529",
    abstract = "Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention - entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.",
}
@inproceedings{ager-etal-2018-modelling,
    title = "Modelling Salient Features as Directions in Fine-Tuned Semantic Spaces",
    author = "Ager, Thomas  and
      Ku{\v{z}}elka, Ond{\v{r}}ej  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1051",
    doi = "10.18653/v1/K18-1051",
    pages = "530--540",
    abstract = "In this paper we consider semantic spaces consisting of objects from some particular domain (e.g. IMDB movie reviews). Various authors have observed that such semantic spaces often model salient features (e.g. how scary a movie is) as directions. These feature directions allow us to rank objects according to how much they have the corresponding feature, and can thus play an important role in interpretable classifiers, recommendation systems, or entity-oriented search engines, among others. Methods for learning semantic spaces, however, are mostly aimed at modelling similarity. In this paper, we argue that there is an inherent trade-off between capturing similarity and faithfully modelling features as directions. Following this observation, we propose a simple method to fine-tune existing semantic spaces, with the aim of improving the quality of their feature directions. Crucially, our method is fully unsupervised, requiring only a bag-of-words representation of the objects as input.",
}
@inproceedings{yoshikawa-iwakura-2018-model,
    title = "Model Transfer with Explicit Knowledge of the Relation between Class Definitions",
    author = "Yoshikawa, Hiyori  and
      Iwakura, Tomoya",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1052",
    doi = "10.18653/v1/K18-1052",
    pages = "541--550",
    abstract = "This paper investigates learning methods for multi-class classification using labeled data for the target classification scheme and another labeled data for a similar but different classification scheme (support scheme). We show that if we have prior knowledge about the relation between support and target classification schemes in the form of a class correspondence table, we can use it to improve the model performance further than the simple multi-task learning approach. Instead of learning the individual classification layers for the support and target schemes, the proposed method converts the class label of each example on the support scheme into a set of candidate class labels on the target scheme via the class correspondence table, and then uses the candidate labels to learn the classification layer for the target scheme. We evaluate the proposed method on two tasks in NLP. The experimental results show that our method effectively learns the target schemes especially for the classes that have a tight connection to certain support classes.",
}
@inproceedings{zemlyanskiy-sha-2018-aiming,
    title = "Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner",
    author = "Zemlyanskiy, Yury  and
      Sha, Fei",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1053",
    doi = "10.18653/v1/K18-1053",
    pages = "551--561",
    abstract = "There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.",
}
@inproceedings{chen-etal-2018-neural,
    title = "Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis",
    author = "Chen, Yufei  and
      Huang, Sheng  and
      Wang, Fang  and
      Cao, Junjie  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1054",
    doi = "10.18653/v1/K18-1054",
    pages = "562--572",
    abstract = "We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser. Our parser targets 1-endpoint-crossing, pagenumber-2 graphs which are a good fit to semantic dependency graphs, and utilizes an efficient dynamic programming algorithm for decoding. For disambiguation, the parser associates words with BiLSTM vectors and utilizes these vectors to assign scores to candidate dependencies. We conduct experiments on the data sets from SemEval 2015 as well as Chinese CCGBank. Our parser achieves very competitive results for both English and Chinese. To improve the parsing performance on cross-domain texts, we propose a data-oriented method to explore the linguistic generality encoded in English Resource Grammar, which is a precisionoriented, hand-crafted HPSG grammar, in an implicit way. Experiments demonstrate the effectiveness of our data-oriented method across a wide range of conditions.",
}
@inproceedings{xu-etal-2018-random,
    title = "From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information",
    author = "Xu, Hengru  and
      Li, Shen  and
      Hu, Renfen  and
      Li, Si  and
      Gao, Sheng",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1055",
    doi = "10.18653/v1/K18-1055",
    pages = "573--582",
    abstract = "Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the units are dropped randomly according to the same probability, we aim to use explicit instructions based on global information of the dataset to guide the training process. With GI-Dropout, the model is supposed to pay more attention to inapparent features or patterns. Experiments demonstrate the effectiveness of the dropout with global information on seven text classification tasks, including sentiment analysis and topic classification.",
}
@inproceedings{he-etal-2018-sequence,
    title = "Sequence to Sequence Mixture Model for Diverse Machine Translation",
    author = "He, Xuanli  and
      Haffari, Gholamreza  and
      Norouzi, Mohammad",
    booktitle = "Proceedings of the 22nd Conference on Computational Natural Language Learning",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K18-1056",
    doi = "10.18653/v1/K18-1056",
    pages = "583--592",
    abstract = "Sequence to sequence (SEQ2SEQ) models lack diversity in their generated translations. This can be attributed to their limitations in capturing lexical and syntactic variations in parallel corpora, due to different styles, genres, topics, or ambiguity of human translation process. In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model. Each mixture component selects its own training dataset via optimization of the marginal log-likelihood, which leads to a soft clustering of the parallel corpus. Experiments on four language pairs demonstrate the superiority of our mixture model compared to SEQ2SEQ model with the standard and diversity encouraged beam search. Our mixture model incurs negligible additional parameters and no extra computation in the decoding time.",
}
@article{cl-2018-linguistics-44-issue-4,
    title = "Computational Linguistics, Volume 44, Issue 4 - {D}ecember 2018",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4000",
}
@article{steedman-2018-lost,
    title = "The Lost Combinator",
    author = "Steedman, Mark",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4001",
    doi = "10.1162/coli_a_00328",
    pages = "613--629",
    abstract = "",
}
@article{zinn-2018-squib,
    title = "{S}quib: The Language Resource Switchboard",
    author = "Zinn, Claus",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4002",
    doi = "10.1162/coli_a_00329",
    pages = "631--639",
    abstract = "The CLARIN research infrastructure gives users access to an increasingly rich and diverse set of language-related resources and tools. Whereas there is ample support for searching resources using metadata-based search, or full-text search, or for aggregating resources into virtual collections, there is little support for users to help them process resources in one way or another. In spite of the large number of tools that process texts in many different languages, there is no single point of access where users can find tools to fit their needs and the resources they have. In this squib, we present the Language Resource Switchboard (LRS), which helps users to discover tools that can process their resources. For this, the LRS identifies all applicable tools for a given resource, lists the tasks the tools can achieve, and invokes the selected tool in such a way so that processing can start immediately with little or no prior tool parameterization.",
}
@article{wieling-etal-2018-squib,
    title = "{S}quib: Reproducibility in Computational Linguistics: Are We Willing to Share?",
    author = "Wieling, Martijn  and
      Rawee, Josine  and
      van Noord, Gertjan",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4003",
    doi = "10.1162/coli_a_00330",
    pages = "641--649",
    abstract = "This study focuses on an essential precondition for reproducibility in computational linguistics: the willingness of authors to share relevant source code and data. Ten years after Ted Pedersen{'}s influential {``}Last Words{''} contribution in Computational Linguistics, we investigate to what extent researchers in computational linguistics are willing and able to share their data and code. We surveyed all 395 full papers presented at the 2011 and 2016 ACL Annual Meetings, and identified whether links to data and code were provided. If working links were not provided, authors were requested to provide this information. Although data were often available, code was shared less often. When working links to code or data were not provided in the paper, authors provided the code in about one third of cases. For a selection of ten papers, we attempted to reproduce the results using the provided data and code. We were able to reproduce the results approximately for six papers. For only a single paper did we obtain the exact same results. Our findings show that even though the situation appears to have improved comparing 2016 to 2011, empiricism in computational linguistics still largely remains a matter of faith. Nevertheless, we are somewhat optimistic about the future. Ensuring reproducibility is not only important for the field as a whole, but also seems worthwhile for individual researchers: The median citation count for studies with working links to the source code is higher.",
}
@article{weischedel-boschee-2018-last,
    title = "Last Words: What Can Be Accomplished with the State of the Art in Information Extraction? A Personal View",
    author = "Weischedel, Ralph  and
      Boschee, Elizabeth",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4004",
    doi = "10.1162/coli_a_00331",
    pages = "651--658",
    abstract = "Though information extraction (IE) research has more than a 25-year history, F1 scores remain low. Thus, one could question continued investment in IE research. In this article, we present three applications where information extraction of entities, relations, and/or events has been used, and note the common features that seem to have led to success. We also identify key research challenges whose solution seems essential for broader successes. Because a few practical deployments already exist and because breakthroughs on particular challenges would greatly broaden the technology{'}s deployment, further R and D investments are justified.",
}
@article{wan-2018-book,
    title = "Book Review: Automatic Text Simplification by Horacio Saggion",
    author = "Wan, Xiaojun",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4005",
    doi = "10.1162/coli_r_00332",
    pages = "659--661",
}
@article{benamara-etal-2018-introduction,
    title = "Introduction to the Special Issue on Language in Social Media: Exploiting Discourse and Other Contextual Information",
    author = "Benamara, Farah  and
      Inkpen, Diana  and
      Taboada, Maite",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4006",
    doi = "10.1162/coli_a_00333",
    pages = "663--681",
    abstract = "Social media content is changing the way people interact with each other and share information, personal messages, and opinions about situations, objects, and past experiences. Most social media texts are short online conversational posts or comments that do not contain enough information for natural language processing (NLP) tools, as they are often accompanied by non-linguistic contextual information, including meta-data (e.g., the user{'}s profile, the social network of the user, and their interactions with other users). Exploiting such different types of context and their interactions makes the automatic processing of social media texts a challenging research task. Indeed, simply applying traditional text mining tools is clearly sub-optimal, as, typically, these tools take into account neither the interactive dimension nor the particular nature of this data, which shares properties with both spoken and written language. This special issue contributes to a deeper understanding of the role of these interactions to process social media data from a new perspective in discourse interpretation. This introduction first provides the necessary background to understand what context is from both the linguistic and computational linguistic perspectives, then presents the most recent context-based approaches to NLP for social media. We conclude with an overview of the papers accepted in this special issue, highlighting what we believe are the future directions in processing social media texts.",
}
@article{kiesling-etal-2018-interactional,
    title = "Interactional Stancetaking in Online Forums",
    author = "Kiesling, Scott F.  and
      Pavalanathan, Umashanthi  and
      Fitzpatrick, Jim  and
      Han, Xiaochuang  and
      Eisenstein, Jacob",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4007",
    doi = "10.1162/coli_a_00334",
    pages = "683--718",
    abstract = "Language is shaped by the relationships between the speaker/writer and the audience, the object of discussion, and the talk itself. In turn, language is used to reshape these relationships over the course of an interaction. Computational researchers have succeeded in operationalizing sentiment, formality, and politeness, but each of these constructs captures only some aspects of social and relational meaning. Theories of interactional stancetaking have been put forward as holistic accounts, but until now, these theories have been applied only through detailed qualitative analysis of (portions of) a few individual conversations. In this article, we propose a new computational operationalization of interpersonal stancetaking. We begin with annotations of three linked stance dimensions{---}affect, investment, and alignment{---}on 68 conversation threads from the online platform Reddit. Using these annotations, we investigate thread structure and linguistic properties of stancetaking in online conversations. We identify lexical features that characterize the extremes along each stancetaking dimension, and show that these stancetaking properties can be predicted with moderate accuracy from bag-of-words features, even with a relatively small labeled training set. These quantitative analyses are supplemented by extensive qualitative analysis, highlighting the compatibility of computational and qualitative methods in synthesizing evidence about the creation of interactional meaning.",
}
@article{li-etal-2018-joint-model,
    title = "A Joint Model of Conversational Discourse Latent Topics on Microblogs",
    author = "Li, Jing  and
      Song, Yan  and
      Wei, Zhongyu  and
      Wong, Kam-Fai",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4008",
    doi = "10.1162/coli_a_00335",
    pages = "719--754",
    abstract = "Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.",
}
@article{ghosh-etal-2018-sarcasm,
    title = "Sarcasm Analysis Using Conversation Context",
    author = "Ghosh, Debanjan  and
      Fabbri, Alexander R.  and
      Muresan, Smaranda",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4009",
    doi = "10.1162/coli_a_00336",
    pages = "755--792",
    abstract = "Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker{'}s sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection? (2) can we identify what part of conversation context triggered the sarcastic reply? and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic? To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network, outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn, or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these data sets, ranging from their size to the nature of the gold-label annotations. To address the latter two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.",
}
@article{van-hee-etal-2018-usually,
    title = "We Usually Don{'}t Like Going to the Dentist: Using Common Sense to Detect Irony on {T}witter",
    author = "Van Hee, Cynthia  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4010",
    doi = "10.1162/coli_a_00337",
    pages = "793--832",
    abstract = "Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., {``}flight delays,{''} {``}sitting the whole day at the doctor{'}s office{''}), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.",
}
@article{cocarascu-toni-2018-combining,
    title = "Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets",
    author = "Cocarascu, Oana  and
      Toni, Francesca",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4011",
    doi = "10.1162/coli_a_00338",
    pages = "833--858",
    abstract = "The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for relation{--}based argument mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement toward a statement is a useful step toward determining its truthfulness. Furthermore, we use our method for extracting bipolar argumentation frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets.",
}
@article{joty-mohiuddin-2018-modeling,
    title = "Modeling Speech Acts in Asynchronous Conversations: A Neural-{CRF} Approach",
    author = "Joty, Shafiq  and
      Mohiuddin, Tasnim",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4012",
    doi = "10.1162/coli_a_00339",
    pages = "859--894",
    abstract = "Participants in an asynchronous conversation (e.g., forum, e-mail) interact with each other at different times, performing certain communicative acts, called speech acts (e.g., question, request). In this article, we propose a hybrid approach to speech act recognition in asynchronous conversations. Our approach works in two main steps: a long short-term memory recurrent neural network (LSTM-RNN) first encodes each sentence separately into a task-specific distributed representation, and this is then used in a conditional random field (CRF) model to capture the conversational dependencies between sentences. The LSTM-RNN model uses pretrained word embeddings learned from a large conversational corpus and is trained to classify sentences into speech act types. The CRF model can consider arbitrary graph structures to model conversational dependencies in an asynchronous conversation. In addition, to mitigate the problem of limited annotated data in the asynchronous domains, we adapt the LSTM-RNN model to learn from synchronous conversations (e.g., meetings), using domain adversarial training of neural networks. Empirical evaluation shows the effectiveness of our approach over existing ones: (i) LSTM-RNNs provide better task-specific representations, (ii) conversational word embeddings benefit the LSTM-RNNs more than the off-the-shelf ones, (iii) adversarial training gives better domain-invariant representations, and (iv) the global CRF model improves over local models.",
}
@article{nn-2018-reviewers,
    title = "Reviewers for Volume 44",
    journal = "Computational Linguistics",
    volume = "44",
    number = "4",
    month = dec,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-4013",
    doi = "10.1162/coli_x_00340",
    pages = "895--896",
}
@article{cl-2018-linguistics-44-issue,
    title = "Computational Linguistics, Volume 44, Issue 3 - September 2018",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3000",
}
@article{webber-2018-obituary,
    title = "{O}bituary: Aravind {K}. Joshi",
    author = "Webber, Bonnie",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3001",
    doi = "10.1162/coli_a_00321",
    pages = "387--392",
}
@article{reiter-2018-structured,
    title = "A Structured Review of the Validity of {BLEU}",
    author = "Reiter, Ehud",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3002",
    doi = "10.1162/coli_a_00322",
    pages = "393--401",
    abstract = "The BLEU metric has been widely used in NLP for over 15 years to evaluate NLP systems, especially in machine translation and natural language generation. I present a structured review of the evidence on whether BLEU is a valid evaluation technique{---}in other words, whether BLEU scores correlate with real-world utility and user-satisfaction of NLP systems; this review covers 284 correlations reported in 34 papers. Overall, the evidence supports using BLEU for diagnostic evaluation of MT systems (which is what it was originally proposed for), but does not support using BLEU outside of MT, for evaluation of individual texts, or for scientific hypothesis testing.",
}
@article{malmasi-dras-2018-native,
    title = "Native Language Identification With Classifier Stacking and Ensembles",
    author = "Malmasi, Shervin  and
      Dras, Mark",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3003",
    doi = "10.1162/coli_a_00323",
    pages = "403--446",
    abstract = "Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.",
}
@article{kuhlmann-etal-2018-complexity,
    title = "On the Complexity of {CCG} Parsing",
    author = "Kuhlmann, Marco  and
      Satta, Giorgio  and
      Jonsson, Peter",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3004",
    doi = "10.1162/coli_a_00324",
    pages = "447--482",
    abstract = "We study the parsing complexity of Combinatory Categorial Grammar (CCG) in the formalism of Vijay-Shanker and Weir (1994). As our main result, we prove that any parsing algorithm for this formalism will take in the worst case exponential time when the size of the grammar, and not only the length of the input sentence, is included in the analysis. This sets the formalism of Vijay-Shanker and Weir (1994) apart from weakly equivalent formalisms such as Tree Adjoining Grammar, for which parsing can be performed in time polynomial in the combined size of grammar and input sentence. Our results contribute to a refined understanding of the class of mildly context-sensitive grammars, and inform the search for new, mildly context-sensitive versions of CCG.",
}
@article{riedl-biemann-2018-using,
    title = "Using Semantics for Granularities of Tokenization",
    author = "Riedl, Martin  and
      Biemann, Chris",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3005",
    doi = "10.1162/coli_a_00325",
    pages = "483--524",
    abstract = "Depending on downstream applications, it is advisable to extend the notion of tokenization from low-level character-based token boundary detection to identification of meaningful and useful language units. This entails both identifying units composed of several single words that form a several single words that form a, as well as splitting single-word compounds into their meaningful parts. In this article, we introduce unsupervised and knowledge-free methods for these two tasks. The main novelty of our research is based on the fact that methods are primarily based on distributional similarity, of which we use two flavors: a sparse count-based and a dense neural-based distributional semantic model. First, we introduce DRUID, which is a method for detecting MWEs. The evaluation on MWE-annotated data sets in two languages and newly extracted evaluation data sets for 32 languages shows that DRUID compares favorably over previous methods not utilizing distributional information. Second, we present SECOS, an algorithm for decompounding close compounds. In an evaluation of four dedicated decompounding data sets across four languages and on data sets extracted from Wiktionary for 14 languages, we demonstrate the superiority of our approach over unsupervised baselines, sometimes even matching the performance of previous language-specific and supervised methods. In a final experiment, we show how both decompounding and MWE information can be used in information retrieval. Here, we obtain the best results when combining word information with MWEs and the compound parts in a bag-of-words retrieval set-up. Overall, our methodology paves the way to automatic detection of lexical units beyond standard tokenization techniques without language-specific preprocessing steps such as POS tagging.",
}
@article{naim-etal-2018-feature,
    title = "Feature-Based Decipherment for Machine Translation",
    author = "Naim, Iftekhar  and
      Riley, Parker  and
      Gildea, Daniel",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3006",
    doi = "10.1162/coli_a_00326",
    pages = "525--546",
    abstract = "Orthographic similarities across languages provide a strong signal for unsupervised probabilistic transduction (decipherment) for closely related language pairs. The existing decipherment models, however, are not well suited for exploiting these orthographic similarities. We propose a log-linear model with latent variables that incorporates orthographic similarity features. Maximum likelihood training is computationally expensive for the proposed log-linear model. To address this challenge, we perform approximate inference via Markov chain Monte Carlo sampling and contrastive divergence. Our results show that the proposed log-linear model with contrastive divergence outperforms the existing generative decipherment models by exploiting the orthographic features. The model both scales to large vocabularies and preserves accuracy in low- and no-resource contexts.",
}
@article{kolhatkar-etal-2018-survey,
    title = "{S}urvey: Anaphora With Non-nominal Antecedents in Computational Linguistics: a {S}urvey",
    author = "Kolhatkar, Varada  and
      Roussel, Adam  and
      Dipper, Stefanie  and
      Zinsmeister, Heike",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-3007",
    doi = "10.1162/coli_a_00327",
    pages = "547--612",
    abstract = "This article provides an extensive overview of the literature related to the phenomenon of non-nominal-antecedent anaphora (also known as abstract anaphora or discourse deixis), a type of anaphora in which an anaphor like {``}that{''} refers to an antecedent (marked in boldface) that is syntactically non-nominal, such as the first sentence in {``}It{'}s way too hot here. That{'}s why I{'}m moving to Alaska.{''} Annotating and automatically resolving these cases of anaphora is interesting in its own right because of the complexities involved in identifying non-nominal antecedents, which typically represent abstract objects such as events, facts, and propositions. There is also practical value in the resolution of non-nominal-antecedent anaphora, as this would help computational systems in machine translation, summarization, and question answering, as well as, conceivably, any other task dependent on some measure of text understanding. Most of the existing approaches to anaphora annotation and resolution focus on nominal-antecedent anaphora, classifying many of the cases where the antecedents are syntactically non-nominal as non-anaphoric. There has been some work done on this topic, but it remains scattered and difficult to collect and assess. With this article, we hope to bring together and synthesize work done in disparate contexts up to now in order to identify fundamental problems and draw conclusions from an overarching perspective. Having a good picture of the current state of the art in this field can help researchers direct their efforts to where they are most necessary. Because of the great variety of theoretical approaches that have been brought to bear on the problem, there is an equally diverse array of terminologies that are used to describe it, so we will provide an overview and discussion of these terminologies. We also describe the linguistic properties of non-nominal-antecedent anaphora, examine previous annotation efforts that have addressed this topic, and present the computational approaches that aim at resolving non-nominal-antecedent anaphora automatically. We close with a review of the remaining open questions in this area and some of our recommendations for future research.",
}
@article{cl-2018-linguistics-44,
    title = "Computational Linguistics, Volume 44, Issue 2 - June 2018",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2000",
}
@article{morey-etal-2018-dependency,
    title = "A Dependency Perspective on {RST} Discourse Parsing and Evaluation",
    author = "Morey, Mathieu  and
      Muller, Philippe  and
      Asher, Nicholas",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2001",
    doi = "10.1162/COLI_a_00314",
    pages = "197--235",
    abstract = "Computational text-level discourse analysis mostly happens within Rhetorical Structure Theory (RST), whose structures have classically been presented as constituency trees, and relies on data from the RST Discourse Treebank (RST-DT); as a result, the RST discourse parsing community has largely borrowed from the syntactic constituency parsing community. The standard evaluation procedure for RST discourse parsers is thus a simplified variant of PARSEVAL, and most RST discourse parsers use techniques that originated in syntactic constituency parsing. In this article, we isolate a number of conceptual and computational problems with the constituency hypothesis. We then examine the consequences, for the implementation and evaluation of RST discourse parsers, of adopting a dependency perspective on RST structures, a view advocated so far only by a few approaches to discourse parsing. While doing that, we show the importance of the notion of headedness of RST structures. We analyze RST discourse parsing as dependency parsing by adapting to RST a recent proposal in syntactic parsing that relies on head-ordered dependency trees, a representation isomorphic to headed constituency trees. We show how to convert the original trees from the RST corpus, RST-DT, and their binarized versions used by all existing RST parsers to head-ordered dependency trees. We also propose a way to convert existing simple dependency parser output to constituent trees. This allows us to evaluate and to compare approaches from both constituent-based and dependency-based perspectives in a unified framework, using constituency and dependency metrics. We thus propose an evaluation framework to compare extant approaches easily and uniformly, something the RST parsing community has lacked up to now. We can also compare parsers{'} predictions to each other across frameworks. This allows us to characterize families of parsing strategies across the different frameworks, in particular with respect to the notion of headedness. Our experiments provide evidence for the conceptual similarities between dependency parsers and shift-reduce constituency parsers, and confirm that dependency parsing constitutes a viable approach to RST discourse parsing.",
}
@article{hou-etal-2018-unrestricted,
    title = "Unrestricted Bridging Resolution",
    author = "Hou, Yufang  and
      Markert, Katja  and
      Strube, Michael",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2002",
    doi = "10.1162/COLI_a_00315",
    pages = "237--284",
    abstract = "In contrast to identity anaphors, which indicate coreference between a noun phrase and its antecedent, bridging anaphors link to their antecedent(s) via lexico-semantic, frame, or encyclopedic relations. Bridging resolution involves recognizing bridging anaphors and finding links to antecedents. In contrast to most prior work, we tackle both problems. Our work also follows a more wide-ranging definition of bridging than most previous work and does not impose any restrictions on the type of bridging anaphora or relations between anaphor and antecedent. We create a corpus (ISNotes) annotated for information status (IS), bridging being one of the IS subcategories. The annotations reach high reliability for all categories and marginal reliability for the bridging subcategory. We use a two-stage statistical global inference method for bridging resolution. Given all mentions in a document, the first stage, bridging anaphora recognition, recognizes bridging anaphors as a subtask of learning fine-grained IS. We use a cascading collective classification method where (i) collective classification allows us to investigate relations among several mentions and autocorrelation among IS classes and (ii) cascaded classification allows us to tackle class imbalance, important for minority classes such as bridging. We show that our method outperforms current methods both for IS recognition overall as well as for bridging, specifically. The second stage, bridging antecedent selection, finds the antecedents for all predicted bridging anaphors. We investigate the phenomenon of semantically or syntactically related bridging anaphors that share the same antecedent, a phenomenon we call sibling anaphors. We show that taking sibling anaphors into account in a joint inference model improves antecedent selection performance. In addition, we develop semantic and salience features for antecedent selection and suggest a novel method to build the candidate antecedent list for an anaphor, using the discourse scope of the anaphor. Our model outperforms previous work significantly.",
}
@article{morrill-valentin-2018-spurious,
    title = "Spurious Ambiguity and Focalization",
    author = "Morrill, Glyn  and
      Valent{\'\i}n, Oriol",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2003",
    doi = "10.1162/COLI_a_00316",
    pages = "285--327",
    abstract = "Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize. In this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.",
}
@article{voeten-van-zaanen-2018-influence,
    title = "The Influence of Context on the Learning of Metrical Stress Systems Using Finite-State Machines",
    author = "Voeten, Cesko  and
      van Zaanen, Menno",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2004",
    doi = "10.1162/COLI_a_00317",
    pages = "329--348",
    abstract = "Languages vary in the way stress is assigned to syllables within words. This article investigates the learnability of stress systems in a wide range of languages. The stress systems can be described using finite-state automata with symbols indicating levels of stress (primary, secondary, or no stress). Finite-state automata have been the focus of research in the area of grammatical inference for some time now. It has been shown that finite-state machines are learnable from examples using state-merging. One such approach, which aims to learn k-testable languages, has been applied to stress systems with some success. The family of k-testable languages has been shown to be efficiently learnable (in polynomial time). Here, we extend this approach to k, l-local languages by taking not only left context, but also right context, into account. We consider empirical results testing the performance of our learner using various amounts of context (corresponding to varying definitions of phonological locality). Our results show that our approach of learning stress patterns using state-merging is more reliant on left context than on right context. Additionally, some stress systems fail to be learned by our learner using either the left-context k-testable or the left-and-right-context k, l-local learning system. A more complex merging strategy, and hence grammar representation, is required for these stress systems.",
}
@article{can-manandhar-2018-tree,
    title = "Tree Structured {D}irichlet Processes for Hierarchical Morphological Segmentation",
    author = "Can, Burcu  and
      Manandhar, Suresh",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2005",
    doi = "10.1162/COLI_a_00318",
    pages = "349--374",
    abstract = "This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process. Tree hierarchies are learned along with the corresponding morphological paradigms simultaneously. Our model is evaluated on Morpho Challenge and shows competitive performance when compared to state-of-the-art unsupervised morphological segmentation systems. Although we apply this model for morphological segmentation, the model itself can also be used for hierarchical clustering of other types of data.",
}
@article{huang-2018-domain,
    title = {Domain-Sensitive Temporal Tagging By Jannik Str{\"o}tgen, {M}ichael Gertz},
    author = "Huang, Ruihong",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2006",
    doi = "10.1162/COLI_r_00319",
    pages = "375--377",
}
@article{merlo-2018-festina,
    title = "Festina Lente: A Farewell from the Editor",
    author = "Merlo, Paola",
    journal = "Computational Linguistics",
    volume = "44",
    number = "2",
    month = jun,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-2007",
    doi = "10.1162/COLI_e_00320",
    pages = "379--385",
}
@article{cl-2018-linguistics,
    title = "Computational Linguistics, Volume 44, Issue 1 - {A}pril 2018",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1000",
}
@article{grosz-2018-smart,
    title = "Smart Enough to Talk With Us? Foundations and Challenges for Dialogue Capable {AI} Systems",
    author = "Grosz, Barbara J.",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1001",
    doi = "10.1162/COLI_a_00313",
    pages = "1--15",
}
@article{sanchez-etal-2018-derivational,
    title = "On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden {M}arkov Models",
    author = "S{\'a}nchez, Joan Andreu  and
      Rocha, Martha Alicia  and
      Romero, Ver{\'o}nica  and
      Villegas, Mauricio",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1002",
    doi = "10.1162/COLI_a_00306",
    pages = "17--37",
    abstract = "Probabilistic finite-state automata are a formalism that is widely used in many problems of automatic speech recognition and natural language processing. Probabilistic finite-state automata are closely related to other finite-state models as weighted finite-state automata, word lattices, and hidden Markov models. Therefore, they share many similar properties and problems. Entropy measures of finite-state models have been investigated in the past in order to study the information capacity of these models. The derivational entropy quantifies the uncertainty that the model has about the probability distribution it represents. The derivational entropy in a finite-state automaton is computed from the probability that is accumulated in all of its individual state sequences. The computation of the entropy from a weighted finite-state automaton requires a normalized model. This article studies an efficient computation of the derivational entropy of left-to-right probabilistic finite-state automata, and it introduces an efficient algorithm for normalizing weighted finite-state automata. The efficient computation of the derivational entropy is also extended to continuous hidden Markov models.",
}
@article{manshadi-etal-2018-notion,
    title = "A Notion of Semantic Coherence for Underspecified Semantic Representation",
    author = "Manshadi, Mehdi  and
      Gildea, Daniel  and
      Allen, James F.",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1003",
    doi = "10.1162/COLI_a_00307",
    pages = "39--83",
    abstract = "The general problem of finding satisfying solutions to constraint-based underspecified representations of quantifier scope is NP-complete. Existing frameworks, including Dominance Graphs, Minimal Recursion Semantics, and Hole Semantics, have struggled to balance expressivity and tractability in order to cover real natural language sentences with efficient algorithms. We address this trade-off with a general principle of coherence, which requires that every variable introduced in the domain of discourse must contribute to the overall semantics of the sentence. We show that every underspecified representation meeting this criterion can be efficiently processed, and that our set of representations subsumes all previously identified tractable sets.",
}
@article{gildea-etal-2018-cache,
    title = "Cache Transition Systems for Graph Parsing",
    author = "Gildea, Daniel  and
      Satta, Giorgio  and
      Peng, Xiaochang",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1004",
    doi = "10.1162/COLI_a_00308",
    pages = "85--118",
    abstract = "Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition. We find empirically that small cache sizes cover a high percentage of sentences in existing semantic corpora.",
}
@article{chiang-etal-2018-weighted,
    title = "Weighted {DAG} Automata for Semantic Graphs",
    author = "Chiang, David  and
      Drewes, Frank  and
      Gildea, Daniel  and
      Lopez, Adam  and
      Satta, Giorgio",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1005",
    doi = "10.1162/COLI_a_00309",
    pages = "119--186",
    abstract = "Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that parallels the frameworks of finite automata for strings and finite tree automata for trees. A possible starting point for such a framework is the formalism of directed acyclic graph (DAG) automata, defined by Kamimura and Slutzki and extended by Quernheim and Knight. In this article, we study the latter in depth, demonstrating several new results, including a practical recognition algorithm that can be used for inference and learning with models defined on DAG automata. We also propose an extension to graphs with unbounded node degree and show that our results carry over to the extended formalism.",
}
@article{duh-2018-book,
    title = "Book Review: {B}ayesian Analysis in Natural Language Processing by Shay {C}ohen",
    author = "Duh, Kevin",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1006",
    doi = "10.1162/COLI_r_00310",
    pages = "187--189",
}
@article{strapparava-2018-metaphor,
    title = "{M}etaphor: A Computational Perspective by Tony Veale, Ekaterina {S}hutova and Beata Beigman Klebanov",
    author = "Strapparava, Carlo",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1007",
    doi = "10.1162/COLI_r_00311",
    pages = "191--192",
}
@article{liu-zhang-2018-neural,
    title = "Neural Network Methods for Natural Language Processing by Yoav {G}oldberg",
    author = "Liu, Yang  and
      Zhang, Meng",
    journal = "Computational Linguistics",
    volume = "44",
    number = "1",
    month = mar,
    year = "2018",
    url = "https://www.aclweb.org/anthology/J18-1008",
    doi = "10.1162/",
    pages = "193--195",
}
@proceedings{emnlp-2018-,
    title = "",
    editor = "{Mausam}  and
      Wang, Lu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = oct # "-" # nov,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
}
@inproceedings{zhang-2018-joint,
    title = "Joint models for {NLP}",
    author = "Zhang, Yue",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = oct # "-" # nov,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-3001",
    abstract = "Joint models have received much research attention in NLP, allowing relevant tasks to share common information while avoiding error propagation in multi-stage pepelines. Several main approaches have been taken by statistical joint modeling, while neural models allow parameter sharing and adversarial training. This tutorial reviews main approaches to joint modeling for both statistical and neural methods.",
}
@inproceedings{lopez-gilroy-2018-graph,
    title = "Graph Formalisms for Meaning Representations",
    author = "Lopez, Adam  and
      Gilroy, Sorcha",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = oct # "-" # nov,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-3002",
    abstract = "In this tutorial we will focus on Hyperedge Replacement Languages (HRL; Drewes et al. 1997), a context-free graph rewriting system. HRL are one of the most popular graph formalisms to be studied in NLP (Chiang et al., 2013; Peng et al., 2015; Bauer and Rambow, 2016). We will discuss HRL by formally defining them, studying several examples, discussing their properties, and providing exercises for the tutorial. While HRL have been used in NLP in the past, there is some speculation that they are more expressive than is necessary for graphs representing natural language (Drewes, 2017). Part of our own research has been exploring what restrictions of HRL could yield languages that are more useful for NLP and also those that have desirable properties for NLP models, such as being closed under intersection. With that in mind, we also plan to discuss Regular Graph Languages (RGL; Courcelle 1991), a subfamily of HRL which are closed under intersection. The definition of RGL is relatively simple after being introduced to HRL. We do not plan on discussing any proofs of why RGL are also a subfamily of MSOL, as described in Gilroy et al. (2017b). We will briefly mention the other formalisms shown in Figure 1 such as MSOL and DAGAL but this will focus on their properties rather than any formal definitions.",
}
@inproceedings{gardner-etal-2018-writing,
    title = "Writing Code for {NLP} Research",
    author = "Gardner, Matt  and
      Neumann, Mark  and
      Grus, Joel  and
      Lourie, Nicholas",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = oct # "-" # nov,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-3003",
    abstract = "Doing modern NLP research requires writing code. Good code enables fast prototyping, easy debugging, controlled experiments, and accessible visualizations that help researchers understand what a model is doing. Bad code leads to research that is at best hard to reproduce and extend, and at worst simply incorrect. Indeed, there is a growing recognition of the importance of having good tools to assist good research in our field, as the upcoming workshop on open source software for NLP demonstrates. This tutorial aims to share best practices for writing code for NLP research, drawing on the instructors' experience designing the recently-released AllenNLP toolkit, a PyTorch-based library for deep learning NLP research. We will explain how a library with the right abstractions and components enables better code and better science, using models implemented in AllenNLP as examples. Participants will learn how to write research code in a way that facilitates good science and easy experimentation, regardless of what framework they use.",
}
@inproceedings{rush-etal-2018-deep,
    title = "Deep Latent Variable Models of Natural Language",
    author = "Rush, Alexander  and
      Kim, Yoon  and
      Wiseman, Sam",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = oct # "-" # nov,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-3004",
    abstract = "The proposed tutorial will cover deep latent variable models both in the case where exact inference over the latent variables is tractable and when it is not. The former case includes neural extensions of unsupervised tagging and parsing models. Our discussion of the latter case, where inference cannot be performed tractably, will restrict itself to continuous latent variables. In particular, we will discuss recent developments both in neural variational inference (e.g., relating to Variational Auto-encoders) and in implicit density modeling (e.g., relating to Generative Adversarial Networks). We will highlight the challenges of applying these families of methods to NLP problems, and discuss recent successes and best practices.",
}
@inproceedings{sachan-etal-2018-standardized,
    title = "Standardized Tests as benchmarks for Artificial Intelligence",
    author = "Sachan, Mrinmaya  and
      Seo, Minjoon  and
      Hajishirzi, Hannaneh  and
      Xing, Eric",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = oct # "-" # nov,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-3005",
    abstract = "Standardized tests have recently been proposed as replacements to the Turing test as a driver for progress in AI (Clark, 2015). These include tests on understanding passages and stories and answering questions about them (Richardson et al., 2013; Rajpurkar et al., 2016a, inter alia), science question answering (Schoenick et al., 2016, inter alia), algebra word problems (Kushman et al., 2014, inter alia), geometry problems (Seo et al., 2015; Sachan et al., 2016), visual question answering (Antol et al., 2015), etc. Many of these tests require sophisticated understanding of the world, aiming to push the boundaries of AI. For this tutorial, we broadly categorize these tests into two categories: open domain tests such as reading comprehensions and elementary school tests where the goal is to find the support for an answer from the student curriculum, and closed domain tests such as intermediate level math and science tests (algebra, geometry, Newtonian physics problems, etc.). Unlike open domain tests, closed domain tests require the system to have significant domain knowledge and reasoning capabilities. For example, geometry questions typically involve a number of geometry primitives (lines, quadrilaterals, circles, etc) and require students to use axioms and theorems of geometry (Pythagoras theorem, alternating angles, etc) to solve them. These closed domains often have a formal logical basis and the question can be mapped to a formal language by semantic parsing. The formal question representation can then provided as an input to an expert system to solve the question.",
}
@inproceedings{wu-yan-2018-deep,
    title = "Deep Chit-Chat: Deep Learning for {C}hat{B}ots",
    author = "Wu, Wei  and
      Yan, Rui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = oct # "-" # nov,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-3006",
    abstract = "The tutorial is based on the long-term efforts on building conversational models with deep learning approaches for chatbots. We will summarize the fundamental challenges in modeling open domain dialogues, clarify the difference from modeling goal-oriented dialogues, and give an overview of state-of-the-art methods for open domain conversation including both retrieval-based methods and generation-based methods. In addition to these, our tutorial will also cover some new trends of research of chatbots, such as how to design a reasonable evaluation system and how to ``control'' conversations from a chatbot with some specific information such as personas, styles, and emotions, etc.",
}
@proceedings{emnlp-2018-2018-empirical,
    title = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    editor = "Blanco, Eduardo  and
      Lu, Wei",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2000",
}
@inproceedings{tanveer-ture-2018-syntaviz,
    title = "{S}ynta{V}iz: Visualizing Voice Queries through a Syntax-Driven Hierarchical Ontology",
    author = "Tanveer, Md Iftekhar  and
      Ture, Ferhan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2001",
    doi = "10.18653/v1/D18-2001",
    pages = "1--6",
    abstract = "This paper describes SyntaViz, a visualization interface specifically designed for analyzing natural-language queries that were created by users of a voice-enabled product. SyntaViz provides a platform for browsing the ontology of user queries from a syntax-driven perspective, providing quick access to high-impact failure points of the existing intent understanding system and evidence for data-driven decisions in the development cycle. A case study on Xfinity X1 (a voice-enabled entertainment platform from Comcast) reveals that SyntaViz helps developers identify multiple action items in a short amount of time without any special training. SyntaViz has been open-sourced for the benefit of the community.",
}
@inproceedings{yin-neubig-2018-tranx,
    title = "{TRANX}: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation",
    author = "Yin, Pengcheng  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2002",
    doi = "10.18653/v1/D18-2002",
    pages = "7--12",
    abstract = "We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system based on the abstract syntax description language for the target MR, which gives it two major advantages: (1) it is highly accurate, using information from the syntax of the target MR to constrain the output space and model the information flow, and (2) it is highly generalizable, and can easily be applied to new types of MR by just writing a new abstract syntax description corresponding to the allowable structures in the MR. Experiments on four different semantic parsing and code generation tasks show that our system is generalizable, extensible, and effective, registering strong results compared to existing neural semantic parsers.",
}
@inproceedings{dou-etal-2018-data2text,
    title = "{D}ata2{T}ext Studio: Automated Text Generation from Structured Data",
    author = "Dou, Longxu  and
      Qin, Guanghui  and
      Wang, Jinpeng  and
      Yao, Jin-Ge  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2003",
    doi = "10.18653/v1/D18-2003",
    pages = "13--18",
    abstract = "Data2Text Studio is a platform for automated text generation from structured data. It is equipped with a Semi-HMMs model to extract high-quality templates and corresponding trigger conditions from parallel data automatically, which improves the interactivity and interpretability of the generated text. In addition, several easy-to-use tools are provided for developers to edit templates of pre-trained models, and APIs are released for developers to call the pre-trained model to generate texts in third-party applications. We conduct experiments on RotoWire datasets for template extraction and text generation. The results show that our model achieves improvements on both tasks.",
}
@inproceedings{mamou-etal-2018-term,
    title = "Term Set Expansion based {NLP} Architect by {I}ntel {AI} Lab",
    author = "Mamou, Jonathan  and
      Pereg, Oren  and
      Wasserblat, Moshe  and
      Eirew, Alon  and
      Green, Yael  and
      Guskin, Shira  and
      Izsak, Peter  and
      Korat, Daniel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2004",
    doi = "10.18653/v1/D18-2004",
    pages = "19--24",
    abstract = "We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to-end workflow. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. SetExpander has been used successfully in real-life use cases including integration into an automated recruitment system and an issues and defects resolution system.",
}
@inproceedings{ozenc-etal-2018-moraz,
    title = "{M}or{A}z: an Open-source Morphological Analyzer for {A}zerbaijani {T}urkish",
    author = {{\"O}zen{\c{c}}, Berke  and
      Ehsani, Razieh  and
      Solak, Ercan},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2005",
    doi = "10.18653/v1/D18-2005",
    pages = "25--29",
    abstract = "MorAz is an open-source morphological analyzer for Azerbaijani Turkish. The analyzer is available through both as a website for interactive exploration and as a RESTful web service for integration into a natural language processing pipeline. MorAz implements the morphology of Azerbaijani Turkish in two-level using Helsinki finite-state transducer and wraps the analyzer with python scripts in a Django instance.",
}
@inproceedings{loginova-neumann-2018-interactive,
    title = "An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering {LSTM}",
    author = {Loginova, Ekaterina  and
      Neumann, G{\"u}nter},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2006",
    doi = "10.18653/v1/D18-2006",
    pages = "30--35",
    abstract = "We present a visualisation tool which aims to illuminate the inner workings of an LSTM model for question answering. It plots heatmaps of neurons{'} firings and allows a user to check the dependency between neurons and manual features. The system possesses an interactive web-interface and can be adapted to other models and domains.",
}
@inproceedings{liu-etal-2018-visual,
    title = "Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension",
    author = "Liu, Shusen  and
      Li, Tao  and
      Li, Zhimin  and
      Srikumar, Vivek  and
      Pascucci, Valerio  and
      Bremer, Peer-Timo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2007",
    doi = "10.18653/v1/D18-2007",
    pages = "36--41",
    abstract = "Neural networks models have gained unprecedented popularity in natural language processing due to their state-of-the-art performance and the flexible end-to-end training scheme. Despite their advantages, the lack of interpretability hinders the deployment and refinement of the models. In this work, we present a flexible visualization library for creating customized visual analytic environments, in which the user can investigate and interrogate the relationships among the input, the model internals (i.e., attention), and the output predictions, which in turn shed light on the model decision-making process.",
}
@inproceedings{adel-etal-2018-dere,
    title = "{DERE}: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction",
    author = "Adel, Heike  and
      Bostan, Laura Ana Maria  and
      Papay, Sean  and
      Pad{\'o}, Sebastian  and
      Klinger, Roman",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2008",
    doi = "10.18653/v1/D18-2008",
    pages = "42--47",
    abstract = "Most machine learning systems for natural language processing are tailored to specific tasks. As a result, comparability of models across tasks is missing and their applicability to new tasks is limited. This affects end users without machine learning experience as well as model developers. To address these limitations, we present DERE, a novel framework for declarative specification and compilation of template-based information extraction. It uses a generic specification language for the task and for data annotations in terms of spans and frames. This formalism enables the representation of a large variety of natural language processing challenges. The backend can be instantiated by different models, following different paradigms. The clear separation of frame specification and model backend will ease the implementation of new models and the evaluation of different models across different tasks. Furthermore, it simplifies transfer learning, joint learning across tasks and/or domains as well as the assessment of model generalizability. DERE is available as open-source software.",
}
@inproceedings{yimam-biemann-2018-demonstrating,
    title = "Demonstrating {P}ar4{S}em - A Semantic Writing Aid with Adaptive Paraphrasing",
    author = "Yimam, Seid Muhie  and
      Biemann, Chris",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2009",
    doi = "10.18653/v1/D18-2009",
    pages = "48--53",
    abstract = "In this paper, we present Par4Sem, a semantic writing aid tool based on adaptive paraphrasing. Unlike many annotation tools that are primarily used to collect training examples, Par4Sem is integrated into a real word application, in this case a writing aid tool, in order to collect training examples from usage data. Par4Sem is a tool, which supports an adaptive, iterative, and interactive process where the underlying machine learning models are updated for each iteration using new training examples from usage data. After motivating the use of ever-learning tools in NLP applications, we evaluate Par4Sem by adopting it to a text simplification task through mere usage.",
}
@inproceedings{tolmachev-etal-2018-juman,
    title = "{J}uman++: A Morphological Analysis Toolkit for Scriptio Continua",
    author = "Tolmachev, Arseny  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2010",
    doi = "10.18653/v1/D18-2010",
    pages = "54--59",
    abstract = "We present a three-part toolkit for developing morphological analyzers for languages without natural word boundaries. The first part is a C++11/14 lattice-based morphological analysis library that uses a combination of linear and recurrent neural net language models for analysis. The other parts are a tool for exposing problems in the trained model and a partial annotation tool. Our morphological analyzer of Japanese achieves new SOTA on Jumandic-based corpora while being 250 times faster than the previous one. We also perform a small experiment and quantitive analysis and experience of using development tools. All components of the toolkit is open source and available under a permissive Apache 2 License.",
}
@inproceedings{ajjour-etal-2018-visualization,
    title = "Visualization of the Topic Space of Argument Search Results in args.me",
    author = {Ajjour, Yamen  and
      Wachsmuth, Henning  and
      Kiesel, Dora  and
      Riehmann, Patrick  and
      Fan, Fan  and
      Castiglia, Giuliano  and
      Adejoh, Rosemary  and
      Fr{\"o}hlich, Bernd  and
      Stein, Benno},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2011",
    doi = "10.18653/v1/D18-2011",
    pages = "60--65",
    abstract = "In times of fake news and alternative facts, pro and con arguments on controversial topics are of increasing importance. Recently, we presented args.me as the first search engine for arguments on the web. In its initial version, args.me ranked arguments solely by their relevance to a topic queried for, making it hard to learn about the diverse topical aspects covered by the search results. To tackle this shortcoming, we integrated a visualization interface for result exploration in args.me that provides an instant overview of the main aspects in a barycentric coordinate system. This topic space is generated ad-hoc from controversial issues on Wikipedia and argument-specific LDA models. In two case studies, we demonstrate how individual arguments can be found easily through interactions with the visualization, such as highlighting and filtering.",
}
@inproceedings{kudo-richardson-2018-sentencepiece,
    title = "{S}entence{P}iece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing",
    author = "Kudo, Taku  and
      Richardson, John",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2012",
    doi = "10.18653/v1/D18-2012",
    pages = "66--71",
    abstract = "This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at \url{https://github.com/google/sentencepiece}.",
}
@inproceedings{ning-etal-2018-cogcomptime,
    title = "{C}og{C}omp{T}ime: A Tool for Understanding Time in Natural Language",
    author = "Ning, Qiang  and
      Zhou, Ben  and
      Feng, Zhili  and
      Peng, Haoruo  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2013",
    doi = "10.18653/v1/D18-2013",
    pages = "72--77",
    abstract = "Automatic extraction of temporal information is important for natural language understanding. It involves two basic tasks: (1) Understanding time expressions that are mentioned explicitly in text (e.g., February 27, 1998 or tomorrow), and (2) Understanding temporal information that is conveyed implicitly via relations. This paper introduces CogCompTime, a system that has these two important functionalities. It incorporates the most recent progress, achieves state-of-the-art performance, and is publicly available at \url{http://cogcomp.org/page/publication_view/844}.",
}
@inproceedings{wiedemann-etal-2018-multilingual,
    title = "A Multilingual Information Extraction Pipeline for Investigative Journalism",
    author = "Wiedemann, Gregor  and
      Yimam, Seid Muhie  and
      Biemann, Chris",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2014",
    doi = "10.18653/v1/D18-2014",
    pages = "78--83",
    abstract = "We introduce an advanced information extraction pipeline to automatically process very large collections of unstructured textual data for the purpose of investigative journalism. The pipeline serves as a new input processor for the upcoming major release of our New/s/leak 2.0 software, which we develop in cooperation with a large German news organization. The use case is that journalists receive a large collection of files up to several Gigabytes containing unknown contents. Collections may originate either from official disclosures of documents, e.g. Freedom of Information Act requests, or unofficial data leaks.",
}
@inproceedings{peter-etal-2018-sisyphus,
    title = "Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition",
    author = "Peter, Jan-Thorsten  and
      Beck, Eugen  and
      Ney, Hermann",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2015",
    doi = "10.18653/v1/D18-2015",
    pages = "84--89",
    abstract = "Training and testing many possible parameters or model architectures of state-of-the-art machine translation or automatic speech recognition system is a cumbersome task. They usually require a long pipeline of commands reaching from pre-processing the training data to post-processing and evaluating the output.",
}
@inproceedings{lakomkin-etal-2018-kt,
    title = "{KT}-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from {Y}ou{T}ube Videos",
    author = "Lakomkin, Egor  and
      Magg, Sven  and
      Weber, Cornelius  and
      Wermter, Stefan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2016",
    doi = "10.18653/v1/D18-2016",
    pages = "90--95",
    abstract = "We describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and post-processing steps, which extract samples that can be used for training end-to-end neural speech recognition systems. In our experiments, we demonstrate that a single-core version of the crawler can obtain around 150 hours of transcribed speech within a day, containing an estimated 3.5{\%} word error rate in the transcriptions. Automatically collected samples contain reading and spontaneous speech recorded in various conditions including background noise and music, distant microphone recordings, and a variety of accents and reverberation. When training a deep neural network on speech recognition, we observed around 40{\%} word error rate reduction on the Wall Street Journal dataset by integrating 200 hours of the collected samples into the training set.",
}
@inproceedings{zhang-etal-2018-visualizing,
    title = "Visualizing Group Dynamics based on Multiparty Meeting Understanding",
    author = "Zhang, Ni  and
      Zhang, Tongtao  and
      Bhattacharya, Indrani  and
      Ji, Heng  and
      Radke, Rich",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2017",
    doi = "10.18653/v1/D18-2017",
    pages = "96--101",
    abstract = "Group discussions are usually aimed at sharing opinions, reaching consensus and making good decisions based on group knowledge. During a discussion, participants might adjust their own opinions as well as tune their attitudes towards others{'} opinions, based on the unfolding interactions. In this paper, we demonstrate a framework to visualize such dynamics; at each instant of a conversation, the participants{'} opinions and potential influence on their counterparts is easily visualized. We use multi-party meeting opinion mining based on bipartite graphs to extract opinions and calculate mutual influential factors, using the Lunar Survival Task as a study case.",
}
@inproceedings{boratko-etal-2018-interface,
    title = "An Interface for Annotating Science Questions",
    author = "Boratko, Michael  and
      Padigela, Harshit  and
      Mikkilineni, Divyendra  and
      Yuvraj, Pritish  and
      Das, Rajarshi  and
      McCallum, Andrew  and
      Chang, Maria  and
      Fokoue, Achille  and
      Kapanipathi, Pavan  and
      Mattei, Nicholas  and
      Musa, Ryan  and
      Talamadupula, Kartik  and
      Witbrock, Michael",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2018",
    doi = "10.18653/v1/D18-2018",
    pages = "102--107",
    abstract = "Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set. That work includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them. However, it does not include clear definitions of these types, nor does it offer information about the quality of the labels or the annotation process used. In this paper, we introduce a novel interface for human annotation of science question-answer pairs with their respective knowledge and reasoning types, in order that the classification of new questions may be improved. We build on the classification schema proposed by prior work on the ARC dataset, and evaluate the effectiveness of our interface with a preliminary study involving 10 participants.",
}
@inproceedings{nghiem-ananiadou-2018-aplenty,
    title = "{APL}enty: annotation tool for creating high-quality datasets using active and proactive learning",
    author = "Nghiem, Minh-Quoc  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2019",
    doi = "10.18653/v1/D18-2019",
    pages = "108--113",
    abstract = "In this paper, we present APLenty, an annotation tool for creating high-quality sequence labeling datasets using active and proactive learning. A major innovation of our tool is the integration of automatic annotation with active learning and proactive learning. This makes the task of creating labeled datasets easier, less time-consuming and requiring less human effort. APLenty is highly flexible and can be adapted to various other tasks.",
}
@inproceedings{sorokin-gurevych-2018-interactive,
    title = "Interactive Instance-based Evaluation of Knowledge Base Question Answering",
    author = "Sorokin, Daniil  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2020",
    doi = "10.18653/v1/D18-2020",
    pages = "114--119",
    abstract = "Most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we present a tool that aids in debugging of question answering systems that construct a structured semantic representation for the input question. Previous work has largely focused on building question answering interfaces or evaluation frameworks that unify multiple data sets. The primary objective of our system is to enable interactive debugging of model predictions on individual instances (questions) and to simplify manual error analysis. Our interactive interface helps researchers to understand the shortcomings of a particular model, qualitatively analyze the complete pipeline and compare different models. A set of sit-by sessions was used to validate our interface design.",
}
@inproceedings{patel-etal-2018-magnitude,
    title = "{M}agnitude: A Fast, Efficient Universal Vector Embedding Utility Package",
    author = "Patel, Ajay  and
      Sands, Alexander  and
      Callison-Burch, Chris  and
      Apidianaki, Marianna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2021",
    doi = "10.18653/v1/D18-2021",
    pages = "120--126",
    abstract = "Vector space embedding models like word2vec, GloVe, and fastText are extremely popular representations in natural language processing (NLP) applications. We present Magnitude, a fast, lightweight tool for utilizing and processing embeddings. Magnitude is an open source Python package with a compact vector storage file format that allows for efficient manipulation of huge numbers of embeddings. Magnitude performs common operations up to 60 to 6,000 times faster than Gensim. Magnitude introduces several novel features for improved robustness like out-of-vocabulary lookups.",
}
@inproceedings{boullosa-etal-2018-integrating,
    title = "Integrating Knowledge-Supported Search into the {INCE}p{TION} Annotation Platform",
    author = "Boullosa, Beto  and
      Eckart de Castilho, Richard  and
      Kumar, Naveen  and
      Klie, Jan-Christoph  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2022",
    doi = "10.18653/v1/D18-2022",
    pages = "127--132",
    abstract = "Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such tasks benefit from text annotation tools that integrate a search which covers the text, the annotations, as well as the knowledge resource. However, to the best of our knowledge, no current tools integrate knowledge-supported search as well as entity linking support. We address this gap by introducing knowledge-supported search functionality into the INCEpTION text annotation platform. In our approach, cross-document references are created by linking entity mentions to a knowledge base in the form of a structured hierarchical vocabulary. The resulting annotations are then indexed to enable fast and yet complex queries taking into account the text, the annotations, and the vocabulary structure.",
}
@inproceedings{wang-etal-2018-cytonmt,
    title = "{C}yton{MT}: an Efficient Neural Machine Translation Open-source Toolkit Implemented in {C}++",
    author = "Wang, Xiaolin  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2023",
    doi = "10.18653/v1/D18-2023",
    pages = "133--138",
    abstract = "This paper presents an open-source neural machine translation toolkit named CytonMT. The toolkit is built from scratch only using C++ and NVIDIA{'}s GPU-accelerated libraries. The toolkit features training efficiency, code simplicity and translation quality. Benchmarks show that cytonMT accelerates the training speed by 64.5{\%} to 110.8{\%} on neural networks of various sizes, and achieves competitive translation quality.",
}
@inproceedings{han-etal-2018-openke,
    title = "{O}pen{KE}: An Open Toolkit for Knowledge Embedding",
    author = "Han, Xu  and
      Cao, Shulin  and
      Lv, Xin  and
      Lin, Yankai  and
      Liu, Zhiyuan  and
      Sun, Maosong  and
      Li, Juanzi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2024",
    doi = "10.18653/v1/D18-2024",
    pages = "139--144",
    abstract = "We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on \url{http://openke.thunlp.org/}.",
}
@inproceedings{labutov-etal-2018-lia,
    title = "{LIA}: A Natural Language Programmable Personal Assistant",
    author = "Labutov, Igor  and
      Srivastava, Shashank  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2025",
    doi = "10.18653/v1/D18-2025",
    pages = "145--150",
    abstract = "We present LIA, an intelligent personal assistant that can be programmed using natural language. Our system demonstrates multiple competencies towards learning from human-like interactions. These include the ability to be taught reusable conditional procedures, the ability to be taught new knowledge about the world (concepts in an ontology) and the ability to be taught how to ground that knowledge in a set of sensors and effectors. Building such a system highlights design questions regarding the overall architecture that such an agent should have, as well as questions about parsing and grounding language in situational contexts. We outline key properties of this architecture, and demonstrate a prototype that embodies them in the form of a personal assistant on an Android device.",
}
@inproceedings{raux-etal-2018-pizzapal,
    title = "{P}izza{P}al: Conversational Pizza Ordering using a High-Density Conversational {AI} Platform",
    author = "Raux, Antoine  and
      Ma, Yi  and
      Yang, Paul  and
      Wong, Felicia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2026",
    doi = "10.18653/v1/D18-2026",
    pages = "151--156",
    abstract = "This paper describes PizzaPal, a voice-only agent for ordering pizza, as well as the Conversational AI architecture built at b4.ai. Based on the principles of high-density conversational AI, it supports natural and flexible interactions through neural conversational language understanding, robust dialog state tracking, and hierarchical task decomposition.",
}
@inproceedings{raghuvanshi-etal-2018-developing,
    title = "Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing",
    author = "Raghuvanshi, Arushi  and
      Carroll, Lucien  and
      Raghunathan, Karthik",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2027",
    doi = "10.18653/v1/D18-2027",
    pages = "157--162",
    abstract = "We demonstrate an end-to-end approach for building conversational interfaces from prototype to production that has proven to work well for a number of applications across diverse verticals. Our architecture improves on the standard domain-intent-entity classification hierarchy and dialogue management architecture by leveraging shallow semantic parsing. We observe that NLU systems for industry applications often require more structured representations of entity relations than provided by the standard hierarchy, yet without requiring full semantic parses which are often inaccurate on real-world conversational data. We distinguish two kinds of semantic properties that can be provided through shallow semantic parsing: entity groups and entity roles. We also provide live demos of conversational apps built for two different use cases: food ordering and meeting control.",
}
@inproceedings{vadapalli-etal-2018-science,
    title = "When science journalism meets artificial intelligence : An interactive demonstration",
    author = "Vadapalli, Raghuram  and
      Syed, Bakhtiyar  and
      Prabhu, Nishant  and
      Srinivasan, Balaji Vasan  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2028",
    doi = "10.18653/v1/D18-2028",
    pages = "163--168",
    abstract = "We present an online interactive tool that generates titles of blog titles and thus take the first step toward automating science journalism. Science journalism aims to transform jargon-laden scientific articles into a form that the common reader can comprehend while ensuring that the underlying meaning of the article is retained. In this work, we present a tool, which, given the title and abstract of a research paper will generate a blog title by mimicking a human science journalist. The tool makes use of a model trained on a corpus of 87,328 pairs of research papers and their corresponding blogs, built from two science news aggregators. The architecture of the model is a two-stage mechanism which generates blog titles. Evaluation using standard metrics indicate the viability of the proposed system.",
}
@inproceedings{cer-etal-2018-universal,
    title = "Universal Sentence Encoder for {E}nglish",
    author = "Cer, Daniel  and
      Yang, Yinfei  and
      Kong, Sheng-yi  and
      Hua, Nan  and
      Limtiaco, Nicole  and
      St. John, Rhomni  and
      Constant, Noah  and
      Guajardo-Cespedes, Mario  and
      Yuan, Steve  and
      Tar, Chris  and
      Strope, Brian  and
      Kurzweil, Ray",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-2029",
    doi = "10.18653/v1/D18-2029",
    pages = "169--174",
    abstract = "We present easy-to-use TensorFlow Hub sentence embedding models having good task transfer performance. Model variants allow for trade-offs between accuracy and compute resources. We report the relationship between model complexity, resources, and transfer performance. Comparisons are made with baselines without transfer learning and to baselines that incorporate word-level transfer. Transfer learning using sentence-level embeddings is shown to outperform models without transfer learning and often those that use only word-level transfer. We show good transfer task performance with minimal training data and obtain encouraging results on word embedding association tests (WEAT) of model bias.",
}
@proceedings{emnlp-2018-2018,
    title = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1000",
}
@inproceedings{coavoux-etal-2018-privacy,
    title = "Privacy-preserving Neural Representations of Text",
    author = "Coavoux, Maximin  and
      Narayan, Shashi  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1001",
    doi = "10.18653/v1/D18-1001",
    pages = "1--10",
    abstract = "This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of attack: an attacker eavesdrops on the hidden representations of a neural text classifier and tries to recover information about the input text. Such scenario may arise in situations when the computation of a neural network is shared across multiple devices, e.g. some hidden representation is computed by a user{'}s device and sent to a cloud-based model. We measure the privacy of a hidden representation by the ability of an attacker to predict accurately specific private information from it and characterize the tradeoff between the privacy and the utility of neural representations. Finally, we propose several defense methods based on modified training objectives and show that they improve the privacy of neural representations.",
}
@inproceedings{elazar-goldberg-2018-adversarial,
    title = "Adversarial Removal of Demographic Attributes from Text Data",
    author = "Elazar, Yanai  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1002",
    doi = "10.18653/v1/D18-1002",
    pages = "11--21",
    abstract = "Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in{---}and can be recovered from{---}the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to{---}and likely condition on{---}demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features.",
}
@inproceedings{popat-etal-2018-declare,
    title = "{D}e{C}lar{E}: Debunking Fake News and False Claims using Evidence-Aware Deep Learning",
    author = "Popat, Kashyap  and
      Mukherjee, Subhabrata  and
      Yates, Andrew  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1003",
    doi = "10.18653/v1/D18-1003",
    pages = "22--32",
    abstract = "Misinformation such as fake news is one of the big challenges of our society. Research on automated fact-checking has proposed methods based on supervised learning, but these approaches do not consider external evidence apart from labeled training instances. Recent approaches counter this deficit by considering external sources related to a claim. However, these methods require substantial feature modeling and rich lexicons. This paper overcomes these limitations of prior work with an end-to-end model for evidence-aware credibility assessment of arbitrary textual claims, without any human intervention. It presents a neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources. It also derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user. Experiments with four datasets and ablation studies show the strength of our method.",
}
@inproceedings{wang-jurgens-2018-going,
    title = "It{'}s going to be okay: Measuring Access to Support in Online Communities",
    author = "Wang, Zijian  and
      Jurgens, David",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1004",
    doi = "10.18653/v1/D18-1004",
    pages = "33--45",
    abstract = "People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one{'}s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and name. We apply these methods to create a new massive corpus of 102M online interactions with gender-labeled users, each rated by degree of supportiveness. Our analysis shows wide-spread and consistent disparity in support: identifying as a woman is associated with higher rates of support - but also higher rates of disparagement.",
}
@inproceedings{chang-etal-2018-detecting,
    title = "Detecting Gang-Involved Escalation on Social Media Using Context",
    author = "Chang, Serina  and
      Zhong, Ruiqi  and
      Adams, Ethan  and
      Lee, Fei-Tzin  and
      Varia, Siddharth  and
      Patton, Desmond  and
      Frey, William  and
      Kedzie, Chris  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1005",
    doi = "10.18653/v1/D18-1005",
    pages = "46--56",
    abstract = "Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user{'}s recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.",
}
@inproceedings{tandon-etal-2018-reasoning,
    title = "Reasoning about Actions and State Changes by Injecting Commonsense Knowledge",
    author = "Tandon, Niket  and
      Dalvi, Bhavana  and
      Grus, Joel  and
      Yih, Wen-tau  and
      Bosselut, Antoine  and
      Clark, Peter",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1006",
    doi = "10.18653/v1/D18-1006",
    pages = "57--66",
    abstract = "Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. Although several recent systems have shown impressive progress in this task, their predictions can be globally inconsistent or highly improbable. In this paper, we show how the predicted effects of actions in the context of a paragraph can be improved in two ways: (1) by incorporating global, commonsense constraints (e.g., a non-existent entity cannot be destroyed), and (2) by biasing reading with preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier methods, we treat the problem as a neural structured prediction task, allowing hard and soft constraints to steer the model away from unlikely predictions. We show that the new model significantly outperforms earlier systems on a benchmark dataset for procedural text comprehension (+8{\%} relative gain), and that it also avoids some of the nonsensical predictions that earlier systems make.",
}
@inproceedings{poliak-etal-2018-collecting,
    title = "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
    author = "Poliak, Adam  and
      Haldar, Aparajita  and
      Rudinger, Rachel  and
      Hu, J. Edward  and
      Pavlick, Ellie  and
      White, Aaron Steven  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1007",
    doi = "10.18653/v1/D18-1007",
    pages = "67--81",
    abstract = "We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at \url{https://www.decomp.net}, and will grow over time as additional resources are recast and added from novel sources.",
}
@inproceedings{lamm-etal-2018-textual,
    title = "Textual Analogy Parsing: What{'}s Shared and What{'}s Compared among Analogous Facts",
    author = "Lamm, Matthew  and
      Chaganty, Arun  and
      Manning, Christopher D.  and
      Jurafsky, Dan  and
      Liang, Percy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1008",
    doi = "10.18653/v1/D18-1008",
    pages = "82--92",
    abstract = "To understand a sentence like {``}whereas only 10{\%} of White Americans live at or below the poverty line, 28{\%} of African Americans do{''} it is important not only to identify individual facts, e.g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e.g., the disparity between them. In this paper, we propose the task of Textual Analogy Parsing (TAP) to model this higher-order meaning. Given a sentence such as the one above, TAP outputs a frame-style meaning representation which explicitly specifies what is shared (e.g., poverty rates) and what is compared (e.g., White Americans vs. African Americans, 10{\%} vs. 28{\%}) between its component facts. Such a meaning representation can enable new applications that rely on discourse understanding such as automated chart generation from quantitative text. We present a new dataset for TAP, baselines, and a model that successfully uses an ILP to enforce the structural constraints of the problem.",
}
@inproceedings{zellers-etal-2018-swag,
    title = "{SWAG}: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference",
    author = "Zellers, Rowan  and
      Bisk, Yonatan  and
      Schwartz, Roy  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1009",
    doi = "10.18653/v1/D18-1009",
    pages = "93--104",
    abstract = "Given a partial description like {``}she opened the hood of the car,{''} humans can reason about the situation and anticipate what might come next ({''}then, she examined the engine{''}). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88{\%}), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.",
}
@inproceedings{yin-roth-2018-twowingos,
    title = "{T}wo{W}ing{OS}: A Two-Wing Optimization Strategy for Evidential Claim Verification",
    author = "Yin, Wenpeng  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1010",
    doi = "10.18653/v1/D18-1010",
    pages = "105--114",
    abstract = "Determining whether a given claim is supported by evidence is a fundamental NLP problem that is best modeled as Textual Entailment. However, given a large collection of text, finding evidence that could support or refute a given claim is a challenge in itself, amplified by the fact that different evidence might be needed to support or refute a claim. Nevertheless, most prior work decouples evidence finding from determining the truth value of the claim given the evidence. We propose to consider these two aspects jointly. We develop TwoWingOS (two-wing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence. Given the claim, TwoWingOS attempts to identify a subset of the evidence candidates; given the predicted evidence, it then attempts to determine the truth value of the corresponding claim entailment problem. We treat this problem as coupled optimization problems, training a joint model for it. TwoWingOS offers two advantages: (i) Unlike pipeline systems it facilitates flexible-size evidence set, and (ii) Joint training improves both the claim entailment and the evidence identification. Experiments on a benchmark dataset show state-of-the-art performance.",
}
@inproceedings{wang-etal-2018-associative,
    title = "Associative Multichannel Autoencoder for Multimodal Word Representation",
    author = "Wang, Shaonan  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1011",
    doi = "10.18653/v1/D18-1011",
    pages = "115--124",
    abstract = "In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs. Inspired by the re-constructive and associative nature of human memory, we propose a novel associative multichannel autoencoder (AMA). Our model first learns the associations between textual and perceptual modalities, so as to predict the missing perceptual information of concepts. Then the textual and predicted perceptual representations are fused through reconstructing their original and associated embeddings. Using a gating mechanism our model assigns different weights to each modality according to the different concepts. Results on six benchmark concept similarity tests show that the proposed method significantly outperforms strong unimodal baselines and state-of-the-art multimodal models.",
}
@inproceedings{pasunuru-bansal-2018-game,
    title = "Game-Based Video-Context Dialogue",
    author = "Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1012",
    doi = "10.18653/v1/D18-1012",
    pages = "125--136",
    abstract = "Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. Some recent work has investigated static image-based dialogue. However, several real-world human interactions also involve dynamic visual context (similar to videos) as well as dialogue exchanges among multiple speakers. To move closer towards such multimodal conversational skills and visually-situated applications, we introduce a new video-context, many-speaker dialogue dataset based on live-broadcast soccer game videos and chats from Twitch.tv. This challenging testbed allows us to develop visually-grounded dialogue models that should generate relevant temporal and spatial event language from the live video, while also being relevant to the chat history. For strong baselines, we also present several discriminative and generative models, e.g., based on tridirectional attention flow (TriDAF). We evaluate these models via retrieval ranking-recall, automatic phrase-matching metrics, as well as human evaluation studies. We also present dataset analyses, model ablations, and visualizations to understand the contribution of different modalities and model components.",
}
@inproceedings{liu-etal-2018-simnet,
    title = "sim{N}et: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions",
    author = "Liu, Fenglin  and
      Ren, Xuancheng  and
      Liu, Yuanxin  and
      Wang, Houfeng  and
      Sun, Xu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1013",
    doi = "10.18653/v1/D18-1013",
    pages = "137--149",
    abstract = "The encode-decoder framework has shown recent success in image captioning. Visual attention, which is good at detailedness, and semantic attention, which is good at comprehensiveness, have been separately proposed to ground the caption on the image. In this paper, we propose the Stepwise Image-Topic Merging Network (simNet) that makes use of the two kinds of attention at the same time. At each time step when generating the caption, the decoder adaptively merges the attentive information in the extracted topics and the image according to the generated context, so that the visual information and the semantic information can be effectively combined. The proposed approach is evaluated on two benchmark datasets and reaches the state-of-the-art performances.",
}
@inproceedings{liang-etal-2018-multimodal,
    title = "Multimodal Language Analysis with Recurrent Multistage Fusion",
    author = "Liang, Paul Pu  and
      Liu, Ziyin  and
      Bagher Zadeh, AmirAli  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1014",
    doi = "10.18653/v1/D18-1014",
    pages = "150--161",
    abstract = "Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into multiple stages, each of them focused on a subset of multimodal signals for specialized, effective fusion. Cross-modal interactions are modeled using this multistage fusion approach which builds upon intermediate representations of previous stages. Temporal and intra-modal interactions are modeled by integrating our proposed fusion approach with a system of recurrent neural networks. The RMFN displays state-of-the-art performance in modeling human multimodal language across three public datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition. We provide visualizations to show that each stage of fusion focuses on a different subset of multimodal signals, learning increasingly discriminative multimodal representations.",
}
@inproceedings{chen-etal-2018-temporally,
    title = "Temporally Grounding Natural Sentence in Video",
    author = "Chen, Jingyuan  and
      Chen, Xinpeng  and
      Ma, Lin  and
      Jie, Zequn  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1015",
    doi = "10.18653/v1/D18-1015",
    pages = "162--171",
    abstract = "We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frame-by-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the state-of-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.",
}
@inproceedings{chen-etal-2018-preco,
    title = "{P}re{C}o: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution",
    author = "Chen, Hong  and
      Fan, Zhenhua  and
      Lu, Hao  and
      Yuille, Alan  and
      Rong, Shu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1016",
    doi = "10.18653/v1/D18-1016",
    pages = "172--181",
    abstract = "We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of 38K documents and 12.5M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at \url{https://preschool-lab.github.io/PreCo/}.",
}
@inproceedings{cao-etal-2018-adversarial,
    title = "Adversarial Transfer Learning for {C}hinese Named Entity Recognition with Self-Attention Mechanism",
    author = "Cao, Pengfei  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun  and
      Liu, Shengping",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1017",
    doi = "10.18653/v1/D18-1017",
    pages = "182--192",
    abstract = "Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) task have many similar word boundaries. There are also specificities in each task. However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS. In this paper, we propose a novel adversarial transfer learning framework to make full use of task-shared boundaries information and prevent the task-specific features of CWS. Besides, since arbitrary character can provide important cues when predicting entity type, we exploit self-attention to explicitly capture long range dependencies between two tokens. Experimental results on two different widely used datasets show that our proposed model significantly and consistently outperforms other state-of-the-art methods.",
}
@inproceedings{moosavi-strube-2018-using,
    title = "Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1018",
    doi = "10.18653/v1/D18-1018",
    pages = "193--203",
    abstract = "Coreference resolution is an intermediate step for text understanding. It is used in tasks and domains for which we do not necessarily have coreference annotated corpora. Therefore, generalization is of special importance for coreference resolution. However, while recent coreference resolvers have notable improvements on the CoNLL dataset, they struggle to generalize properly to new domains or datasets. In this paper, we investigate the role of linguistic features in building more generalizable coreference resolvers. We show that generalization improves only slightly by merely using a set of additional linguistic features. However, employing features and subsets of their values that are informative for coreference resolution, considerably improves generalization. Thanks to better generalization, our system achieves state-of-the-art results in out-of-domain evaluations, e.g., on WikiCoref, our system, which is trained on CoNLL, achieves on-par performance with a system designed for this dataset.",
}
@inproceedings{wang-lu-2018-neural,
    title = "Neural Segmental Hypergraphs for Overlapping Mention Recognition",
    author = "Wang, Bailin  and
      Lu, Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1019",
    doi = "10.18653/v1/D18-1019",
    pages = "204--214",
    abstract = "In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference. We also present a theoretical analysis to formally assess how our representation is better than alternative representations reported in the literature in terms of representational power. Coupled with neural networks for feature learning, our model achieves the state-of-the-art performance in three benchmark datasets annotated with overlapping mentions.",
}
@inproceedings{chen-etal-2018-variational,
    title = "Variational Sequential Labelers for Semi-Supervised Learning",
    author = "Chen, Mingda  and
      Tang, Qingming  and
      Livescu, Karen  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1020",
    doi = "10.18653/v1/D18-1020",
    pages = "215--226",
    abstract = "We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data.",
}
@inproceedings{cao-etal-2018-joint,
    title = "Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision",
    author = "Cao, Yixin  and
      Hou, Lei  and
      Li, Juanzi  and
      Liu, Zhiyuan  and
      Li, Chengjiang  and
      Chen, Xu  and
      Dong, Tiansi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1021",
    doi = "10.18653/v1/D18-1021",
    pages = "227--237",
    abstract = "Jointly representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpus, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitative and quantitative, demonstrate the significance of our method.",
}
@inproceedings{ziser-reichart-2018-deep,
    title = "Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance",
    author = "Ziser, Yftah  and
      Reichart, Roi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1022",
    doi = "10.18653/v1/D18-1022",
    pages = "238--249",
    abstract = "While cross-domain and cross-language transfer have long been prominent topics in NLP research, their combination has hardly been explored. In this work we consider this problem, and propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks (particularly LSTMs and CNNs) and bilingual word embeddings, with the goal of training a model on labeled data from one (language, domain) pair so that it can be effectively applied to another (language, domain) pair. We consider two setups, differing with respect to the unlabeled data available for model training. In the full setup the model has access to unlabeled data from both pairs, while in the lazy setup, which is more realistic for truly resource-poor languages, unlabeled data is available for both domains but only for the source language. We design our model for the lazy setup so that for a given target domain, it can train once on the source language and then be applied to any target language without re-training. In experiments with nine English-German and nine English-French domain pairs our best model substantially outperforms previous models even when it is trained in the lazy setup and previous models are trained in the full setup.",
}
@inproceedings{huang-etal-2018-multi,
    title = "Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding",
    author = "Huang, Lifu  and
      Cho, Kyunghyun  and
      Zhang, Boliang  and
      Ji, Heng  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1023",
    doi = "10.18653/v1/D18-1023",
    pages = "250--260",
    abstract = "We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space via which all available resources and knowledge can be shared across multiple languages. Beyond word alignment, we introduce multiple cluster-level alignments and enforce the word clusters to be consistently distributed across multiple languages. We exploit three signals for clustering: (1) neighbor words in the monolingual word embedding space; (2) character-level information; and (3) linguistic properties (e.g., apposition, locative suffix) derived from linguistic structure knowledge bases available for thousands of languages. We introduce a new cluster-consistent correlational neural network to construct the common semantic space by aligning words as well as clusters. Intrinsic evaluation on monolingual and multilingual QVEC tasks shows our approach achieves significantly higher correlation with linguistic features which are extracted from manually crafted lexical resources than state-of-the-art multi-lingual embedding learning methods do. Using low-resource language name tagging as a case study for extrinsic evaluation, our approach achieves up to 14.6{\%} absolute F-score gain over the state of the art on cross-lingual direct transfer. Our approach is also shown to be robust even when the size of bilingual dictionary is small.",
}
@inproceedings{chen-cardie-2018-unsupervised,
    title = "Unsupervised Multilingual Word Embeddings",
    author = "Chen, Xilun  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1024",
    doi = "10.18653/v1/D18-1024",
    pages = "261--270",
    abstract = "Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and opens many new possibilities for low-resource languages. Prior art for learning UMWEs, however, merely relies on a number of independently trained Unsupervised Bilingual Word Embeddings (UBWEs) to obtain multilingual embeddings. These methods fail to leverage the interdependencies that exist among many languages. To address this shortcoming, we propose a fully unsupervised framework for learning MWEs that directly exploits the relations between all language pairs. Our model substantially outperforms previous approaches in the experiments on multilingual word translation and cross-lingual word similarity. In addition, our model even beats supervised approaches trained with cross-lingual resources.",
}
@inproceedings{chi-chen-2018-cluse,
    title = "{CLUSE}: Cross-Lingual Unsupervised Sense Embeddings",
    author = "Chi, Ta-Chung  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1025",
    doi = "10.18653/v1/D18-1025",
    pages = "271--281",
    abstract = "This paper proposes a modularized sense induction and representation learning model that jointly learns bilingual sense embeddings that align well in the vector space, where the cross-lingual signal in the English-Chinese parallel corpus is exploited to capture the collocation and distributed characteristics in the language pair. The model is evaluated on the Stanford Contextual Word Similarity (SCWS) dataset to ensure the quality of monolingual sense embeddings. In addition, we introduce Bilingual Contextual Word Similarity (BCWS), a large and high-quality dataset for evaluating cross-lingual sense embeddings, which is the first attempt of measuring whether the learned embeddings are indeed aligned well in the vector space. The proposed approach shows the superior quality of sense embeddings evaluated in both monolingual and bilingual spaces.",
}
@inproceedings{ponti-etal-2018-adversarial,
    title = "Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization",
    author = "Ponti, Edoardo Maria  and
      Vuli{\'c}, Ivan  and
      Glava{\v{s}}, Goran  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1026",
    doi = "10.18653/v1/D18-1026",
    pages = "282--293",
    abstract = "Semantic specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with a adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three languages and on three tasks: word similarity, dialog state tracking, and lexical simplification. We report consistent improvements over distributional word vectors and vectors specialized by other state-of-the-art specialization frameworks. Finally, we also propose a cross-lingual transfer method for zero-shot specialization which successfully specializes a full target distributional space without any lexical knowledge in the target language and without any bilingual data.",
}
@inproceedings{doval-etal-2018-improving,
    title = "Improving Cross-Lingual Word Embeddings by Meeting in the Middle",
    author = "Doval, Yerai  and
      Camacho-Collados, Jose  and
      Espinosa-Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1027",
    doi = "10.18653/v1/D18-1027",
    pages = "294--304",
    abstract = "Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. Recently, it has been shown that these embeddings can be effectively learned by aligning two disjoint monolingual vector spaces through linear transformations, using no more than a small bilingual dictionary as supervision. In this work, we propose to apply an additional transformation after the initial alignment step, which moves cross-lingual synonyms towards a middle point between them. By applying this transformation our aim is to obtain a better cross-lingual integration of the vector spaces. In addition, and perhaps surprisingly, the monolingual spaces also improve by this transformation. This is in contrast to the original alignment, which is typically learned such that the structure of the monolingual spaces is preserved. Our experiments confirm that the resulting cross-lingual embeddings outperform state-of-the-art models in both monolingual and cross-lingual evaluation tasks.",
}
@inproceedings{faruqui-etal-2018-wikiatomicedits,
    title = "{W}iki{A}tomic{E}dits: A Multilingual Corpus of {W}ikipedia Edits for Modeling Language and Discourse",
    author = "Faruqui, Manaal  and
      Pavlick, Ellie  and
      Tenney, Ian  and
      Das, Dipanjan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1028",
    doi = "10.18653/v1/D18-1028",
    pages = "305--315",
    abstract = "We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw text. We release the full corpus as a resource to aid ongoing research in semantics, discourse, and representation learning.",
}
@inproceedings{gerz-etal-2018-relation,
    title = "On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling",
    author = "Gerz, Daniela  and
      Vuli{\'c}, Ivan  and
      Ponti, Edoardo Maria  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1029",
    doi = "10.18653/v1/D18-1029",
    pages = "316--327",
    abstract = "A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological profiles of the world{'}s languages. In this work, we analyse the implications of this variation on the language modeling (LM) task. We present a large-scale study of state-of-the art n-gram based and neural language models on 50 typologically diverse languages covering a wide variety of morphological systems. Operating in the full vocabulary LM setup focused on word-level prediction, we demonstrate that a coarse typology of morphological systems is predictive of absolute LM performance. Moreover, fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the meaning ambiguity of character n-grams. Our study strongly suggests that these features have to be taken into consideration during the construction of next-level language-agnostic LM architectures, capable of handling morphologically complex languages such as Tamil or Korean.",
}
@inproceedings{zhang-etal-2018-fast-compact,
    title = "A Fast, Compact, Accurate Model for Language Identification of Codemixed Text",
    author = "Zhang, Yuan  and
      Riesa, Jason  and
      Gillick, Daniel  and
      Bakalov, Anton  and
      Baldridge, Jason  and
      Weiss, David",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1030",
    doi = "10.18653/v1/D18-1030",
    pages = "328--337",
    abstract = "We address fine-grained multilingual language identification: providing a language code for every token in a sentence, including codemixed text containing multiple languages. Such text is prevalent online, in documents, social media, and message boards. We show that a feed-forward network with a simple globally constrained decoder can accurately and rapidly label both codemixed and monolingual text in 100 languages and 100 language pairs. This model outperforms previously published multilingual approaches in terms of both accuracy and speed, yielding an 800x speed-up and a 19.5{\%} averaged absolute gain on three codemixed datasets. It furthermore outperforms several benchmark systems on monolingual language identification.",
}
@inproceedings{wang-etal-2018-personalized,
    title = "Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning",
    author = "Wang, Weichao  and
      Feng, Shi  and
      Gao, Wei  and
      Wang, Daling  and
      Zhang, Yifei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1031",
    doi = "10.18653/v1/D18-1031",
    pages = "338--348",
    abstract = "Sentiment expression in microblog posts can be affected by user{'}s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user{'}s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.",
}
@inproceedings{wang-etal-2018-cross-lingual,
    title = "Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks",
    author = "Wang, Zhichun  and
      Lv, Qingsong  and
      Lan, Xiaohan  and
      Zhang, Yu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1032",
    doi = "10.18653/v1/D18-1032",
    pages = "349--357",
    abstract = "Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual KG alignment is the task of matching entities with their counterparts in different languages, which is an important way to enrich the cross-lingual links in multilingual KGs. In this paper, we propose a novel approach for cross-lingual KG alignment via graph convolutional networks (GCNs). Given a set of pre-aligned entities, our approach trains GCNs to embed entities of each language into a unified vector space. Entity alignments are discovered based on the distances between entities in the embedding space. Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments. In the experiments on aligning real multilingual KGs, our approach gets the best performance compared with other embedding-based KG alignment approaches.",
}
@inproceedings{qi-etal-2018-cross,
    title = "Cross-lingual Lexical Sememe Prediction",
    author = "Qi, Fanchao  and
      Lin, Yankai  and
      Sun, Maosong  and
      Zhu, Hao  and
      Xie, Ruobing  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1033",
    doi = "10.18653/v1/D18-1033",
    pages = "358--368",
    abstract = "Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages. We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. Experimental results on real-world datasets show that our proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingual sememe prediction. The codes and data of this paper are available at \url{https://github.com/thunlp/CL-SP}.",
}
@inproceedings{xie-etal-2018-neural,
    title = "Neural Cross-Lingual Named Entity Recognition with Minimal Resources",
    author = "Xie, Jiateng  and
      Yang, Zhilin  and
      Neubig, Graham  and
      Smith, Noah A.  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1034",
    doi = "10.18653/v1/D18-1034",
    pages = "369--379",
    abstract = "For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings. To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order. We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches. We also evaluate the challenges of applying these methods to Uyghur, a low-resource language.",
}
@inproceedings{chen-etal-2018-stable,
    title = "A Stable and Effective Learning Strategy for Trainable Greedy Decoding",
    author = "Chen, Yun  and
      Li, Victor O.K.  and
      Cho, Kyunghyun  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1035",
    doi = "10.18653/v1/D18-1035",
    pages = "380--390",
    abstract = "Beam search is a widely used approximate search strategy for neural network decoders, and it generally outperforms simple greedy decoding on tasks like machine translation. However, this improvement comes at substantial computational cost. In this paper, we propose a flexible new method that allows us to reap nearly the full benefits of beam search with nearly no additional computational cost. The method revolves around a small neural network actor that is trained to observe and manipulate the hidden state of a previously-trained decoder. To train this actor network, we introduce the use of a pseudo-parallel corpus built using the output of beam search on a base model, ranked by a target quality metric like BLEU. Our method is inspired by earlier work on this problem, but requires no reinforcement learning, and can be trained reliably on a range of models. Experiments on three parallel corpora and three architectures show that the method yields substantial improvements in translation quality and speed over each base system.",
}
@inproceedings{zhao-etal-2018-addressing,
    title = "Addressing Troublesome Words in Neural Machine Translation",
    author = "Zhao, Yang  and
      Zhang, Jiajun  and
      He, Zhongjun  and
      Zong, Chengqing  and
      Wu, Hua",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1036",
    doi = "10.18653/v1/D18-1036",
    pages = "391--400",
    abstract = "One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words. To address this problem, we propose a novel memoryenhanced NMT method. First, we investigate different strategies to define and detect the troublesome words. Then, a contextual memory is constructed to memorize which target words should be produced in what situations. Finally, we design a hybrid model to dynamically access the contextual memory so as to correctly translate the troublesome words. The extensive experiments on Chinese-to-English and English-to-German translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling troublesome words.",
}
@inproceedings{gu-etal-2018-top,
    title = "Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing",
    author = "G{\=u}, Jetic  and
      Shavarani, Hassan S.  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1037",
    doi = "10.18653/v1/D18-1037",
    pages = "401--413",
    abstract = "The addition of syntax-aware decoding in Neural Machine Translation (NMT) systems requires an effective tree-structured neural network, a syntax-aware attention model and a language generation model that is sensitive to sentence structure. Recent approaches resort to sequential decoding by adding additional neural network units to capture bottom-up structural information, or serialising structured data into sequence. We exploit a top-down tree-structured model called DRNN (Doubly-Recurrent Neural Networks) first proposed by Alvarez-Melis and Jaakola (2017) to create an NMT model called Seq2DRNN that combines a sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. Unlike previous approaches to syntax-based NMT which use dependency parsing models our method uses constituency parsing which we argue provides useful information for translation. In addition, we use the syntactic structure of the sentence to add new connections to the tree-structured decoder neural network (Seq2DRNN+SynC). We compare our NMT model with sequential and state of the art syntax-based NMT models and show that our model produces more fluent translations with better reordering. Since our model is capable of doing translation and constituency parsing at the same time we also compare our parsing accuracy against other neural parsing models.",
}
@inproceedings{chen-etal-2018-xl,
    title = "{XL}-{NBT}: A Cross-lingual Neural Belief Tracking Framework",
    author = "Chen, Wenhu  and
      Chen, Jianshu  and
      Su, Yu  and
      Wang, Xin  and
      Yu, Dong  and
      Yan, Xifeng  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1038",
    doi = "10.18653/v1/D18-1038",
    pages = "414--424",
    abstract = "Task-oriented dialog systems are becoming pervasive, and many companies heavily rely on them to complement human agents for customer service in call centers. With globalization, the need for providing cross-lingual customer support becomes more urgent than ever. However, cross-lingual support poses great challenges{---}it requires a large amount of additional annotated data from native speakers. In order to bypass the expensive human annotation and achieve the first step towards the ultimate goal of building a universal dialog system, we set out to build a cross-lingual state tracking framework. Specifically, we assume that there exists a source language with dialog belief tracking annotations while the target languages have no annotated dialog data of any form. Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data. We then distill and transfer its own knowledge to the student state tracker in target languages. We specifically discuss two types of common parallel resources: bilingual corpus and bilingual dictionary, and design different transfer learning strategies accordingly. Experimentally, we successfully use English state tracker as the teacher to transfer its knowledge to both Italian and German trackers and achieve promising results.",
}
@inproceedings{platanios-etal-2018-contextual,
    title = "Contextual Parameter Generation for Universal Neural Machine Translation",
    author = "Platanios, Emmanouil Antonios  and
      Sachan, Mrinmaya  and
      Neubig, Graham  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1039",
    doi = "10.18653/v1/D18-1039",
    pages = "425--435",
    abstract = "We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allowing for language specific parameterization, and that can also be used for domain adaptation. Our approach requires no changes to the model architecture of a standard NMT system, but instead introduces a new component, the contextual parameter generator (CPG), that generates the parameters of the system (e.g., weights in a neural network). This parameter generator accepts source and target language embeddings as input, and generates the parameters for the encoder and the decoder, respectively. The rest of the model remains unchanged and is shared across all languages. We show how this simple modification enables the system to use monolingual data for training and also perform zero-shot translation. We further show it is able to surpass state-of-the-art performance for both the IWSLT-15 and IWSLT-17 datasets and that the learned language embeddings are able to uncover interesting relationships between languages.",
}
@inproceedings{fadaee-monz-2018-back,
    title = "Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation",
    author = "Fadaee, Marzieh  and
      Monz, Christof",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1040",
    doi = "10.18653/v1/D18-1040",
    pages = "436--446",
    abstract = "Neural Machine Translation has achieved state-of-the-art performance for several language pairs using a combination of parallel and synthetic data. Synthetic data is often generated by back-translating sentences randomly sampled from monolingual data using a reverse translation model. While back-translation has been shown to be very effective in many cases, it is not entirely clear why. In this work, we explore different aspects of back-translation, and show that words with high prediction loss during training benefit most from the addition of synthetic data. We introduce several variations of sampling strategies targeting difficult-to-predict words using prediction losses and frequencies of words. In addition, we also target the contexts of difficult words and sample sentences that are similar in context. Experimental results for the WMT news translation task show that our method improves translation quality by up to 1.7 and 1.2 Bleu points over back-translation using random sampling for German-English and English-German, respectively.",
}
@inproceedings{zeng-etal-2018-multi,
    title = "Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination",
    author = "Zeng, Jiali  and
      Su, Jinsong  and
      Wen, Huating  and
      Liu, Yang  and
      Xie, Jun  and
      Yin, Yongjing  and
      Zhao, Jianqiang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1041",
    doi = "10.18653/v1/D18-1041",
    pages = "447--457",
    abstract = "With great practical value, the study of Multi-domain Neural Machine Translation (NMT) mainly focuses on using mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. Intuitively, words in a sentence are related to its domain to varying degrees, so that they will exert disparate impacts on the multi-domain NMT modeling. Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT. To this end, we jointly model NMT with monolingual attention-based domain classification tasks and improve NMT as follows: 1) Based on the sentence representations produced by a domain classifier and an adversarial domain classifier, we generate two gating vectors and use them to construct domain-specific and domain-shared annotations, for later translation predictions via different attention models; 2) We utilize the attention weights derived from target-side domain classifier to adjust the weights of target words in the training objective, enabling domain-related words to have greater impacts during model training. Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. Source codes of this paper are available on Github \url{https://github.com/DeepLearnXMU/WDCNMT}.",
}
@inproceedings{ruder-etal-2018-discriminative,
    title = "A Discriminative Latent-Variable Model for Bilingual Lexicon Induction",
    author = "Ruder, Sebastian  and
      Cotterell, Ryan  and
      Kementchedjhieva, Yova  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1042",
    doi = "10.18653/v1/D18-1042",
    pages = "458--468",
    abstract = "We introduce a novel discriminative latent-variable model for the task of bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a state-of-the-art embedding-based approach. To train the model, we derive an efficient Viterbi EM algorithm. We provide empirical improvements on six language pairs under two metrics and show that the prior theoretically and empirically helps to mitigate the hubness problem. We also demonstrate how previous work may be viewed as a similarly fashioned latent-variable model, albeit with a different prior.",
}
@inproceedings{hoshen-wolf-2018-non,
    title = "Non-Adversarial Unsupervised Word Translation",
    author = "Hoshen, Yedid  and
      Wolf, Lior",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1043",
    doi = "10.18653/v1/D18-1043",
    pages = "469--478",
    abstract = "Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks of generative adversarial models: sensitivity to hyper-parameters, long training time and lack of interpretability. In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods. We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment. Extensive experiments on word translation of European and Non-European languages show that our method achieves better performance than recent state-of-the-art deep adversarial approaches and is competitive with the supervised baseline. It is also efficient, easy to parallelize on CPU and interpretable.",
}
@inproceedings{wang-etal-2018-semi-autoregressive,
    title = "Semi-Autoregressive Neural Machine Translation",
    author = "Wang, Chunqi  and
      Zhang, Ji  and
      Chen, Haiqing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1044",
    doi = "10.18653/v1/D18-1044",
    pages = "479--488",
    abstract = "Existing approaches to neural machine translation are typically autoregressive models. While these models attain state-of-the-art translation quality, they are suffering from low parallelizability and thus slow at decoding long sequences. In this paper, we propose a novel model for fast sequence generation {---} the semi-autoregressive Transformer (SAT). The SAT keeps the autoregressive property in global but relieves in local and thus are able to produce multiple successive words in parallel at each time step. Experiments conducted on English-German and Chinese-English translation tasks show that the SAT achieves a good balance between translation quality and decoding speed. On WMT{'}14 English-German translation, the SAT achieves 5.58{\mbox{$\times$}} speedup while maintaining 88{\%} translation quality, significantly better than the previous non-autoregressive methods. When produces two words at each time step, the SAT is almost lossless (only 1{\%} degeneration in BLEU score).",
}
@inproceedings{edunov-etal-2018-understanding,
    title = "Understanding Back-Translation at Scale",
    author = "Edunov, Sergey  and
      Ott, Myle  and
      Auli, Michael  and
      Grangier, David",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1045",
    doi = "10.18653/v1/D18-1045",
    pages = "489--500",
    abstract = "An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find that in all but resource poor settings back-translations obtained via sampling or noised beam outputs are most effective. Our analysis shows that sampling or noisy synthetic data gives a much stronger training signal than data generated by beam or greedy search. We also compare how synthetic data compares to genuine bitext and study various domain effects. Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMT{'}14 English-German test set.",
}
@inproceedings{upadhyay-etal-2018-bootstrapping,
    title = "Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages",
    author = "Upadhyay, Shyam  and
      Kodner, Jordan  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1046",
    doi = "10.18653/v1/D18-1046",
    pages = "501--511",
    abstract = "Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction. Existing approaches to transliteration generation require a large ({\textgreater}5000) number of training examples. This difficulty contrasts with transliteration discovery, a somewhat easier task that involves picking a plausible transliteration from a given list. In this work, we present a bootstrapping algorithm that uses constrained discovery to improve generation, and can be used with as few as 500 training examples, which we show can be sourced from annotators in a matter of hours. This opens the task to languages for which large number of training examples are unavailable. We evaluate transliteration generation performance itself, as well the improvement it brings to cross-lingual candidate generation for entity linking, a typical downstream task. We present a comprehensive evaluation of our approach on nine languages, each written in a unique script.",
}
@inproceedings{nakashole-2018-norma,
    title = "{NORMA}: Neighborhood Sensitive Maps for Multilingual Word Embeddings",
    author = "Nakashole, Ndapa",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1047",
    doi = "10.18653/v1/D18-1047",
    pages = "512--522",
    abstract = "Inducing multilingual word embeddings by learning a linear map between embedding spaces of different languages achieves remarkable accuracy on related languages. However, accuracy drops substantially when translating between distant languages. Given that languages exhibit differences in vocabulary, grammar, written form, or syntax, one would expect that embedding spaces of different languages have different structures especially for distant languages. With the goal of capturing such differences, we propose a method for learning neighborhood sensitive maps, NORMA. Our experiments show that NORMA outperforms current state-of-the-art methods for word translation between distant languages.",
}
@inproceedings{geng-etal-2018-adaptive,
    title = "Adaptive Multi-pass Decoder for Neural Machine Translation",
    author = "Geng, Xinwei  and
      Feng, Xiaocheng  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1048",
    doi = "10.18653/v1/D18-1048",
    pages = "523--532",
    abstract = "Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored. In this paper, we propose a novel architecture called adaptive multi-pass decoder, which introduces a flexible multi-pass polishing mechanism to extend the capacity of NMT via reinforcement learning. More specifically, we adopt an extra policy network to automatically choose a suitable and effective number of decoding passes, according to the complexity of source sentences and the quality of the generated translations. Extensive experiments on Chinese-English translation demonstrate the effectiveness of our proposed adaptive multi-pass decoder upon the conventional NMT with a significant improvement about 1.55 BLEU.",
}
@inproceedings{zhang-etal-2018-improving,
    title = "Improving the Transformer Translation Model with Document-Level Context",
    author = "Zhang, Jiacheng  and
      Luan, Huanbo  and
      Sun, Maosong  and
      Zhai, Feifei  and
      Xu, Jingfang  and
      Zhang, Min  and
      Liu, Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1049",
    doi = "10.18653/v1/D18-1049",
    pages = "533--542",
    abstract = "Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge. In this work, we extend the Transformer model with a new context encoder to represent document-level context, which is then incorporated into the original encoder and decoder. As large-scale document-level parallel corpora are usually not available, we introduce a two-step training method to take full advantage of abundant sentence-level parallel corpora and limited document-level parallel corpora. Experiments on the NIST Chinese-English datasets and the IWSLT French-English datasets show that our approach improves over Transformer significantly.",
}
@inproceedings{michel-neubig-2018-mtnt,
    title = "{MTNT}: A Testbed for Machine Translation of Noisy Text",
    author = "Michel, Paul  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1050",
    doi = "10.18653/v1/D18-1050",
    pages = "543--553",
    abstract = "Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus previous work has resorted to evaluating on synthetically created datasets. In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (\url{www.reddit.com}) and professionally sourced translations. We commissioned translations of English comments into French and Japanese, as well as French and Japanese comments into English, on the order of 7k-37k sentences per language pair. We qualitatively and quantitatively examine the types of noise included in this dataset, then demonstrate that existing MT models fail badly on a number of noise-related phenomena, even after performing adaptation on a small training set of in-domain data. This indicates that this dataset can provide an attractive testbed for methods tailored to handling noisy text in MT.",
}
@inproceedings{petrochuk-zettlemoyer-2018-simplequestions,
    title = "{S}imple{Q}uestions Nearly Solved: A New Upperbound and Baseline Approach",
    author = "Petrochuk, Michael  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1051",
    doi = "10.18653/v1/D18-1051",
    pages = "554--558",
    abstract = "The SimpleQuestions dataset is one of the most commonly used benchmarks for studying single-relation factoid questions. In this paper, we present new evidence that this benchmark can be nearly solved by standard methods. First, we show that ambiguity in the data bounds performance at 83.4{\%}; many questions have more than one equally plausible interpretation. Second, we introduce a baseline that sets a new state-of-the-art performance level at 78.1{\%} accuracy, despite using standard methods. Finally, we report an empirical analysis showing that the upperbound is loose; roughly a quarter of the remaining errors are also not resolvable from the linguistic signal. Together, these results suggest that the SimpleQuestions dataset is nearly solved.",
}
@inproceedings{seo-etal-2018-phrase,
    title = "Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension",
    author = "Seo, Minjoon  and
      Kwiatkowski, Tom  and
      Parikh, Ankur  and
      Farhadi, Ali  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1052",
    doi = "10.18653/v1/D18-1052",
    pages = "559--564",
    abstract = "We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder. This formulation addresses a key challenge in machine comprehension by building a standalone representation of the document discourse. It additionally leads to a significant scalability advantage since the encoding of the answer candidate phrases in the document can be pre-computed and indexed offline for efficient retrieval. We experiment with baseline models for the new task, which achieve a reasonable accuracy but significantly underperform unconstrained QA models. We invite the QA research community to engage in Phrase-Indexed Question Answering (PIQA, pika) for closing the gap. The leaderboard is at: \url{nlp.cs.washington.edu/piqa}",
}
@inproceedings{lee-etal-2018-ranking,
    title = "Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering",
    author = "Lee, Jinhyuk  and
      Yun, Seongjun  and
      Kim, Hyunjae  and
      Ko, Miyoung  and
      Kang, Jaewoo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1053",
    doi = "10.18653/v1/D18-1053",
    pages = "565--569",
    abstract = "Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four open-domain QA datasets by 7.8{\%} on average.",
}
@inproceedings{indurthi-etal-2018-cut,
    title = "Cut to the Chase: A Context Zoom-in Network for Reading Comprehension",
    author = "Indurthi, Sathish Reddy  and
      Yu, Seunghak  and
      Back, Seohyun  and
      Cuay{\'a}huitl, Heriberto",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1054",
    doi = "10.18653/v1/D18-1054",
    pages = "570--575",
    abstract = "In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset {`}NarrativeQA{'}. The proposed architecture outperforms state-of-the-art results by 12.62{\%} (ROUGE-L) relative improvement.",
}
@inproceedings{kratzwald-feuerriegel-2018-adaptive,
    title = "Adaptive Document Retrieval for Deep Question Answering",
    author = "Kratzwald, Bernhard  and
      Feuerriegel, Stefan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1055",
    doi = "10.18653/v1/D18-1055",
    pages = "576--581",
    abstract = "State-of-the-art systems in deep question answering proceed as follows: (1)an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer. Yet the exact interplay between both components is poorly understood, especially concerning the number of candidate documents that should be retrieved. We show that choosing a static number of documents - as used in prior research - suffers from a noise-information trade-off and yields suboptimal results. As a remedy, we propose an adaptive document retrieval model. This learns the optimal candidate number for document retrieval, conditional on the size of the corpus and the query. We report extensive experimental results showing that our adaptive approach outperforms state-of-the-art methods on multiple benchmark datasets, as well as in the context of corpora with variable sizes.",
}
@inproceedings{hartmann-etal-2018-unsupervised,
    title = "Why is unsupervised alignment of {E}nglish embeddings from different algorithms so hard?",
    author = "Hartmann, Mareike  and
      Kementchedjhieva, Yova  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1056",
    doi = "10.18653/v1/D18-1056",
    pages = "582--586",
    abstract = "This paper presents a challenge to the community: Generative adversarial networks (GANs) can perfectly align independent English word embeddings induced using the same algorithm, based on distributional information alone; but fails to do so, for two different embeddings algorithms. Why is that? We believe understanding why, is key to understand both modern word embedding algorithms and the limitations and instability dynamics of GANs. This paper shows that (a) in all these cases, where alignment fails, there exists a linear transform between the two embeddings (so algorithm biases do not lead to non-linear differences), and (b) similar effects can not easily be obtained by varying hyper-parameters. One plausible suggestion based on our initial experiments is that the differences in the inductive biases of the embedding algorithms lead to an optimization landscape that is riddled with local optima, leading to a very small basin of convergence, but we present this more as a challenge paper than a technical contribution.",
}
@inproceedings{zhuang-etal-2018-quantifying,
    title = "Quantifying Context Overlap for Training Word Embeddings",
    author = "Zhuang, Yimeng  and
      Xie, Jinghui  and
      Zheng, Yinhe  and
      Zhu, Xuan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1057",
    doi = "10.18653/v1/D18-1057",
    pages = "587--593",
    abstract = "Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data to enhance the learning of word embeddings by joint training with existing neural word embedding models. Experimental results show that better word vectors can be obtained for word similarity tasks and some downstream NLP tasks by the enhanced approach.",
}
@inproceedings{washio-kato-2018-neural,
    title = "Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space",
    author = "Washio, Koki  and
      Kato, Tsuneaki",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1058",
    doi = "10.18653/v1/D18-1058",
    pages = "594--600",
    abstract = "Capturing the semantic relations of words in a vector space contributes to many natural language processing tasks. One promising approach exploits lexico-syntactic patterns as features of word pairs. In this paper, we propose a novel model of this pattern-based approach, neural latent relational analysis (NLRA). NLRA can generalize co-occurrences of word pairs and lexico-syntactic patterns, and obtain embeddings of the word pairs that do not co-occur. This overcomes the critical data sparseness problem encountered in previous pattern-based models. Our experimental results on measuring relational similarity demonstrate that NLRA outperforms the previous pattern-based models. In addition, when combined with a vector offset model, NLRA achieves a performance comparable to that of the state-of-the-art model that exploits additional semantic relational data.",
}
@inproceedings{zhao-etal-2018-generalizing,
    title = "Generalizing Word Embeddings using Bag of Subwords",
    author = "Zhao, Jinman  and
      Mudgal, Sidharth  and
      Liang, Yingyu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1059",
    doi = "10.18653/v1/D18-1059",
    pages = "601--606",
    abstract = "We approach the problem of generalizing pre-trained word embeddings beyond fixed-size vocabularies without using additional contextual information. We propose a subword-level word vector generation model that views words as bags of character $n$-grams. The model is simple, fast to train and provides good vectors for rare or unseen words. Experiments show that our model achieves state-of-the-art performances in English word similarity task and in joint prediction of part-of-speech tag and morphosyntactic attributes in 23 languages, suggesting our model{'}s ability in capturing the relationship between words{'} textual representations and their embeddings.",
}
@inproceedings{gao-etal-2018-neural-metaphor,
    title = "Neural Metaphor Detection in Context",
    author = "Gao, Ge  and
      Choi, Eunsol  and
      Choi, Yejin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1060",
    doi = "10.18653/v1/D18-1060",
    pages = "607--613",
    abstract = "We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.",
}
@inproceedings{plank-agic-2018-distant,
    title = "Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging",
    author = "Plank, Barbara  and
      Agi{\'c}, {\v{Z}}eljko",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1061",
    doi = "10.18653/v1/D18-1061",
    pages = "614--620",
    abstract = "a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework. The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data.",
}
@inproceedings{dou-etal-2018-unsupervised,
    title = "Unsupervised Bilingual Lexicon Induction via Latent Variable Models",
    author = "Dou, Zi-Yi  and
      Zhou, Zhi-Hao  and
      Huang, Shujian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1062",
    doi = "10.18653/v1/D18-1062",
    pages = "621--626",
    abstract = "Bilingual lexicon extraction has been studied for decades and most previous methods have relied on parallel corpora or bilingual dictionaries. Recent studies have shown that it is possible to build a bilingual dictionary by aligning monolingual word embedding spaces in an unsupervised way. With the recent advances in generative models, we propose a novel approach which builds cross-lingual dictionaries via latent variable models and adversarial training with no parallel corpora. To demonstrate the effectiveness of our approach, we evaluate our approach on several language pairs and the experimental results show that our model could achieve competitive and even superior performance compared with several state-of-the-art models.",
}
@inproceedings{mukherjee-etal-2018-learning,
    title = "Learning Unsupervised Word Translations Without Adversaries",
    author = "Mukherjee, Tanmoy  and
      Yamada, Makoto  and
      Hospedales, Timothy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1063",
    doi = "10.18653/v1/D18-1063",
    pages = "627--632",
    abstract = "Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyper-parameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised {--} no seed dictionary or parallel corpora required; and introduces no adversary {--} therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.",
}
@inproceedings{masumura-etal-2018-adversarial,
    title = "Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification",
    author = "Masumura, Ryo  and
      Shinohara, Yusuke  and
      Higashinaka, Ryuichiro  and
      Aono, Yushi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1064",
    doi = "10.18653/v1/D18-1064",
    pages = "633--639",
    abstract = "This paper proposes an adversarial training method for the multi-task and multi-lingual joint modeling needed for utterance intent classification. In joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. This is achieved by introducing both language-specific networks shared among different tasks and task-specific networks shared among different languages. However, the shared networks are often specialized in majority tasks or languages, so performance degradation must be expected for some minor data sets. In order to improve the invariance of shared networks, the proposed method introduces both language-specific task adversarial networks and task-specific language adversarial networks; both are leveraged for purging the task or language dependencies of the shared networks. The effectiveness of the adversarial training proposal is demonstrated using Japanese and English data sets for three different utterance intent classification tasks.",
}
@inproceedings{shankar-etal-2018-surprisingly,
    title = "Surprisingly Easy Hard-Attention for Sequence to Sequence Learning",
    author = "Shankar, Shiv  and
      Garg, Siddhant  and
      Sarawagi, Sunita",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1065",
    doi = "10.18653/v1/D18-1065",
    pages = "640--645",
    abstract = "In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning. The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention. On five translation tasks we show effortless and consistent gains in BLEU compared to existing attention mechanisms.",
}
@inproceedings{chen-etal-2018-joint,
    title = "Joint Learning for Emotion Classification and Emotion Cause Detection",
    author = "Chen, Ying  and
      Hou, Wenjun  and
      Cheng, Xiyao  and
      Li, Shoushan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1066",
    doi = "10.18653/v1/D18-1066",
    pages = "646--651",
    abstract = "We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.",
}
@inproceedings{caragea-etal-2018-exploring,
    title = "Exploring Optimism and Pessimism in {T}witter Using Deep Learning",
    author = "Caragea, Cornelia  and
      Dinu, Liviu P.  and
      Dumitru, Bogdan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1067",
    doi = "10.18653/v1/D18-1067",
    pages = "652--658",
    abstract = "Identifying optimistic and pessimistic viewpoints and users from Twitter is useful for providing better social support to those who need such support, and for minimizing the negative influence among users and maximizing the spread of positive attitudes and ideas. In this paper, we explore a range of deep learning models to predict optimism and pessimism in Twitter at both tweet and user level and show that these models substantially outperform traditional machine learning classifiers used in prior work. In addition, we show evidence that a sentiment classifier would not be sufficient for accurately predicting optimism and pessimism in Twitter. Last, we study the verb tense usage as well as the presence of polarity words in optimistic and pessimistic tweets.",
}
@inproceedings{lamprinidis-etal-2018-predicting,
    title = "Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning",
    author = "Lamprinidis, Sotiris  and
      Hardt, Daniel  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1068",
    doi = "10.18653/v1/D18-1068",
    pages = "659--664",
    abstract = "Newspapers need to attract readers with headlines, anticipating their readers{'} preferences. These preferences rely on topical, structural, and lexical factors. We model each of these factors in a multi-task GRU network to predict headline popularity. We find that pre-trained word embeddings provide significant improvements over untrained embeddings, as do the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging. However, we also find that performance is very similar to that of a simple Logistic Regression model over character n-grams. Feature analysis reveals structural patterns of headline popularity, including the use of forward-looking deictic expressions and second person pronouns.",
}
@inproceedings{chen-etal-2018-hybrid,
    title = "Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates",
    author = "Chen, Di  and
      Du, Jiachen  and
      Bing, Lidong  and
      Xu, Ruifeng",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1069",
    doi = "10.18653/v1/D18-1069",
    pages = "665--670",
    abstract = "Inferring the agreement/disagreement relation in debates, especially in online debates, is one of the fundamental tasks in argumentation mining. The expressions of agreement/disagreement usually rely on argumentative expressions in text as well as interactions between participants in debates. Previous works usually lack the capability of jointly modeling these two factors. To alleviate this problem, this paper proposes a hybrid neural attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users. Experimental results on three (dis)agreement inference datasets show that our model outperforms the state-of-the-art models.",
}
@inproceedings{hovy-fornaciari-2018-increasing,
    title = "Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information",
    author = "Hovy, Dirk  and
      Fornaciari, Tommaso",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1070",
    doi = "10.18653/v1/D18-1070",
    pages = "671--677",
    abstract = "Most text-classification approaches represent the input based on textual features, either feature-based or continuous. However, this ignores strong non-linguistic similarities like homophily: people within a demographic group use language more similar to each other than to non-group members. We use homophily cues to retrofit text-based author representations with non-linguistic information, and introduce a trade-off parameter. This approach increases in-class similarity between authors, and improves classification performance by making classes more linearly separable. We evaluate the effect of our method on two author-attribute prediction tasks with various training-set sizes and parameter settings. We find that our method can significantly improve classification performance, especially when the number of labels is large and limited labeled data is available. It is potentially applicable as preprocessing step to any text-classification task.",
}
@inproceedings{li-sun-2018-syntactically,
    title = "A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation",
    author = "Li, Jingyuan  and
      Sun, Xiao",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1071",
    doi = "10.18653/v1/D18-1071",
    pages = "678--683",
    abstract = "Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (E-SCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines.",
}
@inproceedings{shi-etal-2018-auto,
    title = "Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning",
    author = "Shi, Chen  and
      Chen, Qi  and
      Sha, Lei  and
      Li, Sujian  and
      Sun, Xu  and
      Wang, Houfeng  and
      Zhang, Lintao",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1072",
    doi = "10.18653/v1/D18-1072",
    pages = "684--689",
    abstract = "The lack of labeled data is one of the main challenges when building a task-oriented dialogue system. Existing dialogue datasets usually rely on human labeling, which is expensive, limited in size, and in low coverage. In this paper, we instead propose our framework auto-dialabel to automatically cluster the dialogue intents and slots. In this framework, we collect a set of context features, leverage an autoencoder for feature assembly, and adapt a dynamic hierarchical clustering method for intent and slot labeling. Experimental results show that our framework can promote human labeling cost to a great extent, achieve good intent clustering accuracy (84.1{\%}), and provide reasonable and instructive slot labeling results.",
}
@inproceedings{parthasarathi-pineau-2018-extending,
    title = "Extending Neural Generative Conversational Model using External Knowledge Sources",
    author = "Parthasarathi, Prasanna  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1073",
    doi = "10.18653/v1/D18-1073",
    pages = "690--695",
    abstract = "The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents trained with the Reddit News dataset, and consider incorporating external knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our experiments show faster training time and improved perplexity when leveraging external knowledge.",
}
@inproceedings{huang-etal-2018-modeling,
    title = "Modeling Temporality of Human Intentions by Domain Adaptation",
    author = "Huang, Xiaolei  and
      Liu, Lixing  and
      Carey, Kate  and
      Woolley, Joshua  and
      Scherer, Stefan  and
      Borsari, Brian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1074",
    doi = "10.18653/v1/D18-1074",
    pages = "696--701",
    abstract = "Categorizing patient{'}s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patients might show different behaviors during the beginning versus the end). This paper proposes a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). We deploy Bi-LSTM and topic model jointly to learn language usage change across different time sessions. We conduct experiments on the MI corpora to show the promising improvement after considering temporality in the classification task.",
}
@inproceedings{luo-etal-2018-auto,
    title = "An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation",
    author = "Luo, Liangchen  and
      Xu, Jingjing  and
      Lin, Junyang  and
      Zeng, Qi  and
      Sun, Xu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1075",
    doi = "10.18653/v1/D18-1075",
    pages = "702--707",
    abstract = "Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs. To address this problem, we propose an Auto-Encoder Matching (AEM) model to learn such dependency. The model contains two auto-encoders and one mapping module. The auto-encoders learn the semantic representations of inputs and responses, and the mapping module learns to connect the utterance-level representations. Experimental results from automatic and human evaluations demonstrate that our model is capable of generating responses of high coherence and fluency compared to baseline models.",
}
@inproceedings{zhou-etal-2018-dataset,
    title = "A Dataset for Document Grounded Conversations",
    author = "Zhou, Kangyan  and
      Prabhumoye, Shrimai  and
      Black, Alan W",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1076",
    doi = "10.18653/v1/D18-1076",
    pages = "708--713",
    abstract = "This paper introduces a document grounded dataset for conversations. We define {``}Document Grounded Conversations{''} as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.",
}
@inproceedings{ryu-etal-2018-domain,
    title = "Out-of-domain Detection based on Generative Adversarial Network",
    author = "Ryu, Seonghan  and
      Koo, Sangjun  and
      Yu, Hwanjo  and
      Lee, Gary Geunbae",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1077",
    doi = "10.18653/v1/D18-1077",
    pages = "714--718",
    abstract = "The main goal of this paper is to develop out-of-domain (OOD) detection for dialog systems. We propose to use only in-domain (IND) sentences to build a generative adversarial network (GAN) of which the discriminator generates low scores for OOD sentences. To improve basic GANs, we apply feature matching loss in the discriminator, use domain-category analysis as an additional task in the discriminator, and remove the biases in the generator. Thereby, we reduce the huge effort of collecting OOD sentences for training OOD detection. For evaluation, we experimented OOD detection on a multi-domain dialog system. The experimental results showed the proposed method was most accurate compared to the existing methods.",
}
@inproceedings{mirkin-etal-2018-listening,
    title = "Listening Comprehension over Argumentative Content",
    author = "Mirkin, Shachar  and
      Moshkowich, Guy  and
      Orbach, Matan  and
      Kotlerman, Lili  and
      Kantor, Yoav  and
      Lavee, Tamar  and
      Jacovi, Michal  and
      Bilu, Yonatan  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1078",
    doi = "10.18653/v1/D18-1078",
    pages = "719--724",
    abstract = "This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.",
}
@inproceedings{xu-etal-2018-using,
    title = "Using active learning to expand training data for implicit discourse relation recognition",
    author = "Xu, Yang  and
      Hong, Yu  and
      Ruan, Huibin  and
      Yao, Jianmin  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1079",
    doi = "10.18653/v1/D18-1079",
    pages = "725--731",
    abstract = "We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans. Implicit relation recognition is challenging due to the lack of explicit relational clues. The increasingly popular neural network techniques have been proven effective for semantic encoding, whereby widely employed to boost semantic relation discrimination. However, learning to predict semantic relations at a deep level heavily relies on a great deal of training data, but the scale of the publicly available data in this field is limited. In this paper, we follow Rutherford and Xue (2015) to expand the training data set using the corpus of explicitly-related arguments, by arbitrarily dropping the overtly presented discourse connectives. On the basis, we carry out an experiment of sampling, in which a simple active learning approach is used, so as to take the informative instances for data expansion. The goal is to verify whether the selective use of external data not only reduces the time consumption of retraining but also ensures a better system performance. Using the expanded training data, we retrain a convolutional neural network (CNN) based classifer which is a simplified version of Qin et al. (2016){'}s stacking gated relation recognizer. Experimental results show that expanding the training set with small-scale carefully-selected external data yields substantial performance gain, with the improvements of about 4{\%} for accuracy and 3.6{\%} for F-score. This allows a weak classifier to achieve a comparable performance against the state-of-the-art systems.",
}
@inproceedings{botha-etal-2018-learning,
    title = "Learning To Split and Rephrase From {W}ikipedia Edit History",
    author = "Botha, Jan A.  and
      Faruqui, Manaal  and
      Alex, John  and
      Baldridge, Jason  and
      Das, Dipanjan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1080",
    doi = "10.18653/v1/D18-1080",
    pages = "732--737",
    abstract = "Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia{'}s edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.",
}
@inproceedings{sulem-etal-2018-bleu,
    title = "{BLEU} is Not Suitable for the Evaluation of Text Simplification",
    author = "Sulem, Elior  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1081",
    doi = "10.18653/v1/D18-1081",
    pages = "738--744",
    abstract = "BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.",
}
@inproceedings{pei-li-2018-s2spmn,
    title = "{S}2{SPMN}: A Simple and Effective Framework for Response Generation with Relevant Information",
    author = "Pei, Jiaxin  and
      Li, Chenliang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1082",
    doi = "10.18653/v1/D18-1082",
    pages = "745--750",
    abstract = "How to generate relevant and informative responses is one of the core topics in response generation area. Following the task formulation of machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. To realize this mapping, existing works tend to design intuitive but complex models. However, the relevant information existed in large dialogue corpus is mainly overlooked. In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation. Specifically, we devise two simple approaches in S2SPMN to select the relevant information (named prototypes) from the dialogue corpus. These prototypes are then saved into prototype memory network (PMN). Furthermore, a hierarchical attention mechanism is devised to extract the semantic information from the PMN to assist the response generation process. Empirical studies reveal the advantage of our model over several classical and strong baselines.",
}
@inproceedings{guo-etal-2018-improving,
    title = "Improving Reinforcement Learning Based Image Captioning with Natural Language Prior",
    author = "Guo, Tszhang  and
      Chang, Shiyu  and
      Yu, Mo  and
      Bai, Kun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1083",
    doi = "10.18653/v1/D18-1083",
    pages = "751--756",
    abstract = "Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing. However, this shaped reward introduces learning biases, which reduces the readability of generated text. In addition, the large sample space makes training unstable and slow.To alleviate these issues, we propose a simple coherent solution that constrains the action space using an n-gram language prior. Quantitative and qualitative evaluations on benchmarks show that RL with the simple add-on module performs favorably against its counterpart in terms of both readability and speed of convergence. Human evaluation results show that our model is more human readable and graceful. The implementation will become publicly available upon the acceptance of the paper.",
}
@inproceedings{melas-kyriazi-etal-2018-training,
    title = "Training for Diversity in Image Paragraph Captioning",
    author = "Melas-Kyriazi, Luke  and
      Rush, Alexander  and
      Han, George",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1084",
    doi = "10.18653/v1/D18-1084",
    pages = "757--761",
    abstract = "Image paragraph captioning models aim to produce detailed descriptions of a source image. These models use similar techniques as standard image captioning models, but they have encountered issues in text generation, notably a lack of diversity between sentences, that have limited their effectiveness. In this work, we consider applying sequence-level training for this task. We find that standard self-critical training produces poor results, but when combined with an integrated penalty on trigram repetition produces much more diverse paragraphs. This simple training approach improves on the best result on the Visual Genome paragraph captioning dataset from 16.9 to 30.6 CIDEr, with gains on METEOR and BLEU as well, without requiring any architectural changes.",
}
@inproceedings{shafieibavani-etal-2018-graph,
    title = "A Graph-theoretic Summary Evaluation for {ROUGE}",
    author = "ShafieiBavani, Elaheh  and
      Ebrahimi, Mohammad  and
      Wong, Raymond  and
      Chen, Fang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1085",
    doi = "10.18653/v1/D18-1085",
    pages = "762--767",
    abstract = "ROUGE is one of the first and most widely used evaluation metrics for text summarization. However, its assessment merely relies on surface similarities between peer and model summaries. Consequently, ROUGE is unable to fairly evaluate summaries including lexical variations and paraphrasing. We propose a graph-based approach adopted into ROUGE to evaluate summaries based on both lexical and semantic similarities. Experiment results over TAC AESOP datasets show that exploiting the lexico-semantic similarity of the words used in summaries would significantly help ROUGE correlate better with human judgments.",
}
@inproceedings{hardy-vlachos-2018-guided,
    title = "Guided Neural Language Generation for Abstractive Summarization using {A}bstract {M}eaning {R}epresentation",
    author = "Hardy, Hardy  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1086",
    doi = "10.18653/v1/D18-1086",
    pages = "768--773",
    abstract = "Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which we guide using the source document. We demonstrate that this guidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. We also find that the summarization performance on later parses is 2 ROUGE-2 points higher than that of a well-established neural encoder-decoder approach trained on a larger dataset.",
}
@inproceedings{shapira-etal-2018-evaluating,
    title = "Evaluating Multiple System Summary Lengths: A Case Study",
    author = "Shapira, Ori  and
      Gabay, David  and
      Ronen, Hadar  and
      Bar-Ilan, Judit  and
      Amsterdamer, Yael  and
      Nenkova, Ani  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1087",
    doi = "10.18653/v1/D18-1087",
    pages = "774--778",
    abstract = "Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this paper, we raise the research question of whether reference summaries of a single length can be used to reliably evaluate system summaries of multiple lengths. For that, we have analyzed a couple of datasets as a case study, using several variants of the ROUGE metric that are standard in summarization evaluation. Our findings indicate that the evaluation protocol in question is indeed competitive. This result paves the way to practically evaluating varying-length summaries with simple, possibly existing, summarization benchmarks.",
}
@inproceedings{zhang-etal-2018-neural-latent,
    title = "Neural Latent Extractive Document Summarization",
    author = "Zhang, Xingxing  and
      Lapata, Mirella  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1088",
    doi = "10.18653/v1/D18-1088",
    pages = "779--784",
    abstract = "Extractive summarization models need sentence level labels, which are usually created with rule-based methods since most summarization datasets only have document summary pairs. These labels might be suboptimal. We propose a latent variable extractive model, where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training, the loss can come directly from gold summaries. Experiments on CNN/Dailymail dataset show our latent extractive model outperforms a strong extractive baseline trained on rule-based labels and also performs competitively with several recent models.",
}
@inproceedings{zhang-etal-2018-abstractiveness,
    title = "On the Abstractiveness of Neural Document Summarization",
    author = "Zhang, Fangfang  and
      Yao, Jin-ge  and
      Yan, Rui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1089",
    doi = "10.18653/v1/D18-1089",
    pages = "785--790",
    abstract = "Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries. We attempted to verify the degree of abstractiveness of modern neural abstractive summarization systems by calculating overlaps in terms of various types of units. Upon the observation that many abstractive systems tend to be near-extractive in practice, we also implemented a pure copy system, which achieved comparable results as abstractive summarizers while being far more computationally efficient. These findings suggest the possibility for future efforts towards more efficient systems that could better utilize the vocabulary in the original document.",
}
@inproceedings{wang-etal-2018-automatic,
    title = "Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning",
    author = "Wang, Yucheng  and
      Wei, Zhongyu  and
      Zhou, Yaqian  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1090",
    doi = "10.18653/v1/D18-1090",
    pages = "791--797",
    abstract = "Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework.",
}
@inproceedings{faruqui-das-2018-identifying,
    title = "Identifying Well-formed Natural Language Questions",
    author = "Faruqui, Manaal  and
      Das, Dipanjan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1091",
    doi = "10.18653/v1/D18-1091",
    pages = "798--803",
    abstract = "Understanding search queries is a hard problem as it involves dealing with {``}word salad{''} text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. Here, we introduce a new task of identifying a well-formed natural language question. We construct and release a dataset of 25,100 publicly available questions classified into well-formed and non-wellformed categories and report an accuracy of 70.7{\%} on the test set. We also show that our classifier can be used to improve the performance of neural sequence-to-sequence models for generating questions for reading comprehension.",
}
@inproceedings{ravi-kozareva-2018-self,
    title = "Self-Governing Neural Networks for On-Device Short Text Classification",
    author = "Ravi, Sujith  and
      Kozareva, Zornitsa",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1092",
    doi = "10.18653/v1/D18-1092",
    pages = "804--810",
    abstract = "Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.",
}
@inproceedings{shimura-etal-2018-hft,
    title = "{HFT}-{CNN}: Learning Hierarchical Category Structure for Multi-label Short Text Categorization",
    author = "Shimura, Kazuya  and
      Li, Jiyi  and
      Fukumoto, Fumiyo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1093",
    doi = "10.18653/v1/D18-1093",
    pages = "811--816",
    abstract = "We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the pre-defined categories to tackle the data sparsity problem. The lower the HS level, the less the categorization performance. Because the number of training data per category in a lower level is much smaller than that in an upper level. We propose an approach which can effectively utilize the data in the upper levels to contribute the categorization in the lower levels by applying the Convolutional Neural Network (CNN) with a fine-tuning technique. The results using two benchmark datasets show that proposed method, Hierarchical Fine-Tuning based CNN (HFT-CNN) is competitive with the state-of-the-art CNN based methods.",
}
@inproceedings{sinha-etal-2018-hierarchical,
    title = "A Hierarchical Neural Attention-based Text Classifier",
    author = "Sinha, Koustuv  and
      Dong, Yue  and
      Cheung, Jackie Chi Kit  and
      Ruths, Derek",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1094",
    doi = "10.18653/v1/D18-1094",
    pages = "817--823",
    abstract = "Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.",
}
@inproceedings{lund-etal-2018-labeled,
    title = "Labeled Anchors and a Scalable, Transparent, and Interactive Classifier",
    author = "Lund, Jeffrey  and
      Cowley, Stephen  and
      Fearn, Wilson  and
      Hales, Emily  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1095",
    doi = "10.18653/v1/D18-1095",
    pages = "824--829",
    abstract = "We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.",
}
@inproceedings{ding-etal-2018-coherence,
    title = "Coherence-Aware Neural Topic Modeling",
    author = "Ding, Ran  and
      Nallapati, Ramesh  and
      Xiang, Bing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1096",
    doi = "10.18653/v1/D18-1096",
    pages = "830--836",
    abstract = "Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.",
}
@inproceedings{watson-etal-2018-utilizing,
    title = "Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models",
    author = "Watson, Daniel  and
      Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1097",
    doi = "10.18653/v1/D18-1097",
    pages = "837--843",
    abstract = "Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the complexity of the language increase the difficulty of the problem. To address these challenges, we use a sequence-to-sequence model with character-based attention, which in addition to its self-learned character embeddings, uses word embeddings pre-trained with an approach that also models subword information. This provides the neural model with access to more linguistic information especially suitable for text normalization, without large parallel corpora. We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.",
}
@inproceedings{bhatia-etal-2018-topic,
    title = "Topic Intrusion for Automatic Topic Model Evaluation",
    author = "Bhatia, Shraey  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1098",
    doi = "10.18653/v1/D18-1098",
    pages = "844--849",
    abstract = "Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task {---} the task of guessing an outlier topic given a document and a few topics {---} and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.",
}
@inproceedings{mysore-gopinath-etal-2018-supervised,
    title = "Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents",
    author = "Mysore Gopinath, Abhijith Athreya  and
      Wilson, Shomir  and
      Sadeh, Norman",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1099",
    doi = "10.18653/v1/D18-1099",
    pages = "850--855",
    abstract = "The text in many web documents is organized into a hierarchy of section titles and corresponding prose content, a structure which provides potentially exploitable information on discourse structure and topicality. However, this organization is generally discarded during text collection, and collecting it is not straightforward: the same visual organization can be implemented in a myriad of different ways in the underlying HTML. To remedy this, we present a flexible system for automatically extracting the hierarchical section titles and prose organization of web documents irrespective of differences in HTML representation. This system uses features from syntax, semantics, discourse and markup to build two models which classify HTML text into section titles and prose text. When tested on three different domains of web text, our domain-independent system achieves an overall precision of 0.82 and a recall of 0.98. The domain-dependent variation produces very high precision (0.99) at the expense of recall (0.75). These results exhibit a robust level of accuracy suitable for enhancing question answering, information extraction, and summarization.",
}
@inproceedings{wang-etal-2018-switchout,
    title = "{S}witch{O}ut: an Efficient Data Augmentation Algorithm for Neural Machine Translation",
    author = "Wang, Xinyi  and
      Pham, Hieu  and
      Dai, Zihang  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1100",
    doi = "10.18653/v1/D18-1100",
    pages = "856--861",
    abstract = "In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix.",
}
@inproceedings{kim-etal-2018-improving,
    title = "Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder",
    author = "Kim, Yunsu  and
      Geng, Jiahui  and
      Ney, Hermann",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1101",
    doi = "10.18653/v1/D18-1101",
    pages = "862--868",
    abstract = "Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.",
}
@inproceedings{kambhatla-etal-2018-decipherment,
    title = "Decipherment of Substitution Ciphers with Neural Language Models",
    author = "Kambhatla, Nishant  and
      Mansouri Bigvand, Anahita  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1102",
    doi = "10.18653/v1/D18-1102",
    pages = "869--874",
    abstract = "Decipherment of homophonic substitution ciphers using language models is a well-studied task in NLP. Previous work in this topic scores short local spans of possible plaintext decipherments using n-gram language models. The most widely used technique is the use of beam search with n-gram language models proposed by Nuhn et al.(2013). We propose a beam search algorithm that scores the entire candidate plaintext at each step of the decipherment using a neural language model. We augment beam search with a novel rest cost estimation that exploits the prediction power of a neural language model. We compare against the state of the art n-gram based methods on many different decipherment tasks. On challenging ciphers such as the Beale cipher we provide significantly better error rates with much smaller beam sizes.",
}
@inproceedings{neubig-hu-2018-rapid,
    title = "Rapid Adaptation of Neural Machine Translation to New Languages",
    author = "Neubig, Graham  and
      Hu, Junjie",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1103",
    doi = "10.18653/v1/D18-1103",
    pages = "875--880",
    abstract = "This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual {``}seed models{''}, which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of {``}similar-language regularization{''}, where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings.",
}
@inproceedings{wuebker-etal-2018-compact,
    title = "Compact Personalized Models for Neural Machine Translation",
    author = "Wuebker, Joern  and
      Simianer, Patrick  and
      DeNero, John",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1104",
    doi = "10.18653/v1/D18-1104",
    pages = "881--886",
    abstract = "We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters can be frozen during adaptation with minimal or no reduction in translation quality by encouraging structured sparsity in the set of offset tensors during learning via group lasso regularization. We evaluate this technique for both batch and incremental adaptation across multiple data sets and language pairs. Our system architecture{--}combining a state-of-the-art self-attentive model with compact domain adaptation{--}provides high quality personalized machine translation that is both space and time efficient.",
}
@inproceedings{ravi-kozareva-2018-self-governing,
    title = "Self-Governing Neural Networks for On-Device Short Text Classification",
    author = "Ravi, Sujith  and
      Kozareva, Zornitsa",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1105",
    doi = "10.18653/v1/D18-1105",
    pages = "887--893",
    abstract = "Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.",
}
@inproceedings{kim-kim-2018-supervised,
    title = "Supervised Domain Enablement Attention for Personalized Domain Classification",
    author = "Kim, Joo-Kyung  and
      Kim, Young-Bum",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1106",
    doi = "10.18653/v1/D18-1106",
    pages = "894--899",
    abstract = "In large-scale domain classification for natural language understanding, leveraging each user{'}s domain enablement information, which refers to the preferred or authenticated domains by the user, with attention mechanism has been shown to improve the overall domain classification performance. In this paper, we propose a supervised enablement attention mechanism, which utilizes sigmoid activation for the attention weighting so that the attention can be computed with more expressive power without the weight sum constraint of softmax attention. The attention weights are explicitly encouraged to be similar to the corresponding elements of the output one-hot vector, and self-distillation is used to leverage the attention information of the other enabled domains. By evaluating on the actual utterances from a large-scale IPDA, we show that our approach significantly improves domain classification performance",
}
@inproceedings{song-etal-2018-deep,
    title = "A Deep Neural Network Sentence Level Classification Method with Context Information",
    author = "Song, Xingyi  and
      Petrak, Johann  and
      Roberts, Angus",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1107",
    doi = "10.18653/v1/D18-1107",
    pages = "900--904",
    abstract = "In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context-LSTM-CNN, that makes use of potentially large contexts. The method also utilizes long-range dependencies within the sentence being classified, using an LSTM, and short-span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.",
}
@inproceedings{niculae-etal-2018-towards,
    title = "Towards Dynamic Computation Graphs via Sparse Latent Structure",
    author = "Niculae, Vlad  and
      Martins, Andr{\'e} F. T.  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1108",
    doi = "10.18653/v1/D18-1108",
    pages = "905--911",
    abstract = "Deep NLP models benefit from underlying structures in the data{---}e.g., parse trees{---}typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Using the recently proposed SparseMAP inference, which retrieves a sparse distribution over latent structures, we propose a novel approach for end-to-end learning of latent structure predictors jointly with a downstream predictor. To the best of our knowledge, our method is the first to enable unrestricted dynamic computation graph construction from the global latent structure, while maintaining differentiability.",
}
@inproceedings{yang-2018-convolutional,
    title = "Convolutional Neural Networks with Recurrent Neural Filters",
    author = "Yang, Yi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1109",
    doi = "10.18653/v1/D18-1109",
    pages = "912--917",
    abstract = "We introduce a class of convolutional neural networks (CNNs) that utilize recurrent neural networks (RNNs) as convolution filters. A convolution filter is typically implemented as a linear affine transformation followed by a non-linear function, which fails to account for language compositionality. As a result, it limits the use of high-order filters that are often warranted for natural language processing tasks. In this work, we model convolution filters with RNNs that naturally capture compositionality and long-term dependencies in language. We show that simple CNN architectures equipped with recurrent neural filters (RNFs) achieve results that are on par with the best published ones on the Stanford Sentiment Treebank and two answer sentence selection datasets.",
}
@inproceedings{xu-etal-2018-exploiting,
    title = "Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model",
    author = "Xu, Kun  and
      Wu, Lingfei  and
      Wang, Zhiguo  and
      Yu, Mo  and
      Chen, Liwei  and
      Sheinin, Vadim",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1110",
    doi = "10.18653/v1/D18-1110",
    pages = "918--924",
    abstract = "Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features; then employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.",
}
@inproceedings{hayati-etal-2018-retrieval,
    title = "Retrieval-Based Neural Code Generation",
    author = "Hayati, Shirley Anugrah  and
      Olivier, Raphael  and
      Avvaru, Pravalika  and
      Yin, Pengcheng  and
      Tomasic, Anthony  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1111",
    doi = "10.18653/v1/D18-1111",
    pages = "925--930",
    abstract = "In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce RECODE, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.",
}
@inproceedings{xu-etal-2018-sql,
    title = "{SQL}-to-Text Generation with Graph-to-Sequence Model",
    author = "Xu, Kun  and
      Wu, Lingfei  and
      Wang, Zhiguo  and
      Feng, Yansong  and
      Sheinin, Vadim",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1112",
    doi = "10.18653/v1/D18-1112",
    pages = "931--936",
    abstract = "Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we propose a graph-to-sequence model to encode the global structure information into node embeddings. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance.",
}
@inproceedings{colin-gardent-2018-generating,
    title = "Generating Syntactic Paraphrases",
    author = "Colin, Emilie  and
      Gardent, Claire",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1113",
    doi = "10.18653/v1/D18-1113",
    pages = "937--943",
    abstract = "We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, text-to-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.",
}
@inproceedings{rudinger-etal-2018-neural,
    title = "Neural-{D}avidsonian Semantic Proto-role Labeling",
    author = "Rudinger, Rachel  and
      Teichert, Adam  and
      Culkin, Ryan  and
      Zhang, Sheng  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1114",
    doi = "10.18653/v1/D18-1114",
    pages = "944--955",
    abstract = "We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call NeuralDavidsonian: predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.",
}
@inproceedings{bak-oh-2018-conversational,
    title = "Conversational Decision-Making Model for Predicting the King{'}s Decision in the Annals of the {J}oseon Dynasty",
    author = "Bak, JinYeong  and
      Oh, Alice",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1115",
    doi = "10.18653/v1/D18-1115",
    pages = "956--961",
    abstract = "Styles of leaders when they make decisions in groups vary, and the different styles affect the performance of the group. To understand the key words and speakers associated with decisions, we initially formalize the problem as one of predicting leaders{'} decisions from discussion with group members. As a dataset, we introduce conversational meeting records from a historical corpus, and develop a hierarchical RNN structure with attention and pre-trained speaker embedding in the form of a, Conversational Decision Making Model (CDMM). The CDMM outperforms other baselines to predict leaders{'} final decisions from the data. We explain why CDMM works better than other methods by showing the key words and speakers discovered from the attentions as evidence.",
}
@inproceedings{wang-etal-2018-toward,
    title = "Toward Fast and Accurate Neural Discourse Segmentation",
    author = "Wang, Yizhong  and
      Li, Sujian  and
      Yang, Jingfeng",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1116",
    doi = "10.18653/v1/D18-1116",
    pages = "962--967",
    abstract = "Discourse segmentation, which segments texts into Elementary Discourse Units, is a fundamental step in discourse analysis. Previous discourse segmenters rely on complicated hand-crafted features and are not practical in actual use. In this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF framework. To improve its accuracy, we address the problem of data insufficiency by transferring a word representation model that is trained on a large corpus. We also propose a restricted self-attention mechanism in order to capture useful information within a neighborhood. Experiments on the RST-DT corpus show that our model is significantly faster than previous methods, while achieving new state-of-the-art performance.",
}
@inproceedings{gella-etal-2018-dataset,
    title = "A Dataset for Telling the Stories of Social Media Videos",
    author = "Gella, Spandana  and
      Lewis, Mike  and
      Rohrbach, Marcus",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1117",
    doi = "10.18653/v1/D18-1117",
    pages = "968--974",
    abstract = "Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.",
}
@inproceedings{yao-etal-2018-cascaded,
    title = "Cascaded Mutual Modulation for Visual Reasoning",
    author = "Yao, Yiqun  and
      Xu, Jiaming  and
      Wang, Feng  and
      Xu, Bo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1118",
    doi = "10.18653/v1/D18-1118",
    pages = "975--980",
    abstract = "Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions. We propose CMM: Cascaded Mutual Modulation as a novel end-to-end visual reasoning model. CMM includes a multi-step comprehension process for both question and image. In each step, we use a Feature-wise Linear Modulation (FiLM) technique to enable textual/visual pipeline to mutually control each other. Experiments show that CMM significantly outperforms most related models, and reach state-of-the-arts on two visual reasoning benchmarks: CLEVR and NLVR, collected from both synthetic and natural languages. Ablation studies confirm the effectiveness of CMM to comprehend natural language logics under the guidence of images. Our code is available at \url{https://github.com/FlamingHorizon/CMM-VR}.",
}
@inproceedings{bouchacourt-baroni-2018-agents,
    title = "How agents see things: On visual representations in an emergent language game",
    author = "Bouchacourt, Diane  and
      Baroni, Marco",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1119",
    doi = "10.18653/v1/D18-1119",
    pages = "981--985",
    abstract = "There is growing interest in the language developed by agents interacting in emergent-communication settings. Earlier studies have focused on the agents{'} symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017), and investigate the representations the agents develop during their evolving interaction. We find that the agents establish successful communication by inducing visual representations that almost perfectly align with each other, but, surprisingly, do not capture the conceptual properties of the objects depicted in the input images. We conclude that, if we care about developing language-like communication systems, we must pay more attention to the visual semantics agents associate to the symbols they use.",
}
@inproceedings{zhang-etal-2018-attention,
    title = "Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction",
    author = "Zhang, Ningyu  and
      Deng, Shumin  and
      Sun, Zhanling  and
      Chen, Xi  and
      Zhang, Wei  and
      Chen, Huajun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1120",
    doi = "10.18653/v1/D18-1120",
    pages = "986--992",
    abstract = "A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.",
}
@inproceedings{xin-etal-2018-put,
    title = "Put It Back: Entity Typing with Language Model Enhancement",
    author = "Xin, Ji  and
      Zhu, Hao  and
      Han, Xu  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1121",
    doi = "10.18653/v1/D18-1121",
    pages = "993--998",
    abstract = "Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from \url{https://github.com/thunlp/LME}.",
}
@inproceedings{orr-etal-2018-event,
    title = "Event Detection with Neural Networks: A Rigorous Empirical Evaluation",
    author = "Orr, Walker  and
      Tadepalli, Prasad  and
      Fern, Xiaoli",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1122",
    doi = "10.18653/v1/D18-1122",
    pages = "999--1004",
    abstract = "Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally led the state-of-the-art, the differences in performance between different architectures have not been rigorously studied. In this paper we present a novel GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. We show that it is competitive with other neural network architectures through empirical evaluations under different random initializations and training-validation-test splits of ACE2005 dataset.",
}
@inproceedings{zhang-etal-2018-pubse,
    title = "{P}ub{SE}: A Hierarchical Model for Publication Extraction from Academic Homepages",
    author = "Zhang, Yiqing  and
      Qi, Jianzhong  and
      Zhang, Rui  and
      Yin, Chuandong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1123",
    doi = "10.18653/v1/D18-1123",
    pages = "1005--1010",
    abstract = "Publication information in a researcher{'}s academic homepage provides insights about the researcher{'}s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8{\%} in F1-score.",
}
@inproceedings{wang-etal-2018-neural-transition,
    title = "A Neural Transition-based Model for Nested Mention Recognition",
    author = "Wang, Bailin  and
      Lu, Wei  and
      Wang, Yu  and
      Jin, Hongxia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1124",
    doi = "10.18653/v1/D18-1124",
    pages = "1011--1017",
    abstract = "It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We first map a sentence with nested mentions to a designated forest where each mention corresponds to a constituent of the forest. Our shift-reduce based system then learns to construct the forest structure in a bottom-up manner through an action sequence whose maximal length is guaranteed to be three times of the sentence length. Based on Stack-LSTM which is employed to efficiently and effectively represent the states of the system in a continuous space, our system is further incorporated with a character-based component to capture letter-level patterns. Our model gets the state-of-the-art performances in ACE datasets, showing its effectiveness in detecting nested mentions.",
}
@inproceedings{shi-etal-2018-genre,
    title = "Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction",
    author = "Shi, Ge  and
      Feng, Chong  and
      Huang, Lifu  and
      Zhang, Boliang  and
      Ji, Heng  and
      Liao, Lejian  and
      Huang, Heyan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1125",
    doi = "10.18653/v1/D18-1125",
    pages = "1018--1023",
    abstract = "Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1{\%} absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7{\%} absolute F1 gain. We make all programs of our model publicly available for research purpose",
}
@inproceedings{mueller-durrett-2018-effective,
    title = "Effective Use of Context in Noisy Entity Linking",
    author = "Mueller, David  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1126",
    doi = "10.18653/v1/D18-1126",
    pages = "1024--1029",
    abstract = "To disambiguate between closely related concepts, entity linking systems need to effectively distill cues from their context, which may be quite noisy. We investigate several techniques for using these cues in the context of noisy entity linking on short texts. Our starting point is a state-of-the-art attention-based model from prior work; while this model{'}s attention typically identifies context that is topically relevant, it fails to identify some of the most indicative surface strings, especially those exhibiting lexical overlap with the true title. Augmenting the model with convolutional networks over characters still leaves it largely unable to pick up on these cues compared to sparse features that target them directly, indicating that automatically learning how to identify relevant character-level context features is a hard problem. Our final system outperforms past work on the WikilinksNED test set by 2.8{\%} absolute.",
}
@inproceedings{liu-etal-2018-exploiting-contextual,
    title = "Exploiting Contextual Information via Dynamic Memory Network for Event Detection",
    author = "Liu, Shaobo  and
      Cheng, Rui  and
      Yu, Xiaoming  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1127",
    doi = "10.18653/v1/D18-1127",
    pages = "1030--1035",
    abstract = "The task of event detection involves identifying and categorizing event triggers. Contextual information has been shown effective on the task. However, existing methods which utilize contextual information only process the context once. We argue that the context can be better exploited by processing the context multiple times, allowing the model to perform complex reasoning and to generate better context representation, thus improving the overall performance. Meanwhile, dynamic memory network (DMN) has demonstrated promising capability in capturing contextual information and has been applied successfully to various tasks. In light of the multi-hop mechanism of the DMN to model the context, we propose the trigger detection dynamic memory network (TD-DMN) to tackle the event detection problem. We performed a five-fold cross-validation on the ACE-2005 dataset and experimental results show that the multi-hop mechanism does improve the performance and the proposed model achieves best F1 score compared to the state-of-the-art methods.",
}
@inproceedings{chandrasekaran-etal-2018-explanations,
    title = "Do explanations make {VQA} models more predictable to a human?",
    author = "Chandrasekaran, Arjun  and
      Prabhu, Viraj  and
      Yadav, Deshraj  and
      Chattopadhyay, Prithvijit  and
      Parikh, Devi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1128",
    doi = "10.18653/v1/D18-1128",
    pages = "1036--1042",
    abstract = "A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable {`}explanations{'} of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model {---} its responses as well as failures {---} more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.",
}
@inproceedings{ponza-etal-2018-facts,
    title = "Facts That Matter",
    author = "Ponza, Marco  and
      Del Corro, Luciano  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1129",
    doi = "10.18653/v1/D18-1129",
    pages = "1043--1048",
    abstract = "This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. We also present SalIE, the first fact salience system. SalIE is unsupervised and knowledge agnostic, based on open information extraction to detect facts in natural language text, PageRank to determine their relevance, and clustering to promote diversity. We compare SalIE with several baselines (including positional, standard for saliency tasks), and in an extrinsic evaluation, with state-of-the-art automatic text summarizers. SalIE outperforms baselines and text summarizers showing that facts are an effective way to compress information.",
}
@inproceedings{hoang-etal-2018-entity,
    title = "Entity Tracking Improves Cloze-style Reading Comprehension",
    author = "Hoang, Luong  and
      Wiseman, Sam  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1130",
    doi = "10.18653/v1/D18-1130",
    pages = "1049--1055",
    abstract = "Recent work has improved on modeling for reading comprehension tasks with simple approaches such as the Attention Sum-Reader; however, automatic systems still significantly trail human performance. Analysis suggests that many of the remaining hard instances are related to the inability to track entity-references throughout documents. This work focuses on these hard entity tracking cases with two extensions: (1) additional entity features, and (2) training with a multi-task tracking objective. We show that these simple modifications improve performance both independently and in combination, and we outperform the previous state of the art on the LAMBADA dataset by 8 pts, particularly on difficult entity examples. We also effectively match the performance of more complicated models on the named entity portion of the CBT dataset.",
}
@inproceedings{shah-etal-2018-adversarial,
    title = "Adversarial Domain Adaptation for Duplicate Question Detection",
    author = "Shah, Darsh  and
      Lei, Tao  and
      Moschitti, Alessandro  and
      Romeo, Salvatore  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1131",
    doi = "10.18653/v1/D18-1131",
    pages = "1056--1063",
    abstract = "We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6{\%} over the best baseline across multiple pairs of domains.",
}
@inproceedings{wang-etal-2018-translating,
    title = "Translating a Math Word Problem to a Expression Tree",
    author = "Wang, Lei  and
      Wang, Yan  and
      Cai, Deng  and
      Zhang, Dongxiang  and
      Liu, Xiaojiang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1132",
    doi = "10.18653/v1/D18-1132",
    pages = "1064--1069",
    abstract = "Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.",
}
@inproceedings{nicosia-moschitti-2018-semantic,
    title = "Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection",
    author = "Nicosia, Massimo  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1133",
    doi = "10.18653/v1/D18-1133",
    pages = "1070--1076",
    abstract = "State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention. In this paper, instead of focusing on architecture engineering, we take advantage of small amounts of labelled data that model semantic phenomena in text to encode matching features directly in the word representations. This greatly boosts the accuracy of our reference network, while keeping the model simple and fast to train. Our approach also beats a tree kernel model that uses similar input encodings, and neural models which use advanced attention and compare-aggregate mechanisms.",
}
@inproceedings{elgohary-etal-2018-dataset,
    title = "A dataset and baselines for sequential open-domain question answering",
    author = "Elgohary, Ahmed  and
      Zhao, Chen  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1134",
    doi = "10.18653/v1/D18-1134",
    pages = "1077--1083",
    abstract = "Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at \url{http://sequential.qanta.org}.",
}
@inproceedings{ionescu-butnaru-2018-improving,
    title = "Improving the results of string kernels in sentiment analysis and {A}rabic dialect identification by adapting them to your test set",
    author = "Ionescu, Radu Tudor  and
      Butnaru, Andrei M.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1135",
    doi = "10.18653/v1/D18-1135",
    pages = "1084--1090",
    abstract = "Recently, string kernels have obtained state-of-the-art results in various text classification tasks such as Arabic dialect identification or native language identification. In this paper, we apply two simple yet effective transductive learning approaches to further improve the results of string kernels. The first approach is based on interpreting the pairwise string kernel similarities between samples in the training set and samples in the test set as features. Our second approach is a simple self-training method based on two learning iterations. In the first iteration, a classifier is trained on the training set and tested on the test set, as usual. In the second iteration, a number of test samples (to which the classifier associated higher confidence scores) are added to the training set for another round of training. However, the ground-truth labels of the added test samples are not necessary. Instead, we use the labels predicted by the classifier in the first training iteration. By adapting string kernels to the test set, we report significantly better accuracy rates in English polarity classification and Arabic dialect identification.",
}
@inproceedings{huang-carley-2018-parameterized,
    title = "Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification",
    author = "Huang, Binxuan  and
      Carley, Kathleen",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1136",
    doi = "10.18653/v1/D18-1136",
    pages = "1091--1096",
    abstract = "We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspect-specific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.",
}
@inproceedings{yu-etal-2018-improving,
    title = "Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network",
    author = "Yu, Jianfei  and
      Marujo, Lu{\'\i}s  and
      Jiang, Jing  and
      Karuturi, Pradeep  and
      Brendel, William",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1137",
    doi = "10.18653/v1/D18-1137",
    pages = "1097--1102",
    abstract = "In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.",
}
@inproceedings{zhang-etal-2018-learning-sentiment,
    title = "Learning Sentiment Memories for Sentiment Modification without Parallel Data",
    author = "Zhang, Yi  and
      Xu, Jingjing  and
      Yang, Pengcheng  and
      Sun, Xu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1138",
    doi = "10.18653/v1/D18-1138",
    pages = "1103--1108",
    abstract = "The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content but different sentiments are usually unavailable. Due to the lack of such parallel data, it is hard to extract sentiment independent content and reverse the sentiment in an unsupervised way. Previous work usually can not reconcile sentiment transformation and content preservation. In this paper, motivated by the fact the non-emotional context (e.g., {``}staff{''}) provides strong cues for the occurrence of emotional words (e.g., {``}friendly{''}), we propose a novel method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. Experiments show that our method substantially improves the content preservation degree and achieves the state-of-the-art performance.",
}
@inproceedings{schmitt-etal-2018-joint,
    title = "Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks",
    author = "Schmitt, Martin  and
      Steinheber, Simon  and
      Schreiber, Konrad  and
      Roth, Benjamin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1139",
    doi = "10.18653/v1/D18-1139",
    pages = "1109--1114",
    abstract = "In this work, we propose a new model for aspect-based sentiment analysis. In contrast to previous approaches, we jointly model the detection of aspects and the classification of their polarity in an end-to-end trainable neural network. We conduct experiments with different neural architectures and word representations on the recent GermEval 2017 dataset. We were able to show considerable performance gains by using the joint modeling approach in all settings compared to pipeline approaches. The combination of a convolutional neural network and fasttext embeddings outperformed the best submission of the shared task in 2017, establishing a new state of the art.",
}
@inproceedings{kolchinski-potts-2018-representing,
    title = "Representing Social Media Users for Sarcasm Detection",
    author = "Kolchinski, Y. Alex  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1140",
    doi = "10.18653/v1/D18-1140",
    pages = "1115--1121",
    abstract = "We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors{'} propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.",
}
@inproceedings{verma-etal-2018-syntactical,
    title = "Syntactical Analysis of the Weaknesses of Sentiment Analyzers",
    author = "Verma, Rohil  and
      Kim, Samuel  and
      Walter, David",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1141",
    doi = "10.18653/v1/D18-1141",
    pages = "1122--1127",
    abstract = "We carry out a syntactic analysis of two state-of-the-art sentiment analyzers, Google Cloud Natural Language and Stanford CoreNLP, to assess their classification accuracy on sentences with negative polarity items. We were motivated by the absence of studies investigating sentiment analyzer performance on sentences with polarity items, a common construct in human language. Our analysis focuses on two sentential structures: downward entailment and non-monotone quantifiers; and demonstrates weaknesses of Google Natural Language and CoreNLP in capturing polarity item information. We describe the particular syntactic phenomenon that these analyzers fail to understand that any ideal sentiment analyzer must. We also provide a set of 150 test sentences that any ideal sentiment analyzer must be able to understand.",
}
@inproceedings{moorthy-etal-2018-nike,
    title = "Is {N}ike female? Exploring the role of sound symbolism in predicting brand name gender",
    author = "Moorthy, Sridhar  and
      Pogacar, Ruth  and
      Khan, Samin  and
      Xu, Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1142",
    doi = "10.18653/v1/D18-1142",
    pages = "1128--1132",
    abstract = "Are brand names such as Nike female or male? Previous research suggests that the sound of a person{'}s first name is associated with the person{'}s gender, but no research has tried to use this knowledge to assess the gender of brand names. We present a simple computational approach that uses sound symbolism to address this open issue. Consistent with previous research, a model trained on various linguistic features of name endings predicts human gender with high accuracy. Applying this model to a data set of over a thousand commercially-traded brands in 17 product categories, our results reveal an overall bias toward male names, cutting across both male-oriented product categories as well as female-oriented categories. In addition, we find variation within categories, suggesting that firms might be seeking to imbue their brands with differentiating characteristics as part of their competitive strategy.",
}
@inproceedings{lee-etal-2018-improving,
    title = "Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging",
    author = "Lee, Nayeon  and
      Wu, Chien-Sheng  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1143",
    doi = "10.18653/v1/D18-1143",
    pages = "1133--1138",
    abstract = "Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80{\%}, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.",
}
@inproceedings{kano-etal-2018-harnessing,
    title = "Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations",
    author = "Kano, Ryuji  and
      Miura, Yasuhide  and
      Taniguchi, Motoki  and
      Chen, Yan-Ying  and
      Chen, Francine  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1144",
    doi = "10.18653/v1/D18-1144",
    pages = "1139--1145",
    abstract = "We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.",
}
@inproceedings{rouhizadeh-etal-2018-identifying,
    title = "Identifying Locus of Control in Social Media Language",
    author = "Rouhizadeh, Masoud  and
      Jaidka, Kokil  and
      Smith, Laura  and
      Schwartz, H. Andrew  and
      Buffone, Anneke  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1145",
    doi = "10.18653/v1/D18-1145",
    pages = "1146--1152",
    abstract = "Individuals express their locus of control, or {``}control{''}, in their language when they identify whether or not they are in control of their circumstances. Although control is a core concept underlying rhetorical style, it is not clear whether control is expressed by how or by what authors write. We explore the roles of syntax and semantics in expressing users{'} sense of control {--}i.e. being {``}controlled by{''} or {``}in control of{''} their circumstances{--} in a corpus of annotated Facebook posts. We present rich insights into these linguistic aspects and find that while the language signaling control is easy to identify, it is more challenging to label it is internally or externally controlled, with lexical features outperforming syntactic features at the task. Our findings could have important implications for studying self-expression in social media.",
}
@inproceedings{hu-2018-somm,
    title = "{S}omm: Into the Model",
    author = "Hu, Shengli",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1146",
    doi = "10.18653/v1/D18-1146",
    pages = "1153--1159",
    abstract = "To what extent could the sommelier profession, or wine stewardship, be displaced by machine leaning algorithms? There are at least three essential skills that make a qualified sommelier: wine theory, blind tasting, and beverage service, as exemplified in the rigorous certification processes of certified sommeliers and above (advanced and master) with the most authoritative body in the industry, the Court of Master Sommelier (hereafter CMS). We propose and train corresponding machine learning models that match these skills, and compare algorithmic results with real data collected from a large group of wine professionals. We find that our machine learning models outperform human sommeliers on most tasks {---} most notably in the section of blind tasting, where hierarchically supervised Latent Dirichlet Allocation outperforms sommeliers{'} judgment calls by over 6{\%} in terms of F1-score; and in the section of beverage service, especially wine and food pairing, a modified Siamese neural network based on BiLSTM achieves better results than sommeliers by 2{\%}. This demonstrates, contrary to popular opinion in the industry, that the sommelier profession is at least to some extent automatable, barring economic (Kleinberg et al., 2017) and psychological (Dietvorst et al., 2015) complications.",
}
@inproceedings{khanpour-caragea-2018-fine,
    title = "Fine-Grained Emotion Detection in Health-Related Online Posts",
    author = "Khanpour, Hamed  and
      Caragea, Cornelia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1147",
    doi = "10.18653/v1/D18-1147",
    pages = "1160--1166",
    abstract = "Detecting fine-grained emotions in online health communities provides insightful information about patients{'} emotional states. However, current computational approaches to emotion detection from health-related posts focus only on identifying messages that contain emotions, with no emphasis on the emotion type, using a set of handcrafted features. In this paper, we take a step further and propose to detect fine-grained emotion types from health-related posts and show how high-level and abstract features derived from deep neural networks combined with lexicon-based features can be employed to detect emotions.",
}
@inproceedings{giorgi-etal-2018-remarkable,
    title = "The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions",
    author = "Giorgi, Salvatore  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Buffone, Anneke  and
      Rieman, Daniel  and
      Ungar, Lyle  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1148",
    doi = "10.18653/v1/D18-1148",
    pages = "1167--1172",
    abstract = "Nowcasting based on social media text promises to provide unobtrusive and near real-time predictions of community-level outcomes. These outcomes are typically regarding people, but the data is often aggregated without regard to users in the Twitter populations of each community. This paper describes a simple yet effective method for building community-level models using Twitter language aggregated by user. Results on four different U.S. county-level tasks, spanning demographic, health, and psychological outcomes show large and consistent improvements in prediction accuracies (e.g. from Pearson r=.73 to .82 for median income prediction or r=.37 to .47 for life satisfaction prediction) over the standard approach of aggregating all tweets. We make our aggregated and anonymized community-level data, derived from 37 billion tweets {--} over 1 billion of which were mapped to counties, available for research.",
}
@inproceedings{lee-etal-2018-deterministic,
    title = "Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement",
    author = "Lee, Jason  and
      Mansimov, Elman  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1149",
    doi = "10.18653/v1/D18-1149",
    pages = "1173--1182",
    abstract = "We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine translation (En-De and En-Ro) and image caption generation, and observe that it significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart.",
}
@inproceedings{huang-etal-2018-large,
    title = "Large Margin Neural Language Model",
    author = "Huang, Jiaji  and
      Li, Yi  and
      Ping, Wei  and
      Huang, Liang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1150",
    doi = "10.18653/v1/D18-1150",
    pages = "1183--1191",
    abstract = "We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. However, we demonstrate that PPL may not be the best metric to optimize in some tasks, and further propose a large margin formulation. The proposed method aims to enlarge the margin between the {``}good{''} and {``}bad{''} sentences in a task-specific sense. It is trained end-to-end and can be widely applied to tasks that involve re-scoring of generated text. Compared with minimum-PPL training, our method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation.",
}
@inproceedings{marvin-linzen-2018-targeted,
    title = "Targeted Syntactic Evaluation of Language Models",
    author = "Marvin, Rebecca  and
      Linzen, Tal",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1151",
    doi = "10.18653/v1/D18-1151",
    pages = "1192--1202",
    abstract = "We present a data set for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence. The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items. We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one. In an experiment using this data set, an LSTM language model performed poorly on many of the constructions. Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM{'}s accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online. This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.",
}
@inproceedings{peng-etal-2018-rational,
    title = "Rational Recurrences",
    author = "Peng, Hao  and
      Schwartz, Roy  and
      Thomson, Sam  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1152",
    doi = "10.18653/v1/D18-1152",
    pages = "1203--1214",
    abstract = "Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches. Recently, connections have been shown between convolutional neural networks (CNNs) and weighted finite state automata (WFSAs), leading to new interpretations and insights. In this work, we show that some recurrent neural networks also share this connection to WFSAs. We characterize this connection formally, defining rational recurrences to be recurrent hidden state update functions that can be written as the Forward calculation of a finite set of WFSAs. We show that several recent neural models use rational recurrences. Our analysis provides a fresh view of these models and facilitates devising new neural architectures that draw inspiration from WFSAs. We present one such model, which performs better than two recent baselines on language modeling and text classification. Our results demonstrate that transferring intuitions from classical models like WFSAs can be an effective approach to designing and understanding neural models.",
}
@inproceedings{liu-etal-2018-efficient-contextualized,
    title = "Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling",
    author = "Liu, Liyuan  and
      Ren, Xiang  and
      Shang, Jingbo  and
      Gu, Xiaotao  and
      Peng, Jian  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1153",
    doi = "10.18653/v1/D18-1153",
    pages = "1215--1225",
    abstract = "Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture linguistic information of multifarious levels, large-size LMs are required; but for a specific task, only parts of these information are useful. Such large-sized LMs, even in the inference stage, may cause heavy computation workloads, making them too time-consuming for large-scale applications. Here we propose to compress bulky LMs while preserving useful information with regard to a specific task. As different layers of the model keep different information, we develop a layer selection method for model pruning using sparsity-inducing regularization. By introducing the dense connectivity, we can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. In model training, LMs are learned with layer-wise dropouts for better robustness. Experiments on two benchmark datasets demonstrate the effectiveness of our method.",
}
@inproceedings{liu-etal-2018-automatic-event,
    title = "Automatic Event Salience Identification",
    author = "Liu, Zhengzhong  and
      Xiong, Chenyan  and
      Mitamura, Teruko  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1154",
    doi = "10.18653/v1/D18-1154",
    pages = "1226--1236",
    abstract = "Identifying the salience (i.e. importance) of discourse units is an important task in language understanding. While events play important roles in text documents, little research exists on analyzing their saliency status. This paper empirically studies Event Salience and proposes two salience detection models based on discourse relations. The first is a feature based salience model that incorporates cohesion among discourse units. The second is a neural model that captures more complex interactions between discourse units. In our new large-scale event salience corpus, both methods significantly outperform the strong frequency baseline, while our neural model further improves the feature based one by a large margin. Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e.g., scripts and frame structures).",
}
@inproceedings{leeuwenberg-moens-2018-temporal,
    title = "Temporal Information Extraction by Predicting Relative Time-lines",
    author = "Leeuwenberg, Artuur  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1155",
    doi = "10.18653/v1/D18-1155",
    pages = "1237--1246",
    abstract = "The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.",
}
@inproceedings{liu-etal-2018-jointly,
    title = "Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation",
    author = "Liu, Xiao  and
      Luo, Zhunchen  and
      Huang, Heyan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1156",
    doi = "10.18653/v1/D18-1156",
    pages = "1247--1256",
    abstract = "Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.",
}
@inproceedings{vashishth-etal-2018-reside,
    title = "{RESIDE}: Improving Distantly-Supervised Neural Relation Extraction using Side Information",
    author = "Vashishth, Shikhar  and
      Joshi, Rishabh  and
      Prayaga, Sai Suman  and
      Bhattacharyya, Chiranjib  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1157",
    doi = "10.18653/v1/D18-1157",
    pages = "1257--1266",
    abstract = "Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany). RE models usually ignore such readily available side information. In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available. Through extensive experiments on benchmark datasets, we demonstrate RESIDE{'}s effectiveness. We have made RESIDE{'}s source code available to encourage reproducible research.",
}
@inproceedings{chen-etal-2018-collective,
    title = "Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms",
    author = "Chen, Yubo  and
      Yang, Hang  and
      Liu, Kang  and
      Zhao, Jun  and
      Jia, Yantao",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1158",
    doi = "10.18653/v1/D18-1158",
    pages = "1267--1276",
    abstract = "Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierachical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.",
}
@inproceedings{shi-lee-2018-valency,
    title = "Valency-Augmented Dependency Parsing",
    author = "Shi, Tianze  and
      Lee, Lillian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1159",
    doi = "10.18653/v1/D18-1159",
    pages = "1277--1291",
    abstract = "We present a complete, automated, and efficient approach for utilizing valency analysis in making dependency parsing decisions. It includes extraction of valency patterns, a probabilistic model for tagging these patterns, and a joint decoding process that explicitly considers the number and types of each token{'}s syntactic dependents. On 53 treebanks representing 41 languages in the Universal Dependencies data, we find that incorporating valency information yields higher precision and F1 scores on the core arguments (subjects and complements) and functional relations (e.g., auxiliaries) that we employ for valency analysis. Precision on core arguments improves from 80.87 to 85.43. We further show that our approach can be applied to an ostensibly different formalism and dataset, Tree Adjoining Grammar as extracted from the Penn Treebank; there, we outperform the previous state-of-the-art labeled attachment score by 0.7. Finally, we explore the potential of extending valency patterns beyond their traditional domain by confirming their helpfulness in improving PP attachment decisions.",
}
@inproceedings{he-etal-2018-unsupervised,
    title = "Unsupervised Learning of Syntactic Structure with Invertible Neural Projections",
    author = "He, Junxian  and
      Neubig, Graham  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1160",
    doi = "10.18653/v1/D18-1160",
    pages = "1292--1302",
    abstract = "Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so long as the prior is well-behaved. In experiments we instantiate our approach with both Markov and tree-structured priors, evaluating on two tasks: part-of-speech (POS) induction, and unsupervised dependency parsing without gold POS annotation. On the Penn Treebank, our Markov-structured model surpasses state-of-the-art results on POS induction. Similarly, we find that our tree-structured model achieves state-of-the-art performance on unsupervised dependency parsing for the difficult training condition where neither gold POS annotation nor punctuation-based constraints are available.",
}
@inproceedings{fernandez-gonzalez-gomez-rodriguez-2018-dynamic,
    title = "Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1161",
    doi = "10.18653/v1/D18-1161",
    pages = "1303--1313",
    abstract = "We introduce novel dynamic oracles for training two of the most accurate known shift-reduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.",
}
@inproceedings{gomez-rodriguez-vilares-2018-constituent,
    title = "Constituent Parsing as Sequence Labeling",
    author = "G{\'o}mez-Rodr{\'\i}guez, Carlos  and
      Vilares, David",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1162",
    doi = "10.18653/v1/D18-1162",
    pages = "1314--1324",
    abstract = "We introduce a method to reduce constituent parsing to sequence labeling. For each word $w_t$, it generates a label that encodes: (1) the number of ancestors in the tree that the words $w_t$ and $w_{t+1}$ have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90{\%} F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.",
}
@inproceedings{wang-eisner-2018-synthetic,
    title = "Synthetic Data Made to Order: The Case of Parsing",
    author = "Wang, Dingquan  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1163",
    doi = "10.18653/v1/D18-1163",
    pages = "1325--1337",
    abstract = "To approximately parse an unfamiliar language, it helps to have a treebank of a similar language. But what if the closest available treebank still has the wrong word order? We show how to (stochastically) permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. The parameters of the permutation model can be evaluated for quality by dynamic programming and tuned by gradient descent (up to a local optimum). This optimization procedure yields trees for a new artificial language that resembles the target language. We show that delexicalized parsers for the target language can be successfully trained using such {``}made to order{''} artificial languages.",
}
@inproceedings{li-etal-2018-tell,
    title = "Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions",
    author = "Li, Qing  and
      Fu, Jianlong  and
      Yu, Dongfei  and
      Mei, Tao  and
      Luo, Jiebo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1164",
    doi = "10.18653/v1/D18-1164",
    pages = "1338--1346",
    abstract = "In Visual Question Answering, most existing approaches adopt the pipeline of representing an image via pre-trained CNNs, and then using the uninterpretable CNN features in conjunction with the question to predict the answer. Although such end-to-end models might report promising performance, they rarely provide any insight, apart from the answer, into the VQA process. In this work, we propose to break up the end-to-end VQA into two steps: explaining and reasoning, in an attempt towards a more explainable VQA by shedding light on the intermediate results between these two steps. To that end, we first extract attributes and generate descriptions as explanations for an image. Next, a reasoning module utilizes these explanations in place of the image to infer an answer. The advantages of such a breakdown include: (1) the attributes and captions can reflect what the system extracts from the image, thus can provide some insights for the predicted answer; (2) these intermediate results can help identify the inabilities of the image understanding or the answer inference part when the predicted answer is wrong. We conduct extensive experiments on a popular VQA dataset and our system achieves comparable performance with the baselines, yet with added benefits of explanability and the inherent ability to further improve with higher quality explanations.",
}
@inproceedings{padmakumar-etal-2018-learning,
    title = "Learning a Policy for Opportunistic Active Learning",
    author = "Padmakumar, Aishwarya  and
      Stone, Peter  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1165",
    doi = "10.18653/v1/D18-1165",
    pages = "1347--1357",
    abstract = "Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.",
}
@inproceedings{yagcioglu-etal-2018-recipeqa,
    title = "{R}ecipe{QA}: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes",
    author = "Yagcioglu, Semih  and
      Erdem, Aykut  and
      Erdem, Erkut  and
      Ikizler-Cinbis, Nazli",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1166",
    doi = "10.18653/v1/D18-1166",
    pages = "1358--1368",
    abstract = "Understanding and reasoning about cooking recipes is a fruitful research direction towards enabling machines to interpret procedural text. In this work, we introduce RecipeQA, a dataset for multimodal comprehension of cooking recipes. It comprises of approximately 20K instructional recipes with multiple modalities such as titles, descriptions and aligned set of images. With over 36K automatically generated question-answer pairs, we design a set of comprehension and reasoning tasks that require joint understanding of images and text, capturing the temporal flow of events and making sense of procedural knowledge. Our preliminary results indicate that RecipeQA will serve as a challenging test bed and an ideal benchmark for evaluating machine comprehension systems. The data and leaderboard are available at \url{http://hucvl.github.io/recipeqa}.",
}
@inproceedings{lei-etal-2018-tvqa,
    title = "{TVQA}: Localized, Compositional Video Question Answering",
    author = "Lei, Jie  and
      Yu, Licheng  and
      Bansal, Mohit  and
      Berg, Tamara",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1167",
    doi = "10.18653/v1/D18-1167",
    pages = "1369--1379",
    abstract = "Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA. In this paper, we present TVQA, a large-scale video QA dataset based on 6 popular TV shows. TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video. Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts. We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task. The dataset is publicly available at \url{http://tvqa.cs.unc.edu}.",
}
@inproceedings{hendricks-etal-2018-localizing,
    title = "Localizing Moments in Video with Temporal Language",
    author = "Hendricks, Lisa Anne  and
      Wang, Oliver  and
      Shechtman, Eli  and
      Sivic, Josef  and
      Darrell, Trevor  and
      Russell, Bryan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1168",
    doi = "10.18653/v1/D18-1168",
    pages = "1380--1390",
    abstract = "Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language).",
}
@inproceedings{pilehvar-etal-2018-card,
    title = "Card-660: {C}ambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models",
    author = "Pilehvar, Mohammad Taher  and
      Kartsaklis, Dimitri  and
      Prokhorov, Victor  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1169",
    doi = "10.18653/v1/D18-1169",
    pages = "1391--1401",
    abstract = "Rare word representation has recently enjoyed a surge of interest, owing to the crucial role that effective handling of infrequent words can play in accurate semantic understanding. However, there is a paucity of reliable benchmarks for evaluation and comparison of these techniques. We show in this paper that the only existing benchmark (the Stanford Rare Word dataset) suffers from low-confidence annotations and limited vocabulary; hence, it does not constitute a solid comparison framework. In order to fill this evaluation gap, we propose Cambridge Rare word Dataset (Card-660), an expert-annotated word similarity dataset which provides a highly reliable, yet challenging, benchmark for rare word representation techniques. Through a set of experiments we show that even the best mainstream word embeddings, with millions of words in their vocabularies, are unable to achieve performances higher than 0.43 (Pearson correlation) on the dataset, compared to a human-level upperbound of 0.90. We release the dataset and the annotation materials at \url{https://pilehvar.github.io/card-660/}.",
}
@inproceedings{luo-etal-2018-leveraging,
    title = "Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention",
    author = "Luo, Fuli  and
      Liu, Tianyu  and
      He, Zexue  and
      Xia, Qiaolin  and
      Sui, Zhifang  and
      Chang, Baobao",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1170",
    doi = "10.18653/v1/D18-1170",
    pages = "1402--1411",
    abstract = "The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context. Traditional supervised methods only use labeled data (context), while missing rich lexical knowledge such as the gloss which defines the meaning of a word sense. Recent studies have shown that incorporating glosses into neural networks for WSD has made significant improvement. However, the previous models usually build the context representation and gloss representation separately. In this paper, we find that the learning for the context and gloss representation can benefit from each other. Gloss can help to highlight the important words in the context, thus building a better context representation. Context can also help to locate the key words in the gloss of the correct word sense. Therefore, we introduce a co-attention mechanism to generate co-dependent representations for the context and gloss. Furthermore, in order to capture both word-level and sentence-level information, we extend the attention mechanism in a hierarchical fashion. Experimental results show that our model achieves the state-of-the-art results on several standard English all-words WSD test datasets.",
}
@inproceedings{do-dinh-etal-2018-weeding,
    title = "Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations",
    author = "Do Dinh, Erik-L{\^a}n  and
      Wieland, Hannah  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1171",
    doi = "10.18653/v1/D18-1171",
    pages = "1412--1424",
    abstract = "We encounter metaphors every day, but only a few jump out on us and make us stumble. However, little effort has been devoted to investigating more novel metaphors in comparison to general metaphor detection efforts. We attribute this gap primarily to the lack of larger datasets that distinguish between conventionalized, i.e., very common, and novel metaphors. The goal of this paper is to alleviate this situation by introducing a crowdsourced novel metaphor annotation layer for an existing metaphor corpus. Further, we analyze our corpus and investigate correlations between novelty and features that are typically used in metaphor detection, such as concreteness ratings and more semantic features like the Potential for Metaphoricity. Finally, we present a baseline approach to assess novelty in metaphors based on our annotations.",
}
@inproceedings{gornerup-gillblad-2018-streaming,
    title = "Streaming word similarity mining on the cheap",
    author = {G{\"o}rnerup, Olof  and
      Gillblad, Daniel},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1172",
    doi = "10.18653/v1/D18-1172",
    pages = "1425--1434",
    abstract = "Accurately and efficiently estimating word similarities from text is fundamental in natural language processing. In this paper, we propose a fast and lightweight method for estimating similarities from streams by explicitly counting second-order co-occurrences. The method rests on the observation that words that are highly correlated with respect to such counts are also highly similar with respect to first-order co-occurrences. Using buffers of co-occurred words per word to count second-order co-occurrences, we can then estimate similarities in a single pass over data without having to do prohibitively expensive similarity calculations. We demonstrate that this approach is scalable, converges rapidly, behaves robustly under parameter changes, and that it captures word similarities on par with those given by state-of-the-art word embeddings.",
}
@inproceedings{sun-etal-2018-memory,
    title = "Memory, Show the Way: Memory Based Few Shot Word Representation Learning",
    author = "Sun, Jingyuan  and
      Wang, Shaonan  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1173",
    doi = "10.18653/v1/D18-1173",
    pages = "1435--1444",
    abstract = "Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation. This is in stark contrast with human who can guess the meaning of a word from one or a few referents only. In this paper, we propose Mem2Vec, a memory based embedding learning method capable of acquiring high quality word representations from fairly limited context. Our method directly adapts the representations produced by a DSM with a longterm memory to guide its guess of a novel word. Based on a pre-trained embedding space, the proposed method delivers impressive performance on two challenging few-shot word similarity tasks. Embeddings learned with our method also lead to considerable improvements over strong baselines on NER and sentiment classification.",
}
@inproceedings{grzegorczyk-kurdziel-2018-disambiguated,
    title = "Disambiguated skip-gram model",
    author = "Grzegorczyk, Karol  and
      Kurdziel, Marcin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1174",
    doi = "10.18653/v1/D18-1174",
    pages = "1445--1454",
    abstract = "We present disambiguated skip-gram: a neural-probabilistic model for learning multi-sense distributed representations of words. Disambiguated skip-gram jointly estimates a skip-gram-like context word prediction model and a word sense disambiguation model. Unlike previous probabilistic models for learning multi-sense word embeddings, disambiguated skip-gram is end-to-end differentiable and can be interpreted as a simple feed-forward neural network. We also introduce an effective pruning strategy for the embeddings learned by disambiguated skip-gram. This allows us to control the granularity of representations learned by our model. In experimental evaluation disambiguated skip-gram improves state-of-the are results in several word sense induction benchmarks.",
}
@inproceedings{wang-etal-2018-picking,
    title = "Picking Apart Story Salads",
    author = "Wang, Su  and
      Holgate, Eric  and
      Durrett, Greg  and
      Erk, Katrin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1175",
    doi = "10.18653/v1/D18-1175",
    pages = "1455--1465",
    abstract = "During natural disasters and conflicts, information about what happened is often confusing and messy, and distributed across many sources. We would like to be able to automatically identify relevant information and assemble it into coherent narratives of what happened. To make this task accessible to neural models, we introduce \textit{Story Salads}, mixtures of multiple documents that can be generated at scale. By exploiting the Wikipedia hierarchy, we can generate salads that exhibit challenging inference problems. Story salads give rise to a novel, challenging clustering task, where the objective is to group sentences from the same narratives. We demonstrate that simple bag-of-words similarity clustering falls short on this task, and that it is necessary to take into account global context and coherence.",
}
@inproceedings{kiela-etal-2018-dynamic,
    title = "Dynamic Meta-Embeddings for Improved Sentence Representations",
    author = "Kiela, Douwe  and
      Wang, Changhan  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1176",
    doi = "10.18653/v1/D18-1176",
    pages = "1466--1477",
    abstract = "While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce dynamic meta-embeddings, a simple yet effective method for the supervised learning of embedding ensembles, which leads to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new light on the usage of word embeddings in NLP systems.",
}
@inproceedings{ailem-etal-2018-probabilistic,
    title = "A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images",
    author = "Ailem, Melissa  and
      Zhang, Bowen  and
      Bellet, Aurelien  and
      Denis, Pascal  and
      Sha, Fei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1177",
    doi = "10.18653/v1/D18-1177",
    pages = "1478--1487",
    abstract = "Several recent studies have shown the benefits of combining language and perception to infer word embeddings. These multimodal approaches either simply combine pre-trained textual and visual representations (e.g. features extracted from convolutional neural networks), or use the latter to bias the learning of textual word embeddings. In this work, we propose a novel probabilistic model to formalize how linguistic and perceptual inputs can work in concert to explain the observed word-context pairs in a text corpus. Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data. Extensive experimental studies validate the proposed model. Concretely, on the tasks of assessing pairwise word similarity and image/caption retrieval, our approach attains equally competitive or stronger results when compared to other state-of-the-art multimodal models.",
}
@inproceedings{fares-etal-2018-transfer,
    title = "Transfer and Multi-Task Learning for Noun{--}Noun Compound Interpretation",
    author = "Fares, Murhaf  and
      Oepen, Stephan  and
      Velldal, Erik",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1178",
    doi = "10.18653/v1/D18-1178",
    pages = "1488--1498",
    abstract = "In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun{--}noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.",
}
@inproceedings{peters-etal-2018-dissecting,
    title = "Dissecting Contextual Word Embeddings: Architecture and Representation",
    author = "Peters, Matthew  and
      Neumann, Mark  and
      Zettlemoyer, Luke  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1179",
    doi = "10.18653/v1/D18-1179",
    pages = "1499--1509",
    abstract = "Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated.",
}
@inproceedings{gong-etal-2018-preposition,
    title = "Preposition Sense Disambiguation and Representation",
    author = "Gong, Hongyu  and
      Mu, Jiaqi  and
      Bhat, Suma  and
      Viswanath, Pramod",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1180",
    doi = "10.18653/v1/D18-1180",
    pages = "1510--1521",
    abstract = "Prepositions are highly polysemous, and their variegated senses encode significant semantic information. In this paper we match each preposition{'}s left- and right context, and their interplay to the geometry of the word vectors to the left and right of the preposition. Extracting these features from a large corpus and using them with machine learning models makes for an efficient preposition sense disambiguation (PSD) algorithm, which is comparable to and better than state-of-the-art on two benchmark datasets. Our reliance on no linguistic tool allows us to scale the PSD algorithm to a large corpus and learn sense-specific preposition representations. The crucial abstraction of preposition senses as word representations permits their use in downstream applications{--}phrasal verb paraphrasing and preposition selection{--}with new state-of-the-art results.",
}
@inproceedings{bosc-vincent-2018-auto,
    title = "Auto-Encoding Dictionary Definitions into Consistent Word Embeddings",
    author = "Bosc, Tom  and
      Vincent, Pascal",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1181",
    doi = "10.18653/v1/D18-1181",
    pages = "1522--1532",
    abstract = "Monolingual dictionaries are widespread and semantically rich resources. This paper presents a simple model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. It exploits the inherent recursivity of dictionaries by encouraging consistency between the representations it uses as inputs and the representations it produces as outputs. The resulting embeddings are shown to capture semantic similarity better than regular distributional methods and other dictionary-based methods. In addition, our method shows strong performance when trained exclusively on dictionary data and generalizes in one shot.",
}
@inproceedings{stanovsky-hopkins-2018-spot,
    title = "Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources",
    author = "Stanovsky, Gabriel  and
      Hopkins, Mark",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1182",
    doi = "10.18653/v1/D18-1182",
    pages = "1533--1542",
    abstract = "We propose Odd-Man-Out, a novel task which aims to test different properties of word representations. An Odd-Man-Out puzzle is composed of 5 (or more) words, and requires the system to choose the one which does not belong with the others. We show that this simple setup is capable of teasing out various properties of different popular lexical resources (like WordNet and pre-trained word embeddings), while being intuitive enough to annotate on a large scale. In addition, we propose a novel technique for training multi-prototype word representations, based on unsupervised clustering of ELMo embeddings, and show that it surpasses all other representations on all Odd-Man-Out collections.",
}
@inproceedings{liu-etal-2018-neural,
    title = "Neural Multitask Learning for Simile Recognition",
    author = "Liu, Lizhen  and
      Hu, Xiao  and
      Song, Wei  and
      Fu, Ruiji  and
      Liu, Ting  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1183",
    doi = "10.18653/v1/D18-1183",
    pages = "1543--1553",
    abstract = "Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e., the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule-based and feature-based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.",
}
@inproceedings{liu-etal-2018-structured,
    title = "Structured Alignment Networks for Matching Sentences",
    author = "Liu, Yang  and
      Gardner, Matt  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1184",
    doi = "10.18653/v1/D18-1184",
    pages = "1554--1564",
    abstract = "Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence. When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors. We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures. Using a structured attention mechanism, our model matches candidate spans in the first sentence to candidate spans in the second sentence, simultaneously discovering the tree structure of each sentence. Our model is fully differentiable and trained only on the matching objective. We evaluate this model on two tasks, natural entailment detection and answer sentence selection, and find that modeling latent tree structures results in superior performance. Analysis of the learned sentence structures shows they can reflect some syntactic phenomena.",
}
@inproceedings{tay-etal-2018-compare,
    title = "Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference",
    author = "Tay, Yi  and
      Luu, Anh Tuan  and
      Hui, Siu Cheung",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1185",
    doi = "10.18653/v1/D18-1185",
    pages = "1565--1575",
    abstract = "This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. Secondly, we adopt factorization layers for efficient and expressive compression of alignment vectors into scalar features, which are then used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving competitive performance on all. A lightweight parameterization of our model also enjoys a 3 times reduction in parameter size compared to the existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining competitive performance. Additionally, visual analysis shows that our propagated features are highly interpretable.",
}
@inproceedings{gong-etal-2018-convolutional,
    title = "Convolutional Interaction Network for Natural Language Inference",
    author = "Gong, Jingjing  and
      Qiu, Xipeng  and
      Chen, Xinchi  and
      Liang, Dong  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1186",
    doi = "10.18653/v1/D18-1186",
    pages = "1576--1585",
    abstract = "Attention-based neural models have achieved great success in natural language inference (NLI). In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. Specifically, CIN encodes one sentence with the filters dynamically generated based on another sentence. Since the filters may be designed to have various numbers and sizes, CIN can capture more complicated interaction patterns. Experiments on three large datasets demonstrate CIN{'}s efficacy.",
}
@inproceedings{romanov-shivade-2018-lessons,
    title = "Lessons from Natural Language Inference in the Clinical Domain",
    author = "Romanov, Alexey  and
      Shivade, Chaitanya",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1187",
    doi = "10.18653/v1/D18-1187",
    pages = "1586--1596",
    abstract = "State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task (NLI), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.",
}
@inproceedings{guo-etal-2018-question,
    title = "Question Generation from {SQL} Queries Improves Neural Semantic Parsing",
    author = "Guo, Daya  and
      Sun, Yibo  and
      Tang, Duyu  and
      Duan, Nan  and
      Yin, Jian  and
      Chi, Hong  and
      Cao, James  and
      Chen, Peng  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1188",
    doi = "10.18653/v1/D18-1188",
    pages = "1597--1607",
    abstract = "In this paper, we study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.",
}
@inproceedings{zhong-etal-2018-semregex,
    title = "{S}em{R}egex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications",
    author = "Zhong, Zexuan  and
      Guo, Jiaqi  and
      Yang, Wei  and
      Peng, Jian  and
      Xie, Tao  and
      Lou, Jian-Guang  and
      Liu, Ting  and
      Zhang, Dongmei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1189",
    doi = "10.18653/v1/D18-1189",
    pages = "1608--1618",
    abstract = "Recent research proposes syntax-based approaches to address the problem of generating programs from natural language specifications. These approaches typically train a sequence-to-sequence learning model using a syntax-based objective: maximum likelihood estimation (MLE). Such syntax-based approaches do not effectively address the goal of generating semantically correct programs, because these approaches fail to handle Program Aliasing, i.e., semantically equivalent programs may have many syntactically different forms. To address this issue, in this paper, we propose a semantics-based approach named SemRegex. SemRegex provides solutions for a subtask of the program-synthesis problem: generating regular expressions from natural language. Different from the existing syntax-based approaches, SemRegex trains the model by maximizing the expected semantic correctness of the generated regular expressions. The semantic correctness is measured using the DFA-equivalence oracle, random test cases, and distinguishing test cases. The experiments on three public datasets demonstrate the superiority of SemRegex over the existing state-of-the-art approaches.",
}
@inproceedings{herzig-berant-2018-decoupling,
    title = "Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing",
    author = "Herzig, Jonathan  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1190",
    doi = "10.18653/v1/D18-1190",
    pages = "1619--1629",
    abstract = "Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can parse utterances in unseen domains while only being trained on examples in other source domains. First, we map an utterance to an abstract, domain independent, logical form that represents the structure of the logical form, but contains slots instead of KB constants. Then, we replace slots with KB constants via lexical alignment scores and global inference. Our model reaches an average accuracy of 53.4{\%} on 7 domains in the OVERNIGHT dataset, substantially better than other zero-shot baselines, and performs as good as a parser trained on over 30{\%} of the target domain examples.",
}
@inproceedings{ouchi-etal-2018-span,
    title = "A Span Selection Model for Semantic Role Labeling",
    author = "Ouchi, Hiroki  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1191",
    doi = "10.18653/v1/D18-1191",
    pages = "1630--1642",
    abstract = "We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use span-level features, that are difficult to use in token-based BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.",
}
@inproceedings{iyer-etal-2018-mapping,
    title = "Mapping Language to Code in Programmatic Context",
    author = "Iyer, Srinivasan  and
      Konstas, Ioannis  and
      Cheung, Alvin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1192",
    doi = "10.18653/v1/D18-1192",
    pages = "1643--1652",
    abstract = "Source code is rarely written in isolation. It depends significantly on the programmatic context, such as the class that the code would reside in. To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class. This task is challenging because the desired code can vary greatly depending on the functionality the class provides (e.g., a sort function may or may not be available when we are asked to {``}return the smallest element{''} in a particular member variable list). We introduce CONCODE, a new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment. We also present a detailed error analysis suggesting that there is significant room for future work on this task.",
}
@inproceedings{yu-etal-2018-syntaxsqlnet,
    title = "{S}yntax{SQLN}et: Syntax Tree Networks for Complex and Cross-Domain Text-to-{SQL} Task",
    author = "Yu, Tao  and
      Yasunaga, Michihiro  and
      Yang, Kai  and
      Zhang, Rui  and
      Wang, Dongxu  and
      Li, Zifan  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1193",
    doi = "10.18653/v1/D18-1193",
    pages = "1653--1663",
    abstract = "Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task. SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL generation path history and table-aware column attention encoders. We evaluate SyntaxSQLNet on a new large-scale text-to-SQL corpus containing databases with multiple tables and complex SQL queries containing multiple SQL clauses and nested queries. We use a database split setting where databases in the test set are unseen during training. Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 9.5{\%} in exact matching accuracy. To our knowledge, we are the first to study this complex text-to-SQL task. Our task and models with the latest updates are available at \url{https://yale-lily.github.io/seq2sql/spider}.",
}
@inproceedings{zhang-etal-2018-cross,
    title = "Cross-lingual Decompositional Semantic Parsing",
    author = "Zhang, Sheng  and
      Ma, Xutai  and
      Rudinger, Rachel  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1194",
    doi = "10.18653/v1/D18-1194",
    pages = "1664--1675",
    abstract = "We introduce the task of cross-lingual decompositional semantic parsing: mapping content provided in a source language into a decompositional semantic analysis based on a target language. We present: (1) a form of decompositional semantic analysis designed to allow systems to target varying levels of structural complexity (shallow to deep analysis), (2) an evaluation metric to measure the similarity between system output and reference semantic analysis, (3) an end-to-end model with a novel annotating mechanism that supports intra-sentential coreference, and (4) an evaluation dataset on which our model outperforms strong baselines by at least 1.75 F1 score.",
}
@inproceedings{labutov-etal-2018-learning,
    title = "Learning to Learn Semantic Parsers from Natural Language Supervision",
    author = "Labutov, Igor  and
      Yang, Bishan  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1195",
    doi = "10.18653/v1/D18-1195",
    pages = "1676--1690",
    abstract = "As humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency. Inspired by this observation, we propose a learning algorithm for training semantic parsers from supervision (feedback) expressed in natural language. Our algorithm learns a semantic parser from users{'} corrections such as {``}no, what I really meant was before his job, not after{''}, by also simultaneously learning to parse this natural language feedback in order to leverage it as a form of supervision. Unlike supervision with gold-standard logical forms, our method does not require the user to be familiar with the underlying logical formalism, and unlike supervision from denotation, it does not require the user to know the correct answer to their query. This makes our learning algorithm naturally scalable in settings where existing conversational logs are available and can be leveraged as training data. We construct a novel dataset of natural language feedback in a conversational setting, and show that our method is effective at learning a semantic parser from such natural language supervision.",
}
@inproceedings{dunietz-etal-2018-deepcx,
    title = "{D}eep{C}x: A transition-based approach for shallow semantic parsing with complex constructional triggers",
    author = "Dunietz, Jesse  and
      Carbonell, Jaime  and
      Levin, Lori",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1196",
    doi = "10.18653/v1/D18-1196",
    pages = "1691--1701",
    abstract = "This paper introduces the surface construction labeling (SCL) task, which expands the coverage of Shallow Semantic Parsing (SSP) to include frames triggered by complex constructions. We present DeepCx, a neural, transition-based system for SCL. As a test case for the approach, we apply DeepCx to the task of tagging causal language in English, which relies on a wider variety of constructions than are typically addressed in SSP. We report substantial improvements over previous tagging efforts on a causal language dataset. We also propose ways DeepCx could be extended to still more difficult constructions and to other semantic domains once appropriate datasets become available.",
}
@inproceedings{yavuz-etal-2018-takes,
    title = "What It Takes to Achieve 100{\%} Condition Accuracy on {W}iki{SQL}",
    author = "Yavuz, Semih  and
      Gur, Izzeddin  and
      Su, Yu  and
      Yan, Xifeng",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1197",
    doi = "10.18653/v1/D18-1197",
    pages = "1702--1711",
    abstract = "WikiSQL is a newly released dataset for studying the natural language sequence to SQL translation problem. The SQL queries in WikiSQL are simple: Each involves one relation and does not have any join operation. Despite of its simplicity, none of the publicly reported structured query generation models can achieve an accuracy beyond 62{\%}, which is still far from enough for practical use. In this paper, we ask two questions, {``}Why is the accuracy still low for such simple queries?{''} and {``}What does it take to achieve 100{\%} accuracy on WikiSQL?{''} To limit the scope of our study, we focus on the WHERE clause in SQL. The answers will help us gain insights about the directions we should explore in order to further improve the translation accuracy. We will then investigate alternative solutions to realize the potential ceiling performance on WikiSQL. Our proposed solution can reach up to 88.6{\%} condition accuracy on the WikiSQL dataset.",
}
@inproceedings{guo-lu-2018-better,
    title = "Better Transition-Based {AMR} Parsing with a Refined Search Space",
    author = "Guo, Zhijiang  and
      Lu, Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1198",
    doi = "10.18653/v1/D18-1198",
    pages = "1712--1722",
    abstract = "This paper introduces a simple yet effective transition-based system for Abstract Meaning Representation (AMR) parsing. We argue that a well-defined search space involved in a transition system is crucial for building an effective parser. We propose to conduct the search in a refined search space based on a new compact AMR graph and an improved oracle. Our end-to-end parser achieves the state-of-the-art performance on various datasets with minimal additional information.",
}
@inproceedings{liu-hwa-2018-heuristically,
    title = "Heuristically Informed Unsupervised Idiom Usage Recognition",
    author = "Liu, Changsheng  and
      Hwa, Rebecca",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1199",
    doi = "10.18653/v1/D18-1199",
    pages = "1723--1731",
    abstract = "Many idiomatic expressions can be interpreted figuratively or literally depending on their contexts. This paper proposes an unsupervised learning method for recognizing the intended usages of idioms. We treat the usages as a latent variable in probabilistic models and train them in a linguistically motivated feature space. Crucially, we show that distributional semantics is a helpful heuristic for distinguishing the literal usage of idioms, giving us a way to formulate a literal usage metric to estimate the likelihood that the idiom is intended literally. This information then serves as a form of distant supervision to guide the unsupervised training process for the probabilistic models. Experiments show that our overall model performs competitively against supervised methods.",
}
@inproceedings{dubossarsky-etal-2018-coming,
    title = "Coming to Your Senses: on Controls and Evaluation Sets in Polysemy Research",
    author = "Dubossarsky, Haim  and
      Grossman, Eitan  and
      Weinshall, Daphna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1200",
    doi = "10.18653/v1/D18-1200",
    pages = "1732--1740",
    abstract = "The point of departure of this article is the claim that sense-specific vectors provide an advantage over normal vectors due to the polysemy that they presumably represent. This claim is based on performance gains observed in gold standard evaluation tests such as word similarity tasks. We demonstrate that this claim, at least as it is instantiated in prior art, is unfounded in two ways. Furthermore, we provide empirical data and an analytic discussion that may account for the previously reported improved performance. First, we show that ground-truth polysemy degrades performance in word similarity tasks. Therefore word similarity tasks are not suitable as an evaluation test for polysemy representation. Second, random assignment of words to senses is shown to improve performance in the same task. This and additional results point to the conclusion that performance gains as reported in previous work may be an artifact of random sense assignment, which is equivalent to sub-sampling and multiple estimation of word vector representations. Theoretical analysis shows that this may on its own be beneficial for the estimation of word similarity, by reducing the bias in the estimation of the cosine distance.",
}
@inproceedings{pinter-eisenstein-2018-predicting,
    title = "Predicting Semantic Relations using Global Graph Properties",
    author = "Pinter, Yuval  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1201",
    doi = "10.18653/v1/D18-1201",
    pages = "1741--1751",
    abstract = "Semantic graphs, such as WordNet, are resources which curate natural language on two distinguishable layers. On the local level, individual relations between synsets (semantic building blocks) such as hypernymy and meronymy enhance our understanding of the words used to express their meanings. Globally, analysis of graph-theoretic properties of the entire net sheds light on the structure of human language as a whole. In this paper, we combine global and local properties of semantic graphs through the framework of Max-Margin Markov Graph Models (M3GM), a novel extension of Exponential Random Graph Model (ERGM) that scales to large multi-relational graphs. We demonstrate how such global modeling improves performance on the local task of predicting semantic relations between synsets, yielding new state-of-the-art results on the WN18RR dataset, a challenging version of WordNet link prediction in which {``}easy{''} reciprocal cases are removed. In addition, the M3GM model identifies multirelational motifs that are characteristic of well-formed lexical semantic ontologies.",
}
@inproceedings{cocos-etal-2018-learning,
    title = "Learning Scalar Adjective Intensity from Paraphrases",
    author = "Cocos, Anne  and
      Wharton, Skyler  and
      Pavlick, Ellie  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1202",
    doi = "10.18653/v1/D18-1202",
    pages = "1752--1762",
    abstract = "Adjectives like {``}warm{''}, {``}hot{''}, and {``}scalding{''} all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language. We propose a new paraphrase-based method to automatically learn the relative intensity relation that holds between a pair of scalar adjectives. Our approach analyzes over 36k adjectival pairs from the Paraphrase Database under the assumption that, for example, paraphrase pair {``}really hot{''} {\textless}{--}{\textgreater} {``}scalding{''} suggests that {``}hot{''} {\textless} {``}scalding{''}. We show that combining this paraphrase evidence with existing, complementary pattern- and lexicon-based approaches improves the quality of systems for automatically ordering sets of scalar adjectives and inferring the polarity of indirect answers to {``}yes/no{''} questions.",
}
@inproceedings{yokoi-etal-2018-pointwise,
    title = "Pointwise {HSIC}: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions",
    author = "Yokoi, Sho  and
      Kobayashi, Sosuke  and
      Fukumizu, Kenji  and
      Suzuki, Jun  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1203",
    doi = "10.18653/v1/D18-1203",
    pages = "1763--1775",
    abstract = "In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alternative to pointwise mutual information (PMI). As well as deriving PMI from mutual information, we derive this new measure from the Hilbert{--}Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC). PHSIC can be interpreted as a smoothed variant of PMI that allows various similarity metrics (e.g., sentence embeddings) to be plugged in as kernels. Moreover, PHSIC can be estimated by simple and fast (linear in the size of the data) matrix calculations regardless of whether we use linear or nonlinear kernels. Empirically, in a dialogue response selection task, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. In addition, we also demonstrate that PHSIC is beneficial as a criterion of a data selection task for machine translation owing to its ability to give high (low) scores to a consistent (inconsistent) pair with other pairs.",
}
@inproceedings{wang-etal-2018-neural-related,
    title = "Neural Related Work Summarization with a Joint Context-driven Attention Mechanism",
    author = "Wang, Yongzhen  and
      Liu, Xiaozhong  and
      Gao, Zheng",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1204",
    doi = "10.18653/v1/D18-1204",
    pages = "1776--1786",
    abstract = "Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.",
}
@inproceedings{li-etal-2018-improving,
    title = "Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling",
    author = "Li, Wei  and
      Xiao, Xinyan  and
      Lyu, Yajuan  and
      Wang, Yuanzhuo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1205",
    doi = "10.18653/v1/D18-1205",
    pages = "1787--1796",
    abstract = "Information selection is the most important component in document summarization task. In this paper, we propose to extend the basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. Specifically, our information selection layer consists of two parts: gated global information filtering and local sentence selection. Unnecessary information in the original document is first globally filtered, then salient sentences are selected locally while generating each summary sentence sequentially. To optimize the information selection process directly, distantly-supervised training guided by the golden summary is also imported. Experimental results demonstrate that the explicit modeling and optimizing of the information selection process improves document summarization performance significantly, which enables our model to generate more informative and concise summaries, and thus significantly outperform state-of-the-art neural abstractive methods.",
}
@inproceedings{narayan-etal-2018-dont,
    title = "Don{'}t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
    author = "Narayan, Shashi  and
      Cohen, Shay B.  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1206",
    doi = "10.18653/v1/D18-1206",
    pages = "1797--1807",
    abstract = "We introduce {``}extreme summarization{''}, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question {``}What is the article about?{''}. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article{'}s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.",
}
@inproceedings{kryscinski-etal-2018-improving,
    title = "Improving Abstraction in Text Summarization",
    author = "Kry{\'s}ci{\'n}ski, Wojciech  and
      Paulus, Romain  and
      Xiong, Caiming  and
      Socher, Richard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1207",
    doi = "10.18653/v1/D18-1207",
    pages = "1808--1817",
    abstract = "Abstractive text summarization aims to shorten long text documents into a human readable form that contains the most important facts from the original document. However, the level of actual abstraction as measured by novel phrases that do not appear in the source document remains low in existing approaches. We propose two techniques to improve the level of abstraction of generated summaries. First, we decompose the decoder into a contextual network that retrieves relevant parts of the source document, and a pretrained language model that incorporates prior knowledge about language generation. Second, we propose a novelty metric that is optimized directly through policy learning to encourage the generation of novel phrases. Our model achieves results comparable to state-of-the-art models, as determined by ROUGE scores and human evaluations, while achieving a significantly higher level of abstraction as measured by n-gram overlap with the source document.",
}
@inproceedings{kedzie-etal-2018-content,
    title = "Content Selection in Deep Learning Models of Summarization",
    author = "Kedzie, Chris  and
      McKeown, Kathleen  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1208",
    doi = "10.18653/v1/D18-1208",
    pages = "1818--1828",
    abstract = "We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over simpler models. These results suggest that it is easier to create a summarizer for a new domain than previous work suggests and bring into question the benefit of deep learning models for summarization for those domains that do have massive datasets (i.e., news). At the same time, they suggest important questions for new research in summarization; namely, new forms of sentence representations or external knowledge sources are needed that are better suited to the sumarization task.",
}
@inproceedings{shen-etal-2018-improved,
    title = "Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment",
    author = "Shen, Dinghan  and
      Zhang, Xinyuan  and
      Henao, Ricardo  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1209",
    doi = "10.18653/v1/D18-1209",
    pages = "1829--1838",
    abstract = "Network embeddings, which learns low-dimensional representations for each vertex in a large-scale network, have received considerable attention in recent years. For a wide range of applications, vertices in a network are typically accompanied by rich textual information such as user profiles, paper abstracts, etc. In this paper, we propose to incorporate semantic features into network embeddings by matching important words between text sequences for all pairs of vertices. We introduce an word-by-word alignment framework that measures the compatibility of embeddings between word pairs, and then adaptively accumulates these alignment features with a simple yet effective aggregation function. In experiments, we evaluate the proposed framework on three real-world benchmarks for downstream tasks, including link prediction and multi-label vertex classification. The experimental results demonstrate that our model outperforms state-of-the-art network embedding methods by a large margin.",
}
@inproceedings{shen-etal-2018-learning,
    title = "Learning Context-Sensitive Convolutional Filters for Text Processing",
    author = "Shen, Dinghan  and
      Min, Martin Renqiang  and
      Li, Yitong  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1210",
    doi = "10.18653/v1/D18-1210",
    pages = "1839--1848",
    abstract = "Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a small meta network to learn context-sensitive convolutional filters for text processing. The role of meta network is to abstract the contextual information of a sentence or document into a set of input-sensitive filters. We further generalize this framework to model sentence pairs, where a bidirectional filter generation mechanism is introduced to encapsulate co-dependent sentence representations. In our benchmarks on four different tasks, including ontology classification, sentiment analysis, answer sentence selection, and paraphrase identification, our proposed model, a modified CNN with context-sensitive filters, consistently outperforms the standard CNN and attention-based CNN baselines. By visualizing the learned context-sensitive filters, we further validate and rationalize the effectiveness of proposed framework.",
}
@inproceedings{mcdonald-etal-2018-deep,
    title = "Deep Relevance Ranking Using Enhanced Document-Query Interactions",
    author = "McDonald, Ryan  and
      Brokos, George  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1211",
    doi = "10.18653/v1/D18-1211",
    pages = "1849--1860",
    abstract = "We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR{'}s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.",
}
@inproceedings{li-cheng-2018-learning,
    title = "Learning Neural Representation for {CLIR} with Adversarial Framework",
    author = "Li, Bo  and
      Cheng, Ping",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1212",
    doi = "10.18653/v1/D18-1212",
    pages = "1861--1870",
    abstract = "The existing studies in cross-language information retrieval (CLIR) mostly rely on general text representation models (e.g., vector space model or latent semantic analysis). These models are not optimized for the target retrieval task. In this paper, we follow the success of neural representation in natural language processing (NLP) and develop a novel text representation model based on adversarial learning, which seeks a task-specific embedding space for CLIR. Adversarial learning is implemented as an interplay between the generator process and the discriminator process. In order to adapt adversarial learning to CLIR, we design three constraints to direct representation learning, which are (1) a matching constraint capturing essential characteristics of cross-language ranking, (2) a translation constraint bridging language gaps, and (3) an adversarial constraint forcing both language and media invariant to be reached more efficiently and effectively. Through the joint exploitation of these constraints in an adversarial manner, the underlying cross-language semantics relevant to retrieval tasks are better preserved in the embedding space. Standard CLIR experiments show that our model significantly outperforms state-of-the-art continuous space models and is better than the strong machine translation baseline.",
}
@inproceedings{ray-etal-2018-ad3,
    title = "{AD}3: Attentive Deep Document Dater",
    author = "Ray, Swayambhu Nath  and
      Dasgupta, Shib Sankar  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1213",
    doi = "10.18653/v1/D18-1213",
    pages = "1871--1880",
    abstract = "Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can{'}t be trusted. Thus, predicting creation time from document content itself is an important task. In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines.",
}
@inproceedings{alvarez-melis-jaakkola-2018-gromov,
    title = "{G}romov-{W}asserstein Alignment of Word Embedding Spaces",
    author = "Alvarez-Melis, David  and
      Jaakkola, Tommi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1214",
    doi = "10.18653/v1/D18-1214",
    pages = "1881--1890",
    abstract = "Cross-lingual or cross-domain correspondences play key roles in tasks ranging from machine translation to transfer learning. Recently, purely unsupervised methods operating on monolingual embeddings have become effective alignment tools. Current state-of-the-art methods, however, involve multiple steps, including heuristic post-hoc refinement strategies. In this paper, we cast the correspondence problem directly as an optimal transport (OT) problem, building on the idea that word embeddings arise from metric recovery algorithms. Indeed, we exploit the Gromov-Wasserstein distance that measures how similarities between pairs of words relate across languages. We show that our OT objective can be estimated efficiently, requires little or no tuning, and results in performance comparable with the state-of-the-art in various unsupervised word translation tasks.",
}
@inproceedings{wang-poon-2018-deep,
    title = "Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision",
    author = "Wang, Hai  and
      Poon, Hoifung",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1215",
    doi = "10.18653/v1/D18-1215",
    pages = "1891--1902",
    abstract = "Deep learning has emerged as a versatile tool for a wide range of NLP tasks, due to its superior capacity in representation learning. But its applicability is limited by the reliance on annotated examples, which are difficult to produce at scale. Indirect supervision has emerged as a promising direction to address this bottleneck, either by introducing labeling functions to automatically generate noisy examples from unlabeled text, or by imposing constraints over interdependent label decisions. A plethora of methods have been proposed, each with respective strengths and limitations. Probabilistic logic offers a unifying language to represent indirect supervision, but end-to-end modeling with probabilistic logic is often infeasible due to intractable inference and learning. In this paper, we propose deep probabilistic logic (DPL) as a general framework for indirect supervision, by composing probabilistic logic with deep learning. DPL models label decisions as latent variables, represents prior knowledge on their relations using weighted first-order logical formulas, and alternates between learning a deep neural network for the end task and refining uncertain formula weights for indirect supervision, using variational EM. This framework subsumes prior indirect supervision methods as special cases, and enables novel combination via infusion of rich domain and linguistic knowledge. Experiments on biomedical machine reading demonstrate the promise of this approach.",
}
@inproceedings{bao-etal-2018-deriving,
    title = "Deriving Machine Attention from Human Rationales",
    author = "Bao, Yujia  and
      Chang, Shiyu  and
      Yu, Mo  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1216",
    doi = "10.18653/v1/D18-1216",
    pages = "1903--1913",
    abstract = "Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectively. To this end, we start with discrete human-annotated rationales and map them into continuous attention. Our central hypothesis is that this mapping is general across domains, and thus can be transferred from resource-rich domains to low-resource ones. Our model jointly learns a domain-invariant representation and induces the desired mapping between rationales and attention. Our empirical results validate this hypothesis and show that our approach delivers significant gains over state-of-the-art baselines, yielding over 15{\%} average error reduction on benchmark datasets.",
}
@inproceedings{clark-etal-2018-semi,
    title = "Semi-Supervised Sequence Modeling with Cross-View Training",
    author = "Clark, Kevin  and
      Luong, Minh-Thang  and
      Manning, Christopher D.  and
      Le, Quoc",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1217",
    doi = "10.18653/v1/D18-1217",
    pages = "1914--1925",
    abstract = "Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data. On labeled examples, standard supervised learning is used. On unlabeled examples, CVT teaches auxiliary prediction modules that see restricted views of the input (e.g., only part of a sentence) to match the predictions of the full model seeing the whole input. Since the auxiliary modules and the full model share intermediate representations, this in turn improves the full model. Moreover, we show that CVT is particularly effective when combined with multi-task learning. We evaluate CVT on five sequence tagging tasks, machine translation, and dependency parsing, achieving state-of-the-art results.",
}
@inproceedings{paun-etal-2018-probabilistic,
    title = "A Probabilistic Annotation Model for Crowdsourcing Coreference",
    author = "Paun, Silviu  and
      Chamberlain, Jon  and
      Kruschwitz, Udo  and
      Yu, Juntao  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1218",
    doi = "10.18653/v1/D18-1218",
    pages = "1926--1937",
    abstract = "The availability of large scale annotated corpora for coreference is essential to the development of the field. However, creating resources at the required scale via expert annotation would be too expensive. Crowdsourcing has been proposed as an alternative; but this approach has not been widely used for coreference. This paper addresses one crucial hurdle on the way to make this possible, by introducing a new model of annotation for aggregating crowdsourced anaphoric annotations. The model is evaluated along three dimensions: the accuracy of the inferred mention pairs, the quality of the post-hoc constructed silver chains, and the viability of using the silver chains as an alternative to the expert-annotated chains in training a state of the art coreference system. The results suggest that our model can extract from crowdsourced annotations coreference chains of comparable quality to those obtained with expert annotation.",
}
@inproceedings{hou-2018-deterministic,
    title = "A Deterministic Algorithm for Bridging Anaphora Resolution",
    author = "Hou, Yufang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1219",
    doi = "10.18653/v1/D18-1219",
    pages = "1938--1948",
    abstract = "Previous work on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013) use syntactic preposition patterns to calculate word relatedness. However, such patterns only consider NPs{'} head nouns and hence do not fully capture the semantics of NPs. Recently, Hou (2018) created word embeddings (embeddings{\_}PP) to capture associative similarity (i.e., relatedness) between nouns by exploring the syntactic structure of noun phrases. But embeddings{\_}PP only contains word representations for nouns. In this paper, we create new word vectors by combining embeddings{\_}PP with GloVe. This new word embeddings (embeddings{\_}bridging) are a more general lexical knowledge resource for bridging and allow us to represent the meaning of an NP beyond its head easily. We therefore develop a deterministic approach for bridging anaphora resolution, which represents the semantics of an NP based on its head noun and modifications. We show that this simple approach achieves the competitive results compared to the best system in Hou et al. (2013) which explores Markov Logic Networks to model the problem. Additionally, we further improve the results for bridging anaphora resolution reported in Hou (2018) by combining our simple deterministic approach with Hou et al. (2013){'}s best system MLN II.",
}
@inproceedings{emami-etal-2018-knowledge,
    title = "A Knowledge Hunting Framework for Common Sense Reasoning",
    author = "Emami, Ali  and
      De La Cruz, Noelia  and
      Trischler, Adam  and
      Suleman, Kaheer  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1220",
    doi = "10.18653/v1/D18-1220",
    pages = "1949--1958",
    abstract = "We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.",
}
@inproceedings{kartsaklis-etal-2018-mapping,
    title = "Mapping Text to Knowledge Graph Entities using Multi-Sense {LSTM}s",
    author = "Kartsaklis, Dimitri  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1221",
    doi = "10.18653/v1/D18-1221",
    pages = "1959--1970",
    abstract = "This paper addresses the problem of mapping natural language text to knowledge base entities. The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph. The compositional model is an LSTM equipped with a dynamic disambiguation mechanism on the input word embeddings (a Multi-Sense LSTM), addressing polysemy issues. Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities. The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.",
}
@inproceedings{lv-etal-2018-differentiating,
    title = "Differentiating Concepts and Instances for Knowledge Graph Embedding",
    author = "Lv, Xin  and
      Hou, Lei  and
      Li, Juanzi  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1222",
    doi = "10.18653/v1/D18-1222",
    pages = "1971--1979",
    abstract = "Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e.,instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from \url{https://github.com/davidlvxin/TransC}.",
}
@inproceedings{xiong-etal-2018-one,
    title = "One-Shot Relational Learning for Knowledge Graphs",
    author = "Xiong, Wenhan  and
      Yu, Mo  and
      Chang, Shiyu  and
      Guo, Xiaoxiao  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1223",
    doi = "10.18653/v1/D18-1223",
    pages = "1980--1990",
    abstract = "Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs{'} coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.",
}
@inproceedings{zhang-etal-2018-regular,
    title = "Regular Expression Guided Entity Mention Mining from Noisy Web Data",
    author = "Zhang, Shanshan  and
      He, Lihong  and
      Vucetic, Slobodan  and
      Dragut, Eduard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1224",
    doi = "10.18653/v1/D18-1224",
    pages = "1991--2000",
    abstract = "Many important entity types in web documents, such as dates, times, email addresses, and course numbers, follow or closely resemble patterns that can be described by Regular Expressions (REs). Due to a vast diversity of web documents and ways in which they are being generated, even seemingly straightforward tasks such as identifying mentions of date in a document become very challenging. It is reasonable to claim that it is impossible to create a RE that is capable of identifying such entities from web documents with perfect precision and recall. Rather than abandoning REs as a go-to approach for entity detection, this paper explores ways to combine the expressive power of REs, ability of deep learning to learn from large data, and human-in-the loop approach into a new integrated framework for entity identification from web data. The framework starts by creating or collecting the existing REs for a particular type of an entity. Those REs are then used over a large document corpus to collect weak labels for the entity mentions and a neural network is trained to predict those RE-generated weak labels. Finally, a human expert is asked to label a small set of documents and the neural network is fine tuned on those documents. The experimental evaluation on several entity identification problems shows that the proposed framework achieves impressive accuracy, while requiring very modest human effort.",
}
@inproceedings{dasgupta-etal-2018-hyte,
    title = "{H}y{TE}: Hyperplane-based Temporally aware Knowledge Graph Embedding",
    author = "Dasgupta, Shib Sankar  and
      Ray, Swayambhu Nath  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1225",
    doi = "10.18653/v1/D18-1225",
    pages = "2001--2011",
    abstract = "Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods. Relational facts in KG often show temporal dynamics, e.g., the fact (Cristiano{\_}Ronaldo, playsFor, Manchester{\_}United) is valid only from 2003 to 2009. Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements. In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations. Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods.",
}
@inproceedings{lin-lu-2018-neural,
    title = "Neural Adaptation Layers for Cross-domain Named Entity Recognition",
    author = "Lin, Bill Yuchen  and
      Lu, Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1226",
    doi = "10.18653/v1/D18-1226",
    pages = "2012--2022",
    abstract = "Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets. However, despite significant resources required for training such models, the performance of a model trained on one domain typically degrades dramatically when applied to a different domain, yet extracting entities from new emerging domains such as social media can be of significant interest. In this paper, we empirically investigate effective methods for conveniently adapting an existing, well-trained neural NER model for a new domain. Unlike existing approaches, we propose lightweight yet effective methods for performing domain adaptation for neural models. Specifically, we introduce adaptation layers on top of existing neural architectures, where no re-training using the source domain data is required. We conduct extensive empirical studies and show that our approach significantly outperforms state-of-the-art methods.",
}
@inproceedings{dai-etal-2018-entity,
    title = "Entity Linking within a Social Media Platform: A Case Study on Yelp",
    author = "Dai, Hongliang  and
      Song, Yangqiu  and
      Qiu, Liwei  and
      Liu, Rijia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1227",
    doi = "10.18653/v1/D18-1227",
    pages = "2023--2032",
    abstract = "In this paper, we study a new entity linking problem where both the entity mentions and the target entities are within a same social media platform. Compared with traditional entity linking problems that link mentions to a knowledge base, this new problem have less information about the target entities. However, if we can successfully link mentions to entities within a social media platform, we can improve a lot of applications such as comparative study in business intelligence and opinion leader finding. To study this problem, we constructed a dataset called Yelp-EL, where the business mentions in Yelp reviews are linked to their corresponding businesses on the platform. We conducted comprehensive experiments and analysis on this dataset with a learning to rank model that takes different types of features as input, as well as a few state-of-the-art entity linking approaches. Our experimental results show that two types of features that are not available in traditional entity linking: social features and location features, can be very helpful for this task.",
}
@inproceedings{patel-etal-2018-annotation,
    title = "Annotation of a Large Clinical Entity Corpus",
    author = "Patel, Pinal  and
      Davey, Disha  and
      Panchal, Vishal  and
      Pathak, Parth",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1228",
    doi = "10.18653/v1/D18-1228",
    pages = "2033--2042",
    abstract = "Having an entity annotated corpus of the clinical domain is one of the basic requirements for detection of clinical entities using machine learning (ML) approaches. Past researches have shown the superiority of statistical/ML approaches over the rule based approaches. But in order to take full advantage of the ML approaches, an accurately annotated corpus becomes an essential requirement. Though there are a few annotated corpora available either on a small data set, or covering a narrower domain (like cancer patients records, lab reports), annotation of a large data set representing the entire clinical domain has not been created yet. In this paper, we have described in detail the annotation guidelines, annotation process and our approaches in creating a CER (clinical entity recognition) corpus of 5,160 clinical documents from forty different clinical specialities. The clinical entities range across various types such as diseases, procedures, medications, medical devices and so on. We have classified them into eleven categories for annotation. Our annotation also reflects the relations among the group of entities that constitute larger concepts altogether.",
}
@inproceedings{berger-etal-2018-visual,
    title = "Visual Supervision in Bootstrapped Information Extraction",
    author = "Berger, Matthew  and
      Nagesh, Ajay  and
      Levine, Joshua  and
      Surdeanu, Mihai  and
      Zhang, Helen",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1229",
    doi = "10.18653/v1/D18-1229",
    pages = "2043--2053",
    abstract = "We challenge a common assumption in active learning, that a list-based interface populated by informative samples provides for efficient and effective data annotation. We show how a 2D scatterplot populated with diverse and representative samples can yield improved models given the same time budget. We consider this for bootstrapping-based information extraction, in particular named entity classification, where human and machine jointly label data. To enable effective data annotation in a scatterplot, we have developed an embedding-based bootstrapping model that learns the distributional similarity of entities through the patterns that match them in a large data corpus, while being discriminative with respect to human-labeled and machine-promoted entities. We conducted a user study to assess the effectiveness of these different interfaces, and analyze bootstrapping performance in terms of human labeling accuracy, label quantity, and labeling consensus across multiple users. Our results suggest that supervision acquired from the scatterplot interface, despite being noisier, yields improvements in classification performance compared with the list interface, due to a larger quantity of supervision acquired.",
}
@inproceedings{shang-etal-2018-learning,
    title = "Learning Named Entity Tagger using Domain-Specific Dictionary",
    author = "Shang, Jingbo  and
      Liu, Liyuan  and
      Gu, Xiaotao  and
      Ren, Xiang  and
      Ren, Teng  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1230",
    doi = "10.18653/v1/D18-1230",
    pages = "2054--2064",
    abstract = "Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks.",
}
@inproceedings{zhou-etal-2018-zero,
    title = "Zero-Shot Open Entity Typing as Type-Compatible Grounding",
    author = "Zhou, Ben  and
      Khashabi, Daniel  and
      Tsai, Chen-Tse  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1231",
    doi = "10.18653/v1/D18-1231",
    pages = "2065--2076",
    abstract = "The problem of entity-typing has been studied predominantly as a supervised learning problems, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets they often lack the flexibility to transfer across text genres and to generalize to new type taxonomies. In this work we propose a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Given a type taxonomy, the entries of which we define as Boolean functions of freebase {``}types,{''} we ground a given mention to a set of \textit{type-compatible} Wikipedia entries, and then infer the target mention{'}s type using an inference algorithm that makes use of the types of these entries. We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and on a dataset in the biological domain. Our system is shown to be competitive with state-of-the-art supervised NER systems, and to outperform them on out-of-training datasets. We also show that our system significantly outperforms other zero-shot fine typing systems.",
}
@inproceedings{hu-etal-2018-attention,
    title = "Attention-Guided Answer Distillation for Machine Reading Comprehension",
    author = "Hu, Minghao  and
      Peng, Yuxing  and
      Wei, Furu  and
      Huang, Zhen  and
      Li, Dongsheng  and
      Yang, Nan  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1232",
    doi = "10.18653/v1/D18-1232",
    pages = "2077--2086",
    abstract = "Despite that current reading comprehension systems have achieved significant advancements, their promising performances are often obtained at the cost of making an ensemble of numerous models. Besides, existing approaches are also vulnerable to adversarial attacks. This paper tackles these problems by leveraging knowledge distillation, which aims to transfer knowledge from an ensemble model to a single model. We first demonstrate that vanilla knowledge distillation applied to answer span prediction is effective for reading comprehension systems. We then propose two novel approaches that not only penalize the prediction on confusing answers but also guide the training with alignment information distilled from the ensemble. Experiments show that our best student model has only a slight drop of 0.4{\%} F1 on the SQuAD test set compared to the ensemble teacher, while running 12x faster during inference. It even outperforms the teacher on adversarial SQuAD datasets and NarrativeQA benchmark.",
}
@inproceedings{saeidi-etal-2018-interpretation,
    title = "Interpretation of Natural Language Rules in Conversational Machine Reading",
    author = {Saeidi, Marzieh  and
      Bartolo, Max  and
      Lewis, Patrick  and
      Singh, Sameer  and
      Rockt{\"a}schel, Tim  and
      Sheldon, Mike  and
      Bouchard, Guillaume  and
      Riedel, Sebastian},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1233",
    doi = "10.18653/v1/D18-1233",
    pages = "2087--2097",
    abstract = "Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader{'}s background knowledge. One example is the task of interpreting regulations to answer {``}Can I...?{''} or {``}Do I have to...?{''} questions such as {``}I am working in Canada. Do I have to carry on paying UK National Insurance?{''} after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as {``}How long have you been working abroad?{''} when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.",
}
@inproceedings{hu-etal-2018-state,
    title = "A State-transition Framework to Answer Complex Questions over Knowledge Base",
    author = "Hu, Sen  and
      Zou, Lei  and
      Zhang, Xinbo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1234",
    doi = "10.18653/v1/D18-1234",
    pages = "2098--2108",
    abstract = "Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions. To address that, in this paper, we propose a State Transition-based approach to translate a complex natural language question N to a semantic query graph (SQG), which is used to match the underlying knowledge graph to find the answers to question N. In order to generate SQG, we propose four primitive operations (expand, fold, connect and merge) and a learning-based state transition approach. Extensive experiments on several benchmarks (such as QALD, WebQuestions and ComplexQuestions) with two knowledge bases (DBpedia and Freebase) confirm the superiority of our approach compared with state-of-the-arts.",
}
@inproceedings{liu-etal-2018-multi,
    title = "A Multi-answer Multi-task Framework for Real-world Machine Reading Comprehension",
    author = "Liu, Jiahua  and
      Wei, Wan  and
      Sun, Maosong  and
      Chen, Hao  and
      Du, Yantao  and
      Lin, Dekang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1235",
    doi = "10.18653/v1/D18-1235",
    pages = "2109--2118",
    abstract = "The task of machine reading comprehension (MRC) has evolved from answering simple questions from well-edited text to answering real questions from users out of web data. In the real-world setting, full-body text from multiple relevant documents in the top search results are provided as context for questions from user queries, including not only questions with a single, short, and factual answer, but also questions about reasons, procedures, and opinions. In this case, multiple answers could be equally valid for a single question and each answer may occur multiple times in the context, which should be taken into consideration when we build MRC system. We propose a multi-answer multi-task framework, in which different loss functions are used for multiple reference answers. Minimum Risk Training is applied to solve the multi-occurrence problem of a single answer. Combined with a simple heuristic passage extraction strategy for overlong documents, our model increases the ROUGE-L score on the DuReader dataset from 44.18, the previous state-of-the-art, to 51.09.",
}
@inproceedings{sun-etal-2018-logician,
    title = "Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain",
    author = "Sun, Mingming  and
      Li, Xu  and
      Li, Ping",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1236",
    doi = "10.18653/v1/D18-1236",
    pages = "2119--2130",
    abstract = "We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain. Then, we develop an agent, called Orator, to accomplish the OIN task, and assemble the Orator and the recently proposed OIE agent {---} Logician into a dual system to utilize the duality structure with a reinforcement learning paradigm. Experimental results reveal the dual structure between OIE and OIN tasks helps to build better both OIE agents and OIN agents.",
}
@inproceedings{back-etal-2018-memoreader,
    title = "{M}emo{R}eader: Large-Scale Reading Comprehension through Neural Memory Controller",
    author = "Back, Seohyun  and
      Yu, Seunghak  and
      Indurthi, Sathish Reddy  and
      Kim, Jihie  and
      Choo, Jaegul",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1237",
    doi = "10.18653/v1/D18-1237",
    pages = "2131--2140",
    abstract = "Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text. Existing approaches made a significant progress comparable to human-level performance, but they are still limited in understanding, up to a few paragraphs, failing to properly comprehend lengthy document. In this paper, we propose a novel deep neural network architecture to handle a long-range dependency in RC tasks. In detail, our method has two novel aspects: (1) an advanced memory-augmented architecture and (2) an expanded gated recurrent unit with dense connections that mitigate potential information distortion occurring in the memory. Our proposed architecture is widely applicable to other models. We have performed extensive experiments with well-known benchmark datasets such as TriviaQA, QUASAR-T, and SQuAD. The experimental results demonstrate that the proposed method outperforms existing methods, especially for lengthy documents.",
}
@inproceedings{tay-etal-2018-multi,
    title = "Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension",
    author = "Tay, Yi  and
      Luu, Anh Tuan  and
      Hui, Siu Cheung",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1238",
    doi = "10.18653/v1/D18-1238",
    pages = "2141--2151",
    abstract = "Sequence encoders are crucial components in many neural architectures for learning to read and comprehend. This paper presents a new compositional encoder for reading comprehension (RC). Our proposed encoder is not only aimed at being fast but also expressive. Specifically, the key novelty behind our encoder is that it explicitly models across multiple granularities using a new dilated composition mechanism. In our approach, gating functions are learned by modeling relationships and reasoning over multi-granular sequence information, enabling compositional learning that is aware of both long and short term information. We conduct experiments on three RC datasets, showing that our proposed encoder demonstrates very promising results both as a standalone encoder as well as a complementary building block. Empirical results show that simple Bi-Attentive architectures augmented with our proposed encoder not only achieves state-of-the-art / highly competitive results but is also considerably faster than other published works.",
}
@inproceedings{gupta-lewis-2018-neural,
    title = "Neural Compositional Denotational Semantics for Question Answering",
    author = "Gupta, Nitish  and
      Lewis, Mike",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1239",
    doi = "10.18653/v1/D18-1239",
    pages = "2152--2161",
    abstract = "Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question. For example, to interpret {``}not green{''}, the model represents {``}green{''} as a set of KG entities and {``}not{''} as a trainable ungrounded vector{---}and then uses this vector to parameterize a composition function that performs a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent from end-task supervision. The model learns a variety of challenging semantic operators, such as quantifiers, disjunctions and composed relations, and infers latent syntactic structure. It also generalizes well to longer questions than seen in its training data, in contrast to RNN, its tree-based variants, and semantic parsing baselines.",
}
@inproceedings{tymoshenko-moschitti-2018-cross,
    title = "Cross-Pair Text Representations for Answer Sentence Selection",
    author = "Tymoshenko, Kateryna  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1240",
    doi = "10.18653/v1/D18-1240",
    pages = "2162--2173",
    abstract = "High-level semantics tasks, e.g., paraphrasing, textual entailment or question answering, involve modeling of text pairs. Before the emergence of neural networks, this has been mostly performed using intra-pair features, which incorporate similarity scores or rewrite rules computed between the members within the same pair. In this paper, we compute scalar products between vectors representing similarity between members of different pairs, in place of simply using a single vector for each pair. This allows us to obtain a representation specific to any pair of pairs, which delivers the state of the art in answer sentence selection. Most importantly, our approach can outperform much more complex algorithms based on neural networks.",
}
@inproceedings{choi-etal-2018-quac,
    title = "{Q}u{AC}: Question Answering in Context",
    author = "Choi, Eunsol  and
      He, He  and
      Iyyer, Mohit  and
      Yatskar, Mark  and
      Yih, Wen-tau  and
      Choi, Yejin  and
      Liang, Percy  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1241",
    doi = "10.18653/v1/D18-1241",
    pages = "2174--2184",
    abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at \url{http://quac.ai}.",
}
@inproceedings{luo-etal-2018-knowledge,
    title = "Knowledge Base Question Answering via Encoding of Complex Query Graphs",
    author = "Luo, Kangqi  and
      Lin, Fengli  and
      Luo, Xusheng  and
      Zhu, Kenny",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1242",
    doi = "10.18653/v1/D18-1242",
    pages = "2185--2194",
    abstract = "Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions.",
}
@inproceedings{liu-etal-2018-neural-relation,
    title = "Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning",
    author = "Liu, Tianyi  and
      Zhang, Xinsong  and
      Zhou, Wanhao  and
      Jia, Weijia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1243",
    doi = "10.18653/v1/D18-1243",
    pages = "2195--2204",
    abstract = "Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases. However, the automatically constructed datasets comprise amounts of low-quality sentences containing noisy words, which is neglected by current distant supervised methods resulting in unacceptable precisions. To mitigate this problem, we propose a novel word-level distant supervised approach for relation extraction. We first build Sub-Tree Parse(STP) to remove noisy words that are irrelevant to relations. Then we construct a neural network inputting the sub-tree while applying the entity-wise attention to identify the important semantic features of relational words in each instance. To make our model more robust against noisy words, we initialize our network with a priori knowledge learned from the relevant task of entity classification by transfer learning. We conduct extensive experiments using the corpora of New York Times(NYT) and Freebase. Experiments show that our approach is effective and improves the area of Precision/Recall(PR) from 0.35 to 0.39 over the state-of-the-art work.",
}
@inproceedings{zhang-etal-2018-graph,
    title = "Graph Convolution over Pruned Dependency Trees Improves Relation Extraction",
    author = "Zhang, Yuhao  and
      Qi, Peng  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1244",
    doi = "10.18653/v1/D18-1244",
    pages = "2205--2215",
    abstract = "Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.",
}
@inproceedings{du-etal-2018-multi,
    title = "Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction",
    author = "Du, Jinhua  and
      Han, Jingguang  and
      Way, Andy  and
      Wan, Dadong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1245",
    doi = "10.18653/v1/D18-1245",
    pages = "2216--2225",
    abstract = "Attention mechanism is often used in deep neural networks for distantly supervised relation extraction (DS-RE) to distinguish valid from noisy instances. However, traditional 1-D vector attention model is insufficient for learning of different contexts in the selection of valid instances to predict the relationship for an entity pair. To alleviate this issue, we propose a novel multi-level structured (2-D matrix) self-attention mechanism for DS-RE in a multi-instance learning (MIL) framework using bidirectional recurrent neural networks (BiRNN). In the proposed method, a structured word-level self-attention learns a 2-D matrix where each row vector represents a weight distribution for different aspects of an instance regarding two entities. Targeting the MIL issue, the structured sentence-level attention learns a 2-D matrix where each row vector represents a weight distribution on selection of different valid instances. Experiments conducted on two publicly available DS-RE datasets show that the proposed framework with multi-level structured self-attention mechanism significantly outperform baselines in terms of PR curves, P@N and F1 measures.",
}
@inproceedings{song-etal-2018-n,
    title = "N-ary Relation Extraction using Graph-State {LSTM}",
    author = "Song, Linfeng  and
      Zhang, Yue  and
      Wang, Zhiguo  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1246",
    doi = "10.18653/v1/D18-1246",
    pages = "2226--2235",
    abstract = "Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences. Typical methods formulate an input as a \textit{document graph}, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph into two DAGs, adopting a DAG-structured LSTM for each. Though being able to model rich linguistic knowledge by leveraging graph edges, important information can be lost in the splitting procedure. We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing. Compared with DAG LSTMs, our graph LSTM keeps the original graph structure, and speeds up computation by allowing more parallelization. On a standard benchmark, our model shows the best result in the literature.",
}
@inproceedings{han-etal-2018-hierarchical,
    title = "Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention",
    author = "Han, Xu  and
      Yu, Pengfei  and
      Liu, Zhiyuan  and
      Sun, Maosong  and
      Li, Peng",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1247",
    doi = "10.18653/v1/D18-1247",
    pages = "2236--2245",
    abstract = "Distantly supervised relation extraction employs existing knowledge graphs to automatically collect training data. While distant supervision is effective to scale relation extraction up to large-scale corpora, it inevitably suffers from the wrong labeling problem. Many efforts have been devoted to identifying valid instances from noisy data. However, most existing methods handle each relation in isolation, regardless of rich semantic correlations located in relation hierarchies. In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme. The multiple layers of our hierarchical attention scheme provide coarse-to-fine granularity to better identify valid instances, which is especially effective for extracting those long-tail relations. The experimental results on a large-scale benchmark dataset demonstrate that our models are capable of modeling the hierarchical information of relations and significantly outperform other baselines. The source code of this paper can be obtained from \url{https://github.com/thunlp/HNRE}.",
}
@inproceedings{wang-etal-2018-label,
    title = "Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding",
    author = "Wang, Guanying  and
      Zhang, Wen  and
      Wang, Ruoxu  and
      Zhou, Yalin  and
      Chen, Xi  and
      Zhang, Wei  and
      Zhu, Hai  and
      Chen, Huajun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1248",
    doi = "10.18653/v1/D18-1248",
    pages = "2246--2255",
    abstract = "Distant supervision is an effective method to generate large scale labeled data for relation extraction, which assumes that if a pair of entities appears in some relation of a Knowledge Graph (KG), all sentences containing those entities in a large unlabeled corpus are then labeled with that relation to train a relation classifier. However, when the pair of entities has multiple relationships in the KG, this assumption may produce noisy relation labels. This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly. Specifically, we make use of the type information and the translation law derived from typical KG embedding model to learn embeddings for certain sentence patterns. As the supervision signal is only determined by the two aligned entities, neither hard relation labels nor extra noise-reduction model for the bag of sentences is needed in this way. The experiments show that the approach performs well in current distant supervision dataset.",
}
@inproceedings{sun-etal-2018-extracting,
    title = "Extracting Entities and Relations with Joint Minimum Risk Training",
    author = "Sun, Changzhi  and
      Wu, Yuanbin  and
      Lan, Man  and
      Sun, Shiliang  and
      Wang, Wenting  and
      Lee, Kuang-Chih  and
      Wu, Kewen",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1249",
    doi = "10.18653/v1/D18-1249",
    pages = "2256--2265",
    abstract = "We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.",
}
@inproceedings{le-etal-2018-large,
    title = "Large-scale Exploration of Neural Relation Classification Architectures",
    author = "Le, Hoang-Quynh  and
      Can, Duy-Cat  and
      Vu, Sinh T.  and
      Dang, Thanh Hai  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1250",
    doi = "10.18653/v1/D18-1250",
    pages = "2266--2277",
    abstract = "Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our {`}Man for All Seasons{'} approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task.",
}
@inproceedings{chinnappa-blanco-2018-possessors,
    title = "Possessors Change Over Time: A Case Study with Artworks",
    author = "Chinnappa, Dhivya  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1251",
    doi = "10.18653/v1/D18-1251",
    pages = "2278--2287",
    abstract = "This paper presents a corpus and experimental results to extract possession relations over time. We work with Wikipedia articles about artworks, and extract possession relations along with temporal information indicating when these relations are true. The annotation scheme yields many possessors over time for a given artwork, and experimental results show that an LSTM ensemble can automate the task.",
}
@inproceedings{shore-skantze-2018-using,
    title = "Using Lexical Alignment and Referring Ability to Address Data Sparsity in Situated Dialog Reference Resolution",
    author = "Shore, Todd  and
      Skantze, Gabriel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1252",
    doi = "10.18653/v1/D18-1252",
    pages = "2288--2297",
    abstract = "Referring to entities in situated dialog is a collaborative process, whereby interlocutors often expand, repair and/or replace referring expressions in an iterative process, converging on conceptual pacts of referring language use in doing so. Nevertheless, much work on exophoric reference resolution (i.e. resolution of references to entities outside of a given text) follows a literary model, whereby individual referring expressions are interpreted as unique identifiers of their referents given the state of the dialog the referring expression is initiated. In this paper, we address this collaborative nature to improve dialogic reference resolution in two ways: First, we trained a words-as-classifiers logistic regression model of word semantics and incrementally adapt the model to idiosyncratic language between dyad partners during evaluation of the dialog. We then used these semantic models to learn the general referring ability of each word, which is independent of referent features. These methods facilitate accurate automatic reference resolution in situated dialog without annotation of referring expressions, even with little background data.",
}
@inproceedings{tang-etal-2018-subgoal,
    title = "Subgoal Discovery for Hierarchical Dialogue Policy Learning",
    author = "Tang, Da  and
      Li, Xiujun  and
      Gao, Jianfeng  and
      Wang, Chong  and
      Li, Lihong  and
      Jebara, Tony",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1253",
    doi = "10.18653/v1/D18-1253",
    pages = "2298--2309",
    abstract = "Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that discovers and exploits the hidden structure of the task to enable efficient policy learning. First, given successful example dialogues, we propose the Subgoal Discovery Network (SDN) to divide a complex goal-oriented task into a set of simpler subgoals in an unsupervised fashion. We then use these subgoals to learn a multi-level policy by hierarchical reinforcement learning. We demonstrate our method by building a dialogue agent for the composite task of travel planning. Experiments with simulated and real users show that our approach performs competitively against a state-of-the-art method that requires human-defined subgoals. Moreover, we show that the learned subgoals are often human comprehensible.",
}
@inproceedings{haponchyk-etal-2018-supervised,
    title = "Supervised Clustering of Questions into Intents for Dialog System Applications",
    author = "Haponchyk, Iryna  and
      Uva, Antonio  and
      Yu, Seunghak  and
      Uryupina, Olga  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1254",
    doi = "10.18653/v1/D18-1254",
    pages = "2310--2321",
    abstract = "Modern automated dialog systems require complex dialog managers able to deal with user intent triggered by high-level semantic questions. In this paper, we propose a model for automatically clustering questions into user intents to help the design tasks. Since questions are short texts, uncovering their semantics to group them together can be very challenging. We approach the problem by using powerful semantic classifiers from question duplicate/matching research along with a novel idea of supervised clustering methods based on structured output. We test our approach on two intent clustering corpora, showing an impressive improvement over previous methods for two languages/domains.",
}
@inproceedings{moghe-etal-2018-towards,
    title = "Towards Exploiting Background Knowledge for Building Conversation Systems",
    author = "Moghe, Nikita  and
      Arora, Siddhartha  and
      Banerjee, Suman  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1255",
    doi = "10.18653/v1/D18-1255",
    pages = "2322--2332",
    abstract = "Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them. This has resulted in the development of models which treat conversation as a sequence-to-sequence generation task (i.e., given a sequence of utterances generate the response sequence). This is not only an overly simplistic view of conversation but it is also emphatically different from the way humans converse by heavily relying on their background knowledge about the topic (as opposed to simply relying on the previous sequence of utterances). For example, it is common for humans to (involuntarily) produce utterances which are copied or suitably modified from background articles they have read about the topic. To facilitate the development of such natural conversation models which mimic the human process of conversing, we create a new dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie. We establish baseline results on this dataset (90K utterances from 9K conversations) using three different models: (i) pure generation based models which ignore the background knowledge (ii) generation based models which learn to copy information from the background knowledge when required and (iii) span prediction based models which predict the appropriate response span in the background knowledge.",
}
@inproceedings{he-etal-2018-decoupling,
    title = "Decoupling Strategy and Generation in Negotiation Dialogues",
    author = "He, He  and
      Chen, Derek  and
      Balakrishnan, Anusha  and
      Liang, Percy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1256",
    doi = "10.18653/v1/D18-1256",
    pages = "2333--2343",
    abstract = "We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing {\$}50) and the execution of that strategy (e.g., generating {``}The bike is brand new. Selling for just {\$}50!{''}). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.",
}
@inproceedings{xie-etal-2018-large,
    title = "Large-scale Cloze Test Dataset Created by Teachers",
    author = "Xie, Qizhe  and
      Lai, Guokun  and
      Dai, Zihang  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1257",
    doi = "10.18653/v1/D18-1257",
    pages = "2344--2356",
    abstract = "Cloze tests are widely adopted in language exams to evaluate students{'} language proficiency. In this paper, we propose the first large-scale human-created cloze test dataset CLOTH, containing questions used in middle-school and high-school language exams. With missing blanks carefully created by teachers and candidate choices purposely designed to be nuanced, CLOTH requires a deeper language understanding and a wider attention span than previously automatically-generated cloze datasets. We test the performance of dedicatedly designed baseline models including a language model trained on the One Billion Word Corpus and show humans outperform them by a significant margin. We investigate the source of the performance gap, trace model deficiencies to some distinct properties of CLOTH, and identify the limited ability of comprehending the long-term context to be the key bottleneck.",
}
@inproceedings{pampari-etal-2018-emrqa,
    title = "emr{QA}: A Large Corpus for Question Answering on Electronic Medical Records",
    author = "Pampari, Anusri  and
      Raghavan, Preethi  and
      Liang, Jennifer  and
      Peng, Jian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1258",
    doi = "10.18653/v1/D18-1258",
    pages = "2357--2368",
    abstract = "We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million questions-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.",
}
@inproceedings{yang-etal-2018-hotpotqa,
    title = "{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering",
    author = "Yang, Zhilin  and
      Qi, Peng  and
      Zhang, Saizheng  and
      Bengio, Yoshua  and
      Cohen, William  and
      Salakhutdinov, Ruslan  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1259",
    doi = "10.18653/v1/D18-1259",
    pages = "2369--2380",
    abstract = "Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems{'} ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.",
}
@inproceedings{mihaylov-etal-2018-suit,
    title = "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering",
    author = "Mihaylov, Todor  and
      Clark, Peter  and
      Khot, Tushar  and
      Sabharwal, Ashish",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1260",
    doi = "10.18653/v1/D18-1260",
    pages = "2381--2391",
    abstract = "We present a new kind of question answering dataset, OpenBookQA, modeled after open book exams for assessing human understanding of a subject. The open book that comes with our questions is a set of 1326 elementary level science facts. Roughly 6000 questions probe an understanding of these facts and their application to novel situations. This requires combining an open book fact (e.g., metals conduct electricity) with broad common knowledge (e.g., a suit of armor is made of metal) obtained from other sources. While existing QA datasets over documents or knowledge bases, being generally self-contained, focus on linguistic understanding, OpenBookQA probes a deeper understanding of both the topic{---}in the context of common knowledge{---}and the language it is expressed in. Human performance on OpenBookQA is close to 92{\%}, but many state-of-the-art pre-trained QA methods perform surprisingly poorly, worse than several simple neural baselines we develop. Our oracle experiments designed to circumvent the knowledge retrieval bottleneck demonstrate the value of both the open book and additional facts. We leave it as a challenge to solve the retrieval problem in this multi-hop setting and to close the large gap to human performance.",
}
@inproceedings{nematzadeh-etal-2018-evaluating,
    title = "Evaluating Theory of Mind in Question Answering",
    author = "Nematzadeh, Aida  and
      Burns, Kaylee  and
      Grant, Erin  and
      Gopnik, Alison  and
      Griffiths, Tom",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1261",
    doi = "10.18653/v1/D18-1261",
    pages = "2392--2400",
    abstract = "We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs. Our tasks are inspired by theory-of-mind experiments that examine whether children are able to reason about the beliefs of others, in particular when those beliefs differ from reality. We evaluate a number of recent neural models with memory augmentation. We find that all fail on our tasks, which require keeping track of inconsistent states of the world; moreover, the models{'} accuracy decreases notably when random sentences are introduced to the tasks at test.",
}
@inproceedings{li-etal-2018-unified,
    title = "A Unified Syntax-aware Framework for Semantic Role Labeling",
    author = "Li, Zuchao  and
      He, Shexia  and
      Cai, Jiaxun  and
      Zhang, Zhuosheng  and
      Zhao, Hai  and
      Liu, Gongshen  and
      Li, Linlin  and
      Si, Luo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1262",
    doi = "10.18653/v1/D18-1262",
    pages = "2401--2411",
    abstract = "Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be so important for SRL with the emerging much smaller gap between syntax-aware and syntax-agnostic SRL. To comprehensively explore the role of syntax for SRL task, we extend existing models and propose a unified framework to investigate more effective and more diverse ways of incorporating syntax into sequential neural networks. Exploring the effect of syntactic input quality on SRL performance, we confirm that high-quality syntactic parse could still effectively enhance syntactically-driven SRL. Using empirically optimized integration strategy, we even enlarge the gap between syntax-aware and syntax-agnostic SRL. Our framework achieves state-of-the-art results on CoNLL-2009 benchmarks both for English and Chinese, substantially outperforming all previous models.",
}
@inproceedings{stanovsky-dagan-2018-semantics,
    title = "Semantics as a Foreign Language",
    author = "Stanovsky, Gabriel  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1263",
    doi = "10.18653/v1/D18-1263",
    pages = "2412--2421",
    abstract = "We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency graph structure. Following, we design a neural sequence-to-sequence framework which can effectively recover our graph linearizations, performing almost on-par with previous SDP state-of-the-art while requiring less parallel training annotations. Beyond SDP, our linearization technique opens the door to integration of graph-based semantic representations as features in neural models for downstream applications.",
}
@inproceedings{liu-etal-2018-amr,
    title = "An {AMR} Aligner Tuned by Transition-based Parser",
    author = "Liu, Yijia  and
      Che, Wanxiang  and
      Zheng, Bo  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1264",
    doi = "10.18653/v1/D18-1264",
    pages = "2422--2430",
    abstract = "In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest-scored achievable AMR graph. Experimental results show that our aligner outperforms the rule-based aligner in previous work by achieving higher alignment F1 score and consistently improving two open-sourced AMR parsers. Based on our aligner and transition system, we develop a transition-based AMR parser that parses a sentence into its AMR graph directly. An ensemble of our parsers with only words and POS tags as input leads to 68.4 Smatch F1 score, which outperforms the current state-of-the-art parser.",
}
@inproceedings{jie-lu-2018-dependency,
    title = "Dependency-based Hybrid Trees for Semantic Parsing",
    author = "Jie, Zhanming  and
      Lu, Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1265",
    doi = "10.18653/v1/D18-1265",
    pages = "2431--2441",
    abstract = "We propose a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable meaning representations. Unlike previous state-of-the-art models, the semantic information is interpreted as the latent dependency between the natural language words in our joint representation. Such dependency information can capture the interactions between the semantics and natural language words. We integrate a neural component into our model and propose an efficient dynamic-programming algorithm to perform tractable inference. Through extensive experiments on the standard multilingual GeoQuery dataset with eight languages, we demonstrate that our proposed approach is able to achieve state-of-the-art performance across several languages. Analysis also justifies the effectiveness of using our new dependency-based representation.",
}
@inproceedings{misra-etal-2018-policy,
    title = "Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations",
    author = "Misra, Dipendra  and
      Chang, Ming-Wei  and
      He, Xiaodong  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1266",
    doi = "10.18653/v1/D18-1266",
    pages = "2442--2452",
    abstract = "Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0{\%} absolute on exact match accuracy.",
}
@inproceedings{mallinson-etal-2018-sentence,
    title = "Sentence Compression for Arbitrary Languages via Multilingual Pivoting",
    author = "Mallinson, Jonathan  and
      Sennrich, Rico  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1267",
    doi = "10.18653/v1/D18-1267",
    pages = "2453--2464",
    abstract = "In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.",
}
@inproceedings{xu-etal-2018-unsupervised-cross,
    title = "Unsupervised Cross-lingual Transfer of Word Embedding Spaces",
    author = "Xu, Ruochen  and
      Yang, Yiming  and
      Otani, Naoki  and
      Wu, Yuexin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1268",
    doi = "10.18653/v1/D18-1268",
    pages = "2465--2474",
    abstract = "Cross-lingual transfer of word embeddings aims to establish the semantic mappings among words in different languages by learning the transformation functions over the corresponding word embedding spaces. Successfully solving this problem would benefit many downstream tasks such as to translate text classification models from resource-rich languages (e.g. English) to low-resource languages. Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages. This paper proposes an unsupervised learning approach that does not require any cross-lingual labeled data. Given two monolingual word embedding spaces for any language pair, our algorithm optimizes the transformation functions in both directions simultaneously based on distributional matching as well as minimizing the back-translation losses. We use a neural network implementation to calculate the Sinkhorn distance, a well-defined distributional similarity measure, and optimize our objective through back-propagation. Our evaluation on benchmark datasets for bilingual lexicon induction and cross-lingual word similarity prediction shows stronger or competitive performance of the proposed method compared to other state-of-the-art supervised and unsupervised baseline methods over many language pairs.",
}
@inproceedings{conneau-etal-2018-xnli,
    title = "{XNLI}: Evaluating Cross-lingual Sentence Representations",
    author = "Conneau, Alexis  and
      Rinott, Ruty  and
      Lample, Guillaume  and
      Williams, Adina  and
      Bowman, Samuel  and
      Schwenk, Holger  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1269",
    doi = "10.18653/v1/D18-1269",
    pages = "2475--2485",
    abstract = "State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 14 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.",
}
@inproceedings{upadhyay-etal-2018-joint,
    title = "Joint Multilingual Supervision for Cross-lingual Entity Linking",
    author = "Upadhyay, Shyam  and
      Gupta, Nitish  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1270",
    doi = "10.18653/v1/D18-1270",
    pages = "2486--2495",
    abstract = "Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challenging, owing to limited availability of resources as supervision. We address this challenge by developing the first XEL approach that combines supervision from multiple languages jointly. This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language. Extensive evaluation on three benchmark datasets across 8 languages shows that our approach significantly improves over the current state-of-the-art. We also provide analyses in two limited resource settings: (a) zero-shot setting, when no supervision in the target language is available, and in (b) low-resource setting, when some supervision in the target language is available. Our analysis provides insights into the limitations of zero-shot XEL approaches in realistic scenarios, and shows the value of joint supervision in low-resource settings.",
}
@inproceedings{ge-etal-2018-fine,
    title = "Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition",
    author = "Ge, Tao  and
      Dou, Qing  and
      Ji, Heng  and
      Cui, Lei  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1271",
    doi = "10.18653/v1/D18-1271",
    pages = "2496--2506",
    abstract = "This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.",
}
@inproceedings{diao-etal-2018-weca,
    title = "{WECA}: A {W}ord{N}et-Encoded Collocation-Attention Network for Homographic Pun Recognition",
    author = "Diao, Yufeng  and
      Lin, Hongfei  and
      Wu, Di  and
      Yang, Liang  and
      Xu, Kan  and
      Yang, Zhihao  and
      Wang, Jian  and
      Zhang, Shaowu  and
      Xu, Bo  and
      Zhang, Dongyu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1272",
    doi = "10.18653/v1/D18-1272",
    pages = "2507--2516",
    abstract = "Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.",
}
@inproceedings{wang-etal-2018-hybrid,
    title = "A Hybrid Approach to Automatic Corpus Generation for {C}hinese Spelling Check",
    author = "Wang, Dingmin  and
      Song, Yan  and
      Li, Jing  and
      Han, Jialong  and
      Zhang, Haisong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1273",
    doi = "10.18653/v1/D18-1273",
    pages = "2517--2527",
    abstract = "Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples{'} daily lives. However, to utilize data-driven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCR- and ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach.",
}
@inproceedings{chollampatt-ng-2018-neural,
    title = "Neural Quality Estimation of Grammatical Error Correction",
    author = "Chollampatt, Shamil  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1274",
    doi = "10.18653/v1/D18-1274",
    pages = "2528--2539",
    abstract = "Grammatical error correction (GEC) systems deployed in language learning environments are expected to accurately correct errors in learners{'} writing. However, in practice, they often produce spurious corrections and fail to correct many errors, thereby misleading learners. This necessitates the estimation of the quality of output sentences produced by GEC systems so that instructors can selectively intervene and re-correct the sentences which are poorly corrected by the system and ensure that learners get accurate feedback. We propose the first neural approach to automatic quality estimation of GEC output sentences that does not employ any hand-crafted features. Our system is trained in a supervised manner on learner sentences and corresponding GEC system outputs with quality score labels computed using human-annotated references. Our neural quality estimation models for GEC show significant improvements over a strong feature-based baseline. We also show that a state-of-the-art GEC system can be improved when quality scores are used as features for re-ranking the N-best candidates.",
}
@inproceedings{gui-etal-2018-transferring,
    title = "Transferring from Formal Newswire Domain with Hypernet for {T}witter {POS} Tagging",
    author = "Gui, Tao  and
      Zhang, Qi  and
      Gong, Jingjing  and
      Peng, Minlong  and
      Liang, Di  and
      Ding, Keyu  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1275",
    doi = "10.18653/v1/D18-1275",
    pages = "2540--2549",
    abstract = "Part-of-Speech (POS) tagging for Twitter has received considerable attention in recent years. Because most POS tagging methods are based on supervised models, they usually require a large amount of labeled data for training. However, the existing labeled datasets for Twitter are much smaller than those for newswire text. Hence, to help POS tagging for Twitter, most domain adaptation methods try to leverage newswire datasets by learning the shared features between the two domains. However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles. Therefore, POS tagging for the formal Twitter context can be learned together with the newswire dataset, while POS tagging for the informal Twitter context should be learned separately. To achieve this task, in this work, we propose a hypernetwork-based method to generate different parameters to separately model contexts with different expression styles. Experimental results on three different datasets show that our approach achieves better performance than state-of-the-art methods in most cases.",
}
@inproceedings{krishna-etal-2018-free,
    title = "Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in {S}anskrit",
    author = "Krishna, Amrith  and
      Santra, Bishal  and
      Bandaru, Sasi Prasanth  and
      Sahu, Gaurav  and
      Sharma, Vishnu Dutt  and
      Satuluri, Pavankumar  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1276",
    doi = "10.18653/v1/D18-1276",
    pages = "2550--2561",
    abstract = "The configurational information in sentences of a free word order language such as Sanskrit is of limited use. Thus, the context of the entire sentence will be desirable even for basic processing tasks such as word segmentation. We propose a structured prediction framework that jointly solves the word segmentation and morphological tagging tasks in Sanskrit. We build an energy based model where we adopt approaches generally employed in graph based parsing techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06{\%}) while using less than one tenth of the task-specific training data. We find that the use of a graph based approach instead of a traditional lattice-based sequential labelling approach leads to a percentage gain of 12.6{\%} in F-Score for the segmentation task.",
}
@inproceedings{elkahky-etal-2018-challenge,
    title = "A Challenge Set and Methods for Noun-Verb Ambiguity",
    author = "Elkahky, Ali  and
      Webster, Kellie  and
      Andor, Daniel  and
      Pitler, Emily",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1277",
    doi = "10.18653/v1/D18-1277",
    pages = "2562--2572",
    abstract = "English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97{\%}+ accuracy on the WSJ Penn Treebank since 2002. These mistakes have been difficult to quantify and make taggers less useful to downstream tasks such as translation and text-to-speech synthesis. This paper creates a new dataset of over 30,000 naturally-occurring non-trivial examples of noun-verb ambiguity. Taggers within 1{\%} of each other when measured on the WSJ have accuracies ranging from 57{\%} to 75{\%} accuracy on this challenge set. Enhancing the strongest existing tagger with contextual word embeddings and targeted training data improves its accuracy to 89{\%}, a 14{\%} absolute (52{\%} relative) improvement. Downstream, using just this enhanced tagger yields a 28{\%} reduction in error over the prior best learned model for homograph disambiguation for textto-speech synthesis.",
}
@inproceedings{vania-etal-2018-character,
    title = "What do character-level models learn about morphology? The case of dependency parsing",
    author = "Vania, Clara  and
      Grivas, Andreas  and
      Lopez, Adam",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1278",
    doi = "10.18653/v1/D18-1278",
    pages = "2573--2583",
    abstract = "When parsing morphologically-rich languages with neural models, it is beneficial to model input at the character level, and it has been claimed that this is because character-level models learn morphology. We test these claims by comparing character-level models to an oracle with access to explicit morphological analysis on twelve languages with varying morphological typologies. Our results highlight many strengths of character-level models, but also show that they are poor at disambiguating some words, particularly in the face of case syncretism. We then demonstrate that explicitly modeling morphological case improves our best model, showing that character-level models can benefit from targeted forms of explicit morphological modeling.",
}
@inproceedings{xin-etal-2018-learning,
    title = "Learning Better Internal Structure of Words for Sequence Labeling",
    author = "Xin, Yingwei  and
      Hart, Ethan  and
      Mahajan, Vibhuti  and
      Ruvini, Jean-David",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1279",
    doi = "10.18653/v1/D18-1279",
    pages = "2584--2593",
    abstract = "Character-based neural models have recently proven very useful for many NLP tasks. However, there is a gap of sophistication between methods for learning representations of sentences and words. While, most character models for learning representations of sentences are deep and complex, models for learning representations of words are shallow and simple. Also, in spite of considerable research on learning character embeddings, it is still not clear which kind of architecture is the best for capturing character-to-word representations. To address these questions, we first investigate the gaps between methods for learning word and sentence representations. We conduct detailed experiments and comparisons on different state-of-the-art convolutional models, and also investigate the advantages and disadvantages of their constituents. Furthermore, we propose IntNet, a funnel-shaped wide convolutional neural architecture with no down-sampling for learning representations of the internal structure of words by composing their characters from limited, supervised training corpora. We evaluate our proposed model on six sequence labeling datasets, including named entity recognition, part-of-speech tagging, and syntactic chunking. Our in-depth analysis shows that IntNet significantly outperforms other character embedding models and obtains new state-of-the-art performance without relying on any external knowledge or resources.",
}
@inproceedings{hazarika-etal-2018-icon,
    title = "{ICON}: Interactive Conversational Memory Network for Multimodal Emotion Detection",
    author = "Hazarika, Devamanyu  and
      Poria, Soujanya  and
      Mihalcea, Rada  and
      Cambria, Erik  and
      Zimmermann, Roger",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1280",
    doi = "10.18653/v1/D18-1280",
    pages = "2594--2604",
    abstract = "Emotion recognition in conversations is crucial for building empathetic machines. Present works in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. Such memories generate contextual summaries which aid in predicting the emotional orientation of utterance-videos. Our model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.",
}
@inproceedings{hinami-satoh-2018-discriminative,
    title = "Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation",
    author = "Hinami, Ryota  and
      Satoh, Shin{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1281",
    doi = "10.18653/v1/D18-1281",
    pages = "2605--2615",
    abstract = "Thanks to the success of object detection technology, we can retrieve objects of the specified classes even from huge image collections. However, the current state-of-the-art object detectors (such as Faster R-CNN) can only handle pre-specified classes. In addition, large amounts of positive and negative visual samples are required for training. In this paper, we address the problem of open-vocabulary object retrieval and localization, where the target object is specified by a textual query (e.g., a word or phrase). We first propose Query-Adaptive R-CNN, a simple extension of Faster R-CNN adapted to open-vocabulary queries, by transforming the text embedding vector into an object classifier and localization regressor. Then, for discriminative training, we then propose negative phrase augmentation (NPA) to mine hard negative samples which are visually similar to the query and at the same time semantically mutually exclusive of the query. The proposed method can retrieve and localize objects specified by a textual query from one million images in only 0.5 seconds with high precision.",
}
@inproceedings{silberer-pinkal-2018-grounding,
    title = "Grounding Semantic Roles in Images",
    author = "Silberer, Carina  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1282",
    doi = "10.18653/v1/D18-1282",
    pages = "2616--2626",
    abstract = "We address the task of visual semantic role labeling (vSRL), the identification of the participants of a situation or event in a visual scene, and their labeling with their semantic relations to the event or situation. We render candidate participants as image regions of objects, and train a model which learns to ground roles in the regions which depict the corresponding participant. Experimental results demonstrate that we can train a vSRL model without reliance on prohibitive image-based role annotations, by utilizing noisy data which we extract automatically from image captions using a linguistic SRL system. Furthermore, our model induces frame{---}semantic visual representations, and their comparison to previous work on supervised visual verb sense disambiguation yields overall better results.",
}
@inproceedings{yang-etal-2018-commonsense,
    title = "Commonsense Justification for Action Explanation",
    author = "Yang, Shaohua  and
      Gao, Qiaozi  and
      Sadiya, Sari  and
      Chai, Joyce",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1283",
    doi = "10.18653/v1/D18-1283",
    pages = "2627--2637",
    abstract = "To enable collaboration and communication between humans and agents, this paper investigates learning to acquire commonsense evidence for action justification. In particular, we have developed an approach based on the generative Conditional Variational Autoencoder(CVAE) that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. Our empirical results have shown that, compared to a typical attention-based model, CVAE achieves significantly higher performance in both action prediction and justification. A human subject study further shows that the commonsense evidence gathered by CVAE can be communicated to humans to achieve a significantly higher common ground between humans and agents.",
}
@inproceedings{chu-etal-2018-learning,
    title = "Learning Personas from Dialogue with Attentive Memory Networks",
    author = "Chu, Eric  and
      Vijayaraghavan, Prashanth  and
      Roy, Deb",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1284",
    doi = "10.18653/v1/D18-1284",
    pages = "2638--2646",
    abstract = "The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings. The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.",
}
@inproceedings{ross-etal-2018-grounding,
    title = "Grounding language acquisition by training semantic parsers using captioned videos",
    author = "Ross, Candace  and
      Barbu, Andrei  and
      Berzak, Yevgeni  and
      Myanganbayar, Battushig  and
      Katz, Boris",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1285",
    doi = "10.18653/v1/D18-1285",
    pages = "2647--2656",
    abstract = "We develop a semantic parser that is trained in a grounded setting using pairs of videos captioned with sentences. This setting is both data-efficient, requiring little annotation, and similar to the experience of children where they observe their environment and listen to speakers. The semantic parser recovers the meaning of English sentences despite not having access to any annotated sentences. It does so despite the ambiguity inherent in vision where a sentence may refer to any combination of objects, object properties, relations or actions taken by any agent in a video. For this task, we collected a new dataset for grounded language acquisition. Learning a grounded semantic parser {---} turning sentences into logical forms using captioned videos {---} can significantly expand the range of data that parsers can be trained on, lower the effort of training a semantic parser, and ultimately lead to a better understanding of child language acquisition.",
}
@inproceedings{zang-etal-2018-translating,
    title = "Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation",
    author = "Zang, Xiaoxue  and
      Pokle, Ashwini  and
      V{\'a}zquez, Marynel  and
      Chen, Kevin  and
      Niebles, Juan Carlos  and
      Soto, Alvaro  and
      Savarese, Silvio",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1286",
    doi = "10.18653/v1/D18-1286",
    pages = "2657--2666",
    abstract = "We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model{'}s performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.",
}
@inproceedings{misra-etal-2018-mapping,
    title = "Mapping Instructions to Actions in 3{D} Environments with Visual Goal Prediction",
    author = "Misra, Dipendra  and
      Bennett, Andrew  and
      Blukis, Valts  and
      Niklasson, Eyvind  and
      Shatkhin, Max  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1287",
    doi = "10.18653/v1/D18-1287",
    pages = "2667--2678",
    abstract = "We propose to decompose instruction execution to goal prediction and action generation. We design a model that maps raw visual observations to goals using LINGUNET, a language-conditioned image generation network, and then generates the actions required to complete them. Our model is trained from demonstration only without external resources. To evaluate our approach, we introduce two benchmarks for instruction following: LANI, a navigation task; and CHAI, where an agent executes household instructions. Our evaluation demonstrates the advantages of our model decomposition, and illustrates the challenges posed by our new benchmarks.",
}
@inproceedings{shain-schuler-2018-deconvolutional,
    title = "Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects",
    author = "Shain, Cory  and
      Schuler, William",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1288",
    doi = "10.18653/v1/D18-1288",
    pages = "2679--2689",
    abstract = "Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects. However, time series may violate the assumptions of these models through temporal diffusion, where stimulus presentation has a lingering influence on the response as the rest of the experiment unfolds. This paper proposes a new statistical model that borrows from digital signal processing by recasting the predictors and response as convolutionally-related signals, using recent advances in machine learning to fit latent impulse response functions (IRFs) of arbitrary shape. A synthetic experiment shows successful recovery of true latent IRFs, and psycholinguistic experiments reveal plausible, replicable, and fine-grained estimates of latent temporal dynamics, with comparable or improved prediction quality to widely-used alternatives.",
}
@inproceedings{brunato-etal-2018-sentence,
    title = "Is this Sentence Difficult? Do you Agree?",
    author = "Brunato, Dominique  and
      De Mattei, Lorenzo  and
      Dell{'}Orletta, Felice  and
      Iavarone, Benedetta  and
      Venturi, Giulia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1289",
    doi = "10.18653/v1/D18-1289",
    pages = "2690--2699",
    abstract = "In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity. We collect a large corpus of sentences rated with judgments of complexity for two typologically-different languages, Italian and English. We test our approach in two experimental scenarios aimed to investigate the contribution of a wide set of lexical, morpho-syntactic and syntactic phenomena in predicting i) the degree of agreement among annotators independently from the assigned judgment and ii) the perception of sentence complexity.",
}
@inproceedings{malca-reichart-2018-neural,
    title = "Neural Transition Based Parsing of Web Queries: An Entity Based Approach",
    author = "Malca, Rivka  and
      Reichart, Roi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1290",
    doi = "10.18653/v1/D18-1290",
    pages = "2700--2710",
    abstract = "Web queries with question intent manifest a complex syntactic structure and the processing of this structure is important for their interpretation. Pinter et al. (2016) has formalized the grammar of these queries and proposed semi-supervised algorithms for the adaptation of parsers originally designed to parse according to the standard dependency grammar, so that they can account for the unique forest grammar of queries. However, their algorithms rely on resources typically not available outside of big web corporates. We propose a new BiLSTM query parser that: (1) Explicitly accounts for the unique grammar of web queries; and (2) Utilizes named entity (NE) information from a BiLSTM NE tagger, that can be jointly trained with the parser. In order to train our model we annotate the query treebank of Pinter et al. (2016) with NEs. When trained on 2500 annotated queries our parser achieves UAS of 83.5{\%} and segmentation F1-score of 84.5, substantially outperforming existing state-of-the-art parsers.",
}
@inproceedings{smith-etal-2018-investigation,
    title = "An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and {POS} Tags in Dependency Parsing",
    author = "Smith, Aaron  and
      de Lhoneux, Miryam  and
      Stymne, Sara  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1291",
    doi = "10.18653/v1/D18-1291",
    pages = "2711--2720",
    abstract = "We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser. While previous studies have shown POS information to be less important in the presence of character models, we show that in fact there are complex interactions between all three techniques. In isolation each produces large improvements over a baseline system using randomly initialised word embeddings only, but combining them quickly leads to diminishing returns. We categorise words by frequency, POS tag and language in order to systematically investigate how each of the techniques affects parsing quality. For many word categories, applying any two of the three techniques is almost as good as the full combined system. Character models tend to be more important for low-frequency open-class words, especially in morphologically rich languages, while POS tags can help disambiguate high-frequency function words. We also show that large character embedding sizes help even for languages with small character sets, especially in morphologically rich languages.",
}
@inproceedings{jin-etal-2018-depth,
    title = "Depth-bounding is effective: Improvements and evaluation of unsupervised {PCFG} induction",
    author = "Jin, Lifeng  and
      Doshi-Velez, Finale  and
      Miller, Timothy  and
      Schuler, William  and
      Schwartz, Lane",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1292",
    doi = "10.18653/v1/D18-1292",
    pages = "2721--2731",
    abstract = "There have been several recent attempts to improve the accuracy of grammar induction systems by bounding the recursive complexity of the induction model. Modern depth-bounded grammar inducers have been shown to be more accurate than early unbounded PCFG inducers, but this technique has never been compared against unbounded induction within the same system, in part because most previous depth-bounding models are built around sequence models, the complexity of which grows exponentially with the maximum allowed depth. The present work instead applies depth bounds within a chart-based Bayesian PCFG inducer, where bounding can be switched on and off, and then samples trees with or without bounding. Results show that depth-bounding is indeed significantly effective in limiting the search space of the inducer and thereby increasing accuracy of resulting parsing model, independent of the contribution of modern Bayesian induction techniques. Moreover, parsing results on English, Chinese and German show that this bounded model is able to produce parse trees more accurately than or competitively with state-of-the-art constituency grammar induction models.",
}
@inproceedings{cognetta-etal-2018-incremental,
    title = "Incremental Computation of Infix Probabilities for Probabilistic Finite Automata",
    author = "Cognetta, Marco  and
      Han, Yo-Sub  and
      Kwon, Soon Chan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1293",
    doi = "10.18653/v1/D18-1293",
    pages = "2732--2741",
    abstract = "In natural language processing, a common task is to compute the probability of a phrase appearing in a document or to calculate the probability of all phrases matching a given pattern. For instance, one computes affix (prefix, suffix, infix, etc.) probabilities of a string or a set of strings with respect to a probability distribution of patterns. The problem of computing infix probabilities of strings when the pattern distribution is given by a probabilistic context-free grammar or by a probabilistic finite automaton is already solved, yet it was open to compute the infix probabilities in an incremental manner. The incremental computation is crucial when a new query is built from a previous query. We tackle this problem and suggest a method that computes infix probabilities incrementally for probabilistic finite automata by representing all the probabilities of matching strings as a series of transition matrix calculations. We show that the proposed approach is theoretically faster than the previous method and, using real world data, demonstrate that our approach has vastly better performance in practice.",
}
@inproceedings{zhang-etal-2018-syntax,
    title = "Syntax Encoding with Application in Authorship Attribution",
    author = "Zhang, Richong  and
      Hu, Zhiyuan  and
      Guo, Hongyu  and
      Mao, Yongyi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1294",
    doi = "10.18653/v1/D18-1294",
    pages = "2742--2753",
    abstract = "We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation. The proposed syntax encoding scheme is provably information-lossless. In specific, an embedding vector is constructed for each word in the sentence, encoding the path in the syntax tree corresponding to the word. The one-to-one correspondence between these {``}syntax-embedding{''} vectors and the words (hence their embedding vectors) in the sentence makes it easy to integrate such a representation with all word-level NLP models. We empirically show the benefits of the syntax embeddings on the Authorship Attribution domain, where our approach improves upon the prior art and achieves new performance records on five benchmarking data sets.",
}
@inproceedings{hellwig-nehrdich-2018-sanskrit,
    title = "{S}anskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks",
    author = "Hellwig, Oliver  and
      Nehrdich, Sebastian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1295",
    doi = "10.18653/v1/D18-1295",
    pages = "2754--2763",
    abstract = "The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi). Tokenization of Sanskrit depends on local phonetic and distant semantic features that are incorporated using convolutional and recurrent elements. Contrary to most previous systems, our models do not require feature engineering or extern linguistic resources, but operate solely on parallel versions of raw and segmented text. The models discussed in this paper clearly improve over previous approaches to Sanskrit word segmentation. As they are language agnostic, we will demonstrate that they also outperform the state of the art for the related task of German compound splitting.",
}
@inproceedings{xiong-etal-2018-session,
    title = "Session-level Language Modeling for Conversational Speech",
    author = "Xiong, Wayne  and
      Wu, Lingfeng  and
      Zhang, Jun  and
      Stolcke, Andreas",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1296",
    doi = "10.18653/v1/D18-1296",
    pages = "2764--2768",
    abstract = "We propose to generalize language models for conversational speech recognition to allow them to operate across utterance boundaries and speaker changes, thereby capturing conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence. The model consists of a long-short-term memory (LSTM) recurrent network that reads the entire word-level history of a conversation, as well as information about turn taking and speaker overlap, in order to predict each next word. The model is applied in a rescoring framework, where the word history prior to the current utterance is approximated with preliminary recognition results. In experiments in the conversational telephone speech domain (Switchboard) we find that such a model gives substantial perplexity reductions over a standard LSTM-LM with utterance scope, as well as improvements in word error rate.",
}
@inproceedings{liu-etal-2018-towards-less,
    title = "Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method",
    author = "Liu, Yahui  and
      Bi, Wei  and
      Gao, Jun  and
      Liu, Xiaojiang  and
      Yao, Jian  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1297",
    doi = "10.18653/v1/D18-1297",
    pages = "2769--2774",
    abstract = "Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks. However, they tend to generate generic/dull responses, leading to unsatisfying dialogue experience. We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus. The objective function used in standard sequence-to-sequence models will be dominated by loss terms with generic patterns. Inspired by this observation, we introduce a statistical re-weighting method that assigns different weights for the multiple responses of the same query, and trains the common neural generation model with the weights. Experimental results on a large Chinese dialogue corpus show that our method improves the acceptance rate of generated responses compared with several baseline models and significantly reduces the number of generated generic responses.",
}
@inproceedings{mazare-etal-2018-training,
    title = "Training Millions of Personalized Dialogue Agents",
    author = "Mazar{\'e}, Pierre-Emmanuel  and
      Humeau, Samuel  and
      Raison, Martin  and
      Bordes, Antoine",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1298",
    doi = "10.18653/v1/D18-1298",
    pages = "2775--2779",
    abstract = "Current dialogue systems fail at being engaging for users, especially when trained end-to-end without relying on proactive reengaging scripted strategies. Zhang et al. (2018) showed that the engagement level of end-to-end dialogue models increases when conditioning them on text personas providing some personalized back-story to the model. However, the dataset used in Zhang et al. (2018) is synthetic and only contains around 1k different personas. In this paper we introduce a new dataset providing 5 million personas and 700 million persona-based dialogues. Our experiments show that, at this scale, training using personas still improves the performance of end-to-end systems. In addition, we show that other tasks benefit from the wide coverage of our dataset by fine-tuning our model on the data from Zhang et al. (2018) and achieving state-of-the-art results.",
}
@inproceedings{ren-etal-2018-towards,
    title = "Towards Universal Dialogue State Tracking",
    author = "Ren, Liliang  and
      Xie, Kaige  and
      Chen, Lu  and
      Yu, Kai",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1299",
    doi = "10.18653/v1/D18-1299",
    pages = "2780--2786",
    abstract = "Dialogue state tracker is the core part of a spoken dialogue system. It estimates the beliefs of possible user{'}s goals at every dialogue turn. However, for most current approaches, it{'}s difficult to scale to large dialogue domains. They have one or more of following limitations: (a) Some models don{'}t work in the situation where slot values in ontology changes dynamically; (b) The number of model parameters is proportional to the number of slots; (c) Some models extract features based on hand-crafted lexicons. To tackle these challenges, we propose StateNet, a universal dialogue state tracker. It is independent of the number of values, shares parameters across all slots, and uses pre-trained word vectors instead of explicit semantic dictionaries. Our experiments on two datasets show that our approach not only overcomes the limitations, but also significantly outperforms the performance of state-of-the-art approaches.",
}
@inproceedings{gupta-etal-2018-semantic-parsing,
    title = "Semantic Parsing for Task Oriented Dialog using Hierarchical Representations",
    author = "Gupta, Sonal  and
      Shah, Rushin  and
      Mohit, Mrinal  and
      Kumar, Anuj  and
      Lewis, Mike",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1300",
    doi = "10.18653/v1/D18-1300",
    pages = "2787--2792",
    abstract = "Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on task oriented intent and slot-filling work has been restricted to one intent per query and one slot label per token, and thus cannot model complex compositional requests. Alternative semantic parsing systems have represented queries as logical forms, but these are challenging to annotate and parse. We propose a hierarchical annotation scheme for semantic parsing that allows the representation of compositional queries, and can be efficiently and accurately parsed by standard constituency parsing models. We release a dataset of 44k annotated queries (\url{http://fb.me/semanticparsingdialog}), and show that parsing models outperform sequence-to-sequence approaches on this dataset.",
}
@inproceedings{schluter-2018-glass,
    title = "The glass ceiling in {NLP}",
    author = "Schluter, Natalie",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1301",
    doi = "10.18653/v1/D18-1301",
    pages = "2793--2798",
    abstract = "In this paper, we provide empirical evidence based on a rigourously studied mathematical model for bi-populated networks, that a glass ceiling within the field of NLP has developed since the mid 2000s.",
}
@inproceedings{park-etal-2018-reducing,
    title = "Reducing Gender Bias in Abusive Language Detection",
    author = "Park, Ji Ho  and
      Shin, Jamin  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1302",
    doi = "10.18653/v1/D18-1302",
    pages = "2799--2804",
    abstract = "Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, {``}You are a good woman{''} was considered {``}sexist{''} when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure them on models trained with different datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce model bias by 90-98{\%} and can be extended to correct model bias in other scenarios.",
}
@inproceedings{karlekar-bansal-2018-safecity,
    title = "{S}afe{C}ity: Understanding Diverse Forms of Sexual Harassment Personal Stories",
    author = "Karlekar, Sweta  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1303",
    doi = "10.18653/v1/D18-1303",
    pages = "2805--2811",
    abstract = "With the recent rise of {\#}MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. In order to push forward the fight against such harassment and abuse, we present the task of automatically categorizing and analyzing various forms of sexual harassment, based on stories shared on the online forum SafeCity. For the labels of groping, ogling, and commenting, our single-label CNN-RNN model achieves an accuracy of 86.5{\%}, and our multi-label model achieves a Hamming score of 82.5{\%}. Furthermore, we present analysis using LIME, first-derivative saliency heatmaps, activation clustering, and embedding visualization to interpret neural model predictions and demonstrate how this helps extract features that can help automatically fill out incident reports, identify unsafe areas, avoid unsafe practices, and {`}pin the creeps{'}.",
}
@inproceedings{pou-prom-rudzicz-2018-learning,
    title = "Learning multiview embeddings for assessing dementia",
    author = "Pou-Prom, Chlo{\'e}  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1304",
    doi = "10.18653/v1/D18-1304",
    pages = "2812--2817",
    abstract = "As the incidence of Alzheimer{'}s Disease (AD) increases, early detection becomes crucial. Unfortunately, datasets for AD assessment are often sparse and incomplete. In this work, we leverage the multiview nature of a small AD dataset, DementiaBank, to learn an embedding that captures different modes of cognitive impairment. We apply generalized canonical correlation analysis (GCCA) to our dataset and demonstrate the added benefit of using multiview embeddings in two downstream tasks: identifying AD and predicting clinical scores. By including multiview embeddings, we obtain an F1 score of 0.82 in the classification task and a mean absolute error of 3.42 in the regression task. Furthermore, we show that multiview embeddings can be obtained from other datasets as well.",
}
@inproceedings{hua-etal-2018-wikiconv,
    title = "{W}iki{C}onv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community",
    author = "Hua, Yiqing  and
      Danescu-Niculescu-Mizil, Cristian  and
      Taraborelli, Dario  and
      Thain, Nithum  and
      Sorensen, Jeffery  and
      Dixon, Lucas",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1305",
    doi = "10.18653/v1/D18-1305",
    pages = "2818--2823",
    abstract = "We present a corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations - including not only comments and replies, but also their modifications, deletions and restorations - this data offers an unprecedented view of online conversation. Our framework is designed to be language agnostic, and we show that it extracts high quality data in both Chinese and English. This level of detail supports new research questions pertaining to the process (and challenges) of large-scale online collaboration. We illustrate the corpus{'} potential with two case studies on English Wikipedia that highlight new perspectives on earlier work. First, we explore how a person{'}s conversational behavior depends on how they relate to the discussion{'}s venue. Second, we show that community moderation of toxic behavior happens at a higher rate than previously estimated.",
}
@inproceedings{greenberg-etal-2018-marginal,
    title = "Marginal Likelihood Training of {B}i{LSTM}-{CRF} for Biomedical Named Entity Recognition from Disjoint Label Sets",
    author = "Greenberg, Nathan  and
      Bansal, Trapit  and
      Verga, Patrick  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1306",
    doi = "10.18653/v1/D18-1306",
    pages = "2824--2829",
    abstract = "Extracting typed entity mentions from text is a fundamental component to language understanding and reasoning. While there exist substantial labeled text datasets for multiple subsets of biomedical entity types{---}such as genes and proteins, or chemicals and diseases{---}it is rare to find large labeled datasets containing labels for all desired entity types together. This paper presents a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. Our approach employs marginal likelihood training to insist on labels that are present in the data, while filling in {``}missing labels{''}. This allows us to leverage all the available data within a single model. In experimental results on the Biocreative V CDR (chemicals/diseases), Biocreative VI ChemProt (chemicals/proteins) and MedMentions (19 entity types) datasets, we show that joint training on multiple datasets improves NER F1 over training in isolation, and our methods achieve state-of-the-art results.",
}
@inproceedings{bekoulis-etal-2018-adversarial,
    title = "Adversarial training for multi-context joint entity and relation extraction",
    author = "Bekoulis, Giannis  and
      Deleu, Johannes  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1307",
    doi = "10.18653/v1/D18-1307",
    pages = "2830--2836",
    abstract = "Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).",
}
@inproceedings{singh-etal-2018-structured,
    title = "Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding",
    author = "Singh, Gaurav  and
      Thomas, James  and
      Marshall, Iain  and
      Shawe-Taylor, John  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1308",
    doi = "10.18653/v1/D18-1308",
    pages = "2837--2842",
    abstract = "We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.",
}
@inproceedings{sohrab-miwa-2018-deep,
    title = "Deep Exhaustive Model for Nested Named Entity Recognition",
    author = "Sohrab, Mohammad Golam  and
      Miwa, Makoto",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1309",
    doi = "10.18653/v1/D18-1309",
    pages = "2843--2849",
    abstract = "We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity mentions and classify them with deep neural networks. To reduce the computational costs and capture the information of the contexts around the regions, the model represents the regions using the outputs of shared underlying bidirectional long short-term memory. We evaluate our exhaustive model on the GENIA and JNLPBA corpora in biomedical domain, and the results show that our model outperforms state-of-the-art models on nested and flat NER, achieving 77.1{\%} and 78.4{\%} respectively in terms of F-score, without any external knowledge resources.",
}
@inproceedings{wu-etal-2018-evaluating,
    title = "Evaluating the Utility of Hand-crafted Features in Sequence Labelling",
    author = "Wu, Minghao  and
      Liu, Fei  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1310",
    doi = "10.18653/v1/D18-1310",
    pages = "2850--2856",
    abstract = "Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component. We evaluate on the task of named entity recognition (NER), where we show that including manual features for part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a F 1 of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models. We also present an ablation study showing the importance of auto-encoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60{\%}, while retaining the same predictive accuracy.",
}
@inproceedings{wang-etal-2018-improved,
    title = "Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data",
    author = "Wang, Wenhui  and
      Chang, Baobao  and
      Mansur, Mairgup",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1311",
    doi = "10.18653/v1/D18-1311",
    pages = "2857--2863",
    abstract = "Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35{\%} UAS and 95.25{\%} LAS.",
}
@inproceedings{dehouck-denis-2018-framework,
    title = "A Framework for Understanding the Role of Morphology in {U}niversal {D}ependency Parsing",
    author = "Dehouck, Mathieu  and
      Denis, Pascal",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1312",
    doi = "10.18653/v1/D18-1312",
    pages = "2864--2870",
    abstract = "This paper presents a simple framework for characterizing morphological complexity and how it encodes syntactic information. In particular, we propose a new measure of morpho-syntactic complexity in terms of governor-dependent preferential attachment that explains parsing performance. Through experiments on dependency parsing with data from Universal Dependencies (UD), we show that representations derived from morphological attributes deliver important parsing performance improvements over standard word form embeddings when trained on the same datasets. We also show that the new morpho-syntactic complexity measure is predictive of the gains provided by using morphological attributes over plain forms on parsing scores, making it a tool to distinguish languages using morphology as a syntactic marker from others.",
}
@inproceedings{bisazza-tump-2018-lazy,
    title = "The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation",
    author = "Bisazza, Arianna  and
      Tump, Clara",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1313",
    doi = "10.18653/v1/D18-1313",
    pages = "2871--2876",
    abstract = "Neural sequence-to-sequence models have proven very effective for machine translation, but at the expense of model interpretability. To shed more light into the role played by linguistic structure in the process of neural machine translation, we perform a fine-grained analysis of how various source-side morphological features are captured at different levels of the NMT encoder while varying the target language. Differently from previous work, we find no correlation between the accuracy of source morphology encoding and translation quality. We do find that morphological features are only captured in context and only to the extent that they are directly transferable to the target words.",
}
@inproceedings{makarov-clematide-2018-imitation,
    title = "Imitation Learning for Neural Morphological String Transduction",
    author = "Makarov, Peter  and
      Clematide, Simon",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1314",
    doi = "10.18653/v1/D18-1314",
    pages = "2877--2882",
    abstract = "We employ imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. Previous approaches to training this type of model either rely on an external character aligner for the production of gold action sequences, which results in a suboptimal model due to the unwarranted dependence on a single gold action sequence despite spurious ambiguity, or require warm starting with an MLE model. Our approach only requires a simple expert policy, eliminating the need for a character aligner or warm start. It also addresses familiar MLE training biases and leads to strong and state-of-the-art performance on several benchmarks.",
}
@inproceedings{silfverberg-hulden-2018-encoder,
    title = "An Encoder-Decoder Approach to the Paradigm Cell Filling Problem",
    author = "Silfverberg, Miikka  and
      Hulden, Mans",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1315",
    doi = "10.18653/v1/D18-1315",
    pages = "2883--2889",
    abstract = "The Paradigm Cell Filling Problem in morphology asks to complete word inflection tables from partial ones. We implement novel neural models for this task, evaluating them on 18 data sets in 8 languages, showing performance that is comparable with previous work with far less training data. We also publish a new dataset for this task and code implementing the system described in this paper.",
}
@inproceedings{alzantot-etal-2018-generating,
    title = "Generating Natural Language Adversarial Examples",
    author = "Alzantot, Moustafa  and
      Sharma, Yash  and
      Elgohary, Ahmed  and
      Ho, Bo-Jhang  and
      Srivastava, Mani  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1316",
    doi = "10.18653/v1/D18-1316",
    pages = "2890--2896",
    abstract = "Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. In the image domain, these perturbations can often be made virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree. However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document. Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97{\%} and 70{\%}, respectively. We additionally demonstrate that 92.3{\%} of the successful sentiment analysis adversarial examples are classified to their original label by 20 human annotators, and that the examples are perceptibly quite similar. Finally, we discuss an attempt to use adversarial training as a defense, but fail to yield improvement, demonstrating the strength and diversity of our adversarial examples. We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.",
}
@inproceedings{li-etal-2018-multi-head,
    title = "Multi-Head Attention with Disagreement Regularization",
    author = "Li, Jian  and
      Tu, Zhaopeng  and
      Yang, Baosong  and
      Lyu, Michael R.  and
      Zhang, Tong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1317",
    doi = "10.18653/v1/D18-1317",
    pages = "2897--2903",
    abstract = "Multi-head attention is appealing for the ability to jointly attend to information from different representation subspaces at different positions. In this work, we introduce a disagreement regularization to explicitly encourage the diversity among multiple attention heads. Specifically, we propose three types of disagreement regularization, which respectively encourage the subspace, the attended positions, and the output representation associated with each attention head to be different from other heads. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness and universality of the proposed approach.",
}
@inproceedings{siddhant-lipton-2018-deep,
    title = "Deep {B}ayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study",
    author = "Siddhant, Aditya  and
      Lipton, Zachary C.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1318",
    doi = "10.18653/v1/D18-1318",
    pages = "2904--2909",
    abstract = "Several recent papers investigate Active Learning (AL) for mitigating the data dependence of deep learning for natural language processing. However, the applicability of AL to real-world problems remains an open question. While in supervised learning, practitioners can try many different methods, evaluating each against a validation set before selecting a model, AL affords no such luxury. Over the course of one AL run, an agent annotates its dataset exhausting its labeling budget. Thus, given a new task, we have no opportunity to compare models and acquisition functions. This paper provides a large-scale empirical study of deep active learning, addressing multiple tasks and, for each, multiple datasets, multiple models, and a full suite of acquisition functions. We find that across all settings, Bayesian active learning by disagreement, using uncertainty estimates provided either by Dropout or Bayes-by-Backprop significantly improves over i.i.d. baselines and usually outperforms classic uncertainty sampling.",
}
@inproceedings{chirkova-etal-2018-bayesian,
    title = "{B}ayesian Compression for Natural Language Processing",
    author = "Chirkova, Nadezhda  and
      Lobacheva, Ekaterina  and
      Vetrov, Dmitry",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1319",
    doi = "10.18653/v1/D18-1319",
    pages = "2910--2915",
    abstract = "In natural language processing, a lot of the tasks are successfully solved with recurrent neural networks, but such models have a huge number of parameters. The majority of these parameters are often concentrated in the embedding layer, which size grows proportionally to the vocabulary length. We propose a Bayesian sparsification technique for RNNs which allows compressing the RNN dozens or hundreds of times without time-consuming hyperparameters tuning. We also generalize the model for vocabulary sparsification to filter out unnecessary words and compress the RNN even further. We show that the choice of the kept words is interpretable.",
}
@inproceedings{nguyen-etal-2018-multimodal,
    title = "Multimodal neural pronunciation modeling for spoken languages with logographic origin",
    author = "Nguyen, Minh  and
      Ngo, Gia H.  and
      Chen, Nancy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1320",
    doi = "10.18653/v1/D18-1320",
    pages = "2916--2922",
    abstract = "Graphemes of most languages encode pronunciation, though some are more explicit than others. Languages like Spanish have a straightforward mapping between its graphemes and phonemes, while this mapping is more convoluted for languages like English. Spoken languages such as Cantonese present even more challenges in pronunciation modeling: (1) they do not have a standard written form, (2) the closest graphemic origins are logographic Han characters, of which only a subset of these logographic characters implicitly encodes pronunciation. In this work, we propose a multimodal approach to predict the pronunciation of Cantonese logographic characters, using neural networks with a geometric representation of logographs and pronunciation of cognates in historically related languages. The proposed framework improves performance by 18.1{\%} and 25.0{\%} respective to unimodal and multimodal baselines.",
}
@inproceedings{huang-zhao-2018-chinese,
    title = "{C}hinese {P}inyin Aided {IME}, Input What You Have Not Keystroked Yet",
    author = "Huang, Yafang  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1321",
    doi = "10.18653/v1/D18-1321",
    pages = "2923--2929",
    abstract = "Chinese pinyin input method engine (IME) converts pinyin into character so that Chinese characters can be conveniently inputted into computer through common keyboard. IMEs work relying on its core component, pinyin-to-character conversion (P2C). Usually Chinese IMEs simply predict a list of character sequences for user choice only according to user pinyin input at each turn. However, Chinese inputting is a multi-turn online procedure, which can be supposed to be exploited for further user experience promoting. This paper thus for the first time introduces a sequence-to-sequence model with gated-attention mechanism for the core task in IMEs. The proposed neural P2C model is learned by encoding previous input utterance as extra context to enable our IME capable of predicting character sequence with incomplete pinyin input. Our model is evaluated in different benchmark datasets showing great user experience improvement compared to traditional models, which demonstrates the first engineering practice of building Chinese aided IME.",
}
@inproceedings{noraset-etal-2018-estimating,
    title = "Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models",
    author = "Noraset, Thanapon  and
      Downey, Doug  and
      Bing, Lidong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1322",
    doi = "10.18653/v1/D18-1322",
    pages = "2930--2935",
    abstract = "Recurrent neural network language models (RNNLMs) are the current standard-bearer for statistical language modeling. However, RNNLMs only estimate probabilities for complete sequences of text, whereas some applications require context-independent phrase probabilities instead. In this paper, we study how to compute an RNNLM{'}s em marginal probability: the probability that the model assigns to a short sequence of text when the preceding context is not known. We introduce a simple method of altering the RNNLM training to make the model more accurate at marginal estimation. Our experiments demonstrate that the technique is effective compared to baselines including the traditional RNNLM probability and an importance sampling approach. Finally, we show how we can use the marginal estimation to improve an RNNLM by training the marginals to match n-gram probabilities from a larger corpus.",
}
@inproceedings{gulordava-etal-2018-represent,
    title = "How to represent a word and predict it, too: Improving tied architectures for language modelling",
    author = "Gulordava, Kristina  and
      Aina, Laura  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1323",
    doi = "10.18653/v1/D18-1323",
    pages = "2936--2941",
    abstract = "Recent state-of-the-art neural language models share the representations of words given by the input and output mappings. We propose a simple modification to these architectures that decouples the hidden state from the word embedding prediction. Our architecture leads to comparable or better results compared to previous tied models and models without tying, with a much smaller number of parameters. We also extend our proposal to word2vec models, showing that tying is appropriate for general word prediction tasks.",
}
@inproceedings{ford-etal-2018-importance,
    title = "The Importance of Generation Order in Language Modeling",
    author = "Ford, Nicolas  and
      Duckworth, Daniel  and
      Norouzi, Mohammad  and
      Dahl, George",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1324",
    doi = "10.18653/v1/D18-1324",
    pages = "2942--2946",
    abstract = "Neural language models are a critical component of state-of-the-art systems for machine translation, summarization, audio transcription, and other tasks. These language models are almost universally autoregressive in nature, generating sentences one token at a time from left to right. This paper studies the influence of token generation order on model quality via a novel two-pass language model that produces partially-filled sentence {``}templates{''} and then fills in missing tokens. We compare various strategies for structuring these two passes and observe a surprisingly large variation in model quality. We find the most effective strategy generates function words in the first pass followed by content words in the second. We believe these experimental results justify a more extensive investigation of the generation order for neural language models.",
}
@inproceedings{miculicich-etal-2018-document,
    title = "Document-Level Neural Machine Translation with Hierarchical Attention Networks",
    author = "Miculicich, Lesly  and
      Ram, Dhananjay  and
      Pappas, Nikolaos  and
      Henderson, James",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1325",
    doi = "10.18653/v1/D18-1325",
    pages = "2947--2954",
    abstract = "Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model{'}s own previous hidden states. Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.",
}
@inproceedings{wang-etal-2018-three,
    title = "Three Strategies to Improve One-to-Many Multilingual Translation",
    author = "Wang, Yining  and
      Zhang, Jiajun  and
      Zhai, Feifei  and
      Xu, Jingfang  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1326",
    doi = "10.18653/v1/D18-1326",
    pages = "2955--2960",
    abstract = "Due to the benefits of model compactness, multilingual translation (including many-to-one, many-to-many and one-to-many) based on a universal encoder-decoder architecture attracts more and more attention. However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models. In this work, we introduce three strategies to improve one-to-many multilingual translation by balancing the shared and unique features. Within the architecture of one decoder for all target languages, we first exploit the use of unique initial states for different target languages. Then, we employ language-dependent positional embeddings. Finally and especially, we propose to divide the hidden cells of the decoder into shared and language-dependent ones. The extensive experiments demonstrate that our proposed methods can obtain remarkable improvements over the strong baselines. Moreover, our strategies can achieve comparable or even better performance than the individually trained translation models.",
}
@inproceedings{currey-heafield-2018-multi,
    title = "Multi-Source Syntactic Neural Machine Translation",
    author = "Currey, Anna  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1327",
    doi = "10.18653/v1/D18-1327",
    pages = "2961--2966",
    abstract = "We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses. This is achieved by employing separate encoders for the sequential and parsed versions of the same source sentence; the resulting representations are then combined using a hierarchical attention mechanism. The proposed model improves over both seq2seq and parsed baselines by over 1 BLEU on the WMT17 English-German task. Further analysis shows that our multi-source syntactic model is able to translate successfully without any parsed input, unlike standard parsed methods. In addition, performance does not deteriorate as much on long sentences as for the baselines.",
}
@inproceedings{pham-etal-2018-fixing,
    title = "Fixing Translation Divergences in Parallel Corpora for Neural {MT}",
    author = "Pham, MinhQuang  and
      Crego, Josep  and
      Senellart, Jean  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1328",
    doi = "10.18653/v1/D18-1328",
    pages = "2967--2973",
    abstract = "Corpus-based approaches to machine translation rely on the availability of clean parallel corpora. Such resources are scarce, and because of the automatic processes involved in their preparation, they are often noisy. This paper describes an unsupervised method for detecting translation divergences in parallel sentences. We rely on a neural network that computes cross-lingual sentence similarity scores, which are then used to effectively filter out divergent translations. Furthermore, similarity scores predicted by the network are used to identify and fix some partial divergences, yielding additional parallel segments. We evaluate these methods for English-French and English-German machine translation tasks, and show that using filtered/corrected corpora actually improves MT performance.",
}
@inproceedings{elliott-2018-adversarial,
    title = "Adversarial Evaluation of Multimodal Machine Translation",
    author = "Elliott, Desmond",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1329",
    doi = "10.18653/v1/D18-1329",
    pages = "2974--2978",
    abstract = "The promise of combining language and vision in multimodal machine translation is that systems will produce better translations by leveraging the image data. However, the evidence surrounding whether the images are useful is unconvincing due to inconsistencies between text-similarity metrics and human judgements. We present an adversarial evaluation to directly examine the utility of the image data in this task. Our evaluation tests whether systems perform better when paired with congruent images or incongruent images. This evaluation shows that only one out of three publicly available systems is sensitive to this perturbation of the data. We recommend that multimodal translation systems should be able to pass this sanity check in the future.",
}
@inproceedings{joulin-etal-2018-loss,
    title = "Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion",
    author = "Joulin, Armand  and
      Bojanowski, Piotr  and
      Mikolov, Tomas  and
      J{\'e}gou, Herv{\'e}  and
      Grave, Edouard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1330",
    doi = "10.18653/v1/D18-1330",
    pages = "2979--2984",
    abstract = "Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a quadratic problem to learn a orthogonal matrix aligning a bilingual lexicon, and use a retrieval criterion for inference. In this paper, we propose an unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. Our experiments on standard benchmarks show that our approach outperforms the state of the art on word translation, with the biggest improvements observed for distant language pairs such as English-Chinese.",
}
@inproceedings{lin-etal-2018-learning-concentrate,
    title = "Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation",
    author = "Lin, Junyang  and
      Sun, Xu  and
      Ren, Xuancheng  and
      Li, Muyu  and
      Su, Qi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1331",
    doi = "10.18653/v1/D18-1331",
    pages = "2985--2990",
    abstract = "Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism. However, the conventional attention mechanism treats the decoding at each time step equally with the same matrix, which is problematic since the softness of the attention for different types of words (e.g. content words and function words) should differ. Therefore, we propose a new model with a mechanism called Self-Adaptive Control of Temperature (SACT) to control the softness of attention by means of an attention temperature. Experimental results on the Chinese-English translation and English-Vietnamese translation demonstrate that our model outperforms the baseline models, and the analysis and the case study show that our model can attend to the most relevant elements in the source-side contexts and generate the translation of high quality.",
}
@inproceedings{bogoychev-etal-2018-accelerating,
    title = "Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation",
    author = "Bogoychev, Nikolay  and
      Heafield, Kenneth  and
      Aji, Alham Fikri  and
      Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1332",
    doi = "10.18653/v1/D18-1332",
    pages = "2991--2996",
    abstract = "In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly. In order to achieve further speedup we introduce a technique that delays gradient updates effectively increasing the mini-batch size. Unfortunately with the increase of mini-batch size we worsen the stale gradient problem in asynchronous stochastic gradient descent (SGD) which makes the model convergence poor. We introduce local optimizers which mitigate the stale gradient problem and together with fine tuning our momentum we are able to train a shallow machine translation system 27{\%} faster than an optimized baseline with negligible penalty in BLEU.",
}
@inproceedings{wang-etal-2018-learning-jointly,
    title = "Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism",
    author = "Wang, Longyue  and
      Tu, Zhaopeng  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1333",
    doi = "10.18653/v1/D18-1333",
    pages = "2997--3002",
    abstract = "Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. Recently, Wang et al. (2018) proposed a novel reconstruction-based approach to alleviating dropped pronoun (DP) translation problems for neural machine translation models. In this work, we improve the original model from two perspectives. First, we employ a shared reconstructor to better exploit encoder and decoder representations. Second, we jointly learn to translate and predict DPs in an end-to-end manner, to avoid the errors propagated from an external DP prediction model. Experimental results show that our approach significantly improves both translation performance and DP prediction accuracy.",
}
@inproceedings{vanmassenhove-etal-2018-getting,
    title = "Getting Gender Right in Neural Machine Translation",
    author = "Vanmassenhove, Eva  and
      Hardmeier, Christian  and
      Way, Andy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1334",
    doi = "10.18653/v1/D18-1334",
    pages = "3003--3008",
    abstract = "Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such difference is related to the way gender is expressed in a language. Saying {``}I am happy{''} in English, does not encode any additional knowledge of the speaker that uttered the sentence. However, many other languages do have grammatical gender systems and so such knowledge would be encoded. In order to correctly translate such a sentence into, say, French, the inherent gender information needs to be retained/recovered. The same sentence would become either {``}Je suis heureux{''}, for a male speaker or {``}Je suis heureuse{''} for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.) also influence our use of language in terms of word choices or syntactic constructions (Tannen, 1991; Pennebaker et al., 2003). We integrate gender information into NMT systems. Our contribution is two-fold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that adding a gender feature to an NMT system significantly improves the translation quality for some language pairs.",
}
@inproceedings{bahar-etal-2018-towards,
    title = "Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation",
    author = "Bahar, Parnia  and
      Brix, Christopher  and
      Ney, Hermann",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1335",
    doi = "10.18653/v1/D18-1335",
    pages = "3009--3015",
    abstract = "This work investigates an alternative model for neural machine translation (NMT) and proposes a novel architecture, where we employ a multi-dimensional long short-term memory (MDLSTM) for translation modelling. In the state-of-the-art methods, source and target sentences are treated as one-dimensional sequences over time, while we view translation as a two-dimensional (2D) mapping using an MDLSTM layer to define the correspondence between source and target words. We extend beyond the current sequence to sequence backbone NMT models to a 2D structure in which the source and target sentences are aligned with each other in a 2D grid. Our proposed topology shows consistent improvements over attention-based sequence to sequence model on two WMT 2017 tasks, German{\textless}-{\textgreater}English.",
}
@inproceedings{libovicky-helcl-2018-end,
    title = "End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Helcl, Jind{\v{r}}ich",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1336",
    doi = "10.18653/v1/D18-1336",
    pages = "3016--3021",
    abstract = "Autoregressive decoding is the only part of sequence-to-sequence models that prevents them from massive parallelization at inference time. Non-autoregressive models enable the decoder to generate all output symbols independently in parallel. We present a novel non-autoregressive architecture based on connectionist temporal classification and evaluate it on the task of neural machine translation. Unlike other non-autoregressive methods which operate in several steps, our model can be trained end-to-end. We conduct experiments on the WMT English-Romanian and English-German datasets. Our models achieve a significant speedup over the autoregressive models, keeping the translation quality comparable to other non-autoregressive models.",
}
@inproceedings{alinejad-etal-2018-prediction,
    title = "Prediction Improves Simultaneous Neural Machine Translation",
    author = "Alinejad, Ashkan  and
      Siahbani, Maryam  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1337",
    doi = "10.18653/v1/D18-1337",
    pages = "3022--3027",
    abstract = "Simultaneous speech translation aims to maintain translation quality while minimizing the delay between reading input and incrementally producing the output. We propose a new general-purpose prediction action which predicts future words in the input to improve quality and minimize delay in simultaneous translation. We train this agent using reinforcement learning with a novel reward function. Our agent with prediction has better translation quality and less delay compared to an agent-based simultaneous translation system without prediction.",
}
@inproceedings{bapna-etal-2018-training,
    title = "Training Deeper Neural Machine Translation Models with Transparent Attention",
    author = "Bapna, Ankur  and
      Chen, Mia  and
      Firat, Orhan  and
      Cao, Yuan  and
      Wu, Yonghui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1338",
    doi = "10.18653/v1/D18-1338",
    pages = "3028--3033",
    abstract = "While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models used for both text and vision applications. In this work we attempt to train significantly (2-3x) deeper Transformer and Bi-RNN encoders for machine translation. We propose a simple modification to the attention mechanism that eases the optimization of deeper models, and results in consistent gains of 0.7-1.1 BLEU on the benchmark WMT{'}14 English-German and WMT{'}15 Czech-English tasks for both architectures.",
}
@inproceedings{knowles-koehn-2018-context,
    title = "Context and Copying in Neural Machine Translation",
    author = "Knowles, Rebecca  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1339",
    doi = "10.18653/v1/D18-1339",
    pages = "3034--3041",
    abstract = "Neural machine translation systems with subword vocabularies are capable of translating or copying unknown words. In this work, we show that they learn to copy words based on both the context in which the words appear as well as features of the words themselves. In contexts that are particularly copy-prone, they even copy words that they have already learned they should translate. We examine the influence of context and subword features on this and other types of copying behavior.",
}
@inproceedings{cao-xiong-2018-encoding,
    title = "Encoding Gated Translation Memory into Neural Machine Translation",
    author = "Cao, Qian  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1340",
    doi = "10.18653/v1/D18-1340",
    pages = "3042--3047",
    abstract = "Translation memories (TM) facilitate human translators to reuse existing repetitive translation fragments. In this paper, we propose a novel method to combine the strengths of both TM and neural machine translation (NMT) for high-quality translation. We treat the target translation of a TM match as an additional reference input and encode it into NMT with an extra encoder. A gating mechanism is further used to balance the impact of the TM match on the NMT decoder. Experiment results on the UN corpus demonstrate that when fuzzy matches are higher than 50{\%}, the quality of NMT translation can be significantly improved by over 10 BLEU points.",
}
@inproceedings{vu-haffari-2018-automatic,
    title = "Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach",
    author = "Vu, Thuy-Trang  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1341",
    doi = "10.18653/v1/D18-1341",
    pages = "3048--3053",
    abstract = "Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output. In this paper, we present a neural programmer-interpreter approach to this task, resembling the way that human perform post-editing using discrete edit operations, wich we refer to as programs. Our model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores.",
}
@inproceedings{yang-etal-2018-breaking,
    title = "Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation",
    author = "Yang, Yilin  and
      Huang, Liang  and
      Ma, Mingbo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1342",
    doi = "10.18653/v1/D18-1342",
    pages = "3054--3059",
    abstract = "Beam search is widely used in neural machine translation, and usually improves translation quality compared to greedy search. It has been widely observed that, however, beam sizes larger than 5 hurt translation quality. We explain why this happens, and propose several methods to address this problem. Furthermore, we discuss the optimal stopping criteria for these methods. Results show that our hyperparameter-free methods outperform the widely-used hyperparameter-free heuristic of length normalization by +2.0 BLEU, and achieve the best results among all methods on Chinese-to-English translation.",
}
@inproceedings{yaghoobzadeh-schutze-2018-multi,
    title = "Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing",
    author = {Yaghoobzadeh, Yadollah  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1343",
    doi = "10.18653/v1/D18-1343",
    pages = "3060--3066",
    abstract = "Accurate and complete knowledge bases (KBs) are paramount in NLP. We employ mul-itiview learning for increasing the accuracy and coverage of entity type information in KBs. We rely on two metaviews: language and representation. For language, we consider high-resource and low-resource languages from Wikipedia. For representation, we consider representations based on the context distribution of the entity (i.e., on its embedding), on the entity{'}s name (i.e., on its surface form) and on its description in Wikipedia. The two metaviews language and representation can be freely combined: each pair of language and representation (e.g., German embedding, English description, Spanish name) is a distinct view. Our experiments on entity typing with fine-grained classes demonstrate the effectiveness of multiview learning. We release MVET, a large multiview {---} and, in particular, multilingual {---} entity typing dataset we created. Mono- and multilingual fine-grained entity typing systems can be evaluated on this dataset.",
}
@inproceedings{pratapa-etal-2018-word,
    title = "Word Embeddings for Code-Mixed Language Processing",
    author = "Pratapa, Adithya  and
      Choudhury, Monojit  and
      Sitaram, Sunayana",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1344",
    doi = "10.18653/v1/D18-1344",
    pages = "3067--3072",
    abstract = "We compare three existing bilingual word embedding approaches, and a novel approach of training skip-grams on synthetic code-mixed text generated through linguistic models of code-mixing, on two tasks - sentiment analysis and POS tagging for code-mixed text. Our results show that while CVM and CCA based embeddings perform as well as the proposed embedding technique on semantic and syntactic tasks respectively, the proposed approach provides the best performance for both tasks overall. Thus, this study demonstrates that existing bilingual embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embedding from the code-mixed text.",
}
@inproceedings{yu-etal-2018-strength,
    title = "On the Strength of Character Language Models for Multilingual Named Entity Recognition",
    author = "Yu, Xiaodong  and
      Mayhew, Stephen  and
      Sammons, Mark  and
      Roth, Dan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1345",
    doi = "10.18653/v1/D18-1345",
    pages = "3073--3077",
    abstract = "Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and nonname tokens in text, nor whether this property holds across multiple languages. This paper analyzes the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens. We demonstrate that CLMs provide a simple and powerful model for capturing these differences, identifying named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an off-the-shelf NER system for multiple languages.",
}
@inproceedings{garg-etal-2018-code,
    title = "Code-switched Language Models Using Dual {RNN}s and Same-Source Pretraining",
    author = "Garg, Saurabh  and
      Parekh, Tanmay  and
      Jyothi, Preethi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1346",
    doi = "10.18653/v1/D18-1346",
    pages = "3078--3083",
    abstract = "This work focuses on building language models (LMs) for code-switched text. We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately 2) Pretraining the LM using synthetic text from a generative model estimated using the training data. We demonstrate the effectiveness of our proposed techniques by reporting perplexities on a Mandarin-English task and derive significant reductions in perplexity.",
}
@inproceedings{ball-garrette-2018-part,
    title = "Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification",
    author = "Ball, Kelsey  and
      Garrette, Dan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1347",
    doi = "10.18653/v1/D18-1347",
    pages = "3084--3089",
    abstract = "Code-switching, the use of more than one language within a single utterance, is ubiquitous in much of the world, but remains a challenge for NLP largely due to the lack of representative data for training models. In this paper, we present a novel model architecture that is trained exclusively on monolingual resources, but can be applied to unseen code-switched text at inference time. The model accomplishes this by jointly maintaining separate word representations for each of the possible languages, or scripts in the case of transliteration, allowing each to contribute to inferences without forcing the model to commit to a language. Experiments on Hindi-English part-of-speech tagging demonstrate that our approach outperforms standard models when training on monolingual text without transliteration, and testing on code-switched text with alternate scripts.",
}
@inproceedings{xia-etal-2018-zero,
    title = "Zero-shot User Intent Detection via Capsule Neural Networks",
    author = "Xia, Congying  and
      Zhang, Chenwei  and
      Yan, Xiaohui  and
      Chang, Yi  and
      Yu, Philip",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1348",
    doi = "10.18653/v1/D18-1348",
    pages = "3090--3099",
    abstract = "User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users{'} utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: IntentCapsNet that extracts semantic features from utterances and aggregates them to discriminate existing intents, and IntentCapsNet-ZSL which gives IntentCapsNet the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.",
}
@inproceedings{jin-szolovits-2018-hierarchical,
    title = "Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts",
    author = "Jin, Di  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1349",
    doi = "10.18653/v1/D18-1349",
    pages = "3100--3109",
    abstract = "Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2{\%}-3{\%} on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.",
}
@inproceedings{zhao-etal-2018-investigating,
    title = "Investigating Capsule Networks with Dynamic Routing for Text Classification",
    author = "Zhao, Wei  and
      Ye, Jianbo  and
      Yang, Min  and
      Lei, Zeyang  and
      Zhang, Suofei  and
      Zhao, Zhou",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1350",
    doi = "10.18653/v1/D18-1350",
    pages = "3110--3119",
    abstract = "In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain {``}background{''} information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.",
}
@inproceedings{zeng-etal-2018-topic,
    title = "Topic Memory Networks for Short Text Classification",
    author = "Zeng, Jichuan  and
      Li, Jing  and
      Song, Yan  and
      Gao, Cuiyun  and
      Lyu, Michael R.  and
      King, Irwin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1351",
    doi = "10.18653/v1/D18-1351",
    pages = "3120--3131",
    abstract = "Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.",
}
@inproceedings{rios-kavuluru-2018-shot,
    title = "Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces",
    author = "Rios, Anthony  and
      Kavuluru, Ramakanth",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1352",
    doi = "10.18653/v1/D18-1352",
    pages = "3132--3142",
    abstract = "Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III. For few-shot labels we achieve improvements of 6.2{\%} and 4.8{\%} in R@10 for MIMIC II and MIMIC III, respectively, over prior efforts; the corresponding R@10 improvements for zero-shot labels are 17.3{\%} and 19{\%}.",
}
@inproceedings{yi-etal-2018-automatic,
    title = "Automatic Poetry Generation with Mutual Reinforcement Learning",
    author = "Yi, Xiaoyuan  and
      Sun, Maosong  and
      Li, Ruoyu  and
      Li, Wenhao",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1353",
    doi = "10.18653/v1/D18-1353",
    pages = "3143--3153",
    abstract = "Poetry is one of the most beautiful forms of human language art. As a crucial step towards computer creativity, automatic poetry generation has drawn researchers{'} attention for decades. In recent years, some neural models have made remarkable progress in this task. However, they are all based on maximum likelihood estimation, which only learns common patterns of the corpus and results in loss-evaluation mismatch. Human experts evaluate poetry in terms of some specific criteria, instead of word-level likelihood. To handle this problem, we directly model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning, so as to motivate the model to pursue higher scores. Besides, inspired by writing theories, we propose a novel mutual reinforcement learning schema. We simultaneously train two learners (generators) which learn not only from the teacher (rewarder) but also from each other to further improve performance. We experiment on Chinese poetry. Based on a strong basic model, our method achieves better results and outperforms the current state-of-the-art method.",
}
@inproceedings{du-etal-2018-variational,
    title = "Variational Autoregressive Decoder for Neural Response Generation",
    author = "Du, Jiachen  and
      Li, Wenjie  and
      He, Yulan  and
      Xu, Ruifeng  and
      Bing, Lidong  and
      Wang, Xuan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1354",
    doi = "10.18653/v1/D18-1354",
    pages = "3154--3163",
    abstract = "Combining the virtues of probability graphic models and neural networks, Conditional Variational Auto-encoder (CVAE) has shown promising performance in applications such as response generation. However, existing CVAE-based models often generate responses from a single latent variable which may not be sufficient to model high variability in responses. To solve this problem, we propose a novel model that sequentially introduces a series of latent variables to condition the generation of each word in the response sequence. In addition, the approximate posteriors of these latent variables are augmented with a backward Recurrent Neural Network (RNN), which allows the latent variables to capture long-term dependencies of future tokens in generation. To facilitate training, we supplement our model with an auxiliary objective that predicts the subsequent bag of words. Empirical experiments conducted on Opensubtitle and Reddit datasets show that the proposed model leads to significant improvement on both relevance and diversity over state-of-the-art baselines.",
}
@inproceedings{zhao-etal-2018-integrating,
    title = "Integrating Transformer and Paraphrase Rules for Sentence Simplification",
    author = "Zhao, Sanqiang  and
      Meng, Rui  and
      He, Daqing  and
      Saptono, Andi  and
      Parmanto, Bambang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1355",
    doi = "10.18653/v1/D18-1355",
    pages = "3164--3173",
    abstract = "Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we propose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state-of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at \url{https://github.com/Sanqiang/text_simplification}.",
}
@inproceedings{wiseman-etal-2018-learning,
    title = "Learning Neural Templates for Text Generation",
    author = "Wiseman, Sam  and
      Shieber, Stuart  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1356",
    doi = "10.18653/v1/D18-1356",
    pages = "3174--3187",
    abstract = "While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.",
}
@inproceedings{zheng-etal-2018-multi,
    title = "Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation",
    author = "Zheng, Renjie  and
      Ma, Mingbo  and
      Huang, Liang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1357",
    doi = "10.18653/v1/D18-1357",
    pages = "3188--3197",
    abstract = "Neural text generation, including neural machine translation, image captioning, and summarization, has been quite successful recently. However, during training time, typically only one reference is considered for each example, even though there are often multiple references available, e.g., 4 references in NIST MT evaluations, and 5 references in image captioning data. We first investigate several different ways of utilizing multiple human references during training. But more importantly, we then propose an algorithm to generate exponentially many pseudo-references by first compressing existing human references into lattices and then traversing them to generate new pseudo-references. These approaches lead to substantial improvements over strong baselines in both machine translation (+1.5 BLEU) and image captioning (+3.1 BLEU / +11.7 CIDEr).",
}
@inproceedings{zhang-etal-2018-knowledge,
    title = "Knowledge Graph Embedding with Hierarchical Relation Structure",
    author = "Zhang, Zhao  and
      Zhuang, Fuzhen  and
      Qu, Meng  and
      Lin, Fen  and
      He, Qing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1358",
    doi = "10.18653/v1/D18-1358",
    pages = "3198--3207",
    abstract = "The rapid development of knowledge graphs (KGs), such as Freebase and WordNet, has changed the paradigm for AI-related applications. However, even though these KGs are impressively large, most of them are suffering from incompleteness, which leads to performance degradation of AI applications. Most existing researches are focusing on knowledge graph embedding (KGE) models. Nevertheless, those models simply embed entities and relations into latent vectors without leveraging the rich information from the relation structure. Indeed, relations in KGs conform to a three-layer hierarchical relation structure (HRS), i.e., semantically similar relations can make up relation clusters and some relations can be further split into several fine-grained sub-relations. Relation clusters, relations and sub-relations can fit in the top, the middle and the bottom layer of three-layer HRS respectively. To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS. Particularly, our approach is capable to extend other KGE models. Finally, the experiment results clearly validate the effectiveness of the proposed approach against baselines.",
}
@inproceedings{pezeshkpour-etal-2018-embedding,
    title = "Embedding Multimodal Relational Data for Knowledge Base Completion",
    author = "Pezeshkpour, Pouya  and
      Chen, Liyan  and
      Singh, Sameer",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1359",
    doi = "10.18653/v1/D18-1359",
    pages = "3208--3218",
    abstract = "Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, images, and numerical values. In this paper, we propose multimodal knowledge base embeddings (MKBE) that use different neural encoders for this variety of observed data, and combine them with existing relational models to learn embeddings of the entities and multimodal data. Further, using these learned embedings and different neural decoders, we introduce a novel multimodal imputation model to generate missing multimodal values, like text and images, from information in the knowledge base. We enrich existing relational datasets to create two novel benchmarks that contain additional information such as textual descriptions and images of the original entities. We demonstrate that our models utilize this additional information effectively to provide more accurate link prediction, achieving state-of-the-art results with a considerable gap of 5-7{\%} over existing methods. Further, we evaluate the quality of our generated multimodal values via a user study.",
}
@inproceedings{luan-etal-2018-multi,
    title = "Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction",
    author = "Luan, Yi  and
      He, Luheng  and
      Ostendorf, Mari  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1360",
    doi = "10.18653/v1/D18-1360",
    pages = "3219--3232",
    abstract = "We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.",
}
@inproceedings{hu-etal-2018-playing,
    title = "Playing 20 Question Game with Policy-Based Reinforcement Learning",
    author = "Hu, Huang  and
      Wu, Xianchao  and
      Luo, Bingfeng  and
      Tao, Chongyang  and
      Xu, Can  and
      Wu, Wei  and
      Chen, Zhan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1361",
    doi = "10.18653/v1/D18-1361",
    pages = "3233--3242",
    abstract = "The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity. In the game, the answerer first thinks of an object such as a famous person or a kind of animal. Then the questioner tries to guess the object by asking 20 questions. In a Q20 game system, the user is considered as the answerer while the system itself acts as the questioner which requires a good strategy of question selection to figure out the correct object and win the game. However, the optimal policy of question selection is hard to be derived due to the complexity and volatility of the game environment. In this paper, we propose a novel policy-based Reinforcement Learning (RL) method, which enables the questioner agent to learn the optimal policy of question selection through continuous interactions with users. To facilitate training, we also propose to use a reward network to estimate the more informative reward. Compared to previous methods, our RL method is robust to noisy answers and does not rely on the Knowledge Base of objects. Experimental results show that our RL method clearly outperforms an entropy-based engineering system and has competitive performance in a noisy-free simulation environment.",
}
@inproceedings{lin-etal-2018-multi-hop,
    title = "Multi-Hop Knowledge Graph Reasoning with Reward Shaping",
    author = "Lin, Xi Victoria  and
      Socher, Richard  and
      Xiong, Caiming",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1362",
    doi = "10.18653/v1/D18-1362",
    pages = "3243--3253",
    abstract = "Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is used for training, the agent can be misled by spurious search trajectories that incidentally lead to the correct answer. We propose two modeling advances to address both issues: (1) we reduce the impact of false negative supervision by adopting a pretrained one-hop embedding model to estimate the reward of unobserved facts; (2) we counter the sensitivity to spurious paths of on-policy RL by forcing the agent to explore a diverse set of paths using randomly generated edge masks. Our approach significantly improves over existing path-based KGQA models on several benchmark datasets and is comparable or better than embedding-based models.",
}
@inproceedings{kann-schutze-2018-neural,
    title = "Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting",
    author = {Kann, Katharina  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1363",
    doi = "10.18653/v1/D18-1363",
    pages = "3254--3264",
    abstract = "Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets. We address paradigm completion, the morphological task of, given a partial paradigm, generating all missing forms. We propose two new methods for the minimal-resource setting: (i) Paradigm transduction: Since we assume only few paradigms available for training, neural seq2seq models are able to capture relationships between paradigm cells, but are tied to the idiosyncracies of the training set. Paradigm transduction mitigates this problem by exploiting the input subset of inflected forms at test time. (ii) Source selection with high precision (SHIP): Multi-source models which learn to automatically select one or multiple sources to predict a target inflection do not perform well in the minimal-resource setting. SHIP is an alternative to identify a reliable source if training data is limited. On a 52-language benchmark dataset, we outperform the previous state of the art by up to 9.71{\%} absolute accuracy.",
}
@inproceedings{magri-2018-implicational,
    title = "Implicational Universals in Stochastic Constraint-Based Phonology",
    author = "Magri, Giorgio",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1364",
    doi = "10.18653/v1/D18-1364",
    pages = "3265--3274",
    abstract = "This paper focuses on the most basic implicational universals in phonological theory, called T-orders after Anttila and Andrus (2006). It shows that the T-orders predicted by stochastic (and partial order) Optimality Theory coincide with those predicted by categorical OT. Analogously, the T-orders predicted by stochastic Harmonic Grammar coincide with those predicted by categorical HG. In other words, these stochastic constraint-based frameworks do not tamper with the typological structure induced by the original categorical frameworks.",
}
@inproceedings{godin-etal-2018-explaining,
    title = "Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?",
    author = "Godin, Fr{\'e}deric  and
      Demuynck, Kris  and
      Dambre, Joni  and
      De Neve, Wesley  and
      Demeester, Thomas",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1365",
    doi = "10.18653/v1/D18-1365",
    pages = "3275--3284",
    abstract = "Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations. To that end, we extend the contextual decomposition technique (Murdoch et al. 2018) to convolutional neural networks which allows us to compare convolutional neural networks and bidirectional long short-term memory networks. We evaluate and compare these models for the task of morphological tagging on three morphologically different languages and show that these models implicitly discover understandable linguistic rules.",
}
@inproceedings{chaudhary-etal-2018-adapting,
    title = "Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations",
    author = "Chaudhary, Aditi  and
      Zhou, Chunting  and
      Levin, Lori  and
      Neubig, Graham  and
      Mortensen, David R.  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1366",
    doi = "10.18653/v1/D18-1366",
    pages = "3285--3295",
    abstract = "Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU.",
}
@inproceedings{troiano-etal-2018-computational,
    title = "A Computational Exploration of Exaggeration",
    author = {Troiano, Enrica  and
      Strapparava, Carlo  and
      {\"O}zbal, G{\"o}zde  and
      Tekiro{\u{g}}lu, Serra Sinem},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1367",
    doi = "10.18653/v1/D18-1367",
    pages = "3296--3304",
    abstract = "Several NLP studies address the problem of figurative language, but among non-literal phenomena, they have neglected exaggeration. This paper presents a first computational approach to this figure of speech. We explore the possibility to automatically detect exaggerated sentences. First, we introduce HYPO, a corpus containing overstatements (or hyperboles) collected on the web and validated via crowdsourcing. Then, we evaluate a number of models trained on HYPO, and bring evidence that the task of hyperbole identification can be successfully performed based on a small set of semantic features.",
}
@inproceedings{dai-huang-2018-building,
    title = "Building Context-aware Clause Representations for Situation Entity Type Classification",
    author = "Dai, Zeyu  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1368",
    doi = "10.18653/v1/D18-1368",
    pages = "3305--3315",
    abstract = "Capabilities to categorize a clause based on the type of situation entity (e.g., events, states and generic statements) the clause introduces to the discourse can benefit many NLP applications. Observing that the situation entity type of a clause depends on discourse functions the clause plays in a paragraph and the interpretation of discourse functions depends heavily on paragraph-wide contexts, we propose to build context-aware clause representations for predicting situation entity types of clauses. Specifically, we propose a hierarchical recurrent neural network model to read a whole paragraph at a time and jointly learn representations for all the clauses in the paragraph by extensively modeling context influences and inter-dependencies of clauses. Experimental results show that our model achieves the state-of-the-art performance for clause-level situation entity classification on the genre-rich MASC+Wiki corpus, which approaches human-level performance.",
}
@inproceedings{seonwoo-etal-2018-hierarchical,
    title = "Hierarchical {D}irichlet {G}aussian Marked {H}awkes Process for Narrative Reconstruction in Continuous Time Domain",
    author = "Seonwoo, Yeon  and
      Oh, Alice  and
      Park, Sungjoon",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1369",
    doi = "10.18653/v1/D18-1369",
    pages = "3316--3325",
    abstract = "In news and discussions, many articles and posts are provided without their related previous articles or posts. Hence, it is difficult to understand the context from which the articles and posts have occurred. In this paper, we propose the Hierarchical Dirichlet Gaussian Marked Hawkes process (HD-GMHP) for reconstructing the narratives and thread structures of news articles and discussion posts. HD-GMHP unifies three modeling strategies in previous research: temporal characteristics, triggering event relations, and meta information of text in news articles and discussion threads. To show the effectiveness of the model, we perform experiments in narrative reconstruction and thread reconstruction with real world datasets: articles from the New York Times and a corpus of Wikipedia conversations. The experimental results show that HD-GMHP outperforms the baselines of LDA, HDP, and HDHP for both tasks.",
}
@inproceedings{lauscher-etal-2018-investigating,
    title = "Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models",
    author = "Lauscher, Anne  and
      Glava{\v{s}}, Goran  and
      Ponzetto, Simone Paolo  and
      Eckert, Kai",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1370",
    doi = "10.18653/v1/D18-1370",
    pages = "3326--3338",
    abstract = "Exponential growth in the number of scientific publications yields the need for effective automatic analysis of rhetorical aspects of scientific writing. Acknowledging the argumentative nature of scientific text, in this work we investigate the link between the argumentative structure of scientific publications and rhetorical aspects such as discourse categories or citation contexts. To this end, we (1) augment a corpus of scientific publications annotated with four layers of rhetoric annotations with argumentation annotations and (2) investigate neural multi-task learning architectures combining argument extraction with a set of rhetorical classification tasks. By coupling rhetorical classifiers with the extraction of argumentative components in a joint multi-task learning setting, we obtain significant performance gains for different rhetorical analysis tasks.",
}
@inproceedings{zhang-xue-2018-neural,
    title = "Neural Ranking Models for Temporal Dependency Structure Parsing",
    author = "Zhang, Yuchen  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1371",
    doi = "10.18653/v1/D18-1371",
    pages = "3339--3349",
    abstract = "We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure. We evaluate our parser on two domains: news reports and narrative stories. In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches. In an end-to-end evaluation setup where time expressions and events are automatically recognized, our parser beats two strong baselines on both data domains. Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this area.",
}
@inproceedings{son-etal-2018-causal,
    title = "Causal Explanation Analysis on Social Media",
    author = "Son, Youngseo  and
      Bayas, Nipun  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1372",
    doi = "10.18653/v1/D18-1372",
    pages = "3350--3359",
    abstract = "Understanding causal explanations - reasons given for happenings in one{'}s life - has been found to be an important psychological factor linked to physical and mental health. Causal explanations are often studied through manual identification of phrases over limited samples of personal writing. Automatic identification of causal explanations in social media, while challenging in relying on contextual and sequential cues, offers a larger-scale alternative to expensive manual ratings and opens the door for new applications (e.g. studying prevailing beliefs about causes, such as climate change). Here, we explore automating causal explanation analysis, building on discourse parsing, and presenting two novel subtasks: causality detection (determining whether a causal explanation exists at all) and causal explanation identification (identifying the specific phrase that is the explanation). We achieve strong accuracies for both tasks but find different approaches best: an SVM for causality prediction (F1 = 0.791) and a hierarchy of Bidirectional LSTMs for causal explanation identification (F1 = 0.853). Finally, we explore applications of our complete pipeline (F1 = 0.868), showing demographic differences in mentions of causal explanation and that the association between a word and sentiment can change when it is used within a causal explanation.",
}
@inproceedings{wang-etal-2018-lrmm,
    title = "{LRMM}: Learning to Recommend with Missing Modalities",
    author = "Wang, Cheng  and
      Niepert, Mathias  and
      Li, Hui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1373",
    doi = "10.18653/v1/D18-1373",
    pages = "3360--3370",
    abstract = "Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.",
}
@inproceedings{lukasik-zens-2018-content,
    title = "Content Explorer: Recommending Novel Entities for a Document Writer",
    author = "Lukasik, Michal  and
      Zens, Richard",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1374",
    doi = "10.18653/v1/D18-1374",
    pages = "3371--3380",
    abstract = "Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research. Automated tools could help significantly in this discovery process and increase the productivity of the writer. In this paper, we formulate the problem of recommending topics to a writer. We consider this as a supervised learning problem and run a user study to validate this approach. We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set. We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.",
}
@inproceedings{maharjan-etal-2018-genre,
    title = "A Genre-Aware Attention Model to Improve the Likability Prediction of Books",
    author = "Maharjan, Suraj  and
      Montes, Manuel  and
      Gonz{\'a}lez, Fabio A.  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1375",
    doi = "10.18653/v1/D18-1375",
    pages = "3381--3391",
    abstract = "Likability prediction of books has many uses. Readers, writers, as well as the publishing industry, can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architecture that incorporates genre supervision to assign weights to individual feature types. Our proposed method is capable of dynamically tailoring weights given to feature types based on the characteristics of each book. Our architecture achieves competitive results and even outperforms state-of-the-art for this task.",
}
@inproceedings{chan-king-2018-thread,
    title = "Thread Popularity Prediction and Tracking with a Permutation-invariant Model",
    author = "Chan, Hou Pong  and
      King, Irwin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1376",
    doi = "10.18653/v1/D18-1376",
    pages = "3392--3401",
    abstract = "The task of thread popularity prediction and tracking aims to recommend a few popular comments to subscribed users when a batch of new comments arrive in a discussion thread. This task has been formulated as a reinforcement learning problem, in which the reward of the agent is the sum of positive responses received by the recommended comments. In this work, we propose a novel approach to tackle this problem. First, we propose a deep neural network architecture to model the expected cumulative reward (Q-value) of a recommendation (action). Unlike the state-of-the-art approach, which treats an action as a sequence, our model uses an attention mechanism to integrate information from a set of comments. Thus, the prediction of Q-value is invariant to the permutation of the comments, which leads to a more consistent agent behavior. Second, we employ a greedy procedure to approximate the action that maximizes the predicted Q-value from a combinatorial action space. Different from the state-of-the-art approach, this procedure does not require an additional pre-trained model to generate candidate actions. Experiments on five real-world datasets show that our approach outperforms the state-of-the-art.",
}
@inproceedings{majumder-etal-2018-iarm,
    title = "{IARM}: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis",
    author = "Majumder, Navonil  and
      Poria, Soujanya  and
      Gelbukh, Alexander  and
      Akhtar, Md. Shad  and
      Cambria, Erik  and
      Ekbal, Asif",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1377",
    doi = "10.18653/v1/D18-1377",
    pages = "3402--3411",
    abstract = "Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6{\%} on average in two distinct domains: restaurant and laptop.",
}
@inproceedings{zhang-singh-2018-limbic,
    title = "{L}imbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations",
    author = "Zhang, Zhe  and
      Singh, Munindar",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1378",
    doi = "10.18653/v1/D18-1378",
    pages = "3412--3422",
    abstract = "We propose Limbic, an unsupervised probabilistic model that addresses the problem of discovering aspects and sentiments and associating them with authors of opinionated texts. Limbic combines three ideas, incorporating authors, discourse relations, and word embeddings. For discourse relations, Limbic adopts a generative process regularized by a Markov Random Field. To promote words with high semantic similarity into the same topic, Limbic captures semantic regularities from word embeddings via a generalized P{\'o}lya Urn process. We demonstrate that Limbic (1) discovers aspects associated with sentiments with high lexical diversity; (2) outperforms state-of-the-art models by a substantial margin in topic cohesion and sentiment classification.",
}
@inproceedings{yang-etal-2018-interpretable,
    title = "An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking",
    author = "Yang, Yang  and
      Zhou, Deyu  and
      He, Yulan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1379",
    doi = "10.18653/v1/D18-1379",
    pages = "3423--3432",
    abstract = "Text might express or evoke multiple emotions with varying intensities. As such, it is crucial to predict and rank multiple relevant emotions by their intensities. Moreover, as emotions might be evoked by hidden topics, it is important to unveil and incorporate such topical information to understand how the emotions are evoked. We proposed a novel interpretable neural network approach for relevant emotion ranking. Specifically, motivated by transfer learning, the neural network is initialized to make the hidden layer approximate the behavior of topic models. Moreover, a novel error function is defined to optimize the whole neural network for relevant emotion ranking. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label learning methods. Moreover, the extracted emotion-associated topic words indeed represent emotion-evoking events and are in line with our common-sense knowledge.",
}
@inproceedings{fan-etal-2018-multi,
    title = "Multi-grained Attention Network for Aspect-Level Sentiment Classification",
    author = "Fan, Feifan  and
      Feng, Yansong  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1380",
    doi = "10.18653/v1/D18-1380",
    pages = "3433--3442",
    abstract = "We propose a novel multi-grained attention network (MGAN) model for aspect level sentiment classification. Existing approaches mostly adopt coarse-grained attention mechanism, which may bring information loss if the aspect has multiple words or larger context. We propose a fine-grained attention mechanism, which can capture the word-level interaction between aspect and context. And then we leverage the fine-grained and coarse-grained attention mechanisms to compose the MGAN framework. Moreover, unlike previous works which train each aspect with its context separately, we design an aspect alignment loss to depict the aspect-level interactions among the aspects that have the same context. We evaluate the proposed approach on three datasets: laptop and restaurant are from SemEval 2014, and the last one is a twitter dataset. Experimental results show that the multi-grained attention network consistently outperforms the state-of-the-art methods on all three datasets. We also conduct experiments to evaluate the effectiveness of aspect alignment loss, which indicates the aspect-level interactions can bring extra useful information and further improve the performance.",
}
@inproceedings{tay-etal-2018-attentive,
    title = "Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification",
    author = "Tay, Yi  and
      Luu, Anh Tuan  and
      Hui, Siu Cheung  and
      Su, Jian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1381",
    doi = "10.18653/v1/D18-1381",
    pages = "3443--3453",
    abstract = "This paper proposes a new neural architecture that exploits readily available sentiment lexicon resources. The key idea is that that incorporating a word-level prior can aid in the representation learning process, eventually improving model performance. To this end, our model employs two distinctly unique components, i.e., (1) we introduce a lexicon-driven contextual attention mechanism to imbue lexicon words with long-range contextual information and (2), we introduce a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence. Via extensive experiments, we show that our approach outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.",
}
@inproceedings{ghosal-etal-2018-contextual,
    title = "Contextual Inter-modal Attention for Multi-modal Sentiment Analysis",
    author = "Ghosal, Deepanway  and
      Akhtar, Md Shad  and
      Chauhan, Dushyant  and
      Poria, Soujanya  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1382",
    doi = "10.18653/v1/D18-1382",
    pages = "3454--3466",
    abstract = "Multi-modal sentiment analysis offers various challenges, one being the effective combination of different input modalities, namely text, visual and acoustic. In this paper, we propose a recurrent neural network based multi-modal attention framework that leverages the contextual information for utterance-level sentiment prediction. The proposed approach applies attention on multi-modal multi-utterance representations and tries to learn the contributing features amongst them. We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets, viz. CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) corpus and the recently released CMU Multi-modal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Evaluation results show the effectiveness of our proposed approach with the accuracies of 82.31{\%} and 79.80{\%} for the MOSI and MOSEI datasets, respectively. These are approximately 2 and 1 points performance improvement over the state-of-the-art models for the datasets.",
}
@inproceedings{he-etal-2018-adaptive,
    title = "Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification",
    author = "He, Ruidan  and
      Lee, Wee Sun  and
      Ng, Hwee Tou  and
      Dahlmeier, Daniel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1383",
    doi = "10.18653/v1/D18-1383",
    pages = "3467--3476",
    abstract = "We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations {---} entropy minimization and self-ensemble bootstrapping {---} to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.",
}
@inproceedings{luo-etal-2018-extra,
    title = "{E}xt{RA}: Extracting Prominent Review Aspects from Customer Feedback",
    author = "Luo, Zhiyi  and
      Huang, Shanshan  and
      Xu, Frank F.  and
      Lin, Bill Yuchen  and
      Shi, Hanyuan  and
      Zhu, Kenny",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1384",
    doi = "10.18653/v1/D18-1384",
    pages = "3477--3486",
    abstract = "Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost every day. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.",
}
@inproceedings{wen-etal-2018-cross,
    title = "Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content",
    author = "Wen, Weiming  and
      Su, Songwen  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1385",
    doi = "10.18653/v1/D18-1385",
    pages = "3487--3496",
    abstract = "With the increasing popularity of smart devices, rumors with multimedia content become more and more common on social networks. The multimedia information usually makes rumors look more convincing. Therefore, finding an automatic approach to verify rumors with multimedia content is a pressing task. Previous rumor verification research only utilizes multimedia as input features. We propose not to use the multimedia content but to find external information in other news platforms pivoting on it. We introduce a new features set, cross-lingual cross-platform features that leverage the semantic similarity between the rumors and the external information. When implemented, machine learning methods utilizing such features achieved the state-of-the-art rumor verification results.",
}
@inproceedings{carton-etal-2018-extractive,
    title = "Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts",
    author = "Carton, Samuel  and
      Mei, Qiaozhu  and
      Resnick, Paul",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1386",
    doi = "10.18653/v1/D18-1386",
    pages = "3497--3507",
    abstract = "We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via hard attention, we add an adversarial layer which scans the residual of the attention for remaining predictive signal. Motivated by the important domain of detecting personal attacks in social media comments, we additionally demonstrate the importance of manually setting a semantically appropriate {``}default{''} behavior for the model by explicitly manipulating its bias term. We develop a validation set of human-annotated personal attacks to evaluate the impact of these changes.",
}
@inproceedings{lebanoff-liu-2018-automatic,
    title = "Automatic Detection of Vague Words and Sentences in Privacy Policies",
    author = "Lebanoff, Logan  and
      Liu, Fei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1387",
    doi = "10.18653/v1/D18-1387",
    pages = "3508--3517",
    abstract = "Website privacy policies represent the single most important source of information for users to gauge how their personal data are collected, used and shared by companies. However, privacy policies are often vague and people struggle to understand the content. Their opaqueness poses a significant challenge to both users and policy regulators. In this paper, we seek to identify vague content in privacy policies. We construct the first corpus of human-annotated vague words and sentences and present empirical studies on automatic vagueness detection. In particular, we investigate context-aware and context-agnostic models for predicting vague words, and explore auxiliary-classifier generative adversarial networks for characterizing sentence vagueness. Our experimental results demonstrate the effectiveness of proposed approaches. Finally, we provide suggestions for resolving vagueness and improving the usability of privacy policies.",
}
@inproceedings{kulkarni-etal-2018-multi,
    title = "Multi-view Models for Political Ideology Detection of News Articles",
    author = "Kulkarni, Vivek  and
      Ye, Junting  and
      Skiena, Steve  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1388",
    doi = "10.18653/v1/D18-1388",
    pages = "3518--3527",
    abstract = "A news article{'}s title, content and link structure often reveal its political ideology. However, most existing works on automatic political ideology detection only leverage textual cues. Drawing inspiration from recent advances in neural inference, we propose a novel attention based multi-view model to leverage cues from all of the above views to identify the ideology evinced by a news article. Our model draws on advances in representation learning in natural language processing and network science to capture cues from both textual content and the network structure of news articles. We empirically evaluate our model against a battery of baselines and show that our model outperforms state of the art by 10 percentage points F1 score.",
}
@inproceedings{baly-etal-2018-predicting,
    title = "Predicting Factuality of Reporting and Bias of News Media Sources",
    author = "Baly, Ramy  and
      Karadzhov, Georgi  and
      Alexandrov, Dimitar  and
      Glass, James  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1389",
    doi = "10.18653/v1/D18-1389",
    pages = "3528--3539",
    abstract = "We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. This is an under-studied, but arguably important research problem, both in its own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news media, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baseline, and reveal the importance of each feature type.",
}
@inproceedings{zhong-etal-2018-legal,
    title = "Legal Judgment Prediction via Topological Learning",
    author = "Zhong, Haoxi  and
      Guo, Zhipeng  and
      Tu, Cunchao  and
      Xiao, Chaojun  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1390",
    doi = "10.18653/v1/D18-1390",
    pages = "3540--3549",
    abstract = "Legal Judgment Prediction (LJP) aims to predict the judgment result based on the facts of a case and becomes a promising application of artificial intelligence techniques in the legal field. In real-world scenarios, legal judgment usually consists of multiple subtasks, such as the decisions of applicable law articles, charges, fines, and the term of penalty. Moreover, there exist topological dependencies among these subtasks. While most existing works only focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks, we formalize the dependencies among subtasks as a Directed Acyclic Graph (DAG) and propose a topological multi-task learning framework, TopJudge, which incorporates multiple subtasks and DAG dependencies into judgment prediction. We conduct experiments on several real-world large-scale datasets of criminal cases in the civil law system. Experimental results show that our model achieves consistent and significant improvements over baselines on all judgment prediction tasks. The source code can be obtained from \url{https://github.com/thunlp/TopJudge}.",
}
@inproceedings{qian-etal-2018-hierarchical,
    title = "Hierarchical {CVAE} for Fine-Grained Hate Speech Classification",
    author = "Qian, Jing  and
      ElSherief, Mai  and
      Belding, Elizabeth  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1391",
    doi = "10.18653/v1/D18-1391",
    pages = "3550--3559",
    abstract = "Existing work on automated hate speech detection typically focuses on binary classification or on differentiating among a small set of categories. In this paper, we propose a novel method on a fine-grained hate speech classification task, which focuses on differentiating among 40 hate groups of 13 different hate group categories. We first explore the Conditional Variational Autoencoder (CVAE) as a discriminative model and then extend it to a hierarchical architecture to utilize the additional hate category information for more accurate prediction. Experimentally, we show that incorporating the hate category information for training can significantly improve the classification performance and our proposed model outperforms commonly-used discriminative models.",
}
@inproceedings{zamani-etal-2018-residualized,
    title = "Residualized Factor Adaptation for Community Social Media Prediction Tasks",
    author = "Zamani, Mohammadzaman  and
      Schwartz, H. Andrew  and
      Lynn, Veronica  and
      Giorgi, Salvatore  and
      Balasubramanian, Niranjan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1392",
    doi = "10.18653/v1/D18-1392",
    pages = "3560--3569",
    abstract = "Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the language originates. For example, it may be inaccurate to assume people in Mobile, Alabama, where the population is relatively older, will use words the same way as those from San Francisco, where the median age is younger with a higher rate of college education. In this paper, we present residualized factor adaptation, a novel approach to community prediction tasks which both (a) effectively integrates community attributes, as well as (b) adapts linguistic features to community attributes (factors). We use eleven demographic and socioeconomic attributes, and evaluate our approach over five different community-level predictive tasks, spanning health (heart disease mortality, percent fair/poor health), psychology (life satisfaction), and economics (percent housing price increase, foreclosure rate). Our evaluation shows that residualized factor adaptation significantly improves 4 out of 5 community-level outcome predictions over prior state-of-the-art for incorporating socio-demographic contexts.",
}
@inproceedings{field-etal-2018-framing,
    title = "Framing and Agenda-setting in {R}ussian News: a Computational Analysis of Intricate Political Strategies",
    author = "Field, Anjalie  and
      Kliger, Doron  and
      Wintner, Shuly  and
      Pan, Jennifer  and
      Jurafsky, Dan  and
      Tsvetkov, Yulia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1393",
    doi = "10.18653/v1/D18-1393",
    pages = "3570--3580",
    abstract = "Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and {``}fake news{''}. Here, we draw on two concepts from political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered). We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia. We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.",
}
@inproceedings{kleinberg-etal-2018-identifying,
    title = "Identifying the sentiment styles of {Y}ou{T}ube{'}s vloggers",
    author = "Kleinberg, Bennett  and
      Mozes, Maximilian  and
      van der Vegt, Isabelle",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1394",
    doi = "10.18653/v1/D18-1394",
    pages = "3581--3590",
    abstract = "Vlogs provide a rich public source of data in a novel setting. This paper examined the continuous sentiment styles employed in 27,333 vlogs using a dynamic intra-textual approach to sentiment analysis. Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog{'}s narrative time. We provide a taxonomy of these seven continuous sentiment styles and found that vlogs whose sentiment builds up towards a positive ending are the most prevalent in our sample. Gender was associated with preferences for different continuous sentiment trajectories. This paper discusses the findings with respect to previous work and concludes with an outlook towards possible uses of the corpus, method and findings of this paper for related areas of research.",
}
@inproceedings{goldin-etal-2018-native,
    title = "Native Language Identification with User Generated Content",
    author = "Goldin, Gili  and
      Rabinovich, Ella  and
      Wintner, Shuly",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1395",
    doi = "10.18653/v1/D18-1395",
    pages = "3591--3601",
    abstract = "We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English). Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. We provide a detailed analysis of the features that sheds light on differences between native and nonnative speakers, and among nonnative speakers with different backgrounds.",
}
@inproceedings{wu-etal-2018-beyond,
    title = "Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter",
    author = "Wu, Lijun  and
      Tan, Xu  and
      He, Di  and
      Tian, Fei  and
      Qin, Tao  and
      Lai, Jianhuang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1396",
    doi = "10.18653/v1/D18-1396",
    pages = "3602--3611",
    abstract = "Neural machine translation usually adopts autoregressive models and suffers from exposure bias as well as the consequent error propagation problem. Many previous works have discussed the relationship between error propagation and the \textit{accuracy drop} (i.e., the left part of the translated sentence is often better than its right part in left-to-right decoding models) problem. In this paper, we conduct a series of analyses to deeply understand this problem and get several interesting findings. (1) The role of error propagation on accuracy drop is overstated in the literature, although it indeed contributes to the accuracy drop problem. (2) Characteristics of a language play a more important role in causing the accuracy drop: the left part of the translation result in a right-branching language (e.g., English) is more likely to be more accurate than its right part, while the right part is more accurate for a left-branching language (e.g., Japanese). Our discoveries are confirmed on different model structures including Transformer and RNN, and in other sequence generation tasks such as text summarization.",
}
@inproceedings{wu-etal-2018-study,
    title = "A Study of Reinforcement Learning for Neural Machine Translation",
    author = "Wu, Lijun  and
      Tian, Fei  and
      Qin, Tao  and
      Lai, Jianhuang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1397",
    doi = "10.18653/v1/D18-1397",
    pages = "3612--3621",
    abstract = "Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully RL training is challenging, especially in real-world systems where deep models and large datasets are leveraged. In this paper, taking several large-scale translation tasks as testbeds, we conduct a systematic study on how to train better NMT models using reinforcement learning. We provide a comprehensive comparison of several important factors (e.g., baseline reward, reward shaping) in RL training. Furthermore, to fill in the gap that it remains unclear whether RL is still beneficial when monolingual data is used, we propose a new method to leverage RL to further boost the performance of NMT systems trained with source/target monolingual data. By integrating all our findings, we obtain competitive results on WMT14 English-German, WMT17 English-Chinese, and WMT17 Chinese-English translation tasks, especially setting a state-of-the-art performance on WMT17 Chinese-English translation task.",
}
@inproceedings{gu-etal-2018-meta,
    title = "Meta-Learning for Low-Resource Neural Machine Translation",
    author = "Gu, Jiatao  and
      Wang, Yong  and
      Chen, Yun  and
      Li, Victor O. K.  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1398",
    doi = "10.18653/v1/D18-1398",
    pages = "3622--3631",
    abstract = "In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn, et al., 2017) for low-resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem where we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages. We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro,Lv, Fi, Tr and Ko) as target tasks. We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples. For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT{'}16 by seeing only 16,000 translated words ({\textasciitilde}600 parallel sentences)",
}
@inproceedings{artetxe-etal-2018-unsupervised,
    title = "Unsupervised Statistical Machine Translation",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1399",
    doi = "10.18653/v1/D18-1399",
    pages = "3632--3642",
    abstract = "While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018). Despite the potential of this approach for low-resource settings, existing systems are far behind their supervised counterparts, limiting their practical interest. In this paper, we propose an alternative approach based on phrase-based Statistical Machine Translation (SMT) that significantly closes the gap with supervised systems. Our method profits from the modular architecture of SMT: we first induce a phrase table from monolingual corpora through cross-lingual embedding mappings, combine it with an n-gram language model, and fine-tune hyperparameters through an unsupervised MERT variant. In addition, iterative backtranslation improves results further, yielding, for instance, 14.08 and 26.22 BLEU points in WMT 2014 English-German and English-French, respectively, an improvement of more than 7-10 BLEU points over previous unsupervised systems, and closing the gap with supervised SMT (Moses trained on Europarl) down to 2-5 BLEU points. Our implementation is available at \url{https://github.com/artetxem/monoses}.",
}
@inproceedings{zhou-etal-2018-visual,
    title = "A Visual Attention Grounding Neural Model for Multimodal Machine Translation",
    author = "Zhou, Mingyang  and
      Cheng, Runxiang  and
      Lee, Yong Jae  and
      Yu, Zhou",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1400",
    doi = "10.18653/v1/D18-1400",
    pages = "3643--3653",
    abstract = "We introduce a novel multimodal machine translation model that utilizes parallel visual and textual information. Our model jointly optimizes the learning of a shared visual-language embedding and a translator. The model leverages a visual attention grounding mechanism that links the visual semantics with the corresponding textual semantics. Our approach achieves competitive state-of-the-art results on the Multi30K and the Ambiguous COCO datasets. We also collected a new multilingual multimodal product description dataset to simulate a real-world international online shopping scenario. On this dataset, our visual attention grounding model outperforms other methods by a large margin.",
}
@inproceedings{shen-etal-2018-sentiment,
    title = "Sentiment Classification towards Question-Answering with Hierarchical Matching Network",
    author = "Shen, Chenlin  and
      Sun, Changlong  and
      Wang, Jingjing  and
      Kang, Yangyang  and
      Li, Shoushan  and
      Liu, Xiaozhong  and
      Si, Luo  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1401",
    doi = "10.18653/v1/D18-1401",
    pages = "3654--3663",
    abstract = "In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.",
}
@inproceedings{stab-etal-2018-cross,
    title = "Cross-topic Argument Mining from Heterogeneous Sources",
    author = "Stab, Christian  and
      Miller, Tristan  and
      Schiller, Benjamin  and
      Rai, Pranav  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1402",
    doi = "10.18653/v1/D18-1402",
    pages = "3664--3674",
    abstract = "Argument mining is a core technology for automating argument search in large document collections. Despite its usefulness for this task, most current approaches are designed for use only with specific text types and fall short when applied to heterogeneous texts. In this paper, we propose a new sentential annotation scheme that is reliably applicable by crowd workers to arbitrary Web texts. We source annotations for over 25,000 instances covering eight controversial topics. We show that integrating topic information into bidirectional long short-term memory networks outperforms vanilla BiLSTMs by more than 3 percentage points in F1 in two- and three-label cross-topic settings. We also show that these results can be further improved by leveraging additional data for topic relevance using multi-task learning.",
}
@inproceedings{angelidis-lapata-2018-summarizing,
    title = "Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised",
    author = "Angelidis, Stefanos  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1403",
    doi = "10.18653/v1/D18-1403",
    pages = "3675--3686",
    abstract = "We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.",
}
@inproceedings{saravia-etal-2018-carer,
    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
    author = "Saravia, Elvis  and
      Liu, Hsien-Chi Toby  and
      Huang, Yen-Hao  and
      Wu, Junlin  and
      Chen, Yi-Shin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1404",
    doi = "10.18653/v1/D18-1404",
    pages = "3687--3697",
    abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}
@inproceedings{ma-collins-2018-noise,
    title = "Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency",
    author = "Ma, Zhuang  and
      Collins, Michael",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1405",
    doi = "10.18653/v1/D18-1405",
    pages = "3698--3707",
    abstract = "Noise Contrastive Estimation (NCE) is a powerful parameter estimation method for log-linear models, which avoids calculation of the partition function or its derivatives at each training step, a computationally demanding step in many cases. It is closely related to negative sampling methods, now widely used in NLP. This paper considers NCE-based estimation of conditional models. Conditional models are frequently encountered in practice; however there has not been a rigorous theoretical analysis of NCE in this setting, and we will argue there are subtle but important questions when generalizing NCE to the conditional case. In particular, we analyze two variants of NCE for conditional models: one based on a classification objective, the other based on a ranking objective. We show that the ranking-based variant of NCE gives consistent parameter estimates under weaker assumptions than the classification-based method; we analyze the statistical efficiency of the ranking-based and classification-based variants of NCE; finally we describe experiments on synthetic data and language modeling showing the effectiveness and tradeoffs of both methods.",
}
@inproceedings{yavuz-etal-2018-calcs,
    title = "{C}a{L}cs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization",
    author = "Yavuz, Semih  and
      Chiu, Chung-Cheng  and
      Nguyen, Patrick  and
      Wu, Yonghui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1406",
    doi = "10.18653/v1/D18-1406",
    pages = "3708--3718",
    abstract = "Maximum-likelihood estimation (MLE) is one of the most widely used approaches for training structured prediction models for text-generation based natural language processing applications. However, besides exposure bias, models trained with MLE suffer from wrong objective problem where they are trained to maximize the word-level correct next step prediction, but are evaluated with respect to sequence-level discrete metrics such as ROUGE and BLEU. Several variants of policy-gradient methods address some of these problems by optimizing for final discrete evaluation metrics and showing improvements over MLE training for downstream tasks like text summarization and machine translation. However, policy-gradient methods suffers from high sample variance, making the training process very difficult and unstable. In this paper, we present an alternative direction towards mitigating this problem by introducing a new objective (CaLcs) based on a differentiable surrogate of longest common subsequence (LCS) measure that captures sequence-level structure similarity. Experimental results on abstractive summarization and machine translation validate the effectiveness of the proposed approach.",
}
@inproceedings{feng-etal-2018-pathologies,
    title = "Pathologies of Neural Models Make Interpretations Difficult",
    author = "Feng, Shi  and
      Wallace, Eric  and
      Grissom II, Alvin  and
      Iyyer, Mohit  and
      Rodriguez, Pedro  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1407",
    doi = "10.18653/v1/D18-1407",
    pages = "3719--3728",
    abstract = "One way to interpret neural model predictions is to highlight the most important input features{---}for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word{'}s importance is determined by either input perturbation{---}measuring the decrease in model confidence when that word is removed{---}or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction, without accuracy loss on regular examples.",
}
@inproceedings{wu-etal-2018-phrase,
    title = "Phrase-level Self-Attention Networks for Universal Sentence Encoding",
    author = "Wu, Wei  and
      Wang, Houfeng  and
      Liu, Tianyu  and
      Ma, Shuming",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1408",
    doi = "10.18653/v1/D18-1408",
    pages = "3729--3738",
    abstract = "Universal sentence encoding is a hot topic in recent NLP research. Attention mechanism has been an integral part in many sentence encoding models, allowing the models to capture context dependencies regardless of the distance between the elements in the sequence. Fully attention-based models have recently attracted enormous interest due to their highly parallelizable computation and significantly less training time. However, the memory consumption of their models grows quadratically with the sentence length, and the syntactic information is neglected. To this end, we propose Phrase-level Self-Attention Networks (PSAN) that perform self-attention across words inside a phrase to capture context dependencies at the phrase level, and use the gated memory updating mechanism to refine each word{'}s representation hierarchically with longer-term context dependencies captured in a larger phrase. As a result, the memory consumption can be reduced because the self-attention is performed at the phrase level instead of the sentence level. At the same time, syntactic information can be easily integrated in the model. Experiment results show that PSAN can achieve the state-of-the-art performance across a plethora of NLP tasks including binary and multi-class classification, natural language inference and sentence similarity.",
}
@inproceedings{dong-etal-2018-banditsum,
    title = "{B}andit{S}um: Extractive Summarization as a Contextual Bandit",
    author = "Dong, Yue  and
      Shen, Yikang  and
      Crawford, Eric  and
      van Hoof, Herke  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1409",
    doi = "10.18653/v1/D18-1409",
    pages = "3739--3748",
    abstract = "In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.",
}
@inproceedings{maddela-xu-2018-word,
    title = "A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification",
    author = "Maddela, Mounica  and
      Xu, Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1410",
    doi = "10.18653/v1/D18-1410",
    pages = "3749--3760",
    abstract = "Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).",
}
@inproceedings{qin-etal-2018-learning,
    title = "Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data",
    author = "Qin, Guanghui  and
      Yao, Jin-Ge  and
      Wang, Xuening  and
      Wang, Jinpeng  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1411",
    doi = "10.18653/v1/D18-1411",
    pages = "3761--3771",
    abstract = "Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms. In this paper, we attempt at learning explicit latent semantic annotations from paired structured tables and texts, establishing correspondences between various types of values and texts. We model the joint probability of data fields, texts, phrasal spans, and latent annotations with an adapted semi-hidden Markov model, and impose a soft statistical constraint to further improve the performance. As a by-product, we leverage the induced annotations to extract templates for language generation. Experimental results suggest the feasibility of the setting in this study, as well as the effectiveness of our proposed framework.",
}
@inproceedings{swayamdipta-etal-2018-syntactic,
    title = "Syntactic Scaffolds for Semantic Structures",
    author = "Swayamdipta, Swabha  and
      Thomson, Sam  and
      Lee, Kenton  and
      Zettlemoyer, Luke  and
      Dyer, Chris  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1412",
    doi = "10.18653/v1/D18-1412",
    pages = "3772--3782",
    abstract = "We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We improve over strong baselines on PropBank semantics, frame semantics, and coreference resolution, achieving competitive performance on all three tasks.",
}
@inproceedings{weber-etal-2018-hierarchical,
    title = "Hierarchical Quantized Representations for Script Generation",
    author = "Weber, Noah  and
      Shekhar, Leena  and
      Balasubramanian, Niranjan  and
      Chambers, Nathanael",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1413",
    doi = "10.18653/v1/D18-1413",
    pages = "3783--3792",
    abstract = "Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold. One of the challenges to learning scripts is the hierarchical nature of the knowledge. For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen. To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables. We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value. This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting. Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modeling-based method.",
}
@inproceedings{lin-etal-2018-semantic,
    title = "Semantic Role Labeling for Learner {C}hinese: the Importance of Syntactic Parsing and {L}2-{L}1 Parallel Data",
    author = "Lin, Zi  and
      Duan, Yuguang  and
      Zhao, Yuanyuan  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1414",
    doi = "10.18653/v1/D18-1414",
    pages = "3793--3802",
    abstract = "This paper studies semantic parsing for interlanguage (L2), taking semantic role labeling (SRL) as a case task and learner Chinese as a case language. We first manually annotate the semantic roles for a set of learner texts to derive a gold standard for automatic SRL. Based on the new data, we then evaluate three off-the-shelf SRL systems, i.e., the PCFGLA-parser-based, neural-parser-based and neural-syntax-agnostic systems, to gauge how successful SRL for learner Chinese can be. We find two non-obvious facts: 1) the L1-sentence-trained systems performs rather badly on the L2 data; 2) the performance drop from the L1 data to the L2 data of the two parser-based systems is much smaller, indicating the importance of syntactic parsing in SRL for interlanguages. Finally, the paper introduces a new agreement-based model to explore the semantic coherency information in the large-scale L2-L1 parallel data. We then show such information is very effective to enhance SRL for learner texts. Our model achieves an F-score of 72.06, which is a 2.02 point improvement over the best baseline.",
}
@inproceedings{wang-etal-2018-teacher,
    title = "A Teacher-Student Framework for Maintainable Dialog Manager",
    author = "Wang, Weikang  and
      Zhang, Jiajun  and
      Zhang, Han  and
      Hwang, Mei-Yuh  and
      Zong, Chengqing  and
      Li, Zhifei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1415",
    doi = "10.18653/v1/D18-1415",
    pages = "3803--3812",
    abstract = "Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems. However, extending RL-based systems to handle new intents and slots requires a system redesign. The high maintenance cost makes it difficult to apply RL methods to practical systems on a large scale. To address this issue, we propose a practical teacher-student framework to extend RL-based dialog systems without retraining from scratch. Specifically, the {``}student{''} is an extended dialog manager based on a new ontology, and the {``}teacher{''} is existing resources used for guiding the learning process of the {``}student{''}. By specifying constraints held in the new dialog manager, we transfer knowledge of the {``}teacher{''} to the {``}student{''} without additional resources. Experiments show that the performance of the extended system is comparable to the system trained from scratch. More importantly, the proposed framework makes no assumption about the unsupported intents and slots, which makes it possible to improve RL-based systems incrementally.",
}
@inproceedings{su-etal-2018-discriminative,
    title = "Discriminative Deep {D}yna-{Q}: Robust Planning for Dialogue Policy Learning",
    author = "Su, Shang-Yu  and
      Li, Xiujun  and
      Gao, Jianfeng  and
      Liu, Jingjing  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1416",
    doi = "10.18653/v1/D18-1416",
    pages = "3813--3823",
    abstract = "This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ{'}s high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data. Experiments show that D3Q significantly outperforms DDQ by controlling the quality of simulated experience used for planning. The effectiveness and robustness of D3Q is further demonstrated in a domain extension setting, where the agent{'}s capability of adapting to a changing environment is tested.",
}
@inproceedings{li-etal-2018-self,
    title = "A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding",
    author = "Li, Changliang  and
      Li, Liang  and
      Qi, Ji",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1417",
    doi = "10.18653/v1/D18-1417",
    pages = "3824--3833",
    abstract = "Spoken Language Understanding (SLU), which typically involves intent determination and slot filling, is a core component of spoken dialogue systems. Joint learning has shown to be effective in SLU given that slot tags and intents are supposed to share knowledge with each other. However, most existing joint learning methods only consider joint learning by sharing parameters on surface level rather than semantic level. In this work, we propose a novel self-attentive model with gate mechanism to fully utilize the semantic correlation between slot and intent. Our model first obtains intent-augmented embeddings based on neural network with self-attention mechanism. And then the intent semantic representation is utilized as the gate for labelling slot tags. The objectives of both tasks are optimized simultaneously via joint learning in an end-to-end way. We conduct experiment on popular benchmark ATIS. The results show that our model achieves state-of-the-art and outperforms other popular methods by a large margin in terms of both intent detection error rate and slot filling F1-score. This paper gives a new perspective for research on SLU.",
}
@inproceedings{rajendran-etal-2018-learning,
    title = "Learning End-to-End Goal-Oriented Dialog with Multiple Answers",
    author = "Rajendran, Janarthanan  and
      Ganhotra, Jatin  and
      Singh, Satinder  and
      Polymenakos, Lazaros",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1418",
    doi = "10.18653/v1/D18-1418",
    pages = "3834--3843",
    abstract = "In a dialog, there could be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of end-to-end goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5{\%} per-dialog accuracy on original-bAbI dialog tasks to 30.3{\%} on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3{\%} per-dialog accuracy on permuted-bAbI dialog tasks. We also release permuted-bAbI dialog tasks, our proposed testbed, to the community for evaluating dialog systems in a goal-oriented setting.",
}
@inproceedings{wei-etal-2018-airdialogue,
    title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
    author = "Wei, Wei  and
      Le, Quoc  and
      Dai, Andrew  and
      Li, Jia",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1419",
    doi = "10.18653/v1/D18-1419",
    pages = "3844--3854",
    abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.",
}
@inproceedings{liao-etal-2018-quase,
    title = "{Q}ua{SE}: Sequence Editing under Quantifiable Guidance",
    author = "Liao, Yi  and
      Bing, Lidong  and
      Li, Piji  and
      Shi, Shuming  and
      Lam, Wai  and
      Zhang, Tong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1420",
    doi = "10.18653/v1/D18-1420",
    pages = "3855--3864",
    abstract = "We propose the task of Quantifiable Sequence Editing (QuaSE): editing an input sequence to generate an output sequence that satisfies a given numerical outcome value measuring a certain property of the sequence, with the requirement of keeping the main content of the input sequence. For example, an input sequence could be a word sequence, such as review sentence and advertisement text. For a review sentence, the outcome could be the review rating; for an advertisement, the outcome could be the click-through rate. The major challenge in performing QuaSE is how to perceive the outcome-related wordings, and only edit them to change the outcome. In this paper, the proposed framework contains two latent factors, namely, outcome factor and content factor, disentangled from the input sentence to allow convenient editing to change the outcome and keep the content. Our framework explores the pseudo-parallel sentences by modeling their content similarity and outcome differences to enable a better disentanglement of the latent factors, which allows generating an output to better satisfy the desired outcome and keep the content. The dual reconstruction structure further enhances the capability of generating expected output by exploiting the couplings of latent factors of pseudo-parallel sentences. For evaluation, we prepared a dataset of Yelp review sentences with the ratings as outcome. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.",
}
@inproceedings{li-etal-2018-paraphrase,
    title = "Paraphrase Generation with Deep Reinforcement Learning",
    author = "Li, Zichao  and
      Jiang, Xin  and
      Shang, Lifeng  and
      Li, Hang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1421",
    doi = "10.18653/v1/D18-1421",
    pages = "3865--3878",
    abstract = "Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new framework for the task, which consists of a generator and an evaluator, both of which are learned from data. The generator, built as a sequence-to-sequence learning model, can produce paraphrases given a sentence. The evaluator, constructed as a deep matching model, can judge whether two sentences are paraphrases of each other. The generator is first trained by deep learning and then further fine-tuned by reinforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two methods based on supervised learning and inverse reinforcement learning respectively, depending on the type of available training data. Experimental results on two datasets demonstrate the proposed models (the generators) can produce more accurate paraphrases and outperform the state-of-the-art methods in paraphrase generation in both automatic evaluation and human evaluation.",
}
@inproceedings{nie-etal-2018-operation,
    title = "Operation-guided Neural Networks for High Fidelity Data-To-Text Generation",
    author = "Nie, Feng  and
      Wang, Jinpeng  and
      Yao, Jin-Ge  and
      Pan, Rong  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1422",
    doi = "10.18653/v1/D18-1422",
    pages = "3879--3889",
    abstract = "Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.",
}
@inproceedings{li-etal-2018-generating-classical,
    title = "Generating Classical {C}hinese Poems via Conditional Variational Autoencoder and Adversarial Training",
    author = "Li, Juntao  and
      Song, Yan  and
      Zhang, Haisong  and
      Chen, Dongmin  and
      Shi, Shuming  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1423",
    doi = "10.18653/v1/D18-1423",
    pages = "3890--3900",
    abstract = "It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording. Although much attention has been paid to this task and promising progress is made, there exist notable gaps between automatically generated ones with those created by humans, especially on the aspects of term novelty and thematic consistency. Towards filling the gap, in this paper, we propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation, where the autoencoder part generates poems with novel terms and a discriminator is applied to adversarially learn their thematic consistency with their titles. Experimental results on a large poetry corpus confirm the validity and effectiveness of our model, where its automatic and human evaluation scores outperform existing models.",
}
@inproceedings{zhao-etal-2018-paragraph,
    title = "Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks",
    author = "Zhao, Yao  and
      Ni, Xiaochuan  and
      Ding, Yuanyuan  and
      Ke, Qifa",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1424",
    doi = "10.18653/v1/D18-1424",
    pages = "3901--3910",
    abstract = "Question generation, the task of automatically creating questions that can be answered by a certain span of text within a given passage, is important for question-answering and conversational systems in digital assistants such as Alexa, Cortana, Google Assistant and Siri. Recent sequence to sequence neural models have outperformed previous rule-based systems. Existing models mainly focused on using one or two sentences as the input. Long text has posed challenges for sequence to sequence neural models in question generation {--} worse performances were reported if using the whole paragraph (with multiple sentences) as the input. In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. In this paper, we propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. With sentence-level inputs, our model outperforms previous approaches with either sentence-level or paragraph-level inputs. Furthermore, our model can effectively utilize paragraphs as inputs, pushing the state-of-the-art result from 13.9 to 16.3 (BLEU{\_}4).",
}
@inproceedings{yu-etal-2018-spider,
    title = "{S}pider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-{SQL} Task",
    author = "Yu, Tao  and
      Zhang, Rui  and
      Yang, Kai  and
      Yasunaga, Michihiro  and
      Wang, Dongxu  and
      Li, Zifan  and
      Ma, James  and
      Li, Irene  and
      Yao, Qingning  and
      Roman, Shanelle  and
      Zhang, Zilin  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1425",
    doi = "10.18653/v1/D18-1425",
    pages = "3911--3921",
    abstract = "We present \textit{Spider}, a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables covering 138 different domains. We define a new complex and cross-domain semantic parsing and text-to-SQL task so that different complicated SQL queries and databases appear in train and test sets. In this way, the task requires the model to generalize well to both new SQL queries and new database schemas. Therefore, Spider is distinct from most of the previous semantic parsing tasks because they all use a single database and have the exact same program in the train set and the test set. We experiment with various state-of-the-art models and the best model achieves only 9.7{\%} exact matching accuracy on a database split setting. This shows that Spider presents a strong challenge for future research. Our dataset and task with the most recent updates are publicly available at \url{https://yale-lily.github.io/seq2sql/spider}.",
}
@inproceedings{freitag-roy-2018-unsupervised,
    title = "Unsupervised Natural Language Generation with Denoising Autoencoders",
    author = "Freitag, Markus  and
      Roy, Scott",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1426",
    doi = "10.18653/v1/D18-1426",
    pages = "3922--3929",
    abstract = "Generating text from structured data is important for various tasks such as question answering and dialog systems. We show that in at least one domain, without any supervision and only based on unlabeled text, we are able to build a Natural Language Generation (NLG) system with higher performance than supervised approaches. In our approach, we interpret the structured data as a corrupt representation of the desired output and use a denoising auto-encoder to reconstruct the sentence. We show how to introduce noise into training examples that do not contain structured data, and that the resulting denoising auto-encoder generalizes to generate correct sentences when given structured data.",
}
@inproceedings{sun-etal-2018-answer,
    title = "Answer-focused and Position-aware Neural Question Generation",
    author = "Sun, Xingwu  and
      Liu, Jing  and
      Lyu, Yajuan  and
      He, Wei  and
      Ma, Yanjun  and
      Wang, Shi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1427",
    doi = "10.18653/v1/D18-1427",
    pages = "3930--3939",
    abstract = "In this paper, we focus on the problem of question generation (QG). Recent neural network-based approaches employ the sequence-to-sequence model which takes an answer and its context as input and generates a relevant question as output. However, we observe two major issues with these approaches: (1) The generated interrogative words (or question words) do not match the answer type. (2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer. To address these two issues, we propose an answer-focused and position-aware neural question generation model. (1) By answer-focused, we mean that we explicitly model question word generation by incorporating the answer embedding, which can help generate an interrogative word matching the answer type. (2) By position-aware, we mean that we model the relative distance between the context words and the answer. Hence the model can be aware of the position of the context words when copying them to generate a question. We conduct extensive experiments to examine the effectiveness of our model. The experimental results show that our model significantly improves the baseline and outperforms the state-of-the-art system.",
}
@inproceedings{xu-etal-2018-diversity,
    title = "Diversity-Promoting {GAN}: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation",
    author = "Xu, Jingjing  and
      Ren, Xuancheng  and
      Lin, Junyang  and
      Sun, Xu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1428",
    doi = "10.18653/v1/D18-1428",
    pages = "3940--3949",
    abstract = "Existing text generation methods tend to produce repeated and {''}boring{''} expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for {''}novel{''} and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines.",
}
@inproceedings{nema-khapra-2018-towards,
    title = "Towards a Better Metric for Evaluating Question Generation Systems",
    author = "Nema, Preksha  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1429",
    doi = "10.18653/v1/D18-1429",
    pages = "3950--3959",
    abstract = "There has always been criticism for using $n$-gram based similarity metrics, such as BLEU, NIST, \textit{etc}, for evaluating the performance of NLG systems. However, these metrics continue to remain popular and are recently being used for evaluating the performance of systems which automatically generate questions from documents, knowledge graphs, images, \textit{etc}. Given the rising interest in such automatic question generation (AQG) systems, it is important to objectively examine whether these metrics are suitable for this task. In particular, it is important to verify whether such metrics used for evaluating AQG systems focus on \textit{answerability} of the generated question by preferring questions which contain all relevant information such as question type (Wh-types), entities, relations, \textit{etc}. In this work, we show that current automatic evaluation metrics based on $n$-gram similarity do not always correlate well with human judgments about \textit{answerability} of a question. To alleviate this problem and as a first step towards better evaluation metrics for AQG, we introduce a scoring function to capture \textit{answerability} and show that when this scoring function is integrated with existing metrics, they correlate significantly better with human judgments. The scripts and data developed as a part of this work are made publicly available.",
}
@inproceedings{yang-etal-2018-stylistic,
    title = "Stylistic {C}hinese Poetry Generation via Unsupervised Style Disentanglement",
    author = "Yang, Cheng  and
      Sun, Maosong  and
      Yi, Xiaoyuan  and
      Li, Wenhao",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1430",
    doi = "10.18653/v1/D18-1430",
    pages = "3960--3969",
    abstract = "The ability to write diverse poems in different styles under the same poetic imagery is an important characteristic of human poetry writing. Most previous works on automatic Chinese poetry generation focused on improving the coherency among lines. Some work explored style transfer but suffered from expensive expert labeling of poem styles. In this paper, we target on stylistic poetry generation in a fully unsupervised manner for the first time. We propose a novel model which requires no supervised style labeling by incorporating mutual information, a concept in information theory, into modeling. Experimental results show that our model is able to generate stylistic poems without losing fluency and coherency.",
}
@inproceedings{baheti-etal-2018-generating,
    title = "Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints",
    author = "Baheti, Ashutosh  and
      Ritter, Alan  and
      Li, Jiwei  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1431",
    doi = "10.18653/v1/D18-1431",
    pages = "3970--3980",
    abstract = "Neural conversation models tend to generate safe, generic responses for most inputs. This is due to the limitations of likelihood-based decoding objectives in generation tasks with diverse outputs, such as conversation. To address this challenge, we propose a simple yet effective approach for incorporating side information in the form of distributional constraints over the generated responses. We propose two constraints that help generate more content rich responses that are based on a model of syntax and topics (Griffiths et al., 2005) and semantic similarity (Arora et al., 2016). We evaluate our approach against a variety of competitive baselines, using both automatic metrics and human judgments, showing that our proposed approach generates responses that are much less generic without sacrificing plausibility. A working demo of our code can be found at \url{https://github.com/abaheti95/DC-NeuralConversation}.",
}
@inproceedings{xu-etal-2018-better,
    title = "Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity",
    author = "Xu, Xinnuo  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Konstas, Ioannis  and
      Rieser, Verena",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1432",
    doi = "10.18653/v1/D18-1432",
    pages = "3981--3991",
    abstract = "We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity. Experiments on the OpenSubtitles corpus show a substantial improvement over competitive neural models in terms of BLEU score as well as metrics of coherence and diversity.",
}
@inproceedings{whitehead-etal-2018-incorporating,
    title = "Incorporating Background Knowledge into Video Description Generation",
    author = "Whitehead, Spencer  and
      Ji, Heng  and
      Bansal, Mohit  and
      Chang, Shih-Fu  and
      Voss, Clare",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1433",
    doi = "10.18653/v1/D18-1433",
    pages = "3992--4001",
    abstract = "Most previous efforts toward video captioning focus on generating generic descriptions, such as, {``}A man is talking.{''} We collect a news video dataset to generate enriched descriptions that include important background knowledge, such as named entities and related events, which allows the user to fully understand the video content. We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents. Then, given the video as well as the extracted events and entities, we generate a description using a Knowledge-aware Video Description network. The model learns to incorporate entities found in the topically related documents into the description via an entity pointer network and the generation procedure is guided by the event and entity types from the topically related documents through a knowledge gate, which is a gating mechanism added to the model{'}s decoder that takes a one-hot vector of these types. We evaluate our approach on the new dataset of news videos we have collected, establishing the first benchmark for this dataset as well as proposing a new metric to evaluate these descriptions.",
}
@inproceedings{patro-etal-2018-multimodal,
    title = "Multimodal Differential Network for Visual Question Generation",
    author = "Patro, Badri Narayana  and
      Kumar, Sandeep  and
      Kurmi, Vinod Kumar  and
      Namboodiri, Vinay",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1434",
    doi = "10.18653/v1/D18-1434",
    pages = "4002--4012",
    abstract = "Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).",
}
@inproceedings{lu-etal-2018-entity,
    title = "Entity-aware Image Caption Generation",
    author = "Lu, Di  and
      Whitehead, Spencer  and
      Huang, Lifu  and
      Ji, Heng  and
      Chang, Shih-Fu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1435",
    doi = "10.18653/v1/D18-1435",
    pages = "4013--4023",
    abstract = "Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images. In this paper we propose a new task which aims to generate informative image captions, given images and hashtags as input. We propose a simple but effective approach to tackle this problem. We first train a convolutional neural networks - long short term memory networks (CNN-LSTM) model to generate a template caption based on the input image. Then we use a knowledge graph based collective inference algorithm to fill in the template with specific named entities retrieved via the hashtags. Experiments on a new benchmark dataset collected from Flickr show that our model generates news-style image descriptions with much richer information. Our model outperforms unimodal baselines significantly with various evaluation metrics.",
}
@inproceedings{jhamtani-berg-kirkpatrick-2018-learning,
    title = "Learning to Describe Differences Between Pairs of Similar Images",
    author = "Jhamtani, Harsh  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1436",
    doi = "10.18653/v1/D18-1436",
    pages = "4024--4034",
    abstract = "In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a first-pass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.",
}
@inproceedings{rohrbach-etal-2018-object,
    title = "Object Hallucination in Image Captioning",
    author = "Rohrbach, Anna  and
      Hendricks, Lisa Anne  and
      Burns, Kaylee  and
      Darrell, Trevor  and
      Saenko, Kate",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1437",
    doi = "10.18653/v1/D18-1437",
    pages = "4035--4045",
    abstract = "Despite continuously improving performance, contemporary image captioning models are prone to {``}hallucinating{''} objects that are not actually in a scene. One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance. In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination. We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination. We investigate these questions on the standard image captioning benchmark, MSCOCO, using a diverse set of models. Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors.",
}
@inproceedings{chen-zhuge-2018-abstractive,
    title = "Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical {RNN}",
    author = "Chen, Jingqiang  and
      Zhuge, Hai",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1438",
    doi = "10.18653/v1/D18-1438",
    pages = "4046--4056",
    abstract = "Rapid growth of multi-modal documents on the Internet makes multi-modal summarization research necessary. Most previous research summarizes texts or images separately. Recent neural summarization research shows the strength of the Encoder-Decoder model in text summarization. This paper proposes an abstractive text-image summarization model using the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously, and then to align the sentences and images in summaries. A multi-modal attentional mechanism is proposed to attend original sentences, images, and captions when decoding. The DailyMail dataset is extended by collecting images and captions from the Web. Experiments show our model outperforms the neural abstractive and extractive text summarization methods that do not consider images. In addition, our model can generate informative summaries of images.",
}
@inproceedings{chen-etal-2018-keyphrase,
    title = "Keyphrase Generation with Correlation Constraints",
    author = "Chen, Jun  and
      Zhang, Xiaoming  and
      Wu, Yu  and
      Yan, Zhao  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1439",
    doi = "10.18653/v1/D18-1439",
    pages = "4057--4066",
    abstract = "In this paper, we study automatic keyphrase generation. Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting in duplication and coverage issues. To solve these problems, we propose a new sequence-to-sequence architecture for keyphrase generation named CorrRNN, which captures correlation among multiple keyphrases in two ways. First, we employ a coverage vector to indicate whether the word in the source document has been summarized by previous phrases to improve the coverage for keyphrases. Second, preceding phrases are taken into account to eliminate duplicate phrases and improve result coherence. Experiment results show that our model significantly outperforms the state-of-the-art method on benchmark datasets in terms of both accuracy and diversity.",
}
@inproceedings{jiang-bansal-2018-closed,
    title = "Closed-Book Training to Improve Summarization Encoder Memory",
    author = "Jiang, Yichen  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1440",
    doi = "10.18653/v1/D18-1440",
    pages = "4067--4077",
    abstract = "A good neural sequence-to-sequence summarization model should have a strong encoder that can distill and memorize the important information from long input texts so that the decoder can generate salient summaries based on the encoder{'}s memory. In this paper, we aim to improve the memorization capabilities of the encoder of a pointer-generator model by adding an additional {`}closed-book{'} decoder without attention and pointer mechanisms. Such a decoder forces the encoder to be more selective in the information encoded in its memory state because the decoder can{'}t rely on the extra information provided by the attention and possibly copy modules, and hence improves the entire model. On the CNN/Daily Mail dataset, our 2-decoder model outperforms the baseline significantly in terms of ROUGE and METEOR metrics, for both cross-entropy and reinforced setups (and on human evaluation). Moreover, our model also achieves higher scores in a test-only DUC-2002 generalizability setup. We further present a memory ability test, two saliency metrics, as well as several sanity-check ablations (based on fixed-encoder, gradient-flow cut, and model capacity) to prove that the encoder of our 2-decoder model does in fact learn stronger memory representations than the baseline encoder.",
}
@inproceedings{li-etal-2018-improving-neural,
    title = "Improving Neural Abstractive Document Summarization with Structural Regularization",
    author = "Li, Wei  and
      Xiao, Xinyan  and
      Lyu, Yajuan  and
      Wang, Yuanzhuo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1441",
    doi = "10.18653/v1/D18-1441",
    pages = "4078--4087",
    abstract = "Recent neural sequence-to-sequence models have shown significant progress on short text summarization. However, for document summarization, they fail to capture the long-term structure of both documents and multi-sentence summaries, resulting in information loss and repetitions. In this paper, we propose to leverage the structural information of both documents and multi-sentence summaries to improve the document summarization performance. Specifically, we import both structural-compression and structural-coverage regularization into the summarization process in order to capture the information compression and information coverage properties, which are the two most important structural properties of document summarization. Experimental results demonstrate that the structural regularization improves the document summarization performance significantly, which enables our model to generate more informative and concise summaries, and thus significantly outperforms state-of-the-art neural abstractive methods.",
}
@inproceedings{chen-etal-2018-iterative,
    title = "Iterative Document Representation Learning Towards Summarization with Polishing",
    author = "Chen, Xiuying  and
      Gao, Shen  and
      Tao, Chongyang  and
      Song, Yan  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1442",
    doi = "10.18653/v1/D18-1442",
    pages = "4088--4097",
    abstract = "In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.",
}
@inproceedings{gehrmann-etal-2018-bottom,
    title = "Bottom-Up Abstractive Summarization",
    author = "Gehrmann, Sebastian  and
      Deng, Yuntian  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1443",
    doi = "10.18653/v1/D18-1443",
    pages = "4098--4109",
    abstract = "Neural summarization produces outputs that are fluent and readable, but which can be poor at content selection, for instance often copying full sentences from the source document. This work explores the use of data-efficient content selectors to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences making it easy to transfer a trained summarizer to a new domain.",
}
@inproceedings{liu-etal-2018-controlling,
    title = "Controlling Length in Abstractive Summarization Using a Convolutional Neural Network",
    author = "Liu, Yizhu  and
      Luo, Zhiyi  and
      Zhu, Kenny",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1444",
    doi = "10.18653/v1/D18-1444",
    pages = "4110--4119",
    abstract = "Convolutional neural networks (CNNs) have met great success in abstractive summarization, but they cannot effectively generate summaries of desired lengths. Because generated summaries are used in difference scenarios which may have space or length constraints, the ability to control the summary length in abstractive summarization is an important problem. In this paper, we propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. The results show that this approach generates high-quality summaries with user defined length, and outperforms the baselines consistently in terms of ROUGE score, length variations and semantic similarity.",
}
@inproceedings{gao-etal-2018-april,
    title = "{APRIL}: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning",
    author = "Gao, Yang  and
      Meyer, Christian M.  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1445",
    doi = "10.18653/v1/D18-1445",
    pages = "4120--4130",
    abstract = "We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users{'} preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at \url{https://github.com/UKPLab/emnlp2018-april}.",
}
@inproceedings{lebanoff-etal-2018-adapting,
    title = "Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization",
    author = "Lebanoff, Logan  and
      Song, Kaiqiang  and
      Liu, Fei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1446",
    doi = "10.18653/v1/D18-1446",
    pages = "4131--4141",
    abstract = "Generating a text abstract from a set of documents remains a challenging task. The neural encoder-decoder framework has recently been exploited to summarize single documents, but its success can in part be attributed to the availability of large parallel data automatically acquired from the Web. In contrast, parallel data for multi-document summarization are scarce and costly to obtain. There is a pressing need to adapt an encoder-decoder model trained on single-document summarization data to work with multiple-document input. In this paper, we present an initial investigation into a novel adaptation method. It exploits the maximal marginal relevance method to select representative sentences from multi-document input, and leverages an abstractive encoder-decoder model to fuse disparate sentences to an abstractive summary. The adaptation method is robust and itself requires no training data. Our system compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors.",
}
@inproceedings{ye-wang-2018-semi,
    title = "Semi-Supervised Learning for Neural Keyphrase Generation",
    author = "Ye, Hai  and
      Wang, Lu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1447",
    doi = "10.18653/v1/D18-1447",
    pages = "4142--4153",
    abstract = "We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich domains. In this paper, we propose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for learning. Two strategies are proposed. First, unlabeled documents are first tagged with synthetic keyphrases obtained from unsupervised keyphrase extraction methods or a self-learning algorithm, and then combined with labeled samples for training. Furthermore, we investigate a multi-task learning framework to jointly learn to generate keyphrases as well as the titles of the articles. Experimental results show that our semi-supervised learning-based methods outperform a state-of-the-art model trained with labeled data only.",
}
@inproceedings{zhu-etal-2018-msmo,
    title = "{MSMO}: Multimodal Summarization with Multimodal Output",
    author = "Zhu, Junnan  and
      Li, Haoran  and
      Liu, Tianshang  and
      Zhou, Yu  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1448",
    doi = "10.18653/v1/D18-1448",
    pages = "4154--4164",
    abstract = "Multimodal summarization has drawn much attention due to the rapid growth of multimedia data. The output of the current multimodal summarization systems is usually represented in texts. However, we have found through experiments that multimodal output can significantly improve user satisfaction for informativeness of summaries. In this paper, we propose a novel task, multimodal summarization with multimodal output (MSMO). To handle this task, we first collect a large-scale dataset for MSMO research. We then propose a multimodal attention model to jointly generate text and select the most relevant image from the multimodal input. Finally, to evaluate multimodal outputs, we construct a novel multimodal automatic evaluation (MMAE) method which considers both intra-modality salience and inter-modality relevance. The experimental results show the effectiveness of MMAE.",
}
@inproceedings{kobayashi-2018-frustratingly,
    title = "Frustratingly Easy Model Ensemble for Abstractive Summarization",
    author = "Kobayashi, Hayato",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1449",
    doi = "10.18653/v1/D18-1449",
    pages = "4165--4176",
    abstract = "Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling ensemble models. In this paper, we propose an alternative, simple but effective unsupervised ensemble method, \textit{post-ensemble}, that combines multiple models by selecting a majority-like output in post-processing. We theoretically prove that our method is closely related to kernel density estimation based on the von Mises-Fisher kernel. Experimental results on a news-headline-generation task show that the proposed method performs better than the current ensemble methods.",
}
@inproceedings{hirao-etal-2018-automatic,
    title = "Automatic Pyramid Evaluation Exploiting {EDU}-based Extractive Reference Summaries",
    author = "Hirao, Tsutomu  and
      Kamigaito, Hidetaka  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1450",
    doi = "10.18653/v1/D18-1450",
    pages = "4177--4186",
    abstract = "This paper tackles automation of the pyramid method, a reliable manual evaluation framework. To construct a pyramid, we transform human-made reference summaries into extractive reference summaries that consist of Elementary Discourse Units (EDUs) obtained from source documents and then weight every EDU by counting the number of extractive reference summaries that contain the EDU. A summary is scored by the correspondences between EDUs in the summary and those in the pyramid. Experiments on DUC and TAC data sets show that our methods strongly correlate with various manual evaluations.",
}
@inproceedings{wang-lee-2018-learning,
    title = "Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks",
    author = "Wang, Yaushian  and
      Lee, Hung-Yi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1451",
    doi = "10.18653/v1/D18-1451",
    pages = "4187--4195",
    abstract = "Auto-encoders compress input data into a latent-space representation and reconstruct the original data from the representation. This latent representation is not easily interpreted by humans. In this paper, we propose training an auto-encoder that encodes input text into human-readable sentences, and unpaired abstractive summarization is thereby achieved. The auto-encoder is composed of a generator and a reconstructor. The generator encodes the input text into a shorter word sequence, and the reconstructor recovers the generator input from the generator output. To make the generator output human-readable, a discriminator restricts the output of the generator to resemble human-written sentences. By taking the generator output as the summary of the input text, abstractive summarization is achieved without document-summary pairs as training data. Promising results are shown on both English and Chinese corpora.",
}
@inproceedings{joty-etal-2018-joint,
    title = "Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings",
    author = "Joty, Shafiq  and
      M{\`a}rquez, Llu{\'\i}s  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1452",
    doi = "10.18653/v1/D18-1452",
    pages = "4196--4207",
    abstract = "We address jointly two important tasks for Question Answering in community forums: given a new question, (i) find related existing questions, and (ii) find relevant answers to this new question. We further use an auxiliary task to complement the previous two, i.e., (iii) find good answers with respect to the thread question in a question-comment thread. We use deep neural networks (DNNs) to learn meaningful task-specific embeddings, which we then incorporate into a conditional random field (CRF) model for the multitask setting, performing joint learning over a complex graph structure. While DNNs alone achieve competitive results when trained to produce the embeddings, the CRF, which makes use of the embeddings and the dependencies between the tasks, improves the results significantly and consistently across a variety of evaluation metrics, thus showing the complementarity of DNNs and structured learning.",
}
@inproceedings{sugawara-etal-2018-makes,
    title = "What Makes Reading Comprehension Questions Easier?",
    author = "Sugawara, Saku  and
      Inui, Kentaro  and
      Sekine, Satoshi  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1453",
    doi = "10.18653/v1/D18-1453",
    pages = "4208--4219",
    abstract = "A challenge in creating a dataset for machine reading comprehension (MRC) is to collect questions that require a sophisticated understanding of language to answer beyond using superficial cues. In this work, we investigate what makes questions easier across recent 12 MRC datasets with three question styles (answer extraction, description, and multiple choice). We propose to employ simple heuristics to split each dataset into easy and hard subsets and examine the performance of two baseline models for each of the subsets. We then manually annotate questions sampled from each subset with both validity and requisite reasoning skills to investigate which skills explain the difference between easy and hard questions. From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions. These results suggest that one might overestimate recent advances in MRC.",
}
@inproceedings{bauer-etal-2018-commonsense,
    title = "Commonsense for Generative Multi-Hop Question Answering Tasks",
    author = "Bauer, Lisa  and
      Wang, Yicheng  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1454",
    doi = "10.18653/v1/D18-1454",
    pages = "4220--4230",
    abstract = "Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model{'}s performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.",
}
@inproceedings{sun-etal-2018-open,
    title = "Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text",
    author = "Sun, Haitian  and
      Dhingra, Bhuwan  and
      Zaheer, Manzil  and
      Mazaitis, Kathryn  and
      Salakhutdinov, Ruslan  and
      Cohen, William",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1455",
    doi = "10.18653/v1/D18-1455",
    pages = "4231--4242",
    abstract = "Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone. In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus. Building on recent advances in graph representation learning we propose a novel model, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and KB entities and relations. We construct a suite of benchmark tasks for this problem, varying the difficulty of questions, the amount of training data, and KB completeness. We show that GRAFT-Net is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting.",
}
@inproceedings{kundu-ng-2018-nil,
    title = "A Nil-Aware Answer Extraction Framework for Question Answering",
    author = "Kundu, Souvik  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1456",
    doi = "10.18653/v1/D18-1456",
    pages = "4243--4252",
    abstract = "Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA). However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage. A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage. In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer. If the associated passage does not contain any valid answer, the QA system will correctly return Nil. We propose a novel nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step. We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an end-to-end fashion. Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer. Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.",
}
@inproceedings{dou-etal-2018-exploiting,
    title = "Exploiting Deep Representations for Neural Machine Translation",
    author = "Dou, Zi-Yi  and
      Tu, Zhaopeng  and
      Wang, Xing  and
      Shi, Shuming  and
      Zhang, Tong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1457",
    doi = "10.18653/v1/D18-1457",
    pages = "4253--4262",
    abstract = "Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures. However, only the top layers of encoder and decoder are leveraged in the subsequent process, which misses the opportunity to exploit the useful information embedded in other layers. In this work, we propose to simultaneously expose all of these signals with layer aggregation and multi-layer attention mechanisms. In addition, we introduce an auxiliary regularization term to encourage different layers to capture diverse information. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation data demonstrate the effectiveness and universality of the proposed approach.",
}
@inproceedings{tang-etal-2018-self,
    title = "Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures",
    author = {Tang, Gongbo  and
      M{\"u}ller, Mathias  and
      Rios, Annette  and
      Sennrich, Rico},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1458",
    doi = "10.18653/v1/D18-1458",
    pages = "4263--4272",
    abstract = "Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation. CNNs and self-attentional networks can connect distant words via shorter network paths than RNNs, and it has been speculated that this improves their ability to model long-range dependencies. However, this theoretical argument has not been tested empirically, nor have alternative explanations for their strong performance been explored in-depth. We hypothesize that the strong performance of CNNs and self-attentional networks could also be due to their ability to extract semantic features from the source text, and we evaluate RNNs, CNNs and self-attention networks on two tasks: subject-verb agreement (where capturing long-range dependencies is required) and word sense disambiguation (where semantic feature extraction is required). Our experimental results show that: 1) self-attentional networks and CNNs do not outperform RNNs in modeling subject-verb agreement over long distances; 2) self-attentional networks perform distinctly better than RNNs and CNNs on word sense disambiguation.",
}
@inproceedings{zhang-etal-2018-simplifying,
    title = "Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks",
    author = "Zhang, Biao  and
      Xiong, Deyi  and
      Su, Jinsong  and
      Lin, Qian  and
      Zhang, Huiji",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1459",
    doi = "10.18653/v1/D18-1459",
    pages = "4273--4283",
    abstract = "In this paper, we propose an additionsubtraction twin-gated recurrent network (ATR) to simplify neural machine translation. The recurrent units of ATR are heavily simplified to have the smallest number of weight matrices among units of all existing gated RNNs. With the simple addition and subtraction operation, we introduce a twin-gated mechanism to build input and forget gates which are highly correlated. Despite this simplification, the essential non-linearities and capability of modeling long-distance dependencies are preserved. Additionally, the proposed ATR is more transparent than LSTM/GRU due to the simplification. Forward self-attention can be easily established in ATR, which makes the proposed network interpretable. Experiments on WMT14 translation tasks demonstrate that ATR-based neural machine translation can yield competitive performance on English-German and English-French language pairs in terms of both translation quality and speed. Further experiments on NIST Chinese-English translation, natural language inference and Chinese word segmentation verify the generality and applicability of ATR on different natural language processing tasks.",
}
@inproceedings{zhang-etal-2018-speeding,
    title = "Speeding Up Neural Machine Translation Decoding by Cube Pruning",
    author = "Zhang, Wen  and
      Huang, Liang  and
      Feng, Yang  and
      Shen, Lei  and
      Liu, Qun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1460",
    doi = "10.18653/v1/D18-1460",
    pages = "4284--4294",
    abstract = "Although neural machine translation has achieved promising results, it suffers from slow translation speed. The direct consequence is that a trade-off has to be made between translation quality and speed, thus its performance can not come into full play. We apply cube pruning, a popular technique to speed up dynamic programming, into neural machine translation to speed up the translation. To construct the equivalence class, similar target hidden states are combined, leading to less RNN expansion operations on the target side and less softmax operations over the large target vocabulary. The experiments show that, at the same or even better translation quality, our method can translate faster compared with naive beam search by 3.3x on GPUs and 3.5x on CPUs.",
}
@inproceedings{cherry-etal-2018-revisiting,
    title = "Revisiting Character-Based Neural Machine Translation with Capacity and Compression",
    author = "Cherry, Colin  and
      Foster, George  and
      Bapna, Ankur  and
      Firat, Orhan  and
      Macherey, Wolfgang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1461",
    doi = "10.18653/v1/D18-1461",
    pages = "4295--4305",
    abstract = "Translating characters instead of words or word-fragments has the potential to simplify the processing pipeline for neural machine translation (NMT), and improve results by eliminating hyper-parameters and manual feature engineering. However, it results in longer sequences in which each symbol contains less information, creating both modeling and computational challenges. In this paper, we show that the modeling problem can be solved by standard sequence-to-sequence architectures of sufficient depth, and that deep models operating at the character level outperform identical models operating over word fragments. This result implies that alternative architectures for handling character input are better viewed as methods for reducing computation time than as improved ways of modeling longer sequences. From this perspective, we evaluate several techniques for character-level NMT, verify that they do not match the performance of our deep character baseline model, and evaluate the performance versus computation time tradeoffs they offer. Within this framework, we also perform the first evaluation for NMT of conditional computation over time, in which the model learns which timesteps can be skipped, rather than having them be dictated by a fixed schedule specified before training begins.",
}
@inproceedings{xu-etal-2018-skeleton,
    title = "A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation",
    author = "Xu, Jingjing  and
      Ren, Xuancheng  and
      Zhang, Yi  and
      Zeng, Qi  and
      Cai, Xiaoyan  and
      Sun, Xu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1462",
    doi = "10.18653/v1/D18-1462",
    pages = "4306--4315",
    abstract = "Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1{\%} in human evaluation.",
}
@inproceedings{shen-etal-2018-nexus,
    title = "{NEXUS} Network: Connecting the Preceding and the Following in Dialogue Generation",
    author = "Shen, Xiaoyu  and
      Su, Hui  and
      Li, Wenjie  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1463",
    doi = "10.18653/v1/D18-1463",
    pages = "4316--4327",
    abstract = "Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular in building end-to-end trainable dialogue systems. Though highly efficient in learning the backbone of human-computer communications, they suffer from the problem of strongly favoring short generic responses. In this paper, we argue that a good response should smoothly connect both the preceding dialogue history and the following conversations. We strengthen this connection by mutual information maximization. To sidestep the non-differentiability of discrete natural language tokens, we introduce an auxiliary continuous code space and map such code space to a learnable prior distribution for generation purpose. Experiments on two dialogue datasets validate the effectiveness of our model, where the generated responses are closely related to the dialogue context and lead to more interactive conversations.",
}
@inproceedings{mesgar-strube-2018-neural,
    title = "A Neural Local Coherence Model for Text Quality Assessment",
    author = "Mesgar, Mohsen  and
      Strube, Michael",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1464",
    doi = "10.18653/v1/D18-1464",
    pages = "4328--4339",
    abstract = "We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text. We represent the semantics of a sentence by a vector and capture its state at each word of the sentence. We model what relates two adjacent sentences based on the two most similar semantic states, each of which is in one of the sentences. We encode the perceived coherence of a text by a vector, which represents patterns of changes in salient information that relates adjacent sentences. Our experiments demonstrate that our approach is beneficial for two downstream tasks: Readability assessment, in which our model achieves new state-of-the-art results; and essay scoring, in which the combination of our coherence vectors and other task-dependent features significantly improves the performance of a strong essay scorer.",
}
@inproceedings{cui-etal-2018-deep,
    title = "Deep Attentive Sentence Ordering Network",
    author = "Cui, Baiyun  and
      Li, Yingming  and
      Chen, Ming  and
      Zhang, Zhongfei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1465",
    doi = "10.18653/v1/D18-1465",
    pages = "4340--4349",
    abstract = "In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation of the sentence set. With this representation, a pointer network is exploited to generate an ordered sequence. The proposed model is evaluated on Sentence Ordering and Order Discrimination tasks. The extensive experimental results demonstrate its effectiveness and superiority to the state-of-the-art methods.",
}
@inproceedings{staliunaite-etal-2018-getting,
    title = "Getting to {``}Hearer-old{''}: Charting Referring Expressions Across Time",
    author = "Stali{\=u}nait{\.e}, Ieva  and
      Rohde, Hannah  and
      Webber, Bonnie  and
      Louis, Annie",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1466",
    doi = "10.18653/v1/D18-1466",
    pages = "4350--4359",
    abstract = "When a reader is first introduced to an entity, its referring expression must describe the entity. For entities that are widely known, a single word or phrase often suffices. This paper presents the first study of how expressions that refer to the same entity develop over time. We track thousands of person and organization entities over 20 years of New York Times (NYT). As entities move from hearer-new (first introduction to the NYT audience) to hearer-old (common knowledge) status, we show empirically that the referring expressions along this trajectory depend on the type of the entity, and exhibit linguistic properties related to becoming common knowledge (e.g., shorter length, less use of appositives, more definiteness). These properties can also be used to build a model to predict how long it will take for an entity to reach hearer-old status. Our results reach 10-30{\%} absolute improvement over a majority-class baseline.",
}
@inproceedings{stewart-eisenstein-2018-making,
    title = "Making {``}fetch{''} happen: The influence of social and linguistic context on nonstandard word growth and decline",
    author = "Stewart, Ian  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1467",
    doi = "10.18653/v1/D18-1467",
    pages = "4360--4370",
    abstract = "In an online community, new words come and go: today{'}s {``}haha{''} may be replaced by tomorrow{'}s {``}lol.{''} Changes in online writing are usually studied as a social process, with innovations diffusing through a network of individuals in a speech community. But unlike other types of innovation, language change is shaped and constrained by the grammatical system in which it takes part. To investigate the role of social and structural factors in language change, we undertake a large-scale analysis of the frequencies of non-standard words in Reddit. Dissemination across many linguistic contexts is a predictor of success: words that appear in more linguistic contexts grow faster and survive longer. Furthermore, social dissemination plays a less important role in explaining word growth and decline than previously hypothesized.",
}
@inproceedings{murawaki-2018-analyzing,
    title = "Analyzing Correlated Evolution of Multiple Features Using Latent Representations",
    author = "Murawaki, Yugo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1468",
    doi = "10.18653/v1/D18-1468",
    pages = "4371--4382",
    abstract = "Statistical phylogenetic models have allowed the quantitative analysis of the evolution of a single categorical feature and a pair of binary features, but correlated evolution involving multiple discrete features is yet to be explored. Here we propose latent representation-based analysis in which (1) a sequence of discrete surface features is projected to a sequence of independent binary variables and (2) phylogenetic inference is performed on the latent space. In the experiments, we analyze the features of linguistic typology, with a special focus on the order of subject, object and verb. Our analysis suggests that languages sharing the same word order are not necessarily a coherent group but exhibit varying degrees of diachronic stability depending on other features.",
}
@inproceedings{hovy-purschke-2018-capturing,
    title = "Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting",
    author = "Hovy, Dirk  and
      Purschke, Christoph",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1469",
    doi = "10.18653/v1/D18-1469",
    pages = "4383--4394",
    abstract = "Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theory-driven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible.",
}
@inproceedings{rashid-blanco-2018-characterizing,
    title = "Characterizing Interactions and Relationships between People",
    author = "Rashid, Farzana  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1470",
    doi = "10.18653/v1/D18-1470",
    pages = "4395--4404",
    abstract = "This paper presents a set of dimensions to characterize the association between two people. We distinguish between interactions (when somebody refers to somebody in a conversation) and relationships (a sequence of interactions). We work with dialogue scripts from the TV show Friends, and do not impose any restrictions on the interactions and relationships. We introduce and analyze a new corpus, and present experimental results showing that the task can be automated.",
}
@inproceedings{holgate-etal-2018-swear,
    title = "Why Swear? Analyzing and Inferring the Intentions of Vulgar Expressions",
    author = "Holgate, Eric  and
      Cachola, Isabel  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1471",
    doi = "10.18653/v1/D18-1471",
    pages = "4405--4414",
    abstract = "Vulgar words are employed in language use for several different functions, ranging from expressing aggression to signaling group identity or the informality of the communication. This versatility of usage of a restricted set of words is challenging for downstream applications and has yet to be studied quantitatively or using natural language processing techniques. We introduce a novel data set of 7,800 tweets from users with known demographic traits where all instances of vulgar words are annotated with one of the six categories of vulgar word use. Using this data set, we present the first analysis of the pragmatic aspects of vulgarity and how they relate to social factors. We build a model able to predict the category of a vulgar word based on the immediate context it appears in with 67.4 macro F1 across six classes. Finally, we demonstrate the utility of modeling the type of vulgar word use in context by using this information to achieve state-of-the-art performance in hate speech detection on a benchmark data set.",
}
@inproceedings{eger-etal-2018-time,
    title = "Is it Time to Swish? Comparing Deep Learning Activation Functions Across {NLP} tasks",
    author = "Eger, Steffen  and
      Youssef, Paul  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1472",
    doi = "10.18653/v1/D18-1472",
    pages = "4415--4424",
    abstract = "Activation functions play a crucial role in neural networks because they are the nonlinearities which have been attributed to the success story of deep learning. One of the currently most popular activation functions is ReLU, but several competitors have recently been proposed or {`}discovered{'}, including LReLU functions and swish. While most works compare newly proposed activation functions on few tasks (usually from image classification) and against few competitors (usually ReLU), we perform the first largescale comparison of 21 activation functions across eight different NLP tasks. We find that a largely unknown activation function performs most stably across all tasks, the so-called penalized tanh function. We also show that it can successfully replace the sigmoid and tanh gates in LSTM cells, leading to a 2 percentage point (pp) improvement over the standard choices on a challenging NLP task.",
}
@inproceedings{wu-etal-2018-hard,
    title = "Hard Non-Monotonic Attention for Character-Level Transduction",
    author = "Wu, Shijie  and
      Shapiro, Pamela  and
      Cotterell, Ryan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1473",
    doi = "10.18653/v1/D18-1473",
    pages = "4425--4438",
    abstract = "Character-level string-to-string transduction is an important component of various NLP tasks. The goal is to map an input string to an output string, where the strings may be of different lengths and have characters taken from different alphabets. Recent approaches have used sequence-to-sequence models with an attention mechanism to learn which parts of the input string the model should focus on during the generation of the output string. Both soft attention and hard monotonic attention have been used, but hard non-monotonic attention has only been used in other sequence modeling tasks and has required a stochastic approximation to compute the gradient. In this work, we introduce an exact, polynomial-time algorithm for marginalizing over the exponential number of non-monotonic alignments between two strings, showing that hard attention models can be viewed as neural reparameterizations of the classical IBM Model 1. We compare soft and hard non-monotonic attention experimentally and find that the exact algorithm significantly improves performance over the stochastic approximation and outperforms soft attention.",
}
@inproceedings{fu-ma-2018-speed,
    title = "Speed Reading: Learning to Read {F}or{B}ackward via Shuttle",
    author = "Fu, Tsu-Jui  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1474",
    doi = "10.18653/v1/D18-1474",
    pages = "4439--4448",
    abstract = "We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension. In contrast to previous work, LSTM-Shuttle not only reads shuttling forward but also goes back. Shuttling forward enables high efficiency, and going backward gives the model a chance to recover lost information, ensuring better prediction. We evaluate LSTM-Shuttle on sentiment analysis, news classification, and cloze on IMDB, Rotten Tomatoes, AG, and Children{'}s Book Test datasets. We show that LSTM-Shuttle predicts both better and more quickly. To demonstrate how LSTM-Shuttle actually behaves, we also analyze the shuttling operation and present a case study.",
}
@inproceedings{yang-etal-2018-modeling,
    title = "Modeling Localness for Self-Attention Networks",
    author = "Yang, Baosong  and
      Tu, Zhaopeng  and
      Wong, Derek F.  and
      Meng, Fandong  and
      Chao, Lidia S.  and
      Zhang, Tong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1475",
    doi = "10.18653/v1/D18-1475",
    pages = "4449--4458",
    abstract = "Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.",
}
@inproceedings{katti-etal-2018-chargrid,
    title = "{C}hargrid: Towards Understanding 2{D} Documents",
    author = {Katti, Anoop R  and
      Reisswig, Christian  and
      Guder, Cordula  and
      Brarda, Sebastian  and
      Bickel, Steffen  and
      H{\"o}hne, Johannes  and
      Faddoul, Jean Baptiste},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1476",
    doi = "10.18653/v1/D18-1476",
    pages = "4459--4469",
    abstract = "We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.",
}
@inproceedings{lei-etal-2018-simple,
    title = "Simple Recurrent Units for Highly Parallelizable Recurrence",
    author = "Lei, Tao  and
      Zhang, Yu  and
      Wang, Sida I.  and
      Dai, Hui  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1477",
    doi = "10.18653/v1/D18-1477",
    pages = "4470--4481",
    abstract = "Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5{---}9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model (Vaswani et al., 2017) on translation by incorporating SRU into the architecture.",
}
@inproceedings{li-etal-2018-nprf,
    title = "{NPRF}: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval",
    author = "Li, Canjia  and
      Sun, Yingfei  and
      He, Ben  and
      Wang, Le  and
      Hui, Kai  and
      Yates, Andrew  and
      Sun, Le  and
      Xu, Jungang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1478",
    doi = "10.18653/v1/D18-1478",
    pages = "4482--4491",
    abstract = "Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.",
}
@inproceedings{tay-etal-2018-co,
    title = "Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences",
    author = "Tay, Yi  and
      Luu, Anh Tuan  and
      Hui, Siu Cheung",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1479",
    doi = "10.18653/v1/D18-1479",
    pages = "4492--4502",
    abstract = "Learning a matching function between two text sequences is a long standing problem in NLP research. This task enables many potential applications such as question answering and paraphrase identification. This paper proposes Co-Stack Residual Affinity Networks (CSRAN), a new and universal neural architecture for this problem. CSRAN is a deep architecture, involving stacked (multi-layered) recurrent encoders. Stacked/Deep architectures are traditionally difficult to train, due to the inherent weaknesses such as difficulty with feature propagation and vanishing gradients. CSRAN incorporates two novel components to take advantage of the stacked architecture. Firstly, it introduces a new bidirectional alignment mechanism that learns affinity weights by fusing sequence pairs across stacked hierarchies. Secondly, it leverages a multi-level attention refinement component between stacked recurrent layers. The key intuition is that, by leveraging information across all network hierarchies, we can not only improve gradient flow but also improve overall performance. We conduct extensive experiments on six well-studied text sequence matching datasets, achieving state-of-the-art performance on all.",
}
@inproceedings{xu-durrett-2018-spherical,
    title = "Spherical Latent Spaces for Stable Variational Autoencoders",
    author = "Xu, Jiacheng  and
      Durrett, Greg",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1480",
    doi = "10.18653/v1/D18-1480",
    pages = "4503--4513",
    abstract = "A hallmark of variational autoencoders (VAEs) for text processing is their combination of powerful encoder-decoder models, such as LSTMs, with simple latent distributions, typically multivariate Gaussians. These models pose a difficult optimization problem: there is an especially bad local optimum where the variational posterior always equals the prior and the model does not use the latent variable at all, a kind of {``}collapse{''} which is encouraged by the KL divergence term of the objective. In this work, we experiment with another choice of latent distribution, namely the von Mises-Fisher (vMF) distribution, which places mass on the surface of the unit hypersphere. With this choice of prior and posterior, the KL divergence term now only depends on the variance of the vMF distribution, giving us the ability to treat it as a fixed hyperparameter. We show that doing so not only averts the KL collapse, but consistently gives better likelihoods than Gaussians across a range of modeling conditions, including recurrent language modeling and bag-of-words document modeling. An analysis of the properties of our vMF representations shows that they learn richer and more nuanced structures in their latent representations than their Gaussian counterparts.",
}
@inproceedings{zhang-etal-2018-learning-universal,
    title = "Learning Universal Sentence Representations with Mean-Max Attention Autoencoder",
    author = "Zhang, Minghua  and
      Wu, Yunfang  and
      Li, Weikang  and
      Li, Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1481",
    doi = "10.18653/v1/D18-1481",
    pages = "4514--4523",
    abstract = "In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max attention autoencoder (mean-max AAE) within the encoder-decoder framework. Our autoencoder rely entirely on the MultiHead self-attention mechanism to reconstruct the input sequence. In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input. To enable the information to steer the reconstruction process dynamically, the decoder performs attention over the mean-max representation. By training our model on a large collection of unlabelled data, we obtain high-quality representations of sentences. Experimental results on a broad range of 10 transfer tasks demonstrate that our model outperforms the state-of-the-art unsupervised single methods, including the classical skip-thoughts and the advanced skip-thoughts+LN model. Furthermore, compared with the traditional recurrent neural network, our mean-max AAE greatly reduce the training time.",
}
@inproceedings{wu-etal-2018-word,
    title = "Word Mover{'}s Embedding: From {W}ord2{V}ec to Document Embedding",
    author = "Wu, Lingfei  and
      Yen, Ian En-Hsu  and
      Xu, Kun  and
      Xu, Fangli  and
      Balakrishnan, Avinash  and
      Chen, Pin-Yu  and
      Ravikumar, Pradeep  and
      Witbrock, Michael J.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1482",
    doi = "10.18653/v1/D18-1482",
    pages = "4524--4534",
    abstract = "While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called Word Mover{'}s Distance (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is expensive to compute, and it is hard to extend its use beyond a KNN classifier. In this paper, we propose the Word Mover{'}s Embedding (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. In our experiments on 9 benchmark text classification datasets and 22 textual similarity tasks, the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.",
}
@inproceedings{miranda-etal-2018-multilingual,
    title = "Multilingual Clustering of Streaming News",
    author = "Miranda, Sebasti{\~a}o  and
      Znoti{\c{n}}{\v{s}}, Art{\=u}rs  and
      Cohen, Shay B.  and
      Barzdins, Guntis",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1483",
    doi = "10.18653/v1/D18-1483",
    pages = "4535--4544",
    abstract = "Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual clusters. Unlike typical clustering approaches that report results on datasets with a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. In our formulation, the monolingual clusters group together documents while the crosslingual clusters group together monolingual clusters, one per language that appears in the stream. Our method is simple to implement, computationally efficient and produces state-of-the-art results on datasets in German, English and Spanish.",
}
@inproceedings{zhang-etal-2018-multi,
    title = "Multi-Task Label Embedding for Text Classification",
    author = "Zhang, Honglun  and
      Xiao, Liqiang  and
      Chen, Wenqing  and
      Wang, Yongkun  and
      Jin, Yaohui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1484",
    doi = "10.18653/v1/D18-1484",
    pages = "4545--4553",
    abstract = "Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, a large body of previous work treats labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential label information. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. Our model utilizes semantic correlations among tasks and makes it convenient to scale or transfer when new tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our model can effectively improve the performances of related tasks with semantic representations of labels and additional information from each other.",
}
@inproceedings{lin-etal-2018-semantic-unit,
    title = "Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification",
    author = "Lin, Junyang  and
      Su, Qi  and
      Yang, Pengcheng  and
      Ma, Shuming  and
      Sun, Xu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1485",
    doi = "10.18653/v1/D18-1485",
    pages = "4554--4564",
    abstract = "We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning. The model generates higher-level semantic unit representations with multi-level dilated convolution as well as a corresponding hybrid attention mechanism that extracts both the information at the word-level and the level of the semantic unit. Our designed dilated convolution effectively reduces dimension and supports an exponential expansion of receptive fields without loss of local information, and the attention-over-attention mechanism is able to capture more summary relevant information from the source context. Results of our experiments show that the proposed model has significant advantages over the baseline models on the dataset RCV1-V2 and Ren-CECps, and our analysis demonstrates that our model is competitive to the deterministic hierarchical models and it is more robust to classifying low-frequency labels",
}
@inproceedings{xiao-etal-2018-mcapsnet,
    title = "{MC}aps{N}et: Capsule Network for Text with Multi-Task Learning",
    author = "Xiao, Liqiang  and
      Zhang, Honglun  and
      Chen, Wenqing  and
      Wang, Yongkun  and
      Jin, Yaohui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1486",
    doi = "10.18653/v1/D18-1486",
    pages = "4565--4574",
    abstract = "Multi-task learning has an ability to share the knowledge among related tasks and implicitly increase the training data. However, it has long been frustrated by the interference among tasks. This paper investigates the performance of capsule network for text, and proposes a capsule-based multi-task learning architecture, which is unified, simple and effective. With the advantages of capsules for feature clustering, proposed task routing algorithm can cluster the features for each task in the network, which helps reduce the interference among tasks. Experiments on six text classification datasets demonstrate the effectiveness of our models and their characteristics for feature clustering.",
}
@inproceedings{keith-oconnor-2018-uncertainty,
    title = "Uncertainty-aware generative models for inferring document class prevalence",
    author = "Keith, Katherine  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1487",
    doi = "10.18653/v1/D18-1487",
    pages = "4575--4585",
    abstract = "Prevalence estimation is the task of inferring the relative frequency of classes of unlabeled examples in a group{---}for example, the proportion of a document collection with positive sentiment. Previous work has focused on aggregating and adjusting discriminative individual classifiers to obtain prevalence point estimates. But imperfect classifier accuracy ought to be reflected in uncertainty over the predicted prevalence for scientifically valid inference. In this work, we present (1) a generative probabilistic modeling approach to prevalence estimation, and (2) the construction and evaluation of prevalence confidence intervals; in particular, we demonstrate that an off-the-shelf discriminative classifier can be given a generative re-interpretation, by backing out an implicit individual-level likelihood function, which can be used to conduct fast and simple group-level Bayesian inference. Empirically, we demonstrate our approach provides better confidence interval coverage than an alternative, and is dramatically more robust to shifts in the class prior between training and testing.",
}
@inproceedings{wood-doughty-etal-2018-challenges,
    title = "Challenges of Using Text Classifiers for Causal Inference",
    author = "Wood-Doughty, Zach  and
      Shpitser, Ilya  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1488",
    doi = "10.18653/v1/D18-1488",
    pages = "4586--4598",
    abstract = "Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.",
}
@inproceedings{takase-etal-2018-direct,
    title = "Direct Output Connection for a High-Rank Language Model",
    author = "Takase, Sho  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1489",
    doi = "10.18653/v1/D18-1489",
    pages = "4599--4609",
    abstract = "This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also middle layers. This method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018). Our proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets. Moreover, we indicate our proposed method contributes to application tasks: machine translation and headline generation.",
}
@inproceedings{jamshid-lou-etal-2018-disfluency,
    title = "Disfluency Detection using Auto-Correlational Neural Networks",
    author = "Jamshid Lou, Paria  and
      Anderson, Peter  and
      Johnson, Mark",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1490",
    doi = "10.18653/v1/D18-1490",
    pages = "4610--4619",
    abstract = "In recent years, the natural language processing community has moved away from task-specific feature engineering, i.e., researchers discovering ad-hoc feature representations for various tasks, in favor of general-purpose methods that learn the input representation by themselves. However, state-of-the-art approaches to disfluency detection in spontaneous speech transcripts currently still depend on an array of hand-crafted features, and other representations derived from the output of pre-existing systems such as language models or dependency parsers. As an alternative, this paper proposes a simple yet effective model for automatic disfluency detection, called an auto-correlational neural network (ACNN). The model uses a convolutional neural network (CNN) and augments it with a new auto-correlation operator at the lowest layer that can capture the kinds of {``}rough copy{''} dependencies that are characteristic of repair disfluencies in speech. In experiments, the ACNN model outperforms the baseline CNN on a disfluency detection task with a 5{\%} increase in f-score, which is close to the previous best result on this task.",
}
@inproceedings{mehta-etal-2018-pyramidal,
    title = "Pyramidal Recurrent Unit for Language Modeling",
    author = "Mehta, Sachin  and
      Koncel-Kedziorski, Rik  and
      Rastegari, Mohammad  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1491",
    doi = "10.18653/v1/D18-1491",
    pages = "4620--4630",
    abstract = "LSTMs are powerful tools for modeling contextual information, as evidenced by their success at the task of language modeling. However, modeling contexts in very high dimensional space can lead to poor generalizability. We introduce the Pyramidal Recurrent Unit (PRU), which enables learning representations in high dimensional space with more generalization power and fewer parameters. PRUs replace the linear transformation in LSTMs with more sophisticated interactions such as pyramidal or grouped linear transformations. This architecture gives strong results on word-level language modeling while reducing parameters significantly. In particular, PRU improves the perplexity of a recent state-of-the-art language model by up to 1.3 points while learning 15-20{\%} fewer parameters. For similar number of model parameters, PRU outperforms all previous RNN models that exploit different gating mechanisms and transformations. We provide a detailed examination of the PRU and its behavior on the language modeling tasks. Our code is open-source and available at \url{https://sacmehta.github.io/PRU/}.",
}
@inproceedings{shi-etal-2018-tree,
    title = "On Tree-Based Neural Sentence Modeling",
    author = "Shi, Haoyue  and
      Zhou, Hao  and
      Chen, Jiaze  and
      Li, Lei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1492",
    doi = "10.18653/v1/D18-1492",
    pages = "4631--4641",
    abstract = "Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at \url{https://github.com/ExplorerFreda/TreeEnc}.",
}
@inproceedings{gu-etal-2018-language,
    title = "Language Modeling with Sparse Product of Sememe Experts",
    author = "Gu, Yihong  and
      Yan, Jun  and
      Zhu, Hao  and
      Liu, Zhiyuan  and
      Xie, Ruobing  and
      Sun, Maosong  and
      Lin, Fen  and
      Lin, Leyu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1493",
    doi = "10.18653/v1/D18-1493",
    pages = "4642--4651",
    abstract = "Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but not necessarily atomic semantic units. Inspired by HowNet, we use sememes, the minimum semantic units in human languages, to represent the implicit semantics behind words for language modeling, named Sememe-Driven Language Model (SDLM). More specifically, to predict the next word, SDLM first estimates the sememe distribution given textual context. Afterwards, it regards each sememe as a distinct semantic expert, and these experts jointly identify the most probable senses and the corresponding word. In this way, SDLM enables language models to work beyond word-level manipulation to fine-grained sememe-level semantics, and offers us more powerful tools to fine-tune language models and improve the interpretability as well as the robustness of language models. Experiments on language modeling and the downstream application of headline generation demonstrate the significant effectiveness of SDLM.",
}
@inproceedings{huang-etal-2018-siamese,
    title = "{S}iamese Network-Based Supervised Topic Modeling",
    author = "Huang, Minghui  and
      Rao, Yanghui  and
      Liu, Yuwei  and
      Xie, Haoran  and
      Wang, Fu Lee",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1494",
    doi = "10.18653/v1/D18-1494",
    pages = "4652--4662",
    abstract = "Label-specific topics can be widely used for supporting personality psychology, aspect-level sentiment analysis, and cross-domain sentiment classification. To generate label-specific topics, several supervised topic models which adopt likelihood-driven objective functions have been proposed. However, it is hard for them to get a precise estimation on both topic discovery and supervised learning. In this study, we propose a supervised topic model based on the Siamese network, which can trade off label-specific word distributions with document-specific label distributions in a uniform framework. Experiments on real-world datasets validate that our model performs competitive in topic discovery quantitatively and qualitatively. Furthermore, the proposed model can effectively predict categorical or real-valued labels for new documents by generating word embeddings from a label-specific topical space.",
}
@inproceedings{zhu-etal-2018-graphbtm,
    title = "{G}raph{BTM}: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model",
    author = "Zhu, Qile  and
      Feng, Zheng  and
      Li, Xiaolin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1495",
    doi = "10.18653/v1/D18-1495",
    pages = "4663--4672",
    abstract = "Discovering the latent topics within texts has been a fundamental task for many applications. However, conventional topic models suffer different problems in different settings. The Latent Dirichlet Allocation (LDA) may not work well for short texts due to the data sparsity (i.e. the sparse word co-occurrence patterns in short documents). The Biterm Topic Model (BTM) learns topics by modeling the word-pairs named biterms in the whole corpus. This assumption is very strong when documents are long with rich topic information and do not exhibit the transitivity of biterms. In this paper, we propose a novel way called GraphBTM to represent biterms as graphs and design a Graph Convolutional Networks (GCNs) with residual connections to extract transitive features from biterms. To overcome the data sparsity of LDA and the strong assumption of BTM, we sample a fixed number of documents to form a mini-corpus as a sample. We also propose a dataset called All News extracted from 15 news publishers, in which documents are much longer than 20 Newsgroups. We present an amortized variational inference method for GraphBTM. Our method generates more coherent topics compared with previous approaches. Experiments show that the sampling strategy improves performance by a large margin.",
}
@inproceedings{srivatsan-etal-2018-modeling,
    title = "Modeling Online Discourse with Coupled Distributed Topics",
    author = "Srivatsan, Nikita  and
      Wojtowicz, Zachary  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1496",
    doi = "10.18653/v1/D18-1496",
    pages = "4673--4682",
    abstract = "In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.",
}
@inproceedings{jain-etal-2018-learning,
    title = "Learning Disentangled Representations of Texts with Application to Biomedical Abstracts",
    author = "Jain, Sarthak  and
      Banner, Edward  and
      van de Meent, Jan-Willem  and
      Marshall, Iain J.  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1497",
    doi = "10.18653/v1/D18-1497",
    pages = "4683--4693",
    abstract = "We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and interpretability. To induce disentangled embeddings, we propose an adversarial objective based on the (dis)similarity between triplets of documents with respect to specific aspects. Our motivating application is embedding biomedical abstracts describing clinical trials in a manner that disentangles the populations, interventions, and outcomes in a given trial. We show that our method learns representations that encode these clinically salient aspects, and that these can be effectively used to perform aspect-specific retrieval. We demonstrate that the approach generalizes beyond our motivating application in experiments on two multi-aspect review corpora.",
}
@inproceedings{guo-etal-2018-multi,
    title = "Multi-Source Domain Adaptation with Mixture of Experts",
    author = "Guo, Jiang  and
      Shah, Darsh  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1498",
    doi = "10.18653/v1/D18-1498",
    pages = "4694--4703",
    abstract = "We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources. The key idea is to explicitly capture the relationship between a target example and different source domains. This relationship, expressed by a point-to-set metric, determines how to combine predictors trained on various domains. The metric is learned in an unsupervised fashion using meta-training. Experimental results on sentiment analysis and part-of-speech tagging demonstrate that our approach consistently outperforms multiple baselines and can robustly handle negative transfer.",
}
@inproceedings{van-schijndel-linzen-2018-neural,
    title = "A Neural Model of Adaptation in Reading",
    author = "van Schijndel, Marten  and
      Linzen, Tal",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1499",
    doi = "10.18653/v1/D18-1499",
    pages = "4704--4710",
    abstract = "It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.",
}
@inproceedings{lalor-etal-2018-understanding,
    title = "Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study",
    author = "Lalor, John P.  and
      Wu, Hao  and
      Munkhdalai, Tsendsuren  and
      Yu, Hong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1500",
    doi = "10.18653/v1/D18-1500",
    pages = "4711--4716",
    abstract = "Interpreting the performance of deep learning models beyond test set accuracy is challenging. Characteristics of individual data points are often not considered during evaluation, and each data point is treated equally. In this work we examine the impact of a test set question{'}s difficulty to determine if there is a relationship between difficulty and performance. We model difficulty using well-studied psychometric methods on human response patterns. Experiments on Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the likelihood of answering a question correctly is impacted by the question{'}s difficulty. In addition, as DNNs are trained on larger datasets easy questions start to have a higher probability of being answered correctly than harder questions.",
}
@inproceedings{white-etal-2018-lexicosyntactic,
    title = "Lexicosyntactic Inference in Neural Models",
    author = "White, Aaron Steven  and
      Rudinger, Rachel  and
      Rawlins, Kyle  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1501",
    doi = "10.18653/v1/D18-1501",
    pages = "4717--4724",
    abstract = "We investigate neural models{'} ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.",
}
@inproceedings{watcharawittayakul-etal-2018-dual,
    title = "Dual Fixed-Size Ordinally Forgetting Encoding ({FOFE}) for Competitive Neural Language Models",
    author = "Watcharawittayakul, Sedtawut  and
      Xu, Mingbin  and
      Jiang, Hui",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1502",
    doi = "10.18653/v1/D18-1502",
    pages = "4725--4730",
    abstract = "In this paper, we propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) (Zhang et al., 2015b) in neural languages modelling, called dual-FOFE. The main idea of dual-FOFE is that it allows to use two different forgetting factors so that it can avoid the trade-off in choosing either a small or large values for the single forgetting factor. In our experiments, we have compared the dual-FOFE based neural network language models (NNLM) against the original FOFE counterparts and various traditional NNLMs. Our results on the challenging Google Billion word corpus show that both FOFE and dual FOFE yield very strong performance while significantly reducing the computational complexity over other NNLMs. Furthermore, the proposed dual-FOFE method further gives over 10{\%} improvement in perplexity over the original FOFE model.",
}
@inproceedings{tran-etal-2018-importance,
    title = "The Importance of Being Recurrent for Modeling Hierarchical Structure",
    author = "Tran, Ke  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1503",
    doi = "10.18653/v1/D18-1503",
    pages = "4731--4736",
    abstract = "Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks (Blevins et al., 2018) such as language modeling (Linzen et al., 2016; Gulordava et al., 2018) and neural machine translation (Shi et al., 2016). In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017; Vaswani et al., 2017). In this work, we compare the two architectures{---}recurrent versus non-recurrent{---}with respect to their ability to model hierarchical structure and find that recurrency is indeed important for this purpose. The code and data used in our experiments is available at \url{https://github.com/ ketranm/fan_vs_rnn}",
}
@inproceedings{ma-etal-2018-joint,
    title = "Joint Learning for Targeted Sentiment Analysis",
    author = "Ma, Dehong  and
      Li, Sujian  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1504",
    doi = "10.18653/v1/D18-1504",
    pages = "4737--4742",
    abstract = "Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes. Previous works only exploit word embeddings as features and do not explore more potentials of neural networks when jointly learning the two tasks. In this paper, we carefully design the hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model to learn abstract features for both tasks, and we propose a HSBi-GRU based joint model which allows the target label to have influence on their sentiment label. Experimental results on two datasets show that our joint learning model can outperform other baselines and demonstrate the effectiveness of HSBi-GRU in learning abstract features.",
}
@inproceedings{krishna-etal-2018-revisiting,
    title = "Revisiting the Importance of Encoding Logic Rules in Sentiment Classification",
    author = "Krishna, Kalpesh  and
      Jyothi, Preethi  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1505",
    doi = "10.18653/v1/D18-1505",
    pages = "4743--4751",
    abstract = "We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their accuracies must be averaged over far more random seeds than what has traditionally been reported. With proper averaging in place, we notice that the distillation model described in Hu et al. (2016), which incorporates explicit logic rules for sentiment classification, is ineffective. In contrast, using contextualized ELMo embeddings (Peters et al., 2018a) instead of logic rules yields significantly better performance. Additionally, we provide analysis and visualizations that demonstrate ELMo{'}s ability to implicitly learn logic rules. Finally, a crowdsourced analysis reveals how ELMo outperforms baseline models even on sentences with ambiguous sentiment labels.",
}
@inproceedings{li-etal-2018-co,
    title = "A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness",
    author = "Li, Xiangju  and
      Song, Kaisong  and
      Feng, Shi  and
      Wang, Daling  and
      Zhang, Yifei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1506",
    doi = "10.18653/v1/D18-1506",
    pages = "4752--4757",
    abstract = "Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.",
}
@inproceedings{buechel-etal-2018-modeling,
    title = "Modeling Empathy and Distress in Reaction to News Stories",
    author = "Buechel, Sven  and
      Buffone, Anneke  and
      Slaff, Barry  and
      Ungar, Lyle  and
      Sedoc, Jo{\~a}o",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1507",
    doi = "10.18653/v1/D18-1507",
    pages = "4758--4765",
    abstract = "Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.",
}
@inproceedings{barbieri-etal-2018-interpretable,
    title = "Interpretable Emoji Prediction via Label-Wise Attention {LSTM}s",
    author = "Barbieri, Francesco  and
      Espinosa-Anke, Luis  and
      Camacho-Collados, Jose  and
      Schockaert, Steven  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1508",
    doi = "10.18653/v1/D18-1508",
    pages = "4766--4771",
    abstract = "Human language has evolved towards newer forms of communication such as social media, where emojis (i.e., ideograms bearing a visual meaning) play a key role. While there is an increasing body of work aimed at the computational modeling of emoji semantics, there is currently little understanding about what makes a computational model represent or predict a given emoji in a certain way. In this paper we propose a label-wise attention mechanism with which we attempt to better understand the nuances underlying emoji prediction. In addition to advantages in terms of interpretability, we show that our proposed architecture improves over standard baselines in emoji prediction, and does particularly well when predicting infrequent emojis.",
}
@inproceedings{wang-etal-2018-tree,
    title = "A Tree-based Decoder for Neural Machine Translation",
    author = "Wang, Xinyi  and
      Pham, Hieu  and
      Yin, Pengcheng  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1509",
    doi = "10.18653/v1/D18-1509",
    pages = "4772--4777",
    abstract = "Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes some specific types of linguistically-inspired tree structures, like constituency and dependency parse trees. This is often done via a standard RNN decoder that operates on a linearized target tree structure. However, it is an open question of what specific linguistic formalism, if any, is the best structural representation for NMT. In this paper, we (1) propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side, and (2) experiment with various target tree structures. Our experiments show the surprising result that our model delivers the best improvements with balanced binary trees constructed without any linguistic knowledge; this model outperforms standard seq2seq models by up to 2.1 BLEU points, and other methods for incorporating target-side syntax by up to 0.7 BLEU.",
}
@inproceedings{shao-etal-2018-greedy,
    title = "Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation",
    author = "Shao, Chenze  and
      Chen, Xilin  and
      Feng, Yang",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1510",
    doi = "10.18653/v1/D18-1510",
    pages = "4778--4784",
    abstract = "Neural machine translation (NMT) models are usually trained with the word-level loss using the teacher forcing algorithm, which not only evaluates the translation improperly but also suffers from exposure bias. Sequence-level training under the reinforcement framework can mitigate the problems of the word-level loss, but its performance is unstable due to the high variance of the gradient estimation. On these grounds, we present a method with a differentiable sequence-level training objective based on probabilistic n-gram matching which can avoid the reinforcement framework. In addition, this method performs greedy search in the training which uses the predicted words as context just as at inference to alleviate the problem of exposure bias. Experiment results on the NIST Chinese-to-English translation tasks show that our method significantly outperforms the reinforcement-based algorithms and achieves an improvement of 1.5 BLEU points on average over a strong baseline system.",
}
@inproceedings{zhang-etal-2018-exploring,
    title = "Exploring Recombination for Efficient Decoding of Neural Machine Translation",
    author = "Zhang, Zhisong  and
      Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1511",
    doi = "10.18653/v1/D18-1511",
    pages = "4785--4790",
    abstract = "In Neural Machine Translation (NMT), the decoder can capture the features of the entire prediction history with neural connections and representations. This means that partial hypotheses with different prefixes will be regarded differently no matter how similar they are. However, this might be inefficient since some partial hypotheses can contain only local differences that will not influence future predictions. In this work, we introduce recombination in NMT decoding based on the concept of the {``}equivalence{''} of partial hypotheses. Heuristically, we use a simple n-gram suffix based equivalence function and adapt it into beam search decoding. Through experiments on large-scale Chinese-to-English and English-to-Germen translation tasks, we show that the proposed method can obtain similar translation quality with a smaller beam size, making NMT decoding more efficient.",
}
@inproceedings{laubli-etal-2018-machine,
    title = "Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation",
    author = {L{\"a}ubli, Samuel  and
      Sennrich, Rico  and
      Volk, Martin},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1512",
    doi = "10.18653/v1/D18-1512",
    pages = "4791--4796",
    abstract = "Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese{--}English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences. Our findings emphasise the need to shift towards document-level evaluation as machine translation improves to the degree that errors which are hard or impossible to spot at the sentence-level become decisive in discriminating quality of different translation outputs.",
}
@inproceedings{guillou-hardmeier-2018-automatic,
    title = "Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point",
    author = "Guillou, Liane  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1513",
    doi = "10.18653/v1/D18-1513",
    pages = "4797--4802",
    abstract = "We compare the performance of the APT and AutoPRF metrics for pronoun translation against a manually annotated dataset comprising human judgements as to the correctness of translations of the PROTEST test suite. Although there is some correlation with the human judgements, a range of issues limit the performance of the automated metrics. Instead, we recommend the use of semi-automatic metrics and test suites in place of fully automatic metrics.",
}
@inproceedings{han-etal-2018-fewrel,
    title = "{F}ew{R}el: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation",
    author = "Han, Xu  and
      Zhu, Hao  and
      Yu, Pengfei  and
      Wang, Ziyun  and
      Yao, Yuan  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1514",
    doi = "10.18653/v1/D18-1514",
    pages = "4803--4809",
    abstract = "We present a Few-Shot Relation Classification Dataset (dataset), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research.",
}
@inproceedings{gonzalez-etal-2018-strong,
    title = "A strong baseline for question relevancy ranking",
    author = "Gonzalez, Ana  and
      Augenstein, Isabelle  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1515",
    doi = "10.18653/v1/D18-1515",
    pages = "4810--4815",
    abstract = "The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks {--} a task that amounts to question relevancy ranking {--} involve complex pipelines and manual feature engineering. Despite this, many of these still fail at beating the IR baseline, i.e., the rankings provided by Google{'}s search engine. We present a strong baseline for question relevancy ranking by training a simple multi-task feed forward network on a bag of 14 distance measures for the input question pair. This baseline model, which is fast to train and uses only language-independent features, outperforms the best shared task systems on the task of retrieving relevant previously asked questions.",
}
@inproceedings{garcia-duran-etal-2018-learning,
    title = "Learning Sequence Encoders for Temporal Knowledge Graph Completion",
    author = "Garc{\'\i}a-Dur{\'a}n, Alberto  and
      Duman{\v{c}}i{\'c}, Sebastijan  and
      Niepert, Mathias",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1516",
    doi = "10.18653/v1/D18-1516",
    pages = "4816--4821",
    abstract = "Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license.",
}
@inproceedings{lu-nguyen-2018-similar,
    title = "Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching",
    author = "Lu, Weiyi  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1517",
    doi = "10.18653/v1/D18-1517",
    pages = "4822--4828",
    abstract = "Event detection (ED) and word sense disambiguation (WSD) are two similar tasks in that they both involve identifying the classes (i.e. event types or word senses) of some word in a given sentence. It is thus possible to extract the knowledge hidden in the data for WSD, and utilize it to improve the performance on ED. In this work, we propose a method to transfer the knowledge learned on WSD to ED by matching the neural representations learned for the two tasks. Our experiments on two widely used datasets for ED demonstrate the effectiveness of the proposed method.",
}
@inproceedings{luo-glass-2018-learning,
    title = "Learning Word Representations with Cross-Sentence Dependency for End-to-End Co-reference Resolution",
    author = "Luo, Hongyin  and
      Glass, Jim",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1518",
    doi = "10.18653/v1/D18-1518",
    pages = "4829--4833",
    abstract = "In this work, we present a word embedding model that learns cross-sentence dependency for improving end-to-end co-reference resolution (E2E-CR). While the traditional E2E-CR model generates word representations by running long short-term memory (LSTM) recurrent neural networks on each sentence of an input article or conversation separately, we propose linear sentence linking and attentional sentence linking models to learn cross-sentence dependency. Both sentence linking strategies enable the LSTMs to make use of valuable information from context sentences while calculating the representation of the current input word. With this approach, the LSTMs learn word embeddings considering knowledge not only from the current sentence but also from the entire input document. Experiments show that learning cross-sentence dependency enriches information contained by the word representations, and improves the performance of the co-reference resolution model compared with our baseline.",
}
@inproceedings{chen-etal-2018-word,
    title = "Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings",
    author = "Chen, Hong-You  and
      Lee, Cheng-Syuan  and
      Liao, Keng-Te  and
      Lin, Shou-De",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1519",
    doi = "10.18653/v1/D18-1519",
    pages = "4834--4839",
    abstract = "Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly.",
}
@inproceedings{utsumi-2018-refining,
    title = "Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation",
    author = "Utsumi, Akira",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1520",
    doi = "10.18653/v1/D18-1520",
    pages = "4840--4846",
    abstract = "In this paper, we propose a simple method for refining pretrained word embeddings using layer-wise relevance propagation. Given a target semantic representation one would like word vectors to reflect, our method first trains the mapping between the original word vectors and the target representation using a neural network. Estimated target values are then propagated backward toward word vectors, and a relevance score is computed for each dimension of word vectors. Finally, the relevance score vectors are used to refine the original word vectors so that they are projected into the subspace that reflects the information relevant to the target representation. The evaluation experiment using binary classification of word pairs demonstrates that the refined vectors by our method achieve the higher performance than the original vectors.",
}
@inproceedings{zhao-etal-2018-learning,
    title = "Learning Gender-Neutral Word Embeddings",
    author = "Zhao, Jieyu  and
      Zhou, Yichao  and
      Li, Zeyu  and
      Wang, Wei  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1521",
    doi = "10.18653/v1/D18-1521",
    pages = "4847--4853",
    abstract = "Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications. However, embeddings trained on human-generated corpora have been demonstrated to inherit strong gender stereotypes that reflect social constructs. To address this concern, in this paper, we propose a novel training procedure for learning gender-neutral word embeddings. Our approach aims to preserve gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence. Based on the proposed method, we generate a Gender-Neutral variant of GloVe (GN-GloVe). Quantitative and qualitative experiments demonstrate that GN-GloVe successfully isolates gender information without sacrificing the functionality of the embedding model.",
}
@inproceedings{rabinovich-etal-2018-learning,
    title = "Learning Concept Abstractness Using Weak Supervision",
    author = "Rabinovich, Ella  and
      Sznajder, Benjamin  and
      Spector, Artem  and
      Shnayderman, Ilya  and
      Aharonov, Ranit  and
      Konopnicki, David  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1522",
    doi = "10.18653/v1/D18-1522",
    pages = "4854--4859",
    abstract = "We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal linguistic clues and the contextual usage of a concept as manifested in textual data, we train sufficiently powerful classifiers, obtaining high correlation with human labels. The results imply the applicability of this approach to additional properties of concepts, additional languages, and resource-scarce scenarios.",
}
@inproceedings{amrami-goldberg-2018-word,
    title = "Word Sense Induction with Neural bi{LM} and Symmetric Patterns",
    author = "Amrami, Asaf  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1523",
    doi = "10.18653/v1/D18-1523",
    pages = "4860--4867",
    abstract = "An established method for Word Sense Induction (WSI) uses a language model to predict probable substitutes for target words, and induces senses by clustering these resulting substitute vectors. We replace the ngram-based language model (LM) with a recurrent one. Beyond being more accurate, the use of the recurrent LM allows us to effectively query it in a creative way, using what we call dynamic symmetric patterns. The combination of the RNN-LM and the dynamic symmetric patterns results in strong substitute vectors for WSI, allowing to surpass the current state-of-the-art on the SemEval 2013 WSI shared task by a large margin.",
}
@inproceedings{kiros-chan-2018-inferlite,
    title = "{I}nfer{L}ite: Simple Universal Sentence Representations from Natural Language Inference Data",
    author = "Kiros, Jamie  and
      Chan, William",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1524",
    doi = "10.18653/v1/D18-1524",
    pages = "4868--4874",
    abstract = "Natural language inference has been shown to be an effective supervised task for learning generic sentence embeddings. In order to better understand the components that lead to effective representations, we propose a lightweight version of InferSent, called InferLite, that does not use any recurrent layers and operates on a collection of pre-trained word embeddings. We show that a simple instance of our model that makes no use of context, word ordering or position can still obtain competitive performance on the majority of downstream prediction tasks, with most performance gaps being filled by adding local contextual information through temporal convolutions. Our models can be trained in under 1 hour on a single GPU and allows for fast inference of new representations. Finally we describe a semantic hashing layer that allows our model to learn generic binary codes for sentences.",
}
@inproceedings{kovaleva-etal-2018-similarity,
    title = "Similarity-Based Reconstruction Loss for Meaning Representation",
    author = "Kovaleva, Olga  and
      Rumshisky, Anna  and
      Romanov, Alexey",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1525",
    doi = "10.18653/v1/D18-1525",
    pages = "4875--4880",
    abstract = "This paper addresses the problem of representation learning. Using an autoencoder framework, we propose and evaluate several loss functions that can be used as an alternative to the commonly used cross-entropy reconstruction loss. The proposed loss functions use similarities between words in the embedding space, and can be used to train any neural model for text generation. We show that the introduced loss functions amplify semantic diversity of reconstructed sentences, while preserving the original meaning of the input. We test the derived autoencoder-generated representations on paraphrase detection and language inference tasks and demonstrate performance improvement compared to the traditional cross-entropy loss.",
}
@inproceedings{abdou-etal-2018-learn,
    title = "What can we learn from Semantic Tagging?",
    author = "Abdou, Mostafa  and
      Kulmizev, Artur  and
      Ravishankar, Vinit  and
      Abzianidze, Lasha  and
      Bos, Johan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1526",
    doi = "10.18653/v1/D18-1526",
    pages = "4881--4889",
    abstract = "We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting which shows consistent gains across all tasks.",
}
@inproceedings{han-etal-2018-conditional,
    title = "Conditional Word Embedding and Hypothesis Testing via {B}ayes-by-Backprop",
    author = "Han, Rujun  and
      Gill, Michael  and
      Spirling, Arthur  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1527",
    doi = "10.18653/v1/D18-1527",
    pages = "4890--4895",
    abstract = "Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant.",
}
@inproceedings{yaneva-etal-2018-classifying,
    title = "Classifying Referential and Non-referential It Using Gaze",
    author = "Yaneva, Victoria  and
      Ha, Le An  and
      Evans, Richard  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1528",
    doi = "10.18653/v1/D18-1528",
    pages = "4896--4901",
    abstract = "When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones. In this paper we use eye-tracking data to learn how humans perform this disambiguation and use this knowledge to improve the automatic classification of it. We show that by using gaze data and a POS-tagger we are able to significantly outperform a common baseline and classify between three categories of it with an accuracy comparable to that of linguistic-based approaches. In addition, the discriminatory power of specific gaze features informs the way humans process the pronoun, which, to the best of our knowledge, has not been explored using data from a natural reading task.",
}
@inproceedings{ma-etal-2018-state,
    title = "State-of-the-art {C}hinese Word Segmentation with {B}i-{LSTM}s",
    author = "Ma, Ji  and
      Ganchev, Kuzman  and
      Weiss, David",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1529",
    doi = "10.18653/v1/D18-1529",
    pages = "4902--4908",
    abstract = "A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neuralnetwork architectures. Furthermore, our error analysis shows that out-of-vocabulary words remain challenging for neural-network models, and many of the remaining errors are unlikely to be fixed through architecture changes. Instead, more effort should be made on exploring resources for further improvement.",
}
@inproceedings{aralikatte-etal-2018-sanskrit,
    title = "{S}anskrit Sandhi Splitting using seq2(seq)2",
    author = "Aralikatte, Rahul  and
      Gantayat, Neelamadhav  and
      Panwar, Naveen  and
      Sankaran, Anush  and
      Mani, Senthil",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1530",
    doi = "10.18653/v1/D18-1530",
    pages = "4909--4914",
    abstract = "In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95{\%} accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5{\%} accuracy, outperforming the state-of-art by 20{\%}. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well.",
}
@inproceedings{sun-deng-2018-unsupervised,
    title = "Unsupervised Neural Word Segmentation for {C}hinese via Segmental Language Modeling",
    author = "Sun, Zhiqing  and
      Deng, Zhi-Hong",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1531",
    doi = "10.18653/v1/D18-1531",
    pages = "4915--4920",
    abstract = "Previous traditional approaches to unsupervised Chinese word segmentation (CWS) can be roughly classified into discriminative and generative models. The former uses the carefully designed goodness measures for candidate segmentation, while the latter focuses on finding the optimal segmentation of the highest generative probability. However, while there exists a trivial way to extend the discriminative models into neural version by using neural language models, those of generative ones are non-trivial. In this paper, we propose the segmental language models (SLMs) for CWS. Our approach explicitly focuses on the segmental nature of Chinese, as well as preserves several properties of language models. In SLMs, a context encoder encodes the previous context and a segment decoder generates each segment incrementally. As far as we know, we are the first to propose a neural model for unsupervised CWS and achieve competitive performance to the state-of-the-art statistical models on four different datasets from SIGHAN 2005 bakeoff.",
}
@inproceedings{kondratyuk-etal-2018-lemmatag,
    title = "{L}emma{T}ag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with {BRNN}s",
    author = "Kondratyuk, Daniel  and
      Gaven{\v{c}}iak, Tom{\'a}{\v{s}}  and
      Straka, Milan  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1532",
    doi = "10.18653/v1/D18-1532",
    pages = "4921--4928",
    abstract = "We present LemmaTag, a featureless neural network architecture that jointly generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. We demonstrate that both tasks benefit from sharing the encoding part of the network, predicting tag subcategories, and using the tagger output as an input to the lemmatizer. We evaluate our model across several languages with complex morphology, which surpasses state-of-the-art accuracy in both part-of-speech tagging and lemmatization in Czech, German, and Arabic.",
}
@inproceedings{shillingford-parker-jones-2018-recovering,
    title = "Recovering Missing Characters in Old {H}awaiian Writing",
    author = "Shillingford, Brendan  and
      Parker Jones, Oiwi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1533",
    doi = "10.18653/v1/D18-1533",
    pages = "4929--4934",
    abstract = "In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, transliterating between older and newer texts is a laborious task when performed manually. We introduce two related methods to help solve this transliteration problem automatically. One approach is implemented, end-to-end, using finite state transducers (FSTs). The other is a hybrid deep learning approach, which approximately composes an FST with a recurrent neural network language model.",
}
@inproceedings{schluter-varab-2018-data,
    title = "When data permutations are pathological: the case of neural natural language inference",
    author = "Schluter, Natalie  and
      Varab, Daniel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1534",
    doi = "10.18653/v1/D18-1534",
    pages = "4935--4939",
    abstract = "Consider two competitive machine learning models, one of which was considered state-of-the art, and the other a competitive baseline. Suppose that by just permuting the examples of the training set, say by reversing the original order, by shuffling, or by mini-batching, you could report substantially better/worst performance for the system of your choice, by multiple percentage points. In this paper, we illustrate this scenario for a trending NLP task: Natural Language Inference (NLI). We show that for the two central NLI corpora today, the learning process of neural systems is far too sensitive to permutations of the data. In doing so we reopen the question of how to judge a good neural architecture for NLI, given the available dataset and perhaps, further, the soundness of the NLI task itself in its current state.",
}
@inproceedings{kang-etal-2018-bridging,
    title = "Bridging Knowledge Gaps in Neural Entailment via Symbolic Models",
    author = "Kang, Dongyeop  and
      Khot, Tushar  and
      Sabharwal, Ashish  and
      Clark, Peter",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1535",
    doi = "10.18653/v1/D18-1535",
    pages = "4940--4945",
    abstract = "Most textual entailment models focus on lexical gaps between the premise text and the hypothesis, but rarely on knowledge gaps. We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts. Our new architecture combines standard neural entailment models with a knowledge lookup module. To facilitate this lookup, we propose a fact-level decomposition of the hypothesis, and verifying the resulting sub-facts against both the textual premise and the structured KB. Our model, NSNet, learns to aggregate predictions from these heterogeneous data formats. On the SciTail dataset, NSNet outperforms a simpler combination of the two predictions by 3{\%} and the base entailment model by 5{\%}.",
}
@inproceedings{chen-etal-2018-bq,
    title = "The {BQ} Corpus: A Large-scale Domain-specific {C}hinese Corpus For Sentence Semantic Equivalence Identification",
    author = "Chen, Jing  and
      Chen, Qingcai  and
      Liu, Xin  and
      Yang, Haijun  and
      Lu, Daohe  and
      Tang, Buzhou",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1536",
    doi = "10.18653/v1/D18-1536",
    pages = "4946--4951",
    abstract = "This paper introduces the Bank Question (BQ) corpus, a Chinese corpus for sentence semantic equivalence identification (SSEI). The BQ corpus contains 120,000 question pairs from 1-year online bank custom service logs. To efficiently process and annotate questions from such a large scale of logs, this paper proposes a clustering based annotation method to achieve questions with the same intent. First, the deduplicated questions with the same answer are clustered into stacks by the Word Mover{'}s Distance (WMD) based Affinity Propagation (AP) algorithm. Then, the annotators are asked to assign the clustered questions into different intent categories. Finally, the positive and negative question pairs for SSEI are selected in the same intent category and between different intent categories respectively. We also present six SSEI benchmark performance on our corpus, including state-of-the-art algorithms. As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research. The corpus is available in public.",
}
@inproceedings{ghaeini-etal-2018-interpreting,
    title = "Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference",
    author = "Ghaeini, Reza  and
      Fern, Xiaoli  and
      Tadepalli, Prasad",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1537",
    doi = "10.18653/v1/D18-1537",
    pages = "4952--4957",
    abstract = "Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study on a popular neural model for NLI. In particular, we propose to interpret the intermediate layers of NLI models by visualizing the saliency of attention and LSTM gating signals. We present several examples for which our methods are able to reveal interesting insights and identify the critical information contributing to the model decisions.",
}
@inproceedings{mehta-etal-2018-towards,
    title = "Towards Semi-Supervised Learning for Deep Semantic Role Labeling",
    author = "Mehta, Sanket Vaibhav  and
      Lee, Jay Yoon  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1538",
    doi = "10.18653/v1/D18-1538",
    pages = "4958--4963",
    abstract = "Neural models have shown several state-of-the-art performances on Semantic Role Labeling (SRL). However, the neural models require an immense amount of semantic-role corpora and are thus not well suited for low-resource languages or domains. The paper proposes a semi-supervised semantic role labeling method that outperforms the state-of-the-art in limited SRL training corpora. The method is based on explicitly enforcing syntactic constraints by augmenting the training objective with a syntactic-inconsistency loss component and uses SRL-unlabeled instances to train a joint-objective LSTM. On CoNLL-2012 English section, the proposed semi-supervised training with 1{\%}, 10{\%} SRL-labeled data and varying amounts of SRL-unlabeled data achieves +1.58, +0.78 F1, respectively, over the pre-trained models that were trained on SOTA architecture with ELMo on the same SRL-labeled data. Additionally, by using the syntactic-inconsistency loss on inference time, the proposed model achieves +3.67, +2.1 F1 over pre-trained model on 1{\%}, 10{\%} SRL-labeled data, respectively.",
}
@inproceedings{ferguson-etal-2018-identifying,
    title = "Identifying Domain Adjacent Instances for Semantic Parsers",
    author = "Ferguson, James  and
      Christensen, Janara  and
      Li, Edward  and
      Gonz{\`a}lez, Edgar",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1539",
    doi = "10.18653/v1/D18-1539",
    pages = "4964--4969",
    abstract = "When the semantics of a sentence are not representable in a semantic parser{'}s output schema, parsing will inevitably fail. Detection of these instances is commonly treated as an out-of-domain classification problem. However, there is also a more subtle scenario in which the test data is drawn from the same domain. In addition to formalizing this problem of domain-adjacency, we present a comparison of various baselines that could be used to solve it. We also propose a new simple sentence representation that emphasizes words which are unexpected. This approach improves the performance of a downstream semantic parser run on in-domain and domain-adjacent instances.",
}
@inproceedings{pasupat-etal-2018-mapping,
    title = "Mapping natural language commands to web elements",
    author = "Pasupat, Panupong  and
      Jiang, Tian-Shun  and
      Liu, Evan  and
      Guu, Kelvin  and
      Liang, Percy",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1540",
    doi = "10.18653/v1/D18-1540",
    pages = "4970--4976",
    abstract = "The web provides a rich, open-domain environment with textual, structural, and spatial properties. We propose a new task for grounding language in this environment: given a natural language command (e.g., {``}click on the second article{''}), choose the correct element on the web page (e.g., a hyperlink or text box). We collected a dataset of over 50,000 commands that capture various phenomena such as functional references (e.g. {``}find who made this site{''}), relational reasoning (e.g. {``}article by john{''}), and visual reasoning (e.g. {``}top-most article{''}). We also implemented and analyzed three baseline models that capture different phenomena present in the dataset.",
}
@inproceedings{kasewa-etal-2018-wronging,
    title = "Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection",
    author = "Kasewa, Sudhanshu  and
      Stenetorp, Pontus  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1541",
    doi = "10.18653/v1/D18-1541",
    pages = "4977--4983",
    abstract = "Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturally-occurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5{\%} F0.5 score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 F1 score, indicating that our model generates mostly human-like instances.",
}
@inproceedings{van-der-goot-van-noord-2018-modeling,
    title = "Modeling Input Uncertainty in Neural Network Dependency Parsing",
    author = "van der Goot, Rob  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1542",
    doi = "10.18653/v1/D18-1542",
    pages = "4984--4991",
    abstract = "Recently introduced neural network parsers allow for new approaches to circumvent data sparsity issues by modeling character level information and by exploiting raw data in a semi-supervised setting. Data sparsity is especially prevailing when transferring to non-standard domains. In this setting, lexical normalization has often been used in the past to circumvent data sparsity. In this paper, we investigate whether these new neural approaches provide similar functionality as lexical normalization, or whether they are complementary. We provide experimental results which show that a separate normalization component improves performance of a neural network parser even if it has access to character level information as well as external word embeddings. Further improvements are obtained by a straightforward but novel approach in which the top-N best candidates provided by the normalization component are available to the parser.",
}
@inproceedings{de-lhoneux-etal-2018-parameter,
    title = "Parameter sharing between dependency parsers for related languages",
    author = "de Lhoneux, Miryam  and
      Bjerva, Johannes  and
      Augenstein, Isabelle  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1543",
    doi = "10.18653/v1/D18-1543",
    pages = "4992--4997",
    abstract = "Previous work has suggested that parameter sharing between transition-based neural dependency parsers for related languages can lead to better performance, but there is no consensus on what parameters to share. We present an evaluation of 27 different parameter sharing strategies across 10 languages, representing five pairs of related languages, each pair from a different language family. We find that sharing transition classifier parameters always helps, whereas the usefulness of sharing word and/or character LSTM parameters varies. Based on this result, we propose an architecture where the transition classifier is shared, and the sharing of word and character parameters is controlled by a parameter that can be tuned on validation data. This model is linguistically motivated and obtains significant improvements over a monolingually trained baseline. We also find that sharing transition classifier parameters helps when training a parser on unrelated language pairs, but we find that, in the case of unrelated languages, sharing too many parameters does not help.",
}
@inproceedings{htut-etal-2018-grammar,
    title = "Grammar Induction with Neural Language Models: An Unusual Replication",
    author = "Htut, Phu Mon  and
      Cho, Kyunghyun  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1544",
    doi = "10.18653/v1/D18-1544",
    pages = "4998--5003",
    abstract = "A substantial thread of recent work on latent tree learning has attempted to develop neural network models with parse-valued latent variables and train them on non-parsing tasks, in the hope of having them discover interpretable tree structure. In a recent paper, Shen et al. (2018) introduce such a model and report near-state-of-the-art results on the target task of language modeling, and the first strong latent tree learning result on constituency parsing. In an attempt to reproduce these results, we discover issues that make the original results hard to trust, including tuning and even training on what is effectively the test set. Here, we attempt to reproduce these results in a fair experiment and to extend them to two new datasets. We find that the results of this work are robust: All variants of the model under study outperform all latent tree learning baselines, and perform competitively with symbolic grammar induction systems. We find that this model represents the first empirical success for latent tree learning, and that neural network language modeling warrants further study as a setting for grammar induction.",
}
@inproceedings{sahin-steedman-2018-data,
    title = "Data Augmentation via Dependency Tree Morphing for Low-Resource Languages",
    author = {{\c{S}}ahin, G{\"o}zde G{\"u}l  and
      Steedman, Mark},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1545",
    doi = "10.18653/v1/D18-1545",
    pages = "5004--5009",
    abstract = "Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We {``}crop{''} sentences by removing dependency links, and we {``}rotate{''} sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.",
}
@inproceedings{kaushik-lipton-2018-much,
    title = "How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks",
    author = "Kaushik, Divyansh  and
      Lipton, Zachary C.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1546",
    doi = "10.18653/v1/D18-1546",
    pages = "5010--5015",
    abstract = "Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for leaderboard dominance, basic questions about the difficulty of many popular benchmarks remain unanswered. In this paper, we establish sensible baselines for the bAbI, SQuAD, CBT, CNN, and Who-did-What datasets, finding that question- and passage-only models often perform surprisingly well. On 14 out of 20 bAbI tasks, passage-only models achieve greater than 50{\%} accuracy, sometimes matching the full model. Interestingly, while CBT provides 20-sentence passages, only the last is needed for accurate prediction. By comparison, SQuAD and CNN appear better-constructed.",
}
@inproceedings{budzianowski-etal-2018-multiwoz,
    title = "{M}ulti{WOZ} - A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset for Task-Oriented Dialogue Modelling",
    author = "Budzianowski, Pawe{\l}  and
      Wen, Tsung-Hsien  and
      Tseng, Bo-Hsiang  and
      Casanueva, I{\~n}igo  and
      Ultes, Stefan  and
      Ramadan, Osman  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1547",
    doi = "10.18653/v1/D18-1547",
    pages = "5016--5026",
    abstract = "Even though machine learning has become the major scene in dialogue research community, the real breakthrough has been blocked by the scale of data available.To address this fundamental obstacle, we introduce the Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.At a size of 10k dialogues, it is at least one order of magnitude larger than all previous annotated task-oriented corpora.The contribution of this work apart from the open-sourced dataset is two-fold:firstly, a detailed description of the data collection procedure along with a summary of data structure and analysis is provided. The proposed data-collection pipeline is entirely based on crowd-sourcing without the need of hiring professional annotators;secondly, a set of benchmark results of belief tracking, dialogue act and response generation is reported, which shows the usability of the data and sets a baseline for future studies.",
}
@inproceedings{strubell-etal-2018-linguistically,
    title = "Linguistically-Informed Self-Attention for Semantic Role Labeling",
    author = "Strubell, Emma  and
      Verga, Patrick  and
      Andor, Daniel  and
      Weiss, David  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1548",
    doi = "10.18653/v1/D18-1548",
    pages = "5027--5038",
    abstract = "Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10{\%} reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.",
}
@inproceedings{lample-etal-2018-phrase,
    title = "Phrase-Based {\&} Neural Unsupervised Machine Translation",
    author = "Lample, Guillaume  and
      Ott, Myle  and
      Conneau, Alexis  and
      Denoyer, Ludovic  and
      Ranzato, Marc{'}Aurelio",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1549",
    doi = "10.18653/v1/D18-1549",
    pages = "5039--5049",
    abstract = "Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT{'}14 English-French and WMT{'}16 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available.",
}
@proceedings{coling-2018-international-linguistics-tutorial,
    title = "Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts",
    editor = "Scott, Donia  and
      Walker, Marilyn  and
      Fung, Pascale",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-3000",
}
@inproceedings{murray-etal-2018-nlp,
    title = "{NLP} for Conversations: Sentiment, Summarization, and Group Dynamics",
    author = "Murray, Gabriel  and
      Carenini, Giuseppe  and
      Joty, Shafiq",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-3001",
    pages = "1--4",
}
@inproceedings{dakota-kubler-2018-practical,
    title = "Practical Parsing for Downstream Applications",
    author = {Dakota, Daniel  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-3002",
    pages = "5--7",
}
@inproceedings{baker-etal-2018-frame,
    title = "Frame Semantics across Languages: Towards a Multilingual {F}rame{N}et",
    author = "Baker, Collin F.  and
      Ellsworth, Michael  and
      Petruck, Miriam R. L.  and
      Swayamdipta, Swabha",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-3003",
    pages = "9--12",
}
@inproceedings{chien-2018-deep,
    title = "Deep {B}ayesian Learning and Understanding",
    author = "Chien, Jen-Tzung",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-3004",
    pages = "13--18",
}
@inproceedings{stajner-saggion-2018-data,
    title = "Data-Driven Text Simplification",
    author = "{\v{S}}tajner, Sanja  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-3005",
    pages = "19--23",
}
@inproceedings{chen-etal-2018-deep,
    title = "Deep Learning for Dialogue Systems",
    author = {Chen, Yun-Nung  and
      Celikyilmaz, Asli  and
      Hakkani-T{\"u}r, Dilek},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-3006",
    pages = "25--31",
}
@proceedings{coling-2018-international-linguistics,
    title = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    editor = "Zhao, Dongyan",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2000",
}
@inproceedings{ciosici-assent-2018-abbreviation,
    title = "Abbreviation Expander - a Web-based System for Easy Reading of Technical Documents",
    author = "Ciosici, Manuel R.  and
      Assent, Ira",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2001",
    pages = "1--4",
    abstract = "Abbreviations and acronyms are a part of textual communication in most domains. However, abbreviations are not necessarily defined in documents that employ them. Understanding all abbreviations used in a given document often requires extensive knowledge of the target domain and the ability to disambiguate based on context. This creates considerable entry barriers to newcomers and difficulties in automated document processing. Existing abbreviation expansion systems or tools require substantial technical knowledge for set up or make strong assumptions which limit their use in practice. Here, we present Abbreviation Expander, a system that builds on state of the art methods for identification of abbreviations, acronyms and their definitions and a novel disambiguator for abbreviation expansion in an easily accessible web-based solution.",
}
@inproceedings{klie-etal-2018-inception,
    title = "The {INCE}p{TION} Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation",
    author = "Klie, Jan-Christoph  and
      Bugert, Michael  and
      Boullosa, Beto  and
      Eckart de Castilho, Richard  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2002",
    pages = "5--9",
    abstract = "We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (e.g., concept linking, fact linking, knowledge base population, semantic frame annotation). These tasks are very time consuming and demanding for annotators, especially when knowledge bases are used. We address these issues by developing an annotation platform that incorporates machine learning capabilities which actively assist and guide annotators. The platform is both generic and modular. It targets a range of research domains in need of semantic annotation, such as digital humanities, bioinformatics, or linguistics. INCEpTION is publicly available as open-source software.",
}
@inproceedings{hellrich-etal-2018-jeseme,
    title = "{J}e{S}em{E}: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena",
    author = "Hellrich, Johannes  and
      Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2003",
    pages = "10--14",
    abstract = "We here introduce a substantially extended version of JeSemE, an interactive website for visually exploring computationally derived time-variant information on word meanings and lexical emotions assembled from five large diachronic text corpora. JeSemE is designed for scholars in the (digital) humanities as an alternative to consulting manually compiled, printed dictionaries for such information (if available at all). This tool uniquely combines state-of-the-art distributional semantics with a nuanced model of human emotions, two information streams we deem beneficial for a data-driven interpretation of texts in the humanities.",
}
@inproceedings{liu-etal-2018-know,
    title = "{T}-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional {C}hinese Medicine",
    author = "Liu, Ziqing  and
      Peng, Enwei  and
      Yan, Shixing  and
      Li, Guozheng  and
      Hao, Tianyong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2004",
    pages = "15--19",
    abstract = "T-Know is a knowledge service system based on the constructed knowledge graph of Traditional Chinese Medicine (TCM). Using authorized and anonymized clinical records, medicine clinical guidelines, teaching materials, classic medical books, academic publications, etc., as data resources, the system extracts triples from free texts to build a TCM knowledge graph by our developed natural language processing methods. On the basis of the knowledge graph, a deep learning algorithm is implemented for single-round question understanding and multiple-round dialogue. In addition, the TCM knowledge graph also is used to support human-computer interactive knowledge retrieval by normalizing search keywords to medical terminology.",
}
@inproceedings{nam-etal-2018-korean,
    title = "A {K}orean Knowledge Extraction System for Enriching a {KB}ox",
    author = "Nam, Sangha  and
      Kim, Eun-kyung  and
      Kim, Jiho  and
      Jung, Yoosung  and
      Han, Kijong  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2005",
    pages = "20--24",
    abstract = "The increased demand for structured knowledge has created considerable interest in knowledge extraction from natural language sentences. This study presents a new Korean knowledge extraction system and web interface for enriching a KBox knowledge base that expands based on the Korean DBpedia. The aim is to create an endpoint where knowledge can be extracted and added to KBox anytime and anywhere.",
}
@inproceedings{luo-liu-2018-real,
    title = "Real-time Scholarly Retweeting Prediction System",
    author = "Luo, Zhunchen  and
      Liu, Xiao",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2006",
    pages = "25--29",
    abstract = "Twitter has become one of the most import channels to spread latest scholarly information because of its fast information spread speed. How to predict whether a scholarly tweet will be retweeted is a key task in understanding the message propagation within large user communities. Hence, we present the real-time scholarly retweeting prediction system that retrieves scholarly tweets which will be retweeted. First, we filter scholarly tweets from tracking a tweet stream. Then, we extract Tweet Scholar Blocks indicating metadata of papers. At last, we combine scholarly features with the Tweet Scholar Blocks to predict whether a scholarly tweet will be retweeted. Our system outperforms chosen baseline systems. Additionally, our system has the potential to predict scientific impact in real-time.",
}
@inproceedings{yerebakan-etal-2018-document,
    title = "Document Representation Learning for Patient History Visualization",
    author = "Yerebakan, Halid Ziya  and
      Shinagawa, Yoshihisa  and
      Bhatia, Parmeet  and
      Zhan, Yiqiang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2007",
    pages = "30--33",
    abstract = "We tackle the problem of generating a diagrammatic summary of a set of documents each of which pertains to loosely related topics. In particular, we aim at visualizing the medical histories of patients. In medicine, choosing relevant reports from a patient{'}s past exams for comparison provide valuable information for precise treatment planning. Manually finding the relevant reports for comparison studies from a large database is time-consuming, which could result overlooking of some critical information. This task can be automated by defining similarity among documents which is a nontrivial task since these documents are often stored in an unstructured text format. To facilitate this, we have used a representation learning algorithm that creates a semantic representation space for documents where the clinically related documents lie close to each other. We have utilized referral information to weakly supervise a LSTM network to learn this semantic space. The abstract representations within this semantic space are not only useful to visualize disease progressions corresponding to the relevant report groups of a patient, but are also beneficial to analyze diseases at the population level. The proposed key tool here is clustering of documents based on the document similarity whose metric is learned from corpora.",
}
@inproceedings{preiss-stevenson-2018-hide,
    title = "{H}i{DE}: a Tool for Unrestricted Literature Based Discovery",
    author = "Preiss, Judita  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2008",
    pages = "34--37",
    abstract = "As the quantity of publications increases daily, researchers are forced to narrow their attention to their own specialism and are therefore less likely to make new connections with other areas. Literature based discovery (LBD) supports the identification of such connections. A number of LBD tools are available, however, they often suffer from limitations such as constraining possible searches or not producing results in real-time. We introduce HiDE (Hidden Discovery Explorer), an online knowledge browsing tool which allows fast access to hidden knowledge generated from all abstracts in Medline. HiDE is fast enough to allow users to explore the full range of hidden connections generated by an LBD system. The tool employs a novel combination of two approaches to LBD: a graph-based approach which allows hidden knowledge to be generated on a large scale and an inference algorithm to identify the most promising (most likely to be non trivial) information. Available at https://skye.shef.ac.uk/kdisc",
}
@inproceedings{van-cranenburgh-2018-active,
    title = "Active {DOP}: A constituency treebank annotation tool with online learning",
    author = "van Cranenburgh, Andreas",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2009",
    pages = "38--42",
    abstract = "We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags. Candidate analyses are generated by an exemplar-based parsing model that immediately learns from each new annotated sentence during annotation. This makes it suitable for situations in which only a limited seed treebank is available, or a radically different domain is being annotated. The tool offers the possibility to experiment with and evaluate active learning methods to speed up annotation in a naturalistic setting, i.e., measuring actual annotation costs and tracking specific user interactions. The code is made available under the GNU GPL license at https://github.com/andreasvc/activedop.",
}
@inproceedings{ma-etal-2018-crst,
    title = "{CRST}: a Claim Retrieval System in {T}witter",
    author = "Ma, Wenjia  and
      Chao, WenHan  and
      Luo, Zhunchen  and
      Jiang, Xin",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2010",
    pages = "43--47",
    abstract = "For controversial topics, collecting argumentation-containing tweets which tend to be more convincing will help researchers analyze public opinions. Meanwhile, claim is the heart of argumentation. Hence, we present the first real-time claim retrieval system CRST that retrieves tweets containing claims for a given topic from Twitter. We propose a claim-oriented ranking module which can be divided into the offline topic-independent learning to rank model and the online topic-dependent lexicon model. Our system outperforms previous claim retrieval system and argument mining system. Moreover, the claim-oriented ranking module can be easily adapted to new topics without any manual process or external information, guaranteeing the practicability of our system.",
}
@inproceedings{kim-etal-2018-utilizing,
    title = "Utilizing Graph Measure to Deduce Omitted Entities in Paragraphs",
    author = "Kim, Eun-kyung  and
      Han, Kijong  and
      Kim, Jiho  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2011",
    pages = "48--52",
    abstract = "This demo deals with the problem of capturing omitted arguments in relation extraction given a proper knowledge base for entities of interest. This paper introduces the concept of a salient entity and use this information to deduce omitted entities in the paragraph which allows improving the relation extraction quality. The main idea to compute salient entities is to construct a graph on the given information (by identifying the entities but without parsing it), rank it with standard graph measures and embed it in the context of the sentences.",
}
@inproceedings{muller-strube-2018-transparent,
    title = "Transparent, Efficient, and Robust Word Embedding Access with {WOMBAT}",
    author = {M{\"u}ller, Mark-Christoph  and
      Strube, Michael},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2012",
    pages = "53--57",
    abstract = "We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code. WOMBAT addresses common research problems, including unified access, scaling, and robust and reproducible preprocessing. Code that uses WOMBAT for accessing word embeddings is not only cleaner, more readable, and easier to reuse, but also much more efficient than code using standard in-memory methods: a Python script using WOMBAT for evaluating seven large word embedding collections (8.7M embedding vectors in total) on a simple SemEval sentence similarity task involving 250 raw sentence pairs completes in under ten seconds end-to-end on a standard notebook computer.",
}
@inproceedings{mamou-etal-2018-setexpander,
    title = "{S}et{E}xpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings",
    author = "Mamou, Jonathan  and
      Pereg, Oren  and
      Wasserblat, Moshe  and
      Dagan, Ido  and
      Goldberg, Yoav  and
      Eirew, Alon  and
      Green, Yael  and
      Guskin, Shira  and
      Izsak, Peter  and
      Korat, Daniel",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2013",
    pages = "58--62",
    abstract = "We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to end workflow for term set expansion. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. SetExpander has been used for solving real-life use cases including integration in an automated recruitment system and an issues and defects resolution system. A video demo of SetExpander is available at https://drive.google.com/open?id=1e545bB87Autsch36DjnJHmq3HWfSd1Rv .",
}
@inproceedings{iwakura-etal-2018-detecting,
    title = "Detecting Heavy Rain Disaster from Social and Physical Sensor",
    author = "Iwakura, Tomoya  and
      Okajima, Seiji  and
      Igata, Nobuyuki  and
      Takeda, Kunihiro  and
      Yamakage, Yuzuru  and
      Morita, Naoshi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2014",
    pages = "63--67",
    abstract = "We present our system that assists to detect heavy rain disaster, which is being used in real world in Japan. Our system selects tweets about heavy rain disaster with a document classifier. Then, the locations mentioned in the selected tweets are estimated by a location estimator. Finally, combined the selected tweets with amount of rainfall given by physical sensors and a statistical analysis, our system provides users with visualized results for detecting heavy rain disaster.",
}
@inproceedings{ciobanu-dinu-2018-simulating,
    title = "Simulating Language Evolution: a Tool for Historical Linguistics",
    author = "Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2015",
    pages = "68--72",
    abstract = "Language change across space and time is one of the main concerns in historical linguistics. In this paper, we develop a language evolution simulator: a web-based tool for word form production to assist in historical linguistics, in studying the evolution of the languages. Given a word in a source language, the system automatically predicts how the word evolves in a target language. The method that we propose is language-agnostic and does not use any external knowledge, except for the training word pairs.",
}
@inproceedings{chuan-an-etal-2018-unified,
    title = "A Unified {R}v{NN} Framework for End-to-End {C}hinese Discourse Parsing",
    author = "Chuan-An, Lin  and
      Huang, Hen-Hsen  and
      Chen, Zi-Yuan  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2016",
    pages = "73--77",
    abstract = "This paper demonstrates an end-to-end Chinese discourse parser. We propose a unified framework based on recursive neural network (RvNN) to jointly model the subtasks including elementary discourse unit (EDU) segmentation, tree structure construction, center labeling, and sense labeling. Experimental results show our parser achieves the state-of-the-art performance in the Chinese Discourse Treebank (CDTB) dataset. We release the source code with a pre-trained model for the NLP community. To the best of our knowledge, this is the first open source toolkit for Chinese discourse parsing. The standalone toolkit can be integrated into subsequent applications without the need of external resources such as syntactic parser.",
}
@inproceedings{spala-etal-2018-web,
    title = "A Web-based Framework for Collecting and Assessing Highlighted Sentences in a Document",
    author = "Spala, Sasha  and
      Dernoncourt, Franck  and
      Chang, Walter  and
      Dockhorn, Carl",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2017",
    pages = "78--81",
    abstract = "Automatically highlighting a text aims at identifying key portions that are the most important to a reader. In this paper, we present a web-based framework designed to efficiently and scalably crowdsource two independent but related tasks: collecting highlight annotations, and comparing the performance of automated highlighting systems. The first task is necessary to understand human preferences and train supervised automated highlighting systems. The second task yields a more accurate and fine-grained evaluation than existing automated performance metrics.",
}
@inproceedings{lo-etal-2018-cool,
    title = "Cool {E}nglish: a Grammatical Error Correction System Based on Large Learner Corpora",
    author = "Lo, Yu-Chun  and
      Chen, Jhih-Jie  and
      Yang, Chingyu  and
      Chang, Jason",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2018",
    pages = "82--85",
    abstract = "This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained by EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.",
}
@inproceedings{federmann-2018-appraise,
    title = "Appraise Evaluation Framework for Machine Translation",
    author = "Federmann, Christian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2019",
    pages = "86--88",
    abstract = "We present Appraise, an open-source framework for crowd-based annotation tasks, notably for evaluation of machine translation output. This is the software used to run the yearly evaluation campaigns for shared tasks at the WMT Conference on Machine Translation. It has also been used at IWSLT 2017 and, recently, to measure human parity for machine translation for Chinese to English news text. The demo will present the full end-to-end lifecycle of an Appraise evaluation campaign, from task creation to annotation and interpretation of results.",
}
@inproceedings{dessloch-etal-2018-kit,
    title = "{KIT} Lecture Translator: Multilingual Speech Translation with One-Shot Learning",
    author = {Dessloch, Florian  and
      Ha, Thanh-Le  and
      M{\"u}ller, Markus  and
      Niehues, Jan  and
      Nguyen, Thai-Son  and
      Pham, Ngoc-Quan  and
      Salesky, Elizabeth  and
      Sperber, Matthias  and
      St{\"u}ker, Sebastian  and
      Zenkel, Thomas  and
      Waibel, Alexander},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2020",
    pages = "89--93",
    abstract = "In today{'}s globalized world we have the ability to communicate with people across the world. However, in many situations the language barrier still presents a major issue. For example, many foreign students coming to KIT to study are initially unable to follow a lecture in German. Therefore, we offer an automatic simultaneous interpretation service for students. To fulfill this task, we have developed a low-latency translation system that is adapted to lectures and covers several language pairs. While the switch from traditional Statistical Machine Translation to Neural Machine Translation (NMT) significantly improved performance, to integrate NMT into the speech translation framework required several adjustments. We have addressed the run-time constraints and different types of input. Furthermore, we utilized one-shot learning to easily add new topic-specific terms to the system. Besides better performance, NMT also enabled us increase our covered languages through multilingual NMT. {\%} Combining these techniques, we are able to provide an adapted speech translation system for several European languages.",
}
@inproceedings{cetto-etal-2018-graphene-context,
    title = "{G}raphene: a Context-Preserving Open Information Extraction System",
    author = "Cetto, Matthias  and
      Niklaus, Christina  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2021",
    pages = "94--98",
    abstract = "We introduce Graphene, an Open IE system whose goal is to generate accurate, meaningful and complete propositions that may facilitate a variety of downstream semantic applications. For this purpose, we transform syntactically complex input sentences into clean, compact structures in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them in order to maintain their semantic relationship. In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.",
}
@inproceedings{cheng-etal-2018-languagenet,
    title = "{L}anguage{N}et: Learning to Find Sense Relevant Example Sentences",
    author = "Cheng, Shang-Chien  and
      Chen, Jhih-Jie  and
      Yang, Chingyu  and
      Chang, Jason",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2022",
    pages = "99--102",
    abstract = "In this paper, we present a system, LanguageNet, which can help second language learners to search for different meanings and usages of a word. We disambiguate word senses based on the pairs of an English word and its corresponding Chinese translations in a parallel corpus, UM-Corpus. The process involved performing word alignment, learning vector space representations of words and training a classifier to distinguish words into groups of senses. LanguageNet directly shows the definition of a sense, bilingual synonyms and sense relevant examples.",
}
@inproceedings{dasgupta-etal-2018-automatic,
    title = "Automatic Curation and Visualization of Crime Related Information from Incrementally Crawled Multi-source News Reports",
    author = "Dasgupta, Tirthankar  and
      Dey, Lipika  and
      Saha, Rupsa  and
      Naskar, Abir",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2023",
    pages = "103--107",
    abstract = "In this paper, we demonstrate a system for the automatic extraction and curation of crime-related information from multi-source digitally published News articles collected over a period of five years. We have leveraged the use of deep convolution recurrent neural network model to analyze crime articles to extract different crime related entities and events. The proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime ontology. We have done experiments with a collection of 5000 crime-reporting News articles span over time, and multiple sources. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes.",
}
@inproceedings{zhu-etal-2018-lingke,
    title = "{L}ingke: a Fine-grained Multi-turn Chatbot for Customer Service",
    author = "Zhu, Pengfei  and
      Zhang, Zhuosheng  and
      Li, Jiangtong  and
      Huang, Yafang  and
      Zhao, Hai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2024",
    pages = "108--112",
    abstract = "Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.",
}
@inproceedings{madnani-etal-2018-writing,
    title = "Writing Mentor: Self-Regulated Writing Feedback for Struggling Writers",
    author = "Madnani, Nitin  and
      Burstein, Jill  and
      Elliot, Norbert  and
      Beigman Klebanov, Beata  and
      Napolitano, Diane  and
      Andreyev, Slava  and
      Schwartz, Maxwell",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2025",
    pages = "113--117",
    abstract = "Writing Mentor is a free Google Docs add-on designed to provide feedback to struggling writers and help them improve their writing in a self-paced and self-regulated fashion. Writing Mentor uses natural language processing (NLP) methods and resources to generate feedback in terms of features that research into post-secondary struggling writers has classified as developmental (Burstein et al., 2016b). These features span many writing sub-constructs (use of sources, claims, and evidence; topic development; coherence; and knowledge of English conventions). Prelimi- nary analysis indicates that users have a largely positive impression of Writing Mentor in terms of usability and potential impact on their writing.",
}
@inproceedings{gartner-etal-2018-nlatool,
    title = "{NLAT}ool: an Application for Enhanced Deep Text Understanding",
    author = {G{\"a}rtner, Markus  and
      Mayer, Sven  and
      Schwind, Valentin  and
      H{\"a}mmerle, Eric  and
      Turcan, Emine  and
      Rheinwald, Florin  and
      Murawski, Gustav  and
      Lischke, Lars  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2026",
    pages = "118--122",
    abstract = "Today, we see an ever growing number of tools supporting text annotation. Each of these tools is optimized for specific use-cases such as named entity recognition. However, we see large growing knowledge bases such as Wikipedia or the Google Knowledge Graph. In this paper, we introduce NLATool, a web application developed using a human-centered design process. The application combines supporting text annotation and enriching the text with additional information from a number of sources directly within the application. The tool assists users to efficiently recognize named entities, annotate text, and automatically provide users additional information while solving deep text understanding tasks.",
}
@inproceedings{itegulov-etal-2018-sensala,
    title = "{S}ensala: a Dynamic Semantics System for Natural Language Processing",
    author = "Itegulov, Daniyar  and
      Lebedeva, Ekaterina  and
      Woltzenlogel Paleo, Bruno",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2027",
    pages = "123--127",
    abstract = "Here we describe Sensala , an open source framework for the semantic interpretation of natural language that provides the logical meaning of a given text. The framework{'}s theory is based on a lambda calculus with exception handling and uses contexts, continuations, events and dependent types to handle a wide range of complex linguistic phenomena, such as donkey anaphora, verb phrase anaphora, propositional anaphora, presuppositions and implicatures.",
}
@inproceedings{yu-etal-2018-device,
    title = "On-Device Neural Language Model Based Word Prediction",
    author = "Yu, Seunghak  and
      Kulkarni, Nilesh  and
      Lee, Haejun  and
      Kim, Jihie",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2028",
    pages = "128--131",
    abstract = "Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation. However, deploying huge language models for the mobile device such as on-device keyboards poses computation as a bottle-neck due to their puny computation capacities. In this work, we propose an on-device neural language model based word prediction method that optimizes run-time memory and also provides a real-time prediction environment. Our model size is 7.40MB and has average prediction time of 6.47 ms. Our proposed model outperforms the existing methods for word prediction in terms of keystroke savings and word prediction rate and has been successfully commercialized.",
}
@inproceedings{kovatchev-etal-2018-warp,
    title = "{WARP}-Text: a Web-Based Tool for Annotating Relationships between Pairs of Texts",
    author = "Kovatchev, Venelin  and
      Mart{\'\i}, M. Ant{\`o}nia  and
      Salam{\'o}, Maria",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2029",
    pages = "132--136",
    abstract = "We present WARP-Text, an open-source web-based tool for annotating relationships between pairs of texts. WARP-Text supports multi-layer annotation and custom definitions of inter-textual and intra-textual relationships. Annotation can be performed at different granularity levels (such as sentences, phrases, or tokens). WARP-Text has an intuitive user-friendly interface both for project managers and annotators. WARP-Text fills a gap in the currently available NLP toolbox, as open-source alternatives for annotation of pairs of text are not readily available. WARP-Text has already been used in several annotation tasks and can be of interest to the researchers working in the areas of Paraphrasing, Entailment, Simplification, and Summarization, among others.",
}
@inproceedings{shiue-etal-2018-chinese,
    title = "A {C}hinese Writing Correction System for Learning {C}hinese as a Foreign Language",
    author = "Shiue, Yow-Ting  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2030",
    pages = "137--141",
    abstract = "We present a Chinese writing correction system for learning Chinese as a foreign language. The system takes a wrong input sentence and generates several correction suggestions. It also retrieves example Chinese sentences with English translations, helping users understand the correct usages of certain grammar patterns. This is the first available Chinese writing error correction system based on the neural machine translation framework. We discuss several design choices and show empirical results to support our decisions.",
}
@inproceedings{baumartz-etal-2018-ltv,
    title = "{LTV}: Labeled Topic Vector",
    author = "Baumartz, Daniel  and
      Uslu, Tolga  and
      Mehler, Alexander",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2031",
    pages = "142--145",
    abstract = "In this paper we present LTV, a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC), an international standard for topic classification in libraries. We introduce nnDDC, a largely language-independent natural network-based classifier for DDC, which we optimized using a wide range of linguistic features to achieve an F-score of 87.4{\%}. To show that our approach is language-independent, we evaluate nnDDC using up to 40 different languages. We derive a topic model based on nnDDC, which generates probability distributions over semantic units for any input on sense-, word- and text-level. Unlike related approaches, however, these probabilities are estimated by means of nnDDC so that each dimension of the resulting vector representation is uniquely labeled by a DDC class. In this way, we introduce a neural network-based Classifier-Induced Semantic Space (nnCISS).",
}
@inproceedings{jiang-etal-2018-interpretable,
    title = "Interpretable Rationale Augmented Charge Prediction System",
    author = "Jiang, Xin  and
      Ye, Hai  and
      Luo, Zhunchen  and
      Chao, WenHan  and
      Ma, Wenjia",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2032",
    pages = "146--151",
    abstract = "This paper proposes a neural based system to solve the essential interpretability problem existing in text classification, especially in charge prediction task. First, we use a deep reinforcement learning method to extract rationales which mean short, readable and decisive snippets from input text. Then a rationale augmented classification model is proposed to elevate the prediction accuracy. Naturally, the extracted rationales serve as the introspection explanation for the prediction result of the model, enhancing the transparency of the model. Experimental results demonstrate that our system is able to extract readable rationales in a high consistency with manual annotation and is comparable with the attention model in prediction accuracy.",
}
@inproceedings{khan-etal-2018-cross,
    title = "A Cross-lingual Messenger with Keyword Searchable Phrases for the Travel Domain",
    author = "Khan, Shehroze  and
      Kim, Jihyun  and
      Zulfikarpasic, Tarik  and
      Chen, Peter  and
      Habash, Nizar",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2033",
    pages = "152--156",
    abstract = "We present Qutr (Query Translator), a smart cross-lingual communication application for the travel domain. Qutr is a real-time messaging app that automatically translates conversations while supporting keyword-to-sentence matching. Qutr relies on querying a database that holds commonly used pre-translated travel-domain phrases and phrase templates in different languages with the use of keywords. The query matching supports paraphrases, incomplete keywords and some input spelling errors. The application addresses common cross-lingual communication issues such as translation accuracy, speed, privacy, and personalization.",
}
@inproceedings{nair-etal-2018-towards,
    title = "Towards Automated Extraction of Business Constraints from Unstructured Regulatory Text",
    author = "Nair, Rahul  and
      Levacher, Killian  and
      Stephenson, Martin",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2034",
    pages = "157--160",
    abstract = "Large organizations spend considerable resources in reviewing regulations and ensuring that their business processes are compliant with the law. To make compliance workflows more efficient and responsive, we present a system for machine-driven annotations of legal documents. A set of natural language processing pipelines are designed and aimed at addressing some key questions in this domain: (a) is this (new) regulation relevant for me? (b) what set of requirements does this law impose?, and (c) what is the regulatory intent of a law? The system is currently undergoing user trials within our organization.",
}
@inproceedings{do-etal-2018-flexible,
    title = "A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages",
    author = "Do, Quynh Ngoc Thi  and
      Leeuwenberg, Artuur  and
      Heyman, Geert  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-2035",
    pages = "161--165",
    abstract = "This paper presents a flexible and open source framework for deep semantic role labeling. We aim at facilitating easy exploration of model structures for multiple languages with different characteristics. It provides flexibility in its model construction in terms of word representation, sequence representation, output modeling, and inference styles and comes with clear output visualization. The framework is available under the Apache 2.0 license.",
}
@proceedings{coling-2018-international,
    title = "Proceedings of the 27th International Conference on Computational Linguistics",
    editor = "Bender, Emily M.  and
      Derczynski, Leon  and
      Isabelle, Pierre",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1000",
}
@inproceedings{jahan-etal-2018-new,
    title = "A New Approach to {A}nimacy Detection",
    author = "Jahan, Labiba  and
      Chauhan, Geeticka  and
      Finlayson, Mark",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1001",
    pages = "1--12",
    abstract = "Animacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, co-reference resolution, semantic role labeling, and others. Prior work treated animacy as a word-level property, and has developed statistical classifiers to classify words as either animate or inanimate. We discuss why this approach to the problem is ill-posed, and present a new approach based on classifying the animacy of co-reference chains. We show that simple voting approaches to inferring the animacy of a chain from its constituent words perform relatively poorly, and then present a hybrid system merging supervised machine learning (ML) and a small number of hand-built rules to compute the animacy of referring expressions and co-reference chains. This method achieves state of the art performance. The supervised ML component leverages features such as word embeddings over referring expressions, parts of speech, and grammatical and semantic roles. The rules take into consideration parts of speech and the hypernymy structure encoded in WordNet. The system achieves an F1 of 0.88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0.75 for classifying the animacy of coreference chains themselves. We release our training and test dataset, which includes 142 texts (all narratives) comprising 156,154 words, 34,698 referring expressions, and 10,941 co-reference chains. We test the method on a subset of the OntoNotes dataset, showing using manual sampling that animacy classification is 90{\%} +/- 2{\%} accurate for coreference chains, and 92{\%} +/- 1{\%} for referring expressions. The data also contains 46 folktales, which present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, trees that talk). We show that our system is able to detect the animacy of these unusual referents with an F1 of 0.95.",
}
@inproceedings{yin-etal-2018-zero,
    title = "Zero Pronoun Resolution with Attention-based Neural Network",
    author = "Yin, Qingyu  and
      Zhang, Yu  and
      Zhang, Weinan  and
      Liu, Ting  and
      Wang, William Yang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1002",
    pages = "13--23",
    abstract = "Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents. Typically, contextual information is utilized to encode the zero pronouns since they are simply gaps that contain no actual content. To better utilize contexts of the zero pronouns, we here introduce the self-attention mechanism for encoding zero pronouns. With the help of the multiple hops of attention, our model is able to focus on some informative parts of the associated texts and therefore produces an efficient way of encoding the zero pronouns. In addition, an attention-based recurrent neural network is proposed for encoding candidate antecedents by their contents. Experiment results are encouraging: our proposed attention-based model gains the best performance on the Chinese portion of the OntoNotes corpus, substantially surpasses existing Chinese zero pronoun resolution baseline systems.",
}
@inproceedings{zhou-choi-2018-exist,
    title = "They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking",
    author = "Zhou, Ethan  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1003",
    pages = "24--34",
    abstract = "This paper analyzes arguably the most challenging yet under-explored aspect of resolution tasks such as coreference resolution and entity linking, that is the resolution of plural mentions. Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities. To tackle this aspect, we take the character identification corpus from the SemEval 2018 shared task that consists of entity annotation for singular mentions, and expand it by adding annotation for plural mentions. We then introduce a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions, and also a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning. Adjusted evaluation metrics are proposed for these tasks as well to handle the uniqueness of plural mentions. Our experiments show that the new coreference resolution and entity linking models significantly outperform traditional models designed only for singular mentions. To the best of our knowledge, this is the first time that plural mentions are thoroughly analyzed for these two resolution tasks.",
}
@inproceedings{meng-rumshisky-2018-triad,
    title = "Triad-based Neural Network for Coreference Resolution",
    author = "Meng, Yuanliang  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1004",
    pages = "35--43",
    abstract = "We propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical constraints of all three mentions into account, and thus makes more accurate predictions than the traditional pairwise approach. Depending on system choices, the affinity scores can be further used in clustering or mention ranking. Our experiments show that a standard hierarchical clustering using the scores produces state-of-art results with MUC and B 3 metrics on the English portion of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features and is easy to train and use. The triads can also be easily extended to polyads of higher orders. To our knowledge, this is the first neural network system to model mutual dependency of more than two members at mention level.",
}
@inproceedings{xu-etal-2018-unsupervised,
    title = "Unsupervised Morphology Learning with Statistical Paradigms",
    author = "Xu, Hongzhi  and
      Marcus, Mitchell  and
      Yang, Charles  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1005",
    pages = "44--54",
    abstract = "This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e.g., suffixes) that can be applied to a homogeneous set of words (e.g., nouns or verbs). Our algorithm identifies statistically reliable paradigms from the morphological segmentation result of a probabilistic model, and chooses reliable suffixes from them. The new suffixes can be fed back iteratively to improve the accuracy of the probabilistic model. Finally, the unreliable paradigms are subjected to pruning to eliminate unreliable morphological relations between words. The paradigm-based algorithm significantly improves segmentation accuracy. Our method achieves start-of-the-art results on experiments using the Morpho-Challenge data, including English, Turkish, and Finnish.",
}
@inproceedings{mager-etal-2018-challenges,
    title = "Challenges of language technologies for the indigenous languages of the {A}mericas",
    author = "Mager, Manuel  and
      Gutierrez-Vasques, Ximena  and
      Sierra, Gerardo  and
      Meza-Ruiz, Ivan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1006",
    pages = "55--69",
    abstract = "Indigenous languages of the American continent are highly diverse. However, they have received little attention from the technological perspective. In this paper, we review the research, the digital resources and the available NLP systems that focus on these languages. We present the main challenges and research questions that arise when distant languages and low-resource scenarios are faced. We would like to encourage NLP research in linguistically rich and diverse areas like the Americas.",
}
@inproceedings{muis-etal-2018-low,
    title = "Low-resource Cross-lingual Event Type Detection via Distant Supervision with Minimal Effort",
    author = "Muis, Aldrian Obaja  and
      Otani, Naoki  and
      Vyas, Nidhi  and
      Xu, Ruochen  and
      Yang, Yiming  and
      Mitamura, Teruko  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1007",
    pages = "70--82",
    abstract = "The use of machine learning for NLP generally requires resources for training. Tasks performed in a low-resource language usually rely on labeled data in another, typically resource-rich, language. However, there might not be enough labeled data even in a resource-rich language such as English. In such cases, one approach is to use a hand-crafted approach that utilizes only a small bilingual dictionary with minimal manual verification to create distantly supervised data. Another is to explore typical machine learning techniques, for example adversarial training of bilingual word representations. We find that in event-type detection task{---}the task to classify [parts of] documents into a fixed set of labels{---}they give about the same performance. We explore ways in which the two methods can be complementary and also see how to best utilize a limited budget for manual annotation to maximize performance gain.",
}
@inproceedings{makarov-clematide-2018-neural,
    title = "Neural Transition-based String Transduction for Limited-Resource Setting in Morphology",
    author = "Makarov, Peter  and
      Clematide, Simon",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1008",
    pages = "83--93",
    abstract = "We present a neural transition-based model that uses a simple set of edit actions (copy, delete, insert) for morphological transduction tasks such as inflection generation, lemmatization, and reinflection. In a large-scale evaluation on four datasets and dozens of languages, our approach consistently outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. Learning to apply a generic copy action enables our approach to generalize quickly from a few data points. We successfully leverage minimum risk training to compensate for the weaknesses of MLE parameter learning and neutralize the negative effects of training a pipeline with a separate character aligner.",
}
@inproceedings{matsubayashi-inui-2018-distance,
    title = "Distance-Free Modeling of Multi-Predicate Interactions in End-to-End {J}apanese Predicate-Argument Structure Analysis",
    author = "Matsubayashi, Yuichiroh  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1009",
    pages = "94--106",
    abstract = "Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese. In this paper, we propose new Japanese PAS analysis models that integrate the label prediction information of arguments in multiple PASs by extending the input and last layers of a standard deep bidirectional recurrent neural network (bi-RNN) model. In these models, using the mechanisms of pooling and attention, we aim to directly capture the potential interactions among multiple PASs, without being disturbed by the word order and distance. Our experiments show that the proposed models improve the prediction accuracy specifically for cases where the predicate and argument are in an indirect dependency relation and achieve a new state of the art in the overall $F_1$ on a standard benchmark corpus.",
}
@inproceedings{rehbein-ruppenhofer-2018-sprucing,
    title = "Sprucing up the trees {--} Error detection in treebanks",
    author = "Rehbein, Ines  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1010",
    pages = "107--118",
    abstract = "We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data.",
}
@inproceedings{teng-zhang-2018-two,
    title = "Two Local Models for Neural Constituent Parsing",
    author = "Teng, Zhiyang  and
      Zhang, Yue",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1011",
    pages = "119--132",
    abstract = "Non-local features have been exploited by syntactic parsers for capturing dependencies between sub output structures. Such features have been a key to the success of state-of-the-art statistical parsers. With the rise of deep learning, however, it has been shown that local output decisions can give highly competitive accuracies, thanks to the power of dense neural input representations that embody global syntactic information. We investigate two conceptually simple local neural models for constituent parsing, which make local decisions to constituent spans and CFG rules, respectively. Consistent with previous findings along the line, our best model gives highly competitive results, achieving the labeled bracketing F1 scores of 92.4{\%} on PTB and 87.3{\%} on CTB 5.1.",
}
@inproceedings{chowdhury-zamparelli-2018-rnn,
    title = "{RNN} Simulations of Grammaticality Judgments on Long-distance Dependencies",
    author = "Chowdhury, Shammur Absar  and
      Zamparelli, Roberto",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1012",
    pages = "133--144",
    abstract = "The paper explores the ability of LSTM networks trained on a language modeling task to detect linguistic structures which are ungrammatical due to extraction violations (extra arguments and subject-relative clause island violations), and considers its implications for the debate on language innatism. The results show that the current RNN model can correctly classify (un)grammatical sentences, in certain conditions, but it is sensitive to linguistic processing factors and probably ultimately unable to induce a more abstract notion of grammaticality, at least in the domain we tested.",
}
@inproceedings{eidelman-etal-2018-predictable,
    title = "How Predictable is Your State? Leveraging Lexical and Contextual Information for Predicting Legislative Floor Action at the State Level",
    author = "Eidelman, Vladimir  and
      Kornilova, Anastassia  and
      Argyle, Daniel",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1013",
    pages = "145--160",
    abstract = "Modeling U.S. Congressional legislation and roll-call votes has received significant attention in previous literature, and while legislators across 50 state governments and D.C. propose over 100,000 bills each year, enacting over 30{\%} of them on average, state level analysis has received relatively less attention due in part to the difficulty in obtaining the necessary data. Since each state legislature is guided by their own procedures, politics and issues, however, it is difficult to qualitatively asses the factors that affect the likelihood of a legislative initiative succeeding. We present several methods for modeling the likelihood of a bill receiving floor action across all 50 states and D.C. We utilize the lexical content of over 1 million bills, along with contextual legislature and legislator derived features to build our predictive models, allowing a comparison of what factors are important to the lawmaking process. Furthermore, we show that these signals hold complementary predictive power, together achieving an average improvement in accuracy of 18{\%} over state specific baselines.",
}
@inproceedings{geva-berant-2018-learning,
    title = "Learning to Search in Long Documents Using Document Structure",
    author = "Geva, Mor  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1014",
    pages = "161--176",
    abstract = "Reading comprehension models are based on recurrent neural networks that sequentially process the document tokens. As interest turns to answering more complex questions over longer documents, sequential reading of large portions of text becomes a substantial bottleneck. Inspired by how humans use document structure, we propose a novel framework for reading comprehension. We represent documents as trees, and model an agent that learns to interleave quick navigation through the document tree with more expensive answer extraction. To encourage exploration of the document tree, we propose a new algorithm, based on Deep Q-Network (DQN), which strategically samples tree nodes at training time. Empirically we find our algorithm improves question answering performance compared to DQN and a strong information-retrieval (IR) baseline, and that ensembling our model with the IR baseline results in further gains in performance.",
}
@inproceedings{hong-etal-2018-incorporating,
    title = "Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition",
    author = "Hong, Yu  and
      Xu, Yang  and
      Ruan, Huibin  and
      Zou, Bowei  and
      Yao, Jianmin  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1015",
    pages = "177--189",
    abstract = "Event relation recognition is a challenging language processing task. It is required to determine the relation class of a pair of query events, such as causality, under the condition that there isn{'}t any reliable clue for use. We follow the traditional statistical approach in this paper, speculating the relation class of the target events based on the relation-class distributions on the similar events. There is minimal supervision used during the speculation process. In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events. We test our method on the ACE-R2 corpus and compared our model with the fully-supervised neural network models. Experimental results show that we achieve a comparable performance to CNN while slightly better than LSTM.",
}
@inproceedings{yamada-etal-2018-representation,
    title = "Representation Learning of Entities and Documents from Knowledge Base Descriptions",
    author = "Yamada, Ikuya  and
      Shindo, Hiroyuki  and
      Takefuji, Yoshiyasu",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1016",
    pages = "190--201",
    abstract = "In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.",
}
@inproceedings{kulkarni-etal-2018-simple,
    title = "Simple Neologism Based Domain Independent Models to Predict Year of Authorship",
    author = "Kulkarni, Vivek  and
      Tian, Yingtao  and
      Dandiwala, Parth  and
      Skiena, Steve",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1017",
    pages = "202--212",
    abstract = "We present domain independent models to date documents based only on neologism usage patterns. Our models capture patterns of neologism usage over time to date texts, provide insights into temporal locality of word usage over a span of 150 years, and generalize to various domains like News, Fiction, and Non-Fiction with competitive performance. Quite intriguingly, we show that by modeling only the distribution of usage counts over neologisms (the model being agnostic of the particular words themselves), we achieve competitive performance using several orders of magnitude fewer features (only 200 input features) compared to state of the art models some of which use 200K features.",
}
@inproceedings{huang-etal-2018-neural,
    title = "Neural Math Word Problem Solver with Reinforcement Learning",
    author = "Huang, Danqing  and
      Liu, Jing  and
      Lin, Chin-Yew  and
      Yin, Jian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1018",
    pages = "213--223",
    abstract = "Sequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no feature engineering and can generate equations that do not exist in training data. However, our experimental analysis reveals that this model suffers from two shortcomings: (1) generate spurious numbers; (2) generate numbers at wrong positions. In this paper, we propose incorporating copy and alignment mechanism to the sequence-to-sequence model (namely CASS) to address these shortcomings. To train our model, we apply reinforcement learning to directly optimize the solution accuracy. It overcomes the {``}train-test discrepancy{''} issue of maximum likelihood estimation, which uses the surrogate objective of maximizing equation likelihood during training while the evaluation metric is solution accuracy (non-differentiable) at test time. Furthermore, to explore the effectiveness of our neural model, we use our model output as a feature and incorporate it into the feature-based model. Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.",
}
@inproceedings{lee-yeung-2018-personalizing,
    title = "Personalizing Lexical Simplification",
    author = "Lee, John  and
      Yeung, Chak Yan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1019",
    pages = "224--232",
    abstract = "A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users{'} differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution or ranked list of substitutions for all users. We evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels, and measure the benefits of using complex word identification (CWI) models to personalize the system. Experimental results show that even a simple personalized CWI model, based on graded vocabulary lists, can help the system avoid some unnecessary simplifications and produce more readable output.",
}
@inproceedings{kuznetsov-gurevych-2018-text,
    title = "From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources",
    author = "Kuznetsov, Ilia  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1020",
    pages = "233--244",
    abstract = "Distributional word representations (often referred to as word embeddings) are omnipresent in modern NLP. Early work has focused on building representations for word types, and recent studies show that lemmatization and part of speech (POS) disambiguation of targets in isolation improve the performance of word embeddings on a range of downstream tasks. However, the reasons behind these improvements, the qualitative effects of these operations and the combined performance of lemmatized and POS disambiguated targets are less studied. This work aims to close this gap and puts previous findings into a general perspective. We examine the effect of lemmatization and POS typing on word embedding performance in a novel resource-based evaluation scenario, as well as on standard similarity benchmarks. We show that these two operations have complimentary qualitative and vocabulary-level effects and are best used in combination. We find that the improvement is more pronounced for verbs and show how lemmatization and POS typing implicitly target some of the verb-specific issues. We claim that the observed improvement is a result of better conceptual alignment between word embeddings and lexical resources, stressing the need for conceptually plausible modeling of word embedding targets.",
}
@inproceedings{bingel-etal-2018-lexi,
    title = "{L}exi: A tool for adaptive, personalized text simplification",
    author = "Bingel, Joachim  and
      Paetzold, Gustavo  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1021",
    pages = "245--258",
    abstract = "Most previous research in text simplification has aimed to develop generic solutions, assuming very homogeneous target audiences with consistent intra-group simplification needs. We argue that this assumption does not hold, and that instead we need to develop simplification systems that adapt to the individual needs of specific users. As a first step towards personalized simplification, we propose a framework for adaptive lexical simplification and introduce Lexi, a free open-source and easily extensible tool for adaptive, personalized text simplification. Lexi is easily installed as a browser extension, enabling easy access to the service for its users.",
}
@inproceedings{jiang-etal-2018-identifying,
    title = "Identifying Emergent Research Trends by Key Authors and Phrases",
    author = "Jiang, Shenhao  and
      Prasad, Animesh  and
      Kan, Min-Yen  and
      Sugiyama, Kazunari",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1022",
    pages = "259--269",
    abstract = "Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.",
}
@inproceedings{lan-jiang-2018-embedding,
    title = "Embedding {W}ord{N}et Knowledge for Textual Entailment",
    author = "Lan, Yunshi  and
      Jiang, Jing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1023",
    pages = "270--281",
    abstract = "In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call {``}entailment vectors.{''} We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that using these special entailment word vectors, we can significantly improve the performance of textual entailment compared with a baseline that uses only standard word2vec vectors. The final performance of our model is close to or above the state of the art, but our method does not rely on any manually-crafted rules or extensive syntactic features.",
}
@inproceedings{jin-etal-2018-attributed,
    title = "Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases",
    author = "Jin, Hailong  and
      Hou, Lei  and
      Li, Juanzi  and
      Dong, Tiansi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1024",
    pages = "282--292",
    abstract = "Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are unfortunately incomplete even in some large knowledge bases. Limitations of existing methods are either ignoring the structure and type information in KB or requiring large scale annotated corpus. To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively. Extensive experiments on two real DBpedia datasets show that our proposed method significantly outperforms 8 state-of-the-art methods, with 4.0{\%} and 5.2{\%} improvement in Mi-F1 and Ma-F1, respectively.",
}
@inproceedings{li-etal-2018-joint,
    title = "Joint Learning from Labeled and Unlabeled Data for Information Retrieval",
    author = "Li, Bo  and
      Cheng, Ping  and
      Jia, Le",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1025",
    pages = "293--302",
    abstract = "Recently, a significant number of studies have focused on neural information retrieval (IR) models. One category of works use unlabeled data to train general word embeddings based on term proximity, which can be integrated into traditional IR models. The other category employs labeled data (e.g. click-through data) to train end-to-end neural IR models consisting of layers for target-specific representation learning. The latter idea accounts better for the IR task and is favored by recent research works, which is the one we will follow in this paper. We hypothesize that general semantics learned from unlabeled data can complement task-specific representation learned from labeled data of limited quality, and that a combination of the two is favorable. To this end, we propose a learning framework which can benefit from both labeled and more abundant unlabeled data for representation learning in the context of IR. Through a joint learning fashion in a single neural framework, the learned representation is optimized to minimize both the supervised loss on query-document matching and the unsupervised loss on text reconstruction. Standard retrieval experiments on TREC collections indicate that the joint learning methodology leads to significant better performance of retrieval over several strong baselines for IR.",
}
@inproceedings{weiss-meurers-2018-modeling,
    title = "Modeling the Readability of {G}erman Targeting Adults and Children: An empirically broad analysis and its cross-corpus validation",
    author = "Wei{\ss}, Zarah  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1026",
    pages = "303--317",
    abstract = "We analyze two novel data sets of German educational media texts targeting adults and children. The analysis is based on 400 automatically extracted measures of linguistic complexity from a wide range of linguistic domains. We show that both data sets exhibit broad linguistic adaptation to the target audience, which generalizes across both data sets. Our most successful binary classification model for German readability robustly shows high accuracy between 89.4{\%}{--}98.9{\%} for both data sets. To our knowledge, this comprehensive German readability model is the first for which robust cross-corpus performance has been shown. The research also contributes resources for German readability assessment that are externally validated as successful for different target audiences: we compiled a new corpus of German news broadcast subtitles, the Tagesschau/Logo corpus, and crawled a GEO/GEOlino corpus substantially enlarging the data compiled by Hancke et al. 2012.",
}
@inproceedings{stajner-hulpus-2018-automatic,
    title = "Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs",
    author = "{\v{S}}tajner, Sanja  and
      Hulpu{\c{s}}, Ioana",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1027",
    pages = "318--330",
    abstract = "Complexity of texts is usually assessed only at the lexical and syntactic levels. Although it is known that conceptual complexity plays a significant role in text understanding, no attempts have been made at assessing it automatically. We propose to automatically estimate the conceptual complexity of texts by exploiting a number of graph-based measures on a large knowledge base. By using a high-quality language learners corpus for English, we show that graph-based measures of individual text concepts, as well as the way they relate to each other in the knowledge graph, have a high discriminative power when distinguishing between two versions of the same text. Furthermore, when used as features in a binary classification task aiming to choose the simpler of two versions of the same text, our measures achieve high performance even in a default setup.",
}
@inproceedings{yimam-biemann-2018-par4sim,
    title = "{P}ar4{S}im {--} Adaptive Paraphrasing for Text Simplification",
    author = "Yimam, Seid Muhie  and
      Biemann, Chris",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1028",
    pages = "331--342",
    abstract = "Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e. how users have employed the system for the task of simplification. Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88{\%} up to 75.70{\%} based on the NDCG@10 evaluation metrics. To our knowledge, this is the first study where an NLP component is adaptively improved through usage.",
}
@inproceedings{sari-etal-2018-topic,
    title = "Topic or Style? Exploring the Most Useful Features for Authorship Attribution",
    author = "Sari, Yunita  and
      Stevenson, Mark  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1029",
    pages = "343--353",
    abstract = "Approaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals{'} writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used.",
}
@inproceedings{le-etal-2018-deep,
    title = "A Deep Dive into Word Sense Disambiguation with {LSTM}",
    author = "Le, Minh  and
      Postma, Marten  and
      Urbani, Jacopo  and
      Vossen, Piek",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1030",
    pages = "354--365",
    abstract = "LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD). In particular, the technique proposed by Yuan et al. (2016) returned state-of-the-art performance in several benchmarks, but neither the training data nor the source code was released. This paper presents the results of a reproduction study and analysis of this technique using only openly available datasets (GigaWord, SemCor, OMSTI) and software (TensorFlow). Our study showed that similar results can be obtained with much less data than hinted at by Yuan et al. (2016). Detailed analyses shed light on the strengths and weaknesses of this method. First, adding more unannotated training data is useful, but is subject to diminishing returns. Second, the model can correctly identify both popular and unpopular meanings. Finally, the limited sense coverage in the annotated datasets is a major limitation. All code and trained models are made freely available.",
}
@inproceedings{jiang-etal-2018-enriching,
    title = "Enriching Word Embeddings with Domain Knowledge for Readability Assessment",
    author = "Jiang, Zhiwei  and
      Gu, Qing  and
      Yin, Yafeng  and
      Chen, Daoxu",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1031",
    pages = "366--378",
    abstract = "In this paper, we present a method which learns the word embedding for readability assessment. For the existing word embedding models, they typically focus on the syntactic or semantic relations of words, while ignoring the reading difficulty, thus they may not be suitable for readability assessment. Hence, we provide the knowledge-enriched word embedding (KEWE), which encodes the knowledge on reading difficulty into the representation of words. Specifically, we extract the knowledge on word-level difficulty from three perspectives to construct a knowledge graph, and develop two word embedding models to incorporate the difficulty context derived from the knowledge graph to define the loss functions. Experiments are designed to apply KEWE for readability assessment on both English and Chinese datasets, and the results demonstrate both effectiveness and potential of KEWE.",
}
@inproceedings{jana-etal-2018-wikiref,
    title = "{W}iki{R}ef: Wikilinks as a route to recommending appropriate references for scientific {W}ikipedia pages",
    author = "Jana, Abhik  and
      Kanojiya, Pranjal  and
      Goyal, Pawan  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1032",
    pages = "379--389",
    abstract = "The exponential increase in the usage of Wikipedia as a key source of scientific knowledge among the researchers is making it absolutely necessary to metamorphose this knowledge repository into an integral and self-contained source of information for direct utilization. Unfortunately, the references which support the content of each Wikipedia entity page, are far from complete. Why are the reference section ill-formed for most Wikipedia pages? Is this section edited as frequently as the other sections of a page? Can there be appropriate surrogates that can automatically enhance the reference section? In this paper, we propose a novel two step approach {--} WikiRef {--} that (i) leverages the wikilinks present in a scientific Wikipedia target page and, thereby, (ii) recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikilinks. In the first step, we build a classifier to ascertain whether a wikilink is a potential source of reference or not. In the following step, we recommend references to the target page from the reference section of the wikilinks that are classified as potential sources of references in the first step. We perform an extensive evaluation of our approach on datasets from two different domains {--} Computer Science and Physics. For Computer Science we achieve a notably good performance with a precision@1 of 0.44 for reference recommendation as opposed to 0.38 obtained from the most competitive baseline. For the Physics dataset, we obtain a similar performance boost of 10{\%} with respect to the most competitive baseline.",
}
@inproceedings{alharthi-etal-2018-authorship,
    title = "Authorship Identification for Literary Book Recommendations",
    author = "Alharthi, Haifa  and
      Inkpen, Diana  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1033",
    pages = "390--400",
    abstract = "Book recommender systems can help promote the practice of reading for pleasure, which has been declining in recent years. One factor that influences reading preferences is writing style. We propose a system that recommends books after learning their authors{'} style. To our knowledge, this is the first work that applies the information learned by an author-identification model to book recommendations. We evaluated the system according to a top-k recommendation scenario. Our system gives better accuracy when compared with many state-of-the-art methods. We also conducted a qualitative analysis by checking if similar books/authors were annotated similarly by experts.",
}
@inproceedings{leal-etal-2018-nontrivial,
    title = "A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in {P}ortuguese",
    author = "Leal, Sidney Evaldo  and
      Duran, Magali Sanches  and
      Alu{\'\i}sio, Sandra Maria",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1034",
    pages = "401--413",
    abstract = "Effective textual communication depends on readers being proficient enough to comprehend texts, and texts being clear enough to be understood by the intended audience, in a reading task. When the meaning of textual information and instructions is not well conveyed, many losses and damages may occur. Among the solutions to alleviate this problem is the automatic evaluation of sentence readability, task which has been receiving a lot of attention due to its large applicability. However, a shortage of resources, such as corpora for training and evaluation, hinders the full development of this task. In this paper, we generate a nontrivial sentence corpus in Portuguese. We evaluate three scenarios for building it, taking advantage of a parallel corpus of simplification, in which each sentence triplet is aligned and has simplification operations annotated, being ideal for justifying possible mistakes of future methods. The best scenario of our corpus PorSimplesSent is composed of 4,888 pairs, which is bigger than a similar corpus for English; all the three versions of it are publicly available. We created four baselines for PorSimplesSent and made available a pairwise ranking method, using 17 linguistic and psycholinguistic features, which correctly identifies the ranking of sentence pairs with an accuracy of 74.2{\%}.",
}
@inproceedings{du-etal-2018-adopting,
    title = "Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference",
    author = "Du, Qianlong  and
      Zong, Chengqing  and
      Su, Keh-Yih",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1035",
    pages = "414--425",
    abstract = "This paper proposes to perform natural language inference with Word-Pair-Dependency-Triplets. Most previous DNN-based approaches either ignore syntactic dependency among words, or directly use tree-LSTM to generate sentence representation with irrelevant information. To overcome the problems mentioned above, we adopt Word-Pair-Dependency-Triplets to improve alignment and inference judgment. To be specific, instead of comparing each triplet from one passage with the merged information of another passage, we first propose to perform comparison directly between the triplets of the given passage-pair to make the judgement more interpretable. Experimental results show that the performance of our approach is better than most of the approaches that use tree structures, and is comparable to other state-of-the-art approaches.",
}
@inproceedings{lei-etal-2018-cooperative,
    title = "Cooperative Denoising for Distantly Supervised Relation Extraction",
    author = "Lei, Kai  and
      Chen, Daoyuan  and
      Li, Yaliang  and
      Du, Nan  and
      Yang, Min  and
      Fan, Wei  and
      Shen, Ying",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1036",
    pages = "426--436",
    abstract = "Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novelCOopeRativeDenoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.",
}
@inproceedings{zou-etal-2018-adversarial,
    title = "Adversarial Feature Adaptation for Cross-lingual Relation Classification",
    author = "Zou, Bowei  and
      Xu, Zengzhuang  and
      Hong, Yu  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1037",
    pages = "437--448",
    abstract = "Relation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most existing methods focus on exploiting mono-lingual data, e.g., in English, due to the lack of annotated data in other languages. In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data. Such a feature adaptation approach enables feature imitation via the competition between a relation classification network and a rival discriminator. Experimental results on the ACE 2005 multilingual training corpus, treating English as the source language and Chinese the target, demonstrate the effectiveness of our proposed approach, yielding an improvement of 5.7{\%} over the state-of-the-art.",
}
@inproceedings{zhang-zhao-2018-one,
    title = "One-shot Learning for Question-Answering in {G}aokao History Challenge",
    author = "Zhang, Zhuosheng  and
      Zhao, Hai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1038",
    pages = "449--461",
    abstract = "Answering questions from university admission exams (Gaokao in Chinese) is a challenging AI task since it requires effective representation to capture complicated semantic relations between questions and answers. In this work, we propose a hybrid neural model for deep question-answering task from history examinations. Our model employs a cooperative gated neural network to retrieve answers with the assistance of extra labels given by a neural turing machine labeler. Empirical study shows that the labeler works well with only a small training dataset and the gated mechanism is good at fetching the semantic representation of lengthy answers. Experiments on question answering demonstrate the proposed model obtains substantial performance gains over various neural model baselines in terms of multiple evaluation metrics.",
}
@inproceedings{guo-etal-2018-dynamic,
    title = "Dynamic Multi-Level Multi-Task Learning for Sentence Simplification",
    author = "Guo, Han  and
      Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1039",
    pages = "462--476",
    abstract = "Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel {`}multi-level{'} layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task{'}s semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model{'}s learned entailment and paraphrasing skills.",
}
@inproceedings{fukunaga-etal-2018-interpretation,
    title = "Interpretation of Implicit Conditions in Database Search Dialogues",
    author = "Fukunaga, Shunya  and
      Nishikawa, Hitoshi  and
      Tokunaga, Takenobu  and
      Yokono, Hikaru  and
      Takahashi, Tetsuro",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1040",
    pages = "477--486",
    abstract = "Targeting the database search dialogue, we propose to utilise information in the user utterances that do not directly mention the database (DB) field of the backend database system but are useful for constructing database queries. We call this kind of information implicit conditions. Interpreting the implicit conditions enables the dialogue system more natural and efficient in communicating with humans. We formalised the interpretation of the implicit conditions as classifying user utterances into the related DB field while identifying the evidence for that classification at the same time. Introducing this new task is one of the contributions of this paper. We implemented two models for this task: an SVM-based model and an RCNN-based model. Through the evaluation using a corpus of simulated dialogues between a real estate agent and a customer, we found that the SVM-based model showed better performance than the RCNN-based model.",
}
@inproceedings{hu-etal-2018-shot,
    title = "Few-Shot Charge Prediction with Discriminative Legal Attributes",
    author = "Hu, Zikun  and
      Li, Xiang  and
      Tu, Cunchao  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1041",
    pages = "487--498",
    abstract = "Automatic charge prediction aims to predict the final charges according to the fact descriptions in criminal cases and plays a crucial role in legal assistant systems. Existing works on charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. Moreover, these exist many confusing charge pairs, whose fact descriptions are fairly similar to each other. To address these issues, we introduce several discriminative attributes of charges as the internal mapping between fact descriptions and charges. These attributes provide additional information for few-shot charges, as well as effective signals for distinguishing confusing charges. More specifically, we propose an attribute-attentive charge prediction model to infer the attributes and charges simultaneously. Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines. Specifically, our model outperforms other baselines by more than 50{\%} in the few-shot scenario. Our codes and datasets can be obtained from https://github.com/thunlp/attribute{\_}charge.",
}
@inproceedings{gupta-etal-2018-taxonomy,
    title = "Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy",
    author = "Gupta, Deepak  and
      Pujari, Rajkumar  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak  and
      Maitra, Anutosh  and
      Jain, Tom  and
      Sengupta, Shubhashis",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1042",
    pages = "499--513",
    abstract = "In this paper, we propose a hybrid technique for semantic question matching. It uses a proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning or taxonomy-based knowledge alone.",
}
@inproceedings{hosu-etal-2018-natural,
    title = "Natural Language Interface for Databases Using a Dual-Encoder Model",
    author = "Hosu, Ionel Alexandru  and
      Iacob, Radu Cristian Alexandru  and
      Brad, Florin  and
      Ruseti, Stefan  and
      Rebedea, Traian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1043",
    pages = "514--524",
    abstract = "We propose a sketch-based two-step neural model for generating structured queries (SQL) based on a user{'}s request in natural language. The sketch is obtained by using placeholders for specific entities in the SQL query, such as column names, table names, aliases and variables, in a process similar to semantic parsing. The first step is to apply a sequence-to-sequence (SEQ2SEQ) model to determine the most probable SQL sketch based on the request in natural language. Then, a second network designed as a dual-encoder SEQ2SEQ model using both the text query and the previously obtained sketch is employed to generate the final SQL query. Our approach shows improvements over previous approaches on two recent large datasets (WikiSQL and SENLIDB) suitable for data-driven solutions for natural language interfaces for databases.",
}
@inproceedings{xu-etal-2018-employing,
    title = "Employing Text Matching Network to Recognise Nuclearity in {C}hinese Discourse",
    author = "Xu, Sheng  and
      Li, Peifeng  and
      Zhou, Guodong  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1044",
    pages = "525--535",
    abstract = "The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) that encodes the discourse units and the paragraphs by combining Bi-LSTM and CNN to capture both global dependency information and local n-gram information. Moreover, it introduces three components of text matching, the Cosine, Bilinear and Single Layer Network, to incorporate various similarities and interactions among the discourse units. Experimental results on the Chinese Discourse TreeBank show that our proposed TMN model significantly outperforms various strong baselines in both micro-F1 and macro-F1.",
}
@inproceedings{chu-etal-2018-joint,
    title = "Joint Modeling of Structure Identification and Nuclearity Recognition in Macro {C}hinese {D}iscourse {T}reebank",
    author = "Chu, Xiaomin  and
      Jiang, Feng  and
      Zhou, Yi  and
      Zhou, Guodong  and
      Zhu, Qiaoming",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1045",
    pages = "536--546",
    abstract = "Discourse parsing is a challenging task and plays a critical role in discourse analysis. This paper focus on the macro level discourse structure analysis, which has been less studied in the previous researches. We explore a macro discourse structure presentation schema to present the macro level discourse structure, and propose a corresponding corpus, named Macro Chinese Discourse Treebank. On these bases, we concentrate on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition. In order to reduce the error transmission between the associated tasks, we adopt a joint model of the two tasks, and an Integer Linear Programming approach is proposed to achieve global optimization with various kinds of constraints.",
}
@inproceedings{guo-etal-2018-implicit,
    title = "Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning",
    author = "Guo, Fengyu  and
      He, Ruifang  and
      Jin, Di  and
      Dang, Jianwu  and
      Wang, Longbiao  and
      Li, Xiangang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1046",
    pages = "547--558",
    abstract = "Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc. Most previous methods encode two discourse arguments separately, the ones considering pair specific clues ignore the bidirectional interactions between two arguments and the sparsity of pair patterns. In this paper, we propose a novel neural Tensor network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition. (1) We mine the most correlated word pairs from two discourse arguments to model pair specific clues, and integrate them as interactive attention into argument representations produced by the bidirectional long short-term memory network. Meanwhile, (2) the neural tensor network with sparse constraint is proposed to explore the deeper and the more important pair patterns so as to fully recognize discourse relations. The experimental results on PDTB show that our proposed TIASL framework is effective.",
}
@inproceedings{yu-etal-2018-transition,
    title = "Transition-based Neural {RST} Parsing with Implicit Syntax Features",
    author = "Yu, Nan  and
      Zhang, Meishan  and
      Fu, Guohong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1047",
    pages = "559--570",
    abstract = "Syntax has been a useful source of information for statistical RST discourse parsing. Under the neural setting, a common approach integrates syntax by a recursive neural network (RNN), requiring discrete output trees produced by a supervised syntax parser. In this paper, we propose an implicit syntax feature extraction approach, using hidden-layer vectors extracted from a neural syntax parser. In addition, we propose a simple transition-based model as the baseline, further enhancing it with dynamic oracle. Experiments on the standard dataset show that our baseline model with dynamic oracle is highly competitive. When implicit syntax features are integrated, we are able to obtain further improvements, better than using explicit Tree-RNN.",
}
@inproceedings{bai-zhao-2018-deep,
    title = "Deep Enhanced Representation for Implicit Discourse Relation Recognition",
    author = "Bai, Hongxiao  and
      Zhao, Hai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1048",
    pages = "571--583",
    abstract = "Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived from surface features from the input sentence pairs. Thus, properly representing the text is very crucial to this task. In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels. The proposed deeper model is evaluated on the benchmark treebank and achieves state-of-the-art accuracy with greater than 48{\%} in 11-way and F1 score greater than 50{\%} in 4-way classifications for the first time according to our best knowledge.",
}
@inproceedings{kishimoto-etal-2018-knowledge,
    title = "A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification",
    author = "Kishimoto, Yudai  and
      Murawaki, Yugo  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1049",
    pages = "584--595",
    abstract = "Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper, we address this problem by augmenting the input text with external knowledge and context and by adopting a neural network model that can effectively handle the augmented text. Experiments show that external knowledge did improve the classification accuracy. Contextual information provided no significant gain for implicit discourse relations, but it did for explicit ones.",
}
@inproceedings{kuang-etal-2018-modeling,
    title = "Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches",
    author = "Kuang, Shaohui  and
      Xiong, Deyi  and
      Luo, Weihua  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1050",
    pages = "596--606",
    abstract = "Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text. Current neural machine translation (NMT) systems translate a text in a conventional sentence-by-sentence fashion, ignoring such cross-sentence links and dependencies. This may lead to generate an incoherent target text for a coherent source text. In order to handle this issue, we propose a cache-based approach to modeling coherence for neural machine translation by capturing contextual information either from recently translated sentences or the entire document. Particularly, we explore two types of caches: a dynamic cache, which stores words from the best translation hypotheses of preceding sentences, and a topic cache, which maintains a set of target-side topical words that are semantically related to the document to be translated. On this basis, we build a new layer to score target words in these two caches with a cache-based neural model. Here the estimated probabilities from the cache-based neural model are combined with NMT probabilities into the final word prediction probabilities via a gating mechanism. Finally, the proposed cache-based neural model is trained jointly with NMT system in an end-to-end manner. Experiments and analysis presented in this paper demonstrate that the proposed cache-based model achieves substantial improvements over several state-of-the-art SMT and NMT baselines.",
}
@inproceedings{kuang-xiong-2018-fusing,
    title = "Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model",
    author = "Kuang, Shaohui  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1051",
    pages = "607--617",
    abstract = "Neural machine translation (NMT) systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring inter-sentence information. This may make the translation of a sentence ambiguous or even inconsistent with the translations of neighboring sentences. In order to handle this issue, we propose an inter-sentence gate model that uses the same encoder to encode two adjacent sentences and controls the amount of information flowing from the preceding sentence to the translation of the current sentence with an inter-sentence gate. In this way, our proposed model can capture the connection between sentences and fuse recency from neighboring sentences into neural machine translation. On several NIST Chinese-English translation tasks, our experiments demonstrate that the proposed inter-sentence gate model achieves substantial improvements over the baseline.",
}
@inproceedings{morishita-etal-2018-improving,
    title = "Improving Neural Machine Translation by Incorporating Hierarchical Subword Features",
    author = "Morishita, Makoto  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1052",
    pages = "618--629",
    abstract = "This paper focuses on subword-based Neural Machine Translation (NMT). We hypothesize that in the NMT model, the appropriate subword units for the following three modules (layers) can differ: (1) the encoder embedding layer, (2) the decoder embedding layer, and (3) the decoder output layer. We find the subword based on Sennrich et al. (2016) has a feature that a large vocabulary is a superset of a small vocabulary and modify the NMT model enables the incorporation of several different subword units in a single embedding layer. We refer these small subword features as hierarchical subword features. To empirically investigate our assumption, we compare the performance of several different subword units and hierarchical subword features for both the encoder and decoder embedding layers. We confirmed that incorporating hierarchical subword features in the encoder consistently improves BLEU scores on the IWSLT evaluation datasets.",
}
@inproceedings{merhav-ash-2018-design,
    title = "Design Challenges in Named Entity Transliteration",
    author = "Merhav, Yuval  and
      Ash, Stephen",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1053",
    pages = "630--640",
    abstract = "We analyze some of the fundamental design challenges that impact the development of a multilingual state-of-the-art named entity transliteration system, including curating bilingual named entity datasets and evaluation of multiple transliteration methods. We empirically evaluate the transliteration task using the traditional weighted finite state transducer (WFST) approach against two neural approaches: the encoder-decoder recurrent neural network method and the recent, non-sequential Transformer method. In order to improve availability of bilingual named entity transliteration datasets, we release personal name bilingual dictionaries mined from Wikidata for English to Russian, Hebrew, Arabic, and Japanese Katakana. Our code and dictionaries are publicly available.",
}
@inproceedings{lakew-etal-2018-comparison,
    title = "A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation",
    author = "Lakew, Surafel Melaku  and
      Cettolo, Mauro  and
      Federico, Marcello",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1054",
    pages = "641--652",
    abstract = "Recently, neural machine translation (NMT) has been extended to multilinguality, that is to handle more than one translation direction with a single system. Multilingual NMT showed competitive performance against pure bilingual systems. Notably, in low-resource settings, it proved to work effectively and efficiently, thanks to shared representation space that is forced across languages and induces a sort of transfer-learning. Furthermore, multilingual NMT enables so-called zero-shot inference across language pairs never seen at training time. Despite the increasing interest in this framework, an in-depth analysis of what a multilingual NMT model is capable of and what it is not is still missing. Motivated by this, our work (i) provides a quantitative and comparative analysis of the translations produced by bilingual, multilingual and zero-shot systems; (ii) investigates the translation quality of two of the currently dominant neural architectures in MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively explores how the closeness between languages influences the zero-shot translation. Our analysis leverages multiple professional post-edits of automatic translations by several different systems and focuses both on automatic standard metrics (BLEU and TER) and on widely used error categories, which are lexical, morphology, and word order errors.",
}
@inproceedings{ebrahimi-etal-2018-adversarial,
    title = "On Adversarial Examples for Character-Level Neural Machine Translation",
    author = "Ebrahimi, Javid  and
      Lowd, Daniel  and
      Dou, Dejing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1055",
    pages = "653--663",
    abstract = "Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Due to the difficulty of creating white-box adversarial examples for discrete text input, most analyses of the robustness of NLP models have been done through black-box adversarial examples. We investigate adversarial examples for character-level neural machine translation (NMT), and contrast black-box adversaries with a novel white-box adversary, which employs differentiable string-edit operations to rank adversarial changes. We propose two novel types of attacks which aim to remove or change a word in a translation, rather than simply break the NMT. We demonstrate that white-box adversarial examples are significantly stronger than their black-box counterparts in different attack scenarios, which show more serious vulnerabilities than previously known. In addition, after performing adversarial training, which takes only 3 times longer than regular training, we can improve the model{'}s robustness significantly.",
}
@inproceedings{ilievski-etal-2018-systematic,
    title = "Systematic Study of Long Tail Phenomena in Entity Linking",
    author = "Ilievski, Filip  and
      Vossen, Piek  and
      Schlobach, Stefan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1056",
    pages = "664--674",
    abstract = "State-of-the-art entity linkers achieve high accuracy scores with probabilistic methods. However, these scores should be considered in relation to the properties of the datasets they are evaluated on. Until now, there has not been a systematic investigation of the properties of entity linking datasets and their impact on system performance. In this paper we report on a series of hypotheses regarding the long tail phenomena in entity linking datasets, their interaction, and their impact on system performance. Our systematic study of these hypotheses shows that evaluation datasets mainly capture head entities and only incidentally cover data from the tail, thus encouraging systems to overfit to popular/frequent and non-ambiguous cases. We find the most difficult cases of entity linking among the infrequent candidates of ambiguous forms. With our findings, we hope to inspire future designs of both entity linking systems and evaluation datasets. To support this goal, we provide a list of recommended actions for better inclusion of tail cases.",
}
@inproceedings{cao-etal-2018-neural,
    title = "Neural Collective Entity Linking",
    author = "Cao, Yixin  and
      Hou, Lei  and
      Li, Juanzi  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1057",
    pages = "675--686",
    abstract = "Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL apply Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.",
}
@inproceedings{bhutani-etal-2018-exploiting,
    title = "Exploiting Structure in Representation of Named Entities using Active Learning",
    author = "Bhutani, Nikita  and
      Qian, Kun  and
      Li, Yunyao  and
      Jagadish, H. V.  and
      Hernandez, Mauricio  and
      Vasa, Mitesh",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1058",
    pages = "687--699",
    abstract = "Fundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations arise in complex ways that cannot be captured using textual similarity metrics. However, entities have underlying structures, typically shared by entities of the same entity type, that can help reason over their name variations. Discovering, learning and manipulating these structures typically requires high manual effort in the form of large amounts of labeled training data and handwritten transformation programs. In this work, we propose an active-learning based framework that drastically reduces the labeled data required to learn the structures of entities. We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels. Our experiments show that our framework consistently outperforms both handwritten programs and supervised learning models. We also demonstrate the utility of our framework in relation extraction and entity resolution tasks.",
}
@inproceedings{al-olimat-etal-2018-practical,
    title = "A Practical Incremental Learning Framework For Sparse Entity Extraction",
    author = "Al-Olimat, Hussein  and
      Gustafson, Steven  and
      Mackay, Jason  and
      Thirunarayan, Krishnaprasad  and
      Sheth, Amit",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1059",
    pages = "700--710",
    abstract = "This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation. We present a framework that integrates Entity Set Expansion (ESE) and Active Learning (AL) to reduce the annotation cost of sparse data and provide an online evaluation method as feedback. This incremental and interactive learning framework allows for rapid annotation and subsequent extraction of sparse data while maintaining high accuracy. We evaluate our framework on three publicly available datasets and show that it drastically reduces the cost of sparse entity annotation by an average of 85{\%} and 45{\%} to reach 0.9 and 1.0 F-Scores respectively. Moreover, the method exhibited robust performance across all datasets.",
}
@inproceedings{mai-etal-2018-empirical,
    title = "An Empirical Study on Fine-Grained Named Entity Recognition",
    author = "Mai, Khai  and
      Pham, Thai-Hoang  and
      Nguyen, Minh Trung  and
      Nguyen, Tuan Duc  and
      Bollegala, Danushka  and
      Sasano, Ryohei  and
      Sekine, Satoshi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1060",
    pages = "711--722",
    abstract = "Named entity recognition (NER) has attracted a substantial amount of research. Recently, several neural network-based models have been proposed and achieved high performance. However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for non-English languages. It is still an open question whether there is a model that is robust across various settings or the proper model varies depending on the language, the number of named entity categories, and the size of training datasets. This paper first presents an empirical comparison of FG-NER models for English and Japanese and demonstrates that LSTM+CNN+CRF (Ma and Hovy, 2016), one of the state-of-the-art methods for English NER, also works well for English FG-NER but does not work well for Japanese, a language that has a large number of character types. To tackle this problem, we propose a method to improve the neural network-based Japanese FG-NER performance by removing the CNN layer and utilizing dictionary and category embeddings. Experiment results show that the proposed method improves Japanese FG-NER F-score from 66.76{\%} to 75.18{\%}.",
}
@inproceedings{zhang-etal-2018-higher,
    title = "Does Higher Order {LSTM} Have Better Accuracy for Segmenting and Labeling Sequence Data?",
    author = "Zhang, Yi  and
      Sun, Xu  and
      Ma, Shuming  and
      Yang, Yang  and
      Ren, Xuancheng",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1061",
    pages = "723--733",
    abstract = "Existing neural models usually predict the tag of the current token independent of the neighboring tags. The popular LSTM-CRF model considers the tag dependencies between every two consecutive tags. However, it is hard for existing neural models to take longer distance dependencies between tags into consideration. The scalability is mainly limited by the complex model structures and the cost of dynamic programming during training. In our work, we first design a new model called {``}high order LSTM{''} to predict multiple tags for the current token which contains not only the current tag but also the previous several tags. We call the number of tags in one prediction as {``}order{''}. Then we propose a new method called Multi-Order BiLSTM (MO-BiLSTM) which combines low order and high order LSTMs together. MO-BiLSTM keeps the scalability to high order models with a pruning technique. We evaluate MO-BiLSTM on all-phrase chunking and NER datasets. Experiment results show that MO-BiLSTM achieves the state-of-the-art result in chunking and highly competitive results in two NER datasets.",
}
@inproceedings{al-saleh-menai-2018-ant,
    title = "Ant Colony System for Multi-Document Summarization",
    author = "Al-Saleh, Asma  and
      Menai, Mohamed El Bachir",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1062",
    pages = "734--744",
    abstract = "This paper proposes an extractive multi-document summarization approach based on an ant colony system to optimize the information coverage of summary sentences. The implemented system was evaluated on both English and Arabic versions of the corpus of the Text Analysis Conference 2011 MultiLing Pilot by using ROUGE metrics. The evaluation results are promising in comparison to those of the participating systems. Indeed, our system achieved the best scores based on several ROUGE metrics.",
}
@inproceedings{cerisara-etal-2018-multi,
    title = "Multi-task dialog act and sentiment recognition on Mastodon",
    author = "Cerisara, Christophe  and
      Jafaritazehjani, Somayeh  and
      Oluokun, Adedayo  and
      Le, Hoa T.",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1063",
    pages = "745--754",
    abstract = "Because of license restrictions, it often becomes impossible to strictly reproduce most research results on Twitter data already a few months after the creation of the corpus. This situation worsened gradually as time passes and tweets become inaccessible. This is a critical issue for reproducible and accountable research on social media. We partly solve this challenge by annotating a new Twitter-like corpus from an alternative large social medium with licenses that are compatible with reproducible experiments: Mastodon. We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network on joint sentiment and dialog act recognition. We experimentally demonstrate that transfer learning may be efficiently achieved between both tasks, and further analyze some specific correlations between sentiments and dialogues on social media. Both the annotated corpus and deep network are released with an open-source license.",
}
@inproceedings{rogers-etal-2018-rusentiment,
    title = "{R}u{S}entiment: An Enriched Sentiment Analysis Dataset for Social Media in {R}ussian",
    author = "Rogers, Anna  and
      Romanov, Alexey  and
      Rumshisky, Anna  and
      Volkova, Svitlana  and
      Gronas, Mikhail  and
      Gribov, Alex",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1064",
    pages = "755--763",
    abstract = "This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss{'} kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.",
}
@inproceedings{goldberger-melamud-2018-self,
    title = "Self-Normalization Properties of Language Modeling",
    author = "Goldberger, Jacob  and
      Melamud, Oren",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1065",
    pages = "764--773",
    abstract = "Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. In the context of language modeling, this property is particularly appealing as it may significantly reduce run-times due to large word vocabularies. In this study, we provide a comprehensive investigation of language modeling self-normalization. First, we theoretically analyze the inherent self-normalization properties of Noise Contrastive Estimation (NCE) language models. Then, we compare them empirically to softmax-based approaches, which are self-normalized using explicit regularization, and suggest a hybrid model with compelling properties. Finally, we uncover a surprising negative correlation between self-normalization and perplexity across the board, as well as some regularity in the observed errors, which may potentially be used for improving self-normalization algorithms in the future.",
}
@inproceedings{gu-etal-2018-position,
    title = "A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis",
    author = "Gu, Shuqin  and
      Zhang, Lipeng  and
      Hou, Yuexian  and
      Song, Yin",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1066",
    pages = "774--784",
    abstract = "Aspect-level sentiment analysis aims to distinguish the sentiment polarity of each specific aspect term in a given sentence. Both industry and academia have realized the importance of the relationship between aspect term and sentence, and made attempts to model the relationship by designing a series of attention models. However, most existing methods usually neglect the fact that the position information is also crucial for identifying the sentiment polarity of the aspect term. When an aspect term occurs in a sentence, its neighboring words should be given more attention than other words with long distance. Therefore, we propose a position-aware bidirectional attention network (PBAN) based on bidirectional GRU. PBAN not only concentrates on the position information of aspect terms, but also mutually models the relation between aspect term and sentence by employing bidirectional attention mechanism. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our proposed PBAN model.",
}
@inproceedings{kohita-etal-2018-dynamic,
    title = "Dynamic Feature Selection with Attention in Incremental Parsing",
    author = "Kohita, Ryosuke  and
      Noji, Hiroshi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1067",
    pages = "785--794",
    abstract = "One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context. In this work, we present a simple technique to maximally utilize the local features with an attention mechanism, which works as context- dependent dynamic feature selection. Our model learns, for example, which tokens should a parser focus on, to decide the next action. Our multilingual experiment shows its effectiveness across many languages. We also present an experiment with augmented test dataset and demon- strate it helps to understand the model{'}s behavior on locally ambiguous points.",
}
@inproceedings{krishna-etal-2018-vocabulary,
    title = "Vocabulary Tailored Summary Generation",
    author = "Krishna, Kundan  and
      Murhekar, Aniket  and
      Sharma, Saumitra  and
      Srinivasan, Balaji Vasan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1068",
    pages = "795--805",
    abstract = "Neural sequence-to-sequence models have been successfully extended for summary generation.However, existing frameworks generate a single summary for a given input and do not tune the summaries towards any additional constraints/preferences. Such a tunable framework is desirable to account for linguistic preferences of the specific audience who will consume the summary. In this paper, we propose a neural framework to generate summaries constrained to a vocabulary-defined linguistic preferences of a target audience. The proposed method accounts for the generation context by tuning the summary words at the time of generation. Our evaluations indicate that the proposed approach tunes summaries to the target vocabulary while still maintaining a superior summary quality against a state-of-the-art word embedding based lexical substitution algorithm, suggesting the feasibility of the proposed approach. We demonstrate two applications of the proposed approach - to generate understandable summaries with simpler words, and readable summaries with shorter words.",
}
@inproceedings{sun-etal-2018-reading,
    title = "Reading Comprehension with Graph-based Temporal-Casual Reasoning",
    author = "Sun, Yawei  and
      Cheng, Gong  and
      Qu, Yuzhong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1069",
    pages = "806--817",
    abstract = "Complex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text based on dependencies, and rank answers by aligning event graphs. In particular, the alignments are constrained by graph-based reasoning to ensure temporal and causal agreement. Our focused approach self-adaptively complements existing solutions; it is automatically triggered only when applicable. Experiments on RACE and MCTest show that state-of-the-art methods are notably improved by using our approach as an add-on.",
}
@inproceedings{barnes-etal-2018-projecting,
    title = "Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains",
    author = "Barnes, Jeremy  and
      Klinger, Roman  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1070",
    pages = "818--830",
    abstract = "Domain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to this problem are structural correspondence learning and autoencoders. However, they either require long training times or suffer greatly on highly divergent domains. Inspired by recent advances in cross-lingual sentiment analysis, we provide a novel perspective and cast the domain adaptation problem as an embedding projection task. Our model takes as input two mono-domain embedding spaces and learns to project them to a bi-domain space, which is jointly optimized to (1) project across domains and to (2) predict sentiment. We perform domain adaptation experiments on 20 source-target domain pairs for sentiment classification and report novel state-of-the-art results on 11 domain pairs, including the Amazon domain adaptation datasets and SemEval 2013 and 2016 datasets. Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains. Our code is available at https://github.com/jbarnesspain/domain{\_}blse",
}
@inproceedings{eger-etal-2018-cross,
    title = "Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!",
    author = "Eger, Steffen  and
      Daxenberger, Johannes  and
      Stab, Christian  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1071",
    pages = "831--844",
    abstract = "Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at http://github.com/UKPLab/coling2018-xling{\_}argument{\_}mining.",
}
@inproceedings{wu-etal-2018-hl,
    title = "{HL}-{E}nc{D}ec: A Hybrid-Level Encoder-Decoder for Neural Response Generation",
    author = "Wu, Sixing  and
      Zhang, Dawei  and
      Li, Ying  and
      Xie, Xing  and
      Wu, Zhonghai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1072",
    pages = "845--856",
    abstract = "Recent years have witnessed a surge of interest on response generation for neural conversation systems. Most existing models are implemented by following the Encoder-Decoder framework and operate sentences of conversations at word-level. The word-level model is suffering from the Unknown Words Issue and the Preference Issue, which seriously impact the quality of generated responses, for example, generated responses may become irrelevant or too general (i.e. safe responses). To address these issues, this paper proposes a hybrid-level Encoder-Decoder model (HL-EncDec), which not only utilizes the word-level features but also character-level features. We conduct several experiments to evaluate HL-EncDec on a Chinese corpus, experimental results show our model significantly outperforms other non-word-level models in automatic metrics and human annotations and is able to generate more informative responses. We also conduct experiments with a small-scale English dataset to show the generalization ability.",
}
@inproceedings{wang-etal-2018-multi,
    title = "Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension",
    author = "Wang, Liang  and
      Li, Sujian  and
      Zhao, Wei  and
      Shen, Kewei  and
      Sun, Meng  and
      Jia, Ruoyu  and
      Liu, Jingming",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1073",
    pages = "857--867",
    abstract = "Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years. In this paper, we design a novel multi-perspective framework, which can be seen as the joint training of heterogeneous experts and aggregate context information from different perspectives. Each perspective is modeled by a simple aggregation module. The outputs of multiple aggregation modules are fed into a one-timestep pointer network to get the final answer. At the same time, to tackle the problem of insufficient labeled data, we propose an efficient sampling mechanism to automatically generate more training examples by matching the distribution of candidates between labeled and unlabeled data. We conduct our experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017), which consists of nearly 100k questions designed by professional teachers. Results show that our method achieves new state-of-the-art performance over previous strong baselines.",
}
@inproceedings{zou-etal-2018-lexicon,
    title = "A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis",
    author = "Zou, Yicheng  and
      Gui, Tao  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1074",
    pages = "868--877",
    abstract = "Attention mechanisms have been leveraged for sentiment classification tasks because not all words have the same importance. However, most existing attention models did not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. To achieve the above target, in this work, we propose a novel lexicon-based supervised attention model (LBSA), which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations. Compared with general attention models, our model has better interpretability and less noise. Experimental results on three large-scale sentiment classification datasets showed that the proposed method outperforms previous methods.",
}
@inproceedings{araki-mitamura-2018-open,
    title = "Open-Domain Event Detection using Distant Supervision",
    author = "Araki, Jun  and
      Mitamura, Teruko",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1075",
    pages = "878--891",
    abstract = "This paper introduces open-domain event detection, a new event detection paradigm to address issues of prior work on restricted domains and event annotation. The goal is to detect all kinds of events regardless of domains. Given the absence of training data, we propose a distant supervision method that is able to generate high-quality training data. Using a manually annotated event corpus as gold standard, our experiments show that despite no direct supervision, the model outperforms supervised models. This result indicates that the distant supervision enables robust event detection in various domains, while obviating the need for human annotation of events.",
}
@inproceedings{chen-etal-2018-semi,
    title = "Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing",
    author = "Chen, Bo  and
      An, Bo  and
      Sun, Le  and
      Han, Xianpei",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1076",
    pages = "892--904",
    abstract = "Semantic parsers critically rely on accurate and high-coverage lexicons. However, traditional semantic parsers usually utilize annotated logical forms to learn the lexicon, which often suffer from the lexicon coverage problem. In this paper, we propose a graph-based semi-supervised learning framework that makes use of large text corpora and lexical resources. This framework first constructs a graph with a phrase similarity model learned by utilizing many text corpora and lexical resources. Next, graph propagation algorithm identifies the label distribution of unlabeled phrases from labeled ones. We evaluate our approach on two benchmarks: Webquestions and Free917. The results show that, in both datasets, our method achieves substantial improvement when comparing to the base system that does not utilize the learned lexicon, and gains competitive results when comparing to state-of-the-art systems.",
}
@inproceedings{shafieibavani-etal-2018-summarization,
    title = "Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings",
    author = "ShafieiBavani, Elaheh  and
      Ebrahimi, Mohammad  and
      Wong, Raymond  and
      Chen, Fang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1077",
    pages = "905--914",
    abstract = "We present a new summary evaluation approach that does not require human model summaries. Our approach exploits the compositional capabilities of corpus-based and lexical resource-based word embeddings to develop the features reflecting coverage, diversity, informativeness, and coherence of summaries. The features are then used to train a learning model for predicting the summary content quality in the absence of gold models. We evaluate the proposed metric in replicating the human assigned scores for summarization systems and summaries on data from query-focused and update summarization tasks in TAC 2008 and 2009. The results show that our feature combination provides reliable estimates of summary content quality when model summaries are not available.",
}
@inproceedings{jimenez-zafra-etal-2018-review,
    title = "A review of {S}panish corpora annotated with negation",
    author = "Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Morante, Roser  and
      Martin, Maite  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1078",
    pages = "915--924",
    abstract = "The availability of corpora annotated with negation information is essential to develop negation processing systems in any language. However, there is a lack of these corpora even for languages like English, and when there are corpora available they are small and the annotations are not always compatible across corpora. In this paper we review the existing corpora annotated with negation in Spanish with the purpose of first, gathering the information to make it available for other researchers and, second, analyzing how compatible are the corpora and how has the linguistic phenomenon been addressed. Our final aim is to develop a supervised negation processing system for Spanish, for which we need training and test data. Our analysis shows that it will not be possible to merge the small corpora existing for Spanish due to lack of compatibility in the annotations.",
}
@inproceedings{li-etal-2018-document,
    title = "Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings",
    author = "Li, Junjie  and
      Yang, Haitong  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1079",
    pages = "925--936",
    abstract = "Document-level multi-aspect sentiment classification aims to predict user{'}s sentiment polarities for different aspects of a product in a review. Existing approaches mainly focus on text information. However, the authors (i.e. users) and overall ratings of reviews are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects in this paper. Therefore, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall ratings jointly. Specifically, HUARN adopts a hierarchical architecture to encode word, sentence, and document level information. Then, user attention and aspect attention are introduced into building sentence and document level representation. The document representation is combined with user and overall rating information to predict aspect ratings of a review. Diverse aspects are treated differently and a multi-task framework is adopted. Empirical results on two real-world datasets show that HUARN achieves state-of-the-art performances.",
}
@inproceedings{hazem-morin-2018-leveraging,
    title = "Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora",
    author = "Hazem, Amir  and
      Morin, Emmanuel",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1080",
    pages = "937--949",
    abstract = "Recent evaluations on bilingual lexicon extraction from specialized comparable corpora have shown contrasted performance while using word embedding models. This can be partially explained by the lack of large specialized comparable corpora to build efficient representations. Within this context, we try to answer the following questions: First, (i) among the state-of-the-art embedding models, whether trained on specialized corpora or pre-trained on large general data sets, which one is the most appropriate model for bilingual terminology extraction? Second (ii) is it worth it to combine multiple embeddings trained on different data sets? For that purpose, we propose the first systematic evaluation of different word embedding models for bilingual terminology extraction from specialized comparable corpora. We emphasize how the character-based embedding model outperforms other models on the quality of the extracted bilingual lexicons. Further more, we propose a new efficient way to combine different embedding models learned from specialized and general-domain data sets. Our approach leads to higher performance than the best individual embedding model.",
}
@inproceedings{agrawal-etal-2018-learning,
    title = "Learning Emotion-enriched Word Representations",
    author = "Agrawal, Ameeta  and
      An, Aijun  and
      Papagelis, Manos",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1081",
    pages = "950--961",
    abstract = "Most word representation learning methods are based on the distributional hypothesis in linguistics, according to which words that are used and occur in the same contexts tend to possess similar meanings. As a consequence, emotionally dissimilar words, such as {``}happy{''} and {``}sad{''} occurring in similar contexts would purport more similar meaning than emotionally similar words, such as {``}happy{''} and {``}joy{''}. This complication leads to rather undesirable outcome in predictive tasks that relate to affect (emotional state), such as emotion classification and emotion similarity. In order to address this limitation, we propose a novel method of obtaining emotion-enriched word representations, which projects emotionally similar words into neighboring spaces and emotionally dissimilar ones far apart. The proposed approach leverages distant supervision to automatically obtain a large training dataset of text documents and two recurrent neural network architectures for learning the emotion-enriched representations. Through extensive evaluation on two tasks, including emotion classification and emotion similarity, we demonstrate that the proposed representations outperform several competitive general-purpose and affective word representations.",
}
@inproceedings{van-der-lee-etal-2018-evaluating,
    title = "Evaluating the text quality, human likeness and tailoring component of {PASS}: A {D}utch data-to-text system for soccer",
    author = "van der Lee, Chris  and
      Verduijn, Bart  and
      Krahmer, Emiel  and
      Wubben, Sander",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1082",
    pages = "962--972",
    abstract = "We present an evaluation of PASS, a data-to-text system that generates Dutch soccer reports from match statistics which are automatically tailored towards fans of one club or the other. The evaluation in this paper consists of two studies. An intrinsic human-based evaluation of the system{'}s output is described in the first study. In this study it was found that compared to human-written texts, computer-generated texts were rated slightly lower on style-related text components (fluency and clarity) and slightly higher in terms of the correctness of given information. Furthermore, results from the first study showed that tailoring was accurately recognized in most cases, and that participants struggled with correctly identifying whether a text was written by a human or computer. The second study investigated if tailoring affects perceived text quality, for which no results were garnered. This lack of results might be due to negative preconceptions about computer-generated texts which were found in the first study.",
}
@inproceedings{nakanishi-etal-2018-answerable,
    title = "Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension",
    author = "Nakanishi, Mao  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1083",
    pages = "973--983",
    abstract = "Machine-reading comprehension (MRC) has recently attracted attention in the fields of natural language processing and machine learning. One of the problematic presumptions with current MRC technologies is that each question is assumed to be answerable by looking at a given text passage. However, to realize human-like language comprehension ability, a machine should also be able to distinguish not-answerable questions (NAQs) from answerable questions. To develop this functionality, a dataset incorporating hard-to-detect NAQs is vital; however, its manual construction would be expensive. This paper proposes a dataset creation method that alters an existing MRC dataset, the Stanford Question Answering Dataset, and describes the resulting dataset. The value of this dataset is likely to increase if each NAQ in the dataset is properly classified with the difficulty of identifying it as an NAQ. This difficulty level would allow researchers to evaluate a machine{'}s NAQ detection performance more precisely. Therefore, we propose a method for automatically assigning difficulty level labels, which measures the similarity between a question and the target text passage. Our NAQ detection experiments demonstrate that the resulting dataset, having difficulty level annotations, is valid and potentially useful in the development of advanced MRC models.",
}
@inproceedings{emmery-etal-2018-style,
    title = "Style Obfuscation by Invariance",
    author = "Emmery, Chris  and
      Manjavacas Arevalo, Enrique  and
      Chrupa{\l}a, Grzegorz",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1084",
    pages = "984--996",
    abstract = "The task of obfuscating writing style using sequence models has previously been investigated under the framework of obfuscation-by-transfer, where the input text is explicitly rewritten in another style. A side effect of this framework are the frequent major alterations to the semantic content of the input. In this work, we propose obfuscation-by-invariance, and investigate to what extent models trained to be explicitly style-invariant preserve semantics. We evaluate our architectures in parallel and non-parallel settings, and compare automatic and human evaluations on the obfuscated sentences. Our experiments show that the performance of a style classifier can be reduced to chance level, while the output is evaluated to be of equal quality to models applying style-transfer. Additionally, human evaluation indicates a trade-off between the level of obfuscation and the observed quality of the output in terms of meaning preservation and grammaticality.",
}
@inproceedings{ye-etal-2018-encoding,
    title = "Encoding Sentiment Information into Word Vectors for Sentiment Analysis",
    author = "Ye, Zhe  and
      Li, Fang  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1085",
    pages = "997--1007",
    abstract = "General-purpose pre-trained word embeddings have become a mainstay of natural language processing, and more recently, methods have been proposed to encode external knowledge into word embeddings to benefit specific downstream tasks. The goal of this paper is to encode sentiment knowledge into pre-trained word vectors to improve the performance of sentiment analysis. Our proposed method is based on a convolutional neural network (CNN) and an external sentiment lexicon. Experiments on four popular sentiment analysis datasets show that this method improves the accuracy of sentiment analysis compared to a number of benchmark methods.",
}
@inproceedings{niu-etal-2018-multi,
    title = "Multi-Task Neural Models for Translating Between Styles Within and Across Languages",
    author = "Niu, Xing  and
      Rao, Sudha  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1086",
    pages = "1008--1021",
    abstract = "Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models achieve state-of-the-art performance for formality transfer and are able to perform formality-sensitive translation without being explicitly trained on style-annotated translation examples.",
}
@inproceedings{prolo-2018-towards,
    title = "Towards a Language for Natural Language Treebank Transductions",
    author = "Prolo, Carlos A.",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1087",
    pages = "1022--1032",
    abstract = "This paper describes a transduction language suitable for natural language treebank transformations and motivates its application to tasks that have been used and described in the literature. The language, which is the basis for a tree transduction tool allows for clean, precise and concise description of what has been very confusingly, ambiguously, and incompletely textually described in the literature also allowing easy non-hard-coded implementation. We also aim at getting feedback from the NLP community to eventually converge to a de facto standard for such transduction language.",
}
@inproceedings{li-etal-2018-generating,
    title = "Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training",
    author = "Li, Zhongyang  and
      Ding, Xiao  and
      Liu, Ting",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1088",
    pages = "1033--1043",
    abstract = "Story generation is a challenging problem in artificial intelligence (AI) and has received a lot of interests in the natural language processing (NLP) community. Most previous work tried to solve this problem using Sequence to Sequence (Seq2Seq) model trained with Maximum Likelihood Estimation (MLE). However, the pure MLE training objective much limits the power of Seq2Seq model in generating high-quality storys. In this paper, we propose using adversarial training augmented Seq2Seq model to generate reasonable and diversified story endings given a story context. Our model includes a generator that defines the policy of generating a story ending, and a discriminator that labels story endings as human-generated or machine-generated. Carefully designed human and automatic evaluation metrics demonstrate that our adversarial training augmented Seq2Seq model can generate more reasonable and diversified story endings compared to purely MLE-trained Seq2Seq model. Moreover, our model achieves better performance on the task of Story Cloze Test with an accuracy of 62.6{\%} compared with state-of-the-art baseline methods.",
}
@inproceedings{li-wan-2018-point,
    title = "Point Precisely: Towards Ensuring the Precision of Data in Generated Texts Using Delayed Copy Mechanism",
    author = "Li, Liunian  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1089",
    pages = "1044--1055",
    abstract = "The task of data-to-text generation aims to generate descriptive texts conditioned on a number of database records, and recent neural models have shown significant progress on this task. The attention based encoder-decoder models with copy mechanism have achieved state-of-the-art results on a few data-to-text datasets. However, such models still face the problem of putting incorrect data records in the generated texts, especially on some more challenging datasets like RotoWire. In this paper, we propose a two-stage approach with a delayed copy mechanism to improve the precision of data records in the generated texts. Our approach first adopts an encoder-decoder model to generate a template text with data slots to be filled and then leverages a proposed delayed copy mechanism to fill in the slots with proper data records. Our delayed copy mechanism can take into account all the information of the input data records and the full generated template text by using double attention, position-aware attention and a pairwise ranking loss. The two models in the two stages are trained separately. Evaluation results on the RotoWire dataset verify the efficacy of our proposed approach to generate better templates and copy data records more precisely.",
}
@inproceedings{kreutz-daelemans-2018-enhancing,
    title = "Enhancing General Sentiment Lexicons for Domain-Specific Use",
    author = "Kreutz, Tim  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1090",
    pages = "1056--1064",
    abstract = "Lexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity classification. SentProp is a framework for inducing domain-specific polarities from word embeddings. We elaborate on SentProp by evaluating its use for enhancing DuOMan, a general-purpose lexicon, for use in the political domain. By adding only top sentiment bearing words from the vocabulary and applying small polarity shifts in the general-purpose lexicon, we increase accuracy in an in-domain classification task. The enhanced lexicon performs worse than the original lexicon in an out-domain task, showing that the words we added and the polarity shifts we applied are domain-specific and do not translate well to an out-domain setting.",
}
@inproceedings{yu-etal-2018-operation,
    title = "An Operation Network for Abstractive Sentence Compression",
    author = "Yu, Naitong  and
      Zhang, Jie  and
      Huang, Minlie  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1091",
    pages = "1065--1076",
    abstract = "Sentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase the words, which are more coherent to human sentence compression. In this paper, we propose Operation Network, a neural network approach for abstractive sentence compression, which combines the advantages of both delete-based and generate-based sentence compression models. The central idea of Operation Network is to model the sentence compression process as an editing procedure. First, unnecessary words are deleted from the source sentence, then new words are either generated from a large vocabulary or copied directly from the source sentence. A compressed sentence can be obtained by a series of such edit operations (delete, copy and generate). Experiments show that Operation Network outperforms state-of-the-art baselines.",
}
@inproceedings{zhu-qian-2018-enhanced,
    title = "Enhanced Aspect Level Sentiment Classification with Auxiliary Memory",
    author = "Zhu, Peisong  and
      Qian, Tieyun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1092",
    pages = "1077--1087",
    abstract = "In aspect level sentiment classification, there are two common tasks: to identify the sentiment of an aspect (category) or a term. As specific instances of aspects, terms explicitly occur in sentences. It is beneficial for models to focus on nearby context words. In contrast, as high level semantic concepts of terms, aspects usually have more generalizable representations. However, conventional methods cannot utilize the information of aspects and terms at the same time, because few datasets are annotated with both aspects and terms. In this paper, we propose a novel deep memory network with auxiliary memory to address this problem. In our model, a main memory is used to capture the important context words for sentiment classification. In addition, we build an auxiliary memory to implicitly convert aspects and terms to each other, and feed both of them to the main memory. With the interaction between two memories, the features of aspects and terms can be learnt simultaneously. We compare our model with the state-of-the-art methods on four datasets from different domains. The experimental results demonstrate the effectiveness of our model.",
}
@inproceedings{mishra-etal-2018-author,
    title = "Author Profiling for Abuse Detection",
    author = "Mishra, Pushkar  and
      Del Tredici, Marco  and
      Yannakoudakis, Helen  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1093",
    pages = "1088--1098",
    abstract = "The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet. Previous research suggests that such abusive content tends to come from users who share a set of common stereotypes and form communities around them. The current state-of-the-art approaches to abuse detection are oblivious to user and community information and rely entirely on textual (i.e., lexical and semantic) cues. In this paper, we propose a novel approach to this problem that incorporates community-based profiling features of Twitter users. Experimenting with a dataset of 16k tweets, we show that our methods significantly outperform the current state of the art in abuse detection. Further, we conduct a qualitative analysis of model characteristics. We release our code, pre-trained models and all the resources used in the public domain.",
}
@inproceedings{madnani-cahill-2018-automated,
    title = "Automated Scoring: Beyond Natural Language Processing",
    author = "Madnani, Nitin  and
      Cahill, Aoife",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1094",
    pages = "1099--1109",
    abstract = "In this position paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks which usually involve applying the latest machine learning techniques to publicly available data in order to obtain the best accuracy. Automated scoring systems warrant significant cross-discipline collaboration of which natural language processing and machine learning are just two of many important components. Such systems have multiple stakeholders with different but valid perspectives that can often times be at odds with each other. Our position is that it is essential for us as NLP researchers to understand and incorporate these perspectives in our research and work towards a mutually satisfactory solution in order to build automated scoring systems that are accurate, fair, unbiased, and useful.",
}
@inproceedings{yang-etal-2018-aspect,
    title = "Aspect and Sentiment Aware Abstractive Review Summarization",
    author = "Yang, Min  and
      Qu, Qiang  and
      Shen, Ying  and
      Liu, Qiao  and
      Zhao, Wei  and
      Zhu, Jia",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1095",
    pages = "1110--1120",
    abstract = "Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction. However, to date, no work is towards the abstractive review summarization that is essential for business organizations and individual consumers to make informed decisions. This work takes the lead to study the aspect/sentiment-aware abstractive review summarization by exploring multi-factor attentions. Specifically, we propose an interactive attention mechanism to interactively learns the representations of context words, sentiment words and aspect words within the reviews, acted as an encoder. The learned sentiment and aspect representations are incorporated into the decoder to generate aspect/sentiment-aware review summaries via an attention fusion network. In addition, the abstractive summarizer is jointly trained with the text categorization task, which helps learn a category-specific text encoder, locating salient aspect information and exploring the variations of style and wording of content with respect to different text categories. The experimental results on a real-life dataset demonstrate that our model achieves impressive results compared to other strong competitors.",
}
@inproceedings{he-etal-2018-effective,
    title = "Effective Attention Modeling for Aspect-Level Sentiment Classification",
    author = "He, Ruidan  and
      Lee, Wee Sun  and
      Ng, Hwee Tou  and
      Dahlmeier, Daniel",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1096",
    pages = "1121--1131",
    abstract = "Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.",
}
@inproceedings{moore-rayson-2018-bringing,
    title = "Bringing replication and reproduction together with generalisability in {NLP}: Three reproduction studies for Target Dependent Sentiment Analysis",
    author = "Moore, Andrew  and
      Rayson, Paul",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1097",
    pages = "1132--1144",
    abstract = "Lack of repeatability and generalisability are two significant threats to continuing scientific development in Natural Language Processing. Language models and learning methods are so complex that scientific conference papers no longer contain enough space for the technical depth required for replication or reproduction. Taking Target Dependent Sentiment Analysis as a case study, we show how recent work in the field has not consistently released code, or described settings for learning methods in enough detail, and lacks comparability and generalisability in train, test or validation data. To investigate generalisability and to enable state of the art comparative evaluations, we carry out the first reproduction studies of three groups of complementary methods and perform the first large-scale mass evaluation on six different English datasets. Reflecting on our experiences, we recommend that future replication or reproduction experiments should always consider a variety of datasets alongside documenting and releasing their methods and published code in order to minimise the barriers to both repeatability and generalisability. We have released our code with a model zoo on GitHub with Jupyter Notebooks to aid understanding and full documentation, and we recommend that others do the same with their papers at submission time through an anonymised GitHub account.",
}
@inproceedings{hsu-etal-2018-multilevel,
    title = "Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences",
    author = "Hsu, Shiou Tian  and
      Chaudhary, Mandar  and
      Samatova, Nagiza",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1098",
    pages = "1145--1155",
    abstract = "Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence) during the process. However, existing generative networks used to extract rationales come with a trade-off between extracting diversified rationales and achieving good classification results. In this paper, we propose a multilevel heuristic approach to regulate rationale extraction to avoid extracting monotonous rationales without compromising classification performance. In our model, rationale selection is regularized by a semi-supervised process and features from different levels: word, syntax, sentence, and corpus. We evaluate our approach on the SemEval 2010 dataset that includes 19 relation classes and the quality of extracted rationales with our manually-labeled rationales. Experiments show a significant improvement in classification performance and a 20{\%} gain in rationale interpretability compared to state-of-the-art approaches.",
}
@inproceedings{wang-etal-2018-adversarial,
    title = "Adversarial Multi-lingual Neural Relation Extraction",
    author = "Wang, Xiaozhi  and
      Han, Xu  and
      Lin, Yankai  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1099",
    pages = "1156--1166",
    abstract = "Multi-lingual relation extraction aims to find unknown relational facts from text in various languages. Existing models cannot well capture the consistency and diversity of relation patterns in different languages. To address these issues, we propose an adversarial multi-lingual neural relation extraction (AMNRE) model, which builds both consistent and individual representations for each sentence to consider the consistency and diversity among languages. Further, we adopt an adversarial training strategy to ensure those consistent sentence representations could effectively extract the language-consistent relation patterns. The experimental results on real-world datasets demonstrate that our AMNRE model significantly outperforms the state-of-the-art models. The source code of this paper can be obtained from https://github.com/thunlp/AMNRE.",
}
@inproceedings{ren-etal-2018-neural,
    title = "Neural Relation Classification with Text Descriptions",
    author = "Ren, Feiliang  and
      Zhou, Di  and
      Liu, Zhihui  and
      Li, Yongcheng  and
      Zhao, Rongsheng  and
      Liu, Yongkang  and
      Liang, Xiaobo",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1100",
    pages = "1167--1177",
    abstract = "Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which the relations of the labeled entity pairs are given. However, these methods usually suffer from the data sparsity issue greatly. On the other hand, we notice that it is very easily to obtain some concise text descriptions for almost all of the entities in a relation classification task. The text descriptions can provide helpful supplementary information for relation classification. But they are ignored by most of existing methods. In this paper, we propose DesRC, a new neural relation classification method which integrates entities{'} text descriptions into deep neural networks models. We design a two-level attention mechanism to select the most useful information from the {``}intra-sentence{''} aspect and the {``}cross-sentence{''} aspect. Besides, the adversarial training method is also used to further improve the classification per-formance. Finally, we evaluate the proposed method on the SemEval 2010 dataset. Extensive experiments show that our method achieves much better experimental results than other state-of-the-art relation classification methods.",
}
@inproceedings{liao-etal-2018-abstract,
    title = "{A}bstract {M}eaning {R}epresentation for Multi-Document Summarization",
    author = "Liao, Kexin  and
      Lebanoff, Logan  and
      Liu, Fei",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1101",
    pages = "1178--1190",
    abstract = "Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.",
}
@inproceedings{nayeem-etal-2018-abstractive,
    title = "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion",
    author = "Nayeem, Mir Tafseer  and
      Fuad, Tanvir Ahmed  and
      Chali, Yllias",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1102",
    pages = "1191--1204",
    abstract = "In this work, we aim at developing an unsupervised abstractive summarization system in the multi-document setting. We design a paraphrastic sentence fusion model which jointly performs sentence fusion and paraphrasing using skip-gram word embedding model at the sentence level. Our model improves the information coverage and at the same time abstractiveness of the generated sentences. We conduct our experiments on the human-generated multi-sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Furthermore, we apply our sentence level model to implement an abstractive multi-document summarization system where documents usually contain a related set of sentences. We also propose an optimal solution for the classical summary length limit problem which was not addressed in the past research. For the document level summary, we conduct experiments on the datasets of two different domains (e.g., news article and user reviews) which are well suited for multi-document abstractive summarization. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods.",
}
@inproceedings{tran-nguyen-2018-adversarial,
    title = "Adversarial Domain Adaptation for Variational Neural Language Generation in Dialogue Systems",
    author = "Tran, Van-Khanh  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1103",
    pages = "1205--1217",
    abstract = "Domain Adaptation arises when we aim at learning from source domain a model that can perform acceptably well on a different target domain. It is especially crucial for Natural Language Generation (NLG) in Spoken Dialogue Systems when there are sufficient annotated data in the source domain, but there is a limited labeled data in the target domain. How to effectively utilize as much of existing abilities from source domains is a crucial issue in domain adaptation. In this paper, we propose an adversarial training procedure to train a Variational encoder-decoder based language generator via multiple adaptation steps. In this procedure, a model is first trained on a source domain data and then fine-tuned on a small set of target domain utterances under the guidance of two proposed critics. Experimental results show that the proposed method can effectively leverage the existing knowledge in the source domain to adapt to another related domain by using only a small amount of in-domain data.",
}
@inproceedings{shekhar-etal-2018-ask,
    title = "Ask No More: Deciding when to guess in referential visual dialogue",
    author = {Shekhar, Ravi  and
      Baumg{\"a}rtner, Tim  and
      Venkatesh, Aashish  and
      Bruni, Elia  and
      Bernardi, Raffaella  and
      Fernandez, Raquel},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1104",
    pages = "1218--1233",
    abstract = "Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. Our analyses show that adding a decision making component produces dialogues that are less repetitive and that include fewer unnecessary questions, thus potentially leading to more efficient and less unnatural interactions.",
}
@inproceedings{hou-etal-2018-sequence,
    title = "Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding",
    author = "Hou, Yutai  and
      Liu, Yijia  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1105",
    pages = "1234--1245",
    abstract = "In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance without considering its relation with other utterances, we propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance{'}s same semantic alternatives in the training data. A novel diversity rank is incorporated into the utterance representation to make the model produce diverse utterances and these diversely augmented utterances help to improve the language understanding module. Experimental results on the Airline Travel Information System dataset and a newly created semantic frame annotation on Stanford Multi-turn, Multi-domain Dialogue Dataset show that our framework achieves significant improvements of 6.38 and 10.04 F-scores respectively when only a training set of hundreds utterances is represented. Case studies also confirm that our method generates diverse utterances.",
}
@inproceedings{kumar-etal-2018-dialogue,
    title = "Dialogue-act-driven Conversation Model : An Experimental Study",
    author = "Kumar, Harshit  and
      Agarwal, Arvind  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1106",
    pages = "1246--1256",
    abstract = "The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts {--}when used along with context given in the form of dialogue history{--} improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8{\%} for the task of next utterance selection on a newly introduced Daily Dialogue dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialog act information.",
}
@inproceedings{chen-etal-2018-structured,
    title = "Structured Dialogue Policy with Graph Neural Networks",
    author = "Chen, Lu  and
      Tan, Bowen  and
      Long, Sishan  and
      Yu, Kai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1107",
    pages = "1257--1268",
    abstract = "Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark.",
}
@inproceedings{liang-etal-2018-jtav,
    title = "{JTAV}: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features",
    author = "Liang, Hongru  and
      Wang, Haozheng  and
      Wang, Jun  and
      You, Shaodi  and
      Sun, Zhe  and
      Wei, Jin-Mao  and
      Yang, Zhenglu",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1108",
    pages = "1269--1280",
    abstract = "Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrate our proposed model outperforms the state-of-the-art approaches by a large margin.",
}
@inproceedings{zou-etal-2018-memd,
    title = "{MEMD}: A Diversity-Promoting Learning Framework for Short-Text Conversation",
    author = "Zou, Meng  and
      Li, Xihan  and
      Liu, Haokun  and
      Deng, Zhihong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1109",
    pages = "1281--1291",
    abstract = "Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years. However, conventional neural encoder-decoder models tend to generate commonplace responses like {``}I don{'}t know{''} regardless of what the input is. In this paper, we analyze this problem from a new perspective: latent vectors. Based on it, we propose an easy-to-extend learning framework named MEMD (Multi-Encoder to Multi-Decoder), in which an auxiliary encoder and an auxiliary decoder are introduced to provide necessary training guidance without resorting to extra data or complicating network{'}s inner structure. Experimental results demonstrate that our method effectively improve the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies.",
}
@inproceedings{zhang-etal-2018-refining,
    title = "Refining Source Representations with Relation Networks for Neural Machine Translation",
    author = "Zhang, Wen  and
      Hu, Jiawei  and
      Feng, Yang  and
      Liu, Qun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1110",
    pages = "1292--1303",
    abstract = "Although neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is an inherent disadvantage of recurrent neural network structure, and disregarding relationship between source words during encoding step. Whereas in practice, the former information and relationship are often useful in current step. We target on solving these problems and thus introduce relation networks to learn better representations of the source. The relation networks are able to facilitate memorization capability of recurrent neural network via associating source words with each other, this would also help retain their relationships. Then the source representations and all the relations are fed into the attention component together while decoding, with the main encoder-decoder framework unchanged. Experiments on several datasets show that our method can improve the translation performance significantly over the conventional encoder-decoder model and even outperform the approach involving supervised syntactic knowledge.",
}
@inproceedings{chu-wang-2018-survey,
    title = "A Survey of Domain Adaptation for Neural Machine Translation",
    author = "Chu, Chenhui  and
      Wang, Rui",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1111",
    pages = "1304--1319",
    abstract = "Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.",
}
@inproceedings{tang-etal-2018-evaluation,
    title = "An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization",
    author = "Tang, Gongbo  and
      Cap, Fabienne  and
      Pettersson, Eva  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1112",
    pages = "1320--1331",
    abstract = "In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and different neural network architectures. Our results show that NMT models are much better than SMT models in terms of character error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical spelling normalization. Transformer models perform better only when provided with more training data. We also find that subword-level models with a small subword vocabulary are better than character-level models. In addition, we propose a hybrid method which further improves the performance of historical spelling normalization.",
}
@inproceedings{salameh-etal-2018-fine,
    title = "Fine-Grained {A}rabic Dialect Identification",
    author = "Salameh, Mohammad  and
      Bouamor, Houda  and
      Habash, Nizar",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1113",
    pages = "1332--1344",
    abstract = "Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic (6-way classification). This paper presents the first results on a fine-grained dialect classification task covering 25 specific cities from across the Arab World, in addition to Standard Arabic {--} a very challenging task. We build several classification systems and explore a large space of features. Our results show that we can identify the exact city of a speaker at an accuracy of 67.9{\%} for sentences with an average length of 7 words (a 9{\%} relative error reduction over the state-of-the-art technique for Arabic dialect identification) and reach more than 90{\%} when we consider 16 words. We also report on additional insights from a data analysis of similarity and difference across Arabic dialects.",
}
@inproceedings{kim-klinger-2018-feels,
    title = "Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions",
    author = "Kim, Evgeny  and
      Klinger, Roman",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1114",
    pages = "1345--1359",
    abstract = "Most approaches to emotion analysis in fictional texts focus on detecting the emotion expressed in text. We argue that this is a simplification which leads to an overgeneralized interpretation of the results, as it does not take into account who experiences an emotion and why. Emotions play a crucial role in the interaction between characters and the events they are involved in. Until today, no specific corpora that capture such an interaction were available for literature. We aim at filling this gap and present a publicly available corpus based on Project Gutenberg, REMAN (Relational EMotion ANnotation), manually annotated for spans which correspond to emotion trigger phrases and entities/events in the roles of experiencers, targets, and causes of the emotion. We provide baseline results for the automatic prediction of these relational structures and show that emotion lexicons are not able to encompass the high variability of emotion expressions and demonstrate that statistical models benefit from joint modeling of emotions with its roles in all subtasks. The corpus that we provide enables future research on the recognition of emotions and associated entities in text. It supports qualitative literary studies and digital humanities. The corpus is available at http://www.ims.uni-stuttgart.de/data/reman .",
}
@inproceedings{ribeiro-etal-2018-local,
    title = "Local String Transduction as Sequence Labeling",
    author = "Ribeiro, Joana  and
      Narayan, Shashi  and
      Cohen, Shay B.  and
      Carreras, Xavier",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1115",
    pages = "1360--1371",
    abstract = "We show that the general problem of string transduction can be reduced to the problem of sequence labeling. While character deletion and insertions are allowed in string transduction, they do not exist in sequence labeling. We show how to overcome this difference. Our approach can be used with any sequence labeling algorithm and it works best for problems in which string transduction imposes a strong notion of locality (no long range dependencies). We experiment with spelling correction for social media, OCR correction, and morphological inflection, and we see that it behaves better than seq2seq models and yields state-of-the-art results in several cases.",
}
@inproceedings{aghaebrahimian-2018-deep,
    title = "Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction",
    author = "Aghaebrahimian, Ahmad",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1116",
    pages = "1372--1383",
    abstract = "Wikipedia provides an invaluable source of parallel multilingual data, which are in high demand for various sorts of linguistic inquiry, including both theoretical and practical studies. We introduce a novel end-to-end neural model for large-scale parallel data harvesting from Wikipedia. Our model is language-independent, robust, and highly scalable. We use our system for collecting parallel German-English, French-English and Persian-English sentences. Human evaluations at the end show the strong performance of this model in collecting high-quality parallel data. We also propose a statistical framework which extends the results of our human evaluation to other language pairs. Our model also obtained a state-of-the-art result on the German-English dataset of BUCC 2017 shared task on parallel sentence extraction from comparable corpora.",
}
@inproceedings{kutuzov-etal-2018-diachronic,
    title = "Diachronic word embeddings and semantic shifts: a survey",
    author = "Kutuzov, Andrey  and
      {\O}vrelid, Lilja  and
      Szymanski, Terrence  and
      Velldal, Erik",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1117",
    pages = "1384--1397",
    abstract = "Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.",
}
@inproceedings{he-etal-2018-interaction,
    title = "Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention",
    author = "He, Ruifang  and
      Zhang, Xuefei  and
      Jin, Di  and
      Wang, Longbiao  and
      Dang, Jianwu  and
      Li, Xiangang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1118",
    pages = "1398--1409",
    abstract = "Traditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. They ignore that one discusses diverse topics when dynamically interacting with different people. Moreover, people who talk about the same topic have different effects on the topic. In this paper, we propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. A conversation network linking users based on reposting and replying relationship is constructed to mine the dynamic user behaviours. We model dynamic interactions and user attention so as to learn interaction-aware edge embeddings with social context. Then they are incorporated into neural variational inference for generating the more consistent topics. The experiments on three real-world datasets show that our proposed model is effective.",
}
@inproceedings{wang-etal-2018-cross,
    title = "Cross-media User Profiling with Joint Textual and Social User Embedding",
    author = "Wang, Jingjing  and
      Li, Shoushan  and
      Jiang, Mingqi  and
      Wu, Hanqian  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1119",
    pages = "1410--1420",
    abstract = "In realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross-media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross-media user-word network to capture the relationship among users through the textual information and a modified cross-media user-user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross-media user profiling tasks, i.e., cross-media gender classification and cross-media age regression.",
}
@inproceedings{zaremoodi-haffari-2018-incorporating,
    title = "Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model",
    author = "Zaremoodi, Poorya  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1120",
    pages = "1421--1429",
    abstract = "Incorporating syntactic information in Neural Machine Translation (NMT) can lead to better reorderings, particularly useful when the language pairs are syntactically highly divergent or when the training bitext is not large. Previous work on using syntactic information, provided by top-1 parse trees generated by (inevitably error-prone) parsers, has been promising. In this paper, we propose a forest-to-sequence NMT model to make use of exponentially many parse trees of the source sentence to compensate for the parser errors. Our method represents the collection of parse trees as a packed forest, and learns a neural transducer to translate from the input forest to the target sentence. Experiments on English to German, Chinese and Farsi translation tasks show the superiority of our approach over the sequence-to-sequence and tree-to-sequence neural translation models.",
}
@inproceedings{li-etal-2018-ensure,
    title = "Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization",
    author = "Li, Haoran  and
      Zhu, Junnan  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1121",
    pages = "1430--1441",
    abstract = "In this paper, we investigate the sentence summarization task that produces a summary from a source sentence. Neural sequence-to-sequence models have gained considerable success for this task, while most existing approaches only focus on improving the informativeness of the summary, which ignore the correctness, i.e., the summary should not contain unrelated information with respect to the source sentence. We argue that correctness is an essential requirement for summarization systems. Considering a correct summary is semantically entailed by the source sentence, we incorporate entailment knowledge into abstractive summarization models. We propose an entailment-aware encoder under multi-task framework (i.e., summarization generation and entailment recognition) and an entailment-aware decoder by entailment Reward Augmented Maximum Likelihood (RAML) training. Experiment results demonstrate that our models significantly outperform baselines from the aspects of informativeness and correctness.",
}
@inproceedings{gregoire-langlais-2018-extracting,
    title = "Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation",
    author = "Gr{\'e}goire, Francis  and
      Langlais, Philippe",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1122",
    pages = "1442--1453",
    abstract = "Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose a bidirectional recurrent neural network based approach to extract parallel sentences from collections of multilingual texts. Our experiments with noisy parallel corpora show that we can achieve promising results against a competitive baseline by removing the need of specific feature engineering or additional external resources. To justify the utility of our approach, we extract sentence pairs from Wikipedia articles to train machine translation systems and show significant improvements in translation performance.",
}
@inproceedings{zhang-etal-2018-fast,
    title = "Fast and Accurate Reordering with {ITG} Transition {RNN}",
    author = "Zhang, Hao  and
      Ng, Axel  and
      Sproat, Richard",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1123",
    pages = "1454--1463",
    abstract = "Attention-based sequence-to-sequence neural network models learn to jointly align and translate. The quadratic-time attention mechanism is powerful as it is capable of handling arbitrary long-distance reordering, but computationally expensive. In this paper, towards making neural translation both accurate and efficient, we follow the traditional pre-reordering approach to decouple reordering from translation. We add a reordering RNN that shares the input encoder with the decoder. The RNNs are trained jointly with a multi-task loss function and applied sequentially at inference time. The task of the reordering model is to predict the permutation of the input words following the target language word order. After reordering, the attention in the decoder becomes more peaked and monotonic. For reordering, we adopt the Inversion Transduction Grammars (ITG) and propose a transition system to parse input to trees for reordering. We harness the ITG transition system with RNN. With the modeling power of RNN, we achieve superior reordering accuracy without any feature engineering. In experiments, we apply the model to the task of text normalization. Compared to a strong baseline of attention-based RNN, our ITG RNN re-ordering model can reach the same reordering accuracy with only 1/10 of the training data and is 2.5x faster in decoding.",
}
@inproceedings{wang-etal-2018-neural,
    title = "Neural Machine Translation with Decoding History Enhanced Attention",
    author = "Wang, Mingxuan  and
      Xie, Jun  and
      Tan, Zhixing  and
      Su, Jinsong  and
      Xiong, Deyi  and
      Bian, Chao",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1124",
    pages = "1464--1473",
    abstract = "Neural machine translation with source-side attention have achieved remarkable performance. however, there has been little work exploring to attend to the target-side which can potentially enhance the memory capbility of NMT. We reformulate a Decoding History Enhanced Attention mechanism (DHEA) to render NMT model better at selecting both source-side and target-side information. DHA enables dynamic control of the ratios at which source and target contexts contribute to the generation of target words, offering a way to weakly induce structure relations among both source and target tokens. It also allows training errors to be directly back-propagated through short-cut connections and effectively alleviates the gradient vanishing problem. The empirical study on Chinese-English translation shows that our model with proper configuration can improve by 0:9 BLEU upon Transformer and the best reported results in the dataset. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.",
}
@inproceedings{granet-etal-2018-transfer,
    title = "Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources",
    author = "Granet, Adeline  and
      Morin, Emmanuel  and
      Mouch{\`e}re, Harold  and
      Quiniou, Solen  and
      Viard-Gaudin, Christian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1125",
    pages = "1474--1484",
    abstract = "Lack of data can be an issue when beginning a new study on historical handwritten documents. In order to deal with this, we present the character-based decoder part of a multilingual approach based on transductive transfer learning for a historical handwriting recognition task on Italian Comedy Registers. The decoder must build a sequence of characters that corresponds to a word from a vector of letter-ngrams. As learning data, we created a new dataset from untapped resources that covers the same domain and period of our Italian Comedy data, as well as resources from common domains, periods, or languages. We obtain a 97.42{\%} Character Recognition Rate and a 86.57{\%} Word Recognition Rate on our Italian Comedy data, despite a lexical coverage of 67{\%} between the Italian Comedy data and the training data. These results show that an efficient system can be obtained by a carefully selecting the datasets used for the transfer learning.",
}
@inproceedings{cohan-etal-2018-smhd,
    title = "{SMHD}: a Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions",
    author = "Cohan, Arman  and
      Desmet, Bart  and
      Yates, Andrew  and
      Soldaini, Luca  and
      MacAvaney, Sean  and
      Goharian, Nazli",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1126",
    pages = "1485--1497",
    abstract = "Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users{'} language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.",
}
@inproceedings{potthast-etal-2018-crowdsourcing,
    title = "Crowdsourcing a Large Corpus of Clickbait on {T}witter",
    author = "Potthast, Martin  and
      Gollub, Tim  and
      Komlossy, Kristof  and
      Schuster, Sebastian  and
      Wiegmann, Matti  and
      Garces Fernandez, Erika Patricia  and
      Hagen, Matthias  and
      Stein, Benno",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1127",
    pages = "1498--1507",
    abstract = "Clickbait has become a nuisance on social media. To address the urging task of clickbait detection, we constructed a new corpus of 38,517 annotated Twitter tweets, the Webis Clickbait Corpus 2017. To avoid biases in terms of publisher and topic, tweets were sampled from the top 27 most retweeted news publishers, covering a period of 150 days. Each tweet has been annotated on 4-point scale by five annotators recruited at Amazon{'}s Mechanical Turk. The corpus has been employed to evaluate 12 clickbait detectors submitted to the Clickbait Challenge 2017. Download: https://webis.de/data/webis-clickbait-17.html Challenge: https://clickbait-challenge.org",
}
@inproceedings{otani-etal-2018-cross,
    title = "Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion",
    author = "Otani, Naoki  and
      Kiyomaru, Hirokazu  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1128",
    pages = "1508--1520",
    abstract = "Considerable effort has been devoted to building commonsense knowledge bases. However, they are not available in many languages because the construction of KBs is expensive. To bridge the gap between languages, this paper addresses the problem of projecting the knowledge in English, a resource-rich language, into other languages, where the main challenge lies in projection ambiguity. This ambiguity is partially solved by machine translation and target-side knowledge base completion, but neither of them is adequately reliable by itself. We show their combination can project English commonsense knowledge into Japanese and Chinese with high precision. Our method also achieves a top-10 accuracy of 90{\%} on the crowdsourced English{--}Japanese benchmark. Furthermore, we use our method to obtain 18,747 facts of accurate Japanese commonsense within a very short period.",
}
@inproceedings{cuong-xu-2018-assessing,
    title = "Assessing Quality Estimation Models for Sentence-Level Prediction",
    author = "Cuong, Hoang  and
      Xu, Jia",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1129",
    pages = "1521--1533",
    abstract = "This paper provides an evaluation of a wide range of advanced sentence-level Quality Estimation models, including Support Vector Regression, Ride Regression, Neural Networks, Gaussian Processes, Bayesian Neural Networks, Deep Kernel Learning and Deep Gaussian Processes. Beside the accurateness, our main concerns are also the robustness of Quality Estimation models. Our work raises the difficulty in building strong models. Specifically, we show that Quality Estimation models often behave differently in Quality Estimation feature space, depending on whether the scale of feature space is small, medium or large. We also show that Quality Estimation models often behave differently in evaluation settings, depending on whether test data come from the same domain as the training data or not. Our work suggests several strong candidates to use in different circumstances.",
}
@inproceedings{preotiuc-pietro-ungar-2018-user,
    title = "User-Level Race and Ethnicity Predictors from {T}witter Text",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1130",
    pages = "1534--1545",
    abstract = "User demographic inference from social media text has the potential to improve a range of downstream applications, including real-time passive polling or quantifying demographic bias. This study focuses on developing models for user-level race and ethnicity prediction. We introduce a data set of users who self-report their race/ethnicity through a survey, in contrast to previous approaches that use distantly supervised data or perceived labels. We develop predictive models from text which accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC and make these available to the research community.",
}
@inproceedings{karimi-etal-2018-multi,
    title = "Multi-Source Multi-Class Fake News Detection",
    author = "Karimi, Hamid  and
      Roy, Proteek  and
      Saba-Sadiya, Sari  and
      Tang, Jiliang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1131",
    pages = "1546--1557",
    abstract = "Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years. Fake news is typically written intentionally to mislead readers, which determines that fake news detection merely based on news content is tremendously challenging. Meanwhile, fake news could contain true evidence to mock true news and presents different degrees of fakeness, which further exacerbates the detection difficulty. On the other hand, the spread of fake news produces various types of data from different perspectives. These multiple sources provide rich contextual information about fake news and offer unprecedented opportunities for advanced fake news detection. In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources. In particular, we introduce approaches to combine information from multiple sources and to discriminate between different degrees of fakeness, and propose a Multi-source Multi-class Fake news Detection framework MMFD, which combines automated feature extraction, multi-source fusion and automated degrees of fakeness detection into a coherent and interpretable model. Experimental results on the real-world data demonstrate the effectiveness of the proposed framework and extensive experiments are further conducted to understand the working of the proposed framework.",
}
@inproceedings{do-dinh-etal-2018-killing,
    title = "Killing Four Birds with Two Stones: Multi-Task Learning for Non-Literal Language Detection",
    author = "Do Dinh, Erik-L{\^a}n  and
      Eger, Steffen  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1132",
    pages = "1558--1569",
    abstract = "Non-literal language phenomena such as idioms or metaphors are commonly studied in isolation from each other in NLP. However, often similar definitions and features are being used for different phenomena, challenging the distinction. Instead, we propose to view the detection problem as a generalized non-literal language classification problem. In this paper we investigate multi-task learning for related non-literal language phenomena. We show that in contrast to simply joining the data of multiple tasks, multi-task learning consistently improves upon four metaphor and idiom detection tasks in two languages, English and German. Comparing two state-of-the-art multi-task learning architectures, we also investigate when soft parameter sharing and learned information flow can be beneficial for our related tasks. We make our adapted code publicly available.",
}
@inproceedings{choudhary-etal-2018-twitter,
    title = "{T}witter corpus of Resource-Scarce Languages for Sentiment Analysis and Multilingual Emoji Prediction",
    author = "Choudhary, Nurendra  and
      Singh, Rajat  and
      Anvesh Rao, Vijjini  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1133",
    pages = "1570--1577",
    abstract = "In this paper, we leverage social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users on social media platforms. We present an approach to extract social media microblogs such as tweets (Twitter). In this paper, we create corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations.",
}
@inproceedings{rama-wichmann-2018-towards,
    title = "Towards identifying the optimal datasize for lexically-based {B}ayesian inference of linguistic phylogenies",
    author = "Rama, Taraka  and
      Wichmann, S{\o}ren",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1134",
    pages = "1578--1590",
    abstract = "Bayesian linguistic phylogenies are standardly based on cognate matrices for words referring to a fix set of meanings{---}typically around 100-200. To this day there has not been any empirical investigation into which datasize is optimal. Here we determine, across a set of language families, the optimal number of meanings required for the best performance in Bayesian phylogenetic inference. We rank meanings by stability, infer phylogenetic trees using first the most stable meaning, then the two most stable meanings, and so on, computing the quartet distance of the resulting tree to the tree proposed by language family experts at each step of datasize increase. When a gold standard tree is not available we propose to instead compute the quartet distance between the tree based on the n-most stable meaning and the one based on the n + 1-most stable meanings, increasing n from 1 to N − 1, where N is the total number of meanings. The assumption here is that the value of n for which the quartet distance begins to stabilize is also the value at which the quality of the tree ceases to improve. We show that this assumption is borne out. The results of the two methods vary across families, and the optimal number of meanings appears to correlate with the number of languages under consideration.",
}
@inproceedings{del-tredici-fernandez-2018-road,
    title = "The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities",
    author = "Del Tredici, Marco  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1135",
    pages = "1591--1603",
    abstract = "We investigate the birth and diffusion of lexical innovations in a large dataset of online social communities. We build on sociolinguistic theories and focus on the relation between the spread of a novel term and the social role of the individuals who use it, uncovering characteristics of innovators and adopters. Finally, we perform a prediction task that allows us to anticipate whether an innovation will successfully spread within a community.",
}
@inproceedings{ciobanu-dinu-2018-ab,
    title = "Ab Initio: Automatic {L}atin Proto-word Reconstruction",
    author = "Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1136",
    pages = "1604--1614",
    abstract = "Proto-word reconstruction is central to the study of language evolution. It consists of recreating the words in an ancient language from its modern daughter languages. In this paper we investigate automatic word form reconstruction for Latin proto-words. Having modern word forms in multiple Romance languages (French, Italian, Spanish, Portuguese and Romanian), we infer the form of their common Latin ancestors. Our approach relies on the regularities that occurred when the Latin words entered the modern languages. We leverage information from all modern languages, building an ensemble system for proto-word reconstruction. We use conditional random fields for sequence labeling, but we conduct preliminary experiments with recurrent neural networks as well. We apply our method on multiple datasets, showing that our method improves on previous results, having also the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.",
}
@inproceedings{silfverberg-etal-2018-computational,
    title = "A Computational Model for the Linguistic Notion of Morphological Paradigm",
    author = "Silfverberg, Miikka  and
      Liu, Ling  and
      Hulden, Mans",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1137",
    pages = "1615--1626",
    abstract = "In supervised learning of morphological patterns, the strategy of generalizing inflectional tables into more abstract paradigms through alignment of the longest common subsequence found in an inflection table has been proposed as an efficient method to deduce the inflectional behavior of unseen word forms. In this paper, we extend this notion of morphological {`}paradigm{'} from earlier work and provide a formalization that more accurately matches linguist intuitions about what an inflectional paradigm is. Additionally, we propose and evaluate a mechanism for learning full human-readable paradigm specifications from incomplete data{---}a scenario when we only have access to a few inflected forms for each lexeme, and want to reconstruct the missing inflections as well as generalize and group the witnessed patterns into a model of more abstract paradigmatic behavior of lexemes.",
}
@inproceedings{bouraoui-etal-2018-relation,
    title = "Relation Induction in Word Embeddings Revisited",
    author = "Bouraoui, Zied  and
      Jameel, Shoaib  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1138",
    pages = "1627--1637",
    abstract = "Given a set of instances of some relation, the relation induction task is to predict which other word pairs are likely to be related in the same way. While it is natural to use word embeddings for this task, standard approaches based on vector translations turn out to perform poorly. To address this issue, we propose two probabilistic relation induction models. The first model is based on translations, but uses Gaussians to explicitly model the variability of these translations and to encode soft constraints on the source and target words that may be chosen. In the second model, we use Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words, which is considerably weaker than the assumption underlying translation based models.",
}
@inproceedings{akbik-etal-2018-contextual,
    title = "Contextual String Embeddings for Sequence Labeling",
    author = "Akbik, Alan  and
      Blythe, Duncan  and
      Vollgraf, Roland",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1139",
    pages = "1638--1649",
    abstract = "Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis of previous characters, such models have been shown to automatically internalize linguistic concepts such as words, sentences, subclauses and even sentiment. In this paper, we propose to leverage the internal states of a trained character language model to produce a novel type of word embedding which we refer to as contextual string embeddings. Our proposed embeddings have the distinct properties that they (a) are trained without any explicit notion of words and thus fundamentally model words as sequences of characters, and (b) are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use. We conduct a comparative evaluation against previous embeddings and find that our embeddings are highly useful for downstream tasks: across four classic sequence labeling tasks we consistently outperform the previous state-of-the-art. In particular, we significantly outperform previous work on English and German named entity recognition (NER), allowing us to report new state-of-the-art F1-scores on the CoNLL03 shared task. We release all code and pre-trained language models in a simple-to-use framework to the research community, to enable reproduction of these experiments and application of our proposed embeddings to other tasks: https://github.com/zalandoresearch/flair",
}
@inproceedings{bollegala-bao-2018-learning,
    title = "Learning Word Meta-Embeddings by Autoencoding",
    author = "Bollegala, Danushka  and
      Bao, Cong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1140",
    pages = "1650--1661",
    abstract = "Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those methods capture complementary aspects of lexical semantics. Therefore, we believe that it is important to combine the existing word embeddings to produce more accurate and complete meta-embeddings of words. We model the meta-embedding learning problem as an autoencoding problem, where we would like to learn a meta-embedding space that can accurately reconstruct all source embeddings simultaneously. Thereby, the meta-embedding space is enforced to capture complementary information in different source embeddings via a coherent common embedding space. We propose three flavours of autoencoded meta-embeddings motivated by different requirements that must be satisfied by a meta-embedding. Our experimental results on a series of benchmark evaluations show that the proposed autoencoded meta-embeddings outperform the existing state-of-the-art meta-embeddings in multiple tasks.",
}
@inproceedings{lee-etal-2018-gensense,
    title = "{G}en{S}ense: A Generalized Sense Retrofitting Model",
    author = "Lee, Yang-Yin  and
      Yen, Ting-Yu  and
      Huang, Hen-Hsen  and
      Shiue, Yow-Ting  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1141",
    pages = "1662--1671",
    abstract = "With the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To address this issue, some researches propose sense embedding learning algorithms. In this paper, we present a generalized model from existing sense retrofitting model. The generalization takes three major components: semantic relations between the senses, the relation strength and the semantic strength. In the experiment, we show that the generalized model can outperform previous approaches in three types of experiment: semantic relatedness, contextual word similarity and semantic difference.",
}
@inproceedings{bahuleyan-etal-2018-variational,
    title = "Variational Attention for Sequence-to-Sequence Models",
    author = "Bahuleyan, Hareesh  and
      Mou, Lili  and
      Vechtomova, Olga  and
      Poupart, Pascal",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1142",
    pages = "1672--1682",
    abstract = "The variational encoder-decoder (VED) encodes source information as a set of random variables using a neural network, which in turn is decoded into target data using another neural network. In natural language processing, sequence-to-sequence (Seq2Seq) models typically serve as encoder-decoder networks. When combined with a traditional (deterministic) attention mechanism, the variational latent space may be bypassed by the attention model, and thus becomes ineffective. In this paper, we propose a variational attention mechanism for VED, where the attention vector is also modeled as Gaussian distributed random variables. Results on two experiments show that, without loss of quality, our proposed method alleviates the bypassing phenomenon as it increases the diversity of generated sentences.",
}
@inproceedings{wang-etal-2018-new,
    title = "A New Concept of Deep Reinforcement Learning based Augmented General Tagging System",
    author = "Wang, Yu  and
      Patel, Abhishek  and
      Jin, Hongxia",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1143",
    pages = "1683--1693",
    abstract = "In this paper, a new deep reinforcement learning based augmented general tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence labeling model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance by modeling the data with minority tags. The new system is evaluated on SLU and NLU sequence labeling tasks using ATIS and CoNLL-2003 benchmark datasets, to demonstrate the new system{'}s outstanding performance on general tagging tasks. Evaluated by F1 scores, it shows that the new system outperforms the current state-of-the-art model on ATIS dataset by 1.9{\%} and that on CoNLL-2003 dataset by 1.4{\%}.",
}
@inproceedings{felt-etal-2018-learning,
    title = "Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types",
    author = "Felt, Paul  and
      Ringger, Eric  and
      Boyd-Graber, Jordan  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1144",
    pages = "1694--1704",
    abstract = "Annotated corpora enable supervised machine learning and data analysis. To reduce the cost of manual annotation, tasks are often assigned to internet workers whose judgments are reconciled by crowdsourcing models. We approach the problem of crowdsourcing using a framework for learning from rich prior knowledge, and we identify a family of crowdsourcing models with the novel ability to combine annotations with differing structures: e.g., document labels and word labels. Annotator judgments are given in the form of the predicted expected value of measurement functions computed over annotations and the data, unifying annotation models. Our model, a specific instance of this framework, compares favorably with previous work. Furthermore, it enables active sample selection, jointly selecting annotator, data item, and annotation structure to reduce annotation effort.",
}
@inproceedings{kabdolov-etal-2018-reproducing,
    title = "Reproducing and Regularizing the {SCRN} Model",
    author = "Kabdolov, Olzhas  and
      Assylbekov, Zhenisbek  and
      Takhanov, Rustem",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1145",
    pages = "1705--1716",
    abstract = "We reproduce the Structurally Constrained Recurrent Network (SCRN) model, and then regularize it using the existing widespread techniques, such as naive dropout, variational dropout, and weight tying. We show that when regularized and optimized appropriately the SCRN model can achieve performance comparable with the ubiquitous LSTM model in language modeling task on English data, while outperforming it on non-English data.",
}
@inproceedings{song-etal-2018-structure,
    title = "Structure-Infused Copy Mechanisms for Abstractive Summarization",
    author = "Song, Kaiqiang  and
      Zhao, Lin  and
      Liu, Fei",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1146",
    pages = "1717--1729",
    abstract = "Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.",
}
@inproceedings{van-miltenburg-etal-2018-measuring,
    title = "Measuring the Diversity of Automatic Image Descriptions",
    author = "van Miltenburg, Emiel  and
      Elliott, Desmond  and
      Vossen, Piek",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1147",
    pages = "1730--1741",
    abstract = "Automatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of generic descriptions as a lack of diversity in the output, which we quantify using established metrics and two new metrics that frame image description as a word recall task. This framing allows us to evaluate system performance on the head of the vocabulary, as well as on the long tail, where system performance degrades. We use these metrics to examine the diversity of the sentences generated by nine state-of-the-art systems on the MS COCO data set. We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives. However, the adversarially-trained models only produce more types from the head of the vocabulary and not the tail. Besides vocabulary-based methods, we also look at the compositional capacity of the systems, specifically their ability to create compound nouns and prepositional phrases of different lengths. We conclude that there is still much room for improvement, and offer a toolkit to measure progress towards the goal of generating more diverse image descriptions.",
}
@inproceedings{higurashi-etal-2018-extractive,
    title = "Extractive Headline Generation Based on Learning to Rank for Community Question Answering",
    author = "Higurashi, Tatsuru  and
      Kobayashi, Hayato  and
      Masuyama, Takeshi  and
      Murao, Kazuma",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1148",
    pages = "1742--1753",
    abstract = "User-generated content such as the questions on community question answering (CQA) forums does not always come with appropriate headlines, in contrast to the news articles used in various headline generation tasks. In such cases, we cannot use paired supervised data, e.g., pairs of articles and headlines, to learn a headline generation model. To overcome this problem, we propose an extractive headline generation method based on learning to rank for CQA that extracts the most informative substring from each question as its headline. Experimental results show that our method outperforms several baselines, including a prefix-based method, which is widely used in real services.",
}
@inproceedings{li-etal-2018-multi,
    title = "A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task",
    author = "Li, Qian  and
      Li, Ziwei  and
      Wei, Jin-Mao  and
      Gu, Yanhui  and
      Jatowt, Adam  and
      Yang, Zhenglu",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1149",
    pages = "1754--1762",
    abstract = "Enabling a mechanism to understand a temporal story and predict its ending is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). In this paper, we develop a multi-attention-based neural network (MANN) with well-designed optimizations, like Highway Network, and concatenated features with embedding representations into the hierarchical neural network model. Considering the particulars of the specific task, we thoughtfully extend MANN with external knowledge resources, exceeding state-of-the-art results obviously. Furthermore, we develop a thorough understanding of our model through a careful hand analysis on a subset of the stories. We identify what traits of MANN contribute to its outperformance and how external knowledge is obtained in such an ending prediction task.",
}
@inproceedings{fan-etal-2018-reinforcement,
    title = "A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators",
    author = "Fan, Zhihao  and
      Wei, Zhongyu  and
      Wang, Siyuan  and
      Liu, Yang  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1150",
    pages = "1763--1774",
    abstract = "Visual Question Generation (VQG) aims to ask natural questions about an image automatically. Existing research focus on training model to fit the annotated data set that makes it indifferent from other language generation tasks. We argue that natural questions need to have two specific attributes from the perspectives of content and linguistic respectively, namely, natural and human-written. Inspired by the setting of discriminator in adversarial learning, we propose two discriminators, one for each attribute, to enhance the training. We then use the reinforcement learning framework to incorporate scores from the two discriminators as the reward to guide the training of the question generator. Experimental results on a benchmark VQG dataset show the effectiveness and robustness of our model compared to some state-of-the-art models in terms of both automatic and human evaluation metrics.",
}
@inproceedings{brazinskas-etal-2018-embedding,
    title = "Embedding Words as Distributions with a {B}ayesian Skip-gram Model",
    author = "Bra{\v{z}}inskas, Arthur  and
      Havrylov, Serhii  and
      Titov, Ivan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1151",
    pages = "1775--1789",
    abstract = "We introduce a method for embedding words as probability densities in a low-dimensional space. Rather than assuming that a word embedding is fixed across the entire text collection, as in standard word embedding methods, in our Bayesian model we generate it from a word-specific prior density for each occurrence of a given word. Intuitively, for each word, the prior density encodes the distribution of its potential {`}meanings{'}. These prior densities are conceptually similar to Gaussian embeddings of {\.e}wcitevilnis2014word. Interestingly, unlike the Gaussian embeddings, we can also obtain context-specific densities: they encode uncertainty about the sense of a word given its context and correspond to the approximate posterior distributions within our model. The context-dependent densities have many potential applications: for example, we show that they can be directly used in the lexical substitution task. We describe an effective estimation method based on the variational autoencoding framework. We demonstrate the effectiveness of our embedding technique on a range of standard benchmarks.",
}
@inproceedings{ettinger-etal-2018-assessing,
    title = "Assessing Composition in Sentence Vector Representations",
    author = "Ettinger, Allyson  and
      Elgohary, Ahmed  and
      Phillips, Colin  and
      Resnik, Philip",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1152",
    pages = "1790--1801",
    abstract = "An important component of achieving language understanding is mastering the composition of sentence meaning, but an immediate challenge to solving this problem is the opacity of sentence vector representations produced by current neural sentence composition models. We present a method to address this challenge, developing tasks that directly target compositional meaning information in sentence vector representations with a high degree of precision and control. To enable the creation of these controlled tasks, we introduce a specialized sentence generation system that produces large, annotated sentence sets meeting specified syntactic, semantic and lexical constraints. We describe the details of the method and generation system, and then present results of experiments applying our method to probe for compositional information in embeddings from a number of existing sentence composition models. We find that the method is able to extract useful information about the differing capacities of these models, and we discuss the implications of our results with respect to these systems{'} capturing of sentence information. We make available for public use the datasets used for these experiments, as well as the generation system.",
}
@inproceedings{zhang-etal-2018-subword,
    title = "Subword-augmented Embedding for Cloze Reading Comprehension",
    author = "Zhang, Zhuosheng  and
      Huang, Yafang  and
      Zhao, Hai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1153",
    pages = "1802--1814",
    abstract = "Representation learning is the foundation of machine reading comprehension. In state-of-the-art models, deep learning methods broadly use word and character level representations. However, character is not naturally the minimal linguistic unit. In addition, with a simple concatenation of character and word embedding, previous models actually give suboptimal solution. In this paper, we propose to use subword rather than character for word embedding enhancement. We also empirically explore different augmentation strategies on subword-augmented embedding to enhance the cloze-style reading comprehension model (reader). In detail, we present a reader that uses subword-level representation to augment word embedding with a short list to handle rare words effectively. A thorough examination is conducted to evaluate the comprehensive performance and generalization ability of the proposed reader. Experimental results show that the proposed approach helps the reader significantly outperform the state-of-the-art baselines on various public datasets.",
}
@inproceedings{chen-etal-2018-enhancing,
    title = "Enhancing Sentence Embedding with Generalized Pooling",
    author = "Chen, Qian  and
      Ling, Zhen-Hua  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1154",
    pages = "1815--1826",
    abstract = "Pooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding. We propose vector-based multi-head attention that includes the widely used max pooling, mean pooling, and scalar self-attention as special cases. The model benefits from properly designed penalization terms to reduce redundancy in multi-head attention. We evaluate the proposed model on three different tasks: natural language inference (NLI), author profiling, and sentiment classification. The experiments show that the proposed model achieves significant improvement over strong sentence-encoding-based methods, resulting in state-of-the-art performances on four datasets. The proposed approach can be easily implemented for more problems than we discuss in this paper.",
}
@inproceedings{ponkiya-etal-2018-treat,
    title = "Treat us like the sequences we are: Prepositional Paraphrasing of Noun Compounds using {LSTM}",
    author = "Ponkiya, Girishkumar  and
      Patel, Kevin  and
      Bhattacharyya, Pushpak  and
      Palshikar, Girish",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1155",
    pages = "1827--1836",
    abstract = "Interpreting noun compounds is a challenging task. It involves uncovering the underlying predicate which is dropped in the formation of the compound. In most cases, this predicate is of the form VERB+PREP. It has been observed that uncovering the preposition is a significant step towards uncovering the predicate. In this paper, we attempt to paraphrase noun compounds using prepositions. We consider noun compounds and their corresponding prepositional paraphrases as parallelly aligned sequences of words. This enables us to adapt different architectures from cross-lingual embedding literature. We choose the architecture where we create representations of both noun compound (source sequence) and its corresponding prepositional paraphrase (target sequence), such that their sim- ilarity is high. We use LSTMs to learn these representations. We use these representations to decide the correct preposition. Our experiments show that this approach performs considerably well on different datasets of noun compounds that are manually annotated with prepositions.",
}
@inproceedings{hazarika-etal-2018-cascade,
    title = "{CASCADE}: Contextual Sarcasm Detection in Online Discussion Forums",
    author = "Hazarika, Devamanyu  and
      Poria, Soujanya  and
      Gorantla, Sruthi  and
      Cambria, Erik  and
      Zimmermann, Roger  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1156",
    pages = "1837--1848",
    abstract = "The literature in automated sarcasm detection has mainly focused on lexical-, syntactic- and semantic-level analysis of text. However, a sarcastic sentence can be expressed with contextual presumptions, background and commonsense knowledge. In this paper, we propose a ContextuAl SarCasm DEtector (CASCADE), which adopts a hybrid approach of both content- and context-driven modeling for sarcasm detection in online social media discussions. For the latter, CASCADE aims at extracting contextual information from the discourse of a discussion thread. Also, since the sarcastic nature and form of expression can vary from person to person, CASCADE utilizes user embeddings that encode stylometric and personality features of users. When used along with content-based feature extractors such as convolutional neural networks, we see a significant boost in the classification performance on a large Reddit corpus.",
}
@inproceedings{cattle-ma-2018-recognizing,
    title = "Recognizing Humour using Word Associations and Humour Anchor Extraction",
    author = "Cattle, Andrew  and
      Ma, Xiaojuan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1157",
    pages = "1849--1858",
    abstract = "This paper attempts to marry the interpretability of statistical machine learning approaches with the more robust models of joke structure and joke semantics capable of being learned by neural models. Specifically, we explore the use of semantic relatedness features based on word associations, rather than the more common Word2Vec similarity, on a binary humour identification task and identify several factors that make word associations a better fit for humour. We also explore the effects of using joke structure, in the form of humour anchors (Yang et al., 2015), for improving the performance of semantic features and show that, while an intriguing idea, humour anchors contain several pitfalls that can hurt performance.",
}
@inproceedings{hanselowski-etal-2018-retrospective,
    title = "A Retrospective Analysis of the Fake News Challenge Stance-Detection Task",
    author = "Hanselowski, Andreas  and
      PVS, Avinesh  and
      Schiller, Benjamin  and
      Caspelherr, Felix  and
      Chaudhuri, Debanjan  and
      Meyer, Christian M.  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1158",
    pages = "1859--1874",
    abstract = "The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1{'}s experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1{'}s proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods{'} ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research.",
}
@inproceedings{liu-etal-2018-exploiting,
    title = "Exploiting Syntactic Structures for Humor Recognition",
    author = "Liu, Lizhen  and
      Zhang, Donghai  and
      Song, Wei",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1159",
    pages = "1875--1883",
    abstract = "Humor recognition is an interesting and challenging task in natural language processing. This paper proposes to exploit syntactic structure features to enhance humor recognition. Our method achieves significant improvements compared with humor theory driven baselines. We found that some syntactic structure features consistently correlate with humor, which indicate interesting linguistic phenomena. Both the experimental results and the analysis demonstrate that humor can be viewed as a kind of style and content independent syntactic structures can help identify humor and have good interpretability.",
}
@inproceedings{you-etal-2018-attribute,
    title = "An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection",
    author = "You, Zhenni  and
      Qian, Tieyun  and
      Liu, Bing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1160",
    pages = "1884--1895",
    abstract = "Spam detection has long been a research topic in both academic and industry due to its wide applications. Previous studies are mainly focused on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. Such features are either ineffective or take long time to collect and thus are hard to be applied to cold-start spam review detection tasks. Recent advance leveraged the neural network to encode the textual and behavior features for the cold-start problem. However, the abundant attribute information are largely neglected by the existing framework. In this paper, we propose a novel deep learning architecture for incorporating entities and their inherent attributes from various domains into a unified framework. Specifically, our model not only encodes the entities of reviewer, item, and review, but also their attributes such as location, date, price ranges. Furthermore, we present a domain classifier to adapt the knowledge from one domain to the other. With the abundant attributes in existing entities and knowledge in other domains, we successfully solve the problem of data scarcity in the cold-start settings. Experimental results on two Yelp datasets prove that our proposed framework significantly outperforms the state-of-the-art methods.",
}
@inproceedings{ghaddar-langlais-2018-robust,
    title = "Robust Lexical Features for Improved Neural Network Named-Entity Recognition",
    author = "Ghaddar, Abbas  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1161",
    pages = "1896--1907",
    abstract = "Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception of gazetteers. In this work, we show that this is unfair: lexical features are actually quite useful. We propose to embed words and entity types into a low-dimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia. From this, we compute {---} offline {---} a feature vector representing each word. When used with a vanilla recurrent neural network model, this representation yields substantial improvements. We establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while matching state-of-the-art performance with a F1 score of 91.73 on the over-studied CONLL-2003 dataset.",
}
@inproceedings{li-yang-2018-pseudo,
    title = "A Pseudo Label based Dataless Naive {B}ayes Algorithm for Text Classification with Seed Words",
    author = "Li, Ximing  and
      Yang, Bo",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1162",
    pages = "1908--1917",
    abstract = "Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset.",
}
@inproceedings{shimizu-etal-2018-visual,
    title = "Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps",
    author = "Shimizu, Nobuyuki  and
      Rong, Na  and
      Miyazaki, Takashi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1163",
    pages = "1918--1928",
    abstract = "Visual question answering (VQA) is a challenging task that requires a computer system to understand both a question and an image. While there is much research on VQA in English, there is a lack of datasets for other languages, and English annotation is not directly applicable in those languages. To deal with this, we have created a Japanese VQA dataset by using crowdsourced annotation with images from the Visual Genome dataset. This is the first such dataset in Japanese. As another contribution, we propose a cross-lingual method for making use of English annotation to improve a Japanese VQA system. The proposed method is based on a popular VQA method that uses an attention mechanism. We use attention maps generated from English questions to help improve the Japanese VQA task. The proposed method experimentally performed better than simply using a monolingual corpus, which demonstrates the effectiveness of using attention maps to transfer cross-lingual information.",
}
@inproceedings{baumann-etal-2018-style,
    title = "Style Detection for Free Verse Poetry from Text and Speech",
    author = "Baumann, Timo  and
      Hussein, Hussein  and
      Meyer-Sickendiek, Burkhard",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1164",
    pages = "1929--1940",
    abstract = "Modern and post-modern free verse poems feature a large and complex variety in their poetic prosodies that falls along a continuum from a more fluent to a more disfluent and choppy style. As the poets of modernism overcame rhyme and meter, they oriented themselves in these two opposing directions, creating a free verse spectrum that calls for new analyses of prosodic forms. We present a method, grounded in philological analysis and current research on cognitive (dis)fluency, for automatically analyzing this spectrum. We define and relate six classes of poetic styles (ranging from parlando to lettristic decomposition) by their gradual differentiation. Based on this discussion, we present a model for automatic prosodic classification of spoken free verse poetry that uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We evaluate our model on a large corpus of German author-read post-modern poetry and find that classes can reliably be differentiated, reaching a weighted f-measure of 0.73, when combining textual and phonetic evidence. In our further analyses, we validate the model{'}s decision-making process, the philologically hypothesized continuum of fluency and investigate the relative importance of various features.",
}
@inproceedings{wang-etal-2018-neural-question,
    title = "A Neural Question Answering Model Based on Semi-Structured Tables",
    author = "Wang, Hao  and
      Zhang, Xiaodong  and
      Ma, Shuming  and
      Sun, Xu  and
      Wang, Houfeng  and
      Wang, Mengxiang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1165",
    pages = "1941--1951",
    abstract = "Most question answering (QA) systems are based on raw text and structured knowledge graph. However, raw text corpora are hard for QA system to understand, and structured knowledge graph needs intensive manual work, while it is relatively easy to obtain semi-structured tables from many sources directly, or build them automatically. In this paper, we build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. Our system answers queries by two steps. First, it finds the most similar tables. Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer. The system is evaluated with TabMCQ dataset, and gets a huge improvement compared to the state of the art.",
}
@inproceedings{liu-etal-2018-lcqmc,
    title = "{LCQMC}:A Large-scale {C}hinese Question Matching Corpus",
    author = "Liu, Xin  and
      Chen, Qingcai  and
      Deng, Chong  and
      Zeng, Huajun  and
      Chen, Jing  and
      Li, Dongfang  and
      Tang, Buzhou",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1166",
    pages = "1952--1962",
    abstract = "The lack of large-scale question matching corpora greatly limits the development of matching methods in question answering (QA) system, especially for non-English languages. To ameliorate this situation, in this paper, we introduce a large-scale Chinese question matching corpus (named LCQMC), which is released to the public1. LCQMC is more general than paraphrase corpus as it focuses on intent matching rather than paraphrase. How to collect a large number of question pairs in variant linguistic forms, which may present the same intent, is the key point for such corpus construction. In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs. After this process, a question matching corpus that contains 260,068 question pairs is constructed. In order to verify the LCQMC corpus, we split it into three parts, i.e., a training set containing 238,766 question pairs, a development set with 8,802 question pairs, and a test set with 12,500 question pairs, and test several well-known sentence matching methods on it. The experimental results not only demonstrate the good quality of LCQMC but also provide solid baseline performance for further researches on this corpus.",
}
@inproceedings{worsham-kalita-2018-genre,
    title = "Genre Identification and the Compositional Effect of Genre in Literature",
    author = "Worsham, Joseph  and
      Kalita, Jugal",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1167",
    pages = "1963--1973",
    abstract = "Recent advances in Natural Language Processing are finding ways to place an emphasis on the hierarchical nature of text instead of representing language as a flat sequence or unordered collection of words or letters. A human reader must capture multiple levels of abstraction and meaning in order to formulate an understanding of a document. In this paper, we address the problem of developing approaches which are capable of working with extremely large and complex literary documents to perform Genre Identification. The task is to assign the literary classification to a full-length book belonging to a corpus of literature, where the works on average are well over 200,000 words long and genre is an abstract thematic concept. We introduce the Gutenberg Dataset for Genre Identification. Additionally, we present a study on how current deep learning models compare to traditional methods for this task. The results are presented as a baseline along with findings on how using an ensemble of chapters can significantly improve results in deep learning methods. The motivation behind the ensemble of chapters method is discussed as the compositionality of subtexts which make up a larger work and contribute to the overall genre.",
}
@inproceedings{rodriguez-etal-2018-transfer,
    title = "Transfer Learning for Entity Recognition of Novel Classes",
    author = "Rodriguez, Juan Diego  and
      Caldwell, Adam  and
      Liu, Alexander",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1168",
    pages = "1974--1985",
    abstract = "In this reproduction paper, we replicate and extend several past studies on transfer learning for entity recognition. In particular, we are interested in entity recognition problems where the class labels in the source and target domains are different. Our work is the first direct comparison of these previously published approaches in this problem setting. In addition, we perform experiments on seven new source/target corpus pairs, nearly doubling the total number of corpus pairs that have been studied in all past work combined. Our results empirically demonstrate when each of the published approaches tends to do well. In particular, simpler approaches often work best when there is very little labeled target data, while neural transfer approaches tend to do better when there is more labeled target data.",
}
@inproceedings{al-olimat-etal-2018-location,
    title = "Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models",
    author = "Al-Olimat, Hussein  and
      Thirunarayan, Krishnaprasad  and
      Shalin, Valerie  and
      Sheth, Amit",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1169",
    pages = "1986--1997",
    abstract = "Extracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names. Variability, particularly systematic variability in location names (Carroll, 1983), challenges the identification task. Some of this variability can be anticipated as operations within a statistical language model, in this case drawn from gazetteers such as OpenStreetMap (OSM), Geonames, and DBpedia. This permits evaluation of an observed n-gram in Twitter targeted text as a legitimate location name variant from the same location-context. Using n-gram statistics and location-related dictionaries, our Location Name Extraction tool (LNEx) handles abbreviations and automatically filters and augments the location names in gazetteers (handling name contractions and auxiliary contents) to help detect the boundaries of multi-word location names and thereby delimit them in texts. We evaluated our approach on 4,500 event-specific tweets from three targeted streams to compare the performance of LNEx against that of ten state-of-the-art taggers that rely on standard semantic, syntactic and/or orthographic features. LNEx improved the average F-Score by 33-179{\%}, outperforming all taggers. Further, LNEx is capable of stream processing.",
}
@inproceedings{wang-etal-2018-apva,
    title = "The {APVA}-{TURBO} Approach To Question Answering in Knowledge Base",
    author = "Wang, Yue  and
      Zhang, Richong  and
      Xu, Cheng  and
      Mao, Yongyi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1170",
    pages = "1998--2009",
    abstract = "In this paper, we study the problem of question answering over knowledge base. We identify that the primary bottleneck in this problem is the difficulty in accurately predicting the relations connecting the subject entity to the object entities. We advocate a new model architecture, APVA, which includes a verification mechanism responsible for checking the correctness of predicted relations. The APVA framework naturally supports a well-principled iterative training procedure, which we call turbo training. We demonstrate via experiments that the APVA-TUBRO approach drastically improves the question answering performance.",
}
@inproceedings{zhou-etal-2018-interpretable,
    title = "An Interpretable Reasoning Network for Multi-Relation Question Answering",
    author = "Zhou, Mantong  and
      Huang, Minlie  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1171",
    pages = "2010--2022",
    abstract = "Multi-relation Question Answering is a challenging task, due to the requirement of elaborated analysis on questions and reasoning over multiple fact triples in knowledge base. In this paper, we present a novel model called Interpretable Reasoning Network that employs an interpretable, hop-by-hop reasoning process for question answering. The model dynamically decides which part of an input question should be analyzed at each hop; predicts a relation that corresponds to the current parsed results; utilizes the predicted relation to update the question representation and the state of the reasoning process; and then drives the next-hop reasoning. Experiments show that our model yields state-of-the-art results on two datasets. More interestingly, the model can offer traceable and observable intermediate predictions for reasoning analysis and failure diagnosis, thereby allowing manual manipulation in predicting the final answer.",
}
@inproceedings{liu-etal-2018-task,
    title = "Task-oriented Word Embedding for Text Classification",
    author = "Liu, Qian  and
      Huang, Heyan  and
      Gao, Yang  and
      Wei, Xiaochi  and
      Tian, Yuxin  and
      Liu, Luyang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1172",
    pages = "2023--2032",
    abstract = "Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods.",
}
@inproceedings{zhao-etal-2018-adaptive,
    title = "Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification",
    author = "Zhao, Jianyu  and
      Zhan, Zhiqiang  and
      Yang, Qichuan  and
      Zhang, Yang  and
      Hu, Changjian  and
      Li, Zhensheng  and
      Zhang, Liuxin  and
      He, Zhiqiang",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1173",
    pages = "2033--2043",
    abstract = "Representation learning is a key issue for most Natural Language Processing (NLP) tasks. Most existing representation models either learn little structure information or just rely on pre-defined structures, leading to degradation of performance and generalization capability. This paper focuses on learning both local semantic and global structure representations for text classification. In detail, we propose a novel Sandwich Neural Network (SNN) to learn semantic and structure representations automatically without relying on parsers. More importantly, semantic and structure information contribute unequally to the text representation at corpus and instance level. To solve the fusion problem, we propose two strategies: Adaptive Learning Sandwich Neural Network (AL-SNN) and Self-Attention Sandwich Neural Network (SA-SNN). The former learns the weights at corpus level, and the latter further combines attention mechanism to assign the weights at instance level. Experimental results demonstrate that our approach achieves competitive performance on several text classification tasks, including sentiment analysis, question type classification and subjectivity classification. Specifically, the accuracies are MR (82.1{\%}), SST-5 (50.4{\%}), TREC (96{\%}) and SUBJ (93.9{\%}).",
}
@inproceedings{fell-etal-2018-lyrics,
    title = "Lyrics Segmentation: Textual Macrostructure Detection using Convolutions",
    author = "Fell, Michael  and
      Nechaev, Yaroslav  and
      Cabrio, Elena  and
      Gandon, Fabien",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1174",
    pages = "2044--2054",
    abstract = "Lyrics contain repeated patterns that are correlated with the repetitions found in the music they accompany. Repetitions in song texts have been shown to enable lyrics segmentation {--} a fundamental prerequisite of automatically detecting the building blocks (e.g. chorus, verse) of a song text. In this article we improve on the state-of-the-art in lyrics segmentation by applying a convolutional neural network to the task, and experiment with novel features as a step towards deeper macrostructure detection of lyrics.",
}
@inproceedings{xiao-etal-2018-learning,
    title = "Learning What to Share: Leaky Multi-Task Network for Text Classification",
    author = "Xiao, Liqiang  and
      Zhang, Honglun  and
      Chen, Wenqing  and
      Wang, Yongkun  and
      Jin, Yaohui",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1175",
    pages = "2055--2065",
    abstract = "Neural network based multi-task learning has achieved great success on many NLP problems, which focuses on sharing knowledge among tasks by linking some layers to enhance the performance. However, most existing approaches suffer from the interference between tasks because they lack of selection mechanism for feature sharing. In this way, the feature spaces of tasks may be easily contaminated by helpless features borrowed from others, which will confuse the models for making correct prediction. In this paper, we propose a multi-task convolutional neural network with the Leaky Unit, which has memory and forgetting mechanism to filter the feature flows between tasks. Experiments on five different datasets for text classification validate the benefits of our approach.",
}
@inproceedings{levy-etal-2018-towards,
    title = "Towards an argumentative content search engine using weak supervision",
    author = "Levy, Ran  and
      Bogin, Ben  and
      Gretz, Shai  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1176",
    pages = "2066--2081",
    abstract = "Searching for sentences containing claims in a large text corpus is a key component in developing an argumentative content search engine. Previous works focused on detecting claims in a small set of documents or within documents enriched with argumentative content. However, pinpointing relevant claims in massive unstructured corpora, received little attention. A step in this direction was taken in (Levy et al. 2017), where the authors suggested using a weak signal to develop a relatively strict query for claim{--}sentence detection. Here, we leverage this work to define weak signals for training DNNs to obtain significantly greater performance. This approach allows to relax the query and increase the potential coverage. Our results clearly indicate that the system is able to successfully generalize from the weak signal, outperforming previously reported results in terms of both precision and coverage. Finally, we adapt our system to solve a recent argument mining task of identifying argumentative sentences in Web texts retrieved from heterogeneous sources, and obtain F1 scores comparable to the supervised baseline.",
}
@inproceedings{gungor-etal-2018-improving,
    title = "Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags",
    author = {G{\"u}ng{\"o}r, Onur  and
      Uskudarli, Suzan  and
      G{\"u}ng{\"o}r, Tunga},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1177",
    pages = "2082--2092",
    abstract = "Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator{'}s performance is shown to be competitive.",
}
@inproceedings{azmy-etal-2018-farewell,
    title = "Farewell {F}reebase: Migrating the {S}imple{Q}uestions Dataset to {DB}pedia",
    author = "Azmy, Michael  and
      Shi, Peng  and
      Lin, Jimmy  and
      Ilyas, Ihab",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1178",
    pages = "2093--2103",
    abstract = "Question answering over knowledge graphs is an important problem of interest both commercially and academically. There is substantial interest in the class of natural language questions that can be answered via the lookup of a single fact, driven by the availability of the popular SimpleQuestions dataset. The problem with this dataset, however, is that answer triples are provided from Freebase, which has been defunct for several years. As a result, it is difficult to build {``}real-world{''} question answering systems that are operationally deployable. Furthermore, a defunct knowledge graph means that much of the infrastructure for querying, browsing, and manipulating triples no longer exists. To address this problem, we present SimpleDBpediaQA, a new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia. Although this mapping is conceptually straightforward, there are a number of nuances that make the task non-trivial, owing to the different conceptual organizations of the two knowledge graphs. To lay the foundation for future research using this dataset, we leverage recent work to provide simple yet strong baselines with and without neural networks.",
}
@inproceedings{bostan-klinger-2018-analysis,
    title = "An Analysis of Annotated Corpora for Emotion Classification in Text",
    author = "Bostan, Laura-Ana-Maria  and
      Klinger, Roman",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1179",
    pages = "2104--2119",
    abstract = "Several datasets have been annotated and published for classification of emotions. They differ in several ways: (1) the use of different annotation schemata (e. g., discrete label sets, including joy, anger, fear, or sadness or continuous values including valence, or arousal), (2) the domain, and, (3) the file formats. This leads to several research gaps: supervised models often only use a limited set of available resources. Additionally, no previous work has compared emotion corpora in a systematic manner. We aim at contributing to this situation with a survey of the datasets, and aggregate them in a common file format with a common annotation schema. Based on this aggregation, we perform the first cross-corpus classification experiments in the spirit of future research enabled by this paper, in order to gain insight and a better understanding of differences of models inferred from the data. This work also simplifies the choice of the most appropriate resources for developing a model for a novel domain. One result from our analysis is that a subset of corpora is better classified with models trained on a different corpus. For none of the corpora, training on all data altogether is better than using a subselection of the resources. Our unified corpus is available at http://www.ims.uni-stuttgart.de/data/unifyemotion.",
}
@inproceedings{sachan-etal-2018-investigating,
    title = "Investigating the Working of Text Classifiers",
    author = "Sachan, Devendra  and
      Zaheer, Manzil  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1180",
    pages = "2120--2131",
    abstract = "Text classification is one of the most widely studied tasks in natural language processing. Motivated by the principle of compositionality, large multilayer neural network models have been employed for this task in an attempt to effectively utilize the constituent expressions. Almost all of the reported work train large networks using discriminative approaches, which come with a caveat of no proper capacity control, as they tend to latch on to any signal that may not generalize. Using various recent state-of-the-art approaches for text classification, we explore whether these models actually learn to compose the meaning of the sentences or still just focus on some keywords or lexicons for classifying the document. To test our hypothesis, we carefully construct datasets where the training and test splits have no direct overlap of such lexicons, but overall language structure would be similar. We study various text classifiers and observe that there is a big performance drop on these datasets. Finally, we show that even simple models with our proposed regularization techniques, which disincentivize focusing on key lexicons, can substantially improve classification accuracy.",
}
@inproceedings{lai-etal-2018-review,
    title = "A Review on Deep Learning Techniques Applied to Answer Selection",
    author = "Lai, Tuan Manh  and
      Bui, Trung  and
      Li, Sheng",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1181",
    pages = "2132--2144",
    abstract = "Given a question and a set of candidate answers, answer selection is the task of identifying which of the candidates answers the question correctly. It is an important problem in natural language processing, with applications in many areas. Recently, many deep learning based methods have been proposed for the task. They produce impressive performance without relying on any feature engineering or expensive external resources. In this paper, we aim to provide a comprehensive review on deep learning methods applied to answer selection.",
}
@inproceedings{yadav-bethard-2018-survey,
    title = "A Survey on Recent Advances in Named Entity Recognition from Deep Learning models",
    author = "Yadav, Vikas  and
      Bethard, Steven",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1182",
    pages = "2145--2158",
    abstract = "Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.",
}
@inproceedings{yang-etal-2018-distantly,
    title = "Distantly Supervised {NER} with Partial Annotation Learning and Reinforcement Learning",
    author = "Yang, Yaosheng  and
      Chen, Wenliang  and
      Li, Zhenghua  and
      He, Zhengqiu  and
      Zhang, Min",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1183",
    pages = "2159--2169",
    abstract = "A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data. One solution is to utilize the method of distant supervision, which has been widely used in relation extraction, to automatically populate annotated training data without humancost. The distant supervision assumption here is that if a string in text is included in a predefined dictionary of entities, the string might be an entity. However, this kind of auto-generated data suffers from two main problems: incomplete and noisy annotations, which affect the performance of NER models. In this paper, we propose a novel approach which can partially solve the above problems of distant supervision for NER. In our approach, to handle the incomplete problem, we apply partial annotation learning to reduce the effect of unknown labels of characters. As for noisy annotation, we design an instance selector based on reinforcement learning to distinguish positive sentences from auto-generated annotations. In experiments, we create two datasets for Chinese named entity recognition in two domains with the help of distant supervision. The experimental results show that the proposed approach obtains better performance than the comparison systems on both two datasets.",
}
@inproceedings{shahbazi-etal-2018-joint,
    title = "Joint Neural Entity Disambiguation with Output Space Search",
    author = "Shahbazi, Hamed  and
      Fern, Xiaoli  and
      Ghaeini, Reza  and
      Ma, Chao  and
      Obeidat, Rasha Mohammad  and
      Tadepalli, Prasad",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1184",
    pages = "2170--2180",
    abstract = "In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Given an input document, we start from a complete solution constructed by a local model and conduct a search in the space of possible corrections to improve the local solution from a global view point. Our search utilizes a heuristic function to focus more on the least confident local decisions and a pruning function to score the global solutions based on their local fitness and the global coherences among the predicted entities. Experimental results on CoNLL 2003 and TAC 2010 benchmarks verify the effectiveness of our model.",
}
@inproceedings{chen-moschitti-2018-learning,
    title = "Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models",
    author = "Chen, Lingzhen  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1185",
    pages = "2181--2191",
    abstract = "In this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting {--} a Transfer Learning (TL) setting. We train an initial model on source data and transfer it to a model that can recognize new NE categories in the target data during a subsequent step, when the source data is no longer available. Our solution consists in: (i) to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs; (ii) to leave the rest of the architecture unchanged, such that it is initialized with parameters transferred from the initial model; and (iii) to fine tune the network on the target data. Most importantly, we design a new NER approach based on sequence to sequence (Seq2Seq) models, which can intuitively work better in our progressive setting. We compare our approach with a Bidirectional LSTM, which is a strong neural NER model. Our experiments show that the Seq2Seq model performs very well on the standard NER setting and it is more robust in the progressive setting. Our approach can recognize previously unseen NE categories while preserving the knowledge of the seen data.",
}
@inproceedings{yu-etal-2018-responding,
    title = "Responding {E}-commerce Product Questions via Exploiting {QA} Collections and Reviews",
    author = "Yu, Qian  and
      Lam, Wai  and
      Wang, Zihao",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1186",
    pages = "2192--2203",
    abstract = "Providing instant responses for product questions in E-commerce sites can significantly improve satisfaction of potential consumers. We propose a new framework for automatically responding product questions newly posed by users via exploiting existing QA collections and review collections in a coordinated manner. Our framework can return a ranked list of snippets serving as the automated response for a given question, where each snippet can be a sentence from reviews or an existing question-answer pair. One major subtask in our framework is question-based response review ranking. Learning for response review ranking is challenging since there is no labeled response review available. The collection of existing QA pairs are exploited as distant supervision for learning to rank responses. With proposed distant supervision paradigm, the learned response ranking model makes use of the knowledge in the QA pairs and the corresponding retrieved review lists. Extensive experiments on datasets collected from a real-world commercial E-commerce site demonstrate the effectiveness of our proposed framework.",
}
@inproceedings{khosla-etal-2018-aff2vec,
    title = "{A}ff2{V}ec: Affect{--}Enriched Distributional Word Representations",
    author = "Khosla, Sopan  and
      Chhaya, Niyati  and
      Chawla, Kushal",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1187",
    pages = "2204--2218",
    abstract = "Human communication includes information, opinions and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and to some extent affective content generation, the area of affective word distributions is not well studied. Synsets and lexica capture semantic relationships across words. These models, however, lack in encoding affective or emotional word interpretations. Our proposed model, Aff2Vec, provides a method for enriched word embeddings that are representative of affective interpretations of words. Aff2Vec outperforms the state-of-the-art in intrinsic word-similarity tasks. Further, the use of Aff2Vec representations outperforms baseline embeddings in downstream natural language understanding tasks including sentiment analysis, personality detection, and frustration prediction.",
}
@inproceedings{kunneman-etal-2018-aspect,
    title = "Aspect-based summarization of pros and cons in unstructured product reviews",
    author = "Kunneman, Florian  and
      Wubben, Sander  and
      van den Bosch, Antal  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1188",
    pages = "2219--2229",
    abstract = "We developed three systems for generating pros and cons summaries of product reviews. Automating this task eases the writing of product reviews, and offers readers quick access to the most important information. We compared SynPat, a system based on syntactic phrases selected on the basis of valence scores, against a neural-network-based system trained to map bag-of-words representations of reviews directly to pros and cons, and the same neural system trained on clusters of word-embedding encodings of similar pros and cons. We evaluated the systems in two ways: first on held-out reviews with gold-standard pros and cons, and second by asking human annotators to rate the systems{'} output on relevance and completeness. In the second evaluation, the gold-standard pros and cons were assessed along with the system output. We find that the human-generated summaries are not deemed as significantly more relevant or complete than the SynPat systems; the latter are scored higher than the human-generated summaries on a precision metric. The neural approaches yield a lower performance in the human assessment, and are outperformed by the baseline.",
}
@inproceedings{toledo-ronen-etal-2018-learning,
    title = "Learning Sentiment Composition from Sentiment Lexicons",
    author = "Toledo-Ronen, Orith  and
      Bar-Haim, Roy  and
      Halfon, Alon  and
      Jochim, Charles  and
      Menczel, Amir  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1189",
    pages = "2230--2241",
    abstract = "Sentiment composition is a fundamental sentiment analysis problem. Previous work relied on manual rules and manually-created lexical resources such as negator lists, or learned a composition function from sentiment-annotated phrases or sentences. We propose a new approach for learning sentiment composition from a large, unlabeled corpus, which only requires a word-level sentiment lexicon for supervision. We automatically generate large sentiment lexicons of bigrams and unigrams, from which we induce a set of lexicons for a variety of sentiment composition processes. The effectiveness of our approach is confirmed through manual annotation, as well as sentiment classification experiments with both phrase-level and sentence-level benchmarks.",
}
@inproceedings{amram-etal-2018-representations,
    title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew",
    author = "Amram, Adam  and
      Ben David, Anat  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1190",
    pages = "2242--2252",
    abstract = "This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.",
}
@inproceedings{van-son-etal-2018-scoring,
    title = "Scoring and Classifying Implicit Positive Interpretations: A Challenge of Class Imbalance",
    author = "van Son, Chantal  and
      Morante, Roser  and
      Aroyo, Lora  and
      Vossen, Piek",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1191",
    pages = "2253--2264",
    abstract = "This paper reports on a reimplementation of a system on detecting implicit positive meaning from negated statements. In the original regression experiment, different positive interpretations per negation are scored according to their likelihood. We convert the scores to classes and report our results on both the regression and classification tasks. We show that a baseline taking the mean score or most frequent class is hard to beat because of class imbalance in the dataset. Our error analysis indicates that an approach that takes the information structure into account (i.e. which information is new or contrastive) may be promising, which requires looking beyond the syntactic and semantic characteristics of negated statements.",
}
@inproceedings{fan-etal-2018-exploratory,
    title = "Exploratory Neural Relation Classification for Domain Knowledge Acquisition",
    author = "Fan, Yan  and
      Wang, Chengyu  and
      He, Xiaofeng",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1192",
    pages = "2265--2276",
    abstract = "The state-of-the-art methods for relation classification are primarily based on deep neural net- works. This kind of supervised learning method suffers from not only limited training data, but also the large number of low-frequency relations in specific domains. In this paper, we propose the task of exploratory relation classification for domain knowledge harvesting. The goal is to learn a classifier on pre-defined relations and discover new relations expressed in texts. A dynamically structured neural network is introduced to classify entity pairs to a continuously expanded relation set. We further propose the similarity sensitive Chinese restaurant process to discover new relations. Experiments conducted on a large corpus show the effectiveness of our neural network, while new relations are discovered with high precision and recall.",
}
@inproceedings{nguyen-nguyen-2018-killed,
    title = "Who is Killed by Police: Introducing Supervised Attention for Hierarchical {LSTM}s",
    author = "Nguyen, Minh  and
      Nguyen, Thien Huu",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1193",
    pages = "2277--2287",
    abstract = "Finding names of people killed by police has become increasingly important as police shootings get more and more public attention (police killing detection). Unfortunately, there has been not much work in the literature addressing this problem. The early work in this field (Keith etal., 2017) proposed a distant supervision framework based on Expectation Maximization (EM) to deal with the multiple appearances of the names in documents. However, such EM-based framework cannot take full advantages of deep learning models, necessitating the use of handdesigned features to improve the detection performance. In this work, we present a novel deep learning method to solve the problem of police killing recognition. The proposed method relies on hierarchical LSTMs to model the multiple sentences that contain the person names of interests, and introduce supervised attention mechanisms based on semantical word lists and dependency trees to upweight the important contextual words. Our experiments demonstrate the benefits of the proposed model and yield the state-of-the-art performance for police killing detection.",
}
@inproceedings{saha-mausam-2018-open,
    title = "Open Information Extraction from Conjunctive Sentences",
    author = "Saha, Swarnadeep  and
      {Mausam}",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1194",
    pages = "2288--2299",
    abstract = "We develop CALM, a coordination analyzer that improves upon the conjuncts identified from dependency parses. It uses a language model based scoring and several linguistic constraints to search over hierarchical conjunct boundaries (for nested coordination). By splitting a conjunctive sentence around these conjuncts, CALM outputs several simple sentences. We demonstrate the value of our coordination analyzer in the end task of Open Information Extraction (Open IE). State-of-the-art Open IE systems lose substantial yield due to ineffective processing of conjunctive sentences. Our Open IE system, CALMIE, performs extraction over the simple sentences identified by CALM to obtain up to 1.8x yield with a moderate increase in precision compared to extractions from original sentences.",
}
@inproceedings{cetto-etal-2018-graphene,
    title = "{G}raphene: Semantically-Linked Propositions in Open Information Extraction",
    author = "Cetto, Matthias  and
      Niklaus, Christina  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1195",
    pages = "2300--2311",
    abstract = "We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.",
}
@inproceedings{nagesh-surdeanu-2018-exploration,
    title = "An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification",
    author = "Nagesh, Ajay  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1196",
    pages = "2312--2324",
    abstract = "Several semi-supervised representation learning methods have been proposed recently that mitigate the drawbacks of traditional bootstrapping: they reduce the amount of semantic drift introduced by iterative approaches through one-shot learning; others address the sparsity of data through the learning of custom, dense representation for the information modeled. In this work, we are the first to adapt three of these methods, most of which have been originally proposed for image processing, to an information extraction task, specifically, named entity classification. Further, we perform a rigorous comparative analysis on two distinct datasets. Our analysis yields several important observations. First, all representation learning methods outperform state-of-the-art semi-supervised methods that do not rely on representation learning. To the best of our knowledge, we report the latest state-of-the-art results on the semi-supervised named entity classification task. Second, one-shot learning methods clearly outperform iterative representation learning approaches. Lastly, one of the best performers relies on the mean teacher framework (Tarvainen and Valpola, 2017), a simple teacher/student approach that is independent of the underlying task-specific model.",
}
@inproceedings{beinborn-etal-2018-multimodal,
    title = "Multimodal Grounding for Language Processing",
    author = "Beinborn, Lisa  and
      Botschen, Teresa  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1197",
    pages = "2325--2339",
    abstract = "This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.",
}
@inproceedings{naik-etal-2018-stress,
    title = "Stress Test Evaluation for Natural Language Inference",
    author = "Naik, Aakanksha  and
      Ravichander, Abhilasha  and
      Sadeh, Norman  and
      Rose, Carolyn  and
      Neubig, Graham",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1198",
    pages = "2340--2353",
    abstract = "Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a benchmark task for natural language understanding. Existing models perform well at standard datasets for NLI, achieving impressive results across different genres of text. However, the extent to which these models understand the semantic content of sentences is unclear. In this work, we propose an evaluation methodology consisting of automatically constructed {``}stress tests{''} that allow us to examine whether systems have the ability to make real inferential decisions. Our evaluation of six sentence-encoder models on these stress tests reveals strengths and weaknesses of these models with respect to challenging linguistic phenomena, and suggests important directions for future work in this area.",
}
@inproceedings{vu-etal-2018-grounded,
    title = "Grounded Textual Entailment",
    author = "Vu, Hoa Trong  and
      Greco, Claudio  and
      Erofeeva, Aliia  and
      Jafaritazehjan, Somayeh  and
      Linders, Guido  and
      Tanti, Marc  and
      Testoni, Alberto  and
      Bernardi, Raffaella  and
      Gatt, Albert",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1199",
    pages = "2354--2368",
    abstract = "Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise P entails a hypothesis H iff in all worlds where P is true, H is also true. Statistical models view this relationship probabilistically, addressing it in terms of whether a human would likely infer H from P. In this paper, we wish to bridge these two perspectives, by arguing for a visually-grounded version of the Textual Entailment task. Specifically, we ask whether models can perform better if, in addition to P and H, there is also an image (corresponding to the relevant {``}world{''} or {``}situation{''}). We use a multimodal version of the SNLI dataset (Bowman et al., 2015) and we compare {``}blind{''} and visually-augmented models of textual entailment. We show that visual information is beneficial, but we also conduct an in-depth error analysis that reveals that current multimodal models are not performing {``}grounding{''} in an optimal fashion.",
}
@inproceedings{yin-etal-2018-recurrent,
    title = "Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs",
    author = {Yin, Wenpeng  and
      Yaghoobzadeh, Yadollah  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1200",
    pages = "2369--2378",
    abstract = "Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two entities connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answering.",
}
@inproceedings{gu-etal-2018-hybrid,
    title = "Hybrid Attention based Multimodal Network for Spoken Language Classification",
    author = "Gu, Yue  and
      Yang, Kangning  and
      Fu, Shiyu  and
      Chen, Shuhong  and
      Li, Xinyu  and
      Marsic, Ivan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1201",
    pages = "2379--2390",
    abstract = "We examine the utility of linguistic content and vocal characteristics for multimodal deep learning in human spoken language understanding. We present a deep multimodal network with both feature attention and modality attention to classify utterance-level speech data. The proposed hybrid attention architecture helps the system focus on learning informative representations for both modality-specific feature extraction and model fusion. The experimental results show that our system achieves state-of-the-art or competitive results on three published multimodal datasets. We also demonstrated the effectiveness and generalization of our system on a medical speech dataset from an actual trauma scenario. Furthermore, we provided a detailed comparison and analysis of traditional approaches and deep learning methods on both feature extraction and fusion.",
}
@inproceedings{nagata-etal-2018-exploring,
    title = "Exploring the Influence of Spelling Errors on Lexical Variation Measures",
    author = "Nagata, Ryo  and
      Sato, Taisei  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1202",
    pages = "2391--2398",
    abstract = "This paper explores the influence of spelling errors on lexical variation measures. Lexical richness measures such as Type-Token Ration (TTR) and Yule{'}s K are often used for learner English analysis and assessment. When applied to learner English, however, they can be unreliable because of the spelling errors appearing in it. Namely, they are, directly or indirectly, based on the counts of distinct word types, and spelling errors undesirably increase the number of distinct words. This paper introduces and examines the hypothesis that lexical richness measures become unstable in learner English because of spelling errors. Specifically, it tests the hypothesis on English learner corpora of three groups (middle school, high school, and college students). To be precise, it estimates the difference in TTR and Yule{'}s K caused by spelling errors, by calculating their values before and after spelling errors are manually corrected. Furthermore, it examines the results theoretically and empirically to deepen the understanding of the influence of spelling errors on them.",
}
@inproceedings{sun-etal-2018-stance,
    title = "Stance Detection with Hierarchical Attention Network",
    author = "Sun, Qingying  and
      Wang, Zhongqing  and
      Zhu, Qiaoming  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1203",
    pages = "2399--2409",
    abstract = "Stance detection aims to assign a stance label (for or against) to a post toward a specific target. Recently, there is a growing interest in using neural models to detect stance of documents. Most of these works model the sequence of words to learn document representation. However, much linguistic information, such as polarity and arguments of the document, is correlated with the stance of the document, and can inspire us to explore the stance. Hence, we present a neural model to fully employ various linguistic information to construct the document representation. In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information. The experimental results on two datasets demonstrate the effectiveness of the proposed hierarchical attention neural model.",
}
@inproceedings{shiue-etal-2018-correcting,
    title = "Correcting {C}hinese Word Usage Errors for Learning {C}hinese as a Second Language",
    author = "Shiue, Yow-Ting  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1204",
    pages = "2410--2422",
    abstract = "With more and more people around the world learning Chinese as a second language, the need of Chinese error correction tools is increasing. In the HSK dynamic composition corpus, word usage error (WUE) is the most common error type. In this paper, we build a neural network model that considers both target erroneous token and context to generate a correction vector and compare it against a candidate vocabulary to propose suitable corrections. To deal with potential alternative corrections, the top five proposed candidates are judged by native Chinese speakers. For more than 91{\%} of the cases, our system can propose at least one acceptable correction within a list of five candidates. To the best of our knowledge, this is the first research addressing general-type Chinese WUE correction. Our system can help non-native Chinese learners revise their sentences by themselves.",
}
@inproceedings{lengerich-etal-2018-retrofitting,
    title = "Retrofitting Distributional Embeddings to Knowledge Graphs with Functional Relations",
    author = "Lengerich, Ben  and
      Maas, Andrew  and
      Potts, Christopher",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1205",
    pages = "2423--2436",
    abstract = "Knowledge graphs are a versatile framework to encode richly structured data relationships, but it can be challenging to combine these graphs with unstructured data. Methods for retrofitting pre-trained entity representations to the structure of a knowledge graph typically assume that entities are embedded in a connected space and that relations imply similarity. However, useful knowledge graphs often contain diverse entities and relations (with potentially disjoint underlying corpora) which do not accord with these assumptions. To overcome these limitations, we present Functional Retrofitting, a framework that generalizes current retrofitting methods by explicitly modeling pairwise relations. Our framework can directly incorporate a variety of pairwise penalty functions previously developed for knowledge graph completion. Further, it allows users to encode, learn, and extract information about relation semantics. We present both linear and neural instantiations of the framework. Functional Retrofitting significantly outperforms existing retrofitting methods on complex knowledge graphs and loses no accuracy on simpler graphs (in which relations do imply similarity). Finally, we demonstrate the utility of the framework by predicting new drug{--}disease treatment pairs in a large, complex health knowledge graph.",
}
@inproceedings{zhang-etal-2018-context,
    title = "Context-Sensitive Generation of Open-Domain Conversational Responses",
    author = "Zhang, Weinan  and
      Cui, Yiming  and
      Wang, Yifa  and
      Zhu, Qingfu  and
      Li, Lingzhi  and
      Zhou, Lianqiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1206",
    pages = "2437--2447",
    abstract = "Despite the success of existing works on single-turn conversation generation, taking the coherence in consideration, human conversing is actually a context-sensitive process. Inspired by the existing studies, this paper proposed the static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses. Experimental results on two public datasets show that the proposed static attention based approach outperforms all the baselines on automatic and human evaluation.",
}
@inproceedings{liu-etal-2018-lstm,
    title = "A {LSTM} Approach with Sub-Word Embeddings for {M}ongolian Phrase Break Prediction",
    author = "Liu, Rui  and
      Bao, Feilong  and
      Gao, Guanglai  and
      Zhang, Hui  and
      Wang, Yonghe",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1207",
    pages = "2448--2455",
    abstract = "In this paper, we first utilize the word embedding that focuses on sub-word units to the Mongolian Phrase Break (PB) prediction task by using Long-Short-Term-Memory (LSTM) model. Mongolian is an agglutinative language. Each root can be followed by several suffixes to form probably millions of words, but the existing Mongolian corpus is not enough to build a robust entire word embedding, thus it suffers a serious data sparse problem and brings a great difficulty for Mongolian PB prediction. To solve this problem, we look at sub-word units in Mongolian word, and encode their information to a meaningful representation, then fed it to LSTM to decode the best corresponding PB label. Experimental results show that the proposed model significantly outperforms traditional CRF model using manually features and obtains 7.49{\%} F-Measure gain.",
}
@inproceedings{uresova-etal-2018-synonymy,
    title = "Synonymy in Bilingual Context: The {C}z{E}ng{C}lass Lexicon",
    author = "Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}ov{\'a}, Eva  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1208",
    pages = "2456--2469",
    abstract = "This paper describes CzEngClass, a bilingual lexical resource being built to investigate verbal synonymy in bilingual context and to relate semantic roles common to one synonym class to verb arguments (verb valency). In addition, the resource is linked to existing resources with the same of a similar aim: English and Czech WordNet, FrameNet, PropBank, VerbNet (SemLink), and valency lexicons for Czech and English (PDT-Vallex, Vallex, and EngVallex). There are several goals of this work and resource: (a) to provide gold standard data for automatic experiments in the future (such as automatic discovery of synonym classes, word sense disambiguation, assignment of classes to occurrences of verbs in text, coreferential linking of verb and event arguments in text, etc.), (b) to build a core (bilingual) lexicon linked to existing resources, for comparative studies and possibly for training automatic tools, and (c) to enrich the annotation of a parallel treebank, the Prague Czech English Dependency Treebank, which so far contained valency annotation but has not linked synonymous senses of verbs together. The method used for extracting the synonym classes is a semi-automatic process with a substantial amount of manual work during filtering, role assignment to classes and individual Class members{'} arguments, and linking to the external lexical resources. We present the first version with 200 classes (about 1800 verbs) and evaluate interannotator agreement using several metrics.",
}
@inproceedings{jiao-etal-2018-convolutional,
    title = "Convolutional Neural Network for Universal Sentence Embeddings",
    author = "Jiao, Xiaoqi  and
      Wang, Fang  and
      Feng, Dan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1209",
    pages = "2470--2481",
    abstract = "This paper proposes a simple CNN model for creating general-purpose sentence embeddings that can transfer easily across domains and can also act as effective initialization for downstream tasks. Recently, averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, these models represent a sentence, only in terms of features of words or uni-grams in it. In contrast, our model (CSE) utilizes both features of words and n-grams to encode sentences, which is actually a generalization of these bag-of-words models. The extensive experiments demonstrate that CSE performs better than average models in transfer learning setting and exceeds the state of the art in supervised learning setting by initializing the parameters with the pre-trained sentence embeddings.",
}
@inproceedings{matteson-etal-2018-rich,
    title = "Rich Character-Level Information for {K}orean Morphological Analysis and Part-of-Speech Tagging",
    author = "Matteson, Andrew  and
      Lee, Chanhee  and
      Kim, Youngbum  and
      Lim, Heuiseok",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1210",
    pages = "2482--2492",
    abstract = "Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise utilizes comprehensive prior linguistic knowledge (i.e., a dictionary of known morphological transformation forms, or actions). These models have been created with the assumption that character-level, dictionary-less morphological analysis was intractable due to the number of actions required. We present, in this study, a multi-stage action-based model that can perform morphological transformation and part-of-speech tagging using arbitrary units of input and apply it to the case of character-level Korean morphological analysis. Among models that do not employ prior linguistic knowledge, we achieve state-of-the-art word and sentence-level tagging accuracy with the Sejong Korean corpus using our proposed data-driven Bi-LSTM model.",
}
@inproceedings{hakami-etal-2018-pairdiff,
    title = "Why does {P}air{D}iff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection",
    author = "Hakami, Huda  and
      Hayashi, Kohei  and
      Bollegala, Danushka",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1211",
    pages = "2493--2504",
    abstract = "Representing the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion and relational information retrieval. A simple, yet surprisingly accurate method for representing a relation between two words is to compute the vector offset (PairDiff) between their corresponding word embeddings. Despite the empirical success, it remains unclear as to whether PairDiff is the best operator for obtaining a relational representation from word embeddings. We conduct a theoretical analysis of generalised bilinear operators that can be used to measure the l2 relational distance between two word-pairs. We show that, if the word embed- dings are standardised and uncorrelated, such an operator will be independent of bilinear terms, and can be simplified to a linear form, where PairDiff is a special case. For numerous word embedding types, we empirically verify the uncorrelation assumption, demonstrating the general applicability of our theoretical result. Moreover, we experimentally discover PairDiff from the bilinear relational compositional operator on several benchmark analogy datasets.",
}
@inproceedings{wang-goutte-2018-real,
    title = "Real-time Change Point Detection using On-line Topic Models",
    author = "Wang, Yunli  and
      Goutte, Cyril",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1212",
    pages = "2505--2515",
    abstract = "Detecting changes within an unfolding event in real time from news articles or social media enables to react promptly to serious issues in public safety, public health or natural disasters. In this study, we use on-line Latent Dirichlet Allocation (LDA) to model shifts in topics, and apply on-line change point detection (CPD) algorithms to detect when significant changes happen. We describe an on-line Bayesian change point detection algorithm that we use to detect topic changes from on-line LDA output. Extensive experiments on social media data and news articles show the benefits of on-line LDA versus standard LDA, and of on-line change point detection compared to off-line algorithms. This yields F-scores up to 52{\%} on the detection of significant real-life changes from these document streams.",
}
@inproceedings{schulder-etal-2018-automatically,
    title = "Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for {G}erman",
    author = "Schulder, Marc  and
      Wiegand, Michael  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1213",
    pages = "2516--2528",
    abstract = "In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb {``}abandon{''} in {``}abandon all hope{''}. This is similar to how negation words like {``}not{''} can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available.",
}
@inproceedings{anastasopoulos-etal-2018-part,
    title = "Part-of-Speech Tagging on an Endangered Language: a Parallel {G}riko-{I}talian Resource",
    author = "Anastasopoulos, Antonios  and
      Lekakou, Marika  and
      Quer, Josep  and
      Zimianiti, Eleni  and
      DeBenedetto, Justin  and
      Chiang, David",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1214",
    pages = "2529--2539",
    abstract = "Most work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual endangered language, Griko. We present a resource that contains 114 narratives in Griko, along with sentence-level translations in Italian, and provides gold annotations for the test set. Based on a previously collected small corpus, we investigate several traditional methods, as well as methods that take advantage of monolingual data or project cross-lingual POS tags. We show that the combination of a semi-supervised method with cross-lingual transfer is more appropriate for this extremely challenging setting, with the best tagger achieving an accuracy of 72.9{\%}. With an applied active learning scheme, which we use to collect sentence-level annotations over the test set, we achieve improvements of more than 21 percentage points.",
}
@inproceedings{wang-etal-2018-one,
    title = "One vs. Many {QA} Matching with both Word-level and Sentence-level Attention Network",
    author = "Wang, Lu  and
      Li, Shoushan  and
      Sun, Changlong  and
      Si, Luo  and
      Liu, Xiaozhong  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1215",
    pages = "2540--2550",
    abstract = "Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community. In this paper, we first build a novel QA matching corpus with informal text which is collected from a product reviewing website. Then, we propose a novel QA matching approach, namely One vs. Many Matching, which aims to address the novel scenario where one question sentence often has an answer with multiple sentences. Furthermore, we improve our matching approach by employing both word-level and sentence-level attentions for solving the noisy problem in the informal text. Empirical studies demonstrate the effectiveness of the proposed approach to question-answer matching.",
}
@inproceedings{kim-etal-2018-learning,
    title = "Learning to Generate Word Representations using Subword Information",
    author = "Kim, Yeachan  and
      Kim, Kang-Min  and
      Lee, Ji-Min  and
      Lee, SangKeun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1216",
    pages = "2551--2561",
    abstract = "Distributed representations of words play a major role in the field of natural language processing by encoding semantic and syntactic information of words. However, most existing works on learning word representations typically regard words as individual atomic units and thus are blind to subword information in words. This further gives rise to a difficulty in representing out-of-vocabulary (OOV) words. In this paper, we present a character-based word representation approach to deal with this limitation. The proposed model learns to generate word representations from characters. In our model, we employ a convolutional neural network and a highway network over characters to extract salient features effectively. Unlike previous models that learn word representations from a large corpus, we take a set of pre-trained word embeddings and generalize it to word entries, including OOV words. To demonstrate the efficacy of the proposed model, we perform both an intrinsic and an extrinsic task which are word similarity and language modeling, respectively. Experimental results show clearly that the proposed model significantly outperforms strong baseline models that regard words or their subwords as atomic units. For example, we achieve as much as 18.5{\%} improvement on average in perplexity for morphologically rich languages compared to strong baselines in the language modeling task.",
}
@inproceedings{bin-zia-etal-2018-urdu,
    title = "{U}rdu Word Segmentation using Conditional Random Fields ({CRF}s)",
    author = "Bin Zia, Haris  and
      Raza, Agha Ali  and
      Athar, Awais",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1217",
    pages = "2562--2569",
    abstract = "State-of-the-art Natural Language Processing algorithms rely heavily on efficient word segmentation. Urdu is amongst languages for which word segmentation is a complex task as it exhibits space omission as well as space insertion issues. This is partly due to the Arabic script which although cursive in nature, consists of characters that have inherent joining and non-joining attributes regardless of word boundary. This paper presents a word segmentation system for Urdu which uses a Conditional Random Field sequence modeler with orthographic, linguistic and morphological features. Our proposed model automatically learns to predict white space as word boundary as well as Zero Width Non-Joiner (ZWNJ) as sub-word boundary. Using a manually annotated corpus, our model achieves F1 score of 0.97 for word boundary identification and 0.85 for sub-word boundary identification tasks. We have made our code and corpus publicly available to make our results reproducible.",
}
@inproceedings{billami-etal-2018-resyf,
    title = "{R}e{S}yf: a {F}rench lexicon with ranked synonyms",
    author = "Billami, Mokhtar B.  and
      Fran{\c{c}}ois, Thomas  and
      Gala, N{\'u}ria",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1218",
    pages = "2570--2581",
    abstract = "In this article, we present ReSyf, a lexical resource of monolingual synonyms ranked according to their difficulty to be read and understood by native learners of French. The synonyms come from an existing lexical network and they have been semantically disambiguated and refined. A ranking algorithm, based on a wide range of linguistic features and validated through an evaluation campaign with human annotators, automatically sorts the synonyms corresponding to a given word sense by reading difficulty. ReSyf is freely available and will be integrated into a web platform for reading assistance. It can also be applied to perform lexical simplification of French texts.",
}
@inproceedings{pasquer-etal-2018-youve,
    title = "If you{'}ve seen some, you{'}ve seen them all: Identifying variants of multiword expressions",
    author = "Pasquer, Caroline  and
      Savary, Agata  and
      Ramisch, Carlos  and
      Antoine, Jean-Yves",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1219",
    pages = "2582--2594",
    abstract = "Multiword expressions, especially verbal ones (VMWEs), show idiosyncratic variability, which is challenging for NLP applications, hence the need for VMWE identification. We focus on the task of variant identification, i.e. identifying variants of previously seen VMWEs, whatever their surface form. We model the problem as a classification task. Syntactic subtrees with previously seen combinations of lemmas are first extracted, and then classified on the basis of features relevant to morpho-syntactic variation of VMWEs. Feature values are both absolute, i.e. hold for a particular VMWE candidate, and relative, i.e. based on comparing a candidate with previously seen VMWEs. This approach outperforms a baseline by 4 percent points of F-measure on a French corpus.",
}
@inproceedings{hao-paul-2018-learning,
    title = "Learning Multilingual Topics from Incomparable Corpora",
    author = "Hao, Shudong  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1220",
    pages = "2595--2609",
    abstract = "Multilingual topic models enable crosslingual tasks by extracting consistent topics from multilingual corpora. Most models require parallel or comparable training corpora, which limits their ability to generalize. In this paper, we first demystify the knowledge transfer mechanism behind multilingual topic models by defining an alternative but equivalent formulation. Based on this analysis, we then relax the assumption of training data required by most existing models, creating a model that only requires a dictionary for training. Experiments show that our new method effectively learns coherent multilingual topics from partially and fully incomparable corpora with limited amounts of dictionary resources.",
}
@inproceedings{charbonnier-wartena-2018-using,
    title = "Using Word Embeddings for Unsupervised Acronym Disambiguation",
    author = "Charbonnier, Jean  and
      Wartena, Christian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1221",
    pages = "2610--2619",
    abstract = "Scientific papers from all disciplines contain many abbreviations and acronyms. In many cases these acronyms are ambiguous. We present a method to choose the contextual correct definition of an acronym that does not require training for each acronym and thus can be applied to a large number of different acronyms with only few instances. We constructed a set of 19,954 examples of 4,365 ambiguous acronyms from image captions in scientific papers along with their contextually correct definition from different domains. We learn word embeddings for all words in the corpus and compare the averaged context vector of the words in the expansion of an acronym with the weighted average vector of the words in the context of the acronym. We show that this method clearly outperforms (classical) cosine similarity. Furthermore, we show that word embeddings learned from a 1 billion word corpus of scientific texts outperform word embeddings learned on much large general corpora.",
}
@inproceedings{littell-etal-2018-indigenous,
    title = "Indigenous language technologies in {C}anada: Assessment, challenges, and successes",
    author = "Littell, Patrick  and
      Kazantseva, Anna  and
      Kuhn, Roland  and
      Pine, Aidan  and
      Arppe, Antti  and
      Cox, Christopher  and
      Junker, Marie-Odile",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1222",
    pages = "2620--2632",
    abstract = "In this article, we discuss which text, speech, and image technologies have been developed, and would be feasible to develop, for the approximately 60 Indigenous languages spoken in Canada. In particular, we concentrate on technologies that may be feasible to develop for most or all of these languages, not just those that may be feasible for the few most-resourced of these. We assess past achievements and consider future horizons for Indigenous language transliteration, text prediction, spell-checking, approximate search, machine translation, speech recognition, speaker diarization, speech synthesis, optical character recognition, and computer-aided language learning.",
}
@inproceedings{byamugisha-etal-2018-pluralizing,
    title = "Pluralizing Nouns across Agglutinating {B}antu Languages",
    author = "Byamugisha, Joan  and
      Keet, C. Maria  and
      DeRenzi, Brian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1223",
    pages = "2633--2643",
    abstract = "Text generation may require the pluralization of nouns, such as in context-sensitive user interfaces and in natural language generation more broadly. While this has been solved for the widely-used languages, this is still a challenge for the languages in the Bantu language family. Pluralization results obtained for isiZulu and Runyankore showed there were similarities in approach, including the need to combine syntax with semantics, despite belonging to different language zones. This suggests that bootstrapping and generalizability might be feasible. We investigated this systematically for seven languages across three different Guthrie language zones. The first outcome is that Meinhof{'}s 1948 specification of the noun classes are indeed inadequate for computational purposes for all examined languages, due to non-determinism in prefixes, and we thus redefined the characteristic noun class tables of 29 noun classes into 53. The second main result is that the generic pluralizer achieved over 93{\%} accuracy in coverage testing and over 94{\%} on a random sample. This is comparable to the language-specific isiZulu and Runyankore pluralizers.",
}
@inproceedings{kazeminejad-etal-2018-automatically,
    title = "Automatically Extracting Qualia Relations for the Rich Event Ontology",
    author = "Kazeminejad, Ghazaleh  and
      Bonial, Claire  and
      Brown, Susan Windisch  and
      Palmer, Martha",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1224",
    pages = "2644--2652",
    abstract = "Commonsense, real-world knowledge about the events that entities or {``}things in the world{''} are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw everyday inferences about the world. Here, we focus on automatically extracting information about (1) the events that typically bring about certain entities (origins), (2) the events that are the typical functions of entities, and (3) part-whole relationships in entities. These correspond to the agentive, telic and constitutive qualia central to the Generative Lexicon. We describe our motivations and methods for extracting these qualia relations from the Suggested Upper Merged Ontology (SUMO) and show that human annotators overwhelmingly find the information extracted to be reasonable. Because ontologies provide a way of structuring this information and making it accessible to agents and computational systems generally, efforts are underway to incorporate the extracted information to an ontology hub of Natural Language Processing semantic role labeling resources, the Rich Event Ontology.",
}
@inproceedings{espinosa-anke-schockaert-2018-seven,
    title = "{S}e{V}e{N}: Augmenting Word Embeddings with Unsupervised Relation Vectors",
    author = "Espinosa-Anke, Luis  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1225",
    pages = "2653--2665",
    abstract = "We present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these relations are represented as vectors in a continuous vector space. We propose a simple pipeline for learning such relation vectors, which is based on word vector averaging in combination with an ad hoc autoencoder. We show that by explicitly encoding relational information in a dedicated vector space we can capture aspects of word meaning that are complementary to what is captured by word embeddings. For example, by examining clusters of relation vectors, we observe that relational similarities can be identified at a more abstract level than with traditional word vector differences. Finally, we test the effectiveness of semantic vector networks in two tasks: measuring word similarity and neural text categorization. SeVeN is available at bitbucket.org/luisespinosa/seven.",
}
@inproceedings{aldarmaki-diab-2018-evaluation,
    title = "Evaluation of Unsupervised Compositional Representations",
    author = "Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1226",
    pages = "2666--2677",
    abstract = "We evaluated various compositional models, from bag-of-words representations to compositional RNN-based models, on several extrinsic supervised and unsupervised evaluation benchmarks. Our results confirm that weighted vector averaging can outperform context-sensitive models in most benchmarks, but structural features encoded in RNN models can also be useful in certain classification tasks. We analyzed some of the evaluation datasets to identify the aspects of meaning they measure and the characteristics of the various models that explain their performance variance.",
}
@inproceedings{iwatsuki-aizawa-2018-using,
    title = "Using Formulaic Expressions in Writing Assistance Systems",
    author = "Iwatsuki, Kenichi  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1227",
    pages = "2678--2689",
    abstract = "Formulaic expressions (FEs) used in scholarly papers, such as {`}there has been little discussion about{'}, are helpful for non-native English speakers. However, it is time-consuming for users to manually search for an appropriate expression every time they want to consult FE dictionaries. For this reason, we tackle the task of semantic searches of FE dictionaries. At the start of our research, we identified two salient difficulties in this task. First, the paucity of example sentences in existing FE dictionaries results in a shortage of context information, which is necessary for acquiring semantic representation of FEs. Second, while a semantic category label is assigned to each FE in many FE dictionaries, it is difficult to predict the labels from user input, forcing users to manually designate the semantic category when searching. To address these difficulties, we propose a new framework for semantic searches of FEs and propose a new method to leverage both existing dictionaries and domain sentence corpora. Further, we expand an existing FE dictionary to consider building a more comprehensive and domain-specific FE dictionary and to verify the effectiveness of our method.",
}
@inproceedings{rogers-etal-2018-whats,
    title = "What{'}s in Your Embedding, And How It Predicts Task Performance",
    author = "Rogers, Anna  and
      Hosur Ananthakrishna, Shashwath  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1228",
    pages = "2690--2703",
    abstract = "Attempts to find a single technique for general-purpose intrinsic evaluation of word embeddings have so far not been successful. We present a new approach based on scaled-up qualitative analysis of word vector neighborhoods that quantifies interpretable characteristics of a given model (e.g. its preference for synonyms or shared morphological forms as nearest neighbors). We analyze 21 such factors and show how they correlate with performance on 14 extrinsic and intrinsic task datasets (and also explain the lack of correlation between some of them). Our approach enables multi-faceted evaluation, parameter search, and generally {--} a more principled, hypothesis-driven approach to development of distributional semantic representations.",
}
@inproceedings{o-etal-2018-word,
    title = "Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph",
    author = "O, Dongsuk  and
      Kwon, Sunjae  and
      Kim, Kyungsun  and
      Ko, Youngjoong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1229",
    pages = "2704--2714",
    abstract = "Word sense disambiguation (WSD) is the task to determine the word sense according to its context. Many existing WSD studies have been using an external knowledge-based unsupervised approach because it has fewer word set constraints than supervised approaches requiring training data. In this paper, we propose a new WSD method to generate the context of an ambiguous word by using similarities between an ambiguous word and words in the input document. In addition, to leverage our WSD method, we further propose a new word similarity calculation method based on the semantic network structure of BabelNet. We evaluate the proposed methods on the SemEval-13 and SemEval-15 for English WSD dataset. Experimental results demonstrate that the proposed WSD method significantly improves the baseline WSD method. Furthermore, our WSD system outperforms the state-of-the-art WSD systems in the Semeval-13 dataset. Finally, it has higher performance than the state-of-the-art unsupervised knowledge-based WSD system in the average performance of both datasets.",
}
@inproceedings{patro-etal-2018-learning,
    title = "Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator",
    author = "Patro, Badri Narayana  and
      Kurmi, Vinod Kumar  and
      Kumar, Sandeep  and
      Namboodiri, Vinay",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1230",
    pages = "2715--2729",
    abstract = "In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.",
}
@inproceedings{chollampatt-ng-2018-reassessment,
    title = "A Reassessment of Reference-Based Grammatical Error Correction Metrics",
    author = "Chollampatt, Shamil  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1231",
    pages = "2730--2741",
    abstract = "Several metrics have been proposed for evaluating grammatical error correction (GEC) systems based on grammaticality, fluency, and adequacy of the output sentences. Previous studies of the correlation of these metrics with human quality judgments were inconclusive, due to the lack of appropriate significance tests, discrepancies in the methods, and choice of datasets used. In this paper, we re-evaluate reference-based GEC metrics by measuring the system-level correlations with humans on a large dataset of human judgments of GEC outputs, and by properly conducting statistical significance tests. Our results show no significant advantage of GLEU over MaxMatch (M2), contradicting previous studies that claim GLEU to be superior. For a finer-grained analysis, we additionally evaluate these metrics for their agreement with human judgments at the sentence level. Our sentence-level analysis indicates that comparing GLEU and M2, one metric may be more useful than the other depending on the scenario. We further qualitatively analyze these metrics and our findings show that apart from being less interpretable and non-deterministic, GLEU also produces counter-intuitive scores in commonly occurring test examples.",
}
@inproceedings{gong-etal-2018-information,
    title = "Information Aggregation via Dynamic Routing for Sequence Encoding",
    author = "Gong, Jingjing  and
      Qiu, Xipeng  and
      Wang, Shaojing  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1232",
    pages = "2742--2752",
    abstract = "While much progress has been made in how to encode a text sequence into a sequence of vectors, less attention has been paid to how to aggregate these preceding vectors (outputs of RNN/CNN) into fixed-size encoding vector. Usually, a simple max or average pooling is used, which is a bottom-up and passive way of aggregation and lack of guidance by task information. In this paper, we propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy. The dynamic routing policy is dynamically deciding that what and how much information need be transferred from each word to the final encoding of the text sequence. Following the work of Capsule Network, we design two dynamic routing policies to aggregate the outputs of RNN/CNN encoding layer into a final encoding vector. Compared to the other aggregation methods, dynamic routing can refine the messages according to the state of final encoding vector. Experimental results on five text classification tasks show that our method outperforms other aggregating models by a significant margin. Related source code is released on our github page.Related source code is released on our github page.",
}
@inproceedings{cai-etal-2018-full,
    title = "A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware?",
    author = "Cai, Jiaxun  and
      He, Shexia  and
      Li, Zuchao  and
      Zhao, Hai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1233",
    pages = "2753--2765",
    abstract = "Semantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling. Previous studies usually formulate the entire SRL problem into two or more subtasks. For the first time, this paper introduces an end-to-end neural model which unifiedly tackles the predicate disambiguation and the argument labeling in one shot. Using a biaffine scorer, our model directly predicts all semantic role labels for all given word pairs in the sentence without relying on any syntactic parse information. Specifically, we augment the BiLSTM encoder with a non-linear transformation to further distinguish the predicate and the argument in a given sentence, and model the semantic role labeling process as a word pair classification task by employing the biaffine attentional mechanism. Though the proposed model is syntax-agnostic with local decoder, it outperforms the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009 benchmarks for both English and Chinese. To our best knowledge, we report the first syntax-agnostic SRL model that surpasses all known syntax-aware models.",
}
@inproceedings{patchala-bhatnagar-2018-authorship,
    title = "Authorship Attribution By Consensus Among Multiple Features",
    author = "Patchala, Jagadeesh  and
      Bhatnagar, Raj",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1234",
    pages = "2766--2777",
    abstract = "Most existing research on authorship attribution uses various lexical, syntactic and semantic features. In this paper we demonstrate an effective template-based approach for combining various syntactic features of a document for authorship analysis. The parse-tree based features that we propose are independent of the topic of a document and reflect the innate writing styles of authors. We show that the use of templates including sub-trees of parse trees in conjunction with other syntactic features result in improved author attribution rates. Another contribution is the demonstration that Dempster{'}s rule based combination of evidence from syntactic features performs better than other evidence-combination methods. We also demonstrate that our methodology works well for the case where actual author is not included in the candidate author set.",
}
@inproceedings{kim-etal-2018-modeling,
    title = "Modeling with Recurrent Neural Networks for Open Vocabulary Slots",
    author = "Kim, Jun-Seong  and
      Kim, Junghoe  and
      Park, SeungUn  and
      Lee, Kwangyong  and
      Lee, Yoonju",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1235",
    pages = "2778--2790",
    abstract = "Dealing with {`}open-vocabulary{'} slots has been among the challenges in the natural language area. While recent studies on attention-based recurrent neural network (RNN) models have performed well in completing several language related tasks such as spoken language understanding and dialogue systems, there has been a lack of attempts to address filling slots that take on values from a virtually unlimited set. In this paper, we propose a new RNN model that can capture the vital concept: Understanding the role of a word may vary according to how long a reader focuses on a particular part of a sentence. The proposed model utilizes a long-term aware attention structure, positional encoding primarily considering the relative distance between words, and multi-task learning of a character-based language model and an intent detection model. We show that the model outperforms the existing RNN models with respect to discovering {`}open-vocabulary{'} slots without any external information, such as a named entity database or knowledge base. In particular, we confirm that it performs better with a greater number of slots in a dataset, including unknown words, by evaluating the models on a dataset of several domains. In addition, the proposed model also demonstrates superior performance with regard to intent detection.",
}
@inproceedings{van-der-aa-etal-2018-challenges,
    title = "Challenges and Opportunities of Applying Natural Language Processing in Business Process Management",
    author = "van der Aa, Han  and
      Carmona, Josep  and
      Leopold, Henrik  and
      Mendling, Jan  and
      Padr{\'o}, Llu{\'\i}s",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1236",
    pages = "2791--2801",
    abstract = "The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.",
}
@inproceedings{ghosal-etal-2018-novelty,
    title = "Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection",
    author = "Ghosal, Tirthankar  and
      Edithal, Vignesh  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak  and
      Tsatsaronis, George  and
      Chivukula, Srinivasa Satya Sameer Kumar",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1237",
    pages = "2802--2813",
    abstract = "The rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at a deep semantic level. Techniques for detecting such semantic redundancy at the document level are scarce. In this work we propose a deep Convolutional Neural Networks (CNN) based model to classify a document as novel or redundant with respect to a set of relevant documents already seen by the system. The system is simple and do not require any manual feature engineering. Our novel scheme encodes relevant and relative information from both source and target texts to generate an intermediate representation which we coin as the Relative Document Vector (RDV). The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5{\%} in terms of accuracy. We further demonstrate the effectiveness of our approach on a standard paraphrase detection dataset where paraphrased passages closely resemble to semantically redundant documents.",
}
@inproceedings{sundararajan-woodard-2018-represents,
    title = "What represents {``}style{''} in authorship attribution?",
    author = "Sundararajan, Kalaivani  and
      Woodard, Damon",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1238",
    pages = "2814--2822",
    abstract = "Authorship attribution typically uses all information representing both content and style whereas attribution based only on stylistic aspects may be robust in cross-domain settings. This paper analyzes different linguistic aspects that may help represent style. Specifically, we study the role of syntax and lexical words (nouns, verbs, adjectives and adverbs) in representing style. We use a purely syntactic language model to study the significance of sentence structures in both single-domain and cross-domain attribution, i.e. cross-topic and cross-genre attribution. We show that syntax may be helpful for cross-genre attribution while cross-topic attribution and single-domain may benefit from additional lexical information. Further, pure syntactic models may not be effective by themselves and need to be used in combination with other robust models. To study the role of word choice, we perform attribution by masking all words or specific topic words corresponding to nouns, verbs, adjectives and adverbs. Using a single-domain dataset, IMDB1M reviews, we demonstrate the heavy influence of common nouns and proper nouns in attribution, thereby highlighting topic interference. Using cross-domain Guardian10 dataset, we show that some common nouns, verbs, adjectives and adverbs may help with stylometric attribution as demonstrated by masking topic words corresponding to these parts-of-speech. As expected, it was observed that proper nouns are heavily influenced by content and cross-domain attribution will benefit from completely masking them.",
}
@inproceedings{duan-etal-2018-learning,
    title = "Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction",
    author = "Duan, Junwen  and
      Zhang, Yue  and
      Ding, Xiao  and
      Chang, Ching-Yun  and
      Liu, Ting",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1239",
    pages = "2823--2833",
    abstract = "Texts from the Internet serve as important data sources for financial market modeling. Early statistical approaches rely on manually defined features to capture lexical, sentiment and event information, which suffers from feature sparsity. Recent work has considered learning dense representations for news titles and abstracts. Compared to news titles, full documents can contain more potentially helpful information, but also noise compared to events and sentences, which has been less investigated in previous work. To fill this gap, we propose a novel target-specific abstract-guided news document representation model. The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling. Results show that document representations can give better performance for estimating cumulative abnormal returns of companies when compared to titles and abstracts. Our model is especially effective when it used to combine information from multiple document sources compared to the sentence-level baselines.",
}
@inproceedings{an-etal-2018-model,
    title = "Model-Free Context-Aware Word Composition",
    author = "An, Bo  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1240",
    pages = "2834--2845",
    abstract = "Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method.",
}
@inproceedings{li-2018-learning,
    title = "Learning Features from Co-occurrences: A Theoretical Analysis",
    author = "Li, Yanpeng",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1241",
    pages = "2846--2854",
    abstract = "Representing a word by its co-occurrences with other words in context is an effective way to capture the meaning of the word. However, the theory behind remains a challenge. In this work, taking the example of a word classification task, we give a theoretical analysis of the approaches that represent a word X by a function f(P(C|X)), where C is a context feature, P(C|X) is the conditional probability estimated from a text corpus, and the function f maps the co-occurrence measure to a prediction score. We investigate the impact of context feature C and the function f . We also explain the reasons why using the co-occurrences with multiple context features may be better than just using a single one. In addition, based on the analysis, we propose a hypothesis about the conditional probability on zero probability events.",
}
@inproceedings{liu-etal-2018-towards,
    title = "Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms",
    author = "Liu, Jingshu  and
      Morin, Emmanuel  and
      Saldarriaga, Pe{\~n}a",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1242",
    pages = "2855--2866",
    abstract = "Extracting a bilingual terminology for multi-word terms from comparable corpora has not been widely researched. In this work we propose a unified framework for aligning bilingual terms independently of the term lengths. We also introduce some enhancements to the context-based and the neural network based approaches. Our experiments show the effectiveness of our enhancements of previous works and the system can be adapted in specialized domains.",
}
@inproceedings{athanasiou-etal-2018-neural,
    title = "Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations",
    author = "Athanasiou, Nikos  and
      Iosif, Elias  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1243",
    pages = "2867--2878",
    abstract = "Neural activation models have been proposed in the literature that use a set of example words for which fMRI measurements are available in order to find a mapping between word semantics and localized neural activations. Successful mappings let us expand to the full lexicon of concrete nouns using the assumption that similarity of meaning implies similar neural activation patterns. In this paper, we propose a computational model that estimates semantic similarity in the neural activation space and investigates the relative performance of this model for various natural language processing tasks. Despite the simplicity of the proposed model and the very small number of example words used to bootstrap it, the neural activation semantic model performs surprisingly well compared to state-of-the-art word embeddings. Specifically, the neural activation semantic model performs better than the state-of-the-art for the task of semantic similarity estimation between very similar or very dissimilar words, while performing well on other tasks such as entailment and word categorization. These are strong indications that neural activation semantic models can not only shed some light into human cognition but also contribute to computation models for certain tasks.",
}
@inproceedings{kar-etal-2018-folksonomication,
    title = "{F}olksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network",
    author = "Kar, Sudipta  and
      Maharjan, Suraj  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1244",
    pages = "2879--2891",
    abstract = "Folksonomy of movies covers a wide range of heterogeneous information about movies, like the genre, plot structure, visual experiences, soundtracks, metadata, and emotional experiences from watching a movie. Being able to automatically generate or predict tags for movies can help recommendation engines improve retrieval of similar movies, and help viewers know what to expect from a movie in advance. In this work, we explore the problem of creating tags for movies from plot synopses. We propose a novel neural network model that merges information from synopses and emotion flows throughout the plots to predict a set of tags for movies. We compare our system with multiple baselines and found that the addition of emotion flows boosts the performance of the network by learning {\mbox{$\approx$}}18{\%} more tags than a traditional machine learning system.",
}
@inproceedings{buechel-hahn-2018-emotion,
    title = "Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level",
    author = "Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1245",
    pages = "2892--2904",
    abstract = "Emotion Representation Mapping (ERM) has the goal to convert existing emotion ratings from one representation format into another one, e.g., mapping Valence-Arousal-Dominance annotations for words or sentences into Ekman{'}s Basic Emotions and vice versa. ERM can thus not only be considered as an alternative to Word Emotion Induction (WEI) techniques for automatic emotion lexicon construction but may also help mitigate problems that come from the proliferation of emotion representation formats in recent years. We propose a new neural network approach to ERM that not only outperforms the previous state-of-the-art. Equally important, we present a refined evaluation methodology and gather strong evidence that our model yields results which are (almost) as reliable as human annotations, even in cross-lingual settings. Based on these results we generate new emotion ratings for 13 typologically diverse languages and claim that they have near-gold quality, at least.",
}
@inproceedings{tafreshi-diab-2018-emotion,
    title = "Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning",
    author = "Tafreshi, Shabnam  and
      Diab, Mona",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1246",
    pages = "2905--2913",
    abstract = "Detection and classification of emotion categories expressed by a sentence is a challenging task due to subjectivity of emotion. To date, most of the models are trained and evaluated on single genre and when used to predict emotion in different genre their performance drops by a large margin. To address the issue of robustness, we model the problem within a joint multi-task learning framework. We train this model with a multigenre emotion corpus to predict emotions across various genre. Each genre is represented as a separate task, we use soft parameter shared layers across the various tasks. our experimental results show that this model improves the results across the various genres, compared to a single genre training in the same neural net architecture.",
}
@inproceedings{kulshreshtha-etal-2018-emotional,
    title = "How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs",
    author = "Kulshreshtha, Devang  and
      Goel, Pranav  and
      Kumar Singh, Anil",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1247",
    pages = "2914--2926",
    abstract = "Social media based micro-blogging sites like Twitter have become a common source of real-time information (impacting organizations and their strategies, and are used for expressing emotions and opinions. Automated analysis of such content therefore rises in importance. To this end, we explore the viability of using deep neural networks on the specific task of emotion intensity prediction in tweets. We propose a neural architecture combining convolutional and fully connected layers in a non-sequential manner - done for the first time in context of natural language based tasks. Combined with lexicon-based features along with transfer learning, our model achieves state-of-the-art performance, outperforming the previous system by 0.044 or 4.4{\%} Pearson correlation on the WASSA{'}17 EmoInt shared task dataset. We investigate the performance of deep multi-task learning models trained for all emotions at once in a unified architecture and get encouraging results. Experiments performed on evaluating correlation between emotion pairs offer interesting insights into the relationship between them.",
}
@inproceedings{cachola-etal-2018-expressively,
    title = "Expressively vulgar: The socio-dynamics of vulgarity and its effects on sentiment analysis in social media",
    author = "Cachola, Isabel  and
      Holgate, Eric  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1248",
    pages = "2927--2938",
    abstract = "Vulgarity is a common linguistic expression and is used to perform several linguistic functions. Understanding their usage can aid both linguistic and psychological phenomena as well as benefit downstream natural language processing applications such as sentiment analysis. This study performs a large-scale, data-driven empirical analysis of vulgar words using social media data. We analyze the socio-cultural and pragmatic aspects of vulgarity using tweets from users with known demographics. Further, we collect sentiment ratings for vulgar tweets to study the relationship between the use of vulgar words and perceived sentiment and show that explicitly modeling vulgar words can boost sentiment analysis performance.",
}
@inproceedings{howell-zamaraeva-2018-clausal,
    title = "Clausal Modifiers in the Grammar Matrix",
    author = "Howell, Kristen  and
      Zamaraeva, Olga",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1249",
    pages = "2939--2952",
    abstract = "We extend the coverage of an existing grammar customization system to clausal modifiers, also referred to as adverbial clauses. We present an analysis, taking a typologically-driven approach to account for this phenomenon across the world{'}s languages, which we implement in the Grammar Matrix customization system (Bender et al., 2002, 2010). Testing our analysis on testsuites from five genetically and geographically diverse languages that were not considered in development, we achieve 88.4{\%} coverage and 1.5{\%} overgeneration.",
}
@inproceedings{yu-liu-2018-sliced,
    title = "Sliced Recurrent Neural Networks",
    author = "Yu, Zeping  and
      Liu, Gongshen",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1250",
    pages = "2953--2964",
    abstract = "Recurrent neural networks have achieved great success in many NLP tasks. However, they have difficulty in parallelization because of the recurrent structure, so it takes much time to train RNNs. In this paper, we introduce sliced recurrent neural networks (SRNNs), which could be parallelized by slicing the sequences into many subsequences. SRNNs have the ability to obtain high-level information through multiple layers with few extra parameters. We prove that the standard RNN is a special case of the SRNN when we use linear activation functions. Without changing the recurrent units, SRNNs are 136 times as fast as standard RNNs and could be even faster when we train longer sequences. Experiments on six large-scale sentiment analysis datasets show that SRNNs achieve better performance than standard RNNs.",
}
@inproceedings{changpinyo-etal-2018-multi,
    title = "Multi-Task Learning for Sequence Tagging: An Empirical Study",
    author = "Changpinyo, Soravit  and
      Hu, Hexiang  and
      Sha, Fei",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1251",
    pages = "2965--2977",
    abstract = "We study three general multi-task learning (MTL) approaches on 11 sequence tagging tasks. Our extensive empirical results show that in about 50{\%} of the cases, jointly learning all 11 tasks improves upon either independent or pairwise learning of the tasks. We also show that pairwise MTL can inform us what tasks can benefit others or what tasks can be benefited if they are learned jointly. In particular, we identify tasks that can always benefit others as well as tasks that can always be harmed by others. Interestingly, one of our MTL approaches yields embeddings of the tasks that reveal the natural clustering of semantic and syntactic tasks. Our inquiries have opened the doors to further utilization of MTL in NLP.",
}
@inproceedings{moss-etal-2018-using,
    title = "Using {J}-{K}-fold Cross Validation To Reduce Variance When Tuning {NLP} Models",
    author = "Moss, Henry  and
      Leslie, David  and
      Rayson, Paul",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1252",
    pages = "2978--2989",
    abstract = "K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires random partitioning of the data and so our performance estimates are in fact stochastic, with variability that can be substantial for natural language processing tasks. We demonstrate that these unstable estimates cannot be relied upon for effective parameter tuning. The resulting tuned parameters are highly sensitive to how our data is partitioned, meaning that we often select sub-optimal parameter choices and have serious reproducibility issues. Instead, we propose to use the less variable J-K-fold CV, in which J independent K-fold cross validations are used to assess performance. Our main contributions are extending J-K-fold CV from performance estimation to parameter tuning and investigating how to choose J and K. We argue that variability is more important than bias for effective tuning and so advocate lower choices of K than are typically seen in the NLP literature and instead use the saved computation to increase J. To demonstrate the generality of our recommendations we investigate a wide range of case-studies: sentiment classification (both general and target-specific), part-of-speech tagging and document classification.",
}
@inproceedings{kohn-2018-incremental,
    title = "Incremental Natural Language Processing: Challenges, Strategies, and Evaluation",
    author = {K{\"o}hn, Arne},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1253",
    pages = "2990--3003",
    abstract = "Incrementality is ubiquitous in human-human interaction and beneficial for human-computer interaction. It has been a topic of research in different parts of the NLP community, mostly with focus on the specific topic at hand even though incremental systems have to deal with similar challenges regardless of domain. In this survey, I consolidate and categorize the approaches, identifying similarities and differences in the computation and data, and show trade-offs that have to be considered. A focus lies on evaluating incremental systems because the standard metrics often fail to capture the incremental properties of a system and coming up with a suitable evaluation scheme is non-trivial.",
}
@inproceedings{moon-etal-2018-gold,
    title = "Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions",
    author = "Moon, Lori  and
      Christodoulopoulos, Christos  and
      Fisher, Cynthia  and
      Franco, Sandra  and
      Roth, Dan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1254",
    pages = "3004--3014",
    abstract = "This paper describes the augmentation of an existing corpus of child-directed speech. The resulting corpus is a gold-standard labeled corpus for supervised learning of semantic role labels in adult-child dialogues. Semantic role labeling (SRL) models assign semantic roles to sentence constituents, thus indicating who has done what to whom (and in what way). The current corpus is derived from the Adam files in the Brown corpus (Brown 1973) of the CHILDES corpora, and augments the partial annotation described in Connor et al. (2010). It provides labels for both semantic arguments of verbs and semantic arguments of prepositions. The semantic role labels and senses of verbs follow Propbank guidelines Kingsbury and Palmer, 2002; Gildea and Palmer 2002; Palmer et al., 2005) and those for prepositions follow Srikumar and Roth (2011). The corpus was annotated by two annotators. Inter-annotator agreement is given separately for prepositions and verbs, and for adult speech and child speech. Overall, across child and adult samples, including verbs and prepositions, the kappa score for sense is 72.6, for the number of semantic-role-bearing arguments, the kappa score is 77.4, for identical semantic role labels on a given argument, the kappa score is 91.1, for the span of semantic role labels, and the kappa for agreement is 93.9. The sense and number of arguments was often open to multiple interpretations in child speech, due to the rapidly changing discourse and omission of constituents in production. Annotators used a discourse context window of ten sentences before and ten sentences after the target utterance to determine the annotation labels. The derived corpus is available for use in CHAT (MacWhinney, 2000) and XML format.",
}
@inproceedings{wang-etal-2018-multi-layer,
    title = "Multi-layer Representation Fusion for Neural Machine Translation",
    author = "Wang, Qiang  and
      Li, Fuxue  and
      Xiao, Tong  and
      Li, Yanyang  and
      Li, Yinqiao  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1255",
    pages = "3015--3026",
    abstract = "Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.",
}
@inproceedings{mi-etal-2018-toward,
    title = "Toward Better Loanword Identification in {U}yghur Using Cross-lingual Word Embeddings",
    author = "Mi, Chenggang  and
      Yang, Yating  and
      Wang, Lei  and
      Zhou, Xi  and
      Jiang, Tonghai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1256",
    pages = "3027--3037",
    abstract = "To enrich vocabulary of low resource settings, we proposed a novel method which identify loanwords in monolingual corpora. More specifically, we first use cross-lingual word embeddings as the core feature to generate semantically related candidates based on comparable corpora and a small bilingual lexicon; then, a log-linear model which combines several shallow features such as pronunciation similarity and hybrid language model features to predict the final results. In this paper, we use Uyghur as the receipt language and try to detect loanwords in four donor languages: Arabic, Chinese, Persian and Russian. We conduct two groups of experiments to evaluate the effectiveness of our proposed approach: loanword identification and OOV translation in four language pairs and eight translation directions (Uyghur-Arabic, Arabic-Uyghur, Uyghur-Chinese, Chinese-Uyghur, Uyghur-Persian, Persian-Uyghur, Uyghur-Russian, and Russian-Uyghur). Experimental results on loanword identification show that our method outperforms other baseline models significantly. Neural machine translation models integrating results of loanword identification experiments achieve the best results on OOV translation(with 0.5-0.9 BLEU improvements)",
}
@inproceedings{li-etal-2018-adaptive,
    title = "Adaptive Weighting for Neural Machine Translation",
    author = "Li, Yachao  and
      Li, Junhui  and
      Zhang, Min",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1257",
    pages = "3038--3048",
    abstract = "In the popular sequence to sequence (seq2seq) neural machine translation (NMT), there exist many weighted sum models (WSMs), each of which takes a set of input and generates one output. However, the weights in a WSM are independent of each other and fixed for all inputs, suggesting that by ignoring different needs of inputs, the WSM lacks effective control on the influence of each input. In this paper, we propose adaptive weighting for WSMs to control the contribution of each input. Specifically, we apply adaptive weighting for both GRU and the output state in NMT. Experimentation on Chinese-to-English translation and English-to-German translation demonstrates that the proposed adaptive weighting is able to much improve translation accuracy by achieving significant improvement of 1.49 and 0.92 BLEU points for the two translation tasks. Moreover, we discuss in-depth on what type of information is encoded in the encoder and how information influences the generation of target words in the decoder.",
}
@inproceedings{gebhardt-2018-generic,
    title = "Generic refinement of expressive grammar formalisms with an application to discontinuous constituent parsing",
    author = "Gebhardt, Kilian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1258",
    pages = "3049--3063",
    abstract = "We formulate a generalization of Petrov et al. (2006){'}s split/merge algorithm for interpreted regular tree grammars (Koller and Kuhlmann, 2011), which capture a large class of grammar formalisms. We evaluate its effectiveness empirically on the task of discontinuous constituent parsing with two mildly context-sensitive grammar formalisms: linear context-free rewriting systems (Vijay-Shanker et al., 1987) as well as hybrid grammars (Nederhof and Vogler, 2014).",
}
@inproceedings{song-etal-2018-double,
    title = "Double Path Networks for Sequence to Sequence Learning",
    author = "Song, Kaitao  and
      Tan, Xu  and
      He, Di  and
      Lu, Jianfeng  and
      Qin, Tao  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1259",
    pages = "3064--3074",
    abstract = "Encoder-decoder based Sequence to Sequence learning (S2S) has made remarkable progress in recent years. Different network architectures have been used in the encoder/decoder. Among them, Convolutional Neural Networks (CNN) and Self Attention Networks (SAN) are the prominent ones. The two architectures achieve similar performances but use very different ways to encode and decode context: CNN use convolutional layers to focus on the local connectivity of the sequence, while SAN uses self-attention layers to focus on global semantics. In this work we propose Double Path Networks for Sequence to Sequence learning (DPN-S2S), which leverage the advantages of both models by using double path information fusion. During the encoding step, we develop a double path architecture to maintain the information coming from different paths with convolutional layers and self-attention layers separately. To effectively use the encoded context, we develop a gated attention fusion module and use it to automatically pick up the information needed during the decoding step, which is also a double path network. By deeply integrating the two paths, both types of information are combined and well exploited. Experiments show that our proposed method can significantly improve the performance of sequence to sequence learning over state-of-the-art systems.",
}
@inproceedings{nguyen-etal-2018-empirical,
    title = "An Empirical Investigation of Error Types in {V}ietnamese Parsing",
    author = "Nguyen, Quy  and
      Miyao, Yusuke  and
      Noji, Hiroshi  and
      Nguyen, Nhung",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1260",
    pages = "3075--3089",
    abstract = "Syntactic parsing plays a crucial role in improving the quality of natural language processing tasks. Although there have been several research projects on syntactic parsing in Vietnamese, the parsing quality has been far inferior than those reported in major languages, such as English and Chinese. In this work, we evaluated representative constituency parsing models on a Vietnamese Treebank to look for the most suitable parsing method for Vietnamese. We then combined the advantages of automatic and manual analysis to investigate errors produced by the experimented parsers and find the reasons for them. Our analysis focused on three possible sources of parsing errors, namely limited training data, part-of-speech (POS) tagging errors, and ambiguous constructions. As a result, we found that the last two sources, which frequently appear in Vietnamese text, significantly attributed to the poor performance of Vietnamese parsing.",
}
@inproceedings{labeau-allauzen-2018-learning,
    title = "Learning with Noise-Contrastive Estimation: Easing training by learning to scale",
    author = "Labeau, Matthieu  and
      Allauzen, Alexandre",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1261",
    pages = "3090--3101",
    abstract = "Noise-Contrastive Estimation (NCE) is a learning criterion that is regularly used to train neural language models in place of Maximum Likelihood Estimation, since it avoids the computational bottleneck caused by the output softmax. In this paper, we analyse and explain some of the weaknesses of this objective function, linked to the mechanism of self-normalization, by closely monitoring comparative experiments. We then explore several remedies and modifications to propose tractable and efficient NCE training strategies. In particular, we propose to make the scaling factor a trainable parameter of the model, and to use the noise distribution to initialize the output bias. These solutions, yet simple, yield stable and competitive performances in either small and large scale language modelling tasks.",
}
@inproceedings{abate-etal-2018-parallel,
    title = "Parallel Corpora for bi-lingual {E}nglish-{E}thiopian Languages Statistical Machine Translation",
    author = "Abate, Solomon Teferra  and
      Melese, Michael  and
      Tachbelie, Martha Yifiru  and
      Meshesha, Million  and
      Atinafu, Solomon  and
      Mulugeta, Wondwossen  and
      Assabie, Yaregal  and
      Abera, Hafte  and
      Ephrem, Binyam  and
      Abebe, Tewodros  and
      Tsegaye, Wondimagegnhue  and
      Lemma, Amanuel  and
      Andargie, Tsegaye  and
      Shifaw, Seifedin",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1262",
    pages = "3102--3111",
    abstract = "In this paper, we describe an attempt towards the development of parallel corpora for English and Ethiopian Languages, such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge{'}ez. The corpora are used for conducting a bi-directional statistical machine translation experiments. The BLEU scores of the bi-directional Statistical Machine Translation (SMT) systems show a promising result. The morphological richness of the Ethiopian languages has a great impact on the performance of SMT specially when the targets are Ethiopian languages. Now we are working towards an optimal alignment for a bi-directional English-Ethiopian languages SMT.",
}
@inproceedings{blackwood-etal-2018-multilingual,
    title = "Multilingual Neural Machine Translation with Task-Specific Attention",
    author = "Blackwood, Graeme  and
      Ballesteros, Miguel  and
      Ward, Todd",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1263",
    pages = "3112--3122",
    abstract = "Multilingual machine translation addresses the task of translating between multiple source and target languages. We propose task-specific attention models, a simple but effective technique for improving the quality of sequence-to-sequence neural multilingual translation. Our approach seeks to retain as much of the parameter sharing generalization of NMT models as possible, while still allowing for language-specific specialization of the attention model to a particular language-pair or task. Our experiments on four languages of the Europarl corpus show that using a target-specific model of attention provides consistent gains in translation quality for all possible translation directions, compared to a model in which all parameters are shared. We observe improved translation quality even in the (extreme) low-resource zero-shot translation directions for which the model never saw explicitly paired parallel data.",
}
@inproceedings{dellert-2018-combining,
    title = "Combining Information-Weighted Sequence Alignment and Sound Correspondence Models for Improved Cognate Detection",
    author = "Dellert, Johannes",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1264",
    pages = "3123--3133",
    abstract = "Methods for automated cognate detection in historical linguistics invariably build on some measure of form similarity which is designed to capture the remaining systematic similarities between cognate word forms after thousands of years of divergence. A wide range of clustering and classification algorithms has been explored for the purpose, whereas possible improvements on the level of pairwise form similarity measures have not been the main focus of research. The approach presented in this paper improves on this core component of cognate detection systems by a novel combination of information weighting, a technique for putting less weight on reoccurring morphological material, with sound correspondence modeling by means of pointwise mutual information. In evaluations on expert cognacy judgments over a subset of the IPA-encoded NorthEuraLex database, the combination of both techniques is shown to lead to considerable improvements in average precision for binary cognate detection, and modest improvements for distance-based cognate clustering.",
}
@inproceedings{passban-etal-2018-tailoring,
    title = "Tailoring Neural Architectures for Translating from Morphologically Rich Languages",
    author = "Passban, Peyman  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1265",
    pages = "3134--3145",
    abstract = "A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. When translating from morphologically rich languages (MRLs), a source word could be mapped to several words or even a full sentence on the target side, which means an MCW should not be treated as an atomic unit. In order to provide better translations for MRLs, we boost the existing neural machine translation (NMT) architecture with a double- channel encoder and a double-attentive decoder. The main goal targeted in this research is to provide richer information on the encoder side and redesign the decoder accordingly to benefit from such information. Our experimental results demonstrate that we could achieve our goal as the proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English.",
}
@inproceedings{ive-etal-2018-deepquest,
    title = "deep{Q}uest: A Framework for Neural-based Quality Estimation",
    author = "Ive, Julia  and
      Blain, Fr{\'e}d{\'e}ric  and
      Specia, Lucia",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1266",
    pages = "3146--3157",
    abstract = "Predicting Machine Translation (MT) quality can help in many practical tasks such as MT post-editing. The performance of Quality Estimation (QE) methods has drastically improved recently with the introduction of neural approaches to the problem. However, thus far neural approaches have only been designed for word and sentence-level prediction. We present a neural framework that is able to accommodate neural QE approaches at these fine-grained levels and generalize them to the level of documents. We test the framework with two sentence-level neural QE approaches: a state of the art approach that requires extensive pre-training, and a new light-weight approach that we propose, which employs basic encoders. Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks. To our knowledge, this is the first neural architecture for document-level QE. In addition, for the first time we apply QE models to the output of both statistical and neural MT systems for a series of European languages and highlight the new challenges resulting from the use of neural MT.",
}
@inproceedings{kabbach-etal-2018-butterfly,
    title = "Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking",
    author = "Kabbach, Alexandre  and
      Ribeyre, Corentin  and
      Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1267",
    pages = "3158--3169",
    abstract = "Knowing the state-of-the-art for a particular task is an essential component of any computational linguistics investigation. But can we be truly confident that the current state-of-the-art is indeed the best performing model? In this paper, we study the case of frame semantic parsing, a well-established task with multiple shared datasets. We show that in spite of all the care taken to provide a standard evaluation resource, small variations in data processing can have dramatic consequences for ranking parser performance. This leads us to propose an open-source standardized processing pipeline, which can be shared and reused for robust model comparison.",
}
@inproceedings{sadeghi-scheutz-2018-sensitivity,
    title = "Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited {B}ayesian Cross-Situational Word Learning Model",
    author = "Sadeghi, Sepideh  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1268",
    pages = "3170--3180",
    abstract = "We present a variation of the incremental and memory-limited algorithm in (Sadeghi et al., 2017) for Bayesian cross-situational word learning and evaluate the model in terms of its functional performance and its sensitivity to input order. We show that the functional performance of our sub-optimal model on corpus data is close to that of its optimal counterpart (Frank et al., 2009), while only the sub-optimal model is capable of predicting the input order effects reported in experimental studies.",
}
@inproceedings{zhang-xiong-2018-sentence,
    title = "Sentence Weighting for Neural Machine Translation Domain Adaptation",
    author = "Zhang, Shiqi  and
      Xiong, Deyi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1269",
    pages = "3181--3190",
    abstract = "In this paper, we propose a new sentence weighting method for the domain adaptation of neural machine translation. We introduce a domain similarity metric to evaluate the relevance between a sentence and an available entire domain dataset. The similarity of each sentence to the target domain is calculated with various methods. The computed similarity is then integrated into the training objective to weight sentences. The adaptation results on both IWSLT Chinese-English TED task and a task with only synthetic training parallel data show that our sentence weighting method is able to achieve an significant improvement over strong baselines.",
}
@inproceedings{aufrant-etal-2018-quantifying,
    title = "Quantifying training challenges of dependency parsers",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1270",
    pages = "3191--3202",
    abstract = "Not all dependencies are equal when training a dependency parser: some are straightforward enough to be learned with only a sample of data, others embed more complexity. This work introduces a series of metrics to quantify those differences, and thereby to expose the shortcomings of various parsing algorithms and strategies. Apart from a more thorough comparison of parsing systems, these new tools also prove useful for characterizing the information conveyed by cross-lingual parsers, in a quantitative but still interpretable way.",
}
@inproceedings{li-etal-2018-seq2seq,
    title = "Seq2seq Dependency Parsing",
    author = "Li, Zuchao  and
      Cai, Jiaxun  and
      He, Shexia  and
      Zhao, Hai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1271",
    pages = "3203--3214",
    abstract = "This paper presents a sequence to sequence (seq2seq) dependency parser by directly predicting the relative position of head for each given word, which therefore results in a truly end-to-end seq2seq dependency parser for the first time. Enjoying the advantage of seq2seq modeling, we enrich a series of embedding enhancement, including firstly introduced subword and node2vec augmentation. Meanwhile, we propose a beam search decoder with tree constraint and subroot decomposition over the sequence to furthermore enhance our seq2seq parser. Our parser is evaluated on benchmark treebanks, being on par with the state-of-the-art parsers by achieving 94.11{\%} UAS on PTB and 88.78{\%} UAS on CTB, respectively.",
}
@inproceedings{kadar-etal-2018-revisiting,
    title = "Revisiting the Hierarchical Multiscale {LSTM}",
    author = "K{\'a}d{\'a}r, {\'A}kos  and
      C{\^o}t{\'e}, Marc-Alexandre  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1272",
    pages = "3215--3227",
    abstract = "Hierarchical Multiscale LSTM (Chung et. al., 2016) is a state-of-the-art language model that learns interpretable structure from character-level input. Such models can provide fertile ground for (cognitive) computational linguistics studies. However, the high complexity of the architecture, training and implementations might hinder its applicability. We provide a detailed reproduction and ablation study of the architecture, shedding light on some of the potential caveats of re-purposing complex deep-learning architectures. We further show that simplifying certain aspects of the architecture can in fact improve its performance. We also investigate the linguistic units (segments) learned by various levels of the model, and argue that their quality does not correlate with the overall performance of the model on language modeling.",
}
@inproceedings{lee-etal-2018-character,
    title = "Character-Level Feature Extraction with Densely Connected Networks",
    author = "Lee, Chanhee  and
      Kim, Young-Bum  and
      Lee, Dongyub  and
      Lim, Heuiseok",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1273",
    pages = "3228--3239",
    abstract = "Generating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generating hand-crafted features, methods that utilize neural architectures such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) to automatically extract such features have been proposed and have shown great results. However, CNN generates position-independent features, and RNN is slow since it needs to process the characters sequentially. In this paper, we propose a novel method of using a densely connected network to automatically extract character-level features. The proposed method does not require any language or task specific assumptions, and shows robustness and effectiveness while being faster than CNN- or RNN-based methods. Evaluating this method on three sequence labeling tasks - slot tagging, Part-of-Speech (POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art performance with a 96.62 F1-score and 97.73{\%} accuracy on slot tagging and POS tagging, respectively, and comparable performance to the state-of-the-art 91.13 F1-score on NER.",
}
@inproceedings{ugawa-etal-2018-neural,
    title = "Neural Machine Translation Incorporating Named Entity",
    author = "Ugawa, Arata  and
      Tamura, Akihiro  and
      Ninomiya, Takashi  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1274",
    pages = "3240--3250",
    abstract = "This study proposes a new neural machine translation (NMT) model based on the encoder-decoder model that incorporates named entity (NE) tags of source-language sentences. Conventional NMT models have two problems enumerated as follows: (i) they tend to have difficulty in translating words with multiple meanings because of the high ambiguity, and (ii) these models{'}abilitytotranslatecompoundwordsseemschallengingbecausetheencoderreceivesaword, a part of the compound word, at each time step. To alleviate these problems, the encoder of the proposed model encodes the input word on the basis of its NE tag at each time step, which could reduce the ambiguity of the input word. Furthermore,the encoder introduces a chunk-level LSTM layer over a word-level LSTM layer and hierarchically encodes a source-language sentence to capture a compound NE as a chunk on the basis of the NE tags. We evaluate the proposed model on an English-to-Japanese translation task with the ASPEC, and English-to-Bulgarian and English-to-Romanian translation tasks with the Europarl corpus. The evaluation results show that the proposed model achieves up to 3.11 point improvement in BLEU.",
}
@inproceedings{gupta-etal-2018-semantic,
    title = "Semantic Parsing for Technical Support Questions",
    author = "Gupta, Abhirut  and
      Ray, Anupama  and
      Dasgupta, Gargi  and
      Singh, Gautam  and
      Aggarwal, Pooja  and
      Mohapatra, Prateeti",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1275",
    pages = "3251--3259",
    abstract = "Technical support problems are very complex. In contrast to regular web queries (that contain few keywords) or factoid questions (which are a few sentences), these problems usually include attributes like a detailed description of what is failing (symptom), steps taken in an effort to remediate the failure (activity), and sometimes a specific request or ask (intent). Automating support is the task of automatically providing answers to these problems given a corpus of solution documents. Traditional approaches to this task rely on information retrieval and are keyword based; looking for keyword overlap between the question and solution documents and ignoring these attributes. We present an approach for semantic parsing of technical questions that uses grammatical structure to extract these attributes as a baseline, and a CRF based model that can improve performance considerably in the presence of annotated data for training. We also demonstrate that combined with reasoning, these attributes help outperform retrieval baselines.",
}
@inproceedings{lin-etal-2018-deconvolution,
    title = "Deconvolution-Based Global Decoding for Neural Machine Translation",
    author = "Lin, Junyang  and
      Sun, Xu  and
      Ren, Xuancheng  and
      Ma, Shuming  and
      Su, Jinsong  and
      Su, Qi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1276",
    pages = "3260--3271",
    abstract = "A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. As the studies of linguistics have proved that language is not linear word sequence but sequence of complex structure, translation at each step should be conditioned on the whole target-side context. To tackle the problem, we propose a new NMT model that decodes the sequence with the guidance of its structural prediction of the context of the target sequence. Our model generates translation based on the structural prediction of the target-side context so that the translation can be freed from the bind of sequential order. Experimental results demonstrate that our model is more competitive compared with the state-of-the-art methods, and the analysis reflects that our model is also robust to translating sentences of different lengths and it also reduces repetition with the instruction from the target-side context for decoding.",
}
@inproceedings{hao-etal-2018-pattern,
    title = "Pattern-revising Enhanced Simple Question Answering over Knowledge Bases",
    author = "Hao, Yanchao  and
      Liu, Hao  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1277",
    pages = "3272--3282",
    abstract = "Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks. Simple questions constitute a large part of questions queried on the web, still being a challenge to QA systems. In this work, we propose to conduct pattern extraction and entity linking first, and put forward pattern revising procedure to mitigate the error propagation problem. In order to learn to rank candidate subject-predicate pairs to enable the relevant facts retrieval given a question, we propose to do joint fact selection enhanced by relation detection. Multi-level encodings and multi-dimension information are leveraged to strengthen the whole procedure. The experimental results demonstrate that our approach sets a new record in this task, outperforming the current state-of-the-art by an absolute large margin.",
}
@inproceedings{tayyar-madabushi-etal-2018-integrating,
    title = "Integrating Question Classification and Deep Learning for improved Answer Selection",
    author = "Tayyar Madabushi, Harish  and
      Lee, Mark  and
      Barnden, John",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1278",
    pages = "3283--3294",
    abstract = "We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.",
}
@inproceedings{deng-etal-2018-knowledge,
    title = "Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge",
    author = "Deng, Yang  and
      Shen, Ying  and
      Yang, Min  and
      Li, Yaliang  and
      Du, Nan  and
      Fan, Wei  and
      Lei, Kai",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1279",
    pages = "3295--3305",
    abstract = "Answer selection is an important but challenging task. Significant progresses have been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data. In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains. Specifically, we design a knowledge module to integrate the knowledge-based representational learning into answer selection models. The learned knowledge-based representations are shared by source and target domains, which not only leverages large amounts of cross-domain data, but also benefits from a regularization effect that leads to more general representations to help tasks in new domains. To verify the effectiveness of our model, we use SQuAD-T dataset as the source domain and three other datasets (i.e., Yahoo QA, TREC QA and InsuranceQA) as the target domains. The experimental results demonstrate that KAN has remarkable applicability and generality, and consistently outperforms the strong competitors by a noticeable margin for cross-domain answer selection.",
}
@inproceedings{sorokin-gurevych-2018-modeling,
    title = "Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering",
    author = "Sorokin, Daniil  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1280",
    pages = "3306--3317",
    abstract = "The most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. We show on two data sets that the graph networks outperform all baseline models that do not explicitly model the structure. The error analysis confirms that our approach can successfully process complex semantic parses.",
}
@inproceedings{amidei-etal-2018-rethinking,
    title = "Rethinking the Agreement in Human Evaluation Tasks",
    author = "Amidei, Jacopo  and
      Piwek, Paul  and
      Willis, Alistair",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1281",
    pages = "3318--3329",
    abstract = "Human evaluations are broadly thought to be more valuable the higher the inter-annotator agreement. In this paper we examine this idea. We will describe our experiments and analysis within the area of Automatic Question Generation. Our experiments show how annotators diverge in language annotation tasks due to a range of ineliminable factors. For this reason, we believe that annotation schemes for natural language generation tasks that are aimed at evaluating language quality need to be treated with great care. In particular, an unchecked focus on reduction of disagreement among annotators runs the danger of creating generation goals that reward output that is more distant from, rather than closer to, natural human-like language. We conclude the paper by suggesting a new approach to the use of the agreement metrics in natural language generation evaluation tasks.",
}
@inproceedings{ghaeini-etal-2018-dependent,
    title = "Dependent Gated Reading for Cloze-Style Question Answering",
    author = "Ghaeini, Reza  and
      Fern, Xiaoli  and
      Shahbazi, Hamed  and
      Tadepalli, Prasad",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1282",
    pages = "3330--3345",
    abstract = "We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interdependency between the document and the query. In this paper, we propose a novel dependent gated reading bidirectional GRU network (DGR) to efficiently model the relationship between the document and the query during encoding and decision making. Our evaluation shows that DGR obtains highly competitive performance on well-known machine comprehension benchmarks such as the Children{'}s Book Test (CBT-NE and CBT-CN) and Who DiD What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate our model by ablation and attention studies.",
}
@inproceedings{thorne-vlachos-2018-automated,
    title = "Automated Fact Checking: Task Formulations, Methods and Future Directions",
    author = "Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1283",
    pages = "3346--3359",
    abstract = "The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in a variety of disciplines including natural language processing, machine learning, knowledge representation, databases, and journalism. While there has been substantial progress, relevant papers and articles have been published in research communities that are often unaware of each other and use inconsistent terminology, thus impeding understanding and further progress. In this paper we survey automated fact checking research stemming from natural language processing and related disciplines, unifying the task formulations and methodologies across papers and authors. Furthermore, we highlight the use of evidence as an important distinguishing factor among them cutting across task formulations and methods. We conclude with proposing avenues for future NLP research on automated fact checking.",
}
@inproceedings{dungs-etal-2018-rumour,
    title = "Can Rumour Stance Alone Predict Veracity?",
    author = "Dungs, Sebastian  and
      Aker, Ahmet  and
      Fuhr, Norbert  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1284",
    pages = "3360--3370",
    abstract = "Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets{'} times as the only features for modelling true and false rumours achieve F1 scores in the range of 80{\%}, outperforming those approaches where stance is used jointly with content and user based features.",
}
@inproceedings{de-sarkar-etal-2018-attending,
    title = "Attending Sentences to detect Satirical Fake News",
    author = "De Sarkar, Sohan  and
      Yang, Fan  and
      Mukherjee, Arjun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1285",
    pages = "3371--3380",
    abstract = "Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sentence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial performance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news.",
}
@inproceedings{sasaki-etal-2018-predicting,
    title = "Predicting Stances from Social Media Posts using Factorization Machines",
    author = "Sasaki, Akira  and
      Hanawa, Kazuaki  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1286",
    pages = "3381--3390",
    abstract = "Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. An important step to analyze the discussions on social media and to assist in healthy decision-making is stance detection. This paper presents an approach to detect the stance of a user toward a topic based on their stances toward other topics and the social media posts of the user. We apply factorization machines, a widely used method in item recommendation, to model user preferences toward topics from the social media data. The experimental results demonstrate that users{'} posts are useful to model topic preferences and therefore predict stances of silent users.",
}
@inproceedings{perez-rosas-etal-2018-automatic,
    title = "Automatic Detection of Fake News",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Kleinberg, Bennett  and
      Lefevre, Alexandra  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1287",
    pages = "3391--3401",
    abstract = "The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analyses on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors, and show that we can achieve accuracies of up to 76{\%}. In addition, we provide comparative analyses of the automatic and manual identification of fake news.",
}
@inproceedings{kochkina-etal-2018-one,
    title = "All-in-one: Multi-task Learning for Rumour Verification",
    author = "Kochkina, Elena  and
      Liakata, Maria  and
      Zubiaga, Arkaitz",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1288",
    pages = "3402--3413",
    abstract = "Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stance classification, leading to the final outcome of determining the veracity of a rumour. In previous work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification. We examine the connection between the dataset properties and the outcomes of the multi-task learning models used.",
}
@inproceedings{groth-etal-2018-open,
    title = "Open Information Extraction on Scientific Text: An Evaluation",
    author = "Groth, Paul  and
      Lauruhn, Mike  and
      Scerri, Antony  and
      Daniel Jr., Ron",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1289",
    pages = "3414--3423",
    abstract = "Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems using a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.",
}
@inproceedings{k-sarma-sethares-2018-simple,
    title = "Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains.",
    author = "K Sarma, Prathusha  and
      Sethares, William",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1290",
    pages = "3424--3435",
    abstract = "Standard word embedding algorithms learn vector representations from large corpora of text documents in an unsupervised fashion. However, the quality of word embeddings learned from these algorithms is affected by the size of training data sets. Thus, applications of these algorithms in domains with only moderate amounts of available data is limited. In this paper we introduce an algorithm that learns word embeddings jointly with a classifier. Our algorithm is called SWESA (Supervised Word Embeddings for Sentiment Analysis). SWESA leverages document label information to learn vector representations of words from a modest corpus of text documents by solving an optimization problem that minimizes a cost function with respect to both word embeddings and the weight vector used for classification. Experiments on several real world data sets show that SWESA has superior performance on domains with limited data, when compared to previously suggested approaches to word embeddings and sentiment analysis tasks.",
}
@inproceedings{leeuwenberg-moens-2018-word,
    title = "Word-Level Loss Extensions for Neural Temporal Relation Classification",
    author = "Leeuwenberg, Artuur  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1291",
    pages = "3436--3447",
    abstract = "Unsupervised pre-trained word embeddings are used effectively for many tasks in natural language processing to leverage unlabeled textual data. Often these embeddings are either used as initializations or as fixed word representations for task-specific classification models. In this work, we extend our classification model{'}s task loss with an unsupervised auxiliary loss on the word-embedding level of the model. This is to ensure that the learned word representations contain both task-specific features, learned from the supervised loss component, and more general features learned from the unsupervised loss component. We evaluate our approach on the task of temporal relation extraction, in particular, narrative containment relation extraction from clinical records, and show that continued training of the embeddings on the unsupervised objective together with the task objective gives better task-specific embeddings, and results in an improvement over the state of the art on the THYME dataset, using only a general-domain part-of-speech tagger as linguistic resource.",
}
@inproceedings{yeung-lee-2018-personalized,
    title = "Personalized Text Retrieval for Learners of {C}hinese as a Foreign Language",
    author = "Yeung, Chak Yan  and
      Lee, John",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1292",
    pages = "3448--3455",
    abstract = "This paper describes a personalized text retrieval algorithm that helps language learners select the most suitable reading material in terms of vocabulary complexity. The user first rates their knowledge of a small set of words, chosen by a graph-based active learning model. The system trains a complex word identification model on this set, and then applies the model to find texts that contain the desired proportion of new, challenging, and familiar vocabulary. In an evaluation on learners of Chinese as a foreign language, we show that this algorithm is effective in identifying simpler texts for low-proficiency learners, and more challenging ones for high-proficiency learners.",
}
@inproceedings{markov-etal-2018-punctuation,
    title = "Punctuation as Native Language Interference",
    author = "Markov, Ilia  and
      Nastase, Vivi  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1293",
    pages = "3456--3466",
    abstract = "In this paper, we describe experiments designed to explore and evaluate the impact of punctuation marks on the task of native language identification. Punctuation is specific to each language, and is part of the indicators that overtly represent the manner in which each language organizes and conveys information. Our experiments are organized in various set-ups: the usual multi-class classification for individual languages, also considering classification by language groups, across different proficiency levels, topics and even cross-corpus. The results support our hypothesis that punctuation marks are persistent and robust indicators of the native language of the author, which do not diminish in influence even when a high proficiency level in a non-native language is achieved.",
}
@inproceedings{zilio-etal-2018-investigating,
    title = "Investigating Productive and Receptive Knowledge: A Profile for Second Language Learning",
    author = "Zilio, Leonardo  and
      Wilkens, Rodrigo  and
      Fairon, C{\'e}drick",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1294",
    pages = "3467--3478",
    abstract = "The literature frequently addresses the differences in receptive and productive vocabulary, but grammar is often left unacknowledged in second language acquisition studies. In this paper, we used two corpora to investigate the divergences in the behavior of pedagogically relevant grammatical structures in reception and production texts. We further improved the divergence scores observed in this investigation by setting a polarity to them that indicates whether there is overuse or underuse of a grammatical structure by language learners. This led to the compilation of a language profile that was later combined with vocabulary and readability features for classifying reception and production texts in three classes: beginner, intermediate, and advanced. The results of the automatic classification task in both production (0.872 of F-measure) and reception (0.942 of F-measure) were comparable to the current state of the art. We also attempted to automatically attribute a score to texts produced by learners, and the correlation results were encouraging, but there is still a good amount of room for improvement in this task. The developed language profile will serve as input for a system that helps language learners to activate more of their passive knowledge in writing texts.",
}
@inproceedings{chu-etal-2018-iparaphrasing,
    title = "i{P}araphrasing: Extracting Visually Grounded Paraphrases via an Image",
    author = "Chu, Chenhui  and
      Otani, Mayu  and
      Nakashima, Yuta",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1295",
    pages = "3479--3492",
    abstract = "A paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this paper, we propose a novel task iParaphrasing to extract visually grounded paraphrases (VGPs), which are different phrasal expressions describing the same visual concept in an image. These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning. How to model the similarity between VGPs is the key of iParaphrasing. We apply various existing methods as well as propose a novel neural network-based method with image attention, and report the results of the first attempt toward iParaphrasing.",
}
@inproceedings{jiang-etal-2018-mcdtb,
    title = "{MCDTB}: A Macro-level {C}hinese Discourse {T}ree{B}ank",
    author = "Jiang, Feng  and
      Xu, Sheng  and
      Chu, Xiaomin  and
      Li, Peifeng  and
      Zhu, Qiaoming  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1296",
    pages = "3493--3504",
    abstract = "In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus. Fol-lowing RST (Rhetorical Structure Theory), we annotate the macro discourse information, including discourse structure, nuclearity and relationship, and the additional discourse information, including topic sentences, lead and abstract, to make the macro discourse annotation more objective and accurate. Finally, we annotated 720 articles with a Kappa value greater than 0.6. Preliminary experiments on this corpus verify the computability of MCDTB.",
}
@inproceedings{srinivasan-etal-2018-corpus,
    title = "Corpus-based Content Construction",
    author = "Srinivasan, Balaji Vasan  and
      Maneriker, Pranav  and
      Krishna, Kundan  and
      Modani, Natwar",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1297",
    pages = "3505--3515",
    abstract = "Enterprise content writers are engaged in writing textual content for various purposes. Often, the text being written may already be present in the enterprise corpus in the form of past articles and can be re-purposed for the current needs. In the absence of suitable tools, authors manually curate/create such content (sometimes from scratch) which reduces their productivity. To address this, we propose an automatic approach to generate an initial version of the author{'}s intended text based on an input content snippet. Starting with a set of extracted textual fragments related to the snippet based on the query words in it, the proposed approach builds the desired text from these fragment by simultaneously optimizing the information coverage, relevance, diversity and coherence in the generated content. Evaluations on standard datasets shows improved performance against existing baselines on several metrics.",
}
@inproceedings{roesiger-etal-2018-bridging,
    title = "Bridging resolution: Task definition, corpus resources and rule-based experiments",
    author = "Roesiger, Ina  and
      Riester, Arndt  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1298",
    pages = "3516--3528",
    abstract = "Recent work on bridging resolution has so far been based on the corpus ISNotes (Markert et al. 2012), as this was the only corpus available with unrestricted bridging annotation. Hou et al. 2014{'}s rule-based system currently achieves state-of-the-art performance on this corpus, as learning-based approaches suffer from the lack of available training data. Recently, a number of new corpora with bridging annotations have become available. To test the generalisability of the approach by Hou et al. 2014, we apply a slightly extended rule-based system to these corpora. Besides the expected out-of-domain effects, we also observe low performance on some of the in-domain corpora. Our analysis shows that this is the result of two very different phenomena being defined as bridging, namely referential and lexical bridging. We also report that filtering out gold or predicted coreferent anaphors before applying the bridging resolution system helps improve bridging resolution.",
}
@inproceedings{wang-etal-2018-semi,
    title = "Semi-Supervised Disfluency Detection",
    author = "Wang, Feng  and
      Chen, Wei  and
      Yang, Zhen  and
      Dong, Qianqian  and
      Xu, Shuang  and
      Xu, Bo",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1299",
    pages = "3529--3538",
    abstract = "While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.",
}
@inproceedings{mezza-etal-2018-iso,
    title = "{ISO}-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents",
    author = "Mezza, Stefano  and
      Cervone, Alessandra  and
      Stepanov, Evgeny  and
      Tortoreto, Giuliano  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1300",
    pages = "3539--3551",
    abstract = "Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers{'} intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.",
}
@inproceedings{alikhani-stone-2018-arrows,
    title = "Arrows are the Verbs of Diagrams",
    author = "Alikhani, Malihe  and
      Stone, Matthew",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1301",
    pages = "3552--3563",
    abstract = "Arrows are a key ingredient of schematic pictorial communication. This paper investigates the interpretation of arrows through linguistic, crowdsourcing and machine-learning methodology. Our work establishes a novel analogy between arrows and verbs: we advocate representing arrows in terms of qualitatively different structural and semantic frames, and resolving frames to specific interpretations using shallow world knowledge.",
}
@inproceedings{zamaraeva-etal-2018-improving,
    title = "Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection",
    author = "Zamaraeva, Olga  and
      Howell, Kristen  and
      Rhine, Adam",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1302",
    pages = "3564--3575",
    abstract = "We use a broad coverage, linguistically precise English Resource Grammar (ERG) to detect negation scope in sentences taken from pathology reports. We show that incorporating this information in feature extraction has a positive effect on classification of the reports with respect to cancer laterality compared with NegEx, a commonly used tool for negation detection. We analyze the differences between NegEx and ERG results on our dataset and how these differences indicate some directions for future work.",
}
@inproceedings{wang-etal-2018-bridge,
    title = "Bridge Video and Text with Cascade Syntactic Structure",
    author = "Wang, Guolong  and
      Qin, Zheng  and
      Xu, Kaiping  and
      Huang, Kai  and
      Ye, Shuxiong",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1303",
    pages = "3576--3585",
    abstract = "We present a video captioning approach that encodes features by progressively completing syntactic structure (LSTM-CSS). To construct basic syntactic structure (i.e., subject, predicate, and object), we use a Conditional Random Field to label semantic representations (i.e., motions, objects). We argue that in order to improve the comprehensiveness of the description, the local features within object regions can be used to generate complementary syntactic elements (e.g., attribute, adverbial). Inspired by redundancy of human receptors, we utilize a Region Proposal Network to focus on the object regions. To model the final temporal dynamics, Recurrent Neural Network with Path Embeddings is adopted. We demonstrate the effectiveness of LSTM-CSS on generating natural sentences: 42.3{\%} and 28.5{\%} in terms of BLEU@4 and METEOR. Superior performance when compared to state-of-the-art methods are reported on a large video description dataset (i.e., MSR-VTT-2016).",
}
@inproceedings{masumura-etal-2018-multi,
    title = "Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling",
    author = "Masumura, Ryo  and
      Tanaka, Tomohiro  and
      Higashinaka, Ryuichiro  and
      Masataki, Hirokazu  and
      Aono, Yushi",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1304",
    pages = "3586--3596",
    abstract = "This paper is an initial study on multi-task and multi-lingual joint learning for lexical utterance classification. A major problem in constructing lexical utterance classification modules for spoken dialogue systems is that individual data resources are often limited or unbalanced among tasks and/or languages. Various studies have examined joint learning using neural-network based shared modeling; however, previous joint learning studies focused on either cross-task or cross-lingual knowledge transfer. In order to simultaneously support both multi-task and multi-lingual joint learning, our idea is to explicitly divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and task-specific components that can be shared between different languages. In addition, in order to effectively transfer knowledge between different task data sets and different language data sets, this paper proposes a partially-shared modeling method that possesses both shared components and components specific to individual data sets. We demonstrate the effectiveness of proposed method using Japanese and English data sets with three different lexical utterance classification tasks.",
}
@inproceedings{bai-etal-2018-source,
    title = "Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language",
    author = "Bai, He  and
      Zhou, Yu  and
      Zhang, Jiajun  and
      Zhao, Liang  and
      Hwang, Mei-Yuh  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1305",
    pages = "3597--3607",
    abstract = "To deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. An SLU corpus is a monolingual corpus with domain/intent/slot labels. Translating the original SLU corpus into the target language is an attractive strategy. However, SLU corpora consist of plenty of semantic labels (slots), which general-purpose translators cannot handle well, not to mention additional culture differences. This paper focuses on the language transferring task given a small in-domain parallel SLU corpus. The in-domain parallel corpus can be used as the first adaptation on the general translator. But more importantly, we show how to use reinforcement learning (RL) to further adapt the adapted translator, where translated sentences with more proper slot tags receive higher rewards. Our reward is derived from the source input sentence exclusively, unlike reward via actor-critical methods or computing reward with a ground truth target sentence. Hence we can adapt the translator the second time, using the big monolingual SLU corpus from the source language. We evaluate our approach on Chinese to English language transferring for SLU systems. The experimental results show that the generated English SLU corpus via adaptation and reinforcement learning gives us over 97{\%} in the slot F1 score and over 84{\%} accuracy in domain classification. It demonstrates the effectiveness of the proposed language transferring method. Compared with naive translation, our proposed method improves domain classification accuracy by relatively 22{\%}, and the slot filling F1 score by relatively more than 71{\%}.",
}
@inproceedings{wang-etal-2018-prospective,
    title = "A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation",
    author = "Wang, Zongsheng  and
      Bai, Yunzhi  and
      Wu, Bowen  and
      Xu, Zhen  and
      Wang, Zhuoran  and
      Wang, Baoxun",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1306",
    pages = "3608--3618",
    abstract = "Generative dialog models usually adopt beam search as the inference method to generate responses. However, small-width beam search only focuses on the limited current optima. This deficiency named as myopic bias ultimately suppresses the diversity and probability of generated responses. Although increasing the beam width mitigates the myopic bias, it also proportionally slows down the inference efficiency. To alleviate the myopic bias in small-width beam search, this paper proposes a Prospective-Performance Network (PPN) to predict the future reward of the given partially-generated response, and the future reward is defined by the expectation of the partial response appearing in the top-ranked responses given by a larger-width beam search. Enhanced by PPN, the decoder can promote the results with great potential during the beam search phase. The experimental results on both Chinese and English corpora show that our method is promising to increase the quality and diversity of generated responses, with inference efficiency well maintained.",
}
@inproceedings{xing-etal-2018-adaptive,
    title = "Adaptive Multi-Task Transfer Learning for {C}hinese Word Segmentation in Medical Text",
    author = "Xing, Junjie  and
      Zhu, Kenny  and
      Zhang, Shaodian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1307",
    pages = "3619--3630",
    abstract = "Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop when dealing with domain text, especially for a domain with lots of special terms and diverse writing styles, such as the biomedical domain. However, building domain-specific CWS requires extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant knowledge from high resource to low resource domains. Extensive experiments show that our model achieves consistently higher accuracy than the single-task CWS and other transfer learning baselines, especially when there is a large disparity between source and target domains.",
}
@inproceedings{sato-etal-2018-addressee,
    title = "Addressee and Response Selection for Multilingual Conversation",
    author = "Sato, Motoki  and
      Ouchi, Hiroki  and
      Tsuboi, Yuta",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1308",
    pages = "3631--3644",
    abstract = "Developing conversational systems that can converse in many languages is an interesting challenge for natural language processing. In this paper, we introduce multilingual addressee and response selection. In this task, a conversational system predicts an appropriate addressee and response for an input message in multiple languages. A key to developing such multilingual responding systems is how to utilize high-resource language data to compensate for low-resource language data. We present several knowledge transfer methods for conversational systems. To evaluate our methods, we create a new multilingual conversation dataset. Experiments on the dataset demonstrate the effectiveness of our methods.",
}
@inproceedings{liu-etal-2018-graph,
    title = "Graph Based Decoding for Event Sequencing and Coreference Resolution",
    author = "Liu, Zhengzhong  and
      Mitamura, Teruko  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1309",
    pages = "3645--3657",
    abstract = "Events in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding structure for automated Event Coreference is not suitable for Event Sequencing. To this end, we propose a graph-based decoding algorithm that is applicable to both tasks. The new decoding algorithm supports flexible feature sets for both tasks. Empirically, our event coreference system has achieved state-of-the-art performance on the TAC-KBP 2015 event coreference task and our event sequencing system beats a strong temporal-based, oracle-informed baseline. We discuss the challenges of studying these event relations.",
}
@inproceedings{van-miltenburg-etal-2018-didec,
    title = "{DIDEC}: The {D}utch Image Description and Eye-tracking Corpus",
    author = "van Miltenburg, Emiel  and
      K{\'a}d{\'a}r, {\'A}kos  and
      Koolen, Ruud  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1310",
    pages = "3658--3669",
    abstract = "We present a corpus of spoken Dutch image descriptions, paired with two sets of eye-tracking data: Free viewing, where participants look at images without any particular purpose, and Description viewing, where we track eye movements while participants produce spoken descriptions of the images they are viewing. This paper describes the data collection procedure and the corpus itself, and provides an initial analysis of self-corrections in image descriptions. We also present two studies showing the potential of this data. Though these studies mainly serve as an example, we do find two interesting results: (1) the eye-tracking data for the description viewing task is more coherent than for the free-viewing task; (2) variation in image descriptions (also called {`}image specificity{'}; Jas and Parikh, 2015) is only moderately correlated across different languages. Our corpus can be used to gain a deeper understanding of the image description task, particularly how visual attention is correlated with the image description process.",
}
@inproceedings{simonson-davis-2018-narrative,
    title = "Narrative Schema Stability in News Text",
    author = "Simonson, Dan  and
      Davis, Anthony",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1311",
    pages = "3670--3680",
    abstract = "We investigate the stability of narrative schemas (Chambers and Jurafsky, 2009) automatically induced from a news corpus, representing recurring narratives in a corpus. If such techniques produce meaningful results, we should expect that small changes to the corpus will result in only small changes to the induced schemas. We describe experiments involving successive ablation of a corpus and cross-validation at each stage of ablation, on schemas generated by three different techniques over a general news corpus and topically-specific subcorpora. We also develop a method for evaluating the similarity between sets of narrative schemas, and thus the stability of the schema induction algorithms. This stability analysis affirms the heterogeneous/homogeneous document category hypothesis first presented in Simonson and Davis (2016), whose technique is problematically limited. Additionally, increased ablation leads to increasing stability, so the smaller the remaining corpus, the more stable schema generation appears to be. We surmise that as a corpus grows larger, novel and more varied narratives continue to appear and stability declines, though at some point this decline levels off as new additions to the corpus consist essentially of {``}more of the same.{''}",
}
@inproceedings{yusupov-kuratov-2018-nips,
    title = "{NIPS} Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager",
    author = "Yusupov, Idris  and
      Kuratov, Yurii",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1312",
    pages = "3681--3692",
    abstract = "We present bot{\#}1337: a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI). The aim of the competition was to implement a bot capable of conversing with humans based on a given passage of text. To enable conversation, we implemented a set of skills for our bot, including chit-chat, topic detection, text summarization, question answering and question generation. The system has been trained in a supervised setting using a dialogue manager to select an appropriate skill for generating a response. The latter allows a developer to focus on the skill implementation rather than the finite state machine based dialog manager. The proposed system bot{\#}1337 won the competition with an average dialogue quality score of 2.78 out of 5 given by human evaluators. Source code and trained models for the bot{\#}1337 are available on GitHub.",
}
@inproceedings{ogorman-etal-2018-amr,
    title = "{AMR} Beyond the Sentence: the Multi-sentence {AMR} corpus",
    author = "O{'}Gorman, Tim  and
      Regan, Michael  and
      Griffitt, Kira  and
      Hermjakob, Ulf  and
      Knight, Kevin  and
      Palmer, Martha",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1313",
    pages = "3693--3702",
    abstract = "There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.",
}
@inproceedings{ji-etal-2018-incorporating,
    title = "Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model",
    author = "Ji, Lu  and
      Wei, Zhongyu  and
      Hu, Xiangkun  and
      Liu, Yang  and
      Zhang, Qi  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1314",
    pages = "3703--3714",
    abstract = "In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.",
}
@inproceedings{shi-etal-2018-learning,
    title = "Learning Visually-Grounded Semantics from Contrastive Adversarial Samples",
    author = "Shi, Haoyue  and
      Mao, Jiayuan  and
      Xiao, Tete  and
      Jiang, Yuning  and
      Sun, Jian",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1315",
    pages = "3715--3727",
    abstract = "We study the problem of grounding distributional representations of texts on the visual domain, namely visual-semantic embeddings (VSE for short). Begin with an insightful adversarial attack on VSE embeddings, we show the limitation of current frameworks and image-text datasets (e.g., MS-COCO) both quantitatively and qualitatively. The large gap between the number of possible constitutions of real-world semantics and the size of parallel data, to a large extent, restricts the model to establish a strong link between textual semantics and visual concepts. We alleviate this problem by augmenting the MS-COCO image captioning datasets with textual contrastive adversarial samples. These samples are synthesized using language priors of human and the WordNet knowledge base, and enforce the model to ground learned embeddings to concrete concepts within the image. This simple but powerful technique brings a noticeable improvement over the baselines on a diverse set of downstream tasks, in addition to defending known-type adversarial attacks. Codes are available at https://github.com/ExplorerFreda/VSE-C.",
}
@inproceedings{li-etal-2018-structured,
    title = "Structured Representation Learning for Online Debate Stance Prediction",
    author = "Li, Chang  and
      Porco, Aldo  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1316",
    pages = "3728--3739",
    abstract = "Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users{'} conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.",
}
@inproceedings{zhang-etal-2018-modeling,
    title = "Modeling Multi-turn Conversation with Deep Utterance Aggregation",
    author = "Zhang, Zhuosheng  and
      Li, Jiangtong  and
      Zhu, Pengfei  and
      Zhao, Hai  and
      Liu, Gongshen",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1317",
    pages = "3740--3752",
    abstract = "Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus.",
}
@inproceedings{wachsmuth-etal-2018-argumentation,
    title = "Argumentation Synthesis following Rhetorical Strategies",
    author = "Wachsmuth, Henning  and
      Stede, Manfred  and
      El Baff, Roxanne  and
      Al-Khatib, Khalid  and
      Skeppstedt, Maria  and
      Stein, Benno",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1318",
    pages = "3753--3765",
    abstract = "Persuasion is rarely achieved through a loose set of arguments alone. Rather, an effective delivery of arguments follows a rhetorical strategy, combining logical reasoning with appeals to ethics and emotion. We argue that such a strategy means to select, arrange, and phrase a set of argumentative discourse units. In this paper, we model rhetorical strategies for the computational synthesis of effective argumentation. In a study, we let 26 experts synthesize argumentative texts with different strategies for 10 topics. We find that the experts agree in the selection significantly more when following the same strategy. While the texts notably vary for different strategies, especially their arrangement remains stable. The results suggest that our model enables a strategical synthesis.",
}
@inproceedings{banerjee-etal-2018-dataset,
    title = "A Dataset for Building Code-Mixed Goal Oriented Conversation Systems",
    author = "Banerjee, Suman  and
      Moghe, Nikita  and
      Arora, Siddhartha  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1319",
    pages = "3766--3780",
    abstract = "There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc. Most of the existing datasets for building such conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch between them resulting in code-mixed conversations. For example, a Hindi speaking user looking to book a restaurant would typically ask, {``}Kya tum is restaurant mein ek table book karne mein meri help karoge?{''} ({``}Can you help me in booking a table at this restaurant?{''}). To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations. Specifically, we take the text from the DSTC2 restaurant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English. We also establish initial baselines on this dataset using existing state of the art models. This dataset along with our baseline implementations will be made publicly available for research purposes.",
}
@inproceedings{wen-etal-2018-sequence,
    title = "Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation",
    author = "Wen, Haoyang  and
      Liu, Yijia  and
      Che, Wanxiang  and
      Qin, Libo  and
      Liu, Ting",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1320",
    pages = "3781--3792",
    abstract = "Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset shows that our framework significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.",
}
@inproceedings{mitra-etal-2018-incorporating,
    title = "Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering",
    author = "Mitra, Sayantan  and
      Hasanuzzaman, Mohammed  and
      Saha, Sriparna  and
      Way, Andy",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1321",
    pages = "3793--3805",
    abstract = "Current paper explores the use of multi-view learning for search result clustering. A web-snippet can be represented using multiple views. Apart from textual view cued by both the semantic and syntactic information, a complimentary view extracted from images contained in the web-snippets is also utilized in the current framework. A single consensus partitioning is finally obtained after consulting these two individual views by the deployment of a multiobjective based clustering technique. Several objective functions including the values of a cluster quality measure measuring the goodness of partitionings obtained using different views and an agreement-disagreement index, quantifying the amount of oneness among multiple views in generating partitionings are optimized simultaneously using AMOSA. In order to detect the number of clusters automatically, concepts of variable length solutions and a vast range of permutation operators are introduced in the clustering process. Finally, a set of alternative partitioning are obtained on the final Pareto front by the proposed multi-view based multiobjective technique. Experimental results by the proposed approach on several benchmark test datasets of SRC with respect to different performance metrics evidently establish the power of visual and text-based views in achieving better search result clustering.",
}
@inproceedings{miura-etal-2018-integrating,
    title = "Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts",
    author = "Miura, Yasuhide  and
      Kano, Ryuji  and
      Taniguchi, Motoki  and
      Taniguchi, Tomoki  and
      Misawa, Shotaro  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1322",
    pages = "3806--3818",
    abstract = "We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5{\%} in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.",
}
@inproceedings{ercan-yildiz-2018-anlamver,
    title = "{A}nlam{V}er: Semantic Model Evaluation Dataset for {T}urkish - Word Similarity and Relatedness",
    author = {Ercan, G{\"o}khan  and
      Y{\i}ld{\i}z, Olcay Taner},
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1323",
    pages = "3819--3836",
    abstract = "In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.",
}
@inproceedings{przepiorkowski-patejuk-2018-arguments,
    title = "Arguments and Adjuncts in {U}niversal {D}ependencies",
    author = "Przepi{\'o}rkowski, Adam  and
      Patejuk, Agnieszka",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1324",
    pages = "3837--3852",
    abstract = "The aim of this paper is to argue for a coherent Universal Dependencies approach to the core vs. non-core distinction. We demonstrate inconsistencies in the current version 2 of UD in this respect {--} mostly resulting from the preservation of the argument{--}adjunct dichotomy despite the declared avoidance of this distinction {--} and propose a relatively conservative modification of UD that is free from these problems.",
}
@inproceedings{ruppenhofer-etal-2018-distinguishing,
    title = "Distinguishing affixoid formations from compounds",
    author = "Ruppenhofer, Josef  and
      Wiegand, Michael  and
      Wilm, Rebecca  and
      Markert, Katja",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1325",
    pages = "3853--3865",
    abstract = "We study German affixoids, a type of morpheme in between affixes and free stems. Several properties have been associated with them {--} increased productivity; a bleached semantics, which is often evaluative and/or intensifying and thus of relevance to sentiment analysis; and the existence of a free morpheme counterpart {--} but not been validated empirically. In experiments on a new data set that we make available, we put these key assumptions from the morphological literature to the test and show that despite the fact that affixoids generate many low-frequency formations, we can classify these as affixoid or non-affixoid instances with a best F1-score of 74{\%}.",
}
@inproceedings{niklaus-etal-2018-survey,
    title = "A Survey on Open Information Extraction",
    author = "Niklaus, Christina  and
      Cetto, Matthias  and
      Freitas, Andr{\'e}  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1326",
    pages = "3866--3878",
    abstract = "We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address. In addition, we provide a critique of the commonly applied evaluation procedures for assessing the performance of Open IE systems and highlight some directions for future work.",
}
@inproceedings{yang-etal-2018-design,
    title = "Design Challenges and Misconceptions in Neural Sequence Labeling",
    author = "Yang, Jie  and
      Liang, Shuailong  and
      Zhang, Yue",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1327",
    pages = "3879--3889",
    abstract = "We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.",
}
@inproceedings{lan-xu-2018-neural,
    title = "Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering",
    author = "Lan, Wuwei  and
      Xu, Wei",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1328",
    pages = "3890--3902",
    abstract = "In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. Although most of these models have claimed state-of-the-art performance, the original papers often reported on only one or two selected datasets. We provide a systematic study and show that (i) encoding contextual information by LSTM and inter-sentence interactions are critical, (ii) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter datasets, (iii) the Enhanced Sequential Inference Model is the best so far for larger datasets, while the Pairwise Word Interaction Model achieves the best performance when less data is available. We release our implementations as an open-source toolkit.",
}
@inproceedings{thompson-mimno-2018-authorless,
    title = "Authorless Topic Models: Biasing Models Away from Known Structure",
    author = "Thompson, Laure  and
      Mimno, David",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1329",
    pages = "3903--3914",
    abstract = "Most previous work in unsupervised semantic modeling in the presence of metadata has assumed that our goal is to make latent dimensions more correlated with metadata, but in practice the exact opposite is often true. Some users want topic models that highlight differences between, for example, authors, but others seek more subtle connections across authors. We introduce three metrics for identifying topics that are highly correlated with metadata, and demonstrate that this problem affects between 30 and 50{\%} of the topics in models trained on two real-world collections, regardless of the size of the model. We find that we can predict which words cause this phenomenon and that by selectively subsampling these words we dramatically reduce topic-metadata correlation, improve topic stability, and maintain or even improve model quality.",
}
@inproceedings{yang-etal-2018-sgm,
    title = "{SGM}: Sequence Generation Model for Multi-label Classification",
    author = "Yang, Pengcheng  and
      Sun, Xu  and
      Li, Wei  and
      Ma, Shuming  and
      Wu, Wei  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/C18-1330",
    pages = "3915--3926",
    abstract = "Multi-label classification is an important yet challenging task in natural language processing. It is more complex than single-label classification in that the labels tend to be correlated. Existing methods tend to ignore the correlations between labels. Besides, different parts of the text can contribute differently for predicting different labels, which is not considered by existing models. In this paper, we propose to view the multi-label classification task as a sequence generation problem, and apply a sequence generation model with a novel decoder structure to solve it. Extensive experimental results show that our proposed methods outperform previous work by a substantial margin. Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.",
}
@proceedings{lilt-2018-,
    title = "",
    booktitle = "Linguistic Issues in Language Technology, Volume 16, 2018",
    month = jul,
    year = "2018",
    publisher = "CSLI Publications",
}
@inproceedings{bernardy-2018-recurrent,
    title = "Can Recurrent Neural Networks Learn Nested Recursion?",
    author = "Bernardy, Jean-Phillipe",
    booktitle = "Linguistic Issues in Language Technology, Volume 16, 2018",
    month = jul,
    year = "2018",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2018.lilt-16.1",
    abstract = "Context-free grammars (CFG) were one of the first formal tools used to model natural languages, and they remain relevant today as the basis of several frameworks. A key ingredient of CFG is the presence of nested recursion. In this paper, we investigate experimentally the capability of several recurrent neural networks (RNNs) to learn nested recursion. More precisely, we measure an upper bound of their capability to do so, by simplifying the task to learning a generalized Dyck language, namely one composed of matching parentheses of various kinds. To do so, we present the RNNs with a set of random strings having a given maximum nesting depth and test its ability to predict the kind of closing parenthesis when facing deeper nested strings. We report mixed results: when generalizing to deeper nesting levels, the accuracy of standard RNNs is significantly higher than random, but still far from perfect. Additionally, we propose some non-standard stack-based models which can approach perfect accuracy, at the cost of robustness.",
}
@proceedings{jep-taln-recital-2018-actes-de-la,
    title = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    editor = "S{\'e}billot, Pascale  and
      Claveau, Vincent",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.0",
}
@inproceedings{monnin-hamon-2018-construction,
    title = "Construction de patrons lexico-syntaxiques d{'}extraction pour l{'}acquisition de connaissances {\`a} partir du web (Relation pattern extraction and information extraction from the web)",
    author = "Monnin, Chlo{\'e}  and
      Hamon, Olivier",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.1",
    pages = "3--16",
    abstract = "Cet article pr{\'e}sente une m{\'e}thode permettant de collecter sur le web des informations compl{\'e}mentaires {\`a} une information pr{\'e}d{\'e}finie, afin de remplir une base de connaissances. Notre m{\'e}thode utilise des patrons lexico-syntaxiques, servant {\`a} la fois de requ{\^e}tes de recherche et de patrons d{'}extraction permettant l{'}analyse de documents non structur{\'e}s. Pour ce faire, il nous a fallu d{\'e}finir au pr{\'e}alable les crit{\`e}res pertinents issus des analyses dans l{'}objectif de faciliter la d{\'e}couverte de nouvelles valeurs.",
    language = "French",
}
@inproceedings{yan-2018-analysis,
    title = "Analysis of Inferences in {C}hinese for Opinion Mining",
    author = "Yan, Liyun",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.2",
    pages = "17--26",
    abstract = "Analysis of Inferences in Chinese for Opinion Mining Opinion mining is an essential activity for economic watch, made easier by social networks and ad hoc forums. The analysis generally relies on lexicon of sentiments. Nevertheless, some opinions are expressed through inferences. In this paper, we propose a classification of inferences used in Chinese in tourist comments, for an opinion mining task, based on three levels of analysis (semantic realization, modality of realization and production mode). We proved the interest to analyze the distinct types of inferences to identify the polarity of opinions expressed in corpora. We also present some results based on word embeddings.",
}
@inproceedings{wauquier-2018-analyse,
    title = "Analyse des noms agentifs dans les espaces vectoriels distributionnels (Analysis of agent nouns in vector space models)",
    author = "Wauquier, Marine",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.3",
    pages = "27--40",
    abstract = "Notre {\'e}tude s{'}inscrit dans le cadre d{'}une th{\`e}se ayant pour but d{'}exploiter les mod{\`e}les distributionnels pour d{\'e}crire s{\'e}mantiquement des classes de mots d{\'e}finies selon des crit{\`e}res morphologiques. Nous utilisons des indices morphologiques et formels fournis par une base lexicale pour cibler les noms agentifs d{\'e}verbaux construits par suffixation en -eur. Nous montrons qu{'}il est possible de constituer un repr{\'e}sentant prototypique de la classe s{\'e}mantique des noms agentifs en -eur dans les mod{\`e}les distributionnels. L{'}{\'e}tude de ce repr{\'e}sentant met en {\'e}vidence que l{'}information s{\'e}mantique v{\'e}hicul{\'e}e par le suffixe varie en fonction du corpus d{'}{\'e}tude et du degr{\'e} de lexicalisation des d{\'e}riv{\'e}s.",
    language = "French",
}
@inproceedings{lamercerie-2018-analyse,
    title = "Analyse formelle d{'}exigences en langue naturelle pour la conception de syst{\`e}mes cyber-physiques (Formal analysis of natural language requirements for the design of cyber-physical systems )",
    author = "Lamercerie, Aur{\'e}lien",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.4",
    pages = "41--54",
    abstract = "Cet article explore la construction de repr{\'e}sentations formelles d{'}{\'e}nonc{\'e}s en langue naturelle. Le passage d{'}un langage naturel {\`a} une repr{\'e}sentation logique est r{\'e}alis{\'e} avec un formalisme grammatical, reliant l{'}analyse syntaxique de l{'}{\'e}nonc{\'e} {\`a} une repr{\'e}sentation s{\'e}mantique. Nous ciblons l{'}aspect comportemental des cahiers des charges pour les syst{\`e}mes cyber-physiques, c{'}est-{\`a}-dire tout type de syst{\`e}mes dans lesquels des composants logiciels interagissent {\'e}troitement avec un environnement physique. Dans ce cadre, l{'}enjeu serait d{'}apporter une aide au concepteur. Il s{'}agit de permettre de simuler et v{\'e}rifier, par des m{\'e}thodes automatiques ou assist{\'e}es, des cahiers des charges {``}syst{\`e}mes{''} exprim{\'e}s en langue naturelle. Cet article pr{\'e}sente des solutions existantes qui pourraient {\^e}tre combin{\'e}es en vue de la r{\'e}solution de la probl{\'e}matique expos{\'e}e.",
    language = "French",
}
@inproceedings{lamsiyah-etal-2018-resume,
    title = "R{\'e}sum{\'e} automatique guid{\'e} de textes: {\'E}tat de l{'}art et perspectives (Guided Summarization : State-of-the-art and perspectives )",
    author = "Lamsiyah, Salima  and
      Ouatik El Alaoui, Said  and
      Espinasse, Bernard",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.5",
    pages = "55--72",
    abstract = "Les syst{\`e}mes de r{\'e}sum{\'e} automatique de textes (SRAT) consistent {\`a} produire une repr{\'e}sentation condens{\'e}e et pertinente {\`a} partir d{'}un ou de plusieurs documents textuels. La majorit{\'e} des SRAT sont bas{\'e}s sur des approches extractives. La tendance actuelle consiste {\`a} s{'}orienter vers les approches abstractives. Dans ce contexte, le r{\'e}sum{\'e} guid{\'e} d{\'e}fini par la campagne d{'}{\'e}valuation internationale TAC (Text Analysis Conference) en 2010, vise {\`a} encourager la recherche sur ce type d{'}approche, en se basant sur des techniques d{'}analyse en profondeur de textes. Dans ce papier, nous nous penchons sur le r{\'e}sum{\'e} automatique guid{\'e} de textes. Dans un premier temps, nous d{\'e}finissons les diff{\'e}rentes caract{\'e}ristiques et contraintes li{\'e}es {\`a} cette t{\^a}che. Ensuite, nous dressons un {\'e}tat de l{'}art des principaux syst{\`e}mes existants en mettant l{'}accent sur les travaux les plus r{\'e}cents, et en les classifiant selon les approches adopt{\'e}es, les techniques utilis{\'e}es, et leurs {\'e}valuations sur des corpus de r{\'e}f{\'e}rences. Enfin, nous proposons les grandes {\'e}tapes d{'}une m{\'e}thode sp{\'e}cifique devant permettre le d{\'e}veloppement d{'}un nouveau type de syst{\`e}mes de r{\'e}sum{\'e} guid{\'e}.",
    language = "French",
}
@inproceedings{mekki-etal-2018-identification,
    title = "Identification de descripteurs pour la caract{\'e}risation de registres (Feature identification for register characterization)",
    author = "Mekki, Jade  and
      Battistelli, Delphine  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      B{\'e}chet, Nicolas",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.6",
    pages = "73--84",
    abstract = "L{'}article pr{\'e}sente une {\'e}tude des descripteurs linguistiques pour la caract{\'e}risation d{'}un texte selon son registre de langue (familier, courant, soutenu). Cette {\'e}tude a pour but de poser un premier jalon pour des t{\^a}ches futures sur le sujet (classification, extraction de motifs discriminants). {\`A} partir d{'}un {\'e}tat de l{'}art men{\'e} sur la notion de registre dans la litt{\'e}rature linguistique et sociolinguistique, nous avons identifi{\'e} une liste de 72 descripteurs pertinents. Dans cet article, nous pr{\'e}sentons les 30 premiers que nous avons pu valider sur un corpus de textes fran{\c{c}}ais de registres distincts.",
    language = "French",
}
@inproceedings{zhai-2018-construction,
    title = "Construction d{'}un corpus multilingue annot{\'e} en relations de traduction (Construction of a multilingual corpus annotated with translation relations )",
    author = "Zhai, Yuming",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.7",
    pages = "85--100",
    abstract = "Les relations de traduction, qui distinguent la traduction litt{\'e}rale d{'}autres proc{\'e}d{\'e}s, constituent un sujet d{'}{\'e}tude important pour les traducteurs humains (Chuquet {\&} Paillard, 1989). Or les traitements automatiques fond{\'e}s sur des relations entre langues, tels que la traduction automatique ou la m{\'e}thode de g{\'e}n{\'e}ration de paraphrases par {\'e}quivalence de traduction, ne les ont pas exploit{\'e}es explicitement jusqu{'}{\`a} pr{\'e}sent. Dans ce travail, nous pr{\'e}sentons une cat{\'e}gorisation des relations de traduction et nous les annotons dans un corpus parall{\`e}le multilingue (anglais, fran{\c{c}}ais, chinois) de pr{\'e}sentations orales, les TED Talks. Notre objectif {\`a} plus long terme sera d{'}en faire la d{\'e}tection de mani{\`e}re automatique afin de pouvoir les int{\'e}grer comme caract{\'e}ristiques importantes pour la recherche de segments monolingues en relation d{'}{\'e}quivalence (paraphrases) ou d{'}implication. Le corpus annot{\'e} r{\'e}sultant de notre travail sera mis {\`a} disposition de la communaut{\'e}.",
    language = "French",
}
@inproceedings{huynh-neptune-2018-automatic,
    title = "Automatic image annotation : the case of deforestation",
    author = "Huynh, Duy  and
      Neptune, Nathalie",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.8",
    pages = "101--116",
    abstract = "Automatic image annotation : the case of deforestation. This paper aims to present the state of the art of the methods that are used for automatic annotation of earth observation image for deforestation detection. We are interested in the various challenges that the field covers and we present the state of the art methods and the future research that we are considering.",
}
@inproceedings{deturck-2018-detection,
    title = "D{\'e}tection d{'}influenceurs dans des m{\'e}dias sociaux (Influencer detection in social medias)",
    author = "Deturck, K{\'e}vin",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.9",
    pages = "117--130",
    abstract = "Les influenceurs ont la capacit{\'e} d{'}avoir un impact sur d{'}autres individus lorsqu{'}ils interagissent avec eux. D{\'e}tecter les influenceurs permet d{'}identifier les quelques individus {\`a} cibler pour toucher largement un r{\'e}seau. Il est possible d{'}analyser les interactions dans un m{\'e}dia social du point de vue de leur structure ou de leur contenu. Dans nos travaux de th{\`e}se, nous abordons ces deux aspects. Nous pr{\'e}sentons d{'}abord une {\'e}valuation de diff{\'e}rentes mesures de centralit{\'e} sur la structure d{'}interactions extraites de Twitter puis nous analysons l{'}impact de la taille du graphe de suivi sur la performance de mesures de centralit{\'e}. Nous abordons l{'}aspect linguistique pour identifier le changement d{'}avis comme un effet de l{'}influence depuis les messages d{'}un forum.",
    language = "French",
}
@inproceedings{randriatsitohaina-2018-extraction,
    title = "Extraction d{'}interactions entre aliment et m{\'e}dicament : Etat de l{'}art et premiers r{\'e}sultats (Extraction of food-drug interactions : State of the art and first results)",
    author = "Randriatsitohaina, Tsanta",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.10",
    pages = "131--144",
    abstract = "Dans cet article, nous nous int{\'e}ressons {\`a} l{'}extraction des interactions entre m{\'e}dicaments et aliments, une t{\^a}che qui s{'}apparente {\`a} l{'}extraction de relations entre termes dans les textes de sp{\'e}cialit{\'e}. De nombreuses approches ont {\'e}t{\'e} propos{\'e}es pour extraire des relations {\`a} partir de textes : des patrons lexico-syntaxiques, de la classification supervis{\'e}e, et plus r{\'e}cemment de l{'}apprentissage profond. A partir de cet {\'e}tat de l{'}art, nous pr{\'e}sentons une m{\'e}thode bas{\'e}e sur un apprentissage supervis{\'e} et les r{\'e}sultats d{'}une premi{\`e}re s{\'e}rie d{'}exp{\'e}riences. Malgr{\'e} le d{\'e}s{\'e}quilibre des classes, les r{\'e}sultats sont encourageants. Nous avons ainsi pu identifier les classifieurs les plus performants suivant les {\'e}tapes. Nous avons {\'e}galement observ{\'e} l{'}impact important des cat{\'e}gories s{\'e}mantiques des termes comme descripteurs.",
    language = "French",
}
@inproceedings{brassier-etal-2018-classification,
    title = "Classification par paires de mention pour la r{\'e}solution des cor{\'e}f{\'e}rences en fran{\c{c}}ais parl{\'e} interactif (Mention-pair classification for corefence resolution on spontaneous spoken {F}rench)",
    author = {Brassier, Ma{\"e}lle  and
      Puret, Alexis  and
      Voisin-Marras, Augustin  and
      Grobol, Lo{\"\i}c},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.11",
    pages = "145--158",
    abstract = "Cet article pr{\'e}sente et analyse les premiers r{\'e}sultats obtenus par notre laboratoire pour la construction d{'}un mod{\`e}le de r{\'e}solution des cor{\'e}f{\'e}rences en fran{\c{c}}ais {\`a} l{'}aide de techniques de classifications parmi lesquelles les arbres de d{\'e}cision et les s{\'e}parateurs {\`a} vaste marge. Ce syst{\`e}me a {\'e}t{\'e} entra{\^\i}n{\'e} sur le corpus ANCOR et s{'}inspire de travaux ant{\'e}rieurs r{\'e}alis{\'e}s au laboratoire LATTICE (syst{\`e}me CROC). Nous pr{\'e}sentons les exp{\'e}rimentations que nous avons men{\'e}es pour am{\'e}liorer le syst{\`e}me en passant par des classifieurs sp{\'e}cifiques {\`a} chaque type de situation interactive, puis chaque type de relation de cor{\'e}f{\'e}rence.",
    language = "French",
}
@inproceedings{cardon-2018-approche,
    title = "Approche lexicale de la simplification automatique de textes m{\'e}dicaux (Lexical approach for the automatic simplification of medical texts)",
    author = "Cardon, Remi",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.12",
    pages = "159--174",
    abstract = "Notre travail traite de la simplification automatique de textes. Ce type d{'}application vise {\`a} rendre des contenus difficiles {\`a} comprendre plus lisibles. {\`A} partir de trois corpus comparables du domaine m{\'e}dical, d{'}un lexique existant et d{'}une terminologie du domaine, nous proc{\'e}dons {\`a} des analyses et {\`a} des modifications en vue de la simplification lexicale de textes m{\'e}dicaux. L{'}alignement manuel des phrases provenant de ces corpus comparables fournit des donn{\'e}es de r{\'e}f{\'e}rence et permet d{'}analyser les proc{\'e}d{\'e}s de simplification mis en place. La substitution lexicale avec la ressource existante permet d{'}effectuer de premiers tests de simplification lexicale et indique que des ressources plus sp{\'e}cifiques sont n{\'e}cessaires pour traiter les textes m{\'e}dicaux. L{'}{\'e}valuation des substitutions est effectu{\'e}e avec trois crit{\`e}res : grammaticalit{\'e}, simplification et s{\'e}mantique. Elle indique que la grammaticalit{\'e} est plut{\^o}t bien sauvegard{\'e}e, alors que la s{\'e}mantique et la simplicit{\'e} sont plus difficiles {\`a} g{\'e}rer lors des substitutions avec ce type de m{\'e}thodes.",
    language = "French",
}
@inproceedings{mothe-etal-2018-classification,
    title = "Classification multi-label {\`a} grande dimension pour la d{\'e}tection de concepts m{\'e}dicaux (Large multi-label classification for medical concepts detection)",
    author = "Mothe, Josiane  and
      Ny Hoavy, Nomena  and
      Randrianarivony, Mamitiana-Ignace",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-recital.13",
    pages = "175--184",
    abstract = "Dans ce papier, nous pr{\'e}sentons une m{\'e}thode pour associer de fa{\c{c}}on automatique des concepts {\`a} des images. Nous nous focalisons plus particuli{\`e}rement sur des images m{\'e}dicales {\`a} annoter avec des concepts UMLS. Nous avons d{\'e}velopp{\'e} deux mod{\`e}les de transfert d{'}apprentissage {\`a} partir des r{\'e}seaux CNN VGG19 et ResNet50 . Nous avons utilis{\'e} des mod{\`e}les avec des techniques simples et que nous avons optimis{\'e}s pour l{'}apprentissage. Les r{\'e}sultats que nous avons obtenus en utilisant les donn{\'e}es de la t{\^a}che ImageCLEF 2017 sont encourageants et comparables {\`a} ceux des autres participants.",
    language = "French",
}
@proceedings{jep-taln-recital-2018-actes,
    title = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    editor = "S{\'e}billot, Pascale  and
      Claveau, Vincent",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.0",
}
@inproceedings{grabar-etal-2018-etude,
    title = "{\'E}tude de la lisibilit{\'e} des documents de sant{\'e} avec des m{\'e}thodes d{'}oculom{\'e}trie (Study of readability of health documents with eye-tracking methods)",
    author = "Grabar, Natalia  and
      Farce, Emmanuel  and
      Sparrow, Laurent",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.1",
    pages = "3--18",
    abstract = "Le domaine m{\'e}dical fait partie de la vie quotidienne pour des raisons de sant{\'e}, mais la disponibilit{\'e} des informations m{\'e}dicales ne garantit pas leur compr{\'e}hension correcte par les patients. Plusieurs {\'e}tudes ont d{\'e}montr{\'e} qu{'}il existe une difficult{\'e} r{\'e}elle dans la compr{\'e}hension de contenus m{\'e}dicaux par les patients. Nous proposons d{'}exploiter les m{\'e}thodes d{'}oculom{\'e}trie pour {\'e}tudier ces questions et pour d{\'e}tecter quelles unit{\'e}s linguistiques posent des difficult{\'e}s de compr{\'e}hension. Pour cela, des textes m{\'e}dicaux en version originale et simplifi{\'e}e sont exploit{\'e}s. L{'}oculom{\'e}trie permet de suivre le regard des participants de l{'}{\'e}tude et de r{\'e}v{\'e}ler les indicateurs de lecture, comme la dur{\'e}e des fixations, les r{\'e}gressions et les saccades. Les r{\'e}sultats indiquent qu{'}il existe une diff{\'e}rence statistiquement significative lors de la lecture des versions originales et simplifi{\'e}es des documents de sant{\'e} test{\'e}s.",
    language = "French",
}
@inproceedings{liu-etal-2018-alignement,
    title = "Alignement de termes de longueur variable en corpus comparables sp{\'e}cialis{\'e}s (Alignment of variable length terms in specialized comparable corpora)",
    author = "Liu, Jingshu  and
      Morin, Emmanuel  and
      Pe{\~n}a Saldarriaga, Sebasti{\'a}n",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.2",
    pages = "19--32",
    abstract = "Nous proposons dans cet article une adaptation de l{'}approche compositionnelle {\'e}tendue capable d{'}aligner des termes de longueurs variables {\`a} partir de corpus comparables, en modifiant la repr{\'e}sentation des termes complexes. Nous proposons {\'e}galement de nouveaux modes de pond{\'e}ration pour l{'}approche standard qui am{\'e}liorent les r{\'e}sultats des approches {\'e}tat de l{'}art pour les termes simples et complexes en domaine de sp{\'e}cialit{\'e}.",
    language = "French",
}
@inproceedings{pierrejean-tanguy-2018-etude,
    title = "Etude de la reproductibilit{\'e} des word embeddings : rep{\'e}rage des zones stables et instables dans le lexique (Reproducibility of word embeddings : identifying stable and unstable zones in the semantic space)",
    author = "Pierrejean, B{\'e}n{\'e}dicte  and
      Tanguy, Ludovic",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.3",
    pages = "33--46",
    abstract = "Les mod{\`e}les vectoriels de s{\'e}mantique distributionnelle (ou word embeddings), notamment ceux produits par les m{\'e}thodes neuronales, posent des questions de reproductibilit{\'e} et donnent des repr{\'e}sentations diff{\'e}rentes {\`a} chaque utilisation, m{\^e}me sans modifier leurs param{\`e}tres. Nous pr{\'e}sentons ici un ensemble d{'}exp{\'e}rimentations permettant de mesurer cette instabilit{\'e}, {\`a} la fois globalement et localement. Globalement, nous avons mesur{\'e} le taux de variation du voisinage des mots sur trois corpus diff{\'e}rents, qui est estim{\'e} autour de 17{\%} pour les 25 plus proches voisins d{'}un mot. Localement, nous avons identifi{\'e} et caract{\'e}ris{\'e} certaines zones de l{'}espace s{\'e}mantique qui montrent une relative stabilit{\'e}, ainsi que des cas de grande instabilit{\'e}.",
    language = "French",
}
@inproceedings{loukatou-etal-2018-modeling,
    title = "Modeling infant segmentation of two morphologically diverse languages",
    author = "Loukatou, Georgia-Rengina  and
      Stoll, Sabine  and
      Blasi, Damian  and
      Cristia, Alejandrina",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.4",
    pages = "47--60",
    abstract = "A rich literature explores unsupervised segmentation algorithms infants could use to parse their input, mainly focusing on English, an analytic language where word, morpheme, and syllable boundaries often coincide. Synthetic languages, where words are multi-morphemic, may present unique difficulties for segmentation. Our study tests corpora of two languages selected to differ in the extent of complexity of their morphological structure, Chintang and Japanese. We use three conceptually diverse word segmentation algorithms and we evaluate them on both word- and morpheme-level representations. As predicted, results for the simpler Japanese are better than those for the more complex Chintang. However, the difference is small compared to the effect of the algorithm (with the lexical algorithm outperforming sub-lexical ones) and the level (scores were lower when evaluating on words versus morphemes). There are also important interactions between language, model, and evaluation level, which ought to be considered in future work.",
}
@inproceedings{burlot-yvon-2018-evaluation,
    title = "{\'E}valuation morphologique pour la traduction automatique : adaptation au fran{\c{c}}ais (Morphological Evaluation for Machine Translation : Adaptation to {F}rench)",
    author = "Burlot, Franck  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.5",
    pages = "61--74",
    abstract = "Le nouvel {\'e}tat de l{'}art en traduction automatique (TA) s{'}appuie sur des m{\'e}thodes neuronales, qui diff{\'e}rent profond{\'e}ment des m{\'e}thodes utilis{\'e}es ant{\'e}rieurement. Les m{\'e}triques automatiques classiques sont mal adapt{\'e}es pour rendre compte de la nature du saut qualitatif observ{\'e}. Cet article propose un protocole d{'}{\'e}valuation pour la traduction de l{'}anglais vers le fran{\c{c}}ais sp{\'e}cifiquement focalis{\'e} sur la comp{\'e}tence morphologique des syst{\`e}mes de TA, en {\'e}tudiant leurs performances sur diff{\'e}rents ph{\'e}nom{\`e}nes grammaticaux.",
    language = "French",
}
@inproceedings{magistry-etal-2018-etiquetage,
    title = "{\'E}tiquetage en parties du discours de langues peu dot{\'e}es par sp{\'e}cialisation des plongements lexicaux ({POS} tagging for low-resource languages by adapting word embeddings )",
    author = "Magistry, Pierre  and
      Ligozat, Anne-Laure  and
      Rosset, Sophie",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.6",
    pages = "75--86",
    abstract = "Cet article pr{\'e}sente une nouvelle m{\'e}thode d{'}{\'e}tiquetage en parties du discours adapt{\'e}e aux langues peu dot{\'e}es : la d{\'e}finition du contexte utilis{\'e} pour construire les plongements lexicaux est adapt{\'e}e {\`a} la t{\^a}che, et de nouveaux vecteurs sont cr{\'e}{\'e}s pour les mots inconnus. Les exp{\'e}riences men{\'e}es sur le picard, le malgache et l{'}alsacien montrent que cette m{\'e}thode am{\'e}liore l{'}{\'e}tat de l{'}art pour ces trois langues peu dot{\'e}es.",
    language = "French",
}
@inproceedings{marcia-tellier-2018-modelisation,
    title = "Mod{\'e}lisation des processus d{'}acquisition syntaxique par jeux de langage entre agents artificiels (Modeling Syntactic Acquisition by Language Games between Artificial Agents )",
    author = "Marcia, Marie  and
      Tellier, Isabelle",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.7",
    pages = "87--100",
    abstract = "Dans cet article, nous pr{\'e}sentons une mod{\'e}lisation de la situation d{'}acquisition de la syntaxe de sa langue maternelle par un enfant inspir{\'e}e des {``}jeux de langages{''} de Luc Steels. Le mod{\`e}le suppose que l{'}enfant a acc{\`e}s {\`a} une repr{\'e}sentation s{\'e}mantique des {\'e}nonc{\'e}s qui lui sont adress{\'e}s, et qu{'}il doit r{\'e}agir en d{\'e}signant la t{\^e}te syntaxique de ces {\'e}nonc{\'e}s. Nous d{\'e}crivons des exp{\'e}riences exploitant des donn{\'e}es du corpus CHILDES et mettant en jeu un processus d{'}acquisition simple mais efficace.",
    language = "French",
}
@inproceedings{nyzam-etal-2018-mots,
    title = "{MOTS} : un outil modulaire pour le r{\'e}sum{\'e} automatique ({MOTS} : A Modular Framework for Automatic Summarization )",
    author = "Nyzam, Valentin  and
      Rodrigues, Christophe  and
      Bossard, Aur{\'e}lien",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.8",
    pages = "101--114",
    abstract = "Cet article pr{\'e}sente un syst{\`e}me open source et modulaire pour le r{\'e}sum{\'e} automatique : MOTS, d{\'e}velopp{\'e} en Java. Son architecture permet d{'}impl{\'e}menter et tester de nouvelles m{\'e}thodes de r{\'e}sum{\'e} automatique et de les comparer avec des m{\'e}thodes existantes dans un cadre unifi{\'e}. Ce syst{\`e}me, le premier compl{\`e}tement modulaire pour le r{\'e}sum{\'e} automatique permet {\`a} l{'}heure actuelle de d{\'e}finir plus de cent combinaisons de modules afin de r{\'e}sumer automatiquement des textes en langage naturel.",
    language = "French",
}
@inproceedings{el-amel-boussaha-etal-2018-ordonnancement,
    title = "Ordonnancement de r{\'e}ponses dans les syst{\`e}mes de dialogue bas{\'e} sur une similarit{\'e} contexte/r{\'e}ponse (Response ranking in dialogue systems based on context-response similarity)",
    author = "El Amel Boussaha, Basma  and
      Hernandez, Nicolas  and
      Jacquin, Christine  and
      Morin, Emmanuel",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.9",
    pages = "115--128",
    abstract = "Construire des syst{\`e}mes de dialogue qui conversent avec les humains afin de les aider dans leurs t{\^a}ches quotidiennes est devenu une priorit{\'e}. Certains de ces syst{\`e}mes produisent des dialogues en cherchant le meilleur {\'e}nonc{\'e} (r{\'e}ponse) parmi un ensemble d{'}{\'e}nonc{\'e}s candidats. Le choix de la r{\'e}ponse est conditionn{\'e} par l{'}historique de la conversation appel{\'e} contexte. Ces syst{\`e}mes ordonnent les {\'e}nonc{\'e}s candidats par leur ad{\'e}quation au contexte, le meilleur est ensuite choisi. Les approches existantes {\`a} base de r{\'e}seaux de neurones profonds sont performantes pour cette t{\^a}che. Dans cet article, nous am{\'e}liorons une approche {\'e}tat de l{'}art {\`a} base d{'}un dual encodeur LSTM. En se basant sur la similarit{\'e} s{\'e}mantique entre le contexte et la r{\'e}ponse, notre approche apprend {\`a} mieux distinguer les bonnes r{\'e}ponses des mauvaises. Les r{\'e}sultats exp{\'e}rimentaux sur un large corpus de chats d{'}Ubuntu montrent une am{\'e}lioration significative de 7, 6 et 2 points sur le Rappel@(1, 2 et 5) respectivement par rapport au meilleur syst{\`e}me {\'e}tat de l{'}art.",
    language = "French",
}
@inproceedings{kodelja-etal-2018-integration,
    title = "Int{\'e}gration de contexte global par amor{\c{c}}age pour la d{\'e}tection d{'}{\'e}v{\'e}nements (Integrating global context via bootstrapping for event detection)",
    author = "Kodelja, Dorian  and
      Besan{\c{c}}on, Romaric  and
      Ferret, Olivier",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.10",
    pages = "129--142",
    abstract = {Les approches neuronales obtiennent depuis plusieurs ann{\'e}es des r{\'e}sultats int{\'e}ressants en extraction d{'}{\'e}v{\'e}nements. Cependant, les approches d{\'e}velopp{\'e}es dans ce cadre se limitent g{\'e}n{\'e}ralement {\`a} un contexte phrastique. Or, si certains types d{'}{\'e}v{\'e}nements sont ais{\'e}ment identifiables {\`a} ce niveau, l{'}exploitation d{'}indices pr{\'e}sents dans d{'}autres phrases est parfois n{\'e}cessaire pour permettre de d{\'e}sambigu{\"\i}ser des {\'e}v{\'e}nements. Dans cet article, nous proposons ainsi l{'}int{\'e}gration d{'}une repr{\'e}sentation d{'}un contexte plus large pour am{\'e}liorer l{'}apprentissage d{'}un r{\'e}seau convolutif. Cette repr{\'e}sentation est obtenue par amor{\c{c}}age en exploitant les r{\'e}sultats d{'}un premier mod{\`e}le convolutif op{\'e}rant au niveau phrastique. Dans le cadre d{'}une {\'e}valuation r{\'e}alis{\'e}e sur les donn{\'e}es de la campagne TAC 2017, nous montrons que ce mod{\`e}le global obtient un gain significatif par rapport au mod{\`e}le local, ces deux mod{\`e}les {\'e}tant eux-m{\^e}mes comp{\'e}titifs par rapport aux r{\'e}sultats de TAC 2017. Nous {\'e}tudions {\'e}galement en d{\'e}tail le gain de performance de notre nouveau mod{\`e}le au travers de plusieurs exp{\'e}riences compl{\'e}mentaires.},
    language = "French",
}
@inproceedings{lecorve-etal-2018-construction,
    title = "Construction conjointe d{'}un corpus et d{'}un classifieur pour les registres de langue en fran{\c{c}}ais (Joint building of a corpus and a classifier for language registers in {F}rench)",
    author = "Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Ayats, Hugo  and
      Beno{\^\i}t, Fournier  and
      Mekki, Jade  and
      Chevelu, Jonathan  and
      Battistelli, Delphine  and
      B{\'e}chet, Nicolas",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.11",
    pages = "143--156",
    abstract = "Les registres de langue sont un trait stylistique marquant dans l{'}appr{\'e}ciation d{'}un texte ou d{'}un discours. Cependant, il sont encore peu {\'e}tudi{\'e}s en traitement automatique des langues. Dans cet article, nous pr{\'e}sentons une approche semi-supervis{\'e}e permettant la construction conjointe d{'}un corpus de textes {\'e}tiquet{\'e}s en registres et d{'}un classifieur associ{\'e}. Cette approche s{'}appuie sur un ensemble initial et restreint de donn{\'e}es expertes. Via une collecte automatique et massive de pages web, l{'}approche proc{\`e}de par it{\'e}rations en alternant l{'}apprentissage d{'}un classifieur interm{\'e}diaire et l{'}annotation de nouveaux textes pour augmenter le corpus {\'e}tiquet{\'e}. Nous appliquons cette approche aux registres familier, courant et soutenu. {\`A} l{'}issue du processus de construction, le corpus {\'e}tiquet{\'e} regroupe 800 000 textes et le classifieur, un r{\'e}seau de neurones, pr{\'e}sente un taux de bonne classification de 87 {\%}.",
    language = "French",
}
@inproceedings{vial-etal-2018-approche,
    title = {Approche supervis{\'e}e {\`a} base de cellules {LSTM} bidirectionnelles pour la d{\'e}sambigu{\"\i}sation lexicale ({LSTM} Based Supervised Approach for Word Sense Disambiguation)},
    author = {Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin  and
      Schwab, Didier},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.12",
    pages = "157--170",
    abstract = {En d{\'e}sambigu{\"\i}sation lexicale, l{'}utilisation des r{\'e}seaux de neurones est encore peu pr{\'e}sente et tr{\`e}s r{\'e}cente. Cette direction est pourtant tr{\`e}s prometteuse, tant les r{\'e}sultats obtenus par ces premiers syst{\`e}mes arrivent syst{\'e}matiquement en t{\^e}te des campagnes d{'}{\'e}valuation, malgr{\'e} une marge d{'}am{\'e}lioration qui semble encore importante. Nous pr{\'e}sentons dans cet article une nouvelle architecture {\`a} base de r{\'e}seaux de neurones pour la d{\'e}sambigu{\"\i}sation lexicale. Notre syst{\`e}me est {\`a} la fois moins complexe {\`a} entra{\^\i}ner que les syst{\`e}mes neuronaux existants et il obtient des r{\'e}sultats {\'e}tat de l{'}art sur la plupart des t{\^a}ches d{'}{\'e}valuation de la d{\'e}sambigu{\"\i}sation lexicale en anglais. L{'}accent est port{\'e} sur la reproductibilit{\'e} de notre syst{\`e}me et de nos r{\'e}sultats, par l{'}utilisation d{'}un mod{\`e}le de vecteurs de mots, de corpus d{'}apprentissage et d{'}{\'e}valuation librement accessibles.},
    language = "French",
}
@inproceedings{delecraz-etal-2018-correction,
    title = "Correction automatique d{'}attachements pr{\'e}positionnels par utilisation de traits visuels ({PP}-attachement resolution using visual features)",
    author = "Delecraz, S{\'e}bastien  and
      Becerra-Bonache, Leonor  and
      Favre, Beno{\^\i}t  and
      Nasr, Alexis  and
      Bechet, Fr{\'e}d{\'e}ric",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.13",
    pages = "171--182",
    abstract = {La d{\'e}sambigu{\"\i}sation des rattachements pr{\'e}positionnels est une t{\^a}che syntaxique qui demande des connaissances s{\'e}mantiques, pouvant {\^e}tre extraites d{'}une image associ{\'e}e au texte trait{\'e}. Nous pr{\'e}sentons et analysons les difficult{\'e}s de cette t{\^a}che pour laquelle nous construisons un syst{\`e}me complet entra{\^\i}n{\'e} sur une version {\'e}tendue des annotations du corpus Flickr30k Entities. Lorsque la s{\'e}mantique lexicale n{'}est pas disponible, l{'}information visuelle apporte 3 {\%} d{'}am{\'e}lioration.},
    language = "French",
}
@inproceedings{granet-etal-2018-decodeur,
    title = "D{\'e}codeur neuronal pour la transcription de documents manuscrits anciens (Neural decoder for the transcription of historical handwritten documents)",
    author = "Granet, Adeline  and
      Morin, Emmanuel  and
      Mouch{\`e}re, Harold  and
      Quiniou, Solen  and
      Viard-Gaudin, Christian",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-long.14",
    pages = "183--195",
    abstract = "L{'}absence de donn{\'e}es annot{\'e}es peut {\^e}tre une difficult{\'e} majeure lorsque l{'}on s{'}int{\'e}resse {\`a} l{'}analyse de documents manuscrits anciens. Pour contourner cette difficult{\'e}, nous proposons de diviser le probl{\`e}me en deux, afin de pouvoir s{'}appuyer sur des donn{\'e}es plus facilement accessibles. Dans cet article nous pr{\'e}sentons la partie d{\'e}codeur d{'}un encodeur-d{\'e}codeur multimodal utilisant l{'}apprentissage par transfert de connaissances pour la transcription des titres de pi{\`e}ces de la Com{\'e}die Italienne. Le d{\'e}codeur transforme un vecteur de n-grammes au niveau caract{\`e}res en une s{\'e}quence de caract{\`e}res correspondant {\`a} un mot. L{'}apprentissage par transfert de connaissances est r{\'e}alis{\'e} principalement {\`a} partir d{'}une nouvelle ressource inexploit{\'e}e contemporaine {\`a} la Com{\'e}die-Italienne et th{\'e}matiquement proche ; ainsi que d{'}autres ressources couvrant d{'}autres domaines, des langages diff{\'e}rents et m{\^e}me des p{\'e}riodes diff{\'e}rentes. Nous obtenons 97,27{\%} de caract{\`e}res bien reconnus sur les donn{\'e}es de la Com{\'e}die-Italienne, ainsi que 86,57{\%} de mots correctement g{\'e}n{\'e}r{\'e}s malgr{\'e} une couverture de 67,58{\%} uniquement entre la Com{\'e}die-Italienne et l{'}ensemble d{'}apprentissage. Les exp{\'e}riences montrent qu{'}un tel syst{\`e}me peut {\^e}tre une approche efficace dans le cadre d{'}apprentissage par transfert.",
    language = "French",
}
@proceedings{jep-taln-recital-2018-actes-de-la-taln,
    title = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    editor = "S{\'e}billot, Pascale  and
      Claveau, Vincent",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.0",
}
@inproceedings{bothua-etal-2018-curiositext,
    title = "{C}uriosi{T}ext : application web d{'}aide au peuplement d{'}ontologies m{\'e}tiers comme ressources lexicales bas{\'e}e sur {W}ord2{V}ec ({C}uriosi{T}ext: a web application based on {W}ord2{V}ec helping with the population of ontologies (serving as lexical resources))",
    author = "Bothua, Meryl  and
      Lagarde, Delphine  and
      Pierre, Laurent",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.1",
    pages = "193--196",
    abstract = "Suite {\`a} la mise en place d{'}une cha{\^\i}ne traitement destin{\'e}e {\`a} extraire automatiquement des actions de maintenance r{\'e}alis{\'e}es sur des composants dans des comptes rendus, nous avons cherch{\'e} {\`a} constituer des ressources lexicales {\`a} partir de textes souvent mal normalis{\'e}s sur le plan linguistique. Nous avons ainsi d{\'e}velopp{\'e} une application web, CuriosiText, qui permet de lancer un traitement Word2Vec et de peupler semi automatiquement une ontologie m{\'e}tier avec les termes similaires correctement d{\'e}tect{\'e}s. Des relations m{\'e}tiers sp{\'e}cifiques peuvent {\'e}galement {\^e}tre ajout{\'e}es.",
    language = "French",
}
@inproceedings{brunet-manquat-esperanca-rodier-2018-accole,
    title = "{ACCOL{\'E}} : Annotation Collaborative d{'}erreurs de traduction pour {CO}rpus a{L}ign{{\'E}}s ({ACCOL{\'E}}: A Collaborative Platform of Error Annotation for Aligned Corpus)",
    author = "Brunet-Manquat, Francis  and
      Esperan{\c{c}}a-Rodier, Emmanuelle",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.2",
    pages = "197--200",
    abstract = "La plateforme ACCOL{\'E} (Annotation Collaborative d{'}erreurs de traduction pour COrpus aLign{\'E}s) propose une palette de services innovants permettant de r{\'e}pondre aux besoins modernes d{'}analyse d{'}erreurs de traduction : gestion simplifi{\'e}e des corpus et des typologies d{'}erreurs, annotation d{'}erreurs efficace, collaboration et/ou supervision lors de l{'}annotation, recherche de mod{\`e}le d{'}erreurs dans les annotations.",
    language = "French",
}
@inproceedings{cartier-etal-2018-neonaute,
    title = "N{\'e}onaute, Enrichissement s{\'e}mantique pour la recherche d{'}information ()",
    author = {Cartier, Emmanuel  and
      Galand, Lo{\"\i}c  and
      Stirling, Peter  and
      Aubry, Sara},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.3",
    pages = "201--204",
    abstract = "Avec l{'}explosion du nombre de documents num{\'e}riques accessibles, les besoins en outils pour l{'}enrichissement s{\'e}mantique des donn{\'e}es textuelles, ainsi que des fonctionnalit{\'e}s avanc{\'e}es de recherche et d{'}exploration des collections, se font sentir. Cette combinaison entre les domaines de la recherche d{'}information et du traitement automatique des langues est l{'}une des caract{\'e}ristiques du projet N{\'e}onaute. Ce projet, financ{\'e} par la DGLFLF 1 en 2017 (appel Langues et num{\'e}rique), regroupe la Biblioth{\`e}que nationale de France (BnF), le LIPN - RCLN (CNRS UMR 7030) et l{'}Universit{\'e} de Strasbourg (LILPA, EA 1339). Son objectif principal est de doter les observateurs de la langue fran{\c{c}}aise d{'}un moteur de recherche s{'}appuyant sur une collection de sites de presse d{'}actualit{\'e}, collect{\'e}s automatiquement par la BnF au titre de sa mission de d{\'e}p{\^o}t l{\'e}gal de l{'}internet. Sur cette collection, le projet vise {\`a} proposer un moteur de recherche de nouvelle g{\'e}n{\'e}ration, disposant d{'}une indexation enrichie par l{'}analyse automatique des textes (analyse morphosyntaxique, entit{\'e}s nomm{\'e}es, th{\'e}matiques), d{'}une part, et d{'}outils de recherche, d{'}exploration et de visualisation multidimensionnelle interactive des r{\'e}sultats, d{'}autre part.",
    language = "French",
}
@inproceedings{de-chalendar-2018-nouveautes,
    title = "Nouveaut{\'e}s de l{'}analyseur linguistique {LIMA} (What{'}s New in the {LIMA} Language Analyzer)",
    author = {de Chalendar, Ga{\"e}l},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.4",
    pages = "205--208",
    abstract = "LIMA est un analyseur linguistique libre d{'}envergure industrielle. Nous pr{\'e}sentons ici ses {\'e}volutions depuis la derni{\`e}re publication en 2014.",
    language = "French",
}
@inproceedings{dupont-2018-un,
    title = "Un outil d{'}{\'e}tiquetage rapide et un corpus libre en entit{\'e}s nomm{\'e}es du Fran{\c{c}}ais (A fast tagging tool and a free {F}rench named entity corpus)",
    author = "Dupont, Yoann",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.5",
    pages = "209--210",
    abstract = "Dans cet article, nous pr{\'e}sentons un outil pour effectuer l{'}{\'e}tiquetage rapide de textes bruts. Il peut charger des documents annot{\'e}s depuis divers formats, notamment BRAT et GATE. Il se base sur des raccourcis claviers intuitifs et la diffusion d{'}annotation {\`a} l{'}{\'e}chelle du document. Il permet d{'}entra{\^\i}ner des syst{\`e}mes par apprentissage que l{'}on peut alors utiliser pour pr{\'e}annoter les textes.",
    language = "French",
}
@inproceedings{hernandez-2018-pyrata,
    title = "{P}y{RATA}, Python Rule-based fe{A}ture s{T}ructure Analysis",
    author = "Hernandez, Nicolas",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.6",
    pages = "211--212",
    abstract = "Nous pr{\'e}sentons PyRATA (Python Rules-based feAture sTructure Analysis) un module Python (version 3) diffus{\'e} sous licence Apache V2 et disponible sur github 4 et dans les d{\'e}pots pypi 5 . PyRATA a pour objectif de permettre de l{'}analyse {\`a} base de r{\`e}gles sur des donn{\'e}es structur{\'e}es. Le langage de PyRATA offre une expressivit{\'e} qui couvre les fonctionnalit{\'e}s propos{\'e}es par les modules alternatifs et davantage. Con{\c{c}}u pour {\^e}tre intuitif, la syntaxe des motifs et l{'}interface de programmation (API) suivent les d{\'e}finitions de standards existants, respectivement la syntaxe des expressions r{\'e}guli{\`e}res de Perl et l{'}API du module Python re. PyRATA travaille sur des structures de donn{\'e}es simples et natives de Python : une liste de dictionnaires (c-{\`a}-d une liste de tables d{'}associations). Cela lui permet de traiter des donn{\'e}es de diff{\'e}rentes natures (textuelles ou non) telles qu{'}une liste de mots, une liste de phrases, une liste de messages d{'}un fil de discussion, une liste d{'}{\'e}v{\'e}nements d{'}un agenda... Cette sp{\'e}cificit{\'e} le rend ind{\'e}pendant de la nature des annotations (a fortiori linguistiques) associ{\'e}es {\`a} la donn{\'e}e manipul{\'e}e. Ce travail a {\'e}t{\'e} financ{\'e} par le projet ANR 2016 PASTEL.",
}
@inproceedings{hadj-salah-etal-2018-un,
    title = "Un corpus en arabe annot{\'e} manuellement avec des sens {W}ord{N}et ({A}rabic Manually Sense Annotated Corpus with {W}ord{N}et Senses)",
    author = "Hadj Salah, Marwa  and
      Blanchon, Herv{\'e}  and
      Zrigui, Mounir  and
      Schwab, Didier",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-demo.7",
    pages = "213--216",
    abstract = {OntoNotes comprend le seul corpus manuellement annot{\'e} en sens librement disponible pour l{'}arabe. Elle reste peu connue et utilis{\'e}e certainement parce que le projet s{'}est achev{\'e} sans lier cet inventaire au Princeton WordNet qui lui aurait ouvert l{'}acc{\`e}s {\`a} son riche {\'e}cosyst{\`e}me. Dans cet article, nous pr{\'e}sentons une version {\'e}tendue de OntoNotes Release 5.0 que nous avons cr{\'e}{\'e}e en suivant une m{\'e}thodologie de construction semi-automatique. Il s{'}agit d{'}une mise {\`a} jour de la partie arabe annot{\'e}e en sens du corpus en ajoutant l{'}alignement vers le Princeton WordNet 3.0. Cette ressource qui comprend plus de 12 500 mots annot{\'e}s est librement disponible pour la communaut{\'e}. Nous esp{\'e}rons qu{'}elle deviendra un standard pour l{'}{\'e}valuation de la d{\'e}sambigu{\"\i}sation lexicale de l{'}arabe.},
    language = "French",
}
@proceedings{jep-taln-recital-2018-actes-de-la-taln-2,
    title = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    editor = "S{\'e}billot, Pascale  and
      Claveau, Vincent",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.0",
}
@inproceedings{paroubek-etal-2018-deft2018,
    title = "{DEFT}2018 : recherche d{'}information et analyse de sentiments dans des tweets concernant les transports en {{\^I}}le de {F}rance ({DEFT}2018 : Information Retrieval and Sentiment Analysis in Tweets about Public Transportation in {{\^I}}le de {F}rance Region )",
    author = "Paroubek, Patrick  and
      Grouin, Cyril  and
      Bellot, Patrice  and
      Claveau, Vincent  and
      Eshkol-Taravella, Iris  and
      Fraisse, Amel  and
      Jackiewicz, Agata  and
      Karoui, Jihen  and
      Monceaux, Laura  and
      Torres-Moreno, Juan-Manuel",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.1",
    pages = "219--230",
    abstract = "Cet article pr{\'e}sente l{'}{\'e}dition 2018 de la campagne d{'}{\'e}valuation DEFT (D{\'e}fi Fouille de Textes). A partir d{'}un corpus de tweets, quatre t{\^a}ches ont {\'e}t{\'e} propos{\'e}es : identifier les tweets sur la th{\'e}matique des transports, puis parmi ces derniers, identifier la polarit{\'e} (n{\'e}gatif, neutre, positif, mixte), identifier les marqueurs de sentiment et la cible, et enfin, annoter compl{\`e}tement chaque tweet en source et cible des sentiments exprim{\'e}s. Douze {\'e}quipes ont particip{\'e}, majoritairement sur les deux premi{\`e}res t{\^a}ches. Sur l{'}identification de la th{\'e}matique des transports, la micro F-mesure varie de 0,827 {\`a} 0,908. Sur l{'}identification de la polarit{\'e} globale, la micro F-mesure varie de 0,381 {\`a} 0,823.",
    language = "French",
}
@inproceedings{suignard-etal-2018-participation,
    title = "Participation d{'}{EDF} {R}{\&}{D} {\`a} {DEFT} 2018 (Here the title in {E}nglish)",
    author = "Suignard, Philippe  and
      Charaudeau, Lou  and
      Boumghar, Manel  and
      Bothua, Meryl  and
      Lagarde, Delphine",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.2",
    pages = "231--238",
    abstract = "Ce papier d{\'e}crit la participation d{'}EDF R{\&}D {\`a} la campagne d{'}{\'e}valuation DEFT 2018. Notre {\'e}quipe a particip{\'e} aux deux premi{\`e}res t{\^a}ches : classification des tweets en transport/non-transport (T{\^a}che T1) et d{\'e}tection de la polarit{\'e} globale des tweets (T{\^a}che T2). Nous avons utilis{\'e} 3 m{\'e}thodes diff{\'e}rentes s{'}appuyant sur Word2Vec, CNN et LSTM. Aucune donn{\'e}e suppl{\'e}mentaire, autre que les donn{\'e}es d{'}apprentissage, n{'}a {\'e}t{\'e} utilis{\'e}e. Notre {\'e}quipe obtient des r{\'e}sultats tr{\`e}s corrects et se classe 1{\`e}re {\'e}quipe non acad{\'e}mique. Les m{\'e}thodes propos{\'e}es sont facilement transposables {\`a} d{'}autres t{\^a}ches de classification de textes courts et peuvent int{\'e}resser plusieurs entit{\'e}s du groupe EDF.",
    language = "French",
}
@inproceedings{jacques-etal-2018-clac,
    title = "{CL}a{C} @ {DEFT} 2018: Sentiment analysis of tweets on transport from {{\^I}}le-de-{F}rance",
    author = "Jacques, Simon  and
      Farahnak, Farhood  and
      Kosseim, Leila",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.3",
    pages = "239--248",
    abstract = "CLaC @ DEFT 2018: Analysis of tweets on transport on the {\^I}le-de-France This paper describes the system deployed by the CLaC lab at Concordia University in Montreal for the DEFT 2018 shared task. The competition consisted in four different tasks; however, due to lack of time, we only participated in the first two. We participated with a system based on conventional supervised learning methods: a support vector machine classifier and an artificial neural network. For task 1, our best approach achieved an F-measure of 87.61{\%}; while at task 2, we achieve 51.03{\%}, situating our system below the average of the other participants.",
}
@inproceedings{buscaldi-etal-2018-modeles,
    title = "Mod{\`e}les en Caract{\`e}res pour la D{\'e}tection de Polarit{\'e} dans les Tweets (Character-level Models for Polarity Detection in Tweets )",
    author = {Buscaldi, Davide  and
      Le Roux, Joseph  and
      Lejeune, Ga{\"e}l},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.4",
    pages = "249--258",
    abstract = "Dans cet article, nous pr{\'e}sentons notre contribution au D{\'e}fi Fouille de Textes 2018 au travers de trois m{\'e}thodes originales pour la classification th{\'e}matique et la d{\'e}tection de polarit{\'e} dans des tweets en fran{\c{c}}ais. Nous y avons ajout{\'e} un syst{\`e}me de vote. Notre premi{\`e}re m{\'e}thode est fond{\'e}e sur des lexiques (mots et emojis), les n-grammes de caract{\`e}res et un classificateur {\`a} vaste marge (ou SVM). tandis que les deux autres sont des m{\'e}thodes endog{\`e}nes fond{\'e}es sur l{'}extraction de caract{\'e}ristiques au grain caract{\`e}res : un mod{\`e}le {\`a} m{\'e}moire {\`a} court-terme persistante (ou BiLSTM pour Bidirectionnal Long Short-Term Memory) et perceptron multi-couche d{'}une part et un mod{\`e}le de s{\'e}quences de caract{\`e}res ferm{\'e}es fr{\'e}quentes et classificateur SVM d{'}autre part. Le BiLSTM a produit de loin les meilleurs r{\'e}sultats puisqu{'}il a obtenu la premi{\`e}re place sur la t{\^a}che 1, classification binaire de tweets selon qu{'}ils traitent ou non des transports, et la troisi{\`e}me place sur la t{\^a}che 2, classification de la polarit{\'e} en 4 classes. Ce r{\'e}sultat est d{'}autant plus int{\'e}ressant que la m{\'e}thode propos{\'e}e est faiblement param{\'e}trique, totalement endog{\`e}ne et qu{'}elle n{'}implique aucun pr{\'e}-traitement.",
    language = "French",
}
@inproceedings{sileo-etal-2018-concatenation,
    title = "Concat{\'e}nation de r{\'e}seaux de neurones pour la classification de tweets, {DEFT}2018 (Concatenation of neural networks for tweets classification, {DEFT}2018 )",
    author = "Sileo, Damien  and
      Van de Cruys, Tim  and
      Muller, Philippe  and
      Pradel, Camille",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.5",
    pages = "259--264",
    abstract = "Nous pr{\'e}sentons le syst{\`e}me utilis{\'e} par l{'}{\'e}quipe Melodi/Synapse D{\'e}veloppement dans la comp{\'e}tition DEFT2018 portant sur la classification de th{\'e}matique ou de sentiments de tweets en fran{\c{c}}ais. On propose un syst{\`e}me unique pour les deux approches qui combine concat{\'e}nativement deux m{\'e}thodes d{'}embedding et trois mod{\`e}les de repr{\'e}sentation s{\'e}quence. Le syst{\`e}me se classe 1/13 en analyse de sentiments et 4/13 en classification th{\'e}matique.",
    language = "French",
}
@inproceedings{minard-etal-2018-participation,
    title = "Participation de l{'}{IRISA} {\`a} {D}e{FT} 2018 : classification et annotation d{'}opinion dans des tweets ({IRISA} at {D}e{FT} 2018: classifying and tagging opinion in tweets )",
    author = "Minard, Anne-Lyse  and
      Raymond, Christian  and
      Claveau, Vincent",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.6",
    pages = "265--278",
    abstract = "Cet article d{\'e}crit les syst{\`e}mes d{\'e}velopp{\'e}s par l{'}{\'e}quipe LinkMedia de l{'}IRISA pour la campagne d{'}{\'e}valuation DeFT 2018 portant sur l{'}analyse d{'}opinion dans des tweets en fran{\c{c}}ais. L{'}{\'e}quipe a particip{\'e} {\`a} 3 des 4 t{\^a}ches de la campagne : (i) classification des tweets selon s{'}ils concernent les transports ou non, (ii) classification des tweets selon leur polarit{\'e} et (iii) annotation des marqueurs d{'}opinion et de l{'}objet {\`a} propos duquel est exprim{\'e}e l{'}opinion. Nous avons utilis{\'e} un algorithme de boosting d{'}arbres de d{\'e}cision et des r{\'e}seaux de neurones r{\'e}currents (RNN) pour traiter les t{\^a}ches 1 et 2. Pour la t{\^a}che 3 nous avons exp{\'e}riment{\'e} l{'}utilisation de r{\'e}seaux de neurones r{\'e}currents associ{\'e}s {\`a} des CRF. Ces approches donnent des r{\'e}sultats proches, avec un l{\'e}ger avantage aux RNN, et ont permis d{'}{\^e}tre parmi les premiers class{\'e}s pour chacune des t{\^a}ches.",
    language = "French",
}
@inproceedings{dias-etal-2018-deft,
    title = "{DEFT} 2018: Attention s{\'e}lective pour classification de microblogs ({DEFT} 2018 : Selective Attention for Microblogging Classification )",
    author = "Dias, Charles-Emmanuel  and
      de Forsan de Gainon Gabriac, Clara  and
      Gallinari, Patrick  and
      Guigue, Vincent",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.7",
    pages = "279--286",
    abstract = "Dans le cadre de l{'}atelier DEFT 2018 nous nous sommes int{\'e}ress{\'e}s {\`a} la classification de microblogs (ici, des tweets) r{\'e}dig{\'e}s en fran{\c{c}}ais. Ici, nous proposons une m{\'e}thode se basant sur un r{\'e}seau hi{\'e}rarchique de neurones r{\'e}current avec attention. La sp{\'e}cificit{\'e} de notre architecture est de prendre en compte {--}via un mechanisme d{'}attention et de portes{--} les hashtags et les mentions directes (e.g., @user), sp{\'e}cifiques aux microblogs. Notre mod{\`e}le a obtenu de tr{\`e}s bon r{\'e}sultats sur la premi{\`e}re t{\^a}che et des r{\'e}sultats comp{\'e}titifs sur la seconde.",
    language = "French",
}
@inproceedings{graceffa-etal-2018-notre,
    title = "Notre tweet premi{\`e}re fois au {DEFT}-2018 : syst{\`e}mes de d{\'e}tection de polarit{\'e} et de transports (Systems for detecting polarity and public transport discussions in {F}rench tweets)",
    author = "Graceffa, David  and
      Ramond, Armelle  and
      Dusserre, Emmanuelle  and
      Kalitvianski, Ruslan  and
      Ruhlmann, Mathieu  and
      Padr{\'o}, Muntsa",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.8",
    pages = "287--298",
    abstract = "Cet article d{\'e}crit les syst{\`e}mes de l{'}{\'e}quipe Eloquant pour la cat{\'e}gorisation de tweets en fran{\c{c}}ais dans les t{\^a}ches 1 (d{\'e}tection de la th{\'e}matique transports en commun) et 2 (d{\'e}tection de la polarit{\'e} globale) du DEFT 2018. Nos syst{\`e}mes reposent sur un enrichissement s{\'e}mantique, l{'}apprentissage automatique et, pour la t{\^a}che 1 une approche symbolique. Nous avons effectu{\'e} deux runs pour chacune des t{\^a}ches. Nos meilleures F-mesures (0.897 pour la t{\^a}che 1 et 0.800 pour la t{\^a}che 2) sont au-dessus de la moyenne globale pour chaque t{\^a}che, et nous placent dans les 30{\%} sup{\'e}rieurs de tous les runs pour la t{\^a}che 2.",
    language = "French",
}
@inproceedings{sainson-etal-2018-lse,
    title = "{LSE} au {DEFT} 2018 : Classification de tweets bas{\'e}e sur les r{\'e}seaux de neurones profonds ({LSE} at {DEFT} 2018 : Sentiment analysis model based on deep learning)",
    author = "Sainson, Antoine  and
      Linsenmaier, Hugo  and
      Majed, Alexandre  and
      Cadet, Xavier  and
      Bouchekif, Abdessalam",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.9",
    pages = "299--310",
    abstract = "Dans ce papier, nous d{\'e}crivons les syst{\`e}mes d{\'e}velopp{\'e}s au LSE pour le DEFT 2018 sur les t{\^a}ches 1 et 2 qui consistent {\`a} classifier des tweets. La premi{\`e}re t{\^a}che consiste {\`a} d{\'e}terminer si un message concerne les transports ou non. La deuxi{\`e}me, consiste {\`a} classifier les tweets selon leur polarit{\'e} globale. Pour les deux t{\^a}ches nous avons d{\'e}velopp{\'e} des syst{\`e}mes bas{\'e}s sur des r{\'e}seaux de neurones convolutifs (CNN) et r{\'e}currents (LSTM, BLSTM et GRU). Chaque mot d{'}un tweet donn{\'e} est repr{\'e}sent{\'e} par un vecteur dense appris {\`a} partir des donn{\'e}es relativement proches de celles de la comp{\'e}tition. Le score final officiel est de 0.891 pour la t{\^a}che 1 et de 0.781 pour la t{\^a}che 2.",
    language = "French",
}
@inproceedings{monnin-etal-2018-syllabs,
    title = "Syllabs@{DEFT}2018 : combinaison de m{\'e}thodes de classification supervis{\'e}es (Syllabs@{DEFT}2018: Combination of Supervised Classification Methods)",
    author = "Monnin, Chlo{\'e}  and
      Quern{\'e}, Olivier  and
      Hamon, Olivier",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.10",
    pages = "311--318",
    abstract = "Nous pr{\'e}sentons la participation de Syllabs {\`a} la t{\^a}che de classification de tweets dans le domaine du transport lors de DEFT 2018. Pour cette premi{\`e}re participation {\`a} une campagne DEFT, nous avons choisi de tester plusieurs algorithmes de classification {\'e}tat de l{'}art. Apr{\`e}s une {\'e}tape de pr{\'e}traitement commune {\`a} l{'}ensemble des algorithmes, nous effectuons un apprentissage sur le seul contenu des tweets. Les r{\'e}sultats {\'e}tant somme toute assez proches, nous effectuons un vote majoritaire sur les trois algorithmes ayant obtenus les meilleurs r{\'e}sultats.",
    language = "French",
}
@inproceedings{mohamed-azmy-etal-2018-lirmm,
    title = "{LIRMM}@{DEFT}-2018 {--} Mod{\`e}le de classification de la vectorisation des documents ({LIRMM} {DEFT}-2018 {--} Document Vectorization Classification model )",
    author = "Mohamed Azmy, Waleed  and
      Moulahi, Bilel  and
      Bringay, Sandra  and
      Servajean, Maximilien",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.11",
    pages = "319--322",
    abstract = "Dans ce papier, nous d{\'e}crivons notre participation au d{\'e}fi d{'}analyse de texte DEFT 2018. Nous avons particip{\'e} {\`a} deux t{\^a}ches : (i) classification transport/non-transport et (ii) analyse de polarit{\'e} globale des tweets : positifs, negatifs, neutres et mixtes. Nous avons exploit{\'e} un r{\'e}seau de neurone bas{\'e} sur un perceptron multicouche mais utilisant une seule couche cach{\'e}e.",
    language = "French",
}
@inproceedings{htait-2018-adapted,
    title = "Adapted Sentiment Similarity Seed Words For {F}rench Tweets{'} Polarity Classification",
    author = "Htait, Amal",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 2 - D{\'e}monstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-deft.12",
    pages = "323--328",
    abstract = "We present, in this paper, our contribution in DEFT 2018 task 2 : {``}Global polarity{''}, determining the overall polarity (Positive, Negative, Neutral or MixPosNeg) of tweets regarding public transport, in French language. Our system is based on a list of sentiment seed-words adapted for French public transport tweets. These seed-words are extracted from DEFT{'}s training annotated dataset, and the sentiment relations between seed-words and other terms are captured by cosine measure of their word embeddings representations, using a French language word embeddings model of 683k words. Our semi-supervised system achieved an F1-measure equals to 0.64.",
}
@proceedings{jep-taln-recital-2018-actes-de,
    title = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    editor = "S{\'e}billot, Pascale  and
      Claveau, Vincent",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.0",
}
@inproceedings{tyers-ravishankar-2018-prototype,
    title = "A prototype dependency treebank for {B}reton",
    author = "Tyers, Francis M  and
      Ravishankar, Vinit",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.1",
    pages = "197--204",
    abstract = "This paper describes the development of the first syntactically-annotated corpus of Breton. The corpus is part of the Universal Dependencies project. In the paper we describe how the corpus was prepared, some Breton-specific constructions that required special treatment, and in addition we give results for parsing Breton using a number of off-the-shelf data-driven parsers.",
}
@inproceedings{boyer-neveol-2018-detection,
    title = "D{\'e}tection automatique de phrases en domaine de sp{\'e}cialit{\'e} en fran{\c{c}}ais (Sentence boundary detection for specialized domains in {F}rench )",
    author = "Boyer, Arthur  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.2",
    pages = "205--214",
    abstract = "La d{\'e}tection de fronti{\`e}res de phrase est g{\'e}n{\'e}ralement consid{\'e}r{\'e} comme un probl{\`e}me r{\'e}solu. Cependant, les outils performant sur des textes en domaine g{\'e}n{\'e}ral, ne le sont pas forcement sur des domaines sp{\'e}cialis{\'e}s, ce qui peut engendrer des d{\'e}gradations de performance des outils intervenant en aval dans une cha{\^\i}ne de traitement automatique s{'}appuyant sur des textes d{\'e}coup{\'e}s en phrases. Dans cet article, nous {\'e}valuons 5 outils de segmentation en phrase sur 3 corpus issus de diff{\'e}rent domaines. Nous r{\'e}-entrainerons l{'}un de ces outils sur un corpus de sp{\'e}cialit{\'e} pour {\'e}tudier l{'}adaptation en domaine. Notamment, nous utilisons un nouveau corpus biom{\'e}dical annot{\'e} sp{\'e}cifiquement pour cette t{\^a}che. La detection de fronti{\`e}res de phrase {\`a} l{'}aide d{'}un mod{\`e}le OpenNLP entra{\^\i}n{\'e} sur un corpus clinique offre une F-mesure de .73, contre .66 pour la version standard de l{'}outil.",
    language = "French",
}
@inproceedings{barhoumi-etal-2018-des,
    title = "Des repr{\'e}sentations continues de mots pour l{'}analyse d{'}opinions en arabe: une {\'e}tude qualitative (Word embeddings for {A}rabic sentiment analysis : a qualitative study)",
    author = "Barhoumi, Amira  and
      Camelin, Nathalie  and
      Est{\`e}ve, Yannick",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.3",
    pages = "215--224",
    abstract = "Nous nous int{\'e}ressons, dans cet article, {\`a} la d{\'e}tection d{'}opinions dans la langue arabe. Ces derni{\`e}res ann{\'e}es, l{'}utilisation de l{'}apprentissage profond a am{\'e}lior{\'e} des performances de nombreux syst{\`e}mes automatiques dans une grande vari{\'e}t{\'e} de domaines (analyse d{'}images, reconnaissance de la parole, traduction automatique, . . .) et {\'e}galement celui de l{'}analyse d{'}opinions en anglais. Ainsi, nous avons {\'e}tudi{\'e} l{'}apport de deux architectures (CNN et LSTM) dans notre cadre sp{\'e}cifique. Nous avons {\'e}galement test{\'e} et compar{\'e} plusieurs types de repr{\'e}sentations continues de mots (embeddings) disponibles en langue arabe, qui ont permis d{'}obtenir de bons r{\'e}sultats. Nous avons analys{\'e} les erreurs de notre syst{\`e}me et la pertinence de ces embeddings. Cette analyse m{\`e}ne {\`a} plusieurs perspectives int{\'e}ressantes de travail, au sujet notamment de la constitution automatique de ressources expert et d{'}une construction pertinente des embeddings sp{\'e}cifiques {\`a} la t{\^a}che d{'}analyse d{'}opinions.",
    language = "French",
}
@inproceedings{auguste-etal-2018-evaluation,
    title = "Evaluation automatique de la satisfaction client {\`a} partir de conversations de type {``}chat{''} par r{\'e}seaux de neurones r{\'e}currents avec m{\'e}canisme d{'}attention (Customer satisfaction prediction with attention-based {RNN}s from a chat contact center corpus)",
    author = "Auguste, Jeremy  and
      Charlet, Delphine  and
      Damnati, G{\'e}raldine  and
      Favre, Benoit  and
      Bechet, Frederic",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.4",
    pages = "225--232",
    abstract = "Cet article pr{\'e}sente des m{\'e}thodes permettant l{'}{\'e}valuation de la satisfaction client {\`a} partir de tr{\`e}s vastes corpus de conversation de type {``}chat{''} entre des clients et des op{\'e}rateurs. Extraire des connaissances dans ce contexte demeure un d{\'e}fi pour les m{\'e}thodes de traitement automatique des langues de par la dimension interactive et les propri{\'e}t{\'e}s de ce nouveau type de langage {\`a} l{'}intersection du langage {\'e}crit et parl{\'e}. Nous pr{\'e}sentons une {\'e}tude utilisant des r{\'e}ponses {\`a} des sondages utilisateurs comme supervision faible permettant de pr{\'e}dire la satisfaction des usagers d{'}un service en ligne d{'}assistance technique et commerciale.",
    language = "French",
}
@inproceedings{magallon-etal-2018-detection,
    title = "D{\'e}tection d{'}erreurs dans des transcriptions {OCR} de documents historiques par r{\'e}seaux de neurones r{\'e}currents multi-niveau (Combining character level and word level {RNN}s for post-{OCR} error detection)",
    author = "Magallon, Thibault  and
      Bechet, Frederic  and
      Favre, Benoit",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.5",
    pages = "233--240",
    abstract = "Le traitement {\`a} posteriori de transcriptions OCR cherche {\`a} d{\'e}tecter les erreurs dans les sorties d{'}OCR pour tenter de les corriger, deux t{\^a}ches {\'e}valu{\'e}es par la comp{\'e}tition ICDAR-2017 Post-OCR Text Correction. Nous pr{\'e}senterons dans ce papier un syst{\`e}me de d{\'e}tection d{'}erreurs bas{\'e} sur un mod{\`e}le {\`a} r{\'e}seaux r{\'e}currents combinant une analyse du texte au niveau des mots et des caract{\`e}res en deux temps. Ce syst{\`e}me a {\'e}t{\'e} class{\'e} second dans trois cat{\'e}gories {\'e}valu{\'e}es parmi 11 candidats lors de la comp{\'e}tition.",
    language = "French",
}
@inproceedings{park-2018-le,
    title = "Le benchmarking de la reconnaissance d{'}entit{\'e}s nomm{\'e}es pour le fran{\c{c}}ais (Benchmarking for {F}rench {NER})",
    author = "Park, Jungyeul",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.6",
    pages = "241--250",
    abstract = "Cet article pr{\'e}sente une t{\^a}che du benchmarking de la reconnaissance de l{'}entit{\'e} nomm{\'e}e (REN) pour le fran{\c{c}}ais. Nous entrainons et {\'e}valuons plusieurs algorithmes d{'}{\'e}tiquetage de s{\'e}quence, et nous am{\'e}liorons les r{\'e}sultats de REN avec une approche fond{\'e}e sur l{'}utilisation de l{'}apprentissage semi-supervis{\'e} et du reclassement. Nous obtenons jusqu{'}{\`a} 77.95{\%}, am{\'e}liorant ainsi le r{\'e}sultat de plus de 34 points par rapport du r{\'e}sultat de base du mod{\`e}le.",
    language = "French",
}
@inproceedings{park-2018-une,
    title = "Une note sur l{'}analyse du constituant pour le fran{\c{c}}ais (A Note on constituent parsing for {F}rench)",
    author = "Park, Jungyeul",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.7",
    pages = "251--260",
    abstract = "Cet article traite des analyses d{'}erreurs quantitatives et qualitatives sur les r{\'e}sultats de l{'}analyse syntaxique des constituants pour le fran{\c{c}}ais. Pour cela, nous {\'e}tendons l{'}approche de Kummerfeld et al. (2012) pour fran{\c{c}}ais, et nous pr{\'e}sentons les d{\'e}tails de l{'}analyse. Nous entra{\^\i}nons les syst{\`e}mes d{'}analyse syntaxique statistiques et neuraux avec le corpus arbor{\'e} pour fran{\c{c}}ais, et nous {\'e}valuons les r{\'e}sultats d{'}analyse. Le corpus arbor{\'e} pour le fran{\c{c}}ais fournit des {\'e}tiquettes syntagmatiques {\`a} grain fin, et les caract{\'e}ristiques grammaticales du corpus affectent des erreurs d{'}analyse syntaxique.",
    language = "French",
}
@inproceedings{ben-khelil-etal-2018-interface,
    title = "Interface syntaxe-s{\'e}mantique au moyen d{'}une grammaire d{'}arbres adjoints pour l{'}{\'e}tiquetage s{\'e}mantique de l{'}arabe (Syntax-semantic interface using Tree-adjoining grammar for {A}rabic semantic labeling)",
    author = "Ben Khelil, Cherifa  and
      Ben Othmane Zribi, Chiraz  and
      Duchier, Denys  and
      Parmentier, Yannick",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.8",
    pages = "261--270",
    abstract = "Dans une grammaire formelle, le lien entre l{'}information s{\'e}mantique et sa structure syntaxique correspondante peut {\^e}tre {\'e}tabli en utilisant une interface syntaxe/s{\'e}mantique qui permettra la construction du sens de la phrase. L{'}{\'e}tiquetage de r{\^o}les s{\'e}mantiques aide {\`a} r{\'e}aliser cette t{\^a}che en associant automatiquement des r{\^o}les s{\'e}mantiques {\`a} chaque argument du pr{\'e}dicat d{'}une phrase. Dans ce papier, nous pr{\'e}sentons une nouvelle approche qui permet la construction d{'}une telle interface pour une grammaire d{'}arbres adjoints de l{'}arabe. Cette grammaire a {\'e}t{\'e} g{\'e}n{\'e}r{\'e}e semi automatiquement {\`a} partir d{'}une m{\'e}ta-grammaire. Nous d{\'e}taillons le processus d{'}interfa{\c{c}}age entre le niveau syntaxique et le niveau s{\'e}mantique moyennant la s{\'e}mantique des cadres et comment avons-nous proc{\'e}d{\'e} {\`a} l{'}{\'e}tiquetage de r{\^o}les s{\'e}mantiques en utilisant la ressource lexicale ArabicVerbNet.",
    language = "French",
}
@inproceedings{gaillat-etal-2018-finsentia,
    title = "{F}in{S}enti{A}: Sentiment Analysis in {E}nglish Financial Microblogs",
    author = "Gaillat, Thomas  and
      Sousa, Annanda  and
      Zarrouk, Manel  and
      Davis, Brian",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.9",
    pages = "271--280",
    abstract = "FinSentiA: Sentiment Analysis in English Financial Microblogs The objective of this paper is to report on the building of a Sentiment Analysis (SA) system dedicated to financial microblogs in English. The purpose of our work is to build a financial classifier that predicts the sentiment of stock investors in microblog platforms such as StockTwits and Twitter. Our contribution shows that it is possible to conduct such tasks in order to provide fine grained SA of financial microblogs. We extracted financial entities with relevant contexts and assigned scores on a continuous scale by adopting a deep learning method for the classification.",
}
@inproceedings{park-2018-loptimisation,
    title = "L{'}optimisation du plongement de mots pour le fran{\c{c}}ais : une application de la classification des phrases (Optimization of Word Embeddings for {F}rench : an Application of Sentence Classification)",
    author = "Park, Jungyeul",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.10",
    pages = "281--292",
    abstract = "Nous proposons trois nouvelles m{\'e}thodes pour construire et optimiser des plongements de mots pour le fran{\c{c}}ais. Nous utilisons les r{\'e}sultats de l{'}{\'e}tiquetage morpho-syntaxique, de la d{\'e}tection des expressions multi-mots et de la lemmatisation pour un espace vectoriel continu. Pour l{'}{\'e}valuation, nous utilisons ces vecteurs sur une t{\^a}che de classification de phrases et les comparons avec le vecteur du syst{\`e}me de base. Nous explorons {\'e}galement l{'}approche d{'}adaptation de domaine pour construire des vecteurs. Malgr{\'e} un petit nombre de vocabulaires et la petite taille du corpus d{'}apprentissage, les vecteurs sp{\'e}cialis{\'e}s par domaine obtiennent de meilleures performances que les vecteurs hors domaine.",
    language = "French",
}
@inproceedings{ben-othmane-zribi-2018-word2vec,
    title = "{W}ord2{V}ec vs {LSA} pour la d{\'e}tection des erreurs orthographiques produisant un d{\'e}r{\`e}glement s{\'e}mantique en arabe ({W}ord2{V}ec vs {LSA} for detecting semantic errors in {A}rabic language)",
    author = "Ben Othmane Zribi, Chiraz",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.11",
    pages = "293--302",
    abstract = "Les mots en arabe sont tr{\`e}s proches lexicalement les uns des autres. La probabilit{\'e} de tomber sur un mot correct en commettant une erreur typographique est plus importante que pour le fran{\c{c}}ais ou pour l{'}anglais. Nous nous int{\'e}ressons dans cet article {\`a} d{\'e}tecter les erreurs orthographiques plus pr{\'e}cis{\'e}ment, celles g{\'e}n{\'e}rant des mots lexicalement corrects mais causant un d{\'e}r{\`e}glement s{\'e}mantique au niveau de la phrase. Nous d{\'e}crivons et comparons deux m{\'e}thodes se basant sur la repr{\'e}sentation vectorielle du sens des mots. La premi{\`e}re m{\'e}thode utilise l{'}analyse s{\'e}mantique latente (LSA). La seconde s{'}appuie sur le mod{\`e}le Word2Vec et plus particuli{\`e}rement l{'}architecture Skip-Gram. Les exp{\'e}rimentations ont montr{\'e} que Skip-Gram surpasse LSA.",
    language = "French",
}
@inproceedings{kooli-pigneul-2018-analyse,
    title = "Analyse de sentiments {\`a} base d{'}aspects par combinaison de r{\'e}seaux profonds : application {\`a} des avis en fran{\c{c}}ais (A combination of deep learning methods for aspect-based sentiment analysis : application to {F}rench reviews)",
    author = "Kooli, Nihel  and
      Pigneul, Erwan",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.12",
    pages = "303--310",
    abstract = "Cet article propose une approche d{'}analyse de sentiments {\`a} base d{'}aspects dans un texte d{'}opinion. Cette approche se base sur deux {\'e}tapes principales : l{'}extraction d{'}aspects et la classification du sentiment relatif {\`a} chaque aspect. Pour l{'}extraction d{'}aspects, nous proposons une nouvelle approche qui combine un CNN pour l{'}apprentissage de repr{\'e}sentation de caract{\`e}res, un b-LSTM pour joindre l{'}apprentissage de repr{\'e}sentation de caract{\`e}res et de mots et un CRF pour l{'}{\'e}tiquetage des s{\'e}quences de mots en entit{\'e}s. Pour la classification de sentiments, nous utilisons un r{\'e}seau {\`a} m{\'e}moire d{'}attention pour associer un sentiment (positif, n{\'e}gatif ou neutre) {\`a} une expression d{'}aspect donn{\'e}e. Les exp{\'e}rimentations sur des corpus d{'}avis (publics et industriels) en langue fran{\c{c}}aise ont montr{\'e} des performances qui d{\'e}passent les m{\'e}thodes existantes.",
    language = "French",
}
@inproceedings{linhares-pontes-etal-2018-predicting,
    title = "Predicting the Semantic Textual Similarity with {S}iamese {CNN} and {LSTM}",
    author = "Linhares Pontes, Elvys  and
      Huet, St{\'e}phane  and
      Linhares, Andr{\'e}a Carneiro  and
      Torres-Moreno, Juan-Manuel",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.13",
    pages = "311--320",
    abstract = "Semantic Textual Similarity (STS) is the basis of many applications in Natural Language Processing (NLP). Our system combines convolution and recurrent neural networks to measure the semantic similarity of sentences. It uses a convolution network to take account of the local context of words and an LSTM to consider the global context of sentences. This combination of networks helps to preserve the relevant information of sentences and improves the calculation of the similarity between sentences. Our model has achieved good results and is competitive with the best state-of-the-art systems.",
}
@inproceedings{aliane-etal-2018-levaluation,
    title = "L{'}{\'e}valuation des repr{\'e}sentations vectorielles de mots en utilisant {W}ord{N}et (Evaluating word representations using {W}ord{N}et)",
    author = "Aliane, Nourredine  and
      Mariage, Jean-Jacques  and
      Bernard, Gilles",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.14",
    pages = "321--328",
    abstract = "Les m{\'e}thodes d{'}{\'e}valuation actuelles des repr{\'e}sentations vectorielles de mots utilisent g{\'e}n{\'e}ralement un jeu de donn{\'e}es restreint et biais{\'e}. Pour pallier {\`a} ce probl{\`e}me nous pr{\'e}sentons une nouvelle approche, bas{\'e}e sur la similarit{\'e} entre les synsets associ{\'e}s aux mots dans la volumineuse base de donn{\'e}es lexicale WordNet. Notre m{\'e}thode d{'}{\'e}valuation consiste dans un premier temps {\`a} classer automatiquement les repr{\'e}sentions vectorielles de mots {\`a} l{'}aide d{'}un algorithme de clustering, puis {\`a} {\'e}valuer la coh{\'e}rence s{\'e}mantique et syntaxique des clusters produits. Cette {\'e}valuation est effectu{\'e}e en calculant la similarit{\'e} entre les mots de chaque cluster, pris deux {\`a} deux, en utilisant des mesures de similarit{\'e} entre les mots dans WordNet propos{\'e}es par NLTK (wup {\_}similarity). Nous obtenons, pour chaque cluster, une valeur entre 0 et 1. Un cluster dont la valeur est 1 est un cluster dont tous les mots appartiennent au m{\^e}me synset. Nous calculons ensuite la moyenne des mesures de tous les clusters. Nous avons utilis{\'e} notre nouvelle approche pour {\'e}tudier et comparer trois m{\'e}thodes de repr{\'e}sentations vectorielles : une m{\'e}thode traditionnelle, WebSOM et deux m{\'e}thodes r{\'e}centes, word2vec (Skip-Gram et CBOW) et GloVe, sur trois corpus : en anglais, en fran{\c{c}}ais et en arabe.",
    language = "French",
}
@inproceedings{hadj-salah-etal-2018-traduction,
    title = {Traduction automatique de corpus en anglais annot{\'e}s en sens pour la d{\'e}sambigu{\"\i}sation lexicale d{'}une langue moins bien dot{\'e}e, l{'}exemple de l{'}arabe (Automatic Translation of {E}nglish Sense Annotated Corpora for Word Sense Disambiguation of a Less Well-endowed Language, the Example of {A}rabic)},
    author = {Hadj Salah, Marwa  and
      Vial, Lo{\"\i}c  and
      Blanchon, Herv{\'e}  and
      Zrigui, Mounir  and
      Schwab, Didier},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.15",
    pages = "329--336",
    abstract = {Les corpus annot{\'e}s en sens sont des ressources cruciales pour la t{\^a}che de d{\'e}sambigu{\"\i}sation lexicale (Word Sense Disambiguation). La plupart des langues n{'}en poss{\`e}dent pas ou trop peu pour pouvoir construire des syst{\`e}mes robustes. Nous nous int{\'e}ressons ici {\`a} la langue arabe et pr{\'e}sentons 12 corpus annot{\'e}s en sens, fabriqu{\'e}s automatiquement {\`a} partir de 12 corpus en langue anglaise. Nous {\'e}valuons la qualit{\'e} de nos syst{\`e}mes de d{\'e}sambigu{\"\i}sation gr{\^a}ce {\`a} un corpus d{'}{\'e}valuation en arabe nouvellement disponible.},
    language = "French",
}
@inproceedings{bigeard-etal-2018-detection,
    title = "D{\'e}tection de m{\'e}susages de m{\'e}dicaments dans les r{\'e}seaux sociaux (Detection of drug misuse in social media)",
    author = "Bigeard, Elise  and
      Grabar, Natalia  and
      Thiessard, Frantz",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.16",
    pages = "337--346",
    abstract = "Un m{\'e}susage appara{\^\i}t lorsqu{'}un patient ne respecte pas sa prescription et fait des actions pouvant mener {\`a} des effets nocifs. Bien que ces situations soient dangereuses, les patients ne signalent g{\'e}n{\'e}ralement pas les m{\'e}susages {\`a} leurs m{\'e}decins. Il est donc n{\'e}cessaire d{'}{\'e}tudier d{'}autres sources d{'}information pour d{\'e}couvrir ce qui se passe en r{\'e}alit{\'e}. Nous proposons d{'}{\'e}tudier les forums de sant{\'e} en ligne. L{'}objectif de notre travail consiste {\`a} explorer les forums de sant{\'e} avec des m{\'e}thodes de classification supervis{\'e}e afin d{'}identifier les messages contenant un m{\'e}susage de m{\'e}dicament. Notre m{\'e}thode permet de d{\'e}tecter les m{\'e}susages avec une F-mesure allant jusqu{'}{\`a} 0,810. Cette m{\'e}thode peut aider dans la d{\'e}tection de m{\'e}susages et la construction d{'}un corpus exploitable par les experts pour {\'e}tudier les types de m{\'e}susages commis par les patients.",
    language = "French",
}
@inproceedings{wagner-etal-2018-utilisation,
    title = {Utilisation de Repr{\'e}sentations Distribu{\'e}es de Relations pour la D{\'e}sambigu{\"\i}sation d{'}Entit{\'e}s Nomm{\'e}es (Exploiting Relation Embeddings to Improve Entity Linking )},
    author = "Wagner, Nicolas  and
      Besan{\c{c}}on, Romaric  and
      Ferret, Olivier",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.17",
    pages = "347--356",
    abstract = {L{'}identification des entit{\'e}s nomm{\'e}es dans un texte est une {\'e}tape fondamentale pour de nombreuses t{\^a}ches d{'}extraction d{'}information. Pour avoir une identification compl{\`e}te, une {\'e}tape de d{\'e}sambigu{\"\i}sation des entit{\'e}s similaires doit {\^e}tre r{\'e}alis{\'e}e. Celle-ci s{'}appuie souvent sur la seule description textuelle des entit{\'e}s. Or, les bases de connaissances contiennent des informations plus riches, sous la forme de relations entre les entit{\'e}s : cette information peut {\'e}galement {\^e}tre exploit{\'e}e pour am{\'e}liorer la d{\'e}sambigu{\"\i}sation des entit{\'e}s. Nous proposons dans cet article une approche d{'}apprentissage de repr{\'e}sentations distribu{\'e}es de ces relations et leur utilisation pour la t{\^a}che de d{\'e}sambigu{\"\i}sation d{'}entit{\'e}s nomm{\'e}es. Nous montrons le gain de cette m{\'e}thode sur un corpus d{'}{\'e}valuation standard, en anglais, issu de la t{\^a}che de d{\'e}sambigu{\"\i}sation d{'}entit{\'e}s de la campagne TAC-KBP.},
    language = "French",
}
@inproceedings{blin-2018-traduction,
    title = "Traduction automatique du japonais vers le fran{\c{c}}ais Bilan et perspectives (Machine Translation from {J}apanese to {F}rench - Review and Prospects)",
    author = "Blin, Raoul",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.18",
    pages = "357--364",
    abstract = "Nous {\'e}tudions la possibilit{\'e} de construire un dispositif de traduction automatique neuronale du japonais vers le fran{\c{c}}ais, capable d{'}obtenir des r{\'e}sultats {\`a} la hauteur de l{'}{\'e}tat de l{'}art, sachant que l{'}on ne peut disposer de grands corpus align{\'e}s bilingues. Nous proposons un {\'e}tat de l{'}art et relevons de nombreux signes d{'}am{\'e}lioration de la qualit{\'e} des traductions, en comparaison aux traductions statistiques jusque-l{\`a} pr{\'e}dominantes. Nous testons ensuite un des baselines librement disponibles, OpenNMT, qui produit des r{\'e}sultats encourageants. Sur la base de cette exp{\'e}rience, nous proposons plusieurs pistes pour am{\'e}liorer {\`a} terme la traduction et pour compenser le manque de corpus.",
    language = "French",
}
@inproceedings{ferret-2018-des,
    title = "Des pseudo-sens pour am{\'e}liorer l{'}extraction de synonymes {\`a} partir de plongements lexicaux (Pseudo-senses for improving the extraction of synonyms from word embeddings)",
    author = "Ferret, Olivier",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.19",
    pages = "365--374",
    abstract = "Au-del{\`a} des mod{\`e}les destin{\'e}s {\`a} construire des plongements lexicaux {\`a} partir de corpus, des m{\'e}thodes de sp{\'e}cialisation de ces repr{\'e}sentations selon diff{\'e}rentes orientations ont {\'e}t{\'e} propos{\'e}es. Une part importante d{'}entre elles repose sur l{'}utilisation de connaissances externes. Dans cet article, nous proposons Pseudofit, une nouvelle m{\'e}thode de sp{\'e}cialisation de plongements lexicaux focalis{\'e}e sur la similarit{\'e} s{\'e}mantique et op{\'e}rant sans connaissances externes. Pseudofit s{'}appuie sur la notion de pseudo-sens afin d{'}obtenir plusieurs repr{\'e}sentations pour un m{\^e}me mot et utilise cette pluralit{\'e} pour rendre plus g{\'e}n{\'e}riques les plongements initiaux. Nous illustrons l{'}int{\'e}r{\^e}t de Pseudofit pour l{'}extraction de synonymes et nous explorons dans ce cadre diff{\'e}rentes variantes visant {\`a} en am{\'e}liorer les r{\'e}sultats.",
    language = "French",
}
@inproceedings{sini-etal-2018-annotation,
    title = "Annotation automatique des types de discours dans des livres audio en vue d{'}une oralisation par un syst{\`e}me de synth{\`e}se (Automatic annotation of discourse types in audio-books)",
    author = "Sini, Aghilas  and
      Delais-Roussarie, Elisabeth  and
      Lolive, Damien",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.20",
    pages = "375--382",
    abstract = "Pour synth{\'e}tiser automatiquement et de mani{\`e}re expressive des livres audio, il est n{\'e}cessaire de conna{\^\i}tre le type des discours {\`a} oraliser. Ceci {\'e}tant, dans un roman ou une nouvelle, les perspectives narratives et les types de discours {\'e}voluent souvent entre de la narration, du r{\'e}citatif, du discours direct, du discours rapport{\'e}, voire des dialogues. Dans ce travail, nous allons pr{\'e}senter un outil qui a {\'e}t{\'e} d{\'e}velopp{\'e} {\`a} partir de l{'}analyse d{'}un corpus de livres audio (extraits de Madame Bovary et des Myst{\`e}res de Paris) et qui prend comme unit{\'e} de base pour l{'}analyse le paragraphe. Cet outil permet donc non seulement de d{\'e}terminer automatiquement les types de discours (narration, discours direct, dialogue), et donc de savoir qui parle, mais {\'e}galement d{'}annoter l{'}extension des modifications discursives. Ce dernier point est important, notamment dans le cas d{'}incises de citation o{\`u} le narrateur reprend la parole dans une s{\'e}quence au discours direct. Dans sa forme actuelle, l{'}outil atteint un taux de 89 {\%} de bonne d{\'e}tection.",
    language = "French",
}
@inproceedings{bechikh-ali-etal-2018-impact,
    title = "Impact du Pr{\'e}traitement Linguistique sur l{'}Analyse de Sentiment du Dialecte Tunisien ()",
    author = "Bechikh Ali, Chedi  and
      Mulki, Hala  and
      Haddad, Hatem",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.21",
    pages = "383--392",
    abstract = "",
    language = "French",
}
@inproceedings{bawden-etal-2018-detecting,
    title = "Detecting context-dependent sentences in parallel corpora",
    author = "Bawden, Rachel  and
      Lavergne, Thomas  and
      Rosset, Sophie",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.22",
    pages = "393--400",
    abstract = "In this article, we provide several approaches to the automatic identification of parallel sentences that require sentence-external linguistic context to be correctly translated. Our long-term goal is to automatically construct a test set of context-dependent sentences in order to evaluate machine translation models designed to improve the translation of contextual, discursive phenomena. We provide a discussion and critique that show that current approaches do not allow us to achieve our goal, and suggest that for now evaluating individual phenomena is likely the best solution.",
}
@inproceedings{carbou-etal-2018-predicting,
    title = "Predicting failure of a mediated conversation in the context of asymetric role dialogues",
    author = "Carbou, Romain  and
      Charlet, Delphine  and
      Damnati, G{\'e}raldine  and
      Landragin, Fr{\'e}d{\'e}ric  and
      L{\'e}on Bouraoui, Jean",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.23",
    pages = "401--408",
    abstract = "In a human-to-human conversation between a user and his interlocutor in an assistance center, we suppose a context where the conclusion of the dialog can characterize a notion of success or failure, explicitly annotated or deduced. The study involves different approaches expected to have an influence on predictive classification model of failures. On the one hand, we will aim at taking into account the asymmetry of the speakers{'} roles in the modelling of the lexical distribution. On the other hand, we will determine whether the part of the lexicon most closely relating to the domain of customer assistance studied here, modifies the quality of the prediction. We will eventually assess the perspectives of generalization to morphologically comparable corpora.",
}
@inproceedings{dalloux-etal-2018-portee,
    title = "Port{\'e}e de la n{\'e}gation : d{\'e}tection par apprentissage supervis{\'e} en fran{\c{c}}ais et portugais br{\'e}silien (Negation scope : sequence labeling by supervised learning in {F}rench and {B}razilian-{P}ortuguese)",
    author = "Dalloux, Cl{\'e}ment  and
      Claveau, Vincent  and
      Grabar, Natalia  and
      Moro, Claudia",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.24",
    pages = "409--418",
    abstract = "La d{\'e}tection automatique de la n{\'e}gation fait souvent partie des pr{\'e}-requis dans les syst{\`e}mes d{'}extraction d{'}information, notamment dans le domaine biom{\'e}dical. Cet article pr{\'e}sente nos contributions concernant la d{\'e}tection de la port{\'e}e de la n{\'e}gation en fran{\c{c}}ais et portugais br{\'e}silien. Nous pr{\'e}sentons d{'}une part deux corpus principalement constitu{\'e}s d{'}extraits de protocoles d{'}essais cliniques en fran{\c{c}}ais et portugais br{\'e}silien, d{\'e}di{\'e}s aux crit{\`e}res d{'}inclusion de patients. Les marqueurs de n{\'e}gation et leurs port{\'e}es y ont {\'e}t{\'e} annot{\'e}s manuellement. Nous pr{\'e}sentons d{'}autre part une approche par r{\'e}seau de neurones r{\'e}currents pour extraire les port{\'e}es.",
    language = "French",
}
@inproceedings{mdhaffar-etal-2018-le,
    title = "Le corpus {PASTEL} pour le traitement automatique de cours magistraux ({PASTEL} corpus for automatic processing of lectures)",
    author = "Mdhaffar, Salima  and
      Laurent, Antoine  and
      Est{\`e}ve, Yannick",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.25",
    pages = "419--426",
    abstract = "Le projet PASTEL {\'e}tudie l{'}acceptabilit{\'e} et l{'}utilisabilit{\'e} des transcriptions automatiques dans le cadre d{'}enseignements magistraux. Il s{'}agit d{'}outiller les apprenants pour enrichir de mani{\`e}re synchrone et automatique les informations auxquelles ils peuvent avoir acc{\`e}s durant la s{\'e}ance. Cet enrichissement s{'}appuie sur des traitements automatiques du langage naturel effectu{\'e}s sur les transcriptions automatiques. Nous pr{\'e}sentons dans cet article un travail portant sur l{'}annotation d{'}enregistrements de cours magistraux enregistr{\'e}s dans le cadre du projet CominOpenCourseware. Ces annotations visent {\`a} effectuer des exp{\'e}riences de transcription automatique, segmentation th{\'e}matique, appariement automatique en temps r{\'e}el avec des ressources externes... Ce corpus comprend plus de neuf heures de parole annot{\'e}es. Nous pr{\'e}sentons {\'e}galement des exp{\'e}riences pr{\'e}liminaires r{\'e}alis{\'e}es pour {\'e}valuer l{'}adaptation automatique de notre syst{\`e}me de reconnaissance de la parole.",
    language = "French",
}
@inproceedings{landomiel-etal-2018-apprendre,
    title = "Apprendre de la litt{\'e}rature scientifique : Les r{\'e}seaux de signalisation en biologie syst{\'e}mique (Literature-based discovery: Signaling Systems in Systemic Biology)",
    author = "Landomiel, Flavie  and
      Gu{\'e}rineau, Cathy  and
      Gupta, Anubhav  and
      Maurel, Denis  and
      Poupon, Anne",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.26",
    pages = "427--436",
    abstract = "Cet article a pour but de montrer la faisabilit{\'e} d{'}un syst{\`e}me de fouille de texte pour alimenter un moteur d{'}inf{\'e}rences capable de construire, {\`a} partir de pr{\'e}dicats extraits des articles scientifiques, un r{\'e}seau de signalisation en biologie syst{\'e}mique. Cette fouille se r{\'e}alise en deux {\'e}tapes : la recherche de phrases d{'}int{\'e}r{\^e}t dans un grand corpus scientifique, puis la construction automatique de pr{\'e}dicats. Ces deux {\'e}tapes utilisent un syst{\`e}me de cascades de transducteurs.",
    language = "French",
}
@inproceedings{neifar-etal-2018-detection,
    title = "D{\'e}tection des couples de termes translitt{\'e}r{\'e}s {\`a} partir d{'}un corpus parall{\`e}le anglais-arabe ()",
    author = "Neifar, Wafa  and
      Hamon, Thierry  and
      Zweigenbaum, Pierre  and
      Ellouze, Mariem  and
      Belguith, Lamia-Hadrich",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.27",
    pages = "437--446",
    abstract = "",
    language = "French",
}
@inproceedings{ramadier-lafourcade-2018-utilisation,
    title = "Utilisation d{'}une base de connaissances de sp{\'e}cialit{\'e} et de sens commun pour la simplification de comptes-rendus radiologiques (Radiological text simplification using a general knowledge base)",
    author = "Ramadier, Lionel  and
      Lafourcade, Mathieu",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.28",
    pages = "447--454",
    abstract = "Dans le domaine m{\'e}dical, la simplification des textes est {\`a} la fois une t{\^a}che souhaitable pour les patients et scientifiquement stimulante pour le domaine du traitement automatique du langage naturel. En effet, les comptes rendus m{\'e}dicaux peuvent {\^e}tre difficile {\`a} comprendre pour les non sp{\'e}cialistes, essentiellement {\`a} cause de termes m{\'e}dicaux sp{\'e}cifiques (prurit, par exemple). La substitution de ces termes par des mots du langage courant peut aider le patient {\`a} une meilleure compr{\'e}hension. Dans cet article, nous pr{\'e}sentons une m{\'e}thode de simplification dans le domaine m{\'e}dical (en fran{\c{c}}ais) bas{\'e}e sur un r{\'e}seau lexico-s{\'e}mantique. Nous traitons cette difficult{\'e} s{\'e}mantique par le remplacement du terme m{\'e}dical difficile par un synonyme ou terme qui lui est li{\'e} s{\'e}mantiquement {\`a} l{'}aide d{'}un r{\'e}seau lexico-s{\'e}mantique fran{\c{c}}ais. Nous pr{\'e}sentons dans ce papier, une telle m{\'e}thode ainsi que son {\'e}valuation.",
    language = "French",
}
@inproceedings{labeau-allauzen-2018-algorithmes,
    title = "Algorithmes {\`a} base d{'}{\'e}chantillonage pour l{'}entra{\^\i}nement de mod{\`e}les de langue neuronaux (Here the title in {E}nglish)",
    author = "Labeau, Matthieu  and
      Allauzen, Alexandre",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.29",
    pages = "455--464",
    abstract = "L{'}estimation contrastive bruit{\'e}e (NCE) et l{'}{\'e}chantillonage par importance (IS) sont des proc{\'e}dures d{'}entra{\^\i}nement bas{\'e}es sur l{'}{\'e}chantillonage, que l{'}on utilise habituellement {\`a} la place de l{'}estimation du maximum de vraisemblance (MLE) pour {\'e}viter le calcul du softmax lorsque l{'}on entra{\^\i}ne des mod{\`e}les de langue neuronaux. Dans cet article, nous cherchons {\`a} r{\'e}sumer le fonctionnement de ces algorithmes, et leur utilisation dans la litt{\'e}rature du TAL. Nous les comparons exp{\'e}rimentalement, et pr{\'e}sentons des mani{\`e}res de faciliter l{'}entra{\^\i}nement du NCE.",
    language = "French",
}
@inproceedings{alizadeh-etal-2018-etude,
    title = "{\'E}tude Exp{\'e}rimentale d{'}Extraction d{'}Information dans des Retranscriptions de R{\'e}unions (An Experimental Approach For Information Extraction in Multi-Party Dialogue Discourse)",
    author = "Alizadeh, Pegah  and
      Cellier, Peggy  and
      Charnois, Thierry  and
      Cremilleux, Bruno  and
      Zimmermann, Albrecht",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.30",
    pages = "465--472",
    abstract = "Nous nous int{\'e}ressons dans cet article {\`a} l{'}extraction de th{\`e}mes {\`a} partir de retranscriptions textuelles de r{\'e}unions. Ce type de corpus est bruit{\'e}, il manque de formatage, il est peu structur{\'e} avec plusieurs locuteurs qui interviennent et l{'}information y est souvent {\'e}parpill{\'e}e. Nous pr{\'e}sentons une {\'e}tude exp{\'e}rimentale utilisant des m{\'e}thodes fond{\'e}es sur la mesure tf-idf et l{'}extraction de topics sur un corpus r{\'e}el de r{\'e}f{\'e}rence (le corpus AMI) pour l{'}{\'e}tude de r{\'e}unions. Nous comparons nos r{\'e}sultats avec les r{\'e}sum{\'e}s fournis par le corpus.",
    language = "French",
}
@inproceedings{rosales-nunez-wisniewski-2018-analyse,
    title = "Analyse morpho-syntaxique en pr{\'e}sence d{'}alternance codique ({P}o{S} tagging of Code Switching)",
    author = "Rosales N{\'u}{\~n}ez, Jos{\'e} Carlos  and
      Wisniewski, Guillaume",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.31",
    pages = "473--480",
    abstract = "L{'}alternance codique est le ph{\'e}nom{\`e}ne qui consiste {\`a} alterner les langues au cours d{'}une m{\^e}me conversation ou d{'}une m{\^e}me phrase. Avec l{'}augmentation du volume g{\'e}n{\'e}r{\'e} par les utilisateurs, ce ph{\'e}nom{\`e}ne essentiellement oral, se retrouve de plus en plus dans les textes {\'e}crits, n{\'e}cessitant d{'}adapter les t{\^a}ches et mod{\`e}les de traitement automatique de la langue {\`a} ce nouveau type d{'}{\'e}nonc{\'e}s. Ce travail pr{\'e}sente la collecte et l{'}annotation en partie du discours d{'}un corpus d{'}{\'e}nonc{\'e}s comportant des alternances codiques et {\'e}value leur impact sur la t{\^a}che d{'}analyse morpho-syntaxique.",
    language = "French",
}
@inproceedings{grouin-2018-simplification,
    title = "Simplification de sch{\'e}mas d{'}annotation : un aller sans retour ? (Annotation scheme simplification : a one way trip with no return ?)",
    author = "Grouin, Cyril",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.32",
    pages = "481--488",
    abstract = {Dans cet article, nous comparons l{'}impact de la simplification d{'}un sch{\'e}ma d{'}annotation sur un syst{\`e}me de rep{\'e}rage d{'}entit{\'e}s nomm{\'e}es (REN). Une simplification consiste {\`a} rassembler les types d{'}entit{\'e}s nomm{\'e}es (EN) sous deux types g{\'e}n{\'e}riques (personne et lieu), l{'}autre revient {\`a} mieux d{\'e}finir chaque type d{'}EN. Nous observons une am{\'e}lioration des r{\'e}sultats sur les deux versions simplifi{\'e}es. Nous {\'e}tudions {\'e}galement la possibilit{\'e} de retrouver le niveau de d{\'e}tail des types d{'}EN du sch{\'e}ma d{'}origine {\`a} partir des versions simplifi{\'e}es. L{'}utilisation de r{\`e}gles de conversion permet de recouvrer les types d{'}EN d{'}origine, mais il reste une forme d{'}ambigu{\"\i}t{\'e} contextuelle qu{'}il est impossible de lever au moyen de r{\`e}gles.},
    language = "French",
}
@inproceedings{wang-etal-2018-apprentissage,
    title = "Apprentissage d{\'e}s{\'e}quilibr{\'e} pour la d{\'e}tection des signaux de l{'}implication durable dans les conversations en parfumerie (Automatic detection of positive enduring involvement signals in fragrance products reviews)",
    author = {Wang, Yizhe  and
      Nouvel, Damien  and
      Patin, Ga{\"e}l  and
      Leenhardt, Marguerite},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.33",
    pages = "489--498",
    abstract = "Une simple d{\'e}tection d{'}opinions positives ou n{\'e}gatives ne satisfait plus les chercheurs et les entreprises. Le monde des affaires est {\`a} la recherche d{'}un «aper{\c{c}}u des affaires». Beaucoup de m{\'e}thodes peuvent {\^e}tre utilis{\'e}es pour traiter le probl{\`e}me. Cependant, leurs performances, lorsque les classes ne sont pas {\'e}quilibr{\'e}es, peuvent {\^e}tre d{\'e}grad{\'e}es. Notre travail se concentre sur l{'}{\'e}tude des techniques visant {\`a} traiter les donn{\'e}es d{\'e}s{\'e}quilibr{\'e}es en parfumerie. Cinq m{\'e}thodes ont {\'e}t{\'e} compar{\'e}es : Smote, Adasyn, Tomek links, Smote-TL et la modification du poids des classe. L{'}algorithme d{'}apprentissage choisi est le SVM et l{'}{\'e}valuation est r{\'e}alis{\'e}e par le calcul des scores de pr{\'e}cision, de rappel et de f-mesure. Selon les r{\'e}sultats exp{\'e}rimentaux, la m{\'e}thode en ajustant le poids sur des co{\^u}t d{'}erreurs avec SVM, nous permet d{'}obtenir notre meilleure F-mesure.",
    language = "French",
}
@inproceedings{gari-soler-etal-2018-comparative,
    title = "A comparative study of word embeddings and other features for lexical complexity detection in {F}rench",
    author = "Gar{\'\i} Soler, Aina  and
      Apidianaki, Marianna  and
      Allauzen, Alexandre",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.34",
    pages = "499--508",
    abstract = "Lexical complexity detection is an important step for automatic text simplification which serves to make informed lexical substitutions. In this study, we experiment with word embeddings for measuring the complexity of French words and combine them with other features that have been shown to be well-suited for complexity prediction. Our results on a synonym ranking task show that embeddings perform better than other features in isolation, but do not outperform frequency-based systems in this language.",
}
@inproceedings{guellil-etal-2018-approche,
    title = "Approche Hybride pour la translit{\'e}ration de l{'}{A}rabizi Alg{\'e}rien : une {\'e}tude pr{\'e}liminaire (A hybrid approach for the transliteration of {A}lgerian {A}rabizi: A primary study)",
    author = "Guellil, Imane  and
      Faical, Azouaou  and
      Benali, Fodil  and
      Eddine Hachani, Ala  and
      Saadane, Houda",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.35",
    pages = "509--518",
    abstract = "Dans cet article, nous pr{\'e}sentons une approche hybride pour la translit{\'e}ration de l{'}arabizi alg{\'e}rien. Nous avons {\'e}labor{\'e} un ensemble de r{\`e}gles permettant le passage de l{'}arabizi vers l{'}arabe. {\'A} partir de ces r{\`e}gles nous g{\'e}n{\'e}rons un ensemble de candidats pour la translit{\'e}ration de chaque mot en arabizi vers l{'}arabe, et un parmi ces candidats sera ensuite identifi{\'e} et extrait comme le meilleur candidat. Cette approche a {\'e}t{\'e} exp{\'e}riment{\'e}e en utilisant trois corpus de tests. Les r{\'e}sultats obtenus montrent une am{\'e}lioration du score de pr{\'e}cision qui {\'e}tait pour le meilleur des cas de l{'}ordre de 75,11{\%}. Ces r{\'e}sultats ont aussi permis de v{\'e}rifier que notre approche est tr{\`e}s comp{\'e}titive par rapport aux travaux traitant de la translit{\'e}ration de l{'}arabizi en g{\'e}n{\'e}ral.",
    language = "French",
}
@inproceedings{domingues-2018-lieu,
    title = "Lieu et nom de lieu, du texte vers sa repr{\'e}sentation cartographique (Place and place name, from text to its cartographic portrayal)",
    author = "Domingu{\`e}s, Catherine",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.36",
    pages = "519--528",
    abstract = "Les lieux constituent une information structurante de nombreux textes (r{\'e}cits, romans, articles journalistiques, guides touristiques, itin{\'e}raires de randonn{\'e}es, etc.) et leur recensement et leur analyse doit tenir compte des aspects th{\'e}matiques abord{\'e}s dans les textes. Le travail propos{\'e} ici s{'}inscrit dans les domaines de la linguistique de corpus et de la cartographie. La d{\'e}finition de lieu est augment{\'e}e de celle d{'}objet localis{\'e} et la d{\'e}signation de ces lieux peut alors {\^e}tre construite sur un nom propre ou un nom commun. Des exp{\'e}rimentations sont men{\'e}es afin d{'}identifier les lieux noms propres avec des gazetiers et les lieux noms communs gr{\^a}ce {\`a} un mod{\`e}le d{'}apprentissage automatique. Les r{\'e}sultats sont discut{\'e}s sous la forme d{'}une comparaison entre les caract{\'e}ristiques linguistiques des noms de lieux et les propri{\'e}t{\'e}s visuelles que devront satisfaire leur repr{\'e}sentation cartographique.",
    language = "French",
}
@inproceedings{plu-etal-2018-jeuxdeliens,
    title = "{J}eux{D}e{L}iens: Word Embeddings and Path-Based Similarity for Entity Linking using the {F}rench {J}eux{D}e{M}ots Lexical Semantic Network",
    author = {Plu, Julien  and
      Cousot, Kevin  and
      Lafourcade, Mathieu  and
      Troncy, Rapha{\"e}l  and
      Rizzo, Giuseppe},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.37",
    pages = "529--538",
    abstract = "Entity linking systems typically rely on encyclopedic knowledge bases such as DBpedia or Freebase. In this paper, we use, instead, a French lexical-semantic network named JeuxDeMots to jointly type and link entities. Our approach combines word embeddings and a path-based similarity resulting in encouraging results over a set of documents from the French Le Monde newspaper.",
}
@inproceedings{guibon-etal-2018-de,
    title = "De l{'}usage r{\'e}el des emojis {\`a} une pr{\'e}diction de leurs cat{\'e}gories (From Emoji Usage to Emoji-Category Prediction)",
    author = {Guibon, Ga{\"e}l  and
      Ochs, Magalie  and
      Bellot, Patrice},
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.38",
    pages = "539--546",
    abstract = "L{'}utilisation des emojis dans les messageries sociales n{'}a eu de cesse d{'}augmenter ces derni{\`e}res ann{\'e}es. Plusieurs travaux r{\'e}cents ont port{\'e} sur la pr{\'e}diction d{'}emojis afin d{'}{\'e}pargner {\`a} l{'}utillisateur le parcours de librairies d{'}emojis de plus en plus cons{\'e}quentes. Nous proposons une m{\'e}thode permettant de r{\'e}cup{\'e}rer automatiquement les cat{\'e}gories d{'}emojis {\`a} partir de leur contexte d{'}utilisation afin d{'}am{\'e}liorer la pr{\'e}diction finale. Pour ce faire nous utilisons des plongements lexicaux en consid{\'e}rant les emojis comme des mots pr{\'e}sents dans des tweets. Nous appliquons ensuite un regroupement automatique restreint aux emojis visages afin de v{\'e}rifier l{'}ad{\'e}quation des r{\'e}sultats avec la th{\'e}orie d{'}Ekman. L{'}approche est reproductible et applicable sur tous types d{'}emojis, ou lorsqu{'}il est n{\'e}cessaire de pr{\'e}dire de nombreuses classes.",
    language = "French",
}
@inproceedings{brun-2018-transfert,
    title = "Transfert de ressources s{\'e}mantiques pour l{'}analyse de sentiments au niveau des aspects (In this paper, we address the problem of automatic polarity detection in the context of Aspect Based)",
    author = "Brun, Caroline",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.39",
    pages = "547--556",
    abstract = "Dans cet article, nous abordons le probl{\`e}me de la d{\'e}tection de la polarit{\'e} pour l{'}analyse de sentiments au niveau des aspects dans un contexte bilingue : nous proposons d{'}adapter le composant de d{\'e}tection de polarit{\'e} d{'}un syst{\`e}me pr{\'e}existant d{'}analyse de sentiments au niveau des aspects, tr{\`e}s performant pour la t{\^a}che, et reposant sur l{'}utilisation de ressources s{\'e}mantiques riches pour une langue donn{\'e}e, {\`a} une langue s{\'e}mantiquement moins richement dot{\'e}e. L{'}id{\'e}e sous-jacente est de r{\'e}duire le besoin de supervision n{\'e}cessaire {\`a} la construction des ressources s{\'e}mantiques essentielles {\`a} notre syst{\`e}me. {\`A} cette fin, la langue source, peu dot{\'e}e, est traduite vers la langue cible, et les traductions parall{\`e}les sont ensuite align{\'e}es mot {\`a} mot. Les informations s{\'e}mantiques riches sont alors extraites de la langue cible par le syst{\`e}me de d{\'e}tection de polarit{\'e}, et ces informations sont ensuite align{\'e}es vers la langue source. Nous pr{\'e}sentons les diff{\'e}rentes {\'e}tapes de cette exp{\'e}rience, ainsi que l{'}{\'e}valuation finale. Nous concluons par quelques perspectives.",
    language = "French",
}
@inproceedings{gabor-etal-2018-apport,
    title = "Apport des d{\'e}pendances syntaxiques et des patrons s{\'e}quentiels {\`a} l{'}extraction de relations ()",
    author = "G{\'a}bor, Kata  and
      Lechevrel, Nad{\`e}ge  and
      Tellier, Isabelle  and
      Buscaldi, Davide  and
      Zargayouna, Haifa  and
      Charnois, Thierry",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.40",
    pages = "557--566",
    abstract = "",
    language = "French",
}
@inproceedings{wisniewski-yvon-2018-divergences,
    title = "Divergences entre annotations dans le projet {U}niversal {D}ependencies et leur impact sur l{'}{\'e}valuation des performance d{'}{\'e}tiquetage morpho-syntaxique (Evaluating Annotation Divergences in the {UD} Project)",
    author = "Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.41",
    pages = "567--576",
    abstract = "Ce travail montre que la d{\'e}gradation des performances souvent observ{\'e}e lors de l{'}application d{'}un analyseur morpho-syntaxique {\`a} des donn{\'e}es hors domaine r{\'e}sulte souvent d{'}incoh{\'e}rences entre les annotations des ensembles de test et d{'}apprentissage. Nous montrons comment le principe de variation des annotations, introduit par Dickinson {\&} Meurers (2003) pour identifier automatiquement les erreurs d{'}annotation, peut {\^e}tre utilis{\'e} pour identifier ces incoh{\'e}rences et {\'e}valuer leur impact sur les performances des analyseurs morpho-syntaxiques.",
    language = "French",
}
@inproceedings{perrotin-etal-2018-annotation,
    title = "Annotation en Actes de Dialogue pour les Conversations d{'}Assistance en Ligne (Dialog Acts Annotations for Online Chats)",
    author = "Perrotin, Robin  and
      Nasr, Alexis  and
      Auguste, Jeremy",
    booktitle = "Actes de la Conf{\'e}rence TALN. Volume 1 - Articles longs, articles courts de TALN",
    month = "5",
    year = "2018",
    address = "Rennes, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2018.jeptalnrecital-court.42",
    pages = "577--584",
    abstract = "Les conversations techniques en ligne sont un type de productions linguistiques qui par de nombreux aspects se d{\'e}marquent des objets plus usuellement {\'e}tudi{\'e}s en traitement automatique des langues : il s{'}agit de dialogues {\'e}crits entre deux locuteurs qui servent de support {\`a} la r{\'e}solution coop{\'e}rative des probl{\`e}mes des usagers. Nous proposons de d{\'e}crire ici ces conversations par un {\'e}tiquetage en actes de dialogue sp{\'e}cifiquement con{\c{c}}u pour les conversations en ligne. Diff{\'e}rents syst{\`e}mes de pr{\'e}dictions ont {\'e}t{\'e} {\'e}valu{\'e}s ainsi qu{'}une m{\'e}thode permettant de s{'}abstraire des sp{\'e}cificit{\'e}s lexicales du corpus d{'}apprentissage.",
    language = "French",
}
@proceedings{gwc-2018-global,
    title = "Proceedings of the 9th Global Wordnet Conference",
    editor = "Bond, Francis  and
      Vossen, Piek  and
      Fellbaum, Christiane",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.0",
}
@inproceedings{rahit-etal-2018-banglanet,
    title = "{B}angla{N}et: Towards a {W}ord{N}et for {B}engali Language",
    author = "Rahit, K.M. Tahsin Hassan  and
      Hasan, Khandaker Tabin  and
      Amin, Md. Al-  and
      Ahmed, Zahiduddin",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.1",
    pages = "1--9",
    abstract = "Despite being a popular language in the world, the Bengali language lacks in having a good wordnet. This restricts us to do NLP related research work in Bengali. Most of the today{'}s wordnets are developed by following expand approach. One of the key challenges of this approach is the cross-lingual word-sense disambiguation. In our research work, we make semantic relation between Bengali wordnet and Princeton WordNet based on well-established research work in other languages. The algorithm will derive relations between concepts as well. One of our key objectives is to provide a panel for lexicographers so that they can validate and contribute to the wordnet.",
}
@inproceedings{mondal-etal-2018-wme,
    title = "{WME} 3.0: An Enhanced and Validated Lexicon of Medical Concepts",
    author = "Mondal, Anupam  and
      Das, Dipankar  and
      Cambria, Erik  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.2",
    pages = "10--16",
    abstract = "Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners. Thus, in the present work, we are motivated to design a new lexicon, WME 3.0 (WordNet of Medical Events), which contains over 10,000 medical concepts along with their part of speech, gloss (descriptive explanations), polarity score, sentiment, similar sentiment words, category, affinity score and gravity score features. In addition, the manual annotators help to validate the overall as well as individual category level of medical concepts of WME 3.0 using Cohen{'}s Kappa agreement metric. The agreement score indicates almost correct identification of medical concepts and their assigned features in WME 3.0.",
}
@inproceedings{methol-etal-2018-using,
    title = "Using Context to Improve the {S}panish {W}ord{N}et Translation",
    author = "Methol, Alfonso  and
      L{\'o}pez, Guillermo  and
      {\'A}lvarez, Juan  and
      Chiruzzo, Luis  and
      Wonsever, Dina",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.3",
    pages = "17--24",
    abstract = "We present some strategies for improving the Spanish version of WordNet, part of the MCR, selecting new lemmas for the Spanish synsets by translating the lemmas of the corresponding English synsets. We used four simple selectors that resulted in a considerable improvement of the Spanish WordNet coverage, but with relatively lower precision, then we defined two context based selectors that improved the precision of the translations.",
}
@inproceedings{alvez-rigau-2018-towards,
    title = "Towards Cross-checking {W}ord{N}et and {SUMO} Using Meronymy",
    author = "{\'A}lvez, Javier  and
      Rigau, German",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.4",
    pages = "25--33",
    abstract = "We describe the practical application of a black-box testing methodology for the validation of the knowledge encoded in WordNet, SUMO and their mapping by using automated theorem provers. In this paper,weconcentrateonthepart-whole information provided by WordNet and create a large set of tests on the basis of few question patterns. From our preliminary evaluation results, we report on some of the detected inconsistencies.",
}
@inproceedings{loukachevitch-etal-2018-comparing,
    title = "Comparing Two Thesaurus Representations for {R}ussian",
    author = "Loukachevitch, Natalia  and
      Lashevich, German  and
      Dobrov, Boris",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.5",
    pages = "34--43",
    abstract = "In the paper we presented a new Russian wordnet, RuWordNet, which was semi-automatically obtained by transformation of the existing Russian thesaurus RuThes. At the first step, the basic structure of wordnets was reproduced: synsets{'} hierarchy for each part of speech and the basic set of relations between synsets (hyponym-hypernym, part-whole, antonyms). At the second stage, we added causation, entailment and domain relations between synsets. Also derivation relations were established for single words and the component structure for phrases included in RuWordNet. The described procedure of transformation highlights the specific features of each type of thesaurus representations.",
}
@inproceedings{maziarz-piasecki-2018-towards,
    title = "Towards Mapping Thesauri onto pl{W}ord{N}et",
    author = "Maziarz, Marek  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.6",
    pages = "44--52",
    abstract = "plWordNet, the wordnet of Polish, has become a very comprehensive description of the Polish lexical system. This paper presents a plan of its semi-automated integration with thesauri, terminological databases and ontologies, as a further necessary step in its development. This will improve linking of plWordNet into Linked Open Data, and facilitate applications in, e.g., WSD, keyword extraction or automated metadata generation. We present an overview of resources relevant to Polish and a plan for their linking to plWordNet.",
}
@inproceedings{mititelu-2018-investigating,
    title = "Investigating {E}nglish Affixes and their Productivity with {P}rinceton {W}ord{N}et",
    author = "Mititelu, Verginica",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.7",
    pages = "53--60",
    abstract = "Such a rich language resource like Princeton WordNet, containing linguistic information of different types (semantic, lexical, syntactic, derivational, dialectal, etc.), is a thesaurus which is worth both being used in various language-enabled applications and being explored in order to study a language. In this paper we show how we used Princeton WordNet version 3.0 to study the English affixes. We extracted pairs of base-derived words and identified the affixes by means of which the derived words were created from their bases. We distinguished among four types of derivation depending on the type of overlapping between the senses of the base word and those of the derived word that are linked by derivational relations in Princeton WordNet. We studied the behaviour of affixes with respect to these derivation types. Drawing on these data, we inferred about their productivity.",
}
@inproceedings{mccrae-2018-mapping,
    title = "Mapping {W}ord{N}et Instances to {W}ikipedia",
    author = "McCrae, John P.",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.8",
    pages = "61--68",
    abstract = "Lexical resource differ from encyclopaedic resources and represent two distinct types of resource covering general language and named entities respectively. However, many lexical resources, including Princeton WordNet, contain many proper nouns, referring to named entities in the world yet it is not possible or desirable for a lexical resource to cover all named entities that may reasonably occur in a text. In this paper, we propose that instead of including synsets for instance concepts PWN should instead provide links to Wikipedia articles describing the concept. In order to enable this we have created a gold-quality mapping between all of the 7,742 instances in PWN and Wikipedia (where such a mapping is possible). As such, this resource aims to provide a gold standard for link discovery, while also allowing PWN to distinguish itself from other resources such as DBpedia or BabelNet. Moreover, this linking connects PWN to the Linguistic Linked Open Data cloud, thus creating a richer, more usable resource for natural language processing.",
}
@inproceedings{koeva-etal-2018-mapping,
    title = "Mapping {W}ord{N}et Concepts with {CPA} Ontology",
    author = "Koeva, Svetla  and
      Dimitrova, Cvetana  and
      Stefanova, Valentina  and
      Hristov, Dimitar",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.9",
    pages = "69--76",
    abstract = "The paper discusses the enrichment of WordNet data through merging of WordNet concepts and Corpus Pattern Analysis (CPA) semantic types. The 253 CPA semantic types are mapped to the respective WordNet concepts. As a result of mapping, the hyponyms of a synset to which a CPA semantic type is mapped inherit not only the respective WordNet semantic primitive but also the CPA semantic type.",
}
@inproceedings{chakravarthi-etal-2018-improving,
    title = "Improving Wordnets for Under-Resourced Languages Using Machine Translation",
    author = "Chakravarthi, Bharathi Raja  and
      Arcan, Mihael  and
      McCrae, John P.",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.10",
    pages = "77--86",
    abstract = "Wordnets are extensively used in natural language processing, but the current approaches for manually building a wordnet from scratch involves large research groups for a long period of time, which are typically not available for under-resourced languages. Even if wordnet-like resources are available for under-resourced languages, they are often not easily accessible, which can alter the results of applications using these resources. Our proposed method presents an expand approach for improving and generating wordnets with the help of machine translation. We apply our methods to improve and extend wordnets for the Dravidian languages, i.e., Tamil, Telugu, Kannada, which are severly under-resourced languages. We report evaluation results of the generated wordnet senses in term of precision for these languages. In addition to that, we carried out a manual evaluation of the translations for the Tamil language, where we demonstrate that our approach can aid in improving wordnet resources for under-resourced Dravidian languages.",
}
@inproceedings{ruas-grosky-2018-semantic,
    title = "Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains",
    author = "Ruas, Terry  and
      Grosky, William",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.11",
    pages = "87--96",
    abstract = "The meaning of a sentence in a document is more easily determined if its constituent words exhibit cohesion with respect to their individual semantics. This paper explores the degree of cohesion among a document{'}s words using lexical chains as a semantic representation of its meaning. Using a combination of diverse types of lexical chains, we develop a text document representation that can be used for semantic document retrieval. For our approach, we develop two kinds of lexical chains: (i) a multilevel flexible chain representation of the extracted semantic values, which is used to construct a fixed segmentation of these chains and constituent words in the text; and (ii) a fixed lexical chain obtained directly from the initial semantic representation from a document. The extraction and processing of concepts is performed using WordNet as a lexical database. The segmentation then uses these lexical chains to model the dispersion of concepts in the document. Representing each document as a high-dimensional vector, we use spherical k-means clustering to demonstrate that our approach performs better than previous techniques.",
}
@inproceedings{pease-cheung-2018-toward,
    title = "Toward a Semantic Concordancer",
    author = "Pease, Adam  and
      Cheung, Andrew",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.12",
    pages = "97--104",
    abstract = "Concordancers are an accepted and valuable part of the tool set of linguists and lexicographers. They allow the user to see the context of use of a word or phrase in a corpus. A large enough corpus, such as the Corpus Of Contemporary American English, provides the data needed to enumerate all common uses or meanings. One challenge is that there may be too many results for short search phrases or common words when only a specific context is desired. However, finding meaningful groupings of usage may be impractical if it entails enumerating long lists of possible values, such as city names. If a tool existed that could create some semantic abstractions, it would free the lexicographer from the need to resort to customized development of analysis software. To address this need, we have developed a Semantic Concordancer that uses dependency parsing and the Suggested Upper Merged Ontology (SUMO) to support linguistic analysis at a level of semantic abstraction above the original textual elements. We show how this facility can be employed to analyze the use of English prepositions by non-native speakers. We briefly introduce condordancers and then describe the corpora on which we applied this work. Next we provide a detailed description of the NLP pipeline followed by how this captures detailed semantics. We show how the semantics can be used to analyze errors in the use of English prepositions by non-native speakers of English. Then we provide a description of a tool that allows users to build semantic search specifications from a set of English examples and how those results can be employed to build rules that translate sentences into logical forms. Finally, we summarize our conclusions and mention future work.",
}
@inproceedings{delfino-etal-2018-using,
    title = "Using {O}pen{W}ordnet-{PT} for Question Answering on Legal Domain",
    author = "Delfino, Pedro  and
      Cuconato, Bruno  and
      Passos, Guillherme Paulino  and
      Zaverucha, Gerson  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.13",
    pages = "105--112",
    abstract = "In order to practice a legal profession in Brazil, law graduates must be approved in the OAB national unified bar exam. For their topic coverage and national reach, the OAB exams provide an excellent benchmark for the performance of legal information systems, as it provides objective metrics and are challenging even for humans, as only 20{\%} of its candidates are approved. After constructing a new data set on the exams and doing shallow experiments on it, we now employ the OpenWordnet-PT to verify whether using word senses and relations we can improve previous results. We discuss the results, possible future ideas and the additions to the OpenWordnet-PT that we made.",
}
@inproceedings{dziob-piasecki-2018-implementation,
    title = "Implementation of the Verb Model in pl{W}ord{N}et 4.0",
    author = "Dziob, Agnieszka  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.14",
    pages = "113--122",
    abstract = "The paper presents an expansion of the verb model for plWordNet {--} the wordnet of Polish. A modified system of constitutive features (register, aspect and verb classes), synset and lexical relations is presented. A special attention is given to the proposed new relations and changes in the verb classification. We discuss also the results of its verification by application to the description of a relatively large sample of Polish verbs. The model introduces a new class of relations, namely non-constitutive synset relations that are shared among lexical units, but describe, not define synsets. The proposed model is compared to the entailment relations in other wordnets, and the description of verbs based on valency frames.",
}
@inproceedings{shukla-shukla-2018-public,
    title = "Public Apologies in {I}ndia - Semantics, Sentiment and Emotion",
    author = "Shukla, Sangeeta  and
      Shukla, Rajita",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.15",
    pages = "123--135",
    abstract = "This paper reports a pilot study related to public apologies in India, with reference to certain keywords found in them. The study is of importance as the choice of lexical items holds importance which goes beyond the surface meaning of the words. The analysis of the lexical items has been done using interlinked digital lexical resources which, in future, can lend this study to computational tasks related to opinion mining, sentiment analysis and document classification. The study attempts an in-depth psycholinguistic analysis of whether the apology conveys a sincerity of intent or is it a mere ritualistic exercise to control and repair damage.",
}
@inproceedings{batita-zrigui-2018-derivational,
    title = "Derivational Relations in {A}rabic {W}ord{N}et",
    author = "Batita, Mohamed Ali  and
      Zrigui, Mounir",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.16",
    pages = "136--144",
    abstract = "When derivational relations deficiency exists in a wordnet, such as the Arabic WordNet, it makes it very difficult to exploit in the natural language processing community. Such deficiency is raised when many wordnets follow the same development path of Princeton WordNet. A rule-based approach for Arabic derivational relations is proposed in this paper to deal with this deficiency. The proposed approach is explained step by step. It involves the gathering of lexical entries that share the same root, into a bag of words. Rules are then used to affect the appropriate derivational relations, i.e. to relate existing words in the AWN, involving part-of-speech switch. The method is implemented using Java. Manual verification by a lexicographer takes place to ensure good results. The described approach gave good results. It could be useful for other morphologically complex languages as well.",
}
@inproceedings{muniz-etal-2018-extending,
    title = "Extending {W}ordnet to Geological Times",
    author = "Muniz, Henrique  and
      Chalub, Fabricio  and
      Rademaker, Alexandre  and
      Paiva, Valeria De",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.17",
    pages = "145--152",
    abstract = "This paper describes work extending Princeton WordNet to the domain of geological texts, associated with the time periods of the geological eras of the Earth History. We intend this extension to be considered as an example for any other domain extension that we might want to pursue. To provide this extension, we first produce a textual version of Princeton WordNet. Then we map a fragment of the International Commission on Stratigraphy (ICS) ontologies to WordNet and create the appropriate new synsets. We check the extended ontology on a small corpus of sentences from Gas and Oil technical reports and realize that more work needs to be done, as we need new words, new senses and new compounds in our extended WordNet.",
}
@inproceedings{zasko-zielinska-piasecki-2018-towards,
    title = "Towards Emotive Annotation in pl{W}ord{N}et 4.0",
    author = "Za{\'s}ko-Zieli{\'n}ska, Monika  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.18",
    pages = "153--162",
    abstract = "The paper presents an approach to building a very large emotive lexicon for Polish based on plWordNet. An expanded annotation model is discussed, in which lexical units (word senses) are annotated with basic emotions, fundamental human values and sentiment polarisation. The annotation process is performed manually in the 2+1 scheme by pairs of linguists and psychologies. Guidelines referring to the usage in corpora, substitution tests as well linguistic properties of lexical units (e.g. derivational associations) are discussed. Application of the model in a substantial extension of the emotive annotation of plWordNet is presented. The achieved high inter-annotator agreement shows that with relatively small workload a promising emotive resource can be created.",
}
@inproceedings{breen-etal-2018-company,
    title = "The Company They Keep: Extracting {J}apanese Neologisms Using Language Patterns",
    author = "Breen, James  and
      Baldwin, Timothy  and
      Bond, Francis",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.19",
    pages = "163--171",
    abstract = "We describe an investigation into the identification and extraction of unrecorded potential lexical items in Japanese text by detecting text passages containing selected language patterns typically associated with such items. We identified a set of suitable patterns, then tested them with two large collections of text drawn from the WWW and Twitter. Samples of the extracted items were evaluated, and it was demonstrated that the approach has considerable potential for identifying terms for later lexicographic analysis.",
}
@inproceedings{vu-etal-2018-lexical,
    title = "Lexical-semantic resources: yet powerful resources for automatic personality classification",
    author = "Vu, Xuan-Son  and
      Flekova, Lucie  and
      Jiang, Lili  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.20",
    pages = "172--181",
    abstract = "In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.",
}
@inproceedings{pedersen-etal-2018-towards,
    title = "Towards a principled approach to sense clustering {--} a case study of wordnet and dictionary senses in {D}anish",
    author = "Pedersen, Bolette  and
      Agirrezabal, Manex  and
      Nimb, Sanni  and
      Olsen, Ida  and
      Olsen, Sussi",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.21",
    pages = "182--189",
    abstract = "Our aim is to develop principled methods for sense clustering which can make existing lexical resources practically useful in NLP {--} not too fine-grained to be operational and yet finegrained enough to be worth the trouble. Where traditional dictionaries have a highly structured sense inventory typically describing the vocabulary by means of mainand subsenses, wordnets are generally fine-grained and unstructured. We present a series of clustering and annotation experiments with 10 of the most polysemous nouns in Danish. We combine the structured information of a traditional Danish dictionary with the ontological types found in the Danish wordnet, DanNet. This constellation enables us to automatically cluster senses in a principled way and improve inter-annotator agreement and wsd performance.",
}
@inproceedings{naskret-etal-2018-wordnetloom,
    title = "{W}ordnet{L}oom {--} a Multilingual {W}ordnet Editing System Focused on Graph-based Presentation",
    author = "Naskr{\k{e}}t, Tomasz  and
      Dziob, Agnieszka  and
      Piasecki, Maciej  and
      Saedi, Chakaveh  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.22",
    pages = "190--199",
    abstract = "The paper presents a new re-built and expanded, version 2.0 of WordnetLoom {--} an open wordnet editor. It facilitates work on a multilingual system of wordnets, is based on efficient software architecture of thin client, and offers more flexibility in enriching wordnet representation. This new version is built on the experience collected during the use of the previous one for more than 10 years of plWordNet development. We discuss its extensions motivated by the collected experience. A special focus is given to the development of a variant for the needs of MultiWordnet of Portuguese, which is based on a very different wordnet development model.",
}
@inproceedings{kwong-2018-translation,
    title = "Translation Equivalence and Synonymy: Preserving the Synsets in Cross-lingual Wordnets",
    author = "Kwong, Oi Yee",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.23",
    pages = "200--208",
    abstract = "The Princeton WordNet for English was founded on the synonymy relation, and multilingual wordnets are primarily developed by creating equivalent synsets in the respective languages. The process would often rely on translation equivalents obtained from existing bilingual dictionaries. This paper discusses some observations from the Chinese Open Wordnet, especially from the adjective subnet, to illuminate potential blind spots of the approach which may lead to the formation of non-synsets in the new wordnet. With cross-linguistic differences duly taken into account, alternative representations of cross-lingual lexical relations are proposed to better capture the language-specific properties. It is also suggested that such cross-lingual representation encompassing the cognitive as well as linguistic aspects of meaning is beneficial for a lexical resource to be used by both humans and computers.",
}
@inproceedings{rudnicka-etal-2018-lexical,
    title = "Lexical Perspective on {W}ordnet to {W}ordnet Mapping",
    author = "Rudnicka, Ewa  and
      Bond, Francis  and
      Grabowski, {\L}ukasz  and
      Piasecki, Maciej  and
      Piotrowski, Tadeusz",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.24",
    pages = "209--218",
    abstract = "The paper presents a feature-based model of equivalence targeted at (manual) sense linking between Princeton WordNet and plWordNet. The model incorporates insights from lexicographic and translation theories on bilingual equivalence and draws on the results of earlier synset-level mapping of nouns between Princeton WordNet and plWordNet. It takes into account all basic aspects of language such as form, meaning and function and supplements them with (parallel) corpus frequency and translatability. Three types of equivalence are distinguished, namely strong, regular and weak depending on the conformity with the proposed features. The presented solutions are language-neutral and they can be easily applied to language pairs other than Polish and English. Sense-level mapping is a more fine-grained mapping than the existing synset mappings and is thus of great potential to human and machine translation.",
}
@inproceedings{vossen-etal-2018-referencenet,
    title = "{R}eference{N}et: a semantic-pragmatic network for capturing reference relations.",
    author = "Vossen, Piek  and
      Ilievski, Filip  and
      Postrma, Marten",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.25",
    pages = "219--228",
    abstract = "In this paper, we present ReferenceNet: a semantic-pragmatic network of reference relations between synsets. Synonyms are assumed to be exchangeable in similar contexts and also word embeddings are based on sharing of local contexts represented as vectors. Co-referring words, however, tend to occur in the same topical context but in different local contexts. In addition, they may express different concepts related through topical coherence, and through author framing and perspective. In this paper, we describe how reference relations can be added to WordNet and how they can be acquired. We evaluate two methods of extracting event coreference relations using WordNet relations against a manual annotation of 38 documents within the same topical domain of gun violence. We conclude that precision is reasonable but recall is lower because the WordNet hierarchy does not sufficiently capture the required coherence and perspective relations.",
}
@inproceedings{piasecki-etal-2018-wordnet,
    title = "{W}ordnet-based Evaluation of Large Distributional Models for {P}olish",
    author = "Piasecki, Maciej  and
      Czachor, Gabriela  and
      Janz, Arkadiusz  and
      Kaszewski, Dominik  and
      K{\k{e}}dzia, Pawe{\l}",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.26",
    pages = "229--238",
    abstract = "The paper presents construction of large scale test datasets for word embeddings on the basis of a very large wordnet. They were next applied for evaluation of word embedding models and used to assess and compare the usefulness of different word embeddings extracted from a very large corpus of Polish. We analysed also and compared several publicly available models described in literature. In addition, several large word embeddings models built on the basis of a very large Polish corpus are presented.",
}
@inproceedings{nam-etal-2018-distant,
    title = "Distant Supervision for Relation Extraction with Multi-sense Word Embedding",
    author = "Nam, Sangha  and
      Han, Kijong  and
      Kim, Eun-Kyung  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.27",
    pages = "239--244",
    abstract = "Distant supervision can automatically generate labeled data between a large-scale corpus and a knowledge base without utilizing human efforts. Therefore, many studies have used the distant supervision approach in relation extraction tasks. However, existing studies have a disadvantage in that they do not reflect the homograph in the word embedding used as an input of the relation extraction model. Thus, it can be seen that the relation extraction model learns without grasping the meaning of the word accurately. In this paper, we propose a relation extraction model with multi-sense word embedding. We learn multi-sense word embedding using a word sense disambiguation module. In addition, we use convolutional neural network and piecewise max pooling convolutional neural network relation extraction models that efficiently grasp key features in sentences. To evaluate the performance of the proposed model, two additional methods of word embedding were learned and compared. Accordingly, our method showed the highest performance among them.",
}
@inproceedings{mahendra-etal-2018-cross,
    title = "Cross-Lingual and Supervised Learning Approach for {I}ndonesian Word Sense Disambiguation Task",
    author = "Mahendra, Rahmad  and
      Septiantri, Heninggar  and
      Wibowo, Haryo Akbarianto  and
      Manurung, Ruli  and
      Adriani, Mirna",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.28",
    pages = "245--250",
    abstract = "Ambiguity is a problem we frequently face in Natural Language Processing. Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word. However, research in WSD for Indonesian is still rare to find. The availability of English-Indonesian parallel corpora and WordNet for both languages can be used as training data for WSD by applying Cross-Lingual WSD method. This training data is used as an input to build a model using supervised machine learning algorithms. Our research also examines the use of Word Embedding features to build the WSD model.",
}
@inproceedings{czachor-etal-2018-recognition,
    title = "Recognition of Hyponymy and Meronymy Relations in Word Embeddings for {P}olish",
    author = "Czachor, Gabriela  and
      Piasecki, Maciej  and
      Janz, Arkadiusz",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.29",
    pages = "251--258",
    abstract = "Word embeddings were used for the extraction of hyponymy relation in several approaches, but also it was recently shown that they should not work, in fact. In our work we verified both claims using a very large wordnet of Polish as a gold standard for lexico-semantic relations and word embeddings extracted from a very large corpus of Polish. We showed that a hyponymy extraction method based on linear regression classifiers trained on clusters of vectors can be successfully applied on large scale. We presented also a possible explanation for contradictory findings in the literature. Moreover, in order to show the feasibility of the method we extended it to the recognition of meronymy.",
}
@inproceedings{oele-noord-2018-simple,
    title = "Simple Embedding-Based Word Sense Disambiguation",
    author = "Oele, Dieke  and
      Noord, Gertjan Van",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.30",
    pages = "259--265",
    abstract = "We present a simple knowledge-based WSD method that uses word and sense embeddings to compute the similarity between the gloss of a sense and the context of the word. Our method is inspired by the Lesk algorithm as it exploits both the context of the words and the definitions of the senses. It only requires large unlabeled corpora and a sense inventory such as WordNet, and therefore does not rely on annotated data. We explore whether additional extensions to Lesk are compatible with our method. The results of our experiments show that by lexically extending the amount of words in the gloss and context, although it works well for other implementations of Lesk, harms our method. Using a lexical selection method on the context words, on the other hand, improves it. The combination of our method with lexical selection enables our method to outperform state-of the art knowledge-based systems.",
}
@inproceedings{patel-etal-2018-semi,
    title = "Semi-automatic {W}ord{N}et Linking using Word Embeddings",
    author = "Patel, Kevin  and
      Kanojia, Diptesh  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.31",
    pages = "266--271",
    abstract = "Wordnets are rich lexico-semantic resources. Linked wordnets are extensions of wordnets, which link similar concepts in wordnets of different languages. Such resources are extremely useful in many Natural Language Processing (NLP) applications, primarily those based on knowledge-based approaches. In such approaches, these resources are considered as gold standard/oracle. Thus, it is crucial that these resources hold correct information. Thereby, they are created by human experts. However, manual maintenance of such resources is a tedious and costly affair. Thus techniques that can aid the experts are desirable. In this paper, we propose an approach to link wordnets. Given a synset of the source language, the approach returns a ranked list of potential candidate synsets in the target language from which the human expert can choose the correct one(s). Our technique is able to retrieve a winner synset in the top 10 ranked list for 60{\%} of all synsets and 70{\%} of noun synsets.",
}
@inproceedings{vasanthakumar-bond-2018-multilingual,
    title = "Multilingual {W}ordnet sense Ranking using nearest context",
    author = "Vasanthakumar, E Umamaheswari  and
      Bond, Francis",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.32",
    pages = "272--283",
    abstract = "In this paper, we combine methods to estimate sense rankings from raw text with recent work on word embeddings to provide sense ranking estimates for the entries in the Open Multilingual WordNet (OMW). The existing Word2Vec pre-trained models from Polygot2 are only built for single word entries, we, therefore, re-train them with multiword expressions from the wordnets, so that multiword expressions can also be ranked. Thus this trained model gives embeddings for both single words and multiwords. The resulting lexicon gives a WSD baseline for five languages. The results are evaluated for Semcor sense corpora for 5 languages using Word2Vec and Glove models. The Glove model achieves an average accuracy of 0.47 and Word2Vec achieves 0.31 for languages such as English, Italian, Indonesian, Chinese and Japanese. The experimentation on OMW sense ranking proves that the rank correlation is generally similar to the human ranking. Hence distributional semantics can aid in Wordnet Sense Ranking.",
}
@inproceedings{simov-etal-2018-grammatical,
    title = "Grammatical Role Embeddings for Enhancements of Relation Density in the {P}rinceton {W}ord{N}et",
    author = "Simov, Kiril  and
      Popov, Alexander  and
      Simova, Iliana  and
      Osenova, Petya",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.33",
    pages = "284--292",
    abstract = "In this paper we present an approach for training verb subatom embeddings. For each verb we learn several embeddings rather than only one. These embeddings include the verb itself as well as embeddings for each grammatical role of this verb. To give an example, for the verb {`}to give{'} we learn four embeddings: one for the lemma {`}give{'}, one for the subject, one for the direct object and one for the indirect object. We have exploited these grammatical role embeddings in order to add new syntagmatic relations to WordNet. The evaluation of the new relations quality has been done extrinsically through the Knowledge-based Word Sense Disambiguation task.",
}
@inproceedings{patel-bhattacharyya-2018-iterative,
    title = "An Iterative Approach for Unsupervised Most Frequent Sense Detection using {W}ord{N}et and Word Embeddings",
    author = "Patel, Kevin  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.34",
    pages = "293--297",
    abstract = "Given a word, what is the most frequent sense in which it occurs in a given corpus? Most Frequent Sense (MFS) is a strong baseline for unsupervised word sense disambiguation. If we have large amounts of sense-annotated corpora, MFS can be trivially created. However, sense-annotated corpora are a rarity. In this paper, we propose a method which can compute MFS from raw corpora. Our approach iteratively exploits the semantic congruity among related words in corpus. Our method performs better compared to another similar work.",
}
@inproceedings{mills-etal-2018-automatic,
    title = "Automatic Identification of Basic-Level Categories",
    author = "Mills, Chad  and
      Bond, Francis  and
      Levow, Gina-Anne",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.35",
    pages = "298--305",
    abstract = "Basic-level categories have been shown to be both psychologically significant and useful in a wide range of practical applications. We build a rule-based system to identify basic-level categories in WordNet, achieving 77{\%} accuracy on a test set derived from prior psychological experiments. With additional annotations we found our system also has low precision, in part due to the existence of many categories that do not fit into the three classes (superordinate, basic-level, and subordinate) relied on in basic-level category research.",
}
@inproceedings{bosch-griesel-2018-african,
    title = "{A}frican {W}ordnet: facilitating language learning in {A}frican languages",
    author = "Bosch, Sonja  and
      Griesel, Marissa",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.36",
    pages = "306--313",
    abstract = "The development of the African Wordnet (AWN) has reached a stage of maturity where the first steps towards an application can be attempted. The AWN is based on the expand method, and to compensate for the general resource scarceness of the African languages, various development strategies were used. The aim of this paper is to investigate the usefulness of the current isiZulu Wordnet in an application such as language learning. The advantage of incorporating the wordnet of a language into a language learning system is that it provides learners with an integrated application to enhance their learning experience by means of the unique sense identification features of wordnets. In this paper it will be demonstrated by means of a variety of examples within the context of a basic free online course how the isiZulu Wordnet can offer the language learner improved decision support.",
}
@inproceedings{redkar-etal-2018-hindi,
    title = "{H}indi {W}ordnet for Language Teaching: Experiences and Lessons Learnt",
    author = "Redkar, Hanumant  and
      Shukla, Rajita  and
      Singh, Sandhya  and
      Saraswati, Jaya  and
      Kashyap, Laxmi  and
      Kanojia, Diptesh  and
      Jyothi, Preethi  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.37",
    pages = "314--323",
    abstract = "This paper reports the work related to making Hindi Wordnet1 available as a digital resource for language learning and teaching, and the experiences and lessons that were learnt during the process. The language data of the Hindi Wordnet has been suitably modified and enhanced to make it into a language learning aid. This aid is based on modern pedagogical axioms and is aligned to the learning objectives of the syllabi of the school education in India. To make it into a comprehensive language tool, grammatical information has also been encoded, as far as these can be marked on the lexical items. The delivery of information is multi-layered, multi-sensory and is available across multiple digital platforms. The front end has been designed to offer an eye-catching user-friendly interface which is suitable for learners starting from age six onward. Preliminary testing of the tool has been done and it has been modified as per the feedbacks that were received. Above all, the entire exercise has offered gainful insights into learning based on associative networks and how knowledge based on such networks can be made available to modern learners.",
}
@inproceedings{lohk-etal-2018-experiment,
    title = "An Experiment: Using {G}oogle {T}ranslate and Semantic Mirrors to Create Synsets with Many Lexical Units",
    author = "Lohk, Ahti  and
      Tombak, Mati  and
      Vare, Kadri",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.38",
    pages = "324--328",
    abstract = "One of the fundamental building blocks of a wordnet is synonym sets or synsets, which group together similar word meanings or synonyms. These synsets can consist either one or more synonyms. This paper describes an automatic method for composing synsets with multiple synonyms by using Google Translate and Semantic Mirrors{'} method. Also, we will give an overview of the results and discuss the advantages of the proposed method from wordnet{'}s point of view.",
}
@inproceedings{kocon-etal-2018-context,
    title = "Context-sensitive Sentiment Propagation in {W}ord{N}et",
    author = "Koco{\'n}, Jan  and
      Janz, Arkadiusz  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.39",
    pages = "329--334",
    abstract = "In this paper we present a comprehensive overview of recent methods of the sentiment propagation in a wordnet. Next, we propose a fully automated method called Classifier-based Polarity Propagation, which utilises a very rich set of features, where most of them are based on wordnet relation types, multi-level bag-of-synsets and bag-of-polarities. We have evaluated our solution using manually annotated part of plWordNet 3.1 emo, which contains more than 83k manual sentiment annotations, covering more than 41k synsets. We have demonstrated that in comparison to existing rule-based methods using a specific narrow set of semantic relations our method has achieved statistically significant and better results starting with the same seed synsets.",
}
@inproceedings{pedersen-etal-2018-elexis,
    title = "{ELEXIS} - a {E}uropean infrastructure fostering cooperation and information exchange among lexicographical research communities",
    author = "Pedersen, Bolette  and
      McCrae, John  and
      Tiberius, Carole  and
      Krek, Simon",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.40",
    pages = "335--340",
    abstract = "The paper describes objectives, concept and methodology for ELEXIS, a European infrastructure fostering cooperation and information exchange among lexicographical research communities. The infrastructure is a newly granted project under the Horizon 2020 INFRAIA call, with the topic Integrating Activities for Starting Communities. The project is planned to start in January 2018.",
}
@inproceedings{slaughter-etal-2018-enchancing,
    title = "Enchancing the Collaborative Interlingual Index for Digital Humanities: Cross-linguistic Analysis in the Domain of Theology",
    author = "Slaughter, Laura  and
      Wang, Wenjie  and
      Costa, Luis Morgado Da  and
      Bond, Francis",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.41",
    pages = "341--346",
    abstract = "We aim to support digital humanities work related to the study of sacred texts. To do this, we propose to build a cross-lingual wordnet within the do-main of theology. We target the Collaborative Interlingual Index (CILI) directly instead of each individual wordnet. The paper presents background for this proposal: (1) an overview of concepts relevant to theology and (2) a summary of the domain-associated issues observed in the Princeton WordNet (PWN). We have found that definitions for concepts in this domain can be too restrictive, inconsistent, and unclear. Necessary synsets are missing, with the PWN being skewed towards Christianity. We argue that tackling problems in a single domain is a better method for improving CILI. By focusing on a single topic rather than a single language, this will result in the proper construction of definitions, romanization/translation of lemmas, and also improvements in use of/creation of a cross-lingual domain hierarchy.",
}
@inproceedings{orav-etal-2018-estonian,
    title = "{E}stonian {W}ordnet: Current State and Future Prospects",
    author = "Orav, Heili  and
      Vare, Kadri  and
      Zupping, Sirli",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.42",
    pages = "347--351",
    abstract = "This paper presents Estonian Wordnet (EstWN) with its latest developments. We are focusing on the time period of 2011{--}2017 because during this time EstWN project was supported by the National Programme for Estonian Language Technology (NPELT1). We describe which were the goals at the beginning of 2011 and what are the accomplishments today. This paper serves as a summarizing report about the progress of EstWN during this programme. While building EstWN we have been concentrating on the fact, that EstWN as a valuable Estonian resource would also be compatible in a common multilingual framework.",
}
@inproceedings{sojat-etal-2018-expansion,
    title = "Further expansion of the {C}roatian {W}ord{N}et",
    author = "{\v{S}}ojat, Kre{\v{s}}imir  and
      Filko, Matea  and
      Oliver, Antoni",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.43",
    pages = "352--357",
    abstract = "In this paper a semi-automatic procedure for the expansion of the Croatian Wordnet (CroWN) is presented. An English-Croatian dictionary was used in order to translate monosemous PWN 3.0 English variants. The precision values of the automatic process is low (about 30{\%}), but the results proved valuable for the enlargment of CroWN. After manual validation, 10,884 new synset-variant pairs were added to CroWN, achieving a total of 62,075 synset-variant pairs.",
}
@inproceedings{chang-etal-2018-linking,
    title = "Linking {W}ord{N}et to 3{D} Shapes",
    author = "Chang, Angel X  and
      Mago, Rishi  and
      Krishna, Pranav  and
      Savva, Manolis  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.44",
    pages = "358--363",
    abstract = "We describe a project to link the Princeton WordNet to 3D representations of real objects and scenes. The goal is to establish a dataset that helps us to understand how people categorize everyday common objects via their parts, attributes, and context. This paper describes the annotation and data collection effort so far as well as ideas for future work.",
}
@inproceedings{ogrodniczuk-etal-2018-multislownik,
    title = "Multis{\l}ownik: Linking pl{W}ord{N}et-based Lexical Data for Lexicography and Educational Purposes",
    author = "Ogrodniczuk, Maciej  and
      Bili{\'n}ska, Joanna  and
      Bronk, Zbigniew  and
      Kiera{\'s}, Witold",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.45",
    pages = "364--371",
    abstract = "Multis{\l}ownik is an automated integrator of Polish lexical data retrieved from multiple available online sources intended to be used in various scenarios requiring access to such data, most prominently dictionary creation, linguistic studies and education. In contrast to many available internet dictionaries Multis{\l}ownik is WordNet-centric, capturing the core definitions from S{\l}owosiec ́, the Polish WordNet, and linking external resources to particular synsets. The paper provides details of construction of the resource, discussed the difficulties related to linking different logical structures of underlying data and investigates two sample scenarios for using the resulting platform.",
}
@inproceedings{mrini-bond-2018-putting,
    title = "Putting Figures on Influences on {M}oroccan {D}arija from {A}rabic, {F}rench and {S}panish using the {W}ord{N}et",
    author = "Mrini, Khalil  and
      Bond, Francis",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.46",
    pages = "372--377",
    abstract = "Moroccan Darija is a variant of Arabic with many influences. Using the Open Multilingual WordNet (OMW), we compare the lemmas in the Moroccan Darija Wordnet (MDW) with the standard Arabic, French and Spanish ones. We then compared the lemmas in each synset with their translation equivalents. Transliteration is used to bridge alphabet differences and match lemmas in the closest phonological way. The results put figures on the similarity Moroccan Darija has with Arabic, French and Spanish: respectively 42.0{\%}, 2.8{\%} and 2.2{\%}.",
}
@inproceedings{panjwani-etal-2018-pyiwn,
    title = "pyiwn: A Python based {API} to access {I}ndian Language {W}ord{N}ets",
    author = "Panjwani, Ritesh  and
      Kanojia, Diptesh  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.47",
    pages = "378--383",
    abstract = "Indian language WordNets have their individual web-based browsing interfaces along with a common interface for IndoWordNet. These interfaces prove to be useful for language learners and in an educational domain, however, they do not provide the functionality of connecting to them and browsing their data through a lucid application programming interface or an API. In this paper, we present our work on creating such an easy-to-use framework which is bundled with the data for Indian language WordNets and provides NLTK WordNet interface like core functionalities in Python. Additionally, we use a pre-built speech synthesis system for Hindi language and augment Hindi data with audios for words, glosses, and example sentences. We provide a detailed usage of our API and explain the functions for ease of the user. Also, we package the IndoWordNet data along with the source code and provide it openly for the purpose of research. We aim to provide all our work as an open source framework for further development.",
}
@inproceedings{lee-hsieh-2018-sinitic,
    title = "Sinitic {W}ordnet: Laying the Groundwork with {C}hinese Varieties Written in Traditional Characters",
    author = "Lee, Chih-Yao  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.48",
    pages = "384--387",
    abstract = "The present work seeks to make the logographic nature of Chinese script a relevant research ground in wordnet studies. While wordnets are not so much about words as about the concepts represented in words, synset formation inevitably involves the use of orthographic and/or phonetic representations to serve as headword for a given concept. For wordnets of Chinese languages, if their synsets are mapped with each other, the connection from logographic forms to lexicalized concepts can be explored backwards to, for instance, help trace the development of cognates in different varieties of Chinese. The Sinitic Wordnet project is an attempt to construct such an integrated wordnet that aggregates three Chinese varieties that are widely spoken in Taiwan and all written in traditional Chinese characters.",
}
@inproceedings{kanojia-etal-2018-synthesizing,
    title = "Synthesizing Audio for {H}indi {W}ord{N}et",
    author = "Kanojia, Diptesh  and
      Jyothi, Preethi  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.49",
    pages = "388--393",
    abstract = "In this paper, we describe our work on the creation of a voice model using a speech synthesis system for the Hindi Language. We use pre-existing {``}voices{''}, use publicly available speech corpora to create a {``}voice{''} using the Festival Speech Synthesis System (Black, 1997). Our contribution is two-fold: (1) We scrutinize multiple speech synthesis systems and provide an extensive report on the currently available state-of-the-art systems. We also develop voices using the existing implementations of the aforementioned systems, and (2) We use these voices to generate sample audios for randomly chosen words; manually evaluate the audio generated, and produce audio for all WordNet words using the winner voice model. We also produce audios for the Hindi WordNet Glosses and Example sentences. We describe our efforts to use pre-existing implementations for WaveNet - a model to generate raw audio using neural nets (Oord et al., 2016) and generate speech for Hindi. Our lexicographers perform a manual evaluation of the audio generated using multiple voices. A qualitative and quantitative analysis reveals that the voice model generated by us performs the best with an accuracy of 0.44.",
}
@inproceedings{hicks-etal-2018-toward,
    title = "Toward Constructing the National Cancer Institute Thesaurus Derived {W}ord{N}et (ncit{WN})",
    author = {Hicks, Amanda  and
      Sepp{\"a}l{\"a}, Selja  and
      Bond, Francis},
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.50",
    pages = "394--400",
    abstract = "We describe preliminary work in the creation of the first specialized vocabulary to be integrated into the Open Multilingual Wordnet (OMW). The NCIt Derived WordNet (ncitWN) is based on the National Cancer Institute Thesaurus (NCIt), a controlled biomedical terminology that includes formal class restrictions and English definitions developed by groups of clinicians and terminologists. The ncitWN is created by converting the NCIt to the WordNet Lexical Markup Framework and adding semantic types. We report the development of a prototype ncitWN and first steps towards integrating it into the OMW.",
}
@inproceedings{mccrae-etal-2018-towards,
    title = "Towards a Crowd-Sourced {W}ord{N}et for Colloquial {E}nglish",
    author = "McCrae, John P.  and
      Wood, Ian  and
      Hicks, Amanda",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.51",
    pages = "401--406",
    abstract = "Princeton WordNet is one of the most widely-used resources for natural language processing, but is updated only infrequently and cannot keep up with the fast-changing usage of the English language on social media platforms such as Twitter. The Colloquial WordNet aims to provide an open platform whereby anyone can contribute, while still following the structure of WordNet. Many crowd-sourced lexical resources often have significant quality issues, and as such care must be taken in the design of the interface to ensure quality. In this paper, we present the development of a platform that can be opened on the Web to any lexicographer who wishes to contribute to this resource and the lexicographic methodology applied by this interface.",
}
@inproceedings{huminski-zhang-2018-wordnet,
    title = "{W}ord{N}et Troponymy and Extraction of {``}Manner-Result{''} Relations",
    author = "Huminski, Aliaksandr  and
      Zhang, Hao",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.52",
    pages = "407--411",
    abstract = "Commonsense knowledge bases need to have relations that allow to predict the consequences of specific actions (say, if John stabbed Peter, Peter might be killed) and to unfold the possible actions for the specific results (Peter was killed. It could happen because of poisoning, stabbing, shooting, etc.) This kind of causal relations are established between manner verbs and result verbs: manner-result relations. We offer a procedure on how to extract manner-result relations from WordNet through the analysis of the troponym glosses. The procedure of extraction includes three steps and the results are based on the analysis of the whole set of verbs in WordNet.",
}
@inproceedings{angioni-etal-2018-sardanet,
    title = "{S}arda{N}et: a Linguistic Resource for {S}ardinian Language",
    author = "Angioni, Manuela  and
      Tuveri, Franco  and
      Virdis, Maurizio  and
      Lai, Laura Lucia  and
      Maltesi, Micol Elisa",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.53",
    pages = "412--419",
    abstract = "This paper describes the process of building SardaNet, a linguistic resource for Sardinian language including the different linguistic varieties in Sardinia. SardaNet aims at identifying the semantic relations between Sardinian terms, by manually mapping existing WordNet entries to Sardinian word senses. The work, still in progress, has been developed in collaboration with the University of Cagliari. After discussing some linguistic peculiarities, the paper presents the basic steps of the construction process, the method and the tools involved, the issues encountered during the development and the current version of SardaNet.",
}
@inproceedings{khalghani-shamsfard-2018-extraction,
    title = "Extraction of Verbal Synsets and Relations for {F}ars{N}et",
    author = "Khalghani, Fatemeh  and
      Shamsfard, Mehrnoush",
    booktitle = "Proceedings of the 9th Global Wordnet Conference",
    month = jan,
    year = "2018",
    address = "Nanyang Technological University (NTU), Singapore",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2018.gwc-1.54",
    pages = "420--428",
    abstract = "WordNet or ontology development for resource-poor languages like Persian, requires composition of several strategies and employment of appropriate heuristics. Lexical and linguistic structured resources are limited for Persian and there is a lot of diversity and structural and syntagmatic complexities. This paper proposes a system for extraction of verbal synsets and relations to extend FarsNet (Persian WordNet). The proposed method extracts verbal words and concepts using noun and adjective words and synsets. It exploits the data from digital lexicon glossaries, which leads to the identification of 6890 proper verbal words and 2790 verbal synsets, with 91{\%} and 67{\%} precision respectively. The proposed system also extracts relations such as semantic roles of verbal arguments (instrument, location, agent, and patient) and also {``}related-to{''} (unlabeled) relations and co-occurrence among verbs and other concepts. For this purpose, a combination of linguistic approaches such as morphological analysis of words, semantic analysis, and use of key phrases and syntactic and semantic patterns, corpus-based approach, statistical techniques and co-occurrence analysis have been utilized. The presented strategy extracts 5600 proper relations between the existing concepts in FarsNet 2.0 with 76{\%} precision.",
}
@proceedings{paclic-2017-pacific,
    title = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    editor = "Roxas, Rachel Edita",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1000",
}
@inproceedings{kim-2017-modeling,
    title = "Modeling Answering Strategies for the Polar Questions across Languages",
    author = "Kim, Jong-Bok",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1001",
    pages = "2--9",
}
@inproceedings{fujisaki-2017-international,
    title = "International Speech Communication Association Distinguished Lecture: Principles and Design of a System for Academic Information Retrieval based on Human-Machine Dialogue",
    author = "Fujisaki, Hiroya",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1002",
    pages = "10--10",
}
@inproceedings{moody-2017-assessing,
    title = "Assessing Authenticity in Media Englishes and the Englishes of Popular Culture",
    author = "Moody, Andrew",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1003",
    pages = "11--11",
}
@inproceedings{ilao-2017-corpus,
    title = "Corpus Linguistic Analysis for Language Planning",
    author = "Ilao, Joel",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1004",
    pages = "12--12",
}
@inproceedings{tanangkingsing-2017-modality,
    title = "Modality Markers in {C}ebuano and {T}agalog",
    author = "Tanangkingsing, Michael",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1005",
    pages = "13--13",
}
@inproceedings{asahara-2017-reading,
    title = "Between Reading Time and Information Structure",
    author = "Asahara, Masayuki",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1006",
    pages = "15--24",
}
@inproceedings{rieser-2017-doubt,
    title = "Doubt, incredulity, and particles in {J}apanese falling interrogatives",
    author = "Rieser, Lukas",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1007",
    pages = "25--33",
}
@inproceedings{ella-dita-2017-phrasal,
    title = "The Phrasal-Prepositional Verbs in {P}hilippine {E}nglish: A Corpus-based Analysis",
    author = "Ella, Jennibelle  and
      Dita, Shirley",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1008",
    pages = "34--41",
}
@inproceedings{nakamura-2017-type,
    title = "A Type-Logical Approach to Potential Consructions in {J}apanese",
    author = "Nakamura, Hiroaki",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1009",
    pages = "42--49",
}
@inproceedings{ferrer-etal-2017-standard,
    title = "Standard and nonstandard lexicon in aviation {E}nglish: A corpus linguistic study",
    author = "Ferrer, Ramsey  and
      Empinado, Jollene  and
      Calico, Eloisa Marie  and
      Floro, Jan Yharie",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1010",
    pages = "50--55",
}
@inproceedings{hou-huang-2017-stylometric,
    title = "Stylometric Studies based on Tone and Word Length Motifs",
    author = "Hou, Renkui  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1011",
    pages = "56--63",
}
@inproceedings{rahman-purwarianti-2017-ensemble,
    title = "Ensemble Technique Utilization for {I}ndonesian Dependency Parser",
    author = "Rahman, Arief  and
      Purwarianti, Ayu",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1012",
    pages = "64--71",
}
@inproceedings{ohtani-2017-raising,
    title = "Raising to Object in {J}apanese: An {HPSG} Analysis",
    author = "Ohtani, Akira",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1013",
    pages = "72--80",
}
@inproceedings{go-nocon-2017-using,
    title = "Using {S}tanford Part-of-Speech Tagger for the Morphologically-rich {F}ilipino Language",
    author = "Go, Matthew Phillip  and
      Nocon, Nicco",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1014",
    pages = "81--88",
}
@inproceedings{shan-etal-2017-unsupervised,
    title = "Unsupervised Bilingual Segmentation using {MDL} for Machine Translation",
    author = "Shan, Bin  and
      Wang, Hao  and
      Lepage, Yves",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1015",
    pages = "89--96",
}
@inproceedings{pham-le-hong-2017-importance,
    title = "The Importance of Automatic Syntactic Features in {V}ietnamese Named Entity Recognition",
    author = "Pham, Thai-Hoang  and
      Le-Hong, Phuong",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1016",
    pages = "97--103",
}
@inproceedings{seraku-2017-multiple,
    title = "Multiple Nominative Constructions in {J}apanese: An Incremental Grammar Perspective",
    author = "Seraku, Tohru",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1017",
    pages = "104--113",
}
@inproceedings{wang-lepage-2017-btg,
    title = "{BTG}-based Machine Translation with Simple Reordering Model using Structured Perceptron",
    author = "Wang, Hao  and
      Lepage, Yves",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1018",
    pages = "114--123",
}
@inproceedings{hkiri-etal-2017-arabic,
    title = "{A}rabic-{E}nglish Text Translation Leveraging Hybrid {NER}",
    author = "Hkiri, Emna  and
      Mallat, Souheyl  and
      Zrigui, Mounir",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1019",
    pages = "124--131",
}
@inproceedings{cahutay-vicente-2017-actuallydepressed,
    title = "{\#}{A}ctually{D}epressed: Characterization of Depressed Tumblr Users{'} Online Behavior from Rules Generation Machine Learning Technique",
    author = "Cahutay, Czarina Rae  and
      Vicente, Aileen Joan",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1020",
    pages = "132--139",
}
@inproceedings{su-etal-2017-parallel,
    title = "A Parallel Recurrent Neural Network for Language Modeling with {POS} Tags",
    author = "Su, Chao  and
      Huang, Heyan  and
      Shi, Shumin  and
      Guo, Yuhang  and
      Wu, Hao",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1021",
    pages = "140--147",
}
@inproceedings{warnita-lestari-2017-identifying,
    title = "Identifying Deception in {I}ndonesian Transcribed Interviews through Lexical-based Approach",
    author = "Warnita, Tifani  and
      Lestari, Dessi Puji",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1022",
    pages = "148--154",
}
@inproceedings{lan-2017-foreign,
    title = "Foreign Influence and Sound Change: A Case Study of {C}antonese Alveolar Affricates",
    author = "Lan, Yizhou",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1023",
    pages = "155--160",
}
@inproceedings{labidi-etal-2017-unsupervised,
    title = "Unsupervised Method for Improving {A}rabic Speech Recognition Systems",
    author = "Labidi, Mohamed  and
      Maraoui, Mohsen  and
      Zrigui, Mounir",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1024",
    pages = "161--168",
}
@inproceedings{oshima-2017-remarks,
    title = "Remarks on epistemically biased questions",
    author = "Oshima, David Yoshikazu",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1025",
    pages = "169--177",
}
@inproceedings{park-pan-2017-blocking,
    title = "The blocking effect and {K}orean caki",
    author = "Park, Hyunjun  and
      Pan, Haihua",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1026",
    pages = "178--186",
}
@inproceedings{yoo-2017-expressing,
    title = "Expressing prediction and epistemicity with {K}orean -(ul) kes i and {M}andarin {C}hinese hui",
    author = "Yoo, Eunson",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1027",
    pages = "187--195",
}
@inproceedings{takehisa-2017-remarks,
    title = "Remarks on Denominal -Ed Adjectives",
    author = "Takehisa, Tomokazu",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1028",
    pages = "196--205",
}
@inproceedings{kim-etal-2017-subjecthood,
    title = "Subjecthood and Grammatical Relations in {K}orean: An Experimental Study with Honorific Agreement and Plural Copying",
    author = "Kim, Ji-Hye  and
      Lee, Yong-Hun  and
      Yoon, James Hye-Suk",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1029",
    pages = "206--213",
}
@inproceedings{caparas-2017-stylistic,
    title = "A Stylistic Analysis of a {P}hilippine Essay, {``}The Will of the River{''}",
    author = "Caparas, Pilar",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1030",
    pages = "214--221",
}
@inproceedings{gao-lee-2017-corpus,
    title = "A Corpus-based Analysis of Near-Synonymous Sentence-final Particles in {M}andarin {C}hinese: {``}bale{''} and {``}eryi{''}",
    author = "Gao, Xuefeng  and
      Lee, Yat-Mei",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1031",
    pages = "222--230",
}
@inproceedings{synkova-etal-2017-extracting,
    title = "Extracting a Lexicon of Discourse Connectives in {C}zech from an Annotated Corpus",
    author = "Synkov{\'a}, Pavl{\'\i}na  and
      Rysov{\'a}, Magdal{\'e}na  and
      Pol{\'a}kov{\'a}, Lucie  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1032",
    pages = "232--240",
}
@inproceedings{neo-gao-2017-word,
    title = "Word Learning by Young Bilinguals: Understanding the Denotation and Connotation Differences of {``}Cut{''} Verbs in {E}nglish and {C}hinese",
    author = "Neo, Keng Hwee  and
      Gao, Helena",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1033",
    pages = "241--248",
}
@inproceedings{jo-2017-corpus,
    title = "A corpus-based study on synesthesia in {K}orean ordinary language",
    author = "Jo, Charmhun",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1034",
    pages = "249--254",
}
@inproceedings{genuino-mabuan-2017-intrusions,
    title = "Intrusions of {M}asbate Lexicon in Local Bilingual Tabloid",
    author = "Genuino, Cecilia  and
      Mabuan, Romualdo",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1035",
    pages = "255--264",
}
@inproceedings{mabuan-ebron-jr-2017-facebook,
    title = "{F}acebook Integration into University Classes: Opportunities and Challenges",
    author = "Mabuan, Romualdo  and
      Ebron Jr., Gregorio",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1036",
    pages = "265--273",
}
@inproceedings{mahmoud-zrigui-2017-semantic,
    title = "Semantic Similarity Analysis for Paraphrase Identification in {A}rabic Texts",
    author = "Mahmoud, Adnen  and
      Zrigui, Mounir",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1037",
    pages = "274--281",
}
@inproceedings{dabre-etal-2017-empirical,
    title = "An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation",
    author = "Dabre, Raj  and
      Nakagawa, Tetsuji  and
      Kazawa, Hideto",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1038",
    pages = "282--286",
}
@inproceedings{mawalim-etal-2017-rule,
    title = "Rule-based Reordering and Post-Processing for {I}ndonesian-{K}orean Statistical Machine Translation",
    author = "Mawalim, Candy Olivia  and
      Lestari, Dessi Puji  and
      Purwarianti, Ayu",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1039",
    pages = "287--295",
}
@inproceedings{liu-matsumoto-2017-sentence,
    title = "Sentence Complexity Estimation for {C}hinese-speaking Learners of {J}apanese",
    author = "Liu, Jun  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1040",
    pages = "296--302",
}
@inproceedings{ahrens-zeng-2017-conceptualizing,
    title = "Conceptualizing {EDUCATION} in {H}ong {K}ong and {C}hina (1984-2014)",
    author = "Ahrens, Kathleen  and
      Zeng, Huiheng",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1041",
    pages = "303--311",
}
@inproceedings{zhou-etal-2017-multi,
    title = "Multi-dimensional Meanings of Subjective Adverbs - Case Study of {M}andarin {C}hinese Adverb Pianpian",
    author = "Zhou, Mi  and
      Yao, Yao  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1042",
    pages = "312--318",
}
@inproceedings{abreu-2017-morphosyntactic,
    title = "Morphosyntactic Analysis of the Pronominal System of {S}outhern {A}lta",
    author = "Abreu, Marvin",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1043",
    pages = "319--328",
}
@inproceedings{cho-2017-wh,
    title = "Wh-island Effects in {K}orean Scrambling Constructions",
    author = "Cho, Juyeon",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1044",
    pages = "329--335",
}
@inproceedings{hanawa-etal-2017-crowdsourcing,
    title = "A Crowdsourcing Approach for Annotating Causal Relation Instances in {W}ikipedia",
    author = "Hanawa, Kazuaki  and
      Sasaki, Akira  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1045",
    pages = "336--345",
}
@inproceedings{bation-etal-2017-automatic,
    title = "Automatic Categorization of {T}agalog Documents Using Support Vector Machines",
    author = "Bation, April Dae  and
      Vicente, Aileen Joan  and
      Manguilimotan, Erlyn",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1046",
    pages = "346--353",
}
@inproceedings{jiang-huang-2017-lexicalization,
    title = "Lexicalization, Separation and transitivity: A comparative study of {M}andarin {VO} compound Variations",
    author = "Jiang, Menghan  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1047",
    pages = "354--362",
}
@inproceedings{miyazaki-etal-2017-extracting,
    title = "Extracting Important Tweets for News Writers using Recurrent Neural Network with Attention Mechanism and Multi-task Learning",
    author = "Miyazaki, Taro  and
      Toriumi, Shin  and
      Takei, Yuka  and
      Yamada, Ichiro  and
      Goto, Jun",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1048",
    pages = "363--369",
}
@inproceedings{takei-etal-2017-tweet,
    title = "Tweet Extraction for News Production Considering Unreality",
    author = "Takei, Yuka  and
      Miyazaki, Taro  and
      Yamada, Ichiro  and
      Goto, Jun",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1049",
    pages = "370--375",
}
@inproceedings{de-la-paz-etal-2017-discovering,
    title = "Discovering Conversation Spaces in the Public Discourse of Gender Violence: a Comparative Between Two Different Contexts",
    author = "De La Paz, Meliza  and
      Estuar, Maria Regina  and
      Victorino, John Noel",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1050",
    pages = "376--383",
}
@inproceedings{trieu-etal-2017-investigating,
    title = "Investigating Phrase-Based and Neural-Based Machine Translation on Low-Resource Settings",
    author = "Trieu, Hai Long  and
      Tran, Duc-Vu  and
      Nguyen, Le Minh",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1051",
    pages = "384--391",
}
@inproceedings{shinnou-etal-2017-japanese,
    title = "{J}apanese all-words {WSD} system using the {K}yoto Text Analysis {T}ool{K}it",
    author = "Shinnou, Hiroyuki  and
      Komiya, Kanako  and
      Sasaki, Minoru  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1052",
    pages = "392--399",
}
@inproceedings{lin-2017-revisiting,
    title = "Revisiting Tones in {T}wic {E}ast {D}inka",
    author = "Lin, Yu-Leng",
    booktitle = "Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation",
    month = nov,
    year = "2017",
    publisher = "The National University (Phillippines)",
    url = "https://www.aclweb.org/anthology/Y17-1053",
    pages = "400--406",
}
@proceedings{ws-2017-language-technology,
    title = "Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope",
    editor = "Dinu, Anca  and
      Osenova, Petya  and
      Vertan, Cristina",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "https://www.aclweb.org/anthology/W17-8100",
    doi = "0.26615/978-954-452-046-5_",
}
@inproceedings{malahov-etal-2017-diachronic,
    title = "A Diachronic Corpus for {R}omanian ({R}o{D}ia)",
    author = "Malahov, Ludmila  and
      M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lina  and
      Colesnicov, Alexandru",
    booktitle = "Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "http://doi.org/10.26615/978-954-452-046-5_001",
    doi = "0.26615/978-954-452-046-5_001",
    pages = "1--9",
    abstract = "This paper describes a Romanian Dependency Treebank, built at the Al. I. Cuza University (UAIC), and a special OCR techniques used to build it. The corpus has rich morphological and syntactic annotation. There are few annotated representative corpora in Romanian, and the existent ones are mainly focused on the contemporary Romanian standard. The corpus described below is focused on the non-standard aspects of the language, the Regional and the Old Romanian. Having the intention to participate at the PROIEL project, which aligns oldest New Testaments, we annotate the first printed Romanian New Testament (Alba Iulia, 1648). We began by applying the UAIC tools for the morphological and syntactic processing of Contemporary Romanian over the book{'}s first quarter (second edition). By carefully manually correcting the result of the automated annotation (having a modest accuracy) we obtained a sub-corpus for the training of tools for the Old Romanian processing. But the first edition of the New Testament is written in Cyrillic letters. The existence of books printed in the Old Cyrillic alphabet is a common problem for Romania and The Republic of Moldova, countries where the Romanian is spoken; a problem to solve by the joint efforts of the NLP researchers in the two countries.",
}
@inproceedings{bobicev-etal-2017-tools,
    title = "Tools for Building a Corpus to Study the Historical and Geographical Variation of the {R}omanian Language",
    author = "Bobicev, Victoria  and
      M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lina  and
      Perez, Cenel Augusto",
    booktitle = "Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "http://doi.org/10.26615/978-954-452-046-5_002",
    doi = "0.26615/978-954-452-046-5_002",
    pages = "10--19",
    abstract = "Contemporary standard language corpora are ideal for NLP. There are few morphologically and syntactically annotated corpora for Romanian, and those existing or in progress only deal with the Contemporary Romanian standard. However, the necessity to study the dynamics of natural languages gave rise to balanced corpora, containing non-standard texts. In this paper, we describe the creation of tools for processing non-standard Romanian to build a big balanced corpus. We want to preserve in annotated form as many early stages of language as possible. We have already built a corpus in Old Romanian. We also intend to include the South-Danube dialects, remote to the standard language, along with regional forms closer to the standard. We try to preserve data about endangered idioms such as Aromanian, Meglenoromanian and Istroromanian dialects, and calculate the distance between different regional variants, including the language spoken in the Republic of Moldova. This distance, as well as the mutual understanding between the speakers, is the correct criterion for the classification of idioms as different languages, or as dialects, or as regional variants close to the standard.",
}
@inproceedings{declerck-etal-2017-multilingual,
    title = "Multilingual Ontologies for the Representation and Processing of Folktales",
    author = {Declerck, Thierry  and
      Aman, Anastasija  and
      Banzer, Martin  and
      Mach{\'a}{\v{c}}ek, Dominik  and
      Sch{\"a}fer, Lisa  and
      Skachkova, Natalia},
    booktitle = "Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "http://doi.org/10.26615/978-954-452-046-5_003",
    doi = "0.26615/978-954-452-046-5_003",
    pages = "20--23",
    abstract = "We describe work done in the field of folkloristics and consisting in creating ontologies based on well-established studies proposed by {``}classical{''} folklorists. This work is supporting the availability of a huge amount of digital and structured knowledge on folktales to digital humanists. The ontological encoding of past and current motif-indexation and classification systems for folktales was in the first step limited to English language data. This led us to focus on making those newly generated formal knowledge sources available in a few more languages, like German, Russian and Bulgarian. We stress the importance of achieving this multilingual extension of our ontologies at a larger scale, in order for example to support the automated analysis and classification of such narratives in a large variety of languages, as those are getting more and more accessible on the Web.",
}
@inproceedings{dinu-etal-2017-annotation,
    title = "On the annotation of vague expressions: a case study on {R}omanian historical texts",
    author = "Dinu, Anca  and
      von Hahn, Walther  and
      Vertan, Cristina",
    booktitle = "Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "http://doi.org/10.26615/978-954-452-046-5_004",
    doi = "0.26615/978-954-452-046-5_004",
    pages = "24--31",
    abstract = "Current approaches in Digital .Humanities tend to ignore a central as-pect of any hermeneutic introspection: the intrinsic vagueness of analyzed texts. Especially when dealing with his-torical documents neglecting vague-ness has important implications on the interpretation of the results. In this pa-per we present current limitation of an-notation approaches and describe a current methodology for annotating vagueness for historical Romanian texts.",
}
@inproceedings{stambolieva-etal-2017-language,
    title = "Language Technologies in Teaching Bugarian at Primary and Secondary School Level: the {NBU} Platform of Language Teaching ({PLT})",
    author = "Stambolieva, Maria  and
      Ivanova, Valentina  and
      Raykova, Mariana  and
      Hadjikoteva, Milka  and
      Neykova, Mariya",
    booktitle = "Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "http://doi.org/10.26615/978-954-452-046-5_005",
    doi = "0.26615/978-954-452-046-5_005",
    pages = "32--38",
    abstract = "The NBU Language Teaching Platform (PLT) was initially designed for teaching foreign languages for specific purposes; at a second stage, some of its functionalities were extended to answer the needs of teaching general foreign language. New functionalities have now been created for the purpose of providing e-support for Bulgarian language and literature teaching at primary and secondary school level. The article presents the general structure of the platform and the functionalities specifically developed to match the standards and expected results set by the Ministry of Education. The E-platform integrates: 1/ an environment for creating, organizing and maintaining electronic text archives, for extracting text corpora and aligning corpora; 2/ a linguistic database; 3/ a concordancer; 4/ a set of modules for the generation and editing of practice exercises for each text or corpus; 5/ functionalities for export from the platform and import to other educational platforms. For Moodle, modules were created for test generation, performance assessment and feedback. The PLT allows centralized presentation of abundant teaching content, control of the educational process, fast and reliable feedback on performance.",
}
@inproceedings{moise-2017-natural,
    title = "Natural Language Processing in Political Campaigns",
    author = "Moise, Cristina",
    booktitle = "Proceedings of the First Workshop on Language technology for Digital Humanities in Central and (South-)Eastern {E}urope",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "http://doi.org/10.26615/978-954-452-046-5_006",
    doi = "0.26615/978-954-452-046-5_006",
    pages = "39--43",
    abstract = "This paper overviews the Majoritas ecosystem, providing a complete overview of political campaigns assessment aimed to assist politicians and their staff in delivering consistent and personalized message within social media.",
}
@proceedings{ws-2017-biomedical,
    title = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    editor = "Boytcheva, Svetla  and
      Cohen, Kevin Bretonnel  and
      Savova, Guergana  and
      Angelova, Galia",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/W17-8000",
    doi = "10.26615/978-954-452-044-1_",
}
@inproceedings{eric-2017-document,
    title = "Document retrieval and question answering in medical documents. A large-scale corpus challenge.",
    author = "Eric, Curea",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_001",
    doi = "10.26615/978-954-452-044-1_001",
    pages = "1--7",
    abstract = "Whenever employed on large datasets, information retrieval works by isolating a subset of documents from the larger dataset and then proceeding with low-level processing of the text. This is usually carried out by means of adding index-terms to each document in the collection. In this paper we deal with automatic document classification and index-term detection applied on large-scale medical corpora. In our methodology we employ a linear classifier and we test our results on the BioASQ training corpora, which is a collection of 12 million MeSH-indexed medical abstracts. We cover both term-indexing, result retrieval and result ranking based on distributed word representations.",
}
@inproceedings{mitrofan-ion-2017-adapting,
    title = "Adapting the {TTL} {R}omanian {POS} Tagger to the Biomedical Domain",
    author = "Mitrofan, Maria  and
      Ion, Radu",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_002",
    doi = "10.26615/978-954-452-044-1_002",
    pages = "8--14",
    abstract = "This paper presents the adaptation of the Hidden Markov Models-based TTL part-of-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97{\%} when TTL{'}s baseline model is updated with training information from a Romanian biomedical corpus. This corpus is developed in the context of the CoRoLa (a reference corpus for the contemporary Romanian language) project. Informative description and statistics of the Romanian biomedical corpus are also provided.",
}
@inproceedings{freitag-etal-2017-discourse,
    title = "Discourse-Wide Extraction of Assay Frames from the Biological Literature",
    author = "Freitag, Dayne  and
      Kalmar, Paul  and
      Yeh, Eric",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_003",
    doi = "10.26615/978-954-452-044-1_003",
    pages = "15--23",
    abstract = "We consider the problem of populating multi-part knowledge frames from textual information distributed over multiple sentences in a document. We present a corpus constructed by aligning papers from the cellular signaling literature to a collection of approximately 50,000 reference frames curated by hand as part of a decade-long project. We present and evaluate two approaches to the challenging problem of reconstructing these frames, which formalize biological assays described in the literature. One approach is based on classifying candidate records nominated by sentence-local entity co-occurrence. In the second approach, we introduce a novel virtual register machine traverses an article and generates frames, trained on our reference data. Our evaluations show that success in the task ultimately hinges on an integration of evidence spread across the discourse.",
}
@inproceedings{zubke-2017-classification,
    title = "Classification based extraction of numeric values from clinical narratives",
    author = "Zubke, Maximilian",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_004",
    doi = "10.26615/978-954-452-044-1_004",
    pages = "24--31",
    abstract = "The robust extraction of numeric values from clinical narratives is a well known problem in clinical data warehouses. In this paper we describe a dynamic and domain-independent approach to deliver numerical described values from clinical narratives. In contrast to alternative systems, we neither use manual defined rules nor any kind of ontologies or nomenclatures. Instead we propose a topic-based system, that tackles the information extraction as a text classification problem. Hence we use machine learning to identify the crucial context features of a topic-specific numeric value by a given set of example sentences, so that the manual effort reduces to the selection of appropriate sample sentences. We describe context features of a certain numeric value by term frequency vectors which are generated by multiple document segmentation procedures. Due to this simultaneous segmentation approaches, there can be more than one context vector for a numeric value. In those cases, we choose the context vector with the highest classification confidence and suppress the rest. To test our approach, we used a dataset from a german hospital containing 12,743 narrative reports about laboratory results of Leukemia patients. We used Support Vector Machines (SVM) for classification and achieved an average accuracy of 96{\%} on a manually labeled subset of 2073 documents, using 10-fold cross validation. This is a significant improvement over an alternative rule based system.",
}
@inproceedings{grabar-hamon-2017-understanding,
    title = "Understanding of unknown medical words",
    author = "Grabar, Natalia  and
      Hamon, Thierry",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_005",
    doi = "10.26615/978-954-452-044-1_005",
    pages = "32--41",
    abstract = "We assume that unknown words with internal structure (affixed words or compounds) can provide speakers with linguistic cues as for their meaning, and thus help their decoding and understanding. To verify this hypothesis, we propose to work with a set of French medical words. These words are annotated by five annotators. Then, two kinds of analysis are performed: analysis of the evolution of understandable and non-understandable words (globally and according to some suffixes) and analysis of clusters created with unsupervised algorithms on basis of linguistic and extra-linguistic features of the studied words. Our results suggest that, according to linguistic sensitivity of annotators, technical words can be decoded and become understandable. As for the clusters, some of them distinguish between understandable and non-understandable words. Resources built in this work will be made freely available for the research purposes.",
}
@inproceedings{yimam-etal-2017-entity,
    title = "Entity-Centric Information Access with Human in the Loop for the Biomedical Domain",
    author = "Yimam, Seid Muhie  and
      Remus, Steffen  and
      Panchenko, Alexander  and
      Holzinger, Andreas  and
      Biemann, Chris",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_006",
    doi = "10.26615/978-954-452-044-1_006",
    pages = "42--48",
    abstract = "In this paper, we describe the concept of entity-centric information access for the biomedical domain. With entity recognition technologies approaching acceptable levels of accuracy, we put forward a paradigm of document browsing and searching where the entities of the domain and their relations are explicitly modeled to provide users the possibility of collecting exhaustive information on relations of interest. We describe three working prototypes along these lines: NEW/S/LEAK, which was developed for investigative journalists who need a quick overview of large leaked document collections; STORYFINDER, which is a personalized organizer for information found in web pages that allows adding entities as well as relations, and is capable of personalized information management; and adaptive annotation capabilities of WEBANNO, which is a general-purpose linguistic annotation tool. We will discuss future steps towards the adaptation of these tools to biomedical data, which is subject to a recently started project on biomedical knowledge acquisition. A key difference to other approaches is the centering around the user in a Human-in-the-Loop machine learning approach, where users define and extend categories and enable the system to improve via feedback and interaction.",
}
@inproceedings{bellon-rodriguez-esteban-2017-one,
    title = "One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text",
    author = "Bellon, Victor  and
      Rodriguez-Esteban, Raul",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_007",
    doi = "10.26615/978-954-452-044-1_007",
    pages = "49--54",
    abstract = "We explored a new approach to named entity recognition based on hundreds of machine learning models, each trained to distinguish a single entity, and showed its application to gene name identification (GNI). The rationale for our approach, which we named {``}one model per entity{''} (OMPE), was that increasing the number of models would make the learning task easier for each individual model. Our training strategy leveraged freely-available database annotations instead of manually-annotated corpora. While its performance in our proof-of-concept was disappointing, we believe that there is enough room for improvement that such approaches could reach competitive performance while eliminating the cost of creating costly training corpora.",
}
@inproceedings{thorne-klinger-2017-towards,
    title = "Towards Confidence Estimation for Typed Protein-Protein Relation Extraction",
    author = "Thorne, Camilo  and
      Klinger, Roman",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_008",
    doi = "10.26615/978-954-452-044-1_008",
    pages = "55--63",
    abstract = "Systems which build on top of information extraction are typically challenged to extract knowledge that, while correct, is not yet well-known. We hypothesize that a good confidence measure for relational information has the property that such interesting information is found between information extracted with very high confidence and very low confidence. We discuss confidence estimation for the domain of biomedical protein-protein relation discovery in biomedical literature. As facts reported in papers take some time to be validated and recorded in biomedical databases, such task gives rise to large quantities of unknown but potentially true candidate relations. It is thus important to rank them based on supporting evidence rather than discard them. In this paper, we discuss this task and propose different approaches for confidence estimation and a pipeline to evaluate such methods. We show that the most straight-forward approach, a combination of different confidence measures from pipeline modules seems not to work well. We discuss this negative result and pinpoint potential future research directions.",
}
@inproceedings{boytcheva-etal-2017-identification,
    title = "Identification of Risk Factors in Clinical Texts through Association Rules",
    author = "Boytcheva, Svetla  and
      Nikolova, Ivelina  and
      Angelova, Galia  and
      Angelov, Zhivko",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_009",
    doi = "10.26615/978-954-452-044-1_009",
    pages = "64--72",
    abstract = "We describe a method which extracts Association Rules from texts in order to recognise verbalisations of risk factors. Usually some basic vocabulary about risk factors is known but medical conditions are expressed in clinical narratives with much higher variety. We propose an approach for data-driven learning of specialised medical vocabulary which, once collected, enables early alerting of potentially affected patients. The method is illustrated by experimens with clinical records of patients with Chronic Obstructive Pulmonary Disease (COPD) and comorbidity of CORD, Diabetes Melitus and Schizophrenia. Our input data come from the Bulgarian Diabetic Register, which is built using a pseudonymised collection of outpatient records for about 500,000 diabetic patients. The generated Association Rules for CORD are analysed in the context of demographic, gender, and age information. Valuable anounts of meaningful words, signalling risk factors, are discovered with high precision and confidence.",
}
@inproceedings{hamon-etal-2017-pomelo,
    title = "{POMELO}: {M}edline corpus with manually annotated food-drug interactions",
    author = "Hamon, Thierry  and
      Tabanou, Vincent  and
      Mougin, Fleur  and
      Grabar, Natalia  and
      Thiessard, Frantz",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_010",
    doi = "10.26615/978-954-452-044-1_010",
    pages = "73--80",
    abstract = "When patients take more than one medication, they may be at risk of drug interactions, which means that a given drug can cause unexpected effects when taken in combination with other drugs. Similar effects may occur when drugs are taken together with some food or beverages. For instance, grapefruit has interactions with several drugs, because its active ingredients inhibit enzymes involved in the drugs metabolism and can then cause an excessive dosage of these drugs. Yet, information on food/drug interactions is poorly researched. The current research is mainly provided by the medical domain and a very tentative work is provided by computer sciences and NLP domains. One factor that motivates the research is related to the availability of the annotated corpora and the reference data. The purpose of our work is to describe the rationale and approach for creation and annotation of scientific corpus with information on food/drug interactions. This corpus contains 639 MEDLINE citations (titles and abstracts), corresponding to 5,752 sentences. It is manually annotated by two experts. The corpus is named POMELO. This annotated corpus will be made available for the research purposes.",
}
@inproceedings{radev-etal-2017-annotation,
    title = "Annotation of Clinical Narratives in {B}ulgarian language",
    author = "Radev, Ivajlo  and
      Simov, Kiril  and
      Angelova, Galia  and
      Boytcheva, Svetla",
    booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-044-1_011",
    doi = "10.26615/978-954-452-044-1_011",
    pages = "81--87",
    abstract = "In this paper we describe annotation process of clinical texts with morphosyntactic and semantic information. The corpus contains 1,300 discharge letters in Bulgarian language for patients with Endocrinology and Metabolic disorders. The annotated corpus will be used as a Gold standard for information extraction evaluation of test corpus of 6,200 discharge letters. The annotation is performed within Clark system {---} an XML Based System For Corpora Development. It provides mechanism for semi-automatic annotation first running a pipeline for Bulgarian morphosyntactic annotation and a cascaded regular grammar for semantic annotation is run, then rules for cleaning of frequent errors are applied. At the end the result is manually checked. At the end we hope also to be able to adapted the morphosyntactic tagger to the domain of clinical narratives as well.",
}
@proceedings{ws-2017-human,
    title = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    editor = "Temnikova, Irina  and
      Orasan, Constantin  and
      Pastor, Gloria Corpas  and
      Vogel, Stephan",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://www.aclweb.org/anthology/W17-7900",
    doi = "10.26615/978-954-452-042-7_",
}
@inproceedings{scansani-etal-2017-enhancing,
    title = "Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources",
    author = "Scansani, Randy  and
      Bernardini, Silvia  and
      Ferraresi, Adriano  and
      Gaspari, Federico  and
      Soffritti, Marcello",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_001",
    doi = "10.26615/978-954-452-042-7_001",
    pages = "1--10",
    abstract = "This paper describes an approach to translating course unit descriptions from Italian and German into English, using a phrase-based machine translation (MT) system. The genre is very prominent among those requiring translation by universities in European countries in which English is a non-native language. For each language combination, an in-domain bilingual corpus including course unit and degree program descriptions is used to train an MT engine, whose output is then compared to a baseline engine trained on the Europarl corpus. In a subsequent experiment, a bilingual terminology database is added to the training sets in both engines and its impact on the output quality is evaluated based on BLEU and post-editing score. Results suggest that the use of domain-specific corpora boosts the engines quality for both language combinations, especially for German-English, whereas adding terminological resources does not seem to bring notable benefits.",
}
@inproceedings{toledo-baez-etal-2017-experiments,
    title = "Experiments in Non-Coherent Post-editing",
    author = "Toledo B{\'a}ez, Cristina  and
      Schaeffer, Moritz  and
      Carl, Michael",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_002",
    doi = "10.26615/978-954-452-042-7_002",
    pages = "11--20",
    abstract = "Market pressure on translation productivity joined with technological innovation is likely to fragment and decontextualise translation jobs even more than is cur-rently the case. Many different translators increasingly work on one document at different places, collaboratively working in the cloud. This paper investigates the effect of decontextualised source texts on behaviour by comparing post-editing of sequentially ordered sentences with shuffled sentences from two different texts. The findings suggest that there is little or no effect of the decontextualised source texts on behaviour.",
}
@inproceedings{ahrenberg-2017-comparing,
    title = "Comparing Machine Translation and Human Translation: A Case Study",
    author = "Ahrenberg, Lars",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_003",
    doi = "10.26615/978-954-452-042-7_003",
    pages = "21--28",
    abstract = "As machine translation technology improves comparisons to human performance are often made in quite general and exaggerated terms. Thus, it is important to be able to account for differences accurately. This paper reports a simple, descriptive scheme for comparing translations and applies it to two translations of a British opinion article published in March, 2017. One is a human translation (HT) into Swedish, and the other a machine translation (MT). While the comparison is limited to one text, the results are indicative of current limitations in MT.",
}
@inproceedings{ustaszewski-stauder-2017-transbank,
    title = "{T}rans{B}ank: Metadata as the Missing Link between {NLP} and Traditional Translation Studies",
    author = "Ustaszewski, Michael  and
      Stauder, Andy",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_004",
    doi = "10.26615/978-954-452-042-7_004",
    pages = "29--35",
    abstract = "Despite the growing importance of data in translation, there is no data repository that equally meets the requirements of translation industry and academia alike. Therefore, we plan to develop a freely available, multilingual and expandable bank of translations and their source texts aligned at the sentence level. Special emphasis will be placed on the labelling of metadata that precisely describe the relations between translated texts and their originals. This metadata-centric approach gives users the opportunity to compile and download custom corpora on demand. Such a general-purpose data repository may help to bridge the gap between translation theory and the language industry, including translation technology providers and NLP.",
}
@inproceedings{temnikova-etal-2017-interpreting,
    title = "Interpreting Strategies Annotation in the {WAW} Corpus",
    author = "Temnikova, Irina  and
      Abdelali, Ahmed  and
      Hedaya, Samy  and
      Vogel, Stephan  and
      Al Daher, Aishah",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_005",
    doi = "10.26615/978-954-452-042-7_005",
    pages = "36--43",
    abstract = "With the aim to teach our automatic speech-to-text translation system human interpreting strategies, our first step is to identify which interpreting strategies are most often used in the language pair of our interest (English-Arabic). In this article we run an automatic analysis of a corpus of parallel speeches and their human interpretations, and provide the results of manually annotating the human interpreting strategies in a sample of the corpus. We give a glimpse of the corpus, whose value surpasses the fact that it contains a high number of scientific speeches with their interpretations from English into Arabic, as it also provides rich information about the interpreters. We also discuss the difficulties, which we encountered on our way, as well as our solutions to them: our methodology for manual re-segmentation and alignment of parallel segments, the choice of annotation tool, and the annotation procedure. Our annotation findings explain the previously extracted specific statistical features of the interpreted corpus (compared with a translation one) as well as the quality of interpretation provided by different interpreters.",
}
@inproceedings{silvestre-baquero-mitkov-2017-translation,
    title = "Translation Memory Systems Have a Long Way to Go",
    author = "Silvestre Baquero, Andrea  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_006",
    doi = "10.26615/978-954-452-042-7_006",
    pages = "44--51",
    abstract = "The TM memory systems changed the work of translators and now the translators not benefiting from these tools are a tiny minority. These tools operate on fuzzy (surface) matching mostly and cannot benefit from already translated texts which are synonymous to (or paraphrased versions of) the text to be translated. The match score is mostly based on character-string similarity, calculated through Levenshtein distance. The TM tools have difficulties with detecting similarities even in sentences which represent a minor revision of sentences already available in the translation memory. This shortcoming of the current TM systems was the subject of the present study and was empirically proven in the experiments we conducted. To this end, we compiled a small translation memory (English-Spanish) and applied several lexical and syntactic transformation rules to the source sentences with both English and Spanish being the source language. The results of this study show that current TM systems have a long way to go and highlight the need for TM systems equipped with NLP capabilities which will offer the translator the advantage of he/she not having to translate a sentence again if an almost identical sentence has already been already translated.",
}
@inproceedings{elgabou-kazakov-2017-building,
    title = "Building Dialectal {A}rabic Corpora",
    author = "Elgabou, Hani  and
      Kazakov, Dimitar",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_007",
    doi = "10.26615/978-954-452-042-7_007",
    pages = "52--57",
    abstract = "The aim of this research is to identify local Arabic dialects in texts from social media (Twitter) and link them to specific geographic areas. Dialect identification is studied as a subset of the task of language identification. The proposed method is based on unsupervised learning using simultaneously lexical and geographic distance. While this study focusses on Libyan dialects, the approach is general, and could produce resources to support human translators and interpreters when dealing with vernaculars rather than standard Arabic.",
}
@inproceedings{mrini-benjamin-2017-towards,
    title = "Towards Producing Human-Validated Translation Resources for the {F}ula language through {W}ord{N}et Linking",
    author = "Mrini, Khalil  and
      Benjamin, Martin",
    booktitle = "Proceedings of the Workshop Human-Informed Translation and Interpreting Technology",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "Association for Computational Linguistics, Shoumen, Bulgaria",
    url = "https://doi.org/10.26615/978-954-452-042-7_008",
    doi = "10.26615/978-954-452-042-7_008",
    pages = "58--64",
    abstract = "We propose methods to link automatically parsed linguistic data to the WordNet. We apply these methods on a trilingual dictionary in Fula, English and French. Dictionary entry parsing is used to collect the linguistic data. Then we connect it to the Open Multilingual WordNet (OMW) through two attempts, and use confidence scores to quantify accuracy. We obtained 11,000 entries in parsing and linked about 58{\%} to the OMW on the first attempt, and an additional 14{\%} in the second one. These links are due to be validated by Fula speakers before being added to the Kamusi Project{'}s database.",
}
@proceedings{ws-2017-knowledge,
    title = "Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with {RANLP} 2017",
    editor = "Zervanou, Kalliopi  and
      Osenova, Petya  and
      Wandl-Vogt, Eveline  and
      Cristea, Dan",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "https://www.aclweb.org/anthology/W17-7800",
    doi = "10.26615/978-954-452-040-3_",
}
@inproceedings{lejtovicz-dorn-2017-connecting,
    title = "Connecting people digitally - a semantic web based approach to linking heterogeneous data sets",
    author = "Lejtovicz, Katalin  and
      Dorn, Amelie",
    booktitle = "Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-040-3_001",
    doi = "10.26615/978-954-452-040-3_001",
    pages = "1--8",
    abstract = {In this paper we present a semantic enrichment approach for linking two distinct data sets: the {\"O}BL (Austrian Biographical Dictionary) and the DB{\"O} (Database of Bavarian Dialects in Austria). Although the data sets are different in their content and in the structuring of data, they contain similar common {``}entities{''} such as names of persons. Here we describe the semantic enrichment process of how these data sets can be inter-linked through URIs (Uniform Resource Identifiers) taking person names as a concrete example. Moreover, we also point to societal benefits of applying such semantic enrichment methods in order to open and connect our resources to various services.},
}
@inproceedings{colhon-etal-2017-multiform,
    title = "A Multiform Balanced Dependency Treebank for {R}omanian",
    author = "Colhon, Mihaela  and
      M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lina  and
      Mititelu, C{\u{a}}t{\u{a}}lin",
    booktitle = "Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-040-3_002",
    doi = "10.26615/978-954-452-040-3_002",
    pages = "9--18",
    abstract = "The UAIC-RoDia-DepTb is a balanced treebank, containing texts in non-standard language: 2,575 chats sentences, old Romanian texts (a Gospel printed in 1648, a codex of laws printed in 1818, a novel written in 1910), regional popular poetry, legal texts, Romanian and foreign fiction, quotations. The proportions are comparable; each of these types of texts is represented by subsets of at least 1,000 phrases, so that the parser can be trained on their peculiarities. The annotation of the treebank started in 2007, and it has classical tags, such as those in school grammar, with the intention of using the resource for didactic purposes. The classification of circumstantial modifiers is rich in semantic information. We present in this paper the development in progress of this resource which has been automatically annotated and entirely manually corrected. We try to add new texts, and to make it available in more formats, by keeping all the morphological and syntactic information annotated, and adding logical-semantic information. We will describe here two conversions, from the classic syntactic format into Universal Dependencies format and into a logical-semantic layer, which will be shortly presented.",
}
@inproceedings{fokkens-etal-2017-grasp,
    title = "{GR}a{SP}: Grounded Representation and Source Perspective",
    author = "Fokkens, Antske  and
      Vossen, Piek  and
      Rospocher, Marco  and
      Hoekstra, Rinke  and
      van Hage, Willem Robert",
    booktitle = "Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-040-3_003",
    doi = "10.26615/978-954-452-040-3_003",
    pages = "19--25",
    abstract = "When people or organizations provide information, they make choices regarding what information they include and how they present it. The combination of these two aspects (the content and stance provided by the source) represents a perspective. Investigating differences in perspective can provide various useful insights in the reliability of information, the way perspectives change over time, shared beliefs among groups of a similar social or political background and contrasts between other groups, etc. This paper introduces GRaSP, a generic framework for modeling perspectives and their sources.",
}
@inproceedings{stambolieva-2017-educational,
    title = "Educational Content Generation for Business and Administration {FL} Courses with the {NBU} {PLT} Platform",
    author = "Stambolieva, Maria",
    booktitle = "Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-040-3_004",
    doi = "10.26615/978-954-452-040-3_004",
    pages = "26--30",
    abstract = "The paper presents part of an ongoing project of the Laboratory for Language Technologies of New Bulgarian University {--} {``}An e-Platform for Language Teaching (PLT){''} {--} the development of corpus-based teaching content for Business English courses. The presentation offers information on: 1/ corpus creation and corpus management with PLT; 2/ PLT corpus annotation; 3/ language task generation and the Language Task Bank (LTB); 4/ content transfer to the NBU Moodle platform, test generation and feedback on student performance.",
}
@inproceedings{kazakov-etal-2017-machine,
    title = "Machine Learning Models of Universal Grammar Parameter Dependencies",
    author = "Kazakov, Dimitar  and
      Cordoni, Guido  and
      Ceolin, Andrea  and
      Irimia, Monica-Alexandrina  and
      Kim, Shin-Sook  and
      Michelioudakis, Dimitris  and
      Radkevich, Nina  and
      Guardiano, Cristina  and
      Longobardi, Giuseppe",
    booktitle = "Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-040-3_005",
    doi = "10.26615/978-954-452-040-3_005",
    pages = "31--37",
    abstract = "The use of parameters in the description of natural language syntax has to balance between the need to discriminate among (sometimes subtly different) languages, which can be seen as a cross-linguistic version of Chomsky{'}s (1964) descriptive adequacy, and the complexity of the acquisition task that a large number of parameters would imply, which is a problem for explanatory adequacy. Here we present a novel approach in which a machine learning algorithm is used to find dependencies in a table of parameters. The result is a dependency graph in which some of the parameters can be fully predicted from others. These empirical findings can be then subjected to linguistic analysis, which may either refute them by providing typological counter-examples of languages not included in the original dataset, dismiss them on theoretical grounds, or uphold them as tentative empirical laws worth of further study.",
}
@proceedings{ws-2017-natural-language,
    title = "Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with {RANLP} 2017",
    editor = "Makary, Mireille  and
      Oakes, Michael",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Inc.",
    url = "https://www.aclweb.org/anthology/W17-7700",
    doi = "10.26615/978-954-452-038-0_",
}
@inproceedings{pisarevskaya-etal-2017-deception,
    title = "Deception Detection for the {R}ussian Language: Lexical and Syntactic Parameters",
    author = "Pisarevskaya, Dina  and
      Litvinova, Tatiana  and
      Litvinova, Olga",
    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-038-0_001",
    doi = "10.26615/978-954-452-038-0_001",
    pages = "1--10",
    abstract = "The field of automated deception detection in written texts is methodologically challenging. Different linguistic levels (lexics, syntax and semantics) are basically used for different types of English texts to reveal if they are truthful or deceptive. Such parameters as POS tags and POS tags n-grams, punctuation marks, sentiment polarity of words, psycholinguistic features, fragments of syntaсtic structures are taken into consideration. The importance of different types of parameters was not compared for the Russian language before and should be investigated before moving to complex models and higher levels of linguistic processing. On the example of the Russian Deception Bank Corpus we estimate the impact of three groups of features (POS features including bigrams, sentiment and psycholinguistic features, syntax and readability features) on the successful deception detection and find out that POS features can be used for binary text classification, but the results should be double-checked and, if possible, improved.",
}
@inproceedings{cercel-etal-2017-oiqa,
    title = "o{IQ}a: An Opinion Influence Oriented Question Answering Framework with Applications to Marketing Domain",
    author = "Cercel, Dumitru-Clementin  and
      Onose, Cristian  and
      Trausan-Matu, Stefan  and
      Pop, Florin",
    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-038-0_002",
    doi = "10.26615/978-954-452-038-0_002",
    pages = "11--18",
    abstract = "Understanding questions and answers in QA system is a major challenge in the domain of natural language processing. In this paper, we present a question answering system that influences the human opinions in a conversation. The opinion words are quantified by using a lexicon-based method. We apply Latent Semantic Analysis and the cosine similarity measure between candidate answers and each question to infer the answer of the chatbot.",
}
@inproceedings{sanchan-etal-2017-automatic,
    title = "Automatic Summarization of Online Debates",
    author = "Sanchan, Nattapong  and
      Aker, Ahmet  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-038-0_003",
    doi = "10.26615/978-954-452-038-0_003",
    pages = "19--27",
    abstract = "Debate summarization is one of the novel and challenging research areas in automatic text summarization which has been largely unexplored. In this paper, we develop a debate summarization pipeline to summarize key topics which are discussed or argued in the two opposing sides of online debates. We view that the generation of debate summaries can be achieved by clustering, cluster labeling, and visualization. In our work, we investigate two different clustering approaches for the generation of the summaries. In the first approach, we generate the summaries by applying purely term-based clustering and cluster labeling. The second approach makes use of X-means for clustering and Mutual Information for labeling the clusters. Both approaches are driven by ontologies. We visualize the results using bar charts. We think that our results are a smooth entry for users aiming to receive the first impression about what is discussed within a debate topic containing waste number of argumentations.",
}
@inproceedings{salem-etal-2017-game,
    title = "A Game with a Purpose for Automatic Detection of Children{'}s Speech Disabilities using Limited Speech Resources",
    author = "Salem, Reem  and
      Elmahdy, Mohamed  and
      Abdennadher, Slim  and
      Hamed, Injy",
    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-038-0_004",
    doi = "10.26615/978-954-452-038-0_004",
    pages = "28--34",
    abstract = "Speech therapists and researchers are becoming more concerned with the use of computer-based systems in the therapy of speech disorders. In this paper, we propose a computer-based game with a purpose (GWAP) for speech therapy of Egyptian speaking children suffering from Dyslalia. Our aim is to detect if a certain phoneme is pronounced correctly. An Egyptian Arabic speech corpus has been collected. A baseline acoustic model was trained using the Egyptian corpus. In order to benefit from existing large amounts of Modern Standard Arabic (MSA) resources, MSA acoustic models were adapted with the collected Egyptian corpus. An independent testing set that covers common speech disorders has been collected for Egyptian speakers. Results show that adapted acoustic models give better recognition accuracy which could be relied on in the game and that children show more interest in playing the game than in visiting the therapist. A noticeable progress in children Dyslalia appeared with the proposed system.",
}
@proceedings{ws-2017-international-treebanks,
    title = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    editor = "Haji{\v{c}}, Jan",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7600",
}
@inproceedings{candito-2017-annotating,
    title = "Annotating and parsing to semantic frames: feedback from the {F}rench {F}rame{N}et project",
    author = "Candito, Marie",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7601",
    pages = "v",
}
@inproceedings{ovrelid-2017-downstream,
    title = "Downstream use of syntactic analysis: does representation matter?",
    author = "{\O}vrelid, Lilja",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7602",
    pages = "vi",
}
@inproceedings{de-kok-etal-2017-distributional,
    title = "Distributional regularities of verbs and verbal adjectives: Treebank evidence and broader implications",
    author = {de Kok, Dani{\"e}l  and
      Fischer, Patricia  and
      Dima, Corina  and
      Hinrichs, Erhard},
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7603",
    pages = "1--9",
}
@inproceedings{tyers-etal-2017-ud,
    title = "{UD} Annotatrix: An annotation tool for {U}niversal {D}ependencies",
    author = "Tyers, Francis M.  and
      Sheyanova, Mariya  and
      Washington, Jonathan North",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7604",
    pages = "10--17",
}
@inproceedings{passarotti-gonzalez-saavedra-2017-treebanked,
    title = "The Treebanked Conspiracy. Actors and Actions in Bellum Catilinae",
    author = "Passarotti, Marco  and
      Gonz{\'a}lez Saavedra, Berta",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7605",
    pages = "18--26",
}
@inproceedings{kunilovskaya-kutuzov-2017-universal,
    title = "{U}niversal {D}ependencies-based syntactic features in detecting human translation varieties",
    author = "Kunilovskaya, Maria  and
      Kutuzov, Andrey",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7606",
    pages = "27--36",
}
@inproceedings{cetoli-etal-2017-graph,
    title = "Graph Convolutional Networks for Named Entity Recognition",
    author = "Cetoli, Alberto  and
      Bragaglia, Stefano  and
      O{'}Harney, Andrew  and
      Sloan, Marc",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7607",
    pages = "37--45",
}
@inproceedings{odijk-etal-2017-extensions,
    title = "Extensions to the {G}r{ETEL} Treebank Query Application",
    author = "Odijk, Jan  and
      van der Klis, Martijn  and
      Spoel, Sheean",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7608",
    pages = "46--55",
}
@inproceedings{bejcek-etal-2017-relation,
    title = "The Relation of Form and Function in Linguistic Theory and in a Multilayer Treebank",
    author = "Bej{\v{c}}ek, Eduard  and
      Haji{\v{c}}ov{\'a}, Eva  and
      Mikulov{\'a}, Marie  and
      Panevov{\'a}, Jarmila",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7609",
    pages = "56--63",
}
@inproceedings{savary-cordeiro-2017-literal,
    title = "Literal readings of multiword expressions: as scarce as hen{'}s teeth",
    author = "Savary, Agata  and
      Cordeiro, Silvio Ricardo",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7610",
    pages = "64--72",
}
@inproceedings{klyueva-etal-2017-querying,
    title = "Querying Multi-word Expressions Annotation with {CQL}",
    author = "Klyueva, Natalia  and
      Vernerov{\'a}, Anna  and
      Qasemizadeh, Behrang",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7611",
    pages = "73--79",
}
@inproceedings{lyashevskaya-panteleeva-2017-realec,
    title = "{REALEC} learner treebank: annotation principles and evaluation of automatic parsing",
    author = "Lyashevskaya, Olga  and
      Panteleeva, Irina",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7612",
    pages = "80--87",
}
@inproceedings{tio-saenz-metola-rodriguez-2017-semiautomatic,
    title = "A semiautomatic lemmatisation procedure for treebanks. {O}ld {E}nglish strong and weak verbs",
    author = "T{\'\i}o S{\'a}enz, Marta  and
      Metola Rodr{\'\i}guez, Dar{\'\i}o",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7613",
    pages = "88--94",
}
@inproceedings{rehbein-bildhauer-2017-data,
    title = "Data point selection for genre-aware parsing",
    author = "Rehbein, Ines  and
      Bildhauer, Felix",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7614",
    pages = "95--105",
}
@inproceedings{rosa-zabokrtsky-2017-error,
    title = "Error Analysis of Cross-lingual Tagging and Parsing",
    author = "Rosa, Rudolf  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7615",
    pages = "106--118",
}
@inproceedings{rama-vajjala-2017-telugu,
    title = "A {T}elugu treebank based on a grammar book",
    author = "Rama, Taraka  and
      Vajjala, Sowmya",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7616",
    pages = "119--128",
}
@inproceedings{osenova-simov-2017-recent,
    title = "Recent Developments within {B}ul{T}ree{B}ank",
    author = "Osenova, Petya  and
      Simov, Kiril",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7617",
    pages = "129--137",
}
@inproceedings{aplonova-tyers-2017-towards,
    title = "Towards a dependency-annotated treebank for {B}ambara",
    author = "Aplonova, Ekaterina  and
      Tyers, Francis M.",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7618",
    pages = "138--145",
}
@inproceedings{steiner-2017-merging,
    title = "Merging the Trees - Building a Morphological Treebank for {G}erman from Two Resources",
    author = "Steiner, Petra",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7619",
    pages = "146--160",
}
@inproceedings{sogaard-2017-think,
    title = "What {I} think when {I} think about treebanks",
    author = "S{\o}gaard, Anders",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7620",
    pages = "161--166",
}
@inproceedings{maranduc-etal-2017-syntactic,
    title = "Syntactic Semantic Correspondence in Dependency Grammar",
    author = "M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lina  and
      Mititelu, C{\u{a}}t{\u{a}}lin  and
      Bobicev, Victoria",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7621",
    pages = "167--180",
}
@inproceedings{kahane-etal-2017-multi,
    title = "Multi-word annotation in syntactic treebanks - Propositions for {U}niversal {D}ependencies",
    author = "Kahane, Sylvain  and
      Courtin, Marine  and
      Gerdes, Kim",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7622",
    pages = "181--189",
}
@inproceedings{ravishankar-2017-universal,
    title = "A {U}niversal {D}ependencies Treebank for {M}arathi",
    author = "Ravishankar, Vinit",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7623",
    pages = "190--200",
}
@inproceedings{alzetta-etal-2017-dangerous,
    title = "Dangerous Relations in Dependency Treebanks",
    author = "Alzetta, Chiara  and
      Dell{'}Orletta, Felice  and
      Montemagni, Simonetta  and
      Venturi, Giulia",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7624",
    pages = "201--210",
}
@inproceedings{nn-2017-author,
    title = "Author Index",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7625",
    pages = "211--212",
}
@inproceedings{nn-2017-keyword,
    title = "Keyword Index",
    booktitle = "Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories",
    year = "2017",
    address = "Prague, Czech Republic",
    url = "https://www.aclweb.org/anthology/W17-7626",
    pages = "213--214",
}
@proceedings{ws-2017-international-natural-language,
    title = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    editor = "Bandyopadhyay, Sivaji",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7500",
}
@inproceedings{schuller-2017-keynote,
    title = "Keynote Lecture 1: {NLP} in Tomorrow{'}s Profiling - Words May Fail You",
    author = {Schuller, Bj{\"o}rn W.},
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7501",
    pages = "1",
}
@inproceedings{roy-2017-deriving,
    title = "Deriving Word Prosody from Orthography in {H}indi",
    author = "Roy, Somnath",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7502",
    pages = "2--12",
}
@inproceedings{agrawal-etal-2017-three,
    title = "Three-phase training to address data sparsity in Neural Machine Translation",
    author = "Agrawal, Ruchit  and
      Shekhar, Mihir  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7503",
    pages = "13--22",
}
@inproceedings{jha-etal-2017-reference,
    title = "Reference Scope Identification for Citances Using Convolutional Neural Networks",
    author = "Jha, Saurav  and
      Chaurasia, Aanchal  and
      Sudhakar, Akhilesh  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7504",
    pages = "23--32",
}
@inproceedings{agrawal-etal-2017-vis,
    title = "A vis-{\`a}-vis evaluation of {MT} paradigms for linguistically distant languages",
    author = "Agrawal, Ruchit  and
      Ali, Jahfar  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7505",
    pages = "33--42",
}
@inproceedings{chatterjee-etal-2017-textual,
    title = "Textual Relations and Topic-Projection: Issues in Text Categorization",
    author = "Chatterjee, Lahari  and
      Karmakar, Samir  and
      Datta, Abahan",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7506",
    pages = "43--49",
}
@inproceedings{mishra-etal-2017-pos,
    title = "{POS} Tagging For Resource Poor Languages Through Feature Projection",
    author = "Mishra, Pruthwik  and
      Mujadia, Vandan  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7507",
    pages = "50--55",
}
@inproceedings{kocmi-bojar-2017-exploration,
    title = "An Exploration of Word Embedding Initialization in Deep-Learning Tasks",
    author = "Kocmi, Tom  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7508",
    pages = "56--64",
}
@inproceedings{choudhury-etal-2017-curriculum,
    title = "Curriculum Design for Code-switching: Experiments with Language Identification and Language Modeling with Deep Neural Networks",
    author = "Choudhury, Monojit  and
      Bali, Kalika  and
      Sitaram, Sunayana  and
      Baheti, Ashutosh",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7509",
    pages = "65--74",
}
@inproceedings{pratapa-choudhury-2017-quantitative,
    title = "Quantitative Characterization of Code Switching Patterns in Complex Multi-Party Conversations: A Case Study on {H}indi Movie Scripts",
    author = "Pratapa, Adithya  and
      Choudhury, Monojit",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7510",
    pages = "75--84",
}
@inproceedings{phadte-thakkar-2017-towards,
    title = "Towards Normalising {K}onkani-{E}nglish Code-Mixed Social Media Text",
    author = "Phadte, Akshata  and
      Thakkar, Gaurish",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7511",
    pages = "85--94",
}
@inproceedings{pandey-etal-2017-towards,
    title = "Towards developing a phonetically balanced code-mixed speech corpus for {H}indi-{E}nglish {ASR}",
    author = "Pandey, Ayushi  and
      Srivastava, Brij Mohan Lal  and
      Gangashetty, Suryakanth",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7512",
    pages = "95--101",
}
@inproceedings{ng-2017-keynote,
    title = "Keynote Lecture 2: Grammatical Error Correction: Past, Present and Future",
    author = "Ng, Hwee Tou",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7513",
    pages = "102",
}
@inproceedings{patil-etal-2017-hybrid,
    title = "Hybrid Approach for {M}arathi Named Entity Recognition",
    author = "Patil, Nita  and
      Patil, Ajay  and
      Pawar, B.V.",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7514",
    pages = "103--111",
}
@inproceedings{jain-etal-2017-sentiment,
    title = "Sentiment Analysis: An Empirical Comparative Study of Various Machine Learning Approaches",
    author = "Jain, Swapnil  and
      Malviya, Shrikant  and
      Mishra, Rohit  and
      Tiwary, Uma Shanker",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7515",
    pages = "112--121",
}
@inproceedings{jwalapuram-mamidi-2017-handling,
    title = "Handling Multi-Sentence Queries in a Domain Independent Dialogue System",
    author = "Jwalapuram, Prathyusha  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7516",
    pages = "122--130",
}
@inproceedings{saikh-etal-2017-document,
    title = "Document Level Novelty Detection: Textual Entailment Lends a Helping Hand",
    author = "Saikh, Tanik  and
      Ghosal, Tirthankar  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7517",
    pages = "131--140",
}
@inproceedings{kanojia-etal-2017-statement,
    title = "Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose",
    author = "Kanojia, Diptesh  and
      Wani, Nikhil  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7518",
    pages = "141--145",
}
@inproceedings{mandal-naskar-2017-natural,
    title = "Natural Language Programing with Automatic Code Generation towards Solving Addition-Subtraction Word Problems",
    author = "Mandal, Sourav  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7519",
    pages = "146--154",
}
@inproceedings{p-2017-unsupervised,
    title = "Unsupervised Separation of Transliterable and Native Words for {M}alayalam",
    author = "P, Deepak",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7520",
    pages = "155--164",
}
@inproceedings{s-n-2017-known,
    title = "Known Strangers: Cross Linguistic Patterns in Multilingual Multidirectional Dictionaries",
    author = "S, Rejitha K.  and
      N., Rajesha",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7521",
    pages = "165--171",
}
@inproceedings{goyal-goyal-2017-tutorial,
    title = "Tutorial for Deaf {--} Teaching {P}unjabi Alphabet using Synthetic Animations",
    author = "Goyal, Lalit  and
      Goyal, Vishal",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7522",
    pages = "172--177",
}
@inproceedings{dey-etal-2017-semtagger,
    title = "{S}em{T}agger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on {T}witter",
    author = "Dey, Kuntal  and
      Shrivastava, Ritvik  and
      Kaushik, Saroj  and
      Subramaniam, L Venkata",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7523",
    pages = "178--187",
}
@inproceedings{sundaram-khemani-2017-reasoning,
    title = "Reasoning with Sets to Solve Simple Word Problems Automatically",
    author = "Sundaram, Sowmya S  and
      Khemani, Deepak",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7524",
    pages = "188--196",
}
@inproceedings{nayel-shashirekha-2017-improving,
    title = "Improving {NER} for Clinical Texts by Ensemble Approach using Segment Representations",
    author = "Nayel, Hamada  and
      Shashirekha, H. L.",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7525",
    pages = "197--204",
}
@inproceedings{dwivedi-shrivastava-2017-beyond,
    title = "Beyond {W}ord2{V}ec: Embedding Words and Phrases in Same Vector Space",
    author = "Dwivedi, Vijay Prakash  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7526",
    pages = "205--211",
}
@inproceedings{mondal-etal-2017-relationship,
    title = "Relationship Extraction based on Category of Medical Concepts from Lexical Contexts",
    author = "Mondal, Anupam  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7527",
    pages = "212--219",
}
@inproceedings{priyanga-etal-2017-sinhala,
    title = "{S}inhala Word Joiner",
    author = "Priyanga, Rajith  and
      Ranatunga, Surangika  and
      Dias, Gihan",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7528",
    pages = "220--226",
}
@inproceedings{asthana-ekbal-2017-supervised,
    title = "Supervised Methods For Ranking Relations In Web Search",
    author = "Asthana, Sumit  and
      Ekbal, Asif",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7529",
    pages = "227--235",
}
@inproceedings{jayan-etal-2017-malayalam,
    title = "{M}alayalam {V}erb{F}rames",
    author = "Jayan, Jisha P  and
      Nair, Asha S  and
      V, Govindaru",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7530",
    pages = "236--244",
}
@inproceedings{redkar-etal-2017-hindi-shabdamitra,
    title = "{H}indi Shabdamitra: A {W}ordnet based {E}-Learning Tool for Language Learning and Teaching",
    author = "Redkar, Hanumant  and
      Singh, Sandhya  and
      Gorasia, Dhara  and
      Somasundaram, Meenakshi  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7531",
    pages = "245--254",
}
@inproceedings{kumar-etal-2017-pessimist,
    title = "{``}A pessimist sees the difficulty in every opportunity; an optimist sees the opportunity in every difficulty{''} {--} Understanding the psycho-sociological influences to it",
    author = "Kumar, Updendra  and
      Rana, Vishal Kumar  and
      PYKL, Srinivas  and
      Das, Amitava",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7532",
    pages = "255--264",
}
@inproceedings{danda-etal-2017-end,
    title = "End to End Dialog System for {T}elugu",
    author = "Danda, Prathyusha  and
      Jwalapuram, Prathyusha  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7533",
    pages = "265--272",
}
@inproceedings{chawla-etal-2017-investigating,
    title = "Investigating how well contextual features are captured by bi-directional recurrent neural network models",
    author = "Chawla, Kushal  and
      Sahu, Sunil Kumar  and
      Anand, Ashish",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7534",
    pages = "273--282",
}
@inproceedings{kopparapu-anantaram-2017-correcting,
    title = "Correcting General Purpose {ASR} Errors using Posteriors",
    author = "Kopparapu, Sunil Kumar  and
      Anantaram, C.",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7535",
    pages = "283--289",
}
@inproceedings{patra-etal-2017-retrieving,
    title = "Retrieving Similar Lyrics for Music Recommendation System",
    author = "Patra, Braja Gopal  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7536",
    pages = "290--297",
}
@inproceedings{mahapatra-naskar-2017-unsupervised,
    title = "Unsupervised Morpheme Segmentation Through Numerical Weighting and Thresholding",
    author = "Mahapatra, Joy  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7537",
    pages = "298--304",
}
@inproceedings{hingmire-etal-2017-experiments,
    title = "Experiments with Domain Dependent Dialogue Act Classification using Open-Domain Dialogue Corpora",
    author = "Hingmire, Swapnil  and
      Shrivastava, Apoorv  and
      Palshikar, Girish  and
      Srivastava, Saurabh",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7538",
    pages = "305--311",
}
@inproceedings{tiwari-naskar-2017-normalization,
    title = "Normalization of Social Media Text using Deep Neural Networks",
    author = "Tiwari, Ajay Shankar  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7539",
    pages = "312--321",
}
@inproceedings{thakker-etal-2017-acronym,
    title = "Acronym Expansion: A General Approach Using Deep Learning",
    author = "Thakker, Aditya  and
      Barot, Suhail  and
      Bagul, Sudhir",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7540",
    pages = "322--327",
}
@inproceedings{nongmeikapam-etal-2017-exploring,
    title = "Exploring an Efficient Handwritten {M}anipuri Meetei-Mayek Character Recognition Using Gradient Feature Extractor and Cosine Distance Based Multiclass k-Nearest Neighbor Classifier",
    author = "Nongmeikapam, Kishorjit  and
      Kumar, Wahengbam  and
      Singh, Mithlesh Prasad",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7541",
    pages = "328--337",
}
@inproceedings{roul-arora-2017-modified,
    title = "A Modified Cosine-Similarity based Log Kernel for Support Vector Machines in the Domain of Text Classification",
    author = "Roul, Rajendra Kumar  and
      Arora, Kushagr",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7542",
    pages = "338--347",
}
@inproceedings{s-etal-2017-document,
    title = "Document Embedding Generation for Cyber-Aggressive Comment Detection using Supervised Machine Learning Approach",
    author = "S, Shylaja S  and
      Narayanan, Abhishek  and
      Venugopal, Abhijith  and
      Prasad, Abhishek",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7543",
    pages = "348--355",
}
@inproceedings{banerjee-etal-2017-coarticulatory,
    title = "Coarticulatory propensity in Khalkha {M}ongolian",
    author = "Banerjee, Ushashi  and
      Dutta, Indranil  and
      S, Irfan",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7544",
    pages = "356--361",
}
@inproceedings{das-das-2017-developing,
    title = "Developing Lexicon and Classifier for Personality Identification in Texts",
    author = "Das, Kumar Gourav  and
      Das, Dipankar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7545",
    pages = "362--372",
}
@inproceedings{rai-etal-2017-linguistic,
    title = "Linguistic approach based Transfer Learning for Sentiment Classification in {H}indi",
    author = "Rai, Vartika  and
      Vijay, Sakshee  and
      Misra, Dipti",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7546",
    pages = "373--382",
}
@inproceedings{rao-etal-2017-scalable,
    title = "Scalable Bio-Molecular Event Extraction System towards Knowledge Acquisition",
    author = "Rao, Pattabhi RK  and
      Gopalan, Sindhuja  and
      Devi, Sobha Lalitha",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7547",
    pages = "383--391",
}
@inproceedings{ram-devi-2017-co,
    title = "Co-reference Resolution in {T}amil Text",
    author = "Ram, Vijay Sundar  and
      Devi, Sobha Lalitha",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7548",
    pages = "392--401",
}
@inproceedings{gopalan-etal-2017-cross,
    title = "Cross Linguistic Variations in Discourse Relations among {I}ndian Languages",
    author = "Gopalan, Sindhuja  and
      S, Lakshmi  and
      Devi, Sobha Lalitha",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7549",
    pages = "402--407",
}
@inproceedings{nagaraju-etal-2017-rule,
    title = "Rule Based Approch of Clause Boundary Identification in {T}elugu",
    author = "Nagaraju, Ganthoti  and
      Sakkan, Thennarasu  and
      Mala, Christopher",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7550",
    pages = "408--416",
}
@inproceedings{varma-2017-keynote,
    title = "Keynote Lecture 3: Towards Abstractive Summarization",
    author = "Varma, Vasudeva",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7551",
    pages = "417",
}
@inproceedings{solomon-etal-2017-mentions,
    title = "{``}Who Mentions Whom?{''}- Understanding the Psycho-Sociological Aspects of {T}witter Mention Network",
    author = "Solomon, R Sudhesh  and
      Narayan, Abhay  and
      L, Srinivas P Y K  and
      Das, Amitava",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7552",
    pages = "418--426",
}
@inproceedings{sinha-dasgupta-2017-study,
    title = "Study on Visual Word Recognition in {B}angla across Different Reader Groups",
    author = "Sinha, Manjira  and
      Dasgupta, Tirthankar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7553",
    pages = "427--434",
}
@inproceedings{kundu-choudhury-2017-demystifying,
    title = "Demystifying Topology of Autopilot Thoughts: A Computational Analysis of Linguistic Patterns of Psychological Aspects in Mental Health",
    author = "Kundu, Bibekananda  and
      Choudhury, Sanjay",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7554",
    pages = "435--446",
}
@inproceedings{paul-das-2017-deep,
    title = "A Deep Dive into Identification of Characters from {M}ahabharata",
    author = "Paul, Apurba  and
      Das, Dipankar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7555",
    pages = "447--455",
}
@inproceedings{prijatelj-etal-2017-neural,
    title = "Neural Networks for Semantic Textual Similarity",
    author = "Prijatelj, Derek  and
      Kalita, Jugal  and
      Ventura, Jonathan",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7556",
    pages = "456--465",
}
@inproceedings{prakhya-etal-2017-open,
    title = "Open Set Text Classification Using {CNN}s",
    author = "Prakhya, Sridhama  and
      Venkataram, Vinodini  and
      Kalita, Jugal",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7557",
    pages = "466--475",
}
@inproceedings{woldemariam-etal-2017-predicting,
    title = "Predicting User Competence from Linguistic Data",
    author = {Woldemariam, Yonas  and
      Bj{\"o}rklund, Henrik  and
      Bensch, Suna},
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7558",
    pages = "476--484",
}
@inproceedings{sudhakar-singh-2017-neural,
    title = "Neural Morphological Disambiguation Using Surface and Contextual Morphological Awareness",
    author = "Sudhakar, Akhilesh  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7559",
    pages = "485--494",
}
@inproceedings{k-etal-2017-word,
    title = "Word Sense Disambiguation for {M}alayalam in a Conditional Random Field Framework",
    author = "K, Junaida M  and
      Jayan, Jisha P  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7560",
    pages = "495--502",
}
@inproceedings{rani-etal-2017-semisupervied,
    title = "Semisupervied Data Driven Word Sense Disambiguation for Resource-poor Languages",
    author = "Rani, Pratibha  and
      Pudi, Vikram  and
      Sharma, Dipti M.",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7561",
    pages = "503--512",
}
@inproceedings{gollapudi-choppella-2017-notion,
    title = "Notion of Semantics in Computer Science - A Systematic Literature Review",
    author = "Gollapudi, Sai Prasad Vrj  and
      Choppella, Venkatesh",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7562",
    pages = "513--522",
}
@inproceedings{smrz-otrusina-2017-semantic,
    title = "Semantic Enrichment Across Language: A Case Study of {C}zech Bibliographic Databases",
    author = "Smrz, Pavel  and
      Otrusina, Lubomir",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W17-7563",
    pages = "523--532",
}
@proceedings{ws-2017-joint-iso,
    title = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7400",
}
@inproceedings{hoek-scholman-2017-evaluating,
    title = "Evaluating discourse annotation: Some recent insights and new approaches",
    author = "Hoek, Jet  and
      Scholman, Merel",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7401",
}
@inproceedings{delmonte-marchesi-2017-semantically,
    title = "A semantically-based approach to the annotation of narrative style",
    author = "Delmonte, Rodolfo  and
      Marchesi, Giulia",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7402",
}
@inproceedings{mpouli-2017-annotating,
    title = "Annotating similes in literary texts",
    author = "Mpouli, Suzanne",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7403",
}
@inproceedings{bunt-etal-2017-revisiting,
    title = "Revisiting the {ISO} standard for dialogue act annotation",
    author = "Bunt, Harry  and
      Petukhova, Volha  and
      Fang, Alex Chengyu",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7404",
}
@inproceedings{gilmartin-etal-2017-annotation,
    title = "Annotation of greeting, introduction, and leavetaking in dialogues",
    author = "Gilmartin, Emer  and
      Spillane, Brendan  and
      O{'}Reilly, Maria  and
      Saam, Christian  and
      Su, Ketong  and
      Cowan, Benjamin R.  and
      Levacher, Killian  and
      Devesa, Arturo Calvo  and
      Cerrato, Lodana  and
      Campbell, Nick  and
      Wade, Vincent",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7405",
}
@inproceedings{lapina-petukhova-2017-classification,
    title = "Classification of modal meaning in negotiation dialogues",
    author = "Lapina, Valeria  and
      Petukhova, Volha",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7406",
}
@inproceedings{liesenfeld-2017-project,
    title = "Project Notes on building a conversational parser on top of a text parser: Towards a causal language tagger for spoken {C}hinese",
    author = "Liesenfeld, Andreas",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7407",
}
@inproceedings{hao-etal-2017-representation,
    title = "The representation and extraction of qunatitative information",
    author = "Hao, Tianyong  and
      We, Yunyan  and
      Qiang, Jiaqi  and
      Wang, Haitao  and
      Lee, Kiyong",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7408",
}
@inproceedings{bunt-2017-towards,
    title = "Towards interoperable annotation of quantification",
    author = "Bunt, Harry",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7409",
}
@inproceedings{menard-barriere-2017-pacte,
    title = "{PACTE}: A colloaborative platform for textual annotation",
    author = "M{\'e}nard, Pierre Andr{\'e}  and
      Barri{\`e}re, Caroline",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7410",
}
@inproceedings{grobol-etal-2017-interoperable,
    title = "Interoperable annotation of (co)references in the Democrat project",
    author = {Grobol, Lo{\"\i}c  and
      Landragin, Fr{\'e}d{\'e}ric  and
      Heiden, Serge},
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7411",
}
@inproceedings{lee-2017-four,
    title = "Four types of emporal signals",
    author = "Lee, Kiyong",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7412",
}
@inproceedings{antoine-etal-2017-temporal,
    title = "Temporal@{ODIL} project: Adapting {ISO}-{T}ime{ML} to syntactic treebanks for the temporal annotation of spoken speech",
    author = {Antoine, Jean-Yves  and
      Wasczuk, Jakub  and
      Lefeuvre-Haftermeyer, Ana{\"\i}s  and
      Abouda, Lotfi  and
      Schang, Emmanuel  and
      Savary, Agata},
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7413",
}
@inproceedings{woods-etal-2017-towards,
    title = "Towards efficient string processing of annotated events",
    author = "Woods, David  and
      Fernando, Tim  and
      Vogel, Carl",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7414",
}
@inproceedings{pustejovsky-lee-2017-enriching,
    title = "Enriching the Notion of Path in {ISO}-Space",
    author = "Pustejovsky, James  and
      Lee, Kiyong",
    booktitle = "Proceedings of the 13th Joint {ISO}-{ACL} Workshop on Interoperable Semantic Annotation ({ISA}-13)",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7415",
}
@proceedings{ws-2017-semantic,
    title = "Proceedings of the 2nd Workshop on Semantic Deep Learning ({S}em{D}eep-2)",
    editor = "Gromann, Dagmar  and
      Declerck, Thierry  and
      Heigl, Georg",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7300",
}
@inproceedings{saeidi-etal-2017-effect,
    title = "The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings",
    author = "Saeidi, Marzieh  and
      Kulkarni, Ritwik  and
      Togia, Theodosia  and
      Sama, Michele",
    booktitle = "Proceedings of the 2nd Workshop on Semantic Deep Learning ({S}em{D}eep-2)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7301",
    pages = "1--8",
}
@inproceedings{garcia-gasulla-etal-2017-building,
    title = "Building Graph Representations of Deep Vector Embeddings",
    author = "Garcia-Gasulla, Dario  and
      Vilalta, Armand  and
      Par{\'e}s, Ferran  and
      Moreno, Jonathan  and
      Ayguad{\'e}, Eduard  and
      Labarta, Jes{\'u}s  and
      Cort{\'e}s, Ulises  and
      Suzumura, Toyotaro",
    booktitle = "Proceedings of the 2nd Workshop on Semantic Deep Learning ({S}em{D}eep-2)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7302",
    pages = "9--15",
}
@inproceedings{scharffe-2017-class,
    title = "Class Disjointness Constraints as Specific Objective Functions in Neural Network Classifiers",
    author = "Scharffe, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2nd Workshop on Semantic Deep Learning ({S}em{D}eep-2)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7303",
    pages = "16--23",
}
@inproceedings{vilalta-etal-2017-full,
    title = "Full-Network Embedding in a Multimodal Embedding Pipeline",
    author = "Vilalta, Armand  and
      Garcia-Gasulla, Dario  and
      Par{\'e}s, Ferran  and
      Moreno, Jonathan  and
      Ayguad{\'e}, Eduard  and
      Labarta, Jesus  and
      Cort{\'e}s, Ulises  and
      Suzumura, Toyotaro",
    booktitle = "Proceedings of the 2nd Workshop on Semantic Deep Learning ({S}em{D}eep-2)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7304",
    pages = "24--32",
}
@inproceedings{al-kassar-armetta-2017-extracting,
    title = "Extracting Tags from Large Raw Texts Using End-to-End Memory Networks",
    author = "Al Kassar, Feras  and
      Armetta, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the 2nd Workshop on Semantic Deep Learning ({S}em{D}eep-2)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7305",
    pages = "33--40",
}
@inproceedings{van-noord-bos-2017-dealing,
    title = "Dealing with Co-reference in Neural Semantic Parsing",
    author = "van Noord, Rik  and
      Bos, Johan",
    booktitle = "Proceedings of the 2nd Workshop on Semantic Deep Learning ({S}em{D}eep-2)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7306",
    pages = "41--49",
}
@proceedings{ws-2017-computing,
    title = "Proceedings of the Computing Natural Language Inference Workshop",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7200",
}
@inproceedings{breitholtz-etal-2017-incrementality,
    title = "Incrementality all the way up",
    author = "Breitholtz, Ellen  and
      Howes, Christine  and
      Cooper, Robin",
    booktitle = "Proceedings of the Computing Natural Language Inference Workshop",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7201",
}
@inproceedings{catta-mirzapour-2017-quantifier,
    title = "Quantifier Scoping and Semantic Preferences",
    author = "Catta, Davide  and
      Mirzapour, Mehdi",
    booktitle = "Proceedings of the Computing Natural Language Inference Workshop",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7202",
}
@inproceedings{chatzikyriakidis-etal-2017-overview,
    title = "An overview of Natural Language Inference Data Collection: The way forward?",
    author = "Chatzikyriakidis, Stergios  and
      Cooper, Robin  and
      Dobnik, Simon  and
      Larsson, Staffan",
    booktitle = "Proceedings of the Computing Natural Language Inference Workshop",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7203",
}
@inproceedings{cousot-lafourcade-2017-explicative,
    title = "Explicative Path Finding in a Semantic Network",
    author = "Cousot, K{\'e}vin  and
      Lafourcade, Mathieu",
    booktitle = "Proceedings of the Computing Natural Language Inference Workshop",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7204",
}
@inproceedings{kalouli-etal-2017-correcting,
    title = "Correcting Contradictions",
    author = "Kalouli, Aikaterini-Lida  and
      de Paiva, Valeria  and
      Real, Livy",
    booktitle = "Proceedings of the Computing Natural Language Inference Workshop",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7205",
}
@inproceedings{chapuis-lafourcade-2017-identifying,
    title = "Identifying Polysemous Words and Inferring Sense Glosses in a Semantic Network",
    author = "Chapuis, Maxime  and
      Lafourcade, Mathieu",
    booktitle = "Proceedings of the Computing Natural Language Inference Workshop",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7206",
}
@proceedings{ws-2017-iwcs-foundations,
    title = "Proceedings of the {IWCS} workshop on Foundations of Situated and Multimodal Communication",
    editor = "Asher, Nicholas  and
      Hunter, Julie  and
      Lascarides, Alex",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7100",
}
@inproceedings{bhaskar-etal-2017-exploring,
    title = "Exploring Multi-Modal {T}ext+{I}mage Models to Distinguish between Abstract and Concrete Nouns",
    author = {Bhaskar, Sai Abishek  and
      K{\"o}per, Maximilian  and
      Schulte Im Walde, Sabine  and
      Frassinelli, Diego},
    booktitle = "Proceedings of the {IWCS} workshop on Foundations of Situated and Multimodal Communication",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7101",
}
@inproceedings{lucking-2017-indexicals,
    title = "Indexicals as Weak Descriptors",
    author = {L{\"u}cking, Andy},
    booktitle = "Proceedings of the {IWCS} workshop on Foundations of Situated and Multimodal Communication",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7102",
}
@inproceedings{pustejovsky-etal-2017-creating,
    title = "Creating Common Ground through Multimodal Simulations",
    author = "Pustejovsky, James  and
      Krishnaswamy, Nikhil  and
      Draper, Bruce  and
      Narayana, Pradyumna  and
      Bangar, Rahul",
    booktitle = "Proceedings of the {IWCS} workshop on Foundations of Situated and Multimodal Communication",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7103",
}
@inproceedings{van-der-sluis-etal-2017-text,
    title = "Text-Picture Relations in Multimodal Instructions",
    author = "van der Sluis, Ielka  and
      Eppinga, Anne Nienke  and
      Redeker, Gisela",
    booktitle = "Proceedings of the {IWCS} workshop on Foundations of Situated and Multimodal Communication",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-7104",
}
@proceedings{ws-2017-language-ontology,
    title = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    editor = "Frontini, Francesca  and
      Gr{\v{c}}i{\'c} Simeunovi{\'c}, Larisa  and
      Vintar, {\v{S}}pela  and
      Khan, Anas Fahad  and
      Parvisi, Artemis",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7000",
}
@inproceedings{valeriano-oncevay-marcos-2017-exploratory,
    title = "Exploratory Analysis for Ontology Learning from Social Events on Social Media Streaming in {S}panish",
    author = "Valeriano, Enrique  and
      Oncevay-Marcos, Arturo",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7001",
}
@inproceedings{hatty-etal-2017-creating,
    title = "Creating a gold standard corpus for terminological annotation from online forum data",
    author = {H{\"a}tty, Anna  and
      Tannert, Simon  and
      Heid, Ulrich},
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7002",
}
@inproceedings{janin-portine-2017-conceptual,
    title = "A conceptual ontology in the water domain of knowledge to bridge the lexical semantics of stratified discursive strata",
    author = "Janin, Jean-Louis  and
      Portine, Henri",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7003",
}
@inproceedings{fize-etal-2017-geodict,
    title = "{G}eo{D}ict: an integrated gazetteer",
    author = "Fize, Jacques  and
      Shrivastava, Gaurav  and
      M{\'e}nard, Pierre Andr{\'e}",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7004",
}
@inproceedings{bernier-colborne-etal-2017-fine,
    title = "Fine-grained domain classification of text using {TERMIUM} Plus",
    author = "Bernier-Colborne, Gabriel  and
      Barri{\`e}re, Caroline  and
      M{\'e}nard, Pierre Andr{\'e}",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7005",
}
@inproceedings{pernes-romary-2017-tbx,
    title = "{TBX} in {ODD}: Schema-agnostic specification and documentation for {T}erm{B}ase e{X}change",
    author = "Pernes, Stefan  and
      Romary, Laurent",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7006",
}
@inproceedings{tchechmedjiev-jonquet-2017-enrichment,
    title = "Enrichment of {F}rench Biomedical Ontologies with {UMLS} Concepts and Semantic Types for Biomedical Named Entity Recognition Though Ontological Semantic Annotation",
    author = "Tchechmedjiev, Andon  and
      Jonquet, Cl{\'e}ment",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7007",
}
@inproceedings{renau-etal-2017-experiments,
    title = "Experiments in taxonomy induction in {S}panish and {F}rench",
    author = "Renau, Irene  and
      Nazar, Rogelio  and
      Mar{\'\i}n, Rafael",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7008",
}
@inproceedings{bril-etal-2017-statistical,
    title = "A statistical model for morphology inspired by the {A}mis language",
    author = "Bril, Isabelle  and
      Lassoued, Achraf  and
      de Rougemont, Michel",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7009",
}
@inproceedings{bellandi-etal-2017-developing,
    title = "Developing {L}ex{O}: a Collaborative Editor of Multilingual Lexica and Termino-Ontological Resources in the Humanities",
    author = "Bellandi, Andrea  and
      Giovannetti, Emiliano  and
      Piccini, Silvia  and
      Weingart, Anja",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7010",
}
@inproceedings{mugelli-etal-2017-designing,
    title = "Designing an Ontology for the Study of Ritual in {A}ncient {G}reek Tragedy",
    author = "Mugelli, Gloria  and
      Bellandi, Andrea  and
      Boschetti, Federico  and
      Khan, Anas Fahad",
    booktitle = "Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop ({LOTKS} 2017)",
    month = sep,
    year = "2017",
    address = "Montpellier, France",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-7011",
}
@proceedings{ws-2017-iwcs-2017,
    title = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    editor = "Gardent, Claire  and
      Retor{\'e}, Christian",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6900",
}
@inproceedings{abzianidze-bos-2017-towards,
    title = "Towards Universal Semantic Tagging",
    author = "Abzianidze, Lasha  and
      Bos, Johan",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6901",
}
@inproceedings{bick-2017-propbank,
    title = "{P}ropbank Annotation of {D}anish Noun Frames",
    author = "Bick, Eckhard",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6902",
}
@inproceedings{bizzoni-lappin-2017-deep,
    title = "Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase",
    author = "Bizzoni, Yuri  and
      Lappin, Shalom",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6903",
}
@inproceedings{boleda-etal-2017-living,
    title = "Living a discrete life in a continuous world: Reference in cross-modal entity tracking",
    author = "Boleda, Gemma  and
      Pad{\'o}, Sebastian  and
      Pham, Nghia The  and
      Baroni, Marco",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6904",
}
@inproceedings{bos-2017-indexicals,
    title = "Indexicals and Compositionality: Inside-Out or Outside-In?",
    author = "Bos, Johan",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6905",
}
@inproceedings{delmonte-marchesini-2017-semantically,
    title = "A Semantically-Based Computational Approach to Narrative Structure",
    author = "Delmonte, Rodolfo  and
      Marchesini, Giulia",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6906",
}
@inproceedings{di-maro-etal-2017-graph,
    title = "Graph Databases for Designing High-Performance Speech Recognition Grammars",
    author = "Di Maro, Maria  and
      Valentino, Marco  and
      Riccio, Anna  and
      Origlia, Antonio",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6907",
}
@inproceedings{dusserre-padro-2017-bigger,
    title = "Bigger does not mean better! We prefer specificity",
    author = "Dusserre, Emmanuelle  and
      Padr{\'o}, Muntsa",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6908",
}
@inproceedings{falke-gurevych-2017-utilizing,
    title = "Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining",
    author = "Falke, Tobias  and
      Gurevych, Iryna",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6909",
}
@inproceedings{frassinelli-etal-2017-contextual,
    title = "Contextual Characteristics of Concrete and Abstract Words",
    author = "Frassinelli, Diego  and
      Naumann, Daniela  and
      Utt, Jason  and
      Schulte m Walde, Sabine",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6910",
}
@inproceedings{gergel-etal-2017-surprisal,
    title = "Surprisal and Satisfaction: Towards an Information-theoretic Characterization of Presuppositions with a Diachronic Application",
    author = "Gergel, Remus  and
      Kopf-Giammanco, Martin  and
      Masloh, Julia",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6911",
}
@inproceedings{hasegawa-etal-2017-incorporating,
    title = "Incorporating visual features into word embeddings: A bimodal autoencoder-based approach",
    author = "Hasegawa, Mika  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6912",
}
@inproceedings{howes-eshghi-2017-feedback,
    title = "Feedback relevance spaces: The organisation of increments in conversation",
    author = "Howes, Christine  and
      Eshghi, Arash",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6913",
}
@inproceedings{kallmeyer-osswald-2017-modeling,
    title = "Modeling Quantification with Polysemous Nouns",
    author = "Kallmeyer, Laura  and
      Osswald, Rainer",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6914",
}
@inproceedings{kalouli-etal-2017-textual,
    title = "Textual Inference: getting logic from humans",
    author = "Kalouli, Aikaterini-Lida  and
      Real, Livy  and
      de Paiva, Valeria",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6915",
}
@inproceedings{khan-etal-2017-situating,
    title = "Situating Word Senses in their Historical Context with Linked Data",
    author = "Khan, Fahad  and
      Bowers, Jack  and
      Frontini, Francesca",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6916",
}
@inproceedings{klenner-2017-object,
    title = "An Object-oriented Model of Role Framing and Attitude Prediction",
    author = "Klenner, Manfred",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6917",
}
@inproceedings{kracht-aboamer-2017-argument,
    title = "Argument Structure and Referent Systems",
    author = "Kracht, Marcus  and
      Aboamer, Yousuf",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6918",
}
@inproceedings{krishnaswamy-etal-2017-communicating,
    title = "Communicating and Acting: Understanding Gesture in Simulation Semantics",
    author = "Krishnaswamy, Nikhil  and
      Narayana, Pradyumna  and
      Wang, Isaac  and
      Rim, Kyeongmin  and
      Bangar, Rahul  and
      Patil, Dhruva  and
      Mulay, Gururaj  and
      Beveridge, Ross  and
      Ruiz, Jaime  and
      Draper, Bruce  and
      Pustejovsky, James",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6919",
}
@inproceedings{lafourcade-brun-2017-ambiguss,
    title = "Ambiguss, a game for building a Sense Annotated Corpus for {F}rench",
    author = "Lafourcade, Mathieu  and
      Brun, Nathalie Le",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6920",
}
@inproceedings{lamm-eric-2017-pragmatics,
    title = "The Pragmatics of Indirect Commands in Collaborative Discourse",
    author = "Lamm, Matthew  and
      Eric, Mihail",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6921",
}
@inproceedings{lapesa-etal-2017-doggies,
    title = "Are doggies really nicer than dogs? The impact of morphological derivation on emotional valence in {G}erman",
    author = "Lapesa, Gabriella  and
      Pad{\'o}, Sebastian  and
      Pross, Tillmann  and
      Rossdeutscher, Antje",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6922",
}
@inproceedings{larsson-2017-compositionality,
    title = "Compositionality for perceptual classification",
    author = "Larsson, Staffan",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6923",
}
@inproceedings{li-summers-stay-2017-dual,
    title = "Dual Embeddings and Metrics for Relational Similarity",
    author = "Li, Dandan  and
      Summers-Stay, Douglas",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6924",
}
@inproceedings{lierler-etal-2017-action,
    title = "Action Languages and Question Answering",
    author = "Lierler, Yuliya  and
      Inclezan, Daniela  and
      Gelfond, Michael",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6925",
}
@inproceedings{linz-etal-2017-using,
    title = "Using Neural Word Embeddings in the Analysis of the Clinical Semantic Verbal Fluency Task",
    author = {Linz, Nicklas  and
      Tr{\"o}ger, Johannes  and
      Alexandersson, Jan  and
      K{\"o}nig, Alexandra},
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6926",
}
@inproceedings{martinez-camara-etal-2017-neural,
    title = "Neural Disambiguation of Causal Lexical Markers Based on Context",
    author = "Mart{\'\i}nez-C{\'a}mara, Eugenio  and
      Shwartz, Vered  and
      Gurevych, Iryna  and
      Dagan, Ido",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6927",
}
@inproceedings{melymuka-etal-2017-modeling,
    title = "Modeling Derivational Morphology in {U}krainian",
    author = "Melymuka, Mariia  and
      Lapesa, Gabriella  and
      Kisselew, Max  and
      Pad{\'o}, Sebastian",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6928",
}
@inproceedings{miyazawa-miyao-2017-evaluation,
    title = "Evaluation Metrics for Automatically Generated Metaphorical Expressions",
    author = "Miyazawa, Akira  and
      Miyao, Yusuke",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6929",
}
@inproceedings{nulty-2017-network,
    title = "Network Visualisations for Exploring Political Concepts",
    author = "Nulty, Paul",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6930",
}
@inproceedings{oele-van-noord-2017-distributional,
    title = "Distributional {L}esk: Effective Knowledge-Based Word Sense Disambiguation",
    author = "Oele, Dieke  and
      van Noord, Gertjan",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6931",
}
@inproceedings{petersen-hellwig-2017-unsupervised,
    title = "Unsupervised Induction of Compositional Types for {E}nglish Adjective-Noun Pairs",
    author = "Petersen, Wiebke  and
      Hellwig, Oliver",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6932",
}
@inproceedings{riedl-biemann-2017-theres,
    title = "There{'}s no {`}Count or Predict{'} but task-based selection for distributional models",
    author = "Riedl, Martin  and
      Biemann, Chris",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6933",
}
@inproceedings{roth-2017-role,
    title = "Role Semantics for Better Models of Implicit Discourse Relations",
    author = "Roth, Michael",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6934",
}
@inproceedings{ruckle-gurevych-2017-representation,
    title = "Representation Learning for Answer Selection with {LSTM}-Based Importance Weighting",
    author = {R{\"u}ckl{\'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6935",
}
@inproceedings{rudinger-etal-2017-skip,
    title = "Skip-Prop: Representing Sentences with One Vector Per Proposition",
    author = "Rudinger, Rachel  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6936",
}
@inproceedings{sasaki-etal-2017-handling,
    title = "Handling Multiword Expressions in Causality Estimation",
    author = "Sasaki, Shota  and
      Takase, Sho  and
      Inoue, Naoya  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6937",
}
@inproceedings{shekhar-etal-2017-vision,
    title = "Vision and Language Integration: Moving beyond Objects",
    author = "Shekhar, Ravi  and
      Pezzelle, Sandro  and
      Herbelot, Aur{\'e}lie  and
      Nabi, Moin  and
      Sangineto, Enver  and
      Bernardi, Raffaella",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6938",
}
@inproceedings{smith-etal-2017-see,
    title = "Can You See the (Linguistic) Difference? Exploring Mass/Count Distinction in Vision",
    author = "Smith, David Addison  and
      Pezzelle, Sandro  and
      Franzon, Francesca  and
      Zanini, Chiara  and
      Bernardi, Raffaella",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6939",
}
@inproceedings{vial-etal-2017-sense,
    title = "Sense Embeddings in Knowledge-Based Word Sense Disambiguation",
    author = {Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin  and
      Schwab, Didier},
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6940",
}
@inproceedings{wilkens-etal-2017-lexsubnc,
    title = "{L}ex{S}ub{NC}: A Dataset of Lexical Substitution for Nominal Compounds",
    author = "Wilkens, Rodrigo  and
      Zilio, Leonardo  and
      Cordeiro, Silvio Ricardo  and
      Paula, Felipe  and
      Ramisch, Carlos  and
      Idiart, Marco  and
      Villavicencio, Aline",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6941",
}
@inproceedings{wittmann-etal-2017-exploring,
    title = "Exploring Soft-Clustering for {G}erman (Particle) Verbs across Frequency Ranges",
    author = {Wittmann, Moritz  and
      K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6942",
}
@inproceedings{zadrozny-etal-2017-towards,
    title = "Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines",
    author = "Zadrozny, Wlodek  and
      Hematialam, Hossein  and
      Garbayo, Luciana",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6943",
}
@inproceedings{zhang-etal-2017-evaluation,
    title = "An Evaluation of {P}red{P}att and Open {IE} via Stage 1 Semantic Role Labeling",
    author = "Zhang, Sheng  and
      Rudinger, Rachel  and
      Van Durme, Benjamin",
    booktitle = "{IWCS} 2017 {---} 12th International Conference on Computational Semantics {---} Short papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6944",
}
@proceedings{ws-2017-iwcs,
    title = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    editor = "Gardent, Claire  and
      Retor{\'e}, Christian",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6800",
}
@inproceedings{bernardy-chatzikyriakidis-2017-type,
    title = "A Type-Theoretical system for the {F}ra{C}a{S} test suite: Grammatical Framework meets Coq",
    author = "Bernardy, Jean-Philippe  and
      Chatzikyriakidis, Stergios",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6801",
}
@inproceedings{castello-stede-2017-extracting,
    title = "Extracting word lists for domain-specific implicit opinions from corpora",
    author = "Castell{\'o}, N{\'u}ria Bertomeu  and
      Stede, Manfred",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6802",
}
@inproceedings{chersoni-etal-2017-structure,
    title = "Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?",
    author = "Chersoni, Emmanuele  and
      Santus, Enrico  and
      Blache, Philippe  and
      Lenci, Alessandro",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6803",
}
@inproceedings{del-tredici-fernandez-2017-semantic,
    title = "Semantic Variation in Online Communities of Practice",
    author = "Del Tredici, Marco  and
      Fern{\'a}ndez, Raquel",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6804",
}
@inproceedings{diller-etal-2017-defeasible,
    title = "Defeasible {A}ce{R}ules: A Prototype",
    author = "Diller, Martin  and
      Wyner, Adam  and
      Strass, Hannes",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6805",
}
@inproceedings{emerson-copestake-2017-semantic,
    title = "Semantic Composition via Probabilistic Model Theory",
    author = "Emerson, Guy  and
      Copestake, Ann",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6806",
}
@inproceedings{falk-martin-2017-towards-inferential,
    title = "Towards an Inferential Lexicon of Event Selecting Predicates for {F}rench",
    author = "Falk, Ingrid  and
      Martin, Fabienne",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6807",
}
@inproceedings{ghanimifard-dobnik-2017-learning,
    title = "Learning to Compose Spatial Relations with Grounded Neural Language Models",
    author = "Ghanimifard, Mehdi  and
      Dobnik, Simon",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6808",
}
@inproceedings{glavas-etal-2017-sentences,
    title = "If Sentences Could See: Investigating Visual Information for Semantic Textual Similarity",
    author = "Glava{\v{s}}, Goran  and
      Vuli{\'c}, Ivan  and
      Ponzetto, Simone Paolo",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6809",
}
@inproceedings{groschwitz-etal-2017-constrained,
    title = "A constrained graph algebra for semantic parsing with {AMR}s",
    author = "Groschwitz, Jonas  and
      Fowlie, Meaghan  and
      Johnson, Mark  and
      Koller, Alexander",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6810",
}
@inproceedings{hellwig-2017-coarse,
    title = "Coarse Semantic Classification of Rare Nouns Using Cross-Lingual Data and Recurrent Neural Networks",
    author = "Hellwig, Oliver",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6811",
}
@inproceedings{kamel-etal-2017-extracting,
    title = "Extracting hypernym relations from {W}ikipedia disambiguation pages : comparing symbolic and machine learning approaches",
    author = "Kamel, Mouna  and
      Trojahn, Cassia  and
      Ghamnia, Adel  and
      Aussenac-Gilles, Nathalie  and
      Fabre, C{\'e}cile",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6812",
}
@inproceedings{mcgregor-etal-2017-geometric,
    title = "A Geometric Method for Detecting Semantic Coercion",
    author = "McGregor, Stephen  and
      Jezek, Elisabetta  and
      Purver, Matthew  and
      Wiggins, Geraint",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6813",
}
@inproceedings{rohde-etal-2017-exploring,
    title = "Exploring Substitutability through Discourse Adverbials and Multiple Judgments",
    author = "Rohde, Hannah  and
      Dickinson, Anna  and
      Schneider, Nathan  and
      Louis, Annie  and
      Webber, Bonnie",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6814",
}
@inproceedings{rooth-2017-finite,
    title = "Finite state intensional semantics",
    author = "Rooth, Mats",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6815",
}
@inproceedings{vidal-2017-conditional,
    title = "When Conditional Logic met Connexive Logic",
    author = "Vidal, Mathieu",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6816",
}
@inproceedings{zymla-2017-comprehensive,
    title = "Comprehensive annotation of cross-linguistic variation in tense and aspect categories",
    author = "Zymla, Mark-Matthias",
    booktitle = "{IWCS} 2017 - 12th International Conference on Computational Semantics - Long papers",
    year = "2017",
    url = "https://www.aclweb.org/anthology/W17-6817",
}
@proceedings{ws-2017-brazilian,
    title = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    editor = "Paetzold, Gustavo Henrique  and
      Pinheiro, Vl{\'a}dia",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6600",
}
@inproceedings{nau-etal-2017-uma,
    title = "Uma Ferramenta para Identificar Desvios de Linguagem na L{\'\i}ngua Portuguesa (A tool to identify the linguistic deviations in the {P}ortuguese Language)[In {P}ortuguese]",
    author = "Nau, Jonathan  and
      Filho, Aluizio Haendchen  and
      Passero, Guilherme  and
      Cavaco, Vinicius",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6601",
    pages = "12--16",
}
@inproceedings{rodrigues-etal-2017-estudo,
    title = "Estudo explorat{\'o}rio de categorias gramaticais com potencial de indicadores para a An{\'a}lise de Sentimentos (An Exploratory study of grammatical categories as potential indicators for Sentiment Analysis)[In {P}ortuguese]",
    author = "Rodrigues, J{\'u}lia  and
      Pagano, Adriana  and
      Paraiso, Emerson",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6602",
    pages = "17--21",
}
@inproceedings{martins-martins-2017-geracao,
    title = "Gera{\c{c}}{\~a}o de perguntas e respostas para a base de conhecimento de um chatterbot educacional (Application to generate questions and answers for an educational chatterbot)[In {P}ortuguese]",
    author = "Martins, Joyce  and
      Martins, Camila",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6603",
    pages = "22--26",
}
@inproceedings{moraes-etal-2017-study,
    title = "A study on irony within the context of 7x1-{PT} corpus",
    author = "Moraes, Silvia  and
      Machado, Rackel  and
      Redecker, Matheus  and
      Cadaval, Rafael  and
      Meneguzzi, Felipe",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6604",
    pages = "27--31",
}
@inproceedings{anchieta-etal-2017-improving,
    title = "Improving Opinion Summarization by Assessing Sentence Importance in On-line Reviews",
    author = "Anchi{\^e}ta, Rafael  and
      Sousa, Rogerio Figueredo  and
      Moura, Raimundo  and
      Pardo, Thiago",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6605",
    pages = "32--36",
}
@inproceedings{santos-etal-2017-wheel,
    title = "Wheel of Life: an initial investigation. Topic-Related Polarity Visualization in Personal Stories",
    author = "Santos, Henrique  and
      Vieira, Renata  and
      Pinho, Greice  and
      Pinheiro, Jackson",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6606",
    pages = "37--41",
}
@inproceedings{vitorio-etal-2017-investigating,
    title = "Investigating Opinion Mining through Language Varieties: a Case Study of {B}razilian and {E}uropean {P}ortuguese tweets",
    author = "Vit{\'o}rio, Douglas  and
      Souza, Ellen Polliana Ramos  and
      Pereira, Ingryd  and
      Oliveira, Adriano",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6607",
    pages = "43--52",
}
@inproceedings{andrade-etal-2017-rule,
    title = "A Rule-based Semantic Annotator: Adding top-level ontology Tags",
    author = "Andrade, Guidson Coelho  and
      Oliveira, Alcione  and
      Moreira, Alexandra",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6608",
    pages = "53--62",
}
@inproceedings{amaral-etal-2017-processo,
    title = "Processo de constru{\c{c}}{\~a}o de um corpus anotado com Entidades Geol{\'o}gicas visando {REN} (Building an annotated corpus with geological entities for {NER})[In {P}ortuguese]",
    author = "Amaral, Daniela  and
      Collovini, Sandra  and
      Figueira, Anny  and
      Vieira, Renata  and
      Vieira, Renata  and
      Gonzalez, Marco",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6609",
    pages = "63--72",
}
@inproceedings{antunes-etal-2017-formacao,
    title = "Forma{\c{c}}{\~a}o de gent{\'\i}licos a partir de top{\^o}nimos: descri{\c{c}}{\~a}o lingu{\'\i}stica e aprendizado autom{\'a}tico (Formation of Demonyms from Toponyms: Linguistic Description and Machine Learning)[In {P}ortuguese]",
    author = "Antunes, Roger Alfredo Marci Rodrigues  and
      Pardo, Thiago  and
      Almeida, Gladis Maria Barcelos",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6610",
    pages = "73--82",
}
@inproceedings{vaz-etal-2017-visualizacao,
    title = "Visualiza{\c{c}}{\~a}o de gloss{\'a}rio em sistemas de recupera{\c{c}}{\~a}o de informa{\c{c}}{\~a}o (Glossary visualization in information retrieval systems)[In {P}ortuguese]",
    author = "Vaz, Glauber  and
      Oliveira, Leandro Mendon{\c{c}}a  and
      Pierozzi, Jr., Ivo",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6611",
    pages = "83--92",
}
@inproceedings{silva-etal-2017-avaliando,
    title = "Avaliando a similaridade sem{\^a}ntica entre frases curtas atrav{\'e}s de uma abordagem h{\'\i}brida (A hybrid approach to measure Semantic Textual Similarity between short sentences in {B}razilian {P}ortuguese)[In {P}ortuguese]",
    author = "Silva, Allan  and
      Rigo, Sandro  and
      Alves, Isa Mara  and
      Barbosa, Jorge",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6612",
    pages = "93--102",
}
@inproceedings{carvalho-etal-2017-comparative,
    title = "A Comparative Study for Sentiment Analysis on Election {B}razilian News",
    author = "Carvalho, Caio Magno Aguiar  and
      Nagano, Hitoshi  and
      Barros, Allan Kardec",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6613",
    pages = "103--111",
}
@inproceedings{lima-etal-2017-influencia,
    title = "Influ{\^e}ncia de T{\'e}cnicas N{\~a}o-supervisionadas de Redu{\c{c}}{\~a}o de Dimensionalidade para Organiza{\c{c}}{\~a}o Flex{\'\i}vel de Documentos (The Unsupervised Dimensionality Reduction Weight on Flexible Document Organization)[In {P}ortuguese]",
    author = "Lima, Beatriz  and
      Eust{\'a}quio, Fernanda  and
      Nogueira, Tatiane",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6614",
    pages = "112--121",
}
@inproceedings{hartmann-etal-2017-portuguese,
    title = "{P}ortuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks",
    author = "Hartmann, Nathan  and
      Fonseca, Erick  and
      Shulby, Christopher  and
      Treviso, Marcos  and
      Silva, J{\'e}ssica  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6615",
    pages = "122--131",
}
@inproceedings{barbosa-claro-2017-utilizando,
    title = "Utilizando Features Lingu{\'\i}sticas Gen{\'e}ricas para Classifica{\c{c}}{\~a}o de Triplas Relacionais em Portugu{\^e}s (Generic Linguistic Features for Relational Triples Classification in {P}ortuguese)[In {P}ortuguese]",
    author = "Barbosa, George  and
      Claro, Daniela Barreiro",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6616",
    pages = "132--141",
}
@inproceedings{bard-etal-2017-normalizador,
    title = "Normalizador de Texto para Lingua Portuguesa baseado em Modelo de Linguagem (A Normalizer based on Language Model for Texts in {P}ortuguese)[In {P}ortuguese]",
    author = "Bard, Patrick  and
      Luis, Renan Lopes  and
      Moraes, Silvia",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6617",
    pages = "142--150",
}
@inproceedings{treviso-etal-2017-evaluating,
    title = "Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts",
    author = "Treviso, Marcos  and
      Shulby, Christopher  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6618",
    pages = "151--160",
}
@inproceedings{feitosa-pinheiro-2017-analise,
    title = "An{\'a}lise de Medidas de Similaridade Sem{\^a}ntica na Tarefa de Reconhecimento de Implica{\c{c}}{\~a}o Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In {P}ortuguese]",
    author = "Feitosa, David  and
      Pinheiro, Vl{\'a}dia",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6619",
    pages = "161--170",
}
@inproceedings{barreira-etal-2017-framefor,
    title = "{F}rame{FOR} {--} Uma Base de Conhecimento de Frames Sem{\^a}nticos para Per{\'\i}cias de Inform{\'a}tica ({F}rame{FOR} - a Knowledge Base of Semantic Frames for Digital Forensics)[In {P}ortuguese]",
    author = "Barreira, Ravi  and
      Pinheiro, Vl{\'a}dia  and
      Furtado, Vasco",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6620",
    pages = "171--180",
}
@inproceedings{soares-etal-2017-implementacao,
    title = "A Implementa{\c{c}}{\~a}o de uma Minigram{\'a}tica do Portugu{\^e}s Brasileiro sob a Perspectiva da {LFG} (Implementation of a grammar fragment of {B}razilian {P}ortuguese in the {L}exical-{F}unctional {G}rammar formalism)[In {P}ortuguese]",
    author = "Soares, Daniel  and
      Nogueira, Francisco  and
      Alencar, Leonel Figueiredo",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6621",
    pages = "185--192",
}
@inproceedings{peron-correa-torrent-2017-constituicao,
    title = "Constitui{\c{c}}{\~a}o de Um Dicion{\'a}rio Eletr{\^o}nico Tril{\'\i}ngue Fundado em Frames a partir da Extra{\c{c}}{\~a}o Autom{\'a}tica de Candidatos a Termos do Dom{\'\i}nio do Turismo (The Constitution of a Trilingual Eletronic Dictionary Based on Frames from the Automatic Extraction of Candidate Terms of the Tourism Domain)[In {P}ortuguese]",
    author = "Peron-Corr{\^e}a, Simone Rodrigues  and
      Torrent, Tiago Timponi",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6622",
    pages = "193--200",
}
@inproceedings{costa-torrent-2017-modelagem,
    title = "A Modelagem Computacional do Dom{\'\i}nio dos Esportes na {F}rame{N}et Brasil (The Computational Modeling of the Sports Domain in {F}rame{N}et Brasil)[In {P}ortuguese]",
    author = "Costa, Alexandre Diniz  and
      Torrent, Tiago Timponi",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6623",
    pages = "201--208",
}
@inproceedings{marcao-etal-2017-descricao,
    title = "Descri{\c{c}}{\~a}o e modelagem de constru{\c{c}}{\~o}es interrogativas {QU}- em Portugu{\^e}s Brasileiro para o desenvolvimento de um chatbot (Description and modeling of interrogative constructs {QU}- in {B}razilian {P}ortuguese for the development of a chatbot)[In {P}ortuguese]",
    author = "Mar{\c{c}}{\~a}o, Nat{\'a}lia Duarte  and
      Torrent, Tiago Timponi  and
      Matos, Ely Edison da Silva",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6624",
    pages = "209--216",
}
@inproceedings{almeida-torrent-2017-construcoes,
    title = "Constru{\c{c}}{\~o}es de Estrutura Argumental no {\^a}mbito do Constructicon da {F}rame{N}et Brasil: proposta de uma modelagem lingu{\'\i}stico-computacional (Structural Constructs of Arguments in the Context of the Construction of {F}rame{N}et Brasil: a proposal for a computational-linguistic modeling)[In {P}ortuguese]",
    author = "Almeida, V{\^a}nia Gomes  and
      Torrent, Tiago Timponi",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6625",
    pages = "217--223",
}
@inproceedings{ramos-2017-investigacao,
    title = "Investiga{\c{c}}{\~a}o Preliminar Sobre a Pros{\'o}dia Sem{\^a}ntica de Verbos de Elocu{\c{c}}{\~a}o: o Caso do Verbo {``}Confessar{''} (A Preliminar Investigation About the Semantic Prosody of Elocution Verbs: the Case for the Verb {``}Confess{''})[In {P}ortuguese]",
    author = "Ramos, Barbara C.",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6626",
    pages = "224--231",
}
@inproceedings{paiva-torrent-2017-uma,
    title = "Uma Proposta Metodol{\'o}gica para a Categoriza{\c{c}}{\~a}o Automatizada de Atra{\c{c}}{\~o}es Tur{\'\i}sticas a partir de Coment{\'a}rios de Usu{\'a}rios em Plataformas Online (A Methodological Proposition for the Automatic Categorization of Touristic Attractions from User Comments in Online Platforms)[In {P}ortuguese]",
    author = "Paiva, Vanessa Maria Ramos Lopes  and
      Torrent, Tiago Timponi",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6627",
    pages = "232--239",
}
@inproceedings{barros-etal-2017-sofrer,
    title = "Sofrer uma ofensa, Receber uma advert{\^e}ncia: Verbos-suporte Conversos de {`}Fazer{'} no Portugu{\^e}s do Brasil (Suffering an offense, Receiving a citation: Supporting Vectors Converted from {`}To do{'} in {B}razilian {P}ortuguese)[In {P}ortuguese]",
    author = "Barros, Cla{\'u}dia D.  and
      Calcia, Nathalia P.  and
      Vale, Oto A.",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6628",
    pages = "240--246",
}
@inproceedings{reis-baptista-2017-os,
    title = "Os Prov{\'e}rbios em manuais de ensino de Portugu{\^e}s L{\'\i}ngua N{\~a}o Materna (The Proverbs of teaching manuals in Non-Native {P}ortuguese)[In {P}ortuguese]",
    author = "Reis, S{\'o}nia  and
      Baptista, Jorge",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6629",
    pages = "247--255",
}
@inproceedings{bossaglia-etal-2017-prosody,
    title = "Prosody, syntax, and pragmatics: insubordination in spoken {B}razilian {P}ortuguese",
    author = "Bossaglia, Giulia  and
      Mello, Heliana  and
      Raso, Tommaso",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6630",
    pages = "256--265",
}
@inproceedings{rodrigues-etal-2017-bases,
    title = "As bases de dados verbais {ADESSE} e {V}i{PE}r: uma an{\'a}lise constrastiva das constru{\c{c}}{\~o}es locativas em espanhol e em portugu{\^e}s (The verbal databases {ADESSE} and {V}i{PE}r: a contrastive analysis of locative constructs in {S}panish and {P}ortuguese)[In {P}ortuguese]",
    author = "Rodrigues, Roana  and
      Vale, Oto  and
      Alonso Alemany, Laura",
    booktitle = "Proceedings of the 11th {B}razilian Symposium in Information and Human Language Technology",
    month = oct,
    year = "2017",
    address = "Uberl{\^a}ndia, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W17-6631",
    pages = "266--273",
}
@proceedings{ws-2017-international-dependency,
    title = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    editor = "Montemagni, Simonetta  and
      Nivre, Joakim",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6500",
}
@inproceedings{goldberg-2017-capturing,
    title = "Capturing Dependency Syntax with {``}Deep{''} Sequential Models",
    author = "Goldberg, Yoav",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6501",
    pages = "1",
}
@inproceedings{hajicova-2017-syntax,
    title = "Syntax-Semantics Interface: A Plea for a Deep Dependency Sentence Structure",
    author = "Haji{\v{c}}ov{\'a}, Eva",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6502",
    pages = "2--3",
}
@inproceedings{andresen-zinsmeister-2017-benefit,
    title = "The Benefit of Syntactic vs. Linear N-grams for Linguistic Description",
    author = "Andresen, Melanie  and
      Zinsmeister, Heike",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6503",
    pages = "4--14",
}
@inproceedings{boguslavsky-2017-predicate,
    title = "On the Predicate-Argument Structure: Internal and Absorbing Scope",
    author = "Boguslavsky, Igor",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6504",
    pages = "15--24",
}
@inproceedings{brunato-dellorletta-2017-order,
    title = "On the order of Words in {I}talian: a Study on Genre vs Complexity",
    author = "Brunato, Dominique  and
      Dell{'}Orletta, Felice",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6505",
    pages = "25--31",
}
@inproceedings{burga-etal-2017-revising,
    title = "Revising the {METU}-Sabanc{\i} {T}urkish Treebank: An Exercise in Surface-Syntactic Annotation of Agglutinative Languages",
    author = {Burga, Alicia  and
      {\"O}ktem, Alp  and
      Wanner, Leo},
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6506",
    pages = "32--41",
}
@inproceedings{candito-etal-2017-enhanced,
    title = "Enhanced {UD} Dependencies with Neutralized Diathesis Alternation",
    author = "Candito, Marie  and
      Guillaume, Bruno  and
      Perrier, Guy  and
      Seddah, Djam{\'e}",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6507",
    pages = "42--53",
}
@inproceedings{chen-gerdes-2017-classifying,
    title = "Classifying Languages by Dependency Structure. Typologies of Delexicalized {U}niversal {D}ependency Treebanks",
    author = "Chen, Xinying  and
      Gerdes, Kim",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6508",
    pages = "54--63",
}
@inproceedings{gokirmak-tyers-2017-dependency,
    title = "A Dependency Treebank for {K}urmanji {K}urdish",
    author = {G{\"o}k{\i}rmak, Memduh  and
      Tyers, Francis M.},
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6509",
    pages = "64--72",
}
@inproceedings{kahane-etal-2017-limitations,
    title = "What are the limitations on the flux of syntactic dependencies? Evidence from {UD} treebanks",
    author = "Kahane, Sylvain  and
      Yan, Chunxiao  and
      Botalla, Marie-Am{\'e}lie",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6510",
    pages = "73--82",
}
@inproceedings{kanerva-etal-2017-fully,
    title = "Fully Delexicalized Contexts for Syntax-Based Word Embeddings",
    author = "Kanerva, Jenna  and
      Pyysalo, Sampo  and
      Ginter, Filip",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6511",
    pages = "83--91",
}
@inproceedings{kozhukhar-2017-universal,
    title = "{U}niversal {D}ependencies for {D}argwa Mehweb",
    author = "Kozhukhar, Alexandra",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6512",
    pages = "92--99",
}
@inproceedings{macutek-etal-2017-menzerath,
    title = "Menzerath-Altmann Law in Syntactic Dependency Structure",
    author = "Ma{\v{c}}utek, J{\'a}n  and
      {\v{C}}ech, Radek  and
      Mili{\v{c}}ka, Ji{\v{r}}{\'\i}",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6513",
    pages = "100--107",
}
@inproceedings{de-marneffe-etal-2017-assessing,
    title = "Assessing the Annotation Consistency of the {U}niversal {D}ependencies Corpora",
    author = "de Marneffe, Marie-Catherine  and
      Grioni, Matias  and
      Kanerva, Jenna  and
      Ginter, Filip",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6514",
    pages = "108--115",
}
@inproceedings{mazziotta-kahane-2017-extent,
    title = "To What Extent is Immediate Constituency Analysis Dependency-Based? A Survey of Foundational Texts",
    author = "Mazziotta, Nicolas  and
      Kahane, Sylvain",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6515",
    pages = "116--126",
}
@inproceedings{melcuk-2017-dependency,
    title = "Dependency Structure of Binary Conjunctions(of the {IF}{\ldots}, {THEN}{\ldots} Type)",
    author = "Mel{'}{\v{c}}uk, Igor",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6516",
    pages = "127--134",
}
@inproceedings{miletic-urieli-2017-non,
    title = "Non-Projectivity in {S}erbian: Analysis of Formal and Linguistic Properties",
    author = "Miletic, Aleksandra  and
      Urieli, Assaf",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6517",
    pages = "135--144",
}
@inproceedings{milicevic-2017-prices,
    title = "Prices go Up, Surge, Jump, Spike, Skyrocket, Go through the Roof{\ldots} Intensifier Collocations with Parametric Nouns of Type {PRICE}",
    author = "Mili{\'c}evi{\'c}, Jasmina",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6518",
    pages = "145--153",
}
@inproceedings{niu-2017-chinese,
    title = "{C}hinese Descriptive and Resultative {V}-de Constructions. A Dependency-based Analysis",
    author = "Niu, Ruochen",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6519",
    pages = "154--164",
}
@inproceedings{osborne-niu-2017-component,
    title = "The Component Unit. Introducing a Novel Unit of Syntactic Analysis",
    author = "Osborne, Timothy  and
      Niu, Ruochen",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6520",
    pages = "165--175",
}
@inproceedings{osborne-reeve-2017-control,
    title = "Control vs. Raising in {E}nglish. A Dependency Grammar Account",
    author = "Osborne, Timothy  and
      Reeve, Matthew",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6521",
    pages = "176--186",
}
@inproceedings{park-2017-segmentation,
    title = "Segmentation Granularity in Dependency Representations for {K}orean",
    author = "Park, Jungyeul",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6522",
    pages = "187--196",
}
@inproceedings{rademaker-etal-2017-universal,
    title = "{U}niversal {D}ependencies for {P}ortuguese",
    author = "Rademaker, Alexandre  and
      Chalub, Fabricio  and
      Real, Livy  and
      Freitas, Cl{\'a}udia  and
      Bick, Eckhard  and
      de Paiva, Valeria",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6523",
    pages = "197--206",
}
@inproceedings{rambelli-etal-2017-udlex,
    title = "{UDL}ex: Towards Cross-language Subcategorization Lexicons",
    author = "Rambelli, Giulia  and
      Lenci, Alessandro  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6524",
    pages = "207--2017",
}
@inproceedings{rehbein-etal-2017-universal,
    title = "{U}niversal {D}ependencies are Hard to Parse {--} or are They?",
    author = "Rehbein, Ines  and
      Steen, Julius  and
      Do, Bich-Ngoc  and
      Frank, Anette",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6525",
    pages = "218--228",
}
@inproceedings{sanguinetti-etal-2017-annotating,
    title = "Annotating {I}talian Social Media Texts in {U}niversal {D}ependencies",
    author = "Sanguinetti, Manuela  and
      Bosco, Cristina  and
      Mazzei, Alessandro  and
      Lavelli, Alberto  and
      Tamburini, Fabio",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6526",
    pages = "229--239",
}
@inproceedings{simko-vincze-2017-hungarian,
    title = "{H}ungarian Copula Constructions in Dependency Syntax and Parsing",
    author = "Simk{\'o}, Katalin Ilona  and
      Vincze, Veronika",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6527",
    pages = "240--247",
}
@inproceedings{tamburini-2017-semgrex,
    title = "Semgrex-Plus: a Tool for Automatic Dependency-Graph Rewriting",
    author = "Tamburini, Fabio",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6528",
    pages = "248--254",
}
@inproceedings{tandon-sharma-2017-unity,
    title = "Unity in Diversity: A Unified Parsing Strategy for Major {I}ndian Languages",
    author = "Tandon, Juhi  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6529",
    pages = "255--265",
}
@inproceedings{wong-etal-2017-quantitative,
    title = "Quantitative Comparative Syntax on the {C}antonese-{M}andarin Parallel Dependency Treebank",
    author = "Wong, Tak-sum  and
      Gerdes, Kim  and
      Leung, Herman  and
      Lee, John",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6530",
    pages = "266--275",
}
@inproceedings{yadav-etal-2017-understanding,
    title = "Understanding Constraints on Non-Projectivity Using Novel Measures",
    author = "Yadav, Himanshu  and
      Vaidya, Ashwini  and
      Husain, Samar",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6531",
    pages = "276--286",
}
@inproceedings{zeman-2017-core,
    title = "Core Arguments in {U}niversal {D}ependencies",
    author = "Zeman, Daniel",
    booktitle = "Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)",
    month = sep,
    year = "2017",
    address = "Pisa,Italy",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-6532",
    pages = "287--296",
}
@proceedings{ws-2017-international-parsing,
    title = "Proceedings of the 15th International Conference on Parsing Technologies",
    editor = "Miyao, Yusuke  and
      Sagae, Kenji",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6300",
}
@inproceedings{kawahara-etal-2017-automatically,
    title = "Automatically Acquired Lexical Knowledge Improves {J}apanese Joint Morphological and Dependency Analysis",
    author = "Kawahara, Daisuke  and
      Hayashibe, Yuta  and
      Morita, Hajime  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6301",
    pages = "1--10",
    abstract = "This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.",
}
@inproceedings{yu-bohnet-2017-dependency,
    title = "Dependency Language Models for Transition-based Dependency Parsing",
    author = "Yu, Juntao  and
      Bohnet, Bernd",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6302",
    pages = "11--17",
    abstract = "In this paper, we present an approach to improve the accuracy of a strong transition-based dependency parser by exploiting dependency language models that are extracted from a large parsed corpus. We integrated a small number of features based on the dependency language models into the parser. To demonstrate the effectiveness of the proposed approach, we evaluate our parser on standard English and Chinese data where the base parser could achieve competitive accuracy scores. Our enhanced parser achieved state-of-the-art accuracy on Chinese data and competitive results on English data. We gained a large absolute improvement of one point (UAS) on Chinese and 0.5 points for English.",
}
@inproceedings{falenska-cetinoglu-2017-lexicalized,
    title = "Lexicalized vs. Delexicalized Parsing in Low-Resource Scenarios",
    author = {Falenska, Agnieszka  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem},
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6303",
    pages = "18--24",
    abstract = "We present a systematic analysis of lexicalized vs. delexicalized parsing in low-resource scenarios, and propose a methodology to choose one method over another under certain conditions. We create a set of simulation experiments on 41 languages and apply our findings to 9 low-resource languages. Experimental results show that our methodology chooses the best approach in 8 out of 9 cases.",
}
@inproceedings{sagot-martinez-alonso-2017-improving,
    title = "Improving neural tagging with lexical information",
    author = "Sagot, Beno{\^\i}t  and
      Mart{\'\i}nez Alonso, H{\'e}ctor",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6304",
    pages = "25--31",
    abstract = "Neural part-of-speech tagging has achieved competitive results with the incorporation of character-based and pre-trained word embeddings. In this paper, we show that a state-of-the-art bi-LSTM tagger can benefit from using information from morphosyntactic lexicons as additional input. The tagger, trained on several dozen languages, shows a consistent, average improvement when using lexical information, even when also using character-based embeddings, thus showing the complementarity of the different sources of lexical information. The improvements are particularly important for the smaller datasets.",
}
@inproceedings{madhyastha-etal-2017-prepositional,
    title = "Prepositional Phrase Attachment over Word Embedding Products",
    author = "Madhyastha, Pranava Swaroop  and
      Carreras, Xavier  and
      Quattoni, Ariadna",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6305",
    pages = "32--43",
    abstract = "We present a low-rank multi-linear model for the task of solving prepositional phrase attachment ambiguity (PP task). Our model exploits tensor products of word embeddings, capturing all possible conjunctions of latent embeddings. Our results on a wide range of datasets and task settings show that tensor products are the best compositional operation and that a relatively simple multi-linear model that uses only word embeddings of lexical features can outperform more complex non-linear architectures that exploit the same information. Our proposed model gives the current best reported performance on an out-of-domain evaluation and performs competively on out-of-domain dependency parsing datasets.",
}
@inproceedings{lee-etal-2017-l1,
    title = "{L}1-{L}2 Parallel Dependency Treebank as Learner Corpus",
    author = "Lee, John  and
      Li, Keying  and
      Leung, Herman",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6306",
    pages = "44--49",
    abstract = "This opinion paper proposes the use of parallel treebank as learner corpus. We show how an L1-L2 parallel treebank {---} i.e., parse trees of non-native sentences, aligned to the parse trees of their target hypotheses {---} can facilitate retrieval of sentences with specific learner errors. We argue for its benefits, in terms of corpus re-use and interoperability, over a conventional learner corpus annotated with error tags. As a proof of concept, we conduct a case study on word-order errors made by learners of Chinese as a foreign language. We report precision and recall in retrieving a range of word-order error categories from L1-L2 tree pairs annotated in the Universal Dependency framework.",
}
@inproceedings{lee-don-2017-splitting,
    title = "Splitting Complex {E}nglish Sentences",
    author = "Lee, John  and
      Don, J. Buddhika K. Pathirage",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6307",
    pages = "50--55",
    abstract = "This paper applies parsing technology to the task of syntactic simplification of English sentences, focusing on the identification of text spans that can be removed from a complex sentence. We report the most comprehensive evaluation to-date on this task, using a dataset of sentences that exhibit simplification based on coordination, subordination, punctuation/parataxis, adjectival clauses, participial phrases, and appositive phrases. We train a decision tree with features derived from text span length, POS tags and dependency relations, and show that it significantly outperforms a parser-only baseline.",
}
@inproceedings{tanaka-etal-2017-hierarchical,
    title = "Hierarchical Word Structure-based Parsing: A Feasibility Study on {UD}-style Dependency Parsing in {J}apanese",
    author = "Tanaka, Takaaki  and
      Hayashi, Katsuhiko  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6308",
    pages = "56--60",
    abstract = "In applying word-based dependency parsing such as Universal Dependencies (UD) to Japanese, the uncertainty of word segmentation emerges for defining a word unit of the dependencies. We introduce the following hierarchical word structures to dependency parsing in Japanese: morphological units (a short unit word, SUW) and syntactic units (a long unit word, LUW). An SUW can be used to segment a sentence consistently, while it is too short to represent syntactic construction. An LUW is a unit including functional multiwords and LUW-based analysis facilitates the capturing of syntactic structure and makes parsing results more precise than SUW-based analysis. This paper describes the results of a feasibility study on the ability and the effectiveness of parsing methods based on hierarchical word structure (LUW chunking+parsing) in comparison to single layer word structure (SUW parsing). We also show joint analysis of LUW-chunking and dependency parsing improves the performance of identifying predicate-argument structures, while there is not much difference between overall results of them. not much difference between overall results of them.",
}
@inproceedings{bhat-etal-2017-leveraging,
    title = "Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling",
    author = "Bhat, Riyaz A.  and
      Bhat, Irshad  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6309",
    pages = "61--66",
    abstract = "We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-art non-linear transition-based parsing system on a new dataset containing 506 dependency trees for sentences from Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual speakers. We show that a dependency parser trained on a newswire treebank is strongly biased towards the canonical structures and degrades when applied to conversational data. Inspired by Transformational Generative Grammar (Chomsky, 1965), we mitigate the sampling bias by generating all theoretically possible alternative word orders of a clause from the existing (kernel) structures in the treebank. Training our parser on canonical and transformed structures improves performance on conversational data by around 9{\%} LAS over the baseline newswire parser.",
}
@inproceedings{sogaard-2017-using,
    title = "Using hyperlinks to improve multilingual partial parsers",
    author = "S{\o}gaard, Anders",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6310",
    pages = "67--71",
    abstract = "Syntactic annotation is costly and not available for the vast majority of the world{'}s languages. We show that sometimes we can do away with less labeled data by exploiting more readily available forms of mark-up. Specifically, we revisit an idea from Valentin Spitkovsky{'}s work (2010), namely that hyperlinks typically bracket syntactic constituents or chunks. We strengthen his results by showing that not only can hyperlinks help in low resource scenarios, exemplified here by Quechua, but learning from hyperlinks can also improve state-of-the-art NLP models for English newswire. We also present out-of-domain evaluation on English Ontonotes 4.0.",
}
@inproceedings{delecraz-etal-2017-correcting,
    title = "Correcting prepositional phrase attachments using multimodal corpora",
    author = "Delecraz, Sebastien  and
      Nasr, Alexis  and
      Bechet, Frederic  and
      Favre, Benoit",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6311",
    pages = "72--77",
    abstract = "PP-attachments are an important source of errors in parsing natural language. We propose in this article to use data coming from a multimodal corpus, combining textual, visual and conceptual information, as well as a correction strategy, to propose alternative attachments in the output of a parser.",
}
@inproceedings{kurtz-kuhlmann-2017-exploiting,
    title = "Exploiting Structure in Parsing to 1-Endpoint-Crossing Graphs",
    author = "Kurtz, Robin  and
      Kuhlmann, Marco",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6312",
    pages = "78--87",
    abstract = "Deep dependency parsing can be cast as the search for maximum acyclic subgraphs in weighted digraphs. Because this search problem is intractable in the general case, we consider its restriction to the class of 1-endpoint-crossing (1ec) graphs, which has high coverage on standard data sets. Our main contribution is a characterization of 1ec graphs as a subclass of the graphs with pagenumber at most 3. Building on this we show how to extend an existing parsing algorithm for 1-endpoint-crossing trees to the full class. While the runtime complexity of the extended algorithm is polynomial in the length of the input sentence, it features a large constant, which poses a challenge for practical implementations.",
}
@inproceedings{kohita-etal-2017-effective,
    title = "Effective Online Reordering with Arc-Eager Transitions",
    author = "Kohita, Ryosuke  and
      Noji, Hiroshi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6313",
    pages = "88--98",
    abstract = "We present a new transition system with word reordering for unrestricted non-projective dependency parsing. Our system is based on decomposed arc-eager rather than arc-standard, which allows more flexible ambiguity resolution between a local projective and non-local crossing attachment. In our experiment on Universal Dependencies 2.0, we find our parser outperforms the ordinary swap-based parser particularly on languages with a large amount of non-projectivity.",
}
@inproceedings{de-lhoneux-etal-2017-arc,
    title = "Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle",
    author = "de Lhoneux, Miryam  and
      Stymne, Sara  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6314",
    pages = "99--104",
    abstract = "In this paper, we extend the arc-hybrid system for transition-based parsing with a swap transition that enables reordering of the words and construction of non-projective trees. Although this extension breaks the arc-decomposability of the transition system, we show how the existing dynamic oracle for this system can be modified and combined with a static oracle only for the swap transition. Experiments on 5 languages show that the new system gives competitive accuracy and is significantly better than a system trained with a purely static oracle.",
}
@inproceedings{liu-zhang-2017-encoder,
    title = "Encoder-Decoder Shift-Reduce Syntactic Parsing",
    author = "Liu, Jiangming  and
      Zhang, Yue",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6315",
    pages = "105--114",
    abstract = "Encoder-decoder neural networks have been used for many NLP tasks, such as neural machine translation. They have also been applied to constituent parsing by using bracketed tree structures as a target language, translating input sentences into syntactic trees. A more commonly used method to linearize syntactic trees is the shift-reduce system, which uses a sequence of transition-actions to build trees. We empirically investigate the effectiveness of applying the encoder-decoder network to transition-based parsing. On standard benchmarks, our system gives comparable results to the stack LSTM parser for dependency parsing, and significantly better results compared to the aforementioned parser for constituent parsing, which uses bracketed tree formats.",
}
@inproceedings{ballesteros-carreras-2017-arc,
    title = "Arc-Standard Spinal Parsing with Stack-{LSTM}s",
    author = "Ballesteros, Miguel  and
      Carreras, Xavier",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6316",
    pages = "115--121",
    abstract = "We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees. The parser uses Stack-LSTMs that compose constituent nodes with dependency-based derivations. In experiments, we show that this model adapts to different styles of dependency relations, but this choice has little effect for predicting constituent structure, suggesting that LSTMs induce useful states by themselves.",
}
@inproceedings{teichmann-etal-2017-coarse,
    title = "Coarse-To-Fine Parsing for Expressive Grammar Formalisms",
    author = "Teichmann, Christoph  and
      Koller, Alexander  and
      Groschwitz, Jonas",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6317",
    pages = "122--127",
    abstract = "We generalize coarse-to-fine parsing to grammar formalisms that are more expressive than PCFGs and/or describe languages of trees or graphs. We evaluate our algorithm on PCFG, PTAG, and graph parsing. While we achieve the expected performance gains on PCFGs, coarse-to-fine does not help for PTAG and can even slow down parsing for graphs. We discuss the implications of this finding.",
}
@inproceedings{do-rehbein-2017-evaluating,
    title = "Evaluating {LSTM} models for grammatical function labelling",
    author = "Do, Bich-Ngoc  and
      Rehbein, Ines",
    booktitle = "Proceedings of the 15th International Conference on Parsing Technologies",
    month = sep,
    year = "2017",
    address = "Pisa, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6318",
    pages = "128--133",
    abstract = "To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history. We present different ways to encode the history, using different LSTM architectures, and show that our models yield significant improvements, resulting in a LAS for German that is close to the best result from the SPMRL 2014 shared task (without the reranker).",
}
@proceedings{ws-2017-international-tree,
    title = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    editor = "Kuhlmann, Marco  and
      Scheffler, Tatjana",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6200",
}
@inproceedings{koller-2017-feature,
    title = "A Feature Structure Algebra for {FTAG}",
    author = "Koller, Alexander",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6201",
    pages = "1--10",
}
@inproceedings{fowlie-koller-2017-parsing,
    title = "Parsing Minimalist Languages with Interpreted Regular Tree Grammars",
    author = "Fowlie, Meaghan  and
      Koller, Alexander",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6202",
    pages = "11--20",
}
@inproceedings{burkhardt-etal-2017-depictives,
    title = "Depictives in {E}nglish: An {LTAG} Approach",
    author = "Burkhardt, Benjamin  and
      Lichte, Timm  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6203",
    pages = "21--30",
}
@inproceedings{aggazzotti-shieber-2017-reflexives,
    title = "Reflexives and Reciprocals in Synchronous {T}ree {A}djoining {G}rammar",
    author = "Aggazzotti, Cristina  and
      Shieber, Stuart M.",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6204",
    pages = "31--42",
}
@inproceedings{han-sarkar-2017-coordination,
    title = "Coordination in {TAG} without the Conjoin Operation",
    author = "Han, Chung-hye  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6205",
    pages = "43--52",
}
@inproceedings{storoshenko-2017-scope,
    title = "Scope, Time, and Predicate Restriction in {B}lackfoot using {MC}-{STAG}",
    author = "Storoshenko, Dennis Ryan",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6206",
    pages = "53--60",
}
@inproceedings{kallmeyer-osswald-2017-combining,
    title = "Combining Predicate-Argument Structure and Operator Projection: Clause Structure in Role and Reference Grammar",
    author = "Kallmeyer, Laura  and
      Osswald, Rainer",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6207",
    pages = "61--70",
}
@inproceedings{white-etal-2017-parsing,
    title = "Parsing with Dynamic Continuized {CCG}",
    author = "White, Michael  and
      Charlow, Simon  and
      Needle, Jordan  and
      Bumford, Dylan",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6208",
    pages = "71--83",
}
@inproceedings{waszczuk-etal-2017-multiword,
    title = "Multiword Expression-Aware {A}* {TAG} Parsing Revisited",
    author = "Waszczuk, Jakub  and
      Savary, Agata  and
      Parmentier, Yannick",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6209",
    pages = "84--93",
}
@inproceedings{berglund-etal-2017-single,
    title = "Single-Rooted {DAG}s in Regular {DAG} Languages: {P}arikh Image and Path Languages",
    author = {Berglund, Martin  and
      Bj{\"o}rklund, Henrik  and
      Drewes, Frank},
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6210",
    pages = "94--101",
}
@inproceedings{drewes-jonsson-2017-contextual,
    title = "Contextual Hyperedge Replacement Grammars for {A}bstract {M}eaning {R}epresentations",
    author = "Drewes, Frank  and
      Jonsson, Anna",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6211",
    pages = "102--111",
}
@inproceedings{corro-le-roux-2017-transforming,
    title = "Transforming Dependency Structures to {LTAG} Derivation Trees",
    author = "Corro, Caio  and
      Le Roux, Joseph",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6212",
    pages = "112--121",
}
@inproceedings{friedman-etal-2017-linguistically,
    title = "Linguistically Rich Vector Representations of Supertags for {TAG} Parsing",
    author = "Friedman, Dan  and
      Kasai, Jungo  and
      McCoy, R. Thomas  and
      Frank, Robert  and
      Davis, Forrest  and
      Rambow, Owen",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6213",
    pages = "122--131",
}
@inproceedings{xu-etal-2017-tag,
    title = "{TAG} Parser Evaluation using Textual Entailments",
    author = "Xu, Pauli  and
      Frank, Robert  and
      Kasai, Jungo  and
      Rambow, Owen",
    booktitle = "Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6214",
    pages = "132--141",
}
@proceedings{ws-2017-sighan,
    title = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
    editor = "Zhang, Yue  and
      Sui, Zhifang",
    month = dec,
    year = "2017",
    address = "Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6000",
}
@inproceedings{wang-etal-2017-group,
    title = "Group Linguistic Bias Aware Neural Response Generation",
    author = "Wang, Jianan  and
      Wang, Xin  and
      Li, Fang  and
      Xu, Zhen  and
      Wang, Zhuoran  and
      Wang, Baoxun",
    booktitle = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
    month = dec,
    year = "2017",
    address = "Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6001",
    pages = "1--10",
    abstract = "For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder-decoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.",
}
@inproceedings{bao-etal-2017-neural,
    title = "Neural Regularized Domain Adaptation for {C}hinese Word Segmentation",
    author = "Bao, Zuyi  and
      Li, Si  and
      Xu, Weiran  and
      Gao, Sheng",
    booktitle = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
    month = dec,
    year = "2017",
    address = "Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6002",
    pages = "11--20",
    abstract = "For Chinese word segmentation, the large-scale annotated corpora mainly focus on newswire and only a handful of annotated data is available in other domains such as patents and literature. Considering the limited amount of annotated target domain data, it is a challenge for segmenters to learn domain-specific information while avoid getting over-fitted at the same time. In this paper, we propose a neural regularized domain adaptation method for Chinese word segmentation. The teacher networks trained in source domain are employed to regularize the training process of the student network by preserving the general knowledge. In the experiments, our neural regularized domain adaptation method achieves a better performance comparing to previous methods.",
}
@inproceedings{benajiba-etal-2017-sentimental,
    title = "The Sentimental Value of {C}hinese Sub-Character Components",
    author = "Benajiba, Yassine  and
      Biran, Or  and
      Weng, Zhiliang  and
      Zhang, Yong  and
      Sun, Jin",
    booktitle = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
    month = dec,
    year = "2017",
    address = "Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6003",
    pages = "21--29",
    abstract = "Sub-character components of Chinese characters carry important semantic information, and recent studies have shown that utilizing this information can improve performance on core semantic tasks. In this paper, we hypothesize that in addition to semantic information, sub-character components may also carry emotional information, and that utilizing it should improve performance on sentiment analysis tasks. We conduct a series of experiments on four Chinese sentiment data sets and show that we can significantly improve the performance in various tasks over that of a character-level embeddings baseline. We then focus on qualitatively assessing multiple examples and trying to explain how the sub-character components affect the results in each case.",
}
@inproceedings{li-etal-2017-chinese,
    title = "{C}hinese Answer Extraction Based on {POS} Tree and Genetic Algorithm",
    author = "Li, Shuihua  and
      Zhang, Xiaoming  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
    month = dec,
    year = "2017",
    address = "Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6004",
    pages = "30--36",
    abstract = "Answer extraction is the most important part of a chinese web-based question answering system. In order to enhance the robustness and adaptability of answer extraction to new domains and eliminate the influence of the incomplete and noisy search snippets, we propose two new answer exraction methods. We utilize text patterns to generate Part-of-Speech (POS) patterns. In addition, a method is proposed to construct a POS tree by using these POS patterns. The POS tree is useful to candidate answer extraction of web-based question answering. To retrieve a efficient POS tree, the similarities between questions are used to select the question-answer pairs whose questions are similar to the unanswered question. Then, the POS tree is improved based on these question-answer pairs. In order to rank these candidate answers, the weights of the leaf nodes of the POS tree are calculated using a heuristic method. Moreover, the Genetic Algorithm (GA) is used to train the weights. The experimental results of 10-fold crossvalidation show that the weighted POS tree trained by GA can improve the accuracy of answer extraction.",
}
@inproceedings{huang-etal-2017-learning-parenthetical,
    title = "Learning from Parenthetical Sentences for Term Translation in Machine Translation",
    author = "Huang, Guoping  and
      Zhang, Jiajun  and
      Zhou, Yu  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 9th {SIGHAN} Workshop on {C}hinese Language Processing",
    month = dec,
    year = "2017",
    address = "Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-6005",
    pages = "37--45",
    abstract = "Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific machine translation (MT) tasks. However, it{'}s a challenging task to translate them correctly for the huge number of pre-existing terms and the endless new terms. To achieve better term translation quality, it is necessary to inject external term knowledge into the underlying MT system. Fortunately, there are plenty of term translation knowledge in parenthetical sentences on the Internet. In this paper, we propose a simple, straightforward and effective framework to improve term translation by learning from parenthetical sentences. This framework includes: (1) a focused web crawler; (2) a parenthetical sentence filter, acquiring parenthetical sentences including bilingual term pairs; (3) a term translation knowledge extractor, extracting bilingual term translation candidates; (4) a probability learner, generating the term translation table for MT decoders. The extensive experiments demonstrate that our proposed framework significantly improves the translation quality of terms and sentences.",
}
@proceedings{ws-2017-natural,
    title = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    editor = "Tseng, Yuen-Hsien  and
      Chen, Hsin-Hsi  and
      Lee, Lung-Hao  and
      Yu, Liang-Chih",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5900",
}
@inproceedings{winder-etal-2017-ntucle,
    title = "{NTUCLE}: Developing a Corpus of Learner {E}nglish to Provide Writing Support for Engineering Students",
    author = "Winder, Roger Vivek Placidus  and
      MacKinnon, Joseph  and
      Li, Shu Yun  and
      Lin, Benedict Christopher Tzer Liang  and
      Heah, Carmel Lee Hah  and
      Morgado da Costa, Lu{\'\i}s  and
      Kuribayashi, Takayuki  and
      Bond, Francis",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5901",
    pages = "1--11",
    abstract = "This paper describes the creation of a new annotated learner corpus. The aim is to use this corpus to develop an automated system for corrective feedback on students{'} writing. With this system, students will be able to receive timely feedback on language errors before they submit their assignments for grading. A corpus of assignments submitted by first year engineering students was compiled, and a new error tag set for the NTU Corpus of Learner English (NTUCLE) was developed based on that of the NUS Corpus of Learner English (NUCLE), as well as marking rubrics used at NTU. After a description of the corpus, error tag set and annotation process, the paper presents the results of the annotation exercise as well as follow up actions. The final error tag set, which is significantly larger than that for the NUCLE error categories, is then presented before a brief conclusion summarising our experience and future plans.",
}
@inproceedings{hana-hladka-2017-understanding,
    title = "Understanding Non-Native Writings: Can a Parser Help?",
    author = "Hana, Jirka  and
      Hladk{\'a}, Barbora",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5902",
    pages = "12--16",
    abstract = "We present a pilot study on parsing non-native texts written by learners of Czech. We performed experiments that have shown that at least high-level syntactic functions, like subject, predicate, and object, can be assigned based on a parser trained on standard native language.",
}
@inproceedings{jiang-lee-2017-carrier,
    title = "{C}arrier Sentence Selection for Fill-in-the-blank Items",
    author = "Jiang, Shu  and
      Lee, John",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5903",
    pages = "17--22",
    abstract = "Fill-in-the-blank items are a common form of exercise in computer-assisted language learning systems. To automatically generate an effective item, the system must be able to select a high-quality carrier sentence that illustrates the usage of the target word. Previous approaches for carrier sentence selection have considered sentence length, vocabulary difficulty, the position of the target word and the presence of finite verbs. This paper investigates the utility of word co-occurrence statistics and lexical similarity as selection criteria. In an evaluation on generating fill-in-the-blank items for learning Chinese as a foreign language, we show that these two criteria can improve carrier sentence quality.",
}
@inproceedings{redkar-etal-2017-hindi,
    title = "{H}indi Shabdamitra: A {W}ordnet based {E}-Learning Tool for Language Learning and Teaching",
    author = "Redkar, Hanumant  and
      Singh, Sandhya  and
      Somasundaram, Meenakshi  and
      Gorasia, Dhara  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5904",
    pages = "23--28",
    abstract = "In today{'}s technology driven digital era, education domain is undergoing a transformation from traditional approaches to more learner controlled and flexible methods of learning. This transformation has opened the new avenues for interdisciplinary research in the field of educational technology and natural language processing in developing quality digital aids for learning and teaching. The tool presented here - Hindi Shabhadamitra, developed using Hindi Wordnet for Hindi language learning, is one such e-learning tool. It has been developed as a teaching and learning aid suitable for formal school based curriculum and informal setup for self learning users. Besides vocabulary, it also provides word based grammar along with images and pronunciation for better learning and retention. This aid demonstrates that how a rich lexical resource like wordnet can be systematically remodeled for practical usage in the educational domain.",
}
@inproceedings{fung-etal-2017-nlptea,
    title = "{NLPTEA} 2017 Shared Task {--} {C}hinese Spelling Check",
    author = "Fung, Gabriel  and
      Debosschere, Maxime  and
      Wang, Dingmin  and
      Li, Bo  and
      Zhu, Jia  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5905",
    pages = "29--34",
    abstract = "This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017. The goal of this task is to develop a computer-assisted system to automatically diagnose typing errors in traditional Chinese sentences written by students. We defined six types of errors which belong to two categories. Given a sentence, the system should detect where the errors are, and for each detected error determine its type and provide correction suggestions. We designed, constructed, and released a benchmark dataset for this task.",
}
@inproceedings{yeh-etal-2017-chinese,
    title = "{C}hinese Spelling Check based on N-gram and String Matching Algorithm",
    author = "Yeh, Jui-Feng  and
      Chang, Li-Ting  and
      Liu, Chan-Yi  and
      Hsu, Tsung-Wei",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5906",
    pages = "35--38",
    abstract = "This paper presents a Chinese spelling check approach based on language models combined with string match algorithm to treat the problems resulted from the influence caused by Cantonese mother tone. N-grams first used to detecting the probability of sentence constructed by the writers, a string matching algorithm called Knuth-Morris-Pratt (KMP) Algorithm is used to detect and correct the error. According to the experimental results, the proposed approach can detect the error and provide the corresponding correction.",
}
@inproceedings{zhao-etal-2017-n,
    title = "N-gram Model for {C}hinese Grammatical Error Diagnosis",
    author = "Zhao, Jianbo  and
      Liu, Hao  and
      Bao, Zuyi  and
      Bai, Xiaopeng  and
      Li, Si  and
      Lin, Zhiqing",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5907",
    pages = "39--44",
    abstract = "Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis.This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task. The experiment results show that the proposed method is good at correction of Chinese grammatical errors.",
}
@inproceedings{horbach-etal-2017-influence,
    title = "The Influence of Spelling Errors on Content Scoring Performance",
    author = "Horbach, Andrea  and
      Ding, Yuning  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5908",
    pages = "45--53",
    abstract = "Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown. We therefore investigate the influence of spelling errors on content scoring performance using the example of the ASAP corpus. We conduct an annotation study on the nature of spelling errors in the ASAP dataset and utilize these finding in machine learning experiments that measure the influence of spelling errors on automatic content scoring. Our main finding is that scoring methods using both token and character n-gram features are robust against spelling errors up to the error frequency in ASAP.",
}
@inproceedings{mizumoto-nagata-2017-analyzing,
    title = "Analyzing the Impact of Spelling Errors on {POS}-Tagging and Chunking in Learner {E}nglish",
    author = "Mizumoto, Tomoya  and
      Nagata, Ryo",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5909",
    pages = "54--58",
    abstract = "Part-of-speech (POS) tagging and chunking have been used in tasks targeting learner English; however, to the best our knowledge, few studies have evaluated their performance and no studies have revealed the causes of POS-tagging/chunking errors in detail. Therefore, we investigate performance and analyze the causes of failure. We focus on spelling errors that occur frequently in learner English. We demonstrate that spelling errors reduced POS-tagging performance by 0.23{\%} owing to spelling errors, and that a spell checker is not necessary for POS-tagging/chunking of learner English.",
}
@inproceedings{zampieri-etal-2017-complex,
    title = "Complex Word Identification: Challenges in Data Annotation and System Performance",
    author = "Zampieri, Marcos  and
      Malmasi, Shervin  and
      Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5910",
    pages = "59--63",
    abstract = "This paper revisits the problem of complex word identification (CWI) following up the SemEval CWI shared task. We use ensemble classifiers to investigate how well computational methods can discriminate between complex and non-complex words. Furthermore, we analyze the classification performance to understand what makes lexical complexity challenging. Our findings show that most systems performed poorly on the SemEval CWI dataset, and one of the reasons for that is the way in which human annotation was performed.",
}
@inproceedings{shioda-etal-2017-suggesting,
    title = "Suggesting Sentences for {ESL} using Kernel Embeddings",
    author = "Shioda, Kent  and
      Komachi, Mamoru  and
      Ikeya, Rue  and
      Mochihashi, Daichi",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5911",
    pages = "64--68",
    abstract = "Sentence retrieval is an important NLP application for English as a Second Language (ESL) learners. ESL learners are familiar with web search engines, but generic web search results may not be adequate for composing documents in a specific domain. However, if we build our own search system specialized to a domain, it may be subject to the data sparseness problem. Recently proposed word2vec partially addresses the data sparseness problem, but fails to extract sentences relevant to queries owing to the modeling of the latent intent of the query. Thus, we propose a method of retrieving example sentences using kernel embeddings and N-gram windows. This method implicitly models latent intent of query and sentences, and alleviates the problem of noisy alignment. Our results show that our method achieved higher precision in sentence retrieval for ESL in the domain of a university press release corpus, as compared to a previous unsupervised method used for a semantic textual similarity task.",
}
@inproceedings{bedi-etal-2017-event,
    title = "Event Timeline Generation from History Textbooks",
    author = "Bedi, Harsimran  and
      Patil, Sangameshwar  and
      Hingmire, Swapnil  and
      Palshikar, Girish",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA} 2017)",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5912",
    pages = "69--77",
    abstract = "Event timeline serves as the basic structure of history, and it is used as a disposition of key phenomena in studying history as a subject in secondary school. In order to enable a student to understand a historical phenomenon as a series of connected events, we present a system for automatic event timeline generation from history textbooks. Additionally, we propose Message Sequence Chart (MSC) and time-map based visualization techniques to visualize an event timeline. We also identify key computational challenges in developing natural language processing based applications for history textbooks.",
}
@proceedings{ws-2017-international-digital,
    title = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    editor = "Jonnagaddala, Jitendra  and
      Dai, Hong-Jie  and
      Chang, Yung-Chun",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5800",
}
@inproceedings{skeppstedt-etal-2017-automatic,
    title = "Automatic detection of stance towards vaccination in online discussion forums",
    author = "Skeppstedt, Maria  and
      Kerren, Andreas  and
      Stede, Manfred",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5801",
    pages = "1--8",
    abstract = "A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance {`}against{'} or {`}for{'} vaccination, or as {`}undecided{'}. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance {`}against{'} vaccination from stance {`}for{'} vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Future work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features.",
}
@inproceedings{abd-yusof-etal-2017-analysing,
    title = "Analysing the Causes of Depressed Mood from Depression Vulnerable Individuals",
    author = "Abd Yusof, Noor Fazilla  and
      Lin, Chenghua  and
      Guerin, Frank",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5802",
    pages = "9--17",
    abstract = "We develop a computational model to discover the potential causes of depression by analysing the topics in a usergenerated text. We show the most prominent causes, and how these causes evolve over time. Also, we highlight the differences in causes between students with low and high neuroticism. Our studies demonstrate that the topics reveal valuable clues about the causes contributing to depressed mood. Identifying causes can have a significant impact on improving the quality of depression care; thereby providing greater insights into a patient{'}s state for pertinent treatment recommendations. Hence, this study significantly expands the ability to discover the potential factors that trigger depression, making it possible to increase the efficiency of depression treatment.",
}
@inproceedings{takeuchi-etal-2017-multivariate,
    title = "Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation",
    author = "Takeuchi, Ryo  and
      Iso, Hayate  and
      Ito, Kaoru  and
      Wakamiya, Shoko  and
      Aramaki, Eiji",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5803",
    pages = "18--25",
    abstract = "To date, various Twitter-based event detection systems have been proposed. Most of their targets, however, share common characteristics. They are seasonal or global events such as earthquakes and flu pandemics. In contrast, this study targets unseasonal and local disease events. Our system investigates the frequencies of disease-related words such as {``}nausea{''},{``}chill{''},and {``}diarrhea{''} and estimates the number of patients using regression of these word frequencies. Experiments conducted using Japanese 47 areas from January 2017 to April 2017 revealed that the detection of small and unseasonal event is extremely difficult (overall performance: 0.13). However, we found that the event scale and the detection performance show high correlation in the specified cases (in the phase of patient increasing or decreasing). The results also suggest that when 150 and more patients appear in a high population area, we can expect that our social sensors detect this outbreak. Based on these results, we can infer that social sensors can reliably detect unseasonal and local disease events under certain conditions, just as they can for seasonal or global events.",
}
@inproceedings{huang-etal-2017-incorporating,
    title = "Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms",
    author = "Huang, Yi-Jie  and
      Su, Chu Hsien  and
      Chang, Yi-Chun  and
      Ting, Tseng-Hsin  and
      Fu, Tzu-Yuan  and
      Wang, Rou-Min  and
      Dai, Hong-Jie  and
      Chang, Yung-Chun  and
      Jonnagaddala, Jitendra  and
      Hsu, Wen-Lian",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5804",
    pages = "26--32",
    abstract = "The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper{'}s pregnancy stage. Another interesting application is to use the social media platforms to analyze users{'} health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets.",
}
@inproceedings{wang-etal-2017-using,
    title = "Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information",
    author = "Wang, Chen-Kai  and
      Singh, Onkar  and
      Tang, Zhao-Li  and
      Dai, Hong-Jie",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5805",
    pages = "33--38",
    abstract = "Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it{'}s often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to out-breaks in real time. In this paper we apply the long short-term memory recurrent neural net-work (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1{\%} of the highest score achieved by SVM with oversampling technique.",
}
@inproceedings{adam-etal-2017-zikahack,
    title = "{Z}ika{H}ack 2016: A digital disease detection competition",
    author = "Adam, Dillon C  and
      Jonnagaddala, Jitendra  and
      Han-Chen, Daniel  and
      Batongbacal, Sean  and
      Almeida, Luan  and
      Zhu, Jing Z  and
      Yang, Jenny J  and
      Mundekkat, Jumail M  and
      Badman, Steven  and
      Chughtai, Abrar  and
      MacIntyre, C Raina",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5806",
    pages = "39--46",
    abstract = "Effective response to infectious diseases outbreaks relies on the rapid and early detection of those outbreaks. Invalidated, yet timely and openly available digital information can be used for the early detection of outbreaks. Public health surveillance authorities can exploit these early warnings to plan and co-ordinate rapid surveillance and emergency response programs. In 2016, a digital disease detection competition named ZikaHack was launched. The objective of the competition was for multidisciplinary teams to design, develop and demonstrate innovative digital disease detection solutions to retrospectively detect the 2015-16 Brazilian Zika virus outbreak earlier than traditional surveillance methods. In this paper, an overview of the ZikaHack competition is provided. The challenges and lessons learned in organizing this competition are also discussed for use by other researchers interested in organizing similar competitions.",
}
@inproceedings{kim-etal-2017-method,
    title = "A Method to Generate a Machine-Labeled Data for Biomedical Named Entity Recognition with Various Sub-Domains",
    author = "Kim, Juae  and
      Kwon, Sunjae  and
      Ko, Youngjoong  and
      Seo, Jungyun",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5807",
    pages = "47--51",
    abstract = "Biomedical Named Entity (NE) recognition is a core technique for various works in the biomedical domain. In previous studies, using machine learning algorithm shows better performance than dictionary-based and rule-based approaches because there are too many terminological variations of biomedical NEs and new biomedical NEs are constantly generated. To achieve the high performance with a machine-learning algorithm, good-quality corpora are required. However, it is difficult to obtain the good-quality corpora because an-notating a biomedical corpus for ma-chine-learning is extremely time-consuming and costly. In addition, most previous corpora are insufficient for high-level tasks because they cannot cover various domains. Therefore, we propose a method for generating a large amount of machine-labeled data that covers various domains. To generate a large amount of machine-labeled data, firstly we generate an initial machine-labeled data by using a chunker and MetaMap. The chunker is developed to extract only biomedical NEs with manually annotated data. MetaMap is used to annotate the category of bio-medical NE. Then we apply the self-training approach to bootstrap the performance of initial machine-labeled data. In our experiments, the biomedical NE recognition system that is trained with our proposed machine-labeled data achieves much high performance. As a result, our system outperforms biomedical NE recognition system that using MetaMap only with 26.03{\%}p improvements on F1-score.",
}
@inproceedings{tu-etal-2017-enhancing,
    title = "Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble",
    author = "Tu, Jing Cyun  and
      Lai, Po-Ting  and
      Tsai, Richard Tzong-Han",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5808",
    pages = "52--56",
    abstract = "The study of drug-drug interaction (DDI) is important in the drug discovering. Both PubMed and DrugBank are rich resources to retrieve DDI information which is usually represented in plain text. Automatically extracting DDI pairs from text improves the quality of drug discov-ering. In this paper, we presented a study that focuses on the DDI classification. We normalized the drug names, and developed both sentence-level and corpus-level features for DDI classification. A classifier ensemble approach is used for the unbalance DDI labels problem. Our approach achieved an F-score of 65.4{\%} on SemEval 2013 DDI test set. The experimental results also show the effects of proposed corpus-level features in the DDI task.",
}
@inproceedings{warikoo-etal-2017-chemical,
    title = "Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model",
    author = "Warikoo, Neha  and
      Chang, Yung-Chun  and
      Hsu, Wen-Lian",
    booktitle = "Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 ({DDDSM}-2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5809",
    pages = "57--64",
    abstract = "In this work, we introduce a novel feature engineering approach named {``}algebraic invariance{''} to identify discriminative patterns for learning relation pair features for the chemical-disease relation (CDR) task of BioCreative V. Our method exploits the existing structural similarity of the key concepts of relation descriptions from the CDR corpus to generate robust linguistic patterns for SVM tree kernel-based learning. Preprocessing of the training data classifies the entity pairs as either related or unrelated to build instance types for both inter-sentential and intra-sentential scenarios. An invariant function is proposed to process and optimally cluster similar patterns for both positive and negative instances. The learning model for CDR pairs is based on the SVM tree kernel approach, which generates feature trees and vectors and is modeled on suitable invariance based patterns, bringing brevity, precision and context to the identifier features. Results demonstrate that our method outperformed other compared approaches, achieved a high recall rate of 85.08{\%}, and averaged an F1-score of 54.34{\%} without the use of any additional knowledge bases.",
}
@proceedings{ws-2017-asian,
    title = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    editor = "Nakazawa, Toshiaki  and
      Goto, Isao",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5700",
}
@inproceedings{nakazawa-etal-2017-overview,
    title = "Overview of the 4th Workshop on {A}sian Translation",
    author = "Nakazawa, Toshiaki  and
      Higashiyama, Shohei  and
      Ding, Chenchen  and
      Mino, Hideya  and
      Goto, Isao  and
      Kazawa, Hideto  and
      Oda, Yusuke  and
      Neubig, Graham  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5701",
    pages = "1--54",
    abstract = "This paper presents the results of the shared tasks from the 4th workshop on Asian translation (WAT2017) including J↔E, J↔C scientific paper translation subtasks, C↔J, K↔J, E↔J patent translation subtasks, H↔E mixed domain subtasks, J↔E newswire subtasks and J↔E recipe subtasks. For the WAT2017, 12 institutions participated in the shared tasks. About 300 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.",
}
@inproceedings{takeno-etal-2017-controlling,
    title = "Controlling Target Features in Neural Machine Translation via Prefix Constraints",
    author = "Takeno, Shunsuke  and
      Nagata, Masaaki  and
      Yamamoto, Kazuhide",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5702",
    pages = "55--63",
    abstract = "We propose \textit{prefix constraints}, a novel method to enforce constraints on target sentences in neural machine translation. It places a sequence of special tokens at the beginning of target sentence (target prefix), while side constraints places a special token at the end of source sentence (source suffix). Prefix constraints can be predicted from source sentence jointly with target sentence, while side constraints (Sennrich et al., 2016) must be provided by the user or predicted by some other methods. In both methods, special tokens are designed to encode arbitrary features on target-side or metatextual information. We show that prefix constraints are more flexible than side constraints and can be used to control the behavior of neural machine translation, in terms of output length, bidirectional decoding, domain adaptation, and unaligned target word generation.",
}
@inproceedings{sekizawa-etal-2017-improving,
    title = "Improving {J}apanese-to-{E}nglish Neural Machine Translation by Paraphrasing the Target Language",
    author = "Sekizawa, Yuuki  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5703",
    pages = "64--69",
    abstract = "Neural machine translation (NMT) produces sentences that are more fluent than those produced by statistical machine translation (SMT). However, NMT has a very high computational cost because of the high dimensionality of the output layer. Generally, NMT restricts the size of vocabulary, which results in infrequent words being treated as out-of-vocabulary (OOV) and degrades the performance of the translation. In evaluation, we achieved a statistically significant BLEU score improvement of 0.55-0.77 over the baselines including the state-of-the-art method.",
}
@inproceedings{imankulova-etal-2017-improving,
    title = "Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel Corpus",
    author = "Imankulova, Aizhan  and
      Sato, Takayuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5704",
    pages = "70--78",
    abstract = "Large-scale parallel corpora are indispensable to train highly accurate machine translators. However, manually constructed large-scale parallel corpora are not freely available in many language pairs. In previous studies, training data have been expanded using a pseudo-parallel corpus obtained using machine translation of the monolingual corpus in the target language. However, in low-resource language pairs in which only low-accuracy machine translation systems can be used, translation quality is reduces when a pseudo-parallel corpus is used naively. To improve machine translation performance with low-resource language pairs, we propose a method to expand the training data effectively via filtering the pseudo-parallel corpus using a quality estimation based on back-translation. As a result of experiments with three language pairs using small, medium, and large parallel corpora, language pairs with fewer training data filtered out more sentence pairs and improved BLEU scores more significantly.",
}
@inproceedings{fujita-sumita-2017-japanese,
    title = "{J}apanese to {E}nglish/{C}hinese/{K}orean Datasets for Translation Quality Estimation and Automatic Post-Editing",
    author = "Fujita, Atsushi  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5705",
    pages = "79--88",
    abstract = "Aiming at facilitating the research on quality estimation (QE) and automatic post-editing (APE) of machine translation (MT) outputs, especially for those among Asian languages, we have created new datasets for Japanese to English, Chinese, and Korean translations. As the source text, actual utterances in Japanese were extracted from the log data of our speech translation service. MT outputs were then given by phrase-based statistical MT systems. Finally, human evaluators were employed to grade the quality of MT outputs and to post-edit them. This paper describes the characteristics of the created datasets and reports on our benchmarking experiments on word-level QE, sentence-level QE, and APE conducted using the created datasets.",
}
@inproceedings{morishita-etal-2017-ntt,
    title = "{NTT} Neural Machine Translation Systems at {WAT} 2017",
    author = "Morishita, Makoto  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5706",
    pages = "89--94",
    abstract = "In this year, we participated in four translation subtasks at WAT 2017. Our model structure is quite simple but we used it with well-tuned hyper-parameters, leading to a significant improvement compared to the previous state-of-the-art system. We also tried to make use of the unreliable part of the provided parallel corpus by back-translating and making a synthetic corpus. Our submitted system achieved the new state-of-the-art performance in terms of the BLEU score, as well as human evaluation.",
}
@inproceedings{wang-etal-2017-xmu,
    title = "{XMU} Neural Machine Translation Systems for {WAT} 2017",
    author = "Wang, Boli  and
      Tan, Zhixing  and
      Hu, Jinming  and
      Chen, Yidong  and
      Shi, Xiaodong",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5707",
    pages = "95--98",
    abstract = "This paper describes the Neural Machine Translation systems of Xiamen University for the shared translation tasks of WAT 2017. Our systems are based on the Encoder-Decoder framework with attention. We participated in three subtasks. We experimented subword segmentation, synthetic training data and model ensembling. Experiments show that all these methods can give substantial improvements.",
}
@inproceedings{neishi-etal-2017-bag,
    title = "A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch Size",
    author = "Neishi, Masato  and
      Sakuma, Jin  and
      Tohda, Satoshi  and
      Ishiwatari, Shonosuke  and
      Yoshinaga, Naoki  and
      Toyoda, Masashi",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5708",
    pages = "99--109",
    abstract = "In this paper, we describe the team UT-IIS{'}s system and results for the WAT 2017 translation tasks. We further investigated several tricks including a novel technique for initializing embedding layers using only the parallel corpus, which increased the BLEU score by 1.28, found a practical large batch size of 256, and gained insights regarding hyperparameter settings. Ultimately, our system obtained a better result than the state-of-the-art system of WAT 2016. Our code is available on \url{https://github.com/nem6ishi/wat17}.",
}
@inproceedings{long-etal-2017-patent,
    title = "Patent {NMT} integrated with Large Vocabulary Phrase Translation by {SMT} at {WAT} 2017",
    author = "Long, Zi  and
      Kimura, Ryuichiro  and
      Utsuro, Takehito  and
      Mitsuhashi, Tomoharu  and
      Yamamoto, Mikio",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5709",
    pages = "110--118",
    abstract = "Neural machine translation (NMT) cannot handle a larger vocabulary because the training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy. The selected phrases are then replaced with tokens during training and post-translated by the phrase translation table of SMT. In this paper, we apply the method proposed by Long et al. (2017) to the WAT 2017 Japanese-Chinese and Japanese-English patent datasets. Evaluation on Japanese-to-Chinese, Chinese-to-Japanese, Japanese-to-English and English-to-Japanese patent sentence translation proved the effectiveness of phrases selected with branching entropy, where the NMT model of Long et al.(2017) achieves a substantial improvement over a baseline NMT model without the technique proposed by Long et al.(2017).",
}
@inproceedings{ehara-2017-smt,
    title = "{SMT} reranked {NMT}",
    author = "Ehara, Terumasa",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5710",
    pages = "119--126",
    abstract = "System architecture, experimental settings and experimental results of the EHR team for the WAT2017 tasks are described. We participate in three tasks: JPCen-ja, JPCzh-ja and JPCko-ja. Although the basic architecture of our system is NMT, reranking technique is conducted using SMT results. One of the major drawback of NMT is under-translation and over-translation. On the other hand, SMT infrequently makes such translations. So, using reranking of n-best NMT outputs by the SMT output, discarding such translations can be expected. We can improve BLEU score from 46.03 to 47.08 by this technique in JPCzh-ja task.",
}
@inproceedings{imamura-sumita-2017-ensemble,
    title = "Ensemble and Reranking: Using Multiple Models in the {NICT}-2 Neural Machine Translation System at {WAT}2017",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5711",
    pages = "127--134",
    abstract = "In this paper, we describe the NICT-2 neural machine translation system evaluated at WAT2017. This system uses multiple models as an ensemble and combines models with opposite decoding directions by reranking (called bi-directional reranking). In our experimental results on small data sets, the translation quality improved when the number of models was increased to 32 in total and did not saturate. In the experiments on large data sets, improvements of 1.59-3.32 BLEU points were achieved when six-model ensembles were combined by the bi-directional reranking.",
}
@inproceedings{oda-etal-2017-simple,
    title = "A Simple and Strong Baseline: {NAIST}-{NICT} Neural Machine Translation System for {WAT}2017 {E}nglish-{J}apanese Translation Task",
    author = "Oda, Yusuke  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5712",
    pages = "135--139",
    abstract = "This paper describes the details about the NAIST-NICT machine translation system for WAT2017 English-Japanese Scientific Paper Translation Task. The system consists of a language-independent tokenizer and an attentional encoder-decoder style neural machine translation model. According to the official results, our system achieves higher translation accuracy than any systems submitted previous campaigns despite simple model architecture.",
}
@inproceedings{kinoshita-etal-2017-comparison,
    title = "Comparison of {SMT} and {NMT} trained with large Patent Corpora: {J}apio at {WAT}2017",
    author = "Kinoshita, Satoshi  and
      Oshio, Tadaaki  and
      Mitsuhashi, Tomoharu",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5713",
    pages = "140--145",
    abstract = "Japio participates in patent subtasks (JPC-EJ/JE/CJ/KJ) with phrase-based statistical machine translation (SMT) and neural machine translation (NMT) systems which are trained with its own patent corpora in addition to the subtask corpora provided by organizers of WAT2017. In EJ and CJ subtasks, SMT and NMT systems whose sizes of training corpora are about 50 million and 10 million sentence pairs respectively achieved comparable scores for automatic evaluations, but NMT systems were superior to SMT systems for both official and in-house human evaluations.",
}
@inproceedings{cromieres-etal-2017-kyoto,
    title = "{K}yoto {U}niversity Participation to {WAT} 2017",
    author = "Cromieres, Fabien  and
      Dabre, Raj  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5714",
    pages = "146--153",
    abstract = "We describe here our approaches and results on the WAT 2017 shared translation tasks. Following our good results with Neural Machine Translation in the previous shared task, we continue this approach this year, with incremental improvements in models and training methods. We focused on the ASPEC dataset and could improve the state-of-the-art results for Chinese-to-Japanese and Japanese-to-Chinese translations.",
}
@inproceedings{kocmi-etal-2017-cuni,
    title = "{CUNI} {NMT} System for {WAT} 2017 Translation Tasks",
    author = "Kocmi, Tom  and
      Vari{\v{s}}, Du{\v{s}}an  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5715",
    pages = "154--159",
    abstract = "The paper presents this year{'}s CUNI submissions to the WAT 2017 Translation Task focusing on the Japanese-English translation, namely Scientific papers subtask, Patents subtask and Newswire subtask. We compare two neural network architectures, the standard sequence-to-sequence with attention (Seq2Seq) and an architecture using convolutional sentence encoder (FBConv2Seq), both implemented in the NMT framework Neural Monkey that we currently participate in developing. We also compare various types of preprocessing of the source Japanese sentences and their impact on the overall results. Furthermore, we include the results of our experiments with out-of-domain data obtained by combining the corpora provided for each subtask.",
}
@inproceedings{matsumura-komachi-2017-tokyo,
    title = "{T}okyo Metropolitan University Neural Machine Translation System for {WAT} 2017",
    author = "Matsumura, Yukio  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5716",
    pages = "160--166",
    abstract = "In this paper, we describe our neural machine translation (NMT) system, which is based on the attention-based NMT and uses long short-term memories (LSTM) as RNN. We implemented beam search and ensemble decoding in the NMT system. The system was tested on the 4th Workshop on Asian Translation (WAT 2017) shared tasks. In our experiments, we participated in the scientific paper subtasks and attempted Japanese-English, English-Japanese, and Japanese-Chinese translation tasks. The experimental results showed that implementation of beam search and ensemble decoding can effectively improve the translation quality.",
}
@inproceedings{singh-etal-2017-comparing,
    title = "Comparing Recurrent and Convolutional Architectures for {E}nglish-{H}indi Neural Machine Translation",
    author = "Singh, Sandhya  and
      Panjwani, Ritesh  and
      Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5717",
    pages = "167--170",
    abstract = "In this paper, we empirically compare the two encoder-decoder neural machine translation architectures: convolutional sequence to sequence model (ConvS2S) and recurrent sequence to sequence model (RNNS2S) for English-Hindi language pair as part of IIT Bombay{'}s submission to WAT2017 shared task. We report the results for both English-Hindi and Hindi-English direction of language pair.",
}
@proceedings{ws-2017-curation,
    title = "Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora",
    editor = "Afli, Haithem  and
      Liu, Chao-Hong",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5600",
}
@inproceedings{park-etal-2017-building,
    title = "Building a Better Bitext for Structurally Different Languages through Self-training",
    author = {Park, Jungyeul  and
      Dugast, Lo{\"\i}c  and
      Hong, Jeen-Pyo  and
      Shin, Chang-Uk  and
      Cha, Jeong-Won},
    booktitle = "Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5601",
    pages = "1--10",
    abstract = "We propose a novel method to bootstrap the construction of parallel corpora for new pairs of structurally different languages. We do so by combining the use of a pivot language and self-training. A pivot language enables the use of existing translation models to bootstrap the alignment and a self-training procedure enables to achieve better alignment, both at the document and sentence level. We also propose several evaluation methods for the resulting alignment.",
}
@inproceedings{afli-etal-2017-multinews,
    title = "{M}ulti{N}ews: A Web collection of an Aligned Multimodal and Multilingual Corpus",
    author = "Afli, Haithem  and
      Lohar, Pintu  and
      Way, Andy",
    booktitle = "Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5602",
    pages = "11--15",
    abstract = "Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the availability of a specific multimodal data that includes images and texts. In this paper, we present a collection of a Multimodal corpus of comparable texts and their images in 9 languages from the web news articles of Euronews website. This corpus has found widespread use in the NLP community in Multilingual and multimodal tasks. Here, we focus on its acquisition of the images and text data and their multilingual alignment.",
}
@inproceedings{zhou-etal-2017-learning,
    title = "Learning Phrase Embeddings from Paraphrases with {GRU}s",
    author = "Zhou, Zhihao  and
      Huang, Lifu  and
      Ji, Heng",
    booktitle = "Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/W17-5603",
    pages = "16--23",
    abstract = "Learning phrase representations has been widely explored in many Natural Language Processing tasks (e.g., Sentiment Analysis, Machine Translation) and has shown promising improvements. Previous studies either learn non-compositional phrase representations with general word embedding learning techniques or learn compositional phrase representations based on syntactic structures, which either require huge amounts of human annotations or cannot be easily generalized to all phrases. In this work, we propose to take advantage of large-scaled paraphrase database and present a pairwise-GRU framework to generate compositional phrase representations. Our framework can be re-used to generate representations for any phrases. Experimental results show that our framework achieves state-of-the-art results on several phrase similarity tasks.",
}
@proceedings{ws-2017-sigdial,
    title = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    editor = "Jokinen, Kristiina  and
      Stede, Manfred  and
      DeVault, David  and
      Louis, Annie",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5500",
    doi = "10.18653/v1/W17-55",
}
@inproceedings{laali-kosseim-2017-automatic,
    title = "Automatic Mapping of {F}rench Discourse Connectives to {PDTB} Discourse Relations",
    author = "Laali, Majid  and
      Kosseim, Leila",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5501",
    doi = "10.18653/v1/W17-5501",
    pages = "1--6",
    abstract = "In this paper, we present an approach to exploit phrase tables generated by statistical machine translation in order to map French discourse connectives to discourse relations. Using this approach, we created DisCoRel, a lexicon of French discourse connectives and their PDTB relations. When evaluated against LEXCONN, DisCoRel achieves a recall of 0.81 and an Average Precision of 0.68 for the Concession and Condition relations.",
}
@inproceedings{prasad-etal-2017-towards,
    title = "Towards Full Text Shallow Discourse Relation Annotation: Experiments with Cross-Paragraph Implicit Relations in the {PDTB}",
    author = "Prasad, Rashmi  and
      Forbes Riley, Katherine  and
      Lee, Alan",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5502",
    doi = "10.18653/v1/W17-5502",
    pages = "7--16",
    abstract = "Full text discourse parsing relies on texts comprehensively annotated with discourse relations. To this end, we address a significant gap in the inter-sentential discourse relations annotated in the Penn Discourse Treebank (PDTB), namely the class of cross-paragraph implicit relations, which account for 30{\%} of inter-sentential relations in the corpus. We present our annotation study to explore the incidence rate of adjacent vs. non-adjacent implicit relations in cross-paragraph contexts, and the relative degree of difficulty in annotating them. Our experiments show a high incidence of non-adjacent relations that are difficult to annotate reliably, suggesting the practicality of backing off from their annotation to reduce noise for corpus-based studies. Our resulting guidelines follow the PDTB adjacency constraint for implicits while employing an underspecified representation of non-adjacent implicits, and yield 62{\%} inter-annotator agreement on this task.",
}
@inproceedings{larsson-2017-user,
    title = "User-initiated Sub-dialogues in State-of-the-art Dialogue Systems",
    author = "Larsson, Staffan",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5503",
    doi = "10.18653/v1/W17-5503",
    pages = "17--22",
    abstract = "We test state of the art dialogue systems for their behaviour in response to user-initiated sub-dialogues, i.e. interactions where a system question is responded to with a question or request from the user, who thus initiates a sub-dialogue. We look at sub-dialogues both within a single app (where the sub-dialogue concerns another topic in the original domain) and across apps (where the sub-dialogue concerns a different domain). The overall conclusion of the tests is that none of the systems can be said to deal appropriately with user-initiated sub-dialogues.",
}
@inproceedings{prange-etal-2017-multimodal,
    title = "A Multimodal Dialogue System for Medical Decision Support inside Virtual Reality",
    author = "Prange, Alexander  and
      Chikobava, Margarita  and
      Poller, Peter  and
      Barz, Michael  and
      Sonntag, Daniel",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5504",
    doi = "10.18653/v1/W17-5504",
    pages = "23--26",
    abstract = "We present a multimodal dialogue system that allows doctors to interact with a medical decision support system in virtual reality (VR). We integrate an interactive visualization of patient records and radiology image data, as well as therapy predictions. Therapy predictions are computed in real-time using a deep learning model.",
}
@inproceedings{zhao-etal-2017-generative,
    title = "Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability",
    author = "Zhao, Tiancheng  and
      Lu, Allen  and
      Lee, Kyusong  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5505",
    doi = "10.18653/v1/W17-5505",
    pages = "27--36",
    abstract = "Generative encoder-decoder models offer great promise in developing domain-general dialog systems. However, they have mainly been applied to open-domain conversations. This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models. This framework enables encoder-decoder models to accomplish slot-value independent decision-making and interact with external databases. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery. The models were trained on both real-user data from a bus information system and human-human chat data. Results show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users.",
}
@inproceedings{eric-etal-2017-key,
    title = "Key-Value Retrieval Networks for Task-Oriented Dialogue",
    author = "Eric, Mihail  and
      Krishnan, Lakshmi  and
      Charette, Francois  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5506",
    doi = "10.18653/v1/W17-5506",
    pages = "37--49",
    abstract = "Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. We also release a new dataset of 3,031 dialogues that are grounded through underlying knowledge bases and span three distinct tasks in the in-car personal assistant space: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our architecture is simultaneously trained on data from all domains and significantly outperforms a competitive rule-based system and other existing neural dialogue architectures on the provided domains according to both automatic and human evaluation metrics.",
}
@inproceedings{ono-etal-2017-lexical,
    title = "Lexical Acquisition through Implicit Confirmations over Multiple Dialogues",
    author = "Ono, Kohei  and
      Takeda, Ryu  and
      Nichols, Eric  and
      Nakano, Mikio  and
      Komatani, Kazunori",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5507",
    doi = "10.18653/v1/W17-5507",
    pages = "50--59",
    abstract = "We address the problem of acquiring the ontological categories of unknown terms through implicit confirmation in dialogues. We develop an approach that makes implicit confirmation requests with an unknown term{'}s predicted category. Our approach does not degrade user experience with repetitive explicit confirmations, but the system has difficulty determining if information in the confirmation request can be correctly acquired. To overcome this challenge, we propose a method for determining whether or not the predicted category is correct, which is included in an implicit confirmation request. Our method exploits multiple user responses to implicit confirmation requests containing the same ontological category. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly predicted categories than when only single user responses were considered.",
}
@inproceedings{kato-etal-2017-utterance,
    title = "Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders",
    author = "Kato, Tsuneo  and
      Nagai, Atsushi  and
      Noda, Naoki  and
      Sumitomo, Ryosuke  and
      Wu, Jianming  and
      Yamamoto, Seiichi",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5508",
    doi = "10.18653/v1/W17-5508",
    pages = "60--64",
    abstract = "Recursive autoencoders (RAEs) for compositionality of a vector space model were applied to utterance intent classification of a smartphone-based Japanese-language spoken dialogue system. Though the RAEs express a nonlinear operation on the vectors of child nodes, the operation is considered to be different intrinsically depending on types of child nodes. To relax the difference, a data-driven untying of autoencoders (AEs) is proposed. The experimental result of the utterance intent classification showed an improved accuracy with the proposed method compared with the basic tied RAE and untied RAE based on a manual rule.",
}
@inproceedings{ultes-etal-2017-reward,
    title = "Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning",
    author = "Ultes, Stefan  and
      Budzianowski, Pawe{\l}  and
      Casanueva, I{\~n}igo  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Rojas-Barahona, Lina M.  and
      Su, Pei-Hao  and
      Wen, Tsung-Hsien  and
      Ga{\v{s}}i{\'c}, Milica  and
      Young, Steve",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5509",
    doi = "10.18653/v1/W17-5509",
    pages = "65--70",
    abstract = "Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length. In this work, we propose a structured method for finding a good balance between these components by searching for the optimal reward component weighting. To render this search feasible, we use multi-objective reinforcement learning to significantly reduce the number of training dialogues required. We apply our proposed method to find optimized component weights for six domains and compare them to a default baseline.",
}
@inproceedings{dubuisson-duplessis-etal-2017-automatic,
    title = "Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction",
    author = "Dubuisson Duplessis, Guillaume  and
      Clavel, Chlo{\'e}  and
      Landragin, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5510",
    doi = "10.18653/v1/W17-5510",
    pages = "71--81",
    abstract = "This work aims at characterising verbal alignment processes for improving virtual agent communicative capabilities. We propose computationally inexpensive measures of verbal alignment based on expression repetition in dyadic textual dialogues. Using these measures, we present a contrastive study between Human-Human and Human-Agent dialogues on a negotiation task. We exhibit quantitative differences in the strength and orientation of verbal alignment showing the ability of our approach to characterise important aspects of verbal alignment.",
}
@inproceedings{williams-liden-2017-demonstration,
    title = "Demonstration of interactive teaching for end-to-end dialog control with hybrid code networks",
    author = "Williams, Jason D.  and
      Liden, Lars",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5511",
    doi = "10.18653/v1/W17-5511",
    pages = "82--85",
    abstract = "This is a demonstration of interactive teaching for practical end-to-end dialog systems driven by a recurrent neural network. In this approach, a developer teaches the network by interacting with the system and providing on-the-spot corrections. Once a system is deployed, a developer can also correct mistakes in logged dialogs. This demonstration shows both of these teaching methods applied to dialog systems in three domains: pizza ordering, restaurant information, and weather forecasts.",
}
@inproceedings{budzianowski-etal-2017-sub,
    title = "Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning",
    author = "Budzianowski, Pawe{\l}  and
      Ultes, Stefan  and
      Su, Pei-Hao  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Wen, Tsung-Hsien  and
      Casanueva, I{\~n}igo  and
      Rojas-Barahona, Lina M.  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5512",
    doi = "10.18653/v1/W17-5512",
    pages = "86--92",
    abstract = "Human conversation is inherently complex, often spanning many different topics/domains. This makes policy learning for dialogue systems very challenging. Standard flat reinforcement learning methods do not provide an efficient framework for modelling such dialogues. In this paper, we focus on the under-explored problem of multi-domain dialogue management. First, we propose a new method for hierarchical reinforcement learning using the option framework. Next, we show that the proposed architecture learns faster and arrives at a better policy than the existing flat ones do. Moreover, we show how pretrained policies can be adapted to more complex systems with an additional set of new actions. In doing that, we show that our approach has the potential to facilitate policy optimisation for more sophisticated multi-domain dialogue systems.",
}
@inproceedings{truong-etal-2017-maca,
    title = "{MACA}: A Modular Architecture for Conversational Agents",
    author = "Truong, Hoai Phuoc  and
      Parthasarathi, Prasanna  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5513",
    doi = "10.18653/v1/W17-5513",
    pages = "93--102",
    abstract = "We propose a software architecture designed to ease the implementation of dialogue systems. The Modular Architecture for Conversational Agents (MACA) uses a plug-n-play style that allows quick prototyping, thereby facilitating the development of new techniques and the reproduction of previous work. The architecture separates the domain of the conversation from the agent{'}s dialogue strategy, and as such can be easily extended to multiple domains. MACA provides tools to host dialogue agents on Amazon Mechanical Turk (mTurk) for data collection and allows processing of other sources of training data. The current version of the framework already incorporates several domains and existing dialogue strategies from the recent literature.",
}
@inproceedings{bapna-etal-2017-sequential,
    title = "Sequential Dialogue Context Modeling for Spoken Language Understanding",
    author = {Bapna, Ankur  and
      T{\"u}r, Gokhan  and
      Hakkani-T{\"u}r, Dilek  and
      Heck, Larry},
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5514",
    doi = "10.18653/v1/W17-5514",
    pages = "103--114",
    abstract = "Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.",
}
@inproceedings{krause-etal-2017-redundancy,
    title = "Redundancy Localization for the Conversationalization of Unstructured Responses",
    author = "Krause, Sebastian  and
      Kozhevnikov, Mikhail  and
      Malmi, Eric  and
      Pighin, Daniele",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5515",
    doi = "10.18653/v1/W17-5515",
    pages = "115--126",
    abstract = "Conversational agents offer users a natural-language interface to accomplish tasks, entertain themselves, or access information. Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic, and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources. Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research, one of the key issues of which is preventing the agent{'}s responses from sounding repetitive. Targeting this issue, we propose a new task, known as redundancy localization, which aims to pinpoint semantic overlap between text passages. To help address it systematically, we formalize the task, prepare a public dataset with fine-grained redundancy labels, and propose a model utilizing a weak training signal defined over the results of a passage-retrieval system on web texts. The proposed model demonstrates superior performance compared to a state-of-the-art entailment model and yields encouraging results when applied to a real-world dialogue.",
}
@inproceedings{lala-etal-2017-attentive,
    title = "Attentive listening system with backchanneling, response generation and flexible turn-taking",
    author = "Lala, Divesh  and
      Milhorat, Pierrick  and
      Inoue, Koji  and
      Ishida, Masanari  and
      Takanashi, Katsuya  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5516",
    doi = "10.18653/v1/W17-5516",
    pages = "127--136",
    abstract = "Attentive listening systems are designed to let people, especially senior people, keep talking to maintain communication ability and mental health. This paper addresses key components of an attentive listening system which encourages users to talk smoothly. First, we introduce continuous prediction of end-of-utterances and generation of backchannels, rather than generating backchannels after end-point detection of utterances. This improves subjective evaluations of backchannels. Second, we propose an effective statement response mechanism which detects focus words and responds in the form of a question or partial repeat. This can be applied to any statement. Moreover, a flexible turn-taking mechanism is designed which uses backchannels or fillers when the turn-switch is ambiguous. These techniques are integrated into a humanoid robot to conduct attentive listening. We test the feasibility of the system in a pilot experiment and show that it can produce coherent dialogues during conversation.",
}
@inproceedings{braunger-maier-2017-natural,
    title = "Natural Language Input for In-Car Spoken Dialog Systems: How Natural is Natural?",
    author = "Braunger, Patricia  and
      Maier, Wolfgang",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5517",
    doi = "10.18653/v1/W17-5517",
    pages = "137--146",
    abstract = "Recent spoken dialog systems are moving away from command and control towards a more intuitive and natural style of interaction. In order to choose an appropriate system design which allows the system to deal with naturally spoken user input, a definition of what exactly constitutes naturalness in user input is important. In this paper, we examine how different user groups naturally speak to an automotive spoken dialog system (SDS). We conduct a user study in which we collect freely spoken user utterances for a wide range of use cases in German. By means of a comparative study of the utterances from the study with interpersonal utterances, we provide criteria what constitutes naturalness in the user input of an state-of-the-art automotive SDS.",
}
@inproceedings{su-etal-2017-sample,
    title = "Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management",
    author = "Su, Pei-Hao  and
      Budzianowski, Pawe{\l}  and
      Ultes, Stefan  and
      Ga{\v{s}}i{\'c}, Milica  and
      Young, Steve",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5518",
    doi = "10.18653/v1/W17-5518",
    pages = "147--157",
    abstract = "Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learn deep RL-based dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.",
}
@inproceedings{agarwal-dymetman-2017-surprisingly,
    title = "A surprisingly effective out-of-the-box char2char model on the {E}2{E} {NLG} Challenge dataset",
    author = "Agarwal, Shubham  and
      Dymetman, Marc",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5519",
    doi = "10.18653/v1/W17-5519",
    pages = "158--163",
    abstract = "We train a char2char model on the E2E NLG Challenge data, by exploiting {``}out-of-the-box{''} the recently released tfseq2seq framework, using some of the standard options offered by this tool. With minimal effort, and in particular without delexicalization, tokenization or lowercasing, the obtained raw predictions, according to a small scale human evaluation, are excellent on the linguistic side and quite reasonable on the adequacy side, the primary downside being the possible omissions of semantic material. However, in a significant number of cases (more than 70{\%}), a perfect solution can be found in the top-20 predictions, indicating promising directions for solving the remaining issues.",
}
@inproceedings{rach-etal-2017-interaction,
    title = "Interaction Quality Estimation Using Long Short-Term Memories",
    author = "Rach, Niklas  and
      Minker, Wolfgang  and
      Ultes, Stefan",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5520",
    doi = "10.18653/v1/W17-5520",
    pages = "164--169",
    abstract = "For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS), the dialogue history is of significant importance. Previous works included this information manually in the form of precomputed temporal features into the classification process. Here, we employ a deep learning architecture based on Long Short-Term Memories (LSTM) to extract this information automatically from the data, thus estimating IQ solely by using current exchange features. We show that it is thereby possible to achieve competitive results as in a scenario where manually optimized temporal features have been included.",
}
@inproceedings{lee-etal-2017-dialport,
    title = "{D}ial{P}ort, Gone Live: An Update After A Year of Development",
    author = "Lee, Kyusong  and
      Zhao, Tiancheng  and
      Du, Yulun  and
      Cai, Edward  and
      Lu, Allen  and
      Pincus, Eli  and
      Traum, David  and
      Ultes, Stefan  and
      Rojas-Barahona, Lina M.  and
      Gasic, Milica  and
      Young, Steve  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5521",
    doi = "10.18653/v1/W17-5521",
    pages = "170--173",
    abstract = "DialPort collects user data for connected spoken dialog systems. At present six systems are linked to a central portal that directs the user to the applicable system and suggests systems that the user may be interested in. User data has started to flow into the system.",
}
@inproceedings{braun-etal-2017-evaluating,
    title = "Evaluating Natural Language Understanding Services for Conversational Question Answering Systems",
    author = "Braun, Daniel  and
      Hernandez Mendez, Adrian  and
      Matthes, Florian  and
      Langen, Manfred",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5522",
    doi = "10.18653/v1/W17-5522",
    pages = "174--185",
    abstract = "Conversational interfaces recently gained a lot of attention. One of the reasons for the current hype is the fact that chatbots (one particularly popular form of conversational interfaces) nowadays can be created without any programming knowledge, thanks to different toolkits and so-called Natural Language Understanding (NLU) services. While these NLU services are already widely used in both, industry and science, so far, they have not been analysed systematically. In this paper, we present a method to evaluate the classification performance of NLU services. Moreover, we present two new corpora, one consisting of annotated questions and one consisting of annotated questions with the corresponding answers. Based on these corpora, we conduct an evaluation of some of the most popular NLU services. Thereby we want to enable both, researchers and companies to make more educated decisions about which service they should use.",
}
@inproceedings{ghosh-etal-2017-role,
    title = "The Role of Conversation Context for Sarcasm Detection in Online Interactions",
    author = "Ghosh, Debanjan  and
      Richard Fabbri, Alexander  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5523",
    doi = "10.18653/v1/W17-5523",
    pages = "186--196",
    abstract = {Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker{'}s sarcastic intent is not always obvious without additional context. Focusing on social media discussions, we investigate two issues: (1) does modeling of conversation context help in sarcasm detection and (2) can we understand what part of conversation context triggered the sarcastic reply. To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response. We show that the conditional LSTM network (Rockt{\"a}schel et al. 2015) and LSTM networks with sentence level attention on context and response outperform the LSTM model that reads only the response. To address the second issue, we present a qualitative analysis of attention weights produced by the LSTM models with attention and discuss the results compared with human performance on the task.},
}
@inproceedings{yu-etal-2017-voila,
    title = "{VOILA}: An Optimised Dialogue System for Interactively Learning Visually-Grounded Word Meanings (Demonstration System)",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5524",
    doi = "10.18653/v1/W17-5524",
    pages = "197--200",
    abstract = "We present VOILA: an optimised, multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human user. VOILA is: (1) able to learn new visual categories interactively from users from scratch; (2) trained on real human-human dialogues in the same domain, and so is able to conduct natural spontaneous dialogue; (3) optimised to find the most effective trade-off between the accuracy of the visual categories it learns and the cost it incurs to users. VOILA is deployed on Furhat, a human-like, multi-modal robot head with back-projection of the face, and a graphical virtual character.",
}
@inproceedings{novikova-etal-2017-e2e,
    title = "The {E}2{E} Dataset: New Challenges For End-to-End Generation",
    author = "Novikova, Jekaterina  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Rieser, Verena",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5525",
    doi = "10.18653/v1/W17-5525",
    pages = "201--206",
    abstract = "This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.",
}
@inproceedings{el-asri-etal-2017-frames,
    title = "{F}rames: a corpus for adding memory to goal-oriented dialogue systems",
    author = "El Asri, Layla  and
      Schulz, Hannes  and
      Sharma, Shikhar  and
      Zumer, Jeremie  and
      Harris, Justin  and
      Fine, Emery  and
      Mehrotra, Rahul  and
      Suleman, Kaheer",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5526",
    doi = "10.18653/v1/W17-5526",
    pages = "207--219",
    abstract = "This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.",
}
@inproceedings{skantze-2017-towards,
    title = "Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using {LSTM} Recurrent Neural Networks",
    author = "Skantze, Gabriel",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5527",
    doi = "10.18653/v1/W17-5527",
    pages = "220--230",
    abstract = "Previous models of turn-taking have mostly been trained for specific turn-taking decisions, such as discriminating between turn shifts and turn retention in pauses. In this paper, we present a predictive, continuous model of turn-taking using Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN). The model is trained on human-human dialogue data to predict upcoming speech activity in a future time window. We show how this general model can be applied to two different tasks that it was not specifically trained for. First, to predict whether a turn-shift will occur or not in pauses, where the model achieves a better performance than human observers, and better than results achieved with more traditional models. Second, to make a prediction at speech onset whether the utterance will be a short backchannel or a longer utterance. Finally, we show how the hidden layer in the network can be used as a feature vector for turn-taking decisions in a human-robot interaction scenario.",
}
@inproceedings{tran-etal-2017-neural,
    title = "Neural-based Natural Language Generation in Dialogue using {RNN} Encoder-Decoder with Semantic Aggregation",
    author = "Tran, Van-Khanh  and
      Nguyen, Le-Minh  and
      Tojo, Satoshi",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5528",
    doi = "10.18653/v1/W17-5528",
    pages = "231--240",
    abstract = "Natural language generation (NLG) is an important component in spoken dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder which is an extension of an Recurrent Neural Network based Encoder-Decoder architecture. The proposed Semantic Aggregator consists of two components: an Aligner and a Refiner. The Aligner is a conventional attention calculated over the encoded input information, while the Refiner is another attention or gating mechanism stacked over the attentive Aligner in order to further select and aggregate the semantic elements. The proposed model can be jointly trained both sentence planning and surface realization to produce natural language utterances. The model was extensively assessed on four different NLG domains, in which the experimental results showed that the proposed generator consistently outperforms the previous methods on all the NLG domains.",
}
@inproceedings{lopez-gambino-etal-2017-beyond,
    title = "Beyond On-hold Messages: Conversational Time-buying in Task-oriented Dialogue",
    author = "L{\'o}pez Gambino, Soledad  and
      Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5529",
    doi = "10.18653/v1/W17-5529",
    pages = "241--246",
    abstract = "A common convention in graphical user interfaces is to indicate a {``}wait state{''}, for example while a program is preparing a response, through a changed cursor state or a progress bar. What should the analogue be in a spoken conversational system? To address this question, we set up an experiment in which a human information provider (IP) was given their information only in a delayed and incremental manner, which systematically created situations where the IP had the turn but could not provide task-related information. Our data analysis shows that 1) IPs bridge the gap until they can provide information by re-purposing a whole variety of task- and grounding-related communicative actions (e.g. echoing the user{'}s request, signaling understanding, asserting partially relevant information), rather than being silent or explicitly asking for time (e.g. {``}please wait{''}), and that 2) IPs combined these actions productively to ensure an ongoing conversation. These results, we argue, indicate that natural conversational interfaces should also be able to manage their time flexibly using a variety of conversational resources.",
}
@inproceedings{ortega-vu-2017-neural,
    title = "Neural-based Context Representation Learning for Dialog Act Classification",
    author = "Ortega, Daniel  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5530",
    doi = "10.18653/v1/W17-5530",
    pages = "247--252",
    abstract = "We explore context representation learning methods in neural-based models for dialog act classification. We propose and compare extensively different methods which combine recurrent neural network architectures and attention mechanisms (AMs) at different context levels. Our experimental results on two benchmark datasets show consistent improvements compared to the models without contextual information and reveal that the most suitable AM in the architecture depends on the nature of the dataset.",
}
@inproceedings{noseworthy-etal-2017-predicting,
    title = "Predicting Success in Goal-Driven Human-Human Dialogues",
    author = "Noseworthy, Michael  and
      Cheung, Jackie Chi Kit  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5531",
    doi = "10.18653/v1/W17-5531",
    pages = "253--262",
    abstract = "In goal-driven dialogue systems, success is often defined based on a structured definition of the goal. This requires that the dialogue system be constrained to handle a specific class of goals and that there be a mechanism to measure success with respect to that goal. However, in many human-human dialogues the diversity of goals makes it infeasible to define success in such a way. To address this scenario, we consider the task of automatically predicting success in goal-driven human-human dialogues using only the information communicated between participants in the form of text. We build a dataset from stackoverflow.com which consists of exchanges between two users in the technical domain where ground-truth success labels are available. We then propose a turn-based hierarchical neural network model that can be used to predict success without requiring a structured goal definition. We show this model outperforms rule-based heuristics and other baselines as it is able to detect patterns over the course of a dialogue and capture notions such as gratitude.",
}
@inproceedings{johnson-etal-2017-generating,
    title = "Generating and Evaluating Summaries for Partial Email Threads: Conversational {B}ayesian Surprise and Silver Standards",
    author = "Johnson, Jordon  and
      Masrani, Vaden  and
      Carenini, Giuseppe  and
      Ng, Raymond",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5532",
    doi = "10.18653/v1/W17-5532",
    pages = "263--272",
    abstract = "We define and motivate the problem of summarizing partial email threads. This problem introduces the challenge of generating reference summaries for partial threads when human annotation is only available for the threads as a whole, particularly when the human-selected sentences are not uniformly distributed within the threads. We propose an oracular algorithm for generating these reference summaries with arbitrary length, and we are making the resulting dataset publicly available. In addition, we apply a recent unsupervised method based on Bayesian Surprise that incorporates background knowledge into partial thread summarization, extend it with conversational features, and modify the mechanism by which it handles redundancy. Experiments with our method indicate improved performance over the baseline for shorter partial threads; and our results suggest that the potential benefits of background knowledge to partial thread summarization should be further investigated with larger datasets.",
}
@inproceedings{yaghoubzadeh-kopp-2017-enabling,
    title = "Enabling robust and fluid spoken dialogue with cognitively impaired users",
    author = "Yaghoubzadeh, Ramin  and
      Kopp, Stefan",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5533",
    doi = "10.18653/v1/W17-5533",
    pages = "273--283",
    abstract = "We present the flexdiam dialogue management architecture, which was developed in a series of projects dedicated to tailoring spoken interaction to the needs of users with cognitive impairments in an everyday assistive domain, using a multimodal front-end. This hybrid DM architecture affords incremental processing of uncertain input, a flexible, mixed-initiative information grounding process that can be adapted to users{'} cognitive capacities and interactive idiosyncrasies, and generic mechanisms that foster transitions in the joint discourse state that are understandable and controllable by those users, in order to effect a robust interaction for users with varying capacities.",
}
@inproceedings{bruni-fernandez-2017-adversarial,
    title = "Adversarial evaluation for open-domain dialogue generation",
    author = "Bruni, Elia  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5534",
    doi = "10.18653/v1/W17-5534",
    pages = "284--288",
    abstract = "We investigate the potential of adversarial evaluation methods for open-domain dialogue generation systems, comparing the performance of a discriminative agent to that of humans on the same task. Our results show that the task is hard, both for automated models and humans, but that a discriminative agent can learn patterns that lead to above-chance performance.",
}
@inproceedings{nejat-etal-2017-exploring,
    title = "Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis",
    author = "Nejat, Bita  and
      Carenini, Giuseppe  and
      Ng, Raymond",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5535",
    doi = "10.18653/v1/W17-5535",
    pages = "289--298",
    abstract = "Discourse Parsing and Sentiment Analysis are two fundamental tasks in Natural Language Processing that have been shown to be mutually beneficial. In this work, we design and compare two Neural Based models for jointly learning both tasks. In the proposed approach, we first create a vector representation for all the text segments in the input sentence. Next, we apply three different Recursive Neural Net models: one for discourse structure prediction, one for discourse relation prediction and one for sentiment analysis. Finally, we combine these Neural Nets in two different joint models: Multi-tasking and Pre-training. Our results on two standard corpora indicate that both methods result in improvements in each task but Multi-tasking has a bigger impact than Pre-training. Specifically for Discourse Parsing, we see improvements in the prediction of the set of contrastive relations.",
}
@inproceedings{sano-etal-2017-predicting,
    title = "Predicting Causes of Reformulation in Intelligent Assistants",
    author = "Sano, Shumpei  and
      Kaji, Nobuhiro  and
      Sassano, Manabu",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5536",
    doi = "10.18653/v1/W17-5536",
    pages = "299--309",
    abstract = "Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.",
}
@inproceedings{oraby-etal-2017-serious,
    title = "Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog",
    author = "Oraby, Shereen  and
      Harrison, Vrindavan  and
      Misra, Amita  and
      Riloff, Ellen  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5537",
    doi = "10.18653/v1/W17-5537",
    pages = "310--319",
    abstract = "Effective models of social dialog must understand a broad range of rhetorical and figurative devices. Rhetorical questions (RQs) are a type of figurative language whose aim is to achieve a pragmatic goal, such as structuring an argument, being persuasive, emphasizing a point, or being ironic. While there are computational models for other forms of figurative language, rhetorical questions have received little attention to date. We expand a small dataset from previous work, presenting a corpus of 10,270 RQs from debate forums and Twitter that represent different discourse functions. We show that we can clearly distinguish between RQs and sincere questions (0.76 F1). We then show that RQs can be used both sarcastically and non-sarcastically, observing that non-sarcastic (other) uses of RQs are frequently argumentative in forums, and persuasive in tweets. We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0.76 F1 for {``}sarcastic{''} and 0.77 F1 for {``}other{''} in forums, and 0.83 F1 for both {``}sarcastic{''} and {``}other{''} in tweets. We supplement our quantitative experiments with an in-depth characterization of the linguistic variation in RQs.",
}
@inproceedings{jang-etal-2017-finding,
    title = "Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames",
    author = "Jang, Hyeju  and
      Maki, Keith  and
      Hovy, Eduard  and
      Ros{\'e}, Carolyn",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5538",
    doi = "10.18653/v1/W17-5538",
    pages = "320--330",
    abstract = "In this paper, we present a novel and highly effective method for induction and application of metaphor frame templates as a step toward detecting metaphor in extended discourse. We infer implicit facets of a given metaphor frame using a semi-supervised bootstrapping approach on an unlabeled corpus. Our model applies this frame facet information to metaphor detection, and achieves the state-of-the-art performance on a social media dataset when building upon other proven features in a nonlinear machine learning model. In addition, we illustrate the mechanism through which the frame and topic information enable the more accurate metaphor detection.",
}
@inproceedings{manuvinakurike-etal-2017-using,
    title = "Using Reinforcement Learning to Model Incrementality in a Fast-Paced Dialogue Game",
    author = "Manuvinakurike, Ramesh  and
      DeVault, David  and
      Georgila, Kallirroi",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5539",
    doi = "10.18653/v1/W17-5539",
    pages = "331--341",
    abstract = "We apply Reinforcement Learning (RL) to the problem of incremental dialogue policy learning in the context of a fast-paced dialogue game. We compare the policy learned by RL with a high-performance baseline policy which has been shown to perform very efficiently (nearly as well as humans) in this dialogue game. The RL policy outperforms the baseline policy in offline simulations (based on real user data). We provide a detailed comparison of the RL policy and the baseline policy, including information about how much effort and time it took to develop each one of them. We also highlight the cases where the RL policy performs better, and show that understanding the RL policy can provide valuable insights which can inform the creation of an even better rule-based policy.",
}
@inproceedings{hu-walker-2017-inferring,
    title = "Inferring Narrative Causality between Event Pairs in Films",
    author = "Hu, Zhichao  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5540",
    doi = "10.18653/v1/W17-5540",
    pages = "342--351",
    abstract = "To understand narrative, humans draw inferences about the underlying relations between narrative events. Cognitive theories of narrative understanding define these inferences as four different types of causality, that include pairs of events A, B where A physically causes B (X drop, X break), to pairs of events where A causes emotional state B (Y saw X, Y felt fear). Previous work on learning narrative relations from text has either focused on {``}strict{''} physical causality, or has been vague about what relation is being learned. This paper learns pairs of causal events from a corpus of film scene descriptions which are action rich and tend to be told in chronological order. We show that event pairs induced using our methods are of high quality and are judged to have a stronger causal relation than event pairs from Rel-Grams.",
}
@inproceedings{leuski-artstein-2017-lessons,
    title = "Lessons in Dialogue System Deployment",
    author = "Leuski, Anton  and
      Artstein, Ron",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5541",
    doi = "10.18653/v1/W17-5541",
    pages = "352--355",
    abstract = "We analyze deployment of an interactive dialogue system in an environment where deep technical expertise might not be readily available. The initial version was created using a collection of research tools. We summarize a number of challenges with its deployment at two museums and describe a new system that simplifies the installation and user interface; reduces reliance on 3rd-party software; and provides a robust data collection mechanism.",
}
@inproceedings{yoshino-etal-2017-information,
    title = "Information Navigation System with Discovering User Interests",
    author = "Yoshino, Koichiro  and
      Suzuki, Yu  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5542",
    doi = "10.18653/v1/W17-5542",
    pages = "356--359",
    abstract = "We demonstrate an information navigation system for sightseeing domains that has a dialogue interface for discovering user interests for tourist activities. The system discovers interests of a user with focus detection on user utterances, and proactively presents related information to the discovered user interest. A partially observable Markov decision process (POMDP)-based dialogue manager, which is extended with user focus states, controls the behavior of the system to provide information with several dialogue acts for providing information. We transferred the belief-update function and the policy of the manager from other system trained on a different domain to show the generality of defined dialogue acts for our information navigation system.",
}
@inproceedings{rahimtoroghi-etal-2017-modelling,
    title = "Modelling Protagonist Goals and Desires in First-Person Narrative",
    author = "Rahimtoroghi, Elahe  and
      Wu, Jiaqi  and
      Wang, Ruimin  and
      Anand, Pranav  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5543",
    doi = "10.18653/v1/W17-5543",
    pages = "360--369",
    abstract = "Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes. However, to date, there has been limited work on computational models for this problem. We introduce a new dataset, DesireDB, which includes gold-standard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context. We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.",
}
@inproceedings{brixey-etal-2017-shihbot,
    title = "{SHIH}bot: A {F}acebook chatbot for Sexual Health Information on {HIV}/{AIDS}",
    author = "Brixey, Jacqueline  and
      Hoegen, Rens  and
      Lan, Wei  and
      Rusow, Joshua  and
      Singla, Karan  and
      Yin, Xusen  and
      Artstein, Ron  and
      Leuski, Anton",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5544",
    doi = "10.18653/v1/W17-5544",
    pages = "370--373",
    abstract = "We present the implementation of an autonomous chatbot, SHIHbot, deployed on Facebook, which answers a wide variety of sexual health questions on HIV/AIDS. The chatbot{'}s response database is com-piled from professional medical and public health resources in order to provide reliable information to users. The system{'}s backend is NPCEditor, a response selection platform trained on linked questions and answers; to our knowledge this is the first retrieval-based chatbot deployed on a large public social network.",
}
@inproceedings{ravichander-etal-2017-say,
    title = "How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing",
    author = "Ravichander, Abhilasha  and
      Manzini, Thomas  and
      Grabmair, Matthias  and
      Neubig, Graham  and
      Francis, Jonathan  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5545",
    doi = "10.18653/v1/W17-5545",
    pages = "374--383",
    abstract = "Building dialogue interfaces for real-world scenarios often entails training semantic parsers starting from zero examples. How can we build datasets that better capture the variety of ways users might phrase their queries, and what queries are actually realistic? Wang et al. (2015) proposed a method to build semantic parsing datasets by generating canonical utterances using a grammar and having crowdworkers paraphrase them into natural wording. A limitation of this approach is that it induces bias towards using similar language as the canonical utterances. In this work, we present a methodology that elicits meaningful and lexically diverse queries from users for semantic parsing tasks. Starting from a seed lexicon and a generative grammar, we pair logical forms with mixed text-image representations and ask crowdworkers to paraphrase and confirm the plausibility of the queries that they generated. We use this method to build a semantic parsing dataset from scratch for a dialog agent in a smart-home simulation. We find evidence that this dataset, which we have named SmartHome, is demonstrably more lexically diverse and difficult to parse than existing domain-specific semantic parsing datasets.",
}
@inproceedings{lison-bibauw-2017-dialogues,
    title = "Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models",
    author = "Lison, Pierre  and
      Bibauw, Serge",
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5546",
    doi = "10.18653/v1/W17-5546",
    pages = "384--394",
    abstract = "Neural conversational models require substantial amounts of dialogue data to estimate their parameters and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.",
}
@inproceedings{hohn-2017-data,
    title = "A data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language",
    author = {H{\"o}hn, Sviatlana},
    booktitle = "Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue",
    month = aug,
    year = "2017",
    address = {Saarbr{\"u}cken, Germany},
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5547",
    doi = "10.18653/v1/W17-5547",
    pages = "395--405",
    abstract = "This article describes a model of other-initiated self-repair for a chatbot that helps to practice conversation in a foreign language. The model was developed using a corpus of instant messaging conversations between German native and non-native speakers. Conversation Analysis helped to create computational models from a small number of examples. The model has been validated in an AIML-based chatbot. Unlike typical retrieval-based dialogue systems, the explanations are generated at run-time from a linguistic database.",
}
@proceedings{ws-2017-building-linguistically,
    title = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    editor = "Bender, Emily  and
      Daum{\'e} III, Hal  and
      Ettinger, Allyson  and
      Rao, Sudha",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5400",
    doi = "10.18653/v1/W17-54",
}
@inproceedings{ettinger-etal-2017-towards,
    title = "Towards Linguistically Generalizable {NLP} Systems: A Workshop and Shared Task",
    author = "Ettinger, Allyson  and
      Rao, Sudha  and
      Daum{\'e} III, Hal  and
      Bender, Emily M.",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5401",
    doi = "10.18653/v1/W17-5401",
    pages = "1--10",
    abstract = "This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task. The goal of this workshop was to bring together researchers in NLP and linguistics with a carefully designed shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data. We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned.",
}
@inproceedings{schneider-etal-2017-analysing,
    title = "Analysing Errors of Open Information Extraction Systems",
    author = {Schneider, Rudolf  and
      Oberhauser, Tom  and
      Klatt, Tobias  and
      Gers, Felix A.  and
      L{\"o}ser, Alexander},
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5402",
    doi = "10.18653/v1/W17-5402",
    pages = "11--18",
    abstract = "We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations. In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt. In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal important research directions for a next generation on OIE systems.",
}
@inproceedings{peters-etal-2017-massively,
    title = "Massively Multilingual Neural Grapheme-to-Phoneme Conversion",
    author = "Peters, Ben  and
      Dehdari, Jon  and
      van Genabith, Josef",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5403",
    doi = "10.18653/v1/W17-5403",
    pages = "19--26",
    abstract = "Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems. Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach to g2p which is trained on spelling{--}pronunciation pairs in hundreds of languages. The system shares a single encoder and decoder across all languages, allowing it to utilize the intrinsic similarities between different writing systems. We show an 11{\%} improvement in phoneme error rate over an approach based on adapting high-resource monolingual g2p models to low-resource languages. Our model is also much more compact relative to previous approaches.",
}
@inproceedings{li-etal-2017-bibi,
    title = "{BIBI} System Description: Building with {CNN}s and Breaking with Deep Reinforcement Learning",
    author = "Li, Yitong  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5404",
    doi = "10.18653/v1/W17-5404",
    pages = "27--32",
    abstract = "This paper describes our submission to the sentiment analysis sub-task of {``}Build It, Break It: The Language Edition (BIBI){''}, on both the builder and breaker sides. As a builder, we use convolutional neural nets, trained on both phrase and sentence data. As a breaker, we use Q-learning to learn minimal change pairs, and apply a token substitution method automatically. We analyse the results to gauge the robustness of NLP systems.",
}
@inproceedings{mahler-etal-2017-breaking,
    title = "Breaking {NLP}: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems",
    author = "Mahler, Taylor  and
      Cheung, Willy  and
      Elsner, Micha  and
      King, David  and
      de Marneffe, Marie-Catherine  and
      Shain, Cory  and
      Stevens-Guille, Symon  and
      White, Michael",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5405",
    doi = "10.18653/v1/W17-5405",
    pages = "33--39",
    abstract = "This paper describes our {``}breaker{''} submission to the 2017 EMNLP {``}Build It Break It{''} shared task on sentiment analysis. In order to cause the {``}builder{''} systems to make incorrect predictions, we edited items in the blind test data according to linguistically interpretable strategies that allow us to assess the ease with which the builder systems learn various components of linguistic structure. On the whole, our submitted pairs break all systems at a high rate (72.6{\%}), indicating that sentiment analysis as an NLP task may still have a lot of ground to cover. Of the breaker strategies that we consider, we find our semantic and pragmatic manipulations to pose the most substantial difficulties for the builder systems.",
}
@inproceedings{ferres-etal-2017-adaptable,
    title = "An Adaptable Lexical Simplification Architecture for Major {I}bero-{R}omance Languages",
    author = "Ferr{\'e}s, Daniel  and
      Saggion, Horacio  and
      G{\'o}mez Guinovart, Xavier",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5406",
    doi = "10.18653/v1/W17-5406",
    pages = "40--47",
    abstract = "Lexical Simplification is the task of reducing the lexical complexity of textual documents by replacing difficult words with easier to read (or understand) expressions while preserving the original meaning. The development of robust pipelined multilingual architectures able to adapt to new languages is of paramount importance in lexical simplification. This paper describes and evaluates a modular hybrid linguistic-statistical Lexical Simplifier that deals with the four major Ibero-Romance Languages: Spanish, Portuguese, Catalan, and Galician. The architecture of the system is the same for the four languages addressed, only the language resources used during simplification are language specific.",
}
@inproceedings{jurczyk-choi-2017-cross,
    title = "Cross-genre Document Retrieval: Matching between Conversational and Formal Writings",
    author = "Jurczyk, Tomasz  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5407",
    doi = "10.18653/v1/W17-5407",
    pages = "48--53",
    abstract = "This paper challenges a cross-genre document retrieval task, where the queries are in formal writing and the target documents are in conversational writing. In this task, a query, is a sentence extracted from either a summary or a plot of an episode in a TV show, and the target document consists of transcripts from the corresponding episode. To establish a strong baseline, we employ the current state-of-the-art search engine to perform document retrieval on the dataset collected for this work. We then introduce a structure reranking approach to improve the initial ranking by utilizing syntactic and semantic structures generated by NLP tools. Our evaluation shows an improvement of more than 4{\%} when the structure reranking is applied, which is very promising.",
}
@inproceedings{mukku-mamidi-2017-actsa,
    title = "{ACTSA}: Annotated Corpus for {T}elugu Sentiment Analysis",
    author = "Mukku, Sandeep Sricharan  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5408",
    doi = "10.18653/v1/W17-5408",
    pages = "54--58",
    abstract = "Sentiment analysis deals with the task of determining the polarity of a document or sentence and has received a lot of attention in recent years for the English language. With the rapid growth of social media these days, a lot of data is available in regional languages besides English. Telugu is one such regional language with abundant data available in social media, but it{'}s hard to find a labelled data of sentences for Telugu Sentiment Analysis. In this paper, we describe an effort to build a gold-standard annotated corpus of Telugu sentences to support Telugu Sentiment Analysis. The corpus, named ACTSA (Annotated Corpus for Telugu Sentiment Analysis) has a collection of Telugu sentences taken from different sources which were then pre-processed and manually annotated by native Telugu speakers using our annotation guidelines. In total, we have annotated 5457 sentences, which makes our corpus the largest resource currently available. The corpus and the annotation guidelines are made publicly available.",
}
@inproceedings{cho-2017-strawman,
    title = "{S}trawman: An Ensemble of Deep Bag-of-Ngrams for Sentiment Analysis",
    author = "Cho, Kyunghyun",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5409",
    doi = "10.18653/v1/W17-5409",
    pages = "59--60",
    abstract = "This paper describes a builder entry, named {``}strawman{''}, to the sentence-level sentiment analysis task of the {``}Build It, Break It{''} shared task of the First Workshop on Building Linguistically Generalizable NLP Systems. The goal of a builder is to provide an automated sentiment analyzer that would serve as a target for breakers whose goal is to find pairs of minimally-differing sentences that break the analyzer.",
}
@inproceedings{staliunaite-bonfil-2017-breaking,
    title = "Breaking Sentiment Analysis of Movie Reviews",
    author = "Stali{\=u}nait{\.e}, Ieva  and
      Bonfil, Ben",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5410",
    doi = "10.18653/v1/W17-5410",
    pages = "61--64",
    abstract = "The current paper covers several strategies we used to {`}break{'} predictions of sentiment analysis systems participating in the BLGNLP2017 workshop. Specifically, we identify difficulties of participating systems in understanding modals, subjective judgments, world-knowledge based references and certain differences in syntax and perspective.",
}
@proceedings{ws-2017-evaluating,
    title = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    editor = "Bowman, Samuel  and
      Goldberg, Yoav  and
      Hill, Felix  and
      Lazaridou, Angeliki  and
      Levy, Omer  and
      Reichart, Roi  and
      S{\o}gaard, Anders",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5300",
    doi = "10.18653/v1/W17-53",
}
@inproceedings{nangia-etal-2017-repeval,
    title = "The {R}ep{E}val 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations",
    author = "Nangia, Nikita  and
      Williams, Adina  and
      Lazaridou, Angeliki  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5301",
    doi = "10.18653/v1/W17-5301",
    pages = "1--10",
    abstract = "This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5{\%} accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genre-matched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domain-independent representations for sentence meaning.",
}
@inproceedings{che-etal-2017-traversal,
    title = "Traversal-Free Word Vector Evaluation in Analogy Space",
    author = "Che, Xiaoyin  and
      Ring, Nico  and
      Raschkowski, Willi  and
      Yang, Haojin  and
      Meinel, Christoph",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5302",
    doi = "10.18653/v1/W17-5302",
    pages = "11--15",
    abstract = "In this paper, we propose an alternative evaluating metric for word analogy questions (A to B is as C to D) in word vector evaluation. Different from the traditional method which predicts the fourth word by the given three, we measure the similarity directly on the {``}relations{''} of two pairs of given words, just as shifting the relation vectors into a new analogy space. Cosine and Euclidean distances are then calculated as measurements. Observation and experiments shows the proposed analogy space evaluation could offer a more comprehensive evaluating result on word vectors with word analogy questions. Meanwhile, computational complexity are remarkably reduced by avoiding traversing the vocabulary.",
}
@inproceedings{gurnani-2017-hypothesis,
    title = "Hypothesis Testing based Intrinsic Evaluation of Word Embeddings",
    author = "Gurnani, Nishant",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5303",
    doi = "10.18653/v1/W17-5303",
    pages = "16--20",
    abstract = "We introduce the cross-match test - an exact, distribution free, high-dimensional hypothesis test as an intrinsic evaluation metric for word embeddings. We show that cross-match is an effective means of measuring the distributional similarity between different vector representations and of evaluating the statistical significance of different vector embedding models. Additionally, we find that cross-match can be used to provide a quantitative measure of linguistic similarity for selecting bridge languages for machine translation. We demonstrate that the results of the hypothesis test align with our expectations and note that the framework of two sample hypothesis testing is not limited to word embeddings and can be extended to all vector representations.",
}
@inproceedings{auguste-etal-2017-evaluation,
    title = "Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks",
    author = "Auguste, Jeremy  and
      Rey, Arnaud  and
      Favre, Benoit",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5304",
    doi = "10.18653/v1/W17-5304",
    pages = "21--26",
    abstract = "This work presents a framework for word similarity evaluation grounded on cognitive sciences experimental data. Word pair similarities are compared to reaction times of subjects in large scale lexical decision and naming tasks under semantic priming. Results show that GloVe embeddings lead to significantly higher correlation with experimental measurements than other controlled and off-the-shelf embeddings, and that the choice of a training corpus is less important than that of the algorithm. Comparison of rankings with other datasets shows that the cognitive phenomenon covers more aspects than simply word relatedness or similarity.",
}
@inproceedings{gulati-agrawal-2017-playing,
    title = "Playing with Embeddings : Evaluating embeddings for Robot Language Learning through {MUD} Games",
    author = "Gulati, Anmol  and
      Agrawal, Kumar Krishna",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5305",
    doi = "10.18653/v1/W17-5305",
    pages = "27--30",
    abstract = "Acquiring language provides a ubiquitous mode of communication, across humans and robots. To this effect, distributional representations of words based on co-occurrence statistics, have provided significant advancements ranging across machine translation to comprehension. In this paper, we study the suitability of using general purpose word-embeddings for language learning in robots. We propose using text-based games as a proxy to evaluating word embedding on real robots. Based in a risk-reward setting, we review the effectiveness of the embeddings in navigating tasks in fantasy games, as an approximation to their performance on more complex scenarios, like language assisted robot navigation.",
}
@inproceedings{sulea-2017-recognizing,
    title = "Recognizing Textual Entailment in {T}witter Using Word Embeddings",
    author = "{\c{S}}ulea, Octavia-Maria",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5306",
    doi = "10.18653/v1/W17-5306",
    pages = "31--35",
    abstract = "In this paper, we investigate the application of machine learning techniques and word embeddings to the task of Recognizing Textual Entailment (RTE) in Social Media. We look at a manually labeled dataset consisting of user generated short texts posted on Twitter (tweets) and related to four recent media events (the Charlie Hebdo shooting, the Ottawa shooting, the Sydney Siege, and the German Wings crash) and test to what extent neural techniques and embeddings are able to distinguish between tweets that entail or contradict each other or that claim unrelated things. We obtain comparable results to the state of the art in a train-test setting, but we show that, due to the noisy aspect of the data, results plummet in an evaluation strategy crafted to better simulate a real-life train-test scenario.",
}
@inproceedings{chen-etal-2017-recurrent-neural,
    title = "Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference",
    author = "Chen, Qian  and
      Zhu, Xiaodan  and
      Ling, Zhen-Hua  and
      Wei, Si  and
      Jiang, Hui  and
      Inkpen, Diana",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5307",
    doi = "10.18653/v1/W17-5307",
    pages = "36--40",
    abstract = "The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task. This paper describes our system (alpha) that is ranked among the top in the Shared Task, on both the in-domain test set (obtaining a 74.9{\%} accuracy) and on the cross-domain test set (also attaining a 74.9{\%} accuracy), demonstrating that the model generalizes well to the cross-domain data. Our model is equipped with intra-sentence gated-attention composition which helps achieve a better performance. In addition to submitting our model to the Shared Task, we have also tested it on the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy of 85.5{\%}, which is the best reported result on SNLI when cross-sentence attention is not allowed, the same condition enforced in RepEval 2017.",
}
@inproceedings{nie-bansal-2017-shortcut,
    title = "Shortcut-Stacked Sentence Encoders for Multi-Domain Inference",
    author = "Nie, Yixin  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5308",
    doi = "10.18653/v1/W17-5308",
    pages = "41--45",
    abstract = "We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship between these two sentences as that of entailment, contradiction, or neural. Our Shortcut-Stacked sentence encoders achieve strong improvements over existing encoders on matched and mismatched multi-domain natural language inference (top single-model result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)). Moreover, they achieve the new state-of-the-art encoding result on the original SNLI dataset (Bowman et al., 2015).",
}
@inproceedings{yang-etal-2017-character,
    title = "Character-level Intra Attention Network for Natural Language Inference",
    author = "Yang, Han  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5309",
    doi = "10.18653/v1/W17-5309",
    pages = "46--50",
    abstract = "Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.",
}
@inproceedings{balazs-etal-2017-refining,
    title = "Refining Raw Sentence Representations for Textual Entailment Recognition via Attention",
    author = "Balazs, Jorge  and
      Marrese-Taylor, Edison  and
      Loyola, Pablo  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5310",
    doi = "10.18653/v1/W17-5310",
    pages = "51--55",
    abstract = "In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism. Finally it combines the refined representations of both sentences into a single vector to be used for classification. With this model we obtained test accuracies of 72.057{\%} and 72.055{\%} in the matched and mismatched evaluation tracks respectively, outperforming the LSTM baseline, and obtaining performances similar to a model that relies on shared information between sentences (ESIM). When using an ensemble both accuracies increased to 72.247{\%} and 72.827{\%} respectively.",
}
@inproceedings{vu-etal-2017-lct,
    title = "{LCT}-{MALTA}{'}s Submission to {R}ep{E}val 2017 Shared Task",
    author = "Vu, Hoa Trong  and
      Pham, Thuong-Hai  and
      Bai, Xiaoyu  and
      Tanti, Marc  and
      van der Plas, Lonneke  and
      Gatt, Albert",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5311",
    doi = "10.18653/v1/W17-5311",
    pages = "56--60",
    abstract = "System using BiLSTM and max pooling. Embedding is enhanced by POS, character and dependency info.",
}
@proceedings{ws-2017-approaches,
    title = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      van der Goot, Erik",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5200",
    doi = "10.18653/v1/W17-52",
}
@inproceedings{joshi-2017-detecting,
    title = "Detecting Sarcasm Using Different Forms Of Incongruity",
    author = "Joshi, Aditya",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5201",
    doi = "10.18653/v1/W17-5201",
    pages = "1",
    abstract = "Sarcasm is a form of verbal irony that is intended to express contempt or ridicule. Often quoted as a challenge to sentiment analysis, sarcasm involves use of words of positive or no polarity to convey negative sentiment. Incongruity has been observed to be at the heart of sarcasm understanding in humans. Our work in sarcasm detection identifies different forms of incongruity and employs different machine learning techniques to capture them. This talk will describe the approach, datasets and challenges in sarcasm detection using different forms of incongruity. We identify two forms of incongruity: incongruity which can be understood based on the target text and common background knowledge, and incongruity which can be understood based on the target text and additional, specific context. The former involves use of sentiment-based features, word embeddings, and topic models. The latter involves creation of author{'}s historical context based on their historical data, and creation of conversational context for sarcasm detection of dialogue.",
}
@inproceedings{barnes-etal-2017-assessing,
    title = "Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets",
    author = "Barnes, Jeremy  and
      Klinger, Roman  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5202",
    doi = "10.18653/v1/W17-5202",
    pages = "2--12",
    abstract = "There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets. In some papers, however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across different tasks and datasets. In this paper, we contribute to this situation by comparing several models on six different benchmarks, which belong to different domains and additionally have different levels of granularity (binary, 3-class, 4-class and 5-class). We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (\textit{i.e.}, with more than two classes). Incorporating sentiment information into word embeddings during training gives good results for datasets that are lexically similar to the training data. With our experiments, we contribute to a better understanding of the performance of different model architectures on different data sets. Consequently, we detect novel state-of-the-art results on the \textit{SenTube} datasets.",
}
@inproceedings{schuff-etal-2017-annotation,
    title = "Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus",
    author = "Schuff, Hendrik  and
      Barnes, Jeremy  and
      Mohme, Julian  and
      Pad{\'o}, Sebastian  and
      Klinger, Roman",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5203",
    doi = "10.18653/v1/W17-5203",
    pages = "13--23",
    abstract = "There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification). For the more challenging task of detecting discrete emotions following the definitions of Ekman and Plutchik, however, there are much fewer data sets, and notably no resources for the social media domain. This paper contributes to closing this gap by extending the \textit{SemEval 2016 stance and sentiment dataset}with emotion annotation. We (a) analyse annotation reliability and annotation merging; (b) investigate the relation between emotion annotation and the other annotation layers (stance, sentiment); (c) report modelling results as a baseline for future work.",
}
@inproceedings{hartung-etal-2017-ranking,
    title = "Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups",
    author = "Hartung, Matthias  and
      Klinger, Roman  and
      Schmidtke, Franziska  and
      Vogel, Lars",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5204",
    doi = "10.18653/v1/W17-5204",
    pages = "24--33",
    abstract = "Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extremist motives such as mobilization, recruiting or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their monitoring efforts, mostly relying on manual workflows. We propose a machine learning approach to support manual attempts towards identifying right-wing extremist content in German Twitter profiles. Based on a fine-grained conceptualization of right-wing extremism, we frame the task as ranking each individual profile on a continuum spanning different degrees of right-wing extremism, based on a nearest neighbour approach. A quantitative evaluation reveals that our ranking model yields robust performance (up to 0.81 F$_1$ score) when being used for predicting discrete class labels. At the same time, the model provides plausible continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements.",
}
@inproceedings{mohammad-bravo-marquez-2017-wassa,
    title = "{WASSA}-2017 Shared Task on Emotion Intensity",
    author = "Mohammad, Saif  and
      Bravo-Marquez, Felipe",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5205",
    doi = "10.18653/v1/W17-5205",
    pages = "34--49",
    abstract = "We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best{--}worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.",
}
@inproceedings{koper-etal-2017-ims,
    title = "{IMS} at {E}mo{I}nt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning",
    author = {K{\"o}per, Maximilian  and
      Kim, Evgeny  and
      Klinger, Roman},
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5206",
    doi = "10.18653/v1/W17-5206",
    pages = "50--57",
    abstract = "Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms. We combine three main information sources in a random forrest regressor, namely (1), manually created resources, (2) automatically extended lexicons, and (3) the output of a neural network (CNN-LSTM) for sentence regression. All three feature sets perform similarly well in isolation ({\mbox{$\approx$}} .67 macro average Pearson correlation). The combination achieves .72 on the official test set (ranked 2nd out of 22 participants). Our analysis reveals that performance is increased by providing cross-emotional intensity predictions. The automatic extension of lexicon features benefit from domain specific embeddings. Complementary ratings for affective norms increase the impact of lexicon features. Our resources (ratings for 1.6 million twitter specific words) and our implementation is publicly available at \url{http://www.ims.uni-stuttgart.de/data/ims_emoint}.",
}
@inproceedings{goel-etal-2017-prayas,
    title = "Prayas at {E}mo{I}nt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets",
    author = "Goel, Pranav  and
      Kulshreshtha, Devang  and
      Jain, Prayas  and
      Shukla, Kaushal Kumar",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5207",
    doi = "10.18653/v1/W17-5207",
    pages = "58--65",
    abstract = "The paper describes the best performing system for EmoInt - a shared task to predict the intensity of emotions in tweets. Intensity is a real valued score, between 0 and 1. The emotions are classified as - anger, fear, joy and sadness. We apply three different deep neural network based models, which approach the problem from essentially different directions. Our final performance quantified by an average pearson correlation score of 74.7 and an average spearman correlation score of 73.5 is obtained using an ensemble of the three models. We outperform the baseline model of the shared task by 9.9{\%} and 9.4{\%} pearson and spearman correlation scores respectively.",
}
@inproceedings{gurevych-2017-latest,
    title = "Latest News in Computational Argumentation: Surfing on the Deep Learning Wave, Scuba Diving in the Abyss of Fundamental Questions",
    author = "Gurevych, Iryna",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5208",
    doi = "10.18653/v1/W17-5208",
    pages = "66",
    abstract = "Mining arguments from natural language texts, parsing argumentative structures, and assessing argument quality are among the recent challeng-es tackled in computational argumentation. While advanced deep learning models provide state-of-the-art performance in many of these tasks, much attention is also paid to the underly-ing fundamental questions. How are arguments expressed in natural language across genres and domains? What is the essence of an argument{'}s claim? Can we reliably annotate convincingness of an argument? How can we approach logic and common-sense reasoning in argumentation? This talk highlights some recent advances in computa-tional argumentation and shows why researchers must be both {``}surfers{''} and {``}scuba divers{''}.",
}
@inproceedings{vilares-etal-2017-towards,
    title = "Towards Syntactic {I}berian Polarity Classification",
    author = "Vilares, David  and
      Garcia, Marcos  and
      Alonso, Miguel A.  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5209",
    doi = "10.18653/v1/W17-5209",
    pages = "67--73",
    abstract = "Lexicon-based methods using syntactic rules for polarity classification rely on parsers that are dependent on the language and on treebank guidelines. Thus, rules are also dependent and require adaptation, especially in multilingual scenarios. We tackle this challenge in the context of the Iberian Peninsula, releasing the first symbolic syntax-based Iberian system with rules shared across five official languages: Basque, Catalan, Galician, Portuguese and Spanish. The model is made available.",
}
@inproceedings{boltuzic-snajder-2017-toward,
    title = "Toward Stance Classification Based on Claim Microstructures",
    author = "Boltu{\v{z}}i{\'c}, Filip  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5210",
    doi = "10.18653/v1/W17-5210",
    pages = "74--80",
    abstract = "Claims are the building blocks of arguments and the reasons underpinning opinions, thus analyzing claims is important for both argumentation mining and opinion mining. We propose a framework for representing claims as microstructures, which express the beliefs, judgments, and policies about the relations between domain-specific concepts. In a proof-of-concept study, we manually build microstructures for over 800 claims extracted from an online debate. We test the so-obtained microstructures on the task of claim stance classification, achieving considerable improvements over text-based baselines.",
}
@inproceedings{wu-etal-2017-linguistic,
    title = "Linguistic Reflexes of Well-Being and Happiness in Echo",
    author = "Wu, Jiaqi  and
      Walker, Marilyn  and
      Anand, Pranav  and
      Whittaker, Steve",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5211",
    doi = "10.18653/v1/W17-5211",
    pages = "81--91",
    abstract = "Different theories posit different sources for feelings of well-being and happiness. Appraisal theory grounds our emotional responses in our goals and desires and their fulfillment, or lack of fulfillment. Self-Determination theory posits that the basis for well-being rests on our assessments of our competence, autonomy and social connection. And surveys that measure happiness empirically note that people require their basic needs to be met for food and shelter, but beyond that tend to be happiest when socializing, eating or having sex. We analyze a corpus of private micro-blogs from a well-being application called Echo, where users label each written post about daily events with a happiness score between 1 and 9. Our goal is to ground the linguistic descriptions of events that users experience in theories of well-being and happiness, and then examine the extent to which different theoretical accounts can explain the variance in the happiness scores. We show that recurrent event types, such as obligation and incompetence, which affect people{'}s feelings of well-being are not captured in current lexical or semantic resources.",
}
@inproceedings{pekar-binner-2017-forecasting,
    title = "Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media",
    author = "Pekar, Viktor  and
      Binner, Jane",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5212",
    doi = "10.18653/v1/W17-5212",
    pages = "92--101",
    abstract = "Consumer spending is an important macroeconomic indicator that is used by policy-makers to judge the health of an economy. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11{\%} to 18{\%} for all the three models, in comparison to models that used only autoregressive predictors.",
}
@inproceedings{marrese-taylor-etal-2017-mining,
    title = "Mining fine-grained opinions on closed captions of {Y}ou{T}ube videos with an attention-{RNN}",
    author = "Marrese-Taylor, Edison  and
      Balazs, Jorge  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5213",
    doi = "10.18653/v1/W17-5213",
    pages = "102--111",
    abstract = "Video reviews are the natural evolution of written product reviews. In this paper we target this phenomenon and introduce the first dataset created from closed captions of YouTube product review videos as well as a new attention-RNN model for aspect extraction and joint aspect extraction and sentiment classification. Our model provides state-of-the-art performance on aspect extraction without requiring the usage of hand-crafted features on the SemEval ABSA corpus, while it outperforms the baseline on the joint task. In our dataset, the attention-RNN model outperforms the baseline for both tasks, but we observe important performance drops for all models in comparison to SemEval. These results, as well as further experiments on domain adaptation for aspect extraction, suggest that differences between speech and written text, which have been discussed extensively in the literature, also extend to the domain of product reviews, where they are relevant for fine-grained opinion mining.",
}
@inproceedings{balahur-2017-understanding,
    title = "Understanding human values and their emotional effect",
    author = "Balahur, Alexandra",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5214",
    doi = "10.18653/v1/W17-5214",
    pages = "112",
    abstract = "Emotions can be triggered by various factors. According to the Appraisal Theories (De Rivera, 1977; Frijda, 1986; Ortony et al., 1988; Johnson-Laird and Oatley, 1989) emotions are elicited and differentiated on the basis of the cognitive evaluation of the personal significance of a situation, object or event based on {``}appraisal criteria{''} (intrinsic characteristics of objects and events, significance of events to individual needs and goals, individual{'}s ability to cope with the consequences of the event, compatibility of event with social or personal standards, norms and values). These differences in values can trigger reactions such as anger, disgust (contempt), sadness, etc., because these behaviors are evaluated by the public as being incompatible with their social/personal standards, norms or values. Such arguments are frequently present both in mainstream media, as well as social media, building a society-wide view, attitude and emotional reaction towards refugees/immigrants. In this demo, I will talk about experiments to annotate and detect factual arguments that are linked to human needs/motivations from text and in consequence trigger emotion in the media audience and propose a new task for next year{'}s WASSA.",
}
@inproceedings{padilla-lopez-cap-2017-ever,
    title = "Did you ever read about Frogs drinking Coffee? Investigating the Compositionality of Multi-Emoji Expressions",
    author = "Padilla L{\'o}pez, Rebeca  and
      Cap, Fabienne",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5215",
    doi = "10.18653/v1/W17-5215",
    pages = "113--117",
    abstract = "In this work, we present a first attempt to investigate multi-emoji expressions and whether they behave similarly to multiword expressions in terms of non-compositionality. We focus on the combination of the frog and the hot beverage emoji, but also show some preliminary results for other non-compositional emoji combinations. We use off-the-shelf sentiment analysers as well as manual classifications to approach the compositionality of these emoji combinations.",
}
@inproceedings{donato-paggio-2017-investigating,
    title = "Investigating Redundancy in Emoji Use: Study on a {T}witter Based Corpus",
    author = "Donato, Giulia  and
      Paggio, Patrizia",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5216",
    doi = "10.18653/v1/W17-5216",
    pages = "118--126",
    abstract = "In this paper we present an annotated corpus created with the aim of analyzing the informative behaviour of emoji {--} an issue of importance for sentiment analysis and natural language processing. The corpus consists of 2475 tweets all containing at least one emoji, which has been annotated using one of the three possible classes: Redundant, Non Redundant, and Non Redundant + POS. We explain how the corpus was collected, describe the annotation procedure and the interface developed for the task. We provide an analysis of the corpus, considering also possible predictive features, discuss the problematic aspects of the annotation, and suggest future improvements.",
}
@inproceedings{halder-etal-2017-modeling,
    title = "Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach",
    author = "Halder, Kishaloy  and
      Poddar, Lahari  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5217",
    doi = "10.18653/v1/W17-5217",
    pages = "127--135",
    abstract = "Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual{'}s posts. We analyze a real-world dataset from the Mental Health section of HealthBoards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely. We study the problem of predicting a patient{'}s emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.",
}
@inproceedings{de-clercq-etal-2017-towards,
    title = "Towards an integrated pipeline for aspect-based sentiment analysis in various domains",
    author = "De Clercq, Orph{\'e}e  and
      Lefever, Els  and
      Jacobs, Gilles  and
      Carpels, Tijl  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5218",
    doi = "10.18653/v1/W17-5218",
    pages = "136--142",
    abstract = "This paper presents an integrated ABSA pipeline for Dutch that has been developed and tested on qualitative user feedback coming from three domains: retail, banking and human resources. The two latter domains provide service-oriented data, which has not been investigated before in ABSA. By performing in-domain and cross-domain experiments the validity of our approach was investigated. We show promising results for the three ABSA subtasks, aspect term extraction, aspect category classification and aspect polarity classification.",
}
@inproceedings{mohanty-etal-2017-building,
    title = "Building a {S}enti{W}ord{N}et for {O}dia",
    author = "Mohanty, Gaurav  and
      Kannan, Abishek  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5219",
    doi = "10.18653/v1/W17-5219",
    pages = "143--148",
    abstract = "As a discipline of Natural Language Processing, Sentiment Analysis is used to extract and analyze subjective information present in natural language data. The task of Sentiment Analysis has acquired wide commercial uses including social media monitoring tasks, survey responses, review systems, etc. Languages like English have several resources which aid in the task of Sentiment Analysis. SentiWordNet and Subjectivity WordList are examples of such tools and resources. With more data being available in native vernacular, language-specific SentiWordNet(s) have become essential. For resource poor languages, creating such SentiWordNet(s) is a difficult task to achieve. One solution is to use available resources in English and translate the final source lexicon to target lexicon via machine translation. Machine translation systems for the English-Odia language pair have not yet been developed. In this paper, we discuss a method to create a SentiWordNet for Odia, which is resource-poor, by only using resources which are currently available for Indian languages. The lexicon created, would serve as a tool for Sentiment Analysis related task specific to Odia data.",
}
@inproceedings{shin-etal-2017-lexicon,
    title = "Lexicon Integrated {CNN} Models with Attention for Sentiment Analysis",
    author = "Shin, Bonggun  and
      Lee, Timothy  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5220",
    doi = "10.18653/v1/W17-5220",
    pages = "149--158",
    abstract = "With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentiment analysis that integrates lexicon embeddings and an attention mechanism into Convolutional Neural Networks. Our approach performs separate convolutions for word and lexicon embeddings and provides a global view of the document using attention. Our models are experimented on both the SemEval{'}16 Task 4 dataset and the Stanford Sentiment Treebank and show comparative or better results against the existing state-of-the-art systems. Our analysis shows that lexicon embeddings allow building high-performing models with much smaller word embeddings, and the attention mechanism effectively dims out noisy words for sentiment analysis.",
}
@inproceedings{arras-etal-2017-explaining,
    title = "Explaining Recurrent Neural Network Predictions in Sentiment Analysis",
    author = {Arras, Leila  and
      Montavon, Gr{\'e}goire  and
      M{\"u}ller, Klaus-Robert  and
      Samek, Wojciech},
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5221",
    doi = "10.18653/v1/W17-5221",
    pages = "159--168",
    abstract = "Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.",
}
@inproceedings{lakomkin-etal-2017-gradascent,
    title = "{G}rad{A}scent at {E}mo{I}nt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection",
    author = "Lakomkin, Egor  and
      Bothe, Chandrakant  and
      Wermter, Stefan",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5222",
    doi = "10.18653/v1/W17-5222",
    pages = "169--174",
    abstract = "The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity values of tweet messages. Given the text of a tweet and its emotion category (anger, joy, fear, and sadness), the participants were asked to build a system that assigns emotion intensity values. Emotion intensity estimation is a challenging problem given the short length of the tweets, the noisy structure of the text and the lack of annotated data. To solve this problem, we developed an ensemble of two neural models, processing input on the character. and word-level with a lexicon-driven system. The correlation scores across all four emotions are averaged to determine the bottom-line competition metric, and our system ranks place forth in full intensity range and third in 0.5-1 range of intensity among 23 systems at the time of writing (June 2017).",
}
@inproceedings{andryushechkin-etal-2017-nuig,
    title = "{NUIG} at {E}mo{I}nt-2017: {B}i{LSTM} and {SVR} Ensemble to Detect Emotion Intensity",
    author = "Andryushechkin, Vladimir  and
      Wood, Ian  and
      O{'} Neill, James",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5223",
    doi = "10.18653/v1/W17-5223",
    pages = "175--179",
    abstract = "This paper describes the entry NUIG in the WASSA 2017 (8th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis) shared task on emotion recognition. The NUIG system used an SVR (SVM regression) and BLSTM ensemble, utilizing primarily n-grams (for SVR features) and tweet word embeddings (for BLSTM features). Experiments were carried out on several other candidate features, some of which were added to the SVR model. Parameter selection for the SVR model was run as a grid search whilst parameters for the BLSTM model were selected through a non-exhaustive ad-hoc search.",
}
@inproceedings{giannakopoulos-etal-2017-unsupervised,
    title = "Unsupervised Aspect Term Extraction with {B}-{LSTM} {\&} {CRF} using Automatically Labelled Datasets",
    author = "Giannakopoulos, Athanasios  and
      Musat, Claudiu  and
      Hossmann, Andreea  and
      Baeriswyl, Michael",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5224",
    doi = "10.18653/v1/W17-5224",
    pages = "180--188",
    abstract = "Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA) contest. The small amount of available datasets for supervised ATE and the costly human annotation for aspect term labelling give rise to the need for unsupervised ATE. In this paper, we introduce an architecture that achieves top-ranking performance for supervised ATE. Moreover, it can be used efficiently as feature extractor and classifier for unsupervised ATE. Our second contribution is a method to automatically construct datasets for ATE. We train a classifier on our automatically labelled datasets and evaluate it on the human annotated SemEval ABSA test sets. Compared to a strong rule-based baseline, we obtain a dramatically higher F-score and attain precision values above 80{\%}. Our unsupervised method beats the supervised ABSA baseline from SemEval, while preserving high precision scores.",
}
@inproceedings{santos-vieira-2017-pln,
    title = "{PLN}-{PUCRS} at {E}mo{I}nt-2017: Psycholinguistic features for emotion intensity prediction in tweets",
    author = "Santos, Henrique  and
      Vieira, Renata",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5225",
    doi = "10.18653/v1/W17-5225",
    pages = "189--192",
    abstract = "Linguistic Inquiry and Word Count (LIWC) is a rich dictionary that map words into several psychological categories such as Affective, Social, Cognitive, Perceptual and Biological processes. In this work, we have used LIWC psycholinguistic categories to train regression models and predict emotion intensity in tweets for the EmoInt-2017 task. Results show that LIWC features may boost emotion intensity prediction on the basis of a low dimension set.",
}
@inproceedings{meisheri-etal-2017-textmining,
    title = "Textmining at {E}mo{I}nt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of {E}nglish Tweets",
    author = "Meisheri, Hardik  and
      Saha, Rupsa  and
      Sinha, Priyanka  and
      Dey, Lipika",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5226",
    doi = "10.18653/v1/W17-5226",
    pages = "193--199",
    abstract = "This paper describes our approach to the Emotion Intensity shared task. A parallel architecture of Convolutional Neural Network (CNN) and Long short term memory networks (LSTM) alongwith two sets of features are extracted which aid the network in judging emotion intensity. Experiments on different models and various features sets are described and analysis on results has also been presented.",
}
@inproceedings{zhang-etal-2017-ynu,
    title = "{YNU}-{HPCC} at {E}mo{I}nt-2017: Using a {CNN}-{LSTM} Model for Sentiment Intensity Prediction",
    author = "Zhang, You  and
      Yuan, Hang  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5227",
    doi = "10.18653/v1/W17-5227",
    pages = "200--204",
    abstract = "In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has two combined parts: CNN extracts local n-gram features within tweets and LSTM composes the features to capture long-distance dependency across tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as baseline algorithms. Our introduced model showed good performance in the experimental results.",
}
@inproceedings{duppada-hiray-2017-seernet,
    title = "Seernet at {E}mo{I}nt-2017: Tweet Emotion Intensity Estimator",
    author = "Duppada, Venkatesh  and
      Hiray, Sushant",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5228",
    doi = "10.18653/v1/W17-5228",
    pages = "205--211",
    abstract = "The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines various independent feature extractors, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in leaderboard of WASSA-2017 Shared Task on Emotion Intensity.",
}
@inproceedings{akhtar-etal-2017-iitp,
    title = "{IITP} at {E}mo{I}nt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features",
    author = "Akhtar, Md Shad  and
      Sawant, Palaash  and
      Ekbal, Asif  and
      Pawar, Jyoti  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5229",
    doi = "10.18653/v1/W17-5229",
    pages = "212--218",
    abstract = "This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017). We propose a Long short term memory (LSTM) based architecture cascaded with Support Vector Regressor (SVR) for intensity prediction. We also employ Particle Swarm Optimization (PSO) based feature selection algorithm for obtaining an optimized feature set for training and evaluation. System evaluation shows interesting results on the four emotion datasets i.e. anger, fear, joy and sadness. In comparison to the other participating teams our system was ranked 5th in the competition.",
}
@inproceedings{madisetty-desarkar-2017-nsemo,
    title = "{NSE}mo at {E}mo{I}nt-2017: An Ensemble to Predict Emotion Intensity in Tweets",
    author = "Madisetty, Sreekanth  and
      Desarkar, Maunendra Sankar",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5230",
    doi = "10.18653/v1/W17-5230",
    pages = "219--224",
    abstract = "In this paper, we describe a method to predict emotion intensity in tweets. Our approach is an ensemble of three regression methods. The first method uses content-based features (hashtags, emoticons, elongated words, etc.). The second method considers word n-grams and character n-grams for training. The final method uses lexicons, word embeddings, word n-grams, character n-grams for training the model. An ensemble of these three methods gives better performance than individual methods. We applied our method on WASSA emotion dataset. Achieved results are as follows: average Pearson correlation is 0.706, average Spearman correlation is 0.696, average Pearson correlation for gold scores in range 0.5 to 1 is 0.539, and average Spearman correlation for gold scores in range 0.5 to 1 is 0.514.",
}
@inproceedings{moreno-ortiz-2017-tecnolengua,
    title = "{T}ecnolengua {L}ingmotif at {E}mo{I}nt-2017: A lexicon-based approach",
    author = "Moreno-Ortiz, Antonio",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5231",
    doi = "10.18653/v1/W17-5231",
    pages = "225--232",
    abstract = "In this paper we describe Tecnolengua Group{'}s participation in the shared task on emotion intensity at WASSA 2017. We used the Lingmotif tool and a new, complementary tool, Lingmotif Learn, which we developed for this occasion. We based our intensity predictions for the four test datasets entirely on Lingmotif{'}s TSS (text sentiment score) feature. We also developed mechanisms for dealing with the idiosyncrasies of Twitter text. Results were comparatively poor, but the experience meant a good opportunity for us to identify issues in our score calculation for short texts, a genre for which the Lingmotif tool was not originally designed.",
}
@inproceedings{marrese-taylor-matsuo-2017-emoatt,
    title = "{E}mo{A}tt at {E}mo{I}nt-2017: Inner attention sentence embedding for Emotion Intensity",
    author = "Marrese-Taylor, Edison  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5232",
    doi = "10.18653/v1/W17-5232",
    pages = "233--237",
    abstract = "In this paper we describe a deep learning system that has been designed and built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a representation learning approach based on inner attention on top of an RNN. Results show that our model offers good capabilities and is able to successfully identify emotion-bearing words to predict intensity without leveraging on lexicons, obtaining the 13t place among 22 shared task competitors.",
}
@inproceedings{he-etal-2017-yzu,
    title = "{YZU}-{NLP} at {E}mo{I}nt-2017: Determining Emotion Intensity Using a Bi-directional {LSTM}-{CNN} Model",
    author = "He, Yuanye  and
      Yu, Liang-Chih  and
      Lai, K. Robert  and
      Liu, Weiyi",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5233",
    doi = "10.18653/v1/W17-5233",
    pages = "238--242",
    abstract = "The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient.",
}
@inproceedings{jiang-han-2017-dmgroup,
    title = "{DMG}roup at {E}mo{I}nt-2017: Emotion Intensity Using Ensemble Method",
    author = "Jiang, Song  and
      Han, Xiaotian",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5234",
    doi = "10.18653/v1/W17-5234",
    pages = "243--248",
    abstract = "In this paper, we present a novel ensemble learning architecture for emotion intensity analysis, particularly a novel framework of ensemble method. The ensemble method has two stages and each stage includes several single machine learning models. In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation. In stage2, we use two regression models including linear regression and XGBoost. The result of stage1 serves as the input of stage2, so the two different type models (linear and non-linear) in stage2 can describe the input in two opposite aspects. We also added a method for analyzing and splitting multi-words hashtags and appending them to the emotion intensity corpus before feeding it to our model. Our model achieves 0.571 Pearson-measure for the average of four emotions.",
}
@inproceedings{john-vechtomova-2017-uwat,
    title = "{UW}at-Emote at {E}mo{I}nt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings",
    author = "John, Vineet  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5235",
    doi = "10.18653/v1/W17-5235",
    pages = "249--254",
    abstract = "This paper describes the UWaterloo affect prediction system developed for EmoInt-2017. We delve into our feature selection approach for affect intensity, affect presence, sentiment intensity and sentiment presence lexica alongside pre-trained word embeddings, which are utilized to extract emotion intensity signals from tweets in an ensemble learning approach. The system employs emotion specific model training, and utilizes distinct models for each of the emotion corpora in isolation. Our system utilizes gradient boosted regression as the primary learning technique to predict the final emotion intensities.",
}
@inproceedings{buscaldi-priego-2017-lipn,
    title = "{LIPN}-{UAM} at {E}mo{I}nt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination",
    author = "Buscaldi, Davide  and
      Priego, Belem",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5236",
    doi = "10.18653/v1/W17-5236",
    pages = "255--258",
    abstract = "This paper presents the combined LIPN-UAM participation in the WASSA 2017 Shared Task on Emotion Intensity. In particular, the paper provides some highlights on the Tweetaneuse system that was presented to the shared task. We combined lexicon-based features with sentence-level vector representations to implement a random forest regressor.",
}
@inproceedings{r-etal-2017-deepcybernet,
    title = "deep{C}yb{E}r{N}et at {E}mo{I}nt-2017: Deep Emotion Intensities in Tweets",
    author = "R, Vinayakumar  and
      B, Premjith  and
      S, Sachin Kumar  and
      KP, Soman  and
      Poornachandran, Prabaharan",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5237",
    doi = "10.18653/v1/W17-5237",
    pages = "259--263",
    abstract = "This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods are apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.",
}
@proceedings{ws-2017-argument,
    title = "Proceedings of the 4th Workshop on Argument Mining",
    editor = "Habernal, Ivan  and
      Gurevych, Iryna  and
      Ashley, Kevin  and
      Cardie, Claire  and
      Green, Nancy  and
      Litman, Diane  and
      Petasis, Georgios  and
      Reed, Chris  and
      Slonim, Noam  and
      Walker, Vern",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5100",
    doi = "10.18653/v1/W17-51",
}
@inproceedings{fierro-etal-2017-200k,
    title = "200{K}+ Crowdsourced Political Arguments for a New {C}hilean Constitution",
    author = "Fierro, Constanza  and
      Fuentes, Claudio  and
      P{\'e}rez, Jorge  and
      Quezada, Mauricio",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5101",
    doi = "10.18653/v1/W17-5101",
    pages = "1--10",
    abstract = "In this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods with neural network architectures. The experiments show that some of the manual tasks are suitable for automatization. In particular, the best methods achieve a 90{\%} top-5 accuracy in a multi-class classification of arguments, and 65{\%} macro-averaged F1-score for tagging arguments according to a three-part argumentation model.",
}
@inproceedings{hidey-etal-2017-analyzing,
    title = "Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum",
    author = "Hidey, Christopher  and
      Musi, Elena  and
      Hwang, Alyssa  and
      Muresan, Smaranda  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5102",
    doi = "10.18653/v1/W17-5102",
    pages = "11--21",
    abstract = "Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e.g., claims, premises) and their argumentative relations (e.g., support, attack). Less emphasis has been placed on analyzing the semantic types of argument components. We propose a two-tiered annotation scheme to label claims and premises and their semantic types in an online persuasive forum, Change My View, with the long-term goal of understanding what makes a message persuasive. Premises are annotated with the three types of persuasive modes: ethos, logos, pathos, while claims are labeled as interpretation, evaluation, agreement, or disagreement, the latter two designed to account for the dialogical nature of our corpus. We aim to answer three questions: 1) can humans reliably annotate the semantic types of argument components? 2) are types of premises/claims positioned in recurrent orders? and 3) are certain types of claims and/or premises more likely to appear in persuasive messages than in non-persuasive messages?",
}
@inproceedings{yamada-etal-2017-annotation,
    title = "Annotation of argument structure in {J}apanese legal documents",
    author = "Yamada, Hiroaki  and
      Teufel, Simone  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5103",
    doi = "10.18653/v1/W17-5103",
    pages = "22--31",
    abstract = "We propose a method for the annotation of Japanese civil judgment documents, with the purpose of creating flexible summaries of these. The first step, described in the current paper, concerns content selection, i.e., the question of which material should be extracted initially for the summary. In particular, we utilize the hierarchical argument structure of the judgment documents. Our main contributions are a) the design of an annotation scheme that stresses the connection between legal points (called issue topics) and argument structure, b) an adaptation of rhetorical status to suit the Japanese legal system and c) the definition of a linked argument structure based on legal sub-arguments. In this paper, we report agreement between two annotators on several aspects of the overall task.",
}
@inproceedings{bar-haim-etal-2017-improving,
    title = "Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization",
    author = "Bar-Haim, Roy  and
      Edelstein, Lilach  and
      Jochim, Charles  and
      Slonim, Noam",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5104",
    doi = "10.18653/v1/W17-5104",
    pages = "32--38",
    abstract = "Stance classification is a core component in on-demand argument construction pipelines. Previous work on claim stance classification relied on background knowledge such as manually-composed sentiment lexicons. We show that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon. We also developed a set of contextual features that further improves the state-of-the-art for this task.",
}
@inproceedings{lawrence-reed-2017-mining,
    title = "Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models",
    author = "Lawrence, John  and
      Reed, Chris",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5105",
    doi = "10.18653/v1/W17-5105",
    pages = "39--48",
    abstract = "This paper presents a method of extracting argumentative structure from natural language text. The approach presented is based on the way in which we understand an argument being made, not just from the words said, but from existing contextual knowledge and understanding of the broader issues. We leverage high-precision, low-recall techniques in order to automatically build a large corpus of inferential statements related to the text{'}s topic. These statements are then used to produce a matrix representing the inferential relationship between different aspects of the topic. From this matrix, we are able to determine connectedness and directionality of inference between statements in the original text. By following this approach, we obtain results that compare favourably to those of other similar techniques to classify premise-conclusion pairs (with results 22 points above baseline), but without the requirement of large volumes of annotated, domain specific data.",
}
@inproceedings{wachsmuth-etal-2017-building,
    title = "Building an Argument Search Engine for the Web",
    author = "Wachsmuth, Henning  and
      Potthast, Martin  and
      Al-Khatib, Khalid  and
      Ajjour, Yamen  and
      Puschmann, Jana  and
      Qu, Jiani  and
      Dorsch, Jonas  and
      Morari, Viorel  and
      Bevendorff, Janek  and
      Stein, Benno",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5106",
    doi = "10.18653/v1/W17-5106",
    pages = "49--59",
    abstract = "Computational argumentation is expected to play a critical role in the future of web search. To make this happen, many search-related questions must be revisited, such as how people query for arguments, how to mine arguments from the web, or how to rank them. In this paper, we develop an argument search framework for studying these and further questions. The framework allows for the composition of approaches to acquiring, mining, assessing, indexing, querying, retrieving, ranking, and presenting arguments while relying on standard infrastructure and interfaces. Based on the framework, we build a prototype search engine, called args, that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources. The framework and the argument search engine are intended as an environment for collaborative research on computational argumentation and its practical evaluation.",
}
@inproceedings{hou-jochim-2017-argument,
    title = "Argument Relation Classification Using a Joint Inference Model",
    author = "Hou, Yufang  and
      Jochim, Charles",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5107",
    doi = "10.18653/v1/W17-5107",
    pages = "60--66",
    abstract = "In this paper, we address the problem of argument relation classification where argument units are from different texts. We design a joint inference method for the task by modeling argument relation classification and stance classification jointly. We show that our joint model improves the results over several strong baselines.",
}
@inproceedings{aker-zhang-2017-projection,
    title = "Projection of Argumentative Corpora from Source to Target Languages",
    author = "Aker, Ahmet  and
      Zhang, Huangpan",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5108",
    doi = "10.18653/v1/W17-5108",
    pages = "67--72",
    abstract = "Argumentative corpora are costly to create and are available in only few languages with English dominating the area. In this paper we release the first publicly available Mandarin argumentative corpus. The corpus is created by exploiting the idea of comparable corpora from Statistical Machine Translation. We use existing corpora in English and manually map the claims and premises to comparable corpora in Mandarin. We also implement a simple solution to automate this approach with the view of creating argumentative corpora in other less-resourced languages. In this way we introduce a new task of multi-lingual argument mapping that can be evaluated using our English-Mandarin argumentative corpus. The preliminary results of our automatic argument mapper mirror the simplicity of our approach, but provide a baseline for further improvements.",
}
@inproceedings{green-2017-manual,
    title = "Manual Identification of Arguments with Implicit Conclusions Using Semantic Rules for Argument Mining",
    author = "Green, Nancy",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5109",
    doi = "10.18653/v1/W17-5109",
    pages = "73--78",
    abstract = "This paper describes a pilot study to evaluate human analysts{'} ability to identify the argumentation scheme and premises of an argument having an implicit conclusion. In preparation for the study, argumentation scheme definitions were crafted for genetics research articles. The schemes were defined in semantic terms, following a proposal to use semantic rules to mine arguments in that literature.",
}
@inproceedings{levy-etal-2017-unsupervised,
    title = "Unsupervised corpus{--}wide claim detection",
    author = "Levy, Ran  and
      Gretz, Shai  and
      Sznajder, Benjamin  and
      Hummel, Shay  and
      Aharonov, Ranit  and
      Slonim, Noam",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5110",
    doi = "10.18653/v1/W17-5110",
    pages = "79--84",
    abstract = "Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration. Previous works on mining argumentative content have assumed that a set of relevant documents is given in advance. Here, we present a first corpus{--} wide claim detection framework, that can be directly applied to massive corpora. Using simple and intuitive empirical observations, we derive a claim sentence query by which we are able to directly retrieve sentences in which the prior probability to include topic-relevant claims is greatly enhanced. Next, we employ simple heuristics to rank the sentences, leading to an unsupervised corpus{--}wide claim detection system, with precision that outperforms previously reported results on the task of claim detection given relevant documents and labeled data.",
}
@inproceedings{saint-dizier-2017-using,
    title = "Using Question-Answering Techniques to Implement a Knowledge-Driven Argument Mining Approach",
    author = "Saint-Dizier, Patrick",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5111",
    doi = "10.18653/v1/W17-5111",
    pages = "85--90",
    abstract = "This short paper presents a first implementation of a knowledge-driven argument mining approach. The major processing steps and language resources of the system are surveyed. An indicative evaluation outlines challenges and improvement directions.",
}
@inproceedings{aker-etal-2017-works,
    title = "What works and what does not: Classifier and feature analysis for argument mining",
    author = "Aker, Ahmet  and
      Sliwa, Alfred  and
      Ma, Yuan  and
      Lui, Ruishen  and
      Borad, Niravkumar  and
      Ziyaei, Seyedeh  and
      Ghobadi, Mina",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5112",
    doi = "10.18653/v1/W17-5112",
    pages = "91--96",
    abstract = "This paper offers a comparative analysis of the performance of different supervised machine learning methods and feature sets on argument mining tasks. Specifically, we address the tasks of extracting argumentative segments from texts and predicting the structure between those segments. Eight classifiers and different combinations of six feature types reported in previous work are evaluated. The results indicate that overall best performing features are the structural ones. Although the performance of classifiers varies depending on the feature combinations and corpora used for training and testing, Random Forest seems to be among the best performing classifiers. These results build a basis for further development of argument mining techniques and can guide an implementation of argument mining into different applications such as argument based search.",
}
@inproceedings{ferrara-etal-2017-unsupervised,
    title = "Unsupervised Detection of Argumentative Units though Topic Modeling Techniques",
    author = "Ferrara, Alfio  and
      Montanelli, Stefano  and
      Petasis, Georgios",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5113",
    doi = "10.18653/v1/W17-5113",
    pages = "97--107",
    abstract = "In this paper we present a new unsupervised approach, {``}Attraction to Topics{''} {--} A2T , for the detection of argumentative units, a sub-task of argument mining. Motivated by the importance of topic identification in manual annotation, we examine whether topic modeling can be used for performing unsupervised detection of argumentative sentences, and to what extend topic modeling can be used to classify sentences as claims and premises. Preliminary evaluation results suggest that topic information can be successfully used for the detection of argumentative sentences, at least for corpora used for evaluation. Our approach has been evaluated on two English corpora, the first of which contains 90 persuasive essays, while the second is a collection of 340 documents from user generated content.",
}
@inproceedings{lawrence-reed-2017-using,
    title = "Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates",
    author = "Lawrence, John  and
      Reed, Chris",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5114",
    doi = "10.18653/v1/W17-5114",
    pages = "108--117",
    abstract = "In this paper we consider the insights that can be gained by considering large scale argument networks and the complex interactions between their constituent propositions. We investigate metrics for analysing properties of these networks, illustrating these using a corpus of arguments taken from the 2016 US Presidential Debates. We present techniques for determining these features directly from natural language text and show that there is a strong correlation between these automatically identified features and the argumentative structure contained within the text. Finally, we combine these metrics with argument mining techniques and show how the identification of argumentative relations can be improved by considering the larger context in which they occur.",
}
@inproceedings{ajjour-etal-2017-unit,
    title = "Unit Segmentation of Argumentative Texts",
    author = "Ajjour, Yamen  and
      Chen, Wei-Fan  and
      Kiesel, Johannes  and
      Wachsmuth, Henning  and
      Stein, Benno",
    booktitle = "Proceedings of the 4th Workshop on Argument Mining",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5115",
    doi = "10.18653/v1/W17-5115",
    pages = "118--128",
    abstract = "The segmentation of an argumentative text into argument units and their non-argumentative counterparts is the first step in identifying the argumentative structure of the text. Despite its importance for argument mining, unit segmentation has been approached only sporadically so far. This paper studies the major parameters of unit segmentation systematically. We explore the effectiveness of various features, when capturing words separately, along with their neighbors, or even along with the entire text. Each such context is reflected by one machine learning model that we evaluate within and across three domains of texts. Among the models, our new deep learning approach capturing the entire text turns out best within all domains, with an F-score of up to 88.54. While structural features generalize best across domains, the domain transfer remains hard, which points to major challenges of unit segmentation.",
}
@proceedings{ws-2017-innovative,
    title = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    editor = "Tetreault, Joel  and
      Burstein, Jill  and
      Leacock, Claudia  and
      Yannakoudakis, Helen",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5000",
    doi = "10.18653/v1/W17-50",
}
@inproceedings{pado-2017-question,
    title = "Question Difficulty {--} How to Estimate Without Norming, How to Use for Automated Grading",
    author = "Pad{\'o}, Ulrike",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5001",
    doi = "10.18653/v1/W17-5001",
    pages = "1--10",
    abstract = "Question difficulty estimates guide test creation, but are too costly for small-scale testing. We empirically verify that Bloom{'}s Taxonomy, a standard tool for difficulty estimation during question creation, reliably predicts question difficulty observed after testing in a short-answer corpus. We also find that difficulty is mirrored in the amount of variation in student answers, which can be computed before grading. We show that question difficulty and its approximations are useful for \textit{automated grading}, allowing us to identify the optimal feature set for grading each question even in an unseen-question setting.",
}
@inproceedings{jin-etal-2017-combining,
    title = "Combining {CNN}s and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System",
    author = "Jin, Lifeng  and
      White, Michael  and
      Jaffe, Evan  and
      Zimmerman, Laura  and
      Danforth, Douglas",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5002",
    doi = "10.18653/v1/W17-5002",
    pages = "11--21",
    abstract = "For medical students, virtual patient dialogue systems can provide useful training opportunities without the cost of employing actors to portray standardized patients. This work utilizes word- and character-based convolutional neural networks (CNNs) for question identification in a virtual patient dialogue system, outperforming a strong word- and character-based logistic regression baseline. While the CNNs perform well given sufficient training data, the best system performance is ultimately achieved by combining CNNs with a hand-crafted pattern matching system that is robust to label sparsity, providing a 10{\%} boost in system accuracy and an error reduction of 47{\%} as compared to the pattern-matching system alone.",
}
@inproceedings{beigman-klebanov-etal-2017-continuous,
    title = "Continuous fluency tracking and the challenges of varying text complexity",
    author = "Beigman Klebanov, Beata  and
      Loukina, Anastassia  and
      Sabatini, John  and
      O{'}Reilly, Tenaha",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5003",
    doi = "10.18653/v1/W17-5003",
    pages = "22--32",
    abstract = "This paper is a preliminary report on using text complexity measurement in the service of a new educational application. We describe a reading intervention where a child takes turns reading a book aloud with a virtual reading partner. Our ultimate goal is to provide meaningful feedback to the parent or the teacher by continuously tracking the child{'}s improvement in reading fluency. We show that this would not be a simple endeavor, due to an intricate relationship between text complexity from the point of view of comprehension and reading rate.",
}
@inproceedings{rei-yannakoudakis-2017-auxiliary,
    title = "Auxiliary Objectives for Neural Error Detection Models",
    author = "Rei, Marek  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5004",
    doi = "10.18653/v1/W17-5004",
    pages = "33--43",
    abstract = "We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objectives allow it to be optimised more efficiently and achieve better performance.",
}
@inproceedings{loughnane-etal-2017-linked,
    title = "Linked Data for Language-Learning Applications",
    author = "Loughnane, Robyn  and
      McCurdy, Kate  and
      Kolb, Peter  and
      Selent, Stefan",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5005",
    doi = "10.18653/v1/W17-5005",
    pages = "44--51",
    abstract = "The use of linked data within language-learning applications is an open research question. A research prototype is presented that applies linked-data principles to store linguistic annotation generated from language-learning content using a variety of NLP tools. The result is a database that links learning content, linguistic annotation and open-source resources, on top of which a diverse range of tools for language-learning applications can be built.",
}
@inproceedings{lugini-litman-2017-predicting,
    title = "Predicting Specificity in Classroom Discussion",
    author = "Lugini, Luca  and
      Litman, Diane",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5006",
    doi = "10.18653/v1/W17-5006",
    pages = "52--61",
    abstract = "High quality classroom discussion is important to student development, enhancing abilities to express claims, reason about other students{'} claims, and retain information for longer periods of time. Previous small-scale studies have shown that one indicator of classroom discussion quality is specificity. In this paper we tackle the problem of predicting specificity for classroom discussions. We propose several methods and feature sets capable of outperforming the state of the art in specificity prediction. Additionally, we provide a set of meaningful, interpretable features that can be used to analyze classroom discussions at a pedagogical level.",
}
@inproceedings{malmasi-etal-2017-report,
    title = "A Report on the 2017 Native Language Identification Shared Task",
    author = "Malmasi, Shervin  and
      Evanini, Keelan  and
      Cahill, Aoife  and
      Tetreault, Joel  and
      Pugh, Robert  and
      Hamill, Christopher  and
      Napolitano, Diane  and
      Qian, Yao",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5007",
    doi = "10.18653/v1/W17-5007",
    pages = "62--75",
    abstract = "Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is typically framed as a classification task where the set of L1s is known a priori. Two previous shared tasks on NLI have been organized where the aim was to identify the L1 of learners of English based on essays (2013) and spoken responses (2016) they provided during a standardized assessment of academic English proficiency. The 2017 shared task combines the inputs from the two prior tasks for the first time. There are three tracks: NLI on the essay only, NLI on the spoken response only (based on a transcription of the response and i-vector acoustic features), and NLI using both responses. We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks. In this paper, we report the results of the shared task. A total of 19 teams competed across the three different sub-tasks. The fusion track showed that combining the written and spoken responses provides a large boost in prediction accuracy. Multiple classifier systems (e.g. ensembles and meta-classifiers) were the most effective in all tasks, with most based on traditional classifiers (e.g. SVMs) with lexical/syntactic features.",
}
@inproceedings{satria-tokunaga-2017-evaluation,
    title = "Evaluation of Automatically Generated Pronoun Reference Questions",
    author = "Satria, Arief Yudha  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5008",
    doi = "10.18653/v1/W17-5008",
    pages = "76--85",
    abstract = "This study provides a detailed analysis of evaluation of English pronoun reference questions which are created automatically by machine. Pronoun reference questions are multiple choice questions that ask test takers to choose an antecedent of a target pronoun in a reading passage from four options. The evaluation was performed from two perspectives: the perspective of English teachers and that of English learners. Item analysis suggests that machine-generated questions achieve comparable quality with human-made questions. Correlation analysis revealed a strong correlation between the scores of machine-generated questions and that of human-made questions.",
}
@inproceedings{chen-lee-2017-predicting,
    title = "Predicting Audience{'}s Laughter During Presentations Using Convolutional Neural Network",
    author = "Chen, Lei  and
      Lee, Chong Min",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5009",
    doi = "10.18653/v1/W17-5009",
    pages = "86--90",
    abstract = "Public speakings play important roles in schools and work places and properly using humor contributes to effective presentations. For the purpose of automatically evaluating speakers{'} humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The advantages of the CNN method are both getting higher detection accuracies and being able to learn essential features automatically.",
}
@inproceedings{caines-etal-2017-collecting,
    title = "Collecting fluency corrections for spoken learner {E}nglish",
    author = "Caines, Andrew  and
      Flint, Emma  and
      Buttery, Paula",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5010",
    doi = "10.18653/v1/W17-5010",
    pages = "91--100",
    abstract = "We present crowdsourced collection of error annotations for transcriptions of spoken learner English. Our emphasis in data collection is on fluency corrections, a more complete correction than has traditionally been aimed for in grammatical error correction research (GEC). Fluency corrections require improvements to the text, taking discourse and utterance level semantics into account: the result is a more naturalistic, holistic version of the original. We propose that this shifted emphasis be reflected in a new name for the task: {`}holistic error correction{'} (HEC). We analyse crowdworker behaviour in HEC and conclude that the method is useful with certain amendments for future work.",
}
@inproceedings{burstein-etal-2017-exploring,
    title = "Exploring Relationships Between Writing {\&} Broader Outcomes With Automated Writing Evaluation",
    author = "Burstein, Jill  and
      McCaffrey, Dan  and
      Beigman Klebanov, Beata  and
      Ling, Guangming",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5011",
    doi = "10.18653/v1/W17-5011",
    pages = "101--108",
    abstract = "Writing is a challenge, especially for at-risk students who may lack the prerequisite writing skills required to persist in U.S. 4-year postsecondary (college) institutions. Educators teaching postsecondary courses requiring writing could benefit from a better understanding of writing achievement and its role in postsecondary success. In this paper, novel exploratory work examined how automated writing evaluation (AWE) can inform our understanding of the relationship between postsecondary writing skill and broader success outcomes. An exploratory study was conducted using test-taker essays from a standardized writing assessment of postsecondary student learning outcomes. Findings showed that for the essays, AWE features were found to be predictors of broader outcomes measures: college success and learning outcomes measures. Study findings illustrate AWE{'}s potential to support educational analytics {--} i.e., relationships between writing skill and broader outcomes {--} taking a step toward moving AWE beyond writing assessment and instructional use cases.",
}
@inproceedings{sheng-etal-2017-investigation,
    title = "An Investigation into the Pedagogical Features of Documents",
    author = "Sheng, Emily  and
      Natarajan, Prem  and
      Gordon, Jonathan  and
      Burns, Gully",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5012",
    doi = "10.18653/v1/W17-5012",
    pages = "109--120",
    abstract = "Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the {``}pedagogical value{''} of the document to the learner. While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of {``}pedagogical roles{''} of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.",
}
@inproceedings{yaneva-etal-2017-combining,
    title = "Combining Multiple Corpora for Readability Assessment for People with Cognitive Disabilities",
    author = "Yaneva, Victoria  and
      Or{\u{a}}san, Constantin  and
      Evans, Richard  and
      Rohanian, Omid",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5013",
    doi = "10.18653/v1/W17-5013",
    pages = "121--132",
    abstract = "Given the lack of large user-evaluated corpora in disability-related NLP research (e.g. text simplification or readability assessment for people with cognitive disabilities), the question of choosing suitable training data for NLP models is not straightforward. The use of large generic corpora may be problematic because such data may not reflect the needs of the target population. The use of the available user-evaluated corpora may be problematic because these datasets are not large enough to be used as training data. In this paper we explore a third approach, in which a large generic corpus is combined with a smaller population-specific corpus to train a classifier which is evaluated using two sets of unseen user-evaluated data. One of these sets, the ASD Comprehension corpus, is developed for the purposes of this study and made freely available. We explore the effects of the size and type of the training data used on the performance of the classifiers, and the effects of the type of the unseen test datasets on the classification performance.",
}
@inproceedings{tolmachev-kurohashi-2017-automatic,
    title = "Automatic Extraction of High-Quality Example Sentences for Word Learning Using a Determinantal Point Process",
    author = "Tolmachev, Arseny  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5014",
    doi = "10.18653/v1/W17-5014",
    pages = "133--142",
    abstract = "Flashcard systems are effective tools for learning words but have their limitations in teaching word usage. To overcome this problem, we propose a novel flashcard system that shows a new example sentence on each repetition. This extension requires high-quality example sentences, automatically extracted from a huge corpus. To do this, we use a Determinantal Point Process which scales well to large data and allows to naturally represent sentence similarity and quality as features. Our human evaluation experiment on Japanese language indicates that the proposed method successfully extracted high-quality example sentences.",
}
@inproceedings{jiang-lee-2017-distractor,
    title = "Distractor Generation for {C}hinese Fill-in-the-blank Items",
    author = "Jiang, Shu  and
      Lee, John",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5015",
    doi = "10.18653/v1/W17-5015",
    pages = "143--148",
    abstract = "This paper reports the first study on automatic generation of distractors for fill-in-the-blank items for learning Chinese vocabulary. We investigate the quality of distractors generated by a number of criteria, including part-of-speech, difficulty level, spelling, word co-occurrence and semantic similarity. Evaluations show that a semantic similarity measure, based on the word2vec model, yields distractors that are significantly more plausible than those generated by baseline methods.",
}
@inproceedings{farag-etal-2017-error,
    title = "An Error-Oriented Approach to Word Embedding Pre-Training",
    author = "Farag, Youmna  and
      Rei, Marek  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5016",
    doi = "10.18653/v1/W17-5016",
    pages = "149--158",
    abstract = "We propose a novel word embedding pre-training approach that exploits writing errors in learners{'} scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination between correct and corrupt word contexts in addition to the generic commonly-used embeddings pre-trained on large corpora. The comparison is achieved by using the aforementioned models to bootstrap a neural network that learns to predict a holistic score for scripts. Furthermore, we investigate augmenting our model with error corrections and monitor the impact on performance. Our results show that our error-oriented approach outperforms other comparable ones which is further demonstrated when training on more data. Additionally, extending the model with corrections provides further performance gains when data sparsity is an issue.",
}
@inproceedings{riordan-etal-2017-investigating,
    title = "Investigating neural architectures for short answer scoring",
    author = "Riordan, Brian  and
      Horbach, Andrea  and
      Cahill, Aoife  and
      Zesch, Torsten  and
      Lee, Chong Min",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5017",
    doi = "10.18653/v1/W17-5017",
    pages = "159--168",
    abstract = "Neural approaches to automated essay scoring have recently shown state-of-the-art performance. The automated essay scoring task typically involves a broad notion of writing quality that encompasses content, grammar, organization, and conventions. This differs from the short answer content scoring task, which focuses on content accuracy. The inputs to neural essay scoring models {--} ngrams and embeddings {--} are arguably well-suited to evaluate content in short answer scoring tasks. We investigate how several basic neural approaches similar to those used for automated essay scoring perform on short answer scoring. We show that neural architectures can outperform a strong non-neural baseline, but performance and optimal parameter settings vary across the more diverse types of prompts typical of short answer scoring.",
}
@inproceedings{tack-etal-2017-human,
    title = "Human and Automated {CEFR}-based Grading of Short Answers",
    author = {Tack, Ana{\"\i}s  and
      Fran{\c{c}}ois, Thomas  and
      Roekhaut, Sophie  and
      Fairon, C{\'e}drick},
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5018",
    doi = "10.18653/v1/W17-5018",
    pages = "169--179",
    abstract = "This paper is concerned with the task of automatically assessing the written proficiency level of non-native (L2) learners of English. Drawing on previous research on automated L2 writing assessment following the Common European Framework of Reference for Languages (CEFR), we investigate the possibilities and difficulties of deriving the CEFR level from short answers to open-ended questions, which has not yet been subjected to numerous studies up to date. The object of our study is twofold: to examine the intricacy involved with both human and automated CEFR-based grading of short answers. On the one hand, we describe the compilation of a learner corpus of short answers graded with CEFR levels by three certified Cambridge examiners. We mainly observe that, although the shortness of the answers is reported as undermining a clear-cut evaluation, the length of the answer does not necessarily correlate with inter-examiner disagreement. On the other hand, we explore the development of a soft-voting system for the automated CEFR-based grading of short answers and draw tentative conclusions about its use in a computer-assisted testing (CAT) setting.",
}
@inproceedings{sakaguchi-etal-2017-gec,
    title = "{GEC} into the future: Where are we going and how do we get there?",
    author = "Sakaguchi, Keisuke  and
      Napoles, Courtney  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5019",
    doi = "10.18653/v1/W17-5019",
    pages = "180--187",
    abstract = "The field of grammatical error correction (GEC) has made tremendous bounds in the last ten years, but new questions and obstacles are revealing themselves. In this position paper, we discuss the issues that need to be addressed and provide recommendations for the field to continue to make progress, and propose a new shared task. We invite suggestions and critiques from the audience to make the new shared task a community-driven venture.",
}
@inproceedings{rei-2017-detecting,
    title = "Detecting Off-topic Responses to Visual Prompts",
    author = "Rei, Marek",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5020",
    doi = "10.18653/v1/W17-5020",
    pages = "188--197",
    abstract = "Automated methods for essay scoring have made great progress in recent years, achieving accuracies very close to human annotators. However, a known weakness of such automated scorers is not taking into account the semantic relevance of the submitted text. While there is existing work on detecting answer relevance given a textual prompt, very little previous research has been done to incorporate visual writing prompts. We propose a neural architecture and several extensions for detecting off-topic responses to visual prompts and evaluate it on a dataset of texts written by language learners.",
}
@inproceedings{ircing-etal-2017-combining,
    title = "Combining Textual and Speech Features in the {NLI} Task Using State-of-the-Art Machine Learning Techniques",
    author = "Ircing, Pavel  and
      {\v{S}}vec, Jan  and
      Zaj{\'\i}c, Zbyn{\v{e}}k  and
      Hladk{\'a}, Barbora  and
      Holub, Martin",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5021",
    doi = "10.18653/v1/W17-5021",
    pages = "198--209",
    abstract = "We summarize the involvement of our CEMI team in the {''}NLI Shared Task 2017{''}, which deals with both textual and speech input data. We submitted the results achieved by using three different system architectures; each of them combines multiple supervised learning models trained on various feature sets. As expected, better results are achieved with the systems that use both the textual data and the spoken responses. Combining the input data of two different modalities led to a rather dramatic improvement in classification performance. Our best performing method is based on a set of feed-forward neural networks whose hidden-layer outputs are combined together using a softmax layer. We achieved a macro-averaged F1 score of 0.9257 on the evaluation (unseen) test set and our team placed first in the main task together with other three teams.",
}
@inproceedings{mohammadi-etal-2017-native,
    title = "Native Language Identification Using a Mixture of Character and Word N-grams",
    author = "Mohammadi, Elham  and
      Veisi, Hadi  and
      Amini, Hessam",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5022",
    doi = "10.18653/v1/W17-5022",
    pages = "210--216",
    abstract = "Native language identification (NLI) is the task of determining an author{'}s native language, based on a piece of his/her writing in a second language. In recent years, NLI has received much attention due to its challenging nature and its applications in language pedagogy and forensic linguistics. We participated in the NLI2017 shared task under the name UT-DSP. In our effort to implement a method for native language identification, we made use of a fusion of character and word N-grams, and achieved an optimal F1-Score of 77.64{\%}, using both essay and speech transcription datasets.",
}
@inproceedings{chan-etal-2017-ensemble,
    title = "Ensemble Methods for Native Language Identification",
    author = "Chan, Sophia  and
      Honari Jahromi, Maryam  and
      Benetti, Benjamin  and
      Lakhani, Aazim  and
      Fyshe, Alona",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5023",
    doi = "10.18653/v1/W17-5023",
    pages = "217--223",
    abstract = "Our team{---}Uvic-NLP{---}explored and evaluated a variety of lexical features for Native Language Identification (NLI) within the framework of ensemble methods. Using a subset of the highest performing features, we train Support Vector Machines (SVM) and Fully Connected Neural Networks (FCNN) as base classifiers, and test different methods for combining their outputs. Restricting our scope to the closed essay track in the NLI Shared Task 2017, we find that our best SVM ensemble achieves an F1 score of 0.8730 on the test set.",
}
@inproceedings{ionescu-popescu-2017-string,
    title = "Can string kernels pass the test of time in Native Language Identification?",
    author = "Ionescu, Radu Tudor  and
      Popescu, Marius",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5024",
    doi = "10.18653/v1/W17-5024",
    pages = "224--234",
    abstract = "We describe a machine learning approach for the 2017 shared task on Native Language Identification (NLI). The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from essays or speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided by the shared task organizers. For the learning stage, we choose Kernel Discriminant Analysis (KDA) over Kernel Ridge Regression (KRR), because the former classifier obtains better results than the latter one on the development set. In our previous work, we have used a similar machine learning approach to achieve state-of-the-art NLI results. The goal of this paper is to demonstrate that our shallow and simple approach based on string kernels (with minor improvements) can pass the test of time and reach state-of-the-art performance in the 2017 NLI shared task, despite the recent advances in natural language processing. We participated in all three tracks, in which the competitors were allowed to use only the essays (essay track), only the speech transcripts (speech track), or both (fusion track). Using only the data provided by the organizers for training our models, we have reached a macro F1 score of 86.95{\%} in the closed essay track, a macro F1 score of 87.55{\%} in the closed speech track, and a macro F1 score of 93.19{\%} in the closed fusion track. With these scores, our team (UnibucKernel) ranked in the first group of teams in all three tracks, while attaining the best scores in the speech and the fusion tracks.",
}
@inproceedings{bjerva-etal-2017-neural,
    title = "Neural Networks and Spelling Features for Native Language Identification",
    author = {Bjerva, Johannes  and
      Grigonyt{\.e}, Gintar{\.e}  and
      {\"O}stling, Robert  and
      Plank, Barbara},
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5025",
    doi = "10.18653/v1/W17-5025",
    pages = "235--239",
    abstract = "We present the RUG-SU team{'}s submission at the Native Language Identification Shared Task 2017. We combine several approaches into an ensemble, based on spelling error features, a simple neural network using word representations, a deep residual network using word and character features, and a system based on a recurrent neural network. Our best system is an ensemble of neural networks, reaching an F1 score of 0.8323. Although our system is not the highest ranking one, we do outperform the baseline by far.",
}
@inproceedings{vajjala-banerjee-2017-study,
    title = "A study of N-gram and Embedding Representations for Native Language Identification",
    author = "Vajjala, Sowmya  and
      Banerjee, Sagnik",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5026",
    doi = "10.18653/v1/W17-5026",
    pages = "240--248",
    abstract = "We report on our experiments with N-gram and embedding based feature representations for Native Language Identification (NLI) as a part of the NLI Shared Task 2017 (team name: NLI-ISU). Our best performing system on the test set for written essays had a macro F1 of 0.8264 and was based on word uni, bi and trigram features. We explored n-grams covering word, character, POS and word-POS mixed representations for this task. For embedding based feature representations, we employed both word and document embeddings. We had a relatively poor performance with all embedding representations compared to n-grams, which could be because of the fact that embeddings capture semantic similarities whereas L1 differences are more stylistic in nature.",
}
@inproceedings{sari-etal-2017-shallow,
    title = "A Shallow Neural Network for Native Language Identification with Character N-grams",
    author = "Sari, Yunita  and
      Rifqi Fatchurrahman, Muhammad  and
      Dwiastuti, Meisyarah",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5027",
    doi = "10.18653/v1/W17-5027",
    pages = "249--254",
    abstract = "This paper describes the systems submitted by GadjahMada team to the Native Language Identification (NLI) Shared Task 2017. Our models used a continuous representation of character n-grams which are learned jointly with feed-forward neural network classifier. Character n-grams have been proved to be effective for style-based identification tasks including NLI. Results on the test set demonstrate that the proposed model performs very well on essay and fusion tracks by obtaining more than 0.8 on both F-macro score and accuracy.",
}
@inproceedings{rama-coltekin-2017-fewer,
    title = "Fewer features perform well at Native Language Identification task",
    author = {Rama, Taraka  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5028",
    doi = "10.18653/v1/W17-5028",
    pages = "255--260",
    abstract = "This paper describes our results at the NLI shared task 2017. We participated in essays, speech, and fusion task that uses text, speech, and i-vectors for the task of identifying the native language of the given input. In the essay track, a linear SVM system using word bigrams and character 7-grams performed the best. In the speech track, an LDA classifier based only on i-vectors performed better than a combination system using text features from speech transcriptions and i-vectors. In the fusion task, we experimented with systems that used combination of i-vectors with higher order n-grams features, combination of i-vectors with word unigrams, a mean probability ensemble, and a stacked ensemble system. Our finding is that word unigrams in combination with i-vectors achieve higher score than systems trained with larger number of $n$-gram features. Our best-performing systems achieved F1-scores of 87.16{\%}, 83.33{\%} and 91.75{\%} on the essay track, the speech track and the fusion track respectively.",
}
@inproceedings{gordon-etal-2017-structured,
    title = "Structured Generation of Technical Reading Lists",
    author = "Gordon, Jonathan  and
      Aguilar, Stephen  and
      Sheng, Emily  and
      Burns, Gully",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5029",
    doi = "10.18653/v1/W17-5029",
    pages = "261--270",
    abstract = "Learners need to find suitable documents to read and prioritize them in an appropriate order. We present a method of automatically generating reading lists, selecting documents based on their pedagogical value to the learner and ordering them using the structure of concepts in the domain. Resulting reading lists related to computational linguistics were evaluated by advanced learners and judged to be near the quality of those generated by domain experts. We provide an open-source implementation of our method to enable future work on reading list generation.",
}
@inproceedings{stajner-etal-2017-effects,
    title = "Effects of Lexical Properties on Viewing Time per Word in Autistic and Neurotypical Readers",
    author = "{\v{S}}tajner, Sanja  and
      Yaneva, Victoria  and
      Mitkov, Ruslan  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5030",
    doi = "10.18653/v1/W17-5030",
    pages = "271--281",
    abstract = "Eye tracking studies from the past few decades have shaped the way we think of word complexity and cognitive load: words that are long, rare and ambiguous are more difficult to read. However, online processing techniques have been scarcely applied to investigating the reading difficulties of people with autism and what vocabulary is challenging for them. We present parallel gaze data obtained from adult readers with autism and a control group of neurotypical readers and show that the former required higher cognitive effort to comprehend the texts as evidenced by three gaze-based measures. We divide all words into four classes based on their viewing times for both groups and investigate the relationship between longer viewing times and word length, word frequency, and four cognitively-based measures (word concreteness, familiarity, age of acquisition and imagability).",
}
@inproceedings{ostling-grigonyte-2017-transparent,
    title = "Transparent text quality assessment with convolutional neural networks",
    author = {{\"O}stling, Robert  and
      Grigonyte, Gintare},
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5031",
    doi = "10.18653/v1/W17-5031",
    pages = "282--286",
    abstract = "We present a very simple model for text quality assessment based on a deep convolutional neural network, where the only supervision required is one corpus of user-generated text of varying quality, and one contrasting text corpus of consistently high quality. Our model is able to provide local quality assessments in different parts of a text, which allows visual feedback about where potentially problematic parts of the text are located, as well as a way to evaluate which textual features are captured by our model. We evaluate our method on two corpora: a large corpus of manually graded student essays and a longitudinal corpus of language learner written production, and find that the text quality metric learned by our model is a fairly strong predictor of both essay grade and learner proficiency level.",
}
@inproceedings{rei-etal-2017-artificial,
    title = "Artificial Error Generation with Machine Translation and Syntactic Patterns",
    author = "Rei, Marek  and
      Felice, Mariano  and
      Yuan, Zheng  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5032",
    doi = "10.18653/v1/W17-5032",
    pages = "287--292",
    abstract = "Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose treating error generation as a machine translation task, where grammatically correct text is translated to contain errors. In addition, we explore a system for extracting textual patterns from an annotated corpus, which can then be used to insert errors into grammatically correct sentences. Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.",
}
@inproceedings{kochmar-shutova-2017-modelling,
    title = "Modelling semantic acquisition in second language learning",
    author = "Kochmar, Ekaterina  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5033",
    doi = "10.18653/v1/W17-5033",
    pages = "293--302",
    abstract = "Using methods of statistical analysis, we investigate how semantic knowledge is acquired in English as a second language and evaluate the pace of development across a number of predicate types and content word combinations, as well as across the levels of language proficiency and native languages. Our exploratory study helps identify the most problematic areas for language learners with different backgrounds and at different stages of learning.",
}
@inproceedings{stasaski-hearst-2017-multiple,
    title = "Multiple Choice Question Generation Utilizing An Ontology",
    author = "Stasaski, Katherine  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5034",
    doi = "10.18653/v1/W17-5034",
    pages = "303--312",
    abstract = "Ontologies provide a structured representation of concepts and the relationships which connect them. This work investigates how a pre-existing educational Biology ontology can be used to generate useful practice questions for students by using the connectivity structure in a novel way. It also introduces a novel way to generate multiple-choice distractors from the ontology, and compares this to a baseline of using embedding representations of nodes. An assessment by an experienced science teacher shows a significant advantage over a baseline when using the ontology for distractor generation. A subsequent study with three science teachers on the results of a modified question generation algorithm finds significant improvements. An in-depth analysis of the teachers{'} comments yields useful insights for any researcher working on automated question generation for educational applications.",
}
@inproceedings{wolska-clausen-2017-simplifying,
    title = "Simplifying metaphorical language for young readers: A corpus study on news text",
    author = "Wolska, Magdalena  and
      Clausen, Yulia",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5035",
    doi = "10.18653/v1/W17-5035",
    pages = "313--318",
    abstract = "The paper presents first results of an ongoing project on text simplification focusing on linguistic metaphors. Based on an analysis of a parallel corpus of news text professionally simplified for different grade levels, we identify six types of simplification choices falling into two broad categories: preserving metaphors or dropping them. An annotation study on almost 300 source sentences with metaphors (grade level 12) and their simplified counterparts (grade 4) is conducted. The results show that most metaphors are preserved and when they are dropped, the semantic content tends to be preserved rather than dropped, however, it is reworded without metaphorical language. In general, some of the expected tendencies in complexity reduction, measured with psycholinguistic variables linked to metaphor comprehension, are observed, suggesting good prospect for machine learning-based metaphor simplification.",
}
@inproceedings{nadeem-ostendorf-2017-language,
    title = "Language Based Mapping of Science Assessment Items to Skills",
    author = "Nadeem, Farah  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5036",
    doi = "10.18653/v1/W17-5036",
    pages = "319--326",
    abstract = "Knowledge of the association between assessment questions and the skills required to solve them is necessary for analysis of student learning. This association, often represented as a Q-matrix, is either hand-labeled by domain experts or learned as latent variables given a large student response data set. As a means of automating the match to formal standards, this paper uses neural text classification methods, leveraging the language in the standards documents to identify online text for a proxy training task. Experiments involve identifying the topic and crosscutting concepts of middle school science questions leveraging multi-task training. Results show that it is possible to automatically build a Q-matrix without student response data and using a modest number of hand-labeled questions.",
}
@inproceedings{chollampatt-ng-2017-connecting,
    title = "Connecting the Dots: Towards Human-Level Grammatical Error Correction",
    author = "Chollampatt, Shamil  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5037",
    doi = "10.18653/v1/W17-5037",
    pages = "327--333",
    abstract = "We build a grammatical error correction (GEC) system primarily based on the state-of-the-art statistical machine translation (SMT) approach, using task-specific features and tuning, and further enhance it with the modeling power of neural network joint models. The SMT-based system is weak in generalizing beyond patterns seen during training and lacks granularity below the word level. To address this issue, we incorporate a character-level SMT component targeting the misspelled words that the original SMT-based system fails to correct. Our final system achieves 53.14{\%} F 0.5 score on the benchmark CoNLL-2014 test set, an improvement of 3.62{\%} F 0.5 over the best previous published score.",
}
@inproceedings{chinkina-meurers-2017-question,
    title = "Question Generation for Language Learning: From ensuring texts are read to supporting learning",
    author = "Chinkina, Maria  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5038",
    doi = "10.18653/v1/W17-5038",
    pages = "334--344",
    abstract = "In Foreign Language Teaching and Learning (FLTL), questions are systematically used to assess the learner{'}s understanding of a text. Computational linguistic approaches have been developed to generate such questions automatically given a text (e.g., Heilman, 2011). In this paper, we want to broaden the perspective on the different functions questions can play in FLTL and discuss how automatic question generation can support the different uses. Complementing the focus on meaning and comprehension, we want to highlight the fact that questions can also be used to make learners notice form aspects of the linguistic system and their interpretation. Automatically generating questions that target linguistic forms and grammatical categories in a text in essence supports incidental focus-on-form (Loewen, 2005) in a meaning-focused reading task. We discuss two types of questions serving this purpose, how they can be generated automatically; and we report on a crowd-sourcing evaluation comparing automatically generated to manually written questions targeting particle verbs, a challenging linguistic form for learners of English.",
}
@inproceedings{napoles-callison-burch-2017-systematically,
    title = "Systematically Adapting Machine Translation for Grammatical Error Correction",
    author = "Napoles, Courtney  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5039",
    doi = "10.18653/v1/W17-5039",
    pages = "345--356",
    abstract = "n this work we adapt machine translation (MT) to grammatical error correction, identifying how components of the statistical MT pipeline can be modified for this task and analyzing how each modification impacts system performance. We evaluate the contribution of each of these components with standard evaluation metrics and automatically characterize the morphological and lexical transformations made in system output. Our model rivals the current state of the art using a fraction of the training data.",
}
@inproceedings{horbach-etal-2017-fine,
    title = "Fine-grained essay scoring of a complex writing task for native speakers",
    author = "Horbach, Andrea  and
      Scholten-Akoun, Dirk  and
      Ding, Yuning  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5040",
    doi = "10.18653/v1/W17-5040",
    pages = "357--366",
    abstract = "Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays. We present a new dataset of essays written by highly proficient German native speakers that is scored using a fine-grained rubric with the goal to provide detailed feedback. Our experiments with two state-of-the-art scoring systems (a neural and a SVM-based one) show a large drop in performance compared to existing datasets. This demonstrates the need for such datasets that allow to guide research on more elaborate essay scoring methods.",
}
@inproceedings{goutte-leger-2017-exploring,
    title = "Exploring Optimal Voting in Native Language Identification",
    author = "Goutte, Cyril  and
      L{\'e}ger, Serge",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5041",
    doi = "10.18653/v1/W17-5041",
    pages = "367--373",
    abstract = "We describe the submissions entered by the National Research Council Canada in the NLI-2017 evaluation. We mainly explored the use of voting, and various ways to optimize the choice and number of voting systems. We also explored the use of features that rely on no linguistic preprocessing. Long ngrams of characters obtained from raw text turned out to yield the best performance on all textual input (written essays and speech transcripts). Voting ensembles turned out to produce small performance gains, with little difference between the various optimization strategies we tried. Our top systems achieved accuracies of 87{\%} on the essay track, 84{\%} on the speech track, and close to 92{\%} by combining essays, speech and i-vectors in the fusion track.",
}
@inproceedings{markov-etal-2017-cic,
    title = "{CIC}-{FBK} Approach to Native Language Identification",
    author = "Markov, Ilia  and
      Chen, Lingzhen  and
      Strapparava, Carlo  and
      Sidorov, Grigori",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5042",
    doi = "10.18653/v1/W17-5042",
    pages = "374--381",
    abstract = "We present the CIC-FBK system, which took part in the Native Language Identification (NLI) Shared Task 2017. Our approach combines features commonly used in previous NLI research, i.e., word n-grams, lemma n-grams, part-of-speech n-grams, and function words, with recently introduced character n-grams from misspelled words, and features that are novel in this task, such as typed character n-grams, and syntactic n-grams of words and of syntactic relation tags. We use log-entropy weighting scheme and perform classification using the Support Vector Machines (SVM) algorithm. Our system achieved 0.8808 macro-averaged F1-score and shared the 1st rank in the NLI Shared Task 2017 scoring.",
}
@inproceedings{kulmizev-etal-2017-power,
    title = "The Power of Character N-grams in Native Language Identification",
    author = "Kulmizev, Artur  and
      Blankers, Bo  and
      Bjerva, Johannes  and
      Nissim, Malvina  and
      van Noord, Gertjan  and
      Plank, Barbara  and
      Wieling, Martijn",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5043",
    doi = "10.18653/v1/W17-5043",
    pages = "382--389",
    abstract = "In this paper, we explore the performance of a linear SVM trained on language independent character features for the NLI Shared Task 2017. Our basic system (GRONINGEN) achieves the best performance (87.56 F1-score) on the evaluation set using only 1-9 character n-grams as features. We compare this against several ensemble and meta-classifiers in order to examine how the linear system fares when combined with other, especially non-linear classifiers. Special emphasis is placed on the topic bias that exists by virtue of the assessment essay prompt distribution.",
}
@inproceedings{li-zou-2017-classifier,
    title = "Classifier Stacking for Native Language Identification",
    author = "Li, Wen  and
      Zou, Liang",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5044",
    doi = "10.18653/v1/W17-5044",
    pages = "390--397",
    abstract = "This paper reports our contribution (team WLZ) to the NLI Shared Task 2017 (essay track). We first extract lexical and syntactic features from the essays, perform feature weighting and selection, and train linear support vector machine (SVM) classifiers each on an individual feature type. The output of base classifiers, as probabilities for each class, are then fed into a multilayer perceptron to predict the native language of the author. We also report the performance of each feature type, as well as the best features of a type. Our system achieves an accuracy of 86.55{\%}, which is among the best performing systems of this shared task.",
}
@inproceedings{zampieri-etal-2017-native,
    title = "Native Language Identification on Text and Speech",
    author = "Zampieri, Marcos  and
      Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5045",
    doi = "10.18653/v1/W17-5045",
    pages = "398--404",
    abstract = "This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI). The system was submitted to the NLI Shared Task 2017 fusion track which featured students essays and spoken responses in form of audio transcriptions and iVectors by non-native English speakers of eleven native languages. Our system competed in the challenge under the team name ZCD and was based on an ensemble of SVM classifiers trained on character n-grams achieving 83.58{\%} accuracy and ranking 3rd in the shared task.",
}
@inproceedings{smiley-kubler-2017-native,
    title = "Native Language Identification using Phonetic Algorithms",
    author = {Smiley, Charese  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5046",
    doi = "10.18653/v1/W17-5046",
    pages = "405--412",
    abstract = "In this paper, we discuss the results of the IUCL system in the NLI Shared Task 2017. For our system, we explore a variety of phonetic algorithms to generate features for Native Language Identification. These features are contrasted with one of the most successful type of features in NLI, character n-grams. We find that although phonetic features do not perform as well as character n-grams alone, they do increase overall F1 score when used together with character n-grams.",
}
@inproceedings{oh-etal-2017-deep,
    title = "A deep-learning based native-language classification by using a latent semantic analysis for the {NLI} Shared Task 2017",
    author = "Oh, Yoo Rhee  and
      Jeon, Hyung-Bae  and
      Song, Hwa Jeon  and
      Lee, Yun-Kyung  and
      Park, Jeon-Gue  and
      Lee, Yun-Keun",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5047",
    doi = "10.18653/v1/W17-5047",
    pages = "413--422",
    abstract = "This paper proposes a deep-learning based native-language identification (NLI) using a latent semantic analysis (LSA) as a participant (ETRI-SLP) of the NLI Shared Task 2017 where the NLI Shared Task 2017 aims to detect the native language of an essay or speech response of a standardized assessment of English proficiency for academic purposes. To this end, we use the six unit forms of a text data such as character 4/5/6-grams and word 1/2/3-grams. For each unit form of text data, we convert it into a count-based vector, extract a 2000-rank LSA feature, and perform a linear discriminant analysis (LDA) based dimension reduction. From the count-based vector or the LSA-LDA feature, we also obtain the output prediction values of a support vector machine (SVM) based classifier, the output prediction values of a deep neural network (DNN) based classifier, and the bottleneck values of a DNN based classifier. In order to incorporate the various kinds of text-based features and a speech-based i-vector feature, we design two DNN based ensemble classifiers for late fusion and early fusion, respectively. From the NLI experiments, the F1 (macro) scores are obtained as 0.8601, 0.8664, and 0.9220 for the essay track, the speech track, and the fusion track, respectively. The proposed method has comparable performance to the top-ranked teams for the speech and fusion tracks, although it has slightly lower performance for the essay track.",
}
@inproceedings{kepler-etal-2017-fusion,
    title = "Fusion of Simple Models for Native Language Identification",
    author = "Kepler, Fabio  and
      F. Astudillo, Ramon  and
      Abad, Alberto",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5048",
    doi = "10.18653/v1/W17-5048",
    pages = "423--429",
    abstract = "In this paper we describe the approaches we explored for the 2017 Native Language Identification shared task. We focused on simple word and sub-word units avoiding heavy use of hand-crafted features. Following recent trends, we explored linear and neural networks models to attempt to compensate for the lack of rich feature use. Initial efforts yielded f1-scores of 82.39{\%} and 83.77{\%} in the development and test sets of the fusion track, and were officially submitted to the task as team L2F. After the task was closed, we carried on further experiments and relied on a late fusion strategy for combining our simple proposed approaches with modifications of the baselines provided by the task. As expected, the i-vectors based sub-system dominates the performance of the system combinations, and results in the major contributor to our achieved scores. Our best combined system achieves 90.1{\%} and 90.2{\%} f1-score in the development and test sets of the fusion track, respectively.",
}
@inproceedings{cimino-dellorletta-2017-stacked,
    title = "Stacked Sentence-Document Classifier Approach for Improving Native Language Identification",
    author = "Cimino, Andrea  and
      Dell{'}Orletta, Felice",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5049",
    doi = "10.18653/v1/W17-5049",
    pages = "430--437",
    abstract = "In this paper, we describe the approach of the ItaliaNLP Lab team to native language identification and discuss the results we submitted as participants to the essay track of NLI Shared Task 2017. We introduce for the first time a 2-stacked sentence-document architecture for native language identification that is able to exploit both local sentence information and a wide set of general-purpose features qualifying the lexical and grammatical structure of the whole document. When evaluated on the official test set, our sentence-document stacked architecture obtained the best result among all the participants of the essay track with an F1 score of 0.8818.",
}
@inproceedings{gonzalez-garduno-sogaard-2017-using,
    title = "Using Gaze to Predict Text Readability",
    author = "Gonz{\'a}lez-Gardu{\~n}o, Ana Valeria  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5050",
    doi = "10.18653/v1/W17-5050",
    pages = "438--443",
    abstract = "We show that text readability prediction improves significantly from hard parameter sharing with models predicting first pass duration, total fixation duration and regression duration. Specifically, we induce multi-task Multilayer Perceptrons and Logistic Regression models over sentence representations that capture various aggregate statistics, from two different text readability corpora for English, as well as the Dundee eye-tracking corpus. Our approach leads to significant improvements over Single task learning and over previous systems. In addition, our improvements are consistent across train sample sizes, making our approach especially applicable to small datasets.",
}
@inproceedings{laarmann-quante-etal-2017-annotating,
    title = "Annotating Orthographic Target Hypotheses in a {G}erman {L}1 Learner Corpus",
    author = "Laarmann-Quante, Ronja  and
      Ortmann, Katrin  and
      Ehlert, Anna  and
      Vogel, Maurice  and
      Dipper, Stefanie",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5051",
    doi = "10.18653/v1/W17-5051",
    pages = "444--456",
    abstract = "NLP applications for learners often rely on annotated learner corpora. Thereby, it is important that the annotations are both meaningful for the task, and consistent and reliable. We present a new longitudinal L1 learner corpus for German (handwritten texts collected in grade 2{--}4), which is transcribed and annotated with a target hypothesis that strictly only corrects orthographic errors, and is thereby tailored to research and tool development for orthographic issues in primary school. While for most corpora, transcription and target hypothesis are not evaluated, we conducted a detailed inter-annotator agreement study for both tasks. Although we achieved high agreement, our discussion of cases of disagreement shows that even with detailed guidelines, annotators differ here and there for different reasons, which should also be considered when working with transcriptions and target hypotheses of other corpora, especially if no explicit guidelines for their construction are known.",
}
@inproceedings{madnani-etal-2017-large,
    title = "A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring",
    author = "Madnani, Nitin  and
      Loukina, Anastassia  and
      Cahill, Aoife",
    booktitle = "Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-5052",
    doi = "10.18653/v1/W17-5052",
    pages = "457--467",
    abstract = "We explore various supervised learning strategies for automated scoring of content knowledge for a large corpus of 130 different content-based questions spanning four subject areas (Science, Math, English Language Arts, and Social Studies) and containing over 230,000 responses scored by human raters. Based on our analyses, we provide specific recommendations for content scoring. These are based on patterns observed across multiple questions and assessments and are, therefore, likely to generalize to other scenarios and prove useful to the community as automated content scoring becomes more popular in schools and classrooms.",
}
@proceedings{ws-2017-stylistic,
    title = "Proceedings of the Workshop on Stylistic Variation",
    editor = "Brooke, Julian  and
      Solorio, Thamar  and
      Koppel, Moshe",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4900",
    doi = "10.18653/v1/W17-49",
}
@inproceedings{xu-2017-shakespeare,
    title = "From Shakespeare to {T}witter: What are Language Styles all about?",
    author = "Xu, Wei",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4901",
    doi = "10.18653/v1/W17-4901",
    pages = "1--9",
    abstract = "As natural language processing research is growing and largely driven by the availability of data, we expanded research from news and small-scale dialog corpora to web and social media. User-generated data and crowdsourcing opened the door for investigating human language of various styles with more statistical power and real-world applications. In this position/survey paper, I will review and discuss seven language styles that I believe to be important and interesting to study: influential work in the past, challenges at the present, and potential impact for the future.",
}
@inproceedings{jhamtani-etal-2017-shakespearizing,
    title = "Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models",
    author = "Jhamtani, Harsh  and
      Gangal, Varun  and
      Hovy, Eduard  and
      Nyberg, Eric",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4902",
    doi = "10.18653/v1/W17-4902",
    pages = "10--19",
    abstract = "Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose. However, applying stylistic variations is still by and large a manual process, and there have been little efforts towards automating it. In this paper we explore automated methods to transform text from modern English to Shakespearean English using an end to end trainable neural model with pointers to enable copy action. To tackle limited amount of parallel data, we pre-train embeddings of words by leveraging external dictionaries mapping Shakespearean words to modern English words as well as additional text. Our methods are able to get a BLEU score of 31+, an improvement of {\mbox{$\approx$}} 6 points above the strongest baseline. We publicly release our code to foster further research in this area.",
}
@inproceedings{niu-carpuat-2017-discovering,
    title = "Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases",
    author = "Niu, Xing  and
      Carpuat, Marine",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4903",
    doi = "10.18653/v1/W17-4903",
    pages = "20--27",
    abstract = "Detecting and analyzing stylistic variation in language is relevant to diverse Natural Language Processing applications. In this work, we investigate whether salient dimensions of style variations are embedded in standard distributional vector spaces of word meaning. We hypothesizes that distances between embeddings of lexical paraphrases can help isolate style from meaning variations and help identify latent style dimensions. We conduct a qualitative analysis of latent style dimensions, and show the effectiveness of identified style subspaces on a lexical formality prediction task.",
}
@inproceedings{oraby-etal-2017-harvesting,
    title = "Harvesting Creative Templates for Generating Stylistically Varied Restaurant Reviews",
    author = "Oraby, Shereen  and
      Homayon, Sheideh  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4904",
    doi = "10.18653/v1/W17-4904",
    pages = "28--36",
    abstract = "Many of the creative and figurative elements that make language exciting are lost in translation in current natural language generation engines. In this paper, we explore a method to harvest templates from positive and negative reviews in the restaurant domain, with the goal of vastly expanding the types of stylistic variation available to the natural language generator. We learn hyperbolic adjective patterns that are representative of the strongly-valenced expressive language commonly used in either positive or negative reviews. We then identify and delexicalize entities, and use heuristics to extract generation templates from review sentences. We evaluate the learned templates against more traditional review templates, using subjective measures of convincingness, interestingness, and naturalness. Our results show that the learned templates score highly on these measures. Finally, we analyze the linguistic categories that characterize the learned positive and negative templates. We plan to use the learned templates to improve the conversational style of dialogue systems in the restaurant domain.",
}
@inproceedings{braud-sogaard-2017-writing,
    title = "Is writing style predictive of scientific fraud?",
    author = "Braud, Chlo{\'e}  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4905",
    doi = "10.18653/v1/W17-4905",
    pages = "37--42",
    abstract = "The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators. The results seem to suggest that writing style is predictive of scientific fraud. We revisit these initial experiments, and show that the leave-one-out testing procedure they used likely leads to a slight over-estimate of the predictability, but also that simple models can outperform their proposed model by some margin. We go on to explore more abstract linguistic features, such as linguistic complexity and discourse structure, only to obtain negative results. Upon analyzing our models, we do see some interesting patterns, though: Scientific fraud, for examples, contains less comparison, as well as different types of hedging and ways of presenting logical reasoning.",
}
@inproceedings{bizzoni-etal-2017-deep,
    title = "{``}Deep{''} Learning : Detecting Metaphoricity in Adjective-Noun Pairs",
    author = "Bizzoni, Yuri  and
      Chatzikyriakidis, Stergios  and
      Ghanimifard, Mehdi",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4906",
    doi = "10.18653/v1/W17-4906",
    pages = "43--52",
    abstract = "Metaphor is one of the most studied and widespread figures of speech and an essential element of individual style. In this paper we look at metaphor identification in Adjective-Noun pairs. We show that using a single neural network combined with pre-trained vector embeddings can outperform the state of the art in terms of accuracy. In specific, the approach presented in this paper is based on two ideas: a) transfer learning via using pre-trained vectors representing adjective noun pairs, and b) a neural network as a model of composition that predicts a metaphoricity score as output. We present several different architectures for our system and evaluate their performances. Variations on dataset size and on the kinds of embeddings are also investigated. We show considerable improvement over the previous approaches both in terms of accuracy and w.r.t the size of annotated training data.",
}
@inproceedings{hitschler-etal-2017-authorship,
    title = "Authorship Attribution with Convolutional Neural Networks and {POS}-Eliding",
    author = "Hitschler, Julian  and
      van den Berg, Esther  and
      Rehbein, Ines",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4907",
    doi = "10.18653/v1/W17-4907",
    pages = "53--58",
    abstract = "We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict the authors of scientific publications in the same discipline at levels well above chance.",
}
@inproceedings{shoemark-etal-2017-topic,
    title = "Topic and audience effects on distinctively {S}cottish vocabulary usage in {T}witter data",
    author = "Shoemark, Philippa  and
      Kirby, James  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4908",
    doi = "10.18653/v1/W17-4908",
    pages = "59--68",
    abstract = "Sociolinguistic research suggests that speakers modulate their language style in response to their audience. Similar effects have recently been claimed to occur in the informal written context of Twitter, with users choosing less region-specific and non-standard vocabulary when addressing larger audiences. However, these studies have not carefully controlled for the possible confound of topic: that is, tweets addressed to a broad audience might also tend towards topics that engender a more formal style. In addition, it is not clear to what extent previous results generalize to different samples of users. Using mixed-effects models, we show that audience and topic have independent effects on the rate of distinctively Scottish usage in two demographically distinct Twitter user samples. However, not all effects are consistent between the two groups, underscoring the importance of replicating studies on distinct user samples before drawing strong conclusions from social media data.",
}
@inproceedings{litvinova-etal-2017-differences,
    title = "Differences in type-token ratio and part-of-speech frequencies in male and female {R}ussian written texts",
    author = "Litvinova, Tatiana  and
      Seredin, Pavel  and
      Litvinova, Olga  and
      Zagorovskaya, Olga",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4909",
    doi = "10.18653/v1/W17-4909",
    pages = "69--73",
    abstract = "The differences in the frequencies of some parts of speech (POS), particularly function words, and lexical diversity in male and female speech have been pointed out in a number of papers. The classifiers using exclusively context-independent parameters have proved to be highly effective. However, there are still issues that have to be addressed as a lot of studies are performed for English and the genre and topic of texts is sometimes neglected. The aim of this paper is to investigate the association between context-independent parameters of Russian written texts and the gender of their authors and to design predictive re-gression models. A number of correlations were found. The obtained data is in good agreement with the results obtained for other languages. The model based on 5 parameters with the highest correlation coefficients was designed.",
}
@inproceedings{haider-palmer-2017-modeling,
    title = "Modeling Communicative Purpose with Functional Style: Corpus and Features for {G}erman Genre and Register Analysis",
    author = "Haider, Thomas  and
      Palmer, Alexis",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4910",
    doi = "10.18653/v1/W17-4910",
    pages = "74--84",
    abstract = "While there is wide acknowledgement in NLP of the utility of document characterization by genre, it is quite difficult to determine a definitive set of features or even a comprehensive list of genres. This paper addresses both issues. First, with prototype semantics, we develop a hierarchical taxonomy of discourse functions. We implement the taxonomy by developing a new text genre corpus of contemporary German to perform a text based comparative register analysis. Second, we extract a host of style features, both deep and shallow, aiming beyond linguistically motivated features at situational correlates in texts. The feature sets are used for supervised text genre classification, on which our models achieve high accuracy. The combination of the corpus typology and feature sets allows us to characterize types of communicative purpose in a comparative setup, by qualitative interpretation of style feature loadings of a regularized discriminant analysis. Finally, to determine the dependence of genre on topics (which are arguably the distinguishing factor of sub-genre), we compare and combine our style models with Latent Dirichlet Allocation features across different corpus settings with unstable topics.",
}
@inproceedings{lin-walker-2017-stylistic,
    title = "Stylistic Variation in Television Dialogue for Natural Language Generation",
    author = "Lin, Grace  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4911",
    doi = "10.18653/v1/W17-4911",
    pages = "85--93",
    abstract = "Conversation is a critical component of storytelling, where key information is often revealed by what/how a character says it. We focus on the issue of character voice and build stylistic models with linguistic features related to natural language generation decisions. Using a dialogue corpus of the television series, The Big Bang Theory, we apply content analysis to extract relevant linguistic features to build character-based stylistic models, and we test the model-fit through an user perceptual experiment with Amazon{'}s Mechanical Turk. The results are encouraging in that human subjects tend to perceive the generated utterances as being more similar to the character they are modeled on, than to another random character.",
}
@inproceedings{ficler-goldberg-2017-controlling,
    title = "Controlling Linguistic Style Aspects in Neural Language Generation",
    author = "Ficler, Jessica  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4912",
    doi = "10.18653/v1/W17-4912",
    pages = "94--104",
    abstract = "Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts. We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content.",
}
@inproceedings{andresen-zinsmeister-2017-approximating,
    title = "Approximating Style by N-gram-based Annotation",
    author = "Andresen, Melanie  and
      Zinsmeister, Heike",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4913",
    doi = "10.18653/v1/W17-4913",
    pages = "105--115",
    abstract = "The concept of style is much debated in theoretical as well as empirical terms. From an empirical perspective, the key question is how to operationalize style and thus make it accessible for annotation and quantification. In authorship attribution, many different approaches have successfully resolved this issue at the cost of linguistic interpretability: The resulting algorithms may be able to distinguish one language variety from the other, but do not give us much information on their distinctive linguistic properties. We approach the issue of interpreting stylistic features by extracting linear and syntactic n-grams that are distinctive for a language variety. We present a study that exemplifies this process by a comparison of the German academic languages of linguistics and literary studies. Overall, our findings show that distinctive n-grams can be related to linguistic categories. The results suggest that the style of German literary studies is characterized by nominal structures and the style of linguistics by verbal ones.",
}
@inproceedings{manjavacas-etal-2017-assessing,
    title = "Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution",
    author = "Manjavacas, Enrique  and
      De Gussem, Jeroen  and
      Daelemans, Walter  and
      Kestemont, Mike",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4914",
    doi = "10.18653/v1/W17-4914",
    pages = "116--125",
    abstract = "Recent applications of neural language models have led to an increased interest in the automatic generation of natural language. However impressive, the evaluation of neurally generated text has so far remained rather informal and anecdotal. Here, we present an attempt at the systematic assessment of one aspect of the quality of neurally generated text. We focus on a specific aspect of neural language generation: its ability to reproduce authorial writing styles. Using established models for authorship attribution, we empirically assess the stylistic qualities of neurally generated text. In comparison to conventional language models, neural models generate fuzzier text, that is relatively harder to attribute correctly. Nevertheless, our results also suggest that neurally generated text offers more valuable perspectives for the augmentation of training data.",
}
@proceedings{ws-2017-discourse,
    title = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    editor = {Webber, Bonnie  and
      Popescu-Belis, Andrei  and
      Tiedemann, J{\"o}rg},
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4800",
    doi = "10.18653/v1/W17-48",
}
@inproceedings{loaiciga-etal-2017-findings,
    title = "Findings of the 2017 {D}isco{MT} Shared Task on Cross-lingual Pronoun Prediction",
    author = {Lo{\'a}iciga, Sharid  and
      Stymne, Sara  and
      Nakov, Preslav  and
      Hardmeier, Christian  and
      Tiedemann, J{\"o}rg  and
      Cettolo, Mauro  and
      Versley, Yannick},
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4801",
    doi = "10.18653/v1/W17-4801",
    pages = "1--16",
    abstract = "We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the target-language lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document. We offered four subtasks, each for a different language pair and translation direction: English-to-French, English-to-German, German-to-English, and Spanish-to-English. Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that most participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.",
}
@inproceedings{miculicich-werlen-popescu-belis-2017-validation,
    title = "Validation of an Automatic Metric for the Accuracy of Pronoun Translation ({APT})",
    author = "Miculicich Werlen, Lesly  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4802",
    doi = "10.18653/v1/W17-4802",
    pages = "17--25",
    abstract = "In this paper, we define and assess a reference-based metric to evaluate the accuracy of pronoun translation (APT). The metric automatically aligns a candidate and a reference translation using GIZA++ augmented with specific heuristics, and then counts the number of identical or different pronouns, with provision for legitimate variations and omitted pronouns. All counts are then combined into one score. The metric is applied to the results of seven systems (including the baseline) that participated in the DiscoMT 2015 shared task on pronoun translation from English to French. The APT metric reaches around 0.993-0.999 Pearson correlation with human judges (depending on the parameters of APT), while other automatic metrics such as BLEU, METEOR, or those specific to pronouns used at DiscoMT 2015 reach only 0.972-0.986 Pearson correlation.",
}
@inproceedings{born-etal-2017-using,
    title = "Using a Graph-based Coherence Model in Document-Level Machine Translation",
    author = "Born, Leo  and
      Mesgar, Mohsen  and
      Strube, Michael",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4803",
    doi = "10.18653/v1/W17-4803",
    pages = "26--35",
    abstract = "Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube, (2016) with Docent (Hardmeier et al., 2012, Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.",
}
@inproceedings{muller-2017-treatment,
    title = "Treatment of Markup in Statistical Machine Translation",
    author = {M{\"u}ller, Mathias},
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4804",
    doi = "10.18653/v1/W17-4804",
    pages = "36--46",
    abstract = "We present work on handling XML markup in Statistical Machine Translation (SMT). The methods we propose can be used to effectively preserve markup (for instance inline formatting or structure) and to place markup correctly in a machine-translated segment. We evaluate our approaches with parallel data that naturally contains markup or where markup was inserted to create synthetic examples. In our experiments, hybrid reinsertion has proven the most accurate method to handle markup, while alignment masking and alignment reinsertion should be regarded as viable alternatives. We provide implementations of all the methods described and they are freely available as an open-source framework.",
}
@inproceedings{stymne-etal-2017-bilstm,
    title = "A {B}i{LSTM}-based System for Cross-lingual Pronoun Prediction",
    author = "Stymne, Sara  and
      Lo{\'a}iciga, Sharid  and
      Cap, Fabienne",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4805",
    doi = "10.18653/v1/W17-4805",
    pages = "47--53",
    abstract = "We describe the Uppsala system for the 2017 DiscoMT shared task on cross-lingual pronoun prediction. The system is based on a lower layer of BiLSTMs reading the source and target sentences respectively. Classification is based on the BiLSTM representation of the source and target positions for the pronouns. In addition we enrich our system with dependency representations from an external parser and character representations of the source sentence. We show that these additions perform well for German and Spanish as source languages. Our system is competitive and is in first or second place for all language pairs.",
}
@inproceedings{jean-etal-2017-neural,
    title = "Neural Machine Translation for Cross-Lingual Pronoun Prediction",
    author = "Jean, Sebastien  and
      Lauly, Stanislas  and
      Firat, Orhan  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4806",
    doi = "10.18653/v1/W17-4806",
    pages = "54--57",
    abstract = "In this paper we present our systems for the DiscoMT 2017 cross-lingual pronoun prediction shared task. For all four language pairs, we trained a standard attention-based neural machine translation system as well as three variants that incorporate information from the preceding source sentence. We show that our systems, which are not specifically designed for pronoun prediction and may be used to generate complete sentence translations, generally achieve competitive results on this task.",
}
@inproceedings{hardmeier-2017-predicting,
    title = "Predicting Pronouns with a Convolutional Network and an N-gram Model",
    author = "Hardmeier, Christian",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4807",
    doi = "10.18653/v1/W17-4807",
    pages = "58--62",
    abstract = "This paper describes the UU-Hardmeier system submitted to the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The system is an ensemble of convolutional neural networks combined with a source-aware n-gram language model.",
}
@inproceedings{luotolahti-etal-2017-cross,
    title = "Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0",
    author = "Luotolahti, Juhani  and
      Kanerva, Jenna  and
      Ginter, Filip",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4808",
    doi = "10.18653/v1/W17-4808",
    pages = "63--66",
    abstract = "In this paper we present our system in the DiscoMT 2017 Shared Task on Crosslingual Pronoun Prediction. Our entry builds on our last year{'}s success, our system based on deep recurrent neural networks outperformed all the other systems with a clear margin. This year we investigate whether different pre-trained word embeddings can be used to improve the neural systems, and whether the recently published Gated Convolutions outperform the Gated Recurrent Units used last year.",
}
@inproceedings{grishina-2017-combining,
    title = "Combining the output of two coreference resolution systems for two source languages to improve annotation projection",
    author = "Grishina, Yulia",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4809",
    doi = "10.18653/v1/W17-4809",
    pages = "67--72",
    abstract = "Although parallel coreference corpora can to a high degree support the development of SMT systems, there are no large-scale parallel datasets available due to the complexity of the annotation task and the variability in annotation schemes. In this study, we exploit an annotation projection method to combine the output of two coreference resolution systems for two different source languages (English, German) in order to create an annotated corpus for a third language (Russian). We show that our technique is superior to projecting annotations from a single source language, and we provide an in-depth analysis of the projected annotations in order to assess the perspectives of our approach.",
}
@inproceedings{lapshinova-koltunski-hardmeier-2017-discovery,
    title = "Discovery of Discourse-Related Language Contrasts through Alignment Discrepancies in {E}nglish-{G}erman Translation",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4810",
    doi = "10.18653/v1/W17-4810",
    pages = "73--81",
    abstract = "In this paper, we analyse alignment discrepancies for discourse structures in English-German parallel data {--} sentence pairs, in which discourse structures in target or source texts have no alignment in the corresponding parallel sentences. The discourse-related structures are designed in form of linguistic patterns based on the information delivered by automatic part-of-speech and dependency annotation. In addition to alignment errors (existing structures left unaligned), these alignment discrepancies can be caused by language contrasts or through the phenomena of explicitation and implicitation in the translation process. We propose a new approach including new type of resources for corpus-based language contrast analysis and apply it to study and classify the contrasts found in our English-German parallel corpus. As unaligned discourse structures may also result in the loss of discourse information in the MT training data, we hope to deliver information in support of discourse-aware machine translation (MT).",
}
@inproceedings{tiedemann-scherrer-2017-neural,
    title = "Neural Machine Translation with Extended Context",
    author = {Tiedemann, J{\"o}rg  and
      Scherrer, Yves},
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4811",
    doi = "10.18653/v1/W17-4811",
    pages = "82--92",
    abstract = "We investigate the use of extended context in attention-based neural machine translation. We base our experiments on translated movie subtitles and discuss the effect of increasing the segments beyond single translation units. We study the use of extended source language context as well as bilingual context extensions. The models learn to distinguish between information from different segments and are surprisingly robust with respect to translation quality. In this pilot study, we observe interesting cross-sentential attention patterns that improve textual coherence in translation at least in some selected cases.",
}
@inproceedings{li-etal-2017-translating,
    title = "Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment",
    author = "Li, Hongzheng  and
      Langlais, Philippe  and
      Jin, Yaohong",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4812",
    doi = "10.18653/v1/W17-4812",
    pages = "93--98",
    abstract = "Implicit discourse connectives and relations are distributed more widely in Chinese texts, when translating into English, such connectives are usually translated explicitly. Towards Chinese-English MT, in this paper we describe cross-lingual annotation and alignment of dis-course connectives in a parallel corpus, describing related surveys and findings. We then conduct some evaluation experiments to testify the translation of implicit connectives and whether representing implicit connectives explicitly in source language can improve the final translation performance significantly. Preliminary results show it has little improvement by just inserting explicit connectives for implicit relations.",
}
@inproceedings{mascarell-2017-lexical,
    title = "Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation",
    author = "Mascarell, Laura",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4813",
    doi = "10.18653/v1/W17-4813",
    pages = "99--109",
    abstract = "Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German-to-English show that our method produces correct translations in up to 88{\%} of the changes, improving the translation in 36{\%}-48{\%} of them over the baseline.",
}
@inproceedings{sim-smith-2017-integrating,
    title = "On Integrating Discourse in Machine Translation",
    author = "Sim Smith, Karin",
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4814",
    doi = "10.18653/v1/W17-4814",
    pages = "110--121",
    abstract = "As the quality of Machine Translation (MT) improves, research on improving discourse in automatic translations becomes more viable. This has resulted in an increase in the amount of work on discourse in MT. However many of the existing models and metrics have yet to integrate these insights. Part of this is due to the evaluation methodology, based as it is largely on matching to a single reference. At a time when MT is increasingly being used in a pipeline for other tasks, the semantic element of the translation process needs to be properly integrated into the task. Moreover, in order to take MT to another level, it will need to judge output not based on a single reference translation, but based on notions of fluency and of adequacy {--} ideally with reference to the source text.",
}
@proceedings{ws-2017-machine,
    title = "Proceedings of the Second Conference on Machine Translation",
    editor = "Bojar, Ond{\v{r}}ej  and
      Buck, Christian  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      Koehn, Philipp  and
      Kreutzer, Julia",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4700",
    doi = "10.18653/v1/W17-47",
}
@inproceedings{pu-etal-2017-sense,
    title = "Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering",
    author = "Pu, Xiao  and
      Pappas, Nikolaos  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4701",
    doi = "10.18653/v1/W17-4701",
    pages = "1--10",
}
@inproceedings{rios-gonzales-etal-2017-improving,
    title = "Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings",
    author = "Rios Gonzales, Annette  and
      Mascarell, Laura  and
      Sennrich, Rico",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4702",
    doi = "10.18653/v1/W17-4702",
    pages = "11--19",
}
@inproceedings{burlot-etal-2017-word,
    title = "Word Representations in Factored Neural Machine Translation",
    author = {Burlot, Franck  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Barrault, Lo{\"\i}c  and
      Bougares, Fethi  and
      Yvon, Fran{\c{c}}ois},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4703",
    doi = "10.18653/v1/W17-4703",
    pages = "20--31",
}
@inproceedings{tamchyna-etal-2017-modeling,
    title = "Modeling Target-Side Inflection in Neural Machine Translation",
    author = "Tamchyna, Ale{\v{s}}  and
      Weller-Di Marco, Marion  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4704",
    doi = "10.18653/v1/W17-4704",
    pages = "32--42",
}
@inproceedings{burlot-yvon-2017-evaluating,
    title = "Evaluating the morphological competence of Machine Translation Systems",
    author = "Burlot, Franck  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4705",
    doi = "10.18653/v1/W17-4705",
    pages = "43--55",
}
@inproceedings{huck-etal-2017-target,
    title = "Target-side Word Segmentation Strategies for Neural Machine Translation",
    author = "Huck, Matthias  and
      Riess, Simon  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4706",
    doi = "10.18653/v1/W17-4706",
    pages = "56--67",
}
@inproceedings{nadejde-etal-2017-predicting,
    title = "Predicting Target Language {CCG} Supertags Improves Neural Machine Translation",
    author = "N{\u{a}}dejde, Maria  and
      Reddy, Siva  and
      Sennrich, Rico  and
      Dwojak, Tomasz  and
      Junczys-Dowmunt, Marcin  and
      Koehn, Philipp  and
      Birch, Alexandra",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4707",
    doi = "10.18653/v1/W17-4707",
    pages = "68--79",
}
@inproceedings{niehues-cho-2017-exploiting,
    title = "Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning",
    author = "Niehues, Jan  and
      Cho, Eunah",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4708",
    doi = "10.18653/v1/W17-4708",
    pages = "80--89",
}
@inproceedings{miura-etal-2017-tree,
    title = "Tree as a Pivot: Syntactic Matching Methods in Pivot Translation",
    author = "Miura, Akiva  and
      Neubig, Graham  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4709",
    doi = "10.18653/v1/W17-4709",
    pages = "90--98",
}
@inproceedings{miceli-barone-etal-2017-deep,
    title = "Deep architectures for Neural Machine Translation",
    author = "Miceli Barone, Antonio Valerio  and
      Helcl, Jind{\v{r}}ich  and
      Sennrich, Rico  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4710",
    doi = "10.18653/v1/W17-4710",
    pages = "99--107",
}
@inproceedings{alkhouli-ney-2017-biasing,
    title = "Biasing Attention-Based Recurrent Neural Networks Using External Alignment Information",
    author = "Alkhouli, Tamer  and
      Ney, Hermann",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4711",
    doi = "10.18653/v1/W17-4711",
    pages = "108--117",
}
@inproceedings{britz-etal-2017-effective,
    title = "Effective Domain Mixing for Neural Machine Translation",
    author = "Britz, Denny  and
      Le, Quoc  and
      Pryzant, Reid",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4712",
    doi = "10.18653/v1/W17-4712",
    pages = "118--126",
}
@inproceedings{farajian-etal-2017-multi,
    title = "Multi-Domain Neural Machine Translation through Unsupervised Adaptation",
    author = "Farajian, M. Amin  and
      Turchi, Marco  and
      Negri, Matteo  and
      Federico, Marcello",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4713",
    doi = "10.18653/v1/W17-4713",
    pages = "127--137",
}
@inproceedings{chinea-rios-etal-2017-adapting,
    title = "Adapting Neural Machine Translation with Parallel Synthetic Data",
    author = "Chinea-R{\'\i}os, Mara  and
      Peris, {\'A}lvaro  and
      Casacuberta, Francisco",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4714",
    doi = "10.18653/v1/W17-4714",
    pages = "138--147",
}
@inproceedings{currey-etal-2017-copied,
    title = "Copied Monolingual Data Improves Low-Resource Neural Machine Translation",
    author = "Currey, Anna  and
      Miceli Barone, Antonio Valerio  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4715",
    doi = "10.18653/v1/W17-4715",
    pages = "148--156",
}
@inproceedings{chatterjee-etal-2017-guiding,
    title = "Guiding Neural Machine Translation Decoding with External Knowledge",
    author = "Chatterjee, Rajen  and
      Negri, Matteo  and
      Turchi, Marco  and
      Federico, Marcello  and
      Specia, Lucia  and
      Blain, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4716",
    doi = "10.18653/v1/W17-4716",
    pages = "157--168",
}
@inproceedings{bojar-etal-2017-findings,
    title = "Findings of the 2017 Conference on Machine Translation ({WMT}17)",
    author = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huang, Shujian  and
      Huck, Matthias  and
      Koehn, Philipp  and
      Liu, Qun  and
      Logacheva, Varvara  and
      Monz, Christof  and
      Negri, Matteo  and
      Post, Matt  and
      Rubino, Raphael  and
      Specia, Lucia  and
      Turchi, Marco",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4717",
    doi = "10.18653/v1/W17-4717",
    pages = "169--214",
}
@inproceedings{elliott-etal-2017-findings,
    title = "Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description",
    author = {Elliott, Desmond  and
      Frank, Stella  and
      Barrault, Lo{\"\i}c  and
      Bougares, Fethi  and
      Specia, Lucia},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4718",
    doi = "10.18653/v1/W17-4718",
    pages = "215--233",
}
@inproceedings{jimeno-yepes-etal-2017-findings,
    title = "Findings of the {WMT} 2017 Biomedical Translation Shared Task",
    author = "Jimeno Yepes, Antonio  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Verspoor, Karin  and
      Bojar, Ond{\v{r}}ej  and
      Boyer, Arthur  and
      Grozea, Cristian  and
      Haddow, Barry  and
      Kittner, Madeleine  and
      Lichtblau, Yvonne  and
      Pecina, Pavel  and
      Roller, Roland  and
      Rosa, Rudolf  and
      Siu, Amy  and
      Thomas, Philippe  and
      Trescher, Saskia",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4719",
    doi = "10.18653/v1/W17-4719",
    pages = "234--247",
}
@inproceedings{sudarikov-etal-2017-cuni,
    title = "{CUNI} submission in {WMT}17: Chimera goes neural",
    author = "Sudarikov, Roman  and
      Mare{\v{c}}ek, David  and
      Kocmi, Tom  and
      Vari{\v{s}}, Du{\v{s}}an  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4720",
    doi = "10.18653/v1/W17-4720",
    pages = "248--256",
}
@inproceedings{burlot-etal-2017-limsi,
    title = "{LIMSI}@{WMT}{'}17",
    author = "Burlot, Franck  and
      Safari, Pooyan  and
      Labeau, Matthieu  and
      Allauzen, Alexandre  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4721",
    doi = "10.18653/v1/W17-4721",
    pages = "257--264",
}
@inproceedings{deng-etal-2017-systran,
    title = "{SYSTRAN} Purely Neural {MT} Engines for {WMT}2017",
    author = "Deng, Yongchao  and
      Kim, Jungi  and
      Klein, Guillaume  and
      Kobus, Catherine  and
      Segal, Natalia  and
      Servan, Christophe  and
      Wang, Bo  and
      Zhang, Dakun  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4722",
    doi = "10.18653/v1/W17-4722",
    pages = "265--270",
}
@inproceedings{di-gangi-etal-2017-fbks,
    title = "{FBK}{'}s Participation to the {E}nglish-to-{G}erman News Translation Task of {WMT} 2017",
    author = "Di Gangi, Mattia Antonino  and
      Bertoldi, Nicola  and
      Federico, Marcello",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4723",
    doi = "10.18653/v1/W17-4723",
    pages = "271--275",
}
@inproceedings{ding-etal-2017-jhu,
    title = "The {JHU} Machine Translation Systems for {WMT} 2017",
    author = "Ding, Shuoyang  and
      Khayrallah, Huda  and
      Koehn, Philipp  and
      Post, Matt  and
      Kumar, Gaurav  and
      Duh, Kevin",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4724",
    doi = "10.18653/v1/W17-4724",
    pages = "276--282",
}
@inproceedings{escolano-etal-2017-talp,
    title = "The {TALP}-{UPC} Neural Machine Translation System for {G}erman/{F}innish-{E}nglish Using the Inverse Direction Model in Rescoring",
    author = "Escolano, Carlos  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4725",
    doi = "10.18653/v1/W17-4725",
    pages = "283--287",
}
@inproceedings{garcia-martinez-etal-2017-lium,
    title = "{LIUM} Machine Translation Systems for {WMT}17 News Translation Task",
    author = {Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Caglayan, Ozan  and
      Aransa, Walid  and
      Bardet, Adrien  and
      Bougares, Fethi  and
      Barrault, Lo{\"\i}c},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4726",
    doi = "10.18653/v1/W17-4726",
    pages = "288--295",
}
@inproceedings{gronroos-etal-2017-extending,
    title = "Extending hybrid word-character neural machine translation with multi-task learning of morphological analysis",
    author = {Gr{\"o}nroos, Stig-Arne  and
      Virpioja, Sami  and
      Kurimo, Mikko},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4727",
    doi = "10.18653/v1/W17-4727",
    pages = "296--302",
}
@inproceedings{gwinnup-etal-2017-afrl,
    title = "The {AFRL-MITLL} {WMT17} Systems: Old, New, Borrowed, {BLEU}",
    author = "Gwinnup, Jeremy  and
      Anderson, Timothy  and
      Erdmann, Grant  and
      Young, Katherine  and
      Kazi, Michaeel  and
      Salesky, Elizabeth  and
      Thompson, Brian  and
      Taylor, Jonathan",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4728",
    doi = "10.18653/v1/W17-4728",
    pages = "303--309",
}
@inproceedings{holtz-etal-2017-university,
    title = "{U}niversity of {R}ochester {WMT} 2017 {NMT} System Submission",
    author = "Holtz, Chester  and
      Ke, Chuyang  and
      Gildea, Daniel",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4729",
    doi = "10.18653/v1/W17-4729",
    pages = "310--314",
}
@inproceedings{huck-etal-2017-lmu,
    title = "{LMU} {M}unich{'}s Neural Machine Translation Systems for News Articles and Health Information Texts",
    author = "Huck, Matthias  and
      Braune, Fabienne  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4730",
    doi = "10.18653/v1/W17-4730",
    pages = "315--322",
}
@inproceedings{hurskainen-tiedemann-2017-rule,
    title = "Rule-based Machine translation from {E}nglish to {F}innish",
    author = {Hurskainen, Arvi  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4731",
    doi = "10.18653/v1/W17-4731",
    pages = "323--329",
}
@inproceedings{lo-etal-2017-nrc,
    title = "{NRC} Machine Translation System for {WMT} 2017",
    author = "Lo, Chi-kiu  and
      Chen, Boxing  and
      Cherry, Colin  and
      Foster, George  and
      Larkin, Samuel  and
      Stewart, Darlene  and
      Kuhn, Roland",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4732",
    doi = "10.18653/v1/W17-4732",
    pages = "330--337",
}
@inproceedings{ostling-etal-2017-helsinki,
    title = "The {H}elsinki Neural Machine Translation System",
    author = {{\"O}stling, Robert  and
      Scherrer, Yves  and
      Tiedemann, J{\"o}rg  and
      Tang, Gongbo  and
      Nieminen, Tommi},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4733",
    doi = "10.18653/v1/W17-4733",
    pages = "338--347",
}
@inproceedings{peter-etal-2017-qt21,
    title = "The {QT}21 Combined Machine Translation System for {E}nglish to {L}atvian",
    author = "Peter, Jan-Thorsten  and
      Ney, Hermann  and
      Bojar, Ond{\v{r}}ej  and
      Pham, Ngoc-Quan  and
      Niehues, Jan  and
      Waibel, Alex  and
      Burlot, Franck  and
      Yvon, Fran{\c{c}}ois  and
      Pinnis, M{\=a}rcis  and
      {\v{S}}ics, Valters  and
      Bastings, Jasmijn  and
      Rios, Miguel  and
      Aziz, Wilker  and
      Williams, Philip  and
      Blain, Fr{\'e}d{\'e}ric  and
      Specia, Lucia",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4734",
    doi = "10.18653/v1/W17-4734",
    pages = "348--357",
}
@inproceedings{peter-etal-2017-rwth,
    title = "The {RWTH} {A}achen {U}niversity {E}nglish-{G}erman and {G}erman-{E}nglish Machine Translation System for {WMT} 2017",
    author = "Peter, Jan-Thorsten  and
      Guta, Andreas  and
      Alkhouli, Tamer  and
      Bahar, Parnia  and
      Rosendahl, Jan  and
      Rossenbach, Nick  and
      Gra{\c{c}}a, Miguel  and
      Ney, Hermann",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4735",
    doi = "10.18653/v1/W17-4735",
    pages = "358--365",
}
@inproceedings{pham-etal-2017-karlsruhe,
    title = "The Karlsruhe Institute of Technology Systems for the News Translation Task in {WMT} 2017",
    author = "Pham, Ngoc-Quan  and
      Niehues, Jan  and
      Ha, Thanh-Le  and
      Cho, Eunah  and
      Sperber, Matthias  and
      Waibel, Alexander",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4736",
    doi = "10.18653/v1/W17-4736",
    pages = "366--373",
}
@inproceedings{pinnis-etal-2017-tildes,
    title = "Tilde{'}s Machine Translation Systems for {WMT} 2017",
    author = "Pinnis, M{\=a}rcis  and
      Kri{\v{s}}lauks, Rihards  and
      Miks, Toms  and
      Deksne, Daiga  and
      {\v{S}}ics, Valters",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4737",
    doi = "10.18653/v1/W17-4737",
    pages = "374--381",
}
@inproceedings{rikters-etal-2017-c,
    title = "{C}-3{MA}: {T}artu-{R}iga-{Z}urich Translation Systems for {WMT}17",
    author = "Rikters, Mat{\=\i}ss  and
      Amrhein, Chantal  and
      Del, Maksym  and
      Fishel, Mark",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4738",
    doi = "10.18653/v1/W17-4738",
    pages = "382--388",
}
@inproceedings{sennrich-etal-2017-university,
    title = "The {U}niversity of {E}dinburgh{'}s Neural {MT} Systems for {WMT}17",
    author = "Sennrich, Rico  and
      Birch, Alexandra  and
      Currey, Anna  and
      Germann, Ulrich  and
      Haddow, Barry  and
      Heafield, Kenneth  and
      Miceli Barone, Antonio Valerio  and
      Williams, Philip",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4739",
    doi = "10.18653/v1/W17-4739",
    pages = "389--399",
}
@inproceedings{tan-etal-2017-xmu,
    title = "{XMU} Neural Machine Translation Systems for {WMT} 17",
    author = "Tan, Zhixing  and
      Wang, Boli  and
      Hu, Jinming  and
      Chen, Yidong  and
      Shi, Xiaodong",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4740",
    doi = "10.18653/v1/W17-4740",
    pages = "400--404",
}
@inproceedings{trieu-etal-2017-jaist,
    title = "The {JAIST} Machine Translation Systems for {WMT} 17",
    author = "Trieu, Hai-Long  and
      Pham, Trung-Tin  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4741",
    doi = "10.18653/v1/W17-4741",
    pages = "405--409",
}
@inproceedings{wang-etal-2017-sogou,
    title = "Sogou Neural Machine Translation Systems for {WMT}17",
    author = "Wang, Yuguang  and
      Cheng, Shanbo  and
      Jiang, Liyang  and
      Yang, Jiajun  and
      Chen, Wei  and
      Li, Muze  and
      Shi, Lin  and
      Wang, Yanfeng  and
      Yang, Hongtao",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4742",
    doi = "10.18653/v1/W17-4742",
    pages = "410--415",
}
@inproceedings{wolk-marasek-2017-pjiits,
    title = "{PJIIT}{'}s systems for {WMT} 2017 Conference",
    author = "Wolk, Krzysztof  and
      Marasek, Krzysztof",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4743",
    doi = "10.18653/v1/W17-4743",
    pages = "416--421",
}
@inproceedings{xu-etal-2017-hunter,
    title = "Hunter {MT}: A Course for Young Researchers in {WMT}17",
    author = "Xu, Jia  and
      Kuang, Yi Zong  and
      Baijoo, Shondell  and
      Lee, Jacob Hyun  and
      Shahzad, Uman  and
      Ahmed, Mir  and
      Lancaster, Meredith  and
      Carlan, Chris",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4744",
    doi = "10.18653/v1/W17-4744",
    pages = "422--427",
}
@inproceedings{zhang-etal-2017-casict,
    title = "{CASICT}-{DCU} Neural Machine Translation Systems for {WMT}17",
    author = "Zhang, Jinchao  and
      Porkaew, Peerachet  and
      Hu, Jiawei  and
      Zhao, Qiuye  and
      Liu, Qun",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4745",
    doi = "10.18653/v1/W17-4745",
    pages = "428--431",
}
@inproceedings{caglayan-etal-2017-lium,
    title = "{LIUM}-{CVC} Submissions for {WMT}17 Multimodal Translation Task",
    author = {Caglayan, Ozan  and
      Aransa, Walid  and
      Bardet, Adrien  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Bougares, Fethi  and
      Barrault, Lo{\"\i}c  and
      Masana, Marc  and
      Herranz, Luis  and
      van de Weijer, Joost},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4746",
    doi = "10.18653/v1/W17-4746",
    pages = "432--439",
}
@inproceedings{calixto-etal-2017-dcu,
    title = "{DCU} System Report on the {WMT} 2017 Multi-modal Machine Translation Task",
    author = "Calixto, Iacer  and
      Dutta Chowdhury, Koel  and
      Liu, Qun",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4747",
    doi = "10.18653/v1/W17-4747",
    pages = "440--444",
}
@inproceedings{duselis-etal-2017-afrl,
    title = "The {AFRL}-{OSU} {WMT}17 Multimodal Translation System: An Image Processing Approach",
    author = "Duselis, John  and
      Hutt, Michael  and
      Gwinnup, Jeremy  and
      Davis, James  and
      Sandvick, Joshua",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4748",
    doi = "10.18653/v1/W17-4748",
    pages = "445--449",
}
@inproceedings{helcl-libovicky-2017-cuni,
    title = "{CUNI} System for the {WMT}17 Multimodal Translation Task",
    author = "Helcl, Jind{\v{r}}ich  and
      Libovick{\'y}, Jind{\v{r}}ich",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4749",
    doi = "10.18653/v1/W17-4749",
    pages = "450--457",
}
@inproceedings{jaffe-2017-generating,
    title = "Generating Image Descriptions using Multilingual Data",
    author = "Jaffe, Alan",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4750",
    doi = "10.18653/v1/W17-4750",
    pages = "458--464",
}
@inproceedings{ma-etal-2017-osu,
    title = "{OSU} Multimodal Machine Translation System Report",
    author = "Ma, Mingbo  and
      Li, Dapeng  and
      Zhao, Kai  and
      Huang, Liang",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4751",
    doi = "10.18653/v1/W17-4751",
    pages = "465--469",
}
@inproceedings{madhyastha-etal-2017-sheffield,
    title = "{S}heffield {M}ulti{MT}: Using Object Posterior Predictions for Multimodal Machine Translation",
    author = "Madhyastha, Pranava Swaroop  and
      Wang, Josiah  and
      Specia, Lucia",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4752",
    doi = "10.18653/v1/W17-4752",
    pages = "470--476",
}
@inproceedings{zhang-etal-2017-nict,
    title = "{NICT}-{NAIST} System for {WMT}17 Multimodal Translation Task",
    author = "Zhang, Jingyi  and
      Utiyama, Masao  and
      Sumita, Eiichro  and
      Neubig, Graham  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4753",
    doi = "10.18653/v1/W17-4753",
    pages = "477--482",
}
@inproceedings{duma-menzel-2017-automatic,
    title = "Automatic Threshold Detection for Data Selection in Machine Translation",
    author = "Duma, Mirela-Stefania  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4754",
    doi = "10.18653/v1/W17-4754",
    pages = "483--488",
}
@inproceedings{bojar-etal-2017-results,
    title = "Results of the {WMT}17 Metrics Shared Task",
    author = "Bojar, Ond{\v{r}}ej  and
      Graham, Yvette  and
      Kamran, Amir",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4755",
    doi = "10.18653/v1/W17-4755",
    pages = "489--513",
}
@inproceedings{sokolov-etal-2017-shared,
    title = "A Shared Task on Bandit Learning for Machine Translation",
    author = {Sokolov, Artem  and
      Kreutzer, Julia  and
      Sunderland, Kellen  and
      Danchenko, Pavel  and
      Szymaniak, Witold  and
      F{\"u}rstenau, Hagen  and
      Riezler, Stefan},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4756",
    doi = "10.18653/v1/W17-4756",
    pages = "514--524",
}
@inproceedings{bojar-etal-2017-results-wmt17,
    title = "Results of the {WMT}17 Neural {MT} Training Task",
    author = "Bojar, Ond{\v{r}}ej  and
      Helcl, Jind{\v{r}}ich  and
      Kocmi, Tom  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Musil, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4757",
    doi = "10.18653/v1/W17-4757",
    pages = "525--533",
}
@inproceedings{avramidis-2017-sentence,
    title = "Sentence-level quality estimation by predicting {HTER} as a multi-component metric",
    author = "Avramidis, Eleftherios",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4758",
    doi = "10.18653/v1/W17-4758",
    pages = "534--539",
}
@inproceedings{bicici-2017-predicting,
    title = "Predicting Translation Performance with Referential Translation Machines",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4759",
    doi = "10.18653/v1/W17-4759",
    pages = "540--544",
}
@inproceedings{blain-etal-2017-bilexical,
    title = "Bilexical Embeddings for Quality Estimation",
    author = "Blain, Fr{\'e}d{\'e}ric  and
      Scarton, Carolina  and
      Specia, Lucia",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4760",
    doi = "10.18653/v1/W17-4760",
    pages = "545--550",
}
@inproceedings{chen-etal-2017-improving,
    title = "Improving Machine Translation Quality Estimation with Neural Network Features",
    author = "Chen, Zhiming  and
      Tan, Yiming  and
      Zhang, Chenlin  and
      Xiang, Qingyu  and
      Zhang, Lilin  and
      Li, Maoxi  and
      Wang, Mingwen",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4761",
    doi = "10.18653/v1/W17-4761",
    pages = "551--555",
}
@inproceedings{duma-menzel-2017-uhh,
    title = "{UHH} Submission to the {WMT}17 Quality Estimation Shared Task",
    author = "Duma, Melania  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4762",
    doi = "10.18653/v1/W17-4762",
    pages = "556--561",
}
@inproceedings{kim-etal-2017-predictor,
    title = "Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation",
    author = "Kim, Hyun  and
      Lee, Jong-Hyeok  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4763",
    doi = "10.18653/v1/W17-4763",
    pages = "562--568",
}
@inproceedings{martins-etal-2017-unbabels,
    title = "Unbabel{'}s Participation in the {WMT}17 Translation Quality Estimation Shared Task",
    author = "Martins, Andr{\'e} F. T.  and
      Kepler, Fabio  and
      Monteiro, Jos{\'e}",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4764",
    doi = "10.18653/v1/W17-4764",
    pages = "569--574",
}
@inproceedings{paetzold-specia-2017-feature,
    title = "Feature-Enriched Character-Level Convolutions for Text Regression",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4765",
    doi = "10.18653/v1/W17-4765",
    pages = "575--581",
}
@inproceedings{duma-menzel-2017-uhh-submission,
    title = "{UHH} Submission to the {WMT}17 Metrics Shared Task",
    author = "Duma, Melania  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4766",
    doi = "10.18653/v1/W17-4766",
    pages = "582--588",
}
@inproceedings{lo-2017-meant,
    title = "{MEANT} 2.0: Accurate semantic {MT} evaluation for any output language",
    author = "Lo, Chi-kiu",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4767",
    doi = "10.18653/v1/W17-4767",
    pages = "589--597",
}
@inproceedings{ma-etal-2017-blend,
    title = "{B}lend: a Novel Combined {MT} Metric Based on Direct Assessment {---} {CASICT}-{DCU} submission to {WMT}17 Metrics Task",
    author = "Ma, Qingsong  and
      Graham, Yvette  and
      Wang, Shugen  and
      Liu, Qun",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4768",
    doi = "10.18653/v1/W17-4768",
    pages = "598--603",
}
@inproceedings{marecek-etal-2017-cuni,
    title = "{CUNI} Experiments for {WMT}17 Metrics Task",
    author = {Mare{\v{c}}ek, David  and
      Bojar, Ond{\v{r}}ej  and
      H{\"u}bsch, Ond{\v{r}}ej  and
      Rosa, Rudolf  and
      Vari{\v{s}}, Du{\v{s}}an},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4769",
    doi = "10.18653/v1/W17-4769",
    pages = "604--611",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{tattar-fishel-2017-bleu2vec,
    title = "bleu2vec: the Painfully Familiar Metric on Continuous Vector Space Steroids",
    author = {T{\"a}ttar, Andre  and
      Fishel, Mark},
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4771",
    doi = "10.18653/v1/W17-4771",
    pages = "619--622",
}
@inproceedings{berard-etal-2017-lig,
    title = "{LIG}-{CRIS}t{AL} Submission for the {WMT} 2017 Automatic Post-Editing Task",
    author = "B{\'e}rard, Alexandre  and
      Besacier, Laurent  and
      Pietquin, Olivier",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4772",
    doi = "10.18653/v1/W17-4772",
    pages = "623--629",
}
@inproceedings{chatterjee-etal-2017-multi,
    title = "Multi-source Neural Automatic Post-Editing: {FBK}{'}s participation in the {WMT} 2017 {APE} shared task",
    author = "Chatterjee, Rajen  and
      Farajian, M. Amin  and
      Negri, Matteo  and
      Turchi, Marco  and
      Srivastava, Ankit  and
      Pal, Santanu",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4773",
    doi = "10.18653/v1/W17-4773",
    pages = "630--638",
}
@inproceedings{junczys-dowmunt-junczys-dowmunt-2017-amu,
    title = "The {AMU}-{UE}din Submission to the {WMT} 2017 Shared Task on Automatic Post-Editing",
    author = "Junczys-Dowmunt, Marcin  and
      Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4774",
    doi = "10.18653/v1/W17-4774",
    pages = "639--646",
}
@inproceedings{hokamp-2017-ensembling,
    title = "Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation",
    author = "Hokamp, Chris",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4775",
    doi = "10.18653/v1/W17-4775",
    pages = "647--654",
}
@inproceedings{tan-etal-2017-neural,
    title = "Neural Post-Editing Based on Quality Estimation",
    author = "Tan, Yiming  and
      Chen, Zhiming  and
      Huang, Liu  and
      Zhang, Lilin  and
      Li, Maoxi  and
      Wang, Mingwen",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4776",
    doi = "10.18653/v1/W17-4776",
    pages = "655--660",
}
@inproceedings{varis-bojar-2017-cuni,
    title = "{CUNI} System for {WMT}17 Automatic Post-Editing Task",
    author = "Vari{\v{s}}, Du{\v{s}}an  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4777",
    doi = "10.18653/v1/W17-4777",
    pages = "661--666",
}
@inproceedings{sharaf-etal-2017-umd,
    title = "The {UMD} Neural Machine Translation Systems at {WMT}17 Bandit Learning Task",
    author = "Sharaf, Amr  and
      Feng, Shi  and
      Nguyen, Khanh  and
      Brantley, Kiant{\'e}  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4778",
    doi = "10.18653/v1/W17-4778",
    pages = "667--673",
}
@inproceedings{wisniewski-2017-limsi,
    title = "{LIMSI} Submission for {WMT}{'}17 Shared Task on Bandit Learning",
    author = "Wisniewski, Guillaume",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4779",
    doi = "10.18653/v1/W17-4779",
    pages = "674--679",
}
@inproceedings{abdou-etal-2017-variable,
    title = "Variable Mini-Batch Sizing and Pre-Trained Embeddings",
    author = "Abdou, Mostafa  and
      Glon{\v{c}}{\'a}k, Vladan  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4780",
    doi = "10.18653/v1/W17-4780",
    pages = "680--686",
}
@inproceedings{erdmann-etal-2017-afrl,
    title = "The {AFRL} {WMT}17 Neural Machine Translation Training Task Submission",
    author = "Erdmann, Grant  and
      Young, Katherine  and
      Gwinnup, Jeremy",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4781",
    doi = "10.18653/v1/W17-4781",
    pages = "687--691",
}
@proceedings{ws-2017-speech,
    title = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    editor = "Ruiz, Nicholas  and
      Bangalore, Srinivas",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4600",
    doi = "10.18653/v1/W17-46",
}
@inproceedings{chowdhury-etal-2017-functions,
    title = "Functions of Silences towards Information Flow in Spoken Conversation",
    author = "Chowdhury, Shammur Absar  and
      Stepanov, Evgeny  and
      Danieli, Morena  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4601",
    doi = "10.18653/v1/W17-4601",
    pages = "1--9",
    abstract = "Silence is an integral part of the most frequent turn-taking phenomena in spoken conversations. Silence is sized and placed within the conversation flow and it is coordinated by the speakers along with the other speech acts. The objective of this analytical study is twofold: to explore the functions of silence with duration of one second and above, towards information flow in a dyadic conversation utilizing the sequences of dialog acts present in the turns surrounding the silence itself; and to design a feature space useful for clustering the silences using a hierarchical concept formation algorithm. The resulting clusters are manually grouped into functional categories based on their similarities. It is observed that the silence plays an important role in response preparation, also can indicate speakers{'} hesitation or indecisiveness. It is also observed that sometimes long silences can be used deliberately to get a forced response from another speaker thus making silence a multi-functional and an important catalyst towards information flow.",
}
@inproceedings{jagfeld-vu-2017-encoding,
    title = "Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking",
    author = "Jagfeld, Glorianna  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4602",
    doi = "10.18653/v1/W17-4602",
    pages = "10--17",
    abstract = "This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks. We demonstrate the utility of our approach for the task of dialog state tracking in spoken dialog systems that relies on automatic speech recognition output. Encoding confusion networks outperforms encoding the best hypothesis of the automatic speech recognition in a neural system for dialog state tracking on the well-known second Dialog State Tracking Challenge dataset.",
}
@inproceedings{ghaly-mandel-2017-analyzing,
    title = "Analyzing Human and Machine Performance In Resolving Ambiguous Spoken Sentences",
    author = "Ghaly, Hussein  and
      Mandel, Michael",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4603",
    doi = "10.18653/v1/W17-4603",
    pages = "18--26",
    abstract = "Written sentences can be more ambiguous than spoken sentences. We investigate this difference for two different types of ambiguity: prepositional phrase (PP) attachment and sentences where the addition of commas changes the meaning. We recorded a native English speaker saying several of each type of sentence both with and without disambiguating contextual information. These sentences were then presented either as text or audio and either with or without context to subjects who were asked to select the proper interpretation of the sentence. Results suggest that comma-ambiguous sentences are easier to disambiguate than PP-attachment-ambiguous sentences, possibly due to the presence of clear prosodic boundaries, namely silent pauses. Subject performance for sentences with PP-attachment ambiguity without context was 52{\%} for text only while it was 72.4{\%} for audio only, suggesting that audio has more disambiguating information than text. Using an analysis of acoustic features of two PP-attachment sentences, a simple classifier was implemented to resolve the PP-attachment ambiguity being early or late closure with a mean accuracy of 80{\%}.",
}
@inproceedings{caines-etal-2017-parsing,
    title = "Parsing transcripts of speech",
    author = "Caines, Andrew  and
      McCarthy, Michael  and
      Buttery, Paula",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4604",
    doi = "10.18653/v1/W17-4604",
    pages = "27--36",
    abstract = "We present an analysis of parser performance on speech data, comparing word type and token frequency distributions with written data, and evaluating parse accuracy by length of input string. We find that parser performance tends to deteriorate with increasing length of string, more so for spoken than for written texts. We train an alternative parsing model with added speech data and demonstrate improvements in accuracy on speech-units, with no deterioration in performance on written text.",
}
@inproceedings{stiefel-vu-2017-enriching,
    title = "Enriching {ASR} Lattices with {POS} Tags for Dependency Parsing",
    author = "Stiefel, Moritz  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4605",
    doi = "10.18653/v1/W17-4605",
    pages = "37--47",
    abstract = "Parsing speech requires a richer representation than 1-best or n-best hypotheses, e.g. lattices. Moreover, previous work shows that part-of-speech (POS) tags are a valuable resource for parsing. In this paper, we therefore explore a joint modeling approach of automatic speech recognition (ASR) and POS tagging to enrich ASR word lattices. To that end, we manipulate the ASR process from the pronouncing dictionary onward to use word-POS pairs instead of words. We evaluate ASR, POS tagging and dependency parsing (DP) performance demonstrating a successful lattice-based integration of ASR and POS tagging.",
}
@inproceedings{palm-etal-2017-end,
    title = "End-to-End Information Extraction without Token-Level Supervision",
    author = "Palm, Rasmus Berg  and
      Hovy, Dirk  and
      Laws, Florian  and
      Winther, Ole",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4606",
    doi = "10.18653/v1/W17-4606",
    pages = "48--52",
    abstract = "Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly to create, and consequently, not available for many real-life IE tasks. To make matters worse, token-level labels are usually not the desired output, but just an intermediary step. End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels. We propose an E2E model based on pointer networks, which can be trained directly on pairs of raw input and output text. We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT movie corpus and compare to neural baselines that do use token-level labels. We achieve competitive results, within a few percentage points of the baselines, showing the feasibility of E2E information extraction without the need for token-level labels. This opens up new possibilities, as for many tasks currently addressed by human extractors, raw input and output data are available, but not token-level labels.",
}
@inproceedings{anastasopoulos-etal-2017-spoken,
    title = "Spoken Term Discovery for Language Documentation using Translations",
    author = "Anastasopoulos, Antonios  and
      Bansal, Sameer  and
      Chiang, David  and
      Goldwater, Sharon  and
      Lopez, Adam",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4607",
    doi = "10.18653/v1/W17-4607",
    pages = "53--58",
    abstract = "Vast amounts of speech data collected for language documentation and research remain untranscribed and unsearchable, but often a small amount of speech may have text translations available. We present a method for partially labeling additional speech with translations in this scenario. We modify an unsupervised speech-to-translation alignment model and obtain prototype speech segments that match the translation words, which are in turn used to discover terms in the unlabelled data. We evaluate our method on a Spanish-English speech translation corpus and on two corpora of endangered languages, Arapaho and Ainu, demonstrating its appropriateness and applicability in an actual very-low-resource scenario.",
}
@inproceedings{melese-etal-2017-amharic,
    title = "{A}mharic-{E}nglish Speech Translation in Tourism Domain",
    author = "Melese, Michael  and
      Besacier, Laurent  and
      Meshesha, Million",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4608",
    doi = "10.18653/v1/W17-4608",
    pages = "59--66",
    abstract = "This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and Amharic-English Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model(AM). Likewise,SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4{\%} of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4{\%} word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42{\%}. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95{\%}) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.",
}
@inproceedings{loukina-etal-2017-speech,
    title = "Speech- and Text-driven Features for Automated Scoring of {E}nglish Speaking Tasks",
    author = "Loukina, Anastassia  and
      Madnani, Nitin  and
      Cahill, Aoife",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4609",
    doi = "10.18653/v1/W17-4609",
    pages = "67--77",
    abstract = "We consider the automatic scoring of a task for which both the content of the response as well its spoken fluency are important. We combine features from a text-only content scoring system originally designed for written responses with several categories of acoustic features. Although adding any single category of acoustic features to the text-only system on its own does not significantly improve performance, adding all acoustic features together does yield a small but significant improvement. These results are consistent for responses to open-ended questions and to questions focused on some given source material.",
}
@inproceedings{roesiger-etal-2017-improving,
    title = "Improving coreference resolution with automatically predicted prosodic information",
    author = "Roesiger, Ina  and
      Stehwien, Sabrina  and
      Riester, Arndt  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the Workshop on Speech-Centric Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4610",
    doi = "10.18653/v1/W17-4610",
    pages = "78--83",
    abstract = "Adding manually annotated prosodic information, specifically pitch accents and phrasing, to the typical text-based feature set for coreference resolution has previously been shown to have a positive effect on German data. Practical applications on spoken language, however, would rely on automatically predicted prosodic information. In this paper we predict pitch accents (and phrase boundaries) using a convolutional neural network (CNN) model from acoustic features extracted from the speech signal. After an assessment of the quality of these automatic prosodic annotations, we show that they also significantly improve coreference resolution.",
}
@proceedings{ws-2017-new,
    title = "Proceedings of the Workshop on New Frontiers in Summarization",
    editor = "Wang, Lu  and
      Cheung, Jackie Chi Kit  and
      Carenini, Giuseppe  and
      Liu, Fei",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4500",
    doi = "10.18653/v1/W17-45",
}
@inproceedings{ping-chen-2017-video,
    title = "Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments",
    author = "Ping, Qing  and
      Chen, Chaomei",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4501",
    doi = "10.18653/v1/W17-4501",
    pages = "1--11",
    abstract = "With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lag-calibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping. Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.",
}
@inproceedings{hoque-carenini-2017-multimedia,
    title = "Multimedia Summary Generation from Online Conversations: Current Approaches and Future Directions",
    author = "Hoque, Enamul  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4502",
    doi = "10.18653/v1/W17-4502",
    pages = "12--19",
    abstract = "With the proliferation of Web-based social media, asynchronous conversations have become very common for supporting online communication and collaboration. Yet the increasing volume and complexity of conversational data often make it very difficult to get insights about the discussions. We consider combining textual summary with visual representation of conversational data as a promising way of supporting the user in exploring conversations. In this paper, we report our current work on developing visual interfaces that present multimedia summary combining text and visualization for online conversations and how our solutions have been tailored for a variety of domain problems. We then discuss the key challenges and opportunities for future work in this research space.",
}
@inproceedings{tilk-alumae-2017-low,
    title = "Low-Resource Neural Headline Generation",
    author = {Tilk, Ottokar  and
      Alum{\"a}e, Tanel},
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4503",
    doi = "10.18653/v1/W17-4503",
    pages = "20--26",
    abstract = "Recent neural headline generation models have shown great results, but are generally trained on very large datasets. We focus our efforts on improving headline quality on smaller datasets by the means of pretraining. We propose new methods that enable pre-training all the parameters of the model and utilize all available text, resulting in improvements by up to 32.4{\%} relative in perplexity and 2.84 points in ROUGE.",
}
@inproceedings{pasunuru-etal-2017-towards,
    title = "Towards Improving Abstractive Summarization via Entailment Generation",
    author = "Pasunuru, Ramakanth  and
      Guo, Han  and
      Bansal, Mohit",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4504",
    doi = "10.18653/v1/W17-4504",
    pages = "27--32",
    abstract = "Abstractive summarization, the task of rewriting and compressing a document into a short summary, has achieved considerable success with neural sequence-to-sequence models. However, these models can still benefit from stronger natural language inference skills, since a correct summary is logically entailed by the input document, i.e., it should not contain any contradictory or unrelated information. We incorporate such knowledge into an abstractive summarization model via multi-task learning, where we share its decoder parameters with those of an entailment generation model. We achieve promising initial improvements based on multiple metrics and datasets (including a test-only setting). The domain mismatch between the entailment (captions) and summarization (news) datasets suggests that the model is learning some domain-agnostic inference skills.",
}
@inproceedings{ling-rush-2017-coarse,
    title = "Coarse-to-Fine Attention Models for Document Summarization",
    author = "Ling, Jeffrey  and
      Rush, Alexander",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4505",
    doi = "10.18653/v1/W17-4505",
    pages = "33--42",
    abstract = "Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization. We propose a novel coarse-to-fine attention model that hierarchically reads a document, using coarse attention to select top-level chunks of text and fine attention to read the words of the chosen chunks. While the computation for training standard attention models scales linearly with source sequence length, our method scales with the number of top-level chunks and can handle much longer sequences. Empirically, we find that while coarse-to-fine attention models lag behind state-of-the-art baselines, our method achieves the desired behavior of sparsely attending to subsets of the document for generation.",
}
@inproceedings{singla-etal-2017-automatic,
    title = "Automatic Community Creation for Abstractive Spoken Conversations Summarization",
    author = "Singla, Karan  and
      Stepanov, Evgeny  and
      Bayer, Ali Orkan  and
      Carenini, Giuseppe  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4506",
    doi = "10.18653/v1/W17-4506",
    pages = "43--47",
    abstract = "Summarization of spoken conversations is a challenging task, since it requires deep understanding of dialogs. Abstractive summarization techniques rely on linking the summary sentences to sets of original conversation sentences, i.e. communities. Unfortunately, such linking information is rarely available or requires trained annotators. We propose and experiment automatic community creation using cosine similarity on different levels of representation: raw text, WordNet SynSet IDs, and word embeddings. We show that the abstractive summarization systems with automatic communities significantly outperform previously published results on both English and Italian corpora.",
}
@inproceedings{tixier-etal-2017-combining,
    title = "Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization",
    author = "Tixier, Antoine  and
      Meladianos, Polykarpos  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4507",
    doi = "10.18653/v1/W17-4507",
    pages = "48--58",
    abstract = "We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research. The framework allows summaries to be generated in a greedy way while preserving near-optimal performance guarantees. Our main contribution is the novel coverage reward term of the objective function optimized by the greedy algorithm. This component builds on the graph-of-words representation of text and the k-core decomposition algorithm to assign meaningful scores to words. We evaluate our approach on the AMI and ICSI meeting speech corpora, and on the DUC2001 news corpus. We reach state-of-the-art performance on all datasets. Results indicate that our method is particularly well-suited to the meeting domain.",
}
@inproceedings{volske-etal-2017-tl,
    title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
    author = {V{\"o}lske, Michael  and
      Potthast, Martin  and
      Syed, Shahbaz  and
      Stein, Benno},
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4508",
    doi = "10.18653/v1/W17-4508",
    pages = "59--63",
    abstract = "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
}
@inproceedings{miller-mccoy-2017-topic,
    title = "Topic Model Stability for Hierarchical Summarization",
    author = "Miller, John  and
      McCoy, Kathleen",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4509",
    doi = "10.18653/v1/W17-4509",
    pages = "64--73",
    abstract = "We envisioned responsive generic hierarchical text summarization with summaries organized by section and paragraph based on hierarchical structure topic models. But we had to be sure that topic models were stable for the sampled corpora. To that end we developed a methodology for aligning multiple hierarchical structure topic models run over the same corpus under similar conditions, calculating a representative centroid model, and reporting stability of the centroid model. We ran stability experiments for standard corpora and a development corpus of Global Warming articles. We found flat and hierarchical structures of two levels plus the root offer stable centroid models, but hierarchical structures of three levels plus the root didn{'}t seem stable enough for use in hierarchical summarization.",
}
@inproceedings{peyrard-etal-2017-learning,
    title = "Learning to Score System Summaries for Better Content Selection Evaluation.",
    author = "Peyrard, Maxime  and
      Botschen, Teresa  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4510",
    doi = "10.18653/v1/W17-4510",
    pages = "74--84",
    abstract = "The evaluation of summaries is a challenging but crucial task of the summarization field. In this work, we propose to learn an automatic scoring metric based on the human judgements available as part of classical summarization datasets like TAC-2008 and TAC-2009. Any existing automatic scoring metrics can be included as features, the model learns the combination exhibiting the best correlation with human judgments. The reliability of the new metric is tested in a further manual evaluation where we ask humans to evaluate summaries covering the whole scoring spectrum of the metric. We release the trained metric as an open-source tool.",
}
@inproceedings{gholipour-ghalandari-2017-revisiting,
    title = "Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization",
    author = "Gholipour Ghalandari, Demian",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4511",
    doi = "10.18653/v1/W17-4511",
    pages = "85--90",
    abstract = "The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries instead of sentences and use a simple greedy algorithm to find the best summary. Furthermore, we show possibilities to scale up to larger input document collections by selecting a small number of sentences from each document prior to constructing the summary. Experiments were done on the DUC2004 dataset for multi-document summarization. We observe a higher performance over the original model, on par with more complex state-of-the-art methods.",
}
@inproceedings{li-etal-2017-reader,
    title = "Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset",
    author = "Li, Piji  and
      Bing, Lidong  and
      Lam, Wai",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4512",
    doi = "10.18653/v1/W17-4512",
    pages = "91--99",
    abstract = "We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset.",
}
@inproceedings{hua-wang-2017-pilot,
    title = "A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization",
    author = "Hua, Xinyu  and
      Wang, Lu",
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4513",
    doi = "10.18653/v1/W17-4513",
    pages = "100--106",
    abstract = "We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.",
}
@proceedings{ws-2017-noisy,
    title = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    editor = "Derczynski, Leon  and
      Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4400",
    doi = "10.18653/v1/W17-44",
}
@inproceedings{williams-2017-boundary,
    title = "Boundary-based {MWE} segmentation with text partitioning",
    author = "Williams, Jake",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4401",
    doi = "10.18653/v1/W17-4401",
    pages = "1--10",
    abstract = "This submission describes the development of a fine-grained, text-chunking algorithm for the task of comprehensive MWE segmentation. This task notably focuses on the identification of colloquial and idiomatic language. The submission also includes a thorough model evaluation in the context of two recent shared tasks, spanning 19 different languages and many text domains, including noisy, user-generated text. Evaluations exhibit the presented model as the best overall for purposes of MWE segmentation, and open-source software is released with the submission (although links are withheld for purposes of anonymity). Additionally, the authors acknowledge the existence of a pre-print document on arxiv.org, which should be avoided to maintain anonymity in review.",
}
@inproceedings{barbieri-etal-2017-towards,
    title = "Towards the Understanding of Gaming Audiences by Modeling Twitch Emotes",
    author = "Barbieri, Francesco  and
      Espinosa-Anke, Luis  and
      Ballesteros, Miguel  and
      Soler-Company, Juan  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4402",
    doi = "10.18653/v1/W17-4402",
    pages = "11--20",
    abstract = "Videogame streaming platforms have become a paramount example of noisy user-generated text. These are websites where gaming is broadcasted, and allows interaction with viewers via integrated chatrooms. Probably the best known platform of this kind is Twitch, which has more than 100 million monthly viewers. Despite these numbers, and unlike other platforms featuring short messages (e.g. Twitter), Twitch has not received much attention from the Natural Language Processing community. In this paper we aim at bridging this gap by proposing two important tasks specific to the Twitch platform, namely (1) Emote prediction; and (2) Trolling detection. In our experiments, we evaluate three models: a BOW baseline, a logistic supervised classifiers based on word embeddings, and a bidirectional long short-term memory recurrent neural network (LSTM). Our results show that the LSTM model outperforms the other two models, where explicit features with proven effectiveness for similar tasks were encoded.",
}
@inproceedings{gridach-etal-2017-churn,
    title = "Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge",
    author = "Gridach, Mourad  and
      Haddad, Hatem  and
      Mulki, Hala",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4403",
    doi = "10.18653/v1/W17-4403",
    pages = "21--30",
    abstract = "For brands, gaining new customer is more expensive than keeping an existing one. Therefore, the ability to keep customers in a brand is becoming more challenging these days. Churn happens when a customer leaves a brand to another competitor. Most of the previous work considers the problem of churn prediction using the Call Detail Records (CDRs). In this paper, we use micro-posts to classify customers into churny or non-churny. We explore the power of convolutional neural networks (CNNs) since they achieved state-of-the-art in various computer vision and NLP applications. However, the robustness of end-to-end models has some limitations such as the availability of a large amount of labeled data and uninterpretability of these models. We investigate the use of CNNs augmented with structured logic rules to overcome or reduce this issue. We developed our system called Churn{\_}teacher by using an iterative distillation method that transfers the knowledge, extracted using just the combination of three logic rules, directly into the weight of the DNNs. Furthermore, we used weight normalization to speed up training our convolutional neural networks. Experimental results showed that with just these three rules, we were able to get state-of-the-art on publicly available Twitter dataset about three Telecom brands.",
}
@inproceedings{van-der-goot-etal-2017-normalize,
    title = "To normalize, or not to normalize: The impact of normalization on Part-of-Speech tagging",
    author = "van der Goot, Rob  and
      Plank, Barbara  and
      Nissim, Malvina",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4404",
    doi = "10.18653/v1/W17-4404",
    pages = "31--39",
    abstract = "Does normalization help Part-of-Speech (POS) tagging accuracy on noisy, non-canonical data? To the best of our knowledge, little is known on the actual impact of normalization in a real-world scenario, where gold error detection is not available. We investigate the effect of automatic normalization on POS tagging of tweets. We also compare normalization to strategies that leverage large amounts of unlabeled data kept in its raw form. Our results show that normalization helps, but does not add consistently beyond just word embedding layer initialization. The latter approach yields a tagging model that is competitive with a Twitter state-of-the-art tagger.",
}
@inproceedings{andy-etal-2017-constructing,
    title = "Constructing an Alias List for Named Entities during an Event",
    author = "Andy, Anietie  and
      Dredze, Mark  and
      Rwebangira, Mugizi  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4405",
    doi = "10.18653/v1/W17-4405",
    pages = "40--44",
    abstract = "In certain fields, real-time knowledge from events can help in making informed decisions. In order to extract pertinent real-time knowledge related to an event, it is important to identify the named entities and their corresponding aliases related to the event. The problem of identifying aliases of named entities that spike has remained unexplored. In this paper, we introduce an algorithm, EntitySpike, that identifies entities that spike in popularity in tweets from a given time period, and constructs an alias list for these spiked entities. EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event. Each entity is encoded as a vector using this temporal heuristic. We show how these entity-vectors can be used to create a named entity alias list. We evaluated our algorithm on a dataset of temporally ordered tweets from a single event, the 2013 Grammy Awards show. We carried out various experiments on tweets that were published in the same time period and show that our algorithm identifies most entity name aliases and outperforms a competitive baseline.",
}
@inproceedings{xing-paul-2017-incorporating,
    title = "Incorporating Metadata into Content-Based User Embeddings",
    author = "Xing, Linzi  and
      Paul, Michael J.",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4406",
    doi = "10.18653/v1/W17-4406",
    pages = "45--49",
    abstract = "Low-dimensional vector representations of social media users can benefit applications like recommendation systems and user attribute inference. Recent work has shown that user embeddings can be improved by combining different types of information, such as text and network data. We propose a data augmentation method that allows novel feature types to be used within off-the-shelf embedding models. Experimenting with the task of friend recommendation on a dataset of 5,019 Twitter users, we show that our approach can lead to substantial performance gains with the simple addition of network and geographic features.",
}
@inproceedings{emmery-etal-2017-simple,
    title = "Simple Queries as Distant Labels for Predicting Gender on {T}witter",
    author = "Emmery, Chris  and
      Chrupa{\l}a, Grzegorz  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4407",
    doi = "10.18653/v1/W17-4407",
    pages = "50--55",
    abstract = "The majority of research on extracting missing user attributes from social media profiles use costly hand-annotated labels for supervised learning. Distantly supervised methods exist, although these generally rely on knowledge gathered using external sources. This paper demonstrates the effectiveness of gathering distant labels for self-reported gender on Twitter using simple queries. We confirm the reliability of this query heuristic by comparing with manual annotation. Moreover, using these labels for distant supervision, we demonstrate competitive model performance on the same data as models trained on manual annotations. As such, we offer a cheap, extensible, and fast alternative that can be employed beyond the task of gender classification.",
}
@inproceedings{blodgett-etal-2017-dataset,
    title = "A Dataset and Classifier for Recognizing Social Media {E}nglish",
    author = "Blodgett, Su Lin  and
      Wei, Johnny  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4408",
    doi = "10.18653/v1/W17-4408",
    pages = "56--61",
    abstract = "While language identification works well on standard texts, it performs much worse on social media language, in particular dialectal language{---}even for English. First, to support work on English language identification, we contribute a new dataset of tweets annotated for English versus non-English, with attention to ambiguity, code-switching, and automatic generation issues. It is randomly sampled from all public messages, avoiding biases towards pre-existing language classifiers. Second, we find that a demographic language model{---}which identifies messages with language similar to that used by several U.S. ethnic populations on Twitter{---}can be used to improve English language identification performance when combined with a traditional supervised language identifier. It increases recall with almost no loss of precision, including, surprisingly, for English messages written by non-U.S. authors. Our dataset and identifier ensemble are available online.",
}
@inproceedings{salehi-sogaard-2017-evaluating,
    title = "Evaluating hypotheses in geolocation on a very large sample of {T}witter",
    author = "Salehi, Bahar  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4409",
    doi = "10.18653/v1/W17-4409",
    pages = "62--67",
    abstract = "Recent work in geolocation has made several hypotheses about what linguistic markers are relevant to detect where people write from. In this paper, we examine six hypotheses against a corpus consisting of all geo-tagged tweets from the US, or whose geo-tags could be inferred, in a 19{\%} sample of Twitter history. Our experiments lend support to all six hypotheses, including that spelling variants and hashtags are strong predictors of location. We also study what kinds of common nouns are predictive of location after controlling for named entities such as dolphins or sharks",
}
@inproceedings{bowen-etal-2017-effect,
    title = "The Effect of Error Rate in Artificially Generated Data for Automatic Preposition and Determiner Correction",
    author = "Bowen, Fraser  and
      Dehdari, Jon  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4410",
    doi = "10.18653/v1/W17-4410",
    pages = "68--76",
    abstract = "In this research we investigate the impact of mismatches in the density and type of error between training and test data on a neural system correcting preposition and determiner errors. We use synthetically produced training data to control error density and type, and {``}real{''} error data for testing. Our results show it is possible to combine error types, although prepositions and determiners behave differently in terms of how much error should be artificially introduced into the training data in order to get the best results.",
}
@inproceedings{nagpal-etal-2017-entity,
    title = "An Entity Resolution Approach to Isolate Instances of Human Trafficking Online",
    author = "Nagpal, Chirag  and
      Miller, Kyle  and
      Boecking, Benedikt  and
      Dubrawski, Artur",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4411",
    doi = "10.18653/v1/W17-4411",
    pages = "77--84",
    abstract = "Human trafficking is a challenging law enforcement problem, and traces of victims of such activity manifest as {`}escort advertisements{'} on various online forums. Given the large, heterogeneous and noisy structure of this data, building models to predict instances of trafficking is a convoluted task. In this paper we propose an entity resolution pipeline using a notion of proxy labels, in order to extract clusters from this data with prior history of human trafficking activity. We apply this pipeline to 5M records from backpage.com and report on the performance of this approach, challenges in terms of scalability, and some significant domain specific characteristics of our resolved entities.",
}
@inproceedings{tursun-cakici-2017-noisy,
    title = "Noisy {U}yghur Text Normalization",
    author = "Tursun, Osman  and
      Cakici, Ruket",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4412",
    doi = "10.18653/v1/W17-4412",
    pages = "85--93",
    abstract = "Uyghur is the second largest and most actively used social media language in China. However, a non-negligible part of Uyghur text appearing in social media is unsystematically written with the Latin alphabet, and it continues to increase in size. Uyghur text in this format is incomprehensible and ambiguous even to native Uyghur speakers. In addition, Uyghur texts in this form lack the potential for any kind of advancement for the NLP tasks related to the Uyghur language. Restoring and preventing noisy Uyghur text written with unsystematic Latin alphabets will be essential to the protection of Uyghur language and improving the accuracy of Uyghur NLP tasks. To this purpose, in this work we propose and compare the noisy channel model and the neural encoder-decoder model as normalizing methods.",
}
@inproceedings{welbl-etal-2017-crowdsourcing,
    title = "Crowdsourcing Multiple Choice Science Questions",
    author = "Welbl, Johannes  and
      Liu, Nelson F.  and
      Gardner, Matt",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4413",
    doi = "10.18653/v1/W17-4413",
    pages = "94--106",
    abstract = "We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions. We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.",
}
@inproceedings{flint-etal-2017-text,
    title = "A Text Normalisation System for Non-Standard {E}nglish Words",
    author = "Flint, Emma  and
      Ford, Elliot  and
      Thomas, Olivia  and
      Caines, Andrew  and
      Buttery, Paula",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4414",
    doi = "10.18653/v1/W17-4414",
    pages = "107--115",
    abstract = "This paper investigates the problem of text normalisation; specifically, the normalisation of non-standard words (NSWs) in English. Non-standard words can be defined as those word tokens which do not have a dictionary entry, and cannot be pronounced using the usual letter-to-phoneme conversion rules; e.g. lbs, 99.3{\%}, {\#}EMNLP2017. NSWs pose a challenge to the proper functioning of text-to-speech technology, and the solution is to spell them out in such a way that they can be pronounced appropriately. We describe our four-stage normalisation system made up of components for detection, classification, division and expansion of NSWs. Performance is favourabe compared to previous work in the field (Sproat et al. 2001, Normalization of non-standard words), as well as state-of-the-art text-to-speech software. Further, we update Sproat et al.{'}s NSW taxonomy, and create a more customisable system where users are able to input their own abbreviations and specify into which variety of English (currently available: British or American) they wish to normalise.",
}
@inproceedings{salehi-etal-2017-huntsville,
    title = "Huntsville, hospitals, and hockey teams: Names can reveal your location",
    author = "Salehi, Bahar  and
      Hovy, Dirk  and
      Hovy, Eduard  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4415",
    doi = "10.18653/v1/W17-4415",
    pages = "116--121",
    abstract = "Geolocation is the task of identifying a social media user{'}s primary location, and in natural language processing, there is a growing literature on to what extent automated analysis of social media posts can help. However, not all content features are equally revealing of a user{'}s location. In this paper, we evaluate nine name entity (NE) types. Using various metrics, we find that GEO-LOC, FACILITY and SPORT-TEAM are more informative for geolocation than other NE types. Using these types, we improve geolocation accuracy and reduce distance error over various famous text-based methods.",
}
@inproceedings{jang-etal-2017-improving,
    title = "Improving Document Clustering by Removing Unnatural Language",
    author = "Jang, Myungha  and
      Choi, Jinho D.  and
      Allan, James",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4416",
    doi = "10.18653/v1/W17-4416",
    pages = "122--130",
    abstract = "Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can bean important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of un-natural language detection on NLP tasks such as document clustering. We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories. First, we create a new annotated corpus by collecting slides and papers in various for-mats, PPT, PDF, and HTML, where unnatural language components are annotated into four categories. We then explore features available from plain text to build a statistical model that can handle any format as long as it is converted into plain text. Our experiments show that re-moving unnatural language components gives an absolute improvement in document cluster-ing by up to 15{\%}. Our corpus and tool are publicly available",
}
@inproceedings{bhargava-etal-2017-lithium,
    title = "Lithium {NLP}: A System for Rich Information Extraction from Noisy User Generated Text on Social Media",
    author = "Bhargava, Preeti  and
      Spasojevic, Nemanja  and
      Hu, Guoning",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4417",
    doi = "10.18653/v1/W17-4417",
    pages = "131--139",
    abstract = "In this paper, we describe the Lithium Natural Language Processing (NLP) system - a resource-constrained, high-throughput and language-agnostic system for information extraction from noisy user generated text on social media. Lithium NLP extracts a rich set of information including entities, topics, hashtags and sentiment from text. We discuss several real world applications of the system currently incorporated in Lithium products. We also compare our system with existing commercial and academic NLP systems in terms of performance, information extracted and languages supported. We show that Lithium NLP is at par with and in some cases, outperforms state-of-the-art commercial NLP systems.",
}
@inproceedings{derczynski-etal-2017-results,
    title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
    author = "Derczynski, Leon  and
      Nichols, Eric  and
      van Erp, Marieke  and
      Limsopatham, Nut",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4418",
    doi = "10.18653/v1/W17-4418",
    pages = "140--147",
    abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'} hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
}
@inproceedings{aguilar-etal-2017-multi,
    title = "A Multi-task Approach for Named Entity Recognition in Social Media Data",
    author = "Aguilar, Gustavo  and
      Maharjan, Suraj  and
      L{\'o}pez-Monroy, Adrian Pastor  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4419",
    doi = "10.18653/v1/W17-4419",
    pages = "148--153",
    abstract = "Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86{\%} entity F1-score and a 40.24{\%} surface F1-score.",
}
@inproceedings{jansson-liu-2017-distributed,
    title = "Distributed Representation, {LDA} Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media",
    author = "Jansson, Patrick  and
      Liu, Shuhua",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4420",
    doi = "10.18653/v1/W17-4420",
    pages = "154--159",
    abstract = "This paper reports our participation in the W-NUT 2017 shared task on emerging and rare entity recognition from user generated noisy text such as tweets, online reviews and forum discussions. To accomplish this challenging task, we explore an approach that combines LDA topic modelling with deep learning on word level and character level embeddings. The LDA topic modelling generates topic representation for each tweet which is used as a feature for each word in the tweet. The deep learning component consists of two-layer bidirectional LSTM and a CRF output layer. Our submitted result performed at 39.98 (F1) on entity and 37.77 on surface forms. Our new experiments after submission reached a best performance of 41.81 on entity and 40.57 on surface forms.",
}
@inproceedings{lin-etal-2017-multi,
    title = "Multi-channel {B}i{LSTM}-{CRF} Model for Emerging Named Entity Recognition in Social Media",
    author = "Lin, Bill Y.  and
      Xu, Frank  and
      Luo, Zhiyi  and
      Zhu, Kenny",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4421",
    doi = "10.18653/v1/W17-4421",
    pages = "160--165",
    abstract = "In this paper, we present our multi-channel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on Noisy User-generated Text (W-NUT). We propose a novel approach, which incorporates comprehensive word representations with multi-channel information and Conditional Random Fields (CRF) into a traditional Bidirectional Long Short-Term Memory (BiLSTM) neural network without using any additional hand-craft features such as gazetteers. In comparison with other systems participating in the shared task, our system won the 2nd place.",
}
@inproceedings{von-daniken-cieliebak-2017-transfer,
    title = "Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets",
    author = {von D{\"a}niken, Pius  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4422",
    doi = "10.18653/v1/W17-4422",
    pages = "166--171",
    abstract = "We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1-score of 39.33.",
}
@inproceedings{williams-santia-2017-context,
    title = "Context-Sensitive Recognition for Emerging and Rare Entities",
    author = "Williams, Jake  and
      Santia, Giovanni",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4423",
    doi = "10.18653/v1/W17-4423",
    pages = "172--176",
    abstract = "This paper is a shared task system description for the 2017 W-NUT shared task on Rare and Emerging Named Entities. Our paper describes the development and application of a novel algorithm for named entity recognition that relies only on the contexts of word forms. A comparison against the other submitted systems is provided.",
}
@inproceedings{sikdar-gamback-2017-feature,
    title = "A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities",
    author = {Sikdar, Utpal Kumar  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4424",
    doi = "10.18653/v1/W17-4424",
    pages = "177--181",
    abstract = "Detecting previously unseen named entities in text is a challenging task. The paper describes how three initial classifier models were built using Conditional Random Fields (CRFs), Support Vector Machines (SVMs) and a Long Short-Term Memory (LSTM) recurrent neural network. The outputs of these three classifiers were then used as features to train another CRF classifier working as an ensemble. 5-fold cross-validation based on training and development data for the emerging and rare named entity recognition shared task showed precision, recall and F1-score of 66.87{\%}, 46.75{\%} and 54.97{\%}, respectively. For surface form evaluation, the CRF ensemble-based system achieved precision, recall and F1 scores of 65.18{\%}, 45.20{\%} and 53.30{\%}. When applied to unseen test data, the model reached 47.92{\%} precision, 31.97{\%} recall and 38.55{\%} F1-score for entity level evaluation, with the corresponding surface form evaluation values of 44.91{\%}, 30.47{\%} and 36.31{\%}.",
}
@proceedings{ws-2017-structured,
    title = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    editor = "Chang, Kai-Wei  and
      Chang, Ming-Wei  and
      Srikumar, Vivek  and
      Rush, Alexander M.",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4300",
    doi = "10.18653/v1/W17-43",
}
@inproceedings{strubell-mccallum-2017-dependency,
    title = "Dependency Parsing with Dilated Iterated Graph {CNN}s",
    author = "Strubell, Emma  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4301",
    doi = "10.18653/v1/W17-4301",
    pages = "1--6",
    abstract = "Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale. Recent advances in GPU hardware have enabled neural networks to achieve significant gains over the previous best models, these models still fail to leverage GPUs{'} capability for massive parallelism due to their requirement of sequential processing of the sentence. In response, we propose Dilated Iterated Graph Convolutional Neural Networks (DIG-CNNs) for graph-based dependency parsing, a graph convolutional architecture that allows for efficient end-to-end GPU parsing. In experiments on the English Penn TreeBank benchmark, we show that DIG-CNNs perform on par with some of the best neural network parsers.",
}
@inproceedings{stratos-2017-entity,
    title = "Entity Identification as Multitasking",
    author = "Stratos, Karl",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4302",
    doi = "10.18653/v1/W17-4302",
    pages = "7--11",
    abstract = "Standard approaches in entity identification hard-code boundary detection and type prediction into labels and perform Viterbi. This has two disadvantages: 1. the runtime complexity grows quadratically in the number of types, and 2. there is no natural segment-level representation. In this paper, we propose a neural architecture that addresses these disadvantages. We frame the problem as multitasking, separating boundary detection and type prediction but optimizing them jointly. Despite its simplicity, this architecture performs competitively with fully structured models such as BiLSTM-CRFs while scaling linearly in the number of types. Furthermore, by construction, the model induces type-disambiguating embeddings of predicted mentions.",
}
@inproceedings{bradbury-socher-2017-towards,
    title = "Towards Neural Machine Translation with Latent Tree Attention",
    author = "Bradbury, James  and
      Socher, Richard",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4303",
    doi = "10.18653/v1/W17-4303",
    pages = "12--16",
    abstract = "Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing. We introduce such a model for the task of machine translation, pairing a recurrent neural network grammar encoder with a novel attentional RNNG decoder and applying policy gradient reinforcement learning to induce unsupervised tree structures on both the source and target. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline.",
}
@inproceedings{sharaf-daume-iii-2017-structured,
    title = "Structured Prediction via Learning to Search under Bandit Feedback",
    author = "Sharaf, Amr  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4304",
    doi = "10.18653/v1/W17-4304",
    pages = "17--26",
    abstract = "We present an algorithm for structured prediction under online bandit feedback. The learner repeatedly predicts a sequence of actions, generating a structured output. It then observes feedback for that output and no others. We consider two cases: a pure bandit setting in which it only observes a loss, and more fine-grained feedback in which it observes a loss for every action. We find that the fine-grained feedback is necessary for strong empirical performance, because it allows for a robust variance-reduction strategy. We empirically compare a number of different algorithms and exploration methods and show the efficacy of BLS on sequence labeling and dependency parsing tasks.",
}
@inproceedings{qian-etal-2017-syntax,
    title = "Syntax Aware {LSTM} model for Semantic Role Labeling",
    author = "Qian, Feng  and
      Sha, Lei  and
      Chang, Baobao  and
      Liu, Lu-chen  and
      Zhang, Ming",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4305",
    doi = "10.18653/v1/W17-4305",
    pages = "27--32",
    abstract = "In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an architecture engineering way. Experiments demonstrate that on Chinese Proposition Bank (CPB) 1.0, SA-LSTM improves F1 by 2.06{\%} than ordinary bi-LSTM with feature engineered dependency relation information, and gives state-of-the-art F1 of 79.92{\%}. On English CoNLL 2005 dataset, SA-LSTM brings improvement (2.1{\%}) to bi-LSTM model and also brings slight improvement (0.3{\%}) when added to the state-of-the-art model.",
}
@inproceedings{kordjamshidi-etal-2017-spatial,
    title = "Spatial Language Understanding with Multimodal Graphs using Declarative Learning based Programming",
    author = "Kordjamshidi, Parisa  and
      Rahgooy, Taher  and
      Manzoor, Umar",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4306",
    doi = "10.18653/v1/W17-4306",
    pages = "33--43",
    abstract = "This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) that aims at extraction of formal spatial meaning from text. Here, we report the results of initial efforts towards exploiting visual information in the form of images to help spatial language understanding. We discuss the way of designing new models in the framework of declarative learning-based programming (DeLBP). The DeLBP framework facilitates combining modalities and representing various data in a unified graph. The learning and inference models exploit the structure of the unified graph as well as the global first order domain constraints beyond the data to predict the semantics which forms a structured meaning representation of the spatial context. Continuous representations are used to relate the various elements of the graph originating from different modalities. We improved over the state-of-the-art results on SpRL.",
}
@inproceedings{meerkamp-zhou-2017-boosting,
    title = "Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision",
    author = "Meerkamp, Philipp  and
      Zhou, Zhengyi",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4307",
    doi = "10.18653/v1/W17-4307",
    pages = "44--51",
    abstract = "We present an architecture to boost the precision of existing information extraction systems. This is achieved by augmenting the existing parser, which may be constraint-based or hybrid statistical, with a character-level neural network. Our architecture combines the ability of constraint-based or hybrid extraction systems to easily incorporate domain knowledge with the ability of deep neural networks to leverage large amounts of data to learn complex features. The network is trained using a measure of consistency between extracted data and existing databases as a form of cheap, noisy supervision. Our architecture does not require large scale manual annotation or a system rewrite. It has led to large precision improvements over an existing, highly-tuned production information extraction system used at Bloomberg LP for financial language text.",
}
@inproceedings{serban-etal-2017-piecewise-latent,
    title = "Piecewise Latent Variables for Neural Variational Text Processing",
    author = "Serban, Iulian Vlad  and
      Ororbia II, Alexander  and
      Pineau, Joelle  and
      Courville, Aaron",
    booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4308",
    doi = "10.18653/v1/W17-4308",
    pages = "52--62",
    abstract = "Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.",
}
@proceedings{ws-2017-2017,
    title = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    editor = "Popescu, Octavian  and
      Strapparava, Carlo",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4200",
    doi = "10.18653/v1/W17-42",
}
@inproceedings{di-buono-etal-2017-predicting,
    title = "Predicting News Values from Headline Text and Emotions",
    author = "di Buono, Maria Pia  and
      {\v{S}}najder, Jan  and
      Dalbelo Ba{\v{s}}i{\'c}, Bojana  and
      Glava{\v{s}}, Goran  and
      Tutek, Martin  and
      Milic-Frayling, Natasa",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4201",
    doi = "10.18653/v1/W17-4201",
    pages = "1--6",
    abstract = "We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models {--} an SVM and a CNN {--} to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information.",
}
@inproceedings{hardt-rambow-2017-predicting,
    title = "Predicting User Views in Online News",
    author = "Hardt, Daniel  and
      Rambow, Owen",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4202",
    doi = "10.18653/v1/W17-4202",
    pages = "7--12",
    abstract = "We analyze user viewing behavior on an online news site. We collect data from 64,000 news articles, and use text features to predict frequency of user views. We compare predictiveness of the headline and {``}teaser{''} (viewed before clicking) and the body (viewed after clicking). Both are predictive of clicking behavior, with the full article text being most predictive.",
}
@inproceedings{potash-etal-2017-tracking,
    title = "Tracking Bias in News Sources Using Social Media: the Russia-{U}kraine Maidan Crisis of 2013{--}2014",
    author = "Potash, Peter  and
      Romanov, Alexey  and
      Gronas, Mikhail  and
      Rumshisky, Anna  and
      Gronas, Mikhail",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4203",
    doi = "10.18653/v1/W17-4203",
    pages = "13--18",
    abstract = "This paper addresses the task of identifying the bias in news articles published during a political or social conflict. We create a silver-standard corpus based on the actions of users in social media. Specifically, we reconceptualize bias in terms of how likely a given article is to be shared or liked by each of the opposing sides. We apply our methodology to a dataset of links collected in relation to the Russia-Ukraine Maidan crisis from 2013-2014. We show that on the task of predicting which side is likely to prefer a given article, a Naive Bayes classifier can record 90.3{\%} accuracy looking only at domain names of the news sources. The best accuracy of 93.5{\%} is achieved by a feed forward neural network. We also apply our methodology to gold-labeled set of articles annotated for bias, where the aforementioned Naive Bayes classifier records 82.6{\%} accuracy and a feed-forward neural networks records 85.6{\%} accuracy.",
}
@inproceedings{cucchiarelli-etal-2017-write,
    title = "What to Write? A topic recommender for journalists",
    author = "Cucchiarelli, Alessandro  and
      Morbidoni, Christian  and
      Stilo, Giovanni  and
      Velardi, Paola",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4204",
    doi = "10.18653/v1/W17-4204",
    pages = "19--24",
    abstract = "In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers{'} communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter andWikipedia, either not covered or poorly covered in the published news articles.",
}
@inproceedings{jiang-etal-2017-comparing,
    title = "Comparing Attitudes to Climate Change in the Media using sentiment analysis based on {L}atent {D}irichlet {A}llocation",
    author = "Jiang, Ye  and
      Song, Xingyi  and
      Harrison, Jackie  and
      Quegan, Shaun  and
      Maynard, Diana",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4205",
    doi = "10.18653/v1/W17-4205",
    pages = "25--30",
    abstract = "News media typically present biased accounts of news stories, and different publications present different angles on the same event. In this research, we investigate how different publications differ in their approach to stories about climate change, by examining the sentiment and topics presented. To understand these attitudes, we find sentiment targets by combining Latent Dirichlet Allocation (LDA) with SentiWordNet, a general sentiment lexicon. Using LDA, we generate topics containing keywords which represent the sentiment targets, and then annotate the data using SentiWordNet before regrouping the articles based on topic similarity. Preliminary analysis identifies clearly different attitudes on the same issue presented in different news sources. Ongoing work is investigating how systematic these attitudes are between different publications, and how these may change over time.",
}
@inproceedings{bois-etal-2017-language,
    title = "Language-based Construction of Explorable News Graphs for Journalists",
    author = "Bois, R{\'e}mi  and
      Gravier, Guillaume  and
      Jamet, Eric  and
      Morin, Emmanuel  and
      S{\'e}billot, Pascale  and
      Robert, Maxime",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4206",
    doi = "10.18653/v1/W17-4206",
    pages = "31--36",
    abstract = "Faced with ever-growing news archives, media professionals are in need of advanced tools to explore the information surrounding specific events. This problem is most commonly answered by browsing news datasets, going from article to article and viewing unaltered original content. In this article, we introduce an efficient way to generate links between news items, allowing such browsing through an easily explorable graph, and enrich this graph by automatically typing links in order to inform the user on the nature of the relation between two news pieces. User evaluations are conducted on real world data with journalists in order to assess for the interest of both the graph representation and link typing in a press reviewing task, showing the system to be of significant help for their work.",
}
@inproceedings{van-meersbergen-etal-2017-storyteller,
    title = "{S}toryteller: Visual Analytics of Perspectives on Rich Text Interpretations",
    author = "van Meersbergen, Maarten  and
      Vossen, Piek  and
      van der Zwaan, Janneke  and
      Fokkens, Antske  and
      van Hage, Willem  and
      Leemans, Inger  and
      Maks, Isa",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4207",
    doi = "10.18653/v1/W17-4207",
    pages = "37--45",
    abstract = "Complexity of event data in texts makes it difficult to assess its content, especially when considering larger collections in which different sources report on the same or similar situations. We present a system that makes it possible to visually analyze complex event and emotion data extracted from texts. We show that we can abstract from different data models for events and emotions to a single data model that can show the complex relations in four dimensions. The visualization has been applied to analyze 1) dynamic developments in how people both conceive and express emotions in theater plays and 2) how stories are told from the perspectyive of their sources based on rich event data extracted from news or biographies.",
}
@inproceedings{tamori-etal-2017-analyzing,
    title = "Analyzing the Revision Logs of a {J}apanese Newspaper for Article Quality Assessment",
    author = "Tamori, Hideaki  and
      Hitomi, Yuta  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4208",
    doi = "10.18653/v1/W17-4208",
    pages = "46--50",
    abstract = "We address the issue of the quality of journalism and analyze daily article revision logs from a Japanese newspaper company. The revision logs contain data that can help reveal the requirements of quality journalism such as the types and number of edit operations and aspects commonly focused in revision. This study also discusses potential applications such as quality assessment and automatic article revision as our future research directions.",
}
@inproceedings{pavlopoulos-etal-2017-improved,
    title = "Improved Abusive Comment Moderation with User Embeddings",
    author = "Pavlopoulos, John  and
      Malakasiotis, Prodromos  and
      Bakagianni, Juli  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4209",
    doi = "10.18653/v1/W17-4209",
    pages = "51--55",
    abstract = "Experimenting with a dataset of approximately 1.6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases. We observe improvements in all cases, with user embeddings leading to the biggest performance gains.",
}
@inproceedings{chesney-etal-2017-incongruent,
    title = "Incongruent Headlines: Yet Another Way to Mislead Your Readers",
    author = "Chesney, Sophie  and
      Liakata, Maria  and
      Poesio, Massimo  and
      Purver, Matthew",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4210",
    doi = "10.18653/v1/W17-4210",
    pages = "56--61",
    abstract = "This paper discusses the problem of incongruent headlines: those which do not accurately represent the information contained in the article with which they occur. We emphasise that this phenomenon should be considered separately from recognised problematic headline types such as clickbait and sensationalism, arguing that existing natural language processing (NLP) methods applied to these related concepts are not appropriate for the automatic detection of headline incongruence, as an analysis beyond stylistic traits is necessary. We therefore suggest a number of alternative methodologies that may be appropriate to the task at hand as a foundation for future work in this area. In addition, we provide an analysis of existing data sets which are related to this work, and motivate the need for a novel data set in this domain.",
}
@inproceedings{ribeiro-etal-2017-unsupervised,
    title = "Unsupervised Event Clustering and Aggregation from Newswire and Web Articles",
    author = "Ribeiro, Swen  and
      Ferret, Olivier  and
      Tannier, Xavier",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4211",
    doi = "10.18653/v1/W17-4211",
    pages = "62--67",
    abstract = "In this paper, we present an unsupervised pipeline approach for clustering news articles based on identified event instances in their content. We leverage press agency newswire and monolingual word alignment techniques to build meaningful and linguistically varied clusters of articles from the web in the perspective of a broader event type detection task. We validate our approach on a manually annotated corpus of Web articles.",
}
@inproceedings{moreno-schneider-etal-2017-semantic,
    title = "Semantic Storytelling, Cross-lingual Event Detection and other Semantic Services for a Newsroom Content Curation Dashboard",
    author = "Moreno-Schneider, Julian  and
      Srivastava, Ankit  and
      Bourgonje, Peter  and
      Wabnitz, David  and
      Rehm, Georg",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4212",
    doi = "10.18653/v1/W17-4212",
    pages = "68--73",
    abstract = "We present a prototypical content curation dashboard, to be used in the newsroom, and several of its underlying semantic content analysis components (such as named entity recognition, entity linking, summarisation and temporal expression analysis). The idea is to enable journalists (a) to process incoming content (agency reports, twitter feeds, reports, blog posts, social media etc.) and (b) to create new articles more easily and more efficiently. The prototype system also allows the automatic annotation of events in incoming content for the purpose of supporting journalists in identifying important, relevant or meaningful events and also to adapt the content currently in production accordingly in a semi-automatic way. One of our long-term goals is to support journalists building up entire storylines with automatic means. In the present prototype they are generated in a backend service using clustering methods that operate on the extracted events.",
}
@inproceedings{pisarevskaya-2017-deception,
    title = "Deception Detection in News Reports in the {R}ussian Language: Lexics and Discourse",
    author = "Pisarevskaya, Dina",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4213",
    doi = "10.18653/v1/W17-4213",
    pages = "74--79",
    abstract = "News verification and automated fact checking tend to be very important issues in our world. The research is initial. We collected a corpus for Russian (174 news reports, truthful and fake ones). We held two experiments, for both we applied SVMs algorithm (linear/rbf kernel) and Random Forest to classify the news reports into 2 classes: truthful/deceptive. In the first experiment, we used 18 markers on lexics level, mostly frequencies of POS tags in texts. In the second experiment, on discourse level we used frequencies of rhetorical relations types in texts. The classification task in the first experiment is solved better by SVMs (rbf kernel) (f-measure 0.65). The model based on RST features shows best results with Random Forest Classifier (f-measure 0.54) and should be modified. In the next research, the combination of different deception detection markers for the Russian language should be taken in order to make a better predictive model.",
}
@inproceedings{thorne-etal-2017-fake,
    title = "Fake news stance detection using stacked ensemble of classifiers",
    author = "Thorne, James  and
      Chen, Mingjie  and
      Myrianthous, Giorgos  and
      Pu, Jiashu  and
      Wang, Xiaoxuan  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4214",
    doi = "10.18653/v1/W17-4214",
    pages = "80--83",
    abstract = "Fake news has become a hotly debated topic in journalism. In this paper, we present our entry to the 2017 Fake News Challenge which models the detection of fake news as a stance classification task that finished in 11th place on the leader board. Our entry is an ensemble system of classifiers developed by students in the context of their coursework. We show how we used the stacking ensemble method for this purpose and obtained improvements in classification accuracy exceeding each of the individual models{'} performance on the development data. Finally, we discuss aspects of the experimental setup of the challenge.",
}
@inproceedings{bourgonje-etal-2017-clickbait,
    title = "From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles",
    author = "Bourgonje, Peter  and
      Moreno Schneider, Julian  and
      Rehm, Georg",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4215",
    doi = "10.18653/v1/W17-4215",
    pages = "84--89",
    abstract = "We present a system for the detection of the stance of headlines with regard to their corresponding article bodies. The approach can be applied in fake news, especially clickbait detection scenarios. The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information. We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines. On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 89.59.",
}
@inproceedings{tang-kageura-2017-fighting,
    title = "{`}Fighting{'} or {`}Conflict{'}? An Approach to Revealing Concepts of Terms in Political Discourse",
    author = "Tang, Linyuan  and
      Kageura, Kyo",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4216",
    doi = "10.18653/v1/W17-4216",
    pages = "90--94",
    abstract = "Previous work on the epistemology of fact-checking indicated the dilemma between the needs of binary answers for the public and ambiguity of political discussion. Determining concepts represented by terms in political discourse can be considered as a Word-Sense Disambiguation (WSD) task. The analysis of political discourse, however, requires identifying precise concepts of terms from relatively small data. This work attempts to provide a basic framework for revealing concepts of terms in political discourse with explicit contextual information. The framework consists of three parts: 1) extracting important terms, 2) generating concordance for each term with stipulative definitions and explanations, and 3) agglomerating similar information of the term by hierarchical clustering. Utterances made by Prime Minister Abe Shinzo in the Diet of Japan are used to examine our framework. Importantly, we revealed the conceptual inconsistency of the term Sonritsu-kiki-jitai. The framework was proved to work, but only for a small number of terms due to lack of explicit contextual information.",
}
@inproceedings{toprak-etal-2017-news,
    title = "A News Chain Evaluation Methodology along with a Lattice-based Approach for News Chain Construction",
    author = {Toprak, Mustafa  and
      {\"O}zkahraman, {\"O}zer  and
      Tekir, Selma},
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4217",
    doi = "10.18653/v1/W17-4217",
    pages = "95--99",
    abstract = "Chain construction is an important requirement for understanding news and establishing the context. A news chain can be defined as a coherent set of articles that explains an event or a story. There{'}s a lack of well-established methods in this area. In this work, we propose a methodology to evaluate the {``}goodness{''} of a given news chain and implement a concept lattice-based news chain construction method by Hossain et al.. The methodology part is vital as it directly affects the growth of research in this area. Our proposed methodology consists of collected news chains from different studies and two {``}goodness{''} metrics, minedge and dispersion coefficient respectively. We assess the utility of the lattice-based news chain construction method by our proposed methodology.",
}
@inproceedings{kolhatkar-taboada-2017-using,
    title = "Using {N}ew {Y}ork {T}imes Picks to Identify Constructive Comments",
    author = "Kolhatkar, Varada  and
      Taboada, Maite",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4218",
    doi = "10.18653/v1/W17-4218",
    pages = "100--105",
    abstract = "We examine the extent to which we are able to automatically identify constructive online comments. We build several classifiers using New York Times Picks as positive examples and non-constructive thread comments from the Yahoo News Annotated Comments Corpus as negative examples of constructive online comments. We evaluate these classifiers on a crowd-annotated corpus containing 1,121 comments. Our best classifier achieves a top F1 score of 0.84.",
}
@inproceedings{li-etal-2017-nlp,
    title = "An {NLP} Analysis of Exaggerated Claims in Science News",
    author = "Li, Yingya  and
      Zhang, Jieke  and
      Yu, Bei",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4219",
    doi = "10.18653/v1/W17-4219",
    pages = "106--111",
    abstract = "The discrepancy between science and media has been affecting the effectiveness of science communication. Original findings from science publications may be distorted with altered claim strength when reported to the public, causing misinformation spread. This study conducts an NLP analysis of exaggerated claims in science news, and then constructed prediction models for identifying claim strength levels in science reporting. The results demonstrate different writing styles journal articles and news/press releases use for reporting scientific findings. Preliminary prediction models reached promising result with room for further improvement.",
}
@proceedings{ws-2017-subword,
    title = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    editor = "Faruqui, Manaal  and
      Schuetze, Hinrich  and
      Trancoso, Isabel  and
      Yaghoobzadeh, Yadollah",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4100",
    doi = "10.18653/v1/W17-41",
}
@inproceedings{labeau-allauzen-2017-character,
    title = "Character and Subword-Based Word Representation for Neural Language Modeling Prediction",
    author = "Labeau, Matthieu  and
      Allauzen, Alexandre",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4101",
    doi = "10.18653/v1/W17-4101",
    pages = "1--13",
    abstract = "Most of neural language models use different kinds of embeddings for word prediction. While word embeddings can be associated to each word in the vocabulary or derived from characters as well as factored morphological decomposition, these word representations are mainly used to parametrize the input, i.e. the context of prediction. This work investigates the effect of using subword units (character and factored morphological decomposition) to build output representations for neural language modeling. We present a case study on Czech, a morphologically-rich language, experimenting with different input and output representations. When working with the full training vocabulary, despite unstable training, our experiments show that augmenting the output word representations with character-based embeddings can significantly improve the performance of the model. Moreover, reducing the size of the output look-up table, to let the character-based embeddings represent rare words, brings further improvement.",
}
@inproceedings{kunchukuttan-bhattacharyya-2017-learning,
    title = "Learning variable length units for {SMT} between related languages via Byte Pair Encoding",
    author = "Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4102",
    doi = "10.18653/v1/W17-4102",
    pages = "14--24",
    abstract = "We explore the use of segments learnt using Byte Pair Encoding (referred to as BPE units) as basic units for statistical machine translation between related languages and compare it with orthographic syllables, which are currently the best performing basic units for this translation task. BPE identifies the most frequent character sequences as basic units, while orthographic syllables are linguistically motivated pseudo-syllables. We show that BPE units modestly outperform orthographic syllables as units of translation, showing up to 11{\%} increase in BLEU score. While orthographic syllables can be used only for languages whose writing systems use vowel representations, BPE is writing system independent and we show that BPE outperforms other units for non-vowel writing systems too. Our results are supported by extensive experimentation spanning multiple language families and writing systems.",
}
@inproceedings{lejeune-cartier-2017-character,
    title = "Character Based Pattern Mining for Neology Detection",
    author = {Lejeune, Ga{\"e}l  and
      Cartier, Emmanuel},
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4103",
    doi = "10.18653/v1/W17-4103",
    pages = "25--30",
    abstract = "Detecting neologisms is essential in real-time natural language processing applications. Not only can it enable to follow the lexical evolution of languages, but it is also essential for updating linguistic resources and parsers. In this paper, neology detection is considered as a classification task where a system has to assess whether a given lexical item is an actual neologism or not. We propose a combination of an unsupervised data mining technique and a supervised machine learning approach. It is inspired by current researches in stylometry and on token-level and character-level patterns. We train and evaluate our system on a manually designed reference dataset in French and Russian. We show that this approach is able to largely outperform state-of-the-art neology detection systems. Furthermore, character-level patterns exhibit good properties for multilingual extensions of the system.",
}
@inproceedings{ponomareva-etal-2017-automated,
    title = "Automated Word Stress Detection in {R}ussian",
    author = "Ponomareva, Maria  and
      Milintsevich, Kirill  and
      Chernyak, Ekaterina  and
      Starostin, Anatoly",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4104",
    doi = "10.18653/v1/W17-4104",
    pages = "31--35",
    abstract = "In this study we address the problem of automated word stress detection in Russian using character level models and no part-speech-taggers. We use a simple bidirectional RNN with LSTM nodes and achieve accuracy of 90{\%} or higher. We experiment with two training datasets and show that using the data from an annotated corpus is much more efficient than using only a dictionary, since it allows to retain the context of the word and its morphological features.",
}
@inproceedings{choi-etal-2017-syllable,
    title = "A Syllable-based Technique for Word Embeddings of {K}orean Words",
    author = "Choi, Sanghyuk  and
      Kim, Taeuk  and
      Seol, Jinseok  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4105",
    doi = "10.18653/v1/W17-4105",
    pages = "36--40",
    abstract = "Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation. However, popular models that learn such embeddings are unaware of the morphology of words, so it is not directly applicable to highly agglutinative languages such as Korean. We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors. Our model successfully produces morphologically meaningful representation of Korean words compared to the original Skip-gram embeddings. The results also show that it is quite robust to the Out-of-Vocabulary problem.",
}
@inproceedings{shin-lee-2017-supersense,
    title = "Supersense Tagging with a Combination of Character, Subword, and Word-level Representations",
    author = "Shin, Youhyun  and
      Lee, Sang-goo",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4106",
    doi = "10.18653/v1/W17-4106",
    pages = "41--45",
    abstract = "Recently, there has been increased interest in utilizing characters or subwords for natural language processing (NLP) tasks. However, the effect of utilizing character, subword, and word-level information simultaneously has not been examined so far. In this paper, we propose a model to leverage various levels of input features to improve on the performance of an supersense tagging task. Detailed analysis of experimental results show that different levels of input representation offer distinct characteristics that explain performance discrepancy among different tasks.",
}
@inproceedings{silfverberg-hulden-2017-weakly,
    title = "Weakly supervised learning of allomorphy",
    author = "Silfverberg, Miikka  and
      Hulden, Mans",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4107",
    doi = "10.18653/v1/W17-4107",
    pages = "46--56",
    abstract = "Most NLP resources that offer annotations at the word segment level provide morphological annotation that includes features indicating tense, aspect, modality, gender, case, and other inflectional information. Such information is rarely aligned to the relevant parts of the words{---}i.e. the allomorphs, as such annotation would be very costly. These unaligned weak labelings are commonly provided by annotated NLP corpora such as treebanks in various languages. Although they lack alignment information, the presence/absence of labels at the word level is also consistent with the amount of supervision assumed to be provided to L1 and L2 learners. In this paper, we explore several methods to learn this latent alignment between parts of word forms and the grammatical information provided. All the methods under investigation favor hypotheses regarding allomorphs of morphemes that re-use a small inventory, i.e. implicitly minimize the number of allomorphs that a morpheme can be realized as. We show that the provided information offers a significant advantage for both word segmentation and the learning of allomorphy.",
}
@inproceedings{mogren-johansson-2017-character,
    title = "Character-based recurrent neural networks for morphological relational reasoning",
    author = "Mogren, Olof  and
      Johansson, Richard",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4108",
    doi = "10.18653/v1/W17-4108",
    pages = "57--63",
    abstract = "We present a model for predicting word forms based on \textit{morphological relational reasoning} with analogies. While previous work has explored tasks such as morphological inflection and reinflection, these models rely on an explicit enumeration of morphological features, which may not be available in all cases. To address the task of predicting a word form given a \textit{demo relation} (a pair of word forms) and a \textit{query word}, we devise a character-based recurrent neural network architecture using three separate encoders and a decoder. We also investigate a multiclass learning setup, where the prediction of the relation type label is used as an auxiliary task. Our results show that the exact form can be predicted for English with an accuracy of 94.7{\%}. For Swedish, which has a more complex morphology with more inflectional patterns for nouns and verbs, the accuracy is 89.3{\%}. We also show that using the auxiliary task of learning the relation type speeds up convergence and improves the prediction accuracy for the word generation task.",
}
@inproceedings{dai-cai-2017-glyph,
    title = "Glyph-aware Embedding of {C}hinese Characters",
    author = "Dai, Falcon  and
      Cai, Zheng",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4109",
    doi = "10.18653/v1/W17-4109",
    pages = "64--69",
    abstract = "Given the advantage and recent success of English character-level and subword-unit models in several NLP tasks, we consider the equivalent modeling problem for Chinese. Chinese script is logographic and many Chinese logograms are composed of common substructures that provide semantic, phonetic and syntactic hints. In this work, we propose to explicitly incorporate the visual appearance of a character{'}s glyph in its representation, resulting in a novel glyph-aware embedding of Chinese characters. Being inspired by the success of convolutional neural networks in computer vision, we use them to incorporate the spatio-structural patterns of Chinese glyphs as rendered in raw pixels. In the context of two basic Chinese NLP tasks of language modeling and word segmentation, the model learns to represent each character{'}s task-relevant semantic and syntactic information in the character-level embedding.",
}
@inproceedings{jin-kann-2017-exploring,
    title = "Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models",
    author = "Jin, Huiming  and
      Kann, Katharina",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4110",
    doi = "10.18653/v1/W17-4110",
    pages = "70--75",
    abstract = "Multi-task training is an effective method to mitigate the data sparsity problem. It has recently been applied for cross-lingual transfer learning for paradigm completion{---}the task of producing inflected forms of lemmata{---}with sequence-to-sequence networks. However, it is still vague how the model transfers knowledge across languages, as well as if and which information is shared. To investigate this, we propose a set of data-dependent experiments using an existing encoder-decoder recurrent neural network for the task. Our results show that indeed the performance gains surpass a pure regularization effect and that knowledge about language and morphology can be transferred.",
}
@inproceedings{kann-schutze-2017-unlabeled,
    title = "Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models",
    author = {Kann, Katharina  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4111",
    doi = "10.18653/v1/W17-4111",
    pages = "76--81",
    abstract = "We present a semi-supervised way of training a character-based encoder-decoder recurrent neural network for morphological reinflection{---}the task of generating one inflected wordform from another. This is achieved by using unlabeled tokens or random strings as training data for an autoencoding task, adapting a network for morphological reinflection, and performing multi-task training. We thus use limited labeled data more effectively, obtaining up to 9.92{\%} improvement over state-of-the-art baselines for 8 different languages.",
}
@inproceedings{thaine-penn-2017-vowel,
    title = "Vowel and Consonant Classification through Spectral Decomposition",
    author = "Thaine, Patricia  and
      Penn, Gerald",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4112",
    doi = "10.18653/v1/W17-4112",
    pages = "82--91",
    abstract = "We consider two related problems in this paper. Given an undeciphered alphabetic writing system or mono-alphabetic cipher, determine: (1) which of its letters are vowels and which are consonants; and (2) whether the writing system is a vocalic alphabet or an abjad. We are able to show that a very simple spectral decomposition based on character co-occurrences provides nearly perfect performance with respect to answering both question types.",
}
@inproceedings{yu-etal-2017-syllable,
    title = "Syllable-level Neural Language Model for Agglutinative Language",
    author = "Yu, Seunghak  and
      Kulkarni, Nilesh  and
      Lee, Haejun  and
      Kim, Jihie",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4113",
    doi = "10.18653/v1/W17-4113",
    pages = "92--96",
    abstract = "We introduce a novel method to diminish the problem of out of vocabulary words by introducing an embedding method which leverages the agglutinative property of language. We propose additional embedding derived from syllables and morphemes for the words to improve the performance of language model. We apply the above method to input prediction tasks and achieve state of the art performance in terms of Key Stroke Saving (KSS) w.r.t. to existing device input prediction methods.",
}
@inproceedings{misawa-etal-2017-character,
    title = "Character-based Bidirectional {LSTM}-{CRF} with words and characters for {J}apanese Named Entity Recognition",
    author = "Misawa, Shotaro  and
      Taniguchi, Motoki  and
      Miura, Yasuhide  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4114",
    doi = "10.18653/v1/W17-4114",
    pages = "97--102",
    abstract = "Recently, neural models have shown superior performance over conventional models in NER tasks. These models use CNN to extract sub-word information along with RNN to predict a tag for each word. However, these models have been tested almost entirely on English texts. It remains unclear whether they perform similarly in other languages. We worked on Japanese NER using neural models and discovered two obstacles of the state-of-the-art model. First, CNN is unsuitable for extracting Japanese sub-word information. Secondly, a model predicting a tag for each word cannot extract an entity when a part of a word composes an entity. The contributions of this work are (1) verifying the effectiveness of the state-of-the-art NER model for Japanese, (2) proposing a neural model for predicting a tag for each character using word and character information. Experimentally obtained results demonstrate that our model outperforms the state-of-the-art neural English NER model in Japanese.",
}
@inproceedings{vylomova-etal-2017-word,
    title = "Word Representation Models for Morphologically Rich Languages in Neural Machine Translation",
    author = "Vylomova, Ekaterina  and
      Cohn, Trevor  and
      He, Xuanli  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4115",
    doi = "10.18653/v1/W17-4115",
    pages = "103--108",
    abstract = "Out-of-vocabulary words present a great challenge for Machine Translation. Recently various character-level compositional models were proposed to address this issue. In current research we incorporate two most popular neural architectures, namely LSTM and CNN, into hard- and soft-attentional models of translation for character-level representation of the source. We propose semantic and morphological intrinsic evaluation of encoder-level representations. Our analysis of the learned representations reveals that character-based LSTM seems to be better at capturing morphological aspects compared to character-based CNN. We also show that hard-attentional model provides better character-level representations compared to vanilla one.",
}
@inproceedings{alva-oncevay-2017-spell,
    title = "Spell-Checking based on Syllabification and Character-level Graphs for a {P}eruvian Agglutinative Language",
    author = "Alva, Carlo  and
      Oncevay, Arturo",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4116",
    doi = "10.18653/v1/W17-4116",
    pages = "109--116",
    abstract = "There are several native languages in Peru which are mostly agglutinative. These languages are transmitted from generation to generation mainly in oral form, causing different forms of writing across different communities. For this reason, there are recent efforts to standardize the spelling in the written texts, and it would be beneficial to support these tasks with an automatic tool such as an spell-checker. In this way, this spelling corrector is being developed based on two steps: an automatic rule-based syllabification method and a character-level graph to detect the degree of error in a misspelled word. The experiments were realized on Shipibo-konibo, a highly agglutinative and amazonian language, and the results obtained have been promising in a dataset built for the purpose.",
}
@inproceedings{do-etal-2017-need,
    title = "What do we need to know about an unknown word when parsing {G}erman",
    author = "Do, Bich-Ngoc  and
      Rehbein, Ines  and
      Frank, Anette",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4117",
    doi = "10.18653/v1/W17-4117",
    pages = "117--123",
    abstract = "We propose a new type of subword embedding designed to provide more information about unknown compounds, a major source for OOV words in German. We present an extrinsic evaluation where we use the compound embeddings as input to a neural dependency parser and compare the results to the ones obtained with other types of embeddings. Our evaluation shows that adding compound embeddings yields a significant improvement of 2{\%} LAS over using word embeddings when no POS information is available. When adding POS embeddings to the input, however, the effect levels out. This suggests that it is not the missing information about the semantics of the unknown words that causes problems for parsing German, but the lack of morphological information for unknown words. To augment our evaluation, we also test the new embeddings in a language modelling task that requires both syntactic and semantic information.",
}
@inproceedings{yu-etal-2017-general,
    title = "A General-Purpose Tagger with Convolutional Neural Networks",
    author = "Yu, Xiang  and
      Falenska, Agnieszka  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4118",
    doi = "10.18653/v1/W17-4118",
    pages = "124--129",
    abstract = "We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the-art results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the out-of-vocabulary problem; it performs well on artificially unnormalized texts.",
}
@inproceedings{stratos-2017-reconstruction,
    title = "Reconstruction of Word Embeddings from Sub-Word Parameters",
    author = "Stratos, Karl",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4119",
    doi = "10.18653/v1/W17-4119",
    pages = "130--135",
    abstract = "Pre-trained word embeddings improve the performance of a neural model at the cost of increasing the model size. We propose to benefit from this resource without paying the cost by operating strictly at the sub-lexical level. Our approach is quite simple: before task-specific training, we first optimize sub-word parameters to reconstruct pre-trained word embeddings using various distance measures. We report interesting results on a variety of tasks: word similarity, word analogy, and part-of-speech tagging.",
}
@inproceedings{barros-etal-2017-inflection,
    title = "Inflection Generation for {S}panish Verbs using Supervised Learning",
    author = "Barros, Cristina  and
      Gkatzia, Dimitra  and
      Lloret, Elena",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4120",
    doi = "10.18653/v1/W17-4120",
    pages = "136--141",
    abstract = "We present a novel supervised approach to inflection generation for verbs in Spanish. Our system takes as input the verb{'}s lemma form and the desired features such as person, number, tense, and is able to predict the appropriate grammatical conjugation. Even though our approach learns from fewer examples comparing to previous work, it is able to deal with all the Spanish moods (indicative, subjunctive and imperative) in contrast to previous work which only focuses on indicative and subjunctive moods. We show that in an intrinsic evaluation, our system achieves 99{\%} accuracy, outperforming (although not significantly) two competitive state-of-art systems. The successful results obtained clearly indicate that our approach could be integrated into wider approaches related to text generation in Spanish.",
}
@inproceedings{tomar-etal-2017-neural,
    title = "Neural Paraphrase Identification of Questions with Noisy Pretraining",
    author = {Tomar, Gaurav Singh  and
      Duque, Thyago  and
      T{\"a}ckstr{\"o}m, Oscar  and
      Uszkoreit, Jakob  and
      Das, Dipanjan},
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4121",
    doi = "10.18653/v1/W17-4121",
    pages = "142--147",
    abstract = "We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that a variant of the decomposable attention model (replacing the word embeddings of the decomposable attention model of Parikh et al. 2016 with character n-gram representations) results in accurate performance on this task, while being far simpler than many competing neural architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically collected question paraphrases, it obtains the best reported performance on the dataset.",
}
@inproceedings{nguyen-etal-2017-sub,
    title = "Sub-character Neural Language Modelling in {J}apanese",
    author = "Nguyen, Viet  and
      Brooke, Julian  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4122",
    doi = "10.18653/v1/W17-4122",
    pages = "148--153",
    abstract = "In East Asian languages such as Japanese and Chinese, the semantics of a character are (somewhat) reflected in its sub-character elements. This paper examines the effect of using sub-characters for language modeling in Japanese. This is achieved by decomposing characters according to a range of character decomposition datasets, and training a neural language model over variously decomposed character representations. Our results indicate that language modelling can be improved through the inclusion of sub-characters, though this result depends on a good choice of decomposition dataset and the appropriate granularity of decomposition.",
}
@inproceedings{costa-jussa-etal-2017-byte,
    title = "Byte-based Neural Machine Translation",
    author = "Costa-juss{\`a}, Marta R.  and
      Escolano, Carlos  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4123",
    doi = "10.18653/v1/W17-4123",
    pages = "154--158",
    abstract = "This paper presents experiments comparing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural machine translation system is to build multi-lingual neural machine translation systems that can share the same vocabulary. We compare the performance of both systems in several language pairs and we see that the performance in test is similar for most language pairs while the training time is slightly reduced in the case of byte-based neural machine translation.",
}
@inproceedings{jebbara-cimiano-2017-improving,
    title = "Improving Opinion-Target Extraction with Character-Level Word Embeddings",
    author = "Jebbara, Soufian  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4124",
    doi = "10.18653/v1/W17-4124",
    pages = "159--167",
    abstract = "Fine-grained sentiment analysis is receiving increasing attention in recent years. Extracting opinion target expressions (OTE) in reviews is often an important step in fine-grained, aspect-based sentiment analysis. Retrieving this information from user-generated text, however, can be difficult. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. In this work, we investigate whether character-level models can improve the performance for the identification of opinion target expressions. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system{'}s performance. Specifically, we obtain an increase by 3.3 points F1-score with respect to our baseline model. In further experiments, we reveal encoded character patterns of the learned embeddings and give a nuanced view of the performance differences of both models.",
}
@proceedings{ws-2017-international-finite,
    title = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    editor = "Drewes, Frank",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4000",
    doi = "10.18653/v1/W17-4000",
}
@inproceedings{mihov-schulz-2017-failure,
    title = "Failure Transducers and Applications in Knowledge-Based Text Processing",
    author = "Mihov, Stoyan  and
      Schulz, Klaus U.",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4001",
    doi = "10.18653/v1/W17-4001",
    pages = "1--9",
}
@inproceedings{hellsten-etal-2017-transliterated,
    title = "Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers",
    author = "Hellsten, Lars  and
      Roark, Brian  and
      Goyal, Prasoon  and
      Allauzen, Cyril  and
      Beaufays, Fran{\c{c}}oise  and
      Ouyang, Tom  and
      Riley, Michael  and
      Rybach, David",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4002",
    doi = "10.18653/v1/W17-4002",
    pages = "10--19",
}
@inproceedings{hao-2017-harmonic,
    title = "Harmonic Serialism and Finite-State {O}ptimality {T}heory",
    author = "Hao, Yiding",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4003",
    doi = "10.18653/v1/W17-4003",
    pages = "20--29",
}
@inproceedings{yli-jyra-2017-bounded,
    title = "Bounded-Depth High-Coverage Search Space for Noncrossing Parses",
    author = {Yli-Jyr{\"a}, Anssi},
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4004",
    doi = "10.18653/v1/W17-4007",
    pages = "30--40",
}
@inproceedings{wurm-petitjean-2017-multi,
    title = "Multi-tape Computing with Synchronous Relations",
    author = "Wurm, Christian  and
      Petitjean, Simon",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4005",
    doi = "10.18653/v1/W17-4005",
    pages = "41--49",
}
@inproceedings{ravishankar-tyers-2017-finite,
    title = "Finite-State Morphological Analysis for {M}arathi",
    author = "Ravishankar, Vinit  and
      Tyers, Francis M.",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4006",
    doi = "10.18653/v1/W17-4006",
    pages = "50--55",
}
@inproceedings{sharma-singh-2017-word,
    title = "Word Transduction for Addressing the {OOV} Problem in Machine Translation for Similar Resource-Scarce Languages",
    author = "Sharma, Shashikant  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4007",
    doi = "10.18653/v1/W17-4009",
    pages = "56--63",
}
@inproceedings{ehsani-etal-2017-fst,
    title = "A {FST} Description of Noun and Verb Morphology of Azarbaijani {T}urkish",
    author = {Ehsani, Razieh  and
      {\"O}zen{\c{c}}, Berke  and
      Solak, Ercan},
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4008",
    doi = "10.18653/v1/W17-4008",
    pages = "62--68",
}
@inproceedings{liu-hulden-2017-evaluation,
    title = "Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from {W}iktionary",
    author = "Liu, Ling  and
      Hulden, Mans",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4009",
    doi = "10.18653/v1/W17-4004",
    pages = "69--74",
}
@inproceedings{minock-2017-evaluating,
    title = "Evaluating an Automata Approach to Query Containment",
    author = "Minock, Michael",
    booktitle = "Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing ({FSMNLP} 2017)",
    month = sep,
    year = "2017",
    address = "Ume{\aa}, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4010",
    doi = "10.18653/v1/W17-4010",
    pages = "75--79",
}
@proceedings{ws-2017-creativity,
    title = "Proceedings of the Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2017)",
    editor = "Gon{\c{c}}alo Oliveira, Hugo  and
      Burtenshaw, Ben  and
      Kestemont, Mike  and
      De Smedt, Tom",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3900",
    doi = "10.18653/v1/W17-3900",
}
@inproceedings{astigarraga-etal-2017-poets,
    title = "Poet{'}s Little Helper: A methodology for computer-based poetry generation. A case study for the {B}asque language",
    author = "Astigarraga, Aitzol  and
      Mar{\'\i}a Mart{\'\i}nez-Otzeta, Jos{\'e}  and
      Rodriguez, Igor  and
      Sierra, Basilio  and
      Lazkano, Elena",
    booktitle = "Proceedings of the Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2017)",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3901",
    doi = "10.18653/v1/W17-3901",
    pages = "2--10",
}
@inproceedings{goncalo-oliveira-2017-o,
    title = "{O} Poeta Artificial 2.0: Increasing Meaningfulness in a Poetry Generation {T}witter bot",
    author = "Gon{\c{c}}alo Oliveira, Hugo",
    booktitle = "Proceedings of the Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2017)",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3902",
    doi = "10.18653/v1/W17-3902",
    pages = "11--20",
}
@inproceedings{gervas-2017-template,
    title = "Template-Free Construction of Rhyming Poems with Thematic Cohesion",
    author = "Gerv{\'a}s, Pablo",
    booktitle = "Proceedings of the Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2017)",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3903",
    doi = "10.18653/v1/W17-3903",
    pages = "21--28",
}
@inproceedings{manjavacas-etal-2017-synthetic,
    title = "Synthetic Literature: Writing Science Fiction in a Co-Creative Process",
    author = "Manjavacas, Enrique  and
      Karsdorp, Folgert  and
      Burtenshaw, Ben  and
      Kestemont, Mike",
    booktitle = "Proceedings of the Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2017)",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3904",
    doi = "10.18653/v1/W17-3904",
    pages = "29--37",
}
@inproceedings{theofanis-chourdakis-reiss-2017-constructing,
    title = "Constructing narrative using a generative model and continuous action policies",
    author = "Theofanis Chourdakis, Emmanouil  and
      Reiss, Joshua",
    booktitle = "Proceedings of the Workshop on Computational Creativity in Natural Language Generation ({CC}-{NLG} 2017)",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3905",
    doi = "10.18653/v1/W17-3905",
    pages = "38--43",
}
@proceedings{ws-2017-linguistic-resources,
    title = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    editor = "Kocijan, Kristina  and
      Machonis, Peter  and
      Silberztein, Max",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3800",
    doi = "10.18653/v1/W17-38",
}
@inproceedings{silberztein-2017-foaf,
    title = "From {FOAF} to {E}nglish: Linguistic Contribution to Web Semantics",
    author = "Silberztein, Max",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3801",
    doi = "10.18653/v1/W17-3801",
    pages = "1--9",
}
@inproceedings{garcia-mendez-etal-2017-lexicon,
    title = "Lexicon for Natural Language Generation in {S}panish Adapted to Alternative and Augmentative Communication",
    author = "Garc{\'\i}a-M{\'e}ndez, Silvia  and
      Fern{\'a}ndez-Gavilanes, Milagros  and
      Costa-Montenegro, Enrique  and
      Juncal-Mart{\'\i}nez, Jonathan  and
      Gonz{\'a}lez-Casta{\~n}o, Francisco J.",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3802",
    doi = "10.18653/v1/W17-3802",
    pages = "11--15",
}
@inproceedings{bessaies-etal-2017-generating,
    title = "Generating Answering Patterns from Factoid {A}rabic Questions",
    author = "Bessaies, Essia  and
      Mesfar, Slim  and
      Ben Ghezala, Henda",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3803",
    doi = "10.18653/v1/W17-3803",
    pages = "17--24",
}
@inproceedings{kocijan-etal-2017-language,
    title = "Language Generation from {DB} Query",
    author = "Kocijan, Kristina  and
      Bekavac, Bo{\v{z}}o  and
      {\v{S}}ojat, Kre{\v{s}}imir",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3804",
    doi = "10.18653/v1/W17-3804",
    pages = "25--32",
}
@inproceedings{machonis-2017-using,
    title = "Using Electronic Dictionaries and {N}oo{J} to Generate Sentences Containing {E}nglish Phrasal Verbs",
    author = "Machonis, Peter A.",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3805",
    doi = "10.18653/v1/W17-3805",
    pages = "33--37",
}
@inproceedings{fehri-dardour-2017-generating,
    title = "Generating Text with Correct Verb Conjugation: Proposal for a New Automatic Conjugator with {N}oo{J}",
    author = "Fehri, H{\'e}la  and
      Dardour, Sondes",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3806",
    doi = "10.18653/v1/W17-3806",
    pages = "39--42",
}
@inproceedings{ghorbel-2017-formalization,
    title = "Formalization of Speech Verbs with {N}oo{J} for Machine Translation: the {F}rench Verb accuser",
    author = "Ghorbel, Jouda",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3807",
    doi = "10.18653/v1/W17-3807",
    pages = "43--47",
}
@inproceedings{bououd-fafi-2017-using,
    title = "Using Serious Games to Correct {F}rench Dictations: Proposal for a New {U}nity3{D}/{N}oo{J} Connector",
    author = "Bououd, Ikram  and
      Fafi, Rania",
    booktitle = "Proceedings of the Linguistic Resources for Automatic Natural Language Generation - {L}i{RA}@{NLG}",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3808",
    doi = "10.18653/v1/W17-3808",
    pages = "49--52",
}
@proceedings{ws-2017-explainable,
    title = "Proceedings of the 1st Workshop on Explainable Computational Intelligence ({XCI} 2017)",
    editor = "Pereira-Fari{\~n}a, M.  and
      Reed, C.",
    month = sep,
    year = "2017",
    address = "Dundee, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3700",
    doi = "10.18653/v1/W37-1",
}
@inproceedings{baum-etal-2017-two,
    title = "Two Challenges for {CI} Trustworthiness and How to Address Them",
    author = {Baum, Kevin  and
      K{\"o}hl, Maximilian A.  and
      Schmidt, Eva},
    booktitle = "Proceedings of the 1st Workshop on Explainable Computational Intelligence ({XCI} 2017)",
    month = sep,
    year = "2017",
    address = "Dundee, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3701",
    doi = "10.18653/v1/W17-3701",
}
@inproceedings{white-etal-2017-simple,
    title = "A Simple Method for Clarifying Sentences with Coordination Ambiguities",
    author = "White, Michael  and
      Duan, Manjuan  and
      King, David L.",
    booktitle = "Proceedings of the 1st Workshop on Explainable Computational Intelligence ({XCI} 2017)",
    month = sep,
    year = "2017",
    address = "Dundee, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3702",
    doi = "10.18653/v1/W17-3702",
}
@inproceedings{horacek-2017-requirements,
    title = "Requirements for Conceptual Representations of Explanations and How Reasoning Systems Can Serve Them",
    author = "Horacek, Helmut",
    booktitle = "Proceedings of the 1st Workshop on Explainable Computational Intelligence ({XCI} 2017)",
    month = sep,
    year = "2017",
    address = "Dundee, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3703",
    doi = "10.18653/v1/W17-3703",
}
@inproceedings{alonso-trivino-2017-essay,
    title = "An Essay on Self-explanatory Computational Intelligence: A Linguistic Model of Data Processing Systems",
    author = "Alonso, Jose M.  and
      Trivino, Gracian",
    booktitle = "Proceedings of the 1st Workshop on Explainable Computational Intelligence ({XCI} 2017)",
    month = sep,
    year = "2017",
    address = "Dundee, United Kingdom",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3704",
    doi = "10.18653/v1/W17-3704",
}
@proceedings{ws-2017-recent,
    title = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    editor = "Taboada, M.  and
      da Cunha, I.  and
      Maziero, E.G.  and
      Cardoso, P.  and
      Antonio, J.D.  and
      Iruskieta, M.",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3600",
    doi = "10.18653/v1/W17-36",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{imaz-iruskieta-2017-deliberation,
    title = "Deliberation as Genre: Mapping Argumentation through Relational Discourse Structure",
    author = "Imaz, Oier  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3601",
    doi = "10.18653/v1/W17-3601",
    pages = "1--10",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{das-etal-2017-good,
    title = "The Good, the Bad, and the Disagreement: Complex ground truth in rhetorical structure analysis",
    author = "Das, Debopam  and
      Stede, Manfred  and
      Taboada, Maite",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3602",
    doi = "10.18653/v1/W17-3602",
    pages = "11--19",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{zeldes-2017-distributional,
    title = "A Distributional View of Discourse Encapsulation: Multifactorial Prediction of Coreference Density in {RST}",
    author = "Zeldes, Amir",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3603",
    doi = "10.18653/v1/W17-3603",
    pages = "20--28",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{toldova-etal-2017-rhetorical,
    title = "Rhetorical relations markers in {R}ussian {RST} Treebank",
    author = "Toldova, Svetlana  and
      Pisarevskaya, Dina  and
      Ananyeva, Margarita  and
      Kobozeva, Maria  and
      Nasedkin, Alexander  and
      Nikiforova, Sofia  and
      Pavlova, Irina  and
      Shelepov, Alexey",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3604",
    doi = "10.18653/v1/W17-3604",
    pages = "29--33",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{paulino-sierra-2017-applying,
    title = "Applying the {R}hetorical {S}tructure {T}heory in {A}lzheimer patients{'} speech",
    author = "Paulino, Anayeli  and
      Sierra, Gerardo",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3605",
    doi = "10.18653/v1/W17-3605",
    pages = "34--38",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{alkorta-etal-2017-using,
    title = "Using lexical level information in discourse structures for {B}asque sentiment analysis",
    author = "Alkorta, Jon  and
      Gojenola, Koldo  and
      Iruskieta, Mikel  and
      Taboada, Maite",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3606",
    doi = "10.18653/v1/W17-3606",
    pages = "39--47",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{gonzalez-dios-etal-2017-framework,
    title = "Framework for the Analysis of Simplified Texts Taking Discourse into Account: the {B}asque Causal Relations as Case Study",
    author = "Gonzalez-Dios, Itziar  and
      Diaz de Ilarraza, Arantza  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3607",
    doi = "10.18653/v1/W17-3607",
    pages = "48--57",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{popoola-2017-using,
    title = "Using {R}hetorical {S}tructure {T}heory for Detection of Fake Online Reviews",
    author = "Popoola, Olu",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3608",
    doi = "10.18653/v1/W17-3608",
    pages = "58--63",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{antonio-santin-2017-haters,
    title = "{``}Haters gonna hate{''}: challenges for sentiment analysis of {F}acebook comments in {B}razilian {P}ortuguese",
    author = "Antonio, Juliano D.  and
      Santin, Ana Carolina L.",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3609",
    doi = "10.18653/v1/W17-3609",
    pages = "64--72",
    ISBN = "978-1-945626-78-4",
}
@inproceedings{cao-etal-2017-discourse,
    title = "Discourse Segmentation for Building a {RST} {C}hinese Treebank",
    author = "Cao, Shuyuan  and
      Xue, Nianwen  and
      da Cunha, Iria  and
      Iruskieta, Mikel  and
      Wang, Chuan",
    booktitle = "Proceedings of the 6th Workshop on Recent Advances in {RST} and Related Formalisms",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3610",
    doi = "10.18653/v1/W17-3610",
    pages = "73--81",
    ISBN = "978-1-945626-78-4",
}
@proceedings{ws-2017-international-natural,
    title = "Proceedings of the 10th International Conference on Natural Language Generation",
    editor = "Alonso, Jose M.  and
      Bugar{\'\i}n, Alberto  and
      Reiter, Ehud",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3500",
    doi = "10.18653/v1/W17-35",
}
@inproceedings{castro-ferreira-etal-2017-linguistic,
    title = "Linguistic realisation as machine translation: Comparing different {MT} models for {AMR}-to-text generation",
    author = "Castro Ferreira, Thiago  and
      Calixto, Iacer  and
      Wubben, Sander  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3501",
    doi = "10.18653/v1/W17-3501",
    pages = "1--10",
    abstract = "In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT). We systematically study the effects of 3 AMR preprocessing steps (Delexicalisation, Compression, and Linearisation) applied before the MT phase. Our results show that preprocessing indeed helps, although the benefits differ for the two MT models.",
}
@inproceedings{goncalo-oliveira-2017-survey,
    title = "A Survey on Intelligent Poetry Generation: Languages, Features, Techniques, Reutilisation and Evaluation",
    author = "Gon{\c{c}}alo Oliveira, Hugo",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3502",
    doi = "10.18653/v1/W17-3502",
    pages = "11--20",
    abstract = "Poetry generation is becoming popular among researchers of Natural Language Generation, Computational Creativity and, broadly, Artificial Intelligence. To produce text that may be regarded as poetry, poetry generation systems are typically knowledge-intensive and have to deal with several levels of language, from lexical to semantics. Interest on the topic resulted in the development of several poetry generators described in the literature, with different features covered or handled differently, by a broad range of alternative approaches, as well as different perspectives on evaluation, another challenging aspect due the underlying subjectivity. This paper surveys intelligent poetry generators around a set of relevant axis for poetry generation {--} targeted languages, form and content features, techniques, reutilisation of material, and evaluation {--} and aims to organise work developed on this topic so far.",
}
@inproceedings{van-miltenburg-etal-2017-cross,
    title = "Cross-linguistic differences and similarities in image descriptions",
    author = "van Miltenburg, Emiel  and
      Elliott, Desmond  and
      Vossen, Piek",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3503",
    doi = "10.18653/v1/W17-3503",
    pages = "21--30",
    abstract = "Automatic image description systems are commonly trained and evaluated on large image description datasets. Recently, researchers have started to collect such datasets for languages other than English. An unexplored question is how different these datasets are from English and, if there are any differences, what causes them to differ. This paper provides a cross-linguistic comparison of Dutch, English, and German image descriptions. We find that these descriptions are similar in many respects, but the familiarity of crowd workers with the subjects of the images has a noticeable influence on the specificity of the descriptions.",
}
@inproceedings{yao-etal-2017-content,
    title = "Content Selection for Real-time Sports News Construction from Commentary Texts",
    author = "Yao, Jin-ge  and
      Zhang, Jianmin  and
      Wan, Xiaojun  and
      Xiao, Jianguo",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3504",
    doi = "10.18653/v1/W17-3504",
    pages = "31--40",
    abstract = "We study the task of constructing sports news report automatically from live commentary and focus on content selection. Rather than receiving every piece of text of a sports match before news construction, as in previous related work, we novelly verify the feasibility of a more challenging but more useful setting to generate news report on the fly by treating live text input as a stream. Specifically, we design various scoring functions to address different requirements of the task. The near submodularity of scoring functions makes it possible to adapt efficient greedy algorithms even in stream data settings. Experiments suggest that our proposed framework can already produce comparable results compared with previous work that relies on a supervised learning-to-rank model with heavy feature engineering.",
}
@inproceedings{barros-etal-2017-improving,
    title = "Improving the Naturalness and Expressivity of Language Generation for {S}panish",
    author = "Barros, Cristina  and
      Gkatzia, Dimitra  and
      Lloret, Elena",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3505",
    doi = "10.18653/v1/W17-3505",
    pages = "41--50",
    abstract = "We present a flexible Natural Language Generation approach for Spanish, focused on the surface realisation stage, which integrates an inflection module in order to improve the naturalness and expressivity of the generated language. This inflection module inflects the verbs using an ensemble of trainable algorithms whereas the other types of words (e.g. nouns, determiners, etc) are inflected using hand-crafted rules. We show that our approach achieves 2{\%} higher accuracy than two state-of-art inflection generation approaches. Furthermore, our proposed approach also predicts an extra feature: the inflection of the imperative mood, which was not taken into account by previous work. We also present a user evaluation, where we demonstrate that the proposed method significantly improves the perceived naturalness of the generated language.",
}
@inproceedings{tanti-etal-2017-role,
    title = "What is the Role of Recurrent Neural Networks ({RNN}s) in an Image Caption Generator?",
    author = "Tanti, Marc  and
      Gatt, Albert  and
      Camilleri, Kenneth",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3506",
    doi = "10.18653/v1/W17-3506",
    pages = "51--60",
    abstract = "Image captioning has evolved into a core task for Natural Language Generation and has also proved to be an important testbed for deep learning approaches to handling multimodal representations. Most contemporary approaches rely on a combination of a convolutional network to handle image features, and a recurrent network to encode linguistic information. The latter is typically viewed as the primary {``}generation{''} component. Beyond this high-level characterisation, a CNN+RNN model supports a variety of architectural designs. The dominant model in the literature is one in which visual features encoded by a CNN are {``}injected{''} as part of the linguistic encoding process, driving the RNN{'}s linguistic choices. By contrast, it is possible to envisage an architecture in which visual and linguistic features are encoded separately, and merged at a subsequent stage. In this paper, we address two related questions: (1) Is direct injection the best way of combining multimodal information, or is a late merging alternative better for the image captioning task? (2) To what extent should a recurrent network be viewed as actually generating, rather than simply encoding, linguistic information?",
}
@inproceedings{mendez-etal-2017-exploring,
    title = "Exploring the Behavior of Classic {REG} Algorithms in the Description of Characters in 3{D} Images",
    author = "M{\'e}ndez, Gonzalo  and
      Herv{\'a}s, Raquel  and
      Bautista, Susana  and
      Rabad{\'a}n, Adri{\'a}n  and
      Rodr{\'\i}guez, Teresa",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3507",
    doi = "10.18653/v1/W17-3507",
    pages = "61--69",
    abstract = "Describing people and characters can be very useful in different contexts, such as computational narrative or image description for the visually impaired. However, a review of the existing literature shows that the automatic generation of people descriptions has not received much attention. Our work focuses on the description of people in snapshots from a 3D environment. First, we have conducted a survey to identify the way in which people describe other people under different conditions. We have used the information extracted from this survey to design several Referring Expression Generation algorithms which produce similar results. We have evaluated these algorithms with users in order to identify which ones generate the best description for specific characters in different situations. The evaluation has shown that, in order to generate good descriptions, a combination of different algorithms has to be used depending on the features and situation of the person to be described.",
}
@inproceedings{goncalo-oliveira-etal-2017-co,
    title = "Co-{P}oe{T}ry{M}e: a Co-Creative Interface for the Composition of Poetry",
    author = "Gon{\c{c}}alo Oliveira, Hugo  and
      Mendes, Tiago  and
      Boavida, Ana",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3508",
    doi = "10.18653/v1/W17-3508",
    pages = "70--71",
    abstract = "Co-PoeTryMe is a web application for poetry composition, guided by the user, though with the help of automatic features, such as the generation of full (editable) drafts, as well as the acquisition of additional well-formed lines, or semantically-related words, possibly constrained by the number of syllables, rhyme, or polarity. Towards the final poem, the latter can replace lines or words in the draft.",
}
@inproceedings{zarriess-etal-2017-refer,
    title = "Refer-i{TTS}: A System for Referring in Spoken Installments to Objects in Real-World Images",
    author = "Zarrie{\ss}, Sina  and
      L{\'o}pez Gambino, M. Soledad  and
      Schlangen, David",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3509",
    doi = "10.18653/v1/W17-3509",
    pages = "72--73",
    abstract = "Current referring expression generation systems mostly deliver their output as one-shot, written expressions. We present on-going work on incremental generation of spoken expressions referring to objects in real-world images. This approach extends upon previous work using the words-as-classifier model for generation. We implement this generator in an incremental dialogue processing framework such that we can exploit an existing interface to incremental text-to-speech synthesis. Our system generates and synthesizes referring expressions while continuously observing non-verbal user reactions.",
}
@inproceedings{schilder-2017-finding,
    title = "Finding the {``}right{''} answers for customers",
    author = "Schilder, Frank",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3510",
    doi = "10.18653/v1/W17-3510",
    pages = "74",
    abstract = "This talk will present a few NLG systems developed within Thomson Reuters providing information to professionals such as lawyers, accountants or traders. Based on the experience developing these system, I will discuss the usefulness of automatic metrics, crowd-sourced evaluation, corpora studies and expert reviews. I will conclude with exploring the question of whether developers of NLG systems need to follow ethical guidelines and how those guidelines could be established.",
}
@inproceedings{williams-scheutz-2017-referring,
    title = "Referring Expression Generation under Uncertainty: Algorithm and Evaluation Framework",
    author = "Williams, Tom  and
      Scheutz, Matthias",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3511",
    doi = "10.18653/v1/W17-3511",
    pages = "75--84",
    abstract = "For situated agents to effectively engage in natural-language interactions with humans, they must be able to refer to entities such as people, locations, and objects. While classic referring expression generation (REG) algorithms like the Incremental Algorithm (IA) assume perfect, complete, and accessible knowledge of all referents, this is not always possible. In this work, we show how a previously presented consultant framework (which facilitates reference resolution when knowledge is uncertain, heterogeneous and distributed) can be used to extend the IA to produce DIST-PIA, a domain-independent algorithm for REG under uncertain, heterogeneous, and distributed knowledge. We also present a novel framework that can be used to evaluate such REG algorithms without conflating the performance of the algorithm with the performance of classifiers it employs.",
}
@inproceedings{alharbi-gotoh-2017-natural,
    title = "Natural Language Descriptions for Human Activities in Video Streams",
    author = "Alharbi, Nouf  and
      Gotoh, Yoshihiko",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3512",
    doi = "10.18653/v1/W17-3512",
    pages = "85--94",
    abstract = "There has been continuous growth in the volume and ubiquity of video material. It has become essential to define video semantics in order to aid the searchability and retrieval of this data. We present a framework that produces textual descriptions of video, based on the visual semantic content. Detected action classes rendered as verbs, participant objects converted to noun phrases, visual properties of detected objects rendered as adjectives and spatial relations between objects rendered as prepositions. Further, in cases of zero-shot action recognition, a language model is used to infer a missing verb, aided by the detection of objects and scene settings. These extracted features are converted into textual descriptions using a template-based approach. The proposed video descriptions framework evaluated on the NLDHA dataset using ROUGE scores and human judgment evaluation.",
}
@inproceedings{van-der-lee-etal-2017-pass,
    title = "{PASS}: A {D}utch data-to-text system for soccer, targeted towards specific audiences",
    author = "van der Lee, Chris  and
      Krahmer, Emiel  and
      Wubben, Sander",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3513",
    doi = "10.18653/v1/W17-3513",
    pages = "95--104",
    abstract = "We present PASS, a data-to-text system that generates Dutch soccer reports from match statistics. One of the novel elements of PASS is the fact that the system produces corpus-based texts tailored towards fans of one club or the other, which can most prominently be observed in the tone of voice used in the reports. Furthermore, the system is open source and uses a modular design, which makes it relatively easy for people to add extensions. Human-based evaluation shows that people are generally positive towards PASS in regards to its clarity and fluency, and that the tailoring is accurately recognized in most cases.",
}
@inproceedings{byamugisha-etal-2017-evaluation,
    title = "Evaluation of a {R}unyankore grammar engine for healthcare messages",
    author = "Byamugisha, Joan  and
      Keet, C. Maria  and
      DeRenzi, Brian",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3514",
    doi = "10.18653/v1/W17-3514",
    pages = "105--113",
    abstract = "Natural Language Generation (NLG) can be used to generate personalized health information, which is especially useful when provided in one{'}s own language. However, the NLG technique widely used in different domains and languages{---}templates{---}was shown to be inapplicable to Bantu languages, due to their characteristic agglutinative structure. We present here our use of the grammar engine NLG technique to generate text in Runyankore, a Bantu language indigenous to Uganda. Our grammar engine adds to previous work in this field with new rules for cardinality constraints, prepositions in roles, the passive, and phonological conditioning. We evaluated the generated text with linguists and non-linguists, who regarded most text as grammatically correct and understandable; and over 60{\%} of them regarded all the text generated by our system to have been authored by a human being.",
}
@inproceedings{boleda-2017-talking,
    title = "Talking about the world with a distributed model",
    author = "Boleda, Gemma",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3515",
    doi = "10.18653/v1/W17-3515",
    pages = "114",
    abstract = "We use language to talk about the world, and so reference is a crucial property of language. However, modeling reference is particularly difficult, as it involves both continuous and discrete as-pects of language. For instance, referring expressions like {``}the big mug{''} or {``}it{''} typically contain content words ({``}big{''}, {``}mug{''}), which are notoriously fuzzy or vague in their meaning, and also fun-ction words ({``}the{''}, {``}it{''}) that largely serve as discrete pointers. Data-driven, distributed models based on distributional semantics or deep learning excel at the former, but struggle with the latter, and the reverse is true for symbolic models. I present ongoing work on modeling reference with a distribu-ted model aimed at capturing both aspects, and learns to refer directly from reference acts.",
}
@inproceedings{richardson-etal-2017-code2text,
    title = "The {C}ode2{T}ext Challenge: Text Generation in Source Libraries",
    author = "Richardson, Kyle  and
      Zarrie{\ss}, Sina  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3516",
    doi = "10.18653/v1/W17-3516",
    pages = "115--119",
    abstract = "We propose a new shared task for tactical data-to-text generation in the domain of source code libraries. Specifically, we focus on text generation of function descriptions from example software projects. Data is drawn from existing resources used for studying the related problem of semantic parser induction, and spans a wide variety of both natural languages and programming languages. In this paper, we describe these existing resources, which will serve as training and development data for the task, and discuss plans for building new independent test sets.",
}
@inproceedings{mille-etal-2017-shared,
    title = "Shared Task Proposal: Multilingual Surface Realization Using {U}niversal {D}ependency Trees",
    author = "Mille, Simon  and
      Bohnet, Bernd  and
      Wanner, Leo  and
      Belz, Anja",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3517",
    doi = "10.18653/v1/W17-3517",
    pages = "120--123",
    abstract = "We propose a shared task on multilingual Surface Realization, i.e., on mapping unordered and uninflected universal dependency trees to correctly ordered and inflected sentences in a number of languages. A second deeper input will be available in which, in addition, functional words, fine-grained PoS and morphological information will be removed from the input trees. The first shared task on Surface Realization was carried out in 2011 with a similar setup, with a focus on English. We think that it is time for relaunching such a shared task effort in view of the arrival of Universal Dependencies annotated treebanks for a large number of languages on the one hand, and the increasing dominance of Deep Learning, which proved to be a game changer for NLP, on the other hand.",
}
@inproceedings{gardent-etal-2017-webnlg,
    title = "The {W}eb{NLG} Challenge: Generating Text from {RDF} Data",
    author = "Gardent, Claire  and
      Shimorina, Anastasia  and
      Narayan, Shashi  and
      Perez-Beltrachini, Laura",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3518",
    doi = "10.18653/v1/W17-3518",
    pages = "124--133",
    abstract = "The WebNLG challenge consists in mapping sets of RDF triples to text. It provides a common benchmark on which to train, evaluate and compare {``}microplanners{''}, i.e. generation systems that verbalise a given content by making a range of complex interacting choices including referring expression generation, aggregation, lexicalisation, surface realisation and sentence segmentation. In this paper, we introduce the microplanning task, describe data preparation, introduce our evaluation methodology, analyse participant results and provide a brief description of the participating systems.",
}
@inproceedings{reiter-2017-commercial,
    title = "A Commercial Perspective on Reference",
    author = "Reiter, Ehud",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3519",
    doi = "10.18653/v1/W17-3519",
    pages = "134--138",
    abstract = "I briefly describe some of the commercial work which XXX is doing in referring expression algorithms, and highlight differences between what is commercially important (at least to XXX) and the NLG research literature. In particular, XXX is less interested in generic reference algorithms than in high-quality algorithms for specific types of references, such as components of machines, named entities, and dates.",
}
@inproceedings{koller-engonopoulos-2017-integrated,
    title = "Integrated sentence generation using charts",
    author = "Koller, Alexander  and
      Engonopoulos, Nikos",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3520",
    doi = "10.18653/v1/W17-3520",
    pages = "139--143",
    abstract = "Integrating surface realization and the generation of referring expressions into a single algorithm can improve the quality of the generated sentences. Existing algorithms for doing this, such as SPUD and CRISP, are search-based and can be slow or incomplete. We offer a chart-based algorithm for integrated sentence generation and demonstrate its runtime efficiency.",
}
@inproceedings{ramos-soto-etal-2017-adapting,
    title = "Adapting {S}imple{NLG} to {S}panish",
    author = "Ramos-Soto, Alejandro  and
      Janeiro-Gallardo, Julio  and
      Bugar{\'\i}n Diz, Alberto",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3521",
    doi = "10.18653/v1/W17-3521",
    pages = "144--148",
    abstract = "We describe SimpleNLG-ES, an adaptation of the SimpleNLG realization library for the Spanish language. Our implementation is based on the bilingual English-French SimpleNLG-EnFr adaptation. The library has been tested using a battery of examples that ensure that the most common syntax, morphology and orthography rules for Spanish are met. The library is currently being used in three different projects for the development of data-to-text systems in the meteorological, statistical data information, and business intelligence application domains.",
}
@inproceedings{howcroft-etal-2017-g,
    title = "{G}-{TUNA}: a corpus of referring expressions in {G}erman, including duration information",
    author = "Howcroft, David  and
      Vogels, Jorrig  and
      Demberg, Vera",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3522",
    doi = "10.18653/v1/W17-3522",
    pages = "149--153",
    abstract = "Corpora of referring expressions elicited from human participants in a controlled environment are an important resource for research on automatic referring expression generation. We here present G-TUNA, a new corpus of referring expressions for German. Using the furniture stimuli set developed for the TUNA and D-TUNA corpora, our corpus extends on these corpora by providing data collected in a simulated driving dual-task setting, and additionally provides exact duration annotations for the spoken referring expressions. This corpus will hence allow researchers to analyze the interaction between referring expression length and speech rate, under conditions where the listener is under high vs. low cognitive load.",
}
@inproceedings{byamugisha-etal-2017-toward,
    title = "Toward an {NLG} System for {B}antu languages: first steps with {R}unyankore (demo)",
    author = "Byamugisha, Joan  and
      Keet, C. Maria  and
      DeRenzi, Brian",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3523",
    doi = "10.18653/v1/W17-3523",
    pages = "154--155",
    abstract = "There are many domain-specific and language-specific NLG systems, of which it may be possible to adapt to related domains and languages. The languages in the Bantu language family have their own set of features distinct from other major groups, which therefore severely limits the options to bootstrap an NLG system from existing ones. We present here our first proof-of-concept application for knowledge-to-text NLG as a plugin to the Protege 5.x ontology development system, tailored to Runyankore, a Bantu language indigenous to Uganda. It comprises a basic annotation model for linguistic information such as noun class, an implementation of existing verbalisation rules and a CFG for verbs, and a basic interface for data entry.",
}
@inproceedings{weissgraeber-madsack-2017-working,
    title = "A working, non-trivial, topically indifferent {NLG} System for 17 languages",
    author = "Wei{\ss}graeber, Robert  and
      Madsack, Andreas",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3524",
    doi = "10.18653/v1/W17-3524",
    pages = "156--157",
    abstract = "A fully fledged practical working application for a rule-based NLG system is presented that is able to create non-trivial, human sounding narrative from structured data, in any language and for any topic.",
}
@inproceedings{mathur-etal-2017-generating,
    title = "Generating titles for millions of browse pages on an e-Commerce site",
    author = "Mathur, Prashant  and
      Ueffing, Nicola  and
      Leusch, Gregor",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3525",
    doi = "10.18653/v1/W17-3525",
    pages = "158--167",
    abstract = "We present two approaches to generate titles for browse pages in five different languages, namely English, German, French, Italian and Spanish. These browse pages are structured search pages in an e-commerce domain. We first present a rule-based approach to generate these browse page titles. In addition, we also present a hybrid approach which uses a phrase-based statistical machine translation engine on top of the rule-based system to assemble the best title. For the two languages English and German we have access to a large amount of already available rule-based generated and curated titles. For these languages we present an automatic post-editing approach which learns how to post-edit the rule-based titles into curated titles.",
}
@inproceedings{zang-wan-2017-towards,
    title = "Towards Automatic Generation of Product Reviews from Aspect-Sentiment Scores",
    author = "Zang, Hongyu  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3526",
    doi = "10.18653/v1/W17-3526",
    pages = "168--177",
    abstract = "Data-to-text generation is very essential and important in machine writing applications. The recent deep learning models, like Recurrent Neural Networks (RNNs), have shown a bright future for relevant text generation tasks. However, rare work has been done for automatic generation of long reviews from user opinions. In this paper, we introduce a deep neural network model to generate long Chinese reviews from aspect-sentiment scores representing users{'} opinions. We conduct our study within the framework of encoder-decoder networks, and we propose a hierarchical structure with aligned attention in the Long-Short Term Memory (LSTM) decoder. Experiments show that our model outperforms retrieval based baseline methods, and also beats the sequential generation models in qualitative evaluations.",
}
@inproceedings{doust-piwek-2017-model,
    title = "A model of suspense for narrative generation",
    author = "Doust, Richard  and
      Piwek, Paul",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3527",
    doi = "10.18653/v1/W17-3527",
    pages = "178--187",
    abstract = "Most work on automatic generation of narratives, and more specifically suspenseful narrative, has focused on detailed domain-specific modelling of character psychology and plot structure. Recent work in computational linguistics on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas as a starting point for modelling and measuring suspense. We propose a domain-independent model for tracking suspense in a story which can be used to predict the audience{'}s suspense response on a sentence-by-sentence basis at the content determination stage of narrative generation. The model lends itself as the theoretical foundation for a suspense module that is compatible with alternative narrative generation theories. The proposal is evaluated by human judges{'} normalised average scores correlate strongly with predicted values.",
}
@inproceedings{leppanen-etal-2017-data,
    title = "Data-Driven News Generation for Automated Journalism",
    author = {Lepp{\"a}nen, Leo  and
      Munezero, Myriam  and
      Granroth-Wilding, Mark  and
      Toivonen, Hannu},
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3528",
    doi = "10.18653/v1/W17-3528",
    pages = "188--197",
    abstract = "Despite increasing amounts of data and ever improving natural language generation techniques, work on automated journalism is still relatively scarce. In this paper, we explore the field and challenges associated with building a journalistic natural language generation system. We present a set of requirements that should guide system design, including transparency, accuracy, modifiability and transferability. Guided by the requirements, we present a data-driven architecture for automated journalism that is largely domain and language independent. We illustrate its practical application in the production of news articles about the 2017 Finnish municipal elections in three languages, demonstrating the successfulness of the data-driven, modular approach of the design. We then draw some lessons for future automated journalism.",
}
@inproceedings{kafle-etal-2017-data,
    title = "Data Augmentation for Visual Question Answering",
    author = "Kafle, Kushal  and
      Yousefhussien, Mohammed  and
      Kanan, Christopher",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3529",
    doi = "10.18653/v1/W17-3529",
    pages = "198--202",
    abstract = "Data augmentation is widely used to train deep neural networks for image classification tasks. Simply flipping images can help learning tremendously by increasing the number of training images by a factor of two. However, little work has been done studying data augmentation in natural language processing. Here, we describe two methods for data augmentation for Visual Question Answering (VQA). The first uses existing semantic annotations to generate new questions. The second method is a generative approach using recurrent neural networks. Experiments show that the proposed data augmentation improves performance of both baseline and state-of-the-art VQA algorithms.",
}
@inproceedings{gatius-2017-personalized,
    title = "Personalized Questions, Answers and Grammars: Aiding the Search for Relevant Web Information",
    author = "Gatius, Marta",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3530",
    doi = "10.18653/v1/W17-3530",
    pages = "203--207",
    abstract = "This work proposes an organization of knowledge to facilitate the generation of personalized questions, answers and grammars from web documents. To reduce the human effort needed in the generation of the linguistic resources for a new domain, the general aspects that can be reuse across domains are separated from those more specific. The proposed approach is based on the representation of the main domain concepts as a set of attributes. These attributes are related to a syntactico-semantic taxonomy representing the general relationships between conceptual and linguistic knowledge. User models are incorporated by distinguishing different user groups and relating each group to the appropriate conceptual attributes. Then, the data is extracted from the web documents and represented as instances of the domain concepts. Questions, answers and grammars are generated from these instances.",
}
@inproceedings{hasler-etal-2017-comparison,
    title = "A Comparison of Neural Models for Word Ordering",
    author = "Hasler, Eva  and
      Stahlberg, Felix  and
      Tomalin, Marcus  and
      de Gispert, Adri{\`a}  and
      Byrne, Bill",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3531",
    doi = "10.18653/v1/W17-3531",
    pages = "208--212",
    abstract = "We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models. We evaluate the model on a large German WMT data set where it significantly outperforms existing models. We also describe a novel search strategy for LM-based word ordering and report results on the English Penn Treebank. Our best model setup outperforms prior work both in terms of speed and quality.",
}
@inproceedings{van-deemter-etal-2017-investigating,
    title = "Investigating the content and form of referring expressions in {M}andarin: introducing the Mtuna corpus",
    author = "van Deemter, Kees  and
      Sun, Le  and
      Sybesma, Rint  and
      Li, Xiao  and
      Chen, Bo  and
      Yang, Muyun",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3532",
    doi = "10.18653/v1/W17-3532",
    pages = "213--217",
    abstract = "East Asian languages are thought to handle reference differently from languages such as English, particularly in terms of the marking of definiteness and number. We present the first Data-Text corpus for Referring Expressions in Mandarin, and we use this corpus to test some initial hypotheses inspired by the theoretical linguistics literature. Our findings suggest that function words deserve more attention in Referring Expressions Generation than they have so far received, and they have a bearing on the debate about whether different languages make different trade-offs between clarity and brevity.",
}
@inproceedings{muszynska-copestake-2017-realization,
    title = "Realization of long sentences using chunking",
    author = "Muszy{\'n}ska, Ewa  and
      Copestake, Ann",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3533",
    doi = "10.18653/v1/W17-3533",
    pages = "218--222",
    abstract = "We propose sentence chunking as a way to reduce the time and memory costs of realization of long sentences. During chunking we divide the semantic representation of a sentence into smaller components which can be processed and recombined without loss of information. Our meaning representation of choice is the Dependency Minimal Recursion Semantics (DMRS). We show that realizing chunks of a sentence and combining the results of such realizations increases the coverage for long sentences, significantly reduces the resources required and does not affect the quality of the realization.",
}
@inproceedings{braun-etal-2017-satos,
    title = "{S}a{T}o{S}: Assessing and Summarising Terms of Services from {G}erman Webshops",
    author = "Braun, Daniel  and
      Scepankova, Elena  and
      Holl, Patrick  and
      Matthes, Florian",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3534",
    doi = "10.18653/v1/W17-3534",
    pages = "223--227",
    abstract = "Every time we buy something online, we are confronted with Terms of Services. However, only a few people actually read these terms, before accepting them, often to their disadvantage. In this paper, we present the SaToS browser plugin which summarises and simplifies Terms of Services from German webshops.",
}
@inproceedings{inglis-etal-2017-textually,
    title = "Textually Summarising Incomplete Data",
    author = "Inglis, Stephanie  and
      Reiter, Ehud  and
      Sripada, Somayajulu",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3535",
    doi = "10.18653/v1/W17-3535",
    pages = "228--232",
    abstract = "Many data-to-text NLG systems work with data sets which are incomplete, ie some of the data is missing. We have worked with data journalists to understand how they describe incomplete data, and are building NLG algorithms based on these insights. A pilot evaluation showed mixed results, and highlighted several areas where we need to improve our system.",
}
@inproceedings{castro-ferreira-paraboni-2017-improving,
    title = "Improving the generation of personalised descriptions",
    author = "Castro Ferreira, Thiago  and
      Paraboni, Ivandr{\'e}",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3536",
    doi = "10.18653/v1/W17-3536",
    pages = "233--237",
    abstract = "Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly. In this work we propose a simple personalised method for this task, in which speakers are grouped into profiles according to their referential behaviour. Intrinsic evaluation shows that the use of speaker{'}s profiles generally outperforms the personalised method found in previous work.",
}
@inproceedings{perez-beltrachini-gardent-2017-analysing,
    title = "Analysing Data-To-Text Generation Benchmarks",
    author = "Perez-Beltrachini, Laura  and
      Gardent, Claire",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3537",
    doi = "10.18653/v1/W17-3537",
    pages = "238--242",
    abstract = "A generation system can only be as good as the data it is trained on. In this short paper, we propose a methodology for analysing data-to-text corpora used for training Natural Language Generation (NLG) systems. We apply this methodology to three existing benchmarks. We conclude by eliciting a set of criteria for the creation of a data-to-text benchmark which could help better support the development, evaluation and comparison of linguistically sophisticated data-to-text generators.",
}
@inproceedings{alonso-etal-2017-linguistic,
    title = "Linguistic Description of Complex Phenomena with the r{LDCP} {R} Package",
    author = "Alonso, Jose  and
      Conde-Clemente, Patricia  and
      Trivino, Gracian",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3538",
    doi = "10.18653/v1/W17-3538",
    pages = "243--244",
    abstract = "Monitoring and analysis of complex phenomena attract the attention of both academy and industry. Dealing with data produced by complex phenomena requires the use of advance computational intelligence techniques. Namely, linguistic description of complex phenomena constitutes a mature research line. It is supported by the Computational Theory of Perceptions grounded on the Fuzzy Sets Theory. Its aim is the development of computational systems with the ability to generate vague descriptions of the world in a similar way how humans do. This is a human-centric and multi-disciplinary research work. Moreover, its success is a matter of careful design; thus, developers play a key role. The rLDCP R package was designed to facilitate the development of new applications. This demo introduces the use of rLDCP, for both beginners and advance developers, in practical use cases.",
}
@inproceedings{mille-wanner-2017-demo,
    title = "A demo of {FORG}e: the {P}ompeu {F}abra Open Rule-based Generator",
    author = "Mille, Simon  and
      Wanner, Leo",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3539",
    doi = "10.18653/v1/W17-3539",
    pages = "245--246",
    abstract = "This demo paper presents the multilingual deep sentence generator developed by the TALN group at Universitat Pompeu Fabra, implemented as a series of rule-based graph-transducers for the syntacticization of the input graphs, the resolution of morphological agreements, and the linearization of the trees.",
}
@inproceedings{marin-etal-2017-referential,
    title = "Referential Success of Set Referring Expressions with Fuzzy Properties",
    author = "Mar{\'\i}n, Nicol{\'a}s  and
      Rivas-Gervilla, Gustavo  and
      S{\'a}nchez, Daniel",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3540",
    doi = "10.18653/v1/W17-3540",
    pages = "247--251",
    abstract = "We introduce the properties to be satisfied by measures of referential success of set referring expressions with fuzzy properties. We define families of measures on the basis of n-cardinality measures and we illustrate some of them with a toy example.",
}
@inproceedings{herzig-etal-2017-neural,
    title = "Neural Response Generation for Customer Service based on Personality Traits",
    author = "Herzig, Jonathan  and
      Shmueli-Scheuer, Michal  and
      Sandbank, Tommy  and
      Konopnicki, David",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3541",
    doi = "10.18653/v1/W17-3541",
    pages = "252--256",
    abstract = "We present a neural response generation model that generates responses conditioned on a target personality. The model learns high level features based on the target personality, and uses them to update its hidden state. Our model achieves performance improvements in both perplexity and BLEU scores over a baseline sequence-to-sequence model, and is validated by human judges.",
}
@inproceedings{brad-rebedea-2017-neural,
    title = "Neural Paraphrase Generation using Transfer Learning",
    author = "Brad, Florin  and
      Rebedea, Traian",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3542",
    doi = "10.18653/v1/W17-3542",
    pages = "257--261",
    abstract = "Progress in statistical paraphrase generation has been hindered for a long time by the lack of large monolingual parallel corpora. In this paper, we adapt the neural machine translation approach to paraphrase generation and perform transfer learning from the closely related task of entailment generation. We evaluate the model on the Microsoft Research Paraphrase (MSRP) corpus and show that the model is able to generate sentences that capture part of the original meaning, but fails to pick up on important words or to show large lexical variation.",
}
@proceedings{ws-2017-mathematics,
    title = "Proceedings of the 15th Meeting on the Mathematics of Language",
    editor = "Kanazawa, Makoto  and
      de Groote, Philippe  and
      Sadrzadeh, Mehrnoosh",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3400",
    doi = "10.18653/v1/W17-34",
}
@inproceedings{shimada-2017-unique,
    title = "{BE} Is Not the Unique Homomorphism That Makes the Partee Triangle Commute",
    author = "Shimada, Junri",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3401",
    doi = "10.18653/v1/W17-3401",
    pages = "1--10",
}
@inproceedings{hoenen-etal-2017-many,
    title = "How Many Stemmata with Root Degree k?",
    author = "Hoenen, Armin  and
      Eger, Steffen  and
      Gehrke, Ralf",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3402",
    doi = "10.18653/v1/W17-3402",
    pages = "11--21",
}
@inproceedings{jardine-2017-logical,
    title = "On the Logical Complexity of Autosegmental Representations",
    author = "Jardine, Adam",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3403",
    doi = "10.18653/v1/W17-3403",
    pages = "22--35",
}
@inproceedings{rogers-lambert-2017-extracting,
    title = "Extracting Forbidden Factors from Regular Stringsets",
    author = "Rogers, James  and
      Lambert, Dakotah",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3404",
    doi = "10.18653/v1/W17-3404",
    pages = "36--46",
}
@inproceedings{cohen-2017-latent,
    title = "Latent-Variable {PCFG}s: Background and Applications",
    author = "Cohen, Shay",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3405",
    doi = "10.18653/v1/W17-3405",
    pages = "47--58",
}
@inproceedings{francez-2017-proof,
    title = "A Proof-Theoretic Semantics for Transitive Verbs with an Implicit Object",
    author = "Francez, Nissim",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3406",
    doi = "10.18653/v1/W17-3406",
    pages = "59--67",
}
@inproceedings{parikh-2017-speak,
    title = "Why We Speak",
    author = "Parikh, Rohit",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3407",
    doi = "10.18653/v1/W17-3407",
    pages = "68--74",
}
@inproceedings{icard-etal-2017-monotonicity,
    title = "A Monotonicity Calculus and Its Completeness",
    author = "Icard, Thomas  and
      Moss, Lawrence  and
      Tune, William",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3408",
    doi = "10.18653/v1/W17-3408",
    pages = "75--87",
}
@inproceedings{drewes-2017-dag,
    title = "{DAG} Automata for Meaning Representation",
    author = "Drewes, Frank",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3409",
    doi = "10.18653/v1/W17-3409",
    pages = "88--99",
}
@inproceedings{gilroy-etal-2017-introducing,
    title = "(Re)introducing Regular Graph Languages",
    author = "Gilroy, Sorcha  and
      Lopez, Adam  and
      Maneth, Sebastian  and
      Simonaitis, Pijus",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3410",
    doi = "10.18653/v1/W17-3410",
    pages = "100--113",
}
@inproceedings{graf-2017-graph,
    title = "Graph Transductions and Typological Gaps in Morphological Paradigms",
    author = "Graf, Thomas",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3411",
    doi = "10.18653/v1/W17-3411",
    pages = "114--126",
}
@inproceedings{clark-2017-introducing,
    title = "Introducing Structure into Neural Network-Based Semantic Models",
    author = "Clark, Stephen",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3412",
    doi = "10.18653/v1/W17-3412",
    pages = "127",
}
@inproceedings{kuznetsov-etal-2017-count,
    title = "Count-Invariance Including Exponentials",
    author = "Kuznetsov, Stepan  and
      Morrill, Glyn  and
      Valent{\'\i}n, Oriol",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3413",
    doi = "10.18653/v1/W17-3413",
    pages = "128--139",
}
@inproceedings{kuznetsov-okhotin-2017-conjunctive,
    title = "Conjunctive Categorial Grammars",
    author = "Kuznetsov, Stepan  and
      Okhotin, Alexander",
    booktitle = "Proceedings of the 15th Meeting on the Mathematics of Language",
    month = jul,
    year = "2017",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3414",
    doi = "10.18653/v1/W17-3414",
    pages = "140--151",
}
@proceedings{ws-2017-neural,
    title = "Proceedings of the First Workshop on Neural Machine Translation",
    editor = "Luong, Thang  and
      Birch, Alexandra  and
      Neubig, Graham  and
      Finch, Andrew",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3200",
    doi = "10.18653/v1/W17-32",
}
@inproceedings{shu-nakayama-2017-empirical,
    title = "An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation",
    author = "Shu, Raphael  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3201",
    doi = "10.18653/v1/W17-3201",
    pages = "1--10",
    abstract = "Recently, the attention mechanism plays a key role to achieve high performance for Neural Machine Translation models. However, as it computes a score function for the encoder states in all positions at each decoding step, the attention model greatly increases the computational complexity. In this paper, we investigate the adequate vision span of attention models in the context of machine translation, by proposing a novel attention framework that is capable of reducing redundant score computation dynamically. The term {``}vision span{''}{'} means a window of the encoder states considered by the attention model in one step. In our experiments, we found that the average window size of vision span can be reduced by over 50{\%} with modest loss in accuracy on English-Japanese and German-English translation tasks.",
}
@inproceedings{niehues-etal-2017-analyzing,
    title = "Analyzing Neural {MT} Search and Model Performance",
    author = "Niehues, Jan  and
      Cho, Eunah  and
      Ha, Thanh-Le  and
      Waibel, Alex",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3202",
    doi = "10.18653/v1/W17-3202",
    pages = "11--17",
    abstract = "In this paper, we offer an in-depth analysis about the modeling and search performance. We address the question if a more complex search algorithm is necessary. Furthermore, we investigate the question if more complex models which might only be applicable during rescoring are promising. By separating the search space and the modeling using n-best list reranking, we analyze the influence of both parts of an NMT system independently. By comparing differently performing NMT systems, we show that the better translation is already in the search space of the translation systems with less performance. This results indicate that the current search algorithms are sufficient for the NMT systems. Furthermore, we could show that even a relatively small $n$-best list of 50 hypotheses already contain notably better translations.",
}
@inproceedings{denkowski-neubig-2017-stronger,
    title = "Stronger Baselines for Trustable Results in Neural Machine Translation",
    author = "Denkowski, Michael  and
      Neubig, Graham",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3203",
    doi = "10.18653/v1/W17-3203",
    pages = "18--27",
    abstract = "Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectural and algorithmic improvements that lead to significant gains over {``}vanilla{''} NMT implementations. However, these new techniques are rarely evaluated in the context of previously published techniques, specifically those that are widely used in state-of-the-art production and shared-task systems. As a result, it is often difficult to determine whether improvements from research will carry over to systems deployed for real-world use. In this work, we recommend three specific methods that are relatively easy to implement and result in much stronger experimental systems. Beyond reporting significantly higher BLEU scores, we conduct an in-depth analysis of where improvements originate and what inherent weaknesses of basic NMT models are being addressed. We then compare the relative gains afforded by several other techniques proposed in the literature when starting with vanilla systems versus our stronger baselines, showing that experimental conclusions may change depending on the baseline chosen. This indicates that choosing a strong baseline is crucial for reporting reliable experimental results.",
}
@inproceedings{koehn-knowles-2017-six,
    title = "Six Challenges for Neural Machine Translation",
    author = "Koehn, Philipp  and
      Knowles, Rebecca",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3204",
    doi = "10.18653/v1/W17-3204",
    pages = "28--39",
    abstract = "We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.",
}
@inproceedings{chen-etal-2017-cost,
    title = "Cost Weighting for Neural Machine Translation Domain Adaptation",
    author = "Chen, Boxing  and
      Cherry, Colin  and
      Foster, George  and
      Larkin, Samuel",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3205",
    doi = "10.18653/v1/W17-3205",
    pages = "40--46",
    abstract = "In this paper, we propose a new domain adaptation technique for neural machine translation called cost weighting, which is appropriate for adaptation scenarios in which a small in-domain data set and a large general-domain data set are available. Cost weighting incorporates a domain classifier into the neural machine translation training algorithm, using features derived from the encoder representation in order to distinguish in-domain from out-of-domain data. Classifier probabilities are used to weight sentences according to their domain similarity when updating the parameters of the neural translation model. We compare cost weighting to two traditional domain adaptation techniques developed for statistical machine translation: data selection and sub-corpus weighting. Experiments on two large-data tasks show that both the traditional techniques and our novel proposal lead to significant gains, with cost weighting outperforming the traditional methods.",
}
@inproceedings{goto-tanaka-2017-detecting,
    title = "Detecting Untranslated Content for Neural Machine Translation",
    author = "Goto, Isao  and
      Tanaka, Hideki",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3206",
    doi = "10.18653/v1/W17-3206",
    pages = "47--55",
    abstract = "Despite its promise, neural machine translation (NMT) has a serious problem in that source content may be mistakenly left untranslated. The ability to detect untranslated content is important for the practical use of NMT. We evaluate two types of probability with which to detect untranslated content: the cumulative attention (ATN) probability and back translation (BT) probability from the target sentence to the source sentence. Experiments on detecting untranslated content in Japanese-English patent translations show that ATN and BT are each more effective than random choice, BT is more effective than ATN, and the combination of the two provides further improvements. We also confirmed the effectiveness of using ATN and BT to rerank the n-best NMT outputs.",
}
@inproceedings{freitag-al-onaizan-2017-beam,
    title = "Beam Search Strategies for Neural Machine Translation",
    author = "Freitag, Markus  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3207",
    doi = "10.18653/v1/W17-3207",
    pages = "56--60",
    abstract = "The basic concept in Neural Machine Translation (NMT) is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is then using a simple left-to-right beam-search decoder to generate new translations that approximately maximize the trained conditional probability. The current beam search strategy generates the target sentence word by word from left-to-right while keeping a fixed amount of active candidates at each time step. First, this simple search is less adaptive as it also expands candidates whose scores are much worse than the current best. Secondly, it does not expand hypotheses if they are not within the best scoring candidates, even if their scores are close to the best one. The latter one can be avoided by increasing the beam size until no performance improvement can be observed. While you can reach better performance, this has the drawback of a slower decoding speed. In this paper, we concentrate on speeding up the decoder by applying a more flexible beam search strategy whose candidate size may vary at each time step depending on the candidate scores. We speed up the original decoder by up to 43{\%} for the two language pairs German to English and Chinese to English without losing any translation quality.",
}
@inproceedings{morishita-etal-2017-empirical,
    title = "An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation",
    author = "Morishita, Makoto  and
      Oda, Yusuke  and
      Neubig, Graham  and
      Yoshino, Koichiro  and
      Sudoh, Katsuhito  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3208",
    doi = "10.18653/v1/W17-3208",
    pages = "61--68",
    abstract = "Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes. During the mini-batched training process, it is necessary to pad shorter sentences in a mini-batch to be equal in length to the longest sentence therein for efficient computation. Previous work has noted that sorting the corpus based on the sentence length before making mini-batches reduces the amount of padding and increases the processing speed. However, despite the fact that mini-batch creation is an essential step in NMT training, widely used NMT toolkits implement disparate strategies for doing so, which have not been empirically validated or compared. This work investigates mini-batch creation strategies with experiments over two different datasets. Our results suggest that the choice of a mini-batch creation strategy has a large effect on NMT training and some length-based sorting strategies do not always work well compared with simple shuffling.",
}
@inproceedings{carpuat-etal-2017-detecting,
    title = "Detecting Cross-Lingual Semantic Divergence for Neural Machine Translation",
    author = "Carpuat, Marine  and
      Vyas, Yogarshi  and
      Niu, Xing",
    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3209",
    doi = "10.18653/v1/W17-3209",
    pages = "69--79",
    abstract = "Parallel corpora are often not as parallel as one might assume: non-literal translations and noisy translations abound, even in curated corpora routinely used for training and evaluation. We use a cross-lingual textual entailment system to distinguish sentence pairs that are parallel in meaning from those that are not, and show that filtering out divergent examples from training improves translation quality.",
}
@proceedings{ws-2017-linguistics-clinical,
    title = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    editor = "Hollingshead, Kristy  and
      Ireland, Molly E.  and
      Loveys, Kate",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3100",
    doi = "10.18653/v1/W17-31",
}
@inproceedings{morales-etal-2017-cross,
    title = "A Cross-modal Review of Indicators for Depression Detection Systems",
    author = "Morales, Michelle  and
      Scherer, Stefan  and
      Levitan, Rivka",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3101",
    doi = "10.18653/v1/W17-3101",
    pages = "1--12",
    abstract = "Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines. As a result, promising depression detection systems have been reported. This paper surveys these efforts by presenting the first cross-modal review of depression detection systems and discusses best practices and most promising approaches to this task.",
}
@inproceedings{niederhoffer-etal-2017-wildest,
    title = "In your wildest dreams: the language and psychological features of dreams",
    author = "Niederhoffer, Kate  and
      Schler, Jonathan  and
      Crutchley, Patrick  and
      Loveys, Kate  and
      Coppersmith, Glen",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3102",
    doi = "10.18653/v1/W17-3102",
    pages = "13--25",
    abstract = "In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.",
}
@inproceedings{guo-etal-2017-corpus,
    title = "A Corpus Analysis of Social Connections and Social Isolation in Adolescents Suffering from Depressive Disorders",
    author = "Guo, Jia-Wen  and
      Mowery, Danielle L  and
      Lai, Djin  and
      Sward, Katherine  and
      Conway, Mike",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3103",
    doi = "10.18653/v1/W17-3103",
    pages = "26--31",
    abstract = "Social connection and social isolation are associated with depressive symptoms, particularly in adolescents and young adults, but how these concepts are documented in clinical notes is unknown. This pilot study aimed to identify the topics relevant to social connection and isolation by analyzing 145 clinical notes from patients with depression diagnosis. We found that providers, including physicians, nurses, social workers, and psychologists, document descriptions of both social connection and social isolation.",
}
@inproceedings{jamil-etal-2017-monitoring,
    title = "Monitoring Tweets for Depression to Detect At-risk Users",
    author = "Jamil, Zunaira  and
      Inkpen, Diana  and
      Buddhitha, Prasadith  and
      White, Kenton",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3104",
    doi = "10.18653/v1/W17-3104",
    pages = "32--40",
    abstract = "We propose an automated system that can identify at-risk users from their public social media activity, more specifically, from Twitter. The data that we collected is from the {\#}BellLetsTalk campaign, which is a wide-reaching, multi-year program designed to break the silence around mental illness and support mental health across Canada. To achieve our goal, we trained a user-level classifier that can detect at-risk users that achieves a reasonable precision and recall. We also trained a tweet-level classifier that predicts if a tweet indicates depression. This task was much more difficult due to the imbalanced data. In the dataset that we labeled, we came across 5{\%} depression tweets and 95{\%} non-depression tweets. To handle this class imbalance, we used undersampling methods. The resulting classifier had high recall, but low precision. Therefore, we only use this classifier to compute the estimated percentage of depressed tweets and to add this value as a feature for the user-level classifier.",
}
@inproceedings{mikal-etal-2017-investigating,
    title = "Investigating Patient Attitudes Towards the use of Social Media Data to Augment Depression Diagnosis and Treatment: a Qualitative Study",
    author = "Mikal, Jude  and
      Hurst, Samantha  and
      Conway, Mike",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3105",
    doi = "10.18653/v1/W17-3105",
    pages = "41--47",
    abstract = "In this paper, we use qualitative research methods to investigate the attitudes of social media users towards the (opt-in) integration of social media data with routine mental health care and diagnosis. Our investigation was based on secondary analysis of a series of five focus groups with Twitter users, including three groups consisting of participants with a self-reported history of depression, and two groups consisting of participants without a self reported history of depression. Our results indicate that, overall, research participants were enthusiastic about the possibility of using social media (in conjunction with automated Natural Language Processing algorithms) for mood tracking under the supervision of a mental health practitioner. However, for at least some participants, there was skepticism related to how well social media represents the mental health of users, and hence its usefulness in the clinical context.",
}
@inproceedings{roemmele-etal-2017-natural,
    title = "Natural-language Interactive Narratives in Imaginal Exposure Therapy for Obsessive-Compulsive Disorder",
    author = "Roemmele, Melissa  and
      Mardo, Paola  and
      Gordon, Andrew",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3106",
    doi = "10.18653/v1/W17-3106",
    pages = "48--57",
    abstract = "Obsessive-compulsive disorder (OCD) is an anxiety-based disorder that affects around 2.5{\%} of the population. A common treatment for OCD is exposure therapy, where the patient repeatedly confronts a feared experience, which has the long-term effect of decreasing their anxiety. Some exposures consist of reading and writing stories about an imagined anxiety-provoking scenario. In this paper, we present a technology that enables patients to interactively contribute to exposure stories by supplying natural language input (typed or spoken) that advances a scenario. This interactivity could potentially increase the patient{'}s sense of immersion in an exposure and contribute to its success. We introduce the NLP task behind processing inputs to predict new events in the scenario, and describe our initial approach. We then illustrate the future possibility of this work with an example of an exposure scenario authored with our application.",
}
@inproceedings{shen-rudzicz-2017-detecting,
    title = "Detecting Anxiety through {R}eddit",
    author = "Shen, Judy Hanwen  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3107",
    doi = "10.18653/v1/W17-3107",
    pages = "58--65",
    abstract = "Previous investigations into detecting mental illnesses through social media have predominately focused on detecting depression through Twitter corpora. In this paper, we study anxiety disorders through personal narratives collected through the popular social media website, Reddit. We build a substantial data set of typical and anxiety-related posts, and we apply N-gram language modeling, vector embeddings, topic analysis, and emotional norms to generate features that accurately classify posts related to binary levels of anxiety. We achieve an accuracy of 91{\%} with vector-space word embeddings, and an accuracy of 98{\%} when combined with lexicon-based features.",
}
@inproceedings{kshirsagar-etal-2017-detecting,
    title = "Detecting and Explaining Crisis",
    author = "Kshirsagar, Rohan  and
      Morris, Robert  and
      Bowman, Samuel",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3108",
    doi = "10.18653/v1/W17-3108",
    pages = "66--73",
    abstract = "Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately can have profound consequences. However, detecting a general state of crisis without explaining why has limited applications. An explanation in this context is a coherent, concise subset of the text that rationalizes the crisis detection. We explore several methods to detect and explain crisis using a combination of neural and non-neural techniques. We evaluate these techniques on a unique data set obtained from Koko, an anonymous emotional support network available through various messaging applications. We annotate a small subset of the samples labeled with crisis with corresponding explanations. Our best technique significantly outperforms the baseline for detection and explanation.",
}
@inproceedings{iserman-ireland-2017-dictionary,
    title = "A Dictionary-Based Comparison of Autobiographies by People and Murderous Monsters",
    author = "Iserman, Micah  and
      Ireland, Molly",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3109",
    doi = "10.18653/v1/W17-3109",
    pages = "74--84",
    abstract = "People typically assume that killers are mentally ill or fundamentally different from the rest of humanity. Similarly, people often associate mental health conditions (such as schizophrenia or autism) with violence and otherness - treatable perhaps, but not empathically understandable. We take a dictionary approach to explore word use in a set of autobiographies, comparing the narratives of 2 killers (Adolf Hitler and Elliot Rodger) and 39 non-killers. Although results suggest several dimensions that differentiate these autobiographies - such as sentiment, temporal orientation, and references to death - they appear to reflect subject matter rather than psychology per se. Additionally, the Rodger text shows roughly typical developmental arcs in its use of words relating to friends, family, sex, and affect. From these data, we discuss the challenges of understanding killers and people in general.",
}
@inproceedings{loveys-etal-2017-small,
    title = "Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language",
    author = "Loveys, Kate  and
      Crutchley, Patrick  and
      Wyatt, Emily  and
      Coppersmith, Glen",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3110",
    doi = "10.18653/v1/W17-3110",
    pages = "85--95",
    abstract = "Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.",
}
@proceedings{ws-2017-abusive,
    title = "Proceedings of the First Workshop on Abusive Language Online",
    editor = "Waseem, Zeerak  and
      Chung, Wendy Hui Kyong  and
      Hovy, Dirk  and
      Tetreault, Joel",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3000",
    doi = "10.18653/v1/W17-30",
}
@inproceedings{clarke-grieve-2017-dimensions,
    title = "Dimensions of Abusive Language on {T}witter",
    author = "Clarke, Isobelle  and
      Grieve, Jack",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3001",
    doi = "10.18653/v1/W17-3001",
    pages = "1--10",
    abstract = "In this paper, we use a new categorical form of multidimensional register analysis to identify the main dimensions of functional linguistic variation in a corpus of abusive language, consisting of racist and sexist Tweets. By analysing the use of a wide variety of parts-of-speech and grammatical constructions, as well as various features related to Twitter and computer-mediated communication, we discover three dimensions of linguistic variation in this corpus, which we interpret as being related to the degree of interactive, antagonistic and attitudinal language exhibited by individual Tweets. We then demonstrate that there is a significant functional difference between racist and sexist Tweets, with sexists Tweets tending to be more interactive and attitudinal than racist Tweets.",
}
@inproceedings{kolhatkar-taboada-2017-constructive,
    title = "Constructive Language in News Comments",
    author = "Kolhatkar, Varada  and
      Taboada, Maite",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3002",
    doi = "10.18653/v1/W17-3002",
    pages = "11--17",
    abstract = "We discuss the characteristics of constructive news comments, and present methods to identify them. First, we define the notion of constructiveness. Second, we annotate a corpus for constructiveness. Third, we explore whether available argumentation corpora can be useful to identify constructiveness in news comments. Our model trained on argumentation corpora achieves a top accuracy of 72.59{\%} (baseline=49.44{\%}) on our crowd-annotated test data. Finally, we examine the relation between constructiveness and toxicity. In our crowd-annotated data, 21.42{\%} of the non-constructive comments and 17.89{\%} of the constructive comments are toxic, suggesting that non-constructive comments are not much more toxic than constructive comments.",
}
@inproceedings{su-etal-2017-rephrasing,
    title = "Rephrasing Profanity in {C}hinese Text",
    author = "Su, Hui-Po  and
      Huang, Zhen-Jie  and
      Chang, Hao-Tsung  and
      Lin, Chuan-Jie",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3003",
    doi = "10.18653/v1/W17-3003",
    pages = "18--24",
    abstract = "This paper proposes a system that can detect and rephrase profanity in Chinese text. Rather than just masking detected profanity, we want to revise the input sentence by using inoffensive words while keeping their original meanings. 29 of such rephrasing rules were invented after observing sentences on real-word social websites. The overall accuracy of the proposed system is 85.56{\%}",
}
@inproceedings{pavlopoulos-etal-2017-deep,
    title = "Deep Learning for User Comment Moderation",
    author = "Pavlopoulos, John  and
      Malakasiotis, Prodromos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3004",
    doi = "10.18653/v1/W17-3004",
    pages = "25--35",
    abstract = "Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of EnglishWikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.",
}
@inproceedings{serra-etal-2017-class,
    title = "Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words",
    author = "Serr{\`a}, Joan  and
      Leontiadis, Ilias  and
      Spathis, Dimitris  and
      Stringhini, Gianluca  and
      Blackburn, Jeremy  and
      Vakali, Athena",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3005",
    doi = "10.18653/v1/W17-3005",
    pages = "36--40",
    abstract = "Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or non-blacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the {`}ability to describe{'} seen documents to the {`}ability to predict{'} unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4-11{\%}.",
}
@inproceedings{park-fung-2017-one,
    title = "One-step and Two-step Classification for Abusive Language Detection on {T}witter",
    author = "Park, Ji Ho  and
      Fung, Pascale",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3006",
    doi = "10.18653/v1/W17-3006",
    pages = "41--45",
    abstract = "Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.",
}
@inproceedings{fiser-etal-2017-legal,
    title = "Legal Framework, Dataset and Annotation Schema for Socially Unacceptable Online Discourse Practices in {S}lovene",
    author = "Fi{\v{s}}er, Darja  and
      Erjavec, Toma{\v{z}}  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3007",
    doi = "10.18653/v1/W17-3007",
    pages = "46--51",
    abstract = "In this paper we present the legal framework, dataset and annotation schema of socially unacceptable discourse practices on social networking platforms in Slovenia. On this basis we aim to train an automatic identification and classification system with which we wish contribute towards an improved methodology, understanding and treatment of such practices in the contemporary, increasingly multicultural information society.",
}
@inproceedings{mubarak-etal-2017-abusive,
    title = "Abusive Language Detection on {A}rabic Social Media",
    author = "Mubarak, Hamdy  and
      Darwish, Kareem  and
      Magdy, Walid",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3008",
    doi = "10.18653/v1/W17-3008",
    pages = "52--56",
    abstract = "In this paper, we present our work on detecting abusive language on Arabic social media. We extract a list of obscene words and hashtags using common patterns used in offensive and rude communications. We also classify Twitter users according to whether they use any of these words or not in their tweets. We expand the list of obscene words using this classification, and we report results on a newly created dataset of classified Arabic tweets (obscene, offensive, and clean). We make this dataset freely available for research, in addition to the list of obscene words and hashtags. We are also publicly releasing a large corpus of classified user comments that were deleted from a popular Arabic news site due to violations the site{'}s rules and guidelines.",
}
@inproceedings{wright-etal-2017-vectors,
    title = "Vectors for Counterspeech on {T}witter",
    author = "Wright, Lucas  and
      Ruths, Derek  and
      Dillon, Kelly P  and
      Saleem, Haji Mohammad  and
      Benesch, Susan",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3009",
    doi = "10.18653/v1/W17-3009",
    pages = "57--62",
    abstract = "A study of conversations on Twitter found that some arguments between strangers led to favorable change in discourse and even in attitudes. The authors propose that such exchanges can be usefully distinguished according to whether individuals or groups take part on each side, since the opportunity for a constructive exchange of views seems to vary accordingly.",
}
@inproceedings{safi-samghabadi-etal-2017-detecting,
    title = "Detecting Nastiness in Social Media",
    author = "Safi Samghabadi, Niloofar  and
      Maharjan, Suraj  and
      Sprague, Alan  and
      Diaz-Sprague, Raquel  and
      Solorio, Thamar",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3010",
    doi = "10.18653/v1/W17-3010",
    pages = "63--72",
    abstract = "Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others. There is a lack of adequate resources to detect and hinder its occurrence. In this paper, we present our initial NLP approach to detect invective posts as a first step to eventually detect and deter cyberbullying. We crawl data containing profanities and then determine whether or not it contains invective. Annotations on this data are improved iteratively by in-lab annotations and crowdsourcing. We pursue different NLP approaches containing various typical and some newer techniques to distinguish the use of swear words in a neutral way from those instances in which they are used in an insulting way. We also show that this model not only works for our data set, but also can be successfully applied to different data sets.",
}
@inproceedings{kennedy-etal-2017-technology,
    title = "Technology Solutions to Combat Online Harassment",
    author = "Kennedy, George  and
      McCollough, Andrew  and
      Dixon, Edward  and
      Bastidas, Alexei  and
      Ryan, John  and
      Loo, Chris  and
      Sahay, Saurav",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3011",
    doi = "10.18653/v1/W17-3011",
    pages = "73--77",
    abstract = "This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams. Online harassment goes under-reported due to the reliance on humans to identify and report harassment, reporting that is further slowed by requirements to fill out forms providing context. In addition, the time for moderators to respond and apply human judgment can take days, but response times in terms of minutes are needed in the online context. Though some of the major social media companies have been doing proprietary work in automating the detection of harassment, there are few tools available for use by the public. In addition, the amount of labeled online harassment data and availability of cross-platform online harassment datasets is limited. We present the methodology used to create a harassment dataset and classifier and the dataset used to help the system learn what harassment looks like.",
}
@inproceedings{waseem-etal-2017-understanding,
    title = "Understanding Abuse: A Typology of Abusive Language Detection Subtasks",
    author = "Waseem, Zeerak  and
      Davidson, Thomas  and
      Warmsley, Dana  and
      Weber, Ingmar",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3012",
    doi = "10.18653/v1/W17-3012",
    pages = "78--84",
    abstract = "As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and discuss the implications of this for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.",
}
@inproceedings{gamback-sikdar-2017-using,
    title = "Using Convolutional Neural Networks to Classify Hate-Speech",
    author = {Gamb{\"a}ck, Bj{\"o}rn  and
      Sikdar, Utpal Kumar},
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3013",
    doi = "10.18653/v1/W17-3013",
    pages = "85--90",
    abstract = "The paper introduces a deep learning-based Twitter hate-speech text classification system. The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech. Four Convolutional Neural Network models were trained on resp. character 4-grams, word vectors based on semantic information built using word2vec, randomly generated word vectors, and word vectors combined with character n-grams. The feature set was down-sized in the networks by max-pooling, and a softmax function used to classify tweets. Tested by 10-fold cross-validation, the model based on word2vec embeddings performed best, with higher precision than recall, and a 78.3{\%} F-score.",
}
@inproceedings{palmer-etal-2017-illegal,
    title = "Illegal is not a Noun: Linguistic Form for Detection of Pejorative Nominalizations",
    author = "Palmer, Alexis  and
      Robinson, Melissa  and
      Phillips, Kristy K.",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-3014",
    doi = "10.18653/v1/W17-3014",
    pages = "91--100",
    abstract = "This paper focuses on a particular type of abusive language, targeting expressions in which typically neutral adjectives take on pejorative meaning when used as nouns - compare {`}gay people{'} to {`}the gays{'}. We first collect and analyze a corpus of hand-curated, expert-annotated pejorative nominalizations for four target adjectives: female, gay, illegal, and poor. We then collect a second corpus of automatically-extracted and POS-tagged, crowd-annotated tweets. For both corpora, we find support for the hypothesis that some adjectives, when nominalized, take on negative meaning. The targeted constructions are non-standard yet widely-used, and part-of-speech taggers mistag some nominal forms as adjectives. We implement a tool called NomCatcher to correct these mistaggings, and find that the same tool is effective for identifying new adjectives subject to transformation via nominalization into abusive language.",
}
@proceedings{ws-2017-nlp-social,
    title = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    editor = {Hovy, Dirk  and
      Volkova, Svitlana  and
      Bamman, David  and
      Jurgens, David  and
      O{'}Connor, Brendan  and
      Tsur, Oren  and
      Do{\u{g}}ru{\"o}z, A. Seza},
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2900",
    doi = "10.18653/v1/W17-29",
}
@inproceedings{ljubesic-etal-2017-language,
    title = "Language-independent Gender Prediction on {T}witter",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Fi{\v{s}}er, Darja  and
      Erjavec, Toma{\v{z}}",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2901",
    doi = "10.18653/v1/W17-2901",
    pages = "1--6",
    abstract = "In this paper we present a set of experiments and analyses on predicting the gender of Twitter users based on language-independent features extracted either from the text or the metadata of users{'} tweets. We perform our experiments on the TwiSty dataset containing manual gender annotations for users speaking six different languages. Our classification results show that, while the prediction model based on language-independent features performs worse than the bag-of-words model when training and testing on the same language, it regularly outperforms the bag-of-words model when applied to different languages, showing very stable results across various languages. Finally we perform a comparative analysis of feature effect sizes across the six languages and show that differences in our features correspond to cultural distances.",
}
@inproceedings{jha-mamidi-2017-compliment,
    title = "When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data",
    author = "Jha, Akshita  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2902",
    doi = "10.18653/v1/W17-2902",
    pages = "7--16",
    abstract = "Sexism is prevalent in today{'}s society, both offline and online, and poses a credible threat to social equality with respect to gender. According to ambivalent sexism theory (Glick and Fiske, 1996), it comes in two forms: Hostile and Benevolent. While hostile sexism is characterized by an explicitly negative attitude, benevolent sexism is more subtle. Previous works on computationally detecting sexism present online are restricted to identifying the hostile form. Our objective is to investigate the less pronounced form of sexism demonstrated online. We achieve this by creating and analyzing a dataset of tweets that exhibit benevolent sexism. By using Support Vector Machines (SVM), sequence-to-sequence models and FastText classifier, we classify tweets into {`}Hostile{'}, {`}Benevolent{'} or {`}Others{'} class depending on the kind of sexism they exhibit. We have been able to achieve an F1-score of 87.22{\%} using FastText classifier. Our work helps analyze and understand the much prevalent ambivalent sexism in social media.",
}
@inproceedings{preotiuc-pietro-etal-2017-personality,
    title = "Personality Driven Differences in Paraphrase Preference",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Carpenter, Jordan  and
      Ungar, Lyle",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2903",
    doi = "10.18653/v1/W17-2903",
    pages = "17--26",
    abstract = "Personality plays a decisive role in how people behave in different scenarios, including online social media. Researchers have used such data to study how personality can be predicted from language use. In this paper, we study phrase choice as a particular stylistic linguistic difference, as opposed to the mostly topical differences identified previously. Building on previous work on demographic preferences, we quantify differences in paraphrase choice from a massive Facebook data set with posts from over 115,000 users. We quantify the predictive power of phrase choice in user profiling and use phrase choice to study psycholinguistic hypotheses. This work is relevant to future applications that aim to personalize text generation to specific personality types.",
}
@inproceedings{martin-2017-community2vec,
    title = "community2vec: Vector representations of online communities encode semantic relationships",
    author = "Martin, Trevor",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2904",
    doi = "10.18653/v1/W17-2904",
    pages = "27--31",
    abstract = "Vector embeddings of words have been shown to encode meaningful semantic relationships that enable solving of complex analogies. This vector embedding concept has been extended successfully to many different domains and in this paper we both create and visualize vector representations of an unstructured collection of online communities based on user participation. Further, we quantitatively and qualitatively show that these representations allow solving of semantically meaningful community analogies and also other more general types of relationships. These results could help improve community recommendation engines and also serve as a tool for sociological studies of community relatedness.",
}
@inproceedings{addawood-etal-2017-telling,
    title = "Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics",
    author = "Addawood, Aseel  and
      Rezapour, Rezvaneh  and
      Abdar, Omid  and
      Diesner, Jana",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2905",
    doi = "10.18653/v1/W17-2905",
    pages = "32--41",
    abstract = "In this paper, we evaluate the predictability of tweets associated with controversial versus non-controversial topics. As a first step, we crowd-sourced the scoring of a predefined set of topics on a Likert scale from non-controversial to controversial. Our feature set entails and goes beyond sentiment features, e.g., by leveraging empathic language and other features that have been previously used but are new for this particular study. We find focusing on the structural characteristics of tweets to be beneficial for this task. Using a combination of emphatic, language-specific, and Twitter-specific features for supervised learning resulted in 87{\%} accuracy (F1) for cross-validation of the training set and 63.4{\%} accuracy when using the test set. Our analysis shows that features specific to Twitter or social media, in general, are more prevalent in tweets on controversial topics than in non-controversial ones. To test the premise of the paper, we conducted two additional sets of experiments, which led to mixed results. This finding will inform our future investigations into the relationship between language use on social media and the perceived controversiality of topics.",
}
@inproceedings{glavas-etal-2017-cross,
    title = "Cross-Lingual Classification of Topics in Political Texts",
    author = "Glava{\v{s}}, Goran  and
      Nanni, Federico  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2906",
    doi = "10.18653/v1/W17-2906",
    pages = "42--46",
    abstract = "In this paper, we propose an approach for cross-lingual topical coding of sentences from electoral manifestos of political parties in different languages. To this end, we exploit continuous semantic text representations and induce a joint multilingual semantic vector spaces to enable supervised learning using manually-coded sentences across different languages. Our experimental results show that classifiers trained on multilingual data yield performance boosts over monolingual topic classification.",
}
@inproceedings{zielinski-mutschke-2017-mining,
    title = "Mining Social Science Publications for Survey Variables",
    author = "Zielinski, Andrea  and
      Mutschke, Peter",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2907",
    doi = "10.18653/v1/W17-2907",
    pages = "47--52",
    abstract = "Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding a significant increase in performance over the random baseline.",
}
@inproceedings{prabhumoye-etal-2017-linguistic,
    title = "Linguistic Markers of Influence in Informal Interactions",
    author = "Prabhumoye, Shrimai  and
      Choudhary, Samridhi  and
      Spiliopoulou, Evangelia  and
      Bogart, Christopher  and
      Rose, Carolyn  and
      Black, Alan W",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2908",
    doi = "10.18653/v1/W17-2908",
    pages = "53--62",
    abstract = "There has been a long standing interest in understanding {`}Social Influence{'} both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts as influential or non-influential. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15{\%}. Our results illustrate the important correlation between the structure of the language and its potential to influence others.",
}
@inproceedings{tatman-etal-2017-non,
    title = "Non-lexical Features Encode Political Affiliation on {T}witter",
    author = "Tatman, Rachael  and
      Stewart, Leo  and
      Paullada, Amandalynne  and
      Spiro, Emma",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2909",
    doi = "10.18653/v1/W17-2909",
    pages = "63--67",
    abstract = "Previous work on classifying Twitter users{'} political alignment has mainly focused on lexical and social network features. This study provides evidence that political affiliation is also reflected in features which have been previously overlooked: users{'} discourse patterns (proportion of Tweets that are retweets or replies) and their rate of use of capitalization and punctuation. We find robust differences between politically left- and right-leaning communities with respect to these discourse and sub-lexical features, although they are not enough to train a high-accuracy classifier.",
}
@inproceedings{murray-2017-modelling,
    title = "Modelling Participation in Small Group Social Sequences with {M}arkov Rewards Analysis",
    author = "Murray, Gabriel",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2910",
    doi = "10.18653/v1/W17-2910",
    pages = "68--72",
    abstract = "We explore a novel computational approach for analyzing member participation in small group social sequences. Using a complex state representation combining information about dialogue act types, sentiment expression, and participant roles, we explore which sequence states are associated with high levels of member participation. Using a Markov Rewards framework, we associate particular states with immediate positive and negative rewards, and employ a Value Iteration algorithm to calculate the expected value of all states. In our findings, we focus on discourse states belonging to team leaders and project managers which are either very likely or very unlikely to lead to participation from the rest of the group members.",
}
@inproceedings{yoder-etal-2017-code,
    title = "Code-Switching as a Social Act: The Case of {A}rabic {W}ikipedia Talk Pages",
    author = "Yoder, Michael  and
      Rijhwani, Shruti  and
      Ros{\'e}, Carolyn  and
      Levin, Lori",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2911",
    doi = "10.18653/v1/W17-2911",
    pages = "73--82",
    abstract = "Code-switching has been found to have social motivations in addition to syntactic constraints. In this work, we explore the social effect of code-switching in an online community. We present a task from the Arabic Wikipedia to capture language choice, in this case code-switching between Arabic and other languages, as a predictor of social influence in collaborative editing. We find that code-switching is positively associated with Wikipedia editor success, particularly borrowing technical language on pages with topics less directly related to Arabic-speaking regions.",
}
@inproceedings{wood-doughty-etal-2017-twitter,
    title = "How Does {T}witter User Behavior Vary Across Demographic Groups?",
    author = "Wood-Doughty, Zach  and
      Smith, Michael  and
      Broniatowski, David  and
      Dredze, Mark",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2912",
    doi = "10.18653/v1/W17-2912",
    pages = "83--89",
    abstract = "Demographically-tagged social media messages are a common source of data for computational social science. While these messages can indicate differences in beliefs and behaviors between demographic groups, we do not have a clear understanding of how different demographic groups use platforms such as Twitter. This paper presents a preliminary analysis of how groups{'} differing behaviors may confound analyses of the groups themselves. We analyzed one million Twitter users by first inferring demographic attributes, and then measuring several indicators of Twitter behavior. We find differences in these indicators across demographic groups, suggesting that there may be underlying differences in how different demographic groups use Twitter.",
}
@inproceedings{johnson-etal-2017-ideological,
    title = "Ideological Phrase Indicators for Classification of Political Discourse Framing on {T}witter",
    author = "Johnson, Kristen  and
      Lee, I-Ta  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2913",
    doi = "10.18653/v1/W17-2913",
    pages = "90--99",
    abstract = "Politicians carefully word their statements in order to influence how others view an issue, a political strategy called framing. Simultaneously, these frames may also reveal the beliefs or positions on an issue of the politician. Simple language features such as unigrams, bigrams, and trigrams are important indicators for identifying the general frame of a text, for both longer congressional speeches and shorter tweets of politicians. However, tweets may contain multiple unigrams across different frames which limits the effectiveness of this approach. In this paper, we present a joint model which uses both linguistic features of tweets and ideological phrase indicators extracted from a state-of-the-art embedding-based model to predict the general frame of political tweets.",
}
@proceedings{ws-2017-language,
    title = "Proceedings of the First Workshop on Language Grounding for Robotics",
    editor = "Bansal, Mohit  and
      Matuszek, Cynthia  and
      Andreas, Jacob  and
      Artzi, Yoav  and
      Bisk, Yonatan",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2800",
    doi = "10.18653/v1/W17-28",
}
@inproceedings{lindes-etal-2017-grounding,
    title = "Grounding Language for Interactive Task Learning",
    author = "Lindes, Peter  and
      Mininger, Aaron  and
      Kirk, James R.  and
      Laird, John E.",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2801",
    doi = "10.18653/v1/W17-2801",
    pages = "1--9",
    abstract = "This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agents perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain along with test versions of the world model and ontology and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.",
}
@inproceedings{yu-etal-2017-learning,
    title = "Learning how to Learn: An Adaptive Dialogue Agent for Incrementally Learning Visually Grounded Word Meanings",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2802",
    doi = "10.18653/v1/W17-2802",
    pages = "10--19",
    abstract = "We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users, and achieve good learning performance (i.e. accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus {--} a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.",
}
@inproceedings{thomason-etal-2017-guiding,
    title = "Guiding Interaction Behaviors for Multi-modal Grounded Language Learning",
    author = "Thomason, Jesse  and
      Sinapov, Jivko  and
      Mooney, Raymond",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2803",
    doi = "10.18653/v1/W17-2803",
    pages = "20--24",
    abstract = "Multi-modal grounded language learning connects language predicates to physical properties of objects in the world. Sensing with multiple modalities, such as audio, haptics, and visual colors and shapes while performing interaction behaviors like lifting, dropping, and looking on objects enables a robot to ground non-visual predicates like {``}empty{''} as well as visual predicates like {``}red{''}. Previous work has established that grounding in multi-modal space improves performance on object retrieval from human descriptions. In this work, we gather behavior annotations from humans and demonstrate that these improve language grounding performance by allowing a system to focus on relevant behaviors for words like {``}white{''} or {``}half-full{''} that can be understood by looking or lifting, respectively. We also explore adding modality annotations (whether to focus on audio or haptics when performing a behavior), which improves performance, and sharing information between linguistically related predicates (if {``}green{''} is a color, {``}white{''} is a color), which improves grounding recall but at the cost of precision.",
}
@inproceedings{vanzo-etal-2017-structured,
    title = "Structured Learning for Context-aware Spoken Language Understanding of Robotic Commands",
    author = "Vanzo, Andrea  and
      Croce, Danilo  and
      Basili, Roberto  and
      Nardi, Daniele",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2804",
    doi = "10.18653/v1/W17-2804",
    pages = "25--34",
    abstract = "Service robots are expected to operate in specific environments, where the presence of humans plays a key role. A major feature of such robotics platforms is thus the ability to react to spoken commands. This requires the understanding of the user utterance with an accuracy able to trigger the robot reaction. Such correct interpretation of linguistic exchanges depends on physical, cognitive and language-dependent aspects related to the environment. In this work, we present the empirical evaluation of an adaptive Spoken Language Understanding chain for robotic commands, that explicitly depends on the operational environment during both the learning and recognition stages. The effectiveness of such a context-sensitive command interpretation is tested against an extension of an already existing corpus of commands, that introduced explicit perceptual knowledge: this enabled deeper measures proving that more accurate disambiguation capabilities can be actually obtained.",
}
@inproceedings{alomari-etal-2017-natural,
    title = "Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands",
    author = "Alomari, Muhannad  and
      Duckworth, Paul  and
      Hawasly, Majd  and
      Hogg, David C.  and
      Cohn, Anthony G.",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2805",
    doi = "10.18653/v1/W17-2805",
    pages = "35--43",
    abstract = "We present a cognitively plausible system capable of acquiring knowledge in language and vision from pairs of short video clips and linguistic descriptions. The aim of this work is to teach a robot manipulator how to execute natural language commands by demonstration. This is achieved by first learning a set of visual {`}concepts{'} that abstract the visual feature spaces into concepts that have human-level meaning. Second, learning the mapping/grounding between words and the extracted visual concepts. Third, inducing grammar rules via a semantic representation known as Robot Control Language (RCL). We evaluate our approach against state-of-the-art supervised and unsupervised grounding and grammar induction systems, and show that a robot can learn to execute never seen-before commands from pairs of unlabelled linguistic and visual inputs.",
}
@inproceedings{pisl-marecek-2017-communication,
    title = "Communication with Robots using Multilayer Recurrent Networks",
    author = "Pi{\v{s}}l, Bed{\v{r}}ich  and
      Mare{\v{c}}ek, David",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2806",
    doi = "10.18653/v1/W17-2806",
    pages = "44--48",
    abstract = "In this paper, we describe an improvement on the task of giving instructions to robots in a simulated block world using unrestricted natural language commands.",
}
@inproceedings{hristov-etal-2017-grounding,
    title = "Grounding Symbols in Multi-Modal Instructions",
    author = "Hristov, Yordan  and
      Penkov, Svetlin  and
      Lascarides, Alex  and
      Ramamoorthy, Subramanian",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2807",
    doi = "10.18653/v1/W17-2807",
    pages = "49--57",
    abstract = "As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability{---}for instance, learning to ground symbols in the physical world. Realistically, this task must cope with small datasets consisting of a particular users{'} contextual assignment of meaning to terms. We present a method for processing a raw stream of cross-modal input{---}i.e., linguistic instructions, visual perception of a scene and a concurrent trace of 3D eye tracking fixations{---}to produce the segmentation of objects with a correspondent association to high-level concepts. To test our framework we present experiments in a table-top object manipulation scenario. Our results show our model learns the user{'}s notion of colour and shape from a small number of physical demonstrations, generalising to identifying physical referents for novel combinations of the words.",
}
@inproceedings{marge-etal-2017-exploring,
    title = "Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task",
    author = "Marge, Matthew  and
      Bonial, Claire  and
      Foots, Ashley  and
      Hayes, Cory  and
      Henry, Cassidy  and
      Pollard, Kimberly  and
      Artstein, Ron  and
      Voss, Clare  and
      Traum, David",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2808",
    doi = "10.18653/v1/W17-2808",
    pages = "58--66",
    abstract = "Robot-directed communication is variable, and may change based on human perception of robot capabilities. To collect training data for a dialogue system and to investigate possible communication changes over time, we developed a Wizard-of-Oz study that (a) simulates a robot{'}s limited understanding, and (b) collects dialogues where human participants build a progressively better mental model of the robot{'}s understanding. With ten participants, we collected ten hours of human-robot dialogue. We analyzed the structure of instructions that participants gave to a remote robot before it responded. Our findings show a general initial preference for including metric information (e.g., move forward 3 feet) over landmarks (e.g., move to the desk) in motion commands, but this decreased over time, suggesting changes in perception.",
}
@inproceedings{karamcheti-etal-2017-tale,
    title = "A Tale of Two {DRAGGN}s: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions",
    author = "Karamcheti, Siddharth  and
      Williams, Edward Clem  and
      Arumugam, Dilip  and
      Rhee, Mina  and
      Gopalan, Nakul  and
      Wong, Lawson L.S.  and
      Tellex, Stefanie",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2809",
    doi = "10.18653/v1/W17-2809",
    pages = "67--75",
    abstract = "Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.",
}
@inproceedings{lucy-gauthier-2017-distributional,
    title = "Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning",
    author = "Lucy, Li  and
      Gauthier, Jon",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2810",
    doi = "10.18653/v1/W17-2810",
    pages = "76--85",
    abstract = "Distributional word representation methods exploit word co-occurrences to build compact vector encodings of words. While these representations enjoy widespread use in modern natural language processing, it is unclear whether they accurately encode all necessary facets of conceptual meaning. In this paper, we evaluate how well these representations can predict perceptual and conceptual features of concrete concepts, drawing on two semantic norm datasets sourced from human participants. We find that several standard word representations fail to encode many salient perceptual features of concepts, and show that these deficits correlate with word-word similarity prediction errors. Our analyses provide motivation for grounded and embodied language learning approaches, which may help to remedy these deficits.",
}
@inproceedings{novikova-etal-2017-sympathy,
    title = "Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction",
    author = "Novikova, Jekaterina  and
      Dondrup, Christian  and
      Papaioannou, Ioannis  and
      Lemon, Oliver",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2811",
    doi = "10.18653/v1/W17-2811",
    pages = "86--94",
    abstract = "Recognition of social signals, coming from human facial expressions or prosody of human speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users{'} impressions of the robot after a conversation. We find that happiness in the user{'}s recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that the facial expression emotional features and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learning-based adaptive human-robot dialogue systems.",
}
@inproceedings{narayan-chen-etal-2017-towards,
    title = "Towards Problem Solving Agents that Communicate and Learn",
    author = "Narayan-Chen, Anjali  and
      Graber, Colin  and
      Das, Mayukh  and
      Islam, Md Rakibul  and
      Dan, Soham  and
      Natarajan, Sriraam  and
      Doppa, Janardhan Rao  and
      Hockenmaier, Julia  and
      Palmer, Martha  and
      Roth, Dan",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2812",
    doi = "10.18653/v1/W17-2812",
    pages = "95--103",
    abstract = "Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI. These agents need to translate between utterances and actionable meaning representations that can be interpreted by task-specific problem solvers in a context-dependent manner. They should also be able to learn such actionable interpretations for new predicates on the fly. We define an agent architecture for this scenario and present a series of experiments in the Blocks World domain that illustrate how our architecture supports language learning and problem solving in this domain.",
}
@proceedings{ws-2017-events,
    title = "Proceedings of the Events and Stories in the News Workshop",
    editor = "Caselli, Tommaso  and
      Miller, Ben  and
      van Erp, Marieke  and
      Vossen, Piek  and
      Palmer, Martha  and
      Hovy, Eduard  and
      Mitamura, Teruko  and
      Caswell, David",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2700",
    doi = "10.18653/v1/W17-27",
}
@inproceedings{laban-hearst-2017-newslens,
    title = "news{L}ens: building and visualizing long-ranging news stories",
    author = "Laban, Philippe  and
      Hearst, Marti",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2701",
    doi = "10.18653/v1/W17-2701",
    pages = "1--9",
    abstract = "We propose a method to aggregate and organize a large, multi-source dataset of news articles into a collection of major stories, and automatically name and visualize these stories in a working system. The approach is able to run online, as new articles are added, processing 4 million news articles from 20 news sources, and extracting 80000 major stories, some of which span several years. The visual interface consists of lanes of timelines, each annotated with information that is deemed important for the story, including extracted quotations. The working system allows a user to search and navigate 8 years of story information.",
}
@inproceedings{wang-goutte-2017-detecting,
    title = "Detecting Changes in {T}witter Streams using Temporal Clusters of Hashtags",
    author = "Wang, Yunli  and
      Goutte, Cyril",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2702",
    doi = "10.18653/v1/W17-2702",
    pages = "10--14",
    abstract = "Detecting events from social media data has important applications in public security, political issues, and public health. Many studies have focused on detecting specific or unspecific events from Twitter streams. However, not much attention has been paid to detecting changes, and their impact, in online conversations related to an event. We propose methods for detecting such changes, using clustering of temporal profiles of hashtags, and three change point detection algorithms. The methods were tested on two Twitter datasets: one covering the 2014 Ottawa shooting event, and one covering the Sochi winter Olympics. We compare our approach to a baseline consisting of detecting change from raw counts in the conversation. We show that our method produces large gains in change detection accuracy on both datasets.",
}
@inproceedings{spiliopoulou-etal-2017-event,
    title = "Event Detection Using Frame-Semantic Parser",
    author = "Spiliopoulou, Evangelia  and
      Hovy, Eduard  and
      Mitamura, Teruko",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2703",
    doi = "10.18653/v1/W17-2703",
    pages = "15--20",
    abstract = "Recent methods for Event Detection focus on Deep Learning for automatic feature generation and feature ranking. However, most of those approaches fail to exploit rich semantic information, which results in relatively poor recall. This paper is a small {\&} focused contribution, where we introduce an Event Detection and classification system, based on deep semantic information retrieved from a frame-semantic parser. Our experiments show that our system achieves higher recall than state-of-the-art systems. Further, we claim that enhancing our system with deep learning techniques like feature ranking can achieve even better results, as it can benefit from both approaches.",
}
@inproceedings{huang-kurohashi-2017-improving,
    title = "Improving Shared Argument Identification in {J}apanese Event Knowledge Acquisition",
    author = "Huang, Yin Jou  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2704",
    doi = "10.18653/v1/W17-2704",
    pages = "21--30",
    abstract = "Event knowledge represents the knowledge of causal and temporal relations between events. Shared arguments of event knowledge encode patterns of role shifting in successive events. A two-stage framework was proposed for the task of Japanese event knowledge acquisition, in which related event pairs are first extracted, and shared arguments are then identified to form the complete event knowledge. This paper focuses on the second stage of this framework, and proposes a method to improve the shared argument identification of related event pairs. We constructed a gold dataset for shared argument learning. By evaluating our system on this gold dataset, we found that our proposed model outperformed the baseline models by a large margin.",
}
@inproceedings{kutuzov-etal-2017-tracing,
    title = "Tracing armed conflicts with diachronic word embedding models",
    author = "Kutuzov, Andrey  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2705",
    doi = "10.18653/v1/W17-2705",
    pages = "31--36",
    abstract = "Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts in particular words. In this paper, we evaluate some of these approaches on the new task of predicting the dynamics of global armed conflicts on a year-to-year basis, using a dataset from the conflict research field as the gold standard and the Gigaword news corpus as the training data. The results show that much work still remains in extracting {`}cultural{'} semantic shifts from diachronic word embedding models. At the same time, we present a new task complete with an evaluation set and introduce the {`}anchor words{'} method which outperforms previous approaches on this set.",
}
@inproceedings{segers-etal-2017-circumstantial,
    title = "The Circumstantial Event Ontology ({CEO})",
    author = "Segers, Roxane  and
      Caselli, Tommaso  and
      Vossen, Piek",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2706",
    doi = "10.18653/v1/W17-2706",
    pages = "37--41",
    abstract = "In this paper we describe the ongoing work on the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes. The circumstantial relations are designed manually, based on the shared properties of each event class. We discuss and contrast two types of event circumstantial relations: semantic circumstantial relations and episodic circumstantial relations. Further, we show the metamodel and the current contents of the ontology and outline the evaluation of the CEO.",
}
@inproceedings{rehm-etal-2017-event,
    title = "Event Detection and Semantic Storytelling: Generating a Travelogue from a large Collection of Personal Letters",
    author = {Rehm, Georg  and
      Moreno Schneider, Julian  and
      Bourgonje, Peter  and
      Srivastava, Ankit  and
      Nehring, Jan  and
      Berger, Armin  and
      K{\"o}nig, Luca  and
      R{\"a}uchle, S{\"o}ren  and
      Gerth, Jens},
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2707",
    doi = "10.18653/v1/W17-2707",
    pages = "42--51",
    abstract = "We present an approach at identifying a specific class of events, movement action events (MAEs), in a data set that consists of ca. 2,800 personal letters exchanged by the German architect Erich Mendelsohn and his wife, Luise. A backend system uses these and other semantic analysis results as input for an authoring environment that digital curators can use to produce new pieces of digital content. In our example case, the human expert will receive recommendations from the system with the goal of putting together a travelogue, i.e., a description of the trips and journeys undertaken by the couple. We describe the components and architecture and also apply the system to news data.",
}
@inproceedings{hu-etal-2017-inference,
    title = "Inference of Fine-Grained Event Causality from Blogs and Films",
    author = "Hu, Zhichao  and
      Rahimtoroghi, Elahe  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2708",
    doi = "10.18653/v1/W17-2708",
    pages = "52--58",
    abstract = "Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80{\%} of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.",
}
@inproceedings{atkinson-etal-2017-creation,
    title = "On the Creation of a Security-Related Event Corpus",
    author = "Atkinson, Martin  and
      Piskorski, Jakub  and
      Tanev, Hristo  and
      Zavarella, Vanni",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2709",
    doi = "10.18653/v1/W17-2709",
    pages = "59--65",
    abstract = "This paper reports on an effort of creating a corpus of structured information on security-related events automatically extracted from on-line news, part of which has been manually curated. The main motivation behind this effort is to provide material to the NLP community working on event extraction that could be used both for training and evaluation purposes.",
}
@inproceedings{ahn-2017-inducing,
    title = "Inducing Event Types and Roles in Reverse: Using Function to Discover Theme",
    author = "Ahn, Natalie",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2710",
    doi = "10.18653/v1/W17-2710",
    pages = "66--76",
    abstract = "With growing interest in automated event extraction, there is an increasing need to overcome the labor costs of hand-written event templates, entity lists, and annotated corpora. In the last few years, more inductive approaches have emerged, seeking to discover unknown event types and roles in raw text. The main recent efforts use probabilistic generative models, as in topic modeling, which are formally concise but do not always yield stable or easily interpretable results. We argue that event schema induction can benefit from greater structure in the process and in linguistic features that distinguish words{'} functions and themes. To maximize our use of limited data, we reverse the typical schema induction steps and introduce new similarity measures, building an intuitive process for inducing the structure of unknown events.",
}
@inproceedings{caselli-vossen-2017-event,
    title = "The Event {S}tory{L}ine Corpus: A New Benchmark for Causal and Temporal Relation Extraction",
    author = "Caselli, Tommaso  and
      Vossen, Piek",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2711",
    doi = "10.18653/v1/W17-2711",
    pages = "77--86",
    abstract = "This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.",
}
@inproceedings{brown-etal-2017-rich,
    title = "The Rich Event Ontology",
    author = "Brown, Susan  and
      Bonial, Claire  and
      Obrst, Leo  and
      Palmer, Martha",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2712",
    doi = "10.18653/v1/W17-2712",
    pages = "87--97",
    abstract = "In this paper we describe a new lexical semantic resource, The Rich Event On-tology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations. By unifying the FrameNet, VerbNet, Automatic Content Extraction, and Rich Entities, Relations and Events resources, the ontology serves as a shared hub for the disparate annotation schemas and therefore enables the combination of SRL training data into a larger, more diverse corpus. By adding temporal and causal relational information not found in any of the independent resources, the ontology facilitates reasoning on and across documents, revealing relationships between events that come together in temporal and causal chains to build more complex scenarios. We envision the open resource serving as a valuable tool for both moving from the ontology to text to query for event types and scenarios of interest, and for moving from text to the ontology to access interpretations of events using the combined semantic information housed there.",
}
@inproceedings{croft-etal-2017-integrating,
    title = "Integrating Decompositional Event Structures into Storylines",
    author = "Croft, William  and
      Pe{\v{s}}kov{\'a}, Pavl{\'\i}na  and
      Regan, Michael",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2713",
    doi = "10.18653/v1/W17-2713",
    pages = "98--109",
    abstract = "Storyline research links together events in stories and specifies shared participants in those stories. In these analyses, an atomic event is assumed to be a single clause headed by a single verb. However, many analyses of verbal semantics assume a decompositional analysis of events expressed in single clauses. We present a formalization of a decompositional analysis of events in which each participant in a clausal event has their own temporally extended subevent, and the subevents are related through causal and other interactions. This decomposition allows us to represent storylines as an evolving set of interactions between participants over time.",
}
@proceedings{ws-2017-representation,
    title = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    editor = "Blunsom, Phil  and
      Bordes, Antoine  and
      Cho, Kyunghyun  and
      Cohen, Shay  and
      Dyer, Chris  and
      Grefenstette, Edward  and
      Hermann, Karl Moritz  and
      Rimell, Laura  and
      Weston, Jason  and
      Yih, Scott",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2600",
    doi = "10.18653/v1/W17-26",
}
@inproceedings{gamallo-2017-sense,
    title = "Sense Contextualization in a Dependency-Based Compositional Distributional Model",
    author = "Gamallo, Pablo",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2601",
    doi = "10.18653/v1/W17-2601",
    pages = "1--9",
    abstract = "Little attention has been paid to distributional compositional methods which employ syntactically structured vector models. As word vectors belonging to different syntactic categories have incompatible syntactic distributions, no trivial compositional operation can be applied to combine them into a new compositional vector. In this article, we generalize the method described by Erk and Pad{\'o} (2009) by proposing a dependency-base framework that contextualize not only lemmas but also selectional preferences. The main contribution of the article is to expand their model to a fully compositional framework in which syntactic dependencies are put at the core of semantic composition. We claim that semantic composition is mainly driven by syntactic dependencies. Each syntactic dependency generates two new compositional vectors representing the contextualized sense of the two related lemmas. The sequential application of the compositional operations associated to the dependencies results in as many contextualized vectors as lemmas the composite expression contains. At the end of the semantic process, we do not obtain a single compositional vector representing the semantic denotation of the whole composite expression, but one contextualized vector for each lemma of the whole expression. Our method avoids the troublesome high-order tensor representations by defining lemmas and selectional restrictions as first-order tensors (i.e. standard vectors). A corpus-based experiment is performed to both evaluate the quality of the compositional vectors built with our strategy, and to compare them to other approaches on distributional compositional semantics. The experiments show that our dependency-based compositional method performs as (or even better than) the state-of-the-art.",
}
@inproceedings{horn-2017-context,
    title = "Context encoders as a simple but powerful extension of word2vec",
    author = "Horn, Franziska",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2602",
    doi = "10.18653/v1/W17-2602",
    pages = "10--14",
    abstract = "With a strikingly simple architecture and the ability to learn meaningful word embeddings efficiently from texts containing billions of words, word2vec remains one of the most popular neural language models used today. However, as only a single embedding is learned for every word in the vocabulary, the model fails to optimally represent words with multiple meanings and, additionally, it is not possible to create embeddings for new (out-of-vocabulary) words on the spot. Based on an intuitive interpretation of the continuous bag-of-words (CBOW) word2vec model{'}s negative sampling training objective in terms of predicting context based similarities, we motivate an extension of the model we call context encoders (ConEc). By multiplying the matrix of trained word2vec embeddings with a word{'}s average context vector, out-of-vocabulary (OOV) embeddings and representations for words with multiple meanings can be created based on the words{'} local contexts. The benefits of this approach are illustrated by using these word embeddings as features in the CoNLL 2003 named entity recognition (NER) task.",
}
@inproceedings{yuan-etal-2017-machine,
    title = "Machine Comprehension by Text-to-Text Neural Question Generation",
    author = "Yuan, Xingdi  and
      Wang, Tong  and
      Gulcehre, Caglar  and
      Sordoni, Alessandro  and
      Bachman, Philip  and
      Zhang, Saizheng  and
      Subramanian, Sandeep  and
      Trischler, Adam",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2603",
    doi = "10.18653/v1/W17-2603",
    pages = "15--25",
    abstract = "We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD.",
}
@inproceedings{wang-etal-2017-emergent,
    title = "Emergent Predication Structure in Hidden State Vectors of Neural Readers",
    author = "Wang, Hai  and
      Onishi, Takeshi  and
      Gimpel, Kevin  and
      McAllester, David",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2604",
    doi = "10.18653/v1/W17-2604",
    pages = "26--36",
    abstract = "A significant number of neural architectures for reading comprehension have recently been developed and evaluated on large cloze-style datasets. We present experiments supporting the emergence of {``}predication structure{''} in the hidden state vectors of these readers. More specifically, we provide evidence that the hidden state vectors represent atomic formulas $\Phi[c]$ where $\Phi$ is a semantic property (predicate) and $c$ is a constant symbol entity identifier.",
}
@inproceedings{cheri-bhattacharyya-2017-towards,
    title = "Towards Harnessing Memory Networks for Coreference Resolution",
    author = "Cheri, Joe  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2605",
    doi = "10.18653/v1/W17-2605",
    pages = "37--42",
    abstract = "Coreference resolution task demands comprehending a discourse, especially for anaphoric mentions which require semantic information for resolving antecedents. We investigate into how memory networks can be helpful for coreference resolution when posed as question answering problem. The comprehension capability of memory networks assists coreference resolution, particularly for the mentions that require semantic and context information. We experiment memory networks for coreference resolution, with 4 synthetic datasets generated for coreference resolution with varying difficulty levels. Our system{'}s performance is compared with a traditional coreference resolution system to show why memory network can be promising for coreference resolution.",
}
@inproceedings{liang-etal-2017-combining,
    title = "Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text",
    author = "Liang, Dongyun  and
      Xu, Weiran  and
      Zhao, Yinge",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2606",
    doi = "10.18653/v1/W17-2606",
    pages = "43--47",
    abstract = "Word representation models have achieved great success in natural language processing tasks, such as relation classification. However, it does not always work on informal text, and the morphemes of some misspelling words may carry important short-distance semantic information. We propose a hybrid model, combining the merits of word-level and character-level representations to learn better representations on informal text. Experiments on two dataset of relation classification, SemEval-2010 Task8 and a large-scale one we compile from informal text, show that our model achieves a competitive result in the former and state-of-the-art with the other.",
}
@inproceedings{fan-etal-2017-transfer,
    title = "Transfer Learning for Neural Semantic Parsing",
    author = "Fan, Xing  and
      Monti, Emilio  and
      Mathias, Lambert  and
      Dreyer, Markus",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2607",
    doi = "10.18653/v1/W17-2607",
    pages = "48--56",
    abstract = "The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence model and compare their performance with the independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to the target task with smaller labeled data. We see an absolute accuracy gain ranging from 1.0{\%} to 4.4{\%} in in our in-house data set and we also see good gains ranging from 2.5{\%} to 7.0{\%} on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.",
}
@inproceedings{shen-etal-2017-modeling,
    title = "Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion",
    author = "Shen, Yelong  and
      Huang, Po-Sen  and
      Chang, Ming-Wei  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2608",
    doi = "10.18653/v1/W17-2608",
    pages = "57--68",
    abstract = "Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step relations directly on top of observed triplets could be costly. Hence, a manually designed procedure is often used when training the models. In this paper, we propose Implicit ReasoNets (IRNs), which is designed to perform multi-step inference implicitly through a controller and shared memory. Without a human-designed inference procedure, IRNs use training data to learn to perform multi-step inference in an embedding neural space through the shared memory and controller. While the inference procedure does not explicitly operate on top of observed triplets, our proposed model outperforms all previous approaches on the popular FB15k benchmark by more than 5.7{\%}.",
}
@inproceedings{kadlec-etal-2017-knowledge,
    title = "Knowledge Base Completion: Baselines Strike Back",
    author = "Kadlec, Rudolf  and
      Bajgar, Ondrej  and
      Kleindienst, Jan",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2609",
    doi = "10.18653/v1/W17-2609",
    pages = "69--74",
    abstract = "Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets like FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline {---} our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported.",
}
@inproceedings{brarda-etal-2017-sequential,
    title = "Sequential Attention: A Context-Aware Alignment Function for Machine Reading",
    author = "Brarda, Sebastian  and
      Yeres, Philip  and
      Bowman, Samuel",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2610",
    doi = "10.18653/v1/W17-2610",
    pages = "75--80",
    abstract = "In this paper we propose a neural network model with a novel Sequential Attention layer that extends soft attention by assigning weights to words in an input sequence in a way that takes into account not just how well that word matches a query, but how well surrounding words match. We evaluate this approach on the task of reading comprehension (on the Who did What and CNN datasets) and show that it dramatically improves a strong baseline{---}the Stanford Reader{---}and is competitive with the state of the art.",
}
@inproceedings{rygl-etal-2017-semantic,
    title = "Semantic Vector Encoding and Similarity Search Using Fulltext Search Engines",
    author = "Rygl, Jan  and
      Pomik{\'a}lek, Jan  and
      {\v{R}}eh{\r{u}}{\v{r}}ek, Radim  and
      R{\r{u}}{\v{z}}i{\v{c}}ka, Michal  and
      Novotn{\'y}, V{\'\i}t  and
      Sojka, Petr",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2611",
    doi = "10.18653/v1/W17-2611",
    pages = "81--90",
    abstract = "Vector representations and vector space modeling (VSM) play a central role in modern machine learning. We propose a novel approach to {`}vector similarity searching{'} over dense semantic representations of words and documents that can be deployed on top of traditional inverted-index-based fulltext engines, taking advantage of their robustness, stability, scalability and ubiquity. We show that this approach allows the indexing and querying of dense vectors in text domains. This opens up exciting avenues for major efficiency gains, along with simpler deployment, scaling and monitoring. The end result is a fast and scalable vector database with a tunable trade-off between vector search performance and quality, backed by a standard fulltext engine such as Elasticsearch. We empirically demonstrate its querying performance and quality by applying this solution to the task of semantic searching over a dense vector representation of the entire English Wikipedia.",
}
@inproceedings{peng-dredze-2017-multi,
    title = "Multi-task Domain Adaptation for Sequence Tagging",
    author = "Peng, Nanyun  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2612",
    doi = "10.18653/v1/W17-2612",
    pages = "91--100",
    abstract = "Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain.",
}
@inproceedings{upadhyay-etal-2017-beyond,
    title = "Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context",
    author = "Upadhyay, Shyam  and
      Chang, Kai-Wei  and
      Taddy, Matt  and
      Kalai, Adam  and
      Zou, James",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2613",
    doi = "10.18653/v1/W17-2613",
    pages = "101--110",
    abstract = "Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense of a word, by exploiting crosslingual signals to aid sense identification. We present a multi-view Bayesian non-parametric algorithm which improves multi-sense wor d embeddings by (a) using multilingual (i.e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner. Ours is the first approach with the ability to leverage multilingual corpora efficiently for multi-sense representation learning. Experiments show that multilingual training significantly improves performance over monolingual and bilingual training, by allowing us to combine different parallel corpora to leverage multilingual context. Multilingual training yields comparable performance to a state of the art monolingual model trained on five times more training data.",
}
@inproceedings{chen-etal-2017-doctag2vec,
    title = "{D}oc{T}ag2{V}ec: An Embedding Based Multi-label Learning Approach for Document Tagging",
    author = "Chen, Sheng  and
      Soni, Akshay  and
      Pappu, Aasish  and
      Mehdad, Yashar",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2614",
    doi = "10.18653/v1/W17-2614",
    pages = "111--120",
    abstract = "Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec {--} two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple k-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.",
}
@inproceedings{grzegorczyk-kurdziel-2017-binary,
    title = "Binary Paragraph Vectors",
    author = "Grzegorczyk, Karol  and
      Kurdziel, Marcin",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2615",
    doi = "10.18653/v1/W17-2615",
    pages = "121--130",
    abstract = "Recently Le {\&} Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. Inspired by this work, we present Binary Paragraph Vector models: simple neural networks that learn short binary codes for fast information retrieval. We show that binary paragraph vectors outperform autoencoder-based binary codes, despite using fewer bits. We also evaluate their precision in transfer learning settings, where binary codes are inferred for documents unrelated to the training corpus. Results from these experiments indicate that binary paragraph vectors can capture semantics relevant for various domain-specific documents. Finally, we present a model that simultaneously learns short binary codes and longer, real-valued representations. This model can be used to rapidly retrieve a short list of highly relevant documents from a large document collection.",
}
@inproceedings{moirangthem-etal-2017-representing,
    title = "Representing Compositionality based on Multiple Timescales Gated Recurrent Neural Networks with Adaptive Temporal Hierarchy for Character-Level Language Models",
    author = "Moirangthem, Dennis Singh  and
      Son, Jegyung  and
      Lee, Minho",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2616",
    doi = "10.18653/v1/W17-2616",
    pages = "131--138",
    abstract = "A novel character-level neural language model is proposed in this paper. The proposed model incorporates a biologically inspired temporal hierarchy in the architecture for representing multiple compositions of language in order to handle longer sequences for the character-level language model. The temporal hierarchy is introduced in the language model by utilizing a Gated Recurrent Neural Network with multiple timescales. The proposed model incorporates a timescale adaptation mechanism for enhancing the performance of the language model. We evaluate our proposed model using the popular Penn Treebank and Text8 corpora. The experiments show that the use of multiple timescales in a Neural Language Model (NLM) enables improved performance despite having fewer parameters and with no additional computation requirements. Our experiments also demonstrate the ability of the adaptive temporal hierarchies to represent multiple compositonality without the help of complex hierarchical architectures and shows that better representation of the longer sequences lead to enhanced performance of the probabilistic language model.",
}
@inproceedings{madhyastha-espana-bonet-2017-learning,
    title = "Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation",
    author = "Madhyastha, Pranava Swaroop  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2617",
    doi = "10.18653/v1/W17-2617",
    pages = "139--145",
    abstract = "We propose a simple log-bilinear softmax-based model to deal with vocabulary expansion in machine translation. Our model uses word embeddings trained on significantly large unlabelled monolingual corpora and learns over a fairly small, word-to-word bilingual dictionary. Given an out-of-vocabulary source word, the model generates a probabilistic list of possible translations in the target language using the trained bilingual embeddings. We integrate these translation options into a standard phrase-based statistical machine translation system and obtain consistent improvements in translation quality on the English{--}Spanish language pair. When tested over an out-of-domain testset, we get a significant improvement of 3.9 BLEU points.",
}
@inproceedings{botschen-etal-2017-prediction,
    title = "Prediction of Frame-to-Frame Relations in the {F}rame{N}et Hierarchy with Frame Embeddings",
    author = "Botschen, Teresa  and
      Mousselly-Sergieh, Hatem  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2618",
    doi = "10.18653/v1/W17-2618",
    pages = "146--156",
    abstract = "Automatic completion of frame-to-frame (F2F) relations in the FrameNet (FN) hierarchy has received little attention, although they incorporate meta-level commonsense knowledge and are used in downstream approaches. We address the problem of sparsely annotated F2F relations. First, we examine whether the manually defined F2F relations emerge from text by learning text-based frame embeddings. Our analysis reveals insights about the difficulty of reconstructing F2F relations purely from text. Second, we present different systems for predicting F2F relations; our best-performing one uses the FN hierarchy to train on and to ground embeddings in. A comparison of systems and embeddings exposes the crucial influence of knowledge-based embeddings to a system{'}s performance in predicting F2F relations.",
}
@inproceedings{schwenk-douze-2017-learning,
    title = "Learning Joint Multilingual Sentence Representations with Neural Machine Translation",
    author = "Schwenk, Holger  and
      Douze, Matthijs",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2619",
    doi = "10.18653/v1/W17-2619",
    pages = "157--167",
    abstract = "In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new cross-lingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.",
}
@inproceedings{kunze-etal-2017-transfer,
    title = "Transfer Learning for Speech Recognition on a Budget",
    author = "Kunze, Julius  and
      Kirsch, Louis  and
      Kurenkov, Ilia  and
      Krug, Andreas  and
      Johannsmeier, Jens  and
      Stober, Sebastian",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2620",
    doi = "10.18653/v1/W17-2620",
    pages = "168--177",
    abstract = "End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources. We explore transfer learning based on model adaptation as an approach for training ASR models under constrained GPU memory, throughput and training data. We conduct several systematic experiments adapting a Wav2Letter convolutional neural network originally trained for English ASR to the German language. We show that this technique allows faster training on consumer-grade resources while requiring less training data in order to achieve the same accuracy, thereby lowering the cost of training ASR models in other languages. Model introspection revealed that small adaptations to the network{'}s weights were sufficient for good performance, especially for inner layers.",
}
@inproceedings{asaadi-rudolph-2017-gradual,
    title = "Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis",
    author = "Asaadi, Shima  and
      Rudolph, Sebastian",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2621",
    doi = "10.18653/v1/W17-2621",
    pages = "178--185",
    abstract = "Learning word representations to capture the semantics and compositionality of language has received much research interest in natural language processing. Beyond the popular vector space models, matrix representations for words have been proposed, since then, matrix multiplication can serve as natural composition operation. In this work, we investigate the problem of learning matrix representations of words. We present a learning approach for compositional matrix-space models for the task of sentiment analysis. We show that our approach, which learns the matrices gradually in two steps, outperforms other approaches and a gradient-descent baseline in terms of quality and computational cost.",
}
@inproceedings{godin-etal-2017-improving,
    title = "Improving Language Modeling using Densely Connected Recurrent Neural Networks",
    author = "Godin, Fr{\'e}deric  and
      Dambre, Joni  and
      De Neve, Wesley",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2622",
    doi = "10.18653/v1/W17-2622",
    pages = "186--190",
    abstract = "In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks. We evaluate our proposed architecture on the Penn Treebank language modeling task. We show that we can obtain similar perplexity scores with six times fewer parameters compared to a standard stacked 2-layer LSTM model trained with dropout (Zaremba et al., 2014). In contrast with the current usage of skip connections, we show that densely connecting only a few stacked layers with skip connections already yields significant perplexity reductions.",
}
@inproceedings{trischler-etal-2017-newsqa,
    title = "{N}ews{QA}: A Machine Comprehension Dataset",
    author = "Trischler, Adam  and
      Wang, Tong  and
      Yuan, Xingdi  and
      Harris, Justin  and
      Sordoni, Alessandro  and
      Bachman, Philip  and
      Suleman, Kaheer",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2623",
    doi = "10.18653/v1/W17-2623",
    pages = "191--200",
    abstract = "We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text in the articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. Analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (13.3{\%} F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available online.",
}
@inproceedings{phillips-etal-2017-intrinsic,
    title = "Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in {T}witter Streams",
    author = "Phillips, Lawrence  and
      Shaffer, Kyle  and
      Arendt, Dustin  and
      Hodas, Nathan  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2624",
    doi = "10.18653/v1/W17-2624",
    pages = "201--210",
    abstract = "Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning. These changes can be estimated using word representations in context, over time and across locations. A number of methods have been proposed to track these spatiotemporal changes but no general method exists to evaluate the quality of these representations. Previous work largely focused on qualitative evaluation, which we improve by proposing a set of visualizations that highlight changes in text representation over both space and time. We demonstrate usefulness of novel spatiotemporal representations to explore and characterize specific aspects of the corpus of tweets collected from European countries over a two-week period centered around the terrorist attacks in Brussels in March 2016. In addition, we quantitatively evaluate spatiotemporal representations by feeding them into a downstream classification task {--} event type prediction. Thus, our work is the first to provide both intrinsic (qualitative) and extrinsic (quantitative) evaluation of text representations for spatiotemporal trends.",
}
@inproceedings{tang-etal-2017-rethinking,
    title = "Rethinking Skip-thought: A Neighborhood based Approach",
    author = "Tang, Shuai  and
      Jin, Hailin  and
      Fang, Chen  and
      Wang, Zhaowen  and
      de Sa, Virginia",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2625",
    doi = "10.18653/v1/W17-2625",
    pages = "211--218",
    abstract = "We study the skip-thought model with neighborhood information as weak supervision. More specifically, we propose a skip-thought neighbor model to consider the adjacent sentences as a neighborhood. We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks. Both quantitative comparison and qualitative investigation are conducted. We empirically show that, our skip-thought neighbor model performs as well as the skip-thought model on evaluation tasks. In addition, we found that, incorporating an autoencoder path in our model didn{'}t aid our model to perform better, while it hurts the performance of the skip-thought model.",
}
@inproceedings{schulz-etal-2017-frame,
    title = "A Frame Tracking Model for Memory-Enhanced Dialogue Systems",
    author = "Schulz, Hannes  and
      Zumer, Jeremie  and
      El Asri, Layla  and
      Sharma, Shikhar",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2626",
    doi = "10.18653/v1/W17-2626",
    pages = "219--227",
    abstract = "Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems. An example is the frame tracking task, which requires recording multiple frames, one for each user goal set during the dialogue. This allows a user, for instance, to compare items corresponding to different goals. This paper proposes a model which takes as input the list of frames created so far during the dialogue, the current user utterance as well as the dialogue acts, slot types, and slot values associated with this utterance. The model then outputs the frame being referenced by each triple of dialogue act, slot type, and slot value. We show that on the recently published Frames dataset, this model significantly outperforms a previously proposed rule-based baseline. In addition, we propose an extensive analysis of the frame tracking task by dividing it into sub-tasks and assessing their difficulty with respect to our model.",
}
@inproceedings{gulcehre-etal-2017-plan,
    title = "Plan, Attend, Generate: Character-Level Neural Machine Translation with Planning",
    author = "Gulcehre, Caglar  and
      Dutil, Francis  and
      Trischler, Adam  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2627",
    doi = "10.18653/v1/W17-2627",
    pages = "228--234",
    abstract = "We investigate the integration of a planning mechanism into an encoder-decoder architecture with attention. We develop a model that can plan ahead when it computes alignments between the source and target sequences not only for a single time-step but for the next k time-steps as well by constructing a matrix of proposed future alignments and a commitment vector that governs whether to follow or recompute the plan. This mechanism is inspired by strategic attentive reader and writer (STRAW) model, a recent neural architecture for planning with hierarchical reinforcement learning that can also learn higher level temporal abstractions. Our proposed model is end-to-end trainable with differentiable operations. We show that our model outperforms strong baselines on character-level translation task from WMT{'}15 with fewer parameters and computes alignments that are qualitatively intuitive.",
}
@inproceedings{michel-etal-2017-geometry,
    title = "Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations",
    author = "Michel, Paul  and
      Ravichander, Abhilasha  and
      Rijhwani, Shruti",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2628",
    doi = "10.18653/v1/W17-2628",
    pages = "235--240",
    abstract = "We investigate the pertinence of methods from algebraic topology for text data analysis. These methods enable the development of mathematically-principled isometric-invariant mappings from a set of vectors to a document embedding, which is stable with respect to the geometry of the document in the selected metric space. In this work, we evaluate the utility of these topology-based document representations in traditional NLP tasks, specifically document clustering and sentiment classification. We find that the embeddings do not benefit text analysis. In fact, performance is worse than simple techniques like tf-idf, indicating that the geometry of the document does not provide enough variability for classification on the basis of topic or sentiment in the chosen datasets.",
}
@inproceedings{subramanian-etal-2017-adversarial,
    title = "Adversarial Generation of Natural Language",
    author = "Subramanian, Sandeep  and
      Rajeswar, Sai  and
      Dutil, Francis  and
      Pal, Chris  and
      Courville, Aaron",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2629",
    doi = "10.18653/v1/W17-2629",
    pages = "241--251",
    abstract = "Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.",
}
@inproceedings{shen-etal-2017-deep,
    title = "Deep Active Learning for Named Entity Recognition",
    author = "Shen, Yanyao  and
      Yun, Hyokun  and
      Lipton, Zachary  and
      Kronrod, Yakov  and
      Anandkumar, Animashree",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2630",
    doi = "10.18653/v1/W17-2630",
    pages = "252--256",
    abstract = "Deep neural networks have advanced the state of the art in named entity recognition. However, under typical training procedures, advantages over classical methods emerge only with large datasets. As a result, deep learning is employed only when large public datasets or a large budget for manually labeling data is available. In this work, we show otherwise: by combining deep learning with active learning, we can outperform classical methods even with a significantly smaller amount of training data.",
}
@inproceedings{johansen-socher-2017-learning,
    title = "Learning when to skim and when to read",
    author = "Johansen, Alexander  and
      Socher, Richard",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2631",
    doi = "10.18653/v1/W17-2631",
    pages = "257--264",
    abstract = "Many recent advances in deep learning for natural language processing have come at increasing computational cost, but the power of these state-of-the-art models is not needed for every example in a dataset. We demonstrate two approaches to reducing unnecessary computation in cases where a fast but weak baseline classier and a stronger, slower model are both available. Applying an AUC-based metric to the task of sentiment classification, we find significant efficiency gains with both a probability-threshold method for reducing computational cost and one that uses a secondary decision network.",
}
@inproceedings{tu-etal-2017-learning,
    title = "Learning to Embed Words in Context for Syntactic Tasks",
    author = "Tu, Lifu  and
      Gimpel, Kevin  and
      Livescu, Karen",
    booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2632",
    doi = "10.18653/v1/W17-2632",
    pages = "265--275",
    abstract = "We present models for embedding words in the context of surrounding words. Such models, which we refer to as token embeddings, represent the characteristics of a word that are specific to a given context, such as word sense, syntactic category, and semantic role. We explore simple, efficient token embedding models based on standard neural network architectures. We learn token embeddings on a large amount of unannotated text and evaluate them as features for part-of-speech taggers and dependency parsers trained on much smaller amounts of annotated data. We find that predictors endowed with token embeddings consistently outperform baseline predictors across a range of context window and training set sizes.",
}
@proceedings{ws-2017-building,
    title = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    editor = "Sharoff, Serge  and
      Zweigenbaum, Pierre  and
      Rapp, Reinhard",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2500",
    doi = "10.18653/v1/W17-25",
}
@inproceedings{langlais-2017-users,
    title = "Users and Data: The Two Neglected Children of Bilingual Natural Language Processing Research",
    author = "Langlais, Phillippe",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2501",
    doi = "10.18653/v1/W17-2501",
    pages = "1--5",
    abstract = "Despite numerous studies devoted to mining parallel material from bilingual data, we have yet to see the resulting technologies wholeheartedly adopted by professional translators and terminologists alike. I argue that this state of affairs is mainly due to two factors: the emphasis published authors put on models (even though data is as important), and the conspicuous lack of concern for actual end-users.",
}
@inproceedings{ferrero-etal-2017-deep,
    title = "Deep Investigation of Cross-Language Plagiarism Detection Methods",
    author = "Ferrero, J{\'e}r{\'e}my  and
      Besacier, Laurent  and
      Schwab, Didier  and
      Agn{\`e}s, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2502",
    doi = "10.18653/v1/W17-2502",
    pages = "6--15",
    abstract = "This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.",
}
@inproceedings{grover-mitra-2017-sentence,
    title = "Sentence Alignment using Unfolding Recursive Autoencoders",
    author = "Grover, Jeenu  and
      Mitra, Pabitra",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2503",
    doi = "10.18653/v1/W17-2503",
    pages = "16--20",
    abstract = "In this paper, we propose a novel two step algorithm for sentence alignment in monolingual corpora using Unfolding Recursive Autoencoders. First, we use unfolding recursive auto-encoders (RAE) to learn feature vectors for phrases in syntactical tree of the sentence. To compare two sentences we use a similarity matrix which has dimensions proportional to the size of the two sentences. Since the similarity matrix generated to compare two sentences has varying dimension due to different sentence lengths, a dynamic pooling layer is used to map it to a matrix of fixed dimension. The resulting matrix is used to calculate the similarity scores between the two sentences. The second step of the algorithm captures the contexts in which the sentences occur in the document by using a dynamic programming algorithm for global alignment.",
}
@inproceedings{bloodgood-strauss-2017-acquisition,
    title = "Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords",
    author = "Bloodgood, Michael  and
      Strauss, Benjamin",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2504",
    doi = "10.18653/v1/W17-2504",
    pages = "21--25",
    abstract = "With the advent of informal electronic communications such as social media, colloquial languages that were historically unwritten are being written for the first time in heavily code-switched environments. We present a method for inducing portions of translation lexicons through the use of expert knowledge in these settings where there are approximately zero resources available other than a language informant, potentially not even large amounts of monolingual data. We investigate inducing a Moroccan Darija-English translation lexicon via French loanwords bridging into English and find that a useful lexicon is induced for human-assisted translation and statistical machine translation.",
}
@inproceedings{milajevs-2017-toward,
    title = "Toward a Comparable Corpus of {L}atvian, {R}ussian and {E}nglish Tweets",
    author = "Milajevs, Dmitrijs",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2505",
    doi = "10.18653/v1/W17-2505",
    pages = "26--30",
    abstract = "Twitter has become a rich source for linguistic data. Here, a possibility of building a trilingual Latvian-Russian-English corpus of tweets from Riga, Latvia is investigated. Such a corpus, once constructed, might be of great use for multiple purposes including training machine translation models, examining cross-lingual phenomena and studying the population of Riga. This pilot study shows that it is feasible to build such a resource by collecting and analysing a pilot corpus, which is made publicly available and can be used to construct a large comparable corpus.",
}
@inproceedings{oktem-etal-2017-automatic,
    title = "Automatic Extraction of Parallel Speech Corpora from Dubbed Movies",
    author = {{\"O}ktem, Alp  and
      Farr{\'u}s, Mireia  and
      Wanner, Leo},
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2506",
    doi = "10.18653/v1/W17-2506",
    pages = "31--35",
    abstract = "This paper presents a methodology to extract parallel speech corpora based on any language pair from dubbed movies, together with an application framework in which some corresponding prosodic parameters are extracted. The obtained parallel corpora are especially suitable for speech-to-speech translation applications when a prosody transfer between source and target languages is desired.",
}
@inproceedings{neves-2017-parallel,
    title = "A parallel collection of clinical trials in {P}ortuguese and {E}nglish",
    author = "Neves, Mariana",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2507",
    doi = "10.18653/v1/W17-2507",
    pages = "36--40",
    abstract = "Parallel collections of documents are crucial resources for training and evaluating machine translation (MT) systems. Even though large collections are available for certain domains and language pairs, these are still scarce in the biomedical domain. We developed a parallel corpus of clinical trials in Portuguese and English. The documents are derived from the Brazilian Clinical Trials Registry and the corpus currently contains a total of 1188 documents. In this paper, we describe the corpus construction and discuss the quality of the translation and the sentence alignment that we obtained.",
}
@inproceedings{azpeitia-etal-2017-weighted,
    title = "Weighted Set-Theoretic Alignment of Comparable Sentences",
    author = "Azpeitia, Andoni  and
      Etchegoyhen, Thierry  and
      Mart{\'\i}nez Garcia, Eva",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2508",
    doi = "10.18653/v1/W17-2508",
    pages = "41--45",
    abstract = "This article presents the STACCw system for the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. The original STACC approach, based on set-theoretic operations over bags of words, had been previously shown to be efficient and portable across domains and alignment scenarios. Wedescribe an extension of this approach with a new weighting scheme and show that it provides significant improvements on the datasets provided for the shared task.",
}
@inproceedings{gregoire-langlais-2017-bucc,
    title = "{BUCC} 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora",
    author = "Gr{\'e}goire, Francis  and
      Langlais, Philippe",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2509",
    doi = "10.18653/v1/W17-2509",
    pages = "46--50",
    abstract = "This paper describes our participation in BUCC 2017 shared task: identifying parallel sentences in comparable corpora. Our goal is to leverage continuous vector representations and distributional semantics with a minimal use of external preprocessing and postprocessing tools. We report experiments that were conducted after transmitting our results.",
}
@inproceedings{zhang-zweigenbaum-2017-znlp,
    title = "z{NLP}: Identifying Parallel Sentences in {C}hinese-{E}nglish Comparable Corpora",
    author = "Zhang, Zheng  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2510",
    doi = "10.18653/v1/W17-2510",
    pages = "51--55",
    abstract = "This paper describes the zNLP system for the BUCC 2017 shared task. Our system identifies parallel sentence pairs in Chinese-English comparable corpora by translating word-by-word Chinese sentences into English, using the search engine Solr to select near-parallel sentences and then by using an SVM classifier to identify true parallel sentences from the previous results. It obtains an F1-score of 45{\%} (resp. 32{\%}) on the test (training) set.",
}
@inproceedings{mahata-etal-2017-bucc2017,
    title = "{BUCC}2017: A Hybrid Approach for Identifying Parallel Sentences in Comparable Corpora",
    author = "Mahata, Sainik  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2511",
    doi = "10.18653/v1/W17-2511",
    pages = "56--59",
    abstract = "A Statistical Machine Translation (SMT) system is always trained using large parallel corpus to produce effective translation. Not only is the corpus scarce, it also involves a lot of manual labor and cost. Parallel corpus can be prepared by employing comparable corpora where a pair of corpora is in two different languages pointing to the same domain. In the present work, we try to build a parallel corpus for French-English language pair from a given comparable corpus. The data and the problem set are provided as part of the shared task organized by BUCC 2017. We have proposed a system that first translates the sentences by heavily relying on Moses and then group the sentences based on sentence length similarity. Finally, the one to one sentence selection was done based on Cosine Similarity algorithm.",
}
@inproceedings{zweigenbaum-etal-2017-overview,
    title = "Overview of the Second {BUCC} Shared Task: Spotting Parallel Sentences in Comparable Corpora",
    author = "Zweigenbaum, Pierre  and
      Sharoff, Serge  and
      Rapp, Reinhard",
    booktitle = "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2512",
    doi = "10.18653/v1/W17-2512",
    pages = "60--67",
    abstract = "This paper presents the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. It recalls the design of the datasets, presents their final construction and statistics and the methods used to evaluate system results. 13 runs were submitted to the shared task by 4 teams, covering three of the four proposed language pairs: French-English (7 runs), German-English (3 runs), and Chinese-English (3 runs). The best F-scores as measured against the gold standard were 0.84 (German-English), 0.80 (French-English), and 0.43 (Chinese-English). Because of the design of the dataset, in which not all gold parallel sentence pairs are known, these are only minimum values. We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard. Adding them to the gold standard leads to revised estimates for the French-English F-scores of at most +1.5pt. This suggests that the BUCC 2017 datasets provide a reasonable approximate evaluation of the parallel sentence spotting task.",
}
@proceedings{ws-2017-textgraphs,
    title = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    editor = "Riedl, Martin  and
      Somasundaran, Swapna  and
      Glava{\v{s}}, Goran  and
      Hovy, Eduard",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2400",
    doi = "10.18653/v1/W17-24",
}
@inproceedings{marinho-etal-2017-calligraphy,
    title = "On the {``}Calligraphy{''} of Books",
    author = "Marinho, Vanessa Queiroz  and
      de Arruda, Henrique Ferraz  and
      Sinelli, Thales  and
      Costa, Luciano da Fontoura  and
      Amancio, Diego Raphael",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2401",
    doi = "10.18653/v1/W17-2401",
    pages = "1--10",
    abstract = "Authorship attribution is a natural language processing task that has been widely studied, often by considering small order statistics. In this paper, we explore a complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative. Indeed, as reported in this work, such an approach allowed the identification of the dominant narrative structure of the studied authors. This has been achieved due to the ability of the mesoscopic approach to take into account relationships between different, not necessarily adjacent, parts of the text, which is able to capture the story flow. The potential of the proposed approach has been illustrated through principal component analysis, a comparison with the chance baseline method, and network visualization. Such visualizations reveal individual characteristics of the authors, which can be understood as a kind of calligraphy.",
}
@inproceedings{mathew-etal-2017-adapting,
    title = "Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences",
    author = "Mathew, Binny  and
      Maity, Suman Kalyan  and
      Sarkar, Pratip  and
      Mukherjee, Animesh  and
      Goyal, Pawan",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2402",
    doi = "10.18653/v1/W17-2402",
    pages = "11--20",
    abstract = "Word senses are not static and may have temporal, spatial or corpus-specific scopes. Identifying such scopes might benefit the existing WSD systems largely. In this paper, while studying corpus specific word senses, we adapt three existing predominant and novel-sense discovery algorithms to identify these corpus-specific senses. We make use of text data available in the form of millions of digitized books and newspaper archives as two different sources of corpora and propose automated methods to identify corpus-specific word senses at various time points. We conduct an extensive and thorough human judgement experiment to rigorously evaluate and compare the performance of these approaches. Post adaptation, the output of the three algorithms are in the same format and the accuracy results are also comparable, with roughly 45-60{\%} of the reported corpus-specific senses being judged as genuine.",
}
@inproceedings{hernandez-gonzalez-etal-2017-merging,
    title = "Merging knowledge bases in different languages",
    author = "Hern{\'a}ndez-Gonz{\'a}lez, Jer{\'o}nimo  and
      Hruschka Jr., Estevam R.  and
      Mitchell, Tom M.",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2403",
    doi = "10.18653/v1/W17-2403",
    pages = "21--29",
    abstract = "Recently, different systems which learn to populate and extend a knowledge base (KB) from the web in different languages have been presented. Although a large set of concepts should be learnt independently from the language used to read, there are facts which are expected to be more easily gathered in local language (e.g., culture or geography). A system that merges KBs learnt in different languages will benefit from the complementary information as long as common beliefs are identified, as well as from redundancy present in web pages written in different languages. In this paper, we deal with the problem of identifying equivalent beliefs (or concepts) across language specific KBs, assuming that they share the same ontology of categories and relations. In a case study with two KBs independently learnt from different inputs, namely web pages written in English and web pages written in Portuguese respectively, we report on the results of two methodologies: an approach based on personalized PageRank and an inference technique to find out common relevant paths through the KBs. The proposed inference technique efficiently identifies relevant paths, outperforming the baseline (a dictionary-based classifier) in the vast majority of tested categories.",
}
@inproceedings{trost-klakow-2017-parameter,
    title = "Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings",
    author = "Trost, Thomas Alexander  and
      Klakow, Dietrich",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2404",
    doi = "10.18653/v1/W17-2404",
    pages = "30--38",
    abstract = "Word embeddings are high-dimensional vector representations of words and are thus difficult to interpret. In order to deal with this, we introduce an unsupervised parameter free method for creating a hierarchical graphical clustering of the full ensemble of word vectors and show that this structure is a geometrically meaningful representation of the original relations between the words. This newly obtained representation can be used for better understanding and thus improving the embedding algorithm and exhibits semantic meaning, so it can also be utilized in a variety of language processing tasks like categorization or measuring similarity.",
}
@inproceedings{fukui-etal-2017-spectral,
    title = "Spectral Graph-Based Method of Multimodal Word Embedding",
    author = "Fukui, Kazuki  and
      Oshikiri, Takamasa  and
      Shimodaira, Hidetoshi",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2405",
    doi = "10.18653/v1/W17-2405",
    pages = "39--44",
    abstract = "In this paper, we propose a novel method for multimodal word embedding, which exploit a generalized framework of multi-view spectral graph embedding to take into account visual appearances or scenes denoted by words in a corpus. We evaluated our method through word similarity tasks and a concept-to-image search task, having found that it provides word representations that reflect visual information, while somewhat trading-off the performance on the word similarity tasks. Moreover, we demonstrate that our method captures multimodal linguistic regularities, which enable recovering relational similarities between words and images by vector arithmetics.",
}
@inproceedings{baker-ellsworth-2017-graph,
    title = "Graph Methods for Multilingual {F}rame{N}ets",
    author = "Baker, Collin F.  and
      Ellsworth, Michael",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2406",
    doi = "10.18653/v1/W17-2406",
    pages = "45--50",
    abstract = "This paper introduces a new, graph-based view of the data of the FrameNet project, which we hope will make it easier to understand the mixture of semantic and syntactic information contained in FrameNet annotation. We show how English FrameNet and other Frame Semantic resources can be represented as sets of interconnected graphs of frames, frame elements, semantic types, and annotated instances of them in text. We display examples of the new graphical representation based on the annotations, which combine Frame Semantics and Construction Grammar, thus capturing most of the syntax and semantics of each sentence. We consider how graph theory could help researchers to make better use of FrameNet data for tasks such as automatic Frame Semantic role labeling, paraphrasing, and translation. Finally, we describe the development of FrameNet-like lexical resources for other languages in the current Multilingual FrameNet project. which seeks to discover cross-lingual alignments, both in the lexicon (for frames and lexical units within frames) and across parallel or comparable texts. We conclude with an example showing graphically the semantic and syntactic similarities and differences between parallel sentences in English and Japanese. We will release software for displaying such graphs from the current data releases.",
}
@inproceedings{nayeem-chali-2017-extract,
    title = "Extract with Order for Coherent Multi-Document Summarization",
    author = "Nayeem, Mir Tafseer  and
      Chali, Yllias",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2407",
    doi = "10.18653/v1/W17-2407",
    pages = "51--56",
    abstract = "In this work, we aim at developing an extractive summarizer in the multi-document setting. We implement a rank based sentence selection using continuous vector representations along with key-phrases. Furthermore, we propose a model to tackle summary coherence for increasing readability. We conduct experiments on the Document Understanding Conference (DUC) 2004 datasets using ROUGE toolkit. Our experiments demonstrate that the methods bring significant improvements over the state of the art methods in terms of informativity and coherence.",
}
@inproceedings{alkhereyf-rambow-2017-work,
    title = "Work Hard, Play Hard: Email Classification on the Avocado and {E}nron Corpora",
    author = "Alkhereyf, Sakhar  and
      Rambow, Owen",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2408",
    doi = "10.18653/v1/W17-2408",
    pages = "57--65",
    abstract = "In this paper, we present an empirical study of email classification into two main categories {``}Business{''} and {``}Personal{''}. We train on the Enron email corpus, and test on the Enron and Avocado email corpora. We show that information from the email exchange networks improves the performance of classification. We represent the email exchange networks as social networks with graph structures. For this classification task, we extract social networks features from the graphs in addition to lexical features from email content and we compare the performance of SVM and Extra-Trees classifiers using these features. Combining graph features with lexical features improves the performance on both classifiers. We also provide manually annotated sets of the Avocado and Enron email corpora as a supplementary contribution.",
}
@inproceedings{krishna-etal-2017-graph,
    title = "A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in {S}anskrit",
    author = "Krishna, Amrith  and
      Satuluri, Pavankumar  and
      Ponnada, Harshavardhan  and
      Ahmed, Muneeb  and
      Arora, Gulab  and
      Hiware, Kaustubh  and
      Goyal, Pawan",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2409",
    doi = "10.18653/v1/W17-2409",
    pages = "66--75",
    abstract = "Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 {\%} to that of the entire dataset.",
}
@inproceedings{putra-tokunaga-2017-evaluating,
    title = "Evaluating text coherence based on semantic similarity graph",
    author = "Putra, Jan Wira Gotama  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of {T}ext{G}raphs-11: the Workshop on Graph-based Methods for Natural Language Processing",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2410",
    doi = "10.18653/v1/W17-2410",
    pages = "76--85",
    abstract = "Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of semantic vectors representing sentences. We provide three graph construction methods establishing an edge from a given vertex to a preceding adjacent vertex, to a single similar vertex, or to multiple similar vertices. We evaluated our methods in the document discrimination task and the insertion task by comparing our proposed methods to the supervised (Entity Grid) and unsupervised (Entity Graph) baselines. In the document discrimination task, our method outperformed the unsupervised baseline but could not do the supervised baseline, while in the insertion task, our method outperformed both baselines.",
}
@proceedings{ws-2017-bionlp,
    title = "{B}io{NLP} 2017",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2300",
    doi = "10.18653/v1/W17-23",
}
@inproceedings{adams-etal-2017-target,
    title = "Target word prediction and paraphasia classification in spoken discourse",
    author = "Adams, Joel  and
      Bedrick, Steven  and
      Fergadiotis, Gerasimos  and
      Gorman, Kyle  and
      van Santen, Jan",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2301",
    doi = "10.18653/v1/W17-2301",
    pages = "1--8",
    abstract = "We present a system for automatically detecting and classifying phonologically anomalous productions in the speech of individuals with aphasia. Working from transcribed discourse samples, our system identifies neologisms, and uses a combination of string alignment and language models to produce a lattice of plausible words that the speaker may have intended to produce. We then score this lattice according to various features, and attempt to determine whether the anomalous production represented a phonemic error or a genuine neologism. This approach has the potential to be expanded to consider other types of paraphasic errors, and could be applied to a wide variety of screening and therapeutic applications.",
}
@inproceedings{asada-etal-2017-extracting,
    title = "Extracting Drug-Drug Interactions with Attention {CNN}s",
    author = "Asada, Masaki  and
      Miwa, Makoto  and
      Sasaki, Yutaka",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2302",
    doi = "10.18653/v1/W17-2302",
    pages = "9--18",
    abstract = "We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12{\%}, which is competitive with the state-of-the-art models.",
}
@inproceedings{newman-griffis-etal-2017-insights,
    title = "Insights into Analogy Completion from the Biomedical Domain",
    author = "Newman-Griffis, Denis  and
      Lai, Albert  and
      Fosler-Lussier, Eric",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2303",
    doi = "10.18653/v1/W17-2303",
    pages = "19--28",
    abstract = "Analogy completion has been a popular task in recent years for evaluating the semantic properties of word embeddings, but the standard methodology makes a number of assumptions about analogies that do not always hold, either in recent benchmark datasets or when expanding into other domains. Through an analysis of analogies in the biomedical domain, we identify three assumptions: that of a Single Answer for any given analogy, that the pairs involved describe the Same Relationship, and that each pair is Informative with respect to the other. We propose modifying the standard methodology to relax these assumptions by allowing for multiple correct answers, reporting MAP and MRR in addition to accuracy, and using multiple example pairs. We further present BMASS, a novel dataset for evaluating linguistic regularities in biomedical embeddings, and demonstrate that the relationships described in the dataset pose significant semantic challenges to current word embedding methods.",
}
@inproceedings{peng-lu-2017-deep,
    title = "Deep learning for extracting protein-protein interactions from biomedical literature",
    author = "Peng, Yifan  and
      Lu, Zhiyong",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2304",
    doi = "10.18653/v1/W17-2304",
    pages = "29--38",
    abstract = "State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN provides up to 6{\%} F1-score improvement over rich feature-based methods and single-kernel methods. In addition, McDepCNN achieves 24.4{\%} relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12{\%} improvement in F1-score over kernel-based methods on {``}difficult{''} instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.",
}
@inproceedings{rajani-etal-2017-stacking,
    title = "Stacking With Auxiliary Features for Entity Linking in the Medical Domain",
    author = "Rajani, Nazneen Fatema  and
      Bornea, Mihaela  and
      Barker, Ken",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2305",
    doi = "10.18653/v1/W17-2305",
    pages = "39--47",
    abstract = "Linking spans of natural language text to concepts in a structured source is an important task for many problems. It allows intelligent systems to leverage rich knowledge available in those sources (such as concept properties and relations) to enhance the semantics of the mentions of these concepts in text. In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System. Different approaches have different strengths: some are precision-oriented, some recall-oriented; some better at considering context but more prone to hallucination. The variety of techniques suggests that ensembling could outperform component technologies at this task. In this paper, we describe our process for building a Stacking ensemble using additional, auxiliary features for Entity Linking in the medical domain. We report experiments that show that naive ensembling does not always outperform component Entity Linking systems, that stacking usually outperforms naive ensembling, and that auxiliary features added to the stacker further improve its performance on three distinct datasets. Our best model produces state-of-the-art results on several medical datasets.",
}
@inproceedings{nentidis-etal-2017-results,
    title = "Results of the fifth edition of the {B}io{ASQ} Challenge",
    author = "Nentidis, Anastasios  and
      Bougiatiotis, Konstantinos  and
      Krithara, Anastasia  and
      Paliouras, Georgios  and
      Kakadiaris, Ioannis",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2306",
    doi = "10.18653/v1/W17-2306",
    pages = "48--57",
    abstract = "The goal of the BioASQ challenge is to engage researchers into creating cuttingedge biomedical information systems. Specifically, it aims at the promotion of systems and methodologies that are able to deal with a plethora of different tasks in the biomedical domain. This is achieved through the organization of challenges. The fifth challenge consisted of three tasks: semantic indexing, question answering and a new task on information extraction. In total, 29 teams with more than 95 systems participated in the challenge. Overall, as in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the art systems are continuously improving, pushing the frontier of research.",
}
@inproceedings{chandu-etal-2017-tackling,
    title = "Tackling Biomedical Text Summarization: {OAQA} at {B}io{ASQ} 5{B}",
    author = "Chandu, Khyathi  and
      Naik, Aakanksha  and
      Chandrasekar, Aditya  and
      Yang, Zi  and
      Gupta, Niloy  and
      Nyberg, Eric",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2307",
    doi = "10.18653/v1/W17-2307",
    pages = "58--66",
    abstract = "In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data. We describe our techniques with an emphasis on ideal answer generation, where the goal is to produce a relevant, precise, non-redundant, query-oriented summary from multiple relevant documents. We make use of extractive summarization techniques to address this task and experiment with different biomedical ontologies and various algorithms including agglomerative clustering, Maximum Marginal Relevance (MMR) and sentence compression. We propose a novel word embedding based tf-idf similarity metric and a soft positional constraint which improve our system performance. We evaluate our techniques on test batch 4 from the fourth edition of the challenge. Our best system achieves a ROUGE-2 score of 0.6534 and ROUGE-SU4 score of 0.6536.",
}
@inproceedings{molla-2017-macquarie,
    title = "{M}acquarie {U}niversity at {B}io{ASQ} 5b {--} Query-based Summarisation Techniques for Selecting the Ideal Answers",
    author = "Moll{\'a}, Diego",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2308",
    doi = "10.18653/v1/W17-2308",
    pages = "67--75",
    abstract = "Macquarie University{'}s contribution to the BioASQ challenge (Task 5b Phase B) focused on the use of query-based extractive summarisation techniques for the generation of the ideal answers. Four runs were submitted, with approaches ranging from a trivial system that selected the first $n$ snippets, to the use of deep learning approaches under a regression framework. Our experiments and the ROUGE results of the five test batches of BioASQ indicate surprisingly good results for the trivial approach. Overall, most of our runs on the first three test batches achieved the best ROUGE-SU4 results in the challenge.",
}
@inproceedings{wiese-etal-2017-neural,
    title = "Neural Question Answering at {B}io{ASQ} 5{B}",
    author = "Wiese, Georg  and
      Weissenborn, Dirk  and
      Neves, Mariana",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2309",
    doi = "10.18653/v1/W17-2309",
    pages = "76--79",
    abstract = "This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.",
}
@inproceedings{mehryary-etal-2017-end,
    title = "End-to-End System for Bacteria Habitat Extraction",
    author = {Mehryary, Farrokh  and
      Hakala, Kai  and
      Kaewphan, Suwisa  and
      Bj{\"o}rne, Jari  and
      Salakoski, Tapio  and
      Ginter, Filip},
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2310",
    doi = "10.18653/v1/W17-2310",
    pages = "80--90",
    abstract = "We introduce an end-to-end system capable of named-entity detection, normalization and relation extraction for extracting information about bacteria and their habitats from biomedical literature. Our system is based on deep learning, CRF classifiers and vector space models. We train and evaluate the system on the BioNLP 2016 Shared Task Bacteria Biotope data. The official evaluation shows that the joint performance of our entity detection and relation extraction models outperforms the winning team of the Shared Task by 19pp on F1-score, establishing a new top score for the task. We also achieve state-of-the-art results in the normalization task. Our system is open source and freely available at \url{https://github.com/TurkuNLP/BHE}.",
}
@inproceedings{cook-etal-2017-creation,
    title = "Creation and evaluation of a dictionary-based tagger for virus species and proteins",
    author = "Cook, Helen  and
      B{\=e}rzi{\c{n}}{\v{s}}, R{\=u}dolfs  and
      Rodr{\i}guez, Cristina Leal  and
      Cejuela, Juan Miguel  and
      Jensen, Lars Juhl",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2311",
    doi = "10.18653/v1/W17-2311",
    pages = "91--98",
    abstract = "ext mining automatically extracts information from the literature with the goal of making it available for further analysis, for example by incorporating it into biomedical databases. A key first step towards this goal is to identify and normalize the named entities, such as proteins and species, which are mentioned in text. Despite the large detrimental impact that viruses have on human and agricultural health, very little previous text-mining work has focused on identifying virus species and proteins in the literature. Here, we present an improved dictionary-based system for viral species and the first dictionary for viral proteins, which we benchmark on a new corpus of 300 manually annotated abstracts. We achieve 81.0{\%} precision and 72.7{\%} recall at the task of recognizing and normalizing viral species and 76.2{\%} precision and 34.9{\%} recall on viral proteins. These results are achieved despite the many challenges involved with the names of viral species and, especially, proteins. This work provides a foundation that can be used to extract more complicated relations about viruses from the literature.",
}
@inproceedings{ferre-etal-2017-representation,
    title = "Representation of complex terms in a vector space structured by an ontology for a normalization task",
    author = "Ferr{\'e}, Arnaud  and
      Zweigenbaum, Pierre  and
      N{\'e}dellec, Claire",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2312",
    doi = "10.18653/v1/W17-2312",
    pages = "99--106",
    abstract = "We propose in this paper a semi-supervised method for labeling terms of texts with concepts of a domain ontology. The method generates continuous vector representations of complex terms in a semantic space structured by the ontology. The proposed method relies on a distributional semantics approach, which generates initial vectors for each of the extracted terms. Then these vectors are embedded in the vector space constructed from the structure of the ontology. This embedding is carried out by training a linear model. Finally, we apply a distance calculation to determine the proximity between vectors of terms and vectors of concepts and thus to assign ontology labels to terms. We have evaluated the quality of these representations for a normalization task by using the concepts of an ontology as semantic labels. Normalization of terms is an important step to extract a part of the information containing in texts, but the vector space generated might find other applications. The performance of this method is comparable to that of the state of the art for this task of standardization, opening up encouraging prospects.",
}
@inproceedings{mcinnes-pedersen-2017-improving,
    title = "Improving Correlation with Human Judgments by Integrating Semantic Similarity with Second{--}Order Vectors",
    author = "McInnes, Bridget  and
      Pedersen, Ted",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2313",
    doi = "10.18653/v1/W17-2313",
    pages = "107--116",
    abstract = "Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co{--}occurrence frequencies or statistical measures of association to weight the importance of particular co{--}occurrences. In this paper, we extend these methods by incorporating a measure of semantic similarity based on a human curated taxonomy into a second{--}order vector representation. This results in a measure of semantic relatedness that combines both the contextual information available in a corpus{--}based vector space representation with the semantic knowledge found in a biomedical ontology. Our results show that incorporating semantic similarity into a second order co-occurrence matrices improves correlation with human judgments for both similarity and relatedness, and that our method compares favorably to various different word embedding methods that have recently been evaluated on the same reference standards we have used.",
}
@inproceedings{li-etal-2017-proactive,
    title = "Proactive Learning for Named Entity Recognition",
    author = "Li, Maolin  and
      Nguyen, Nhung  and
      Ananiadou, Sophia",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2314",
    doi = "10.18653/v1/W17-2314",
    pages = "117--125",
    abstract = "The goal of active learning is to minimise the cost of producing an annotated dataset, in which annotators are assumed to be perfect, i.e., they always choose the correct labels. However, in practice, annotators are not infallible, and they are likely to assign incorrect labels to some instances. Proactive learning is a generalisation of active learning that can model different kinds of annotators. Although proactive learning has been applied to certain labelling tasks, such as text classification, there is little work on its application to named entity (NE) tagging. In this paper, we propose a proactive learning method for producing NE annotated corpora, using two annotators with different levels of expertise, and who charge different amounts based on their levels of experience. To optimise both cost and annotation quality, we also propose a mechanism to present multiple sentences to annotators at each iteration. Experimental results for several corpora show that our method facilitates the construction of high-quality NE labelled datasets at minimal cost.",
}
@inproceedings{rao-etal-2017-biomedical,
    title = "Biomedical Event Extraction using {A}bstract {M}eaning {R}epresentation",
    author = "Rao, Sudha  and
      Marcu, Daniel  and
      Knight, Kevin  and
      Daum{\'e} III, Hal",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2315",
    doi = "10.18653/v1/W17-2315",
    pages = "126--135",
    abstract = "We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results.",
}
@inproceedings{klein-etal-2017-detecting,
    title = "Detecting Personal Medication Intake in {T}witter: An Annotated Corpus and Baseline Classification System",
    author = "Klein, Ari  and
      Sarker, Abeed  and
      Rouhizadeh, Masoud  and
      O{'}Connor, Karen  and
      Gonzalez, Graciela",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2316",
    doi = "10.18653/v1/W17-2316",
    pages = "136--142",
    abstract = "Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this in-formation would require the ability to distinguish indications of personal medication in-take in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication at a specific time. To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification.",
}
@inproceedings{fivez-etal-2017-unsupervised,
    title = "Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings",
    author = "Fivez, Pieter  and
      {\v{S}}uster, Simon  and
      Daelemans, Walter",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2317",
    doi = "10.18653/v1/W17-2317",
    pages = "143--148",
    abstract = "We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating a weighted cosine similarity between the vectorized representation of a candidate and the misspelling context. We greatly outperform two baseline off-the-shelf spelling correction tools on a manually annotated MIMIC-III test set, and counter the frequency bias of an optimized noisy channel model, showing that neural embeddings can be successfully exploited to include context-awareness in a spelling correction model.",
}
@inproceedings{bhatia-etal-2017-characterization,
    title = "Characterization of Divergence in Impaired Speech of {ALS} Patients",
    author = "Bhatia, Archna  and
      Dorr, Bonnie  and
      Hollingshead, Kristy  and
      Phillips, Samuel L.  and
      McKenzie, Barbara",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2318",
    doi = "10.18653/v1/W17-2318",
    pages = "149--158",
    abstract = "Approximately 80{\%} to 95{\%} of patients with Amyotrophic Lateral Sclerosis (ALS) eventually develop speech impairments, such as defective articulation, slow laborious speech and hypernasality. The relationship between impaired speech and asymptomatic speech may be seen as a divergence from a baseline. This relationship can be characterized in terms of measurable combinations of phonological characteristics that are indicative of the degree to which the two diverge. We demonstrate that divergence measurements based on phonological characteristics of speech correlate with physiological assessments of ALS. Speech-based assessments offer benefits over commonly-used physiological assessments in that they are inexpensive, non-intrusive, and do not require trained clinical personnel for administering and interpreting the results.",
}
@inproceedings{salloum-etal-2017-deep,
    title = "Deep Learning for Punctuation Restoration in Medical Reports",
    author = "Salloum, Wael  and
      Finley, Greg  and
      Edwards, Erik  and
      Miller, Mark  and
      Suendermann-Oeft, David",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2319",
    doi = "10.18653/v1/W17-2319",
    pages = "159--164",
    abstract = "In clinical dictation, speakers try to be as concise as possible to save time, often resulting in utterances without explicit punctuation commands. Since the end product of a dictated report, e.g. an out-patient letter, does require correct orthography, including exact punctuation, the latter need to be restored, preferably by automated means. This paper describes a method for punctuation restoration based on a state-of-the-art stack of NLP and machine learning techniques including B-RNNs with an attention mechanism and late fusion, as well as a feature extraction technique tailored to the processing of medical terminology using a novel vocabulary reduction model. To the best of our knowledge, the resulting performance is superior to that reported in prior art on similar tasks.",
}
@inproceedings{miller-etal-2017-unsupervised,
    title = "Unsupervised Domain Adaptation for Clinical Negation Detection",
    author = "Miller, Timothy  and
      Bethard, Steven  and
      Amiri, Hadi  and
      Savova, Guergana",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2320",
    doi = "10.18653/v1/W17-2320",
    pages = "165--170",
    abstract = "Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.",
}
@inproceedings{islamaj-dogan-etal-2017-biocreative,
    title = "{B}io{C}reative {VI} Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations",
    author = "Islamaj Do{\u{g}}an, Rezarta  and
      Chatr-aryamontri, Andrew  and
      Kim, Sun  and
      Wei, Chih-Hsuan  and
      Peng, Yifan  and
      Comeau, Donald  and
      Lu, Zhiyong",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2321",
    doi = "10.18653/v1/W17-2321",
    pages = "171--175",
    abstract = "The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI). In order to support this track with an effective train-ing dataset with limited curator time, the track organizers carefully reviewed Pub-Med articles from two different sources: curated public PPI databases, and the re-sults of state-of-the-art public text mining tools. We detail here the data collection, manual review and annotation process and describe this training corpus charac-teristics. We also describe a corpus per-formance baseline. This analysis will provide useful information to developers and researchers for comparing and devel-oping innovative text mining approaches for the BioCreative VI challenge and other Precision Medicine related applica-tions.",
}
@inproceedings{lever-jones-2017-painless,
    title = "Painless Relation Extraction with Kindred",
    author = "Lever, Jake  and
      Jones, Steven",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2322",
    doi = "10.18653/v1/W17-2322",
    pages = "176--183",
    abstract = "Relation extraction methods are essential for creating robust text mining tools to help researchers find useful knowledge in the vast published literature. Easy-to-use and generalizable methods are needed to encourage an ecosystem in which researchers can easily use shared resources and build upon each others{'} methods. We present the Kindred Python package for relation extraction. It builds upon methods from the most successful tools in the recent BioNLP Shared Task to predict high-quality predictions with low computational cost. It also integrates with PubAnnotation, PubTator, and BioNLP Shared Task data in order to allow easy development and application of relation extraction models.",
}
@inproceedings{li-etal-2017-noise,
    title = "Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction",
    author = "Li, Gang  and
      Wu, Cathy  and
      Vijay-Shanker, K.",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2323",
    doi = "10.18653/v1/W17-2323",
    pages = "184--193",
    abstract = "Distant supervision has been applied to automatically generate labeled data for biomedical relation extraction. Noise exists in both positively and negatively-labeled data and affects the performance of supervised machine learning methods. In this paper, we propose three novel heuristics based on the notion of proximity, trigger word and confidence of patterns to leverage lexical and syntactic information to reduce the level of noise in the distantly labeled data. Experiments on three different tasks, extraction of protein-protein-interaction, miRNA-gene regulation relation and protein-localization event, show that the proposed methods can improve the F-score over the baseline by 6, 10 and 14 points for the three tasks, respectively. We also show that when the models are configured to output high-confidence results, high precisions can be obtained using the proposed methods, making them promising for facilitating manual curation for databases.",
}
@inproceedings{polsley-etal-2017-role,
    title = "Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing",
    author = "Polsley, Seth  and
      Tahir, Atif  and
      Raju, Muppala  and
      Akinleye, Akintayo  and
      Steward, Duane",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2324",
    doi = "10.18653/v1/W17-2324",
    pages = "194--199",
    abstract = "Electronic medical records (EMR) have largely replaced hand-written patient files in healthcare. The growing pool of EMR data presents a significant resource in medical research, but the U.S. Health Insurance Portability and Accountability Act (HIPAA) mandates redacting medical records before performing any analysis on the same. This process complicates obtaining medical data and can remove much useful information from the record. As part of a larger project involving ontology-driven medical processing, we employ a method of recognizing protected health information (PHI) that maps to ontological terms. We then use the relationships defined in the ontology to redact medical texts so that roles and semantics of terms are retained without compromising anonymity. The method is evaluated by clinical experts on several hundred medical documents, achieving up to a 98.8{\%} f-score, and has already shown promise for retaining semantic information in later processing.",
}
@inproceedings{yim-etal-2017-annotation,
    title = "Annotation of pain and anesthesia events for surgery-related processes and outcomes extraction",
    author = "Yim, Wen-wai  and
      Tedesco, Dario  and
      Curtin, Catherine  and
      Hernandez-Boussard, Tina",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2325",
    doi = "10.18653/v1/W17-2325",
    pages = "200--205",
    abstract = "Pain and anesthesia information are crucial elements to identifying surgery-related processes and outcomes. However pain is not consistently recorded in the electronic medical record. Even when recorded, the rich complex granularity of the pain experience may be lost. Similarly, anesthesia information is recorded using local electronic collection systems; though the accuracy and completeness of the information is unknown. We propose an annotation schema to capture pain, pain management, and anesthesia event information.",
}
@inproceedings{gupta-etal-2017-identifying,
    title = "Identifying Comparative Structures in Biomedical Text",
    author = "Gupta, Samir  and
      Mahmood, A.S.M. Ashique  and
      Ross, Karen  and
      Wu, Cathy  and
      Vijay-Shanker, K.",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2326",
    doi = "10.18653/v1/W17-2326",
    pages = "206--215",
    abstract = "Comparison sentences are very commonly used by authors in biomedical literature to report results of experiments. In such comparisons, authors typically make observations under two different scenarios. In this paper, we present a system to automatically identify such comparative sentences and their components i.e. the compared entities, the scale of the comparison and the aspect on which the entities are being compared. Our methodology is based on dependencies obtained by applying a parser to extract a wide range of comparison structures. We evaluated our system for its effectiveness in identifying comparisons and their components. The system achieved a F-score of 0.87 for comparison sentence identification and 0.77-0.81 for identifying its components.",
}
@inproceedings{kayal-etal-2017-tagging,
    title = "Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models",
    author = "Kayal, Subhradeep  and
      Afzal, Zubair  and
      Tsatsaronis, George  and
      Katrenko, Sophia  and
      Coupet, Pascal  and
      Doornenbal, Marius  and
      Gregory, Michelle",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2327",
    doi = "10.18653/v1/W17-2327",
    pages = "216--221",
    abstract = "In this paper we present a solution for tagging funding bodies and grants in scientific articles using a combination of trained sequential learning models, namely conditional random fields (CRF), hidden markov models (HMM) and maximum entropy models (MaxEnt), on a benchmark set created in-house. We apply the trained models to address the BioASQ challenge 5c, which is a newly introduced task that aims to solve the problem of funding information extraction from scientific articles. Results in the dry-run data set of BioASQ task 5c show that the suggested approach can achieve a micro-recall of more than 85{\%} in tagging both funding bodies and grants.",
}
@inproceedings{mohan-etal-2017-deep,
    title = "Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs",
    author = "Mohan, Sunil  and
      Fiorini, Nicolas  and
      Kim, Sun  and
      Lu, Zhiyong",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2328",
    doi = "10.18653/v1/W17-2328",
    pages = "222--231",
    abstract = "We describe a Deep Learning approach to modeling the relevance of a document{'}s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document{'}s relevance to the query. Despite the small amount of training data, this approach produces a more robust predictor than computing similarities between semantic vector representations of the query and document, and also results in significant improvements over traditional IR text factors. In the future, we plan to explore its application in improving PubMed search.",
}
@inproceedings{masrani-etal-2017-detecting,
    title = "Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia",
    author = "Masrani, Vaden  and
      Murray, Gabriel  and
      Field, Thalia  and
      Carenini, Giuseppe",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2329",
    doi = "10.18653/v1/W17-2329",
    pages = "232--237",
    abstract = "We investigate if writers with dementia can be automatically distinguished from those without by analyzing linguistic markers in written text, in the form of blog posts. We have built a corpus of several thousand blog posts, some by people with dementia and others by people with loved ones with dementia. We use this dataset to train and test several machine learning methods, and achieve prediction performance at a level far above the baseline.",
}
@inproceedings{ganesan-etal-2017-protein,
    title = "Protein Word Detection using Text Segmentation Techniques",
    author = "Ganesan, Devi  and
      Tendulkar, Ashish V.  and
      Chakraborti, Sutanu",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2330",
    doi = "10.18653/v1/W17-2330",
    pages = "238--246",
    abstract = "Literature in Molecular Biology is abundant with linguistic metaphors. There have been works in the past that attempt to draw parallels between linguistics and biology, driven by the fundamental premise that proteins have a language of their own. Since word detection is crucial to the decipherment of any unknown language, we attempt to establish a problem mapping from natural language text to protein sequences at the level of words. Towards this end, we explore the use of an unsupervised text segmentation algorithm to the task of extracting {``}biological words{''} from protein sequences. In particular, we demonstrate the effectiveness of using domain knowledge to complement data driven approaches in the text segmentation task, as well as in its biological counterpart. We also propose a novel extrinsic evaluation measure for protein words through protein family classification.",
}
@inproceedings{kusa-spranger-2017-external,
    title = "External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the m{TOR} pathway",
    author = "Kusa, Wojciech  and
      Spranger, Michael",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2331",
    doi = "10.18653/v1/W17-2331",
    pages = "247--256",
    abstract = "This paper evaluates the impact of various event extraction systems on automatic pathway curation using the popular mTOR pathway. We quantify the impact of training data sets as well as different machine learning classifiers and show that some improve the quality of automatically extracted pathways.",
}
@inproceedings{apostolova-velez-2017-toward,
    title = "Toward Automated Early Sepsis Alerting: Identifying Infection Patients from Nursing Notes",
    author = "Apostolova, Emilia  and
      Velez, Tom",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2332",
    doi = "10.18653/v1/W17-2332",
    pages = "257--262",
    abstract = "Severe sepsis and septic shock are conditions that affect millions of patients and have close to 50{\%} mortality rate. Early identification of at-risk patients significantly improves outcomes. Electronic surveillance tools have been developed to monitor structured Electronic Medical Records and automatically recognize early signs of sepsis. However, many sepsis risk factors (e.g. symptoms and signs of infection) are often captured only in free text clinical notes. In this study, we developed a method for automatic monitoring of nursing notes for signs and symptoms of infection. We utilized a creative approach to automatically generate an annotated dataset. The dataset was used to create a Machine Learning model that achieved an F1-score ranging from 79 to 96{\%}.",
}
@inproceedings{zhang-etal-2017-enhancing,
    title = "Enhancing Automatic {ICD}-9-{CM} Code Assignment for Medical Texts with {P}ub{M}ed",
    author = "Zhang, Danchen  and
      He, Daqing  and
      Zhao, Sanqiang  and
      Li, Lei",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2333",
    doi = "10.18653/v1/W17-2333",
    pages = "263--271",
    abstract = "Assigning a standard ICD-9-CM code to disease symptoms in medical texts is an important task in the medical domain. Automating this process could greatly reduce the costs. However, the effectiveness of an automatic ICD-9-CM code classifier faces a serious problem, which can be triggered by unbalanced training data. Frequent diseases often have more training data, which helps its classification to perform better than that of an infrequent disease. However, a disease{'}s frequency does not necessarily reflect its importance. To resolve this training data shortage problem, we propose to strategically draw data from PubMed to enrich the training data when there is such need. We validate our method on the CMC dataset, and the evaluation results indicate that our method can significantly improve the code assignment classifiers{'} performance at the macro-averaging level.",
}
@inproceedings{henry-etal-2017-evaluating,
    title = "Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation",
    author = "Henry, Sam  and
      Cuffy, Clint  and
      McInnes, Bridget",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2334",
    doi = "10.18653/v1/W17-2334",
    pages = "272--281",
    abstract = "In this paper, we present an analysis of feature extraction methods via dimensionality reduction for the task of biomedical Word Sense Disambiguation (WSD). We modify the vector representations in the 2-MRD WSD algorithm, and evaluate four dimensionality reduction methods: Word Embeddings using Continuous Bag of Words and Skip Gram, Singular Value Decomposition (SVD), and Principal Component Analysis (PCA). We also evaluate the effects of vector size on the performance of each of these methods. Results are evaluated on five standard evaluation datasets (Abbrev.100, Abbrev.200, Abbrev.300, NLM-WSD, and MSH-WSD). We find that vector sizes of 100 are sufficient for all techniques except SVD, for which a vector size of 1500 is referred. We also show that SVD performs on par with Word Embeddings for all but one dataset.",
}
@inproceedings{mowery-etal-2017-investigating,
    title = "Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study",
    author = "Mowery, Danielle  and
      South, Brett  and
      Patterson, Olga  and
      Zhu, Shu-Hong  and
      Conway, Mike",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2335",
    doi = "10.18653/v1/W17-2335",
    pages = "282--286",
    abstract = "In this paper, we present pilot work on characterising the documentation of electronic cigarettes (e-cigarettes) in the United States Veterans Administration Electronic Health Record. The Veterans Health Administration is the largest health care system in the United States with 1,233 health care facilities nationwide, serving 8.9 million veterans per year. We identified a random sample of 2000 Veterans Administration patients, coded as current tobacco users, from 2008 to 2014. Using simple keyword matching techniques combined with qualitative analysis, we investigated the prevalence and distribution of e-cigarette terms in these clinical notes, discovering that for current smokers, 11.9{\%} of patient records contain an e-cigarette related term.",
}
@inproceedings{salloum-etal-2017-automated,
    title = "Automated Preamble Detection in Dictated Medical Reports",
    author = "Salloum, Wael  and
      Finley, Greg  and
      Edwards, Erik  and
      Miller, Mark  and
      Suendermann-Oeft, David",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2336",
    doi = "10.18653/v1/W17-2336",
    pages = "287--295",
    abstract = "Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of state-of-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.",
}
@inproceedings{sarrouti-ouatik-el-alaoui-2017-biomedical,
    title = "A Biomedical Question Answering System in {B}io{ASQ} 2017",
    author = "Sarrouti, Mourad  and
      Ouatik El Alaoui, Said",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2337",
    doi = "10.18653/v1/W17-2337",
    pages = "296--301",
    abstract = "Question answering, the identification of short accurate answers to users questions, is a longstanding challenge widely studied over the last decades in the open domain. However, it still requires further efforts in the biomedical domain. In this paper, we describe our participation in phase B of task 5b in the 2017 BioASQ challenge using our biomedical question answering system. Our system, dealing with four types of questions (i.e., yes/no, factoid, list, and summary), is based on (1) a dictionary-based approach for generating the exact answers of yes/no questions, (2) UMLS metathesaurus and term frequency metric for extracting the exact answers of factoid and list questions, and (3) the BM25 model and UMLS concepts for retrieving the ideal answers (i.e., paragraph-sized summaries). Preliminary results show that our system achieves good and competitive results in both exact and ideal answers extraction tasks as compared with the participating systems.",
}
@inproceedings{patel-etal-2017-adapting,
    title = "Adapting Pre-trained Word Embeddings For Use In Medical Coding",
    author = "Patel, Kevin  and
      Patel, Divya  and
      Golakiya, Mansi  and
      Bhattacharyya, Pushpak  and
      Birari, Nilesh",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2338",
    doi = "10.18653/v1/W17-2338",
    pages = "302--306",
    abstract = "Word embeddings are a crucial component in modern NLP. Pre-trained embeddings released by different groups have been a major reason for their popularity. However, they are trained on generic corpora, which limits their direct use for domain specific tasks. In this paper, we propose a method to add task specific information to pre-trained word embeddings. Such information can improve their utility. We add information from medical coding data, as well as the first level from the hierarchy of ICD-10 medical code set to different pre-trained word embeddings. We adapt CBOW algorithm from the word2vec package for our purpose. We evaluated our approach on five different pre-trained word embeddings. Both the original word embeddings, and their modified versions (the ones with added information) were used for automated review of medical coding. The modified word embeddings give an improvement in f-score by 1{\%} on the 5-fold evaluation on a private medical claims dataset. Our results show that adding extra information is possible and beneficial for the task at hand.",
}
@inproceedings{baker-korhonen-2017-initializing,
    title = "Initializing neural networks for hierarchical multi-label text classification",
    author = "Baker, Simon  and
      Korhonen, Anna",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2339",
    doi = "10.18653/v1/W17-2339",
    pages = "307--315",
    abstract = "Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary classifier is trained for each label in the taxonomy or ontology where all instances not belonging to the class are considered negative examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the additional computational cost of training parallel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such as hypernymy. This approach elegantly lends itself to hierarchical classification. We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentence- and document-level classification. Our evaluation shows promising results for this approach.",
}
@inproceedings{v-s-s-patchigolla-etal-2017-biomedical,
    title = "Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models",
    author = "V S S Patchigolla, Rahul  and
      Sahu, Sunil  and
      Anand, Ashish",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2340",
    doi = "10.18653/v1/W17-2340",
    pages = "316--321",
    abstract = "Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.",
}
@inproceedings{lin-etal-2017-representations,
    title = "Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks",
    author = "Lin, Chen  and
      Miller, Timothy  and
      Dligach, Dmitriy  and
      Bethard, Steven  and
      Savova, Guergana",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2341",
    doi = "10.18653/v1/W17-2341",
    pages = "322--327",
    abstract = "Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.",
}
@inproceedings{karimi-etal-2017-automatic,
    title = "Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods",
    author = "Karimi, Sarvnaz  and
      Dai, Xiang  and
      Hassanzadeh, Hamed  and
      Nguyen, Anthony",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2342",
    doi = "10.18653/v1/W17-2342",
    pages = "328--332",
    abstract = "Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.",
}
@inproceedings{campillos-llanos-etal-2017-automatic,
    title = "Automatic classification of doctor-patient questions for a virtual patient record query task",
    author = "Campillos Llanos, Leonardo  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2343",
    doi = "10.18653/v1/W17-2343",
    pages = "333--341",
    abstract = "We present the work-in-progress of automating the classification of doctor-patient questions in the context of a simulated consultation with a virtual patient. We classify questions according to the computational strategy (rule-based or other) needed for looking up data in the clinical record. We compare {`}traditional{'} machine learning methods (Gaussian and Multinomial Naive Bayes, and Support Vector Machines) and a neural network classifier (FastText). We obtained the best results with the SVM using semantic annotations, whereas the neural classifier achieved promising results without it.",
}
@inproceedings{neves-etal-2017-assessing,
    title = "Assessing the performance of {O}lelo, a real-time biomedical question answering application",
    author = "Neves, Mariana  and
      Eckert, Fabian  and
      Folkerts, Hendrik  and
      Uflacker, Matthias",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2344",
    doi = "10.18653/v1/W17-2344",
    pages = "342--350",
    abstract = "Question answering (QA) can support physicians and biomedical researchers to find answers to their questions in the scientific literature. Such systems process large collections of documents in real time and include many natural language processing (NLP) procedures. We recently developed Olelo, a QA system for biomedicine which includes various NLP components, such as question processing, document and passage retrieval, answer processing and multi-document summarization. In this work, we present an evaluation of our system on the the fifth BioASQ challenge. We participated with the current state of the application and with an extension based on semantic role labeling that we are currently investigating. In addition to the BioASQ evaluation, we compared our system to other on-line biomedical QA systems in terms of the response time and the quality of the answers.",
}
@inproceedings{maharana-yetisgen-2017-clinical,
    title = "Clinical Event Detection with Hybrid Neural Architecture",
    author = "Maharana, Adyasha  and
      Yetisgen, Meliha",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2345",
    doi = "10.18653/v1/W17-2345",
    pages = "351--355",
    abstract = "Event detection from clinical notes has been traditionally solved with rule based and statistical natural language processing (NLP) approaches that require extensive domain knowledge and feature engineering. In this paper, we have explored the feasibility of approaching this task with recurrent neural networks, clinical word embeddings and introduced a hybrid architecture to improve detection for entities with smaller representation in the dataset. A comparative analysis is also done which reveals the complementary behavior of neural networks and conditional random fields in clinical entity detection.",
}
@inproceedings{naik-etal-2017-extracting,
    title = "Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision",
    author = "Naik, Aakanksha  and
      Bogart, Chris  and
      Rose, Carolyn",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2346",
    doi = "10.18653/v1/W17-2346",
    pages = "356--364",
    abstract = "In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text available. Creating systems which can automatically extract important medical events and create disease progression timelines for users from such text can help in patient health monitoring as well as studying links between medical events and users{'} participation in support groups. Prior work in this domain has used manually constructed keyword sets to detect medical events. In this work, our aim is to perform medical event detection using minimal supervision in order to develop a more general timeline construction system. Our system achieves an accuracy of 55.17{\%}, which is 92{\%} of the performance achieved by a supervised baseline system.",
}
@inproceedings{moen-etal-2017-detecting,
    title = "Detecting mentions of pain and acute confusion in {F}innish clinical text",
    author = {Moen, Hans  and
      Hakala, Kai  and
      Mehryary, Farrokh  and
      Peltonen, Laura-Maria  and
      Salakoski, Tapio  and
      Ginter, Filip  and
      Salanter{\"a}, Sanna},
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2347",
    doi = "10.18653/v1/W17-2347",
    pages = "365--372",
    abstract = "We study and compare two different approaches to the task of automatic assignment of predefined classes to clinical free-text narratives. In the first approach this is treated as a traditional mention-level named-entity recognition task, while the second approach treats it as a sentence-level multi-label classification task. Performance comparison across these two approaches is conducted in the form of sentence-level evaluation and state-of-the-art methods for both approaches are evaluated. The experiments are done on two data sets consisting of Finnish clinical text, manually annotated with respect to the topics pain and acute confusion. Our results suggest that the mention-level named-entity recognition approach outperforms sentence-level classification overall, but the latter approach still manages to achieve the best prediction scores on several annotation classes.",
}
@inproceedings{jin-etal-2017-multi,
    title = "A Multi-strategy Query Processing Approach for Biomedical Question Answering: {USTB}{\_}{PRIR} at {B}io{ASQ} 2017 Task 5{B}",
    author = "Jin, Zan-Xia  and
      Zhang, Bo-Wen  and
      Fang, Fan  and
      Zhang, Le-Le  and
      Yin, Xu-Cheng",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2348",
    doi = "10.18653/v1/W17-2348",
    pages = "373--380",
    abstract = "This paper describes the participation of USTB{\_}PRIR team in the 2017 BioASQ 5B on question answering, including document retrieval, snippet retrieval, and concept retrieval task. We introduce different multimodal query processing strategies to enrich query terms and assign different weights to them. Specifically, sequential dependence model (SDM), pseudo-relevance feedback (PRF), fielded sequential dependence model (FSDM) and Divergence from Randomness model (DFRM) are respectively performed on different fields of PubMed articles, sentences extracted from relevant articles, the five terminologies or ontologies (MeSH, GO, Jochem, Uniprot and DO) to achieve better search performances. Preliminary results show that our systems outperform others in the document and snippet retrieval task in the first two batches.",
}
@proceedings{ws-2017-joint-sighum,
    title = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    editor = "Alex, Beatrice  and
      Degaetano-Ortlieb, Stefania  and
      Feldman, Anna  and
      Kazantseva, Anna  and
      Reiter, Nils  and
      Szpakowicz, Stan",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2200",
    doi = "10.18653/v1/W17-22",
}
@inproceedings{kesarwani-etal-2017-metaphor,
    title = "Metaphor Detection in a Poetry Corpus",
    author = "Kesarwani, Vaibhav  and
      Inkpen, Diana  and
      Szpakowicz, Stan  and
      Tanasescu, Chris",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2201",
    doi = "10.18653/v1/W17-2201",
    pages = "1--9",
    abstract = "Metaphor is indispensable in poetry. It showcases the poet{'}s creativity, and contributes to the overall emotional pertinence of the poem while honing its specific rhetorical impact. Previous work on metaphor detection relies on either rule-based or statistical models, none of them applied to poetry. Our method focuses on metaphor detection in a poetry corpus. It combines rule-based and statistical models (word embeddings) to develop a new classification system. Our system has achieved a precision of 0.759 and a recall of 0.804 in identifying one type of metaphor in poetry.",
}
@inproceedings{page-perron-etal-2017-machine,
    title = "Machine Translation and Automated Analysis of the {S}umerian Language",
    author = "Pag{\'e}-Perron, {\'E}milie  and
      Sukhareva, Maria  and
      Khait, Ilya  and
      Chiarcos, Christian",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2202",
    doi = "10.18653/v1/W17-2202",
    pages = "10--16",
    abstract = "This paper presents a newly funded international project for machine translation and automated analysis of ancient cuneiform languages where NLP specialists and Assyriologists collaborate to create an information retrieval system for Sumerian. This research is conceived in response to the need to translate large numbers of administrative texts that are only available in transcription, in order to make them accessible to a wider audience. The methodology includes creation of a specialized NLP pipeline and also the use of linguistic linked open data to increase access to the results.",
}
@inproceedings{kim-etal-2017-investigating,
    title = "Investigating the Relationship between Literary Genres and Emotional Plot Development",
    author = "Kim, Evgeny  and
      Pad{\'o}, Sebastian  and
      Klinger, Roman",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2203",
    doi = "10.18653/v1/W17-2203",
    pages = "17--26",
    abstract = "Literary genres are commonly viewed as being defined in terms of content and stylistic features. In this paper, we focus on one particular class of lexical features, namely emotion information, and investigate the hypothesis that emotion-related information correlates with particular genres. Using genre classification as a testbed, we compare a model that computes lexicon-based emotion scores globally for complete stories with a model that tracks emotion arcs through stories on a subset of Project Gutenberg with five genres. Our main findings are: (a), the global emotion model is competitive with a large-vocabulary bag-of-words genre classifier (80{\%}F1); (b), the emotion arc model shows a lower performance (59 {\%} F1) but shows complementary behavior to the global model, as indicated by a very good performance of an oracle model (94 {\%} F1) and an improved performance of an ensemble model (84 {\%} F1); (c), genres differ in the extent to which stories follow the same emotional arcs, with particularly uniform behavior for anger (mystery) and fear (adventures, romance, humor, science fiction).",
}
@inproceedings{ruiz-etal-2017-enjambment,
    title = "Enjambment Detection in a Large Diachronic Corpus of {S}panish Sonnets",
    author = "Ruiz, Pablo  and
      Mart{\'\i}nez Cant{\'o}n, Clara  and
      Poibeau, Thierry  and
      Gonz{\'a}lez-Blanco, Elena",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2204",
    doi = "10.18653/v1/W17-2204",
    pages = "27--32",
    abstract = "Enjambment takes place when a syntactic unit is broken up across two lines of poetry, giving rise to different stylistic effects. In Spanish literary studies, there are unclear points about the types of stylistic effects that can arise, and under which linguistic conditions. To systematically gather evidence about this, we developed a system to automatically identify enjambment (and its type) in Spanish. For evaluation, we manually annotated a reference corpus covering different periods. As a scholarly corpus to apply the tool, from public HTML sources we created a diachronic corpus covering four centuries of sonnets (3750 poems), and we analyzed the occurrence of enjambment across stanzaic boundaries in different periods. Besides, we found examples that highlight limitations in current definitions of enjambment.",
}
@inproceedings{kelleher-keane-2017-plotting,
    title = "Plotting {M}arkson{'}s {``}Mistress{''}",
    author = "Kelleher, Conor  and
      Keane, Mark",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2205",
    doi = "10.18653/v1/W17-2205",
    pages = "33--39",
    abstract = "The post-modern novel {``}Wittgenstein{'}s Mistress{''} by David Markson (1988) presents the reader with a very challenging non-linear narrative, that itself appears to one of the novel{'}s themes. We present a distant reading of this work designed to complement a close reading of it by David Foster Wallace (1990). Using a combination of text analysis, entity recognition and networks, we plot repetitive structures in the novel{'}s narrative relating them to its critical analysis.",
}
@inproceedings{seemann-etal-2017-annotation,
    title = "Annotation Challenges for Reconstructing the Structural Elaboration of {M}iddle {L}ow {G}erman",
    author = {Seemann, Nina  and
      Merten, Marie-Luis  and
      Geierhos, Michaela  and
      Tophinke, Doris  and
      H{\"u}llermeier, Eyke},
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2206",
    doi = "10.18653/v1/W17-2206",
    pages = "40--45",
    abstract = "In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA - a web-based annotation tool for historical and other non-standard language data - to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations.",
}
@inproceedings{hench-2017-phonological,
    title = "Phonological Soundscapes in Medieval Poetry",
    author = "Hench, Christopher",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2207",
    doi = "10.18653/v1/W17-2207",
    pages = "46--56",
    abstract = "The oral component of medieval poetry was integral to its performance and reception. Yet many believe that the medieval voice has been forever lost, and any attempts at rediscovering it are doomed to failure due to scribal practices, manuscript mouvance, and linguistic normalization in editing practices. This paper offers a method to abstract from this noise and better understand relative differences in phonological soundscapes by considering syllable qualities. The presented syllabification method and soundscape analysis offer themselves as cross-disciplinary tools for low-resource languages. As a case study, we examine medieval German lyric and argue that the heavily debated lyrical {`}I{'} follows a unique trajectory through soundscapes, shedding light on the performance and practice of these poets.",
}
@inproceedings{blessing-etal-2017-end,
    title = "An End-to-end Environment for Research Question-Driven Entity Extraction and Network Analysis",
    author = "Blessing, Andre  and
      Echelmeyer, Nora  and
      John, Markus  and
      Reiter, Nils",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2208",
    doi = "10.18653/v1/W17-2208",
    pages = "57--67",
    abstract = "This paper presents an approach to extract co-occurrence networks from literary texts. It is a deliberate decision not to aim for a fully automatic pipeline, as the literary research questions need to guide both the definition of the nature of the things that co-occur as well as how to decide co-occurrence. We showcase the approach on a Middle High German romance, \textit{Parzival}. Manual inspection and discussion shows the huge impact various choices have.",
}
@inproceedings{degaetano-ortlieb-teich-2017-modeling,
    title = "Modeling intra-textual variation with entropy and surprisal: topical vs. stylistic patterns",
    author = "Degaetano-Ortlieb, Stefania  and
      Teich, Elke",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2209",
    doi = "10.18653/v1/W17-2209",
    pages = "68--77",
    abstract = "We present a data-driven approach to investigate intra-textual variation by combining entropy and surprisal. With this approach we detect linguistic variation based on phrasal lexico-grammatical patterns across sections of research articles. Entropy is used to detect patterns typical of specific sections. Surprisal is used to differentiate between more and less informationally-loaded patterns as well as type of information (topical vs. stylistic). While we here focus on research articles in biology/genetics, the methodology is especially interesting for digital humanities scholars, as it can be applied to any text type or domain and combined with additional variables (e.g. time, author or social group).",
}
@inproceedings{dinu-uban-2017-finding,
    title = "Finding a Character{'}s Voice: Stylome Classification on Literary Characters",
    author = "Dinu, Liviu P.  and
      Uban, Ana Sabina",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2210",
    doi = "10.18653/v1/W17-2210",
    pages = "78--82",
    abstract = "We investigate in this paper the problem of classifying the stylome of characters in a literary work. Previous research in the field of authorship attribution has shown that the writing style of an author can be characterized and distinguished from that of other authors automatically. In this paper we take a look at the less approached problem of how the styles of different characters can be distinguished, trying to verify if an author managed to create believable characters with individual styles. We present the results of some initial experiments developed on the novel {``}Liaisons Dangereuses{''}, showing that a simple bag of words model can be used to classify the characters.",
}
@inproceedings{di-buono-2017-ontology,
    title = "An Ontology-Based Method for Extracting and Classifying Domain-Specific Compositional Nominal Compounds",
    author = "di Buono, Maria Pia",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2211",
    doi = "10.18653/v1/W17-2211",
    pages = "83--88",
    abstract = "In this paper, we present our preliminary study on an ontology-based method to extract and classify compositional nominal compounds in specific domains of knowledge. This method is based on the assumption that, applying a conceptual model to represent knowledge domain, it is possible to improve the extraction and classification of lexicon occurrences for that domain in a semi-automatic way. We explore the possibility of extracting and classifying a specific construction type (nominal compounds) spanning a specific domain (Cultural Heritage) and a specific language (Italian).",
}
@inproceedings{walther-sagot-2017-speeding,
    title = "Speeding up corpus development for linguistic research: language documentation and acquisition in {R}omansh Tuatschin",
    author = "Walther, G{\'e}raldine  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2212",
    doi = "10.18653/v1/W17-2212",
    pages = "89--94",
    abstract = "In this paper, we present ongoing work for developing language resources and basic NLP tools for an undocumented variety of Romansh, in the context of a language documentation and language acquisition project. Our tools are meant to improve the speed and reliability of corpus annotations for noisy data involving large amounts of code-switching, occurrences of child-speech and orthographic noise. Being able to increase the efficiency of language resource development for language documentation and acquisition research also constitutes a step towards solving the data sparsity issues with which researchers have been struggling.",
}
@inproceedings{sukhareva-etal-2017-distantly,
    title = "Distantly Supervised {POS} Tagging of Low-Resource Languages under Extreme Data Sparsity: The Case of {H}ittite",
    author = {Sukhareva, Maria  and
      Fuscagni, Francesco  and
      Daxenberger, Johannes  and
      G{\"o}rke, Susanne  and
      Prechel, Doris  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2213",
    doi = "10.18653/v1/W17-2213",
    pages = "95--104",
    abstract = "This paper presents a statistical approach to automatic morphosyntactic annotation of Hittite transcripts. Hittite is an extinct Indo-European language using the cuneiform script. There are currently no morphosyntactic annotations available for Hittite, so we explored methods of distant supervision. The annotations were projected from parallel German translations of the Hittite texts. In order to reduce data sparsity, we applied stemming of German and Hittite texts. As there is no off-the-shelf Hittite stemmer, a stemmer for Hittite was developed for this purpose. The resulting annotation projections were used to train a POS tagger, achieving an accuracy of 69{\%} on a test sample. To our knowledge, this is the first attempt of statistical POS tagging of a cuneiform language.",
}
@inproceedings{krishna-etal-2017-dataset,
    title = "A Dataset for {S}anskrit Word Segmentation",
    author = "Krishna, Amrith  and
      Satuluri, Pavan Kumar  and
      Goyal, Pawan",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2214",
    doi = "10.18653/v1/W17-2214",
    pages = "105--114",
    abstract = "The last decade saw a surge in digitisation efforts for ancient manuscripts in Sanskrit. Due to various linguistic peculiarities inherent to the language, even the preliminary tasks such as word segmentation are non-trivial in Sanskrit. Elegant models for Word Segmentation in Sanskrit are indispensable for further syntactic and semantic processing of the manuscripts. Current works in word segmentation for Sanskrit, though commendable in their novelty, often have variations in their objective and evaluation criteria. In this work, we set the record straight. We formally define the objectives and the requirements for the word segmentation task. In order to encourage research in the field and to alleviate the time and effort required in pre-processing, we release a dataset of 115,000 sentences for word segmentation. For each sentence in the dataset we include the input character sequence, ground truth segmentation, and additionally lexical and morphological information about all the phonetically possible segments for the given sentence. In this work, we also discuss the linguistic considerations made while generating the candidate space of the possible segments.",
}
@inproceedings{ogrodniczuk-kopec-2017-lexical,
    title = "Lexical Correction of {P}olish {T}witter Political Data",
    author = "Ogrodniczuk, Maciej  and
      Kope{\'c}, Mateusz",
    booktitle = "Proceedings of the Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2215",
    doi = "10.18653/v1/W17-2215",
    pages = "115--125",
    abstract = "Language processing architectures are often evaluated in near-to-perfect conditions with respect to processed content. The tools which perform sufficiently well on electronic press, books and other type of non-interactive content may poorly handle littered, colloquial and multilingual textual data which make the majority of communication today. This paper aims at investigating how Polish Twitter data (in a slightly controlled {`}political{'} flavour) differs from expectation of linguistic tools and how they could be corrected to be ready for processing by standard language processing chains available for Polish. The setting includes specialised components for spelling correction of tweets as well as hashtag and username decoding.",
}
@proceedings{ws-2017-vision,
    title = "Proceedings of the Sixth Workshop on Vision and Language",
    editor = "Belz, Anya  and
      Erdem, Erkut  and
      Pastra, Katerina  and
      Mikolajczyk, Krystian",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2000",
    doi = "10.18653/v1/W17-20",
}
@inproceedings{yu-etal-2017-burchak,
    title = "The {BURCHAK} corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Mills, Gregory  and
      Lemon, Oliver",
    booktitle = "Proceedings of the Sixth Workshop on Vision and Language",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2001",
    doi = "10.18653/v1/W17-2001",
    pages = "1--10",
    abstract = "We motivate and describe a new freely available human-human dialogue data set for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; anon.) with a novel task, where a Learner needs to learn invented visual attribute words (such as {``}burchak{''} for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self- and other-correction, mid-sentence continuations, interruptions, turn overlaps, fillers, hedges and many kinds of ellipsis. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental dialogue data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78{\%} turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.",
}
@inproceedings{birmingham-muscat-2017-use,
    title = "The Use of Object Labels and Spatial Prepositions as Keywords in a Web-Retrieval-Based Image Caption Generation System",
    author = "Birmingham, Brandon  and
      Muscat, Adrian",
    booktitle = "Proceedings of the Sixth Workshop on Vision and Language",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2002",
    doi = "10.18653/v1/W17-2002",
    pages = "11--20",
    abstract = "In this paper, a retrieval-based caption generation system that searches the web for suitable image descriptions is studied. Google{'}s reverse image search is used to find potentially relevant web multimedia content for query images. Sentences are extracted from web pages and the likelihood of the descriptions is computed to select one sentence from the retrieved text documents. The search mechanism is modified to replace the caption generated by Google with a caption composed of labels and spatial prepositions as part of the query{'}s text alongside the image. The object labels are obtained using an off-the-shelf R-CNN and a machine learning model is developed to predict the prepositions. The effect on the caption generation system performance when using the generated text is investigated. Both human evaluations and automatic metrics are used to evaluate the retrieved descriptions. Results show that the web-retrieval-based approach performed better when describing single-object images with sentences extracted from stock photography websites. On the other hand, images with two image objects were better described with template-generated sentences composed of object labels and prepositions.",
}
@inproceedings{nurani-venkitasubramanian-etal-2017-learning,
    title = "Learning to Recognize Animals by Watching Documentaries: Using Subtitles as Weak Supervision",
    author = "Nurani Venkitasubramanian, Aparna  and
      Tuytelaars, Tinne  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the Sixth Workshop on Vision and Language",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2003",
    doi = "10.18653/v1/W17-2003",
    pages = "21--30",
    abstract = "We investigate animal recognition models learned from wildlife video documentaries by using the weak supervision of the textual subtitles. This is a particularly challenging setting, since i) the animals occur in their natural habitat and are often largely occluded and ii) subtitles are to a large degree complementary to the visual content, providing a very weak supervisory signal. This is in contrast to most work on integrated vision and language in the literature, where textual descriptions are tightly linked to the image content, and often generated in a curated fashion for the task at hand. In particular, we investigate different image representations and models, including a support vector machine on top of activations of a pretrained convolutional neural network, as well as a Naive Bayes framework on a {`}bag-of-activations{'} image representation, where each element of the bag is considered separately. This representation allows key components in the image to be isolated, in spite of largely varying backgrounds and image clutter, without an object detection or image segmentation step. The methods are evaluated based on how well they transfer to unseen camera-trap images captured across diverse topographical regions under different environmental conditions and illumination settings, involving a large domain shift.",
}
@inproceedings{calixto-etal-2017-human,
    title = "Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on {E}-Commerce Listing Titles",
    author = "Calixto, Iacer  and
      Stein, Daniel  and
      Matusov, Evgeny  and
      Castilho, Sheila  and
      Way, Andy",
    booktitle = "Proceedings of the Sixth Workshop on Vision and Language",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2004",
    doi = "10.18653/v1/W17-2004",
    pages = "31--37",
    abstract = "In this paper, we study how humans perceive the use of images as an additional knowledge source to machine-translate user-generated product listings in an e-commerce company. We conduct a human evaluation where we assess how a multi-modal neural machine translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attention-based NMT and a phrase-based statistical machine translation (PBSMT) model. We evaluate translations obtained with different systems and also discuss the data set of user-generated product listings, which in our case comprises both product listings and associated images. We found that humans preferred translations obtained with a PBSMT system to both text-only and multi-modal NMT over 56{\%} of the time. Nonetheless, human evaluators ranked translations from a multi-modal NMT model as better than those of a text-only NMT over 88{\%} of the time, which suggests that images do help NMT in this use-case.",
}
@inproceedings{ramisa-etal-2017-breakingnews,
    title = "The {B}reaking{N}ews Dataset",
    author = "Ramisa, Arnau  and
      Yan, Fei  and
      Moreno-Noguer, Francesc  and
      Mikolajczyk, Krystian",
    booktitle = "Proceedings of the Sixth Workshop on Vision and Language",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2005",
    doi = "10.18653/v1/W17-2005",
    pages = "38--39",
    abstract = "We present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (e.g. GPS coordinates and popularity metrics). The tenuous connection between the images and text in news data is appropriate to take work at the intersection of Computer Vision and Natural Language Processing to the next step, hence we hope this dataset will help spur progress in the field.",
}
@inproceedings{paggio-etal-2017-automatic,
    title = "Automatic identification of head movements in video-recorded conversations: can words help?",
    author = "Paggio, Patrizia  and
      Navarretta, Costanza  and
      Jongejan, Bart",
    booktitle = "Proceedings of the Sixth Workshop on Vision and Language",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2006",
    doi = "10.18653/v1/W17-2006",
    pages = "40--42",
    abstract = "We present an approach where an SVM classifier learns to classify head movements based on measurements of velocity, acceleration, and the third derivative of position with respect to time, jerk. Consequently, annotations of head movements are added to new video data. The results of the automatic annotation are evaluated against manual annotations in the same data and show an accuracy of 68{\%} with respect to these. The results also show that using jerk improves accuracy. We then conduct an investigation of the overlap between temporal sequences classified as either movement or non-movement and the speech stream of the person performing the gesture. The statistics derived from this analysis show that using word features may help increase the accuracy of the model.",
}
@inproceedings{rubio-romano-etal-2017-multi,
    title = "Multi-Modal Fashion Product Retrieval",
    author = "Rubio Romano, Antonio  and
      Yu, LongLong  and
      Simo-Serra, Edgar  and
      Moreno-Noguer, Francesc",
    booktitle = "Proceedings of the Sixth Workshop on Vision and Language",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-2007",
    doi = "10.18653/v1/W17-2007",
    pages = "43--45",
    abstract = "Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space. We compare against existing approaches and show significant improvements in retrieval tasks on a large-scale e-commerce dataset.",
}
@proceedings{ws-2017-sense,
    title = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    editor = "Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1900",
    doi = "10.18653/v1/W17-19",
}
@inproceedings{gamallo-pereira-farina-2017-compositional,
    title = "Compositional Semantics using Feature-Based Models from {W}ord{N}et",
    author = "Gamallo, Pablo  and
      Pereira-Fari{\~n}a, Mart{\'\i}n",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1901",
    doi = "10.18653/v1/W17-1901",
    pages = "1--11",
    abstract = "This article describes a method to build semantic representations of composite expressions in a compositional way by using WordNet relations to represent the meaning of words. The meaning of a target word is modelled as a vector in which its semantically related words are assigned weights according to both the type of the relationship and the distance to the target word. Word vectors are compositionally combined by syntactic dependencies. Each syntactic dependency triggers two complementary compositional functions: the named head function and dependent function. The experiments show that the proposed compositional method outperforms the state-of-the-art for both intransitive subject-verb and transitive subject-verb-object constructions.",
}
@inproceedings{khodak-etal-2017-automated,
    title = "Automated {W}ord{N}et Construction Using Word Embeddings",
    author = "Khodak, Mikhail  and
      Risteski, Andrej  and
      Fellbaum, Christiane  and
      Arora, Sanjeev",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1902",
    doi = "10.18653/v1/W17-1902",
    pages = "12--23",
    abstract = "We present a fully unsupervised method for automated construction of WordNets based upon recent advances in distributional representations of sentences and word-senses combined with readily available machine translation tools. The approach requires very few linguistic resources and is thus extensible to multiple target languages. To evaluate our method we construct two 600-word testsets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches. Our method exceeds the best language-specific and multi-lingual automated WordNets in F-score for both languages. The databases we construct for French and Russian, both languages without large publicly available manually constructed WordNets, will be publicly released along with the testsets.",
}
@inproceedings{koper-schulte-im-walde-2017-improving,
    title = "Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1903",
    doi = "10.18653/v1/W17-1903",
    pages = "24--30",
    abstract = "Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for larger units by propagating abstractness to verb-noun pairs which lead to better metaphor detection iii) we overcome the limitation of learning a single rating per word and show that multi-sense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3million English words and multi-words as well as automatically created sense specific abstractness ratings",
}
@inproceedings{ling-etal-2017-improving,
    title = "Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge",
    author = "Ling, Yuan  and
      An, Yuan  and
      Hasan, Sadid",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1904",
    doi = "10.18653/v1/W17-1904",
    pages = "31--36",
    abstract = "This paper presents a novel approach to the task of automatically inferring the most probable diagnosis from a given clinical narrative. Structured Knowledge Bases (KBs) can be useful for such complex tasks but not sufficient. Hence, we leverage a vast amount of unstructured free text to integrate with structured KBs. The key innovative ideas include building a concept graph from both structured and unstructured knowledge sources and ranking the diagnosis concepts using the enhanced word embedding vectors learned from integrated sources. Experiments on the TREC CDS and HumanDx datasets showed that our methods improved the results of clinical diagnosis inference.",
}
@inproceedings{kanada-etal-2017-classifying,
    title = "Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations",
    author = "Kanada, Kentaro  and
      Kobayashi, Tetsunori  and
      Hayashi, Yoshihiko",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1905",
    doi = "10.18653/v1/W17-1905",
    pages = "37--46",
    abstract = "This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by {``}sense representations{''} (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.",
}
@inproceedings{king-cook-2017-supervised,
    title = "Supervised and unsupervised approaches to measuring usage similarity",
    author = "King, Milton  and
      Cook, Paul",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1906",
    doi = "10.18653/v1/W17-1906",
    pages = "47--52",
    abstract = "Usage similarity (USim) is an approach to determining word meaning in context that does not rely on a sense inventory. Instead, pairs of usages of a target lemma are rated on a scale. In this paper we propose unsupervised approaches to USim based on embeddings for words, contexts, and sentences, and achieve state-of-the-art results over two USim datasets. We further consider supervised approaches to USim, and find that although they outperform unsupervised approaches, they are unable to generalize to lemmas that are unseen in the training data.",
}
@inproceedings{ezeani-etal-2017-lexical,
    title = "Lexical Disambiguation of {I}gbo using Diacritic Restoration",
    author = "Ezeani, Ignatius  and
      Hepple, Mark  and
      Onyenwe, Ikechukwu",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1907",
    doi = "10.18653/v1/W17-1907",
    pages = "53--60",
    abstract = "Properly written texts in Igbo, a low-resource African language, are rich in both orthographic and tonal diacritics. Diacritics are essential in capturing the distinctions in pronunciation and meaning of words, as well as in lexical disambiguation. Unfortunately, most electronic texts in diacritic languages are written without diacritics. This makes diacritic restoration a necessary step in corpus building and language processing tasks for languages with diacritics. In our previous work, we built some n-gram models with simple smoothing techniques based on a closed-world assumption. However, as a classification task, diacritic restoration is well suited for and will be more generalisable with machine learning. This paper, therefore, presents a more standard approach to dealing with the task which involves the application of machine learning algorithms.",
}
@inproceedings{el-haj-etal-2017-creating,
    title = "Creating and Validating Multilingual Semantic Representations for Six Languages: Expert versus Non-Expert Crowds",
    author = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Piao, Scott  and
      Wattam, Stephen",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1908",
    doi = "10.18653/v1/W17-1908",
    pages = "61--71",
    abstract = "Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approaches is a challenging time-consuming manual task. This has traditionally been performed by linguistic experts: a slow and expensive process. We present an experiment in which we adapt and evaluate crowdsourcing methods employing native speakers to generate a list of coarse-grained senses under a common multilingual semantic taxonomy for sets of words in six languages. 451 non-experts (including 427 Mechanical Turk workers) and 15 expert participants semantically annotated 250 words manually for Arabic, Chinese, English, Italian, Portuguese and Urdu lexicons. In order to avoid erroneous (spam) crowdsourced results, we used a novel task-specific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous senses.",
}
@inproceedings{panchenko-etal-2017-using,
    title = "Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation",
    author = "Panchenko, Alexander  and
      Faralli, Stefano  and
      Ponzetto, Simone Paolo  and
      Biemann, Chris",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1909",
    doi = "10.18653/v1/W17-1909",
    pages = "72--78",
    abstract = "We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.",
}
@inproceedings{kober-etal-2017-one,
    title = "One Representation per Word - Does it make Sense for Composition?",
    author = "Kober, Thomas  and
      Weeds, Julie  and
      Wilkie, John  and
      Reffin, Jeremy  and
      Weir, David",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1910",
    doi = "10.18653/v1/W17-1910",
    pages = "79--90",
    abstract = "In this paper, we investigate whether an a priori disambiguation of word senses is strictly necessary or whether the meaning of a word in context can be disambiguated through composition alone. We evaluate the performance of off-the-shelf single-vector and multi-sense vector models on a benchmark phrase similarity task and a novel task for word-sense discrimination. We find that single-sense vector models perform as well or better than multi-sense vector models despite arguably less clean elementary representations. Our findings furthermore show that simple composition functions such as pointwise addition are able to recover sense specific information from a single-sense vector model remarkably well.",
}
@inproceedings{jang-myaeng-2017-elucidating,
    title = "Elucidating Conceptual Properties from Word Embeddings",
    author = "Jang, Kyoung-Rok  and
      Myaeng, Sung-Hyon",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1911",
    doi = "10.18653/v1/W17-1911",
    pages = "91--95",
    abstract = "In this paper, we introduce a method of identifying the components (i.e. dimensions) of word embeddings that strongly signifies properties of a word. By elucidating such properties hidden in word embeddings, we could make word embeddings more interpretable, and also could perform property-based meaning comparison. With the capability, we can answer questions like {``}To what degree a given word has the property cuteness?{''} or {``}In what perspective two words are similar?{''}. We verify our method by examining how the strength of property-signifying components correlates with the degree of prototypicality of a target word.",
}
@inproceedings{mensa-etal-2017-ttcs,
    title = "{TTCS}$^{\mathcal{E}}$: a Vectorial Resource for Computing Conceptual Similarity",
    author = "Mensa, Enrico  and
      Radicioni, Daniele P.  and
      Lieto, Antonio",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1912",
    doi = "10.18653/v1/W17-1912",
    pages = "96--101",
    abstract = "In this paper we introduce the TTCS$^{\mathcal{E}}$, a linguistic resource that relies on BabelNet, NASARI and ConceptNet, that has now been used to compute the conceptual similarity between concept pairs. The conceptual representation herein provides uniform access to concepts based on BabelNet synset IDs, and consists of a vector-based semantic representation which is compliant with the Conceptual Spaces, a geometric framework for common-sense knowledge representation and reasoning. The TTCS$^{\mathcal{E}}$ has been evaluated in a preliminary experimentation on a conceptual similarity task.",
}
@inproceedings{gregori-panunzi-2017-measuring,
    title = "Measuring the {I}talian-{E}nglish lexical gap for action verbs and its impact on translation",
    author = "Gregori, Lorenzo  and
      Panunzi, Alessandro",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1913",
    doi = "10.18653/v1/W17-1913",
    pages = "102--109",
    abstract = "This paper describes a method to measure the lexical gap of action verbs in Italian and English by using the IMAGACT ontology of action. The fine-grained categorization of action concepts of the data source allowed to have wide overview of the relation between concepts in the two languages. The calculated lexical gap for both English and Italian is about 30{\%} of the action concepts, much higher than previous results. Beyond this general numbers a deeper analysis has been performed in order to evaluate the impact that lexical gaps can have on translation. In particular a distinction has been made between the cases in which the presence of a lexical gap affects translation correctness and completeness at a semantic level. The results highlight a high percentage of concepts that can be considered hard to translate (about 18{\%} from English to Italian and 20{\%} from Italian to English) and confirms that action verbs are a critical lexical class for translation tasks.",
}
@inproceedings{cocos-etal-2017-word,
    title = "Word Sense Filtering Improves Embedding-Based Lexical Substitution",
    author = "Cocos, Anne  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1914",
    doi = "10.18653/v1/W17-1914",
    pages = "110--119",
    abstract = "The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense. We show that a filtering mechanism based on a sense inventory optimized for substitutability can improve the results of these models. Our sense inventory is constructed using a clustering method which generates paraphrase clusters that are congruent with lexical substitution annotations in a development set. The results show that lexical substitution can still benefit from senses which can improve the output of vector space paraphrase ranking models.",
}
@inproceedings{wawer-mykowiecka-2017-supervised,
    title = "Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms",
    author = "Wawer, Aleksander  and
      Mykowiecka, Agnieszka",
    booktitle = "Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1915",
    doi = "10.18653/v1/W17-1915",
    pages = "120--125",
    abstract = "This paper compares two approaches to word sense disambiguation using word embeddings trained on unambiguous synonyms. The first is unsupervised method based on computing log probability from sequences of word embedding vectors, taking into account ambiguous word senses and guessing correct sense from context. The second method is supervised. We use a multilayer neural network model to learn a context-sensitive transformation that maps an input vector of ambiguous word into an output vector representing its sense. We evaluate both methods on corpora with manual annotations of word senses from the Polish wordnet (plWordnet).",
}
@proceedings{ws-2017-semantics,
    title = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    editor = "Blanco, Eduardo  and
      Morante, Roser  and
      Saur{\'\i}, Roser",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1800",
    doi = "10.18653/v1/W17-18",
}
@inproceedings{calderwood-etal-2017-understanding,
    title = "Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions",
    author = "Calderwood, Alexander  and
      Pruett, Elizabeth A.  and
      Ptucha, Raymond  and
      Homan, Christopher  and
      Ovesdotter Alm, Cecilia",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1801",
    doi = "10.18653/v1/W17-1801",
    pages = "1--9",
    abstract = "Interpersonal violence (IPV) is a prominent sociological problem that affects people of all demographic backgrounds. By analyzing how readers interpret, perceive, and react to experiences narrated in social media posts, we explore an understudied source for discourse about abuse. We asked readers to annotate Reddit posts about relationships with vs. without IPV for stakeholder roles and emotion, while measuring their galvanic skin response (GSR), pulse, and facial expression. We map annotations to coreference resolution output to obtain a labeled coreference chain for stakeholders in texts, and apply automated semantic role labeling for analyzing IPV discourse. Findings provide insights into how readers process roles and emotion in narratives. For example, abusers tend to be linked with violent actions and certain affect states. We train classifiers to predict stakeholder categories of coreference chains. We also find that subjects{'} GSR noticeably changed for IPV texts, suggesting that co-collected measurement-based data about annotators can be used to support text annotation.",
}
@inproceedings{kim-schubert-2017-intension,
    title = "Intension, Attitude, and Tense Annotation in a High-Fidelity Semantic Representation",
    author = "Kim, Gene  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1802",
    doi = "10.18653/v1/W17-1802",
    pages = "10--15",
    abstract = "This paper describes current efforts in developing an annotation schema and guidelines for sentences in Episodic Logic (EL). We focus on important distinctions for representing modality, attitudes, and tense and present an annotation schema that makes these distinctions. EL has proved competitive with other logical formulations in speed and inference-enablement, while expressing a wider array of natural language phenomena including intensional modification of predicates and sentences, propositional attitudes, and tense and aspect.",
}
@inproceedings{falk-martin-2017-towards,
    title = "Towards a lexicon of event-selecting predicates for a {F}rench {F}act{B}ank",
    author = "Falk, Ingrid  and
      Martin, Fabienne",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1803",
    doi = "10.18653/v1/W17-1803",
    pages = "16--21",
    abstract = "This paper presents ongoing work for the construction of a French FactBank and a lexicon of French event-selecting predicates (ESPs), by applying the factuality detection algorithm introduced in (Saur{\'\i} and Pustejovsky, 2012). This algorithm relies on a lexicon of ESPs, specifying how these predicates influence the polarity of their embedded events. For this pilot study, we focused on French factive and implicative verbs, and capitalised on a lexical resource for the English counterparts of these verbs provided by the CLSI Group (Nairn et al., 2006; Karttunen, 2012).",
}
@inproceedings{fancellu-etal-2017-universal,
    title = "{U}niversal {D}ependencies to Logical Form with Negation Scope",
    author = "Fancellu, Federico  and
      Reddy, Siva  and
      Lopez, Adam  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1804",
    doi = "10.18653/v1/W17-1804",
    pages = "22--32",
    abstract = "Many language technology applications would benefit from the ability to represent negation and its scope on top of widely-used linguistic resources. In this paper, we investigate the possibility of obtaining a first-order logic representation with negation scope marked using \textit{Universal Dependencies}. To do so, we enhance \textit{UDepLambda}, a framework that converts dependency graphs to logical forms. The resulting \textit{UDepLambda$\lnot$ }is able to handle phenomena related to scope by means of an higher-order type theory, relevant not only to negation but also to universal quantification and other complex semantic phenomena. The initial conversion we did for English is promising, in that one can represent the scope of negation also in the presence of more complex phenomena such as universal quantifiers.",
}
@inproceedings{bos-2017-meaning,
    title = "Meaning Banking beyond Events and Roles",
    author = "Bos, Johan",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1805",
    doi = "10.18653/v1/W17-1805",
    pages = "33",
    abstract = "In this talk I will discuss the analysis of several semantic phenomena that need meaning representations that can describe attributes of propositional contexts. I will do this in a version of Discourse Representation Theory, using a universal semantic tagset developed as part of a project that aims to produce a large meaning bank (a semantically-annotated corpus) for four languages (English, Dutch, German and Italian).",
}
@inproceedings{altuna-etal-2017-scope,
    title = "The Scope and Focus of Negation: A Complete Annotation Framework for {I}talian",
    author = "Altuna, Bego{\~n}a  and
      Minard, Anne-Lyse  and
      Speranza, Manuela",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1806",
    doi = "10.18653/v1/W17-1806",
    pages = "34--42",
    abstract = "In this paper we present a complete framework for the annotation of negation in Italian, which accounts for both negation scope and negation focus, and also for language-specific phenomena such as negative concord. In our view, the annotation of negation complements more comprehensive Natural Language Processing tasks, such as temporal information processing and sentiment analysis. We applied the proposed framework and the guidelines built on top of it to the annotation of written texts, namely news articles and tweets, thus producing annotated data for a total of over 36,000 tokens.",
}
@inproceedings{marimon-etal-2017-annotation,
    title = "Annotation of negation in the {IULA} {S}panish Clinical Record Corpus",
    author = "Marimon, Montserrat  and
      Vivaldi, Jorge  and
      Bel, N{\'u}ria",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1807",
    doi = "10.18653/v1/W17-1807",
    pages = "43--52",
    abstract = "This paper presents the IULA Spanish Clinical Record Corpus, a corpus of 3,194 sentences extracted from anonymized clinical records and manually annotated with negation markers and their scope. The corpus was conceived as a resource to support clinical text-mining systems, but it is also a useful resource for other Natural Language Processing systems handling clinical texts: automatic encoding of clinical records, diagnosis support, term extraction, among others, as well as for the study of clinical texts. The corpus is publicly available with a CC-BY-SA 3.0 license.",
}
@inproceedings{cruz-etal-2017-annotating,
    title = "Annotating Negation in {S}panish Clinical Texts",
    author = "Cruz, Noa  and
      Morante, Roser  and
      Ma{\~n}a L{\'o}pez, Manuel J.  and
      Mata V{\'a}zquez, Jacinto  and
      Parra Calder{\'o}n, Carlos L.",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1808",
    doi = "10.18653/v1/W17-1808",
    pages = "53--58",
    abstract = "In this paper we present on-going work on annotating negation in Spanish clinical documents. A corpus of anamnesis and radiology reports has been annotated by two domain expert annotators with negation markers and negated events. The Dice coefficient for inter-annotator agreement is higher than 0.94 for negation markers and higher than 0.72 for negated events. The corpus will be publicly released when the annotation process is finished, constituting the first corpus annotated with negation for Spanish clinical reports available for the NLP community.",
}
@inproceedings{he-etal-2017-neural,
    title = "Neural Networks for Negation Cue Detection in {C}hinese",
    author = "He, Hangfeng  and
      Fancellu, Federico  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1809",
    doi = "10.18653/v1/W17-1809",
    pages = "59--63",
    abstract = "Negation cue detection involves identifying the span inherently expressing negation in a negative sentence. In Chinese, negative cue detection is complicated by morphological proprieties of the language. Previous work has shown that negative cue detection in Chinese can benefit from specific lexical and morphemic features, as well as cross-lingual information. We show here that they are not necessary: A bi-directional LSTM can perform equally well, with minimal feature engineering. In particular, the use of a character-based model allows us to capture characteristics of negation cues in Chinese using word-embedding information only. Not only does our model performs on par with previous work, further error analysis clarifies what problems remain to be addressed.",
}
@inproceedings{enger-etal-2017-open,
    title = "An open-source tool for negation detection: a maximum-margin approach",
    author = "Enger, Martine  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the Workshop Computational Semantics Beyond Events and Roles",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1810",
    doi = "10.18653/v1/W17-1810",
    pages = "64--69",
    abstract = "This paper presents an open-source toolkit for negation detection. It identifies negation cues and their corresponding scope in either raw or parsed text using maximum-margin classification. The system design draws on best practice from the existing literature on negation detection, aiming for a simple and portable system that still achieves competitive performance. Pre-trained models and experimental results are provided for English.",
}
@proceedings{ws-2017-multiword,
    title = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    editor = "Markantonatou, Stella  and
      Ramisch, Carlos  and
      Savary, Agata  and
      Vincze, Veronika",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1700",
    doi = "10.18653/v1/W17-17",
}
@inproceedings{barancikova-kettnerova-2017-paradi,
    title = "{P}ara{D}i: Dictionary of Paraphrases of {C}zech Complex Predicates with Light Verbs",
    author = "Baran{\v{c}}{\'\i}kov{\'a}, Petra  and
      Kettnerov{\'a}, V{\'a}clava",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1701",
    doi = "10.18653/v1/W17-1701",
    pages = "1--10",
    abstract = "We present a new freely available dictionary of paraphrases of Czech complex predicates with light verbs, ParaDi. Candidates for single predicative paraphrases of selected complex predicates have been extracted automatically from large monolingual data using word2vec. They have been manually verified and further refined. We demonstrate one of many possible applications of ParaDi in an experiment with improving machine translation quality.",
}
@inproceedings{chesney-etal-2017-multi,
    title = "Multi-word Entity Classification in a Highly Multilingual Environment",
    author = "Chesney, Sophie  and
      Jacquet, Guillaume  and
      Steinberger, Ralf  and
      Piskorski, Jakub",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1702",
    doi = "10.18653/v1/W17-1702",
    pages = "11--20",
    abstract = "This paper describes an approach for the classification of millions of existing multi-word entities (MWEntities), such as organisation or event names, into thirteen category types, based only on the tokens they contain. In order to classify our very large in-house collection of multilingual MWEntities into an application-oriented set of entity categories, we trained and tested distantly-supervised classifiers in 43 languages based on MWEntities extracted from BabelNet. The best-performing classifier was the multi-class SVM using a TF.IDF-weighted data representation. Interestingly, one unique classifier trained on a mix of all languages consistently performed better than classifiers trained for individual languages, reaching an averaged F1-value of 88.8{\%}. In this paper, we present the training and test data, including a human evaluation of its accuracy, describe the methods used to train the classifiers, and discuss the results.",
}
@inproceedings{garcia-etal-2017-using,
    title = "Using bilingual word-embeddings for multilingual collocation extraction",
    author = "Garcia, Marcos  and
      Garc{\'\i}a-Salido, Marcos  and
      Alonso-Ramos, Margarita",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1703",
    doi = "10.18653/v1/W17-1703",
    pages = "21--30",
    abstract = "This paper presents a new strategy for multilingual collocation extraction which takes advantage of parallel corpora to learn bilingual word-embeddings. Monolingual collocation candidates are retrieved using Universal Dependencies, while the distributional models are then applied to search for equivalents of the elements of each collocation in the target languages. The proposed method extracts not only collocation equivalents with direct translation between languages, but also other cases where the collocations in the two languages are not literal translations of each other. Several experiments -evaluating collocations with three syntactic patterns- in English, Spanish, and Portuguese show that our approach can effectively extract large pairs of bilingual equivalents with an average precision of about 90{\%}. Moreover, preliminary results on comparable corpora suggest that the distributional models can be applied for identifying new bilingual collocations in different domains.",
}
@inproceedings{savary-etal-2017-parseme,
    title = "The {PARSEME} Shared Task on Automatic Identification of Verbal Multiword Expressions",
    author = "Savary, Agata  and
      Ramisch, Carlos  and
      Cordeiro, Silvio  and
      Sangati, Federico  and
      Vincze, Veronika  and
      QasemiZadeh, Behrang  and
      Candito, Marie  and
      Cap, Fabienne  and
      Giouli, Voula  and
      Stoyanova, Ivelina  and
      Doucet, Antoine",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1704",
    doi = "10.18653/v1/W17-1704",
    pages = "31--47",
    abstract = "Multiword expressions (MWEs) are known as a {``}pain in the neck{''} for NLP due to their idiosyncratic behaviour. While some categories of MWEs have been addressed by many studies, verbal MWEs (VMWEs), such as to take a decision, to break one{'}s heart or to turn off, have been rarely modelled. This is notably due to their syntactic variability, which hinders treating them as {``}words with spaces{''}. We describe an initiative meant to bring about substantial progress in understanding, modelling and processing VMWEs. It is a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for 18 languages. Its main outcome is a multilingual 5-million-word annotated corpus which underlies a shared task on automatic identification of VMWEs. This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems.",
}
@inproceedings{simko-etal-2017-uszeged,
    title = "{US}zeged: Identifying Verbal Multiword Expressions with {POS} Tagging and Parsing Techniques",
    author = "Simk{\'o}, Katalin Ilona  and
      Kov{\'a}cs, Vikt{\'o}ria  and
      Vincze, Veronika",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1705",
    doi = "10.18653/v1/W17-1705",
    pages = "48--53",
    abstract = "The paper describes our system submitted for the Workshop on Multiword Expressions{'} shared task on automatic identification of verbal multiword expressions. It uses POS tagging and dependency parsing to identify single- and multi-token verbal MWEs in text. Our system is language independent and competed on nine of the eighteen languages. Our paper describes how our system works and gives its error analysis for the languages it was submitted for.",
}
@inproceedings{foufi-etal-2017-parsing,
    title = "Parsing and {MWE} Detection: Fips at the {PARSEME} Shared Task",
    author = "Foufi, Vasiliki  and
      Nerima, Luka  and
      Wehrli, {\'E}ric",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1706",
    doi = "10.18653/v1/W17-1706",
    pages = "54--59",
    abstract = "Identifying multiword expressions (MWEs) in a sentence in order to ensure their proper processing in subsequent applications, like machine translation, and performing the syntactic analysis of the sentence are interrelated processes. In our approach, priority is given to parsing alternatives involving collocations, and hence collocational information helps the parser through the maze of alternatives, with the aim to lead to substantial improvements in the performance of both tasks (collocation identification and parsing), and in that of a subsequent task (machine translation). In this paper, we are going to present our system and the procedure that we have followed in order to participate to the open track of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs) in running texts.",
}
@inproceedings{klyueva-etal-2017-neural,
    title = "Neural Networks for Multi-Word Expression Detection",
    author = "Klyueva, Natalia  and
      Doucet, Antoine  and
      Straka, Milan",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1707",
    doi = "10.18653/v1/W17-1707",
    pages = "60--65",
    abstract = "In this paper we describe the MUMULS system that participated to the 2017 shared task on automatic identification of verbal multiword expressions (VMWEs). The MUMULS system was implemented using a supervised approach based on recurrent neural networks using the open source library TensorFlow. The model was trained on a data set containing annotated VMWEs as well as morphological and syntactic information. The MUMULS system performed the identification of VMWEs in 15 languages, it was one of few systems that could categorize VMWEs type in nearly all languages.",
}
@inproceedings{bott-schulte-im-walde-2017-factoring,
    title = "Factoring Ambiguity out of the Prediction of Compositionality for {G}erman Multi-Word Expressions",
    author = "Bott, Stefan  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1708",
    doi = "10.18653/v1/W17-1708",
    pages = "66--72",
    abstract = {Ambiguity represents an obstacle for distributional semantic models(DSMs), which typically subsume the contexts of all word senses within one vector. While individual vector space approaches have been concerned with sense discrimination (e.g., Sch{\"u}tze 1998, Erk 2009, Erk and Pado 2010), such discrimination has rarely been integrated into DSMs across semantic tasks. This paper presents a soft-clustering approach to sense discrimination that filters sense-irrelevant features when predicting the degrees of compositionality for German noun-noun compounds and German particle verbs.},
}
@inproceedings{findlay-2017-multiword,
    title = "Multiword expressions and lexicalism: the view from {LFG}",
    author = "Findlay, Jamie Y.",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1709",
    doi = "10.18653/v1/W17-1709",
    pages = "73--79",
    abstract = "Multiword expressions (MWEs) pose a problem for lexicalist theories like Lexical Functional Grammar (LFG), since they are prima facie counterexamples to a strong form of the lexical integrity principle, which entails that a lexical item can only be realised as a single, syntactically atomic word. In this paper, I demonstrate some of the problems facing any strongly lexicalist account of MWEs, and argue that the lexical integrity principle must be weakened. I conclude by sketching a formalism which integrates a Tree Adjoining Grammar into the LFG architecture, taking advantage of this relaxation.",
}
@inproceedings{geeraert-etal-2017-understanding,
    title = "Understanding Idiomatic Variation",
    author = "Geeraert, Kristina  and
      Baayen, R. Harald  and
      Newman, John",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1710",
    doi = "10.18653/v1/W17-1710",
    pages = "80--90",
    abstract = "This study investigates the processing of idiomatic variants through an eye-tracking experiment. Four types of idiom variants were included, in addition to the canonical form and the literal meaning. Results suggest that modifications to idioms, modulo obvious effects of length differences, are not more difficult to process than the canonical forms themselves. This fits with recent corpus findings.",
}
@inproceedings{vargas-etal-2017-discovering,
    title = "Discovering Light Verb Constructions and their Translations from Parallel Corpora without Word Alignment",
    author = "Vargas, Natalie  and
      Ramisch, Carlos  and
      Caseli, Helena",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1711",
    doi = "10.18653/v1/W17-1711",
    pages = "91--96",
    abstract = "We propose a method for joint unsupervised discovery of multiword expressions (MWEs) and their translations from parallel corpora. First, we apply independent monolingual MWE extraction in source and target languages simultaneously. Then, we calculate translation probability, association score and distributional similarity of co-occurring pairs. Finally, we rank all translations of a given MWE using a linear combination of these features. Preliminary experiments on light verb constructions show promising results.",
}
@inproceedings{mandravickaite-krilavicius-2017-identification,
    title = "Identification of Multiword Expressions for {L}atvian and {L}ithuanian: Hybrid Approach",
    author = "Mandravickait{\.e}, Justina  and
      Krilavi{\v{c}}ius, Tomas",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1712",
    doi = "10.18653/v1/W17-1712",
    pages = "97--101",
    abstract = "We discuss an experiment on automatic identification of bi-gram multi-word expressions in parallel Latvian and Lithuanian corpora. Raw corpora, lexical association measures (LAMs) and supervised machine learning (ML) are used due to deficit and quality of lexical resources (e.g., POS-tagger, parser) and tools. While combining LAMs with ML is rather effective for other languages, it has shown some nice results for Lithuanian and Latvian as well. Combining LAMs with ML we have achieved 92,4{\%} precision and 52,2{\%} recall for Latvian and 95,1{\%} precision and 77,8{\%} recall for Lithuanian.",
}
@inproceedings{cap-2017-show,
    title = "Show Me Your Variance and {I} Tell You Who You Are - Deriving Compound Compositionality from Word Alignments",
    author = "Cap, Fabienne",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1713",
    doi = "10.18653/v1/W17-1713",
    pages = "102--107",
    abstract = "We use word alignment variance as an indicator for the non-compositionality of German and English noun compounds. Our work-in-progress results are on their own not competitive with state-of-the art approaches, but they show that alignment variance is correlated with compositionality and thus worth a closer look in the future.",
}
@inproceedings{cabezas-garcia-san-martin-2017-semantic,
    title = "Semantic annotation to characterize contextual variation in terminological noun compounds: a pilot study",
    author = "Cabezas-Garc{\'\i}a, Melania  and
      San Mart{\'\i}n, Antonio",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1714",
    doi = "10.18653/v1/W17-1714",
    pages = "108--113",
    abstract = "Noun compounds (NCs) are semantically complex and not fully compositional, as is often assumed. This paper presents a pilot study regarding the semantic annotation of environmental NCs with a view to accessing their semantics and exploring their domain-based contextual variation. Our results showed that the semantic annotation of NCs afforded important insights into how context impacts their conceptualization.",
}
@inproceedings{maldonado-etal-2017-detection,
    title = "Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking",
    author = "Maldonado, Alfredo  and
      Han, Lifeng  and
      Moreau, Erwan  and
      Alsulaimani, Ashjan  and
      Chowdhury, Koel Dutta  and
      Vogel, Carl  and
      Liu, Qun",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1715",
    doi = "10.18653/v1/W17-1715",
    pages = "114--120",
    abstract = "A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented. The system mainly exploits universal syntactic dependency features through a Conditional Random Fields (CRF) sequence model. The system competed in the Closed Track at the PARSEME VMWE Shared Task 2017, ranking 2nd place in most languages on full VMWE-based evaluation and 1st in three languages on token-based evaluation. In addition, this paper presents an option to re-rank the 10 best CRF-predicted sequences via semantic vectors, boosting its scores above other systems in the competition. We also show that all systems in the competition would struggle to beat a simple lookup baseline system and argue for a more purpose-specific evaluation scheme.",
}
@inproceedings{boros-etal-2017-data,
    title = "A data-driven approach to verbal multiword expression detection. {PARSEME} Shared Task system description paper",
    author = "Boros, Tiberiu  and
      Pipa, Sonia  and
      Barbu Mititelu, Verginica  and
      Tufis, Dan",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1716",
    doi = "10.18653/v1/W17-1716",
    pages = "121--126",
    abstract = "{``}Multiword expressions{''} are groups of words acting as a morphologic, syntactic and semantic unit in linguistic analysis. Verbal multiword expressions represent the subgroup of multiword expressions, namely that in which a verb is the syntactic head of the group considered in its canonical (or dictionary) form. All multiword expressions are a great challenge for natural language processing, but the verbal ones are particularly interesting for tasks such as parsing, as the verb is the central element in the syntactic organization of a sentence. In this paper we introduce our data-driven approach to verbal multiword expressions which was objectively validated during the PARSEME shared task on verbal multiword expressions identification. We tested our approach on 12 languages, and we provide detailed information about corpora composition, feature selection process, validation procedure and performance on all languages.",
}
@inproceedings{al-saied-etal-2017-atilf,
    title = "The {ATILF}-{LLF} System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger",
    author = "Al Saied, Hazem  and
      Constant, Matthieu  and
      Candito, Marie",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1717",
    doi = "10.18653/v1/W17-1717",
    pages = "127--132",
    abstract = "We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first for all but two languages (Hungarian and Romanian).",
}
@inproceedings{taslimipoor-etal-2017-investigating,
    title = "Investigating the Opacity of Verb-Noun Multiword Expression Usages in Context",
    author = "Taslimipoor, Shiva  and
      Rohanian, Omid  and
      Mitkov, Ruslan  and
      Fazly, Afsaneh",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1718",
    doi = "10.18653/v1/W17-1718",
    pages = "133--138",
    abstract = "This study investigates the supervised token-based identification of Multiword Expressions (MWEs). This is an ongoing research to exploit the information contained in the contexts in which different instances of an expression could occur. This information is used to investigate the question of whether an expression is literal or MWE. Lexical and syntactic context features derived from vector representations are shown to be more effective over traditional statistical measures to identify tokens of MWEs.",
}
@inproceedings{bhatia-etal-2017-compositionality,
    title = "Compositionality in Verb-Particle Constructions",
    author = "Bhatia, Archna  and
      Teng, Choh Man  and
      Allen, James",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1719",
    doi = "10.18653/v1/W17-1719",
    pages = "139--148",
    abstract = "We are developing a broad-coverage deep semantic lexicon for a system that parses sentences into a logical form expressed in a rich ontology that supports reasoning. In this paper we look at verb-particle constructions (VPCs), and the extent to which they can be treated compositionally vs idiomatically. First we distinguish between the different types of VPCs based on their compositionality and then present a set of heuristics for classifying specific instances as compositional or not. We then identify a small set of general sense classes for particles when used compositionally and discuss the resulting lexical representations that are being added to the lexicon. By treating VPCs as compositional whenever possible, we attain broad coverage in a compact way, and also enable interpretations of novel VPC usages not explicitly present in the lexicon.",
}
@inproceedings{inurrieta-etal-2017-rule,
    title = "Rule-Based Translation of {S}panish Verb-Noun Combinations into {B}asque",
    author = "I{\~n}urrieta, Uxoa  and
      Aduriz, Itziar  and
      D{\'\i}az de Ilarraza, Arantza  and
      Labaka, Gorka  and
      Sarasola, Kepa",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1720",
    doi = "10.18653/v1/W17-1720",
    pages = "149--154",
    abstract = "This paper presents a method to improve the translation of Verb-Noun Combinations (VNCs) in a rule-based Machine Translation (MT) system for Spanish-Basque. Linguistic information about a set of VNCs is gathered from the public database Konbitzul, and it is integrated into the MT system, leading to an improvement in BLEU, NIST and TER scores, as well as the results being evidently better according to human evaluators.",
}
@inproceedings{vincze-2017-verb,
    title = "Verb-Particle Constructions in Questions",
    author = "Vincze, Veronika",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1721",
    doi = "10.18653/v1/W17-1721",
    pages = "155--160",
    abstract = "In this paper, we investigate the behavior of verb-particle constructions in English questions. We present a small dataset that contains questions and verb-particle construction candidates. We demonstrate that there are significant differences in the distribution of WH-words, verbs and prepositions/particles in sentences that contain VPCs and sentences that contain only verb + prepositional phrase combinations both by statistical means and in machine learning experiments. Hence, VPCs and non-VPCs can be effectively separated from each other by using a rich feature set, containing several novel features.",
}
@inproceedings{weller-di-marco-2017-simple,
    title = "Simple Compound Splitting for {G}erman",
    author = "Weller-Di Marco, Marion",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1722",
    doi = "10.18653/v1/W17-1722",
    pages = "161--166",
    abstract = "This paper presents a simple method for German compound splitting that combines a basic frequency-based approach with a form-to-lemma mapping to approximate morphological operations. With the exception of a small set of hand-crafted rules for modeling transitional elements, this approach is resource-poor. In our evaluation, the simple splitter outperforms a splitter relying on rich morphological resources.",
}
@inproceedings{scholivet-ramisch-2017-identification,
    title = "Identification of Ambiguous Multiword Expressions Using Sequence Models and Lexical Resources",
    author = "Scholivet, Manon  and
      Ramisch, Carlos",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1723",
    doi = "10.18653/v1/W17-1723",
    pages = "167--175",
    abstract = "We present a simple and efficient tagger capable of identifying highly ambiguous multiword expressions (MWEs) in French texts. It is based on conditional random fields (CRF), using local context information as features. We show that this approach can obtain results that, in some cases, approach more sophisticated parser-based MWE identification methods without requiring syntactic trees from a treebank. Moreover, we study how well the CRF can take into account external information coming from a lexicon.",
}
@inproceedings{tutin-kraif-2017-comparing,
    title = "Comparing Recurring Lexico-Syntactic Trees ({RLT}s) and Ngram Techniques for Extended Phraseology Extraction",
    author = "Tutin, Agn{\`e}s  and
      Kraif, Olivier",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1724",
    doi = "10.18653/v1/W17-1724",
    pages = "176--180",
    abstract = "This paper aims at assessing to what extent a syntax-based method (Recurring Lexico-syntactic Trees (RLT) extraction) allows us to extract large phraseological units such as prefabricated routines, e.g. {``}as previously said{''} or {``}as far as we/I know{''} in scientific writing. In order to evaluate this method, we compare it to the classical ngram extraction technique, on a subset of recurring segments including speech verbs in a French corpus of scientific writing. Results show that the LRT extraction technique is far more efficient for extended MWEs such as routines or collocations but performs more poorly for surface phenomena such as syntactic constructions or fully frozen expressions.",
}
@inproceedings{constant-martinez-alonso-2017-benchmarking,
    title = "Benchmarking Joint Lexical and Syntactic Analysis on Multiword-Rich Data",
    author = "Constant, Matthieu  and
      Martinez Alonso, H{\'e}ctor",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1725",
    doi = "10.18653/v1/W17-1725",
    pages = "181--186",
    abstract = "This article evaluates the extension of a dependency parser that performs joint syntactic analysis and multiword expression identification. We show that, given sufficient training data, the parser benefits from explicit multiword information and improves overall labeled accuracy score in eight of the ten evaluation cases.",
}
@inproceedings{chan-etal-2017-semi,
    title = "Semi-Automated Resolution of Inconsistency for a Harmonized Multiword Expression and Dependency Parse Annotation",
    author = "Chan, King  and
      Brooke, Julian  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1726",
    doi = "10.18653/v1/W17-1726",
    pages = "187--193",
    abstract = "This paper presents a methodology for identifying and resolving various kinds of inconsistency in the context of merging dependency and multiword expression (MWE) annotations, to generate a dependency treebank with comprehensive MWE annotations. Candidates for correction are identified using a variety of heuristics, including an entirely novel one which identifies violations of MWE constituency in the dependency tree, and resolved by arbitration with minimal human intervention. Using this technique, we identified and corrected several hundred errors across both parse and MWE annotations, representing changes to a significant percentage (well over 10{\%}) of the MWE instances in the joint corpus.",
}
@inproceedings{buljan-snajder-2017-combining,
    title = "Combining Linguistic Features for the Detection of {C}roatian Multiword Expressions",
    author = "Buljan, Maja  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1727",
    doi = "10.18653/v1/W17-1727",
    pages = "194--199",
    abstract = "As multiword expressions (MWEs) exhibit a range of idiosyncrasies, their automatic detection warrants the use of many different features. Tsvetkov and Wintner (2014) proposed a Bayesian network model that combines linguistically motivated features and also models their interactions. In this paper, we extend their model with new features and apply it to Croatian, a morphologically complex and a relatively free word order language, achieving a satisfactory performance of 0.823 F1-score. Furthermore, by comparing against (semi)naive Bayes models, we demonstrate that manually modeling feature interactions is indeed important. We make our annotated dataset of Croatian MWEs freely available.",
}
@inproceedings{koper-schulte-im-walde-2017-complex,
    title = "Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 13th Workshop on Multiword Expressions ({MWE} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1728",
    doi = "10.18653/v1/W17-1728",
    pages = "200--206",
    abstract = "This paper compares a neural network DSM relying on textual co-occurrences with a multi-modal model integrating visual information. We focus on nominal vs. verbal compounds, and zoom into lexical, empirical and perceptual target properties to explore the contribution of the visual modality. Our experiments show that (i) visual features contribute differently for verbs than for nouns, and (ii) images complement textual information, if (a) the textual modality by itself is poor and appropriate image subsets are used, or (b) the textual modality by itself is rich and large (potentially noisy) images are added.",
}
@proceedings{ws-2017-acl,
    title = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    editor = "Hovy, Dirk  and
      Spruit, Shannon  and
      Mitchell, Margaret  and
      Bender, Emily M.  and
      Strube, Michael  and
      Wallach, Hanna",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1600",
    doi = "10.18653/v1/W17-16",
}
@inproceedings{larson-2017-gender,
    title = "Gender as a Variable in Natural-Language Processing: Ethical Considerations",
    author = "Larson, Brian",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1601",
    doi = "10.18653/v1/W17-1601",
    pages = "1--11",
    abstract = "Researchers and practitioners in natural-language processing (NLP) and related fields should attend to ethical principles in study design, ascription of categories/variables to study participants, and reporting of findings or results. This paper discusses theoretical and ethical frameworks for using gender as a variable in NLP studies and proposes four guidelines for researchers and practitioners. The principles outlined here should guide practitioners, researchers, and peer reviewers, and they may be applicable to other social categories, such as race, applied to human beings connected to NLP research.",
}
@inproceedings{koolen-van-cranenburgh-2017-stereotypes,
    title = "These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution",
    author = "Koolen, Corina  and
      van Cranenburgh, Andreas",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1602",
    doi = "10.18653/v1/W17-1602",
    pages = "12--22",
    abstract = "Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target audience of a text. More fundamentally, such classification efforts risk invoking stereotyping and essentialism. We explore this issue in two datasets of Dutch literary novels, using commonly used descriptive (LIWC, topic modeling) and predictive (machine learning) methods. Our results show the importance of controlling for variables in the corpus and we argue for taking care not to overgeneralize from the results.",
}
@inproceedings{mieskes-2017-quantitative,
    title = "A Quantitative Study of Data in the {NLP} community",
    author = "Mieskes, Margot",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1603",
    doi = "10.18653/v1/W17-1603",
    pages = "23--29",
    abstract = "We present results on a quantitative analysis of publications in the NLP domain on collecting, publishing and availability of research data. We find that a wide range of publications rely on data crawled from the web, but few give details on how potentially sensitive data was treated. Additionally, we find that while links to repositories of data are given, they often do not work even a short time after publication. We put together several suggestions on how to improve this situation based on publications from the NLP domain, but also other research areas.",
}
@inproceedings{leidner-plachouras-2017-ethical,
    title = "Ethical by Design: Ethics Best Practices for Natural Language Processing",
    author = "Leidner, Jochen L.  and
      Plachouras, Vassilis",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1604",
    doi = "10.18653/v1/W17-1604",
    pages = "30--40",
    abstract = "Natural language processing (NLP) systems analyze and/or generate human language, typically on users{'} behalf. One natural and necessary question that needs to be addressed in this context, both in research projects and in production settings, is the question how ethical the work is, both regarding the process and its outcome. Towards this end, we articulate a set of issues, propose a set of best practices, notably a process featuring an ethics review board, and sketch and how they could be meaningfully applied. Our main argument is that ethical outcomes ought to be achieved by design, i.e. by following a process aligned by ethical values. We also offer some response options for those facing ethics issues. While a number of previous works exist that discuss ethical issues, in particular around big data and machine learning, to the authors{'} knowledge this is the first account of NLP and ethics from the perspective of a principled process.",
}
@inproceedings{madnani-etal-2017-building,
    title = "Building Better Open-Source Tools to Support Fairness in Automated Scoring",
    author = "Madnani, Nitin  and
      Loukina, Anastassia  and
      von Davier, Alina  and
      Burstein, Jill  and
      Cahill, Aoife",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1605",
    doi = "10.18653/v1/W17-1605",
    pages = "41--52",
    abstract = "Automated scoring of written and spoken responses is an NLP application that can significantly impact lives especially when deployed as part of high-stakes tests such as the GRE® and the TOEFL®. Ethical considerations require that automated scoring algorithms treat all test-takers fairly. The educational measurement community has done significant research on fairness in assessments and automated scoring systems must incorporate their recommendations. The best way to do that is by making available automated, non-proprietary tools to NLP researchers that directly incorporate these recommendations and generate the analyses needed to help identify and resolve biases in their scoring systems. In this paper, we attempt to provide such a solution.",
}
@inproceedings{tatman-2017-gender,
    title = "Gender and Dialect Bias in {Y}ou{T}ube{'}s Automatic Captions",
    author = "Tatman, Rachael",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1606",
    doi = "10.18653/v1/W17-1606",
    pages = "53--59",
    abstract = "This project evaluates the accuracy of YouTube{'}s automatically-generated captions across two genders and five dialect groups. Speakers{'} dialect and gender was controlled for by using videos uploaded as part of the {``}accent tag challenge{''}, where speakers explicitly identify their language background. The results show robust differences in accuracy across both gender and dialect, with lower accuracy for 1) women and 2) speakers from Scotland. This finding builds on earlier research finding that speaker{'}s sociolinguistic identity may negatively impact their ability to use automatic speech recognition, and demonstrates the need for sociolinguistically-stratified validation of systems.",
}
@inproceedings{lewis-etal-2017-integrating,
    title = "Integrating the Management of Personal Data Protection and Open Science with Research Ethics",
    author = "Lewis, Dave  and
      Moorkens, Joss  and
      Fatema, Kaniz",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1607",
    doi = "10.18653/v1/W17-1607",
    pages = "60--65",
    abstract = "We examine the impact of the EU General Data Protection Regulation and the push from research funders to provide open access research data on the current practices in Language Technology Research. We analyse the challenges that arise and the opportunities to address many of them through the use of existing open data practices. We discuss the impact of this also on current practice in research ethics.",
}
@inproceedings{parra-escartin-etal-2017-ethical,
    title = "Ethical Considerations in {NLP} Shared Tasks",
    author = "Parra Escart{\'\i}n, Carla  and
      Reijers, Wessel  and
      Lynn, Teresa  and
      Moorkens, Joss  and
      Way, Andy  and
      Liu, Chao-Hong",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1608",
    doi = "10.18653/v1/W17-1608",
    pages = "66--73",
    abstract = "Shared tasks are increasingly common in our field, and new challenges are suggested at almost every conference and workshop. However, as this has become an established way of pushing research forward, it is important to discuss how we researchers organise and participate in shared tasks, and make that information available to the community to allow further research improvements. In this paper, we present a number of ethical issues along with other areas of concern that are related to the competitive nature of shared tasks. As such issues could potentially impact on research ethics in the Natural Language Processing community, we also propose the development of a framework for the organisation of and participation in shared tasks that can help mitigate against these issues arising.",
}
@inproceedings{rudinger-etal-2017-social,
    title = "Social Bias in Elicited Natural Language Inferences",
    author = "Rudinger, Rachel  and
      May, Chandler  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1609",
    doi = "10.18653/v1/W17-1609",
    pages = "74--79",
    abstract = "We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The SNLI human-elicitation protocol makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples.",
}
@inproceedings{suster-etal-2017-short,
    title = "A Short Review of Ethical Challenges in Clinical Natural Language Processing",
    author = "{\v{S}}uster, Simon  and
      Tulkens, St{\'e}phan  and
      Daelemans, Walter",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1610",
    doi = "10.18653/v1/W17-1610",
    pages = "80--87",
    abstract = "Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this potential has remained largely untapped due to slow progress primarily caused by strict data access policies for researchers. In this paper, we discuss the concern for privacy and the measures it entails. We also suggest sources of less sensitive data. Finally, we draw attention to biases that can compromise the validity of empirical research and lead to socially harmful applications.",
}
@inproceedings{schnoebelen-2017-goal,
    title = "Goal-Oriented Design for Ethical Machine Learning and {NLP}",
    author = "Schnoebelen, Tyler",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1611",
    doi = "10.18653/v1/W17-1611",
    pages = "88--93",
    abstract = "The argument made in this paper is that to act ethically in machine learning and NLP requires focusing on goals. NLP projects are often classificatory systems that deal with human subjects, which means that goals from people affected by the systems should be included. The paper takes as its core example a model that detects criminality, showing the problems of training data, categories, and outcomes. The paper is oriented to the kinds of critiques on power and the reproduction of inequality that are found in social theory, but it also includes concrete suggestions on how to put goal-oriented design into practice.",
}
@inproceedings{benton-etal-2017-ethical,
    title = "Ethical Research Protocols for Social Media Health Research",
    author = "Benton, Adrian  and
      Coppersmith, Glen  and
      Dredze, Mark",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1612",
    doi = "10.18653/v1/W17-1612",
    pages = "94--102",
    abstract = "Social media have transformed data-driven research in political science, the social sciences, health, and medicine. Since health research often touches on sensitive topics that relate to ethics of treatment and patient privacy, similar ethical considerations should be acknowledged when using social media data in health research. While much has been said regarding the ethical considerations of social media research, health research leads to an additional set of concerns. We provide practical suggestions in the form of guidelines for researchers working with social media data in health research. These guidelines can inform an IRB proposal for researchers new to social media health research.",
}
@inproceedings{smiley-etal-2017-say,
    title = "Say the Right Thing Right: Ethics Issues in Natural Language Generation Systems",
    author = "Smiley, Charese  and
      Schilder, Frank  and
      Plachouras, Vassilis  and
      Leidner, Jochen L.",
    booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1613",
    doi = "10.18653/v1/W17-1613",
    pages = "103--108",
    abstract = "We discuss the ethical implications of Natural Language Generation systems. We use one particular system as a case study to identify and classify issues, and we provide an ethics checklist, in the hope that future system designers may benefit from conducting their own ethics reviews based on our checklist.",
}
@proceedings{ws-2017-coreference,
    title = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    editor = "Ogrodniczuk, Maciej  and
      Ng, Vincent",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1500",
    doi = "10.18653/v1/W17-15",
}
@inproceedings{moosavi-strube-2017-use,
    title = "Use Generalized Representations, But Do Not Forget Surface Features",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1501",
    doi = "10.18653/v1/W17-1501",
    pages = "1--7",
    abstract = "Only a year ago, all state-of-the-art coreference resolvers were using an extensive amount of surface features. Recently, there was a paradigm shift towards using word embeddings and deep neural networks, where the use of surface features is very limited. In this paper, we show that a simple SVM model with surface features outperforms more complex neural models for detecting anaphoric mentions. Our analysis suggests that using generalized representations and surface features have different strength that should be both taken into account for improving coreference resolution.",
}
@inproceedings{soraluze-etal-2017-enriching,
    title = "Enriching {B}asque Coreference Resolution System using Semantic Knowledge sources",
    author = "Soraluze, Ander  and
      Arregi, Olatz  and
      Arregi, Xabier  and
      D{\'\i}az de Ilarraza, Arantza",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1502",
    doi = "10.18653/v1/W17-1502",
    pages = "8--16",
    abstract = "In this paper we present a Basque coreference resolution system enriched with semantic knowledge. An error analysis carried out revealed the deficiencies that the system had in resolving coreference cases in which semantic or world knowledge is needed. We attempt to improve the deficiencies using two semantic knowledge sources, specifically Wikipedia and WordNet.",
}
@inproceedings{ogrodniczuk-niton-2017-improving,
    title = "Improving {P}olish Mention Detection with Valency Dictionary",
    author = "Ogrodniczuk, Maciej  and
      Nito{\'n}, Bart{\l}omiej",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1503",
    doi = "10.18653/v1/W17-1503",
    pages = "17--23",
    abstract = "This paper presents results of an experiment integrating information from valency dictionary of Polish into a mention detection system. Two types of information is acquired: positions of syntactic schemata for nominal and verbal constructs and secondary prepositions present in schemata. The syntactic schemata are used to prevent (for verbal realizations) or encourage (for nominal groups) constructing mentions from phrases filling multiple schema positions, the secondary prepositions {--} to filter out artificial mentions created from their nominal components. Mention detection is evaluated against the manual annotation of the Polish Coreference Corpus in two settings: taking into account only mention heads or exact borders.",
}
@inproceedings{amsili-seminck-2017-google,
    title = "A {G}oogle-Proof Collection of {F}rench {W}inograd Schemas",
    author = "Amsili, Pascal  and
      Seminck, Olga",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1504",
    doi = "10.18653/v1/W17-1504",
    pages = "24--29",
    abstract = "This article presents the first collection of French Winograd Schemas. Winograd Schemas form anaphora resolution problems that can only be resolved with extensive world knowledge. For this reason the Winograd Schema Challenge has been proposed as an alternative to the Turing Test. A very important feature of Winograd Schemas is that it should be impossible to resolve them with statistical information about word co-occurrences: they should be Google-proof. We propose a measure of Google-proofness based on Mutual Information, and demonstrate the method on our collection of French Winograd Schemas.",
}
@inproceedings{miculicich-werlen-popescu-belis-2017-using,
    title = "Using Coreference Links to Improve {S}panish-to-{E}nglish Machine Translation",
    author = "Miculicich Werlen, Lesly  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1505",
    doi = "10.18653/v1/W17-1505",
    pages = "30--40",
    abstract = "In this paper, we present a proof-of-concept implementation of a coreference-aware decoder for document-level machine translation. We consider that better translations should have coreference links that are closer to those in the source text, and implement this criterion in two ways. First, we define a similarity measure between source and target coreference structures, by projecting the target ones onto the source and reusing existing coreference metrics. Based on this similarity measure, we re-rank the translation hypotheses of a baseline system for each sentence. Alternatively, to address the lack of diversity of mentions in the MT hypotheses, we focus on mention pairs and integrate their coreference scores with MT ones, resulting in post-editing decisions for mentions. The experimental results for Spanish to English MT on the AnCora-ES corpus show that the second approach yields a substantial increase in the accuracy of pronoun translation, with BLEU scores remaining constant.",
}
@inproceedings{grishina-stede-2017-multi,
    title = "Multi-source annotation projection of coreference chains: assessing strategies and testing opportunities",
    author = "Grishina, Yulia  and
      Stede, Manfred",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1506",
    doi = "10.18653/v1/W17-1506",
    pages = "41--50",
    abstract = "In this paper, we examine the possibility of using annotation projection from multiple sources for automatically obtaining coreference annotations in the target language. We implement a multi-source annotation projection algorithm and apply it on an English-German-Russian parallel corpus in order to transfer coreference chains from two sources to the target side. Operating in two settings {--} a low-resource and a more linguistically-informed one {--} we show that automatic coreference transfer could benefit from combining information from multiple languages, and assess the quality of both the extraction and the linking of target coreference mentions.",
}
@inproceedings{grishina-2017-corbon,
    title = "{CORBON} 2017 Shared Task: Projection-Based Coreference Resolution",
    author = "Grishina, Yulia",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1507",
    doi = "10.18653/v1/W17-1507",
    pages = "51--55",
    abstract = "The CORBON 2017 Shared Task, organised as part of the Coreference Resolution Beyond OntoNotes workshop at EACL 2017, presented a new challenge for multilingual coreference resolution: we offer a projection-based setting in which one is supposed to build a coreference resolver for a new language exploiting little or even no knowledge of it, with our languages of interest being German and Russian. We additionally offer a more traditional setting, targeting the development of a multilingual coreference resolver without any restrictions on the resources and methods used. In this paper, we describe the task setting and provide the results of one participant who successfully completed the task, comparing their results to the closely related previous research. Analysing the task setting and the results, we discuss the major challenges and make suggestions on the future directions of coreference evaluation.",
}
@inproceedings{novak-etal-2017-projection,
    title = "Projection-based Coreference Resolution Using Deep Syntax",
    author = "Nov{\'a}k, Michal  and
      Nedoluzhko, Anna  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k",
    booktitle = "Proceedings of the 2nd Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1508",
    doi = "10.18653/v1/W17-1508",
    pages = "56--64",
    abstract = "The paper describes the system for coreference resolution in German and Russian, trained exclusively on coreference relations project ed through a parallel corpus from English. The resolver operates on the level of deep syntax and makes use of multiple specialized models. It achieves 32 and 22 points in terms of CoNLL score for Russian and German, respectively. Analysis of the evaluation results show that the resolver for Russian is able to preserve 66{\%} of the English resolver{'}s quality in terms of CoNLL score. The system was submitted to the Closed track of the CORBON 2017 Shared task.",
}
@proceedings{ws-2017-balto,
    title = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    editor = "Erjavec, Toma{\v{z}}  and
      Piskorski, Jakub  and
      Pivovarova, Lidia  and
      {\v{S}}najder, Jan  and
      Steinberger, Josef  and
      Yangarber, Roman",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1400",
    doi = "10.18653/v1/W17-14",
}
@inproceedings{sharoff-2017-toward,
    title = "Toward Pan-{S}lavic {NLP}: Some Experiments with Language Adaptation",
    author = "Sharoff, Serge",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1401",
    doi = "10.18653/v1/W17-1401",
    pages = "1--2",
    abstract = "There is great variation in the amount of NLP resources available for Slavonic languages. For example, the Universal Dependency treebank (Nivre et al., 2016) has about 2 MW of training resources for Czech, more than 1 MW for Russian, while only 950 words for Ukrainian and nothing for Belorussian, Bosnian or Macedonian. Similarly, the Autodesk Machine Translation dataset only covers three Slavonic languages (Czech, Polish and Russian). In this talk I will discuss a general approach, which can be called Language Adaptation, similarly to Domain Adaptation. In this approach, a model for a particular language processing task is built by lexical transfer of cognate words and by learning a new feature representation for a lesser-resourced (recipient) language starting from a better-resourced (donor) language. More specifically, I will demonstrate how language adaptation works in such training scenarios as Translation Quality Estimation, Part-of-Speech tagging and Named Entity Recognition.",
}
@inproceedings{kutuzov-etal-2017-clustering,
    title = "Clustering of {R}ussian Adjective-Noun Constructions using Word Embeddings",
    author = "Kutuzov, Andrey  and
      Kuzmenko, Elizaveta  and
      Pivovarova, Lidia",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1402",
    doi = "10.18653/v1/W17-1402",
    pages = "3--13",
    abstract = "This paper presents a method of automatic construction extraction from a large corpus of Russian. The term {`}construction{'} here means a multi-word expression in which a variable can be replaced with another word from the same semantic class, for example, {`}a glass of [water/juice/milk]{'}. We deal with constructions that consist of a noun and its adjective modifier. We propose a method of grouping such constructions into semantic classes via 2-step clustering of word vectors in distributional models. We compare it with other clustering techniques and evaluate it against A Russian-English Collocational Dictionary of the Human Body that contains manually annotated groups of constructions with nouns meaning human body parts. The best performing method is used to cluster all adjective-noun bigrams in the Russian National Corpus. Results of this procedure are publicly available and can be used for building Russian construction dictionary as well as to accelerate theoretical studies of constructions.",
}
@inproceedings{alagic-snajder-2017-preliminary,
    title = "A Preliminary Study of {C}roatian Lexical Substitution",
    author = "Alagi{\'c}, Domagoj  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1403",
    doi = "10.18653/v1/W17-1403",
    pages = "14--19",
    abstract = "Lexical substitution is a task of determining a meaning-preserving replacement for a word in context. We report on a preliminary study of this task for the Croatian language on a small-scale lexical sample dataset, manually annotated using three different annotation schemes. We compare the annotations, analyze the inter-annotator agreement, and observe a number of interesting language specific details in the obtained lexical substitutes. Furthermore, we apply a recently-proposed, dependency-based lexical substitution model to our dataset. The model achieves a P@3 score of 0.35, which indicates the difficulty of the task.",
}
@inproceedings{savary-waszczuk-2017-projecting,
    title = "Projecting Multiword Expression Resources on a {P}olish Treebank",
    author = "Savary, Agata  and
      Waszczuk, Jakub",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1404",
    doi = "10.18653/v1/W17-1404",
    pages = "20--26",
    abstract = "Multiword expressions (MWEs) are linguistic objects containing two or more words and showing idiosyncratic behavior at different levels. Treebanks with annotated MWEs enable studies of such properties, as well as training and evaluation of MWE-aware parsers. However, few treebanks contain full-fledged MWE annotations. We show how this gap can be bridged in Polish by projecting 3 MWE resources on a constituency treebank.",
}
@inproceedings{rabus-scherrer-2017-lexicon,
    title = "Lexicon Induction for Spoken {R}usyn {--} Challenges and Results",
    author = "Rabus, Achim  and
      Scherrer, Yves",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1405",
    doi = "10.18653/v1/W17-1405",
    pages = "27--32",
    abstract = "This paper reports on challenges and results in developing NLP resources for spoken Rusyn. Being a Slavic minority language, Rusyn does not have any resources to make use of. We propose to build a morphosyntactic dictionary for Rusyn, combining existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish. We adapt these resources to Rusyn by using vowel-sensitive Levenshtein distance, hand-written language-specific transformation rules, and combinations of the two. Compared to an exact match baseline, we increase the coverage of the resulting morphological dictionary by up to 77.4{\%} relative (42.9{\%} absolute), which results in a tagging recall increased by 11.6{\%} relative (9.1{\%} absolute). Our research confirms and expands the results of previous studies showing the efficiency of using NLP resources from neighboring languages for low-resourced languages.",
}
@inproceedings{dobrovoljc-etal-2017-universal,
    title = "The {U}niversal {D}ependencies Treebank for {S}lovenian",
    author = "Dobrovoljc, Kaja  and
      Erjavec, Toma{\v{z}}  and
      Krek, Simon",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1406",
    doi = "10.18653/v1/W17-1406",
    pages = "33--38",
    abstract = "This paper introduces the Universal Dependencies Treebank for Slovenian. We overview the existing dependency treebanks for Slovenian and then detail the conversion of the ssj200k treebank to the framework of Universal Dependencies version 2. We explain the mapping of part-of-speech categories, morphosyntactic features, and the dependency relations, focusing on the more problematic language-specific issues. We conclude with a quantitative overview of the treebank and directions for further work.",
}
@inproceedings{samardzic-etal-2017-universal,
    title = "{U}niversal {D}ependencies for {S}erbian in Comparison with {C}roatian and Other {S}lavic Languages",
    author = "Samard{\v{z}}i{\'c}, Tanja  and
      Starovi{\'c}, Mirjana  and
      Agi{\'c}, {\v{Z}}eljko  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1407",
    doi = "10.18653/v1/W17-1407",
    pages = "39--44",
    abstract = "The paper documents the procedure of building a new Universal Dependencies (UDv2) treebank for Serbian starting from an existing Croatian UDv1 treebank and taking into account the other Slavic UD annotation guidelines. We describe the automatic and manual annotation procedures, discuss the annotation of Slavic-specific categories (case governing quantifiers, reflexive pronouns, question particles) and propose an approach to handling deverbal nouns in Slavic languages.",
}
@inproceedings{sorokin-2017-spelling,
    title = "Spelling Correction for Morphologically Rich Language: a Case Study of {R}ussian",
    author = "Sorokin, Alexey",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1408",
    doi = "10.18653/v1/W17-1408",
    pages = "45--53",
    abstract = "We present an algorithm for automatic correction of spelling errors on the sentence level, which uses noisy channel model and feature-based reranking of hypotheses. Our system is designed for Russian and clearly outperforms the winner of SpellRuEval-2016 competition. We show that language model size has the greatest influence on spelling correction quality. We also experiment with different types of features and show that morphological and semantic information also improves the accuracy of spellchecking.",
}
@inproceedings{gombar-etal-2017-debunking,
    title = "Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for {C}roatian",
    author = "Gombar, Paula  and
      Medi{\'c}, Zoran  and
      Alagi{\'c}, Domagoj  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1409",
    doi = "10.18653/v1/W17-1409",
    pages = "54--59",
    abstract = "Sentiment lexicons are widely used as an intuitive and inexpensive way of tackling sentiment classification, often within a simple lexicon word-counting approach or as part of a supervised model. However, it is an open question whether these approaches can compete with supervised models that use only word-representation features. We address this question in the context of domain-specific sentiment classification for Croatian. We experiment with the graph-based acquisition of sentiment lexicons, analyze their quality, and investigate how effectively they can be used in sentiment classification. Our results indicate that, even with as few as 500 labeled instances, a supervised model substantially outperforms a word-counting model. We also observe that adding lexicon-based features does not significantly improve supervised sentiment classification.",
}
@inproceedings{ljubesic-etal-2017-adapting,
    title = "Adapting a State-of-the-Art Tagger for {S}outh {S}lavic Languages to Non-Standard Text",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Erjavec, Toma{\v{z}}  and
      Fi{\v{s}}er, Darja",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1410",
    doi = "10.18653/v1/W17-1410",
    pages = "60--68",
    abstract = "In this paper we present the adaptations of a state-of-the-art tagger for South Slavic languages to non-standard texts on the example of the Slovene language. We investigate the impact of introducing in-domain training data as well as additional supervision through external resources or tools like word clusters and word normalization. We remove more than half of the error of the standard tagger when applied to non-standard texts by training it on a combination of standard and non-standard training data, while enriching the data representation with external resources removes additional 11 percent of the error. The final configuration achieves tagging accuracy of 87.41{\%} on the full morphosyntactic description, which is, nevertheless, still quite far from the accuracy of 94.27{\%} achieved on standard text.",
}
@inproceedings{rotim-snajder-2017-comparison,
    title = "Comparison of Short-Text Sentiment Analysis Methods for {C}roatian",
    author = "Rotim, Leon  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1411",
    doi = "10.18653/v1/W17-1411",
    pages = "69--75",
    abstract = "We focus on the task of supervised sentiment classification of short and informal texts in Croatian, using two simple yet effective methods: word embeddings and string kernels. We investigate whether word embeddings offer any advantage over corpus- and preprocessing-free string kernels, and how these compare to bag-of-words baselines. We conduct a comparison on three different datasets, using different preprocessing methods and kernel functions. Results show that, on two out of three datasets, word embeddings outperform string kernels, which in turn outperform word and n-gram bag-of-words baselines.",
}
@inproceedings{piskorski-etal-2017-first,
    title = "The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in {S}lavic Languages",
    author = "Piskorski, Jakub  and
      Pivovarova, Lidia  and
      {\v{S}}najder, Jan  and
      Steinberger, Josef  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1412",
    doi = "10.18653/v1/W17-1412",
    pages = "76--85",
    abstract = "This paper describes the outcomes of the first challenge on multilingual named entity recognition that aimed at recognizing mentions of named entities in web documents in Slavic languages, their normalization/lemmatization, and cross-language matching. It was organised in the context of the 6th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2017 conference. Although eleven teams signed up for the evaluation, due to the complexity of the task(s) and short time available for elaborating a solution, only two teams submitted results on time. The reported evaluation figures reflect the relatively higher level of complexity of named entity-related tasks in the context of processing texts in Slavic languages. Since the duration of the challenge goes beyond the date of the publication of this paper and updated picture of the participating systems and their corresponding performance can be found on the web page of the challenge.",
}
@inproceedings{marcinczuk-etal-2017-liner2,
    title = "{L}iner2 {---} a Generic Framework for Named Entity Recognition",
    author = "Marci{\'n}czuk, Micha{\l}  and
      Koco{\'n}, Jan  and
      Oleksy, Marcin",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1413",
    doi = "10.18653/v1/W17-1413",
    pages = "86--91",
    abstract = "In the paper we present an adaptation of Liner2 framework to solve the BSNLP 2017 shared task on multilingual named entity recognition. The tool is tuned to recognize and lemmatize named entities for Polish.",
}
@inproceedings{mayfield-etal-2017-language,
    title = "Language-Independent Named Entity Analysis Using Parallel Projection and Rule-Based Disambiguation",
    author = "Mayfield, James  and
      McNamee, Paul  and
      Costello, Cash",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1414",
    doi = "10.18653/v1/W17-1414",
    pages = "92--96",
    abstract = "The 2017 shared task at the Balto-Slavic NLP workshop requires identifying coarse-grained named entities in seven languages, identifying each entity{'}s base form, and clustering name mentions across the multilingual set of documents. The fact that no training data is provided to systems for building supervised classifiers further adds to the complexity. To complete the task we first use publicly available parallel texts to project named entity recognition capability from English to each evaluation language. We ignore entirely the subtask of identifying non-inflected forms of names. Finally, we create cross-document entity identifiers by clustering named mentions using a procedure-based approach.",
}
@inproceedings{chernyak-2017-comparison,
    title = "Comparison of String Similarity Measures for Obscenity Filtering",
    author = "Chernyak, Ekaterina",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1415",
    doi = "10.18653/v1/W17-1415",
    pages = "97--101",
    abstract = "In this paper we address the problem of filtering obscene lexis in Russian texts. We use string similarity measures to find words similar or identical to words from a stop list and establish both a test collection and a baseline for the task. Our experiments show that a novel string similarity measure based on the notion of an annotated suffix tree outperforms some of the other well known measures.",
}
@inproceedings{mandravickaite-krilavicius-2017-stylometric,
    title = "Stylometric Analysis of Parliamentary Speeches: Gender Dimension",
    author = "Mandravickait{\.e}, Justina  and
      Krilavi{\v{c}}ius, Tomas",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1416",
    doi = "10.18653/v1/W17-1416",
    pages = "102--107",
    abstract = "Relation between gender and language has been studied by many authors, however, there is still some uncertainty left regarding gender influence on language usage in the professional environment. Often, the studied data sets are too small or texts of individual authors are too short in order to capture differences of language usage wrt gender successfully. This study draws from a larger corpus of speeches transcripts of the Lithuanian Parliament (1990-2013) to explore language differences of political debates by gender via stylometric analysis. Experimental set up consists of stylistic features that indicate lexical style and do not require external linguistic tools, namely the most frequent words, in combination with unsupervised machine learning algorithms. Results show that gender differences in the language use remain in professional environment not only in usage of function words, preferred linguistic constructions, but in the presented topics as well.",
}
@inproceedings{buraya-etal-2017-towards,
    title = "Towards Never Ending Language Learning for Morphologically Rich Languages",
    author = "Buraya, Kseniya  and
      Pivovarova, Lidia  and
      Budkov, Sergey  and
      Filchenkov, Andrey",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1417",
    doi = "10.18653/v1/W17-1417",
    pages = "108--118",
    abstract = "This work deals with ontology learning from unstructured Russian text. We implement one of components Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologicaly rich free-word-order language. We demonstrate that this method may be successfully applied to Russian data. In addition we perform several additional experiments comparing different settings of the training process. We demonstrate that utilizing of morphological features significantly improves the system precision while using of seed patterns helps to improve the coverage.",
}
@inproceedings{verhoeven-etal-2017-gender,
    title = "Gender Profiling for {S}lovene {T}witter communication: the Influence of Gender Marking, Content and Style",
    author = "Verhoeven, Ben  and
      {\v{S}}krjanec, Iza  and
      Pollak, Senja",
    booktitle = "Proceedings of the 6th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1418",
    doi = "10.18653/v1/W17-1418",
    pages = "119--125",
    abstract = "We present results of the first gender classification experiments on Slovene text to our knowledge. Inspired by the TwiSty corpus and experiments (Verhoeven et al., 2016), we employed the Janes corpus (Erjavec et al., 2016) and its gender annotations to perform gender classification experiments on Twitter text comparing a token-based and a lemma-based approach. We find that the token-based approach (92.6{\%} accuracy), containing gender markings related to the author, outperforms the lemma-based approach by about 5{\%}. Especially in the lemmatized version, we also observe stylistic and content-based differences in writing between men (e.g. more profane language, numerals and beer mentions) and women (e.g. more pronouns, emoticons and character flooding). Many of our findings corroborate previous research on other languages.",
}
@proceedings{ws-2017-arabic,
    title = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    editor = "Habash, Nizar  and
      Diab, Mona  and
      Darwish, Kareem  and
      El-Hajj, Wassim  and
      Al-Khalifa, Hend  and
      Bouamor, Houda  and
      Tomeh, Nadi  and
      El-Haj, Mahmoud",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1300",
    doi = "10.18653/v1/W17-13",
}
@inproceedings{adouane-dobnik-2017-identification,
    title = "Identification of Languages in {A}lgerian {A}rabic Multilingual Documents",
    author = "Adouane, Wafia  and
      Dobnik, Simon",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1301",
    doi = "10.18653/v1/W17-1301",
    pages = "1--8",
    abstract = "This paper presents a language identification system designed to detect the language of each word, in its context, in a multilingual documents as generated in social media by bilingual/multilingual communities, in our case speakers of Algerian Arabic. We frame the task as a sequence tagging problem and use supervised machine learning with standard methods like HMM and Ngram classification tagging. We also experiment with a lexicon-based method. Combining all the methods in a fall-back mechanism and introducing some linguistic rules, to deal with unseen tokens and ambiguous words, gives an overall accuracy of 93.14{\%}. Finally, we introduced rules for language identification from sequences of recognised words.",
}
@inproceedings{darwish-etal-2017-arabic,
    title = "{A}rabic Diacritization: Stats, Rules, and Hacks",
    author = "Darwish, Kareem  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1302",
    doi = "10.18653/v1/W17-1302",
    pages = "9--17",
    abstract = "In this paper, we present a new and fast state-of-the-art Arabic diacritizer that guesses the diacritics of words and then their case endings. We employ a Viterbi decoder at word-level with back-off to stem, morphological patterns, and transliteration and sequence labeling based diacritization of named entities. For case endings, we use Support Vector Machine (SVM) based ranking coupled with morphological patterns and linguistic rules to properly guess case endings. We achieve a low word level diacritization error of 3.29{\%} and 12.77{\%} without and with case endings respectively on a new multi-genre free of copyright test set. We are making the diacritizer available for free for research purposes.",
}
@inproceedings{nagoudi-schwab-2017-semantic,
    title = "Semantic Similarity of {A}rabic Sentences with Word Embeddings",
    author = "Nagoudi, El Moatez Billah  and
      Schwab, Didier",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1303",
    doi = "10.18653/v1/W17-1303",
    pages = "18--24",
    abstract = "Semantic textual similarity is the basis of countless applications and plays an important role in diverse areas, such as information retrieval, plagiarism detection, information extraction and machine translation. This article proposes an innovative word embedding-based system devoted to calculate the semantic similarity in Arabic sentences. The main idea is to exploit vectors as word representations in a multidimensional space in order to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. The performance of our proposed system is confirmed through the Pearson correlation between our assigned semantic similarity scores and human judgments.",
}
@inproceedings{borg-gatt-2017-morphological,
    title = "Morphological Analysis for the {M}altese Language: The challenges of a hybrid system",
    author = "Borg, Claudia  and
      Gatt, Albert",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1304",
    doi = "10.18653/v1/W17-1304",
    pages = "25--34",
    abstract = "Maltese is a morphologically rich language with a hybrid morphological system which features both concatenative and non-concatenative processes. This paper analyses the impact of this hybridity on the performance of machine learning techniques for morphological labelling and clustering. In particular, we analyse a dataset of morphologically related word clusters to evaluate the difference in results for concatenative and non-concatenative clusters. We also describe research carried out in morphological labelling, with a particular focus on the verb category. Two evaluations were carried out, one using an unseen dataset, and another one using a gold standard dataset which was manually labelled. The gold standard dataset was split into concatenative and non-concatenative to analyse the difference in results between the two morphological systems.",
}
@inproceedings{khalifa-etal-2017-morphological,
    title = "A Morphological Analyzer for {G}ulf {A}rabic Verbs",
    author = "Khalifa, Salam  and
      Hassan, Sara  and
      Habash, Nizar",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1305",
    doi = "10.18653/v1/W17-1305",
    pages = "35--45",
    abstract = "We present CALIMAGLF, a Gulf Arabic morphological analyzer currently covering over 2,600 verbal lemmas. We describe in detail the process of building the analyzer starting from phonetic dictionary entries to fully inflected orthographic paradigms and associated lexicon and orthographic variants. We evaluate the coverage of CALIMA-GLF against Modern Standard Arabic and Egyptian Arabic analyzers on part of a Gulf Arabic novel. CALIMA-GLF verb analysis token recall for identifying correct POS tag outperforms both the Modern Standard Arabic and Egyptian Arabic analyzers by over 27.4{\%} and 16.9{\%} absolute, respectively.",
}
@inproceedings{samih-etal-2017-neural,
    title = "A Neural Architecture for Dialectal {A}rabic Segmentation",
    author = "Samih, Younes  and
      Attia, Mohammed  and
      Eldesouki, Mohamed  and
      Abdelali, Ahmed  and
      Mubarak, Hamdy  and
      Kallmeyer, Laura  and
      Darwish, Kareem",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1306",
    doi = "10.18653/v1/W17-1306",
    pages = "46--54",
    abstract = "The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general. Segmentation of words into its constituent parts is an important processing building block. In this paper, we show how a segmenter can be trained using only 350 annotated tweets using neural networks without any normalization or use of lexical features or lexical resources. We deal with segmentation as a sequence labeling problem at the character level. We show experimentally that our model can rival state-of-the-art methods that rely on additional resources.",
}
@inproceedings{medhaffar-etal-2017-sentiment,
    title = "Sentiment Analysis of {T}unisian Dialects: Linguistic Ressources and Experiments",
    author = "Medhaffar, Salima  and
      Bougares, Fethi  and
      Est{\`e}ve, Yannick  and
      Hadrich-Belguith, Lamia",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1307",
    doi = "10.18653/v1/W17-1307",
    pages = "55--61",
    abstract = "Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we are interested in the SA of the Tunisian Dialect. We utilize Machine Learning techniques to determine the polarity of comments written in Tunisian Dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian Dialect corpus of 17.000 comments from Facebook. This corpus allows us a significant accuracy improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas.",
}
@inproceedings{el-ballouli-etal-2017-cat,
    title = "{CAT}: Credibility Analysis of {A}rabic Content on {T}witter",
    author = "El Ballouli, Rim  and
      El-Hajj, Wassim  and
      Ghandour, Ahmad  and
      Elbassuoni, Shady  and
      Hajj, Hazem  and
      Shaban, Khaled",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1308",
    doi = "10.18653/v1/W17-1308",
    pages = "62--71",
    abstract = "Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author{'}s profile and timeline. To train and test CAT, we annotated for credibility a data set of 9,000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21{\%} in weighted average F-measure. We also conducted experiments to highlight the importance of the user-based features as opposed to the content-based features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.",
}
@inproceedings{alamri-teahan-2017-new,
    title = "A New Error Annotation for Dyslexic texts in {A}rabic",
    author = "Alamri, Maha  and
      Teahan, William J",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1309",
    doi = "10.18653/v1/W17-1309",
    pages = "72--78",
    abstract = "This paper aims to develop a new classification of errors made in Arabic by those suffering from dyslexia to be used in the annotation of the Arabic dyslexia corpus (BDAC). The dyslexic error classification for Arabic texts (DECA) comprises a list of spelling errors extracted from previous studies and a collection of texts written by people with dyslexia that can provide a framework to help analyse specific errors committed by dyslexic writers. The classification comprises 37 types of errors, grouped into nine categories. The paper also discusses building a corpus of dyslexic Arabic texts that uses the error annotation scheme and provides an analysis of the errors that were found in the texts.",
}
@inproceedings{ahmed-etal-2017-unsupervised,
    title = "An Unsupervised Speaker Clustering Technique based on {SOM} and {I}-vectors for Speech Recognition Systems",
    author = "Ahmed, Hany  and
      Elaraby, Mohamed  and
      M. Mousa, Abdullah  and
      Elhosiny, Mostafa  and
      Abdou, Sherif  and
      Rashwan, Mohsen",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1310",
    doi = "10.18653/v1/W17-1310",
    pages = "79--83",
    abstract = "In this paper, we introduce an enhancement for speech recognition systems using an unsupervised speaker clustering technique. The proposed technique is mainly based on I-vectors and Self-Organizing Map Neural Network(SOM).The input to the proposed algorithm is a set of speech utterances. For each utterance, we extract 100-dimensional I-vector and then SOM is used to group the utterances to different speakers. In our experiments, we compared our technique with Normalized Cross Likelihood ratio Clustering (NCLR). Results show that the proposed technique reduces the speaker error rate in comparison with NCLR. Finally, we have experimented the effect of speaker clustering on Speaker Adaptive Training (SAT) in a speech recognition system implemented to test the performance of the proposed technique. It was noted that the proposed technique reduced the WER over clustering speakers with NCLR.",
}
@inproceedings{fashwan-alansary-2017-shakkil,
    title = "{SHAKKIL}: An Automatic Diacritization System for {M}odern {S}tandard {A}rabic Texts",
    author = "Fashwan, Amany  and
      Alansary, Sameh",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1311",
    doi = "10.18653/v1/W17-1311",
    pages = "84--93",
    abstract = "This paper sheds light on a system that would be able to diacritize Arabic texts automatically (SHAKKIL). In this system, the diacritization problem will be handled through two levels; morphological and syntactic processing levels. The adopted morphological disambiguation algorithm depends on four layers; Uni-morphological form layer, rule-based morphological disambiguation layer, statistical-based disambiguation layer and Out Of Vocabulary (OOV) layer. The adopted syntactic disambiguation algorithms is concerned with detecting the case ending diacritics depending on a rule based approach simulating the shallow parsing technique. This will be achieved using an annotated corpus for extracting the Arabic linguistic rules, building the language models and testing the system output. This system is considered as a good trial of the interaction between rule-based approach and statistical approach, where the rules can help the statistics in detecting the right diacritization and vice versa. At this point, the morphological Word Error Rate (WER) is 4.56{\%} while the morphological Diacritic Error Rate (DER) is 1.88{\%} and the syntactic WER is 9.36{\%}. The best WER is 14.78{\%} compared to the best-published results, of (Abandah, 2015); 11.68{\%}, (Rashwan, et al., 2015); 12.90{\%} and (Metwally, Rashwan, {\&} Atiya, 2016); 13.70{\%}.",
}
@inproceedings{albogamy-etal-2017-arabic,
    title = "{A}rabic Tweets Treebanking and Parsing: A Bootstrapping Approach",
    author = "Albogamy, Fahad  and
      Ramsay, Allan  and
      Ahmed, Hanady",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1312",
    doi = "10.18653/v1/W17-1312",
    pages = "94--99",
    abstract = "In this paper, we propose using a {``}bootstrapping{''} method for constructing a dependency treebank of Arabic tweets. This method uses a rule-based parser to create a small treebank of one thousand Arabic tweets and a data-driven parser to create a larger treebank by using the small treebank as a seed training set. We are able to create a dependency treebank from unlabelled tweets without any manual intervention. Experiments results show that this method can improve the speed of training the parser and the accuracy of the resulting parsers.",
}
@inproceedings{khwileh-etal-2017-identifying,
    title = "Identifying Effective Translations for Cross-lingual {A}rabic-to-{E}nglish User-generated Speech Search",
    author = "Khwileh, Ahmad  and
      Afli, Haithem  and
      Jones, Gareth  and
      Way, Andy",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1313",
    doi = "10.18653/v1/W17-1313",
    pages = "100--109",
    abstract = "Cross Language Information Retrieval (CLIR) systems are a valuable tool to enable speakers of one language to search for content of interest expressed in a different language. A group for whom this is of particular interest is bilingual Arabic speakers who wish to search for English language content using information needs expressed in Arabic queries. A key challenge in CLIR is crossing the language barrier between the query and the documents. The most common approach to bridging this gap is automated query translation, which can be unreliable for vague or short queries. In this work, we examine the potential for improving CLIR effectiveness by predicting the translation effectiveness using Query Performance Prediction (QPP) techniques. We propose a novel QPP method to estimate the quality of translation for an Arabic-English Cross-lingual User-generated Speech Search (CLUGS) task. We present an empirical evaluation that demonstrates the quality of our method on alternative translation outputs extracted from an Arabic-to-English Machine Translation system developed for this task. Finally, we show how this framework can be integrated in CLUGS to find relevant translations for improved retrieval performance.",
}
@inproceedings{baly-etal-2017-characterization,
    title = "A Characterization Study of {A}rabic {T}witter Data with a Benchmarking for State-of-the-Art Opinion Mining Models",
    author = "Baly, Ramy  and
      Badaro, Gilbert  and
      El-Khoury, Georges  and
      Moukalled, Rawan  and
      Aoun, Rita  and
      Hajj, Hazem  and
      El-Hajj, Wassim  and
      Habash, Nizar  and
      Shaban, Khaled",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1314",
    doi = "10.18653/v1/W17-1314",
    pages = "110--118",
    abstract = "Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic{'}s complex morphology.",
}
@inproceedings{zhang-etal-2017-robust,
    title = "Robust Dictionary Lookup in Multiple Noisy Orthographies",
    author = "Zhang, Lingliang  and
      Habash, Nizar  and
      Toussaint, Godfried",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1315",
    doi = "10.18653/v1/W17-1315",
    pages = "119--129",
    abstract = "We present the MultiScript Phonetic Search algorithm to address the problem of language learners looking up unfamiliar words that they heard. We apply it to Arabic dictionary lookup with noisy queries done using both the Arabic and Roman scripts. Our algorithm is based on a computational phonetic distance metric that can be optionally machine learned. To benchmark our performance, we created the ArabScribe dataset, containing 10,000 noisy transcriptions of random Arabic dictionary words. Our algorithm outperforms Google Translate{'}s {``}did you mean{''} feature, as well as the Yamli smart Arabic keyboard.",
}
@inproceedings{darwish-etal-2017-arabic-pos,
    title = "{A}rabic {POS} Tagging: Don{'}t Abandon Feature Engineering Just Yet",
    author = "Darwish, Kareem  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed  and
      Eldesouki, Mohamed",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1316",
    doi = "10.18653/v1/W17-1316",
    pages = "130--137",
    abstract = "This paper focuses on comparing between using Support Vector Machine based ranking (SVM-Rank) and Bidirectional Long-Short-Term-Memory (bi-LSTM) neural-network based sequence labeling in building a state-of-the-art Arabic part-of-speech tagging system. Using SVM-Rank leads to state-of-the-art results, but with a fair amount of feature engineering. Using bi-LSTM, particularly when combined with word embeddings, may lead to competitive POS-tagging results by automatically deducing latent linguistic features. However, we show that augmenting bi-LSTM sequence labeling with some of the features that we used for the SVM-Rank based tagger yields to further improvements. We also show that gains that realized by using embeddings may not be additive with the gains achieved by the features. We are open-sourcing both the SVM-Rank and the bi-LSTM based systems for free.",
}
@inproceedings{bougrine-etal-2017-toward,
    title = "Toward a Web-based Speech Corpus for {A}lgerian Dialectal {A}rabic Varieties",
    author = "Bougrine, Soumia  and
      Chorana, Aicha  and
      Lakhdari, Abdallah  and
      Cherroun, Hadda",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1317",
    doi = "10.18653/v1/W17-1317",
    pages = "138--146",
    abstract = "The success of machine learning for automatic speech processing has raised the need for large scale datasets. However, collecting such data is often a challenging task as it implies significant investment involving time and money cost. In this paper, we devise a recipe for building largescale Speech Corpora by harnessing Web resources namely YouTube, other Social Media, Online Radio and TV. We illustrate our methodology by building KALAM{'}DZ, An Arabic Spoken corpus dedicated to Algerian dialectal varieties. The preliminary version of our dataset covers all major Algerian dialects. In addition, we make sure that this material takes into account numerous aspects that foster its richness. In fact, we have targeted various speech topics. Some automatic and manual annotations are provided. They gather useful information related to the speakers and sub-dialect information at the utterance level. Our corpus encompasses the 8 major Algerian Arabic sub-dialects with 4881 speakers and more than 104.4 hours segmented in utterances of at least 6 s.",
}
@inproceedings{abdul-mageed-2017-segments,
    title = "Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space",
    author = "Abdul-Mageed, Muhammad",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1318",
    doi = "10.18653/v1/W17-1318",
    pages = "147--156",
    abstract = "Although there is by now a considerable amount of research on subjectivity and sentiment analysis on morphologically-rich languages, it is still unclear how lexical information can best be modeled in these languages. To bridge this gap, we build effective models exploiting exclusively gold- and machine-segmented lexical input and successfully employ syntactically motivated feature selection to improve classification. Our best models achieve significantly above the baselines, with 67.93{\%} and 69.37{\%} accuracies for subjectivity and sentiment classification respectively.",
}
@inproceedings{menacer-etal-2017-enhanced,
    title = "An enhanced automatic speech recognition system for {A}rabic",
    author = "Menacer, Mohamed Amine  and
      Mella, Odile  and
      Fohr, Dominique  and
      Jouvet, Denis  and
      Langlois, David  and
      Smaili, Kamel",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1319",
    doi = "10.18653/v1/W17-1319",
    pages = "157--165",
    abstract = "Automatic speech recognition for Arabic is a very challenging task. Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance. In this article, we focus on Modern Standard Arabic (MSA) speech recognition. We introduce the challenges related to Arabic language, namely the complex morphology nature of the language and the absence of the short vowels in written text, which leads to several potential vowelization for each graphemes, which is often conflicting. We develop an ASR system for MSA by using Kaldi toolkit. Several acoustic and language models are trained. We obtain a Word Error Rate (WER) of 14.42 for the baseline system and 12.2 relative improvement by rescoring the lattice and by rewriting the output with the right Z hamoza above or below Alif.",
}
@inproceedings{taji-etal-2017-universal,
    title = "{U}niversal {D}ependencies for {A}rabic",
    author = "Taji, Dima  and
      Habash, Nizar  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1320",
    doi = "10.18653/v1/W17-1320",
    pages = "166--176",
    abstract = "We describe the process of creating NUDAR, a Universal Dependency treebank for Arabic. We present the conversion from the Penn Arabic Treebank to the Universal Dependency syntactic representation through an intermediate dependency representation. We discuss the challenges faced in the conversion of the trees, the decisions we made to solve them, and the validation of our conversion. We also present initial parsing results on NUDAR.",
}
@inproceedings{al-badrashiny-etal-2017-layered,
    title = "A Layered Language Model based Hybrid Approach to Automatic Full Diacritization of {A}rabic",
    author = "Al-Badrashiny, Mohamed  and
      Hawwari, Abdelati  and
      Diab, Mona",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1321",
    doi = "10.18653/v1/W17-1321",
    pages = "177--184",
    abstract = "In this paper we present a system for automatic Arabic text diacritization using three levels of analysis granularity in a layered back off manner. We build and exploit diacritized language models (LM) for each of three different levels of granularity: surface form, morphologically segmented into prefix/stem/suffix, and character level. For each of the passes, we use Viterbi search to pick the most probable diacritization per word in the input. We start with the surface form LM, followed by the morphological level, then finally we leverage the character level LM. Our system outperforms all of the published systems evaluated against the same training and test data. It achieves a 10.87{\%} WER for complete full diacritization including lexical and syntactic diacritization, and 3.0{\%} WER for lexical diacritization, ignoring syntactic diacritization.",
}
@inproceedings{almarwani-diab-2017-arabic,
    title = "{A}rabic Textual Entailment with Word Embeddings",
    author = "Almarwani, Nada  and
      Diab, Mona",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1322",
    doi = "10.18653/v1/W17-1322",
    pages = "185--190",
    abstract = "Determining the textual entailment between texts is important in many NLP tasks, such as summarization, question answering, and information extraction and retrieval. Various methods have been suggested based on external knowledge sources; however, such resources are not always available in all languages and their acquisition is typically laborious and very costly. Distributional word representations such as word embeddings learned over large corpora have been shown to capture syntactic and semantic word relationships. Such models have contributed to improving the performance of several NLP tasks. In this paper, we address the problem of textual entailment in Arabic. We employ both traditional features and distributional representations. Crucially, we do not depend on any external resources in the process. Our suggested approach yields state of the art performance on a standard data set, ArbTE, achieving an accuracy of 76.2 {\%} compared to state of the art of 69.3 {\%}.",
}
@proceedings{ws-2017-nlp,
    title = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    editor = {Nakov, Preslav  and
      Zampieri, Marcos  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Malmasi, Shevin  and
      Ali, Ahmed},
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1200",
    doi = "10.18653/v1/W17-12",
}
@inproceedings{zampieri-etal-2017-findings,
    title = "Findings of the {V}ar{D}ial Evaluation Campaign 2017",
    author = {Zampieri, Marcos  and
      Malmasi, Shervin  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Nakov, Preslav  and
      Ali, Ahmed  and
      Tiedemann, J{\"o}rg  and
      Scherrer, Yves  and
      Aepli, No{\"e}mi},
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1201",
    doi = "10.18653/v1/W17-1201",
    pages = "1--15",
    abstract = "We present the results of the VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects, which we organized as part of the fourth edition of the VarDial workshop at EACL{'}2017. This year, we included four shared tasks: Discriminating between Similar Languages (DSL), Arabic Dialect Identification (ADI), German Dialect Identification (GDI), and Cross-lingual Dependency Parsing (CLP). A total of 19 teams submitted runs across the four tasks, and 15 of them wrote system description papers.",
}
@inproceedings{donoso-sanchez-2017-dialectometric,
    title = "Dialectometric analysis of language variation in {T}witter",
    author = "Donoso, Gonzalo  and
      S{\'a}nchez, David",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1202",
    doi = "10.18653/v1/W17-1202",
    pages = "16--25",
    abstract = "In the last few years, microblogging platforms such as Twitter have given rise to a deluge of textual data that can be used for the analysis of informal communication between millions of individuals. In this work, we propose an information-theoretic approach to geographic language variation using a corpus based on Twitter. We test our models with tens of concepts and their associated keywords detected in Spanish tweets geolocated in Spain. We employ dialectometric measures (cosine similarity and Jensen-Shannon divergence) to quantify the linguistic distance on the lexical level between cells created in a uniform grid over the map. This can be done for a single concept or in the general case taking into account an average of the considered variants. The latter permits an analysis of the dialects that naturally emerge from the data. Interestingly, our results reveal the existence of two dialect macrovarieties. The first group includes a region-specific speech spoken in small towns and rural areas whereas the second cluster encompasses cities that tend to use a more uniform variety. Since the results obtained with the two different metrics qualitatively agree, our work suggests that social media corpora can be efficiently used for dialectometric analyses.",
}
@inproceedings{rama-etal-2017-computational,
    title = "Computational analysis of {G}ondi dialects",
    author = {Rama, Taraka  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Sofroniev, Pavel},
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1203",
    doi = "10.18653/v1/W17-1203",
    pages = "26--35",
    abstract = "This paper presents a computational analysis of Gondi dialects spoken in central India. We present a digitized data set of the dialect area, and analyze the data using different techniques from dialectometry, deep learning, and computational biology. We show that the methods largely agree with each other and with the earlier non-computational analyses of the language group.",
}
@inproceedings{dipper-waldenberger-2017-investigating,
    title = "Investigating Diatopic Variation in a Historical Corpus",
    author = "Dipper, Stefanie  and
      Waldenberger, Sandra",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1204",
    doi = "10.18653/v1/W17-1204",
    pages = "36--45",
    abstract = "This paper investigates diatopic variation in a historical corpus of German. Based on equivalent word forms from different language areas, replacement rules and mappings are derived which describe the relations between these word forms. These rules and mappings are then interpreted as reflections of morphological, phonological or graphemic variation. Based on sample rules and mappings, we show that our approach can replicate results from historical linguistics. While previous studies were restricted to predefined word lists, or confined to single authors or texts, our approach uses a much wider range of data available in historical corpora.",
}
@inproceedings{rosso-2017-author,
    title = "Author Profiling at {PAN}: from Age and Gender Identification to Language Variety Identification (invited talk)",
    author = "Rosso, Paolo",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1205",
    doi = "10.18653/v1/W17-1205",
    pages = "46",
    abstract = "Author profiling is the study of how language is shared by people, a problem of growing importance in applications dealing with security, in order to understand who could be behind an anonymous threat message, and marketing, where companies may be interested in knowing the demographics of people that in online reviews liked or disliked their products. In this talk we will give an overview of the PAN shared tasks that since 2013 have been organised at CLEF and FIRE evaluation forums, mainly on age and gender identification in social media, although also personality recognition in Twitter as well as in code sources was also addressed. In 2017 the PAN author profiling shared task addresses jointly gender and language variety identification in Twitter where tweets have been annotated with authors{'} gender and their specific variation of their native language: English (Australia, Canada, Great Britain, Ireland, New Zealand, United States), Spanish (Argentina, Chile, Colombia, Mexico, Peru, Spain, Venezuela), Portuguese (Brazil, Portugal), and Arabic (Egypt, Gulf, Levantine, Maghrebi).",
}
@inproceedings{feleke-2017-similarity,
    title = "The similarity and Mutual Intelligibility between {A}mharic and {T}igrigna Varieties",
    author = "Feleke, Tekabe Legesse",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1206",
    doi = "10.18653/v1/W17-1206",
    pages = "47--54",
    abstract = "The present study has examined the similarity and the mutual intelligibility between Amharic and Tigrigna using three tools namely Levenshtein distance, intelligibility test and questionnaires. The study has shown that both Tigrigna varieties have almost equal phonetic and lexical distances from Amharic. The study also indicated that Amharic speakers understand less than 50{\%} of the two varieties. Furthermore, the study showed that Amharic speakers are more positive about the Ethiopian Tigrigna variety than the Eritrean Variety. However, their attitude towards the two varieties does not have an impact on their intelligibility. The Amharic speakers{'} familiarity to the Tigrigna varieties is largely dependent on the genealogical relation between Amharic and the two Tigrigna varieties.",
}
@inproceedings{costa-jussa-2017-catalan,
    title = "Why {C}atalan-{S}panish Neural Machine Translation? Analysis, comparison and combination with standard Rule and Phrase-based technologies",
    author = "Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1207",
    doi = "10.18653/v1/W17-1207",
    pages = "55--62",
    abstract = "Catalan and Spanish are two related languages given that both derive from Latin. They share similarities in several linguistic levels including morphology, syntax and semantics. This makes them particularly interesting for the MT task. Given the recent appearance and popularity of neural MT, this paper analyzes the performance of this new approach compared to the well-established rule-based and phrase-based MT systems. Experiments are reported on a large database of 180 million words. Results, in terms of standard automatic measures, show that neural MT clearly outperforms the rule-based and phrase-based MT system on in-domain test set, but it is worst in the out-of-domain test set. A naive system combination specially works for the latter. In-domain manual analysis shows that neural MT tends to improve both adequacy and fluency, for example, by being able to generate more natural translations instead of literal ones, choosing to the adequate target word when the source word has several translations and improving gender agreement. However, out-of-domain manual analysis shows how neural MT is more affected by unknown words or contexts.",
}
@inproceedings{hassani-2017-kurdish,
    title = "{K}urdish Interdialect Machine Translation",
    author = "Hassani, Hossein",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1208",
    doi = "10.18653/v1/W17-1208",
    pages = "63--72",
    abstract = "This research suggests a method for machine translation among two Kurdish dialects. We chose the two widely spoken dialects, Kurmanji and Sorani, which are considered to be mutually unintelligible. Also, despite being spoken by about 30 million people in different countries, Kurdish is among less-resourced languages. The research used bi-dialectal dictionaries and showed that the lack of parallel corpora is not a major obstacle in machine translation between the two dialects. The experiments showed that the machine translated texts are comprehensible to those who do not speak the dialect. The research is the first attempt for inter-dialect machine translation in Kurdish and particularly could help in making online texts in one dialect comprehensible to those who only speak the target dialect. The results showed that the translated texts are in 71{\%} and 79{\%} cases rated as understandable for Kurmanji and Sorani respectively. They are rated as slightly-understandable in 29{\%} cases for Kurmanji and 21{\%} for Sorani.",
}
@inproceedings{williams-dagli-2017-twitter,
    title = "{T}witter Language Identification Of Similar Languages And Dialects Without Ground Truth",
    author = "Williams, Jennifer  and
      Dagli, Charlie",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1209",
    doi = "10.18653/v1/W17-1209",
    pages = "73--83",
    abstract = "We present a new method to bootstrap filter Twitter language ID labels in our dataset for automatic language identification (LID). Our method combines geo-location, original Twitter LID labels, and Amazon Mechanical Turk to resolve missing and unreliable labels. We are the first to compare LID classification performance using the MIRA algorithm and langid.py. We show classifier performance on different versions of our dataset with high accuracy using only Twitter data, without ground truth, and very few training examples. We also show how Platt Scaling can be use to calibrate MIRA classifier output values into a probability distribution over candidate classes, making the output more intuitive. Our method allows for fine-grained distinctions between similar languages and dialects and allows us to rediscover the language composition of our Twitter dataset.",
}
@inproceedings{scherrer-rabus-2017-multi,
    title = "Multi-source morphosyntactic tagging for spoken {R}usyn",
    author = "Scherrer, Yves  and
      Rabus, Achim",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1210",
    doi = "10.18653/v1/W17-1210",
    pages = "84--92",
    abstract = "This paper deals with the development of morphosyntactic taggers for spoken varieties of the Slavic minority language Rusyn. As neither annotated corpora nor parallel corpora are electronically available for Rusyn, we propose to combine existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish and adapt them to Rusyn. Using MarMoT as tagging toolkit, we show that a tagger trained on a balanced set of the four source languages outperforms single language taggers by about 9{\%}, and that additional automatically induced morphosyntactic lexicons lead to further improvements. The best observed accuracies for Rusyn are 82.4{\%} for part-of-speech tagging and 75.5{\%} for full morphological tagging.",
}
@inproceedings{hanani-etal-2017-identifying,
    title = "Identifying dialects with textual and acoustic cues",
    author = "Hanani, Abualsoud  and
      Qaroush, Aziz  and
      Taylor, Stephen",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1211",
    doi = "10.18653/v1/W17-1211",
    pages = "93--101",
    abstract = "We describe several systems for identifying short samples of Arabic or Swiss-German dialects, which were prepared for the shared task of the 2017 DSL Workshop (Zampieri et al., 2017). The Arabic data comprises both text and acoustic files, and our best run combined both. The Swiss-German data is text-only. Coincidently, our best runs achieved a accuracy of nearly 63{\%} on both the Swiss-German and Arabic dialects tasks.",
}
@inproceedings{jauhiainen-etal-2017-evaluating,
    title = "Evaluating {H}e{LI} with Non-Linear Mappings",
    author = "Jauhiainen, Tommi  and
      Lind{\'e}n, Krister  and
      Jauhiainen, Heidi",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1212",
    doi = "10.18653/v1/W17-1212",
    pages = "102--108",
    abstract = "In this paper we describe the non-linear mappings we used with the Helsinki language identification method, HeLI, in the 4th edition of the Discriminating between Similar Languages (DSL) shared task, which was organized as part of the VarDial 2017 workshop. Our SUKI team participated on the closed track together with 10 other teams. Our system reached the 7th position in the track. We describe the HeLI method and the non-linear mappings in mathematical notation. The HeLI method uses a probabilistic model with character n-grams and word-based backoff. We also describe our trials using the non-linear mappings instead of relative frequencies and we present statistics about the back-off function of the HeLI method.",
}
@inproceedings{gamallo-etal-2017-perplexity,
    title = "A Perplexity-Based Method for Similar Languages Discrimination",
    author = "Gamallo, Pablo  and
      Pichel, Jose Ramom  and
      Alegria, I{\~n}aki",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1213",
    doi = "10.18653/v1/W17-1213",
    pages = "109--114",
    abstract = "This article describes the system submitted by the Citius{\_}Ixa{\_}Imaxin team to the VarDial 2017 (DSL and GDI tasks). The strategy underlying our system is based on a language distance computed by means of model perplexity. The best model configuration we have tested is a voting system making use of several $n$-grams models of both words and characters, even if word unigrams turned out to be a very competitive model with reasonable results in the tasks we have participated. An error analysis has been performed in which we identified many test examples with no linguistic evidences to distinguish among the variants.",
}
@inproceedings{bestgen-2017-improving,
    title = "Improving the Character Ngram Model for the {DSL} Task with {BM}25 Weighting and Less Frequently Used Feature Sets",
    author = "Bestgen, Yves",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1214",
    doi = "10.18653/v1/W17-1214",
    pages = "115--123",
    abstract = "This paper describes the system developed by the Centre for English Corpus Linguistics (CECL) to discriminating similar languages, language varieties and dialects. Based on a SVM with character and POStag n-grams as features and the BM25 weighting scheme, it achieved 92.7{\%} accuracy in the Discriminating between Similar Languages (DSL) task, ranking first among eleven systems but with a lead over the next three teams of only 0.2{\%}. A simpler version of the system ranked second in the German Dialect Identification (GDI) task thanks to several ad hoc postprocessing steps. Complementary analyses carried out by a cross-validation procedure suggest that the BM25 weighting scheme could be competitive in this type of tasks, at least in comparison with the sublinear TF-IDF. POStag n-grams also improved the system performance.",
}
@inproceedings{criscuolo-aluisio-2017-discriminating,
    title = "Discriminating between Similar Languages with Word-level Convolutional Neural Networks",
    author = "Criscuolo, Marcelo  and
      Alu{\'\i}sio, Sandra Maria",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1215",
    doi = "10.18653/v1/W17-1215",
    pages = "124--130",
    abstract = "Discriminating between Similar Languages (DSL) is a challenging task addressed at the VarDial Workshop series. We report on our participation in the DSL shared task with a two-stage system. In the first stage, character n-grams are used to separate language groups, then specialized classifiers distinguish similar language varieties. We have conducted experiments with three system configurations and submitted one run for each. Our main approach is a word-level convolutional neural network (CNN) that learns task-specific vectors with minimal text preprocessing. We also experiment with multi-layer perceptron (MLP) networks and another hybrid configuration. Our best run achieved an accuracy of 90.76{\%}, ranking 8th among 11 participants and getting very close to the system that ranked first (less than 2 points). Even though the CNN model could not achieve the best results, it still makes a viable approach to discriminating between similar languages.",
}
@inproceedings{tiedemann-2017-cross,
    title = "Cross-lingual dependency parsing for closely related languages - {H}elsinki{'}s submission to {V}ar{D}ial 2017",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1216",
    doi = "10.18653/v1/W17-1216",
    pages = "131--136",
    abstract = "This paper describes the submission from the University of Helsinki to the shared task on cross-lingual dependency parsing at VarDial 2017. We present work on annotation projection and treebank translation that gave good results for all three target languages in the test set. In particular, Slovak seems to work well with information coming from the Czech treebank, which is in line with related work. The attachment scores for cross-lingual models even surpass the fully supervised models trained on the target language treebank. Croatian is the most difficult language in the test set and the improvements over the baseline are rather modest. Norwegian works best with information coming from Swedish whereas Danish contributes surprisingly little.",
}
@inproceedings{gomez-etal-2017-discriminating,
    title = "Discriminating between Similar Languages Using a Combination of Typed and Untyped Character N-grams and Words",
    author = "Gomez, Helena  and
      Markov, Ilia  and
      Baptista, Jorge  and
      Sidorov, Grigori  and
      Pinto, David",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1217",
    doi = "10.18653/v1/W17-1217",
    pages = "137--145",
    abstract = "This paper presents the cic{\_}ualg{'}s system that took part in the Discriminating between Similar Languages (DSL) shared task, held at the VarDial 2017 Workshop. This year{'}s task aims at identifying 14 languages across 6 language groups using a corpus of excerpts of journalistic texts. Two classification approaches were compared: a single-step (all languages) approach and a two-step (language group and then languages within the group) approach. Features exploited include lexical features (unigrams of words) and character n-grams. Besides traditional (untyped) character n-grams, we introduce typed character n-grams in the DSL task. Experiments were carried out with different feature representation methods (binary and raw term frequency), frequency threshold values, and machine-learning algorithms {--} Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB). Our best run in the DSL task achieved 91.46{\%} accuracy.",
}
@inproceedings{coltekin-rama-2017-tubingen,
    title = {{T}{\"u}bingen system in {V}ar{D}ial 2017 shared task: experiments with language identification and cross-lingual parsing},
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Rama, Taraka},
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1218",
    doi = "10.18653/v1/W17-1218",
    pages = "146--155",
    abstract = "This paper describes our systems and results on VarDial 2017 shared tasks. Besides three language/dialect discrimination tasks, we also participated in the cross-lingual dependency parsing (CLP) task using a simple methodology which we also briefly describe in this paper. For all the discrimination tasks, we used linear SVMs with character and word features. The system achieves competitive results among other systems in the shared task. We also report additional experiments with neural network models. The performance of neural network models was close but always below the corresponding SVM classifiers in the discrimination tasks. For the cross-lingual parsing task, we experimented with an approach based on automatically translating the source treebank to the target language, and training a parser on the translated treebank. We used off-the-shelf tools for both translation and parsing. Despite achieving better-than-baseline results, our scores in CLP tasks were substantially lower than the scores of the other participants.",
}
@inproceedings{medvedeva-etal-2017-sparse,
    title = "When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages",
    author = "Medvedeva, Maria  and
      Kroon, Martin  and
      Plank, Barbara",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1219",
    doi = "10.18653/v1/W17-1219",
    pages = "156--163",
    abstract = "We present the results of our participation in the VarDial 4 shared task on discriminating closely related languages. Our submission includes simple traditional models using linear support vector machines (SVMs) and a neural network (NN). The main idea was to leverage language group information. We did so with a two-layer approach in the traditional model and a multi-task objective in the neural network case. Our results confirm earlier findings: simple traditional models outperform neural networks consistently for this task, at least given the amount of systems we could examine in the available time. Our two-layer linear SVM ranked 2nd in the shared task.",
}
@inproceedings{malmasi-zampieri-2017-german,
    title = "{G}erman Dialect Identification in Interview Transcriptions",
    author = "Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1220",
    doi = "10.18653/v1/W17-1220",
    pages = "164--169",
    abstract = "This paper presents three systems submitted to the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2017. The task consists of training models to identify the dialect of Swiss-German speech transcripts. The dialects included in the GDI dataset are Basel, Bern, Lucerne, and Zurich. The three systems we submitted are based on: a plurality ensemble, a mean probability ensemble, and a meta-classifier trained on character and word n-grams. The best results were obtained by the meta-classifier achieving 68.1{\%} accuracy and 66.2{\%} F1-score, ranking first among the 10 teams which participated in the GDI shared task.",
}
@inproceedings{clematide-makarov-2017-cluzh,
    title = "{CLUZH} at {V}ar{D}ial {GDI} 2017: Testing a Variety of Machine Learning Tools for the Classification of {S}wiss {G}erman Dialects",
    author = "Clematide, Simon  and
      Makarov, Peter",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1221",
    doi = "10.18653/v1/W17-1221",
    pages = "170--177",
    abstract = {Our submissions for the GDI 2017 Shared Task are the results from three different types of classifiers: Na{\"\i}ve Bayes, Conditional Random Fields (CRF), and Support Vector Machine (SVM). Our CRF-based run achieves a weighted F1 score of 65{\%} (third rank) being beaten by the best system by 0.9{\%}. Measured by classification accuracy, our ensemble run (Na{\"\i}ve Bayes, CRF, SVM) reaches 67{\%} (second rank) being 1{\%} lower than the best system. We also describe our experiments with Recurrent Neural Network (RNN) architectures. Since they performed worse than our non-neural approaches we did not include them in the submission.},
}
@inproceedings{malmasi-zampieri-2017-arabic,
    title = "{A}rabic Dialect Identification Using i{V}ectors and {ASR} Transcripts",
    author = "Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1222",
    doi = "10.18653/v1/W17-1222",
    pages = "178--183",
    abstract = "This paper presents the systems submitted by the MAZA team to the Arabic Dialect Identification (ADI) shared task at the VarDial Evaluation Campaign 2017. The goal of the task is to evaluate computational models to identify the dialect of Arabic utterances using both audio and text transcriptions. The ADI shared task dataset included Modern Standard Arabic (MSA) and four Arabic dialects: Egyptian, Gulf, Levantine, and North-African. The three systems submitted by MAZA are based on combinations of multiple machine learning classifiers arranged as (1) voting ensemble; (2) mean probability ensemble; (3) meta-classifier. The best results were obtained by the meta-classifier achieving 71.7{\%} accuracy, ranking second among the six teams which participated in the ADI shared task.",
}
@inproceedings{barbaresi-2017-discriminating,
    title = "Discriminating between Similar Languages using Weighted Subword Features",
    author = "Barbaresi, Adrien",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1223",
    doi = "10.18653/v1/W17-1223",
    pages = "184--189",
    abstract = "The present contribution revolves around a contrastive subword n-gram model which has been tested in the Discriminating between Similar Languages shared task. I present and discuss the method used in this 14-way language identification task comprising varieties of 6 main language groups. It features the following characteristics: (1) the preprocessing and conversion of a collection of documents to sparse features; (2) weighted character n-gram profiles; (3) a multinomial Bayesian classifier. Meaningful bag-of-n-grams features can be used as a system in a straightforward way, my approach outperforms most of the systems used in the DSL shared task (3rd rank).",
}
@inproceedings{van-der-lee-van-den-bosch-2017-exploring,
    title = "Exploring Lexical and Syntactic Features for Language Variety Identification",
    author = "van der Lee, Chris  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1224",
    doi = "10.18653/v1/W17-1224",
    pages = "190--199",
    abstract = "We present a method to discriminate between texts written in either the Netherlandic or the Flemish variant of the Dutch language. The method draws on a feature bundle representing text statistics, syntactic features, and word $n$-grams. Text statistics include average word length and sentence length, while syntactic features include ratios of function words and part-of-speech $n$-grams. The effectiveness of the classifier was measured by classifying Dutch subtitles developed for either Dutch or Flemish television. Several machine learning algorithms were compared as well as feature combination methods in order to find the optimal generalization performance. A machine-learning meta classifier based on AdaBoost attained the best F-score of 0.92.",
}
@inproceedings{ionescu-butnaru-2017-learning,
    title = "Learning to Identify {A}rabic and {G}erman Dialects using Multiple Kernels",
    author = "Ionescu, Radu Tudor  and
      Butnaru, Andrei",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1225",
    doi = "10.18653/v1/W17-1225",
    pages = "200--209",
    abstract = "We present a machine learning approach for the Arabic Dialect Identification (ADI) and the German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided only for the Arabic data. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Our approach is shallow and simple, but the empirical results obtained in the shared tasks prove that it achieves very good results. Indeed, we ranked on the first place in the ADI Shared Task with a weighted F1 score of 76.32{\%} (4.62{\%} above the second place) and on the fifth place in the GDI Shared Task with a weighted F1 score of 63.67{\%} (2.57{\%} below the first place).",
}
@inproceedings{rosa-etal-2017-slavic,
    title = "{S}lavic Forest, {N}orwegian Wood",
    author = "Rosa, Rudolf  and
      Zeman, Daniel  and
      Mare{\v{c}}ek, David  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k",
    booktitle = "Proceedings of the Fourth Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1226",
    doi = "10.18653/v1/W17-1226",
    pages = "210--219",
    abstract = "We once had a corp, or should we say, it once had us They showed us its tags, isn{'}t it great, unified tags They asked us to parse and they told us to use everything So we looked around and we noticed there was near nothing We took other langs, bitext aligned: words one-to-one We played for two weeks, and then they said, here is the test The parser kept training till morning, just until deadline So we had to wait and hope what we get would be just fine And, when we awoke, the results were done, we saw we{'}d won So, we wrote this paper, isn{'}t it good, Norwegian wood.",
}
@proceedings{ws-2017-international,
    title = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1100",
    doi = "10.18653/v1/W17-11",
}
@inproceedings{schmidt-wiegand-2017-survey,
    title = "A Survey on Hate Speech Detection using Natural Language Processing",
    author = "Schmidt, Anna  and
      Wiegand, Michael",
    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1101",
    doi = "10.18653/v1/W17-1101",
    pages = "1--10",
    abstract = "This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.",
}
@inproceedings{tian-etal-2017-facebook,
    title = "{F}acebook sentiment: Reactions and Emojis",
    author = "Tian, Ye  and
      Galery, Thiago  and
      Dulcinati, Giulio  and
      Molimpakis, Emilia  and
      Sun, Chao",
    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1102",
    doi = "10.18653/v1/W17-1102",
    pages = "11--16",
    abstract = "Emojis are used frequently in social media. A widely assumed view is that emojis express the emotional state of the user, which has led to research focusing on the expressiveness of emojis independent from the linguistic context. We argue that emojis and the linguistic texts can modify the meaning of each other. The overall communicated meaning is not a simple sum of the two channels. In order to study the meaning interplay, we need data indicating the overall sentiment of the entire message as well as the sentiment of the emojis stand-alone. We propose that Facebook Reactions are a good data source for such a purpose. FB reactions (e.g. {``}Love{''} and {``}Angry{''}) indicate the readers{'} overall sentiment, against which we can investigate the types of emojis used the comments under different reaction profiles. We present a data set of 21,000 FB posts (57 million reactions and 8 million comments) from public media pages across four countries.",
}
@inproceedings{deriu-etal-2017-potential,
    title = "Potential and Limitations of Cross-Domain Sentiment Classification",
    author = "Deriu, Jan Milan  and
      Weilenmann, Martin  and
      Von Gruenigen, Dirk  and
      Cieliebak, Mark",
    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1103",
    doi = "10.18653/v1/W17-1103",
    pages = "17--24",
    abstract = "In this paper we investigate the cross-domain performance of a current state-of-the-art sentiment analysis systems. For this purpose we train a convolutional neural network (CNN) on data from different domains and evaluate its performance on other domains. Furthermore, we evaluate the usefulness of combining a large amount of different smaller annotated corpora to a large corpus. Our results show that more sophisticated approaches are required to train a system that works equally well on various domains.",
}
@inproceedings{mckelvey-etal-2017-aligning,
    title = "Aligning Entity Names with Online Aliases on {T}witter",
    author = "McKelvey, Kevin  and
      Goutzounis, Peter  and
      da Cruz, Stephen  and
      Chambers, Nathanael",
    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1104",
    doi = "10.18653/v1/W17-1104",
    pages = "25--35",
    abstract = "This paper presents new models that automatically align online aliases with their real entity names. Many research applications rely on identifying entity names in text, but people often refer to entities with unexpected nicknames and aliases. For example, The King and King James are aliases for Lebron James, a professional basketball player. Recent work on entity linking attempts to resolve mentions to knowledge base entries, like a wikipedia page, but linking is unfortunately limited to well-known entities with pre-built pages. This paper asks a more basic question: can aliases be aligned without background knowledge of the entity? Further, can the semantics surrounding alias mentions be used to inform alignments? We describe statistical models that make decisions based on the lexicographic properties of the aliases with their semantic context in a large corpus of tweets. We experiment on a database of Twitter users and their usernames, and present the first human evaluation for this task. Alignment accuracy approaches human performance at 81{\%}, and we show that while lexicographic features are most important, the semantic context of an alias further improves classification accuracy.",
}
@inproceedings{vakulenko-etal-2017-character,
    title = "Character-based Neural Embeddings for Tweet Clustering",
    author = "Vakulenko, Svitlana  and
      Nixon, Lyndon  and
      Lupu, Mihai",
    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1105",
    doi = "10.18653/v1/W17-1105",
    pages = "36--44",
    abstract = "In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks. The proposed approach overcomes the limitations related to the vocabulary explosion in the word-based models and allows for the seamless processing of the multilingual content. Our evaluation results and code are available on-line: \url{https://github.com/vendi12/tweet2vec_clustering}.",
}
@inproceedings{cieliebak-etal-2017-twitter,
    title = "A {T}witter Corpus and Benchmark Resources for {G}erman Sentiment Analysis",
    author = "Cieliebak, Mark  and
      Deriu, Jan Milan  and
      Egger, Dominic  and
      Uzdilli, Fatih",
    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1106",
    doi = "10.18653/v1/W17-1106",
    pages = "45--51",
    abstract = "In this paper we present SB10k, a new corpus for sentiment analysis with approx. 10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available.",
}
@proceedings{ws-2017-multiling,
    title = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    editor = "Giannakopoulos, George  and
      Lloret, Elena  and
      Conroy, John M.  and
      Steinberger, Josef  and
      Litvak, Marina  and
      Rankel, Peter  and
      Favre, Benoit",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1000",
    doi = "10.18653/v1/W17-10",
}
@inproceedings{giannakopoulos-etal-2017-multiling,
    title = "{M}ulti{L}ing 2017 Overview",
    author = "Giannakopoulos, George  and
      Conroy, John  and
      Kubina, Jeff  and
      Rankel, Peter A.  and
      Lloret, Elena  and
      Steinberger, Josef  and
      Litvak, Marina  and
      Favre, Benoit",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1001",
    doi = "10.18653/v1/W17-1001",
    pages = "1--6",
    abstract = "In this brief report we present an overview of the MultiLing 2017 effort and workshop, as implemented within EACL 2017. MultiLing is a community-driven initiative that pushes the state-of-the-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. This year the scope of the workshop was widened, bringing together researchers that work on summarization across sources, languages and genres. We summarize the main tasks planned and implemented this year, the contributions received, and we also provide insights on next steps.",
}
@inproceedings{xu-etal-2017-decoupling,
    title = "Decoupling Encoder and Decoder Networks for Abstractive Document Summarization",
    author = "Xu, Ying  and
      Lau, Jey Han  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1002",
    doi = "10.18653/v1/W17-1002",
    pages = "7--11",
    abstract = "Abstractive document summarization seeks to automatically generate a summary for a document, based on some abstract {``}understanding{''} of the original document. State-of-the-art techniques traditionally use attentive encoder{--}decoder architectures. However, due to the large number of parameters in these models, they require large training datasets and long training times. In this paper, we propose decoupling the encoder and decoder networks, and training them separately. We encode documents using an unsupervised document encoder, and then feed the document vector to a recurrent neural network decoder. With this decoupled architecture, we decrease the number of parameters in the decoder substantially, and shorten its training time. Experiments show that the decoupled model achieves comparable performance with state-of-the-art models for in-domain documents, but less well for out-of-domain documents.",
}
@inproceedings{rossiello-etal-2017-centroid,
    title = "Centroid-based Text Summarization through Compositionality of Word Embeddings",
    author = "Rossiello, Gaetano  and
      Basile, Pierpaolo  and
      Semeraro, Giovanni",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1003",
    doi = "10.18653/v1/W17-1003",
    pages = "12--21",
    abstract = "The textual similarity is a crucial aspect for many extractive text summarization methods. A bag-of-words representation does not allow to grasp the semantic relationships between concepts when comparing strongly related sentences with no words in common. To overcome this issue, in this paper we propose a centroid-based method for text summarization that exploits the compositional capabilities of word embeddings. The evaluations on multi-document and multilingual datasets prove the effectiveness of the continuous vector representation of words compared to the bag-of-words model. Despite its simplicity, our method achieves good performance even in comparison to more complex deep learning models. Our method is unsupervised and it can be adopted in other summarization tasks.",
}
@inproceedings{litvak-vanetik-2017-query,
    title = "Query-based summarization using {MDL} principle",
    author = "Litvak, Marina  and
      Vanetik, Natalia",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1004",
    doi = "10.18653/v1/W17-1004",
    pages = "22--31",
    abstract = "Query-based text summarization is aimed at extracting essential information that answers the query from original text. The answer is presented in a minimal, often predefined, number of words. In this paper we introduce a new unsupervised approach for query-based extractive summarization, based on the minimum description length (MDL) principle that employs Krimp compression algorithm (Vreeken et al., 2011). The key idea of our approach is to select frequent word sets related to a given query that compress document sentences better and therefore describe the document better. A summary is extracted by selecting sentences that best cover query-related frequent word sets. The approach is evaluated based on the DUC 2005 and DUC 2006 datasets which are specifically designed for query-based summarization (DUC, 2005 2006). It competes with the best results.",
}
@inproceedings{li-etal-2017-word,
    title = "Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums",
    author = "Li, Lei  and
      Mao, Liyuan  and
      Chen, Moye",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1005",
    doi = "10.18653/v1/W17-1005",
    pages = "32--36",
    abstract = "Multiple grammatical and semantic features are adopted in content linking and argument/sentiment labeling for online forums in this paper. There are mainly two different methods for content linking. First, we utilize the deep feature obtained from Word Embedding Model in deep learning and compute sentence similarity. Second, we use multiple traditional features to locate candidate linking sentences, and then adopt a voting method to obtain the final result. LDA topic modeling is used to mine latent semantic feature and K-means clustering is implemented for argument labeling, while features from sentiment dictionaries and rule-based sentiment analysis are integrated for sentiment labeling. Experimental results have shown that our methods are valid.",
}
@inproceedings{lloret-etal-2017-ultra,
    title = "Ultra-Concise Multi-genre Summarisation of Web2.0: towards Intelligent Content Generation",
    author = "Lloret, Elena  and
      Boldrini, Ester  and
      Mart{\'\i}nez-Barco, Patricio  and
      Palomar, Manuel",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1006",
    doi = "10.18653/v1/W17-1006",
    pages = "37--46",
    abstract = "The electronic Word of Mouth has become the most powerful communication channel thanks to the wide usage of the Social Media. Our research proposes an approach towards the production of automatic ultra-concise summaries from multiple Web 2.0 sources. We exploit user-generated content from reviews and microblogs in different domains, and compile and analyse four types of ultra-concise summaries: a)positive information, b) negative information; c) both or d) objective information. The appropriateness and usefulness of our model is demonstrated by its successful results and great potential in real-life applications, thus meaning a relevant advancement of the state-of-the-art approaches.",
}
@inproceedings{ellouze-etal-2017-machine,
    title = "Machine Learning Approach to Evaluate {M}ulti{L}ingual Summaries",
    author = "Ellouze, Samira  and
      Jaoua, Maher  and
      Hadrich Belguith, Lamia",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-1007",
    doi = "10.18653/v1/W17-1007",
    pages = "47--54",
    abstract = "The present paper introduces a new MultiLing text summary evaluation method. This method relies on machine learning approach which operates by combining multiple features to build models that predict the human score (overall responsiveness) of a new summary. We have tried several single and {``}ensemble learning{''} classifiers to build the best model. We have experimented our method in summary level evaluation where we evaluate each text summary separately. The correlation between built models and human score is better than the correlation between baselines and manual score.",
}
@proceedings{ws-2017-linking,
    title = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    editor = "Roth, Michael  and
      Mostafazadeh, Nasrin  and
      Chambers, Nathanael  and
      Louis, Annie",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0900",
    doi = "10.18653/v1/W17-09",
}
@inproceedings{wanzare-etal-2017-inducing,
    title = "Inducing Script Structure from Crowdsourced Event Descriptions via Semi-Supervised Clustering",
    author = "Wanzare, Lilian  and
      Zarcone, Alessandra  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0901",
    doi = "10.18653/v1/W17-0901",
    pages = "1--11",
    abstract = "We present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets (representing event types) and inducing their temporal order. Our approach exploits semantic and positional similarity and allows for flexible event order, thus overcoming the rigidity of previous approaches. We incorporate crowdsourced alignments as prior knowledge and show that exploiting a small number of alignments results in a substantial improvement in cluster quality over state-of-the-art models and provides an appropriate basis for the induction of temporal order. We also show a coverage study to demonstrate the scalability of our approach.",
}
@inproceedings{wities-etal-2017-consolidated,
    title = "A Consolidated Open Knowledge Representation for Multiple Texts",
    author = "Wities, Rachel  and
      Shwartz, Vered  and
      Stanovsky, Gabriel  and
      Adler, Meni  and
      Shapira, Ori  and
      Upadhyay, Shyam  and
      Roth, Dan  and
      Martinez Camara, Eugenio  and
      Gurevych, Iryna  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0902",
    doi = "10.18653/v1/W17-0902",
    pages = "12--24",
    abstract = "We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions using entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to give semantic applications an easy handle on consolidated information across multiple texts.",
}
@inproceedings{ponti-korhonen-2017-event,
    title = "Event-Related Features in Feedforward Neural Networks Contribute to Identifying Causal Relations in Discourse",
    author = "Ponti, Edoardo Maria  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0903",
    doi = "10.18653/v1/W17-0903",
    pages = "25--30",
    abstract = "Causal relations play a key role in information extraction and reasoning. Most of the times, their expression is ambiguous or implicit, i.e. without signals in the text. This makes their identification challenging. We aim to improve their identification by implementing a Feedforward Neural Network with a novel set of features for this task. In particular, these are based on the position of event mentions and the semantics of events and participants. The resulting classifier outperforms strong baselines on two datasets (the Penn Discourse Treebank and the CSTNews corpus) annotated with different schemes and containing examples in two languages, English and Portuguese. This result demonstrates the importance of events for identifying discourse relations.",
}
@inproceedings{klenner-etal-2017-stance,
    title = "Stance Detection in {F}acebook Posts of a {G}erman Right-wing Party",
    author = "Klenner, Manfred  and
      Tuggener, Don  and
      Clematide, Simon",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0904",
    doi = "10.18653/v1/W17-0904",
    pages = "31--40",
    abstract = "We argue that in order to detect stance, not only the explicit attitudes of the stance holder towards the targets are crucial. It is the whole narrative the writer drafts that counts, including the way he hypostasizes the discourse referents: as benefactors or villains, as victims or beneficiaries. We exemplify the ability of our system to identify targets and detect the writer{'}s stance towards them on the basis of about 100 000 Facebook posts of a German right-wing party. A reader and writer model on top of our verb-based attitude extraction directly reveal stance conflicts.",
}
@inproceedings{chambers-2017-behind,
    title = "Behind the Scenes of an Evolving Event Cloze Test",
    author = "Chambers, Nathanael",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0905",
    doi = "10.18653/v1/W17-0905",
    pages = "41--45",
    abstract = "This paper analyzes the narrative event cloze test and its recent evolution. The test removes one event from a document{'}s chain of events, and systems predict the missing event. Originally proposed to evaluate learned knowledge of event scenarios (e.g., scripts and frames), most recent work now builds ngram-like language models (LM) to beat the test. This paper argues that the test has slowly/unknowingly been altered to accommodate LMs.5 Most notably, tests are auto-generated rather than by hand, and no effort is taken to include core script events. Recent work is not clear on evaluation goals and contains contradictory results. We implement several models, and show that the test{'}s bias to high-frequency events explains the inconsistencies. We conclude with recommendations on how to return to the test{'}s original intent, and offer brief suggestions on a path forward.",
}
@inproceedings{mostafazadeh-etal-2017-lsdsem,
    title = "{LSDS}em 2017 Shared Task: The Story Cloze Test",
    author = "Mostafazadeh, Nasrin  and
      Roth, Michael  and
      Louis, Annie  and
      Chambers, Nathanael  and
      Allen, James",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0906",
    doi = "10.18653/v1/W17-0906",
    pages = "46--51",
    abstract = "The LSDSem{'}17 shared task is the Story Cloze Test, a new evaluation for story understanding and script learning. This test provides a system with a four-sentence story and two possible endings, and the system must choose the correct ending to the story. Successful narrative understanding (getting closer to human performance of 100{\%}) requires systems to link various levels of semantics to commonsense knowledge. A total of eight systems participated in the shared task, with a variety of approaches including.",
}
@inproceedings{schwartz-etal-2017-story,
    title = "Story Cloze Task: {UW} {NLP} System",
    author = "Schwartz, Roy  and
      Sap, Maarten  and
      Konstas, Ioannis  and
      Zilles, Leila  and
      Choi, Yejin  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0907",
    doi = "10.18653/v1/W17-0907",
    pages = "52--55",
    abstract = "This paper describes University of Washington NLP{'}s submission for the Linking Models of Lexical, Sentential and Discourse-level Semantics (LSDSem 2017) shared task{---}the Story Cloze Task. Our system is a linear classifier with a variety of features, including both the scores of a neural language model and style features. We report 75.2{\%} accuracy on the task. A further discussion of our results can be found in Schwartz et al. (2017).",
}
@inproceedings{bugert-etal-2017-lsdsem,
    title = "{LSDS}em 2017: Exploring Data Generation Methods for the Story Cloze Test",
    author = {Bugert, Michael  and
      Puzikov, Yevgeniy  and
      R{\"u}ckl{\'e}, Andreas  and
      Eckle-Kohler, Judith  and
      Martin, Teresa  and
      Mart{\'\i}nez-C{\'a}mara, Eugenio  and
      Sorokin, Daniil  and
      Peyrard, Maxime  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0908",
    doi = "10.18653/v1/W17-0908",
    pages = "56--61",
    abstract = "The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus.",
}
@inproceedings{flor-somasundaran-2017-sentiment,
    title = "Sentiment Analysis and Lexical Cohesion for the Story Cloze Task",
    author = "Flor, Michael  and
      Somasundaran, Swapna",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0909",
    doi = "10.18653/v1/W17-0909",
    pages = "62--67",
    abstract = "We present two NLP components for the Story Cloze Task {--} dictionary-based sentiment analysis and lexical cohesion. While previous research found no contribution from sentiment analysis to the accuracy on this task, we demonstrate that sentiment is an important aspect. We describe a new approach, using a rule that estimates sentiment congruence in a story. Our sentiment-based system achieves strong results on this task. Our lexical cohesion system achieves accuracy comparable to previously published baseline results. A combination of the two systems achieves better accuracy than published baselines. We argue that sentiment analysis should be considered an integral part of narrative comprehension.",
}
@inproceedings{schenk-chiarcos-2017-resource,
    title = "Resource-Lean Modeling of Coherence in Commonsense Stories",
    author = "Schenk, Niko  and
      Chiarcos, Christian",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0910",
    doi = "10.18653/v1/W17-0910",
    pages = "68--73",
    abstract = "We present a resource-lean neural recognizer for modeling coherence in commonsense stories. Our lightweight system is inspired by successful attempts to modeling discourse relations and stands out due to its simplicity and easy optimization compared to prior approaches to narrative script learning. We evaluate our approach in the Story Cloze Test demonstrating an absolute improvement in accuracy of 4.7{\%} over state-of-the-art implementations.",
}
@inproceedings{roemmele-etal-2017-rnn,
    title = "An {RNN}-based Binary Classifier for the Story Cloze Test",
    author = "Roemmele, Melissa  and
      Kobayashi, Sosuke  and
      Inoue, Naoya  and
      Gordon, Andrew",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0911",
    doi = "10.18653/v1/W17-0911",
    pages = "74--80",
    abstract = "The Story Cloze Test consists of choosing a sentence that best completes a story given two choices. In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct. The classifier is trained to distinguish correct story endings given in the training data from incorrect ones that we artificially generate. Our experiments evaluate different methods for generating these negative examples, as well as different embedding-based representations of the stories. Our best result obtains 67.2{\%} accuracy on the test set, outperforming the existing top baseline of 58.5{\%}.",
}
@inproceedings{goel-singh-2017-iit,
    title = "{IIT} ({BHU}): System Description for {LSDS}em{'}17 Shared Task",
    author = "Goel, Pranav  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0912",
    doi = "10.18653/v1/W17-0912",
    pages = "81--86",
    abstract = "This paper describes an ensemble system submitted as part of the LSDSem Shared Task 2017 - the Story Cloze Test. The main conclusion from our results is that an approach based on semantic similarity alone may not be enough for this task. We test various approaches and compare them with two ensemble systems. One is based on voting and the other on logistic regression based classifier. Our final system is able to outperform the previous state of the art for the Story Cloze test. Another very interesting observation is the performance of sentiment based approach which works almost as well on its own as our final ensemble system.",
}
@inproceedings{mihaylov-frank-2017-story,
    title = "Story Cloze Ending Selection Baselines and Data Examination",
    author = "Mihaylov, Todor  and
      Frank, Anette",
    booktitle = "Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0913",
    doi = "10.18653/v1/W17-0913",
    pages = "87--92",
    abstract = "This paper describes two supervised baseline systems for the Story Cloze Test Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using features based on word embeddings and semantic similarity computation. We further implement a neural LSTM system with different encoding strategies that try to model the relation between the story and the provided endings. Our experiments show that a model using representation features based on average word embedding vectors over the given story words and the candidate ending sentences words, joint with similarity features between the story and candidate ending representations performed better than the neural models. Our best model based on achieves an accuracy of 72.42, ranking 3rd in the official evaluation.",
}
@proceedings{ws-2017-linguistic,
    title = "Proceedings of the 11th Linguistic Annotation Workshop",
    editor = "Schneider, Nathan  and
      Xue, Nianwen",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0800",
    doi = "10.18653/v1/W17-08",
}
@inproceedings{buechel-hahn-2017-readers,
    title = "Readers vs. Writers vs. Texts: Coping with Different Perspectives of Text Understanding in Emotion Annotation",
    author = "Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0801",
    doi = "10.18653/v1/W17-0801",
    pages = "1--12",
    abstract = "We here examine how different perspectives of understanding written discourse, like the reader{'}s, the writer{'}s or the text{'}s point of view, affect the quality of emotion annotations. We conducted a series of annotation experiments on two corpora, a popular movie review corpus and a genre- and domain-balanced corpus of standard English. We found statistical evidence that the writer{'}s perspective yields superior annotation quality overall. However, the quality one perspective yields compared to the other(s) seems to depend on the domain the utterance originates from. Our data further suggest that the popular movie review data set suffers from an atypical bimodal distribution which may decrease model performance when used as a training resource.",
}
@inproceedings{napoles-etal-2017-finding,
    title = "Finding Good Conversations Online: The {Y}ahoo {N}ews Annotated Comments Corpus",
    author = "Napoles, Courtney  and
      Tetreault, Joel  and
      Pappu, Aasish  and
      Rosato, Enrica  and
      Provenzale, Brian",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0802",
    doi = "10.18653/v1/W17-0802",
    pages = "13--23",
    abstract = "This work presents a dataset and annotation scheme for the new task of identifying {``}good{''} conversations that occur online, which we call ERICs: Engaging, Respectful, and/or Informative Conversations. We develop a taxonomy to reflect features of entire threads and individual comments which we believe contribute to identifying ERICs; code a novel dataset of Yahoo News comment threads (2.4k threads and 10k comments) and 1k threads from the Internet Argument Corpus; and analyze the features characteristic of ERICs. This is one of the largest annotated corpora of online human dialogues, with the most detailed set of annotations. It will be valuable for identifying ERICs and other aspects of argumentation, dialogue, and discourse.",
}
@inproceedings{scholman-demberg-2017-crowdsourcing,
    title = "Crowdsourcing discourse interpretations: On the influence of context and the reliability of a connective insertion task",
    author = "Scholman, Merel  and
      Demberg, Vera",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0803",
    doi = "10.18653/v1/W17-0803",
    pages = "24--33",
    abstract = "Traditional discourse annotation tasks are considered costly and time-consuming, and the reliability and validity of these tasks is in question. In this paper, we investigate whether crowdsourcing can be used to obtain reliable discourse relation annotations. We also examine the influence of context on the reliability of the data. The results of a crowdsourced connective insertion task showed that the method can be used to obtain reliable annotations: The majority of the inserted connectives converged with the original label. Further, the method is sensitive to the fact that multiple senses can often be inferred for a single relation. Regarding the presence of context, the results show no significant difference in distributions of insertions between conditions overall. However, a by-item comparison revealed several characteristics of segments that determine whether the presence of context makes a difference in annotations. The findings discussed in this paper can be taken as evidence that crowdsourcing can be used as a valuable method to obtain insights into the sense(s) of relations.",
}
@inproceedings{cetinoglu-2017-code,
    title = "A Code-Switching Corpus of {T}urkish-{G}erman Conversations",
    author = {{\c{C}}etino{\u{g}}lu, {\"O}zlem},
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0804",
    doi = "10.18653/v1/W17-0804",
    pages = "34--40",
    abstract = "We present a code-switching corpus of Turkish-German that is collected by recording conversations of bilinguals. The recordings are then transcribed in two layers following speech and orthography conventions, and annotated with sentence boundaries and intersentential, intrasentential, and intra-word switch points. The total amount of data is 5 hours of speech which corresponds to 3614 sentences. The corpus aims at serving as a resource for speech or text analysis, as well as a collection for linguistic inquiries.",
}
@inproceedings{martinez-alonso-etal-2017-annotating,
    title = "Annotating omission in statement pairs",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Delamaire, Amaury  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0805",
    doi = "10.18653/v1/W17-0805",
    pages = "41--45",
    abstract = "We focus on the identification of omission in statement pairs. We compare three annotation schemes, namely two different crowdsourcing schemes and manual expert annotation. We show that the simplest of the two crowdsourcing approaches yields a better annotation quality than the more complex one. We use a dedicated classifier to assess whether the annotators{'} behavior can be explained by straightforward linguistic features. The classifier benefits from a modeling that uses lexical information beyond length and overlap measures. However, for our task, we argue that expert and not crowdsourcing-based annotation is the best compromise between annotation cost and quality.",
}
@inproceedings{bary-etal-2017-annotating,
    title = "Annotating Speech, Attitude and Perception Reports",
    author = "Bary, Corien  and
      Hess, Leopold  and
      Thijs, Kees  and
      Berck, Peter  and
      Hendrickx, Iris",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0806",
    doi = "10.18653/v1/W17-0806",
    pages = "46--56",
    abstract = "We present REPORTS, an annotation scheme for the annotation of speech, attitude and perception reports. Such a scheme makes it possible to annotate the various text elements involved in such reports (e.g. embedding entity, complement, complement head) and their relations in a uniform way, which in turn facilitates the automatic extraction of information on, for example, complementation and vocabulary distribution. We also present the Ancient Greek corpus RAG (Thucydides{'} History of the Peloponnesian War), to which we have applied this scheme using the annotation tool BRAT. We discuss some of the issues, both theoretical and practical, that we encountered, show how the corpus helps in answering specific questions, and conclude that REPORTS fitted in well with our needs.",
}
@inproceedings{fujita-etal-2017-consistent,
    title = "Consistent Classification of Translation Revisions: A Case Study of {E}nglish-{J}apanese Student Translations",
    author = "Fujita, Atsushi  and
      Tanabe, Kikuko  and
      Toyoshima, Chiho  and
      Yamamoto, Mayuka  and
      Kageura, Kyo  and
      Hartley, Anthony",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0807",
    doi = "10.18653/v1/W17-0807",
    pages = "57--66",
    abstract = "Consistency is a crucial requirement in text annotation. It is especially important in educational applications, as lack of consistency directly affects learners{'} motivation and learning performance. This paper presents a quality assessment scheme for English-to-Japanese translations produced by learner translators at university. We constructed a revision typology and a decision tree manually through an application of the OntoNotes method, i.e., an iteration of assessing learners{'} translations and hypothesizing the conditions for consistent decision making, as well as re-organizing the typology. Intrinsic evaluation of the created scheme confirmed its potential contribution to the consistent classification of identified erroneous text spans, achieving visibly higher Cohen{'}s kappa values, up to 0.831, than previous work. This paper also describes an application of our scheme to an English-to-Japanese translation exercise course for undergraduate students at a university in Japan.",
}
@inproceedings{eckart-de-castilho-etal-2017-representation,
    title = "Representation and Interchange of Linguistic Annotation. An In-Depth, Side-by-Side Comparison of Three Designs",
    author = "Eckart de Castilho, Richard  and
      Ide, Nancy  and
      Lapponi, Emanuele  and
      Oepen, Stephan  and
      Suderman, Keith  and
      Velldal, Erik  and
      Verhagen, Marc",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0808",
    doi = "10.18653/v1/W17-0808",
    pages = "67--75",
    abstract = "For decades, most self-respecting linguistic engineering initiatives have designed and implemented custom representations for various layers of, for example, morphological, syntactic, and semantic analysis. Despite occasional efforts at harmonization or even standardization, our field today is blessed with a multitude of ways of encoding and exchanging linguistic annotations of these types, both at the levels of {`}abstract syntax{'}, naming choices, and of course file formats. To a large degree, it is possible to work within and across design plurality by conversion, and often there may be good reasons for divergent design reflecting differences in use. However, it is likely that some abstract commonalities across choices of representation are obscured by more superficial differences, and conversely there is no obvious procedure to tease apart what actually constitute contentful vs. mere technical divergences. In this study, we seek to conceptually align three representations for common types of morpho-syntactic analysis, pinpoint what in our view constitute contentful differences, and reflect on the underlying principles and specific requirements that led to individual choices. We expect that a more in-depth understanding of these choices across designs may led to increased harmonization, or at least to more informed design of future representations.",
}
@inproceedings{zeyrek-kurfali-2017-tdb,
    title = "{TDB} 1.1: Extensions on {T}urkish Discourse Bank",
    author = "Zeyrek, Deniz  and
      Kurfal{\i}, Murathan",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0809",
    doi = "10.18653/v1/W17-0809",
    pages = "76--81",
    abstract = "This paper presents the recent developments on Turkish Discourse Bank (TDB). First, the resource is summarized and an evaluation is presented. Then, TDB 1.1, i.e. enrichments on 10{\%} of the corpus are described (namely, senses for explicit discourse connectives, and new annotations for three discourse relation types - implicit relations, entity relations and alternative lexicalizations). The method of annotation is explained and the data are evaluated.",
}
@inproceedings{di-buono-etal-2017-two,
    title = "Two Layers of Annotation for Representing Event Mentions in News Stories",
    author = "di Buono, Maria Pia  and
      Tutek, Martin  and
      {\v{S}}najder, Jan  and
      Glava{\v{s}}, Goran  and
      Dalbelo Ba{\v{s}}i{\'c}, Bojana  and
      Mili{\'c}-Frayling, Nata{\v{s}}a",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0810",
    doi = "10.18653/v1/W17-0810",
    pages = "82--90",
    abstract = "In this paper, we describe our preliminary study on annotating event mention as a part of our research on high-precision news event extraction models. To this end, we propose a two-layer annotation scheme, designed to separately capture the functional and conceptual aspects of event mentions. We hypothesize that the precision of models can be improved by modeling and extracting separately the different aspects of news events, and then combining the extracted information by leveraging the complementarities of the models. In addition, we carry out a preliminary annotation using the proposed scheme and analyze the annotation quality in terms of inter-annotator agreement.",
}
@inproceedings{akhtar-etal-2017-word,
    title = "Word Similarity Datasets for {I}ndian Languages: Annotation and Baseline Systems",
    author = "Akhtar, Syed Sarfaraz  and
      Gupta, Arihant  and
      Vajpayee, Avijit  and
      Srivastava, Arjit  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0811",
    doi = "10.18653/v1/W17-0811",
    pages = "91--94",
    abstract = "With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity datasets of six Indian languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These languages are most spoken Indian languages worldwide after Hindi and Bengali. For the construction of these datasets, our approach relies on translation and re-annotation of word similarity datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity datasets.",
}
@inproceedings{dunietz-etal-2017-corpus,
    title = "The {BEC}au{SE} Corpus 2.0: Annotating Causality and Overlapping Relations",
    author = "Dunietz, Jesse  and
      Levin, Lori  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0812",
    doi = "10.18653/v1/W17-0812",
    pages = "95--104",
    abstract = "Language of cause and effect captures an essential component of the semantics of a text. However, causal language is also intertwined with other semantic relations, such as temporal precedence and correlation. This makes it difficult to determine when causation is the primary intended meaning. This paper presents BECauSE 2.0, a new version of the BECauSE corpus with exhaustively annotated expressions of causal language, but also seven semantic relations that are frequently co-present with causation. The new corpus shows high inter-annotator agreement, and yields insights both about the linguistic expressions of causation and about the process of annotating co-present semantic relations.",
}
@inproceedings{rehbein-ruppenhofer-2017-catching,
    title = "Catching the Common Cause: Extraction and Annotation of Causal Relations and their Participants",
    author = "Rehbein, Ines  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0813",
    doi = "10.18653/v1/W17-0813",
    pages = "105--114",
    abstract = "In this paper, we present a simple, yet effective method for the automatic identification and extraction of causal relations from text, based on a large English-German parallel corpus. The goal of this effort is to create a lexical resource for German causal relations. The resource will consist of a lexicon that describes constructions that trigger causality as well as the participants of the causal event, and will be augmented by a corpus with annotated instances for each entry, that can be used as training data to develop a system for automatic classification of causal relations. Focusing on verbs, our method harvested a set of 100 different lexical triggers of causality, including support verb constructions. At the moment, our corpus includes over 1,000 annotated instances. The lexicon and the annotated data will be made available to the research community.",
}
@inproceedings{hartmann-etal-2017-assessing,
    title = "Assessing {SRL} Frameworks with Automatic Training Data Expansion",
    author = "Hartmann, Silvana  and
      M{\'u}jdricza-Maydt, {\'E}va  and
      Kuznetsov, Ilia  and
      Gurevych, Iryna  and
      Frank, Anette",
    booktitle = "Proceedings of the 11th Linguistic Annotation Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0814",
    doi = "10.18653/v1/W17-0814",
    pages = "115--121",
    abstract = "We present the first experiment-based study that explicitly contrasts the three major semantic role labeling frameworks. As a prerequisite, we create a dataset labeled with parallel FrameNet-, PropBank-, and VerbNet-style labels for German. We train a state-of-the-art SRL tool for German for the different annotation styles and provide a comparative analysis across frameworks. We further explore the behavior of the frameworks with automatic training data generation. VerbNet provides larger semantic expressivity than PropBank, and we find that its generalization capacity approaches PropBank in SRL training, but it benefits less from training data expansion than the sparse-data affected FrameNet.",
}
@proceedings{ws-2017-cognitive,
    title = "Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2017)",
    editor = "Gibson, Ted  and
      Linzen, Tal  and
      Sayeed, Asad  and
      van Schijndel, Martin  and
      Schuler, William",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0700",
    doi = "10.18653/v1/W17-07",
}
@inproceedings{nelson-etal-2017-entropy,
    title = "Entropy Reduction correlates with temporal lobe activity",
    author = "Nelson, Matthew  and
      Dehaene, Stanislas  and
      Pallier, Christophe  and
      Hale, John",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0701",
    doi = "10.18653/v1/W17-0701",
    pages = "1--10",
    abstract = "Using the Entropy Reduction incremental complexity metric, we relate high gamma power signals from the brains of epileptic patients to incremental stages of syntactic analysis in English and French. We find that signals recorded intracranially from the anterior Inferior Temporal Sulcus (aITS) and the posterior Inferior Temporal Gyrus (pITG) correlate with word-by-word Entropy Reduction values derived from phrase structure grammars for those languages. In the anterior region, this correlation persists even in combination with surprisal co-predictors from PCFG and ngram models. The result confirms the idea that the brain{'}s temporal lobe houses a parsing function, one whose incremental processing difficulty profile reflects changes in grammatical uncertainty.",
}
@inproceedings{perkins-etal-2017-learning,
    title = "Learning an Input Filter for Argument Structure Acquisition",
    author = "Perkins, Laurel  and
      Feldman, Naomi  and
      Lidz, Jeffrey",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0702",
    doi = "10.18653/v1/W17-0702",
    pages = "11--19",
    abstract = "How do children learn a verb{'}s argument structure when their input contains nonbasic clauses that obscure verb transitivity? Here we present a new model that infers verb transitivity by learning to filter out non-basic clauses that were likely parsed in error. In simulations with child-directed speech, we show that this model accurately categorizes the majority of 50 frequent transitive, intransitive and alternating verbs, and jointly learns appropriate parameters for filtering parsing errors. Our model is thus able to filter out problematic data for verb learning without knowing in advance which data need to be filtered.",
}
@inproceedings{burchill-jaeger-2017-grounding,
    title = "Grounding sound change in ideal observer models of perception",
    author = "Burchill, Zachary  and
      Jaeger, T. Florian",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0703",
    doi = "10.18653/v1/W17-0703",
    pages = "20--28",
    abstract = "An important predictor of historical sound change, functional load, fails to capture insights from speech perception. Building on ideal observer models of word recognition, we devise a new definition of functional load that incorporates both a priori predictability and perceptual information. We explore this new measure with a simple model and find that it outperforms traditional measures.",
}
@inproceedings{tatman-2017-oh,
    title = "{``}Oh, {I}{'}ve Heard That Before{''}: Modelling Own-Dialect Bias After Perceptual Learning by Weighting Training Data",
    author = "Tatman, Rachael",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0704",
    doi = "10.18653/v1/W17-0704",
    pages = "29--34",
    abstract = "Human listeners are able to quickly and robustly adapt to new accents and do so by using information about speaker{'}s identities. This paper will present experimental evidence that, even considering information about speaker{'}s identities, listeners retain a strong bias towards the acoustics of their own dialect after dialect learning. Participants{'} behaviour was accurately mimicked by a classifier which was trained on more cases from the base dialect and fewer from the target dialect. This suggests that imbalanced training data may result in automatic speech recognition errors consistent with those of speakers from populations over-represented in the training data.",
}
@inproceedings{doucette-2017-inherent,
    title = "Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation",
    author = "Doucette, Amanda",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0705",
    doi = "10.18653/v1/W17-0705",
    pages = "35--40",
    abstract = "A recurrent neural network model of phonological pattern learning is proposed. The model is a relatively simple neural network with one recurrent layer, and displays biases in learning that mimic observed biases in human learning. Single-feature patterns are learned faster than two-feature patterns, and vowel or consonant-only patterns are learned faster than patterns involving vowels and consonants, mimicking the results of laboratory learning experiments. In non-recurrent models, capturing these biases requires the use of alpha features or some other representation of repeated features, but with a recurrent neural network, these elaborations are not necessary.",
}
@inproceedings{orita-2017-predicting,
    title = "Predicting {J}apanese scrambling in the wild",
    author = "Orita, Naho",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics ({CMCL} 2017)",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0706",
    doi = "10.18653/v1/W17-0706",
    pages = "41--45",
    abstract = "Japanese speakers have a choice between canonical SOV and scrambled OSV word order to express the same meaning. Although previous experiments examine the influence of one or two factors for scrambling in a controlled setting, it is not yet known what kinds of multiple effects contribute to scrambling. This study uses naturally distributed data to test the multiple effects on scrambling simultaneously. A regression analysis replicates the NP length effect and suggests the influence of noun types, but it provides no evidence for syntactic priming, given-new ordering, and the animacy effect. These findings only show evidence for sentence-internal factors, but we find no evidence that discourse level factors play a role.",
}
@proceedings{ws-2017-linguistics,
    title = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    editor = "Tyers, Francis M.  and
      Rie{\ss}ler, Michael  and
      Pirinen, Tommi A.  and
      Trosterud, Trond",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0600",
    doi = "10.18653/v1/W17-06",
}
@inproceedings{rueter-hamalainen-2017-synchronized,
    title = "Synchronized Mediawiki based analyzer dictionary development",
    author = {Rueter, Jack  and
      H{\"a}m{\"a}l{\"a}inen, Mika},
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0601",
    doi = "10.18653/v1/W17-0601",
    pages = "1--7",
}
@inproceedings{rueter-2017-demo,
    title = "{DEMO}: Giellatekno Open-source click-in-text dictionaries for bringing closely related languages into contact.",
    author = "Rueter, Jack",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0602",
    doi = "10.18653/v1/W17-0602",
    pages = "8--9",
}
@inproceedings{simon-mus-2017-languages,
    title = "Languages under the influence: Building a database of Uralic languages",
    author = "Simon, Eszter  and
      Mus, Nikolett",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0603",
    doi = "10.18653/v1/W17-0603",
    pages = "10--24",
}
@inproceedings{gerstenberger-etal-2017-instant-annotations,
    title = "Instant Annotations {--} Applying {NLP} Methods to the Annotation of Spoken Language Documentation Corpora",
    author = "Gerstenberger, Ciprian  and
      Partanen, Niko  and
      Rie{\ss}ler, Michael  and
      Wilbur, Joshua",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0604",
    doi = "10.18653/v1/W17-0604",
    pages = "25--36",
}
@inproceedings{chaminade-poibeau-2017-preliminary,
    title = "Preliminary Experiments concerning Verbal Predicative Structure Extraction from a Large {F}innish Corpus",
    author = "Chaminade, Guersande  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0605",
    doi = "10.18653/v1/W17-0605",
    pages = "37--55",
}
@inproceedings{horvath-etal-2017-language,
    title = "Language technology resources and tools for {M}ansi: an overview",
    author = "Horv{\'a}th, Csilla  and
      Szil{\'a}gyi, Norbert  and
      Vincze, Veronika  and
      Nagy, {\'A}goston",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0606",
    doi = "10.18653/v1/W17-0606",
    pages = "56--65",
}
@inproceedings{tyers-sheyanova-2017-annotation,
    title = "Annotation schemes in {N}orth {S}{\'a}mi dependency parsing",
    author = "Tyers, Francis M.  and
      Sheyanova, Mariya",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0607",
    doi = "10.18653/v1/W17-0607",
    pages = "66--75",
}
@inproceedings{reino-trosterud-etal-2017-morphological,
    title = "A morphological analyser for Kven",
    author = {Reino Trosterud, Sindre  and
      Trosterud, Trond  and
      R{\"a}is{\"a}nen, Anna-Kaisa  and
      Niiranen, Leena  and
      Haavisto, Mervi  and
      Maliniemi, Kaisa},
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages",
    month = jan,
    year = "2017",
    address = "St. Petersburg, Russia",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0608",
    doi = "10.18653/v1/W17-0608",
    pages = "76--88",
}
@proceedings{ws-2017-nodalida-2017,
    title = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    editor = "Bouma, Gerlof  and
      Adesam, Yvonne",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0500",
}
@inproceedings{dipper-2017-variance,
    title = "Variance in Historical Data: How bad is it and how can we profit from it for historical linguistics?",
    author = "Dipper, Stefanie",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0501",
    pages = "1--1",
}
@inproceedings{janssen-etal-2017-improving,
    title = "Improving {POS} Tagging in {O}ld {S}panish Using {TEITOK}",
    author = "Janssen, Maarten  and
      Ausensi, Josep  and
      Fontana, Josep",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0502",
    pages = "2--6",
}
@inproceedings{knappen-etal-2017-making,
    title = "The Making of the Royal Society Corpus",
    author = {Knappen, J{\"o}rg  and
      Fischer, Stefan  and
      Kermes, Hannah  and
      Teich, Elke  and
      Fankhauser, Peter},
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0503",
    pages = "7--11",
}
@inproceedings{korchagina-2017-normalizing,
    title = "Normalizing Medieval {G}erman Texts: from rules to deep learning",
    author = "Korchagina, Natalia",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0504",
    pages = "12--17",
}
@inproceedings{moritz-buchler-2017-ambiguity,
    title = "Ambiguity in Semantically Related Word Substitutions: an investigation in historical {B}ible translations",
    author = {Moritz, Maria  and
      B{\"u}chler, Marco},
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0505",
    pages = "18--23",
}
@inproceedings{passarotti-etal-2017-lemlat,
    title = "The Lemlat 3.0 Package for Morphological Analysis of {L}atin",
    author = "Passarotti, Marco  and
      Budassi, Marco  and
      Litta, Eleonora  and
      Ruffolo, Paolo",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0506",
    pages = "24--31",
}
@inproceedings{schatzle-etal-2017-histobankvis,
    title = "{H}isto{B}ank{V}is: Detecting Language Change via Data Visualization",
    author = {Sch{\"a}tzle, Christin  and
      Hund, Michael  and
      Dennig, Frederik  and
      Butt, Miriam  and
      Keim, Daniel},
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0507",
    pages = "32--39",
}
@inproceedings{schneider-etal-2017-comparing,
    title = "Comparing Rule-based and {SMT}-based Spelling Normalisation for {E}nglish Historical Texts",
    author = "Schneider, Gerold  and
      Pettersson, Eva  and
      Percillier, Michael",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0508",
    pages = "40--46",
}
@inproceedings{schraagen-etal-2017-data,
    title = "Data-driven Morphology and Sociolinguistics for Early {M}odern {D}utch",
    author = "Schraagen, Marijn  and
      van Koppen, Marjo  and
      Dietz, Feike",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0509",
    pages = "47--53",
}
@inproceedings{vesanto-etal-2017-applying,
    title = "Applying {BLAST} to Text Reuse Detection in {F}innish Newspapers and Journals, 1771-1910",
    author = "Vesanto, Aleksi  and
      Nivala, Asko  and
      Rantala, Heli  and
      Salakoski, Tapio  and
      Salmi, Hannu  and
      Ginter, Filip",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://www.aclweb.org/anthology/W17-0510",
    pages = "54--58",
}
@proceedings{ws-2017-nodalida,
    title = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    editor = "de Marneffe, Marie-Catherine  and
      Nivre, Joakim  and
      Schuster, Sebastian",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0400",
}
@inproceedings{agic-2017-cross,
    title = "Cross-Lingual Parser Selection for Low-Resource Languages",
    author = "Agi{\'c}, {\v{Z}}eljko",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0401",
    pages = "1--10",
}
@inproceedings{ahrenberg-2017-swedish,
    title = "{S}wedish Prepositions are not Pure Function Words",
    author = "Ahrenberg, Lars",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0402",
    pages = "11--18",
}
@inproceedings{bouma-van-noord-2017-increasing,
    title = "Increasing Return on Annotation Investment: The Automatic Construction of a {U}niversal {D}ependency Treebank for {D}utch",
    author = "Bouma, Gosse  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0403",
    pages = "19--26",
}
@inproceedings{coltekin-etal-2017-converting,
    title = {Converting the {T}{\"u}{B}a-{D}/{Z} Treebank of {G}erman to {U}niversal {D}ependencies},
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Campbell, Ben  and
      Hinrichs, Erhard  and
      Telljohann, Heike},
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0404",
    pages = "27--37",
}
@inproceedings{dirix-etal-2017-universal,
    title = "{U}niversal {D}ependencies for {A}frikaans",
    author = "Dirix, Peter  and
      Augustinus, Liesbeth  and
      van Niekerk, Daniel  and
      Van Eynde, Frank",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0405",
    pages = "38--47",
}
@inproceedings{droganova-zeman-2017-elliptic,
    title = "Elliptic Constructions: Spotting Patterns in {UD} Treebanks",
    author = "Droganova, Kira  and
      Zeman, Daniel",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0406",
    pages = "48--57",
}
@inproceedings{hennig-kohn-2017-dependency,
    title = "Dependency Tree Transformation with Tree Transducers",
    author = {Hennig, Felix  and
      K{\"o}hn, Arne},
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0407",
    pages = "58--66",
}
@inproceedings{lee-etal-2017-towards,
    title = "Towards {U}niversal {D}ependencies for Learner {C}hinese",
    author = "Lee, John  and
      Leung, Herman  and
      Li, Keying",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0408",
    pages = "67--71",
}
@inproceedings{levshina-2017-syntactic,
    title = "Does Syntactic Informativity Predict Word Length? A Cross-Linguistic Study Based on the {U}niversal {D}ependencies Corpora",
    author = "Levshina, Natalia",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0409",
    pages = "72--78",
}
@inproceedings{muischnek-muurisep-2017-estonian,
    title = "{E}stonian Copular and Existential Constructions as an {UD} Annotation Problem",
    author = {Muischnek, Kadri  and
      M{\"u}{\"u}risep, Kaili},
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0410",
    pages = "79--85",
}
@inproceedings{nivre-fang-2017-universal,
    title = "{U}niversal {D}ependency Evaluation",
    author = "Nivre, Joakim  and
      Fang, Chiao-Ting",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0411",
    pages = "86--95",
}
@inproceedings{popel-etal-2017-udapi,
    title = "{U}dapi: Universal {API} for {U}niversal {D}ependencies",
    author = "Popel, Martin  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Vojtek, Martin",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0412",
    pages = "96--101",
}
@inproceedings{prokopidis-papageorgiou-2017-universal,
    title = "{U}niversal {D}ependencies for {G}reek",
    author = "Prokopidis, Prokopis  and
      Papageorgiou, Haris",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0413",
    pages = "102--106",
}
@inproceedings{ranta-kolachina-2017-universal,
    title = "From {U}niversal {D}ependencies to Abstract Syntax",
    author = "Ranta, Aarne  and
      Kolachina, Prasanth",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0414",
    pages = "107--116",
}
@inproceedings{schluter-agic-2017-empirically,
    title = "Empirically Sampling {U}niversal {D}ependencies",
    author = "Schluter, Natalie  and
      Agi{\'c}, {\v{Z}}eljko",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0415",
    pages = "117--122",
}
@inproceedings{schuster-etal-2017-gapping,
    title = "Gapping Constructions in {U}niversal {D}ependencies v2",
    author = "Schuster, Sebastian  and
      Lamm, Matthew  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0416",
    pages = "123--132",
}
@inproceedings{senuma-aizawa-2017-toward,
    title = "Toward {U}niversal {D}ependencies for {A}inu",
    author = "Senuma, Hajime  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0417",
    pages = "133--139",
}
@inproceedings{silfverberg-hulden-2017-automatic,
    title = "Automatic Morpheme Segmentation and Labeling in {U}niversal {D}ependencies Resources",
    author = "Silfverberg, Miikka  and
      Hulden, Mans",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0418",
    pages = "140--145",
}
@inproceedings{wisniewski-lacroix-2017-systematic,
    title = "A Systematic Comparison of Syntactic Representations of Dependency Parsing",
    author = "Wisniewski, Guillaume  and
      Lacroix, Oph{\'e}lie",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Universal Dependencies ({UDW} 2017)",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0419",
    pages = "146--152",
}
@proceedings{ws-2017-joint,
    title = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    editor = {Volodina, Elena  and
      Grigonyt{\.e}, Gintar{\.e}  and
      Pil{\'a}n, Ildik{\'o}  and
      Bj{\"o}rkenstam, Kristina Nilsson  and
      Borin, Lars},
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0300",
}
@inproceedings{adams-stymne-2017-learning,
    title = "Learning with learner corpora: Using the {TLE} for native language identification",
    author = "Adams, Allison  and
      Stymne, Sara",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0301",
    pages = "1--7",
}
@inproceedings{chen-meurers-2017-challenging,
    title = "Challenging learners in their individual zone of proximal development using pedagogic developmental benchmarks of syntactic complexity",
    author = "Chen, Xiaobin  and
      Meurers, Detmar",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0302",
    pages = "8--17",
}
@inproceedings{graen-schneider-2017-crossing,
    title = "Crossing the border twice: Reimporting prepositions to alleviate {L}1-specific transfer errors",
    author = {Gra{\"e}n, Johannes  and
      Schneider, Gerold},
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0303",
    pages = "18--26",
}
@inproceedings{katinskaia-etal-2017-revita,
    title = "{R}evita: a system for language learning and supporting endangered languages",
    author = "Katinskaia, Anisia  and
      Nouri, Javad  and
      Yangarber, Roman",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0304",
    pages = "27--35",
}
@inproceedings{rudzewitz-etal-2017-developing,
    title = "Developing a web-based workbook for {E}nglish supporting the interaction of students and teachers",
    author = {Rudzewitz, Bj{\"o}rn  and
      Ziai, Ramon  and
      De Kuthy, Kordula  and
      Meurers, Detmar},
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0305",
    pages = "36--46",
}
@inproceedings{stymne-etal-2017-annotating,
    title = "Annotating errors in student texts: First experiences and experiments",
    author = "Stymne, Sara  and
      Pettersson, Eva  and
      Megyesi, Be{\'a}ta  and
      Palm{\'e}r, Anne",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0306",
    pages = "47--60",
}
@inproceedings{uibo-etal-2017-building,
    title = "Building and using language resources and infrastructure to develop e-learning programs for a minority language",
    author = "Uibo, Heli  and
      Rueter, Jack  and
      Iva, Sulev",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W17-0307",
    pages = "61--67",
}
@proceedings{ws-2017-nordic,
    title = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    editor = {Tiedemann, J{\"o}rg  and
      Tahmasebi, Nina},
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0200",
}
@inproceedings{velldal-etal-2017-joint,
    title = "Joint {UD} Parsing of {N}orwegian {B}okm{\aa}l and Nynorsk",
    author = "Velldal, Erik  and
      {\O}vrelid, Lilja  and
      Hohle, Petter",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0201",
    pages = "1--10",
}
@inproceedings{kolachina-etal-2017-replacing,
    title = "Replacing {OOV} Words For Dependency Parsing With Distributional Semantics",
    author = "Kolachina, Prasanth  and
      Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0202",
    pages = "11--19",
}
@inproceedings{basirat-nivre-2017-real,
    title = "Real-valued Syntactic Word Vectors ({RSV}) for Greedy Neural Dependency Parsing",
    author = "Basirat, Ali  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0203",
    pages = "20--28",
}
@inproceedings{kettunen-lofberg-2017-tagging,
    title = "Tagging Named Entities in 19th Century and {M}odern {F}innish Newspaper Material with a {F}innish Semantic Tagger",
    author = {Kettunen, Kimmo  and
      L{\"o}fberg, Laura},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0204",
    pages = "29--36",
}
@inproceedings{dubremetz-nivre-2017-machine,
    title = "Machine Learning for Rhetorical Figure Detection: More Chiasmus with Less Annotation",
    author = "Dubremetz, Marie  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0205",
    pages = "37--45",
}
@inproceedings{wallin-nugues-2017-coreference,
    title = "Coreference Resolution for {S}wedish and {G}erman using Distant Supervision",
    author = "Wallin, Alexander  and
      Nugues, Pierre",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0206",
    pages = "46--55",
}
@inproceedings{koskenniemi-2017-aligning,
    title = "Aligning phonemes using finte-state methods",
    author = "Koskenniemi, Kimmo",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0207",
    pages = "56--64",
}
@inproceedings{leino-kurimo-2017-acoustic,
    title = "Acoustic Model Compression with {MAP} adaptation",
    author = "Leino, Katri  and
      Kurimo, Mikko",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0208",
    pages = "65--69",
}
@inproceedings{drobac-etal-2017-ocr,
    title = "{OCR} and post-correction of historical {F}innish texts",
    author = "Drobac, Senka  and
      Kauppinen, Pekka  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0209",
    pages = "70--76",
}
@inproceedings{steinskog-etal-2017-twitter,
    title = "{T}witter Topic Modeling by Tweet Aggregation",
    author = {Steinskog, Asbj{\o}rn  and
      Therkelsen, Jonas  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0210",
    pages = "77--86",
}
@inproceedings{sodergren-nugues-2017-multilingual,
    title = "A Multilingual Entity Linker Using {P}age{R}ank and Semantic Graphs",
    author = {S{\"o}dergren, Anton  and
      Nugues, Pierre},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0211",
    pages = "87--95",
}
@inproceedings{muromagi-etal-2017-linear,
    title = "Linear Ensembles of Word Embedding Models",
    author = {Murom{\"a}gi, Avo  and
      Sirts, Kairit  and
      Laur, Sven},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0212",
    pages = "96--104",
}
@inproceedings{massimiliano-cecchini-etal-2017-using,
    title = "Using Pseudowords for Algorithm Comparison: An Evaluation Framework for Graph-based Word Sense Induction",
    author = "Massimiliano Cecchini, Flavio  and
      Biemann, Chris  and
      Riedl, Martin",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0213",
    pages = "105--114",
}
@inproceedings{pirinen-etal-2017-north,
    title = "{N}orth-{S}{\'a}mi to {F}innish rule-based machine translation system",
    author = "Pirinen, Tommi  and
      Tyers, Francis M.  and
      Trosterud, Trond  and
      Johnson, Ryan  and
      Unhammer, Kevin  and
      Puolakainen, Tiina",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0214",
    pages = "115--122",
}
@inproceedings{antonsen-etal-2017-machine,
    title = "Machine translation with North Saami as a pivot language",
    author = "Antonsen, Lene  and
      Gerstenberger, Ciprian  and
      Kappfjell, Maja  and
      Nyst{\o} Rahka, Sandra  and
      Olthuis, Marja-Liisa  and
      Trosterud, Trond  and
      Tyers, Francis M.",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0215",
    pages = "123--131",
}
@inproceedings{nasman-etal-2017-swegram,
    title = "{SWEGRAM} {--} A Web-Based Tool for Automatic Annotation and Analysis of {S}wedish Texts",
    author = {N{\"a}sman, Jesper  and
      Megyesi, Be{\'a}ta  and
      Palm{\'e}r, Anne},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0216",
    pages = "132--141",
}
@inproceedings{hohle-etal-2017-optimizing,
    title = "Optimizing a {P}o{S} Tagset for {N}orwegian Dependency Parsing",
    author = "Hohle, Petter  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0217",
    pages = "142--151",
}
@inproceedings{laippala-etal-2017-creating,
    title = "Creating register sub-corpora for the {F}innish {I}nternet Parsebank",
    author = {Laippala, Veronika  and
      Luotolahti, Juhani  and
      Kyr{\"o}l{\"a}inen, Aki-Juhani  and
      Salakoski, Tapio  and
      Ginter, Filip},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0218",
    pages = "152--161",
}
@inproceedings{dobnik-de-graaf-2017-kille,
    title = "{KILLE}: a Framework for Situated Agents for Learning Language Through Interaction",
    author = "Dobnik, Simon  and
      de Graaf, Erik",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0219",
    pages = "162--171",
}
@inproceedings{kokkinakis-etal-2017-data,
    title = "Data Collection from Persons with Mild Forms of Cognitive Impairment and Healthy Controls - Infrastructure for Classification and Prediction of Dementia",
    author = {Kokkinakis, Dimitrios  and
      Lundholm Fors, Kristina  and
      Bj{\"o}rkner, Eva  and
      Nordlund, Arto},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0220",
    pages = "172--182",
}
@inproceedings{jauhiainen-etal-2017-evaluation,
    title = "Evaluation of language identification methods using 285 languages",
    author = "Jauhiainen, Tommi  and
      Lind{\'e}n, Krister  and
      Jauhiainen, Heidi",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0221",
    pages = "183--191",
}
@inproceedings{orasmaa-kaalep-2017-create,
    title = "Can We Create a Tool for General Domain Event Analysis?",
    author = "Orasmaa, Siim  and
      Kaalep, Heiki-Jaan",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0222",
    pages = "192--201",
}
@inproceedings{bick-2017-treebank,
    title = "From Treebank to {P}ropbank: A Semantic-Role and {V}erb{N}et Corpus for {D}anish",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0223",
    pages = "202--210",
}
@inproceedings{bjerva-ostling-2017-cross,
    title = "Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations",
    author = {Bjerva, Johannes  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0224",
    pages = "211--215",
}
@inproceedings{bjerva-2017-will,
    title = "Will my auxiliary tagging task help? Estimating Auxiliary Tasks Effectivity in Multi-Task Learning",
    author = "Bjerva, Johannes",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0225",
    pages = "216--220",
}
@inproceedings{borstell-ostling-2017-iconic,
    title = "Iconic Locations in {S}wedish {S}ign {L}anguage: Mapping Form to Meaning with Lexical Databases",
    author = {B{\"o}rstell, Carl  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0226",
    pages = "221--225",
}
@inproceedings{klang-nugues-2017-docforia,
    title = "{D}ocforia: A Multilayer Document Model",
    author = "Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0227",
    pages = "226--230",
}
@inproceedings{venekoski-vankka-2017-finnish,
    title = "{F}innish resources for evaluating language model semantics",
    author = "Venekoski, Viljami  and
      Vankka, Jouko",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0228",
    pages = "231--236",
}
@inproceedings{steingrimsson-etal-2017-malromur,
    title = "{M}{\'a}lr{\'o}mur: A Manually Verified Corpus of Recorded {I}celandic Speech",
    author = {Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Gu{\dh}nason, J{\'o}n  and
      Helgad{\'o}ttir, Sigr{\'u}n  and
      R{\"o}gnvaldsson, Eir{\'\i}kur},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0229",
    pages = "237--240",
}
@inproceedings{stymne-2017-effect,
    title = "The Effect of Translationese on Tuning for Statistical Machine Translation",
    author = "Stymne, Sara",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0230",
    pages = "241--246",
}
@inproceedings{graen-etal-2017-multilingwis2,
    title = "Multilingwis{\mbox{$^2$}} {--} Explore Your Parallel Corpus",
    author = {Gra{\"e}n, Johannes  and
      Sandoz, Dominique  and
      Volk, Martin},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0231",
    pages = "247--250",
}
@inproceedings{noklestad-etal-2017-modernised,
    title = "A modernised version of the Glossa corpus search system",
    author = "N{\o}klestad, Anders  and
      Hagen, Kristin  and
      Bondi Johannessen, Janne  and
      Kosek, Micha{\l}  and
      Priestley, Joel",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0232",
    pages = "251--254",
}
@inproceedings{luotolahti-etal-2017-dep,
    title = "{D}ep{\_}search: Efficient Search Tool for Large Dependency Parsebanks",
    author = "Luotolahti, Juhani  and
      Kanerva, Jenna  and
      Ginter, Filip",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0233",
    pages = "255--258",
}
@inproceedings{pyysalo-2017-proto,
    title = "{P}roto-{I}ndo-{E}uropean Lexicon: The Generative Etymological Dictionary of {I}ndo-{E}uropean Languages",
    author = "Pyysalo, Jouna",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0234",
    pages = "259--262",
}
@inproceedings{rozis-skadins-2017-tilde,
    title = "Tilde {MODEL} - Multilingual Open Data for {EU} Languages",
    author = "Rozis, Roberts  and
      Skadi{\c{n}}{\v{s}}, Raivis",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0235",
    pages = "263--265",
}
@inproceedings{ek-knuutinen-2017-mainstreaming,
    title = "Mainstreaming {A}ugust Strindberg with Text Normalization",
    author = "Ek, Adam  and
      Knuutinen, Sofia",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0236",
    pages = "266--270",
}
@inproceedings{fares-etal-2017-word,
    title = "Word vectors, reuse, and replicability: Towards a community repository of large-text resources",
    author = "Fares, Murhaf  and
      Kutuzov, Andrey  and
      Oepen, Stephan  and
      Velldal, Erik",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0237",
    pages = "271--276",
}
@inproceedings{koistinen-etal-2017-improving,
    title = "Improving Optical Character Recognition of {F}innish Historical Newspapers with a Combination of Fraktur {\&} Antiqua Models and Image Preprocessing",
    author = {Koistinen, Mika  and
      Kettunen, Kimmo  and
      P{\"a}{\"a}kk{\"o}nen, Tuula},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0238",
    pages = "277--283",
}
@inproceedings{lison-kutuzov-2017-redefining,
    title = "Redefining Context Windows for Word Embedding Models: An Experimental Study",
    author = "Lison, Pierre  and
      Kutuzov, Andrey",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0239",
    pages = "284--288",
}
@inproceedings{persson-2017-effect,
    title = "The Effect of Excluding Out of Domain Training Data from Supervised Named-Entity Recognition",
    author = "Persson, Adam",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0240",
    pages = "289--292",
}
@inproceedings{salway-etal-2017-quote,
    title = "Quote Extraction and Attribution from {N}orwegian Newspapers",
    author = "Salway, Andrew  and
      Meurer, Paul  and
      Hofland, Knut  and
      Reigem, {\O}ystein",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0241",
    pages = "293--297",
}
@inproceedings{sand-etal-2017-wordnet,
    title = "{W}ordnet extension via word embeddings: Experiments on the {N}orwegian {W}ordnet",
    author = "Sand, Heidi  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0242",
    pages = "298--302",
}
@inproceedings{ostling-etal-2017-universal,
    title = "{U}niversal {D}ependencies for {S}wedish {S}ign {L}anguage",
    author = {{\"O}stling, Robert  and
      B{\"o}rstell, Carl  and
      G{\"a}rdenfors, Moa  and
      Wir{\'e}n, Mats},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0243",
    pages = "303--308",
}
@inproceedings{falkenjack-etal-2017-services,
    title = "Services for text simplification and analysis",
    author = {Falkenjack, Johan  and
      Rennes, Evelina  and
      Fahlborg, Daniel  and
      Johansson, Vida  and
      J{\"o}nsson, Arne},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0244",
    pages = "309--313",
}
@inproceedings{graen-bless-2017-exploring,
    title = "Exploring Properties of Intralingual and Interlingual Association Measures Visually",
    author = {Gra{\"e}n, Johannes  and
      Bless, Christof},
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0245",
    pages = "314--317",
}
@inproceedings{juel-henrichsen-2017-talerum,
    title = "{TALERUM} - Learning {D}anish by Doing {D}anish",
    author = "Juel Henrichsen, Peter",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0246",
    pages = "318--321",
}
@inproceedings{ranta-etal-2017-cross,
    title = "Cross-Lingual Syntax: Relating Grammatical Framework with {U}niversal {D}ependencies",
    author = "Ranta, Aarne  and
      Kolachina, Prasanth  and
      Hallgren, Thomas",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0247",
    pages = "322--325",
}
@inproceedings{rosen-etal-2017-exploring,
    title = "Exploring Treebanks with {INESS} Search",
    author = "Ros{\'e}n, Victoria  and
      Dyvik, Helge  and
      Meurer, Paul  and
      De Smedt, Koenraad",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0248",
    pages = "326--329",
}
@inproceedings{vesanto-etal-2017-system,
    title = "A System for Identifying and Exploring Text Repetition in Large Historical Document Corpora",
    author = "Vesanto, Aleksi  and
      Ginter, Filip  and
      Salmi, Hannu  and
      Nivala, Asko  and
      Salakoski, Tapio",
    booktitle = "Proceedings of the 21st Nordic Conference on Computational Linguistics",
    month = may,
    year = "2017",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0249",
    pages = "330--333",
}
@proceedings{ws-2017-use,
    title = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    editor = "Arppe, Antti  and
      Good, Jeff  and
      Hulden, Mans  and
      Lachler, Jordan  and
      Palmer, Alexis  and
      Schwartz, Lane",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0100",
    doi = "10.18653/v1/W17-01",
}
@inproceedings{bowers-etal-2017-morphological,
    title = "A Morphological Parser for Odawa",
    author = "Bowers, Dustin  and
      Arppe, Antti  and
      Lachler, Jordan  and
      Moshagen, Sjur  and
      Trosterud, Trond",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0101",
    doi = "10.18653/v1/W17-0101",
    pages = "1--9",
}
@inproceedings{kazeminejad-etal-2017-creating,
    title = "Creating lexical resources for polysynthetic languages{---}the case of {A}rapaho",
    author = "Kazeminejad, Ghazaleh  and
      Cowell, Andrew  and
      Hulden, Mans",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0102",
    doi = "10.18653/v1/W17-0102",
    pages = "10--18",
}
@inproceedings{thieberger-tuohy-2017-small,
    title = "From Small to Big Data: paper manuscripts to {RDF} triples of {A}ustralian Indigenous Vocabularies",
    author = "Thieberger, Nick  and
      Tuohy, Conal",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0103",
    doi = "10.18653/v1/W17-0103",
    pages = "19--23",
}
@inproceedings{um-2017-issues,
    title = "Issues in digital text representation, on-line dissemination, sharing and re-use for {A}frican minority languages",
    author = "Um, Emmanuel Ngu{\'e}",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0104",
    doi = "10.18653/v1/W17-0104",
    pages = "24--32",
}
@inproceedings{holton-etal-2017-developing,
    title = "Developing collection management tools to create more robust and reliable linguistic data",
    author = "Holton, Gary  and
      Hooshiar, Kavon  and
      Thieberger, Nick",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0105",
    doi = "10.18653/v1/W17-0105",
    pages = "33--38",
}
@inproceedings{levow-etal-2017-streamlined,
    title = "{STREAMLI}n{ED} Challenges: Aligning Research Interests with Shared Tasks",
    author = "Levow, Gina-Anne  and
      Bender, Emily M.  and
      Littell, Patrick  and
      Howell, Kristen  and
      Chelliah, Shobhana  and
      Crowgey, Joshua  and
      Garrette, Dan  and
      Good, Jeff  and
      Hargus, Sharon  and
      Inman, David  and
      Maxwell, Michael  and
      Tjalve, Michael  and
      Xia, Fei",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0106",
    doi = "10.18653/v1/W17-0106",
    pages = "39--47",
}
@inproceedings{bell-bell-2017-work,
    title = "Work With What You{'}ve Got",
    author = "Bell, Lucy  and
      Bell, Lawrence",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0107",
    doi = "10.18653/v1/W17-0107",
    pages = "48--51",
}
@inproceedings{arppe-etal-2017-converting,
    title = "Converting a comprehensive lexical database into a computational model: The case of {E}ast {C}ree verb inflection",
    author = "Arppe, Antti  and
      Junker, Marie-Odile  and
      Torkornoo, Delasie",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0108",
    doi = "10.18653/v1/W17-0108",
    pages = "52--56",
}
@inproceedings{gerstenberger-etal-2017-instant,
    title = "Instant annotations in {ELAN} corpora of spoken and written {K}omi, an endangered language of the Barents Sea region",
    author = "Gerstenberger, Ciprian  and
      Partanen, Niko  and
      Rie{\ss}ler, Michael",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0109",
    doi = "10.18653/v1/W17-0109",
    pages = "57--66",
}
@inproceedings{howell-etal-2017-inferring,
    title = "Inferring Case Systems from {IGT}: Enriching the Enrichment",
    author = "Howell, Kristen  and
      Bender, Emily M.  and
      Lockwood, Michel  and
      Xia, Fei  and
      Zamaraeva, Olga",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0110",
    doi = "10.18653/v1/W17-0110",
    pages = "67--75",
}
@inproceedings{kodner-etal-2017-case,
    title = "Case Studies in the Automatic Characterization of Grammars from Small Wordlists",
    author = "Kodner, Jordan  and
      Caplan, Spencer  and
      Xu, Hongzhi  and
      Marcus, Mitchell P.  and
      Yang, Charles",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0111",
    doi = "10.18653/v1/W17-0111",
    pages = "76--84",
}
@inproceedings{maxwell-bills-2017-endangered,
    title = "Endangered Data for Endangered Languages: Digitizing Print dictionaries",
    author = "Maxwell, Michael  and
      Bills, Aric",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0112",
    doi = "10.18653/v1/W17-0112",
    pages = "85--91",
}
@inproceedings{meyer-2017-computationally,
    title = "A computationally-assisted procedure for discovering poetic organization within oral tradition",
    author = "Meyer, David",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0113",
    doi = "10.18653/v1/W17-0113",
    pages = "92--100",
}
@inproceedings{micher-2017-improving,
    title = "Improving Coverage of an {I}nuktitut Morphological Analyzer Using a Segmental Recurrent Neural Network",
    author = "Micher, Jeffrey",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0114",
    doi = "10.18653/v1/W17-0114",
    pages = "101--106",
}
@inproceedings{miller-elsner-2017-click,
    title = "Click reduction in fluent speech: a semi-automated analysis of {M}angetti Dune !{X}ung",
    author = "Miller, Amanda  and
      Elsner, Micha",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0115",
    doi = "10.18653/v1/W17-0115",
    pages = "107--115",
}
@inproceedings{rytting-yelle-2017-decca,
    title = "{DECCA} Repurposed: Detecting transcription inconsistencies without an orthographic standard",
    author = "Rytting, C. Anton  and
      Yelle, Julie",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0116",
    doi = "10.18653/v1/W17-0116",
    pages = "116--121",
}
@inproceedings{saltzman-2017-jejueo,
    title = "{J}ejueo talking dictionary: A collaborative online database for language revitalization",
    author = "Saltzman, Moira",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0117",
    doi = "10.18653/v1/W17-0117",
    pages = "122--129",
}
@inproceedings{zamaraeva-etal-2017-computational,
    title = "Computational Support for Finding Word Classes: A Case Study of {A}bui",
    author = "Zamaraeva, Olga  and
      Kratochv{\'\i}l, Franti{\v{s}}ek  and
      Bender, Emily M.  and
      Xia, Fei  and
      Howell, Kristen",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0118",
    doi = "10.18653/v1/W17-0118",
    pages = "130--140",
}
@inproceedings{littell-etal-2017-waldayu,
    title = "Waldayu and Waldayu Mobile: Modern digital dictionary interfaces for endangered languages",
    author = "Littell, Patrick  and
      Pine, Aidan  and
      Davis, Henry",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0119",
    doi = "10.18653/v1/W17-0119",
    pages = "141--150",
}
@inproceedings{little-2017-connecting,
    title = "Connecting Documentation and Revitalization: A New Approach to Language Apps",
    author = "Little, Alexa N.",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0120",
    doi = "10.18653/v1/W17-0120",
    pages = "151--155",
}
@inproceedings{bettinson-bird-2017-developing,
    title = "Developing a Suite of Mobile Applications for Collaborative Language Documentation",
    author = "Bettinson, Mat  and
      Bird, Steven",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0121",
    doi = "10.18653/v1/W17-0121",
    pages = "156--164",
}
@inproceedings{kempton-2017-cross,
    title = "Cross-language forced alignment to assist community-based linguistics for low resource languages",
    author = "Kempton, Timothy",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0122",
    doi = "10.18653/v1/W17-0122",
    pages = "165--169",
}
@inproceedings{anastasopoulos-chiang-2017-case,
    title = "A case study on using speech-to-translation alignments for language documentation",
    author = "Anastasopoulos, Antonios  and
      Chiang, David",
    booktitle = "Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages",
    month = mar,
    year = "2017",
    address = "Honolulu",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-0123",
    doi = "10.18653/v1/W17-0123",
    pages = "170--178",
}
@proceedings{alta-2017-australasian,
    title = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    editor = "Wong, Jojo Sze-Meng  and
      Haffari, Gholamreza",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1000",
}
@inproceedings{dos-santos-pinheiro-dras-2017-stock,
    title = "Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading",
    author = "dos Santos Pinheiro, Leonardo  and
      Dras, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1001",
    pages = "6--15",
}
@inproceedings{liu-etal-2017-improving,
    title = "Improving End-to-End Memory Networks with Unified Weight Tying",
    author = "Liu, Fei  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1002",
    pages = "16--24",
}
@inproceedings{subramanian-etal-2017-joint,
    title = "Joint Sentence-Document Model for Manifesto Text Analysis",
    author = "Subramanian, Shivashankar  and
      Cohn, Trevor  and
      Baldwin, Timothy  and
      Brooke, Julian",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1003",
    pages = "25--33",
}
@inproceedings{liu-etal-2017-leveraging,
    title = "Leveraging Linguistic Resources for Improving Neural Text Classification",
    author = "Liu, Ming  and
      Haffari, Gholamreza  and
      Buntine, Wray  and
      Ananda-Rajah, Michelle",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1004",
    pages = "34--42",
}
@inproceedings{shen-etal-2017-hybrid,
    title = "A Hybrid Model for Quality Assessment of {W}ikipedia Articles",
    author = "Shen, Aili  and
      Qi, Jianzhong  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1005",
    pages = "43--52",
}
@inproceedings{adams-etal-2017-phonemic,
    title = "Phonemic Transcription of Low-Resource Tonal Languages",
    author = "Adams, Oliver  and
      Cohn, Trevor  and
      Neubig, Graham  and
      Michaud, Alexis",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1006",
    pages = "53--60",
}
@inproceedings{ishihara-2017-comparative,
    title = "A Comparative Study of Two Statistical Modelling Approaches for Estimating Multivariate Likelihood Ratios in Forensic Voice Comparison",
    author = "Ishihara, Shunichi",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1007",
    pages = "61--69",
}
@inproceedings{cheng-etal-2017-automatic,
    title = "Automatic Negation and Speculation Detection in Veterinary Clinical Text",
    author = "Cheng, Katherine  and
      Baldwin, Timothy  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1008",
    pages = "70--78",
}
@inproceedings{dai-etal-2017-medication,
    title = "Medication and Adverse Event Extraction from Noisy Text",
    author = "Dai, Xiang  and
      Karimi, Sarvnaz  and
      Paris, Cecile",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1009",
    pages = "79--87",
}
@inproceedings{kim-2017-incremental,
    title = "Incremental Knowledge Acquisition Approach for Information Extraction on both Semi-Structured and Unstructured Text from the Open Domain Web",
    author = "Kim, Maria Myung Hee",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1010",
    pages = "88--96",
}
@inproceedings{hajiabadi-etal-2017-extending,
    title = "On Extending Neural Networks with Loss Ensembles for Text Classification",
    author = "Hajiabadi, Hamideh  and
      Molla-Aliod, Diego  and
      Monsefi, Reza",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1011",
    pages = "98--102",
}
@inproceedings{molla-aliod-2017-towards,
    title = "Towards the Use of Deep Reinforcement Learning with Global Policy for Query-based Extractive Summarisation",
    author = "Moll{\'a}-Aliod, Diego",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1012",
    pages = "103--107",
}
@inproceedings{nguyen-etal-2017-word,
    title = "From Word Segmentation to {POS} Tagging for {V}ietnamese",
    author = "Nguyen, Dat Quoc  and
      Vu, Thanh  and
      Nguyen, Dai Quoc  and
      Dras, Mark  and
      Johnson, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1013",
    pages = "108--113",
}
@inproceedings{molla-aliod-cassidy-2017-overview,
    title = "Overview of the 2017 {ALTA} Shared Task: Correcting {OCR} Errors",
    author = "Moll{\'a}-Aliod, Diego  and
      Cassidy, Steve",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1014",
    pages = "115--118",
}
@inproceedings{khirbat-2017-ocr,
    title = "{OCR} Post-Processing Text Correction using Simulated Annealing ({OPT}e{CA})",
    author = "Khirbat, Gitansh",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1015",
    pages = "119--123",
}
@inproceedings{wang-2017-superocr,
    title = "{S}uper{OCR} for {ALTA} 2017 Shared Task",
    author = "Wang, Yufei",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2017",
    month = dec,
    year = "2017",
    address = "Brisbane, Australia",
    url = "https://www.aclweb.org/anthology/U17-1016",
    pages = "124--128",
}
@proceedings{semeval-2017-international,
    title = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2000",
    doi = "10.18653/v1/S17-2",
}
@inproceedings{cer-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
    author = "Cer, Daniel  and
      Diab, Mona  and
      Agirre, Eneko  and
      Lopez-Gazpio, I{\~n}igo  and
      Specia, Lucia",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2001",
    doi = "10.18653/v1/S17-2001",
    pages = "1--14",
    abstract = "Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in \textit{all language tracks}. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the \textit{STS Benchmark} is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012-2017).",
}
@inproceedings{camacho-collados-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity",
    author = "Camacho-Collados, Jose  and
      Pilehvar, Mohammad Taher  and
      Collier, Nigel  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2002",
    doi = "10.18653/v1/S17-2002",
    pages = "15--26",
    abstract = "This paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish. High quality datasets were manually curated for the five languages with high inter-annotator agreements (consistently in the 0.9 ballpark). These were used for semi-automatic construction of ten cross-lingual datasets. 17 teams participated in the task, submitting 24 systems in subtask 1 and 14 systems in subtask 2. Results show that systems that combine statistical knowledge from text corpora, in the form of word embeddings, and external knowledge from lexical resources are best performers in both subtasks. More information can be found on the task website: \url{http://alt.qcri.org/semeval2017/task2/}",
}
@inproceedings{nakov-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 3: Community Question Answering",
    author = "Nakov, Preslav  and
      Hoogeveen, Doris  and
      M{\`a}rquez, Llu{\'\i}s  and
      Moschitti, Alessandro  and
      Mubarak, Hamdy  and
      Baldwin, Timothy  and
      Verspoor, Karin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2003",
    doi = "10.18653/v1/S17-2003",
    pages = "27--48",
    abstract = "We describe SemEval{--}2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016: (A) Question{--}Comment Similarity, (B) Question{--}Question Similarity, (C) Question{--}External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A{--}D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A{--}C.",
}
@inproceedings{potash-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 6: {\#}{H}ashtag{W}ars: Learning a Sense of Humor",
    author = "Potash, Peter  and
      Romanov, Alexey  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2004",
    doi = "10.18653/v1/S17-2004",
    pages = "49--57",
    abstract = "This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the {`}Hashtag Wars{'} segment of the TV show @midnight. The results are evaluated in two subtasks that require the participants to generate either the correct pairwise comparisons of tweets (subtask A), or the correct ranking of the tweets (subtask B) in terms of how funny they are. 7 teams participated in subtask A, and 5 teams participated in subtask B. The best accuracy in subtask A was 0.675. The best (lowest) rank edit distance for subtask B was 0.872.",
}
@inproceedings{miller-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 7: Detection and Interpretation of {E}nglish Puns",
    author = "Miller, Tristan  and
      Hempelmann, Christian  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2005",
    doi = "10.18653/v1/S17-2005",
    pages = "58--68",
    abstract = "A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-per-context assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems{'} methodologies, resources, and results.",
}
@inproceedings{derczynski-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 8: {R}umour{E}val: Determining rumour veracity and support for rumours",
    author = "Derczynski, Leon  and
      Bontcheva, Kalina  and
      Liakata, Maria  and
      Procter, Rob  and
      Wong Sak Hoi, Geraldine  and
      Zubiaga, Arkaitz",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2006",
    doi = "10.18653/v1/S17-2006",
    pages = "69--76",
    abstract = "Media is full of false claims. Even Oxford Dictionaries named {``}post-truth{''} as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the nature of the discourse around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics {--} each having their own families of claims and replies {--} and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.",
}
@inproceedings{wu-etal-2017-bit,
    title = "{BIT} at {S}em{E}val-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity",
    author = "Wu, Hao  and
      Huang, Heyan  and
      Jian, Ping  and
      Guo, Yuhang  and
      Su, Chao",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2007",
    doi = "10.18653/v1/S17-2007",
    pages = "77--84",
    abstract = "This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task. One is an unsupervised system and the other two are supervised systems which simply employ the unsupervised one. All our systems mainly depend on the (SIS), which is constructed based on the semantic hierarchical taxonomy in WordNet, to compute non-overlapping information content (IC) of sentences. Our team ranked 2nd among 31 participating teams by the primary score of Pearson correlation coefficient (PCC) mean of 7 tracks and achieved the best performance on Track 1 (AR-AR) dataset.",
}
@inproceedings{speer-lowry-duda-2017-conceptnet,
    title = "{C}oncept{N}et at {S}em{E}val-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge",
    author = "Speer, Robyn  and
      Lowry-Duda, Joanna",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2008",
    doi = "10.18653/v1/S17-2008",
    pages = "85--89",
    abstract = "This paper describes Luminoso{'}s participation in SemEval 2017 Task 2, {``}Multilingual and Cross-lingual Semantic Word Similarity{''}, with a system based on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses on general knowledge that relates the meanings of words and phrases. Our submission to SemEval was an update of previous work that builds high-quality, multilingual word embeddings from a combination of ConceptNet and distributional semantics. Our system took first place in both subtasks. It ranked first in 4 out of 5 of the separate languages, and also ranked first in all 10 of the cross-lingual language pairs.",
}
@inproceedings{nandi-etal-2017-iit,
    title = "{IIT}-{UHH} at {S}em{E}val-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification",
    author = "Nandi, Titas  and
      Biemann, Chris  and
      Yimam, Seid Muhie  and
      Gupta, Deepak  and
      Kohail, Sarah  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2009",
    doi = "10.18653/v1/S17-2009",
    pages = "90--97",
    abstract = "In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3. We develop a Support Vector Machine (SVM) based system that makes use of textual, domain-specific, word-embedding and topic-modeling features. In addition, we propose a novel method for dialogue chain identification in comment threads. Our primary submission won subtask C, outperforming other systems in all the primary evaluation metrics. We performed well in other English subtasks, ranking third in subtask A and eighth in subtask B. We also developed open source toolkits for all the three English subtasks by the name cQARank [\url{https://github.com/TitasNandi/cQARank}].",
}
@inproceedings{donahue-etal-2017-humorhawk,
    title = "{H}umor{H}awk at {S}em{E}val-2017 Task 6: Mixing Meaning and Sound for Humor Recognition",
    author = "Donahue, David  and
      Romanov, Alexey  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2010",
    doi = "10.18653/v1/S17-2010",
    pages = "98--102",
    abstract = "This paper describes the winning system for SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. Humor detection has up until now been predominantly addressed using feature-based approaches. Our system utilizes recurrent deep learning methods with dense embeddings to predict humorous tweets from the @midnight show {\#}HashtagWars. In order to include both meaning and sound in the analysis, GloVe embeddings are combined with a novel phonetic representation to serve as input to an LSTM component. The output is combined with a character-based CNN model, and an XGBoost component in an ensemble model which achieves 0.675 accuracy on the evaluation data.",
}
@inproceedings{doogan-etal-2017-idiom,
    title = "Idiom Savant at {S}emeval-2017 Task 7: Detection and Interpretation of {E}nglish Puns",
    author = "Doogan, Samuel  and
      Ghosh, Aniruddha  and
      Chen, Hanyang  and
      Veale, Tony",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2011",
    doi = "10.18653/v1/S17-2011",
    pages = "103--108",
    abstract = "This paper describes our system, entitled Idiom Savant, for the 7th Task of the Semeval 2017 workshop, {``}Detection and interpretation of English Puns{''}. Our system consists of two probabilistic models for each type of puns using Google n-gram and Word2Vec. Our system achieved f-score of calculating, 0.663, and 0.07 in homographic puns and 0.8439, 0.6631, and 0.0806 in heterographic puns in task 1, task 2, and task 3 respectively.",
}
@inproceedings{ferrero-etal-2017-compilig,
    title = "{C}ompi{LIG} at {S}em{E}val-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity",
    author = "Ferrero, J{\'e}r{\'e}my  and
      Besacier, Laurent  and
      Schwab, Didier  and
      Agn{\`e}s, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2012",
    doi = "10.18653/v1/S17-2012",
    pages = "109--114",
    abstract = "We present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must estimate their semantic similarity by a score between 0 and 5. In our submission, we use syntax-based, dictionary-based, context-based, and MT-based methods. We also combine these methods in unsupervised and supervised way. Our best run ranked 1st on track 4a with a correlation of 83.02{\%} with human annotations.",
}
@inproceedings{al-natsheh-etal-2017-udl,
    title = "{U}d{L} at {S}em{E}val-2017 Task 1: Semantic Textual Similarity Estimation of {E}nglish Sentence Pairs Using Regression Model over Pairwise Features",
    author = "Al-Natsheh, Hussein T.  and
      Martinet, Lucie  and
      Muhlenbach, Fabrice  and
      Zighed, Djamel Abdelkader",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2013",
    doi = "10.18653/v1/S17-2013",
    pages = "115--119",
    abstract = "This paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of a given set of sentence pairs in English. The best run out of three submitted runs of our model achieved a Pearson correlation score of 0.8004 compared to a hidden human annotation of 250 pairs. We used random forest ensemble learning to map an expandable set of extracted pairwise features into a semantic similarity estimated value bounded between 0 and 5. Most of these features were calculated using word embedding vectors similarity to align Part of Speech (PoS) and Name Entities (NE) tagged tokens of each sentence pair. Among other pairwise features, we experimented a classical tf-idf weighted Bag of Words (BoW) vector model but with character-based range of n-grams instead of words. This sentence vector BoW-based feature gave a relatively high importance value percentage in the feature importances analysis of the ensemble learning.",
}
@inproceedings{maharjan-etal-2017-dt,
    title = "{DT}{\_}{T}eam at {S}em{E}val-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and {G}aussian Mixture Model Output",
    author = "Maharjan, Nabin  and
      Banjade, Rajendra  and
      Gautam, Dipesh  and
      Tamang, Lasang J.  and
      Rus, Vasile",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2014",
    doi = "10.18653/v1/S17-2014",
    pages = "120--124",
    abstract = "We describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity scores calculated using word and chunk alignments, word/sentence embeddings, and Gaussian Mixture Model(GMM). The correlation between our system{'}s output and the human judgments were up to 0.8536, which is more than 10{\%} above baseline, and almost as good as the best performing system which was at 0.8547 correlation (the difference is just about 0.1{\%}). Also, our system produced leading results when evaluated with a separate STS benchmark dataset. The word alignment and sentence embeddings based features were found to be very effective.",
}
@inproceedings{hassan-etal-2017-fcicu,
    title = "{FCICU} at {S}em{E}val-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach",
    author = "Hassan, Basma  and
      AbdelRahman, Samir  and
      Bahgat, Reem  and
      Farag, Ibrahim",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2015",
    doi = "10.18653/v1/S17-2015",
    pages = "125--129",
    abstract = "This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, in which a proposed alignment similarity method coupled with new usage of a semantic network (BabelNet) is used. Additionally, a previously proposed integration between sense-based and sur-face-based semantic textual similarity approach is applied together with our proposed approach. For all the tracks in Task1, Run1 is a string kernel with alignments metric and Run2 is a sense-based alignment similarity method. The first run is ranked 10th, and the second is ranked 12th in the primary track, with correlation 0.619 and 0.617 respectively",
}
@inproceedings{shao-2017-hcti,
    title = "{HCTI} at {S}em{E}val-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity",
    author = "Shao, Yang",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2016",
    doi = "10.18653/v1/S17-2016",
    pages = "130--133",
    abstract = "This paper describes our convolutional neural network (CNN) system for Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generated semantic vector of every sentence by max pooling every dimension of their word vectors. There are mainly two trick points in our system. One is that we trained a CNN to transfer GloVe word vectors to a more proper form for STS task before pooling. Another is that we trained a fully-connected neural network (FCNN) to transfer difference of two semantic vectors to probability of every similarity score. We decided all hyper parameters empirically. In spite of the simplicity of our neural network system, we achieved a good accuracy and ranked 3rd in primary track of SemEval 2017.",
}
@inproceedings{nagoudi-etal-2017-lim,
    title = "{LIM}-{LIG} at {S}em{E}val-2017 Task1: Enhancing the Semantic Similarity for {A}rabic Sentences with Vectors Weighting",
    author = "Nagoudi, El Moatez Billah  and
      Ferrero, J{\'e}r{\'e}my  and
      Schwab, Didier",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2017",
    doi = "10.18653/v1/S17-2017",
    pages = "134--138",
    abstract = "This article describes our proposed system named LIM-LIG. This system is designed for SemEval 2017 Task1: Semantic Textual Similarity (Track1). LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences. The main idea is to exploit the word representations as vectors in a multidimensional space to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. LIM-LIG system achieves a Pearson{'}s correlation of 0.74633, ranking 2nd among all participants in the Arabic monolingual pairs STS task organized within the SemEval 2017 evaluation campaign",
}
@inproceedings{spiewak-etal-2017-opi,
    title = "{OPI}-{JSA} at {S}em{E}val-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity",
    author = "{\'S}piewak, Martyna  and
      Sobecki, Piotr  and
      Kara{\'s}, Daniel",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2018",
    doi = "10.18653/v1/S17-2018",
    pages = "139--143",
    abstract = "Semantic Textual Similarity (STS) evaluation assesses the degree to which two parts of texts are similar, based on their semantic evaluation. In this paper, we describe three models submitted to STS SemEval 2017. Given two English parts of a text, each of proposed methods outputs the assessment of their semantic similarity. We propose an approach for computing monolingual semantic textual similarity based on an ensemble of three distinct methods. Our model consists of recursive neural network (RNN) text auto-encoders ensemble with supervised a model of vectorized sentences using reduced part of speech (PoS) weighted word embeddings as well as unsupervised a method based on word coverage (TakeLab). Additionally, we enrich our model with additional features that allow disambiguation of ensemble methods based on their efficiency. We have used Multi-Layer Perceptron as an ensemble classifier basing on estimations of trained Gradient Boosting Regressors. Results of our research proves that using such ensemble leads to a higher accuracy due to a fact that each member-algorithm tends to specialize in particular type of sentences. Simple model based on PoS weighted Word2Vec word embeddings seem to improve performance of more complex RNN based auto-encoders in the ensemble. In the monolingual English-English STS subtask our Ensemble based model achieved mean Pearson correlation of .785 compared with human annotators.",
}
@inproceedings{espana-bonet-barron-cedeno-2017-lump,
    title = "Lump at {S}em{E}val-2017 Task 1: Towards an Interlingua Semantic Similarity",
    author = "Espa{\~n}a-Bonet, Cristina  and
      Barr{\'o}n-Cede{\~n}o, Alberto",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2019",
    doi = "10.18653/v1/S17-2019",
    pages = "144--149",
    abstract = "This is the Lump team participation at SemEval 2017 Task 1 on Semantic Textual Similarity. Our supervised model relies on features which are multilingual or interlingual in nature. We include lexical similarities, cross-language explicit semantic analysis, internal representations of multilingual neural networks and interlingual word embeddings. Our representations allow to use large datasets in language pairs with many instances to better classify instances in smaller language pairs avoiding the necessity of translating into a single language. Hence we can deal with all the languages in the task: Arabic, English, Spanish, and Turkish.",
}
@inproceedings{meng-etal-2017-qlut,
    title = "{QLUT} at {S}em{E}val-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings",
    author = "Meng, Fanqing  and
      Lu, Wenpeng  and
      Zhang, Yuteng  and
      Cheng, Jinyong  and
      Du, Yuehan  and
      Han, Shuwang",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2020",
    doi = "10.18653/v1/S17-2020",
    pages = "150--153",
    abstract = "This paper reports the details of our submissions in the task 1 of SemEval 2017. This task aims at assessing the semantic textual similarity of two sentences or texts. We submit three unsupervised systems based on word embeddings. The differences between these runs are the various preprocessing on evaluation data. The best performance of these systems on the evaluation of Pearson correlation is 0.6887. Unsurprisingly, results of our runs demonstrate that data preprocessing, such as tokenization, lemmatization, extraction of content words and removing stop words, is helpful and plays a significant role in improving the performance of models.",
}
@inproceedings{bjerva-ostling-2017-ressim,
    title = "{R}es{S}im at {S}em{E}val-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity",
    author = {Bjerva, Johannes  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2021",
    doi = "10.18653/v1/S17-2021",
    pages = "154--158",
    abstract = "Shared Task 1 at SemEval-2017 deals with assessing the semantic similarity between sentences, either in the same or in different languages. In our system submission, we employ multilingual word representations, in which similar words in different languages are close to one another. Using such representations is advantageous, since the increasing amount of available parallel data allows for the application of such methods to many of the languages in the world. Hence, semantic similarity can be inferred even for languages for which no annotated data exists. Our system is trained and evaluated on all language pairs included in the shared task (English, Spanish, Arabic, and Turkish). Although development results are promising, our system does not yield high performance on the shared task test sets.",
}
@inproceedings{liu-etal-2017-itnlp,
    title = "{ITNLP}-{A}i{KF} at {S}em{E}val-2017 Task 1: Rich Features Based {SVR} for Semantic Textual Similarity Computing",
    author = "Liu, Wenjie  and
      Sun, Chengjie  and
      Lin, Lei  and
      Liu, Bingquan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2022",
    doi = "10.18653/v1/S17-2022",
    pages = "159--163",
    abstract = "Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair. We proposed a new system, ITNLP-AiKF, which applies in the SemEval 2017 Task1 Semantic Textual Similarity track 5 English monolingual pairs. In our system, rich features are involved, including Ontology based, word embedding based, Corpus based, Alignment based and Literal based feature. We leveraged the features to predict sentence pair similarity by a Support Vector Regression (SVR) model. In the result, a Pearson Correlation of 0.8231 is achieved by our system, which is a competitive result in the contest of this track.",
}
@inproceedings{zhuang-chang-2017-neobility,
    title = "Neobility at {S}em{E}val-2017 Task 1: An Attention-based Sentence Similarity Model",
    author = "Zhuang, WenLi  and
      Chang, Ernie",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2023",
    doi = "10.18653/v1/S17-2023",
    pages = "164--169",
    abstract = "This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.",
}
@inproceedings{duma-menzel-2017-sef,
    title = "{SEF}@{UHH} at {S}em{E}val-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector",
    author = "Duma, Mirela-Stefania  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2024",
    doi = "10.18653/v1/S17-2024",
    pages = "170--174",
    abstract = "This paper describes our unsupervised knowledge-free approach to the SemEval-2017 Task 1 Competition. The proposed method makes use of Paragraph Vector for assessing the semantic similarity between pairs of sentences. We experimented with various dimensions of the vector and three state-of-the-art similarity metrics. Given a cross-lingual task, we trained models corresponding to its two languages and combined the models by averaging the similarity scores. The results of our submitted runs are above the median scores for five out of seven test sets by means of Pearson Correlation. Moreover, one of our system runs performed best on the Spanish-English-WMT test set ranking first out of 53 runs submitted in total by all participants.",
}
@inproceedings{kohail-etal-2017-sts,
    title = "{STS}-{UHH} at {S}em{E}val-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble",
    author = "Kohail, Sarah  and
      Salama, Amr Rekaby  and
      Biemann, Chris",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2025",
    doi = "10.18653/v1/S17-2025",
    pages = "175--179",
    abstract = "This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, and supervised approach, which combines dependency graph similarity and coverage features with lexical similarity measures using regression methods. We also present a way on ensembling both models. Out of 84 submitted runs, our team best multi-lingual run has been ranked 12th in overall performance with correlation of 0.61, 7th among 31 participating teams.",
}
@inproceedings{barrow-peskov-2017-umdeep,
    title = "{UMD}eep at {S}em{E}val-2017 Task 1: End-to-End Shared Weight {LSTM} Model for Semantic Textual Similarity",
    author = "Barrow, Joe  and
      Peskov, Denis",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2026",
    doi = "10.18653/v1/S17-2026",
    pages = "180--184",
    abstract = "We describe a modified shared-LSTM network for the Semantic Textual Similarity (STS) task at SemEval-2017. The network builds on previously explored Siamese network architectures. We treat max sentence length as an additional hyperparameter to be tuned (beyond learning rate, regularization, and dropout). Our results demonstrate that hand-tuning max sentence training length significantly improves final accuracy. After optimizing hyperparameters, we train the network on the multilingual semantic similarity task using pre-translated sentences. We achieved a correlation of 0.4792 for all the subtasks. We achieved the fourth highest team correlation for Task 4b, which was our best relative placement.",
}
@inproceedings{henderson-etal-2017-mitre,
    title = "{MITRE} at {S}em{E}val-2017 Task 1: Simple Semantic Similarity",
    author = "Henderson, John  and
      Merkhofer, Elizabeth  and
      Strickhart, Laura  and
      Zarrella, Guido",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2027",
    doi = "10.18653/v1/S17-2027",
    pages = "185--190",
    abstract = "This paper describes MITRE{'}s participation in the Semantic Textual Similarity task (SemEval-2017 Task 1), which evaluated machine learning approaches to the identification of similar meaning among text snippets in English, Arabic, Spanish, and Turkish. We detail the techniques we explored ranging from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Linear regression is used to tie the systems together into an ensemble submitted for evaluation. The resulting system is capable of matching human similarity ratings of image captions with correlations of 0.73 to 0.83 in monolingual settings and 0.68 to 0.78 in cross-lingual conditions, demonstrating the power of relatively simple approaches.",
}
@inproceedings{tian-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 1: Leverage Kernel-based Traditional {NLP} features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity",
    author = "Tian, Junfeng  and
      Zhou, Zhiheng  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2028",
    doi = "10.18653/v1/S17-2028",
    pages = "191--197",
    abstract = "To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16{\%} in primary track.",
}
@inproceedings{lee-etal-2017-purduenlp,
    title = "{P}urdue{NLP} at {S}em{E}val-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings",
    author = "Lee, I-Ta  and
      Goindani, Mahak  and
      Li, Chang  and
      Jin, Di  and
      Johnson, Kristen Marie  and
      Zhang, Xiao  and
      Pacheco, Maria Leonor  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2029",
    doi = "10.18653/v1/S17-2029",
    pages = "198--202",
    abstract = "This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.",
}
@inproceedings{bicici-2017-rtm,
    title = "{RTM} at {S}em{E}val-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2030",
    doi = "10.18653/v1/S17-2030",
    pages = "203--207",
    abstract = "We use referential translation machines for predicting the semantic similarity of text in all STS tasks which contain Arabic, English, Spanish, and Turkish this year. RTMs pioneer a language independent approach to semantic similarity and remove the need to access any task or domain specific information or resource. RTMs become 6th out of 52 submissions in Spanish to English STS. We average prediction scores using weights based on the training performance to improve the overall performance.",
}
@inproceedings{arroyo-fernandez-meza-ruiz-2017-lipn,
    title = "{LIPN}-{IIMAS} at {S}em{E}val-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity",
    author = "Arroyo-Fern{\'a}ndez, Ignacio  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2031",
    doi = "10.18653/v1/S17-2031",
    pages = "208--212",
    abstract = "In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).",
}
@inproceedings{fialho-etal-2017-l2f,
    title = "{L}2{F}/{INESC}-{ID} at {S}em{E}val-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity",
    author = "Fialho, Pedro  and
      Patinho Rodrigues, Hugo  and
      Coheur, Lu{\'\i}sa  and
      Quaresma, Paulo",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2032",
    doi = "10.18653/v1/S17-2032",
    pages = "213--219",
    abstract = "This paper describes our approach to the SemEval-2017 {``}Semantic Textual Similarity{''} and {``}Multilingual Word Similarity{''} tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Although without state of the art results, we introduce semantic structures in textual similarity and analyze their impact. Regarding word similarity, we target the English language and combine WordNet information with Word Embeddings. Without matching the best systems, our approach proved to be simple and effective.",
}
@inproceedings{he-etal-2017-hccl,
    title = "{HCCL} at {S}em{E}val-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity",
    author = "He, Junqing  and
      Wu, Long  and
      Zhao, Xuemin  and
      Yan, Yonghong",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2033",
    doi = "10.18653/v1/S17-2033",
    pages = "220--225",
    abstract = "In this paper, we introduce an approach to combining word embeddings and machine translation for multilingual semantic word similarity, the task2 of SemEval-2017. Thanks to the unsupervised transliteration model, our cross-lingual word embeddings encounter decreased sums of OOVs. Our results are produced using only monolingual Wikipedia corpora and a limited amount of sentence-aligned data. Although relatively little resources are utilized, our system ranked 3rd in the monolingual subtask and can be the 6th in the cross-lingual subtask.",
}
@inproceedings{gamallo-2017-citius,
    title = "{C}itius at {S}em{E}val-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts",
    author = "Gamallo, Pablo",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2034",
    doi = "10.18653/v1/S17-2034",
    pages = "226--229",
    abstract = "This article describes the distributional strategy submitted by the Citius team to the SemEval 2017 Task 2. Even though the team participated in two subtasks, namely monolingual and cross-lingual word similarity, the article is mainly focused on the cross-lingual subtask. Our method uses comparable corpora and syntactic dependencies to extract count-based and transparent bilingual distributional contexts. The evaluation of the results show that our method is competitive with other cross-lingual strategies, even those using aligned and parallel texts.",
}
@inproceedings{melka-bernard-2017-jmp8,
    title = "Jmp8 at {S}em{E}val-2017 Task 2: A simple and general distributional approach to estimate word similarity",
    author = "Melka, Josu{\'e}  and
      Bernard, Gilles",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2035",
    doi = "10.18653/v1/S17-2035",
    pages = "230--234",
    abstract = "We have built a simple corpus-based system to estimate words similarity in multiple languages with a count-based approach. After training on Wikipedia corpora, our system was evaluated on the multilingual subtask of SemEval-2017 Task 2 and achieved a good level of performance, despite its great simplicity. Our results tend to demonstrate the power of the distributional approach in semantic similarity tasks, even without knowledge of the underlying language. We also show that dimensionality reduction has a considerable impact on the results.",
}
@inproceedings{meng-etal-2017-qlut-semeval,
    title = "{QLUT} at {S}em{E}val-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base",
    author = "Meng, Fanqing  and
      Lu, Wenpeng  and
      Zhang, Yuteng  and
      Jian, Ping  and
      Shi, Shumin  and
      Huang, Heyan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2036",
    doi = "10.18653/v1/S17-2036",
    pages = "235--238",
    abstract = "This paper shows the details of our system submissions in the task 2 of SemEval 2017. We take part in the subtask 1 of this task, which is an English monolingual subtask. This task is designed to evaluate the semantic word similarity of two linguistic items. The results of runs are assessed by standard Pearson and Spearman correlation, contrast with official gold standard set. The best performance of our runs is 0.781 (Final). The techniques of our runs mainly make use of the word embeddings and the knowledge-based method. The results demonstrate that the combined method is effective for the computation of word similarity, while the word embeddings and the knowledge-based technique, respectively, needs more deeply improvement in details.",
}
@inproceedings{jimenez-etal-2017-rufino,
    title = "{RUFINO} at {S}em{E}val-2017 Task 2: Cross-lingual lexical similarity by extending {PMI} and word embeddings systems with a {S}wadesh{'}s-like list",
    author = "Jimenez, Sergio  and
      Due{\~n}as, George  and
      Gaitan, Lorena  and
      Segura, Jorge",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2037",
    doi = "10.18653/v1/S17-2037",
    pages = "239--244",
    abstract = "The RUFINO team proposed a non-supervised, conceptually-simple and low-cost approach for addressing the Multilingual and Cross-lingual Semantic Word Similarity challenge at SemEval 2017. The proposed systems were cross-lingual extensions of popular monolingual lexical similarity approaches such as PMI and word2vec. The extensions were possible by means of a small parallel list of concepts similar to the Swadesh{'}s list, which we obtained in a semi-automatic way. In spite of its simplicity, our approach showed to be effective obtaining statistically-significant and consistent results in all datasets proposed for the task. Besides, we provide some research directions for improving this novel and affordable approach.",
}
@inproceedings{mensa-etal-2017-merali,
    title = "{MERALI} at {S}em{E}val-2017 Task 2 Subtask 1: a Cognitively Inspired approach",
    author = "Mensa, Enrico  and
      Radicioni, Daniele P.  and
      Lieto, Antonio",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2038",
    doi = "10.18653/v1/S17-2038",
    pages = "245--249",
    abstract = "In this paper we report on the participation of the MERALI system to the SemEval Task 2 Subtask 1. The MERALI system approaches conceptual similarity through a simple, cognitively inspired, heuristics; it builds on a linguistic resource, the TTCS-e, that relies on BabelNet, NASARI and ConceptNet. The linguistic resource in fact contains a novel mixture of common-sense and encyclopedic knowledge. The obtained results point out that there is ample room for improvement, so that they are used to elaborate on present limitations and on future steps.",
}
@inproceedings{qasemizadeh-kallmeyer-2017-hhu,
    title = "{HHU} at {S}em{E}val-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment",
    author = "QasemiZadeh, Behrang  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2039",
    doi = "10.18653/v1/S17-2039",
    pages = "250--255",
    abstract = "This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our unsupervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task{'}s organizers.",
}
@inproceedings{ranjbar-etal-2017-mahtab,
    title = "Mahtab at {S}em{E}val-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity",
    author = "Ranjbar, Niloofar  and
      Mashhadirajab, Fatemeh  and
      Shamsfard, Mehrnoush  and
      Hosseini pour, Rayeheh  and
      Vahid pour, Aryan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2040",
    doi = "10.18653/v1/S17-2040",
    pages = "256--260",
    abstract = "In this paper, we describe our proposed method for measuring semantic similarity for a given pair of words at SemEval-2017 monolingual semantic word similarity task. We use a combination of knowledge-based and corpus-based techniques. We use FarsNet, the Persian Word Net, besides deep learning techniques to extract the similarity of words. We evaluated our proposed approach on Persian (Farsi) test data at SemEval-2017. It outperformed the other participants and ranked the first in the challenge.",
}
@inproceedings{delli-bovi-raganato-2017-sew,
    title = "Sew-Embed at {S}em{E}val-2017 Task 2: Language-Independent Concept Representations from a Semantically Enriched {W}ikipedia",
    author = "Delli Bovi, Claudio  and
      Raganato, Alessandro",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2041",
    doi = "10.18653/v1/S17-2041",
    pages = "261--266",
    abstract = "This paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations developed by Raganato et al. (2016), and propose an embedded augmentation of their explicit high-dimensional vectors, which we obtain by plugging in an arbitrary word (or sense) embedding representation, and computing a weighted average in the continuous vector space. We evaluate Sew-Embed with two different off-the-shelf embedding representations, and report their performances across all monolingual and cross-lingual benchmarks available for the task. Despite its simplicity, especially compared with supervised or overly tuned approaches, Sew-Embed achieves competitive results in the cross-lingual setting (3rd best result in the global ranking of subtask 2, score 0.56).",
}
@inproceedings{rotari-etal-2017-wild,
    title = "Wild Devs{'} at {S}em{E}val-2017 Task 2: Using Neural Networks to Discover Word Similarity",
    author = "Rotari, R{\u{a}}zvan-Gabriel  and
      Hulub, Ionu{\textcommabelow{t}}  and
      Oprea, {\textcommabelow{S}}tefan  and
      Pl{\u{a}}mad{\u{a}}-Onofrei, Mihaela  and
      Loren{\c{t}}, Alina Beatrice  and
      Preisler, Raluca  and
      Iftene, Adrian  and
      Trandab{\u{a}}{\textcommabelow{t}}, Diana",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2042",
    doi = "10.18653/v1/S17-2042",
    pages = "267--270",
    abstract = "This paper presents Wild Devs{'} participation in the SemEval-2017 Task 2 {``}Multi-lingual and Cross-lingual Semantic Word Similarity{''}, which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs.",
}
@inproceedings{qwaider-etal-2017-trentoteam,
    title = "{T}rento{T}eam at {S}em{E}val-2017 Task 3: An application of {G}rice Maxims in Ranking Community Question Answers",
    author = "Qwaider, Mohammed R. H.  and
      Freihat, Abed Alhakim  and
      Giunchiglia, Fausto",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2043",
    doi = "10.18653/v1/S17-2043",
    pages = "271--274",
    abstract = "In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al.,2017).We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy.Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis.Our system obtained a comparable resultsto Machine learning systems.",
}
@inproceedings{el-adlouni-etal-2017-upc,
    title = "{UPC}-{USMBA} at {S}em{E}val-2017 Task 3: Combining multiple approaches for {CQA} for {A}rabic",
    author = "El Adlouni, Yassine  and
      Lahbari, Imane  and
      Rodr{\'\i}guez, Horacio  and
      Meknassi, Mohammed  and
      El Alaoui, Said Ouatik  and
      Ennahnahi, Noureddine",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2044",
    doi = "10.18653/v1/S17-2044",
    pages = "275--279",
    abstract = "This paper presents a description of the participation of the UPC-USMBA team in the SemEval 2017 Task 3, subtask D, Arabic. Our approach for facing the task is based on a combination of a set of atomic classifiers. The atomic classifiers include lexical string based, based on vectorial representations and rulebased. Several combination approaches have been tried.",
}
@inproceedings{feng-etal-2017-beihang,
    title = "Beihang-{MSRA} at {S}em{E}val-2017 Task 3: A Ranking System with Neural Matching Features for Community Question Answering",
    author = "Feng, Wenzheng  and
      Wu, Yu  and
      Wu, Wei  and
      Li, Zhoujun  and
      Zhou, Ming",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2045",
    doi = "10.18653/v1/S17-2045",
    pages = "280--286",
    abstract = "This paper presents the system in SemEval-2017 Task 3, Community Question Answering (CQA). We develop a ranking system that is capable of capturing semantic relations between text pairs with little word overlap. In addition to traditional NLP features, we introduce several neural network based matching features which enable our system to measure text similarity beyond lexicons. Our system significantly outperforms baseline methods and holds the second place in Subtask A and the fifth place in Subtask B, which demonstrates its efficacy on answer selection and question retrieval.",
}
@inproceedings{rodrigues-couto-2017-mors,
    title = "{M}o{RS} at {S}em{E}val-2017 Task 3: Easy to use {SVM} in Ranking Tasks",
    author = "Rodrigues, Miguel J.  and
      Couto, Francisco M.",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2046",
    doi = "10.18653/v1/S17-2046",
    pages = "287--291",
    abstract = "This paper describes our system, dubbed MoRS (Modular Ranking System), pronounced {`}Morse{'}, which participated in Task 3 of SemEval-2017. We used MoRS to perform the Community Question Answering Task 3, which consisted on reordering a set of comments according to their usefulness in answering the question in the thread. This was made for a large collection of questions created by a user community. As for this challenge we wanted to go back to simple, easy-to-use, and somewhat forgotten technologies that we think, in the hands of non-expert people, could be reused in their own data sets. Some of our techniques included the annotation of text, the retrieval of meta-data for each comment, POS tagging and Named Entity Recognition, among others. These gave place to syntactical analysis and semantic measurements. Finally we show and discuss our results and the context of our approach, which is part of a more comprehensive system in development, named MoQA.",
}
@inproceedings{xie-etal-2017-eica,
    title = "{EICA} Team at {S}em{E}val-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering",
    author = "Xie, Yufei  and
      Wang, Maoquan  and
      Ma, Jing  and
      Jiang, Jian  and
      Lu, Zhao",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2047",
    doi = "10.18653/v1/S17-2047",
    pages = "292--298",
    abstract = "We describe our system for participating in SemEval-2017 Task 3 on Community Question Answering. Our approach relies on combining a rich set of various types of features: semantic and metadata. The most important group turned out to be the metadata feature and the semantic vectors trained on QatarLiving data. In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13.48 and accuracy of 97.08. In Subtask A, our primary submission get into the top 50{\%}.",
}
@inproceedings{attardi-etal-2017-fa3l,
    title = "{FA}3{L} at {S}em{E}val-2017 Task 3: A {T}h{R}ee Embeddings Recurrent Neural Network for Question Answering",
    author = "Attardi, Giuseppe  and
      Carta, Antonio  and
      Errica, Federico  and
      Madotto, Andrea  and
      Pannitto, Ludovica",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2048",
    doi = "10.18653/v1/S17-2048",
    pages = "299--304",
    abstract = "In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking purposes of the Task. The score obtained on the official test data shows promising results.",
}
@inproceedings{qi-etal-2017-scir,
    title = "{SCIR}-{QA} at {S}em{E}val-2017 Task 3: {CNN} Model Based on Similar and Dissimilar Information between Keywords for Question Similarity",
    author = "Qi, Le  and
      Zhang, Yu  and
      Liu, Ting",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2049",
    doi = "10.18653/v1/S17-2049",
    pages = "305--309",
    abstract = "We describe a method of calculating the similarity of questions in community QA. Question in cQA are usually very long and there are a lot of useless information about calculating the similarity of questions. Therefore,we implement a CNN model based on similar and dissimilar information between question{'}s keywords. We extract the keywords of questions, and then model the similar and dissimilar information between the keywords, and use the CNN model to calculate the similarity.",
}
@inproceedings{goyal-2017-learningtoquestion,
    title = "{L}earning{T}o{Q}uestion at {S}em{E}val 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features",
    author = "Goyal, Naman",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2050",
    doi = "10.18653/v1/S17-2050",
    pages = "310--314",
    abstract = "This paper describes our official entry LearningToQuestion for SemEval 2017 task 3 community question answer, subtask B. The objective is to rerank questions obtained in web forum as per their similarity to original question. Our system uses pairwise learning to rank methods on rich set of hand designed and representation learning features. We use various semantic features that help our system to achieve promising results on the task. The system achieved second highest results on official metrics MAP and good results on other search metrics.",
}
@inproceedings{charlet-damnati-2017-simbow-semeval,
    title = "{S}im{B}ow at {S}em{E}val-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering",
    author = "Charlet, Delphine  and
      Damnati, G{\'e}raldine",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2051",
    doi = "10.18653/v1/S17-2051",
    pages = "315--319",
    abstract = "This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a soft-cosine that takes into account relations between words. According to the type of relation matrix embedded in the soft-cosine, semantic or lexical relations can be considered. Our system ranked first among the official submissions of subtask B.",
}
@inproceedings{zhang-etal-2017-furongwang,
    title = "{F}u{R}ong{W}ang at {S}em{E}val-2017 Task 3: Deep Neural Networks for Selecting Relevant Answers in Community Question Answering",
    author = "Zhang, Sheng  and
      Cheng, Jiajun  and
      Wang, Hui  and
      Zhang, Xin  and
      Li, Pei  and
      Ding, Zhaoyun",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2052",
    doi = "10.18653/v1/S17-2052",
    pages = "320--325",
    abstract = "We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3). Convolutional neural networks and bi-directional long-short term memory networks are applied in our methods to extract semantic information from questions and answers (comments). In addition, in order to take the full advantage of question-comment semantic relevance, we deploy interaction layer and augmented features before calculating the similarity. The results show that our methods have the great effectiveness for both subtask A and subtask C.",
}
@inproceedings{filice-etal-2017-kelp,
    title = "{K}e{LP} at {S}em{E}val-2017 Task 3: Learning Pairwise Patterns in Community Question Answering",
    author = "Filice, Simone  and
      Da San Martino, Giovanni  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2053",
    doi = "10.18653/v1/S17-2053",
    pages = "326--333",
    abstract = "This paper describes the KeLP system participating in the SemEval-2017 community Question Answering (cQA) task. The system is a refinement of the kernel-based sentence pair modeling we proposed for the previous year challenge. It is implemented within the Kernel-based Learning Platform called KeLP, from which we inherit the team{'}s name. Our primary submission ranked first in subtask A, and third in subtasks B and C, being the only systems appearing in the top-3 ranking for all the English subtasks. This shows that the proposed framework, which has minor variations among the three subtasks, is extremely flexible and effective in tackling learning tasks defined on sentence pairs.",
}
@inproceedings{deriu-cieliebak-2017-swissalps,
    title = "{S}wiss{A}lps at {S}em{E}val-2017 Task 3: Attention-based Convolutional Neural Network for Community Question Answering",
    author = "Deriu, Jan Milan  and
      Cieliebak, Mark",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2054",
    doi = "10.18653/v1/S17-2054",
    pages = "334--338",
    abstract = "In this paper we propose a system for reranking answers for a given question. Our method builds on a siamese CNN architecture which is extended by two attention mechanisms. The approach was evaluated on the datasets of the SemEval-2017 competition for Community Question Answering (cQA), where it achieved 7th place obtaining a MAP score of 86:24 points on the Question-Comment Similarity subtask.",
}
@inproceedings{saina-etal-2017-takelab,
    title = "{T}ake{L}ab-{QA} at {S}em{E}val-2017 Task 3: Classification Experiments for Answer Retrieval in Community {QA}",
    author = "{\v{S}}aina, Filip  and
      Kukurin, Toni  and
      Pulji{\'c}, Lukrecija  and
      Karan, Mladen  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2055",
    doi = "10.18653/v1/S17-2055",
    pages = "339--343",
    abstract = "In this paper we present the TakeLab-QA entry to SemEval 2017 task 3, which is a question-comment re-ranking problem. We present a classification based approach, including two supervised learning models {--} Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). We use features based on different semantic similarity models (e.g., Latent Dirichlet Allocation), as well as features based on several types of pre-trained word embeddings. Moreover, we also use some hand-crafted task-specific features. For training, our system uses no external labeled data apart from that provided by the organizers. Our primary submission achieves a MAP-score of 81.14 and F1-score of 66.99 {--} ranking us 10th on the SemEval 2017 task 3, subtask A.",
}
@inproceedings{almarwani-diab-2017-gw,
    title = "{GW}{\_}{QA} at {S}em{E}val-2017 Task 3: Question Answer Re-ranking on {A}rabic Fora",
    author = "Almarwani, Nada  and
      Diab, Mona",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2056",
    doi = "10.18653/v1/S17-2056",
    pages = "344--348",
    abstract = "This paper describes our submission to SemEval-2017 Task 3 Subtask D, {``}Question Answer Ranking in Arabic Community Question Answering{''}. In this work, we applied a supervised machine learning approach to automatically re-rank a set of QA pairs according to their relevance to a given question. We employ features based on latent semantic models, namely WTMF, as well as a set of lexical features based on string lengths and surface level matching. The proposed system ranked first out of 3 submissions, with a MAP score of 61.16{\%}.",
}
@inproceedings{ben-abacha-demner-fushman-2017-nlm,
    title = "{NLM}{\_}{NIH} at {S}em{E}val-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering",
    author = "Ben Abacha, Asma  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2057",
    doi = "10.18653/v1/S17-2057",
    pages = "349--352",
    abstract = "This paper describes our participation in SemEval-2017 Task 3 on Community Question Answering (cQA). The Question Similarity subtask (B) aims to rank a set of related questions retrieved by a search engine according to their similarity to the original question. We adapted our feature-based system for Recognizing Question Entailment (RQE) to the question similarity task. Tested on cQA-B-2016 test data, our RQE system outperformed the best system of the 2016 challenge in all measures with 77.47 MAP and 80.57 Accuracy. On cQA-B-2017 test data, performances of all systems dropped by around 30 points. Our primary system obtained 44.62 MAP, 67.27 Accuracy and 47.25 F1 score. The cQA-B-2017 best system achieved 47.22 MAP and 42.37 F1 score. Our system is ranked sixth in terms of MAP and third in terms of F1 out of 13 participating teams.",
}
@inproceedings{koreeda-etal-2017-bunji,
    title = "bunji at {S}em{E}val-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features",
    author = "Koreeda, Yuta  and
      Hashito, Takuya  and
      Niwa, Yoshiki  and
      Sato, Misa  and
      Yanase, Toshihiko  and
      Kurotsuchi, Kenzo  and
      Yanai, Kohsuke",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2058",
    doi = "10.18653/v1/S17-2058",
    pages = "353--359",
    abstract = "This paper describes a text-ranking system developed by bunji team in SemEval-2017 Task 3: Community Question Answering, Subtask A and C. The goal of the task is to re-rank the comments in a question-and-answer forum such that useful comments for answering the question are ranked high. We proposed a method that combines neural similarity features and hand-crafted comment plausibility features, and we modeled inter-comments relationship using conditional random field. Our approach obtained the fifth place in the Subtask A and the second place in the Subtask C.",
}
@inproceedings{torki-etal-2017-qu,
    title = "{QU}-{BIGIR} at {S}em{E}val 2017 Task 3: Using Similarity Features for {A}rabic Community Question Answering Forums",
    author = "Torki, Marwan  and
      Hasanain, Maram  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2059",
    doi = "10.18653/v1/S17-2059",
    pages = "360--364",
    abstract = "In this paper we describe our QU-BIGIR system for the Arabic subtask D of the SemEval 2017 Task 3. Our approach builds on our participation in the past version of the same subtask. This year, our system uses different similarity measures that encodes lexical and semantic pairwise similarity of text pairs. In addition to well known similarity measures such as cosine similarity, we use other measures based on the summary statistics of word embedding representation for a given text. To rank a list of candidate question answer pairs for a given question, we learn a linear SVM classifier over our similarity features. Our best resulting run came second in subtask D with a very competitive performance to the first-ranking system.",
}
@inproceedings{wu-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task",
    author = "Wu, Guoshun  and
      Sheng, Yixuan  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2060",
    doi = "10.18653/v1/S17-2060",
    pages = "365--369",
    abstract = "This paper describes the systems we submitted to the task 3 (Community Question Answering) in SemEval 2017 which contains three subtasks on English corpora, i.e., subtask A: Question-Comment Similarity, subtask B: Question-Question Similarity, and subtask C: Question-External Comment Similarity. For subtask A, we combined two different methods to represent question-comment pair, i.e., supervised model using traditional features and Convolutional Neural Network. For subtask B, we utilized the information of snippets returned from Search Engine with question subject as query. For subtask C, we ranked the comments by multiplying the probability of the pair related question comment being Good by the reciprocal rank of the related question.",
}
@inproceedings{agustian-takamura-2017-uinsuska,
    title = "{UINSUSKA}-{T}i{T}ech at {S}em{E}val-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for {CQA}",
    author = "Agustian, Surya  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2061",
    doi = "10.18653/v1/S17-2061",
    pages = "370--374",
    abstract = "The majority of core techniques to solve many problems in Community Question Answering (CQA) task rely on similarity computation. This work focuses on similarity between two sentences (or questions in subtask B) based on word embeddings. We exploit words importance levels in sentences or questions for similarity features, for classification and ranking with machine learning. Using only 2 types of similarity metric, our proposed method has shown comparable results with other complex systems. This method on subtask B 2017 dataset is ranked on position 7 out of 13 participants. Evaluation on 2016 dataset is on position 8 of 12, outperforms some complex systems. Further, this finding is explorable and potential to be used as baseline and extensible for many tasks in CQA and other textual similarity based system.",
}
@inproceedings{galbraith-etal-2017-talla,
    title = "Talla at {S}em{E}val-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection",
    author = "Galbraith, Byron  and
      Pratap, Bhanu  and
      Shank, Daniel",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2062",
    doi = "10.18653/v1/S17-2062",
    pages = "375--379",
    abstract = "This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7{\%} out of max achievable 67.0{\%} on the test set.",
}
@inproceedings{han-toner-2017-qub,
    title = "{QUB} at {S}em{E}val-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in {T}witter",
    author = "Han, Xiwu  and
      Toner, Gregory",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2063",
    doi = "10.18653/v1/S17-2063",
    pages = "380--384",
    abstract = {This paper presents our submission to SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Na{\"\i}ve Bayes Multinomial with standard text-representation features, we approached Subtask B as a sequence of imbalanced classification problems, and optimized our system per the macro-average recall. Subtask A was then solved via the Semi-Ranking results. On the final test, our system was ranked 10th for Subtask A, and 3rd for Subtask B.},
}
@inproceedings{yan-pedersen-2017-duluth,
    title = "{D}uluth at {S}em{E}val-2017 Task 6: Language Models in Humor Detection",
    author = "Yan, Xinru  and
      Pedersen, Ted",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2064",
    doi = "10.18653/v1/S17-2064",
    pages = "385--389",
    abstract = "This paper describes the Duluth system that participated in SemEval-2017 Task 6 {\#}HashtagWars: Learning a Sense of Humor. The system participated in Subtasks A and B using N-gram language models, ranking highly in the task evaluation. This paper discusses the results of our system in the development and evaluation stages and from two post-evaluation runs.",
}
@inproceedings{baziotis-etal-2017-datastories,
    title = "{D}ata{S}tories at {S}em{E}val-2017 Task 6: {S}iamese {LSTM} with Attention for Humorous Text Comparison",
    author = "Baziotis, Christos  and
      Pelekis, Nikos  and
      Doulkeridis, Christos",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2065",
    doi = "10.18653/v1/S17-2065",
    pages = "390--395",
    abstract = "In this paper we present a deep-learning system that competed at SemEval-2017 Task 6 ''{\#}HashtagWars: Learning a Sense of Humor{''}. We participated in Subtask A, in which the goal was, given two Twitter messages, to identify which one is funnier. We propose a Siamese architecture with bidirectional Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism. Our system works on the token-level, leveraging word embeddings trained on a big collection of unlabeled Twitter messages. We ranked 2nd in 7 teams. A post-completion improvement of our model, achieves state-of-the-art results on {\#}HashtagWars dataset.",
}
@inproceedings{kukovacec-etal-2017-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2017 Task 6: {\#}{R}anking{H}umor{I}n4{P}ages",
    author = "Kukova{\v{c}}ec, Marin  and
      Malenica, Juraj  and
      Mr{\v{s}}i{\'c}, Ivan  and
      {\v{S}}ajatovi{\'c}, Antonio  and
      Alagi{\'c}, Domagoj  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2066",
    doi = "10.18653/v1/S17-2066",
    pages = "396--400",
    abstract = "This paper describes our system for humor ranking in tweets within the SemEval 2017 Task 6: {\#}HashtagWars (6A and 6B). For both subtasks, we use an off-the-shelf gradient boosting model built on a rich set of features, handcrafted to provide the model with the external knowledge needed to better predict the humor in the text. The features capture various cultural references and specific humor patterns. Our system ranked 2nd (officially 7th) among 10 submissions on the Subtask A and 2nd among 9 submissions on the Subtask B.",
}
@inproceedings{cattle-ma-2017-srhr,
    title = "{SRHR} at {S}em{E}val-2017 Task 6: Word Associations for Humour Recognition",
    author = "Cattle, Andrew  and
      Ma, Xiaojuan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2067",
    doi = "10.18653/v1/S17-2067",
    pages = "401--406",
    abstract = "This paper explores the role of semantic relatedness features, such as word associations, in humour recognition. Specifically, we examine the task of inferring pairwise humour judgments in Twitter hashtag wars. We examine a variety of word association features derived from University of Southern Florida Free Association Norms (USF) and the Edinburgh Associative Thesaurus (EAT) and find that word association-based features outperform Word2Vec similarity, a popular semantic relatedness measure. Our system achieves an accuracy of 56.42{\%} using a combination of unigram perplexity, bigram perplexity, EAT difference (tweet-avg), USF forward (max), EAT difference (word-avg), USF difference (word-avg), EAT forward (min), USF difference (tweet-max), and EAT backward (min).",
}
@inproceedings{flescan-lovin-arseni-etal-2017-warteam,
    title = "{\#}{W}ar{T}eam at {S}em{E}val-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets",
    author = "Fle{\textcommabelow{s}}can-Lovin-Arseni, Iuliana Alexandra  and
      Turcu, Ramona Andreea  and
      S{\^\i}rbu, Cristina  and
      Alexa, Larisa  and
      Amarandei, Sandra Maria  and
      Herciu, Nichita  and
      Scutaru, Constantin  and
      Trandab{\u{a}}{\textcommabelow{t}}, Diana  and
      Iftene, Adrian",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2068",
    doi = "10.18653/v1/S17-2068",
    pages = "407--410",
    abstract = {This paper presents the participation of {\#}WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. {\#}WarTeam{'}s system uses a neural network (TensorFlow) having inputs from a Na{\"\i}ve Bayes humor classifier and a sentiment analyzer.},
}
@inproceedings{mahajan-zaveri-2017-svnit,
    title = "{SVNIT} @ {S}em{E}val 2017 Task-6: Learning a Sense of Humor Using Supervised Approach",
    author = "Mahajan, Rutal  and
      Zaveri, Mukesh",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2069",
    doi = "10.18653/v1/S17-2069",
    pages = "411--415",
    abstract = {This paper describes the system devel-oped for SemEval 2017 task 6: {\#}HashTagWars -Learning a Sense of Hu-mor. Learning to recognize sense of hu-mor is the important task for language understanding applications. Different set of features based on frequency of words, structure of tweets and semantics are used in this system to identify the presence of humor in tweets. Supervised machine learning approaches, Multilayer percep-tron and Na{\"\i}ve Bayes are used to classify the tweets in to three level of sense of humor. For given Hashtag, the system finds the funniest tweet and predicts the amount of funniness of all the other tweets. In official submitted runs, we have achieved 0.506 accuracy using mul-tilayer perceptron in subtask-A and 0.938 distance in subtask-B. Using Na{\"\i}ve bayes in subtask-B, the system achieved 0.949 distance. Apart from official runs, this system have scored 0.751 accuracy in subtask-A using SVM. But still there is a wide room for improvement in system.},
}
@inproceedings{pedersen-2017-duluth,
    title = "{D}uluth at {S}em{E}val-2017 Task 7 : Puns Upon a Midnight Dreary, Lexical Semantics for the Weak and Weary",
    author = "Pedersen, Ted",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2070",
    doi = "10.18653/v1/S17-2070",
    pages = "416--420",
    abstract = "This paper describes the Duluth systems that participated in SemEval-2017 Task 7 : Detection and Interpretation of English Puns. The Duluth systems participated in all three subtasks, and relied on methods that included word sense disambiguation and measures of semantic relatedness.",
}
@inproceedings{vechtomova-2017-uwaterloo,
    title = "{UW}aterloo at {S}em{E}val-2017 Task 7: Locating the Pun Using Syntactic Characteristics and Corpus-based Metrics",
    author = "Vechtomova, Olga",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2071",
    doi = "10.18653/v1/S17-2071",
    pages = "421--425",
    abstract = "The paper presents a system for locating a pun word. The developed method calculates a score for each word in a pun, using a number of components, including its Inverse Document Frequency (IDF), Normalized Pointwise Mutual Information (NPMI) with other words in the pun text, its position in the text, part-of-speech and some syntactic features. The method achieved the best performance in the Heterographic category and the second best in the Homographic. Further analysis showed that IDF is the most useful characteristic, whereas the count of words with which the given word has high NPMI has a negative effect on performance.",
}
@inproceedings{mikhalkova-karyakin-2017-punfields,
    title = "{P}un{F}ields at {S}em{E}val-2017 Task 7: Employing {R}oget{'}s Thesaurus in Automatic Pun Recognition and Interpretation",
    author = "Mikhalkova, Elena  and
      Karyakin, Yuri",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2072",
    doi = "10.18653/v1/S17-2072",
    pages = "426--431",
    abstract = "The article describes a model of automatic interpretation of English puns, based on Roget{'}s Thesaurus, and its implementation, PunFields. In a pun, the algorithm discovers two groups of words that belong to two main semantic fields. The fields become a semantic vector based on which an SVM classifier learns to recognize puns. A rule-based model is then applied for recognition of intentionally ambiguous (target) words and their definitions. In SemEval Task 7 PunFields shows a considerably good result in pun classification, but requires improvement in searching for the target word and its definition.",
}
@inproceedings{pramanick-das-2017-ju,
    title = "{JU} {CSE} {NLP} @ {S}em{E}val 2017 Task 7: Employing Rules to Detect and Interpret {E}nglish Puns",
    author = "Pramanick, Aniket  and
      Das, Dipankar",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2073",
    doi = "10.18653/v1/S17-2073",
    pages = "432--435",
    abstract = "System description. Implementation of HMM and Cyclic Dependency Network.",
}
@inproceedings{sevgili-etal-2017-n,
    title = "N-Hance at {S}em{E}val-2017 Task 7: A Computational Approach using Word Association for Puns",
    author = {Sevgili, {\"O}zge  and
      Ghotbi, Nima  and
      Tekir, Selma},
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2074",
    doi = "10.18653/v1/S17-2074",
    pages = "436--439",
    abstract = "This paper presents a system developed for SemEval-2017 Task 7, Detection and Interpretation of English Puns consisting of three subtasks; pun detection, pun location, and pun interpretation, respectively. The system stands on recognizing a distinctive word which has a high association with the pun in the given sentence. The intended humorous meaning of pun is identified through the use of this word. Our official results confirm the potential of this approach.",
}
@inproceedings{hurtado-etal-2017-elirf,
    title = "{EL}i{RF}-{UPV} at {S}em{E}val-2017 Task 7: Pun Detection and Interpretation",
    author = "Hurtado, Llu{\'\i}s-F.  and
      Segarra, Encarna  and
      Pla, Ferran  and
      Carrasco, Pascual  and
      Gonz{\'a}lez, Jos{\'e}-{\'A}ngel",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2075",
    doi = "10.18653/v1/S17-2075",
    pages = "440--443",
    abstract = "This paper describes the participation of ELiRF-UPV team at task 7 (subtask 2: homographic pun detection and subtask 3: homographic pun interpretation) of SemEval2017. Our approach is based on the use of word embeddings to find related words in a sentence and a version of the Lesk algorithm to establish relationships between synsets. The results obtained are in line with those obtained by the other participants and they encourage us to continue working on this problem.",
}
@inproceedings{oele-evang-2017-buzzsaw,
    title = "{B}uzz{S}aw at {S}em{E}val-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic {E}nglish Puns with Sense Embeddings",
    author = "Oele, Dieke  and
      Evang, Kilian",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2076",
    doi = "10.18653/v1/S17-2076",
    pages = "444--448",
    abstract = "This paper describes our system participating in the SemEval-2017 Task 7, for the subtasks of homographic pun location and homographic pun interpretation. For pun interpretation, we use a knowledge-based Word Sense Disambiguation (WSD) method based on sense embeddings. Pun-based jokes can be divided into two parts, each containing information about the two distinct senses of the pun. To exploit this structure we split the context that is input to the WSD system into two local contexts and find the best sense for each of them. We use the output of pun interpretation for pun location. As we expect the two meanings of a pun to be very dissimilar, we compute sense embedding cosine distances for each sense-pair and select the word that has the highest distance. We describe experiments on different methods of splitting the context and compare our method to several baselines. We find evidence supporting our hypotheses and obtain competitive results for pun interpretation.",
}
@inproceedings{vadehra-2017-uwav,
    title = "{UWAV} at {S}em{E}val-2017 Task 7: Automated feature-based system for locating puns",
    author = "Vadehra, Ankit",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2077",
    doi = "10.18653/v1/S17-2077",
    pages = "449--452",
    abstract = "In this paper we describe our system created for SemEval-2017 Task 7: Detection and Interpretation of English Puns. We tackle subtask 1, pun detection, by leveraging features selected from sentences to design a classifier that can disambiguate between the presence or absence of a pun. We address subtask 2, pun location, by utilizing a decision flow structure that uses presence or absence of certain features to decide the next action. The results obtained by our system are encouraging, considering the simplicity of the system. We consider this system as a precursor for deeper exploration on efficient feature selection for pun detection.",
}
@inproceedings{xiu-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate {E}nglish Puns",
    author = "Xiu, Yuhuan  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2078",
    doi = "10.18653/v1/S17-2078",
    pages = "453--456",
    abstract = "This paper describes our submissions to task 7 in SemEval 2017, i.e., Detection and Interpretation of English Puns. We participated in the first two subtasks, which are to detect and locate English puns respectively. For subtask 1, we presented a supervised system to determine whether or not a sentence contains a pun using similarity features calculated on sense vectors or cluster center vectors. For subtask 2, we established an unsupervised system to locate the pun by scoring each word in the sentence and we assumed that the word with the smallest score is the pun.",
}
@inproceedings{indurthi-oota-2017-fermi,
    title = "Fermi at {S}em{E}val-2017 Task 7: Detection and Interpretation of Homographic puns in {E}nglish Language",
    author = "Indurthi, Vijayasaradhi  and
      Oota, Subba Reddy",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2079",
    doi = "10.18653/v1/S17-2079",
    pages = "457--460",
    abstract = "This paper describes our system for detection and interpretation of English puns. We participated in 2 subtasks related to homographic puns achieve comparable results for these tasks. Through the paper we provide detailed description of the approach, as well as the results obtained in the task. Our models achieved a F1-score of 77.65{\%} for Subtask 1 and 52.15{\%} for Subtask 2.",
}
@inproceedings{bahuleyan-vechtomova-2017-uwaterloo,
    title = "{UW}aterloo at {S}em{E}val-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features",
    author = "Bahuleyan, Hareesh  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2080",
    doi = "10.18653/v1/S17-2080",
    pages = "461--464",
    abstract = "This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017. Identifying rumours, especially for breaking news events as they unfold, is a challenging task due to the absence of sufficient information about the exact rumour stories circulating on social media. Determining the stance of Twitter users towards rumourous messages could provide an indirect way of identifying potential rumours. The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier. With an accuracy of 0.78, our system achieved the second best performance on subtask-A of RumourEval.",
}
@inproceedings{chen-etal-2017-ikm,
    title = "{IKM} at {S}em{E}val-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification",
    author = "Chen, Yi-Chin  and
      Liu, Zhao-Yang  and
      Kao, Hung-Yu",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2081",
    doi = "10.18653/v1/S17-2081",
    pages = "465--469",
    abstract = "This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B.",
}
@inproceedings{enayet-el-beltagy-2017-niletmrg,
    title = "{N}ile{TMRG} at {S}em{E}val-2017 Task 8: Determining Rumour and Veracity Support for Rumours on {T}witter.",
    author = "Enayet, Omar  and
      El-Beltagy, Samhaa R.",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2082",
    doi = "10.18653/v1/S17-2082",
    pages = "470--474",
    abstract = "Final submission for NileTMRG on RumourEval 2017.",
}
@inproceedings{kochkina-etal-2017-turing,
    title = "{T}uring at {S}em{E}val-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-{LSTM}",
    author = "Kochkina, Elena  and
      Liakata, Maria  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2083",
    doi = "10.18653/v1/S17-2083",
    pages = "475--480",
    abstract = "This paper describes team Turing{'}s submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A.",
}
@inproceedings{garcia-lozano-etal-2017-mama,
    title = "Mama Edha at {S}em{E}val-2017 Task 8: Stance Classification with {CNN} and Rules",
    author = {Garc{\'\i}a Lozano, Marianela  and
      Lilja, Hanna  and
      Tj{\"o}rnhammar, Edward  and
      Karasalo, Maja},
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2084",
    doi = "10.18653/v1/S17-2084",
    pages = "481--485",
    abstract = "For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9{\%} on the competition{'}s test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification.",
}
@inproceedings{srivastava-etal-2017-dfki,
    title = "{DFKI}-{DKT} at {S}em{E}val-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics",
    author = "Srivastava, Ankit  and
      Rehm, Georg  and
      Moreno Schneider, Julian",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2085",
    doi = "10.18653/v1/S17-2085",
    pages = "486--490",
    abstract = "We describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) participated in two subtasks: Subtask A (determining the stance of a message) and Subtask B (determining veracity of a message, closed variant). In both cases, our implementation consisted of a Multivariate Logistic Regression (Maximum Entropy) classifier coupled with hand-written patterns and rules (heuristics) applied in a post-process cascading fashion. We provide a detailed analysis of the system performance and report on variants of our systems that were not part of the official submission.",
}
@inproceedings{wang-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models",
    author = "Wang, Feixiang  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2086",
    doi = "10.18653/v1/S17-2086",
    pages = "491--496",
    abstract = "This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods, incorporating rich features. Since training data is imbalanced, we specifically designed a two-step classifier to address subtask A .",
}
@inproceedings{singh-etal-2017-iitp,
    title = "{IITP} at {S}em{E}val-2017 Task 8 : A Supervised Approach for Rumour Evaluation",
    author = "Singh, Vikram  and
      Narayan, Sunny  and
      Akhtar, Md Shad  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2087",
    doi = "10.18653/v1/S17-2087",
    pages = "497--501",
    abstract = "This paper describes our system participation in the SemEval-2017 Task 8 {`}RumourEval: Determining rumour veracity and support for rumours{'}. The objective of this task was to predict the stance and veracity of the underlying rumour. We propose a supervised classification approach employing several lexical, content and twitter specific features for learning. Evaluation shows promising results for both the problems.",
}
@inproceedings{rosenthal-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter",
    author = "Rosenthal, Sara  and
      Farra, Noura  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2088",
    doi = "10.18653/v1/S17-2088",
    pages = "502--518",
    abstract = "This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.",
}
@inproceedings{cortis-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News",
    author = "Cortis, Keith  and
      Freitas, Andr{\'e}  and
      Daudert, Tobias  and
      Huerlimann, Manuela  and
      Zarrouk, Manel  and
      Handschuh, Siegfried  and
      Davis, Brian",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2089",
    doi = "10.18653/v1/S17-2089",
    pages = "519--535",
    abstract = "This paper discusses the {``}Fine-Grained Sentiment Analysis on Financial Microblogs and News{''} task as part of SemEval-2017, specifically under the {``}Detecting sentiment, humour, and truth{''} theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.",
}
@inproceedings{may-priyadarshi-2017-semeval,
    title = "{S}em{E}val-2017 Task 9: {A}bstract {M}eaning {R}epresentation Parsing and Generation",
    author = "May, Jonathan  and
      Priyadarshi, Jay",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2090",
    doi = "10.18653/v1/S17-2090",
    pages = "536--545",
    abstract = "In this report we summarize the results of the 2017 AMR SemEval shared task. The task consisted of two separate yet related subtasks. In the parsing subtask, participants were asked to produce Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the biomedical domain. In the generation subtask, participants were asked to generate English sentences given AMR graphs in the news/forum domain. A total of five sites participated in the parsing subtask, and four participated in the generation subtask. Along with a description of the task and the participants{'} systems, we show various score ablations and some sample outputs.",
}
@inproceedings{augenstein-etal-2017-semeval,
    title = "{S}em{E}val 2017 Task 10: {S}cience{IE} - Extracting Keyphrases and Relations from Scientific Publications",
    author = "Augenstein, Isabelle  and
      Das, Mrinal  and
      Riedel, Sebastian  and
      Vikraman, Lakshmi  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2091",
    doi = "10.18653/v1/S17-2091",
    pages = "546--555",
    abstract = "We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.",
}
@inproceedings{sales-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 11: End-User Development using Natural Language",
    author = "Sales, Juliano  and
      Handschuh, Siegfried  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2092",
    doi = "10.18653/v1/S17-2092",
    pages = "556--564",
    abstract = "This task proposes a challenge to support the interaction between users and applications, micro-services and software APIs using natural language. The task aims for supporting the evaluation and evolution of the discussions surrounding the natural language processing approaches within the context of end-user natural language programming, under scenarios of high semantic heterogeneity/gap.",
}
@inproceedings{bethard-etal-2017-semeval,
    title = "{S}em{E}val-2017 Task 12: Clinical {T}emp{E}val",
    author = "Bethard, Steven  and
      Savova, Guergana  and
      Palmer, Martha  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2093",
    doi = "10.18653/v1/S17-2093",
    pages = "565--572",
    abstract = "Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were evaluated on clinical and pathology notes from Mayo Clinic cancer patients, annotated with an extension of TimeML for the clinical domain. 11 teams participated in the tasks, with the best systems achieving F1 scores above 0.55 for time expressions, above 0.70 for event expressions, and above 0.40 for temporal relations. Most tasks observed about a 20 point drop over Clinical TempEval 2016, where systems were trained and evaluated on the same domain (colon cancer).",
}
@inproceedings{cliche-2017-bb,
    title = "{BB}{\_}twtr at {S}em{E}val-2017 Task 4: {T}witter Sentiment Analysis with {CNN}s and {LSTM}s",
    author = "Cliche, Mathieu",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2094",
    doi = "10.18653/v1/S17-2094",
    pages = "573--580",
    abstract = "In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.",
}
@inproceedings{moore-rayson-2017-lancaster,
    title = "{L}ancaster A at {S}em{E}val-2017 Task 5: Evaluation metrics matter: predicting sentiment from financial news headlines",
    author = "Moore, Andrew  and
      Rayson, Paul",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2095",
    doi = "10.18653/v1/S17-2095",
    pages = "581--585",
    abstract = "This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Term Memory (BLSTM). We found an improvement of 4-6{\%} using the LSTM model over the SVR and came fourth in the track. We report a number of different evaluations using a finance specific word embedding model and reflect on the effects of using different evaluation metrics.",
}
@inproceedings{lampouras-vlachos-2017-sheffield,
    title = "{S}heffield at {S}em{E}val-2017 Task 9: Transition-based language generation from {AMR}.",
    author = "Lampouras, Gerasimos  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2096",
    doi = "10.18653/v1/S17-2096",
    pages = "586--591",
    abstract = "This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relies on the fact that an AMR graph can be considered structurally similar to a dependency tree, with a focus on content rather than function words. An added benefit to this approach is the greater amount of data we can take advantage of to train the parse-to-text linearizer. Our submitted run on the test data achieved a BLEU score of 3.32 and a Trueskill score of -22.04 on automatic and human evaluation respectively.",
}
@inproceedings{ammar-etal-2017-ai2,
    title = "The {AI}2 system at {S}em{E}val-2017 Task 10 ({S}cience{IE}): semi-supervised end-to-end entity and relation extraction",
    author = "Ammar, Waleed  and
      Peters, Matthew  and
      Bhagavatula, Chandra  and
      Power, Russell",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2097",
    doi = "10.18653/v1/S17-2097",
    pages = "592--596",
    abstract = "This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).",
}
@inproceedings{tourille-etal-2017-limsi,
    title = "{LIMSI}-{COT} at {S}em{E}val-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives",
    author = "Tourille, Julien  and
      Ferret, Olivier  and
      Tannier, Xavier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2098",
    doi = "10.18653/v1/S17-2098",
    pages = "597--602",
    abstract = "In this paper we present our participation to SemEval 2017 Task 12. We used a neural network based approach for entity and temporal relation extraction, and experimented with two domain adaptation strategies. We achieved competitive performance for both tasks.",
}
@inproceedings{baly-etal-2017-omam,
    title = "{OMAM} at {S}em{E}val-2017 Task 4: Evaluation of {E}nglish State-of-the-Art Sentiment Analysis Models for {A}rabic and a New Topic-based Model",
    author = "Baly, Ramy  and
      Badaro, Gilbert  and
      Hamdi, Ali  and
      Moukalled, Rawan  and
      Aoun, Rita  and
      El-Khoury, Georges  and
      Al Sallab, Ahmad  and
      Hajj, Hazem  and
      Habash, Nizar  and
      Shaban, Khaled  and
      El-Hajj, Wassim",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2099",
    doi = "10.18653/v1/S17-2099",
    pages = "603--610",
    abstract = "While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the {``}OMAM{''} systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topic-based approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in subtask D.",
}
@inproceedings{correa-junior-etal-2017-nilc,
    title = "{NILC}-{USP} at {S}em{E}val-2017 Task 4: A Multi-view Ensemble for {T}witter Sentiment Analysis",
    author = "Corr{\^e}a J{\'u}nior, Edilson Anselmo  and
      Marinho, Vanessa Queiroz  and
      dos Santos, Leandro Borges",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2100",
    doi = "10.18653/v1/S17-2100",
    pages = "611--615",
    abstract = "This paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). Our system is a voting ensemble, where each base classifier is trained in a different feature space. The first space is a bag-of-words model and has a Linear SVM as base classifier. The second and third spaces are two different strategies of combining word embeddings to represent sentences and use a Linear SVM and a Logistic Regressor as base classifiers. The proposed system was ranked 18th out of 38 systems considering F1 score and 20th considering recall.",
}
@inproceedings{yang-etal-2017-deepsa,
    title = "deep{SA} at {S}em{E}val-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in {T}witter",
    author = "Yang, Tzu-Hsuan  and
      Tseng, Tzu-Hsuan  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2101",
    doi = "10.18653/v1/S17-2101",
    pages = "616--620",
    abstract = "In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy.",
}
@inproceedings{yin-etal-2017-nnembs,
    title = "{NNEMB}s at {S}em{E}val-2017 Task 4: Neural {T}witter Sentiment Classification: a Simple Ensemble Method with Different Embeddings",
    author = "Yin, Yichun  and
      Song, Yangqiu  and
      Zhang, Ming",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2102",
    doi = "10.18653/v1/S17-2102",
    pages = "621--625",
    abstract = "Recently, neural twitter sentiment classification has become one of state-of-thearts, which relies less feature engineering work compared with traditional methods. In this paper, we propose a simple and effective ensemble method to further boost the performances of neural models. We collect several word embedding sets which are publicly released (often are learned on different corpus) or constructed by running Skip-gram on released large-scale corpus. We make an assumption that different word embeddings cover different words and encode different semantic knowledge, thus using them together can improve the generalizations and performances of neural models. In the SemEval 2017, our method ranks 1st in Accuracy, 5th in AverageR. Meanwhile, the additional comparisons demonstrate the superiority of our model over these ones based on only one word embedding set. We release our code for the method duplicability.",
}
@inproceedings{gupta-yang-2017-crystalnest,
    title = "{C}rystal{N}est at {S}em{E}val-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification",
    author = "Gupta, Raj Kumar  and
      Yang, Yinping",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2103",
    doi = "10.18653/v1/S17-2103",
    pages = "626--633",
    abstract = "This paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect sarcasm in order to enhance the performance of sentiment classification. We first constructed an affect-cognition-sociolinguistics sarcasm features model and trained a SVM-based classifier for detecting sarcastic expressions from general tweets. For sentiment prediction, we developed CrystalNest{--} a two-level cascade classification system using features combining sarcasm score derived from our sarcasm classifier, sentiment scores from Alchemy, NRC lexicon, n-grams, word embedding vectors, and part-of-speech features. We found that the sarcasm detection derived features consistently benefited key sentiment analysis evaluation metrics, in different degrees, across four subtasks A-D.",
}
@inproceedings{jimenez-zafra-etal-2017-sinai,
    title = "{SINAI} at {S}em{E}val-2017 Task 4: User based classification",
    author = "Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Montejo-R{\'a}ez, Arturo  and
      Martin, Maite  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2104",
    doi = "10.18653/v1/S17-2104",
    pages = "634--639",
    abstract = "This document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on a two point scale (positive or negative sentiment). Our main contribution is the integration of user information in the classification process. A SVM model is trained with Word2Vec vectors from user{'}s tweets extracted from his timeline. The obtained results show that user-specific classifiers trained on tweets from user timeline can introduce noise as they are error prone because they are classified by an imperfect system. This encourages us to explore further integration of user information for author-based Sentiment Analysis.",
}
@inproceedings{sarker-gonzalez-2017-hlp,
    title = "{HLP}@{UP}enn at {S}em{E}val-2017 Task 4{A}: A simple, self-optimizing text classification system combining dense and sparse vectors",
    author = "Sarker, Abeed  and
      Gonzalez, Graciela",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2105",
    doi = "10.18653/v1/S17-2105",
    pages = "640--643",
    abstract = "We present a simple supervised text classification system that combines sparse and dense vector representations of words, and generalized representations of words via clusters. The sparse vectors are generated from word n-gram sequences (1-3). The dense vector representations of words (embeddings) are learned by training a neural network to predict neighboring words in a large unlabeled dataset. To classify a text segment, the different representations of it are concatenated, and the classification is performed using Support Vector Machines (SVM). Our system is particularly intended for use by non-experts of natural language processing and machine learning, and, therefore, the system does not require any manual tuning of parameters or weights. Given a training set, the system automatically generates the training vectors, optimizes the relevant hyper-parameters for the SVM classifier, and trains the classification model. We evaluated this system on the SemEval-2017 English sentiment analysis task. In terms of average F1-score, our system obtained 8th position out of 39 submissions (F1-score: 0.632, average recall: 0.637, accuracy: 0.646).",
}
@inproceedings{dovdon-saias-2017-ej,
    title = "ej-sa-2017 at {S}em{E}val-2017 Task 4: Experiments for Target oriented Sentiment Analysis in {T}witter",
    author = "Dovdon, Enkhzol  and
      Saias, Jos{\'e}",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2106",
    doi = "10.18653/v1/S17-2106",
    pages = "644--647",
    abstract = "This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system.",
}
@inproceedings{troncy-etal-2017-sentime,
    title = "{S}enti{ME}++ at {S}em{E}val-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification",
    author = {Troncy, Rapha{\"e}l  and
      Palumbo, Enrico  and
      Sygkounas, Efstratios  and
      Rizzo, Giuseppe},
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2107",
    doi = "10.18653/v1/S17-2107",
    pages = "648--652",
    abstract = "In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A {``}Sentiment Analysis in Twitter{''} that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30{\%} F1-score, ranking 12th out of 38 participants.",
}
@inproceedings{rozental-fleischer-2017-amobee,
    title = "{A}mobee at {S}em{E}val-2017 Task 4: Deep Learning System for Sentiment Detection on {T}witter",
    author = "Rozental, Alon  and
      Fleischer, Daniel",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2108",
    doi = "10.18653/v1/S17-2108",
    pages = "653--658",
    abstract = "This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the different sub-tasks. The algorithm reached the 3rd place on the 5-label classification task (sub-task C).",
}
@inproceedings{laskari-sanampudi-2017-twina,
    title = "{TWINA} at {S}em{E}val-2017 Task 4: {T}witter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier",
    author = "Laskari, Naveen Kumar  and
      Sanampudi, Suresh Kumar",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2109",
    doi = "10.18653/v1/S17-2109",
    pages = "659--663",
    abstract = "This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification {--} Two point scale) and 4D (two-point scale Tweet quantification). We implemented ensemble based Gradient Boost Trees classification method for both the tasks. Our system could perform well for the task 4D and ranked 13th among 15 teams, for the task 4B our model ranked 23rd position.",
}
@inproceedings{mulki-etal-2017-tw,
    title = "Tw-{S}t{AR} at {S}em{E}val-2017 Task 4: Sentiment Classification of {A}rabic Tweets",
    author = "Mulki, Hala  and
      Haddad, Hatem  and
      Gridach, Mourad  and
      Babaoglu, Ismail",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2110",
    doi = "10.18653/v1/S17-2110",
    pages = "664--669",
    abstract = "In this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled {``}Sentiment analysis in Twitter{''}, specifically subtask 4A-Arabic. We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies. In both models, Arabic tweets were preprocessed first then various schemes of bag-of-N-grams were extracted to be used as features. The final submission was selected upon the best performance achieved by the supervised learning-based model. However, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work.",
}
@inproceedings{onyibe-habash-2017-omam,
    title = "{OMAM} at {S}em{E}val-2017 Task 4: {E}nglish Sentiment Analysis with Conditional Random Fields",
    author = "Onyibe, Chukwuyem  and
      Habash, Nizar",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2111",
    doi = "10.18653/v1/S17-2111",
    pages = "670--674",
    abstract = "We describe a supervised system that uses optimized Condition Random Fields and lexical features to predict the sentiment of a tweet. The system was submitted to the English version of all subtasks in SemEval-2017 Task 4.",
}
@inproceedings{kolovou-etal-2017-tweester,
    title = "Tweester at {S}em{E}val-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in {T}witter",
    author = "Kolovou, Athanasia  and
      Kokkinos, Filippos  and
      Fergadis, Aris  and
      Papalampidi, Pinelopi  and
      Iosif, Elias  and
      Malandrakis, Nikolaos  and
      Palogiannidi, Elisavet  and
      Papageorgiou, Haris  and
      Narayanan, Shrikanth  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2112",
    doi = "10.18653/v1/S17-2112",
    pages = "675--682",
    abstract = "In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter. Specifically the proposed system participated both to tweet polarity classification (two-, three- and five class) and tweet quantification (two and five-class) tasks.",
}
@inproceedings{karpov-2017-nru,
    title = "{NRU}-{HSE} at {S}em{E}val-2017 Task 4: Tweet Quantification Using Deep Learning Architecture",
    author = "Karpov, Nikolay",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2113",
    doi = "10.18653/v1/S17-2113",
    pages = "683--688",
    abstract = "In many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment analysis, when proportions of instances are changing over time. In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task). Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture. All the techniques are evaluated using the SemEval-2017 Task4 dataset and source code, mentioned in this paper and available online in the Python programming language. The results of an application of the quantification techniques are discussed.",
}
@inproceedings{zhao-etal-2017-mi,
    title = "{MI}{\&}{T} Lab at {S}em{E}val-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification",
    author = "Zhao, Jingjing  and
      Yang, Yan  and
      Xu, Bing",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2114",
    doi = "10.18653/v1/S17-2114",
    pages = "689--693",
    abstract = "A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.",
}
@inproceedings{jabreel-moreno-2017-sitaka,
    title = "{S}i{TAKA} at {S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter Based on a Rich Set of Features",
    author = "Jabreel, Mohammed  and
      Moreno, Antonio",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2115",
    doi = "10.18653/v1/S17-2115",
    pages = "694--699",
    abstract = "This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017. The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets.",
}
@inproceedings{hamdan-2017-senti17,
    title = "{S}enti17 at {S}em{E}val-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification",
    author = "Hamdan, Hussam",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2116",
    doi = "10.18653/v1/S17-2116",
    pages = "700--703",
    abstract = "This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Softmax on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4{\%} average recall.",
}
@inproceedings{symeonidis-etal-2017-duth,
    title = "{DUTH} at {S}em{E}val-2017 Task 4: A Voting Classification Approach for {T}witter Sentiment Analysis",
    author = "Symeonidis, Symeon  and
      Effrosynidis, Dimitrios  and
      Kordonis, John  and
      Arampatzis, Avi",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2117",
    doi = "10.18653/v1/S17-2117",
    pages = "704--708",
    abstract = "This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and combined supervised machine learning methods with classical linguistic resources, including bag-of-words and sentiment lexicon features.",
}
@inproceedings{s-etal-2017-ssn,
    title = "{SSN}{\_}{MLRG}1 at {S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter Using Multi-Kernel {G}aussian Process Classifier",
    author = "S, Angel Deborah  and
      Rajendram, S Milton  and
      Mirnalinee, T T",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2118",
    doi = "10.18653/v1/S17-2118",
    pages = "709--712",
    abstract = "The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment analysis of tweets. Since tweets on the same topic, made at different times, may exhibit different emotions, their properties such as smoothness and periodicity also vary with time. Our experiments show that, compared to single kernel, multiple kernels are effective in learning the simultaneous presence of multiple properties.",
}
@inproceedings{wang-etal-2017-ynudlg-semeval,
    title = "{YNUDLG} at {S}em{E}val-2017 Task 4: A {GRU}-{SVM} Model for Sentiment Classification and Quantification in {T}witter",
    author = "Wang, Ming  and
      Chu, Biao  and
      Liu, Qingxun  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2119",
    doi = "10.18653/v1/S17-2119",
    pages = "713--717",
    abstract = "Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g.,positive or negative). In this paper we describe our deep learning system(combining GRU and SVM) to solve both two-, three- and five-tweet polarity classifications. We first trained a gated recurrent neural network using pre-trained word embeddings, then we extracted features from GRU layer and input these features into support vector machine to fulfill both the classification and quantification subtasks. The proposed approach achieved 37th, 19th, and 14rd places in subtasks A, B and C, respectively.",
}
@inproceedings{htait-etal-2017-lsis,
    title = "{LSIS} at {S}em{E}val-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For {E}nglish and {A}rabic Tweet Polarity Classification",
    author = "Htait, Amal  and
      Fournier, S{\'e}bastien  and
      Bellot, Patrice",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2120",
    doi = "10.18653/v1/S17-2120",
    pages = "718--722",
    abstract = "We present, in this paper, our contribution in SemEval2017 task 4 : {``}Sentiment Analysis in Twitter{''}, subtask A: {``}Message Polarity Classification{''}, for English and Arabic languages. Our system is based on a list of sentiment seed words adapted for tweets. The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations (word2vec). These seed words are extracted from datasets of annotated tweets available online. Our tests, using these seed words, show significant improvement in results compared to the use of Turney and Littman{'}s (2003) seed words, on polarity classification of tweet messages.",
}
@inproceedings{gonzalez-etal-2017-elirf,
    title = "{EL}i{RF}-{UPV} at {S}em{E}val-2017 Task 4: Sentiment Analysis using Deep Learning",
    author = "Gonz{\'a}lez, Jos{\'e}-{\'A}ngel  and
      Pla, Ferran  and
      Hurtado, Llu{\'\i}s-F.",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2121",
    doi = "10.18653/v1/S17-2121",
    pages = "723--727",
    abstract = "This paper describes the participation of ELiRF-UPV team at task 4 of SemEval2017. Our approach is based on the use of convolutional and recurrent neural networks and the combination of general and specific word embeddings with polarity lexicons. We participated in all of the proposed subtasks both for English and Arabic languages using the same system with small variations.",
}
@inproceedings{hao-etal-2017-xjsa,
    title = "{XJSA} at {S}em{E}val-2017 Task 4: A Deep System for Sentiment Classification in {T}witter",
    author = "Hao, Yazhou  and
      Lan, YangYang  and
      Li, Yufei  and
      Li, Chen",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2122",
    doi = "10.18653/v1/S17-2122",
    pages = "728--731",
    abstract = "This paper describes the XJSA System submission from XJTU. Our system was created for SemEval2017 Task 4 {--} subtask A which is very popular and fundamental. The system is based on convolutional neural network and word embedding. We used two pre-trained word vectors and adopt a dynamic strategy for k-max pooling.",
}
@inproceedings{yoon-etal-2017-adullam,
    title = "Adullam at {S}em{E}val-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention",
    author = "Yoon, Joosung  and
      Lyu, Kigon  and
      Kim, Hyeoncheol",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2123",
    doi = "10.18653/v1/S17-2123",
    pages = "732--736",
    abstract = "We propose a sentiment analyzer for the prediction of document-level sentiments of English micro-blog messages from Twitter. The proposed method is based on lexicon integrated convolutional neural networks with attention (LCA). Its performance was evaluated using the datasets provided by SemEval competition (Task 4). The proposed sentiment analyzer obtained an average F1 of 55.2{\%}, an average recall of 58.9{\%} and an accuracy of 61.4{\%}.",
}
@inproceedings{wang-etal-2017-eica,
    title = "{EICA} at {S}em{E}val-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification",
    author = "Wang, Maoquan  and
      Chen, Shiyun  and
      Xie, Yufei  and
      Zhao, Lu",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2124",
    doi = "10.18653/v1/S17-2124",
    pages = "737--740",
    abstract = "This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative.",
}
@inproceedings{li-etal-2017-funsentiment,
    title = "fun{S}entiment at {S}em{E}val-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts",
    author = "Li, Quanzhi  and
      Nourbakhsh, Armineh  and
      Liu, Xiaomo  and
      Fang, Rui  and
      Shah, Sameena",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2125",
    doi = "10.18653/v1/S17-2125",
    pages = "741--746",
    abstract = "This paper describes the approach we used for SemEval-2017 Task 4: Sentiment Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has become attractive and been used in some applications recently, but it is still a challenging research task. In our approach, we take the left and right context of a target into consideration when generating polarity classification features. We use two types of word embeddings in our classifiers: the general word embeddings learned from 200 million tweets, and sentiment-specific word embeddings learned from 10 million tweets using distance supervision. We also incorporate a text feature model in our algorithm. This model produces features based on text negation, tf.idf weighting scheme, and a Rocchio text classification method. We participated in four subtasks (B, C, D {\&} E for English), all of which are about topic-based message polarity classification. Our team is ranked {\#}6 in subtask B, {\#}3 by MAEu and {\#}9 by MAEm in subtask C, {\#}3 using RAE and {\#}6 using KLD in subtask D, and {\#}3 in subtask E.",
}
@inproceedings{baziotis-etal-2017-datastories-semeval,
    title = "{D}ata{S}tories at {S}em{E}val-2017 Task 4: Deep {LSTM} with Attention for Message-level and Topic-based Sentiment Analysis",
    author = "Baziotis, Christos  and
      Pelekis, Nikos  and
      Doulkeridis, Christos",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2126",
    doi = "10.18653/v1/S17-2126",
    pages = "747--754",
    abstract = "In this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 {``}Sentiment Analysis in Twitter{''}. We participated in all subtasks for English tweets, involving message-level and topic-based sentiment polarity classification and quantification. We use Long Short-Term Memory (LSTM) networks augmented with two kinds of attention mechanisms, on top of word embeddings pre-trained on a big collection of Twitter messages. Also, we present a text processing tool suitable for social network messages, which performs tokenization, word normalization, segmentation and spell correction. Moreover, our approach uses no hand-crafted features or sentiment lexicons. We ranked 1st (tie) in Subtask A, and achieved very competitive results in the rest of the Subtasks. Both the word embeddings and our text processing tool are available to the research community.",
}
@inproceedings{balikas-2017-twise,
    title = "{T}wi{S}e at {S}em{E}val-2017 Task 4: Five-point {T}witter Sentiment Classification and Quantification",
    author = "Balikas, Georgios",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2127",
    doi = "10.18653/v1/S17-2127",
    pages = "755--759",
    abstract = "The paper describes the participation of the team {``}TwiSE{''} in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled {``}Sentiment Analysis in Twitter{''} for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked \textit{5/15} in Subtask C and \textit{2/12} in Subtask E.",
}
@inproceedings{rouvier-2017-lia,
    title = "{LIA} at {S}em{E}val-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification",
    author = "Rouvier, Mickael",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2128",
    doi = "10.18653/v1/S17-2128",
    pages = "760--765",
    abstract = "This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system is an ensemble of Deep Neural Network (DNN) models: Convolutional Neural Network (CNN) and Recurrent Neural Network Long Short-Term Memory (RNN-LSTM). We initialize the input representation of DNN with different sets of embeddings trained on large datasets. The ensemble of DNNs are combined using a score-level fusion approach. The system ranked 2nd at SemEval-2017 and obtained an average recall of 67.6{\%}.",
}
@inproceedings{muller-etal-2017-topicthunder,
    title = "{T}opic{T}hunder at {S}em{E}val-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision",
    author = {M{\"u}ller, Simon  and
      Huonder, Tobias  and
      Deriu, Jan  and
      Cieliebak, Mark},
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2129",
    doi = "10.18653/v1/S17-2129",
    pages = "766--770",
    abstract = "In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN.With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4th place.",
}
@inproceedings{miranda-jimenez-etal-2017-ingeotec,
    title = "{INGEOTEC} at {S}em{E}val 2017 Task 4: A {B}4{MSA} Ensemble based on Genetic Programming for {T}witter Sentiment Analysis",
    author = "Miranda-Jim{\'e}nez, Sabino  and
      Graff, Mario  and
      Tellez, Eric Sadit  and
      Moctezuma, Daniela",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2130",
    doi = "10.18653/v1/S17-2130",
    pages = "771--776",
    abstract = "This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values predicted by B4MSA systems using a non-linear function evolved using a Genetic Programming system, EvoDAG. With this approach, the best performances reached by our system were macro-recall 0.68 (English) and 0.477 (Arabic) which set us in sixth and fourth positions in the results table, respectively.",
}
@inproceedings{ayata-etal-2017-busem,
    title = "{BUSEM} at {S}em{E}val-2017 Task 4{A} Sentiment Analysis with Word Embedding and Long Short Term Memory {RNN} Approaches",
    author = "Ayata, Deger  and
      Saraclar, Murat  and
      Ozgur, Arzucan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2131",
    doi = "10.18653/v1/S17-2131",
    pages = "777--783",
    abstract = "This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system uses word embeddings for feature representation and Support Vector Machine, Random Forest and Naive Bayes algorithms for classification of Twitter messages into negative, neutral and positive polarity. The second system is based on Long Short Term Memory Recurrent Neural Networks and uses word indexes as sequence of inputs for feature representation.",
}
@inproceedings{lozic-etal-2017-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in {T}witter",
    author = "Lozi{\'c}, David  and
      {\v{S}}ari{\'c}, Doria  and
      Toki{\'c}, Ivan  and
      Medi{\'c}, Zoran  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2132",
    doi = "10.18653/v1/S17-2132",
    pages = "784--789",
    abstract = "This paper describes the system we submitted to SemEval-2017 Task 4 (Sentiment Analysis in Twitter), specifically subtasks A, B, and D. Our main focus was topic-based message polarity classification on a two-point scale (subtask B). The system we submitted uses a Support Vector Machine classifier with rich set of features, ranging from standard to more creative, task-specific features, including a series of rating-based features as well as features that account for sentimental reminiscence of past topics and deceased famous people. Our system ranked 14th out of 39 submissions in subtask A, 5th out of 24 submissions in subtask B, and 3rd out of 16 submissions in subtask D.",
}
@inproceedings{el-beltagy-etal-2017-niletmrg,
    title = "{N}ile{TMRG} at {S}em{E}val-2017 Task 4: {A}rabic Sentiment Analysis",
    author = "El-Beltagy, Samhaa R.  and
      El Kalamawy, Mona  and
      Soliman, Abu Bakr",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2133",
    doi = "10.18653/v1/S17-2133",
    pages = "790--795",
    abstract = "This paper describes two systems that were used by the NileTMRG for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. NileTMRG participated in three Arabic related subtasks which are: Subtask A (Message Polarity Classification), Subtask B (Topic-Based Message Polarity classification) and Subtask D (Tweet quantification). For subtask A, we made use of NU{'}s sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers. The first classifier was a convolutional neural network that used trained (word2vec) word embeddings. The second classifier consisted of a MultiLayer Perceptron while the third classifier was a Logistic regression model that takes the same input as the second classifier. Voting between the three classifiers was used to determine the final outcome. In all three Arabic related tasks in which NileTMRG participated, the team ranked at number one.",
}
@inproceedings{zhang-etal-2017-ynu-hpcc,
    title = "{YNU}-{HPCC} at {S}em{E}val 2017 Task 4: Using A Multi-Channel {CNN}-{LSTM} Model for Sentiment Classification",
    author = "Zhang, Haowei  and
      Wang, Jin  and
      Zhang, Jixian  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2134",
    doi = "10.18653/v1/S17-2134",
    pages = "796--801",
    abstract = "In this paper, we propose a multi-channel convolutional neural network-long short-term memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter. Un-like a conventional CNN, the proposed model applies a multi-channel strategy that uses several filters of different length to extract active local n-gram features in different scales. This information is then sequentially composed using LSTM. By combining both CNN and LSTM, we can consider both local information within tweets and long-distance dependency across tweets in the classification process. Officially released results show that our system outperforms the baseline algo-rithm.",
}
@inproceedings{deshmane-friedrichs-2017-tsa,
    title = "{TSA}-{INF} at {S}em{E}val-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for {T}witter Sentiment Analysis",
    author = "Deshmane, Amit Ajit  and
      Friedrichs, Jasper",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2135",
    doi = "10.18653/v1/S17-2135",
    pages = "802--806",
    abstract = "This paper describes the submission of team TSA-INF to SemEval-2017 Task 4 Subtask A. The submitted system is an ensemble of three varying deep learning architectures for sentiment analysis. The core of the architecture is a convolutional neural network that performs well on text classification as is. The second subsystem is a gated recurrent neural network implementation. Additionally, the third system integrates opinion lexicons directly into a convolution neural network architecture. The resulting ensemble of the three architectures achieved a top ten ranking with a macro-averaged recall of 64.3{\%}. Additional results comparing variations of the submitted system are not conclusive enough to determine a best architecture, but serve as a benchmark for further implementations.",
}
@inproceedings{abreu-etal-2017-ucsc,
    title = "{UCSC}-{NLP} at {S}em{E}val-2017 Task 4: Sense n-grams for Sentiment Analysis in {T}witter",
    author = "Abreu, Jos{\'e}  and
      Castro, Iv{\'a}n  and
      Mart{\'\i}nez, Claudia  and
      Oliva, Sebasti{\'a}n  and
      Guti{\'e}rrez, Yoan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2136",
    doi = "10.18653/v1/S17-2136",
    pages = "807--811",
    abstract = "This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute to this task, i.e. co-occurrences of WordNet senses in the tweet, and the polarity. Furthermore, we evaluated the effect of discarding a large set of features based on char-grams reported in preceding works. Based on these elements, we developed a SVM system, which exploring SentiWordNet as a polarity lexicon. It achieves an $F_1=0.624$of average. Among 39 submissions to this task, we ranked 10th.",
}
@inproceedings{zhou-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for {T}witter Message Polarity Classification",
    author = "Zhou, Yunxiao  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2137",
    doi = "10.18653/v1/S17-2137",
    pages = "812--816",
    abstract = "This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.",
}
@inproceedings{mansar-etal-2017-fortia,
    title = "Fortia-{FBK} at {S}em{E}val-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines",
    author = "Mansar, Youness  and
      Gatti, Lorenzo  and
      Ferradans, Sira  and
      Guerini, Marco  and
      Staiano, Jacopo",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2138",
    doi = "10.18653/v1/S17-2138",
    pages = "817--822",
    abstract = "In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance.",
}
@inproceedings{s-etal-2017-ssn-mlrg1,
    title = "{SSN}{\_}{MLRG}1 at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel {G}aussian Process Regression Model",
    author = "S, Angel Deborah  and
      Rajendram, S Milton  and
      Mirnalinee, T T",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2139",
    doi = "10.18653/v1/S17-2139",
    pages = "823--826",
    abstract = "The system developed by the SSN{\_}MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies and stocks. Since the comments made at different times about the same companies and stocks may display different emotions, their properties such as smoothness and periodicity may vary. Our experiments show that while single kernel Gaussian Process can learn certain properties well, Multiple Kernel Gaussian Process are effective in learning the presence of different properties simultaneously.",
}
@inproceedings{nasim-2017-iba,
    title = "{IBA}-Sys at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News",
    author = "Nasim, Zarmeen",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2140",
    doi = "10.18653/v1/S17-2140",
    pages = "827--831",
    abstract = "This paper presents the details of our system IBA-Sys that participated in SemEval Task: Fine-grained sentiment analysis on Financial Microblogs and News. Our system participated in both tracks. For microblogs track, a supervised learning approach was adopted and the regressor was trained using XgBoost regression algorithm on lexicon features. For news headlines track, an ensemble of regressors was used to predict sentiment score. One regressor was trained using TF-IDF features and another was trained using the n-gram features. The source code is available at Github.",
}
@inproceedings{cabanski-etal-2017-hhu,
    title = "{HHU} at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Data using Machine Learning Methods",
    author = "Cabanski, Tobias  and
      Romberg, Julia  and
      Conrad, Stefan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2141",
    doi = "10.18653/v1/S17-2141",
    pages = "832--836",
    abstract = "In this Paper a system for solving SemEval-2017 Task 5 is presented. This task is divided into two tracks where the sentiment of microblog messages and news headlines has to be predicted. Since two submissions were allowed, two different machine learning methods were developed to solve this task, a support vector machine approach and a recurrent neural network approach. To feed in data for these approaches, different feature extraction methods are used, mainly word representations and lexica. The best submissions for both tracks are provided by the recurrent neural network which achieves a F1-score of 0.729 in track 1 and 0.702 in track 2.",
}
@inproceedings{zini-etal-2017-inf,
    title = "{INF}-{UFRGS} at {S}em{E}val-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines",
    author = "Zini, Tiago  and
      Becker, Karin  and
      Dias, Marcelo",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2142",
    doi = "10.18653/v1/S17-2142",
    pages = "837--841",
    abstract = "This paper describes a supervised solution for detecting the polarity scores of tweets or headline news in the financial domain, submitted to the SemEval 2017 Fine-Grained Sentiment Analysis on Financial Microblogs and News Task. The premise is that it is possible to understand market reaction over a company stock by measuring the positive/negative sentiment contained in the financial tweets and news headlines, where polarity is measured in a continuous scale ranging from -1.0 (very bearish) to 1.0 (very bullish). Our system receives as input the textual content of tweets or news headlines, together with their ids, stock cashtag or name of target company, and the polarity score gold standard for the training dataset. Our solution retrieves features from these text instances using n-gram, hashtags, sentiment score calculated by a external APIs and others features to train a regression model capable to detect continuous score of these sentiments with precision.",
}
@inproceedings{pivovarova-etal-2017-hcs,
    title = "{HCS} at {S}em{E}val-2017 Task 5: Polarity detection in business news using convolutional neural networks",
    author = "Pivovarova, Lidia  and
      Escoter, Lloren{\c{c}}  and
      Klami, Arto  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2143",
    doi = "10.18653/v1/S17-2143",
    pages = "842--846",
    abstract = "Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.",
}
@inproceedings{chen-etal-2017-nlg301,
    title = "{NLG}301 at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News",
    author = "Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2144",
    doi = "10.18653/v1/S17-2144",
    pages = "847--851",
    abstract = "Short length, multi-targets, target relation-ship, monetary expressions, and outside reference are characteristics of financial tweets. This paper proposes methods to extract target spans from a tweet and its referencing web page. Total 15 publicly available sentiment dictionaries and one sentiment dictionary constructed from training set, containing sentiment scores in binary or real numbers, are used to compute the sentiment scores of text spans. Moreover, the correlation coeffi-cients of the price return between any two stocks are learned with the price data from Bloomberg. They are used to capture the relationships between the interesting tar-get and other stocks mentioned in a tweet. The best result of our method in both sub-task are 56.68{\%} and 55.43{\%}, evaluated by evaluation method 2.",
}
@inproceedings{li-etal-2017-funsentiment-semeval,
    title = "fun{S}entiment at {S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from {S}tock{T}wits and {T}witter",
    author = "Li, Quanzhi  and
      Shah, Sameena  and
      Nourbakhsh, Armineh  and
      Fang, Rui  and
      Liu, Xiaomo",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2145",
    doi = "10.18653/v1/S17-2145",
    pages = "852--856",
    abstract = "This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs. We use three types of word embeddings in our algorithm: word embeddings learned from 200 million tweets, sentiment-specific word embeddings learned from 10 million tweets using distance supervision, and word embeddings learned from 20 million StockTwits messages. In our approach, we also take the left and right context of the target company into consideration when generating polarity prediction features. All the features generated from different word embeddings and contexts are integrated together to train our algorithm",
}
@inproceedings{tabari-etal-2017-sentiheros,
    title = "{S}enti{H}eros at {S}em{E}val-2017 Task 5: An application of Sentiment Analysis on Financial Tweets",
    author = "Tabari, Narges  and
      Seyeditabari, Armin  and
      Zadrozny, Wlodek",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2146",
    doi = "10.18653/v1/S17-2146",
    pages = "857--860",
    abstract = "Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task.",
}
@inproceedings{symeonidis-etal-2017-duth-semeval,
    title = "{DUTH} at {S}em{E}val-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles",
    author = "Symeonidis, Symeon  and
      Kordonis, John  and
      Effrosynidis, Dimitrios  and
      Arampatzis, Avi",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2147",
    doi = "10.18653/v1/S17-2147",
    pages = "861--865",
    abstract = "We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B. Our approach to determine the sentiment of Microblog Messages and News Statements {\&} Headlines is based on linguistic preprocessing, feature engineering, and supervised machine learning techniques. To train our model, we used Neural Network Regression, Linear Regression, Boosted Decision Tree Regression and Decision Forrest Regression classifiers to forecast sentiment scores. At the end, we present an error measure, so as to improve the performance about forecasting methods of the system.",
}
@inproceedings{rotim-etal-2017-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news",
    author = "Rotim, Leon  and
      Tutek, Martin  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2148",
    doi = "10.18653/v1/S17-2148",
    pages = "866--871",
    abstract = "This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5{--}subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733.",
}
@inproceedings{john-vechtomova-2017-uw,
    title = "{UW}-{F}in{S}ent at {S}em{E}val-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation",
    author = "John, Vineet  and
      Vechtomova, Olga",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2149",
    doi = "10.18653/v1/S17-2149",
    pages = "872--876",
    abstract = "This paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper describes the document vectorization and sentiment score prediction techniques used, as well as the design and implementation decisions taken while building the system for this task. The system uses text vectorization models, such as N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the sentiment scores. Amongst the methods examined, unigrams and bigrams coupled with simple linear regression obtained the best baseline accuracy. The paper also explores data augmentation methods to supplement the training dataset. This system was designed for Subtask 2 (News Statements and Headlines).",
}
@inproceedings{kar-etal-2017-ritual,
    title = "{R}i{TUAL}-{UH} at {S}em{E}val-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks",
    author = "Kar, Sudipta  and
      Maharjan, Suraj  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2150",
    doi = "10.18653/v1/S17-2150",
    pages = "877--882",
    abstract = "In this paper, we present our systems for the {``}SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News{''}. In our system, we combined hand-engineered lexical, sentiment and metadata features, the representations learned from Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) with Attention model applied on top. With this architecture we obtained weighted cosine similarity scores of 0.72 and 0.74 for subtask-1 and subtask-2, respectively. Using the official scoring system, our system ranked the second place for subtask-2 and eighth place for the subtask-1. It ranked first for both of the subtasks by the scores achieved by an alternate scoring system.",
}
@inproceedings{schouten-etal-2017-commit,
    title = "{COMMIT} at {S}em{E}val-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines",
    author = "Schouten, Kim  and
      Frasincar, Flavius  and
      de Jong, Franciska",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2151",
    doi = "10.18653/v1/S17-2151",
    pages = "883--887",
    abstract = "This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the required regression, and besides unigrams and a sentiment tool, we use various ontology-based features. To this end we created a domain ontology that models various concepts from the financial domain. This allows us to model the sentiment of actions depending on which entity they are affecting (e.g., {`}decreasing debt{'} is positive, but {`}decreasing profit{'} is negative). The presented approach yielded a cosine distance of 0.6810 on the official test data, resulting in the 12th position.",
}
@inproceedings{jiang-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain",
    author = "Jiang, Mengxiao  and
      Lan, Man  and
      Wu, Yuanbin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2152",
    doi = "10.18653/v1/S17-2152",
    pages = "888--893",
    abstract = "This paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and news headline domain respectively. To settle this problem, we extract four types of effective features, including linguistic features, sentiment lexicon features, domain-specific features and word embedding features. Then we employ these features to construct models by using ensemble regression algorithms. Our submissions rank 1st and rank 5th in subtask 1 and subtask 2 respectively.",
}
@inproceedings{kumar-etal-2017-iitpb,
    title = "{IITPB} at {S}em{E}val-2017 Task 5: Sentiment Prediction in Financial Text",
    author = "Kumar, Abhishek  and
      Sethi, Abhishek  and
      Akhtar, Md Shad  and
      Ekbal, Asif  and
      Biemann, Chris  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2153",
    doi = "10.18653/v1/S17-2153",
    pages = "894--898",
    abstract = "This paper reports team IITPB{'}s participation in the SemEval 2017 Task 5 on {`}Fine-grained sentiment analysis on financial microblogs and news{'}. We developed 2 systems for the two tracks. One system was based on an ensemble of Support Vector Classifier and Logistic Regression. This system relied on Distributional Thesaurus (DT), word embeddings and lexicon features to predict a floating sentiment value between -1 and +1. The other system was based on Support Vector Regression using word embeddings, lexicon features, and PMI scores as features. The system was ranked 5th in track 1 and 8th in track 2.",
}
@inproceedings{ghosal-etal-2017-iitp,
    title = "{IITP} at {S}em{E}val-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis",
    author = "Ghosal, Deepanway  and
      Bhatnagar, Shobhit  and
      Akhtar, Md Shad  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2154",
    doi = "10.18653/v1/S17-2154",
    pages = "899--903",
    abstract = "In this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) along with feature based models to identify optimistic or pessimistic sentiments associated with companies and stocks in financial texts. We build our system to participate in a competition organized by Semantic Evaluation 2017 International Workshop. We combined predictions from various models using an artificial neural network to determine the opinion towards an entity in (a) Microblog Messages and (b) News Headlines data. Our models achieved a cosine similarity score of 0.751 and 0.697 for the above two tracks giving us the rank of 2nd and 7th best team respectively.",
}
@inproceedings{saleiro-etal-2017-feup,
    title = "{FEUP} at {S}em{E}val-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings",
    author = "Saleiro, Pedro  and
      Mendes Rodrigues, Eduarda  and
      Soares, Carlos  and
      Oliveira, Eug{\'e}nio",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2155",
    doi = "10.18653/v1/S17-2155",
    pages = "904--908",
    abstract = "This paper presents the approach developed at the Faculty of Engineering of University of Porto, to participate in SemEval 2017, Task 5: Fine-grained Sentiment Analysis on Financial Microblogs and News. The task consisted in predicting a real continuous variable from -1.0 to +1.0 representing the polarity and intensity of sentiment concerning companies/stocks mentioned in short texts. We modeled the task as a regression analysis problem and combined traditional techniques such as pre-processing short texts, bag-of-words representations and lexical-based features with enhanced financial specific bag-of-embeddings. We used an external collection of tweets and news headlines mentioning companies/stocks from S{\&}P 500 to create financial word embeddings which are able to capture domain-specific syntactic and semantic similarities. The resulting approach obtained a cosine similarity score of 0.69 in sub-task 5.1 - Microblogs and 0.68 in sub-task 5.2 - News Headlines.",
}
@inproceedings{nguyen-nguyen-2017-uit,
    title = "{UIT}-{DANGNT}-{CLNLP} at {S}em{E}val-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving {CAMR}",
    author = "Nguyen, Khoa  and
      Nguyen, Dang",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2156",
    doi = "10.18653/v1/S17-2156",
    pages = "909--913",
    abstract = "This paper describes the improvements that we have applied on CAMR baseline parser (Wang et al., 2016) at Task 8 of SemEval-2016. Our objective is to increase the performance of CAMR when parsing sentences from scientific articles, especially articles of biology domain more accurately. To achieve this goal, we built two wrapper layers for CAMR. The first layer, which covers the input data, will normalize, add necessary information to the input sentences to make the input dependency parser and the aligner better handle reference citations, scientific figures, formulas, etc. The second layer, which covers the output data, will modify and standardize output data based on a list of scientific concept fixing patterns. This will help CAMR better handle biological concepts which are not in the training dataset. Finally, after applying our approach, CAMR has scored 0.65 F-score on the test set of Biomedical training data and 0.61 F-score on the official blind test dataset.",
}
@inproceedings{buys-blunsom-2017-oxford,
    title = "{O}xford at {S}em{E}val-2017 Task 9: Neural {AMR} Parsing with Pointer-Augmented Attention",
    author = "Buys, Jan  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2157",
    doi = "10.18653/v1/S17-2157",
    pages = "914--919",
    abstract = "We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59{\%} Smatch on the SemEval test set.",
}
@inproceedings{mille-etal-2017-forge,
    title = "{FORG}e at {S}em{E}val-2017 Task 9: Deep sentence generation based on a sequence of graph transducers",
    author = "Mille, Simon  and
      Carlini, Roberto  and
      Burga, Alicia  and
      Wanner, Leo",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2158",
    doi = "10.18653/v1/S17-2158",
    pages = "920--923",
    abstract = "We present the contribution of Universitat Pompeu Fabra{'}s NLP group to the SemEval Task 9.2 (AMR-to-English Generation). The proposed generation pipeline comprises: (i) a series of rule-based graph-transducers for the syntacticization of the input graphs and the resolution of morphological agreements, and (ii) an off-the-shelf statistical linearization component.",
}
@inproceedings{gruzitis-etal-2017-rigotrio,
    title = "{RIGOTRIO} at {S}em{E}val-2017 Task 9: Combining Machine Learning and Grammar Engineering for {AMR} Parsing and Generation",
    author = "Gruzitis, Normunds  and
      Gosko, Didzis  and
      Barzdins, Guntis",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2159",
    doi = "10.18653/v1/S17-2159",
    pages = "924--928",
    abstract = "By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017 Task 9 established AMR as a powerful semantic interlingua. We strengthen the interlingual aspect of AMR by applying the multilingual Grammatical Framework (GF) for AMR-to-text generation. Our current rule-based GF approach completely covered only 12.3{\%} of the test AMRs, therefore we combined it with state-of-the-art JAMR Generator to see if the combination increases or decreases the overall performance. The combined system achieved the automatic BLEU score of 18.82 and the human Trueskill score of 107.2, to be compared to the plain JAMR Generator results. As for AMR parsing, we added NER extensions to our SemEval-2016 general-domain AMR parser to handle the biomedical genre, rich in organic compound names, achieving Smatch F1=54.0{\%}.",
}
@inproceedings{van-noord-bos-2017-meaning,
    title = "The Meaning Factory at {S}em{E}val-2017 Task 9: Producing {AMR}s with Neural Semantic Parsing",
    author = "van Noord, Rik  and
      Bos, Johan",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2160",
    doi = "10.18653/v1/S17-2160",
    pages = "929--933",
    abstract = "We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based neural semantic parsing models, the overall accuracy is still lower than a state-of-the-art AMR parser. An ensemble combining our neural semantic parser with an existing, traditional parser, yields a small gain in performance.",
}
@inproceedings{wang-li-2017-pku,
    title = "{PKU}{\_}{ICL} at {S}em{E}val-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge",
    author = "Wang, Liang  and
      Li, Sujian",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2161",
    doi = "10.18653/v1/S17-2161",
    pages = "934--937",
    abstract = "This paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed approach utilizes external knowledge to enrich feature representation of candidate keyphrase, including Wikipedia, IEEE taxonomy and pre-trained word embeddings etc. Ensemble of unsupervised models, random forest and linear models are used for candidate keyphrase ranking and keyphrase type classification. Our system achieves the 3rd place in subtask A and 4th place in subtask B.",
}
@inproceedings{marsi-etal-2017-ntnu,
    title = "{NTNU}-1@{S}cience{IE} at {S}em{E}val-2017 Task 10: Identifying and Labelling Keyphrases with Conditional Random Fields",
    author = "Marsi, Erwin  and
      Sikdar, Utpal Kumar  and
      Marco, Cristina  and
      Barik, Biswanath  and
      S{\ae}tre, Rune",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2162",
    doi = "10.18653/v1/S17-2162",
    pages = "938--941",
    abstract = "We present NTNU{'}s systems for Task A (prediction of keyphrases) and Task B (labelling as Material, Process or Task) at SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our approach relies on supervised machine learning using Conditional Random Fields. Our system yields a micro F-score of 0.34 for Tasks A and B combined on the test data. For Task C (relation extraction), we relied on an independently developed system described in (Barik and Marsi, 2017). For the full Scenario 1 (including relations), our approach reaches a micro F-score of 0.33 (5th place). Here we describe our systems, report results and discuss errors.",
}
@inproceedings{eger-etal-2017-eelection,
    title = "{EELECTION} at {S}em{E}val-2017 Task 10: Ensemble of n{E}ural Learners for k{E}yphrase {C}lassifica{TION}",
    author = "Eger, Steffen  and
      Do Dinh, Erik-L{\^a}n  and
      Kuznetsov, Ilia  and
      Kiaeeha, Masoud  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2163",
    doi = "10.18653/v1/S17-2163",
    pages = "942--946",
    abstract = "This paper describes our approach to the SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications, specifically to Subtask (B): Classification of identified keyphrases. We explored three different deep learning approaches: a character-level convolutional neural network (CNN), a stacked learner with an MLP meta-classifier, and an attention based Bi-LSTM. From these approaches, we created an ensemble of differently hyper-parameterized systems, achieving a micro-$F_1$-score of 0.63 on the test data. Our approach ranks 2nd (score of 1st placed system: 0.64) out of four according to this official score. However, we erroneously trained 2 out of 3 neural nets (the stacker and the CNN) on only roughly 15{\%} of the full data, namely, the original development set. When trained on the full data (training+development), our ensemble has a micro-$F_{1}$-score of 0.69. Our code is available from \url{https://github.com/UKPLab/semeval2017-scienceie}.",
}
@inproceedings{segura-bedmar-etal-2017-labda,
    title = "{LABDA} at {S}em{E}val-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the {BANNER} tool and the {UMLS} Semantic Network",
    author = "Segura-Bedmar, Isabel  and
      Col{\'o}n-Ruiz, Crist{\'o}bal  and
      Mart{\'\i}nez, Paloma",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2164",
    doi = "10.18653/v1/S17-2164",
    pages = "947--950",
    abstract = "This paper describes the system presented by the LABDA group at SemEval 2017 Task 10 ScienceIE, specifically for the subtasks of identification and classification of keyphrases from scientific articles. For the task of identification, we use the BANNER tool, a named entity recognition system, which is based on conditional random fields (CRF) and has obtained successful results in the biomedical domain. To classify keyphrases, we study the UMLS semantic network and propose a possible linking between the keyphrase types and the UMLS semantic groups. Based on this semantic linking, we create a dictionary for each keyphrase type. Then, a feature indicating if a token is found in one of these dictionaries is incorporated to feature set used by the BANNER tool. The final results on the test dataset show that our system still needs to be improved, but the conditional random fields and, consequently, the BANNER system can be used as a first approximation to identify and classify keyphrases.",
}
@inproceedings{lee-etal-2017-ntnu,
    title = "The {NTNU} System at {S}em{E}val-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields",
    author = "Lee, Lung-Hao  and
      Lee, Kuei-Ching  and
      Tseng, Yuen-Hsien",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2165",
    doi = "10.18653/v1/S17-2165",
    pages = "951--955",
    abstract = "This study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extracted features to identify keyphrases along with categorized labels and their relations from scientific publications. A total of 16 teams participated in evaluation scenario 1 (subtasks A, B, and C), with only 7 teams competing in all sub-tasks. Our best micro-averaging F1 across the three subtasks is 0.23, ranking in the middle among all 16 submissions.",
}
@inproceedings{liu-etal-2017-mayonlp,
    title = "{M}ayo{NLP} at {S}em{E}val 2017 Task 10: Word Embedding Distance Pattern for Keyphrase Classification in Scientific Publications",
    author = "Liu, Sijia  and
      Shen, Feichen  and
      Chaudhary, Vipin  and
      Liu, Hongfang",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2166",
    doi = "10.18653/v1/S17-2166",
    pages = "956--960",
    abstract = "In this paper, we present MayoNLP{'}s results from the participation in the ScienceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models. Word Embedding Distance Pattern, which uses the head noun word embedding to generate distance patterns based on labeled keyphrases, is proposed as an incremental feature set to enhance the conventional Named Entity Recognition feature sets. Support vector machine is used as the supervised classifier for keyphrase classification. Our system achieved an overall F1 score of 0.67 for keyphrase classification and 0.64 for keyphrase classification and relation detection.",
}
@inproceedings{kern-etal-2017-know,
    title = "Know-Center at {S}em{E}val-2017 Task 10: Sequence Classification with the {CODE} Annotator",
    author = "Kern, Roman  and
      Falk, Stefan  and
      Rexha, Andi",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2167",
    doi = "10.18653/v1/S17-2167",
    pages = "961--964",
    abstract = "This paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label them with one of the three categories: Task, Process, and Material. These scientific publications are selected from Computer Science, Material Sciences, and Physics domains. We followed a supervised approach for both subtasks by using a sequential classifier (CRF - Conditional Random Fields). For generating our solution we used a web-based application implemented in the EU-funded research project, named CODE. Our system achieved an F1 score of 0.39 for the Subtask 1 and 0.28 for the Subtask 2.",
}
@inproceedings{barik-marsi-2017-ntnu,
    title = "{NTNU}-2 at {S}em{E}val-2017 Task 10: Identifying Synonym and Hyponym Relations among Keyphrases in Scientific Documents",
    author = "Barik, Biswanath  and
      Marsi, Erwin",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2168",
    doi = "10.18653/v1/S17-2168",
    pages = "965--968",
    abstract = "This paper presents our relation extraction system for subtask C of SemEval-2017 Task 10: ScienceIE. Assuming that the keyphrases are already annotated in the input data, our work explores a wide range of linguistic features, applies various feature selection techniques, optimizes the hyper parameters and class weights and experiments with different problem formulations (single classification model vs individual classifiers for each keyphrase type, single-step classifier vs pipeline classifier for hyponym relations). Performance of five popular classification algorithms are evaluated for each problem formulation along with feature selection. The best setting achieved an F1 score of 71.0{\%} for synonym and 30.0{\%} for hyponym relation on the test data.",
}
@inproceedings{suarez-paniagua-etal-2017-labda,
    title = "{LABDA} at {S}em{E}val-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network",
    author = "Su{\'a}rez-Paniagua, V{\'\i}ctor  and
      Segura-Bedmar, Isabel  and
      Mart{\'\i}nez, Paloma",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2169",
    doi = "10.18653/v1/S17-2169",
    pages = "969--972",
    abstract = "In this paper, we describe our participation at the subtask of extraction of relationships between two identified keyphrases. This task can be very helpful in improving search engines for scientific articles. Our approach is based on the use of a convolutional neural network (CNN) trained on the training dataset. This deep learning model has already achieved successful results for the extraction relationships between named entities. Thus, our hypothesis is that this model can be also applied to extract relations between keyphrases. The official results of the task show that our architecture obtained an F1-score of 0.38{\%} for Keyphrases Relation Classification. This performance is lower than the expected due to the generic preprocessing phase and the basic configuration of the CNN model, more complex architectures are proposed as future work to increase the classification rate.",
}
@inproceedings{prasad-kan-2017-wing,
    title = "{WING}-{NUS} at {S}em{E}val-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling",
    author = "Prasad, Animesh  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2170",
    doi = "10.18653/v1/S17-2170",
    pages = "973--977",
    abstract = "We describe an end-to-end pipeline processing approach for SemEval 2017{'}s Task 10 to extract keyphrases and their relations from scientific publications. We jointly identify and classify keyphrases by modeling the subtasks as sequential labeling. Our system utilizes standard, surface-level features along with the adjacent word features, and performs conditional decoding on whole text to extract keyphrases. We focus only on the identification and typing of keyphrases (Subtasks A and B, together referred as extraction), but provide an end-to-end system inclusive of keyphrase relation identification (Subtask C) for completeness. Our top performing configuration achieves an $F_1$ of 0.27 for the end-to-end keyphrase extraction and relation identification scenario on the final test data, and compares on par to other top ranked systems for keyphrase extraction. Our system outperforms other techniques that do not employ global decoding and hence do not account for dependencies between keyphrases. We believe this is crucial for keyphrase classification in the given context of scientific document mining.",
}
@inproceedings{lee-etal-2017-mit,
    title = "{MIT} at {S}em{E}val-2017 Task 10: Relation Extraction with Convolutional Neural Networks",
    author = "Lee, Ji Young  and
      Dernoncourt, Franck  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2171",
    doi = "10.18653/v1/S17-2171",
    pages = "978--984",
    abstract = "Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts. Artificial neural networks have recently been explored for relation extraction. In this work, we continue this line of work and present a system based on a convolutional neural network to extract relations. Our model ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).",
}
@inproceedings{tsujimura-etal-2017-tti,
    title = "{TTI}-{COIN} at {S}em{E}val-2017 Task 10: Investigating Embeddings for End-to-End Relation Extraction from Scientific Papers",
    author = "Tsujimura, Tomoki  and
      Miwa, Makoto  and
      Sasaki, Yutaka",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2172",
    doi = "10.18653/v1/S17-2172",
    pages = "985--989",
    abstract = "This paper describes our TTI-COIN system that participated in SemEval-2017 Task 10. We investigated appropriate embeddings to adapt a neural end-to-end entity and relation extraction system LSTM-ER to this task. We participated in the full task setting of the entity segmentation, entity classification and relation classification (scenario 1) and the setting of relation classification only (scenario 3). The system was directly applied to the scenario 1 without modifying the codes thanks to its generality and flexibility. Our evaluation results show that the choice of appropriate pre-trained embeddings affected the performance significantly. With the best embeddings, our system was ranked third in the scenario 1 with the micro F1 score of 0.38. We also confirm that our system can produce the micro F1 score of 0.48 for the scenario 3 on the test data, and this score is close to the score of the 3rd ranked system in the task.",
}
@inproceedings{berend-2017-szte,
    title = "{SZTE}-{NLP} at {S}em{E}val-2017 Task 10: A High Precision Sequence Model for Keyphrase Extraction Utilizing Sparse Coding for Feature Generation",
    author = "Berend, G{\'a}bor",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2173",
    doi = "10.18653/v1/S17-2173",
    pages = "990--994",
    abstract = "In this paper we introduce our system participating at the 2017 SemEval shared task on keyphrase extraction from scientific documents. We aimed at the creation of a keyphrase extraction approach which relies on as little external resources as possible. Without applying any hand-crafted external resources, and only utilizing a transformed version of word embeddings trained at Wikipedia, our proposed system manages to perform among the best participating systems in terms of precision.",
}
@inproceedings{hernandez-etal-2017-lipn,
    title = "{LIPN} at {S}em{E}val-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model",
    author = "Hernandez, Simon David  and
      Buscaldi, Davide  and
      Charnois, Thierry",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2174",
    doi = "10.18653/v1/S17-2174",
    pages = "995--999",
    abstract = "This paper describes the system used by the team LIPN in SemEval 2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications. The team participated in Scenario 1, that includes three subtasks, Identification of keyphrases (Subtask A), Classification of identified keyphrases (Subtask B) and Extraction of relationships between two identified keyphrases (Subtask C). The presented system was mainly focused on the use of part-of-speech tag sequences to filter candidate keyphrases for Subtask A. Subtasks A and B were addressed as a sequence labeling problem using Conditional Random Fields (CRFs) and even though Subtask C was out of the scope of this approach, one rule was included to identify synonyms.",
}
@inproceedings{kubis-etal-2017-eudamu,
    title = "{EUDAMU} at {S}em{E}val-2017 Task 11: Action Ranking and Type Matching for End-User Development",
    author = "Kubis, Marek  and
      Sk{\'o}rzewski, Pawe{\l}  and
      Zi{\k{e}}tkiewicz, Tomasz",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2175",
    doi = "10.18653/v1/S17-2175",
    pages = "1000--1004",
    abstract = "The paper describes a system for end-user development using natural language. Our approach uses a ranking model to identify the actions to be executed followed by reference and parameter matching models to select parameter values that should be set for the given commands. We discuss the results of evaluation and possible improvements for future work.",
}
@inproceedings{p-r-etal-2017-hitachi,
    title = "Hitachi at {S}em{E}val-2017 Task 12: System for temporal information extraction from clinical notes",
    author = "P R, Sarath  and
      R, Manikandan  and
      Niwa, Yoshiki",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2176",
    doi = "10.18653/v1/S17-2176",
    pages = "1005--1009",
    abstract = "This paper describes the system developed for the task of temporal information extraction from clinical narratives in the context of the 2017 Clinical TempEval challenge. Clinical TempEval 2017 addressed the problem of temporal reasoning in the clinical domain by providing annotated clinical notes, pathology and radiology reports in line with Clinical TempEval challenges 2015/16, across two different evaluation phases focusing on cross domain adaptation. Our team focused on subtasks involving extractions of temporal spans and relations for which the developed systems showed average F-score of 0.45 and 0.47 across the two phases of evaluations.",
}
@inproceedings{huang-etal-2017-ntu,
    title = "{NTU}-1 at {S}em{E}val-2017 Task 12: Detection and classification of temporal events in clinical data with domain adaptation",
    author = "Huang, Po-Yu  and
      Huang, Hen-Hsen  and
      Wang, Yu-Wun  and
      Huang, Ching  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2177",
    doi = "10.18653/v1/S17-2177",
    pages = "1010--1013",
    abstract = "This study proposes a system to participate in the Clinical TempEval 2017 shared task, a part of the SemEval 2017 Tasks. Domain adaptation was the main challenge this year. We took part in the supervised domain adaption where data of 591 records of colon cancer patients and 30 records of brain cancer patients from Mayo clinic were given and we are asked to analyze the records from brain cancer patients. Based on the THYME corpus released by the organizer of Clinical TempEval, we propose a framework that automatically analyzes clinical temporal events in a fine-grained level. Support vector machine (SVM) and conditional random field (CRF) were implemented in our system for different subtasks, including detecting clinical relevant events and time expression, determining their attributes, and identifying their relations with each other within the document. The results showed the capability of domain adaptation of our system.",
}
@inproceedings{long-etal-2017-xjnlp,
    title = "{XJNLP} at {S}em{E}val-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model",
    author = "Long, Yu  and
      Li, Zhijing  and
      Wang, Xuan  and
      Li, Chen",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2178",
    doi = "10.18653/v1/S17-2178",
    pages = "1014--1018",
    abstract = "Temporality is crucial in understanding the course of clinical events from a patient{'}s electronic health recordsand temporal processing is becoming more and more important for improving access to content.SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.",
}
@inproceedings{lamurias-etal-2017-ulisboa,
    title = "{ULISBOA} at {S}em{E}val-2017 Task 12: Extraction and classification of temporal expressions and events",
    author = "Lamurias, Andre  and
      Sousa, Diana  and
      Pereira, Sofia  and
      Clarke, Luka  and
      Couto, Francisco M.",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2179",
    doi = "10.18653/v1/S17-2179",
    pages = "1019--1023",
    abstract = "This paper presents our approach to participate in the SemEval 2017 Task 12: Clinical TempEval challenge, specifically in the event and time expressions span and attribute identification subtasks (ES, EA, TS, TA). Our approach consisted in training Conditional Random Fields (CRF) classifiers using the provided annotations, and in creating manually curated rules to classify the attributes of each event and time expression. We used a set of common features for the event and time CRF classifiers, and a set of features specific to each type of entity, based on domain knowledge. Training only on the source domain data, our best F-scores were 0.683 and 0.485 for event and time span identification subtasks. When adding target domain annotations to the training data, the best F-scores obtained were 0.729 and 0.554, for the same subtasks. We obtained the second highest F-score of the challenge on the event polarity subtask (0.708). The source code of our system, Clinical Timeline Annotation (CiTA), is available at \url{https://github.com/lasigeBioTM/CiTA}.",
}
@inproceedings{macavaney-etal-2017-guir,
    title = "{GUIR} at {S}em{E}val-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction",
    author = "MacAvaney, Sean  and
      Cohan, Arman  and
      Goharian, Nazli",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2180",
    doi = "10.18653/v1/S17-2180",
    pages = "1024--1029",
    abstract = "Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text. We present a system for this task that uses supervised learning for the extraction of temporal expression and event spans with corresponding attributes and narrative container relations. Approaches include conditional random fields and decision tree ensembles, using lexical, syntactic, semantic, distributional, and rule-based features. Our system received best or second best scores in TIMEX3 span, EVENT span, and CONTAINS relation extraction.",
}
@inproceedings{leeuwenberg-moens-2017-kuleuven,
    title = "{KUL}euven-{LIIR} at {S}em{E}val-2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records",
    author = "Leeuwenberg, Artuur  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-2181",
    doi = "10.18653/v1/S17-2181",
    pages = "1030--1034",
    abstract = "In this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for event and temporal expression detection, and a structured perceptron for extracting temporal relations. Moreover, we present and analyze the results from our submissions, and verify the effectiveness of several system components. Our system performed above average for all subtasks in both phases.",
}
@proceedings{semeval-2017-joint,
    title = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    editor = "Ide, Nancy  and
      Herbelot, Aur{\'e}lie  and
      M{\`a}rquez, Llu{\'\i}s",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1000",
    doi = "10.18653/v1/S17-1",
}
@inproceedings{finley-etal-2017-analogies,
    title = "What Analogies Reveal about Word Vectors and their Compositionality",
    author = "Finley, Gregory  and
      Farmer, Stephanie  and
      Pakhomov, Serguei",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1001",
    doi = "10.18653/v1/S17-1001",
    pages = "1--11",
    abstract = "Analogy completion via vector arithmetic has become a common means of demonstrating the compositionality of word embeddings. Previous work have shown that this strategy works more reliably for certain types of analogical word relationships than for others, but these studies have not offered a convincing account for why this is the case. We arrive at such an account through an experiment that targets a wide variety of analogy questions and defines a baseline condition to more accurately measure the efficacy of our system. We find that the most reliably solvable analogy categories involve either 1) the application of a morpheme with clear syntactic effects, 2) male{--}female alternations, or 3) named entities. These broader types do not pattern cleanly along a syntactic{--}semantic divide. We suggest instead that their commonality is distributional, in that the difference between the distributions of two words in any given pair encompasses a relatively small number of word types. Our study offers a needed explanation for why analogy tests succeed and fail where they do and provides nuanced insight into the relationship between word distributions and the theoretical linguistic domains of syntax and semantics.",
}
@inproceedings{rajana-etal-2017-learning,
    title = "Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network",
    author = "Rajana, Sneha  and
      Callison-Burch, Chris  and
      Apidianaki, Marianna  and
      Shwartz, Vered",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1002",
    doi = "10.18653/v1/S17-1002",
    pages = "12--21",
    abstract = "Recognizing and distinguishing antonyms from other types of semantic relations is an essential part of language understanding systems. In this paper, we present a novel method for deriving antonym pairs using paraphrase pairs containing negation markers. We further propose a neural network model, AntNET, that integrates morphological features indicative of antonymy into a path-based relation detection algorithm. We demonstrate that our model outperforms state-of-the-art models in distinguishing antonyms from other semantic relations and is capable of efficiently handling multi-word expressions.",
}
@inproceedings{ponti-etal-2017-decoding,
    title = "Decoding Sentiment from Distributed Representations of Sentences",
    author = "Ponti, Edoardo Maria  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1003",
    doi = "10.18653/v1/S17-1003",
    pages = "22--32",
    abstract = "Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no {`}one-size-fits-all{'} representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bi-directional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.",
}
@inproceedings{vyas-carpuat-2017-detecting,
    title = "Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection",
    author = "Vyas, Yogarshi  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1004",
    doi = "10.18653/v1/S17-1004",
    pages = "33--43",
    abstract = "We introduce WHiC, a challenging testbed for detecting hypernymy, an asymmetric relation between words. While previous work has focused on detecting hypernymy between word types, we ground the meaning of words in specific contexts drawn from WordNet examples, and require predictions to be sensitive to changes in contexts. WHiC lets us analyze complementary properties of two approaches of inducing vector representations of word meaning in context. We show that such contextualized word representations also improve detection of a wider range of semantic relations in context.",
}
@inproceedings{chen-sun-2017-domain,
    title = "Domain-Specific New Words Detection in {C}hinese",
    author = "Chen, Ao  and
      Sun, Maosong",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1005",
    doi = "10.18653/v1/S17-1005",
    pages = "44--53",
    abstract = "With the explosive growth of Internet, more and more domain-specific environments appear, such as forums, blogs, MOOCs and etc. Domain-specific words appear in these areas and always play a critical role in the domain-specific NLP tasks. This paper aims at extracting Chinese domain-specific new words automatically. The extraction of domain-specific new words has two parts including both new words in this domain and the especially important words. In this work, we propose a joint statistical model to perform these two works simultaneously. Compared to traditional new words detection models, our model doesn{'}t need handcraft features which are labor intensive. Experimental results demonstrate that our joint model achieves a better performance compared with the state-of-the-art methods.",
}
@inproceedings{gharbieh-etal-2017-deep,
    title = "Deep Learning Models For Multiword Expression Identification",
    author = "Gharbieh, Waseem  and
      Bhavsar, Virendrakumar  and
      Cook, Paul",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1006",
    doi = "10.18653/v1/S17-1006",
    pages = "54--64",
    abstract = "Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.",
}
@inproceedings{mohammad-bravo-marquez-2017-emotion,
    title = "Emotion Intensities in Tweets",
    author = "Mohammad, Saif  and
      Bravo-Marquez, Felipe",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1007",
    doi = "10.18653/v1/S17-1007",
    pages = "65--77",
    abstract = "This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best{--}worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity; and, the extent to which two emotions are similar in terms of how they manifest in language.",
}
@inproceedings{asghar-etal-2017-deep,
    title = "Deep Active Learning for Dialogue Generation",
    author = "Asghar, Nabiha  and
      Poupart, Pascal  and
      Jiang, Xin  and
      Li, Hang",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1008",
    doi = "10.18653/v1/S17-1008",
    pages = "78--83",
    abstract = "We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.",
}
@inproceedings{cocos-etal-2017-mapping,
    title = "Mapping the Paraphrase Database to {W}ord{N}et",
    author = "Cocos, Anne  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1009",
    doi = "10.18653/v1/S17-1009",
    pages = "84--90",
    abstract = "WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic structure of WordNet that would make it more directly useful for downstream tasks. We present a method for mapping words from PPDB to WordNet synsets with 89{\%} accuracy. The mapping also lays important groundwork for incorporating WordNet{'}s relations into PPDB so as to increase its utility for semantic reasoning in applications.",
}
@inproceedings{feng-etal-2017-semantic,
    title = "Semantic Frame Labeling with Target-based Neural Model",
    author = "Feng, Yukun  and
      Yu, Dong  and
      Xu, Jian  and
      Liu, Chunhua",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1010",
    doi = "10.18653/v1/S17-1010",
    pages = "91--96",
    abstract = "This paper explores the automatic learning of distributed representations of the target{'}s context for semantic frame labeling with target-based neural model. We constrain the whole sentence as the model{'}s input without feature extraction from the sentence. This is different from many previous works in which local feature extraction of the targets is widely used. This constraint makes the task harder, especially with long sentences, but also makes our model easily applicable to a range of resources and other similar tasks. We evaluate our model on several resources and get the state-of-the-art result on subtask 2 of SemEval 2015 task 15. Finally, we extend the task to word-sense disambiguation task and we also achieve a strong result in comparison to state-of-the-art work.",
}
@inproceedings{ferraro-etal-2017-frame,
    title = "Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles",
    author = "Ferraro, Francis  and
      Poliak, Adam  and
      Cotterell, Ryan  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1011",
    doi = "10.18653/v1/S17-1011",
    pages = "97--103",
    abstract = "We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that consistently encodes syntactic-semantic content better, with multiple 10{\%} gains over baselines.",
}
@inproceedings{gupta-etal-2017-distributed,
    title = "Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible",
    author = "Gupta, Abhijeet  and
      Boleda, Gemma  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1012",
    doi = "10.18653/v1/S17-1012",
    pages = "104--109",
    abstract = "Word embeddings are supposed to provide easy access to semantic relations such as {``}male of{''} (man{--}woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings.",
}
@inproceedings{maredia-etal-2017-comparing,
    title = "Comparing Approaches for Automatic Question Identification",
    author = "Maredia, Angel  and
      Schechtman, Kara  and
      Levitan, Sarah Ita  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1013",
    doi = "10.18653/v1/S17-1013",
    pages = "110--114",
    abstract = "Collecting spontaneous speech corpora that are open-ended, yet topically constrained, is increasingly popular for research in spoken dialogue systems and speaker state, inter alia. Typically, these corpora are labeled by human annotators, either in the lab or through crowd-sourcing; however, this is cumbersome and time-consuming for large corpora. We present four different approaches to automatically tagging a corpus when general topics of the conversations are known. We develop these approaches on the Columbia X-Cultural Deception corpus and find accuracy that significantly exceeds the baseline. Finally, we conduct a cross-corpus evaluation by testing the best performing approach on the Columbia/SRI/Colorado corpus.",
}
@inproceedings{medic-etal-2017-free,
    title = "Does Free Word Order Hurt? Assessing the Practical Lexical Function Model for {C}roatian",
    author = "Medi{\'c}, Zoran  and
      {\v{S}}najder, Jan  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1014",
    doi = "10.18653/v1/S17-1014",
    pages = "115--120",
    abstract = "The Practical Lexical Function (PLF) model is a model of computational distributional semantics that attempts to strike a balance between expressivity and learnability in predicting phrase meaning and shows competitive results. We investigate how well the PLF carries over to free word order languages, given that it builds on observations of predicate-argument combinations that are harder to recover in free word order languages. We evaluate variants of the PLF for Croatian, using a new lexical substitution dataset. We find that the PLF works about as well for Croatian as for English, but demonstrate that its strength lies in modeling verbs, and that the free word order affects the less robust PLF variant.",
}
@inproceedings{nguyen-etal-2017-mixture,
    title = "A Mixture Model for Learning Multi-Sense Word Embeddings",
    author = "Nguyen, Dai Quoc  and
      Nguyen, Dat Quoc  and
      Modi, Ashutosh  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1015",
    doi = "10.18653/v1/S17-1015",
    pages = "121--127",
    abstract = "Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.",
}
@inproceedings{ostermann-etal-2017-aligning,
    title = "Aligning Script Events with Narrative Texts",
    author = "Ostermann, Simon  and
      Roth, Michael  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1016",
    doi = "10.18653/v1/S17-1016",
    pages = "128--134",
    abstract = "Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks. In this paper, we consider two recent datasets which provide a rich and general representation of script events in terms of paraphrase sets. We introduce the task of mapping event mentions in narrative texts to such script event types, and present a model for this task that exploits rich linguistic representations as well as information on temporal ordering. The results of our experiments demonstrate that this complex task is indeed feasible.",
}
@inproceedings{rogers-etal-2017-many,
    title = "The (too Many) Problems of Analogical Reasoning with Word Vectors",
    author = "Rogers, Anna  and
      Drozd, Aleksandr  and
      Li, Bofang",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1017",
    doi = "10.18653/v1/S17-1017",
    pages = "135--148",
    abstract = "This paper explores the possibilities of analogical reasoning with vector space models. Given two pairs of words with the same relation (e.g. man:woman :: king:queen), it was proposed that the offset between one pair of the corresponding word vectors can be used to identify the unknown member of the other pair (king - man + woman = queen). We argue against such {``}linguistic regularities{''} as a model for linguistic relations in vector space models and as a benchmark, and we show that the vector offset (as well as two other, better-performing methods) suffers from dependence on vector similarity.",
}
@inproceedings{shutova-etal-2017-semantic,
    title = "Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions",
    author = "Shutova, Ekaterina  and
      Wundsam, Andreas  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1018",
    doi = "10.18653/v1/S17-1018",
    pages = "149--154",
    abstract = "Frame-semantic parsing and semantic role labelling, that aim to automatically assign semantic roles to arguments of verbs in a sentence, have become an active strand of research in NLP. However, to date these methods have relied on a predefined inventory of semantic roles. In this paper, we present a method to automatically learn argument role inventories for verbs from large corpora of text, images and videos. We evaluate the method against manually constructed role inventories in FrameNet and show that the visual model outperforms the language-only model and operates with a high precision.",
}
@inproceedings{shwartz-etal-2017-acquiring,
    title = "Acquiring Predicate Paraphrases from News Tweets",
    author = "Shwartz, Vered  and
      Stanovsky, Gabriel  and
      Dagan, Ido",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1019",
    doi = "10.18653/v1/S17-1019",
    pages = "155--160",
    abstract = "We present a simple method for ever-growing extraction of predicate paraphrases from news headlines in Twitter. Analysis of the output of ten weeks of collection shows that the accuracy of paraphrases with different support levels is estimated between 60-86{\%}. We also demonstrate that our resource is to a large extent complementary to existing resources, providing many novel paraphrases. Our resource is publicly available, continuously expanding based on daily news.",
}
@inproceedings{talmor-etal-2017-evaluating,
    title = "Evaluating Semantic Parsing against a Simple Web-based Question Answering Model",
    author = "Talmor, Alon  and
      Geva, Mor  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1020",
    doi = "10.18653/v1/S17-1020",
    pages = "161--167",
    abstract = "Semantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence. However, datasets for semantic parsing contain many factoid questions that can be answered from a single web document. In this paper, we propose to evaluate semantic parsing-based question answering models by comparing them to a question answering baseline that queries the web and extracts the answer only from web snippets, without access to the target knowledge-base. We investigate this approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional language, and find that our model obtains reasonable performance (∼35 F1 compared to 41 F1 of state-of-the-art). We find in our analysis that our model performs well on complex questions involving conjunctions, but struggles on questions that involve relation composition and superlatives.",
}
@inproceedings{chersoni-etal-2017-logical,
    title = "Logical Metonymy in a Distributional Model of Sentence Comprehension",
    author = "Chersoni, Emmanuele  and
      Lenci, Alessandro  and
      Blache, Philippe",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1021",
    doi = "10.18653/v1/S17-1021",
    pages = "168--177",
    abstract = "In theoretical linguistics, logical metonymy is defined as the combination of an event-subcategorizing verb with an entity-denoting direct object (e.g., The author began the book), so that the interpretation of the VP requires the retrieval of a covert event (e.g., writing). Psycholinguistic studies have revealed extra processing costs for logical metonymy, a phenomenon generally explained with the introduction of new semantic structure. In this paper, we present a general distributional model for sentence comprehension inspired by the Memory, Unification and Control model by Hagoort (2013,2016). We show that our distributional framework can account for the extra processing costs of logical metonymy and can identify the covert event in a classification task.",
}
@inproceedings{hwang-etal-2017-double,
    title = "Double Trouble: The Problem of Construal in Semantic Annotation of Adpositions",
    author = "Hwang, Jena D.  and
      Bhatia, Archna  and
      Han, Na-Rae  and
      O{'}Gorman, Tim  and
      Srikumar, Vivek  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1022",
    doi = "10.18653/v1/S17-1022",
    pages = "178--188",
    abstract = "We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that an adposition{'}s lexical contribution is equivalent to the role/relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition{'}s lexical function so they can be annotated at scale{---}supporting automatic, statistical processing of domain-general language{---}and discuss how this representation would allow for a simpler inventory of labels.",
}
@inproceedings{kiss-etal-2017-issues,
    title = "Issues of Mass and Count: Dealing with {`}Dual-Life{'} Nouns",
    author = "Kiss, Tibor  and
      Pelletier, Francis Jeffry  and
      Husi{\'c}, Halima  and
      Poppek, Johanna",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1023",
    doi = "10.18653/v1/S17-1023",
    pages = "189--198",
    abstract = "The topics of mass and count have been studied for many decades in philosophy (e.g., Quine, 1960; Pelletier, 1975), linguistics (e.g., McCawley, 1975; Allen, 1980; Krifka, 1991) and psychology (e.g., Middleton et al, 2004; Barner et al, 2009). More recently, interest from within computational linguistics has studied the issues involved (e.g., Pustejovsky, 1991; Bond, 2005; Schmidtke {\&} Kuperman, 2016), to name just a few. As is pointed out in these works, there are many difficult conceptual issues involved in the study of this contrast. In this article we study one of these issues {--} the {``}Dual-Life{''} of being simultaneously +mass and +count {--} by means of an unusual combination of human annotation, online lexical resources, and online corpora.",
}
@inproceedings{gilroy-etal-2017-parsing,
    title = "Parsing Graphs with Regular Graph Grammars",
    author = "Gilroy, Sorcha  and
      Lopez, Adam  and
      Maneth, Sebastian",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1024",
    doi = "10.18653/v1/S17-1024",
    pages = "199--208",
    abstract = "Recently, several datasets have become available which represent natural language phenomena as graphs. Hyperedge Replacement Languages (HRL) have been the focus of much attention as a formalism to represent the graphs in these datasets. Chiang et al. (2013) prove that HRL graphs can be parsed in polynomial time with respect to the size of the input graph. We believe that HRL are more expressive than is necessary to represent semantic graphs and we propose the use of Regular Graph Languages (RGL; Courcelle 1991), which is a subfamily of HRL, as a possible alternative. We provide a top-down parsing algorithm for RGL that runs in time linear in the size of the input graph.",
}
@inproceedings{jauhar-hovy-2017-embedded,
    title = "Embedded Semantic Lexicon Induction with Joint Global and Local Optimization",
    author = "Jauhar, Sujay Kumar  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1025",
    doi = "10.18653/v1/S17-1025",
    pages = "209--219",
    abstract = "Creating annotated frame lexicons such as PropBank and FrameNet is expensive and labor intensive. We present a method to induce an embedded frame lexicon in an minimally supervised fashion using nothing more than unlabeled predicate-argument word pairs. We hypothesize that aggregating such pair selectional preferences across training leads us to a global understanding that captures predicate-argument frame structure. Our approach revolves around a novel integration between a predictive embedding model and an Indian Buffet Process posterior regularizer. We show, through our experimental evaluation, that we outperform baselines on two tasks and can learn an embedded frame lexicon that is able to capture some interesting generalities in relation to hand-crafted semantic frames.",
}
@inproceedings{eichler-etal-2017-generating,
    title = "Generating Pattern-Based Entailment Graphs for Relation Extraction",
    author = "Eichler, Kathrin  and
      Xu, Feiyu  and
      Uszkoreit, Hans  and
      Krause, Sebastian",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1026",
    doi = "10.18653/v1/S17-1026",
    pages = "220--229",
    abstract = "Relation extraction is the task of recognizing and extracting relations between entities or concepts in texts. A common approach is to exploit existing knowledge to learn linguistic patterns expressing the target relation and use these patterns for extracting new relation mentions. Deriving relation patterns automatically usually results in large numbers of candidates, which need to be filtered to derive a subset of patterns that reliably extract correct relation mentions. We address the pattern selection task by exploiting the knowledge represented by entailment graphs, which capture semantic relationships holding among the learned pattern candidates. This is motivated by the fact that a pattern may not express the target relation explicitly, but still be useful for extracting instances for which the relation holds, because its meaning entails the meaning of the target relation. We evaluate the usage of both automatically generated and gold-standard entailment graphs in a relation extraction scenario and present favorable experimental results, exhibiting the benefits of structuring and selecting patterns based on entailment graphs.",
}
@inproceedings{becker-etal-2017-classifying,
    title = "Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention",
    author = "Becker, Maria  and
      Staniek, Michael  and
      Nastase, Vivi  and
      Palmer, Alexis  and
      Frank, Anette",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1027",
    doi = "10.18653/v1/S17-1027",
    pages = "230--240",
    abstract = "Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deep-learning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. Apart from implicitly capturing task relevant features, the advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable. We present experiments for English and German that achieve competitive performance. We present a novel take on modeling and exploiting genre information and showcase the adaptation of our system from one language to another.",
}
@inproceedings{sarioglu-kayi-etal-2017-predictive,
    title = "Predictive Linguistic Features of Schizophrenia",
    author = "Sarioglu Kayi, Efsun  and
      Diab, Mona  and
      Pauselli, Luca  and
      Compton, Michael  and
      Coppersmith, Glen",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1028",
    doi = "10.18653/v1/S17-1028",
    pages = "241--250",
    abstract = "Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self proclaimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.",
}
@inproceedings{sachan-xing-2017-learning,
    title = "Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks",
    author = "Sachan, Mrinmaya  and
      Xing, Eric",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1029",
    doi = "10.18653/v1/S17-1029",
    pages = "251--261",
    abstract = "Humans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In this paper, we introduce the task of question answering using natural language demonstrations where the question answering system is provided with detailed demonstrative solutions to questions in natural language. As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks. We collect a new dataset of demonstrative geometry solutions from textbooks and explore approaches that learn to interpret these demonstrations as well as to use these interpretations to solve geometry problems. Our approaches show improvements over the best previously published system for solving geometry problems.",
}
@inproceedings{antonio-rodrigues-etal-2017-ways,
    title = "Ways of Asking and Replying in Duplicate Question Detection",
    author = "Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Saedi, Chakaveh  and
      Maraev, Vladislav  and
      Silva, Jo{\~a}o  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S17-1030",
    doi = "10.18653/v1/S17-1030",
    pages = "262--270",
    abstract = "This paper presents the results of systematic experimentation on the impact in duplicate question detection of different types of questions across both a number of established approaches and a novel, superior one used to address this language processing task. This study permits to gain a novel insight on the different levels of robustness of the diverse detection methods with respect to different conditions of their application, including the ones that approximate real usage scenarios.",
}
@proceedings{ranlp-2017-student,
    title = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    editor = "Kovatchev, Venelin  and
      Temnikova, Irina  and
      Gencheva, Pepa  and
      Kiprov, Yasen  and
      Nikolova, Ivelina",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R17-2000",
    doi = "10.26615/issn.1314-9156.2017_",
}
@inproceedings{clairet-2017-dish,
    title = "Dish Classification using Knowledge based Dietary Conflict Detection",
    author = "Clairet, Nadia",
    booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/issn.1314-9156.2017_001",
    doi = "10.26615/issn.1314-9156.2017_001",
    pages = "1--9",
    abstract = "The present paper considers the problem of dietary conflict detection from dish titles. The proposed method explores the semantics associated with the dish title in order to discover a certain or possible incompatibility of a particular dish with a particular diet. Dish titles are parts of the elusive and metaphoric gastronomy language, their processing can be viewed as a combination of short text and domain-specific texts analysis. We build our algorithm on the basis of a common knowledge lexical semantic network and show how such network can be used for domain specific short text processing.",
}
@inproceedings{daudert-2017-analysing,
    title = "Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy",
    author = "Daudert, Tobias",
    booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/issn.1314-9156.2017_002",
    doi = "10.26615/issn.1314-9156.2017_002",
    pages = "10--16",
    abstract = "In today{'}s world, globalisation is not only affecting inter-culturalism but also linking markets across the globe. Given that all markets are affecting each other and are not only driven by fundamental data but also by sentiments, sentiment analysis regarding the markets becomes a tool to predict, anticipate, and milden future economic crises such as the one we faced in 2008. In this paper, an approach to improve sentiment analysis by exploiting relationships among different kinds of sentiment, together with supplementary information, from and across various data sources is proposed.",
}
@inproceedings{jwalapuram-2017-evaluating,
    title = "Evaluating Dialogs based on {G}rice{'}s Maxims",
    author = "Jwalapuram, Prathyusha",
    booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/issn.1314-9156.2017_003",
    doi = "10.26615/issn.1314-9156.2017_003",
    pages = "17--24",
    abstract = "There is no agreed upon standard for the evaluation of conversational dialog systems, which are well-known to be hard to evaluate due to the difficulty in pinning down metrics that will correspond to human judgements and the subjective nature of human judgment itself. We explored the possibility of using Grice{'}s Maxims to evaluate effective communication in conversation. We collected some system generated dialogs from popular conversational chatbots across the spectrum and conducted a survey to see how the human judgements based on Gricean maxims correlate, and if such human judgments can be used as an effective evaluation metric for conversational dialog.",
}
@inproceedings{popov-2017-word,
    title = "Word Sense Disambiguation with Recurrent Neural Networks",
    author = "Popov, Alexander",
    booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/issn.1314-9156.2017_004",
    doi = "10.26615/issn.1314-9156.2017_004",
    pages = "25--34",
    abstract = "This paper presents a neural network architecture for word sense disambiguation (WSD). The architecture employs recurrent neural layers and more specifically LSTM cells, in order to capture information about word order and to easily incorporate distributed word representations (embeddings) as features, without having to use a fixed window of text. The paper demonstrates that the architecture is able to compete with the most successful supervised systems for WSD and that there is an abundance of possible improvements to take it to the current state of the art. In addition, it explores briefly the potential of combining different types of embeddings as input features; it also discusses possible ways for generating {``}artificial corpora{''} from knowledge bases {--} for the purpose of producing training data and in relation to possible applications of embedding lemmas and word senses in the same space.",
}
@inproceedings{rohanian-2017-multi,
    title = "Multi-Document Summarization of {P}ersian Text using Paragraph Vectors",
    author = "Rohanian, Morteza",
    booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/issn.1314-9156.2017_005",
    doi = "10.26615/issn.1314-9156.2017_005",
    pages = "35--40",
    abstract = "A multi-document summarizer finds the key topics from multiple textual sources and organizes information around them. In this paper we propose a summarization method for Persian text using paragraph vectors that can represent textual units of arbitrary lengths. We use these vectors to calculate the semantic relatedness between documents, cluster them to a number of predetermined groups, weight them based on their distance to the centroids and the intra-cluster homogeneity and take out the key paragraphs. We compare the final summaries with the gold-standard summaries of 21 digital topics using the ROUGE evaluation metric. Experimental results show the advantages of using paragraph vectors over earlier attempts at developing similar methods for a low resource language like Persian.",
}
@inproceedings{simeonova-2017-gradient,
    title = "Gradient Emotional Analysis",
    author = "Simeonova, Lilia",
    booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/issn.1314-9156.2017_006",
    doi = "10.26615/issn.1314-9156.2017_006",
    pages = "41--45",
    abstract = {Over the past few years a lot of research has been done on sentiment analysis, however, the emotional analysis, being so subjective, is not a well examined dis-cipline. The main focus of this proposal is to categorize a given sentence in two dimensions - sentiment and arousal. For this purpose two techniques will be com-bined {--} Machine Learning approach and Lexicon-based approach. The first di-mension will give the sentiment value {--} positive versus negative. This will be re-solved by using Na{\"\i}ve Bayes Classifier. The second and more interesting dimen-sion will determine the level of arousal. This will be achieved by evaluation of given a phrase or sentence based on lexi-con with affective ratings for 14 thousand English words.},
}
@inproceedings{tran-2017-applying,
    title = "Applying Deep Neural Network to Retrieve Relevant Civil Law Articles",
    author = "Tran, Anh Hang Nga",
    booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/issn.1314-9156.2017_007",
    doi = "10.26615/issn.1314-9156.2017_007",
    pages = "46--48",
    abstract = "The paper aims to achieve the legal question answering information retrieval (IR) task at Competition on Legal Information Extraction/Entailment (COLIEE) 2017. Our proposal methodology for the task is to utilize deep neural network, natural language processing and word2vec. The system was evaluated using training and testing data from the competition on legal information extraction/entailment (COLIEE). Our system mainly focuses on giving relevant civil law articles for given bar exams. The corpus of legal questions is drawn from Japanese Legal Bar exam queries. We implemented a combined deep neural network with additional features NLP and word2vec to gain the corresponding civil law articles based on a given bar exam {`}Yes/No{'} questions. This paper focuses on clustering words-with-relation in order to acquire relevant civil law articles. All evaluation processes were done on the COLIEE 2017 training and test data set. The experimental result shows a very promising result.",
}
@proceedings{ranlp-2017-international,
    title = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://www.aclweb.org/anthology/R17-1000",
}
@inproceedings{abualhaija-etal-2017-parameter,
    title = "Parameter Transfer across Domains for Word Sense Disambiguation",
    author = "Abualhaija, Sallam  and
      Tahmasebi, Nina  and
      Forin, Diane  and
      Zimmermann, Karl-Heinz",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_001",
    doi = "10.26615/978-954-452-049-6_001",
    pages = "1--8",
    abstract = "Word sense disambiguation is defined as finding the corresponding sense for a target word in a given context, which comprises a major step in text applications. Recently, it has been addressed as an optimization problem. The idea behind is to find a sequence of senses that corresponds to the words in a given context with a maximum semantic similarity. Metaheuristics like simulated annealing and D-Bees provide approximate good-enough solutions, but are usually influenced by the starting parameters. In this paper, we study the parameter tuning for both algorithms within the word sense disambiguation problem. The experiments are conducted on different datasets to cover different disambiguation scenarios. We show that D-Bees is robust and less sensitive towards the initial parameters compared to simulated annealing, hence, it is sufficient to tune the parameters once and reuse them for different datasets, domains or languages.",
}
@inproceedings{aburaed-etal-2017-sentence,
    title = "What Sentence are you Referring to and Why? Identifying Cited Sentences in Scientific Literature",
    author = "AbuRa{'}ed, Ahmed  and
      Chiruzzo, Luis  and
      Saggion, Horacio",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_002",
    doi = "10.26615/978-954-452-049-6_002",
    pages = "9--17",
    abstract = "In the current context of scientific information overload, text mining tools are of paramount importance for researchers who have to read scientific papers and assess their value. Current citation networks, which link papers by citation relationships (reference and citing paper), are useful to quantitatively understand the value of a piece of scientific work, however they are limited in that they do not provide information about what specific part of the reference paper the citing paper is referring to. This qualitative information is very important, for example, in the context of current community-based scientific summarization activities. In this paper, and relying on an annotated dataset of co-citation sentences, we carry out a number of experiments aimed at, given a citation sentence, automatically identify a part of a reference paper being cited. Additionally our algorithm predicts the specific reason why such reference sentence has been cited out of five possible reasons.",
}
@inproceedings{agirrezabal-etal-2017-comparison,
    title = "A Comparison of Feature-Based and Neural Scansion of Poetry",
    author = "Agirrezabal, Manex  and
      Alegria, I{\~n}aki  and
      Hulden, Mans",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_003",
    doi = "10.26615/978-954-452-049-6_003",
    pages = "18--23",
    abstract = "Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.",
}
@inproceedings{ahmadnia-etal-2017-persian,
    title = "{P}ersian-{S}panish Low-Resource Statistical Machine Translation Through {E}nglish as Pivot Language",
    author = "Ahmadnia, Benyamin  and
      Serrano, Javier  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_004",
    doi = "10.26615/978-954-452-049-6_004",
    pages = "24--30",
    abstract = "This paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian-Spanish low-resource Statistical Machine Translation (SMT). In this case, English is used as the bridging language, and the Persian-English SMT is combined with the English-Spanish one, where the relatively large corpora of each may be used in support of the Persian-Spanish pairing. Our results indicate that the pivot language technique outperforms the direct SMT processes currently in use between Persian and Spanish. Furthermore, we investigate the sentence translation pivot strategy and the phrase translation in turn, and demonstrate that, in the context of the Persian-Spanish SMT system, the phrase-level pivoting outperforms the sentence-level pivoting. Finally we suggest a method called combination model in which the standard direct model and the best triangulation pivoting model are blended in order to reach a high-quality translation.",
}
@inproceedings{aker-etal-2017-simple,
    title = "Simple Open Stance Classification for Rumour Analysis",
    author = "Aker, Ahmet  and
      Derczynski, Leon  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_005",
    doi = "10.26615/978-954-452-049-6_005",
    pages = "31--39",
    abstract = "Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.",
}
@inproceedings{aker-etal-2017-extensible,
    title = "An Extensible Multilingual Open Source Lemmatizer",
    author = "Aker, Ahmet  and
      Petrak, Johann  and
      Sabbah, Firas",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_006",
    doi = "10.26615/978-954-452-049-6_006",
    pages = "40--45",
    abstract = "We present GATE DictLemmatizer, a multilingual open source lemmatizer for the GATE NLP framework that currently supports English, German, Italian, French, Dutch, and Spanish, and is easily extensible to other languages. The software is freely available under the LGPL license. The lemmatization is based on the Helsinki Finite-State Transducer Technology (HFST) and lemma dictionaries automatically created from Wiktionary. We evaluate the performance of the lemmatizers against TreeTagger, which is only freely available for research purposes. Our evaluation shows that DictLemmatizer achieves similar or even better results than TreeTagger for languages where there is support from HFST. The performance drops when there is no support from HFST and the entire lemmatization process is based on lemma dictionaries. However, the results are still satisfactory given the fact that DictLemmatizer isopen-source and can be easily extended to other languages. The software for extending the lemmatizer by creating word lists from Wiktionary dictionaries is also freely available as open-source software.",
}
@inproceedings{albogamy-ramsay-2017-universal,
    title = "{U}niversal {D}ependencies for {A}rabic Tweets",
    author = "Albogamy, Fahad  and
      Ramsay, Allan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_007",
    doi = "10.26615/978-954-452-049-6_007",
    pages = "46--51",
    abstract = "To facilitate cross-lingual studies, there is an increasing interest in identifying linguistic universals. Recently, a new universal scheme was designed as a part of universal dependency project. In this paper, we map the Arabic tweets dependency treebank (ATDT) to the Universal Dependency (UD) scheme to compare it to other language resources and for the purpose of cross-lingual studies.",
}
@inproceedings{almansor-al-ani-2017-translating,
    title = "Translating Dialectal {A}rabic as Low Resource Language using Word Embedding",
    author = "Almansor, Ebtesam H  and
      Al-Ani, Ahmed",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_008",
    doi = "10.26615/978-954-452-049-6_008",
    pages = "52--57",
    abstract = "A number of machine translation methods have been proposed in recent years to deal with the increasingly important problem of automatic translation between texts of different languages or languages and their dialects. These methods have produced promising results when applied to some of the widely studied languages. Existing translation methods are mainly implemented using rule-based and static machine translation approaches. Rule based approaches utilize language translation rules that can either be constructed by an expert, which is quite difficult when dealing with dialects, or rely on rule construction algorithms, which require very large parallel datasets. Statistical approaches also require large parallel datasets to build the translation models. However, large parallel datasets do not exist for languages with low resources, such as the Arabic language and its dialects. In this paper we propose an algorithm that attempts to overcome this limitation, and apply it to translate the Egyptian dialect (EGY) to Modern Standard Arabic (MSA). Monolingual corpus was collected for both MSA and EGY and a relatively small parallel language pair set was built to train the models. The proposed method utilizes Word embedding as it requires monolingual data rather than parallel corpus. Both Continuous Bag of Words and Skip-gram were used to build word vectors. The proposed method was validated on four different datasets using a four-fold cross validation approach.",
}
@inproceedings{almiman-ramsay-2017-using,
    title = "Using {E}nglish Dictionaries to generate Commonsense Knowledge in Natural Language",
    author = "Almiman, Ali  and
      Ramsay, Allan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_009",
    doi = "10.26615/978-954-452-049-6_009",
    pages = "58--63",
    abstract = "This paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by using definitions from English dictionaries. Because the definitions in English dictionaries are not prepared to be transformed into inference rules, some preprocessing steps were taken to turn each relation of word:definition in dictionaries into an inference rule in the form left-hand side ⇒ right-hand side. In this paper, we applied this mechanism using two dictionaries: The MacMillan Dictionary and WordNet definitions. A random set of 200 inference rules were extracted equally from the two dictionaries, and then we used human judgment as to whether these rules are {`}True{'} or not. For the MacMillan Dictionary the precision reaches 0.74 with 0.508 recall, and the WordNet definitions resulted in 0.73 precision with 0.09 recall.",
}
@inproceedings{almiman-ramsay-2017-hybrid,
    title = "A Hybrid System to apply Natural Language Inference over Dependency Trees",
    author = "Almiman, Ali  and
      Ramsay, Allan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_010",
    doi = "10.26615/978-954-452-049-6_010",
    pages = "64--70",
    abstract = "This paper presents the development of a natural language inference engine that benefits from two current standard approaches; i.e., shallow and deep approaches. This system combines two non-deterministic algorithms: the approximate matching from the shallow approach and a theorem prover from the deep approach for handling multi-step inference tasks. The theorem prover is customized to accept dependency trees and apply inference rules to these trees. The inference rules are automatically generated as syllogistic rules from our test data (FraCaS test suite). The theorem prover exploits a non-deterministic matching algorithm within a standard backward chaining inference engine. We employ continuation programming as a way of seamlessly handling the combination of these two non-deterministic algorithms. Testing the matching algorithm on {``}Generalized quantifiers{''} and {``}adjectives{''} topics in FraCaS (MacCartney and Manning 2007), we achieved an accuracy of 92.8{\%} of the single-premise cases. For the multi-steps of inference, we checked the validity of our syllogistic rules and then extracted four generic instances that can be applied to more than one problem.",
}
@inproceedings{barbu-2017-ensembles,
    title = "Ensembles of Classifiers for Cleaning Web Parallel Corpora and Translation Memories",
    author = "Barbu, Eduard",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_011",
    doi = "10.26615/978-954-452-049-6_011",
    pages = "71--77",
    abstract = "The last years witnessed an increasing interest in the automatic methods for spotting false translation units in translation memories. This problem presents a great interest to industry as there are many translation memories that contain errors. A closely related line of research deals with identifying sentences that do not align in the parallel corpora mined from the web. The task of spotting false translations is modeled as a binary classification problem. It is known that in certain conditions the ensembles of classifiers improve over the performance of the individual members. In this paper we benchmark the most popular ensemble of classifiers: Majority Voting, Bagging, Stacking and Ada Boost at the task of spotting false translation units for translation memories and parallel web corpora. We want to know if for this specific problem any ensemble technique improves the performance of the individual classifiers and if there is a difference between the data in translation memories and parallel web corpora with respect to this task.",
}
@inproceedings{basaldella-etal-2017-exploiting,
    title = "Exploiting and Evaluating a Supervised, Multilanguage Keyphrase Extraction pipeline for under-resourced languages",
    author = "Basaldella, Marco  and
      Helmy, Muhammad  and
      Antolli, Elisa  and
      Popescu, Mihai Horia  and
      Serra, Giuseppe  and
      Tasso, Carlo",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_012",
    doi = "10.26615/978-954-452-049-6_012",
    pages = "78--85",
    abstract = "This paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement pipelines for Arabic, Italian, Portuguese, and Romanian, and we build test collections for languages which lack one. Then, we add a Machine Learning module trained on a well-known English language corpus and we evaluate the performance not only over English but on the other languages as well. Finally, we repeat the same evaluation after training the pipeline over an Arabic language corpus to check whether using a language-specific corpus brings a further improvement in performance. On the five languages we analyzed, results show an improvement in performance when using a machine learning algorithm, even if such algorithm is not trained and tested on the same language.",
}
@inproceedings{bastawisy-elmahdy-2017-multi,
    title = "Multi-Lingual Phrase-Based Statistical Machine Translation for {A}rabic-{E}nglish",
    author = "Bastawisy, Ahmed  and
      Elmahdy, Mohamed",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_013",
    doi = "10.26615/978-954-452-049-6_013",
    pages = "86--89",
    abstract = "In this paper, we implement a multilingual Statistical Machine Translation (SMT) system for Arabic-English Translation. Arabic Text can be categorized into standard and dialectal Arabic. These two forms of Arabic differ significantly. Different mono-lingual and multi-lingual hybrid SMT approaches are compared. Mono-lingual systems do always results in better translation accuracy in one Arabic form and poor accuracy in the other. Multi-lingual SMT models that are trained with pooled parallel MSA/dialectal data result in better accuracy. However, since the available parallel MSA data are much larger compared to dialectal data, multilingual models are biased to MSA. We propose in the work, a multi-lingual combination of different mono-lingual systems using an Arabic form classifier. The outcome of the classier directs the system to use the appropriate mono-lingual models (standard, dialectal, or mixture). Testing the different SMT systems shows that the proposed classifier-based SMT system outperforms mono-lingual and data pooled multi-lingual systems.",
}
@inproceedings{benikova-zesch-2017-different,
    title = "Same same, but different: Compositionality of paraphrase granularity levels",
    author = "Benikova, Darina  and
      Zesch, Torsten",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_014",
    doi = "10.26615/978-954-452-049-6_014",
    pages = "90--96",
    abstract = "Paraphrases exist on different granularity levels, the most frequently used one being the sentential level. However, we argue that working on the sentential level is not optimal for both machines and humans, and that it would be easier and more efficient to work on sub-sentential levels. To prove this, we quantify and analyze the difference between paraphrases on both sentence and sub-sentence level in order to show the significance of the problem. First results on a preliminary dataset seem to confirm our hypotheses.",
}
@inproceedings{bobicev-sokolova-2017-inter,
    title = "Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective",
    author = "Bobicev, Victoria  and
      Sokolova, Marina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_015",
    doi = "10.26615/978-954-452-049-6_015",
    pages = "97--102",
    abstract = "Manual text annotation is an essential part of Big Text analytics. Although annotators work with limited parts of data sets, their results are extrapolated by automated text classification and affect the final classification results. Reliability of annotations and adequacy of assigned labels are especially important in the case of sentiment annotations. In the current study we examine inter-annotator agreement in multi-class, multi-label sentiment annotation of messages. We used several annotation agreement measures, as well as statistical analysis and Machine Learning to assess the resulting annotations.",
}
@inproceedings{boros-etal-2017-fast,
    title = "Fast and Accurate Decision Trees for Natural Language Processing Tasks",
    author = "Boros, Tiberiu  and
      Dumitrescu, Stefan Daniel  and
      Pipa, Sonia",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_016",
    doi = "10.26615/978-954-452-049-6_016",
    pages = "103--110",
    abstract = "Decision trees have been previously employed in many machine-learning tasks such as part-of-speech tagging, lemmatization, morphological-attribute resolution, letter-to-sound conversion and statistical-parametric speech synthesis. In this paper we introduce an optimized tree-computation algorithm, which is based on the original ID3 algorithm. We also introduce a tree-pruning method that uses a development set to delete nodes from over-fitted models. The later mentioned algorithm also uses a results caching method for speed-up. Our algorithm is almost 200 times faster than a naive implementation and yields accurate results on our test datasets.",
}
@inproceedings{bossard-rodrigues-2017-evolutionary,
    title = "An Evolutionary Algorithm for Automatic Summarization",
    author = "Bossard, Aur{\'e}lien  and
      Rodrigues, Christophe",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_017",
    doi = "10.26615/978-954-452-049-6_017",
    pages = "111--120",
    abstract = "This paper proposes a novel method to select sentences for automatic summarization based on an evolutionary algorithm. The algorithm explores candidate summaries space following an objective function computed over ngrams probability distributions of the candidate summary and the source documents. This method does not consider a summary as a stack of independent sentences but as a whole text, and makes use of advances in unsupervised summarization evaluation. We compare this sentence extraction method to one of the best existing methods which is based on integer linear programming, and show its efficiency on three different acknowledged corpora.",
}
@inproceedings{boyanov-etal-2017-building,
    title = "Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics",
    author = "Boyanov, Martin  and
      Nakov, Preslav  and
      Moschitti, Alessandro  and
      Da San Martino, Giovanni  and
      Koychev, Ivan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_018",
    doi = "10.26615/978-954-452-049-6_018",
    pages = "121--129",
    abstract = "We propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbot systems. The evaluation shows that the model achieves a MAP of 63.5{\%} on the extrinsic task. Moreover, our manual evaluation demonstrates that the model can answer correctly 49.5{\%} of the questions when they are similar in style to how questions are asked in the forum, and 47.3{\%} of the questions, when they are more conversational in style.",
}
@inproceedings{boytcheva-etal-2017-mining,
    title = "Mining Association Rules from Clinical Narratives",
    author = "Boytcheva, Svetla  and
      Nikolova, Ivelina  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_019",
    doi = "10.26615/978-954-452-049-6_019",
    pages = "130--138",
    abstract = "Shallow text analysis (Text Mining) uses mainly Information Extraction techniques. The low resource languages do not allow application of such traditional techniques with sufficient accuracy and recall on big data. In contrast, Data Mining approaches provide an opportunity to make deep analysis and to discover new knowledge. Frequent pattern mining approaches are used mainly for structured information in databases and are a quite challenging task in text mining. Unfortunately, most frequent pattern mining approaches do not use contextual information for extracted patterns: general patterns are extracted regardless of the context. We propose a method that processes raw informal texts (from health discussion forums) and formal texts (outpatient records) in Bulgarian language. In addition we use some context information and small terminological lexicons to generalize extracted frequent patterns. This allows to map informal expression of medical terminology to the formal one and to generate automatically resources.",
}
@inproceedings{calixto-liu-2017-sentence,
    title = "Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing",
    author = "Calixto, Iacer  and
      Liu, Qun",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_020",
    doi = "10.26615/978-954-452-049-6_020",
    pages = "139--148",
    abstract = "We propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce an objective function that uses pairwise ranking adapted to the case of three or more input sources. We compare our model against different baselines, and evaluate the robustness of our embeddings on image{--}sentence ranking (ISR), semantic textual similarity (STS), and neural machine translation (NMT). We find that the additional multilingual signals lead to improvements on all three tasks, and we highlight that our model can be used to consistently improve the adequacy of translations generated with NMT models when re-ranking n-best lists.",
}
@inproceedings{calleja-etal-2017-role,
    title = "Role-based model for Named Entity Recognition",
    author = "Calleja, Pablo  and
      Garc{\'\i}a-Castro, Ra{\'u}l  and
      Aguado-de-Cea, Guadalupe  and
      G{\'o}mez-P{\'e}rez, Asunci{\'o}n",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_021",
    doi = "10.26615/978-954-452-049-6_021",
    pages = "149--156",
    abstract = "Named Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. This work proposes a NER model that relies on role classification models that support recognizing entities with a specific role. The proposed model has been implemented in two use cases using Spanish drug Summary of Product Characteristics: identification of therapeutic indications and identification of adverse reactions. The results show how precision is increased using a NER model that is oriented towards a specific role and discards entities out of scope.",
}
@inproceedings{canales-etal-2017-towards,
    title = "Towards the Improvement of Automatic Emotion Pre-annotation with Polarity and Subjective Information",
    author = "Canales, Lea  and
      Daelemans, Walter  and
      Boldrini, Ester  and
      Mart{\'\i}nez-Barco, Patricio",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_022",
    doi = "10.26615/978-954-452-049-6_022",
    pages = "157--163",
    abstract = "Emotion detection has a high potential positive impact on the benefit of business, society, politics or education. Given this, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection: emotional corpora annotation. This will be tackled by proposing a semi-automatic methodology. It consists in two main phases: (1) an automatic process to pre-annotate the unlabelled sentences with a reduced number of emotional categories; and (2) a manual process of refinement where human annotators will determine which is the dominant emotion between the pre-defined set. Our objective in this paper is to show the pre-annotation process, as well as to evaluate the usability of subjective and polarity information in this process. The evaluation performed confirms clearly the benefits of employing the polarity and subjective information on emotion detection and thus endorses the relevance of our approach.",
}
@inproceedings{chen-bangalore-2017-underspecification,
    title = "Underspecification in Natural Language Understanding for Dialog Automation",
    author = "Chen, John  and
      Bangalore, Srinivas",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_023",
    doi = "10.26615/978-954-452-049-6_023",
    pages = "164--170",
    abstract = "With the increasing number of communication platforms that offer variety of ways of connecting two interlocutors, there is a resurgence of chat-based dialog systems. These systems, typically known as \textit{chatbots} have been successfully applied in a range of consumer and enterprise applications. A key technology in such chat-bots is robust natural language understanding (NLU) which can significantly influence and impact the efficacy of the conversation and ultimately the user-experience. While NLU is far from perfect, this paper illustrates the role of \textit{underspecification} and its impact on successful dialog completion.",
}
@inproceedings{chiru-decea-2017-identification,
    title = "Identification and Classification of the Most Important Moments in Students{'} Collaborative Chats",
    author = "Chiru, Costin  and
      Decea, Remus",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_024",
    doi = "10.26615/978-954-452-049-6_024",
    pages = "171--176",
    abstract = "In this paper, we present an application for the automatic identification of the important moments that might occur during students{'} collaborative chats. The moments are detected based on the input received from the user, who may choose to perform an analysis on the topics that interest him/her. Moreover, the application offers various types of suggestive and intuitive graphics that aid the user in identification of such moments. There are two main aspects that are considered when identifying important moments: the concepts{'} frequency and distribution throughout the conversation and the chat tempo, which is analyzed for identifying intensively debated concepts. By the tempo of the chat we understand the rate at which the ideas are input by the chat participants, expressed by the utterances{'} timestamps.",
}
@inproceedings{cotik-etal-2017-annotation,
    title = "Annotation of Entities and Relations in {S}panish Radiology Reports",
    author = "Cotik, Viviana  and
      Filippo, Dar{\'\i}o  and
      Roller, Roland  and
      Uszkoreit, Hans  and
      Xu, Feiyu",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_025",
    doi = "10.26615/978-954-452-049-6_025",
    pages = "177--184",
    abstract = "Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge. Supervised machine learning methods are very popular to address information extraction, but are usually domain and language dependent. To train new classification models, annotated data is required. Moreover, annotated data is also required as an evaluation resource of information extraction algorithms. However, one major drawback of processing clinical data is the low availability of annotated datasets. For this reason we performed a manual annotation of radiology reports written in Spanish. This paper presents the corpus, the annotation schema, the annotation guidelines and further insight of the data.",
}
@inproceedings{dakota-kubler-2017-towards,
    title = "Towards Replicability in Parsing",
    author = {Dakota, Daniel  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_026",
    doi = "10.26615/978-954-452-049-6_026",
    pages = "185--194",
    abstract = "We investigate parsing replicability across 7 languages (and 8 treebanks), showing that choices concerning the use of grammatical functions in parsing or evaluation, the influence of the rare word threshold, as well as choices in test sentences and evaluation script options have considerable and often unexpected effects on parsing accuracies. All of those choices need to be carefully documented if we want to ensure replicability.",
}
@inproceedings{davoodi-kosseim-2017-automatic,
    title = "Automatic Identification of {A}lt{L}exes using Monolingual Parallel Corpora",
    author = "Davoodi, Elnaz  and
      Kosseim, Leila",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_027",
    doi = "10.26615/978-954-452-049-6_027",
    pages = "195--200",
    abstract = "The automatic identification of discourse relations is still a challenging task in natural language processing. Discourse connectives, such as since or but, are the most informative cues to identify explicit relations; however discourse parsers typically use a closed inventory of such connectives. As a result, discourse relations signalled by markers outside these inventories (i.e. AltLexes) are not detected as effectively. In this paper, we propose a novel method to leverage parallel corpora in text simplification and lexical resources to automatically identify alternative lexicalizations that signal discourse relation. When applied to the Simple Wikipedia and Newsela corpora along with WordNet and the PPDB, the method allowed the automatic discovery of 91 AltLexes.",
}
@inproceedings{dinu-etal-2017-stylistic,
    title = "On the stylistic evolution from communism to democracy: {S}olomon {M}arcus study case",
    author = "Dinu, Anca  and
      Dinu, Liviu P.  and
      Dumitru, Bogdan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_028",
    doi = "10.26615/978-954-452-049-6_028",
    pages = "201--207",
    abstract = "In this article we propose a stylistic analysis of Solomon Marcus{'} non-scientific published texts, gathered in six volumes, aiming to uncover some of his quantitative and qualitative fingerprints. Moreover, we compare and cluster two distinct periods of time in his writing style: 22 years of communist regime (1967-1989) and 27 years of democracy (1990-2016). The distributional analysis of Marcus{'} text reveals that the passing from the communist regime period to democracy is sharply marked by two complementary changes in Marcus{'} writing: in the pre-democracy period, the communist norms of writing style demanded on the one hand long phrases, long words and clich{\'e}s, and on the other hand, a short list of preferred {``}official{''} topics; in democracy tendency was towards shorten phrases and words while approaching a broader area of topics.",
}
@inproceedings{edouard-etal-2017-building,
    title = "Building timelines of soccer matches from {T}witter",
    author = "Edouard, Amosse  and
      Cabrio, Elena  and
      Tonelli, Sara  and
      Le-Thanh, Nhan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_029",
    doi = "10.26615/978-954-452-049-6_029",
    pages = "208--213",
    abstract = "This demo paper presents a system that builds a timeline with salient actions of a soccer game, based on the tweets posted by users. It combines information provided by external knowledge bases to enrich the content of tweets and applies graph theory to model relations between actions (e.g. goals, penalties) and participants of a game (e.g. players, teams). In the demo, a web application displays in nearly real-time the actions detected from tweets posted by users for a given match of Euro 2016. Our tools are freely available at \url{https://bitbucket.org/eamosse/event_tracking}.",
}
@inproceedings{edouard-etal-2017-youll,
    title = "You{'}ll Never Tweet Alone: Building Sports Match Timelines from Microblog Posts",
    author = "Edouard, Amosse  and
      Cabrio, Elena  and
      Tonelli, Sara  and
      Le-Thanh, Nhan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_030",
    doi = "10.26615/978-954-452-049-6_030",
    pages = "214--221",
    abstract = "In this paper, we propose an approach to build a timeline with actions in a sports game based on tweets. We combine information provided by external knowledge bases to enrich the content of the tweets, and apply graph theory to model relations between actions and participants in a game. We demonstrate the validity of our approach using tweets collected during the EURO 2016 Championship and evaluate the output against live summaries produced by sports channels.",
}
@inproceedings{edouard-etal-2017-graph,
    title = "Graph-based Event Extraction from {T}witter",
    author = "Edouard, Amosse  and
      Cabrio, Elena  and
      Tonelli, Sara  and
      Le-Thanh, Nhan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_031",
    doi = "10.26615/978-954-452-049-6_031",
    pages = "222--230",
    abstract = "Detecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly focused on detecting spikes in clusters around specific keywords or Named Entities (NE). However, one of the main drawbacks of such approaches is the difficulty in understanding when the same keywords describe different events. In this paper, we propose a novel approach that exploits NE mentions in tweets and their entity context to create a temporal event graph. Then, using simple graph theory techniques and a PageRank-like algorithm, we process the event graphs to detect clusters of tweets describing the same events. Experiments on two gold standard datasets show that our approach achieves state-of-the-art results both in terms of evaluation performances and the quality of the detected events.",
}
@inproceedings{fernandez-etal-2017-opinion,
    title = "Opinion Mining in Social Networks versus Electoral Polls",
    author = "Fern{\'a}ndez, Javi  and
      Llopis, Fernando  and
      Guti{\'e}rrez, Yoan  and
      Mart{\'\i}nez-Barco, Patricio  and
      D{\'\i}ez, {\'A}lvaro",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_032",
    doi = "10.26615/978-954-452-049-6_032",
    pages = "231--237",
    abstract = "The recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential elections with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity to make predictions in this context. In this paper we present our approximation and compare it with a real case: the 2017 French presidential election.",
}
@inproceedings{galarreta-etal-2017-corpus,
    title = "Corpus Creation and Initial {SMT} Experiments between {S}panish and {S}hipibo-konibo",
    author = "Galarreta, Ana-Paula  and
      Melgar, Andr{\'e}s  and
      Oncevay, Arturo",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_033",
    doi = "10.26615/978-954-452-049-6_033",
    pages = "238--244",
    abstract = "In this paper, we present the first attempts to develop a machine translation (MT) system between Spanish and Shipibo-konibo (es-shp). There are very few digital texts written in Shipibo-konibo and even less bilingual texts that can be aligned, hence we had to create a parallel corpus using both bilingual and monolingual texts. We will describe how this corpus was made, as well as the process we followed to improve the quality of the sentences used to build a statistical MT model or SMT. The results obtained surpassed the baseline proposed (dictionary based) and made a promising result for further development considering the size of corpus used. Finally, it is expected that this MT system can be reinforced with the use of additional linguistic rules and automatic language processing functions that are being implemented.",
}
@inproceedings{galieva-etal-2017-russian,
    title = "{R}ussian-{T}atar Socio-Political Thesaurus: Methodology, Challenges, the Status of the Project",
    author = "Galieva, Alfiya  and
      Nevzorova, Olga  and
      Yakubova, Dilyara",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_034",
    doi = "10.26615/978-954-452-049-6_034",
    pages = "245--252",
    abstract = "This paper discusses the general methodology and important practical aspects of implementing a new bilingual lexical resource {--} the Russian-Tatar Socio-Political Thesaurus that is being developed on the basis of the Russian RuThes thesaurus format as a hierarchy of concepts viewed as units of thought. Each concept is linked with a set of language expressions (words and collocations) referring to it in texts (text entries). Currently the Russian-Tatar Socio-Political Thesaurus includes 6,000 concepts, while new concepts and text entries are being constantly added to it. The paper outlines main challenges of translating concept names and their text entries into Tatar, and describes ways of reflecting the specificity of the Tatar lexical-semantic system.",
}
@inproceedings{galitsky-ilvovsky-2017-chat,
    title = "On a Chat Bot Finding Answers with Optimal Rhetoric Representation",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_035",
    doi = "10.26615/978-954-452-049-6_035",
    pages = "253--259",
    abstract = "We demo a chat bot with the focus on complex, multi-sentence questions that enforce what we call rhetoric agreement of answers with questions. Chat bot finds answers which are not only relevant by topic but also match the question by style, argumentation patterns, communication means, experience level and other attributes. The system achieves rhetoric agreement by learning pairs of discourse trees (DTs) for question (Q) and answer (A). We build a library of best answer DTs for most types of complex questions. To better recognize a valid rhetoric agreement between Q and A, DTs are extended with the labels for communicative actions. An algorithm for finding the best DT for an A, given a Q, is evaluated.",
}
@inproceedings{gao-huang-2017-detecting,
    title = "Detecting Online Hate Speech Using Context Aware Models",
    author = "Gao, Lei  and
      Huang, Ruihong",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_036",
    doi = "10.26615/978-954-452-049-6_036",
    pages = "260--266",
    abstract = "In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3{\%} to 4{\%} in F1 score and combining these two models further improve the performance by another 7{\%} in F1 score.",
}
@inproceedings{gencheva-etal-2017-context,
    title = "A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates",
    author = "Gencheva, Pepa  and
      Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Koychev, Ivan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_037",
    doi = "10.26615/978-954-452-049-6_037",
    pages = "267--276",
    abstract = "In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new corpus of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.",
}
@inproceedings{gromann-declerck-2017-hashtag,
    title = "Hashtag Processing for Enhanced Clustering of Tweets",
    author = "Gromann, Dagmar  and
      Declerck, Thierry",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_038",
    doi = "10.26615/978-954-452-049-6_038",
    pages = "277--283",
    abstract = "Rich data provided by tweets have beenanalyzed, clustered, and explored in a variety of studies. Typically those studies focus on named entity recognition, entity linking, and entity disambiguation or clustering. Tweets and hashtags are generally analyzed on sentential or word level but not on a compositional level of concatenated words. We propose an approach for a closer analysis of compounds in hashtags, and in the long run also of other types of text sequences in tweets, in order to enhance the clustering of such text documents. Hashtags have been used before as primary topic indicators to cluster tweets, however, their segmentation and its effect on clustering results have not been investigated to the best of our knowledge. Our results with a standard dataset from the Text REtrieval Conference (TREC) show that segmented and harmonized hashtags positively impact effective clustering.",
}
@inproceedings{guillen-etal-2017-natural,
    title = "Natural Language Processing Technologies for Document Profiling",
    author = "Guill{\'e}n, Antonio  and
      Guti{\'e}rrez, Yoan  and
      Mu{\~n}oz, Rafael",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_039",
    doi = "10.26615/978-954-452-049-6_039",
    pages = "284--290",
    abstract = "Nowadays, search for documents on the Internet is becoming increasingly difficult. The reason is the amount of content published by users (articles, comments, blogs, reviews). How to facilitate that the users can find their required documents? What would be necessary to provide useful document meta-data for supporting search engines? In this article, we present a study of some Natural Language Processing (NLP) technologies that can be useful for facilitating the proper identification of documents according to the user needs. For this purpose, it is designed a document profile that will be able to represent semantic meta-data extracted from documents by using NLP technologies. The research is basically focused on the study of different NLP technologies in order to support the creation our novel document profile proposal from semantic perspectives.",
}
@inproceedings{hazem-etal-2017-mappsent,
    title = "{M}app{S}ent: a Textual Mapping Approach for Question-to-Question Similarity",
    author = "Hazem, Amir  and
      El Amel Boussaha, Basma  and
      Hernandez, Nicolas",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_040",
    doi = "10.26615/978-954-452-049-6_040",
    pages = "291--300",
    abstract = "Since the advent of word embedding methods, the representation of longer pieces of texts such as sentences and paragraphs is gaining more and more interest, especially for textual similarity tasks. Mikolov et al. (2013) have demonstrated that words and phrases exhibit linear structures that allow to meaningfully combine words by an element-wise addition of their vector representations. Recently, Arora et al. (2017) have shown that removing the projections of the weighted average sum of word embedding vectors on their first principal components, outperforms sophisticated supervised methods including RNN{'}s and LSTM{'}s. Inspired by Mikolov et al. (2013) and Arora et al. (2017) findings and by a bilingual word mapping technique presented in Artetxe et al. (2016), we introduce MappSent, a novel approach for textual similarity. Based on a linear sentence embedding representation, its principle is to build a matrix that maps sentences in a joint-subspace where similar sets of sentences are pushed closer. We evaluate our approach on the SemEval 2016/2017 question-to-question similarity task and show that overall MappSent achieves competitive results and outperforms in most cases state-of-art methods.",
}
@inproceedings{hercig-lenc-2017-impact,
    title = "The Impact of Figurative Language on Sentiment Analysis",
    author = "Hercig, Tom{\'a}{\v{s}}  and
      Lenc, Ladislav",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_041",
    doi = "10.26615/978-954-452-049-6_041",
    pages = "301--308",
    abstract = "Figurative language such as irony, sarcasm, and metaphor is considered a significant challenge in sentiment analysis. These figurative devices can sculpt the affect of an utterance and test the limits of sentiment analysis of supposedly literal texts. We explore the effect of figurative language on sentiment analysis. We incorporate the figurative language indicators into the sentiment analysis process and compare the results with and without the additional information about them. We evaluate on the SemEval-2015 Task 11 data and outperform the first team with our convolutional neural network model and additional training data in terms of mean squared error and we follow closely behind the first place in terms of cosine similarity.",
}
@inproceedings{hooda-kosseim-2017-argument,
    title = "Argument Labeling of Explicit Discourse Relations using {LSTM} Neural Networks",
    author = "Hooda, Sohail  and
      Kosseim, Leila",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_042",
    doi = "10.26615/978-954-452-049-6_042",
    pages = "309--315",
    abstract = "Argument labeling of explicit discourse relations is a challenging task. The state of the art systems achieve slightly above 55{\%} F-measure but require hand-crafted features. In this paper, we propose a Long Short Term Memory (LSTM) based model for argument labeling. We experimented with multiple configurations of our model. Using the PDTB dataset, our best model achieved an F1 measure of 23.05{\%} without any feature engineering. This is significantly higher than the 20.52{\%} achieved by the state of the art RNN approach, but significantly lower than the feature based state of the art systems. On the other hand, because our approach learns only from the raw dataset, it is more widely applicable to multiple textual genres and languages.",
}
@inproceedings{hu-etal-2017-non,
    title = "Non-Deterministic Segmentation for {C}hinese Lattice Parsing",
    author = {Hu, Hai  and
      Dakota, Daniel  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_043",
    doi = "10.26615/978-954-452-049-6_043",
    pages = "316--324",
    abstract = "Parsing Chinese critically depends on correct word segmentation for the parser since incorrect segmentation inevitably causes incorrect parses. We investigate a pipeline approach to segmentation and parsing using word lattices as parser input. We compare CRF-based and lexicon-based approaches to word segmentation. Our results show that the lattice parser is capable of selecting the correction segmentation from thousands of options, thus drastically reducing the number of unparsed sentence. Lexicon-based parsing models have a better coverage than the CRF-based approach, but the many options are more difficult to handle. We reach our best result by using a lexicon from the n-best CRF analyses, combined with highly probable words.",
}
@inproceedings{kanishcheva-bobicev-2017-good,
    title = "Good News vs. Bad News: What are they talking about?",
    author = "Kanishcheva, Olga  and
      Bobicev, Victoria",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_044",
    doi = "10.26615/978-954-452-049-6_044",
    pages = "325--333",
    abstract = "Today{'}s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad but have to be delivered in neutral words as pure facts. Although there are applications for sentiment analysis of news, the task of news analysis is still a very actual problem because the latest news impacts people{'}s lives daily. In this paper, we explored the problem of sentiment analysis for Ukrainian and Russian news, developed a corpus of Ukrainian and Russian news and annotated each text using one of three categories: positive, negative and neutral. Each text was marked by at least three independent annotators via the web interface, the inter-annotator agreement was analyzed and the final label for each text was computed. These texts were used in the machine learning experiments. Further, we investigated what kinds of named entities such as Locations, Organizations, Persons are perceived as good or bad by the readers and which of them were the cause for text annotation ambiguity.",
}
@inproceedings{karadzhov-etal-2017-built,
    title = "We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!",
    author = "Karadzhov, Georgi  and
      Gencheva, Pepa  and
      Nakov, Preslav  and
      Koychev, Ivan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_045",
    doi = "10.26615/978-954-452-049-6_045",
    pages = "334--343",
    abstract = "It is completely amazing! Fake news and {``}click baits{''} have totally invaded the cyberspace. Let us face it: everybody hates them for three simple reasons. Reason {\#}2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you know, somebody has to stop it. So, we did this research, and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever! Seriously, come have a look, we have it all: neural networks, attention mechanism, sentiment lexicons, author profiling, you name it. Lexical features, semantic features, we absolutely have it all. And we have totally tested it, trust us! We have results, and numbers, really big numbers. The best numbers ever! Oh, and analysis, absolutely top notch analysis. Interested? Come read the shocking truth about fake news and clickbait in the Bulgarian cyberspace. You won{'}t believe what we have found!",
}
@inproceedings{karadzhov-etal-2017-fully,
    title = "Fully Automated Fact Checking Using External Sources",
    author = "Karadzhov, Georgi  and
      Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Koychev, Ivan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_046",
    doi = "10.26615/978-954-452-049-6_046",
    pages = "344--353",
    abstract = "Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.",
}
@inproceedings{kaushik-etal-2017-making,
    title = "Making Travel Smarter: Extracting Travel Information From Email Itineraries Using Named Entity Recognition",
    author = "Kaushik, Divyansh  and
      Gupta, Shashank  and
      Raju, Chakradhar  and
      Dias, Reuben  and
      Ghosh, Sanjib",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_047",
    doi = "10.26615/978-954-452-049-6_047",
    pages = "354--362",
    abstract = "The purpose of this research is to address the problem of extracting information from travel itineraries and discuss the challenges faced in the process. Business-to-customer emails like booking confirmations and e-tickets are usually machine generated by filling slots in pre-defined templates which improve the presentation of such emails but also make the emails more complex in structure. Extracting the relevant information from these emails would let users track their journeys and important updates on applications installed on their devices to give them a consolidated over view of their itineraries and also save valuable time. We investigate the use of an HMM-based named entity recognizer on such emails which we will use to label and extract relevant entities. NER in such emails is challenging as these itineraries offer less useful contextual information. We also propose a rich set of features which are integrated into the model and are specific to our domain. The result from our model is a list of lists containing the relevant information extracted from ones itinerary.",
}
@inproceedings{kedzia-etal-2017-graph,
    title = "Graph-Based Approach to Recognizing {CST} Relations in {P}olish Texts",
    author = "K{\k{e}}dzia, Pawe{\l}  and
      Piasecki, Maciej  and
      Janz, Arkadiusz",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_048",
    doi = "10.26615/978-954-452-049-6_048",
    pages = "363--371",
    abstract = "This paper presents an supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. In the proposed, graph-based representation is constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relation extracted from text. Similarity between sentences is calculated from graph, and the similarity values are input to classifiers trained by Logistic Model Tree. Several different configurations of graph, as well as graph similarity methods were analysed for this tasks. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.",
}
@inproceedings{kobus-etal-2017-domain,
    title = "Domain Control for Neural Machine Translation",
    author = "Kobus, Catherine  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_049",
    doi = "10.26615/978-954-452-049-6_049",
    pages = "372--378",
    abstract = "Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have already been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains. The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data. In addition, model parameters do not need to be re-estimated for each domain, making this effective to real use cases. Evaluation is carried out on English-to-French translation for two different testing scenarios. We first consider the case where an end-user performs translations on a known domain. Secondly, we consider the scenario where the domain is not known and predicted at the sentence level before translating. Results show consistent accuracy improvements for both conditions.",
}
@inproceedings{kocmi-bojar-2017-curriculum,
    title = "Curriculum Learning and Minibatch Bucketing in Neural Machine Translation",
    author = "Kocmi, Tom  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_050",
    doi = "10.26615/978-954-452-049-6_050",
    pages = "379--386",
    abstract = "We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT). We focus on two types of such orderings: (1) ensuring that each minibatch contains sentences similar in some aspect and (2) gradual inclusion of some sentence types as the training progresses (so called {``}curriculum learning{''}). In our English-to-Czech experiments, the internal homogeneity of minibatches has no effect on the training but some of our {``}curricula{''} achieve a small improvement over the baseline.",
}
@inproceedings{kocon-marcinczuk-2017-improved,
    title = "Improved Recognition and Normalisation of {P}olish Temporal Expressions",
    author = "Koco{\'n}, Jan  and
      Marci{\'n}czuk, Micha{\l}",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_051",
    doi = "10.26615/978-954-452-049-6_051",
    pages = "387--393",
    abstract = "In this article we present the result of the recent research in the recognition and normalisation of Polish temporal expressions. The temporal information extracted from the text plays major role in many information extraction systems, like question answering, event recognition or discourse analysis. We proposed a new method for the temporal expressions normalisation, called Cascade of Partial Rules. Here we describe results achieved by updated version of Liner2 machine learning system.",
}
@inproceedings{konkol-2017-joint,
    title = "Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees",
    author = "Konkol, Michal",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_052",
    doi = "10.26615/978-954-452-049-6_052",
    pages = "394--400",
    abstract = "In this paper, we introduce WoRel, a model that jointly learns word embeddings and a semantic representation of word relations. The model learns from plain text sentences and their dependency parse trees. The word embeddings produced by WoRel outperform Skip-Gram and GloVe in word similarity and syntactical word analogy tasks and have comparable results on word relatedness and semantic word analogy tasks. We show that the semantic representation of relations enables us to express the meaning of phrases and is a promising research direction for semantics at the sentence level.",
}
@inproceedings{konopik-etal-2017-czech,
    title = "{C}zech Dataset for Semantic Similarity and Relatedness",
    author = "Konop{\'\i}k, Miloslav  and
      Pra{\v{z}}{\'a}k, Ond{\v{r}}ej  and
      Steinberger, David",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_053",
    doi = "10.26615/978-954-452-049-6_053",
    pages = "401--406",
    abstract = "This paper introduces a Czech dataset for semantic similarity and semantic relatedness. The dataset contains word pairs with hand annotated scores that indicate the semantic similarity and semantic relatedness of the words. The dataset contains 953 word pairs compiled from 9 different sources. It contains words and their contexts taken from real text corpora including extra examples when the words are ambiguous. The dataset is annotated by 5 independent annotators. The average Spearman correlation coefficient of the annotation agreement is $r = 0.81$. We provide reference evaluation experiments with several methods for computing semantic similarity and relatedness.",
}
@inproceedings{laali-kosseim-2017-improving,
    title = "Improving Discourse Relation Projection to Build Discourse Annotated Corpora",
    author = "Laali, Majid  and
      Kosseim, Leila",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_054",
    doi = "10.26615/978-954-452-049-6_054",
    pages = "407--416",
    abstract = "The naive approach to annotation projection is not effective to project discourse annotations from one language to another because implicit relations are often changed to explicit ones and vice-versa in the translation. In this paper, we propose a novel approach based on the intersection between statistical word-alignment models to identify unsupported discourse annotations. This approach identified 65{\%} of the unsupported annotations in the English-French parallel sentences from Europarl. By filtering out these unsupported annotations, we induced the first PDTB-style discourse annotated corpus for French from Europarl. We then used this corpus to train a classifier to identify the discourse-usage of French discourse connectives and show a 15{\%} improvement of F1-score compared to the classifier trained on the non-filtered annotations.",
}
@inproceedings{lafourcade-le-brun-2017-extracting,
    title = "Extracting semantic relations via the combination of inferences, schemas and cooccurrences",
    author = "Lafourcade, Mathieu  and
      Le Brun, Nathalie",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_055",
    doi = "10.26615/978-954-452-049-6_055",
    pages = "417--423",
    abstract = "Extracting semantic relations from texts is a good way to build and supply a knowledge base, an indispensable resource for text analysis. We propose and evaluate the combination of three ways of producing lexical-semantic relations.",
}
@inproceedings{lafourcade-etal-2017-mice,
    title = "If mice were reptiles, then reptiles could be mammals or How to detect errors in the {J}eux{D}e{M}ots lexical network?",
    author = "Lafourcade, Mathieu  and
      Joubert, Alain  and
      Le Brun, Nathalie",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_056",
    doi = "10.26615/978-954-452-049-6_056",
    pages = "424--430",
    abstract = "Correcting errors in a data set is a critical issue. This task can be either hand-made by experts, or by crowdsourcing methods, or automatically done using algorithms. Although the rate of errors present in the JeuxDeMots network is rather low, it is important to reduce it. We present here automatic methods for detecting potential secondary errors that would result from automatic inference mechanisms when they rely on an initial error manually detected. Encouraging results also invite us to consider strategies that would automatically detect {``}erroneous{''} initial relations, which could lead to the automatic detection of the majority of errors in the network.",
}
@inproceedings{lenc-kral-2017-word,
    title = "Word Embeddings for Multi-label Document Classification",
    author = "Lenc, Ladislav  and
      Kr{\'a}l, Pavel",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_057",
    doi = "10.26615/978-954-452-049-6_057",
    pages = "431--437",
    abstract = "In this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label classification scenario. The embeddings are used in three convolutional neural network topologies. The experiments are realized on the Czech {\v{C}}TK and English Reuters-21578 standard corpora. We compare the results of word2vec static and trainable embeddings with randomly initialized word vectors. We conclude that initialization does not play an important role for classification. However, learning of word vectors is crucial to obtain good results.",
}
@inproceedings{li-dickinson-2017-gender,
    title = "Gender Prediction for {C}hinese Social Media Data",
    author = "Li, Wen  and
      Dickinson, Markus",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_058",
    doi = "10.26615/978-954-452-049-6_058",
    pages = "438--445",
    abstract = "Social media provides users a platform to publish messages and socialize with others, and microblogs have gained more users than ever in recent years. With such usage, user profiling is a popular task in computational linguistics and text mining. Different approaches have been used to predict users{'} gender, age, and other information, but most of this work has been done on English and other Western languages. The goal of this project is to predict the gender of users based on their posts on Weibo, a Chinese micro-blogging platform. Given issues in Chinese word segmentation, we explore character and word n-grams as features for this task, as well as using character and word embeddings for classification. Given how the data is extracted, we approach the task on a per-post basis, and we show the difficulties of the task for both humans and computers. Nonetheless, we present encouraging results and point to future improvements.",
}
@inproceedings{liao-xie-2017-statistical,
    title = "A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing",
    author = "Liao, Zhihua  and
      Xie, Yan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_059",
    doi = "10.26615/978-954-452-049-6_059",
    pages = "446--451",
    abstract = "In this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the lambda-logical expression structure into a form suitable for the mechanics of statistical machine translation and useful for modeling. We show that our model is able to yield new state-of-the-art results on both standard datasets with simple features.",
}
@inproceedings{londhe-srihari-2017-summarizing,
    title = "Summarizing World Speak : A Preliminary Graph Based Approach",
    author = "Londhe, Nikhil  and
      Srihari, Rohini",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_060",
    doi = "10.26615/978-954-452-049-6_060",
    pages = "452--458",
    abstract = "Social media platforms play a crucial role in piecing together global news stories via their corresponding online discussions. Thus, in this work, we introduce the problem of automatically summarizing massively multilingual microblog text streams. We discuss the challenges involved in both generating summaries as well as evaluating them. We introduce a simple word graph based approach that utilizes node neighborhoods to identify keyphrases and thus in turn, pick summary candidates. We also demonstrate the effectiveness of our method in generating precise summaries as compared to other popular techniques.",
}
@inproceedings{loukachevitch-gerasimova-2017-human,
    title = "Human Associations Help to Detect Conventionalized Multiword Expressions",
    author = "Loukachevitch, Natalia  and
      Gerasimova, Anastasia",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_061",
    doi = "10.26615/978-954-452-049-6_061",
    pages = "459--466",
    abstract = "In this paper we show that if we want to obtain human evidence about conventionalization of some phrases, we should ask native speakers about associations they have to a given phrase and its component words. We have shown that if component words of a phrase have each other as frequent associations, then this phrase can be considered as conventionalized. Another type of conventionalized phrases can be revealed using two factors: low entropy of phrase associations and low intersection of component word and phrase associations. The association experiments were performed for the Russian language.",
}
@inproceedings{malmasi-zampieri-2017-detecting,
    title = "Detecting Hate Speech in Social Media",
    author = "Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_062",
    doi = "10.26615/978-954-452-049-6_062",
    pages = "467--472",
    abstract = "In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78{\%} accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.",
}
@inproceedings{marcinczuk-etal-2017-inforex,
    title = "{I}nforex {---} a collaborative system for text corpora annotation and analysis",
    author = "Marci{\'n}czuk, Micha{\l}  and
      Oleksy, Marcin  and
      Koco{\'n}, Jan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_063",
    doi = "10.26615/978-954-452-049-6_063",
    pages = "473--482",
    abstract = "We report a first major upgrade of Inforex {---} a web-based system for qualitative and collaborative text corpora annotation and analysis. Inforex is a part of Polish CLARIN infrastructure. It is integrated with a digital repository for storing and publishing language resources and allows to visualize, browse and annotate text corpora stored in the repository. As a result of a series of workshops for researches from humanities and social sciences fields we improved the graphical interface to make the system more friendly and readable for non-experienced users. We also implemented a new functionality for gold standard annotation which includes private annotations and annotation agreement by a super-annotator.",
}
@inproceedings{marcinczuk-2017-lemmatization,
    title = "Lemmatization of Multi-word Common Noun Phrases and Named Entities in {P}olish",
    author = "Marci{\'n}czuk, Micha{\l}",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_064",
    doi = "10.26615/978-954-452-049-6_064",
    pages = "483--491",
    abstract = "In the paper we present a tool for lemmatization of multi-word common noun phrases and named entities for Polish called LemmaPL. The tool is based on a set of manually crafted rules and heuristics utilizing a set of dictionaries (including morphological, named entities and inflection patterns). The accuracy of lemmatization obtained by the tool reached 97.99{\%} on a dataset with multi-word common noun phrases and 86.17{\%} for case-sensitive evaluation on a dataset with named entities.",
}
@inproceedings{mi-etal-2017-log,
    title = "Log-linear Models for {U}yghur Segmentation in Spoken Language Translation",
    author = "Mi, Chenggang  and
      Yang, Yating  and
      Dong, Rui  and
      Zhou, Xi  and
      Wang, Lei  and
      Li, Xiao  and
      Jiang, Tonghai",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_065",
    doi = "10.26615/978-954-452-049-6_065",
    pages = "492--500",
    abstract = "To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach. Instead of learning model only from monolingual annotated corpus, this approach optimizes Uyghur segmentation for spoken translation based on both bilingual and monolingual corpus. Our approach relies on several features such as traditional conditional random field (CRF) feature, bilingual word alignment feature and monolingual suffixword co-occurrence feature. Experimental results shown that our proposed segmentation model for Uyghur spoken translation achieved 1.6 BLEU score improvements compared with the state-of-the-art baseline.",
}
@inproceedings{mitrofan-2017-bootstrapping,
    title = "Bootstrapping a {R}omanian Corpus for Medical Named Entity Recognition",
    author = "Mitrofan, Maria",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_066",
    doi = "10.26615/978-954-452-049-6_066",
    pages = "501--509",
    abstract = "Named Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts. Due to the fact that for the Romanian language there are only a few linguistic resources specific to the biomedical domain, it was created a sub-corpus specific to this domain. In this paper we present a newly developed Romanian sub-corpus for medical-domain NER, which is a valuable asset for the field of biomedical text processing. We provide a description of the sub-corpus, informative statistics about data-composition and we evaluate an automatic NER tool on the newly created resource.",
}
@inproceedings{moreno-etal-2017-domain,
    title = "A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information",
    author = "Moreno, Isabel  and
      Rom{\'a}-Ferri, Mar{\'\i}a Teresa  and
      Moreda Pozo, Paloma",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_067",
    doi = "10.26615/978-954-452-049-6_067",
    pages = "510--518",
    abstract = "This paper presents a Named Entity Classification system, which employs machine learning. Our methodology employs local entity information and profiles as feature set. All features are generated in an unsupervised manner. It is tested on two different data sets: (i) DrugSemantics Spanish corpus (Overall F1 = 74.92), whose results are in-line with the state of the art without employing external domain-specific resources. And, (ii) English CONLL2003 dataset (Overall F1 = 81.40), although our results are lower than previous work, these are reached without external knowledge or complex linguistic analysis. Last, using the same configuration for the two corpora, the difference of overall F1 is only 6.48 points (DrugSemantics = 74.92 versus CoNLL2003 = 81.40). Thus, this result supports our hypothesis that our approach is language and domain independent and does not require any external knowledge or complex linguistic analysis.",
}
@inproceedings{mukherjee-kubler-2017-similarity,
    title = "Similarity Based Genre Identification for {POS} Tagging Experts {\&} Dependency Parsing",
    author = {Mukherjee, Atreyee  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_068",
    doi = "10.26615/978-954-452-049-6_068",
    pages = "519--526",
    abstract = "POS tagging and dependency parsing achieve good results for homogeneous datasets. However, these tasks are much more difficult on heterogeneous datasets. In (Mukherjee et al. 2016, 2017), we address this issue by creating genre experts for both POS tagging and parsing. We use topic modeling to automatically separate training and test data into genres and to create annotation experts per genre by training separate models for each topic. However, this approach assumes that topic modeling is performed jointly on training and test sentences each time a new test sentence is encountered. We extend this work by assigning new test sentences to their genre expert by using similarity metrics. We investigate three different types of methods: 1) based on words highly associated with a genre by the topic modeler, 2) using a k-nearest neighbor classification approach, and 3) using perplexity to determine the closest topic. The results show that the choice of similarity metric has an effect on results and that we can reach comparable accuracies to the joint topic modeling in POS tagging and dependency parsing, thus providing a viable and efficient approach to POS tagging and parsing a sentence by its genre expert.",
}
@inproceedings{naderi-hirst-2017-recognizing,
    title = "Recognizing Reputation Defence Strategies in Critical Political Exchanges",
    author = "Naderi, Nona  and
      Hirst, Graeme",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_069",
    doi = "10.26615/978-954-452-049-6_069",
    pages = "527--535",
    abstract = "We propose a new task of automatically detecting reputation defence strategies in the field of computational argumentation. We cast the problem as relation classification, where given a pair of reputation threat and reputation defence, we determine the reputation defence strategy. We annotate a dataset of parliamentary questions and answers with reputation defence strategies. We then propose a model based on supervised learning to address the detection of these strategies, and report promising experimental results.",
}
@inproceedings{naderi-hirst-2017-classifying,
    title = "Classifying Frames at the Sentence Level in News Articles",
    author = "Naderi, Nona  and
      Hirst, Graeme",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_070",
    doi = "10.26615/978-954-452-049-6_070",
    pages = "536--542",
    abstract = "Previous approaches to generic frame classification analyze frames at the document level. Here, we propose a supervised based approach based on deep neural networks and distributional representations for classifying frames at the sentence level in news articles. We conduct our experiments on the publicly available Media Frames Corpus compiled from the U.S. Newspapers. Using (B)LSTMs and GRU networks to represent the meaning of frames, we demonstrate that our approach yields at least 14-point improvement over several baseline methods.",
}
@inproceedings{nakov-vogel-2017-robust,
    title = "Robust Tuning Datasets for Statistical Machine Translation",
    author = "Nakov, Preslav  and
      Vogel, Stephan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_071",
    doi = "10.26615/978-954-452-049-6_071",
    pages = "543--550",
    abstract = "We explore the idea of automatically crafting a tuning dataset for Statistical Machine Translation (SMT) that makes the hyper-parameters of the SMT system more robust with respect to some specific deficiencies of the parameter tuning algorithms. This is an under-explored research direction, which can allow better parameter tuning. In this paper, we achieve this goal by selecting a subset of the available sentence pairs, which are more suitable for specific combinations of optimizers, objective functions, and evaluation measures. We demonstrate the potential of the idea with the pairwise ranking optimization (PRO) optimizer, which is known to yield too short translations. We show that the learning problem can be alleviated by tuning on a subset of the development set, selected based on sentence length. In particular, using the longest 50{\%} of the tuning sentences, we achieve two-fold tuning speedup, and improvements in BLEU score that rival those of alternatives, which fix BLEU+1{'}s smoothing instead.",
}
@inproceedings{nakov-etal-2017-trust,
    title = "Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums",
    author = "Nakov, Preslav  and
      Mihaylova, Tsvetomila  and
      M{\`a}rquez, Llu{\'\i}s  and
      Shiroya, Yashkumar  and
      Koychev, Ivan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_072",
    doi = "10.26615/978-954-452-049-6_072",
    pages = "551--560",
    abstract = "We address information credibility in community forums, in a setting in which the credibility of an answer posted in a question thread by a particular user has to be predicted. First, we motivate the problem and we create a publicly available annotated English corpus by crowdsourcing. Second, we propose a large set of features to predict the credibility of the answers. The features model the user, the answer, the question, the thread as a whole, and the interaction between them. Our experiments with ranking SVMs show that the credibility labels can be predicted with high performance according to several standard IR ranking metrics, thus supporting the potential usage of this layer of credibility information in practical applications. The features modeling the profile of the user (in particular trollness) turn out to be most important, but embedding features modeling the answer and the similarity between the question and the answer are also very relevant. Overall, half of the gap between the baseline performance and the perfect classifier can be covered using the proposed features.",
}
@inproceedings{osenova-simov-2017-bulgarian,
    title = "{B}ulgarian-{E}nglish and {E}nglish-{B}ulgarian Machine Translation: System Design and Evaluation",
    author = "Osenova, Petya  and
      Simov, Kiril",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_073",
    doi = "10.26615/978-954-452-049-6_073",
    pages = "561--568",
    abstract = "The paper presents a deep factored machine translation (MT) system between English and Bulgarian languages in both directions. The MT system is hybrid. It consists of three main steps: (1) the source-language text is linguistically annotated, (2) it is translated to the target language with the Moses system, and (3) translation is post-processed with the help of the transferred linguistic annotation from the source text. Besides automatic evaluation we performed manual evaluation over a domain test suite of sentences demonstrating certain phenomena like imperatives, questions, etc.",
}
@inproceedings{paul-das-2017-identification,
    title = "Identification of Character Adjectives from {M}ahabharata",
    author = "Paul, Apurba  and
      Das, Dipankar",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_074",
    doi = "10.26615/978-954-452-049-6_074",
    pages = "569--576",
    abstract = "The present paper describes the identification of prominent characters and their adjectives from Indian mythological epic, Mahabharata, written in English texts. However, in contrast to the tra-ditional approaches of named entity identifica-tion, the present system extracts hidden attributes associated with each of the characters (e.g., character adjectives). We observed distinct phrase level linguistic patterns that hint the pres-ence of characters in different text spans. Such six patterns were used in order to extract the cha-racters. On the other hand, a distinguishing set of novel features (e.g., multi-word expression, nodes and paths of parse tree, immediate ancestors etc.) was employed. Further, the correlation of the features is also measured in order to identify the important features. Finally, we applied various machine learning algorithms (e.g., Naive Bayes, KNN, Logistic Regression, Decision Tree, Random Forest etc.) along with deep learning to classify the patterns as characters or non-characters in order to achieve decent accuracy. Evaluation shows that phrase level linguistic patterns as well as the adopted features are highly active in capturing characters and their adjectives.",
}
@inproceedings{perez-estruch-etal-2017-learning,
    title = "Learning Multimodal Gender Profile using Neural Networks",
    author = "P{\'e}rez Estruch, Carlos  and
      Paredes Palacios, Roberto  and
      Rosso, Paolo",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_075",
    doi = "10.26615/978-954-452-049-6_075",
    pages = "577--582",
    abstract = "Gender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because of the little difference in the use of language between genders. This situation has led to the need of taking into account other information apart from textual data, favoring the emergence of multimodal data. The aim of this paper is to apply neural networks to perform data fusion, using an existing multimodal corpus, the NUS-MSS data set, that (not only) contains text data, but also image and location information. We improved previous results in terms of macro accuracy (87.8{\%}) obtaining the state-of-the-art performance of 91.3{\%}.",
}
@inproceedings{piasecki-etal-2017-recognition,
    title = "Recognition of Genuine {P}olish Suicide Notes",
    author = "Piasecki, Maciej  and
      M{\l}ynarczyk, Ksenia  and
      Koco{\'n}, Jan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_076",
    doi = "10.26615/978-954-452-049-6_076",
    pages = "583--591",
    abstract = "In this article we present the result of the recent research in the recognition of genuine Polish suicide notes (SNs). We provide useful method to distinguish between SNs and other types of discourse, including counterfeited SNs. The method uses a wide range of word-based and semantic features and it was evaluated using Polish Corpus of Suicide Notes, which contains 1244 genuine SNs, expanded with manually prepared set of 334 counterfeited SNs and 2200 letter-like texts from the Internet. We utilized the algorithm to create the class-related sense dictionaries to improve the result of SNs classification. The obtained results show that there are fundamental differences between genuine SNs and counterfeited SNs. The applied method of the sense dictionary construction appeared to be the best way of improving the model.",
}
@inproceedings{prazak-konopik-2017-cross,
    title = "Cross-Lingual {SRL} Based upon {U}niversal {D}ependencies",
    author = "Pra{\v{z}}{\'a}k, Ond{\v{r}}ej  and
      Konop{\'\i}k, Miloslav",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_077",
    doi = "10.26615/978-954-452-049-6_077",
    pages = "592--600",
    abstract = "In this paper, we introduce a cross-lingual Semantic Role Labeling (SRL) system with language independent features based upon Universal Dependencies. We propose two methods to convert SRL annotations from monolingual dependency trees into universal dependency trees. Our SRL system is based upon cross-lingual features derived from universal dependency trees and a supervised learning that utilizes a maximum entropy classifier. We design experiments to verify whether the Universal Dependencies are suitable for the cross-lingual SRL. The results are very promising and they open new interesting research paths for the future.",
}
@inproceedings{rohanian-etal-2017-using,
    title = "Using Gaze Data to Predict Multiword Expressions",
    author = "Rohanian, Omid  and
      Taslimipoor, Shiva  and
      Yaneva, Victoria  and
      Ha, Le An",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_078",
    doi = "10.26615/978-954-452-049-6_078",
    pages = "601--609",
    abstract = "In recent years gaze data has been increasingly used to improve and evaluate NLP models due to the fact that it carries information about the cognitive processing of linguistic phenomena. In this paper we conduct a preliminary study towards the automatic identification of multiword expressions based on gaze features from native and non-native speakers of English. We report comparisons between a part-of-speech (POS) and frequency baseline to: i) a prediction model based solely on gaze data and ii) a combined model of gaze data, POS and frequency. In spite of the challenging nature of the task, best performance was achieved by the latter. Furthermore, we explore how the type of gaze data (from native versus non-native speakers) affects the prediction, showing that data from the two groups is discriminative to an equal degree for the task. Finally, we show that late processing measures are more predictive than early ones, which is in line with previous research on idioms and other formulaic structures.",
}
@inproceedings{ruckle-gurevych-2017-real,
    title = "Real-Time News Summarization with Adaptation to Media Attention",
    author = {R{\"u}ckl{\'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_079",
    doi = "10.26615/978-954-452-049-6_079",
    pages = "610--617",
    abstract = "Real-time summarization of news events (RTS) allows persons to stay up-to-date on important topics that develop over time. With the occurrence of major sub-events, media attention increases and a large number of news articles are published. We propose a summarization approach that detects such changes and selects a suitable summarization configuration at run-time. In particular, at times with high media attention, our approach exploits the redundancy in content to produce a more precise summary and avoid emitting redundant information. We find that our approach significantly outperforms a strong non-adaptive RTS baseline in terms of the emitted summary updates and achieves the best results on a recent web-scale dataset. It can successfully be applied to a different real-world dataset without requiring additional modifications.",
}
@inproceedings{rudrapal-das-2017-measuring,
    title = "Measuring the Limit of Semantic Divergence for {E}nglish Tweets.",
    author = "Rudrapal, Dwijen  and
      Das, Amitava",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_080",
    doi = "10.26615/978-954-452-049-6_080",
    pages = "618--624",
    abstract = "In human language, an expression could be conveyed in many ways by different people. Even that the same person may express same sentence quite differently when addressing different audiences, using different modalities, or using different syntactic variations or may use different set of vocabulary. The possibility of such endless surface form of text while the meaning of the text remains almost same, poses many challenges for Natural Language Processing (NLP) systems like question-answering system, machine translation system and text summarization. This research paper is an endeavor to understand the characteristic of such endless semantic divergence. In this research work we develop a corpus of 1525 semantic divergent sentences for 200 English tweets.",
}
@inproceedings{ruppenhofer-etal-2017-evaluating,
    title = "Evaluating the morphological compositionality of polarity",
    author = "Ruppenhofer, Josef  and
      Steiner, Petra  and
      Wiegand, Michael",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_081",
    doi = "10.26615/978-954-452-049-6_081",
    pages = "625--633",
    abstract = "Unknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very productive processes of derivation and compounding and many German hapax words, which are likely to bear sentiment, are morphologically complex. We present results of supervised classification experiments on new datasets with morphological parses and polarity annotations.",
}
@inproceedings{rysova-etal-2017-introducing,
    title = "Introducing {EVALD} {--} Software Applications for Automatic Evaluation of Discourse in {C}zech",
    author = "Rysov{\'a}, Kate{\v{r}}ina  and
      Rysov{\'a}, Magdal{\'e}na  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}  and
      Nov{\'a}k, Michal",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_082",
    doi = "10.26615/978-954-452-049-6_082",
    pages = "634--641",
    abstract = "In the paper, we introduce two software applications for automatic evaluation of coherence in Czech texts called EVALD {--} Evaluator of Discourse. The first one {--} EVALD 1.0 {--} evaluates texts written by native speakers of Czech on a five-step scale commonly used at Czech schools (grade 1 is the best, grade 5 is the worst). The second application is EVALD 1.0 for Foreigners assessing texts by non-native speakers of Czech using six-step scale (A1{--}C2) according to CEFR. Both appli-cations are available online at \url{https://lindat.mff.cuni.cz/services/evald-foreign/}.",
}
@inproceedings{salton-etal-2017-idiom,
    title = "Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier",
    author = "Salton, Giancarlo  and
      Ross, Robert  and
      Kelleher, John",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_083",
    doi = "10.26615/978-954-452-049-6_083",
    pages = "642--651",
    abstract = "In our work we address limitations in the state-of-the-art in idiom type identification. We investigate different approaches for a lexical fixedness metric, a component of the state-of the-art model. We also show that our Machine Learning based approach to the idiom type identification task achieves an F1-score of 0.85, an improvement of 11 points over the state-of the-art.",
}
@inproceedings{satthar-etal-2017-calibration,
    title = "A Calibration Method for Evaluation of Sentiment Analysis",
    author = "Satthar, F. Sharmila  and
      Evans, Roger  and
      Uchyigit, Gulden",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_084",
    doi = "10.26615/978-954-452-049-6_084",
    pages = "652--660",
    abstract = "Sentiment analysis is the computational task of extracting sentiment from a text document {--} for example whether it expresses a positive, negative or neutral opinion. Various approaches have been introduced in recent years, using a range of different techniques to extract sentiment information from a document. Measuring these methods against a gold standard dataset is a useful way to evaluate such systems. However, different sentiment analysis techniques represent sentiment values in different ways, such as discrete categorical classes or continuous numerical sentiment scores. This creates a challenge for evaluating and comparing such systems; in particular assessing numerical scores against datasets that use fixed classes is difficult, because the numerical outputs have to be mapped onto the ordered classes. This paper proposes a novel calibration technique that uses precision vs. recall curves to set class thresholds to optimize a continuous sentiment analyser{'}s performance against a discrete gold standard dataset. In experiments mapping a continuous score onto a three-class classification of movie reviews, we show that calibration results in a substantial increase in f-score when compared to a non-calibrated mapping.",
}
@inproceedings{semmar-laib-2017-building,
    title = "Building Multiword Expressions Bilingual Lexicons for Domain Adaptation of an Example-Based Machine Translation System",
    author = "Semmar, Nasredine  and
      Laib, Mariama",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_085",
    doi = "10.26615/978-954-452-049-6_085",
    pages = "661--670",
    abstract = "We describe in this paper a hybrid ap-proach to build automatically bilingual lexicons of Multiword Expressions (MWEs) from parallel corpora. We more specifically investigate the impact of using a domain-specific bilingual lexicon of MWEs on domain adaptation of an Example-Based Machine Translation (EBMT) system. We conducted experiments on the English-French language pair and two kinds of texts: in-domain texts from Europarl (European Parliament proceedings) and out-of-domain texts from Emea (European Medicines Agency documents) and Ecb (European Central Bank corpus). The obtained results indicate that integrating domain-specific bilingual lexicons of MWEs improves translation quality of the EBMT system when texts to translate are related to the specific domain and induces a relatively slight deterioration of translation quality when translating general-purpose texts.",
}
@inproceedings{simaki-etal-2017-identifying,
    title = "Identifying the Authors{'} National Variety of {E}nglish in Social Media Texts",
    author = "Simaki, Vasiliki  and
      Simakis, Panagiotis  and
      Paradis, Carita  and
      Kerren, Andreas",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_086",
    doi = "10.26615/978-954-452-049-6_086",
    pages = "671--678",
    abstract = "In this paper, we present a study for the identification of authors{'} national variety of English in texts from social media. In data from Facebook and Twitter, information about the author{'}s social profile is annotated, and the national English variety (US, UK, AUS, CAN, NNS) that each author uses is attributed. We tested four feature types: formal linguistic features, POS features, lexicon-based features related to the different varieties, and data-based features from each English variety. We used various machine learning algorithms for the classification experiments, and we implemented a feature selection process. The classification accuracy achieved, when the 31 highest ranked features were used, was up to 77.32{\%}. The experimental results are evaluated, and the efficacy of the ranked features discussed.",
}
@inproceedings{simov-etal-2017-towards,
    title = "Towards Lexical Chains for Knowledge-Graph-based Word Embeddings",
    author = "Simov, Kiril  and
      Boytcheva, Svetla  and
      Osenova, Petya",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_087",
    doi = "10.26615/978-954-452-049-6_087",
    pages = "679--685",
    abstract = "Word vectors with varying dimensionalities and produced by different algorithms have been extensively used in NLP. The corpora that the algorithms are trained on can contain either natural language text (e.g. Wikipedia or newswire articles) or artificially-generated pseudo corpora due to natural data sparseness. We exploit Lexical Chain based templates over Knowledge Graph for generating pseudo-corpora with controlled linguistic value. These corpora are then used for learning word embeddings. A number of experiments have been conducted over the following test sets: WordSim353 Similarity, WordSim353 Relatedness and SimLex-999. The results show that, on the one hand, the incorporation of many-relation lexical chains improves results, but on the other hand, unrestricted-length chains remain difficult to handle with respect to their huge quantity.",
}
@inproceedings{simova-uszkoreit-2017-word,
    title = "Word Embeddings as Features for Supervised Coreference Resolution",
    author = "Simova, Iliana  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_088",
    doi = "10.26615/978-954-452-049-6_088",
    pages = "686--693",
    abstract = "A common reason for errors in coreference resolution is the lack of semantic information to help determine the compatibility between mentions referring to the same entity. Distributed representations, which have been shown successful in encoding relatedness between words, could potentially be a good source of such knowledge. Moreover, being obtained in an unsupervised manner, they could help address data sparsity issues in labeled training data at a small cost. In this work we investigate whether and to what extend features derived from word embeddings can be successfully used for supervised coreference resolution. We experiment with several word embedding models, and several different types of embeddingbased features, including embedding cluster and cosine similarity-based features. Our evaluations show improvements in the performance of a supervised state-of-theart coreference system.",
}
@inproceedings{steinberger-etal-2017-cross,
    title = "Cross-lingual Flames Detection in News Discussions",
    author = "Steinberger, Josef  and
      Brychc{\'\i}n, Tom{\'a}{\v{s}}  and
      Hercig, Tom{\'a}{\v{s}}  and
      Krejzl, Peter",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_089",
    doi = "10.26615/978-954-452-049-6_089",
    pages = "694--700",
    abstract = "We introduce Flames Detector, an online system for measuring flames, i.e. strong negative feelings or emotions, insults or other verbal offences, in news commentaries across five languages. It is designed to assist journalists, public institutions or discussion moderators to detect news topics which evoke wrangles. We propose a machine learning approach to flames detection and calculate an aggregated score for a set of comment threads. The demo application shows the most flaming topics of the current period in several language variants. The search functionality gives a possibility to measure flames in any topic specified by a query. The evaluation shows that the flame detection in discussions is a difficult task, however, the application can already reveal interesting information about the actual news discussions.",
}
@inproceedings{steinberger-etal-2017-pyramid,
    title = "Pyramid-based Summary Evaluation Using {A}bstract {M}eaning {R}epresentation",
    author = "Steinberger, Josef  and
      Krejzl, Peter  and
      Brychc{\'\i}n, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_090",
    doi = "10.26615/978-954-452-049-6_090",
    pages = "701--706",
    abstract = "We propose a novel metric for evaluating summary content coverage. The evaluation framework follows the Pyramid approach to measure how many summarization content units, considered important by human annotators, are contained in an automatic summary. Our approach automatizes the evaluation process, which does not need any manual intervention on the evaluated summary side. Our approach compares abstract meaning representations of each content unit mention and each summary sentence. We found that the proposed metric complements well the widely-used ROUGE metrics.",
}
@inproceedings{steinberger-etal-2017-large,
    title = "Large-scale news entity sentiment analysis",
    author = "Steinberger, Ralf  and
      Hegele, Stefanie  and
      Tanev, Hristo  and
      Della Rocca, Leonida",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_091",
    doi = "10.26615/978-954-452-049-6_091",
    pages = "707--715",
    abstract = "We work on detecting positive or negative sentiment towards named entities in very large volumes of news articles. The aim is to monitor changes over time, as well as to work towards media bias detection by com-paring differences across news sources and countries. With view to applying the same method to dozens of languages, we use lin-guistically light-weight methods: searching for positive and negative terms in bags of words around entity mentions (also consid-ering negation). Evaluation results are good and better than a third-party baseline sys-tem, but precision is not sufficiently high to display the results publicly in our multilin-gual news analysis system Europe Media Monitor (EMM). In this paper, we focus on describing our effort to improve the English language results by avoiding the biggest sources of errors. We also present new work on using a syntactic parser to identify safe opinion recognition rules, such as predica-tive structures in which sentiment words di-rectly refer to an entity. The precision of this method is good, but recall is very low.",
}
@inproceedings{sulea-etal-2017-predicting,
    title = "Predicting the Law Area and Decisions of {F}rench {S}upreme {C}ourt Cases",
    author = "{\c{S}}ulea, Octavia-Maria  and
      Zampieri, Marcos  and
      Vela, Mihaela  and
      van Genabith, Josef",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_092",
    doi = "10.26615/978-954-452-049-6_092",
    pages = "716--722",
    abstract = "In this paper, we investigate the application of text classification methods to predict the law area and the decision of cases judged by the French Supreme Court. We also investigate the influence of the time period in which a ruling was made over the textual form of the case description and the extent to which it is necessary to mask the judge{'}s motivation for a ruling to emulate a real-world test scenario. We report results of 96{\%} f1 score in predicting a case ruling, 90{\%} f1 score in predicting the law area of a case, and 75.9{\%} f1 score in estimating the time span when a ruling has been issued using a linear Support Vector Machine (SVM) classifier trained on lexical features.",
}
@inproceedings{sumalvico-2017-unsupervised,
    title = "Unsupervised Learning of Morphology with Graph Sampling",
    author = "Sumalvico, Maciej",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_093",
    doi = "10.26615/978-954-452-049-6_093",
    pages = "723--732",
    abstract = "We introduce a language-independent, graph-based probabilistic model of morphology, which uses transformation rules operating on whole words instead of the traditional morphological segmentation. The morphological analysis of a set of words is expressed through a graph having words as vertices and structural relationships between words as edges. We define a probability distribution over such graphs and develop a sampler based on the Metropolis-Hastings algorithm. The sampling is applied in order to determine the strength of morphological relationships between words, filter out accidental similarities and reduce the set of rules necessary to explain the data. The model is evaluated on the task of finding pairs of morphologically similar words, as well as generating new words. The results are compared to a state-of-the-art segmentation-based approach.",
}
@inproceedings{sweeney-padmanabhan-2017-multi,
    title = "Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach",
    author = "Sweeney, Colm  and
      Padmanabhan, Deepak",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_094",
    doi = "10.26615/978-954-452-049-6_094",
    pages = "733--740",
    abstract = "The sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow semantic understanding without the task of creating large amounts of annotated test data. One problem with this type of binary classification, is that the sentiment output will be in the form of {`}1{'} (positive) or {`}0{'} (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. This paper plans to enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique to appoint a total score that indicates the polarity of opinion in relation to a particular entity or entities. This type of sentiment classification is a way of associating a given entity with the adjectives, adverbs, and verbs describing it, and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities.",
}
@inproceedings{tahmasebi-risse-2017-finding,
    title = "Finding Individual Word Sense Changes and their Delay in Appearance",
    author = "Tahmasebi, Nina  and
      Risse, Thomas",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_095",
    doi = "10.26615/978-954-452-049-6_095",
    pages = "741--749",
    abstract = "We present a method for detecting word sense changes by utilizing automatically induced word senses. Our method works on the level of individual senses and allows a word to have e.g. one stable sense and then add a novel sense that later experiences change. Senses are grouped based on polysemy to find linguistic concepts and we can find broadening and narrowing as well as novel (polysemous and homonymic) senses. We evaluate on a testset, present recall and estimates of the time between expected and found change.",
}
@inproceedings{thomas-etal-2017-streaming,
    title = "Streaming Text Analytics for Real-Time Event Recognition",
    author = "Thomas, Philippe  and
      Kirschnick, Johannes  and
      Hennig, Leonhard  and
      Ai, Renlong  and
      Schmeier, Sven  and
      Hemsen, Holmer  and
      Xu, Feiyu  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_096",
    doi = "10.26615/978-954-452-049-6_096",
    pages = "750--757",
    abstract = "A huge body of continuously growing written knowledge is available on the web in the form of social media posts, RSS feeds, and news articles. Real-time information extraction from such high velocity, high volume text streams requires scalable, distributed natural language processing pipelines. We introduce such a system for fine-grained event recognition within the big data framework Flink, and demonstrate its capabilities for extracting and geo-locating mobility- and industry-related events from heterogeneous text sources. Performance analyses conducted on several large datasets show that our system achieves high throughput and maintains low latency, which is crucial when events need to be detected and acted upon in real-time. We also present promising experimental results for the event extraction component of our system, which recognizes a novel set of event types. The demo system is available at \url{http://dfki.de/sd4m-sta-demo/}.",
}
@inproceedings{tokunaga-etal-2017-eye,
    title = "An Eye-tracking Study of Named Entity Annotation",
    author = "Tokunaga, Takenobu  and
      Nishikawa, Hitoshi  and
      Iwakura, Tomoya",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_097",
    doi = "10.26615/978-954-452-049-6_097",
    pages = "758--764",
    abstract = "Utilising effective features in machine learning-based natural language processing (NLP) is crucial in achieving good performance for a given NLP task. The paper describes a pilot study on the analysis of eye-tracking data during named entity (NE) annotation, aiming at obtaining insights into effective features for the NE recognition task. The eye gaze data were collected from 10 annotators and analysed regarding working time and fixation distribution. The results of the preliminary qualitative analysis showed that human annotators tend to look at broader contexts around the target NE than recent state-of-the-art automatic NE recognition systems and to use predicate argument relations to identify the NE categories.",
}
@inproceedings{tsekouras-etal-2017-graph,
    title = "A Graph-based Text Similarity Measure That Employs Named Entity Information",
    author = "Tsekouras, Leonidas  and
      Varlamis, Iraklis  and
      Giannakopoulos, George",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_098",
    doi = "10.26615/978-954-452-049-6_098",
    pages = "765--771",
    abstract = "Text comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities{'} information extracted from the texts and the n-gram graphs{'} model for representing documents. Using OpenCalais as a named-entity recognition service and the JINSECT toolkit for constructing and managing n-gram graphs, the text similarity measure is embedded in a text clustering algorithm (k-Means). The evaluation of the produced clusters with various clustering validity metrics shows that the extraction of named entities at a first step can be profitable for the time-performance of similarity measures that are based on the n-gram graph representation without affecting the overall performance of the NLP task.",
}
@inproceedings{wawer-mykowiecka-2017-detecting,
    title = "Detecting Metaphorical Phrases in the {P}olish Language",
    author = "Wawer, Aleksander  and
      Mykowiecka, Agnieszka",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_099",
    doi = "10.26615/978-954-452-049-6_099",
    pages = "772--777",
    abstract = "In this paper we describe experiments with automated detection of metaphors in the Polish language. We focus our analysis on noun phrases composed of an adjective and a noun, and distinguish three types of expressions: with literal sense, with metaphorical sense, and expressions both literal and methaphorical (context-dependent). We propose a method of automatically recognizing expression type using word embeddings and neural networks. We evaluate multiple neural network architectures and demonstrate that the method significantly outperforms strong baselines.",
}
@inproceedings{weegar-etal-2017-efficient,
    title = "Efficient Encoding of Pathology Reports Using Natural Language Processing",
    author = "Weegar, Rebecka  and
      Nyg{\aa}rd, Jan F  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_100",
    doi = "10.26615/978-954-452-049-6_100",
    pages = "778--783",
    abstract = "In this article we present a system that extracts information from pathology reports. The reports are written in Norwegian and contain free text describing prostate biopsies. Currently, these reports are manually coded for research and statistical purposes by trained experts at the Cancer Registry of Norway where the coders extract values for a set of predefined fields that are specific for prostate cancer. The presented system is rule based and achieves an average F-score of 0.91 for the fields Gleason grade, Gleason score, the number of biopsies that contain tumor tissue, and the orientation of the biopsies. The system also identifies reports that contain ambiguity or other content that should be reviewed by an expert. The system shows potential to encode the reports considerably faster, with less resources, and similar high quality to the manual encoding.",
}
@inproceedings{yang-etal-2017-neural-reranking,
    title = "Neural Reranking for Named Entity Recognition",
    author = "Yang, Jie  and
      Zhang, Yue  and
      Dong, Fei",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_101",
    doi = "10.26615/978-954-452-049-6_101",
    pages = "784--792",
    abstract = "We propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, {``}PER was born in LOC{''} can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.",
}
@inproceedings{yao-etal-2017-online,
    title = "Online Deception Detection Refueled by Real World Data Collection",
    author = "Yao, Wenlin  and
      Dai, Zeyu  and
      Huang, Ruihong  and
      Caverlee, James",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_102",
    doi = "10.26615/978-954-452-049-6_102",
    pages = "793--802",
    abstract = "The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high quality deceptive and truthful online reviews1 from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features {--} advertising speak and writing complexity scores {--} deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers{'} writing styles.",
}
@inproceedings{yao-etal-2017-weakly,
    title = "A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously",
    author = "Yao, Wenlin  and
      Nettyam, Saipravallika  and
      Huang, Ruihong",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_103",
    doi = "10.26615/978-954-452-049-6_103",
    pages = "803--812",
    abstract = "Capabilities of detecting temporal and causal relations between two events can benefit many applications. Most of existing temporal relation classifiers were trained in a supervised manner. Instead, we explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts and these rich contexts can be used to train a contextual temporal relation classifier, which can further recognize new temporal relation contexts and identify new regular event pairs. We focus on detecting after and before temporal relations and design a weakly supervised learning approach that extracts thousands of regular event pairs and learns a contextual temporal relation classifier simultaneously. Evaluation shows that the acquired regular event pairs are of high quality and contain rich commonsense knowledge and domain specific knowledge. In addition, the weakly supervised trained temporal relation classifier achieves comparable performance with the state-of-the-art supervised systems.",
}
@inproceedings{yimam-etal-2017-multilingual,
    title = "Multilingual and Cross-Lingual Complex Word Identification",
    author = "Yimam, Seid Muhie  and
      {\v{S}}tajner, Sanja  and
      Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_104",
    doi = "10.26615/978-954-452-049-6_104",
    pages = "813--822",
    abstract = "Complex Word Identification (CWI) is an important task in lexical simplification and text accessibility. Due to the lack of CWI datasets, previous works largely depend on Simple English Wikipedia and edit histories for obtaining {`}gold standard{'} annotations, which are of doubtable quality, and limited only to English. We collect complex words/phrases (CP) for English, German and Spanish, annotated by both native and non-native speakers, and propose language independent features that can be used to train multilingual and cross-lingual CWI models. We show that the performance of cross-lingual CWI systems (using a model trained on one language and applying it on the other languages) is comparable to the performance of monolingual CWI systems.",
}
@inproceedings{yordanova-2017-automatic,
    title = "Automatic Generation of Situation Models for Plan Recognition Problems",
    author = "Yordanova, Kristina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_105",
    doi = "10.26615/978-954-452-049-6_105",
    pages = "823--830",
    abstract = "Recent attempts at behaviour understanding through language grounding have shown that it is possible to automatically generate models for planning problems from textual instructions. One drawback of these approaches is that they either do not make use of the semantic structure behind the model elements identified in the text, or they manually incorporate a collection of concepts with semantic relationships between them. We call this collection of knowledge situation model. The situation model introduces additional context information to the model. It could also potentially reduce the complexity of the planning problem compared to models that do not use situation models. To address this problem, we propose an approach that automatically generates the situation model from textual instructions. The approach is able to identify various hierarchical, spatial, directional, and causal relations. We use the situation model to automatically generate planning problems in a PDDL notation and we show that the situation model reduces the complexity of the PDDL model in terms of number of operators and branching factor compared to planning models that do not make use of situation models.",
}
@inproceedings{yordanova-2017-simple,
    title = "A Simple Model for Improving the Performance of the {S}tanford Parser for Action Detection in Textual Instructions",
    author = "Yordanova, Kristina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_106",
    doi = "10.26615/978-954-452-049-6_106",
    pages = "831--838",
    abstract = "Different approaches for behaviour understanding rely on textual instructions to generate models of human behaviour. These approaches usually use state of the art parsers to obtain the part of speech (POS) meaning and dependencies of the words in the instructions. For them it is essential that the parser is able to correctly annotate the instructions and especially the verbs as they describe the actions of the person. State of the art parsers usually make errors when annotating textual instructions, as they have short sentence structure often in imperative form. The inability of the parser to identify the verbs results in the inability of behaviour understanding systems to identify the relevant actions. To address this problem, we propose a simple rule-based model that attempts to correct any incorrectly annotated verbs. We argue that the model is able to significantly improve the parser{'}s performance without the need of additional training data. We evaluate our approach by extracting the actions from 61 textual instructions annotated only with the Stanford parser and once again after applying our model. The results show a significant improvement in the recognition rate when applying the rules (75{\%} accuracy compared to 68{\%} without the rules, p-value {\textless} 0.001).",
}
@inproceedings{zilio-etal-2017-using,
    title = "Using {NLP} for Enhancing Second Language Acquisition",
    author = "Zilio, Leonardo  and
      Wilkens, Rodrigo  and
      Fairon, C{\'e}drick",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd.",
    url = "https://doi.org/10.26615/978-954-452-049-6_107",
    doi = "10.26615/978-954-452-049-6_107",
    pages = "839--846",
    abstract = "This study presents SMILLE, a system that draws on the Noticing Hypothesis and on input enhancements, addressing the lack of salience of grammatical infor mation in online documents chosen by a given user. By means of input enhancements, the system can draw the user{'}s attention to grammar, which could possibly lead to a higher intake per input ratio for metalinguistic information. The system receives as input an online document and submits it to a combined processing of parser and hand-written rules for detecting its grammatical structures. The input text can be freely chosen by the user, providing a more engaging experience and reflecting the user{'}s interests. The system can enhance a total of 107 fine-grained types of grammatical structures that are based on the CEFR. An evaluation of some of those structures resulted in an overall precision of 87{\%}.",
}
@article{tacl-2017-transactions,
    title = "Transactions of the Association for Computational Linguistics, Volume 5",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1000",
}
@article{smith-etal-2017-evaluating,
    title = "Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Topic Labels",
    author = "Smith, Alison  and
      Lee, Tak Yeon  and
      Poursabzi-Sangdeh, Forough  and
      Boyd-Graber, Jordan  and
      Elmqvist, Niklas  and
      Findlater, Leah",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1001",
    doi = "10.1162/tacl_a_00042",
    pages = "1--16",
    abstract = "Probabilistic topic models are important tools for indexing, summarizing, and analyzing large document collections by their themes. However, promoting end-user understanding of topics remains an open research problem. We compare labels generated by users given four topic visualization techniques{---}word lists, word lists with bars, word clouds, and network graphs{---}against each other and against automatically generated labels. Our basis of comparison is participant ratings of how well labels describe documents from the topic. Our study has two phases: a labeling phase where participants label visualized topics and a validation phase where different participants select which labels best describe the topics{'} documents. Although all visualizations produce similar quality labels, simple visualizations such as word lists allow participants to quickly understand topics, while complex visualizations take longer but expose multi-word expressions that simpler visualizations obscure. Automatic labels lag behind user-created labels, but our dataset of manually labeled topics highlights linguistic patterns (e.g., hypernyms, phrases) that can be used to improve automatic topic labeling algorithms.",
}
@article{anderson-etal-2017-visually,
    title = "Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns",
    author = "Anderson, Andrew J.  and
      Kiela, Douwe  and
      Clark, Stephen  and
      Poesio, Massimo",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1002",
    doi = "10.1162/tacl_a_00043",
    pages = "17--30",
    abstract = "Important advances have recently been made using computational semantic models to decode brain activity patterns associated with concepts; however, this work has almost exclusively focused on concrete nouns. How well these models extend to decoding abstract nouns is largely unknown. We address this question by applying state-of-the-art computational models to decode functional Magnetic Resonance Imaging (fMRI) activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns. One of the models we use is linguistic, exploiting the recent word2vec skipgram approach trained on Wikipedia. The second is visually grounded, using deep convolutional neural networks trained on Google Images. Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically. Splitting the fMRI data according to human concreteness ratings, we indeed observe that both models significantly decode the most concrete nouns; however, accuracy is significantly greater using the text-based models for the most abstract nouns. More generally this confirms that current computational models are sufficiently advanced to assist in investigating the representational structure of abstract concepts in the brain.",
}
@article{modi-etal-2017-modeling,
    title = "Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction",
    author = "Modi, Ashutosh  and
      Titov, Ivan  and
      Demberg, Vera  and
      Sayeed, Asad  and
      Pinkal, Manfred",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1003",
    doi = "10.1162/tacl_a_00044",
    pages = "31--44",
    abstract = "Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.",
}
@article{liu-zhang-2017-shift,
    title = "Shift-Reduce Constituent Parsing with Neural Lookahead Features",
    author = "Liu, Jiangming  and
      Zhang, Yue",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1004",
    doi = "10.1162/tacl_a_00045",
    pages = "45--58",
    abstract = "Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which consists of a sequence of non-local constituents. On the other hand, during incremental parsing, constituent information on the right hand side of the current word is not utilized, which is a relative weakness of shift-reduce parsing. To address this limitation, we leverage a fast neural model to extract lookahead features. In particular, we build a bidirectional LSTM model, which leverages full sentence information to predict the hierarchy of constituents that each word starts and ends. The results are then passed to a strong transition-based constituent parser as lookahead features. The resulting parser gives 1.3{\%} absolute improvement in WSJ and 2.3{\%} in CTB compared to the baseline, giving the highest reported accuracies for fully-supervised parsing.",
}
@article{chang-collins-2017-polynomial,
    title = "A Polynomial-Time Dynamic Programming Algorithm for Phrase-Based Decoding with a Fixed Distortion Limit",
    author = "Chang, Yin-Wen  and
      Collins, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1005",
    doi = "10.1162/tacl_a_00046",
    pages = "59--71",
    abstract = "Decoding of phrase-based translation models in the general case is known to be NP-complete, by a reduction from the traveling salesman problem (Knight, 1999). In practice, phrase-based systems often impose a hard distortion limit that limits the movement of phrases during translation. However, the impact on complexity after imposing such a constraint is not well studied. In this paper, we describe a dynamic programming algorithm for phrase-based decoding with a fixed distortion limit. The runtime of the algorithm is O(nd!lhd+1) where n is the sentence length, d is the distortion limit, l is a bound on the number of phrases starting at any position in the sentence, and h is related to the maximum number of target language translations for any source word. The algorithm makes use of a novel representation that gives a new perspective on decoding of phrase-based models.",
}
@article{futrell-etal-2017-generative,
    title = "A Generative Model of Phonotactics",
    author = "Futrell, Richard  and
      Albright, Adam  and
      Graff, Peter  and
      O{'}Donnell, Timothy J.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1006",
    doi = "10.1162/tacl_a_00047",
    pages = "73--86",
    abstract = "We present a probabilistic model of phonotactics, the set of well-formed phoneme sequences in a language. Unlike most computational models of phonotactics (Hayes and Wilson, 2008; Goldsmith and Riggle, 2012), we take a fully generative approach, modeling a process where forms are built up out of subparts by phonologically-informed structure building operations. We learn an inventory of subparts by applying stochastic memoization (Johnson et al., 2007; Goodman et al., 2008) to a generative process for phonemes structured as an and-or graph, based on concepts of feature hierarchy from generative phonology (Clements, 1985; Dresher, 2009). Subparts are combined in a way that allows tier-based feature interactions. We evaluate our models{'} ability to capture phonotactic distributions in the lexicons of 14 languages drawn from the WOLEX corpus (Graff, 2012). Our full model robustly assigns higher probabilities to held-out forms than a sophisticated N-gram model for all languages. We also present novel analyses that probe model behavior in more detail.",
}
@article{tu-etal-2017-context,
    title = "Context Gates for Neural Machine Translation",
    author = "Tu, Zhaopeng  and
      Liu, Yang  and
      Lu, Zhengdong  and
      Liu, Xiaohua  and
      Li, Hang",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1007",
    doi = "10.1162/tacl_a_00048",
    pages = "87--99",
    abstract = "In neural machine translation (NMT), generation of a target word depends on both source and target contexts. We find that source contexts have a direct impact on the adequacy of a translation while target contexts affect the fluency. Intuitively, generation of a content word should rely more on the source context and generation of a functional word should rely more on the target context. Due to the lack of effective control over the influence from source and target contexts, conventional NMT tends to yield fluent but inadequate translations. To address this problem, we propose context gates which dynamically control the ratios at which source and target contexts contribute to the generation of target words. In this way, we can enhance both the adequacy and fluency of NMT with more careful control of the information flow from contexts. Experiments show that our approach significantly improves upon a standard attention-based NMT system by +2.3 BLEU points.",
}
@article{peng-etal-2017-cross,
    title = "Cross-Sentence N-ary Relation Extraction with Graph {LSTM}s",
    author = "Peng, Nanyun  and
      Poon, Hoifung  and
      Quirk, Chris  and
      Toutanova, Kristina  and
      Yih, Wen-tau",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1008",
    doi = "10.1162/tacl_a_00049",
    pages = "101--115",
    abstract = "Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.",
}
@article{dunietz-etal-2017-automatically,
    title = "Automatically Tagging Constructions of Causation and Their Slot-Fillers",
    author = "Dunietz, Jesse  and
      Levin, Lori  and
      Carbonell, Jaime",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1009",
    doi = "10.1162/tacl_a_00050",
    pages = "117--133",
    abstract = {This paper explores extending shallow semantic parsing beyond lexical-unit triggers, using causal relations as a test case. Semantic parsing becomes difficult in the face of the wide variety of linguistic realizations that causation can take on. We therefore base our approach on the concept of constructions from the linguistic paradigm known as Construction Grammar (CxG). In CxG, a construction is a form/function pairing that can rely on arbitrary linguistic and semantic features. Rather than codifying all aspects of each construction{'}s form, as some attempts to employ CxG in NLP have done, we propose methods that offload that problem to machine learning. We describe two supervised approaches for tagging causal constructions and their arguments. Both approaches combine automatically induced pattern-matching rules with statistical classifiers that learn the subtler parameters of the constructions. Our results show that these approaches are promising: they significantly outperform na{\"\i}ve baselines for both construction recognition and cause and effect head matches.},
}
@article{bojanowski-etal-2017-enriching,
    title = "Enriching Word Vectors with Subword Information",
    author = "Bojanowski, Piotr  and
      Grave, Edouard  and
      Joulin, Armand  and
      Mikolov, Tomas",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1010",
    doi = "10.1162/tacl_a_00051",
    pages = "135--146",
    abstract = "Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.",
}
@article{wang-eisner-2017-fine,
    title = "Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning",
    author = "Wang, Dingquan  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1011",
    doi = "10.1162/tacl_a_00052",
    pages = "147--161",
    abstract = "We show how to predict the basic word-order facts of a novel language given only a corpus of part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, how often adjectives follow their nouns, and in general the directionalities of all dependency relations. Such typological properties could be helpful in grammar induction. While such a problem is usually regarded as unsupervised learning, our innovation is to treat it as supervised learning, using a large collection of realistic synthetic languages as training data. The supervised learner must identify surface features of a language{'}s POS sequence (hand-engineered or neural features) that correlate with the language{'}s deeper structure (latent trees). In the experiment, we show: 1) Given a small set of real languages, it helps to add many synthetic languages to the training data. 2) Our system is robust even when the POS sequences include noise. 3) Our system on this task outperforms a grammar induction baseline by a large margin.",
}
@article{teng-zhang-2017-head,
    title = "Head-Lexicalized Bidirectional Tree {LSTM}s",
    author = "Teng, Zhiyang  and
      Zhang, Yue",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1012",
    doi = "10.1162/tacl_a_00053",
    pages = "163--177",
    abstract = "Sequential LSTMs have been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of constituent nodes, making direct use of input word information only for leaf nodes. This is different from sequential LSTMs, which contain references to input words for each node. In this paper, we propose a method for automatic head-lexicalization for tree-structure LSTMs, propagating head words from leaf nodes to every constituent node. In addition, enabled by head lexicalization, we build a tree LSTM in the top-down direction, which corresponds to bidirectional sequential LSTMs in structure. Experiments show that both extensions give better representations of tree structures. Our final model gives the best results on the Stanford Sentiment Treebank and highly competitive results on the TREC question type classification task.",
}
@article{fujii-etal-2017-nonparametric,
    title = "Nonparametric {B}ayesian Semi-supervised Word Segmentation",
    author = "Fujii, Ryo  and
      Domoto, Ryo  and
      Mochihashi, Daichi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1013",
    doi = "10.1162/tacl_a_00054",
    pages = "179--189",
    abstract = "This paper presents a novel hybrid generative/discriminative model of word segmentation based on nonparametric Bayesian methods. Unlike ordinary discriminative word segmentation which relies only on labeled data, our semi-supervised model also leverages a huge amounts of unlabeled text to automatically learn new {``}words{''}, and further constrains them by using a labeled data to segment non-standard texts such as those found in social networking services. Specifically, our hybrid model combines a discriminative classifier (CRF; Lafferty et al. (2001) and unsupervised word segmentation (NPYLM; Mochihashi et al. (2009)), with a transparent exchange of information between these two model structures within the semi-supervised framework (JESS-CM; Suzuki and Isozaki (2008)). We confirmed that it can appropriately segment non-standard texts like those in Twitter and Weibo and has nearly state-of-the-art accuracy on standard datasets in Japanese, Chinese, and Thai.",
}
@article{kim-etal-2017-joint,
    title = "Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora",
    author = "Kim, Jooyeon  and
      Kim, Dongwoo  and
      Oh, Alice",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1014",
    doi = "10.1162/tacl_a_00055",
    pages = "191--204",
    abstract = "Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author{'}s influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.",
}
@article{martins-etal-2017-pushing,
    title = "Pushing the Limits of Translation Quality Estimation",
    author = "Martins, Andr{\'e} F. T.  and
      Junczys-Dowmunt, Marcin  and
      Kepler, Fabio N.  and
      Astudillo, Ram{\'o}n  and
      Hokamp, Chris  and
      Grundkiewicz, Roman",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1015",
    doi = "10.1162/tacl_a_00056",
    pages = "205--218",
    abstract = "Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways. However, this potential is currently limited by the relatively low accuracy of existing systems. In this paper, we achieve remarkable improvements by exploiting synergies between the related tasks of word-level quality estimation and automatic post-editing. First, we stack a new, carefully engineered, neural model into a rich feature-based word-level quality estimation system. Then, we use the output of an automatic post-editing system as an extra feature, obtaining striking results on WMT16: a word-level FMULT1 score of 57.47{\%} (an absolute gain of +7.95{\%} over the current state of the art), and a Pearson correlation score of 65.56{\%} for sentence-level HTER prediction (an absolute gain of +13.36{\%}).",
}
@article{wang-etal-2017-winning,
    title = "Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes",
    author = "Wang, Lu  and
      Beauchamp, Nick  and
      Shugars, Sarah  and
      Qin, Kechen",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1016",
    doi = "10.1162/tacl_a_00057",
    pages = "219--232",
    abstract = "Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model{'}s combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74{\%} accuracy, significantly outperforming linguistic features alone (66{\%}). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.",
}
@article{dalvi-mishra-etal-2017-domain,
    title = "Domain-Targeted, High Precision Knowledge Extraction",
    author = "Dalvi Mishra, Bhavana  and
      Tandon, Niket  and
      Clark, Peter",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1017",
    doi = "10.1162/tacl_a_00058",
    pages = "233--246",
    abstract = "Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable resource for our task already exists; existing resources are either too noisy, too named-entity centric, or too incomplete, and typically have not been constructed with a clear scope or purpose. To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that produces high precision knowledge targeted to a particular domain - in our case, elementary science. To measure the KB{'}s coverage of the target domain{'}s knowledge (its {``}comprehensiveness{''} with respect to science) we measure recall with respect to an independent corpus of domain text, and show that our pipeline produces output with over 80{\%} precision and 23{\%} recall with respect to that target, a substantially higher coverage of tuple-expressible science knowledge than other comparable resources. We have made the KB publicly available.",
}
@article{berend-2017-sparse,
    title = "Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling",
    author = "Berend, G{\'a}bor",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1018",
    doi = "10.1162/tacl_a_00059",
    pages = "247--261",
    abstract = "In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8{\%} of its average POS tagging accuracy when trained at 1.2{\%} of the total available training data, i.e. 150 sentences per language.",
}
@article{vieira-eisner-2017-learning,
    title = "Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing",
    author = "Vieira, Tim  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1019",
    doi = "10.1162/tacl_a_00060",
    pages = "263--278",
    abstract = "Pruning hypotheses during dynamic programming is commonly used to speed up inference in settings such as parsing. Unlike prior work, we train a pruning policy under an objective that measures end-to-end performance: we search for a fast and accurate policy. This poses a difficult machine learning problem, which we tackle with the lols algorithm. lols training must continually compute the effects of changing pruning decisions: we show how to make this efficient in the constituency parsing setting, via dynamic programming and change propagation algorithms. We find that optimizing end-to-end performance in this way leads to a better Pareto frontier{---}i.e., parsers which are more accurate for a given runtime.",
}
@article{rasooli-collins-2017-cross,
    title = "Cross-Lingual Syntactic Transfer with Limited Resources",
    author = "Rasooli, Mohammad Sadegh  and
      Collins, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1020",
    doi = "10.1162/tacl_a_00061",
    pages = "279--293",
    abstract = "We describe a simple but effective method for cross-lingual syntactic transfer of dependency parsers, in the scenario where a large amount of translation data is not available. This method makes use of three steps: 1) a method for deriving cross-lingual word clusters, which can then be used in a multilingual parser; 2) a method for transferring lexical information from a target language to source language treebanks; 3) a method for integrating these steps with the density-driven annotation projection method of Rasooli and Collins (2015). Experiments show improvements over the state-of-the-art in several languages used in previous work, in a setting where the only source of translation data is the Bible, a considerably smaller corpus than the Europarl corpus used in previous work. Results using the Europarl corpus as a source of translation data show additional improvements over the results of Rasooli and Collins (2015). We conclude with results on 38 datasets from the Universal Dependencies corpora.",
}
@article{yang-eisenstein-2017-overcoming,
    title = "Overcoming Language Variation in Sentiment Analysis with Social Attention",
    author = "Yang, Yi  and
      Eisenstein, Jacob",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1021",
    doi = "10.1162/tacl_a_00062",
    pages = "295--307",
    abstract = "Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author{'}s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.",
}
@article{mrksic-etal-2017-semantic,
    title = "Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints",
    author = "Mrk{\v{s}}i{\'c}, Nikola  and
      Vuli{\'c}, Ivan  and
      {\'O} S{\'e}aghdha, Diarmuid  and
      Leviant, Ira  and
      Reichart, Roi  and
      Ga{\v{s}}i{\'c}, Milica  and
      Korhonen, Anna  and
      Young, Steve",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1022",
    doi = "10.1162/tacl_a_00063",
    pages = "309--324",
    abstract = "We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.",
}
@article{monroe-etal-2017-colors,
    title = "Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding",
    author = "Monroe, Will  and
      Hawkins, Robert X.D.  and
      Goodman, Noah D.  and
      Potts, Christopher",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1023",
    doi = "10.1162/tacl_a_00064",
    pages = "325--338",
    abstract = "We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework. Experiments show that this combined pragmatic model interprets color descriptions more accurately than the classifiers from which it is built, and that much of this improvement results from combining the speaker and listener perspectives. We observe that pragmatic reasoning helps primarily in the hardest cases: when the model must distinguish very similar colors, or when few utterances adequately express the target color. Our findings make use of a newly-collected corpus of human utterances in color reference games, which exhibit a variety of pragmatic behaviors. We also show that the embedded speaker model reproduces many of these pragmatic behaviors.",
}
@article{johnson-etal-2017-googles,
    title = "{G}oogle{'}s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation",
    author = "Johnson, Melvin  and
      Schuster, Mike  and
      Le, Quoc V.  and
      Krikun, Maxim  and
      Wu, Yonghui  and
      Chen, Zhifeng  and
      Thorat, Nikhil  and
      Vi{\'e}gas, Fernanda  and
      Wattenberg, Martin  and
      Corrado, Greg  and
      Hughes, Macduff  and
      Dean, Jeffrey",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1024",
    doi = "10.1162/tacl_a_00065",
    pages = "339--351",
    abstract = "We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT systems using a single model. On the WMT{'}14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-theart results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT{'}14 and WMT{'}15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and also show some interesting examples when mixing languages.",
}
@article{luo-etal-2017-unsupervised,
    title = "Unsupervised Learning of Morphological Forests",
    author = "Luo, Jiaming  and
      Narasimhan, Karthik  and
      Barzilay, Regina",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1025",
    doi = "10.1162/tacl_a_00066",
    pages = "353--364",
    abstract = "This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edge-wise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of tight morphological families. The resulting objective is solved using Integer Linear Programming (ILP) paired with contrastive estimation. We train the model by alternating between optimizing the local log-linear model and the global ILP objective. We evaluate our system on three tasks: root detection, clustering of morphological families, and segmentation. Our experiments demonstrate that our model yields consistent gains in all three tasks compared with the best published results.",
}
@article{lee-etal-2017-fully,
    title = "Fully Character-Level Neural Machine Translation without Explicit Segmentation",
    author = "Lee, Jason  and
      Cho, Kyunghyun  and
      Hofmann, Thomas",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1026",
    doi = "10.1162/tacl_a_00067",
    pages = "365--378",
    abstract = "Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model that maps a source character sequence to a target character sequence without any segmentation. We employ a character-level convolutional network with max-pooling at the encoder to reduce the length of source representation, allowing the model to be trained at a speed comparable to subword-level models while capturing local regularities. Our character-to-character model outperforms a recently proposed baseline with a subword-level encoder on WMT{'}15 DE-EN and CS-EN, and gives comparable performance on FI-EN and RU-EN. We then demonstrate that it is possible to share a single character-level encoder across multiple languages by training a model on a many-to-one translation task. In this multilingual setting, the character-level encoder significantly outperforms the subword-level encoder on all the language pairs. We observe that on CS-EN, FI-EN and RU-EN, the quality of the multilingual character-level translation even surpasses the models specifically trained on that language pair alone, both in terms of the BLEU score and human judgment.",
}
@article{zhang-etal-2017-ordinal,
    title = "Ordinal Common-sense Inference",
    author = "Zhang, Sheng  and
      Rudinger, Rachel  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1027",
    doi = "10.1162/tacl_a_00068",
    pages = "379--395",
    abstract = "Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural sequence-to-sequence model on this dataset, which we use to score and generate possible inferences. Further, we annotate subsets of previously established datasets via our ordinal annotation protocol in order to then analyze the distinctions between these and what we have constructed.",
}
@article{yamada-etal-2017-learning,
    title = "Learning Distributed Representations of Texts and Entities from Knowledge Base",
    author = "Yamada, Ikuya  and
      Shindo, Hiroyuki  and
      Takeda, Hideaki  and
      Takefuji, Yoshiyasu",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1028",
    doi = "10.1162/tacl_a_00069",
    pages = "397--411",
    abstract = "We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.",
}
@article{liu-zhang-2017-order,
    title = "In-Order Transition-based Constituent Parsing",
    author = "Liu, Jiangming  and
      Zhang, Yue",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1029",
    doi = "10.1162/tacl_a_00070",
    pages = "413--424",
    abstract = "Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction. To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on the WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.",
}
@article{richter-etal-2017-evaluating,
    title = "Evaluating Low-Level Speech Features Against Human Perceptual Data",
    author = "Richter, Caitlin  and
      Feldman, Naomi H.  and
      Salgado, Harini  and
      Jansen, Aren",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1030",
    doi = "10.1162/tacl_a_00071",
    pages = "425--440",
    abstract = "We introduce a method for measuring the correspondence between low-level speech features and human perception, using a cognitive model of speech perception implemented directly on speech recordings. We evaluate two speaker normalization techniques using this method and find that in both cases, speech features that are normalized across speakers predict human data better than unnormalized speech features, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a new framework for evaluating low-level representations of speech on their match to human perception, and lays the groundwork for creating more ecologically valid models of speech perception.",
}
@article{kummerfeld-klein-2017-parsing,
    title = "Parsing with Traces: An {O}(n4) Algorithm and a Structural Representation",
    author = "Kummerfeld, Jonathan K.  and
      Klein, Dan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1031",
    doi = "10.1162/tacl_a_00072",
    pages = "441--454",
    abstract = "General treebank analyses are graph structured, but parsers are typically restricted to tree structures for efficiency and modeling reasons. We propose a new representation and algorithm for a class of graph structures that is flexible enough to cover almost all treebank structures, while still admitting efficient learning and inference. In particular, we consider directed, acyclic, one-endpoint-crossing graph structures, which cover most long-distance dislocation, shared argumentation, and similar tree-violating linguistic phenomena. We describe how to convert phrase structure parses, including traces, to our new representation in a reversible manner. Our dynamic program uniquely decomposes structures, is sound and complete, and covers 97.3{\%} of the Penn English Treebank. We also implement a proof-of-concept parser that recovers a range of null elements and trace types.",
}
@article{brooke-etal-2017-unsupervised,
    title = "Unsupervised Acquisition of Comprehensive Multiword Lexicons using Competition in an n-gram Lattice",
    author = "Brooke, Julian  and
      {\v{S}}najder, Jan  and
      Baldwin, Timothy",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1032",
    doi = "10.1162/tacl_a_00073",
    pages = "455--470",
    abstract = "We present a new model for acquiring comprehensive multiword lexicons from large corpora based on competition among n-gram candidates. In contrast to the standard approach of simple ranking by association measure, in our model n-grams are arranged in a lattice structure based on subsumption and overlap relationships, with nodes inhibiting other nodes in their vicinity when they are selected as a lexical item. We show how the configuration of such a lattice can be optimized tractably, and demonstrate using annotations of sampled n-grams that our method consistently outperforms alternatives by at least 0.05 F-score across several corpora and languages.",
}
@article{dror-etal-2017-replicability,
    title = "Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets",
    author = "Dror, Rotem  and
      Baumer, Gili  and
      Bogomolov, Marina  and
      Reichart, Roi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1033",
    doi = "10.1162/tacl_a_00074",
    pages = "471--486",
    abstract = "With the ever growing amount of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure a consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.",
}
@article{marie-fujita-2017-phrase,
    title = "Phrase Table Induction Using In-Domain Monolingual Data for Domain Adaptation in Statistical Machine Translation",
    author = "Marie, Benjamin  and
      Fujita, Atsushi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1034",
    doi = "10.1162/tacl_a_00075",
    pages = "487--500",
    abstract = "We present a new framework to induce an in-domain phrase table from in-domain monolingual data that can be used to adapt a general-domain statistical machine translation system to the targeted domain. Our method first compiles sets of phrases in source and target languages separately and generates candidate phrase pairs by taking the Cartesian product of the two phrase sets. It then computes inexpensive features for each candidate phrase pair and filters them using a supervised classifier in order to induce an in-domain phrase table. We experimented on the language pair English{--}French, both translation directions, in two domains and obtained consistently better results than a strong baseline system that uses an in-domain bilingual lexicon. We also conducted an error analysis that showed the induced phrase tables proposed useful translations, especially for words and phrases unseen in the parallel data used to train the general-domain baseline system.",
}
@article{mansouri-bigvand-etal-2017-joint,
    title = "Joint Prediction of Word Alignment with Alignment Types",
    author = "Mansouri Bigvand, Anahita  and
      Bu, Te  and
      Sarkar, Anoop",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1035",
    doi = "10.1162/tacl_a_00076",
    pages = "501--514",
    abstract = "Current word alignment models do not distinguish between different types of alignment links. In this paper, we provide a new probabilistic model for word alignment where word alignments are associated with linguistically motivated alignment types. We propose a novel task of joint prediction of word alignment and alignment types and propose novel semi-supervised learning algorithms for this task. We also solve a sub-task of predicting the alignment type given an aligned word pair. In our experimental results, the generative models we introduce to model alignment types significantly outperform the models without alignment types.",
}
@article{zhang-etal-2017-aspect,
    title = "Aspect-augmented Adversarial Networks for Domain Adaptation",
    author = "Zhang, Yuan  and
      Barzilay, Regina  and
      Jaakkola, Tommi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1036",
    doi = "10.1162/tacl_a_00077",
    pages = "515--528",
    abstract = "We introduce a neural method for transfer learning between two (source and target) classification tasks or aspects over the same domain. Rather than training on target labels, we use a few keywords pertaining to source and target aspects indicating sentence relevance instead of document class labels. Documents are encoded by learning to embed and softly select relevant sentences in an aspect-dependent manner. A shared classifier is trained on the source encoded documents and labels, and applied to target encoded documents. We ensure transfer through aspect-adversarial training so that encoded documents are, as sets, aspect-invariant. Experimental results demonstrate that our approach outperforms different baselines and model variants on two datasets, yielding an improvement of 27{\%} on a pathology dataset and 5{\%} on a review dataset.",
}
@article{gallagher-etal-2017-anchored,
    title = "Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge",
    author = "Gallagher, Ryan J.  and
      Reing, Kyle  and
      Kale, David  and
      Ver Steeg, Greg",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "5",
    year = "2017",
    url = "https://www.aclweb.org/anthology/Q17-1037",
    doi = "10.1162/tacl_a_00078",
    pages = "529--542",
    abstract = "While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.",
}
@proceedings{acl-2017-association-linguistics-tutorial,
    title = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    editor = "Popovi{\'c}, Maja  and
      Boyd-Graber, Jordan",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-5000",
}
@inproceedings{poon-etal-2017-nlp,
    title = "{NLP} for Precision Medicine",
    author = "Poon, Hoifung  and
      Quirk, Chris  and
      Toutanova, Kristina  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-5001",
    pages = "1--2",
    abstract = "We will introduce precision medicine and showcase the vast opportunities for NLP in this burgeoning field with great societal impact. We will review pressing NLP problems, state-of-the art methods, and important applications, as well as datasets, medical resources, and practical issues. The tutorial will provide an accessible overview of biomedicine, and does not presume knowledge in biology or healthcare. The ultimate goal is to reduce the entry barrier for NLP researchers to contribute to this exciting domain.",
}
@inproceedings{morency-baltrusaitis-2017-multimodal,
    title = "Multimodal Machine Learning: Integrating Language, Vision and Speech",
    author = "Morency, Louis-Philippe  and
      Baltru{\v{s}}aitis, Tadas",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-5002",
    pages = "3--5",
    abstract = "Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with image and video captioning projects, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities.This tutorial builds upon a recent course taught at Carnegie Mellon University during the Spring 2016 semester (CMU course 11-777) and two tutorials presented at CVPR 2016 and ICMI 2016. The present tutorial will review fundamental concepts of machine learning and deep neural networks before describing the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation {\&} mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. The tutorial will also present state-of-the-art algorithms that were recently proposed to solve multimodal applications such as image captioning, video descriptions and visual question-answer. We will also discuss the current and upcoming challenges.",
}
@inproceedings{zhu-grefenstette-2017-deep,
    title = "Deep Learning for Semantic Composition",
    author = "Zhu, Xiaodan  and
      Grefenstette, Edward",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-5003",
    pages = "6--7",
    abstract = "Learning representation to model the meaning of text has been a core problem in NLP. The last several years have seen extensive interests on distributional approaches, in which text spans of different granularities are encoded as vectors of numerical values. If properly learned, such representation has showed to achieve the state-of-the-art performance on a wide range of NLP problems.In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural network-based modeling for semantic composition, which aims to learn distributed representation for different granularities of text, e.g., phrases, sentences, or even documents, from their sub-component meaning representation, e.g., word embedding.",
}
@inproceedings{chen-etal-2017-deep,
    title = "Deep Learning for Dialogue Systems",
    author = {Chen, Yun-Nung  and
      Celikyilmaz, Asli  and
      Hakkani-T{\"u}r, Dilek},
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-5004",
    pages = "8--14",
    abstract = "In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today's virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to a dialogue system is still challenging. Hence, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue.miulab.tw.",
}
@inproceedings{kordoni-2017-beyond,
    title = "Beyond Words: Deep Learning for Multiword Expressions and Collocations",
    author = "Kordoni, Valia",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-5005",
    pages = "15--16",
    abstract = "Deep learning has recently shown much promise for NLP applications. Traditionally, in most NLP approaches, documents or sentences are represented by a sparse bag-of-words representation. There is now a lot of work which goes beyond this by adopting a distributed representation of words, by constructing a so-called ``neural embedding'' or vector space representation of each word or document. The aim of this tutorial is to go beyond the learning of word vectors and present methods for learning vector representations for Multiword Expressions and bilingual phrase pairs, all of which are useful for various NLP applications.This tutorial aims to provide attendees with a clear notion of the linguistic and distributional characteristics of Multiword Expressions (MWEs), their relevance for the intersection of deep learning and natural language processing, what methods and resources are available to support their use, and what more could be done in the future. Our target audience are researchers and practitioners in machine learning, parsing (syntactic and semantic) and language technology, not necessarily experts in MWEs, who are interested in tasks that involve or could benefit from considering MWEs as a pervasive phenomenon in human language and communication.",
}
@inproceedings{vaughan-2017-tutorial,
    title = "{T}utorial: Making Better Use of the Crowd",
    author = "Vaughan, Jennifer Wortman",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-5006",
    pages = "17--18",
    abstract = "Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business. The natural language processing community was early to embrace crowdsourcing as a tool for quickly and inexpensively obtaining annotated data to train NLP systems. Once this data is collected, it can be handed off to algorithms that learn to perform basic NLP tasks such as translation or parsing. Usually this handoff is where interaction with the crowd ends. The crowd provides the data, but the ultimate goal is to eventually take humans out of the loop. Are there better ways to make use of the crowd?In this tutorial, I will begin with a showcase of innovative uses of crowdsourcing that go beyond data collection and annotation. I will discuss applications to natural language processing and machine learning, hybrid intelligence or {``}human in the loop{''} AI systems that leverage the complementary strengths of humans and machines in order to achieve more than either could achieve alone, and large scale studies of human behavior online. I will then spend the majority of the tutorial diving into recent research aimed at understanding who crowdworkers are, how they behave, and what this should teach us about best practices for interacting with the crowd.",
}
@proceedings{acl-2017-acl-2017,
    title = "Proceedings of {ACL} 2017, System Demonstrations",
    editor = "Bansal, Mohit  and
      Ji, Heng",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4000",
}
@inproceedings{ramm-etal-2017-annotating,
    title = "Annotating tense, mood and voice for {E}nglish, {F}rench and {G}erman",
    author = "Ramm, Anita  and
      Lo{\'a}iciga, Sharid  and
      Friedrich, Annemarie  and
      Fraser, Alexander",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4001",
    pages = "1--6",
}
@inproceedings{marshall-etal-2017-automating,
    title = "Automating Biomedical Evidence Synthesis: {R}obot{R}eviewer",
    author = {Marshall, Iain  and
      Kuiper, Jo{\"e}l  and
      Banner, Edward  and
      Wallace, Byron C.},
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4002",
    pages = "7--12",
}
@inproceedings{zhang-etal-2017-benben,
    title = "{B}enben: A {C}hinese Intelligent Conversational Robot",
    author = "Zhang, Wei-Nan  and
      Liu, Ting  and
      Qin, Bing  and
      Zhang, Yu  and
      Che, Wanxiang  and
      Zhao, Yanyan  and
      Ding, Xiao",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4003",
    pages = "13--18",
}
@inproceedings{ruckle-gurevych-2017-end,
    title = "End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights",
    author = {R{\"u}ckl{\'e}, Andreas  and
      Gurevych, Iryna},
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4004",
    pages = "19--24",
}
@inproceedings{arendt-volkova-2017-esteem,
    title = "{ESTEEM}: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media",
    author = "Arendt, Dustin  and
      Volkova, Svitlana",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4005",
    pages = "25--30",
}
@inproceedings{hellrich-hahn-2017-exploring,
    title = "Exploring Diachronic Lexical Semantics with {J}e{S}em{E}",
    author = "Hellrich, Johannes  and
      Hahn, Udo",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4006",
    pages = "31--36",
}
@inproceedings{nguyen-etal-2017-extended,
    title = "Extended Named Entity Recognition {API} and Its Applications in Language Education",
    author = "Nguyen, Tuan Duc  and
      Mai, Khai  and
      Pham, Thai-Hoang  and
      Nguyen, Minh Trung  and
      Nguyen, Truc-Vien T.  and
      Eguchi, Takashi  and
      Sasano, Ryohei  and
      Sekine, Satoshi",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4007",
    pages = "37--42",
}
@inproceedings{ghazvininejad-etal-2017-hafez,
    title = "{H}afez: an Interactive Poetry Generation System",
    author = "Ghazvininejad, Marjan  and
      Shi, Xing  and
      Priyadarshi, Jay  and
      Knight, Kevin",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4008",
    pages = "43--48",
}
@inproceedings{el-assady-etal-2017-interactive,
    title = "Interactive Visual Analysis of Transcribed Multi-Party Discourse",
    author = "El-Assady, Mennatallah  and
      Hautli-Janisz, Annette  and
      Gold, Valentin  and
      Butt, Miriam  and
      Holzinger, Katharina  and
      Keim, Daniel",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4009",
    pages = "49--54",
}
@inproceedings{ren-etal-2017-life,
    title = "Life-i{N}et: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences",
    author = "Ren, Xiang  and
      Shen, Jiaming  and
      Qu, Meng  and
      Wang, Xuan  and
      Wu, Zeqiu  and
      Zhu, Qi  and
      Jiang, Meng  and
      Tao, Fangbo  and
      Sinha, Saurabh  and
      Liem, David  and
      Ping, Peipei  and
      Weinshilboum, Richard  and
      Han, Jiawei",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4010",
    pages = "55--60",
}
@inproceedings{neves-etal-2017-olelo,
    title = "{O}lelo: A Question Answering Application for Biomedicine",
    author = {Neves, Mariana  and
      Folkerts, Hendrik  and
      Jankrift, Marcel  and
      Niedermeier, Julian  and
      Stachewicz, Toni  and
      Tietb{\"o}hl, S{\"o}ren  and
      Kraus, Milena  and
      Uflacker, Matthias},
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4011",
    pages = "61--66",
}
@inproceedings{klein-etal-2017-opennmt,
    title = "{O}pen{NMT}: Open-Source Toolkit for Neural Machine Translation",
    author = "Klein, Guillaume  and
      Kim, Yoon  and
      Deng, Yuntian  and
      Senellart, Jean  and
      Rush, Alexander",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4012",
    pages = "67--72",
}
@inproceedings{ultes-etal-2017-pydial,
    title = "{P}y{D}ial: A Multi-domain Statistical Dialogue System Toolkit",
    author = "Ultes, Stefan  and
      Rojas-Barahona, Lina M.  and
      Su, Pei-Hao  and
      Vandyke, David  and
      Kim, Dongho  and
      Casanueva, I{\~n}igo  and
      Budzianowski, Pawe{\l}  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Wen, Tsung-Hsien  and
      Ga{\v{s}}i{\'c}, Milica  and
      Young, Steve",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4013",
    pages = "73--78",
}
@inproceedings{tymoshenko-etal-2017-reltextrank,
    title = "{R}el{T}ext{R}ank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations",
    author = "Tymoshenko, Kateryna  and
      Moschitti, Alessandro  and
      Nicosia, Massimo  and
      Severyn, Aliaksei",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4014",
    pages = "79--84",
}
@inproceedings{kessler-2017-scattertext,
    title = "{S}cattertext: a Browser-Based Tool for Visualizing how Corpora Differ",
    author = "Kessler, Jason",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4015",
    pages = "85--90",
}
@inproceedings{faessler-hahn-2017-semedico,
    title = "{S}emedico: A Comprehensive Semantic Search Engine for the Life Sciences",
    author = "Faessler, Erik  and
      Hahn, Udo",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4016",
    pages = "91--96",
}
@inproceedings{cui-etal-2017-superagent,
    title = "{S}uper{A}gent: A Customer Service Chatbot for {E}-commerce Websites",
    author = "Cui, Lei  and
      Huang, Shaohan  and
      Wei, Furu  and
      Tan, Chuanqi  and
      Duan, Chaoqun  and
      Zhou, Ming",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4017",
    pages = "97--102",
}
@inproceedings{hahn-powell-etal-2017-swanson,
    title = "Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph",
    author = "Hahn-Powell, Gus  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4018",
    pages = "103--108",
}
@inproceedings{abend-etal-2017-uccaapp,
    title = "{UCCAA}pp: Web-application for Syntactic and Semantic Phrase-based Annotation",
    author = "Abend, Omri  and
      Yerushalmi, Shai  and
      Rappoport, Ari",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4019",
    pages = "109--114",
}
@inproceedings{tandon-etal-2017-webchild,
    title = "{W}eb{C}hild 2.0 : Fine-Grained Commonsense Knowledge Distillation",
    author = "Tandon, Niket  and
      de Melo, Gerard  and
      Weikum, Gerhard",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4020",
    pages = "115--120",
}
@inproceedings{siddique-etal-2017-zara,
    title = "{Z}ara Returns: Improved Personality Induction and Adaptation by an Empathetic Virtual Agent",
    author = "Siddique, Farhad Bin  and
      Kampman, Onno  and
      Yang, Yang  and
      Dey, Anik  and
      Fung, Pascale",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-4021",
    pages = "121--126",
}
@proceedings{acl-2017-acl,
    title = "Proceedings of {ACL} 2017, Student Research Workshop",
    editor = "Ettinger, Allyson  and
      Gella, Spandana  and
      Labeau, Matthieu  and
      Alm, Cecilia Ovesdotter  and
      Carpuat, Marine  and
      Dredze, Mark",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3000",
}
@inproceedings{carrillo-2017-computational,
    title = "Computational Characterization of Mental States: A Natural Language Processing Approach",
    author = "Carrillo, Facundo",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3001",
    pages = "1--3",
}
@inproceedings{jawahar-2017-improving,
    title = "Improving Distributed Representations of Tweets - Present and Future",
    author = "Jawahar, Ganesh",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3002",
    pages = "4--10",
}
@inproceedings{grover-mitra-2017-bilingual,
    title = "Bilingual Word Embeddings with Bucketed {CNN} for Parallel Sentence Extraction",
    author = "Grover, Jeenu  and
      Mitra, Pabitra",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3003",
    pages = "11--16",
}
@inproceedings{sukthankar-etal-2017-nquery,
    title = "n{Q}uery - A Natural Language Statement to {SQL} Query Generator",
    author = "Sukthankar, Nandan  and
      Maharnawar, Sanket  and
      Deshmukh, Pranay  and
      Haribhakta, Yashodhara  and
      Kamble, Vibhavari",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3004",
    pages = "17--23",
}
@inproceedings{nayak-etal-2017-v,
    title = "{V} for Vocab: An Intelligent {F}lashcard Application",
    author = "Nayak, Nihal V.  and
      Chinchore, Tanmay  and
      Hanumanth Rao, Aishwarya  and
      Martin, Shane Michael  and
      Simha, Sagar Nagaraj  and
      Lingaraju, G. M.  and
      Jamadagni, H. S.",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3005",
    pages = "24--29",
}
@inproceedings{rao-2017-asking,
    title = "Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions",
    author = "Rao, Sudha",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3006",
    pages = "30--35",
}
@inproceedings{suzuki-etal-2017-building,
    title = "Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation Systems",
    author = "Suzuki, Yui  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3007",
    pages = "36--42",
}
@inproceedings{sharma-etal-2017-segmentation,
    title = "Segmentation Guided Attention Networks for Visual Question Answering",
    author = "Sharma, Vasu  and
      Bishnu, Ankita  and
      Patel, Labhesh",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3008",
    pages = "43--48",
}
@inproceedings{ma-etal-2017-text,
    title = "Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks",
    author = "Ma, Kaixin  and
      Xiao, Catherine  and
      Choi, Jinho D.",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3009",
    pages = "49--55",
}
@inproceedings{li-etal-2017-variation,
    title = "Variation Autoencoder Based Network Representation Learning for Classification",
    author = "Li, Hang  and
      Wang, Haozheng  and
      Yang, Zhenglu  and
      Odagaki, Masato",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3010",
    pages = "56--61",
}
@inproceedings{michel-etal-2017-blind,
    title = "Blind Phoneme Segmentation With Temporal Prediction Errors",
    author = "Michel, Paul  and
      Rasanen, Okko  and
      Thiolli{\`e}re, Roland  and
      Dupoux, Emmanuel",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3011",
    pages = "62--68",
}
@inproceedings{aggarwal-mamidi-2017-automatic,
    title = "Automatic Generation of Jokes in {H}indi",
    author = "Aggarwal, Srishti  and
      Mamidi, Radhika",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3012",
    pages = "69--74",
}
@inproceedings{zhang-litman-2017-word,
    title = "Word Embedding for Response-To-Text Assessment of Evidence",
    author = "Zhang, Haoran  and
      Litman, Diane",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3013",
    pages = "75--81",
}
@inproceedings{soleymanzadeh-2017-domain,
    title = "Domain Specific Automatic Question Generation from Text",
    author = "Soleymanzadeh, Katira",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3014",
    pages = "82--88",
}
@inproceedings{ramirez-etal-2017-socceval,
    title = "{S}occ{E}val: An Annotation Schema for Rating Soccer Players",
    author = "Ramirez, Jose  and
      Garber, Matthew  and
      Wang, Xinhao",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3015",
    pages = "89--94",
}
@inproceedings{garber-etal-2017-accent,
    title = "Accent Adaptation for the Air Traffic Control Domain",
    author = "Garber, Matthew  and
      Singer, Meital  and
      Ward, Christopher",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3016",
    pages = "95--99",
}
@inproceedings{fang-etal-2017-generating,
    title = "Generating Steganographic Text with {LSTM}s",
    author = "Fang, Tina  and
      Jaggi, Martin  and
      Argyraki, Katerina",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3017",
    pages = "100--106",
}
@inproceedings{hiraga-2017-predicting,
    title = "Predicting Depression for {J}apanese Blog Text",
    author = "Hiraga, Misato",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3018",
    pages = "107--113",
}
@inproceedings{babkin-nirenburg-2017-fast,
    title = "Fast Forward Through Opportunistic Incremental Meaning Representation Construction",
    author = "Babkin, Petr  and
      Nirenburg, Sergei",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3019",
    pages = "114--119",
}
@inproceedings{sato-etal-2017-modeling,
    title = "Modeling Situations in Neural Chat Bots",
    author = "Sato, Shoetsu  and
      Yoshinaga, Naoki  and
      Toyoda, Masashi  and
      Kitsuregawa, Masaru",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3020",
    pages = "120--127",
}
@inproceedings{espinosa-2017-empirical,
    title = "An Empirical Study on End-to-End Sentence Modelling",
    author = "Espinosa, Kurt Junshean",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3021",
    pages = "128--135",
}
@inproceedings{naaman-etal-2017-varying,
    title = "Varying Linguistic Purposes of Emoji in ({T}witter) Context",
    author = "Na{'}aman, Noa  and
      Provenza, Hannah  and
      Montoya, Orion",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3022",
    pages = "136--141",
}
@inproceedings{wang-2017-negotiation,
    title = "Negotiation of Antibiotic Treatment in Medical Consultations: A Corpus Based Study",
    author = "Wang, Nan",
    booktitle = "Proceedings of {ACL} 2017, Student Research Workshop",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-3023",
    pages = "142--149",
}
@proceedings{acl-2017-association-linguistics,
    title = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2000",
    doi = "10.18653/v1/P17-2",
}
@inproceedings{cheng-miyao-2017-classifying,
    title = "Classifying Temporal Relations by Bidirectional {LSTM} over Dependency Paths",
    author = "Cheng, Fei  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2001",
    doi = "10.18653/v1/P17-2001",
    pages = "1--6",
    abstract = "Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a {``}common root{''} assumption to extend DP representations of cross-sentence links. In the final comparison to two state-of-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge, as well as manually annotated attributes of entities (class, tense, polarity, etc.).",
}
@inproceedings{song-etal-2017-amr,
    title = "{AMR}-to-text Generation with Synchronous Node Replacement Grammar",
    author = "Song, Linfeng  and
      Peng, Xiaochang  and
      Zhang, Yue  and
      Wang, Zhiguo  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2002",
    doi = "10.18653/v1/P17-2002",
    pages = "7--13",
    abstract = "This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.",
}
@inproceedings{moosavi-strube-2017-lexical,
    title = "Lexical Features in Coreference Resolution: To be Used With Caution",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2003",
    doi = "10.18653/v1/P17-2003",
    pages = "14--19",
    abstract = "Lexical features are a major source of information in state-of-the-art coreference resolvers. Lexical features implicitly model some of the linguistic phenomena at a fine granularity level. They are especially useful for representing the context of mentions. In this paper we investigate a drawback of using many lexical features in state-of-the-art coreference resolvers. We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains. Furthermore, we show that the current coreference resolution evaluation is clearly flawed by only evaluating on a specific split of a specific dataset in which there is a notable overlap between the training, development and test sets.",
}
@inproceedings{stanojevic-simaan-2017-alternative,
    title = "Alternative Objective Functions for Training {MT} Evaluation Metrics",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2004",
    doi = "10.18653/v1/P17-2004",
    pages = "20--25",
    abstract = "MT evaluation metrics are tested for correlation with human judgments either at the sentence- or the corpus-level. Trained metrics ignore corpus-level judgments and are trained for high sentence-level correlation only. We show that training only for one objective (sentence or corpus level), can not only harm the performance on the other objective, but it can also be suboptimal for the objective being optimized. To this end we present a metric trained for corpus-level and show empirical comparison against a metric trained for sentence-level exemplifying how their performance may vary per language pair, type and level of judgment. Subsequently we propose a model trained to optimize both objectives simultaneously and show that it is far more stable than{--}and on average outperforms{--}both models on both objectives.",
}
@inproceedings{peyrard-eckle-kohler-2017-principled,
    title = "A Principled Framework for Evaluating Summarizers: Comparing Models of Summary Quality against Human Judgments",
    author = "Peyrard, Maxime  and
      Eckle-Kohler, Judith",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2005",
    doi = "10.18653/v1/P17-2005",
    pages = "26--31",
    abstract = "We present a new framework for evaluating extractive summarizers, which is based on a principled representation as optimization problem. We prove that every extractive summarizer can be decomposed into an objective function and an optimization technique. We perform a comparative analysis and evaluation of several objective functions embedded in well-known summarizers regarding their correlation with human judgments. Our comparison of these correlations across two datasets yields surprising insights into the role and performance of objective functions in the different summarizers.",
}
@inproceedings{prudhommeaux-etal-2017-vector,
    title = "Vector space models for evaluating semantic fluency in autism",
    author = "Prud{'}hommeaux, Emily  and
      van Santen, Jan  and
      Gliner, Douglas",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2006",
    doi = "10.18653/v1/P17-2006",
    pages = "32--37",
    abstract = "A common test administered during neurological examination is the semantic fluency test, in which the patient must list as many examples of a given semantic category as possible under timed conditions. Poor performance is associated with neurological conditions characterized by impairments in executive function, such as dementia, schizophrenia, and autism spectrum disorder (ASD). Methods for analyzing semantic fluency responses at the level of detail necessary to uncover these differences have typically relied on subjective manual annotation. In this paper, we explore automated approaches for scoring semantic fluency responses that leverage ontological resources and distributional semantic models to characterize the semantic fluency responses produced by young children with and without ASD. Using these methods, we find significant differences in the semantic fluency responses of children with ASD, demonstrating the utility of using objective methods for clinical language analysis.",
}
@inproceedings{susanto-lu-2017-neural,
    title = "Neural Architectures for Multilingual Semantic Parsing",
    author = "Susanto, Raymond Hendy  and
      Lu, Wei",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2007",
    doi = "10.18653/v1/P17-2007",
    pages = "38--44",
    abstract = "In this paper, we address semantic parsing in a multilingual context. We train one multilingual model that is capable of parsing natural language sentences from multiple different languages into their corresponding formal semantic representations. We extend an existing sequence-to-tree model to a multi-task learning framework which shares the decoder for generating semantic representations. We report evaluation results on the multilingual GeoQuery corpus and introduce a new multilingual version of the ATIS corpus.",
}
@inproceedings{malinin-etal-2017-incorporating,
    title = "Incorporating Uncertainty into Deep Learning for Spoken Language Assessment",
    author = "Malinin, Andrey  and
      Ragni, Anton  and
      Knill, Kate  and
      Gales, Mark",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2008",
    doi = "10.18653/v1/P17-2008",
    pages = "45--50",
    abstract = "There is a growing demand for automatic assessment of spoken English proficiency. These systems need to handle large variations in input data owing to the wide range of candidate skill levels and L1s, and errors from ASR. Some candidates will be a poor match to the training data set, undermining the validity of the predicted grade. For high stakes tests it is essential for such systems not only to grade well, but also to provide a measure of their uncertainty in their predictions, enabling rejection to human graders. Previous work examined Gaussian Process (GP) graders which, though successful, do not scale well with large data sets. Deep Neural Network (DNN) may also be used to provide uncertainty using Monte-Carlo Dropout (MCD). This paper proposes a novel method to yield uncertainty and compares it to GPs and DNNs with MCD. The proposed approach explicitly teaches a DNN to have low uncertainty on training data and high uncertainty on generated artificial data. On experiments conducted on data from the Business Language Testing Service (BULATS), the proposed approach is found to outperform GPs and DNNs with MCD in uncertainty-based rejection whilst achieving comparable grading performance.",
}
@inproceedings{jurgens-etal-2017-incorporating,
    title = "Incorporating Dialectal Variability for Socially Equitable Language Identification",
    author = "Jurgens, David  and
      Tsvetkov, Yulia  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2009",
    doi = "10.18653/v1/P17-2009",
    pages = "51--57",
    abstract = "Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of {``}socially inclusive{''} NLP tools.",
}
@inproceedings{jagfeld-etal-2017-evaluating,
    title = "Evaluating Compound Splitters Extrinsically with Textual Entailment",
    author = "Jagfeld, Glorianna  and
      Ziering, Patrick  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2010",
    doi = "10.18653/v1/P17-2010",
    pages = "58--63",
    abstract = "Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation. We explore a novel way for the extrinsic evaluation of compound splitters, namely recognizing textual entailment. Compound splitting has great potential for this novel task that is both transparent and well-defined. Moreover, we show that it addresses certain aspects that are either ignored in intrinsic evaluations or compensated for by taskinternal mechanisms in statistical machine translation. We show significant improvements using different compound splitting methods on a German textual entailment dataset.",
}
@inproceedings{gella-keller-2017-analysis,
    title = "An Analysis of Action Recognition Datasets for Language and Vision Tasks",
    author = "Gella, Spandana  and
      Keller, Frank",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2011",
    doi = "10.18653/v1/P17-2011",
    pages = "64--71",
    abstract = "A large amount of recent research has focused on tasks that combine language and vision, resulting in a proliferation of datasets and methods. One such task is action recognition, whose applications include image annotation, scene understanding and image retrieval. In this survey, we categorize the existing approaches based on how they conceptualize this problem and provide a detailed review of existing datasets, highlighting their diversity as well as advantages and disadvantages. We focus on recently developed datasets which link visual information with linguistic resources and provide a fine-grained syntactic and semantic analysis of actions in images.",
}
@inproceedings{eriguchi-etal-2017-learning,
    title = "Learning to Parse and Translate Improves Neural Machine Translation",
    author = "Eriguchi, Akiko  and
      Tsuruoka, Yoshimasa  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2012",
    doi = "10.18653/v1/P17-2012",
    pages = "72--78",
    abstract = "There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RNNG.",
}
@inproceedings{almodaresi-etal-2017-distribution,
    title = "On the Distribution of Lexical Features at Multiple Levels of Analysis",
    author = "Almodaresi, Fatemeh  and
      Ungar, Lyle  and
      Kulkarni, Vivek  and
      Zakeri, Mohsen  and
      Giorgi, Salvatore  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2013",
    doi = "10.18653/v1/P17-2013",
    pages = "79--84",
    abstract = "Natural language processing has increasingly moved from modeling documents and words toward studying the people behind the language. This move to working with data at the user or community level has presented the field with different characteristics of linguistic data. In this paper, we empirically characterize various lexical distributions at different levels of analysis, showing that, while most features are decidedly sparse and non-normal at the message-level (as with traditional NLP), they follow the central limit theorem to become much more Log-normal or even Normal at the user- and county-levels. Finally, we demonstrate that modeling lexical features for the correct level of analysis leads to marked improvements in common social scientific prediction tasks.",
}
@inproceedings{nisioi-etal-2017-exploring,
    title = "Exploring Neural Text Simplification Models",
    author = "Nisioi, Sergiu  and
      {\v{S}}tajner, Sanja  and
      Ponzetto, Simone Paolo  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2014",
    doi = "10.18653/v1/P17-2014",
    pages = "85--91",
    abstract = "We present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems",
}
@inproceedings{dahlmeier-2017-challenges,
    title = "On the Challenges of Translating {NLP} Research into Commercial Products",
    author = "Dahlmeier, Daniel",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2015",
    doi = "10.18653/v1/P17-2015",
    pages = "92--96",
    abstract = "This paper highlights challenges in industrial research related to translating research in natural language processing into commercial products. While the interest in natural language processing from industry is significant, the transfer of research to commercial products is non-trivial and its challenges are often unknown to or underestimated by many researchers. I discuss current obstacles and provide suggestions for increasing the chances for translating research to commercial success based on my experience in industrial research.",
}
@inproceedings{stajner-etal-2017-sentence,
    title = "Sentence Alignment Methods for Improving Text Simplification Systems",
    author = "{\v{S}}tajner, Sanja  and
      Franco-Salvador, Marc  and
      Ponzetto, Simone Paolo  and
      Rosso, Paolo  and
      Stuckenschmidt, Heiner",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2016",
    doi = "10.18653/v1/P17-2016",
    pages = "97--102",
    abstract = "We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.",
}
@inproceedings{jiang-etal-2017-understanding,
    title = "Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection",
    author = "Jiang, Youxuan  and
      Kummerfeld, Jonathan K.  and
      Lasecki, Walter S.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2017",
    doi = "10.18653/v1/P17-2017",
    pages = "103--109",
    abstract = "Linguistically diverse datasets are critical for training and evaluating robust machine learning systems, but data collection is a costly process that often requires experts. Crowdsourcing the process of paraphrase generation is an effective means of expanding natural language datasets, but there has been limited analysis of the trade-offs that arise when designing tasks. In this paper, we present the first systematic study of the key factors in crowdsourcing paraphrase collection. We consider variations in instructions, incentives, data domains, and workflows. We manually analyzed paraphrases for correctness, grammaticality, and linguistic diversity. Our observations provide new insight into the trade-offs between accuracy and diversity in crowd responses that arise as a result of task design, providing guidance for future paraphrase generation procedures.",
}
@inproceedings{qi-manning-2017-arc,
    title = "Arc-swift: A Novel Transition System for Dependency Parsing",
    author = "Qi, Peng  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2018",
    doi = "10.18653/v1/P17-2018",
    pages = "110--117",
    abstract = "Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7{--}7.6{\%} relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.",
}
@inproceedings{cheng-etal-2017-generative,
    title = "A Generative Parser with a Discriminative Recognition Algorithm",
    author = "Cheng, Jianpeng  and
      Lopez, Adam  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2019",
    doi = "10.18653/v1/P17-2019",
    pages = "118--124",
    abstract = "Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.",
}
@inproceedings{wang-etal-2017-hybrid,
    title = "Hybrid Neural Network Alignment and Lexicon Model in Direct {HMM} for Statistical Machine Translation",
    author = "Wang, Weiyue  and
      Alkhouli, Tamer  and
      Zhu, Derui  and
      Ney, Hermann",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2020",
    doi = "10.18653/v1/P17-2020",
    pages = "125--131",
    abstract = "Recently, the neural machine translation systems showed their promising performance and surpassed the phrase-based systems for most translation tasks. Retreating into conventional concepts machine translation while utilizing effective neural models is vital for comprehending the leap accomplished by neural machine translation over phrase-based methods. This work proposes a direct HMM with neural network-based lexicon and alignment models, which are trained jointly using the Baum-Welch algorithm. The direct HMM is applied to rerank the n-best list created by a state-of-the-art phrase-based translation system and it provides improvements by up to 1.0{\%} Bleu scores on two different translation tasks.",
}
@inproceedings{aharoni-goldberg-2017-towards,
    title = "Towards String-To-Tree Neural Machine Translation",
    author = "Aharoni, Roee  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2021",
    doi = "10.18653/v1/P17-2021",
    pages = "132--140",
    abstract = "We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.",
}
@inproceedings{reed-etal-2017-learning,
    title = "Learning Lexico-Functional Patterns for First-Person Affect",
    author = "Reed, Lena  and
      Wu, Jiaqi  and
      Oraby, Shereen  and
      Anand, Pranav  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2022",
    doi = "10.18653/v1/P17-2022",
    pages = "141--147",
    abstract = "Informal first-person narratives are a unique resource for computational models of everyday events and people{'}s affective reactions to them. People blogging about their day tend not to explicitly say I am happy. Instead they describe situations from which other humans can readily infer their affective reactions. However current sentiment dictionaries are missing much of the information needed to make similar inferences. We build on recent work that models affect in terms of lexical predicate functions and affect on the predicate{'}s arguments. We present a method to learn proxies for these functions from first-person narratives. We construct a novel fine-grained test set, and show that the patterns we learn improve our ability to predict first-person affective reactions to everyday events, from a Stanford sentiment baseline of .67F to .75F.",
}
@inproceedings{shu-etal-2017-lifelong,
    title = "Lifelong Learning {CRF} for Supervised Aspect Extraction",
    author = "Shu, Lei  and
      Xu, Hu  and
      Liu, Bing",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2023",
    doi = "10.18653/v1/P17-2023",
    pages = "148--154",
    abstract = "This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their results as knowledge, Conditional Random Fields (CRF) can leverage this knowledge in a lifelong learning manner to extract in a new domain markedly better than the traditional CRF without using this prior knowledge. The key innovation is that even after CRF training, the model can still improve its extraction with experiences in its applications.",
}
@inproceedings{zhang-etal-2017-exploiting,
    title = "Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization",
    author = "Zhang, Ye  and
      Lease, Matthew  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2024",
    doi = "10.18653/v1/P17-2024",
    pages = "155--160",
    abstract = "A fundamental advantage of neural models for NLP is their ability to learn representations from scratch. However, in practice this often means ignoring existing external linguistic resources, e.g., WordNet or domain specific ontologies such as the Unified Medical Language System (UMLS). We propose a general, novel method for exploiting such resources via weight sharing. Prior work on weight sharing in neural networks has considered it largely as a means of model compression. In contrast, we treat weight sharing as a flexible mechanism for incorporating prior knowledge into neural models. We show that this approach consistently yields improved performance on classification tasks compared to baseline strategies that do not exploit weight sharing.",
}
@inproceedings{fried-etal-2017-improving,
    title = "Improving Neural Parsing by Disentangling Model Combination and Reranking Effects",
    author = "Fried, Daniel  and
      Stern, Mitchell  and
      Klein, Dan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2025",
    doi = "10.18653/v1/P17-2025",
    pages = "161--166",
    abstract = "Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficult, they have primarily been used to rescore candidate outputs from base parsers in which decoding is more straightforward. We first present an algorithm for direct search in these generative models. We then demonstrate that the rescoring results are at least partly due to implicit model combination rather than reranking effects. Finally, we show that explicit model combination can improve performance even further, resulting in new state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data and 94.66 F1 when using external data.",
}
@inproceedings{melamud-goldberger-2017-information,
    title = "Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function",
    author = "Melamud, Oren  and
      Goldberger, Jacob",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2026",
    doi = "10.18653/v1/P17-2026",
    pages = "167--171",
    abstract = "In this paper we define a measure of dependency between two random variables, based on the Jensen-Shannon (JS) divergence between their joint distribution and the product of their marginal distributions. Then, we show that word2vec{'}s skip-gram with negative sampling embedding algorithm finds the optimal low-dimensional approximation of this JS dependency measure between the words and their contexts. The gap between the optimal score and the low-dimensional approximation is demonstrated on a standard text corpus.",
}
@inproceedings{kazi-thompson-2017-implicitly,
    title = "Implicitly-Defined Neural Networks for Sequence Labeling",
    author = "Kazi, Michaeel  and
      Thompson, Brian",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2027",
    doi = "10.18653/v1/P17-2027",
    pages = "172--177",
    abstract = "In this work, we propose a novel, implicitly-defined neural network architecture and describe a method to compute its components. The proposed architecture forgoes the causality assumption used to formulate recurrent neural networks and instead couples the hidden states of the network, allowing improvement on problems with complex, long-distance dependencies. Initial experiments demonstrate the new architecture outperforms both the Stanford Parser and baseline bidirectional networks on the Penn Treebank Part-of-Speech tagging task and a baseline bidirectional network on an additional artificial random biased walk task.",
}
@inproceedings{ludusan-etal-2017-role,
    title = "The Role of Prosody and Speech Register in Word Segmentation: A Computational Modelling Perspective",
    author = "Ludusan, Bogdan  and
      Mazuka, Reiko  and
      Bernard, Mathieu  and
      Cristia, Alejandrina  and
      Dupoux, Emmanuel",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2028",
    doi = "10.18653/v1/P17-2028",
    pages = "178--183",
    abstract = "This study explores the role of speech register and prosody for the task of word segmentation. Since these two factors are thought to play an important role in early language acquisition, we aim to quantify their contribution for this task. We study a Japanese corpus containing both infant- and adult-directed speech and we apply four different word segmentation models, with and without knowledge of prosodic boundaries. The results showed that the difference between registers is smaller than previously reported and that prosodic boundary information helps more adult- than infant-directed speech.",
}
@inproceedings{wang-etal-2017-two,
    title = "A Two-Stage Parsing Method for Text-Level Discourse Analysis",
    author = "Wang, Yizhong  and
      Li, Sujian  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2029",
    doi = "10.18653/v1/P17-2029",
    pages = "184--188",
    abstract = "Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance. In this paper, we propose that transition-based model is more appropriate for parsing the naked discourse tree (i.e., identifying span and nuclearity) due to data sparsity. At the same time, we argue that relation labeling can benefit from naked tree structure and should be treated elaborately with consideration of three kinds of relations including within-sentence, across-sentence and across-paragraph relations. Thus, we design a pipelined two-stage parsing method for generating an RST tree from text. Experimental results show that our method achieves state-of-the-art performance, especially on span and nuclearity identification.",
}
@inproceedings{sakaguchi-etal-2017-error,
    title = "Error-repair Dependency Parsing for Ungrammatical Texts",
    author = "Sakaguchi, Keisuke  and
      Post, Matt  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2030",
    doi = "10.18653/v1/P17-2030",
    pages = "189--195",
    abstract = "We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.",
}
@inproceedings{libovicky-helcl-2017-attention,
    title = "Attention Strategies for Multi-Source Sequence-to-Sequence Learning",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Helcl, Jind{\v{r}}ich",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2031",
    doi = "10.18653/v1/P17-2031",
    pages = "196--202",
    abstract = "Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.",
}
@inproceedings{hua-wang-2017-understanding,
    title = "Understanding and Detecting Supporting Arguments of Diverse Types",
    author = "Hua, Xinyu  and
      Wang, Lu",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2032",
    doi = "10.18653/v1/P17-2032",
    pages = "203--208",
    abstract = "We investigate the problem of sentence-level supporting argument detection from relevant documents for user-specified claims. A dataset containing claims and associated citation articles is collected from online debate website idebate.org. We then manually label sentence-level supporting arguments from the documents along with their types as study, factual, opinion, or reasoning. We further characterize arguments of different types, and explore whether leveraging type information can facilitate the supporting arguments detection task. Experimental results show that LambdaMART (Burges, 2010) ranker that uses features informed by argument types yields better performance than the same ranker trained without type information.",
}
@inproceedings{rahimi-etal-2017-neural,
    title = "A Neural Model for User Geolocation and Lexical Dialectology",
    author = "Rahimi, Afshin  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2033",
    doi = "10.18653/v1/P17-2033",
    pages = "209--216",
    abstract = "We propose a simple yet effective text-based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.",
}
@inproceedings{suhr-etal-2017-corpus,
    title = "A Corpus of Natural Language for Visual Reasoning",
    author = "Suhr, Alane  and
      Lewis, Mike  and
      Yeh, James  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2034",
    doi = "10.18653/v1/P17-2034",
    pages = "217--223",
    abstract = "We present a new visual reasoning language dataset, containing 92,244 pairs of examples of natural statements grounded in synthetic images with 3,962 unique sentences. We describe a method of crowdsourcing linguistically-diverse data, and present an analysis of our data. The data demonstrates a broad set of linguistic phenomena, requiring visual and set-theoretic reasoning. We experiment with various models, and show the data presents a strong challenge for future research.",
}
@inproceedings{tourille-etal-2017-neural,
    title = "Neural Architecture for Temporal Relation Extraction: A {B}i-{LSTM} Approach for Detecting Narrative Containers",
    author = "Tourille, Julien  and
      Ferret, Olivier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Tannier, Xavier",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2035",
    doi = "10.18653/v1/P17-2035",
    pages = "224--230",
    abstract = "We present a neural architecture for containment relation identification between medical events and/or temporal expressions. We experiment on a corpus of de-identified clinical notes in English from the Mayo Clinic, namely the THYME corpus. Our model achieves an F-measure of 0.613 and outperforms the best result reported on this corpus to date.",
}
@inproceedings{tian-etal-2017-make,
    title = "How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models",
    author = "Tian, Zhiliang  and
      Yan, Rui  and
      Mou, Lili  and
      Song, Yiping  and
      Feng, Yansong  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2036",
    doi = "10.18653/v1/P17-2036",
    pages = "231--236",
    abstract = "Generative conversational systems are attracting increasing attention in natural language processing (NLP). Recently, researchers have noticed the importance of context information in dialog processing, and built various models to utilize context. However, there is no systematic comparison to analyze how to use context effectively. In this paper, we conduct an empirical study to compare various models and investigate the effect of context information in dialog systems. We also propose a variant that explicitly weights context vectors by context-query relevance, outperforming the other baselines.",
}
@inproceedings{braud-etal-2017-cross,
    title = "Cross-lingual and cross-domain discourse segmentation of entire documents",
    author = "Braud, Chlo{\'e}  and
      Lacroix, Oph{\'e}lie  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2037",
    doi = "10.18653/v1/P17-2037",
    pages = "237--243",
    abstract = "Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only detect intra-sentential segment boundaries, assuming gold standard sentence and token segmentation, and relying on high-quality syntactic parses and rich heuristics that are not generally available across languages and domains. In this paper, we propose statistical discourse segmenters for five languages and three domains that do not rely on gold pre-annotations. We also consider the problem of learning discourse segmenters when no labeled data is available for a language. Our fully supervised system obtains 89.5{\%} F1 for English newswire, with slight drops in performance on other domains, and we report supervised and unsupervised (cross-lingual) results for five languages in total.",
}
@inproceedings{beigman-klebanov-etal-2017-detecting,
    title = "Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?",
    author = "Beigman Klebanov, Beata  and
      Gyawali, Binod  and
      Song, Yi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2038",
    doi = "10.18653/v1/P17-2038",
    pages = "244--249",
    abstract = "Automatic identification of good arguments on a controversial topic has applications in civics and education, to name a few. While in the civics context it might be acceptable to create separate models for each topic, in the context of scoring of students{'} writing there is a preference for a single model that applies to all responses. Given that good arguments for one topic are likely to be irrelevant for another, is a single model for detecting good arguments a contradiction in terms? We investigate the extent to which it is possible to close the performance gap between topic-specific and across-topics models for identification of good arguments.",
}
@inproceedings{wachsmuth-etal-2017-argumentation,
    title = "Argumentation Quality Assessment: Theory vs. Practice",
    author = "Wachsmuth, Henning  and
      Naderi, Nona  and
      Habernal, Ivan  and
      Hou, Yufang  and
      Hirst, Graeme  and
      Gurevych, Iryna  and
      Stein, Benno",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2039",
    doi = "10.18653/v1/P17-2039",
    pages = "250--255",
    abstract = "Argumentation quality is viewed differently in argumentation theory and in practical assessment approaches. This paper studies to what extent the views match empirically. We find that most observations on quality phrased spontaneously are in fact adequately represented by theory. Even more, relative comparisons of arguments in practice correlate with absolute quality ratings based on theory. Our results clarify how the two views can learn from each other.",
}
@inproceedings{ronnqvist-etal-2017-recurrent,
    title = "A Recurrent Neural Model with Attention for the Recognition of {C}hinese Implicit Discourse Relations",
    author = {R{\"o}nnqvist, Samuel  and
      Schenk, Niko  and
      Chiarcos, Christian},
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2040",
    doi = "10.18653/v1/P17-2040",
    pages = "256--262",
    abstract = "We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model{'}s ability to selectively focus on the relevant parts of an input sequence.",
}
@inproceedings{wang-etal-2017-discourse,
    title = "Discourse Annotation of Non-native Spontaneous Spoken Responses Using the {R}hetorical {S}tructure {T}heory Framework",
    author = "Wang, Xinhao  and
      Bruno, James  and
      Molloy, Hillary  and
      Evanini, Keelan  and
      Zechner, Klaus",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2041",
    doi = "10.18653/v1/P17-2041",
    pages = "263--268",
    abstract = "The availability of the Rhetorical Structure Theory (RST) Discourse Treebank has spurred substantial research into discourse analysis of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, non-native spontaneous speech. Considering that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we initiated a research effort to obtain RST annotations of a large number of non-native spoken responses from a standardized assessment of academic English proficiency. The resulting inter-annotator kappa agreements on the three different levels of Span, Nuclearity, and Relation are 0.848, 0.766, and 0.653, respectively. Furthermore, a set of features was explored to evaluate the discourse structure of non-native spontaneous speech based on these annotations; the highest performing feature resulted in a correlation of 0.612 with scores of discourse coherence provided by expert human raters.",
}
@inproceedings{wu-etal-2017-improving,
    title = "Improving Implicit Discourse Relation Recognition with Discourse-specific Word Embeddings",
    author = "Wu, Changxing  and
      Shi, Xiaodong  and
      Chen, Yidong  and
      Su, Jinsong  and
      Wang, Boli",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2042",
    doi = "10.18653/v1/P17-2042",
    pages = "269--274",
    abstract = "We introduce a simple and effective method to learn discourse-specific word embeddings (DSWE) for implicit discourse relation recognition. Specifically, DSWE is learned by performing connective classification on massive explicit discourse data, and capable of capturing discourse relationships between words. On the PDTB data set, using DSWE as features achieves significant improvements over baselines.",
}
@inproceedings{hirao-etal-2017-oracle,
    title = "Oracle Summaries of Compressive Summarization",
    author = "Hirao, Tsutomu  and
      Nishino, Masaaki  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2043",
    doi = "10.18653/v1/P17-2043",
    pages = "275--280",
    abstract = "This paper derives an Integer Linear Programming (ILP) formulation to obtain an oracle summary of the compressive summarization paradigm in terms of ROUGE. The oracle summary is essential to reveal the upper bound performance of the paradigm. Experimental results on the DUC dataset showed that ROUGE scores of compressive oracles are significantly higher than those of extractive oracles and state-of-the-art summarization systems. These results reveal that compressive summarization is a promising paradigm and encourage us to continue with the research to produce informative summaries.",
}
@inproceedings{hasegawa-etal-2017-japanese,
    title = "{J}apanese Sentence Compression with a Large Training Dataset",
    author = "Hasegawa, Shun  and
      Kikuchi, Yuta  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2044",
    doi = "10.18653/v1/P17-2044",
    pages = "281--286",
    abstract = "In English, high-quality sentence compression models by deleting words have been trained on automatically created large training datasets. We work on Japanese sentence compression by a similar approach. To create a large Japanese training dataset, a method of creating English training dataset is modified based on the characteristics of the Japanese language. The created dataset is used to train Japanese sentence compression models based on the recurrent neural network.",
}
@inproceedings{loyola-etal-2017-neural,
    title = "A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes",
    author = "Loyola, Pablo  and
      Marrese-Taylor, Edison  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2045",
    doi = "10.18653/v1/P17-2045",
    pages = "287--292",
    abstract = "We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.",
}
@inproceedings{wei-etal-2017-english,
    title = "{E}nglish Event Detection With Translated Language Features",
    author = "Wei, Sam  and
      Korostil, Igor  and
      Nothman, Joel  and
      Hachey, Ben",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2046",
    doi = "10.18653/v1/P17-2046",
    pages = "293--298",
    abstract = "We propose novel radical features from automatic translation for event extraction. Event detection is a complex language processing task for which it is expensive to collect training data, making generalisation challenging. We derive meaningful subword features from automatic translations into target language. Results suggest this method is particularly useful when using languages with writing systems that facilitate easy decomposition into subword features, e.g., logograms and Cangjie. The best result combines logogram features from Chinese and Japanese with syllable features from Korean, providing an additional 3.0 points f-score when added to state-of-the-art generalisation features on the TAC KBP 2015 Event Nugget task.",
}
@inproceedings{savenkov-agichtein-2017-evinets,
    title = "{E}vi{N}ets: Neural Networks for Combining Evidence Signals for Factoid Question Answering",
    author = "Savenkov, Denis  and
      Agichtein, Eugene",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2047",
    doi = "10.18653/v1/P17-2047",
    pages = "299--304",
    abstract = "A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate. This paper proposes EviNets: a novel neural network architecture for factoid question answering. EviNets scores candidate answer entities by combining the available supporting evidence, e.g., structured knowledge bases and unstructured text documents. EviNets represents each piece of evidence with a dense embeddings vector, scores their relevance to the question, and aggregates the support for each candidate to predict their final scores. Each of the components is generic and allows plugging in a variety of models for semantic similarity scoring and information aggregation. We demonstrate the effectiveness of EviNets in experiments on the existing TREC QA and WikiMovies benchmarks, and on the new Yahoo! Answers dataset introduced in this paper. EviNets can be extended to other information types and could facilitate future work on combining evidence signals for joint reasoning in question answering.",
}
@inproceedings{wolfe-etal-2017-pocket,
    title = "Pocket Knowledge Base Population",
    author = "Wolfe, Travis  and
      Dredze, Mark  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2048",
    doi = "10.18653/v1/P17-2048",
    pages = "305--310",
    abstract = "Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well anew annotations collected for this work. Our methods produce high quality KB from just text with many more entities and relationships than existing KBP systems.",
}
@inproceedings{khot-etal-2017-answering,
    title = "Answering Complex Questions Using Open Information Extraction",
    author = "Khot, Tushar  and
      Sabharwal, Ashish  and
      Clark, Peter",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2049",
    doi = "10.18653/v1/P17-2049",
    pages = "311--316",
    abstract = "While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such knowledge has only been used to answer simple questions with retrieval-based methods. We overcome this limitation by presenting a method for reasoning with Open IE knowledge, allowing more complex questions to be handled. Using a recently proposed support graph optimization framework for QA, we develop a new inference model for Open IE, in particular one that can work effectively with multiple short facts, noise, and the relational structure of tuples. Our model significantly outperforms a state-of-the-art structured solver on complex questions of varying difficulty, while also removing the reliance on manually curated knowledge.",
}
@inproceedings{saha-etal-2017-bootstrapping,
    title = "Bootstrapping for Numerical Open {IE}",
    author = "Saha, Swarnadeep  and
      Pal, Harinder  and
      {Mausam}",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2050",
    doi = "10.18653/v1/P17-2050",
    pages = "317--323",
    abstract = "We design and release BONIE, the first open numerical relation extractor, for extracting Open IE tuples where one of the arguments is a number or a quantity-unit phrase. BONIE uses bootstrapping to learn the specific dependency patterns that express numerical relations in a sentence. BONIE{'}s novelty lies in task-specific customizations, such as inferring implicit relations, which are clear due to context such as units (for e.g., {`}square kilometers{'} suggests area, even if the word {`}area{'} is missing in the sentence). BONIE obtains 1.5x yield and 15 point precision gain on numerical facts over a state-of-the-art Open IE system.",
}
@inproceedings{komninos-manandhar-2017-feature,
    title = "Feature-Rich Networks for Knowledge Base Completion",
    author = "Komninos, Alexandros  and
      Manandhar, Suresh",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2051",
    doi = "10.18653/v1/P17-2051",
    pages = "324--329",
    abstract = "We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed models can better incorporate side information than previously proposed combinations of bilinear models with convolutional neural networks, showing large improvements when scoring the plausibility of unobserved facts with associated textual mentions.",
}
@inproceedings{rabinovich-klein-2017-fine,
    title = "Fine-Grained Entity Typing with High-Multiplicity Assignments",
    author = "Rabinovich, Maxim  and
      Klein, Dan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2052",
    doi = "10.18653/v1/P17-2052",
    pages = "330--334",
    abstract = "As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-prediction approach to this problem and show that our model outperforms unstructured baselines on a new Wikipedia-based fine-grained typing corpus.",
}
@inproceedings{ma-etal-2017-group,
    title = "Group Sparse {CNN}s for Question Classification with Answer Sets",
    author = "Ma, Mingbo  and
      Huang, Liang  and
      Xiang, Bing  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2053",
    doi = "10.18653/v1/P17-2053",
    pages = "335--340",
    abstract = "Question classification is an important task with wide applications. However, traditional techniques treat questions as general sentences, ignoring the corresponding answer data. In order to consider answer information into question modeling, we first introduce novel group sparse autoencoders which refine question representation by utilizing group information in the answer set. We then propose novel group sparse CNNs which naturally learn question representation with respect to their answers by implanting group sparse autoencoders into traditional CNNs. The proposed model significantly outperform strong baselines on four datasets.",
}
@inproceedings{augenstein-sogaard-2017-multi,
    title = "Multi-Task Learning of Keyphrase Boundary Classification",
    author = "Augenstein, Isabelle  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2054",
    doi = "10.18653/v1/P17-2054",
    pages = "341--346",
    abstract = "Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far underexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.",
}
@inproceedings{mirza-etal-2017-cardinal,
    title = "Cardinal Virtues: Extracting Relation Cardinalities from Text",
    author = "Mirza, Paramita  and
      Razniewski, Simon  and
      Darari, Fariz  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2055",
    doi = "10.18653/v1/P17-2055",
    pages = "347--351",
    abstract = "Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3{\%} and 55{\%}, depending on the difficulty of relations.",
}
@inproceedings{stanovsky-etal-2017-integrating,
    title = "Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets",
    author = "Stanovsky, Gabriel  and
      Eckle-Kohler, Judith  and
      Puzikov, Yevgeniy  and
      Dagan, Ido  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2056",
    doi = "10.18653/v1/P17-2056",
    pages = "352--357",
    abstract = "Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available.",
}
@inproceedings{das-etal-2017-question,
    title = "Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks",
    author = "Das, Rajarshi  and
      Zaheer, Manzil  and
      Reddy, Siva  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2057",
    doi = "10.18653/v1/P17-2057",
    pages = "358--365",
    abstract = "Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing Memory networks to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on Spades fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 F1 points.",
}
@inproceedings{goyal-etal-2017-differentiable,
    title = "Differentiable Scheduled Sampling for Credit Assignment",
    author = "Goyal, Kartik  and
      Dyer, Chris  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2058",
    doi = "10.18653/v1/P17-2058",
    pages = "366--371",
    abstract = "We demonstrate that a continuous relaxation of the argmax operation can be used to create a differentiable approximation to greedy decoding in sequence-to-sequence (seq2seq) models. By incorporating this approximation into the scheduled sampling training procedure{--}a well-known technique for correcting exposure bias{--}we introduce a new training objective that is continuous and differentiable everywhere and can provide informative gradients near points where previous decoding decisions change their value. By using a related approximation, we also demonstrate a similar approach to sampled-based training. We show that our approach outperforms both standard cross-entropy training and scheduled sampling procedures in two sequence prediction tasks: named entity recognition and machine translation.",
}
@inproceedings{guo-2017-deep,
    title = "A Deep Network with Visual Text Composition Behavior",
    author = "Guo, Hongyu",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2059",
    doi = "10.18653/v1/P17-2059",
    pages = "372--377",
    abstract = "While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.",
}
@inproceedings{zhou-etal-2017-neural,
    title = "Neural System Combination for Machine Translation",
    author = "Zhou, Long  and
      Hu, Wenpeng  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2060",
    doi = "10.18653/v1/P17-2060",
    pages = "378--384",
    abstract = "Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT). However, SMT is usually better than NMT in translation adequacy. It is therefore a promising direction to combine the advantages of both NMT and SMT. In this paper, we propose a neural system combination framework leveraging multi-source NMT, which takes as input the outputs of NMT and SMT systems and produces the final translation. Extensive experiments on the Chinese-to-English translation task show that our model archives significant improvement by 5.3 BLEU points over the best single system output and 3.4 BLEU points over the state-of-the-art traditional system combination methods.",
}
@inproceedings{chu-etal-2017-empirical,
    title = "An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation",
    author = "Chu, Chenhui  and
      Dabre, Raj  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2061",
    doi = "10.18653/v1/P17-2061",
    pages = "385--391",
    abstract = "In this paper, we propose a novel domain adaptation method named {``}mixed fine tuning{''} for neural machine translation (NMT). We combine two existing approaches namely fine tuning and multi domain NMT. We first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus which is a mix of the in-domain and out-of-domain corpora. All corpora are augmented with artificial tags to indicate specific domains. We empirically compare our proposed method against fine tuning and multi domain methods and discuss its benefits and shortcomings.",
}
@inproceedings{marie-fujita-2017-efficient,
    title = "Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings",
    author = "Marie, Benjamin  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2062",
    doi = "10.18653/v1/P17-2062",
    pages = "392--398",
    abstract = "We propose a new method for extracting pseudo-parallel sentences from a pair of large monolingual corpora, without relying on any document-level information. Our method first exploits word embeddings in order to efficiently evaluate trillions of candidate sentence pairs and then a classifier to find the most reliable ones. We report significant improvements in domain adaptation for statistical machine translation when using a translation model trained on the sentence pairs extracted from in-domain monolingual corpora.",
}
@inproceedings{malmasi-dras-2017-feature,
    title = "Feature Hashing for Language and Dialect Identification",
    author = "Malmasi, Shervin  and
      Dras, Mark",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2063",
    doi = "10.18653/v1/P17-2063",
    pages = "399--403",
    abstract = "We evaluate feature hashing for language identification (LID), a method not previously used for this task. Using a standard dataset, we first show that while feature performance is high, LID data is highly dimensional and mostly sparse ({\textgreater}99.5{\%}) as it includes large vocabularies for many languages; memory requirements grow as languages are added. Next we apply hashing using various hash sizes, demonstrating that there is no performance loss with dimensionality reductions of up to 86{\%}. We also show that using an ensemble of low-dimension hash-based classifiers further boosts performance. Feature hashing is highly useful for LID and holds great promise for future work in this area.",
}
@inproceedings{shiue-etal-2017-detection,
    title = "Detection of {C}hinese Word Usage Errors for Non-Native {C}hinese Learners with Bidirectional {LSTM}",
    author = "Shiue, Yow-Ting  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2064",
    doi = "10.18653/v1/P17-2064",
    pages = "404--410",
    abstract = "Selecting appropriate words to compose a sentence is one common problem faced by non-native Chinese learners. In this paper, we propose (bidirectional) LSTM sequence labeling models and explore various features to detect word usage errors in Chinese sentences. By combining CWINDOW word embedding features and POS information, the best bidirectional LSTM model achieves accuracy 0.5138 and MRR 0.6789 on the HSK dataset. For 80.79{\%} of the test data, the model ranks the ground-truth within the top two at position level.",
}
@inproceedings{ryskina-etal-2017-automatic,
    title = "Automatic Compositor Attribution in the First Folio of Shakespeare",
    author = "Ryskina, Maria  and
      Alpert-Abrams, Hannah  and
      Garrette, Dan  and
      Berg-Kirkpatrick, Taylor",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2065",
    doi = "10.18653/v1/P17-2065",
    pages = "411--416",
    abstract = "Compositor attribution, the clustering of pages in a historical printed document by the individual who set the type, is a bibliographic task that relies on analysis of orthographic variation and inspection of visual details of the printed page. In this paper, we introduce a novel unsupervised model that jointly describes the textual and visual features needed to distinguish compositors. Applied to images of Shakespeare{'}s First Folio, our model predicts attributions that agree with the manual judgements of bibliographers with an accuracy of 87{\%}, even on text that is the output of OCR.",
}
@inproceedings{yoshikawa-etal-2017-stair,
    title = "{STAIR} Captions: Constructing a Large-Scale {J}apanese Image Caption Dataset",
    author = "Yoshikawa, Yuya  and
      Shigeto, Yutaro  and
      Takeuchi, Akikazu",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2066",
    doi = "10.18653/v1/P17-2066",
    pages = "417--421",
    abstract = "In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention. In this paper, we particularly consider generating Japanese captions for images. Since most available caption datasets have been constructed for English language, there are few datasets for Japanese. To tackle this problem, we construct a large-scale Japanese image caption dataset based on images from MS-COCO, which is called STAIR Captions. STAIR Captions consists of 820,310 Japanese captions for 164,062 images. In the experiment, we show that a neural network trained using STAIR Captions can generate more natural and better Japanese captions, compared to those generated using English-Japanese machine translation after generating English captions.",
}
@inproceedings{wang-2017-liar,
    title = "{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection",
    author = "Wang, William Yang",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2067",
    doi = "10.18653/v1/P17-2067",
    pages = "422--426",
    abstract = "Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.",
}
@inproceedings{kato-etal-2017-english,
    title = "{E}nglish Multiword Expression-aware Dependency Parsing Including Named Entities",
    author = "Kato, Akihiko  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2068",
    doi = "10.18653/v1/P17-2068",
    pages = "427--432",
    abstract = "Because syntactic structures and spans of multiword expressions (MWEs) are independently annotated in many English syntactic corpora, they are generally inconsistent with respect to one another, which is harmful to the implementation of an aggregate system. In this work, we construct a corpus that ensures consistency between dependency structures and MWEs, including named entities. Further, we explore models that predict both MWE-spans and an MWE-aware dependency structure. Experimental results show that our joint model using additional MWE-span features achieves an MWE recognition improvement of 1.35 points over a pipeline model.",
}
@inproceedings{kober-etal-2017-improving,
    title = "Improving Semantic Composition with Offset Inference",
    author = "Kober, Thomas  and
      Weeds, Julie  and
      Reffin, Jeremy  and
      Weir, David",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2069",
    doi = "10.18653/v1/P17-2069",
    pages = "433--440",
    abstract = "Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition.",
}
@inproceedings{fadaee-etal-2017-learning,
    title = "Learning Topic-Sensitive Word Representations",
    author = "Fadaee, Marzieh  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2070",
    doi = "10.18653/v1/P17-2070",
    pages = "441--447",
    abstract = "Distributed word representations are widely used for modeling words in NLP tasks. Most of the existing models generate one representation per word and do not consider different meanings of a word. We present two approaches to learn multiple topic-sensitive representations per word by using Hierarchical Dirichlet Process. We observe that by modeling topics and integrating topic distributions for each document we obtain representations that are able to distinguish between different meanings of a given word. Our models yield statistically significant improvements for the lexical substitution task indicating that commonly used single word representations, even when combined with contextual information, are insufficient for this task.",
}
@inproceedings{szymanski-2017-temporal,
    title = "Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings",
    author = "Szymanski, Terrence",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2071",
    doi = "10.18653/v1/P17-2071",
    pages = "448--453",
    abstract = "This paper introduces the concept of temporal word analogies: pairs of words which occupy the same semantic space at different points in time. One well-known property of word embeddings is that they are able to effectively model traditional word analogies ({``}word $w_1$ is to word $w_2$ as word $w_3$ is to word $w_4${''}) through vector addition. Here, I show that temporal word analogies ({``}word $w_1$ at time $t_\alpha$ is like word $w_2$ at time $t_\beta${''}) can effectively be modeled with diachronic word embeddings, provided that the independent embedding spaces from each time period are appropriately transformed into a common vector space. When applied to a diachronic corpus of news articles, this method is able to identify temporal word analogies such as {``}Ronald Reagan in 1987 is like Bill Clinton in 1997{''}, or {``}Walkman in 1987 is like iPod in 2007{''}.",
}
@inproceedings{elrazzaz-etal-2017-methodical,
    title = "Methodical Evaluation of {A}rabic Word Embeddings",
    author = "Elrazzaz, Mohammed  and
      Elbassuoni, Shady  and
      Shaban, Khaled  and
      Helwe, Chadi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2072",
    doi = "10.18653/v1/P17-2072",
    pages = "454--458",
    abstract = "Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic word embeddings. We first build a benchmark for the Arabic language that can be utilized to perform intrinsic evaluation of different word embeddings. We then perform additional extrinsic evaluations of the embeddings based on two NLP tasks.",
}
@inproceedings{rashkin-etal-2017-multilingual,
    title = "Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast",
    author = "Rashkin, Hannah  and
      Bell, Eric  and
      Choi, Yejin  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2073",
    doi = "10.18653/v1/P17-2073",
    pages = "459--464",
    abstract = "People around the globe respond to major real world events through social media. To study targeted public sentiments across many languages and geographic locations, we introduce multilingual connotation frames: an extension from English connotation frames of Rashkin et al. (2016) with 10 additional European languages, focusing on the implied sentiments among event participants engaged in a frame. As a case study, we present large scale analysis on targeted public sentiments toward salient events and entities using 1.2 million multilingual connotation frames extracted from Twitter.",
}
@inproceedings{kiritchenko-mohammad-2017-best,
    title = "Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation",
    author = "Kiritchenko, Svetlana  and
      Mohammad, Saif",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2074",
    doi = "10.18653/v1/P17-2074",
    pages = "465--470",
    abstract = "Rating scales are a widely used method for data annotation; however, they present several challenges, such as difficulty in maintaining inter- and intra-annotator consistency. Best{--}worst scaling (BWS) is an alternative method of annotation that is claimed to produce high-quality annotations while keeping the required number of annotations similar to that of rating scales. However, the veracity of this claim has never been systematically established. Here for the first time, we set up an experiment that directly compares the rating scale method with BWS. We show that with the same total number of annotations, BWS produces significantly more reliable results than the rating scale.",
}
@inproceedings{kim-etal-2017-demographic,
    title = "Demographic Inference on {T}witter using Recursive Neural Networks",
    author = "Kim, Sunghwan Mac  and
      Xu, Qiongkai  and
      Qu, Lizhen  and
      Wan, Stephen  and
      Paris, C{\'e}cile",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2075",
    doi = "10.18653/v1/P17-2075",
    pages = "471--477",
    abstract = "In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one{'}s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-the-art.",
}
@inproceedings{vijayaraghavan-etal-2017-twitter,
    title = "{T}witter Demographic Classification Using Deep Multi-modal Multi-task Learning",
    author = "Vijayaraghavan, Prashanth  and
      Vosoughi, Soroush  and
      Roy, Deb",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2076",
    doi = "10.18653/v1/P17-2076",
    pages = "478--483",
    abstract = "Twitter should be an ideal place to get a fresh read on how different issues are playing with the public, one that{'}s potentially more reflective of democracy in this new media age than traditional polls. Pollsters typically ask people a fixed set of questions, while in social media people use their own voices to speak about whatever is on their minds. However, the demographic distribution of users on Twitter is not representative of the general population. In this paper, we present a demographic classifier for gender, age, political orientation and location on Twitter. We collected and curated a robust Twitter demographic dataset for this task. Our classifier uses a deep multi-modal multi-task learning architecture to reach a state-of-the-art performance, achieving an F1-score of 0.89, 0.82, 0.86, and 0.68 for gender, age, political orientation, and location respectively.",
}
@inproceedings{zhan-etal-2017-network,
    title = "A Network Framework for Noisy Label Aggregation in Social Media",
    author = "Zhan, Xueying  and
      Wang, Yaowei  and
      Rao, Yanghui  and
      Xie, Haoran  and
      Li, Qing  and
      Wang, Fu Lee  and
      Wong, Tak-Lam",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2077",
    doi = "10.18653/v1/P17-2077",
    pages = "484--490",
    abstract = "This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents. To aggregate noisy labels at a small cost, a network framework is proposed by calculating the matching degree of a document{'}s topics and the annotators{'} meta-data. Unlike using the back-propagation algorithm, a probabilistic inference approach is adopted to estimate network parameters. Finally, a new simulation method is designed for validating the effectiveness of the proposed framework in aggregating noisy labels.",
}
@inproceedings{van-der-goot-van-noord-2017-parser,
    title = "Parser Adaptation for Social Media by Integrating Normalization",
    author = "van der Goot, Rob  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2078",
    doi = "10.18653/v1/P17-2078",
    pages = "491--497",
    abstract = "This work explores different approaches of using normalization for parser adaptation. Traditionally, normalization is used as separate pre-processing step. We show that integrating the normalization model into the parsing algorithm is more beneficial. This way, multiple normalization candidates can be leveraged, which improves parsing performance on social media. We test this hypothesis by modifying the Berkeley parser; out-of-the-box it achieves an F1 score of 66.52. Our integrated approach reaches a significant improvement with an F1 score of 67.36, while using the best normalization sequence results in an F1 score of only 66.94.",
}
@inproceedings{qiu-etal-2017-alime,
    title = "{A}li{M}e Chat: A Sequence to Sequence and Rerank based Chatbot Engine",
    author = "Qiu, Minghui  and
      Li, Feng-Lin  and
      Wang, Siyu  and
      Gao, Xing  and
      Chen, Yan  and
      Zhao, Weipeng  and
      Chen, Haiqing  and
      Huang, Jun  and
      Chu, Wei",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2079",
    doi = "10.18653/v1/P17-2079",
    pages = "498--503",
    abstract = "We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models. AliMe Chat uses an attentive Seq2Seq based rerank model to optimize the joint results. Extensive experiments show our engine outperforms both IR and generation based models. We launch AliMe Chat for a real-world industrial application and observe better results than another public chatbot.",
}
@inproceedings{shen-etal-2017-conditional,
    title = "A Conditional Variational Framework for Dialog Generation",
    author = "Shen, Xiaoyu  and
      Su, Hui  and
      Li, Yanran  and
      Li, Wenjie  and
      Niu, Shuzi  and
      Zhao, Yang  and
      Aizawa, Akiko  and
      Long, Guoping",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2080",
    doi = "10.18653/v1/P17-2080",
    pages = "504--509",
    abstract = "Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.",
}
@inproceedings{min-etal-2017-question,
    title = "Question Answering through Transfer Learning from Large Fine-grained Supervision Data",
    author = "Min, Sewon  and
      Seo, Minjoon  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2081",
    doi = "10.18653/v1/P17-2081",
    pages = "510--517",
    abstract = "We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8{\%}. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task.",
}
@inproceedings{abad-etal-2017-self,
    title = "Self-Crowdsourcing Training for Relation Extraction",
    author = "Abad, Azad  and
      Nabi, Moin  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2082",
    doi = "10.18653/v1/P17-2082",
    pages = "518--523",
    abstract = "In this paper we introduce a self-training strategy for crowdsourcing. The training examples are automatically selected to train the crowd workers. Our experimental results show an impact of 5{\%} Improvement in terms of F1 for relation extraction task, compared to the method based on distant supervision.",
}
@inproceedings{tran-etal-2017-generative,
    title = "A Generative Attentional Neural Network Model for Dialogue Act Classification",
    author = "Tran, Quan Hung  and
      Haffari, Gholamreza  and
      Zukerman, Ingrid",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2083",
    doi = "10.18653/v1/P17-2083",
    pages = "524--529",
    abstract = "We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a novel attentional technique and a label to label connection for sequence learning, akin to Hidden Markov Models. The experiments show that both of these innovations lead our model to outperform strong baselines for dialogue act classification on MapTask and Switchboard corpora. We further empirically analyse the effectiveness of each of the new innovations.",
}
@inproceedings{teneva-cheng-2017-salience,
    title = "Salience Rank: Efficient Keyphrase Extraction with Topic Modeling",
    author = "Teneva, Nedelina  and
      Cheng, Weiwei",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2084",
    doi = "10.18653/v1/P17-2084",
    pages = "530--535",
    abstract = "Topical PageRank (TPR) uses latent topic distribution inferred by Latent Dirichlet Allocation (LDA) to perform ranking of noun phrases extracted from documents. The ranking procedure consists of running PageRank K times, where K is the number of topics used in the LDA model. In this paper, we propose a modification of TPR, called Salience Rank. Salience Rank only needs to run PageRank once and extracts comparable or better keyphrases on benchmark datasets. In addition to quality and efficiency benefit, our method has the flexibility to extract keyphrases with varying tradeoffs between topic specificity and corpus specificity.",
}
@inproceedings{lin-etal-2017-list,
    title = "List-only Entity Linking",
    author = "Lin, Ying  and
      Lin, Chin-Yew  and
      Ji, Heng",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2085",
    doi = "10.18653/v1/P17-2085",
    pages = "536--541",
    abstract = "Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB). However, in many applications, the KB may be as simple and sparse as lists of names of the same type (e.g., lists of products). We call it as List-only Entity Linking problem. Fortunately, some mentions may have more cues for linking, which can be used as seed mentions to bridge other mentions and the uninformative entities. In this work, we select most linkable mentions as seed mentions and disambiguate other mentions by comparing them with the seed mentions rather than directly with the entities. Our experiments on linking mentions to seven automatically mined lists show promising results and demonstrate the effectiveness of our approach.",
}
@inproceedings{chen-etal-2017-improving-native,
    title = "Improving Native Language Identification by Using Spelling Errors",
    author = "Chen, Lingzhen  and
      Strapparava, Carlo  and
      Nastase, Vivi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2086",
    doi = "10.18653/v1/P17-2086",
    pages = "542--546",
    abstract = "In this paper, we explore spelling errors as a source of information for detecting the native language of a writer, a previously under-explored area. We note that character n-grams from misspelled words are very indicative of the native language of the author. In combination with other lexical features, spelling error features lead to 1.2{\%} improvement in accuracy on classifying texts in the TOEFL11 corpus by the author{'}s native language, compared to systems participating in the NLI shared task.",
}
@inproceedings{jamshid-lou-johnson-2017-disfluency,
    title = "Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model",
    author = "Jamshid Lou, Paria  and
      Johnson, Mark",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2087",
    doi = "10.18653/v1/P17-2087",
    pages = "547--553",
    abstract = "This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) language model to score the underlying fluent sentences of each analysis. The LSTM language model scores, along with other features, are used in a MaxEnt reranker to identify the most plausible analysis. We show that using an LSTM language model in the reranking process of noisy channel disfluency model improves the state-of-the-art in disfluency detection.",
}
@inproceedings{hayashi-shimbo-2017-equivalence,
    title = "On the Equivalence of Holographic and Complex Embeddings for Link Prediction",
    author = "Hayashi, Katsuhiko  and
      Shimbo, Masashi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2088",
    doi = "10.18653/v1/P17-2088",
    pages = "554--559",
    abstract = "We show the equivalence of two state-of-the-art models for link prediction/knowledge graph completion: Nickel et al{'}s holographic embeddings and Trouillon et al.{'}s complex embeddings. We first consider a spectral version of the holographic embeddings, exploiting the frequency domain in the Fourier transform for efficient computation. The analysis of the resulting model reveals that it can be viewed as an instance of the complex embeddings with a certain constraint imposed on the initial vectors upon training. Conversely, any set of complex embeddings can be converted to a set of equivalent holographic embeddings.",
}
@inproceedings{wang-etal-2017-sentence,
    title = "Sentence Embedding for Neural Machine Translation Domain Adaptation",
    author = "Wang, Rui  and
      Finch, Andrew  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2089",
    doi = "10.18653/v1/P17-2089",
    pages = "560--566",
    abstract = "Although new corpora are becoming increasingly available for machine translation, only those that belong to the same or similar domains are typically able to improve translation performance. Recently Neural Machine Translation (NMT) has become prominent in the field. However, most of the existing domain adaptation methods only focus on phrase-based machine translation. In this paper, we exploit the NMT{'}s internal embedding of the source sentence and use the sentence embedding similarity to select the sentences which are close to in-domain data. The empirical adaptation results on the IWSLT English-French and NIST Chinese-English tasks show that the proposed methods can substantially improve NMT performance by 2.4-9.0 BLEU points, outperforming the existing state-of-the-art baseline by 2.3-4.5 BLEU points.",
}
@inproceedings{fadaee-etal-2017-data,
    title = "Data Augmentation for Low-Resource Neural Machine Translation",
    author = "Fadaee, Marzieh  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2090",
    doi = "10.18653/v1/P17-2090",
    pages = "567--573",
    abstract = "The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.",
}
@inproceedings{shi-knight-2017-speeding,
    title = "Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary",
    author = "Shi, Xing  and
      Knight, Kevin",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2091",
    doi = "10.18653/v1/P17-2091",
    pages = "574--579",
    abstract = "We speed up Neural Machine Translation (NMT) decoding by shrinking run-time target vocabulary. We experiment with two shrinking approaches: Locality Sensitive Hashing (LSH) and word alignments. Using the latter method, we get a 2x overall speed-up over a highly-optimized GPU implementation, without hurting BLEU. On certain low-resource language pairs, the same methods improve BLEU by 0.5 points. We also report a negative result for LSH on GPUs, due to relatively large overhead, though it was successful on CPUs. Compared with Locality Sensitive Hashing (LSH), decoding with word alignments is GPU-friendly, orthogonal to existing speedup methods and more robust across language pairs.",
}
@inproceedings{zhou-etal-2017-chunk,
    title = "Chunk-Based Bi-Scale Decoder for Neural Machine Translation",
    author = "Zhou, Hao  and
      Tu, Zhaopeng  and
      Huang, Shujian  and
      Liu, Xiaohua  and
      Li, Hang  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2092",
    doi = "10.18653/v1/P17-2092",
    pages = "580--586",
    abstract = "In typical neural machine translation (NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.",
}
@inproceedings{fang-cohn-2017-model,
    title = "Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary",
    author = "Fang, Meng  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2093",
    doi = "10.18653/v1/P17-2093",
    pages = "587--593",
    abstract = "Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language, and its associated annotated corpora. However, parallel data is not readily available for many languages, limiting the applicability of these approaches. We address these drawbacks in our framework which takes advantage of cross-lingual word embeddings trained solely on a high coverage dictionary. We propose a novel neural network model for joint training from both sources of data based on cross-lingual word embeddings, and show substantial empirical improvements over baseline techniques. We also propose several active learning heuristics, which result in improvements over competitive benchmark methods.",
}
@inproceedings{delli-bovi-etal-2017-eurosense,
    title = "{E}uro{S}ense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text",
    author = "Delli Bovi, Claudio  and
      Camacho-Collados, Jose  and
      Raganato, Alessandro  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2094",
    doi = "10.18653/v1/P17-2094",
    pages = "594--600",
    abstract = "Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EuroSense, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a language-independent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.",
}
@inproceedings{sajjad-etal-2017-challenging,
    title = "Challenging Language-Dependent Segmentation for {A}rabic: An Application to Machine Translation and Part-of-Speech Tagging",
    author = "Sajjad, Hassan  and
      Dalvi, Fahim  and
      Durrani, Nadir  and
      Abdelali, Ahmed  and
      Belinkov, Yonatan  and
      Vogel, Stephan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2095",
    doi = "10.18653/v1/P17-2095",
    pages = "601--607",
    abstract = "Word segmentation plays a pivotal role in improving any Arabic NLP application. Therefore, a lot of research has been spent in improving its accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii) domain/dialect dependent. We explore three language-independent alternatives to morphological segmentation using: i) data-driven sub-word units, ii) characters as a unit of learning, and iii) word embeddings learned using a character CNN (Convolution Neural Network). On the tasks of Machine Translation and POS tagging, we found these methods to achieve close to, and occasionally surpass state-of-the-art performance. In our analysis, we show that a neural machine translation system is sensitive to the ratio of source and target tokens, and a ratio close to 1 or greater, gives optimal performance.",
}
@inproceedings{cai-etal-2017-fast,
    title = "Fast and Accurate Neural Word Segmentation for {C}hinese",
    author = "Cai, Deng  and
      Zhao, Hai  and
      Zhang, Zhisong  and
      Xin, Yuan  and
      Wu, Yongjian  and
      Huang, Feiyue",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2096",
    doi = "10.18653/v1/P17-2096",
    pages = "608--615",
    abstract = "Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. In this paper, we propose a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.",
}
@inproceedings{cai-etal-2017-pay,
    title = "Pay Attention to the Ending:Strong Neural Baselines for the {ROC} Story Cloze Task",
    author = "Cai, Zheng  and
      Tu, Lifu  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2097",
    doi = "10.18653/v1/P17-2097",
    pages = "616--622",
    abstract = "We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings. We develop a model that uses hierarchical recurrent networks with attention to encode the sentences in the story and score candidate endings. By discarding the large training set and only training on the validation set, we achieve an accuracy of 74.7{\%}. Even when we discard the story plots (sentences before the ending) and only train to choose the better of two endings, we can still reach 72.5{\%}. We then analyze this {``}ending-only{''} task setting. We estimate human accuracy to be 78{\%} and find several types of clues that lead to this high accuracy, including those related to sentiment, negation, and general ending likelihood regardless of the story context.",
}
@inproceedings{herzig-berant-2017-neural,
    title = "Neural Semantic Parsing over Multiple Knowledge-bases",
    author = "Herzig, Jonathan  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2098",
    doi = "10.18653/v1/P17-2098",
    pages = "623--628",
    abstract = "A fundamental challenge in developing semantic parsers is the paucity of strong supervision in the form of language utterances annotated with logical form. In this paper, we propose to exploit structural regularities in language in different domains, and train semantic parsers over multiple knowledge-bases (KBs), while sharing information across datasets. We find that we can substantially improve parsing accuracy by training a single sequence-to-sequence model over multiple KBs, when providing an encoding of the domain at decoding time. Our model achieves state-of-the-art performance on the Overnight dataset (containing eight domains), improves performance over a single KB baseline from 75.6{\%} to 79.6{\%}, while obtaining a 7x reduction in the number of model parameters.",
}
@inproceedings{mu-etal-2017-representing,
    title = "Representing Sentences as Low-Rank Subspaces",
    author = "Mu, Jiaqi  and
      Bhat, Suma  and
      Viswanath, Pramod",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2099",
    doi = "10.18653/v1/P17-2099",
    pages = "629--634",
    abstract = "Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences {--} the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15{\%} on average.",
}
@inproceedings{ma-etal-2017-improving,
    title = "Improving Semantic Relevance for Sequence-to-Sequence Learning of {C}hinese Social Media Text Summarization",
    author = "Ma, Shuming  and
      Sun, Xu  and
      Xu, Jingjing  and
      Wang, Houfeng  and
      Li, Wenjie  and
      Su, Qi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2100",
    doi = "10.18653/v1/P17-2100",
    pages = "635--640",
    abstract = "Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization. We introduce a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the summary representation is produced by a decoder. Besides, the similarity score between the representations is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus.",
}
@inproceedings{sanagavarapu-etal-2017-determining,
    title = "Determining Whether and When People Participate in the Events They Tweet About",
    author = "Sanagavarapu, Krishna Chaitanya  and
      Vempala, Alakananda  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2101",
    doi = "10.18653/v1/P17-2101",
    pages = "641--646",
    abstract = "This paper describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in the future. We present new annotations using 1,096 event mentions, and experimental results showing that the task is challenging.",
}
@inproceedings{volkova-etal-2017-separating,
    title = "Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on {T}witter",
    author = "Volkova, Svitlana  and
      Shaffer, Kyle  and
      Jang, Jin Yea  and
      Hodas, Nathan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2102",
    doi = "10.18653/v1/P17-2102",
    pages = "647--653",
    abstract = "Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that {``}made-up news{''} has caused a {``}great deal of confusion{''} about the facts of current events (Barthel et al., 2016). Fabricated stories in social media, ranging from deliberate propaganda to hoaxes and satire, contributes to this confusion in addition to having serious effects on global stability. In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news {--} satire, hoaxes, clickbait and propaganda. We show that neural network models trained on tweet content and social network interactions outperform lexical models. Unlike previous work on deception detection, we find that adding syntax and grammar features to our models does not improve performance. Incorporating linguistic features improves classification results, however, social interaction features are most informative for finer-grained separation between four types of suspicious news posts.",
}
@inproceedings{son-etal-2017-recognizing,
    title = "Recognizing Counterfactual Thinking in Social Media Texts",
    author = "Son, Youngseo  and
      Buffone, Anneke  and
      Raso, Joe  and
      Larche, Allegra  and
      Janocko, Anthony  and
      Zembroski, Kevin  and
      Schwartz, H Andrew  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2103",
    doi = "10.18653/v1/P17-2103",
    pages = "654--658",
    abstract = "Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.",
}
@inproceedings{hasanuzzaman-etal-2017-temporal,
    title = "Temporal Orientation of Tweets for Predicting Income of Users",
    author = "Hasanuzzaman, Mohammed  and
      Kamila, Sabyasachi  and
      Kaur, Mandeep  and
      Saha, Sriparna  and
      Ekbal, Asif",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2104",
    doi = "10.18653/v1/P17-2104",
    pages = "659--665",
    abstract = "Automatically estimating a user{'}s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user{'}s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.",
}
@inproceedings{toleu-etal-2017-character,
    title = "Character-Aware Neural Morphological Disambiguation",
    author = "Toleu, Alymzhan  and
      Tolegen, Gulmira  and
      Makazhanov, Aibek",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2105",
    doi = "10.18653/v1/P17-2105",
    pages = "666--671",
    abstract = "We develop a language-independent, deep learning-based approach to the task of morphological disambiguation. Guided by the intuition that the correct analysis should be {``}most similar{''} to the context, we propose dense representations for morphological analyses and surface context and a simple yet effective way of combining the two to perform disambiguation. Our approach improves on the language-dependent state of the art for two agglutinative languages (Turkish and Kazakh) and can be potentially applied to other morphologically complex languages.",
}
@inproceedings{yu-vu-2017-character,
    title = "Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages",
    author = "Yu, Xiang  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2106",
    doi = "10.18653/v1/P17-2106",
    pages = "672--678",
    abstract = "We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters. The character composition model shows great improvement over the word-lookup model, especially for parsing agglutinative languages. These improvements are even better than using pre-trained word embeddings from extra data. On the SPMRL data sets, our system outperforms the previous best greedy parser (Ballesteros et. al, 2015) by a margin of 3{\%} on average.",
}
@inproceedings{agic-schluter-2017-train,
    title = "How (not) to train a dependency parser: The curious case of jackknifing part-of-speech taggers",
    author = "Agi{\'c}, {\v{Z}}eljko  and
      Schluter, Natalie",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-2107",
    doi = "10.18653/v1/P17-2107",
    pages = "679--684",
    abstract = "In dependency parsing, jackknifing taggers is indiscriminately used as a simple adaptation strategy. Here, we empirically evaluate when and how (not) to use jackknifing in parsing. On 26 languages, we reveal a preference that conflicts with, and surpasses the ubiquitous ten-folding. We show no clear benefits of tagging the training data in cross-lingual parsing.",
}
@proceedings{acl-2017-association,
    title = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1000",
    doi = "10.18653/v1/P17-1",
}
@inproceedings{liu-etal-2017-adversarial,
    title = "Adversarial Multi-task Learning for Text Classification",
    author = "Liu, Pengfei  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1001",
    doi = "10.18653/v1/P17-1001",
    pages = "1--10",
    abstract = "Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at \url{http://nlp.fudan.edu.cn/data/}.",
}
@inproceedings{eger-etal-2017-neural,
    title = "Neural End-to-End Learning for Computational Argumentation Mining",
    author = "Eger, Steffen  and
      Daxenberger, Johannes  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1002",
    doi = "10.18653/v1/P17-1002",
    pages = "11--22",
    abstract = "We investigate neural techniques for end-to-end computational argumentation mining (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiLSTMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning {`}natural{'} subtasks, in a multi-task learning setup, improves performance.",
}
@inproceedings{liang-etal-2017-neural,
    title = "Neural Symbolic Machines: Learning Semantic Parsers on {F}reebase with Weak Supervision",
    author = "Liang, Chen  and
      Berant, Jonathan  and
      Le, Quoc  and
      Forbus, Kenneth D.  and
      Lao, Ni",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1003",
    doi = "10.18653/v1/P17-1003",
    pages = "23--33",
    abstract = "Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural {``}programmer{''}, i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic {``}computer{''}, i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.",
}
@inproceedings{lin-etal-2017-neural,
    title = "Neural Relation Extraction with Multi-lingual Attention",
    author = "Lin, Yankai  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1004",
    doi = "10.18653/v1/P17-1004",
    pages = "34--43",
    abstract = "Relation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the texts in various languages. To address this issue, we introduce a multi-lingual neural relation extraction framework, which employs mono-lingual attention to utilize the information within mono-lingual texts and further proposes cross-lingual attention to consider the information consistency and complementarity among cross-lingual texts. Experimental results on real-world datasets show that, our model can take advantage of multi-lingual texts and consistently achieve significant improvements on relation extraction as compared with baselines.",
}
@inproceedings{cheng-etal-2017-learning,
    title = "Learning Structured Natural Language Representations for Semantic Parsing",
    author = "Cheng, Jianpeng  and
      Reddy, Siva  and
      Saraswat, Vijay  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1005",
    doi = "10.18653/v1/P17-1005",
    pages = "44--55",
    abstract = "We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.",
}
@inproceedings{vulic-etal-2017-morph,
    title = "Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules",
    author = "Vuli{\'c}, Ivan  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Reichart, Roi  and
      {\'O} S{\'e}aghdha, Diarmuid  and
      Young, Steve  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1006",
    doi = "10.18653/v1/P17-1006",
    pages = "56--68",
    abstract = "Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that {`}inexpensive{'} is a rephrasing for {`}expensive{'} or may not associate {`}acquire{'} with {`}acquires{'}. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.",
}
@inproceedings{gittens-etal-2017-skip,
    title = "Skip-Gram − {Z}ipf + Uniform = Vector Additivity",
    author = "Gittens, Alex  and
      Achlioptas, Dimitris  and
      Mahoney, Michael W.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1007",
    doi = "10.18653/v1/P17-1007",
    pages = "69--76",
    abstract = "In recent years word-embedding models have gained great popularity due to their remarkable performance on several tasks, including word analogy questions and caption generation. An unexpected {``}side-effect{''} of such models is that their vectors often exhibit compositionality, i.e., \textit{adding}two word-vectors results in a vector that is only a small angle away from the vector of a word representing the semantic composite of the original words, e.g., {``}man{''} + {``}royal{''} = {``}king{''}. This work provides a theoretical justification for the presence of additive compositionality in word vectors learned using the Skip-Gram model. In particular, it shows that additive compositionality holds in an even stricter sense (small distance rather than small angle) under certain assumptions on the process generating the corpus. As a corollary, it explains the success of vector calculus in solving word analogies. When these assumptions do not hold, this work describes the correct non-linear composition operator. Finally, this work establishes a connection between the Skip-Gram model and the Sufficient Dimensionality Reduction (SDR) framework of Globerson and Tishby: the parameters of SDR models can be obtained from those of Skip-Gram models simply by adding information on symbol frequencies. This shows that Skip-Gram embeddings are optimal in the sense of Globerson and Tishby and, further, implies that the heuristics commonly used to approximately fit Skip-Gram models can be used to fit SDR models.",
}
@inproceedings{abend-rappoport-2017-state,
    title = "The State of the Art in Semantic Representation",
    author = "Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1008",
    doi = "10.18653/v1/P17-1008",
    pages = "77--89",
    abstract = "Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarify the general goals of research on semantic representation. We address these gaps by critically surveying the state of the art in the field.",
}
@inproceedings{lu-ng-2017-joint,
    title = "Joint Learning for Event Coreference Resolution",
    author = "Lu, Jing  and
      Ng, Vincent",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1009",
    doi = "10.18653/v1/P17-1009",
    pages = "90--101",
    abstract = "While joint models have been developed for many NLP tasks, the vast majority of event coreference resolvers, including the top-performing resolvers competing in the recent TAC KBP 2016 Event Nugget Detection and Coreference task, are pipeline-based, where the propagation of errors from the trigger detection component to the event coreference component is a major performance limiting factor. To address this problem, we propose a model for jointly learning event coreference, trigger detection, and event anaphoricity. Our joint model is novel in its choice of tasks and its features for capturing cross-task interactions. To our knowledge, this is the first attempt to train a mention-ranking model and employ event anaphoricity for event coreference. Our model achieves the best results to date on the KBP 2016 English and Chinese datasets.",
}
@inproceedings{liu-etal-2017-generating,
    title = "Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution",
    author = "Liu, Ting  and
      Cui, Yiming  and
      Yin, Qingyu  and
      Zhang, Wei-Nan  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1010",
    doi = "10.18653/v1/P17-1010",
    pages = "102--111",
    abstract = "Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for better performance. To alleviate the problem above, in this paper, we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution. Furthermore, we successfully transfer the cloze-style reading comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to overcome the gap between the pseudo training data and the real one. Experimental results show that the proposed approach significantly outperforms the state-of-the-art systems with an absolute improvements of 3.1{\%} F-score on OntoNotes 5.0 data.",
}
@inproceedings{song-etal-2017-discourse,
    title = "Discourse Mode Identification in Essays",
    author = "Song, Wei  and
      Wang, Dong  and
      Fu, Ruiji  and
      Liu, Lizhen  and
      Liu, Ting  and
      Hu, Guoping",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1011",
    doi = "10.18653/v1/P17-1011",
    pages = "112--122",
    abstract = "Discourse modes play an important role in writing composition and evaluation. This paper presents a study on the manual and automatic identification of narration,exposition, description, argument and emotion expressing sentences in narrative essays. We annotate a corpus to study the characteristics of discourse modes and describe a neural sequence labeling model for identification. Evaluation results show that discourse modes can be identified automatically with an average F1-score of 0.7. We further demonstrate that discourse modes can be used as features that improve automatic essay scoring (AES). The impacts of discourse modes for AES are also discussed.",
}
@inproceedings{gehring-etal-2017-convolutional,
    title = "A Convolutional Encoder Model for Neural Machine Translation",
    author = "Gehring, Jonas  and
      Auli, Michael  and
      Grangier, David  and
      Dauphin, Yann",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1012",
    doi = "10.18653/v1/P17-1012",
    pages = "123--135",
    abstract = "The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT{'}16 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and on WMT{'}15 English-German we outperform several recently published results. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT{'}14 English-French translation. We speed up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM.",
}
@inproceedings{wang-etal-2017-deep-neural,
    title = "Deep Neural Machine Translation with Linear Associative Unit",
    author = "Wang, Mingxuan  and
      Lu, Zhengdong  and
      Zhou, Jie  and
      Liu, Qun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1013",
    doi = "10.18653/v1/P17-1013",
    pages = "136--145",
    abstract = "Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with its capability in modeling complex functions and capturing complex linguistic structures. However NMT with deep architecture in its encoder or decoder RNNs often suffer from severe gradient diffusion due to the non-linear recurrent activations, which often makes the optimization much more difficult. To address this problem we propose a novel linear associative units (LAU) to reduce the gradient propagation path inside the recurrent unit. Different from conventional approaches (LSTM unit and GRU), LAUs uses linear associative connections between input and output of the recurrent unit, which allows unimpeded information flow through both space and time The model is quite simple, but it is surprisingly effective. Our empirical study on Chinese-English translation shows that our model with proper configuration can improve by 11.7 BLEU upon Groundhog and the best reported on results in the same setting. On WMT14 English-German task and a larger WMT14 English-French task, our model achieves comparable results with the state-of-the-art.",
}
@inproceedings{konstas-etal-2017-neural,
    title = "Neural {AMR}: Sequence-to-Sequence Models for Parsing and Generation",
    author = "Konstas, Ioannis  and
      Iyer, Srinivasan  and
      Yatskar, Mark  and
      Choi, Yejin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1014",
    doi = "10.18653/v1/P17-1014",
    pages = "146--157",
    abstract = "Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.",
}
@inproceedings{ling-etal-2017-program,
    title = "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems",
    author = "Ling, Wang  and
      Yogatama, Dani  and
      Dyer, Chris  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1015",
    doi = "10.18653/v1/P17-1015",
    pages = "158--167",
    abstract = "Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.",
}
@inproceedings{hopkins-kiela-2017-automatically,
    title = "Automatically Generating Rhythmic Verse with Neural Networks",
    author = "Hopkins, Jack  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1016",
    doi = "10.18653/v1/P17-1016",
    pages = "168--178",
    abstract = "We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms. The first approach uses a neural language model trained on a phonetic encoding to learn an implicit representation of both the form and content of English poetry. This model can effectively learn common poetic devices such as rhyme, rhythm and alliteration. The second approach considers poetry generation as a constraint satisfaction problem where a generative neural language model is tasked with learning a representation of content, and a discriminative weighted finite state machine constrains it on the basis of form. By manipulating the constraints of the latter model, we can generate coherent poetry with arbitrary forms and themes. A large-scale extrinsic evaluation demonstrated that participants consider machine-generated poems to be written by humans 54{\%} of the time. In addition, participants rated a machine-generated poem to be the best amongst all evaluated.",
}
@inproceedings{gardent-etal-2017-creating,
    title = "Creating Training Corpora for {NLG} Micro-Planners",
    author = "Gardent, Claire  and
      Shimorina, Anastasia  and
      Narayan, Shashi  and
      Perez-Beltrachini, Laura",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1017",
    doi = "10.18653/v1/P17-1017",
    pages = "179--188",
    abstract = "In this paper, we present a novel framework for semi-automatically creating linguistically challenging micro-planning data-to-text corpora from existing Knowledge Bases. Because our method pairs data of varying size and shape with texts ranging from simple clauses to short texts, a dataset created using this framework provides a challenging benchmark for microplanning. Another feature of this framework is that it can be applied to any large scale knowledge base and can therefore be used to train and learn KB verbalisers. We apply our framework to DBpedia data and compare the resulting dataset with Wen et al. 2016{'}s. We show that while Wen et al.{'}s dataset is more than twice larger than ours, it is less diverse both in terms of input and in terms of text. We thus propose our corpus generation framework as a novel method for creating challenging data sets from which NLG models can be learned which are capable of handling the complex interactions occurring during in micro-planning between lexicalisation, aggregation, surface realisation, referring expression generation and sentence segmentation. To encourage researchers to take up this challenge, we made available a dataset of 21,855 data/text pairs created using this framework in the context of the WebNLG shared task.",
}
@inproceedings{wang-etal-2017-gated,
    title = "Gated Self-Matching Networks for Reading Comprehension and Question Answering",
    author = "Wang, Wenhui  and
      Yang, Nan  and
      Wei, Furu  and
      Chang, Baobao  and
      Zhou, Ming",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1018",
    doi = "10.18653/v1/P17-1018",
    pages = "189--198",
    abstract = "In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD dataset. The single model achieves 71.3{\%} on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9{\%}. At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model.",
}
@inproceedings{he-etal-2017-generating,
    title = "Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning",
    author = "He, Shizhu  and
      Liu, Cao  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1019",
    doi = "10.18653/v1/P17-1019",
    pages = "199--208",
    abstract = "Generating answer with natural language sentence is very important in real-world question answering systems, which needs to obtain a right answer as well as a coherent natural response. In this paper, we propose an end-to-end question answering system called COREQA in sequence-to-sequence learning, which incorporates copying and retrieving mechanisms to generate natural answers within an encoder-decoder framework. Specifically, in COREQA, the semantic units (words, phrases and entities) in a natural answer are dynamically predicted from the vocabulary, copied from the given question and/or retrieved from the corresponding knowledge base jointly. Our empirical study on both synthetic and real-world datasets demonstrates the efficiency of COREQA, which is able to generate correct, coherent and natural answers for knowledge inquired questions.",
}
@inproceedings{choi-etal-2017-coarse,
    title = "Coarse-to-Fine Question Answering for Long Documents",
    author = "Choi, Eunsol  and
      Hewlett, Daniel  and
      Uszkoreit, Jakob  and
      Polosukhin, Illia  and
      Lacoste, Alexandre  and
      Berant, Jonathan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1020",
    doi = "10.18653/v1/P17-1020",
    pages = "209--220",
    abstract = "We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences. Inspired by how people first skim the document, identify relevant parts, and carefully read these parts to produce an answer, we combine a coarse, fast model for selecting relevant sentences and a more expensive RNN for producing the answer from those sentences. We treat sentence selection as a latent variable trained jointly from the answer only using reinforcement learning. Experiments demonstrate state-of-the-art performance on a challenging subset of the WikiReading dataset and on a new dataset, while speeding up the model by 3.5x-6.7x.",
}
@inproceedings{hao-etal-2017-end,
    title = "An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge",
    author = "Hao, Yanchao  and
      Zhang, Yuanzhe  and
      Liu, Kang  and
      He, Shizhu  and
      Liu, Zhanyi  and
      Wu, Hua  and
      Zhao, Jun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1021",
    doi = "10.18653/v1/P17-1021",
    pages = "221--231",
    abstract = "With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge. Meanwhile, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is not easy to express the proper information in the question. Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the cross-attention model to represent the question more precisely. The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach.",
}
@inproceedings{andreas-etal-2017-translating,
    title = "Translating Neuralese",
    author = "Andreas, Jacob  and
      Dragan, Anca  and
      Klein, Dan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1022",
    doi = "10.18653/v1/P17-1022",
    pages = "232--242",
    abstract = "Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies are effective for many tasks, interpretation of their induced communication strategies has remained a challenge. Here we propose to interpret agents{'} messages by translating them. Unlike in typical machine translation problems, we have no parallel data to learn from. Instead we develop a translation model based on the insight that agent messages and natural language strings mean the same thing if they induce the same belief about the world in a listener. We present theoretical guarantees and empirical evidence that our approach preserves both the semantics and pragmatics of messages by ensuring that players communicating through a translation layer do not suffer a substantial loss in reward relative to players with a common language.",
}
@inproceedings{zarriess-schlangen-2017-obtaining,
    title = "Obtaining referential word meanings from visual and distributional information: Experiments on object naming",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1023",
    doi = "10.18653/v1/P17-1023",
    pages = "243--254",
    abstract = "We investigate object naming, which is an important sub-task of referring expression generation on real-world images. As opposed to mutually exclusive labels used in object recognition, object names are more flexible, subject to communicative preferences and semantically related to each other. Therefore, we investigate models of referential word meaning that link visual to lexical information which we assume to be given through distributional word embeddings. We present a model that learns individual predictors for object names that link visual and distributional aspects of word meaning during training. We show that this is particularly beneficial for zero-shot learning, as compared to projecting visual objects directly into the distributional space. In a standard object naming task, we find that different ways of combining lexical and visual information achieve very similar performance, though experiments on model combination suggest that they capture complementary aspects of referential meaning.",
}
@inproceedings{shekhar-etal-2017-foil,
    title = "{FOIL} it! Find One mismatch between Image and Language caption",
    author = "Shekhar, Ravi  and
      Pezzelle, Sandro  and
      Klimovich, Yauhen  and
      Herbelot, Aur{\'e}lie  and
      Nabi, Moin  and
      Sangineto, Enver  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1024",
    doi = "10.18653/v1/P17-1024",
    pages = "255--265",
    abstract = "In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MS-COCO dataset, FOIL-COCO, which associates images with both correct and {`}foil{'} captions, that is, descriptions of the image that are highly similar to the original ones, but contain one single mistake ({`}foil word{'}). We show that current LaVi models fall into the traps of this data and perform badly on three tasks: a) caption classification (correct vs. foil); b) foil word detection; c) foil word correction. Humans, in contrast, have near-perfect performance on those tasks. We demonstrate that merely utilising language cues is not enough to model FOIL-COCO and that it challenges the state-of-the-art by requiring a fine-grained understanding of the relation between text and image.",
}
@inproceedings{forbes-choi-2017-verb,
    title = "Verb Physics: Relative Physical Knowledge of Actions and Objects",
    author = "Forbes, Maxwell  and
      Choi, Yejin",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1025",
    doi = "10.18653/v1/P17-1025",
    pages = "266--276",
    abstract = "Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., {``}My house is bigger than me.{''} However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, {``}Tyler entered his house{''} implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.",
}
@inproceedings{yoshikawa-etal-2017-ccg,
    title = "{A}* {CCG} Parsing with a Supertag and Dependency Factored Model",
    author = "Yoshikawa, Masashi  and
      Noji, Hiroshi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1026",
    doi = "10.18653/v1/P17-1026",
    pages = "277--287",
    abstract = "We propose a new A* CCG parsing model in which the probability of a tree is decomposed into factors of CCG categories and its syntactic dependencies both defined on bi-directional LSTMs. Our factored model allows the precomputation of all probabilities and runs very efficiently, while modeling sentence structures explicitly via dependencies. Our model achieves the state-of-the-art results on English and Japanese CCG parsing.",
}
@inproceedings{fernandez-gonzalez-gomez-rodriguez-2017-full,
    title = "A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1027",
    doi = "10.18653/v1/P17-1027",
    pages = "288--298",
    abstract = "Restricted non-monotonicity has been shown beneficial for the projective arc-eager dependency parser in previous research, as posterior decisions can repair mistakes made in previous states due to the lack of information. In this paper, we propose a novel, fully non-monotonic transition system based on the non-projective Covington algorithm. As a non-monotonic system requires exploration of erroneous actions during the training process, we develop several non-monotonic variants of the recently defined dynamic oracle for the Covington parser, based on tight approximations of the loss. Experiments on datasets from the CoNLL-X and CoNLL-XI shared tasks show that a non-monotonic dynamic oracle outperforms the monotonic version in the majority of languages.",
}
@inproceedings{nguyen-etal-2017-aggregating,
    title = "Aggregating and Predicting Sequence Labels from Crowd Annotations",
    author = "Nguyen, An Thanh  and
      Wallace, Byron  and
      Li, Junyi Jessy  and
      Nenkova, Ani  and
      Lease, Matthew",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1028",
    doi = "10.18653/v1/P17-1028",
    pages = "299--309",
    abstract = "Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory. We evaluate a suite of methods across two different applications and text genres: Named-Entity Recognition in news articles and Information Extraction from biomedical abstracts. Results show improvement over strong baselines. Our source code and data are available online.",
}
@inproceedings{zhou-neubig-2017-multi,
    title = "Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction",
    author = "Zhou, Chunting  and
      Neubig, Graham",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1029",
    doi = "10.18653/v1/P17-1029",
    pages = "310--320",
    abstract = "Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-space variational encoder-decoders, a new model for labeled sequence transduction with semi-supervised learning. The generative model can use neural networks to handle both discrete and continuous latent variables to exploit various features of data. Experiments show that our model provides not only a powerful supervised framework but also can effectively take advantage of the unlabeled data. On the SIGMORPHON morphological inflection benchmark, our model outperforms single-model state-of-art results by a large margin for the majority of languages.",
}
@inproceedings{gan-etal-2017-scalable,
    title = "Scalable {B}ayesian Learning of Recurrent Neural Networks for Language Modeling",
    author = "Gan, Zhe  and
      Li, Chunyuan  and
      Chen, Changyou  and
      Pu, Yunchen  and
      Su, Qinliang  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1030",
    doi = "10.18653/v1/P17-1030",
    pages = "321--331",
    abstract = "Recurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers from overfitting. One reason for this is that stochastic optimization (used for large training sets) does not provide good estimates of model uncertainty. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. It yields a principled Bayesian learning algorithm, adding gradient noise during training (enhancing exploration of the model-parameter space) and model averaging when testing. Extensive experiments on various RNN models and across a broad range of applications demonstrate the superiority of the proposed approach relative to stochastic optimization.",
}
@inproceedings{bollmann-etal-2017-learning,
    title = "Learning attention for historical text normalization by learning to pronounce",
    author = "Bollmann, Marcel  and
      Bingel, Joachim  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1031",
    doi = "10.18653/v1/P17-1031",
    pages = "332--344",
    abstract = "Automated processing of historical texts often relies on pre-normalization to modern word forms. Training encoder-decoder architectures to solve such problems typically requires a lot of training data, which is not available for the named task. We address this problem by using several novel encoder-decoder architectures, including a multi-task learning (MTL) architecture using a grapheme-to-phoneme dictionary as auxiliary data, pushing the state-of-the-art by an absolute 2{\%} increase in performance. We analyze the induced models across 44 different texts from Early New High German. Interestingly, we observe that, as previously conjectured, multi-task learning can learn to focus attention during decoding, in ways remarkably similar to recently proposed attention mechanisms. This, we believe, is an important step toward understanding how MTL works.",
}
@inproceedings{croce-etal-2017-deep,
    title = "Deep Learning in Semantic Kernel Spaces",
    author = "Croce, Danilo  and
      Filice, Simone  and
      Castellucci, Giuseppe  and
      Basili, Roberto",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1032",
    doi = "10.18653/v1/P17-1032",
    pages = "345--354",
    abstract = "Kernel methods enable the direct usage of structured representations of textual data during language learning and inference tasks. Expressive kernels, such as Tree Kernels, achieve excellent performance in NLP. On the other side, deep neural networks have been demonstrated effective in automatically learning feature representations during training. However, their input is tensor data, i.e., they can not manage rich structured information. In this paper, we show that expressive kernels and deep neural networks can be combined in a common framework in order to (i) explicitly model structured information and (ii) learn non-linear decision functions. We show that the input layer of a deep architecture can be pre-trained through the application of the Nystrom low-rank approximation of kernel spaces. The resulting {``}kernelized{''} neural network achieves state-of-the-art accuracy in three different tasks.",
}
@inproceedings{lau-etal-2017-topically,
    title = "Topically Driven Neural Language Model",
    author = "Lau, Jey Han  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1033",
    doi = "10.18653/v1/P17-1033",
    pages = "355--365",
    abstract = "Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the current sentence. Experiments over a range of datasets demonstrate that our model outperforms a pure sentence-based model in terms of language model perplexity, and leads to topics that are potentially more coherent than those produced by a standard LDA topic model. Our model also has the ability to generate related sentences for a topic, providing another way to interpret topics.",
}
@inproceedings{wang-etal-2017-handling,
    title = "Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors",
    author = "Wang, Xuepeng  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1034",
    doi = "10.18653/v1/P17-1034",
    pages = "366--376",
    abstract = "Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for cold-start problem, by learning to represent the new reviewers{'} review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large scale dataset in an unsupervised way.",
}
@inproceedings{mishra-etal-2017-learning,
    title = "Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network",
    author = "Mishra, Abhijit  and
      Dey, Kuntal  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1035",
    doi = "10.18653/v1/P17-1035",
    pages = "377--387",
    abstract = "Cognitive NLP systems- i.e., NLP systems that make use of behavioral data - augment traditional text-based features with cognitive features extracted from eye-movement patterns, EEG signals, brain-imaging etc. Such extraction of features is typically manual. We contend that manual extraction of features may not be the best way to tackle text subtleties that characteristically prevail in complex classification tasks like Sentiment Analysis and Sarcasm Detection, and that even the extraction and choice of features should be delegated to the learning system. We introduce a framework to automatically extract cognitive features from the eye-movement/gaze data of human readers reading the text and use them as features along with textual features for the tasks of sentiment polarity and sarcasm detection. Our proposed framework is based on Convolutional Neural Network (CNN). The CNN learns features from both gaze and text and uses them to classify the input text. We test our technique on published sentiment and sarcasm labeled datasets, enriched with gaze information, to show that using a combination of automatically learned text and gaze features often yields better classification performance over (i) CNN based systems that rely on text input alone and (ii) existing systems that rely on handcrafted gaze and textual features.",
}
@inproceedings{he-etal-2017-unsupervised,
    title = "An Unsupervised Neural Attention Model for Aspect Extraction",
    author = "He, Ruidan  and
      Lee, Wee Sun  and
      Ng, Hwee Tou  and
      Dahlmeier, Daniel",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1036",
    doi = "10.18653/v1/P17-1036",
    pages = "388--397",
    abstract = "Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discovering coherent aspects. The model improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings. Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space. In addition, we use an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects. Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects, and substantially outperforms baseline methods on several evaluation tasks.",
}
@inproceedings{sasaki-etal-2017-topics,
    title = "Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization",
    author = "Sasaki, Akira  and
      Hanawa, Kazuaki  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1037",
    doi = "10.18653/v1/P17-1037",
    pages = "398--408",
    abstract = "We presents in this paper our approach for modeling inter-topic preferences of Twitter users: for example, {``}those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade{''}. This kind of knowledge is useful not only for stance detection across multiple topics but also for various real-world applications including public opinion survey, electoral prediction, electoral campaigns, and online debates. In order to extract users{'} preferences on Twitter, we design linguistic patterns in which people agree and disagree about specific topics (e.g., {``}A is completely wrong{''}). By applying these linguistic patterns to a collection of tweets, we extract statements agreeing and disagreeing with various topics. Inspired by previous work on item recommendation, we formalize the task of modeling inter-topic preferences as matrix factorization: representing users{'} preference as a user-topic matrix and mapping both users and topics onto a latent feature space that abstracts the preferences. Our experimental results demonstrate both that our presented approach is useful in predicting missing preferences of users and that the latent vector representations of topics successfully encode inter-topic preferences.",
}
@inproceedings{chen-etal-2017-automatically,
    title = "Automatically Labeled Data Generation for Large Scale Event Extraction",
    author = "Chen, Yubo  and
      Liu, Shulin  and
      Zhang, Xiang  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1038",
    doi = "10.18653/v1/P17-1038",
    pages = "409--419",
    abstract = "Modern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data. However, hand-labeled training data is expensive to produce, in low coverage of event types, and limited in size, which makes supervised methods hard to extract large scale of events for knowledge base population. To solve the data labeling problem, we propose to automatically label training data for event extraction via world knowledge and linguistic knowledge, which can detect key arguments and trigger words for each event type and employ them to label events in texts automatically. The experimental results show that the quality of our large scale automatically labeled data is competitive with elaborately human-labeled data. And our automatically labeled data can incorporate with human-labeled data, then improve the performance of models learned from these data.",
}
@inproceedings{zhong-etal-2017-time,
    title = "Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules",
    author = "Zhong, Xiaoshi  and
      Sun, Aixin  and
      Cambria, Erik",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1039",
    doi = "10.18653/v1/P17-1039",
    pages = "420--429",
    abstract = "Extracting time expressions from free text is a fundamental task for many applications. We analyze the time expressions from four datasets and find that only a small group of words are used to express time information, and the words in time expressions demonstrate similar syntactic behaviour. Based on the findings, we propose a type-based approach, named SynTime, to recognize time expressions. Specifically, we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related regular expressions over tokens. On the types we design general heuristic rules to recognize time expressions. In recognition, SynTime first identifies the time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions. As a light-weight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text of different types and of different domains. Experiment on benchmark datasets and tweets data shows that SynTime outperforms state-of-the-art methods.",
}
@inproceedings{luo-etal-2017-learning-noise,
    title = "Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix",
    author = "Luo, Bingfeng  and
      Feng, Yansong  and
      Wang, Zheng  and
      Zhu, Zhanxing  and
      Huang, Songfang  and
      Yan, Rui  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1040",
    doi = "10.18653/v1/P17-1040",
    pages = "430--439",
    abstract = "Distant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect the model performance. In this paper, we take a deep look at the application of distant supervision in relation extraction. We show that the dynamic transition matrix can effectively characterize the noise in the training data built by distant supervision. The transition matrix can be effectively trained using a novel curriculum learning based method without any direct supervision about the noise. We thoroughly evaluate our approach under a wide range of extraction scenarios. Experimental results show that our approach consistently improves the extraction results and outperforms the state-of-the-art in various evaluation scenarios.",
}
@inproceedings{yin-neubig-2017-syntactic,
    title = "A Syntactic Neural Model for General-Purpose Code Generation",
    author = "Yin, Pengcheng  and
      Neubig, Graham",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1041",
    doi = "10.18653/v1/P17-1041",
    pages = "440--450",
    abstract = "We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation task without considering the underlying syntax of the target programming language. Informed by previous work in semantic parsing, in this paper we propose a novel neural architecture powered by a grammar model to explicitly capture the target syntax as prior knowledge. Experiments find this an effective way to scale up to generation of complex programs from natural language descriptions, achieving state-of-the-art results that well outperform previous code generation and semantic parsing approaches.",
}
@inproceedings{artetxe-etal-2017-learning,
    title = "Learning bilingual word embeddings with (almost) no bilingual data",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1042",
    doi = "10.18653/v1/P17-1042",
    pages = "451--462",
    abstract = "Most methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax this requirement, with methods that use document-aligned corpora or bilingual dictionaries of a few thousand words instead. In this work, we further reduce the need of bilingual resources using a very simple self-learning approach that can be combined with any dictionary-based mapping technique. Our method exploits the structural similarity of embedding spaces, and works with as little bilingual evidence as a 25 word dictionary or even an automatically generated list of numerals, obtaining results comparable to those of systems that use richer resources.",
}
@inproceedings{foland-martin-2017-abstract,
    title = "{A}bstract {M}eaning {R}epresentation Parsing using {LSTM} Recurrent Neural Networks",
    author = "Foland, William  and
      Martin, James H.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1043",
    doi = "10.18653/v1/P17-1043",
    pages = "463--472",
    abstract = "We present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5{\%}. AMR graphs represent semantic content using linguistic properties such as semantic roles, coreference, negation, and more. The AMR parser does not rely on a syntactic pre-parse, or heavily engineered features, and uses five recurrent neural networks as the key architectural components for inferring AMR graphs.",
}
@inproceedings{he-etal-2017-deep,
    title = "Deep Semantic Role Labeling: What Works and What{'}s Next",
    author = "He, Luheng  and
      Lee, Kenton  and
      Lewis, Mike  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1044",
    doi = "10.18653/v1/P17-1044",
    pages = "473--483",
    abstract = "We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on theCoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10{\%} relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.",
}
@inproceedings{dhingra-etal-2017-towards,
    title = "Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access",
    author = "Dhingra, Bhuwan  and
      Li, Lihong  and
      Li, Xiujun  and
      Gao, Jianfeng  and
      Chen, Yun-Nung  and
      Ahmed, Faisal  and
      Deng, Li",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1045",
    doi = "10.18653/v1/P17-1045",
    pages = "484--495",
    abstract = "This paper proposes KB-InfoBot - a multi-turn dialogue agent which helps users search Knowledge Bases (KBs) without composing complicated queries. Such goal-oriented dialogue agents typically need to interact with an external database to access real-world knowledge. Previous systems achieved this by issuing a symbolic query to the KB to retrieve entries based on their attributes. However, such symbolic operations break the differentiability of the system and prevent end-to-end training of neural dialogue agents. In this paper, we address this limitation by replacing symbolic queries with an induced {``}soft{''} posterior distribution over the KB that indicates which entities the user is interested in. Integrating the soft retrieval process with a reinforcement learner leads to higher task success rate and reward in both simulations and against real users. We also present a fully neural end-to-end agent, trained entirely from user feedback, and discuss its application towards personalized dialogue agents.",
}
@inproceedings{wu-etal-2017-sequential,
    title = "Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots",
    author = "Wu, Yu  and
      Wu, Wei  and
      Xing, Chen  and
      Zhou, Ming  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1046",
    doi = "10.18653/v1/P17-1046",
    pages = "496--505",
    abstract = "We study response selection for multi-turn conversation in retrieval based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among the utterances or important information in the context. We propose a sequential matching network (SMN) to address both problems. SMN first matches a response with each utterance in the context on multiple levels of granularity, and distills important matching information from each pair as a vector with convolution and pooling operations. The vectors are then accumulated in a chronological order through a recurrent neural network (RNN) which models relationships among the utterances. The final matching score is calculated with the hidden states of the RNN. Empirical study on two public data sets shows that SMN can significantly outperform state-of-the-art methods for response selection in multi-turn conversation.",
}
@inproceedings{harwath-glass-2017-learning,
    title = "Learning Word-Like Units from Joint Audio-Visual Analysis",
    author = "Harwath, David  and
      Glass, James",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1047",
    doi = "10.18653/v1/P17-1047",
    pages = "506--517",
    abstract = "Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word {`}lighthouse{'} within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.",
}
@inproceedings{hori-etal-2017-joint,
    title = "Joint {CTC}/attention decoding for end-to-end speech recognition",
    author = "Hori, Takaaki  and
      Watanabe, Shinji  and
      Hershey, John",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1048",
    doi = "10.18653/v1/P17-1048",
    pages = "518--529",
    abstract = "End-to-end automatic speech recognition (ASR) has become a popular alternative to conventional DNN/HMM systems because it avoids the need for linguistic resources such as pronunciation dictionary, tokenization, and context-dependency trees, leading to a greatly simplified model-building process. There are two major types of end-to-end architectures for ASR: attention-based methods use an attention mechanism to perform alignment between acoustic frames and recognized symbols, and connectionist temporal classification (CTC), uses Markov assumptions to efficiently solve sequential problems by dynamic programming. This paper proposes joint decoding algorithm for end-to-end ASR with a hybrid CTC/attention architecture, which effectively utilizes both advantages in decoding. We have applied the proposed method to two ASR benchmarks (spontaneous Japanese and Mandarin Chinese), and showing the comparable performance to conventional state-of-the-art DNN/HMM ASR systems without linguistic resources.",
}
@inproceedings{rabinovich-etal-2017-found,
    title = "Found in Translation: Reconstructing Phylogenetic Language Trees from Translations",
    author = "Rabinovich, Ella  and
      Ordan, Noam  and
      Wintner, Shuly",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1049",
    doi = "10.18653/v1/P17-1049",
    pages = "530--540",
    abstract = "Translation has played an important role in trade, law, commerce, politics, and literature for thousands of years. Translators have always tried to be invisible; ideal translations should look as if they were written originally in the target language. We show that traces of the source language remain in the translation product to the extent that it is possible to uncover the history of the source language by looking only at the translation. Specifically, we automatically reconstruct phylogenetic language trees from monolingual texts (translated from several source languages). The signal of the source language is so powerful that it is retained even after two phases of translation. This strongly indicates that source language interference is the most dominant characteristic of translated texts, overshadowing the more subtle signals of universal properties of translation.",
}
@inproceedings{berzak-etal-2017-predicting,
    title = "Predicting Native Language from Gaze",
    author = "Berzak, Yevgeni  and
      Nakamura, Chie  and
      Flynn, Suzanne  and
      Katz, Boris",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1050",
    doi = "10.18653/v1/P17-1050",
    pages = "541--551",
    abstract = "A fundamental question in language learning concerns the role of a speaker{'}s first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first time that the native language of English learners can be predicted from their gaze fixations when reading English. We provide analysis of classifier uncertainty and learned features, which indicates that differences in English reading are likely to be rooted in linguistic divergences across native languages. The presented framework complements production studies and offers new ground for advancing research on multilingualism.",
}
@inproceedings{sakakini-etal-2017-morse,
    title = "{MORSE}: Semantic-ally Drive-n {MOR}pheme {SE}gment-er",
    author = "Sakakini, Tarek  and
      Bhat, Suma  and
      Viswanath, Pramod",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1051",
    doi = "10.18653/v1/P17-1051",
    pages = "552--561",
    abstract = "We present in this paper a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes. This framework is the first to consider vocabulary-wide syntactico-semantic information for this task. We also analyze the deficiencies of available benchmarking datasets and introduce our own dataset that was created on the basis of compositionality. We validate our algorithm across datasets and present state-of-the-art results.",
}
@inproceedings{johnson-zhang-2017-deep,
    title = "Deep Pyramid Convolutional Neural Networks for Text Categorization",
    author = "Johnson, Rie  and
      Zhang, Tong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1052",
    doi = "10.18653/v1/P17-1052",
    pages = "562--570",
    abstract = "This paper proposes a low-complexity word-level deep convolutional neural network (CNN) architecture for text categorization that can efficiently represent long-range associations in text. In the literature, several deep and complex neural networks have been proposed for this task, assuming availability of relatively large amounts of training data. However, the associated computational complexity increases as the networks go deeper, which poses serious challenges in practical applications. Moreover, it was shown recently that shallow word-level CNNs are more accurate and much faster than the state-of-the-art very deep nets such as character-level CNNs even in the setting of large training data. Motivated by these findings, we carefully studied deepening of word-level CNNs to capture global representations of text, and found a simple network architecture with which the best accuracy can be obtained by increasing the network depth without increasing computational cost by much. We call it deep pyramid CNN. The proposed model with 15 weight layers outperforms the previous best models on six benchmark datasets for sentiment classification and topic categorization.",
}
@inproceedings{yu-etal-2017-improved,
    title = "Improved Neural Relation Detection for Knowledge Base Question Answering",
    author = "Yu, Mo  and
      Yin, Wenpeng  and
      Hasan, Kazi Saidul  and
      dos Santos, Cicero  and
      Xiang, Bing  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1053",
    doi = "10.18653/v1/P17-1053",
    pages = "571--581",
    abstract = "Relation detection is a core component of many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning which detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different levels of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to make the two components enhance each other. Our experimental results show that our approach not only achieves outstanding relation detection performance, but more importantly, it helps our KBQA system achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.",
}
@inproceedings{meng-etal-2017-deep,
    title = "Deep Keyphrase Generation",
    author = "Meng, Rui  and
      Zhao, Sanqiang  and
      Han, Shuguang  and
      He, Daqing  and
      Brusilovsky, Peter  and
      Chi, Yu",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1054",
    doi = "10.18653/v1/P17-1054",
    pages = "582--592",
    abstract = "Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as \textit{deep keyphrase generation} since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at \url{https://github.com/memray/seq2seq-keyphrase}.",
}
@inproceedings{cui-etal-2017-attention,
    title = "Attention-over-Attention Neural Networks for Reading Comprehension",
    author = "Cui, Yiming  and
      Chen, Zhipeng  and
      Wei, Si  and
      Wang, Shijin  and
      Liu, Ting  and
      Hu, Guoping",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1055",
    doi = "10.18653/v1/P17-1055",
    pages = "593--602",
    abstract = "Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The proposed model aims to place another attention mechanism over the document-level attention and induces {``}attended attention{''} for final answer predictions. One advantage of our model is that it is simpler than related works while giving excellent performance. In addition to the primary model, we also propose an N-best re-ranking strategy to double check the validity of the candidates and further improve the performance. Experimental results show that the proposed methods significantly outperform various state-of-the-art systems by a large margin in public datasets, such as CNN and Children{'}s Book Test.",
}
@inproceedings{doyle-etal-2017-alignment,
    title = "Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations",
    author = "Doyle, Gabriel  and
      Goldberg, Amir  and
      Srivastava, Sameer  and
      Frank, Michael",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1056",
    doi = "10.18653/v1/P17-1056",
    pages = "603--612",
    abstract = "Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elusive, poorly measured construct. Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed, dynamic measure of cultural fit based on linguistic alignment, which estimates the influence of one person{'}s word use on another{'}s and distinguishes between two enculturation mechanisms: internalization and self-regulation. We use this measure to trace employees{'} enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals{'} downstream outcomes, especially involuntary exit. Further predictive analyses suggest referential alignment plays an overlooked role in linguistic alignment.",
}
@inproceedings{chrupala-etal-2017-representations,
    title = "Representations of language in a model of visually grounded speech signal",
    author = "Chrupa{\l}a, Grzegorz  and
      Gelderloos, Lieke  and
      Alishahi, Afra",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1057",
    doi = "10.18653/v1/P17-1057",
    pages = "613--622",
    abstract = "We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.",
}
@inproceedings{xu-reitter-2017-spectral,
    title = "Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance",
    author = "Xu, Yang  and
      Reitter, David",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1058",
    doi = "10.18653/v1/P17-1058",
    pages = "623--633",
    abstract = "We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication. We predict the success of collaborative task in English and Danish corpora of task-oriented dialogue. Two features are extracted from the frequency domain representations of the lexical entropy series of each interlocutor, power spectrum overlap (PSO) and relative phase (RP). We find that PSO is a negative predictor of task success, while RP is a positive one. An SVM with these features significantly improved on previous task success prediction models. Our findings suggest that the strategic distribution of information density between interlocutors is relevant to task success.",
}
@inproceedings{ghosh-etal-2017-affect,
    title = "Affect-{LM}: A Neural Language Model for Customizable Affective Text Generation",
    author = "Ghosh, Sayan  and
      Chollet, Mathieu  and
      Laksana, Eugene  and
      Morency, Louis-Philippe  and
      Scherer, Stefan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1059",
    doi = "10.18653/v1/P17-1059",
    pages = "634--642",
    abstract = "Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research effort in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generation of conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM can generate naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction.",
}
@inproceedings{kim-etal-2017-domain,
    title = "Domain Attention with an Ensemble of Experts",
    author = "Kim, Young-Bum  and
      Stratos, Karl  and
      Kim, Dongchan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1060",
    doi = "10.18653/v1/P17-1060",
    pages = "643--653",
    abstract = "An important problem in domain adaptation is to quickly generalize to a new domain with limited supervision given K existing domains. One approach is to retrain a global model across all K + 1 domains using standard techniques, for instance Daum{\'e} III (2009). However, it is desirable to adapt without having to re-estimate a global model from scratch each time a new domain with potentially new intents and slots is added. We describe a solution based on attending an ensemble of domain experts. We assume K domain specific intent and slot models trained on respective domains. When given domain K + 1, our model uses a weighted combination of the K domain experts{'} feedback along with its own opinion to make predictions on the new domain. In experiments, the model significantly outperforms baselines that do not use domain adaptation and also performs better than the full retraining approach.",
}
@inproceedings{zhao-etal-2017-learning,
    title = "Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders",
    author = "Zhao, Tiancheng  and
      Zhao, Ran  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1061",
    doi = "10.18653/v1/P17-1061",
    pages = "654--664",
    abstract = "While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder from word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that capture the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved through introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence of discourse-level decision-making.",
}
@inproceedings{williams-etal-2017-hybrid,
    title = "Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning",
    author = "Williams, Jason D.  and
      Asadi, Kavosh  and
      Zweig, Geoffrey",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1062",
    doi = "10.18653/v1/P17-1062",
    pages = "665--677",
    abstract = "End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as software and system action templates. Compared to existing end-to-end approaches, HCNs considerably reduce the amount of training data required, while retaining the key benefit of inferring a latent representation of dialog state. In addition, HCNs can be optimized with supervised learning, reinforcement learning, or a mixture of both. HCNs attain state-of-the-art performance on the bAbI dialog dataset (Bordes and Weston, 2016), and outperform two commercially deployed customer-facing dialog systems at our company.",
}
@inproceedings{villalba-etal-2017-generating,
    title = "Generating Contrastive Referring Expressions",
    author = "Villalba, Mart{\'\i}n  and
      Teichmann, Christoph  and
      Koller, Alexander",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1063",
    doi = "10.18653/v1/P17-1063",
    pages = "678--687",
    abstract = "The referring expressions (REs) produced by a natural language generation (NLG) system can be misunderstood by the hearer, even when they are semantically correct. In an interactive setting, the NLG system can try to recognize such misunderstandings and correct them. We present an algorithm for generating corrective REs that use contrastive focus ({``}no, the BLUE button{''}) to emphasize the information the hearer most likely misunderstood. We show empirically that these contrastive REs are preferred over REs without contrast marking.",
}
@inproceedings{li-etal-2017-modeling,
    title = "Modeling Source Syntax for Neural Machine Translation",
    author = "Li, Junhui  and
      Xiong, Deyi  and
      Tu, Zhaopeng  and
      Zhu, Muhua  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1064",
    doi = "10.18653/v1/P17-1064",
    pages = "688--697",
    abstract = "Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best performance with an significant improvement of 1.4 BLEU points. Moreover, an in-depth analysis from several perspectives is provided to reveal how source syntax benefits NMT.",
}
@inproceedings{wu-etal-2017-sequence,
    title = "Sequence-to-Dependency Neural Machine Translation",
    author = "Wu, Shuangzhi  and
      Zhang, Dongdong  and
      Yang, Nan  and
      Li, Mu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1065",
    doi = "10.18653/v1/P17-1065",
    pages = "698--707",
    abstract = "Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and Japanese-English translation tasks.",
}
@inproceedings{ma-etal-2017-detect,
    title = "Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning",
    author = "Ma, Jing  and
      Gao, Wei  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1066",
    doi = "10.18653/v1/P17-1066",
    pages = "708--717",
    abstract = "How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate that the proposed kernel-based approach can detect rumors more quickly and accurately than state-of-the-art rumor detection models.",
}
@inproceedings{abdul-mageed-ungar-2017-emonet,
    title = "{E}mo{N}et: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks",
    author = "Abdul-Mageed, Muhammad  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1067",
    doi = "10.18653/v1/P17-1067",
    pages = "718--728",
    abstract = "Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58{\%}). We also extend the task beyond emotion types to model Robert Plutick{'}s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68{\%}.",
}
@inproceedings{preotiuc-pietro-etal-2017-beyond,
    title = "Beyond Binary Labels: Political Ideology Prediction of {T}witter Users",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Liu, Ye  and
      Hopkins, Daniel  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1068",
    doi = "10.18653/v1/P17-1068",
    pages = "729--740",
    abstract = "Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US. This study examines users{'} political ideology using a seven-point scale which enables us to identify politically moderate and neutral users {--} groups which are of particular interest to political scientists and pollsters. Using a novel data set with political ideology labels self-reported through surveys, our goal is two-fold: a) to characterize the groups of politically engaged users through language use on Twitter; b) to build a fine-grained model that predicts political ideology of unseen users. Our results identify differences in both political leaning and engagement and the extent to which each group tweets using political keywords. Finally, we demonstrate how to improve ideology prediction accuracy by exploiting the relationships between the user groups.",
}
@inproceedings{johnson-etal-2017-leveraging,
    title = "Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on {T}witter",
    author = "Johnson, Kristen  and
      Jin, Di  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1069",
    doi = "10.18653/v1/P17-1069",
    pages = "741--752",
    abstract = "Framing is a political strategy in which politicians carefully word their statements in order to control public perception of issues. Previous works exploring political framing typically analyze frame usage in longer texts, such as congressional speeches. We present a collection of weakly supervised models which harness collective classification to predict the frames used in political discourse on the microblogging platform, Twitter. Our global probabilistic models show that by combining both lexical features of tweets and network-based behavioral features of Twitter, we are able to increase the average, unsupervised F1 score by 21.52 points over a lexical baseline alone.",
}
@inproceedings{ji-etal-2017-nested,
    title = "A Nested Attention Neural Hybrid Model for Grammatical Error Correction",
    author = "Ji, Jianshu  and
      Wang, Qinlong  and
      Toutanova, Kristina  and
      Gong, Yongen  and
      Truong, Steven  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1070",
    doi = "10.18653/v1/P17-1070",
    pages = "753--762",
    abstract = "Grammatical error correction (GEC) systems strive to correct both global errors inword order and usage, and local errors inspelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC.Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information, and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset.Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective incorrecting local errors that involve small edits in orthography.",
}
@inproceedings{mrabet-etal-2017-textflow,
    title = "{T}ext{F}low: A Text Similarity Measure based on Continuous Sequences",
    author = "Mrabet, Yassine  and
      Kilicoglu, Halil  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1071",
    doi = "10.18653/v1/P17-1071",
    pages = "763--772",
    abstract = "Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment. While recent advances in deep learning highlighted the relevance of sequential models in natural language generation, existing similarity measures do not fully exploit the sequential nature of language. Examples of such similarity measures include n-grams and skip-grams overlap which rely on distinct slices of the input texts. In this paper we present a novel text similarity measure inspired from a common representation in DNA sequence alignment algorithms. The new measure, called TextFlow, represents input text pairs as continuous curves and uses both the actual position of the words and sequence matching to compute the similarity value. Our experiments on 8 different datasets show very encouraging results in paraphrase detection, textual entailment recognition and ranking relevance.",
}
@inproceedings{tan-etal-2017-friendships,
    title = "Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts",
    author = "Tan, Chenhao  and
      Card, Dallas  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1072",
    doi = "10.18653/v1/P17-1072",
    pages = "773--783",
    abstract = "Understanding how ideas relate to each other is a fundamental question in many domains, ranging from intellectual history to public communication. Because ideas are naturally embedded in texts, we propose the first framework to systematically characterize the relations between ideas based on their occurrence in a corpus of documents, independent of how these ideas are represented. Combining two statistics{---}cooccurrence within documents and prevalence correlation over time{---}our approach reveals a number of different ways in which ideas can cooperate and compete. For instance, two ideas can closely track each other{'}s prevalence over time, and yet rarely cooccur, almost like a {``}cold war{''} scenario. We observe that pairwise cooccurrence and prevalence correlation exhibit different distributions. We further demonstrate that our approach is able to uncover intriguing relations between ideas through in-depth case studies on news articles and research papers.",
}
@inproceedings{wroblewska-krasnowska-kieras-2017-polish,
    title = "{P}olish evaluation dataset for compositional distributional semantics models",
    author = "Wr{\'o}blewska, Alina  and
      Krasnowska-Kiera{\'s}, Katarzyna",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1073",
    doi = "10.18653/v1/P17-1073",
    pages = "784--792",
    abstract = "The paper presents a procedure of building an evaluation dataset. for the validation of compositional distributional semantics models estimated for languages other than English. The procedure generally builds on steps designed to assemble the SICK corpus, which contains pairs of English sentences annotated for semantic relatedness and entailment, because we aim at building a comparable dataset. However, the implementation of particular building steps significantly differs from the original SICK design assumptions, which is caused by both lack of necessary extraneous resources for an investigated language and the need for language-specific transformation rules. The designed procedure is verified on Polish, a fusional language with a relatively free word order, and contributes to building a Polish evaluation dataset. The resource consists of 10K sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish.",
}
@inproceedings{bryant-etal-2017-automatic,
    title = "Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction",
    author = "Bryant, Christopher  and
      Felice, Mariano  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1074",
    doi = "10.18653/v1/P17-1074",
    pages = "793--805",
    abstract = "Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rule-based framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as {``}Good{''} or {``}Acceptable{''} in at least 95{\%} of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.",
}
@inproceedings{sugawara-etal-2017-evaluation,
    title = "Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability",
    author = "Sugawara, Saku  and
      Kido, Yusuke  and
      Yokono, Hikaru  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1075",
    doi = "10.18653/v1/P17-1075",
    pages = "806--817",
    abstract = "Knowing the quality of reading comprehension (RC) datasets is important for the development of natural-language understanding systems. In this study, two classes of metrics were adopted for evaluating RC datasets: prerequisite skills and readability. We applied these classes to six existing datasets, including MCTest and SQuAD, and highlighted the characteristics of the datasets according to each metric and the correlation between the two classes. Our dataset analysis suggests that the readability of RC datasets does not directly affect the question difficulty and that it is possible to create an RC dataset that is easy to read but difficult to answer.",
}
@inproceedings{stern-etal-2017-minimal,
    title = "A Minimal Span-Based Neural Constituency Parser",
    author = "Stern, Mitchell  and
      Andreas, Jacob  and
      Klein, Dan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1076",
    doi = "10.18653/v1/P17-1076",
    pages = "818--827",
    abstract = "In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top-down inference algorithm based on recursive partitioning of the input. We demonstrate empirically that both prediction schemes are competitive with recent work, and when combined with basic extensions to the scoring model are capable of achieving state-of-the-art single-model performance on the Penn Treebank (91.79 F1) and strong performance on the French Treebank (82.23 F1).",
}
@inproceedings{sun-etal-2017-semantic,
    title = "Semantic Dependency Parsing via Book Embedding",
    author = "Sun, Weiwei  and
      Cao, Junjie  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1077",
    doi = "10.18653/v1/P17-1077",
    pages = "828--838",
    abstract = "We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algorithm tocombine pages into a book. Experiments demonstrate the effectiveness of the bookembedding framework across a wide range of conditions. Our parser obtains comparable results with a state-of-the-art transition-based parser.",
}
@inproceedings{yang-etal-2017-neural,
    title = "Neural Word Segmentation with Rich Pretraining",
    author = "Yang, Jie  and
      Zhang, Yue  and
      Dong, Fei",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1078",
    doi = "10.18653/v1/P17-1078",
    pages = "839--849",
    abstract = "Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.",
}
@inproceedings{oda-etal-2017-neural,
    title = "Neural Machine Translation via Binary Code Prediction",
    author = "Oda, Yusuke  and
      Arthur, Philip  and
      Neubig, Graham  and
      Yoshino, Koichiro  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1079",
    doi = "10.18653/v1/P17-1079",
    pages = "850--860",
    abstract = "In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can reduce computation time/memory requirements of the output layer to be logarithmic in vocabulary size in the best case. In addition, we also introduce two advanced approaches to improve the robustness of the proposed model: using error-correcting codes and combining softmax and binary codes. Experiments on two English-Japanese bidirectional translation tasks show proposed models achieve BLEU scores that approach the softmax, while reducing memory usage to the order of less than 1/10 and improving decoding speed on CPUs by x5 to x10.",
}
@inproceedings{belinkov-etal-2017-neural,
    title = "What do Neural Machine Translation Models Learn about Morphology?",
    author = "Belinkov, Yonatan  and
      Durrani, Nadir  and
      Dalvi, Fahim  and
      Sajjad, Hassan  and
      Glass, James",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1080",
    doi = "10.18653/v1/P17-1080",
    pages = "861--872",
    abstract = "Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.",
}
@inproceedings{poria-etal-2017-context,
    title = "Context-Dependent Sentiment Analysis in User-Generated Videos",
    author = "Poria, Soujanya  and
      Cambria, Erik  and
      Hazarika, Devamanyu  and
      Majumder, Navonil  and
      Zadeh, Amir  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1081",
    doi = "10.18653/v1/P17-1081",
    pages = "873--883",
    abstract = "Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10{\%} performance improvement over the state of the art and high robustness to generalizability.",
}
@inproceedings{pavalanathan-etal-2017-multidimensional,
    title = "A Multidimensional Lexicon for Interpersonal Stancetaking",
    author = "Pavalanathan, Umashanthi  and
      Fitzpatrick, Jim  and
      Kiesling, Scott  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1082",
    doi = "10.18653/v1/P17-1082",
    pages = "884--895",
    abstract = "The sociolinguistic construct of stancetaking describes the activities through which discourse participants create and signal relationships to their interlocutors, to the topic of discussion, and to the talk itself. Stancetaking underlies a wide range of interactional phenomena, relating to formality, politeness, affect, and subjectivity. We present a computational approach to stancetaking, in which we build a theoretically-motivated lexicon of stance markers, and then use multidimensional analysis to identify a set of underlying stance dimensions. We validate these dimensions intrinscially and extrinsically, showing that they are internally coherent, match pre-registered hypotheses, and correlate with social phenomena.",
}
@inproceedings{lund-etal-2017-tandem,
    title = "Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling",
    author = "Lund, Jeffrey  and
      Cook, Connor  and
      Seppi, Kevin  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1083",
    doi = "10.18653/v1/P17-1083",
    pages = "896--905",
    abstract = "Interactive topic models are powerful tools for those seeking to understand large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work but lack both a mechanism to inject prior knowledge and lack the intuitive semantics needed for user-facing applications. We propose combinations of words as anchors, going beyond existing single word anchor algorithms{---}an approach we call {``}Tandem Anchors{''}. We begin with a synthetic investigation of this approach then apply the approach to interactive topic modeling in a user study and compare it to interactive and non-interactive approaches. Tandem anchors are faster and more intuitive than existing interactive approaches.",
}
@inproceedings{bakhshandeh-allen-2017-apples,
    title = "Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task",
    author = "Bakhshandeh, Omid  and
      Allen, James",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1084",
    doi = "10.18653/v1/P17-1084",
    pages = "906--916",
    abstract = "Understanding common entities and their attributes is a primary requirement for any system that comprehends natural language. In order to enable learning about common entities, we introduce a novel machine comprehension task, GuessTwo: given a short paragraph comparing different aspects of two real-world semantically-similar entities, a system should guess what those entities are. Accomplishing this task requires deep language understanding which enables inference, connecting each comparison paragraph to different levels of knowledge about world entities and their attributes. So far we have crowdsourced a dataset of more than 14K comparison paragraphs comparing entities from a variety of categories such as fruits and animals. We have designed two schemes for evaluation: open-ended, and binary-choice prediction. For benchmarking further progress in the task, we have collected a set of paragraphs as the test set on which human can accomplish the task with an accuracy of 94.2{\%} on open-ended prediction. We have implemented various models for tackling the task, ranging from semantic-driven to neural models. The semantic-driven approach outperforms the neural models, however, the results indicate that the task is very challenging across the models.",
}
@inproceedings{katiyar-cardie-2017-going,
    title = "Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees",
    author = "Katiyar, Arzoo  and
      Cardie, Claire",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1085",
    doi = "10.18653/v1/P17-1085",
    pages = "917--928",
    abstract = "We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms feature-based joint model by Li and Ji (2014). We also compare our model with an end-to-end tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1{\%} on entity mentions and 2{\%} on relations. Our fine-grained analysis also shows that our model performs significantly better on Agent-Artifact relations, while SPTree performs better on Physical and Part-Whole relations.",
}
@inproceedings{wang-etal-2017-naturalizing,
    title = "Naturalizing a Programming Language via Interactive Learning",
    author = "Wang, Sida I.  and
      Ginn, Samuel  and
      Liang, Percy  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1086",
    doi = "10.18653/v1/P17-1086",
    pages = "929--938",
    abstract = "Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow users to {``}naturalize{''} the core language incrementally by defining alternative, more natural syntax and increasingly complex concepts in terms of compositions of simpler ones. In a voxel world, we show that a community of users can simultaneously teach a common system a diverse language and use it to build hundreds of complex voxel structures. Over the course of three days, these users went from using only the core language to using the naturalized language in 85.9{\%} of the last 10K utterances.",
}
@inproceedings{sedoc-etal-2017-semantic,
    title = "Semantic Word Clusters Using Signed Spectral Clustering",
    author = "Sedoc, Jo{\~a}o  and
      Gallier, Jean  and
      Foster, Dean  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1087",
    doi = "10.18653/v1/P17-1087",
    pages = "939--949",
    abstract = "Vector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each other. For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights. We present a new signed spectral normalized graph cut algorithm, \textit{signed clustering}, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words that simultaneously capture distributional and synonym relations. By using randomized spectral decomposition (Halko et al., 2011) and sparse matrices, our method is both fast and scalable. We validate our clusters using datasets containing human judgments of word pair similarities and show the benefit of using our word clusters for sentiment prediction.",
}
@inproceedings{xie-etal-2017-interpretable,
    title = "An Interpretable Knowledge Transfer Model for Knowledge Base Completion",
    author = "Xie, Qizhe  and
      Ma, Xuezhe  and
      Dai, Zihang  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1088",
    doi = "10.18653/v1/P17-1088",
    pages = "950--962",
    abstract = "Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets{---}WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.",
}
@inproceedings{iyer-etal-2017-learning,
    title = "Learning a Neural Semantic Parser from User Feedback",
    author = "Iyer, Srinivasan  and
      Konstas, Ioannis  and
      Cheung, Alvin  and
      Krishnamurthy, Jayant  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1089",
    doi = "10.18653/v1/P17-1089",
    pages = "963--973",
    abstract = "We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention. To achieve this, we adapt neural sequence models to map utterances directly to SQL with its full expressivity, bypassing any intermediate meaning representations. These models are immediately deployed online to solicit feedback from real users to flag incorrect queries. Finally, the popularity of SQL facilitates gathering annotations for incorrect predictions using the crowd, which is directly used to improve our models. This complete feedback loop, without intermediate representations or database specific engineering, opens up new ways of building high quality semantic parsers. Experiments suggest that this approach can be deployed quickly for any new target domain, as we show by learning a semantic parser for an online academic database from scratch.",
}
@inproceedings{qin-etal-2017-joint,
    title = "Joint Modeling of Content and Discourse Relations in Dialogues",
    author = "Qin, Kechen  and
      Wang, Lu  and
      Kim, Joseph",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1090",
    doi = "10.18653/v1/P17-1090",
    pages = "974--984",
    abstract = "We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrase-based content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members{'} understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.",
}
@inproceedings{niculae-etal-2017-argument,
    title = "Argument Mining with Structured {SVM}s and {RNN}s",
    author = "Niculae, Vlad  and
      Park, Joonsuk  and
      Cardie, Claire",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1091",
    doi = "10.18653/v1/P17-1091",
    pages = "985--995",
    abstract = "We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure. (This is the case in over 20{\%} of the web comments dataset we release.) Our model jointly learns elementary unit type classification and argumentative relation prediction. Moreover, our model supports SVM and RNN parametrizations, can enforce structure constraints (e.g., transitivity), and can express dependencies between adjacent relations and propositions. Our approaches outperform unstructured baselines in both web comments and argumentative essay datasets.",
}
@inproceedings{ji-smith-2017-neural,
    title = "Neural Discourse Structure for Text Categorization",
    author = "Ji, Yangfeng  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1092",
    doi = "10.18653/v1/P17-1092",
    pages = "996--1005",
    abstract = "We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.",
}
@inproceedings{qin-etal-2017-adversarial,
    title = "Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification",
    author = "Qin, Lianhui  and
      Zhang, Zhisong  and
      Zhao, Hai  and
      Hu, Zhiting  and
      Xing, Eric",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1093",
    doi = "10.18653/v1/P17-1093",
    pages = "1006--1017",
    abstract = "Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.",
}
@inproceedings{haponchyk-moschitti-2017-dont,
    title = "Don{'}t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures",
    author = "Haponchyk, Iryna  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1094",
    doi = "10.18653/v1/P17-1094",
    pages = "1018--1028",
    abstract = "An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation for enabling the use and study of more complex loss functions for coreference resolution. Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.",
}
@inproceedings{andrews-etal-2017-bayesian,
    title = "{B}ayesian Modeling of Lexical Resources for Low-Resource Settings",
    author = "Andrews, Nicholas  and
      Dredze, Mark  and
      Van Durme, Benjamin  and
      Eisner, Jason",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1095",
    doi = "10.18653/v1/P17-1095",
    pages = "1029--1039",
    abstract = "Lexical resources such as dictionaries and gazetteers are often used as auxiliary data for tasks such as part-of-speech induction and named-entity recognition. However, discriminative training with lexical features requires annotated data to reliably estimate the lexical feature weights and may result in overfitting the lexical features at the expense of features which generalize better. In this paper, we investigate a more robust approach: we stipulate that the lexicon is the result of an assumed generative process. Practically, this means that we may treat the lexical resources as observations under the proposed generative model. The lexical resources provide training data for the generative model without requiring separate data to estimate lexical feature weights. We evaluate the proposed approach in two settings: part-of-speech induction and low-resource named-entity recognition.",
}
@inproceedings{yang-etal-2017-semi,
    title = "Semi-Supervised {QA} with Generative Domain-Adaptive Nets",
    author = "Yang, Zhilin  and
      Hu, Junjie  and
      Salakhutdinov, Ruslan  and
      Cohen, William",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1096",
    doi = "10.18653/v1/P17-1096",
    pages = "1040--1050",
    abstract = "We study the problem of semi-supervised question answering{---}utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the \textit{Generative Domain-Adaptive Nets}. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text.",
}
@inproceedings{guu-etal-2017-language,
    title = "From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood",
    author = "Guu, Kelvin  and
      Pasupat, Panupong  and
      Liu, Evan  and
      Liang, Percy",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1097",
    doi = "10.18653/v1/P17-1097",
    pages = "1051--1062",
    abstract = "Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by \textit{spurious programs}: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.",
}
@inproceedings{nema-etal-2017-diversity,
    title = "Diversity driven attention model for query-based abstractive summarization",
    author = "Nema, Preksha  and
      Khapra, Mitesh M.  and
      Laha, Anirban  and
      Ravindran, Balaraman",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1098",
    doi = "10.18653/v1/P17-1098",
    pages = "1063--1072",
    abstract = "Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encode-attend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28{\%} (absolute) in ROUGE-L scores.",
}
@inproceedings{see-etal-2017-get,
    title = "Get To The Point: Summarization with Pointer-Generator Networks",
    author = "See, Abigail  and
      Liu, Peter J.  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1099",
    doi = "10.18653/v1/P17-1099",
    pages = "1073--1083",
    abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
}
@inproceedings{peyrard-eckle-kohler-2017-supervised,
    title = "Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization",
    author = "Peyrard, Maxime  and
      Eckle-Kohler, Judith",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1100",
    doi = "10.18653/v1/P17-1100",
    pages = "1084--1094",
    abstract = "We present a new supervised framework that learns to estimate automatic Pyramid scores and uses them for optimization-based extractive multi-document summarization. For learning automatic Pyramid scores, we developed a method for automatic training data generation which is based on a genetic algorithm using automatic Pyramid as the fitness function. Our experimental evaluation shows that our new framework significantly outperforms strong baselines regarding automatic Pyramid, and that there is much room for improvement in comparison with the upper-bound for automatic Pyramid.",
}
@inproceedings{zhou-etal-2017-selective,
    title = "Selective Encoding for Abstractive Sentence Summarization",
    author = "Zhou, Qingyu  and
      Yang, Nan  and
      Wei, Furu  and
      Zhou, Ming",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1101",
    doi = "10.18653/v1/P17-1101",
    pages = "1095--1104",
    abstract = "We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-of-the-art baseline models.",
}
@inproceedings{florescu-caragea-2017-positionrank,
    title = "{P}osition{R}ank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents",
    author = "Florescu, Corina  and
      Caragea, Cornelia",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1102",
    doi = "10.18653/v1/P17-1102",
    pages = "1105--1115",
    abstract = "The large and growing amounts of online scholarly data present both challenges and opportunities to enhance knowledge discovery. One such challenge is to automatically extract a small set of keyphrases from a document that can accurately describe the document{'}s content and can facilitate fast information processing. In this paper, we propose PositionRank, an unsupervised model for keyphrase extraction from scholarly documents that incorporates information from all positions of a word{'}s occurrences into a biased PageRank. Our model obtains remarkable improvements in performance over PageRank models that do not take into account word positions as well as over strong baselines for this task. Specifically, on several datasets of research papers, PositionRank achieves improvements as high as 29.09{\%}.",
}
@inproceedings{lowe-etal-2017-towards,
    title = "Towards an Automatic {T}uring Test: Learning to Evaluate Dialogue Responses",
    author = "Lowe, Ryan  and
      Noseworthy, Michael  and
      Serban, Iulian Vlad  and
      Angelard-Gontier, Nicolas  and
      Bengio, Yoshua  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1103",
    doi = "10.18653/v1/P17-1103",
    pages = "1116--1126",
    abstract = "Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality (Liu et al., 2016). Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem.We present an evaluation model (ADEM)that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model{'}s predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue mod-els unseen during training, an important step for automatic dialogue evaluation.",
}
@inproceedings{hershcovich-etal-2017-transition,
    title = "A Transition-Based Directed Acyclic Graph Parser for {UCCA}",
    author = "Hershcovich, Daniel  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1104",
    doi = "10.18653/v1/P17-1104",
    pages = "1127--1138",
    abstract = "We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.",
}
@inproceedings{rabinovich-etal-2017-abstract,
    title = "Abstract Syntax Networks for Code Generation and Semantic Parsing",
    author = "Rabinovich, Maxim  and
      Stern, Mitchell  and
      Klein, Dan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1105",
    doi = "10.18653/v1/P17-1105",
    pages = "1139--1149",
    abstract = "Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7{\%} exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1{\%}. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.",
}
@inproceedings{ding-etal-2017-visualizing,
    title = "Visualizing and Understanding Neural Machine Translation",
    author = "Ding, Yanzhuo  and
      Liu, Yang  and
      Luan, Huanbo  and
      Sun, Maosong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1106",
    doi = "10.18653/v1/P17-1106",
    pages = "1150--1159",
    abstract = "While neural machine translation (NMT) has made remarkable progress in recent years, it is hard to interpret its internal workings due to the continuous representations and non-linearity of neural networks. In this work, we propose to use layer-wise relevance propagation (LRP) to compute the contribution of each contextual word to arbitrary hidden states in the attention-based encoder-decoder framework. We show that visualization with LRP helps to interpret the internal workings of NMT and analyze translation errors.",
}
@inproceedings{rehbein-ruppenhofer-2017-detecting,
    title = "Detecting annotation noise in automatically labelled data",
    author = "Rehbein, Ines  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1107",
    doi = "10.18653/v1/P17-1107",
    pages = "1160--1170",
    abstract = "We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.",
}
@inproceedings{tan-etal-2017-abstractive,
    title = "Abstractive Document Summarization with a Graph-Based Attentional Neural Model",
    author = "Tan, Jiwei  and
      Wan, Xiaojun  and
      Xiao, Jianguo",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1108",
    doi = "10.18653/v1/P17-1108",
    pages = "1171--1181",
    abstract = "Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.",
}
@inproceedings{cotterell-eisner-2017-probabilistic,
    title = "Probabilistic Typology: Deep Generative Models of Vowel Inventories",
    author = "Cotterell, Ryan  and
      Eisner, Jason",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1109",
    doi = "10.18653/v1/P17-1109",
    pages = "1182--1192",
    abstract = "Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most{---}but not all{---}languages have an /u/ sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.",
}
@inproceedings{chen-etal-2017-adversarial,
    title = "Adversarial Multi-Criteria Learning for {C}hinese Word Segmentation",
    author = "Chen, Xinchi  and
      Shi, Zhan  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1110",
    doi = "10.18653/v1/P17-1110",
    pages = "1193--1203",
    abstract = "Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github.",
}
@inproceedings{kurita-etal-2017-neural,
    title = "Neural Joint Model for Transition-based {C}hinese Syntactic Analysis",
    author = "Kurita, Shuhei  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1111",
    doi = "10.18653/v1/P17-1111",
    pages = "1204--1214",
    abstract = "We present neural network-based joint models for Chinese word segmentation, POS tagging and dependency parsing. Our models are the first neural approaches for fully joint Chinese analysis that is known to prevent the error propagation problem of pipeline models. Although word embeddings play a key role in dependency parsing, they cannot be applied directly to the joint task in the previous work. To address this problem, we propose embeddings of character strings, in addition to words. Experiments show that our models outperform existing systems in Chinese word segmentation and POS tagging, and perform preferable accuracies in dependency parsing. We also explore bi-LSTM models with fewer features.",
}
@inproceedings{buys-blunsom-2017-robust,
    title = "Robust Incremental Neural Semantic Graph Parsing",
    author = "Buys, Jan  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1112",
    doi = "10.18653/v1/P17-1112",
    pages = "1215--1226",
    abstract = "Parsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focussed almost exclusively on bilexical dependencies or domain-specific logical forms. We propose a neural encoder-decoder transition-based parser which is the first full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The model architecture uses stack-based embedding features, predicting graphs jointly with unlexicalized predicates and their token alignments. Our parser is more accurate than attention-based baselines on MRS, and on an additional Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes it an order of magnitude faster than a high-precision grammar-based parser. Further, the 86.69{\%} Smatch score of our MRS parser is higher than the upper-bound on AMR parsing, making MRS an attractive choice as a semantic representation.",
}
@inproceedings{zheng-etal-2017-joint,
    title = "Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme",
    author = "Zheng, Suncong  and
      Wang, Feng  and
      Bao, Hongyun  and
      Hao, Yuexing  and
      Zhou, Peng  and
      Xu, Bo",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1113",
    doi = "10.18653/v1/P17-1113",
    pages = "1227--1236",
    abstract = "Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem.. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What{'}s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.",
}
@inproceedings{xu-etal-2017-local,
    title = "A Local Detection Approach for Named Entity Recognition and Mention Detection",
    author = "Xu, Mingbin  and
      Jiang, Hui  and
      Watcharawittayakul, Sedtawut",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1114",
    doi = "10.18653/v1/P17-1114",
    pages = "1237--1247",
    abstract = "In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, we propose a new local detection approach, which relies on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixed-size representation. Subsequently, a simple feedforward neural network (FFNN) is learned to either reject or predict entity label for each individual text fragment. The proposed method has been evaluated in several popular NER and MD tasks, including CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Tri-lingual Entity Discovery and Linking (EDL) tasks. Our method has yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labeling methods.",
}
@inproceedings{gritta-etal-2017-vancouver,
    title = "{V}ancouver Welcomes You! Minimalist Location Metonymy Resolution",
    author = "Gritta, Milan  and
      Pilehvar, Mohammad Taher  and
      Limsopatham, Nut  and
      Collier, Nigel",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1115",
    doi = "10.18653/v1/P17-1115",
    pages = "1248--1259",
    abstract = "Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.",
}
@inproceedings{miura-etal-2017-unifying,
    title = "Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction",
    author = "Miura, Yasuhide  and
      Taniguchi, Motoki  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1116",
    doi = "10.18653/v1/P17-1116",
    pages = "1260--1272",
    abstract = "We propose a novel geolocation prediction model using a complex neural network. Geolocation prediction in social media has attracted many researchers to use information of various types. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8{\%} increase in accuracy and a maximum of 6.6{\%} increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.",
}
@inproceedings{pasunuru-bansal-2017-multi,
    title = "Multi-Task Video Captioning with Video and Entailment Generation",
    author = "Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1117",
    doi = "10.18653/v1/P17-1117",
    pages = "1273--1283",
    abstract = "Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data. We improve video captioning by sharing knowledge with two related directed-generation tasks: a temporally-directed unsupervised video prediction task to learn richer context-aware video encoder representations, and a logically-directed language entailment generation task to learn better video-entailing caption decoder representations. For this, we present a many-to-many multi-task learning model that shares parameters across the encoders and decoders of the three tasks. We achieve significant improvements and the new state-of-the-art on several standard video captioning datasets using diverse automatic and human evaluations. We also show mutual multi-task improvements on the entailment generation task.",
}
@inproceedings{santos-etal-2017-enriching,
    title = "Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts",
    author = "Santos, Leandro  and
      Corr{\^e}a J{\'u}nior, Edilson Anselmo  and
      Oliveira Jr, Osvaldo  and
      Amancio, Diego  and
      Mansur, Let{\'\i}cia  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1118",
    doi = "10.18653/v1/P17-1118",
    pages = "1284--1296",
    abstract = "Mild Cognitive Impairment (MCI) is a mental disorder difficult to diagnose. Linguistic features, mainly from parsers, have been used to detect MCI, but this is not suitable for large-scale assessments. MCI disfluencies produce non-grammatical speech that requires manual or high precision automatic correction of transcripts. In this paper, we modeled transcripts into complex networks and enriched them with word embedding (CNE) to better represent short texts produced in neuropsychological assessments. The network measurements were applied with well-known classifiers to automatically identify MCI in transcripts, in a binary classification task. A comparison was made with the performance of traditional approaches using Bag of Words (BoW) and linguistic features for three datasets: DementiaBank in English, and Cinderella and Arizona-Battery in Portuguese. Overall, CNE provided higher accuracy than using only complex networks, while Support Vector Machine was superior to other classifiers. CNE provided the highest accuracies for DementiaBank and Cinderella, but BoW was more efficient for the Arizona-Battery dataset probably owing to its short narratives. The approach using linguistic features yielded higher accuracy if the transcriptions of the Cinderella dataset were manually revised. Taken together, the results indicate that complex networks enriched with embedding is promising for detecting MCI in large-scale assessments.",
}
@inproceedings{kim-etal-2017-adversarial,
    title = "Adversarial Adaptation of Synthetic or Stale Data",
    author = "Kim, Young-Bum  and
      Stratos, Karl  and
      Kim, Dongchan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1119",
    doi = "10.18653/v1/P17-1119",
    pages = "1297--1307",
    abstract = "Two types of data shift common in practice are 1. transferring from synthetic data to live user data (a deployment shift), and 2. transferring from stale data to current data (a temporal shift). Both cause a distribution mismatch between training and evaluation, leading to a model that overfits the flawed training data and performs poorly on the test data. We propose a solution to this mismatch problem by framing it as domain adaptation, treating the flawed training dataset as a source domain and the evaluation dataset as a target domain. To this end, we use and build on several recent advances in neural domain adaptation such as adversarial training (Ganinet al., 2016) and domain separation network (Bousmalis et al., 2016), proposing a new effective adversarial training scheme. In both supervised and unsupervised adaptation scenarios, our approach yields clear improvement over strong baselines.",
}
@inproceedings{akasaki-kaji-2017-chat,
    title = "Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems",
    author = "Akasaki, Satoshi  and
      Kaji, Nobuhiro",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1120",
    doi = "10.18653/v1/P17-1120",
    pages = "1308--1319",
    abstract = "Recently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific task-oriented spoken dialogue systems and open-domain non-task-oriented ones. To realize such hybrid dialogue systems, this paper investigates determining whether or not a user is going to have a chat with the system. To address the lack of benchmark datasets for this task, we construct a new dataset consisting of 15,160 utterances collected from the real log data of a commercial intelligent assistant (and will release the dataset to facilitate future research activity). In addition, we investigate using tweets and Web search queries for handling open-domain user utterances, which characterize the task of chat detection. Experimental experiments demonstrated that, while simple supervised methods are effective, the use of the tweets and search queries further improves the F$_1$-score from 86.21 to 87.53.",
}
@inproceedings{tien-nguyen-joty-2017-neural,
    title = "A Neural Local Coherence Model",
    author = "Tien Nguyen, Dat  and
      Joty, Shafiq",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1121",
    doi = "10.18653/v1/P17-1121",
    pages = "1320--1330",
    abstract = "We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.",
}
@inproceedings{cagan-etal-2017-data,
    title = "Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation ({ONLG})",
    author = "Cagan, Tomer  and
      Frank, Stefan L.  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1122",
    doi = "10.18653/v1/P17-1122",
    pages = "1331--1341",
    abstract = "Opinionated Natural Language Generation (ONLG) is a new, challenging, task that aims to automatically generate human-like, subjective, responses to opinionated articles online. We present a data-driven architecture for ONLG that generates subjective responses triggered by users{'} agendas, consisting of topics and sentiments, and based on wide-coverage automatically-acquired generative grammars. We compare three types of grammatical representations that we design for ONLG, which interleave different layers of linguistic information and are induced from a new, enriched dataset we developed. Our evaluation shows that generation with Relational-Realizational (Tsarfaty and Sima{'}an, 2008) inspired grammar gets better language model scores than lexicalized grammars {`}a la Collins (2003), and that the latter gets better human-evaluation scores. We also show that conditioning the generation on topic models makes generated responses more relevant to the document content.",
}
@inproceedings{du-etal-2017-learning,
    title = "Learning to Ask: Neural Question Generation for Reading Comprehension",
    author = "Du, Xinya  and
      Shao, Junru  and
      Cardie, Claire",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1123",
    doi = "10.18653/v1/P17-1123",
    pages = "1342--1352",
    abstract = "We study automatic question generation for sentences from text passages in reading comprehension. We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence- vs. paragraph-level information. In contrast to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequence-to-sequence learning. Automatic evaluation results show that our system significantly outperforms the state-of-the-art rule-based system. In human evaluations, questions generated by our system are also rated as being more natural (\textit{i.e.,}, grammaticality, fluency) and as more difficult to answer (in terms of syntactic and lexical divergence from the original text and reasoning needed to answer).",
}
@inproceedings{p-v-s-meyer-2017-joint,
    title = "Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback",
    author = "P.V.S, Avinesh  and
      Meyer, Christian M.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1124",
    doi = "10.18653/v1/P17-1124",
    pages = "1353--1363",
    abstract = "In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing high-quality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedback-based concept selection in the ILP setup in order to maximize the user-desired content in the summary.",
}
@inproceedings{zhang-etal-2017-flexible,
    title = "Flexible and Creative {C}hinese Poetry Generation Using Neural Memory",
    author = "Zhang, Jiyuan  and
      Feng, Yang  and
      Wang, Dong  and
      Wang, Yang  and
      Abel, Andrew  and
      Zhang, Shiyue  and
      Zhang, Andi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1125",
    doi = "10.18653/v1/P17-1125",
    pages = "1364--1373",
    abstract = "It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. In addition, it is found that the memory mechanism provides interesting flexibility that can be used to generate poems with different styles.",
}
@inproceedings{murakami-etal-2017-learning,
    title = "Learning to Generate Market Comments from Stock Prices",
    author = "Murakami, Soichiro  and
      Watanabe, Akihiko  and
      Miyazawa, Akira  and
      Goshima, Keiichi  and
      Yanase, Toshihiko  and
      Takamura, Hiroya  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1126",
    doi = "10.18653/v1/P17-1126",
    pages = "1374--1384",
    abstract = "This paper presents a novel encoder-decoder model for automatically generating market comments from stock prices. The model first encodes both short- and long-term series of stock prices so that it can mention short- and long-term changes in stock prices. In the decoding phase, our model can also generate a numerical value by selecting an appropriate arithmetic operation such as subtraction or rounding, and applying it to the input stock prices. Empirical experiments show that our best model generates market comments at the fluency and the informativeness approaching human-generated reference texts.",
}
@inproceedings{wang-etal-2017-syntax,
    title = "Can Syntax Help? Improving an {LSTM}-based Sentence Compression Model for New Domains",
    author = "Wang, Liangguo  and
      Jiang, Jing  and
      Chieu, Hai Leong  and
      Ong, Chen Hui  and
      Song, Dandan  and
      Liao, Lejian",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1127",
    doi = "10.18653/v1/P17-1127",
    pages = "1385--1393",
    abstract = "In this paper, we study how to improve the domain adaptability of a deletion-based Long Short-Term Memory (LSTM) neural network model for sentence compression. We hypothesize that syntactic information helps in making such models more robust across domains. We propose two major changes to the model: using explicit syntactic features and introducing syntactic constraints through Integer Linear Programming (ILP). Our evaluation shows that the proposed model works better than the original model as well as a traditional non-neural-network-based model in a cross-domain setting.",
}
@inproceedings{wang-etal-2017-transductive,
    title = "Transductive Non-linear Learning for {C}hinese Hypernym Prediction",
    author = "Wang, Chengyu  and
      Yan, Junchi  and
      Zhou, Aoying  and
      He, Xiaofeng",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1128",
    doi = "10.18653/v1/P17-1128",
    pages = "1394--1404",
    abstract = "Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc. Due to the flexibility of the Chinese language, it is challenging to identify hypernyms in Chinese accurately. Rather than extracting hypernyms from texts, in this paper, we present a transductive learning approach to establish mappings from entities to hypernyms in the embedding space directly. It combines linear and non-linear embedding projection models, with the capacity of encoding arbitrary language-specific rules. Experiments on real-world datasets illustrate that our approach outperforms previous methods for Chinese hypernym prediction.",
}
@inproceedings{xie-xing-2017-constituent,
    title = "A Constituent-Centric Neural Architecture for Reading Comprehension",
    author = "Xie, Pengtao  and
      Xing, Eric",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1129",
    doi = "10.18653/v1/P17-1129",
    pages = "1405--1414",
    abstract = "Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task. In this paper, we study the RC problem on the Stanford Question Answering Dataset (SQuAD). Observing from the training set that most correct answers are centered around constituents in the parse tree, we design a constituent-centric neural architecture where the generation of candidate answers and their representation learning are both based on constituents and guided by the parse tree. Under this architecture, the search space of candidate answers can be greatly reduced without sacrificing the coverage of correct answers and the syntactic, hierarchical and compositional structure among constituents can be well captured, which contributes to better representation learning of the candidate answers. On SQuAD, our method achieves the state of the art performance and the ablation study corroborates the effectiveness of individual modules.",
}
@inproceedings{xu-yang-2017-cross,
    title = "Cross-lingual Distillation for Text Classification",
    author = "Xu, Ruochen  and
      Yang, Yiming",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1130",
    doi = "10.18653/v1/P17-1130",
    pages = "1415--1425",
    abstract = "Cross-lingual text classification(CLTC) is the task of classifying documents written in different languages into the same taxonomy of categories. This paper presents a novel approach to CLTC that builds on model distillation, which adapts and extends a framework originally proposed for model compression. Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available. An adversarial feature adaptation technique is also applied during the model training to reduce distribution mismatch. We conducted experiments on two benchmark CLTC datasets, treating English as the source language and German, French, Japan and Chinese as the unlabeled target languages. The proposed approach had the advantageous or comparable performance of the other state-of-art methods.",
}
@inproceedings{perez-rosas-etal-2017-understanding,
    title = "Understanding and Predicting Empathic Behavior in Counseling Therapy",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada  and
      Resnicow, Kenneth  and
      Singh, Satinder  and
      An, Lawrence",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1131",
    doi = "10.18653/v1/P17-1131",
    pages = "1426--1435",
    abstract = "Counselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants{'} engagement, participants{'} verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80{\%}.",
}
@inproceedings{yang-mitchell-2017-leveraging,
    title = "Leveraging Knowledge Bases in {LSTM}s for Improving Machine Reading",
    author = "Yang, Bishan  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1132",
    doi = "10.18653/v1/P17-1132",
    pages = "1436--1446",
    abstract = "This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.",
}
@inproceedings{pan-etal-2017-prerequisite,
    title = "Prerequisite Relation Learning for Concepts in {MOOC}s",
    author = "Pan, Liangming  and
      Li, Chengjiang  and
      Li, Juanzi  and
      Tang, Jie",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1133",
    doi = "10.18653/v1/P17-1133",
    pages = "1447--1456",
    abstract = "What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can be inferred automatically. In particular, what kinds of information can be leverage to uncover the potential prerequisite relation between knowledge concepts. We first propose a representation learning-based method for learning latent representations of course concepts, and then investigate how different features capture the prerequisite relations between concepts. Our experiments on three datasets form Coursera show that the proposed method achieves significant improvements (+5.9-48.0{\%} by F1-score) comparing with existing methods.",
}
@inproceedings{malmasi-etal-2017-unsupervised,
    title = "Unsupervised Text Segmentation Based on Native Language Characteristics",
    author = "Malmasi, Shervin  and
      Dras, Mark  and
      Johnson, Mark  and
      Du, Lan  and
      Wolska, Magdalena",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1134",
    doi = "10.18653/v1/P17-1134",
    pages = "1457--1469",
    abstract = "Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language. We propose a Bayesian unsupervised text segmentation approach to the latter. While baseline models achieve essentially random segmentation on our task, indicating its difficulty, a Bayesian model that incorporates appropriately compact language models and alternating asymmetric priors can achieve scores on the standard metrics around halfway to perfect segmentation.",
}
@inproceedings{ni-etal-2017-weakly,
    title = "Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection",
    author = "Ni, Jian  and
      Dinu, Georgiana  and
      Florian, Radu",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1135",
    doi = "10.18653/v1/P17-1135",
    pages = "1470--1480",
    abstract = "The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data.",
}
@inproceedings{chakrabarty-etal-2017-context,
    title = "Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks",
    author = "Chakrabarty, Abhisek  and
      Pandit, Onkar Arun  and
      Garain, Utpal",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1136",
    doi = "10.18653/v1/P17-1136",
    pages = "1481--1491",
    abstract = "We introduce a composite deep neural network architecture for supervised and language independent context sensitive lemmatization. The proposed method considers the task as to identify the correct edit tree representing the transformation between a word-lemma pair. To find the lemma of a surface word, we exploit two successive bidirectional gated recurrent structures - the first one is used to extract the character level dependencies and the next one captures the contextual information of the given word. The key advantages of our model compared to the state-of-the-art lemmatizers such as Lemming and Morfette are - (i) it is independent of human decided features (ii) except the gold lemma, no other expensive morphological attribute is required for joint learning. We evaluate the lemmatizer on nine languages - Bengali, Catalan, Dutch, Hindi, Hungarian, Italian, Latin, Romanian and Spanish. It is found that except Bengali, the proposed method outperforms Lemming and Morfette on the other languages. To train the model on Bengali, we develop a gold lemma annotated dataset (having 1,702 sentences with a total of 20,257 word tokens), which is an additional contribution of this work.",
}
@inproceedings{kawakami-etal-2017-learning,
    title = "Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling",
    author = "Kawakami, Kazuya  and
      Dyer, Chris  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1137",
    doi = "10.18653/v1/P17-1137",
    pages = "1492--1502",
    abstract = "Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Although character-level language models offer a partial solution in that they can create word types not attested in the training corpus, they do not capture the {``}bursty{''} distribution of such words. In this paper, we augment a hierarchical LSTM language model that generates sequences of word tokens character by character with a caching mechanism that learns to reuse previously generated words. To validate our model we construct a new open-vocabulary language modeling corpus (the Multilingual Wikipedia Corpus; MWC) from comparable Wikipedia articles in 7 typologically diverse languages and demonstrate the effectiveness of our model across this range of languages.",
}
@inproceedings{kreutzer-etal-2017-bandit,
    title = "Bandit Structured Prediction for Neural Sequence-to-Sequence Learning",
    author = "Kreutzer, Julia  and
      Sokolov, Artem  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1138",
    doi = "10.18653/v1/P17-1138",
    pages = "1503--1513",
    abstract = "Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback. This feedback is received in the form of a task loss evaluation to a predicted output structure, without having access to gold standard structures. We advance this framework by lifting linear bandit learning to neural sequence-to-sequence learning problems using attention-based recurrent neural networks. Furthermore, we show how to incorporate control variates into our learning algorithms for variance reduction and improved generalization. We present an evaluation on a neural machine translation task that shows improvements of up to 5.89 BLEU points for domain adaptation from simulated bandit feedback.",
}
@inproceedings{zhang-etal-2017-prior,
    title = "Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization",
    author = "Zhang, Jiacheng  and
      Liu, Yang  and
      Luan, Huanbo  and
      Xu, Jingfang  and
      Sun, Maosong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1139",
    doi = "10.18653/v1/P17-1139",
    pages = "1514--1523",
    abstract = "Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, which guides the learning processing of the neural translation model. Experiments on Chinese-English dataset show that our approach leads to significant improvements.",
}
@inproceedings{zhang-etal-2017-incorporating,
    title = "Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation",
    author = "Zhang, Jinchao  and
      Wang, Mingxuan  and
      Liu, Qun  and
      Zhou, Jie",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1140",
    doi = "10.18653/v1/P17-1140",
    pages = "1524--1534",
    abstract = "This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance. Our proposed models enable attention mechanism to attend to source words regarding both the semantic requirement and the word reordering penalty. Experiments on Chinese-English translation show that the approaches can improve word alignment quality and achieve significant translation improvements over a basic attention-based NMT by large margins. Compared with previous works on identical corpora, our system achieves the state-of-the-art performance on translation quality.",
}
@inproceedings{hokamp-liu-2017-lexically,
    title = "Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search",
    author = "Hokamp, Chris  and
      Liu, Qun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1141",
    doi = "10.18653/v1/P17-1141",
    pages = "1535--1546",
    abstract = "We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints. The algorithm can be used with any model which generates sequences token by token. Lexical constraints take the form of phrases or words that must be present in the output sequence. This is a very general way to incorporate auxillary knowledge into a model{'}s output without requiring any modification of the parameters or training data. We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation. Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.",
}
@inproceedings{tong-etal-2017-combating,
    title = "Combating Human Trafficking with Multimodal Deep Models",
    author = "Tong, Edmund  and
      Zadeh, Amir  and
      Jones, Cara  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1142",
    doi = "10.18653/v1/P17-1142",
    pages = "1547--1556",
    abstract = "Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to pertain to human trafficking. We present a novel dataset called Trafficking-10k, with more than 10,000 advertisements annotated for this task. The dataset contains two sources of information per advertisement: text and images. For the accurate detection of trafficking advertisements, we designed and trained a deep multimodal model called the Human Trafficking Deep Network (HTDN).",
}
@inproceedings{lim-etal-2017-malwaretextdb,
    title = "{M}alware{T}ext{DB}: A Database for Annotated Malware Articles",
    author = "Lim, Swee Kiat  and
      Muis, Aldrian Obaja  and
      Lu, Wei  and
      Ong, Chen Hui",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1143",
    doi = "10.18653/v1/P17-1143",
    pages = "1557--1567",
    abstract = "Cybersecurity risks and malware threats are becoming increasingly dangerous and common. Despite the severity of the problem, there has been few NLP efforts focused on tackling cybersecurity. In this paper, we discuss the construction of a new database for annotated malware texts. An annotation framework is introduced based on the MAEC vocabulary for defining malware characteristics, along with a database consisting of 39 annotated APT reports with a total of 6,819 sentences. We also use the database to construct models that can potentially help cybersecurity researchers in their data collection and analytics efforts.",
}
@inproceedings{zhang-etal-2017-corpus,
    title = "A Corpus of Annotated Revisions for Studying Argumentative Writing",
    author = "Zhang, Fan  and
      Hashemi, Homa B.  and
      Hwa, Rebecca  and
      Litman, Diane",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1144",
    doi = "10.18653/v1/P17-1144",
    pages = "1568--1578",
    abstract = "This paper presents ArgRewrite, a corpus of between-draft revisions of argumentative essays. Drafts are manually aligned at the sentence level, and the writer{'}s purpose for each revision is annotated with categories analogous to those used in argument mining and discourse analysis. The corpus should enable advanced research in writing comparison and revision analysis, as demonstrated via our own studies of student revision behavior and of automatic revision purpose prediction.",
}
@inproceedings{ustalov-etal-2017-watset,
    title = "{W}atset: Automatic Induction of Synsets from a Graph of Synonyms",
    author = "Ustalov, Dmitry  and
      Panchenko, Alexander  and
      Biemann, Chris",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1145",
    doi = "10.18653/v1/P17-1145",
    pages = "1579--1590",
    abstract = "This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.",
}
@inproceedings{ouchi-etal-2017-neural,
    title = "Neural Modeling of Multi-Predicate Interactions for {J}apanese Predicate Argument Structure Analysis",
    author = "Ouchi, Hiroki  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1146",
    doi = "10.18653/v1/P17-1146",
    pages = "1591--1600",
    abstract = "The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates. However, this approach relies heavily on syntactic information predicted by parsers, and suffers from errorpropagation. To remedy this problem, we introduce a model that uses grid-type recurrent neural networks. The proposed model automatically induces features sensitive to multi-predicate interactions from the word sequence information of a sentence. Experiments on the NAIST Text Corpus demonstrate that without syntactic information, our model outperforms previous syntax-dependent models.",
}
@inproceedings{joshi-etal-2017-triviaqa,
    title = "{T}rivia{QA}: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension",
    author = "Joshi, Mandar  and
      Choi, Eunsol  and
      Weld, Daniel  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1147",
    doi = "10.18653/v1/P17-1147",
    pages = "1601--1611",
    abstract = "We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23{\%} and 40{\%} vs. 80{\%}), suggesting that TriviaQA is a challenging testbed that is worth significant future study.",
}
@inproceedings{richardson-kuhn-2017-learning,
    title = "Learning Semantic Correspondences in Technical Documentation",
    author = "Richardson, Kyle  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1148",
    doi = "10.18653/v1/P17-1148",
    pages = "1612--1622",
    abstract = "We consider the problem of translating high-level textual descriptions to formal representations in technical documentation as part of an effort to model the meaning of such documentation. We focus specifically on the problem of learning translational correspondences between text descriptions and grounded representations in the target documentation, such as formal representation of functions or code templates. Our approach exploits the parallel nature of such documentation, or the tight coupling between high-level text and the low-level representations we aim to learn. Data is collected by mining technical documents for such parallel text-representation pairs, which we use to train a simple semantic parsing model. We report new baseline results on sixteen novel datasets, including the standard library documentation for nine popular programming languages across seven natural languages, and a small collection of Unix utility manuals.",
}
@inproceedings{cao-etal-2017-bridge,
    title = "Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding",
    author = "Cao, Yixin  and
      Huang, Lifu  and
      Ji, Heng  and
      Chen, Xu  and
      Li, Juanzi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1149",
    doi = "10.18653/v1/P17-1149",
    pages = "1623--1633",
    abstract = "Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative and quantitative analysis demonstrate the high quality of the word, entity and multi-prototype mention embeddings. Using entity linking as a study case, we apply our disambiguation method as well as the multi-prototype mention embeddings on the benchmark dataset, and achieve state-of-the-art performance.",
}
@inproceedings{she-chai-2017-interactive,
    title = "Interactive Learning of Grounded Verb Semantics towards Human-Robot Communication",
    author = "She, Lanbo  and
      Chai, Joyce",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1150",
    doi = "10.18653/v1/P17-1150",
    pages = "1634--1644",
    abstract = "To enable human-robot communication and collaboration, previous works represent grounded verb semantics as the potential change of state to the physical world caused by these verbs. Grounded verb semantics are acquired mainly based on the parallel data of the use of a verb phrase and its corresponding sequences of primitive actions demonstrated by humans. The rich interaction between teachers and students that is considered important in learning new skills has not yet been explored. To address this limitation, this paper presents a new interactive learning approach that allows robots to proactively engage in interaction with human partners by asking good questions to learn models for grounded verb semantics. The proposed approach uses reinforcement learning to allow the robot to acquire an optimal policy for its question-asking behaviors by maximizing the long-term reward. Our empirical results have shown that the interactive learning approach leads to more reliable models for grounded verb semantics, especially in the noisy environment which is full of uncertainties. Compared to previous work, the models acquired from interactive learning result in a 48{\%} to 145{\%} performance gain when applied in new situations.",
}
@inproceedings{athiwaratkun-wilson-2017-multimodal,
    title = "Multimodal Word Distributions",
    author = "Athiwaratkun, Ben  and
      Wilson, Andrew",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1151",
    doi = "10.18653/v1/P17-1151",
    pages = "1645--1656",
    abstract = "Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.",
}
@inproceedings{chen-etal-2017-enhanced,
    title = "Enhanced {LSTM} for Natural Language Inference",
    author = "Chen, Qian  and
      Zhu, Xiaodan  and
      Ling, Zhen-Hua  and
      Wei, Si  and
      Jiang, Hui  and
      Inkpen, Diana",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1152",
    doi = "10.18653/v1/P17-1152",
    pages = "1657--1668",
    abstract = "Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6{\%} on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result{---}it further improves the performance even when added to the already very strong model.",
}
@inproceedings{ramakrishna-etal-2017-linguistic,
    title = "Linguistic analysis of differences in portrayal of movie characters",
    author = "Ramakrishna, Anil  and
      Mart{\'\i}nez, Victor R.  and
      Malandrakis, Nikolaos  and
      Singla, Karan  and
      Narayanan, Shrikanth",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1153",
    doi = "10.18653/v1/P17-1153",
    pages = "1669--1678",
    abstract = "We examine differences in portrayal of characters in movies using psycholinguistic and graph theoretic measures computed directly from screenplays. Differences are examined with respect to characters{'} gender, race, age and other metadata. Psycholinguistic metrics are extrapolated to dialogues in movies using a linear regression model built on a set of manually annotated seed words. Interesting patterns are revealed about relationships between genders of production team and the gender ratio of characters. Several correlations are noted between gender, race, age of characters and the linguistic metrics.",
}
@inproceedings{qian-etal-2017-linguistically,
    title = "Linguistically Regularized {LSTM} for Sentiment Classification",
    author = "Qian, Qiao  and
      Huang, Minlie  and
      Lei, Jinhao  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1154",
    doi = "10.18653/v1/P17-1154",
    pages = "1679--1689",
    abstract = "This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which has remarkably degraded performance when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words). In this paper, we propose simple models trained with sentence-level annotation, but also attempt to model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are able to capture the linguistic role of sentiment words, negation words, and intensity words in sentiment expression.",
}
@inproceedings{peled-reichart-2017-sarcasm,
    title = "Sarcasm {SIGN}: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation",
    author = "Peled, Lotem  and
      Reichart, Roi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1155",
    doi = "10.18653/v1/P17-1155",
    pages = "1690--1700",
    abstract = "Sarcasm is a form of speech in which speakers say the opposite of what they truly mean in order to convey a strong sentiment. In other words, {``}Sarcasm is the giant chasm between what I say, and the person who doesn{'}t get it.{''}. In this paper we present the novel task of sarcasm interpretation, defined as the generation of a non-sarcastic utterance conveying the same message as the original sarcastic one. We introduce a novel dataset of 3000 sarcastic tweets, each interpreted by five human judges. Addressing the task as monolingual machine translation (MT), we experiment with MT algorithms and evaluation measures. We then present SIGN: an MT based sarcasm interpretation algorithm that targets sentiment words, a defining element of textual sarcasm. We show that while the scores of n-gram based automatic measures are similar for all interpretation models, SIGN{'}s interpretations are scored higher by humans for adequacy and sentiment polarity. We conclude with a discussion on future research directions for our new task.",
}
@inproceedings{wu-etal-2017-active,
    title = "Active Sentiment Domain Adaptation",
    author = "Wu, Fangzhao  and
      Huang, Yongfeng  and
      Yan, Jun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1156",
    doi = "10.18653/v1/P17-1156",
    pages = "1701--1711",
    abstract = "Domain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may heavily decline if the distributions of sentiment features in source and target domains have significant difference. In this paper, we propose an active sentiment domain adaptation approach to handle this problem. Instead of the source domain sentiment classifiers, our approach adapts the general-purpose sentiment lexicons to target domain with the help of a small number of labeled samples which are selected and annotated in an active learning mode, as well as the domain-specific sentiment similarities among words mined from unlabeled samples of target domain. A unified model is proposed to fuse different types of sentiment information and train sentiment classifier for target domain. Extensive experiments on benchmark datasets show that our approach can train accurate sentiment classifier with less labeled samples.",
}
@inproceedings{rekabsaz-etal-2017-volatility,
    title = "Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based {IR} Models",
    author = {Rekabsaz, Navid  and
      Lupu, Mihai  and
      Baklanov, Artem  and
      D{\"u}r, Alexander  and
      Andersson, Linda  and
      Hanbury, Allan},
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1157",
    doi = "10.18653/v1/P17-1157",
    pages = "1712--1721",
    abstract = "Volatility prediction{---}an essential concept in financial markets{---}has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the mainstream approach to forecast market risk. We therefore study different fusion methods to combine text and market data resources. Our word embedding-based approach significantly outperforms state-of-the-art methods. In addition, we investigate the characteristics of the reports of the companies in different financial sectors.",
}
@inproceedings{tu-etal-2017-cane,
    title = "{CANE}: Context-Aware Network Embedding for Relation Modeling",
    author = "Tu, Cunchao  and
      Liu, Han  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1158",
    doi = "10.18653/v1/P17-1158",
    pages = "1722--1731",
    abstract = "Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors. However, existing NE models aim to learn a fixed context-free embedding for each vertex and neglect the diverse roles when interacting with other vertices. In this paper, we assume that one vertex usually shows different aspects when interacting with different neighbor vertices, and should own different embeddings respectively. Therefore, we present Context-Aware Network Embedding (CANE), a novel NE model to address this issue. CANE learns context-aware embeddings for vertices with mutual attention mechanism and is expected to model the semantic relationships between vertices more precisely. In experiments, we compare our model with existing NE models on three real-world datasets. Experimental results show that CANE achieves significant improvement than state-of-the-art methods on link prediction and comparable performance on vertex classification. The source code and datasets can be obtained from \url{https://github.com/thunlp/CANE}.",
}
@inproceedings{wang-etal-2017-universal,
    title = "{U}niversal {D}ependencies Parsing for Colloquial Singaporean {E}nglish",
    author = "Wang, Hongmin  and
      Zhang, Yue  and
      Chan, GuangYong Leonard  and
      Yang, Jie  and
      Chieu, Hai Leong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1159",
    doi = "10.18653/v1/P17-1159",
    pages = "1732--1744",
    abstract = "Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25{\%} relative error reduction, resulting in a parser of 84.47{\%} accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.",
}
@inproceedings{yli-jyra-gomez-rodriguez-2017-generic,
    title = "Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing",
    author = {Yli-Jyr{\"a}, Anssi  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos},
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1160",
    doi = "10.18653/v1/P17-1160",
    pages = "1745--1755",
    abstract = "We present a simple encoding for unlabeled noncrossing graphs and show how its latent counterpart helps us to represent several families of directed and undirected graphs used in syntactic and semantic parsing of natural language as context-free languages. The families are separated purely on the basis of forbidden patterns in latent encoding, eliminating the need to differentiate the families of non-crossing graphs in inference algorithms: one algorithm works for all when the search space can be controlled in parser input.",
}
@inproceedings{peters-etal-2017-semi,
    title = "Semi-supervised sequence tagging with bidirectional language models",
    author = "Peters, Matthew  and
      Ammar, Waleed  and
      Bhagavatula, Chandra  and
      Power, Russell",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1161",
    doi = "10.18653/v1/P17-1161",
    pages = "1756--1765",
    abstract = "Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pretrained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.",
}
@inproceedings{he-etal-2017-learning,
    title = "Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings",
    author = "He, He  and
      Balakrishnan, Anusha  and
      Eric, Mihail  and
      Liang, Percy",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1162",
    doi = "10.18653/v1/P17-1162",
    pages = "1766--1776",
    abstract = "We study a \textit{symmetric collaborative dialogue} setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.",
}
@inproceedings{mrksic-etal-2017-neural,
    title = "Neural Belief Tracker: Data-Driven Dialogue State Tracking",
    author = "Mrk{\v{s}}i{\'c}, Nikola  and
      {\'O} S{\'e}aghdha, Diarmuid  and
      Wen, Tsung-Hsien  and
      Thomson, Blaise  and
      Young, Steve",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1163",
    doi = "10.18653/v1/P17-1163",
    pages = "1777--1788",
    abstract = "One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user{'}s goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users{'} language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.",
}
@inproceedings{liu-etal-2017-exploiting,
    title = "Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms",
    author = "Liu, Shulin  and
      Chen, Yubo  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1164",
    doi = "10.18653/v1/P17-1164",
    pages = "1789--1798",
    abstract = "This paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely ignored or exploited in an indirect manner in existing detection approaches. In this work, we propose to exploit argument information explicitly for ED via supervised attention mechanisms. In specific, we systematically investigate the proposed model under the supervision of different attention strategies. Experimental results show that our approach advances state-of-the-arts and achieves the best F1 score on ACE 2005 dataset.",
}
@inproceedings{amoualian-etal-2017-topical,
    title = "Topical Coherence in {LDA}-based Models through Induced Segmentation",
    author = "Amoualian, Hesam  and
      Lu, Wei  and
      Gaussier, Eric  and
      Balikas, Georgios  and
      Amini, Massih R.  and
      Clausel, Marianne",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1165",
    doi = "10.18653/v1/P17-1165",
    pages = "1799--1809",
    abstract = "This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.",
}
@inproceedings{ye-etal-2017-jointly,
    title = "Jointly Extracting Relations with Class Ties via Effective Deep Ranking",
    author = "Ye, Hai  and
      Chao, Wenhan  and
      Luo, Zhunchen  and
      Li, Zhoujun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1166",
    doi = "10.18653/v1/P17-1166",
    pages = "1810--1820",
    abstract = "Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties. Our model outperforms the baselines significantly, achieving state-of-the-art performance.",
}
@inproceedings{iyyer-etal-2017-search,
    title = "Search-based Neural Structured Learning for Sequential Question Answering",
    author = "Iyyer, Mohit  and
      Yih, Wen-tau  and
      Chang, Ming-Wei",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1167",
    doi = "10.18653/v1/P17-1167",
    pages = "1821--1831",
    abstract = "Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.",
}
@inproceedings{dhingra-etal-2017-gated,
    title = "Gated-Attention Readers for Text Comprehension",
    author = "Dhingra, Bhuwan  and
      Liu, Hanxiao  and
      Yang, Zhilin  and
      Cohen, William  and
      Salakhutdinov, Ruslan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1168",
    doi = "10.18653/v1/P17-1168",
    pages = "1832--1846",
    abstract = "In this paper we study the problem of answering cloze-style questions over documents. Our model, the Gated-Attention (GA) Reader, integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader. This enables the reader to build query-specific representations of tokens in the document for accurate answer selection. The GA Reader obtains state-of-the-art results on three benchmarks for this task{--}the CNN {\&} Daily Mail news stories and the Who Did What dataset. The effectiveness of multiplicative interaction is demonstrated by an ablation study, and by comparing to alternative compositional operators for implementing the gated-attention.",
}
@inproceedings{ye-etal-2017-determining,
    title = "Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering",
    author = "Ye, Jianbo  and
      Li, Yanran  and
      Wu, Zhaohui  and
      Wang, James Z.  and
      Li, Wenjie  and
      Li, Jia",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1169",
    doi = "10.18653/v1/P17-1169",
    pages = "1847--1856",
    abstract = "Word embeddings have become widely-used in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clustering empirical distributions. By using the Wasserstein distance between distributions, the word-to-word semantic relationship is taken into account in a principled way. The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets. More importantly, the method provides an effective framework for determining when and how much word embeddings contribute to document analysis. Experimental results with multiple embedding models are reported.",
}
@inproceedings{pilehvar-etal-2017-towards,
    title = "Towards a Seamless Integration of Word Senses into Downstream {NLP} Applications",
    author = "Pilehvar, Mohammad Taher  and
      Camacho-Collados, Jose  and
      Navigli, Roberto  and
      Collier, Nigel",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1170",
    doi = "10.18653/v1/P17-1170",
    pages = "1857--1869",
    abstract = "Lexical ambiguity can impede NLP systems from accurate understanding of semantics. Despite its potential benefits, the integration of sense-level information into NLP systems has remained understudied. By incorporating a novel disambiguation algorithm into a state-of-the-art classification model, we create a pipeline to integrate sense-level information into downstream NLP applications. We show that a simple disambiguation of the input text can lead to consistent performance improvement on multiple topic categorization and polarity detection datasets, particularly when the fine granularity of the underlying sense inventory is reduced and the document is sufficiently large. Our results also point to the need for sense representation research to focus more on in vivo evaluations which target the performance in downstream NLP applications rather than artificial benchmarks.",
}
@inproceedings{chen-etal-2017-reading,
    title = "Reading {W}ikipedia to Answer Open-Domain Questions",
    author = "Chen, Danqi  and
      Fisch, Adam  and
      Weston, Jason  and
      Bordes, Antoine",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1171",
    doi = "10.18653/v1/P17-1171",
    pages = "1870--1879",
    abstract = "This paper proposes to tackle open-domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task.",
}
@inproceedings{yu-etal-2017-learning-skim,
    title = "Learning to Skim Text",
    author = "Yu, Adams Wei  and
      Lee, Hongrae  and
      Le, Quoc",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1172",
    doi = "10.18653/v1/P17-1172",
    pages = "1880--1890",
    abstract = "Recurrent Neural Networks are showing much promise in many sub-areas of natural language processing, ranging from document classification to machine translation to automatic question answering. Despite their promise, many recurrent models have to read the whole text word by word, making it slow to handle long documents. For example, it is difficult to use a recurrent network to read a book and answer questions about it. In this paper, we present an approach of reading text while skipping irrelevant information if needed. The underlying model is a recurrent network that learns how far to jump after reading a few words of the input text. We employ a standard policy gradient method to train the model to make discrete jumping decisions. In our benchmarks on four different tasks, including number prediction, sentiment analysis, news article classification and automatic Q{\&}A, our proposed model, a modified LSTM with jumping, is up to 6 times faster than the standard sequential LSTM, while maintaining the same or even better accuracy.",
}
@inproceedings{srikumar-2017-algebra,
    title = "An Algebra for Feature Extraction",
    author = "Srikumar, Vivek",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1173",
    doi = "10.18653/v1/P17-1173",
    pages = "1891--1900",
    abstract = "Though feature extraction is a necessary first step in statistical NLP, it is often seen as a mere preprocessing step. Yet, it can dominate computation time, both during training, and especially at deployment. In this paper, we formalize feature extraction from an algebraic perspective. Our formalization allows us to define a message passing algorithm that can restructure feature templates to be more computationally efficient. We show via experiments on text chunking and relation extraction that this restructuring does indeed speed up feature extraction in practice by reducing redundant computation.",
}
@inproceedings{ishiwatari-etal-2017-chunk,
    title = "Chunk-based Decoder for Neural Machine Translation",
    author = "Ishiwatari, Shonosuke  and
      Yao, Jingtao  and
      Liu, Shujie  and
      Li, Mu  and
      Zhou, Ming  and
      Yoshinaga, Naoki  and
      Kitsuregawa, Masaru  and
      Jia, Weijia",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1174",
    doi = "10.18653/v1/P17-1174",
    pages = "1901--1912",
    abstract = "Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intra-chunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-based decoders for (NMT), each of which consists of a chunk-level decoder and a word-level decoder. The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk. To output a target sentence, the chunk-level decoder generates a chunk representation containing global information, which the word-level decoder then uses as a basis to predict the words inside the chunk. Experimental results show that our proposed decoders can significantly improve translation performance in a WAT {`}16 English-to-Japanese translation task.",
}
@inproceedings{calixto-etal-2017-doubly,
    title = "Doubly-Attentive Decoder for Multi-modal Neural Machine Translation",
    author = "Calixto, Iacer  and
      Liu, Qun  and
      Campbell, Nick",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1175",
    doi = "10.18653/v1/P17-1175",
    pages = "1913--1924",
    abstract = "We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.",
}
@inproceedings{chen-etal-2017-teacher,
    title = "A Teacher-Student Framework for Zero-Resource Neural Machine Translation",
    author = "Chen, Yun  and
      Liu, Yang  and
      Cheng, Yong  and
      Li, Victor O.K.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1176",
    doi = "10.18653/v1/P17-1176",
    pages = "1925--1935",
    abstract = "While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language. Based on the assumption, our method is able to train a source-to-target NMT model ({``}student{''}) without parallel corpora available guided by an existing pivot-to-target NMT model ({``}teacher{''}) on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs.",
}
@inproceedings{chen-etal-2017-improved,
    title = "Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder",
    author = "Chen, Huadong  and
      Huang, Shujian  and
      Chiang, David  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1177",
    doi = "10.18653/v1/P17-1177",
    pages = "1936--1945",
    abstract = "Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.",
}
@inproceedings{pan-etal-2017-cross,
    title = "Cross-lingual Name Tagging and Linking for 282 Languages",
    author = "Pan, Xiaoman  and
      Zhang, Boliang  and
      May, Jonathan  and
      Nothman, Joel  and
      Knight, Kevin  and
      Ji, Heng",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1178",
    doi = "10.18653/v1/P17-1178",
    pages = "1946--1958",
    abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
@inproceedings{zhang-etal-2017-adversarial,
    title = "Adversarial Training for Unsupervised Bilingual Lexicon Induction",
    author = "Zhang, Meng  and
      Liu, Yang  and
      Luan, Huanbo  and
      Sun, Maosong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1179",
    doi = "10.18653/v1/P17-1179",
    pages = "1959--1970",
    abstract = "Word embeddings are well known to capture linguistic regularities of the language on which they are trained. Researchers also observe that these regularities can transfer across languages. However, previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon. In this work, we show that such cross-lingual connection can actually be established without any form of supervision. We achieve this end by formulating the problem as a natural adversarial game, and investigating techniques that are crucial to successful training. We carry out evaluation on the unsupervised bilingual lexicon induction task. Even though this task appears intrinsically cross-lingual, we are able to demonstrate encouraging performance without any cross-lingual clues.",
}
@inproceedings{rijhwani-etal-2017-estimating,
    title = "Estimating Code-Switching on {T}witter with a Novel Generalized Word-Level Language Detection Technique",
    author = "Rijhwani, Shruti  and
      Sequiera, Royal  and
      Choudhury, Monojit  and
      Bali, Kalika  and
      Maddila, Chandra Shekhar",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1180",
    doi = "10.18653/v1/P17-1180",
    pages = "1971--1982",
    abstract = "Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence. Existing models are restricted to code-switching between two specific languages and fail in real-world scenarios as text input rarely has a priori information on the languages used. We present a novel unsupervised word-level language detection technique for code-switched text for an arbitrarily large number of languages, which does not require any manually annotated training data. Our experiments with tweets in seven languages show a 74{\%} relative error reduction in word-level labeling with respect to competitive baselines. We then use this system to conduct a large-scale quantitative analysis of code-switching patterns on Twitter, both global as well as region-specific, with 58M tweets.",
}
@inproceedings{bloodgood-strauss-2017-using,
    title = "Using Global Constraints and Reranking to Improve Cognates Detection",
    author = "Bloodgood, Michael  and
      Strauss, Benjamin",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1181",
    doi = "10.18653/v1/P17-1181",
    pages = "1983--1992",
    abstract = "Global constraints and reranking have not been used in cognates detection research to date. We propose methods for using global constraints by performing rescoring of the score matrices produced by state of the art cognates detection systems. Using global constraints to perform rescoring is complementary to state of the art methods for performing cognates detection and results in significant performance improvements beyond current state of the art performance on publicly available datasets with different language pairs and various conditions such as different levels of baseline state of the art performance and different data size conditions, including with more realistic large data size conditions than have been evaluated with in the past.",
}
@inproceedings{kann-etal-2017-one,
    title = "One-Shot Neural Cross-Lingual Transfer for Paradigm Completion",
    author = {Kann, Katharina  and
      Cotterell, Ryan  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1182",
    doi = "10.18653/v1/P17-1182",
    pages = "1993--2003",
    abstract = "We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task. We use labeled data from a high-resource language to increase performance on a low-resource language. In experiments on 21 language pairs from four different language families, we obtain up to 58{\%} higher accuracy than without transfer and show that even zero-shot and one-shot learning are possible. We further find that the degree of language relatedness strongly influences the ability to transfer morphological knowledge.",
}
@inproceedings{aharoni-goldberg-2017-morphological,
    title = "Morphological Inflection Generation with Hard Monotonic Attention",
    author = "Aharoni, Roee  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1183",
    doi = "10.18653/v1/P17-1183",
    pages = "2004--2015",
    abstract = "We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection. We evaluate the model on three previously studied morphological inflection generation datasets and show that it provides state of the art results in various setups compared to previous neural and non-neural approaches. Finally we present an analysis of the continuous representations learned by both the hard and soft (Bahdanau, 2014) attention models for the task, shedding some light on the features such models extract.",
}
@inproceedings{vania-lopez-2017-characters,
    title = "From Characters to Words to in Between: Do We Capture Morphology?",
    author = "Vania, Clara  and
      Lopez, Adam",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1184",
    doi = "10.18653/v1/P17-1184",
    pages = "2016--2027",
    abstract = "Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results extend previous findings that character representations are effective across typologies, and we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most others. But we also find room for improvement: none of the character-level models match the predictive accuracy of a model with access to true morphological analyses, even when learned from an order of magnitude more data.",
}
@inproceedings{fonarev-etal-2017-riemannian,
    title = "{R}iemannian Optimization for Skip-Gram Negative Sampling",
    author = "Fonarev, Alexander  and
      Grinchuk, Oleksii  and
      Gusev, Gleb  and
      Serdyukov, Pavel  and
      Oseledets, Ivan",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1185",
    doi = "10.18653/v1/P17-1185",
    pages = "2028--2036",
    abstract = "Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in {``}word2vec{''} software, is usually optimized by stochastic gradient descent. However, the optimization of SGNS objective can be viewed as a problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we propose an algorithm that optimizes SGNS objective using Riemannian optimization and demonstrates its superiority over popular competitors, such as the original method to train SGNS and SVD over SPPMI matrix.",
}
@inproceedings{peng-etal-2017-deep,
    title = "Deep Multitask Learning for Semantic Dependency Parsing",
    author = "Peng, Hao  and
      Thomson, Sam  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1186",
    doi = "10.18653/v1/P17-1186",
    pages = "2037--2048",
    abstract = "We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two multitask learning approaches{---}one that shares parameters across formalisms, and one that uses higher-order structures to predict the graphs jointly. We find that both approaches improve performance across formalisms on average, achieving a new state of the art. Our code is open-source and available at \url{https://github.com/Noahs-ARK/NeurboParser}.",
}
@inproceedings{niu-etal-2017-improved,
    title = "Improved Word Representation Learning with Sememes",
    author = "Niu, Yilin  and
      Xie, Ruobing  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1187",
    doi = "10.18653/v1/P17-1187",
    pages = "2049--2058",
    abstract = "Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people manually annotate word sememes and form linguistic common-sense knowledge bases. In this paper, we present that, word sememe information can improve word representation learning (WRL), which maps words into a low-dimensional semantic space and serves as a fundamental step for many NLP tasks. The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately. More specifically, we follow the framework of Skip-gram and present three sememe-encoded models to learn representations of sememes, senses and words, where we apply the attention scheme to detect word senses in various contexts. We conduct experiments on two tasks including word similarity and word analogy, and our models significantly outperform baselines. The results indicate that WRL can benefit from sememes via the attention scheme, and also confirm our models being capable of correctly modeling sememe information.",
}
@inproceedings{liu-etal-2017-learning,
    title = "Learning Character-level Compositionality with Visual Features",
    author = "Liu, Frederick  and
      Lu, Han  and
      Lo, Chieh  and
      Neubig, Graham",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1188",
    doi = "10.18653/v1/P17-1188",
    pages = "2059--2068",
    abstract = "Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry topical content which resulting in embeddings that are coherent in visual space.",
}
@inproceedings{xia-etal-2017-progressive,
    title = "A Progressive Learning Approach to {C}hinese {SRL} Using Heterogeneous Data",
    author = "Xia, Qiaolin  and
      Sha, Lei  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1189",
    doi = "10.18653/v1/P17-1189",
    pages = "2069--2077",
    abstract = "Previous studies on Chinese semantic role labeling (SRL) have concentrated on a single semantically annotated corpus. But the training data of single corpus is often limited. Whereas the other existing semantically annotated corpora for Chinese SRL are scattered across different annotation frameworks. But still, Data sparsity remains a bottleneck. This situation calls for larger training datasets, or effective approaches which can take advantage of highly heterogeneous data. In this paper, we focus mainly on the latter, that is, to improve Chinese SRL by using heterogeneous corpora together. We propose a novel progressive learning model which augments the Progressive Neural Network with Gated Recurrent Adapters. The model can accommodate heterogeneous inputs and effectively transfer knowledge between them. We also release a new corpus, Chinese SemBank, for Chinese SRL. Experiments on CPB 1.0 show that our model outperforms state-of-the-art methods.",
}
@inproceedings{wieting-gimpel-2017-revisiting,
    title = "Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings",
    author = "Wieting, John  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1190",
    doi = "10.18653/v1/P17-1190",
    pages = "2078--2088",
    abstract = "We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b). While they found LSTM recurrent networks to underperform word averaging, we present several developments that together produce the opposite conclusion. These include training on sentence pairs rather than phrase pairs, averaging states to represent sequences, and regularizing aggressively. These improve LSTMs in both transfer learning and supervised settings. We also introduce a new recurrent architecture, the Gated Recurrent Averaging Network, that is inspired by averaging and LSTMs while outperforming them both. We analyze our learned models, finding evidence of preferences for particular parts of speech and dependency relations.",
}
@inproceedings{dasigi-etal-2017-ontology,
    title = "Ontology-Aware Token Embeddings for Prepositional Phrase Attachment",
    author = "Dasigi, Pradeep  and
      Ammar, Waleed  and
      Dyer, Chris  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1191",
    doi = "10.18653/v1/P17-1191",
    pages = "2089--2098",
    abstract = "Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4{\%} absolute points, which amounts to a 34.4{\%} relative reduction in errors.",
}
@inproceedings{pavlick-pasca-2017-identifying,
    title = "Identifying 1950s {A}merican Jazz Musicians: Fine-Grained {I}s{A} Extraction via Modifier Composition",
    author = "Pavlick, Ellie  and
      Pa{\c{s}}ca, Marius",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1192",
    doi = "10.18653/v1/P17-1192",
    pages = "2099--2109",
    abstract = "We present a method for populating fine-grained classes (e.g., {``}1950s American jazz musicians{''}) with instances (e.g., Charles Mingus ). While state-of-the-art methods tend to treat class labels as single lexical units, the proposed method considers each of the individual modifiers in the class label relative to the head. An evaluation on the task of reconstructing Wikipedia category pages demonstrates a {\textgreater}10 point increase in AUC, over a strong baseline relying on widely-used Hearst patterns.",
}
@inproceedings{cao-etal-2017-parsing,
    title = "Parsing to 1-Endpoint-Crossing, Pagenumber-2 Graphs",
    author = "Cao, Junjie  and
      Huang, Sheng  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1193",
    doi = "10.18653/v1/P17-1193",
    pages = "2110--2120",
    abstract = "We study the Maximum Subgraph problem in deep dependency parsing. We consider two restrictions to deep dependency graphs: (a) 1-endpoint-crossing and (b) pagenumber-2. Our main contribution is an exact algorithm that obtains maximum subgraphs satisfying both restrictions simultaneously in time O(n5). Moreover, ignoring one linguistically-rare structure descreases the complexity to O(n4). We also extend our quartic-time algorithm into a practical parser with a discriminative disambiguation model and evaluate its performance on four linguistic data sets used in semantic dependency parsing.",
}
@inproceedings{rei-2017-semi,
    title = "Semi-supervised Multitask Learning for Sequence Labeling",
    author = "Rei, Marek",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1194",
    doi = "10.18653/v1/P17-1194",
    pages = "2121--2130",
    abstract = "We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.",
}
@inproceedings{matsuzaki-etal-2017-semantic,
    title = "Semantic Parsing of Pre-university Math Problems",
    author = "Matsuzaki, Takuya  and
      Ito, Takumi  and
      Iwane, Hidenao  and
      Anai, Hirokazu  and
      Arai, Noriko H.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1195",
    doi = "10.18653/v1/P17-1195",
    pages = "2131--2141",
    abstract = "We have been developing an end-to-end math problem solving system that accepts natural language input. The current paper focuses on how we analyze the problem sentences to produce logical forms. We chose a hybrid approach combining a shallow syntactic analyzer and a manually-developed lexicalized grammar. A feature of the grammar is that it is extensively typed on the basis of a formal ontology for pre-university math. These types are helpful in semantic disambiguation inside and across sentences. Experimental results show that the hybrid system produces a well-formed logical form with 88{\%} precision and 56{\%} recall.",
}
@proceedings{rocling-ijclclp-2017-international-journal,
    title = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    editor = "Lee, Chi-Chun (Jeremy)  and
      Yang, Cheng-Zen",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3000",
}
@inproceedings{lee-yang-2017-forewords,
    title = "Forewords",
    author = "Lee, Chi-Chun Jeremy  and
      Yang, Cheng-Zen",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3001",
}
@inproceedings{lo-etal-2017-yu,
    title = "語音文件檢索使用類神經網路技術 (On the Use of Neural Network Modeling Techniques for Spoken Document Retrieval) [In {C}hinese]",
    author = "Lo, Tien-Hong  and
      Chen, Ying-Wen  and
      Chen, Kuan-Yu  and
      Wang, Hsin-Min  and
      Chen, Berlin",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3002",
}
@inproceedings{weng-etal-2017-question-retrieval,
    title = "Question Retrieval with Distributed Representations and Participant Reputation in Community Question Answering",
    author = "Weng, Sam  and
      Wu, Chun-Kai  and
      Wang, Yu-Chun  and
      Tsai, Richard Tzong-Han",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3003",
}
@inproceedings{tseng-etal-2017-tan-jiu,
    title = "探究使用基於類神經網路之特徵於文本可讀性分類 (Exploring the Use of Neural Network based Features for Text Readability Classification) [In {C}hinese]",
    author = "Tseng, Hou-Chiang  and
      Chen, Berlin  and
      Sung, Yao-Ting",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3004",
}
@inproceedings{li-you-etal-2017-gai-jin,
    title = "改進的向量空間可適性濾波器用於聲學回聲消除 (Acoustic Echo Cancellation Using an Improved Vector-Space-Based Adaptive Filtering Algorithm) [In {C}hinese]",
    author = "Li-You, Jin  and
      Tsao, Yu  and
      Chien, Ying-Ren",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3005",
}
@inproceedings{wu-etal-2017-ji,
    title = "基於鑑別式自編碼解碼器之錄音回放攻擊偵測系統 (A Replay Spoofing Detection System Based on Discriminative Autoencoders) [In {C}hinese]",
    author = "Wu, Chia-Lung  and
      Hsu, Hsiang-Ping  and
      Lu, Yu-Ding  and
      Tsao, Yu  and
      Lee, Hung-Shin  and
      Wang, Hsin-Min",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3006",
}
@inproceedings{chang-2017-yi-zhi,
    title = "以知識表徵方法建構台語聲調群剖析器 (A Knowledge Representation Method to Implement A {T}aiwanese Tone Group Parser) [In {C}hinese]",
    author = "Chang, Yu-Chu",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3007",
}
@inproceedings{chang-etal-2017-novel-trajectory,
    title = "A Novel Trajectory-based Spatial-Temporal Spectral Features for Speech Emotion Recognition",
    author = "Chang, Chun-Min  and
      Lin, Wei-Cheng  and
      Lee, Chi-Chun",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 2, {D}ecember 2017-Special Issue on Selected Papers from {ROCLING} {XXIX}",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-3008",
}
@proceedings{rocling-ijclclp-2017-international,
    title = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 1, June 2017",
    editor = "Tseng, Yuen-Hsien  and
      Chien, Jen-Tzung",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-2000",
}
@inproceedings{liu-etal-2017-dang,
    title = "當代非監督式方法之比較於節錄式語音摘要 (An Empirical Comparison of Contemporary Unsupervised Approaches for Extractive Speech Summarization) [In {C}hinese]",
    author = "Liu, Shih-Hung  and
      Chen, Kuan-Yu  and
      Shih, Kai-Wun  and
      Chen, Berlin  and
      Wang, Hsin-Min  and
      Hsu, Wen-Lian",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 1, June 2017",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-2001",
}
@inproceedings{chen-chung-2017-fan,
    title = "反義詞「多」和「少」在數量名結構中的不對稱現象－－以語料庫為本的分析 (The Asymmetric Occurences of \textit{Dou1} and \textit{Shao3} in the [Numeral + Measure Word/Classifier + Noun] Construction: A Corpus-based Analysis) [In {C}hinese]",
    author = "Chen, Wei-Yu  and
      Chung, Siaw-Fong",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 1, June 2017",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-2002",
}
@inproceedings{xu-etal-2017-approach,
    title = "An Approach to Extract Product Features from {C}hinese Consumer Reviews and Establish Product Feature Structure Tree",
    author = "Xu, Xinsheng  and
      Lin, Jing  and
      Xiao, Ying  and
      Yu, Jianzhe",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 22, Number 1, June 2017",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/O17-2003",
}
@proceedings{rocling-ijclclp-2017-linguistics,
    title = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    editor = "Ku, Lun-Wei  and
      Tsao, Yu",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1000",
}
@inproceedings{chang-2017-yi,
    title = "以知識表徵方法建構台語聲調群剖析器 (A Knowledge Representation Method to Implement A {T}aiwanese Tone Group Parser) [In {C}hinese]",
    author = "Chang, Yu-Chu",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1001",
    pages = "1--4",
}
@inproceedings{deng-etal-2017-yun,
    title = "運用類神經網路方法之語言端點偵測研究 (A Study on Voice Activation Detection by Using Neural Networks) [In {C}hinese]",
    author = "Deng, Yu-Chih  and
      Chiang, Chen-Yu  and
      Pan, Chen-Ming",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1002",
    pages = "5--20",
}
@inproceedings{chen-etal-2017-ji,
    title = "基於卷積類神經網路之廣播節目音訊事件偵測系統 (Automatic Audio Event Detection of Broadcast Radio Programs Based on Convolution Neural Networks) [In {C}hinese]",
    author = "Chen, Jhih-wei  and
      Hsu, Wu-Hua  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1003",
    pages = "21--36",
}
@inproceedings{hung-etal-2017-ji,
    title = "基於次頻道遞迴類神經網路之麥克風陣列電視回聲消除系統 (Subband Recurrent Neural Networks-based Microphone Array Television Echo Cancellation) [In {C}hinese]",
    author = "Hung, Wei-Jung  and
      Su, Shih-An  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1004",
    pages = "37--51",
}
@inproceedings{chang-etal-2017-novel,
    title = "A Novel Trajectory-based Spatial-Temporal Spectral Features for Speech Emotion Recognition",
    author = "Chang, Chun-Min  and
      Lin, Wei-Cheng  and
      Lee, Chi-Chun",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1005",
    pages = "52--52",
}
@inproceedings{hsu-etal-2017-ying,
    title = "應用興趣點辨識技術從 Web 中挖掘新商家資訊 (Mining {POI}s from Web via {POI} recognition and Relation Verification) [In {C}hinese]",
    author = "Hsu, Kuo-Hsin  and
      Chuang, Hsiu-Min  and
      Chou, Chien-Lung  and
      Chang, Chia-Hui",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1006",
    pages = "53--67",
}
@inproceedings{wu-hsieh-2017-exploring,
    title = "Exploring Lavender Tongue from Social Media Texts[In {C}hinese]",
    author = "Wu, Hsiao-Han  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1007",
    pages = "68--80",
}
@inproceedings{huang-etal-2017-shou,
    title = "手機平台 {APP} 之四縣客語輸入法的研發 (Research and Implementation of {S}ixian {H}akka {P}inyin Input Method for Mobile Cell {APP}) [In {C}hinese]",
    author = "Huang, Feng-Long  and
      Liu, Kuei-Sen  and
      Tseng, Sheng-Yi",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1008",
    pages = "81--100",
}
@inproceedings{lee-etal-2017-duo,
    title = "多樣訊雜比之訓練語料於降噪自動編碼器其語音強化功能之初步研究 (A Preliminary Study of Various {SNR}-level Training Data in the Denoising Auto-encoder ({DAE}) Technique for Speech Enhancement) [In {C}hinese]",
    author = "Lee, Shih-Kuang  and
      Wang, Syu-Siang  and
      Tsao, Yu  and
      Hung, Jeih-weih",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1009",
    pages = "101--113",
}
@inproceedings{lu-etal-2017-ji,
    title = "基於鑑別式自編碼解碼器之錄音回放攻擊偵測系統 (A Replay Spoofing Detection System Based on Discriminative Autoencoders) [In {C}hinese]",
    author = "Lu, Yu-Ding  and
      Lee, Hung-Shin  and
      Tsao, Yu  and
      Wang, Hsin-Min",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1010",
    pages = "114--115",
}
@inproceedings{tseng-etal-2017-tan,
    title = "探究不同領域文件之可讀性分析 (Exploring Readability Analysis on Multi-Domain Texts) [In {C}hinese]",
    author = "Tseng, Hou-Chiang  and
      Sung, Yao-Ting  and
      Chen, Berlin",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1011",
    pages = "116--118",
}
@inproceedings{chang-etal-2017-ji,
    title = "基於i-vector與{PLDA}並使用{GMM}-{HMM}強制對位之自動語者分段標記系統 (Speaker Diarization based on {I}-vector {PLDA} Scoring and using {GMM}-{HMM} Forced Alignment) [In {C}hinese]",
    author = "Chang, Cheng-Jo Ray  and
      Lee, Hung-Shin  and
      Wang, Hsin-Min  and
      Jang, Jyh-Shing Roger",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1012",
    pages = "119--135",
}
@inproceedings{chou-etal-2017-amplifying,
    title = "Amplifying a Sense of Emotion toward Drama-Long Short-Term Memory Recurrent Neural Network for dynamic emotion recognition",
    author = "Chou, Huang-Cheng  and
      Chang, Chun-Min  and
      Liu, Yu-Shuo  and
      Kao, Shiuan-Kai  and
      Lee, Chi-Chun",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1013",
    pages = "136--147",
}
@inproceedings{weng-etal-2017-question,
    title = "Question Retrieval with Distributed Representations and Participant Reputation in Community Question Answering",
    author = "Weng, Sam  and
      Wu, Kevin Chun-Kai  and
      Wang, Yu-Chun  and
      Tsai, Richard Tzong-Han",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1014",
    pages = "148--148",
}
@inproceedings{lo-etal-2017-shi,
    title = "使用查詢意向探索與類神經網路於語音文件檢索之研究 (Exploring Query Intent and Neural Network modeling Techniques for Spoken Document Retrieval) [In {C}hinese]",
    author = "Lo, Tien-Hong  and
      Chen, Ying-Wen  and
      Chen, Berlin  and
      Chen, Kuan-Yu  and
      Wang, Hsin-Min",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1015",
    pages = "149--151",
}
@inproceedings{tran-phan-2017-toward,
    title = "Toward Contextual Valence Shifters in {V}ietnamese Reviews",
    author = "Tran, Thien Khai  and
      Phan, Tuoi Thi",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1016",
    pages = "152--159",
}
@inproceedings{chou-chi-2017-ji,
    title = "基於雙工音高感知模型之神經網路旋律抽取演算法 (The duplex model of pitch perception inspired neural network for melody extraction) [In {C}hinese]",
    author = "Chou, Hsin  and
      Chi, Tai-Shih",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1017",
    pages = "160--177",
}
@inproceedings{li-you-etal-2017-gai,
    title = "改進的向量空間可適性濾波器用於聲學回聲消除 (Acoustic Echo Cancellation Using an Improved Vector-Space-Based Adaptive Filtering Algorithm) [In {C}hinese]",
    author = "Li-You, Jin  and
      Tsao, Yu  and
      Chien, Ying-Ren",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1018",
    pages = "178--182",
}
@inproceedings{chung-etal-2017-ptt,
    title = "{PTT} 網站餐廳美食類別擷取之研究 (A Study of Restaurant Information and Food Type Extraction from {PTT}) [In {C}hinese]",
    author = "Chung, Chih-Yu  and
      Chou, Chien-Lung  and
      Chang, Chia-Hui",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1019",
    pages = "183--196",
}
@inproceedings{wang-etal-2017-ji,
    title = "基於半監督式學習之廣播節目語音逐字稿自動轉寫系統 (Automatic Transcription of Broadcast Radio Speech Based on Quality Estimation-Guided Semi-Supervised Training) [In {C}hinese]",
    author = "Wang, Sing-Yue  and
      Hsu, Wu-Hua  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1020",
    pages = "197--212",
}
@inproceedings{liao-etal-2017-wan,
    title = "完全基於類神經網路之語音合成系統初步研究 (A Preliminary Study on Fully Neural Network-based Speech Synthesis System) [In {C}hinese]",
    author = "Liao, Shu-Han  and
      Chai, Ya-Bo  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1021",
    pages = "213--229",
}
@inproceedings{xiao-etal-2017-ying,
    title = "應用詞向量於語言樣式探勘之研究 (Mining Language Patterns Using Word Embeddings) [In {C}hinese]",
    author = "Xiao, Xiang  and
      Ye, Shao-Zhen  and
      Yu, Liang-Chih  and
      Lai, K. Robert",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1022",
    pages = "230--243",
}
@inproceedings{yoon-kim-2017-multi,
    title = "Multi-Channel Lexicon Integrated {CNN}-{B}i{LSTM} Models for Sentiment Analysis",
    author = "Yoon, Joosung  and
      Kim, Hyeoncheol",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1023",
    pages = "244--253",
}
@inproceedings{hsiao-etal-2017-using,
    title = "Using Teacher-Student Model For Emotional Speech Recognition[In {C}hinese]",
    author = "Hsiao, Po-Wei  and
      Hsieh, Po-Chen  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1024",
    pages = "254--263",
}
@inproceedings{adiban-etal-2017-sut,
    title = "{SUT} System Description for Anti-Spoofing 2017 Challenge",
    author = "Adiban, Mohammad  and
      Sameti, Hossein  and
      Maghsoodi, Noushin  and
      Shahsavari, Sajjad",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1025",
    pages = "264--275",
}
@inproceedings{zeinali-etal-2017-sut,
    title = "{SUT} Submission for {NIST} 2016 Speaker Recognition Evaluation: Description and Analysis",
    author = "Zeinali, Hossein  and
      Sameti, Hossein  and
      Maghsoodi, Noushin",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1026",
    pages = "276--286",
}
@inproceedings{wang-etal-2017-yi,
    title = "以語音能量特性發展即時語速偵測裝置-前導型研究 (Real-time monitoring device of phonation speed and volume based on speech energy: A pilot study) [In {C}hinese]",
    author = "Wang, Chi-Te  and
      Lin, Feng-Chuan  and
      Zheng, Wei-Zhung  and
      Fang, Shih-Hau  and
      Tsao, Yu  and
      Lai, Ying-Hui",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1027",
    pages = "287--294",
}
@inproceedings{chathuranga-etal-2017-opinion,
    title = "Opinion Target Extraction for Student Course Feedback",
    author = "Chathuranga, Janaka  and
      Ediriweera, Shanika  and
      Munasinghe, Pranidhith  and
      Hasantha, Ravindu  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1028",
    pages = "295--307",
}
@inproceedings{jihan-etal-2017-multi,
    title = "Multi-Domain Aspect Extraction Using Support Vector Machines",
    author = "Jihan, Nadheesh  and
      Senarath, Yasas  and
      Tennekoon, Dulanjaya  and
      Wickramarathne, Mithila  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1029",
    pages = "308--322",
}
@inproceedings{wang-etal-2017-yi-ruan,
    title = "以軟體為基礎建構語音增強系統使用者介面 (Development of a software-based User-Interface of Speech Enhancement System) [In {C}hinese]",
    author = "Wang, Tao-Wei  and
      Tsao, Yu  and
      Lai, Ying-Hui  and
      Hsu, Hsiang-Ping  and
      Wu, Chia-Lung",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1030",
    pages = "323--331",
}
@inproceedings{lo-etal-2017-ji,
    title = "基於聽覺感知模型之類神經網路及其在語者識別上之應用 (Two-stage Attentional Auditory Model Inspired Neural Network and Its Application to Speaker Identification) [In {C}hinese]",
    author = "Lo, Yu-Wen  and
      Liao, Yuan-Fu  and
      Chi, Tai-Shih",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1031",
    pages = "332--353",
}
@inproceedings{wu-etal-2017-xu,
    title = "序列標記與配對方法用於語音辨識錯誤偵測及修正 (On the Use of Sequence Labeling and Matching Methods for {ASR} Error Detection and Correction) [In {C}hinese]",
    author = "Wu, Chia-Hua  and
      Tsai, Chun-I  and
      Hung, Hsiao-Tsung  and
      Kao, Yu-Chen  and
      Chen, Berlin",
    booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2017)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O17-1032",
    pages = "354--369",
}
@proceedings{conll-2017-conll-2017,
    title = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    editor = "Haji{\v{c}}, Jan  and
      Zeman, Dan",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3000",
    doi = "10.18653/v1/K17-3",
}
@inproceedings{zeman-etal-2017-conll,
    title = "{C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to {U}niversal {D}ependencies",
    author = {Zeman, Daniel  and
      Popel, Martin  and
      Straka, Milan  and
      Haji{\v{c}}, Jan  and
      Nivre, Joakim  and
      Ginter, Filip  and
      Luotolahti, Juhani  and
      Pyysalo, Sampo  and
      Petrov, Slav  and
      Potthast, Martin  and
      Tyers, Francis  and
      Badmaeva, Elena  and
      Gokirmak, Memduh  and
      Nedoluzhko, Anna  and
      Cinkov{\'a}, Silvie  and
      Haji{\v{c}} jr., Jan  and
      Hlav{\'a}{\v{c}}ov{\'a}, Jaroslava  and
      Kettnerov{\'a}, V{\'a}clava  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Kanerva, Jenna  and
      Ojala, Stina  and
      Missil{\"a}, Anna  and
      Manning, Christopher D.  and
      Schuster, Sebastian  and
      Reddy, Siva  and
      Taji, Dima  and
      Habash, Nizar  and
      Leung, Herman  and
      de Marneffe, Marie-Catherine  and
      Sanguinetti, Manuela  and
      Simi, Maria  and
      Kanayama, Hiroshi  and
      de Paiva, Valeria  and
      Droganova, Kira  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Sulubacak, Umut  and
      Uszkoreit, Hans  and
      Macketanz, Vivien  and
      Burchardt, Aljoscha  and
      Harris, Kim  and
      Marheinecke, Katrin  and
      Rehm, Georg  and
      Kayadelen, Tolga  and
      Attia, Mohammed  and
      Elkahky, Ali  and
      Yu, Zhuoran  and
      Pitler, Emily  and
      Lertpradit, Saran  and
      Mandl, Michael  and
      Kirchner, Jesse  and
      Alcalde, Hector Fernandez  and
      Strnadov{\'a}, Jana  and
      Banerjee, Esha  and
      Manurung, Ruli  and
      Stella, Antonio  and
      Shimada, Atsuko  and
      Kwak, Sookyoung  and
      Mendon{\c{c}}a, Gustavo  and
      Lando, Tatiana  and
      Nitisaroj, Rattima  and
      Li, Josie},
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3001",
    doi = "10.18653/v1/K17-3001",
    pages = "1--19",
    abstract = "The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.",
}
@inproceedings{dozat-etal-2017-stanfords,
    title = "{S}tanford{'}s Graph-based Neural Dependency Parser at the {C}o{NLL} 2017 Shared Task",
    author = "Dozat, Timothy  and
      Qi, Peng  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3002",
    doi = "10.18653/v1/K17-3002",
    pages = "20--30",
    abstract = "This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09{\%}), XPOS tagging (82.27{\%}), unlabeled attachment score (81.30{\%}), labeled attachment score (76.30{\%}), and content word labeled attachment score (72.57{\%}).",
}
@inproceedings{shi-etal-2017-combining,
    title = "Combining Global Models for Parsing {U}niversal {D}ependencies",
    author = "Shi, Tianze  and
      Wu, Felix G.  and
      Chen, Xilun  and
      Cheng, Yao",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3003",
    doi = "10.18653/v1/K17-3003",
    pages = "31--39",
    abstract = "We describe our entry, C2L2, to the CoNLL 2017 shared task on parsing Universal Dependencies from raw text. Our system features an ensemble of three global parsing paradigms, one graph-based and two transition-based. Each model leverages character-level bi-directional LSTMs as lexical feature extractors to encode morphological information. Though relying on baseline tokenizers and focusing only on parsing, our system ranked second in the official end-to-end evaluation with a macro-average of 75.00 LAS F1 score over 81 test treebanks. In addition, we had the top average performance on the four surprise languages and on the small treebank subset.",
}
@inproceedings{bjorkelund-etal-2017-ims,
    title = "{IMS} at the {C}o{NLL} 2017 {UD} Shared Task: {CRF}s and Perceptrons Meet Neural Networks",
    author = {Bj{\"o}rkelund, Anders  and
      Falenska, Agnieszka  and
      Yu, Xiang  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3004",
    doi = "10.18653/v1/K17-3004",
    pages = "40--51",
    abstract = "This paper presents the IMS contribution to the CoNLL 2017 Shared Task. In the preprocessing step we employed a CRF POS/morphological tagger and a neural tagger predicting supertags. On some languages, we also applied word segmentation with the CRF tagger and sentence segmentation with a perceptron-based parser. For parsing we took an ensemble approach by blending multiple instances of three parsers with very different architectures. Our system achieved the third place overall and the second place for the surprise languages.",
}
@inproceedings{che-etal-2017-hit,
    title = "The {HIT}-{SCIR} System for End-to-End Parsing of {U}niversal {D}ependencies",
    author = "Che, Wanxiang  and
      Guo, Jiang  and
      Wang, Yuxuan  and
      Zheng, Bo  and
      Zhao, Huaipeng  and
      Liu, Yang  and
      Teng, Dechuan  and
      Liu, Ting",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3005",
    doi = "10.18653/v1/K17-3005",
    pages = "52--62",
    abstract = "This paper describes our system (HIT-SCIR) for the CoNLL 2017 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system includes three pipelined components: \textit{tokenization}, \textit{Part-of-Speech} (POS) \textit{tagging} and \textit{dependency parsing}. We use character-based bidirectional long short-term memory (LSTM) networks for both tokenization and POS tagging. Afterwards, we employ a list-based transition-based algorithm for general non-projective parsing and present an improved Stack-LSTM-based architecture for representing each transition state and making predictions. Furthermore, to parse low/zero-resource languages and cross-domain data, we use a model transfer approach to make effective use of existing resources. We demonstrate substantial gains against the UDPipe baseline, with an average improvement of 3.76{\%} in LAS of all languages. And finally, we rank the 4th place on the official test sets.",
}
@inproceedings{lim-poibeau-2017-system,
    title = "A System for Multilingual Dependency Parsing based on Bidirectional {LSTM} Feature Representations",
    author = "Lim, KyungTae  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3006",
    doi = "10.18653/v1/K17-3006",
    pages = "63--70",
    abstract = "In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with {``}Multilingual Parsing from Raw Text to Universal Dependencies{''}. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).",
}
@inproceedings{sato-etal-2017-adversarial,
    title = "Adversarial Training for Cross-Domain {U}niversal {D}ependency Parsing",
    author = "Sato, Motoki  and
      Manabe, Hitoshi  and
      Noji, Hiroshi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3007",
    doi = "10.18653/v1/K17-3007",
    pages = "71--79",
    abstract = "We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique. Our approach is an extension to the recently proposed adversarial training technique for domain adaptation, which we apply on top of a graph-based neural dependency parsing model on bidirectional LSTMs. In our experiments, we find our baseline graph-based parser already outperforms the official baseline model (UDPipe) by a large margin. Further, by applying our technique to the treebanks of the same language with different domains, we observe an additional gain in the performance, in particular for the domains with less training data.",
}
@inproceedings{kirnap-etal-2017-parsing,
    title = "Parsing with Context Embeddings",
    author = {K{\i}rnap, {\"O}mer  and
      {\"O}nder, Berkay Furkan  and
      Yuret, Deniz},
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3008",
    doi = "10.18653/v1/K17-3008",
    pages = "80--87",
    abstract = "We introduce context embeddings, dense vectors derived from a language model that represent the left/right context of a word instance, and demonstrate that context embeddings significantly improve the accuracy of our transition based parser. Our model consists of a bidirectional LSTM (BiLSTM) based language model that is pre-trained to predict words in plain text, and a multi-layer perceptron (MLP) decision model that uses features from the language model to predict the correct actions for an ArcHybrid transition based parser. We participated in the CoNLL 2017 UD Shared Task as the {``}Ko{\c{c}} University{''} team and our system was ranked 7th out of 33 systems that parsed 81 treebanks in 49 languages.",
}
@inproceedings{straka-strakova-2017-tokenizing,
    title = "Tokenizing, {POS} Tagging, Lemmatizing and Parsing {UD} 2.0 with {UDP}ipe",
    author = "Straka, Milan  and
      Strakov{\'a}, Jana",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3009",
    doi = "10.18653/v1/K17-3009",
    pages = "88--99",
    abstract = "Many natural language processing tasks, including the most advanced ones, routinely start by several basic processing steps {--} tokenization and segmentation, most likely also POS tagging and lemmatization, and commonly parsing as well. A multilingual pipeline performing these steps can be trained using the Universal Dependencies project, which contains annotations of the described tasks for 50 languages in the latest release UD 2.0. We present an update to UDPipe, a simple-to-use pipeline processing CoNLL-U version 2.0 files, which performs these tasks for multiple languages without requiring additional external data. We provide models for all 50 languages of UD 2.0, and furthermore, the pipeline can be trained easily using data in CoNLL-U format. UDPipe is a standalone application in C++, with bindings available for Python, Java, C{\#} and Perl. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, UDPipe was the eight best system, while achieving low running times and moderately sized models.",
}
@inproceedings{vania-etal-2017-uparse,
    title = "{UP}arse: the {E}dinburgh system for the {C}o{NLL} 2017 {UD} shared task",
    author = "Vania, Clara  and
      Zhang, Xingxing  and
      Lopez, Adam",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3010",
    doi = "10.18653/v1/K17-3010",
    pages = "100--110",
    abstract = "This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for low-resource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).",
}
@inproceedings{heinecke-asadullah-2017-multi,
    title = "Multi-Model and Crosslingual Dependency Analysis",
    author = "Heinecke, Johannes  and
      Asadullah, Munshi",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3011",
    doi = "10.18653/v1/K17-3011",
    pages = "111--118",
    abstract = "This paper describes the system of the Team Orange-Deski{\~n}, used for the CoNLL 2017 UD Shared Task in Multilingual Dependency Parsing. We based our approach on an existing open source tool (BistParser), which we modified in order to produce the required output. Additionally we added a kind of pseudo-projectivisation. This was needed since some of the task{'}s languages have a high percentage of non-projective dependency trees. In most cases we also employed word embeddings. For the 4 surprise languages, the data provided seemed too little to train on. Thus we decided to use the training data of typologically close languages instead. Our system achieved a macro-averaged LAS of 68.61{\%} (10th in the overall ranking) which improved to 69.38{\%} after bug fixes.",
}
@inproceedings{kanerva-etal-2017-turkunlp,
    title = "{T}urku{NLP}: Delexicalized Pre-training of Word Embeddings for Dependency Parsing",
    author = "Kanerva, Jenna  and
      Luotolahti, Juhani  and
      Ginter, Filip",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3012",
    doi = "10.18653/v1/K17-3012",
    pages = "119--125",
    abstract = "We present the TurkuNLP entry in the CoNLL 2017 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. The system is based on the UDPipe parser with our focus being in exploring various techniques to pre-train the word embeddings used by the parser in order to improve its performance especially on languages with small training sets. The system ranked 11th among the 33 participants overall, being 8th on the small treebanks, 10th on the large treebanks, 12th on the parallel test sets, and 26th on the surprise languages.",
}
@inproceedings{yu-etal-2017-parse,
    title = "The parse is darc and full of errors: Universal dependency parsing with transition-based and graph-based algorithms",
    author = "Yu, Kuan  and
      Sofroniev, Pavel  and
      Schill, Erik  and
      Hinrichs, Erhard",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3013",
    doi = "10.18653/v1/K17-3013",
    pages = "126--133",
    abstract = "We developed two simple systems for dependency parsing: darc, a transition-based parser, and mstnn, a graph-based parser. We tested our systems in the CoNLL 2017 UD Shared Task, with darc being the official system. Darc ranked 12th among 33 systems, just above the baseline. Mstnn had no official ranking, but its main score was above the 27th. In this paper, we describe our two systems, examine their strengths and weaknesses, and discuss the lessons we learned.",
}
@inproceedings{nguyen-etal-2017-novel,
    title = "A Novel Neural Network Model for Joint {POS} Tagging and Graph-based Dependency Parsing",
    author = "Nguyen, Dat Quoc  and
      Dras, Mark  and
      Johnson, Mark",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3014",
    doi = "10.18653/v1/K17-3014",
    pages = "134--142",
    abstract = "We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: \url{https://github.com/datquocnguyen/jPTDP}",
}
@inproceedings{qian-liu-2017-non,
    title = "A non-{DNN} Feature Engineering Approach to Dependency Parsing {--} {FBAML} at {C}o{NLL} 2017 Shared Task",
    author = "Qian, Xian  and
      Liu, Yang",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3015",
    doi = "10.18653/v1/K17-3015",
    pages = "143--151",
    abstract = "For this year{'}s multilingual dependency parsing shared task, we developed a pipeline system, which uses a variety of features for each of its components. Unlike the recent popular deep learning approaches that learn low dimensional dense features using non-linear classifier, our system uses structured linear classifiers to learn millions of sparse features. Specifically, we trained a linear classifier for sentence boundary prediction, linear chain conditional random fields (CRFs) for tokenization, part-of-speech tagging and morph analysis. A second order graph based parser learns the tree structure (without relations), and fa linear tree CRF then assigns relations to the dependencies in the tree. Our system achieves reasonable performance {--} 67.87{\%} official averaged macro F1 score",
}
@inproceedings{vilares-gomez-rodriguez-2017-non,
    title = "A non-projective greedy dependency parser with bidirectional {LSTM}s",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3016",
    doi = "10.18653/v1/K17-3016",
    pages = "152--162",
    abstract = "The LyS-FASTPARSE team present BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kiperwasser and Goldberg (2016) is used to train a greedy parser with a dynamic oracle to mitigate error propagation. The model participated in the CoNLL 2017 UD Shared Task. In spite of not using any ensemble methods and using the baseline segmentation and PoS tagging, the parser obtained good results on both macro-average LAS and UAS in the big treebanks category (55 languages), ranking 7th out of 33 teams. In the all treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the all and big categories is mainly due to the poor performance on four parallel PUD treebanks, suggesting that some {`}suffixed{'} treebanks (e.g. Spanish-AnCora) perform poorly on cross-treebank settings, which does not occur with the corresponding {`}unsuffixed{'} treebank (e.g. Spanish). By changing that, we obtain the 11th best LAS among all runs (official and unofficial). The code is made available at \url{https://github.com/CoNLL-UD-2017/LyS-FASTPARSE}",
}
@inproceedings{aufrant-etal-2017-limsi,
    title = "{LIMSI}@{C}o{NLL}{'}17: {UD} Shared Task",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3017",
    doi = "10.18653/v1/K17-3017",
    pages = "163--173",
    abstract = "This paper describes LIMSI{'}s submission to the CoNLL 2017 UD Shared Task, which is focused on small treebanks, and how to improve low-resourced parsing only by ad hoc combination of multiple views and resources. We present our approach for low-resourced parsing, together with a detailed analysis of the results for each test treebank. We also report extensive analysis experiments on model selection for the PUD treebanks, and on annotation consistency among UD treebanks.",
}
@inproceedings{dumitrescu-etal-2017-racais,
    title = "{RACAI}{'}s Natural Language Processing pipeline for {U}niversal {D}ependencies",
    author = "Dumitrescu, Stefan Daniel  and
      Boros, Tiberiu  and
      Tufis, Dan",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3018",
    doi = "10.18653/v1/K17-3018",
    pages = "174--181",
    abstract = "This paper presents RACAI{'}s approach, experiments and results at CONLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. We handle raw text and we cover tokenization, sentence splitting, word segmentation, tagging, lemmatization and parsing. All results are reported under strict training, development and testing conditions, in which the corpora provided for the shared tasks is used {``}as is{''}, without any modifications to the composition of the train and development sets.",
}
@inproceedings{das-etal-2017-delexicalized,
    title = "Delexicalized transfer parsing for low-resource languages using transformed and combined treebanks",
    author = "Das, Ayan  and
      Zaffar, Affan  and
      Sarkar, Sudeshna",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3019",
    doi = "10.18653/v1/K17-3019",
    pages = "182--190",
    abstract = "This paper describes our dependency parsing system in CoNLL-2017 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We primarily focus on the low-resource languages (surprise languages). We have developed a framework to combine multiple treebanks to train parsers for low resource languages by delexicalization method. We have applied transformation on source language treebanks based on syntactic features of the low-resource language to improve performance of the parser. In the official evaluation, our system achieves an macro-averaged LAS score of 67.61 and 37.16 on the entire blind test data and the surprise language test data respectively.",
}
@inproceedings{wang-etal-2017-transition-based,
    title = "A Transition-based System for {U}niversal {D}ependency Parsing",
    author = "Wang, Hao  and
      Zhao, Hai  and
      Zhang, Zhisong",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3020",
    doi = "10.18653/v1/K17-3020",
    pages = "191--197",
    abstract = "This paper describes the system for our participation in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In this work, we design a system based on UDPipe1 for universal dependency parsing, where multilingual transition-based models are trained for different treebanks. Our system directly takes raw texts as input, performing several intermediate steps like tokenizing and tagging, and finally generates the corresponding dependency trees. For the special surprise languages for this task, we adopt a delexicalized strategy and predict basing on transfer learning from other related languages. In the final evaluation of the shared task, our system achieves a result of 66.53{\%} in macro-averaged LAS F1-score.",
}
@inproceedings{hornby-etal-2017-corpus,
    title = "Corpus Selection Approaches for Multilingual Parsing from Raw Text to {U}niversal {D}ependencies",
    author = "Hornby, Ryan  and
      Taylor, Clark  and
      Park, Jungyeul",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3021",
    doi = "10.18653/v1/K17-3021",
    pages = "198--206",
    abstract = {This paper describes UALing{'}s approach to the \textit{CoNLL 2017 UD Shared Task} using corpus selection techniques to reduce training data size. The methodology is simple: we use similarity measures to select a corpus from available training data (even from multiple corpora for surprise languages) and use the resulting corpus to complete the parsing task. The training and parsing is done with the baseline UDPipe system (Straka et al., 2016). While our approach reduces the size of training data significantly, it retains performance within 0.5{\%} of the baseline system. Due to the reduction in training data size, our system performs faster than the na{\"\i}ve, complete corpus method. Specifically, our system runs in less than 10 minutes, ranking it among the fastest entries for this task. Our system is available at \url{https://github.com/CoNLL-UD-2017/UALING}.},
}
@inproceedings{de-lhoneux-etal-2017-raw,
    title = "From Raw Text to {U}niversal {D}ependencies - Look, No Tags!",
    author = "de Lhoneux, Miryam  and
      Shao, Yan  and
      Basirat, Ali  and
      Kiperwasser, Eliyahu  and
      Stymne, Sara  and
      Goldberg, Yoav  and
      Nivre, Joakim",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3022",
    doi = "10.18653/v1/K17-3022",
    pages = "207--217",
    abstract = "We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies. Our system is a simple pipeline consisting of two components. The first performs joint word and sentence segmentation on raw text; the second predicts dependency trees from raw words. The parser bypasses the need for part-of-speech tagging, but uses word embeddings based on universal tag distributions. We achieved a macro-averaged LAS F1 of 65.11 in the official test run, which improved to 70.49 after bug fixes. We obtained the 2nd best result for sentence segmentation with a score of 89.03.",
}
@inproceedings{akkus-etal-2017-initial,
    title = "Initial Explorations of {CCG} Supertagging for {U}niversal {D}ependency Parsing",
    author = "Akkus, Burak Kerim  and
      Azizoglu, Heval  and
      Cakici, Ruket",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3023",
    doi = "10.18653/v1/K17-3023",
    pages = "218--227",
    abstract = "In this paper we describe the system by METU team for universal dependency parsing of multilingual text. We use a neural network-based dependency parser that has a greedy transition approach to dependency parsing. CCG supertags contain rich structural information that proves useful in certain NLP tasks. We experiment with CCG supertags as additional features in our experiments. The neural network parser is trained together with dependencies and simplified CCG tags as well as other features provided.",
}
@inproceedings{moor-etal-2017-clcl,
    title = "{CLCL} (Geneva) {DINN} Parser: a Neural Network Dependency Parser Ten Years Later",
    author = "Moor, Christophe  and
      Merlo, Paola  and
      Henderson, James  and
      Wang, Haozhou",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3024",
    doi = "10.18653/v1/K17-3024",
    pages = "228--236",
    abstract = "This paper describes the University of Geneva{'}s submission to the CoNLL 2017 shared task Multilingual Parsing from Raw Text to Universal Dependencies (listed as the CLCL (Geneva) entry). Our submitted parsing system is the grandchild of the first transition-based neural network dependency parser, which was the University of Geneva{'}s entry in the CoNLL 2007 multilingual dependency parsing shared task, with some improvements to speed and portability. These results provide a baseline for investigating how far we have come in the past ten years of work on neural network dependency parsing.",
}
@inproceedings{ji-etal-2017-fast,
    title = "A Fast and Lightweight System for Multilingual Dependency Parsing",
    author = "Ji, Tao  and
      Wu, Yuanbin  and
      Lan, Man",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3025",
    doi = "10.18653/v1/K17-3025",
    pages = "237--242",
    abstract = "We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33{\%}.",
}
@inproceedings{de-la-clergerie-etal-2017-parisnlp,
    title = "The {P}aris{NLP} entry at the {C}on{LL} {UD} Shared Task 2017: A Tale of a {\#}{P}arsing{T}ragedy",
    author = "de La Clergerie, {\'E}ric  and
      Sagot, Beno{\^\i}t  and
      Seddah, Djam{\'e}",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3026",
    doi = "10.18653/v1/K17-3026",
    pages = "243--252",
    abstract = "We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon-based morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc.) and the best combination for each language was selected. Unfortunately, a glitch in the shared task{'}s Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this {\#}ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.",
}
@inproceedings{more-tsarfaty-2017-universal,
    title = "Universal Joint Morph-Syntactic Processing: The {O}pen {U}niversity of {I}srael{'}s Submission to The {C}o{NLL} 2017 Shared Task",
    author = "More, Amir  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3027",
    doi = "10.18653/v1/K17-3027",
    pages = "253--264",
    abstract = "We present the Open University{'}s submission to the CoNLL 2017 Shared Task on multilingual parsing from raw text to Universal Dependencies. The core of our system is a joint morphological disambiguator and syntactic parser which accepts morphologically analyzed surface tokens as input and returns morphologically disambiguated dependency trees as output. Our parser requires a lattice as input, so we generate morphological analyses of surface tokens using a data-driven morphological analyzer that derives its lexicon from the UD training corpora, and we rely on UDPipe for sentence segmentation and surface-level tokenization. We report our official macro-average LAS is 56.56. Although our model is not as performant as many others, it does not make use of neural networks, therefore we do not rely on word embeddings or any other data source other than the corpora themselves. In addition, we show the utility of a lexicon-backed morphological analyzer for the MRL Modern Hebrew. We use our results on Modern Hebrew to argue that the UD community should define a UD-compatible standard for access to lexical resources, which we argue is crucial for MRLs and low resource languages in particular.",
}
@inproceedings{kanayama-etal-2017-semi,
    title = "A Semi-universal Pipelined Approach to the {C}o{NLL} 2017 {UD} Shared Task",
    author = "Kanayama, Hiroshi  and
      Muraoka, Masayasu  and
      Yoshikawa, Katsumasa",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3028",
    doi = "10.18653/v1/K17-3028",
    pages = "265--273",
    abstract = "This paper presents our system submitted for the CoNLL 2017 Shared Task, {``}Multilingual Parsing from Raw Text to Universal Dependencies.{''} We ran the system for all languages with our own fully pipelined components without relying on re-trained baseline systems. To train the dependency parser, we used only the universal part-of-speech tags and distance between words, and applied deterministic rules to assign dependency labels. The simple and delexicalized models are suitable for cross-lingual transfer approaches and a universal language model. Experimental results show that our model performed well in some metrics and leads discussion on topics such as contribution of each component and on syntactic similarities among languages.",
}
@inproceedings{garcia-gamallo-2017-rule,
    title = "A rule-based system for cross-lingual parsing of {R}omance languages with {U}niversal {D}ependencies",
    author = "Garcia, Marcos  and
      Gamallo, Pablo",
    booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-3029",
    doi = "10.18653/v1/K17-3029",
    pages = "274--282",
    abstract = "This article describes MetaRomance, a rule-based cross-lingual parser for Romance languages submitted to CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. The system is an almost delexicalized parser which does not need training data to analyze Romance languages. It contains linguistically motivated rules based on PoS-tag patterns. The rules included in MetaRomance were developed in about 12 hours by one expert with no prior knowledge in Universal Dependencies, and can be easily extended using a transparent formalism. In this paper we compare the performance of MetaRomance with other supervised systems participating in the competition, paying special attention to the parsing of different treebanks of the same language. We also compare our system with a delexicalized parser for Romance languages, and take advantage of the harmonized annotation of Universal Dependencies to propose a language ranking based on the syntactic distance each variety has from Romance languages.",
}
@proceedings{conll-2017-conll,
    title = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    editor = "Hulden, Mans",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2000",
    doi = "10.18653/v1/K17-2",
}
@inproceedings{cotterell-etal-2017-conll,
    title = "{C}o{NLL}-{SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection in 52 Languages",
    author = {Cotterell, Ryan  and
      Kirov, Christo  and
      Sylak-Glassman, John  and
      Walther, G{\'e}raldine  and
      Vylomova, Ekaterina  and
      Xia, Patrick  and
      Faruqui, Manaal  and
      K{\"u}bler, Sandra  and
      Yarowsky, David  and
      Eisner, Jason  and
      Hulden, Mans},
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2001",
    doi = "10.18653/v1/K17-2001",
    pages = "1--30",
}
@inproceedings{bergmanis-etal-2017-training,
    title = "Training Data Augmentation for Low-Resource Morphological Inflection",
    author = {Bergmanis, Toms  and
      Kann, Katharina  and
      Sch{\"u}tze, Hinrich  and
      Goldwater, Sharon},
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2002",
    doi = "10.18653/v1/K17-2002",
    pages = "31--39",
}
@inproceedings{kann-schutze-2017-lmu,
    title = "The {LMU} System for the {C}o{NLL}-{SIGMORPHON} 2017 Shared Task on Universal Morphological Reinflection",
    author = {Kann, Katharina  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2003",
    doi = "10.18653/v1/K17-2003",
    pages = "40--48",
}
@inproceedings{makarov-etal-2017-align,
    title = "Align and Copy: {UZH} at {SIGMORPHON} 2017 Shared Task for Morphological Reinflection",
    author = "Makarov, Peter  and
      Ruzsics, Tatiana  and
      Clematide, Simon",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2004",
    doi = "10.18653/v1/K17-2004",
    pages = "49--57",
}
@inproceedings{zhou-neubig-2017-morphological,
    title = "Morphological Inflection Generation with Multi-space Variational Encoder-Decoders",
    author = "Zhou, Chunting  and
      Neubig, Graham",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2005",
    doi = "10.18653/v1/K17-2005",
    pages = "58--65",
}
@inproceedings{chakrabarty-garain-2017-isi,
    title = "{ISI} at the {SIGMORPHON} 2017 Shared Task on Morphological Reinflection",
    author = "Chakrabarty, Abhisek  and
      Garain, Utpal",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2006",
    doi = "10.18653/v1/K17-2006",
    pages = "66--70",
}
@inproceedings{sudhakar-singh-2017-experiments,
    title = "Experiments on Morphological Reinflection: {C}o{NLL}-2017 Shared Task",
    author = "Sudhakar, Akhilesh  and
      Singh, Anil Kumar",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2007",
    doi = "10.18653/v1/K17-2007",
    pages = "71--78",
}
@inproceedings{nicolai-etal-2017-cant,
    title = "If you can{'}t beat them, join them: the {U}niversity of {A}lberta system description",
    author = "Nicolai, Garrett  and
      Hauer, Bradley  and
      Motallebi, Mohammad  and
      Najafi, Saeed  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2008",
    doi = "10.18653/v1/K17-2008",
    pages = "79--84",
}
@inproceedings{zhu-etal-2017-character,
    title = "Character Sequence-to-Sequence Model with Global Attention for Universal Morphological Reinflection",
    author = "Zhu, Qile  and
      Li, Yanjun  and
      Li, Xiaolin",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2009",
    doi = "10.18653/v1/K17-2009",
    pages = "85--89",
}
@inproceedings{silfverberg-etal-2017-data,
    title = "Data Augmentation for Morphological Reinflection",
    author = "Silfverberg, Miikka  and
      Wiemerslage, Adam  and
      Liu, Ling  and
      Mao, Lingshuang Jack",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2010",
    doi = "10.18653/v1/K17-2010",
    pages = "90--99",
}
@inproceedings{senuma-aizawa-2017-seq2seq,
    title = "Seq2seq for Morphological Reinflection: When Deep Learning Fails",
    author = "Senuma, Hajime  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2011",
    doi = "10.18653/v1/K17-2011",
    pages = "100--109",
}
@inproceedings{ostling-bjerva-2017-su,
    title = "{SU}-{RUG} at the {C}o{NLL}-{SIGMORPHON} 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models",
    author = {{\"O}stling, Robert  and
      Bjerva, Johannes},
    booktitle = "Proceedings of the {C}o{NLL} {SIGMORPHON} 2017 Shared Task: Universal Morphological Reinflection",
    month = aug,
    year = "2017",
    address = "Vancouver",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-2012",
    doi = "10.18653/v1/K17-2012",
    pages = "110--113",
}
@proceedings{conll-2017-natural,
    title = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    editor = "Levy, Roger  and
      Specia, Lucia",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1000",
    doi = "10.18653/v1/K17-1",
}
@inproceedings{dyer-2017-neural,
    title = "Should Neural Network Architecture Reflect Linguistic Structure?",
    author = "Dyer, Chris",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1001",
    doi = "10.18653/v1/K17-1001",
    pages = "1",
    abstract = "I explore the hypothesis that conventional neural network models (e.g., recurrent neural networks) are incorrectly biased for making linguistically sensible generalizations when learning, and that a better class of models is based on architectures that reflect hierarchical structures for which considerable behavioral evidence exists. I focus on the problem of modeling and representing the meanings of sentences. On the generation front, I introduce recurrent neural network grammars (RNNGs), a joint, generative model of phrase-structure trees and sentences. RNNGs operate via a recursive syntactic process reminiscent of probabilistic context-free grammar generation, but decisions are parameterized using RNNs that condition on the entire (top-down, left-to-right) syntactic derivation history, thus relaxing context-free independence assumptions, while retaining a bias toward explaining decisions via {``}syntactically local{''} conditioning contexts. Experiments show that RNNGs obtain better results in generating language than models that don{'}t exploit linguistic structure. On the representation front, I explore unsupervised learning of syntactic structures based on distant semantic supervision using a reinforcement-learning algorithm. The learner seeks a syntactic structure that provides a compositional architecture that produces a good representation for a downstream semantic task. Although the inferred structures are quite different from traditional syntactic analyses, the performance on the downstream tasks surpasses that of systems that use sequential RNNs and tree-structured RNNs based on treebank dependencies. This is joint work with Adhi Kuncoro, Dani Yogatama, Miguel Ballesteros, Phil Blunsom, Ed Grefenstette, Wang Ling, and Noah A. Smith.",
}
@inproceedings{feldman-2017-rational,
    title = "Rational Distortions of Learners{'} Linguistic Input",
    author = "Feldman, Naomi",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1002",
    doi = "10.18653/v1/K17-1002",
    pages = "2",
    abstract = "Language acquisition can be modeled as a statistical inference problem: children use sentences and sounds in their input to infer linguistic structure. However, in many cases, children learn from data whose statistical structure is distorted relative to the language they are learning. Such distortions can arise either in the input itself, or as a result of children{'}s immature strategies for encoding their input. This work examines several cases in which the statistical structure of children{'}s input differs from the language being learned. Analyses show that these distortions of the input can be accounted for with a statistical learning framework by carefully considering the inference problems that learners solve during language acquisition",
}
@inproceedings{enguehard-etal-2017-exploring,
    title = "Exploring the Syntactic Abilities of {RNN}s with Multi-task Learning",
    author = "Enguehard, {\'E}mile  and
      Goldberg, Yoav  and
      Linzen, Tal",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1003",
    doi = "10.18653/v1/K17-1003",
    pages = "3--14",
    abstract = "Recent work has explored the syntactic abilities of RNNs using the subject-verb agreement task, which diagnoses sensitivity to sentence structure. RNNs performed this task well in common cases, but faltered in complex sentences (Linzen et al., 2016). We test whether these errors are due to inherent limitations of the architecture or to the relatively indirect supervision provided by most agreement dependencies in a corpus. We trained a single RNN to perform both the agreement task and an additional task, either CCG supertagging or language modeling. Multi-task training led to significantly lower error rates, in particular on complex sentences, suggesting that RNNs have the ability to evolve more sophisticated syntactic representations than shown before. We also show that easily available agreement training data can improve performance on other syntactic tasks, in particular when only a limited amount of training data is available for those tasks. The multi-task paradigm can also be leveraged to inject grammatical knowledge into language models.",
}
@inproceedings{schwartz-etal-2017-effect,
    title = "The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the {ROC} Story Cloze Task",
    author = "Schwartz, Roy  and
      Sap, Maarten  and
      Konstas, Ioannis  and
      Zilles, Leila  and
      Choi, Yejin  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1004",
    doi = "10.18653/v1/K17-1004",
    pages = "15--25",
    abstract = "A writer{'}s style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze task (Mostafazadeh et al., 2016a), where annotators were assigned similar writing tasks with different constraints: (1) writing an entire story, (2) adding a story ending for a given story context, and (3) adding an incoherent ending to a story. We show that a simple linear classifier informed by stylistic features is able to successfully distinguish among the three cases, without even looking at the story context. In addition, combining our stylistic features with language model predictions reaches state of the art performance on the story cloze challenge. Our results demonstrate that different task framings can dramatically affect the way people write.",
}
@inproceedings{sun-etal-2017-parsing,
    title = "Parsing for Grammatical Relations via Graph Merging",
    author = "Sun, Weiwei  and
      Du, Yantao  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1005",
    doi = "10.18653/v1/K17-1005",
    pages = "26--35",
    abstract = "This paper is concerned with building deep grammatical relation (GR) analysis using data-driven approach. To deal with this problem, we propose graph merging, a new perspective, for building flexible dependency graphs: Constructing complex graphs via constructing simple subgraphs. We discuss two key problems in this perspective: (1) how to decompose a complex graph into simple subgraphs, and (2) how to combine subgraphs into a coherent complex graph. Experiments demonstrate the effectiveness of graph merging. Our parser reaches state-of-the-art performance and is significantly better than two transition-based parsers.",
}
@inproceedings{chen-etal-2017-leveraging,
    title = "Leveraging Eventive Information for Better Metaphor Detection and Classification",
    author = "Chen, I-Hsuan  and
      Long, Yunfei  and
      Lu, Qin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1006",
    doi = "10.18653/v1/K17-1006",
    pages = "36--46",
    abstract = "Metaphor detection has been both challenging and rewarding in natural language processing applications. This study offers a new approach based on eventive information in detecting metaphors by leveraging the Chinese writing system, which is a culturally bound ontological system organized according to the basic concepts represented by radicals. As such, the information represented is available in all Chinese text without pre-processing. Since metaphor detection is another culturally based conceptual representation, we hypothesize that sub-textual information can facilitate the identification and classification of the types of metaphoric events denoted in Chinese text. We propose a set of syntactic conditions crucial to event structures to improve the model based on the classification of radical groups. With the proposed syntactic conditions, the model achieves a performance of 0.8859 in terms of F-scores, making 1.7{\%} of improvement than the same classifier with only Bag-of-word features. Results show that eventive information can improve the effectiveness of metaphor detection. Event information is rooted in every language, and thus this approach has a high potential to be applied to metaphor detection in other languages.",
}
@inproceedings{uryupina-moschitti-2017-collaborative,
    title = "Collaborative Partitioning for Coreference Resolution",
    author = "Uryupina, Olga  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1007",
    doi = "10.18653/v1/K17-1007",
    pages = "47--57",
    abstract = "This paper presents a collaborative partitioning algorithm{---}a novel ensemble-based approach to coreference resolution. Starting from the all-singleton partition, we search for a solution close to the ensemble{'}s outputs in terms of a task-specific similarity measure. Our approach assumes a loose integration of individual components of the ensemble and can therefore combine arbitrary coreference resolvers, regardless of their models. Our experiments on the CoNLL dataset show that collaborative partitioning yields results superior to those attained by the individual components, for ensembles of both strong and weak systems. Moreover, by applying the collaborative partitioning algorithm on top of three state-of-the-art resolvers, we obtain the best coreference performance reported so far in the literature (MELA v08 score of 64.47).",
}
@inproceedings{eshel-etal-2017-named,
    title = "Named Entity Disambiguation for Noisy Text",
    author = "Eshel, Yotam  and
      Cohen, Noam  and
      Radinsky, Kira  and
      Markovitch, Shaul  and
      Yamada, Ikuya  and
      Levy, Omer",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1008",
    doi = "10.18653/v1/K17-1008",
    pages = "58--68",
    abstract = "We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it with a novel method for sampling informative negative examples. We also describe a new way of initializing word and entity embeddings that significantly improves performance. Our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.",
}
@inproceedings{sharp-etal-2017-tell,
    title = "Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification",
    author = "Sharp, Rebecca  and
      Surdeanu, Mihai  and
      Jansen, Peter  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Clark, Peter  and
      Hammond, Michael",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1009",
    doi = "10.18653/v1/K17-1009",
    pages = "69--79",
    abstract = "For many applications of question answering (QA), being able to explain why a given model chose an answer is critical. However, the lack of labeled data for answer justifications makes learning this difficult and expensive. Here we propose an approach that uses answer ranking as distant supervision for learning how to select informative justifications, where justifications serve as inferential connections between the question and the correct answer while often containing little lexical overlap with either. We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection. Our approach is informed by a set of features designed to combine both learned representations and explicit features to capture the connection between questions, answers, and answer justifications. We show that with this end-to-end approach we are able to significantly improve upon a strong IR baseline in both justification ranking (+9{\%} rated highly relevant) and answer selection (+6{\%} P@1).",
}
@inproceedings{khashabi-etal-2017-learning,
    title = "Learning What is Essential in Questions",
    author = "Khashabi, Daniel  and
      Khot, Tushar  and
      Sabharwal, Ashish  and
      Roth, Dan",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1010",
    doi = "10.18653/v1/K17-1010",
    pages = "80--89",
    abstract = "Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies the notion of essential question terms with the goal of improving such QA solvers. We illustrate the importance of essential question terms by showing that humans{'} ability to answer questions drops significantly when essential terms are eliminated from questions.We then develop a classifier that reliably (90{\%} mean average precision) identifies and ranks essential terms in questions. Finally, we use the classifier to demonstrate that the notion of question term essentiality allows state-of-the-art QA solver for elementary-level science questions to make better and more informed decisions,improving performance by up to 5{\%}.We also introduce a new dataset of over 2,200 crowd-sourced essential terms annotated science questions.",
}
@inproceedings{chen-etal-2017-top,
    title = "Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation",
    author = "Chen, Huadong  and
      Huang, Shujian  and
      Chiang, David  and
      Dai, Xinyu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1011",
    doi = "10.18653/v1/K17-1011",
    pages = "90--99",
    abstract = "Pairwise ranking methods are the most widely used discriminative training approaches for structure prediction problems in natural language processing (NLP). Decomposing the problem of ranking hypotheses into pairwise comparisons enables simple and efficient solutions. However, neglecting the global ordering of the hypothesis list may hinder learning. We propose a listwise learning framework for structure prediction problems such as machine translation. Our framework directly models the entire translation list{'}s ordering to learn parameters which may better fit the given listwise samples. Furthermore, we propose top-rank enhanced loss functions, which are more sensitive to ranking errors at higher positions. Experiments on a large-scale Chinese-English translation task show that both our listwise learning framework and top-rank enhanced listwise losses lead to significant improvements in translation quality.",
}
@inproceedings{mancini-etal-2017-embedding,
    title = "Embedding Words and Senses Together via Joint Knowledge-Enhanced Training",
    author = "Mancini, Massimiliano  and
      Camacho-Collados, Jose  and
      Iacobacci, Ignacio  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1012",
    doi = "10.18653/v1/K17-1012",
    pages = "100--111",
    abstract = "Word embeddings are widely used in Natural Language Processing, mainly due to their success in capturing semantic information from massive corpora. However, their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector. We address this issue by proposing a new model which learns word and sense embeddings jointly. Our model exploits large corpora and knowledge from semantic networks in order to produce a unified vector space of word and sense embeddings. We evaluate the main features of our approach both qualitatively and quantitatively in a variety of tasks, highlighting the advantages of the proposed method in comparison to state-of-the-art word- and sense-based models.",
}
@inproceedings{vulic-etal-2017-automatic,
    title = "Automatic Selection of Context Configurations for Improved Class-Specific Word Representations",
    author = "Vuli{\'c}, Ivan  and
      Schwartz, Roy  and
      Rappoport, Ari  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1013",
    doi = "10.18653/v1/K17-1013",
    pages = "112--122",
    abstract = "This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple yet effective framework for an automatic selection of class-specific context configurations. We construct a context configuration space based on universal dependency relations between words, and efficiently search this space with an adapted beam search algorithm. In word similarity tasks for each word class, we show that our framework is both effective and efficient. Particularly, it improves the Spearman{'}s rho correlation with human scores on SimLex-999 over the best previously proposed class-specific contexts by 6 (A), 6 (V) and 5 (N) rho points. With our selected context configurations, we train on only 14{\%} (A), 26.2{\%} (V), and 33.6{\%} (N) of all dependency-based contexts, resulting in a reduced training time. Our results generalise: we show that the configurations our algorithm learns for one English training setup outperform previously proposed context types in another training setup for English. Moreover, basing the configuration space on universal dependencies, it is possible to transfer the learned configurations to German and Italian. We also demonstrate improved per-class results over other context types in these two languages..",
}
@inproceedings{jameel-schockaert-2017-modeling,
    title = "Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding",
    author = "Jameel, Shoaib  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1014",
    doi = "10.18653/v1/K17-1014",
    pages = "123--133",
    abstract = "Vector representations of word meaning have found many applications in the field of natural language processing. Word vectors intuitively represent the average context in which a given word tends to occur, but they cannot explicitly model the diversity of these contexts. Although region representations of word meaning offer a natural alternative to word vectors, only few methods have been proposed that can effectively learn word regions. In this paper, we propose a new word embedding model which is based on SVM regression. We show that the underlying ranking interpretation of word contexts is sufficient to match, and sometimes outperform, the performance of popular methods such as Skip-gram. Furthermore, we show that by using a quadratic kernel, we can effectively learn word regions, which outperform existing unsupervised models for the task of hypernym detection.",
}
@inproceedings{torabi-asr-jones-2017-artificial,
    title = "An Artificial Language Evaluation of Distributional Semantic Models",
    author = "Torabi Asr, Fatemeh  and
      Jones, Michael",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1015",
    doi = "10.18653/v1/K17-1015",
    pages = "134--142",
    abstract = "Recent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is an attempt to reveal the underlying contribution of additional training data and post-processing steps on each type of model in word similarity and relatedness inference tasks. We do so by designing an artificial language framework, training a predictive and a count-based model on data sampled from this grammar, and evaluating the resulting word vectors in paradigmatic and syntagmatic tasks defined with respect to the grammar.",
}
@inproceedings{song-etal-2017-learning,
    title = "Learning Word Representations with Regularization from Prior Knowledge",
    author = "Song, Yan  and
      Lee, Chia-Jung  and
      Xia, Fei",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1016",
    doi = "10.18653/v1/K17-1016",
    pages = "143--152",
    abstract = "Conventional word embeddings are trained with specific criteria (e.g., based on language modeling or co-occurrence) inside a single information source, disregarding the opportunity for further calibration using external knowledge. This paper presents a unified framework that leverages pre-learned or external priors, in the form of a regularizer, for enhancing conventional language model-based embedding learning. We consider two types of regularizers. The first type is derived from topic distribution by running LDA on unlabeled data. The second type is based on dictionaries that are created with human annotation efforts. To effectively learn with the regularizers, we propose a novel data structure, trajectory softmax, in this paper. The resulting embeddings are evaluated by word similarity and sentiment classification. Experimental results show that our learning framework with regularization from prior knowledge improves embedding quality across multiple datasets, compared to a diverse collection of baseline methods.",
}
@inproceedings{dong-etal-2017-attention,
    title = "Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring",
    author = "Dong, Fei  and
      Zhang, Yue  and
      Yang, Jie",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1017",
    doi = "10.18653/v1/K17-1017",
    pages = "153--162",
    abstract = "Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous state-of-the-art methods, demonstrating the effectiveness of the attention mechanism.",
}
@inproceedings{paul-2017-feature,
    title = "Feature Selection as Causal Inference: Experiments with Text Classification",
    author = "Paul, Michael J.",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1018",
    doi = "10.18653/v1/K17-1018",
    pages = "163--172",
    abstract = "This paper proposes a matching technique for learning causal associations between word features and class labels in document classification. The goal is to identify more meaningful and generalizable features than with only correlational approaches. Experiments with sentiment classification show that the proposed method identifies interpretable word associations with sentiment and improves classification performance in a majority of cases. The proposed feature selection method is particularly effective when applied to out-of-domain data.",
}
@inproceedings{peng-etal-2017-joint,
    title = "A Joint Model for Semantic Sequences: Frames, Entities, Sentiments",
    author = "Peng, Haoruo  and
      Chaturvedi, Snigdha  and
      Roth, Dan",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1019",
    doi = "10.18653/v1/K17-1019",
    pages = "173--183",
    abstract = "Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task. These events typically carry multiple aspects of semantics including actions, entities and emotions. Not only does each individual aspect contribute to the meaning of the story, so does the interaction among these aspects. Building on this intuition, we propose to jointly model important aspects of semantic knowledge {--} frames, entities and sentiments {--} via a semantic language model. We achieve this by first representing these aspects{'} semantic units at an appropriate level of abstraction and then using the resulting vector representations for each semantic aspect to learn a joint representation via a neural language model. We show that the joint semantic language model is of high quality and can generate better semantic sequences than models that operate on the word level. We further demonstrate that our joint model can be applied to story cloze test and shallow discourse parsing tasks with improved performance and that each semantic aspect contributes to the model.",
}
@inproceedings{ruzsics-samardzic-2017-neural,
    title = "Neural Sequence-to-sequence Learning of Internal Word Structure",
    author = "Ruzsics, Tatyana  and
      Samard{\v{z}}i{\'c}, Tanja",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1020",
    doi = "10.18653/v1/K17-1020",
    pages = "184--194",
    abstract = "Learning internal word structure has recently been recognized as an important step in various multilingual processing tasks and in theoretical language comparison. In this paper, we present a neural encoder-decoder model for learning canonical morphological segmentation. Our model combines character-level sequence-to-sequence transformation with a language model over canonical segments. We obtain up to 4{\%} improvement over a strong character-level encoder-decoder baseline for three languages. Our model outperforms the previous state-of-the-art for two languages, while eliminating the need for external resources such as large dictionaries. Finally, by comparing the performance of encoder-decoder and classical statistical machine translation systems trained with and without corpus counts, we show that including corpus counts is beneficial to both approaches.",
}
@inproceedings{collins-etal-2017-supervised,
    title = "A Supervised Approach to Extractive Summarisation of Scientific Papers",
    author = "Collins, Ed  and
      Augenstein, Isabelle  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1021",
    doi = "10.18653/v1/K17-1021",
    pages = "195--205",
    abstract = "Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.",
}
@inproceedings{bhatia-etal-2017-automatic,
    title = "An Automatic Approach for Document-level Topic Model Evaluation",
    author = "Bhatia, Shraey  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1022",
    doi = "10.18653/v1/K17-1022",
    pages = "206--215",
    abstract = "Topic models jointly learn topics and document-level topic distribution. Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics. We demonstrate that there can be large discrepancies between topic- and document-level model quality, and that basing model evaluation on topic-level analysis can be highly misleading. We propose a method for automatically predicting topic model quality based on analysis of document-level topic allocations, and provide empirical evidence for its robustness.",
}
@inproceedings{chen-etal-2017-robust,
    title = "Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on {TV} Show Transcripts",
    author = "Chen, Henry Y.  and
      Zhou, Ethan  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1023",
    doi = "10.18653/v1/K17-1023",
    pages = "216--225",
    abstract = "This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annotation errors in an existing corpus so the corpus is clearer and cleaner for statistical learning. We also introduce the agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings for coreference resolution. We then propose another neural model that employs the embeddings learned and creates cluster embeddings for entity linking. Our coreference resolution model shows comparable results to other state-of-the-art systems. Our entity linking model significantly outperforms the previous work, showing the F1 score of 86.76{\%} and the accuracy of 95.30{\%} for character identification.",
}
@inproceedings{joty-etal-2017-cross,
    title = "Cross-language Learning with Adversarial Neural Networks",
    author = "Joty, Shafiq  and
      Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s  and
      Jaradat, Israa",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1024",
    doi = "10.18653/v1/K17-1024",
    pages = "226--237",
    abstract = "We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system.",
}
@inproceedings{renduchintala-etal-2017-knowledge,
    title = "Knowledge Tracing in Sequential Learning of Inflected Vocabulary",
    author = "Renduchintala, Adithya  and
      Koehn, Philipp  and
      Eisner, Jason",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1025",
    doi = "10.18653/v1/K17-1025",
    pages = "238--247",
    abstract = "We present a feature-rich knowledge tracing method that captures a student{'}s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student{'}s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.",
}
@inproceedings{saparov-etal-2017-probabilistic,
    title = "A Probabilistic Generative Grammar for Semantic Parsing",
    author = "Saparov, Abulhair  and
      Saraswat, Vijay  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1026",
    doi = "10.18653/v1/K17-1026",
    pages = "248--259",
    abstract = "We present a generative model of natural language sentences and demonstrate its application to semantic parsing. In the generative process, a logical form sampled from a prior, and conditioned on this logical form, a grammar probabilistically generates the output sentence. Grammar induction using MCMC is applied to learn the grammar given a set of labeled sentences with corresponding logical forms. We develop a semantic parser that finds the logical form with the highest posterior probability exactly. We obtain strong results on the GeoQuery dataset and achieve state-of-the-art F1 on Jobs.",
}
@inproceedings{nicosia-moschitti-2017-learning,
    title = "Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information",
    author = "Nicosia, Massimo  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1027",
    doi = "10.18653/v1/K17-1027",
    pages = "260--270",
    abstract = "Tree kernels (TKs) and neural networks are two effective approaches for automatic feature engineering. In this paper, we combine them by modeling context word similarity in semantic TKs. This way, the latter can operate subtree matching by applying neural-based similarity on tree lexical nodes. We study how to learn representations for the words in context such that TKs can exploit more focused information. We found that neural embeddings produced by current methods do not provide a suitable contextual similarity. Thus, we define a new approach based on a Siamese Network, which produces word representations while learning a binary text similarity. We set the latter considering examples in the same category as similar. The experiments on question and sentiment classification show that our semantic TK highly improves previous results.",
}
@inproceedings{weissenborn-etal-2017-making,
    title = "Making Neural {QA} as Simple as Possible but not Simpler",
    author = "Weissenborn, Dirk  and
      Wiese, Georg  and
      Seiffe, Laura",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1028",
    doi = "10.18653/v1/K17-1028",
    pages = "271--280",
    abstract = "Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.",
}
@inproceedings{wiese-etal-2017-neural-domain,
    title = "Neural Domain Adaptation for Biomedical Question Answering",
    author = "Wiese, Georg  and
      Weissenborn, Dirk  and
      Neves, Mariana",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1029",
    doi = "10.18653/v1/K17-1029",
    pages = "281--289",
    abstract = "Factoid question answering (QA) has recently benefited from the development of deep learning (DL) systems. Neural network models outperform traditional approaches in domains where large datasets exist, such as SQuAD (ca. 100,000 questions) for Wikipedia articles. However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch. For example, the BioASQ dataset for biomedical QA comprises less then 900 factoid (single answer) and list (multiple answers) QA instances. In this work, we adapt a neural QA system trained on a large open-domain dataset (SQuAD, source) to a biomedical dataset (BioASQ, target) by employing various transfer learning techniques. Our network architecture is based on a state-of-the-art QA system, extended with biomedical word embeddings and a novel mechanism to answer list questions. In contrast to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create. Despite this fact, our systems achieve state-of-the-art results on factoid questions and competitive results on list questions.",
}
@inproceedings{hulden-2017-phoneme,
    title = "A phoneme clustering algorithm based on the obligatory contour principle",
    author = "Hulden, Mans",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1030",
    doi = "10.18653/v1/K17-1030",
    pages = "290--300",
    abstract = "This paper explores a divisive hierarchical clustering algorithm based on the well-known Obligatory Contour Principle in phonology. The purpose is twofold: to see if such an algorithm could be used for unsupervised classification of phonemes or graphemes in corpora, and to investigate whether this purported universal constraint really holds for several classes of phonological distinctive features. The algorithm achieves very high accuracies in an unsupervised setting of inferring a consonant-vowel distinction, and also has a strong tendency to detect coronal phonemes in an unsupervised fashion. Remaining classes, however, do not correspond as neatly to phonological distinctive feature splits. While the results offer only mixed support for a universal Obligatory Contour Principle, the algorithm can be very useful for many NLP tasks due to the high accuracy in revealing consonant/vowel/coronal distinctions.",
}
@inproceedings{li-shah-2017-learning,
    title = "Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from {S}tock{T}wits",
    author = "Li, Quanzhi  and
      Shah, Sameena",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1031",
    doi = "10.18653/v1/K17-1031",
    pages = "301--310",
    abstract = "Previous studies have shown that investor sentiment indicators can predict stock market change. A domain-specific sentiment lexicon and sentiment-oriented word embedding model would help the sentiment analysis in financial domain and stock market. In this paper, we present a new approach to learning stock market lexicon from StockTwits, a popular financial social network for investors to share ideas. It learns word polarity by predicting message sentiment, using a neural net-work. The sentiment-oriented word embeddings are learned from tens of millions of StockTwits posts, and this is the first study presenting sentiment-oriented word embeddings for stock market. The experiments of predicting investor sentiment show that our lexicon outperformed other lexicons built by the state-of-the-art methods, and the sentiment-oriented word vector was much better than the general word embeddings.",
}
@inproceedings{raj-etal-2017-learning,
    title = "Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text",
    author = "Raj, Desh  and
      Sahu, Sunil  and
      Anand, Ashish",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1032",
    doi = "10.18653/v1/K17-1032",
    pages = "311--321",
    abstract = "The task of relation classification in the biomedical domain is complex due to the presence of samples obtained from heterogeneous sources such as research articles, discharge summaries, or electronic health records. It is also a constraint for classifiers which employ manual feature engineering. In this paper, we propose a convolutional recurrent neural network (CRNN) architecture that combines RNNs and CNNs in sequence to solve this problem. The rationale behind our approach is that CNNs can effectively identify coarse-grained local features in a sentence, while RNNs are more suited for long-term dependencies. We compare our CRNN model with several baselines on two biomedical datasets, namely the i2b2-2010 clinical relation extraction challenge dataset, and the SemEval-2013 DDI extraction dataset. We also evaluate an attentive pooling technique and report its performance in comparison with the conventional max pooling method. Our results indicate that the proposed model achieves state-of-the-art performance on both datasets.",
}
@inproceedings{sirts-etal-2017-idea,
    title = "Idea density for predicting {A}lzheimer{'}s disease from transcribed speech",
    author = "Sirts, Kairit  and
      Piguet, Olivier  and
      Johnson, Mark",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1033",
    doi = "10.18653/v1/K17-1033",
    pages = "322--332",
    abstract = "Idea Density (ID) measures the rate at which ideas or elementary predications are expressed in an utterance or in a text. Lower ID is found to be associated with an increased risk of developing Alzheimer{'}s disease (AD) (Snowdon et al., 1996; Engelman et al., 2010). ID has been used in two different versions: propositional idea density (PID) counts the expressed ideas and can be applied to any text while semantic idea density (SID) counts pre-defined information content units and is naturally more applicable to normative domains, such as picture description tasks. In this paper, we develop DEPID, a novel dependency-based method for computing PID, and its version DEPID-R that enables to exclude repeating ideas{---}a feature characteristic to AD speech. We conduct the first comparison of automatically extracted PID and SID in the diagnostic classification task on two different AD datasets covering both closed-topic and free-recall domains. While SID performs better on the normative dataset, adding PID leads to a small but significant improvement (+1.7 F-score). On the free-topic dataset, PID performs better than SID as expected (77.6 vs 72.3 in F-score) but adding the features derived from the word embedding clustering underlying the automatic SID increases the results considerably, leading to an F-score of 84.8.",
}
@inproceedings{levy-etal-2017-zero,
    title = "Zero-Shot Relation Extraction via Reading Comprehension",
    author = "Levy, Omer  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1034",
    doi = "10.18653/v1/K17-1034",
    pages = "333--342",
    abstract = "We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.",
}
@inproceedings{zhang-etal-2017-covert,
    title = "The Covert Helps Parse the Overt",
    author = "Zhang, Xun  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1035",
    doi = "10.18653/v1/K17-1035",
    pages = "343--353",
    abstract = "This paper is concerned with whether deep syntactic information can help surface parsing, with a particular focus on empty categories. We design new algorithms to produce dependency trees in which empty elements are allowed, and evaluate the impact of information about empty category on parsing overt elements. Such information is helpful to reduce the approximation error in a structured parsing model, but increases the search space for inference and accordingly the estimation error. To deal with structure-based overfitting, we propose to integrate disambiguation models with and without empty elements, and perform structure regularization via joint decoding. Experiments on English and Chinese TreeBanks with different parsing models indicate that incorporating empty elements consistently improves surface parsing.",
}
@inproceedings{schlechtweg-etal-2017-german,
    title = "{G}erman in Flux: Detecting Metaphoric Change via Word Entropy",
    author = "Schlechtweg, Dominik  and
      Eckmann, Stefanie  and
      Santus, Enrico  and
      Schulte im Walde, Sabine  and
      Hole, Daniel",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1036",
    doi = "10.18653/v1/K17-1036",
    pages = "354--367",
    abstract = "This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change. We build the first diachronic test set for German as a standard for metaphoric change annotation. Our model is unsupervised, language-independent and generalizable to other processes of semantic change.",
}
@inproceedings{alishahi-etal-2017-encoding,
    title = "Encoding of phonology in a recurrent neural model of grounded speech",
    author = "Alishahi, Afra  and
      Barking, Marie  and
      Chrupa{\l}a, Grzegorz",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1037",
    doi = "10.18653/v1/K17-1037",
    pages = "368--378",
    abstract = "We study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects the visual and auditory representations into the same semantic space. We perform a number of analyses on how information about individual phonemes is encoded in the MFCC features extracted from the speech signal, and the activations of the layers of the model. Via experiments with phoneme decoding and phoneme discrimination we show that phoneme representations are most salient in the lower layers of the model, where low-level signals are processed at a fine-grained level, although a large amount of phonological information is retain at the top recurrent layer. We further find out that the attention mechanism following the top recurrent layer significantly attenuates encoding of phonology and makes the utterance embeddings much more invariant to synonymy. Moreover, a hierarchical clustering of phoneme representations learned by the network shows an organizational structure of phonemes similar to those proposed in linguistics.",
}
@inproceedings{duong-etal-2017-multilingual-semantic,
    title = "Multilingual Semantic Parsing And Code-Switching",
    author = "Duong, Long  and
      Afshar, Hadi  and
      Estival, Dominique  and
      Pink, Glen  and
      Cohen, Philip  and
      Johnson, Mark",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1038",
    doi = "10.18653/v1/K17-1038",
    pages = "379--389",
    abstract = "Extending semantic parsing systems to new domains and languages is a highly expensive, time-consuming process, so making effective use of existing resources is critical. In this paper, we describe a transfer learning method using crosslingual word embeddings in a sequence-to-sequence model. On the NLmaps corpus, our approach achieves state-of-the-art accuracy of 85.7{\%} for English. Most importantly, we observed a consistent improvement for German compared with several baseline domain adaptation techniques. As a by-product of this approach, our models that are trained on a combination of English and German utterances perform reasonably well on code-switching utterances which contain a mixture of English and German, even though the training data does not contain any such. As far as we know, this is the first study of code-switching in semantic parsing. We manually constructed the set of code-switching test utterances for the NLmaps corpus and achieve 78.3{\%} accuracy on this dataset.",
}
@inproceedings{le-titov-2017-optimizing,
    title = "Optimizing Differentiable Relaxations of Coreference Evaluation Metrics",
    author = "Le, Phong  and
      Titov, Ivan",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1039",
    doi = "10.18653/v1/K17-1039",
    pages = "390--399",
    abstract = "Coreference evaluation metrics are hard to optimize directly as they are non-differentiable functions, not easily decomposable into elementary decisions. Consequently, most approaches optimize objectives only indirectly related to the end goal, resulting in suboptimal performance. Instead, we propose a differentiable relaxation that lends itself to gradient-based optimisation, thus bypassing the need for reinforcement learning or heuristic modification of cross-entropy. We show that by modifying the training objective of a competitive neural coreference system, we obtain a substantial gain in performance. This suggests that our approach can be regarded as a viable alternative to using reinforcement learning or more computationally expensive imitation learning.",
}
@inproceedings{ziser-reichart-2017-neural,
    title = "Neural Structural Correspondence Learning for Domain Adaptation",
    author = "Ziser, Yftah  and
      Reichart, Roi",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1040",
    doi = "10.18653/v1/K17-1040",
    pages = "400--410",
    abstract = "We introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural networks (NNs). Our model is a three-layer NN that learns to encode the non-pivot features of an input example into a low dimensional representation, so that the existence of pivot features (features that are prominent in both domains and convey useful information for the NLP task) in the example can be decoded from that representation. The low-dimensional representation is then employed in a learning algorithm for the task. Moreover, we show how to inject pre-trained word embeddings into our model in order to improve generalization across examples with similar pivot features. We experiment with the task of cross-domain sentiment classification on 16 domain pairs and show substantial improvements over strong baselines.",
}
@inproceedings{marcheggiani-etal-2017-simple,
    title = "A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling",
    author = "Marcheggiani, Diego  and
      Frolov, Anton  and
      Titov, Ivan",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1041",
    doi = "10.18653/v1/K17-1041",
    pages = "411--420",
    abstract = "We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.",
}
@inproceedings{inoue-etal-2017-joint,
    title = "Joint Prediction of Morphosyntactic Categories for Fine-Grained {A}rabic Part-of-Speech Tagging Exploiting Tag Dictionary Information",
    author = "Inoue, Go  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1042",
    doi = "10.18653/v1/K17-1042",
    pages = "421--431",
    abstract = "Part-of-speech (POS) tagging for morphologically rich languages such as Arabic is a challenging problem because of their enormous tag sets. One reason for this is that in the tagging scheme for such languages, a complete POS tag is formed by combining tags from multiple tag sets defined for each morphosyntactic category. Previous approaches in Arabic POS tagging applied one model for each morphosyntactic tagging task, without utilizing shared information between the tasks. In this paper, we propose an approach that utilizes this information by jointly modeling multiple morphosyntactic tagging tasks with a multi-task learning framework. We also propose a method of incorporating tag dictionary information into our neural models by combining word representations with representations of the sets of possible tags. Our experiments showed that the joint model with tag dictionary information results in an accuracy of 91.38{\%} on the Penn Arabic Treebank data set, with an absolute improvement of 2.11{\%} over the current state-of-the-art tagger.",
}
@inproceedings{samih-etal-2017-learning,
    title = "Learning from Relatives: Unified Dialectal {A}rabic Segmentation",
    author = "Samih, Younes  and
      Eldesouki, Mohamed  and
      Attia, Mohammed  and
      Darwish, Kareem  and
      Abdelali, Ahmed  and
      Mubarak, Hamdy  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1043",
    doi = "10.18653/v1/K17-1043",
    pages = "432--441",
    abstract = "Arabic dialects do not just share a common koin{\'e}, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other. In this paper we build a unified segmentation model where the training data for different dialects are combined and a single model is trained. The model yields higher accuracies than dialect-specific models, eliminating the need for dialect identification before segmentation. We also measure the degree of relatedness between four major Arabic dialects by testing how a segmentation model trained on one dialect performs on the other dialects. We found that linguistic relatedness is contingent with geographical proximity. In our experiments we use SVM-based ranking and bi-LSTM-CRF sequence labeling.",
}
@inproceedings{tran-nguyen-2017-natural,
    title = "Natural Language Generation for Spoken Dialogue System using {RNN} Encoder-Decoder Networks",
    author = "Tran, Van-Khanh  and
      Nguyen, Le-Minh",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1044",
    doi = "10.18653/v1/K17-1044",
    pages = "442--451",
    abstract = "Natural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to select, aggregate semantic elements produced by an attention mechanism over the input elements, and to produce the required utterances. The proposed generator can be jointly trained both sentence planning and surface realization to produce natural language sentences. The proposed model was extensively evaluated on four different NLG datasets. The experimental results showed that the proposed generators not only consistently outperform the previous methods across all the NLG domains but also show an ability to generalize from a new, unseen domain and learn from multi-domain datasets.",
}
@inproceedings{yasunaga-etal-2017-graph,
    title = "Graph-based Neural Multi-Document Summarization",
    author = "Yasunaga, Michihiro  and
      Zhang, Rui  and
      Meelu, Kshitijh  and
      Pareek, Ayush  and
      Srinivasan, Krishnan  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K17-1045",
    doi = "10.18653/v1/K17-1045",
    pages = "452--462",
    abstract = "We propose a neural multi-document summarization system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences that avoid redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon other traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multi-document summarization systems.",
}
@article{cl-2017-linguistics-43-issue-4,
    title = "Computational Linguistics, Volume 43, Issue 4 - {D}ecember 2017",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4000",
}
@article{joty-etal-2017-discourse,
    title = "Discourse Structure in Machine Translation Evaluation",
    author = "Joty, Shafiq  and
      Guzm{\'a}n, Francisco  and
      M{\`a}rquez, Llu{\'\i}s  and
      Nakov, Preslav",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4001",
    doi = "10.1162/COLI_a_00298",
    pages = "683--722",
    abstract = "In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment level and at the system level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular, we show that (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference RST tree is positively correlated with translation quality.",
}
@article{rozovskaya-etal-2017-adapting,
    title = "Adapting to Learner Errors with Minimal Supervision",
    author = "Rozovskaya, Alla  and
      Roth, Dan  and
      Sammons, Mark",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4002",
    doi = "10.1162/COLI_a_00299",
    pages = "723--760",
    abstract = "This article considers the problem of correcting errors made by English as a Second Language writers from a machine learning perspective, and addresses an important issue of developing an appropriate training paradigm for the task, one that accounts for error patterns of non-native writers using minimal supervision. Existing training approaches present a trade-off between large amounts of cheap data offered by the native-trained models and additional knowledge of learner error patterns provided by the more expensive method of training on annotated learner data. We propose a novel training approach that draws on the strengths offered by the two standard training paradigms{---}of training either on native or on annotated learner data{---}and that outperforms both of these standard methods. Using the key observation that parameters relating to error regularities exhibited by non-native writers are relatively simple, we develop models that can incorporate knowledge about error regularities based on a small annotated sample but that are otherwise trained on native English data. The key contribution of this article is the introduction and analysis of two methods for adapting the learned models to error patterns of non-native writers; one method that applies to generative classifiers and a second that applies to discriminative classifiers. Both methods demonstrated state-of-the-art performance in several text correction competitions. In particular, the Illinois system that implements these methods ranked at the top in two recent CoNLL shared tasks on error correction.1 We conduct further evaluation of the proposed approaches studying the effect of using error data from speakers of the same native language, languages that are closely related linguistically, and unrelated languages.",
}
@article{kadar-etal-2017-representation,
    title = "Representation of Linguistic Form and Function in Recurrent Neural Networks",
    author = "K{\'a}d{\'a}r, {\'A}kos  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4003",
    doi = "10.1162/COLI_a_00300",
    pages = "761--780",
    abstract = "We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture consisting of two parallel pathways with shared word embeddings: The Visual pathway is trained on predicting the representations of the visual scene corresponding to an input sentence, and the Textual pathway is trained to predict the next word in the same sentence. We propose a method for estimating the amount of contribution of individual tokens in the input to the final prediction of the networks. Using this method, we show that the Visual pathway pays selective attention to lexical categories and grammatical functions that carry semantic information, and learns to treat word types differently depending on their grammatical function and their position in the sequential structure of the sentence. In contrast, the language models are comparatively more sensitive to words with a syntactic function. Further analysis of the most informative n-gram contexts for each model shows that in comparison with the Visual pathway, the language models react more strongly to abstract contexts that represent syntactic constructions.",
}
@article{vulic-etal-2017-hyperlex,
    title = "{H}yper{L}ex: A Large-Scale Evaluation of Graded Lexical Entailment",
    author = "Vuli{\'c}, Ivan  and
      Gerz, Daniela  and
      Kiela, Douwe  and
      Hill, Felix  and
      Korhonen, Anna",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4004",
    doi = "10.1162/COLI_a_00301",
    pages = "781--835",
    abstract = "We introduce HyperLex{---}a data set and evaluation resource that quantifies the extent of the semantic category membership, that is, type-of relation, also known as hyponymy{--}hypernymy or lexical entailment (LE) relation between 2,616 concept pairs. Cognitive psychology research has established that typicality and category/class membership are computed in human semantic memory as a gradual rather than binary relation. Nevertheless, most NLP research and existing large-scale inventories of concept category membership (WordNet, DBPedia, etc.) treat category membership and LE as binary. To address this, we asked hundreds of native English speakers to indicate typicality and strength of category membership between a diverse range of concept pairs on a crowdsourcing platform. Our results confirm that category membership and LE are indeed more gradual than binary. We then compare these human judgments with the predictions of automatic systems, which reveals a huge gap between human performance and state-of-the-art LE, distributional and representation learning models, and substantial differences between the models themselves. We discuss a pathway for improving semantic models to overcome this discrepancy, and indicate future application areas for improved graded LE systems.",
}
@article{constant-etal-2017-survey,
    title = "{S}urvey: Multiword Expression Processing: A {S}urvey",
    author = {Constant, Mathieu  and
      Eryi{\v{g}}it, G{\"u}l{\c{s}}en  and
      Monti, Johanna  and
      van der Plas, Lonneke  and
      Ramisch, Carlos  and
      Rosner, Michael  and
      Todirascu, Amalia},
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4005",
    doi = "10.1162/COLI_a_00302",
    pages = "837--892",
    abstract = "Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by {``}MWE processing,{''} distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives.",
}
@article{hadiwinoto-2017-book,
    title = "Book Review: Syntax-Based Statistical Machine Translation by Philip {W}illiams, Rico {S}ennrich, Matt Post and Philipp {K}oehn",
    author = "Hadiwinoto, Christian",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4006",
    doi = "10.1162/COLI_r_00303",
    pages = "893--896",
}
@article{nissim-etal-2017-last,
    title = "Last Words: Sharing Is Caring: The Future of Shared Tasks",
    author = "Nissim, Malvina  and
      Abzianidze, Lasha  and
      Evang, Kilian  and
      van der Goot, Rob  and
      Haagsma, Hessel  and
      Plank, Barbara  and
      Wieling, Martijn",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4007",
    doi = "10.1162/COLI_a_00304",
    pages = "897--904",
}
@article{nn-2017-reviewers,
    title = "Reviewers for Volume 43",
    journal = "Computational Linguistics",
    volume = "43",
    number = "4",
    month = dec,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-4008",
    doi = "10.1162/COLI_x_00305",
    pages = "905--905",
}
@article{cl-2017-linguistics-43-issue,
    title = "Computational Linguistics, Volume 43, Issue 3 - September 2017",
    journal = "Computational Linguistics",
    volume = "43",
    number = "3",
    month = sep,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-3000",
}
@article{gebhardt-etal-2017-hybrid,
    title = "Hybrid Grammars for Parsing of Discontinuous Phrase Structures and Non-Projective Dependency Structures",
    author = "Gebhardt, Kilian  and
      Nederhof, Mark-Jan  and
      Vogler, Heiko",
    journal = "Computational Linguistics",
    volume = "43",
    number = "3",
    month = sep,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-3001",
    doi = "10.1162/COLI_a_00291",
    pages = "465--520",
    abstract = "We explore the concept of hybrid grammars, which formalize and generalize a range of existing frameworks for dealing with discontinuous syntactic structures. Covered are both discontinuous phrase structures and non-projective dependency structures. Technically, hybrid grammars are related to synchronous grammars, where one grammar component generates linear structures and another generates hierarchical structures. By coupling lexical elements of both components together, discontinuous structures result. Several types of hybrid grammars are characterized. We also discuss grammar induction from treebanks. The main advantage over existing frameworks is the ability of hybrid grammars to separate discontinuity of the desired structures from time complexity of parsing. This permits exploration of a large variety of parsing algorithms for discontinuous structures, with different properties. This is confirmed by the reported experimental results, which show a wide variety of running time, accuracy, and frequency of parse failures.",
}
@article{deng-xue-2017-translation,
    title = "Translation Divergences in {C}hinese{--}{E}nglish Machine Translation: An Empirical Investigation",
    author = "Deng, Dun  and
      Xue, Nianwen",
    journal = "Computational Linguistics",
    volume = "43",
    number = "3",
    month = sep,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-3002",
    doi = "10.1162/COLI_a_00292",
    pages = "521--565",
    abstract = "In this article, we conduct an empirical investigation of translation divergences between Chinese and English relying on a parallel treebank. To do this, we first devise a hierarchical alignment scheme where Chinese and English parse trees are aligned in a way that eliminates conflicts and redundancies between word alignments and syntactic parses to prevent the generation of spurious translation divergences. Using this Hierarchically Aligned Chinese{--}English Parallel Treebank (HACEPT), we are able to semi-automatically identify and categorize the translation divergences between the two languages and quantify each type of translation divergence. Our results show that the translation divergences are much broader than described in previous studies that are largely based on anecdotal evidence and linguistic knowledge. The distribution of the translation divergences also shows that some high-profile translation divergences that motivate previous research are actually very rare in our data, whereas other translation divergences that have previously received little attention actually exist in large quantities. We also show that HACEPT allows the extraction of syntax-based translation rules, most of which are expressive enough to capture the translation divergences, and point out that the syntactic annotation in existing treebanks is not optimal for extracting such translation rules. We also discuss the implications of our study for attempts to bridge translation divergences by devising shared semantic representations across languages. Our quantitative results lend further support to the observation that although it is possible to bridge some translation divergences with semantic representations, other translation divergences are open-ended, thus building a semantic representation that captures all possible translation divergences may be impractical.",
}
@article{nguyen-eisenstein-2017-kernel,
    title = "A Kernel Independence Test for Geographical Language Variation",
    author = "Nguyen, Dong  and
      Eisenstein, Jacob",
    journal = "Computational Linguistics",
    volume = "43",
    number = "3",
    month = sep,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-3003",
    doi = "10.1162/COLI_a_00293",
    pages = "567--592",
    abstract = "Quantifying the degree of spatial dependence for linguistic variables is a key task for analyzing dialectal variation. However, existing approaches have important drawbacks. First, they are based on parametric models of dependence, which limits their power in cases where the underlying parametric assumptions are violated. Second, they are not applicable to all types of linguistic data: Some approaches apply only to frequencies, others to boolean indicators of whether a linguistic variable is present. We present a new method for measuring geographical language variation, which solves both of these problems. Our approach builds on Reproducing Kernel Hilbert Space (RKHS) representations for nonparametric statistics, and takes the form of a test statistic that is computed from pairs of individual geotagged observations without aggregation into predefined geographical bins. We compare this test with prior work using synthetic data as well as a diverse set of real data sets: a corpus of Dutch tweets, a Dutch syntactic atlas, and a data set of letters to the editor in North American newspapers. Our proposed test is shown to support robust inferences across a broad range of scenarios and types of data.",
}
@article{rothe-schutze-2017-autoextend,
    title = "{A}uto{E}xtend: Combining Word Embeddings with Semantic Resources",
    author = {Rothe, Sascha  and
      Sch{\"u}tze, Hinrich},
    journal = "Computational Linguistics",
    volume = "43",
    number = "3",
    month = sep,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-3004",
    doi = "10.1162/COLI_a_00294",
    pages = "593--617",
    abstract = "We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guarantees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks.",
}
@article{stab-gurevych-2017-parsing,
    title = "Parsing Argumentation Structures in Persuasive Essays",
    author = "Stab, Christian  and
      Gurevych, Iryna",
    journal = "Computational Linguistics",
    volume = "43",
    number = "3",
    month = sep,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-3005",
    doi = "10.1162/COLI_a_00295",
    pages = "619--659",
    abstract = "In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using Integer Linear Programming. We show that our model significantly outperforms challenging heuristic baselines on two different types of discourse. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement.",
}
@article{mathet-2017-agreement,
    title = "The Agreement Measure γcat a Complement to γ Focused on Categorization of a Continuum",
    author = "Mathet, Yann",
    journal = "Computational Linguistics",
    volume = "43",
    number = "3",
    month = sep,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-3006",
    doi = "10.1162/COLI_a_00296",
    pages = "661--681",
    abstract = "Agreement on unitizing, where several annotators freely put units of various sizes and categories on a continuum, is difficult to assess because of the simultaneaous discrepancies in positioning and categorizing. The recent agreement measure γ offers an overall solution that simultaneously takes into account positions and categories. In this article, I propose the additional coefficient γcat, which complements γ by assessing the agreement on categorization of a continuum, putting aside positional discrepancies. When applied to pure categorization (with predefined units), γcat behaves the same way as the famous dedicated Krippendorff{'}s α, even with missing values, which proves its consistency. A variation of γcat is also proposed that provides an in-depth assessment of categorizing for each individual category. The entire family of γ coefficients is implemented in free software.",
}
@article{cl-2017-linguistics-43,
    title = "Computational Linguistics, Volume 43, Issue 2 - June 2017",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2000",
}
@article{irvine-callison-burch-2017-comprehensive,
    title = "A Comprehensive Analysis of Bilingual Lexicon Induction",
    author = "Irvine, Ann  and
      Callison-Burch, Chris",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2001",
    doi = "10.1162/COLI_a_00284",
    pages = "273--310",
    abstract = "Bilingual lexicon induction is the task of inducing word translations from monolingual corpora in two languages. In this article we present the most comprehensive analysis of bilingual lexicon induction to date. We present experiments on a wide range of languages and data sizes. We examine translation into English from 25 foreign languages: Albanian, Azeri, Bengali, Bosnian, Bulgarian, Cebuano, Gujarati, Hindi, Hungarian, Indonesian, Latvian, Nepali, Romanian, Serbian, Slovak, Somali, Spanish, Swedish, Tamil, Telugu, Turkish, Ukrainian, Uzbek, Vietnamese, and Welsh. We analyze the behavior of bilingual lexicon induction on low-frequency words, rather than testing solely on high-frequency words, as previous research has done. Low-frequency words are more relevant to statistical machine translation, where systems typically lack translations of rare words that fall outside of their training data. We systematically explore a wide range of features and phenomena that affect the quality of the translations discovered by bilingual lexicon induction. We provide illustrative examples of the highest ranking translations for orthogonal signals of translation equivalence like contextual similarity and temporal similarity. We analyze the effects of frequency and burstiness, and the sizes of the seed bilingual dictionaries and the monolingual training corpora. Additionally, we introduce a novel discriminative approach to bilingual lexicon induction. Our discriminative model is capable of combining a wide variety of features that individually provide only weak indications of translation equivalence. When feature weights are discriminatively set, these signals produce dramatically higher translation quality than previous approaches that combined signals in an unsupervised fashion (e.g., using minimum reciprocal rank). We also directly compare our model{'}s performance against a sophisticated generative approach, the matching canonical correlation analysis (MCCA) algorithm used by Haghighi et al. (2008). Our algorithm achieves an accuracy of 42{\%} versus MCCA{'}s 15{\%}.",
}
@article{ballesteros-etal-2017-greedy,
    title = "Greedy Transition-Based Dependency Parsing with Stack {LSTM}s",
    author = "Ballesteros, Miguel  and
      Dyer, Chris  and
      Goldberg, Yoav  and
      Smith, Noah A.",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2002",
    doi = "10.1162/COLI_a_00285",
    pages = "311--347",
    abstract = "We introduce a greedy transition-based parser that learns to represent parser states using recurrent neural networks. Our primary innovation that enables us to do this efficiently is a new control structure for sequential neural networks{---}the stack long short-term memory unit (LSTM). Like the conventional stack data structures used in transition-based parsers, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. Our model captures three facets of the parser{'}s state: (i) unbounded look-ahead into the buffer of incoming words, (ii) the complete history of transition actions taken by the parser, and (iii) the complete contents of the stack of partially built tree fragments, including their internal structures. In addition, we compare two different word representations: (i) standard word vectors based on look-up tables and (ii) character-based models of words. Although standard word embedding models work well in all languages, the character-based models improve the handling of out-of-vocabulary words, particularly in morphologically rich languages. Finally, we discuss the use of dynamic oracles in training the parser. During training, dynamic oracles alternate between sampling parser states from the training data and from the model as it is being learned, making the model more robust to the kinds of errors that will be made at test time. Training our model with dynamic oracles yields a linear-time greedy parser with very competitive performance.",
}
@article{sajjad-etal-2017-statistical,
    title = "Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining",
    author = {Sajjad, Hassan  and
      Schmid, Helmut  and
      Fraser, Alexander  and
      Sch{\"u}tze, Hinrich},
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2003",
    doi = "10.1162/COLI_a_00286",
    pages = "349--375",
    abstract = "We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised, and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e., noise). The model is trained on noisy unlabeled data using the EM algorithm. During training the transliteration sub-model learns to generate transliteration pairs and the fixed non-transliteration model generates the noise pairs. After training, the unlabeled data is disambiguated based on the posterior probabilities of the two sub-models. We evaluate our transliteration mining system on data from a transliteration mining shared task and on parallel corpora. For three out of four language pairs, our system outperforms all semi-supervised and supervised systems that participated in the NEWS 2010 shared task. On word pairs extracted from parallel corpora with fewer than 2{\%} transliteration pairs, our system achieves up to 86.7{\%} F-measure with 77.9{\%} precision and 97.8{\%} recall.",
}
@article{chinaei-etal-2017-identifying,
    title = "Identifying and Avoiding Confusion in Dialogue with People with {A}lzheimer{'}s Disease",
    author = "Chinaei, Hamidreza  and
      Currie, Leila Chan  and
      Danks, Andrew  and
      Lin, Hubert  and
      Mehta, Tejas  and
      Rudzicz, Frank",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2004",
    doi = "10.1162/COLI_a_00290",
    pages = "377--406",
    abstract = "Alzheimer{'}s disease (AD) is an increasingly prevalent cognitive disorder in which memory, language, and executive function deteriorate, usually in that order. There is a growing need to support individuals with AD and other forms of dementia in their daily lives, and our goal is to do so through speech-based interaction. Given that 33{\%} of conversations with people with middle-stage AD involve a breakdown in communication, it is vital that automated dialogue systems be able to identify those breakdowns and, if possible, avoid them. In this article, we discuss several linguistic features that are verbal indicators of confusion in AD (including vocabulary richness, parse tree structures, and acoustic cues) and apply several machine learning algorithms to identify dialogue-relevant confusion from speech with up to 82{\%} accuracy. We also learn dialogue strategies to avoid confusion in the first place, which is accomplished using a partially observable Markov decision process and which obtains accuracies (up to 96.1{\%}) that are significantly higher than several baselines. This work represents a major step towards automated dialogue systems for individuals with dementia.",
}
@article{jansen-etal-2017-framing,
    title = "Framing {QA} as Building and Ranking Intersentence Answer Justifications",
    author = "Jansen, Peter  and
      Sharp, Rebecca  and
      Surdeanu, Mihai  and
      Clark, Peter",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2005",
    doi = "10.1162/COLI_a_00287",
    pages = "407--449",
    abstract = "We propose a question answering (QA) approach for standardized science exams that both identifies correct answers and produces compelling human-readable justifications for why those answers are correct. Our method first identifies the actual information needed in a question using psycholinguistic concreteness norms, then uses this information need to construct answer justifications by aggregating multiple sentences from different knowledge bases using syntactic and lexical information. We then jointly rank answers and their justifications using a reranking perceptron that treats justification quality as a latent variable. We evaluate our method on 1,000 multiple-choice questions from elementary school science exams, and empirically demonstrate that it performs better than several strong baselines, including neural network approaches. Our best configuration answers 44{\%} of the questions correctly, where the top justifications for 57{\%} of these correct answers contain a compelling human-readable justification that explains the inference required to arrive at the correct answer. We include a detailed characterization of the justification quality for both our method and a strong baseline, and show that information aggregation is key to addressing the information need in complex questions.",
}
@article{paraboni-etal-2017-squib,
    title = "{S}quib: Effects of Cognitive Effort on the Resolution of Overspecified Descriptions",
    author = "Paraboni, Ivandr{\'e}  and
      Lan, Alex Gwo Jen  and
      de Sant{'}Ana, Matheus Mendes  and
      Coutinho, Fl{\'a}vio Luiz",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2006",
    doi = "10.1162/COLI_a_00288",
    pages = "451--459",
    abstract = "Studies in referring expression generation (REG) have shown different effects of referential overspecification on the resolution of certain descriptions. To further investigate effects of this kind, this article reports two eye-tracking experiments that measure the time required to recognize target objects based on different kinds of information. Results suggest that referential overspecification may be either helpful or detrimental to identification depending on the kind of information that is actually overspecified, an insight that may be useful for the design of more informed hearer-oriented REG algorithms.",
}
@article{ehrmann-2017-book,
    title = "Book Review: Linked Lexical Knowledge Bases Foundations and Applications by Iryna {G}urevych, Judith Eckle-er and {M}ichael Matuschek",
    author = "Ehrmann, Maud",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2007",
    doi = "10.1162/COLI_r_00289",
    pages = "461--463",
}
@article{cl-2017-linguistics,
    title = "Computational Linguistics, Volume 43, Issue 1 - {A}pril 2017",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1000",
}
@article{gardent-perez-beltrachini-2017-statistical,
    title = "A Statistical, Grammar-Based Approach to Microplanning",
    author = "Gardent, Claire  and
      Perez-Beltrachini, Laura",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1001",
    doi = "10.1162/COLI_a_00273",
    pages = "1--30",
    abstract = "Although there has been much work in recent years on data-driven natural language generation, little attention has been paid to the fine-grained interactions that arise during microplanning between aggregation, surface realization, and sentence segmentation. In this article, we propose a hybrid symbolic/statistical approach to jointly model the constraints regulating these interactions. Our approach integrates a small handwritten grammar, a statistical hypertagger, and a surface realization algorithm. It is applied to the verbalization of knowledge base queries and tested on 13 knowledge bases to demonstrate domain independence. We evaluate our approach in several ways. A quantitative analysis shows that the hybrid approach outperforms a purely symbolic approach in terms of both speed and coverage. Results from a human study indicate that users find the output of this hybrid statistic/symbolic system more fluent than both a template-based and a purely symbolic grammar-based approach. Finally, we illustrate by means of examples that our approach can account for various factors impacting aggregation, sentence segmentation, and surface realization.",
}
@article{tripodi-pelillo-2017-game,
    title = "A Game-Theoretic Approach to Word Sense Disambiguation",
    author = "Tripodi, Rocco  and
      Pelillo, Marcello",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1002",
    doi = "10.1162/COLI_a_00274",
    pages = "31--70",
    abstract = "This article presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes. The words simultaneously update their class membership preferences according to the senses that neighboring words are likely to choose. We use distributional information to weigh the influence that each word has on the decisions of the others and semantic similarity information to measure the strength of compatibility among the choices. With this information we can formulate the word sense disambiguation problem as a constraint satisfaction problem and solve it using tools derived from game theory, maintaining the textual coherence. The model is based on two ideas: Similar words should be assigned to similar classes and the meaning of a word does not depend on all the words in a text but just on some of them. The article provides an in-depth motivation of the idea of modeling the word sense disambiguation problem in terms of game theory, which is illustrated by an example. The conclusion presents an extensive analysis on the combination of similarity measures to use in the framework and a comparison with state-of-the-art systems. The results show that our model outperforms state-of-the-art algorithms and can be applied to different tasks and in different scenarios.",
}
@article{shutova-etal-2017-multilingual,
    title = "Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning",
    author = "Shutova, Ekaterina  and
      Sun, Lin  and
      Dar{\'\i}o Guti{\'e}rrez, Elkin  and
      Lichtenstein, Patricia  and
      Narayanan, Srini",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1003",
    doi = "10.1162/COLI_a_00275",
    pages = "71--123",
    abstract = "Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing (NLP) applications. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the systems hard to scale. Recent years have witnessed a rise in statistical approaches to metaphor processing. However, these approaches often require extensive human annotation effort and are predominantly evaluated within a limited domain. In contrast, we experiment with weakly supervised and unsupervised techniques{---}with little or no annotation{---}to generalize higher-level mechanisms of metaphor from distributional properties of concepts. We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings. Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups{---}English, Spanish, and Russian{---}achieving state-of-the-art results with little supervision. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor.",
}
@article{habernal-gurevych-2017-argumentation,
    title = "Argumentation Mining in User-Generated Web Discourse",
    author = "Habernal, Ivan  and
      Gurevych, Iryna",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1004",
    doi = "10.1162/COLI_a_00276",
    pages = "125--179",
    abstract = "The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people{'}s argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.",
}
@article{stilo-velardi-2017-hashtag,
    title = "Hashtag Sense Clustering Based on Temporal Similarity",
    author = "Stilo, Giovanni  and
      Velardi, Paola",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1005",
    doi = "10.1162/COLI_a_00277",
    pages = "181--200",
    abstract = "Hashtags are creative labels used in micro-blogs to characterize the topic of a message/discussion. Regardless of the use for which they were originally intended, hashtags cannot be used as a means to cluster messages with similar content. First, because hashtags are created in a spontaneous and highly dynamic way by users in multiple languages, the same topic can be associated with different hashtags, and conversely, the same hashtag may refer to different topics in different time periods. Second, contrary to common words, hashtag disambiguation is complicated by the fact that no sense catalogs (e.g., Wikipedia or WordNet) are available; and, furthermore, hashtag labels are difficult to analyze, as they often consist of acronyms, concatenated words, and so forth. A common way to determine the meaning of hashtags has been to analyze their context, but, as we have just pointed out, hashtags can have multiple and variable meanings. In this article, we propose a temporal sense clustering algorithm based on the idea that semantically related hashtags have similar and synchronous usage patterns.",
}
@article{benamara-etal-2017-evaluative,
    title = "Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications",
    author = "Benamara, Farah  and
      Taboada, Maite  and
      Mathieu, Yannick",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1006",
    doi = "10.1162/COLI_a_00278",
    pages = "201--264",
    abstract = "The study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including sociology, psychology, economics, linguistics, and computer science. A number of excellent computational linguistics and linguistic surveys of the field exist. Most surveys, however, do not bring the two disciplines together to show how methods from linguistics can benefit computational sentiment analysis systems. In this survey, we show how incorporating linguistic insights, discourse information, and other contextual phenomena, in combination with the statistical exploitation of data, can result in an improvement over approaches that take advantage of only one of these perspectives. We first provide a comprehensive introduction to evaluative language from both a linguistic and computational perspective. We then argue that the standard computational definition of the concept of evaluative language neglects the dynamic nature of evaluation, in which the interpretation of a given evaluation depends on linguistic and extra-linguistic contextual factors. We thus propose a dynamic definition that incorporates update functions. The update functions allow for different contextual aspects to be incorporated into the calculation of sentiment for evaluative words or expressions, and can be applied at all levels of discourse. We explore each level and highlight which linguistic aspects contribute to accurate extraction of sentiment. We end the review by outlining what we believe the future directions of sentiment analysis are, and the role that discourse and contextual information need to play.",
}
@article{kim-2017-book,
    title = "Book Review: Biomedical Natural Language Processing by Kevin Bretonnel {C}ohen and Dina Demner-Fushman",
    author = "Kim, Jin-Dong",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1007",
    doi = "10.1162/COLI_r_00281",
    pages = "265--267",
}
@article{keok-lee-2017-book,
    title = "Book Review: Automatic Detection of Verbal Deception by Eileen Fitzpatrick, Joan Bachenko and Tommaso Fornaciari",
    author = "Keok Lee, Yoong",
    journal = "Computational Linguistics",
    volume = "43",
    number = "1",
    month = apr,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-1008",
    doi = "10.1162/COLI_r_00282",
    pages = "269--271",
}
@proceedings{ijcnlp-2017-ijcnlp-2017-tutorial,
    title = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    editor = "Kurohashi, Sadao  and
      Strube, Michael",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-5000",
}
@inproceedings{che-zhang-2017-deep,
    title = "Deep Learning in Lexical Analysis and Parsing",
    author = "Che, Wanxiang  and
      Zhang, Yue",
    booktitle = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-5001",
    pages = "1--2",
    abstract = "Neural networks, also with a fancy name deep learning, just right can overcome the above {``}feature engineering{''} problem. In theory, they can use non-linear activation functions and multiple layers to automatically find useful features. The novel network structures, such as convolutional or recurrent, help to reduce the difficulty further. These deep learning models have been successfully used for lexical analysis and parsing. In this tutorial, we will give a review of each line of work, by contrasting them with traditional statistical methods, and organizing them in consistent orders.",
}
@inproceedings{de-melo-2017-multilingual,
    title = "Multilingual Vector Representations of Words, Sentences, and Documents",
    author = "de Melo, Gerard",
    booktitle = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-5002",
    pages = "3--5",
    abstract = "Neural vector representations are now ubiquitous in all subfields of natural language processing and text mining. While methods such as word2vec and GloVe are well-known, this tutorial focuses on multilingual and cross-lingual vector representations, of words, but also of sentences and documents as well.",
}
@inproceedings{chen-gao-2017-open,
    title = "Open-Domain Neural Dialogue Systems",
    author = "Chen, Yun-Nung  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-5003",
    pages = "6--10",
    abstract = "In the past decade, spoken dialogue systems have been the most prominent component in today{'}s personal assistants. A lot of devices have incorporated dialogue system modules, which allow users to speak naturally in order to finish tasks more efficiently. The traditional conversational systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. Nevertheless, applying deep learning technologies for building robust and scalable dialogue systems is still a challenging task and an open research area as it requires deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work and the recent state-of-the-art work. Therefore, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building task-oriented and chit-chat dialogue systems, and summarizing the challenges. We target the audience of students and practitioners who have some deep learning background, who want to get more familiar with conversational dialogue systems.",
}
@inproceedings{cromieres-etal-2017-neural,
    title = "Neural Machine Translation: Basics, Practical Aspects and Recent Trends",
    author = "Cromieres, Fabien  and
      Nakazawa, Toshiaki  and
      Dabre, Raj",
    booktitle = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-5004",
    pages = "11--13",
    abstract = "Machine Translation (MT) is a sub-field of NLP which has experienced a number of paradigm shifts since its inception. Up until 2014, Phrase Based Statistical Machine Translation (PBSMT) approaches used to be the state of the art. In late 2014, Neural Machine Translation (NMT) was introduced and was proven to outperform all PBSMT approaches by a significant margin. Since then, the NMT approaches have undergone several transformations which have pushed the state of the art even further. This tutorial is primarily aimed at researchers who are either interested in or are fairly new to the world of NMT and want to obtain a deep understanding of NMT fundamentals. Because it will also cover the latest developments in NMT, it should also be useful to attendees with some experience in NMT.",
}
@inproceedings{paetzold-specia-2017-ultimate,
    title = "The Ultimate Presentation Makeup Tutorial: How to {P}olish your Posters, Slides and Presentations Skills",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-5005",
    pages = "14--15",
    abstract = "There is no question that our research community have, and still has been producing an insurmountable amount of interesting strategies, models and tools to a wide array of problems and challenges in diverse areas of knowledge. But for as long as interesting work has existed, we{'}ve been plagued by a great unsolved mystery: how come there is so much interesting work being published in conferences, but not as many interesting and engaging posters and presentations being featured in them? In this tutorial, we present practical step-by-step makeup solutions for poster, slides and oral presentations in order to help researchers who feel like they are not able to convey the importance of their research to the community in conferences.",
}
@inproceedings{tian-etal-2017-challenge,
    title = "The Challenge of Composition in Distributional and Formal Semantics",
    author = "Tian, Ran  and
      Mineshima, Koji  and
      Mart{\'\i}nez-G{\'o}mez, Pascual",
    booktitle = "Proceedings of the {IJCNLP} 2017, Tutorial Abstracts",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-5006",
    pages = "16--17",
    abstract = "This is tutorial proposal. Abstract is as follows: The principle of compositionality states that the meaning of a complete sentence must be explained in terms of the meanings of its subsentential parts; in other words, each syntactic operation should have a corresponding semantic operation. In recent years, it has been increasingly evident that distributional and formal semantics are complementary in addressing composition; while the distributional/vector-based approach can naturally measure semantic similarity (Mitchell and Lapata, 2010), the formal/symbolic approach has a long tradition within logic-based semantic frameworks (Montague, 1974) and can readily be connected to theorem provers or databases to perform complicated tasks. In this tutorial, we will cover recent efforts in extending word vectors to account for composition and reasoning, the various challenging phenomena observed in composition and addressed by formal semantics, and a hybrid approach that combines merits of the two. Outline and introduction to instructors are found in the submission. Ran Tian has taught a tutorial at the Annual Meeting of the Association for Natural Language Processing in Japan, 2015. The estimated audience size was about one hundred. Only a limited part of the contents in this tutorial is drawn from the previous one. Koji Mineshima has taught a one-week course at the 28th European Summer School in Logic, Language and Information (ESSLLI2016), together with Prof. Daisuke Bekki. Only a few contents are the same with this tutorial. Tutorials on {``}CCG Semantic Parsing{''} have been given in ACL2013, EMNLP2014, and AAAI2015. A coming tutorial on {``}Deep Learning for Semantic Composition{''} will be given in ACL2017. Contents in these tutorials are somehow related to but not overlapping with our proposal.",
}
@proceedings{ijcnlp-2017-ijcnlp-2017,
    title = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    editor = "Liu, Chao-Hong  and
      Nakov, Preslav  and
      Xue, Nianwen",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4000",
}
@inproceedings{rao-etal-2017-ijcnlp,
    title = "{IJCNLP}-2017 Task 1: {C}hinese Grammatical Error Diagnosis",
    author = "Rao, Gaoqi  and
      Zhang, Baolin  and
      Xun, Endong  and
      Lee, Lung-Hao",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4001",
    pages = "1--8",
    abstract = "This paper presents the IJCNLP 2017 shared task for Chinese grammatical error diagnosis (CGED) which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 13 teams registered for this shared task, 5 teams developed the system and submitted a total of 13 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.",
}
@inproceedings{yu-etal-2017-ijcnlp,
    title = "{IJCNLP}-2017 Task 2: Dimensional Sentiment Analysis for {C}hinese Phrases",
    author = "Yu, Liang-Chih  and
      Lee, Lung-Hao  and
      Wang, Jin  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4002",
    pages = "9--16",
    abstract = "This paper presents the IJCNLP 2017 shared task on Dimensional Sentiment Analysis for Chinese Phrases (DSAP) which seeks to identify a real-value sentiment score of Chinese single words and multi-word phrases in the both valence and arousal dimensions. Valence represents the degree of pleasant and unpleasant (or positive and negative) feelings, and arousal represents the degree of excitement and calm. Of the 19 teams registered for this shared task for two-dimensional sentiment analysis, 13 submitted results. We expected that this evaluation campaign could produce more advanced dimensional sentiment analysis techniques, especially for Chinese affective computing. All data sets with gold standards and scoring script are made publicly available to researchers.",
}
@inproceedings{kumar-singh-etal-2017-ijcnlp,
    title = "{IJCNLP}-2017 Task 3: Review Opinion Diversification ({R}ev{O}pi{D}-2017)",
    author = "Kumar Singh, Anil  and
      Thawani, Avijit  and
      Panchal, Mayank  and
      Gupta, Anubhav  and
      McAuley, Julian",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4003",
    pages = "17--25",
    abstract = "Unlike Entity Disambiguation in web search results, Opinion Disambiguation is a relatively unexplored topic. RevOpiD shared task at IJCNLP-2107 aimed to attract attention towards this research problem. In this paper, we summarize the first run of this task and introduce a new dataset that we have annotated for the purpose of evaluating Opinion Mining, Summarization and Disambiguation methods.",
}
@inproceedings{liu-etal-2017-ijcnlp,
    title = "{IJCNLP}-2017 Task 4: Customer Feedback Analysis",
    author = "Liu, Chao-Hong  and
      Moriya, Yasufumi  and
      Poncelas, Alberto  and
      Groves, Declan",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4004",
    pages = "26--33",
    abstract = "This document introduces the IJCNLP 2017 Shared Task on Customer Feedback Analysis. In this shared task we have prepared corpora of customer feedback in four languages, i.e. English, French, Spanish and Japanese. They were annotated in a common meanings categorization, which was improved from an ADAPT-Microsoft pivot study on customer feedback. Twenty teams participated in the shared task and twelve of them have submitted prediction results. The results show that performance of prediction meanings of customer feedback is reasonable well in four languages. Nine system description papers are archived in the shared tasks proceeding.",
}
@inproceedings{guo-etal-2017-ijcnlp,
    title = "{IJCNLP}-2017 Task 5: Multi-choice Question Answering in Examinations",
    author = "Guo, Shangmin  and
      Liu, Kang  and
      He, Shizhu  and
      Liu, Cao  and
      Zhao, Jun  and
      Wei, Zhuoyu",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4005",
    pages = "34--40",
    abstract = "The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1. The questions are all 4-way multi-choice questions writing in Chinese and English respectively that cover a wide range of subjects, e.g. Biology, History, Life Science and etc. And, all questions are restrained within the elementary and middle school level. During the whole procedure of this task, 7 teams submitted 323 runs in total. This paper describes the collected data, the format and size of these questions, formal run statistics and results, overview and performance statistics of different methods",
}
@inproceedings{yang-etal-2017-alibaba,
    title = "{A}libaba at {IJCNLP}-2017 Task 1: Embedding Grammatical Features into {LSTM}s for {C}hinese Grammatical Error Diagnosis Task",
    author = "Yang, Yi  and
      Xie, Pengjun  and
      Tao, Jun  and
      Xu, Guangwei  and
      Li, Linlin  and
      Si, Luo",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4006",
    pages = "41--46",
    abstract = "This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to improve the performance. At the identification level and the position level our system gets the highest F1 scores. At the position level, which is the most difficult level, we perform best on all metrics.",
}
@inproceedings{wu-etal-2017-thu,
    title = "{THU}{\_}{NGN} at {IJCNLP}-2017 Task 2: Dimensional Sentiment Analysis for {C}hinese Phrases with Deep {LSTM}",
    author = "Wu, Chuhan  and
      Wu, Fangzhao  and
      Huang, Yongfeng  and
      Wu, Sixing  and
      Yuan, Zhigang",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4007",
    pages = "47--52",
    abstract = "Predicting valence-arousal ratings for words and phrases is very useful for constructing affective resources for dimensional sentiment analysis. Since the existing valence-arousal resources of Chinese are mainly in word-level and there is a lack of phrase-level ones, the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) task aims to predict the valence-arousal ratings for Chinese affective words and phrases automatically. In this task, we propose an approach using a densely connected LSTM network and word features to identify dimensional sentiment on valence and arousal for words and phrases jointly. We use word embedding as major feature and choose part of speech (POS) and word clusters as additional features to train the dense LSTM network. The evaluation results of our submissions (1st and 2nd in average performance) validate the effectiveness of our system to predict valence and arousal dimensions for Chinese words and phrases.",
}
@inproceedings{mishra-etal-2017-iiit,
    title = "{IIIT}-{H} at {IJCNLP}-2017 Task 3: A Bidirectional-{LSTM} Approach for Review Opinion Diversification",
    author = "Mishra, Pruthwik  and
      Danda, Prathyusha  and
      Kanneganti, Silpa  and
      Lanka, Soujanya",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4008",
    pages = "53--58",
    abstract = "The Review Opinion Diversification (Revopid-2017) shared task focuses on selecting top-k reviews from a set of reviews for a particular product based on a specific criteria. In this paper, we describe our approaches and results for modeling the ranking of reviews based on their usefulness score, this being the first of the three subtasks under this shared task. Instead of posing this as a regression problem, we modeled this as a classification task where we want to identify whether a review is useful or not. We employed a bi-directional LSTM to represent each review and is used with a softmax layer to predict the usefulness score. We chose the review with highest usefulness score, then find its cosine similarity score with rest of the reviews. This is done in order to ensure diversity in the selection of top-k reviews. On the top-5 list prediction, we finished 3rd while in top-10 list one, we are placed 2nd in the shared task. We have discussed the model and the results in detail in the paper.",
}
@inproceedings{elfardy-etal-2017-bingo,
    title = "Bingo at {IJCNLP}-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification",
    author = "Elfardy, Heba  and
      Srivastava, Manisha  and
      Xiao, Wei  and
      Kramer, Jared  and
      Agarwal, Tarun",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4009",
    pages = "59--66",
    abstract = "The ability to automatically and accurately process customer feedback is a necessity in the private sector. Unfortunately, customer feedback can be one of the most difficult types of data to work with due to the sheer volume and variety of services, products, languages, and cultures that comprise the customer experience. In order to address this issue, our team built a suite of classifiers trained on a four-language, multi-label corpus released as part of the shared task on {``}Customer Feedback Analysis{''} at IJCNLP 2017. In addition to standard text preprocessing, we translated each dataset into each other language to increase the size of the training datasets. Additionally, we also used word embeddings in our feature engineering step. Ultimately, we trained classifiers using Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) Recurrent Neural Networks. Overall, we achieved a Macro-Average F-score between 48.7{\%} and 56.0{\%} for the four languages and ranked 3/12 for English, 3/7 for Spanish, 1/8 for French, and 2/7 for Japanese.",
}
@inproceedings{dzendzik-etal-2017-adapt,
    title = "{ADAPT} Centre Cone Team at {IJCNLP}-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task",
    author = "Dzendzik, Daria  and
      Poncelas, Alberto  and
      Vogel, Carl  and
      Liu, Qun",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4010",
    pages = "67--72",
    abstract = "We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi-choice Question Answering in Examinations shared task. The system is based on a logistic regression over the string similarities between question, answer, and additional text. We obtain the highest grade out of six systems: 48.7{\%} accuracy on a validation set (vs. a baseline of 29.45{\%}) and 45.6{\%} on a test set.",
}
@inproceedings{liao-etal-2017-ynu,
    title = "{YNU}-{HPCC} at {IJCNLP}-2017 Task 1: {C}hinese Grammatical Error Diagnosis Using a Bi-directional {LSTM}-{CRF} Model",
    author = "Liao, Quanlei  and
      Wang, Jin  and
      Yang, Jinnan  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4011",
    pages = "73--77",
    abstract = "Building a system to detect Chinese grammatical errors is a challenge for natural-language processing researchers. As Chinese learners are increasing, developing such a system can help them study Chinese more easily. This paper introduces a bi-directional long short-term memory (BiLSTM) - conditional random field (CRF) model to produce the sequences that indicate an error type for every position of a sentence, since we regard Chinese grammatical error diagnosis (CGED) as a sequence-labeling problem.",
}
@inproceedings{li-etal-2017-cvte,
    title = "{CVTE} at {IJCNLP}-2017 Task 1: Character Checking System for {C}hinese Grammatical Error Diagnosis Task",
    author = "Li, Xian  and
      Wang, Peng  and
      Wang, Suixue  and
      Jiang, Guanyu  and
      You, Tianyuan",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4012",
    pages = "78--83",
    abstract = "Grammatical error diagnosis is an important task in natural language processing. This paper introduces CVTE Character Checking System in the NLP-TEA-4 shared task for CGED 2017, we use Bi-LSTM to generate the probability of every character, then take two kinds of strategies to decide whether a character is correct or not. This system is probably more suitable to deal with the error type of bad word selection, which is one of four types of errors, and the rest are words re-dundancy, words missing and words disorder. Finally the second strategy achieves better F1 score than the first one at all of detection level, identification level, position level.",
}
@inproceedings{zhong-wang-2017-ldccnlp,
    title = "{LDCCNLP} at {IJCNLP}-2017 Task 2: Dimensional Sentiment Analysis for {C}hinese Phrases Using Machine Learning",
    author = "Zhong, Peng  and
      Wang, Jingbin",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4013",
    pages = "84--88",
    abstract = "Sentiment analysis on Chinese text has intensively studied. The basic task for related research is to construct an affective lexicon and thereby predict emotional scores of different levels. However, finite lexicon resources make it difficult to effectively and automatically distinguish between various types of sentiment information in Chinese texts. This IJCNLP2017-Task2 competition seeks to automatically calculate Valence and Arousal ratings within the hierarchies of vocabulary and phrases in Chinese. We introduce a regression methodology to automatically recognize continuous emotional values, and incorporate a word embedding technique. In our system, the MAE predictive values of Valence and Arousal were 0.811 and 0.996, respectively, for the sentiment dimension prediction of words in Chinese. In phrase prediction, the corresponding results were 0.822 and 0.489, ranking sixth among all teams.",
}
@inproceedings{li-etal-2017-ckip,
    title = "{CKIP} at {IJCNLP}-2017 Task 2: Neural Valence-Arousal Prediction for Phrases",
    author = "Li, Peng-Hsuan  and
      Ma, Wei-Yun  and
      Wang, Hsin-Yang",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4014",
    pages = "89--94",
    abstract = "CKIP takes part in solving the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) share task of IJCNLP 2017. This task calls for systems that can predict the valence and the arousal of Chinese phrases, which are real values between 1 and 9. To achieve this, functions mapping Chinese character sequences to real numbers are built by regression techniques. In addition, the CKIP phrase Valence-Arousal (VA) predictor depends on knowledge of modifier words and head words. This includes the types of known modifier words, VA of head words, and distributional semantics of both these words. The predictor took the second place out of 13 teams on phrase VA prediction, with 0.444 MAE and 0.935 PCC on valence, and 0.395 MAE and 0.904 PCC on arousal.",
}
@inproceedings{lin-etal-2017-cial,
    title = "{CIAL} at {IJCNLP}-2017 Task 2: An Ensemble Valence-Arousal Analysis System for {C}hinese Words and Phrases",
    author = "Lin, Zheng-Wen  and
      Chang, Yung-Chun  and
      Wang, Chen-Ann  and
      Hsieh, Yu-Lun  and
      Hsu, Wen-Lian",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4015",
    pages = "95--99",
    abstract = "Sentiment lexicon is very helpful in dimensional sentiment applications. Because of countless Chinese words, developing a method to predict unseen Chinese words is required. The proposed method can handle both words and phrases by using an ADVWeight List for word prediction, which in turn improves our performance at phrase level. The evaluation results demonstrate that our system is effective in dimensional sentiment analysis for Chinese phrases. The Mean Absolute Error (MAE) and Pearson{'}s Correlation Coefficient (PCC) for Valence are 0.723 and 0.835, respectively, and those for Arousal are 0.914 and 0.756, respectively.",
}
@inproceedings{zhou-etal-2017-alibaba,
    title = "{A}libaba at {IJCNLP}-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of {C}hinese Phrases",
    author = "Zhou, Xin  and
      Wang, Jian  and
      Xie, Xu  and
      Sun, Changlong  and
      Si, Luo",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4016",
    pages = "100--104",
    abstract = "This paper introduces Team Alibaba{'}s systems participating IJCNLP 2017 shared task No. 2 Dimensional Sentiment Analysis for Chinese Phrases (DSAP). The systems mainly utilize a multi-layer neural networks, with multiple features input such as word embedding, part-of-speech-tagging (POST), word clustering, prefix type, character embedding, cross sentiment input, and AdaBoost method for model training. For word level task our best run achieved MAE 0.545 (ranked 2nd), PCC 0.892 (ranked 2nd) in valence prediction and MAE 0.857 (ranked 1st), PCC 0.678 (ranked 2nd) in arousal prediction. For average performance of word and phrase task we achieved MAE 0.5355 (ranked 3rd), PCC 0.8965 (ranked 3rd) in valence prediction and MAE 0.661 (ranked 3rd), PCC 0.766 (ranked 2nd) in arousal prediction. In the final our submitted system achieved 2nd in mean rank.",
}
@inproceedings{chen-etal-2017-nlpsa,
    title = "{NLPSA} at {IJCNLP}-2017 Task 2: Imagine Scenario: Leveraging Supportive Images for Dimensional Sentiment Analysis",
    author = "Chen, Szu-Min  and
      Chen, Zi-Yuan  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4017",
    pages = "105--111",
    abstract = "Categorical sentiment classification has drawn much attention in the field of NLP, while less work has been conducted for dimensional sentiment analysis (DSA). Recent works for DSA utilize either word embedding, knowledge base features, or bilingual language resources. In this paper, we propose our model for IJCNLP 2017 Dimensional Sentiment Analysis for Chinese Phrases shared task. Our model incorporates word embedding as well as image features, attempting to simulate human{'}s imaging behavior toward sentiment analysis. Though the performance is not comparable to others in the end, we conduct several experiments with possible reasons discussed, and analyze the drawbacks of our model.",
}
@inproceedings{yeh-etal-2017-ncyu,
    title = "{NCYU} at {IJCNLP}-2017 Task 2: Dimensional Sentiment Analysis for {C}hinese Phrases using Vector Representations",
    author = "Yeh, Jui-Feng  and
      Tsai, Jian-Cheng  and
      Wu, Bo-Wei  and
      Kuang, Tai-You",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4018",
    pages = "112--117",
    abstract = "This paper presents two vector representations proposed by National Chiayi University (NCYU) about phrased-based sentiment detection which was used to compete in dimensional sentiment analysis for Chinese phrases (DSACP) at IJCNLP 2017. The vector-based sentiment phraselike unit analysis models are proposed in this article. E-HowNet-based clustering is used to obtain the values of valence and arousal for sentiment words first. An out-of-vocabulary function is also defined in this article to measure the dimensional emotion values for unknown words. For predicting the corresponding values of sentiment phrase-like unit, a vectorbased approach is proposed here. According to the experimental results, we can find the proposed approach is efficacious.",
}
@inproceedings{benajiba-etal-2017-mainiwayai,
    title = "{M}ainiway{AI} at {IJCNLP}-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction",
    author = "Benajiba, Yassine  and
      Sun, Jin  and
      Zhang, Yong  and
      Weng, Zhiliang  and
      Biran, Or",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4019",
    pages = "118--123",
    abstract = "This paper introduces Mainiway AI Labs submitted system for the IJCNLP 2017 shared task on Dimensional Sentiment Analysis of Chinese Phrases (DSAP), and related experiments. Our approach consists of deep neural networks with various architectures, and our best system is a voted ensemble of networks. We achieve a Mean Absolute Error of 0.64 in valence prediction and 0.68 in arousal prediction on the test set, both placing us as the 5th ranked team in the competition.",
}
@inproceedings{lee-etal-2017-nctu,
    title = "{NCTU}-{NTUT} at {IJCNLP}-2017 Task 2: Deep Phrase Embedding using bi-{LSTM}s for Valence-Arousal Ratings Prediction of {C}hinese Phrases",
    author = "Lee, Yen-Hsuan  and
      Yeh, Han-Yun  and
      Wang, Yih-Ru  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4020",
    pages = "124--129",
    abstract = "In this paper, a deep phrase embedding approach using bi-directional long short-term memory (Bi-LSTM) is proposed to predict the valence-arousal ratings of Chinese words and phrases. It adopts a Chinese word segmentation frontend, a local order-aware word, a global phrase embedding representations and a deep regression neural network (DRNN) model. The performance of the proposed method was benchmarked by the IJCNLP 2017 shared task 2. According the official evaluation results, our best system achieved mean rank 6.5 among all 24 submissions.",
}
@inproceedings{lin-chang-2017-ntoua,
    title = "{NTOUA} at {IJCNLP}-2017 Task 2: Predicting Sentiment Scores of {C}hinese Words and Phrases",
    author = "Lin, Chuan-Jie  and
      Chang, Hao-Tsung",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4021",
    pages = "130--133",
    abstract = "This paper describes the approaches of sentimental score prediction in the NTOU DSA system participating in DSAP this year. The modules to predict scores for words are adapted from our system last year. The approach to predict scores for phrases is keyword-based machine learning method. The performance of our system is good in predicting scores of phrases.",
}
@inproceedings{wu-etal-2017-cyut,
    title = "{CYUT} at {IJCNLP}-2017 Task 3: System Report for Review Opinion Diversification",
    author = "Wu, Shih-Hung  and
      Chang, Su-Yu  and
      Chen, Liang-Pu",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4022",
    pages = "134--137",
    abstract = "Review Opinion Diversification (RevOpiD) 2017 is a shared task which is held in International Joint Conference on Natural Language Processing (IJCNLP). The shared task aims at selecting top-k reviews, as a summary, from a set of re-views. There are three subtasks in RevOpiD: helpfulness ranking, rep-resentativeness ranking, and ex-haustive coverage ranking. This year, our team submitted runs by three models. We focus on ranking reviews based on the helpfulness of the reviews. In the first two models, we use linear regression with two different loss functions. First one is least squares, and second one is cross entropy. The third run is a random baseline. For both k=5 and k=10, our second model gets the best scores in the official evaluation metrics.",
}
@inproceedings{dey-etal-2017-junlp,
    title = "{JUNLP} at {IJCNLP}-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification",
    author = "Dey, Monalisa  and
      Mondal, Anupam  and
      Das, Dipankar",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4023",
    pages = "138--142",
    abstract = {IJCNLP-17 Review Opinion Diversification (RevOpiD-2017) task has been designed for ranking the top-k reviews of a product from a set of reviews, which assists in identifying a summarized output to express the opinion of the entire review set. The task is divided into three independent subtasks as subtask-A,subtask-B, and subtask-C. Each of these three subtasks selects the top-k reviews based on helpfulness, representativeness, and exhaustiveness of the opinions expressed in the review set individually. In order to develop the modules and predict the rank of reviews for all three subtasks, we have employed two well-known supervised classifiers namely, Na{\"\i}ve Bayes and Logistic Regression on the top of several extracted features such as the number of nouns, number of verbs, and number of sentiment words etc from the provided datasets. Finally, the organizers have helped to validate the predicted outputs for all three subtasks by using their evaluation metrics. The metrics provide the scores of list size 5 as (0.80 (mth)) for subtask-A, (0.86 (cos), 0.87 (cos d), 0.71 (cpr), 4.98 (a-dcg), and 556.94 (wt)) for subtask B, and (10.94 (unwt) and 0.67 (recall)) for subtask C individually.},
}
@inproceedings{plank-2017-1,
    title = "All-In-1 at {IJCNLP}-2017 Task 4: Short Text Classification with One Model for All Languages",
    author = "Plank, Barbara",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4024",
    pages = "143--148",
    abstract = "We present All-In-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.",
}
@inproceedings{lin-etal-2017-sentinlp,
    title = "{S}enti{NLP} at {IJCNLP}-2017 Task 4: Customer Feedback Analysis Using a {B}i-{LSTM}-{CNN} Model",
    author = "Lin, Shuying  and
      Xie, Huosheng  and
      Yu, Liang-Chih  and
      Lai, K. Robert",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4025",
    pages = "149--154",
    abstract = "The analysis of customer feedback is useful to provide good customer service. There are a lot of online customer feedback are produced. Manual classification is impractical because the high volume of data. Therefore, the automatic classification of the customer feedback is of importance for the analysis system to identify meanings or intentions that the customer express. The aim of shared Task 4 of IJCNLP 2017 is to classify the customer feedback into six tags categorization. In this paper, we present a system that uses word embeddings to express the feature of the sentence in the corpus and the neural network as the classifier to complete the shared task. And then the ensemble method is used to get final predictive result. The proposed method get ranked first among twelve teams in terms of micro-averaged F1 and second for accura-cy metric.",
}
@inproceedings{danda-etal-2017-iiit,
    title = "{IIIT}-{H} at {IJCNLP}-2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches",
    author = "Danda, Prathyusha  and
      Mishra, Pruthwik  and
      Kanneganti, Silpa  and
      Lanka, Soujanya",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4026",
    pages = "155--160",
    abstract = "The IJCNLP 2017 shared task on Customer Feedback Analysis focuses on classifying customer feedback into one of a predefined set of categories or classes. In this paper, we describe our approach to this problem and the results on four languages, i.e. English, French, Japanese and Spanish. Our system implemented a bidirectional LSTM (Graves and Schmidhuber, 2005) using pre-trained glove (Pennington et al., 2014) and fastText (Joulin et al., 2016) embeddings, and SVM (Cortes and Vapnik, 1995) with TF-IDF vectors for classifying the feedback data which is described in the later sections. We also tried different machine learning techniques and compared the results in this paper. Out of the 12 participating teams, our systems obtained 0.65, 0.86, 0.70 and 0.56 exact accuracy score in English, Spanish, French and Japanese respectively. We observed that our systems perform better than the baseline systems in three languages while we match the baseline accuracy for Japanese on our submitted systems. We noticed significant improvements in Japanese in later experiments, matching the highest performing system that was submitted in the shared task, which we will discuss in this paper.",
}
@inproceedings{lohar-etal-2017-adapt,
    title = "{ADAPT} at {IJCNLP}-2017 Task 4: A Multinomial Naive {B}ayes Classification Approach for Customer Feedback Analysis task",
    author = "Lohar, Pintu  and
      Dutta Chowdhury, Koel  and
      Afli, Haithem  and
      Hasanuzzaman, Mohammed  and
      Way, Andy",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4027",
    pages = "161--169",
    abstract = "In this age of the digital economy, promoting organisations attempt their best to engage the customers in the feedback provisioning process. With the assistance of customer insights, an organisation can develop a better product and provide a better service to its customer. In this paper, we analyse the real world samples of customer feedback from Microsoft Office customers in four languages, i.e., English, French, Spanish and Japanese and conclude a five-plus-one-classes categorisation (comment, request, bug, complaint, meaningless and undetermined) for meaning classification. The task is to {\%}access multilingual corpora annotated by the proposed meaning categorization scheme and develop a system to determine what class(es) the customer feedback sentences should be annotated as in four languages. We propose following approaches to accomplish this task: (i) a multinomial naive bayes (MNB) approach for multi-label classification, (ii) MNB with one-vs-rest classifier approach, and (iii) the combination of the multilabel classification-based and the sentiment classification-based approach. Our best system produces F-scores of 0.67, 0.83, 0.72 and 0.7 for English, Spanish, French and Japanese, respectively. The results are competitive to the best ones for all languages and secure 3rd and 5th position for Japanese and French, respectively, among all submitted systems.",
}
@inproceedings{dhyani-2017-ohiostate,
    title = "{O}hio{S}tate at {IJCNLP}-2017 Task 4: Exploring Neural Architectures for Multilingual Customer Feedback Analysis",
    author = "Dhyani, Dushyanta",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4028",
    pages = "170--173",
    abstract = "This paper describes our systems for IJCNLP 2017 Shared Task on Customer Feedback Analysis. We experimented with simple neural architectures that gave competitive performance on certain tasks. This includes shallow CNN and Bi-Directional LSTM architectures with Facebook{'}s Fasttext as a baseline model. Our best performing model was in the Top 5 systems using the Exact-Accuracy and Micro-Average-F1 metrics for the Spanish (85.28{\%} for both) and French (70{\%} and 73.17{\%} respectively) task, and outperformed all the other models on comment (87.28{\%}) and meaningless (51.85{\%}) tags using Micro Average F1 by Tags metric for the French task.",
}
@inproceedings{wang-etal-2017-ynu,
    title = "{YNU}-{HPCC} at {IJCNLP}-2017 Task 4: Attention-based Bi-directional {GRU} Model for Customer Feedback Analysis Task of {E}nglish",
    author = "Wang, Nan  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4029",
    pages = "174--179",
    abstract = "This paper describes our submission to IJCNLP 2017 shared task 4, for predicting the tags of unseen customer feedback sentences, such as comments, complaints, bugs, requests, and meaningless and undetermined statements. With the use of a neural network, a large number of deep learning methods have been developed, which perform very well on text classification. Our ensemble classification model is based on a bi-directional gated recurrent unit and an attention mechanism which shows a 3.8{\%} improvement in classification accuracy. To enhance the model performance, we also compared it with several word-embedding models. The comparative results show that a combination of both word2vec and GloVe achieves the best performance.",
}
@inproceedings{banerjee-etal-2017-nitmz,
    title = "{NITMZ}-{JU} at {IJCNLP}-2017 Task 4: Customer Feedback Analysis",
    author = "Banerjee, Somnath  and
      Pakray, Partha  and
      Manna, Riyanka  and
      Das, Dipankar  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4030",
    pages = "180--183",
    abstract = "In this paper, we describe a deep learning framework for analyzing the customer feedback as part of our participation in the shared task on Customer Feedback Analysis at the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017). A Convolutional Neural Network (CNN) based deep neural network model was employed for the customer feedback task. The proposed system was evaluated on two languages, namely, English and French.",
}
@inproceedings{gupta-etal-2017-iitp,
    title = "{IITP} at {IJCNLP}-2017 Task 4: Auto Analysis of Customer Feedback using {CNN} and {GRU} Network",
    author = "Gupta, Deepak  and
      Lenka, Pabitra  and
      Bedi, Harsimran  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4031",
    pages = "184--193",
    abstract = "Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is in great demand. Users may write feedbacks in any language, and hence mining appropriate information often becomes intractable. Especially in a traditional feature-based supervised model, it is difficult to build a generic system as one has to understand the concerned language for finding the relevant features. In order to overcome this, we propose deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches that do not require handcrafting of features. We evaluate these techniques for analyzing customer feedback sentences on four languages, namely English, French, Japanese and Spanish. Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis. Our model achieved the second rank in French, with an accuracy of 71.75{\%} and third ranks for all the other languages.",
}
@inproceedings{wang-etal-2017-ynudlg,
    title = "{YNUDLG} at {IJCNLP}-2017 Task 5: A {CNN}-{LSTM} Model with Attention for Multi-choice Question Answering in Examinations",
    author = "Wang, Min  and
      Liu, Qingxun  and
      Ding, Peng  and
      Li, Yongbin  and
      Zhou, Xiaobing",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4032",
    pages = "194--198",
    abstract = "In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer. We also incorporating external knowledge by training Word2Vec on Flashcards data, thus we get more compact embedding. Experimental results show that our method achieves better or comparable performance compared with the baseline system. The proposed approach achieves the accuracy of 0.39, 0.42 in English valid set, test set, respectively.",
}
@inproceedings{li-kong-2017-als,
    title = "{ALS} at {IJCNLP}-2017 Task 5: Answer Localization System for Multi-Choice Question Answering in Exams",
    author = "Li, Changliang  and
      Kong, Cunliang",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4033",
    pages = "199--202",
    abstract = "Multi-choice question answering in exams is a typical QA task. To accomplish this task, we present an answer localization method to locate answers shown in web pages, considering structural information and semantic information both. Using this method as basis, we analyze sentences and paragraphs appeared on web pages to get predictions. With this answer localization system, we get effective results on both validation dataset and test dataset.",
}
@inproceedings{hazem-2017-mappsent,
    title = "{M}app{S}ent at {IJCNLP}-2017 Task 5: A Textual Similarity Approach Applied to Multi-choice Question Answering in Examinations",
    author = "Hazem, Amir",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4034",
    pages = "203--207",
    abstract = "In this paper we present MappSent, a textual similarity approach that we applied to the multi-choice question answering in exams shared task. MappSent has initially been proposed for question-to-question similarity hazem2017. In this work, we present the results of two adaptations of MappSent for the question answering task on the English dataset.",
}
@inproceedings{yuan-etal-2017-ynu,
    title = "{YNU}-{HPCC} at {IJCNLP}-2017 Task 5: Multi-choice Question Answering in Exams Using an Attention-based {LSTM} Model",
    author = "Yuan, Hang  and
      Zhang, You  and
      Wang, Jin  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4035",
    pages = "208--212",
    abstract = "A shared task is a typical question answering task that aims to test how accurately the participants can answer the questions in exams. Typically, for each question, there are four candidate answers, and only one of the answers is correct. The existing methods for such a task usually implement a recurrent neural network (RNN) or long short-term memory (LSTM). However, both RNN and LSTM are biased models in which the words in the tail of a sentence are more dominant than the words in the header. In this paper, we propose the use of an attention-based LSTM (AT-LSTM) model for these tasks. By adding an attention mechanism to the standard LSTM, this model can more easily capture long contextual information.",
}
@inproceedings{sarkar-etal-2017-ju,
    title = "{JU} {NITM} at {IJCNLP}-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System",
    author = "Sarkar, Sandip  and
      Das, Dipankar  and
      Pakray, Partha",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-4036",
    pages = "213--216",
    abstract = "This paper describes the participation of the JU NITM team in IJCNLP-2017 Task 5: {``}Multi-choice Question Answering in Examinations{''}. The main aim of this shared task is to choose the correct option for each multi-choice question. Our proposed model includes vector representations as feature and machine learning for classification. At first we represent question and answer in vector space and after that find the cosine similarity between those two vectors. Finally we apply classification approach to find the correct answer. Our system was only developed for the English language, and it obtained an accuracy of 40.07{\%} for test dataset and 40.06{\%} for valid dataset.",
}
@proceedings{ijcnlp-2017-ijcnlp,
    title = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    editor = "Park, Seong-Bae  and
      Supnithi, Thepchai",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3000",
}
@inproceedings{paetzold-etal-2017-massalign,
    title = "{MASSA}lign: Alignment and Annotation of Comparable Documents",
    author = "Paetzold, Gustavo  and
      Alva-Manchego, Fernando  and
      Specia, Lucia",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3001",
    pages = "1--4",
    abstract = "We introduce MASSAlign: a Python library for the alignment and annotation of monolingual comparable documents. MASSAlign offers easy-to-use access to state of the art algorithms for paragraph and sentence-level alignment, as well as novel algorithms for word-level annotation of transformation operations between aligned sentences. In addition, MASSAlign provides a visualization module to display and analyze the alignments and annotations performed.",
}
@inproceedings{van-durme-etal-2017-cadet,
    title = "{CADET}: Computer Assisted Discovery Extraction and Translation",
    author = "Van Durme, Benjamin  and
      Lippincott, Tom  and
      Duh, Kevin  and
      Burchfield, Deana  and
      Poliak, Adam  and
      Costello, Cash  and
      Finin, Tim  and
      Miller, Scott  and
      Mayfield, James  and
      Koehn, Philipp  and
      Harman, Craig  and
      Lawrie, Dawn  and
      May, Chandler  and
      Thomas, Max  and
      Carrell, Annabelle  and
      Chaloux, Julianne  and
      Chen, Tongfei  and
      Comerford, Alex  and
      Dredze, Mark  and
      Glass, Benjamin  and
      Hao, Shudong  and
      Martin, Patrick  and
      Rastogi, Pushpendre  and
      Sankepally, Rashmi  and
      Wolfe, Travis  and
      Tran, Ying-Ying  and
      Zhang, Ted",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3002",
    pages = "5--8",
    abstract = "Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.",
}
@inproceedings{noh-etal-2017-wisereporter,
    title = "{W}ise{R}eporter: A {K}orean Report Generation System",
    author = "Noh, Yunseok  and
      Choi, Su Jeong  and
      Park, Seong-Bae  and
      Park, Se-Young",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3003",
    pages = "9--12",
    abstract = "We demonstrate a report generation system called WiseReporter. The WiseReporter generates a text report of a specific topic which is usually given as a keyword by verbalizing knowledge base facts involving the topic. This demonstration does not demonstate only the report itself, but also the processes how the sentences for the report are generated. We are planning to enhance WiseReporter in the future by adding data analysis based on deep learning architecture and text summarization.",
}
@inproceedings{wang-etal-2017-encyclolink,
    title = "{E}ncyclolink: A Cross-Encyclopedia,Cross-language Article-Linking System and Web-based Search Interface",
    author = "Wang, Yu-Chun  and
      Wong, Ka Ming  and
      Wu, Chun-Kai  and
      Pan, Chao-Lin  and
      Tsai, Richard Tzong-Han",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3004",
    pages = "13--16",
    abstract = "Cross-language article linking (CLAL) is the task of finding corresponding article pairs across encyclopedias of different languages. In this paper, we present Encyclolink, a web-based CLAL search interface designed to help users find equivalent encyclopedia articles in Baidu Baike for a given English Wikipedia article title query. Encyclolink is powered by our cross-encyclopedia entity embedding CLAL system (0.8 MRR). The browser-based Interface provides users with a clear and easily readable preview of the contents of retrieved articles for comparison.",
}
@inproceedings{wang-etal-2017-telecom,
    title = "A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification",
    author = "Wang, Jui-Yang  and
      Kuo, Min-Feng  and
      Han, Jen-Chieh  and
      Shih, Chao-Chuang  and
      Chen, Chun-Hsun  and
      Lee, Po-Ching  and
      Tsai, Richard Tzong-Han",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3005",
    pages = "17--20",
    abstract = "In the paper, we propose an information retrieval based (IR-based) Question Answering (QA) system to assist online customer service staffs respond users in the telecom domain. When user asks a question, the system retrieves a set of relevant answers and ranks them. Moreover, our system uses a novel reranker to enhance the ranking result of information retrieval.It employs the word2vec model to represent the sentences as vectors. It also uses a sub-category feature, predicted by the k-nearest neighbor algorithm. Finally, the system returns the top five candidate answers, making online staffs find answers much more efficiently.",
}
@inproceedings{wang-etal-2017-totemss,
    title = "{TOTEMSS}: Topic-based, Temporal Sentiment Summarisation for {T}witter",
    author = "Wang, Bo  and
      Liakata, Maria  and
      Tsakalidis, Adam  and
      Georgakopoulos Kolaitis, Spiros  and
      Papadopoulos, Symeon  and
      Apostolidis, Lazaros  and
      Zubiaga, Arkaitz  and
      Procter, Rob  and
      Kompatsiaris, Yiannis",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3006",
    pages = "21--24",
    abstract = "We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets. We describe the main elements of the system and illustrate its functionality with two examples of sentiment analysis of topics related to the 2017 UK general election.",
}
@inproceedings{scarton-etal-2017-musst,
    title = "{MUSST}: A Multilingual Syntactic Simplification Tool",
    author = "Scarton, Carolina  and
      Palmero Aprosio, Alessio  and
      Tonelli, Sara  and
      Mart{\'\i}n Wanton, Tamara  and
      Specia, Lucia",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3007",
    pages = "25--28",
    abstract = "We describe MUSST, a multilingual syntactic simplification tool. The tool supports sentence simplifications for English, Italian and Spanish, and can be easily extended to other languages. Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications). The tool was implemented in the context of the European project SIMPATICO on text simplification for Public Administration (PA) texts. Our evaluation on sentences in the PA domain shows that we obtain correct simplifications for 76{\%} of the simplified cases in English, 71{\%} of the cases in Spanish. For Italian, the results are lower (38{\%}) but the tool is still under development.",
}
@inproceedings{wang-etal-2017-xmu-neural,
    title = "{XMU} Neural Machine Translation Online Service",
    author = "Wang, Boli  and
      Tan, Zhixing  and
      Hu, Jinming  and
      Chen, Yidong  and
      Shi, Xiaodong",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3008",
    pages = "29--32",
    abstract = "We demonstrate a neural machine translation web service. Our NMT service provides web-based translation interfaces for a variety of language pairs. We describe the architecture of NMT runtime pipeline and the training details of NMT models. We also show several applications of our online translation interfaces.",
}
@inproceedings{wang-etal-2017-semantics,
    title = "Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking",
    author = "Wang, Longyue  and
      Du, Jinhua  and
      Li, Liangyou  and
      Tu, Zhaopeng  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3009",
    pages = "33--36",
    abstract = "We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a novel grounded semantic method for dialogue understanding and task-order management. TODAY is a case-study demo which can efficiently and accurately assist customers and agents in different languages to reach an agreement in a dialogue for the hotel booking.",
}
@inproceedings{pham-etal-2017-nnvlp,
    title = "{NNVLP}: A Neural Network-Based {V}ietnamese Language Processing Toolkit",
    author = "Pham, Thai-Hoang  and
      Pham, Xuan-Khoai  and
      Nguyen, Tuan-Anh  and
      Le-Hong, Phuong",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3010",
    pages = "37--40",
    abstract = "This paper demonstrates neural network-based toolkit namely NNVLP for essential Vietnamese language processing tasks including part-of-speech (POS) tagging, chunking, Named Entity Recognition (NER). Our toolkit is a combination of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), Conditional Random Field (CRF), using pre-trained word embeddings as input, which outperforms previously published toolkits on these three tasks. We provide both of API and web demo for this toolkit.",
}
@inproceedings{peinelt-etal-2017-classifierguesser,
    title = "{C}lassifier{G}uesser: A Context-based Classifier Prediction System for {C}hinese Language Learners",
    author = "Peinelt, Nicole  and
      Liakata, Maria  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3011",
    pages = "41--44",
    abstract = "Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. In contrast to previous studies, we argue that the choice of classifiers is highly contextual and train context-aware machine learning models based on a novel publicly available dataset, outperforming previous baselines. We further present use cases for our database and models in an interactive demo system.",
}
@inproceedings{lee-etal-2017-automatic,
    title = "Automatic Difficulty Assessment for {C}hinese Texts",
    author = "Lee, John  and
      Liu, Meichun  and
      Lam, Chun Yin  and
      Lau, Tak On  and
      Li, Bing  and
      Li, Keying",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3012",
    pages = "45--48",
    abstract = "We present a web-based interface that automatically assesses reading difficulty of Chinese texts. The system performs word segmentation, part-of-speech tagging and dependency parsing on the input text, and then determines the difficulty levels of the vocabulary items and grammatical constructions in the text. Furthermore, the system highlights the words and phrases that must be simplified or re-written in order to conform to the user-specified target difficulty level. Evaluation results show that the system accurately identifies the vocabulary level of 89.9{\%} of the words, and detects grammar points at 0.79 precision and 0.83 recall.",
}
@inproceedings{wu-etal-2017-verb,
    title = "Verb Replacer: An {E}nglish Verb Error Correction System",
    author = "Wu, Yu-Hsuan  and
      Chen, Jhih-Jie  and
      Chang, Jason",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3013",
    pages = "49--52",
    abstract = "According to the analysis of Cambridge Learner Corpus, using a wrong verb is the most common type of grammatical errors. This paper describes Verb Replacer, a system for detecting and correcting potential verb errors in a given sentence. In our approach, alternative verbs are considered to replace the verb based on an error-annotated corpus and verb-object collocations. The method involves applying regression on channel models, parsing the sentence, identifying the verbs, retrieving a small set of alternative verbs, and evaluating each alternative. Our method combines and improves channel and language models, resulting in high recall of detecting and correcting verb misuse.",
}
@inproceedings{wu-etal-2017-learning,
    title = "Learning Synchronous Grammar Patterns for Assisted Writing for Second Language Learners",
    author = "Wu, Chi-En  and
      Chen, Jhih-Jie  and
      Chang, Jim  and
      Chang, Jason",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3014",
    pages = "53--56",
    abstract = "In this paper, we present a method for extracting Synchronous Grammar Patterns (SGPs) from a given parallel corpus in order to assisted second language learners in writing. A grammar pattern consists of a head word (verb, noun, or adjective) and its syntactic environment. A synchronous grammar pattern describes a grammar pattern in the target language (e.g., English) and its counterpart in an other language (e.g., Mandarin), serving the purpose of native language support. Our method involves identifying the grammar patterns in the target language, aligning these patterns with the target language patterns, and finally filtering valid SGPs. The extracted SGPs with examples are then used to develop a prototype writing assistant system, called WriteAhead/bilingual. Evaluation on a set of randomly selected SGPs shows that our system provides satisfactory writing suggestions for English as a Second Language (ESL) learners.",
}
@inproceedings{li-etal-2017-guess,
    title = "Guess What: A Question Answering Game via On-demand Knowledge Validation",
    author = "Li, Yu-Sheng  and
      Tseng, Chien-Hui  and
      Huang, Chian-Yun  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3015",
    pages = "57--60",
    abstract = "In this paper, we propose an idea of ondemand knowledge validation and fulfill the idea through an interactive Question-Answering (QA) game system, which is named Guess What. An object (e.g. dog) is first randomly chosen by the system, and then a user can repeatedly ask the system questions in natural language to guess what the object is. The system would respond with yes/no along with a confidence score. Some useful hints can also be given if needed. The proposed framework provides a pioneering example of on-demand knowledge validation in dialog environment to address such needs in AI agents/chatbots. Moreover, the released log data that the system gathered can be used to identify the most critical concepts/attributes of an existing knowledge base, which reflects human{'}s cognition about the world.",
}
@inproceedings{xu-etal-2017-stcp,
    title = "{STCP}: Simplified-Traditional {C}hinese Conversion and Proofreading",
    author = "Xu, Jiarui  and
      Ma, Xuezhe  and
      Tsai, Chen-Tse  and
      Hovy, Eduard",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3016",
    pages = "61--64",
    abstract = "This paper aims to provide an effective tool for conversion between Simplified Chinese and Traditional Chinese. We present STCP, a customizable system comprising statistical conversion model, and proofreading web interface. Experiments show that our system achieves comparable character-level conversion performance with the state-of-art systems. In addition, our proofreading interface can effectively support diagnostics and data annotation. STCP is available at \url{http://lagos.lti.cs.cmu.edu:8002/}",
}
@inproceedings{mehta-etal-2017-deep,
    title = "Deep Neural Network based system for solving Arithmetic Word problems",
    author = "Mehta, Purvanshi  and
      Mishra, Pruthwik  and
      Athavale, Vinayak  and
      Shrivastava, Manish  and
      Sharma, Dipti",
    booktitle = "Proceedings of the {IJCNLP} 2017, System Demonstrations",
    month = nov,
    year = "2017",
    address = "Tapei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/I17-3017",
    pages = "65--68",
    abstract = "This paper presents DILTON a system which solves simple arithmetic word problems. DILTON uses a Deep Neural based model to solve math word problems. DILTON divides the question into two parts - worldstate and query. The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation. We report the first deep learning approach for the prediction of operation between two numbers. DILTON learns to predict operations with 88.81{\%} accuracy in a corpus of primary school questions.",
}
@proceedings{ijcnlp-2017-international-joint,
    title = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2000",
}
@inproceedings{watanabe-etal-2017-cky,
    title = "{CKY}-based Convolutional Attention for Neural Machine Translation",
    author = "Watanabe, Taiki  and
      Tamura, Akihiro  and
      Ninomiya, Takashi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2001",
    pages = "1--6",
    abstract = "This paper proposes a new attention mechanism for neural machine translation (NMT) based on convolutional neural networks (CNNs), which is inspired by the CKY algorithm. The proposed attention represents every possible combination of source words (e.g., phrases and structures) through CNNs, which imitates the CKY table in the algorithm. NMT, incorporating the proposed attention, decodes a target sentence on the basis of the attention scores of the hidden states of CNNs. The proposed attention enables NMT to capture alignments from underlying structures of a source sentence without sentence parsing. The evaluations on the Asian Scientific Paper Excerpt Corpus (ASPEC) English-Japanese translation task show that the proposed attention gains 0.66 points in BLEU.",
}
@inproceedings{kamigaito-etal-2017-supervised,
    title = "Supervised Attention for Sequence-to-Sequence Constituency Parsing",
    author = "Kamigaito, Hidetaka  and
      Hayashi, Katsuhiko  and
      Hirao, Tsutomu  and
      Takamura, Hiroya  and
      Okumura, Manabu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2002",
    pages = "7--12",
    abstract = "The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT). Recently, MT performances were improved by incorporating supervised attention into the model. In this paper, we introduce supervised attention to constituency parsing that can be regarded as another translation task. Evaluation results on the PTB corpus showed that the bracketing F-measure was improved by supervised attention.",
}
@inproceedings{aminian-etal-2017-transferring,
    title = "Transferring Semantic Roles Using Translation and Syntactic Information",
    author = "Aminian, Maryam  and
      Rasooli, Mohammad Sadegh  and
      Diab, Mona",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2003",
    pages = "13--19",
    abstract = "Our paper addresses the problem of annotation projection for semantic role labeling for resource-poor languages using supervised annotations from a resource-rich language through parallel data. We propose a transfer method that employs information from source and target syntactic dependencies as well as word alignment density to improve the quality of an iterative bootstrapping method. Our experiments yield a 3.5 absolute labeled F-score improvement over a standard annotation projection method.",
}
@inproceedings{khayrallah-etal-2017-neural,
    title = "Neural Lattice Search for Domain Adaptation in Machine Translation",
    author = "Khayrallah, Huda  and
      Kumar, Gaurav  and
      Duh, Kevin  and
      Post, Matt  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2004",
    pages = "20--25",
    abstract = "Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.",
}
@inproceedings{yawata-etal-2017-analyzing,
    title = "Analyzing Well-Formedness of Syllables in {J}apanese {S}ign {L}anguage",
    author = "Yawata, Satoshi  and
      Miwa, Makoto  and
      Sasaki, Yutaka  and
      Hara, Daisuke",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2005",
    pages = "26--30",
    abstract = "This paper tackles a problem of analyzing the well-formedness of syllables in Japanese Sign Language (JSL). We formulate the problem as a classification problem that classifies syllables into well-formed or ill-formed. We build a data set that contains hand-coded syllables and their well-formedness. We define a fine-grained feature set based on the hand-coded syllables and train a logistic regression classifier on labeled syllables, expecting to find the discriminative features from the trained classifier. We also perform pseudo active learning to investigate the applicability of active learning in analyzing syllables. In the experiments, the best classifier with our combinatorial features achieved the accuracy of 87.0{\%}. The pseudo active learning is also shown to be effective showing that it could reduce about 84{\%} of training instances to achieve the accuracy of 82.0{\%} when compared to the model without active learning.",
}
@inproceedings{patel-bhattacharyya-2017-towards,
    title = "Towards Lower Bounds on Number of Dimensions for Word Embeddings",
    author = "Patel, Kevin  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2006",
    pages = "31--36",
    abstract = "Word embeddings are a relatively new addition to the modern NLP researcher{'}s toolkit. However, unlike other tools, word embeddings are used in a black box manner. There are very few studies regarding various hyperparameters. One such hyperparameter is the dimension of word embeddings. They are rather decided based on a rule of thumb: in the range 50 to 300. In this paper, we show that the dimension should instead be chosen based on corpus statistics. More specifically, we show that the number of pairwise equidistant words of the corpus vocabulary (as defined by some distance/similarity metric) gives a lower bound on the the number of dimensions , and going below this bound results in degradation of quality of learned word embeddings. Through our evaluations on standard word embedding evaluation tasks, we show that for dimensions higher than or equal to the bound, we get better results as compared to the ones below it.",
}
@inproceedings{nguyen-etal-2017-sequence,
    title = "Sequence to Sequence Learning for Event Prediction",
    author = "Nguyen, Dai Quoc  and
      Nguyen, Dat Quoc  and
      Chu, Cuong Xuan  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2007",
    pages = "37--42",
    abstract = "This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively. Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.",
}
@inproceedings{takase-etal-2017-input,
    title = "Input-to-Output Gate to Improve {RNN} Language Models",
    author = "Takase, Sho  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2008",
    pages = "43--48",
    abstract = "This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Output Gate (IOG). IOG has an extremely simple structure, and thus, can be easily combined with any RNN language models. Our experiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG consistently boosts the performance of several different types of current topline RNN language models.",
}
@inproceedings{arnold-etal-2017-counterfactual,
    title = "Counterfactual Language Model Adaptation for Suggesting Phrases",
    author = "Arnold, Kenneth  and
      Chang, Kai-Wei  and
      Kalai, Adam",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2009",
    pages = "49--54",
    abstract = "Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions that writers accept, a related but distinct task to making accurate predictions. Although this task is fundamentally interactive, we propose a counterfactual setting that permits offline training and evaluation. We find that even a simple language model can capture text characteristics that improve acceptability.",
}
@inproceedings{liang-shu-2017-deep,
    title = "Deep Automated Multi-task Learning",
    author = "Liang, Davis  and
      Shu, Yan",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2010",
    pages = "55--60",
    abstract = "Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character prediction, and missing word completion as potential automated tasks. Our results show that training on a primary task in parallel with a secondary automated task improves both the convergence speed and accuracy for the primary task. We suggest two methods for augmenting an existing network with automated tasks and establish better performance in topic prediction, sentiment analysis, and hashtag recommendation. Finally, we show that the MTL models can perform well on datasets that are small and colloquial by nature.",
}
@inproceedings{soni-etal-2017-post,
    title = "Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches",
    author = "Soni, Akshay  and
      Pappu, Aasish  and
      Ni, Jerry Chia-mau  and
      Chevalier, Troy",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2011",
    pages = "61--66",
    abstract = "In Multilabel Learning (MLL) each training instance is associated with a set of labels and the task is to learn a function that maps an unseen instance to its corresponding label set. In this paper, we present a suite of {--} MLL algorithm independent {--} post-processing techniques that utilize the conditional and directional label-dependences in order to make the predictions from any MLL approach more coherent and precise. We solve constraint optimization problem over the output produced by any MLL approach and the result is a refined version of the input predicted label set. Using proposed techniques, we show absolute improvement of 3{\%} on English News and 10{\%} on Chinese E-commerce datasets for P@K metric.",
}
@inproceedings{beck-cohn-2017-learning,
    title = "Learning Kernels over Strings using {G}aussian Processes",
    author = "Beck, Daniel  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2012",
    pages = "67--73",
    abstract = "Non-contiguous word sequences are widely known to be important in modelling natural language. However they not explicitly encoded in common text representations. In this work we propose a model for text processing using string kernels, capable of flexibly representing non-contiguous sequences. Specifically, we derive a vectorised version of the string kernel algorithm and their gradients, allowing efficient hyperparameter optimisation as part of a Gaussian Process framework. Experiments on synthetic data and text regression for emotion analysis show the promise of this technique.",
}
@inproceedings{fujinuma-grissom-ii-2017-substring,
    title = "Substring Frequency Features for Segmentation of {J}apanese Katakana Words with Unlabeled Corpora",
    author = "Fujinuma, Yoshinari  and
      Grissom II, Alvin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2013",
    pages = "74--79",
    abstract = "Word segmentation is crucial in natural language processing tasks for unsegmented languages. In Japanese, many out-of-vocabulary words appear in the phonetic syllabary katakana, making segmentation more difficult due to the lack of clues found in mixed script settings. In this paper, we propose a straightforward approach based on a variant of tf-idf and apply it to the problem of word segmentation in Japanese. Even though our method uses only an unlabeled corpus, experimental results show that it achieves performance comparable to existing methods that use manually labeled corpora. Furthermore, it improves performance of simple word segmentation models trained on a manually labeled corpus.",
}
@inproceedings{hsieh-etal-2017-monpa,
    title = "{MONPA}: Multi-objective Named-entity and Part-of-speech Annotator for {C}hinese using Recurrent Neural Network",
    author = "Hsieh, Yu-Lun  and
      Chang, Yung-Chun  and
      Huang, Yi-Jie  and
      Yeh, Shu-Hao  and
      Chen, Chun-Hung  and
      Hsu, Wen-Lian",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2014",
    pages = "80--85",
    abstract = "Part-of-speech (POS) tagging and named entity recognition (NER) are crucial steps in natural language processing. In addition, the difficulty of word segmentation places additional burden on those who intend to deal with languages such as Chinese, and pipelined systems often suffer from error propagation. This work proposes an end-to-end model using character-based recurrent neural network (RNN) to jointly accomplish segmentation, POS tagging and NER of a Chinese sentence. Experiments on previous word segmentation and NER datasets show that a single model with the proposed architecture is comparable to those trained specifically for each task, and outperforms freely-available softwares. Moreover, we provide a web-based interface for the public to easily access this resource.",
}
@inproceedings{shao-etal-2017-recall,
    title = "Recall is the Proper Evaluation Metric for Word Segmentation",
    author = "Shao, Yan  and
      Hardmeier, Christian  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2015",
    pages = "86--90",
    abstract = "We extensively analyse the correlations and drawbacks of conventionally employed evaluation metrics for word segmentation. Unlike in standard information retrieval, precision favours under-splitting systems and therefore can be misleading in word segmentation. Overall, based on both theoretical and experimental analysis, we propose that precision should be excluded from the standard evaluation metrics and that the evaluation score obtained by using only recall is sufficient and better correlated with the performance of word segmentation systems.",
}
@inproceedings{cotterell-duh-2017-low,
    title = "Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields",
    author = "Cotterell, Ryan  and
      Duh, Kevin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2016",
    pages = "91--96",
    abstract = "Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world{'}s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low-resource languages jointly. Learning character representations for multiple related languages allows knowledge transfer from the high-resource languages to the low-resource ones, improving F1 by up to 9.8 points.",
}
@inproceedings{sato-etal-2017-segment,
    title = "Segment-Level Neural Conditional Random Fields for Named Entity Recognition",
    author = "Sato, Motoki  and
      Shindo, Hiroyuki  and
      Yamada, Ikuya  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2017",
    pages = "97--102",
    abstract = "We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.",
}
@inproceedings{kehat-pustejovsky-2017-integrating,
    title = "Integrating Vision and Language Datasets to Measure Word Concreteness",
    author = "Kehat, Gitit  and
      Pustejovsky, James",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2018",
    pages = "103--108",
    abstract = "We present and take advantage of the inherent visualizability properties of words in visual corpora (the textual components of vision-language datasets) to compute concreteness scores for words. Our simple method does not require hand-annotated concreteness score lists for training, and yields state-of-the-art results when evaluated against concreteness scores lists and previously derived scores, as well as when used for metaphor detection.",
}
@inproceedings{kajiwara-fujita-2017-semantic,
    title = "Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification",
    author = "Kajiwara, Tomoyuki  and
      Fujita, Atsushi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2019",
    pages = "109--115",
    abstract = "This paper examines the usefulness of semantic features based on word alignments for estimating the quality of text simplification. Specifically, we introduce seven types of alignment-based features computed on the basis of word embeddings and paraphrase lexicons. Through an empirical experiment using the QATS dataset, we confirm that we can achieve the state-of-the-art performance only with these features.",
}
@inproceedings{pandey-etal-2017-injecting,
    title = "Injecting Word Embeddings with Another Language{'}s Resource : An Application of Bilingual Embeddings",
    author = "Pandey, Prakhar  and
      Pudi, Vikram  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2020",
    pages = "116--121",
    abstract = "Word embeddings learned from text corpus can be improved by injecting knowledge from external resources, while at the same time also specializing them for similarity or relatedness. These knowledge resources (like WordNet, Paraphrase Database) may not exist for all languages. In this work we introduce a method to inject word embeddings of a language with knowledge resource of another language by leveraging bilingual embeddings. First we improve word embeddings of German, Italian, French and Spanish using resources of English and test them on variety of word similarity tasks. Then we demonstrate the utility of our method by creating improved embeddings for Urdu and Telugu languages using Hindi WordNet, beating the previously established baseline for Urdu.",
}
@inproceedings{corona-etal-2017-improving,
    title = "Improving Black-box Speech Recognition using Semantic Parsing",
    author = "Corona, Rodolfo  and
      Thomason, Jesse  and
      Mooney, Raymond",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2021",
    pages = "122--127",
    abstract = "Speech is a natural channel for human-computer interaction in robotics and consumer applications. Natural language understanding pipelines that start with speech can have trouble recovering from speech recognition errors. Black-box automatic speech recognition (ASR) systems, built for general purpose use, are unable to take advantage of in-domain language models that could otherwise ameliorate these errors. In this work, we present a method for re-ranking black-box ASR hypotheses using an in-domain language model and semantic parser trained for a particular task. Our re-ranking method significantly improves both transcription accuracy and semantic understanding over a state-of-the-art ASR{'}s vanilla output.",
}
@inproceedings{matsubayashi-inui-2017-revisiting,
    title = "Revisiting the Design Issues of Local Models for {J}apanese Predicate-Argument Structure Analysis",
    author = "Matsubayashi, Yuichiroh  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2022",
    pages = "128--133",
    abstract = "The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions. However, the existing global models tend to employ only relatively simple local features; therefore, the overall performance gains are rather limited. The importance of designing a local model is demonstrated in this study by showing that the performance of a sophisticated local model can be considerably improved with recent feature embedding methods and a feature combination learning based on a neural network, outperforming the state-of-the-art global models in F1 on a common benchmark dataset.",
}
@inproceedings{han-etal-2017-natural,
    title = "Natural Language Informs the Interpretation of Iconic Gestures: A Computational Approach",
    author = "Han, Ting  and
      Hough, Julian  and
      Schlangen, David",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2023",
    pages = "134--139",
    abstract = "When giving descriptions, speakers often signify object shape or size with hand gestures. Such so-called {`}iconic{'} gestures represent their meaning through their relevance to referents in the verbal content, rather than having a conventional form. The gesture form on its own is often ambiguous, and the aspect of the referent that it highlights is constrained by what the language makes salient. We show how the verbal content guides gesture interpretation through a computational model that frames the task as a multi-label classification task that maps multimodal utterances to semantic categories, using annotated human-human data.",
}
@inproceedings{beck-2017-modelling,
    title = "Modelling Representation Noise in Emotion Analysis using {G}aussian Processes",
    author = "Beck, Daniel",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2024",
    pages = "140--145",
    abstract = "Emotion Analysis is the task of modelling latent emotions present in natural language. Labelled datasets for this task are scarce so learning good input text representations is not trivial. Using averaged word embeddings is a simple way to leverage unlabelled corpora to build text representations but this approach can be prone to noise either coming from the embedding themselves or the averaging procedure. In this paper we propose a model for Emotion Analysis using Gaussian Processes and kernels that are better suitable for functions that exhibit noisy behaviour. Empirical evaluations in a emotion prediction task show that our model outperforms commonly used baselines for regression.",
}
@inproceedings{li-etal-2017-manually,
    title = "Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis",
    author = "Li, Minglei  and
      Lu, Qin  and
      Long, Yunfei",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2025",
    pages = "146--150",
    abstract = "In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases. We examine how phrases can be represented through manually prepared lexicons, extended lexicons using computational methods, or word embedding. Comparative studies clearly show that word embedding using unsupervised distributional method outperforms manually prepared lexicons no matter what affective models are used in the lexicons. Our conclusion is that although different affective lexicons are cognitively backed by theories, they do not show any advantage over the automatically obtained word embedding.",
}
@inproceedings{xue-etal-2017-mtna,
    title = "{MTNA}: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews",
    author = "Xue, Wei  and
      Zhou, Wubai  and
      Li, Tao  and
      Wang, Qing",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2026",
    pages = "151--156",
    abstract = "Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities. In Aspect Based Sentiment Analysis (ABSA), it is critical to identify aspect categories and extract aspect terms from the sentences of user-generated reviews. However, the two tasks are often treated independently, even though they are closely related. Intuitively, the learned knowledge of one task should inform the other learning task. In this paper, we propose a multi-task learning model based on neural networks to solve them together. We demonstrate the improved performance of our multi-task learning model over the models trained separately on three public dataset released by SemEval workshops.",
}
@inproceedings{yung-etal-2017-discourse,
    title = "Can Discourse Relations be Identified Incrementally?",
    author = "Yung, Frances  and
      Noji, Hiroshi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2027",
    pages = "157--162",
    abstract = "Humans process language word by word and construct partial linguistic structures on the fly before the end of the sentence is perceived. Inspired by this cognitive ability, incremental algorithms for natural language processing tasks have been proposed and demonstrated promising performance. For discourse relation (DR) parsing, however, it is not yet clear to what extent humans can recognize DRs incrementally, because the latent {`}nodes{'} of discourse structure can span clauses and sentences. To answer this question, this work investigates incrementality in discourse processing based on a corpus annotated with DR signals. We find that DRs are dominantly signaled at the boundary between the two constituent discourse units. The findings complement existing psycholinguistic theories on expectation in discourse processing and provide direction for incremental discourse parsing.",
}
@inproceedings{chi-etal-2017-speaker,
    title = "Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning",
    author = "Chi, Ta-Chung  and
      Chen, Po-Chun  and
      Su, Shang-Yu  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2028",
    pages = "163--168",
    abstract = "Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks.",
}
@inproceedings{song-etal-2017-diversifying,
    title = "Diversifying Neural Conversation Model with Maximal Marginal Relevance",
    author = "Song, Yiping  and
      Tian, Zhiliang  and
      Zhao, Dongyan  and
      Zhang, Ming  and
      Yan, Rui",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2029",
    pages = "169--174",
    abstract = "Neural conversation systems, typically using sequence-to-sequence (seq2seq) models, are showing promising progress recently. However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies. Maximum Marginal Relevance (MMR) is a ranking algorithm that has been widely used for subset selection. In this paper, we propose the MMR-BS decoding method, which incorporates MMR into the beam search (BS) process of seq2seq. The MMR-BS method improves the diversity of generated replies without sacrificing their high relevance with the user-issued query. Experiments show that our proposed model achieves the best performance among other comparison methods.",
}
@inproceedings{chaurasia-mooney-2017-dialog,
    title = "Dialog for Language to Code",
    author = "Chaurasia, Shobhit  and
      Mooney, Raymond J.",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2030",
    pages = "175--180",
    abstract = "Generating computer code from natural language descriptions has been a long-standing problem. Prior work in this domain has restricted itself to generating code in one shot from a single description. To overcome this limitation, we propose a system that can engage users in a dialog to clarify their intent until it has all the information to produce correct code. To evaluate the efficacy of dialog in code generation, we focus on synthesizing conditional statements in the form of IFTTT recipes.",
}
@inproceedings{sladoljev-agejev-snajder-2017-using,
    title = "Using Analytic Scoring Rubrics in the Automatic Assessment of College-Level Summary Writing Tasks in {L}2",
    author = "Sladoljev-Agejev, Tamara  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2031",
    pages = "181--186",
    abstract = "Assessing summaries is a demanding, yet useful task which provides valuable information on language competence, especially for second language learners. We consider automated scoring of college-level summary writing task in English as a second language (EL2). We adopt the Reading-for-Understanding (RU) cognitive framework, extended with the Reading-to-Write (RW) element, and use analytic scoring with six rubrics covering content and writing quality. We show that regression models with reference-based and linguistic features considerably outperform the baselines across all the rubrics. Moreover, we find interesting correlations between summary features and analytic rubrics, revealing the links between the RU and RW constructs.",
}
@inproceedings{wang-etal-2017-statistical,
    title = "A Statistical Framework for Product Description Generation",
    author = "Wang, Jinpeng  and
      Hou, Yutai  and
      Liu, Jing  and
      Cao, Yunbo  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2032",
    pages = "187--192",
    abstract = "We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.",
}
@inproceedings{lee-lee-2017-automatic,
    title = "Automatic Text Summarization Using Reinforcement Learning with Embedding Features",
    author = "Lee, Gyoung Ho  and
      Lee, Kong Joo",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2033",
    pages = "193--197",
    abstract = "An automatic text summarization system can automatically generate a short and brief summary that contains a main concept of an original document. In this work, we explore the advantages of simple embedding features in Reinforcement leaning approach to automatic text summarization tasks. In addition, we propose a novel deep learning network for estimating Q-values used in Reinforcement learning. We evaluate our model by using ROUGE scores with DUC 2001, 2002, Wikipedia, ACL-ARC data. Evaluation results show that our model is competitive with the previous models.",
}
@inproceedings{vadapalli-etal-2017-ssas,
    title = "{SSAS}: Semantic Similarity for Abstractive Summarization",
    author = "Vadapalli, Raghuram  and
      J Kurisinkel, Litton  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2034",
    pages = "198--203",
    abstract = "Ideally a metric evaluating an abstract system summary should represent the extent to which the system-generated summary approximates the semantic inference conceived by the reader using a human-written reference summary. Most of the previous approaches relied upon word or syntactic sub-sequence overlap to evaluate system-generated summaries. Such metrics cannot evaluate the summary at semantic inference level. Through this work we introduce the metric of Semantic Similarity for Abstractive Summarization (SSAS), which leverages natural language inference and paraphrasing techniques to frame a novel approach to evaluate system summaries at semantic inference level. SSAS is based upon a weighted composition of quantities representing the level of agreement, contradiction, independence, paraphrasing, and optionally ROUGE score between a system-generated and a human-written summary.",
}
@inproceedings{mnasri-etal-2017-taking,
    title = "Taking into account Inter-sentence Similarity for Update Summarization",
    author = {Mnasri, Ma{\^a}li  and
      de Chalendar, Ga{\"e}l  and
      Ferret, Olivier},
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2035",
    pages = "204--209",
    abstract = "Following Gillick and Favre (2009), a lot of work about extractive summarization has modeled this task by associating two contrary constraints: one aims at maximizing the coverage of the summary with respect to its information content while the other represents its size limit. In this context, the notion of redundancy is only implicitly taken into account. In this article, we extend the framework defined by Gillick and Favre (2009) by examining how and to what extent integrating semantic sentence similarity into an update summarization system can improve its results. We show more precisely the impact of this strategy through evaluations performed on DUC 2007 and TAC 2008 and 2009 datasets.",
}
@inproceedings{masumura-etal-2017-hyperspherical,
    title = "Hyperspherical Query Likelihood Models with Word Embeddings",
    author = "Masumura, Ryo  and
      Asami, Taichi  and
      Masataki, Hirokazu  and
      Sadamitsu, Kugatsu  and
      Nishida, Kyosuke  and
      Higashinaka, Ryuichiro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2036",
    pages = "210--216",
    abstract = "This paper presents an initial study on hyperspherical query likelihood models (QLMs) for information retrieval (IR). Our motivation is to naturally utilize pre-trained word embeddings for probabilistic IR. To this end, key idea is to directly leverage the word embeddings as random variables for directional probabilistic models based on von Mises-Fisher distributions which are familiar to cosine distances. The proposed method enables us to theoretically take semantic similarities between document and target queries into consideration without introducing heuristic expansion techniques. In addition, this paper reveals relationships between hyperspherical QLMs and conventional QLMs. Experiments show document retrieval evaluation results in which a hyperspherical QLM is compared to conventional QLMs and document distance metrics using word or document embeddings.",
}
@inproceedings{kulkarni-etal-2017-dual,
    title = "Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering",
    author = "Kulkarni, Kaustubh  and
      Togashi, Riku  and
      Maeda, Hideyuki  and
      Fujita, Sumio",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2037",
    pages = "217--221",
    abstract = "Embedding based approaches are shown to be effective for solving simple Question Answering (QA) problems in recent works. The major drawback of current approaches is that they look only at the similarity (constraint) between a question and a head, relation pair. Due to the absence of tail (answer) in the questions, these models often require paraphrase datasets to obtain adequate embeddings. In this paper, we propose a dual constraint model which exploits the embeddings obtained by Trans* family of algorithms to solve the simple QA problem without using any additional resources such as paraphrase datasets. The results obtained prove that the embeddings learned using dual constraints are better than those with single constraint models having similar architecture.",
}
@inproceedings{ziegler-etal-2017-efficiency,
    title = "Efficiency-aware Answering of Compositional Questions using Answer Type Prediction",
    author = "Ziegler, David  and
      Abujabal, Abdalghani  and
      Saha Roy, Rishiraj  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2038",
    pages = "222--227",
    abstract = "This paper investigates the problem of answering compositional factoid questions over knowledge bases (KB) under efficiency constraints. The method, called TIPI, (i) decomposes compositional questions, (ii) predicts answer types for individual sub-questions, (iii) reasons over the compatibility of joint types, and finally, (iv) formulates compositional SPARQL queries respecting type constraints. TIPI{'}s answer type predictor is trained using distant supervision, and exploits lexical, syntactic and embedding-based features to compute context- and hierarchy-aware candidate answer types for an input question. Experiments on a recent benchmark show that TIPI results in state-of-the-art performance under the real-world assumption that only a single SPARQL query can be executed over the KB, and substantial reduction in the number of queries in the more general case.",
}
@inproceedings{elsahar-etal-2017-high,
    title = "High Recall Open {IE} for Relation Discovery",
    author = "Elsahar, Hady  and
      Gravier, Christophe  and
      Laforest, Frederique",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2039",
    pages = "228--233",
    abstract = "Relation Discovery discovers predicates (relation types) from a text corpus relying on the co-occurrence of two named entities in the same sentence. This is a very narrowing constraint: it represents only a small fraction of all relation mentions in practice. In this paper we propose a high recall approach for Open IE, which enables covering up to 16 times more sentences in a large corpus. Comparison against OpenIE systems shows that our proposed approach achieves 28{\%} improvement over the highest recall OpenIE system and 6{\%} improvement in precision than the same system.",
}
@inproceedings{dai-etal-2017-using,
    title = "Using Context Events in Neural Network Models for Event Temporal Status Identification",
    author = "Dai, Zeyu  and
      Yao, Wenlin  and
      Huang, Ruihong",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2040",
    pages = "234--239",
    abstract = "Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts. Therefore, we extract dependency chains containing context events and use them as input in neural network models, which consistently outperform previous models using local context words as input. Visualization verifies that the dependency chain representation can effectively capture the context events which are closely related to the target event and play key roles in predicting event temporal status.",
}
@inproceedings{hsieh-etal-2017-identifying,
    title = "Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory",
    author = "Hsieh, Yu-Lun  and
      Chang, Yung-Chun  and
      Chang, Nai-Wen  and
      Hsu, Wen-Lian",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2041",
    pages = "240--245",
    abstract = "In this paper, we propose a recurrent neural network model for identifying protein-protein interactions in biomedical literature. Experiments on two largest public benchmark datasets, AIMed and BioInfer, demonstrate that our approach significantly surpasses state-of-the-art methods with relative improvements of 10{\%} and 18{\%}, respectively. Cross-corpus evaluation also demonstrate that the proposed model remains robust despite using different training data. These results suggest that RNN can effectively capture semantic relationships among proteins as well as generalizes over different corpora, without any feature engineering.",
}
@inproceedings{khanpour-etal-2017-identifying,
    title = "Identifying Empathetic Messages in Online Health Communities",
    author = "Khanpour, Hamed  and
      Caragea, Cornelia  and
      Biyani, Prakhar",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2042",
    pages = "246--251",
    abstract = "Empathy captures one{'}s ability to correlate with and understand others{'} emotional states and experiences. Messages with empathetic content are considered as one of the main advantages for joining online health communities due to their potential to improve people{'}s moods. Unfortunately, to this date, no computational studies exist that automatically identify empathetic messages in online health communities. We propose a combination of Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks, and show that the proposed model outperforms each individual model (CNN and LSTM) as well as several baselines.",
}
@inproceedings{long-etal-2017-fake,
    title = "Fake News Detection Through Multi-Perspective Speaker Profiles",
    author = "Long, Yunfei  and
      Lu, Qin  and
      Xiang, Rong  and
      Li, Minglei  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2043",
    pages = "252--256",
    abstract = "Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5{\%} in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.",
}
@inproceedings{saito-etal-2017-improving,
    title = "Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels",
    author = "Saito, Itsumi  and
      Suzuki, Jun  and
      Nishida, Kyosuke  and
      Sadamitsu, Kugatsu  and
      Kobashikawa, Satoshi  and
      Masumura, Ryo  and
      Matsumoto, Yuji  and
      Tomita, Junji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2044",
    pages = "257--262",
    abstract = "In this study, we investigated the effectiveness of augmented data for encoder-decoder-based neural normalization models. Attention based encoder-decoder models are greatly effective in generating many natural languages. {\%} such as machine translation or machine summarization. In general, we have to prepare for a large amount of training data to train an encoder-decoder model. Unlike machine translation, there are few training data for text-normalization tasks. In this paper, we propose two methods for generating augmented data. The experimental results with Japanese dialect normalization indicate that our methods are effective for an encoder-decoder model and achieve higher BLEU score than that of baselines. We also investigated the oracle performance and revealed that there is sufficient room for improving an encoder-decoder model.",
}
@inproceedings{miura-etal-2017-using,
    title = "Using Social Networks to Improve Language Variety Identification with Neural Networks",
    author = "Miura, Yasuhide  and
      Taniguchi, Tomoki  and
      Taniguchi, Motoki  and
      Misawa, Shotaro  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2045",
    pages = "263--270",
    abstract = "We propose a hierarchical neural network model for language variety identification that integrates information from a social network. Recently, language variety identification has enjoyed heightened popularity as an advanced task of language identification. The proposed model uses additional texts from a social network to improve language variety identification from two perspectives. First, they are used to introduce the effects of homophily. Secondly, they are used as expanded training data for shared layers of the proposed model. By introducing information from social networks, the model improved its accuracy by 1.67-5.56. Compared to state-of-the-art baselines, these improved performances are better in English and comparable in Spanish. Furthermore, we analyzed the cases of Portuguese and Arabic when the model showed weak performances, and found that the effect of homophily is likely to be weak due to sparsity and noises compared to languages with the strong performances.",
}
@inproceedings{zhang-etal-2017-boosting,
    title = "Boosting Neural Machine Translation",
    author = "Zhang, Dakun  and
      Kim, Jungi  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2046",
    pages = "271--276",
    abstract = "Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art performance. This results in very high computation cost, slowing down research and industrialisation. In this paper, we propose to alleviate this problem with several training methods based on data boosting and bootstrap with no modifications to the neural network. It imitates the learning process of humans, which typically spend more time when learning {``}difficult{''} concepts than easier ones. We experiment on an English-French translation task showing accuracy improvements of up to 1.63 BLEU while saving 20{\%} of training time.",
}
@inproceedings{yamagishi-etal-2017-improving,
    title = "Improving {J}apanese-to-{E}nglish Neural Machine Translation by Voice Prediction",
    author = "Yamagishi, Hayahide  and
      Kanouchi, Shin  and
      Sato, Takayuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2047",
    pages = "277--282",
    abstract = "This study reports an attempt to predict the voice of reference using the information from the input sentences or previous input/output sentences. Our previous study presented a voice controlling method to generate sentences for neural machine translation, wherein it was demonstrated that the BLEU score improved when the voice of generated sentence was controlled relative to that of the reference. However, it is impractical to use the reference information because we cannot discern the voice of the correct translation in advance. Thus, this study presents a voice prediction method for generated sentences for neural machine translation. While evaluating on Japanese-to-English translation, we obtain a 0.70-improvement in the BLEU using the predicted voice.",
}
@inproceedings{kunchukuttan-etal-2017-utilizing,
    title = "Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based {SMT}",
    author = "Kunchukuttan, Anoop  and
      Shah, Maulik  and
      Prakash, Pradyot  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2048",
    pages = "283--289",
    abstract = "We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morpheme-level pivot models. It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is used. We also show that combining multiple related language pivot models can rival a direct translation model. Thus, the use of subwords as translation units coupled with multiple related pivot languages can compensate for the lack of a direct parallel corpus.",
}
@inproceedings{mino-etal-2017-key,
    title = "Key-value Attention Mechanism for Neural Machine Translation",
    author = "Mino, Hideya  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2049",
    pages = "290--295",
    abstract = "In this paper, we propose a neural machine translation (NMT) with a key-value attention mechanism on the source-side encoder. The key-value attention mechanism separates the source-side content vector into two types of memory known as the key and the value. The key is used for calculating the attention distribution, and the value is used for encoding the context representation. Experiments on three different tasks indicate that our model outperforms an NMT model with a conventional attention mechanism. Furthermore, we perform experiments with a conventional NMT framework, in which a part of the initial value of a weight matrix is set to zero so that the matrix is as the same initial-state as the key-value attention mechanism. As a result, we obtain comparable results with the key-value attention mechanism without changing the network structure.",
}
@inproceedings{nguyen-chiang-2017-transfer,
    title = "Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation",
    author = "Nguyen, Toan Q.  and
      Chiang, David",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2050",
    pages = "296--301",
    abstract = "We present a simple method to improve neural translation of a low-resource language pair using parallel data from a related, also low-resource, language pair. The method is based on the transfer method of Zoph et al., but whereas their method ignores any source vocabulary overlap, ours exploits it. First, we split words using Byte Pair Encoding (BPE) to increase vocabulary overlap. Then, we train a model on the first language pair and transfer its parameters, including its source word embeddings, to another model and continue training on the second language pair. Our experiments show that transfer learning helps word-based translation only slightly, but when used on top of a much stronger BPE baseline, it yields larger improvements of up to 4.3 BLEU.",
}
@inproceedings{kim-etal-2017-concept,
    title = "Concept Equalization to Guide Correct Training of Neural Machine Translation",
    author = "Kim, Kangil  and
      Shin, Jong-Hun  and
      Na, Seung-Hoon  and
      Jung, SangKeun",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2051",
    pages = "302--307",
    abstract = "Neural machine translation decoders are usually conditional language models to sequentially generate words for target sentences. This approach is limited to find the best word composition and requires help of explicit methods as beam search. To help learning correct compositional mechanisms in NMTs, we propose concept equalization using direct mapping distributed representations of source and target sentences. In a translation experiment from English to French, the concept equalization significantly improved translation quality by 3.00 BLEU points compared to a state-of-the-art NMT model.",
}
@inproceedings{dernoncourt-lee-2017-pubmed,
    title = "{P}ub{M}ed 200k {RCT}: a Dataset for Sequential Sentence Classification in Medical Abstracts",
    author = "Dernoncourt, Franck  and
      Lee, Ji Young",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2052",
    pages = "308--313",
    abstract = "We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short-text classification (i.e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.",
}
@inproceedings{miceli-barone-sennrich-2017-parallel,
    title = "A Parallel Corpus of Python Functions and Documentation Strings for Automated Code Documentation and Code Generation",
    author = "Miceli Barone, Antonio Valerio  and
      Sennrich, Rico",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2053",
    pages = "314--319",
    abstract = "Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest. Progress in these areas has been limited by the low availability of parallel corpora of code and natural language descriptions, which tend to be small and constrained to specific domains. In this work we introduce a large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings ({``}docstrings{''}) generated by scraping open source repositories on GitHub. We describe baseline results for the code documentation and code generation tasks obtained by neural machine translation. We also experiment with data augmentation techniques to further increase the amount of training data. We release our datasets and processing scripts in order to stimulate research in these areas.",
}
@inproceedings{li-wang-2017-building,
    title = "Building Large {C}hinese Corpus for Spoken Dialogue Research in Specific Domains",
    author = "Li, Changliang  and
      Wang, Xiuying",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2054",
    pages = "320--324",
    abstract = "Corpus is a valuable resource for information retrieval and data-driven natural language processing systems,especially for spoken dialogue research in specific domains. However,there is little non-English corpora, particular for ones in Chinese. Spoken by the nation with the largest population in the world, Chinese become increasingly prevalent and popular among millions of people worldwide. In this paper, we build a large-scale and high-quality Chinese corpus, called CSDC (Chinese Spoken Dialogue Corpus). It contains five domains and more than 140 thousand dialogues in all. Each sentence in this corpus is annotated with slot information additionally compared to other corpora. To our best knowledge, this is the largest Chinese spoken dialogue corpus, as well as the first one with slot information. With this corpus, we proposed a method and did a well-designed experiment. The indicative result is reported at last.",
}
@inproceedings{yeung-lee-2017-identifying,
    title = "Identifying Speakers and Listeners of Quoted Speech in Literary Works",
    author = "Yeung, Chak Yan  and
      Lee, John",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2055",
    pages = "325--329",
    abstract = "We present the first study that evaluates both speaker and listener identification for direct speech in literary texts. Our approach consists of two steps: identification of speakers and listeners near the quotes, and dialogue chain segmentation. Evaluation results show that this approach outperforms a rule-based approach that is state-of-the-art on a corpus of literary texts.",
}
@inproceedings{ehara-2017-language,
    title = "Language-Independent Prediction of Psycholinguistic Properties of Words",
    author = "Ehara, Yo",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2056",
    pages = "330--336",
    abstract = "The psycholinguistic properties of words, namely, word familiarity, age of acquisition, concreteness, and imagery, have been reported to be effective for educational natural language-processing tasks. Previous studies on predicting the values of these properties rely on language-dependent features. This paper is the first to propose a practical language-independent method for predicting such values by using only a large raw corpus in a language. Through experiments, our method successfully predicted the values of these properties in two languages. The results for English were competitive with the reported accuracy achieved using features specific to English.",
}
@inproceedings{yoon-kim-2017-correlation,
    title = "Correlation Analysis of Chronic Obstructive Pulmonary Disease ({COPD}) and its Biomarkers Using the Word Embeddings",
    author = "Yoon, Byeong-Hun  and
      Kim, Yu-Seop",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2057",
    pages = "337--342",
    abstract = "It is very costly and time consuming to find new biomarkers for specific diseases in clinical laboratories. In this study, to find new biomarkers most closely related to Chronic Obstructive Pulmonary Disease (COPD), which is widely known as respiratory disease, biomarkers known to be associated with respiratory diseases and COPD itself were converted into word embedding. And their similarities were measured. We used Word2Vec, Canonical Correlation Analysis (CCA), and Global Vector (GloVe) for word embedding. In order to replace the clinical evaluation, the titles and abstracts of papers retrieved from Google Scholars were analyzed and quantified to estimate the performance of the word em-bedding models.",
}
@inproceedings{asano-etal-2017-reference,
    title = "Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems",
    author = "Asano, Hiroki  and
      Mizumoto, Tomoya  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2058",
    pages = "343--348",
    abstract = "In grammatical error correction (GEC), automatically evaluating system outputs requires gold-standard references, which must be created manually and thus tend to be both expensive and limited in coverage. To address this problem, a reference-less approach has recently emerged; however, previous reference-less metrics that only consider the criterion of grammaticality, have not worked as well as reference-based metrics. This study explores the potential of extending a prior grammaticality-based method to establish a reference-less evaluation method for GEC systems. Further, we empirically show that a reference-less metric that combines fluency and meaning preservation with grammaticality provides a better estimate of manual scores than that of commonly used reference-based metrics. To our knowledge, this is the first study that provides empirical evidence that a reference-less metric can replace reference-based metrics in evaluating GEC systems.",
}
@inproceedings{garg-etal-2017-cvbed,
    title = "{CVB}ed: Structuring {CV}s using{W}ord Embeddings",
    author = "Garg, Shweta  and
      Singh, Sudhanshu S  and
      Mishra, Abhijit  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2059",
    pages = "349--354",
    abstract = "Automatic analysis of curriculum vitae (CVs) of applicants is of tremendous importance in recruitment scenarios. The semi-structuredness of CVs, however, makes CV processing a challenging task. We propose a solution towards transforming CVs to follow a unified structure, thereby, paving ways for smoother CV analysis. The problem of restructuring is posed as a section relabeling problem, where each section of a given CV gets reassigned to a predefined label. Our relabeling method relies on semantic relatedness computed between section header, content and labels, based on phrase-embeddings learned from a large pool of CVs. We follow different heuristics to measure semantic relatedness. Our best heuristic achieves an F-score of 93.17{\%} on a test dataset with gold-standard labels obtained using manual annotation.",
}
@inproceedings{li-etal-2017-leveraging-diverse,
    title = "Leveraging Diverse Lexical Chains to Construct Essays for {C}hinese College Entrance Examination",
    author = "Li, Liunian  and
      Wan, Xiaojun  and
      Yao, Jin-ge  and
      Yan, Siming",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2060",
    pages = "355--360",
    abstract = "In this work we study the challenging task of automatically constructing essays for Chinese college entrance examination where the topic is specified in advance. We explore a sentence extraction framework based on diversified lexical chains to capture coherence and richness. Experimental analysis shows the effectiveness of our approach and reveals the importance of information richness in essay writing.",
}
@inproceedings{han-schlangen-2017-draw,
    title = "Draw and Tell: Multimodal Descriptions Outperform Verbal- or Sketch-Only Descriptions in an Image Retrieval Task",
    author = "Han, Ting  and
      Schlangen, David",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2061",
    pages = "361--365",
    abstract = "While language conveys meaning largely symbolically, actual communication acts typically contain iconic elements as well: People gesture while they speak, or may even draw sketches while explaining something. Image retrieval prima facie seems like a task that could profit from combined symbolic and iconic reference, but it is typically set up to work either from language only, or via (iconic) sketches with no verbal contribution. Using a model of grounded language semantics and a model of sketch-to-image mapping, we show that adding even very reduced iconic information to a verbal image description improves recall. Verbal descriptions paired with fully detailed sketches still perform better than these sketches alone. We see these results as supporting the assumption that natural user interfaces should respond to multimodal input, where possible, rather than just language alone.",
}
@inproceedings{sakaguchi-etal-2017-grammatical,
    title = "Grammatical Error Correction with Neural Reinforcement Learning",
    author = "Sakaguchi, Keisuke  and
      Post, Matt  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2062",
    pages = "366--372",
    abstract = "We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.",
}
@inproceedings{ito-etal-2017-coreference,
    title = "Coreference Resolution on Math Problem Text in {J}apanese",
    author = "Ito, Takumi  and
      Matsuzaki, Takuya  and
      Sato, Satoshi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2063",
    pages = "373--377",
    abstract = "This paper describes a coreference resolution system for math problem text. Case frame dictionaries and a math taxonomy are utilized for supplying domain knowledge. The system deals with various anaphoric phenomena beyond well-studied entity coreferences.",
}
@inproceedings{toyama-etal-2017-utilizing,
    title = "Utilizing Visual Forms of {J}apanese Characters for Neural Review Classification",
    author = "Toyama, Yota  and
      Miwa, Makoto  and
      Sasaki, Yutaka",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2064",
    pages = "378--382",
    abstract = "We propose a novel method that exploits visual information of ideograms and logograms in analyzing Japanese review documents. Our method first converts font images of Japanese characters into character embeddings using convolutional neural networks. It then constructs document embeddings from the character embeddings based on Hierarchical Attention Networks, which represent the documents based on attention mechanisms from a character level to a sentence level. The document embeddings are finally used to predict the labels of documents. Our method provides a way to exploit visual features of characters in languages with ideograms and logograms. In the experiments, our method achieved an accuracy comparable to a character embedding-based model while our method has much fewer parameters since it does not need to keep embeddings of thousands of characters.",
}
@inproceedings{wang-etal-2017-multi,
    title = "A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition",
    author = "Wang, Dingquan  and
      Peng, Nanyun  and
      Duh, Kevin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2065",
    pages = "383--388",
    abstract = "We show how to adapt bilingual word embeddings (BWE{'}s) to bootstrap a cross-lingual name-entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi-task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine-tuned for the NER task.",
}
@inproceedings{mitsuda-etal-2017-investigating,
    title = "Investigating the Effect of Conveying Understanding Results in Chat-Oriented Dialogue Systems",
    author = "Mitsuda, Koh  and
      Higashinaka, Ryuichiro  and
      Tomita, Junji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2066",
    pages = "389--394",
    abstract = "In dialogue systems, conveying understanding results of user utterances is important because it enables users to feel understood by the system. However, it is not clear what types of understanding results should be conveyed to users; some utterances may be offensive and some may be too commonsensical. In this paper, we explored the effect of conveying understanding results of user utterances in a chat-oriented dialogue system by an experiment using human subjects. As a result, we found that only certain types of understanding results, such as those related to a user{'}s permanent state, are effective to improve user satisfaction. This paper clarifies the types of understanding results that can be safely uttered by a system.",
}
@inproceedings{ibeke-etal-2017-extracting,
    title = "Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences",
    author = "Ibeke, Ebuka  and
      Lin, Chenghua  and
      Wyner, Adam  and
      Barawi, Mohamad Hardyman",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2067",
    pages = "395--400",
    abstract = "Contrastive opinion mining is essential in identifying, extracting and organising opinions from user generated texts. Most existing studies separate input data into respective collections. In addition, the relationships between the topics extracted and the sentences in the corpus which express the topics are opaque, hindering our understanding of the opinions expressed in the corpus. We propose a novel unified latent variable model (contraLDA) which addresses the above matters. Experimental results show the effectiveness of our model in mining contrasted opinions, outperforming our baselines.",
}
@inproceedings{yimam-etal-2017-cwig3g2,
    title = "{CWIG}3{G}2 - Complex Word Identification Task across Three Text Genres and Two User Groups",
    author = "Yimam, Seid Muhie  and
      {\v{S}}tajner, Sanja  and
      Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2068",
    pages = "401--407",
    abstract = "Complex word identification (CWI) is an important task in text accessibility. However, due to the scarcity of CWI datasets, previous studies have only addressed this problem on Wikipedia sentences and have solely taken into account the needs of non-native English speakers. We collect a new CWI dataset (CWIG3G2) covering three text genres News, WikiNews, and Wikipedia) annotated by both native and non-native English speakers. Unlike previous datasets, we cover single words, as well as complex phrases, and present them for judgment in a paragraph context. We present the first study on cross-genre and cross-group CWI, showing measurable influences in native language and genre types.",
}
@inproceedings{akama-etal-2017-generating,
    title = "Generating Stylistically Consistent Dialog Responses with Transfer Learning",
    author = "Akama, Reina  and
      Inada, Kazuaki  and
      Inoue, Naoya  and
      Kobayashi, Sosuke  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2069",
    pages = "408--412",
    abstract = "We propose a novel, data-driven, and stylistically consistent dialog response generation system. To create a user-friendly system, it is crucial to make generated responses not only appropriate but also stylistically consistent. For leaning both the properties effectively, our proposed framework has two training stages inspired by transfer learning. First, we train the model to generate appropriate responses, and then we ensure that the responses have a specific style. Experimental results demonstrate that the proposed method produces stylistically consistent responses while maintaining the appropriateness of the responses learned in a general domain.",
}
@inproceedings{ni-wang-2017-learning,
    title = "Learning to Explain Non-Standard {E}nglish Words and Phrases",
    author = "Ni, Ke  and
      Wang, William Yang",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2070",
    pages = "413--417",
    abstract = "We describe a data-driven approach for automatically explaining new, non-standard English expressions in a given sentence, building on a large dataset that includes 15 years of crowdsourced examples from UrbanDictionary.com. Unlike prior studies that focus on matching keywords from a slang dictionary, we investigate the possibility of learning a neural sequence-to-sequence model that generates explanations of unseen non-standard English expressions given context. We propose a dual encoder approach{---}a word-level encoder learns the representation of context, and a second character-level encoder to learn the hidden representation of the target non-standard expression. Our model can produce reasonable definitions of new non-standard English expressions given their context with certain confidence.",
}
@inproceedings{chali-etal-2017-towards,
    title = "Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging",
    author = "Chali, Yllias  and
      Tanvee, Moin  and
      Nayeem, Mir Tafseer",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2071",
    pages = "418--424",
    abstract = "We propose a submodular function-based summarization system which integrates three important measures namely importance, coverage, and non-redundancy to detect the important sentences for the summary. We design monotone and submodular functions which allow us to apply an efficient and scalable greedy algorithm to obtain informative and well-covered summaries. In addition, we integrate two abstraction-based methods namely sentence compression and merging for generating an abstractive sentence set. We design our summarization models for both generic and query-focused summarization. Experimental results on DUC-2004 and DUC-2007 datasets show that our generic and query-focused summarizers have outperformed the state-of-the-art summarization systems in terms of ROUGE-1 and ROUGE-2 recall and F-measure.",
}
@inproceedings{fu-etal-2017-domain,
    title = "Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network",
    author = "Fu, Lisheng  and
      Nguyen, Thien Huu  and
      Min, Bonan  and
      Grishman, Ralph",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2072",
    pages = "425--429",
    abstract = "Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor{'}s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domain-specific labeled dataset for adapting the extractor. In response, we present an unsupervised domain adaptation method which only requires labels from the source domain. Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier. The two components are optimized jointly to learn a domain-independent representation for prediction on the target domain. Our model outperforms the state-of-the-art on all three test domains of ACE 2005.",
}
@inproceedings{pereira-etal-2017-lexical,
    title = "Lexical Simplification with the Deep Structured Similarity Model",
    author = "Pereira, Lis  and
      Liu, Xiaodong  and
      Lee, John",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2073",
    pages = "430--435",
    abstract = "We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used for the task.",
}
@inproceedings{hitomi-etal-2017-proofread,
    title = "Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction",
    author = "Hitomi, Yuta  and
      Tamori, Hideaki  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2074",
    pages = "436--441",
    abstract = "This paper explores the idea of robot editors, automated proofreaders that enable journalists to improve the quality of their articles. We propose a novel neural model of multi-task learning that both generates proofread sentences and predicts the editing operations required to rewrite the source sentences and create the proofread ones. The model is trained using logs of the revisions made professional editors revising draft newspaper articles written by journalists. Experiments demonstrate the effectiveness of our multi-task learning approach and the potential value of using revision logs for this task.",
}
@inproceedings{chen-bunescu-2017-exploration,
    title = "An Exploration of Data Augmentation and {RNN} Architectures for Question Ranking in Community Question Answering",
    author = "Chen, Charles  and
      Bunescu, Razvan",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2075",
    pages = "442--447",
    abstract = "The automation of tasks in community question answering (cQA) is dominated by machine learning approaches, whose performance is often limited by the number of training examples. Starting from a neural sequence learning approach with attention, we explore the impact of two data augmentation techniques on question ranking performance: a method that swaps reference questions with their paraphrases, and training on examples automatically selected from external datasets. Both methods are shown to lead to substantial gains in accuracy over a strong baseline. Further improvements are obtained by changing the model architecture to mirror the structure seen in the data.",
}
@inproceedings{xia-yarowsky-2017-deriving,
    title = "Deriving Consensus for Multi-Parallel Corpora: an {E}nglish {B}ible Study",
    author = "Xia, Patrick  and
      Yarowsky, David",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-2076",
    pages = "448--453",
    abstract = "What can you do with multiple noisy versions of the same text? We present a method which generates a single consensus between multi-parallel corpora. By maximizing a function of linguistic features between word pairs, we jointly learn a single corpus-wide multiway alignment: a consensus between 27 versions of the English Bible. We additionally produce English paraphrases, word-level distributions of tags, and consensus dependency parses. Our method is language independent and applicable to any multi-parallel corpora. Given the Bible{'}s unique role as alignable bitext for over 800 of the world{'}s languages, this consensus alignment and resulting resources offer value for multilingual annotation projection, and also shed potential insights into the Bible itself.",
}
@proceedings{ijcnlp-2017-international,
    title = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    editor = "Kondrak, Greg  and
      Watanabe, Taro",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1000",
}
@inproceedings{belinkov-etal-2017-evaluating,
    title = "Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks",
    author = "Belinkov, Yonatan  and
      M{\`a}rquez, Llu{\'\i}s  and
      Sajjad, Hassan  and
      Durrani, Nadir  and
      Dalvi, Fahim  and
      Glass, James",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1001",
    pages = "1--10",
    abstract = "While neural machine translation (NMT) models provide improved translation quality in an elegant framework, it is less clear what they learn about language. Recent work has started evaluating the quality of vector representations learned by NMT models on morphological and syntactic tasks. In this paper, we investigate the representations learned at different layers of NMT encoders. We train NMT systems on parallel data and use the models to extract features for training a classifier on two tasks: part-of-speech and semantic tagging. We then measure the performance of the classifier as a proxy to the quality of the original NMT model for the given task. Our quantitative analysis yields interesting insights regarding representation learning in NMT models. For instance, we find that higher layers are better at learning semantics while lower layers tend to be better for part-of-speech tagging. We also observe little effect of the target language on source-side representations, especially in higher quality models.",
}
@inproceedings{chen-etal-2017-context,
    title = "Context-Aware Smoothing for Neural Machine Translation",
    author = "Chen, Kehai  and
      Wang, Rui  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1002",
    pages = "11--20",
    abstract = "In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information. This means that even if the word is in a different sentence context, it is represented as the fixed vector to learn source representation. Moreover, a large number of Out-Of-Vocabulary (OOV) words, which have different syntax and semantic information, are represented as the same vector representation of {``}unk{''}. To alleviate this problem, we propose a novel context-aware smoothing method to dynamically learn a sentence-specific vector for each word (including OOV words) depending on its local context words in a sentence. The learned context-aware representation is integrated into the NMT to improve the translation performance. Empirical results on NIST Chinese-to-English translation task show that the proposed approach achieves 1.78 BLEU improvements on average over a strong attentional NMT, and outperforms some existing systems.",
}
@inproceedings{nguyen-le-etal-2017-improving,
    title = "Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information",
    author = "Nguyen Le, An  and
      Martinez, Ander  and
      Yoshimoto, Akifumi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1003",
    pages = "21--29",
    abstract = "Sequence to Sequence Neural Machine Translation has achieved significant performance in recent years. Yet, there are some existing issues that Neural Machine Translation still does not solve completely. Two of them are translation for long sentences and the {``}over-translation{''}. To address these two problems, we propose an approach that utilize more grammatical information such as syntactic dependencies, so that the output can be generated based on more abundant information. In our approach, syntactic dependencies is employed in decoding. In addition, the output of the model is presented not as a simple sequence of tokens but as a linearized tree construction. In order to assess the performance, we construct model based on an attention mechanism encoder-decoder model in which the source language is input to the encoder as a sequence and the decoder generates the target language as a linearized dependency tree structure. Experiments on the Europarl-v7 dataset of French-to-English translation demonstrate that our proposed method improves BLEU scores by 1.57 and 2.40 on datasets consisting of sentences with up to 50 and 80 tokens, respectively. Furthermore, the proposed method also solved the two existing problems, ineffective translation for long sentences and over-translation in Neural Machine Translation.",
}
@inproceedings{ghader-monz-2017-attention,
    title = "What does Attention in Neural Machine Translation Pay Attention to?",
    author = "Ghader, Hamidreza  and
      Monz, Christof",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1004",
    pages = "30--39",
    abstract = "Attention in neural machine translation provides the possibility to encode relevant parts of the source sentence at each translation step. As a result, attention is considered to be an alignment model as well. However, there is no work that specifically studies attention and provides analysis of what is being learned by attention models. Thus, the question still remains that how attention is similar or different from the traditional alignment. In this paper, we provide detailed analysis of attention and compare it to traditional alignment. We answer the question of whether attention is only capable of modelling translational equivalent or it captures more information. We show that attention is different from alignment in some cases and is capturing useful information other than alignments.",
}
@inproceedings{kaneko-etal-2017-grammatical,
    title = "Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings",
    author = "Kaneko, Masahiro  and
      Sakaizawa, Yuya  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1005",
    pages = "40--48",
    abstract = "In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address the problem of contextual information by considering learner errors. Specifically, we propose two models: one model that employs grammatical error patterns and another model that considers grammaticality of the target word. We determine grammaticality of n-gram sequence from the annotated error tags and extract grammatical error patterns for word embeddings from large-scale learner corpora. Experimental results show that a bidirectional long-short term memory model initialized by our word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.",
}
@inproceedings{zhang-etal-2017-dependency,
    title = "Dependency Parsing with Partial Annotations: An Empirical Comparison",
    author = "Zhang, Yue  and
      Li, Zhenghua  and
      Lang, Jun  and
      Xia, Qingrong  and
      Zhang, Min",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1006",
    pages = "49--58",
    abstract = "This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA). The first approach is based on a forest-based training objective for two CRF parsers, i.e., a biaffine neural network graph-based parser (Biaffine) and a traditional log-linear graph-based parser (LLGPar). The second approach is based on the idea of constrained decoding for three parsers, i.e., a traditional linear graph-based parser (LGPar), a globally normalized neural network transition-based parser (GN3Par) and a traditional linear transition-based parser (LTPar). For the test phase, constrained decoding is also used for completing partial trees. We conduct experiments on Penn Treebank under three different settings for simulating PA, i.e., random, most uncertain, and divergent outputs from the five parsers. The results show that LLGPar is most effective in directly learning from PA, and other parsers can achieve best performance when PAs are completed into full trees by LLGPar.",
}
@inproceedings{ma-hovy-2017-neural,
    title = "Neural Probabilistic Model for Non-projective {MST} Parsing",
    author = "Ma, Xuezhe  and
      Hovy, Eduard",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1007",
    pages = "59--69",
    abstract = "In this paper, we propose a probabilistic parsing model that defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTMCNNs, which automatically benefits from both word- and character-level representations, by using a combination of bidirectional LSTMs and CNNs. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees. By exploiting Kirchhoff{'}s Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straightforward end-to-end model training procedure via back-propagation. We evaluate our model on 17 different datasets, across 14 different languages. Our parser achieves state-of-the-art parsing performance on nine datasets.",
}
@inproceedings{nishida-nakayama-2017-word,
    title = "Word Ordering as Unsupervised Learning Towards Syntactically Plausible Word Representations",
    author = "Nishida, Noriki  and
      Nakayama, Hideki",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1008",
    pages = "70--79",
    abstract = "The research question we explore in this study is how to obtain syntactically plausible word representations without using human annotations. Our underlying hypothesis is that word ordering tests, or linearizations, is suitable for learning syntactic knowledge about words. To verify this hypothesis, we develop a differentiable model called Word Ordering Network (WON) that explicitly learns to recover correct word order while implicitly acquiring word embeddings representing syntactic knowledge. We evaluate the word embeddings produced by the proposed method on downstream syntax-related tasks such as part-of-speech tagging and dependency parsing. The experimental results demonstrate that the WON consistently outperforms both order-insensitive and order-sensitive baselines on these tasks.",
}
@inproceedings{kajiwara-etal-2017-mipa,
    title = "{MIPA}: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting",
    author = "Kajiwara, Tomoyuki  and
      Komachi, Mamoru  and
      Mochihashi, Daichi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1009",
    pages = "80--89",
    abstract = "We present a pointwise mutual information (PMI)-based approach to formalize paraphrasability and propose a variant of PMI, called MIPA, for the paraphrase acquisition. Our paraphrase acquisition method first acquires lexical paraphrase pairs by bilingual pivoting and then reranks them by PMI and distributional similarity. The complementary nature of information from bilingual corpora and from monolingual corpora makes the proposed method robust. Experimental results show that the proposed method substantially outperforms bilingual pivoting and distributional similarity themselves in terms of metrics such as MRR, MAP, coverage, and Spearman{'}s correlation.",
}
@inproceedings{do-etal-2017-improving,
    title = "Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments",
    author = "Do, Quynh Ngoc Thi  and
      Bethard, Steven  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1010",
    pages = "90--99",
    abstract = "Implicit semantic role labeling (iSRL) is the task of predicting the semantic roles of a predicate that do not appear as explicit arguments, but rather regard common sense knowledge or are mentioned earlier in the discourse. We introduce an approach to iSRL based on a predictive recurrent neural semantic frame model (PRNSFM) that uses a large unannotated corpus to learn the probability of a sequence of semantic arguments given a predicate. We leverage the sequence probabilities predicted by the PRNSFM to estimate selectional preferences for predicates and their arguments. On the NomBank iSRL test set, our approach improves state-of-the-art performance on implicit semantic role labeling with less reliance than prior work on manually constructed language resources.",
}
@inproceedings{lai-etal-2017-natural,
    title = "Natural Language Inference from Multiple Premises",
    author = "Lai, Alice  and
      Bisk, Yonatan  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1011",
    pages = "100--109",
    abstract = "We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard textual entailment.",
}
@inproceedings{wang-ku-2017-enabling,
    title = "Enabling Transitivity for Lexical Inference on {C}hinese Verbs Using Probabilistic Soft Logic",
    author = "Wang, Wei-Chung  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1012",
    pages = "110--119",
    abstract = "To learn more knowledge, enabling transitivity is a vital step for lexical inference. However, most of the lexical inference models with good performance are for nouns or noun phrases, which cannot be directly applied to the inference on events or states. In this paper, we construct the largest Chinese verb lexical inference dataset containing 18,029 verb pairs, where for each pair one of four inference relations are annotated. We further build a probabilistic soft logic (PSL) model to infer verb lexicons using the logic language. With PSL, we easily enable transitivity in two layers, the observed layer and the feature layer, which are included in the knowledge base. We further discuss the effect of transitives within and between these layers. Results show the performance of the proposed PSL model can be improved at least 3.5{\%} (relative) when the transitivity is enabled. Furthermore, experiments show that enabling transitivity in the observed layer benefits the most.",
}
@inproceedings{junczys-dowmunt-grundkiewicz-2017-exploration,
    title = "An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing",
    author = "Junczys-Dowmunt, Marcin  and
      Grundkiewicz, Roman",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1013",
    pages = "120--129",
    abstract = "In this work, we explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputs $mt$ (raw MT output) and $src$ (source language input) in a single neural architecture, modeling $\{mt, src\} \rightarrow pe$ directly. Apart from that, we investigate the influence of hard-attention models which seem to be well-suited for monolingual tasks, as well as combinations of both ideas. We report results on data sets provided during the WMT-2016 shared task on automatic post-editing and can demonstrate that dual-attention models that incorporate all available data in the APE scenario in a single model improve on the best shared task system and on all other published results after the shared task. Dual-attention models that are combined with hard attention remain competitive despite applying fewer changes to the input.",
}
@inproceedings{elliott-kadar-2017-imagination,
    title = "Imagination Improves Multimodal Translation",
    author = "Elliott, Desmond  and
      K{\'a}d{\'a}r, {\'A}kos",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1014",
    pages = "130--141",
    abstract = "We decompose multimodal translation into two sub-tasks: learning to translate and learning visually grounded representations. In a multitask learning framework, translations are learned in an attention-based encoder-decoder, and grounded representations are learned through image representation prediction. Our approach improves translation performance compared to the state of the art on the Multi30K dataset. Furthermore, it is equally effective if we train the image prediction task on the external MS COCO dataset, and we find improvements if we train the translation model on the external News Commentary parallel text.",
}
@inproceedings{dalvi-etal-2017-understanding,
    title = "Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder",
    author = "Dalvi, Fahim  and
      Durrani, Nadir  and
      Sajjad, Hassan  and
      Belinkov, Yonatan  and
      Vogel, Stephan",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1015",
    pages = "142--151",
    abstract = "End-to-end training makes the neural machine translation (NMT) architecture simpler, yet elegant compared to traditional statistical machine translation (SMT). However, little is known about linguistic patterns of morphology, syntax and semantics learned during the training of NMT systems, and more importantly, which parts of the architecture are responsible for learning each of these phenomenon. In this paper we i) analyze how much morphology an NMT decoder learns, and ii) investigate whether injecting target morphology in the decoder helps it to produce better translations. To this end we present three methods: i) simultaneous translation, ii) joint-data learning, and iii) multi-task learning. Our results show that explicit morphological information helps the decoder learn target language morphology and improves the translation quality by 0.2{--}0.6 BLEU points.",
}
@inproceedings{zhang-etal-2017-improving,
    title = "Improving Neural Machine Translation through Phrase-based Forced Decoding",
    author = "Zhang, Jingyi  and
      Utiyama, Masao  and
      Sumita, Eiichro  and
      Neubig, Graham  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1016",
    pages = "152--162",
    abstract = "Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine the advantages of traditional SMT and NMT by exploiting an existing phrase-based SMT model to compute the phrase-based decoding cost for an NMT output and then using the phrase-based decoding cost to rerank the n-best NMT outputs. The main challenge in implementing this approach is that NMT outputs may not be in the search space of the standard phrase-based decoding algorithm, because the search space of phrase-based SMT is limited by the phrase-based translation rule table. We propose a soft forced decoding algorithm, which can always successfully find a decoding path for any NMT output. We show that using the forced decoding cost to rerank the NMT outputs can successfully improve translation quality on four different language pairs.",
}
@inproceedings{wang-xu-2017-convolutional,
    title = "Convolutional Neural Network with Word Embeddings for {C}hinese Word Segmentation",
    author = "Wang, Chunqi  and
      Xu, Bo",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1017",
    pages = "163--172",
    abstract = "Character-based sequence labeling framework is flexible and efficient for Chinese word segmentation (CWS). Recently, many character-based neural models have been applied to CWS. While they obtain good performance, they have two obvious weaknesses. The first is that they heavily rely on manually designed bigram feature, i.e. they are not good at capturing $n$-gram features automatically. The second is that they make no use of full word information. For the first weakness, we propose a convolutional neural model, which is able to capture rich $n$-gram features without any feature engineering. For the second one, we propose an effective approach to integrate the proposed model with word embeddings. We evaluate the model on two benchmark datasets: PKU and MSR. Without any feature engineering, the model obtains competitive performance {---} 95.7{\%} on PKU and 97.3{\%} on MSR. Armed with word embeddings, the model achieves state-of-the-art performance on both datasets {---} 96.5{\%} on PKU and 98.0{\%} on MSR, without using any external labeled resource.",
}
@inproceedings{shao-etal-2017-character,
    title = "Character-based Joint Segmentation and {POS} Tagging for {C}hinese using Bidirectional {RNN}-{CRF}",
    author = {Shao, Yan  and
      Hardmeier, Christian  and
      Tiedemann, J{\"o}rg  and
      Nivre, Joakim},
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1018",
    pages = "173--183",
    abstract = "We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.",
}
@inproceedings{huang-etal-2017-addressing,
    title = "Addressing Domain Adaptation for {C}hinese Word Segmentation with Global Recurrent Structure",
    author = "Huang, Shen  and
      Sun, Xu  and
      Wang, Houfeng",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1019",
    pages = "184--193",
    abstract = "Boundary features are widely used in traditional Chinese Word Segmentation (CWS) methods as they can utilize unlabeled data to help improve the Out-of-Vocabulary (OOV) word recognition performance. Although various neural network methods for CWS have achieved performance competitive with state-of-the-art systems, these methods, constrained by the domain and size of the training corpus, do not work well in domain adaptation. In this paper, we propose a novel BLSTM-based neural network model which incorporates a global recurrent structure designed for modeling boundary features dynamically. Experiments show that the proposed structure can effectively boost the performance of Chinese Word Segmentation, especially OOV-Recall, which brings benefits to domain adaptation. We achieved state-of-the-art results on 6 domains of CNKI articles, and competitive results to the best reported on the 4 domains of SIGHAN Bakeoff 2010 data.",
}
@inproceedings{vishal-etal-2017-information,
    title = "Information Bottleneck Inspired Method For Chat Text Segmentation",
    author = "Vishal, S  and
      Yadav, Mohit  and
      Vig, Lovekesh  and
      Shroff, Gautam",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1020",
    pages = "194--203",
    abstract = "We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints. Furthermore, we utilize critical non-textual clues such as time between two consecutive posts and people mentions within the posts. To ascertain the effectiveness of the proposed method, we have collected data from public Slack conversations and Fresco, a proprietary platform deployed inside our organization. Experiments demonstrate that the proposed method yields an absolute (relative) improvement of as high as 3.23{\%} (11.25{\%}). To facilitate future research, we are releasing manual annotations for segmentation on public Slack conversations.",
}
@inproceedings{wang-etal-2017-distributional,
    title = "Distributional Modeling on a Diet: One-shot Word Learning from Text Only",
    author = "Wang, Su  and
      Roller, Stephen  and
      Erk, Katrin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1021",
    pages = "204--213",
    abstract = "We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative.",
}
@inproceedings{park-myaeng-2017-computational,
    title = "A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding",
    author = "Park, Joohee  and
      Myaeng, Sung-hyon",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1022",
    pages = "214--223",
    abstract = "A distributed representation has become a popular approach to capturing a word meaning. Besides its success and practical value, however, questions arise about the relationships between a true word meaning and its distributed representation. In this paper, we examine such a relationship via polymodal embedding approach inspired by the theory that humans tend to use diverse sources in developing a word meaning. The result suggests that the existing embeddings lack in capturing certain aspects of word meanings which can be significantly improved by the polymodal approach. Also, we show distinct characteristics of different types of words (e.g. concreteness) via computational studies. Finally, we show our proposed embedding method outperforms the baselines in the word similarity measure tasks and the hypernym prediction tasks.",
}
@inproceedings{konkol-etal-2017-geographical,
    title = "Geographical Evaluation of Word Embeddings",
    author = "Konkol, Michal  and
      Brychc{\'\i}n, Tom{\'a}{\v{s}}  and
      Nykl, Michal  and
      Hercig, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1023",
    pages = "224--232",
    abstract = "Word embeddings are commonly compared either with human-annotated word similarities or through improvements in natural language processing tasks. We propose a novel principle which compares the information from word embeddings with reality. We implement this principle by comparing the information in the word embeddings with geographical positions of cities. Our evaluation linearly transforms the semantic space to optimally fit the real positions of cities and measures the deviation between the position given by word embeddings and the real position. A set of well-known word embeddings with state-of-the-art results were evaluated. We also introduce a visualization that helps with error analysis.",
}
@inproceedings{cao-etal-2017-modeling,
    title = "On Modeling Sense Relatedness in Multi-prototype Word Embedding",
    author = "Cao, Yixin  and
      Shi, Jiaxin  and
      Li, Juanzi  and
      Liu, Zhiyuan  and
      Li, Chengjiang",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1024",
    pages = "233--242",
    abstract = "To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model. However, most related work ignores the relatedness among word senses which actually plays an important role. In this paper, we propose a novel approach to capture word sense relatedness in multi-prototype word embedding model. Particularly, we differentiate the original sense and extended senses of a word by introducing their global occurrence information and model their relatedness through the local textual context information. Based on the idea of fuzzy clustering, we introduce a random process to integrate these two types of senses and design two non-parametric methods for word sense induction. To make our model more scalable and efficient, we use an online joint learning framework extended from the Skip-gram model. The experimental results demonstrate that our model outperforms both conventional single-prototype embedding models and other multi-prototype embedding models, and achieves more stable performance when trained on smaller data.",
}
@inproceedings{takeda-komatani-2017-unsupervised,
    title = "Unsupervised Segmentation of Phoneme Sequences based on {P}itman-{Y}or Semi-{M}arkov Model using Phoneme Length Context",
    author = "Takeda, Ryu  and
      Komatani, Kazunori",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1025",
    pages = "243--252",
    abstract = "Unsupervised segmentation of phoneme sequences is an essential process to obtain unknown words during spoken dialogues. In this segmentation, an input phoneme sequence without delimiters is converted into segmented sub-sequences corresponding to words. The Pitman-Yor semi-Markov model (PYSMM) is promising for this problem, but its performance degrades when it is applied to phoneme-level word segmentation. This is because of insufficient cues for the segmentation, e.g., homophones are improperly treated as single entries and their different contexts are also confused. We propose a phoneme-length context model for PYSMM to give a helpful cue at the phoneme-level and to predict succeeding segments more accurately. Our experiments showed that the peak performance with our context model outperformed those without such a context model by 0.045 at most in terms of F-measures of estimated segmentation.",
}
@inproceedings{zhang-wallace-2017-sensitivity,
    title = "A Sensitivity Analysis of (and Practitioners{'} Guide to) Convolutional Neural Networks for Sentence Classification",
    author = "Zhang, Ye  and
      Wallace, Byron",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1026",
    pages = "253--263",
    abstract = "Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (Kim, 2014; Kalchbrenner et al., 2014; Johnson and Zhang, 2014; Zhang et al., 2016). However, these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance, which makes it a modern standard baseline method akin to Support Vector Machine (SVMs) and logistic regression. We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification in real world settings.",
}
@inproceedings{teranishi-etal-2017-coordination,
    title = "Coordination Boundary Identification with Similarity and Replaceability",
    author = "Teranishi, Hiroki  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1027",
    pages = "264--272",
    abstract = "We propose a neural network model for coordination boundary detection. Our method relies on the two common properties - similarity and replaceability in conjuncts - in order to detect both similar pairs of conjuncts and dissimilar pairs of conjuncts. The model improves identification of clause-level coordination using bidirectional RNNs incorporating two properties as features. We show that our model outperforms the existing state-of-the-art methods on the coordination annotated Penn Treebank and Genia corpus without any syntactic information from parsers.",
}
@inproceedings{ferret-2017-turning,
    title = "Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion",
    author = "Ferret, Olivier",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1028",
    pages = "273--283",
    abstract = "In this article, we propose to investigate a new problem consisting in turning a distributional thesaurus into dense word vectors. We propose more precisely a method for performing such task by associating graph embedding and distributed representation adaptation. We have applied and evaluated it for English nouns at a large scale about its ability to retrieve synonyms. In this context, we have also illustrated the interest of the developed method for three different tasks: the improvement of already existing word embeddings, the fusion of heterogeneous representations and the expansion of synsets.",
}
@inproceedings{nieto-pina-johansson-2017-training,
    title = "Training Word Sense Embeddings With Lexicon-based Regularization",
    author = "Nieto-Pi{\~n}a, Luis  and
      Johansson, Richard",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1029",
    pages = "284--294",
    abstract = "We propose to improve word sense embeddings by enriching an automatic corpus-based method with lexicographic data. Information from a lexicon is introduced into the learning algorithm{'}s objective function through a regularizer. The incorporation of lexicographic data yields embeddings that are able to reflect expert-defined word senses, while retaining the robustness, high quality, and coverage of automatic corpus-based methods. These properties are observed in a manual inspection of the semantic clusters that different degrees of regularizer strength create in the vector space. Moreover, we evaluate the sense embeddings in two downstream applications: word sense disambiguation and semantic frame prediction, where they outperform simpler approaches. Our results show that a corpus-based model balanced with lexicographic data learns better representations and improve their performance in downstream tasks.",
}
@inproceedings{alva-manchego-etal-2017-learning,
    title = "Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs",
    author = "Alva-Manchego, Fernando  and
      Bingel, Joachim  and
      Paetzold, Gustavo  and
      Scarton, Carolina  and
      Specia, Lucia",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1030",
    pages = "295--305",
    abstract = "Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data. While the recently introduced Newsela corpus has alleviated the first problem, simplifications still need to be learned directly from parallel text using black-box, end-to-end approaches rather than from explicit annotations. These complex-simple parallel sentence pairs often differ to such a high degree that generalization becomes difficult. End-to-end models also make it hard to interpret what is actually learned from data. We propose a method that decomposes the task of TS into its sub-problems. We devise a way to automatically identify operations in a parallel corpus and introduce a sequence-labeling approach based on these annotations. Finally, we provide insights on the types of transformations that different approaches can model.",
}
@inproceedings{biran-mckeown-2017-domain,
    title = "Domain-Adaptable Hybrid Generation of {RDF} Entity Descriptions",
    author = "Biran, Or  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1031",
    pages = "306--315",
    abstract = "RDF ontologies provide structured data on entities in many domains and continue to grow in size and diversity. While they can be useful as a starting point for generating descriptions of entities, they often miss important information about an entity that cannot be captured as simple relations. In addition, generic approaches to generation from RDF cannot capture the unique style and content of specific domains. We describe a framework for hybrid generation of entity descriptions, which combines generation from RDF data with text extracted from a corpus, and extracts unique aspects of the domain from the corpus to create domain-specific generation systems. We show that each component of our approach significantly increases the satisfaction of readers with the text across multiple applications and domains.",
}
@inproceedings{pouriyeh-etal-2017-es,
    title = "{ES}-{LDA}: Entity Summarization using Knowledge-based Topic Modeling",
    author = "Pouriyeh, Seyedamin  and
      Allahyari, Mehdi  and
      Kochut, Krzysztof  and
      Cheng, Gong  and
      Arabnia, Hamid Reza",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1032",
    pages = "316--325",
    abstract = "With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably. These descriptions are usually lengthy, which makes the utilization of the underlying entities a difficult task. Entity summarization, which aims to create summaries for real-world entities, has gained increasing attention in recent years. In this paper, we propose a probabilistic topic model, ES-LDA, that combines prior knowledge with statistical learning techniques within a single framework to create more reliable and representative summaries for entities. We demonstrate the effectiveness of our approach by conducting extensive experiments and show that our model outperforms the state-of-the-art techniques and enhances the quality of the entity summaries.",
}
@inproceedings{ushiku-etal-2017-procedural,
    title = "Procedural Text Generation from an Execution Video",
    author = "Ushiku, Atsushi  and
      Hashimoto, Hayato  and
      Hashimoto, Atsushi  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1033",
    pages = "326--335",
    abstract = "In recent years, there has been a surge of interest in automatically describing images or videos in a natural language. These descriptions are useful for image/video search, etc. In this paper, we focus on procedure execution videos, in which a human makes or repairs something and propose a method for generating procedural texts from them. Since video/text pairs available are limited in size, the direct application of end-to-end deep learning is not feasible. Thus we propose to train Faster R-CNN network for object recognition and LSTM for text generation and combine them at run time. We took pairs of recipe and cooking video, generated a recipe from a video, and compared it with the original recipe. The experimental results showed that our method can produce a recipe as accurate as the state-of-the-art scene descriptions.",
}
@inproceedings{gan-gong-2017-text,
    title = "Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information",
    author = "Gan, Ling  and
      Gong, Houyu",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1034",
    pages = "336--341",
    abstract = "Tree-structured Long Short-Term Memory (Tree-LSTM) has been proved to be an effective method in the sentiment analysis task. It extracts structural information on text, and uses Long Short-Term Memory (LSTM) cell to prevent gradient vanish. However, though combining the LSTM cell, it is still a kind of model that extracts the structural information and almost not extracts serialization information. In this paper, we propose three new models in order to combine those two kinds of information: the structural information generated by the Constituency Tree-LSTM and the serialization information generated by Long-Short Term Memory neural network. Our experiments show that combining those two kinds of information can give contributes to the performance of the sentiment analysis task compared with the single Constituency Tree-LSTM model and the LSTM model.",
}
@inproceedings{potash-etal-2017-length,
    title = "Length, Interchangeability, and External Knowledge: Observations from Predicting Argument Convincingness",
    author = "Potash, Peter  and
      Bhattacharya, Robin  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1035",
    pages = "342--351",
    abstract = "In this work, we provide insight into three key aspects related to predicting argument convincingness. First, we explicitly display the power that text length possesses for predicting convincingness in an unsupervised setting. Second, we show that a bag-of-words embedding model posts state-of-the-art on a dataset of arguments annotated for convincingness, outperforming an SVM with numerous hand-crafted features as well as recurrent neural network models that attempt to capture semantic composition. Finally, we assess the feasibility of integrating external knowledge when predicting convincingness, as arguments are often more convincing when they contain abundant information and facts. We finish by analyzing the correlations between the various models we propose.",
}
@inproceedings{duan-etal-2017-exploiting,
    title = "Exploiting Document Level Information to Improve Event Detection via Recurrent Neural Networks",
    author = "Duan, Shaoyang  and
      He, Ruifang  and
      Zhao, Wenli",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1036",
    pages = "352--361",
    abstract = "This paper tackles the task of event detection, which involves identifying and categorizing events. The previous work mainly exist two problems: (1) the traditional feature-based methods apply cross-sentence information, yet need taking a large amount of human effort to design complicated feature sets and inference rules; (2) the representation-based methods though overcome the problem of manually extracting features, while just depend on local sentence representation. Considering local sentence context is insufficient to resolve ambiguities in identifying particular event types, therefore, we propose a novel document level Recurrent Neural Networks (DLRNN) model, which can automatically extract cross-sentence clues to improve sentence level event detection without designing complex reasoning rules. Experiment results show that our approach outperforms other state-of-the-art methods on ACE 2005 dataset without external knowledge base.",
}
@inproceedings{zhang-etal-2017-embracing,
    title = "Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging",
    author = "Zhang, Boliang  and
      Lu, Di  and
      Pan, Xiaoman  and
      Lin, Ying  and
      Abudukelimu, Halidanmu  and
      Ji, Heng  and
      Knight, Kevin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1037",
    pages = "362--372",
    abstract = "Current supervised name tagging approaches are inadequate for most low-resource languages due to the lack of annotated data and actionable linguistic knowledge. All supervised learning methods (including deep neural networks (DNN)) are sensitive to noise and thus they are not quite portable without massive clean annotations. We found that the F-scores of DNN-based name taggers drop rapidly (20{\%}-30{\%}) when we replace clean manual annotations with noisy annotations in the training data. We propose a new solution to incorporate many non-traditional language universal resources that are readily available but rarely explored in the Natural Language Processing (NLP) community, such as the World Atlas of Linguistic Structure, CIA names, PanLex and survival guides. We acquire and encode various types of non-traditional linguistic resources into a DNN name tagger. Experiments on three low-resource languages show that feeding linguistic knowledge can make DNN significantly more robust to noise, achieving 8{\%}-22{\%} absolute F-score gains on name tagging without using any human annotation",
}
@inproceedings{skadina-pinnis-2017-nmt,
    title = "{NMT} or {SMT}: Case Study of a Narrow-domain {E}nglish-{L}atvian Post-editing Project",
    author = "Skadi{\c{n}}a, Inguna  and
      Pinnis, M{\=a}rcis",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1038",
    pages = "373--383",
    abstract = "The recent technological shift in machine translation from statistical machine translation (SMT) to neural machine translation (NMT) raises the question of the strengths and weaknesses of NMT. In this paper, we present an analysis of NMT and SMT systems{'} outputs from narrow domain English-Latvian MT systems that were trained on a rather small amount of data. We analyze post-edits produced by professional translators and manually annotated errors in these outputs. Analysis of post-edits allowed us to conclude that both approaches are comparably successful, allowing for an increase in translators{'} productivity, with the NMT system showing slightly worse results. Through the analysis of annotated errors, we found that NMT translations are more fluent than SMT translations. However, errors related to accuracy, especially, mistranslation and omission errors, occur more often in NMT outputs. The word form errors, that characterize the morphological richness of Latvian, are frequent for both systems, but slightly fewer in NMT outputs.",
}
@inproceedings{wang-etal-2017-towards,
    title = "Towards Neural Machine Translation with Partially Aligned Corpora",
    author = "Wang, Yining  and
      Zhao, Yang  and
      Zhang, Jiajun  and
      Zong, Chengqing  and
      Xue, Zhengshan",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1039",
    pages = "384--393",
    abstract = "While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs. In this paper, we address a new translation scenario in which there only exists monolingual corpora and phrase pairs. We propose a new method towards translation with partially aligned sentence pairs which are derived from the phrase pairs and monolingual corpora. To make full use of the partially aligned corpora, we adapt the conventional NMT training method in two aspects. On one hand, different generation strategies are designed for aligned and unaligned target words. On the other hand, a different objective function is designed to model the partially aligned parts. The experiments demonstrate that our method can achieve a relatively good result in such a translation scenario, and tiny bitexts can boost translation quality to a large extent.",
}
@inproceedings{lahiri-etal-2017-identifying,
    title = "Identifying Usage Expression Sentences in Consumer Product Reviews",
    author = "Lahiri, Shibamouli  and
      Vydiswaran, V.G.Vinod  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1040",
    pages = "394--403",
    abstract = "In this paper we introduce the problem of identifying usage expression sentences in a consumer product review. We create a human-annotated gold standard dataset of 565 reviews spanning five distinct product categories. Our dataset consists of more than 3,000 annotated sentences. We further introduce a classification system to label sentences according to whether or not they describe some {``}usage{''}. The system combines lexical, syntactic, and semantic features in a product-agnostic fashion to yield good classification performance. We show the effectiveness of our approach using importance ranking of features, error analysis, and cross-product classification experiments.",
}
@inproceedings{asahara-kato-2017-reading,
    title = "Between Reading Time and Syntactic/Semantic Categories",
    author = "Asahara, Masayuki  and
      Kato, Sachi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1041",
    pages = "404--412",
    abstract = "This article presents a contrastive analysis between reading time and syntactic/semantic categories in Japanese. We overlaid the reading time annotation of BCCWJ-EyeTrack and a syntactic/semantic category information annotation on the {`}Balanced Corpus of Contemporary Written Japanese{'}. Statistical analysis based on a mixed linear model showed that verbal phrases tend to have shorter reading times than adjectives, adverbial phrases, or nominal phrases. The results suggest that the preceding phrases associated with the presenting phrases promote the reading process to shorten the gazing time.",
}
@inproceedings{ghaddar-langlais-2017-winer,
    title = "{W}i{NER}: A {W}ikipedia Annotated Corpus for Named Entity Recognition",
    author = "Ghaddar, Abbas  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1042",
    pages = "413--422",
    abstract = "We revisit the idea of mining Wikipedia in order to generate named-entity annotations. We propose a new methodology that we applied to English Wikipedia to build WiNER, a large, high quality, annotated corpus. We evaluate its usefulness on 6 NER tasks, comparing 4 popular state-of-the art approaches. We show that LSTM-CRF is the approach that benefits the most from our corpus. We report impressive gains with this model when using a small portion of WiNER on top of the CONLL training material. Last, we propose a simple but efficient method for exploiting the full range of WiNER, leading to further improvements.",
}
@inproceedings{lakomkin-etal-2017-reusing,
    title = "Reusing Neural Speech Representations for Auditory Emotion Recognition",
    author = "Lakomkin, Egor  and
      Weber, Cornelius  and
      Magg, Sven  and
      Wermter, Stefan",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1043",
    pages = "423--430",
    abstract = "Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcity of training data, general subjectivity in emotion perception resulting in low annotator agreement, and the uncertainty about which features are the most relevant and robust ones for classification. In this paper, we will tackle the latter problem. Inspired by the recent success of transfer learning methods we propose a set of architectures which utilize neural representations inferred by training on large speech databases for the acoustic emotion recognition task. Our experiments on the IEMOCAP dataset show {\textasciitilde}10{\%} relative improvements in the accuracy and F1-score over the baseline recurrent neural network which is trained end-to-end for emotion recognition.",
}
@inproceedings{tjandra-etal-2017-local,
    title = "Local Monotonic Attention Mechanism for End-to-End Speech And Language Processing",
    author = "Tjandra, Andros  and
      Sakti, Sakriani  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1044",
    pages = "431--440",
    abstract = "Recently, encoder-decoder neural networks have shown impressive performance on many sequence-related tasks. The architecture commonly uses an attentional mechanism which allows the model to learn alignments between the source and the target sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in several tasks, such as automatic speech recognition (ASR), grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results on ASR, G2P and machine translation between two languages with similar sentence structures, demonstrate that the proposed encoder-decoder model with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.",
}
@inproceedings{salton-etal-2017-attentive,
    title = "Attentive Language Models",
    author = "Salton, Giancarlo  and
      Ross, Robert  and
      Kelleher, John",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1045",
    pages = "441--450",
    abstract = "In this paper, we extend Recurrent Neural Network Language Models (RNN-LMs) with an attention mechanism. We show that an {``}attentive{''} RNN-LM (with 11M parameters) achieves a better perplexity than larger RNN-LMs (with 66M parameters) and achieves performance comparable to an ensemble of 10 similar sized RNN-LMs. We also show that an {``}attentive{''} RNN-LM needs less contextual information to achieve similar results to the state-of-the-art on the wikitext2 dataset.",
}
@inproceedings{murawaki-2017-diachrony,
    title = "Diachrony-aware Induction of Binary Latent Representations from Typological Features",
    author = "Murawaki, Yugo",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1046",
    pages = "451--461",
    abstract = "Although features of linguistic typology are a promising alternative to lexical evidence for tracing evolutionary history of languages, a large number of missing values in the dataset pose serious difficulties for statistical modeling. In this paper, we combine two existing approaches to the problem: (1) the synchronic approach that focuses on interdependencies between features and (2) the diachronic approach that exploits phylogenetically- and/or spatially-related languages. Specifically, we propose a Bayesian model that (1) represents each language as a sequence of binary latent parameters encoding inter-feature dependencies and (2) relates a language{'}s parameters to those of its phylogenetic and spatial neighbors. Experiments show that the proposed model recovers missing values more accurately than others and that induced representations retain phylogenetic and spatial signals observed for surface features.",
}
@inproceedings{mostafazadeh-etal-2017-image,
    title = "Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation",
    author = "Mostafazadeh, Nasrin  and
      Brockett, Chris  and
      Dolan, Bill  and
      Galley, Michel  and
      Gao, Jianfeng  and
      Spithourakis, Georgios  and
      Vanderwende, Lucy",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1047",
    pages = "462--472",
    abstract = "The popularity of image sharing on social media and the engagement it creates between users reflect the important role that visual context plays in everyday conversations. We present a novel task, Image Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialog research.",
}
@inproceedings{kobayashi-etal-2017-neural,
    title = "A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse",
    author = "Kobayashi, Sosuke  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1048",
    pages = "473--483",
    abstract = "This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models. We proposed a method for on-the-fly construction and exploitation of word embeddings in both the input and output layers of a neural model by tracking contexts. This extends the dynamic entity representation used in Kobayashi et al. (2016) and incorporates a copy mechanism proposed independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we construct a new task and dataset called Anonymized Language Modeling for evaluating the ability to capture word meanings while reading. Experiments conducted using our novel dataset show that the proposed variant of RNN language model outperformed the baseline model. Furthermore, the experiments also demonstrate that dynamic updates of an output layer help a model predict reappearing entities, whereas those of an input layer are effective to predict words following reappearing entities.",
}
@inproceedings{shi-etal-2017-using,
    title = "Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification",
    author = "Shi, Wei  and
      Yung, Frances  and
      Rubino, Raphael  and
      Demberg, Vera",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1049",
    pages = "484--495",
    abstract = "Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer from the shortage of labeled data. In this paper, we address this problem by procuring additional training data from parallel corpora: When humans translate a text, they sometimes add connectives (a process known as \textit{explicitation}). We automatically back-translate it into an English connective and use it to infer a label with high confidence. We show that a training set several times larger than the original training set can be generated this way. With the extra labeled instances, we show that even a simple bidirectional Long Short-Term Memory Network can outperform the current state-of-the-art.",
}
@inproceedings{wang-etal-2017-tag,
    title = "Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification",
    author = "Wang, Yizhong  and
      Li, Sujian  and
      Yang, Jingfeng  and
      Sun, Xu  and
      Wang, Houfeng",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1050",
    pages = "496--505",
    abstract = "Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text. To tackle this task, recent studies have tried several deep learning methods but few of them exploited the syntactic information. In this work, we explore the idea of incorporating syntactic parse tree into neural networks. Specifically, we employ the Tree-LSTM model and Tree-GRU model, which is based on the tree structure, to encode the arguments in a relation. And we further leverage the constituent tags to control the semantic composition process in these tree-structured neural networks. Experimental results show that our method achieves state-of-the-art performance on PDTB corpus.",
}
@inproceedings{abdalla-hirst-2017-cross,
    title = "Cross-Lingual Sentiment Analysis Without (Good) Translation",
    author = "Abdalla, Mohamed  and
      Hirst, Graeme",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1051",
    pages = "506--515",
    abstract = "Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context. By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.",
}
@inproceedings{gao-etal-2017-implicit,
    title = "Implicit Syntactic Features for Target-dependent Sentiment Analysis",
    author = "Gao, Yuze  and
      Zhang, Yue  and
      Xiao, Tong",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1052",
    pages = "516--524",
    abstract = "Targeted sentiment analysis investigates the sentiment polarities on given target mentions from input texts. Different from sentence level sentiment, it offers more fine-grained knowledge on each entity mention. While early work leveraged syntactic information, recent research has used neural representation learning to induce features automatically, thereby avoiding error propagation of syntactic parsers, which are particularly severe on social media texts. We study a method to leverage syntactic information without explicitly building the parser outputs, by training an encoder-decoder structure parser model on standard syntactic treebanks, and then leveraging its hidden encoder layers when analysing tweets. Such hidden vectors do not contain explicit syntactic outputs, yet encode rich syntactic features. We use them to augment the inputs to a baseline state-of-the-art targeted sentiment classifier, observing significant improvements on various benchmark datasets. We obtain the best accuracies on all test sets.",
}
@inproceedings{tamilselvam-etal-2017-graph,
    title = "Graph Based Sentiment Aggregation using {C}oncept{N}et Ontology",
    author = "Tamilselvam, Srikanth  and
      Nagar, Seema  and
      Mishra, Abhijit  and
      Dey, Kuntal",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1053",
    pages = "525--535",
    abstract = "The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text. The current paper addresses the sentiment aggregation problem, by assigning weights to each aspect appearing in the user-generated content, that are proportionate to the strategic importance of the aspect in the pragmatic domain. The novelty of this paper is in computing the pragmatic significance (weight) of each aspect, using graph centrality measures (applied on domain specific ontology-graphs extracted from ConceptNet), and deeply ingraining these weights while aggregating the sentiments from opinionated text. We experiment over multiple real-life product review data. Our system consistently outperforms the state of the art - by as much as a F-score of 20.39{\%} in one case.",
}
@inproceedings{nguyen-nguyen-2017-sentence,
    title = "Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification",
    author = "Nguyen, Huy Thanh  and
      Nguyen, Minh Le",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1054",
    pages = "536--544",
    abstract = "Tweet-level sentiment classification in Twitter social networking has many challenges: exploiting syntax, semantic, sentiment, and context in tweets. To address these problems, we propose a novel approach to sentiment analysis that uses lexicon features for building lexicon embeddings (LexW2Vs) and generates character attention vectors (CharAVs) by using a Deep Convolutional Neural Network (DeepCNN). Our approach integrates LexW2Vs and CharAVs with continuous word embeddings (ContinuousW2Vs) and dependency-based word embeddings (DependencyW2Vs) simultaneously in order to increase information for each word into a Bidirectional Contextual Gated Recurrent Neural Network (Bi-CGRNN). We evaluate our model on two Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.",
}
@inproceedings{jin-etal-2017-combining-lightly,
    title = "Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising",
    author = "Jin, Yiping  and
      Wanvarie, Dittaya  and
      Le, Phu",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1055",
    pages = "545--554",
    abstract = "In this paper we propose a lightly-supervised framework to rapidly build text classifiers for contextual advertising. Traditionally text classification techniques require labeled training documents for each predefined class. In the scenario of contextual advertising, advertisers often want to target to a specific class of webpages most relevant to their product or service, which may not be covered by a pre-trained classifier. Moreover, the advertisers are interested in whether a webpage is {``}relevant{''} or {``}irrelevant{''}. It is time-consuming to solicit the advertisers for reliable training signals for the negative class. Therefore, it is more suitable to model the problem as a one-class classification problem, in contrast to traditional classification problems where disjoint classes are defined a priori. We first apply two state-of-the-art lightly-supervised classification models, generalized expectation (GE) criteria (Druck et al., 2008) and multinomial naive Bayes (MNB) with priors (Settles, 2011) to one-class classification where the user only needs to provide a small list of labeled words for the target class. To combine the strengths of the two models, we fuse them together by using MNB to automatically enrich the constraints for GE training. We also explore ensemble method to combine classifiers. On a corpus of webpages from real-time bidding requests, the proposed model achieves the highest average F1 of 0.69 and closes more than half of the gap between previous state-of-the-art lightly-supervised models to a fully-supervised MaxEnt model.",
}
@inproceedings{liu-etal-2017-capturing,
    title = "Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields",
    author = "Liu, Fei  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1056",
    pages = "555--565",
    abstract = "Despite successful applications across a broad range of NLP tasks, conditional random fields ({``}CRFs{''}), in particular the linear-chain variant, are only able to model local features. While this has important benefits in terms of inference tractability, it limits the ability of the model to capture long-range dependencies between items. Attempts to extend CRFs to capture long-range dependencies have largely come at the cost of computational complexity and approximate inference. In this work, we propose an extension to CRFs by integrating external memory, taking inspiration from memory networks, thereby allowing CRFs to incorporate information far beyond neighbouring steps. Experiments across two tasks show substantial improvements over strong CRF and LSTM baselines.",
}
@inproceedings{tran-etal-2017-named,
    title = "Named Entity Recognition with Stack Residual {LSTM} and Trainable Bias Decoding",
    author = "Tran, Quan  and
      MacKinlay, Andrew  and
      Jimeno Yepes, Antonio",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1057",
    pages = "566--575",
    abstract = "Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER). We present two innovations to improve the performance of these models. The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks. The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure. Our work improves the state-of-the-art results for both Spanish and English languages on the standard train/development/test split of the CoNLL 2003 Shared Task NER dataset.",
}
@inproceedings{das-ghosh-2017-neuramanteau,
    title = "{N}euramanteau: A Neural Network Ensemble Model for Lexical Blends",
    author = "Das, Kollol  and
      Ghosh, Shaona",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1058",
    pages = "576--583",
    abstract = "The problem of blend formation in generative linguistics is interesting in the context of neologism, their quick adoption in modern life and the creative generative process guiding their formation. Blend quality depends on multitude of factors with high degrees of uncertainty. In this work, we investigate if the modern neural network models can sufficiently capture and recognize the creative blend composition process. We propose recurrent neural network sequence-to-sequence models, that are evaluated on multiple blend datasets available in the literature. We propose an ensemble neural and hybrid model that outperforms most of the baselines and heuristic models upon evaluation on test data.",
}
@inproceedings{ferracane-etal-2017-leveraging,
    title = "Leveraging Discourse Information Effectively for Authorship Attribution",
    author = "Ferracane, Elisa  and
      Wang, Su  and
      Mooney, Raymond",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1059",
    pages = "584--593",
    abstract = "We explore techniques to maximize the effectiveness of discourse information in the task of authorship attribution. We present a novel method to embed discourse features in a Convolutional Neural Network text classifier, which achieves a state-of-the-art result by a significant margin. We empirically investigate several featurization methods to understand the conditions under which discourse features contribute non-trivial performance gains, and analyze discourse embeddings.",
}
@inproceedings{persing-ng-2017-lightly,
    title = "Lightly-Supervised Modeling of Argument Persuasiveness",
    author = "Persing, Isaac  and
      Ng, Vincent",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1060",
    pages = "594--604",
    abstract = "We propose the first lightly-supervised approach to scoring an argument{'}s persuasiveness. Key to our approach is the novel hypothesis that lightly-supervised persuasiveness scoring is possible by explicitly modeling the major errors that negatively impact persuasiveness. In an evaluation on a new annotated corpus of online debate arguments, our approach rivals its fully-supervised counterparts in performance by four scoring metrics when using only 10{\%} of the available training instances.",
}
@inproceedings{luan-etal-2017-multi,
    title = "Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models",
    author = "Luan, Yi  and
      Brockett, Chris  and
      Dolan, Bill  and
      Gao, Jianfeng  and
      Galley, Michel",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1061",
    pages = "605--614",
    abstract = "Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers{'} traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.",
}
@inproceedings{mehri-carenini-2017-chat,
    title = "Chat Disentanglement: Identifying Semantic Reply Relationships with Random Forests and Recurrent Neural Networks",
    author = "Mehri, Shikib  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1062",
    pages = "615--623",
    abstract = "Thread disentanglement is a precursor to any high-level analysis of multiparticipant chats. Existing research approaches the problem by calculating the likelihood of two messages belonging in the same thread. Our approach leverages a newly annotated dataset to identify reply relationships. Furthermore, we explore the usage of an RNN, along with large quantities of unlabeled data, to learn semantic relationships between messages. Our proposed pipeline, which utilizes a reply classifier and an RNN to generate a set of disentangled threads, is novel and performs well against previous work.",
}
@inproceedings{schulder-etal-2017-towards,
    title = "Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features",
    author = "Schulder, Marc  and
      Wiegand, Michael  and
      Ruppenhofer, Josef  and
      Roth, Benjamin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1063",
    pages = "624--633",
    abstract = "We present a major step towards the creation of the first high-coverage lexicon of polarity shifters. In this work, we bootstrap a lexicon of verbs by exploiting various linguistic features. Polarity shifters, such as {``}abandon{''}, are similar to negations (e.g. {``}not{''}) in that they move the polarity of a phrase towards its inverse, as in {``}abandon all hope{''}. While there exist lists of negation words, creating comprehensive lists of polarity shifters is far more challenging due to their sheer number. On a sample of manually annotated verbs we examine a variety of linguistic features for this task. Then we build a supervised classifier to increase coverage. We show that this approach drastically reduces the annotation effort while ensuring a high-precision lexicon. We also show that our acquired knowledge of verbal polarity shifters improves phrase-level sentiment analysis.",
}
@inproceedings{ma-etal-2017-cascading,
    title = "Cascading Multiway Attentions for Document-level Sentiment Classification",
    author = "Ma, Dehong  and
      Li, Sujian  and
      Zhang, Xiaodong  and
      Wang, Houfeng  and
      Sun, Xu",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1064",
    pages = "634--643",
    abstract = "Document-level sentiment classification aims to assign the user reviews a sentiment polarity. Previous methods either just utilized the document content without consideration of user and product information, or did not comprehensively consider what roles the three kinds of information play in text modeling. In this paper, to reasonably use all the information, we present the idea that user, product and their combination can all influence the generation of attentions to words and sentences, when judging the sentiment of a document. With this idea, we propose a cascading multiway attention (CMA) model, where multiple ways of using user and product information are cascaded to influence the generation of attentions on the word and sentence layers. Then, sentences and documents are well modeled by multiple representation vectors, which provide rich information for sentiment classification. Experiments on IMDB and Yelp datasets demonstrate the effectiveness of our model.",
}
@inproceedings{nguyen-nguyen-2017-ensemble,
    title = "An Ensemble Method with Sentiment Features and Clustering Support",
    author = "Nguyen, Huy Tien  and
      Nguyen, Minh Le",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1065",
    pages = "644--653",
    abstract = "Deep learning models have recently been applied successfully in natural language processing, especially sentiment analysis. Each deep learning model has a particular advantage, but it is difficult to combine these advantages into one model, especially in the area of sentiment analysis. In our approach, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) were utilized to learn sentiment-specific features in a freezing scheme. This scenario provides a novel and efficient way for integrating advantages of deep learning models. In addition, we also grouped documents into clusters by their similarity and applied the prediction score of Naive Bayes SVM (NBSVM) method to boost the classification accuracy of each group. The experiments show that our method achieves the state-of-the-art performance on two well-known datasets: IMDB large movie reviews for document level and Pang {\&} Lee movie reviews for sentence level.",
}
@inproceedings{yu-jiang-2017-leveraging,
    title = "Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification",
    author = "Yu, Jianfei  and
      Jiang, Jing",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1066",
    pages = "654--663",
    abstract = "In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.",
}
@inproceedings{wilson-mihalcea-2017-measuring,
    title = "Measuring Semantic Relations between Human Activities",
    author = "Wilson, Steven  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1067",
    pages = "664--673",
    abstract = "The things people do in their daily lives can provide valuable insights into their personality, values, and interests. Unstructured text data on social media platforms are rich in behavioral content, and automated systems can be deployed to learn about human activity on a broad scale if these systems are able to reason about the content of interest. In order to aid in the evaluation of such systems, we introduce a new phrase-level semantic textual similarity dataset comprised of human activity phrases, providing a testbed for automated systems that analyze relationships between phrasal descriptions of people{'}s actions. Our set of 1,000 pairs of activities is annotated by human judges across four relational dimensions including similarity, relatedness, motivational alignment, and perceived actor congruence. We evaluate a set of strong baselines for the task of generating scores that correlate highly with human ratings, and we introduce several new approaches to the phrase-level similarity task in the domain of human activities.",
}
@inproceedings{min-etal-2017-learning,
    title = "Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks",
    author = "Min, Bonan  and
      Jiang, Zhuolin  and
      Freedman, Marjorie  and
      Weischedel, Ralph",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1068",
    pages = "674--684",
    abstract = "Typically, relation extraction models are trained to extract instances of a relation ontology using only training data from a single language. However, the concepts represented by the relation ontology (e.g. ResidesIn, EmployeeOf) are language independent. The numbers of annotated examples available for a given ontology vary between languages. For example, there are far fewer annotated examples in Spanish and Japanese than English and Chinese. Furthermore, using only language-specific training data results in the need to manually annotate equivalently large amounts of training for each new language a system encounters. We propose a deep neural network to learn transferable, discriminative bilingual representation. Experiments on the ACE 2005 multilingual training corpus demonstrate that the joint training process results in significant improvement in relation classification performance over the monolingual counterparts. The learnt representation is discriminative and transferable between languages. When using 10{\%} (25K English words, or 30K Chinese characters) of the training data, our approach results in doubling F1 compared to a monolingual baseline. We achieve comparable performance to the monolingual system trained with 250K English words (or 300K Chinese characters) With 50{\%} of training data.",
}
@inproceedings{hazem-morin-2017-bilingual,
    title = "Bilingual Word Embeddings for Bilingual Terminology Extraction from Specialized Comparable Corpora",
    author = "Hazem, Amir  and
      Morin, Emmanuel",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1069",
    pages = "685--693",
    abstract = "Bilingual lexicon extraction from comparable corpora is constrained by the small amount of available data when dealing with specialized domains. This aspect penalizes the performance of distributional-based approaches, which is closely related to the reliability of word{'}s cooccurrence counts extracted from comparable corpora. A solution to avoid this limitation is to associate external resources with the comparable corpus. Since bilingual word embeddings have recently shown efficient models for learning bilingual distributed representation of words, we explore different word embedding models and show how a general-domain comparable corpus can enrich a specialized comparable corpus via neural networks",
}
@inproceedings{liu-nouvel-2017-bambara,
    title = "A {B}ambara Tonalization System for Word Sense Disambiguation Using Differential Coding, Segmentation and Edit Operation Filtering",
    author = "Liu, Luigi Yu-Cheng  and
      Nouvel, Damien",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1070",
    pages = "694--703",
    abstract = "In many languages such as Bambara or Arabic, tone markers (diacritics) may be written but are actually often omitted. NLP applications are confronted to ambiguities and subsequent difficulties when processing texts. To circumvent this problem, tonalization may be used, as a word sense disambiguation task, relying on context to add diacritics that partially disambiguate words as well as senses. In this paper, we describe our implementation of a Bambara tonalizer that adds tone markers using machine learning (CRFs). To make our tool efficient, we used differential coding, word segmentation and edit operation filtering. We describe our approach that allows tractable machine learning and improves accuracy: our model may be learned within minutes on a 358K-word corpus and reaches 92.3{\%} accuracy.",
}
@inproceedings{zhao-kawahara-2017-joint,
    title = "Joint Learning of Dialog Act Segmentation and Recognition in Spoken Dialog Using Neural Networks",
    author = "Zhao, Tianyu  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1071",
    pages = "704--712",
    abstract = "Dialog act segmentation and recognition are basic natural language understanding tasks in spoken dialog systems. This paper investigates a unified architecture for these two tasks, which aims to improve the model{'}s performance on both of the tasks. Compared with past joint models, the proposed architecture can (1) incorporate contextual information in dialog act recognition, and (2) integrate models for tasks of different levels as a whole, i.e. dialog act segmentation on the word level and dialog act recognition on the segment level. Experimental results show that the joint training system outperforms the simple cascading system and the joint coding system on both dialog act segmentation and recognition tasks.",
}
@inproceedings{wang-etal-2017-predicting,
    title = "Predicting Users{'} Negative Feedbacks in Multi-Turn Human-Computer Dialogues",
    author = "Wang, Xin  and
      Wang, Jianan  and
      Liu, Yuanchao  and
      Wang, Xiaolong  and
      Wang, Zhuoran  and
      Wang, Baoxun",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1072",
    pages = "713--722",
    abstract = "User experience is essential for human-computer dialogue systems. However, it is impractical to ask users to provide explicit feedbacks when the agents{'} responses displease them. Therefore, in this paper, we explore to predict users{'} imminent dissatisfactions caused by intelligent agents by analysing the existing utterances in the dialogue sessions. To our knowledge, this is the first work focusing on this task. Several possible factors that trigger negative emotions are modelled. A relation sequence model (RSM) is proposed to encode the sequence of appropriateness of current response with respect to the earlier utterances. The experimental results show that the proposed structure is effective in modelling emotional risk (possibility of negative feedback) than existing conversation modelling approaches. Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work. Balanced sampling with respect to the last response in the distance supervision data are shown to be reliable for data augmentation.",
}
@inproceedings{madan-joshi-2017-finding,
    title = "Finding Dominant User Utterances And System Responses in Conversations",
    author = "Madan, Dhiraj  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1073",
    pages = "723--732",
    abstract = "There are several dialog frameworks which allow manual specification of intents and rule based dialog flow. The rule based framework provides good control to dialog designers at the expense of being more time consuming and laborious. The job of a dialog designer can be reduced if we could identify pairs of user intents and corresponding responses automatically from prior conversations between users and agents. In this paper we propose an approach to find these frequent user utterances (which serve as examples for intents) and corresponding agent responses. We propose a novel SimCluster algorithm that extends standard K-means algorithm to simultaneously cluster user utterances and agent utterances by taking their adjacency information into account. The method also aligns these clusters to provide pairs of intents and response groups. We compare our results with those produced by using simple Kmeans clustering on a real dataset and observe upto 10{\%} absolute improvement in F1-scores. Through our experiments on synthetic dataset, we show that our algorithm gains more advantage over K-means algorithm when the data has large variance.",
}
@inproceedings{li-etal-2017-end,
    title = "End-to-End Task-Completion Neural Dialogue Systems",
    author = "Li, Xiujun  and
      Chen, Yun-Nung  and
      Li, Lihong  and
      Gao, Jianfeng  and
      Celikyilmaz, Asli",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1074",
    pages = "733--743",
    abstract = "One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues.Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.",
}
@inproceedings{lau-etal-2017-end,
    title = "End-to-end Network for {T}witter Geolocation Prediction and Hashing",
    author = "Lau, Jey Han  and
      Chi, Lianhua  and
      Tran, Khoi-Nguyen  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1075",
    pages = "744--753",
    abstract = "We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the-art systems, our model outperforms them by 2{\%}-6{\%}. Additionally, we propose extensions to the model to compress representation learnt by the network into binary codes. Experiments show that it produces compact codes compared to benchmark hashing algorithms. An implementation of the model is released publicly.",
}
@inproceedings{newell-etal-2017-assessing,
    title = "Assessing the Verifiability of Attributions in News Text",
    author = "Newell, Edward  and
      Schang, Ariane  and
      Margolin, Drew  and
      Ruths, Derek",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1076",
    pages = "754--763",
    abstract = "When reporting the news, journalists rely on the statements of stakeholders, experts, and officials. The attribution of such a statement is verifiable if its fidelity to the source can be confirmed or denied. In this paper, we develop a new NLP task: determining the verifiability of an attribution based on linguistic cues. We operationalize the notion of verifiability as a score between 0 and 1 using human judgments in a comparison-based approach. Using crowdsourcing, we create a dataset of verifiability-scored attributions, and demonstrate a model that achieves an RMSE of 0.057 and Spearman{'}s rank correlation of 0.95 to human-generated scores. We discuss the application of this technique to the analysis of mass media.",
}
@inproceedings{rieman-etal-2017-domain,
    title = "Domain Adaptation from User-level {F}acebook Models to County-level {T}witter Predictions",
    author = "Rieman, Daniel  and
      Jaidka, Kokil  and
      Schwartz, H. Andrew  and
      Ungar, Lyle",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1077",
    pages = "764--773",
    abstract = "Several studies have demonstrated how language models of user attributes, such as personality, can be built by using the Facebook language of social media users in conjunction with their responses to psychology questionnaires. It is challenging to apply these models to make general predictions about attributes of communities, such as personality distributions across US counties, because it requires 1. the potentially inavailability of the original training data because of privacy and ethical regulations, 2. adapting Facebook language models to Twitter language without retraining the model, and 3. adapting from users to county-level collections of tweets. We propose a two-step algorithm, Target Side Domain Adaptation (TSDA) for such domain adaptation when no labeled Twitter/county data is available. TSDA corrects for the different word distributions between Facebook and Twitter and for the varying word distributions across counties by adjusting target side word frequencies; no changes to the trained model are made. In the case of predicting the Big Five county-level personality traits, TSDA outperforms a state-of-the-art domain adaptation method, gives county-level predictions that have fewer extreme outliers, higher year-to-year stability, and higher correlation with county-level outcomes.",
}
@inproceedings{gao-etal-2017-recognizing,
    title = "Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach",
    author = "Gao, Lei  and
      Kuppersmith, Alexis  and
      Huang, Ruihong",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1078",
    pages = "774--782",
    abstract = "In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.",
}
@inproceedings{ni-etal-2017-estimating,
    title = "Estimating Reactions and Recommending Products with Generative Models of Reviews",
    author = "Ni, Jianmo  and
      Lipton, Zachary C.  and
      Vikram, Sharad  and
      McAuley, Julian",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1079",
    pages = "783--791",
    abstract = "Traditional approaches to recommendation focus on learning from large volumes of historical feedback to estimate simple numerical quantities (Will a user click on a product? Make a purchase? etc.). Natural language approaches that model information like product reviews have proved to be incredibly useful in improving the performance of such methods, as reviews provide valuable auxiliary information that can be used to better estimate latent user preferences and item properties. In this paper, rather than using reviews as an inputs to a recommender system, we focus on generating reviews as the model{'}s output. This requires us to efficiently model text (at the character level) to capture the preferences of the user, the properties of the item being consumed, and the interaction between them (i.e., the user{'}s preference). We show that this can model can be used to (a) generate plausible reviews and estimate nuanced reactions; (b) provide personalized rankings of existing reviews; and (c) recommend existing products more effectively.",
}
@inproceedings{ishigaki-etal-2017-summarizing,
    title = "Summarizing Lengthy Questions",
    author = "Ishigaki, Tatsuya  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1080",
    pages = "792--800",
    abstract = "In this research, we propose the task of question summarization. We first analyzed question-summary pairs extracted from a Community Question Answering (CQA) site, and found that a proportion of questions cannot be summarized by extractive approaches but requires abstractive approaches. We created a dataset by regarding the question-title pairs posted on the CQA site as question-summary pairs. By using the data, we trained extractive and abstractive summarization models, and compared them based on ROUGE scores and manual evaluations. Our experimental results show an abstractive method using an encoder-decoder model with a copying mechanism achieves better scores for both ROUGE-2 F-measure and the evaluations by human judges.",
}
@inproceedings{falke-etal-2017-concept,
    title = "Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization",
    author = "Falke, Tobias  and
      Meyer, Christian M.  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1081",
    pages = "801--811",
    abstract = "Concept-map-based multi-document summarization is a variant of traditional summarization that produces structured summaries in the form of concept maps. In this work, we propose a new model for the task that addresses several issues in previous methods. It learns to identify and merge coreferent concepts to reduce redundancy, determines their importance with a strong supervised model and finds an optimal summary concept map via integer linear programming. It is also computationally more efficient than previous methods, allowing us to summarize larger document sets. We evaluate the model on two datasets, finding that it outperforms several approaches from previous work.",
}
@inproceedings{j-kurisinkel-etal-2017-abstractive,
    title = "Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization",
    author = "J Kurisinkel, Litton  and
      Zhang, Yue  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1082",
    pages = "812--821",
    abstract = "Existing work for abstractive multidocument summarization utilise existing phrase structures directly extracted from input documents to generate summary sentences. These methods can suffer from lack of consistence and coherence in merging phrases. We introduce a novel approach for abstractive multidocument summarization through partial dependency tree extraction, recombination and linearization. The method entrusts the summarizer to generate its own topically coherent sequential structures from scratch for effective communication. Results on TAC 2011, DUC-2004 and 2005 show that our system gives competitive results compared with state of the art abstractive summarization approaches in the literature. We also achieve competitive results in linguistic quality assessed by human evaluators.",
}
@inproceedings{judea-strube-2017-event,
    title = "Event Argument Identification on Dependency Graphs with Bidirectional {LSTM}s",
    author = "Judea, Alex  and
      Strube, Michael",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1083",
    pages = "822--831",
    abstract = "In this paper we investigate the performance of event argument identification. We show that the performance is tied to syntactic complexity. Based on this finding, we propose a novel and effective system for event argument identification. Recurrent Neural Networks learn to produce meaningful representations of long and short dependency paths. Convolutional Neural Networks learn to decompose the lexical context of argument candidates. They are combined into a simple system which outperforms a feature-based, state-of-the-art event argument identifier without any manual feature engineering.",
}
@inproceedings{zhang-etal-2017-selective,
    title = "Selective Decoding for Cross-lingual Open Information Extraction",
    author = "Zhang, Sheng  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1084",
    pages = "832--842",
    abstract = "Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard encoder-decoder model, selective decoding significantly increases the performance on a Chinese-English cross-lingual open IE dataset by 3.87-4.49 BLEU and 1.91-5.92 F1. We also extend our approach to low-resource scenarios, and gain promising improvement.",
}
@inproceedings{mcdowell-etal-2017-event,
    title = "Event Ordering with a Generalized Model for Sieve Prediction Ranking",
    author = "McDowell, Bill  and
      Chambers, Nathanael  and
      Ororbia II, Alexander  and
      Reitter, David",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1085",
    pages = "843--853",
    abstract = "This paper improves on several aspects of a sieve-based event ordering architecture, CAEVO (Chambers et al., 2014), which creates globally consistent temporal relations between events and time expressions. First, we examine the usage of word embeddings and semantic role features. With the incorporation of these new features, we demonstrate a 5{\%} relative F1 gain over our replicated version of CAEVO. Second, we reformulate the architecture{'}s sieve-based inference algorithm as a prediction reranking method that approximately optimizes a scoring function computed using classifier precisions. Within this prediction reranking framework, we propose an alternative scoring function, showing an 8.8{\%} relative gain over the original CAEVO. We further include an in-depth analysis of one of the main datasets that is used to evaluate temporal classifiers, and we show how despite using the densest corpus, there is still a danger of overfitting. While this paper focuses on temporal ordering, its results are applicable to other areas that use sieve-based architectures.",
}
@inproceedings{yu-etal-2017-open,
    title = "Open Relation Extraction and Grounding",
    author = "Yu, Dian  and
      Huang, Lifu  and
      Ji, Heng",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1086",
    pages = "854--864",
    abstract = "Previous open Relation Extraction (open RE) approaches mainly rely on linguistic patterns and constraints to extract important relational triples from large-scale corpora. However, they lack of abilities to cover diverse relation expressions or measure the relative importance of candidate triples within a sentence. It is also challenging to name the relation type of a relational triple merely based on context words, which could limit the usefulness of open RE in downstream applications. We propose a novel importance-based open RE approach by exploiting the global structure of a dependency tree to extract salient triples. We design an unsupervised relation type naming method by grounding relational triples to a large-scale Knowledge Base (KB) schema, leveraging KB triples and weighted context words associated with relational triples. Experiments on the English Slot Filling 2013 dataset demonstrate that our approach achieves 8.1{\%} higher F-score over state-of-the-art open RE methods.",
}
@inproceedings{you-etal-2017-extraction,
    title = "Extraction of Gene-Environment Interaction from the Biomedical Literature",
    author = "You, Jinseon  and
      Chung, Jin-Woo  and
      Yang, Wonsuk  and
      Park, Jong C.",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1087",
    pages = "865--874",
    abstract = "Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.",
}
@inproceedings{pan-etal-2017-course,
    title = "Course Concept Extraction in {MOOC}s via Embedding-Based Graph Propagation",
    author = "Pan, Liangming  and
      Wang, Xiaochen  and
      Li, Chengjiang  and
      Li, Juanzi  and
      Tang, Jie",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1088",
    pages = "875--884",
    abstract = "Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education. One challenging issue in MOOCs is how to design effective and fine-grained course concepts such that students with different backgrounds can grasp the essence of the course. In this paper, we conduct a systematic investigation of the problem of course concept extraction for MOOCs. We propose to learn latent representations for candidate concepts via an embedding-based method. Moreover, we develop a graph-based propagation algorithm to rank the candidate concepts based on the learned representations. We evaluate the proposed method using different courses from XuetangX and Coursera. Experimental results show that our method significantly outperforms all the alternative methods (+0.013-0.318 in terms of R-precision; p{\textless}{\textless}0.01, t-test).",
}
@inproceedings{perez-rosas-etal-2017-identity,
    title = "Identity Deception Detection",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Davenport, Quincy  and
      Dai, Anna Mengdan  and
      Abouelenien, Mohamed  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1089",
    pages = "885--894",
    abstract = "This paper addresses the task of detecting identity deception in language. Using a novel identity deception dataset, consisting of real and portrayed identities from 600 individuals, we show that we can build accurate identity detectors targeting both age and gender, with accuracies of up to 88. We also perform an analysis of the linguistic patterns used in identity deception, which lead to interesting insights into identity portrayers.",
}
@inproceedings{ling-etal-2017-learning,
    title = "Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning",
    author = "Ling, Yuan  and
      Hasan, Sadid A.  and
      Datla, Vivek  and
      Qadir, Ashequl  and
      Lee, Kathy  and
      Liu, Joey  and
      Farri, Oladimeji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1090",
    pages = "895--905",
    abstract = "Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario. This paper proposes a novel approach by formulating clinical diagnosis as a reinforcement learning problem. During training, the reinforcement learning agent mimics the clinician{'}s cognitive process and learns the optimal policy to obtain the most appropriate diagnoses for a clinical narrative. This is achieved through an iterative search for candidate diagnoses from external knowledge sources via a sentence-by-sentence analysis of the inherent clinical context. A deep Q-network architecture is trained to optimize a reward function that measures the accuracy of the candidate diagnoses. Experiments on the TREC CDS datasets demonstrate the effectiveness of our system over various non-reinforcement learning-based systems.",
}
@inproceedings{brad-etal-2017-dataset,
    title = "Dataset for a Neural Natural Language Interface for Databases ({NNLIDB})",
    author = "Brad, Florin  and
      Iacob, Radu Cristian Alexandru  and
      Hosu, Ionel Alexandru  and
      Rebedea, Traian",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1091",
    pages = "906--914",
    abstract = "Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability. Moreover, the lack of a large corpus to be used as a standard benchmark has made data-driven approaches difficult to develop and compare. In this paper, we revisit the problem of NLIDBs and recast it as a sequence translation problem. To this end, we introduce a large dataset extracted from the Stack Exchange Data Explorer website, which can be used for training neural natural language interfaces for databases. We also report encouraging baseline results on a smaller manually annotated test corpus, obtained using an attention-based sequence-to-sequence neural network.",
}
@inproceedings{pragst-etal-2017-acquisition,
    title = "Acquisition and Assessment of Semantic Content for the Generation of Elaborateness and Indirectness in Spoken Dialogue Systems",
    author = "Pragst, Louisa  and
      Yoshino, Koichiro  and
      Minker, Wolfgang  and
      Nakamura, Satoshi  and
      Ultes, Stefan",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1092",
    pages = "915--925",
    abstract = "In a dialogue system, the dialogue manager selects one of several system actions and thereby determines the system{'}s behaviour. Defining all possible system actions in a dialogue system by hand is a tedious work. While efforts have been made to automatically generate such system actions, those approaches are mostly focused on providing functional system behaviour. Adapting the system behaviour to the user becomes a difficult task due to the limited amount of system actions available. We aim to increase the adaptability of a dialogue system by automatically generating variants of system actions. In this work, we introduce an approach to automatically generate action variants for elaborateness and indirectness. Our proposed algorithm extracts RDF triplets from a knowledge base and rates their relevance to the original system action to find suitable content. We show that the results of our algorithm are mostly perceived similarly to human generated elaborateness and indirectness and can be used to adapt a conversation to the current user and situation. We also discuss where the results of our algorithm are still lacking and how this could be improved: Taking into account the conversation topic as well as the culture of the user is likely to have beneficial effect on the user{'}s perception.",
}
@inproceedings{hasanuzzaman-etal-2017-demographic,
    title = "Demographic Word Embeddings for Racism Detection on {T}witter",
    author = {Hasanuzzaman, Mohammed  and
      Dias, Ga{\"e}l  and
      Way, Andy},
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1093",
    pages = "926--936",
    abstract = "Most social media platforms grant users freedom of speech by allowing them to freely express their thoughts, beliefs, and opinions. Although this represents incredible and unique communication opportunities, it also presents important challenges. Online racism is such an example. In this study, we present a supervised learning strategy to detect racist language on Twitter based on word embedding that incorporate demographic (Age, Gender, and Location) information. Our methodology achieves reasonable classification accuracy over a gold standard dataset (F1=76.3{\%}) and significantly improves over the classification performance of demographic-agnostic models.",
}
@inproceedings{saito-etal-2017-automatically,
    title = "Automatically Extracting Variant-Normalization Pairs for {J}apanese Text Normalization",
    author = "Saito, Itsumi  and
      Nishida, Kyosuke  and
      Sadamitsu, Kugatsu  and
      Saito, Kuniko  and
      Tomita, Junji",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1094",
    pages = "937--946",
    abstract = "Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatically extracting pairs of a variant word and its normal form from unsegmented text on the basis of a pair-wise similarity approach. We incorporated the acquired variant-normalization pairs into Japanese morphological analysis. The experimental results show that our method can extract widely covered variants from large Twitter data and improve the recall of normalization without degrading the overall accuracy of Japanese morphological analysis.",
}
@inproceedings{zhu-etal-2017-semantic,
    title = "Semantic Document Distance Measures and Unsupervised Document Revision Detection",
    author = "Zhu, Xiaofeng  and
      Klabjan, Diego  and
      Bless, Patrick",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1095",
    pages = "947--956",
    abstract = "In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances. Furthermore, we propose two new document distance measures, word vector-based Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED). Our revision detection system is designed for a large scale corpus and implemented in Apache Spark. We demonstrate that our system can more precisely detect revisions than state-of-the-art methods by utilizing the Wikipedia revision dumps and simulated data sets.",
}
@inproceedings{shen-etal-2017-empirical,
    title = "An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks",
    author = "Shen, Yelong  and
      Liu, Xiaodong  and
      Duh, Kevin  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1096",
    pages = "957--966",
    abstract = "Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empirically investigate the performance of single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The RC model is an end-to-end neural network with iterative attention, and uses reinforcement learning to dynamically control the number of turns. We find that multiple-turn reasoning outperforms single-turn reasoning for all question and answer types; further, we observe that enabling a flexible number of turns generally improves upon a fixed multiple-turn strategy. {\%}across all question types, and is particularly beneficial to questions with lengthy, descriptive answers. We achieve results competitive to the state-of-the-art on these two datasets.",
}
@inproceedings{kobayashi-etal-2017-automated,
    title = "Automated Historical Fact-Checking by Passage Retrieval, Word Statistics, and Virtual Question-Answering",
    author = "Kobayashi, Mio  and
      Ishii, Ai  and
      Hoshino, Chikara  and
      Miyashita, Hiroshi  and
      Matsuzaki, Takuya",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1097",
    pages = "967--975",
    abstract = "This paper presents a hybrid approach to the verification of statements about historical facts. The test data was collected from the world history examinations in a standardized achievement test for high school students. The data includes various kinds of false statements that were carefully written so as to deceive the students while they can be disproven on the basis of the teaching materials. Our system predicts the truth or falsehood of a statement based on text search, word cooccurrence statistics, factoid-style question answering, and temporal relation recognition. These features contribute to the judgement complementarily and achieved the state-of-the-art accuracy.",
}
@inproceedings{hsiao-etal-2017-integrating,
    title = "Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base",
    author = "Hsiao, Wei-Chuan  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1098",
    pages = "976--985",
    abstract = "This paper presents an approach to identify subject, type and property from knowledge base (KB) for answering simple questions. We propose new features to rank entity candidates in KB. Besides, we split a relation in KB into type and property. Each of them is modeled by a bi-directional LSTM. Experimental results show that our model achieves the state-of-the-art performance on the SimpleQuestions dataset. The hard questions in the experiments are also analyzed in detail.",
}
@inproceedings{li-etal-2017-dailydialog,
    title = "{D}aily{D}ialog: A Manually Labelled Multi-turn Dialogue Dataset",
    author = "Li, Yanran  and
      Su, Hui  and
      Shen, Xiaoyu  and
      Li, Wenjie  and
      Cao, Ziqiang  and
      Niu, Shuzi",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1099",
    pages = "986--995",
    abstract = "We develop a high-quality multi-turn dialog dataset, \textbf{DailyDialog}, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on \url{http://yanran.li/dailydialog}",
}
@inproceedings{white-etal-2017-inference,
    title = "Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework",
    author = "White, Aaron Steven  and
      Rastogi, Pushpendre  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1100",
    pages = "996--1005",
    abstract = "We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model{'}s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.",
}
@inproceedings{dhondt-etal-2017-generating,
    title = "Generating a Training Corpus for {OCR} Post-Correction Using Encoder-Decoder Model",
    author = "D{'}hondt, Eva  and
      Grouin, Cyril  and
      Grau, Brigitte",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1101",
    pages = "1006--1014",
    abstract = "In this paper we present a novel approach to the automatic correction of OCR-induced orthographic errors in a given text. While current systems depend heavily on large training corpora or external information, such as domain-specific lexicons or confidence scores from the OCR process, our system only requires a small amount of (relatively) clean training data from a representative corpus to learn a character-based statistical language model using Bidirectional Long Short-Term Memory Networks (biLSTMs). We demonstrate the versatility and adaptability of our system on different text corpora with varying degrees of textual noise, including a real-life OCR corpus in the medical domain.",
}
@inproceedings{pappas-popescu-belis-2017-multilingual,
    title = "Multilingual Hierarchical Attention Networks for Document Classification",
    author = "Pappas, Nikolaos  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1102",
    pages = "1015--1025",
    abstract = "Hierarchical attention networks have recently achieved remarkable performance for document classification in a given language. However, when multilingual document collections are considered, training such models separately for each language entails linear parameter growth and lack of cross-language transfer. Learning a single multilingual model with fewer parameters is therefore a challenging but potentially beneficial objective. To this end, we propose multilingual hierarchical attention networks for learning document structures, with shared encoders and/or shared attention mechanisms across languages, using multi-task learning and an aligned semantic space as input. We evaluate the proposed models on multilingual document classification with disjoint label sets, on a large dataset which we provide, with 600k news documents in 8 languages, and 5k labels. The multilingual models outperform monolingual ones in low-resource as well as full-resource settings, and use fewer parameters, thus confirming their computational efficiency and the utility of cross-language transfer.",
}
@inproceedings{maki-etal-2017-roles,
    title = "Roles and Success in {W}ikipedia Talk Pages: Identifying Latent Patterns of Behavior",
    author = "Maki, Keith  and
      Yoder, Michael  and
      Jo, Yohan  and
      Ros{\'e}, Carolyn",
    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://www.aclweb.org/anthology/I17-1103",
    pages = "1026--1035",
    abstract = "In this work we investigate how role-based behavior profiles of a Wikipedia editor, considered against the backdrop of roles taken up by other editors in discussions, predict the success of the editor at achieving an impact on the associated article. We first contribute a new public dataset including a task predicting the success of Wikipedia editors involved in discussion, measured by an operationalization of the lasting impact of their edits in the article. We then propose a probabilistic graphical model that advances earlier work inducing latent discussion roles using the light supervision of success in the negotiation task. We evaluate the performance of the model and interpret findings of roles and group configurations that lead to certain outcomes on Wikipedia.",
}
@proceedings{eacl-2017-,
    title = "",
    editor = "Klementiev, Alexandre  and
      Specia, Lucia",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
}
@inproceedings{nivre-etal-2017-universal,
    title = "{U}niversal {D}ependencies",
    author = "Nivre, Joakim  and
      Zeman, Daniel  and
      Ginter, Filip  and
      Tyers, Francis",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-5001",
    abstract = "Universal Dependencies (UD) is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages. This tutorial gives an introduction to the UD framework and resources, from basic design principles to annotation guidelines and existing treebanks. We also discuss tools for developing and exploiting UD treebanks and survey applications of UD in NLP and linguistics.",
}
@inproceedings{sennrich-haddow-2017-practical,
    title = "Practical Neural Machine Translation",
    author = "Sennrich, Rico  and
      Haddow, Barry",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-5002",
    abstract = "Neural Machine Translation (NMT) has achieved new breakthroughs in machine translation in recent years. It has dominated recent shared translation tasks in machine translation research, and is also being quickly adopted in industry. The technical differences between NMT and the previously dominant phrase-based statistical approach require that practictioners learn new best practices for building MT systems, ranging from different hardware requirements, new techniques for handling rare words and monolingual data, to new opportunities in continued learning and domain adaptation.This tutorial is aimed at researchers and users of machine translation interested in working with NMT. The tutorial will cover a basic theoretical introduction to NMT, discuss the components of state-of-the-art systems, and provide practical advice for building NMT systems.",
}
@inproceedings{vlachos-etal-2017-imitation,
    title = "Imitation learning for structured prediction in natural language processing",
    author = "Vlachos, Andreas  and
      Lampouras, Gerasimos  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-5003",
    abstract = "Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks.All material associated with the tutorial will be made available through https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/.",
}
@inproceedings{vulic-etal-2017-word,
    title = "Word Vector Space Specialisation",
    author = "Vuli{\'c}, Ivan  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-5004",
    abstract = "Specialising vector spaces to maximise their content with respect to one key property of vector space models (e.g. semantic similarity vs. relatedness or lexical entailment) while mitigating others has become an active and attractive research topic in representation learning. Such specialised vector spaces support different classes of NLP problems. Proposed approaches fall into two broad categories: a) Unsupervised methods which learn from raw textual corpora in more sophisticated ways (e.g. using context selection, extracting co-occurrence information from word patterns, attending over contexts); and b) Knowledge-base driven approaches which exploit available resources to encode external information into distributional vector spaces, injecting knowledge from semantic lexicons (e.g., WordNet, FrameNet, PPDB). In this tutorial, we will introduce researchers to state-of-the-art methods for constructing vector spaces specialised for a broad range of downstream NLP applications. We will deliver a detailed survey of the proposed methods and discuss best practices for intrinsic and application-oriented evaluation of such vector spaces.Throughout the tutorial, we will provide running examples reaching beyond English as the only (and probably the easiest) use-case language, in order to demonstrate the applicability and modelling challenges of current representation learning architectures in other languages.",
}
@inproceedings{roth-srikumar-2017-integer,
    title = "Integer Linear Programming formulations in Natural Language Processing",
    author = "Roth, Dan  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-5005",
    abstract = "Making decisions in natural language processing problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate what assignments are possible. This setting includes a broad range of structured prediction problems such as semantic role labeling, named entity and relation recognition, co-reference resolution, dependency parsing and semantic parsing. The setting is also appropriate for cases that may require making global decisions that involve multiple components, possibly pre-designed or pre-learned, as in event recognition and analysis, summarization, paraphrasing, textual entailment and question answering. In all these cases, it is natural to formulate the decision problem as a constrained optimization problem, with an objective function that is composed of learned models, subject to domain or problem specific constraints.Over the last few years, starting with a couple of papers written by (Roth {\&} Yih, 2004, 2005), dozens of papers have been using the Integer linear programming (ILP) formulation developed there, including several award-winning papers (e.g., (Martins, Smith, {\&} Xing, 2009; Koo, Rush, Collins, Jaakkola, {\&} Sontag., 2010; Berant, Dagan, {\&} Goldberger, 2011)).This tutorial will present the key ingredients of ILP formulations of natural language processing problems, aiming at guiding readers through the key modeling steps, explaining the learning and inference paradigms and exemplifying these by providing examples from the literature. We will cover a range of topics, from the theoretical foundations of learning and inference with ILP models, to practical modeling guides, to software packages and applications.The goal of this tutorial is to introduce the computational framework to broader ACL community, motivate it as a generic framework for learning and inference in global NLP decision problems, present some of the key theoretical and practical issues involved and survey some of the existing applications of it as a way to promote further development of the framework and additional applications. We will also make connections with some of the {``}hot{''} topics in current NLP research and show how they can be used within the general framework proposed here. The tutorial will thus be useful for many of the senior and junior researchers that have interest in global decision problems in NLP, providing a concise overview of recent perspectives and research results.",
}
@inproceedings{pustejovsky-krishnaswamy-2017-building,
    title = "Building Multimodal Simulations for Natural Language",
    author = "Pustejovsky, James  and
      Krishnaswamy, Nikhil",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-5006",
    abstract = "In this tutorial, we introduce a computational framework and modeling language (VoxML) for composing multimodal simulations of natural language expressions within a 3D simulation environment (VoxSim). We demonstrate how to construct voxemes, which are visual object representations of linguistic entities. We also show how to compose events and actions over these objects, within a restricted domain of dynamics. This gives us the building blocks to simulate narratives of multiple events or participate in a multimodal dialogue with synthetic agents in the simulation environment. To our knowledge, this is the first time such material has been presented as a tutorial within the CL community.This will be of relevance to students and researchers interested in modeling actionable language, natural language communication with agents and robots, spatial and temporal constraint solving through language, referring expression generation, embodied cognition, as well as minimal model creation.Multimodal simulation of language, particularly motion expressions, brings together a number of existing lines of research from the computational linguistic, semantics, robotics, and formal logic communities, including action and event representation (Di Eugenio, 1991), modeling gestural correlates to NL expressions (Kipp et al., 2007; Neff et al., 2008), and action event modeling (Kipper and Palmer, 2000; Yang et al., 2015). We combine an approach to event modeling with a scene generation approach akin to those found in work by (Coyne and Sproat, 2001; Siskind, 2011; Chang et al., 2015). Mapping natural language expressions through a formal model and a dynamic logic interpretation into a visualization of the event described provides an environment for grounding concepts and referring expressions that is interpretable by both a computer and a human user. This opens a variety of avenues for humans to communicate with computerized agents and robots, as in (Matuszek et al., 2013; Lauria et al., 2001), (Forbes et al., 2015), and (Deits et al., 2013; Walter et al., 2013; Tellex et al., 2014). Simulation and automatic visualization of events from natural language descriptions and supplementary modalities, such as gestures, allows humans to use their native capabilities as linguistic and visual interpreters to collaborate on tasks with an artificial agent or to put semantic intuitions to the test in an environment where user and agent share a common context.In previous work (Pustejovsky and Krishnaswamy, 2014; Pustejovsky, 2013a), we introduced a method for modeling natural language expressions within a 3D simulation environment built on top of the game development platform Unity (Goldstone, 2009). The goal of that work was to evaluate, through explicit visualizations of linguistic input, the semantic presuppositions inherent in the different lexical choices of an utterance. This work led to two additional lines of research: an explicit encoding for how an object is itself situated relative to its environment; and an operational characterization of how an object changes its location or how an agent acts on an object over time, e.g., its affordance structure. The former has developed into a semantic notion of situational context, called a habitat (Pustejovsky, 2013a; McDonald and Pustejovsky, 2014), while the latter is addressed by dynamic interpretations of event structure (Pustejovsky and Moszkowicz, 2011; Pustejovsky and Krishnaswamy, 2016b; Pustejovsky, 2013b).The requirements on building a visual simulation from language include several components. We require a rich type system for lexical items and their composition, as well as a language for modeling the dynamics of events, based on Generative Lexicon (GL). Further, a minimal embedding space (MES) for the simulation must be determined. This is the 3D region within which the state is configured or the event unfolds. Object-based attributes for participants in a situation or event also need to be specified; e.g., orientation, relative size, default position or pose, etc. The simulation establishes an epistemic condition on the object and event rendering, imposing an implicit point of view (POV). Finally, there must be some sort of agent-dependent embodiment; this determines the relative scaling of an agent and its event participants and their surroundings, as it engages in the environment.In order to construct a robust simulation from linguistic input, an event and its participants must be embedded within an appropriate minimal embedding space. This must sufficiently enclose the event localization, while optionally including space enough for a frame of reference for the event (the viewer{\^a}€{\mbox{$^\mbox{TM}$}}s perspective).We first describe the formal multimodal foundations for the modeling language, VoxML, which creates a minimal simulation from the linguistic input interpreted by the multimodal language, DITL. We then describe VoxSim, the compositional modeling and simulation environment, which maps the minimal VoxML model of the linguistic utterance to a simulation in Unity. This knowledge includes specification of object affordances, e.g., what actions are possible or enabled by use an object.VoxML (Pustejovsky and Krishnaswamy, 2016b; Pustejovsky and Krishnaswamy, 2016a) encodes semantic knowledge of real-world objects represented as 3D models, and of events and attributes related to and enacted over these objects. VoxML goes beyond the limitations of existing 3D visual markup languages by allowing for the encoding of a broad range of semantic knowledge that can be exploited by a simulation platform such as VoxSim.VoxSim (Krishnaswamy and Pustejovsky, 2016a; Krishnaswamy and Pustejovsky, 2016b) uses object and event semantic knowledge to generate animated scenes in real time without a complex animation interface. It uses the Unity game engine for graphics and I/O processing and takes as input a simple natural language utterance. The parsed utterance is semantically interpreted and transformed into a hybrid dynamic logic representation (DITL), and used to generate a minimal simulation of the event when composed with VoxML knowledge. 3D assets and VoxML-modeled nominal objects and events are created with other Unity-based tools, and VoxSim uses the entirety of the composed information to render a visualization of the described event.The tutorial participants will learn how to build simulatable objects, compose dynamic event structures, and simulate the events running over the objects. The toolkit consists of object and program (event) composers and the runtime environment, which allows for the user to directly manipulate the objects, or interact with synthetic agents in VoxSim. As a result of this tutorial, the student will acquire the following skill set: take a novel object geometry from a library and model it in VoxML; apply existing library behaviors (actions or events) to the new VoxML object; model attributes of new objects as well as introduce novel attributes; model novel behaviors over objects.The tutorial modules will be conducted within a build image of the software. Access to libraries will be provided by the instructors. No knowledge of 3D modeling or the Unity platform will be required.",
}
@proceedings{eacl-2017-student,
    title = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    editor = "Kunneman, Florian  and
      I{\~n}urrieta, Uxoa  and
      Camilleri, John J.  and
      Ardanuy, Mariona Coll",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4000",
}
@inproceedings{van-miltenburg-2017-pragmatic,
    title = "Pragmatic descriptions of perceptual stimuli",
    author = "van Miltenburg, Emiel",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4001",
    pages = "1--10",
    abstract = "This research proposal discusses pragmatic factors in image description, arguing that current automatic image description systems do not take these factors into account. I present a general model of the human image description process, and propose to study this process using corpus analysis, experiments, and computational modeling. This will lead to a better characterization of human image description behavior, providing a road map for future research in automatic image description, and the automatic description of perceptual stimuli in general.",
}
@inproceedings{barteld-2017-detecting,
    title = "Detecting spelling variants in non-standard texts",
    author = "Barteld, Fabian",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4002",
    pages = "11--22",
    abstract = "Spelling variation in non-standard language, e.g. computer-mediated communication and historical texts, is usually treated as a deviation from a standard spelling, e.g. 2mr as an non-standard spelling for tomorrow. Consequently, in normalization {--} the standard approach of dealing with spelling variation {--} so-called non-standard words are mapped to their corresponding standard words. However, there is not always a corresponding standard word. This can be the case for single types (like emoticons in computer-mediated communication) or a complete language, e.g. texts from historical languages that did not develop to a standard variety. The approach presented in this thesis proposal deals with spelling variation in absence of reference to a standard. The task is to detect pairs of types that are variants of the same morphological word. An approach for spelling-variant detection is presented, where pairs of potential spelling variants are generated with Levenshtein distance and subsequently filtered by supervised machine learning. The approach is evaluated on historical Low German texts. Finally, further perspectives are discussed.",
}
@inproceedings{marrese-taylor-matsuo-2017-replication,
    title = "Replication issues in syntax-based aspect extraction for opinion mining",
    author = "Marrese-Taylor, Edison  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4003",
    pages = "23--32",
    abstract = "Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.",
}
@inproceedings{pyatkin-webber-2017-discourse,
    title = "Discourse Relations and Conjoined {VP}s: Automated Sense Recognition",
    author = "Pyatkin, Valentina  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4004",
    pages = "33--42",
    abstract = "Sense classification of discourse relations is a sub-task of shallow discourse parsing. Discourse relations can occur both across sentences (\textit{inter-sentential}) and within sentences (\textit{intra-sentential}), and more than one discourse relation can hold between the same units. Using a newly available corpus of discourse-annotated intra-sentential conjoined verb phrases, we demonstrate a sequential classification pipeline for their multi-label sense classification. We assess the importance of each feature used in the classification, the feature scope, and what is lost in moving from gold standard manual parses to the output of an off-the-shelf parser.",
}
@inproceedings{litvinova-etal-2017-deception,
    title = "Deception detection in {R}ussian texts",
    author = "Litvinova, Olga  and
      Seredin, Pavel  and
      Litvinova, Tatiana  and
      Lyell, John",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4005",
    pages = "43--52",
    abstract = "Humans are known to detect deception in speech randomly and it is therefore important to develop tools to enable them to detect deception. The problem of deception detection has been studied for a significant amount of time, however the last 10-15 years have seen methods of computational linguistics being employed. Texts are processed using different NLP tools and then classified as deceptive/truthful using machine learning methods. While most research has been performed for English, Slavic languages have never been a focus of detection deception studies. The paper deals with deception detection in Russian narratives. It employs a specially designed corpus of truthful and deceptive texts on the same topic from each respondent, N = 113. The texts were processed using Linguistic Inquiry and Word Count software that is used in most studies of text-based deception detection. The list of parameters computed using the software was expanded due to the designed users{'} dictionaries. A variety of text classification methods was employed. The accuracy of the model was found to depend on the author{'}s gender and text type (deceptive/truthful).",
}
@inproceedings{seminck-amsili-2017-computational,
    title = "A Computational Model of Human Preferences for Pronoun Resolution",
    author = "Seminck, Olga  and
      Amsili, Pascal",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4006",
    pages = "53--63",
    abstract = "We present a cognitive computational model of pronoun resolution that reproduces the human interpretation preferences of the Subject Assignment Strategy and the Parallel Function Strategy. Our model relies on a probabilistic pronoun resolution system trained on corpus data. Factors influencing pronoun resolution are represented as features weighted by their relative importance. The importance the model gives to the preferences is in line with psycholinguistic studies. We demonstrate the cognitive plausibility of the model by running it on experimental items and simulating antecedent choice and reading times of human participants. Our model can be used as a new means to study pronoun resolution, because it captures the interaction of preferences.",
}
@inproceedings{piotrkowicz-etal-2017-automatic,
    title = "Automatic Extraction of News Values from Headline Text",
    author = "Piotrkowicz, Alicja  and
      Dimitrova, Vania  and
      Markert, Katja",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4007",
    pages = "64--74",
    abstract = "Headlines play a crucial role in attracting audiences{'} attention to online artefacts (e.g. news articles, videos, blogs). The ability to carry out an automatic, large-scale analysis of headlines is critical to facilitate the selection and prioritisation of a large volume of digital content. In journalism studies news content has been extensively studied using manually annotated news values - factors used implicitly and explicitly when making decisions on the selection and prioritisation of news items. This paper presents the first attempt at a fully automatic extraction of news values from headline text. The news values extraction methods are applied on a large headlines corpus collected from The Guardian, and evaluated by comparing it with a manually annotated gold standard. A crowdsourcing survey indicates that news values affect people{'}s decisions to click on a headline, supporting the need for an automatic news values detection.",
}
@inproceedings{chalaguine-schulz-2017-assessing,
    title = "Assessing Convincingness of Arguments in Online Debates with Limited Number of Features",
    author = "Chalaguine, Lisa Andreevna  and
      Schulz, Claudia",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4008",
    pages = "75--83",
    abstract = "We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016). Rather than using argument specific feature values, we measure feature values relative to the average value in the debate, allowing us to determine argument convincingness with fewer features (between 5 and 35) than normally used for natural language processing tasks. We use a simple forward-feeding neural network for this task and achieve an accuracy of 0.77 which is comparable to the accuracy obtained using 64k features and a support vector machine by Habernal and Gurevych.",
}
@inproceedings{perez-melian-etal-2017-zipfs,
    title = "{Z}ipf{'}s and {B}enford{'}s laws in {T}witter hashtags",
    author = "P{\'e}rez Meli{\'a}n, Jos{\'e} Alberto  and
      Conejero, J. Alberto  and
      Ferri Ram{\'\i}rez, C{\`e}sar",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4009",
    pages = "84--93",
    abstract = "Social networks have transformed communication dramatically in recent years through the rise of new platforms and the development of a new language of communication. This landscape requires new forms to describe and predict the behaviour of users in networks. This paper presents an analysis of the frequency distribution of hashtag popularity in Twitter conversations. Our objective is to determine if these frequency distribution follow some well-known frequency distribution that many real-life sets of numerical data satisfy. In particular, we study the similarity of frequency distribution of hashtag popularity with respect to Zipf{'}s law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford{'}s law, is a special case of Zipf{'}s law, a common pattern about the frequency distribution of leading digits. In order to compute correctly the frequency distribution of hashtag popularity, we need to correct many spelling errors that Twitter{'}s users introduce. For this purpose we introduce a new filter to correct hashtag mistake based on string distances. The experiments obtained employing datasets of Twitter streams generated under controlled conditions show that Benford{'}s law and Zipf{'}s law can be used to model hashtag frequency distribution.",
}
@inproceedings{amorim-veloso-2017-multi,
    title = "A Multi-aspect Analysis of Automatic Essay Scoring for {B}razilian {P}ortuguese",
    author = "Amorim, Evelin  and
      Veloso, Adriano",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4010",
    pages = "94--102",
    abstract = "Several methods for automatic essay scoring (AES) for English language have been proposed. However, multi-aspect AES systems for other languages are unusual. Therefore, we propose a multi-aspect AES system to apply on a dataset of Brazilian Portuguese essays, which human experts evaluated according to five aspects defined by Brazilian Government to the National Exam to High School Student (ENEM). These aspects are skills that student must master and every skill is assessed apart from each other. Besides the prediction of each aspect, the feature analysis also was performed for each aspect. The AES system proposed employs several features already employed by AES systems for English language. Our results show that predictions for some aspects performed well with the features we employed, while predictions for other aspects performed poorly. Also, it is possible to note the difference between the five aspects in the detailed feature analysis we performed. Besides these contributions, the eight millions of enrollments every year for ENEM raise some challenge issues for future directions in our research.",
}
@inproceedings{ehren-2017-literal,
    title = "Literal or idiomatic? Identifying the reading of single occurrences of {G}erman multiword expressions using word embeddings",
    author = "Ehren, Rafael",
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4011",
    pages = "103--112",
    abstract = "Non-compositional multiword expressions (MWEs) still pose serious issues for a variety of natural language processing tasks and their ubiquity makes it impossible to get around methods which automatically identify these kind of MWEs. The method presented in this paper was inspired by Sporleder and Li (2009) and is able to discriminate between the literal and non-literal use of an MWE in an unsupervised way. It is based on the assumption that words in a text form cohesive units. If the cohesion of these units is weakened by an expression, it is classified as literal, and otherwise as idiomatic. While Sporleder an Li used \textit{Normalized Google Distance} to modell semantic similarity, the present work examines the use of a variety of different word embeddings.",
}
@inproceedings{hatty-etal-2017-evaluating,
    title = "Evaluating the Reliability and Interaction of Recursively Used Feature Classes for Terminology Extraction",
    author = {H{\"a}tty, Anna  and
      Dorna, Michael  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-4012",
    pages = "113--121",
    abstract = "Feature design and selection is a crucial aspect when treating terminology extraction as a machine learning classification problem. We designed feature classes which characterize different properties of terms based on distributions, and propose a new feature class for components of term candidates. By using random forests, we infer optimal features which are later used to build decision tree classifiers. We evaluate our method using the ACL RD-TEC dataset. We demonstrate the importance of the novel feature class for downgrading termhood which exploits properties of term components. Furthermore, our classification suggests that the identification of reliable term candidates should be performed successively, rather than just once.",
}
@proceedings{eacl-2017-software,
    title = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    editor = "Martins, Andr{\'e}  and
      Pe{\~n}as, Anselmo",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3000",
}
@inproceedings{minock-2017-cover,
    title = "{COVER}: Covering the Semantically Tractable Questions",
    author = "Minock, Michael",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3001",
    pages = "1--4",
    abstract = "In semantic parsing, natural language questions map to expressions in a meaning representation language (MRL) over some fixed vocabulary of predicates. To do this reliably, one must guarantee that for a wide class of natural language questions (the so called semantically tractable questions), correct interpretations are always in the mapped set of possibilities. In this demonstration, we introduce the system COVER which significantly clarifies, revises and extends the basic notion of semantic tractability. COVER achieves coverage of 89{\%} while the earlier PRECISE system achieved coverage of 77{\%} on the well known GeoQuery corpus. Like PRECISE, COVER requires only a simple domain lexicon and integrates off-the-shelf syntactic parsers. Beyond PRECISE, COVER also integrates off-the-shelf theorem provers to provide more accurate results. COVER is written in Python and uses the NLTK.",
}
@inproceedings{uszkoreit-etal-2017-common,
    title = "Common Round: Application of Language Technologies to Large-Scale Web Debates",
    author = {Uszkoreit, Hans  and
      Gabryszak, Aleksandra  and
      Hennig, Leonhard  and
      Steffen, J{\"o}rg  and
      Ai, Renlong  and
      Busemann, Stephan  and
      Dehdari, Jon  and
      van Genabith, Josef  and
      Heigold, Georg  and
      Rethmeier, Nils  and
      Rubino, Raphael  and
      Schmeier, Sven  and
      Thomas, Philippe  and
      Wang, He  and
      Xu, Feiyu},
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3002",
    pages = "5--8",
    abstract = "Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking. However, it is challenging to organize, structure, and navigate a vast number of diverse argumentations and comments collected from many participants over a long time period. In this paper we demonstrate Common Round, a next generation platform for large-scale web debates, which provides functions for eliciting the semantic content and structures from the contributions of participants. In particular, Common Round applies language technologies for the extraction of semantic essence from textual input, aggregation of the formulated opinions and arguments. The platform also provides a cross-lingual access to debates using machine translation.",
}
@inproceedings{list-2017-web,
    title = "A Web-Based Interactive Tool for Creating, Inspecting, Editing, and Publishing Etymological Datasets",
    author = "List, Johann-Mattis",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3003",
    pages = "9--12",
    abstract = "The paper presents the Etymological DICtionary ediTOR (EDICTOR), a free, interactive, web-based tool designed to aid historical linguists in creating, editing, analysing, and publishing etymological datasets. The EDICTOR offers interactive solutions for important tasks in historical linguistics, including facilitated input and segmentation of phonetic transcriptions, quantitative and qualitative analyses of phonetic and morphological data, enhanced interfaces for cognate class assignment and multiple word alignment, and automated evaluation of regular sound correspondences. As a web-based tool written in JavaScript, the EDICTOR can be used in standard web browsers across all major platforms.",
}
@inproceedings{kiesel-etal-2017-wat,
    title = "{WAT}-{SL}: A Customizable Web Annotation Tool for Segment Labeling",
    author = "Kiesel, Johannes  and
      Wachsmuth, Henning  and
      Al-Khatib, Khalid  and
      Stein, Benno",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3004",
    pages = "13--16",
    abstract = "A frequent type of annotations in text corpora are labeled text segments. General-purpose annotation tools tend to be overly comprehensive, often making the annotation process slower and more error-prone. We present WAT-SL, a new web-based tool that is dedicated to segment labeling and highly customizable to the labeling task at hand. We outline its main features and exemplify how we used it for a crowdsourced corpus with labeled argument units.",
}
@inproceedings{uslu-etal-2017-textimager,
    title = "{T}ext{I}mager as a Generic Interface to {R}",
    author = "Uslu, Tolga  and
      Hemati, Wahed  and
      Mehler, Alexander  and
      Baumartz, Daniel",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3005",
    pages = "17--20",
    abstract = "R is a very powerful framework for statistical modeling. Thus, it is of high importance to integrate R with state-of-the-art tools in NLP. In this paper, we present the functionality and architecture of such an integration by means of TextImager. We use the OpenCPU API to integrate R based on our own R-Server. This allows for communicating with R-packages and combining them with TextImager{'}s NLP-components.",
}
@inproceedings{cabrera-etal-2017-grawitas,
    title = "{G}ra{W}i{T}as: a Grammar-based {W}ikipedia Talk Page Parser",
    author = {Cabrera, Benjamin  and
      Steinert, Laura  and
      Ross, Bj{\"o}rn},
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3006",
    pages = "21--24",
    abstract = "Wikipedia offers researchers unique insights into the collaboration and communication patterns of a large self-regulating community of editors. The main medium of direct communication between editors of an article is the article{'}s talk page. However, a talk page file is unstructured and therefore difficult to analyse automatically. A few parsers exist that enable its transformation into a structured data format. However, they are rarely open source, support only a limited subset of the talk page syntax {--} resulting in the loss of content {--} and usually support only one export format. Together with this article we offer a very fast, lightweight, open source parser with support for various output formats. In a preliminary evaluation it achieved a high accuracy. The parser uses a grammar-based approach {--} offering a transparent implementation and easy extensibility.",
}
@inproceedings{nozza-etal-2017-twine,
    title = "{TWINE}: A real-time system for {TW}eet analysis via {IN}formation Extraction",
    author = "Nozza, Debora  and
      Ristagno, Fausto  and
      Palmonari, Matteo  and
      Fersini, Elisabetta  and
      Manchanda, Pikakshi  and
      Messina, Enza",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3007",
    pages = "25--28",
    abstract = "In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and sophisticated systems for capturing and understanding the underlying information enclosed in them. In this paper we present TWINE, a real-time system for the big data analysis and exploration of information extracted from Twitter streams. The proposed system based on a Named Entity Recognition and Linking pipeline and a multi-dimensional spatial geo-localization is managed by a scalable and flexible architecture for an interactive visualization of micropost streams insights. The demo is available at \url{http://twine-mind.cloudapp.net/streaming}.",
}
@inproceedings{gontrum-etal-2017-alto,
    title = "{A}lto: Rapid Prototyping for Parsing and Translation",
    author = "Gontrum, Johannes  and
      Groschwitz, Jonas  and
      Koller, Alexander  and
      Teichmann, Christoph",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3008",
    pages = "29--32",
    abstract = "We present Alto, a rapid prototyping tool for new grammar formalisms. Alto implements generic but efficient algorithms for parsing, translation, and training for a range of monolingual and synchronous grammar formalisms. It can easily be extended to new formalisms, which makes all of these algorithms immediately available for the new formalism.",
}
@inproceedings{boros-etal-2017-cassandra,
    title = "{CASSANDRA}: A multipurpose configurable voice-enabled human-computer-interface",
    author = "Boros, Tiberiu  and
      Dumitrescu, Stefan Daniel  and
      Pipa, Sonia",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3009",
    pages = "33--36",
    abstract = "Voice enabled human computer interfaces (HCI) that integrate automatic speech recognition, text-to-speech synthesis and natural language understanding have become a commodity, introduced by the immersion of smart phones and other gadgets in our daily lives. Smart assistants are able to respond to simple queries (similar to text-based question-answering systems), perform simple tasks (call a number, reject a call etc.) and help organizing appointments. With this paper we introduce a newly created process automation platform that enables the user to control applications and home appliances and to query the system for information using a natural voice interface. We offer an overview of the technologies that enabled us to construct our system and we present different usage scenarios in home and office environments.",
}
@inproceedings{thorne-vlachos-2017-extensible,
    title = "An Extensible Framework for Verification of Numerical Claims",
    author = "Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3010",
    pages = "37--40",
    abstract = "In this paper we present our automated fact checking system demonstration which we developed in order to participate in the Fast and Furious Fact Check challenge. We focused on simple numerical claims such as {``}population of Germany in 2015 was 80 million{''} which comprised a quarter of the test instances in the challenge, achieving 68{\%} accuracy. Our system extends previous work on semantic parsing and claim identification to handle temporal expressions and knowledge bases consisting of multiple tables, while relying solely on automatically generated training data. We demonstrate the extensible nature of our system by evaluating it on relations used in previous work. We make our system publicly available so that it can be used and extended by the community.",
}
@inproceedings{vallejo-huanga-etal-2017-adocs,
    title = "{AD}o{CS}: Automatic Designer of Conference Schedules",
    author = "Vallejo Huanga, Diego Fernando  and
      Morillo Alc{\'\i}var, Paulina Adriana  and
      Ferri Ram{\'\i}rez, C{\`e}sar",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3011",
    pages = "41--44",
    abstract = "Distributing papers into sessions in scientific conferences is a task consisting in grouping papers with common topics and considering the size restrictions imposed by the conference schedule. This problem can be seen as a semi-supervised clustering of scientific papers based on their features. This paper presents a web tool called ADoCS that solves the problem of configuring conference schedules by an automatic clustering of articles by similarity using a new algorithm considering size constraints.",
}
@inproceedings{gamallo-etal-2017-web,
    title = "A Web Interface for Diachronic Semantic Search in {S}panish",
    author = "Gamallo, Pablo  and
      Rodr{\'\i}guez-Torres, Iv{\'a}n  and
      Garcia, Marcos",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3012",
    pages = "45--48",
    abstract = "This article describes a semantic system which is based on distributional models obtained from a chronologically structured language resource, namely Google Books Syntactic Ngrams.The models were created using dependency-based contexts and a strategy for reducing the vector space, which consists in selecting the more informative and relevant word contexts. The system allowslinguists to analize meaning change of Spanish words in the written language across time.",
}
@inproceedings{perez-cuadros-2017-multilingual,
    title = "Multilingual {CALL} Framework for Automatic Language Exercise Generation from Free Text",
    author = "Perez, Naiara  and
      Cuadros, Montse",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3013",
    pages = "49--52",
    abstract = "This paper describes a web-based application to design and answer exercises for language learning. It is available in Basque, Spanish, English, and French. Based on open-source Natural Language Processing (NLP) technology such as word embedding models and word sense disambiguation, the application enables users to automatic create easily and in real time three types of exercises, namely, Fill-in-the-Gaps, Multiple Choice, and Shuffled Sentences questionnaires. These are generated from texts of the users{'} own choice, so they can train their language skills with content of their particular interest.",
}
@inproceedings{henrich-lang-2017-audience,
    title = "Audience Segmentation in Social Media",
    author = "Henrich, Verena  and
      Lang, Alexander",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3014",
    pages = "53--56",
    abstract = "Understanding the social media audience is becoming increasingly important for social media analysis. This paper presents an approach that detects various audience attributes, including author location, demographics, behavior and interests. It works both for a variety of social media sources and for multiple languages. The approach has been implemented within IBM Watson Analytics for Social Media and creates author profiles for more than 300 different analysis domains every day.",
}
@inproceedings{da-cunha-etal-2017-artext,
    title = "The ar{T}ext prototype: An automatic system for writing specialized texts",
    author = "da Cunha, Iria  and
      Montan{\'e}, M. Amor  and
      Hysa, Luis",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3015",
    pages = "57--60",
    abstract = "This article describes an automatic system for writing specialized texts in Spanish. The arText prototype is a free online text editor that includes different types of linguistic information. It is designed for a variety of end users and domains, including specialists and university students working in the fields of medicine and tourism, and laypersons writing to the public administration. ArText provides guidance on how to structure a text, prompts users to include all necessary contents in each section, and detects lexical and discourse problems in the text.",
}
@inproceedings{dalvi-etal-2017-qcri,
    title = "{QCRI} Live Speech Translation System",
    author = "Dalvi, Fahim  and
      Zhang, Yifan  and
      Khurana, Sameer  and
      Durrani, Nadir  and
      Sajjad, Hassan  and
      Abdelali, Ahmed  and
      Mubarak, Hamdy  and
      Ali, Ahmed  and
      Vogel, Stephan",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3016",
    pages = "61--64",
    abstract = "This paper presents QCRI{'}s Arabic-to-English live speech translation system. It features modern web technologies to capture live audio, and broadcasts Arabic transcriptions and English translations simultaneously. Our Kaldi-based ASR system uses the Time Delay Neural Network (TDNN) architecture, while our Machine Translation (MT) system uses both phrase-based and neural frameworks. Although our neural MT system is slower than the phrase-based system, it produces significantly better translations and is memory efficient. The demo is available at \url{https://st.qcri.org/demos/livetranslation}.",
}
@inproceedings{sennrich-etal-2017-nematus,
    title = "{N}ematus: a Toolkit for Neural Machine Translation",
    author = {Sennrich, Rico  and
      Firat, Orhan  and
      Cho, Kyunghyun  and
      Birch, Alexandra  and
      Haddow, Barry  and
      Hitschler, Julian  and
      Junczys-Dowmunt, Marcin  and
      L{\"a}ubli, Samuel  and
      Miceli Barone, Antonio Valerio  and
      Mokry, Jozef  and
      N{\u{a}}dejde, Maria},
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3017",
    pages = "65--68",
    abstract = "We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-performing submissions to shared translation tasks at WMT and IWSLT, and has been used to train systems for production environments.",
}
@inproceedings{boullosa-etal-2017-tool,
    title = "A tool for extracting sense-disambiguated example sentences through user feedback",
    author = "Boullosa, Beto  and
      Eckart de Castilho, Richard  and
      Geyken, Alexander  and
      Lemnitzer, Lothar  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3018",
    pages = "69--72",
    abstract = "This paper describes an application system aimed to help lexicographers in the extraction of example sentences for a given headword based on its different senses. The tool uses classification and clustering methods and incorporates user feedback to refine its results.",
}
@inproceedings{moreno-ortiz-2017-lingmotif,
    title = "{L}ingmotif: Sentiment Analysis for the Digital Humanities",
    author = "Moreno-Ortiz, Antonio",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3019",
    pages = "73--76",
    abstract = "Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmotif can perform SA on any type of input texts, regardless of their length and topic. The analysis is based on the identification of sentiment-laden words and phrases contained in the application{'}s rich core lexicons, and employs context rules to account for sentiment shifters. It offers easy-to-interpret visual representations of quantitative data (text polarity, sentiment intensity, sentiment profile), as well as a detailed, qualitative analysis of the text in terms of its sentiment. Lingmotif can also take user-provided plugin lexicons in order to account for domain-specific sentiment expression. Lingmotif currently analyzes English and Spanish texts.",
}
@inproceedings{menini-etal-2017-ramble,
    title = "{RAMBLE} {ON}: Tracing Movements of Popular Historical Figures",
    author = "Menini, Stefano  and
      Sprugnoli, Rachele  and
      Moretti, Giovanni  and
      Bignotti, Enrico  and
      Tonelli, Sara  and
      Lepri, Bruno",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3020",
    pages = "77--80",
    abstract = "We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories. The code of the extraction pipeline and a navigator are freely available; moreover we display in a demonstrator the outcome of a case study carried out on trajectories of notable persons of the XX Century.",
}
@inproceedings{torr-2017-autobank,
    title = "{A}utobank: a semi-automatic annotation tool for developing deep {M}inimalist {G}rammar treebanks",
    author = "Torr, John",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3021",
    pages = "81--86",
    abstract = "This paper presents Autobank, a prototype tool for constructing a wide-coverage Minimalist Grammar (MG) (Stabler 1997), and semi-automatically converting the Penn Treebank (PTB) into a deep Minimalist treebank. The front end of the tool is a graphical user interface which facilitates the rapid development of a seed set of MG trees via manual reannotation of PTB preterminals with MG lexical categories. The system then extracts various dependency mappings between the source and target trees, and uses these in concert with a non-statistical MG parser to automatically reannotate the rest of the corpus. Autobank thus enables deep treebank conversions (and subsequent modifications) without the need for complex transduction algorithms accompanied by cascades of ad hoc rules; instead, the locus of human effort falls directly on the task of grammar construction itself.",
}
@inproceedings{galitsky-ilvovsky-2017-chatbot,
    title = "Chatbot with a Discourse Structure-Driven Dialogue Management",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3022",
    pages = "87--90",
    abstract = "We build a chat bot with iterative content exploration that leads a user through a personalized knowledge acquisition session. The chat bot is designed as an automated customer support or product recommendation agent assisting a user in learning product features, product usability, suitability, troubleshooting and other related tasks. To control the user navigation through content, we extend the notion of a linguistic discourse tree (DT) towards a set of documents with multiple sections covering a topic. For a given paragraph, a DT is built by DT parsers. We then combine DTs for the paragraphs of documents to form what we call extended DT, which is a basis for interactive content exploration facilitated by the chat bot. To provide cohesive answers, we use a measure of rhetoric agreement between a question and an answer by tree kernel learning of their DTs.",
}
@inproceedings{marsi-etal-2017-marine,
    title = "Marine Variable Linker: Exploring Relations between Changing Variables in Marine Science Literature",
    author = "Marsi, Erwin  and
      Pinar {\O}zturk, Pinar  and
      V. Ardelan, Murat",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3023",
    pages = "91--94",
    abstract = "We report on a demonstration system for text mining of literature in marine science and related disciplines. It automatically extracts variables ({``}CO2{''}) involved in events of change/increase/decrease ({``}increasing CO2{''}), as well as co-occurrence and causal relations among these events ({``}increasing CO2 causes a decrease in pH in seawater{''}), resulting in a big knowledge graph. A web-based graphical user interface targeted at marine scientists facilitates searching, browsing and visualising events and their relations in an interactive way.",
}
@inproceedings{cartier-2017-neoveille,
    title = "Neoveille, a Web Platform for Neologism Tracking",
    author = "Cartier, Emmanuel",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3024",
    pages = "95--98",
    abstract = "This paper details a software designed to track neologisms in seven languages through newspapers monitor corpora. The platform combines state-of-the-art processes to track linguistic changes and a web platform for linguists to create and manage their corpora, accept or reject automatically identified neologisms, describe linguistically the accepted neologisms and follow their lifecycle on the monitor corpora. In the following, after a short state-of-the-art in Neologism Retrieval, Analysis and Life-tracking, we describe the overall architecture of the system. The platform can be freely browsed at \url{www.neoveille.org} where detailed presentation is given. Access to the editing modules is available upon request.",
}
@inproceedings{kutuzov-kuzmenko-2017-building,
    title = "Building Web-Interfaces for Vector Semantic Models with the {W}eb{V}ectors Toolkit",
    author = "Kutuzov, Andrey  and
      Kuzmenko, Elizaveta",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3025",
    pages = "99--103",
    abstract = "In this demo we present WebVectors, a free and open-source toolkit helping to deploy web services which demonstrate and visualize distributional semantic models (widely known as word embeddings). WebVectors can be useful in a very common situation when one has trained a distributional semantics model for one{'}s particular corpus or language (tools for this are now widespread and simple to use), but then there is a need to demonstrate the results to general public over the Web. We show its abilities on the example of the living web services featuring distributional models for English, Norwegian and Russian.",
}
@inproceedings{ferrero-etal-2017-intoevents,
    title = "{I}n{T}o{E}vent{S}: An Interactive Toolkit for Discovering and Building Event Schemas",
    author = "Ferrero, Germ{\'a}n  and
      Primadhanty, Audi  and
      Quattoni, Ariadna",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3026",
    pages = "104--107",
    abstract = "Event Schema Induction is the task of learning a representation of events (e.g., bombing) and the roles involved in them (e.g, victim and perpetrator). This paper presents InToEventS, an interactive tool for learning these schemas. InToEventS allows users to explore a corpus and discover which kind of events are present. We show how users can create useful event schemas using two interactive clustering steps.",
}
@inproceedings{vuppuluri-etal-2017-ice,
    title = "{ICE}: Idiom and Collocation Extractor for Research and Education",
    author = "Vuppuluri, Vasanthi  and
      Baki, Shahryar  and
      Nguyen, An  and
      Verma, Rakesh",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3027",
    pages = "108--111",
    abstract = "Collocation and idiom extraction are well-known challenges with many potential applications in Natural Language Processing (NLP). Our experimental, open-source software system, called ICE, is a python package for flexibly extracting collocations and idioms, currently in English. It also has a competitive POS tagger that can be used alone or as part of collocation/idiom extraction. ICE is available free of cost for research and educational uses in two user-friendly formats. This paper gives an overview of ICE and its performance, and briefly describes the research underlying the extraction algorithms.",
}
@inproceedings{yoneda-etal-2017-bib2vec,
    title = "{B}ib2vec: Embedding-based Search System for Bibliographic Information",
    author = "Yoneda, Takuma  and
      Mori, Koki  and
      Miwa, Makoto  and
      Sasaki, Yutaka",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3028",
    pages = "112--115",
    abstract = "We propose a novel embedding model that represents relationships among several elements in bibliographic information with high representation ability and flexibility. Based on this model, we present a novel search system that shows the relationships among the elements in the ACL Anthology Reference Corpus. The evaluation results show that our model can achieve a high prediction ability and produce reasonable search results.",
}
@inproceedings{liepins-etal-2017-summa,
    title = "The {SUMMA} Platform Prototype",
    author = "Liepins, Renars  and
      Germann, Ulrich  and
      Barzdins, Guntis  and
      Birch, Alexandra  and
      Renals, Steve  and
      Weber, Susanne  and
      van der Kreeft, Peggy  and
      Bourlard, Herv{\'e}  and
      Prieto, Jo{\~a}o  and
      Klejch, Ond{\v{r}}ej  and
      Bell, Peter  and
      Lazaridis, Alexandros  and
      Mendes, Alfonso  and
      Riedel, Sebastian  and
      Almeida, Mariana S. C.  and
      Balage, Pedro  and
      Cohen, Shay B.  and
      Dwojak, Tomasz  and
      Garner, Philip N.  and
      Giefer, Andreas  and
      Junczys-Dowmunt, Marcin  and
      Imran, Hina  and
      Nogueira, David  and
      Ali, Ahmed  and
      Miranda, Sebasti{\~a}o  and
      Popescu-Belis, Andrei  and
      Miculicich Werlen, Lesly  and
      Papasarantopoulos, Nikos  and
      Obamuyide, Abiola  and
      Jones, Clive  and
      Dalvi, Fahim  and
      Vlachos, Andreas  and
      Wang, Yang  and
      Tong, Sibo  and
      Sennrich, Rico  and
      Pappas, Nikolaos  and
      Narayan, Shashi  and
      Damonte, Marco  and
      Durrani, Nadir  and
      Khurana, Sameer  and
      Abdelali, Ahmed  and
      Sajjad, Hassan  and
      Vogel, Stephan  and
      Sheppey, David  and
      Hernon, Chris  and
      Mitchell, Jeff",
    booktitle = "Proceedings of the Software Demonstrations of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-3029",
    pages = "116--119",
    abstract = "We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.",
}
@proceedings{eacl-2017-european-chapter,
    title = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2000",
}
@inproceedings{kohita-etal-2017-multilingual,
    title = "Multilingual Back-and-Forth Conversion between Content and Function Head for Easy Dependency Parsing",
    author = "Kohita, Ryosuke  and
      Noji, Hiroshi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2001",
    pages = "1--7",
    abstract = "Universal Dependencies (UD) is becoming a standard annotation scheme cross-linguistically, but it is argued that this scheme centering on content words is harder to parse than the conventional one centering on function words. To improve the parsability of UD, we propose a back-and-forth conversion algorithm, in which we preprocess the training treebank to increase parsability, and reconvert the parser outputs to follow the UD scheme as a postprocess. We show that this technique consistently improves LAS across languages even with a state-of-the-art parser, in particular on core dependency arcs such as nominal modifier. We also provide an in-depth analysis to understand why our method increases parsability.",
}
@inproceedings{littell-etal-2017-uriel,
    title = "{URIEL} and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors",
    author = "Littell, Patrick  and
      Mortensen, David R.  and
      Lin, Ke  and
      Kairis, Katherine  and
      Turner, Carlisle  and
      Levin, Lori",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2002",
    pages = "8--14",
    abstract = "We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics. The goal of URIEL and lang2vec is to enable multilingual NLP, especially on less-resourced languages and make possible types of experiments (especially but not exclusively related to NLP tasks) that are otherwise difficult or impossible due to the sparsity and incommensurability of the data sources. lang2vec vectors have been shown to reduce perplexity in multilingual language modeling, when compared to one-hot language identification vectors.",
}
@inproceedings{labeau-allauzen-2017-experimental,
    title = "An experimental analysis of Noise-Contrastive Estimation: the noise distribution matters",
    author = "Labeau, Matthieu  and
      Allauzen, Alexandre",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2003",
    pages = "15--20",
    abstract = "Noise Contrastive Estimation (NCE) is a learning procedure that is regularly used to train neural language models, since it avoids the computational bottleneck caused by the output softmax. In this paper, we attempt to explain some of the weaknesses of this objective function, and to draw directions for further developments. Experiments on a small task show the issues raised by an unigram noise distribution, and that a context dependent noise distribution, such as the bigram distribution, can solve these issues and provide stable and data-efficient learning.",
}
@inproceedings{li-etal-2017-robust,
    title = "Robust Training under Linguistic Adversity",
    author = "Li, Yitong  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2004",
    pages = "21--27",
    abstract = "Deep neural networks have achieved remarkable results across many language processing tasks, however they have been shown to be susceptible to overfitting and highly sensitive to noise, including adversarial attacks. In this work, we propose a linguistically-motivated approach for training robust models based on exposing the model to corrupted text examples at training time. We consider several flavours of linguistically plausible corruption, include lexical semantic and syntactic methods. Empirically, we evaluate our method with a convolutional neural model across a range of sentiment analysis datasets. Compared with a baseline and the dropout method, our method achieves better overall performance.",
}
@inproceedings{zamani-schwartz-2017-using,
    title = "Using {T}witter Language to Predict the Real Estate Market",
    author = "Zamani, Mohammadzaman  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2005",
    pages = "28--33",
    abstract = "We explore whether social media can provide a window into community real estate -foreclosure rates and price changes- beyond that of traditional economic and demographic variables. We find language use in Twitter not only predicts real estate outcomes as well as traditional variables across counties, but that including Twitter language in traditional models leads to a significant improvement (e.g. from Pearson r = :50 to r = :59 for price changes). We overcome the challenge of the relative sparsity and noise in Twitter language variables by showing that training on the residual error of the traditional models leads to more accurate overall assessments. Finally, we discover that it is Twitter language related to business (e.g. {`}company{'}, {`}marketing{'}) and technology (e.g. {`}technology{'}, {`}internet{'}), among others, that yield predictive power over economics.",
}
@inproceedings{paetzold-specia-2017-lexical,
    title = "Lexical Simplification with Neural Ranking",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2006",
    pages = "34--40",
    abstract = "We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus - a large set of professionally produced simplifications. We extract candidate substitutions by combining the Newsela corpus with a retrofitted context-aware word embeddings model and rank them using a new neural regression model that learns rankings from annotated data. This strategy leads to the highest Accuracy, Precision and F1 scores to date in standard datasets for the task.",
}
@inproceedings{schluter-2017-limits,
    title = "The limits of automatic summarisation according to {ROUGE}",
    author = "Schluter, Natalie",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2007",
    pages = "41--45",
    abstract = "This paper discusses some central caveats of summarisation, incurred in the use of the ROUGE metric for evaluation, with respect to optimal solutions. The task is NP-hard, of which we give the first proof. Still, as we show empirically for three central benchmark datasets for the task, greedy algorithms empirically seem to perform optimally according to the metric. Additionally, overall quality assurance is problematic: there is no natural upper bound on the quality of summarisation systems, and even humans are excluded from performing optimal summarisation.",
}
@inproceedings{ouyang-etal-2017-crowd,
    title = "Crowd-Sourced Iterative Annotation for Narrative Summarization Corpora",
    author = "Ouyang, Jessica  and
      Chang, Serina  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2008",
    pages = "46--51",
    abstract = "We present an iterative annotation process for producing aligned, parallel corpora of abstractive and extractive summaries for narrative. Our approach uses a combination of trained annotators and crowd-sourcing, allowing us to elicit human-generated summaries and alignments quickly and at low cost. We use crowd-sourcing to annotate aligned phrases with the text-to-text generation techniques needed to transform each phrase into the other. We apply this process to a corpus of 476 personal narratives, which we make available on the Web.",
}
@inproceedings{chu-etal-2017-broad,
    title = "Broad Context Language Modeling as Reading Comprehension",
    author = "Chu, Zewei  and
      Wang, Hai  and
      Gimpel, Kevin  and
      McAllester, David",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2009",
    pages = "52--57",
    abstract = "Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015). We focus here on the LAMBADA dataset (Paperno et al., 2016), a word prediction task requiring broader context than the immediate sentence. We view LAMBADA as a reading comprehension problem and apply comprehension models based on neural networks. Though these models are constrained to choose a word from the context, they improve the state of the art on LAMBADA from 7.3{\%} to 49{\%}. We analyze 100 instances, finding that neural network readers perform well in cases that involve selecting a name from the context based on dialogue or discourse cues but struggle when coreference resolution or external knowledge is needed.",
}
@inproceedings{fancellu-etal-2017-detecting,
    title = "Detecting negation scope is easy, except when it isn{'}t",
    author = "Fancellu, Federico  and
      Lopez, Adam  and
      Webber, Bonnie  and
      He, Hangfeng",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2010",
    pages = "58--63",
    abstract = "Several corpora have been annotated with negation scope{---}the set of words whose meaning is negated by a cue like the word {``}not{''}{---}leading to the development of classifiers that detect negation scope with high accuracy. We show that for nearly all of these corpora, this high accuracy can be attributed to a single fact: they frequently annotate negation scope as a single span of text delimited by punctuation. For negation scopes not of this form, detection accuracy is low and under-sampling the easy training examples does not substantially improve accuracy. We demonstrate that this is partly an artifact of annotation guidelines, and we argue that future negation scope annotation efforts should focus on these more difficult cases.",
}
@inproceedings{zhang-etal-2017-mt,
    title = "{MT}/{IE}: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models",
    author = "Zhang, Sheng  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2011",
    pages = "64--70",
    abstract = "Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, and show that it outperforms the pipeline in a cross-lingual open information extraction setting by 1-4 BLEU and 0.5-0.8 F1.",
}
@inproceedings{rimell-etal-2017-learning,
    title = "Learning to Negate Adjectives with Bilinear Models",
    author = "Rimell, Laura  and
      Mabona, Amandla  and
      Bulat, Luana  and
      Kiela, Douwe",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2012",
    pages = "71--78",
    abstract = "We learn a mapping that negates adjectives by predicting an adjective{'}s antonym in an arbitrary word embedding model. We show that both linear models and neural networks improve on this task when they have access to a vector representing the semantic domain of the input word, e.g. a centroid of temperature words when predicting the antonym of {`}cold{'}. We introduce a continuous class-conditional bilinear neural network which is able to negate adjectives with high precision.",
}
@inproceedings{boleda-etal-2017-instances,
    title = "Instances and concepts in distributional space",
    author = "Boleda, Gemma  and
      Gupta, Abhijeet  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2013",
    pages = "79--85",
    abstract = "Instances ({``}Mozart{''}) are ontologically distinct from concepts or classes ({``}composer{''}). Natural language encompasses both, but instances have received comparatively little attention in distributional semantics. Our results show that instances and concepts differ in their distributional properties. We also establish that instantiation detection ({``}Mozart {--} composer{''}) is generally easier than hypernymy detection ({``}chemist {--} scientist{''}), and that results on the influence of input representation do not transfer from hyponymy to instantiation.",
}
@inproceedings{zarriess-schlangen-2017-child,
    title = "Is this a Child, a Girl or a Car? Exploring the Contribution of Distributional Similarity to Learning Referential Word Meanings",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2014",
    pages = "86--91",
    abstract = "There has recently been a lot of work trying to use images of referents of words for improving vector space meaning representations derived from text. We investigate the opposite direction, as it were, trying to improve visual word predictors that identify objects in images, by exploiting distributional similarity information during training. We show that for certain words (such as entry-level nouns or hypernyms), we can indeed learn better referential word meanings by taking into account their semantic similarity to other words. For other words, there is no or even a detrimental effect, compared to a learning setup that presents even semantically related objects as negative instances.",
}
@inproceedings{white-etal-2017-semantic,
    title = "The Semantic Proto-Role Linking Model",
    author = "White, Aaron Steven  and
      Rawlins, Kyle  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2015",
    pages = "92--98",
    abstract = "We propose the semantic proto-role linking model, which jointly induces both predicate-specific semantic roles and predicate-general semantic proto-roles based on semantic proto-role property likelihood judgments. We use this model to empirically evaluate Dowty{'}s thematic proto-role linking theory.",
}
@inproceedings{cocos-callison-burch-2017-language,
    title = "The Language of Place: Semantic Value from Geospatial Context",
    author = "Cocos, Anne  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2016",
    pages = "99--104",
    abstract = "There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.",
}
@inproceedings{barbieri-etal-2017-emojis,
    title = "Are Emojis Predictable?",
    author = "Barbieri, Francesco  and
      Ballesteros, Miguel  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2017",
    pages = "105--111",
    abstract = "Emojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message. Despite being widely used in social media, their underlying semantics have received little attention from a Natural Language Processing standpoint. In this paper, we investigate the relation between words and emojis, studying the novel task of predicting which emojis are evoked by text-based tweet messages. We train several models based on Long Short-Term Memory networks (LSTMs) in this task. Our experimental results show that our neural model outperforms a baseline as well as humans solving the same task, suggesting that computational models are able to better capture the underlying semantics of emojis.",
}
@inproceedings{kirov-etal-2017-rich,
    title = "A Rich Morphological Tagger for {E}nglish: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax",
    author = "Kirov, Christo  and
      Sylak-Glassman, John  and
      Knowles, Rebecca  and
      Cotterell, Ryan  and
      Post, Matt",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2018",
    pages = "112--117",
    abstract = "A traditional claim in linguistics is that all human languages are equally expressive{---}able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation.",
}
@inproceedings{vylomova-etal-2017-context,
    title = "Context-Aware Prediction of Derivational Word-forms",
    author = "Vylomova, Ekaterina  and
      Cotterell, Ryan  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2019",
    pages = "118--124",
    abstract = "Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose a new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder-decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under lexicon agnostic setting.",
}
@inproceedings{le-godais-etal-2017-comparing,
    title = "Comparing Character-level Neural Language Models Using a Lexical Decision Task",
    author = {Le Godais, Ga{\"e}l  and
      Linzen, Tal  and
      Dupoux, Emmanuel},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2020",
    pages = "125--130",
    abstract = "What is the information captured by neural network models of language? We address this question in the case of character-level recurrent neural language models. These models do not have explicit word representations; do they acquire implicit ones? We assess the lexical capacity of a network using the lexical decision task common in psycholinguistics: the system is required to decide whether or not a string of characters forms a word. We explore how accuracy on this task is affected by the architecture of the network, focusing on cell type (LSTM vs. SRN), depth and width. We also compare these architectural properties to a simple count of the parameters of the network. The overall number of parameters in the network turns out to be the most important predictor of accuracy; in particular, there is little evidence that deeper networks are beneficial for this task.",
}
@inproceedings{lemke-etal-2017-optimal,
    title = "Optimal encoding! - Information Theory constrains article omission in newspaper headlines",
    author = "Lemke, Robin  and
      Horch, Eva  and
      Reich, Ingo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2021",
    pages = "131--135",
    abstract = "In this paper we pursue the hypothesis that the distribution of article omission specifically is constrained by principles of Information Theory (Shannon 1948). In particular, Information Theory predicts a stronger preference for article omission before nouns which are relatively unpredictable in context of the preceding words. We investigated article omission in German newspaper headlines with a corpus and acceptability rating study. Both support our hypothesis: Articles are inserted more often before unpredictable nouns and subjects perceive article omission before predictable nouns as more well-formed than before unpredictable ones. This suggests that information theoretic principles constrain the distribution of article omission in headlines.",
}
@inproceedings{strapparava-mihalcea-2017-computational,
    title = "A Computational Analysis of the Language of Drug Addiction",
    author = "Strapparava, Carlo  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2022",
    pages = "136--142",
    abstract = "We present a computational analysis of the language of drug users when talking about their drug experiences. We introduce a new dataset of over 4,000 descriptions of experiences reported by users of four main drug types, and show that we can predict with an F1-score of up to 88{\%} the drug behind a certain experience. We also perform an analysis of the dominant psycholinguistic processes and dominant emotions associated with each drug type, which sheds light on the characteristics of drug users.",
}
@inproceedings{haponchyk-moschitti-2017-practical,
    title = "A Practical Perspective on Latent Structured Prediction for Coreference Resolution",
    author = "Haponchyk, Iryna  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2023",
    pages = "143--149",
    abstract = "Latent structured prediction theory proposes powerful methods such as Latent Structural SVM (LSSVM), which can potentially be very appealing for coreference resolution (CR). In contrast, only small work is available, mainly targeting the latent structured perceptron (LSP). In this paper, we carried out a practical study comparing for the first time online learning with LSSVM. We analyze the intricacies that may have made initial attempts to use LSSVM fail, i.e., a huge training time and much lower accuracy produced by Kruskal{'}s spanning tree algorithm. In this respect, we also propose a new effective feature selection approach for improving system efficiency. The results show that LSP, if correctly parameterized, produces the same performance as LSSVM, being much more efficient.",
}
@inproceedings{shi-demberg-2017-need,
    title = "On the Need of Cross Validation for Discourse Relation Classification",
    author = "Shi, Wei  and
      Demberg, Vera",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2024",
    pages = "150--156",
    abstract = "The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic. Existing machine learning models for the task train on sections 2-21 of the PDTB and test on section 23, which includes a total of 761 implicit discourse relations. In this paper, we{'}d like to make a methodological point, arguing that the standard test set is too small to draw conclusions about whether the inclusion of certain features constitute a genuine improvement, or whether one got lucky with some properties of the test set, and argue for the adoption of cross validation for the discourse relation classification task by the community.",
}
@inproceedings{press-wolf-2017-using,
    title = "Using the Output Embedding to Improve Language Models",
    author = "Press, Ofir  and
      Wolf, Lior",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2025",
    pages = "157--163",
    abstract = "We study the topmost weight matrix of neural network language models. We show that this matrix constitutes a valid word embedding. When training language models, we recommend tying the input embedding and this output embedding. We analyze the resulting update rules and show that the tied embedding evolves in a more similar way to the output embedding than to the input embedding in the untied model. We also offer a new method of regularizing the output embedding. Our methods lead to a significant reduction in perplexity, as we are able to show on a variety of neural network language models. Finally, we show that weight tying can reduce the size of neural translation models to less than half of their original size without harming their performance.",
}
@inproceedings{bingel-sogaard-2017-identifying,
    title = "Identifying beneficial task relations for multi-task learning in deep neural networks",
    author = "Bingel, Joachim  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2026",
    pages = "164--169",
    abstract = "Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP tasks, mixed results have been reported, and little is known about the conditions under which MTL leads to gains in NLP. This paper sheds light on the specific task relations that can lead to gains from MTL models over single-task setups.",
}
@inproceedings{pande-2017-effective,
    title = "Effective search space reduction for spell correction using character neural embeddings",
    author = "Pande, Harshit",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2027",
    pages = "170--174",
    abstract = "We present a novel, unsupervised, and distance measure agnostic method for search space reduction in spell correction using neural character embeddings. The embeddings are learned by skip-gram word2vec training on sequences generated from dictionary words in a phonetic information-retentive manner. We report a very high performance in terms of both success rates and reduction of search space on the Birkbeck spelling error corpus. To the best of our knowledge, this is the first application of word2vec to spell correction.",
}
@inproceedings{cotterell-etal-2017-explaining,
    title = "Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis",
    author = "Cotterell, Ryan  and
      Poliak, Adam  and
      Van Durme, Benjamin  and
      Eisner, Jason",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2028",
    pages = "175--181",
    abstract = "The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence. We offer a new interpretation of skip-gram based on exponential family PCA-a form of matrix factorization to generalize the skip-gram model to tensor factorization. In turn, this lets us train embeddings through richer higher-order coocurrences, e.g., triples that include positional information (to incorporate syntax) or morphological information (to share parameters across related words). We experiment on 40 languages and show our model improves upon skip-gram.",
}
@inproceedings{cao-clark-2017-latent,
    title = "Latent Variable Dialogue Models and their Diversity",
    author = "Cao, Kris  and
      Clark, Stephen",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2029",
    pages = "182--187",
    abstract = "We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the {`}boring output{'} issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.",
}
@inproceedings{katerenchuk-2017-age,
    title = "Age Group Classification with Speech and Metadata Multimodality Fusion",
    author = "Katerenchuk, Denys",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2030",
    pages = "188--193",
    abstract = "Children comprise a significant proportion of TV viewers and it is worthwhile to customize the experience for them. However, identifying who is a child in the audience can be a challenging task. We present initial studies of a novel method which combines utterances with user metadata. In particular, we develop an ensemble of different machine learning techniques on different subsets of data to improve child detection. Our initial results show an 9.2{\%} absolute improvement over the baseline, leading to a state-of-the-art performance.",
}
@inproceedings{lakomkin-etal-2017-automatically,
    title = "Automatically augmenting an emotion dataset improves classification using audio",
    author = "Lakomkin, Egor  and
      Weber, Cornelius  and
      Wermter, Stefan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2031",
    pages = "194--197",
    abstract = "In this work, we tackle a problem of speech emotion classification. One of the issues in the area of affective computation is that the amount of annotated data is very limited. On the other hand, the number of ways that the same emotion can be expressed verbally is enormous due to variability between speakers. This is one of the factors that limits performance and generalization. We propose a simple method that extracts audio samples from movies using textual sentiment analysis. As a result, it is possible to automatically construct a larger dataset of audio samples with positive, negative emotional and neutral speech. We show that pretraining recurrent neural network on such a dataset yields better results on the challenging EmotiW corpus. This experiment shows a potential benefit of combining textual sentiment analysis with vocal information.",
}
@inproceedings{chen-etal-2017-line,
    title = "On-line Dialogue Policy Learning with Companion Teaching",
    author = "Chen, Lu  and
      Yang, Runzhe  and
      Chang, Cheng  and
      Ye, Zihao  and
      Zhou, Xiang  and
      Yu, Kai",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2032",
    pages = "198--204",
    abstract = "On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios. Poor initial policy can easily lead to bad user experience and consequently fail to attract sufficient users for policy training. A novel framework, companion teaching, is proposed to include a human teacher in the dialogue policy training loop to address the cold start problem. Here, dialogue policy is trained using not only user{'}s reward, but also teacher{'}s example action as well as estimated immediate reward at turn level. Simulation experiments showed that, with small number of human teaching dialogues, the proposed approach can effectively improve user experience at the beginning and smoothly lead to good performance with more user interaction data.",
}
@inproceedings{vodolan-etal-2017-hybrid,
    title = "Hybrid Dialog State Tracker with {ASR} Features",
    author = "Vodol{\'a}n, Miroslav  and
      Kadlec, Rudolf  and
      Kleindienst, Jan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2033",
    pages = "205--210",
    abstract = "This paper presents a hybrid dialog state tracker enhanced by trainable Spoken Language Understanding (SLU) for slot-filling dialog systems. Our architecture is inspired by previously proposed neural-network-based belief-tracking systems. In addition, we extended some parts of our modular architecture with differentiable rules to allow end-to-end training. We hypothesize that these rules allow our tracker to generalize better than pure machine-learning based systems. For evaluation, we used the Dialog State Tracking Challenge (DSTC) 2 dataset - a popular belief tracking testbed with dialogs from restaurant information system. To our knowledge, our hybrid tracker sets a new state-of-the-art result in three out of four categories within the DSTC2.",
}
@inproceedings{nicolai-kondrak-2017-morphological,
    title = "Morphological Analysis without Expert Annotation",
    author = "Nicolai, Garrett  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2034",
    pages = "211--216",
    abstract = "The task of morphological analysis is to produce a complete list of lemma+tag analyses for a given word-form. We propose a discriminative string transduction approach which exploits plain inflection tables and raw text corpora, thus obviating the need for expert annotation. Experiments on four languages demonstrate that our system has much higher coverage than a hand-engineered FST analyzer, and is more accurate than a state-of-the-art morphological tagger.",
}
@inproceedings{kumar-etal-2017-morphological,
    title = "Morphological Analysis of the {D}ravidian Language Family",
    author = "Kumar, Arun  and
      Cotterell, Ryan  and
      Padr{\'o}, Llu{\'\i}s  and
      Oliver, Antoni",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2035",
    pages = "217--222",
    abstract = "The Dravidian languages are one of the most widely spoken language families in the world, yet there are very few annotated resources available to NLP researchers. To remedy this, we create DravMorph, a corpus annotated for morphological segmentation and part-of-speech. Additionally, we exploit novel features and higher-order models to set state-of-the-art results on these corpora on both tasks, beating techniques proposed in the literature by as much as 4 points in segmentation F1.",
}
@inproceedings{camacho-collados-navigli-2017-babeldomains,
    title = "{B}abel{D}omains: Large-Scale Domain Labeling of Lexical Resources",
    author = "Camacho-Collados, Jose  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2036",
    pages = "223--228",
    abstract = "In this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge. We propose an automatic method that uses knowledge from various lexical resources, exploiting both distributional and graph-based clues, to accurately propagate domain information. We evaluate our methodology intrinsically on two lexical resources (WordNet and BabelNet), achieving a precision over 80{\%} in both cases. Finally, we show the potential of BabelDomains in a supervised learning setting, clustering training data by domain for hypernym discovery.",
}
@inproceedings{napoles-etal-2017-jfleg,
    title = "{JFLEG}: A Fluency Corpus and Benchmark for Grammatical Error Correction",
    author = "Napoles, Courtney  and
      Sakaguchi, Keisuke  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2037",
    pages = "229--234",
    abstract = "We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original text more native sounding. We describe the types of corrections made and benchmark four leading GEC systems on this corpus, identifying specific areas in which they do well and how they can improve. JFLEG fulfills the need for a new gold standard to properly assess the current state of GEC.",
}
@inproceedings{habash-etal-2017-parallel,
    title = "A Parallel Corpus for Evaluating Machine Translation between {A}rabic and {E}uropean Languages",
    author = "Habash, Nizar  and
      Zalmout, Nasser  and
      Taji, Dima  and
      Hoang, Hieu  and
      Alzate, Maverick",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2038",
    pages = "235--241",
    abstract = "We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic. Arab-Acquis consists of over 12,000 sentences from the JRC-Acquis (Acquis Communautaire) corpus translated twice by professional translators, once from English and once from French, and totaling over 600,000 words. The corpus follows previous data splits in the literature for tuning, development, and testing. We describe the corpus and how it was created. We also present the first benchmarking results on translating to and from Arabic for 22 European languages.",
}
@inproceedings{abzianidze-etal-2017-parallel,
    title = "The {P}arallel {M}eaning {B}ank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations",
    author = "Abzianidze, Lasha  and
      Bjerva, Johannes  and
      Evang, Kilian  and
      Haagsma, Hessel  and
      van Noord, Rik  and
      Ludmann, Pierre  and
      Nguyen, Duc-Duy  and
      Bos, Johan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2039",
    pages = "242--247",
    abstract = "The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations, assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) segmentation of the text in sentences and lexical items; (ii) syntactic parsing with Combinatory Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semi-supervised manner. The employed annotation models are all language-neutral. Our first results are promising.",
}
@inproceedings{agic-etal-2017-cross,
    title = "Cross-lingual tagger evaluation without test data",
    author = "Agi{\'c}, {\v{Z}}eljko  and
      Plank, Barbara  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2040",
    pages = "248--253",
    abstract = "We address the challenge of cross-lingual POS tagger evaluation in absence of manually annotated test data. We put forth and evaluate two dictionary-based metrics. On the tasks of accuracy prediction and system ranking, we reveal that these metrics are reliable enough to approximate test set-based evaluation, and at the same time lean enough to support assessment for truly low-resource languages.",
}
@inproceedings{cardellino-etal-2017-legal,
    title = "Legal {NERC} with ontologies, {W}ikipedia and curriculum learning",
    author = "Cardellino, Cristian  and
      Teruel, Milagro  and
      Alonso Alemany, Laura  and
      Villata, Serena",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2041",
    pages = "254--259",
    abstract = "In this paper, we present a Wikipedia-based approach to develop resources for the legal domain. We establish a mapping between a legal domain ontology, LKIF (Hoekstra et al. 2007), and a Wikipedia-based ontology, YAGO (Suchanek et al. 2007), and through that we populate LKIF. Moreover, we use the mentions of those entities in Wikipedia text to train a specific Named Entity Recognizer and Classifier. We find that this classifier works well in the Wikipedia, but, as could be expected, performance decreases in a corpus of judgments of the European Court of Human Rights. However, this tool will be used as a preprocess for human annotation. We resort to a technique called {``}curriculum learning{''} aimed to overcome problems of overfitting by learning increasingly more complex concepts. However, we find that in this particular setting, the method works best by learning from most specific to most general concepts, not the other way round.",
}
@inproceedings{sprugnoli-etal-2017-content,
    title = "The Content Types Dataset: a New Resource to Explore Semantic and Functional Characteristics of Texts",
    author = "Sprugnoli, Rachele  and
      Caselli, Tommaso  and
      Tonelli, Sara  and
      Moretti, Giovanni",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2042",
    pages = "260--266",
    abstract = "This paper presents a new resource, called Content Types Dataset, to promote the analysis of texts as a composition of units with specific semantic and functional roles. By developing this dataset, we also introduce a new NLP task for the automatic classification of Content Types. The annotation scheme and the dataset are described together with two sets of classification experiments.",
}
@inproceedings{sari-etal-2017-continuous,
    title = "Continuous N-gram Representations for Authorship Attribution",
    author = "Sari, Yunita  and
      Vlachos, Andreas  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2043",
    pages = "267--273",
    abstract = "This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.",
}
@inproceedings{bekoulis-etal-2017-reconstructing,
    title = "Reconstructing the house from the ad: Structured prediction on real estate classifieds",
    author = "Bekoulis, Giannis  and
      Deleu, Johannes  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2044",
    pages = "274--279",
    abstract = "In this paper, we address the (to the best of our knowledge) new problem of extracting a structured description of real estate properties from their natural language descriptions in classifieds. We survey and present several models to (a) identify important entities of a property (e.g.,rooms) from classifieds and (b) structure them into a tree format, with the entities as nodes and edges representing a part-of relation. Experiments show that a graph-based system deriving the tree from an initially fully connected entity graph, outperforms a transition-based system starting from only the entity nodes, since it better reconstructs the tree.",
}
@inproceedings{farajian-etal-2017-neural,
    title = "Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario",
    author = "Farajian, M. Amin  and
      Turchi, Marco  and
      Negri, Matteo  and
      Bertoldi, Nicola  and
      Federico, Marcello",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2045",
    pages = "280--284",
    abstract = "State-of-the-art neural machine translation (NMT) systems are generally trained on specific domains by carefully selecting the training sets and applying proper domain adaptation techniques. In this paper we consider the real world scenario in which the target domain is not predefined, hence the system should be able to translate text from multiple domains. We compare the performance of a generic NMT system and phrase-based statistical machine translation (PBMT) system by training them on a generic parallel corpus composed of data from different domains. Our results on multi-domain English-French data show that, in these realistic conditions, PBMT outperforms its neural counterpart. This raises the question: is NMT ready for deployment as a generic/multi-purpose MT backbone in real-world settings?",
}
@inproceedings{martschat-markert-2017-improving,
    title = "Improving {ROUGE} for Timeline Summarization",
    author = "Martschat, Sebastian  and
      Markert, Katja",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2046",
    pages = "285--290",
    abstract = "Current evaluation metrics for timeline summarization either ignore the temporal aspect of the task or require strict date matching. We introduce variants of ROUGE that allow alignment of daily summaries via temporal distance or semantic similarity. We argue for the suitability of these variants in a theoretical analysis and demonstrate it in a battery of task-specific tests.",
}
@inproceedings{suzuki-nagata-2017-cutting,
    title = "Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization",
    author = "Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2047",
    pages = "291--297",
    abstract = "This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.",
}
@inproceedings{gatti-etal-2017-sing,
    title = "To Sing like a Mockingbird",
    author = {Gatti, Lorenzo  and
      {\"O}zbal, G{\"o}zde  and
      Stock, Oliviero  and
      Strapparava, Carlo},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2048",
    pages = "298--304",
    abstract = "Musical parody, i.e. the act of changing the lyrics of an existing and very well-known song, is a commonly used technique for creating catchy advertising tunes and for mocking people or events. Here we describe a system for automatically producing a musical parody, starting from a corpus of songs. The system can automatically identify characterizing words and concepts related to a novel text, which are taken from the daily news. These concepts are then used as seeds to appropriately replace part of the original lyrics of a song, using metrical, rhyming and lexical constraints. Finally, the parody can be sung with a singing speech synthesizer, with no intervention from the user.",
}
@inproceedings{hayashi-nagata-2017-k,
    title = "K-best Iterative {V}iterbi Parsing",
    author = "Hayashi, Katsuhiko  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2049",
    pages = "305--310",
    abstract = "This paper presents an efficient and optimal parsing algorithm for probabilistic context-free grammars (PCFGs). To achieve faster parsing, our proposal employs a pruning technique to reduce unnecessary edges in the search space. The key is to conduct repetitively Viterbi inside and outside parsing, while gradually expanding the search space to efficiently compute heuristic bounds used for pruning. Our experimental results using the English Penn Treebank corpus show that the proposed algorithm is faster than the standard CKY parsing algorithm. In addition, we also show how to extend this algorithm to extract k-best Viterbi parse trees.",
}
@inproceedings{de-kok-etal-2017-pp,
    title = "{PP} Attachment: Where do We Stand?",
    author = {de Kok, Dani{\"e}l  and
      Ma, Jianqiang  and
      Dima, Corina  and
      Hinrichs, Erhard},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2050",
    pages = "311--317",
    abstract = "Prepostitional phrase (PP) attachment is a well known challenge to parsing. In this paper, we combine the insights of different works, namely: (1) treating PP attachment as a classification task with an arbitrary number of attachment candidates; (2) using auxiliary distributions to augment the data beyond the hand-annotated training set; (3) using topological fields to get information about the distribution of PP attachment throughout clauses and (4) using state-of-the-art techniques such as word embeddings and neural networks. We show that jointly using these techniques leads to substantial improvements. We also conduct a qualitative analysis to gauge where the ceiling of the task is in a realistic setup.",
}
@inproceedings{aufrant-etal-2017-dont,
    title = "Don{'}t Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2051",
    pages = "318--323",
    abstract = "This paper formalizes a sound extension of dynamic oracles to global training, in the frame of transition-based dependency parsers. By dispensing with the pre-computation of references, this extension widens the training strategies that can be entertained for such parsers; we show this by revisiting two standard training procedures, early-update and max-violation, to correct some of their search space sampling biases. Experimentally, on the SPMRL treebanks, this improvement increases the similarity between the train and test distributions and yields performance improvements up to 0.7 UAS, without any computation overhead.",
}
@inproceedings{bhat-etal-2017-joining,
    title = "Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data",
    author = "Bhat, Irshad  and
      Bhat, Riyaz A.  and
      Shrivastava, Manish  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2052",
    pages = "324--330",
    abstract = "In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Due to lack of an evaluation set for code-mixed structures, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation.",
}
@inproceedings{coavoux-crabbe-2017-multilingual,
    title = "Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks",
    author = "Coavoux, Maximin  and
      Crabb{\'e}, Beno{\^\i}t",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2053",
    pages = "331--336",
    abstract = "We introduce a constituency parser based on a bi-LSTM encoder adapted from recent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character biLSTM (Ballesteros et al., 2015; Plank et al., 2016). We model two important interfaces of constituency parsing with auxiliary tasks supervised at the word level: (i) part-of-speech (POS) and morphological tagging, (ii) functional label prediction. On the SPMRL dataset, our parser obtains above state-of-the-art results on constituency parsing without requiring either predicted POS or morphological tags, and outputs labelled dependency trees.",
}
@inproceedings{pezzelle-etal-2017-precise,
    title = "Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision",
    author = "Pezzelle, Sandro  and
      Marelli, Marco  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2054",
    pages = "337--342",
    abstract = "People can refer to quantities in a visual scene by using either exact cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few, most, all). In humans, these two processes underlie fairly different cognitive and neural mechanisms. Inspired by this evidence, the present study proposes two models for learning the objective meaning of cardinals and quantifiers from visual scenes containing multiple objects. We show that a model capitalizing on a {`}fuzzy{'} measure of similarity is effective for learning quantifiers, whereas the learning of exact cardinals is better accomplished when information about number is provided.",
}
@inproceedings{ficler-goldberg-2017-improving,
    title = "Improving a Strong Neural Parser with Conjunction-Specific Features",
    author = "Ficler, Jessica  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2055",
    pages = "343--348",
    abstract = "While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the extended parser yields an improvement in conj attachment as well as in overall dependency parsing accuracy on the Stanford dependency conversion of the Penn TreeBank.",
}
@inproceedings{pal-etal-2017-neural,
    title = "Neural Automatic Post-Editing Using Prior Alignment and Reranking",
    author = "Pal, Santanu  and
      Naskar, Sudip Kumar  and
      Vela, Mihaela  and
      Liu, Qun  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2056",
    pages = "349--355",
    abstract = "We present a second-stage machine translation (MT) system based on a neural machine translation (NMT) approach to automatic post-editing (APE) that improves the translation quality provided by a first-stage MT system. Our APE system (APE{\_}Sym) is an extended version of an attention based NMT model with bilingual symmetry employing bidirectional models, mt{--}pe and pe{--}mt. APE translations produced by our system show statistically significant improvements over the first-stage MT, phrase-based APE and the best reported score on the WMT 2016 APE dataset by a previous neural APE system. Re-ranking (APE{\_}Rerank) of the n-best translations from the phrase-based APE and APE{\_}Sym systems provides further substantial improvements over the symmetric neural APE model. Human evaluation confirms that the APE{\_}Rerank generated PE translations improve on the previous best neural APE system at WMT 2016.",
}
@inproceedings{graham-etal-2017-improving,
    title = "Improving Evaluation of Document-level Machine Translation Quality Estimation",
    author = "Graham, Yvette  and
      Ma, Qingsong  and
      Baldwin, Timothy  and
      Liu, Qun  and
      Parra, Carla  and
      Scarton, Carolina",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2057",
    pages = "356--361",
    abstract = "Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable. In this paper, we explore the validity of human annotations currently employed in the evaluation of document-level quality estimation for machine translation (MT). We demonstrate the degree to which MT system rankings are dependent on weights employed in the construction of the gold standard, before proposing direct human assessment as a valid alternative. Experiments show direct assessment (DA) scores for documents to be highly reliable, achieving a correlation of above 0.9 in a self-replication experiment, in addition to a substantial estimated cost reduction through quality controlled crowd-sourcing. The original gold standard based on post-edits incurs a 10{--}20 times greater cost than DA.",
}
@inproceedings{stahlberg-etal-2017-neural,
    title = "Neural Machine Translation by Minimising the {B}ayes-risk with Respect to Syntactic Translation Lattices",
    author = "Stahlberg, Felix  and
      de Gispert, Adri{\`a}  and
      Hasler, Eva  and
      Byrne, Bill",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2058",
    pages = "362--368",
    abstract = "We present a novel scheme to combine neural machine translation (NMT) with traditional statistical machine translation (SMT). Our approach borrows ideas from linearised lattice minimum Bayes-risk decoding for SMT. The NMT score is combined with the Bayes-risk of the translation according the SMT lattice. This makes our approach much more flexible than n-best list or lattice rescoring as the neural decoder is not restricted to the SMT search space. We show an efficient and simple way to integrate risk estimation into the NMT decoder which is suitable for word-level as well as subword-unit-level NMT. We test our method on English-German and Japanese-English and report significant gains over lattice rescoring on several data sets for both single and ensembled NMT. The MBR decoder produces entirely new hypotheses far beyond simply rescoring the SMT search space or fixing UNKs in the NMT output.",
}
@inproceedings{huck-etal-2017-producing,
    title = "Producing Unseen Morphological Variants in Statistical Machine Translation",
    author = "Huck, Matthias  and
      Tamchyna, Ale{\v{s}}  and
      Bojar, Ond{\v{r}}ej  and
      Fraser, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2059",
    pages = "369--375",
    abstract = "Translating into morphologically rich languages is difficult. Although the coverage of lemmas may be reasonable, many morphological variants cannot be learned from the training data. We present a statistical translation system that is able to produce these inflected word forms. Different from most previous work, we do not separate morphological prediction from lexical choice into two consecutive steps. Our approach is novel in that it is integrated in decoding and takes advantage of context information from both the source language and the target language sides.",
}
@inproceedings{sennrich-2017-grammatical,
    title = "How Grammatical is Character-level Neural Machine Translation? Assessing {MT} Quality with Contrastive Translation Pairs",
    author = "Sennrich, Rico",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2060",
    pages = "376--382",
    abstract = "Analysing translation quality in regards to specific linguistic phenomena has historically been difficult and time-consuming. Neural machine translation has the attractive property that it can produce scores for arbitrary translations, and we propose a novel method to assess how well NMT systems model specific linguistic phenomena such as agreement over long distances, the production of novel words, and the faithful translation of polarity. The core idea is that we measure whether a reference translation is more probable under a NMT model than a contrastive translation which introduces a specific type of error. We present LingEval97, a large-scale data set of 97000 contrastive translation pairs based on the WMT English-{\textgreater}German translation task, with errors automatically created with simple rules. We report results for a number of systems, and find that recently introduced character-level NMT systems perform better at transliteration than models with byte-pair encoding (BPE) segmentation, but perform more poorly at morphosyntactic agreement, and translating discontiguous units of meaning.",
}
@inproceedings{yang-etal-2017-neural-machine,
    title = "Neural Machine Translation with Recurrent Attention Modeling",
    author = "Yang, Zichao  and
      Hu, Zhiting  and
      Deng, Yuntian  and
      Dyer, Chris  and
      Smola, Alex",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2061",
    pages = "383--387",
    abstract = "Knowing which words have been attended to in previous time steps while generating a translation is a rich source of information for predicting what words will be attended to in the future. We improve upon the attention model of Bahdanau et al. (2014) by explicitly modeling the relationship between previous and subsequent attention levels for each word using one recurrent network per input word. This architecture easily captures informative features, such as fertility and regularities in relative distortion. In experiments, we show our parameterization of attention improves translation quality.",
}
@inproceedings{pilehvar-collier-2017-inducing,
    title = "Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources",
    author = "Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2062",
    pages = "388--393",
    abstract = "We put forward an approach that exploits the knowledge encoded in lexical resources in order to induce representations for words that were not encountered frequently during training. Our approach provides an advantage over the past work in that it enables vocabulary expansion not only for morphological variations, but also for infrequent domain specific terms. We performed evaluations in different settings, showing that the technique can provide consistent improvements on multiple benchmarks across domains.",
}
@inproceedings{lapesa-evert-2017-large,
    title = "Large-scale evaluation of dependency-based {DSM}s: Are they worth the effort?",
    author = "Lapesa, Gabriella  and
      Evert, Stefan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2063",
    pages = "394--400",
    abstract = "This paper presents a large-scale evaluation study of dependency-based distributional semantic models. We evaluate dependency-filtered and dependency-structured DSMs in a number of standard semantic similarity tasks, systematically exploring their parameter space in order to give them a {``}fair shot{''} against window-based models. Our results show that properly tuned window-based DSMs still outperform the dependency-based models in most tasks. There appears to be little need for the language-dependent resources and computational cost associated with syntactic analysis.",
}
@inproceedings{sanchez-riedel-2017-well,
    title = "How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis",
    author = "Sanchez, Ivan  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2064",
    pages = "401--407",
    abstract = "One key property of word embeddings currently under study is their capacity to encode hypernymy. Previous works have used supervised models to recover hypernymy structures from embeddings. However, the overall results do not clearly show how well we can recover such structures. We conduct the first dataset-centric analysis that shows how only the Baroni dataset provides consistent results. We empirically show that a possible reason for its good performance is its alignment to dimensions specific of hypernymy: generality and similarity",
}
@inproceedings{vulic-2017-cross,
    title = "Cross-Lingual Syntactically Informed Distributed Word Representations",
    author = "Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2065",
    pages = "408--414",
    abstract = "We develop a novel cross-lingual word representation model which injects syntactic information through dependency-based contexts into a shared cross-lingual word vector space. The model, termed CL-DepEmb, is based on the following assumptions: (1) dependency relations are largely language-independent, at least for related languages and prominent dependency links such as direct objects, as evidenced by the Universal Dependencies project; (2) word translation equivalents take similar grammatical roles in a sentence and are therefore substitutable within their syntactic contexts. Experiments with several language pairs on word similarity and bilingual lexicon induction, two fundamental semantic tasks emphasising semantic similarity, suggest the usefulness of the proposed syntactically informed cross-lingual word vector spaces. Improvements are observed in both tasks over standard cross-lingual {``}offline mapping{''} baselines trained using the same setup and an equal level of bilingual supervision.",
}
@inproceedings{ferrero-etal-2017-using,
    title = "Using Word Embedding for Cross-Language Plagiarism Detection",
    author = "Ferrero, J{\'e}r{\'e}my  and
      Besacier, Laurent  and
      Schwab, Didier  and
      Agn{\`e}s, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2066",
    pages = "415--421",
    abstract = "This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15{\%} for English-French similarity detection at chunk level (88.5{\%} at sentence level) on a very challenging corpus.",
}
@inproceedings{avraham-goldberg-2017-interplay,
    title = "The Interplay of Semantics and Morphology in Word Embeddings",
    author = "Avraham, Oded  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2067",
    pages = "422--426",
    abstract = "We explore the ability of word embeddings to capture both semantic and morphological similarity, as affected by the different types of linguistic properties (surface form, lemma, morphological tag) used to compose the representation of each word. We train several models, where each uses a different subset of these properties to compose its representations. By evaluating the models on semantic and morphological measures, we reveal some useful insights on the relationship between semantics and morphology.",
}
@inproceedings{joulin-etal-2017-bag,
    title = "Bag of Tricks for Efficient Text Classification",
    author = "Joulin, Armand  and
      Grave, Edouard  and
      Bojanowski, Piotr  and
      Mikolov, Tomas",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2068",
    pages = "427--431",
    abstract = "This paper explores a simple and efficient baseline for text classification. Our experiments show that our fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation. We can train fastText on more than one billion words in less than ten minutes using a standard multicore CPU, and classify half a million sentences among 312K classes in less than a minute.",
}
@inproceedings{schofield-etal-2017-pulling,
    title = "Pulling Out the Stops: Rethinking Stopword Removal for Topic Models",
    author = "Schofield, Alexandra  and
      Magnusson, M{\aa}ns  and
      Mimno, David",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2069",
    pages = "432--436",
    abstract = "It is often assumed that topic models benefit from the use of a manually curated stopword list. Constructing this list is time-consuming and often subject to user judgments about what kinds of words are important to the model and the application. Although stopword removal clearly affects which word types appear as most probable terms in topics, we argue that this improvement is superficial, and that topic inference benefits little from the practice of removing stopwords beyond very frequent terms. Removing corpus-specific stopwords after model inference is more transparent and produces similar results to removing those words prior to inference.",
}
@inproceedings{ramrakhiyani-etal-2017-measuring,
    title = "Measuring Topic Coherence through Optimal Word Buckets",
    author = "Ramrakhiyani, Nitin  and
      Pawar, Sachin  and
      Hingmire, Swapnil  and
      Palshikar, Girish",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2070",
    pages = "437--442",
    abstract = "Measuring topic quality is essential for scoring the learned topics and their subsequent use in Information Retrieval and Text classification. To measure quality of Latent Dirichlet Allocation (LDA) based topics learned from text, we propose a novel approach based on grouping of topic words into buckets (TBuckets). A single large bucket signifies a single coherent theme, in turn indicating high topic coherence. TBuckets uses word embeddings of topic words and employs singular value decomposition (SVD) and Integer Linear Programming based optimization to create coherent word buckets. TBuckets outperforms the state-of-the-art techniques when evaluated using 3 publicly available datasets and on another one proposed in this paper.",
}
@inproceedings{hsu-etal-2017-hybrid,
    title = "A Hybrid {CNN}-{RNN} Alignment Model for Phrase-Aware Sentence Classification",
    author = "Hsu, Shiou Tian  and
      Moon, Changsung  and
      Jones, Paul  and
      Samatova, Nagiza",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2071",
    pages = "443--449",
    abstract = "The success of sentence classification often depends on understanding both the syntactic and semantic properties of word-phrases. Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. In this paper, we propose a structure-independent {`}Gated Representation Alignment{'} (GRA) model that blends a phrase-focused Convolutional Neural Network (CNN) approach with sequence-oriented Recurrent Neural Network (RNN). Our novel alignment mechanism allows the RNN to selectively include phrase information in a word-by-word sentence representation, and to do this without awareness of the syntactic structure. An empirical evaluation of GRA shows higher prediction accuracy (up to 4.6{\%}) of fine-grained sentiment ratings, when compared to other structure-independent baselines. We also show comparable results to several structure-dependent methods. Finally, we analyzed the effect of our alignment mechanism and found that this is critical to the effectiveness of the CNN-RNN hybrid.",
}
@inproceedings{nikolentzos-etal-2017-multivariate,
    title = "Multivariate {G}aussian Document Representation from Word Embeddings for Text Categorization",
    author = "Nikolentzos, Giannis  and
      Meladianos, Polykarpos  and
      Rousseau, Fran{\c{c}}ois  and
      Stavrakas, Yannis  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2072",
    pages = "450--455",
    abstract = "Recently, there has been a lot of activity in learning distributed representations of words in vector spaces. Although there are models capable of learning high-quality distributed representations of words, how to generate vector representations of the same quality for phrases or documents still remains a challenge. In this paper, we propose to model each document as a multivariate Gaussian distribution based on the distributed representations of its words. We then measure the similarity between two documents based on the similarity of their distributions. Experiments on eight standard text categorization datasets demonstrate the effectiveness of the proposed approach in comparison with state-of-the-art methods.",
}
@inproceedings{li-mak-2017-derivation,
    title = "Derivation of Document Vectors from Adaptation of {LSTM} Language Model",
    author = "Li, Wei  and
      Mak, Brian",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2073",
    pages = "456--461",
    abstract = "In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature vector is that it ignores word orders that carry syntactic and semantic relationships among the words in a document. This paper proposes a novel distributed vector representation of a document, which will be labeled as DV-LSTM, and is derived from the result of adapting a long short-term memory recurrent neural network language model by the document. DV-LSTM is expected to capture some high-level sequential information in the document, which other current document representations fail to do. It was evaluated in document genre classification in the Brown Corpus and the BNC Baby Corpus. The results show that DV-LSTM significantly outperforms TF-IDF vector and paragraph vector (PV-DM) in most cases, and their combinations may further improve the classification performance.",
}
@inproceedings{meladianos-etal-2017-real,
    title = "Real-Time Keyword Extraction from Conversations",
    author = "Meladianos, Polykarpos  and
      Tixier, Antoine  and
      Nikolentzos, Ioannis  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2074",
    pages = "462--467",
    abstract = "We introduce a novel method to extract keywords from meeting speech in real-time. Our approach builds on the graph-of-words representation of text and leverages the k-core decomposition algorithm and properties of submodular functions. We outperform multiple baselines in a real-time scenario emulated from the AMI and ICSI meeting corpora. Evaluation is conducted against both extractive and abstractive gold standard using two standard performance metrics and a newer one based on word embeddings.",
}
@inproceedings{eric-manning-2017-copy,
    title = "A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue",
    author = "Eric, Mihail  and
      Manning, Christopher",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2075",
    pages = "468--473",
    abstract = "Task-oriented dialogue focuses on conversational agents that participate in dialogues with user goals on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7{\%} in per-response generation and is on par with the current state-of-the-art on DSTC2, a real-world task-oriented dialogue dataset.",
}
@inproceedings{bansal-etal-2017-towards,
    title = "Towards speech-to-text translation without speech recognition",
    author = "Bansal, Sameer  and
      Kamper, Herman  and
      Lopez, Adam  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2076",
    pages = "474--479",
    abstract = "We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations. We present the first system for this problem applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English speech translation corpus. Our approach uses unsupervised term discovery (UTD) to cluster repeated patterns in the audio, creating a pseudotext, which we pair with translations to create a parallel text and train a simple bag-of-words MT model. We identify the challenges faced by the system, finding that the difficulty of cross-speaker UTD results in low recall, but that our system is still able to correctly translate some content words in test data.",
}
@inproceedings{keizer-etal-2017-evaluating,
    title = "Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents",
    author = "Keizer, Simon  and
      Guhe, Markus  and
      Cuay{\'a}huitl, Heriberto  and
      Efstathiou, Ioannis  and
      Engelbrecht, Klaus-Peter  and
      Dobre, Mihai  and
      Lascarides, Alex  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2077",
    pages = "480--484",
    abstract = "In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game {``}Settlers of Catan{''}. The comparison is based on human subjects playing games against artificial game-playing agents ({`}bots{'}) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.",
}
@inproceedings{brychcin-kral-2017-unsupervised,
    title = "Unsupervised Dialogue Act Induction using {G}aussian Mixtures",
    author = "Brychc{\'\i}n, Tom{\'a}{\v{s}}  and
      Kr{\'a}l, Pavel",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2078",
    pages = "485--490",
    abstract = "This paper introduces a new unsupervised approach for dialogue act induction. Given the sequence of dialogue utterances, the task is to assign them the labels representing their function in the dialogue. Utterances are represented as real-valued vectors encoding their meaning. We model the dialogue as Hidden Markov model with emission probabilities estimated by Gaussian mixtures. We use Gibbs sampling for posterior inference. We present the results on the standard Switchboard-DAMSL corpus. Our algorithm achieves promising results compared with strong supervised baselines and outperforms other unsupervised algorithms.",
}
@inproceedings{han-schlangen-2017-grounding,
    title = "Grounding Language by Continuous Observation of Instruction Following",
    author = "Han, Ting  and
      Schlangen, David",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2079",
    pages = "491--496",
    abstract = "Grounded semantics is typically learnt from utterance-level meaning representations (e.g., successful database retrievals, denoted objects in images, moves in a game). We explore learning word and utterance meanings by continuous observation of the actions of an instruction follower (IF). While an instruction giver (IG) provided a verbal description of a configuration of objects, IF recreated it using a GUI. Aligning these GUI actions to sub-utterance chunks allows a simple maximum entropy model to associate them as chunk meaning better than just providing it with the utterance-final configuration. This shows that semantics useful for incremental (word-by-word) application, as required in natural dialogue, might also be better acquired from incremental settings.",
}
@inproceedings{van-der-klis-etal-2017-mapping,
    title = "Mapping the Perfect via Translation Mining",
    author = {van der Klis, Martijn  and
      Le Bruyn, Bert  and
      de Swart, Henri{\"e}tte},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2080",
    pages = "497--502",
    abstract = {Semantic analyses of the Perfect often defeat their own purpose: by restricting their attention to {`}real{'} perfects (like the English one), they implicitly assume the Perfect has predefined meanings and usages. We turn the tables and focus on form, using data extracted from multilingual parallel corpora to automatically generate semantic maps (Haspelmath, 1997) of the sequence {`}Have/Be + past participle{'} in five European languages (German, English, Spanish, French, Dutch). This technique, which we dub Translation Mining, has been applied before in the lexical domain (W{\"a}lchli and Cysouw, 2012) but we showcase its application at the level of the grammar.},
}
@inproceedings{poliak-etal-2017-efficient,
    title = "Efficient, Compositional, Order-sensitive n-gram Embeddings",
    author = "Poliak, Adam  and
      Rastogi, Pushpendre  and
      Martin, M. Patrick  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2081",
    pages = "503--508",
    abstract = "We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.",
}
@inproceedings{wang-ma-2017-integrating,
    title = "Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement",
    author = "Wang, Hsin-Yang  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2082",
    pages = "509--515",
    abstract = "Distributional word representations are widely used in NLP tasks. These representations are based on an assumption that words with a similar context tend to have a similar meaning. To improve the quality of the context-based embeddings, many researches have explored how to make full use of existing lexical resources. In this paper, we argue that while we incorporate the prior knowledge with context-based embeddings, words with different occurrences should be treated differently. Therefore, we propose to rely on the measurement of information content to control the degree of applying prior knowledge into context-based embeddings - different words would have different learning rates when adjusting their embeddings. In the result, we demonstrate that our embeddings get significant improvements on two different tasks: Word Similarity and Analogical Reasoning.",
}
@inproceedings{klein-etal-2017-improving,
    title = "Improving Neural Knowledge Base Completion with Cross-Lingual Projections",
    author = "Klein, Patrick  and
      Ponzetto, Simone Paolo  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2083",
    pages = "516--522",
    abstract = "In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.",
}
@inproceedings{bulat-etal-2017-modelling,
    title = "Modelling metaphor with attribute-based semantics",
    author = "Bulat, Luana  and
      Clark, Stephen  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2084",
    pages = "523--528",
    abstract = "One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms. In this paper we present the first metaphor identification method that uses representations constructed from property norms. Such norms have been previously shown to provide a cognitively plausible representation of concepts in terms of semantic properties. Our results demonstrate that such property-based semantic representations provide a suitable model of cross-domain knowledge projection in metaphors, outperforming standard distributional models on a metaphor identification task.",
}
@inproceedings{weeds-etal-2017-red,
    title = "When a Red Herring in Not a Red Herring: Using Compositional Methods to Detect Non-Compositional Phrases",
    author = "Weeds, Julie  and
      Kober, Thomas  and
      Reffin, Jeremy  and
      Weir, David",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2085",
    pages = "529--534",
    abstract = "Non-compositional phrases such as \textit{red herring} and weakly compositional phrases such as \textit{spelling bee} are an integral part of natural language (Sag, 2002). They are also the phrases that are difficult, or even impossible, for good compositional distributional models of semantics. Compositionality detection therefore provides a good testbed for compositional methods. We compare an integrated compositional distributional approach, using sparse high dimensional representations, with the ad-hoc compositional approach of applying simple composition operations to state-of-the-art neural embeddings.",
}
@inproceedings{koper-schulte-im-walde-2017-applying,
    title = "Applying Multi-Sense Embeddings for {G}erman Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2086",
    pages = "535--542",
    abstract = "Up to date, the majority of computational models still determines the semantic relatedness between words (or larger linguistic units) on the type level. In this paper, we compare and extend multi-sense embeddings, in order to model and utilise word senses on the token level. We focus on the challenging class of complex verbs, and evaluate the model variants on various semantic tasks: semantic classification; predicting compositionality; and detecting non-literal language usage. While there is no overall best model, all models significantly outperform a word2vec single-sense skip baseline, thus demonstrating the need to distinguish between word senses in a distributional semantic model.",
}
@inproceedings{ustalov-etal-2017-negative,
    title = "Negative Sampling Improves Hypernymy Extraction Based on Projection Learning",
    author = "Ustalov, Dmitry  and
      Arefyev, Nikolay  and
      Biemann, Chris  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2087",
    pages = "543--550",
    abstract = "We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of positive examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.",
}
@inproceedings{sobhani-etal-2017-dataset,
    title = "A Dataset for Multi-Target Stance Detection",
    author = "Sobhani, Parinaz  and
      Inkpen, Diana  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2088",
    pages = "551--557",
    abstract = "Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e.g., stance towards two or more politicians in an election or towards several brands of the same product. In this paper, we focus on the problem of multi-target stance detection. We present a new dataset that we built for this task. Furthermore, We experiment with several neural models on the dataset and show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning, such as cascading classification. We make the new dataset publicly available, in order to facilitate further research in multi-target stance classification.",
}
@inproceedings{gimenez-perez-etal-2017-single,
    title = "Single and Cross-domain Polarity Classification using String Kernels",
    author = "Gim{\'e}nez-P{\'e}rez, Rosa M.  and
      Franco-Salvador, Marc  and
      Rosso, Paolo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2089",
    pages = "558--563",
    abstract = "The polarity classification task aims at automatically identifying whether a subjective text is positive or negative. When the target domain is different from those where a model was trained, we refer to a cross-domain setting. That setting usually implies the use of a domain adaptation method. In this work, we study the single and cross-domain polarity classification tasks from the string kernels perspective. Contrary to classical domain adaptation methods, which employ texts from both domains to detect pivot features, we do not use the target domain for training. Our approach detects the lexical peculiarities that characterise the text polarity and maps them into a domain independent space by means of kernel discriminant analysis. Experimental results show state-of-the-art performance in single and cross-domain polarity classification.",
}
@inproceedings{sedoc-etal-2017-predicting,
    title = "Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering",
    author = "Sedoc, Jo{\~a}o  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2090",
    pages = "564--571",
    abstract = "Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains. We develop a method that automatically extends word-level ratings to unrated words using signed clustering of vector space word representations along with affect ratings. We use our method to determine a word{'}s valence and arousal, which determine its position on the circumplex model of affect, the most popular dimensional model of emotion. Our method achieves superior out-of-sample word rating prediction on both affective dimensions across three different languages when compared to state-of-the-art word similarity based methods. Our method can assist building word ratings for new languages and improve downstream tasks such as sentiment analysis and emotion detection.",
}
@inproceedings{liu-zhang-2017-attention,
    title = "Attention Modeling for Targeted Sentiment",
    author = "Liu, Jiangming  and
      Zhang, Yue",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2091",
    pages = "572--577",
    abstract = "Neural network models have been used for target-dependent sentiment analysis. Previous work focus on learning a target specific representation for a given input sentence which is used for classification. However, they do not explicitly model the contribution of each word in a sentence with respect to targeted sentiment polarities. We investigate an attention model to this end. In particular, a vanilla LSTM model is used to induce an attention value of the whole sentence. The model is further extended to differentiate left and right contexts given a certain target following previous work. Results show that by using attention to model the contribution of each word with respect to the target, our model gives significantly improved results over two standard benchmarks. We report the best accuracy for this task.",
}
@inproceedings{buechel-hahn-2017-emobank,
    title = "{E}mo{B}ank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis",
    author = "Buechel, Sven  and
      Hahn, Udo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2092",
    pages = "578--585",
    abstract = "We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a bi-perspectival and bi-representational design. On the one hand, we distinguish between writer{'}s and reader{'}s emotions, on the other hand, a subset of the corpus complements dimensional VAD annotations with categorical ones based on Basic Emotions. We find evidence for the supremacy of the reader{'}s perspective in terms of IAA and rating intensity, and achieve close-to-human performance when mapping between dimensional and categorical formats.",
}
@inproceedings{kokkinos-potamianos-2017-structural,
    title = "Structural Attention Neural Networks for improved sentiment analysis",
    author = "Kokkinos, Filippos  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2093",
    pages = "586--591",
    abstract = "We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottom-up and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree.",
}
@inproceedings{adel-etal-2017-ranking,
    title = "Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced {T}witter Data",
    author = "Adel, Heike  and
      Chen, Francine  and
      Chen, Yan-Ying",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2094",
    pages = "592--598",
    abstract = "Users often use social media to share their interest in products. We propose to identify purchase stages from Twitter data following the AIDA model (Awareness, Interest, Desire, Action). In particular, we define the task of classifying the purchase stage of each tweet in a user{'}s tweet sequence. We introduce RCRNN, a Ranking Convolutional Recurrent Neural Network which computes tweet representations using convolution over word embeddings and models a tweet sequence with gated recurrent units. Also, we consider various methods to cope with the imbalanced label distribution in our data and show that a ranking layer outperforms class weights.",
}
@inproceedings{li-etal-2017-context,
    title = "Context-Aware Graph Segmentation for Graph-Based Translation",
    author = "Li, Liangyou  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2095",
    pages = "599--604",
    abstract = "In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations left-to-right using beam search. Experiments on Chinese{--}English and German{--}English demonstrate that the context-aware segmentation significantly improves the baseline graph-based model.",
}
@inproceedings{jakubina-langlais-2017-reranking,
    title = "Reranking Translation Candidates Produced by Several Bilingual Word Similarity Sources",
    author = "Jakubina, Laurent  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2096",
    pages = "605--611",
    abstract = "We investigate the reranking of the output of several distributional approaches on the Bilingual Lexicon Induction task. We show that reranking an n-best list produced by any of those approaches leads to very substantial improvements. We further demonstrate that combining several n-best lists by reranking is an effective way of further boosting performance.",
}
@inproceedings{siahbani-sarkar-2017-lexicalized,
    title = "Lexicalized Reordering for Left-to-Right Hierarchical Phrase-based Translation",
    author = "Siahbani, Maryam  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2097",
    pages = "612--618",
    abstract = "Phrase-based and hierarchical phrase-based (Hiero) translation models differ radically in the way reordering is modeled. Lexicalized reordering models play an important role in phrase-based MT and such models have been added to CKY-based decoders for Hiero. Watanabe et al. (2006) proposed a promising decoding algorithm for Hiero (LR-Hiero) that visits input spans in arbitrary order and produces the translation in left to right (LR) order which leads to far fewer language model calls and leads to a considerable speedup in decoding. We introduce a novel shift-reduce algorithm to LR-Hiero to decode with our lexicalized reordering model (LRM) and show that it improves translation quality for Czech-English, Chinese-English and German-English.",
}
@inproceedings{hauer-etal-2017-bootstrapping,
    title = "Bootstrapping Unsupervised Bilingual Lexicon Induction",
    author = "Hauer, Bradley  and
      Nicolai, Garrett  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2098",
    pages = "619--624",
    abstract = "The task of unsupervised lexicon induction is to find translation pairs across monolingual corpora. We develop a novel method that creates seed lexicons by identifying cognates in the vocabularies of related languages on the basis of their frequency and lexical similarity. We apply bidirectional bootstrapping to a method which learns a linear mapping between context-based vector spaces. Experimental results on three language pairs show consistent improvement over prior work.",
}
@inproceedings{weller-di-marco-etal-2017-addressing,
    title = "Addressing Problems across Linguistic Levels in {SMT}: Combining Approaches to Model Morphology, Syntax and Lexical Choice",
    author = "Weller-Di Marco, Marion  and
      Fraser, Alexander  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2099",
    pages = "625--630",
    abstract = "Many errors in phrase-based SMT can be attributed to problems on three linguistic levels: morphological complexity in the target language, structural differences and lexical choice. We explore combinations of linguistically motivated approaches to address these problems in English-to-German SMT and show that they are complementary to one another, but also that the popular verbal pre-ordering can cause problems on the morphological and lexical level. A discriminative classifier can overcome these problems, in particular when enriching standard lexical features with features geared towards verbal inflection.",
}
@inproceedings{luong-etal-2017-machine,
    title = "Machine Translation of {S}panish Personal and Possessive Pronouns Using Anaphora Probabilities",
    author = "Luong, Ngoc Quang  and
      Popescu-Belis, Andrei  and
      Rios Gonzales, Annette  and
      Tuggener, Don",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2100",
    pages = "631--636",
    abstract = "We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system. The probabilities over antecedents are converted into probabilities for the features of translated pronouns, and are integrated with phrase-based MT using an additional translation model for pronouns. The system improves the translation of several Spanish personal and possessive pronouns into English, by solving translation divergencies such as {`}ella{'} vs. {`}she{'}/{`}it{'} or {`}su{'} vs. {`}his{'}/{`}her{'}/{`}its{'}/{`}their{'}. On a test set with 2,286 pronouns, a baseline system correctly translates 1,055 of them, while ours improves this by 41. Moreover, with oracle antecedents, possessives are translated with an accuracy of 83{\%}.",
}
@inproceedings{calixto-etal-2017-using,
    title = "Using Images to Improve Machine-Translating {E}-Commerce Product Listings.",
    author = "Calixto, Iacer  and
      Stein, Daniel  and
      Matusov, Evgeny  and
      Lohar, Pintu  and
      Castilho, Sheila  and
      Way, Andy",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2101",
    pages = "637--643",
    abstract = "In this paper we study the impact of using images to machine-translate user-generated e-commerce product listings. We study how a multi-modal Neural Machine Translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attentional NMT and a Statistical Machine Translation (SMT) model. User-generated product listings often do not constitute grammatical or well-formed sentences. More often than not, they consist of the juxtaposition of short phrases or keywords. We train our models end-to-end as well as use text-only and multi-modal NMT models for re-ranking $n$-best lists generated by an SMT model. We qualitatively evaluate our user-generated training data also analyse how adding synthetic data impacts the results. We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different n-best list sizes.",
}
@inproceedings{ostling-tiedemann-2017-continuous,
    title = "Continuous multilinguality with language vectors",
    author = {{\"O}stling, Robert  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2102",
    pages = "644--649",
    abstract = "Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In contrast, we propose using continuous vector representations of language. We show that these can be learned efficiently with a character-based neural language model, and used to improve inference about language varieties not seen during training. In experiments with 1303 Bible translations into 990 different languages, we empirically explore the capacity of multilingual language models, and also show that the language vectors capture genetic relationships between languages.",
}
@inproceedings{kim-etal-2017-unsupervised,
    title = "Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes",
    author = "Kim, Yunsu  and
      Schamper, Julian  and
      Ney, Hermann",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2103",
    pages = "650--656",
    abstract = "We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity with a simple thresholding scheme for the lexicon. Second, we initialize the lexicon training with word classes, which efficiently boosts the performance. Our methods produced promising results on two large-scale unsupervised translation tasks.",
}
@inproceedings{rios-gonzales-tuggener-2017-co,
    title = "Co-reference Resolution of Elided Subjects and Possessive Pronouns in {S}panish-{E}nglish Statistical Machine Translation",
    author = "Rios Gonzales, Annette  and
      Tuggener, Don",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2104",
    pages = "657--662",
    abstract = "This paper presents a straightforward method to integrate co-reference information into phrase-based machine translation to address the problems of i) elided subjects and ii) morphological underspecification of pronouns when translating from pro-drop languages. We evaluate the method for the language pair Spanish-English and find that translation quality improves with the addition of co-reference information.",
}
@inproceedings{xia-etal-2017-large,
    title = "Large-Scale Categorization of {J}apanese Product Titles Using Neural Attention Models",
    author = "Xia, Yandi  and
      Levine, Aaron  and
      Das, Pradipto  and
      Di Fabbrizio, Giuseppe  and
      Shinzato, Keiji  and
      Datta, Ankur",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2105",
    pages = "663--668",
    abstract = "We propose a variant of Convolutional Neural Network (CNN) models, the Attention CNN (ACNN); for large-scale categorization of millions of Japanese items into thirty-five product categories. Compared to a state-of-the-art Gradient Boosted Tree (GBT) classifier, the proposed model reduces training time from three weeks to three days while maintaining more than 96{\%} accuracy. Additionally, our proposed model characterizes products by imputing attentive focus on word tokens in a language agnostic way. The attention words have been observed to be semantically highly correlated with the predicted categories and give us a choice of automatic feature extraction for downstream processing.",
}
@inproceedings{shrestha-etal-2017-convolutional,
    title = "Convolutional Neural Networks for Authorship Attribution of Short Texts",
    author = "Shrestha, Prasha  and
      Sierra, Sebastian  and
      Gonz{\'a}lez, Fabio  and
      Montes, Manuel  and
      Rosso, Paolo  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2106",
    pages = "669--674",
    abstract = "We present a model to perform authorship attribution of tweets using Convolutional Neural Networks (CNNs) over character n-grams. We also present a strategy that improves model interpretability by estimating the importance of input text fragments in the predicted classification. The experimental evaluation shows that text CNNs perform competitively and are able to outperform previous methods.",
}
@inproceedings{yang-etal-2017-aspect,
    title = "Aspect Extraction from Product Reviews Using Category Hierarchy Information",
    author = "Yang, Yinfei  and
      Chen, Cen  and
      Qiu, Minghui  and
      Bao, Forrest",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2107",
    pages = "675--680",
    abstract = "Aspect extraction abstracts the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of sub-categories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10{\%}.",
}
@inproceedings{soler-company-wanner-2017-relevance,
    title = "On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification",
    author = "Soler-Company, Juan  and
      Wanner, Leo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2108",
    pages = "681--687",
    abstract = "The majority of approaches to author profiling and author identification focus mainly on lexical features, i.e., on the content of a text. We argue that syntactic and discourse features play a significantly more prominent role than they were given in the past. We show that they achieve state-of-the-art performance in author and gender identification on a literary corpus while keeping the feature set small: the used feature set is composed of only 188 features and still outperforms the winner of the PAN 2014 shared task on author verification in the literary genre.",
}
@inproceedings{glavas-etal-2017-unsupervised,
    title = "Unsupervised Cross-Lingual Scaling of Political Texts",
    author = "Glava{\v{s}}, Goran  and
      Nanni, Federico  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2109",
    pages = "688--693",
    abstract = "Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models scale texts based on relative word usage and cannot be used for cross-lingual analyses. Additionally, there is little quantitative evidence that the output of these models correlates with common political dimensions like left-to-right orientation. Experimental results show that the semantically-informed scaling models better predict the party positions than the existing word-based models in two different political dimensions. Furthermore, the proposed models exhibit no drop in performance in the cross-lingual compared to monolingual setting.",
}
@inproceedings{dernoncourt-etal-2017-neural,
    title = "Neural Networks for Joint Sentence Classification in Medical Paper Abstracts",
    author = "Dernoncourt, Franck  and
      Lee, Ji Young  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2110",
    pages = "694--700",
    abstract = "Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model outperforms the state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.",
}
@inproceedings{sorodoc-etal-2017-multimodal,
    title = "Multimodal Topic Labelling",
    author = "Sorodoc, Ionut  and
      Lau, Jey Han  and
      Aletras, Nikolaos  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2111",
    pages = "701--706",
    abstract = "Topics generated by topic models are typically presented as a list of topic terms. Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics. Traditionally, topic label systems focus on a single label modality, e.g. textual labels. In this work we propose a multimodal approach to topic labelling using a simple feedforward neural network. Given a topic and a candidate image or textual label, our method automatically generates a rating for the label, relative to the topic. Experiments show that this multimodal approach outperforms single-modality topic labelling systems.",
}
@inproceedings{yang-etal-2017-detecting,
    title = "Detecting (Un)Important Content for Single-Document News Summarization",
    author = "Yang, Yinfei  and
      Bao, Forrest  and
      Nenkova, Ani",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2112",
    pages = "707--712",
    abstract = "We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the {``}beginning of document{''} heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.",
}
@inproceedings{he-sun-2017-f,
    title = "{F}-Score Driven Max Margin Neural Network for Named Entity Recognition in {C}hinese Social Media",
    author = "He, Hangfeng  and
      Sun, Xu",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2113",
    pages = "713--718",
    abstract = "We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semi-supervised learning model based on B-LSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of F-score driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields 7.44{\%} improvement over previous state-of-the-art result.",
}
@inproceedings{chen-van-durme-2017-discriminative,
    title = "Discriminative Information Retrieval for Question Answering Sentence Selection",
    author = "Chen, Tongfei  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2114",
    pages = "719--725",
    abstract = "We propose a framework for discriminative IR atop linguistic features, trained to improve the recall of answer candidate passage retrieval, the initial step in text-based question answering. We formalize this as an instance of linear feature-based IR, demonstrating a 34{\%}-43{\%} improvement in recall for candidate triage for QA.",
}
@inproceedings{bonadiman-etal-2017-effective,
    title = "Effective shared representations with Multitask Learning for Community Question Answering",
    author = "Bonadiman, Daniele  and
      Uva, Antonio  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2115",
    pages = "726--732",
    abstract = "An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineering features. Unfortunately, DNNs usually require a lot of training data, especially for highly semantic tasks such as community Question Answering (cQA). In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting. We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions. This enables a global representation for comments, new and previous questions. The experiments of our model on a SemEval challenge dataset for cQA show a 20{\%} of relative improvement over standard DNNs.",
}
@inproceedings{song-lee-2017-learning,
    title = "Learning User Embeddings from Emails",
    author = "Song, Yan  and
      Lee, Chia-Jung",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2116",
    pages = "733--738",
    abstract = "Many important email-related tasks, such as email classification or search, highly rely on building quality document representations (e.g., bag-of-words or key phrases) to assist matching and understanding. Despite prior success on representing textual messages, creating quality user representations from emails was overlooked. In this paper, we propose to represent users using embeddings that are trained to reflect the email communication network. Our experiments on Enron dataset suggest that the resulting embeddings capture the semantic distance between users. To assess the quality of embeddings in a real-world application, we carry out auto-foldering task where the lexical representation of an email is enriched with user embedding features. Our results show that folder prediction accuracy is improved when embedding features are present across multiple settings.",
}
@inproceedings{tourille-etal-2017-temporal,
    title = "Temporal information extraction from clinical text",
    author = "Tourille, Julien  and
      Ferret, Olivier  and
      Tannier, Xavier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2117",
    pages = "739--745",
    abstract = "In this paper, we present a method for temporal relation extraction from clinical narratives in French and in English. We experiment on two comparable corpora, the MERLOT corpus and the THYME corpus, and show that a common approach can be used for both languages.",
}
@inproceedings{dligach-etal-2017-neural,
    title = "Neural Temporal Relation Extraction",
    author = "Dligach, Dmitriy  and
      Miller, Timothy  and
      Lin, Chen  and
      Bethard, Steven  and
      Savova, Guergana",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2118",
    pages = "746--751",
    abstract = "We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that encoding relation arguments with XML tags outperforms a traditional position-based encoding.",
}
@inproceedings{karn-etal-2017-end,
    title = "End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification",
    author = {Karn, Sanjeev  and
      Waltinger, Ulli  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2119",
    pages = "752--758",
    abstract = "We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure.",
}
@inproceedings{cotterell-etal-2017-neural,
    title = "Neural Graphical Models over Strings for Principal Parts Morphological Paradigm Completion",
    author = "Cotterell, Ryan  and
      Sylak-Glassman, John  and
      Kirov, Christo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2120",
    pages = "759--765",
    abstract = "Many of the world{'}s languages contain an abundance of inflected forms for each lexeme. A critical task in processing such languages is predicting these inflected forms. We develop a novel statistical model for the problem, drawing on graphical modeling techniques and recent advances in deep learning. We derive a Metropolis-Hastings algorithm to jointly decode the model. Our Bayesian network draws inspiration from principal parts morphological analysis. We demonstrate improvements on 5 languages.",
}
@proceedings{eacl-2017-european,
    title = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    editor = "Lapata, Mirella  and
      Blunsom, Phil  and
      Koller, Alexander",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1000",
}
@inproceedings{liu-perez-2017-gated,
    title = "Gated End-to-End Memory Networks",
    author = "Liu, Fei  and
      Perez, Julien",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1001",
    pages = "1--10",
    abstract = "Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact question-answering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-to-End trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art.",
}
@inproceedings{munkhdalai-yu-2017-neural,
    title = "Neural Tree Indexers for Text Understanding",
    author = "Munkhdalai, Tsendsuren  and
      Yu, Hong",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1002",
    pages = "11--21",
    abstract = "Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic treebased recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binary tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks.",
}
@inproceedings{adel-schutze-2017-exploring,
    title = "Exploring Different Dimensions of Attention for Uncertainty Detection",
    author = {Adel, Heike  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1003",
    pages = "22--34",
    abstract = "Neural networks with attention have proven effective for many natural language processing tasks. In this paper, we develop attention mechanisms for uncertainty detection. In particular, we generalize standardly used attention mechanisms by introducing external attention and sequence-preserving attention. These novel architectures differ from standard approaches in that they use external resources to compute attention weights and preserve sequence information. We compare them to other configurations along different dimensions of attention. Our novel architectures set the new state of the art on a Wikipedia benchmark dataset and perform similar to the state-of-the-art model on a biomedical benchmark which uses a large set of linguistic features.",
}
@inproceedings{al-nabki-etal-2017-classifying,
    title = "Classifying Illegal Activities on Tor Network Based on Web Textual Contents",
    author = "Al Nabki, Mhd Wesam  and
      Fidalgo, Eduardo  and
      Alegre, Enrique  and
      de Paz, Ivan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1004",
    pages = "35--43",
    abstract = "The freedom of the Deep Web offers a safe place where people can express themselves anonymously but they also can conduct illegal activities. In this paper, we present and make publicly available a new dataset for Darknet active domains, which we call {''}Darknet Usage Text Addresses{''} (DUTA). We built DUTA by sampling the Tor network during two months and manually labeled each address into 26 classes. Using DUTA, we conducted a comparison between two well-known text representation techniques crossed by three different supervised classifiers to categorize the Tor hidden services. We also fixed the pipeline elements and identified the aspects that have a critical influence on the classification results. We found that the combination of TFIDF words representation with Logistic Regression classifier achieves 96.6{\%} of 10 folds cross-validation accuracy and a macro F1 score of 93.7{\%} when classifying a subset of illegal activities from DUTA. The good performance of the classifier might support potential tools to help the authorities in the detection of these activities.",
}
@inproceedings{martinez-alonso-plank-2017-multitask,
    title = "When is multitask learning effective? Semantic sequence prediction under varying data conditions",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Plank, Barbara",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1005",
    pages = "44--53",
    abstract = "Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on \textit{when} MTL works and whether there are data characteristics that help to determine the success of MTL. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary task configurations, amongst which a novel setup, and correlate their impact to data-dependent conditions. Our results show that MTL is not always effective, because significant improvements are obtained only for 1 out of 5 tasks. When successful, auxiliary tasks with compact and more uniform label distributions are preferable.",
}
@inproceedings{hartung-etal-2017-learning,
    title = "Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases",
    author = "Hartung, Matthias  and
      Kaupmann, Fabian  and
      Jebbara, Soufian  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1006",
    pages = "54--64",
    abstract = "Word embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of lacking the capabilty of making the underlying semantic relation explicit. In this paper, we investigate the attribute relation that often holds between the constituents of adjective-noun phrases. We use CBOW word embeddings to represent word meaning and learn a compositionality function that combines the individual constituents into a phrase representation, thus capturing the compositional attribute meaning. The resulting embedding model, while being fully interpretable, outperforms count-based distributional vector space models that are tailored to attribute meaning in the two tasks of attribute selection and phrase similarity prediction. Moreover, as the model captures a generalized layer of attribute meaning, it bears the potential to be used for predictions over various attribute inventories without re-training.",
}
@inproceedings{shwartz-etal-2017-hypernyms,
    title = "Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection",
    author = "Shwartz, Vered  and
      Santus, Enrico  and
      Schlechtweg, Dominik",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1007",
    pages = "65--75",
    abstract = "The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. We investigate an extensive number of such unsupervised measures, using several distributional semantic models that differ by context type and feature weighting. We analyze the performance of the different methods based on their linguistic motivation. Comparison to the state-of-the-art supervised methods shows that while supervised methods generally outperform the unsupervised ones, the former are sensitive to the distribution of training instances, hurting their reliability. Being based on general linguistic hypotheses and independent from training data, unsupervised measures are more robust, and therefore are still useful artillery for hypernymy detection.",
}
@inproceedings{nguyen-etal-2017-distinguishing,
    title = "Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network",
    author = "Nguyen, Kim Anh  and
      Schulte im Walde, Sabine  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1008",
    pages = "76--85",
    abstract = "Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSynNET improves the performance over prior pattern-based methods.",
}
@inproceedings{panchenko-etal-2017-unsupervised-mean,
    title = "Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation",
    author = "Panchenko, Alexander  and
      Ruppert, Eugen  and
      Faralli, Stefano  and
      Ponzetto, Simone Paolo  and
      Biemann, Chris",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1009",
    pages = "86--98",
    abstract = "The current trend in NLP is the use of highly opaque models, e.g. neural networks and word embeddings. While these models yield state-of-the-art results on a range of tasks, their drawback is poor interpretability. On the example of word sense induction and disambiguation (WSID), we show that it is possible to develop an interpretable model that matches the state-of-the-art models in accuracy. Namely, we present an unsupervised, knowledge-free WSID approach, which is interpretable at three levels: word sense inventory, sense feature representations, and disambiguation procedure. Experiments show that our model performs on par with state-of-the-art word sense embeddings and other unsupervised systems while offering the possibility to justify its decisions in human-readable form.",
}
@inproceedings{raganato-etal-2017-word,
    title = "Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison",
    author = "Raganato, Alessandro  and
      Camacho-Collados, Jose  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1010",
    pages = "99--110",
    abstract = "Word Sense Disambiguation is a long-standing task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework. In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets.",
}
@inproceedings{guo-etal-2017-effective,
    title = "Which is the Effective Way for {G}aokao: Information Retrieval or Neural Networks?",
    author = "Guo, Shangmin  and
      Zeng, Xiangrong  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1011",
    pages = "111--120",
    abstract = "As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students. In this work, we detailed the Gaokao History Multiple Choice Questions(GKHMC) and proposed two different approaches to address them using various resources. One approach is based on entity search technique (IR approach), the other is based on text entailment approach where we specifically employ deep neural networks(NN approach). The result of experiment on our collected real Gaokao questions showed that they are good at different categories of questions, that is IR approach performs much better at entity questions(EQs) while NN approach shows its advantage on sentence questions(SQs). We achieve state-of-the-art performance and show that it{'}s indispensable to apply hybrid method when participating in the real-world tests.",
}
@inproceedings{bogdanova-etal-2017-cant,
    title = "If You Can{'}t Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking",
    author = "Bogdanova, Dasha  and
      Foster, Jennifer  and
      Dzendzik, Daria  and
      Liu, Qun",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1012",
    pages = "121--131",
    abstract = "We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features. We present a neural network architecture based on a combination of recurrent neural networks that are used to encode questions and answers, and a multilayer perceptron. We show how this approach can be combined with additional features, in particular, the discourse features used by previous research. Our neural approach achieves state-of-the-art performance on a public dataset from Yahoo! Answers and its performance is further improved by incorporating the discourse features. Additionally, we present a new dataset of Ask Ubuntu questions where the hybrid approach also achieves good results.",
}
@inproceedings{das-etal-2017-chains,
    title = "Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks",
    author = "Das, Rajarshi  and
      Neelakantan, Arvind  and
      Belanger, David  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1013",
    pages = "132--141",
    abstract = "Our goal is to combine the rich multi-step inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and relations in text and large-scale knowledge bases (KBs). Neelakantan et al. (2015) use RNNs to compose the distributed semantics of multi-hop paths in KBs; however for multiple reasons, the approach lacks accuracy and practicality. This paper proposes three significant modeling advances: (1) we learn to jointly reason about relations, \textit{entities, and entity-types}; (2) we use neural attention modeling to incorporate \textit{multiple paths}; (3) we learn to \textit{share strength in a single RNN} that represents logical composition across all relations. On a large-scale Freebase+ClueWeb prediction task, we achieve 25{\%} error reduction, and a 53{\%} error reduction on sparse relations due to shared strength. On chains of reasoning in WordNet we reduce error in mean quantile by 84{\%} versus previous state-of-the-art.",
}
@inproceedings{stanovsky-etal-2017-recognizing,
    title = "Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models",
    author = "Stanovsky, Gabriel  and
      Gruhl, Daniel  and
      Mendes, Pablo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1014",
    pages = "142--151",
    abstract = "Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are context-dependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology. We use the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and show the resulting model to be highly accurate (93.4 F1). Furthermore, even when lacking high quality expert annotations, we show that by employing an active learning technique and using purpose built annotation tools, we can train the RNN to perform well (83.9 F1).",
}
@inproceedings{benton-etal-2017-multitask,
    title = "Multitask Learning for Mental Health Conditions with Limited Social Media Data",
    author = "Benton, Adrian  and
      Mitchell, Margaret  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1015",
    pages = "152--162",
    abstract = "Language contains information about the author{'}s demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.",
}
@inproceedings{vulic-etal-2017-evaluation,
    title = "Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation",
    author = "Vuli{\'c}, Ivan  and
      Kiela, Douwe  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1016",
    pages = "163--175",
    abstract = "Recent work on evaluating representation learning architectures in NLP has established a need for evaluation protocols based on subconscious cognitive measures rather than manually tailored intrinsic similarity and relatedness tasks. In this work, we propose a novel evaluation framework that enables large-scale evaluation of such architectures in the free word association (WA) task, which is firmly grounded in cognitive theories of human semantic representation. This evaluation is facilitated by the existence of large manually constructed repositories of word association data. In this paper, we (1) present a detailed analysis of the new quantitative WA evaluation protocol, (2) suggest new evaluation metrics for the WA task inspired by its direct analogy with information retrieval problems, (3) evaluate various state-of-the-art representation models on this task, and (4) discuss the relationship between WA and prior evaluations of semantic representation with well-known similarity and relatedness evaluation sets. We have made the WA evaluation toolkit publicly available.",
}
@inproceedings{wachsmuth-etal-2017-computational,
    title = "Computational Argumentation Quality Assessment in Natural Language",
    author = "Wachsmuth, Henning  and
      Naderi, Nona  and
      Hou, Yufang  and
      Bilu, Yonatan  and
      Prabhakaran, Vinodkumar  and
      Thijm, Tim Alberdingk  and
      Hirst, Graeme  and
      Stein, Benno",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1017",
    pages = "176--187",
    abstract = "Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.",
}
@inproceedings{tuggener-2017-method,
    title = "A method for in-depth comparative evaluation: How (dis)similar are outputs of pos taggers, dependency parsers and coreference resolvers really?",
    author = "Tuggener, Don",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1018",
    pages = "188--198",
    abstract = "This paper proposes a generic method for the comparative evaluation of system outputs. The approach is able to quantify the pairwise differences between two outputs and to unravel in detail what the differences consist of. We apply our approach to three tasks in Computational Linguistics, i.e. POS tagging, dependency parsing, and coreference resolution. We find that system outputs are more distinct than the (often) small differences in evaluation scores seem to suggest.",
}
@inproceedings{kilickaya-etal-2017-evaluating,
    title = "Re-evaluating Automatic Metrics for Image Captioning",
    author = "Kilickaya, Mert  and
      Erdem, Aykut  and
      Ikizler-Cinbis, Nazli  and
      Erdem, Erkut",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1019",
    pages = "199--209",
    abstract = "The task of generating natural language descriptions from images has received a lot of attention in recent years. Consequently, it is becoming increasingly important to evaluate such image captioning approaches in an automatic manner. In this paper, we provide an in-depth evaluation of the existing image captioning metrics through a series of carefully designed experiments. Moreover, we explore the utilization of the recently proposed Word Mover{'}s Distance (WMD) document metric for the purpose of image captioning. Our findings outline the differences and/or similarities between metrics and their relative robustness by means of extensive correlation, accuracy and distraction based evaluations. Our results also demonstrate that WMD provides strong advantages over other metrics.",
}
@inproceedings{baskaya-etal-2017-integrating,
    title = "Integrating Meaning into Quality Evaluation of Machine Translation",
    author = {Ba{\c{s}}kaya, Osman  and
      Yildiz, Eray  and
      Tunao{\u{g}}lu, Doruk  and
      Eren, Mustafa Tolga  and
      Do{\u{g}}ru{\"o}z, A. Seza},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1020",
    pages = "210--219",
    abstract = "Machine translation (MT) quality is evaluated through comparisons between MT outputs and the human translations (HT). Traditionally, this evaluation relies on form related features (e.g. lexicon and syntax) and ignores the transfer of meaning reflected in HT outputs. Instead, we evaluate the quality of MT outputs through meaning related features (e.g. polarity, subjectivity) with two experiments. In the first experiment, the meaning related features are compared to human rankings individually. In the second experiment, combinations of meaning related features and other quality metrics are utilized to predict the same human rankings. The results of our experiments confirm the benefit of these features in predicting human evaluation of translation quality in addition to traditional metrics which focus mainly on form.",
}
@inproceedings{schlichtkrull-sogaard-2017-cross,
    title = "Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages",
    author = "Schlichtkrull, Michael  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1021",
    pages = "220--229",
    abstract = "In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependency parser that can be trained to reproduce matrices of edge scores, which can be directly projected across word alignments. We show that our approach to cross-lingual dependency parsing is not only simpler, but also achieves an absolute improvement of 2.25{\%} averaged across 10 languages compared to the previous state of the art.",
}
@inproceedings{martinez-alonso-etal-2017-parsing,
    title = "Parsing {U}niversal {D}ependencies without training",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Agi{\'c}, {\v{Z}}eljko  and
      Plank, Barbara  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1022",
    pages = "230--240",
    abstract = "We present UDP, the first training-free parser for Universal Dependencies (UD). Our algorithm is based on PageRank and a small set of specific dependency head rules. UDP features two-step decoding to guarantee that function words are attached as leaf nodes. The parser requires no training, and it is competitive with a delexicalized transfer system. UDP offers a linguistically sound unsupervised alternative to cross-lingual parsing for UD. The parser has very few parameters and distinctly robust to domain change across languages.",
}
@inproceedings{dehouck-denis-2017-delexicalized,
    title = "Delexicalized Word Embeddings for Cross-lingual Dependency Parsing",
    author = "Dehouck, Mathieu  and
      Denis, Pascal",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1023",
    pages = "241--250",
    abstract = "This paper presents a new approach to the problem of cross-lingual dependency parsing, aiming at leveraging training data from different source languages to learn a parser in a target language. Specifically, this approach first constructs word vector representations that exploit structural (i.e., dependency-based) contexts but only considering the morpho-syntactic information associated with each word and its contexts. These delexicalized word embeddings, which can be trained on any set of languages and capture features shared across languages, are then used in combination with standard language-specific features to train a lexicalized parser in the target language. We evaluate our approach through experiments on a set of eight different languages that are part the Universal Dependencies Project. Our main results show that using such delexicalized embeddings, either trained in a monolingual or multilingual fashion, achieves significant improvements over monolingual baselines.",
}
@inproceedings{bar-haim-etal-2017-stance,
    title = "Stance Classification of Context-Dependent Claims",
    author = "Bar-Haim, Roy  and
      Bhattacharya, Indrajit  and
      Dinuzzo, Francesco  and
      Saha, Amrita  and
      Slonim, Noam",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1024",
    pages = "251--261",
    abstract = "Recent work has addressed the problem of detecting relevant claims for a given controversial topic. We introduce the complementary task of Claim Stance Classification, along with the first benchmark dataset for this task. We decompose this problem into: (a) open-domain target identification for topic and claim (b) sentiment classification for each target, and (c) open-domain contrast detection between the topic and the claim targets. Manual annotation of the dataset confirms the applicability and validity of our model. We describe an implementation of our model, focusing on a novel algorithm for contrast detection. Our approach achieves promising results, and is shown to outperform several baselines, which represent the common practice of applying a single, monolithic classifier for stance classification.",
}
@inproceedings{karoui-etal-2017-exploring,
    title = "Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study",
    author = "Karoui, Jihen  and
      Benamara, Farah  and
      Moriceau, V{\'e}ronique  and
      Patti, Viviana  and
      Bosco, Cristina  and
      Aussenac-Gilles, Nathalie",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1025",
    pages = "262--272",
    abstract = "This paper provides a linguistic and pragmatic analysis of the phenomenon of irony in order to represent how Twitter{'}s users exploit irony devices within their communication strategies for generating textual contents. We aim to measure the impact of a wide-range of pragmatic phenomena in the interpretation of irony, and to investigate how these phenomena interact with contexts local to the tweet. Informed by linguistic theories, we propose for the first time a multi-layered annotation schema for irony and its application to a corpus of French, English and Italian tweets. We detail each layer, explore their interactions, and discuss our results according to a qualitative and quantitative perspective.",
}
@inproceedings{nozza-etal-2017-multi,
    title = "A Multi-View Sentiment Corpus",
    author = "Nozza, Debora  and
      Fersini, Elisabetta  and
      Messina, Enza",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1026",
    pages = "273--280",
    abstract = "Sentiment Analysis is a broad task that involves the analysis of various aspect of the natural language text. However, most of the approaches in the state of the art usually investigate independently each aspect, i.e. Subjectivity Classification, Sentiment Polarity Classification, Emotion Recognition, Irony Detection. In this paper we present a Multi-View Sentiment Corpus (MVSC), which comprises 3000 English microblog posts related the movie domain. Three independent annotators manually labelled MVSC, following a broad annotation schema about different aspects that can be grasped from natural language text coming from social networks. The contribution is therefore a corpus that comprises five different views for each message, i.e. subjective/objective, sentiment polarity, implicit/explicit, irony, emotion. In order to allow a more detailed investigation on the human labelling behaviour, we provide the annotations of each human annotator involved.",
}
@inproceedings{rutherford-etal-2017-systematic,
    title = "A Systematic Study of Neural Discourse Models for Implicit Discourse Relation",
    author = "Rutherford, Attapol  and
      Demberg, Vera  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1027",
    pages = "281--291",
    abstract = "Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Many neural network models have been proposed to tackle this problem. However, the comparison for this task is not unified, so we could hardly draw clear conclusions about the effectiveness of various architectures. Here, we propose neural network models that are based on feedforward and long-short term memory architecture and systematically study the effects of varying structures. To our surprise, the best-configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Further, we compare our best feedforward system with competitive convolutional and recurrent networks and find that feedforward can actually be more effective. For the first time for this task, we compile and publish outputs from previous neural and non-neural systems to establish the standard for further comparison.",
}
@inproceedings{braud-etal-2017-cross-lingual,
    title = "Cross-lingual {RST} Discourse Parsing",
    author = "Braud, Chlo{\'e}  and
      Coavoux, Maximin  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1028",
    pages = "292--304",
    abstract = "Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on cross-lingual discourse parsing.",
}
@inproceedings{perez-liu-2017-dialog,
    title = "Dialog state tracking, a machine reading approach using Memory Network",
    author = "Perez, Julien  and
      Liu, Fei",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1029",
    pages = "305--314",
    abstract = "In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules. This paper introduces a novel method of dialog state tracking based on the general paradigm of machine reading and proposes to solve it using an End-to-End Memory Network, MemN2N, a memory-enhanced neural network architecture. We evaluate the proposed approach on the second Dialog State Tracking Challenge (DSTC-2) dataset. The corpus has been converted for the occasion in order to frame the hidden state variable inference as a question-answering task based on a sequence of utterances extracted from a dialog. We show that the proposed tracker gives encouraging results. Then, we propose to extend the DSTC-2 dataset with specific reasoning capabilities requirement like counting, list maintenance, yes-no question answering and indefinite knowledge management. Finally, we present encouraging results using our proposed MemN2N based tracking model.",
}
@inproceedings{treviso-etal-2017-sentence,
    title = "Sentence Segmentation in Narrative Transcripts from Neuropsychological Tests using Recurrent Convolutional Neural Networks",
    author = "Treviso, Marcos  and
      Shulby, Christopher  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1030",
    pages = "315--325",
    abstract = "Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language-impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks in order to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech as well as normal, prepared speech and presents better results for healthy elderly (CTL) (F1 = 0.74) and Mild Cognitive Impairment (MCI) patients (F1 = 0.70) than the Conditional Random Fields method (F1 = 0.55 and 0.53, respectively) used in the same context of our study. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by MCI and CTL.",
}
@inproceedings{hough-schlangen-2017-joint,
    title = "Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech",
    author = "Hough, Julian  and
      Schlangen, David",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1031",
    pages = "326--336",
    abstract = "We present the joint task of incremental disfluency detection and utterance segmentation and a simple deep learning system which performs it on transcripts and ASR results. We show how the constraints of the two tasks interact. Our joint-task system outperforms the equivalent individual task systems, provides competitive results and is suitable for future use in conversation agents in the psychiatric domain.",
}
@inproceedings{bergmanis-goldwater-2017-segmentation,
    title = "From Segmentation to Analyses: a Probabilistic Model for Unsupervised Morphology Induction",
    author = "Bergmanis, Toms  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1032",
    pages = "337--346",
    abstract = "A major motivation for unsupervised morphological analysis is to reduce the sparse data problem in under-resourced languages. Most previous work focus on segmenting surface forms into their constituent morphs (taking: tak +ing), but surface form segmentation does not solve the sparse data problem as the analyses of take and taking are not connected to each other. We present a system that adapts the MorphoChains system (Narasimhan et al., 2015) to provide morphological analyses that aim to abstract over spelling differences in functionally similar morphs. This results in analyses that are not compelled to use all the orthographic material of a word (stopping: stop +ing) or limited to only that material (acidified: acid +ify +ed). On average across six typologically varied languages our system has a similar or better F-score on EMMA (a measure of underlying morpheme accuracy) than three strong baselines; moreover, the total number of distinct morphemes identified by our system is on average 12.8{\%} lower than for Morfessor (Virpioja et al., 2013), a state-of-the-art surface segmentation system.",
}
@inproceedings{mukherjee-etal-2017-creating,
    title = "Creating {POS} Tagging and Dependency Parsing Experts via Topic Modeling",
    author = {Mukherjee, Atreyee  and
      K{\"u}bler, Sandra  and
      Scheutz, Matthias},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1033",
    pages = "347--355",
    abstract = "Part of speech (POS) taggers and dependency parsers tend to work well on homogeneous datasets but their performance suffers on datasets containing data from different genres. In our current work, we investigate how to create POS tagging and dependency parsing experts for heterogeneous data by employing topic modeling. We create topic models (using Latent Dirichlet Allocation) to determine genres from a heterogeneous dataset and then train an expert for each of the genres. Our results show that the topic modeling experts reach substantial improvements when compared to the general versions. For dependency parsing, the improvement reaches 2 percent points over the full training baseline when we use two topics.",
}
@inproceedings{vincze-etal-2017-universal,
    title = "{U}niversal {D}ependencies and Morphology for {H}ungarian - and on the Price of Universality",
    author = "Vincze, Veronika  and
      Simk{\'o}, Katalin  and
      Sz{\'a}nt{\'o}, Zsolt  and
      Farkas, Rich{\'a}rd",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1034",
    pages = "356--365",
    abstract = "In this paper, we present how the principles of universal dependencies and morphology have been adapted to Hungarian. We report the most challenging grammatical phenomena and our solutions to those. On the basis of the adapted guidelines, we have converted and manually corrected 1,800 sentences from the Szeged Treebank to universal dependency format. We also introduce experiments on this manually annotated corpus for evaluating automatic conversion and the added value of language-specific, i.e. non-universal, annotations. Our results reveal that converting to universal dependencies is not necessarily trivial, moreover, using language-specific morphological features may have an impact on overall performance.",
}
@inproceedings{peng-etal-2017-addressing,
    title = "Addressing the Data Sparsity Issue in Neural {AMR} Parsing",
    author = "Peng, Xiaochang  and
      Wang, Chuan  and
      Gildea, Daniel  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1035",
    pages = "366--375",
    abstract = "Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural attention model and our results are also competitive against state-of-the-art systems that do not use extra linguistic resources.",
}
@inproceedings{reddy-etal-2017-generating,
    title = "Generating Natural Language Question-Answer Pairs from a Knowledge Graph Using a {RNN} Based Question Generation Model",
    author = "Reddy, Sathish  and
      Raghu, Dinesh  and
      Khapra, Mitesh M.  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1036",
    pages = "376--385",
    abstract = "In recent years, knowledge graphs such as Freebase that capture facts about entities and relationships between them have been used actively for answering factoid questions. In this paper, we explore the problem of automatically generating question answer pairs from a given knowledge graph. The generated question answer (QA) pairs can be used in several downstream applications. For example, they could be used for training better QA systems. To generate such QA pairs, we first extract a set of keywords from entities and relationships expressed in a triple stored in the knowledge graph. From each such set, we use a subset of keywords to generate a natural language question that has a unique answer. We treat this subset of keywords as a sequence and propose a sequence to sequence model using RNN to generate a natural language question from it. Our RNN based model generates QA pairs with an accuracy of 33.61 percent and performs 110.47 percent (relative) better than a state-of-the-art template based method for generating natural language question from keywords. We also do an extrinsic evaluation by using the generated QA pairs to train a QA system and observe that the F1-score of the QA system improves by 5.5 percent (relative) when using automatically generated QA pairs in addition to manually generated QA pairs available for training.",
}
@inproceedings{hirao-etal-2017-enumeration,
    title = "Enumeration of Extractive Oracle Summaries",
    author = "Hirao, Tsutomu  and
      Nishino, Masaaki  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1037",
    pages = "386--396",
    abstract = "To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also propose an algorithm that enumerates all of the oracle summaries for a set of reference summaries to exploit F-measures that evaluate which system summaries contain how many sentences that are extracted as an oracle summary. Our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.",
}
@inproceedings{munkhdalai-yu-2017-neural-semantic,
    title = "Neural Semantic Encoders",
    author = "Munkhdalai, Tsendsuren  and
      Yu, Hong",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1038",
    pages = "397--407",
    abstract = "We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access 1 multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.",
}
@inproceedings{haffari-etal-2017-efficient,
    title = "Efficient Benchmarking of {NLP} {API}s using Multi-armed Bandits",
    author = "Haffari, Gholamreza  and
      Tran, Tuan Dung  and
      Carman, Mark",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1039",
    pages = "408--416",
    abstract = "Comparing NLP systems to select the best one for a task of interest, such as named entity recognition, is critical for practitioners and researchers. A rigorous approach involves setting up a hypothesis testing scenario using the performance of the systems on query documents. However, often the hypothesis testing approach needs to send a lot of document queries to the systems, which can be problematic. In this paper, we present an effective alternative based on the multi-armed bandit (MAB). We propose a hierarchical generative model to represent the uncertainty in the performance measures of the competing systems, to be used by Thompson Sampling to solve the resulting MAB. Experimental results on both synthetic and real data show that our approach requires significantly fewer queries compared to the standard benchmarking technique to identify the best system according to F-measure.",
}
@inproceedings{verwimp-etal-2017-character,
    title = "Character-Word {LSTM} Language Models",
    author = "Verwimp, Lyan  and
      Pelemans, Joris  and
      Van hamme, Hugo  and
      Wambacq, Patrick",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1040",
    pages = "417--427",
    abstract = "We present a Character-Word Long Short-Term Memory Language Model which both reduces the perplexity with respect to a baseline word-level language model and reduces the number of parameters of the model. Character information can reveal structural (dis)similarities between words and can even be used when a word is out-of-vocabulary, thus improving the modeling of infrequent and unknown words. By concatenating word and character embeddings, we achieve up to 2.77{\%} relative improvement on English compared to a baseline model with a similar amount of parameters and 4.57{\%} on Dutch. Moreover, we also outperform baseline word-level models with a larger number of parameters.",
}
@inproceedings{tran-etal-2017-hierarchical,
    title = "A Hierarchical Neural Model for Learning Sequences of Dialogue Acts",
    author = "Tran, Quan Hung  and
      Zukerman, Ingrid  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1041",
    pages = "428--437",
    abstract = "We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs). The input in this task is a sequence of utterances (i.e., conversational contributions) comprising a sequence of tokens, and the output is a sequence of DA labels (one label per utterance). Our model leverages the hierarchical nature of dialogue data by using two nested RNNs that capture long-range dependencies at the dialogue level and the utterance level. This model is combined with an attention mechanism that focuses on salient tokens in utterances. Our experimental results show that our model outperforms strong baselines on two popular datasets, Switchboard and MapTask; and our detailed empirical analysis highlights the impact of each aspect of our model.",
}
@inproceedings{wen-etal-2017-network,
    title = "A Network-based End-to-End Trainable Task-oriented Dialogue System",
    author = "Wen, Tsung-Hsien  and
      Vandyke, David  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Ga{\v{s}}i{\'c}, Milica  and
      Rojas-Barahona, Lina M.  and
      Su, Pei-Hao  and
      Ultes, Stefan  and
      Young, Steve",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1042",
    pages = "438--449",
    abstract = "Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, text-out end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain.",
}
@inproceedings{peng-etal-2017-may,
    title = "May {I} take your order? A Neural Model for Extracting Structured Information from Conversations",
    author = "Peng, Baolin  and
      Seltzer, Michael  and
      Ju, Y.C.  and
      Zweig, Geoffrey  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1043",
    pages = "450--459",
    abstract = "In this paper we tackle a unique and important problem of extracting a structured order from the conversation a customer has with an order taker at a restaurant. This is motivated by an actual system under development to assist in the order taking process. We develop a sequence-to-sequence model that is able to map from unstructured conversational input to the structured form that is conveyed to the kitchen and appears on the customer receipt. This problem is critically different from other tasks like machine translation where sequence-to-sequence models have been used: the input includes two sides of a conversation; the output is highly structured; and logical manipulations must be performed, for example when the customer changes his mind while ordering. We present a novel sequence-to-sequence model that incorporates a special attention-memory gating mechanism and conversational role markers. The proposed model improves performance over both a phrase-based machine translation approach and a standard sequence-to-sequence model.",
}
@inproceedings{muzny-etal-2017-two,
    title = "A Two-stage Sieve Approach for Quote Attribution",
    author = "Muzny, Grace  and
      Fang, Michael  and
      Chang, Angel  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1044",
    pages = "460--470",
    abstract = "We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1.",
}
@inproceedings{hartmann-etal-2017-domain,
    title = "Out-of-domain {F}rame{N}et Semantic Role Labeling",
    author = "Hartmann, Silvana  and
      Kuznetsov, Ilia  and
      Martin, Teresa  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1045",
    pages = "471--482",
    abstract = "Domain dependence of NLP systems is one of the major obstacles to their application in large-scale text analysis, also restricting the applicability of FrameNet semantic role labeling (SRL) systems. Yet, current FrameNet SRL systems are still only evaluated on a single in-domain test set. For the first time, we study the domain dependence of FrameNet SRL on a wide range of benchmark sets. We create a novel test set for FrameNet SRL based on user-generated web text and find that the major bottleneck for out-of-domain FrameNet SRL is the frame identification step. To address this problem, we develop a simple, yet efficient system based on distributed word representations. Our system closely approaches the state-of-the-art in-domain while outperforming the best available frame identification system out-of-domain. We publish our system and test data for research purposes.",
}
@inproceedings{wang-etal-2017-tdparse,
    title = "{TDP}arse: Multi-target-specific sentiment recognition on {T}witter",
    author = "Wang, Bo  and
      Liakata, Maria  and
      Zubiaga, Arkaitz  and
      Procter, Rob",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1046",
    pages = "483--493",
    abstract = "Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for single-target sentiment.",
}
@inproceedings{upadhyay-chang-2017-annotating,
    title = "Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems",
    author = "Upadhyay, Shyam  and
      Chang, Ming-Wei",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1047",
    pages = "494--504",
    abstract = "We propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook. Our proposal is to evaluate such solvers using derivations, which reflect how an equation system was constructed from the word problem. To accomplish this, we develop an algorithm for checking the equivalence between two derivations, and show how derivation annotations can be semi-automatically added to existing datasets. To make our experiments more comprehensive, we include the derivation annotation for DRAW-1K, a new dataset containing 1000 general algebra word problems. In our experiments, we found that the annotated derivations enable a more accurate evaluation of automatic solvers than previously used metrics. We release derivation annotations for over 2300 algebra word problems for future evaluations.",
}
@inproceedings{heigold-etal-2017-extensive,
    title = "An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages",
    author = "Heigold, Georg  and
      Neumann, Guenter  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1048",
    pages = "505--513",
    abstract = "This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarely- and unseen words gracefully. We evaluate on 14 languages and observe consistent gains over a state-of-the-art morphological tagger across all languages except for English and French, where we match the state-of-the-art. We compare two architectures for computing character-based word vectors using recurrent (RNN) and convolutional (CNN) nets. We show that the CNN based approach performs slightly worse and less consistently than the RNN based approach. Small but systematic gains are observed when combining the two architectures by ensembling.",
}
@inproceedings{kann-etal-2017-neural,
    title = "Neural Multi-Source Morphological Reinflection",
    author = {Kann, Katharina  and
      Cotterell, Ryan  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1049",
    pages = "514--524",
    abstract = "We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version. The input consists of (i) a target tag and (ii) multiple pairs of source form and source tag for a lemma. The motivation is that it is beneficial to have access to more than one source form since different source forms can provide complementary information, e.g., different stems. We further present a novel extension to the encoder-decoder recurrent neural architecture, consisting of multiple encoders, to better solve the task. We show that our new architecture outperforms single-source reinflection models and publish our dataset for multi-source morphological reinflection to facilitate future research.",
}
@inproceedings{chatterjee-etal-2017-online,
    title = "Online Automatic Post-editing for {MT} in a Multi-Domain Translation Environment",
    author = "Chatterjee, Rajen  and
      Gebremelak, Gebremedhen  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1050",
    pages = "525--535",
    abstract = "Automatic post-editing (APE) for machine translation (MT) aims to fix recurrent errors made by the MT decoder by learning from correction examples. In controlled evaluation scenarios, the representativeness of the training set with respect to the test data is a key factor to achieve good performance. Real-life scenarios, however, do not guarantee such favorable learning conditions. Ideally, to be integrated in a real professional translation workflow (e.g. to play a role in computer-assisted translation framework), APE tools should be flexible enough to cope with continuous streams of diverse data coming from different domains/genres. To cope with this problem, we propose an online APE framework that is: i) robust to data diversity (i.e. capable to learn and apply correction rules in the right contexts) and ii) able to evolve over time (by continuously extending and refining its knowledge). In a comparative evaluation, with English-German test data coming in random order from two different domains, we show the effectiveness of our approach, which outperforms a strong batch system and the state of the art in online APE.",
}
@inproceedings{damonte-etal-2017-incremental,
    title = "An Incremental Parser for {A}bstract {M}eaning {R}epresentation",
    author = "Damonte, Marco  and
      Cohen, Shay B.  and
      Satta, Giorgio",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1051",
    pages = "536--546",
    abstract = "Abstract Meaning Representation (AMR) is a semantic representation for natural language that embeds annotations related to traditional tasks such as named entity recognition, semantic role labeling, word sense disambiguation and co-reference resolution. We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time. We further propose a test-suite that assesses specific subtasks that are helpful in comparing AMR parsers, and show that our parser is competitive with the state of the art on the LDC2015E86 dataset and that it outperforms state-of-the-art parsers for recovering named entities and handling polarity.",
}
@inproceedings{padmakumar-etal-2017-integrated,
    title = "Integrated Learning of Dialog Strategies and Semantic Parsing",
    author = "Padmakumar, Aishwarya  and
      Thomason, Jesse  and
      Mooney, Raymond J.",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1052",
    pages = "547--557",
    abstract = "Natural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of direct and indirect supervision. We present an approach to integrate the learning of both a dialog strategy using reinforcement learning, and a semantic parser for robust natural language understanding, using only natural dialog interaction for supervision. Experimental results on a simulated task of robot instruction demonstrate that joint learning of both components improves dialog performance over learning either of these components alone.",
}
@inproceedings{chen-palmer-2017-unsupervised,
    title = "Unsupervised {AMR}-Dependency Parse Alignment",
    author = "Chen, Wei-Te  and
      Palmer, Martha",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1053",
    pages = "558--567",
    abstract = "In this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. Our aligner involves several different features, including named entity tags and semantic role labels, and uses Expectation-Maximization training. Results show that our aligner reaches an 87.1{\%} F-Score score with the experimental data, and enhances AMR parsing.",
}
@inproceedings{jin-etal-2017-improving,
    title = "Improving {C}hinese Semantic Role Labeling using High-quality Surface and Deep Case Frames",
    author = "Jin, Gongye  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1054",
    pages = "568--577",
    abstract = "This paper presents a method for applying automatically acquired knowledge to semantic role labeling (SRL). We use a large amount of automatically extracted knowledge to improve the performance of SRL. We present two varieties of knowledge, which we call surface case frames and deep case frames. Although the surface case frames are compiled from syntactic parses and can be used as rich syntactic knowledge, they have limited capability for resolving semantic ambiguity. To compensate the deficiency of the surface case frames, we compile deep case frames from automatic semantic roles. We also consider quality management for both types of knowledge in order to get rid of the noise brought from the automatic analyses. The experimental results show that Chinese SRL can be improved using automatically acquired knowledge and the quality management shows a positive effect on this task.",
}
@inproceedings{yaghoobzadeh-schutze-2017-multi,
    title = "Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities",
    author = {Yaghoobzadeh, Yadollah  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1055",
    pages = "578--589",
    abstract = "Entities are essential elements of natural language. In this paper, we present methods for learning multi-level representations of entities on three complementary levels: character (character patterns in entity names extracted, e.g., by neural networks), word (embeddings of words in entity names) and entity (entity embeddings). We investigate state-of-the-art learning methods on each level and find large differences, e.g., for deep learning models, traditional ngram features and the subword model of fasttext (Bojanowski et al., 2016) on the character level; for word2vec (Mikolov et al., 2013) on the word level; and for the order-aware model wang2vec (Ling et al., 2015a) on the entity level. We confirm experimentally that each level of representation contributes complementary information and a joint representation of all three levels improves the existing embedding based baseline for fine-grained entity typing by a large margin. Additionally, we show that adding information from entity descriptions further improves multi-level representations of entities.",
}
@inproceedings{faralli-etal-2017-contrastmedium,
    title = "The {C}ontrast{M}edium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links",
    author = "Faralli, Stefano  and
      Panchenko, Alexander  and
      Biemann, Chris  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1056",
    pages = "590--600",
    abstract = "In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies. ContrastMedium is able to identify the embedded taxonomy structure from a noisy knowledge graph without explicit human supervision such as, for instance, a set of manually selected input root and leaf concepts. This is achieved by leveraging structural information from a companion reference taxonomy, to which the input knowledge graph is linked (either automatically or manually). When used in conjunction with methods for hypernym acquisition and knowledge base linking, our methodology provides a complete solution for end-to-end taxonomy induction. We conduct experiments using automatically acquired knowledge graphs, as well as a SemEval benchmark, and show that our method is able to achieve high performance on the task of taxonomy induction.",
}
@inproceedings{min-etal-2017-probabilistic,
    title = "Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge",
    author = "Min, Bonan  and
      Freedman, Marjorie  and
      Meltzer, Talya",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1057",
    pages = "601--612",
    abstract = "Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into sub-problems such as mention and named entity recognition, entity linking and relation extraction. However, combining these components has shown to be under-constrained and often produces KBs with supersize entities and common-sense errors in relations (a person has multiple birthdates). The errors are difficult to resolve solely with IE tools but become obvious with world knowledge at the corpus level. By analyzing Freebase and a large text collection, we found that per-relation cardinality and the popularity of entities follow the power-law distribution favoring flat long tails with low-frequency instances. We present a probabilistic joint inference algorithm to incorporate this world knowledge during KB construction. Our approach yields state-of-the-art performance on the TAC Cold Start task, and 42{\%} and 19.4{\%} relative improvements in F1 over our baseline on Cold Start hop-1 and all-hop queries respectively.",
}
@inproceedings{verga-etal-2017-generalizing,
    title = "Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema",
    author = "Verga, Patrick  and
      Neelakantan, Arvind  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1058",
    pages = "613--622",
    abstract = "Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types{---}not only types from multiple structured databases (such as Freebase or Wikipedia infoboxes), but also types expressed as textual patterns from raw text. This prediction is typically modeled as a matrix completion problem, with one type per column, and either one or two entities per row (in the case of entity types or binary relation types, respectively). Factorizing this sparsely observed matrix yields a learned vector embedding for each row and each column. In this paper we explore the problem of making predictions for entities or entity-pairs unseen at training time (and hence without a pre-learned row embedding). We propose an approach having no per-row parameters at all; rather we produce a row vector on the fly using a learned aggregation function of the vectors of the observed columns for that row. We experiment with various aggregation functions, including neural network attention models. Our approach can be understood as a natural language database, in that questions about KB entities are answered by attending to textual or database evidence. In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time.",
}
@inproceedings{dong-etal-2017-learning-generate,
    title = "Learning to Generate Product Reviews from Attributes",
    author = "Dong, Li  and
      Huang, Shaohan  and
      Wei, Furu  and
      Lapata, Mirella  and
      Zhou, Ming  and
      Xu, Ke",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1059",
    pages = "623--632",
    abstract = "Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating. The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors. We also introduce an attention mechanism to jointly generate reviews and align words with input attributes. The proposed model is trained end-to-end to maximize the likelihood of target product reviews given the attributes. We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata. Experiments on the dataset show that our approach outperforms baseline methods and the attention mechanism significantly improves the performance of our model.",
}
@inproceedings{chisholm-etal-2017-learning,
    title = "Learning to generate one-sentence biographies from {W}ikidata",
    author = "Chisholm, Andrew  and
      Radford, Will  and
      Hachey, Ben",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1060",
    pages = "633--642",
    abstract = "We investigate the generation of one-sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries. Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts. The model achieves a BLEU score of 41, improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline. Human preference evaluation suggests the model is nearly as good as the Wikipedia reference. Manual analysis explores content selection, suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information.",
}
@inproceedings{puduppully-etal-2017-transition,
    title = "Transition-Based Deep Input Linearization",
    author = "Puduppully, Ratish  and
      Zhang, Yue  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1061",
    pages = "643--654",
    abstract = "Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to visualize, pipeline approaches suffer from error propagation. In addition, information available across modules cannot be leveraged by all modules. We construct a transition-based model to jointly perform linearization, function word prediction and morphological generation, which considerably improves upon the accuracy compared to a pipelined baseline system. On a standard deep input linearization shared task, our system achieves the best results reported so far.",
}
@inproceedings{castro-ferreira-etal-2017-generating,
    title = "Generating flexible proper name references in text: Data, models and evaluation",
    author = "Castro Ferreira, Thiago  and
      Krahmer, Emiel  and
      Wubben, Sander",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1062",
    pages = "655--664",
    abstract = "This study introduces a statistical model able to generate variations of a proper name by taking into account the person to be mentioned, the discourse context and variation. The model relies on the REGnames corpus, a dataset with 53,102 proper name references to 1,000 people in different discourse contexts. We evaluate the versions of our model from the perspective of how human writers produce proper names, and also how human readers process them. The corpus and the model are publicly available.",
}
@inproceedings{zhang-etal-2017-dependency-parsing,
    title = "Dependency Parsing as Head Selection",
    author = "Zhang, Xingxing  and
      Cheng, Jianpeng  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1063",
    pages = "665--676",
    abstract = "Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call DENSE (as shorthand for \textbf{De}pendency \textbf{N}eural \textbf{Se}lection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, DeNSe generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate DeNSe on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of the approach, our parsers are on par with the state of the art.",
}
@inproceedings{le-fokkens-2017-tackling,
    title = "Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing",
    author = "L{\^e}, Minh  and
      Fokkens, Antske",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1064",
    pages = "677--687",
    abstract = "Error propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In this paper, we apply reinforcement learning to greedy dependency parsing which is known to suffer from error propagation. Reinforcement learning improves accuracy of both labeled and unlabeled dependencies of the Stanford Neural Dependency Parser, a high performance greedy parser, while maintaining its efficiency. We investigate the portion of errors which are the result of error propagation and confirm that reinforcement learning reduces the occurrence of error propagation.",
}
@inproceedings{futrell-levy-2017-noisy,
    title = "Noisy-context surprisal as a human sentence processing cost model",
    author = "Futrell, Richard  and
      Levy, Roger",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1065",
    pages = "688--698",
    abstract = "We use the noisy-channel theory of human sentence comprehension to develop an incremental processing cost model that unifies and extends key features of expectation-based and memory-based models. In this model, which we call noisy-context surprisal, the processing cost of a word is the surprisal of the word given a noisy representation of the preceding context. We show that this model accounts for an outstanding puzzle in sentence comprehension, language-dependent structural forgetting effects (Gibson and Thomas, 1999; Vasishth et al., 2010; Frank et al., 2016), which are previously not well modeled by either expectation-based or memory-based approaches. Additionally, we show that this model derives and generalizes locality effects (Gibson, 1998; Demberg and Keller, 2008), a signature prediction of memory-based models. We give corpus-based evidence for a key assumption in this derivation.",
}
@inproceedings{yin-schutze-2017-task,
    title = "Task-Specific Attentive Pooling of Phrase Alignments Contributes to Sentence Matching",
    author = {Yin, Wenpeng  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1066",
    pages = "699--709",
    abstract = "This work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments deserve more attention in AS. The key to reach this observation lies in phrase detection, phrase representation, phrase alignment, and more importantly how to connect those aligned phrases of different matching degrees with the final classifier. Prior work (i) has limitations in phrase generation and representation, or (ii) conducts alignment at word and phrase levels by handcrafted features or (iii) utilizes a single framework of alignment without considering the characteristics of specific tasks, which limits the framework{'}s effectiveness across tasks. We propose an architecture based on Gated Recurrent Unit that supports (i) representation learning of phrases of arbitrary granularity and (ii) task-specific attentive pooling of phrase alignments between two sentences. Experimental results on TE and AS match our observation and show the effectiveness of our approach.",
}
@inproceedings{martinez-gomez-etal-2017-demand,
    title = "On-demand Injection of Lexical Knowledge for Recognising Textual Entailment",
    author = "Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Mineshima, Koji  and
      Miyao, Yusuke  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1067",
    pages = "710--720",
    abstract = "We approach the recognition of textual entailment using logical semantic representations and a theorem prover. In this setup, lexical divergences that preserve semantic entailment between the source and target texts need to be explicitly stated. However, recognising subsentential semantic relations is not trivial. We address this problem by monitoring the proof of the theorem and detecting unprovable sub-goals that share predicate arguments with logical premises. If a linguistic relation exists, then an appropriate axiom is constructed on-demand and the theorem proving continues. Experiments show that this approach is effective and precise, producing a system that outperforms other logic-based systems and is competitive with state-of-the-art statistical methods.",
}
@inproceedings{lai-hockenmaier-2017-learning,
    title = "Learning to Predict Denotational Probabilities For Modeling Entailment",
    author = "Lai, Alice  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1068",
    pages = "721--730",
    abstract = "We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI.",
}
@inproceedings{maheshwari-etal-2017-societal,
    title = "A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content",
    author = {Maheshwari, Tushar  and
      Reganti, Aishwarya N.  and
      Gupta, Samiksha  and
      Jamatia, Anupam  and
      Kumar, Upendra  and
      Gamb{\"a}ck, Bj{\"o}rn  and
      Das, Amitava},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1069",
    pages = "731--741",
    abstract = "To find out how users{'} social media behaviour and language are related to their ethical practices, the paper investigates applying Schwartz{'} psycholinguistic model of societal sentiment to social media text. The analysis is based on corpora collected from user essays as well as social media (Facebook and Twitter). Several experiments were carried out on the corpora to classify the ethical values of users, incorporating Linguistic Inquiry Word Count analysis, n-grams, topic models, psycholinguistic lexica, speech-acts, and non-linguistic information, while applying a range of machine learners (Support Vector Machines, Logistic Regression, and Random Forests) to identify the best linguistic and non-linguistic features for automatic classification of values and ethics.",
}
@inproceedings{lukin-etal-2017-argument,
    title = "Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion",
    author = "Lukin, Stephanie  and
      Anand, Pranav  and
      Walker, Marilyn  and
      Whittaker, Steve",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1070",
    pages = "742--753",
    abstract = "Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.",
}
@inproceedings{liu-etal-2017-language,
    title = "A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts",
    author = "Liu, Fei  and
      Perez, Julien  and
      Nowson, Scott",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1071",
    pages = "754--764",
    abstract = "There have been many attempts at automatically recognising author personality traits from text, typically incorporating linguistic features with conventional machine learning models, e.g. linear regression or Support Vector Machines. In this work, we propose to use deep-learning-based models with atomic features of text {--} the characters {--} to build hierarchical, vectorial word and sentence representations for the task of trait inference. On a corpus of tweets, this method shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.",
}
@inproceedings{levy-etal-2017-strong,
    title = "A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments",
    author = "Levy, Omer  and
      S{\o}gaard, Anders  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1072",
    pages = "765--774",
    abstract = "While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remain vague. We observe that whether or not an algorithm uses a particular feature set (sentence IDs) accounts for a significant performance gap among these algorithms. This feature set is also used by traditional alignment algorithms, such as IBM Model-1, which demonstrate similar performance to state-of-the-art embedding algorithms on a variety of benchmarks. Overall, we observe that different algorithmic approaches for utilizing the sentence ID feature space result in similar performance. This paper draws both empirical and theoretical parallels between the embedding and alignment literature, and suggests that adding additional sources of information, which go beyond the traditional signal of bilingual sentence-aligned corpora, may substantially improve cross-lingual word embeddings, and that future baselines should at least take such features into account.",
}
@inproceedings{denis-ralaivola-2017-online,
    title = "Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm",
    author = "Denis, Pascal  and
      Ralaivola, Liva",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1073",
    pages = "775--784",
    abstract = "This paper presents a new, efficient method for learning task-specific word vectors using a variant of the Passive-Aggressive algorithm. Specifically, this algorithm learns a word embedding matrix in tandem with the classifier parameters in an online fashion, solving a bi-convex constrained optimization at each iteration. We provide a theoretical analysis of this new algorithm in terms of regret bounds, and evaluate it on both synthetic data and NLP classification problems, including text classification and sentiment analysis. In the latter case, we compare various pre-trained word vectors to initialize our word embedding matrix, and show that the matrix learned by our algorithm vastly outperforms the initial matrix, with performance results comparable or above the state-of-the-art on these tasks.",
}
@inproceedings{schutze-2017-nonsymbolic,
    title = "Nonsymbolic Text Representation",
    author = {Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1074",
    pages = "785--796",
    abstract = "We introduce the first generic text representation model that is completely nonsymbolic, i.e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text. This applies to training the parameters of the model on a training corpus as well as to applying it when computing the representation of a new text. We show that our model performs better than prior work on an information extraction and a text denoising task.",
}
@inproceedings{abhishek-etal-2017-fine,
    title = "Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings",
    author = "Abhishek, Abhishek  and
      Anand, Ashish  and
      Awekar, Amit",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1075",
    pages = "797--807",
    abstract = "Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Our model treats training data as noisy and uses non-parametric variant of hinge loss function. Experiments show that the proposed model outperforms previous state-of-the-art methods on two publicly available datasets, namely FIGER (GOLD) and BBN with an average relative improvement of 2.69{\%} in micro-F1 score. Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems. These approaches of transferring knowledge further improve the performance of respective models.",
}
@inproceedings{zhou-etal-2017-event,
    title = "Event extraction from {T}witter using Non-Parametric {B}ayesian Mixture Model with Word Embeddings",
    author = "Zhou, Deyu  and
      Zhang, Xuan  and
      He, Yulan",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1076",
    pages = "808--817",
    abstract = "To extract structured representations of newsworthy events from Twitter, unsupervised models typically assume that tweets involving the same named entities and expressed using similar words are likely to belong to the same event. Hence, they group tweets into clusters based on the co-occurrence patterns of named entities and topical keywords. However, there are two main limitations. First, they require the number of events to be known beforehand, which is not realistic in practical applications. Second, they don{'}t recognise that the same named entity might be referred to by multiple mentions and tweets using different mentions would be wrongly assigned to different events. To overcome these limitations, we propose a non-parametric Bayesian mixture model with word embeddings for event extraction, in which the number of events can be inferred automatically and the issue of lexical variations for the same named entity can be dealt with properly. Our model has been evaluated on three datasets with sizes ranging between 2,499 and over 60 million tweets. Experimental results show that our model outperforms the baseline approach on all datasets by 5-8{\%} in F-measure.",
}
@inproceedings{pawar-etal-2017-end,
    title = "End-to-end Relation Extraction using Neural Networks and {M}arkov {L}ogic {N}etworks",
    author = "Pawar, Sachin  and
      Bhattacharyya, Pushpak  and
      Palshikar, Girish",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1077",
    pages = "818--827",
    abstract = "End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions. Traditionally, separate predictive models were trained for each of these tasks and were used in a {``}pipeline{''} fashion where output of one model is fed as input to another. But it was observed that addressing some of these tasks jointly results in better performance. We propose a single, joint neural network based model to carry out all the three tasks of boundary identification, entity type classification and relation type classification. This model is referred to as {``}All Word Pairs{''} model (AWP-NN) as it assigns an appropriate label to each word pair in a given sentence for performing end-to-end relation extraction. We also propose to refine output of the AWP-NN model by using inference in Markov Logic Networks (MLN) so that additional domain knowledge can be effectively incorporated. We demonstrate effectiveness of our approach by achieving better end-to-end relation extraction performance than all 4 previous joint modelling approaches, on the standard dataset of ACE 2004.",
}
@inproceedings{heinzerling-etal-2017-trust,
    title = "Trust, but Verify! Better Entity Linking through Automatic Verification",
    author = "Heinzerling, Benjamin  and
      Strube, Michael  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1078",
    pages = "828--838",
    abstract = "We introduce automatic verification as a post-processing step for entity linking (EL). The proposed method trusts EL system results collectively, by assuming entity mentions are mostly linked correctly, in order to create a semantic profile of the given text using geospatial and temporal information, as well as fine-grained entity types. This profile is then used to automatically verify each linked mention individually, i.e., to predict whether it has been linked correctly or not. Verification allows leveraging a rich set of global and pairwise features that would be prohibitively expensive for EL systems employing global inference. Evaluation shows consistent improvements across datasets and systems. In particular, when applied to state-of-the-art systems, our method yields an absolute improvement in linking performance of up to 1.7 F1 on AIDA/CoNLL{'}03 and up to 2.4 F1 on the English TAC KBP 2015 TEDL dataset.",
}
@inproceedings{jochim-deleris-2017-named,
    title = "Named Entity Recognition in the Medical Domain with Constrained {CRF} Models",
    author = "Jochim, Charles  and
      Deleris, L{\'e}a",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1079",
    pages = "839--849",
    abstract = "This paper investigates how to improve performance on information extraction tasks by constraining and sequencing CRF-based approaches. We consider two different relation extraction tasks, both from the medical literature: dependence relations and probability statements. We explore whether adding constraints can lead to an improvement over standard CRF decoding. Results on our relation extraction tasks are promising, showing significant increases in performance from both (i) adding constraints to post-process the output of a baseline CRF, which captures {``}domain knowledge{''}, and (ii) further allowing flexibility in the application of those constraints by leveraging a binary classifier as a pre-processing step.",
}
@inproceedings{yadav-etal-2017-learning,
    title = "Learning and Knowledge Transfer with Memory Networks for Machine Comprehension",
    author = "Yadav, Mohit  and
      Vig, Lovekesh  and
      Shroff, Gautam",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1080",
    pages = "850--859",
    abstract = "Enabling machines to read and comprehend unstructured text remains an unfulfilled goal for NLP research. Recent research efforts on the {``}machine comprehension{''} task have managed to achieve close to ideal performance on simulated data. However, achieving similar levels of performance on small real world datasets has proved difficult; major challenges stem from the large vocabulary size, complex grammar, and, the frequent ambiguities in linguistic structure. On the other hand, the requirement of human generated annotations for training, in order to ensure a sufficiently diverse set of questions is prohibitively expensive. Motivated by these practical issues, we propose a novel curriculum inspired training procedure for Memory Networks to improve the performance for machine comprehension with relatively small volumes of training data. Additionally, we explore various training regimes for Memory Networks to allow knowledge transfer from a closely related domain having larger volumes of labelled data. We also suggest the use of a loss function to incorporate the asymmetric nature of knowledge transfer. Our experiments demonstrate improvements on Dailymail, CNN, and MCTest datasets.",
}
@inproceedings{sarabi-blanco-2017-media,
    title = "If No Media Were Allowed inside the Venue, Was Anybody Allowed?",
    author = "Sarabi, Zahra  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1081",
    pages = "860--869",
    abstract = "This paper presents a framework to understand negation in positive terms. Specifically, we extract positive meaning from negation when the negation cue syntactically modifies a noun or adjective. Our approach is grounded on generating potential positive interpretations automatically, and then scoring them. Experimental results show that interpretations scored high can be reliably identified.",
}
@inproceedings{abualhaija-etal-2017-metaheuristic,
    title = "Metaheuristic Approaches to Lexical Substitution and Simplification",
    author = "Abualhaija, Sallam  and
      Miller, Tristan  and
      Eckle-Kohler, Judith  and
      Gurevych, Iryna  and
      Zimmermann, Karl-Heinz",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1082",
    pages = "870--880",
    abstract = "In this paper, we propose using metaheuristics{---}in particular, simulated annealing and the new D-Bees algorithm{---}to solve word sense disambiguation as an optimization problem within a knowledge-based lexical substitution system. We are the first to perform such an extrinsic evaluation of metaheuristics, for which we use two standard lexical substitution datasets, one English and one German. We find that D-Bees has robust performance for both languages, and performs better than simulated annealing, though both achieve good results. Moreover, the D-Bees{--}based lexical substitution system outperforms state-of-the-art systems on several evaluation metrics. We also show that D-Bees achieves competitive performance in lexical simplification, a variant of lexical substitution.",
}
@inproceedings{mallinson-etal-2017-paraphrasing,
    title = "Paraphrasing Revisited with Neural Machine Translation",
    author = "Mallinson, Jonathan  and
      Sennrich, Rico  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1083",
    pages = "881--893",
    abstract = "Recognizing and generating paraphrases is an important component in many natural language processing applications. A well-established technique for automatically extracting paraphrases leverages bilingual corpora to find meaning-equivalent phrases in a single language by {``}pivoting{''} over a shared translation in another language. In this paper we revisit bilingual pivoting in the context of neural machine translation and present a paraphrasing model based purely on neural networks. Our model represents paraphrases in a continuous space, estimates the degree of semantic relatedness between text segments of arbitrary length, and generates candidate paraphrases for any source input. Experimental results across tasks and datasets show that neural paraphrases outperform those obtained with conventional phrase-based pivoting approaches.",
}
@inproceedings{duong-etal-2017-multilingual,
    title = "Multilingual Training of Crosslingual Word Embeddings",
    author = "Duong, Long  and
      Kanayama, Hiroshi  and
      Ma, Tengfei  and
      Bird, Steven  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1084",
    pages = "894--904",
    abstract = "Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer. Most prior work constructs embeddings for a pair of languages, with English on one side. We investigate methods for building high quality crosslingual word embeddings for many languages in a unified vector space.In this way, we can exploit and combine strength of many languages. We obtained high performance on bilingual lexicon induction, monolingual similarity and crosslingual document classification tasks.",
}
@inproceedings{silva-de-carvalho-nguyen-2017-building,
    title = "Building Lexical Vector Representations from Concept Definitions",
    author = "Silva de Carvalho, Danilo  and
      Nguyen, Minh Le",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1085",
    pages = "905--915",
    abstract = "The use of distributional language representations have opened new paths in solving a variety of NLP problems. However, alternative approaches can take advantage of information unavailable through pure statistical means. This paper presents a method for building vector representations from meaning unit blocks called concept definitions, which are obtained by extracting information from a curated linguistic resource (Wiktionary). The representations obtained in this way can be compared through conventional cosine similarity and are also interpretable by humans. Evaluation was conducted in semantic similarity and relatedness test sets, with results indicating a performance comparable to other methods based on single linguistic resource extraction. The results also indicate noticeable performance gains when combining distributional similarity scores with the ones obtained using this approach. Additionally, a discussion on the proposed method{'}s shortcomings is provided in the analysis of error cases.",
}
@inproceedings{butnaru-etal-2017-shotgunwsd,
    title = "{S}hotgun{WSD}: An unsupervised algorithm for global word sense disambiguation inspired by {DNA} sequencing",
    author = "Butnaru, Andrei  and
      Ionescu, Radu Tudor  and
      Hristea, Florentina",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1086",
    pages = "916--926",
    abstract = "In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).",
}
@inproceedings{kocmi-bojar-2017-lanidenn,
    title = "{L}anide{NN}: Multilingual Language Identification on Character Window",
    author = "Kocmi, Tom  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1087",
    pages = "927--936",
    abstract = "In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. Monolingual language identification assumes that the given document is written in one language. In multilingual language identification, the document is usually in two or three languages and we just want their names. We aim one step further and propose a method for textual language identification where languages can change arbitrarily and the goal is to identify the spans of each of the languages. Our method is based on Bidirectional Recurrent Neural Networks and it performs well in monolingual and multilingual language identification tasks on six datasets covering 131 languages. The method keeps the accuracy also for short documents and across domains, so it is ideal for off-the-shelf use without preparation of training data.",
}
@inproceedings{adams-etal-2017-cross,
    title = "Cross-Lingual Word Embeddings for Low-Resource Language Modeling",
    author = "Adams, Oliver  and
      Makarucha, Adam  and
      Neubig, Graham  and
      Bird, Steven  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1088",
    pages = "937--947",
    abstract = "Most languages have no established writing system and minimal written records. However, textual data is essential for natural language processing, and particularly important for training language models to support speech recognition. Even in cases where text data is missing, there are some languages for which bilingual lexicons are available, since creating lexicons is a fundamental task of documentary linguistics. We investigate the use of such lexicons to improve language models when textual training data is limited to as few as a thousand sentences. The method involves learning cross-lingual word embeddings as a preliminary step in training monolingual language models. Results across a number of languages show that language models are improved by this pre-training. Application to Yongning Na, a threatened language, highlights challenges in deploying the approach in real low-resource environments.",
}
@inproceedings{pu-etal-2017-consistent,
    title = "Consistent Translation of Repeated Nouns using Syntactic and Semantic Cues",
    author = "Pu, Xiao  and
      Mascarell, Laura  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1089",
    pages = "948--957",
    abstract = "We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently, i.e. using the same noun in the target text as well. We train and test classifiers that predict consistent translations based on lexical, syntactic, and semantic features. We first evaluate the accuracy of our classifiers intrinsically, in terms of the accuracy of consistency predictions, over a subset of the UN Corpus. Then, we also evaluate them in combination with phrase-based statistical MT systems for Chinese-to-English and German-to-English. We compare the automatic post-editing of noun translations with the re-ranking of the translation hypotheses based on the classifiers{'} output, and also use these methods in combination. This improves over the baseline and closes up to 50{\%} of the gap in BLEU scores between the baseline and an oracle classifier.",
}
@inproceedings{howcroft-demberg-2017-psycholinguistic,
    title = "Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking",
    author = "Howcroft, David M.  and
      Demberg, Vera",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1090",
    pages = "958--968",
    abstract = "While previous research on readability has typically focused on document-level measures, recent work in areas such as natural language generation has pointed out the need of sentence-level readability measures. Much of psycholinguistics has focused for many years on processing measures that provide difficulty estimates on a word-by-word basis. However, these psycholinguistic measures have not yet been tested on sentence readability ranking tasks. In this paper, we use four psycholinguistic measures: idea density, surprisal, integration cost, and embedding depth to test whether these features are predictive of readability levels. We find that psycholinguistic features significantly improve performance by up to 3 percentage points over a standard document-level readability metric baseline.",
}
@inproceedings{das-etal-2017-web,
    title = "Web-Scale Language-Independent Cataloging of Noisy Product Listings for {E}-Commerce",
    author = "Das, Pradipto  and
      Xia, Yandi  and
      Levine, Aaron  and
      Di Fabbrizio, Giuseppe  and
      Datta, Ankur",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1091",
    pages = "969--979",
    abstract = "The cataloging of product listings through taxonomy categorization is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search recommendations to query understanding. However, manual and rule based approaches to categorization are not scalable. In this paper, we compare several classifiers for categorizing listings in both English and Japanese product catalogs. We show empirically that a combination of words from product titles, navigational breadcrumbs, and list prices, when available, improves results significantly. We outline a novel method using correspondence topic models and a lightweight manual process to reduce noise from mis-labeled data in the training set. We contrast linear models, gradient boosted trees (GBTs) and convolutional neural networks (CNNs), and show that GBTs and CNNs yield the highest gains in error reduction. Finally, we show GBTs applied in a language-agnostic way on a large-scale Japanese e-commerce dataset have improved taxonomy categorization performance over current state-of-the-art based on deep belief network models.",
}
@inproceedings{stab-gurevych-2017-recognizing,
    title = "Recognizing Insufficiently Supported Arguments in Argumentative Essays",
    author = "Stab, Christian  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1092",
    pages = "980--990",
    abstract = "In this paper, we propose a new task for assessing the quality of natural language arguments. The premises of a well-reasoned argument should provide enough evidence for accepting or rejecting its claim. Although this criterion, known as sufficiency, is widely adopted in argumentation theory, there are no empirical studies on its applicability to real arguments. In this work, we show that human annotators substantially agree on the sufficiency criterion and introduce a novel annotated corpus. Furthermore, we experiment with feature-rich SVMs and Convolutional Neural Networks and achieve 84{\%} accuracy for automatically identifying insufficiently supported arguments. The final corpus as well as the annotation guideline are freely available for encouraging future research on argument quality.",
}
@inproceedings{sato-etal-2017-distributed,
    title = "Distributed Document and Phrase Co-embeddings for Descriptive Clustering",
    author = "Sato, Motoki  and
      Brockmeier, Austin J.  and
      Kontonatsios, Georgios  and
      Mu, Tingting  and
      Goulermas, John Y.  and
      Tsujii, Jun{'}ichi  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1093",
    pages = "991--1001",
    abstract = "Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster. In this paper, we present a descriptive clustering approach that employs a distributed representation model, namely the paragraph vector model, to capture semantic similarities between documents and phrases. The proposed method uses a joint representation of phrases and documents (i.e., a co-embedding) to automatically select a descriptive phrase that best represents each document cluster. We evaluate our method by comparing its performance to an existing state-of-the-art descriptive clustering method that also uses co-embedding but relies on a bag-of-words representation. Results obtained on benchmark datasets demonstrate that the paragraph vector-based method obtains superior performance over the existing approach in both identifying clusters and assigning appropriate descriptive labels to them.",
}
@inproceedings{farra-mckeown-2017-smarties,
    title = "{SMART}ies: Sentiment Models for {A}rabic Target entities",
    author = "Farra, Noura  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1094",
    pages = "1002--1013",
    abstract = "We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources. We present a system that is applied to complex posts written in response to Arabic newspaper articles. Our goal is to identify important entity {``}targets{''} within the post along with the polarity expressed about each target. We achieve significant improvements over multiple baselines, demonstrating that the use of specific morphological representations improves the performance of identifying both important targets and their sentiment, and that the use of distributional semantic clusters further boosts performances for these representations, especially when richer linguistic resources are not available.",
}
@inproceedings{segura-bedmar-etal-2017-exploring,
    title = "Exploring Convolutional Neural Networks for Sentiment Analysis of {S}panish tweets",
    author = "Segura-Bedmar, Isabel  and
      Quir{\'o}s, Antonio  and
      Mart{\'\i}nez, Paloma",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1095",
    pages = "1014--1022",
    abstract = "Spanish is the third-most used language on the internet, after English and Chinese, with a total of 7.7{\%} (more than 277 million of users) and a huge internet growth of more than 1,400{\%}. However, most work on sentiment analysis has been focused on English. This paper describes a deep learning system for Spanish sentiment analysis. To the best of our knowledge, this is the first work that explores the use of a convolutional neural network to polarity classification of Spanish tweets.",
}
@inproceedings{mousa-schuller-2017-contextual,
    title = "Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis",
    author = {Mousa, Amr  and
      Schuller, Bj{\"o}rn},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1096",
    pages = "1023--1032",
    abstract = "Traditional learning-based approaches to sentiment analysis of written text use the concept of bag-of-words or bag-of-n-grams, where a document is viewed as a set of terms or short combinations of terms disregarding grammar rules or word order. Novel approaches de-emphasize this concept and view the problem as a sequence classification problem. In this context, recurrent neural networks (RNNs) have achieved significant success. The idea is to use RNNs as discriminative binary classifiers to predict a positive or negative sentiment label at every word position then perform a type of pooling to get a sentence-level polarity. Here, we investigate a novel generative approach in which a separate probability distribution is estimated for every sentiment using language models (LMs) based on long short-term memory (LSTM) RNNs. We introduce a novel type of LM using a modified version of bidirectional LSTM (BLSTM) called contextual BLSTM (cBLSTM), where the probability of a word is estimated based on its full left and right contexts. Our approach is compared with a BLSTM binary classifier. Significant improvements are observed in classifying the IMDB movie review dataset. Further improvements are achieved via model combination.",
}
@inproceedings{sun-etal-2017-large,
    title = "Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network",
    author = "Sun, Changzhi  and
      Wu, Yuanbin  and
      Lan, Man  and
      Sun, Shiliang  and
      Zhang, Qi",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1097",
    pages = "1033--1043",
    abstract = "We investigate the task of open domain opinion relation extraction. Different from works on manually labeled corpus, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is design to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset. The result shows that our model is able to achieve promising performances without any human annotations.",
}
@inproceedings{argueta-chiang-2017-decoding,
    title = "Decoding with Finite-State Transducers on {GPU}s",
    author = "Argueta, Arturo  and
      Chiang, David",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1098",
    pages = "1044--1052",
    abstract = "Weighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such as morphological analysis, part-of-speech tagging, chunking, named entity recognition, speech recognition, and others. Parallelizing finite state algorithms on graphics processing units (GPUs) would benefit many areas of NLP. Although researchers have implemented GPU versions of basic graph algorithms, no work, to our knowledge, has been done on GPU algorithms for weighted finite automata. We introduce a GPU implementation of the Viterbi and forward-backward algorithm, achieving speedups of up to 4x over our serial implementations running on different computer architectures and 3335x over widely used tools such as OpenFST.",
}
@inproceedings{gu-etal-2017-learning,
    title = "Learning to Translate in Real-time with Neural Machine Translation",
    author = "Gu, Jiatao  and
      Neubig, Graham  and
      Cho, Kyunghyun  and
      Li, Victor O.K.",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1099",
    pages = "1053--1062",
    abstract = "Translating in real-time, a.k.a.simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.",
}
@inproceedings{toral-sanchez-cartagena-2017-multifaceted,
    title = "A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions",
    author = "Toral, Antonio  and
      S{\'a}nchez-Cartagena, V{\'\i}ctor M.",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1100",
    pages = "1063--1073",
    abstract = "We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm. To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions. Specifically, we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories. We find out that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Neural machine translation systems are also more accurate at producing inflected forms, but they perform poorly when translating very long sentences.",
}
@inproceedings{rabinovich-etal-2017-personalized,
    title = "Personalized Machine Translation: Preserving Original Author Traits",
    author = "Rabinovich, Ella  and
      Patel, Raj Nath  and
      Mirkin, Shachar  and
      Specia, Lucia  and
      Wintner, Shuly",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1101",
    pages = "1074--1084",
    abstract = "The language that we produce reflects our personality, and various personal and demographic characteristics can be detected in natural language texts. We focus on one particular personal trait of the author, gender, and study how it is manifested in original texts and in translations. We show that author{'}s gender has a powerful, clear signal in originals texts, but this signal is obfuscated in human and machine translation. We then propose simple domain-adaptation techniques that help retain the original gender traits in the translation, without harming the quality of the translation, thereby creating more personalized machine translation systems.",
}
@inproceedings{heyman-etal-2017-bilingual,
    title = "Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations",
    author = "Heyman, Geert  and
      Vuli{\'c}, Ivan  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1102",
    pages = "1085--1095",
    abstract = "We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term memory and deep feed forward networks. The results show that word- and character-level representations each improve state-of-the-art results for BLI, and the best results are obtained by exploiting the synergy between these word- and character-level representations in the classification model.",
}
@inproceedings{escoter-etal-2017-grouping,
    title = "Grouping business news stories based on salience of named entities",
    author = "Escoter, Lloren{\c{c}}  and
      Pivovarova, Lidia  and
      Du, Mian  and
      Katinskaia, Anisia  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1103",
    pages = "1096--1106",
    abstract = "In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles. Depending on the relative importance of the story, the number of versions can reach dozens or hundreds within a day. The text in these versions may be nearly identical or quite different. Linking multiple versions of a story into a single group brings several important benefits to the end-user{--}reducing the cognitive load on the reader, as well as signaling the relative importance of the story. We present a grouping algorithm, and explore several vector-based representations of input documents: from a baseline using keywords, to a method using salience{--}a measure of importance of named entities in the text. We demonstrate that features beyond keywords yield substantial improvements, verified on a manually-annotated corpus of business news stories.",
}
@inproceedings{conneau-etal-2017-deep,
    title = "Very Deep Convolutional Networks for Text Classification",
    author = {Conneau, Alexis  and
      Schwenk, Holger  and
      Barrault, Lo{\"\i}c  and
      Lecun, Yann},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1104",
    pages = "1107--1116",
    abstract = "The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which have pushed the state-of-the-art in computer vision. We present a new architecture (VDCNN) for text processing which operates directly at the character level and uses only small convolutions and pooling operations. We are able to show that the performance of this model increases with the depth: using up to 29 convolutional layers, we report improvements over the state-of-the-art on several public text classification tasks. To the best of our knowledge, this is the first time that very deep convolutional nets have been applied to text processing.",
}
@inproceedings{wachsmuth-etal-2017-pagerank,
    title = "{``}{P}age{R}ank{''} for Argument Relevance",
    author = "Wachsmuth, Henning  and
      Stein, Benno  and
      Ajjour, Yamen",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1105",
    pages = "1117--1127",
    abstract = "Future search engines are expected to deliver pro and con arguments in response to queries on controversial topics. While argument mining is now in the focus of research, the question of how to retrieve the relevant arguments remains open. This paper proposes a radical model to assess relevance objectively at web scale: the relevance of an argument{'}s conclusion is decided by what other arguments reuse it as a premise. We build an argument graph for this model that we analyze with a recursive weighting scheme, adapting key ideas of PageRank. In experiments on a large ground-truth argument graph, the resulting relevance scores correlate with human average judgments. We outline what natural language challenges must be faced at web scale in order to stepwise bring argument relevance to web search engines.",
}
@inproceedings{perez-rosas-etal-2017-predicting,
    title = "Predicting Counselor Behaviors in Motivational Interviewing Encounters",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada  and
      Resnicow, Kenneth  and
      Singh, Satinder  and
      An, Lawrence  and
      Goggin, Kathy J.  and
      Catley, Delwyn",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1106",
    pages = "1128--1137",
    abstract = "As the number of people receiving psycho-therapeutic treatment increases, the automatic evaluation of counseling practice arises as an important challenge in the clinical domain. In this paper, we address the automatic evaluation of counseling performance by analyzing counselors{'} language during their interaction with clients. In particular, we present a model towards the automation of Motivational Interviewing (MI) coding, which is the current gold standard to evaluate MI counseling. First, we build a dataset of hand labeled MI encounters; second, we use text-based methods to extract and analyze linguistic patterns associated with counselor behaviors; and third, we develop an automatic system to predict these behaviors. We introduce a new set of features based on semantic information and syntactic patterns, and show that they lead to accuracy figures of up to 90{\%}, which represent a significant improvement with respect to features used in the past.",
}
@inproceedings{stamatatos-2017-authorship,
    title = "Authorship Attribution Using Text Distortion",
    author = "Stamatatos, Efstathios",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1107",
    pages = "1138--1149",
    abstract = "Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures. The proposed method attempts to mask topic-specific information that is not related to the personal style of authors. Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic.",
}
@inproceedings{leeuwenberg-moens-2017-structured,
    title = "Structured Learning for Temporal Relation Extraction from Clinical Records",
    author = "Leeuwenberg, Artuur  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1108",
    pages = "1150--1158",
    abstract = "We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We employ a structured perceptron, together with integer linear programming constraints for document-level inference during training and prediction to exploit relational properties of temporality, together with global learning of the relations at the document level. Moreover, this study gives insights in the results of integrating constraints for temporal relation extraction when using structured learning and prediction. Our best system outperforms the state-of-the art on both the CONTAINS TLINK task, and the DCTR task.",
}
@inproceedings{yadav-etal-2017-entity,
    title = "Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with {PSO} based Feature Selection",
    author = "Yadav, Shweta  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1109",
    pages = "1159--1170",
    abstract = "Text mining has drawn significant attention in recent past due to the rapid growth in biomedical and clinical records. Entity extraction is one of the fundamental components for biomedical text mining. In this paper, we propose a novel approach of feature selection for entity extraction that exploits the concept of deep learning and Particle Swarm Optimization (PSO). The system utilizes word embedding features along with several other features extracted by studying the properties of the datasets. We obtain an interesting observation that compact word embedding features as determined by PSO are more effective compared to the entire word embedding feature set for entity extraction. The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG, and AiMed. The effectiveness of the proposed approach is evident with significant performance gains over the baseline models as well as the other existing systems. We observe improvements of 7.86{\%}, 5.27{\%} and 7.25{\%} F-measure points over the baseline models for GENIA, GENETAG, and AiMed dataset respectively.",
}
@inproceedings{quirk-poon-2017-distant,
    title = "Distant Supervision for Relation Extraction beyond the Sentence Boundary",
    author = "Quirk, Chris  and
      Poon, Hoifung",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1110",
    pages = "1171--1182",
    abstract = "The growing demand for structured knowledge has led to great interest in relation extraction, especially in cases with limited supervision. However, existing distance supervision approaches only extract relations expressed in single sentences. In general, cross-sentence relation extraction is under-explored, even in the supervised-learning setting. In this paper, we propose the first approach for applying distant supervision to cross-sentence relation extraction. At the core of our approach is a graph representation that can incorporate both standard dependencies and discourse relations, thus providing a unifying way to model relations within and across sentences. We extract features from multiple paths in this graph, increasing accuracy and robustness when confronted with linguistic variation and analysis error. Experiments on an important extraction task for precision medicine show that our approach can learn an accurate cross-sentence extractor, using only a small existing knowledge base and unlabeled text from biomedical research articles. Compared to the existing distant supervision paradigm, our approach extracted twice as many relations at similar precision, thus demonstrating the prevalence of cross-sentence relations and the promise of our approach.",
}
@inproceedings{yaghoobzadeh-etal-2017-noise,
    title = "Noise Mitigation for Neural Entity Typing and Relation Extraction",
    author = {Yaghoobzadeh, Yadollah  and
      Adel, Heike  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1111",
    pages = "1183--1194",
    abstract = "In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing and relation extraction. For the first noise type, we introduce multi-instance multi-label learning algorithms using neural network models, and apply them to fine-grained entity typing for the first time. Our model outperforms the state-of-the-art supervised approach which uses global embeddings of entities. For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction. Our experiments show that probabilistic predictions are more robust than discrete predictions and that joint training of the two tasks performs best.",
}
@inproceedings{takamura-etal-2017-analyzing,
    title = "Analyzing Semantic Change in {J}apanese Loanwords",
    author = "Takamura, Hiroya  and
      Nagata, Ryo  and
      Kawasaki, Yoshifumi",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1112",
    pages = "1195--1204",
    abstract = "We analyze semantic changes in loanwords from English that are used in Japanese (Japanese loanwords). Specifically, we create word embeddings of English and Japanese and map the Japanese embeddings into the English space so that we can calculate the similarity of each Japanese word and each English word. We then attempt to find loanwords that are semantically different from their original, see if known meaning changes are correctly captured, and show the possibility of using our methodology in language education.",
}
@inproceedings{jager-etal-2017-using,
    title = "Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists",
    author = {J{\"a}ger, Gerhard  and
      List, Johann-Mattis  and
      Sofroniev, Pavel},
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1113",
    pages = "1205--1216",
    abstract = "Most current approaches in phylogenetic linguistics require as input multilingual word lists partitioned into sets of etymologically related words (cognates). Cognate identification is so far done manually by experts, which is time consuming and as of yet only available for a small number of well-studied language families. Automatizing this step will greatly expand the empirical scope of phylogenetic methods in linguistics, as raw wordlists (in phonetic transcription) are much easier to obtain than wordlists in which cognate words have been fully identified and annotated, even for under-studied languages. A couple of different methods have been proposed in the past, but they are either disappointing regarding their performance or not applicable to larger datasets. Here we present a new approach that uses support vector machines to unify different state-of-the-art methods for phonetic alignment and cognate detection within a single framework. Training and evaluating these method on a typologically broad collection of gold-standard data shows it to be superior to the existing state of the art.",
}
@inproceedings{maharjan-etal-2017-multi,
    title = "A Multi-task Approach to Predict Likability of Books",
    author = "Maharjan, Suraj  and
      Arevalo, John  and
      Montes, Manuel  and
      Gonz{\'a}lez, Fabio A.  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1114",
    pages = "1217--1227",
    abstract = "We investigate the value of feature engineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new strategies to automatically learn representations from book contents. We evaluate our feature set on two different corpora created from Project Gutenberg books. The first presents a novel approach for generating the gold standard labels for the task and the other is based on prior research. Using a combination of hand-crafted and recurrent neural network learned representations in a dual learning setting, we obtain the best performance of 73.50{\%} weighted F1-score.",
}
@inproceedings{van-cranenburgh-bod-2017-data,
    title = "A Data-Oriented Model of Literary Language",
    author = "van Cranenburgh, Andreas  and
      Bod, Rens",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1115",
    pages = "1228--1238",
    abstract = "We consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, mined from the training set, and a series of hand-picked features. Our model is the first to distinguish degrees of highly and less literary novels using a variety of lexical and syntactic features, and explains 76.0 {\%} of the variation in literary ratings.",
}
@inproceedings{shoemark-etal-2017-aye,
    title = "Aye or naw, whit dae ye hink? {S}cottish independence and linguistic identity on social media",
    author = "Shoemark, Philippa  and
      Sur, Debnil  and
      Shrimpton, Luke  and
      Murray, Iain  and
      Goldwater, Sharon",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1116",
    pages = "1239--1248",
    abstract = "Political surveys have indicated a relationship between a sense of Scottish identity and voting decisions in the 2014 Scottish Independence Referendum. Identity is often reflected in language use, suggesting the intuitive hypothesis that individuals who support Scottish independence are more likely to use distinctively Scottish words than those who oppose it. In the first large-scale study of sociolinguistic variation on social media in the UK, we identify distinctively Scottish terms in a data-driven way, and find that these terms are indeed used at a higher rate by users of pro-independence hashtags than by users of anti-independence hashtags. However, we also find that in general people are less likely to use distinctively Scottish words in tweets with referendum-related hashtags than in their general Twitter activity. We attribute this difference to style shifting relative to audience, aligning with previous work showing that Twitter users tend to use fewer local variants when addressing a broader audience.",
}
@inproceedings{kuncoro-etal-2017-recurrent,
    title = "What Do Recurrent Neural Network Grammars Learn About Syntax?",
    author = "Kuncoro, Adhiguna  and
      Ballesteros, Miguel  and
      Kong, Lingpeng  and
      Dyer, Chris  and
      Neubig, Graham  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1117",
    pages = "1249--1258",
    abstract = "Recurrent neural network grammars (RNNG) are a recently proposed probablistic generative modeling family for natural language. They show state-of-the-art language modeling and parsing performance. We investigate what information they learn, from a linguistic perspective, through various ablations to the model and the data, and by augmenting the model with an attention mechanism (GA-RNNG) to enable closer inspection. We find that explicit modeling of composition is crucial for achieving the best performance. Through the attention mechanism, we find that headedness plays a central role in phrasal representation (with the model{'}s latent attention largely agreeing with predictions made by hand-crafted head rules, albeit with some important differences). By training grammars without nonterminal labels, we find that phrasal representations depend minimally on nonterminals, providing support for the endocentricity hypothesis.",
}
@inproceedings{coavoux-crabbe-2017-incremental,
    title = "Incremental Discontinuous Phrase Structure Parsing with the {GAP} Transition",
    author = "Coavoux, Maximin  and
      Crabb{\'e}, Beno{\^\i}t",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1118",
    pages = "1259--1270",
    abstract = "This article introduces a novel transition system for discontinuous lexicalized constituent parsing called SR-GAP. It is an extension of the shift-reduce algorithm with an additional gap transition. Evaluation on two German treebanks shows that SR-GAP outperforms the previous best transition-based discontinuous parser (Maier, 2015) by a large margin (it is notably twice as accurate on the prediction of discontinuous constituents), and is competitive with the state of the art (Fern{\'a}ndez-Gonz{\'a}lez and Martins, 2015). As a side contribution, we adapt span features (Hall et al., 2014) to discontinuous parsing.",
}
@inproceedings{shimaoka-etal-2017-neural,
    title = "Neural Architectures for Fine-grained Entity Type Classification",
    author = "Shimaoka, Sonse  and
      Stenetorp, Pontus  and
      Inui, Kentaro  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-1119",
    pages = "1271--1280",
    abstract = "In this work, we investigate several neural network architectures for fine-grained entity type classification and make three key contributions. Despite being a natural comparison and addition, previous work on attentive neural architectures have not considered hand-crafted features and we combine these with learnt features and establish that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism learns to attend over syntactic heads and the phrase containing the mention, both of which are known to be strong hand-crafted features for our task. We introduce parameter sharing between labels through a hierarchical encoding method, that in low-dimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We demonstrate that the choice of training data has a drastic impact on performance, which decreases by as much as 9.85{\%} loose micro F1 score for a previously proposed method. Despite this discrepancy, our best model achieves state-of-the-art results with 75.36{\%} loose micro F1 score on the well-established Figer (GOLD) dataset and we report the best results for models trained using publicly available data for the OntoNotes dataset with 64.93{\%} loose micro F1 score.",
}
@proceedings{emnlp-2017-,
    title = "",
    editor = "Birch, Alexandra  and
      Schneider, Nathan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
}
@inproceedings{pasca-2017-acquisition,
    title = "Acquisition, Representation and Usage of Conceptual Hierarchies",
    author = "Pasca, Marius",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-3001",
    abstract = "Through subsumption and instantiation, individual instances ({``}artificial intelligence{''}, {``}the spotted pig{''}) otherwise spanning a wide range of domains can be brought together and organized under conceptual hierarchies. The hierarchies connect more specific concepts ({``}computer science subfields{''}, {``}gastropubs{''}) to more general concepts ({``}academic disciplines{''}, {``}restaurants{''}) through IsA relations. Explicit or implicit properties applicable to, and defining, more general concepts are inherited by their more specific concepts, down to the instances connected to the lower parts of the hierarchies. Subsumption represents a crisp, universally-applicable principle towards consistently representing IsA relations in any knowledge resource. Yet knowledge resources often exhibit significant differences in their scope, representation choices and intended usage, to cause significant differences in their expected usage and impact on various tasks. This tutorial examines the theoretical foundations of subsumption, and its practical embodiment through IsA relations compiled manually or extracted automatically. It addresses IsA relations from their formal definition; through practical choices made in their representation within the larger and more widely-used of the available knowledge resources; to their automatic acquisition from document repositories, as opposed to their manual compilation by human contributors; to their impact in text analysis and information retrieval. As search engines move away from returning a set of links and closer to returning results that more directly answer queries, IsA relations play an increasingly important role towards a better understanding of documents and queries. The tutorial teaches the audience about definitions, assumptions and practical choices related to modeling and representing IsA relations in existing, human-compiled resources of instances, concepts and resulting conceptual hierarchies; methods for automatically extracting sets of instances within unlabeled or labeled concepts, where the concepts may be considered as a flat set or organized hierarchically; and applications of IsA relations in information retrieval.",
}
@inproceedings{bhattacharyya-joshi-2017-computational,
    title = "Computational Sarcasm",
    author = "Bhattacharyya, Pushpak  and
      Joshi, Aditya",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-3002",
    abstract = "Sarcasm is a form of verbal irony that is intended to express contempt or ridicule. Motivated by challenges posed by sarcastic text to sentiment analysis, computational approaches to sarcasm have witnessed a growing interest at NLP forums in the past decade. Computational sarcasm refers to automatic approaches pertaining to sarcasm. The tutorial will provide a bird{'}s-eye view of the research in computational sarcasm for text, while focusing on significant milestones.The tutorial begins with linguistic theories of sarcasm, with a focus on incongruity: a useful notion that underlies sarcasm and other forms of figurative language. Since the most significant work in computational sarcasm is sarcasm detection: predicting whether a given piece of text is sarcastic or not, sarcasm detection forms the focus hereafter. We begin our discussion on sarcasm detection with datasets, touching on strategies, challenges and nature of datasets. Then, we describe algorithms for sarcasm detection: rule-based (where a specific evidence of sarcasm is utilised as a rule), statistical classifier-based (where features are designed for a statistical classifier), a topic model-based technique, and deep learning-based algorithms for sarcasm detection. In case of each of these algorithms, we refer to our work on sarcasm detection and share our learnings. Since information beyond the text to be classified, contextual information is useful for sarcasm detection, we then describe approaches that use such information through conversational context or author-specific context.We then follow it by novel areas in computational sarcasm such as sarcasm generation, sarcasm v/s irony classification, etc. We then summarise the tutorial and describe future directions based on errors reported in past work. The tutorial will end with a demonstration of our work on sarcasm detection.This tutorial will be of interest to researchers investigating computational sarcasm and related areas such as computational humour, figurative language understanding, emotion and sentiment sentiment analysis, etc. The tutorial is motivated by our continually evolving survey paper of sarcasm detection, that is available on arXiv at: Joshi, Aditya, Pushpak Bhattacharyya, and Mark James Carman. {``}Automatic Sarcasm Detection: A Survey.{''} arXiv preprint arXiv:1602.03426 (2016).",
}
@inproceedings{malliaros-vazirgiannis-2017-graph,
    title = "Graph-based Text Representations: Boosting Text Mining, {NLP} and Information Retrieval with Graphs",
    author = "Malliaros, Fragkiskos D.  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-3003",
    abstract = "Graphs or networks have been widely used as modeling tools in Natural Language Processing (NLP), Text Mining (TM) and Information Retrieval (IR). Traditionally, the unigram bag-of-words representation is applied; that way, a document is represented as a multiset of its terms, disregarding dependencies between the terms. Although several variants and extensions of this modeling approach have been proposed (e.g., the n-gram model), the main weakness comes from the underlying term independence assumption. The order of the terms within a document is completely disregarded and any relationship between terms is not taken into account in the final task (e.g., text categorization). Nevertheless, as the heterogeneity of text collections is increasing (especially with respect to document length and vocabulary), the research community has started exploring different document representations aiming to capture more fine-grained contexts of co-occurrence between different terms, challenging the well-established unigram bag-of-words model. To this direction, graphs constitute a well-developed model that has been adopted for text representation. The goal of this tutorial is to offer a comprehensive presentation of recent methods that rely on graph-based text representations to deal with various tasks in NLP and IR. We will describe basic as well as novel graph theoretic concepts and we will examine how they can be applied in a wide range of text-related application domains.All the material associated to the tutorial will be available at: http://fragkiskosm.github.io/projects/graph{\_}text{\_}tutorial",
}
@inproceedings{marcheggiani-etal-2017-semantic,
    title = "Semantic Role Labeling",
    author = "Marcheggiani, Diego  and
      Roth, Michael  and
      Titov, Ivan  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-3004",
    abstract = "This tutorial describes semantic role labelling (SRL), the task of mapping text to shallow semantic representations of eventualities and their participants. The tutorial introduces the SRL task and discusses recent research directions related to the task. The audience of this tutorial will learn about the linguistic background and motivation for semantic roles, and also about a range of computational models for this task, from early approaches to the current state-of-the-art. We will further discuss recently proposed variations to the traditional SRL task, including topics such as semantic proto-role labeling.We also cover techniques for reducing required annotation effort, such as methods exploiting unlabeled corpora (semi-supervised and unsupervised techniques), model adaptation across languages and domains, and methods for crowdsourcing semantic role annotation (e.g., question-answer driven SRL). Methods based on different machine learning paradigms, including neural networks, generative Bayesian models, graph-based algorithms and bootstrapping style techniques.Beyond sentence-level SRL, we discuss work that involves semantic roles in discourse. In particular, we cover data sets and models related to the task of identifying implicit roles and linking them to discourse antecedents. We introduce different approaches to this task from the literature, including models based on coreference resolution, centering, and selectional preferences. We also review how new insights gained through them can be useful for the traditional SRL task.",
}
@inproceedings{gulcehre-chandar-2017-memory,
    title = "Memory Augmented Neural Networks for Natural Language Processing",
    author = "Gulcehre, Caglar  and
      Chandar, Sarath",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-3005",
    abstract = "Designing of general-purpose learning algorithms is a long-standing goal of artificial intelligence. A general purpose AI agent should be able to have a memory that it can store and retrieve information from. Despite the success of deep learning in particular with the introduction of LSTMs and GRUs to this area, there are still a set of complex tasks that can be challenging for conventional neural networks. Those tasks often require a neural network to be equipped with an explicit, external memory in which a larger, potentially unbounded, set of facts need to be stored. They include but are not limited to, reasoning, planning, episodic question-answering and learning compact algorithms. Recently two promising approaches based on neural networks to this type of tasks have been proposed: Memory Networks and Neural Turing Machines.In this tutorial, we will give an overview of this new paradigm of {``}neural networks with memory{''}. We will present a unified architecture for Memory Augmented Neural Networks (MANN) and discuss the ways in which one can address the external memory and hence read/write from it. Then we will introduce Neural Turing Machines and Memory Networks as specific instantiations of this general architecture. In the second half of the tutorial, we will focus on recent advances in MANN which focus on the following questions: How can we read/write from an extremely large memory in a scalable way? How can we design efficient non-linear addressing schemes? How can we do efficient reasoning using large scale memory and an episodic memory? The answer to any one of these questions introduces a variant of MANN. We will conclude the tutorial with several open challenges in MANN and its applications to NLP.We will introduce several applications of MANN in NLP throughout the tutorial. Few examples include language modeling, question answering, visual question answering, and dialogue systems.For updated information and material, please refer to our tutorial website: https://sites.google.com/view/mann-emnlp2017/.",
}
@inproceedings{lu-2017-unified,
    title = "A Unified Framework for Structured Prediction: From Theory to Practice",
    author = "Lu, Wei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-3006",
    abstract = "Structured prediction is one of the most important topics in various fields, including machine learning, computer vision, natural language processing (NLP) and bioinformatics. In this tutorial, we present a novel framework that unifies various structured prediction models.The hidden Markov model (HMM) and the probabilistic context-free grammars (PCFGs) are two classic generative models used for predicting outputs with linear-chain and tree structures, respectively. As HMM{'}s discriminative counterpart, the linear-chain conditional random fields (CRFs) (Lafferty et al., 2001) model was later proposed. Such a model was shown to yield good performance on standard NLP tasks such as information extraction. Several extensions to such a model were then proposed afterward, including the semi-Markov CRFs (Sarawagi and Cohen, 2004), tree CRFs (Cohn and Blunsom, 2005), as well as discriminative parsing models and their latent variable variants (Petrov and Klein, 2007). On the other hand, utilizing a slightly different loss function, one could arrive at the structured support vector machines (Tsochantaridis et al., 2004) and its latent variable variant (Yu and Joachims, 2009) as well. Furthermore, new models that integrate neural networks and graphical models, such as neural CRFs (Do et al., 2010) were also proposed.In this tutorial, we will be discussing how such a wide spectrum of existing structured prediction models can all be implemented under a unified framework (available at here) that involves some basic building blocks. Based on such a framework, we show how some seemingly complicated structured prediction models such as a semantic parsing model (Lu et al., 2008; Lu, 2014) can be implemented conveniently and quickly. Furthermore, we also show that the framework can be used to solve certain structured prediction problems that otherwise cannot be easily handled by conventional structured prediction models. Specifically, we show how to use such a framework to construct models that are capable of predicting non-conventional structures, such as overlapping structures (Lu and Roth, 2015; Muis and Lu, 2016a). We will also discuss how to make use of the framework to build other related models such as topic models and highlight its potential applications in some recent popular tasks (e.g., AMR parsing (Flanigan et al., 2014)).The framework has been extensively used by our research group for developing various structured prediction models, including models for information extraction (Lu and Roth, 2015; Muis and Lu, 2016a; Jie et al., 2017), noun phrase chunking (Muis and Lu, 2016b), semantic parsing (Lu, 2015; Susanto and Lu, 2017), and sentiment analysis (Li and Lu, 2017). It is our hope that this tutorial will be helpful for many natural language processing researchers who are interested in designing their own structured prediction models rapidly. We also hope this tutorial allows researchers to strengthen their understandings on the connections between various structured prediction models, and that the open release of the framework will bring value to the NLP research community and enhance its overall productivity.The material associated with this tutorial will be available at the tutorial web site: https://web.archive.org/web/20180427113151/http://statnlp.org/tutorials/.",
}
@inproceedings{faruqui-etal-2017-cross,
    title = "Cross-Lingual Word Representations: Induction and Evaluation",
    author = "Faruqui, Manaal  and
      S{\o}gaard, Anders  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-3007",
    abstract = "In recent past, NLP as a field has seen tremendous utility of distributional word vector representations as features in downstream tasks. The fact that these word vectors can be trained on unlabeled monolingual corpora of a language makes them an inexpensive resource in NLP. With the increasing use of monolingual word vectors, there is a need for word vectors that can be used as efficiently across multiple languages as monolingually. Therefore, learning bilingual and multilingual word embeddings/vectors is currently an important research topic. These vectors offer an elegant and language-pair independent way to represent content across different languages.This tutorial aims to bring NLP researchers up to speed with the current techniques in cross-lingual word representation learning. We will first discuss how to induce cross-lingual word representations (covering both bilingual and multilingual ones) from various data types and resources (e.g., parallel data, comparable data, non-aligned monolingual data in different languages, dictionaries and theasuri, or, even, images, eye-tracking data). We will then discuss how to evaluate such representations, intrinsically and extrinsically. We will introduce researchers to state-of-the-art methods for constructing cross-lingual word representations and discuss their applicability in a broad range of downstream NLP applications.We will deliver a detailed survey of the current methods, discuss best training and evaluation practices and use-cases, and provide links to publicly available implementations, datasets, and pre-trained models.",
}
@proceedings{emnlp-2017-2017-empirical,
    title = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    editor = "Specia, Lucia  and
      Post, Matt  and
      Paul, Michael",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2000",
    doi = "10.18653/v1/D17-2",
}
@inproceedings{schneider-wooters-2017-nltk,
    title = "The {NLTK} {F}rame{N}et {API}: Designing for Discoverability with a Rich Linguistic Resource",
    author = "Schneider, Nathan  and
      Wooters, Chuck",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2001",
    doi = "10.18653/v1/D17-2001",
    pages = "1--6",
    abstract = "A new Python API, integrated within the NLTK suite, offers access to the FrameNet 1.7 lexical database. The lexicon (structured in terms of frames) as well as annotated sentences can be processed programatically, or browsed with human-readable displays via the interactive Python prompt.",
}
@inproceedings{habernal-etal-2017-argotario,
    title = "{A}rgotario: Computational Argumentation Meets Serious Games",
    author = "Habernal, Ivan  and
      Hannemann, Raffael  and
      Pollak, Christian  and
      Klamm, Christopher  and
      Pauli, Patrick  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2002",
    doi = "10.18653/v1/D17-2002",
    pages = "7--12",
    abstract = "An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies. Fallacious arguments, omnipresent in argumentative discourse, can be deceptive, manipulative, or simply leading to {`}wrong moves{'} in a discussion. Despite their importance, argumentation scholars and NLP researchers with focus on argumentation quality have not yet investigated fallacies empirically. The nonexistence of resources dealing with fallacious argumentation calls for scalable approaches to data acquisition and annotation, for which the serious games methodology offers an appealing, yet unexplored, alternative. We present Argotario, a serious game that deals with fallacies in everyday argumentation. Argotario is a multilingual, open-source, platform-independent application with strong educational aspects, accessible at \url{www.argotario.net}.",
}
@inproceedings{ovesdotter-alm-etal-2017-analysis,
    title = "An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts",
    author = "Ovesdotter Alm, Cecilia  and
      Meyers, Benjamin  and
      Prud{'}hommeaux, Emily",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2003",
    doi = "10.18653/v1/D17-2003",
    pages = "13--18",
    abstract = "We present an educational tool that integrates computational linguistics resources for use in non-technical undergraduate language science courses. By using the tool in conjunction with evidence-driven pedagogical case studies, we strive to provide opportunities for students to gain an understanding of linguistic concepts and analysis through the lens of realistic problems in feasible ways. Case studies tend to be used in legal, business, and health education contexts, but less in the teaching and learning of linguistics. The approach introduced also has potential to encourage students across training backgrounds to continue on to computational language analysis coursework.",
}
@inproceedings{falke-gurevych-2017-graphdocexplore,
    title = "{G}raph{D}oc{E}xplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques",
    author = "Falke, Tobias  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2004",
    doi = "10.18653/v1/D17-2004",
    pages = "19--24",
    abstract = "Graphs have long been proposed as a tool to browse and navigate in a collection of documents in order to support exploratory search. Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested. While experimental evidence that they are indeed helpful exists for some of them, it is largely unknown which type of graph is most helpful for a specific exploratory task. However, carrying out experimental comparisons with human subjects is challenging and time-consuming. Towards this end, we present the \textit{GraphDocExplore} framework. It provides an intuitive web interface for graph-based document exploration that is optimized for experimental user studies. Through a generic graph interface, different methods to extract graphs from text can be plugged into the system. Hence, they can be compared at minimal implementation effort in an environment that ensures controlled comparisons. The system is publicly available under an open-source license.",
}
@inproceedings{stahlberg-etal-2017-sgnmt,
    title = "{SGNMT} {--} A Flexible {NMT} Decoding Platform for Quick Prototyping of New Models and Search Strategies",
    author = "Stahlberg, Felix  and
      Hasler, Eva  and
      Saunders, Danielle  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2005",
    doi = "10.18653/v1/D17-2005",
    pages = "25--30",
    abstract = "This paper introduces SGNMT, our experimental platform for machine translation research. SGNMT provides a generic interface to neural and symbolic scoring modules (predictors) with left-to-right semantic such as translation models like NMT, language models, translation lattices, n-best lists or other kinds of scores and constraints. Predictors can be combined with other predictors to form complex decoding tasks. SGNMT implements a number of search strategies for traversing the space spanned by the predictors which are appropriate for different predictor constellations. Adding new predictors or decoding strategies is particularly easy, making it a very efficient tool for prototyping new research ideas. SGNMT is actively being used by students in the MPhil program in Machine Learning, Speech and Language Technology at the University of Cambridge for course work and theses, as well as for most of the research work in our group.",
}
@inproceedings{yanai-etal-2017-struap,
    title = "{S}tru{AP}: A Tool for Bundling Linguistic Trees through Structure-based Abstract Pattern",
    author = "Yanai, Kohsuke  and
      Sato, Misa  and
      Yanase, Toshihiko  and
      Kurotsuchi, Kenzo  and
      Koreeda, Yuta  and
      Niwa, Yoshiki",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2006",
    doi = "10.18653/v1/D17-2006",
    pages = "31--36",
    abstract = "We present a tool for developing tree structure patterns that makes it easy to define the relations among textual phrases and create a search index for these newly defined relations. By using the proposed tool, users develop tree structure patterns through abstracting syntax trees. The tool features (1) intuitive pattern syntax, (2) unique functions such as recursive call of patterns and use of lexicon dictionaries, and (3) whole workflow support for relation development and validation. We report the current implementation of the tool and its effectiveness.",
}
@inproceedings{mechanic-etal-2017-knowyournyms,
    title = "{K}now{Y}our{N}yms? A Game of Semantic Relationships",
    author = "Mechanic, Ross  and
      Fulgoni, Dean  and
      Cutler, Hannah  and
      Rajana, Sneha  and
      Liu, Zheyuan  and
      Jackson, Bradley  and
      Cocos, Anne  and
      Callison-Burch, Chris  and
      Apidianaki, Marianna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2007",
    doi = "10.18653/v1/D17-2007",
    pages = "37--42",
    abstract = "Semantic relation knowledge is crucial for natural language understanding. We introduce {``}KnowYourNyms?{''}, a web-based game for learning semantic relations. While providing users with an engaging experience, the application collects large amounts of data that can be used to improve semantic relation classifiers. The data also broadly informs us of how people perceive the relationships between words, providing useful insights for research in psychology and linguistics.",
}
@inproceedings{akbik-vollgraf-2017-projector,
    title = "The Projector: An Interactive Annotation Projection Visualization Tool",
    author = "Akbik, Alan  and
      Vollgraf, Roland",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2008",
    doi = "10.18653/v1/D17-2008",
    pages = "43--48",
    abstract = "Previous works proposed annotation projection in parallel corpora to inexpensively generate treebanks or propbanks for new languages. In this approach, linguistic annotation is automatically transferred from a resource-rich source language (SL) to translations in a target language (TL). However, annotation projection may be adversely affected by translational divergences between specific language pairs. For this reason, previous work often required careful qualitative analysis of projectability of specific annotation in order to define strategies to address quality and coverage issues. In this demonstration, we present THE PROJECTOR, an interactive GUI designed to assist researchers in such analysis: it allows users to execute and visually inspect annotation projection in a range of different settings. We give an overview of the GUI, discuss use cases and illustrate how the tool can facilitate discussions with the research community.",
}
@inproceedings{sarnat-etal-2017-interactive,
    title = "Interactive Visualization for Linguistic Structure",
    author = "Sarnat, Aaron  and
      Joshi, Vidur  and
      Petrescu-Prahova, Cristian  and
      Herrasti, Alvaro  and
      Stilson, Brandon  and
      Hopkins, Mark",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2009",
    doi = "10.18653/v1/D17-2009",
    pages = "49--54",
    abstract = "We provide a visualization library and web interface for interactively exploring a parse tree or a forest of parses. The library is not tied to any particular linguistic representation, but provides a general-purpose API for the interactive exploration of hierarchical linguistic structure. To facilitate rapid understanding of a complex structure, the API offers several important features, including expand/collapse functionality, positional and color cues, explicit visual support for sequential structure, and dynamic highlighting to convey node-to-text correspondence.",
}
@inproceedings{schwartz-etal-2017-dlatk,
    title = "{DLATK}: Differential Language Analysis {T}ool{K}it",
    author = "Schwartz, H. Andrew  and
      Giorgi, Salvatore  and
      Sap, Maarten  and
      Crutchley, Patrick  and
      Ungar, Lyle  and
      Eichstaedt, Johannes",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2010",
    doi = "10.18653/v1/D17-2010",
    pages = "55--60",
    abstract = "We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.",
}
@inproceedings{abujabal-etal-2017-quint,
    title = "{QUINT}: Interpretable Question Answering over Knowledge Bases",
    author = "Abujabal, Abdalghani  and
      Saha Roy, Rishiraj  and
      Yahya, Mohamed  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2011",
    doi = "10.18653/v1/D17-2011",
    pages = "61--66",
    abstract = "We present QUINT, a live system for question answering over knowledge bases. QUINT automatically learns role-aligned utterance-query templates from user questions paired with their answers. When QUINT answers a question, it visualizes the complete derivation sequence from the natural language utterance to the final answer. The derivation provides an explanation of how the syntactic structure of the question was used to derive the structure of a SPARQL query, and how the phrases in the question were used to instantiate different parts of the query. When an answer seems unsatisfactory, the derivation provides valuable insights towards reformulating the question.",
}
@inproceedings{richardson-kuhn-2017-function,
    title = "Function Assistant: A Tool for {NL} Querying of {API}s",
    author = "Richardson, Kyle  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2012",
    doi = "10.18653/v1/D17-2012",
    pages = "67--72",
    abstract = "In this paper, we describe Function Assistant, a lightweight Python-based toolkit for querying and exploring source code repositories using natural language. The toolkit is designed to help end-users of a target API quickly find information about functions through high-level natural language queries, or descriptions. For a given text query and background API, the tool finds candidate functions by performing a translation from the text to known representations in the API using the semantic parsing approach of (Richardson and Kuhn, 2017). Translations are automatically learned from example text-code pairs in example APIs. The toolkit includes features for building translation pipelines and query engines for arbitrary source code projects. To explore this last feature, we perform new experiments on 27 well-known Python projects hosted on Github.",
}
@inproceedings{huang-etal-2017-moodswipe,
    title = "{M}ood{S}wipe: A Soft Keyboard that Suggests {M}essage{B}ased on User-Specified Emotions",
    author = "Huang, Chieh-Yang  and
      Labetoulle, Tristan  and
      Huang, Ting-Hao  and
      Chen, Yi-Pei  and
      Chen, Hung-Chen  and
      Srivastava, Vallari  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2013",
    doi = "10.18653/v1/D17-2013",
    pages = "73--78",
    abstract = "We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is to create a convenient user interface to enjoy the technology of emotion classification and text suggestion, and at the same time to collect labeled data automatically for developing more advanced technologies. While users select the MoodSwipe keyboard, they can type as usual but sense the emotion conveyed by their text and receive suggestions for their message as a benefit. In MoodSwipe, the detected emotions serve as the medium for suggested texts, where viewing the latter is the incentive to correcting the former. We conduct several experiments to show the superiority of the emotion classification models trained on the dialog data, and further to verify good emotion cues are important context for text suggestion.",
}
@inproceedings{miller-etal-2017-parlai,
    title = "{P}arl{AI}: A Dialog Research Software Platform",
    author = "Miller, Alexander  and
      Feng, Will  and
      Batra, Dhruv  and
      Bordes, Antoine  and
      Fisch, Adam  and
      Lu, Jiasen  and
      Parikh, Devi  and
      Weston, Jason",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2014",
    doi = "10.18653/v1/D17-2014",
    pages = "79--84",
    abstract = "We introduce ParlAI (pronounced {``}par-lay{''}), an open-source software platform for dialog research implemented in Python, available at \url{http://parl.ai}. Its goal is to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others{'} models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.",
}
@inproceedings{richter-etal-2017-heidelplace,
    title = "{H}eidel{P}lace: An Extensible Framework for Geoparsing",
    author = "Richter, Ludwig  and
      Gei{\ss}, Johanna  and
      Spitz, Andreas  and
      Gertz, Michael",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2015",
    doi = "10.18653/v1/D17-2015",
    pages = "85--90",
    abstract = "Geographic information extraction from textual data sources, called geoparsing, is a key task in text processing and central to subsequent spatial analysis approaches. Several geoparsers are available that support this task, each with its own (often limited or specialized) gazetteer and its own approaches to toponym detection and resolution. In this demonstration paper, we present HeidelPlace, an extensible framework in support of geoparsing. Key features of HeidelPlace include a generic gazetteer model that supports the integration of place information from different knowledge bases, and a pipeline approach that enables an effective combination of diverse modules tailored to specific geoparsing tasks. This makes HeidelPlace a valuable tool for testing and evaluating different gazetteer sources and geoparsing methods. In the demonstration, we show how to set up a geoparsing workflow with HeidelPlace and how it can be used to compare and consolidate the output of different geoparsing approaches.",
}
@inproceedings{panchenko-etal-2017-unsupervised,
    title = "Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation",
    author = "Panchenko, Alexander  and
      Marten, Fide  and
      Ruppert, Eugen  and
      Faralli, Stefano  and
      Ustalov, Dmitry  and
      Ponzetto, Simone Paolo  and
      Biemann, Chris",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2016",
    doi = "10.18653/v1/D17-2016",
    pages = "91--96",
    abstract = "Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.",
}
@inproceedings{dernoncourt-etal-2017-neuroner,
    title = "{N}euro{NER}: an easy-to-use program for named-entity recognition based on neural networks",
    author = "Dernoncourt, Franck  and
      Lee, Ji Young  and
      Szolovits, Peter",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2017",
    doi = "10.18653/v1/D17-2017",
    pages = "97--102",
    abstract = "Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easy-to-use named-entity recognition tool based on ANNs. Users can annotate entities using a graphical web-based user interface (BRAT): the annotations are then used to train an ANN, which in turn predict entities{'} locations and categories in new texts. NeuroNER makes this annotation-training-prediction flow smooth and accessible to anyone.",
}
@inproceedings{papandrea-etal-2017-supwsd,
    title = "{S}up{WSD}: A Flexible Toolkit for Supervised Word Sense Disambiguation",
    author = "Papandrea, Simone  and
      Raganato, Alessandro  and
      Delli Bovi, Claudio",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2018",
    doi = "10.18653/v1/D17-2018",
    pages = "103--108",
    abstract = "In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at \url{http://github.com/SI3P/SupWSD}.",
}
@inproceedings{shapira-etal-2017-interactive,
    title = "Interactive Abstractive Summarization for Event News Tweets",
    author = "Shapira, Ori  and
      Ronen, Hadar  and
      Adler, Meni  and
      Amsterdamer, Yael  and
      Bar-Ilan, Judit  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2019",
    doi = "10.18653/v1/D17-2019",
    pages = "109--114",
    abstract = "We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts. We incorporate a couple of interaction mechanisms, providing a bullet-style summary while allowing to attain the most important information first and interactively drill down to more specific details. A usability study of our implementation, for event news tweets, suggests the utility of our approach for text exploration.",
}
@inproceedings{abzianidze-2017-langpro,
    title = "{L}ang{P}ro: Natural Language Theorem Prover",
    author = "Abzianidze, Lasha",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2020",
    doi = "10.18653/v1/D17-2020",
    pages = "115--120",
    abstract = "LangPro is an automated theorem prover for natural language. Given a set of premises and a hypothesis, it is able to prove semantic relations between them. The prover is based on a version of analytic tableau method specially designed for natural logic. The proof procedure operates on logical forms that preserve linguistic expressions to a large extent. {\%}This property makes the logical forms easily obtainable from syntactic trees. {\%}, in particular, Combinatory Categorial Grammar derivation trees. The nature of proofs is deductive and transparent. On the FraCaS and SICK textual entailment datasets, the prover achieves high results comparable to state-of-the-art.",
}
@inproceedings{lee-etal-2017-interactive,
    title = "Interactive Visualization and Manipulation of Attention-based Neural Machine Translation",
    author = "Lee, Jaesong  and
      Shin, Joong-Hwi  and
      Kim, Jun-Seok",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-2021",
    doi = "10.18653/v1/D17-2021",
    pages = "121--126",
    abstract = "While neural machine translation (NMT) provides high-quality translation, it is still hard to interpret and analyze its behavior. We present an interactive interface for visualizing and intervening behavior of NMT, specifically concentrating on the behavior of beam search mechanism and attention component. The tool (1) visualizes search tree and attention and (2) provides interface to adjust search tree and attention weight (manually or automatically) at real-time. We show the tool gives various methods to understand NMT.",
}
@proceedings{emnlp-2017-2017,
    title = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1000",
    doi = "10.18653/v1/D17-1",
}
@inproceedings{arase-tsujii-2017-monolingual,
    title = "Monolingual Phrase Alignment on Parse Forests",
    author = "Arase, Yuki  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1001",
    doi = "10.18653/v1/D17-1001",
    pages = "1--11",
    abstract = "We propose an efficient method to conduct phrase alignment on parse forests for paraphrase detection. Unlike previous studies, our method identifies syntactic paraphrases under linguistically motivated grammar. In addition, it allows phrases to non-compositionally align to handle paraphrases with non-homographic phrase correspondences. A dataset that provides gold parse trees and their phrase alignments is created. The experimental results confirm that the proposed method conducts highly accurate phrase alignment compared to human performance.",
}
@inproceedings{shi-etal-2017-fast,
    title = "Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set",
    author = "Shi, Tianze  and
      Huang, Liang  and
      Lee, Lillian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1002",
    doi = "10.18653/v1/D17-1002",
    pages = "12--23",
    abstract = "We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case $O(n^3)$ exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present $O(n^3)$ global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the {``}second-best-in-class{''} result on the English Penn Treebank.",
}
@inproceedings{cao-etal-2017-quasi,
    title = "Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs",
    author = "Cao, Junjie  and
      Huang, Sheng  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1003",
    doi = "10.18653/v1/D17-1003",
    pages = "24--34",
    abstract = "We propose a new Maximum Subgraph algorithm for first-order parsing to 1-endpoint-crossing, pagenumber-2 graphs. Our algorithm has two characteristics: (1) it separates the construction for noncrossing edges and crossing edges; (2) in a single construction step, whether to create a new arc is deterministic. These two characteristics make our algorithm relatively easy to be extended to incorporiate crossing-sensitive second-order features. We then introduce a new algorithm for quasi-second-order parsing. Experiments demonstrate that second-order features are helpful for Maximum Subgraph parsing.",
}
@inproceedings{zhang-etal-2017-position,
    title = "Position-aware Attention and Supervised Data Improve Slot Filling",
    author = "Zhang, Yuhao  and
      Zhong, Victor  and
      Chen, Danqi  and
      Angeli, Gabor  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1004",
    doi = "10.18653/v1/D17-1004",
    pages = "35--45",
    abstract = "Organized relational knowledge in the form of {``}knowledge graphs{''} is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F1 score increases markedly from 22.2{\%} to 26.7{\%}.",
}
@inproceedings{liu-etal-2017-heterogeneous,
    title = "Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach",
    author = "Liu, Liyuan  and
      Ren, Xiang  and
      Zhu, Qi  and
      Zhi, Shi  and
      Gui, Huan  and
      Ji, Heng  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1005",
    doi = "10.18653/v1/D17-1005",
    pages = "46--56",
    abstract = "Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.",
}
@inproceedings{wang-etal-2017-integrating,
    title = "Integrating Order Information and Event Relation for Script Event Prediction",
    author = "Wang, Zhongqing  and
      Zhang, Yue  and
      Chang, Ching-Yun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1006",
    doi = "10.18653/v1/D17-1006",
    pages = "57--67",
    abstract = "There has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs have been used to encode full chains of narrative events. The latter has the advantage of learning long-range temporal orders, yet the former is more adaptive to partial orders. We propose a neural model that leverages the advantages of both methods, by using LSTM hidden states as features for event pair modelling. A dynamic memory network is utilized to automatically induce weights on existing events for inferring a subsequent event. Standard evaluation shows that our method significantly outperforms both methods above, giving the best results reported so far.",
}
@inproceedings{tan-etal-2017-entity,
    title = "Entity Linking for Queries by Searching {W}ikipedia Sentences",
    author = "Tan, Chuanqi  and
      Wei, Furu  and
      Ren, Pengjie  and
      Lv, Weifeng  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1007",
    doi = "10.18653/v1/D17-1007",
    pages = "68--77",
    abstract = "We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0{\%} in F1 on the ERD14 dataset and 56.9{\%} on the GERDAQ dataset.",
}
@inproceedings{pasini-navigli-2017-train,
    title = "Train-{O}-{M}atic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data",
    author = "Pasini, Tommaso  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1008",
    doi = "10.18653/v1/D17-1008",
    pages = "78--88",
    abstract = "Annotating large numbers of sentences with senses is the heaviest requirement of current Word Sense Disambiguation. We present Train-O-Matic, a language-independent method for generating millions of sense-annotated training instances for virtually all meanings of words in a language{'}s vocabulary. The approach is fully automatic: no human intervention is required and the only type of human knowledge used is a WordNet-like resource. Train-O-Matic achieves consistently state-of-the-art performance across gold standard datasets and languages, while at the same time removing the burden of manual annotation. All the training data is available for research purposes at \url{http://trainomatic.org}.",
}
@inproceedings{reddy-etal-2017-universal,
    title = "Universal Semantic Parsing",
    author = {Reddy, Siva  and
      T{\"a}ckstr{\"o}m, Oscar  and
      Petrov, Slav  and
      Steedman, Mark  and
      Lapata, Mirella},
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1009",
    doi = "10.18653/v1/D17-1009",
    pages = "89--101",
    abstract = "Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions.",
}
@inproceedings{pinter-etal-2017-mimicking,
    title = "Mimicking Word Embeddings using Subword {RNN}s",
    author = "Pinter, Yuval  and
      Guthrie, Robert  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1010",
    doi = "10.18653/v1/D17-1010",
    pages = "102--112",
    abstract = "Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised character-based model in low resource settings.",
}
@inproceedings{asgari-schutze-2017-past,
    title = "Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages",
    author = {Asgari, Ehsaneddin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1011",
    doi = "10.18653/v1/D17-1011",
    pages = "113--124",
    abstract = "We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tense. We produce analysis results for more than 1000 languages, conducting {--} to the best of our knowledge {--} the largest crosslingual computational study performed to date. We extend existing methodology for leveraging parallel corpora for typological analysis by overcoming a limiting assumption of earlier work: We only require that a linguistic feature is overtly marked in a few of thousands of languages as opposed to requiring that it be marked in all languages under investigation.",
}
@inproceedings{hashimoto-tsuruoka-2017-neural,
    title = "Neural Machine Translation with Source-Side Latent Graph Parsing",
    author = "Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1012",
    doi = "10.18653/v1/D17-1012",
    pages = "125--135",
    abstract = "This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.",
}
@inproceedings{weng-etal-2017-neural,
    title = "Neural Machine Translation with Word Predictions",
    author = "Weng, Rongxiang  and
      Huang, Shujian  and
      Zheng, Zaixiang  and
      Dai, Xinyu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1013",
    doi = "10.18653/v1/D17-1013",
    pages = "136--145",
    abstract = "In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence. These vectors are generated by parameters which are updated by back-propagation of translation errors through time.We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors. In this paper, we propose to use word predictions as a mechanism for direct supervision. More specifically, we require these vectors to be able to predict the vocabulary in target sentence. Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation. It is also helpful in reducing the target side vocabulary and improving the decoding efficiency. Experiments on Chinese-English machine translation task show an average BLEU improvement by 4.53, respectively.",
}
@inproceedings{hoang-etal-2017-towards,
    title = "Towards Decoding as Continuous Optimisation in Neural Machine Translation",
    author = "Hoang, Cong Duy Vu  and
      Haffari, Gholamreza  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1014",
    doi = "10.18653/v1/D17-1014",
    pages = "146--156",
    abstract = "We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We reformulate decoding, a discrete optimization problem, into a continuous problem, such that optimization can make use of efficient gradient-based techniques. Our powerful decoding framework allows for more accurate decoding for standard neural machine translation models, as well as enabling decoding in intractable models such as intersection of several different NMT models. Our empirical results show that our decoding framework is effective, and can leads to substantial improvements in translations, especially in situations where greedy search and beam search are not feasible. Finally, we show how the technique is highly competitive with, and complementary to, reranking.",
}
@inproceedings{kitaev-klein-2017-misty,
    title = "Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space",
    author = "Kitaev, Nikita  and
      Klein, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1015",
    doi = "10.18653/v1/D17-1015",
    pages = "157--166",
    abstract = "We present a model for locating regions in space based on natural language descriptions. Starting with a 3D scene and a sentence, our model is able to associate words in the sentence with regions in the scene, interpret relations such as {`}on top of{'} or {`}next to,{'} and finally locate the region described in the sentence. All components form a single neural network that is trained end-to-end without prior knowledge of object segmentation. To evaluate our model, we construct and release a new dataset consisting of Minecraft scenes with crowdsourced natural language descriptions. We achieve a 32{\%} relative error reduction compared to a strong neural baseline.",
}
@inproceedings{rahimi-etal-2017-continuous,
    title = "Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks",
    author = "Rahimi, Afshin  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1016",
    doi = "10.18653/v1/D17-1016",
    pages = "167--176",
    abstract = "We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.",
}
@inproceedings{yin-ordonez-2017-obj2text,
    title = "{O}bj2{T}ext: Generating Visually Descriptive Language from Object Layouts",
    author = "Yin, Xuwang  and
      Ordonez, Vicente",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1017",
    doi = "10.18653/v1/D17-1017",
    pages = "177--187",
    abstract = "Generating captions for images is a task that has recently received considerable attention. Another type of visual inputs are abstract scenes or object layouts where the only information provided is a set of objects and their locations. This type of imagery is commonly found in many applications in computer graphics, virtual reality, and storyboarding. We explore in this paper OBJ2TEXT, a sequence-to-sequence model that encodes a set of objects and their locations as an input sequence using an LSTM network, and decodes this representation using an LSTM language model. We show in our paper that this model despite using a sequence encoder can effectively represent complex spatial object-object relationships and produce descriptions that are globally coherent and semantically relevant. We test our approach for the task of describing object layouts in the MS-COCO dataset by producing sentences given only object annotations. We additionally show that our model combined with a state-of-the-art object detector can improve the accuracy of an image captioning model.",
}
@inproceedings{lee-etal-2017-end,
    title = "End-to-end Neural Coreference Resolution",
    author = "Lee, Kenton  and
      He, Luheng  and
      Lewis, Mike  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1018",
    doi = "10.18653/v1/D17-1018",
    pages = "188--197",
    abstract = "We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources.",
}
@inproceedings{li-jurafsky-2017-neural,
    title = "Neural Net Models of Open-domain Discourse Coherence",
    author = "Li, Jiwei  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1019",
    doi = "10.18653/v1/D17-1019",
    pages = "198--209",
    abstract = "Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences. We study both discriminative models that learn to distinguish coherent from incoherent discourse, and generative models that produce coherent text, including a novel neural latent-variable Markovian generative model that captures the latent discourse dependencies between sentences in a text. Our work achieves state-of-the-art performance on multiple coherence evaluations, and marks an initial step in generating coherent texts given discourse contexts.",
}
@inproceedings{wang-etal-2017-affinity,
    title = "Affinity-Preserving Random Walk for Multi-Document Summarization",
    author = "Wang, Kexiang  and
      Liu, Tianyu  and
      Sui, Zhifang  and
      Chang, Baobao",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1020",
    doi = "10.18653/v1/D17-1020",
    pages = "210--220",
    abstract = "Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.",
}
@inproceedings{marasovic-etal-2017-mention,
    title = "A Mention-Ranking Model for Abstract Anaphora Resolution",
    author = "Marasovi{\'c}, Ana  and
      Born, Leo  and
      Opitz, Juri  and
      Frank, Anette",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1021",
    doi = "10.18653/v1/D17-1021",
    pages = "221--232",
    abstract = "Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence{--}antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and {--} if disregarding syntax {--} discriminates candidates using deeper features.",
}
@inproceedings{nguyen-etal-2017-hierarchical,
    title = "Hierarchical Embeddings for Hypernymy Detection and Directionality",
    author = {Nguyen, Kim Anh  and
      K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine  and
      Vu, Ngoc Thang},
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1022",
    doi = "10.18653/v1/D17-1022",
    pages = "233--243",
    abstract = "We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality. While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym{--}hyponym distributional hierarchy. Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages. Results on benchmark datasets show that HyperVec outperforms both state-of-the-art unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.",
}
@inproceedings{zhao-etal-2017-ngram2vec,
    title = "{N}gram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics",
    author = "Zhao, Zhe  and
      Liu, Tao  and
      Li, Shen  and
      Li, Bofang  and
      Du, Xiaoyong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1023",
    doi = "10.18653/v1/D17-1023",
    pages = "244--253",
    abstract = "The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The results show that improved word representations are learned from ngram co-occurrence statistics. We also demonstrate that the trained ngram representations are useful in many aspects such as finding antonyms and collocations. Besides, a novel approach of building co-occurrence matrix is proposed to alleviate the hardware burdens brought by ngrams.",
}
@inproceedings{tissier-etal-2017-dict2vec,
    title = "{D}ict2vec : Learning Word Embeddings using Lexical Dictionaries",
    author = "Tissier, Julien  and
      Gravier, Christophe  and
      Habrard, Amaury",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1024",
    doi = "10.18653/v1/D17-1024",
    pages = "254--263",
    abstract = "Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks. The most efficient and popular approaches learn or retrofit such representations using additional external data. Resulting embeddings are generally better than their corpus-only counterparts, although such resources cover a fraction of words in the vocabulary. In this paper, we propose a new approach, Dict2vec, based on one of the largest yet refined datasource for describing words {--} natural language dictionaries. Dict2vec builds new word pairs from dictionary entries so that semantically-related words are moved closer, and negative sampling filters out pairs whose words are unrelated in dictionaries. We evaluate the word representations obtained using Dict2vec on eleven datasets for the word similarity task and on four datasets for a text classification task.",
}
@inproceedings{su-lee-2017-learning,
    title = "Learning {C}hinese Word Representations From Glyphs Of Characters",
    author = "Su, Tzu-Ray  and
      Lee, Hung-Yi",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1025",
    doi = "10.18653/v1/D17-1025",
    pages = "264--273",
    abstract = "In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribution in this paper is that we created several evaluation datasets in traditional Chinese and made them public.",
}
@inproceedings{wieting-etal-2017-learning,
    title = "Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext",
    author = "Wieting, John  and
      Mallinson, Jonathan  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1026",
    doi = "10.18653/v1/D17-1026",
    pages = "274--285",
    abstract = "We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from human-written sentences, finding clear differences in length, the amount of repetition, and the use of rare words.",
}
@inproceedings{yu-etal-2017-joint,
    title = "Joint Embeddings of {C}hinese Words, Characters, and Fine-grained Subcharacter Components",
    author = "Yu, Jinxing  and
      Jian, Xun  and
      Xin, Hao  and
      Song, Yangqiu",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1027",
    doi = "10.18653/v1/D17-1027",
    pages = "286--291",
    abstract = "Word embeddings have attracted much attention recently. Different from alphabetic writing systems, Chinese characters are often composed of subcharacter components which are also semantically informative. In this work, we propose an approach to jointly embed Chinese words as well as their characters and fine-grained subcharacter components. We use three likelihoods to evaluate whether the context words, characters, and components can predict the current target word, and collected 13,253 subcharacter components to demonstrate the existing approaches of decomposing Chinese characters are not enough. Evaluation on both word similarity and word analogy tasks demonstrates the superior performance of our model.",
}
@inproceedings{gupta-etal-2017-exploiting,
    title = "Exploiting Morphological Regularities in Distributional Word Representations",
    author = "Gupta, Arihant  and
      Akhtar, Syed Sarfaraz  and
      Vajpayee, Avijit  and
      Srivastava, Arjit  and
      Jhanwar, Madan Gopal  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1028",
    doi = "10.18653/v1/D17-1028",
    pages = "292--297",
    abstract = "We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word analogy test set with an accuracy of 85{\%} which is 12{\%} higher than the previous best known system.",
}
@inproceedings{wang-etal-2017-exploiting,
    title = "Exploiting Word Internal Structures for Generic {C}hinese Sentence Representation",
    author = "Wang, Shaonan  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1029",
    doi = "10.18653/v1/D17-1029",
    pages = "298--303",
    abstract = "We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures. Our architecture uses two key strategies. The first is a mask gate on characters, learning the relation among characters in a word. The second is a maxpooling operation on words, adaptively finding the optimal mixture of the atomic and compositional word representations. Finally, the proposed architecture is applied to various sentence composition models, which achieves substantial performance gains over baseline models on sentence similarity task.",
}
@inproceedings{herbelot-baroni-2017-high,
    title = "High-risk learning: acquiring new word vectors from tiny data",
    author = "Herbelot, Aur{\'e}lie  and
      Baroni, Marco",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1030",
    doi = "10.18653/v1/D17-1030",
    pages = "304--309",
    abstract = "Distributional semantics models are known to struggle with small data. It is generally accepted that in order to learn {`}a good vector{'} for a word, a model must have sufficient examples of its usage. This contradicts the fact that humans can guess the meaning of a word from a few occurrences only. In this paper, we show that a neural language model such as Word2Vec only necessitates minor modifications to its standard architecture to learn new terms from tiny data, using background knowledge from a previously learnt semantic space. We test our model on word definitions and on a nonce task involving 2-6 sentences{'} worth of context, showing a large increase in performance over state-of-the-art models on the definitional task.",
}
@inproceedings{sanu-etal-2017-word,
    title = "Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding",
    author = "Sanu, Joseph  and
      Xu, Mingbin  and
      Jiang, Hui  and
      Liu, Quan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1031",
    doi = "10.18653/v1/D17-1031",
    pages = "310--315",
    abstract = "In this paper, we propose to learn word embeddings based on the recent fixed-size ordinally forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence into a fixed-size representation. We use FOFE to fully encode the left and right context of each word in a corpus to construct a novel word-context matrix, which is further weighted and factorized using truncated SVD to generate low-dimension word embedding vectors. We evaluate this alternate method in encoding word-context statistics and show the new FOFE method has a notable effect on the resulting word embeddings. Experimental results on several popular word similarity tasks have demonstrated that the proposed method outperforms other SVD models that use canonical count based techniques to generate word context matrices.",
}
@inproceedings{fernandez-etal-2017-vecshare,
    title = "{V}ec{S}hare: A Framework for Sharing Word Representation Vectors",
    author = "Fernandez, Jared  and
      Yu, Zhaocheng  and
      Downey, Doug",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1032",
    doi = "10.18653/v1/D17-1032",
    pages = "316--320",
    abstract = "Many Natural Language Processing (NLP) models rely on distributed vector representations of words. Because the process of training word vectors can require large amounts of data and computation, NLP researchers and practitioners often utilize pre-trained embeddings downloaded from the Web. However, finding the best embeddings for a given task is difficult, and can be computationally prohibitive. We present a framework, called VecShare, that makes it easy to share and retrieve word embeddings on the Web. The framework leverages a public data-sharing infrastructure to host embedding sets, and provides automated mechanisms for retrieving the embeddings most similar to a given corpus. We perform an experimental evaluation of VecShare{'}s similarity strategies, and show that they are effective at efficiently retrieving embeddings that boost accuracy in a document classification task. Finally, we provide an open-source Python library for using the VecShare framework.",
}
@inproceedings{hasan-curry-2017-word,
    title = "Word Re-Embedding via Manifold Dimensionality Retention",
    author = "Hasan, Souleiman  and
      Curry, Edward",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1033",
    doi = "10.18653/v1/D17-1033",
    pages = "321--326",
    abstract = "Word embeddings seek to recover a Euclidean metric space by mapping words into vectors, starting from words co-occurrences in a corpus. Word embeddings may underestimate the similarity between nearby words, and overestimate it between distant words in the Euclidean metric space. In this paper, we re-embed pre-trained word embeddings with a stage of manifold learning which retains dimensionality. We show that this approach is theoretically founded in the metric recovery paradigm, and empirically show that it can improve on state-of-the-art embeddings in word similarity tasks 0.5 - 5.0{\%} points depending on the original space.",
}
@inproceedings{lee-chen-2017-muse,
    title = "{MUSE}: Modularizing Unsupervised Sense Embeddings",
    author = "Lee, Guang-He  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1034",
    doi = "10.18653/v1/D17-1034",
    pages = "327--337",
    abstract = "This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC.",
}
@inproceedings{reimers-gurevych-2017-reporting,
    title = "Reporting Score Distributions Makes a Difference: Performance Study of {LSTM}-networks for Sequence Tagging",
    author = "Reimers, Nils  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1035",
    doi = "10.18653/v1/D17-1035",
    pages = "338--348",
    abstract = "In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant ($p < 10^{-4}$) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F₁-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we present network architectures that produce both superior performance as well as are more stable with respect to the remaining hyperparameters.",
}
@inproceedings{martins-kreutzer-2017-learning,
    title = "Learning What{'}s Easy: Fully Differentiable Neural Easy-First Taggers",
    author = "Martins, Andr{\'e} F. T.  and
      Kreutzer, Julia",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1036",
    doi = "10.18653/v1/D17-1036",
    pages = "349--362",
    abstract = "We introduce a novel neural easy-first decoder that learns to solve sequence tagging tasks in a flexible order. In contrast to previous easy-first decoders, our models are end-to-end differentiable. The decoder iteratively updates a {``}sketch{''} of the predictions over the sequence. At its core is an attention mechanism that controls which parts of the input are strategically the best to process next. We present a new constrained softmax transformation that ensures the same cumulative attention to every word, and show how to efficiently evaluate and backpropagate over it. Our models compare favourably to BILSTM taggers on three sequence tagging tasks.",
}
@inproceedings{kaji-kobayashi-2017-incremental,
    title = "Incremental Skip-gram Model with Negative Sampling",
    author = "Kaji, Nobuhiro  and
      Kobayashi, Hayato",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1037",
    doi = "10.18653/v1/D17-1037",
    pages = "363--371",
    abstract = "This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives. Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update. To address this problem, we present a simple incremental extension of SGNS and provide a thorough theoretical analysis to demonstrate its validity. Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm.",
}
@inproceedings{ruder-plank-2017-learning,
    title = "Learning to select data for transfer learning with {B}ayesian Optimization",
    author = "Ruder, Sebastian  and
      Plank, Barbara",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1038",
    doi = "10.18653/v1/D17-1038",
    pages = "372--382",
    abstract = "Domain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable for respective tasks. Inspired by work on curriculum learning, we propose to learn data selection measures using Bayesian Optimization and evaluate them across models, domains and tasks. Our learned measures outperform existing domain similarity measures significantly on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We show the importance of complementing similarity with diversity, and that learned measures are{--}to some degree{--}transferable across models, domains, and even tasks.",
}
@inproceedings{ramachandran-etal-2017-unsupervised,
    title = "Unsupervised Pretraining for Sequence to Sequence Learning",
    author = "Ramachandran, Prajit  and
      Liu, Peter  and
      Le, Quoc",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1039",
    doi = "10.18653/v1/D17-1039",
    pages = "383--391",
    abstract = "This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main result is that pretraining improves the generalization of seq2seq models. We achieve state-of-the-art results on the WMT English→German task, surpassing a range of methods using both phrase-based machine translation and neural machine translation. Our method achieves a significant improvement of 1.3 BLEU from th previous best models on both WMT{'}14 and WMT{'}15 English→German. We also conduct human evaluations on abstractive summarization and find that our method outperforms a purely supervised learning baseline in a statistically significant manner.",
}
@inproceedings{britz-etal-2017-efficient,
    title = "Efficient Attention using a Fixed-Size Memory Representation",
    author = "Britz, Denny  and
      Guan, Melody  and
      Luong, Minh-Thang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1040",
    doi = "10.18653/v1/D17-1040",
    pages = "392--400",
    abstract = "The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20{\%} for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments.",
}
@inproceedings{park-etal-2017-rotated,
    title = "Rotated Word Vector Representations and their Interpretability",
    author = "Park, Sungjoon  and
      Bak, JinYeong  and
      Oh, Alice",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1041",
    doi = "10.18653/v1/D17-1041",
    pages = "401--411",
    abstract = "Vector representation of words improves performance in various NLP tasks, but the high dimensional word vectors are very difficult to interpret. We apply several rotation algorithms to the vector representation of words to improve the interpretability. Unlike previous approaches that induce sparsity, the rotated vectors are interpretable while preserving the expressive performance of the original vectors. Furthermore, any prebuilt word vector representation can be rotated for improved interpretability. We apply rotation to skipgrams and glove and compare the expressive power and interpretability with the original vectors and the sparse overcomplete vectors. The results show that the rotated vectors outperform the original and the sparse overcomplete vectors for interpretability and expressiveness tasks.",
}
@inproceedings{alvarez-melis-jaakkola-2017-causal,
    title = "A causal framework for explaining the predictions of black-box sequence-to-sequence models",
    author = "Alvarez-Melis, David  and
      Jaakkola, Tommi",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1042",
    doi = "10.18653/v1/D17-1042",
    pages = "412--421",
    abstract = "We interpret the predictions of any black-box structured input-structured output model around a specific input-output pair. Our method returns an {``}explanation{''} consisting of groups of input-output tokens that are causally related. These dependencies are inferred by querying the model with perturbed inputs, generating a graph over tokens from the responses, and solving a partitioning problem to select the most relevant components. We focus the general approach on sequence-to-sequence problems, adopting a variational autoencoder to yield meaningful input perturbations. We test our method across several NLP sequence generation tasks.",
}
@inproceedings{serban-etal-2017-piecewise,
    title = "Piecewise Latent Variables for Neural Variational Text Processing",
    author = "Serban, Iulian Vlad  and
      Ororbia, Alexander G.  and
      Pineau, Joelle  and
      Courville, Aaron",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1043",
    doi = "10.18653/v1/D17-1043",
    pages = "422--432",
    abstract = "Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.",
}
@inproceedings{lavergne-yvon-2017-learning,
    title = "Learning the Structure of Variable-Order {CRF}s: a finite-state perspective",
    author = "Lavergne, Thomas  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1044",
    doi = "10.18653/v1/D17-1044",
    pages = "433--439",
    abstract = "The computational complexity of linear-chain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies. Such situations are not rare and arise when dealing with morphologically rich languages or joint labelling tasks. We extend here recent proposals to consider variable order CRFs. Using an effective finite-state representation of variable-length dependencies, we propose new ways to perform feature selection at large scale and report experimental results where we outperform strong baselines on a tagging task.",
}
@inproceedings{aji-heafield-2017-sparse,
    title = "Sparse Communication for Distributed Gradient Descent",
    author = "Aji, Alham Fikri  and
      Heafield, Kenneth",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1045",
    doi = "10.18653/v1/D17-1045",
    pages = "440--445",
    abstract = "We make distributed stochastic gradient descent faster by exchanging sparse updates instead of dense updates. Gradient updates are positively skewed as most updates are near zero, so we map the 99{\%} smallest updates (by absolute value) to zero then exchange sparse matrices. This method can be combined with quantization to further improve the compression. We explore different configurations and apply them to neural machine translation and MNIST image classification tasks. Most configurations work on MNIST, whereas different configurations reduce convergence rate on the more complex translation task. Our experiments show that we can achieve up to 49{\%} speed up on MNIST and 22{\%} on NMT without damaging the final accuracy or BLEU.",
}
@inproceedings{lu-etal-2017-adagrad,
    title = "Why {ADAGRAD} Fails for Online Topic Modeling",
    author = "Lu, You  and
      Lund, Jeffrey  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1046",
    doi = "10.18653/v1/D17-1046",
    pages = "446--451",
    abstract = "Online topic modeling, i.e., topic modeling with stochastic variational inference, is a powerful and efficient technique for analyzing large datasets, and ADAGRAD is a widely-used technique for tuning learning rates during online gradient optimization. However, these two techniques do not work well together. We show that this is because ADAGRAD uses accumulation of previous gradients as the learning rates{'} denominators. For online topic modeling, the magnitude of gradients is very large. It causes learning rates to shrink very quickly, so the parameters cannot fully converge until the training ends",
}
@inproceedings{chen-etal-2017-recurrent,
    title = "Recurrent Attention Network on Memory for Aspect Sentiment Analysis",
    author = "Chen, Peng  and
      Sun, Zhongqian  and
      Bing, Lidong  and
      Yang, Wei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1047",
    doi = "10.18653/v1/D17-1047",
    pages = "452--461",
    abstract = "We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by a long distance, so that it is more robust against irrelevant information. The results of multiple attentions are non-linearly combined with a recurrent neural network, which strengthens the expressive power of our model for handling more complications. The weighted-memory mechanism not only helps us avoid the labor-intensive feature engineering work, but also provides a tailor-made memory for different opinion targets of a sentence. We examine the merit of our model on four datasets: two are from SemEval2014, i.e. reviews of restaurants and laptops; a twitter dataset, for testing its performance on social media data; and a Chinese news comment dataset, for testing its language sensitivity. The experimental results show that our model consistently outperforms the state-of-the-art methods on different types of data.",
}
@inproceedings{long-etal-2017-cognition,
    title = "A Cognition Based Attention Model for Sentiment Analysis",
    author = "Long, Yunfei  and
      Lu, Qin  and
      Xiang, Rong  and
      Li, Minglei  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1048",
    doi = "10.18653/v1/D17-1048",
    pages = "462--471",
    abstract = "Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.",
}
@inproceedings{poddar-etal-2017-author,
    title = "Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews",
    author = "Poddar, Lahari  and
      Hsu, Wynne  and
      Lee, Mong Li",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1049",
    doi = "10.18653/v1/D17-1049",
    pages = "472--481",
    abstract = "User generated content about products and services in the form of reviews are often diverse and even contradictory. This makes it difficult for users to know if an opinion in a review is prevalent or biased. We study the problem of searching for supporting opinions in the context of reviews. We propose a framework called SURF, that first identifies opinions expressed in a review, and then finds similar opinions from other reviews. We design a novel probabilistic graphical model that captures opinions as a combination of aspect, topic and sentiment dimensions, takes into account the preferences of individual authors, as well as the quality of the entity under review, and encodes the flow of thoughts in a review by constraining the aspect distribution dynamically among successive review segments. We derive a similarity measure that considers both lexical and semantic similarity to find supporting opinions. Experiments on TripAdvisor hotel reviews and Yelp restaurant reviews show that our model outperforms existing methods for modeling opinions, and the proposed framework is effective in finding supporting opinions.",
}
@inproceedings{ghosh-veale-2017-magnets,
    title = "Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal",
    author = "Ghosh, Aniruddha  and
      Veale, Tony",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1050",
    doi = "10.18653/v1/D17-1050",
    pages = "482--491",
    abstract = "Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude. Concision requires wit to produce and wit to understand, which demands from each party knowledge of norms, context and a speaker{'}s mindset. Insight into a speaker{'}s psychological profile at the time of production is a valuable source of context for sarcasm detection. Using a neural architecture, we show significant gains in detection accuracy when knowledge of the speaker{'}s mood at the time of production can be inferred. Our focus is on sarcasm detection on Twitter, and show that the mood exhibited by a speaker over tweets leading up to a new post is as useful a cue for sarcasm as the topical context of the post itself. The work opens the door to an empirical exploration not just of sarcasm in text but of the sarcastic state of mind.",
}
@inproceedings{morales-zhai-2017-identifying,
    title = "Identifying Humor in Reviews using Background Text Sources",
    author = "Morales, Alex  and
      Zhai, Chengxiang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1051",
    doi = "10.18653/v1/D17-1051",
    pages = "492--501",
    abstract = "We study the problem of automatically identifying humorous text from a new kind of text data, i.e., online reviews. We propose a generative language model, based on the theory of incongruity, to model humorous text, which allows us to leverage background text sources, such as Wikipedia entry descriptions, and enables construction of multiple features for identifying humorous reviews. Evaluation of these features using supervised learning for classifying reviews into humorous and non-humorous reviews shows that the features constructed based on the proposed generative model are much more effective than the major features proposed in the existing literature, allowing us to achieve almost 86{\%} accuracy. These humorous review predictions can also supply good indicators for identifying helpful reviews.",
}
@inproceedings{wang-xia-2017-sentiment,
    title = "Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment Supervision",
    author = "Wang, Leyi  and
      Xia, Rui",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1052",
    doi = "10.18653/v1/D17-1052",
    pages = "502--510",
    abstract = "Sentiment lexicon is an important tool for identifying the sentiment polarity of words and texts. How to automatically construct sentiment lexicons has become a research topic in the field of sentiment analysis and opinion mining. Recently there were some attempts to employ representation learning algorithms to construct a sentiment lexicon with sentiment-aware word embedding. However, these methods were normally trained under document-level sentiment supervision. In this paper, we develop a neural architecture to train a sentiment-aware word embedding by integrating the sentiment supervision at both document and word levels, to enhance the quality of word embedding as well as the sentiment lexicon. Experiments on the SemEval 2013-2016 datasets indicate that the sentiment lexicon generated by our approach achieves the state-of-the-art performance in both supervised and unsupervised sentiment classification, in comparison with several strong sentiment lexicon construction methods.",
}
@inproceedings{xu-wan-2017-towards,
    title = "Towards a Universal Sentiment Classifier in Multiple languages",
    author = "Xu, Kui  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1053",
    doi = "10.18653/v1/D17-1053",
    pages = "511--520",
    abstract = "Existing sentiment classifiers usually work for only one specific language, and different classification models are used in different languages. In this paper we aim to build a universal sentiment classifier with a single classification model in multiple different languages. In order to achieve this goal, we propose to learn multilingual sentiment-aware word embeddings simultaneously based only on the labeled reviews in English and unlabeled parallel data available in a few language pairs. It is not required that the parallel data exist between English and any other language, because the sentiment information can be transferred into any language via pivot languages. We present the evaluation results of our universal sentiment classifier in five languages, and the results are very promising even when the parallel data between English and the target languages are not used. Furthermore, the universal single classifier is compared with a few cross-language sentiment classifiers relying on direct parallel data between the source and target languages, and the results show that the performance of our universal sentiment classifier is very promising compared to that of different cross-language classifiers in multiple target languages.",
}
@inproceedings{dou-2017-capturing,
    title = "Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network",
    author = "Dou, Zi-Yi",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1054",
    doi = "10.18653/v1/D17-1054",
    pages = "521--526",
    abstract = "Document-level sentiment classification is a fundamental problem which aims to predict a user{'}s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluated. To address the issue, we propose a deep memory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better performance than several existing methods.",
}
@inproceedings{yang-etal-2017-identifying,
    title = "Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters",
    author = "Yang, Min  and
      Mei, Jincheng  and
      Ji, Heng  and
      Zhao, Wei  and
      Zhao, Zhou  and
      Chen, Xiaojun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1055",
    doi = "10.18653/v1/D17-1055",
    pages = "527--533",
    abstract = "We study the problem of identifying the topics and sentiments and tracking their shifts from social media texts in different geographical regions during emergencies and disasters. We propose a location-based dynamic sentiment-topic model (LDST) which can jointly model topic, sentiment, time and Geolocation information. The experimental results demonstrate that LDST performs very well at discovering topics and sentiments from social media and tracking their shifts in different geographical regions during emergencies and disasters. We will release the data and source code after this work is published.",
}
@inproceedings{yu-etal-2017-refining,
    title = "Refining Word Embeddings for Sentiment Analysis",
    author = "Yu, Liang-Chih  and
      Wang, Jin  and
      Lai, K. Robert  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1056",
    doi = "10.18653/v1/D17-1056",
    pages = "534--539",
    abstract = "Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically fail to capture sufficient sentiment information. This may result in words with similar vector representations having an opposite sentiment polarity (e.g., good and bad), thus degrading sentiment analysis performance. Therefore, this study proposes a word vector refinement model that can be applied to any pre-trained word vectors (e.g., Word2vec and GloVe). The refinement model is based on adjusting the vector representations of words such that they can be closer to both semantically and sentimentally similar words and further away from sentimentally dissimilar words. Experimental results show that the proposed method can improve conventional word embeddings and outperform previously proposed sentiment embeddings for both binary and fine-grained classification on Stanford Sentiment Treebank (SST).",
}
@inproceedings{akhtar-etal-2017-multilayer,
    title = "A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis",
    author = "Akhtar, Md Shad  and
      Kumar, Abhishek  and
      Ghosal, Deepanway  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1057",
    doi = "10.18653/v1/D17-1057",
    pages = "540--546",
    abstract = "In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various deep learning models based on Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). These are trained on top of pre-trained, autoencoder-based, financial word embeddings and lexicon features. An ensemble is constructed by combining these deep learning models and a classical supervised model based on Support Vector Regression (SVR). We evaluate our proposed technique on a benchmark dataset of SemEval-2017 shared task on financial sentiment analysis. The propose model shows impressive results on two datasets, i.e. microblogs and news headlines datasets. Comparisons show that our proposed model performs better than the existing state-of-the-art systems for the above two datasets by 2.0 and 4.1 cosine points, respectively.",
}
@inproceedings{sharma-etal-2017-sentiment,
    title = "Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings",
    author = "Sharma, Raksha  and
      Somani, Arpan  and
      Kumar, Lakshya  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1058",
    doi = "10.18653/v1/D17-1058",
    pages = "547--552",
    abstract = "Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis. For example, {`}master{'}, {`}seasoned{'} and {`}familiar{'} point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semi-supervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman{'}s rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.",
}
@inproceedings{wang-etal-2017-sentiment,
    title = "Sentiment Lexicon Expansion Based on Neural {PU} Learning, Double Dictionary Lookup, and Polarity Association",
    author = "Wang, Yasheng  and
      Zhang, Yang  and
      Liu, Bing",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1059",
    doi = "10.18653/v1/D17-1059",
    pages = "553--563",
    abstract = "Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first poses the problem as a PU learning problem, which is a new formulation. It then proposes a new PU learning method suitable for our problem using a neural network. The results are enhanced further with a new dictionary-based technique and a novel polarity classification technique. Experimental results show that the proposed approach outperforms baseline methods greatly.",
}
@inproceedings{xiong-etal-2017-deeppath,
    title = "{D}eep{P}ath: A Reinforcement Learning Method for Knowledge Graph Reasoning",
    author = "Xiong, Wenhan  and
      Hoang, Thien  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1060",
    doi = "10.18653/v1/D17-1060",
    pages = "564--573",
    abstract = "We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector-space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.",
}
@inproceedings{nogueira-cho-2017-task,
    title = "Task-Oriented Query Reformulation with Reinforcement Learning",
    author = "Nogueira, Rodrigo  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1061",
    doi = "10.18653/v1/D17-1061",
    pages = "574--583",
    abstract = "Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20{\%} in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.",
}
@inproceedings{zhang-lapata-2017-sentence,
    title = "Sentence Simplification with Deep Reinforcement Learning",
    author = "Zhang, Xingxing  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1062",
    doi = "10.18653/v1/D17-1062",
    pages = "584--594",
    abstract = "Sentence simplification aims to make sentences easier to read and understand. Most recent approaches draw on insights from machine translation to learn simplification rewrites from monolingual corpora of complex and simple sentences. We address the simplification problem with an encoder-decoder model coupled with a deep reinforcement learning framework. Our model, which we call DRESS (as shorthand for \textbf{D}eep \textbf{RE}inforcement \textbf{S}entence \textbf{S}implification), explores the space of possible simplifications while learning to optimize a reward function that encourages outputs which are simple, fluent, and preserve the meaning of the input. Experiments on three datasets demonstrate that our model outperforms competitive simplification systems.",
}
@inproceedings{fang-etal-2017-learning,
    title = "Learning how to Active Learn: A Deep Reinforcement Learning Approach",
    author = "Fang, Meng  and
      Li, Yuan  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1063",
    doi = "10.18653/v1/D17-1063",
    pages = "595--605",
    abstract = "Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation to one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning algorithms.",
}
@inproceedings{narayan-etal-2017-split,
    title = "Split and Rephrase",
    author = "Narayan, Shashi  and
      Gardent, Claire  and
      Cohen, Shay B.  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1064",
    doi = "10.18653/v1/D17-1064",
    pages = "606--616",
    abstract = "We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.",
}
@inproceedings{xu-etal-2017-neural,
    title = "Neural Response Generation via {GAN} with an Approximate Embedding Layer",
    author = "Xu, Zhen  and
      Liu, Bingquan  and
      Wang, Baoxun  and
      Sun, Chengjie  and
      Wang, Xiaolong  and
      Wang, Zhuoran  and
      Qi, Chao",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1065",
    doi = "10.18653/v1/D17-1065",
    pages = "617--626",
    abstract = "This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a {``}safe responses{''}), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.",
}
@inproceedings{semeniuta-etal-2017-hybrid,
    title = "A Hybrid Convolutional Variational Autoencoder for Text Generation",
    author = "Semeniuta, Stanislau  and
      Severyn, Aliaksei  and
      Barth, Erhardt",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1066",
    doi = "10.18653/v1/D17-1066",
    pages = "627--637",
    abstract = "In this paper we explore the effect of architectural choices on learning a variational autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components with a recurrent language model. Our architecture exhibits several attractive properties such as faster run time and convergence, ability to better handle long sequences and, more importantly, it helps to avoid the issue of the VAE collapsing to a deterministic model.",
}
@inproceedings{hossain-etal-2017-filling,
    title = "Filling the Blanks (hint: plural noun) for Mad {L}ibs Humor",
    author = "Hossain, Nabil  and
      Krumm, John  and
      Vanderwende, Lucy  and
      Horvitz, Eric  and
      Kautz, Henry",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1067",
    doi = "10.18653/v1/D17-1067",
    pages = "638--647",
    abstract = "Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.",
}
@inproceedings{santus-etal-2017-measuring,
    title = "Measuring Thematic Fit with Distributional Feature Overlap",
    author = "Santus, Enrico  and
      Chersoni, Emmanuele  and
      Lenci, Alessandro  and
      Blache, Philippe",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1068",
    doi = "10.18653/v1/D17-1068",
    pages = "648--658",
    abstract = "In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of verb-specific roles: for every verb, we extract the most salient second order contexts for each of its roles (i.e. the most salient dimensions of typical role fillers), and then we compute thematic fit as a weighted overlap between the top features of candidate fillers and role prototypes. Our experiments show that our method consistently outperforms a baseline re-implementing a state-of-the-art system, and achieves better or comparable results to those reported in the literature for the other unsupervised systems. Moreover, it provides an explicit representation of the features characterizing verb-specific semantic roles.",
}
@inproceedings{mekala-etal-2017-scdv,
    title = "{SCDV} : Sparse Composite Document Vectors using soft clustering over distributional representations",
    author = "Mekala, Dheeraj  and
      Gupta, Vivek  and
      Paranjape, Bhargavi  and
      Karnick, Harish",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1069",
    doi = "10.18653/v1/D17-1069",
    pages = "659--669",
    abstract = "We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embeddings are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG. We also show that SCDV embeddings perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve a significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds - better performance with lower time and space complexity.",
}
@inproceedings{conneau-etal-2017-supervised,
    title = "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data",
    author = {Conneau, Alexis  and
      Kiela, Douwe  and
      Schwenk, Holger  and
      Barrault, Lo{\"\i}c  and
      Bordes, Antoine},
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1070",
    doi = "10.18653/v1/D17-1070",
    pages = "670--680",
    abstract = "Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available.",
}
@inproceedings{yanaka-etal-2017-determining,
    title = "Determining Semantic Textual Similarity using Natural Deduction Proofs",
    author = "Yanaka, Hitomi  and
      Mineshima, Koji  and
      Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1071",
    doi = "10.18653/v1/D17-1071",
    pages = "681--691",
    abstract = "Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. We propose a method for determining semantic textual similarity by combining shallow features with features extracted from natural deduction proofs of bidirectional entailment relations between sentence pairs. For the natural deduction proofs, we use ccg2lambda, a higher-order automatic inference system, which converts Combinatory Categorial Grammar (CCG) derivation trees into semantic representations and conducts natural deduction proofs. Experiments show that our system was able to outperform other logic-based systems and that features derived from the proofs are effective for learning textual similarity.",
}
@inproceedings{gong-etal-2017-multi,
    title = "Multi-Grained {C}hinese Word Segmentation",
    author = "Gong, Chen  and
      Li, Zhenghua  and
      Zhang, Min  and
      Jiang, Xinzhou",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1072",
    doi = "10.18653/v1/D17-1072",
    pages = "692--703",
    abstract = "Traditionally, word segmentation (WS) adopts the single-grained formalism, where a sentence corresponds to a single word sequence. However, Sproat et al. (1997) show that the inter-native-speaker consistency ratio over Chinese word boundaries is only 76{\%}, indicating single-grained WS (SWS) imposes unnecessary challenges on both manual annotation and statistical modeling. Moreover, WS results of different granularities can be complementary and beneficial for high-level applications. This work proposes and addresses multi-grained WS (MWS). We build a large-scale pseudo MWS dataset for model training and tuning by leveraging the annotation heterogeneity of three SWS datasets. Then we manually annotate 1,500 test sentences with true MWS annotations. Finally, we propose three benchmark approaches by casting MWS as constituent parsing and sequence labeling. Experiments and analysis lead to many interesting findings.",
}
@inproceedings{zalmout-habash-2017-dont,
    title = "Don{'}t Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for {A}rabic",
    author = "Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1073",
    doi = "10.18653/v1/D17-1073",
    pages = "704--713",
    abstract = "This paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model the various morphological features. Our experiments show that these models outperform state-of-the-art systems without explicit use of feature engineering. However, adding learning features from a morphological analyzer to model the space of possible analyses provides additional improvement. We make use of the resulting morphological models for scoring and ranking the analyses of the morphological analyzer for morphological disambiguation. The results show significant gains in accuracy across several evaluation metrics. Our system results in 4.4{\%} absolute increase over the state-of-the-art in full morphological analysis accuracy (30.6{\%} relative error reduction), and 10.6{\%} (31.5{\%} relative error reduction) for out-of-vocabulary words.",
}
@inproceedings{cotterell-etal-2017-paradigm,
    title = "Paradigm Completion for Derivational Morphology",
    author = "Cotterell, Ryan  and
      Vylomova, Ekaterina  and
      Khayrallah, Huda  and
      Kirov, Christo  and
      Yarowsky, David",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1074",
    doi = "10.18653/v1/D17-1074",
    pages = "714--720",
    abstract = "The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models adapted from the inflection task are able to learn the range of derivation patterns, and outperform a non-neural baseline by 16.4{\%}. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems.",
}
@inproceedings{stratos-2017-sub,
    title = "A Sub-Character Architecture for {K}orean Language Processing",
    author = "Stratos, Karl",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1075",
    doi = "10.18653/v1/D17-1075",
    pages = "721--726",
    abstract = "We introduce a novel sub-character architecture that exploits a unique compositional structure of the Korean language. Our method decomposes each character into a small set of primitive phonetic units called jamo letters from which character- and word-level representations are induced. The jamo letters divulge syntactic and semantic information that is difficult to access with conventional character-level units. They greatly alleviate the data sparsity problem, reducing the observation space to 1.6{\%} of the original while increasing accuracy in our experiments. We apply our architecture to dependency parsing and achieve dramatic improvement over strong lexical baselines.",
}
@inproceedings{horsmann-zesch-2017-lstms,
    title = "Do {LSTM}s really work so well for {P}o{S} tagging? {--} A replication study",
    author = "Horsmann, Tobias  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1076",
    doi = "10.18653/v1/D17-1076",
    pages = "727--736",
    abstract = "A recent study by Plank et al. (2016) found that LSTM-based PoS taggers considerably improve over the current state-of-the-art when evaluated on the corpora of the Universal Dependencies project that use a coarse-grained tagset. We replicate this study using a fresh collection of 27 corpora of 21 languages that are annotated with fine-grained tagsets of varying size. Our replication confirms the result in general, and we additionally find that the advantage of LSTMs is even bigger for larger tagsets. However, we also find that for the very large tagsets of morphologically rich languages, hand-crafted morphological lexicons are still necessary to reach state-of-the-art performance.",
}
@inproceedings{mcconnaughey-etal-2017-labeled,
    title = "The Labeled Segmentation of Printed Books",
    author = "McConnaughey, Lara  and
      Dai, Jennifer  and
      Bamman, David",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1077",
    doi = "10.18653/v1/D17-1077",
    pages = "737--747",
    abstract = "We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750-1922, and present empirical results comparing the performance of several classes of models. The best-performing model, a bidirectional LSTM with rich features, achieves an overall accuracy of 95.8 and a class-balanced macro F-score of 71.4.",
}
@inproceedings{cotterell-heigold-2017-cross,
    title = "Cross-lingual Character-Level Neural Morphological Tagging",
    author = "Cotterell, Ryan  and
      Heigold, Georg",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1078",
    doi = "10.18653/v1/D17-1078",
    pages = "748--759",
    abstract = "Even for common NLP tasks, sufficient supervision is not available in many languages {--} morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones.",
}
@inproceedings{zhou-etal-2017-word,
    title = "Word-Context Character Embeddings for {C}hinese Word Segmentation",
    author = "Zhou, Hao  and
      Yu, Zhenting  and
      Zhang, Yue  and
      Huang, Shujian  and
      Dai, Xinyu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1079",
    doi = "10.18653/v1/D17-1079",
    pages = "760--766",
    abstract = "Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented data.",
}
@inproceedings{oshikiri-2017-segmentation,
    title = "Segmentation-Free Word Embedding for Unsegmented Languages",
    author = "Oshikiri, Takamasa",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1080",
    doi = "10.18653/v1/D17-1080",
    pages = "767--772",
    abstract = "In this paper, we propose a new pipeline of word embedding for unsegmented languages, called segmentation-free word embedding, which does not require word segmentation as a preprocessing step. Unlike space-delimited languages, unsegmented languages, such as Chinese and Japanese, require word segmentation as a preprocessing step. However, word segmentation, that often requires manually annotated resources, is difficult and expensive, and unavoidable errors in word segmentation affect downstream tasks. To avoid these problems in learning word vectors of unsegmented languages, we consider word co-occurrence statistics over all possible candidates of segmentations based on frequent character n-grams instead of segmented sentences provided by conventional word segmenters. Our experiments of noun category prediction tasks on raw Twitter, Weibo, and Wikipedia corpora show that the proposed method outperforms the conventional approaches that require word segmenters.",
}
@inproceedings{sachan-etal-2017-textbooks,
    title = "From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems",
    author = "Sachan, Mrinmaya  and
      Dubey, Kumar  and
      Xing, Eric",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1081",
    doi = "10.18653/v1/D17-1081",
    pages = "773--784",
    abstract = "Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted from raw textbooks. It leverages the redundancy and shared ordering across multiple textbooks to further refine the harvested axioms. These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.",
}
@inproceedings{lai-etal-2017-race,
    title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations",
    author = "Lai, Guokun  and
      Xie, Qizhe  and
      Liu, Hanxiao  and
      Yang, Yiming  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1082",
    doi = "10.18653/v1/D17-1082",
    pages = "785--794",
    abstract = "We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students{'} ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43{\%}) and the ceiling human performance (95{\%}). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at \url{http://www.cs.cmu.edu/~glai1/data/race/}and the code is available at \url{https://github.com/qizhex/RACE_AR_baselines}.",
}
@inproceedings{hopkins-etal-2017-beyond,
    title = "Beyond Sentential Semantic Parsing: Tackling the Math {SAT} with a Cascade of Tree Transducers",
    author = "Hopkins, Mark  and
      Petrescu-Prahova, Cristian  and
      Levin, Roie  and
      Le Bras, Ronan  and
      Herrasti, Alvaro  and
      Joshi, Vidur",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1083",
    doi = "10.18653/v1/D17-1083",
    pages = "795--804",
    abstract = "We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions {--} the math portion of the Scholastic Aptitude Test (SAT). By using a tree transducer cascade as its basic architecture, our system propagates uncertainty from multiple sources (e.g. coreference resolution or verb interpretation) until it can be confidently resolved. Experiments show the first-ever results 43{\%} recall and 91{\%} precision) on SAT algebra word problems. We also apply our system to the public Dolphin algebra question set, and improve the state-of-the-art F1-score from 73.9{\%} to 77.0{\%}.",
}
@inproceedings{huang-etal-2017-learning,
    title = "Learning Fine-Grained Expressions to Solve Math Word Problems",
    author = "Huang, Danqing  and
      Shi, Shuming  and
      Lin, Chin-Yew  and
      Yin, Jian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1084",
    doi = "10.18653/v1/D17-1084",
    pages = "805--814",
    abstract = "This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4{\%} on the linear Dolphin18K benchmark, which is 10{\%} (54{\%} relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12{\%} (59{\%} relative) on the TS6 benchmark subset.",
}
@inproceedings{liu-etal-2017-structural,
    title = "Structural Embedding of Syntactic Trees for Machine Comprehension",
    author = "Liu, Rui  and
      Hu, Junjie  and
      Wei, Wei  and
      Yang, Zi  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1085",
    doi = "10.18653/v1/D17-1085",
    pages = "815--824",
    abstract = "Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.",
}
@inproceedings{long-etal-2017-world,
    title = "World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical {LSTM}s Using External Descriptions",
    author = "Long, Teng  and
      Bengio, Emmanuel  and
      Lowe, Ryan  and
      Cheung, Jackie Chi Kit  and
      Precup, Doina",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1086",
    doi = "10.18653/v1/D17-1086",
    pages = "825--834",
    abstract = "Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.",
}
@inproceedings{golub-etal-2017-two,
    title = "Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension",
    author = "Golub, David  and
      Huang, Po-Sen  and
      He, Xiaodong  and
      Deng, Li",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1087",
    doi = "10.18653/v1/D17-1087",
    pages = "835--844",
    abstract = "We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed synthesis network with a pretrained model on the SQuAD dataset, we achieve an F1 measure of 46.6{\%} on the challenging NewsQA dataset, approaching performance of in-domain models (F1 measure of 50.0{\%}) and outperforming the out-of-domain baseline by 7.6{\%}, without use of provided annotations.",
}
@inproceedings{wang-etal-2017-deep,
    title = "Deep Neural Solver for Math Word Problems",
    author = "Wang, Yan  and
      Liu, Xiaojiang  and
      Shi, Shuming",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1088",
    doi = "10.18653/v1/D17-1088",
    pages = "845--854",
    abstract = "This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid model that combines the RNN model and a similarity-based retrieval model to achieve additional performance improvement. Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform state-of-the-art statistical learning methods for math word problem solving.",
}
@inproceedings{p-etal-2017-latent,
    title = "Latent Space Embedding for Retrieval in Question-Answer Archives",
    author = "P, Deepak  and
      Garg, Dinesh  and
      Shevade, Shirish",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1089",
    doi = "10.18653/v1/D17-1089",
    pages = "855--865",
    abstract = "Community-driven Question Answering (CQA) systems such as Yahoo! Answers have become valuable sources of reusable information. CQA retrieval enables usage of historical CQA archives to solve new questions posed by users. This task has received much recent attention, with methods building upon literature from translation models, topic models, and deep learning. In this paper, we devise a CQA retrieval technique, LASER-QA, that embeds question-answer pairs within a unified latent space preserving the local neighborhood structure of question and answer spaces. The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval. Through an empirical analysis on various real-world QA datasets, we illustrate the improved effectiveness of LASER-QA over state-of-the-art methods.",
}
@inproceedings{duan-etal-2017-question,
    title = "Question Generation for Question Answering",
    author = "Duan, Nan  and
      Tang, Duyu  and
      Chen, Peng  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1090",
    doi = "10.18653/v1/D17-1090",
    pages = "866--874",
    abstract = "This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.",
}
@inproceedings{dong-etal-2017-learning,
    title = "Learning to Paraphrase for Question Answering",
    author = "Dong, Li  and
      Mallinson, Jonathan  and
      Reddy, Siva  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1091",
    doi = "10.18653/v1/D17-1091",
    pages = "875--886",
    abstract = "Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-to-end using question-answer pairs as a supervision signal. A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. We evaluate our approach on QA over Freebase and answer sentence selection. Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of simple QA models.",
}
@inproceedings{meng-etal-2017-temporal,
    title = "Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an {LSTM}-based Architecture",
    author = "Meng, Yuanliang  and
      Rumshisky, Anna  and
      Romanov, Alexey",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1092",
    doi = "10.18653/v1/D17-1092",
    pages = "887--896",
    abstract = "In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations. A {``}double-checking{''} technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes. An efficient pruning algorithm resolves conflicts globally. Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin. We also conduct intrinsic evaluation and post state-of-the-art results on Timebank-Dense.",
}
@inproceedings{tymoshenko-etal-2017-ranking,
    title = "Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model",
    author = "Tymoshenko, Kateryna  and
      Bonadiman, Daniele  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1093",
    doi = "10.18653/v1/D17-1093",
    pages = "897--902",
    abstract = "Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector Machines (SVMs) is promising as it can exploit both the syntactic patterns captured by TKs and the embeddings learned by CNNs. However, the embeddings are constructed according to a classification function, which is not directly exploitable in the preference ranking algorithm of SVMs. In this work, we propose a new hybrid approach combining preference ranking applied to TKs and pointwise ranking applied to CNNs. We show that our approach produces better results on two well-known and rather different datasets: WikiQA for answer sentence selection and SemEval cQA for comment selection in Community Question Answering.",
}
@inproceedings{yavuz-etal-2017-recovering,
    title = "Recovering Question Answering Errors via Query Revision",
    author = "Yavuz, Semih  and
      Gur, Izzeddin  and
      Su, Yu  and
      Yan, Xifeng",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1094",
    doi = "10.18653/v1/D17-1094",
    pages = "903--909",
    abstract = "The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes. In this work, we propose to crosscheck the corresponding KB relations behind the predicted answers and identify potential inconsistencies. Instead of developing a new model that accepts evidences collected from these relations, we choose to plug them back to the original questions directly and check if the revised question makes sense or not. A bidirectional LSTM is applied to encode revised questions. We develop a scoring mechanism over the revised question encodings to refine the predictions of a base QA system. This approach can improve the F1 score of STAGG (Yih et al., 2015), one of the leading QA systems, from 52.5{\%} to 53.9{\%} on WEBQUESTIONS data.",
}
@inproceedings{delbrouck-dupont-2017-empirical,
    title = "An empirical study on the effectiveness of images in Multimodal Neural Machine Translation",
    author = "Delbrouck, Jean-Benoit  and
      Dupont, St{\'e}phane",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1095",
    doi = "10.18653/v1/D17-1095",
    pages = "910--919",
    abstract = "In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effectiveness of the attention mechanism has also been explored for multi-modal tasks, where it becomes possible to focus both on sentence parts and image regions that they describe. In this paper, we compare several attention mechanism on the multi-modal translation task (English, image → German) and evaluate the ability of the model to make use of images to improve translation. We surpass state-of-the-art scores on the Multi30k data set, we nevertheless identify and report different misbehavior of the machine while translating.",
}
@inproceedings{vijayakumar-etal-2017-sound,
    title = "Sound-{W}ord2{V}ec: Learning Word Representations Grounded in Sounds",
    author = "Vijayakumar, Ashwin  and
      Vedantam, Ramakrishna  and
      Parikh, Devi",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1096",
    doi = "10.18653/v1/D17-1096",
    pages = "920--925",
    abstract = "To be able to interact better with humans, it is crucial for machines to understand sound {--} a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic semantic similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream 6textual tasks which require aural grounding. To this end, we propose sound-word2vec {--} a new embedding scheme that learns specialized word embeddings grounded in sounds. For example, we learn that two seemingly (semantically) unrelated concepts, like leaves and paper are similar due to the similar rustling sounds they make. Our embeddings prove useful in textual tasks requiring aural reasoning like text-based sound retrieval and discovering Foley sound effects (used in movies). Moreover, our embedding space captures interesting dependencies between words and onomatopoeia and outperforms prior work on aurally-relevant word relatedness datasets such as AMEN and ASLex.",
}
@inproceedings{mahendru-etal-2017-promise,
    title = "The Promise of Premise: Harnessing Question Premises in Visual Question Answering",
    author = "Mahendru, Aroma  and
      Prabhu, Viraj  and
      Mohapatra, Akrit  and
      Batra, Dhruv  and
      Lee, Stefan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1097",
    doi = "10.18653/v1/D17-1097",
    pages = "926--935",
    abstract = "In this paper, we make a simple observation that questions about images often contain premises {--} objects and relationships implied by the question {--} and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is false (i.e. not depicted in the image). We leverage this observation to construct a dataset for Question Relevance Prediction and Explanation (QRPE) by searching for false premises. We train novel question relevance detection models and show that models that reason about premises consistently outperform models that do not. We also find that forcing standard VQA models to reason about premises during training can lead to improvements on tasks requiring compositional reasoning.",
}
@inproceedings{anderson-etal-2017-guided,
    title = "Guided Open Vocabulary Image Captioning with Constrained Beam Search",
    author = "Anderson, Peter  and
      Fernando, Basura  and
      Johnson, Mark  and
      Gould, Stephen",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1098",
    doi = "10.18653/v1/D17-1098",
    pages = "936--945",
    abstract = "Existing image captioning models do not generalize well to out-of-domain images containing novel scenes or objects. This limitation severely hinders the use of these models in real world applications dealing with images in the wild. We address this problem using a flexible approach that enables existing deep captioning architectures to take advantage of image taggers at test time, without re-training. Our method uses constrained beam search to force the inclusion of selected tag words in the output, and fixed, pretrained word embeddings to facilitate vocabulary expansion to previously unseen tag words. Using this approach we achieve state of the art results for out-of-domain captioning on MSCOCO (and improved results for in-domain captioning). Perhaps surprisingly, our results significantly outperform approaches that incorporate the same tag predictions into the learning algorithm. We also show that we can significantly improve the quality of generated ImageNet captions by leveraging ground-truth labels.",
}
@inproceedings{zellers-choi-2017-zero,
    title = "Zero-Shot Activity Recognition with Verb Attribute Induction",
    author = "Zellers, Rowan  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1099",
    doi = "10.18653/v1/D17-1099",
    pages = "946--958",
    abstract = "In this paper, we investigate large-scale zero-shot activity recognition by modeling the visual and linguistic attributes of action verbs. For example, the verb {``}salute{''} has several properties, such as being a light movement, a social act, and short in duration. We use these attributes as the internal mapping between visual and textual representations to reason about a previously unseen action. In contrast to much prior work that assumes access to gold standard attributes for zero-shot classes and focuses primarily on object attributes, our model uniquely learns to infer action attributes from dictionary definitions and distributed word representations. Experimental results confirm that action attributes inferred from language can provide a predictive signal for zero-shot prediction of previously unseen activities.",
}
@inproceedings{zarriess-schlangen-2017-deriving,
    title = "Deriving continous grounded meaning representations from referentially structured multimodal contexts",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1100",
    doi = "10.18653/v1/D17-1100",
    pages = "959--965",
    abstract = "Corpora of referring expressions paired with their visual referents are a good source for learning word meanings directly grounded in visual representations. Here, we explore additional ways of extracting from them word representations linked to multi-modal context: through expressions that refer to the same object, and through expressions that refer to different objects in the same scene. We show that continuous meaning representations derived from these contexts capture complementary aspects of similarity, , even if not outperforming textual embeddings trained on very large amounts of raw text when tested on standard similarity benchmarks. We propose a new task for evaluating grounded meaning representations{---}detection of potentially co-referential phrases{---}and show that it requires precise denotational representations of attribute meanings, which our method provides.",
}
@inproceedings{yu-etal-2017-hierarchically,
    title = "Hierarchically-Attentive {RNN} for Album Summarization and Storytelling",
    author = "Yu, Licheng  and
      Bansal, Mohit  and
      Berg, Tamara",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1101",
    doi = "10.18653/v1/D17-1101",
    pages = "966--971",
    abstract = "We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.",
}
@inproceedings{fu-etal-2017-video,
    title = "Video Highlight Prediction Using Audience Chat Reactions",
    author = "Fu, Cheng-Yang  and
      Lee, Joon  and
      Bansal, Mohit  and
      Berg, Alexander",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1102",
    doi = "10.18653/v1/D17-1102",
    pages = "972--978",
    abstract = "Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features and textual analysis of the real-world audience discourse with complex slang, in both English and traditional Chinese. We present a novel dataset based on League of Legends championships recorded from North American and Taiwanese Twitch.tv channels (will be released for further research), and demonstrate strong results on these using multimodal, character-level CNN-RNN model architectures.",
}
@inproceedings{pasunuru-bansal-2017-reinforced,
    title = "Reinforced Video Captioning with Entailment Rewards",
    author = "Pasunuru, Ramakanth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1103",
    doi = "10.18653/v1/D17-1103",
    pages = "979--985",
    abstract = "Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.",
}
@inproceedings{mu-etal-2017-evaluating,
    title = "Evaluating Hierarchies of Verb Argument Structure with Hierarchical Clustering",
    author = "Mu, Jesse  and
      Hartshorne, Joshua K.  and
      O{'}Donnell, Timothy",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1104",
    doi = "10.18653/v1/D17-1104",
    pages = "986--991",
    abstract = "Verbs can only be used with a few specific arrangements of their arguments (syntactic frames). Most theorists note that verbs can be organized into a hierarchy of verb classes based on the frames they admit. Here we show that such a hierarchy is objectively well-supported by the patterns of verbs and frames in English, since a systematic hierarchical clustering algorithm converges on the same structure as the handcrafted taxonomy of VerbNet, a broad-coverage verb lexicon. We also show that the hierarchies capture meaningful psychological dimensions of generalization by predicting novel verb coercions by human participants. We discuss limitations of a simple hierarchical representation and suggest similar approaches for identifying the representations underpinning verb argument structure.",
}
@inproceedings{calixto-liu-2017-incorporating,
    title = "Incorporating Global Visual Features into Attention-based Neural Machine Translation.",
    author = "Calixto, Iacer  and
      Liu, Qun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1105",
    doi = "10.18653/v1/D17-1105",
    pages = "992--1003",
    abstract = "We introduce multi-modal, attention-based neural machine translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to initialise the decoder hidden state. In our experiments, we evaluate translations into English and German, how different strategies to incorporate global image features compare and which ones perform best. We also study the impact that adding synthetic multi-modal, multilingual data brings and find that the additional data have a positive impact on multi-modal NMT models. We report new state-of-the-art results and our best models also significantly improve on a comparable phrase-based Statistical MT (PBSMT) model trained on the Multi30k data set according to all metrics evaluated. To the best of our knowledge, it is the first time a purely neural model significantly improves over a PBSMT model on all metrics evaluated on this data set.",
}
@inproceedings{misra-etal-2017-mapping,
    title = "Mapping Instructions and Visual Observations to Actions with Reinforcement Learning",
    author = "Misra, Dipendra  and
      Langford, John  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1106",
    doi = "10.18653/v1/D17-1106",
    pages = "1004--1015",
    abstract = "We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent{'}s exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.",
}
@inproceedings{fraser-etal-2017-analysis,
    title = "An analysis of eye-movements during reading for the detection of mild cognitive impairment",
    author = "Fraser, Kathleen C.  and
      Lundholm Fors, Kristina  and
      Kokkinakis, Dimitrios  and
      Nordlund, Arto",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1107",
    doi = "10.18653/v1/D17-1107",
    pages = "1016--1026",
    abstract = "We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia. We compare two experimental configurations (reading aloud versus reading silently), as well as two methods of combining information from the two trials (concatenation and merging). Additionally, we annotate the words being read with information about their frequency and syntactic category, and use these annotations to generate new features. Ultimately, we are able to distinguish between participants with and without cognitive impairment with up to 86{\%} accuracy.",
}
@inproceedings{ning-etal-2017-structured,
    title = "A Structured Learning Approach to Temporal Relation Extraction",
    author = "Ning, Qiang  and
      Feng, Zhili  and
      Roth, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1108",
    doi = "10.18653/v1/D17-1108",
    pages = "1027--1037",
    abstract = "Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.",
}
@inproceedings{chaganty-etal-2017-importance,
    title = "Importance sampling for unbiased on-demand evaluation of knowledge base population",
    author = "Chaganty, Arun  and
      Paranjape, Ashwin  and
      Liang, Percy  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1109",
    doi = "10.18653/v1/D17-1109",
    pages = "1038--1048",
    abstract = "Knowledge base population (KBP) systems take in a large document corpus and extract entities and their relations. Thus far, KBP evaluation has relied on judgements on the pooled predictions of existing systems. We show that this evaluation is problematic: when a new system predicts a previously unseen relation, it is penalized even if it is correct. This leads to significant bias against new systems, which counterproductively discourages innovation in the field. Our first contribution is a new importance-sampling based evaluation which corrects for this bias by annotating a new system{'}s predictions on-demand via crowdsourcing. We show this eliminates bias and reduces variance using data from the 2015 TAC KBP task. Our second contribution is an implementation of our method made publicly available as an online KBP evaluation service. We pilot the service by testing diverse state-of-the-art systems on the TAC KBP 2016 corpus and obtain accurate scores in a cost effective manner.",
}
@inproceedings{hui-etal-2017-pacrr,
    title = "{PACRR}: A Position-Aware Neural {IR} Model for Relevance Matching",
    author = "Hui, Kai  and
      Yates, Andrew  and
      Berberich, Klaus  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1110",
    doi = "10.18653/v1/D17-1110",
    pages = "1049--1058",
    abstract = "In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years{'} TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.",
}
@inproceedings{raiman-miller-2017-globally,
    title = "Globally Normalized Reader",
    author = "Raiman, Jonathan  and
      Miller, John",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1111",
    doi = "10.18653/v1/D17-1111",
    pages = "1059--1069",
    abstract = "Rapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bi-directional attention mechanisms or score all possible answer spans, limiting scalability. We propose instead to cast extractive QA as an iterative search problem: select the answer{'}s sentence, start word, and end word. This representation reduces the space of each search step and allows computation to be conditionally allocated to promising search paths. We show that globally normalizing the decision process and back-propagating through beam search makes this representation viable and learning efficient. We empirically demonstrate the benefits of this approach using our model, Globally Normalized Reader (GNR), which achieves the second highest single model performance on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow. We also introduce a data-augmentation method to produce semantically valid examples by aligning named entities to a knowledge base and swapping them with new entities of the same type. This method improves the performance of all models considered in this work and is of independent interest for a variety of NLP tasks.",
}
@inproceedings{elsner-shain-2017-speech,
    title = "Speech segmentation with a neural encoder model of working memory",
    author = "Elsner, Micha  and
      Shain, Cory",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1112",
    doi = "10.18653/v1/D17-1112",
    pages = "1070--1080",
    abstract = "We present the first unsupervised LSTM speech segmenter as a cognitive model of the acquisition of words from unsegmented input. Cognitive biases toward phonological and syntactic predictability in speech are rooted in the limitations of human memory (Baddeley et al., 1998); compressed representations are easier to acquire and retain in memory. To model the biases introduced by these memory limitations, our system uses an LSTM-based encoder-decoder with a small number of hidden units, then searches for a segmentation that minimizes autoencoding loss. Linguistically meaningful segments (e.g. words) should share regular patterns of features that facilitate decoder performance in comparison to random segmentations, and we show that our learner discovers these patterns when trained on either phoneme sequences or raw acoustics. To our knowledge, ours is the first fully unsupervised system to be able to segment both symbolic and acoustic representations of speech.",
}
@inproceedings{bulat-etal-2017-speaking,
    title = "Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain",
    author = "Bulat, Luana  and
      Clark, Stephen  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1113",
    doi = "10.18653/v1/D17-1113",
    pages = "1081--1091",
    abstract = "Research in computational semantics is increasingly guided by our understanding of human semantic processing. However, semantic models are typically studied in the context of natural language processing system performance. In this paper, we present a systematic evaluation and comparison of a range of widely-used, state-of-the-art semantic models in their ability to predict patterns of conceptual representation in the human brain. Our results provide new insights both for the design of computational semantic models and for further research in cognitive neuroscience.",
}
@inproceedings{li-etal-2017-multi,
    title = "Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video",
    author = "Li, Haoran  and
      Zhu, Junnan  and
      Ma, Cong  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1114",
    doi = "10.18653/v1/D17-1114",
    pages = "1092--1102",
    abstract = "The rapid increase of the multimedia data over the Internet necessitates multi-modal summarization from collections of text, image, audio and video. In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic. The key idea is to bridge the semantic gaps between multi-modal contents. For audio information, we design an approach to selectively use its transcription. For vision information, we learn joint representations of texts and images using a neural network. Finally, all the multi-modal aspects are considered to generate the textural summary by maximizing the salience, non-redundancy, readability and coverage through budgeted optimization of submodular functions. We further introduce an MMS corpus in English and Chinese. The experimental results on this dataset demonstrate that our method outperforms other competitive baseline methods.",
}
@inproceedings{zadeh-etal-2017-tensor,
    title = "Tensor Fusion Network for Multimodal Sentiment Analysis",
    author = "Zadeh, Amir  and
      Chen, Minghai  and
      Poria, Soujanya  and
      Cambria, Erik  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1115",
    doi = "10.18653/v1/D17-1115",
    pages = "1103--1114",
    abstract = "Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Networks, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal sentiment analysis.",
}
@inproceedings{joseph-etal-2017-constance,
    title = "{C}on{S}tance: Modeling Annotation Contexts to Improve Stance Classification",
    author = "Joseph, Kenneth  and
      Friedland, Lisa  and
      Hobbs, William  and
      Lazer, David  and
      Tsur, Oren",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1116",
    doi = "10.18653/v1/D17-1116",
    pages = "1115--1124",
    abstract = "Manual annotations are a prerequisite for many applications of machine learning. However, weaknesses in the annotation process itself are easy to overlook. In particular, scholars often choose what information to give to annotators without examining these decisions empirically. For subjective tasks such as sentiment analysis, sarcasm, and stance detection, such choices can impact results. Here, for the task of political stance detection on Twitter, we show that providing too little context can result in noisy and uncertain annotations, whereas providing too strong a context may cause it to outweigh other signals. To characterize and reduce these biases, we develop ConStance, a general model for reasoning about annotations across information conditions. Given conflicting labels produced by multiple annotators seeing the same instances with different contexts, ConStance simultaneously estimates gold standard labels and also learns a classifier for new instances. We show that the classifier learned by ConStance outperforms a variety of baselines at predicting political stance, while the model{'}s interpretable parameters shed light on the effects of each context.",
}
@inproceedings{pavlopoulos-etal-2017-deeper,
    title = "Deeper Attention to Abusive User Content Moderation",
    author = "Pavlopoulos, John  and
      Malakasiotis, Prodromos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1117",
    doi = "10.18653/v1/D17-1117",
    pages = "1125--1135",
    abstract = "Experimenting with a new dataset of 1.6M user comments from a news portal and an existing dataset of 115K Wikipedia talk page comments, we show that an RNN operating on word embeddings outpeforms the previous state of the art in moderation, which used logistic regression or an MLP classifier with character or word n-grams. We also compare against a CNN operating on word embeddings, and a word-list baseline. A novel, deep, classificationspecific attention mechanism improves the performance of the RNN further, and can also highlight suspicious words for free, without including highlighted words in the training data. We consider both fully automatic and semi-automatic moderation.",
}
@inproceedings{dubossarsky-etal-2017-outta,
    title = "Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models",
    author = "Dubossarsky, Haim  and
      Weinshall, Daphna  and
      Grossman, Eitan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1118",
    doi = "10.18653/v1/D17-1118",
    pages = "1136--1145",
    abstract = "This article evaluates three proposed laws of semantic change. Our claim is that in order to validate a putative law of semantic change, the effect should be observed in the genuine condition but absent or reduced in a suitably matched control condition, in which no change can possibly have taken place. Our analysis shows that the effects reported in recent literature must be substantially revised: (i) the proposed negative correlation between meaning change and word frequency is shown to be largely an artefact of the models of word representation used; (ii) the proposed negative correlation between meaning change and prototypicality is shown to be much weaker than what has been claimed in prior art; and (iii) the proposed positive correlation between meaning change and polysemy is largely an artefact of word frequency. These empirical observations are corroborated by analytical proofs that show that count representations introduce an inherent dependence on word frequency, and thus word frequency cannot be evaluated as an independent factor with these representations.",
}
@inproceedings{lynn-etal-2017-human,
    title = "Human Centered {NLP} with User-Factor Adaptation",
    author = "Lynn, Veronica  and
      Son, Youngseo  and
      Kulkarni, Vivek  and
      Balasubramanian, Niranjan  and
      Schwartz, H. Andrew",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1119",
    doi = "10.18653/v1/D17-1119",
    pages = "1146--1155",
    abstract = "We pose the general task of user-factor adaptation {--} adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece of text should be understood within the context of the user that wrote it. We introduce a continuous adaptation technique, suited for real-valued user factors that are common in social science and bringing us closer to personalized NLP, adapting to each user uniquely. We apply this technique with known user factors including age, gender, and personality traits, as well as latent factors, evaluating over five tasks: POS tagging, PP-attachment, sentiment analysis, sarcasm detection, and stance detection. Adaptation provides statistically significant benefits for 3 of the 5 tasks: up to +1.2 points for PP-attachment, +3.4 points for sarcasm, and +3.0 points for stance.",
}
@inproceedings{raganato-etal-2017-neural,
    title = "Neural Sequence Learning Models for Word Sense Disambiguation",
    author = "Raganato, Alessandro  and
      Delli Bovi, Claudio  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1120",
    doi = "10.18653/v1/D17-1120",
    pages = "1156--1167",
    abstract = "Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.",
}
@inproceedings{rosin-etal-2017-learning,
    title = "Learning Word Relatedness over Time",
    author = "Rosin, Guy D.  and
      Adar, Eytan  and
      Radinsky, Kira",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1121",
    doi = "10.18653/v1/D17-1121",
    pages = "1168--1178",
    abstract = "Search systems are often focused on providing relevant results for the {``}now{''}, assuming both corpora and user needs that focus on the present. However, many corpora today reflect significant longitudinal collections ranging from 20 years of the Web to hundreds of years of digitized newspapers and books. Understanding the temporal intent of the user and retrieving the most relevant historical content has become a significant challenge. Common search features, such as query expansion, leverage the relationship between terms but cannot function well across all times when relationships vary temporally. In this work, we introduce a temporal relationship model that is extracted from longitudinal data collections. The model supports the task of identifying, given two words, when they relate to each other. We present an algorithmic framework for this task and show its application for the task of query expansion, achieving high gain.",
}
@inproceedings{shen-etal-2017-inter,
    title = "Inter-Weighted Alignment Network for Sentence Pair Modeling",
    author = "Shen, Gehui  and
      Yang, Yunlun  and
      Deng, Zhi-Hong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1122",
    doi = "10.18653/v1/D17-1122",
    pages = "1179--1189",
    abstract = "Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.",
}
@inproceedings{wang-etal-2017-short,
    title = "A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances",
    author = "Wang, Chengyu  and
      He, Xiaofeng  and
      Zhou, Aoying",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1123",
    doi = "10.18653/v1/D17-1123",
    pages = "1190--1203",
    abstract = "A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a large number of taxonomies have been constructed from human-compiled resources (e.g., Wikipedia), learning taxonomies from text corpora has received a growing interest and is essential for long-tailed and domain-specific knowledge acquisition. In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. We also overview resources for evaluation and discuss challenges for future research.",
}
@inproceedings{liu-etal-2017-idiom,
    title = "Idiom-Aware Compositional Distributed Semantics",
    author = "Liu, Pengfei  and
      Qian, Kaiyu  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1124",
    doi = "10.18653/v1/D17-1124",
    pages = "1204--1213",
    abstract = "Idioms are peculiar linguistic constructions that impose great challenges for representing the semantics of language, especially in current prevailing end-to-end neural models, which assume that the semantics of a phrase or sentence can be literally composed from its constitutive words. In this paper, we propose an idiom-aware distributed semantic model to build representation of sentences on the basis of understanding their contained idioms. Our models are grounded in the literal-first psycholinguistic hypothesis, which can adaptively learn semantic compositionality of a phrase literally or idiomatically. To better evaluate our models, we also construct an idiom-enriched sentiment classification dataset with considerable scale and abundant peculiarities of idioms. The qualitative and quantitative experimental analyses demonstrate the efficacy of our models.",
}
@inproceedings{zhang-etal-2017-macro,
    title = "Macro Grammars and Holistic Triggering for Efficient Semantic Parsing",
    author = "Zhang, Yuchen  and
      Pasupat, Panupong  and
      Liang, Percy",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1125",
    doi = "10.18653/v1/D17-1125",
    pages = "1214--1223",
    abstract = "To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations. We propose a new online learning algorithm that searches faster as training progresses. The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity. On the WikiTableQuestions dataset, we first expand the search space of an existing model to improve the state-of-the-art accuracy from 38.7{\%} to 42.7{\%}, and then use macro grammars and holistic triggering to achieve an 11x speedup and an accuracy of 43.7{\%}.",
}
@inproceedings{lan-etal-2017-continuously,
    title = "A Continuously Growing Dataset of Sentential Paraphrases",
    author = "Lan, Wuwei  and
      Qiu, Siyu  and
      He, Hua  and
      Xu, Wei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1126",
    doi = "10.18653/v1/D17-1126",
    pages = "1224--1234",
    abstract = "A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at {\textasciitilde}70{\%} precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.",
}
@inproceedings{su-yan-2017-cross,
    title = "Cross-domain Semantic Parsing via Paraphrasing",
    author = "Su, Yu  and
      Yan, Xifeng",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1127",
    doi = "10.18653/v1/D17-1127",
    pages = "1235--1246",
    abstract = "Existing studies on semantic parsing mainly focus on the in-domain setting. We formulate cross-domain semantic parsing as a domain adaptation problem: train a semantic parser on some source domains and then adapt it to the target domain. Due to the diversity of logical forms in different domains, this problem presents unique and intriguing challenges. By converting logical forms into canonical utterances in natural language, we reduce semantic parsing to paraphrasing, and develop an attentive sequence-to-sequence paraphrase model that is general and flexible to adapt to different domains. We discover two problems, small micro variance and large macro variance, of pre-trained word embeddings that hinder their direct use in neural networks, and propose standardization techniques as a remedy. On the popular Overnight dataset, which contains eight domains, we show that both cross-domain training and standardized pre-trained word embeddings can bring significant improvement.",
}
@inproceedings{yang-mitchell-2017-joint,
    title = "A Joint Sequential and Relational Model for Frame-Semantic Parsing",
    author = "Yang, Bishan  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1128",
    doi = "10.18653/v1/D17-1128",
    pages = "1247--1256",
    abstract = "We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction.",
}
@inproceedings{wang-xue-2017-getting,
    title = "Getting the Most out of {AMR} Parsing",
    author = "Wang, Chuan  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1129",
    doi = "10.18653/v1/D17-1129",
    pages = "1257--1268",
    abstract = "This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.",
}
@inproceedings{ballesteros-al-onaizan-2017-amr,
    title = "{AMR} Parsing using Stack-{LSTM}s",
    author = "Ballesteros, Miguel  and
      Al-Onaizan, Yaser",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1130",
    doi = "10.18653/v1/D17-1130",
    pages = "1269--1275",
    abstract = "We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.",
}
@inproceedings{zhao-etal-2017-end,
    title = "An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective",
    author = "Zhao, Jie  and
      Su, Yu  and
      Guan, Ziyu  and
      Sun, Huan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1131",
    doi = "10.18653/v1/D17-1131",
    pages = "1276--1282",
    abstract = "Given a question and a set of answer candidates, answer triggering determines whether the candidate set contains any correct answers. If yes, it then outputs a correct one. In contrast to existing pipeline methods which first consider individual candidate answers separately and then make a prediction based on a threshold, we propose an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance. Our objective function penalizes three potential types of error and allows training the framework in an end-to-end manner. Experimental results on the WikiQA benchmark show that our framework outperforms the state of the arts by a 6.6{\%} absolute gain under F1 measure.",
}
@inproceedings{cattle-ma-2017-predicting,
    title = "Predicting Word Association Strengths",
    author = "Cattle, Andrew  and
      Ma, Xiaojuan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1132",
    doi = "10.18653/v1/D17-1132",
    pages = "1283--1288",
    abstract = "This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973). We achieve results of r=0.357 and p=0.379, r=0.344 and p=0.300, and r=0.292 and p=0.363, respectively. We find Word2Vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) cosine similarities, as well as vector offsets, to be the highest performing features. Furthermore, we examine the usefulness of Gaussian embeddings (Vilnis and McCallum, 2014) for predicting word association strength, the first work to do so.",
}
@inproceedings{liu-lapata-2017-learning,
    title = "Learning Contextually Informed Representations for Linear-Time Discourse Parsing",
    author = "Liu, Yang  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1133",
    doi = "10.18653/v1/D17-1133",
    pages = "1289--1298",
    abstract = "Recent advances in RST discourse parsing have focused on two modeling paradigms: (a) high order parsers which jointly predict the tree structure of the discourse and the relations it encodes; or (b) linear-time parsers which are efficient but mostly based on local features. In this work, we propose a linear-time parser with a novel way of representing discourse constituents based on neural networks which takes into account global contextual information and is able to capture long-distance dependencies. Experimental results show that our parser obtains state-of-the art performance on benchmark datasets, while being efficient (with time complexity linear in the number of sentences in the document) and requiring minimal feature engineering.",
}
@inproceedings{lan-etal-2017-multi,
    title = "Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification",
    author = "Lan, Man  and
      Wang, Jianxiang  and
      Wu, Yuanbin  and
      Niu, Zheng-Yu  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1134",
    doi = "10.18653/v1/D17-1134",
    pages = "1299--1308",
    abstract = "We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.",
}
@inproceedings{yin-etal-2017-chinese,
    title = "{C}hinese Zero Pronoun Resolution with Deep Memory Network",
    author = "Yin, Qingyu  and
      Zhang, Yu  and
      Zhang, Weinan  and
      Liu, Ting",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1135",
    doi = "10.18653/v1/D17-1135",
    pages = "1309--1318",
    abstract = "Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents. Meanwhile, representing zero pronouns is challenging since they are merely gaps that convey no actual content. In this paper, we address this issue by building a deep memory network that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents. Consequently, our resolver takes advantage of semantic information by using these continuous distributed representations. Experiments on the OntoNotes 5.0 dataset show that the proposed memory network could substantially outperform the state-of-the-art systems in various experimental settings.",
}
@inproceedings{morey-etal-2017-much,
    title = "How much progress have we made on {RST} discourse parsing? A replication study of recent results on the {RST}-{DT}",
    author = "Morey, Mathieu  and
      Muller, Philippe  and
      Asher, Nicholas",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1136",
    doi = "10.18653/v1/D17-1136",
    pages = "1319--1324",
    abstract = "This article evaluates purported progress over the past years in RST discourse parsing. Several studies report a relative error reduction of 24 to 51{\%} on all metrics that authors attribute to the introduction of distributed representations of discourse units. We replicate the standard evaluation of 9 parsers, 5 of which use distributed representations, from 8 studies published between 2013 and 2017, using their predictions on the test set of the RST-DT. Our main finding is that most recently reported increases in RST discourse parser performance are an artefact of differences in implementations of the evaluation procedure. We evaluate all these parsers with the standard Parseval procedure to provide a more accurate picture of the actual RST discourse parsers performance in standard evaluation settings. Under this more stringent procedure, the gains attributable to distributed representations represent at most a 16{\%} relative error reduction on fully-labelled structures.",
}
@inproceedings{loaiciga-etal-2017-disambiguating,
    title = "What is it? Disambiguating the different readings of the pronoun {`}it{'}",
    author = "Lo{\'a}iciga, Sharid  and
      Guillou, Liane  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1137",
    doi = "10.18653/v1/D17-1137",
    pages = "1325--1331",
    abstract = "In this paper, we address the problem of predicting one of three functions for the English pronoun {`}it{'}: anaphoric, event reference or pleonastic. This disambiguation is valuable in the context of machine translation and coreference resolution. We present experiments using a MAXENT classifier trained on gold-standard data and self-training experiments of an RNN trained on silver-standard data, annotated using the MAXENT classifier. Lastly, we report on an analysis of the strengths of these two models.",
}
@inproceedings{heinzerling-etal-2017-revisiting,
    title = "Revisiting Selectional Preferences for Coreference Resolution",
    author = "Heinzerling, Benjamin  and
      Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1138",
    doi = "10.18653/v1/D17-1138",
    pages = "1332--1339",
    abstract = "Selectional preferences have long been claimed to be essential for coreference resolution. However, they are modeled only implicitly by current coreference resolvers. We propose a dependency-based embedding model of selectional preferences which allows fine-grained compatibility judgments with high coverage. Incorporating our model improves performance, matching state-of-the-art results of a more complex system. However, it comes with a cost that makes it debatable how worthwhile are such improvements.",
}
@inproceedings{wang-etal-2017-learning,
    title = "Learning to Rank Semantic Coherence for Topic Segmentation",
    author = "Wang, Liang  and
      Li, Sujian  and
      Lv, Yajuan  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1139",
    doi = "10.18653/v1/D17-1139",
    pages = "1340--1344",
    abstract = "Topic segmentation plays an important role for discourse parsing and information retrieval. Due to the absence of training data, previous work mainly adopts unsupervised methods to rank semantic coherence between paragraphs for topic segmentation. In this paper, we present an intuitive and simple idea to automatically create a {``}quasi{''} training dataset, which includes a large amount of text pairs from the same or different documents with different semantic coherence. With the training corpus, we design a symmetric CNN neural network to model text pairs and rank the semantic coherence within the learning to rank framework. Experiments show that our algorithm is able to achieve competitive performance over strong baselines on several real-world datasets.",
}
@inproceedings{shnarch-etal-2017-grasp,
    title = "{GRASP}: Rich Patterns for Argumentation Mining",
    author = "Shnarch, Eyal  and
      Levy, Ran  and
      Raykar, Vikas  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1140",
    doi = "10.18653/v1/D17-1140",
    pages = "1345--1350",
    abstract = "GRASP (GReedy Augmented Sequential Patterns) is an algorithm for automatically extracting patterns that characterize subtle linguistic phenomena. To that end, GRASP augments each term of input text with multiple layers of linguistic information. These different facets of the text terms are systematically combined to reveal rich patterns. We report highly promising experimental results in several challenging text analysis tasks within the field of Argumentation Mining. We believe that GRASP is general enough to be useful for other domains too. For example, each of the following sentences includes a claim for a [topic]: 1. Opponents often argue that the open primary is unconstitutional. [Open Primaries] 2. Prof. Smith suggested that affirmative action devalues the accomplishments of the chosen. [Affirmative Action] 3. The majority stated that the First Amendment does not guarantee the right to offend others. [Freedom of Speech] These sentences share almost no words in common, however, they are similar at a more abstract level. A human observer may notice the following underlying common structure, or pattern: [someone][argue/suggest/state][that][topic term][sentiment term]. GRASP aims to automatically capture such underlying structures of the given data. For the above examples it finds the pattern [noun][express][that][noun,topic][sentiment], where [express] stands for all its (in)direct hyponyms, and [noun,topic] means a noun which is also related to the topic.",
}
@inproceedings{al-khatib-etal-2017-patterns,
    title = "Patterns of Argumentation Strategies across Topics",
    author = "Al-Khatib, Khalid  and
      Wachsmuth, Henning  and
      Hagen, Matthias  and
      Stein, Benno",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1141",
    doi = "10.18653/v1/D17-1141",
    pages = "1351--1357",
    abstract = "This paper presents an analysis of argumentation strategies in news editorials within and across topics. Given nearly 29,000 argumentative editorials from the New York Times, we develop two machine learning models, one for determining an editorial{'}s topic, and one for identifying evidence types in the editorial. Based on the distribution and structure of the identified types, we analyze the usage patterns of argumentation strategies among 12 different topics. We detect several common patterns that provide insights into the manifestation of argumentation strategies. Also, our experiments reveal clear correlations between the topics and the detected patterns.",
}
@inproceedings{liu-etal-2017-using,
    title = "Using Argument-based Features to Predict and Analyse Review Helpfulness",
    author = "Liu, Haijing  and
      Gao, Yang  and
      Lv, Pin  and
      Li, Mengxue  and
      Geng, Shiqiang  and
      Li, Minglan  and
      Wang, Hao",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1142",
    doi = "10.18653/v1/D17-1142",
    pages = "1358--1363",
    abstract = "We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01{\%} in average.",
}
@inproceedings{potash-etal-2017-heres,
    title = "Here{'}s My Point: Joint Pointer Architecture for Argument Mining",
    author = "Potash, Peter  and
      Romanov, Alexey  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1143",
    doi = "10.18653/v1/D17-1143",
    pages = "1364--1373",
    abstract = "In order to determine argument structure in text, one must understand how individual components of the overall argument are linked. This work presents the first neural network-based approach to link extraction in argument mining. Specifically, we propose a novel architecture that applies Pointer Network sequence-to-sequence attention modeling to structural prediction in discourse parsing tasks. We then develop a joint model that extends this architecture to simultaneously address the link extraction task and the classification of argument components. The proposed joint model achieves state-of-the-art results on two separate evaluation corpora, showing far superior performance than the previously proposed corpus-specific and heavily feature-engineered models. Furthermore, our results demonstrate that jointly optimizing for both tasks is crucial for high performance.",
}
@inproceedings{cocarascu-toni-2017-identifying,
    title = "Identifying attack and support argumentative relations using deep learning",
    author = "Cocarascu, Oana  and
      Toni, Francesca",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1144",
    doi = "10.18653/v1/D17-1144",
    pages = "1374--1379",
    abstract = "We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. The architecture uses two (unidirectional or bidirectional) Long Short-Term Memory networks and (trained or non-trained) word embeddings, and allows to considerably improve upon existing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining.",
}
@inproceedings{sperber-etal-2017-neural,
    title = "Neural Lattice-to-Sequence Models for Uncertain Inputs",
    author = "Sperber, Matthias  and
      Neubig, Graham  and
      Niehues, Jan  and
      Waibel, Alex",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1145",
    doi = "10.18653/v1/D17-1145",
    pages = "1380--1389",
    abstract = "The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream models are potentially error-prone. Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model. We integrate lattice posterior scores into this architecture by extending the TreeLSTM{'}s child-sum and forget gates and introducing a bias term into the attention mechanism. We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.",
}
@inproceedings{feng-etal-2017-memory,
    title = "Memory-augmented Neural Machine Translation",
    author = "Feng, Yang  and
      Zhang, Shiyue  and
      Zhang, Andi  and
      Wang, Dong  and
      Abel, Andrew",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1146",
    doi = "10.18653/v1/D17-1146",
    pages = "1390--1399",
    abstract = "Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs. This paper presents a novel memory-augmented NMT (M-NMT) architecture, which stores knowledge about how words (usually infrequently encountered ones) should be translated in a memory and then utilizes them to assist the neural model. We use this memory mechanism to combine the knowledge learned from a conventional statistical machine translation system and the rules learned by an NMT system, and also propose a solution for out-of-vocabulary (OOV) words based on this framework. Our experiments on two Chinese-English translation tasks demonstrated that the M-NMT architecture outperformed the NMT baseline by 9.0 and 2.7 BLEU points on the two tasks, respectively. Additionally, we found this architecture resulted in a much more effective OOV treatment compared to competitive methods.",
}
@inproceedings{van-der-wees-etal-2017-dynamic,
    title = "Dynamic Data Selection for Neural Machine Translation",
    author = "van der Wees, Marlies  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1147",
    doi = "10.18653/v1/D17-1147",
    pages = "1400--1410",
    abstract = "Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine translation (PBMT). With the recent increase in popularity of neural machine translation (NMT), we explore in this paper to what extent and how NMT can also benefit from data selection. While state-of-the-art data selection (Axelrod et al., 2011) consistently performs well for PBMT, we show that gains are substantially lower for NMT. Next, we introduce {`}dynamic data selection{'} for NMT, a method in which we vary the selected subset of training data between different training epochs. Our experiments show that the best results are achieved when applying a technique we call {`}gradual fine-tuning{'}, with improvements up to +2.6 BLEU over the original data selection approach and up to +3.1 BLEU over a general baseline.",
}
@inproceedings{dahlmann-etal-2017-neural,
    title = "Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search",
    author = "Dahlmann, Leonard  and
      Matusov, Evgeny  and
      Petrushkov, Pavel  and
      Khadivi, Shahram",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1148",
    doi = "10.18653/v1/D17-1148",
    pages = "1411--1420",
    abstract = "In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase. Phrases added in this way are scored with the NMT model, but also with SMT features including phrase-level translation probabilities and a target language model. Experimental results on German-to-English news domain and English-to-Russian e-commerce domain translation tasks show that using phrase-based models in NMT search improves MT quality by up to 2.3{\%} BLEU absolute as compared to a strong NMT baseline.",
}
@inproceedings{wang-etal-2017-translating,
    title = "Translating Phrases in Neural Machine Translation",
    author = "Wang, Xing  and
      Tu, Zhaopeng  and
      Xiong, Deyi  and
      Zhang, Min",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1149",
    doi = "10.18653/v1/D17-1149",
    pages = "1421--1431",
    abstract = "Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into current neural machine translation (NMT) which reads and generates sentences word by word. In this work, we propose a method to translate phrases in NMT by integrating a phrase memory storing target phrases from a phrase-based statistical machine translation (SMT) system into the encoder-decoder architecture of NMT. At each decoding step, the phrase memory is first re-written by the SMT model, which dynamically generates relevant target phrases with contextual information provided by the NMT model. Then the proposed model reads the phrase memory to make probability estimations for all phrases in the phrase memory. If phrase generation is carried on, the NMT decoder selects an appropriate phrase from the memory to perform phrase translation and updates its decoding state by consuming the words in the selected phrase. Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does. Experiment results on the Chinese to English translation show that the proposed model achieves significant improvements over the baseline on various test sets.",
}
@inproceedings{yang-etal-2017-towards,
    title = "Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation",
    author = "Yang, Baosong  and
      Wong, Derek F.  and
      Xiao, Tong  and
      Chao, Lidia S.  and
      Zhu, Jingbo",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1150",
    doi = "10.18653/v1/D17-1150",
    pages = "1432--1441",
    abstract = "This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder. To maximize the predictive likelihood of target words, a weighted variant of an attention mechanism is used to balance the attentive information between lexical and phrase vectors. Using a tree-based rare word encoding, the proposed model is extended to sub-word level to alleviate the out-of-vocabulary (OOV) problem. Empirical results reveal that the proposed model significantly outperforms sequence-to-sequence attention-based and tree-based neural translation models in English-Chinese translation tasks.",
}
@inproceedings{britz-etal-2017-massive,
    title = "Massive Exploration of Neural Machine Translation Architectures",
    author = "Britz, Denny  and
      Goldie, Anna  and
      Luong, Minh-Thang  and
      Le, Quoc",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1151",
    doi = "10.18653/v1/D17-1151",
    pages = "1442--1451",
    abstract = "Neural Machine Translation (NMT) has shown remarkable progress over the past few years, with production systems now being deployed to end-users. As the field is moving rapidly, it has become unclear which elements of NMT architectures have a significant impact on translation quality. In this work, we present a large-scale analysis of the sensitivity of NMT architectures to common hyperparameters. We report empirical results and variance numbers for several hundred experimental runs, corresponding to over 250,000 GPU hours on a WMT English to German translation task. Our experiments provide practical insights into the relative importance of factors such as embedding size, network depth, RNN cell type, residual connections, attention mechanism, and decoding heuristics. As part of this contribution, we also release an open-source NMT framework in TensorFlow to make it easy for others to reproduce our results and perform their own experiments.",
}
@inproceedings{wijaya-etal-2017-learning,
    title = "Learning Translations via Matrix Completion",
    author = "Wijaya, Derry Tanti  and
      Callahan, Brendan  and
      Hewitt, John  and
      Gao, Jie  and
      Ling, Xiao  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1152",
    doi = "10.18653/v1/D17-1152",
    pages = "1452--1463",
    abstract = "Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completing the matrix. This method harnesses diverse bilingual and monolingual signals, each of which may be incomplete or noisy. Our model achieves state-of-the-art performance for both high and low resource languages.",
}
@inproceedings{nguyen-etal-2017-reinforcement,
    title = "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback",
    author = "Nguyen, Khanh  and
      Daum{\'e} III, Hal  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1153",
    doi = "10.18653/v1/D17-1153",
    pages = "1464--1474",
    abstract = "Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoder-decoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.",
}
@inproceedings{zhang-etal-2017-towards,
    title = "Towards Compact and Fast Neural Machine Translation Using a Combined Method",
    author = "Zhang, Xiaowei  and
      Chen, Wei  and
      Wang, Feng  and
      Xu, Shuang  and
      Xu, Bo",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1154",
    doi = "10.18653/v1/D17-1154",
    pages = "1475--1481",
    abstract = "Neural Machine Translation (NMT) lays intensive burden on computation and memory cost. It is a challenge to deploy NMT models on the devices with limited computation and memory budgets. This paper presents a four stage pipeline to compress model and speed up the decoding for NMT. Our method first introduces a compact architecture based on convolutional encoder and weight shared embeddings. Then weight pruning is applied to obtain a sparse model. Next, we propose a fast sequence interpolation approach which enables the greedy decoding to achieve performance on par with the beam search. Hence, the time-consuming beam search can be replaced by simple greedy decoding. Finally, vocabulary selection is used to reduce the computation of softmax layer. Our final model achieves 10 times speedup, 17 times parameters reduction, less than 35MB storage size and comparable performance compared to the baseline model.",
}
@inproceedings{wang-etal-2017-instance,
    title = "Instance Weighting for Neural Machine Translation Domain Adaptation",
    author = "Wang, Rui  and
      Utiyama, Masao  and
      Liu, Lemao  and
      Chen, Kehai  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1155",
    doi = "10.18653/v1/D17-1155",
    pages = "1482--1488",
    abstract = "Instance weighting has been widely applied to phrase-based machine translation domain adaptation. However, it is challenging to be applied to Neural Machine Translation (NMT) directly, because NMT is not a linear model. In this paper, two instance weighting technologies, i.e., sentence weighting and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation. Empirical results on the IWSLT English-German/French tasks show that the proposed methods can substantially improve NMT performance by up to 2.7-6.7 BLEU points, outperforming the existing baselines by up to 1.6-3.6 BLEU points.",
}
@inproceedings{miceli-barone-etal-2017-regularization,
    title = "Regularization techniques for fine-tuning in neural machine translation",
    author = "Miceli Barone, Antonio Valerio  and
      Haddow, Barry  and
      Germann, Ulrich  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1156",
    doi = "10.18653/v1/D17-1156",
    pages = "1489--1494",
    abstract = "We investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset. In this scenario, overfitting is a major challenge. We investigate a number of techniques to reduce overfitting and improve transfer learning, including regularization techniques such as dropout and L2-regularization towards an out-of-domain prior. In addition, we introduce tuneout, a novel regularization technique inspired by dropout. We apply these techniques, alone and in combination, to neural machine translation, obtaining improvements on IWSLT datasets for English→German and English→Russian. We also investigate the amounts of in-domain training data needed for domain adaptation in NMT, and find a logarithmic relationship between the amount of training data and gain in BLEU score.",
}
@inproceedings{chang-collins-2017-source,
    title = "Source-Side Left-to-Right or Target-Side Left-to-Right? An Empirical Comparison of Two Phrase-Based Decoding Algorithms",
    author = "Chang, Yin-Wen  and
      Collins, Michael",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1157",
    doi = "10.18653/v1/D17-1157",
    pages = "1495--1499",
    abstract = "This paper describes an empirical study of the phrase-based decoding algorithm proposed by Chang and Collins (2017). The algorithm produces a translation by processing the source-language sentence in strictly left-to-right order, differing from commonly used approaches that build the target-language sentence in left-to-right order. Our results show that the new algorithm is competitive with Moses (Koehn et al., 2007) in terms of both speed and BLEU scores.",
}
@inproceedings{domhan-hieber-2017-using,
    title = "Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning",
    author = "Domhan, Tobias  and
      Hieber, Felix",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1158",
    doi = "10.18653/v1/D17-1158",
    pages = "1500--1505",
    abstract = "The performance of Neural Machine Translation (NMT) models relies heavily on the availability of sufficient amounts of parallel data, and an efficient and effective way of leveraging the vastly available amounts of monolingual data has yet to be found. We propose to modify the decoder in a neural sequence-to-sequence model to enable multi-task learning for two strongly related tasks: target-side language modeling and translation. The decoder predicts the next target word through two channels, a target-side language model on the lowest layer, and an attentional recurrent model which is conditioned on the source representation. This architecture allows joint training on both large amounts of monolingual and moderate amounts of bilingual data to improve NMT performance. Initial results in the news domain for three language pairs show moderate but consistent improvements over a baseline trained on bilingual data only.",
}
@inproceedings{marcheggiani-titov-2017-encoding,
    title = "Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling",
    author = "Marcheggiani, Diego  and
      Titov, Ivan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1159",
    doi = "10.18653/v1/D17-1159",
    pages = "1506--1515",
    abstract = "Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.",
}
@inproceedings{krishnamurthy-etal-2017-neural,
    title = "Neural Semantic Parsing with Type Constraints for Semi-Structured Tables",
    author = "Krishnamurthy, Jayant  and
      Dasigi, Pradeep  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1160",
    doi = "10.18653/v1/D17-1160",
    pages = "1516--1526",
    abstract = "We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also introduce a novel method for training our neural model with question-answer supervision. On the WikiTableQuestions data set, our parser achieves a state-of-the-art accuracy of 43.3{\%} for a single model and 45.9{\%} for a 5-model ensemble, improving on the best prior score of 38.7{\%} set by a 15-model ensemble. These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.",
}
@inproceedings{srivastava-etal-2017-joint,
    title = "Joint Concept Learning and Semantic Parsing from Natural Language Explanations",
    author = "Srivastava, Shashank  and
      Labutov, Igor  and
      Mitchell, Tom",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1161",
    doi = "10.18653/v1/D17-1161",
    pages = "1527--1536",
    abstract = "Natural language constitutes a predominant medium for much of human learning and pedagogy. We consider the problem of concept learning from natural language explanations, and a small number of labeled examples of the concept. For example, in learning the concept of a phishing email, one might say {`}this is a phishing email because it asks for your bank account number{'}. Solving this problem involves both learning to interpret open ended natural language statements, and learning the concept itself. We present a joint model for (1) language interpretation (semantic parsing) and (2) concept learning (classification) that does not require labeling statements with logical forms. Instead, the model prefers discriminative interpretations of statements in context of observable features of the data as a weak signal for parsing. On a dataset of email-related concepts, our approach yields across-the-board improvements in classification performance, with a 30{\%} relative improvement in F1 score over competitive methods in the low data regime.",
}
@inproceedings{rei-etal-2017-grasping,
    title = "Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection",
    author = "Rei, Marek  and
      Bulat, Luana  and
      Kiela, Douwe  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1162",
    doi = "10.18653/v1/D17-1162",
    pages = "1537--1546",
    abstract = "The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.",
}
@inproceedings{keith-etal-2017-identifying,
    title = "Identifying civilians killed by police with distantly supervised entity-event extraction",
    author = "Keith, Katherine  and
      Handler, Abram  and
      Pinkham, Michael  and
      Magliozzi, Cara  and
      McDuffie, Joshua  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1163",
    doi = "10.18653/v1/D17-1163",
    pages = "1547--1557",
    abstract = "We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police. We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers. Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases.",
}
@inproceedings{zhang-etal-2017-asking,
    title = "Asking too much? The rhetorical role of questions in political discourse",
    author = "Zhang, Justine  and
      Spirling, Arthur  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1164",
    doi = "10.18653/v1/D17-1164",
    pages = "1558--1572",
    abstract = "Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied. In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse{---}such as the bifurcation in questioning behavior between government and opposition parties{---}and reveals new insights into the effects of a legislator{'}s tenure and political career ambitions.",
}
@inproceedings{vilares-he-2017-detecting,
    title = "Detecting Perspectives in Political Debates",
    author = "Vilares, David  and
      He, Yulan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1165",
    doi = "10.18653/v1/D17-1165",
    pages = "1573--1582",
    abstract = "We explore how to detect people{'}s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled as latent variables. Words associated with topics or perspectives follow different generative routes. Based on the extracted perspectives, we can extract the top associated sentences from text to generate a succinct summary which allows a quick glimpse of the main viewpoints in a document. The model is evaluated on debates from the House of Commons of the UK Parliament, revealing perspectives from the debates without the use of labelled data and obtaining better results than previous related solutions under a variety of evaluations.",
}
@inproceedings{swamy-etal-2017-feeling,
    title = "{``}i have a feeling trump will win..................{''}: Forecasting Winners and Losers from User Predictions on {T}witter",
    author = "Swamy, Sandesh  and
      Ritter, Alan  and
      de Marneffe, Marie-Catherine",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1166",
    doi = "10.18653/v1/D17-1166",
    pages = "1583--1592",
    abstract = "Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome: {``}Leonardo DiCaprio will win Best Actor{''} vs. {``}Leonardo DiCaprio may win{''} or {``}No way Leonardo wins!{''}. Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users{'} explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts{'} predictions and retrospectively identify surprise outcomes.",
}
@inproceedings{gui-etal-2017-question,
    title = "A Question Answering Approach for Emotion Cause Extraction",
    author = "Gui, Lin  and
      Hu, Jiannan  and
      He, Yulan  and
      Xu, Ruifeng  and
      Lu, Qin  and
      Du, Jiachen",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1167",
    doi = "10.18653/v1/D17-1167",
    pages = "1593--1602",
    abstract = "Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01{\%} in F-measure.",
}
@inproceedings{chaturvedi-etal-2017-story,
    title = "Story Comprehension for Predicting What Happens Next",
    author = "Chaturvedi, Snigdha  and
      Peng, Haoruo  and
      Roth, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1168",
    doi = "10.18653/v1/D17-1168",
    pages = "1603--1614",
    abstract = "Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense. In this paper, we present a story comprehension model that explores three distinct semantic aspects: (i) the sequence of events described in the story, (ii) its emotional trajectory, and (iii) its plot consistency. We judge the model{'}s understanding of real-world stories by inquiring if, like humans, it can develop an expectation of what will happen next in a given story. Specifically, we use it to predict the correct ending of a given short story from possible alternatives. The model uses a hidden variable to weigh the semantic aspects in the context of the story. Our experiments demonstrate the potential of our approach to characterize these semantic aspects, and the strength of the hidden variable based approach. The model outperforms the state-of-the-art approaches and achieves best results on a publicly available dataset.",
}
@inproceedings{felbo-etal-2017-using,
    title = "Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm",
    author = "Felbo, Bjarke  and
      Mislove, Alan  and
      S{\o}gaard, Anders  and
      Rahwan, Iyad  and
      Lehmann, Sune",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1169",
    doi = "10.18653/v1/D17-1169",
    pages = "1615--1625",
    abstract = "NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-the-art performance on 8 benchmark datasets within emotion, sentiment and sarcasm detection using a single pretrained model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.",
}
@inproceedings{wang-zhang-2017-opinion,
    title = "Opinion Recommendation Using A Neural Model",
    author = "Wang, Zhongqing  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1170",
    doi = "10.18653/v1/D17-1170",
    pages = "1626--1637",
    abstract = "We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp{'}s own ratings. our methods give better results compared to several pipelines baselines.",
}
@inproceedings{cai-etal-2017-crf,
    title = "{CRF} Autoencoder for Unsupervised Dependency Parsing",
    author = "Cai, Jiong  and
      Jiang, Yong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1171",
    doi = "10.18653/v1/D17-1171",
    pages = "1638--1643",
    abstract = "Unsupervised dependency parsing, which tries to discover linguistic dependency structures from unannotated data, is a very challenging task. Almost all previous work on this task focuses on learning generative models. In this paper, we develop an unsupervised dependency parsing model based on the CRF autoencoder. The encoder part of our model is discriminative and globally normalized which allows us to use rich features as well as universal linguistic priors. We propose an exact algorithm for parsing as well as a tractable learning algorithm. We evaluated the performance of our model on eight multilingual treebanks and found that our model achieved comparable performance with state-of-the-art approaches.",
}
@inproceedings{corro-etal-2017-efficient,
    title = "Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence",
    author = "Corro, Caio  and
      Le Roux, Joseph  and
      Lacroix, Mathieu",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1172",
    doi = "10.18653/v1/D17-1172",
    pages = "1644--1654",
    abstract = "We present a new method for the joint task of tagging and non-projective dependency parsing. We demonstrate its usefulness with an application to discontinuous phrase-structure parsing where decoding lexicalized spines and syntactic derivations is performed jointly. The main contributions of this paper are (1) a reduction from joint tagging and non-projective dependency parsing to the Generalized Maximum Spanning Arborescence problem, and (2) a novel decoding algorithm for this problem through Lagrangian relaxation. We evaluate this model and obtain state-of-the-art results despite strong independence assumptions.",
}
@inproceedings{zheng-2017-incremental,
    title = "Incremental Graph-based Neural Dependency Parsing",
    author = "Zheng, Xiaoqing",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1173",
    doi = "10.18653/v1/D17-1173",
    pages = "1655--1665",
    abstract = "Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages. However, for graph-based neural dependency parsing systems, they either count on the long-term memory and attention mechanism to implicitly capture the high-order features or give up the global exhaustive inference algorithms in order to harness the features over a rich history of parsing decisions. The former might miss out the important features for specific headword predictions without the help of the explicit structural information, and the latter may suffer from the error propagation as false early structural constraints are used to create features when making future predictions. We explore the feasibility of explicitly taking high-order features into account while remaining the main advantage of global inference and learning for graph-based parsing. The proposed parser first forms an initial parse tree by head-modifier predictions based on the first-order factorization. High-order features (such as grandparent, sibling, and uncle) then can be defined over the initial tree, and used to refine the parse tree in an iterative fashion. Experimental results showed that our model (called INDP) archived competitive performance to existing benchmark parsers on both English and Chinese datasets.",
}
@inproceedings{stanojevic-alhama-2017-neural,
    title = "Neural Discontinuous Constituency Parsing",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Alhama, Raquel G.",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1174",
    doi = "10.18653/v1/D17-1174",
    pages = "1666--1676",
    abstract = "One of the most pressing issues in discontinuous constituency transition-based parsing is that the relevant information for parsing decisions could be located in any part of the stack or the buffer. In this paper, we propose a solution to this problem by replacing the structured perceptron model with a recursive neural model that computes a global representation of the configuration, therefore allowing even the most remote parts of the configuration to influence the parsing decisions. We also provide a detailed analysis of how this representation should be built out of sub-representations of its core elements (words, trees and stack). Additionally, we investigate how different types of swap oracles influence the results. Our model is the first neural discontinuous constituency parser, and it outperforms all the previously published models on three out of four datasets while on the fourth it obtains second place by a tiny difference.",
}
@inproceedings{zhang-etal-2017-stack,
    title = "Stack-based Multi-layer Attention for Transition-based Dependency Parsing",
    author = "Zhang, Zhirui  and
      Liu, Shujie  and
      Li, Mu  and
      Zhou, Ming  and
      Chen, Enhong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1175",
    doi = "10.18653/v1/D17-1175",
    pages = "1677--1682",
    abstract = "Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection. In this paper, we propose a stack-based multi-layer attention model for seq2seq learning to better leverage structural linguistics information. In our method, two binary vectors are used to track the decoding stack in transition-based parsing, and multi-layer attention is introduced to capture multiple word dependencies in partial trees. We conduct experiments on PTB and CTB datasets, and the results show that our proposed model achieves state-of-the-art accuracy and significant improvement in labeled precision with respect to the baseline seq2seq model.",
}
@inproceedings{han-etal-2017-dependency,
    title = "Dependency Grammar Induction with Neural Lexicalization and Big Training Data",
    author = "Han, Wenjuan  and
      Jiang, Yong  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1176",
    doi = "10.18653/v1/D17-1176",
    pages = "1683--1688",
    abstract = "We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Dependency Model with Valence (Klein and Manning, 2004) and L-NDMV, our lexicalized extension of the Neural Dependency Model with Valence (Jiang et al., 2016). We find that L-DMV only benefits from very small degrees of lexicalization and moderate sizes of training corpora. L-NDMV can benefit from big training data and lexicalization of greater degrees, especially when enhanced with good model initialization, and it achieves a result that is competitive with the current state-of-the-art.",
}
@inproceedings{jiang-etal-2017-combining,
    title = "Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition",
    author = "Jiang, Yong  and
      Han, Wenjuan  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1177",
    doi = "10.18653/v1/D17-1177",
    pages = "1689--1694",
    abstract = "Unsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maximization algorithm and its variants, or learning discriminative models using the discriminative clustering algorithm. In this paper, we propose a new learning strategy that learns a generative model and a discriminative model jointly based on the dual decomposition method. Our method is simple and general, yet effective to capture the advantages of both models and improve their learning results. We tested our method on the UD treebank and achieved a state-of-the-art performance on thirty languages.",
}
@inproceedings{stern-etal-2017-effective,
    title = "Effective Inference for Generative Neural Parsing",
    author = "Stern, Mitchell  and
      Fried, Daniel  and
      Klein, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1178",
    doi = "10.18653/v1/D17-1178",
    pages = "1695--1700",
    abstract = "Generative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited to reranking the output of external parsers in which decoding is more tractable. We describe an alternative to the conventional action-level beam search used for discriminative neural models that enables us to decode directly in these generative models. We then show that by improving our basic candidate selection strategy and using a coarse pruning function, we can improve accuracy while exploring significantly less of the search space. Applied to the model of Choe and Charniak (2016), our inference procedure obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior state-of-the-art results for single-model systems.",
}
@inproceedings{zhang-etal-2017-semi,
    title = "Semi-supervised Structured Prediction with Neural {CRF} Autoencoder",
    author = "Zhang, Xiao  and
      Jiang, Yong  and
      Peng, Hao  and
      Tu, Kewei  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1179",
    doi = "10.18653/v1/D17-1179",
    pages = "1701--1711",
    abstract = "In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems. Our NCRF-AE consists of two parts: an encoder which is a CRF model enhanced by deep neural networks, and a decoder which is a generative model trying to reconstruct the input. Our model has a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We developed a variation of the EM algorithm for optimizing both the encoder and the decoder simultaneously by decoupling their parameters. Our Experimental results over the Part-of-Speech (POS) tagging task on eight different languages, show that our model can outperform competitive systems in both supervised and semi-supervised scenarios.",
}
@inproceedings{kasai-etal-2017-tag,
    title = "{TAG} Parsing with Neural Networks and Vector Representations of Supertags",
    author = "Kasai, Jungo  and
      Frank, Bob  and
      McCoy, Tom  and
      Rambow, Owen  and
      Nasr, Alexis",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1180",
    doi = "10.18653/v1/D17-1180",
    pages = "1712--1722",
    abstract = "We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely, and uses only the 1-best supertags to parse a sentence, providing further support for the claim that supertagging is {``}almost parsing.{''} We demonstrate that the embedding vector representations the parser induces for supertags possess linguistically interpretable structure, supporting analogies between grammatical structures like those familiar from recent work in distributional semantics. This dense representation of supertags overcomes the drawbacks for statistical models of TAG as compared to CCG parsing, raising the possibility that TAG is a viable alternative for NLP tasks that require the assignment of richer structural descriptions to sentences.",
}
@inproceedings{adel-schutze-2017-global,
    title = "Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification",
    author = {Adel, Heike  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1181",
    doi = "10.18653/v1/D17-1181",
    pages = "1723--1729",
    abstract = "We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.",
}
@inproceedings{zhang-etal-2017-end,
    title = "End-to-End Neural Relation Extraction with Global Optimization",
    author = "Zhang, Meishan  and
      Zhang, Yue  and
      Fu, Guohong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1182",
    doi = "10.18653/v1/D17-1182",
    pages = "1730--1740",
    abstract = "Neural networks have shown promising results for relation extraction. State-of-the-art models cast the task as an end-to-end problem, solved incrementally using a local classifier. Yet previous work using statistical models have demonstrated that global optimization can achieve better performances compared to local classification. We build a globally optimized neural model for end-to-end relation extraction, proposing novel LSTM features in order to better learn context representations. In addition, we present a novel method to integrate syntactic information to facilitate global learning, yet requiring little background on syntactic grammars thus being easy to extend. Experimental results show that our proposed model is highly effective, achieving the best performances on two standard benchmarks.",
}
@inproceedings{ojha-talukdar-2017-kgeval,
    title = "{KGE}val: Accuracy Estimation of Automatically Constructed Knowledge Graphs",
    author = "Ojha, Prakhar  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1183",
    doi = "10.18653/v1/D17-1183",
    pages = "1741--1750",
    abstract = "Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently. Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity. This important problem has largely been ignored in prior research {--} we fill this gap and propose KGEval. KGEval uses coupling constraints to bind facts and crowdsources those few that can infer large parts of the graph. We demonstrate that the objective optimized by KGEval is submodular and NP-hard, allowing guarantees for our approximation algorithm. Through experiments on real-world datasets, we demonstrate that KGEval best estimates KG accuracy compared to other baselines, while requiring significantly lesser number of human evaluations.",
}
@inproceedings{pujara-etal-2017-sparsity,
    title = "Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short",
    author = "Pujara, Jay  and
      Augustine, Eriq  and
      Getoor, Lise",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1184",
    doi = "10.18653/v1/D17-1184",
    pages = "1751--1756",
    abstract = "Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations. One prominent goal of these approaches is to improve the quality of knowledge graphs by removing errors and adding missing facts. Surprisingly, most embedding techniques have been evaluated on benchmark datasets consisting of dense and reliable subsets of human-curated KGs, which tend to be fairly complete and have few errors. In this paper, we consider the problem of applying embedding techniques to KGs extracted from text, which are often incomplete and contain errors. We compare the sparsity and unreliability of different KGs and perform empirical experiments demonstrating how embedding approaches degrade as sparsity and unreliability increase.",
}
@inproceedings{glavas-ponzetto-2017-dual,
    title = "Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations",
    author = "Glava{\v{s}}, Goran  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1185",
    doi = "10.18653/v1/D17-1185",
    pages = "1757--1767",
    abstract = "Detection of lexico-semantic relations is one of the central tasks of computational semantics. Although some fundamental relations (e.g., hypernymy) are asymmetric, most existing models account for asymmetry only implicitly and use the same concept representations to support detection of symmetric and asymmetric relations alike. In this work, we propose the Dual Tensor model, a neural architecture with which we explicitly model the asymmetry and capture the translation between unspecialized and specialized word embeddings via a pair of tensors. Although our Dual Tensor model needs only unspecialized embeddings as input, our experiments on hypernymy and meronymy detection suggest that it can outperform more complex and resource-intensive models. We further demonstrate that the model can account for polysemy and that it exhibits stable performance across languages.",
}
@inproceedings{zeng-etal-2017-incorporating,
    title = "Incorporating Relation Paths in Neural Relation Extraction",
    author = "Zeng, Wenyuan  and
      Lin, Yankai  and
      Liu, Zhiyuan  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1186",
    doi = "10.18653/v1/D17-1186",
    pages = "1768--1777",
    abstract = "Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which also provide rich useful information but not yet employed by relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on real-world datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with strong baselines. The source code of this paper can be obtained from \url{https://github.com/thunlp/PathNRE}.",
}
@inproceedings{wu-etal-2017-adversarial,
    title = "Adversarial Training for Relation Extraction",
    author = "Wu, Yi  and
      Bamman, David  and
      Russell, Stuart",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1187",
    doi = "10.18653/v1/D17-1187",
    pages = "1778--1783",
    abstract = "Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. We evaluate various neural network architectures on two different datasets. Experimental results demonstrate that adversarial training is generally effective for both CNN and RNN models and significantly improves the precision of predicted relations.",
}
@inproceedings{sorokin-gurevych-2017-context,
    title = "Context-Aware Representations for Knowledge Base Relation Extraction",
    author = "Sorokin, Daniil  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1188",
    doi = "10.18653/v1/D17-1188",
    pages = "1784--1789",
    abstract = "We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24 on a held-out set of relations. The code and the dataset to replicate the experiments are made available at \url{https://github.com/ukplab/}.",
}
@inproceedings{liu-etal-2017-soft,
    title = "A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction",
    author = "Liu, Tianyu  and
      Wang, Kexiang  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1189",
    doi = "10.18653/v1/D17-1189",
    pages = "1790--1795",
    abstract = "Distant-supervised relation extraction inevitably suffers from wrong labeling problems because it heuristically labels relational facts with knowledge bases. Previous sentence level denoise models don{'}t achieve satisfying performances because they use hard labels which are determined by distant supervision and immutable during training. To this end, we introduce an entity-pair level denoise method which exploits semantic information from correctly labeled entity pairs to correct wrong labels dynamically during training. We propose a joint score function which combines the relational scores based on the entity-pair representation and the confidence of the hard label to obtain a new label, namely a soft label, for certain entity pair. During training, soft labels instead of hard labels serve as gold labels. Experiments on the benchmark dataset show that our method dramatically reduces noisy instances and outperforms other state-of-the-art systems.",
}
@inproceedings{choubey-huang-2017-sequential,
    title = "A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events",
    author = "Choubey, Prafulla Kumar  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1190",
    doi = "10.18653/v1/D17-1190",
    pages = "1796--1802",
    abstract = "We present a sequential model for temporal relation classification between intra-sentence events. The key observation is that the overall syntactic structure and compositional meanings of the multi-word context between events are important for distinguishing among fine-grained temporal relations. Specifically, our approach first extracts a sequence of context words that indicates the temporal relation between two events, which well align with the dependency path between two event mentions. The context word sequence, together with a parts-of-speech tag sequence and a dependency relation sequence that are generated corresponding to the word sequence, are then provided as input to bidirectional recurrent neural network (LSTM) models. The neural nets learn compositional syntactic and semantic representations of contexts surrounding the two events and predict the temporal relation between them. Evaluation of the proposed approach on TimeBank corpus shows that sequential modeling is capable of accurately recognizing temporal relations between events, which outperforms a neural net model using various discrete features as input that imitates previous feature based models.",
}
@inproceedings{huang-wang-2017-deep,
    title = "Deep Residual Learning for Weakly-Supervised Relation Extraction",
    author = "Huang, Yi Yao  and
      Wang, William Yang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1191",
    doi = "10.18653/v1/D17-1191",
    pages = "1803--1807",
    abstract = "Deep residual learning (ResNet) is a new method for training very deep neural networks using identity mapping for shortcut connections. ResNet has won the ImageNet ILSVRC 2015 classification task, and achieved state-of-the-art performances in many computer vision tasks. However, the effect of residual learning on noisy natural language processing tasks is still not well understood. In this paper, we design a novel convolutional neural network (CNN) with residual learning, and investigate its impacts on the task of distantly supervised noisy relation extraction. In contradictory to popular beliefs that ResNet only works well for very deep networks, we found that even with 9 layers of CNNs, using identity mapping could significantly improve the performance for distantly-supervised relation extraction.",
}
@inproceedings{zhang-wang-2017-noise,
    title = "Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric {B}ayesian Perspective",
    author = "Zhang, Qing  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1192",
    doi = "10.18653/v1/D17-1192",
    pages = "1808--1813",
    abstract = "For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top precision improvements over the traditional state-of-the-art approaches.",
}
@inproceedings{gabor-etal-2017-exploring,
    title = "Exploring Vector Spaces for Semantic Relations",
    author = {G{\'a}bor, Kata  and
      Zargayouna, Ha{\"\i}fa  and
      Tellier, Isabelle  and
      Buscaldi, Davide  and
      Charnois, Thierry},
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1193",
    doi = "10.18653/v1/D17-1193",
    pages = "1814--1823",
    abstract = "Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for analogical similarities. In this paper, we explore the potential of pre-trained word embeddings to identify generic types of semantic relations in an unsupervised experiment. We propose a new relational similarity measure based on the combination of word2vec{'}s CBOW input and output vectors which outperforms concurrent vector representations, when used for unsupervised clustering on SemEval 2010 Relation Classification data.",
}
@inproceedings{kutuzov-etal-2017-temporal,
    title = "Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants",
    author = "Kutuzov, Andrey  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1194",
    doi = "10.18653/v1/D17-1194",
    pages = "1824--1829",
    abstract = "This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words. The set-up is similar to the well-known analogies task, but expanded with a time dimension. To this end, we apply incremental updating of the models with new training texts, including incremental vocabulary expansion, coupled with learned transformation matrices that let us map between members of the relation. The proposed approach is evaluated on the task of predicting insurgent armed groups based on geographical locations. The gold standard data for the time span 1994{--}2010 is extracted from the UCDP Armed Conflicts dataset. The results show that the method is feasible and outperforms the baselines, but also that important work still remains to be done.",
}
@inproceedings{ji-etal-2017-dynamic,
    title = "Dynamic Entity Representations in Neural Language Models",
    author = "Ji, Yangfeng  and
      Tan, Chenhao  and
      Martschat, Sebastian  and
      Choi, Yejin  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1195",
    doi = "10.18653/v1/D17-1195",
    pages = "1830--1839",
    abstract = "Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their mentions. Our model is generative and flexible; it can model an arbitrary number of entities in context while generating each entity mention at an arbitrary length. In addition, it can be used for several different tasks such as language modeling, coreference resolution, and entity prediction. Experimental results with all these tasks demonstrate that our model consistently outperforms strong baselines and prior work.",
}
@inproceedings{basile-tamburini-2017-towards,
    title = "Towards Quantum Language Models",
    author = "Basile, Ivano  and
      Tamburini, Fabio",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1196",
    doi = "10.18653/v1/D17-1196",
    pages = "1840--1849",
    abstract = "This paper presents a new approach for building Language Models using the Quantum Probability Theory, a Quantum Language Model (QLM). It mainly shows that relying on this probability calculus it is possible to build stochastic models able to benefit from quantum correlations due to interference and entanglement. We extensively tested our approach showing its superior performances, both in terms of model perplexity and inserting it into an automatic speech recognition evaluation setting, when compared with state-of-the-art language modelling techniques.",
}
@inproceedings{yang-etal-2017-reference,
    title = "Reference-Aware Language Models",
    author = "Yang, Zichao  and
      Blunsom, Phil  and
      Dyer, Chris  and
      Ling, Wang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1197",
    doi = "10.18653/v1/D17-1197",
    pages = "1850--1859",
    abstract = "We propose a general class of language models that treat reference as discrete stochastic latent variables. This decision allows for the creation of entity mentions by accessing external databases of referents (required by, e.g., dialogue generation) or past internal state (required to explicitly model coreferentiality). Beyond simple copying, our coreference model can additionally refer to a referent using varied mention forms (e.g., a reference to {``}Jane{''} can be realized as {``}she{''}), a characteristic feature of reference in natural languages. Experiments on three representative applications show our model variants outperform models based on deterministic attention and standard language modeling baselines.",
}
@inproceedings{melamud-etal-2017-simple,
    title = "A Simple Language Model based on {PMI} Matrix Approximations",
    author = "Melamud, Oren  and
      Dagan, Ido  and
      Goldberger, Jacob",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1198",
    doi = "10.18653/v1/D17-1198",
    pages = "1860--1865",
    abstract = "In this study, we introduce a new approach for learning language models by training them to estimate word-context pointwise mutual information (PMI), and then deriving the desired conditional probabilities from PMI at test time. Specifically, we show that with minor modifications to word2vec{'}s algorithm, we get principled language models that are closely related to the well-established Noise Contrastive Estimation (NCE) based language models. A compelling aspect of our approach is that our models are trained with the same simple negative sampling objective function that is commonly used in word2vec to learn word embeddings.",
}
@inproceedings{assylbekov-etal-2017-syllable,
    title = "Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones",
    author = "Assylbekov, Zhenisbek  and
      Takhanov, Rustem  and
      Myrzakhmetov, Bagdat  and
      Washington, Jonathan N.",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1199",
    doi = "10.18653/v1/D17-1199",
    pages = "1866--1872",
    abstract = "Syllabification does not seem to improve word-level RNN language modeling quality when compared to character-based segmentation. However, our best syllable-aware language model, achieving performance comparable to the competitive character-aware model, has 18{\%}-33{\%} fewer parameters and is trained 1.2-2.2 times faster.",
}
@inproceedings{frermann-szarvas-2017-inducing,
    title = "Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels",
    author = {Frermann, Lea  and
      Szarvas, Gy{\"o}rgy},
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1200",
    doi = "10.18653/v1/D17-1200",
    pages = "1873--1883",
    abstract = "Automatically understanding the plot of novels is important both for informing literary scholarship and applications such as summarization or recommendation. Various models have addressed this task, but their evaluation has remained largely intrinsic and qualitative. Here, we propose a principled and scalable framework leveraging expert-provided semantic tags (e.g., mystery, pirates) to evaluate plot representations in an extrinsic fashion, assessing their ability to produce locally coherent groupings of novels (micro-clusters) in model space. We present a deep recurrent autoencoder model that learns richly structured multi-view plot representations, and show that they i) yield better micro-clusters than less structured representations; and ii) are interpretable, and thus useful for further literary analysis or labeling of the emerging micro-clusters.",
}
@inproceedings{li-etal-2017-initializing,
    title = "Initializing Convolutional Filters with Semantic Features for Text Classification",
    author = "Li, Shen  and
      Zhao, Zhe  and
      Liu, Tao  and
      Hu, Renfen  and
      Du, Xiaoyong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1201",
    doi = "10.18653/v1/D17-1201",
    pages = "1884--1889",
    abstract = "Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification.",
}
@inproceedings{nikolentzos-etal-2017-shortest,
    title = "Shortest-Path Graph Kernels for Document Similarity",
    author = "Nikolentzos, Giannis  and
      Meladianos, Polykarpos  and
      Rousseau, Fran{\c{c}}ois  and
      Stavrakas, Yannis  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1202",
    doi = "10.18653/v1/D17-1202",
    pages = "1890--1900",
    abstract = "In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents. The proposed measure takes into account both the terms contained in the documents and the relationships between them. By representing each document as a graph-of-words, we are able to model these relationships and then determine how similar two documents are by using a modified shortest-path graph kernel. We evaluate our approach on two tasks and compare it against several baseline approaches using various performance metrics such as DET curves and macro-average F1-score. Experimental results on a range of datasets showed that our proposed approach outperforms traditional techniques and is capable of measuring more accurately the similarity between two documents.",
}
@inproceedings{yang-etal-2017-adapting,
    title = "Adapting Topic Models using Lexical Associations with Tree Priors",
    author = "Yang, Weiwei  and
      Boyd-Graber, Jordan  and
      Resnik, Philip",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1203",
    doi = "10.18653/v1/D17-1203",
    pages = "1901--1906",
    abstract = "Models work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using measures of lexical association. We integrate lexical association into topic optimization using tree priors, which provide a flexible framework that can take advantage of both first order word associations and the higher-order associations captured by word embeddings. Tree priors improve topic interpretability without hurting extrinsic performance.",
}
@inproceedings{parde-nielsen-2017-finding,
    title = "Finding Patterns in Noisy Crowds: Regression-based Annotation Aggregation for Crowdsourced Data",
    author = "Parde, Natalie  and
      Nielsen, Rodney",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1204",
    doi = "10.18653/v1/D17-1204",
    pages = "1907--1912",
    abstract = "Crowdsourcing offers a convenient means of obtaining labeled data quickly and inexpensively. However, crowdsourced labels are often noisier than expert-annotated data, making it difficult to aggregate them meaningfully. We present an aggregation approach that learns a regression model from crowdsourced annotations to predict aggregated labels for instances that have no expert adjudications. The predicted labels achieve a correlation of 0.594 with expert labels on our data, outperforming the best alternative aggregation method by 11.9{\%}. Our approach also outperforms the alternatives on third-party datasets.",
}
@inproceedings{wang-etal-2017-crowd,
    title = "{CROWD}-{IN}-{THE}-{LOOP}: A Hybrid Approach for Annotating Semantic Roles",
    author = "Wang, Chenguang  and
      Akbik, Alan  and
      Chiticariu, Laura  and
      Li, Yunyao  and
      Xia, Fei  and
      Xu, Anbang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1205",
    doi = "10.18653/v1/D17-1205",
    pages = "1913--1922",
    abstract = "Crowdsourcing has proven to be an effective method for generating labeled data for a range of NLP tasks. However, multiple recent attempts of using crowdsourcing to generate gold-labeled training data for semantic role labeling (SRL) reported only modest results, indicating that SRL is perhaps too difficult a task to be effectively crowdsourced. In this paper, we postulate that while producing SRL annotation does require expert involvement in general, a large subset of SRL labeling tasks is in fact appropriate for the crowd. We present a novel workflow in which we employ a classifier to identify difficult annotation tasks and route each task either to experts or crowd workers according to their difficulties. Our experimental evaluation shows that the proposed approach reduces the workload for experts by over two-thirds, and thus significantly reduces the cost of producing SRL annotation at little loss in quality.",
}
@inproceedings{hashimoto-etal-2017-joint,
    title = "A Joint Many-Task Model: Growing a Neural Network for Multiple {NLP} Tasks",
    author = "Hashimoto, Kazuma  and
      Xiong, Caiming  and
      Tsuruoka, Yoshimasa  and
      Socher, Richard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1206",
    doi = "10.18653/v1/D17-1206",
    pages = "1923--1933",
    abstract = "Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. Higher layers include shortcut connections to lower-level task predictions to reflect linguistic hierarchies. We use a simple regularization term to allow for optimizing all model weights to improve one task{'}s loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end model obtains state-of-the-art or competitive results on five different tasks from tagging, parsing, relatedness, and entailment tasks.",
}
@inproceedings{zhang-etal-2017-earth,
    title = "Earth Mover{'}s Distance Minimization for Unsupervised Bilingual Lexicon Induction",
    author = "Zhang, Meng  and
      Liu, Yang  and
      Luan, Huanbo  and
      Sun, Maosong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1207",
    doi = "10.18653/v1/D17-1207",
    pages = "1934--1945",
    abstract = "Cross-lingual natural language processing hinges on the premise that there exists invariance across languages. At the word level, researchers have identified such invariance in the word embedding semantic spaces of different languages. However, in order to connect the separate spaces, cross-lingual supervision encoded in parallel data is typically required. In this paper, we attempt to establish the cross-lingual connection without relying on any cross-lingual supervision. By viewing word embedding spaces as distributions, we propose to minimize their earth mover{'}s distance, a measure of divergence between distributions. We demonstrate the success on the unsupervised bilingual lexicon induction task. In addition, we reveal an interesting finding that the earth mover{'}s distance shows potential as a measure of language difference.",
}
@inproceedings{stahlberg-byrne-2017-unfolding,
    title = "Unfolding and Shrinking Neural Machine Translation Ensembles",
    author = "Stahlberg, Felix  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1208",
    doi = "10.18653/v1/D17-1208",
    pages = "1946--1956",
    abstract = "Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance. Instead of a single neural net, multiple neural nets with the same topology are trained separately, and the decoder generates predictions by averaging over the individual models. Ensembling often improves the quality of the generated translations drastically. However, it is not suitable for production systems because it is cumbersome and slow. This work aims to reduce the runtime to be on par with a single system without compromising the translation quality. First, we show that the ensemble can be unfolded into a single large neural network which imitates the output of the ensemble system. We show that unfolding can already improve the runtime in practice since more work can be done on the GPU. We proceed by describing a set of techniques to shrink the unfolded network by reducing the dimensionality of layers. On Japanese-English we report that the resulting network has the size and decoding speed of a single NMT network but performs on the level of a 3-ensemble system.",
}
@inproceedings{bastings-etal-2017-graph,
    title = "Graph Convolutional Encoders for Syntax-aware Neural Machine Translation",
    author = "Bastings, Jasmijn  and
      Titov, Ivan  and
      Aziz, Wilker  and
      Marcheggiani, Diego  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1209",
    doi = "10.18653/v1/D17-1209",
    pages = "1957--1967",
    abstract = "We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation. We rely on graph-convolutional networks (GCNs), a recent class of neural networks developed for modeling graph-structured data. Our GCNs use predicted syntactic dependency trees of source sentences to produce representations of words (i.e. hidden states of the encoder) that are sensitive to their syntactic neighborhoods. GCNs take word representations as input and produce word representations as output, so they can easily be incorporated as layers into standard encoders (e.g., on top of bidirectional RNNs or convolutional neural networks). We evaluate their effectiveness with English-German and English-Czech translation experiments for different types of encoders and observe substantial improvements over their syntax-agnostic versions in all the considered setups.",
}
@inproceedings{gu-etal-2017-trainable,
    title = "Trainable Greedy Decoding for Neural Machine Translation",
    author = "Gu, Jiatao  and
      Cho, Kyunghyun  and
      Li, Victor O.K.",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1210",
    doi = "10.18653/v1/D17-1210",
    pages = "1968--1978",
    abstract = "Recent research in neural machine translation has largely focused on two aspects; neural network architectures and end-to-end learning algorithms. The problem of decoding, however, has received relatively little attention from the research community. In this paper, we solely focus on the problem of decoding given a trained neural machine translation model. Instead of trying to build a new decoding algorithm for any specific decoding objective, we propose the idea of trainable decoding algorithm in which we train a decoding algorithm to find a translation that maximizes an arbitrary decoding objective. More specifically, we design an actor that observes and manipulates the hidden state of the neural machine translation decoder and propose to train it using a variant of deterministic policy gradient. We extensively evaluate the proposed algorithm using four language pairs and two decoding objectives and show that we can indeed train a trainable greedy decoder that generates a better translation (in terms of a target decoding objective) with minimal computational overhead.",
}
@inproceedings{yang-etal-2017-satirical,
    title = "Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features",
    author = "Yang, Fan  and
      Mukherjee, Arjun  and
      Dragut, Eduard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1211",
    doi = "10.18653/v1/D17-1211",
    pages = "1979--1989",
    abstract = "Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.",
}
@inproceedings{fetahu-etal-2017-fine,
    title = "Fine Grained Citation Span for References in {W}ikipedia",
    author = "Fetahu, Besnik  and
      Markert, Katja  and
      Anand, Avishek",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1212",
    doi = "10.18653/v1/D17-1212",
    pages = "1990--1999",
    abstract = "Verifiability is one of the core editing principles in Wikipedia, where editors are encouraged to provide citations for the added content. For a Wikipedia article determining what content is covered by a citation or the citation span is not trivial, an important aspect for automated citation finding for uncovered content, or fact assessments. We address the problem of determining the citation span in Wikipedia articles. We approach this problem by classifying which textual fragments in an article are covered or hold true given a citation. We propose a sequence classification approach where for a paragraph and a citation, we determine the citation span at a fine-grained level. We provide a thorough experimental evaluation and compare our approach against baselines adopted from the scientific domain, where we show improvement for all evaluation metrics.",
}
@inproceedings{yang-etal-2017-identifying-semantic,
    title = "Identifying Semantic Edit Intentions from Revisions in {W}ikipedia",
    author = "Yang, Diyi  and
      Halfaker, Aaron  and
      Kraut, Robert  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1213",
    doi = "10.18653/v1/D17-1213",
    pages = "2000--2010",
    abstract = "Most studies on human editing focus merely on syntactic revision operations, failing to capture the intentions behind revision changes, which are essential for facilitating the single and collaborative writing process. In this work, we develop in collaboration with Wikipedia editors a 13-category taxonomy of the semantic intention behind edits in Wikipedia articles. Using labeled article edits, we build a computational classifier of intentions that achieved a micro-averaged F1 score of 0.621. We use this model to investigate edit intention effectiveness: how different types of edits predict the retention of newcomers and changes in the quality of articles, two key concerns for Wikipedia today. Our analysis shows that the types of edits that users make in their first session predict their subsequent survival as Wikipedia editors, and articles in different stages need different types of edits.",
}
@inproceedings{hewlett-etal-2017-accurate,
    title = "Accurate Supervised and Semi-Supervised Machine Reading for Long Documents",
    author = "Hewlett, Daniel  and
      Jones, Llion  and
      Lacoste, Alexandre  and
      Gur, Izzeddin",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1214",
    doi = "10.18653/v1/D17-1214",
    pages = "2011--2020",
    abstract = "We introduce a hierarchical architecture for machine reading capable of extracting precise information from long documents. The model divides the document into small, overlapping windows and encodes all windows in parallel with an RNN. It then attends over these window encodings, reducing them to a single encoding, which is decoded into an answer using a sequence decoder. This hierarchical approach allows the model to scale to longer documents without increasing the number of sequential steps. In a supervised setting, our model achieves state of the art accuracy of 76.8 on the WikiReading dataset. We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows. We evaluate models that can reuse autoencoder states and outputs without fine-tuning their weights, allowing for more efficient training and inference.",
}
@inproceedings{jia-liang-2017-adversarial,
    title = "Adversarial Examples for Evaluating Reading Comprehension Systems",
    author = "Jia, Robin  and
      Liang, Percy",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1215",
    doi = "10.18653/v1/D17-1215",
    pages = "2021--2031",
    abstract = "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75{\%} F1 score to 36{\%}; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7{\%}. We hope our insights will motivate the development of new models that understand language more precisely.",
}
@inproceedings{lin-etal-2017-reasoning,
    title = "Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension",
    author = "Lin, Hongyu  and
      Sun, Le  and
      Han, Xianpei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1216",
    doi = "10.18653/v1/D17-1216",
    pages = "2032--2043",
    abstract = "Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.",
}
@inproceedings{yin-etal-2017-document,
    title = "Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension",
    author = "Yin, Yichun  and
      Song, Yangqiu  and
      Zhang, Ming",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1217",
    doi = "10.18653/v1/D17-1217",
    pages = "2044--2054",
    abstract = "Document-level multi-aspect sentiment classification is an important task for customer relation management. In this paper, we model the task as a machine comprehension problem where pseudo question-answer pairs are constructed by a small number of aspect-related keywords and aspect ratings. A hierarchical iterative attention model is introduced to build aspectspecific representations by frequent and repeated interactions between documents and aspect questions. We adopt a hierarchical architecture to represent both word level and sentence level information, and use the attention operations for aspect questions and documents alternatively with the multiple hop mechanism. Experimental results on the TripAdvisor and BeerAdvocate datasets show that our model outperforms classical baselines. We will release our code and data for the method replicability.",
}
@inproceedings{daxenberger-etal-2017-essence,
    title = "What is the Essence of a Claim? Cross-Domain Claim Identification",
    author = "Daxenberger, Johannes  and
      Eger, Steffen  and
      Habernal, Ivan  and
      Stab, Christian  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1218",
    doi = "10.18653/v1/D17-1218",
    pages = "2055--2066",
    abstract = "Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent conceptualization of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.",
}
@inproceedings{du-cardie-2017-identifying,
    title = "Identifying Where to Focus in Reading Comprehension for Neural Question Generation",
    author = "Du, Xinya  and
      Cardie, Claire",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1219",
    doi = "10.18653/v1/D17-1219",
    pages = "2067--2073",
    abstract = "A first step in the task of automatically generating questions for testing reading comprehension is to identify \textit{question-worthy} sentences, i.e. sentences in a text passage that humans find it worthwhile to ask questions about. We propose a hierarchical neural sentence-level sequence tagging model for this task, which existing approaches to question generation have ignored. The approach is fully data-driven {---} with no sophisticated NLP pipelines or any hand-crafted rules/features {---} and compares favorably to a number of baselines when evaluated on the SQuAD data set. When incorporated into an existing neural question generation system, the resulting end-to-end system achieves state-of-the-art performance for paragraph-level question generation for reading comprehension.",
}
@inproceedings{sterckx-etal-2017-break,
    title = "Break it Down for Me: A Study in Automated Lyric Annotation",
    author = "Sterckx, Lucas  and
      Naradowsky, Jason  and
      Byrne, Bill  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1220",
    doi = "10.18653/v1/D17-1220",
    pages = "2074--2080",
    abstract = "Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.",
}
@inproceedings{li-etal-2017-cascaded,
    title = "Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization",
    author = "Li, Piji  and
      Lam, Wai  and
      Bing, Lidong  and
      Guo, Weiwei  and
      Li, Hang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1221",
    doi = "10.18653/v1/D17-1221",
    pages = "2081--2090",
    abstract = "When people recall and digest what they have read for writing summaries, the important content is more likely to attract their attention. Inspired by this observation, we propose a cascaded attention based unsupervised model to estimate the salience information from the text for compressive multi-document summarization. The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience. By adding sparsity constraints on the number of output vectors, we can generate condensed information which can be treated as word salience. Fine-grained and coarse-grained sentence compression strategies are incorporated to produce compressive summaries. Experiments on some benchmark data sets show that our framework achieves better results than the state-of-the-art methods.",
}
@inproceedings{li-etal-2017-deep,
    title = "Deep Recurrent Generative Decoder for Abstractive Text Summarization",
    author = "Li, Piji  and
      Lam, Wai  and
      Bing, Lidong  and
      Wang, Zihao",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1222",
    doi = "10.18653/v1/D17-1222",
    pages = "2091--2100",
    abstract = "We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.",
}
@inproceedings{isonuma-etal-2017-extractive,
    title = "Extractive Summarization Using Multi-Task Learning with Document Classification",
    author = "Isonuma, Masaru  and
      Fujino, Toru  and
      Mori, Junichiro  and
      Matsuo, Yutaka  and
      Sakata, Ichiro",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1223",
    doi = "10.18653/v1/D17-1223",
    pages = "2101--2110",
    abstract = "The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multi-task learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems.",
}
@inproceedings{zhang-wan-2017-towards,
    title = "Towards Automatic Construction of News Overview Articles by News Synthesis",
    author = "Zhang, Jianmin  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1224",
    doi = "10.18653/v1/D17-1224",
    pages = "2111--2116",
    abstract = "In this paper we investigate a new task of automatically constructing an overview article from a given set of news articles about a news event. We propose a news synthesis approach to address this task based on passage segmentation, ranking, selection and merging. Our proposed approach is compared with several typical multi-document summarization methods on the Wikinews dataset, and achieves the best performance on both automatic evaluation and manual evaluation.",
}
@inproceedings{zhao-huang-2017-joint,
    title = "Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank",
    author = "Zhao, Kai  and
      Huang, Liang",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1225",
    doi = "10.18653/v1/D17-1225",
    pages = "2117--2123",
    abstract = "Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing. Most attempts at this problem rely on annotated text segmentations (Elementary Discourse Units, EDUs) and sophisticated sparse or continuous features to extract syntactic information. In this paper we propose the first end-to-end discourse parser that jointly parses in both syntax and discourse levels, as well as the first syntacto-discourse treebank by integrating the Penn Treebank and the RST Treebank. Built upon our recent span-based constituency parser, this joint syntacto-discourse parser requires no preprocessing efforts such as segmentation or feature extraction, making discourse parsing more convenient. Empirically, our parser achieves the state-of-the-art end-to-end discourse parsing accuracy.",
}
@inproceedings{choubey-huang-2017-event,
    title = "Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events",
    author = "Choubey, Prafulla Kumar  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1226",
    doi = "10.18653/v1/D17-1226",
    pages = "2124--2133",
    abstract = "We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish within- and cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of within- and cross-document event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.",
}
@inproceedings{huang-etal-2017-finish,
    title = "When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)",
    author = "Huang, Liang  and
      Zhao, Kai  and
      Ma, Mingbo",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1227",
    doi = "10.18653/v1/D17-1227",
    pages = "2134--2139",
    abstract = "In neural text generation such as neural machine translation, summarization, and image captioning, beam search is widely used to improve the output text quality. However, in the neural generation setting, hypotheses can finish in different steps, which makes it difficult to decide when to end beam search to ensure optimality. We propose a provably optimal beam search algorithm that will always return the optimal-score complete hypothesis (modulo beam size), and finish as soon as the optimality is established. To counter neural generation{'}s tendency for shorter hypotheses, we also introduce a bounded length reward mechanism which allows a modified version of our beam search algorithm to remain optimal. Experiments on neural machine translation demonstrate that our principled beam search algorithm leads to improvement in BLEU score over previously proposed alternatives.",
}
@inproceedings{wang-etal-2017-steering,
    title = "Steering Output Style and Topic in Neural Response Generation",
    author = "Wang, Di  and
      Jojic, Nebojsa  and
      Brockett, Chris  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1228",
    doi = "10.18653/v1/D17-1228",
    pages = "2140--2150",
    abstract = "We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation. This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge. We decompose the neural generation process into empirically easier sub-problems: a faithfulness model and a decoding method based on selective-sampling. We also describe training and sampling algorithms that bias the generation process with a specific language style restriction, or a topic restriction. Human evaluation results show that our proposed methods are able to to restrict style and topic without degrading output quality in conversational tasks.",
}
@inproceedings{tran-etal-2017-preserving,
    title = "Preserving Distributional Information in Dialogue Act Classification",
    author = "Tran, Quan Hung  and
      Zukerman, Ingrid  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1229",
    doi = "10.18653/v1/D17-1229",
    pages = "2151--2156",
    abstract = "This paper introduces a novel training/decoding strategy for sequence labeling. Instead of greedily choosing a label at each time step, and using it for the next prediction, we retain the probability distribution over the current label, and pass this distribution to the next prediction. This approach allows us to avoid the effect of label bias and error propagation in sequence learning/decoding. Our experiments on dialogue act classification demonstrate the effectiveness of this approach. Even though our underlying neural network model is relatively simple, it outperforms more complex neural models, achieving state-of-the-art results on the MapTask and Switchboard corpora.",
}
@inproceedings{li-etal-2017-adversarial,
    title = "Adversarial Learning for Neural Dialogue Generation",
    author = "Li, Jiwei  and
      Monroe, Will  and
      Shi, Tianlin  and
      Jean, S{\'e}bastien  and
      Ritter, Alan  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1230",
    doi = "10.18653/v1/D17-1230",
    pages = "2157--2169",
    abstract = "We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator{---}analagous to the human evaluator in the Turing test{---} to distinguish between the human-generated dialogues and the machine-generated ones. In this generative adversarial network approach, the outputs from the discriminator are used to encourage the system towards more human-like dialogue. Further, we investigate models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines",
}
@inproceedings{liu-etal-2017-using-context,
    title = "Using Context Information for Dialog Act Classification in {DNN} Framework",
    author = "Liu, Yang  and
      Han, Kun  and
      Tan, Zhao  and
      Lei, Yun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1231",
    doi = "10.18653/v1/D17-1231",
    pages = "2170--2178",
    abstract = "Previous work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent studies explored using deep learning neural networks for DA classification, however, it is not clear yet what is the best method for using dialog context or DA sequential information, and how much gain it brings. This paper proposes several ways of using context information for DA classification, all in the deep learning framework. The baseline system classifies each utterance using the convolutional neural networks (CNN). Our proposed methods include using hierarchical models (recurrent neural networks (RNN) or CNN) for DA sequence tagging where the bottom layer takes the sentence CNN representation as input, concatenating predictions from the previous utterances with the CNN vector for classification, and performing sequence decoding based on the predictions from the sentence CNN model. We conduct thorough experiments and comparisons on the Switchboard corpus, demonstrate that incorporating context information significantly improves DA classification, and show that we achieve new state-of-the-art performance for this task.",
}
@inproceedings{jo-etal-2017-modeling,
    title = "Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences",
    author = "Jo, Yohan  and
      Yoder, Michael  and
      Jang, Hyeju  and
      Ros{\'e}, Carolyn",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1232",
    doi = "10.18653/v1/D17-1232",
    pages = "2179--2189",
    abstract = "We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes content-related words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing content-related words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.",
}
@inproceedings{yao-etal-2017-towards,
    title = "Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems",
    author = "Yao, Lili  and
      Zhang, Yaoyuan  and
      Feng, Yansong  and
      Zhao, Dongyan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1233",
    doi = "10.18653/v1/D17-1233",
    pages = "2190--2199",
    abstract = "The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.",
}
@inproceedings{chang-etal-2017-affordable,
    title = "Affordable On-line Dialogue Policy Learning",
    author = "Chang, Cheng  and
      Yang, Runzhe  and
      Chen, Lu  and
      Zhou, Xiang  and
      Yu, Kai",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1234",
    doi = "10.18653/v1/D17-1234",
    pages = "2200--2209",
    abstract = "The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.",
}
@inproceedings{shao-etal-2017-generating,
    title = "Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models",
    author = "Shao, Yuanlong  and
      Gouws, Stephan  and
      Britz, Denny  and
      Goldie, Anna  and
      Strope, Brian  and
      Kurzweil, Ray",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1235",
    doi = "10.18653/v1/D17-1235",
    pages = "2210--2219",
    abstract = "Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. The generation of long, informative, coherent, and diverse responses remains a hard task. In this work, we focus on the single turn setting. We add self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequence-to-sequence models with explicit length-promotion. A back-off strategy produces better responses overall, in the full spectrum of lengths.",
}
@inproceedings{eshghi-etal-2017-bootstrapping,
    title = "Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars",
    author = "Eshghi, Arash  and
      Shalyminov, Igor  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1236",
    doi = "10.18653/v1/D17-1236",
    pages = "2220--2230",
    abstract = "We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue. We hypothesised that the rich linguistic knowledge within the grammar should enable a combinatorially large number of dialogue variations to be processed, even when trained on very few dialogues. Our experiments show that our model can process 74{\%} of the Facebook AI bAbI dataset even when trained on only 0.13{\%} of the data (5 dialogues). It can in addition process 65{\%} of bAbI+, a corpus we created by systematically adding incremental dialogue phenomena such as restarts and self-corrections to bAbI. We compare our model with a state-of-the-art retrieval model, MEMN2N. We find that, in terms of semantic accuracy, the MEMN2N model shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.",
}
@inproceedings{peng-etal-2017-composite,
    title = "Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning",
    author = "Peng, Baolin  and
      Li, Xiujun  and
      Li, Lihong  and
      Gao, Jianfeng  and
      Celikyilmaz, Asli  and
      Lee, Sungjin  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1237",
    doi = "10.18653/v1/D17-1237",
    pages = "2231--2240",
    abstract = "Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. The dialogue manager consists of: (1) a top-level dialogue policy that selects among subtasks or options, (2) a low-level dialogue policy that selects primitive actions to complete the subtask given by the top-level policy, and (3) a global state tracker that helps ensure all cross-subtask constraints be satisfied. Experiments on a travel planning task with simulated and real users show that our approach leads to significant improvements over three baselines, two based on handcrafted rules and the other based on flat deep reinforcement learning.",
}
@inproceedings{novikova-etal-2017-need,
    title = "Why We Need New Evaluation Metrics for {NLG}",
    author = "Novikova, Jekaterina  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Cercas Curry, Amanda  and
      Rieser, Verena",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1238",
    doi = "10.18653/v1/D17-1238",
    pages = "2241--2252",
    abstract = "The majority of NLG evaluation relies on automatic metrics, such as BLEU . In this paper, we motivate the need for novel, system- and data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is data- and system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.",
}
@inproceedings{wiseman-etal-2017-challenges,
    title = "Challenges in Data-to-Document Generation",
    author = "Wiseman, Sam  and
      Shieber, Stuart  and
      Rush, Alexander",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1239",
    doi = "10.18653/v1/D17-1239",
    pages = "2253--2263",
    abstract = "Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task. In particular, we introduce a new, large-scale corpus of data records paired with descriptive documents, propose a series of extractive evaluation methods for analyzing performance, and obtain baseline results using current neural generation methods. Experiments show that these models produce fluent text, but fail to convincingly approximate human-generated documents. Moreover, even templated baselines exceed the performance of these neural models on some metrics, though copy- and reconstruction-based extensions lead to noticeable improvements.",
}
@inproceedings{patro-etal-2017-english,
    title = "All that is {E}nglish may be {H}indi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media",
    author = "Patro, Jasabanta  and
      Samanta, Bidisha  and
      Singh, Saurabh  and
      Basu, Abhipsa  and
      Mukherjee, Prithwish  and
      Choudhury, Monojit  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1240",
    doi = "10.18653/v1/D17-1240",
    pages = "2264--2274",
    abstract = "n this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman{'}s correlation values, our methods perform more than two times better (∼ 0.62) in predicting the borrowing likeliness compared to the best performing baseline (∼ 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88{\%} of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.",
}
@inproceedings{ding-etal-2017-multi,
    title = "Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction",
    author = "Ding, Tao  and
      Bickel, Warren K.  and
      Pan, Shimei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1241",
    doi = "10.18653/v1/D17-1241",
    pages = "2275--2284",
    abstract = "In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems. Since a substance use ground truth is difficult to obtain on a large scale, to maximize system performance, we explore different unsupervised feature learning methods to take advantage of a large amount of unsupervised social media data. We also demonstrate the benefit of using multi-view unsupervised feature learning to combine heterogeneous user information such as Facebook {``}likes{''} and {``}status updates{''} to enhance system performance. Based on our evaluation, our best models achieved 86{\%} AUC for predicting tobacco use, 81{\%} for alcohol use and 84{\%} for illicit drug use, all of which significantly outperformed existing methods. Our investigation has also uncovered interesting relations between a user{'}s social media behavior (e.g., word usage) and substance use.",
}
@inproceedings{garimella-etal-2017-demographic,
    title = "Demographic-aware word associations",
    author = "Garimella, Aparna  and
      Banea, Carmen  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1242",
    doi = "10.18653/v1/D17-1242",
    pages = "2285--2295",
    abstract = "Variations of word associations across different groups of people can provide insights into people{'}s psychologies and their world views. To capture these variations, we introduce the task of demographic-aware word associations. We build a new gold standard dataset consisting of word association responses for approximately 300 stimulus words, collected from more than 800 respondents of different gender (male/female) and from different locations (India/United States), and show that there are significant variations in the word associations made by these groups. We also introduce a new demographic-aware word association model based on a neural net skip-gram architecture, and show how computational methods for measuring word associations that specifically account for writer demographics can outperform generic methods that are agnostic to such information.",
}
@inproceedings{cheng-etal-2017-factored,
    title = "A Factored Neural Network Model for Characterizing Online Discussions in Vector Space",
    author = "Cheng, Hao  and
      Fang, Hao  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1243",
    doi = "10.18653/v1/D17-1243",
    pages = "2296--2306",
    abstract = "We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.",
}
@inproceedings{rashid-blanco-2017-dimensions,
    title = "Dimensions of Interpersonal Relationships: Corpus and Experiments",
    author = "Rashid, Farzana  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1244",
    doi = "10.18653/v1/D17-1244",
    pages = "2307--2316",
    abstract = "This paper presents a corpus and experiments to determine dimensions of interpersonal relationships. We define a set of dimensions heavily inspired by work in social science. We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships. A corpus analysis shows that dimensions can be annotated reliably. Experimental results show that given a pair of people, values to dimensions can be assigned automatically.",
}
@inproceedings{dusmanu-etal-2017-argument,
    title = "Argument Mining on {T}witter: Arguments, Facts and Sources",
    author = "Dusmanu, Mihai  and
      Cabrio, Elena  and
      Villata, Serena",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1245",
    doi = "10.18653/v1/D17-1245",
    pages = "2317--2322",
    abstract = "Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.",
}
@inproceedings{aoki-etal-2017-distinguishing,
    title = "Distinguishing {J}apanese Non-standard Usages from Standard Ones",
    author = "Aoki, Tatsuya  and
      Sasano, Ryohei  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1246",
    doi = "10.18653/v1/D17-1246",
    pages = "2323--2328",
    abstract = "We focus on non-standard usages of common words on social media. In the context of social media, words sometimes have other usages that are totally different from their original. In this study, we attempt to distinguish non-standard usages on social media from standard ones in an unsupervised manner. Our basic idea is that non-standardness can be measured by the inconsistency between the expected meaning of the target word and the given context. For this purpose, we use context embeddings derived from word embeddings. Our experimental results show that the model leveraging the context embedding outperforms other methods and provide us with findings, for example, on how to construct context embeddings and which corpus to use.",
}
@inproceedings{sap-etal-2017-connotation,
    title = "Connotation Frames of Power and Agency in Modern Films",
    author = "Sap, Maarten  and
      Prasettio, Marcella Cindy  and
      Holtzman, Ari  and
      Rashkin, Hannah  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1247",
    doi = "10.18653/v1/D17-1247",
    pages = "2329--2334",
    abstract = "The framing of an action influences how we perceive its actor. We introduce connotation frames of power and agency, a pragmatic formalism organized using frame semantic representations, to model how different levels of power and agency are implicitly projected on actors through their actions. We use the new power and agency frames to measure the subtle, but prevalent, gender bias in the portrayal of modern film characters and provide insights that deviate from the well-known Bechdel test. Our contributions include an extended lexicon of connotation frames along with a web interface that provides a comprehensive analysis through the lens of connotation frames.",
}
@inproceedings{preotiuc-pietro-etal-2017-controlling,
    title = "Controlling Human Perception of Basic User Traits",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Chandra Guntuku, Sharath  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1248",
    doi = "10.18653/v1/D17-1248",
    pages = "2335--2341",
    abstract = "Much of our online communication is text-mediated and, lately, more common with automated agents. Unlike interacting with humans, these agents currently do not tailor their language to the type of person they are communicating to. In this pilot study, we measure the extent to which human perception of basic user trait information {--} gender and age {--} is controllable through text. Using automatic models of gender and age prediction, we estimate which tweets posted by a user are more likely to mis-characterize his traits. We perform multiple controlled crowdsourcing experiments in which we show that we can reduce the human prediction accuracy of gender to almost random {--} an over 20{\%} drop in accuracy. Our experiments show that it is practically feasible for multiple applications such as text generation, text summarization or machine translation to be tailored to specific traits and perceived as such.",
}
@inproceedings{gautrais-etal-2017-topic,
    title = "Topic Signatures in Political Campaign Speeches",
    author = "Gautrais, Cl{\'e}ment  and
      Cellier, Peggy  and
      Quiniou, Ren{\'e}  and
      Termier, Alexandre",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1249",
    doi = "10.18653/v1/D17-1249",
    pages = "2342--2347",
    abstract = "Highlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign. In this paper, we present a method combining standard topic modeling with signature mining for analyzing topic recurrence in speeches of Clinton and Trump during the 2016 American presidential campaign. The results show that the method extracts automatically the main ideas of each candidate and, in addition, provides information about the evolution of these topics during the campaign.",
}
@inproceedings{schwartz-etal-2017-assessing,
    title = "Assessing Objective Recommendation Quality through Political Forecasting",
    author = "Schwartz, H. Andrew  and
      Rouhizadeh, Masoud  and
      Bishop, Michael  and
      Tetlock, Philip  and
      Mellers, Barbara  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1250",
    doi = "10.18653/v1/D17-1250",
    pages = "2348--2357",
    abstract = "Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility. We explore recommendation quality assessment with respect to both subjective (i.e. users{'} ratings) and objective (i.e., did it influence? did it improve decisions?) metrics in a massive online geopolitical forecasting system, ultimately comparing linguistic characteristics of each quality metric. Using a variety of features, we predict all types of quality with better accuracy than the simple yet strong baseline of comment length. Looking at the most predictive content illustrates rater biases; for example, forecasters are subjectively biased in favor of comments mentioning business transactions or dealings as well as material things, even though such comments do not indeed prove any more useful objectively. Additionally, more complex sentence constructions, as evidenced by subordinate conjunctions, are characteristic of comments leading to objective improvements in forecasting.",
}
@inproceedings{shirakawa-etal-2017-never,
    title = "Never Abandon Minorities: Exhaustive Extraction of Bursty Phrases on Microblogs Using Set Cover Problem",
    author = "Shirakawa, Masumi  and
      Hara, Takahiro  and
      Maekawa, Takuya",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1251",
    doi = "10.18653/v1/D17-1251",
    pages = "2358--2367",
    abstract = "We propose a language-independent data-driven method to exhaustively extract bursty phrases of arbitrary forms (e.g., phrases other than simple noun phrases) from microblogs. The burst (i.e., the rapid increase of the occurrence) of a phrase causes the burst of overlapping N-grams including incomplete ones. In other words, bursty incomplete N-grams inevitably overlap bursty phrases. Thus, the proposed method performs the extraction of bursty phrases as the set cover problem in which all bursty N-grams are covered by a minimum set of bursty phrases. Experimental results using Japanese Twitter data showed that the proposed method outperformed word-based, noun phrase-based, and segmentation-based methods both in terms of accuracy and coverage.",
}
@inproceedings{peng-etal-2017-maximum,
    title = "Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision",
    author = "Peng, Haoruo  and
      Chang, Ming-Wei  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1252",
    doi = "10.18653/v1/D17-1252",
    pages = "2368--2378",
    abstract = "Neural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural network-based framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.",
}
@inproceedings{wachsmuth-etal-2017-impact,
    title = "The Impact of Modeling Overall Argumentation with Tree Kernels",
    author = "Wachsmuth, Henning  and
      Da San Martino, Giovanni  and
      Kiesel, Dora  and
      Stein, Benno",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1253",
    doi = "10.18653/v1/D17-1253",
    pages = "2379--2389",
    abstract = "Several approaches have been proposed to model either the explicit sequential structure of an argumentative text or its implicit hierarchical structure. So far, the adequacy of these models of overall argumentation remains unclear. This paper asks what type of structure is actually important to tackle downstream tasks in computational argumentation. We analyze patterns in the overall argumentation of texts from three corpora. Then, we adapt the idea of positional tree kernels in order to capture sequential and hierarchical argumentative structure together for the first time. In systematic experiments for three text classification tasks, we find strong evidence for the impact of both types of structure. Our results suggest that either of them is necessary while their combination may be beneficial.",
}
@inproceedings{gan-etal-2017-learning,
    title = "Learning Generic Sentence Representations Using Convolutional Neural Networks",
    author = "Gan, Zhe  and
      Pu, Yunchen  and
      Henao, Ricardo  and
      Li, Chunyuan  and
      He, Xiaodong  and
      Carin, Lawrence",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1254",
    doi = "10.18653/v1/D17-1254",
    pages = "2390--2400",
    abstract = "We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoder-decoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.",
}
@inproceedings{amiri-etal-2017-repeat,
    title = "Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks",
    author = "Amiri, Hadi  and
      Miller, Timothy  and
      Savova, Guergana",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1255",
    doi = "10.18653/v1/D17-1255",
    pages = "2401--2410",
    abstract = "We present a novel approach for training artificial neural networks. Our approach is inspired by broad evidence in psychology that shows human learners can learn efficiently and effectively by increasing intervals of time between subsequent reviews of previously learned materials (spaced repetition). We investigate the analogy between training neural models and findings in psychology about human memory model and develop an efficient and effective algorithm to train neural models. The core part of our algorithm is a cognitively-motivated scheduler according to which training instances and their {``}reviews{''} are spaced over time. Our algorithm uses only 34-50{\%} of data per epoch, is 2.9-4.8 times faster than standard training, and outperforms competing state-of-the-art baselines. Our code is available at \url{scholar.harvard.edu/hadi/RbF/}.",
}
@inproceedings{gui-etal-2017-part,
    title = "Part-of-Speech Tagging for {T}witter with Adversarial Neural Networks",
    author = "Gui, Tao  and
      Zhang, Qi  and
      Huang, Haoran  and
      Peng, Minlong  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1256",
    doi = "10.18653/v1/D17-1256",
    pages = "2411--2420",
    abstract = "In this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a lack of large scale labeled datasets for this domain. To tackle these challenges, we propose a novel neural network to make use of out-of-domain labeled data, unlabeled in-domain data, and labeled in-domain data. Inspired by adversarial neural networks, the proposed method tries to learn common features through adversarial discriminator. In addition, we hypothesize that domain-specific features of target domain should be preserved in some degree. Hence, the proposed method adopts a sequence-to-sequence autoencoder to perform this task. Experimental results on three different datasets show that our method achieves better performance than state-of-the-art methods.",
}
@inproceedings{li-etal-2017-investigating,
    title = "Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings",
    author = "Li, Bofang  and
      Liu, Tao  and
      Zhao, Zhe  and
      Tang, Buzhou  and
      Drozd, Aleksandr  and
      Rogers, Anna  and
      Du, Xiaoyong",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1257",
    doi = "10.18653/v1/D17-1257",
    pages = "2421--2431",
    abstract = "The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.",
}
@inproceedings{braud-etal-2017-syntax,
    title = "Does syntax help discourse segmentation? Not so much",
    author = "Braud, Chlo{\'e}  and
      Lacroix, Oph{\'e}lie  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1258",
    doi = "10.18653/v1/D17-1258",
    pages = "2432--2442",
    abstract = "Discourse segmentation is the first step in building discourse parsers. Most work on discourse segmentation does not scale to real-world discourse parsing across languages, for two reasons: (i) models rely on constituent trees, and (ii) experiments have relied on gold standard identification of sentence and token boundaries. We therefore investigate to what extent constituents can be replaced with universal dependencies, or left out completely, as well as how state-of-the-art segmenters fare in the absence of sentence boundaries. Our results show that dependency information is less useful than expected, but we provide a fully scalable, robust model that only relies on part-of-speech information, and show that it performs well across languages in the absence of any gold-standard annotation.",
}
@inproceedings{lewis-etal-2017-deal,
    title = "Deal or No Deal? End-to-End Learning of Negotiation Dialogues",
    author = "Lewis, Mike  and
      Yarats, Denis  and
      Dauphin, Yann  and
      Parikh, Devi  and
      Batra, Dhruv",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1259",
    doi = "10.18653/v1/D17-1259",
    pages = "2443--2453",
    abstract = "Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other{'}s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.",
}
@inproceedings{chen-etal-2017-agent,
    title = "Agent-Aware Dropout {DQN} for Safe and Efficient On-line Dialogue Policy Learning",
    author = "Chen, Lu  and
      Zhou, Xiang  and
      Chang, Cheng  and
      Yang, Runzhe  and
      Yu, Kai",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1260",
    doi = "10.18653/v1/D17-1260",
    pages = "2454--2464",
    abstract = "Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a \textit{companion learning} framework to integrate the two approaches for \textit{on-line} dialogue policy learning, in which a pre-defined rule-based policy acts as a {``}teacher{''} and guides a data-driven RL system by giving example actions as well as additional rewards. A novel \textit{agent-aware dropout} Deep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher{'}s experiences. AAD-DQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve both \textit{safety}and \textit{efficiency} of on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus.",
}
@inproceedings{potash-rumshisky-2017-towards,
    title = "Towards Debate Automation: a Recurrent Model for Predicting Debate Winners",
    author = "Potash, Peter  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1261",
    doi = "10.18653/v1/D17-1261",
    pages = "2465--2475",
    abstract = "In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate predictive model, we are able to objectively rate the quality of a statement made at a specific turn in a debate. The model is based on a recurrent neural network architecture with attention, which allows the model to effectively account for the entire debate when making its prediction. Our model achieves state-of-the-art accuracy on a dataset of debate transcripts annotated with audience favorability of the debate teams. Finally, we discuss how future work can leverage our proposed model for the creation of an automated debate agent. We accomplish this by determining the model input that will maximize audience favorability toward a given side of a debate at an arbitrary turn.",
}
@inproceedings{ma-etal-2017-investigation,
    title = "Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation",
    author = "Ma, Qingsong  and
      Graham, Yvette  and
      Baldwin, Timothy  and
      Liu, Qun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1262",
    doi = "10.18653/v1/D17-1262",
    pages = "2476--2485",
    abstract = "Monolingual evaluation of Machine Translation (MT) aims to simplify human assessment by requiring assessors to compare the meaning of the MT output with a reference translation, opening up the task to a much larger pool of genuinely qualified evaluators. Monolingual evaluation runs the risk, however, of bias in favour of MT systems that happen to produce translations superficially similar to the reference and, consistent with this intuition, previous investigations have concluded monolingual assessment to be strongly biased in this respect. On re-examination of past analyses, we identify a series of potential analytical errors that force some important questions to be raised about the reliability of past conclusions, however. We subsequently carry out further investigation into reference bias via direct human assessment of MT adequacy via quality controlled crowd-sourcing. Contrary to both intuition and past conclusions, results for show no significant evidence of reference bias in monolingual evaluation of MT.",
}
@inproceedings{isabelle-etal-2017-challenge,
    title = "A Challenge Set Approach to Evaluating Machine Translation",
    author = "Isabelle, Pierre  and
      Cherry, Colin  and
      Foster, George",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1263",
    doi = "10.18653/v1/D17-1263",
    pages = "2486--2496",
    abstract = "Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system{'}s capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.",
}
@inproceedings{nakashole-flauger-2017-knowledge,
    title = "Knowledge Distillation for Bilingual Dictionary Induction",
    author = "Nakashole, Ndapandula  and
      Flauger, Raphael",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1264",
    doi = "10.18653/v1/D17-1264",
    pages = "2497--2506",
    abstract = "Leveraging zero-shot learning to learn mapping functions between vector spaces of different languages is a promising approach to bilingual dictionary induction. However, methods using this approach have not yet achieved high accuracy on the task. In this paper, we propose a bridging approach, where our main contribution is a knowledge distillation training objective. As teachers, rich resource translation paths are exploited in this role. And as learners, translation paths involving low resource languages learn from the teachers. Our training objective allows seamless addition of teacher translation paths for any given low resource pair. Since our approach relies on the quality of monolingual word embeddings, we also propose to enhance vector representations of both the source and target language with linguistic information. Our experiments on various languages show large performance gains from our distillation training objective, obtaining as high as 17{\%} accuracy improvements.",
}
@inproceedings{bawden-2017-machine,
    title = "Machine Translation, it{'}s a question of style, innit? The case of {E}nglish tag questions",
    author = "Bawden, Rachel",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1265",
    doi = "10.18653/v1/D17-1265",
    pages = "2507--2512",
    abstract = "In this paper, we address the problem of generating English tag questions (TQs) (e.g. it is, isn{'}t it?) in Machine Translation (MT). We propose a post-edition solution, formulating the problem as a multi-class classification task. We present (i) the automatic annotation of English TQs in a parallel corpus of subtitles and (ii) an approach using a series of classifiers to predict TQ forms, which we use to post-edit state-of-the-art MT outputs. Our method provides significant improvements in English TQ translation when translating from Czech, French and German, in turn improving the fluidity, naturalness, grammatical correctness and pragmatic coherence of MT output.",
}
@inproceedings{pourdamghani-knight-2017-deciphering,
    title = "Deciphering Related Languages",
    author = "Pourdamghani, Nima  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1266",
    doi = "10.18653/v1/D17-1266",
    pages = "2513--2518",
    abstract = "We present a method for translating texts between close language pairs. The method does not require parallel data, and it does not require the languages to be written in the same script. We show results for six language pairs: Afrikaans/Dutch, Bosnian/Serbian, Danish/Swedish, Macedonian/Bulgarian, Malaysian/Indonesian, and Polish/Belorussian. We report BLEU scores showing our method to outperform others that do not use parallel data.",
}
@inproceedings{st-arnaud-etal-2017-identifying,
    title = "Identifying Cognate Sets Across Dictionaries of Related Languages",
    author = "St Arnaud, Adam  and
      Beck, David  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1267",
    doi = "10.18653/v1/D17-1267",
    pages = "2519--2528",
    abstract = "We present a system for identifying cognate sets across dictionaries of related languages. The likelihood of a cognate relationship is calculated on the basis of a rich set of features that capture both phonetic and semantic similarity, as well as the presence of regular sound correspondences. The similarity scores are used to cluster words from different languages that may originate from a common proto-word. When tested on the Algonquian language family, our system detects 63{\%} of cognate sets while maintaining cluster purity of 70{\%}.",
}
@inproceedings{malaviya-etal-2017-learning,
    title = "Learning Language Representations for Typology Prediction",
    author = "Malaviya, Chaitanya  and
      Neubig, Graham  and
      Littell, Patrick",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1268",
    doi = "10.18653/v1/D17-1268",
    pages = "2529--2535",
    abstract = "One central mystery of neural NLP is what neural models {``}know{''} about their subject matter. When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Can this knowledge be extracted from the system to fill holes in human scientific knowledge? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existence of parallel texts in more than a thousand languages, we build a massive many-to-one NMT system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages geographic and phylogenetic neighbors.",
}
@inproceedings{mayhew-etal-2017-cheap,
    title = "Cheap Translation for Cross-Lingual Named Entity Recognition",
    author = "Mayhew, Stephen  and
      Tsai, Chen-Tse  and
      Roth, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1269",
    doi = "10.18653/v1/D17-1269",
    pages = "2536--2545",
    abstract = "Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with \textit{very} minimal resources. Our approach makes use of a lexicon to {``}translate{''} annotated data available in one or several high resource language(s) into the target language, and learns a standard monolingual NER model there. Further, when Wikipedia is available in the target language, our method can enhance Wikipedia based methods to yield state-of-the-art NER results; we evaluate on 7 diverse languages, improving the state-of-the-art by an average of 5.5{\%} F1 points. With the minimal resources required, this is an extremely portable cross-lingual NER approach, as illustrated using a truly low-resource language, Uyghur.",
}
@inproceedings{vulic-etal-2017-cross,
    title = "Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation",
    author = "Vuli{\'c}, Ivan  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1270",
    doi = "10.18653/v1/D17-1270",
    pages = "2546--2558",
    abstract = "Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored in this work.",
}
@inproceedings{friedrich-gateva-2017-classification,
    title = "Classification of telicity using cross-linguistic annotation projection",
    author = "Friedrich, Annemarie  and
      Gateva, Damyana",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1271",
    doi = "10.18653/v1/D17-1271",
    pages = "2559--2565",
    abstract = "This paper addresses the automatic recognition of telicity, an aspectual notion. A telic event includes a natural endpoint ({``}she walked home{''}), while an atelic event does not ({``}she walked around{''}). Recognizing this difference is a prerequisite for temporal natural language understanding. In English, this classification task is difficult, as telicity is a covert linguistic category. In contrast, in Slavic languages, aspect is part of a verb{'}s meaning and even available in machine-readable dictionaries. Our contributions are as follows. We successfully leverage additional silver standard training data in the form of projected annotations from parallel English-Czech data as well as context information, improving automatic telicity classification for English significantly compared to previous work. We also create a new data set of English texts manually annotated with telicity.",
}
@inproceedings{lawrence-etal-2017-counterfactual,
    title = "Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation",
    author = "Lawrence, Carolin  and
      Sokolov, Artem  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1272",
    doi = "10.18653/v1/D17-1272",
    pages = "2566--2576",
    abstract = "The goal of counterfactual learning for statistical machine translation (SMT) is to optimize a target SMT system from logged data that consist of user feedback to translations that were predicted by another, historic SMT system. A challenge arises by the fact that risk-averse commercial SMT systems deterministically log the most probable translation. The lack of sufficient exploration of the SMT output space seemingly contradicts the theoretical requirements for counterfactual learning. We show that counterfactual learning from deterministic bandit logs is possible nevertheless by smoothing out deterministic components in learning. This can be achieved by additive and multiplicative control variates that avoid degenerate behavior in empirical risk minimization. Our simulation experiments show improvements of up to 2 BLEU points by counterfactual learning from deterministic bandit feedback.",
}
@inproceedings{wang-etal-2017-learning-fine,
    title = "Learning Fine-grained Relations from {C}hinese User Generated Categories",
    author = "Wang, Chengyu  and
      Fan, Yan  and
      He, Xiaofeng  and
      Zhou, Aoying",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1273",
    doi = "10.18653/v1/D17-1273",
    pages = "2577--2587",
    abstract = "User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.",
}
@inproceedings{huang-etal-2017-improving,
    title = "Improving Slot Filling Performance with Attentive Neural Networks on Dependency Structures",
    author = "Huang, Lifu  and
      Sil, Avirup  and
      Ji, Heng  and
      Florian, Radu",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1274",
    doi = "10.18653/v1/D17-1274",
    pages = "2588--2597",
    abstract = "Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities{\_}of{\_}residence) for a given entity from a large collection of source documents. In this paper we propose an effective DNN architecture for SF with the following new strategies: (1). Take a regularized dependency graph instead of a raw sentence as input to DNN, to compress the wide contexts between query and candidate filler; (2). Incorporate two attention mechanisms: local attention learned from query and candidate filler, and global attention learned from external knowledge bases, to guide the model to better select indicative contexts to determine slot type. Experiments show that this framework outperforms state-of-the-art on both relation extraction (16{\%} absolute F-score gain) and slot filling validation for each individual system (up to 8.5{\%} absolute F-score gain).",
}
@inproceedings{durrett-etal-2017-identifying,
    title = "Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation",
    author = "Durrett, Greg  and
      Kummerfeld, Jonathan K.  and
      Berg-Kirkpatrick, Taylor  and
      Portnoff, Rebecca  and
      Afroz, Sadia  and
      McCoy, Damon  and
      Levchenko, Kirill  and
      Paxson, Vern",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1275",
    doi = "10.18653/v1/D17-1275",
    pages = "2598--2607",
    abstract = "One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own {``}fine-grained domain{''} in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.",
}
@inproceedings{muis-lu-2017-labeling,
    title = "Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators",
    author = "Muis, Aldrian Obaja  and
      Lu, Wei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1276",
    doi = "10.18653/v1/D17-1276",
    pages = "2608--2618",
    abstract = "In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.",
}
@inproceedings{ganea-hofmann-2017-deep,
    title = "Deep Joint Entity Disambiguation with Local Neural Attention",
    author = "Ganea, Octavian-Eugen  and
      Hofmann, Thomas",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1277",
    doi = "10.18653/v1/D17-1277",
    pages = "2619--2629",
    abstract = "We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs.",
}
@inproceedings{gashteovski-etal-2017-minie,
    title = "{M}in{IE}: Minimizing Facts in Open Information Extraction",
    author = "Gashteovski, Kiril  and
      Gemulla, Rainer  and
      del Corro, Luciano",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1278",
    doi = "10.18653/v1/D17-1278",
    pages = "2630--2640",
    abstract = "The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner. In this paper, we propose MinIE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinIE approaches these goals by (1) representing information about polarity, modality, attribution, and quantities with semantic annotations instead of in the actual extraction, and (2) identifying and removing parts that are considered overly specific. We conducted an experimental study with several real-world datasets and found that MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, semantically enriched extractions.",
}
@inproceedings{luan-etal-2017-scientific,
    title = "Scientific Information Extraction with Semi-supervised Neural Tagging",
    author = "Luan, Yi  and
      Ostendorf, Mari  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1279",
    doi = "10.18653/v1/D17-1279",
    pages = "2641--2651",
    abstract = "This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.",
}
@inproceedings{tang-etal-2017-nite,
    title = "{NITE}: A Neural Inductive Teaching Framework for Domain Specific {NER}",
    author = "Tang, Siliang  and
      Zhang, Ning  and
      Zhang, Jinjiang  and
      Wu, Fei  and
      Zhuang, Yueting",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1280",
    doi = "10.18653/v1/D17-1280",
    pages = "2652--2657",
    abstract = "In domain-specific NER, due to insufficient labeled training data, deep models usually fail to behave normally. In this paper, we proposed a novel Neural Inductive TEaching framework (NITE) to transfer knowledge from existing domain-specific NER models into an arbitrary deep neural network in a teacher-student training manner. NITE is a general framework that builds upon transfer learning and multiple instance learning, which collaboratively not only transfers knowledge to a deep student network but also reduces the noise from teachers. NITE can help deep learning methods to effectively utilize existing resources (i.e., models, labeled and unlabeled data) in a small domain. The experiment resulted on Disease NER proved that without using any labeled data, NITE can significantly boost the performance of a CNN-bidirectional LSTM-CRF NER neural network nearly over 30{\%} in terms of F1-score.",
}
@inproceedings{sharma-etal-2017-speeding,
    title = "Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods",
    author = "Sharma, Aditya  and
      Parekh, Zarana  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1281",
    doi = "10.18653/v1/D17-1281",
    pages = "2658--2663",
    abstract = "RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process. RLIE-DQN trains a single agent sequentially, training on one instance at a time. This results in significant training slowdown which is undesirable. We leverage recent advances in parallel RL training using asynchronous methods and propose RLIE-A3C. RLIE-A3C trains multiple agents in parallel and is able to achieve upto 6x training speedup over RLIE-DQN, while suffering no loss in average accuracy.",
}
@inproceedings{li-etal-2017-leveraging,
    title = "Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks",
    author = "Li, Peng-Hsuan  and
      Dong, Ruo-Ping  and
      Wang, Yu-Siang  and
      Chou, Ju-Chieh  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1282",
    doi = "10.18653/v1/D17-1282",
    pages = "2664--2669",
    abstract = "In this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirectional recursive network attached with a convolutional network. Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition. In contrast to classical sequential labeling methods, the system first identifies which text chunks are possible named entities by whether they are linguistic constituents. Then it classifies these chunks with a constituency tree structure by recursively propagating syntactic and semantic information to each constituent node. This method surpasses current state-of-the-art on OntoNotes 5.0 with automatically generated parses.",
}
@inproceedings{strubell-etal-2017-fast,
    title = "Fast and Accurate Entity Recognition with Iterated Dilated Convolutions",
    author = "Strubell, Emma  and
      Verga, Patrick  and
      Belanger, David  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1283",
    doi = "10.18653/v1/D17-1283",
    pages = "2670--2680",
    abstract = "Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard method for obtaining per-token vector representations serving as input to labeling tasks such as NER (often followed by prediction in a linear-chain CRF). Though expressive and accurate, these models fail to fully exploit GPU parallelism, limiting their computational efficiency. This paper proposes a faster alternative to Bi-LSTMs for NER: Iterated Dilated Convolutional Neural Networks (ID-CNNs), which have better capacity than traditional CNNs for large context and structured prediction. Unlike LSTMs whose sequential processing on sentences of length N requires O(N) time even in the face of parallelism, ID-CNNs permit fixed-depth convolutions to run in parallel across entire documents. We describe a distinct combination of network structure, parameter sharing and training procedures that enable dramatic 14-20x test-time speedups while retaining accuracy comparable to the Bi-LSTM-CRF. Moreover, ID-CNNs trained to aggregate context from the entire document are more accurate than Bi-LSTM-CRFs while attaining 8x faster test time speeds.",
}
@inproceedings{gupta-etal-2017-entity,
    title = "Entity Linking via Joint Encoding of Types, Descriptions, and Context",
    author = "Gupta, Nitish  and
      Singh, Sameer  and
      Roth, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1284",
    doi = "10.18653/v1/D17-1284",
    pages = "2681--2690",
    abstract = "For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively {``}embed{''} entities that are new to the KB, and is able to link its mentions accurately.",
}
@inproceedings{he-etal-2017-insight,
    title = "An Insight Extraction System on {B}io{M}edical Literature with Deep Neural Networks",
    author = "He, Hua  and
      Ganjam, Kris  and
      Jain, Navendu  and
      Lundin, Jessica  and
      White, Ryen  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1285",
    doi = "10.18653/v1/D17-1285",
    pages = "2691--2701",
    abstract = "Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.",
}
@inproceedings{nastase-strapparava-2017-word,
    title = "Word Etymology as Native Language Interference",
    author = "Nastase, Vivi  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1286",
    doi = "10.18653/v1/D17-1286",
    pages = "2702--2707",
    abstract = "We present experiments that show the influence of native language on lexical choice when producing text in another language {--} in this particular case English. We start from the premise that non-native English speakers will choose lexical items that are close to words in their native language. This leads us to an etymology-based representation of documents written by people whose mother tongue is an Indo-European language. Based on this representation we grow a language family tree, that matches closely the Indo-European language tree.",
}
@inproceedings{eisenberg-finlayson-2017-simpler,
    title = "A Simpler and More Generalizable Story Detector using Verb and Character Features",
    author = "Eisenberg, Joshua  and
      Finlayson, Mark",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1287",
    doi = "10.18653/v1/D17-1287",
    pages = "2708--2715",
    abstract = "Story detection is the task of determining whether or not a unit of text contains a story. Prior approaches achieved a maximum performance of 0.66 F1, and did not generalize well across different corpora. We present a new state-of-the-art detector that achieves a maximum performance of 0.75 F1 (a 14{\%} improvement), with significantly greater generalizability than previous work. In particular, our detector achieves performance above 0.70 F1 across a variety of combinations of lexically different corpora for training and testing, as well as dramatic improvements (up to 4,000{\%}) in performance when trained on a small, disfluent data set. The new detector uses two basic types of features{--}ones related to events, and ones related to characters{--}totaling 283 specific features overall; previous detectors used tens of thousands of features, and so this detector represents a significant simplification along with increased performance.",
}
@inproceedings{schulz-kuhn-2017-multi,
    title = "Multi-modular domain-tailored {OCR} post-correction",
    author = "Schulz, Sarah  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1288",
    doi = "10.18653/v1/D17-1288",
    pages = "2716--2726",
    abstract = "One of the main obstacles for many Digital Humanities projects is the low data availability. Texts have to be digitized in an expensive and time consuming process whereas Optical Character Recognition (OCR) post-correction is one of the time-critical factors. At the example of OCR post-correction, we show the adaptation of a generic system to solve a specific problem with little data. The system accounts for a diversity of errors encountered in OCRed texts coming from different time periods in the domain of literature. We show that the combination of different approaches, such as e.g. Statistical Machine Translation and spell checking, with the help of a ranking mechanism tremendously improves over single-handed approaches. Since we consider the accessibility of the resulting tool as a crucial part of Digital Humanities collaborations, we describe the workflow we suggest for efficient text recognition and subsequent automatic and manual post-correction",
}
@inproceedings{luo-etal-2017-learning,
    title = "Learning to Predict Charges for Criminal Cases with Legal Basis",
    author = "Luo, Bingfeng  and
      Feng, Yansong  and
      Xu, Jianbo  and
      Zhang, Xiang  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1289",
    doi = "10.18653/v1/D17-1289",
    pages = "2727--2736",
    abstract = "The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.",
}
@inproceedings{schofield-etal-2017-quantifying,
    title = "Quantifying the Effects of Text Duplication on Semantic Models",
    author = "Schofield, Alexandra  and
      Thompson, Laure  and
      Mimno, David",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1290",
    doi = "10.18653/v1/D17-1290",
    pages = "2737--2747",
    abstract = "Duplicate documents are a pervasive problem in text datasets and can have a strong effect on unsupervised models. Methods to remove duplicate texts are typically heuristic or very expensive, so it is vital to know when and why they are needed. We measure the sensitivity of two latent semantic methods to the presence of different levels of document repetition. By artificially creating different forms of duplicate text we confirm several hypotheses about how repeated text impacts models. While a small amount of duplication is tolerable, substantial over-representation of subsets of the text may overwhelm meaningful topical patterns.",
}
@inproceedings{zhuang-etal-2017-identifying,
    title = "Identifying Semantically Deviating Outlier Documents",
    author = "Zhuang, Honglei  and
      Wang, Chi  and
      Tao, Fangbo  and
      Kaplan, Lance  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1291",
    doi = "10.18653/v1/D17-1291",
    pages = "2748--2757",
    abstract = "A document outlier is a document that substantially deviates in semantics from the majority ones in a corpus. Automatic identification of document outliers can be valuable in many applications, such as screening health records for medical mistakes. In this paper, we study the problem of mining semantically deviating document outliers in a given corpus. We develop a generative model to identify frequent and characteristic semantic regions in the word embedding space to represent the given corpus, and a robust outlierness measure which is resistant to noisy content in documents. Experiments conducted on two real-world textual data sets show that our method can achieve an up to 135{\%} improvement over baselines in terms of recall at top-1{\%} of the outlier ranking.",
}
@inproceedings{kang-etal-2017-detecting,
    title = "Detecting and Explaining Causes From Text For a Time Series Event",
    author = "Kang, Dongyeop  and
      Gangal, Varun  and
      Lu, Ang  and
      Chen, Zheng  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1292",
    doi = "10.18653/v1/D17-1292",
    pages = "2758--2767",
    abstract = "Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.",
}
@inproceedings{jiang-etal-2017-novel,
    title = "A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of {MOOC}",
    author = "Jiang, Zhuoxuan  and
      Feng, Shanshan  and
      Cong, Gao  and
      Miao, Chunyan  and
      Li, Xiaoming",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1293",
    doi = "10.18653/v1/D17-1293",
    pages = "2768--2773",
    abstract = "Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs). With massive learners being offered MOOCs, there is a demand that the forum contents within MOOCs need to be classified in order to facilitate both learners and instructors. Therefore we investigate a significant application, which is to associate forum threads to subtitles of video clips. This task can be regarded as a document ranking problem, and the key is how to learn a distinguishable text representation from word sequences and learners{'} behavior sequences. In this paper, we propose a novel cascade model, which can capture both the latent semantics and latent similarity by modeling MOOC data. Experimental results on two real-world datasets demonstrate that our textual representation outperforms state-of-the-art unsupervised counterparts for the application.",
}
@inproceedings{mysore-sathyendra-etal-2017-identifying,
    title = "Identifying the Provision of Choices in Privacy Policy Text",
    author = "Mysore Sathyendra, Kanthashree  and
      Wilson, Shomir  and
      Schaub, Florian  and
      Zimmeck, Sebastian  and
      Sadeh, Norman",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1294",
    doi = "10.18653/v1/D17-1294",
    pages = "2774--2779",
    abstract = "Websites{'} and mobile apps{'} privacy policies, written in natural language, tend to be long and difficult to understand. Information privacy revolves around the fundamental principle of Notice and choice, namely the idea that users should be able to make informed decisions about what information about them can be collected and how it can be used. Internet users want control over their privacy, but their choices are often hidden in long and convoluted privacy policy texts. Moreover, little (if any) prior work has been done to detect the provision of choices in text. We address this challenge of enabling user choice by automatically identifying and extracting pertinent choice language in privacy policies. In particular, we present a two-stage architecture of classification models to identify opt-out choices in privacy policy text, labelling common varieties of choices with a mean F1 score of 0.735. Our techniques enable the creation of systems to help Internet users to learn about their choices, thereby effectuating notice and choice and improving Internet privacy.",
}
@inproceedings{goyal-etal-2017-empirical,
    title = "An Empirical Analysis of Edit Importance between Document Versions",
    author = "Goyal, Tanya  and
      Kelkar, Sachin  and
      Agarwal, Manas  and
      Grover, Jeenu",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1295",
    doi = "10.18653/v1/D17-1295",
    pages = "2780--2784",
    abstract = "In this paper, we present a novel approach to infer significance of various textual edits to documents. An author may make several edits to a document; each edit varies in its impact to the content of the document. While some edits are surface changes and introduce negligible change, other edits may change the content/tone of the document significantly. In this paper, we perform an analysis on the human perceptions of edit importance while reviewing documents from one version to the next. We identify linguistic features that influence edit importance and model it in a regression based setting. We show that the predicted importance by our approach is highly correlated with the human perceived importance, established by a Mechanical Turk study.",
}
@inproceedings{wang-etal-2017-transition,
    title = "Transition-Based Disfluency Detection using {LSTM}s",
    author = "Wang, Shaolei  and
      Che, Wanxiang  and
      Zhang, Yue  and
      Zhang, Meishan  and
      Liu, Ting",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1296",
    doi = "10.18653/v1/D17-1296",
    pages = "2785--2794",
    abstract = "In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information. Compared with sequence labeling methods, it can capture non-local chunk-level features; compared with joint parsing and disfluency detection methods, it is free for noise in syntax. Experiments show that our model achieves state-of-the-art f-score of 87.5{\%} on the commonly used English Switchboard test set, and a set of in-house annotated Chinese data.",
}
@inproceedings{yannakoudakis-etal-2017-neural,
    title = "Neural Sequence-Labelling Models for Grammatical Error Correction",
    author = "Yannakoudakis, Helen  and
      Rei, Marek  and
      Andersen, {\O}istein E.  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1297",
    doi = "10.18653/v1/D17-1297",
    pages = "2795--2806",
    abstract = "We propose an approach to N-best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.",
}
@inproceedings{schmaltz-etal-2017-adapting,
    title = "Adapting Sequence Models for Sentence Correction",
    author = "Schmaltz, Allen  and
      Kim, Yoon  and
      Rush, Alexander  and
      Shieber, Stuart",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1298",
    doi = "10.18653/v1/D17-1298",
    pages = "2807--2813",
    abstract = "In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.",
}
@inproceedings{niu-etal-2017-study,
    title = "A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output",
    author = "Niu, Xing  and
      Martindale, Marianna  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1299",
    doi = "10.18653/v1/D17-1299",
    pages = "2814--2819",
    abstract = "Stylistic variations of language, such as formality, carry speakers{'} intention beyond literal meaning and should be conveyed adequately in translation. We propose to use lexical formality models to control the formality level of machine translation output. We demonstrate the effectiveness of our approach in empirical evaluations, as measured by automatic metrics and human assessments.",
}
@inproceedings{devlin-2017-sharp,
    title = "Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the {CPU}",
    author = "Devlin, Jacob",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1300",
    doi = "10.18653/v1/D17-1300",
    pages = "2820--2825",
    abstract = "Attentional sequence-to-sequence models have become the new standard for machine translation, but one challenge of such models is a significant increase in training and decoding cost compared to phrase-based systems. In this work we focus on efficient decoding, with a goal of achieving accuracy close the state-of-the-art in neural machine translation (NMT), while achieving CPU decoding speed/throughput close to that of a phrasal decoder. We approach this problem from two angles: First, we describe several techniques for speeding up an NMT beam search decoder, which obtain a 4.4x speedup over a very efficient baseline decoder without changing the decoder output. Second, we propose a simple but powerful network architecture which uses an RNN (GRU/LSTM) layer at bottom, followed by a series of stacked fully-connected layers applied at every timestep. This architecture achieves similar accuracy to a deep recurrent model, at a small fraction of the training and decoding cost. By combining these techniques, our best system achieves a very competitive accuracy of 38.3 BLEU on WMT English-French NewsTest2014, while decoding at 100 words/sec on single-threaded CPU. We believe this is the best published accuracy/speed trade-off of an NMT system.",
}
@inproceedings{wang-etal-2017-exploiting-cross,
    title = "Exploiting Cross-Sentence Context for Neural Machine Translation",
    author = "Wang, Longyue  and
      Tu, Zhaopeng  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1301",
    doi = "10.18653/v1/D17-1301",
    pages = "2826--2831",
    abstract = "In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.",
}
@inproceedings{kim-etal-2017-cross,
    title = "Cross-Lingual Transfer Learning for {POS} Tagging without Cross-Lingual Resources",
    author = "Kim, Joo-Kyung  and
      Kim, Young-Bum  and
      Sarikaya, Ruhi  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1302",
    doi = "10.18653/v1/D17-1302",
    pages = "2832--2838",
    abstract = "Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables knowledge transfer from other languages, and private BLSTMs for language-specific representations. The cross-lingual model is trained with language-adversarial training and bidirectional language modeling as auxiliary objectives to better represent language-general information while not losing the information about a specific target language. Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.",
}
@inproceedings{gella-etal-2017-image,
    title = "Image Pivoting for Learning Multilingual Multimodal Representations",
    author = "Gella, Spandana  and
      Sennrich, Rico  and
      Keller, Frank  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1303",
    doi = "10.18653/v1/D17-1303",
    pages = "2839--2845",
    abstract = "In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding. Our model learns a common representation for images and their descriptions in two different languages (which need not be parallel) by considering the image as a pivot between two languages. We introduce a new pairwise ranking loss function which can handle both symmetric and asymmetric similarity between the two modalities. We evaluate our models on image-description ranking for German and English, and on semantic textual similarity of image descriptions in English. In both cases we achieve state-of-the-art performance.",
}
@inproceedings{chen-etal-2017-neural,
    title = "Neural Machine Translation with Source Dependency Representation",
    author = "Chen, Kehai  and
      Wang, Rui  and
      Utiyama, Masao  and
      Liu, Lemao  and
      Tamura, Akihiro  and
      Sumita, Eiichiro  and
      Zhao, Tiejun",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1304",
    doi = "10.18653/v1/D17-1304",
    pages = "2846--2852",
    abstract = "Source dependency information has been successfully introduced into statistical machine translation. However, there are only a few preliminary attempts for Neural Machine Translation (NMT), such as concatenating representations of source word and its dependency label together. In this paper, we propose a novel NMT with source dependency representation to improve translation performance of NMT, especially long sentences. Empirical results on NIST Chinese-to-English translation task show that our method achieves 1.6 BLEU improvements on average over a strong NMT system.",
}
@inproceedings{han-etal-2017-visual,
    title = "Visual Denotations for Recognizing Textual Entailment",
    author = "Han, Dan  and
      Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Mineshima, Koji",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1305",
    doi = "10.18653/v1/D17-1305",
    pages = "2853--2859",
    abstract = "In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments. We propose to map phrases to their visual denotations and compare their meaning in terms of their images. We show that our approach is effective in the task of Recognizing Textual Entailment when combined with specific linguistic and logic features.",
}
@inproceedings{mathur-etal-2017-sequence,
    title = "Sequence Effects in Crowdsourced Annotations",
    author = "Mathur, Nitika  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1306",
    doi = "10.18653/v1/D17-1306",
    pages = "2860--2865",
    abstract = "Manual data annotation is a vital component of NLP research. When designing annotation tasks, properties of the annotation interface can unintentionally lead to artefacts in the resulting dataset, biasing the evaluation. In this paper, we explore sequence effects where annotations of an item are affected by the preceding items. Having assigned one label to an instance, the annotator may be less (or more) likely to assign the same label to the next. During rating tasks, seeing a low quality item may affect the score given to the next item either positively or negatively. We see clear evidence of both types of effects using auto-correlation studies over three different crowdsourced datasets. We then recommend a simple way to minimise sequence effects.",
}
@inproceedings{ture-jojic-2017-need,
    title = "No Need to Pay Attention: Simple Recurrent Neural Networks Work!",
    author = "Ture, Ferhan  and
      Jojic, Oliver",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1307",
    doi = "10.18653/v1/D17-1307",
    pages = "2866--2872",
    abstract = "First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65{\%}{--}76{\%} accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results {---} even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast{'}s X1 entertainment platform with millions of users every day.",
}
@inproceedings{mimno-thompson-2017-strange,
    title = "The strange geometry of skip-gram with negative sampling",
    author = "Mimno, David  and
      Thompson, Laure",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1308",
    doi = "10.18653/v1/D17-1308",
    pages = "2873--2878",
    abstract = "Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood. We find that vector positions are not simply determined by semantic similarity, but rather occupy a narrow cone, diametrically opposed to the context vectors. We show that this geometric concentration depends on the ratio of positive to negative examples, and that it is neither theoretically nor empirically inherent in related embedding algorithms.",
}
@inproceedings{botha-etal-2017-natural,
    title = "Natural Language Processing with Small Feed-Forward Networks",
    author = "Botha, Jan A.  and
      Pitler, Emily  and
      Ma, Ji  and
      Bakalov, Anton  and
      Salcianu, Alex  and
      Weiss, David  and
      McDonald, Ryan  and
      Petrov, Slav",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1309",
    doi = "10.18653/v1/D17-1309",
    pages = "2879--2885",
    abstract = "We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.",
}
@inproceedings{li-lam-2017-deep,
    title = "Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction",
    author = "Li, Xin  and
      Lam, Wai",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1310",
    doi = "10.18653/v1/D17-1310",
    pages = "2886--2892",
    abstract = "We propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for jointly handling the extraction tasks of aspects and opinions via memory interactions. Sentimental sentence constraint is also added for more accurate prediction via another LSTM. Experiment results over two benchmark datasets demonstrate the effectiveness of our framework.",
}
@inproceedings{andreas-klein-2017-analogs,
    title = "Analogs of Linguistic Structure in Deep Representations",
    author = "Andreas, Jacob  and
      Klein, Dan",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1311",
    doi = "10.18653/v1/D17-1311",
    pages = "2893--2897",
    abstract = "We investigate the compositional structure of message vectors computed by a deep network trained on a communication game. By comparing truth-conditional representations of encoder-produced message vectors to human-produced referring expressions, we are able to identify aligned (vector, utterance) pairs with the same meaning. We then search for structured relationships among these aligned pairs to discover simple vector space transformations corresponding to negation, conjunction, and disjunction. Our results suggest that neural representations are capable of spontaneously developing a {``}syntax{''} with functional analogues to qualitative properties of natural language.",
}
@inproceedings{yang-etal-2017-simple,
    title = "A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings",
    author = "Yang, Wei  and
      Lu, Wei  and
      Zheng, Vincent",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1312",
    doi = "10.18653/v1/D17-1312",
    pages = "2898--2904",
    abstract = "Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is less explored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.",
}
@inproceedings{noriega-atala-etal-2017-learning,
    title = "Learning what to read: Focused machine reading",
    author = "Noriega-Atala, Enrique  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Morrison, Clayton  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1313",
    doi = "10.18653/v1/D17-1313",
    pages = "2905--2910",
    abstract = "Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today{'}s scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.",
}
@inproceedings{shu-etal-2017-doc,
    title = "{DOC}: Deep Open Classification of Text Documents",
    author = "Shu, Lei  and
      Xu, Hu  and
      Liu, Bing",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1314",
    doi = "10.18653/v1/D17-1314",
    pages = "2911--2916",
    abstract = "Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.",
}
@inproceedings{gangal-etal-2017-charmanteau,
    title = "{C}harmanteau: Character Embedding Models For Portmanteau Creation",
    author = "Gangal, Varun  and
      Jhamtani, Harsh  and
      Neubig, Graham  and
      Hovy, Eduard  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1315",
    doi = "10.18653/v1/D17-1315",
    pages = "2917--2922",
    abstract = "Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.",
}
@inproceedings{gutierrez-etal-2017-using,
    title = "Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia",
    author = "Guti{\'e}rrez, E. Dar{\'\i}o  and
      Cecchi, Guillermo  and
      Corcoran, Cheryl  and
      Corlett, Philip",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1316",
    doi = "10.18653/v1/D17-1316",
    pages = "2923--2930",
    abstract = "The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures. However, previous research suggests there are disturbances in aspects of the language use of patients with schizophrenia. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier, that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.",
}
@inproceedings{rashkin-etal-2017-truth,
    title = "Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking",
    author = "Rashkin, Hannah  and
      Choi, Eunsol  and
      Jang, Jin Yea  and
      Volkova, Svitlana  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1317",
    doi = "10.18653/v1/D17-1317",
    pages = "2931--2937",
    abstract = "We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.",
}
@inproceedings{menini-etal-2017-topic,
    title = "Topic-Based Agreement and Disagreement in {US} Electoral Manifestos",
    author = "Menini, Stefano  and
      Nanni, Federico  and
      Ponzetto, Simone Paolo  and
      Tonelli, Sara",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1318",
    doi = "10.18653/v1/D17-1318",
    pages = "2938--2944",
    abstract = "We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering. Our approach outperforms both standard techniques like LDA and a state-of-the-art graph-based method, and provides promising initial results for this new task in computational social science.",
}
@inproceedings{xu-koehn-2017-zipporah,
    title = "{Z}ipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora",
    author = "Xu, Hainan  and
      Koehn, Philipp",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1319",
    doi = "10.18653/v1/D17-1319",
    pages = "2945--2950",
    abstract = "We introduce Zipporah, a fast and scalable data cleaning system. We propose a novel type of bag-of-words translation feature, and train logistic regression models to classify good data and synthetic noisy data in the proposed feature space. The trained model is used to score parallel sentences in the data pool for selection. As shown in experiments, Zipporah selects a high-quality parallel corpus from a large, mixed quality data pool. In particular, for one noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of the data over using the entire corpus.",
}
@inproceedings{falke-gurevych-2017-bringing,
    title = "Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps",
    author = "Falke, Tobias  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1320",
    doi = "10.18653/v1/D17-1320",
    pages = "2951--2961",
    abstract = "Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.",
}
@inproceedings{kottur-etal-2017-natural,
    title = "Natural Language Does Not Emerge {`}Naturally{'} in Multi-Agent Dialog",
    author = "Kottur, Satwik  and
      Moura, Jos{\'e}  and
      Lee, Stefan  and
      Batra, Dhruv",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1321",
    doi = "10.18653/v1/D17-1321",
    pages = "2962--2967",
    abstract = "A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, learned without any human supervision! In this paper, using a Task {\&} Talk reference game between two agents as a testbed, we present a sequence of {`}negative{'} results culminating in a {`}positive{'} one {--} showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge {`}naturally{'},despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate.",
}
@inproceedings{yates-etal-2017-depression,
    title = "Depression and Self-Harm Risk Assessment in Online Forums",
    author = "Yates, Andrew  and
      Cohan, Arman  and
      Goharian, Nazli",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1322",
    doi = "10.18653/v1/D17-1322",
    pages = "2968--2978",
    abstract = "Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.",
}
@inproceedings{zhao-etal-2017-men,
    title = "Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints",
    author = "Zhao, Jieyu  and
      Wang, Tianlu  and
      Yatskar, Mark  and
      Ordonez, Vicente  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D17-1323",
    doi = "10.18653/v1/D17-1323",
    pages = "2979--2989",
    abstract = "Language is increasingly being used to de-fine rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33{\%} more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68{\%} at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5{\%} and 40.5{\%} for multilabel classification and visual semantic role labeling, respectively。",
}
@proceedings{lilt-2017-,
    title = "",
    booktitle = "Linguistic Issues in Language Technology, Volume 15, 2017",
    year = "2017",
    publisher = "CSLI Publications",
}
@inproceedings{pustejovsky-joshi-2017-lexical,
    title = "Lexical Factorization and Syntactic Behavior",
    author = "Pustejovsky, James  and
      Joshi, Aravind",
    booktitle = "Linguistic Issues in Language Technology, Volume 15, 2017",
    year = "2017",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2017.lilt-15.1",
    abstract = "In this paper, we examine the correlation between lexical semantics and the syntactic realization of the different components of a word{'}s meaning in natural language. More specifically, we will explore the effect that lexical factorization in verb semantics has on the suppression or expression of semantic features within the sentence. Factorization was a common analytic tool employed in early generative linguistic approaches to lexical decomposition, and continues to play a role in contemporary semantics, in various guises and modified forms. Building on the unpublished analysis of verbs of seeing in Joshi (1972), we argue here that the significance of lexical factorization is twofold: first, current models of verb meaning owe much of their insight to factor-based theories of meaning; secondly, the factorization properties of a lexical item appear to influence, both directly and indirectly, the possible syntactic expressibility of arguments and adjuncts in sentence composition. We argue that this information can be used to compute what we call the factor expression likelihood (FEL) associated with a verb in a sentence. This is the likelihood that the overt syntactic expression of a factor will cooccur with the verb. This has consequences for the compositional mechanisms responsible for computing the meaning of the sentence, as well as significance in the creation of computational models attempting to capture linguistic behavior over large corpora.",
}
@inproceedings{joshi-2017-factorization,
    title = "Factorization of Verbs: An Analysis of Verbs of Seeing",
    author = "Joshi, Aravind",
    booktitle = "Linguistic Issues in Language Technology, Volume 15, 2017",
    year = "2017",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2017.lilt-15.2",
}
@inproceedings{bernardy-lappin-2017-using,
    title = "Using Deep Neural Networks to Learn Syntactic Agreement",
    author = "Bernardy, Jean-Phillipe  and
      Lappin, Shalom",
    booktitle = "Linguistic Issues in Language Technology, Volume 15, 2017",
    year = "2017",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2017.lilt-15.3",
    abstract = "We consider the extent to which different deep neural network (DNN) configurations can learn syntactic relations, by taking up Linzen et al.{'}s (2016) work on subject-verb agreement with LSTM RNNs. We test their methods on a much larger corpus than they used (a ⇠24 million example part of the WaCky corpus, instead of their ⇠1.35 million example corpus, both drawn from Wikipedia). We experiment with several different DNN architectures (LSTM RNNs, GRUs, and CNNs), and alternative parameter settings for these systems (vocabulary size, training to test ratio, number of layers, memory size, drop out rate, and lexical embedding dimension size). We also try out our own unsupervised DNN language model. Our results are broadly compatible with those that Linzen et al. report. However, we discovered some interesting, and in some cases, surprising features of DNNs and language models in their performance of the agreement learning task. In particular, we found that DNNs require large vocabularies to form substantive lexical embeddings in order to learn structural patterns. This finding has interesting consequences for our understanding of the way in which DNNs represent syntactic information. It suggests that DNNs learn syntactic patterns more efficiently through rich lexical embeddings, with semantic as well as syntactic cues, than from training on lexically impoverished strings that highlight structural patterns.",
}
@inproceedings{jezek-2017-dynamic,
    title = "Dynamic Argument Structure",
    author = "Jezek, Elisabetta",
    booktitle = "Linguistic Issues in Language Technology, Volume 15, 2017",
    year = "2017",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2017.lilt-15.4",
    abstract = "This paper presents a new classification of verbs of change and modification, proposing a dynamic interpretation of the lexical semantics of the predicate and its arguments. Adopting the model of dynamic event structure proposed in Pustejovsky (2013), and extending the model of dynamic selection outlined in Pustejovsky and Jezek (2011), we define a verb class in terms of its Dynamic Argument Structure (DAS), a representation which encodes how the participants involved in the change behave as the event unfolds. We address how the logical resources and results of change predicates are realized syntactically, if at all, as well as how the exploitation of the resource results in the initiation or termination of a new object, i.e. the result. We show how DAS can be associated with a dynamically encoded event structure representation, which measures the change making reference to a scalar component, modelled in terms of assignment and/or testing of values of attributes of participants.",
}
@proceedings{jep-taln-recital-2017-actes-des-24eme,
    title = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    editor = "Eshkol-Taravella, Iris  and
      Antoine, Jean-Yves",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.0",
}
@inproceedings{bawden-2017-machine-translation,
    title = "Machine Translation of Speech-Like Texts: Strategies for the Inclusion of Context",
    author = "Bawden, Rachel",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.1",
    pages = "1--14",
    abstract = "Whilst the focus of Machine Translation (MT) has for a long time been the translation of planned, written texts, more and more research is being dedicated to translating speech-like texts (informal or spontaneous discourse or dialogue). To achieve high quality and natural translation of speechlike texts, the integration of context is needed, whether it is extra-linguistic (speaker identity, the interaction between speaker and interlocutor) or linguistic (coreference and stylistic phenomena linked to the spontaneous and informal nature of the texts). However, the integration of contextual information in MT systems remains limited in most current systems. In this paper, we present and critique three experiments for the integration of context into a MT system, each focusing on a different type of context and exploiting a different method: adaptation to speaker gender, cross-lingual pronoun prediction and the generation of tag questions from French into English.",
}
@inproceedings{bigeard-2017-construction,
    title = "Construction de lexiques pour l{'}extraction des mentions de maladies dans les forums de sant{\'e} (Building lexica for extraction of mentions of diseases from healthcare fora)",
    author = "Bigeard, Elise",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.2",
    pages = "15--27",
    abstract = "Les forums de discussion et les r{\'e}seaux sociaux sont des sources potentielles de diff{\'e}rents types d{'}information, qui ne sont en g{\'e}n{\'e}ral pas accessibles par ailleurs. Par exemple, dans les forums de sant{\'e}, il est possible de trouver les informations sur les habitudes et le mode de vie des personnes. Ces informations sont rarement partag{\'e}es avec les m{\'e}decins. Il est donc possible de se fonder sur ces informations pour {\'e}valuer les pratiques r{\'e}elles des patients. Il s{'}agit cependant d{'}une source d{'}information difficile {\`a} traiter, essentiellement {\`a} cause des sp{\'e}cificit{\'e}s linguistiques qu{'}elle pr{\'e}sente. Si une premi{\`e}re {\'e}tape pour l{'}exploration des forums consiste {\`a} indexer les termes m{\'e}dicaux pr{\'e}sents dans les messages avec des concepts issus de terminologies m{\'e}dicales, cela s{'}av{\`e}re extr{\^e}mement compliqu{\'e} car les formulations des patients sont tr{\`e}s diff{\'e}rentes des terminologies officielles. Nous proposons une m{\'e}thode permettant de cr{\'e}er et enrichir des lexiques de termes et expressions d{\'e}signant une maladie ou un trouble, avec un int{\'e}r{\^e}t particulier pour les troubles de l{'}humeur. Nous utilisons des ressources existantes ainsi que des m{\'e}thodes non supervis{\'e}es. Les ressources construites dans le cadre du travail nous permettent d{'}am{\'e}liorer la d{\'e}tection de messages pertinents.",
    language = "French",
}
@inproceedings{colin-2017-creation,
    title = "Cr{\'e}ation automatique d{'}une grammaire syntaxico-s{\'e}mantique (Syntactic-semantic grammar automatic creation)",
    author = "Colin, Emilie",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.3",
    pages = "28--41",
    abstract = "Nous proposons une nouvelle m{\'e}thode pour la cr{\'e}ation automatique de grammaires lexicalis{\'e}es syntaxico-s{\'e}mantiques. A l{'}heure actuelle, la cr{\'e}ation de grammaire r{\'e}sulte soit d{'}un travail manuel soit d{'}un traitement automatis{\'e} de corpus arbor{\'e}. Notre proposition est d{'}extraire {\`a} partir de donn{\'e}es VerbNet une grammaire noyau (formes canoniques des verbes et des groupes nominaux) de l{'}anglais int{\'e}grant une s{\'e}mantique VerbNet. Notre objectif est de profiter des larges ressources existantes pour produire un syst{\`e}me de g{\'e}n{\'e}ration de texte symbolique de qualit{\'e} en domaine restreint.",
    language = "French",
}
@inproceedings{dupont-2017-exploration,
    title = "Exploration de traits pour la reconnaissance d{'}entit{\'e}s nomm{\'e}es du Fran{\c{c}}ais par apprentissage automatique (Feature exploration for {F}rench Named Entity Recognition with Machine Learning)",
    author = "Dupont, Yoann",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.4",
    pages = "42--55",
    abstract = "Dans cet article, nous explorons divers traits propos{\'e}s dans la litt{\'e}rature afin de fournir un d{\'e}tecteur d{'}entit{\'e}s nomm{\'e}es pour le Fran{\c{c}}ais appris automatiquement sur le French Treebank. Nous {\'e}tudions l{'}int{\'e}gration de connaissances en domaine, l{'}apport de la classification des verbes, la gestion des mots inconnus et l{'}int{\'e}gration de traits non locaux. Nous comparons ensuite notre syst{\`e}me aux r{\'e}cents r{\'e}seaux de neurones.",
    language = "French",
}
@inproceedings{wolfarth-2017-aligner,
    title = "Aligner production et normalisation : une premi{\`e}re approche pour l{'}{\'e}tude d{'}{\'e}crits scolaires (To align production and normalization : first approach to study school learner{'}s writings)",
    author = "Wolfarth, Claire",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.5",
    pages = "56--69",
    abstract = "L{'}{\'e}mergence des corpus scolaires et la volont{\'e} d{'}outiller ces corpus sp{\'e}cifiques font apparaitre de nouvelles probl{\'e}matiques de recherche pour le traitement automatique des langues (TAL). Nous exposons ici une recherche qui vise le traitement de productions d{'}apprenants en d{\'e}but d{'}apprentissage de l{'}{\'e}criture, en vue d{'}une annotation et d{'}une exploitation ult{\'e}rieure. {\`A} cette fin, nous proposons d{'}envisager cette {\'e}tape comme une t{\^a}che d{'}alignement entre la production de l{'}apprenant et une normalisation produite manuellement. Ce proc{\'e}d{\'e} permet d{'}augmenter significativement les scores d{'}identification des formes et lemmes produits et am{\'e}liore les perspectives d{'}annotation.",
    language = "French",
}
@inproceedings{ben-khelil-2017-generer,
    title = "G{\'e}n{\'e}rer une grammaire d{'}arbres adjoints pour l{'}arabe {\`a} partir d{'}une m{\'e}ta-grammaire (Generate a tree adjoining grammar for arabic from a meta-grammar)",
    author = "Ben Khelil, Cherifa",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.6",
    pages = "70--80",
    abstract = "La raret{\'e} des ressources num{\'e}riques pour la langue arabe, telles que les grammaires et corpus, rend son traitement plus difficile que les autres langues naturelles. A ce jour il n{'}existe pas une grammaire formelle {\`a} large couverture de l{'}arabe. Dans ce papier, nous pr{\'e}sentons une nouvelle approche qui facilite la description de l{'}arabe avec le formalisme des grammaires d{'}arbres adjoints en utilisant une m{\'e}ta-grammaire. Nous exposons les premiers r{\'e}sultats de notre grammaire ainsi que les probl{\`e}mes rencontr{\'e}s pour son {\'e}valuation.",
    language = "French",
}
@inproceedings{beysson-2017-determinants,
    title = "D{\'e}terminants et quantificateurs g{\'e}n{\'e}ralis{\'e}s dynamiques (Determiners and dynamic generalised quantifiers)",
    author = "Beysson, Cl{\'e}ment",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.7",
    pages = "81--93",
    abstract = "Dans cet article, nous proposons une classification des d{\'e}terminants en {\'e}tudiant leur capacit{\'e} {\`a} introduire de nouveaux r{\'e}f{\'e}rents du discours et l{'}accessibilit{\'e} de ces r{\'e}f{\'e}rents. Cette classification se fonde sur des aspects de logique dynamique (Groenendijk {\&} Stokhof, 1991) dans la tradition montagovienne. Nous montrons ensuite que ces classes raffinent d{'}autres classifications plus linguistiques en {\'e}tudiant chaque esp{\`e}ce de d{\'e}terminants une {\`a} une. L{'}analyse de ces propri{\'e}t{\'e} est un premi{\`e}re {\'e}tape dans la d{\'e}finition des quantificateurs g{\'e}n{\'e}ralis{\'e}s dynamiques n{\'e}cessaires pour d{\'e}noter la s{\'e}mantique des d{\'e}terminants.",
    language = "French",
}
@inproceedings{dalloux-2017-detection,
    title = "D{\'e}tection de l{'}incertitude et de la n{\'e}gation : un {\'e}tat de l{'}art (Identifying uncertainty and negation{'}s cues and scope : State of the art One of the goals of our endeavours is to turn a corpus of medical documents into more easily readable structured data)",
    author = "Dalloux, Cl{\'e}ment",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.8",
    pages = "94--107",
    abstract = "L{'}un des objectifs de nos travaux, {\`a} terme, est de transformer un corpus de documents m{\'e}dicaux en donn{\'e}es structur{\'e}es pour en faciliter l{'}exploitation. Ainsi, il est n{\'e}cessaire non seulement de d{\'e}tecter les concepts m{\'e}dicaux {\'e}voqu{\'e}s, mais aussi d{'}int{\'e}grer un processus capable d{'}identifier le contexte dans lequel est {\'e}voqu{\'e} chaque concept m{\'e}dical. Dans cet article, nous revenons principalement sur les syst{\`e}mes par apprentissage supervis{\'e} qui ont {\'e}t{\'e} propos{\'e} pour la d{\'e}tection de l{'}incertitude et de la n{\'e}gation. Ces dix derni{\`e}res ann{\'e}es, les travaux pour d{\'e}tecter l{'}incertitude et la n{\'e}gation dans les textes en anglais ont donn{\'e} des r{\'e}sultats satisfaisants. Cependant, il existe encore une marge de progression non-n{\'e}gligeable.",
    language = "French",
}
@inproceedings{ferre-2017-normalisation,
    title = "Normalisation de termes complexes par s{\'e}mantique distributionnelle guid{\'e}e par une ontologie (Normalization of complex terms with distributional semantics guided by an ontology)",
    author = "Ferr{\'e}, Arnaud",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.9",
    pages = "108--120",
    abstract = "Nous proposons dans cet article une m{\'e}thode semi-supervis{\'e}e originale pour la cr{\'e}ation de repr{\'e}sentations vectorielles pour des termes (complexes ou non) dans un espace s{\'e}mantique pertinent pour une t{\^a}che de normalisation de termes d{\'e}signant des entit{\'e}s dans un corpus. Notre m{\'e}thode s{'}appuie en partie sur une approche de s{\'e}mantique distributionnelle, celle-ci g{\'e}n{\'e}rant des vecteurs initiaux pour chacun des termes extraits. Ces vecteurs sont alors plong{\'e}s dans un autre espace vectoriel construit {\`a} partir de la structure d{'}une ontologie. Pour la construction de ce second espace vectoriel ontologique, plusieurs m{\'e}thodes sont test{\'e}es et compar{\'e}es. Le plongement s{'}effectue par entra{\^\i}nement d{'}un mod{\`e}le lin{\'e}aire. Un calcul de distance (en utilisant la similarit{\'e} cosinus) est enfin effectu{\'e} pour d{\'e}terminer la proximit{\'e} entre vecteurs de termes et vecteurs de concepts de l{'}ontologie servant {\`a} la normalisation. La performance de cette m{\'e}thode a atteint un rang honorable, ouvrant d{'}encourageantes perspectives.",
    language = "French",
}
@inproceedings{flamein-2017-annotation,
    title = "Annotation automatique des lieux dans l{'}oral spontan{\'e} transcrit (Automatic annotation of places in the transcribed oral)",
    author = "Flamein, H{\'e}l{\`e}ne",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.10",
    pages = "121--134",
    abstract = "Cet article a pour but de pr{\'e}senter une d{\'e}marche g{\'e}n{\'e}raliste pour l{'}annotation automatique des lieux dans l{'}oral transcrit. Cette annotation est effectu{\'e}e sur le corpus ESLO (Enqu{\^e}te SocioLinguistique {\`a} Orl{\'e}ans) et suppose une r{\'e}flexion sur les caract{\'e}ristiques propres {\`a} la d{\'e}signation d{'}un lieu {\`a} l{'}oral. Avant d{'}expliciter la m{\'e}thode employ{\'e}e pour traiter automatiquement notre corpus, nous pr{\'e}senterons le travail pr{\'e}paratoire de la constitution d{'}une convention d{'}annotation et d{'}un corpus de r{\'e}f{\'e}rence indispensable pour l{'}{\'e}valuation du syst{\`e}me.",
    language = "French",
}
@inproceedings{koroleva-2017-vers,
    title = "Vers d{\'e}tection automatique des affirmations inappropri{\'e}es dans les articles scientifiques (Towards automatic detection of inadequate claims in scientific articles)",
    author = "Koroleva, Anna",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.11",
    pages = "135--148",
    abstract = "Dans cet article nous consid{\'e}rons l{'}apport du Traitement Automatique des Langues (TAL) au probl{\`e}me de la d{\'e}tection automatique de « l{'}embellissement » (en anglais « spin ») des r{\'e}sultats de recherche dans les publications scientifiques du domaine biom{\'e}dical. Nous cherchons {\`a} identifier les affirmations inappropri{\'e}es dans les articles, c{'}est-{\`a}-dire les affirmations o{\`u} l{'}effet positif du traitement {\'e}tudi{\'e} est plus grand que celui effectivement prouv{\'e} par la recherche. Apr{\`e}s une description du probl{\`e}me de point de vue du TAL, nous pr{\'e}sentons les pistes de recherche qui nous semblent les plus prometteuses pour automatiser la d{\'e}tection de l{'}embellissement. Ensuite nous analysons l{'}{\'e}tat de l{'}art sur les t{\^a}ches comparables et pr{\'e}sentons les premiers r{\'e}sultats obtenus dans notre projet avec des m{\'e}thodes de base (grammaires locales) pour la t{\^a}che de l{'}extraction des entit{\'e}s sp{\'e}cifiques {\`a} notre objectif.",
    language = "French",
}
@inproceedings{mirzapour-2017-finding,
    title = "Finding Missing Categories in Incomplete Utterances",
    author = "Mirzapour, Mehdi",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.12",
    pages = "149--160",
    abstract = "Finding Missing Categories in Incomplete Utterances This paper introduces an efficient algorithm (O(n4 )) for finding a missing category in an incomplete utterance by using unification technique as when learning categorial grammars, and dynamic programming as in Cocke{--}Younger{--}Kasami algorithm. Using syntax/semantic interface of categorial grammar, this work can be used for deriving possible semantic readings of an incomplete utterance. The paper illustrates the problem with running examples.",
}
@inproceedings{pasquer-2017-expressions,
    title = "Expressions polylexicales verbales : {\'e}tude de la variabilit{\'e} en corpus (Verbal {MWE}s : a corpus-based study of variability)",
    author = "Pasquer, Caroline",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-recital.13",
    pages = "161--174",
    abstract = "La reconnaissance et le traitement appropri{\'e} des expressions polylexicales (EP) constituent un enjeu pour diff{\'e}rentes applications en traitement automatique des langues. Ces expressions sont susceptibles d{'}appara{\^\i}tre sous d{'}autres formes que leur forme canonique, d{'}o{\`u} l{'}int{\'e}r{\^e}t d{'}{\'e}tudier leur profil de variabilit{\'e}. Dans cet article, nous proposons de donner un aper{\c{c}}u de motifs de variation syntaxiques et/ou morphologiques d{'}apr{\`e}s un corpus de 4441 expressions polylexicales verbales (EPV) annot{\'e}es manuellement. L{'}objectif poursuivi est de g{\'e}n{\'e}rer automatiquement les diff{\'e}rentes variantes pour am{\'e}liorer la performance des techniques de traitement automatique des EPV.",
    language = "French",
}
@proceedings{jep-taln-recital-2017-actes,
    title = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    editor = "Eshkol-Taravella, Iris  and
      Antoine, Jean-Yves",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.0",
}
@inproceedings{qader-etal-2017-ajout,
    title = "Ajout automatique de disfluences pour la synth{\`e}se de la parole spontan{\'e}e : formalisation et preuve de concept (Automatic disfluency insertion towards spontaneous {TTS} : formalization and proof of concept)",
    author = "Qader, Raheel  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Lolive, Damien  and
      S{\'e}billot, Pascale",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.1",
    pages = "1--15",
    abstract = "Cet article pr{\'e}sente un travail exploratoire sur l{'}ajout automatique de disfluences, c{'}est-{\`a}-dire de pauses, de r{\'e}p{\'e}titions et de r{\'e}visions, dans les {\'e}nonc{\'e}s en entr{\'e}e d{'}un syst{\`e}me de synth{\`e}se de la parole. L{'}objectif est de conf{\'e}rer aux signaux ainsi synth{\'e}tis{\'e}s un caract{\`e}re plus spontan{\'e} et expressif. Pour cela, nous pr{\'e}sentons une formalisation novatrice du processus de production de disfluences {\`a} travers un m{\'e}canisme de composition de ces disfluences. Cette formalisation se distingue notamment des approches visant la d{\'e}tection ou le nettoyage de disfluences dans des transcriptions, ou de celles en synth{\`e}se de la parole qui ne s{'}int{\'e}ressent qu{'}au seul ajout de pauses. Nous pr{\'e}sentons une premi{\`e}re impl{\'e}mentation de notre processus fond{\'e}e sur des champs al{\'e}atoires conditionnels et des mod{\`e}les de langage, puis conduisons des {\'e}valuations objectives et perceptives. Celles-ci nous permettent de conclure {\`a} la fonctionnalit{\'e} de notre proposition et d{'}en discuter les pistes principales d{'}am{\'e}lioration.",
    language = "French",
}
@inproceedings{burlot-yvon-2017-normalisation,
    title = "Normalisation automatique du vocabulaire source pour traduire depuis une langue {\`a} morphologie riche (Learning Morphological Normalization for Translation from Morphologically Rich Languages)",
    author = "Burlot, Franck  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.2",
    pages = "16--31",
    abstract = "Lorsqu{'}ils sont traduits depuis une langue {\`a} morphologie riche vers l{'}anglais, les mots-formes sources contiennent des marques d{'}informations grammaticales pouvant {\^e}tre jug{\'e}es redondantes par rapport {\`a} l{'}anglais, causant une variabilit{\'e} formelle qui nuit {\`a} l{'}estimation des mod{\`e}les probabilistes. Un moyen bien document{\'e} pour att{\'e}nuer ce probl{\`e}me consiste {\`a} supprimer l{'}information non pertinente de la source en la normalisant. Ce pr{\'e}-traitement est g{\'e}n{\'e}ralement effectu{\'e} de mani{\`e}re d{\'e}terministe, {\`a} l{'}aide de r{\`e}gles produites manuellement. Une telle normalisation est, par essence, sous-optimale et doit {\^e}tre adapt{\'e}e pour chaque paire de langues. Nous pr{\'e}sentons, dans cet article, une m{\'e}thode simple pour rechercher automatiquement une normalisation optimale de la morphologie source par rapport {\`a} la langue cible et montrons que celle-ci peut am{\'e}liorer la traduction automatique.",
    language = "French",
}
@inproceedings{labeau-allauzen-2017-representations,
    title = "Repr{\'e}sentations continues d{\'e}riv{\'e}es des caract{\`e}res pour un mod{\`e}le de langue neuronal {\`a} vocabulaire ouvert (Opening the vocabulary of neural language models with character-level word representations)",
    author = "Labeau, Matthieu  and
      Allauzen, Alexandre",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.3",
    pages = "32--46",
    abstract = "Cet article propose une architecture neuronale pour un mod{\`e}le de langue {\`a} vocabulaire ouvert. Les repr{\'e}sentations continues des mots sont calcul{\'e}es {\`a} la vol{\'e}e {\`a} partir des caract{\`e}res les composant, gr{\`a}ce {\`a} une couche convolutionnelle suivie d{'}une couche de regroupement (pooling). Cela permet au mod{\`e}le de repr{\'e}senter n{'}importe quel mot, qu{'}il fasse partie du contexte ou soit {\'e}valu{\'e} pour la pr{\'e}diction. La fonction objectif est d{\'e}riv{\'e}e de l{'}estimation contrastive bruit{\'e}e (Noise Contrastive Estimation, ou NCE), calculable dans notre cas sans vocabulaire. Nous {\'e}valuons la capacit{\'e} de notre mod{\`e}le {\`a} construire des repr{\'e}sentations continues de mots inconnus sur la t{\^a}che de traduction automatique IWSLT-2016, de l{'}Anglais vers le Tch{\`e}que, en r{\'e}-{\'e}valuant les N meilleures hypoth{\`e}ses (N-best reranking). Les r{\'e}sultats exp{\'e}rimentaux permettent des gains jusqu{'}{\`a} 0,7 point BLEU. Ils montrent aussi la difficult{\'e} d{'}utiliser des repr{\'e}sentations d{\'e}riv{\'e}es des caract{\`e}res pour la pr{\'e}diction.",
    language = "French",
}
@inproceedings{bestgen-2017-utilisation,
    title = "Utilisation d{'}indices phras{\'e}ologiques pour {\'e}valuer des textes en langue {\'e}trang{\`e}re : comparaison des bigrammes et des trigrammes (Collocation measures and automated scoring of foreign language texts : Comparing bigrams and trigrams)",
    author = "Bestgen, Yves",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.4",
    pages = "47--62",
    abstract = "Cette recherche a pour principal objectif d{'}{\'e}valuer l{'}utilit{\'e} de prendre en compte des mesures totalement automatiques de la comp{\'e}tence phras{\'e}ologique pour estimer la qualit{\'e} de textes d{'}apprenants de l{'}anglais langue {\'e}trang{\`e}re. Les analyses, men{\'e}es sur plus de 1000 copies d{'}examen du First Certificate in English, librement mises {\`a} disposition par Yannakoudakis et coll., confirment que l{'}approche qui consiste {\`a} assigner aux bigrammes et aux trigrammes de mots pr{\'e}sents dans un texte des scores d{'}association collocationnelle calcul{\'e}s sur la base d{'}un grand corpus de r{\'e}f{\'e}rence natif est particuli{\`e}rement efficace. Si les indices extraits des trigrammes sont moins efficaces que ceux extraits des bigrammes, ils apportent une contribution utile {\`a} ces derniers. Les analyses soulignent aussi les b{\'e}n{\'e}fices apport{\'e}s par un emploi simultan{\'e} de plusieurs mesures d{'}association collocationnelle.",
    language = "French",
}
@inproceedings{bouzidi-etal-2017-traitement,
    title = "Traitement des Mots Hors Vocabulaire pour la Traduction Automatique de Document {OCR}is{\'e}s en Arabe (This article presents a new system that automatically translates images of arabic documents)",
    author = "Bouzidi, Kamel  and
      Elloumi, Zied  and
      Besacier, Laurent  and
      Lecouteux, Benjamin  and
      Benzeghiba, Mohamed-Faouzi",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.5",
    pages = "63--76",
    abstract = "Cet article pr{\'e}sente un syst{\`e}me original de traduction de documents num{\'e}ris{\'e}s en arabe. Deux modules sont cascad{\'e}s : un syst{\`e}me de reconnaissance optique de caract{\`e}res (OCR) en arabe et un syst{\`e}me de traduction automatique (TA) arabe-fran{\c{c}}ais. Le couplage OCR-TA a {\'e}t{\'e} peu abord{\'e} dans la litt{\'e}rature et l{'}originalit{\'e} de cette {\'e}tude consiste {\`a} proposer un couplage {\'e}troit entre OCR et TA ainsi qu{'}un traitement sp{\'e}cifique des mots hors vocabulaire (MHV) engendr{\'e}s par les erreurs d{'}OCRisation. Le couplage OCR-TA par treillis et notre traitement des MHV par remplacement selon une mesure composite qui prend en compte forme de surface et contexte du mot, permettent une am{\'e}lioration significative des performances de traduction. Les exp{\'e}rimentations sont r{\'e}alis{\'e}s sur un corpus de journaux num{\'e}ris{\'e}s en arabe et permettent d{'}obtenir des am{\'e}liorations en score BLEU de 3,73 et 5,5 sur les corpus de d{\'e}veloppement et de test respectivement.",
    language = "French",
}
@inproceedings{coavoux-crabbe-2017-representation,
    title = "Repr{\'e}sentation et analyse automatique des discontinuit{\'e}s syntaxiques dans les corpus arbor{\'e}s en constituants du fran{\c{c}}ais (Representation and parsing of syntactic discontinuities in {F}rench constituent treebanks)",
    author = "Coavoux, Maximin  and
      Crabb{\'e}, Beno{\^\i}t",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.6",
    pages = "77--92",
    abstract = "Nous pr{\'e}sentons de nouvelles instanciations de trois corpus arbor{\'e}s en constituants du fran{\c{c}}ais, o{\`u} certains ph{\'e}nom{\`e}nes syntaxiques {\`a} l{'}origine de d{\'e}pendances {\`a} longue distance sont repr{\'e}sent{\'e}s directement {\`a} l{'}aide de constituants discontinus. Les arbres obtenus rel{\`e}vent de formalismes grammaticaux l{\'e}g{\`e}rement sensibles au contexte (LCFRS). Nous montrons ensuite qu{'}il est possible d{'}analyser automatiquement de telles structures de mani{\`e}re efficace {\`a} condition de s{'}appuyer sur une m{\'e}thode d{'}inf{\'e}rence approximative. Pour cela, nous pr{\'e}sentons un analyseur syntaxique par transitions, qui r{\'e}alise {\'e}galement l{'}analyse morphologique et l{'}{\'e}tiquetage fonctionnel des mots de la phrase. Enfin, nos exp{\'e}riences montrent que la raret{\'e} des ph{\'e}nom{\`e}nes concern{\'e}s dans les donn{\'e}es fran{\c{c}}aises pose des difficult{\'e}s pour l{'}apprentissage et l{'}{\'e}valuation des structures discontinues.",
    language = "French",
}
@inproceedings{ferret-2017-construire,
    title = "Construire des repr{\'e}sentations denses {\`a} partir de th{\'e}saurus distributionnels (Distributional Thesaurus Embedding and its Applications)",
    author = "Ferret, Olivier",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.7",
    pages = "93--108",
    abstract = "Dans cet article, nous nous int{\'e}ressons {\`a} un nouveau probl{\`e}me, appel{\'e} plongement de th{\'e}saurus, consistant {\`a} transformer un th{\'e}saurus distributionnel en une repr{\'e}sentation dense de mots. Nous proposons de traiter ce probl{\`e}me par une m{\'e}thode fond{\'e}e sur l{'}association d{'}un plongement de graphe et de l{'}injection de relations dans des repr{\'e}sentations denses. Nous avons appliqu{\'e} et {\'e}valu{\'e} cette m{\'e}thode pour un large ensemble de noms en anglais et montr{\'e} que les repr{\'e}sentations denses produites obtiennent de meilleures performances, selon une {\'e}valuation intrins{\`e}que, que les repr{\'e}sentations denses construites selon les m{\'e}thodes de l{'}{\'e}tat de l{'}art sur le m{\^e}me corpus. Nous illustrons aussi l{'}int{\'e}r{\^e}t de la m{\'e}thode d{\'e}velopp{\'e}e pour am{\'e}liorer les repr{\'e}sentations denses existantes {\`a} la fois de fa{\c{c}}on endog{\`e}ne et exog{\`e}ne.",
    language = "French",
}
@inproceedings{qasemizadeh-etal-2017-projection,
    title = "Projection Al{\'e}atoire Non-N{\'e}gative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding",
    author = "Qasemizadeh, Behrang  and
      Kallmeyer, Laura  and
      Herbelot, Aurelie",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.8",
    pages = "109--122",
    abstract = "Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique. We show that weighting methods such as positive pointwise mutual information (PPMI) can be applied to our models after their construction and at a reduced dimensionality. Hence, the proposed technique can efficiently transfer words onto semantically discriminative spaces while demonstrating high computational performance, besides benefits such as ease of update and a simple mechanism for interoperability. We report the performance of our method on several tasks and show that it yields competitive results compared to neural embedding methods in monolingual corpus-based setups.",
}
@inproceedings{billami-gala-2017-creation,
    title = "Cr{\'e}ation et validation de signatures s{\'e}mantiques : application {\`a} la mesure de similarit{\'e} s{\'e}mantique et {\`a} la substitution lexicale (Creating and validating semantic signatures : application for measuring semantic similarity and lexical substitution)",
    author = "Billami, Mokhtar-Boumedyen  and
      Gala, N{\'u}ria",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.9",
    pages = "123--138",
    abstract = "L{'}int{\'e}gration de la notion de similarit{\'e} s{\'e}mantique entre les unit{\'e}s lexicales est essentielle dans diff{\'e}rentes applications de Traitement Automatique des Langues (TAL). De ce fait, elle a re{\c{c}}u un int{\'e}r{\^e}t consid{\'e}rable qui a eu comme cons{\'e}quence le d{\'e}veloppement d{'}une vaste gamme d{'}approches pour en d{\'e}terminer une mesure. Ainsi, plusieurs types de mesures de similarit{\'e} existent, elles utilisent diff{\'e}rentes repr{\'e}sentations obtenues {\`a} partir d{'}informations soit dans des ressources lexicales, soit dans de gros corpus de donn{\'e}es ou bien dans les deux. Dans cet article, nous nous int{\'e}ressons {\`a} la cr{\'e}ation de signatures s{\'e}mantiques d{\'e}crivant des repr{\'e}sentations vectorielles de mots {\`a} partir du r{\'e}seau lexical JeuxDeMots (JDM). L{'}{\'e}valuation de ces signatures est r{\'e}alis{\'e}e sur deux t{\^a}ches diff{\'e}rentes : mesures de similarit{\'e} s{\'e}mantique et substitution lexicale. Les r{\'e}sultats obtenus sont tr{\`e}s satisfaisants et surpassent, dans certains cas, les performances des syst{\`e}mes de l{'}{\'e}tat de l{'}art.",
    language = "French",
}
@inproceedings{millour-etal-2017-vers,
    title = "Vers une solution l{\'e}g{\`e}re de production de donn{\'e}es pour le {TAL} : cr{\'e}ation d{'}un tagger de l{'}alsacien par crowdsourcing b{\'e}n{\'e}vole (Toward a lightweight solution to the language resources bottleneck issue: creating a {POS} tagger for {A}lsatian using voluntary crowdsourcing)",
    author = {Millour, Alice  and
      Fort, Kar{\"e}n  and
      Bernhard, Delphine  and
      Steibl{\'e}, Lucie},
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.10",
    pages = "139--154",
    abstract = "Nous pr{\'e}sentons ici les r{\'e}sultats d{'}une exp{\'e}rience men{\'e}e sur l{'}annotation en parties du discours d{'}un corpus d{'}une langue r{\'e}gionale encore peu dot{\'e}e, l{'}alsacien, via une plateforme de myriadisation (crowdsourcing) b{\'e}n{\'e}vole d{\'e}velopp{\'e}e sp{\'e}cifiquement {\`a} cette fin : Bisame1 . La plateforme, mise en ligne en mai 2016, nous a permis de recueillir 15 846 annotations gr{\^a}ce {\`a} 42 participants. L{'}{\'e}valuation des annotations, r{\'e}alis{\'e}e sur un corpus de r{\'e}f{\'e}rence, montre que la F-mesure des annotations volontaires est de 0, 93. Le tagger entra{\^\i}n{\'e} sur le corpus annot{\'e} atteint lui 82 {\%} d{'}exactitude. Il s{'}agit du premier tagger sp{\'e}cifique {\`a} l{'}alsacien. Cette m{\'e}thode de d{\'e}veloppement de ressources langagi{\`e}res est donc efficace et prometteuse pour certaines langues peu dot{\'e}es, dont un nombre suffisant de locuteurs est connect{\'e} et actif sur le Web. Le code de la plateforme, le corpus annot{\'e} et le tagger sont librement disponibles.",
    language = "French",
}
@inproceedings{antoine-lefeuvre-halftermeyer-2017-sciences,
    title = "Sciences participatives et {TAL}: jusqu{'}o{\`u} ? comment ? pourquoi ? (Citizen science and {NLP} : how far ? how ? why ? This paper investigates the modalities of achievement of citizen science in {NLP}, by considering existing participative projects but also historical studies on the relationships between science and opinion)",
    author = {Antoine, Jean-Yves  and
      Lefeuvre-Halftermeyer, Ana{\"\i}s},
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.11",
    pages = "155--168",
    abstract = "Cet article s{'}interroge sur les modalit{\'e}s de participation citoyenne aux recherches en TALN, {\`a} la lumi{\`e}re des projets actuels en sciences citoyennes mais aussi d{'}{\'e}tudes men{\'e}es sur le sujet en histoire des sciences. Il vise {\`a} montrer comment une science participative est d{\'e}j{\`a} en marche en TALN, {\`a} interroger ses modalit{\'e}s et {\'e}galement {\`a} en circonscrire les limites.",
    language = "French",
}
@inproceedings{sagot-2017-construction,
    title = "Construction automatique d{'}une base de donn{\'e}es {\'e}tymologiques {\`a} partir du wiktionary (Automatic construction of an etymological database using {W}iktionary)",
    author = "Sagot, Beno{\^\i}t",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.12",
    pages = "169--181",
    abstract = "Les ressources lexicales {\'e}lectroniques ne contiennent quasiment jamais d{'}informations {\'e}tymologiques. De telles informations, convenablement formalis{\'e}es, permettraient pourtant de d{\'e}velopper des outils automatiques au service de la linguistique historique et comparative, ainsi que d{'}am{\'e}liorer significativement le traitement automatique de langues anciennes. Nous d{\'e}crivons ici le processus que nous avons mis en {\oe}uvre pour extraire des donn{\'e}es {\'e}tymologiques {\`a} partir des notices {\'e}tymologiques du wiktionary, r{\'e}dig{\'e}es en anglais. Nous avons ainsi produit une base multilingue de pr{\`e}s d{'}un million de lex{\`e}mes et une base de plus d{'}un demi-million de relations {\'e}tymologiques entre lex{\`e}mes.",
    language = "French",
}
@inproceedings{moreno-etal-2017-apprendre,
    title = {Apprendre des repr{\'e}sentations jointes de mots et d{'}entit{\'e}s pour la d{\'e}sambigu{\"\i}sation d{'}entit{\'e}s (Combining Word and Entity Embeddings for Entity Linking)},
    author = "Moreno, Jos{\'e}  and
      Besan{\c{c}}on, Romaric  and
      Beaumont, Romain  and
      D{'}Hondt, Eva  and
      Ligozat, Anne-Laure  and
      Rosset, Sophie  and
      Tannier, Xavier  and
      Grau, Brigitte",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.13",
    pages = "182--195",
    abstract = {La d{\'e}sambigu{\"\i}sation d{'}entit{\'e}s (ou liaison d{'}entit{\'e}s), qui consiste {\`a} relier des mentions d{'}entit{\'e}s d{'}un texte {\`a} des entit{\'e}s d{'}une base de connaissance, est un probl{\`e}me qui se pose, entre autre, pour le peuplement automatique de bases de connaissances {\`a} partir de textes. Une difficult{\'e} de cette t{\^a}che est la r{\'e}solution d{'}ambigu{\"\i}t{\'e}s car les syst{\`e}mes ont {\`a} choisir parmi un nombre important de candidats. Cet article propose une nouvelle approche fond{\'e}e sur l{'}apprentissage joint de repr{\'e}sentations distribu{\'e}es des mots et des entit{\'e}s dans le m{\^e}me espace, ce qui permet d{'}{\'e}tablir un mod{\`e}le robuste pour la comparaison entre le contexte local de la mention d{'}entit{\'e} et les entit{\'e}s candidates.},
    language = "French",
}
@inproceedings{grabar-hamon-2017-analyse,
    title = "Analyse et {\'e}volution de la compr{\'e}hension de termes techniques (Analysis and Evolution of Understanding of Technical Terms)",
    author = "Grabar, Natalia  and
      Hamon, Thierry",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-long.14",
    pages = "196--211",
    abstract = "Nous faisons l{'}hypoth{\`e}se que les mots techniques inconnus dot{\'e}s d{'}une structure interne (mots affix{\'e}s ou compos{\'e}s) peuvent fournir des indices linguistiques {\`a} un locuteur, ce qui peut l{'}aider {\`a} analyser et {\`a} comprendre ces mots. Afin de tester notre hypoth{\`e}se, nous proposons de travailler sur un ensemble de mots techniques provenant du domaine m{\'e}dical. Un grand ensemble de mots techniques est annot{\'e} par cinq annotateurs. Nous effectuons deux types d{'}analyses : l{'}analyse de l{'}{\'e}volution des mots compr{\'e}hensibles et incompr{\'e}hensibles (de mani{\`e}re g{\'e}n{\'e}rale et en fonction de certains suffixes) et l{'}analyse des clusters avec ces mots cr{\'e}{\'e}s par apprentissage non-supervis{\'e}, sur la base des descripteurs linguistiques et extra-linguistiques. Nos r{\'e}sultats indiquent que, selon la sensibilit{\'e} linguistique des annotateurs, les mots techniques peuvent devenir d{\'e}codables et compr{\'e}hensibles. Quant aux clusters, le contenu de certains refl{\`e}te la difficult{\'e} des mots qui les composent et montre {\'e}galement la progression des annotateurs dans leur compr{\'e}hension. La ressource construite est disponible pour la recherche : http://natalia.grabar.free.fr/rated-lexicon.html.",
    language = "French",
}
@proceedings{jep-taln-recital-2017-actes-des-24eme-sur,
    title = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    editor = "Eshkol-Taravella, Iris  and
      Antoine, Jean-Yves",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.0",
}
@inproceedings{boumediane-2017-les,
    title = "Les {TIC} au service de l{'}enseignement : Cas de la formation et auto-formation de la langue amazighe (Information and Communication Technologies for Education: the case of amazigh language teaching and self-teaching)",
    author = "Boumediane, Mounia",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.1",
    pages = "1--7",
    abstract = "Le Centre des {\'E}tudes Informatiques, des Syst{\`e}mes d{'}Information et de Communication (CEISIC), issu de l{'}Institut Royal de la Culture Amazighe (IRCAM), f{\'e}d{\`e}re au sein du portail TALAM, un ensemble de ressources linguistiques informatis{\'e}es et d{'}outils de traitement de la langue d{\'e}di{\'e}es {\`a} l{'}amazighe. Dans ce qui suit, nous pr{\'e}senterons les diff{\'e}rentes ressources, applications et outils linguistiques d{\'e}velopp{\'e}s en langue amazighe pour accompagner toute personne, de diff{\'e}rente tranche d{'}{\^a}ge, assoiff{\'e}e d{'}apprendre la langue Amazighe.",
    language = "French",
}
@inproceedings{suignard-2017-wordsurf,
    title = "Wordsurf : un outil pour naviguer dans un espace de « Word Embeddings » (Wordsurf : a tool to surf in a {``}word embeddings{''} space)",
    author = "Suignard, Philippe",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.2",
    pages = "8--10",
    abstract = "Dans cet article, nous pr{\'e}sentons un outil appel{\'e} « Wordsurf » pour faciliter la phase d{'}exploration et de navigation dans un espace de « Word Embeddings » pr{\'e}alablement entrain{\'e} sur des corpus de textes avec Word2Vec.",
    language = "French",
}
@inproceedings{parmentier-2017-un,
    title = "Un outil pour la manipulation de ressources arbor{\'e}es (A tool for handling tree-based linguistic resources)",
    author = "Parmentier, Yannick",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.3",
    pages = "11--14",
    abstract = "Dans cet article, nous pr{\'e}sentons bri{\`e}vement pytreeview, un outil pour la manipulation de ressources arbor{\'e}es (corpus annot{\'e}s, grammaires {\'e}lectroniques). Initialement con{\c{c}}u pour assiter les utilisateurs linguistes dans leur t{\^a}che de d{\'e}veloppement de grammaires arborescentes, pytreeview a {\'e}t{\'e} {\'e}tendu pour permettre de manipuler des ressources arbor{\'e}es vari{\'e}es (grammaires mais aussi corpus aux formats FTB, PTB, CoNLL, Tiger), afin d{'}en extraire des informations utiles (par exemple la distribution des cadres de sous-cat{\'e}gorisation). pytreeview est actuellement utilis{\'e} dans le cadre d{'}un projet visant l{'}extraction semi-automatique de grammaires abstraites (m{\'e}ta-grammaires) {\`a} partir de corpus arbor{\'e}s.",
    language = "French",
}
@inproceedings{dupont-plancq-2017-un,
    title = "Un {\'e}tiqueteur en ligne du Fran{\c{c}}ais (An online tagger for {F}rench)",
    author = "Dupont, Yoann  and
      Plancq, Cl{\'e}ment",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.4",
    pages = "15--16",
    abstract = "Nous proposons ici une interface en ligne pour {\'e}tiqueter des textes en fran{\c{c}}ais selon trois niveaux d{'}analyses : la morphosyntaxe, le chunking et la reconnaissance des entit{\'e}s nomm{\'e}es. L{'}interface se veut simple et les {\'e}tiquetages r{\'e}utilisables, ces derniers pouvant {\^e}tre export{\'e}s en diff{\'e}rents formats.",
    language = "French",
}
@inproceedings{favre-etal-2017-apprentissage,
    title = "Apprentissage d{'}agents conversationnels pour la gestion de relations clients (Training chatbots for customer relation management)",
    author = "Favre, Benoit  and
      Bechet, Frederic  and
      Damnati, G{\'e}raldine  and
      Charlet, Delphine",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.5",
    pages = "17--18",
    abstract = "Ce travail d{\'e}montre la faisabilit{\'e} d{'}entra{\^\i}ner des chatbots sur des traces de conversations dans le domaine de la relation client. Des syst{\`e}mes {\`a} base de mod{\`e}les de langage, de recherche d{'}information et de traduction sont compar{\'e}s pour la t{\^a}che.",
    language = "French",
}
@inproceedings{servan-etal-2017-conception,
    title = "Conception d{'}une solution de d{\'e}tection d{'}{\'e}v{\'e}nements bas{\'e}e sur {T}witter (Design of a solution for event detection from Tweeter)",
    author = "Servan, Christophe  and
      Kobus, Catherine  and
      Deng, Yongchao  and
      Touffet, Cyril  and
      Kim, Jungi  and
      Kapp, In{\`e}s  and
      Mostefa, Djamel  and
      Crego, Josep  and
      Coquard, Aur{\'e}lien  and
      Senellart, Jean",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.6",
    pages = "19--20",
    abstract = "Cet article pr{\'e}sente un syst{\`e}me d{'}alertes fond{\'e} sur la masse de donn{\'e}es issues de Tweeter. L{'}objectif de l{'}outil est de surveiller l{'}actualit{\'e}, autour de diff{\'e}rents domaines t{\'e}moin incluant les {\'e}v{\'e}nements sportifs ou les catastrophes naturelles. Cette surveillance est transmise {\`a} l{'}utilisateur sous forme d{'}une interface web contenant la liste d{'}{\'e}v{\'e}nements localis{\'e}s sur une carte.",
    language = "French",
}
@inproceedings{guibon-etal-2017-une,
    title = "Une plateforme de recommandation automatique d{'}emojis (An emoji recommandation platform)",
    author = {Guibon, Ga{\"e}l  and
      Ochs, Magalie  and
      Bellot, Patrice},
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.7",
    pages = "21--23",
    abstract = "Nous pr{\'e}sentons une interface de recommandation d{'}emojis porteurs de sentiments qui utilise un mod{\`e}le de pr{\'e}diction appris sur des messages informels priv{\'e}s. Chacun {\'e}tant associ{\'e} {\`a} deux scores de polarit{\'e} pr{\'e}dits. Cette interface permet permet {\'e}galement d{'}enregistrer les choix de l{'}utilisateur pour confirmer ou infirmer la recommandation.",
    language = "French",
}
@inproceedings{nyzam-bossard-2017-un,
    title = "Un outil modulaire libre pour le r{\'e}sum{\'e} automatique (A Modular Open Source Tool for Automatic Summarization)",
    author = "Nyzam, Valentin  and
      Bossard, Aur{\'e}lien",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.8",
    pages = "24--26",
    abstract = "automatique Valentin Nyzam Aur{\'e}lien Bossard LIASD, Universit{\'e} Paris 8 - IUT de Montreuil, 140 rue de la Nouvelle France, 93100 Montreuil, France valentin.nyzam@iut.univ-paris8.fr, aurelien.bossard@iut.univ-paris8.fr R {\'E}SUM{\'E} Nous proposons une d{\'e}monstration d{'}un outil modulaire et {\'e}volutif de r{\'e}sum{\'e} automatique qui impl{\'e}mente trois m{\'e}thodes d{'}extraction de phrases de l{'}{\'e}tat de l{'}art ainsi que sept m{\'e}thodes d{'}{\'e}valuation des phrases. L{'}outil est d{\'e}velopp{\'e} en Java et est d{'}ores-et-d{\'e}j{\`a} disponible sur la plateforme Github.",
    language = "French",
}
@inproceedings{vial-etal-2017-uniformisation,
    title = "Uniformisation de corpus anglais annot{\'e}s en sens (Unification of sense annotated {E}nglish corpora for word sense disambiguation)",
    author = {Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin  and
      Schwab, Didier},
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.9",
    pages = "27--29",
    abstract = {Pour la d{\'e}sambigu{\"\i}sation lexicale en anglais, on compte aujourd{'}hui une quinzaine de corpus annot{\'e}s en sens dans des formats souvent diff{\'e}rents et provenant de diff{\'e}rentes versions du Princeton WordNet. Nous pr{\'e}sentons un format pour uniformiser ces corpus, et nous fournissons {\`a} la communaut{\'e} l{'}ensemble des corpus annot{\'e}s en anglais port{\'e}s {\`a} notre connaissance avec des sens uniformis{\'e}s du Princeton WordNet 3.0, lorsque les droits le permettent et le code source pour construire l{'}ensemble des corpus {\`a} partir des donn{\'e}es originales.},
    language = "French",
}
@inproceedings{nyzam-etal-2017-resumer,
    title = "R{\'e}sumer automatiquement en ligne : d{\'e}monstration d{'}un service web de r{\'e}sum{\'e} multidocument (Summarizing Automatically Online : We propose a demonstration of an automatic multidocument summarization web service)",
    author = "Nyzam, Valentin  and
      Gatto, Nathan  and
      Bossard, Aur{\'e}lien",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.10",
    pages = "30--32",
    abstract = "r automatiquement en ligne : d{\'e}monstration d{'}un service web de r{\'e}sum{\'e} multidocument Valentin Nyzam Nathan Gatto Aur{\'e}lien Bossard LIASD, Universit{\'e} Paris 8 - IUT de Montreuil, 140 rue de la Nouvelle France, 93100 Montreuil, France valentin.nyzam@iut.univ-paris8.fr, nathan.gatto@free.fr, aurelien.bossard@iut.univ-paris8.fr R {\'E}SUM{\'E} Nous proposons une d{\'e}monstration d{'}un webservice de r{\'e}sum{\'e} automatique multidocument. Ce webservice s{'}appuie sur un outil ouvert qui impl{\'e}mente plusieurs algorithmes reconnus de r{\'e}sum{\'e} automatique, et permet de r{\'e}sumer des documents en utilisant des configurations diff{\'e}rentes.",
    language = "French",
}
@inproceedings{norman-etal-2017-traitement,
    title = "Traitement automatique de la langue biom{\'e}dicale au {LIMSI} (Biomedical language processing at {LIMSI})",
    author = "Norman, Christopher  and
      Grouin, Cyril  and
      Lavergne, Thomas  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Zweigenbaum, Pierre",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.11",
    pages = "33--34",
    abstract = "Nous proposons des d{\'e}monstrations de trois outils d{\'e}velopp{\'e}s par le LIMSI en traitement automatique des langues appliqu{\'e} au domaine biom{\'e}dical : la d{\'e}tection de concepts m{\'e}dicaux dans des textes courts, la cat{\'e}gorisation d{'}articles scientifiques pour l{'}assistance {\`a} l{'}{\'e}criture de revues syst{\'e}matiques, et l{'}anonymisation de textes cliniques.",
    language = "French",
}
@inproceedings{chaumartin-2017-proxem,
    title = "Proxem Studio : la plate-forme d{'}analyse s{\'e}mantique qui transforme l{'}utilisateur m{\'e}tier en text scientist ({P}roxem{S}tudio: the semantic analysis platform that turns the business user into a text scientist)",
    author = "Chaumartin, Francois-Regis",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.12",
    pages = "35--36",
    abstract = {Proxem {\'e}dite depuis 2011 une plate-forme d{'}analyse s{\'e}mantique multilingue utilis{\'e} en entreprise pour de multiples usages : relation clients, ressources humaines, veille strat{\'e}gique... La version la plus r{\'e}cente du logiciel, lanc{\'e}e en mars 2017, l{\`e}ve le principal goulet d{'}{\'e}tranglement des outils classiques de text mining : un utilisateur m{\'e}tier devient enfin autonome pour d{\'e}finir lui-m{\^e}me les ressources linguistiques n{\'e}cessaires {\`a} l{'}analyse s{\'e}mantique d{'}un corpus donn{\'e}. Une fois le corpus charg{\'e}, la plate-forme en extrait une terminologie et organise les termes en regroupements hi{\'e}rarchis{\'e}s de proto-concepts ; l{'}utilisateur n{'}a plus qu{'}{\`a} valider ces concepts au niveau de granularit{\'e} qui lui semble pertinent pour constituer un extracteur d{'}entit{\'e}s nomm{\'e}es de granularit{\'e} fine, adapt{\'e} au corpus {\`a} traiter, avec un rappel {\'e}lev{\'e} gr{\^a}ce {\`a} l{'}identification automatique des quasisynonymes. La plate-forme d{\'e}tecte aussi dans ces termes les homonymes potentiels et propose {\`a} l{'}utilisateur des contextes de d{\'e}sambigu{\"\i}sation, fournissant ainsi une bonne pr{\'e}cision.},
    language = "French",
}
@inproceedings{tan-le-etal-2017-translitteration,
    title = "Translitt{\'e}ration automatique pour une paire de langues peu dot{\'e}e ()",
    author = "Tan Le, Ngoc  and
      Sadat, Fatiha  and
      M{\'e}nard, Lucie",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.13",
    pages = "37--40",
    abstract = "La translitt{\'e}ration convertit phon{\'e}tiquement les mots dans une langue source (i.e. fran{\c{c}}ais) en mots {\'e}quivalents dans une langue cible (i.e. vietnamien). Cette conversion n{\'e}cessite un nombre consid{\'e}rable de r{\`e}gles d{\'e}finies par les experts linguistes pour d{\'e}terminer comment les phon{\`e}mes sont align{\'e}s ainsi que prendre en compte le syst{\`e}me de phonologie de la langue cible. La probl{\'e}matique pour les paires de langues peu dot{\'e}es lie {\`a} la p{\'e}nurie des ressources linguistiques. Dans ce travail de recherche, nous pr{\'e}sentons une d{\'e}monstration de conversion de graph{\`e}me en phon{\`e}me pour pallier au probl{\`e}me de translitt{\'e}ration pour une paire de langues peu dot{\'e}e, avec une application sur fran{\c{c}}ais-vietnamien. Notre syst{\`e}me n{\'e}cessite un petit corpus d{'}apprentissage phon{\'e}tique bilingue. Nous avons obtenu des r{\'e}sultats prometteurs, avec un gain de +4,40{\%} de score BLEU, par rapport au syst{\`e}me de base utilisant l{'}approche de traduction automatique statistique.",
    language = "French",
}
@inproceedings{de-malezieux-etal-2017-motor,
    title = "Motor, un outil de segmentation accessible en ligne (Motor, a segmentation tool accessible online)",
    author = "de Mal{\'e}zieux, Guillaume  and
      Lewis-Wong, Jennifer  and
      Berment, Vincent",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 3 - D{\'e}monstrations",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-demo.14",
    pages = "41--42",
    abstract = {Dans cette d{\'e}monstration, nous montrons le fonctionnement des segmenteurs disponibles en ligne pour diverses langues (birman, khmer, lao, tha{\"\i} et tib{\'e}tain) et r{\'e}alis{\'e}s avec l{'}outil Motor.},
    language = "French",
}
@proceedings{jep-taln-recital-2017-actes-des,
    title = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    editor = "Eshkol-Taravella, Iris  and
      Antoine, Jean-Yves",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.0",
}
@inproceedings{candito-etal-2017-annotation,
    title = "Annotation d{'}expressions polylexicales verbales en fran{\c{c}}ais (Annotation of verbal multiword expressions in {F}rench)",
    author = "Candito, Marie  and
      Constant, Mathieu  and
      Ramisch, Carlos  and
      Savary, Agata  and
      Parmentier, Yannick  and
      Pasquer, Caroline  and
      Antoine, Jean-Yves",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.1",
    pages = "1--9",
    abstract = "Nous d{\'e}crivons la partie fran{\c{c}}aise des donn{\'e}es produites dans le cadre de la campagne multilingue PARSEME sur l{'}identification d{'}expressions polylexicales verbales (Savary et al., 2017). Les expressions couvertes pour le fran{\c{c}}ais sont les expressions verbales idiomatiques, les verbes intrins{\`e}quement pronominaux et une g{\'e}n{\'e}ralisation des constructions {\`a} verbe support. Ces ph{\'e}nom{\`e}nes ont {\'e}t{\'e} annot{\'e}s sur le corpus French-UD (Nivre et al., 2016) et le corpus Sequoia (Candito {\&} Seddah, 2012), soit un corpus de 22 645 phrases, pour un total de 4 962 expressions annot{\'e}es. On obtient un ratio d{'}une expression annot{\'e}e tous les 100 tokens environ, avec un fort taux d{'}expressions discontinues (40{\%}).",
    language = "French",
}
@inproceedings{bestgen-2017-evaluation,
    title = "{\'E}valuation de mesures d{'}association pour les bigrammes et les trigrammes au moyen du test exact de Fisher (Using Fisher{'}s Exact Test to Evaluate Association Measures for Bigrams and Trigrams)",
    author = "Bestgen, Yves",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.2",
    pages = "10--18",
    abstract = "Pour d{\'e}terminer si certaines mesures d{'}association lexicale fr{\'e}quemment employ{\'e}es en TAL attribuent des scores {\'e}lev{\'e}s {\`a} des n-grammes que le hasard aurait pu produire aussi souvent qu{'}observ{\'e}, nous avons utilis{\'e} une extension du test exact de Fisher {\`a} des s{\'e}quences de plus de deux mots. Les analyses ont port{\'e} sur un corpus de quatre millions de mots d{'}anglais conversationnel extrait du BNC. Les r{\'e}sultats, bas{\'e}s sur la courbe pr{\'e}cision-rappel et sur la pr{\'e}cision moyenne, montrent que le LL-simple est extr{\^e}mement efficace. IM3 est plus efficace que les autres mesures bas{\'e}es sur les tests d{'}hypoth{\`e}se et atteint m{\^e}me un niveau de performance presque {\'e}gal {\`a} LL-simple pour les trigrammes.",
    language = "French",
}
@inproceedings{dupont-etal-2017-reseaux,
    title = "R{\'e}seaux neuronaux profonds pour l{'}{\'e}tiquetage de s{\'e}quences (Deep Neural Networks for Sequence Labeling)",
    author = "Dupont, Yoann  and
      Dinarelli, Marco  and
      Tellier, Isabelle",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.3",
    pages = "19--27",
    abstract = "Depuis quelques ann{\'e}es les r{\'e}seaux neuronaux se montrent tr{\`e}s efficaces dans toutes les t{\^a}ches de Traitement Automatique des Langues (TAL). R{\'e}cemment, une variante de r{\'e}seau neuronal particuli{\`e}rement adapt{\'e} {\`a} l{'}{\'e}tiquetage de s{\'e}quences textuelles a {\'e}t{\'e} propos{\'e}e, utilisant des repr{\'e}sentations distributionnelles des {\'e}tiquettes. Dans cet article, nous reprenons cette variante et nous l{'}am{\'e}liorons avec une version profonde. Dans cette version, diff{\'e}rentes couches cach{\'e}es permettent de prendre en compte s{\'e}par{\'e}ment les diff{\'e}rents types d{'}informations donn{\'e}es en entr{\'e}e au r{\'e}seau. Nous {\'e}valuons notre mod{\`e}le sur les m{\^e}mes t{\^a}ches que la premi{\`e}re version de r{\'e}seau de laquelle nous nous sommes inspir{\'e}s. Les r{\'e}sultats montrent que notre variante de r{\'e}seau neuronal est plus efficace que les autres, mais aussi qu{'}elle est plus efficace que tous les autres mod{\`e}les {\'e}valu{\'e}s sur ces t{\^a}ches, obtenant l{'}{\'e}tat-de-l{'}art.",
    language = "French",
}
@inproceedings{amsili-seminck-2017-schemas,
    title = "Sch{\'e}mas {W}inograd en fran{\c{c}}ais: une {\'e}tude statistique et comportementale ({W}inograd schemas in {F}rench : a statistical and behavioral study)",
    author = "Amsili, Pascal  and
      Seminck, Olga",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.4",
    pages = "28--35",
    abstract = {Nous pr{\'e}sentons dans cet article une collection de sch{\'e}mas Winograd en fran{\c{c}}ais, adapt{\'e}e de la liste propos{\'e}e par Levesque et al. (2012) pour l{'}anglais. Les sch{\'e}mas Winograd sont des probl{\`e}mes de r{\'e}solution d{'}anaphore con{\c{c}}us pour {\^e}tre IA-complets. Nous montrons que notre collection v{\'e}rifie deux propri{\'e}t{\'e}s cruciales : elle est robuste vis-{\`a}-vis de m{\'e}thodes statistiques simples ({``}Google-proof{''}), tout en {\'e}tant largement d{\'e}pourvue d{'}ambigu{\"\i}t{\'e} pour les sujets humains que nous avons test{\'e}s.},
    language = "French",
}
@inproceedings{grabar-claveau-2017-criteres,
    title = "Crit{\`e}res num{\'e}riques dans les essais cliniques : annotation, d{\'e}tection et normalisation (Numerical criteria in clinical trials : annotation, detection and normalization)",
    author = "Grabar, Natalia  and
      Claveau, Vincent",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.5",
    pages = "36--43",
    abstract = "Les essais cliniques sont un {\'e}l{\'e}ment fondamental pour l{'}{\'e}valuation de nouvelles th{\'e}rapies ou techniques de diagnostic, de leur s{\'e}curit{\'e} et efficacit{\'e}. Ils exigent d{'}avoir un {\'e}chantillon convenable de la population. Le d{\'e}fi consiste alors {\`a} recruter le nombre suffisant de participants avec des caract{\'e}ristiques similaires pour garantir que les r{\'e}sultats des essais sont bien contr{\^o}l{\'e}s et dus aux facteurs {\'e}tudi{\'e}s. C{'}est une t{\^a}che difficile, effectu{\'e}e essentiellement manuellement. Comme les valeurs num{\'e}riques sont une information tr{\`e}s fr{\'e}quente et importante, nous proposons un syst{\`e}me automatique qui vise leur extraction et normalisation.",
    language = "French",
}
@inproceedings{marzinotto-etal-2017-analyse,
    title = "Analyse automatique {F}rame{N}et : une {\'e}tude sur un corpus fran{\c{c}}ais de textes encyclop{\'e}diques ({F}rame{N}et automatic analysis : a study on a {F}rench corpus of encyclopedic texts)",
    author = "Marzinotto, Gabriel  and
      Damnati, G{\'e}raldine  and
      B{\'e}chet, Fr{\'e}d{\'e}ric",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.6",
    pages = "44--51",
    abstract = "Cet article pr{\'e}sente un syst{\`e}me d{'}analyse automatique en cadres s{\'e}mantiques {\'e}valu{\'e} sur un corpus de textes encyclop{\'e}diques d{'}histoire annot{\'e}s selon le formalisme FrameNet. L{'}approche choisie repose sur un mod{\`e}le int{\'e}gr{\'e} d{'}{\'e}tiquetage de s{\'e}quence qui optimise conjointement l{'}identification des cadres, la segmentation et l{'}identification des r{\^o}les s{\'e}mantiques associ{\'e}s. Nous cherchons dans cette {\'e}tude {\`a} analyser la complexit{\'e} de la t{\^a}che selon plusieurs dimensions. Une analyse d{\'e}taill{\'e}e des performances du syst{\`e}me est ainsi propos{\'e}e, {\`a} la fois selon l{'}angle des param{\`e}tres du mod{\`e}le et de la nature des donn{\'e}es.",
    language = "French",
}
@inproceedings{godbert-favre-2017-detection,
    title = "D{\'e}tection de cor{\'e}f{\'e}rences de bout en bout en fran{\c{c}}ais (End-to-end coreference resolution for {F}rench)",
    author = "Godbert, Elisabeth  and
      Favre, Benoit",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.7",
    pages = "52--59",
    abstract = "Notre objectif est l{'}{\'e}laboration d{'}un syst{\`e}me de d{\'e}tection automatique de relations de cor{\'e}f{\'e}rence le plus g{\'e}n{\'e}ral possible, pour le traitement des anaphores pronominales et les cor{\'e}f{\'e}rences directes. Nous d{\'e}crivons dans cet article les diff{\'e}rentes {\'e}tapes de traitement des textes dans le syst{\`e}me que nous avons d{\'e}velopp{\'e} : (i) l{'}annotation en traits lexicaux et syntaxiques par le syst{\`e}me Macaon ; (ii) le rep{\'e}rage des mentions par un mod{\`e}le obtenu par apprentissage sur le corpus ANCOR ; (iii) l{'}annotation s{\'e}mantique des mentions {\`a} partir de deux ressources : le DEM et le LVF ; (iv) l{'}annotation en cor{\'e}f{\'e}rences par un syst{\`e}me {\`a} base de r{\`e}gles. Le syst{\`e}me est {\'e}valu{\'e} sur le corpus ANCOR.",
    language = "French",
}
@inproceedings{semmar-marchand-2017-une,
    title = "Une approche hybride pour la construction de lexiques bilingues d{'}expressions multi-mots {\`a} partir de corpus parall{\`e}les (A hybrid approach to build bilingual lexicons of multiword expressions from parallel corpora)",
    author = "Semmar, Nasredine  and
      Marchand, Morgane",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.8",
    pages = "60--68",
    abstract = "Les expressions multi-mots jouent un r{\^o}le important dans diff{\'e}rentes applications du Traitement Automatique de la Langue telles que la traduction automatique et la recherche d{'}information interlingue. Cet article, d{'}une part, d{\'e}crit une approche hybride pour l{'}acquisition d{'}un lexique bilingue d{'}expressions multi-mots {\`a} partir d{'}un corpus parall{\`e}le anglais-fran{\c{c}}ais, et d{'}autre part, pr{\'e}sente l{'}impact de l{'}utilisation d{'}un lexique bilingue sp{\'e}cialis{\'e} d{'}expressions multi-mots produit par cette approche sur les r{\'e}sultats du syst{\`e}me de traduction statistique libre Moses. Nous avons explor{\'e} deux m{\'e}triques bas{\'e}es sur la co-occurrence pour {\'e}valuer les liens d{'}alignement entre les expressions multi-mots des langues source et cible. Les r{\'e}sultats obtenus montrent que la m{\'e}trique utilisant un dictionnaire bilingue amorce de mots simples am{\'e}liore aussi bien la qualit{\'e} de l{'}alignement d{'}expressions multi-mots que celle de la traduction.",
    language = "French",
}
@inproceedings{bernard-2017-une,
    title = "Une interpr{\'e}tation probabiliste des informations de factivit{\'e} (Factuality information as sets of probabilities)",
    author = "Bernard, Timoth{\'e}e",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.9",
    pages = "69--76",
    abstract = "Nous pr{\'e}sentons une nouvelle formalisation de la factivit{\'e}, la dimension repr{\'e}sentant le degr{\'e} de croyance qu{'}une source {--} l{'}auteur ou tout autre agent mentionn{\'e} dans un texte {--} accorde {\`a} une {\'e}ventualit{\'e} donn{\'e}e. Nous insistons sur l{'}aspect dynamique de cette notion ainsi que sur ses interactions avec la structure discursive. Nous montrons comment une interpr{\'e}tation en termes d{'}ensembles de probabilit{\'e}s permet de s{'}affranchir des principaux probl{\`e}mes que posait la formalisation utilis{\'e}e dans les travaux pr{\'e}c{\'e}dents au calcul d{'}une factivit{\'e} coh{\'e}rente {\`a} l{'}{\'e}chelle du texte dans sa totalit{\'e}.",
    language = "French",
}
@inproceedings{beniamine-2017-une,
    title = "Une approche universelle pour l{'}abstraction automatique d{'}alternances morphophonologiques (A universal algorithm for the automatical abstraction of morphophonological alternations)",
    author = "Beniamine, Sacha",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.10",
    pages = "77--85",
    abstract = "Cet article pr{\'e}sente un algorithme impl{\'e}ment{\'e} pour l{'}inf{\'e}rence de patrons d{'}alternances morphophonologiques entre mots-formes. Il est universel au sens o{\`u} il permet d{'}obtenir des classifications comparables d{'}une langue {\`a} l{'}autre sans pr{\'e}juger des types d{'}alternances. Les patrons constituent une premi{\`e}re {\'e}tape pour les travaux quantitatifs dans l{'}approche Mot et Paradigme de la morphologie.",
    language = "French",
}
@inproceedings{beligne-etal-2017-detection,
    title = "D{\'e}tection automatique de m{\'e}taphores dans des textes de G{\'e}ographie : une {\'e}tude prospective (Automatic detection of metaphors in Geographical research papers : a prospective study)",
    author = "Belign{\'e}, Max  and
      Campar, Aleksandra  and
      Chauchat, Jean-Hugues  and
      Lefeuvre, Melanie  and
      Lefort, Isabelle  and
      Loudcher, Sabine  and
      Velcin, Julien",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.11",
    pages = "86--93",
    abstract = "Cet article s{'}int{\`e}gre dans un projet collaboratif qui vise {\`a} r{\'e}aliser une analyse longitudinale de la production universitaire en G{\'e}ographie. En particulier, nous pr{\'e}sentons les premiers r{\'e}sultats de l{'}application d{'}une m{\'e}thode de d{\'e}tection automatique de m{\'e}taphores bas{\'e}e sur les mod{\`e}les de th{\'e}matiques latentes. Une analyse d{\'e}taill{\'e}e permet de mieux comprendre l{'}impact de certains choix et de r{\'e}fl{\'e}chir aux pistes de recherche que nous serons amen{\'e}s {\`a} explorer pour am{\'e}liorer ces r{\'e}sultats.",
    language = "French",
}
@inproceedings{andreou-petitjean-2017-describing,
    title = "Describing derivational polysemy with {XMG}",
    author = "Andreou, Marios  and
      Petitjean, Simon",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.12",
    pages = "94--101",
    abstract = "In this paper, we model and test the monosemy and polysemy approaches to derivational multiplicity of meaning, using Frame Semantics and XMG. In order to illustrate our claims and proposals, we use data from deverbal nominalizations with the suffix -al on verbs of change of possession (e.g. rental, disbursal). In our XMG implementation, we show that the underspecified meaning of affixes cannot always be reduced to a single unitary meaning and that the polysemy approach to multiplicity of meaning is more judicious compared to the monosemy approach. We also introduce constraints on the potential referents of derivatives. These constraints have the form of type constraints and specify which arguments in the frame of the verbal base are compatible with the referential argument of the derivative. The introduction of type constraints rules out certain readings because frame unification only succeeds if types are compatible.",
}
@inproceedings{sileo-etal-2017-changement,
    title = "Changement stylistique de phrases par apprentissage faiblement supervis{\'e} (Textual Style Transfer using Weakly Supervised Learning)",
    author = "Sileo, Damien  and
      Pradel, Camille  and
      Muller, Philippe  and
      Van de Cruys, Tim",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.13",
    pages = "102--109",
    abstract = "Plusieurs t{\^a}ches en traitement du langage naturel impliquent de modifier des phrases en conservant au mieux leur sens, comme la reformulation, la compression, la simplification, chacune avec leurs propres donn{\'e}es et mod{\`e}les. Nous introduisons ici une m{\'e}thode g{\'e}n{\'e}rale s{'}adressant {\`a} tous ces probl{\`e}mes, utilisant des donn{\'e}es plus simples {\`a} obtenir : un ensemble de phrases munies d{'}indicateurs sur leur style, comme des phrases et le type de sentiment qu{'}elles expriment. Cette m{\'e}thode repose sur un mod{\`e}le d{'}apprentissage de repr{\'e}sentations non supervis{\'e} (un auto-encodeur variationnel), puis sur le changement des repr{\'e}sentations apprises pour correspondre {\`a} un style donn{\'e}. Le r{\'e}sultat est {\'e}valu{\'e} qualitativement, puis quantitativement sur le jeu de donn{\'e}es de compression de phrases Microsoft, avec des r{\'e}sultats encourageants.",
    language = "French",
}
@inproceedings{nagoudi-etal-2017-amelioration,
    title = "Am{\'e}lioration de la similarit{\'e} s{\'e}mantique vectorielle par m{\'e}thodes non-supervis{\'e}es (Improved the Semantic Similarity with Weighting Vectors)",
    author = "Nagoudi, El-Moatez-Billah  and
      Ferrero, J{\'e}r{\'e}my  and
      Schwab, Didier",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.14",
    pages = "110--117",
    abstract = "",
    language = "French",
}
@inproceedings{tarrade-etal-2017-typologies,
    title = "Typologies pour l{'}annotation de textes non standard en fran{\c{c}}ais (Typologies for the annotation of non-standard {F}rench texts)",
    author = "Tarrade, Louise  and
      Lopez, C{\'e}dric  and
      Panckhurst, Rachel  and
      Antoniadis, Geroges",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.15",
    pages = "118--125",
    abstract = "La t{\^a}che de normalisation automatique des messages issus de la communication {\'e}lectronique m{\'e}di{\'e}e requiert une {\'e}tape pr{\'e}alable consistant {\`a} identifier les ph{\'e}nom{\`e}nes linguistiques. Dans cet article, nous proposons deux typologies pour l{'}annotation de textes non standard en fran{\c{c}}ais, relevant respectivement des niveaux morpho-lexical et morpho-syntaxique. Ces typologies ont {\'e}t{\'e} d{\'e}velopp{\'e}es en conciliant les typologies existantes et en les faisant {\'e}voluer en parall{\`e}le d{'}une annotation manuelle de tweets et de SMS.",
    language = "French",
}
@inproceedings{charlet-damnati-2017-simbow,
    title = "Simbow : une mesure de similarit{\'e} s{\'e}mantique entre textes (Simbow : a semantic similarity metric between texts)",
    author = "Charlet, Delphine  and
      Damnati, G{\'e}raldine",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.16",
    pages = "126--133",
    abstract = "Cet article d{\'e}crit une mesure de similarit{\'e} s{\'e}mantique non-supervis{\'e}e qui repose sur l{'}introduction d{'}une matrice de relations entre mots, dans un paradigme de mesure cosinus entre sacs de mots. La m{\'e}trique obtenue, apparent{\'e}e {\`a} soft-cosinus, tient compte des relations entre mots qui peuvent {\^e}tre d{'}ordre lexical ou s{\'e}mantique selon la matrice consid{\'e}r{\'e}e. La mise en {\oe}uvre de cette m{\'e}trique sur la t{\^a}che qui consiste {\`a} mesurer des similarit{\'e}s s{\'e}mantiques entre questions pos{\'e}es sur un forum, a remport{\'e} la campagne d{'}{\'e}valuation SemEval2017. Si l{'}approche soumise {\`a} la campagne est une combinaison supervis{\'e}e de diff{\'e}rentes mesures non-supervis{\'e}es, nous pr{\'e}sentons dans cet article en d{\'e}tail les m{\'e}triques non-supervis{\'e}es, qui pr{\'e}sentent l{'}avantage de produire de bons r{\'e}sultats sans n{\'e}cessiter de ressources sp{\'e}cifiques autres que des donn{\'e}es non annot{\'e}es du domaine consid{\'e}r{\'e}.",
    language = "French",
}
@inproceedings{bartenlian-etal-2017-adaptation,
    title = "Adaptation au domaine pour l{'}analyse morpho-syntaxique (Domain Adaptation for {P}o{S} tagging)",
    author = "Bartenlian, {\'E}l{\'e}onor  and
      Lacour, Margot  and
      Labeau, Matthieu  and
      Allauzen, Alexandre  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.17",
    pages = "134--141",
    abstract = "Ce travail cherche {\`a} comprendre pourquoi les performances d{'}un analyseur morpho-syntaxiques chutent fortement lorsque celui-ci est utilis{\'e} sur des donn{\'e}es hors domaine. Nous montrons {\`a} l{'}aide d{'}une exp{\'e}rience jouet que ce comportement peut {\^e}tre d{\^u} {\`a} un ph{\'e}nom{\`e}ne de masquage des caract{\'e}ristiques lexicalis{\'e}es par les caract{\'e}ristiques non lexicalis{\'e}es. Nous proposons plusieurs mod{\`e}les essayant de r{\'e}duire cet effet.",
    language = "French",
}
@inproceedings{vial-etal-2017-representation,
    title = {Repr{\'e}sentation vectorielle de sens pour la d{\'e}sambigu{\"\i}sation lexicale {\`a} base de connaissances (Sense Embeddings in Knowledge-Based Word Sense Disambiguation)},
    author = {Vial, Lo{\"\i}c  and
      Lecouteux, Benjamin  and
      Schwab, Didier},
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.18",
    pages = "142--149",
    abstract = {Dans cet article, nous proposons une nouvelle m{\'e}thode pour repr{\'e}senter sous forme vectorielle les sens d{'}un dictionnaire. Nous utilisons les termes employ{\'e}s dans leur d{\'e}finition en les projetant dans un espace vectoriel, puis en additionnant les vecteurs r{\'e}sultants, avec des pond{\'e}rations d{\'e}pendantes de leur partie du discours et de leur fr{\'e}quence. Le vecteur de sens r{\'e}sultant est alors utilis{\'e} pour trouver des sens reli{\'e}s, permettant de cr{\'e}er un r{\'e}seau lexical de mani{\`e}re automatique. Le r{\'e}seau obtenu est ensuite {\'e}valu{\'e} par rapport au r{\'e}seau lexical de WordNet, construit manuellement. Pour cela nous comparons l{'}impact des diff{\'e}rents r{\'e}seaux sur un syst{\`e}me de d{\'e}sambigu{\"\i}sation lexicale bas{\'e} sur la mesure de Lesk. L{'}avantage de notre m{\'e}thode est qu{'}elle peut {\^e}tre appliqu{\'e}e {\`a} n{'}importe quelle langue ne poss{\'e}dant pas un r{\'e}seau lexical comme celui de WordNet. Les r{\'e}sultats montrent que notre r{\'e}seau automatiquement g{\'e}n{\'e}r{\'e} permet d{'}am{\'e}liorer le score du syst{\`e}me de base, atteignant quasiment la qualit{\'e} du r{\'e}seau de WordNet.},
    language = "French",
}
@inproceedings{lafourcade-le-brun-2017-parcourir,
    title = "Parcourir, reconna{\^\i}tre et r{\'e}fl{\'e}chir. Combinaison de m{\'e}thodes l{\'e}g{\`e}res pour l{'}extraction de relations s{\'e}mantiques (Browse, recognize and think)",
    author = "Lafourcade, Mathieu  and
      Le Brun, Nathalie",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.19",
    pages = "150--157",
    abstract = "La capture de relations s{\'e}mantiques entre termes {\`a} partir de textes est un moyen privil{\'e}gi{\'e} de constituer/alimenter une base de connaissances, ressource indispensable pour l{'}analyse de textes. Nous proposons et {\'e}valuons la combinaison de trois m{\'e}thodes de production de relations lexicos{\'e}mantiques.",
    language = "French",
}
@inproceedings{joubert-etal-2017-si,
    title = "Si les souris {\'e}taient des reptiles, alors les reptiles pourraient {\^e}tre des mammif{\`e}res ou Comment d{\'e}tecter les anomalies dans le r{\'e}seau {JDM} ? (If mice were reptiles, then the reptiles could be mammals, or How to detect errors in a lexical network?)",
    author = "Joubert, Alain  and
      Lafourcade, Mathieu  and
      Le Brun, Nathalie",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.20",
    pages = "158--164",
    abstract = "La correction des erreurs dans une collection de donn{\'e}es est un probl{\`e}me d{\'e}licat. Elle peut {\^e}tre r{\'e}alis{\'e}e manuellement par un expert, ou en utilisant des m{\'e}thodes de crowdsourcing, ou encore automatiquement au moyen d{'}algorithmes. Nous pr{\'e}sentons ici des m{\'e}thodes automatiques permettant de d{\'e}tecter les erreurs potentielles « secondaires » induites par les m{\'e}canismes automatiques d{'}inf{\'e}rences de relations, lorsqu{'}ils s{'}appuient sur des relations erron{\'e}es « initiales » d{\'e}tect{\'e}es manuellement. Des r{\'e}sultats encourageants, mesur{\'e}s sur le r{\'e}seau JeuxDeMots, nous invitent {\`a} envisager {\'e}galement des strat{\'e}gies qui permettraient de d{\'e}tecter automatiquement les relations erron{\'e}es « initiales », ce qui pourrait conduire {\`a} une d{\'e}tection automatique de la majorit{\'e} des erreurs pr{\'e}sentes dans le r{\'e}seau.",
    language = "French",
}
@inproceedings{fort-etal-2017-vers,
    title = "Vers l{'}annotation par le jeu de corpus (plus) complexes : le cas de la langue de sp{\'e}cialit{\'e} (Towards (more) complex corpora annotation using a game with a purpose : the case of scientific language)",
    author = {Fort, Kar{\"e}n  and
      Guillaume, Bruno  and
      Lefebvre, Nicolas  and
      Ram{\'\i}rez, Laura  and
      Regnault, Mathilde  and
      Collins, Mary  and
      Gavrilova, Oksana  and
      Kristanti, Tanti},
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.21",
    pages = "165--173",
    abstract = "Nous avons pr{\'e}c{\'e}demment montr{\'e} qu{'}il est possible de faire produire des annotations syntaxiques de qualit{\'e} par des participants {\`a} un jeu ayant un but. Nous pr{\'e}sentons ici les r{\'e}sultats d{'}une exp{\'e}rience visant {\`a} {\'e}valuer leur production sur un corpus plus complexe, en langue de sp{\'e}cialit{\'e}, en l{'}occurrence un corpus de textes scientifiques sur l{'}ADN. Nous d{\'e}terminons pr{\'e}cis{\'e}ment la complexit{\'e} de ce corpus, puis nous {\'e}valuons les annotations en syntaxe de d{\'e}pendances produites par les joueurs par rapport {\`a} une r{\'e}f{\'e}rence mise au point par des experts du domaine.",
    language = "French",
}
@inproceedings{tian-etal-2017-detection,
    title = "D{\'e}tection des mots non-standards dans les tweets avec des r{\'e}seaux de neurones (Detecting non-standard words in tweets with neural networks)",
    author = "Tian, Tian  and
      Tellier, Isabelle  and
      Dinarelli, Marco  and
      Cardoso, Pedro",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.22",
    pages = "174--182",
    abstract = "Dans cet article, nous proposons un mod{\`e}le pour d{\'e}tecter dans les textes g{\'e}n{\'e}r{\'e}s par des utilisateurs (en particulier les tweets), les mots non-standards {\`a} corriger. Nous utilisons pour cela des r{\'e}seaux de neurones convolutifs au niveau des caract{\`e}res, associ{\'e}s {\`a} des {``}plongements{''} (embeddings) des mots pr{\'e}sents dans le contexte du mot courant. Nous avons utilis{\'e} pour l{'}{\'e}valuation trois corpus de r{\'e}f{\'e}rence. Nous avons test{\'e} diff{\'e}rents mod{\`e}les qui varient suivant leurs plongements pr{\'e}-entrain{\'e}s, leurs configurations et leurs optimisations. Nous avons finalement obtenu une F1-mesure de 0.972 en validation crois{\'e}e pour la classe des mots non-standards. Cette d{\'e}tection des mots {\`a} corriger est l{'}{\'e}tape pr{\'e}liminaire pour la normalisation des textes non standards comme les tweets.",
    language = "French",
}
@inproceedings{lefeuvre-condamines-2017-mar,
    title = "{MAR}-{REL} : une base de marqueurs de relations conceptuelles pour la d{\'e}tection de Contextes Riches en Connaissances ({MAR}-{REL} : a conceptual relation markers database for Knowledge-Rich Contexts extraction)",
    author = "Lefeuvre, Luce  and
      Condamines, Anne",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.23",
    pages = "183--191",
    abstract = "Les marqueurs de relation conceptuelle sont un moyen efficace de d{\'e}tecter automatiquement en corpus des Contextes Riches en Connaissances. Dans le domaine de la terminologie ou de l{'}ing{\'e}nierie des connaissances, les Contextes Riches en Connaissances peuvent {\^e}tre utiles pour l{'}{\'e}laboration de ressources termino-ontologiques. Bien que la litt{\'e}rature concernant ce sujet soit riche, il n{'}existe pas de recensement syst{\'e}matique ni d{'}{\'e}valuation {\`a} grande {\'e}chelle des marqueurs de relation conceptuelle. Pour ces raisons notamment, nous avons constitu{\'e} une base de marqueurs pour les relations d{'}hyperonymie, de m{\'e}ronymie, et de cause, en fran{\c{c}}ais. Pour chacun de ces marqueurs, son taux de pr{\'e}cision est propos{\'e} pour des corpus qui varient en fonction du domaine et du genre textuel.",
    language = "French",
}
@inproceedings{riou-etal-2017-apprentissage,
    title = "Apprentissage en ligne interactif d{'}un g{\'e}n{\'e}rateur en langage naturel neuronal pour le dialogue homme-machine (On-line Interactive Learning of Natural Language Neural Generation for Human-machine)",
    author = "Riou, Matthieu  and
      Jabaian, Bassam  and
      Huet, St{\'e}phane  and
      Lef{\`e}vre, Fabrice",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.24",
    pages = "192--199",
    abstract = "R{\'e}cemment, de nouveaux mod{\`e}les {\`a} base de r{\'e}seaux de neurones r{\'e}currents ont {\'e}t{\'e} propos{\'e}s pour traiter la g{\'e}n{\'e}ration en langage naturel dans des syst{\`e}mes de dialogue (Wen et al., 2016a). Ces mod{\`e}les demandent une grande quantit{\'e} de donn{\'e}es d{'}apprentissage ; or la collecte et l{'}annotation de ces donn{\'e}es peuvent {\^e}tre laborieuses. Pour r{\'e}pondre {\`a} cette probl{\'e}matique, nous nous int{\'e}ressons ici {\`a} la mise en place d{'}un protocole d{'}apprentissage en ligne bas{\'e} sur un apprentissage par renforcement, permettant d{'}am{\'e}liorer l{'}utilisation d{'}un mod{\`e}le initial appris sur un corpus plus restreint g{\'e}n{\'e}r{\'e} par patrons. Dans cette {\'e}tude exploratoire, nous proposons une approche bas{\'e}e sur un algorithme de bandit contre un adversaire, afin d{'}en {\'e}tudier l{'}int{\'e}r{\^e}t et les limites.",
    language = "French",
}
@inproceedings{grobol-etal-2017-apports,
    title = "Apports des analyses syntaxiques pour la d{\'e}tection automatique de mentions dans un corpus de fran{\c{c}}ais oral (Experiences in using deep and shallow parsing to detect entity mentions in oral {F}rench)",
    author = {Grobol, Lo{\"\i}c  and
      Tellier, Isabelle  and
      de La Clergerie, {\'E}ric  and
      Dinarelli, Marco  and
      Landragin, Fr{\'e}d{\'e}ric},
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.25",
    pages = "200--208",
    abstract = "Cet article pr{\'e}sente trois exp{\'e}riences de d{\'e}tection de mentions dans un corpus de fran{\c{c}}ais oral : ANCOR. Ces exp{\'e}riences utilisent des outils pr{\'e}existants d{'}analyse syntaxique du fran{\c{c}}ais et des m{\'e}thodes issues de travaux sur la cor{\'e}f{\'e}rence, les anaphores et la d{\'e}tection d{'}entit{\'e}s nomm{\'e}es. Bien que ces outils ne soient pas optimis{\'e}s pour le traitement de l{'}oral, la qualit{\'e} de la d{\'e}tection des mentions que nous obtenons est comparable {\`a} l{'}{\'e}tat de l{'}art des syst{\`e}mes con{\c{c}}us pour l{'}{\'e}crit dans d{'}autres langues. Nous concluons en proposant des perspectives pour l{'}am{\'e}lioration des r{\'e}sultats que nous obtenons et la construction d{'}un syst{\`e}me end-to-end pour lequel nos exp{\'e}riences peuvent servir de base de travail.",
    language = "French",
}
@inproceedings{lemaire-2017-un,
    title = "Un nouveau besoin dans l{'}industrie : une aide au « r{\'e}dacteur traduisant » (A new need in companies: writing assistance for {``}translating writers{''})",
    author = "Lemaire, Claire",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.26",
    pages = "209--217",
    abstract = "Nous mettons en relief, gr{\^a}ce {\`a} une exp{\'e}rimentation avec questionnaires et corpus parall{\`e}le, une situation nouvelle en entreprise de r{\'e}daction multilingue, pour laquelle il n{'}existe pas de technologie TAL d{\'e}di{\'e}e. Nous sugg{\'e}rons de tirer profit de cette situation in{\'e}dite de r{\'e}dacteur traduisant, pour utiliser l{'}expertise du r{\'e}dacteur pendant le processus de traduction et nous pr{\'e}conisons de d{\'e}velopper une TA permettant une {\'e}dition en cours de processus.",
    language = "French",
}
@inproceedings{servan-etal-2017-adaptation,
    title = "Adaptation incr{\'e}mentale de mod{\`e}les de traduction neuronaux (Incremental adaptation of neural machine translation models)",
    author = "Servan, Christophe  and
      Crego, Josep  and
      Senellart, Jean",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.27",
    pages = "218--225",
    abstract = "L{'}adaptation au domaine est un verrou scientifique en traduction automatique. Il englobe g{\'e}n{\'e}ralement l{'}adaptation de la terminologie et du style, en particulier pour la post-{\'e}dition humaine dans le cadre d{'}une traduction assist{\'e}e par ordinateur. Avec la traduction automatique neuronale, nous {\'e}tudions une nouvelle approche d{'}adaptation au domaine que nous appelons {``}sp{\'e}cialisation{''} et qui pr{\'e}sente des r{\'e}sultats prometteurs tant dans la vitesse d{'}apprentissage que dans les scores de traduction. Dans cet article, nous proposons d{'}explorer cette approche.",
    language = "French",
}
@inproceedings{zweigenbaum-lavergne-2017-detection,
    title = "D{\'e}tection de concepts et granularit{\'e} de l{'}annotation (Concept detection and annotation granularity )",
    author = "Zweigenbaum, Pierre  and
      Lavergne, Thomas",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.28",
    pages = "226--233",
    abstract = "Nous nous int{\'e}ressons ici {\`a} une t{\^a}che de d{\'e}tection de concepts dans des textes sans exigence particuli{\`e}re de passage par une phase de d{\'e}tection d{'}entit{\'e}s avec leurs fronti{\`e}res. Il s{'}agit donc d{'}une t{\^a}che de cat{\'e}gorisation de textes multi{\'e}tiquette, avec des jeux de donn{\'e}es annot{\'e}s au niveau des textes entiers. Nous faisons l{'}hypoth{\`e}se qu{'}une annotation {\`a} un niveau de granularit{\'e} plus fin, typiquement au niveau de l{'}{\'e}nonc{\'e}, devrait am{\'e}liorer la performance d{'}un d{\'e}tecteur automatique entra{\^\i}n{\'e} sur ces donn{\'e}es. Nous examinons cette hypoth{\`e}se dans le cas de textes courts particuliers : des certificats de d{\'e}c{\`e}s o{\`u} l{'}on cherche {\`a} reconna{\^\i}tre des diagnostics, avec des jeux de donn{\'e}es initialement annot{\'e}s au niveau du certificat entier. Nous constatons qu{'}une annotation au niveau de la « ligne » am{\'e}liore effectivement les r{\'e}sultats, mais aussi que le simple fait d{'}appliquer au niveau de la ligne un classifieur entra{\^\i}n{\'e} au niveau du texte est d{\'e}j{\`a} une source d{'}am{\'e}lioration.",
    language = "French",
}
@inproceedings{norman-etal-2017-tri,
    title = "Tri Automatique de la Litt{\'e}rature pour les Revues Syst{\'e}matiques (Automatically Ranking the Literature in Support of Systematic Reviews)",
    author = "Norman, Christopher  and
      Leeflang, Mariska  and
      Zweigenbaum, Pierre  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.29",
    pages = "234--241",
    abstract = "Les revues syst{\'e}matiques de la litt{\'e}rature dans le domaine biom{\'e}dical reposent essentiellement sur le travail bibliographique manuel d{'}experts. Nous {\'e}valuons les performances de la classification supervis{\'e}e pour la d{\'e}couverte automatique d{'}articles {\`a} l{'}aide de plusieurs d{\'e}finitions des crit{\`e}res d{'}inclusion. Nous appliquons un mod{\`e}le de regression logistique sur deux corpus issus de revues syst{\'e}matiques conduites dans le domaine du traitement automatique de la langue et de l{'}efficacit{\'e} des m{\'e}dicaments. La classification offre une aire sous la courbe moyenne (AUC) de 0.769 si le classifieur est contruit {\`a} partir des jugements experts port{\'e}s sur les titres et r{\'e}sum{\'e}s des articles, et de 0.835 si on utilise les jugements port{\'e}s sur le texte int{\'e}gral. Ces r{\'e}sultats indiquent l{'}importance des jugements port{\'e}s d{\`e}s le d{\'e}but du processus de s{\'e}lection pour d{\'e}velopper un classifieur efficace pour acc{\'e}l{\'e}rer l{'}{\'e}laboration des revues syst{\'e}matiques {\`a} l{'}aide d{'}un algorithme de classification standard.",
    language = "French",
}
@inproceedings{saadane-etal-2017-une,
    title = "Une approche linguistique pour la d{\'e}tection des dialectes arabes (A linguistic approach for the detection of {A}rabic dialects)",
    author = "Sa{\^a}dane, Houda  and
      Nouvel, Damien  and
      Seffih, Hosni  and
      Fluhr, Christian",
    booktitle = "Actes des 24{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts",
    month = "6",
    year = "2017",
    address = "Orl{\'e}ans, France",
    publisher = "ATALA",
    url = "https://www.aclweb.org/anthology/2017.jeptalnrecital-court.30",
    pages = "242--250",
    abstract = "Dans cet article, nous pr{\'e}sentons un processus d{'}identification automatique de l{'}origine dialectale pour la langue arabe de textes {\'e}crits en caract{\`e}res arabes ou en {\'e}criture latine (arabizi). Nous d{\'e}crivons le processus d{'}annotation des ressources construites et du syst{\`e}me de translitt{\'e}ration adopt{\'e}. Deux approches d{'}identification de la langue sont compar{\'e}es : la premi{\`e}re est linguistique et exploite des dictionnaires, la seconde est statistique et repose sur des m{\'e}thodes traditionnelles d{'}apprentissage automatique (n-grammes). L{'}{\'e}valuation de ces approches montre que la m{\'e}thode linguistique donne des r{\'e}sultats satisfaisants, sans {\^e}tre d{\'e}pendante des corpus d{'}apprentissage.",
    language = "French",
}
@proceedings{paclic-2016-pacific-asia-language,
    title = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3000",
}
@inproceedings{ma-etal-2016-retrieval,
    title = "Retrieval Term Prediction Using Deep Learning Methods",
    author = "Ma, Qing  and
      Tanigawa, Ibuki  and
      Murata, Masaki",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3001",
    pages = "309--317",
}
@inproceedings{kamada-2016-japanese,
    title = "{J}apanese Post-verbal Constructions Revisited",
    author = "Kamada, Kohji",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3002",
    pages = "319--328",
}
@inproceedings{ikegami-ohsawa-2016-sentence,
    title = "Sentence Clustering using {P}age{R}ank Topic Model",
    author = "Ikegami, Kenshin  and
      Ohsawa, Yukio",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3003",
    pages = "329--337",
}
@inproceedings{lee-jun-2016-inner,
    title = "The Inner Circle vs. the Outer Circle or {B}ritish {E}nglish vs. {A}merican {E}nglish",
    author = "Lee, Yong-hun  and
      Jun, Ki-suk",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3004",
    pages = "339--346",
}
@inproceedings{kim-etal-2016-correlation,
    title = "A Correlation Analysis of {E}nglish Particle Placement of Three {E}ast {A}sian {EFL} Learners Writings",
    author = "Kim, Ha-Eung  and
      Lee, Gyu-Hyeong  and
      Lee, Yong-hun",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3005",
    pages = "347--354",
}
@inproceedings{sboev-2016-sources,
    title = "The sources of new words and expressions in the {C}hinese {I}nternet language and the ways by which they enter the {I}nternet language",
    author = "Sboev, Aleksandr",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3006",
    pages = "355--361",
}
@inproceedings{liu-etal-2016-sentiment,
    title = "Sentiment Clustering with Topic and Temporal Information from Large Email Dataset",
    author = "Liu, Sisi  and
      Lee, Ickjai  and
      Cai, Guochen",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3007",
    pages = "363--371",
}
@inproceedings{chung-2016-adnominal,
    title = "On What an Adnominal Appendix Modifies in {K}orean Adjunct {RDC}s",
    author = "Chung, Daeho",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3008",
    pages = "373--381",
}
@inproceedings{miao-etal-2016-automatic,
    title = "Automatic Identifying Entity Type in Linked Data",
    author = "Miao, Qingliang  and
      Fang, Ruiyu  and
      Song, Shuangyong  and
      Zheng, Zhongguang  and
      Fang, Lu  and
      Meng, Yao  and
      Sun, Jun",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3009",
    pages = "383--390",
}
@inproceedings{nocon-borra-2016-smtpost,
    title = "{SMTPOST} Using Statistical Machine Translation Approach in {F}ilipino Part-of-Speech Tagging",
    author = "Nocon, Nicco  and
      Borra, Allan",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3010",
    pages = "391--396",
}
@inproceedings{wang-etal-2016-built,
    title = "From built examples to attested examples: a syntax-based query for non-specialists",
    author = "Wang, Ilaine  and
      Kahane, Sylvain  and
      Tellier, Isabelle",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3011",
    pages = "397--405",
}
@inproceedings{li-2016-study,
    title = "A Study of Valence {\&} Argument Integration in {C}hinese Verb-Resultative Complement",
    author = "Li, Anran",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3012",
    pages = "407--415",
}
@inproceedings{sok-adams-2016-phonological,
    title = "Phonological Principles for Automatic Phonetic Transcription of {K}hmer Orthographic Words",
    author = "Sok, Makara  and
      Adams, Larin",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3013",
    pages = "417--424",
}
@inproceedings{takehisa-2016-possessor,
    title = "On the Possessor Interpretation of Non-Agentive Subjects",
    author = "Takehisa, Tomokazu",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3014",
    pages = "425--431",
}
@inproceedings{oco-etal-2016-philippine,
    title = "{P}hilippine Language Resources: Applications, Issues, and Directions",
    author = "Oco, Nathaniel  and
      Syliongka, Leif Romeritch  and
      Allman, Tod  and
      Roxas, Rachel Edita",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3015",
    pages = "433--438",
}
@inproceedings{kang-park-2016-generating,
    title = "Generating a Linguistic Model for Requirement Quality Analysis",
    author = "Kang, Juyeon  and
      Park, Jungyeul",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3016",
    pages = "439--447",
}
@inproceedings{mirovsky-etal-2016-designing,
    title = "Designing {C}ze{DL}ex {--} A Lexicon of {C}zech Discourse Connectives",
    author = "M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}  and
      J{\'\i}nov{\'a}, Pavl{\'\i}na  and
      Rysov{\'a}, Magdal{\'e}na  and
      Pol{\'a}kov{\'a}, Lucie",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3017",
    pages = "449--457",
}
@inproceedings{jiang-etal-2016-transitivity,
    title = "Transitivity in Light Verb Variations in {M}andarin {C}hinese {--} A Comparable Corpus-based Statistical Approach",
    author = "Jiang, Menghan  and
      Shi, Dingxu  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3018",
    pages = "459--468",
}
@inproceedings{he-2016-interaction,
    title = "The Interaction between {SFP}-Ne and {S}p{OA}s in {M}andarin {C}hinese{--}A corpus based approach",
    author = "He, Yifan",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3019",
    pages = "469--475",
}
@inproceedings{kang-2016-semantics,
    title = "On the semantics of {K}orean modalized question",
    author = "Kang, Arum",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3020",
    pages = "477--483",
}
@inproceedings{xiong-huang-2016-synaesthetic,
    title = "The Synaesthetic and Metaphorical Uses of 味 wei {`}taste{'} in {C}hinese Buddhist Suttas",
    author = "Xiong, Jiajuan  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3021",
    pages = "485--492",
}
@inproceedings{park-2016-l2,
    title = "{L}2 Acquisition of {K}orean locative construction by {E}nglish {L}1 speakers: Learnability problem in {K}orean Figure non-alternating verbs",
    author = "Park, Sun Hee",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3022",
    pages = "493--499",
}
@inproceedings{lee-2016-towards,
    title = "Towards a unified account of resultative constructions in {K}orean",
    author = "Lee, Juwon",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3023",
    pages = "501--510",
}
@inproceedings{duann-huang-2016-use,
    title = "The use of body part terms in {T}aiwan and {C}hina: Analyzing 血 xue {`}blood{'} and 骨 gu {`}bone{'} in {C}hinese {G}igaword v. 2.0",
    author = "Duann, Ren-feng  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3024",
    pages = "511--517",
}
@inproceedings{iijima-kobayashi-2016-pomdp,
    title = "A {POMDP}-based Multimodal Interaction System Using a Humanoid Robot",
    author = "Iijima, Sae  and
      Kobayashi, Ichiro",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3025",
    pages = "519--525",
}
@inproceedings{lee-hong-2016-cloud,
    title = "The Cloud of Knowing: Non-factive al-ta {`}know{'} (as a Neg-raiser) in {K}orean",
    author = "Lee, Chungmin  and
      Hong, Seungjin",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3026",
    pages = "527--533",
}
@inproceedings{suzuki-etal-2016-neural,
    title = "Neural Joint Learning for Classifying {W}ikipedia Articles into Fine-grained Named Entity Types",
    author = "Suzuki, Masatoshi  and
      Matsuda, Koji  and
      Sekine, Satoshi  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3027",
    pages = "535--544",
}
@inproceedings{chakraborty-etal-2016-mining,
    title = "Mining Call Center Conversations Exhibiting Similar Affective States",
    author = "Chakraborty, Rupayan  and
      Pandharipande, Meghna  and
      Kopparapu, Sunil Kumar",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-3028",
    pages = "545--553",
}
@proceedings{paclic-2016-pacific-asia,
    title = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2000",
}
@inproceedings{seraku-2016-maximal,
    title = "A {``}Maximal Exclusion{''} Approach to Structural Uncertainty in Dynamic Syntax",
    author = "Seraku, Tohru",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2001",
    pages = "39--47",
}
@inproceedings{park-etal-2016-korean,
    title = "{K}orean Language Resources for Everyone",
    author = "Park, Jungyeul  and
      Hong, Jeen-Pyo  and
      Cha, Jeong-Won",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2002",
    pages = "49--58",
}
@inproceedings{shi-2016-secondary,
    title = "Secondary Predicates in Native and Nonnative Grammars",
    author = "Shi, Enchao",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2003",
    pages = "59--68",
}
@inproceedings{wu-etal-2016-generalized,
    title = "A Generalized Framework for Hierarchical Word Sequence Language Model",
    author = "Wu, Xiaoyi  and
      Duh, Kevin  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2004",
    pages = "69--75",
}
@inproceedings{park-lee-2016-processing,
    title = "Processing {E}nglish {I}sland Sentences by {K}orean {EFL} Learners",
    author = "Park, Yeonkyung  and
      Lee, Yong-hun",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2005",
    pages = "77--84",
}
@inproceedings{phan-etal-2016-multiple,
    title = "Multiple Emotions Detection in Conversation Transcripts",
    author = "Phan, Duc-Anh  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2006",
    pages = "85--94",
}
@inproceedings{park-2016-long,
    title = "Long-distance anaphors and the blocking effect revisited-An {E}ast {A}sian perspective",
    author = "Park, Hyunjun",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2007",
    pages = "95--103",
}
@inproceedings{go-borra-2016-developing,
    title = "Developing an Unsupervised Grammar Checker for {F}ilipino Using Hybrid N-grams as Grammar Rules",
    author = "Go, Matthew Phillip  and
      Borra, Allan",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2008",
    pages = "105--113",
}
@inproceedings{yamaki-etal-2016-supervised,
    title = "Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings",
    author = "Yamaki, Shoma  and
      Shinnou, Hiroyuki  and
      Komiya, Kanako  and
      Sasaki, Minoru",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2009",
    pages = "115--121",
}
@inproceedings{zhang-etal-2016-hssa,
    title = "{HSSA} tree structures for {BTG}-based preordering in machine translation",
    author = "Zhang, Yujia  and
      Wang, Hao  and
      Lepage, Yves",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2010",
    pages = "123--132",
}
@inproceedings{qiu-2016-manner,
    title = "The Manner/Result Complementarity in {C}hinese Motion Verbs Revisited",
    author = "Qiu, Lei",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2011",
    pages = "133--142",
}
@inproceedings{wang-lepage-2016-yet,
    title = "Yet Another Symmetrical and Real-time Word Alignment Method: Hierarchical Sub-sentential Alignment using {F}-measure",
    author = "Wang, Hao  and
      Lepage, Yves",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2012",
    pages = "143--152",
}
@inproceedings{li-etal-2016-event,
    title = "Event Based Emotion Classification for News Articles",
    author = "Li, Minglei  and
      Wang, Da  and
      Lu, Qin  and
      Long, Yunfei",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2013",
    pages = "153--162",
}
@inproceedings{sperlich-etal-2016-interaction,
    title = "The interaction of politeness systems in {K}orean learners of {F}rench",
    author = "Sperlich, Darcy  and
      Leem, Jaiho  and
      Ahn, Eui-Jeen",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2014",
    pages = "163--171",
}
@inproceedings{phi-matsumoto-2016-integrating,
    title = "Integrating Word Embedding Offsets into the Espresso System for Part-Whole Relation Extraction",
    author = "Phi, Van-Thuy  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2015",
    pages = "173--181",
}
@inproceedings{kim-etal-2016-experimental,
    title = "An Experimental Study of Subject Properties in {K}orean Multiple Subject Constructions ({MSC}s)",
    author = "Kim, Ji-Hye  and
      Kim, Eunah  and
      Yoon, James",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2016",
    pages = "183--190",
}
@inproceedings{chen-2016-focal,
    title = "Focal Prominence Underlying Distribution of {M}andarin Minimizers",
    author = "Chen, I-Hsuan",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2017",
    pages = "191--198",
}
@inproceedings{zeman-etal-2016-planting,
    title = "Planting Trees in the Desert: Delexicalized Tagging and Parsing Combined",
    author = "Zeman, Daniel  and
      Mare{\v{c}}ek, David  and
      Yu, Zhiwei  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2018",
    pages = "199--207",
}
@inproceedings{mi-etal-2016-recurrent,
    title = "Recurrent Neural Network Based Loanwords Identification in {U}yghur",
    author = "Mi, Chenggang  and
      Yang, Yating  and
      Zhou, Xi  and
      Wang, Lei  and
      Li, Xiao  and
      Jiang, Tonghai",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2019",
    pages = "209--217",
}
@inproceedings{tomita-2016-solving,
    title = "Solving Event Quantification and Free Variable Problems in Semantics for {M}inimalist {G}rammars",
    author = "Tomita, Yu",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2020",
    pages = "219--227",
}
@inproceedings{santus-etal-2016-testing,
    title = "Testing {APS}yn against Vector Cosine on Similarity Estimation",
    author = "Santus, Enrico  and
      Chersoni, Emmanuele  and
      Lenci, Alessandro  and
      Huang, Chu-Ren  and
      Blache, Philippe",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2021",
    pages = "229--238",
}
@inproceedings{muraoka-etal-2016-recognizing,
    title = "Recognizing Open-Vocabulary Relations between Objects in Images",
    author = "Muraoka, Masayasu  and
      Maharjan, Sumit  and
      Saito, Masaki  and
      Yamaguchi, Kota  and
      Okazaki, Naoaki  and
      Okatani, Takayuki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2022",
    pages = "239--248",
}
@inproceedings{kwong-2016-strong,
    title = "Strong Associations Can Be Weak: Some Thoughts on Cross-lingual Word Webs for Translation",
    author = "Kwong, Oi Yee",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2023",
    pages = "249--257",
}
@inproceedings{trieu-etal-2016-dealing,
    title = "Dealing with Out-Of-Vocabulary Problem in Sentence Alignment Using Word Similarity",
    author = "Trieu, Hai-Long  and
      Nguyen, Le-Minh  and
      Nguyen, Phuong-Thai",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2024",
    pages = "259--266",
}
@inproceedings{zhou-2016-pipeline,
    title = "A Pipeline {J}apanese Entity Linking System with Embedding Features",
    author = "Zhou, Shuangshuang",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2025",
    pages = "267--276",
}
@inproceedings{uemura-etal-2016-toward,
    title = "Toward the automatic extraction of knowledge of usable goods",
    author = "Uemura, Mei  and
      Orita, Naho  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2026",
    pages = "277--285",
}
@inproceedings{hoe-park-2016-syntactic,
    title = "A Syntactic Approach to the 1st Person Restriction of Causal Clauses in {K}orean",
    author = "Hoe, Semoon  and
      Park, Yugyeong",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2027",
    pages = "287--295",
}
@inproceedings{park-2016-towards,
    title = "Towards a {QUD}-Based Analysis of Gapping Constructions",
    author = "Park, Sang-Hee",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-2028",
    pages = "297--305",
}
@proceedings{paclic-2016-pacific,
    title = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1000",
}
@inproceedings{muller-2016-coregram,
    title = "The {C}ore{G}ram Project: Theoretical Linguistics, Theory Development and Verification",
    author = {M{\"u}ller, Stefan},
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1001",
    pages = "3--3",
}
@inproceedings{nenadic-2016-inferring,
    title = "Inferring Methodological Meta-knowledge from Large Biomedical Corpora",
    author = "Nenadic, Goran",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1002",
    pages = "5--5",
}
@inproceedings{sarkar-2016-challenge,
    title = "The Challenge of Simultaneous Speech Translation",
    author = "Sarkar, Anoop",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1003",
    pages = "7--7",
}
@inproceedings{choe-2016-significance,
    title = "The Significance of Background Information in Acceptability Judgements of {K}orean Sentences",
    author = "Choe, Jae-Woong",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1004",
    pages = "11--11",
}
@inproceedings{harada-2016-measuring,
    title = "Measuring Diversified Proficiency of {J}apanese Learners of {E}nglish",
    author = "Harada, Yasunari",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1005",
    pages = "13--14",
}
@inproceedings{huang-2016-endurant,
    title = "Endurant vs Perdurant: Ontological Motivation for Language Variations",
    author = "Huang, Chu-Ren",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1006",
    pages = "15--25",
}
@inproceedings{liu-2016-syntax,
    title = "The Syntax of the {C}hinese Excessive Resultative Construction",
    author = "Liu, Hongyong",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1007",
    pages = "27--34",
}
@inproceedings{dita-2016-grammar,
    title = "The grammar and semantics of disjuncts in World Englishes",
    author = "Dita, Shirley",
    booktitle = "Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Keynote Speeches and Invited Talks",
    month = oct,
    year = "2016",
    address = "Seoul, South Korea",
    url = "https://www.aclweb.org/anthology/Y16-1008",
    pages = "35--35",
}
@proceedings{ws-2016-international-natural-language-generation,
    title = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6600",
    doi = "10.18653/v1/W16-66",
}
@inproceedings{macdonald-siddharthan-2016-summarising,
    title = "Summarising News Stories for Children",
    author = "Macdonald, Iain  and
      Siddharthan, Advaith",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6601",
    doi = "10.18653/v1/W16-6601",
    pages = "1--10",
}
@inproceedings{winer-young-2016-discourse,
    title = "Discourse-Driven Narrative Generation With Bipartite Planning",
    author = "Winer, David  and
      Young, R. Michael",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6602",
    doi = "10.18653/v1/W16-6602",
    pages = "11--20",
}
@inproceedings{pourdamghani-etal-2016-generating,
    title = "Generating {E}nglish from {A}bstract {M}eaning {R}epresentations",
    author = "Pourdamghani, Nima  and
      Knight, Kevin  and
      Hermjakob, Ulf",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6603",
    doi = "10.18653/v1/W16-6603",
    pages = "21--25",
}
@inproceedings{acharya-etal-2016-generating,
    title = "Generating summaries of hospitalizations: A new metric to assess the complexity of medical terms and their definitions",
    author = "Acharya, Sabita  and
      Di Eugenio, Barbara  and
      Boyd, Andrew D.  and
      Dunn Lopez, Karen  and
      Cameron, Richard  and
      Keenan, Gail M",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6604",
    doi = "10.18653/v1/W16-6604",
    pages = "26--30",
}
@inproceedings{van-deemter-2016-designing,
    title = "Designing Algorithms for Referring with Proper Names",
    author = "van Deemter, Kees",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6605",
    doi = "10.18653/v1/W16-6605",
    pages = "31--35",
}
@inproceedings{smiley-etal-2016-plummet,
    title = "When to Plummet and When to Soar: Corpus Based Verb Selection for Natural Language Generation",
    author = "Smiley, Charese  and
      Plachouras, Vassilis  and
      Schilder, Frank  and
      Bretz, Hiroko  and
      Leidner, Jochen  and
      Song, Dezhao",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6606",
    doi = "10.18653/v1/W16-6606",
    pages = "36--39",
}
@inproceedings{choi-2016-sketch,
    title = "Sketch-to-Text Generation: Toward Contextual, Creative, and Coherent Composition",
    author = "Choi, Yejin",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6607",
    doi = "10.18653/v1/W16-6607",
    pages = "40",
}
@inproceedings{wubben-etal-2016-abstractive,
    title = "Abstractive Compression of Captions with Attentive Recurrent Neural Networks",
    author = "Wubben, Sander  and
      Krahmer, Emiel  and
      van den Bosch, Antal  and
      Verberne, Suzan",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6608",
    doi = "10.18653/v1/W16-6608",
    pages = "41--50",
}
@inproceedings{mazidi-tarau-2016-infusing,
    title = "Infusing {NLU} into Automatic Question Generation",
    author = "Mazidi, Karen  and
      Tarau, Paul",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6609",
    doi = "10.18653/v1/W16-6609",
    pages = "51--60",
}
@inproceedings{aker-etal-2016-automatic,
    title = "Automatic label generation for news comment clusters",
    author = "Aker, Ahmet  and
      Paramita, Monica  and
      Kurtic, Emina  and
      Funk, Adam  and
      Barker, Emma  and
      Hepple, Mark  and
      Gaizauskas, Rob",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6610",
    doi = "10.18653/v1/W16-6610",
    pages = "61--69",
}
@inproceedings{ahn-etal-2016-improving,
    title = "Improving Fluency in Narrative Text Generation With Grammatical Transformations and Probabilistic Parsing",
    author = "Ahn, Emily  and
      Morbini, Fabrizio  and
      Gordon, Andrew",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6611",
    doi = "10.18653/v1/W16-6611",
    pages = "70--73",
}
@inproceedings{braun-etal-2016-multilingual,
    title = "The Multilingual Affective Soccer Corpus ({MASC}): Compiling a biased parallel corpus on soccer reportage in {E}nglish, {G}erman and {D}utch",
    author = "Braun, Nadine  and
      Goudbeek, Martijn  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6612",
    doi = "10.18653/v1/W16-6612",
    pages = "74--78",
}
@inproceedings{saint-dizier-2016-challenges,
    title = "Challenges of Argument Mining: Generating an Argument Synthesis based on the Qualia Structure",
    author = "Saint-Dizier, Patrick",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6613",
    doi = "10.18653/v1/W16-6613",
    pages = "79--83",
}
@inproceedings{byamugisha-etal-2016-tense,
    title = "Tense and Aspect in {R}unyankore Using a Context-Free Grammar",
    author = "Byamugisha, Joan  and
      Keet, C. Maria  and
      DeRenzi, Brian",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6614",
    doi = "10.18653/v1/W16-6614",
    pages = "84--88",
}
@inproceedings{baltaretu-castro-ferreira-2016-task,
    title = "Task demands and individual variation in referring expressions",
    author = "Baltaretu, Adriana  and
      Castro Ferreira, Thiago",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6615",
    doi = "10.18653/v1/W16-6615",
    pages = "89--93",
}
@inproceedings{mohammed-etal-2016-category,
    title = "Category-Driven Content Selection",
    author = "Mohammed, Rania  and
      Perez-Beltrachini, Laura  and
      Gardent, Claire",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6616",
    doi = "10.18653/v1/W16-6616",
    pages = "94--98",
}
@inproceedings{lee-hsieh-2016-evaluative,
    title = "Evaluative Pattern Extraction for Automated Text Generation",
    author = "Lee, Chia-Chen  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6617",
    doi = "10.18653/v1/W16-6617",
    pages = "99--103",
}
@inproceedings{li-etal-2016-statistics,
    title = "Statistics-Based Lexical Choice for {NLG} from Quantitative Information",
    author = "Li, Xiao  and
      van Deemter, Kees  and
      Lin, Chenghua",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6618",
    doi = "10.18653/v1/W16-6618",
    pages = "104--108",
}
@inproceedings{yu-etal-2016-incremental,
    title = "Incremental Generation of Visually Grounded Language in Situated Dialogue (demonstration system)",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6619",
    doi = "10.18653/v1/W16-6619",
    pages = "109--110",
}
@inproceedings{narayan-gardent-2016-unsupervised,
    title = "Unsupervised Sentence Simplification Using Deep Semantics",
    author = "Narayan, Shashi  and
      Gardent, Claire",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6620",
    doi = "10.18653/v1/W16-6620",
    pages = "111--120",
}
@inproceedings{moraes-etal-2016-enabling,
    title = "Enabling text readability awareness during the micro planning phase of {NLG} applications",
    author = "Moraes, Priscilla  and
      Mccoy, Kathleen  and
      Carberry, Sandra",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6621",
    doi = "10.18653/v1/W16-6621",
    pages = "121--131",
}
@inproceedings{demberg-2016-adapt,
    title = "How can we adapt generation to the user{'}s cognitive load?",
    author = "Demberg, Vera",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6622",
    doi = "10.18653/v1/W16-6622",
    pages = "132",
}
@inproceedings{jin-le-2016-selecting,
    title = "Selecting Domain-Specific Concepts for Question Generation With Lightly-Supervised Methods",
    author = "Jin, Yiping  and
      Le, Phu",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6623",
    doi = "10.18653/v1/W16-6623",
    pages = "133--142",
}
@inproceedings{mahapatra-etal-2016-statistical,
    title = "Statistical Natural Language Generation from Tabular Non-textual Data",
    author = "Mahapatra, Joy  and
      Naskar, Sudip Kumar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6624",
    doi = "10.18653/v1/W16-6624",
    pages = "143--152",
}
@inproceedings{narayan-etal-2016-paraphrase,
    title = "Paraphrase Generation from Latent-Variable {PCFG}s for Semantic Parsing",
    author = "Narayan, Shashi  and
      Reddy, Siva  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6625",
    doi = "10.18653/v1/W16-6625",
    pages = "153--162",
}
@inproceedings{colin-etal-2016-webnlg,
    title = "The {W}eb{NLG} Challenge: Generating Text from {DBP}edia Data",
    author = "Colin, Emilie  and
      Gardent, Claire  and
      M{'}rabet, Yassine  and
      Narayan, Shashi  and
      Perez-Beltrachini, Laura",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6626",
    doi = "10.18653/v1/W16-6626",
    pages = "163--167",
}
@inproceedings{novikova-rieser-2016-analogue,
    title = "The a{NAL}o{G}u{E} Challenge: Non Aligned Language {GE}neration",
    author = "Novikova, Jekaterina  and
      Rieser, Verena",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6627",
    doi = "10.18653/v1/W16-6627",
    pages = "168--170",
}
@inproceedings{concepcion-etal-2016-challenge,
    title = "A Challenge Proposal for Narrative Generation Using {CNL}s",
    author = "Concepci{\'o}n, Eugenio  and
      M{\'e}ndez, Gonzalo  and
      Gerv{\'a}s, Pablo  and
      Le{\'o}n, Carlos",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6628",
    doi = "10.18653/v1/W16-6628",
    pages = "171--173",
}
@inproceedings{keet-khumalo-2016-verbalization,
    title = "On the verbalization patterns of part-whole relations in isi{Z}ulu",
    author = "Keet, C. Maria  and
      Khumalo, Langa",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6629",
    doi = "10.18653/v1/W16-6629",
    pages = "174--183",
}
@inproceedings{mazzei-etal-2016-simplenlg,
    title = "{S}imple{NLG}-{IT}: adapting {S}imple{NLG} to {I}talian",
    author = "Mazzei, Alessandro  and
      Battaglino, Cristina  and
      Bosco, Cristina",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6630",
    doi = "10.18653/v1/W16-6630",
    pages = "184--192",
}
@inproceedings{wang-gaizauskas-2016-dont,
    title = "Don{'}t Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation",
    author = "Wang, Josiah  and
      Gaizauskas, Robert",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6631",
    doi = "10.18653/v1/W16-6631",
    pages = "193--202",
}
@inproceedings{woo-etal-2016-good,
    title = "Good Automatic Authentication Question Generation",
    author = "Woo, Simon  and
      Li, Zuyao  and
      Mirkovic, Jelena",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6632",
    doi = "10.18653/v1/W16-6632",
    pages = "203--206",
}
@inproceedings{isard-knox-2016-automatic,
    title = "Automatic Generation of Student Report Cards",
    author = "Isard, Amy  and
      Knox, Jeremy",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6633",
    doi = "10.18653/v1/W16-6633",
    pages = "207--211",
}
@inproceedings{godwin-piwek-2016-collecting,
    title = "Collecting Reliable Human Judgements on Machine-Generated Language: The Case of the {QG}-{STEC} Data",
    author = "Godwin, Keith  and
      Piwek, Paul",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6634",
    doi = "10.18653/v1/W16-6634",
    pages = "212--216",
}
@inproceedings{chali-golestanirad-2016-ranking,
    title = "Ranking Automatically Generated Questions Using Common Human Queries",
    author = "Chali, Yllias  and
      Golestanirad, Sina",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6635",
    doi = "10.18653/v1/W16-6635",
    pages = "217--221",
}
@inproceedings{castro-ferreira-etal-2016-towards,
    title = "Towards proper name generation: a corpus analysis",
    author = "Castro Ferreira, Thiago  and
      Wubben, Sander  and
      Krahmer, Emiel",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6636",
    doi = "10.18653/v1/W16-6636",
    pages = "222--226",
}
@inproceedings{ghodsi-denero-2016-analysis,
    title = "An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language",
    author = "Ghodsi, Aneiss  and
      DeNero, John",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6637",
    doi = "10.18653/v1/W16-6637",
    pages = "227--231",
}
@inproceedings{king-white-2016-enhancing,
    title = "Enhancing {PTB} {U}niversal {D}ependencies for Grammar-Based Surface Realization",
    author = "King, David L.  and
      White, Michael",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6638",
    doi = "10.18653/v1/W16-6638",
    pages = "232--236",
}
@inproceedings{belz-etal-2016-effect,
    title = "Effect of Data Annotation, Feature Selection and Model Choice on Spatial Description Generation in {F}rench",
    author = "Belz, Anja  and
      Muscat, Adrian  and
      Birmingham, Brandon  and
      Levacher, Jessie  and
      Pain, Julie  and
      Quinquenel, Adam",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6639",
    doi = "10.18653/v1/W16-6639",
    pages = "237--241",
}
@inproceedings{patinho-rodrigues-etal-2016-qgasp,
    title = "{QGASP}: a Framework for Question Generation Based on Different Levels of Linguistic Information",
    author = "Patinho Rodrigues, Hugo  and
      Coheur, Lu{\'\i}sa  and
      Nyberg, Eric",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6640",
    doi = "10.18653/v1/W16-6640",
    pages = "242--243",
}
@inproceedings{duboue-2016-automatic,
    title = "Automatic Reports from Spreadsheets: Data Analysis for the Rest of Us",
    author = "Duboue, Pablo",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6641",
    doi = "10.18653/v1/W16-6641",
    pages = "244--245",
}
@inproceedings{zarriess-schlangen-2016-towards,
    title = "Towards Generating Colour Terms for Referents in Photographs: Prefer the Expected or the Unexpected?",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6642",
    doi = "10.18653/v1/W16-6642",
    pages = "246--255",
}
@inproceedings{de-oliveira-etal-2016-absolute,
    title = "Absolute and Relative Properties in Geographic Referring Expressions",
    author = "de Oliveira, Rodrigo  and
      Sripada, Somayajulu  and
      Reiter, Ehud",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6643",
    doi = "10.18653/v1/W16-6643",
    pages = "256--264",
}
@inproceedings{novikova-etal-2016-crowd,
    title = "Crowd-sourcing {NLG} Data: Pictures Elicit Better Data.",
    author = "Novikova, Jekaterina  and
      Lemon, Oliver  and
      Rieser, Verena",
    booktitle = "Proceedings of the 9th International Natural Language Generation conference",
    month = sep # " 5-8",
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6644",
    doi = "10.18653/v1/W16-6644",
    pages = "265--273",
}
@proceedings{ws-2016-joint-nlp,
    title = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    editor = {Volodina, Elena  and
      Grigonyt{\.e}, Gintar{\.e}  and
      Pil{\'a}n, Ildik{\'o}  and
      Bj{\"o}rkenstam, Kristina Nilsson  and
      Borin, Lars},
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6500",
}
@inproceedings{alfter-etal-2016-distributions,
    title = "From distributions to labels: A lexical proficiency analysis using learner corpora",
    author = {Alfter, David  and
      Bizzoni, Yuri  and
      Agebj{\"o}rn, Anders  and
      Volodina, Elena  and
      Pil{\'a}n, Ildik{\'o}},
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6501",
    pages = "1--7",
}
@inproceedings{del-rio-etal-2016-towards,
    title = "Towards error annotation in a learner corpus of {P}ortuguese",
    author = "del R{\'\i}o, Iria  and
      Antunes, Sandra  and
      Mendes, Am{\'a}lia  and
      Janssen, Maarten",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6502",
    pages = "8--17",
}
@inproceedings{fibla-etal-2016-word,
    title = "Word comprehension and multilingualism among toddlers: A study using touch screens in daycares",
    author = "Fibla, Laia  and
      Maniel, Charlotte  and
      Cristia, Alejandrina",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6503",
    pages = "18--23",
}
@inproceedings{ganek-eriks-brophy-2016-language,
    title = "The Language {EN}vironment Analysis ({LENA}) system: A literature review",
    author = "Ganek, Hillary  and
      Eriks-Brophy, Alice",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6504",
    pages = "24--32",
}
@inproceedings{gao-2016-perception,
    title = "Perception of lexical tones by {S}wedish learners of {M}andarin",
    author = "Gao, Man",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6505",
    pages = "33--40",
}
@inproceedings{grigonyte-nilsson-bjorkenstam-2016-language,
    title = "Language-independent exploration of repetition and variation in longitudinal child-directed speech: A tool and resources",
    author = {Grigonyt{\.e}, Gintar{\.e}  and
      Nilsson Bj{\"o}rkenstam, Kristina},
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6506",
    pages = "41--50",
}
@inproceedings{meyer-etal-2016-validating,
    title = "Validating bundled gap filling {--} Empirical evidence for ambiguity reduction and language proficiency testing capabilities",
    author = "Meyer, Niklas  and
      Wojatzki, Michael  and
      Zesch, Torsten",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6507",
    pages = "51--59",
}
@inproceedings{ni-chiarain-ni-chasaide-2016-faking,
    title = "Faking Intelligent {CALL}: The {I}rish context and the road ahead",
    author = "N{\'\i} Chiar{\'a}in, Neasa  and
      N{\'\i} Chasaide, Ailbhe",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6508",
    pages = "60--65",
}
@inproceedings{rakhilina-etal-2016-building,
    title = "Building a learner corpus for {R}ussian",
    author = "Rakhilina, Ekaterina  and
      Vyrenkova, Anastasia  and
      Mustakimova, Elmira  and
      Ladygina, Alina  and
      Smirnov, Ivan",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6509",
    pages = "66--75",
}
@inproceedings{volodina-etal-2016-swellex,
    title = "{S}we{LL}ex: Second language learners{'} productive vocabulary",
    author = "Volodina, Elena  and
      Pil{\'a}n, Ildik{\'o}  and
      Llozhi, Lorena  and
      Degryse, Baptiste  and
      Fran{\c{c}}ois, Thomas",
    booktitle = "Proceedings of the joint workshop on {NLP} for Computer Assisted Language Learning and {NLP} for Language Acquisition",
    month = nov,
    year = "2016",
    address = "Ume{\aa}, Sweden",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W16-6510",
    pages = "76--84",
}
@proceedings{ws-2016-deep,
    title = "Proceedings of the 2nd Deep Machine Translation Workshop",
    editor = "Haji{\v{c}}, Jan  and
      van Noord, Gertjan  and
      Branco, Ant{\'o}nio",
    month = oct,
    year = "2016",
    address = "Lisbon, Portugal",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W16-6400",
}
@inproceedings{rosa-etal-2016-moses,
    title = "{M}oses {\&} Treex Hybrid {MT} Systems Bestiary",
    author = "Rosa, Rudolf  and
      Popel, Martin  and
      Bojar, Ond{\v{r}}ej  and
      Mare{\v{c}}ek, David  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 2nd Deep Machine Translation Workshop",
    month = oct,
    year = "2016",
    address = "Lisbon, Portugal",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W16-6401",
    pages = "1--10",
}
@inproceedings{arnoult-simaan-2016-factoring,
    title = "Factoring Adjunction in Hierarchical Phrase-Based {SMT}",
    author = "Arnoult, Sophie  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the 2nd Deep Machine Translation Workshop",
    month = oct,
    year = "2016",
    address = "Lisbon, Portugal",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W16-6402",
    pages = "11--20",
}
@inproceedings{simov-osenova-2016-hybrid,
    title = "A Hybrid Approach for Deep Machine Translation",
    author = "Simov, Kiril  and
      Osenova, Petya",
    booktitle = "Proceedings of the 2nd Deep Machine Translation Workshop",
    month = oct,
    year = "2016",
    address = "Lisbon, Portugal",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W16-6403",
    pages = "21--28",
}
@inproceedings{avramidis-etal-2016-deeper,
    title = "Deeper Machine Translation and Evaluation for {G}erman",
    author = "Avramidis, Eleftherios  and
      Macketanz, Vivien  and
      Burchardt, Aljoscha  and
      Helcl, Jindrich  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the 2nd Deep Machine Translation Workshop",
    month = oct,
    year = "2016",
    address = "Lisbon, Portugal",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W16-6404",
    pages = "29--38",
}
@inproceedings{artetxe-etal-2016-adding,
    title = "Adding syntactic structure to bilingual terminology for improved domain adaptation",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Saedi, Chakaveh  and
      Rodrigues, Jo{\~a}o  and
      Silva, Jo{\~a}o  and
      Branco, Ant{\'o}nio  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 2nd Deep Machine Translation Workshop",
    month = oct,
    year = "2016",
    address = "Lisbon, Portugal",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W16-6405",
    pages = "39--46",
}
@inproceedings{klyueva-kubon-2016-incorporation,
    title = "Incorporation of a valency lexicon into a {T}ecto{MT} pipeline",
    author = "Klyueva, Natalia  and
      Kubo{\v{n}}, Vladislav",
    booktitle = "Proceedings of the 2nd Deep Machine Translation Workshop",
    month = oct,
    year = "2016",
    address = "Lisbon, Portugal",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W16-6406",
    pages = "47--53",
}
@proceedings{ws-2016-international-natural-language-processing,
    title = "Proceedings of the 13th International Conference on Natural Language Processing",
    editor = "Sharma, Dipti Misra  and
      Sangal, Rajeev  and
      Singh, Anil Kumar",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6300",
}
@inproceedings{pouliquen-2016-keynote,
    title = "Keynote Lecture 1: Practical Use of Machine Translation in International Organizations",
    author = "Pouliquen, Bruno",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6301",
    pages = "1",
}
@inproceedings{foley-kalita-2016-integrating,
    title = "Integrating {W}ord{N}et for Multiple Sense Embeddings in Vector Semantics",
    author = "Foley, David  and
      Kalita, Jugal",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6302",
    pages = "2--9",
}
@inproceedings{banik-etal-2016-smt,
    title = "Can {SMT} and {RBMT} Improve each other{'}s Performance?- An Experiment with {E}nglish-{H}indi Translation",
    author = "Banik, Debajyoty  and
      Sen, Sukanta  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6303",
    pages = "10--19",
}
@inproceedings{yee-kalita-2016-composition,
    title = "Composition of Compound Nouns Using Distributional Semantics",
    author = "Yee, Kyra  and
      Kalita, Jugal",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6304",
    pages = "20--29",
}
@inproceedings{kannan-etal-2016-towards,
    title = "Towards Building a {S}enti{W}ord{N}et for {T}amil",
    author = "Kannan, Abishek  and
      Mohanty, Gaurav  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6305",
    pages = "30--35",
}
@inproceedings{akarapu-chowdary-2016-extending,
    title = "Extending {AIDA} framework by incorporating coreference resolution on detected mentions and pruning based on popularity of an entity",
    author = "Akarapu, Samaikya  and
      Chowdary, C Ravindranath",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6306",
    pages = "36--45",
}
@inproceedings{tripathi-etal-2016-sentence,
    title = "Sentence Based Discourse Classification for {H}indi Story Text-to-Speech ({TTS}) System",
    author = "Tripathi, Kumud  and
      Sarkar, Parakrant  and
      Rao, K. Sreenivasa",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6307",
    pages = "46--54",
}
@inproceedings{majumder-etal-2016-biomolecular,
    title = "Biomolecular Event Extraction using a Stacked Generalization based Classifier",
    author = "Majumder, Amit  and
      Ekbal, Asif  and
      Naskar, Sudip Kumar",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6308",
    pages = "55--64",
}
@inproceedings{karmakar-ghosh-2016-syntax,
    title = "Syntax and Pragmatics of Conversation: A Case of {B}angla",
    author = "Karmakar, Samir  and
      Ghosh, Soumya Sankar",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6309",
    pages = "65--70",
}
@inproceedings{obrebski-2016-dependency,
    title = "Dependency grammars as Haskell programs",
    author = "Obr{\k{e}}bski, Tomasz",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6310",
    pages = "71--80",
}
@inproceedings{kumar-etal-2016-improving,
    title = "Improving Document Ranking using Query Expansion and Classification Techniques for Mixed Script Information Retrieval",
    author = "Kumar, Subham  and
      Ray, Anwesh Sinha  and
      Kamila, Sabyasachi  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6311",
    pages = "81--89",
}
@inproceedings{yadav-chowdary-2016-feature,
    title = "Feature based Sentiment Analysis using a Domain Ontology",
    author = "Yadav, Neha  and
      Chowdary, C Ravindranath",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6312",
    pages = "90--98",
}
@inproceedings{das-etal-2016-cross,
    title = "Cross-lingual transfer parser from {H}indi to {B}engali using delexicalization and chunking",
    author = "Das, Ayan  and
      Saha, Agnivo  and
      Sarkar, Sudeshna",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6313",
    pages = "99--108",
}
@inproceedings{bick-2016-constraint,
    title = "Constraint Grammar-based conversion of Dependency Treebanks",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6314",
    pages = "109--114",
}
@inproceedings{sharma-etal-2016-meaning,
    title = "Meaning Matters: Senses of Words are More Informative than Words for Cross-domain Sentiment Analysis",
    author = "Sharma, Raksha  and
      Bhingardive, Sudha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6315",
    pages = "115--119",
}
@inproceedings{mukherjee-etal-2016-pos,
    title = "{POS} Tagging Experts via Topic Modeling",
    author = {Mukherjee, Atreyee  and
      K{\"u}bler, Sandra  and
      Scheutz, Matthias},
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6316",
    pages = "120--128",
}
@inproceedings{karmakar-etal-2016-graph,
    title = "Graph theoretic interpretation of {B}angla traditional grammar",
    author = "Karmakar, Samir  and
      Banerjee, Sayantani  and
      Ghosh, Soumya",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6317",
    pages = "129--136",
}
@inproceedings{jadon-pareek-2016-method,
    title = "A method for Automatic Text Summarization using Consensus of Multiple Similarity Measures and Ranking Techniques",
    author = "Jadon, Mukesh Kumar  and
      Pareek, Ayush",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6318",
    pages = "137--143",
}
@inproceedings{goyal-goyal-2016-automatic,
    title = "Automatic Translation of {E}nglish Text to {I}ndian {S}ign {L}anguage Synthetic Animations",
    author = "Goyal, Lalit  and
      Goyal, Vishal",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6319",
    pages = "144--153",
}
@inproceedings{athavale-etal-2016-towards,
    title = "Towards Deep Learning in {H}indi {NER}: An approach to tackle the Labelled Data Sparsity",
    author = "Athavale, Vinayak  and
      Bharadwaj, Shreenivas  and
      Pamecha, Monik  and
      Prabhu, Ameya  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6320",
    pages = "154--160",
}
@inproceedings{danda-etal-2016-vaidya,
    title = "{V}aidya: A Spoken Dialog System for Health Domain",
    author = "Danda, Prathyusha  and
      Srivastava, Brij Mohan Lal  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6321",
    pages = "161--166",
}
@inproceedings{reganti-etal-2016-cosmopolitan,
    title = "Cosmopolitan {M}umbai, Orthodox {D}elhi, Techcity {B}angalore:Understanding City Specific Societal Sentiment",
    author = "Reganti, Aishwarya N  and
      Maheshwari, Tushar  and
      Kumar, Upendra  and
      Das, Amitava",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6322",
    pages = "167--176",
}
@inproceedings{sproat-2016-keynote,
    title = "Keynote Lecture 2: Neural (and other Machine Learning) Approaches to Text Normalization",
    author = "Sproat, Richard",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6323",
    pages = "177",
}
@inproceedings{roy-etal-2016-wisdom,
    title = "Wisdom of Students: A Consistent Automatic Short Answer Grading Technique",
    author = "Roy, Shourya  and
      Dandapat, Sandipan  and
      Nagesh, Ajay  and
      Narahari, Y.",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6324",
    pages = "178--187",
}
@inproceedings{shweta-etal-2016-recurrent,
    title = "A Recurrent Neural Network Architecture for De-identifying Clinical Records",
    author = "{Shweta}  and
      Kumar, Ankit  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6325",
    pages = "188--197",
}
@inproceedings{sikdar-gamback-2016-twitter,
    title = "{T}witter Named Entity Extraction and Linking Using Differential Evolution",
    author = {Sikdar, Utpal Kumar  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6326",
    pages = "198--207",
}
@inproceedings{carlos-rakshit-2016-learning,
    title = "Learning Non-Linear Functions for Text Classification",
    author = "Carlos, Cohan Sujay  and
      Rakshit, Geetanjali",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6327",
    pages = "208--218",
}
@inproceedings{das-etal-2016-computational,
    title = "A Computational Analysis of {M}ahabharata",
    author = "Das, Debarati  and
      Das, Bhaskarjyoti  and
      Mahesh, Kavi",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6328",
    pages = "219--228",
}
@inproceedings{sohoni-kulkarni-2016-use,
    title = "Use of Features for Accentuation of gha{\~n}anta Words",
    author = "Sohoni, Samir Janardan  and
      Kulkarni, Malhar A.",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6329",
    pages = "229--238",
}
@inproceedings{palshikar-etal-2016-learning,
    title = "Learning to Identify Subjective Sentences",
    author = "Palshikar, Girish K.  and
      Apte, Manoj  and
      Pandita, Deepak  and
      Singh, Vikram",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6330",
    pages = "239--248",
}
@inproceedings{gupta-etal-2016-opinion,
    title = "Opinion Mining in a Code-Mixed Environment: A Case Study with Government Portals",
    author = "Gupta, Deepak  and
      Lamba, Ankit  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6331",
    pages = "249--258",
}
@inproceedings{asnani-pawar-2016-use,
    title = "Use of Semantic Knowledge Base for Enhancement of Coherence of Code-mixed Topic-Based Aspect Clusters",
    author = "Asnani, Kavita  and
      Pawar, Jyoti D",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6332",
    pages = "259--266",
}
@inproceedings{nongmeikapam-bandyopadhyay-2016-genetic,
    title = "Genetic Algorithm ({GA}) Implementation for Feature Selection in {M}anipuri {POS} Tagging",
    author = "Nongmeikapam, Kishorjit  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6333",
    pages = "267--274",
}
@inproceedings{sinha-etal-2016-effect,
    title = "Effect of Syntactic Features in {B}angla Sentence Comprehension",
    author = "Sinha, Manjira  and
      Dasgupta, Tirthankar  and
      Basu, Anupam",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6334",
    pages = "275--284",
}
@inproceedings{roul-rai-2016-new,
    title = "A New Feature Selection Technique Combined with {ELM} Feature Space for Text Classification",
    author = "Roul, Rajendra Kumar  and
      Rai, Pranav",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6335",
    pages = "285--292",
}
@inproceedings{ponkiya-etal-2016-coarse,
    title = "On Why Coarse Class Classification is Bottleneck in Noun Compound Interpretation",
    author = "Ponkiya, Girishkumar  and
      Bhattacharyya, Pushpak  and
      Palshikar, Girish K.",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6336",
    pages = "293--298",
}
@inproceedings{redkar-etal-2016-verbframator,
    title = "{V}erbframator:Semi-Automatic Verb Frame Annotator Tool with Special Reference to {M}arathi",
    author = "Redkar, Hanumant  and
      Singh, Sandhya  and
      Ghag, Nandini  and
      Paranjape, Jai  and
      Joshi, Nilesh  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6337",
    pages = "299--304",
}
@inproceedings{ramesh-etal-2016-towards,
    title = "Towards Building A Domain Agnostic Natural Language Interface to Real-World Relational Databases",
    author = "Ramesh, Sree Harsha  and
      Jain, Jayant  and
      S, Sarath K  and
      Sundaresan, Krishna R",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6338",
    pages = "305--314",
}
@inproceedings{basu-etal-2016-experimental,
    title = "Experimental Study of Vowels in {N}agamese, {A}o and {L}otha: Languages of {N}agaland",
    author = "Basu, Joyanta  and
      Basu, Tulika  and
      Khan, Soma  and
      Pal, Madhab  and
      Roy, Rajib  and
      Basu, Tapan Kumar",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6339",
    pages = "315--323",
}
@inproceedings{roy-2016-perception,
    title = "Perception of Phi-Phrase boundaries in {H}indi.",
    author = "Roy, Somnath",
    booktitle = "Proceedings of the 13th International Conference on Natural Language Processing",
    month = dec,
    year = "2016",
    address = "Varanasi, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W16-6340",
    pages = "324--330",
}
@proceedings{ws-2016-international-natural-language,
    title = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    editor = "Ku, Lun-Wei  and
      Hsu, Jane Yung-jen  and
      Li, Cheng-Te",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6200",
    doi = "10.18653/v1/W16-62",
}
@inproceedings{stowe-etal-2016-identifying,
    title = "Identifying and Categorizing Disaster-Related Tweets",
    author = "Stowe, Kevin  and
      Paul, Michael J.  and
      Palmer, Martha  and
      Palen, Leysia  and
      Anderson, Kenneth",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6201",
    doi = "10.18653/v1/W16-6201",
    pages = "1--6",
}
@inproceedings{doggett-cantarero-2016-identifying,
    title = "Identifying Eyewitness News-worthy Events on {T}witter",
    author = "Doggett, Erika  and
      Cantarero, Alejandro",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6202",
    doi = "10.18653/v1/W16-6202",
    pages = "7--13",
}
@inproceedings{sadeque-etal-2016-leave,
    title = "Why Do They Leave: Modeling Participation in Online Depression Forums",
    author = "Sadeque, Farig  and
      Pedersen, Ted  and
      Solorio, Thamar  and
      Shrestha, Prasha  and
      Rey-Villamizar, Nicolas  and
      Bethard, Steven",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6203",
    doi = "10.18653/v1/W16-6203",
    pages = "14--19",
}
@inproceedings{dredze-etal-2016-twitter,
    title = "{T}witter at the Grammys: A Social Media Corpus for Entity Linking and Disambiguation",
    author = "Dredze, Mark  and
      Andrews, Nicholas  and
      DeYoung, Jay",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6204",
    doi = "10.18653/v1/W16-6204",
    pages = "20--25",
}
@inproceedings{navindgi-etal-2016-steps,
    title = "Steps Toward Automatic Understanding of the Function of Affective Language in Support Groups",
    author = "Navindgi, Amit  and
      Brun, Caroline  and
      Boulard Masson, C{\'e}cile  and
      Nowson, Scott",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6205",
    doi = "10.18653/v1/W16-6205",
    pages = "26--33",
}
@inproceedings{kim-etal-2016-detecting,
    title = "Detecting Social Roles in {T}witter",
    author = "Kim, Sunghwan Mac  and
      Wan, Stephen  and
      Paris, C{\'e}cile",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6206",
    doi = "10.18653/v1/W16-6206",
    pages = "34--40",
}
@inproceedings{jain-2016-identifying,
    title = "Identifying Sensible Participants in Online Discussions",
    author = "Jain, Siddharth",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6207",
    doi = "10.18653/v1/W16-6207",
    pages = "41--47",
}
@inproceedings{eisner-etal-2016-emoji2vec,
    title = "emoji2vec: Learning Emoji Representations from their Description",
    author = {Eisner, Ben  and
      Rockt{\"a}schel, Tim  and
      Augenstein, Isabelle  and
      Bo{\v{s}}njak, Matko  and
      Riedel, Sebastian},
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6208",
    doi = "10.18653/v1/W16-6208",
    pages = "48--54",
}
@inproceedings{fang-etal-2016-learning,
    title = "Learning Latent Local Conversation Modes for Predicting Comment Endorsement in Online Discussions",
    author = "Fang, Hao  and
      Cheng, Hao  and
      Ostendorf, Mari",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6209",
    doi = "10.18653/v1/W16-6209",
    pages = "55--64",
}
@inproceedings{fang-etal-2016-witness,
    title = "Witness Identification in {T}witter",
    author = "Fang, Rui  and
      Nourbakhsh, Armineh  and
      Liu, Xiaomo  and
      Shah, Sameena  and
      Li, Quanzhi",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6210",
    doi = "10.18653/v1/W16-6210",
    pages = "65--73",
}
@inproceedings{hsieh-etal-2016-look,
    title = "How Do {I} Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles",
    author = "Hsieh, Yu-Lun  and
      Chang, Yung-Chun  and
      Chu, Chun-Han  and
      Hsu, Wen-Lian",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6211",
    doi = "10.18653/v1/W16-6211",
    pages = "74--83",
}
@inproceedings{jaech-etal-2016-hierarchical,
    title = "Hierarchical Character-Word Models for Language Identification",
    author = "Jaech, Aaron  and
      Mulcaire, George  and
      Hathi, Shobhit  and
      Ostendorf, Mari  and
      Smith, Noah A.",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6212",
    doi = "10.18653/v1/W16-6212",
    pages = "84--93",
}
@inproceedings{pappas-popescu-belis-2016-human,
    title = "Human versus Machine Attention in Document Classification: A Dataset with Crowdsourced Annotations",
    author = "Pappas, Nikolaos  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media",
    month = nov,
    year = "2016",
    address = "Austin, TX, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6213",
    doi = "10.18653/v1/W16-6213",
    pages = "94--100",
}
@proceedings{ws-2016-international-health,
    title = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    editor = "Grouin, Cyril  and
      Hamon, Thierry  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Zweigenbaum, Pierre",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6100",
    doi = "10.18653/v1/W16-61",
}
@inproceedings{chalapathy-etal-2016-investigation,
    title = "An Investigation of Recurrent Neural Architectures for Drug Name Recognition",
    author = "Chalapathy, Raghavendra  and
      Zare Borzeshi, Ehsan  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6101",
    doi = "10.18653/v1/W16-6101",
    pages = "1--5",
}
@inproceedings{spithourakis-etal-2016-clinical,
    title = "Clinical Text Prediction with Numerically Grounded Conditional Language Models",
    author = "Spithourakis, Georgios  and
      Petersen, Steffen  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6102",
    doi = "10.18653/v1/W16-6102",
    pages = "6--16",
}
@inproceedings{cornegruta-etal-2016-modelling,
    title = "Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks",
    author = "Cornegruta, Savelie  and
      Bakewell, Robert  and
      Withey, Samuel  and
      Montana, Giovanni",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6103",
    doi = "10.18653/v1/W16-6103",
    pages = "17--27",
}
@inproceedings{kokkinakis-etal-2016-data,
    title = "Data Resource Acquisition from People at Various Stages of Cognitive Decline {--} Design and Exploration Considerations",
    author = "Kokkinakis, Dimitrios  and
      Lundholm Fors, Kristina  and
      Nordlund, Arto",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6104",
    doi = "10.18653/v1/W16-6104",
    pages = "28--36",
}
@inproceedings{rey-villamizar-etal-2016-analysis,
    title = "Analysis of Anxious Word Usage on Online Health Forums",
    author = "Rey-Villamizar, Nicolas  and
      Shrestha, Prasha  and
      Sadeque, Farig  and
      Bethard, Steven  and
      Pedersen, Ted  and
      Mukherjee, Arjun  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6105",
    doi = "10.18653/v1/W16-6105",
    pages = "37--42",
}
@inproceedings{yu-etal-2016-retrofitting,
    title = "Retrofitting Word Vectors of {M}e{SH} Terms to Improve Semantic Similarity Measures",
    author = "Yu, Zhiguo  and
      Cohen, Trevor  and
      Wallace, Byron  and
      Bernstam, Elmer  and
      Johnson, Todd",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6106",
    doi = "10.18653/v1/W16-6106",
    pages = "43--51",
}
@inproceedings{kirchhoff-turner-2016-unsupervised,
    title = "Unsupervised Resolution of Acronyms and Abbreviations in Nursing Notes Using Document-Level Context Models",
    author = "Kirchhoff, Katrin  and
      Turner, Anne M.",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6107",
    doi = "10.18653/v1/W16-6107",
    pages = "52--60",
}
@inproceedings{dhondt-etal-2016-low,
    title = "Low-resource {OCR} error detection and correction in {F}rench Clinical Texts",
    author = "D{'}hondt, Eva  and
      Grouin, Cyril  and
      Grau, Brigitte",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6108",
    doi = "10.18653/v1/W16-6108",
    pages = "61--68",
}
@inproceedings{munkhdalai-etal-2016-citation,
    title = "Citation Analysis with Neural Attention Models",
    author = "Munkhdalai, Tsendsuren  and
      Lalor, John P.  and
      Yu, Hong",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6109",
    doi = "10.18653/v1/W16-6109",
    pages = "69--77",
}
@inproceedings{neveol-etal-2016-replicability,
    title = "Replicability of Research in Biomedical Natural Language Processing: a pilot evaluation for a coding task",
    author = "N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Cohen, Kevin  and
      Grouin, Cyril  and
      Robert, Aude",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6110",
    doi = "10.18653/v1/W16-6110",
    pages = "78--84",
}
@inproceedings{collier-2016-nlp,
    title = "{NLP} and Online Health Reports: What do we say and what do we mean?",
    author = "Collier, Nigel",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6111",
    doi = "10.18653/v1/W16-6111",
    pages = "85",
}
@inproceedings{ferracane-etal-2016-leveraging,
    title = "Leveraging coreference to identify arms in medical abstracts: An experimental study",
    author = "Ferracane, Elisa  and
      Marshall, Iain  and
      Wallace, Byron C.  and
      Erk, Katrin",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6112",
    doi = "10.18653/v1/W16-6112",
    pages = "86--95",
}
@inproceedings{zweigenbaum-lavergne-2016-hybrid,
    title = "Hybrid methods for {ICD}-10 coding of death certificates",
    author = "Zweigenbaum, Pierre  and
      Lavergne, Thomas",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6113",
    doi = "10.18653/v1/W16-6113",
    pages = "96--105",
}
@inproceedings{huang-lu-2016-exploring,
    title = "Exploring Query Expansion for Entity Searches in {P}ub{M}ed",
    author = "Huang, Chung-Chi  and
      Lu, Zhiyong",
    booktitle = "Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2016",
    address = "Auxtin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6114",
    doi = "10.18653/v1/W16-6114",
    pages = "106--112",
}
@proceedings{ws-2016-uphill,
    title = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    editor = "Louis, Annie  and
      Roth, Michael  and
      Webber, Bonnie  and
      White, Michael  and
      Zettlemoyer, Luke",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6000",
    doi = "10.18653/v1/W16-60",
}
@inproceedings{sugawara-aizawa-2016-analysis,
    title = "An Analysis of Prerequisite Skills for Reading Comprehension",
    author = "Sugawara, Saku  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6001",
    doi = "10.18653/v1/W16-6001",
    pages = "1--5",
}
@inproceedings{benikova-zesch-2016-bridging,
    title = "Bridging the gap between computable and expressive event representations in Social Media",
    author = "Benikova, Darina  and
      Zesch, Torsten",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6002",
    doi = "10.18653/v1/W16-6002",
    pages = "6--10",
}
@inproceedings{pichotta-mooney-2016-statistical,
    title = "Statistical Script Learning with Recurrent Neural Networks",
    author = "Pichotta, Karl  and
      Mooney, Raymond",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6003",
    doi = "10.18653/v1/W16-6003",
    pages = "11--16",
}
@inproceedings{postma-etal-2016-moving,
    title = "Moving away from semantic overfitting in disambiguation datasets",
    author = "Postma, Marten  and
      Ilievski, Filip  and
      Vossen, Piek  and
      van Erp, Marieke",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6004",
    doi = "10.18653/v1/W16-6004",
    pages = "17--21",
}
@inproceedings{rajagopal-etal-2016-unsupervised,
    title = "Unsupervised Event Coreference for Abstract Words",
    author = "Rajagopal, Dheeraj  and
      Hovy, Eduard  and
      Mitamura, Teruko",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6005",
    doi = "10.18653/v1/W16-6005",
    pages = "22--26",
}
@inproceedings{bakhshandeh-allen-2016-towards,
    title = "Towards Broad-coverage Meaning Representation: The Case of Comparison Structures",
    author = "Bakhshandeh, Omid  and
      Allen, James",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6006",
    doi = "10.18653/v1/W16-6006",
    pages = "27--31",
}
@inproceedings{zhao-etal-2016-dialport,
    title = "{D}ial{P}ort: A General Framework for Aggregating Dialog Systems",
    author = "Zhao, Tiancheng  and
      Lee, Kyusong  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6007",
    doi = "10.18653/v1/W16-6007",
    pages = "32--34",
}
@inproceedings{church-etal-2016-c2d2e2,
    title = "{C}2{D}2{E}2: Using Call Centers to Motivate the Use of Dialog and Diarization in Entity Extraction",
    author = "Church, Ken  and
      Zhu, Weizhong  and
      Pelecanos, Jason",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6008",
    doi = "10.18653/v1/W16-6008",
    pages = "35--38",
}
@inproceedings{do-etal-2016-visualizing,
    title = "Visualizing the Content of a Children{'}s Story in a Virtual World: Lessons Learned",
    author = "Do, Quynh Ngoc Thi  and
      Bethard, Steven  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6009",
    doi = "10.18653/v1/W16-6009",
    pages = "39--42",
}
@inproceedings{kabbara-cheung-2016-stylistic,
    title = "Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks",
    author = "Kabbara, Jad  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6010",
    doi = "10.18653/v1/W16-6010",
    pages = "43--47",
}
@inproceedings{lewis-etal-2016-using,
    title = "Using Language Groundings for Context-Sensitive Text Prediction",
    author = "Lewis, Timothy  and
      Matuszek, Cynthia  and
      Hurst, Amy  and
      Taylor, Matthew",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6011",
    doi = "10.18653/v1/W16-6011",
    pages = "48--52",
}
@inproceedings{ruder-etal-2016-towards,
    title = "Towards a continuous modeling of natural language domains",
    author = "Ruder, Sebastian  and
      Ghaffari, Parsa  and
      Breslin, John G.",
    booktitle = "Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-6012",
    doi = "10.18653/v1/W16-6012",
    pages = "53--57",
}
@proceedings{ws-2016-structured,
    title = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    editor = "Chang, Kai-Wei  and
      Chang, Ming-Wei  and
      Rush, Alexander  and
      Srikumar, Vivek",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5900",
    doi = "10.18653/v1/W16-59",
}
@inproceedings{eisner-2016-inside,
    title = "Inside-Outside and Forward-Backward Algorithms Are Just Backprop (tutorial paper)",
    author = "Eisner, Jason",
    booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5901",
    doi = "10.18653/v1/W16-5901",
    pages = "1--17",
}
@inproceedings{liu-etal-2016-research,
    title = "Research on attention memory networks as a model for learning natural language inference",
    author = "Liu, Zhuang  and
      Huang, Degen  and
      Zhang, Jing  and
      Huang, Kaiyu",
    booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5902",
    doi = "10.18653/v1/W16-5902",
    pages = "18--24",
}
@inproceedings{goyal-eisenstein-2016-joint,
    title = "A Joint Model of Rhetorical Discourse Structure and Summarization",
    author = "Goyal, Naman  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5903",
    doi = "10.18653/v1/W16-5903",
    pages = "25--34",
}
@inproceedings{goyal-dyer-2016-posterior,
    title = "Posterior regularization for Joint Modeling of Multiple Structured Prediction Tasks with Soft Constraints",
    author = "Goyal, Kartik  and
      Dyer, Chris",
    booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5904",
    doi = "10.18653/v1/W16-5904",
    pages = "35--43",
}
@inproceedings{wolfe-etal-2016-study,
    title = "A Study of Imitation Learning Methods for Semantic Role Labeling",
    author = "Wolfe, Travis  and
      Dredze, Mark  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5905",
    doi = "10.18653/v1/W16-5905",
    pages = "44--53",
}
@inproceedings{zhang-etal-2016-introducing,
    title = "Introducing {DRAIL} {--} a Step Towards Declarative Deep Relational Learning",
    author = "Zhang, Xiao  and
      Pacheco, Maria Leonor  and
      Li, Chang  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5906",
    doi = "10.18653/v1/W16-5906",
    pages = "54--62",
}
@inproceedings{tran-etal-2016-unsupervised,
    title = "Unsupervised Neural Hidden {M}arkov Models",
    author = "Tran, Ke M.  and
      Bisk, Yonatan  and
      Vaswani, Ashish  and
      Marcu, Daniel  and
      Knight, Kevin",
    booktitle = "Proceedings of the Workshop on Structured Prediction for {NLP}",
    month = nov,
    year = "2016",
    address = "Austin, TX",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5907",
    doi = "10.18653/v1/W16-5907",
    pages = "63--71",
}
@proceedings{ws-2016-approaches-code,
    title = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    editor = "Diab, Mona  and
      Fung, Pascale  and
      Ghoneim, Mahmoud  and
      Hirschberg, Julia  and
      Solorio, Thamar",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5800",
    doi = "10.18653/v1/W16-58",
}
@inproceedings{cetinoglu-etal-2016-challenges,
    title = "Challenges of Computational Processing of Code-Switching",
    author = {{\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      Schulz, Sarah  and
      Vu, Ngoc Thang},
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5801",
    doi = "10.18653/v1/W16-5801",
    pages = "1--11",
}
@inproceedings{guzman-etal-2016-simple,
    title = "Simple Tools for Exploring Variation in Code-switching for Linguists",
    author = "Guzman, Gualberto A.  and
      Serigos, Jacqueline  and
      Bullock, Barbara E.  and
      Toribio, Almeida Jacqueline",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5802",
    doi = "10.18653/v1/W16-5802",
    pages = "12--20",
}
@inproceedings{piergallini-etal-2016-word,
    title = "Word-Level Language Identification and Predicting Codeswitching Points in {S}wahili-{E}nglish Language Data",
    author = "Piergallini, Mario  and
      Shirvani, Rouzbeh  and
      S. Gautam, Gauri  and
      Chouikha, Mohamed",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5803",
    doi = "10.18653/v1/W16-5803",
    pages = "21--29",
}
@inproceedings{barman-etal-2016-part,
    title = "Part-of-speech Tagging of Code-mixed Social Media Content: Pipeline, Stacking and Joint Modelling",
    author = "Barman, Utsab  and
      Wagner, Joachim  and
      Foster, Jennifer",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5804",
    doi = "10.18653/v1/W16-5804",
    pages = "30--39",
}
@inproceedings{molina-etal-2016-overview,
    title = "Overview for the Second Shared Task on Language Identification in Code-Switched Data",
    author = "Molina, Giovanni  and
      AlGhamdi, Fahad  and
      Ghoneim, Mahmoud  and
      Hawwari, Abdelati  and
      Rey-Villamizar, Nicolas  and
      Diab, Mona  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5805",
    doi = "10.18653/v1/W16-5805",
    pages = "40--49",
}
@inproceedings{samih-etal-2016-multilingual,
    title = "Multilingual Code-switching Identification via {LSTM} Recurrent Neural Networks",
    author = "Samih, Younes  and
      Maharjan, Suraj  and
      Attia, Mohammed  and
      Kallmeyer, Laura  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5806",
    doi = "10.18653/v1/W16-5806",
    pages = "50--59",
}
@inproceedings{jaech-etal-2016-neural,
    title = "A Neural Model for Language Identification in Code-Switched Tweets",
    author = "Jaech, Aaron  and
      Mulcaire, George  and
      Ostendorf, Mari  and
      Smith, Noah A.",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5807",
    doi = "10.18653/v1/W16-5807",
    pages = "60--64",
}
@inproceedings{samih-etal-2016-sawt,
    title = "{SAWT}: Sequence Annotation Web Tool",
    author = "Samih, Younes  and
      Maier, Wolfgang  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5808",
    doi = "10.18653/v1/W16-5808",
    pages = "65--70",
}
@inproceedings{xia-cheung-2016-accurate,
    title = "Accurate {P}inyin-{E}nglish Codeswitched Language Identification",
    author = "Xia, Meng Xuan  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5809",
    doi = "10.18653/v1/W16-5809",
    pages = "71--79",
}
@inproceedings{chanda-etal-2016-unraveling,
    title = "Unraveling the {E}nglish-{B}engali Code-Mixing Phenomenon",
    author = "Chanda, Arunavha  and
      Das, Dipankar  and
      Mazumdar, Chandan",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5810",
    doi = "10.18653/v1/W16-5810",
    pages = "80--89",
}
@inproceedings{ghosh-etal-2016-part,
    title = "Part-of-speech Tagging of Code-Mixed Social Media Text",
    author = "Ghosh, Souvick  and
      Ghosh, Satanu  and
      Das, Dipankar",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5811",
    doi = "10.18653/v1/W16-5811",
    pages = "90--97",
}
@inproceedings{alghamdi-etal-2016-part,
    title = "Part of Speech Tagging for Code Switched Data",
    author = "AlGhamdi, Fahad  and
      Molina, Giovanni  and
      Diab, Mona  and
      Solorio, Thamar  and
      Hawwari, Abdelati  and
      Soto, Victor  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5812",
    doi = "10.18653/v1/W16-5812",
    pages = "98--107",
}
@inproceedings{al-badrashiny-diab-2016-george,
    title = "The {G}eorge {W}ashington {U}niversity System for the Code-Switching Workshop Shared Task 2016",
    author = "Al-Badrashiny, Mohamed  and
      Diab, Mona",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5813",
    doi = "10.18653/v1/W16-5813",
    pages = "108--111",
}
@inproceedings{chanda-etal-2016-columbia,
    title = "{C}olumbia-Jadavpur submission for {EMNLP} 2016 Code-Switching Workshop Shared Task: System description",
    author = "Chanda, Arunavha  and
      Das, Dipankar  and
      Mazumdar, Chandan",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5814",
    doi = "10.18653/v1/W16-5814",
    pages = "112--115",
}
@inproceedings{shirvani-etal-2016-howard,
    title = "The {H}oward {U}niversity System Submission for the Shared Task in Language Identification in {S}panish-{E}nglish Codeswitching",
    author = "Shirvani, Rouzbeh  and
      Piergallini, Mario  and
      Gautam, Gauri Shankar  and
      Chouikha, Mohamed",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5815",
    doi = "10.18653/v1/W16-5815",
    pages = "116--120",
}
@inproceedings{shrestha-2016-codeswitching,
    title = "Codeswitching Detection via Lexical Features in Conditional Random Fields",
    author = "Shrestha, Prajwol",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5816",
    doi = "10.18653/v1/W16-5816",
    pages = "121--126",
}
@inproceedings{sikdar-gamback-2016-language,
    title = "Language Identification in Code-Switched Text Using Conditional Random Fields and Babelnet",
    author = {Sikdar, Utpal Kumar  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5817",
    doi = "10.18653/v1/W16-5817",
    pages = "127--131",
}
@inproceedings{xia-2016-codeswitching,
    title = "Codeswitching language identification using Subword Information Enriched Word Vectors",
    author = "Xia, Meng Xuan",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Code Switching",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5818",
    doi = "10.18653/v1/W16-5818",
    pages = "132--136",
}
@proceedings{ws-2016-computing,
    title = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    editor = "Caselli, Tommaso  and
      Miller, Ben  and
      van Erp, Marieke  and
      Vossen, Piek  and
      Caswell, David",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5700",
    doi = "10.18653/v1/W16-57",
}
@inproceedings{caswell-2016-computable,
    title = "Computable News Ecosystems: Roles for Humans and Machines",
    author = "Caswell, David",
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5701",
    doi = "10.18653/v1/W16-5701",
    pages = "1--8",
}
@inproceedings{bruggermann-etal-2016-storyline,
    title = "Storyline detection and tracking using Dynamic {L}atent {D}irichlet {A}llocation",
    author = {Br{\"u}ggermann, Daniel  and
      Hermey, Yannik  and
      Orth, Carsten  and
      Schneider, Darius  and
      Selzer, Stefan  and
      Spanakis, Gerasimos},
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5702",
    doi = "10.18653/v1/W16-5702",
    pages = "9--19",
}
@inproceedings{poghosyan-ifrim-2016-real,
    title = "Real-time News Story Detection and Tracking with Hashtags",
    author = "Poghosyan, Gevorg  and
      Ifrim, Georgiana",
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5703",
    doi = "10.18653/v1/W16-5703",
    pages = "20--29",
}
@inproceedings{krishnan-eisenstein-2016-nonparametric,
    title = "Nonparametric {B}ayesian Storyline Detection from Microtexts",
    author = "Krishnan, Vinodh  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5704",
    doi = "10.18653/v1/W16-5704",
    pages = "30--35",
}
@inproceedings{eisenberg-finlayson-2016-automatic,
    title = "Automatic Identification of Narrative Diegesis and Point of View",
    author = "Eisenberg, Joshua  and
      Finlayson, Mark",
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5705",
    doi = "10.18653/v1/W16-5705",
    pages = "36--46",
}
@inproceedings{ogorman-etal-2016-richer,
    title = "Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation",
    author = "O{'}Gorman, Tim  and
      Wright-Bettner, Kristin  and
      Palmer, Martha",
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5706",
    doi = "10.18653/v1/W16-5706",
    pages = "47--56",
}
@inproceedings{simonson-davis-2016-nastea,
    title = "{NASTEA}: Investigating Narrative Schemas through Annotated Entities",
    author = "Simonson, Dan  and
      Davis, Anthony",
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5707",
    doi = "10.18653/v1/W16-5707",
    pages = "57--66",
}
@inproceedings{caselli-vossen-2016-storyline,
    title = "The Storyline Annotation and Representation Scheme ({S}ta{R}): A Proposal",
    author = "Caselli, Tommaso  and
      Vossen, Piek",
    booktitle = "Proceedings of the 2nd Workshop on Computing News Storylines ({CNS} 2016)",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5708",
    doi = "10.18653/v1/W16-5708",
    pages = "67--72",
}
@proceedings{ws-2016-nlp-social,
    title = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    editor = {Bamman, David  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Eisenstein, Jacob  and
      Hovy, Dirk  and
      Jurgens, David  and
      O{'}Connor, Brendan  and
      Oh, Alice  and
      Tsur, Oren  and
      Volkova, Svitlana},
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5600",
    doi = "10.18653/v1/W16-56",
}
@inproceedings{joseph-carley-2016-relating,
    title = "Relating semantic similarity and semantic association to how humans label other people",
    author = "Joseph, Kenneth  and
      Carley, Kathleen M.",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5601",
    doi = "10.18653/v1/W16-5601",
    pages = "1--10",
}
@inproceedings{freitas-ji-2016-identifying,
    title = "Identifying News from Tweets",
    author = "Freitas, Jesse  and
      Ji, Heng",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5602",
    doi = "10.18653/v1/W16-5602",
    pages = "11--16",
}
@inproceedings{reddy-knight-2016-obfuscating,
    title = "Obfuscating Gender in Social Media Writing",
    author = "Reddy, Sravana  and
      Knight, Kevin",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5603",
    doi = "10.18653/v1/W16-5603",
    pages = "17--26",
}
@inproceedings{rosenthal-mckeown-2016-social,
    title = "Social Proof: The Impact of Author Traits on Influence Detection",
    author = "Rosenthal, Sara  and
      McKeown, Kathy",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5604",
    doi = "10.18653/v1/W16-5604",
    pages = "27--36",
}
@inproceedings{beieler-2016-generating,
    title = "Generating Politically-Relevant Event Data",
    author = "Beieler, John",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5605",
    doi = "10.18653/v1/W16-5605",
    pages = "37--42",
}
@inproceedings{poulston-etal-2016-user,
    title = "User profiling with geo-located posts and demographic data",
    author = "Poulston, Adam  and
      Stevenson, Mark  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5606",
    doi = "10.18653/v1/W16-5606",
    pages = "43--48",
}
@inproceedings{nay-2016-gov2vec,
    title = "{G}ov2{V}ec: Learning Distributed Representations of Institutions and Their Legal Text",
    author = "Nay, John J.",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5607",
    doi = "10.18653/v1/W16-5607",
    pages = "49--54",
}
@inproceedings{priante-etal-2016-whoami,
    title = "{\#}{W}ho{A}m{I} in 160 Characters? Classifying Social Identities Based on {T}witter Profile Descriptions",
    author = "Priante, Anna  and
      Hiemstra, Djoerd  and
      van den Broek, Tijs  and
      Saeed, Aaqib  and
      Ehrenhard, Michel  and
      Need, Ariana",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5608",
    doi = "10.18653/v1/W16-5608",
    pages = "55--65",
}
@inproceedings{johnson-goldwasser-2016-identifying,
    title = "Identifying Stance by Analyzing Political Discourse on {T}witter",
    author = "Johnson, Kristen  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5609",
    doi = "10.18653/v1/W16-5609",
    pages = "66--75",
}
@inproceedings{wang-etal-2016-learning,
    title = "Learning Linguistic Descriptors of User Roles in Online Communities",
    author = "Wang, Alex  and
      Hamilton, William L.  and
      Leskovec, Jure",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5610",
    doi = "10.18653/v1/W16-5610",
    pages = "76--85",
}
@inproceedings{kim-etal-2016-effects,
    title = "The Effects of Data Collection Methods in {T}witter",
    author = "Kim, Sunghwan Mac  and
      Wan, Stephen  and
      Paris, C{\'e}cile  and
      Jin, Brian  and
      Robinson, Bella",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5611",
    doi = "10.18653/v1/W16-5611",
    pages = "86--91",
}
@inproceedings{rheault-2016-expressions,
    title = "Expressions of Anxiety in Political Texts",
    author = "Rheault, Ludovic",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5612",
    doi = "10.18653/v1/W16-5612",
    pages = "92--101",
}
@inproceedings{makarov-etal-2016-constructing,
    title = "Constructing an Annotated Corpus for Protest Event Mining",
    author = "Makarov, Peter  and
      Lorenzini, Jasmine  and
      Kriesi, Hanspeter",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5613",
    doi = "10.18653/v1/W16-5613",
    pages = "102--107",
}
@inproceedings{knowles-etal-2016-demographer,
    title = "{D}emographer: Extremely Simple Name Demographics",
    author = "Knowles, Rebecca  and
      Carroll, Josh  and
      Dredze, Mark",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5614",
    doi = "10.18653/v1/W16-5614",
    pages = "108--113",
}
@inproceedings{handler-etal-2016-bag,
    title = "Bag of What? Simple Noun Phrase Extraction for Text Analysis",
    author = "Handler, Abram  and
      Denny, Matthew  and
      Wallach, Hanna  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5615",
    doi = "10.18653/v1/W16-5615",
    pages = "114--124",
}
@inproceedings{fraiberger-2016-news,
    title = "News Sentiment and Cross-Country Fluctuations",
    author = "Fraiberger, Samuel",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5616",
    doi = "10.18653/v1/W16-5616",
    pages = "125--131",
}
@inproceedings{coppersmith-etal-2016-clinical,
    title = "The Clinical Panel: Leveraging Psychological Expertise During {NLP} Research",
    author = "Coppersmith, Glen  and
      Hollingshead, Kristy  and
      Schwartz, H. Andrew  and
      Ireland, Molly  and
      Resnik, Rebecca  and
      Loveys, Kate  and
      Foreman, April  and
      Ingraham, Loring",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5617",
    doi = "10.18653/v1/W16-5617",
    pages = "132--137",
}
@inproceedings{waseem-2016-racist,
    title = "Are You a Racist or Am {I} Seeing Things? Annotator Influence on Hate Speech Detection on {T}witter",
    author = "Waseem, Zeerak",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5618",
    doi = "10.18653/v1/W16-5618",
    pages = "138--142",
}
@inproceedings{wilson-etal-2016-disentangling,
    title = "Disentangling Topic Models: A Cross-cultural Analysis of Personal Values through Words",
    author = "Wilson, Steven  and
      Mihalcea, Rada  and
      Boyd, Ryan  and
      Pennebaker, James",
    booktitle = "Proceedings of the First Workshop on {NLP} and Computational Social Science",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5619",
    doi = "10.18653/v1/W16-5619",
    pages = "143--152",
}
@proceedings{ws-2016-inlg,
    title = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    editor = "Purver, Matthew  and
      Gerv{\'a}s, Pablo  and
      Griffiths, Sascha",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5500",
    doi = "10.18653/v1/W16-55",
}
@inproceedings{vaudry-lapalme-2016-assembling,
    title = "Assembling Narratives with Associative Threads",
    author = "Vaudry, Pierre-Luc  and
      Lapalme, Guy",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5501",
    doi = "10.18653/v1/W16-5501",
    pages = "1--10",
}
@inproceedings{kumagai-etal-2016-human,
    title = "Human-like Natural Language Generation Using {M}onte {C}arlo Tree Search",
    author = "Kumagai, Kaori  and
      Kobayashi, Ichiro  and
      Mochihashi, Daichi  and
      Asoh, Hideki  and
      Nakamura, Tomoaki  and
      Nagai, Takayuki",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5502",
    doi = "10.18653/v1/W16-5502",
    pages = "11--18",
}
@inproceedings{gervas-2016-empirical,
    title = "Empirical Determination of Basic Heuristics for Narrative Content Planning",
    author = "Gerv{\'a}s, Pablo",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5503",
    doi = "10.18653/v1/W16-5503",
    pages = "19--26",
}
@inproceedings{winterstein-corneli-2016-x575,
    title = "{X}575: Writing rengas with web services",
    author = "Winterstein, Daniel  and
      Corneli, Joseph",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5504",
    doi = "10.18653/v1/W16-5504",
    pages = "27--30",
}
@inproceedings{sato-2016-challenge,
    title = "A Challenge to the Third Hoshi Shinichi Award",
    author = "Sato, Satoshi",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5505",
    doi = "10.18653/v1/W16-5505",
    pages = "31--35",
}
@inproceedings{pragst-etal-2016-automatic,
    title = "Automatic Modification of Communication Style in Dialogue Management",
    author = "Pragst, Louisa  and
      Miehle, Juliana  and
      Ultes, Stefan  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5506",
    doi = "10.18653/v1/W16-5506",
    pages = "36--40",
}
@inproceedings{concepcion-etal-2016-mining,
    title = "Mining Knowledge in Storytelling Systems for Narrative Generation",
    author = "Concepci{\'o}n, Eugenio  and
      Gerv{\'a}s, Pablo  and
      M{\'e}ndez, Gonzalo",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5507",
    doi = "10.18653/v1/W16-5507",
    pages = "41--50",
}
@inproceedings{mcgregor-etal-2016-process,
    title = "Process Based Evaluation of Computer Generated Poetry",
    author = "McGregor, Stephen  and
      Purver, Matthew  and
      Wiggins, Geraint",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5508",
    doi = "10.18653/v1/W16-5508",
    pages = "51--60",
}
@inproceedings{mazzei-valle-2016-combinatorics,
    title = "Combinatorics vs Grammar: Archeology of Computational Poetry in Tape Mark {I}",
    author = "Mazzei, Alessandro  and
      Valle, Andrea",
    booktitle = "Proceedings of the {INLG} 2016 Workshop on Computational Creativity in Natural Language Generation",
    month = sep,
    year = "2016",
    address = "Edinburgh, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-5509",
    doi = "10.18653/v1/W16-5509",
    pages = "61--70",
}
@proceedings{ws-2016-asian-language,
    title = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    editor = "Hasida, Koiti  and
      Wong, Kam-Fai  and
      Calzorari, Nicoletta  and
      Choi, Key-Sun",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5400",
}
@inproceedings{gotou-etal-2016-extension,
    title = "An extension of {ISO}-Space for annotating object direction",
    author = "Gotou, Daiki  and
      Nishikawa, Hitoshi  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5401",
    pages = "1--9",
    abstract = "In this paper, we extend an existing annotation scheme ISO-Space for annotating necessary spatial information for the task placing an specified object at a specified location with a specified direction according to a natural language instruction. We call such task the spatial placement problem. Our extension particularly focuses on describing the object direction, when the object is placed on the 2D plane. We conducted an annotation experiment in which a corpus of 20 situated dialogues were annotated. The annotation result showed the number of newly introduced tags by our proposal is not negligible. We also implemented an analyser that automatically assigns the proposed tags to the corpus and evaluated its performance. The result showed that the performance for entity tag was quite high ranging from 0.68 to 0.99 in F-measure, but not the case for relation tags, i.e. less than 0.4 in F-measure.",
}
@inproceedings{kaneko-etal-2016-annotation,
    title = "Annotation and Analysis of Discourse Relations, Temporal Relations and Multi-Layered Situational Relations in {J}apanese Texts",
    author = "Kaneko, Kimi  and
      Sugawara, Saku  and
      Mineshima, Koji  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5402",
    pages = "10--19",
    abstract = "This paper proposes a methodology for building a specialized Japanese data set for recognizing temporal relations and discourse relations. In addition to temporal and discourse relations, multi-layered situational relations that distinguish generic and specific states belonging to different layers in a discourse are annotated. Our methodology has been applied to 170 text fragments taken from Wikinews articles in Japanese. The validity of our methodology is evaluated and analyzed in terms of degree of annotator agreement and frequency of errors.",
}
@inproceedings{leung-etal-2016-developing,
    title = "Developing {U}niversal {D}ependencies for {M}andarin {C}hinese",
    author = {Leung, Herman  and
      Poiret, Rafa{\"e}l  and
      Wong, Tak-sum  and
      Chen, Xinying  and
      Gerdes, Kim  and
      Lee, John},
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5403",
    pages = "20--29",
    abstract = "This article proposes a Universal Dependency Annotation Scheme for Mandarin Chinese, including POS tags and dependency analysis. We identify cases of idiosyncrasy of Mandarin Chinese that are difficult to fit into the current schema which has mainly been based on the descriptions of various Indo-European languages. We discuss differences between our scheme and those of the Stanford Chinese Dependencies and the Chinese Dependency Treebank.",
}
@inproceedings{minamiguchi-tsuchiya-2016-developing,
    title = "Developing Corpus of Lecture Utterances Aligned to Slide Components",
    author = "Minamiguchi, Ryo  and
      Tsuchiya, Masatoshi",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5404",
    pages = "30--37",
    abstract = "The approach which formulates the automatic text summarization as a maximum coverage problem with knapsack constraint over a set of textual units and a set of weighted conceptual units is promising. However, it is quite important and difficult to determine the appropriate granularity of conceptual units for this formulation. In order to resolve this problem, we are examining to use components of presentation slides as conceptual units to generate a summary of lecture utterances, instead of other possible conceptual units like base noun phrases or important nouns. This paper explains our developing corpus designed to evaluate our proposing approach, which consists of presentation slides and lecture utterances aligned to presentation slide components.",
}
@inproceedings{nguyen-etal-2016-vsolscsum,
    title = "{VS}o{LSCS}um: Building a {V}ietnamese Sentence-Comment Dataset for Social Context Summarization",
    author = "Nguyen, Minh-Tien  and
      Lai, Dac Viet  and
      Do, Phong-Khac  and
      Tran, Duc-Vu  and
      Nguyen, Minh-Le",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5405",
    pages = "38--48",
    abstract = "This paper presents VSoLSCSum, a Vietnamese linked sentence-comment dataset, which was manually created to treat the lack of standard corpora for social context summarization in Vietnamese. The dataset was collected through the keywords of 141 Web documents in 12 special events, which were mentioned on Vietnamese Web pages. Social users were asked to involve in creating standard summaries and the label of each sentence or comment. The inter-agreement calculated by Cohen{'}s Kappa among raters after validating is 0.685. To illustrate the potential use of our dataset, a learning to rank method was trained by using a set of local and social features. Experimental results indicate that the summary model trained on our dataset outperforms state-of-the-art baselines in both ROUGE-1 and ROUGE-2 in social context summarization.",
}
@inproceedings{asahara-matsumoto-2016-bccwj,
    title = "{BCCWJ}-{D}ep{P}ara: A Syntactic Annotation Treebank on the {`}{B}alanced {C}orpus of {C}ontemporary {W}ritten {J}apanese{'}",
    author = "Asahara, Masayuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5406",
    pages = "49--58",
    abstract = "Paratactic syntactic structures are difficult to represent in syntactic dependency tree structures. As such, we propose an annotation schema for syntactic dependency annotation of Japanese, in which coordinate structures are split from and overlaid on bunsetsu-based (base phrase unit) dependency. The schema represents nested coordinate structures, non-constituent conjuncts, and forward sharing as the set of regions. The annotation was performed on the core data of {`}Balanced Corpus of Contemporary Written Japanese{'}, which comprised about one million words and 1980 samples from six registers, such as newspapers, books, magazines, and web texts.",
}
@inproceedings{chu-etal-2016-sctb,
    title = "{SCTB}: A {C}hinese Treebank in Scientific Domain",
    author = "Chu, Chenhui  and
      Nakazawa, Toshiaki  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5407",
    pages = "59--67",
    abstract = "Treebanks are curial for natural language processing (NLP). In this paper, we present our work for annotating a Chinese treebank in scientific domain (SCTB), to address the problem of the lack of Chinese treebanks in this domain. Chinese analysis and machine translation experiments conducted using this treebank indicate that the annotated treebank can significantly improve the performance on both tasks. This treebank is released to promote Chinese NLP research in scientific domain.",
}
@inproceedings{iwakura-etal-2016-big,
    title = "Big Community Data before World Wide Web Era",
    author = "Iwakura, Tomoya  and
      Takahashi, Tetsuro  and
      Ohtani, Akihiro  and
      Matsui, Kunio",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5408",
    pages = "68--72",
    abstract = "This paper introduces the NIFTY-Serve corpus, a large data archive collected from Japanese discussion forums that operated via a Bulletin Board System (BBS) between 1987 and 2006. This corpus can be used in Artificial Intelligence researches such as Natural Language Processing, Community Analysis, and so on. The NIFTY-Serve corpus differs from data on WWW in three ways; (1) essentially spam- and duplication-free because of strict data collection procedures, (2) historic user-generated data before WWW, and (3) a complete data set because the service now shut down. We also introduce some examples of use of the corpus.",
}
@inproceedings{gunarso-riza-2016-overview,
    title = "An Overview of {BPPT}{'}s {I}ndonesian Language Resources",
    author = "Gunarso, Gunarso  and
      Riza, Hammam",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5409",
    pages = "73--77",
    abstract = "This paper describes various Indonesian language resources that Agency for the Assessment and Application of Technology (BPPT) has developed and collected since mid 80{'}s when we joined MMTS (Multilingual Machine Translation System), an international project coordinated by CICC-Japan to develop a machine translation system for five Asian languages (Bahasa Indonesia, Malay, Thai, Japanese, and Chinese). Since then, we have been actively doing many types of research in the field of statistical machine translation, speech recognition, and speech synthesis which requires many text and speech corpus. Most recent cooperation within ASEAN-IVO is the development of Indonesian ALT (Asian Language Treebank) has added new NLP tools.",
}
@inproceedings{kimura-etal-2016-creating,
    title = "Creating {J}apanese Political Corpus from Local Assembly Minutes of 47 prefectures",
    author = "Kimura, Yasutomo  and
      Takamaru, Keiichi  and
      Tanaka, Takuma  and
      Kobayashi, Akio  and
      Sakaji, Hiroki  and
      Uchida, Yuzu  and
      Ototake, Hokuto  and
      Masuyama, Shigeru",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5410",
    pages = "78--85",
    abstract = "This paper describes a Japanese political corpus created for interdisciplinary political research. The corpus contains the local assembly minutes of 47 prefectures from April 2011 to March 2015. This four-year period coincides with the term of office for assembly members in most autonomies. We analyze statistical data, such as the number of speakers, characters, and words, to clarify the characteristics of local assembly minutes. In addition, we identify problems associated with the different web services used by the autonomies to make the minutes available to the public.",
}
@inproceedings{xu-etal-2016-selective,
    title = "Selective Annotation of Sentence Parts: Identification of Relevant Sub-sentential Units",
    author = "Xu, Ge  and
      Yang, Xiaoyan  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5411",
    pages = "86--94",
    abstract = "Many NLP tasks involve sentence-level annotation yet the relevant information is not encoded at sentence level but at some relevant parts of the sentence. Such tasks include but are not limited to: sentiment expression annotation, product feature annotation, and template annotation for Q{\&}A systems. However, annotation of the full corpus sentence by sentence is resource intensive. In this paper, we propose an approach that iteratively extracts frequent parts of sentences for annotating, and compresses the set of sentences after each round of annotation. Our approach can also be used in preparing training sentences for binary classification (domain-related vs. noise, subjectivity vs. objectivity, etc.), assuming that sentence-type annotation can be predicted by annotation of the most relevant sub-sentences. Two experiments are performed to test our proposal and evaluated in terms of time saved and agreement of annotation.",
}
@inproceedings{yamamura-etal-2016-kyutech,
    title = "The {K}yutech corpus and topic segmentation using a combined method",
    author = "Yamamura, Takashi  and
      Shimada, Kazutaka  and
      Kawahara, Shintaro",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5412",
    pages = "95--104",
    abstract = "Summarization of multi-party conversation is one of the important tasks in natural language processing. In this paper, we explain a Japanese corpus and a topic segmentation task. To the best of our knowledge, the corpus is the first Japanese corpus annotated for summarization tasks and freely available to anyone. We call it {``}the Kyutech corpus.{''} The task of the corpus is a decision-making task with four participants and it contains utterances with time information, topic segmentation and reference summaries. As a case study for the corpus, we describe a method combined with LCSeg and TopicTiling for a topic segmentation task. We discuss the effectiveness and the problems of the combined method through the experiment with the Kyutech corpus.",
}
@inproceedings{shudo-etal-2016-automatic,
    title = "Automatic Evaluation of Commonsense Knowledge for Refining {J}apanese {C}oncept{N}et",
    author = "Shudo, Seiya  and
      Rzepka, Rafal  and
      Araki, Kenji",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5413",
    pages = "105--112",
    abstract = "In this paper we present two methods for automatic common sense knowledge evaluation for Japanese entries in ConceptNet ontology. Our proposed methods utilize text-mining approach: one with relation clue words and WordNet synonyms, and one without. Both methods were tested with a blog corpus. The system based on our proposed methods reached relatively high precision score for three relations (MadeOf, UsedFor, AtLocation), which is comparable with previous research using commercial search engines and simpler input. We analyze errors and discuss problems of common sense evaluation, both manual and automatic and propose ideas for further improvements.",
}
@inproceedings{al-badrashiny-etal-2016-samer,
    title = "{SAMER}: A Semi-Automatically Created Lexical Resource for {A}rabic Verbal Multiword Expressions Tokens Paradigm and their Morphosyntactic Features",
    author = "Al-Badrashiny, Mohamed  and
      Hawwari, Abdelati  and
      Ghoneim, Mahmoud  and
      Diab, Mona",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5414",
    pages = "113--122",
    abstract = "Although MWE are relatively morphologically and syntactically fixed expressions, several types of flexibility can be observed in MWE, verbal MWE in particular. Identifying the degree of morphological and syntactic flexibility of MWE is very important for many Lexicographic and NLP tasks. Adding MWE variants/tokens to a dictionary resource requires characterizing the flexibility among other morphosyntactic features. Carrying out the task manually faces several challenges since it is a very laborious task time and effort wise, as well as it will suffer from coverage limitation. The problem is exacerbated in rich morphological languages where the average word in Arabic could have 12 possible inflection forms. Accordingly, in this paper we introduce a semi-automatic Arabic multiwords expressions resource (SAMER). We propose an automated method that identifies the morphological and syntactic flexibility of Arabic Verbal Multiword Expressions (AVMWE). All observed morphological variants and syntactic pattern alternations of an AVMWE are automatically acquired using large scale corpora. We look for three morphosyntactic aspects of AVMWE types investigating derivational and inflectional variations and syntactic templates, namely: 1) inflectional variation (inflectional paradigm) and calculating degree of flexibility; 2) derivational productivity; and 3) identifying and classifying the different syntactic types. We build a comprehensive list of AVMWE. Every token in the AVMWE list is lemmatized and tagged with POS information. We then search Arabic Gigaword and All ATBs for all possible flexible matches. For each AVMWE type we generate: a) a statistically ranked list of MWE-lexeme inflections and syntactic pattern alternations; b) An abstract syntactic template; and c) The most frequent form. Our technique is validated using a Golden MWE annotated list. The results shows that the quality of the generated resource is 80.04{\%}.",
}
@inproceedings{le-etal-2016-sentiment,
    title = "Sentiment Analysis for Low Resource Languages: A Study on Informal {I}ndonesian Tweets",
    author = "Le, Tuan Anh  and
      Moeljadi, David  and
      Miura, Yasuhide  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5415",
    pages = "123--131",
    abstract = "This paper describes our attempt to build a sentiment analysis system for Indonesian tweets. With this system, we can study and identify sentiments and opinions in a text or document computationally. We used four thousand manually labeled tweets collected in February and March 2016 to build the model. Because of the variety of content in tweets, we analyze tweets into eight groups in total, including pos(itive), neg(ative), and neu(tral). Finally, we obtained 73.2{\%} accuracy with Long Short Term Memory (LSTM) without normalizer.",
}
@proceedings{ws-2016-cognitive-aspects,
    title = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    editor = "Zock, Michael  and
      Lenci, Alessandro  and
      Evert, Stefan",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5300",
}
@inproceedings{biemann-2016-vectors,
    title = "Vectors or Graphs? On Differences of Representations for Distributional Semantic Models",
    author = "Biemann, Chris",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5301",
    pages = "1--7",
    abstract = "Distributional Semantic Models (DSMs) have recently received increased attention, together with the rise of neural architectures for scalable training of dense vector embeddings. While some of the literature even includes terms like {`}vectors{'} and {`}dimensionality{'} in the definition of DSMs, there are some good reasons why we should consider alternative formulations of distributional models. As an instance, I present a scalable graph-based solution to distributional semantics. The model belongs to the family of {`}count-based{'} DSMs, keeps its representation sparse and explicit, and thus fully interpretable. I will highlight some important differences between sparse graph-based and dense vector approaches to DSMs: while dense vector-based models are computationally easier to handle and provide a nice uniform representation that can be compared and combined in many ways, they lack interpretability, provenance and robustness. On the other hand, graph-based sparse models have a more straightforward interpretation, handle sense distinctions more naturally and can straightforwardly be linked to knowledge bases, while lacking the ability to compare arbitrary lexical units and a compositionality operation. Since both representations have their merits, I opt for exploring their combination in the outlook.",
}
@inproceedings{lebani-lenci-2016-beware,
    title = "{``}Beware the Jabberwock, dear reader!{''} Testing the distributional reality of construction semantics",
    author = "Lebani, Gianluca  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5302",
    pages = "8--18",
    abstract = "Notwithstanding the success of the notion of construction, the computational tradition still lacks a way to represent the semantic content of these linguistic entities. Here we present a simple corpus-based model implementing the idea that the meaning of a syntactic construction is intimately related to the semantics of its typical verbs. It is a two-step process, that starts by identifying the typical verbs occurring with a given syntactic construction and building their distributional vectors. We then calculated the weighted centroid of these vectors in order to derive the distributional signature of a construction. In order to assess the goodness of our approach, we replicated the priming effect described by Johnson and Golberg (2013) as a function of the semantic distance between a construction and its prototypical verbs. Additional support for our view comes from a regression analysis showing that our distributional information can be used to model behavioral data collected with a crowdsourced elicitation experiment.",
}
@inproceedings{lopukhina-lopukhin-2016-regular,
    title = "Regular polysemy: from sense vectors to sense patterns",
    author = "Lopukhina, Anastasiya  and
      Lopukhin, Konstantin",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5303",
    pages = "19--23",
    abstract = "Regular polysemy was extensively investigated in lexical semantics, but this phenomenon has been very little studied in distributional semantics. We propose a model for regular polysemy detection that is based on sense vectors and allows to work directly with senses in semantic vector space. Our method is able to detect polysemous words that have the same regular sense alternation as in a given example (a word with two automatically induced senses that represent one polysemy pattern, such as ANIMAL / FOOD). The method works equally well for nouns, verbs and adjectives and achieves average recall of 0.55 and average precision of 0.59 for ten different polysemy patterns.",
}
@inproceedings{shwartz-dagan-2016-path,
    title = "Path-based vs. Distributional Information in Recognizing Lexical Semantic Relations",
    author = "Shwartz, Vered  and
      Dagan, Ido",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5304",
    pages = "24--29",
    abstract = "Recognizing various semantic relations between terms is beneficial for many NLP tasks. While path-based and distributional information sources are considered complementary for this task, the superior results the latter showed recently suggested that the former{'}s contribution might have become obsolete. We follow the recent success of an integrated neural method for hypernymy detection (Shwartz et al., 2016) and extend it to recognize multiple relations. The empirical results show that this method is effective in the multiclass setting as well. We further show that the path-based information source always contributes to the classification, and analyze the cases in which it mostly complements the distributional information.",
}
@inproceedings{santos-etal-2016-semantic,
    title = "Semantic Relation Classification: Task Formalisation and Refinement",
    author = "Santos, Vivian  and
      Huerliman, Manuela  and
      Davis, Brian  and
      Handschuh, Siegfried  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5305",
    pages = "30--39",
    abstract = "The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded, allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.",
}
@inproceedings{attia-etal-2016-power,
    title = "The Power of Language Music: {A}rabic Lemmatization through Patterns",
    author = "Attia, Mohammed  and
      Zirikly, Ayah  and
      Diab, Mona",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5306",
    pages = "40--50",
    abstract = "The interaction between roots and patterns in Arabic has intrigued lexicographers and morphologists for centuries. While roots provide the consonantal building blocks, patterns provide the syllabic vocalic moulds. While roots provide abstract semantic classes, patterns realize these classes in specific instances. In this way both roots and patterns are indispensable for understanding the derivational, morphological and, to some extent, the cognitive aspects of the Arabic language. In this paper we perform lemmatization (a high-level lexical processing) without relying on a lookup dictionary. We use a hybrid approach that consists of a machine learning classifier to predict the lemma pattern for a given stem, and mapping rules to convert stems to their respective lemmas with the vocalization defined by the pattern.",
}
@inproceedings{kageback-salomonsson-2016-word,
    title = "Word Sense Disambiguation using a Bidirectional {LSTM}",
    author = {K{\aa}geb{\"a}ck, Mikael  and
      Salomonsson, Hans},
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5307",
    pages = "51--56",
    abstract = "In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.",
}
@inproceedings{zock-biemann-2016-towards,
    title = "Towards a resource based on users{'} knowledge to overcome the Tip of the Tongue problem.",
    author = "Zock, Michael  and
      Biemann, Chris",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5308",
    pages = "57--68",
    abstract = "Language production is largely a matter of words which, in the case of access problems, can be searched for in an external resource (lexicon, thesaurus). In this kind of dialogue the user provides the momentarily available knowledge concerning the target and the system responds with the best guess(es) it can make given this input. As tip-of-the-tongue (ToT)-studies have shown, people always have some knowledge concerning the target (meaning fragments, number of syllables, ...) even if its complete form is eluding them. We will show here how to tap on this knowledge to build a resource likely to help authors (speakers/writers) to overcome the ToT-problem. Yet, before doing so we need a better understanding of the various kinds of knowledge people have when looking for a word. To this end, we asked crowdworkers to provide some cues to describe a given target and to specify then how each one of them relates to the target, in the hope that this could help others to find the elusive word. Next, we checked how well a given search strategy worked when being applied to differently built lexical networks. The results showed quite dramatic differences, which is not really surprising. After all, different networks are built for different purposes; hence each one of them is more or less suited for a given task. What was more surprising though is the fact that the relational information given by the users did not allow us to find the elusive word in WordNet better than without it.",
}
@inproceedings{santus-etal-2016-cogalex,
    title = "The {C}og{AL}ex-{V} Shared Task on the Corpus-Based Identification of Semantic Relations",
    author = "Santus, Enrico  and
      Gladkova, Anna  and
      Evert, Stefan  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5309",
    pages = "69--79",
    abstract = "The shared task of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) aims at providing a common benchmark for testing current corpus-based methods for the identification of lexical semantic relations (synonymy, antonymy, hypernymy, part-whole meronymy) and at gaining a better understanding of their respective strengths and weaknesses. The shared task uses a challenging dataset extracted from EVALution 1.0, which contains word pairs holding the above-mentioned relations as well as semantically unrelated control items (random). The task is split into two subtasks: (i) identification of related word pairs vs. unrelated ones; (ii) classification of the word pairs according to their semantic relation. This paper describes the subtasks, the dataset, the evaluation metrics, the seven participating systems and their results. The best performing system in subtask 1 is GHHH (F1 = 0.790), while the best system in subtask 2 is LexNet (F1 = 0.445). The dataset and the task description are available at \url{https://sites.google.com/site/cogalex2016/home/shared-task}.",
}
@inproceedings{shwartz-dagan-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: {L}ex{NET} - Integrated Path-based and Distributional Method for the Identification of Semantic Relations",
    author = "Shwartz, Vered  and
      Dagan, Ido",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5310",
    pages = "80--85",
    abstract = "We present a submission to the CogALex 2016 shared task on the corpus-based identification of semantic relations, using LexNET (Shwartz and Dagan, 2016), an integrated path-based and distributional method for semantic relation classification. The reported results in the shared task bring this submission to the third place on subtask 1 (word relatedness), and the first place on subtask 2 (semantic relation classification), demonstrating the utility of integrating the complementary path-based and distributional information sources in recognizing concrete semantic relations. Combined with a common similarity measure, LexNET performs fairly good on the word relatedness task (subtask 1). The relatively low performance of LexNET and all other systems on subtask 2, however, confirms the difficulty of the semantic relation classification task, and stresses the need to develop additional methods for this task.",
}
@inproceedings{attia-etal-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: {GHHH} - Detecting Semantic Relations via Word Embeddings",
    author = "Attia, Mohammed  and
      Maharjan, Suraj  and
      Samih, Younes  and
      Kallmeyer, Laura  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5311",
    pages = "86--91",
    abstract = "This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1{\%} (79.0{\%} for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0{\%} (42.3{\%} when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.",
}
@inproceedings{evert-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: Mach5 {--} A traditional {DSM} approach to semantic relatedness",
    author = "Evert, Stefan",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5312",
    pages = "92--97",
    abstract = "This contribution provides a strong baseline result for the CogALex-V shared task using a traditional {``}count{''}-type DSM (placed in rank 2 out of 7 in subtask 1 and rank 3 out of 6 in subtask 2). Parameter tuning experiments reveal some surprising effects and suggest that the use of random word pairs as negative examples may be problematic, guiding the parameter optimization in an undesirable direction.",
}
@inproceedings{chersoni-etal-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: {ROOT}18",
    author = "Chersoni, Emmanuele  and
      Rambelli, Giulia  and
      Santus, Enrico",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5313",
    pages = "98--103",
    abstract = "In this paper, we describe ROOT 18, a classifier using the scores of several unsupervised distributional measures as features to discriminate between semantically related and unrelated words, and then to classify the related pairs according to their semantic relation (i.e. synonymy, antonymy, hypernymy, part-whole meronymy). Our classifier participated in the CogALex-V Shared Task, showing a solid performance on the first subtask, but a poor performance on the second subtask. The low scores reported on the second subtask suggest that distributional measures are not sufficient to discriminate between multiple semantic relations at once.",
}
@inproceedings{guggilla-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: {CGSRC} - Classifying Semantic Relations using Convolutional Neural Networks",
    author = "Guggilla, Chinnappa",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5314",
    pages = "104--109",
    abstract = "In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared task of corpus-based identification of semantic relations. Proposed approach using CNN-based deep neural networks leveraging pre-compiled word2vec distributional neural embeddings achieved 43.15{\%} weighted-F1 accuracy on subtask-1 (checking existence of a relation between two terms) and 25.24{\%} weighted-F1 accuracy on subtask-2 (classifying relation types).",
}
@inproceedings{luce-etal-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: {LOPE}",
    author = "Luce, Kanan  and
      Yu, Jiaxing  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5315",
    pages = "110--113",
    abstract = "Automatic discovery of semantically-related words is one of the most important NLP tasks, and has great impact on the theoretical psycholinguistic modeling of the mental lexicon. In this shared task, we employ the word embeddings model to testify two thoughts explicitly or implicitly assumed by the NLP community: (1). Word embedding models can reflect syntagmatic similarities in usage between words to distances in projected vector space. (2). Word embedding models can reflect paradigmatic relationships between words.",
}
@inproceedings{wartena-aga-2016-cogalex,
    title = "{C}og{AL}ex-{V} Shared Task: {H}s{H}-Supervised {--} Supervised similarity learning using entry wise product of context vectors",
    author = "Wartena, Christian  and
      Aga, Rosa Tsegaye",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5316",
    pages = "114--118",
    abstract = "The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.",
}
@inproceedings{aoki-nakatani-2016-study,
    title = "A Study of the Bump Alternation in {J}apanese from the Perspective of Extended/Onset Causation",
    author = "Aoki, Natsuno  and
      Nakatani, Kentaro",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5317",
    pages = "119--124",
    abstract = "This paper deals with a seldom studied object/oblique alternation phenomenon in Japanese, which. We call this the bump alternation. This phenomenon, first discussed by Sadanobu (1990), is similar to the English with/against alternation. For example, compare hit the wall with the bat [=immobile-as-direct-object frame] to hit the bat against the wall [=mobile-as-direct-object frame]). However, in the Japanese version, the case frame remains constant. Although we fundamentally question Sadanobu{'}s acceptability judgment, we also claim that the causation type (i.e., whether the event is an instance of onset or extended causation; Talmy, 1988; 2000) could make an improvement. An extended causative interpretation could improve the acceptability of the otherwise awkward immobile-as-direct-object frame. We examined this claim through a rating study, and the results showed an interaction between the Causation type (extended/onset) and the Object type (mobile/immobile) in the direction we predicted. We propose that a perspective shift on what is moving causes the {``}extended causation{''} advantage.",
}
@inproceedings{bott-etal-2016-ghost,
    title = "{G}ho{S}t-{PV}: A Representative Gold Standard of {G}erman Particle Verbs",
    author = "Bott, Stefan  and
      Khvtisavrishvili, Nana  and
      Kisselew, Max  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5318",
    pages = "125--133",
    abstract = "German particle verbs represent a frequent type of multi-word-expression that forms a highly productive paradigm in the lexicon. Similarly to other multi-word expressions, particle verbs exhibit various levels of compositionality. One of the major obstacles for the study of compositionality is the lack of representative gold standards of human ratings. In order to address this bottleneck, this paper presents such a gold standard data set containing 400 randomly selected German particle verbs. It is balanced across several particle types and three frequency bands, and accomplished by human ratings on the degree of semantic compositionality.",
}
@inproceedings{daoud-daoud-2016-discovering,
    title = "Discovering Potential Terminological Relationships from {T}witter{'}s Timed Content",
    author = "Daoud, Mohammad  and
      Daoud, Daoud",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5319",
    pages = "134--144",
    abstract = "This paper presents a method to discover possible terminological relationships from tweets. We match the histories of terms (frequency patterns). Similar history indicates a possible relationship between terms. For example, if two terms (t1, t2) appeared frequently in Twitter at particular days, and there is a {`}similarity{'} in the frequencies over a period of time, then t1 and t2 can be related. Maintaining standard terminological repository with updated relationships can be difficult; especially in a dynamic domain such as social media where thousands of new terms (neology) are coined every day. So we propose to construct a raw repository of lexical units with unconfirmed relationships. We have experimented our method on time-sensitive Arabic terms used by the online Arabic community of Twitter. We draw relationships between these terms by matching their similar frequency patterns (timelines). We use dynamic time warping as a similarity measure. For evaluation, we have selected 630 possible terms (we call them preterms) and we matched the similarity of these terms over a period of 30 days. Around 270 correct relationships were discovered with a precision of 0.61. These relationships were extracted without considering the textual context of the term.",
}
@inproceedings{fonseca-etal-2016-lexfom,
    title = "{L}exfom: a lexical functions ontology model",
    author = "Fonseca, Alexsandro  and
      Sadat, Fatiha  and
      Lareau, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5320",
    pages = "145--155",
    abstract = "A lexical function represents a type of relation that exists between lexical units (words or expressions) in any language. For example, the antonymy is a type of relation that is represented by the lexical function Anti: Anti(big) = small. Those relations include both paradigmatic relations, i.e. vertical relations, such as synonymy, antonymy and meronymy and syntagmatic relations, i.e. horizontal relations, such as objective qualification (legitimate demand), subjective qualification (fruitful analysis), positive evaluation (good review) and support verbs (pay a visit, subject to an interrogation). In this paper, we present the Lexical Functions Ontology Model (lexfom) to represent lexical functions and the relation among lexical units. Lexfom is divided in four modules: lexical function representation (lfrep), lexical function family (lffam), lexical function semantic perspective (lfsem) and lexical function relations (lfrel). Moreover, we show how it combines to Lexical Model for Ontologies (lemon), for the transformation of lexical networks into the semantic web formats. So far, we have implemented 100 simple and 500 complex lexical functions, and encoded about 8,000 syntagmatic and 46,000 paradigmatic relations, for the French language.",
}
@inproceedings{l-homme-etal-2016-proposal,
    title = "A Proposal for combining {``}general{''} and specialized frames",
    author = "L{'} Homme, Marie-Claude  and
      Subirats, Carlos  and
      Robichaud, Beno{\^\i}t",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5321",
    pages = "156--165",
    abstract = "The objectives of the work described in this paper are: 1. To list the differences between a general language resource (namely FrameNet) and a domain-specific resource; 2. To devise solutions to merge their contents in order to increase the coverage of the general resource. Both resources are based on Frame Semantics (Fillmore 1985; Fillmore and Baker 2010) and this raises specific challenges since the theoretical framework and the methodology derived from it provide for both a lexical description and a conceptual representation. We propose a series of strategies that handle both lexical and conceptual (frame) differences and implemented them in the specialized resource. We also show that most differences can be handled in a straightforward manner. However, some more domain specific differences (such as frames defined exclusively for the specialized domain or relations between these frames) are likely to be much more difficult to take into account since some are domain-specific.",
}
@inproceedings{pastena-lenci-2016-antonymy,
    title = "Antonymy and Canonicity: Experimental and Distributional Evidence",
    author = "Pastena, Andreana  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5322",
    pages = "166--175",
    abstract = "The present paper investigates the phenomenon of antonym canonicity by providing new behavioural and distributional evidence on Italian adjectives. Previous studies have showed that some pairs of antonyms are perceived to be better examples of opposition than others, and are so considered representative of the whole category (e.g., Deese, 1964; Murphy, 2003; Paradis et al., 2009). Our goal is to further investigate why such canonical pairs (Murphy, 2003) exist and how they come to be associated. In the literature, two different approaches have dealt with this issue. The lexical-categorical approach (Charles and Miller, 1989; Justeson and Katz, 1991) finds the cause of canonicity in the high co-occurrence frequency of the two adjectives. The cognitive-prototype approach (Paradis et al., 2009; Jones et al., 2012) instead claims that two adjectives form a canonical pair because they are aligned along a simple and salient dimension. Our empirical evidence, while supporting the latter view, shows that the paradigmatic distributional properties of adjectives can also contribute to explain the phenomenon of canonicity, providing a corpus-based correlate of the cognitive notion of salience.",
}
@inproceedings{silva-etal-2016-categorization,
    title = "Categorization of Semantic Roles for Dictionary Definitions",
    author = "Silva, Vivian  and
      Handschuh, Siegfried  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5323",
    pages = "176--184",
    abstract = "Understanding the semantic relationships between terms is a fundamental task in natural language processing applications. While structured resources that can express those relationships in a formal way, such as ontologies, are still scarce, a large number of linguistic resources gathering dictionary definitions is becoming available, but understanding the semantic structure of natural language definitions is fundamental to make them useful in semantic interpretation tasks. Based on an analysis of a subset of WordNet{'}s glosses, we propose a set of semantic roles that compose the semantic structure of a dictionary definition, and show how they are related to the definition{'}s syntactic configuration, identifying patterns that can be used in the development of information extraction frameworks and semantic models.",
}
@inproceedings{tomokiyo-boitet-2016-corpus,
    title = "Corpus and dictionary development for classifiers/quantifiers towards a {F}rench-{J}apanese machine translation",
    author = "Tomokiyo, Mutsuko  and
      Boitet, Christian",
    booktitle = "Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon ({C}og{AL}ex - V)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5324",
    pages = "185--192",
    abstract = "Although quantifiers/classifiers expressions occur frequently in everyday communications or written documents, there is no description for them in classical bilingual paper dictionaries, nor in machine-readable dictionaries. The paper describes a corpus and dictionary development for quantifiers/classifiers, and their usage in the framework of French-Japanese machine translation (MT). They often cause problems of lexical ambiguity and of set phrase recognition during analysis, in particular for a long-distance language pair like French and Japanese. For the development of a dictionary aiming at ambiguity resolution for expressions including quantifiers and classifiers which may be ambiguous with common nouns, we have annotated our corpus with UWs (interlingual lexemes) of UNL (Universal Networking Language) found on the UNL-jp dictionary. The extraction of potential classifiers/quantifiers from corpus is made by UNLexplorer web service. Keywords : classifiers, quantifiers, phraseology study, corpus annotation, UNL (Universal Networking Language), UWs dictionary, Tori Bank, French-Japanese machine translation (MT).",
}
@proceedings{ws-2016-international-worldwide,
    title = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    editor = "Murakami, Yohei  and
      Lin, Donghui  and
      Ide, Nancy  and
      Pustejovsky, James",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5200",
}
@inproceedings{mohanty-etal-2016-kathaa,
    title = "{K}athaa : {NLP} Systems as Edge-Labeled Directed Acyclic {M}ulti{G}raphs",
    author = "Mohanty, Sharada  and
      Wani, Nehal J  and
      Srivastava, Manish  and
      Sharma, Dipti",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5201",
    pages = "1--10",
    abstract = "We present Kathaa, an Open Source web-based Visual Programming Framework for Natural Language Processing (NLP) Systems. Kathaa supports the design, execution and analysis of complex NLP systems by visually connecting NLP components from an easily extensible Module Library. It models NLP systems an edge-labeled Directed Acyclic MultiGraph, and lets the user use publicly co-created modules in their own NLP applications irrespective of their technical proficiency in Natural Language Processing. Kathaa exposes an intuitive web based Interface for the users to interact with and modify complex NLP Systems; and a precise Module definition API to allow easy integration of new state of the art NLP components. Kathaa enables researchers to publish their services in a standardized format to enable the masses to use their services out of the box. The vision of this work is to pave the way for a system like Kathaa, to be the Lego blocks of NLP Research and Applications. As a practical use case we use Kathaa to visually implement the Sampark Hindi-Panjabi Machine Translation Pipeline and the Sampark Hindi-Urdu Machine Translation Pipeline, to demonstrate the fact that Kathaa can handle really complex NLP systems while still being intuitive for the end user.",
}
@inproceedings{ide-etal-2016-lapps,
    title = "{LAPPS}/Galaxy: Current State and Next Steps",
    author = "Ide, Nancy  and
      Suderman, Keith  and
      Nyberg, Eric  and
      Pustejovsky, James  and
      Verhagen, Marc",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5202",
    pages = "11--18",
    abstract = "The US National Science Foundation (NSF) SI2-funded LAPPS/Galaxy project has developed an open-source platform for enabling complex analyses while hiding complexities associated with underlying infrastructure, that can be accessed through a web interface, deployed on any Unix system, or run from the cloud. It provides sophisticated tool integration and history capabilities, a workflow system for building automated multi-step analyses, state-of-the-art evaluation capabilities, and facilities for sharing and publishing analyses. This paper describes the current facilities available in LAPPS/Galaxy and outlines the project{'}s ongoing activities to enhance the framework.",
}
@inproceedings{eckart-de-castilho-2016-automatic,
    title = "Automatic Analysis of Flaws in Pre-Trained {NLP} Models",
    author = "Eckart de Castilho, Richard",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5203",
    pages = "19--27",
    abstract = "Most tools for natural language processing today are based on machine learning and come with pre-trained models. In addition, third-parties provide pre-trained models for popular NLP tools. The predictive power and accuracy of these tools depends on the quality of these models. Downstream researchers often base their results on pre-trained models instead of training their own. Consequently, pre-trained models are an essential resource to our community. However, to be best of our knowledge, no systematic study of pre-trained models has been conducted so far. This paper reports on the analysis of 274 pre-models for six NLP tools and four potential causes of problems: encoding, tokenization, normalization and change over time. The analysis is implemented in the open source tool Model Investigator. Our work 1) allows model consumers to better assess whether a model is suitable for their task, 2) enables tool and model creators to sanity-check their models before distributing them, and 3) enables improvements in tool interoperability by performing automatic adjustments of normalization or other pre-processing based on the models used.",
}
@inproceedings{nakaguchi-etal-2016-combining,
    title = "Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums",
    author = "Nakaguchi, Takao  and
      Otani, Masayuki  and
      Takasaki, Toshiyuki  and
      Ishida, Toru",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5204",
    pages = "28--35",
    abstract = "In this research, we introduce and implement a method that combines human inputters and machine translators. When the languages of the participants vary widely, the cost of simultaneous translation becomes very high. However, the results of simply applying machine translation to speech text do not have the quality that is needed for real use. Thus, we propose a method that people who understand the language of the speaker cooperate with a machine translation service in support of multilingualization by the co-creation of value. We implement a system with this method and apply it to actual presentations. While the quality of direct machine translations is 1.84 (fluency) and 2.89 (adequacy), the system has corresponding values of 3.76 and 3.85.",
}
@inproceedings{assawinjaipetch-etal-2016-recurrent,
    title = "Recurrent Neural Network with Word Embedding for Complaint Classification",
    author = "Assawinjaipetch, Panuwat  and
      Shirai, Kiyoaki  and
      Sornlertlamvanich, Virach  and
      Marukata, Sanparith",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5205",
    pages = "36--43",
    abstract = "Complaint classification aims at using information to deliver greater insights to enhance user experience after purchasing the products or services. Categorized information can help us quickly collect emerging problems in order to provide a support needed. Indeed, the response to the complaint without the delay will grant users highest satisfaction. In this paper, we aim to deliver a novel approach which can clarify the complaints precisely with the aim to classify each complaint into nine predefined classes i.e. acces-sibility, company brand, competitors, facilities, process, product feature, staff quality, timing respec-tively and others. Given the idea that one word usually conveys ambiguity and it has to be interpreted by its context, the word embedding technique is used to provide word features while applying deep learning techniques for classifying a type of complaints. The dataset we use contains 8,439 complaints of one company.",
}
@inproceedings{eli-etal-2016-universal,
    title = "Universal dependencies for {U}yghur",
    author = "Eli, Marhaba  and
      Mushajiang, Weinila  and
      Yibulayin, Tuergen  and
      Abiderexiti, Kahaerjiang  and
      Liu, Yan",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5206",
    pages = "44--50",
    abstract = "The Universal Dependencies (UD) Project seeks to build a cross-lingual studies of treebanks, linguistic structures and parsing. Its goal is to create a set of multilingual harmonized treebanks that are designed according to a universal annotation scheme. In this paper, we report on the conversion of the Uyghur dependency treebank to a UD version of the treebank which we term the Uyghur Universal Dependency Treebank (UyDT). We present the mapping of the Uyghur dependency treebank{'}s labelling scheme to the UD scheme, along with a clear description of the structural changes required in this conversion.",
}
@inproceedings{luong-vu-2016-non,
    title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
    author = "Luong, Hieu-Thi  and
      Vu, Hai-Quan",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5207",
    pages = "51--55",
    abstract = "In this paper we describe a non-expert setup for Vietnamese speech recognition system using Kaldi toolkit. We collected a speech corpus over fifteen hours from about fifty Vietnamese native speakers and using it to test the feasibility of our setup. The essential linguistic components for the Automatic Speech Recognition (ASR) system was prepared basing on the written form of the language instead of expertise knowledge on linguistic and phonology as commonly seen in rich resource languages like English. The modeling of tones by integrating them into the phoneme and using the phonetic decision tree is also discussed. Experimental results showed this setup for ASR systems does yield competitive results while still have potentials for further improvements.",
}
@inproceedings{lu-etal-2016-evaluating,
    title = "Evaluating Ensemble Based Pre-annotation on Named Entity Corpus Construction in {E}nglish and {C}hinese",
    author = "Lu, Tingming  and
      Zhu, Man  and
      Gao, Zhiqiang  and
      Gui, Yaocheng",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5208",
    pages = "56--60",
    abstract = "Annotated corpora are crucial language resources, and pre-annotation is an usual way to reduce the cost of corpus construction. Ensemble based pre-annotation approach combines multiple existing named entity taggers and categorizes annotations into normal annotations with high confidence and candidate annotations with low confidence, to reduce the human annotation time. In this paper, we manually annotate three English datasets under various pre-annotation conditions, report the effects of ensemble based pre-annotation, and analyze the experimental results. In order to verify the effectiveness of ensemble based pre-annotation in other languages, such as Chinese, three Chinese datasets are also tested. The experimental results show that the ensemble based pre-annotation approach significantly reduces the number of annotations which human annotators have to add, and outperforms the baseline approaches in reduction of human annotation time without loss in annotation performance (in terms of F1-measure), on both English and Chinese datasets.",
}
@inproceedings{murakami-etal-2016-ontology,
    title = "An Ontology for Language Service Composability",
    author = "Murakami, Yohei  and
      Nakaguchi, Takao  and
      Lin, Donghui  and
      Ishida, Toru",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5209",
    pages = "61--69",
    abstract = "Fragmentation and recombination is a key to create customized language environments for supporting various intercultural activities. Fragmentation provides various language resource components for the customized language environments and recombination builds each language environment according to user{'}s request by combining these components. To realize this fragmentation and recombination process, existing language resources (both data and programs) should be shared as language services and combined beyond mismatch of their service interfaces. To address this issue, standardization is inevitable: standardized interfaces are necessary for language services as well as data format required for language resources. Therefore, we have constructed a hierarchy of language services based on inheritance of service interfaces, which is called language service ontology. This ontology allows users to create a new customized language service that is compatible with existing ones. Moreover, we have developed a dynamic service binding technology that instantiates various executable customized services from an abstract workflow according to user{'}s request. By using the ontology and service binding together, users can bind the instantiated language service to another abstract workflow for a new customized one.",
}
@inproceedings{kano-2016-platform,
    title = "Between Platform and {API}s: {K}achako {API} for Developers",
    author = "Kano, Yoshinobu",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5210",
    pages = "70--75",
    abstract = "Different types of users require different functions in NLP software. It is difficult for a single platform to cover all types of users. When a framework aims to provide more interoperability, users are required to learn more concepts; users{'} application designs are restricted to be compliant with the framework. While an interoperability framework is useful in certain cases, some types of users will not select the framework due to the learning cost and design restrictions. We suggest a rather simple framework for the interoperability aiming at developers. Reusing an existing NLP platform Kachako, we created an API oriented NLP system. This system loosely couples rich high-end functions, including annotation visualizations, statistical evaluations, an-notation searching, etc. This API do not require users much learning cost, providing customization ability for power users while also allowing easy users to employ many GUI functions.",
}
@proceedings{ws-2016-building,
    title = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    editor = "Ananiadou, Sophia  and
      Batista-Navarro, Riza  and
      Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Thompson, Paul",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5100",
}
@inproceedings{baker-etal-2016-cancer,
    title = "Cancer Hallmark Text Classification Using Convolutional Neural Networks",
    author = "Baker, Simon  and
      Korhonen, Anna  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5101",
    pages = "1--9",
    abstract = "Methods based on deep learning approaches have recently achieved state-of-the-art performance in a range of machine learning tasks and are increasingly applied to natural language processing (NLP). Despite strong results in various established NLP tasks involving general domain texts, there is only limited work applying these models to biomedical NLP. In this paper, we consider a Convolutional Neural Network (CNN) approach to biomedical text classification. Evaluation using a recently introduced cancer domain dataset involving the categorization of documents according to the well-established hallmarks of cancer shows that a basic CNN model can achieve a level of performance competitive with a Support Vector Machine (SVM) trained using complex manually engineered features optimized to the task. We further show that simple modifications to the CNN hyperparameters, initialization, and training process allow the model to notably outperform the SVM, establishing a new state of the art result at this task. We make all of the resources and tools introduced in this study available under open licenses from \url{https://cambridgeltl.github.io/cancer-hallmark-cnn/}.",
}
@inproceedings{limsopatham-collier-2016-learning,
    title = "Learning Orthographic Features in Bi-directional {LSTM} for Biomedical Named Entity Recognition",
    author = "Limsopatham, Nut  and
      Collier, Nigel",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5102",
    pages = "10--19",
    abstract = "End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e.g. newswire), without requiring additional hand-crafted features. However, in biomedical domain, recent studies have shown that hand-engineered features (e.g. orthographic features) should be used to attain effective performance, due to the complexity of biomedical terminology (e.g. the use of acronyms and complex gene names). In this work, we propose a novel approach that allows a neural network model based on a long short-term memory (LSTM) to automatically learn orthographic features and incorporate them into a model for biomedical NER. Importantly, our bi-directional LSTM model learns and leverages orthographic features on an end-to-end basis. We evaluate our approach by comparing against existing neural network models for NER using three well-established biomedical datasets. Our experimental results show that the proposed approach consistently outperforms these strong baselines across all of the three datasets.",
}
@inproceedings{amplayo-song-2016-building,
    title = "Building Content-driven Entity Networks for Scarce Scientific Literature using Content Information",
    author = "Amplayo, Reinald Kim  and
      Song, Min",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5103",
    pages = "20--29",
    abstract = "This paper proposes several network construction methods for collections of scarce scientific literature data. We define scarcity as lacking in value and in volume. Instead of using the paper{'}s metadata to construct several kinds of scientific networks, we use the full texts of the articles and automatically extract the entities needed to construct the networks. Specifically, we present seven kinds of networks using the proposed construction methods: co-occurrence networks for author, keyword, and biological entities, and citation networks for author, keyword, biological, and topic entities. We show two case studies that applies our proposed methods: CADASIL, a rare yet the most common form of hereditary stroke disorder, and Metformin, the first-line medication to the type 2 diabetes treatment. We apply our proposed method to four different applications for evaluation: finding prolific authors, finding important bio-entities, finding meaningful keywords, and discovering influential topics. The results show that the co-occurrence and citation networks constructed using the proposed method outperforms the traditional-based networks. We also compare our proposed networks to traditional citation networks constructed using enough data and infer that even with the same amount of enough data, our methods perform comparably or better than the traditional methods.",
}
@inproceedings{almgren-etal-2016-named,
    title = "Named Entity Recognition in {S}wedish Health Records with Character-Based Deep Bidirectional {LSTM}s",
    author = "Almgren, Simon  and
      Pavlov, Sean  and
      Mogren, Olof",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5104",
    pages = "30--39",
    abstract = "We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a 60{\%} improvement in F 1 score over the baseline, and our system generalizes well between different datasets.",
}
@inproceedings{schulze-neves-2016-entity,
    title = "Entity-Supported Summarization of Biomedical Abstracts",
    author = "Schulze, Frederik  and
      Neves, Mariana",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5105",
    pages = "40--49",
    abstract = "The increasing amount of biomedical information that is available for researchers and clinicians makes it harder to quickly find the right information. Automatic summarization of multiple texts can provide summaries specific to the user{'}s information needs. In this paper we look into the use named-entity recognition for graph-based summarization. We extend the LexRank algorithm with information about named entities and present EntityRank, a multi-document graph-based summarization algorithm that is solely based on named entities. We evaluate our system on a datasets of 1009 human written summaries provided by BioASQ and on 1974 gene summaries, fetched from the Entrez Gene database. The results show that the addition of named-entity information increases the performance of graph-based summarizers and that the EntityRank significantly outperforms the other methods with regard to the ROUGE measures.",
}
@inproceedings{perez-etal-2016-fully,
    title = "Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics",
    author = "P{\'e}rez, Alicia  and
      Casillas, Arantza  and
      Gojenola, Koldo",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5106",
    pages = "50--59",
    abstract = "Electronic health records show great variability since the same concept is often expressed with different terms, either scientific latin forms, common or lay variants and even vernacular naming. Deep learning enables distributional representation of terms in a vector-space, and therefore, related terms tend to be close in the vector space. Accordingly, embedding words through these vectors opens the way towards accounting for semantic relatedness through classical algebraic operations. In this work we propose a simple though efficient unsupervised characterization of Adverse Drug Reactions (ADRs). This approach exploits the embedding representation of the terms involved in candidate ADR events, that is, drug-disease entity pairs. In brief, the ADRs are represented as vectors that link the drug with the disease in their context through a recursive additive model. We discovered that a low-dimensional representation that makes use of the modulus and argument of the embedded representation of the ADR event shows correlation with the manually annotated class. Thus, it can be derived that this characterization results in to be beneficial for further classification tasks as predictive features.",
}
@inproceedings{lavergne-etal-2016-dataset,
    title = "A Dataset for {ICD}-10 Coding of Death Certificates: Creation and Usage",
    author = "Lavergne, Thomas  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Robert, Aude  and
      Grouin, Cyril  and
      Rey, Gr{\'e}goire  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5107",
    pages = "60--69",
    abstract = "Very few datasets have been released for the evaluation of diagnosis coding with the International Classification of Diseases, and only one so far in a language other than English. This paper describes a large-scale dataset prepared from French death certificates, and the problems which needed to be solved to turn it into a dataset suitable for the application of machine learning and natural language processing methods of ICD-10 coding. The dataset includes the free-text statements written by medical doctors, the associated meta-data, the human coder-assigned codes for each statement, as well as the statement segments which supported the coder{'}s decision for each code. The dataset comprises 93,694 death certificates totalling 276,103 statements and 377,677 ICD-10 code assignments (3,457 unique codes). It was made available for an international automated coding shared task, which attracted five participating teams. An extended version of the dataset will be used in a new edition of the shared task.",
}
@inproceedings{shmanina-etal-2016-corpus,
    title = "A Corpus of Tables in Full-Text Biomedical Research Publications",
    author = "Shmanina, Tatyana  and
      Zukerman, Ingrid  and
      Cheam, Ai Lee  and
      Bochynek, Thomas  and
      Cavedon, Lawrence",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5108",
    pages = "70--79",
    abstract = "The development of text mining techniques for biomedical research literature has received increased attention in recent times. However, most of these techniques focus on prose, while much important biomedical data reside in tables. In this paper, we present a corpus created to serve as a gold standard for the development and evaluation of techniques for the automatic extraction of information from biomedical tables. We describe the guidelines used for corpus annotation and the manner in which they were developed. The high inter-annotator agreement achieved on the corpus, and the generic nature of our annotation approach, suggest that the developed guidelines can serve as a general framework for table annotation in biomedical and other scientific domains. The annotated corpus and the guidelines are available at \url{http://www.csse.monash.edu.au/research/umnl/data/index.shtml}.",
}
@inproceedings{zweigenbaum-etal-2016-supervised,
    title = "Supervised classification of end-of-lines in clinical text with no manual annotation",
    author = "Zweigenbaum, Pierre  and
      Grouin, Cyril  and
      Lavergne, Thomas",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5109",
    pages = "80--88",
    abstract = "In some plain text documents, end-of-line marks may or may not mark the boundary of a text unit (e.g., of a paragraph). This vexing problem is likely to impact subsequent natural language processing components, but is seldom addressed in the literature. We propose a method which uses no manual annotation to classify whether end-of-lines must actually be seen as simple spaces (soft line breaks) or as true text unit boundaries. This method, which includes self-training and co-training steps based on token and line length features, achieves 0.943 F-measure on a corpus of short e-books with controlled format, F=0.904 on a random sample of 24 clinical texts with soft line breaks, and F=0.898 on a larger set of mixed clinical texts which may or may not contain soft line breaks, a fairly high value for a method with no manual annotation.",
}
@inproceedings{gopalan-lalitha-devi-2016-biodca,
    title = "{B}io{DCA} Identifier: A System for Automatic Identification of Discourse Connective and Arguments from Biomedical Text",
    author = "Gopalan, Sindhuja  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5110",
    pages = "89--98",
    abstract = "This paper describes a Natural language processing system developed for automatic identification of explicit connectives, its sense and arguments. Prior work has shown that the difference in usage of connectives across corpora affects the cross domain connective identification task negatively. Hence the development of domain specific discourse parser has become indispensable. Here, we present a corpus annotated with discourse relations on Medline abstracts. Kappa score is calculated to check the annotation quality of our corpus. The previous works on discourse analysis in bio-medical data have concentrated only on the identification of connectives and hence we have developed an end-end parser for connective and argument identification using Conditional Random Fields algorithm. The type and sub-type of the connective sense is also identified. The results obtained are encouraging.",
}
@inproceedings{sarker-gonzalez-2016-data,
    title = "Data, tools and resources for mining social media drug chatter",
    author = "Sarker, Abeed  and
      Gonzalez, Graciela",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5111",
    pages = "99--107",
    abstract = "Social media has emerged into a crucial resource for obtaining population-based signals for various public health monitoring and surveillance tasks, such as pharmacovigilance. There is an abundance of knowledge hidden within social media data, and the volume is growing. Drug-related chatter on social media can include user-generated information that can provide insights into public health problems such as abuse, adverse reactions, long-term effects, and multi-drug interactions. Our objective in this paper is to present to the biomedical natural language processing, data science, and public health communities data sets (annotated and unannotated), tools and resources that we have collected and created from social media. The data we present was collected from Twitter using the generic and brand names of drugs as keywords, along with their common misspellings. Following the collection of the data, annotation guidelines were created over several iterations, which detail important aspects of social media data annotation and can be used by future researchers for developing similar data sets. The annotation guidelines were followed to prepare data sets for text classification, information extraction and normalization. In this paper, we discuss the preparation of these guidelines, outline the data sets prepared, and present an overview of our state-of-the-art systems for data collection, supervised classification, and information extraction. In addition to the development of supervised systems for classification and extraction, we developed and released unlabeled data and language models. We discuss the potential uses of these language models in data mining and the large volumes of unlabeled data from which they were generated. We believe that the summaries and repositories we present here of our data, annotation guidelines, models, and tools will be beneficial to the research community as a single-point entry for all these resources, and will promote further research in this area.",
}
@inproceedings{dhondt-etal-2016-detection,
    title = "Detection of Text Reuse in {F}rench Medical Corpora",
    author = "D{'}hondt, Eva  and
      Grouin, Cyril  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Stamatatos, Efstathios  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5112",
    pages = "108--114",
    abstract = "Electronic Health Records (EHRs) are increasingly available in modern health care institutions either through the direct creation of electronic documents in hospitals{'} health information systems, or through the digitization of historical paper records. Each EHR creation method yields the need for sophisticated text reuse detection tools in order to prepare the EHR collections for efficient secondary use relying on Natural Language Processing methods. Herein, we address the detection of two types of text reuse in French EHRs: 1) the detection of updated versions of the same document and 2) the detection of document duplicates that still bear surface differences due to OCR or de-identification processing. We present a robust text reuse detection method to automatically identify redundant document pairs in two French EHR corpora that achieves an overall macro F-measure of 0.68 and 0.60, respectively and correctly identifies all redundant document pairs of interest.",
}
@inproceedings{cotik-etal-2016-negation,
    title = "Negation Detection in Clinical Reports Written in {G}erman",
    author = "Cotik, Viviana  and
      Roller, Roland  and
      Xu, Feiyu  and
      Uszkoreit, Hans  and
      Budde, Klemens  and
      Schmidt, Danilo",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5113",
    pages = "115--124",
    abstract = "An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that are negated or just speculated. The system has been applied to two different types of German clinical texts: clinical notes and discharge summaries. Our approach is built on top of NegEx, a well known algorithm for identifying non-factive mentions of medical findings. In this work, we adjust a previous adaptation of NegEx to German and evaluate the system on our data to detect negation and speculation. The results are compared to a baseline algorithm and are analyzed for both types of clinical documents. Our system achieves an F1-Score above 0.9 on both types of reports.",
}
@inproceedings{dandala-etal-2016-scoring,
    title = "Scoring Disease-Medication Associations using Advanced {NLP}, Machine Learning, and Multiple Content Sources",
    author = "Dandala, Bharath  and
      Devarakonda, Murthy  and
      Bornea, Mihaela  and
      Nielson, Christopher",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5114",
    pages = "125--133",
    abstract = "Effective knowledge resources are critical for developing successful clinical decision support systems that alleviate the cognitive load on physicians in patient care. In this paper, we describe two new methods for building a knowledge resource of disease to medication associations. These methods use fundamentally different content and are based on advanced natural language processing and machine learning techniques. One method uses distributional semantics on large medical text, and the other uses data mining on a large number of patient records. The methods are evaluated using 25,379 unique disease-medication pairs extracted from 100 de-identified longitudinal patient records of a large multi-provider hospital system. We measured recall (R), precision (P), and F scores for positive and negative association prediction, along with coverage and accuracy. While individual methods performed well, a combined stacked classifier achieved the best performance, indicating the limitations and unique value of each resource and method. In predicting positive associations, the stacked combination significantly outperformed the baseline (a distant semi-supervised method on large medical text), achieving F scores of 0.75 versus 0.55 on the pairs seen in the patient records, and F scores of 0.69 and 0.35 on unique pairs.",
}
@inproceedings{vishnyakova-etal-2016-author,
    title = "Author Name Disambiguation in {MEDLINE} Based on Journal Descriptors and Semantic Types",
    author = "Vishnyakova, Dina  and
      Rodriguez-Esteban, Raul  and
      Ozol, Khan  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining ({B}io{T}xt{M}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5115",
    pages = "134--142",
    abstract = "Author name disambiguation (AND) in publication and citation resources is a well-known problem. Often, information about email address and other details in the affiliation is missing. In cases where such information is not available, identifying the authorship of publications becomes very challenging. Consequently, there have been attempts to resolve such cases by utilizing external resources as references. However, such external resources are heterogeneous and are not always reliable regarding the correctness of information. To solve the AND task, especially when information about an author is not complete we suggest the use of new features such as journal descriptors (JD) and semantic types (ST). The evaluation of different feature models shows that their inclusion has an impact equivalent to that of other important features such as email address. Using such features we show that our system outperforms the state of the art.",
}
@proceedings{ws-2016-extra,
    title = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    editor = "Blanco, Eduardo  and
      Morante, Roser  and
      Saur{\'\i}, Roser",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5000",
}
@inproceedings{joshi-etal-2016-thought,
    title = "{`}Who would have thought of that!{'}: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection",
    author = "Joshi, Aditya  and
      Jain, Prayas  and
      Bhattacharyya, Pushpak  and
      Carman, Mark",
    booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5001",
    pages = "1--10",
    abstract = "Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports the first topic model for sarcasm detection, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic-level, and sentiment-level distributions. Our evaluation shows that topics such as {`}work{'}, {`}gun laws{'}, {`}weather{'} are sarcasm-prevalent topics. Our model is also able to discover the mixture of sentiment-bearing words that exist in a text of a given sentiment-related label. Finally, we apply our model to predict sarcasm in tweets. We outperform two prior work based on statistical classifiers with specific features, by around 25{\%}.",
}
@inproceedings{vincze-2016-detecting,
    title = "Detecting Uncertainty Cues in {H}ungarian Social Media Texts",
    author = "Vincze, Veronika",
    booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5002",
    pages = "11--21",
    abstract = "In this paper, we aim at identifying uncertainty cues in Hungarian social media texts. We present our machine learning based uncertainty detector which is based on a rich features set including lexical, morphological, syntactic, semantic and discourse-based features, and we evaluate our system on a small set of manually annotated social media texts. We also carry out cross-domain and domain adaptation experiments using an annotated corpus of standard Hungarian texts and show that domain differences significantly affect machine learning. Furthermore, we argue that differences among uncertainty cue types may also affect the efficiency of uncertainty detection.",
}
@inproceedings{colomer-etal-2016-detecting,
    title = "Detecting Level of Belief in {C}hinese and {S}panish",
    author = "Colomer, Juan Pablo  and
      Lai, Keyu  and
      Rambow, Owen",
    booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5003",
    pages = "22--30",
    abstract = "There has been extensive work on detecting the level of committed belief (also known as {``}factuality{''}) that an author is expressing towards the propositions in his or her utterances. Previous work on English has revealed that this can be done as a sequence tagging task. In this paper, we investigate the same task for Chinese and Spanish, two very different languages from English and from each other.",
}
@inproceedings{lendvai-reichel-2016-contradiction,
    title = "Contradiction Detection for Rumorous Claims",
    author = "Lendvai, Piroska  and
      Reichel, Uwe",
    booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5004",
    pages = "31--40",
    abstract = "The utilization of social media material in journalistic workflows is increasing, demanding automated methods for the identification of mis- and disinformation. Since textual contradiction across social media posts can be a signal of rumorousness, we seek to model how claims in Twitter posts are being textually contradicted. We identify two different contexts in which contradiction emerges: its broader form can be observed across independently posted tweets and its more specific form in threaded conversations. We define how the two scenarios differ in terms of central elements of argumentation: claims and conversation structure. We design and evaluate models for the two scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to represent claims and conversation structure implicitly in a generic inference model, while previous studies used explicit or no representation of these properties. To address noisy text, our classifiers use simple similarity features derived from the string and part-of-speech level. Corpus statistics reveal distribution differences for these features in contradictory as opposed to non-contradictory tweet relations, and the classifiers yield state of the art performance.",
}
@inproceedings{nakov-2016-negation,
    title = "Negation and Modality in Machine Translation",
    author = "Nakov, Preslav",
    booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5005",
    pages = "41",
    abstract = "Negation and modality are two important grammatical phenomena that have attracted recent research attention as they can contribute to extra-propositional meaning aspects, among with factuality, attribution, irony and sarcasm. These aspects go beyond analysis such as semantic role labeling, and modeling them is important as a step towards a higher level of language understanding, which is needed for practical applications such as sentiment analysis. In this talk, I will go beyond English, and I will discuss how negation and modality are expressed in other languages. I will also go beyond sentiment analysis and I will present some challenges that the two phenomena pose for machine translation (MT). In particular, I will demonstrate how contemporary MT systems fail on them, and I will discuss some possible solutions.",
}
@inproceedings{jimenez-zafra-etal-2016-problematic,
    title = "Problematic Cases in the Annotation of Negation in {S}panish",
    author = "Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Martin, Maite  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso  and
      Mart{\'\i}, Toni  and
      Taul{\'e}, Mariona",
    booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5006",
    pages = "42--48",
    abstract = "This paper presents the main sources of disagreement found during the annotation of the Spanish SFU Review Corpus with negation (SFU ReviewSP -NEG). Negation detection is a challenge in most of the task related to NLP, so the availability of corpora annotated with this phenomenon is essential in order to advance in tasks related to this area. A thorough analysis of the problems found during the annotation could help in the study of this phenomenon.",
}
@inproceedings{van-son-etal-2016-building,
    title = "Building a Dictionary of Affixal Negations",
    author = "van Son, Chantal  and
      van Miltenburg, Emiel  and
      Morante, Roser",
    booktitle = "Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics ({E}x{P}ro{M})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-5007",
    pages = "49--56",
    abstract = "This paper discusses the need for a dictionary of affixal negations and regular antonyms to facilitate their automatic detection in text. Without such a dictionary, affixal negations are very difficult to detect. In addition, we show that the set of affixal negations is not homogeneous, and that different NLP tasks may require different subsets. A dictionary can store the subtypes of affixal negations, making it possible to select a certain subset or to make inferences on the basis of these subtypes. We take a first step towards creating a negation dictionary by annotating all direct antonym pairs inWordNet using an existing typology of affixal negations. By highlighting some of the issues that were encountered in this annotation experiment, we hope to provide some insights into the necessary steps of building a negation dictionary.",
}
@proceedings{ws-2016-natural,
    title = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    editor = "Chen, Hsin-Hsi  and
      Tseng, Yuen-Hsien  and
      Ng, Vincent  and
      Lu, Xiaofei",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4900",
}
@inproceedings{liu-matsumoto-2016-simplification,
    title = "Simplification of Example Sentences for Learners of {J}apanese Functional Expressions",
    author = "Liu, Jun  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4901",
    pages = "1--5",
    abstract = "Learning functional expressions is one of the difficulties for language learners, since functional expressions tend to have multiple meanings and complicated usages in various situations. In this paper, we report an experiment of simplifying example sentences of Japanese functional expressions especially for Chinese-speaking learners. For this purpose, we developed {``}Japanese Functional Expressions List{''} and {``}Simple Japanese Replacement List{''}. To evaluate the method, we conduct a small-scale experiment with Chinese-speaking learners on the effectiveness of the simplified example sentences. The experimental results indicate that simplified sentences are helpful in learning Japanese functional expressions.",
}
@inproceedings{kotani-yoshimi-2016-effectiveness,
    title = "Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier",
    author = "Kotani, Katsunori  and
      Yoshimi, Takehiko",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4902",
    pages = "6--10",
    abstract = "In learning Asian languages, learners encounter the problem of character types that are different from those in their first language, for instance, between Chinese characters and the Latin alphabet. This problem also affects listening because learners reconstruct letters from speech sounds. Hence, special attention should be paid to listening practice for learners of Asian languages. However, to our knowledge, few studies have evaluated the ease of listening comprehension (listenability) in Asian languages. Therefore, as a pilot study of listenability in Asian languages, we developed a measurement method for learners of English in order to examine the discriminability of linguistic and learner features. The results showed that the accuracy of our method outperformed a simple majority vote, which suggests that a combination of linguistic and learner features should be used to measure listenability in Asian languages as well as in English.",
}
@inproceedings{pathak-etal-2016-two,
    title = "A Two-Phase Approach Towards Identifying Argument Structure in Natural Language",
    author = "Pathak, Arkanath  and
      Goyal, Pawan  and
      Bhowmick, Plaban",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4903",
    pages = "11--19",
    abstract = "We propose a new approach for extracting argument structure from natural language texts that contain an underlying argument. Our approach comprises of two phases: Score Assignment and Structure Prediction. The Score Assignment phase trains models to classify relations between argument units (Support, Attack or Neutral). To that end, different training strategies have been explored. We identify different linguistic and lexical features for training the classifiers. Through ablation study, we observe that our novel use of word-embedding features is most effective for this task. The Structure Prediction phase makes use of the scores from the Score Assignment phase to arrive at the optimal structure. We perform experiments on three argumentation datasets, namely, AraucariaDB, Debatepedia and Wikipedia. We also propose two baselines and observe that the proposed approach outperforms baseline systems for the final task of Structure Prediction.",
}
@inproceedings{adams-etal-2016-distributed,
    title = "Distributed Vector Representations for Unsupervised Automatic Short Answer Grading",
    author = "Adams, Oliver  and
      Roy, Shourya  and
      Krishnapuram, Raghuram",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4904",
    pages = "20--29",
    abstract = "We address the problem of automatic short answer grading, evaluating a collection of approaches inspired by recent advances in distributional text representations. In addition, we propose an unsupervised approach for determining text similarity using one-to-many alignment of word vectors. We evaluate the proposed technique across two datasets from different domains, namely, computer science and English reading comprehension, that additionally vary between highschool level and undergraduate students. Experiments demonstrate that the proposed technique often outperforms other compositional distributional semantics approaches as well as vector space methods such as latent semantic analysis. When combined with a scoring scheme, the proposed technique provides a powerful tool for tackling the complex problem of short answer grading. We also discuss a number of other key points worthy of consideration in preparing viable, easy-to-deploy automatic short-answer grading systems for the real-world.",
}
@inproceedings{kang-etal-2016-comparison,
    title = "A Comparison of Word Embeddings for {E}nglish and Cross-Lingual {C}hinese Word Sense Disambiguation",
    author = "Kang, Hong Jin  and
      Chen, Tao  and
      Chandrasekaran, Muthu Kumar  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4905",
    pages = "30--39",
    abstract = "Word embeddings are now ubiquitous forms of word representation in natural language processing. There have been applications of word embeddings for monolingual word sense disambiguation (WSD) in English, but few comparisons have been done. This paper attempts to bridge that gap by examining popular embeddings for the task of monolingual English WSD. Our simplified method leads to comparable state-of-the-art performance without expensive retraining. Cross-Lingual WSD {--} where the word senses of a word in a source language come from a separate target translation language {--} can also assist in language learning; for example, when providing translations of target vocabulary for learners. Thus we have also applied word embeddings to the novel task of cross-lingual WSD for Chinese and provide a public dataset for further benchmarking. We have also experimented with using word embeddings for LSTM networks and found surprisingly that a basic LSTM network does not work well. We discuss the ramifications of this outcome.",
}
@inproceedings{lee-etal-2016-overview,
    title = "Overview of {NLP}-{TEA} 2016 Shared Task for {C}hinese Grammatical Error Diagnosis",
    author = "Lee, Lung-Hao  and
      Rao, Gaoqi  and
      Yu, Liang-Chih  and
      Xun, Endong  and
      Zhang, Baolin  and
      Chang, Li-Ping",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4906",
    pages = "40--48",
    abstract = "This paper presents the NLP-TEA 2016 shared task for Chinese grammatical error diagnosis which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 15 teams registered for this shared task, 9 teams developed the system and submitted a total of 36 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.",
}
@inproceedings{zheng-etal-2016-chinese,
    title = "{C}hinese Grammatical Error Diagnosis with Long Short-Term Memory Networks",
    author = "Zheng, Bo  and
      Che, Wanxiang  and
      Guo, Jiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4907",
    pages = "49--56",
    abstract = "Grammatical error diagnosis is an important task in natural language processing. This paper introduces our Chinese Grammatical Error Diagnosis (CGED) system in the NLP-TEA-3 shared task for CGED. The CGED system can diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the CGED task as a sequence labeling task and describe three models, including a CRF-based model, an LSTM-based model and an ensemble model using stacking. We also show in details how we build and train the models. Evaluation includes three levels, which are detection level, identification level and position level. On the CGED-HSK dataset of NLP-TEA-3 shared task, our system presents the best F1-scores in all the three levels and also the best recall in the last two levels.",
}
@inproceedings{liu-etal-2016-automatic,
    title = "Automatic Grammatical Error Detection for {C}hinese based on Conditional Random Field",
    author = "Liu, Yajun  and
      Han, Yingjie  and
      Zhuo, Liyan  and
      Zan, Hongying",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4908",
    pages = "57--62",
    abstract = "In the process of learning and using Chinese, foreigners may have grammatical errors due to negative migration of their native languages. Currently, the computer-oriented automatic detection method of grammatical errors is not mature enough. Based on the evaluating task {---} CGED2016, we select and analyze the classification model and design feature extraction method to obtain grammatical errors including Mission(M), Disorder(W), Selection (S) and Redundant (R) automatically. The experiment results based on the dynamic corpus of HSK show that the Chinese grammatical error automatic detection method, which uses CRF as classification model and n-gram as feature extraction method. It is simple and efficient which play a positive effect on the research of Chinese grammatical error automatic detection and also a supporting and guiding role in the teaching of Chinese as a foreign language.",
}
@inproceedings{chen-etal-2016-cyut,
    title = "{CYUT}-{III} System at {C}hinese Grammatical Error Diagnosis Task",
    author = "Chen, Po-Lin  and
      Wu, Shih-Hung  and
      Chen, Liang-Pu  and
      Yang, Ping-Che",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4909",
    pages = "63--72",
    abstract = "This paper describe the CYUT-III system on grammar error detection in the 2016 NLP-TEA Chinese Grammar Error Detection shared task CGED. In this task a system has to detect four types of errors, in-cluding redundant word error, missing word error, word selection error and word ordering error. Based on the conditional random fields (CRF) model, our system is a linear tagger that can detect the errors in learners{'} essays. Since the system performance depends on the features heavily, in this paper, we are going to report how to integrate the collocation feature into the CRF model. Our system presents the best detection accuracy and Identification accuracy on the TOCFL dataset, which is in traditional Chi-nese. The same system also works well on the simplified Chinese HSK dataset.",
}
@inproceedings{chou-etal-2016-word,
    title = "Word Order Sensitive Embedding Features/Conditional Random Field-based {C}hinese Grammatical Error Detection",
    author = "Chou, Wei-Chieh  and
      Lin, Chin-Kui  and
      Liao, Yuan-Fu  and
      Wang, Yih-Ru",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4910",
    pages = "73--81",
    abstract = "This paper discusses how to adapt two new word embedding features to build a more efficient Chinese Grammatical Error Diagnosis (CGED) systems to assist Chinese foreign learners (CFLs) in improving their written essays. The major idea is to apply word order sensitive Word2Vec approaches including (1) structured skip-gram and (2) continuous window (CWindow) models, because they are more suitable for solving syntax-based problems. The proposed new features were evaluated on the Test of Chinese as a Foreign Language (TOCFL) learner database provided by NLP-TEA-3{\&}CGED shared task. Experimental results showed that the new features did work better than the traditional word order insensitive Word2Vec approaches. Moreover, according to the official evaluation results, our system achieved the lowest (0.1362) false positive (FA) and the highest precision rates in all three measurements.",
}
@inproceedings{roy-etal-2016-fluctuation,
    title = "A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading",
    author = "Roy, Shourya  and
      Dandapat, Sandipan  and
      Narahari, Y.",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4911",
    pages = "82--91",
    abstract = "We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem. A major drawback of the existing techniques is the significant effect that variations in model answers could have on their performances. The proposed fluctuation smoothing approach, based on classical sequential pattern mining, exploits lexical overlap in students{'} answers to any typical question. We empirically demonstrate using multiple datasets that the proposed approach improves the overall performance and significantly reduces (up to 63{\%}) variation in performance (standard deviation) of unsupervised ASAG techniques. We bring in additional benchmarks such as (a) paraphrasing of model answers and (b) using answers by k top performing students as model answers, to amplify the benefits of the proposed approach.",
}
@inproceedings{hading-etal-2016-japanese,
    title = "{J}apanese Lexical Simplification for Non-Native Speakers",
    author = "Hading, Muhaimin  and
      Matsumoto, Yuji  and
      Sakamoto, Maki",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4912",
    pages = "92--96",
    abstract = "This paper introduces Japanese lexical simplification. Japanese lexical simplification is the task of replacing difficult words in a given sentence to produce a new sentence with simple words without changing the original meaning of the sentence. We purpose a method of supervised regression learning to estimate difficulty ordering of words with statistical features obtained from two types of Japanese corpora. For the similarity of words, we use a Japanese thesaurus and dependency-based word embeddings. Evaluation of the proposed method is performed by comparing the difficulty ordering of the words.",
}
@inproceedings{cao-etal-2016-corpus,
    title = "A Corpus-based Approach for {S}panish-{C}hinese Language Learning",
    author = "Cao, Shuyuan  and
      da Cunha, Iria  and
      Iruskieta, Mikel",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4913",
    pages = "97--106",
    abstract = "Due to the huge population that speaks Spanish and Chinese, these languages occupy an important position in the language learning studies. Although there are some automatic translation systems that benefit the learning of both languages, there is enough space to create resources in order to help language learners. As a quick and effective resource that can give large amount language information, corpus-based learning is becoming more and more popular. In this paper we enrich a Spanish-Chinese parallel corpus automatically with part of-speech (POS) information and manually with discourse segmentation (following the Rhetorical Structure Theory (RST) (Mann and Thompson, 1988)). Two search tools allow the Spanish-Chinese language learners to carry out different queries based on tokens and lemmas. The parallel corpus and the research tools are available to the academic community. We propose some examples to illustrate how learners can use the corpus to learn Spanish and Chinese.",
}
@inproceedings{morgado-da-costa-etal-2016-syntactic,
    title = "Syntactic Well-Formedness Diagnosis and Error-Based Coaching in Computer Assisted Language Learning using Machine Translation",
    author = "Morgado da Costa, Luis  and
      Bond, Francis  and
      He, Xiaoling",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4914",
    pages = "107--116",
    abstract = "We present a novel approach to Computer Assisted Language Learning (CALL), using deep syntactic parsers and semantic based machine translation (MT) in diagnosing and providing explicit feedback on language learners{'} errors. We are currently developing a proof of concept system showing how semantic-based machine translation can, in conjunction with robust computational grammars, be used to interact with students, better understand their language errors, and help students correct their grammar through a series of useful feedback messages and guided language drills. Ultimately, we aim to prove the viability of a new integrated rule-based MT approach to disambiguate students{'} intended meaning in a CALL system. This is a necessary step to provide accurate coaching on how to correct ungrammatical input, and it will allow us to overcome a current bottleneck in the field {---} an exponential burst of ambiguity caused by ambiguous lexical items (Flickinger, 2010). From the users{'} interaction with the system, we will also produce a richly annotated Learner Corpus, annotated automatically with both syntactic and semantic information.",
}
@inproceedings{kalitvianski-etal-2016-aligned,
    title = "An Aligned {F}rench-{C}hinese corpus of 10{K} segments from university educational material",
    author = "Kalitvianski, Ruslan  and
      Wang, Lingxiao  and
      Bellynck, Val{\'e}rie  and
      Boitet, Christian",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4915",
    pages = "117--121",
    abstract = "This paper describes a corpus of nearly 10K French-Chinese aligned segments, produced by post-editing machine translated computer science courseware. This corpus was built from 2013 to 2016 within the PROJECT{\_}NAME project, by native Chinese students. The quality, as judged by native speakers, is ad-equate for understanding (far better than by reading only the original French) and for getting better marks. This corpus is annotated at segment-level by a self-assessed quality score. It has been directly used as supplemental training data to build a statistical machine translation system dedicated to that sublanguage, and can be used to extract the specific bilingual terminology. To our knowledge, it is the first corpus of this kind to be released.",
}
@inproceedings{zalmout-etal-2016-analysis,
    title = "Analysis of Foreign Language Teaching Methods: An Automatic Readability Approach",
    author = "Zalmout, Nasser  and
      Saddiki, Hind  and
      Habash, Nizar",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4916",
    pages = "122--130",
    abstract = "Much research in education has been done on the study of different language teaching methods. However, there has been little investigation using computational analysis to compare such methods in terms of readability or complexity progression. In this paper, we make use of existing readability scoring techniques and our own classifiers to analyze the textbooks used in two very different teaching methods for English as a Second Language {--} the grammar-based and the communicative methods. Our analysis indicates that the grammar-based curriculum shows a more coherent readability progression compared to the communicative curriculum. This finding corroborates with the expectations about the differences between these two methods and validates our approach{'}s value in comparing different teaching methods quantitatively.",
}
@inproceedings{chen-etal-2016-generating,
    title = "Generating and Scoring Correction Candidates in {C}hinese Grammatical Error Diagnosis",
    author = "Chen, Shao-Heng  and
      Tsai, Yu-Lin  and
      Lin, Chuan-Jie",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4917",
    pages = "131--139",
    abstract = "Grammatical error diagnosis is an essential part in a language-learning tutoring system. Based on the data sets of Chinese grammar error detection tasks, we proposed a system which measures the likelihood of correction candidates generated by deleting or inserting characters or words, moving substrings to different positions, substituting prepositions with other prepositions, or substituting words with their synonyms or similar strings. Sentence likelihood is measured based on the frequencies of substrings from the space-removed version of Google n-grams. The evaluation on the training set shows that Missing-related and Selection-related candidate generation methods have promising performance. Our final system achieved a precision of 30.28{\%} and a recall of 62.85{\%} in the identification level evaluated on the test set.",
}
@inproceedings{yeh-etal-2016-grammatical,
    title = "Grammatical Error Detection Based on Machine Learning for {M}andarin as Second Language Learning",
    author = "Yeh, Jui-Feng  and
      Hsu, Tsung-Wei  and
      Yeh, Chan-Kun",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4918",
    pages = "140--147",
    abstract = "Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become a word has another semantic. Second, the Mandarin{'}s grammars have flexible rule and special usage. Therefore, the common grammatical errors can classify to missing, redundant, selection and disorder. In this paper, we proposed the structure of the Recurrent Neural Networks using Long Short-term memory (RNN-LSTM). It can detect the error type from the foreign learner writing. The features based on the word vector and part-of-speech vector. In the test data found that our method in the detection level of recall better than the others, even as high as 0.9755. That is because we give the possibility of greater choice in detecting errors.",
}
@inproceedings{huang-wang-2016-bi,
    title = "{B}i-{LSTM} Neural Networks for {C}hinese Grammatical Error Diagnosis",
    author = "Huang, Shen  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4919",
    pages = "148--154",
    abstract = "Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression. In this paper, we present a model based on Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the task as a sequence labeling problem, so as to detect Chinese grammatical errors, to identify the error types and to locate the error positions. In the corpora of this year{'}s shared task, there can be multiple errors in a single offset of a sentence, to address which, we simutaneously train three Bi-LSTM models sharing word embeddings which label Missing, Redundant and Selection errors respectively. We regard word ordering error as a special kind of word selection error which is longer during training phase, and then separate them by length during testing phase. In NLP-TEA 3 shared task for Chinese Grammatical Error Diagnosis(CGED), Our system achieved relatively high F1 for all the three levels in the traditional Chinese track and for the detection level in the Simpified Chinese track.",
}
@inproceedings{yang-etal-2016-chinese,
    title = "{C}hinese Grammatical Error Diagnosis Using Single Word Embedding",
    author = "Yang, Jinnan  and
      Peng, Bo  and
      Wang, Jin  and
      Zhang, Jixian  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications ({NLPTEA}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4920",
    pages = "155--161",
    abstract = "Abstract Automatic grammatical error detection for Chinese has been a big challenge for NLP researchers. Due to the formal and strict grammar rules in Chinese, it is hard for foreign students to master Chinese. A computer-assisted learning tool which can automatically detect and correct Chinese grammatical errors is necessary for those foreign students. Some of the previous works have sought to identify Chinese grammatical errors using template- and learning-based methods. In contrast, this study introduced convolutional neural network (CNN) and long-short term memory (LSTM) for the shared task of Chinese Grammatical Error Diagnosis (CGED). Different from traditional word-based embedding, single word embedding was used as input of CNN and LSTM. The proposed single word embedding can capture both semantic and syntactic information to detect those four type grammatical error. In experimental evaluation, the recall and f1-score of our submitted results Run1 of the TOCFL testing data ranked the fourth place in all submissions in detection-level.",
}
@proceedings{ws-2016-nlp,
    title = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    editor = {Nakov, Preslav  and
      Zampieri, Marcos  and
      Tan, Liling  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Malmasi, Shervin},
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4800",
}
@inproceedings{malmasi-etal-2016-discriminating,
    title = "Discriminating between Similar Languages and {A}rabic Dialect Identification: A Report on the Third {DSL} Shared Task",
    author = {Malmasi, Shervin  and
      Zampieri, Marcos  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Nakov, Preslav  and
      Ali, Ahmed  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4801",
    pages = "1--14",
    abstract = "We present the results of the third edition of the Discriminating between Similar Languages (DSL) shared task, which was organized as part of the VarDial{'}2016 workshop at COLING{'}2016. The challenge offered two subtasks: subtask 1 focused on the identification of very similar languages and language varieties in newswire texts, whereas subtask 2 dealt with Arabic dialect identification in speech transcripts. A total of 37 teams registered to participate in the task, 24 teams submitted test results, and 20 teams also wrote system description papers. High-order character n-grams were the most successful feature, and the best classification approaches included traditional supervised learning methods such as SVM, logistic regression, and language models, while deep learning approaches did not perform very well.",
}
@inproceedings{coltekin-rama-2016-discriminating,
    title = "Discriminating Similar Languages with Linear {SVM}s and Neural Networks",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Rama, Taraka},
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4802",
    pages = "15--24",
    abstract = "This paper describes the systems we experimented with for participating in the discriminating between similar languages (DSL) shared task 2016. We submitted results of a single system based on support vector machines (SVM) with linear kernel and using character ngram features, which obtained the first rank at the closed training track for test set A. Besides the linear SVM, we also report additional experiments with a number of deep learning architectures. Despite our intuition that non-linear deep learning methods should be advantageous, linear models seems to fare better in this task, at least with the amount of data and the amount of effort we spent on tuning these models.",
}
@inproceedings{rama-coltekin-2016-lstm,
    title = "{LSTM} Autoencoders for Dialect Analysis",
    author = {Rama, Taraka  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4803",
    pages = "25--32",
    abstract = "Computational approaches for dialectometry employed Levenshtein distance to compute an aggregate similarity between two dialects belonging to a single language group. In this paper, we apply a sequence-to-sequence autoencoder to learn a deep representation for words that can be used for meaningful comparison across dialects. In contrast to the alignment-based methods, our method does not require explicit alignments. We apply our architectures to three different datasets and show that the learned representations indicate highly similar results with the analyses based on Levenshtein distance and capture the traditional dialectal differences shown by dialectologists.",
}
@inproceedings{zirikly-etal-2016-gw-lt3,
    title = "The {GW}/{LT}3 {V}ar{D}ial 2016 Shared Task System for Dialects and Similar Languages Detection",
    author = "Zirikly, Ayah  and
      Desmet, Bart  and
      Diab, Mona",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4804",
    pages = "33--41",
    abstract = "This paper describes the GW/LT3 contribution to the 2016 VarDial shared task on the identification of similar languages (task 1) and Arabic dialects (task 2). For both tasks, we experimented with Logistic Regression and Neural Network classifiers in isolation. Additionally, we implemented a cascaded classifier that consists of coarse and fine-grained classifiers (task 1) and a classifier ensemble with majority voting for task 2. The submitted systems obtained state-of-the art performance and ranked first for the evaluation on social media data (test sets B1 and B2 for task 1), with a maximum weighted F1 score of 91.94{\%}.",
}
@inproceedings{diab-2016-processing,
    title = "Processing Dialectal {A}rabic: Exploiting Variability and Similarity to Overcome Challenges and Discover Opportunities",
    author = "Diab, Mona",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4805",
    pages = "42",
    abstract = "We recently witnessed an exponential growth in dialectal Arabic usage in both textual data and speech recordings especially in social media. Processing such media is of great utility for all kinds of applications ranging from information extraction to social media analytics for political and commercial purposes to building decision support systems. Compared to other languages, Arabic, especially the informal variety, poses a significant challenge to natural language processing algorithms since it comprises multiple dialects, linguistic code switching, and a lack of standardized orthographies, to top its relatively complex morphology. Inherently, the problem of processing Arabic in the context of social media is the problem of how to handle resource poor languages. In this talk I will go over some of our insights to some of these problems and show how there is a silver lining where we can generalize some of our solutions to other low resource language contexts.",
}
@inproceedings{popovic-etal-2016-language,
    title = "Language Related Issues for Machine Translation between Closely Related {S}outh {S}lavic Languages",
    author = "Popovi{\'c}, Maja  and
      Ar{\v{c}}an, Mihael  and
      Klubi{\v{c}}ka, Filip",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4806",
    pages = "43--52",
    abstract = "Machine translation between closely related languages is less challenging and exibits a smaller number of translation errors than translation between distant languages, but there are still obstacles which should be addressed in order to improve such systems. This work explores the obstacles for machine translation systems between closely related South Slavic languages, namely Croatian, Serbian and Slovenian. Statistical systems for all language pairs and translation directions are trained using parallel texts from different domains, however mainly on spoken language i.e. subtitles. For translation between Serbian and Croatian, a rule-based system is also explored. It is shown that for all language pairs and translation systems, the main obstacles are differences between structural properties.",
}
@inproceedings{adouane-etal-2016-romanized,
    title = "{R}omanized {B}erber and {R}omanized {A}rabic Automatic Language Identification Using Machine Learning",
    author = "Adouane, Wafia  and
      Semmar, Nasredine  and
      Johansson, Richard",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4807",
    pages = "53--61",
    abstract = "The identification of the language of text/speech input is the first step to be able to properly do any language-dependent natural language processing. The task is called Automatic Language Identification (ALI). Being a well-studied field since early 1960{'}s, various methods have been applied to many standard languages. The ALI standard methods require datasets for training and use character/word-based n-gram models. However, social media and new technologies have contributed to the rise of informal and minority languages on the Web. The state-of-the-art automatic language identifiers fail to properly identify many of them. Romanized Arabic (RA) and Romanized Berber (RB) are cases of these informal languages which are under-resourced. The goal of this paper is twofold: detect RA and RB, at a document level, as separate languages and distinguish between them as they coexist in North Africa. We consider the task as a classification problem and use supervised machine learning to solve it. For both languages, character-based 5-grams combined with additional lexicons score the best, F-score of 99.75{\%} and 97.77{\%} for RB and RA respectively.",
}
@inproceedings{ostling-2016-many,
    title = "How Many Languages Can a Language Model Model?",
    author = {{\"O}stling, Robert},
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4808",
    pages = "62",
    abstract = "One of the purposes of the VarDial workshop series is to encourage research into NLP methods that treat human languages as a continuum, by designing models that exploit the similarities between languages and variants. In my work, I am using a continuous vector representation of languages that allows modeling and exploring the language continuum in a very direct way. The basic tool for this is a character-based recurrent neural network language model conditioned on language vectors whose values are learned during training. By feeding the model Bible translations in a thousand languages, not only does the learned vector space capture language similarity, but by interpolating between the learned vectors it is possible to generate text in unattested intermediate forms between the training languages.",
}
@inproceedings{adouane-etal-2016-automatic,
    title = "Automatic Detection of {A}rabicized {B}erber and {A}rabic Varieties",
    author = "Adouane, Wafia  and
      Semmar, Nasredine  and
      Johansson, Richard  and
      Bobicev, Victoria",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4809",
    pages = "63--72",
    abstract = "Automatic Language Identification (ALI) is the detection of the natural language of an input text by a machine. It is the first necessary step to do any language-dependent natural language processing task. Various methods have been successfully applied to a wide range of languages, and the state-of-the-art automatic language identifiers are mainly based on character n-gram models trained on huge corpora. However, there are many languages which are not yet automatically processed, for instance minority and informal languages. Many of these languages are only spoken and do not exist in a written format. Social media platforms and new technologies have facilitated the emergence of written format for these spoken languages based on pronunciation. The latter are not well represented on the Web, commonly referred to as under-resourced languages, and the current available ALI tools fail to properly recognize them. In this paper, we revisit the problem of ALI with the focus on Arabicized Berber and dialectal Arabic short texts. We introduce new resources and evaluate the existing methods. The results show that machine learning models combined with lexicons are well suited for detecting Arabicized Berber and different Arabic varieties and distinguishing between them, giving a macro-average F-score of 92.94{\%}.",
}
@inproceedings{aminian-etal-2016-automatic,
    title = "Automatic Verification and Augmentation of Multilingual Lexicons",
    author = "Aminian, Maryam  and
      Al-Badrashiny, Mohamed  and
      Diab, Mona",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4810",
    pages = "73--81",
    abstract = "We present an approach for automatic verification and augmentation of multilingual lexica. We exploit existing parallel and monolingual corpora to extract multilingual correspondents via tri-angulation. We demonstrate the efficacy of our approach on two publicly available resources: Tharwa, a three-way lexicon comprising Dialectal Arabic, Modern Standard Arabic and English lemmas among other information (Diab et al., 2014); and BabelNet, a multilingual thesaurus comprising over 276 languages including Arabic variant entries (Navigli and Ponzetto, 2012). Our automated approach yields an F1-score of 71.71{\%} in generating correct multilingual correspondents against gold Tharwa, and 54.46{\%} against gold BabelNet without any human intervention.",
}
@inproceedings{kunchukuttan-bhattacharyya-2016-faster,
    title = "Faster Decoding for Subword Level Phrase-based {SMT} between Related Languages",
    author = "Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4811",
    pages = "82--88",
    abstract = "A common and effective way to train translation systems between related languages is to consider sub-word level basic units. However, this increases the length of the sentences resulting in increased decoding time. The increase in length is also impacted by the specific choice of data format for representing the sentences as subwords. In a phrase-based SMT framework, we investigate different choices of decoder parameters as well as data format and their impact on decoding time and translation accuracy. We suggest best options for these settings that significantly improve decoding time with little impact on the translation accuracy.",
}
@inproceedings{malmasi-2016-subdialectal,
    title = "Subdialectal Differences in {S}orani {K}urdish",
    author = "Malmasi, Shervin",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4812",
    pages = "89--96",
    abstract = "In this study we apply classification methods for detecting subdialectal differences in Sorani Kurdish texts produced in different regions, namely Iran and Iraq. As Sorani is a low-resource language, no corpus including texts from different regions was readily available. To this end, we identified data sources that could be leveraged for this task to create a dataset of 200,000 sentences. Using surface features, we attempted to classify Sorani subdialects, showing that sentences from news sources in Iraq and Iran are distinguishable with 96{\%} accuracy. This is the first preliminary study for a dialect that has not been widely studied in computational linguistics, evidencing the possible existence of distinct subdialects.",
}
@inproceedings{popovic-etal-2016-enlarging,
    title = "Enlarging Scarce In-domain {E}nglish-{C}roatian Corpus for {SMT} of {MOOC}s Using {S}erbian",
    author = "Popovi{\'c}, Maja  and
      Cholakov, Kostadin  and
      Kordoni, Valia  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4813",
    pages = "97--105",
    abstract = "Massive Open Online Courses have been growing rapidly in size and impact. Yet the language barrier constitutes a major growth impediment in reaching out all people and educating all citizens. A vast majority of educational material is available only in English, and state-of-the-art machine translation systems still have not been tailored for this peculiar genre. In addition, a mere collection of appropriate in-domain training material is a challenging task. In this work, we investigate statistical machine translation of lecture subtitles from English into Croatian, which is morphologically rich and generally weakly supported, especially for the educational domain. We show that results comparable with publicly available systems trained on much larger data can be achieved if a small in-domain training set is used in combination with additional in-domain corpus originating from the closely related Serbian language.",
}
@inproceedings{malmasi-zampieri-2016-arabic,
    title = "{A}rabic Dialect Identification in Speech Transcripts",
    author = "Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4814",
    pages = "106--113",
    abstract = "In this paper we describe a system developed to identify a set of four regional Arabic dialects (Egyptian, Gulf, Levantine, North African) and Modern Standard Arabic (MSA) in a transcribed speech corpus. We competed under the team name MAZA in the Arabic Dialect Identification sub-task of the 2016 Discriminating between Similar Languages (DSL) shared task. Our system achieved an F1-score of 0.51 in the closed training track, ranking first among the 18 teams that participated in the sub-task. Our system utilizes a classifier ensemble with a set of linear models as base classifiers. We experimented with three different ensemble fusion strategies, with the mean probability approach providing the best performance.",
}
@inproceedings{herman-etal-2016-dsl,
    title = "{DSL} Shared Task 2016: Perfect Is The Enemy of Good Language Discrimination Through Expectation{--}Maximization and Chunk-based Language Model",
    author = "Herman, Ond{\v{r}}ej  and
      Suchomel, V{\'\i}t  and
      Baisa, V{\'\i}t  and
      Rychl{\'y}, Pavel",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4815",
    pages = "114--118",
    abstract = "In this paper we investigate two approaches to discrimination of similar languages: Expectation{--}maximization algorithm for estimating conditional probability P(word|language) and byte level language models similar to compression-based language modelling methods. The accuracy of these methods reached respectively 86.6{\%} and 88.3{\%} on set A of the DSL Shared task 2016 competition.",
}
@inproceedings{bjerva-2016-byte,
    title = "Byte-based Language Identification with Deep Convolutional Networks",
    author = "Bjerva, Johannes",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4816",
    pages = "119--125",
    abstract = "We report on our system for the shared task on discriminating between similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88{\%} accuracy on subtask A, 68.80{\%} accuracy on subtask B1, and 69.80{\%} accuracy on subtask B2. A large difference in accuracy on development data can be observed with relatively minor changes in our network{'}s architecture and hyperparameters. We therefore expect fine-tuning of these parameters to yield higher accuracies.",
}
@inproceedings{hanani-etal-2016-classifying,
    title = "Classifying {ASR} Transcriptions According to {A}rabic Dialect",
    author = "Hanani, Abualsoud  and
      Qaroush, Aziz  and
      Taylor, Stephen",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4817",
    pages = "126--134",
    abstract = "We describe several systems for identifying short samples of Arabic dialects. The systems were prepared for the shared task of the 2016 DSL Workshop. Our best system, an SVM using character tri-gram features, achieved an accuracy on the test data for the task of 0.4279, compared to a baseline of 0.20 for chance guesses or 0.2279 if we had always chosen the same most frequent class in the test set. This compares with the results of the team with the best weighted F1 score, which was an accuracy of 0.5117. The team entries seem to fall into cohorts, with all the teams in a cohort within a standard-deviation of each other, and our three entries are in the third cohort, which is about seven standard deviations from the top.",
}
@inproceedings{ionescu-popescu-2016-unibuckernel,
    title = "{U}nibuc{K}ernel: An Approach for {A}rabic Dialect Identification Based on Multiple String Kernels",
    author = "Ionescu, Radu Tudor  and
      Popescu, Marius",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4818",
    pages = "135--144",
    abstract = "The most common approach in text mining classification tasks is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. Unlike the common approach, we present a method that uses only character p-grams (also known as n-grams) as features for the Arabic Dialect Identification (ADI) Closed Shared Task of the DSL 2016 Challenge. The proposed approach combines several string kernels using multiple kernel learning. In the learning stage, we try both Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR), and we choose KDA as it gives better results in a 10-fold cross-validation carried out on the training set. Our approach is shallow and simple, but the empirical results obtained in the ADI Shared Task prove that it achieves very good results. Indeed, we ranked on the second place with an accuracy of 50.91{\%} and a weighted F1 score of 51.31{\%}. We also present improved results in this paper, which we obtained after the competition ended. Simply by adding more regularization into our model to make it more suitable for test data that comes from a different distribution than training data, we obtain an accuracy of 51.82{\%} and a weighted F1 score of 52.18{\%}. Furthermore, the proposed approach has an important advantage in that it is language independent and linguistic theory neutral, as it does not require any NLP tools.",
}
@inproceedings{belinkov-glass-2016-character,
    title = "A Character-level Convolutional Neural Network for Distinguishing Similar Languages and Dialects",
    author = "Belinkov, Yonatan  and
      Glass, James",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4819",
    pages = "145--152",
    abstract = "Discriminating between closely-related language varieties is considered a challenging and important task. This paper describes our submission to the DSL 2016 shared-task, which included two sub-tasks: one on discriminating similar languages and one on identifying Arabic dialects. We developed a character-level neural network for this task. Given a sequence of characters, our model embeds each character in vector space, runs the sequence through multiple convolutions with different filter widths, and pools the convolutional representations to obtain a hidden vector representation of the text that is used for predicting the language or dialect. We primarily focused on the Arabic dialect identification task and obtained an F1 score of 0.4834, ranking 6th out of 18 participants. We also analyze errors made by our system on the Arabic data in some detail, and point to challenges such an approach is faced with.",
}
@inproceedings{jauhiainen-etal-2016-heli,
    title = "{H}e{LI}, a Word-Based Backoff Method for Language Identification",
    author = "Jauhiainen, Tommi  and
      Lind{\'e}n, Krister  and
      Jauhiainen, Heidi",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4820",
    pages = "153--162",
    abstract = "In this paper we describe the Helsinki language identification method, HeLI, and the resources we created for and used in the 3rd edition of the Discriminating between Similar Languages (DSL) shared task, which was organized as part of the VarDial 2016 workshop. The shared task comprised of a total of 8 tracks, of which we participated in 7. The shared task had a record number of participants, with 17 teams providing results for the closed track of the test set A. Our system reached the 2nd position in 4 tracks (A closed and open, B1 open and B2 open) and in this paper we are focusing on the methods and data used for those tracks. We describe our word-based backoff method in mathematical notation. We also describe how we selected the corpus we used in the open tracks.",
}
@inproceedings{adouane-etal-2016-asirem,
    title = "{ASIREM} Participation at the Discriminating Similar Languages Shared Task 2016",
    author = "Adouane, Wafia  and
      Semmar, Nasredine  and
      Johansson, Richard",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4821",
    pages = "163--169",
    abstract = "This paper presents the system built by ASIREM team for the Discriminating between Similar Languages (DSL) Shared task 2016. It describes the system which uses character-based and word-based n-grams separately. ASIREM participated in both sub-tasks (sub-task 1 and sub-task 2) and in both open and closed tracks. For the sub-task 1 which deals with Discriminating between similar languages and national language varieties, the system achieved an accuracy of 87.79{\%} on the closed track, ending up ninth (the best results being 89.38{\%}). In sub-task 2, which deals with Arabic dialect identification, the system achieved its best performance using character-based n-grams (49.67{\%} accuracy), ranking fourth in the closed track (the best result being 51.16{\%}), and an accuracy of 53.18{\%}, ranking first in the open track.",
}
@inproceedings{gamallo-etal-2016-comparing,
    title = "Comparing Two Basic Methods for Discriminating Between Similar Languages and Varieties",
    author = "Gamallo, Pablo  and
      Alegria, I{\~n}aki  and
      Pichel, Jos{\'e} Ramom  and
      Agirrezabal, Manex",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4822",
    pages = "170--177",
    abstract = "This article describes the systems submitted by the Citius{\_}Ixa{\_}Imaxin team to the Discriminating Similar Languages Shared Task 2016. The systems are based on two different strategies: classification with ranked dictionaries and Naive Bayes classifiers. The results of the evaluation show that ranking dictionaries are more sound and stable across different domains while basic bayesian models perform reasonably well on in-domain datasets, but their performance drops when they are applied on out-of-domain texts.",
}
@inproceedings{goutte-leger-2016-advances,
    title = "Advances in Ngram-based Discrimination of Similar Languages",
    author = "Goutte, Cyril  and
      L{\'e}ger, Serge",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4823",
    pages = "178--184",
    abstract = "We describe the systems entered by the National Research Council in the 2016 shared task on discriminating similar languages. Like previous years, we relied on character ngram features, and a mixture of discriminative and generative statistical classifiers. We mostly investigated the influence of the amount of data on the performance, in the open task, and compared the two-stage approach (predicting language/group, then variant) to a flat approach. Results suggest that ngrams are still state-of-the-art for language and variant identification, and that additional data has a small but decisive impact.",
}
@inproceedings{guggilla-2016-discrimination,
    title = "Discrimination between Similar Languages, Varieties and Dialects using {CNN}- and {LSTM}-based Deep Neural Networks",
    author = "Guggilla, Chinnappa",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4824",
    pages = "185--194",
    abstract = "In this paper, we describe a system (CGLI) for discriminating similar languages, varieties and dialects using convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks. We have participated in the Arabic dialect identification sub-task of DSL 2016 shared task for distinguishing different Arabic language texts under closed submission track. Our proposed approach is language independent and works for discriminating any given set of languages, varieties, and dialects. We have obtained 43.29{\%} weighted-F1 accuracy in this sub-task using CNN approach using default network parameters.",
}
@inproceedings{mcnamee-2016-language,
    title = "Language and Dialect Discrimination Using Compression-Inspired Language Models",
    author = "McNamee, Paul",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4825",
    pages = "195--203",
    abstract = "The DSL 2016 shared task continued previous evaluations from 2014 and 2015 that facilitated the study of automated language and dialect identification. This paper describes results for this year{'}s shared task and from several related experiments conducted at the Johns Hopkins University Human Language Technology Center of Excellence (JHU HLTCOE). Previously the HLTCOE has explored the use of compression-inspired language modeling for language and dialect identification, using news, Wikipedia, blog post, and Twitter corpora. The technique we have relied upon is based on prediction by partial matching (PPM), a state of the art text compression technique. Due to the close relationship between adaptive compression and language modeling, such compression techniques can also be applied to multi-way text classification problems, and previous studies have examined tasks such as authorship attribution, email spam detection, and topical classification. We applied our approach to the multi-class decision that considered each dialect or language as a possibility for the given shared task input line. Results for test-set A were in accord with our expectations, however results for test-sets B and C appear to be markedly worse. We had not anticipated the inclusion of multiple communications in differing languages in test-set B (social media) input lines, and had not expected the test-set C (dialectal Arabic) data to be represented phonetically instead of in native orthography.",
}
@inproceedings{alshutayri-etal-2016-arabic,
    title = "{A}rabic Language {WEKA}-Based Dialect Classifier for {A}rabic Automatic Speech Recognition Transcripts",
    author = "Alshutayri, Areej  and
      Atwell, Eric  and
      Alosaimy, Abdulrahman  and
      Dickins, James  and
      Ingleby, Michael  and
      Watson, Janet",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4826",
    pages = "204--211",
    abstract = "This paper describes an Arabic dialect identification system which we developed for the Discriminating Similar Languages (DSL) 2016 shared task. We classified Arabic dialects by using Waikato Environment for Knowledge Analysis (WEKA) data analytic tool which contains many alternative filters and classifiers for machine learning. We experimented with several classifiers and the best accuracy was achieved using the Sequential Minimal Optimization (SMO) algorithm for training and testing process set to three different feature-sets for each testing process. Our approach achieved an accuracy equal to 42.85{\%} which is considerably worse in comparison to the evaluation scores on the training set of 80-90{\%} and with training set {``}60:40{''} percentage split which achieved accuracy around 50{\%}. We observed that Buckwalter transcripts from the Saarland Automatic Speech Recognition (ASR) system are given without short vowels, though the Buckwalter system has notation for these. We elaborate such observations, describe our methods and analyse the training dataset.",
}
@inproceedings{barbaresi-2016-unsupervised,
    title = "An Unsupervised Morphological Criterion for Discriminating Similar Languages",
    author = "Barbaresi, Adrien",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4827",
    pages = "212--220",
    abstract = "In this study conducted on the occasion of the Discriminating between Similar Languages shared task, I introduce an additional decision factor focusing on the token and subtoken level. The motivation behind this submission is to test whether a morphologically-informed criterion can add linguistically relevant information to global categorization and thus improve performance. The contributions of this paper are (1) a description of the unsupervised, low-resource method; (2) an evaluation and analysis of its raw performance; and (3) an assessment of its impact within a model comprising common indicators used in language identification. I present and discuss the systems used in the task A, a 12-way language identification task comprising varieties of five main language groups. Additionally I introduce a new off-the-shelf Naive Bayes classifier using a contrastive word and subword n-gram model ({``}Bayesline{''}) which outperforms the best submissions.",
}
@inproceedings{eldesouki-etal-2016-qcri,
    title = "{QCRI} @ {DSL} 2016: Spoken {A}rabic Dialect Identification Using Textual Features",
    author = "Eldesouki, Mohamed  and
      Dalvi, Fahim  and
      Sajjad, Hassan  and
      Darwish, Kareem",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4828",
    pages = "221--226",
    abstract = "The paper describes the QCRI submissions to the task of automatic Arabic dialect classification into 5 Arabic variants, namely Egyptian, Gulf, Levantine, North-African, and Modern Standard Arabic (MSA). The training data is relatively small and is automatically generated from an ASR system. To avoid over-fitting on such small data, we carefully selected and designed the features to capture the morphological essence of the different dialects. We submitted four runs to the Arabic sub-task. For all runs, we used a combined feature vector of character bi-grams, tri-grams, 4-grams, and 5-grams. We tried several machine-learning algorithms, namely Logistic Regression, Naive Bayes, Neural Networks, and Support Vector Machines (SVM) with linear and string kernels. However, our submitted runs used SVM with a linear kernel. In the closed submission, we got the best accuracy of 0.5136 and the third best weighted F1 score, with a difference less than 0.002 from the highest score.",
}
@inproceedings{franco-penya-mamani-sanchez-2016-tuning,
    title = "Tuning {B}ayes Baseline for Dialect Detection",
    author = "Franco-Penya, Hector-Hugo  and
      Mamani Sanchez, Liliana",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4829",
    pages = "227--234",
    abstract = "This paper describes an analysis of our submissions to the Dialect Detection Shared Task 2016. We proposed three different systems that involved simplistic features, to name: a Naive-bayes system, a Support Vector Machines-based system and a Tree Kernel-based system. These systems underperform when compared to other submissions in this shared task, since the best one achieved an accuracy of {\textasciitilde}0.834.",
}
@inproceedings{nisioi-etal-2016-vanilla,
    title = "Vanilla Classifiers for Distinguishing between Similar Languages",
    author = "Nisioi, Sergiu  and
      Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4830",
    pages = "235--242",
    abstract = "In this paper we describe the submission of the UniBuc-NLP team for the Discriminating between Similar Languages Shared Task, DSL 2016. We present and analyze the results we obtained in the closed track of sub-task 1 (Similar languages and language varieties) and sub-task 2 (Arabic dialects). For sub-task 1 we used a logistic regression classifier with tf-idf feature weighting and for sub-task 2 a character-based string kernel with an SVM classifier. Our results show that good accuracy scores can be obtained with limited feature and model engineering. While certain limitations are to be acknowledged, our approach worked surprisingly well for out-of-domain, social media data, with 0.898 accuracy (3rd place) for dataset B1 and 0.838 accuracy (4th place) for dataset B2.",
}
@inproceedings{cianflone-kosseim-2016-n,
    title = "N-gram and Neural Language Models for Discriminating Similar Languages",
    author = "Cianflone, Andre  and
      Kosseim, Leila",
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4831",
    pages = "243--250",
    abstract = "This paper describes our submission to the 2016 Discriminating Similar Languages (DSL) Shared Task. We participated in the closed Sub-task 1 with two separate machine learning techniques. The first approach is a character based Convolution Neural Network with an LSTM layer (CLSTM), which achieved an accuracy of 78.45{\%} with minimal tuning. The second approach is a character-based n-gram model of size 7. It achieved an accuracy of 88.45{\%} which is close to the accuracy of 89.38{\%} achieved by the best submission.",
}
@proceedings{ws-2016-international-terminology,
    title = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    editor = "Drouin, Patrick  and
      Grabar, Natalia  and
      Hamon, Thierry  and
      Kageura, Kyo  and
      Takeuchi, Koichi",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4700",
}
@inproceedings{song-2016-analyzing,
    title = "Analyzing Impact, Trend, and Diffusion of Knowledge associated with Neoplasms Research",
    author = "Song, Min",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4701",
    pages = "1",
    abstract = "Cancer (a.k.a neoplasms in a broader sense) is one of the leading causes of death worldwide and its incidence is expected to exacerbate. To respond to the critical need from the society, there have been rigorous attempts for the cancer research community to develop treatment for cancer. Accordingly, we observe a surge in the sheer volume of research products and outcomes in relation to neoplasms. In this talk, we introduce the notion of entitymetrics to provide a new lens for understanding the impact, trend, and diffusion of knowledge associated with neoplasms research. To this end, we collected over two million records from PubMed, the most popular search engine in the medical domain. Coupled with text mining techniques including named entity recognition, sentence boundary detection, string approximate matching, entitymetrics enables us to analyze knowledge diffusion, impact, and trend at various knowledge entity units, such as bio-entity, organization, and country. At the end of the talk, the future applications and possible directions of entitymetrics will be discussed.",
}
@inproceedings{amjadian-etal-2016-local,
    title = "Local-Global Vectors to Improve Unigram Terminology Extraction",
    author = "Amjadian, Ehsan  and
      Inkpen, Diana  and
      Paribakht, Tahereh  and
      Faez, Farahnaz",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4702",
    pages = "2--11",
    abstract = "The present paper explores a novel method that integrates efficient distributed representations with terminology extraction. We show that the information from a small number of observed instances can be combined with local and global word embeddings to remarkably improve the term extraction results on unigram terms. To do so we pass the terms extracted by other tools to a filter made of the local-global embeddings and a classifier which in turn decides whether or not a term candidate is a term. The filter can also be used as a hub to merge different term extraction tools into a single higher-performing system. We compare filters that use the skip-gram architecture and filters that employ the CBOW architecture for the task at hand.",
}
@inproceedings{mykowiecka-etal-2016-recognition,
    title = "Recognition of non-domain phrases in automatically extracted lists of terms",
    author = "Mykowiecka, Agnieszka  and
      Marciniak, Malgorzata  and
      Rychlik, Piotr",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4703",
    pages = "12--20",
    abstract = "In the paper, we address the problem of recognition of non-domain phrases in terminology lists obtained with an automatic term extraction tool. We focus on identification of multi-word phrases that are general terms and discourse function expressions. We tested several methods based on domain corpora comparison and a method based on contexts of phrases identified in a large corpus of general language. We compared the results of the methods to manual annotation. The results show that the task is quite hard as the inter-annotator agreement is low. Several tested methods achieved similar overall results, although the phrase ordering varied between methods. The most successful method with the precision about 0.75 at the half of the tested list was the context based method using a modified contextual diversity coefficient.",
}
@inproceedings{barriere-etal-2016-contextual,
    title = "Contextual term equivalent search using domain-driven disambiguation",
    author = "Barri{\`e}re, Caroline  and
      M{\'e}nard, Pierre Andr{\'e}  and
      Azoulay, Daphn{\'e}e",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4704",
    pages = "21--29",
    abstract = "This article presents a domain-driven algorithm for the task of term sense disambiguation (TSD). TSD aims at automatically choosing which term record from a term bank best represents the meaning of a term occurring in a particular context. In a translation environment, finding the contextually appropriate term record is necessary to access the proper equivalent to be used in the target language text. The term bank TERMIUM Plus, recently published as an open access repository, is chosen as a domain-rich resource for testing our TSD algorithm, using English and French as source and target languages. We devise an experiment using over 1300 English terms found in scientific articles, and show that our domain-driven TSD algorithm is able to bring the best term record, and therefore the best French equivalent, at the average rank of 1.69 compared to a baseline random rank of 3.51.",
}
@inproceedings{iwai-etal-2016-method,
    title = "A Method of Augmenting Bilingual Terminology by Taking Advantage of the Conceptual Systematicity of Terminologies",
    author = "Iwai, Miki  and
      Takeuchi, Koichi  and
      Kageura, Kyo  and
      Ishibashi, Kazuya",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4705",
    pages = "30--40",
    abstract = "In this paper, we propose a method of augmenting existing bilingual terminologies. Our method belongs to a {``}generate and validate{''} framework rather than extraction from corpora. Although many studies have proposed methods to find term translations or to augment terminology within a {``}generate and validate{''} framework, few has taken full advantage of the systematic nature of terminologies. A terminology of a domain represents the conceptual system of the domain fairly systematically, and we contend that making use of the systematicity fully will greatly contribute to the effective augmentation of terminologies. This paper proposes and evaluates a novel method to generate bilingual term candidates by using existing terminologies and delving into their systematicity. Experiments have shown that our method can generate much better term candidate pairs than the existing method and give improved performance for terminology augmentation.",
}
@inproceedings{roesiger-etal-2016-acquisition,
    title = "Acquisition of semantic relations between terms: how far can we get with standard {NLP} tools?",
    author = {Roesiger, Ina  and
      Bettinger, Julia  and
      Sch{\"a}fer, Johannes  and
      Dorna, Michael  and
      Heid, Ulrich},
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4706",
    pages = "41--51",
    abstract = "The extraction of data exemplifying relations between terms can make use, at least to a large extent, of techniques that are similar to those used in standard hybrid term candidate extraction, namely basic corpus analysis tools (e.g. tagging, lemmatization, parsing), as well as morphological analysis of complex words (compounds and derived items). In this article, we discuss the use of such techniques for the extraction of raw material for a description of relations between terms, and we provide internal evaluation data for the devices developed. We claim that user-generated content is a rich source of term variation through paraphrasing and reformulation, and that these provide relational data at the same time as term variants. Germanic languages with their rich word formation morphology may be particularly good candidates for the approach advocated here.",
}
@inproceedings{bernier-colborne-drouin-2016-evaluation,
    title = "Evaluation of distributional semantic models: a holistic approach",
    author = "Bernier-Colborne, Gabriel  and
      Drouin, Patrick",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4707",
    pages = "52--61",
    abstract = "We investigate how both model-related factors and application-related factors affect the accuracy of distributional semantic models (DSMs) in the context of specialized lexicography, and how these factors interact. This holistic approach to the evaluation of DSMs provides valuable guidelines for the use of these models and insight into the kind of semantic information they capture.",
}
@inproceedings{qasemizadeh-2016-study,
    title = "A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification",
    author = "QasemiZadeh, Behrang",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4708",
    pages = "62--72",
    abstract = "We propose and evaluate a method for identifying co-hyponym lexical units in a terminological resource. The principles of term recognition and distributional semantics are combined to extract terms from a similar category of concept. Given a set of candidate terms, random projections are employed to represent them as low-dimensional vectors. These vectors are derived automatically from the frequency of the co-occurrences of the candidate terms and words that appear within windows of text in their proximity (context-windows). In a $k$-nearest neighbours framework, these vectors are classified using a small set of manually annotated terms which exemplify concept categories. We then investigate the interplay between the size of the corpus that is used for collecting the co-occurrences and a number of factors that play roles in the performance of the proposed method: the configuration of context-windows for collecting co-occurrences, the selection of neighbourhood size ($k$), and the choice of similarity metric.",
}
@inproceedings{leon-arauz-etal-2016-pattern,
    title = "Pattern-based Word Sketches for the Extraction of Semantic Relations",
    author = "Le{\'o}n-Ara{\'u}z, Pilar  and
      San Mart{\'\i}n, Antonio  and
      Faber, Pamela",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4709",
    pages = "73--82",
    abstract = "Despite advances in computer technology, terminologists still tend to rely on manual work to extract all the semantic information that they need for the description of specialized concepts. In this paper we propose the creation of new word sketches in Sketch Engine for the extraction of semantic relations. Following a pattern-based approach, new sketch grammars are devel-oped in order to extract some of the most common semantic relations used in the field of ter-minology: generic-specific, part-whole, location, cause and function.",
}
@inproceedings{miyata-kageura-2016-constructing,
    title = "Constructing and Evaluating Controlled Bilingual Terminologies",
    author = "Miyata, Rei  and
      Kageura, Kyo",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4710",
    pages = "83--93",
    abstract = "This paper presents the construction and evaluation of Japanese and English controlled bilingual terminologies that are particularly intended for controlled authoring and machine translation with special reference to the Japanese municipal domain. Our terminologies are constructed by extracting terms from municipal website texts, and the term variations are controlled by defining preferred and proscribed terms for both the source Japanese and the target English. To assess the coverage of the terms/concepts in the municipal domain and validate the quality of the control, we employ a quantitative extrapolation method that estimates the potential vocabulary size. Using Large-Number-of-Rare-Event (LNRE) modelling, we compare two parameters: (1) uncontrolled and controlled and (2) Japanese and English. The results show that our terminologies currently cover about 45{--}65{\%} of the terms and 50{--}65{\%} of the concepts in the municipal domain, and are well controlled. The detailed analysis of growth patterns of terminologies also provides insight into the extent to which we can enlarge the terminologies within the realistic range.",
}
@inproceedings{francopoulo-etal-2016-providing,
    title = "Providing and Analyzing {NLP} Terms for our Community",
    author = "Francopoulo, Gil  and
      Mariani, Joseph  and
      Paroubek, Patrick  and
      Vernier, Fr{\'e}d{\'e}ric",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4711",
    pages = "94--103",
    abstract = "By its own nature, the Natural Language Processing (NLP) community is a priori the best equipped to study the evolution of its own publications, but works in this direction are rare and only recently have we seen a few attempts at charting the field. In this paper, we use the algorithms, resources, standards, tools and common practices of the NLP field to build a list of terms characteristic of ongoing research, by mining a large corpus of scientific publications, aiming at the largest possible exhaustivity and covering the largest possible time span. Study of the evolution of this term list through time reveals interesting insights on the dynamics of field and the availability of the term database and of the corpus (for a large part) make possible many further comparative studies in addition to providing a test field for a new graphic interface designed to perform visual time analytics of large sized thesauri.",
}
@inproceedings{kocbek-etal-2016-evaluating,
    title = "Evaluating a dictionary of human phenotype terms focusing on rare diseases",
    author = "Kocbek, Simon  and
      Fujiwara, Toyofumi  and
      Kim, Jin-Dong  and
      Takagi, Toshihisa  and
      Groza, Tudor",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4712",
    pages = "104--109",
    abstract = "Annotating medical text such as clinical notes with human phenotype descriptors is an important task that can, for example, assist in building patient profiles. To automatically annotate text one usually needs a dictionary of predefined terms. However, do to the variety of human expressiveness, current state-of-the art phenotype concept recognizers and automatic annotators struggle with specific domain issues and challenges. In this paper we present results of an-notating gold standard corpus with a dictionary containing lexical variants for the Human Phenotype Ontology terms. The main purpose of the dictionary is to improve the recall of phenotype concept recognition systems. We compare the method with four other approaches and present results.",
}
@inproceedings{sadoun-2016-semi,
    title = "A semi automatic annotation approach for ontological and terminological knowledge acquisition",
    author = "Sadoun, Driss",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4713",
    pages = "110--120",
    abstract = "We propose a semi-automatic method for the acquisition of specialised ontological and terminological knowledge. An ontology and a terminology are automatically built from domain experts{'} annotations. The ontology formalizes the common and shared conceptual vocabulary of those experts. Its associated terminology defines a glossary linking annotated terms to their semantic categories. These two resources evolve incrementally and are used for an automatic annotation of a new corpus at each iteration. The annotated corpus concerns the evaluation of French higher education and science institutions.",
}
@inproceedings{krishna-hans-2016-understanding,
    title = "Understanding Medical free text: A Terminology driven approach",
    author = "Krishna, Santosh Sai  and
      Hans, Manoj",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4714",
    pages = "121--125",
    abstract = "With many hospitals digitalizing clinical records it has opened opportunities for researchers in NLP, Machine Learning to apply techniques for extracting meaning and make actionable insights. There has been previous attempts in mapping free text to medical nomenclature like UMLS, SNOMED. However, in this paper, we had analyzed diagnosis in clinical reports using ICD10 to achieve a lightweight, real-time predictions by introducing concepts like WordInfo, root word identification. We were able to achieve 68.3{\%} accuracy over clinical records collected from qualified clinicians. Our study would further help the healthcare institutes in organizing their clinical reports based on ICD10 mappings and derive numerous insights to achieve operational efficiency and better medical care.",
}
@proceedings{ws-2016-asian,
    title = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    editor = "Nakazawa, Toshiaki  and
      Mino, Hideya  and
      Ding, Chenchen  and
      Goto, Isao  and
      Neubig, Graham  and
      Kurohashi, Sadao  and
      Riza, Ir. Hammam  and
      Bhattacharyya, Pushpak",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4600",
}
@inproceedings{nakazawa-etal-2016-overview,
    title = "Overview of the 3rd Workshop on {A}sian Translation",
    author = "Nakazawa, Toshiaki  and
      Ding, Chenchen  and
      Mino, Hideya  and
      Goto, Isao  and
      Neubig, Graham  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4601",
    pages = "1--46",
    abstract = "This paper presents the results of the shared tasks from the 3rd workshop on Asian translation (WAT2016) including J ↔ E, J ↔ C scientific paper translation subtasks, C ↔ J, K ↔ J, E ↔ J patent translation subtasks, I ↔ E newswire subtasks and H ↔ E, H ↔ J mixed domain subtasks. For the WAT2016, 15 institutions participated in the shared tasks. About 500 translation results have been submitted to the automatic evaluation server, and selected submissions were manually evaluated.",
}
@inproceedings{long-etal-2016-translation,
    title = "Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation",
    author = "Long, Zi  and
      Utsuro, Takehito  and
      Mitsuhashi, Tomoharu  and
      Yamamoto, Mikio",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4602",
    pages = "47--57",
    abstract = "Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT). Despite its recent success, NMT cannot handle a larger vocabulary because training complexity and decoding complexity proportionally increase with the number of target words. This problem becomes even more serious when translating patent documents, which contain many technical terms that are observed infrequently. In NMTs, words that are out of vocabulary are represented by a single unknown token. In this paper, we propose a method that enables NMT to translate patent sentences comprising a large vocabulary of technical terms. We train an NMT system on bilingual data wherein technical terms are replaced with technical term tokens; this allows it to translate most of the source sentences except technical terms. Further, we use it as a decoder to translate source sentences with technical term tokens and replace the tokens with technical term translations using SMT. We also use it to rerank the 1,000-best SMT translations on the basis of the average of the SMT score and that of the NMT rescoring of the translated sentences with technical term tokens. Our experiments on Japanese-Chinese patent sentences show that the proposed NMT system achieves a substantial improvement of up to 3.1 BLEU points and 2.3 RIBES points over traditional SMT systems and an improvement of approximately 0.6 BLEU points and 0.8 RIBES points over an equivalent NMT system without our proposed technique.",
}
@inproceedings{sato-etal-2016-japanese,
    title = "{J}apanese-{E}nglish Machine Translation of Recipe Texts",
    author = "Sato, Takayuki  and
      Harashima, Jun  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4603",
    pages = "58--67",
    abstract = "Concomitant with the globalization of food culture, demand for the recipes of specialty dishes has been increasing. The recent growth in recipe sharing websites and food blogs has resulted in numerous recipe texts being available for diverse foods in various languages. However, little work has been done on machine translation of recipe texts. In this paper, we address the task of translating recipes and investigate the advantages and disadvantages of traditional phrase-based statistical machine translation and more recent neural machine translation. Specifically, we translate Japanese recipes into English, analyze errors in the translated recipes, and discuss available room for improvements.",
}
@inproceedings{singh-etal-2016-iit,
    title = "{IIT} {B}ombay{'}s {E}nglish-{I}ndonesian submission at {WAT}: Integrating Neural Language Models with {SMT}",
    author = "Singh, Sandhya  and
      Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4604",
    pages = "68--74",
    abstract = "This paper describes the IIT Bombay{'}s submission as a part of the shared task in WAT 2016 for English{--}Indonesian language pair. The results reported here are for both the direction of the language pair. Among the various approaches experimented, Operation Sequence Model (OSM) and Neural Language Model have been submitted for WAT. The OSM approach integrates translation and reordering process resulting in relatively improved translation. Similarly the neural experiment integrates Neural Language Model with Statistical Machine Translation (SMT) as a feature for translation. The Neural Probabilistic Language Model (NPLM) gave relatively high BLEU points for Indonesian to English translation system while the Neural Network Joint Model (NNJM) performed better for English to Indonesian direction of translation system. The results indicate improvement over the baseline Phrase-based SMT by 0.61 BLEU points for English-Indonesian system and 0.55 BLEU points for Indonesian-English translation system.",
}
@inproceedings{hashimoto-etal-2016-domain,
    title = "Domain Adaptation and Attention-Based Unknown Word Replacement in {C}hinese-to-{J}apanese Neural Machine Translation",
    author = "Hashimoto, Kazuma  and
      Eriguchi, Akiko  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4605",
    pages = "75--83",
    abstract = "This paper describes our UT-KAY system that participated in the Workshop on Asian Translation 2016. Based on an Attention-based Neural Machine Translation (ANMT) model, we build our system by incorporating a domain adaptation method for multiple domains and an attention-based unknown word replacement method. In experiments, we verify that the attention-based unknown word replacement method is effective in improving translation scores in Chinese-to-Japanese machine translation. We further show results of manual analysis on the replaced unknown words.",
}
@inproceedings{fuji-etal-2016-global,
    title = "Global Pre-ordering for Improving Sublanguage Translation",
    author = "Fuji, Masaru  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4606",
    pages = "84--93",
    abstract = "When translating formal documents, capturing the sentence structure specific to the sublanguage is extremely necessary to obtain high-quality translations. This paper proposes a novel global reordering method with particular focus on long-distance reordering for capturing the global sentence structure of a sublanguage. The proposed method learns global reordering models from a non-annotated parallel corpus and works in conjunction with conventional syntactic reordering. Experimental results on the patent abstract sublanguage show substantial gains of more than 25 points in the RIBES metric and comparable BLEU scores both for Japanese-to-English and English-to-Japanese translations.",
}
@inproceedings{kanouchi-etal-2016-neural,
    title = "Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation",
    author = "Kanouchi, Shin  and
      Sudoh, Katsuhito  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4607",
    pages = "94--103",
    abstract = "This paper presents an improved lexicalized reordering model for phrase-based statistical machine translation using a deep neural network. Lexicalized reordering suffers from reordering ambiguity, data sparseness and noises in a phrase table. Previous neural reordering model is successful to solve the first and second problems but fails to address the third one. Therefore, we propose new features using phrase translation and word alignment to construct phrase vectors to handle inherently noisy phrase translation pairs. The experimental results show that our proposed method improves the accuracy of phrase reordering. We confirm that the proposed method works well with phrase pairs including NULL alignments.",
}
@inproceedings{li-etal-2016-system,
    title = "System Description of bjtu{\_}nlp Neural Machine Translation System",
    author = "Li, Shaotong  and
      Xu, JinAn  and
      Chen, Yufeng  and
      Zhang, Yujie",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4608",
    pages = "104--110",
    abstract = "This paper presents our machine translation system that developed for the WAT2016 evalua-tion tasks of ja-en, ja-zh, en-ja, zh-ja, JPCja-en, JPCja-zh, JPCen-ja, JPCzh-ja. We build our system based on encoder{--}decoder framework by integrating recurrent neural network (RNN) and gate recurrent unit (GRU), and we also adopt an attention mechanism for solving the problem of information loss. Additionally, we propose a simple translation-specific approach to resolve the unknown word translation problem. Experimental results show that our system performs better than the baseline statistical machine translation (SMT) systems in each task. Moreover, it shows that our proposed approach of unknown word translation performs effec-tively improvement of translation results.",
}
@inproceedings{ehara-2016-translation,
    title = "Translation systems and experimental results of the {EHR} group for {WAT}2016 tasks",
    author = "Ehara, Terumasa",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4609",
    pages = "111--118",
    abstract = "System architecture, experimental settings and experimental results of the group for the WAT2016 tasks are described. We participate in six tasks: en-ja, zh-ja, JPCzh-ja, JPCko-ja, HINDENen-hi and HINDENhi-ja. Although the basic architecture of our sys-tems is PBSMT with reordering, several techniques are conducted. Especially, the system for the HINDENhi-ja task with pivoting by English uses the reordering technique. Be-cause Hindi and Japanese are both OV type languages and English is a VO type language, we can use reordering technique to the pivot language. We can improve BLEU score from 7.47 to 7.66 by the reordering technique for the sentence level pivoting of this task.",
}
@inproceedings{neubig-2016-lexicons,
    title = "Lexicons and Minimum Risk Training for Neural Machine Translation: {NAIST}-{CMU} at {WAT}2016",
    author = "Neubig, Graham",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4610",
    pages = "119--125",
    abstract = "This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian Translation was based on attentional neural machine translation (NMT) models. In addition to the standard NMT model, we make a number of improvements, most notably the use of discrete translation lexicons to improve probability estimates, and the use of minimum risk training to optimize the MT system for BLEU score. As a result, our system achieved the highest translation evaluation scores for the task.",
}
@inproceedings{imamura-sumita-2016-nict,
    title = "{NICT}-2 Translation System for {WAT}2016: Applying Domain Adaptation to Phrase-based Statistical Machine Translation",
    author = "Imamura, Kenji  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4611",
    pages = "126--132",
    abstract = "This paper describes the NICT-2 translation system for the 3rd Workshop on Asian Translation. The proposed system employs a domain adaptation method based on feature augmentation. We regarded the Japan Patent Office Corpus as a mixture of four domain corpora and improved the translation quality of each domain. In addition, we incorporated language models constructed from Google n-grams as external knowledge. Our domain adaptation method can naturally incorporate such external knowledge that contributes to translation quality.",
}
@inproceedings{kinoshita-etal-2016-translation,
    title = "Translation Using {JAPIO} Patent Corpora: {JAPIO} at {WAT}2016",
    author = "Kinoshita, Satoshi  and
      Oshio, Tadaaki  and
      Mitsuhashi, Tomoharu  and
      Ehara, Terumasa",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4612",
    pages = "133--138",
    abstract = "We participate in scientific paper subtask (ASPEC-EJ/CJ) and patent subtask (JPC-EJ/CJ/KJ) with phrase-based SMT systems which are trained with its own patent corpora. Using larger corpora than those prepared by the workshop organizer, we achieved higher BLEU scores than most participants in EJ and CJ translations of patent subtask, but in crowdsourcing evaluation, our EJ translation, which is best in all automatic evaluations, received a very poor score. In scientific paper subtask, our translations are given lower scores than most translations that are produced by translation engines trained with the in-domain corpora. But our scores are higher than those of general-purpose RBMTs and online services. Considering the result of crowdsourcing evaluation, it shows a possibility that CJ SMT system trained with a large patent corpus translates non-patent technical documents at a practical level.",
}
@inproceedings{wang-etal-2016-efficient,
    title = "An Efficient and Effective Online Sentence Segmenter for Simultaneous Interpretation",
    author = "Wang, Xiaolin  and
      Finch, Andrew  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4613",
    pages = "139--148",
    abstract = "Simultaneous interpretation is a very challenging application of machine translation in which the input is a stream of words from a speech recognition engine. The key problem is how to segment the stream in an online manner into units suitable for translation. The segmentation process proceeds by calculating a confidence score for each word that indicates the soundness of placing a sentence boundary after it, and then heuristics are employed to determine the position of the boundaries. Multiple variants of the confidence scoring method and segmentation heuristics were studied. Experimental results show that the best performing strategy is not only efficient in terms of average latency per word, but also achieved end-to-end translation quality close to an offline baseline, and close to oracle segmentation.",
}
@inproceedings{ding-etal-2016-similar,
    title = "Similar {S}outheast {A}sian Languages: Corpus-Based Case Study on {T}hai-{L}aotian and {M}alay-{I}ndonesian",
    author = "Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4614",
    pages = "149--156",
    abstract = "This paper illustrates the similarity between Thai and Laotian, and between Malay and Indonesian, based on an investigation on raw parallel data from Asian Language Treebank. The cross-lingual similarity is investigated and demonstrated on metrics of correspondence and order of tokens, based on several standard statistical machine translation techniques. The similarity shown in this study suggests a possibility on harmonious annotation and processing of the language pairs in future development.",
}
@inproceedings{takeno-etal-2016-integrating,
    title = "Integrating empty category detection into preordering Machine Translation",
    author = "Takeno, Shunsuke  and
      Nagata, Masaaki  and
      Yamamoto, Kazuhide",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4615",
    pages = "157--165",
    abstract = "We propose a method for integrating Japanese empty category detection into the preordering process of Japanese-to-English statistical machine translation. First, we apply machine-learning-based empty category detection to estimate the position and the type of empty categories in the constituent tree of the source sentence. Then, we apply discriminative preordering to the augmented constituent tree in which empty categories are treated as if they are normal lexical symbols. We find that it is effective to filter empty categories based on the confidence of estimation. Our experiments show that, for the IWSLT dataset consisting of short travel conversations, the insertion of empty categories alone improves the BLEU score from 33.2 to 34.3 and the RIBES score from 76.3 to 78.7, which imply that reordering has improved For the KFTT dataset consisting of Wikipedia sentences, the proposed preordering method considering empty categories improves the BLEU score from 19.9 to 20.2 and the RIBES score from 66.2 to 66.3, which shows both translation and reordering have improved slightly.",
}
@inproceedings{cromieres-etal-2016-kyoto,
    title = "{K}yoto {U}niversity Participation to {WAT} 2016",
    author = "Cromieres, Fabien  and
      Chu, Chenhui  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4616",
    pages = "166--174",
    abstract = "We describe here our approaches and results on the WAT 2016 shared translation tasks. We tried to use both an example-based machine translation (MT) system and a neural MT system. We report very good translation results, especially when using neural MT for Chinese-to-Japanese translation.",
}
@inproceedings{eriguchi-etal-2016-character,
    title = "Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation",
    author = "Eriguchi, Akiko  and
      Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4617",
    pages = "175--183",
    abstract = "This paper reports our systems (UT-AKY) submitted in the 3rd Workshop of Asian Translation 2016 (WAT{'}16) and their results in the English-to-Japanese translation task. Our model is based on the tree-to-sequence Attention-based NMT (ANMT) model proposed by Eriguchi et al. (2016). We submitted two ANMT systems: one with a word-based decoder and the other with a character-based decoder. Experimenting on the English-to-Japanese translation task, we have confirmed that the character-based decoder can cover almost the full vocabulary in the target language and generate translations much faster than the word-based model.",
}
@inproceedings{tan-2016-faster,
    title = "Faster and Lighter Phrase-based Machine Translation Baseline",
    author = "Tan, Liling",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4618",
    pages = "184--193",
    abstract = "This paper describes the SENSE machine translation system participation in the Third Workshop for Asian Translation (WAT2016). We share our best practices to build a fast and light phrase-based machine translation (PBMT) models that have comparable results to the baseline systems provided by the organizers. As Neural Machine Translation (NMT) overtakes PBMT as the state-of-the-art, deep learning and new MT practitioners might not be familiar with the PBMT paradigm and we hope that this paper will help them build a PBMT baseline system quickly and easily.",
}
@inproceedings{yang-lepage-2016-improving,
    title = "Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for {C}hinese{--}{J}apanese",
    author = "Yang, Wei  and
      Lepage, Yves",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4619",
    pages = "194--202",
    abstract = "Unlike European languages, many Asian languages like Chinese and Japanese do not have typographic boundaries in written system. Word segmentation (tokenization) that break sentences down into individual words (tokens) is normally treated as the first step for machine translation (MT). For Chinese and Japanese, different rules and segmentation tools lead different segmentation results in different level of granularity between Chinese and Japanese. To improve the translation accuracy, we adjust and balance the granularity of segmentation results around terms for Chinese{--}Japanese patent corpus for training translation model. In this paper, we describe a statistical machine translation (SMT) system which is built on re-tokenized Chinese-Japanese patent training corpus using extracted bilingual multi-word terms.",
}
@inproceedings{yamagishi-etal-2016-controlling,
    title = "Controlling the Voice of a Sentence in {J}apanese-to-{E}nglish Neural Machine Translation",
    author = "Yamagishi, Hayahide  and
      Kanouchi, Shin  and
      Sato, Takayuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4620",
    pages = "203--210",
    abstract = "In machine translation, we must consider the difference in expression between languages. For example, the active/passive voice may change in Japanese-English translation. The same verb in Japanese may be translated into different voices at each translation because the voice of a generated sentence cannot be determined using only the information of the Japanese sentence. Machine translation systems should consider the information structure to improve the coherence of the output by using several topicalization techniques such as passivization. Therefore, this paper reports on our attempt to control the voice of the sentence generated by an encoder-decoder model. To control the voice of the generated sentence, we added the voice information of the target sentence to the source sentence during the training. We then generated sentences with a specified voice by appending the voice information to the source sentence. We observed experimentally whether the voice could be controlled. The results showed that, we could control the voice of the generated sentence with 85.0{\%} accuracy on average. In the evaluation of Japanese-English translation, we obtained a 0.73-point improvement in BLEU score by using gold voice labels.",
}
@inproceedings{sudoh-nagata-2016-chinese,
    title = "{C}hinese-to-{J}apanese Patent Machine Translation based on Syntactic Pre-ordering for {WAT} 2016",
    author = "Sudoh, Katsuhito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4621",
    pages = "211--215",
    abstract = "This paper presents our Chinese-to-Japanese patent machine translation system for WAT 2016 (Group ID: ntt) that uses syntactic pre-ordering over Chinese dependency structures. Chinese words are reordered by a learning-to-rank model based on pairwise classification to obtain word order close to Japanese. In this year{'}s system, two different machine translation methods are compared: traditional phrase-based statistical machine translation and recent sequence-to-sequence neural machine translation with an attention mechanism. Our pre-ordering showed a significant improvement over the phrase-based baseline, but, in contrast, it degraded the neural machine translation baseline.",
}
@inproceedings{sen-etal-2016-iitp,
    title = "{IITP} {E}nglish-{H}indi Machine Translation System at {WAT} 2016",
    author = "Sen, Sukanta  and
      Banik, Debajyoty  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4622",
    pages = "216--222",
    abstract = "In this paper we describe the system that we develop as part of our participation in WAT 2016. We develop a system based on hierarchical phrase-based SMT for English to Hindi language pair. We perform re-ordering and augment bilingual dictionary to improve the performance. As a baseline we use a phrase-based SMT model. The MT models are fine-tuned on the development set, and the best configurations are used to report the evaluation on the test set. Experiments show the BLEU of 13.71 on the benchmark test data. This is better compared to the official baseline BLEU score of 10.79.",
}
@inproceedings{shu-miura-2016-residual,
    title = "Residual Stacking of {RNN}s for Neural Machine Translation",
    author = "Shu, Raphael  and
      Miura, Akiva",
    booktitle = "Proceedings of the 3rd Workshop on {A}sian Translation ({WAT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4623",
    pages = "223--229",
    abstract = "To enhance Neural Machine Translation models, several obvious ways such as enlarging the hidden size of recurrent layers and stacking multiple layers of RNN can be considered. Surprisingly, we observe that using naively stacked RNNs in the decoder slows down the training and leads to degradation in performance. In this paper, We demonstrate that applying residual connections in the depth of stacked RNNs can help the optimization, which is referred to as residual stacking. In empirical evaluation, residual stacking of decoder RNNs gives superior results compared to other methods of enhancing the model with a fixed parameter budget. Our submitted systems in WAT2016 are based on a NMT model ensemble with residual stacking in the decoder. To further improve the performance, we also attempt various methods of system combination in our experiments.",
}
@proceedings{ws-2016-hybrid,
    title = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    editor = "Lambert, Patrik  and
      Babych, Bogdan  and
      Eberle, Kurt  and
      Banchs, Rafael E.  and
      Rapp, Reinhard  and
      Costa-juss{\`a}, Marta R.",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4500",
}
@inproceedings{wang-lepage-2016-combining,
    title = "Combining fast{\_}align with Hierarchical Sub-sentential Alignment for Better Word Alignments",
    author = "Wang, Hao  and
      Lepage, Yves",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4501",
    pages = "1--7",
    abstract = "fast align is a simple and fast word alignment tool which is widely used in state-of-the-art machine translation systems. It yields comparable results in the end-to-end translation experiments of various language pairs. However, fast align does not perform as well as GIZA++ when applied to language pairs with distinct word orders, like English and Japanese. In this paper, given the lexical translation table output by fast align, we propose to realign words using the hierarchical sub-sentential alignment approach. Experimental results show that simple additional processing improves the performance of word alignment, which is measured by counting alignment matches in comparison with fast align. We also report the result of final machine translation in both English-Japanese and Japanese-English. We show our best system provided significant improvements over the baseline as measured by BLEU and RIBES.",
}
@inproceedings{rikters-2016-neural,
    title = "Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation",
    author = "Rikters, Mat{\=\i}ss",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4502",
    pages = "8--15",
    abstract = "This paper presents the comparison of how using different neural network based language modeling tools for selecting the best candidate fragments affects the final output translation quality in a hybrid multi-system machine translation setup. Experiments were conducted by comparing perplexity and BLEU scores on common test cases using the same training data set. A 12-gram statistical language model was selected as a baseline to oppose three neural network based models of different characteristics. The models were integrated in a hybrid system that depends on the perplexity score of a sentence fragment to produce the best fitting translations. The results show a correlation between language model perplexity and BLEU scores as well as overall improvements in BLEU.",
}
@inproceedings{hong-etal-2016-image,
    title = "Image-Image Search for Comparable Corpora Construction",
    author = "Hong, Yu  and
      Yao, Liang  and
      Liu, Mengyi  and
      Zhang, Tongtao  and
      Zhou, Wenxuan  and
      Yao, Jianmin  and
      Ji, Heng",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4503",
    pages = "16--25",
    abstract = "We present a novel method of comparable corpora construction. Unlike the traditional methods which heavily rely on linguistic features, our method only takes image similarity into consid-eration. We use an image-image search engine to obtain similar images, together with the cap-tions in source language and target language. On the basis, we utilize captions of similar imag-es to construct sentence-level bilingual corpora. Experiments on 10,371 target captions show that our method achieves a precision of 0.85 in the top search results.",
}
@inproceedings{angelov-lobanov-2016-predicting,
    title = "Predicting Translation Equivalents in Linked {W}ord{N}ets",
    author = "Angelov, Krasimir  and
      Lobanov, Gleb",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4504",
    pages = "26--32",
    abstract = "We present an algorithm for predicting translation equivalents between two languages, based on the corresponding WordNets. The assumption is that all synsets of one of the languages are linked to the corresponding synsets in the other language. In theory, given the exact sense of a word in a context it must be possible to translate it as any of the words in the linked synset. In practice, however, this does not work well since automatic and accurate sense disambiguation is difficult. Instead it is possible to define a more robust translation relation between the lexemes of the two languages. As far as we know the Finnish WordNet is the only one that includes that relation. Our algorithm can be used to predict the relation for other languages as well. This is useful for instance in hybrid machine translation systems which are usually more dependent on high-quality translation dictionaries.",
}
@inproceedings{wang-merlo-2016-modifications,
    title = "Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings",
    author = "Wang, Haozhou  and
      Merlo, Paola",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4505",
    pages = "33--41",
    abstract = "Traditional machine translation evaluation metrics such as BLEU and WER have been widely used, but these metrics have poor correlations with human judgements because they badly represent word similarity and impose strict identity matching. In this paper, we propose some modifications to the traditional measures based on word embeddings for these two metrics. The evaluation results show that our modifications significantly improve their correlation with human judgements.",
}
@inproceedings{sudarikov-etal-2016-verb,
    title = "Verb sense disambiguation in Machine Translation",
    author = "Sudarikov, Roman  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Holub, Martin  and
      Bojar, Ond{\v{r}}ej  and
      Kr{\'\i}{\v{z}}, Vincent",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4506",
    pages = "42--50",
    abstract = "We describe experiments in Machine Translation using word sense disambiguation (WSD) information. This work focuses on WSD in verbs, based on two different approaches {--} verbal patterns based on corpus pattern analysis and verbal word senses from valency frames. We evaluate several options of using verb senses in the source-language sentences as an additional factor for the Moses statistical machine translation system. Our results show a statistically significant translation quality improvement in terms of the BLEU metric for the valency frames approach, but in manual evaluation, both WSD methods bring improvements.",
}
@inproceedings{beloucif-etal-2016-improving,
    title = "Improving word alignment for low resource languages using {E}nglish monolingual {SRL}",
    author = "Beloucif, Meriem  and
      Saers, Markus  and
      Wu, Dekai",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4507",
    pages = "51--60",
    abstract = "We introduce a new statistical machine translation approach specifically geared to learning translation from low resource languages, that exploits monolingual English semantic parsing to bias inversion transduction grammar (ITG) induction. We show that in contrast to conventional statistical machine translation (SMT) training methods, which rely heavily on phrase memorization, our approach focuses on learning bilingual correlations that help translating low resource languages, by using the output language semantic structure to further narrow down ITG constraints. This approach is motivated by previous research which has shown that injecting a semantic frame based objective function while training SMT models improves the translation quality. We show that including a monolingual semantic objective function during the learning of the translation model leads towards a semantically driven alignment which is more efficient than simply tuning loglinear mixture weights against a semantic frame based evaluation metric in the final stage of statistical machine translation training. We test our approach with three different language pairs and demonstrate that our model biases the learning towards more semantically correct alignments. Both GIZA++ and ITG based techniques fail to capture meaningful bilingual constituents, which is required when trying to learn translation models for low resource languages. In contrast, our proposed model not only improve translation by injecting a monolingual objective function to learn bilingual correlations during early training of the translation model, but also helps to learn more meaningful correlations with a relatively small data set, leading to a better alignment compared to either conventional ITG or traditional GIZA++ based approaches.",
}
@inproceedings{mahesh-etal-2016-using,
    title = "Using Bilingual Segments in Generating Word-to-word Translations",
    author = "Mahesh, Kavitha  and
      Pereira Lopes, Gabriel  and
      Gomes, Lu{\'\i}s",
    booktitle = "Proceedings of the Sixth Workshop on Hybrid Approaches to Translation ({H}y{T}ra6)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4508",
    pages = "61--71",
    abstract = "We defend that bilingual lexicons automatically extracted from parallel corpora, whose entries have been meanwhile validated by linguists and classified as correct or incorrect, should constitute a specific parallel corpora. And, in this paper, we propose to use word-to-word translations to learn morph-units (comprising of bilingual stems and suffixes) from those bilingual lexicons for two language pairs L1-L2 and L1-L3 to induce a bilingual lexicon for the language pair L2-L3, apart from also learning morph-units for this other language pair. The applicability of bilingual morph-units in L1-L2 and L1-L3 is examined from the perspective of pivot-based lexicon induction for language pair L2-L3 with L1 as bridge. While the lexicon is derived by transitivity, the correspondences are identified based on previously learnt bilingual stems and suffixes rather than surface translation forms. The induced pairs are validated using a binary classifier trained on morphological and similarity-based features using an existing, automatically acquired, manually validated bilingual translation lexicon for language pair L2-L3. In this paper, we discuss the use of English (EN)-French (FR) and English (EN)-Portuguese (PT) lexicon of word-to-word translations in generating word-to-word translations for the language pair FR-PT with EN as pivot language. Generated translations are filtered out first using an SVM-based FR-PT classifier and then are manually validated.",
}
@proceedings{ws-2016-open,
    title = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    editor = "Choi, Key-Sun  and
      Unger, Christina  and
      Vossen, Piek  and
      Kim, Jin-Dong  and
      Kando, Noriko  and
      Ngonga Ngomo, Axel-Cyrille",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4400",
}
@inproceedings{chen-etal-2016-using,
    title = "Using {W}ikipedia and Semantic Resources to Find Answer Types and Appropriate Answer Candidate Sets in Question Answering",
    author = "Chen, Po-Chun  and
      Zhuang, Meng-Jie  and
      Lin, Chuan-Jie",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4401",
    pages = "1--10",
    abstract = "This paper proposes a new idea that uses Wikipedia categories as answer types and defines candidate sets inside Wikipedia. The focus of a given question is searched in the hierarchy of Wikipedia main pages. Our searching strategy combines head-noun matching and synonym matching provided in semantic resources. The set of answer candidates is determined by the entry hierarchy in Wikipedia and the hyponymy hierarchy in WordNet. The experimental results show that the approach can find candidate sets in a smaller size but achieve better performance especially for ARTIFACT and ORGANIZATION types, where the performance is better than state-of-the-art Chinese factoid QA systems.",
}
@inproceedings{otani-etal-2016-large,
    title = "Large-Scale Acquisition of Commonsense Knowledge via a Quiz Game on a Dialogue System",
    author = "Otani, Naoki  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao  and
      Kaji, Nobuhiro  and
      Sassano, Manabu",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4402",
    pages = "11--20",
    abstract = "Commonsense knowledge is essential for fully understanding language in many situations. We acquire large-scale commonsense knowledge from humans using a game with a purpose (GWAP) developed on a smartphone spoken dialogue system. We transform the manual knowledge acquisition process into an enjoyable quiz game and have collected over 150,000 unique commonsense facts by gathering the data of more than 70,000 players over eight months. In this paper, we present a simple method for maintaining the quality of acquired knowledge and an empirical analysis of the knowledge acquisition process. To the best of our knowledge, this is the first work to collect large-scale knowledge via a GWAP on a widely-used spoken dialogue system.",
}
@inproceedings{homma-etal-2016-hierarchical,
    title = "A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences",
    author = "Homma, Yukinori  and
      Sadamitsu, Kugatsu  and
      Nishida, Kyosuke  and
      Higashinaka, Ryuichiro  and
      Asano, Hisako  and
      Matsuo, Yoshihiro",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4403",
    pages = "21--29",
    abstract = "This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents. The network classifies each sentence in a document into attribute and condition classes on the basis of word sequences and sentence sequences in the document. Experimental results showed the method using the proposed network significantly outperformed baseline methods by taking semantic representation of word and sentence sequential data into account. We also evaluated the network with two different product domains (insurance and tourism domains) and found that it was effective for both the domains.",
}
@inproceedings{shi-etal-2016-combining,
    title = "Combining Lexical and Semantic-based Features for Answer Sentence Selection",
    author = "Shi, Jing  and
      Xu, Jiaming  and
      Yao, Yiqun  and
      Zheng, Suncong  and
      Xu, Bo",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4404",
    pages = "30--38",
    abstract = "Question answering is always an attractive and challenging task in natural language processing area. There are some open domain question answering systems, such as IBM Waston, which take the unstructured text data as input, in some ways of humanlike thinking process and a mode of artificial intelligence. At the conference on Natural Language Processing and Chinese Computing (NLPCC) 2016, China Computer Federation hosted a shared task evaluation about Open Domain Question Answering. We achieve the 2nd place at the document-based subtask. In this paper, we present our solution, which consists of feature engineering in lexical and semantic aspects and model training methods. As the result of the evaluation shows, our solution provides a valuable and brief model which could be used in modelling question answering or sentence semantic relevance. We hope our solution would contribute to this vast and significant task with some heuristic thinking.",
}
@inproceedings{andy-etal-2016-entity,
    title = "An Entity-Based approach to Answering Recurrent and Non-Recurrent Questions with Past Answers",
    author = "Andy, Anietie  and
      Rwebangira, Mugizi  and
      Sekine, Satoshi",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4405",
    pages = "39--43",
    abstract = "An Entity-based approach to Answering recurrent and non-recurrent questions with Past Answers Abstract Community question answering (CQA) systems such as Yahoo! Answers allow registered-users to ask and answer questions in various question categories. However, a significant percentage of asked questions in Yahoo! Answers are unanswered. In this paper, we propose to reduce this percentage by reusing answers to past resolved questions from the site. Specifically, we propose to satisfy unanswered questions in entity rich categories by searching for and reusing the best answers to past resolved questions with shared needs. For unanswered questions that do not have a past resolved question with a shared need, we propose to use the best answer to a past resolved question with similar needs. Our experiments on a Yahoo! Answers dataset shows that our approach retrieves most of the past resolved questions that have shared and similar needs to unanswered questions.",
}
@inproceedings{perera-nand-2016-answer,
    title = "Answer Presentation in Question Answering over Linked Data using Typed Dependency Subtree Patterns",
    author = "Perera, Rivindu  and
      Nand, Parma",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4406",
    pages = "44--48",
    abstract = "In an era where highly accurate Question Answering (QA) systems are being built using complex Natural Language Processing (NLP) and Information Retrieval (IR) algorithms, presenting the acquired answer to the user akin to a human answer is also crucial. In this paper we present an answer presentation strategy by embedding the answer in a sentence which is developed by incorporating the linguistic structure of the source question extracted through typed dependency parsing. The evaluation using human participants proved that the methodology is human-competitive and can result in linguistically correct sentences for more that 70{\%} of the test dataset acquired from QALD question dataset.",
}
@inproceedings{neves-kraus-2016-biomedlat,
    title = "{B}io{M}ed{LAT} Corpus: Annotation of the Lexical Answer Type for Biomedical Questions",
    author = "Neves, Mariana  and
      Kraus, Milena",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4407",
    pages = "49--58",
    abstract = "Question answering (QA) systems need to provide exact answers for the questions that are posed to the system. However, this can only be achieved through a precise processing of the question. During this procedure, one important step is the detection of the expected type of answer that the system should provide by extracting the headword of the questions and identifying its semantic type. We have annotated the headword and assigned UMLS semantic types to 643 factoid/list questions from the BioASQ training data. We present statistics on the corpus and a preliminary evaluation in baseline experiments. We also discuss the challenges on both the manual annotation and the automatic detection of the headwords and the semantic types. We believe that this is a valuable resource for both training and evaluation of biomedical QA systems. The corpus is available at: \url{https://github.com/mariananeves/BioMedLAT}.",
}
@inproceedings{jokinen-wilcock-2016-double,
    title = "Double Topic Shifts in Open Domain Conversations: Natural Language Interface for a {W}ikipedia-based Robot Application",
    author = "Jokinen, Kristiina  and
      Wilcock, Graham",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4408",
    pages = "59--66",
    abstract = "The paper describes topic shifting in dialogues with a robot that provides information from Wiki-pedia. The work focuses on a double topical construction of dialogue coherence which refers to discourse coherence on two levels: the evolution of dialogue topics via the interaction between the user and the robot system, and the creation of discourse topics via the content of the Wiki-pedia article itself. The user selects topics that are of interest to her, and the system builds a list of potential topics, anticipated to be the next topic, by the links in the article and by the keywords extracted from the article. The described system deals with Wikipedia articles, but could easily be adapted to other digital information providing systems.",
}
@inproceedings{choi-etal-2016-filling,
    title = "Filling a Knowledge Graph with a Crowd",
    author = "Choi, GyuHyeon  and
      Nam, Sangha  and
      Choi, Dongho  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4409",
    pages = "67--71",
    abstract = "",
}
@inproceedings{klang-nugues-2016-pairing,
    title = "Pairing {W}ikipedia Articles Across Languages",
    author = "Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4410",
    pages = "72--76",
    abstract = "Wikipedia has become a reference knowledge source for scores of NLP applications. One of its invaluable features lies in its multilingual nature, where articles on a same entity or concept can have from one to more than 200 different versions. The interlinking of language versions in Wikipedia has undergone a major renewal with the advent of Wikidata, a unified scheme to identify entities and their properties using unique numbers. However, as the interlinking is still manually carried out by thousands of editors across the globe, errors may creep in the assignment of entities. In this paper, we describe an optimization technique to match automatically language versions of articles, and hence entities, that is only based on bags of words and anchors. We created a dataset of all the articles on persons we extracted from Wikipedia in six languages: English, French, German, Russian, Spanish, and Swedish. We report a correct match of at least 94.3{\%} on each pair.",
}
@inproceedings{nam-etal-2016-srdf,
    title = "{SRDF}: Extracting Lexical Knowledge Graph for Preserving Sentence Meaning",
    author = "Nam, Sangha  and
      Choi, GyuHyeon  and
      Hahm, Younggyun  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4411",
    pages = "77--81",
    abstract = "In this paper, we present an open information extraction system so-called SRDF that generates lexical knowledge graphs from unstructured texts. In semantic web, knowledge is expressed in the RDF triple form but the natural language text consist of multiple relations between arguments. For this reason, we combine open information extraction with the reification for the full text extraction to preserve meaning of sentence in our knowledge graph. And also our knowledge graph is designed to adapt for many existing semantic web applications. At the end of this paper, we introduce the result of the experiment and a Korean template generation module developed using SRDF.",
}
@inproceedings{hahm-etal-2016-qaf,
    title = "{QAF}: Frame Semantics-based Question Interpretation",
    author = "Hahm, Younggyun  and
      Nam, Sangha  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4412",
    pages = "82--90",
    abstract = "Natural language questions are interpreted to a sequence of patterns to be matched with instances of patterns in a knowledge base (KB) for answering. A natural language (NL) question answering (QA) system utilizes meaningful patterns matching the syntac-tic/lexical features between the NL questions and KB. In the most of KBs, there are only binary relations in triple form to represent relation between two entities or entity and a value using the domain specific ontology. However, the binary relation representation is not enough to cover complex information in questions, and the ontology vocabulary sometimes does not cover the lexical meaning in questions. Complex meaning needs a knowledge representation to link the binary relation-type triples in KB. In this paper, we propose a frame semantics-based semantic parsing approach as KB-independent question pre-processing. We will propose requirements of question interpretation in the KBQA perspective, and a query form representation based on our proposed format QAF (Ques-tion Answering with the Frame Semantics), which is supposed to cover the requirements. In QAF, frame semantics roles as a model to represent complex information in questions and to disambiguate the lexical meaning in questions to match with the ontology vocabu-lary. Our system takes a question as an input and outputs QAF-query by the process which assigns semantic information in the question to its corresponding frame semantic structure using the semantic parsing rules.",
}
@inproceedings{kano-2016-answering,
    title = "Answering Yes-No Questions by Penalty Scoring in History Subjects of University Entrance Examinations",
    author = "Kano, Yoshinobu",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4413",
    pages = "91--96",
    abstract = "Answering yes{--}no questions is more difficult than simply retrieving ranked search results. To answer yes{--}no questions, especially when the correct answer is no, one must find an objectionable keyword that makes the question{'}s answer no. Existing systems, such as factoid-based ones, cannot answer yes{--}no questions very well because of insufficient handling of such objectionable keywords. We suggest an algorithm that answers yes{--}no questions by assigning an importance to objectionable keywords. Concretely speaking, we suggest a penalized scoring method that finds and makes lower score for parts of documents that include such objectionable keywords. We check a keyword distribution for each part of a document such as a paragraph, calculating the keyword density as a basic score. Then we use an objectionable keyword penalty when a keyword does not appear in a target part but appears in other parts of the document. Our algorithm is robust for open domain problems because it requires no training. We achieved 4.45 point better results in F1 scores than the best score of the NTCIR-10 RITE2 shared task, also obtained the best score in 2014 mock university examination challenge of the Todai Robot project.",
}
@inproceedings{kim-etal-2016-dedicated,
    title = "Dedicated Workflow Management for {OKBQA} Framework",
    author = "Kim, Jiseong  and
      Choi, GyuHyeon  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4414",
    pages = "97--101",
    abstract = "Nowadays, a question answering (QA) system is used in various areas such a quiz show, personal assistant, home device, and so on. The OKBQA framework supports developing a QA system in an intuitive and collaborative ways. To support collaborative development, the framework should be equipped with some functions, e.g., flexible system configuration, debugging supports, intuitive user interface, and so on while considering different developing groups of different domains. This paper presents OKBQA controller, a dedicated workflow manager for OKBQA framework, to boost collaborative development of a QA system.",
}
@proceedings{ws-2016-modeling,
    title = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    editor = "Nissim, Malvina  and
      Patti, Viviana  and
      Plank, Barbara",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4300",
}
@inproceedings{garimella-mihalcea-2016-zooming,
    title = "Zooming in on Gender Differences in Social Media",
    author = "Garimella, Aparna  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4301",
    pages = "1--10",
    abstract = "Men are from Mars and women are from Venus - or so the genre of relationship literature would have us believe. But there is some truth in this idea, and researchers in fields as diverse as psychology, sociology, and linguistics have explored ways to better understand the differences between genders. In this paper, we take another look at the problem of gender discrimination and attempt to move beyond the typical surface-level text classification approach, by (1) identifying semantic and psycholinguistic word classes that reflect systematic differences between men and women and (2) finding differences between genders in the ways they use the same words. We describe several experiments and report results on a large collection of blogs authored by men and women.",
}
@inproceedings{schneevogt-paggio-2016-effect,
    title = "The Effect of Gender and Age Differences on the Recognition of Emotions from Facial Expressions",
    author = "Schneevogt, Daniela  and
      Paggio, Patrizia",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4302",
    pages = "11--19",
    abstract = "Recent studies have demonstrated gender and cultural differences in the recognition of emotions in facial expressions. However, most studies were conducted on American subjects. In this paper, we explore the generalizability of several findings to a non-American culture in the form of Danish subjects. We conduct an emotion recognition task followed by two stereotype questionnaires with different genders and age groups. While recent findings (Krems et al., 2015) suggest that women are biased to see anger in neutral facial expressions posed by females, in our sample both genders assign higher ratings of anger to all emotions expressed by females. Furthermore, we demonstrate an effect of gender on the fear-surprise-confusion observed by Tomkins and McCarter (1964); females overpredict fear, while males overpredict surprise.",
}
@inproceedings{liu-etal-2016-recurrent,
    title = "A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts",
    author = "Liu, Fei  and
      Perez, Julien  and
      Nowson, Scott",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4303",
    pages = "20--29",
    abstract = "Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits compared with prior work. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.",
}
@inproceedings{pool-nissim-2016-distant,
    title = "Distant supervision for emotion detection using {F}acebook reactions",
    author = "Pool, Chris  and
      Nissim, Malvina",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4304",
    pages = "30--39",
    abstract = "We exploit the Facebook reaction feature in a distant supervised fashion to train a support vector machine classifier for emotion detection, using several feature combinations and combining different Facebook pages. We test our models on existing benchmarks for emotion detection and show that employing only information that is derived completely automatically, thus without relying on any handcrafted lexicon as it{'}s usually done, we can achieve competitive results. The results also show that there is large room for improvement, especially by gearing the collection of Facebook pages, with a view to the target domain.",
}
@inproceedings{mullick-etal-2016-graphical,
    title = "A graphical framework to detect and categorize diverse opinions from online news",
    author = "Mullick, Ankan  and
      Goyal, Pawan  and
      Ganguly, Niloy",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4305",
    pages = "40--49",
    abstract = "This paper proposes a graphical framework to extract opinionated sentences which highlight different contexts within a given news article by introducing the concept of diversity in a graphical model for opinion detection.We conduct extensive evaluations and find that the proposed modification leads to impressive improvement in performance and makes the final results of the model much more usable. The proposed method (OP-D) not only performs much better than the other techniques used for opinion detection as well as introducing diversity, but is also able to select opinions from different categories (Asher et al. 2009). By developing a classification model which categorizes the identified sentences into various opinion categories, we find that OP-D is able to push opinions from different categories uniformly among the top opinions.",
}
@inproceedings{skeppstedt-etal-2016-active,
    title = "Active learning for detection of stance components",
    author = "Skeppstedt, Maria  and
      Sahlgren, Magnus  and
      Paradis, Carita  and
      Kerren, Andreas",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4306",
    pages = "50--59",
    abstract = "Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a larger variation in how sentiment is expressed than in how speakers express the other three categories. This larger variation was also shown by the lower recall results achieved by the lexicon-based approach for sentiment than for the categories speculation, contrast and condition.",
}
@inproceedings{kaljahi-foster-2016-detecting,
    title = "Detecting Opinion Polarities using Kernel Methods",
    author = "Kaljahi, Rasoul  and
      Foster, Jennifer",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4307",
    pages = "60--69",
    abstract = "We investigate the application of kernel methods to representing both structural and lexical knowledge for predicting polarity of opinions in consumer product review. We introduce any-gram kernels which model lexical information in a significantly faster way than the traditional n-gram features, while capturing all possible orders of n-grams n in a sequence without the need to explicitly present a pre-specified set of such orders. We also present an effective format to represent constituency and dependency structure together with aspect terms and sentiment polarity scores. Furthermore, we modify the traditional tree kernel function to compute the similarity based on word embedding vectors instead of exact string match and present experiments using the new models.",
}
@inproceedings{cattle-ma-2016-effects,
    title = "Effects of Semantic Relatedness between Setups and Punchlines in {T}witter Hashtag Games",
    author = "Cattle, Andrew  and
      Ma, Xiaojuan",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4308",
    pages = "70--79",
    abstract = "This paper explores humour recognition for Twitter-based hashtag games. Given their popularity, frequency, and relatively formulaic nature, these games make a good target for computational humour research and can leverage Twitter likes and retweets as humour judgments. In this work, we use pair-wise relative humour judgments to examine several measures of semantic relatedness between setups and punchlines on a hashtag game corpus we collected and annotated. Results show that perplexity, Normalized Google Distance, and free-word association-based features are all useful in identifying {``}funnier{''} hashtag game responses. In fact, we provide empirical evidence that funnier punchlines tend to be more obscure, although more obscure punchlines are not necessarily rated funnier. Furthermore, the asymmetric nature of free-word association features allows us to see that while punchlines should be harder to predict given a setup, they should also be relatively easy to understand in context.",
}
@inproceedings{sidarenka-stede-2016-generating,
    title = "Generating Sentiment Lexicons for {G}erman {T}witter",
    author = "Sidarenka, Uladzimir  and
      Stede, Manfred",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4309",
    pages = "80--90",
    abstract = "Despite a substantial progress made in developing new sentiment lexicon generation (SLG) methods for English, the task of transferring these approaches to other languages and domains in a sound way still remains open. In this paper, we contribute to the solution of this problem by systematically comparing semi-automatic translations of common English polarity lists with the results of the original automatic SLG algorithms, which were applied directly to German data. We evaluate these lexicons on a corpus of 7,992 manually annotated tweets. In addition to that, we also collate the results of dictionary- and corpus-based SLG methods in order to find out which of these paradigms is better suited for the inherently noisy domain of social media. Our experiments show that semi-automatic translations notably outperform automatic systems (reaching a macro-averaged F1-score of 0.589), and that dictionary-based techniques produce much better polarity lists as compared to corpus-based approaches (whose best F1-scores run up to 0.479 and 0.419 respectively) even for the non-standard Twitter genre.",
}
@inproceedings{canales-etal-2016-innovative,
    title = "Innovative Semi-Automatic Methodology to Annotate Emotional Corpora",
    author = "Canales, Lea  and
      Strapparava, Carlo  and
      Boldrini, Ester  and
      Mart{\'\i}nez-Barco, Patricio",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4310",
    pages = "91--100",
    abstract = "Detecting depression or personality traits, tutoring and student behaviour systems, or identifying cases of cyber-bulling are a few of the wide range of the applications, in which the automatic detection of emotion is a crucial element. Emotion detection has the potential of high impact by contributing the benefit of business, society, politics or education. Given this context, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection task: the problems of emotional corpora annotation. This will be tackled by proposing of a new semi-automatic methodology. Our innovative methodology consists in two main phases: (1) an automatic process to pre-annotate the unlabelled sentences with a reduced number of emotional categories; and (2) a refinement manual process where human annotators will determine which is the predominant emotion between the emotional categories selected in the phase 1. Our proposal in this paper is to show and evaluate the pre-annotation process to analyse the feasibility and the benefits by the methodology proposed. The results obtained are promising and allow obtaining a substantial improvement of annotation time and cost and confirm the usefulness of our pre-annotation process to improve the annotation task.",
}
@inproceedings{kamijo-etal-2016-personality,
    title = "Personality Estimation from {J}apanese Text",
    author = "Kamijo, Koichi  and
      Nasukawa, Tetsuya  and
      Kitamura, Hideya",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4311",
    pages = "101--109",
    abstract = "We created a model to estimate personality trait from authors{'} text written in Japanese and measured its performance by conducting surveys and analyzing the Twitter data of 1,630 users. We used the Big Five personality traits for personality trait estimation. Our approach is a combination of category- and Word2Vec-based approaches. For the category-based element, we added several unique Japanese categories along with the ones regularly used in the English model, and for the Word2Vec-based element, we used a model called GloVe. We found that some of the newly added categories have a stronger correlation with personality traits than other categories do and that the combination of the category- and Word2Vec-based approaches improves the accuracy of the personality trait estimation compared with the case of using just one of them.",
}
@inproceedings{celli-etal-2016-predicting,
    title = "Predicting {B}rexit: Classifying Agreement is Better than Sentiment and Pollsters",
    author = "Celli, Fabio  and
      Stepanov, Evgeny  and
      Poesio, Massimo  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4312",
    pages = "110--118",
    abstract = "On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data. Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.",
}
@inproceedings{bali-singh-2016-sarcasm,
    title = "Sarcasm Detection : Building a Contextual Hierarchy",
    author = "Bali, Taradheesh  and
      Singh, Navjyoti",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4313",
    pages = "119--127",
    abstract = "The conundrum of understanding and classifying sarcasm has been dealt with by the traditional theorists as an analysis of a sarcastic utterance and the ironic situation that surrounds it. The problem with such an approach is that it is too narrow, as it is unable to sufficiently utilize the two indispensable agents in making such an utterance, viz. the speaker and the listener. It undermines the necessary context required to comprehend a sarcastic utterance. In this paper, we propose a novel approach towards understanding sarcasm in terms of the existing knowledge hierarchy between the two participants, which forms the basis of the context that both agents share. The difference in relationship of the speaker of the sarcastic utterance and the disparate audience found on social media, such as Twitter, is also captured. We then apply our model on a corpus of tweets to achieve significant results and consequently, shed light on subjective nature of context, which is contingent on the relation between the speaker and the listener.",
}
@inproceedings{litvak-etal-2016-social,
    title = "Social and linguistic behavior and its correlation to trait empathy",
    author = "Litvak, Marina  and
      Otterbacher, Jahna  and
      Ang, Chee Siang  and
      Atkins, David",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4314",
    pages = "128--137",
    abstract = "A growing body of research exploits social media behaviors to gauge psychological character-istics, though trait empathy has received little attention. Because of its intimate link to the abil-ity to relate to others, our research aims to predict participants{'} levels of empathy, given their textual and friending behaviors on Facebook. Using Poisson regression, we compared the vari-ance explained in Davis{'} Interpersonal Reactivity Index (IRI) scores on four constructs (em-pathic concern, personal distress, fantasy, perspective taking), by two classes of variables: 1) post content and 2) linguistic style. Our study lays the groundwork for a greater understanding of empathy{'}s role in facilitating interactions on social media.",
}
@inproceedings{ohman-etal-2016-challenges,
    title = "The Challenges of Multi-dimensional Sentiment Analysis Across Languages",
    author = {{\"O}hman, Emily  and
      Honkela, Timo  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4315",
    pages = "138--142",
    abstract = "This paper outlines a pilot study on multi-dimensional and multilingual sentiment analysis of social media content. We use parallel corpora of movie subtitles as a proxy for colloquial language in social media channels and a multilingual emotion lexicon for fine-grained sentiment analyses. Parallel data sets make it possible to study the preservation of sentiments and emotions in translation and our assessment reveals that the lexical approach shows great inter-language agreement. However, our manual evaluation also suggests that the use of purely lexical methods is limited and further studies are necessary to pinpoint the cross-lingual differences and to develop better sentiment classifiers.",
}
@inproceedings{alam-etal-2016-social,
    title = "The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems",
    author = "Alam, Firoj  and
      Celli, Fabio  and
      Stepanov, Evgeny A.  and
      Ghosh, Arindam  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4316",
    pages = "143--152",
    abstract = "In this paper, we address the issue of automatic prediction of readers{'} mood from newspaper articles and comments. As online newspapers are becoming more and more similar to social media platforms, users can provide affective feedback, such as mood and emotion. We have exploited the self-reported annotation of mood categories obtained from the metadata of the Italian online newspaper corriere.it to design and evaluate a system for predicting five different mood categories from news articles and comments: indignation, disappointment, worry, satisfaction, and amusement. The outcome of our experiments shows that overall, bag-of-word-ngrams perform better compared to all other feature sets; however, stylometric features perform better for the mood score prediction of articles. Our study shows that self-reported annotations can be used to design automatic mood prediction systems.",
}
@inproceedings{summa-etal-2016-microblog,
    title = "Microblog Emotion Classification by Computing Similarity in Text, Time, and Space",
    author = "Summa, Anja  and
      Resch, Bernd  and
      Strube, Michael",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4317",
    pages = "153--162",
    abstract = "Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emotion labels. Graph-based semi-supervised learning labels all tweets with an emotion.",
}
@inproceedings{santos-etal-2016-domain,
    title = "A domain-agnostic approach for opinion prediction on speech",
    author = "Santos, Pedro Bispo  and
      Beinborn, Lisa  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4318",
    pages = "163--172",
    abstract = "We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction. We represent the speech signal by mel-frequency cepstral coefficients and apply long short-term memory neural networks to automatically learn temporal regularities in speech. In contrast to previous work, our approach does not require complex feature engineering and works without textual transcripts. As a consequence, it can easily be applied on various speech analysis tasks for different languages and the results show that it can nevertheless be competitive to the state-of-the-art in opinion prediction. In a detailed error analysis for opinion mining we find that our approach performs well in identifying speaker-specific characteristics, but should be combined with additional information if subtle differences in the linguistic content need to be identified.",
}
@inproceedings{lee-etal-2016-make,
    title = "Can We Make Computers Laugh at Talks?",
    author = "Lee, Chong Min  and
      Yoon, Su-Youn  and
      Chen, Lei",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4319",
    pages = "173--181",
    abstract = "Considering the importance of public speech skills, a system which makes a prediction on where audiences laugh in a talk can be helpful to a person who prepares for a talk. We investigated a possibility that a state-of-the-art humor recognition system can be used in detecting sentences inducing laughters in talks. In this study, we used TED talks and laughters in the talks as data. Our results showed that the state-of-the-art system needs to be improved in order to be used in a practical application. In addition, our analysis showed that classifying humorous sentences in talks is very challenging due to close distance between humorous and non-humorous sentences.",
}
@inproceedings{mowery-etal-2016-towards,
    title = "Towards Automatically Classifying Depressive Symptoms from {T}witter Data for Population Health",
    author = "Mowery, Danielle L.  and
      Park, Albert  and
      Bryan, Craig  and
      Conway, Mike",
    booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4320",
    pages = "182--191",
    abstract = "Major depressive disorder, a debilitating and burdensome disease experienced by individuals worldwide, can be defined by several depressive symptoms (e.g., anhedonia (inability to feel pleasure), depressed mood, difficulty concentrating, etc.). Individuals often discuss their experiences with depression symptoms on public social media platforms like Twitter, providing a potentially useful data source for monitoring population-level mental health risk factors. In a step towards developing an automated method to estimate the prevalence of symptoms associated with major depressive disorder over time in the United States using Twitter, we developed classifiers for discerning whether a Twitter tweet represents no evidence of depression or evidence of depression. If there was evidence of depression, we then classified whether the tweet contained a depressive symptom and if so, which of three subtypes: depressed mood, disturbed sleep, or fatigue or loss of energy. We observed that the most accurate classifiers could predict classes with high-to-moderate F1-score performances for no evidence of depression (85), evidence of depression (52), and depressive symptoms (49). We report moderate F1-scores for depressive symptoms ranging from 75 (fatigue or loss of energy) to 43 (disturbed sleep) to 35 (depressed mood). Our work demonstrates baseline approaches for automatically encoding Twitter data with granular depressive symptoms associated with major depressive disorder.",
}
@proceedings{ws-2016-clinical,
    title = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    editor = "Rumshisky, Anna  and
      Roberts, Kirk  and
      Bethard, Steven  and
      Naumann, Tristan",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4200",
}
@inproceedings{weegar-etal-2016-impact,
    title = "The impact of simple feature engineering in multilingual medical {NER}",
    author = "Weegar, Rebecka  and
      Casillas, Arantza  and
      Diaz de Ilarraza, Arantza  and
      Oronoz, Maite  and
      P{\'e}rez, Alicia  and
      Gojenola, Koldo",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4201",
    pages = "1--6",
    abstract = "The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER. Sometimes papers using scientifically sound techniques present raw baselines that could be improved adding simple and cheap features. This work focuses on entity recognition for the clinical domain for three languages: English, Swedish and Spanish. The task is tackled using simple features, starting from the window size, capitalization, prefixes, and moving to POS and semantic tags. This work demonstrates that a simple initial step of feature engineering can improve the baseline results significantly. Hence, the contributions of this paper are: first, a short list of guidelines well supported with experimental results on three languages and, second, a detailed description of the relevance of these features for medical NER.",
}
@inproceedings{chalapathy-etal-2016-bidirectional,
    title = "Bidirectional {LSTM}-{CRF} for Clinical Concept Extraction",
    author = "Chalapathy, Raghavendra  and
      Zare Borzeshi, Ehsan  and
      Piccardi, Massimo",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4202",
    pages = "7--12",
    abstract = "Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.",
}
@inproceedings{aramaki-etal-2016-mednlpdoc,
    title = "{M}ed{NLPD}oc: {J}apanese Shared Task for Clinical {NLP}",
    author = "Aramaki, Eiji  and
      Kano, Yoshinobu  and
      Ohkuma, Tomoko  and
      Morita, Mizuki",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4203",
    pages = "13--16",
    abstract = "Due to the recent replacements of physical documents with electronic medical records (EMR), the importance of information processing in medical fields has been increased. We have been organizing the MedNLP task series in NTCIR-10 and 11. These workshops were the first shared tasks which attempt to evaluate technologies that retrieve important information from medical reports written in Japanese. In this report, we describe the NTCIR-12 MedNLPDoc task which is designed for more advanced and practical use for the medical fields. This task is considered as a multi-labeling task to a patient record. This report presents results of the shared task, discusses and illustrates remained issues in the medical natural language processing field.",
}
@inproceedings{lee-etal-2016-feature,
    title = "Feature-Augmented Neural Networks for Patient Note De-identification",
    author = {Lee, Ji Young  and
      Dernoncourt, Franck  and
      Uzuner, {\"O}zlem  and
      Szolovits, Peter},
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4204",
    pages = "17--22",
    abstract = "Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients{'} privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-the-art results. Unlike other systems, it does not rely on human-engineered features, which allows it to be quickly deployed, but does not leverage knowledge from human experts or from electronic health records (EHRs). In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system. Our results show that the addition of features, especially the EHR-derived features, further improves the state-of-the-art in patient note de-identification, including for some of the most sensitive PHI types such as patient names. Since in a real-life setting patient notes typically come with EHRs, we recommend developers of de-identification systems to leverage the information EHRs contain.",
}
@inproceedings{sahoo-etal-2016-semi,
    title = "Semi-supervised Clustering of Medical Text",
    author = "Sahoo, Pracheta  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Moll{\'a}, Diego  and
      Nandan, Kaushik",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4205",
    pages = "23--31",
    abstract = "Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications. By using the information of a small annotated dataset, semi-supervised clustering can produce clusters that are customized to the application domain. In this paper, we present a semi-supervised clustering technique based on a multi-objective evolutionary algorithm (NSGA-II-clus). We apply this technique to the task of clustering medical publications for Evidence Based Medicine (EBM) and observe an improvement of the results against unsupervised and other semi-supervised clustering techniques.",
}
@inproceedings{yadav-etal-2016-deep,
    title = "Deep Learning Architecture for Patient Data De-identification in Clinical Records",
    author = "Yadav, Shweta  and
      Ekbal, Asif  and
      Saha, Sriparna  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4206",
    pages = "32--41",
    abstract = "Rapid growth in Electronic Medical Records (EMR) has emerged to an expansion of data in the clinical domain. The majority of the available health care information is sealed in the form of narrative documents which form the rich source of clinical information. Text mining of such clinical records has gained huge attention in various medical applications like treatment and decision making. However, medical records enclose patient Private Health Information (PHI) which can reveal the identities of the patients. In order to retain the privacy of patients, it is mandatory to remove all the PHI information prior to making it publicly available. The aim is to de-identify or encrypt the PHI from the patient medical records. In this paper, we propose an algorithm based on deep learning architecture to solve this problem. We perform de-identification of seven PHI terms from the clinical records. Experiments on benchmark datasets show that our proposed approach achieves encouraging performance, which is better than the baseline model developed with Conditional Random Field.",
}
@inproceedings{hasan-etal-2016-neural,
    title = "Neural Clinical Paraphrase Generation with Attention",
    author = "Hasan, Sadid A.  and
      Liu, Bo  and
      Liu, Joey  and
      Qadir, Ashequl  and
      Lee, Kathy  and
      Datla, Vivek  and
      Prakash, Aaditya  and
      Farri, Oladimeji",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4207",
    pages = "42--53",
    abstract = "Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical paraphrase generation is especially vital in building patient-centric clinical decision support (CDS) applications where users are able to understand complex clinical jargons via easily comprehensible alternative paraphrases. This paper presents Neural Clinical Paraphrase Generation (NCPG), a novel approach that casts the task as a monolingual neural machine translation (NMT) problem. We propose an end-to-end neural network built on an attention-based bidirectional Recurrent Neural Network (RNN) architecture with an encoder-decoder framework to perform the task. Conventional bilingual NMT models mostly rely on word-level modeling and are often limited by out-of-vocabulary (OOV) issues. In contrast, we represent the source and target paraphrase pairs as character sequences to address this limitation. To the best of our knowledge, this is the first work that uses attention-based RNNs for clinical paraphrase generation and also proposes an end-to-end character-level modeling for this task. Extensive experiments on a large curated clinical paraphrase corpus show that the attention-based NCPG models achieve improvements of up to 5.2 BLEU points and 0.5 METEOR points over a non-attention based strong baseline for word-level modeling, whereas further gains of up to 6.1 BLEU points and 1.3 METEOR points are obtained by the character-level NCPG models over their word-level counterparts. Overall, our models demonstrate comparable performance relative to the state-of-the-art phrase-based non-neural models.",
}
@inproceedings{roberts-2016-assessing,
    title = "Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical {NLP}",
    author = "Roberts, Kirk",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4208",
    pages = "54--63",
    abstract = "The proliferation of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data-poor clinical NLP domain. In particular, large text corpora are necessary to build high-quality word embeddings, yet often large corpora that are suitably representative of the target clinical data are unavailable. This forces a choice between building embeddings from small clinical corpora and less representative, larger corpora. This paper explores this trade-off, as well as intermediate compromise solutions. Two standard clinical NLP tasks (the i2b2 2010 concept and assertion tasks) are evaluated with commonly used deep learning models (recurrent neural networks and convolutional neural networks) using a set of six corpora ranging from the target i2b2 data to large open-domain datasets. While combinations of corpora are generally found to work best, the single-best corpus is generally task-dependent.",
}
@inproceedings{sakishita-kano-2016-inference,
    title = "Inference of {ICD} Codes from {J}apanese Medical Records by Searching Disease Names",
    author = "Sakishita, Masahito  and
      Kano, Yoshinobu",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4209",
    pages = "64--68",
    abstract = "Importance of utilizing medical information is getting increased as electronic health records (EHRs) are widely used nowadays. We aim to assign international standardized disease codes, ICD-10, to Japanese textual information in EHRs for users to reuse the information accurately. In this paper, we propose methods to automatically extract diagnosis and to assign ICD codes to Japanese medical records. Due to the lack of available training data, we dare employed rule-based methods rather than machine learning. We observed characteristics of medical records carefully, writing rules to make effective methods by hand. We applied our system to the NTCIR-12 MedNLPDoc shared task data where participants are required to assign ICD-10 codes of possible diagnosis in given EHRs. In this shared task, our system achieved the highest F-measure score among all participants in the most severe evaluation criteria. Through comparison with other approaches, we show that our approach could be a useful milestone for the future development of Japanese medical record processing.",
}
@inproceedings{roller-etal-2016-fine,
    title = "A fine-grained corpus annotation schema of {G}erman nephrology records",
    author = "Roller, Roland  and
      Uszkoreit, Hans  and
      Xu, Feiyu  and
      Seiffe, Laura  and
      Mikhailov, Michael  and
      Staeck, Oliver  and
      Budde, Klemens  and
      Halleck, Fabian  and
      Schmidt, Danilo",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4210",
    pages = "69--77",
    abstract = "In this work we present a fine-grained annotation schema to detect named entities in German clinical data of chronically ill patients with kidney diseases. The annotation schema is driven by the needs of our clinical partners and the linguistic aspects of German language. In order to generate annotations within a short period, the work also presents a semi-automatic annotation which uses additional sources of knowledge such as UMLS, to pre-annotate concepts in advance. The presented schema will be used to apply novel techniques from natural language processing and machine learning to support doctors treating their patients by improved information access from unstructured German texts.",
}
@inproceedings{shibata-etal-2016-detecting,
    title = "Detecting {J}apanese Patients with {A}lzheimer{'}s Disease based on Word Category Frequencies",
    author = "Shibata, Daisaku  and
      Wakamiya, Shoko  and
      Kinoshita, Ayae  and
      Aramaki, Eiji",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4211",
    pages = "78--85",
    abstract = "In recent years, detecting Alzheimer disease (AD) in early stages based on natural language processing (NLP) has drawn much attention. To date, vocabulary size, grammatical complexity, and fluency have been studied using NLP metrics. However, the content analysis of AD narratives is still unreachable for NLP. This study investigates features of the words that AD patients use in their spoken language. After recruiting 18 examinees of 53{--}90 years old (mean: 76.89), they were divided into two groups based on MMSE scores. The AD group comprised 9 examinees with scores of 21 or lower. The healthy control group comprised 9 examinees with a score of 22 or higher. Linguistic Inquiry and Word Count (LIWC) classified words were used to categorize the words that the examinees used. The word frequency was found from observation. Significant differences were confirmed for the usage of impersonal pronouns in the AD group. This result demonstrated the basic feasibility of the proposed NLP-based detection approach.",
}
@inproceedings{yamashita-etal-2016-prediction,
    title = "Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records",
    author = "Yamashita, Takanori  and
      Wakata, Yoshifumi  and
      Soejima, Hidehisa  and
      Nakashima, Naoki  and
      Hirokawa, Sachio",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4212",
    pages = "86--90",
    abstract = "The number of unstructured medical records kept in hospital information systems is increasing. The conditions of patients are formulated as outcomes in clinical pathway. A variance of an outcome describes deviations from standards of care like a patient{'}s bad condition. The present paper applied text mining to extract feature words and phrases of the variance from admission records. We report the cases the variances of {``}pain control{''} and {``}no neuropathy worsening{''} in cerebral infarction.",
}
@inproceedings{kreuzthaler-etal-2016-unsupervised,
    title = "Unsupervised Abbreviation Detection in Clinical Narratives",
    author = "Kreuzthaler, Markus  and
      Oleynik, Michel  and
      Avian, Alexander  and
      Schulz, Stefan",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4213",
    pages = "91--98",
    abstract = "Clinical narratives in electronic health record systems are a rich resource of patient-based information. They constitute an ongoing challenge for natural language processing, due to their high compactness and abundance of short forms. German medical texts exhibit numerous ad-hoc abbreviations that terminate with a period character. The disambiguation of period characters is therefore an important task for sentence and abbreviation detection. This task is addressed by a combination of co-occurrence information of word types with trailing period characters, a large domain dictionary, and a simple rule engine, thus merging statistical and dictionary-based disambiguation strategies. An F-measure of 0.95 could be reached by using the unsupervised approach presented in this paper. The results are promising for a domain-independent abbreviation detection strategy, because our approach avoids retraining of models or use case specific feature engineering efforts required for supervised machine learning approaches.",
}
@inproceedings{yuwono-etal-2016-automated,
    title = "Automated Anonymization as Spelling Variant Detection",
    author = "Yuwono, Steven Kester  and
      Ng, Hwee Tou  and
      Ngiam, Kee Yuan",
    booktitle = "Proceedings of the Clinical Natural Language Processing Workshop ({C}linical{NLP})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4214",
    pages = "99--103",
    abstract = "The issue of privacy has always been a concern when clinical texts are used for research purposes. Personal health information (PHI) (such as name and identification number) needs to be removed so that patients cannot be identified. Manual anonymization is not feasible due to the large number of clinical texts to be anonymized. In this paper, we tackle the task of anonymizing clinical texts written in sentence fragments and which frequently contain symbols, abbreviations, and misspelled words. Our clinical texts therefore differ from those in the i2b2 shared tasks which are in prose form with complete sentences. Our clinical texts are also part of a structured database which contains patient name and identification number in structured fields. As such, we formulate our anonymization task as spelling variant detection, exploiting patients{'} personal information in the structured fields to detect their spelling variants in clinical texts. We successfully anonymized clinical texts consisting of more than 200 million words, using minimum edit distance and regular expression patterns.",
}
@proceedings{ws-2016-linguistics-linguistic,
    title = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    editor = "Brunato, Dominique  and
      Dell{'}Orletta, Felice  and
      Venturi, Giulia  and
      Fran{\c{c}}ois, Thomas  and
      Blache, Philippe",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4100",
}
@inproceedings{jimenez-lopez-becerra-bonache-2016-machine,
    title = "Could Machine Learning Shed Light on Natural Language Complexity?",
    author = "Jim{\'e}nez-L{\'o}pez, Maria Dolores  and
      Becerra-Bonache, Leonor",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4101",
    pages = "1--11",
    abstract = "In this paper, we propose to use a subfield of machine learning {--}grammatical inference{--} to measure linguistic complexity from a developmental point of view. We focus on relative complexity by considering a child learner in the process of first language acquisition. The relevance of grammatical inference models for measuring linguistic complexity from a developmental point of view is based on the fact that algorithms proposed in this area can be considered computational models for studying first language acquisition. Even though it will be possible to use different techniques from the field of machine learning as computational models for dealing with linguistic complexity -since in any model we have algorithms that can learn from data-, we claim that grammatical inference models offer some advantages over other tools.",
}
@inproceedings{chersoni-etal-2016-towards,
    title = "Towards a Distributional Model of Semantic Complexity",
    author = "Chersoni, Emmanuele  and
      Blache, Philippe  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4102",
    pages = "12--22",
    abstract = "In this paper, we introduce for the first time a Distributional Model for computing semantic complexity, inspired by the general principles of the Memory, Unification and Control framework(Hagoort, 2013; Hagoort, 2016). We argue that sentence comprehension is an incremental process driven by the goal of constructing a coherent representation of the event represented by the sentence. The composition cost of a sentence depends on the semantic coherence of the event being constructed and on the activation degree of the linguistic constructions. We also report the results of a first evaluation of the model on the Bicknell dataset (Bicknell et al., 2010).",
}
@inproceedings{marcus-etal-2016-cocogen,
    title = "{C}o{C}o{G}en - Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique",
    author = {Marcus, Str{\"o}bel  and
      Kerz, Elma  and
      Wiechmann, Daniel  and
      Neumann, Stella},
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4103",
    pages = "23--31",
    abstract = "We present a novel approach to the automatic assessment of text complexity based on a sliding-window technique that tracks the distribution of complexity within a text. Such distribution is captured by what we term {``}complexity contours{''} derived from a series of measurements for a given linguistic complexity measure. This approach is implemented in an automatic computational tool, CoCoGen {--} Complexity Contour Generator, which in its current version supports 32 indices of linguistic complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.",
}
@inproceedings{van-schijndel-schuler-2016-addressing,
    title = "Addressing surprisal deficiencies in reading time models",
    author = "van Schijndel, Marten  and
      Schuler, William",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4104",
    pages = "32--37",
    abstract = "This study demonstrates a weakness in how n-gram and PCFG surprisal are used to predict reading times in eye-tracking data. In particular, the information conveyed by words skipped during saccades is not usually included in the surprisal measures. This study shows that correcting the surprisal calculation improves n-gram surprisal and that upcoming n-grams affect reading times, replicating previous findings of how lexical frequencies affect reading times. In contrast, the predictivity of PCFG surprisal does not benefit from the surprisal correction despite the fact that lexical sequences skipped by saccades are processed by readers, as demonstrated by the corrected n-gram measure. These results raise questions about the formulation of information-theoretic measures of syntactic processing such as PCFG surprisal and entropy reduction when applied to reading times.",
}
@inproceedings{vajjala-etal-2016-towards,
    title = "Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts",
    author = "Vajjala, Sowmya  and
      Meurers, Detmar  and
      Eitel, Alexander  and
      Scheiter, Katharina",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4105",
    pages = "38--48",
    abstract = "Computational approaches to readability assessment are generally built and evaluated using gold standard corpora labeled by publishers or teachers rather than being grounded in observations about human performance. Considering that both the reading process and the outcome can be observed, there is an empirical wealth that could be used to ground computational analysis of text readability. This will also support explicit readability models connecting text complexity and the reader{'}s language proficiency to the reading process and outcomes. This paper takes a step in this direction by reporting on an experiment to study how the relation between text complexity and reader{'}s language proficiency affects the reading process and performance outcomes of readers after reading We modeled the reading process using three eye tracking variables: fixation count, average fixation count, and second pass reading duration. Our models for these variables explained 78.9{\%}, 74{\%} and 67.4{\%} variance, respectively. Performance outcome was modeled through recall and comprehension questions, and these models explained 58.9{\%} and 27.6{\%} of the variance, respectively. While the online models give us a better understanding of the cognitive correlates of reading with text complexity and language proficiency, modeling of the offline measures can be particularly relevant for incorporating user aspects into readability models.",
}
@inproceedings{shain-etal-2016-memory,
    title = "Memory access during incremental sentence processing causes reading time latency",
    author = "Shain, Cory  and
      van Schijndel, Marten  and
      Futrell, Richard  and
      Gibson, Edward  and
      Schuler, William",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4106",
    pages = "49--58",
    abstract = "Studies on the role of memory as a predictor of reading time latencies (1) differ in their predictions about when memory effects should occur in processing and (2) have had mixed results, with strong positive effects emerging from isolated constructed stimuli and weak or even negative effects emerging from naturally-occurring stimuli. Our study addresses these concerns by comparing several implementations of prominent sentence processing theories on an exploratory corpus and evaluating the most successful of these on a confirmatory corpus, using a new self-paced reading corpus of seemingly natural narratives constructed to contain an unusually high proportion of memory-intensive constructions. We show highly significant and complementary broad-coverage latency effects both for predictors based on the Dependency Locality Theory and for predictors based on a left-corner parsing model of sentence processing. Our results indicate that memory access during sentence processing does take time, but suggest that stimuli requiring many memory access events may be necessary in order to observe the effect.",
}
@inproceedings{gala-ziegler-2016-reducing,
    title = "Reducing lexical complexity as a tool to increase text accessibility for children with dyslexia",
    author = "Gala, N{\'u}ria  and
      Ziegler, Johannes",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4107",
    pages = "59--66",
    abstract = "Lexical complexity plays a central role in readability, particularly for dyslexic children and poor readers because of their slow and laborious decoding and word recognition skills. Although some features to aid readability may be common to most languages (e.g., the majority of {`}easy{'} words are of low frequency), we believe that lexical complexity is mainly language-specific. In this paper, we define lexical complexity for French and we present a pilot study on the effects of text simplification in dyslexic children. The participants were asked to read out loud original and manually simplified versions of a standardized French text corpus and to answer comprehension questions after reading each text. The analysis of the results shows that the simplifications performed were beneficial in terms of reading speed and they reduced the number of reading errors (mainly lexical ones) without a loss in comprehension. Although the number of participants in this study was rather small (N=10), the results are promising and contribute to the development of applications in computational linguistics.",
}
@inproceedings{delmonte-2016-syntactic,
    title = "Syntactic and Lexical Complexity in {I}talian Noncanonical Structures",
    author = "Delmonte, Rodolfo",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4108",
    pages = "67--78",
    abstract = "In this paper we will be dealing with different levels of complexity in the processing of Italian, a Romance language inheriting many properties from Latin which make it an almost free word order language . The paper is concerned with syntactic complexity as measurable on the basis of the cognitive parser that incrementally builds up a syntactic representation to be used by the semantic component. The theory behind will be LFG and parsing preferences will be used to justify one choice both from a principled and a processing point of view. LFG is a transformationless theory in which there is no deep structure separate from surface syntactic structure. This is partially in accordance with constructional theories in which noncanonical structures containing non-argument functions FOCUS/TOPIC are treated as multifunctional constituents. Complexity is computed on a processing basis following suggestions made by Blache and demonstrated by Kluender and Chesi",
}
@inproceedings{shi-etal-2016-real,
    title = "Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation",
    author = "Shi, Haoyue  and
      Li, Caihua  and
      Hu, Junfeng",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4109",
    pages = "79--88",
    abstract = "Previous researches have shown that learning multiple representations for polysemous words can improve the performance of word embeddings on many tasks. However, this leads to another problem. Several vectors of a word may actually point to the same meaning, namely pseudo multi-sense. In this paper, we introduce the concept of pseudo multi-sense, and then propose an algorithm to detect such cases. With the consideration of the detected pseudo multi-sense cases, we try to refine the existing word embeddings to eliminate the influence of pseudo multi-sense. Moreover, we apply our algorithm on previous released multi-sense word embeddings and tested it on artificial word similarity tasks and the analogy task. The result of the experiments shows that diminishing pseudo multi-sense can improve the quality of word representations. Thus, our method is actually an efficient way to reduce linguistic complexity.",
}
@inproceedings{gonzalez-dios-etal-2016-preliminary,
    title = "A Preliminary Study of Statistically Predictive Syntactic Complexity Features and Manual Simplifications in {B}asque",
    author = "Gonzalez-Dios, Itziar  and
      Aranzabe, Mar{\'\i}a Jes{\'u}s  and
      D{\'\i}az de Ilarraza, Arantza",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4110",
    pages = "89--97",
    abstract = "In this paper, we present a comparative analysis of statistically predictive syntactic features of complexity and the treatment of these features by humans when simplifying texts. To that end, we have used a list of the most five statistically predictive features obtained automatically and the Corpus of Basque Simplified Texts (CBST) to analyse how the syntactic phenomena in these features have been manually simplified. Our aim is to go beyond the descriptions of operations found in the corpus and relate the multidisciplinary findings to understand text complexity from different points of view. We also present some issues that can be important when analysing linguistic complexity.",
}
@inproceedings{heilmann-neumann-2016-dynamic,
    title = "Dynamic pause assessment of keystroke logged data for the detection of complexity in translation and monolingual text production",
    author = "Heilmann, Arndt  and
      Neumann, Stella",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4111",
    pages = "98--103",
    abstract = "Pause analysis of key-stroke logged translations is a hallmark of process based translation studies. However, an exact definition of what a cognitively effortful pause during the translation process is has not been found yet (Saldanha and O{'}Brien, 2013). This paper investigates the design of a key-stroke and subject dependent identification system of cognitive effort to track complexity in translation with keystroke logging (cf. also (Dragsted, 2005) (Couto-Vale, in preparation)). It is an elastic measure that takes into account idiosyncratic pause duration of translators as well as further confounds such as bi-gram frequency, letter frequency and some motor tasks involved in writing. The method is compared to a common static threshold of 1000 ms in an analysis of cognitive effort during the translation of grammatical functions from English to German. Additionally, the results are triangulated with eye tracking data for further validation. The findings show that at least for smaller sets of data a dynamic pause assessment may lead to more accurate results than a generic static pause threshold of similar duration.",
}
@inproceedings{falkenjack-jonsson-2016-implicit,
    title = "Implicit readability ranking using the latent variable of a {B}ayesian Probit model",
    author = {Falkenjack, Johan  and
      J{\"o}nsson, Arne},
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4112",
    pages = "104--112",
    abstract = "Data driven approaches to readability analysis for languages other than English has been plagued by a scarcity of suitable corpora. Often, relevant corpora consist only of easy-to-read texts with no rank information or empirical readability scores, making only binary approaches, such as classification, applicable. We propose a Bayesian, latent variable, approach to get the most out of these kinds of corpora. In this paper we present results on using such a model for readability ranking. The model is evaluated on a preliminary corpus of ranked student texts with encouraging results. We also assess the model by showing that it performs readability classification on par with a state of the art classifier while at the same being transparent enough to allow more sophisticated interpretations.",
}
@inproceedings{chen-meurers-2016-ctap,
    title = "{CTAP}: A Web-Based Tool Supporting Automatic Complexity Analysis",
    author = "Chen, Xiaobin  and
      Meurers, Detmar",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4113",
    pages = "113--119",
    abstract = "Informed by research on readability and language acquisition, computational linguists have developed sophisticated tools for the analysis of linguistic complexity. While some tools are starting to become accessible on the web, there still is a disconnect between the features that can in principle be identified based on state-of-the-art computational linguistic analysis, and the analyses a second language acquisition researcher, teacher, or textbook writer can readily obtain and visualize for their own collection of texts. This short paper presents a web-based tool development that aims to meet this challenge. The Common Text Analysis Platform (CTAP) is designed to support fully configurable linguistic feature extraction for a wide range of complexity analyses. It features a user-friendly interface, modularized and reusable analysis component integration, and flexible corpus and feature management. Building on the Unstructured Information Management framework (UIMA), CTAP readily supports integration of state-of-the-art NLP and complexity feature extraction maintaining modularization and reusability. CTAP thereby aims at providing a common platform for complexity analysis, encouraging research collaboration and sharing of feature extraction components{---}to jointly advance the state-of-the-art in complexity analysis in a form that readily supports real-life use by ordinary users.",
}
@inproceedings{pilan-etal-2016-coursebook,
    title = "Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners{'} Writings",
    author = "Pil{\'a}n, Ildik{\'o}  and
      Alfter, David  and
      Volodina, Elena",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4114",
    pages = "120--126",
    abstract = "We bring together knowledge from two different types of language learning data, texts learners read and texts they write, to improve linguistic complexity classification in the latter. Linguistic complexity in the foreign and second language learning context can be expressed in terms of proficiency levels. We show that incorporating features capturing lexical complexity information from reading passages can boost significantly the machine learning based classification of learner-written texts into proficiency levels. With an F1 score of .8 our system rivals state-of-the-art results reported for other languages for this task. Finally, we present a freely available web-based tool for proficiency level classification and lexical complexity visualization for both learner writings and reading texts.",
}
@inproceedings{zaghouani-etal-2016-using,
    title = "Using Ambiguity Detection to Streamline Linguistic Annotation",
    author = "Zaghouani, Wajdi  and
      Hawwari, Abdelati  and
      Alqahtani, Sawsan  and
      Bouamor, Houda  and
      Ghoneim, Mahmoud  and
      Diab, Mona  and
      Oflazer, Kemal",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4115",
    pages = "127--136",
    abstract = "Arabic writing is typically underspecified for short vowels and other markups, referred to as diacritics. In addition to the lexical ambiguity exhibited in most languages, the lack of diacritics in written Arabic adds another layer of ambiguity which is an artifact of the orthography. In this paper, we present the details of three annotation experimental conditions designed to study the impact of automatic ambiguity detection, on annotation speed and quality in a large scale annotation project.",
}
@inproceedings{bjerva-borstell-2016-morphological,
    title = "Morphological Complexity Influences Verb-Object Order in {S}wedish {S}ign {L}anguage",
    author = {Bjerva, Johannes  and
      B{\"o}rstell, Carl},
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4116",
    pages = "137--141",
    abstract = "Computational linguistic approaches to sign languages could benefit from investigating how complexity influences structure. We investigate whether morphological complexity has an effect on the order of Verb (V) and Object (O) in Swedish Sign Language (SSL), on the basis of elicited data from five Deaf signers. We find a significant difference in the distribution of the orderings OV vs. VO, based on an analysis of morphological weight. While morphologically heavy verbs exhibit a general preference for OV, humanness seems to affect the ordering in the opposite direction, with [+human] Objects pushing towards a preference for VO.",
}
@inproceedings{bentz-etal-2016-comparison,
    title = "A Comparison Between Morphological Complexity Measures: Typological Data vs. Language Corpora",
    author = "Bentz, Christian  and
      Ruzsics, Tatyana  and
      Koplenig, Alexander  and
      Samard{\v{z}}i{\'c}, Tanja",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4117",
    pages = "142--153",
    abstract = "Language complexity is an intriguing phenomenon argued to play an important role in both language learning and processing. The need to compare languages with regard to their complexity resulted in a multitude of approaches and methods, ranging from accounts targeting specific structural features to global quantification of variation more generally. In this paper, we investigate the degree to which morphological complexity measures are mutually correlated in a sample of more than 500 languages of 101 language families. We use human expert judgements from the World Atlas of Language Structures (WALS), and compare them to four quantitative measures automatically calculated from language corpora. These consist of three previously defined corpus-derived measures, which are all monolingual, and one new measure based on automatic word-alignment across pairs of languages. We find strong correlations between all the measures, illustrating that both expert judgements and automated approaches converge to similar complexity ratings, and can be used interchangeably.",
}
@inproceedings{albertsson-etal-2016-similarity,
    title = "Similarity-Based Alignment of Monolingual Corpora for Text Simplification Purposes",
    author = {Albertsson, Sarah  and
      Rennes, Evelina  and
      J{\"o}nsson, Arne},
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4118",
    pages = "154--163",
    abstract = "Comparable or parallel corpora are beneficial for many NLP tasks. The automatic collection of corpora enables large-scale resources, even for less-resourced languages, which in turn can be useful for deducing rules and patterns for text rewriting algorithms, a subtask of automatic text simplification. We present two methods for the alignment of Swedish easy-to-read text segments to text segments from a reference corpus. The first method (M1) was originally developed for the task of text reuse detection, measuring sentence similarity by a modified version of a TF-IDF vector space model. A second method (M2), also accounting for part-of-speech tags, was developed, and the methods were compared. For evaluation, a crowdsourcing platform was built for human judgement data collection, and preliminary results showed that cosine similarity relates better to human ranks than the Dice coefficient. We also saw a tendency that including syntactic context to the TF-IDF vector space model is beneficial for this kind of paraphrase alignment task.",
}
@inproceedings{wagner-filho-etal-2016-automatic,
    title = "Automatic Construction of Large Readability Corpora",
    author = "Wagner Filho, Jorge Alberto  and
      Wilkens, Rodrigo  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4119",
    pages = "164--173",
    abstract = "This work presents a framework for the automatic construction of large Web corpora classified by readability level. We compare different Machine Learning classifiers for the task of readability assessment focusing on Portuguese and English texts, analysing the impact of variables like the feature inventory used in the resulting corpus. In a comparison between shallow and deeper features, the former already produce F-measures of over 0.75 for Portuguese texts, but the use of additional features results in even better results, in most cases. For English, shallow features also perform well as do classic readability formulas. Comparing different classifiers for the task, logistic regression obtained, in general, the best results, but with considerable differences between the results for two and those for three-classes, especially regarding the intermediary class. Given the large scale of the resulting corpus, for evaluation we adopt the agreement between different classifiers as an indication of readability assessment certainty. As a result of this work, a large corpus for Brazilian Portuguese was built, including 1.7 million documents and about 1.6 billion tokens, already parsed and annotated with 134 different textual attributes, along with the agreement among the various classifiers.",
}
@inproceedings{bloem-2016-testing,
    title = "Testing the Processing Hypothesis of word order variation using a probabilistic language model",
    author = "Bloem, Jelke",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4120",
    pages = "174--185",
    abstract = "This work investigates the application of a measure of surprisal to modeling a grammatical variation phenomenon between near-synonymous constructions. We investigate a particular variation phenomenon, word order variation in Dutch two-verb clusters, where it has been established that word order choice is affected by processing cost. Several multifactorial corpus studies of Dutch verb clusters have used other measures of processing complexity to show that this factor affects word order choice. This previous work allows us to compare the surprisal measure, which is based on constraint satisfaction theories of language modeling, to those previously used measures, which are more directly linked to empirical observations of processing complexity. Our results show that surprisal does not predict the word order choice by itself, but is a significant predictor when used in a measure of uniform information density (UID). This lends support to the view that human language processing is facilitated not so much by predictable sequences of words but more by sequences of words in which information is spread evenly.",
}
@inproceedings{li-etal-2016-temporal,
    title = "Temporal Lobes as Combinatory Engines for both Form and Meaning",
    author = "Li, Jixing  and
      Brennan, Jonathan  and
      Mahar, Adam  and
      Hale, John",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4121",
    pages = "186--191",
    abstract = "The relative contributions of meaning and form to sentence processing remains an outstanding issue across the language sciences. We examine this issue by formalizing four incremental complexity metrics and comparing them against freely-available ROI timecourses. Syntax-related metrics based on top-down parsing and structural dependency-distance turn out to significantly improve a regression model, compared to a simpler model that formalizes only conceptual combination using a distributional vector-space model. This confirms the view of the anterior temporal lobes as combinatory engines that deal in both form (see e.g. Brennan et al., 2012; Mazoyer, 1993) and meaning (see e.g., Patterson et al., 2007). This same characterization applies to a posterior temporal region in roughly {``}Wernicke{'}s Area.{''}",
}
@inproceedings{mirzaei-etal-2016-automatic,
    title = "Automatic Speech Recognition Errors as a Predictor of {L}2 Listening Difficulties",
    author = "Mirzaei, Maryam Sadat  and
      Meshgi, Kourosh  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4122",
    pages = "192--201",
    abstract = "This paper investigates the use of automatic speech recognition (ASR) errors as indicators of the second language (L2) learners{'} listening difficulties and in doing so strives to overcome the shortcomings of Partial and Synchronized Caption (PSC) system. PSC is a system that generates a partial caption including difficult words detected based on high speech rate, low frequency, and specificity. To improve the choice of words in this system, and explore a better method to detect speech challenges, ASR errors were investigated as a model of the L2 listener, hypothesizing that some of these errors are similar to those of language learners{'} when transcribing the videos. To investigate this hypothesis, ASR errors in transcription of several TED talks were analyzed and compared with PSC{'}s selected words. Both the overlapping and mismatching cases were analyzed to investigate possible improvement for the PSC system. Those ASR errors that were not detected by PSC as cases of learners{'} difficulties were further analyzed and classified into four categories: homophones, minimal pairs, breached boundaries and negatives. These errors were embedded into the baseline PSC to make the enhanced version and were evaluated in an experiment with L2 learners. The results indicated that the enhanced version, which encompasses the ASR errors addresses most of the L2 learners{'} difficulties and better assists them in comprehending challenging video segments as compared with the baseline.",
}
@inproceedings{singh-etal-2016-quantifying,
    title = "Quantifying sentence complexity based on eye-tracking measures",
    author = "Singh, Abhinav Deep  and
      Mehta, Poojan  and
      Husain, Samar  and
      Rajakrishnan, Rajkumar",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4123",
    pages = "202--212",
    abstract = "Eye-tracking reading times have been attested to reflect cognitive processes underlying sentence comprehension. However, the use of reading times in NLP applications is an underexplored area of research. In this initial work we build an automatic system to assess sentence complexity using automatically predicted eye-tracking reading time measures and demonstrate the efficacy of these reading times for a well known NLP task, namely, readability assessment. We use a machine learning model and a set of features known to be significant predictors of reading times in order to learn per-word reading times from a corpus of English text having reading times of human readers. Subsequently, we use the model to predict reading times for novel text in the context of the aforementioned task. A model based only on reading times gave competitive results compared to the systems that use extensive syntactic features to compute linguistic complexity. Our work, to the best of our knowledge, is the first study to show that automatically predicted reading times can successfully model the difficulty of a text and can be deployed in practical text processing applications.",
}
@inproceedings{takahira-etal-2016-upper,
    title = "Upper Bound of Entropy Rate Revisited {---}{A} New Extrapolation of Compressed Large-Scale Corpora{---}",
    author = "Takahira, Ryosuke  and
      Tanaka-Ishii, Kumiko  and
      D{\k{e}}bowski, {\L}ukasz",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4124",
    pages = "213--221",
    abstract = "The article presents results of entropy rate estimation for human languages across six languages by using large, state-of-the-art corpora of up to 7.8 gigabytes. To obtain the estimates for data length tending to infinity, we use an extrapolation function given by an ansatz. Whereas some ansatzes of this kind were proposed in previous research papers, here we introduce a stretched exponential extrapolation function that has a smaller error of fit. In this way, we uncover a possibility that the entropy rates of human languages are positive but 20{\%} smaller than previously reported.",
}
@inproceedings{bentz-berdicevskis-2016-learning,
    title = "Learning pressures reduce morphological complexity: Linking corpus, computational and experimental evidence",
    author = "Bentz, Christian  and
      Berdicevskis, Aleksandrs",
    booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4125",
    pages = "222--232",
    abstract = "The morphological complexity of languages differs widely and changes over time. Pathways of change are often driven by the interplay of multiple competing factors, and are hard to disentangle. We here focus on a paradigmatic scenario of language change: the reduction of morphological complexity from Latin towards the Romance languages. To establish a causal explanation for this phenomenon, we employ three lines of evidence: 1) analyses of parallel corpora to measure the complexity of words in actual language production, 2) applications of NLP tools to further tease apart the contribution of inflectional morphology to word complexity, and 3) experimental data from artificial language learning, which illustrate the learning pressures at play when morphology simplifies. These three lines of evidence converge to show that pressures associated with imperfect language learning are good candidates to causally explain the reduction in morphological complexity in the Latin-to-Romance scenario. More generally, we argue that combining corpus, computational and experimental evidence is the way forward in historical linguistics and linguistic typology.",
}
@proceedings{ws-2016-language,
    title = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    editor = "Hinrichs, Erhard  and
      Hinrichs, Marie  and
      Trippel, Thorsten",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4000",
}
@inproceedings{kuhn-2016-flexible,
    title = "Flexible and Reliable Text Analytics in the Digital Humanities {--} Some Methodological Considerations",
    author = "Kuhn, Jonas",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4001",
    pages = "1",
    abstract = "The availability of Language Technology Resources and Tools generates a considerable methodological potential in the Digital Humanities: aspects of research questions from the Humanities and Social Sciences can be addressed on text collections in ways that were unavailable to traditional approaches. I start this talk by sketching some sample scenarios of Digital Humanities projects which involve various Humanities and Social Science disciplines, noting that the potential for a meaningful contribution to higher-level questions is highest when the employed language technological models are carefully tailored both (a) to characteristics of the given target corpus, and (b) to relevant analytical subtasks feeding the discipline-specific research questions. Keeping up a multidisciplinary perspective, I then point out a recurrent dilemma in Digital Humanities projects that follow the conventional set-up of collaboration: to build high-quality computational models for the data, fixed analytical targets should be specified as early as possible {--} but to be able to respond to Humanities questions as they evolve over the course of analysis, the analytical machinery should be kept maximally flexible. To reach both, I argue for a novel collaborative culture that rests on a more interleaved, continuous dialogue. (Re-)Specification of analytical targets should be an ongoing process in which the Humanities Scholars and Social Scientists play a role that is as important as the Computational Scientists{'} role. A promising approach lies in the identification of re-occurring types of analytical subtasks, beyond linguistic standard tasks, which can form building blocks for text analysis across disciplines, and for which corpus-based characterizations (viz. annotations) can be collected, compared and revised. On such grounds, computational modeling is more directly tied to the evolving research questions, and hence the seemingly opposing needs of reliable target specifications vs. {``}malleable{''} frameworks of analysis can be reconciled. Experimental work following this approach is under way in the Center for Reflected Text Analytics (CRETA) in Stuttgart.",
}
@inproceedings{tjong-kim-sang-2016-finding,
    title = "Finding Rising and Falling Words",
    author = "Tjong Kim Sang, Erik",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4002",
    pages = "2--9",
    abstract = "We examine two different methods for finding rising words (among which neologisms) and falling words (among which archaisms) in decades of magazine texts (millions of words) and in years of tweets (billions of words): one based on correlation coefficients of relative frequencies and time, and one based on comparing initial and final word frequencies of time intervals. We find that smoothing frequency scores improves the precision scores of both methods and that the correlation coefficients perform better on magazine text but worse on tweets. Since the two ranking methods find different words they can be used in side-by-side to study the behavior of words over time.",
}
@inproceedings{sheng-etal-2016-dataset,
    title = "A Dataset for Multimodal Question Answering in the Cultural Heritage Domain",
    author = "Sheng, Shurong  and
      Van Gool, Luc  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4003",
    pages = "10--17",
    abstract = "Multimodal question answering in the cultural heritage domain allows visitors to ask questions in a more natural way and thus provides better user experiences with cultural objects while visiting a museum, landmark or any other historical site. In this paper, we introduce the construction of a golden standard dataset that will aid research of multimodal question answering in the cultural heritage domain. The dataset, which will be soon released to the public, contains multimodal content including images of typical artworks from the fascinating old-Egyptian Amarna period, related image-containing documents of the artworks and over 800 multimodal queries integrating visual and textual questions. The multimodal questions and related documents are all in English. The multimodal questions are linked to relevant paragraphs in the related documents that contain the answer to the multimodal query.",
}
@inproceedings{wohlgenannt-etal-2016-extracting,
    title = "Extracting Social Networks from Literary Text with Word Embedding Tools",
    author = "Wohlgenannt, Gerhard  and
      Chernyak, Ekaterina  and
      Ilvovsky, Dmitry",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4004",
    pages = "18--25",
    abstract = "In this paper a social network is extracted from a literary text. The social network shows, how frequent the characters interact and how similar their social behavior is. Two types of similarity measures are used: the first applies co-occurrence statistics, while the second exploits cosine similarity on different types of word embedding vectors. The results are evaluated by a paid micro-task crowdsourcing survey. The experiments suggest that specific types of word embeddings like word2vec are well-suited for the task at hand and the specific circumstances of literary fiction text.",
}
@inproceedings{kutuzov-etal-2016-exploration,
    title = "Exploration of register-dependent lexical semantics using word embeddings",
    author = "Kutuzov, Andrey  and
      Kuzmenko, Elizaveta  and
      Marakasova, Anna",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4005",
    pages = "26--34",
    abstract = "We present an approach to detect differences in lexical semantics across English language registers, using word embedding models from distributional semantics paradigm. Models trained on register-specific subcorpora of the BNC corpus are employed to compare lists of nearest associates for particular words and draw conclusions about their semantic shifts depending on register in which they are used. The models are evaluated on the task of register classification with the help of the deep inverse regression approach. Additionally, we present a demo web service featuring most of the described models and allowing to explore word meanings in different English registers and to detect register affiliation for arbitrary texts. The code for the service can be easily adapted to any set of underlying models.",
}
@inproceedings{oka-kono-2016-original,
    title = "Original-Transcribed Text Alignment for {M}anyosyu Written by {O}ld {J}apanese Language",
    author = "Oka, Teruaki  and
      Kono, Tomoaki",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4006",
    pages = "35--44",
    abstract = "We are constructing an annotated diachronic corpora of the Japanese language. In part of thiswork, we construct a corpus of Manyosyu, which is an old Japanese poetry anthology. In thispaper, we describe how to align the transcribed text and its original text semiautomatically to beable to cross-reference them in our Manyosyu corpus. Although we align the original charactersto the transcribed words manually, we preliminarily align the transcribed and original charactersby using an unsupervised automatic alignment technique of statistical machine translation toalleviate the work. We found that automatic alignment achieves an F1-measure of 0.83; thus, each poem has 1{--}2 alignment errors. However, finding these errors and modifying them are less workintensiveand more efficient than fully manual annotation. The alignment probabilities can beutilized in this modification. Moreover, we found that we can locate the uncertain transcriptionsin our corpus and compare them to other transcriptions, by using the alignment probabilities.",
}
@inproceedings{belinkov-etal-2016-shamela,
    title = "{S}hamela: A Large-Scale Historical {A}rabic Corpus",
    author = "Belinkov, Yonatan  and
      Magidow, Alexander  and
      Romanov, Maxim  and
      Shmidman, Avi  and
      Koppel, Moshe",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4007",
    pages = "45--53",
    abstract = "Arabic is a widely-spoken language with a rich and long history spanning more than fourteen centuries. Yet existing Arabic corpora largely focus on the modern period or lack sufficient diachronic information. We develop a large-scale, historical corpus of Arabic of about 1 billion words from diverse periods of time. We clean this corpus, process it with a morphological analyzer, and enhance it by detecting parallel passages and automatically dating undated texts. We demonstrate its utility with selected case-studies in which we show its application to the digital humanities.",
}
@inproceedings{buechel-etal-2016-feelings,
    title = "Feelings from the {P}ast{---}{A}dapting Affective Lexicons for Historical Emotion Analysis",
    author = "Buechel, Sven  and
      Hellrich, Johannes  and
      Hahn, Udo",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4008",
    pages = "54--61",
    abstract = "We here describe a novel methodology for measuring affective language in historical text by expanding an affective lexicon and jointly adapting it to prior language stages. We automatically construct a lexicon for word-emotion association of 18th and 19th century German which is then validated against expert ratings. Subsequently, this resource is used to identify distinct emotional patterns and trace long-term emotional trends in different genres of writing spanning several centuries.",
}
@inproceedings{eckhoff-berdicevskis-2016-automatic,
    title = "Automatic parsing as an efficient pre-annotation tool for historical texts",
    author = "Eckhoff, Hanne Martine  and
      Berdi{\v{c}}evskis, Aleksandrs",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4009",
    pages = "62--70",
    abstract = "Historical treebanks tend to be manually annotated, which is not surprising, since state-of-the-art parsers are not accurate enough to ensure high-quality annotation for historical texts. We test whether automatic parsing can be an efficient pre-annotation tool for Old East Slavic texts. We use the TOROT treebank from the PROIEL treebank family. We convert the PROIEL format to the CONLL format and use MaltParser to create syntactic pre-annotation. Using the most conservative evaluation method, which takes into account PROIEL-specific features, MaltParser by itself yields 0.845 unlabelled attachment score, 0.779 labelled attachment score and 0.741 secondary dependency accuracy (note, though, that the test set comes from a relatively simple genre and contains rather short sentences). Experiments with human annotators show that preparsing, if limited to sentences where no changes to word or sentence boundaries are required, increases their annotation rate. For experienced annotators, the speed gain varies from 5.80{\%} to 16.57{\%}, for inexperienced annotators from 14.61{\%} to 32.17{\%} (using conservative estimates). There are no strong reliable differences in the annotation accuracy, which means that there is no reason to suspect that using preparsing might lower the final annotation quality.",
}
@inproceedings{bucur-nisioi-2016-visual,
    title = "A Visual Representation of {W}ittgenstein{'}s {T}ractatus Logico-Philosophicus",
    author = "Bucur, Anca  and
      Nisioi, Sergiu",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4010",
    pages = "71--75",
    abstract = "In this paper we will discuss a method for data visualization together with its potential usefulness in digital humanities and philosophy of language. We compiled a multilingual parallel corpus from different versions of \textit{Wittgenstein{'}s Tractatus Logico-philosophicus}, including the original in German and translations into English, Spanish, French, and Russian. Using this corpus, we compute a similarity measure between propositions and render a visual network of relations for different languages.",
}
@inproceedings{eckart-de-castilho-etal-2016-web,
    title = "A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures",
    author = "Eckart de Castilho, Richard  and
      M{\'u}jdricza-Maydt, {\'E}va  and
      Yimam, Seid Muhie  and
      Hartmann, Silvana  and
      Gurevych, Iryna  and
      Frank, Anette  and
      Biemann, Chris",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4011",
    pages = "76--84",
    abstract = "We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams. New features in this release focus on semantic annotation tasks (e.g. semantic role labelling or event annotation) and allow the tight integration of semantic annotations with syntactic annotations. In particular, we introduce the concept of slot features, a novel constraint mechanism that allows modelling the interaction between semantic and syntactic annotations, as well as a new annotation user interface. The new features were developed and used in an annotation project for semantic roles on German texts. The paper briefly introduces this project and reports on experiences performing annotations with the new tool. On a comparative evaluation, our tool reaches significant speedups over WebAnno 2 for a semantic annotation task.",
}
@inproceedings{erdmann-etal-2016-challenges,
    title = "Challenges and Solutions for {L}atin Named Entity Recognition",
    author = "Erdmann, Alexander  and
      Brown, Christopher  and
      Joseph, Brian  and
      Janse, Mark  and
      Ajaka, Petra  and
      Elsner, Micha  and
      de Marneffe, Marie-Catherine",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4012",
    pages = "85--93",
    abstract = "Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English. Data sparsity in Latin presents a number of challenges for traditional Named Entity Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90{\%} F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality.",
}
@inproceedings{petran-2016-geographical,
    title = "Geographical Visualization of Search Results in Historical Corpora",
    author = "Petran, Florian",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4013",
    pages = "94--100",
    abstract = "We present ANNISVis, a webapp for comparative visualization of geographical distribution of linguistic data, as well as a sample deployment for a corpus of Middle High German texts. Unlike existing geographical visualization solutions, which work with pre-existing data sets, or are bound to specific corpora, ANNISVis allows the user to formulate multiple ad-hoc queries and visualizes them on a map, and it can be configured for any corpus that can be imported into ANNIS. This enables explorative queries of the quantitative aspects of a corpus with geographical features. The tool will be made available to download in open source.",
}
@inproceedings{jongejan-2016-implementation,
    title = "Implementation of a Workflow Management System for Non-Expert Users",
    author = "Jongejan, Bart",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4014",
    pages = "101--108",
    abstract = "In the Danish CLARIN-DK infrastructure, chaining language technology (LT) tools into a workflow is easy even for a non-expert user, because she only needs to specify the input and the desired output of the workflow. With this information and the registered input and output profiles of the available tools, the CLARIN-DK workflow management system (WMS) computes combinations of tools that will give the desired result. This advanced functionality was originally not envisaged, but came within reach by writing the WMS partly in Java and partly in a programming language for symbolic computation, Bracmat. Handling LT tool profiles, including the computation of workflows, is easier with Bracmat{'}s language constructs for tree pattern matching and tree construction than with the language constructs offered by mainstream programming languages.",
}
@inproceedings{afli-way-2016-integrating,
    title = "Integrating Optical Character Recognition and Machine Translation of Historical Documents",
    author = "Afli, Haithem  and
      Way, Andy",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4015",
    pages = "109--116",
    abstract = "Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how good the OCR is, this process introduces recognition errors, which often renders MT ineffective. In this paper, we propose a new OCR to MT framework based on adding a new OCR error correction module to enhance the overall quality of translation. Experimentation shows that our new system correction based on the combination of Language Modeling and Translation methods outperforms the baseline system by nearly 30{\%} relative improvement.",
}
@inproceedings{hunyadi-etal-2016-language,
    title = "Language technology tools and resources for the analysis of multimodal communication",
    author = "Hunyadi, L{\'a}szl{\'o}  and
      V{\'a}radi, Tam{\'a}s  and
      Szekr{\'e}nyes, Istv{\'a}n",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4016",
    pages = "117--124",
    abstract = "In this paper we describe how the complexity of human communication can be analysed with the help of language technology. We present the HuComTech corpus, a multimodal corpus containing 50 hours of videotaped interviews containing a rich annotation of about 2 million items annotated on 33 levels. The corpus serves as a general resource for a wide range of re-search addressing natural conversation between humans in their full complexity. It can benefit particularly digital humanities researchers working in the field of pragmatics, conversational analysis and discourse analysis. We will present a number of tools and automated methods that can help such enquiries. In particular, we will highlight the tool Theme, which is designed to uncover hidden temporal patterns (called T-patterns) in human interaction, and will show how it can applied to the study of multimodal communication.",
}
@inproceedings{baumann-meyer-sickendiek-2016-large,
    title = "Large-scale Analysis of Spoken Free-verse Poetry",
    author = "Baumann, Timo  and
      Meyer-Sickendiek, Burkhard",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4017",
    pages = "125--130",
    abstract = {Most modern and post-modern poems have developed a post-metrical idea of lyrical prosody that employs rhythmical features of everyday language and prose instead of a strict adherence to rhyme and metrical schemes. This development is subsumed under the term free verse prosody. We present our methodology for the large-scale analysis of modern and post-modern poetry in both their written form and as spoken aloud by the author. We employ language processing tools to align text and speech, to generate a null-model of how the poem would be spoken by a na{\"\i}ve reader, and to extract contrastive prosodic features used by the poet. On these, we intend to build our model of free verse prosody, which will help to understand, differentiate and relate the different styles of free verse poetry. We plan to use our processing scheme on large amounts of data to iteratively build models of styles, to validate and guide manual style annotation, to identify further rhythmical categories, and ultimately to broaden our understanding of free verse poetry. In this paper, we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features. We find that our methodology helps to extract differentiating features in the authors{'} speech that can be explained by philological insight. Thus, our automatic method helps to guide the literary analysis and this in turn helps to improve our computational models.},
}
@inproceedings{van-der-sluis-etal-2016-pat,
    title = "{PAT} workbench: Annotation and Evaluation of Text and Pictures in Multimodal Instructions",
    author = "van der Sluis, Ielka  and
      Kloppenburg, Lennart  and
      Redeker, Gisela",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4018",
    pages = "131--139",
    abstract = "This paper presents a tool to investigate the design of multimodal instructions (MIs), i.e., instructions that contain both text and pictures. The benefit of including pictures in information presentation has been established, but the characteristics of those pictures and of their textual counterparts and the rela-tion(s) between them have not been researched in a systematic manner. We present the PAT Work-bench, a tool to store, annotate and retrieve MIs based on a validated coding scheme with currently 42 categories that describe instructions in terms of textual features, pictorial elements, and relations be-tween text and pictures. We describe how the PAT Workbench facilitates collaborative annotation and inter-annotator agreement calculation. Future work on the tool includes expanding its functionality and usability by (i) making the MI annotation scheme dynamic for adding relevant features based on empirical evaluations of the MIs, (ii) implementing algorithms for automatic tagging of MI features, and (iii) implementing automatic MI evaluation algorithms based on results obtained via e.g. crowdsourced assessments of MIs.",
}
@inproceedings{raganato-etal-2016-semantic,
    title = "Semantic Indexing of Multilingual Corpora and its Application on the History Domain",
    author = "Raganato, Alessandro  and
      Camacho-Collados, Jose  and
      Raganato, Antonio  and
      Joung, Yunseo",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4019",
    pages = "140--147",
    abstract = "The increasing amount of multilingual text collections available in different domains makes its automatic processing essential for the development of a given field. However, standard processing techniques based on statistical clues and keyword searches have clear limitations. Instead, we propose a knowledge-based processing pipeline which overcomes most of the limitations of these techniques. This, in turn, enables direct comparison across texts in different languages without the need of translation. In this paper we show the potential of this approach for semantically indexing multilingual text collections in the history domain. In our experiments we used a version of the Bible translated in four different languages, evaluating the precision of our semantic indexing pipeline and showing its reliability on the cross-lingual text retrieval task.",
}
@inproceedings{tiedemann-etal-2016-tagging,
    title = "Tagging {I}ngush - Language Technology For Low-Resource Languages Using Resources From Linguistic Field Work",
    author = {Tiedemann, J{\"o}rg  and
      Nichols, Johanna  and
      Sprouse, Ronald},
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4020",
    pages = "148--155",
    abstract = "This paper presents on-going work on creating NLP tools for under-resourced languages from very sparse training data coming from linguistic field work. In this work, we focus on Ingush, a Nakh-Daghestanian language spoken by about 300,000 people in the Russian republics Ingushetia and Chechnya. We present work on morphosyntactic taggers trained on transcribed and linguistically analyzed recordings and dependency parsers using English glosses to project annotation for creating synthetic treebanks. Our preliminary results are promising, supporting the goal of bootstrapping efficient NLP tools with limited or no task-specific annotated data resources available.",
}
@inproceedings{sadoun-etal-2016-multital,
    title = "The {M}ulti{T}al {NLP} tool infrastructure",
    author = "Sadoun, Driss  and
      Mkhitaryan, Satenik  and
      Nouvel, Damien  and
      Valette, Mathieu",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4021",
    pages = "156--163",
    abstract = "This paper gives an overview of the MultiTal project, which aims to create a research infrastructure that ensures long-term distribution of NLP tools descriptions. The goal is to make NLP tools more accessible and usable to end-users of different disciplines. The infrastructure is built on a meta-data scheme modelling and standardising multilingual NLP tools documentation. The model is conceptualised using an OWL ontology. The formal representation of the ontology allows us to automatically generate organised and structured documentation in different languages for each represented tool.",
}
@inproceedings{khan-etal-2016-tools,
    title = "Tools and Instruments for Building and Querying Diachronic Computational Lexica",
    author = "Khan, Fahad  and
      Bellandi, Andrea  and
      Monachini, Monica",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4022",
    pages = "164--171",
    abstract = "This article describes work on enabling the addition of temporal information to senses of words in linguistic linked open data lexica based on the lemonDia model. Our contribution in this article is twofold. On the one hand, we demonstrate how lemonDia enables the querying of diachronic lexical datasets using OWL-oriented Semantic Web based technologies. On the other hand, we present a preliminary version of an interactive interface intended to help users in creating lexical datasets that model meaning change over time.",
}
@inproceedings{liu-luo-2016-tracking,
    title = "Tracking Words in {C}hinese Poetry of {T}ang and {S}ong Dynasties with the {C}hina {B}iographical {D}atabase",
    author = "Liu, Chao-Lin  and
      Luo, Kuo-Feng",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4023",
    pages = "172--180",
    abstract = "(This is the abstract for the submission.) Large-scale comparisons between the poetry of Tang and Song dynasties shed light on how words and expressions were used and shared among the poets. That some words were used only in the Tang poetry and some only in the Song poetry could lead to interesting research in linguistics. That the most frequent colors are different in the Tang and Song poetry provides a trace of the changing social circumstances in the dynasties. Results of the current work link to research topics of lexicography, semantics, and social transitions. We discuss our findings and present our algorithms for efficient comparisons among the poems, which are crucial for completing billion times of comparisons within acceptable time.",
}
@inproceedings{stahn-etal-2016-using,
    title = "Using {TEI} for textbook research",
    author = "Stahn, Lena-Luise  and
      Hennicke, Steffen  and
      De Luca, Ernesto William",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4024",
    pages = "181--186",
    abstract = "The following paper describes the first steps in the development of an ontology for the textbook research discipline. The aim of the project WorldViews is to establish a digital edition focussing on views of the world depicted in textbooks. For this purpose an initial TEI profile has been formalised and tested as a use case to enable the semantical encoding of the resource {`}textbook{'}. This profile shall provide a basic data model describing major facets of the textbook{'}s structure relevant to historians.",
}
@inproceedings{ogrodniczuk-2016-web,
    title = "Web services and data mining: combining linguistic tools for {P}olish with an analytical platform",
    author = "Ogrodniczuk, Maciej",
    booktitle = "Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities ({LT}4{DH})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-4025",
    pages = "187--195",
    abstract = "In this paper we present a new combination of existing language tools for Polish with a popular data mining platform intended to help researchers from digital humanities perform computational analyses without any programming. The toolset includes RapidMiner Studio, a software solution offering graphical setup of integrated analytical processes and Multiservice, a Web service offering access to several state-of-the-art linguistic tools for Polish. The setting is verified in a simple task of counting frequencies of unknown words in a small corpus.",
}
@proceedings{ws-2016-noisy,
    title = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    editor = "Han, Bo  and
      Ritter, Alan  and
      Derczynski, Leon  and
      Xu, Wei  and
      Baldwin, Tim",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3900",
}
@inproceedings{plank-2016-processing,
    title = "Processing non-canonical or noisy text: fortuitous data to the rescue",
    author = "Plank, Barbara",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3901",
    pages = "1",
    abstract = "Real world data differs radically from the benchmark corpora we use in NLP, resulting in large performance drops. The reason for this problem is obvious: NLP models are trained on limited samples from canonical varieties considered standard. However, there are many dimensions, e.g., sociodemographic, language, genre, sentence type, etc. on which texts can differ from the standard. The solution is not obvious: we cannot control for all factors, and it is not clear how to best go beyond the current practice of training on homogeneous data from a single domain and language. In this talk, I review the notion of canonicity, and how it shapes our community{'}s approach to language. I argue for the use of fortuitous data. Fortuitous data is data out there that just waits to be harvested. It includes data which is in plain sight, but is often neglected, and more distant sources like behavioral data, which first need to be refined. They provide additional contexts and a myriad of opportunities to build more adaptive language technology, some of which I will explore in this talk.",
}
@inproceedings{chang-2016-entity,
    title = "From Entity Linking to Question Answering {--} Recent Progress on Semantic Grounding Tasks",
    author = "Chang, Ming-Wei",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3902",
    pages = "2",
    abstract = "Entity linking and semantic parsing have been shown to be crucial to important applications such as question answering and document understanding. These tasks often require structured learning models, which make predictions on multiple interdependent variables. In this talk, I argue that carefully designed structured learning algorithms play a central role in entity linking and semantic parsing tasks. In particular, I will present several new structured learning models for entity linking, which jointly detect mentions and disambiguate entities as well as capture non-textual information. I will then show how to use a staged search procedure to building a state-of-the-art knowledge base question answering system. Finally, if time permits, I will discuss different supervision protocols for training semantic parsers and the value of labeling semantic parses.",
}
@inproceedings{torisawa-2016-disaana,
    title = "{DISAANA} and {D}-{SUMM}: Large-scale Real Time {NLP} Systems for Analyzing Disaster Related Reports in Tweets",
    author = "Torisawa, Kentaro",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3903",
    pages = "3",
    abstract = "This talk presents two NLP systems that were developed for helping disaster victims and rescue workers in the aftermath of large-scale disasters. DISAANA provides answers to questions such as {``}What is in short supply in Tokyo?{''} and displays locations related to each answer on a map. D-SUMM automatically summarizes a large number of disaster related reports concerning a specified area and helps rescue workers to understand disaster situations from a macro perspective. Both systems are publicly available as Web services. In the aftermath of the 2016 Kumamoto Earthquake (M7.0), the Japanese government actually used DISAANA to analyze the situation.",
}
@inproceedings{ljubesic-fiser-2016-private,
    title = "Private or Corporate? Predicting User Types on {T}witter",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Fi{\v{s}}er, Darja",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3904",
    pages = "4--12",
    abstract = "In this paper we present a series of experiments on discriminating between private and corporate accounts on Twitter. We define features based on Twitter metadata, morphosyntactic tags and surface forms, showing that the simple bag-of-words model achieves single best results that can, however, be improved by building a weighted soft ensemble of classifiers based on each feature type. Investigating the time and language dependence of each feature type delivers quite unexpecting results showing that features based on metadata are neither time- nor language-insensitive as the way the two user groups use the social network varies heavily through time and space.",
}
@inproceedings{martinez-alonso-etal-2016-noisy,
    title = "From Noisy Questions to {M}inecraft Texts: Annotation Challenges in Extreme Syntax Scenario",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Seddah, Djam{\'e}  and
      Sagot, Beno{\^\i}t",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3905",
    pages = "13--23",
    abstract = "User-generated content presents many challenges for its automatic processing. While many of them do come from out-of-vocabulary effects, others spawn from different linguistic phenomena such as unusual syntax. In this work we present a French three-domain data set made up of question headlines from a cooking forum, game chat logs and associated forums from two popular online games (MINECRAFT {\&} LEAGUE OF LEGENDS). We chose these domains because they encompass different degrees of lexical and syntactic compliance with canonical language. We conduct an automatic and manual evaluation of the difficulties of processing these domains for part-of-speech prediction, and introduce a pilot study to determine whether dependency analysis lends itself well to annotate these data. We also discuss the development cost of our data set.",
}
@inproceedings{asakura-etal-2016-disaster,
    title = "Disaster Analysis using User-Generated Weather Report",
    author = "Asakura, Yasunobu  and
      Hangyo, Masatsugu  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3906",
    pages = "24--32",
    abstract = "Information extraction from user-generated text has gained much attention with the growth of the Web.Disaster analysis using information from social media provides valuable, real-time, geolocation information for helping people caught up these in disasters. However, it is not convenient to analyze texts posted on social media because disaster keywords match any texts that contain words. For collecting posts about a disaster from social media, we need to develop a classifier to filter posts irrelevant to disasters. Moreover, because of the nature of social media, we can take advantage of posts that come with GPS information. However, a post does not always refer to an event occurring at the place where it has been posted. Therefore, we propose a new task of classifying whether a flood disaster occurred, in addition to predicting the geolocation of events from user-generated text. We report the annotation of the flood disaster corpus and develop a classifier to demonstrate the use of this corpus for disaster analysis.",
}
@inproceedings{reichel-lendvai-2016-veracity,
    title = "Veracity Computing from Lexical Cues and Perceived Certainty Trends",
    author = "Reichel, Uwe  and
      Lendvai, Piroska",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3907",
    pages = "33--42",
    abstract = "We present a data-driven method for determining the veracity of a set of rumorous claims on social media data. Tweets from different sources pertaining to a rumor are processed on three levels: first, factuality values are assigned to each tweet based on four textual cue categories relevant for our journalism use case; these amalgamate speaker support in terms of polarity and commitment in terms of certainty and speculation. Next, the proportions of these lexical cues are utilized as predictors for tweet certainty in a generalized linear regression model. Subsequently, lexical cue proportions, predicted certainty, as well as their time course characteristics are used to compute veracity for each rumor in terms of the identity of the rumor-resolving tweet and its binary resolution value judgment. The system operates without access to extralinguistic resources. Evaluated on the data portion for which hand-labeled examples were available, it achieves .74 F1-score on identifying rumor resolving tweets and .76 F1-score on predicting if a rumor is resolved as true or false.",
}
@inproceedings{van-der-wees-etal-2016-simple,
    title = "A Simple but Effective Approach to Improve {A}rabizi-to-{E}nglish Statistical Machine Translation",
    author = "van der Wees, Marlies  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3908",
    pages = "43--50",
    abstract = "A major challenge for statistical machine translation (SMT) of Arabic-to-English user-generated text is the prevalence of text written in Arabizi, or Romanized Arabic. When facing such texts, a translation system trained on conventional Arabic-English data will suffer from extremely low model coverage. In addition, Arabizi is not regulated by any official standardization and therefore highly ambiguous, which prevents rule-based approaches from achieving good translation results. In this paper, we improve Arabizi-to-English machine translation by presenting a simple but effective Arabizi-to-Arabic transliteration pipeline that does not require knowledge by experts or native Arabic speakers. We incorporate this pipeline into a phrase-based SMT system, and show that translation quality after automatically transliterating Arabizi to Arabic yields results that are comparable to those achieved after human transliteration.",
}
@inproceedings{andy-etal-2016-name,
    title = "Name Variation in Community Question Answering Systems",
    author = "Andy, Anietie  and
      Sekine, Satoshi  and
      Rwebangira, Mugizi  and
      Dredze, Mark",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3909",
    pages = "51--60",
    abstract = "Name Variation in Community Question Answering Systems Abstract Community question answering systems are forums where users can ask and answer questions in various categories. Examples are Yahoo! Answers, Quora, and Stack Overflow. A common challenge with such systems is that a significant percentage of asked questions are left unanswered. In this paper, we propose an algorithm to reduce the number of unanswered questions in Yahoo! Answers by reusing the answer to the most similar past resolved question to the unanswered question, from the site. Semantically similar questions could be worded differently, thereby making it difficult to find questions that have shared needs. For example, {``}Who is the best player for the Reds?{''} and {``}Who is currently the biggest star at Manchester United?{''} have a shared need but are worded differently; also, {``}Reds{''} and {``}Manchester United{''} are used to refer to the soccer team Manchester United football club. In this research, we focus on question categories that contain a large number of named entities and entity name variations. We show that in these categories, entity linking can be used to identify relevant past resolved questions with shared needs as a given question by disambiguating named entities and matching these questions based on the disambiguated entities, identified entities, and knowledge base information related to these entities. We evaluated our algorithm on a new dataset constructed from Yahoo! Answers. The dataset contains annotated question pairs, (Qgiven, [Qpast, Answer]). We carried out experiments on several question categories and show that an entity-based approach gives good performance when searching for similar questions in entity rich categories.",
}
@inproceedings{wang-etal-2016-whose,
    title = "Whose Nickname is This? Recognizing Politicians from Their Aliases",
    author = "Wang, Wei-Chung  and
      Chen, Hung-Chen  and
      Ji, Zhi-Kai  and
      Hsiao, Hui-I  and
      Chiu, Yu-Shian  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3910",
    pages = "61--69",
    abstract = "Using aliases to refer to public figures is one way to make fun of people, to express sarcasm, or even to sidestep legal issues when expressing opinions on social media. However, linking an alias back to the real name is difficult, as it entails phonemic, graphemic, and semantic challenges. In this paper, we propose a phonemic-based approach and inject semantic information to align aliases with politicians{'} Chinese formal names. The proposed approach creates an HMM model for each name to model its phonemes and takes into account document-level pairwise mutual information to capture the semantic relations to the alias. In this work we also introduce two new datasets consisting of 167 phonemic pairs and 279 mixed pairs of aliases and formal names. Experimental results show that the proposed approach models both phonemic and semantic information and outperforms previous work on both the phonemic and mixed datasets with the best top-1 accuracies of 0.78 and 0.59 respectively.",
}
@inproceedings{jain-etal-2016-towards,
    title = "Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators",
    author = "Jain, Ajit  and
      Kasiviswanathan, Girish  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3911",
    pages = "70--77",
    abstract = "Accurate event detection in social media is very challenging because user generated contents are extremely noisy and sparse in content. Event indicators are generally words or phrases that act as a trigger that help us understand the semantics of the context they occur in. We present a weakly supervised approach that relies on using a single strong event indicator phrase as a seed to acquire a variety of additional event cues. We propose to leverage various types of implicit event indicators, such as props, actors and precursor events, to achieve precise event detection. We experimented with civil unrest events and show that the automatically learnt event indicators are effective in identifying specific types of events.",
}
@inproceedings{albogamy-ramsay-2016-unsupervised,
    title = "Unsupervised Stemmer for {A}rabic Tweets",
    author = "Albogamy, Fahad  and
      Ramsay, Allan",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3912",
    pages = "78--84",
    abstract = "Stemming is an essential processing step in a wide range of high level text processing applications such as information extraction, machine translation and sentiment analysis. It is used to reduce words to their stems. Many stemming algorithms have been developed for Modern Standard Arabic (MSA). Although Arabic tweets and MSA are closely related and share many characteristics, there are substantial differences between them in lexicon and syntax. In this paper, we introduce a light Arabic stemmer for Arabic tweets. Our results show improvements over the performance of a number of well-known stemmers for Arabic.",
}
@inproceedings{su-etal-2016-topic,
    title = "Topic Stability over Noisy Sources",
    author = "Su, Jing  and
      Greene, Derek  and
      Boydell, Ois{\'\i}n",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3913",
    pages = "85--93",
    abstract = "Topic modelling techniques such as LDA have recently been applied to speech transcripts and OCR output. These corpora may contain noisy or erroneous texts which may undermine topic stability. Therefore, it is important to know how well a topic modelling algorithm will perform when applied to noisy data. In this paper we show that different types of textual noise can have diverse effects on the stability of topic models. On the other hand, topic model stability is not consistent with the same type but different levels of noise. We introduce a dictionary filtering approach to address this challenge, with the result that a topic model with the correct number of topics is always identified across different levels of noise.",
}
@inproceedings{pain-etal-2016-analysis,
    title = "Analysis of {T}witter Data for Postmarketing Surveillance in Pharmacovigilance",
    author = "Pain, Julie  and
      Levacher, Jessie  and
      Quinquenel, Adam  and
      Belz, Anja",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3914",
    pages = "94--101",
    abstract = "Postmarketing surveillance (PMS) has the vital aim to monitor effects of drugs after release for use by the general population, but suffers from under-reporting and limited coverage. Automatic methods for detecting drug effect reports, especially for social media, could vastly increase the scope of PMS. Very few automatic PMS methods are currently available, in particular for the messy text types encountered on Twitter. In this paper we describe first results for developing PMS methods specifically for tweets. We describe the corpus of 125,669 tweets we have created and annotated to train and test the tools. We find that generic tools perform well for tweet-level language identification and tweet-level sentiment analysis (both 0.94 F1-Score). For detection of effect mentions we are able to achieve 0.87 F1-Score, while effect-level adverse-vs.-beneficial analysis proves harder with an F1-Score of 0.64. Among other things, our results indicate that MetaMap semantic types provide a very promising basis for identifying drug effect mentions in tweets.",
}
@inproceedings{belainine-etal-2016-named,
    title = "Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets",
    author = "Belainine, Billal  and
      Fonseca, Alexsandro  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3915",
    pages = "102--111",
    abstract = "In social networks services like Twitter, users are overwhelmed with huge amount of social data, most of which are short, unstructured and highly noisy. Identifying accurate information from this huge amount of data is indeed a hard task. Classification of tweets into organized form will help the user to easily access these required information. Our first contribution relates to filtering parts of speech and preprocessing this kind of highly noisy and short data. Our second contribution concerns the named entity recognition (NER) in tweets. Thus, the adaptation of existing language tools for natural languages, noisy and not accurate language tweets, is necessary. Our third contribution involves segmentation of hashtags and a semantic enrichment using a combination of relations from WordNet, which helps the performance of our classification system, including disambiguation of named entities, abbreviations and acronyms. Graph theory is used to cluster the words extracted from WordNet and tweets, based on the idea of connected components. We test our automatic classification system with four categories: politics, economy, sports and the medical field. We evaluate and compare several automatic classification systems using part or all of the items described in our contributions and found that filtering by part of speech and named entity recognition dramatically increase the classification precision to 77.3 {\%}. Moreover, a classification system incorporating segmentation of hashtags and semantic enrichment by two relations from WordNet, synonymy and hyperonymy, increase classification precision up to 83.4 {\%}.",
}
@inproceedings{costa-bertaglia-volpe-nunes-2016-exploring,
    title = "Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization",
    author = "Costa Bertaglia, Thales Felipe  and
      Volpe Nunes, Maria das Gra{\c{c}}as",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3916",
    pages = "112--120",
    abstract = "Text normalization techniques based on rules, lexicons or supervised training requiring large corpora are not scalable nor domain interchangeable, and this makes them unsuitable for normalizing user-generated content (UGC). Current tools available for Brazilian Portuguese make use of such techniques. In this work we propose a technique based on distributed representation of words (or word embeddings). It generates continuous numeric vectors of high-dimensionality to represent words. The vectors explicitly encode many linguistic regularities and patterns, as well as syntactic and semantic word relationships. Words that share semantic similarity are represented by similar vectors. Based on these features, we present a totally unsupervised, expandable and language and domain independent method for learning normalization lexicons from word embeddings. Our approach obtains high correction rate of orthographic errors and internet slang in product reviews, outperforming the current available tools for Brazilian Portuguese.",
}
@inproceedings{boudin-etal-2016-document,
    title = "How Document Pre-processing affects Keyphrase Extraction Performance",
    author = "Boudin, Florian  and
      Mougard, Hugo  and
      Cram, Damien",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3917",
    pages = "121--128",
    abstract = "The SemEval-2010 benchmark dataset has brought renewed attention to the task of automatic keyphrase extraction. This dataset is made up of scientific articles that were automatically converted from PDF format to plain text and thus require careful preprocessing so that irrevelant spans of text do not negatively affect keyphrase extraction performance. In previous work, a wide range of document preprocessing techniques were described but their impact on the overall performance of keyphrase extraction models is still unexplored. Here, we re-assess the performance of several keyphrase extraction models and measure their robustness against increasingly sophisticated levels of document preprocessing.",
}
@inproceedings{ikeda-etal-2016-japanese,
    title = "{J}apanese Text Normalization with Encoder-Decoder Model",
    author = "Ikeda, Taishi  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3918",
    pages = "129--137",
    abstract = "Text normalization is the task of transforming lexical variants to their canonical forms. We model the problem of text normalization as a character-level sequence to sequence learning problem and present a neural encoder-decoder model for solving it. To train the encoder-decoder model, many sentences pairs are generally required. However, Japanese non-standard canonical pairs are scarce in the form of parallel corpora. To address this issue, we propose a method of data augmentation to increase data size by converting existing resources into synthesized non-standard forms using handcrafted rules. We conducted an experiment to demonstrate that the synthesized corpus contributes to stably train an encoder-decoder model and improve the performance of Japanese text normalization.",
}
@inproceedings{strauss-etal-2016-results,
    title = "Results of the {WNUT}16 Named Entity Recognition Shared Task",
    author = "Strauss, Benjamin  and
      Toma, Bethany  and
      Ritter, Alan  and
      de Marneffe, Marie-Catherine  and
      Xu, Wei",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3919",
    pages = "138--144",
    abstract = "This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating. We outline the shared task, annotation process and dataset statistics, and provide a high-level overview of the participating systems for each shared task.",
}
@inproceedings{limsopatham-collier-2016-bidirectional,
    title = "Bidirectional {LSTM} for Named Entity Recognition in {T}witter Messages",
    author = "Limsopatham, Nut  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3920",
    pages = "145--152",
    abstract = "In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the {`}segmentation and categorisation{'} and the {`}segmentation only{'} sub-tasks.",
}
@inproceedings{espinosa-etal-2016-learning,
    title = "Learning to recognise named entities in tweets by exploiting weakly labelled data",
    author = "Espinosa, Kurt Junshean  and
      Batista-Navarro, Riza Theresa  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3921",
    pages = "153--163",
    abstract = "Named entity recognition (NER) in social media (e.g., Twitter) is a challenging task due to the noisy nature of text. As part of our participation in the W-NUT 2016 Named Entity Recognition Shared Task, we proposed an unsupervised learning approach using deep neural networks and leverage a knowledge base (i.e., DBpedia) to bootstrap sparse entity types with weakly labelled data. To further boost the performance, we employed a more sophisticated tagging scheme and applied dropout as a regularisation technique in order to reduce overfitting. Even without hand-crafting linguistic features nor leveraging any of the W-NUT-provided gazetteers, we obtained robust performance with our approach, which ranked third amongst all shared task participants according to the official evaluation on a gold standard named entity-annotated corpus of 3,856 tweets.",
}
@inproceedings{sikdar-gamback-2016-feature,
    title = "Feature-Rich {T}witter Named Entity Recognition and Classification",
    author = {Sikdar, Utpal Kumar  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3922",
    pages = "164--170",
    abstract = "Twitter named entity recognition is the process of identifying proper names and classifying them into some predefined labels/categories. The paper introduces a Twitter named entity system using a supervised machine learning approach, namely Conditional Random Fields. A large set of different features was developed and the system was trained using these. The Twitter named entity task can be divided into two parts: i) Named entity extraction from tweets and ii) Twitter name classification into ten different types. For Twitter named entity recognition on unseen test data, our system obtained the second highest F1 score in the shared task: 63.22{\%}. The system performance on the classification task was worse, with an F1 measure of 40.06{\%} on unseen test data, which was the fourth best of the ten systems participating in the shared task.",
}
@inproceedings{partalas-etal-2016-learning,
    title = "Learning to Search for Recognizing Named Entities in {T}witter",
    author = "Partalas, Ioannis  and
      Lopez, C{\'e}dric  and
      Derbas, Nadia  and
      Kalitvianski, Ruslan",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3923",
    pages = "171--177",
    abstract = "We presented in this work our participation in the 2nd Named Entity Recognition for Twitter shared task. The task has been cast as a sequence labeling one and we employed a learning to search approach in order to tackle it. We also leveraged LOD for extracting rich contextual features for the named-entities. Our submission achieved F-scores of 46.16 and 60.24 for the classification and the segmentation tasks and ranked 2nd and 3rd respectively. The post-analysis showed that LOD features improved substantially the performance of our system as they counter-balance the lack of context in tweets. The shared task gave us the opportunity to test the performance of NER systems in short and noisy textual data. The results of the participated systems shows that the task is far to be considered as a solved one and methods with stellar performance in normal texts need to be revised.",
}
@inproceedings{dugas-nichols-2016-deepnnner,
    title = "{D}eep{NNNER}: Applying {BLSTM}-{CNN}s and Extended Lexicons to Named Entity Recognition in Tweets",
    author = "Dugas, Fabrice  and
      Nichols, Eric",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3924",
    pages = "178--187",
    abstract = "In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task {\#}2: Named Entity Recognition in Twitter. Our shared task submission adopts the bidirectional LSTM-CNN model of Chiu and Nichols (2016), as it has been shown to perform well on both newswire and Web texts. It uses word embeddings trained on large-scale Web text collections together with text normalization to cope with the diversity in Web texts, and lexicons for target named entity classes constructed from publicly-available sources. Extended evaluation comparing the effectiveness of various word embeddings, text normalization, and lexicon settings shows that our system achieves a maximum F1-score of 47.24, performance surpassing that of the shared task{'}s second-ranked system.",
}
@inproceedings{gerguis-etal-2016-asu,
    title = "{ASU}: An Experimental Study on Applying Deep Learning in {T}witter Named Entity Recognition.",
    author = "Gerguis, Michel Naim  and
      Salama, Cherif  and
      El-Kharashi, M. Watheq",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3925",
    pages = "188--196",
    abstract = "This paper describes the ASU system submitted in the COLING W-NUT 2016 Twitter Named Entity Recognition (NER) task. We present an experimental study on applying deep learning to extracting named entities (NEs) from tweets. We built two Long Short-Term Memory (LSTM) models for the task. The first model was built to extract named entities without types while the second model was built to extract and then classify them into 10 fine-grained entity classes. In this effort, we show detailed experimentation results on the effectiveness of word embeddings, brown clusters, part-of-speech (POS) tags, shape features, gazetteers, and local context for the tweet input vector representation to the LSTM model. Also, we present a set of experiments, to better design the network parameters for the Twitter NER task. Our system was ranked the fifth out of ten participants with a final f1-score for the typed classes of 39{\%} and 55{\%} for the non typed ones.",
}
@inproceedings{le-etal-2016-uqam,
    title = "{UQAM}-{NTL}: Named entity recognition in {T}witter messages",
    author = "Le, Ngoc Tan  and
      Mallek, Fatma  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3926",
    pages = "197--202",
    abstract = "This paper describes our system used in the 2nd Workshop on Noisy User-generated Text (WNUT) shared task for Named Entity Recognition (NER) in Twitter, in conjunction with Coling 2016. Our system is based on supervised machine learning by applying Conditional Random Fields (CRF) to train two classifiers for two evaluations. The first evaluation aims at predicting the 10 fine-grained types of named entities; while the second evaluation aims at predicting no type of named entities. The experimental results show that our method has significantly improved Twitter NER performance.",
}
@inproceedings{mishra-diesner-2016-semi,
    title = "Semi-supervised Named Entity Recognition in noisy-text",
    author = "Mishra, Shubhanshu  and
      Diesner, Jana",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3927",
    pages = "203--212",
    abstract = "Many of the existing Named Entity Recognition (NER) solutions are built based on news corpus data with proper syntax. These solutions might not lead to highly accurate results when being applied to noisy, user generated data, e.g., tweets, which can feature sloppy spelling, concept drift, and limited contextualization of terms and concepts due to length constraints. The models described in this paper are based on linear chain conditional random fields (CRFs), use the BIEOU encoding scheme, and leverage random feature dropout for up-sampling the training data. The considered features include word clusters and pre-trained distributed word representations, updated gazetteer features, and global context predictions. The latter feature allows for ingesting the meaning of new or rare tokens into the system via unsupervised learning and for alleviating the need to learn lexicon based features, which usually tend to be high dimensional. In this paper, we report on the solution [ST] we submitted to the WNUT 2016 NER shared task. We also present an improvement over our original submission [SI], which we built by using semi-supervised learning on labelled training data and pre-trained resourced constructed from unlabelled tweet data. Our ST solution achieved an F1 score of 1.2{\%} higher than the baseline (35.1{\%} F1) for the task of extracting 10 entity types. The SI resulted in an increase of 8.2{\%} in F1 score over the base-line (7.08{\%} over ST). Finally, the SI model{'}s evaluation on the test data achieved a F1 score of 47.3{\%} ({\textasciitilde}1.15{\%} increase over the 2nd best submitted solution). Our experimental setup and results are available as a standalone twitter NER tool at \url{https://github.com/napsternxg/TwitterNER}.",
}
@inproceedings{han-etal-2016-twitter,
    title = "{T}witter Geolocation Prediction Shared Task of the 2016 Workshop on Noisy User-generated Text",
    author = "Han, Bo  and
      Rahimi, Afshin  and
      Derczynski, Leon  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3928",
    pages = "213--217",
    abstract = "This paper presents the shared task for English Twitter geolocation prediction in WNUT 2016. We discuss details of task settings, data preparations and participant systems. The derived dataset and performance figures from each system provide baselines for future research in this realm.",
}
@inproceedings{jayasinghe-etal-2016-csiro,
    title = "{CSIRO} {D}ata61 at the {WNUT} Geo Shared Task",
    author = "Jayasinghe, Gaya  and
      Jin, Brian  and
      Mchugh, James  and
      Robinson, Bella  and
      Wan, Stephen",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3929",
    pages = "218--226",
    abstract = "In this paper, we describe CSIRO Data61{'}s participation in the Geolocation shared task at the Workshop for Noisy User-generated Text. Our approach was to use ensemble methods to capitalise on four component methods: heuristics based on metadata, a label propagation method, timezone text classifiers, and an information retrieval approach. The ensembles we explored focused on examining the role of language technologies in geolocation prediction and also in examining the use of hard voting and cascading ensemble methods. Based on the accuracy of city-level predictions, our systems were the best performing submissions at this year{'}s shared task. Furthermore, when estimating the latitude and longitude of a user, our median error distance was accurate to within 30 kilometers.",
}
@inproceedings{chi-etal-2016-geolocation,
    title = "Geolocation Prediction in {T}witter Using Location Indicative Words and Textual Features",
    author = "Chi, Lianhua  and
      Lim, Kwan Hui  and
      Alam, Nebula  and
      Butler, Christopher J.",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3930",
    pages = "227--234",
    abstract = "Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task {``}Geolocation Prediction in Twitter{''} of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, {\#}hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, {\#}hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.",
}
@inproceedings{miura-etal-2016-simple,
    title = "A Simple Scalable Neural Networks based Model for Geolocation Prediction in {T}witter",
    author = "Miura, Yasuhide  and
      Taniguchi, Motoki  and
      Taniguchi, Tomoki  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the 2nd Workshop on Noisy User-generated Text ({WNUT})",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3931",
    pages = "235--239",
    abstract = "This paper describes a model that we submitted to W-NUT 2016 Shared task {\#}1: Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and trained the model with them. In the test run of the task, the model achieved the accuracy of 40.91{\%} and the median distance error of 69.50 km in message-level prediction and the accuracy of 47.55{\%} and the median distance error of 16.13 km in user-level prediction. These results are moderate performances in terms of accuracy and best performances in terms of distance. The results show a promising extension of neural networks based models for geolocation prediction where recent advances in neural networks can be added to enhance our current simple model.",
}
@proceedings{ws-2016-grammar,
    title = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    editor = "Haji{\v{c}}ov{\'a}, Eva  and
      Boguslavsky, Igor",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3800",
}
@inproceedings{apresjan-2016-information,
    title = "Information structure, syntax, and pragmatics and other factors in resolving scope ambiguity",
    author = "Apresjan, Valentina",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3801",
    pages = "1--6",
    abstract = "The paper is a corpus study of the factors involved in disambiguating potential scope ambiguity in sentences with negation and universal quantifier, such as {``}I don{'}t want talk to all these people{''}, which can alternatively mean {`}I don{'}t want to talk to any of these people{'} and {`}I don{'}t want to talk to some of these people{'}. The relevant factors are demonstrated to be largely different from those involved in disambiguating lexical polysemy. They include the syntactic function of the constituent containing {``}all{''} quantifier (subject, direct complement, adjunct), as well as the deepness of its embedding; the status of the main predicate and {``}all{''} constituent with respect to the information structure of the 6utterance (topic vs. focus, given vs. new information); pragmatic implicatures pertaining to the situations described in the utterances.",
}
@inproceedings{baldwin-2016-multiword,
    title = "Multiword Expressions at the Grammar-Lexicon Interface",
    author = "Baldwin, Timothy",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3802",
    pages = "7",
    abstract = "In this talk, I will outline a range of challenges presented by multiword expressions in terms of (lexicalist) precision grammar engineering, and different strategies for accommodating those challenges, in an attempt to strike the right balance in terms of generalisation and over- and under-generation.",
}
@inproceedings{iomdin-2016-microsyntactic,
    title = "Microsyntactic Phenomena as a Computational Linguistics Issue",
    author = "Iomdin, Leonid",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3803",
    pages = "8--17",
    abstract = "Microsyntactic linguistic units, such as syntactic idioms and non-standard syntactic constructions, are poorly represented in linguistic resources, mostly because the former are elements occupying an intermediate position between the lexicon and the grammar and the latter are too specific to be routinely tackled by general grammars. Consequently, many such units produce substantial gaps in systems intended to solve sophisticated computational linguistics tasks, such as parsing, deep semantic analysis, question answering, machine translation, or text generation. They also present obstacles for applying advanced techniques to these tasks, such as machine learning. The paper discusses an approach aimed at bridging such gaps, focusing on the development of monolingual and multilingual corpora where microsyntactic units are to be tagged.",
}
@inproceedings{lopatkova-kettnerova-2016-alternations,
    title = "{A}lternations: From Lexicon to Grammar And Back Again",
    author = "Lopatkov{\'a}, Mark{\'e}ta  and
      Kettnerov{\'a}, V{\'a}clava",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3804",
    pages = "18--27",
    abstract = "An excellent example of a phenomenon bridging a lexicon and a grammar is provided by grammaticalized alternations (e.g., passivization, reflexivity, and reciprocity): these alternations represent productive grammatical processes which are, however, lexically determined. While grammaticalized alternations keep lexical meaning of verbs unchanged, they are usually characterized by various changes in their morphosyntactic structure. In this contribution, we demonstrate on the example of reciprocity and its representation in the valency lexicon of Czech verbs, VALLEX how a linguistic description of complex (and still systemic) changes characteristic of grammaticalized alternations can benefit from an integration of grammatical rules into a valency lexicon. In contrast to other types of grammaticalized alternations, reciprocity in Czech has received relatively little attention although it closely interacts with various linguistic phenomena (e.g., with light verbs, diatheses, and reflexivity).",
}
@inproceedings{mcshane-nirenburg-2016-extra,
    title = "Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents",
    author = "McShane, Marjorie  and
      Nirenburg, Sergei",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3805",
    pages = "28--37",
    abstract = "Language-endowed intelligent agents benefit from leveraging lexical knowledge falling at different points along a spectrum of compositionality. This means that robust computational lexicons should include not only the compositional expectations of argument-taking words, but also non-compositional collocations (idioms), semi-compositional collocations that might be difficult for an agent to interpret (e.g., standard metaphors), and even collocations that could be compositionally analyzed but are so frequently encountered that recording their meaning increases the efficiency of interpretation. In this paper we argue that yet another type of string-to-meaning mapping can also be useful to intelligent agents: remembered semantic analyses of actual text inputs. These can be viewed as super-specific multi-word expressions whose recorded interpretations mimic a person{'}s memories of knowledge previously learned from language input. These differ from typical annotated corpora in two ways. First, they provide a full, context-sensitive semantic interpretation rather than select features. Second, they are are formulated in the ontologically-grounded metalanguage used in a particular agent environment, meaning that the interpretations contribute to the dynamically evolving cognitive capabilites of agents configured in that environment.",
}
@inproceedings{nivre-2016-universal,
    title = "{U}niversal {D}ependencies: A Cross-Linguistic Perspective on Grammar and Lexicon",
    author = "Nivre, Joakim",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3806",
    pages = "38--40",
    abstract = "Universal Dependencies is an initiative to develop cross-linguistically consistent grammatical annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning and parsing research from a language typology perspective. It assumes a dependency-based approach to syntax and a lexicalist approach to morphology, which together entail that the fundamental units of grammatical annotation are words. Words have properties captured by morphological annotation and enter into relations captured by syntactic annotation. Moreover, priority is given to relations between lexical content words, as opposed to grammatical function words. In this position paper, I discuss how this approach allows us to capture similarities and differences across typologically diverse languages.",
}
@inproceedings{pustejovsky-etal-2016-development,
    title = "The Development of Multimodal Lexical Resources",
    author = "Pustejovsky, James  and
      Do, Tuan  and
      Kehat, Gitit  and
      Krishnaswamy, Nikhil",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3807",
    pages = "41--47",
    abstract = "Human communication is a multimodal activity, involving not only speech and written expressions, but intonation, images, gestures, visual clues, and the interpretation of actions through perception. In this paper, we describe the design of a multimodal lexicon that is able to accommodate the diverse modalities that present themselves in NLP applications. We have been developing a multimodal semantic representation, VoxML, that integrates the encoding of semantic, visual, gestural, and action-based features associated with linguistic expressions.",
}
@inproceedings{boguslavsky-2016-non,
    title = "On the Non-canonical Valency Filling",
    author = "Boguslavsky, Igor",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3808",
    pages = "51--60",
    abstract = "Valency slot filling is a semantic glue, which brings together the meanings of words. As regards the position of an argument in the dependency structure with respect to its predicate, there exist three types of valency filling: active (canonical), passive, and discontinuous. Of these, the first type is studied much better than the other two. As a rule, canonical actants are unambiguously marked in the syntactic structure, and each actant corresponds to a unique syntactic position. Linguistic information on which syntactic function an actant might have (subject, direct or indirect object), what its morphological form should be and which prepositions or conjunctions it requires, can be given in the lexicon in the form of government patterns, subcategorization frames, or similar data structures. We concentrate on non-canonical cases of valency filling in Russian, which are characteristic of non-verbal parts of speech, such as adverbs, adjectives, and particles, in the first place. They are more difficult to handle than canonical ones, because the position of the actant in the tree is governed by more complicated rules. A valency may be filled by expressions occupying different syntactic positions, and a syntactic position may accept expressions filling different valencies of the same word. We show how these phenomena can be processed in a semantic analyzer.",
}
@inproceedings{danlos-etal-2016-improvement,
    title = "Improvement of {V}erb{N}et-like resources by frame typing",
    author = "Danlos, Laurence  and
      Constant, Matthieu  and
      Barque, Lucie",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3809",
    pages = "61--70",
    abstract = "Verbenet is a French lexicon developed by {``}translation{''} of its English counterpart {---} VerbNet (Kipper-Schuler, 2005){---}and treatment of the specificities of French syntax (Pradet et al., 2014; Danlos et al., 2016). One difficulty encountered in its development springs from the fact that the list of (potentially numerous) frames has no internal organization. This paper proposes a type system for frames that shows whether two frames are variants of a given alternation. Frame typing facilitates coherence checking of the resource in a {``}virtuous circle{''}. We present the principles underlying a program we developed and used to automatically type frames in VerbeNet. We also show that our system is portable to other languages.",
}
@inproceedings{fucikova-etal-2016-enriching,
    title = "Enriching a Valency Lexicon by Deverbative Nouns",
    author = "Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}, Jan  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3810",
    pages = "71--80",
    abstract = "We present an attempt to automatically identify Czech deverbative nouns using several methods that use large corpora as well as existing lexical resources. The motivation for the task is to extend a verbal valency (i.e., predicate-argument) lexicon by adding nouns that share the valency properties with the base verb, assuming their properties can be derived (even if not trivially) from the underlying verb by deterministic grammatical rules. At the same time, even in inflective languages, not all deverbatives are simply created from their underlying base verb by regular lexical derivation processes. We have thus developed hybrid techniques that use both large parallel corpora and several standard lexical resources. Thanks to the use of parallel corpora, the resulting sets contain also synonyms, which the lexical derivation rules cannot get. For evaluation, we have manually created a small, 100-verb gold data since no such dataset was initially available for Czech.",
}
@inproceedings{iordachioaia-etal-2016-grammar,
    title = "The Grammar of {E}nglish Deverbal Compounds and their Meaning",
    author = "Iord{\u{a}}chioaia, Gianina  and
      van der Plas, Lonneke  and
      Jagfeld, Glorianna",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3811",
    pages = "81--91",
    abstract = "We present an interdisciplinary study on the interaction between the interpretation of noun-noun deverbal compounds (DCs; e.g., task assignment) and the morphosyntactic properties of their deverbal heads in English. Underlying hypotheses from theoretical linguistics are tested with tools and resources from computational linguistics. We start with Grimshaw{'}s (1990) insight that deverbal nouns are ambiguous between argument-supporting nominal (ASN) readings, which inherit verbal arguments (e.g., the assignment of the tasks), and the less verbal and more lexicalized Result Nominal and Simple Event readings (e.g., a two-page assignment). Following Grimshaw, our hypothesis is that the former will realize object arguments in DCs, while the latter will receive a wider range of interpretations like root compounds headed by non-derived nouns (e.g., chocolate box). Evidence from a large corpus assisted by machine learning techniques confirms this hypothesis, by showing that, besides other features, the realization of internal arguments by deverbal heads outside compounds (i.e., the most distinctive ASN-property in Grimshaw 1990) is a good predictor for an object interpretation of non-heads in DCs.",
}
@inproceedings{kahane-lareau-2016-encoding,
    title = "Encoding a syntactic dictionary into a super granular unification grammar",
    author = "Kahane, Sylvain  and
      Lareau, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3812",
    pages = "92--101",
    abstract = "We show how to turn a large-scale syntactic dictionary into a dependency-based unification grammar where each piece of lexical information calls a separate rule, yielding a super granular grammar. Subcategorization, raising and control verbs, auxiliaries and copula, passivization, and tough-movement are discussed. We focus on the semantics-syntax interface and offer a new perspective on syntactic structure.",
}
@inproceedings{morimoto-etal-2016-identification,
    title = "Identification of Flexible Multiword Expressions with the Help of Dependency Structure Annotation",
    author = "Morimoto, Ayaka  and
      Yoshimoto, Akifumi  and
      Kato, Akihiko  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3813",
    pages = "102--109",
    abstract = "This paper presents our ongoing work on compilation of English multi-word expression (MWE) lexicon. We are especially interested in collecting flexible MWEs, in which some other components can intervene the expression such as {``}a number of{''} vs {``}a large number of{''} where a modifier of {``}number{''} can be placed in the expression and inherit the original meaning. We fiest collect possible candidates of flexible English MWEs from the web, and annotate all of their occurrences in the Wall Street Journal portion of Ontonotes corpus. We make use of word dependency strcuture information of the sentences converted from the phrase structure annotation. This process enables semi-automatic annotation of MWEs in the corpus and simultanaously produces the internal and external dependency representation of flexible MWEs.",
}
@inproceedings{nedoluzhko-2016-new,
    title = "A new look at possessive reflexivization: A comparative study between {C}zech and {R}ussian",
    author = "Nedoluzhko, Anna",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3814",
    pages = "110--119",
    abstract = "The paper presents a contrastive description of reflexive possessive pronouns {``}sv{\r{u}}j{''} in Czech and {``}svoj{''} in Russian. The research concerns syntactic, semantic and pragmatic aspects. With our analysis, we shed a new light on the already investigated issue, which comes from a detailed comparison of the phenomenon of possessive reflexivization in two typologically and genetically similar languages. We show that whereas in Czech, the possessive reflexivization is mostly limited to syntactic functions and does not go beyond the grammar, in Russian it gets additional semantic meanings and moves substan-tially towards the lexicon. The obtained knowledge allows us to explain heretofore unclear marginal uses of reflexives in each language.",
}
@inproceedings{rosen-2016-modeling,
    title = "Modeling non-standard language",
    author = "Rosen, Alexandr",
    booktitle = "Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces ({G}ram{L}ex)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3815",
    pages = "120--131",
    abstract = "A specific language as used by different speakers and in different situations has a number of more or less distant varieties. Extending the notion of non-standard language to varieties that do not fit an explicitly or implicitly assumed norm or pattern, we look for methods and tools that could be applied to this domain. The needs start from the theoretical side: categories usable for the analysis of non-standard language are not readily available, and continue to methods and tools required for its detection and diagnostics. A general discussion of issues related to non-standard language is followed by two case studies. The first study presents a taxonomy of morphosyntactic categories as an attempt to analyse non-standard forms produced by non-native learners of Czech. The second study focusses on the role of a rule-based grammar and lexicon in the process of building and using a parsebank.",
}
@proceedings{ws-2016-south,
    title = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    editor = "Wu, Dekai  and
      Bhattacharyya, Pushpak",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3700",
}
@inproceedings{krishna-etal-2016-compound,
    title = "Compound Type Identification in {S}anskrit: What Roles do the Corpus and Grammar Play?",
    author = "Krishna, Amrith  and
      Satuluri, Pavankumar  and
      Sharma, Shubham  and
      Kumar, Apurv  and
      Goyal, Pawan",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3701",
    pages = "1--10",
    abstract = "We propose a classification framework for semantic type identification of compounds in Sanskrit. We broadly classify the compounds into four different classes namely, \textit{Avyay{\=\i}bh{\=a}va}, \textit{Tatpuruṣa}, \textit{Bahuvr{\=\i}hi} and \textit{Dvandva}. Our classification is based on the traditional classification system followed by the ancient grammar treatise \textit{Adṣṭ{\=a}dhy{\=a}y{\=\i}}, proposed by P{\=a}ṇini 25 centuries back. We construct an elaborate features space for our system by combining conditional rules from the grammar \textit{Adṣṭ{\=a}dhy{\=a}y{\=\i}}, semantic relations between the compound components from a lexical database \textit{Amarakoṣa} and linguistic structures from the data using Adaptor Grammars. Our in-depth analysis of the feature space highlight inadequacy of \textit{Adṣṭ{\=a}dhy{\=a}y{\=\i}}, a generative grammar, in classifying the data samples. Our experimental results validate the effectiveness of using lexical databases as suggested by Amba Kulkarni and Anil Kumar, and put forward a new research direction by introducing linguistic patterns obtained from Adaptor grammars for effective identification of compound type. We utilise an ensemble based approach, specifically designed for handling skewed datasets and we {\%}and Experimenting with various classification methods, we achieve an overall accuracy of 0.77 using random forest classifiers.",
}
@inproceedings{kyaw-thu-etal-2016-comparison,
    title = "Comparison of Grapheme-to-Phoneme Conversion Methods on a {M}yanmar Pronunciation Dictionary",
    author = "Kyaw Thu, Ye  and
      Pa Pa, Win  and
      Sagisaka, Yoshinori  and
      Iwahashi, Naoto",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3702",
    pages = "11--22",
    abstract = "Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech (TTS) systems. In this paper, we evaluate seven G2P conversion approaches: Adaptive Regularization of Weight Vectors (AROW) based structured learning (S-AROW), Conditional Random Field (CRF), Joint-sequence models (JSM), phrase-based statistical machine translation (PBSMT), Recurrent Neural Network (RNN), Support Vector Machine (SVM) based point-wise classification, Weighted Finite-state Transducers (WFST) on a manually tagged Myanmar phoneme dictionary. The G2P bootstrapping experimental results were measured with both automatic phoneme error rate (PER) calculation and also manual checking in terms of voiced/unvoiced, tones, consonant and vowel errors. The result shows that CRF, PBSMT and WFST approaches are the best performing methods for G2P conversion on Myanmar language.",
}
@inproceedings{gridach-2016-character,
    title = "Character-Aware Neural Networks for {A}rabic Named Entity Recognition for Social Media",
    author = "Gridach, Mourad",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3703",
    pages = "23--32",
    abstract = "Named Entity Recognition (NER) is the task of classifying or labelling atomic elements in the text into categories such as Person, Location or Organisation. For Arabic language, recognizing named entities is a challenging task because of the complexity and the unique characteristics of this language. In addition, most of the previous work focuses on Modern Standard Arabic (MSA), however, recognizing named entities in social media is becoming more interesting these days. Dialectal Arabic (DA) and MSA are both used in social media, which is deemed as another challenging task. Most state-of-the-art Arabic NER systems count heavily on handcrafted engineering features and lexicons which is time consuming. In this paper, we introduce a novel neural network architecture which benefits both from character- and word-level representations automatically, by using combination of bidirectional LSTM and Conditional Random Field (CRF), eliminating the need for most feature engineering. Moreover, our model relies on unsupervised word representations learned from unannotated corpora. Experimental results demonstrate that our model achieves state-of-the-art performance on publicly available benchmark for Arabic NER for social media and surpassing the previous system by a large margin.",
}
@inproceedings{das-etal-2016-development,
    title = "Development of a {B}engali parser by cross-lingual transfer from {H}indi",
    author = "Das, Ayan  and
      Saha, Agnivo  and
      Sarkar, Sudeshna",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3704",
    pages = "33--43",
    abstract = "In recent years there has been a lot of interest in cross-lingual parsing for developing treebanks for languages with small or no annotated treebanks. In this paper, we explore the development of a cross-lingual transfer parser from Hindi to Bengali using a Hindi parser and a Hindi-Bengali parallel corpus. A parser is trained and applied to the Hindi sentences of the parallel corpus and the parse trees are projected to construct probable parse trees of the corresponding Bengali sentences. Only about 14{\%} of these trees are complete (transferred trees contain all the target sentence words) and they are used to construct a Bengali parser. We relax the criteria of completeness to consider well-formed trees (43{\%} of the trees) leading to an improvement. We note that the words often do not have a one-to-one mapping in the two languages but considering sentences at the chunk-level results in better correspondence between the two languages. Based on this we present a method to use chunking as a preprocessing step and do the transfer on the chunk trees. We find that about 72{\%} of the projected parse trees of Bengali are now well-formed. The resultant parser achieves significant improvement in both Unlabeled Attachment Score (UAS) as well as Labeled Attachment Score (LAS) over the baseline word-level transferred parser.",
}
@inproceedings{kadupitiya-etal-2016-sinhala,
    title = "{S}inhala Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures",
    author = "Kadupitiya, Jcs  and
      Ranathunga, Surangika  and
      Dias, Gihan",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3705",
    pages = "44--53",
    abstract = "Currently, corpus based-similarity, string-based similarity, and knowledge-based similarity techniques are used to compare short phrases. However, no work has been conducted on the similarity of phrases in Sinhala language. In this paper, we present a hybrid methodology to compute the similarity between two Sinhala sentences using a Semantic Similarity Measurement technique (corpus-based similarity measurement plus knowledge-based similarity measurement) that makes use of word order information. Since Sinhala WordNet is still under construction, we used lexical resources in performing this semantic similarity calculation. Evaluation using 4000 sentence pairs yielded an average MSE of 0.145 and a Pearson correla-tion factor of 0.832.",
}
@inproceedings{jawaid-etal-2016-enriching,
    title = "Enriching Source for {E}nglish-to-{U}rdu Machine Translation",
    author = "Jawaid, Bushra  and
      Kamran, Amir  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3706",
    pages = "54--63",
    abstract = "This paper focuses on the generation of case markers for free word order languages that use case markers as phrasal clitics for marking the relationship between the dependent-noun and its head. The generation of such clitics becomes essential task especially when translating from fixed word order languages where syntactic relations are identified by the positions of the dependent-nouns. To address the problem of missing markers on source-side, artificial markers are added in source to improve alignments with its target counterparts. Up to 1 BLEU point increase is observed over the baseline on different test sets for English-to-Urdu.",
}
@inproceedings{behera-etal-2016-imagact4all,
    title = "The {IMAGACT}4{ALL} Ontology of Animated Images: Implications for Theoretical and Machine Translation of Action Verbs from {E}nglish-{I}ndian Languages",
    author = "Behera, Pitambar  and
      Muzaffar, Sharmin  and
      Ojha, Atul Ku.  and
      Jha, Girish",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3707",
    pages = "64--73",
    abstract = "Action verbs are one of the frequently occurring linguistic elements in any given natural language as the speakers use them during every linguistic intercourse. However, each language expresses action verbs in its own inherently unique manner by categorization. One verb can refer to several interpretations of actions and one action can be expressed by more than one verb. The inter-language and intra-language variations create ambiguity for the translation of languages from the source language to target language with respect to action verbs. IMAGACT is a corpus-based ontological platform of action verbs translated from prototypic animated images explained in English and Italian as meta-languages. In this paper, we are presenting the issues and challenges in translating action verbs of Indian languages as target and English as source language by observing the animated images. Among the ten Indian languages which have been annotated so far on the platform are Sanskrit, Hindi, Urdu, Odia (Oriya), Bengali, Manipuri, Tamil, Assamese, Magahi and Marathi. Out of them, Manipuri belongs to the Sino-Tibetan, Tamil comes off the Dravidian and the rest owe their genesis to the Indo-Aryan language family. One of the issues is that the one-word morphological English verbs are translated into most of the Indian languages as verbs having more than one-word form; for instance as in the case of conjunct, compound, serial verbs and so on. We are further presenting a cross-lingual comparison of action verbs among Indian languages. In addition, we are also dealing with the issues in disambiguating animated images by the L1 native speakers using competence-based judgements and the theoretical and machine translation implications they bear.",
}
@inproceedings{lapitan-etal-2016-crowdsourcing,
    title = "Crowdsourcing-based Annotation of Emotions in {F}ilipino and {E}nglish Tweets",
    author = "Lapitan, Fermin Roberto  and
      Batista-Navarro, Riza Theresa  and
      Albacea, Eliezer",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3708",
    pages = "74--82",
    abstract = "The automatic analysis of emotions conveyed in social media content, e.g., tweets, has many beneficial applications. In the Philippines, one of the most disaster-prone countries in the world, such methods could potentially enable first responders to make timely decisions despite the risk of data deluge. However, recognising emotions expressed in Philippine-generated tweets, which are mostly written in Filipino, English or a mix of both, is a non-trivial task. In order to facilitate the development of natural language processing (NLP) methods that will automate such type of analysis, we have built a corpus of tweets whose predominant emotions have been manually annotated by means of crowdsourcing. Defining measures ensuring that only high-quality annotations were retained, we have produced a gold standard corpus of 1,146 emotion-labelled Filipino and English tweets. We validate the value of this manually produced resource by demonstrating that an automatic emotion-prediction method based on the use of a publicly available word-emotion association lexicon was unable to reproduce the labels assigned via crowdsourcing. While we are planning to make a few extensions to the corpus in the near future, its current version has been made publicly available in order to foster the development of emotion analysis methods based on advanced Filipino and English NLP.",
}
@inproceedings{phani-etal-2016-sentiment,
    title = "Sentiment Analysis of Tweets in Three {I}ndian Languages",
    author = "Phani, Shanta  and
      Lahiri, Shibamouli  and
      Biswas, Arindam",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3710",
    pages = "93--102",
    abstract = "In this paper, we describe the results of sentiment analysis on tweets in three Indian languages {--} Bengali, Hindi, and Tamil. We used the recently released SAIL dataset (Patra et al., 2015), and obtained state-of-the-art results in all three languages. Our features are simple, robust, scalable, and language-independent. Further, we show that these simple features provide better results than more complex and language-specific features, in two separate classification tasks. Detailed feature analysis and error analysis have been reported, along with learning curves for Hindi and Bengali.",
}
@inproceedings{behera-etal-2016-dealing,
    title = "Dealing with Linguistic Divergences in {E}nglish-{B}hojpuri Machine Translation",
    author = "Behera, Pitambar  and
      Mourya, Neha  and
      Pandey, Vandana",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3711",
    pages = "103--113",
    abstract = "In Machine Translation, divergence is one of the major barriers which plays a deciding role in determining the efficiency of the system at hand. Translation divergences originate when there is structural discrepancies between the input and the output languages. It can be of various types based on the issues we are addressing to such as linguistic, cultural, communicative and so on. Owing to the fact that two languages owe their origin to different language families, linguistic divergences emerge. The present study attempts at categorizing different types of linguistic divergences: the lexical-semantic and syntactic. In addition, it also helps identify and resolve the divergent linguistic features between English as source language and Bhojpuri as target language pair. Dorr{'}s theoretical framework (1994, 1994a) has been followed in the classification and resolution procedure. Furthermore, so far as the methodology is concerned, we have adhered to the Dorr{'}s Lexical Conceptual Structure for the resolution of divergences. This research will prove to be beneficial for developing efficient MT systems if the mentioned factors are incorporated considering the inherent structural constraints between source and target languages.ated considering the inherent structural constraints between SL and TL pairs.",
}
@inproceedings{nishioka-akasegawa-2016-development,
    title = "The development of a web corpus of {H}indi language and corpus-based comparative studies to {J}apanese",
    author = "Nishioka, Miki  and
      Akasegawa, Shiro",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3712",
    pages = "114--123",
    abstract = "In this paper, we discuss our creation of a web corpus of spoken Hindi (COSH), one of the Indo-Aryan languages spoken mainly in the Indian subcontinent. We also point out notable problems we{'}ve encountered in the web corpus and the special concordancer. After observing the kind of technical problems we encountered, especially regarding annotation tagged by Shiva Reddy{'}s tagger, we argue how they can be solved when using COSH for linguistic studies. Finally, we mention the kinds of linguistic research that we non-native speakers of Hindi can do using the corpus, especially in pragmatics and semantics, and from a comparative viewpoint to Japanese.",
}
@inproceedings{abdul-hameed-etal-2016-automatic,
    title = "Automatic Creation of a Sentence Aligned {S}inhala-{T}amil Parallel Corpus",
    author = "Abdul Hameed, Riyafa  and
      Pathirennehelage, Nadeeshani  and
      Ihalapathirana, Anusha  and
      Ziyad Mohamed, Maryam  and
      Ranathunga, Surangika  and
      Jayasena, Sanath  and
      Dias, Gihan  and
      Fernando, Sandareka",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3713",
    pages = "124--132",
    abstract = "A sentence aligned parallel corpus is an important prerequisite in statistical machine translation. However, manual creation of such a parallel corpus is time consuming, and requires experts fluent in both languages. Automatic creation of a sentence aligned parallel corpus using parallel text is the solution to this problem. In this paper, we present the first ever empirical evaluation carried out to identify the best method to automatically create a sentence aligned Sinhala-Tamil parallel corpus. Annual reports from Sri Lankan government institutions were used as the parallel text for aligning. Despite both Sinhala and Tamil being under-resourced languages, we were able to achieve an F-score value of 0.791 using a hybrid approach that makes use of a bilingual dictionary.",
}
@inproceedings{chen-etal-2016-clustering,
    title = "Clustering-based Phonetic Projection in Mismatched Crowdsourcing Channels for Low-resourced {ASR}",
    author = "Chen, Wenda  and
      Hasegawa-Johnson, Mark  and
      Chen, Nancy  and
      Jyothi, Preethi  and
      Varshney, Lav",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3714",
    pages = "133--141",
    abstract = "Acquiring labeled speech for low-resource languages is a difficult task in the absence of native speakers of the language. One solution to this problem involves collecting speech transcriptions from crowd workers who are foreign or non-native speakers of a given target language. From these mismatched transcriptions, one can derive probabilistic phone transcriptions that are defined over the set of all target language phones using a noisy channel model. This paper extends prior work on deriving probabilistic transcriptions (PTs) from mismatched transcriptions by 1) modelling multilingual channels and 2) introducing a clustering-based phonetic mapping technique to improve the quality of PTs. Mismatched crowdsourcing for multilingual channels has certain properties of projection mapping, e.g., it can be interpreted as a clustering based on singular value decomposition of the segment alignments. To this end, we explore the use of distinctive feature weights, lexical tone confusions, and a two-step clustering algorithm to learn projections of phoneme segments from mismatched multilingual transcriber languages to the target language. We evaluate our techniques using mismatched transcriptions for Cantonese speech acquired from native English and Mandarin speakers. We observe a 5-9{\%} relative reduction in phone error rate for the predicted Cantonese phone transcriptions using our proposed techniques compared with the previous PT method.",
}
@inproceedings{hellwig-2016-improving,
    title = "Improving the Morphological Analysis of Classical {S}anskrit",
    author = "Hellwig, Oliver",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3715",
    pages = "142--151",
    abstract = "The paper describes a new tagset for the morphological disambiguation of Sanskrit, and compares the accuracy of two machine learning methods (Conditional Random Fields, deep recurrent neural networks) for this task, with a special focus on how to model the lexicographic information. It reports a significant improvement over previously published results.",
}
@inproceedings{bhattacharya-etal-2016-query,
    title = "Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters",
    author = "Bhattacharya, Paheli  and
      Goyal, Pawan  and
      Sarkar, Sudeshna",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3716",
    pages = "152--162",
    abstract = "In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve. We propose a technique towards solving this problem with the help of multilingual word clusters obtained from multilingual word embeddings. We use word embeddings of the languages projected to a common vector space on which a community-detection algorithm is applied to find clusters such that words that represent the same concept from different languages fall in the same group. We utilize these multilingual word clusters to perform query translation for Cross-Language Information Retrieval for three languages - English, Hindi and Bengali. We have experimented with the FIRE 2012 and Wikipedia datasets and have shown improvements over several standard methods like dictionary-based method, a transliteration-based model and Google Translate.",
}
@inproceedings{das-etal-2016-study,
    title = "A study of attention-based neural machine translation model on {I}ndian languages",
    author = "Das, Ayan  and
      Yerra, Pranay  and
      Kumar, Ken  and
      Sarkar, Sudeshna",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3717",
    pages = "163--172",
    abstract = "Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT). The use of LSTMs in machine translation has significantly improved the translation performance for longer sentences by being able to capture the context and long range correlations of the sentences in their hidden layers. The attention model based NMT system (Bahdanau et al., 2014) has become the state-of-the-art, performing equal or better than other statistical MT approaches. In this paper, we wish to study the performance of the attention-model based NMT system (Bahdanau et al., 2014) on the Indian language pair, Hindi and Bengali, and do an analysis on the types or errors that occur in case when the languages are morphologically rich and there is a scarcity of large parallel training corpus. We then carry out certain post-processing heuristic steps to improve the quality of the translated statements and suggest further measures that can be carried out.",
}
@inproceedings{fernando-etal-2016-comprehensive,
    title = "Comprehensive Part-Of-Speech Tag Set and {SVM} based {POS} Tagger for {S}inhala",
    author = "Fernando, Sandareka  and
      Ranathunga, Surangika  and
      Jayasena, Sanath  and
      Dias, Gihan",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3718",
    pages = "173--182",
    abstract = "This paper presents a new comprehensive multi-level Part-Of-Speech tag set and a Support Vector Machine based Part-Of-Speech tagger for the Sinhala language. The currently available tag set for Sinhala has two limitations: the unavailability of tags to represent some word classes and the lack of tags to capture inflection based grammatical variations of words. The new tag set, presented in this paper overcomes both of these limitations. The accuracy of available Sinhala Part-Of-Speech taggers, which are based on Hidden Markov Models, still falls far behind state of the art. Our Support Vector Machine based tagger achieved an overall accuracy of 84.68{\%} with 59.86{\%} accuracy for unknown words and 87.12{\%} for known words, when the test set contains 10{\%} of unknown words.",
}
@inproceedings{bakliwal-etal-2016-align,
    title = "Align Me: A framework to generate Parallel Corpus Using {OCR}s and Bilingual Dictionaries",
    author = "Bakliwal, Priyam  and
      V V, Devadath  and
      Jawahar, C V",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3719",
    pages = "183--187",
    abstract = "Multilingual language processing tasks like statistical machine translation and cross language information retrieval rely mainly on availability of accurate parallel corpora. Manual construction of such corpus can be extremely expensive and time consuming. In this paper we present a simple yet efficient method to generate huge amount of reasonably accurate parallel corpus with minimal user efforts. We utilize the availability of large number of English books and their corresponding translations in other languages to build parallel corpus. Optical Character Recognizing systems are used to digitize such books. We propose a robust dictionary based parallel corpus generation system for alignment of multilingual text at different levels of granularity (sentence, paragraphs, etc). We show the performance of our proposed method on a manually aligned dataset of 300 Hindi-English sentences and 100 English-Malayalam sentences.",
}
@inproceedings{qiu-zhu-2016-learning,
    title = "Learning {I}ndonesian-{C}hinese Lexicon with Bilingual Word Embedding Models and Monolingual Signals",
    author = "Qiu, Xinying  and
      Zhu, Gangqin",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3720",
    pages = "188--193",
    abstract = "We present a research on learning Indonesian-Chinese bilingual lexicon using monolingual word embedding and bilingual seed lexicons to build shared bilingual word embedding space. We take the first attempt to examine the impact of different monolingual signals for the choice of seed lexicons on the model performance. We found that although monolingual signals alone do not seem to outperform signals coverings all words, the significant improvement for learning word translation of the same signal types may suggest that linguistic features possess value for further study in distinguishing the semantic margins of the shared word embedding space.",
}
@inproceedings{berment-2016-creating,
    title = "Creating rich online dictionaries for the {L}ao{--}{F}rench language pair, reusable for Machine Translation",
    author = "Berment, Vincent",
    booktitle = "Proceedings of the 6th Workshop on South and Southeast {A}sian Natural Language Processing ({WSSANLP}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/W16-3721",
    pages = "194--197",
    abstract = "In this paper, we present how we generated two rich online bilingual dictionaries {---} Lao-French and French-Lao {---} from unstructured dictionaries in Microsoft Word files. Then we shortly discuss the possible reuse of the lexical data for Machine Translation projects.",
}
@proceedings{ws-2016-special,
    title = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    editor = "Fernandez, Raquel  and
      Minker, Wolfgang  and
      Carenini, Giuseppe  and
      Higashinaka, Ryuichiro  and
      Artstein, Ron  and
      Gainer, Alesia",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3600",
    doi = "10.18653/v1/W16-36",
}
@inproceedings{zhao-eskenazi-2016-towards,
    title = "Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning",
    author = "Zhao, Tiancheng  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3601",
    doi = "10.18653/v1/W16-3601",
    pages = "1--10",
}
@inproceedings{lee-stent-2016-task,
    title = "Task Lineages: Dialog State Tracking for Flexible Interaction",
    author = "Lee, Sungjin  and
      Stent, Amanda",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3602",
    doi = "10.18653/v1/W16-3602",
    pages = "11--21",
}
@inproceedings{liu-lane-2016-joint,
    title = "Joint Online Spoken Language Understanding and Language Modeling With Recurrent Neural Networks",
    author = "Liu, Bing  and
      Lane, Ian",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3603",
    doi = "10.18653/v1/W16-3603",
    pages = "22--30",
}
@inproceedings{oraby-etal-2016-creating,
    title = "Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue",
    author = "Oraby, Shereen  and
      Harrison, Vrindavan  and
      Reed, Lena  and
      Hernandez, Ernesto  and
      Riloff, Ellen  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3604",
    doi = "10.18653/v1/W16-3604",
    pages = "31--41",
}
@inproceedings{barker-etal-2016-sensei,
    title = "The {SENSEI} Annotated Corpus: Human Summaries of Reader Comment Conversations in On-line News",
    author = "Barker, Emma  and
      Paramita, Monica Lestari  and
      Aker, Ahmet  and
      Kurtic, Emina  and
      Hepple, Mark  and
      Gaizauskas, Robert",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3605",
    doi = "10.18653/v1/W16-3605",
    pages = "42--52",
}
@inproceedings{matsuyama-papangelis-2016-special,
    title = "Special Session - The Future Directions of Dialogue-Based Intelligent Personal Assistants",
    author = "Matsuyama, Yoichi  and
      Papangelis, Alexandros",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3606",
    doi = "10.18653/v1/W16-3606",
    pages = "53",
}
@inproceedings{brennan-2016-keynote,
    title = "Keynote - More than meets the ear: Processes that shape dialogue",
    author = "Brennan, Susan",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3607",
    doi = "10.18653/v1/W16-3607",
    pages = "54",
}
@inproceedings{yu-etal-2016-wizard,
    title = "A {W}izard-of-{O}z Study on A Non-Task-Oriented Dialog Systems That Reacts to User Engagement",
    author = "Yu, Zhou  and
      Nicolich-Henkin, Leah  and
      Black, Alan W  and
      Rudnicky, Alexander",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3608",
    doi = "10.18653/v1/W16-3608",
    pages = "55--63",
}
@inproceedings{herzig-etal-2016-classifying,
    title = "Classifying Emotions in Customer Support Dialogues in Social Media",
    author = "Herzig, Jonathan  and
      Feigenblat, Guy  and
      Shmueli-Scheuer, Michal  and
      Konopnicki, David  and
      Rafaeli, Anat  and
      Altman, Daniel  and
      Spivak, David",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3609",
    doi = "10.18653/v1/W16-3609",
    pages = "64--73",
}
@inproceedings{miehle-etal-2016-cultural,
    title = "Cultural Communication Idiosyncrasies in Human-Computer Interaction",
    author = "Miehle, Juliana  and
      Yoshino, Koichiro  and
      Pragst, Louisa  and
      Ultes, Stefan  and
      Nakamura, Satoshi  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3610",
    doi = "10.18653/v1/W16-3610",
    pages = "74--79",
}
@inproceedings{casanueva-etal-2016-using,
    title = "Using phone features to improve dialogue state tracking generalisation to unseen states",
    author = "Casanueva, I{\~n}igo  and
      Hain, Thomas  and
      Nicolao, Mauro  and
      Green, Phil",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3611",
    doi = "10.18653/v1/W16-3611",
    pages = "80--89",
}
@inproceedings{chen-choi-2016-character,
    title = "Character Identification on Multiparty Conversation: Identifying Mentions of Characters in {TV} Shows",
    author = "Chen, Yu-Hsin  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3612",
    doi = "10.18653/v1/W16-3612",
    pages = "90--100",
}
@inproceedings{fatemi-etal-2016-policy,
    title = "Policy Networks with Two-Stage Training for Dialogue Systems",
    author = "Fatemi, Mehdi  and
      El Asri, Layla  and
      Schulz, Hannes  and
      He, Jing  and
      Suleman, Kaheer",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3613",
    doi = "10.18653/v1/W16-3613",
    pages = "101--110",
}
@inproceedings{ravi-artstein-2016-language,
    title = "Language Portability for Dialogue Systems: Translating a Question-Answering System from {E}nglish into {T}amil",
    author = "Ravi, Satheesh  and
      Artstein, Ron",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3614",
    doi = "10.18653/v1/W16-3614",
    pages = "111--116",
}
@inproceedings{forbes-riley-etal-2016-extracting,
    title = "Extracting {PDTB} Discourse Relations from Student Essays",
    author = "Forbes-Riley, Kate  and
      Zhang, Fan  and
      Litman, Diane",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3615",
    doi = "10.18653/v1/W16-3615",
    pages = "117--127",
}
@inproceedings{hayashi-etal-2016-empirical,
    title = "Empirical comparison of dependency conversions for {RST} discourse trees",
    author = "Hayashi, Katsuhiko  and
      Hirao, Tsutomu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3616",
    doi = "10.18653/v1/W16-3616",
    pages = "128--136",
}
@inproceedings{li-etal-2016-role,
    title = "The Role of Discourse Units in Near-Extractive Summarization",
    author = "Li, Junyi Jessy  and
      Thadani, Kapil  and
      Stent, Amanda",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3617",
    doi = "10.18653/v1/W16-3617",
    pages = "137--147",
}
@inproceedings{nicolich-henkin-etal-2016-initiations,
    title = "Initiations and Interruptions in a Spoken Dialog System",
    author = "Nicolich-Henkin, Leah  and
      Ros{\'e}, Carolyn  and
      Black, Alan W",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3618",
    doi = "10.18653/v1/W16-3618",
    pages = "148--156",
}
@inproceedings{hirano-etal-2016-analyzing,
    title = "Analyzing Post-dialogue Comments by Speakers {--} How Do Humans Personalize Their Utterances in Dialogue? {--}",
    author = "Hirano, Toru  and
      Higashinaka, Ryuichiro  and
      Matsuo, Yoshihiro",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3619",
    doi = "10.18653/v1/W16-3619",
    pages = "157--165",
}
@inproceedings{davoodi-kosseim-2016-contribution,
    title = "On the Contribution of Discourse Structure on Text Complexity Assessment",
    author = "Davoodi, Elnaz  and
      Kosseim, Leila",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3620",
    doi = "10.18653/v1/W16-3620",
    pages = "166--174",
}
@inproceedings{nasr-etal-2016-syntactic,
    title = "Syntactic parsing of chat language in contact center conversation corpus",
    author = "Nasr, Alexis  and
      Damnati, Geraldine  and
      Guerraz, Aleksandra  and
      Bechet, Frederic",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3621",
    doi = "10.18653/v1/W16-3621",
    pages = "175--184",
}
@inproceedings{dusek-jurcicek-2016-context,
    title = "A Context-aware Natural Language Generator for Dialogue Systems",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Jur{\v{c}}{\'\i}{\v{c}}ek, Filip",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3622",
    doi = "10.18653/v1/W16-3622",
    pages = "185--190",
}
@inproceedings{blanchard-etal-2016-identifying,
    title = "Identifying Teacher Questions Using Automatic Speech Recognition in Classrooms",
    author = "Blanchard, Nathaniel  and
      Donnelly, Patrick  and
      Olney, Andrew M.  and
      Samei, Borhan  and
      Ward, Brooke  and
      Sun, Xiaoyi  and
      Kelly, Sean  and
      Nystrand, Martin  and
      D{'}Mello, Sidney K.",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3623",
    doi = "10.18653/v1/W16-3623",
    pages = "191--201",
}
@inproceedings{laskowski-2016-framework,
    title = "A framework for the automatic inference of stochastic turn-taking styles",
    author = "Laskowski, Kornel",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3624",
    doi = "10.18653/v1/W16-3624",
    pages = "202--211",
}
@inproceedings{inoue-etal-2016-talking,
    title = "Talking with {ERICA}, an autonomous android",
    author = "Inoue, Koji  and
      Milhorat, Pierrick  and
      Lala, Divesh  and
      Zhao, Tianyu  and
      Kawahara, Tatsuya",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3625",
    doi = "10.18653/v1/W16-3625",
    pages = "212--215",
}
@inproceedings{stoyanchev-etal-2016-rapid,
    title = "Rapid Prototyping of Form-driven Dialogue Systems Using an Open-source Framework",
    author = "Stoyanchev, Svetlana  and
      Lison, Pierre  and
      Bangalore, Srinivas",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3626",
    doi = "10.18653/v1/W16-3626",
    pages = "216--219",
}
@inproceedings{ivanov-etal-2016-lvcsr,
    title = "{LVCSR} System on a Hybrid {GPU}-{CPU} Embedded Platform for Real-Time Dialog Applications",
    author = "Ivanov, Alexei V.  and
      Lange, Patrick L.  and
      Suendermann-Oeft, David",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3627",
    doi = "10.18653/v1/W16-3627",
    pages = "220--223",
}
@inproceedings{matsuyama-etal-2016-socially,
    title = "Socially-Aware Animated Intelligent Personal Assistant Agent",
    author = "Matsuyama, Yoichi  and
      Bhardwaj, Arjun  and
      Zhao, Ran  and
      Romeo, Oscar  and
      Akoju, Sushma  and
      Cassell, Justine",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3628",
    doi = "10.18653/v1/W16-3628",
    pages = "224--227",
}
@inproceedings{mori-araki-2016-selection,
    title = "Selection method of an appropriate response in chat-oriented dialogue systems",
    author = "Mori, Hideaki  and
      Araki, Masahiro",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3629",
    doi = "10.18653/v1/W16-3629",
    pages = "228--231",
}
@inproceedings{manuvinakurike-etal-2016-real,
    title = "Real-Time Understanding of Complex Discriminative Scene Descriptions",
    author = "Manuvinakurike, Ramesh  and
      Kennington, Casey  and
      DeVault, David  and
      Schlangen, David",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3630",
    doi = "10.18653/v1/W16-3630",
    pages = "232--241",
}
@inproceedings{kennington-schlangen-2016-supporting,
    title = "Supporting Spoken Assistant Systems with a Graphical User Interface that Signals Incremental Understanding and Prediction State",
    author = "Kennington, Casey  and
      Schlangen, David",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3631",
    doi = "10.18653/v1/W16-3631",
    pages = "242--251",
}
@inproceedings{manuvinakurike-etal-2016-toward,
    title = "Toward incremental dialogue act segmentation in fast-paced interactive dialogue systems",
    author = "Manuvinakurike, Ramesh  and
      Paetzel, Maike  and
      Qu, Cheng  and
      Schlangen, David  and
      DeVault, David",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3632",
    doi = "10.18653/v1/W16-3632",
    pages = "252--262",
}
@inproceedings{morency-2016-keynote,
    title = "Keynote - Modeling Human Communication Dynamics",
    author = "Morency, Louis-Philippe",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3633",
    doi = "10.18653/v1/W16-3633",
    pages = "263",
}
@inproceedings{lowe-etal-2016-evaluation,
    title = "On the Evaluation of Dialogue Systems with Next Utterance Classification",
    author = "Lowe, Ryan  and
      Serban, Iulian Vlad  and
      Noseworthy, Michael  and
      Charlin, Laurent  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3634",
    doi = "10.18653/v1/W16-3634",
    pages = "264--269",
}
@inproceedings{litman-etal-2016-towards,
    title = "Towards Using Conversations with Spoken Dialogue Systems in the Automated Assessment of Non-Native Speakers of {E}nglish",
    author = "Litman, Diane  and
      Young, Steve  and
      Gales, Mark  and
      Knill, Kate  and
      Ottewell, Karen  and
      van Dalen, Rogier  and
      Vandyke, David",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3635",
    doi = "10.18653/v1/W16-3635",
    pages = "270--275",
}
@inproceedings{misra-etal-2016-measuring,
    title = "Measuring the Similarity of Sentential Arguments in Dialogue",
    author = "Misra, Amita  and
      Ecker, Brian  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3636",
    doi = "10.18653/v1/W16-3636",
    pages = "276--287",
}
@inproceedings{hough-schlangen-2016-investigating,
    title = "Investigating Fluidity for Human-Robot Interaction with Real-time, Real-world Grounding Strategies",
    author = "Hough, Julian  and
      Schlangen, David",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3637",
    doi = "10.18653/v1/W16-3637",
    pages = "288--298",
}
@inproceedings{mehdad-tetreault-2016-characters,
    title = "Do Characters Abuse More Than Words?",
    author = "Mehdad, Yashar  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3638",
    doi = "10.18653/v1/W16-3638",
    pages = "299--303",
}
@inproceedings{kumar-etal-2016-towards,
    title = "Towards a dialogue system that supports rich visualizations of data",
    author = "Kumar, Abhinav  and
      Aurisano, Jillian  and
      Di Eugenio, Barbara  and
      Johnson, Andrew  and
      Gonzalez, Alberto  and
      Leigh, Jason",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3639",
    doi = "10.18653/v1/W16-3639",
    pages = "304--309",
}
@inproceedings{mizukami-etal-2016-analyzing,
    title = "Analyzing the Effect of Entrainment on Dialogue Acts",
    author = "Mizukami, Masahiro  and
      Yoshino, Koichiro  and
      Neubig, Graham  and
      Traum, David  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3640",
    doi = "10.18653/v1/W16-3640",
    pages = "310--318",
}
@inproceedings{miyazaki-etal-2016-towards,
    title = "Towards an Entertaining Natural Language Generation System: Linguistic Peculiarities of {J}apanese Fictional Characters",
    author = "Miyazaki, Chiaki  and
      Hirano, Toru  and
      Higashinaka, Ryuichiro  and
      Matsuo, Yoshihiro",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3641",
    doi = "10.18653/v1/W16-3641",
    pages = "319--328",
}
@inproceedings{li-boyer-2016-reference,
    title = "Reference Resolution in Situated Dialogue with Learned Semantics",
    author = "Li, Xiaolong  and
      Boyer, Kristy",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3642",
    doi = "10.18653/v1/W16-3642",
    pages = "329--338",
}
@inproceedings{yu-etal-2016-training,
    title = "Training an adaptive dialogue policy for interactive learning of visually grounded word meanings",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3643",
    doi = "10.18653/v1/W16-3643",
    pages = "339--349",
}
@inproceedings{rahimtoroghi-etal-2016-learning,
    title = "Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events",
    author = "Rahimtoroghi, Elahe  and
      Hernandez, Ernesto  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3644",
    doi = "10.18653/v1/W16-3644",
    pages = "350--359",
}
@inproceedings{tian-etal-2016-laugh,
    title = "When do we laugh?",
    author = "Tian, Ye  and
      Mazzocconi, Chiara  and
      Ginzburg, Jonathan",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3645",
    doi = "10.18653/v1/W16-3645",
    pages = "360--369",
}
@inproceedings{kobori-etal-2016-small,
    title = "Small Talk Improves User Impressions of Interview Dialogue Systems",
    author = "Kobori, Takahiro  and
      Nakano, Mikio  and
      Nakamura, Tomoaki",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3646",
    doi = "10.18653/v1/W16-3646",
    pages = "370--380",
}
@inproceedings{zhao-etal-2016-automatic,
    title = "Automatic Recognition of Conversational Strategies in the Service of a Socially-Aware Dialog System",
    author = "Zhao, Ran  and
      Sinha, Tanmay  and
      Black, Alan  and
      Cassell, Justine",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3647",
    doi = "10.18653/v1/W16-3647",
    pages = "381--392",
}
@inproceedings{inaba-takahashi-2016-neural,
    title = "Neural Utterance Ranking Model for Conversational Dialogue Systems",
    author = "Inaba, Michimasa  and
      Takahashi, Kenichi",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3648",
    doi = "10.18653/v1/W16-3648",
    pages = "393--403",
}
@inproceedings{yu-etal-2016-strategy,
    title = "Strategy and Policy Learning for Non-Task-Oriented Conversational Systems",
    author = "Yu, Zhou  and
      Xu, Ziyu  and
      Black, Alan W  and
      Rudnicky, Alexander",
    booktitle = "Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2016",
    address = "Los Angeles",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3649",
    doi = "10.18653/v1/W16-3649",
    pages = "404--412",
}
@proceedings{ws-2016-international-natural,
    title = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    editor = "Gardent, Claire  and
      Gangemi, Aldo",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3500",
}
@inproceedings{barros-lloret-2016-generating,
    title = "Generating sets of related sentences from input seed features",
    author = "Barros, Cristina  and
      Lloret, Elena",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3501",
    pages = "1--4",
}
@inproceedings{basile-2016-repository,
    title = "A Repository of Frame Instance Lexicalizations for Generation",
    author = "Basile, Valerio",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3502",
    pages = "5--12",
}
@inproceedings{bourgonje-etal-2016-processing,
    title = "Processing Document Collections to Automatically Extract Linked Data: Semantic Storytelling Technologies for Smart Curation Workflows",
    author = "Bourgonje, Peter  and
      Moreno Schneider, Julian  and
      Rehm, Georg  and
      Sasaki, Felix",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3503",
    pages = "13--16",
}
@inproceedings{duboue-etal-2016-robustness,
    title = "On the Robustness of Standalone Referring Expression Generation Algorithms Using {RDF} Data",
    author = "Duboue, Pablo  and
      Dom{\'\i}nguez, Martin Ariel  and
      Estrella, Paula",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3504",
    pages = "17--24",
}
@inproceedings{lloret-gardent-2016-content,
    title = "Content Selection through Paraphrase Detection: Capturing different Semantic Realisations of the Same Idea",
    author = "Lloret, Elena  and
      Gardent, Claire",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3505",
    pages = "25--28",
}
@inproceedings{mrabet-etal-2016-aligning,
    title = "Aligning Texts and Knowledge Bases with Semantic Sentence Simplification",
    author = "Mrabet, Yassine  and
      Vougiouklis, Pavlos  and
      Kilicoglu, Halil  and
      Gardent, Claire  and
      Demner-Fushman, Dina  and
      Hare, Jonathon  and
      Simperl, Elena",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3506",
    pages = "29--36",
}
@inproceedings{nesterenko-2016-building,
    title = "Building a System for Stock News Generation in {R}ussian",
    author = "Nesterenko, Liubov",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3507",
    pages = "37--40",
}
@inproceedings{perez-beltrachini-etal-2016-content,
    title = "Content selection as semantic-based ontology exploration",
    author = "Perez-Beltrachini, Laura  and
      Gardent, Claire  and
      Revuz, Anselme  and
      Bandyopadhyay, Saptarashmi",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3508",
    pages = "41--45",
}
@inproceedings{sadoun-etal-2016-readme,
    title = "{R}ead{ME} generation from an {OWL} ontology describing {NLP} tools",
    author = "Sadoun, Driss  and
      Mkhitaryan, Satenik  and
      Nouvel, Damien  and
      Valette, Mathieu",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3509",
    pages = "46--49",
}
@inproceedings{sanby-etal-2016-comparing,
    title = "Comparing the Template-Based Approach to {GF}: the case of {A}frikaans",
    author = "Sanby, Lauren  and
      Todd, Ion  and
      Keet, Maria C.",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3510",
    pages = "50--53",
}
@inproceedings{sleimi-gardent-2016-generating,
    title = "Generating Paraphrases from {DBP}edia using Deep Learning",
    author = "Sleimi, Amin  and
      Gardent, Claire",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3511",
    pages = "54--57",
}
@inproceedings{tran-popowich-2016-automatic,
    title = "Automatic Tweet Generation From Traffic Incident Data",
    author = "Tran, Khoa  and
      Popowich, Fred",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3512",
    pages = "59--66",
}
@inproceedings{vicente-lloret-2016-analysing,
    title = "Analysing the Integration of Semantic Web Features for Document Planning across Genres",
    author = "Vicente, Marta  and
      Lloret, Elena",
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web ({W}eb{NLG} 2016)",
    month = sep,
    year = "2016",
    address = "Edinburgh, Scotland",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3513",
    pages = "67--70",
}
@proceedings{ws-2016-european,
    title = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3400",
}
@inproceedings{culo-nitzke-2016-patterns,
    title = "Patterns of Terminological Variation in Post-editing and of Cognate Use in Machine Translation in Contrast to Human Translation",
    author = "{\v{C}}ulo, Oliver  and
      Nitzke, Jean",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3401",
    pages = "106--114",
}
@inproceedings{babych-2016-graphonological,
    title = "Graphonological {L}evenshtein Edit Distance: Application for Automated Cognate Identification",
    author = "Babych, Bogdan",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3402",
    pages = "115--128",
}
@inproceedings{passban-etal-2016-improving,
    title = "Improving Phrase-Based {SMT} Using Cross-Granularity Embedding Similarity",
    author = "Passban, Peyman  and
      Hokamp, Chris  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3403",
    pages = "129--140",
}
@inproceedings{moorkens-way-2016-comparing,
    title = "Comparing Translator Acceptability of {TM} and {SMT} Outputs",
    author = "Moorkens, Joss  and
      Way, Andy",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3404",
    pages = "141--151",
}
@inproceedings{forcada-etal-2016-stand,
    title = "Stand-off Annotation of Web Content as a Legally Safer Alternative to Crawling for Distribution",
    author = "Forcada, Mikel L.  and
      Espl{\`a}-Gomis, Miquel  and
      P{\'e}rez-Ortiz, Juan Antonio",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3405",
    pages = "152--164",
}
@inproceedings{li-etal-2016-combining,
    title = "Combining Translation Memories and Syntax-Based {SMT}: Experiments with Real Industrial Data",
    author = "Li, Liangyou  and
      Escartin, Carla Parra  and
      Liu, Qun",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3406",
    pages = "165--177",
}
@inproceedings{smith-etal-2016-trouble,
    title = "The Trouble with Machine Translation Coherence",
    author = "Smith, Karin Sim  and
      Aziz, Wilker  and
      Specia, Lucia",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3407",
    pages = "178--189",
}
@inproceedings{hoang-bojar-2016-pivoting,
    title = "Pivoting Methods and Data for {C}zech-{V}ietnamese Translation via {E}nglish",
    author = "Hoang, Duc Tam  and
      Bojar, Ondrej",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3408",
    pages = "190--202",
}
@inproceedings{tezcan-etal-2016-detecting,
    title = "Detecting Grammatical Errors in Machine Translation Output Using Dependency Parsing and Treebank Querying",
    author = "Tezcan, Arda  and
      Hoste, Veronique  and
      Macken, Lieve",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3409",
    pages = "203--217",
}
@inproceedings{popovic-etal-2016-potential,
    title = "Potential and Limits of Using Post-edits as Reference Translations for {MT} Evaluation",
    author = "Popovic, Maja  and
      Ar{\v{c}}an, Mihael  and
      Lommel, Arle",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3410",
    pages = "218--229",
}
@inproceedings{stajner-popovic-2016-text,
    title = "Can Text Simplification Help Machine Translation?",
    author = "{\v{S}}tajner, Sanja  and
      Popovic, Maja",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3411",
    pages = "230--242",
}
@inproceedings{etchegoyhen-azpeitia-2016-portable,
    title = "A Portable Method for Parallel and Comparable Document Alignment",
    author = "Etchegoyhen, Thierry  and
      Azpeitia, Andoni",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3412",
    pages = "243--255",
}
@inproceedings{bechara-etal-2016-semantic,
    title = "Semantic Textual Similarity in Quality Estimation",
    author = "Bechara, Hanna  and
      Escartin, Carla Parra  and
      Orasan, Constantin  and
      Specia, Lucia",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3413",
    pages = "256--268",
}
@inproceedings{smith-etal-2016-climbing,
    title = "Climbing Mont {BLEU}: The Strange World of Reachable High-{BLEU} Translations",
    author = "Smith, Aaron  and
      Hardmeier, Christian  and
      Tiedemann, Joerg",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3414",
    pages = "269--281",
}
@inproceedings{domingo-etal-2016-interactive,
    title = "Interactive-Predictive Translation Based on Multiple Word-Segments",
    author = "Domingo, Miguel  and
      Peris, Alvaro  and
      Casacuberta, Francisco",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3415",
    pages = "282--291",
}
@inproceedings{luong-popescu-belis-2016-contextual,
    title = "A Contextual Language Model to Improve Machine Translation of Pronouns by Re-ranking Translation Hypotheses",
    author = "Luong, Ngoc Quang  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3416",
    pages = "292--304",
}
@inproceedings{steele-specia-2016-predicting,
    title = "Predicting and Using Implicit Discourse Elements in {C}hinese-{E}nglish Translation",
    author = "Steele, David  and
      Specia, Lucia",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3417",
    pages = "305--317",
}
@inproceedings{hardmeier-guillou-2016-graphical,
    title = "A Graphical Pronoun Analysis Tool for the {PROTEST} Pronoun Evaluation Test Suite",
    author = "Hardmeier, Christian  and
      Guillou, Liane",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3418",
    pages = "318--330",
}
@inproceedings{schaeffer-etal-2016-measuring,
    title = "Measuring Cognitive Translation Effort with Activity Units",
    author = "Schaeffer, Moritz Jonas  and
      Carl, Michael  and
      Lacruz, Isabel  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3419",
    pages = "331--34195",
}
@inproceedings{hu-cadwell-2016-comparative,
    title = "A Comparative Study of Post-editing Guidelines",
    author = "Hu, Ke  and
      Cadwell, Patrick",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3420",
    pages = "34206--353",
}
@inproceedings{sanchez-cartagena-etal-2016-dealing,
    title = "Dealing with Data Sparseness in {SMT} with Factured Models and Morphological Expansion: a Case Study on {C}roatian",
    author = "S{\'a}nchez-Cartagena, Victor M.  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Klubi{\v{c}}ka, Filip",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3421",
    pages = "354--360",
}
@inproceedings{klubicka-etal-2016-collaborative,
    title = "Collaborative Development of a Rule-Based Machine Translator between {C}roatian and {S}erbian",
    author = "Klubi{\v{c}}ka, Filip  and
      Ram{\'\i}rez-S{\'a}nchez, Gema  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3422",
    pages = "361--367",
}
@inproceedings{toral-etal-2016-assessing,
    title = "Re-assessing the Impact of {SMT} Techniques with Human Evaluation: a Case Study on {E}nglish{---}{C}roatian",
    author = "Toral, Antonio  and
      Rubino, Raphael  and
      Ram{\'\i}rez-S{\'a}nchez, Gema",
    booktitle = "Proceedings of the 19th Annual Conference of the {E}uropean Association for Machine Translation",
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-3423",
    pages = "368--375",
}
@proceedings{ws-2016-international,
    title = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    editor = "Chiang, David  and
      Koller, Alexander",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3300",
}
@inproceedings{torr-stabler-2016-coordination,
    title = "Coordination in {M}inimalist {G}rammars: Excorporation and Across the Board (Head) Movement",
    author = "Torr, John  and
      Stabler, Edward P.",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3301",
    pages = "1--17",
}
@inproceedings{ben-khelil-etal-2016-arabtag,
    title = "{A}rab{TAG}: from a Handcrafted to a Semi-automatically Generated {TAG}",
    author = "Ben Khelil, Ch{\'e}rifa  and
      Duchier, Denys  and
      Parmentier, Yannick  and
      Zribi, Chiraz  and
      Ben Fraj, F{\'e}riel",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3302",
    pages = "18--26",
}
@inproceedings{danlos-etal-2016-interfacing,
    title = "Interfacing Sentential and Discourse {TAG}-based Grammars",
    author = "Danlos, Laurence  and
      Maskharashvili, Aleksandre  and
      Pogodalla, Sylvain",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3303",
    pages = "27--37",
}
@inproceedings{bernard-danlos-2016-modelling,
    title = "Modelling Discourse in {STAG}: Subordinate Conjunctions and Attributing Phrases",
    author = "Bernard, Timoth{\'e}e  and
      Danlos, Laurence",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3304",
    pages = "38--47",
}
@inproceedings{kallmeyer-etal-2016-argument,
    title = "Argument linking in {LTAG}: A constraint-based implementation with {XMG}",
    author = "Kallmeyer, Laura  and
      Lichte, Timm  and
      Osswald, Rainer  and
      Petitjean, Simon",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3305",
    pages = "48--57",
}
@inproceedings{balogh-2016-verbal,
    title = "Verbal fields in {H}ungarian simple sentences and infinitival clausal complements",
    author = "Balogh, Kata",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3306",
    pages = "58--66",
}
@inproceedings{storoshenko-2016-modelling,
    title = "Modelling the ziji Blocking Effect and Constraining Bound Variable Derivations in {MC}-{TAG} with Delayed Locality",
    author = "Storoshenko, Dennis Ryan",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3307",
    pages = "67--76",
}
@inproceedings{sloan-2016-node,
    title = "Node-based Induction of Tree-Substitution Grammars",
    author = "Sloan, Rose",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3308",
    pages = "77--84",
}
@inproceedings{chung-etal-2016-revisiting,
    title = "Revisiting Supertagging and Parsing: How to Use Supertags in Transition-Based Parsing",
    author = "Chung, Wonchang  and
      Mhatre, Suhas Siddhesh  and
      Nasr, Alexis  and
      Rambow, Owen  and
      Bangalore, Srinivas",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3309",
    pages = "85--92",
}
@inproceedings{de-santo-etal-2016-alternate,
    title = "An Alternate View on Strong Lexicalization in {TAG}",
    author = {De Santo, Aniello  and
      Aks{\"e}nova, Al{\"e}na  and
      Graf, Thomas},
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3310",
    pages = "93--102",
}
@inproceedings{bauer-rambow-2016-hyperedge,
    title = "Hyperedge Replacement and Nonprojective Dependency Structures",
    author = "Bauer, Daniel  and
      Rambow, Owen",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3311",
    pages = "103--111",
}
@inproceedings{storoshenko-frank-2016-parasitic,
    title = "Parasitic Gaps and the Heterogeneity of Dependency Formation in {STAG}",
    author = "Storoshenko, Dennis Ryan  and
      Frank, Robert",
    booktitle = "Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms ({TAG}+12)",
    month = jun,
    year = "2016",
    address = {D{\"u}sseldorf, Germany},
    url = "https://www.aclweb.org/anthology/W16-3312",
    pages = "112--120",
}
@proceedings{ws-2016-vision,
    title = "Proceedings of the 5th Workshop on Vision and Language",
    editor = "Belz, Anya  and
      Erdem, Erkut  and
      Mikolajczyk, Krystian  and
      Pastra, Katerina",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3200",
    doi = "10.18653/v1/W16-32",
}
@inproceedings{elhoseiny-etal-2016-automatic,
    title = "Automatic Annotation of Structured Facts in Images",
    author = "Elhoseiny, Mohamed  and
      Cohen, Scott  and
      Chang, Walter  and
      Price, Brian  and
      Elgammal, Ahmed",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3201",
    doi = "10.18653/v1/W16-3201",
    pages = "1--9",
}
@inproceedings{hurlimann-bos-2016-combining,
    title = "Combining Lexical and Spatial Knowledge to Predict Spatial Relations between Objects in Images",
    author = {H{\"u}rlimann, Manuela  and
      Bos, Johan},
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3202",
    doi = "10.18653/v1/W16-3202",
    pages = "10--18",
}
@inproceedings{hodosh-hockenmaier-2016-focused,
    title = "Focused Evaluation for Image Description with Binary Forced-Choice Tasks",
    author = "Hodosh, Micah  and
      Hockenmaier, Julia",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3203",
    doi = "10.18653/v1/W16-3203",
    pages = "19--28",
}
@inproceedings{kilickaya-etal-2016-leveraging,
    title = "Leveraging Captions in the Wild to Improve Object Detection",
    author = "Kilickaya, Mert  and
      Ikizler-Cinbis, Nazli  and
      Erdem, Erkut  and
      Erdem, Aykut",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3204",
    doi = "10.18653/v1/W16-3204",
    pages = "29--38",
}
@inproceedings{alharbi-gotoh-2016-natural,
    title = "Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis",
    author = "Alharbi, Nouf  and
      Gotoh, Yoshihiko",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3205",
    doi = "10.18653/v1/W16-3205",
    pages = "39--47",
}
@inproceedings{yu-etal-2016-interactively,
    title = "Interactively Learning Visually Grounded Word Meanings from a Human Tutor",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3206",
    doi = "10.18653/v1/W16-3206",
    pages = "48--53",
}
@inproceedings{van-miltenburg-etal-2016-pragmatic,
    title = "Pragmatic Factors in Image Description: The Case of Negations",
    author = "van Miltenburg, Emiel  and
      Morante, Roser  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3207",
    doi = "10.18653/v1/W16-3207",
    pages = "54--59",
}
@inproceedings{pezzelle-etal-2016-building,
    title = "Building a Bagpipe with a Bag and a Pipe: Exploring Conceptual Combination in Vision",
    author = "Pezzelle, Sandro  and
      Shekhar, Ravi  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3208",
    doi = "10.18653/v1/W16-3208",
    pages = "60--64",
}
@inproceedings{belz-etal-2016-exploring,
    title = "Exploring Different Preposition Sets, Models and Feature Sets in Automatic Generation of Spatial Image Descriptions",
    author = "Belz, Anja  and
      Muscat, Adrian  and
      Birmingham, Brandon",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3209",
    doi = "10.18653/v1/W16-3209",
    pages = "65--69",
}
@inproceedings{elliott-etal-2016-multi30k,
    title = "{M}ulti30{K}: Multilingual {E}nglish-{G}erman Image Descriptions",
    author = "Elliott, Desmond  and
      Frank, Stella  and
      Sima{'}an, Khalil  and
      Specia, Lucia",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3210",
    doi = "10.18653/v1/W16-3210",
    pages = "70--74",
}
@inproceedings{sorodoc-etal-2016-look,
    title = "{``}Look, some Green Circles!{''}: Learning to Quantify from Images",
    author = "Sorodoc, Ionut  and
      Lazaridou, Angeliki  and
      Boleda, Gemma  and
      Herbelot, Aur{\'e}lie  and
      Pezzelle, Sandro  and
      Bernardi, Raffaella",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3211",
    doi = "10.18653/v1/W16-3211",
    pages = "75--79",
}
@inproceedings{mehler-etal-2016-text2voronoi,
    title = "{T}ext2voronoi: An Image-driven Approach to Differential Diagnosis",
    author = "Mehler, Alexander  and
      Uslu, Tolga  and
      Hemati, Wahed",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3212",
    doi = "10.18653/v1/W16-3212",
    pages = "80--85",
}
@inproceedings{winn-etal-2016-detecting,
    title = "Detecting Visually Relevant Sentences for Fine-Grained Classification",
    author = "Winn, Olivia  and
      Kidambi, Madhavan Kavanur  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 5th Workshop on Vision and Language",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3213",
    doi = "10.18653/v1/W16-3213",
    pages = "86--91",
}
@proceedings{ws-2016-bioasq,
    title = "Proceedings of the Fourth {B}io{ASQ} workshop",
    editor = "Kakadiaris, Ioannis A.  and
      Paliouras, George  and
      Krithara, Anastasia",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3100",
    doi = "10.18653/v1/W16-31",
}
@inproceedings{krithara-etal-2016-results,
    title = "Results of the 4th edition of {B}io{ASQ} Challenge",
    author = "Krithara, Anastasia  and
      Nentidis, Anastasios  and
      Paliouras, Georgios  and
      Kakadiaris, Ioannis",
    booktitle = "Proceedings of the Fourth {B}io{ASQ} workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3101",
    doi = "10.18653/v1/W16-3101",
    pages = "1--7",
}
@inproceedings{zavorin-etal-2016-using,
    title = "Using Learning-To-Rank to Enhance {NLM} Medical Text Indexer Results",
    author = "Zavorin, Ilya  and
      Mork, James  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Fourth {B}io{ASQ} workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3102",
    doi = "10.18653/v1/W16-3102",
    pages = "8--15",
}
@inproceedings{segura-bedmar-etal-2016-labda,
    title = "{LABDA} at the 2016 {B}io{ASQ} challenge task 4a: Semantic Indexing by using {E}lastic{S}earch",
    author = "Segura-Bedmar, Isabel  and
      Carruana, Adri{\'a}n  and
      Mart{\'\i}nez, Paloma",
    booktitle = "Proceedings of the Fourth {B}io{ASQ} workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3103",
    doi = "10.18653/v1/W16-3103",
    pages = "16--22",
}
@inproceedings{yang-etal-2016-learning,
    title = "Learning to Answer Biomedical Questions: {OAQA} at {B}io{ASQ} 4{B}",
    author = "Yang, Zi  and
      Zhou, Yue  and
      Nyberg, Eric",
    booktitle = "Proceedings of the Fourth {B}io{ASQ} workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3104",
    doi = "10.18653/v1/W16-3104",
    pages = "23--37",
}
@inproceedings{schulze-etal-2016-hpi,
    title = "{HPI} Question Answering System in {B}io{ASQ} 2016",
    author = {Schulze, Frederik  and
      Sch{\"u}ler, Ricarda  and
      Draeger, Tim  and
      Dummer, Daniel  and
      Ernst, Alexander  and
      Flemming, Pedro  and
      Perscheid, Cindy  and
      Neves, Mariana},
    booktitle = "Proceedings of the Fourth {B}io{ASQ} workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3105",
    doi = "10.18653/v1/W16-3105",
    pages = "38--44",
}
@inproceedings{lee-etal-2016-ksanswer,
    title = "{KSA}nswer: Question-answering System of Kangwon National University and Sogang University in the 2016 {B}io{ASQ} Challenge",
    author = "Lee, Hyeon-gu  and
      Kim, Minkyoung  and
      Kim, Harksoo  and
      Kim, Juae  and
      Kwon, Sunjae  and
      Seo, Jungyun  and
      Kim, Yi-reun  and
      Choi, Jung-Kyu",
    booktitle = "Proceedings of the Fourth {B}io{ASQ} workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3106",
    doi = "10.18653/v1/W16-3106",
    pages = "45--49",
}
@inproceedings{papagiannopoulou-etal-2016-large,
    title = "Large-Scale Semantic Indexing and Question Answering in Biomedicine",
    author = "Papagiannopoulou, Eirini  and
      Papanikolaou, Yiannis  and
      Dimitriadis, Dimitris  and
      Lagopoulos, Sakis  and
      Tsoumakas, Grigorios  and
      Laliotis, Manos  and
      Markantonatos, Nikos  and
      Vlahavas, Ioannis",
    booktitle = "Proceedings of the Fourth {B}io{ASQ} workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3107",
    doi = "10.18653/v1/W16-3107",
    pages = "50--54",
}
@proceedings{ws-2016-bionlp,
    title = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    editor = "N{\.e}dellec, Claire  and
      Bossy, Robert  and
      Kim, Jin-Dong",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3000",
    doi = "10.18653/v1/W16-30",
}
@inproceedings{chaix-etal-2016-overview,
    title = "Overview of the Regulatory Network of Plant Seed Development ({S}ee{D}ev) Task at the {B}io{NLP} Shared Task 2016.",
    author = "Chaix, Estelle  and
      Dubreucq, Bertrand  and
      Fatihi, Abdelhak  and
      Valsamou, Dialekti  and
      Bossy, Robert  and
      Ba, Mouhamadou  and
      Del{\'e}ger, Louise  and
      Zweigenbaum, Pierre  and
      Bessi{\`e}res, Philippe  and
      Lepiniec, Loic  and
      N{\'e}dellec, Claire",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3001",
    doi = "10.18653/v1/W16-3001",
    pages = "1--11",
}
@inproceedings{deleger-etal-2016-overview,
    title = "Overview of the Bacteria Biotope Task at {B}io{NLP} Shared Task 2016",
    author = "Del{\'e}ger, Louise  and
      Bossy, Robert  and
      Chaix, Estelle  and
      Ba, Mouhamadou  and
      Ferr{\'e}, Arnaud  and
      Bessi{\`e}res, Philippe  and
      N{\'e}dellec, Claire",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3002",
    doi = "10.18653/v1/W16-3002",
    pages = "12--22",
}
@inproceedings{kim-etal-2016-refactoring,
    title = "Refactoring the {G}enia Event Extraction Shared Task Toward a General Framework for {IE}-Driven {KB} Development",
    author = "Kim, Jin-Dong  and
      Wang, Yue  and
      Colic, Nicola  and
      Beak, Seung Han  and
      Kim, Yong Hwan  and
      Song, Min",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3003",
    doi = "10.18653/v1/W16-3003",
    pages = "23--31",
}
@inproceedings{li-etal-2016-litway,
    title = "{L}it{W}ay, Discriminative Extraction for Different Bio-Events",
    author = "Li, Chen  and
      Rao, Zhiqiang  and
      Zhang, Xiangrong",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3004",
    doi = "10.18653/v1/W16-3004",
    pages = "32--41",
}
@inproceedings{lever-jones-2016-verse,
    title = "{VERSE}: Event and Relation Extraction in the {B}io{NLP} 2016 Shared Task",
    author = "Lever, Jake  and
      Jones, Steven JM",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3005",
    doi = "10.18653/v1/W16-3005",
    pages = "42--49",
}
@inproceedings{cook-etal-2016-dictionary,
    title = "A dictionary- and rule-based system for identification of bacteria and habitats in text",
    author = "Cook, Helen V  and
      Pafilis, Evangelos  and
      Jensen, Lars Juhl",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3006",
    doi = "10.18653/v1/W16-3006",
    pages = "50--55",
}
@inproceedings{tiftikci-etal-2016-ontology,
    title = "Ontology-Based Categorization of Bacteria and Habitat Entities using Information Retrieval Techniques",
    author = {Tiftikci, Mert  and
      {\c{S}}ahin, Hakan  and
      B{\"u}y{\"u}k{\"o}z, Berfu  and
      Yay{\i}k{\c{c}}{\i}, Alper  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3007",
    doi = "10.18653/v1/W16-3007",
    pages = "56--63",
}
@inproceedings{grouin-2016-identification,
    title = "Identification of Mentions and Relations between Bacteria and Biotope from {P}ub{M}ed Abstracts",
    author = "Grouin, Cyril",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3008",
    doi = "10.18653/v1/W16-3008",
    pages = "64--72",
}
@inproceedings{mehryary-etal-2016-deep,
    title = "Deep Learning with Minimal Training Data: {T}urku{NLP} Entry in the {B}io{NLP} Shared Task 2016",
    author = {Mehryary, Farrokh  and
      Bj{\"o}rne, Jari  and
      Pyysalo, Sampo  and
      Salakoski, Tapio  and
      Ginter, Filip},
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3009",
    doi = "10.18653/v1/W16-3009",
    pages = "73--81",
}
@inproceedings{c-panyam-etal-2016-seedev,
    title = "{S}ee{D}ev Binary Event Extraction using {SVM}s and a Rich Feature Set",
    author = "C. Panyam, Nagesh  and
      Khirbat, Gitansh  and
      Verspoor, Karin  and
      Cohn, Trevor  and
      Ramamohanarao, Kotagiri",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3010",
    doi = "10.18653/v1/W16-3010",
    pages = "82--87",
}
@inproceedings{lamurias-etal-2016-extraction,
    title = "Extraction of Regulatory Events using Kernel-based Classifiers and Distant Supervision",
    author = "Lamurias, Andre  and
      Rodrigues, Miguel J.  and
      Clarke, Luka A.  and
      Couto, Francisco M.",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3011",
    doi = "10.18653/v1/W16-3011",
    pages = "88--92",
}
@inproceedings{li-etal-2016-dutir,
    title = "{DUTIR} in {B}io{NLP}-{ST} 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations",
    author = "Li, Honglei  and
      Zhang, Jianhai  and
      Wang, Jian  and
      Lin, Hongfei  and
      Yang, Zhihao",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3012",
    doi = "10.18653/v1/W16-3012",
    pages = "93--100",
}
@inproceedings{he-etal-2016-extracting,
    title = "Extracting Biomedical Event Using Feature Selection and Word Representation",
    author = "He, Xinyu  and
      Li, Lishuang  and
      Zheng, Jieqiong  and
      Qin, Meiyue",
    booktitle = "Proceedings of the 4th {B}io{NLP} Shared Task Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-3013",
    doi = "10.18653/v1/W16-3013",
    pages = "101",
}
@proceedings{ws-2016-biomedical,
    title = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Jun-ichi",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2900",
    doi = "10.18653/v1/W16-29",
}
@inproceedings{castano-etal-2016-machine,
    title = "A Machine Learning Approach to Clinical Terms Normalization",
    author = "Casta{\~n}o, Jos{\'e}  and
      Gambarte, Mar{\'\i}a Laura  and
      Park, Hee Joon  and
      del Pilar Avila Williams, Maria  and
      P{\'e}rez, David  and
      Campos, Fernando  and
      Luna, Daniel  and
      Ben{\'\i}tez, Sonia  and
      Berinsky, Hern{\'a}n  and
      Zanetti, Sof{\'\i}a",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2901",
    doi = "10.18653/v1/W16-2901",
    pages = "1--11",
}
@inproceedings{pilehvar-collier-2016-improved,
    title = "Improved Semantic Representation for Domain-Specific Entities",
    author = "Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2902",
    doi = "10.18653/v1/W16-2902",
    pages = "12--16",
}
@inproceedings{shivade-etal-2016-identification,
    title = "Identification, characterization, and grounding of gradable terms in clinical text",
    author = "Shivade, Chaitanya  and
      de Marneffe, Marie-Catherine  and
      Fosler-Lussier, Eric  and
      Lai, Albert M.",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2903",
    doi = "10.18653/v1/W16-2903",
    pages = "17--26",
}
@inproceedings{sheikhshab-etal-2016-graph,
    title = "Graph-based Semi-supervised Gene Mention Tagging",
    author = "Sheikhshab, Golnar  and
      Starks, Elizabeth  and
      Karsan, Aly  and
      Sarkar, Anoop  and
      Birol, Inanc",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2904",
    doi = "10.18653/v1/W16-2904",
    pages = "27--35",
}
@inproceedings{freitag-niekrasz-2016-feature,
    title = "Feature Derivation for Exploitation of Distant Annotation via Pattern Induction against Dependency Parses",
    author = "Freitag, Dayne  and
      Niekrasz, John",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2905",
    doi = "10.18653/v1/W16-2905",
    pages = "36--45",
}
@inproceedings{kilicoglu-2016-inferring,
    title = "Inferring Implicit Causal Relationships in Biomedical Literature",
    author = "Kilicoglu, Halil",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2906",
    doi = "10.18653/v1/W16-2906",
    pages = "46--55",
}
@inproceedings{valenzuela-escarcega-etal-2016-snaptogrid,
    title = "{S}nap{T}o{G}rid: From Statistical to Interpretable Models for Biomedical Information Extraction",
    author = "Valenzuela-Esc{\'a}rcega, Marco A.  and
      Hahn-Powell, Gus  and
      Bell, Dane  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2907",
    doi = "10.18653/v1/W16-2907",
    pages = "56--65",
}
@inproceedings{singh-qi-2016-character,
    title = "Character based String Kernels for Bio-Entity Relation Detection",
    author = "Singh, Ritambhara  and
      Qi, Yanjun",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2908",
    doi = "10.18653/v1/W16-2908",
    pages = "66--71",
}
@inproceedings{siu-etal-2016-disambiguation,
    title = "Disambiguation of entities in {MEDLINE} abstracts by combining {M}e{SH} terms with knowledge",
    author = "Siu, Amy  and
      Ernst, Patrick  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2909",
    doi = "10.18653/v1/W16-2909",
    pages = "72--76",
}
@inproceedings{tulkens-etal-2016-using,
    title = "Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts",
    author = "Tulkens, St{\'e}phan  and
      Suster, Simon  and
      Daelemans, Walter",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2910",
    doi = "10.18653/v1/W16-2910",
    pages = "77--82",
}
@inproceedings{miller-etal-2016-unsupervised,
    title = "Unsupervised Document Classification with Informed Topic Models",
    author = "Miller, Timothy  and
      Dligach, Dmitriy  and
      Savova, Guergana",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2911",
    doi = "10.18653/v1/W16-2911",
    pages = "83--91",
}
@inproceedings{velupillai-etal-2016-vocabulary,
    title = "Vocabulary Development To Support Information Extraction of Substance Abuse from Psychiatry Notes",
    author = "Velupillai, Sumithra  and
      Mowery, Danielle L.  and
      Conway, Mike  and
      Hurdle, John  and
      Kious, Brent",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2912",
    doi = "10.18653/v1/W16-2912",
    pages = "92--101",
}
@inproceedings{hakala-etal-2016-syntactic,
    title = "Syntactic analyses and named entity recognition for {P}ub{M}ed and {P}ub{M}ed Central {---} up-to-the-minute",
    author = "Hakala, Kai  and
      Kaewphan, Suwisa  and
      Salakoski, Tapio  and
      Ginter, Filip",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2913",
    doi = "10.18653/v1/W16-2913",
    pages = "102--107",
}
@inproceedings{lin-etal-2016-improving,
    title = "Improving Temporal Relation Extraction with Training Instance Augmentation",
    author = "Lin, Chen  and
      Miller, Timothy  and
      Dligach, Dmitriy  and
      Bethard, Steven  and
      Savova, Guergana",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2914",
    doi = "10.18653/v1/W16-2914",
    pages = "108--113",
}
@inproceedings{brokos-etal-2016-using,
    title = "Using Centroids of Word Embeddings and Word Mover{'}s Distance for Biomedical Document Retrieval in Question Answering",
    author = "Brokos, Georgios-Ioannis  and
      Malakasiotis, Prodromos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2915",
    doi = "10.18653/v1/W16-2915",
    pages = "114--118",
}
@inproceedings{spranger-etal-2016-measuring,
    title = "Measuring the State of the Art of Automated Pathway Curation Using Graph Algorithms - A Case Study of the m{TOR} Pathway",
    author = "Spranger, Michael  and
      Palaniappan, Sucheendra  and
      Gosh, Samik",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2916",
    doi = "10.18653/v1/W16-2916",
    pages = "119--127",
}
@inproceedings{jiang-etal-2016-construction,
    title = "Construction of a Personal Experience Tweet Corpus for Health Surveillance",
    author = "Jiang, Keyuan  and
      Calix, Ricardo  and
      Gupta, Matrika",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2917",
    doi = "10.18653/v1/W16-2917",
    pages = "128--135",
}
@inproceedings{limsopatham-collier-2016-modelling,
    title = "Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification",
    author = "Limsopatham, Nut  and
      Collier, Nigel",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2918",
    doi = "10.18653/v1/W16-2918",
    pages = "136--140",
}
@inproceedings{yeganova-etal-2016-pubtermvariants,
    title = "{P}ub{T}erm{V}ariants: biomedical term variants and their use for {P}ub{M}ed search",
    author = "Yeganova, Lana  and
      Kim, Won  and
      Kim, Sun  and
      Islamaj Do{\u{g}}an, Rezarta  and
      Liu, Wanli  and
      Comeau, Donald C  and
      Lu, Zhiyong  and
      Wilbur, W John",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2919",
    doi = "10.18653/v1/W16-2919",
    pages = "141--145",
}
@inproceedings{hahn-powell-etal-2016-causal,
    title = "This before That: Causal Precedence in the Biomedical Domain",
    author = "Hahn-Powell, Gus  and
      Bell, Dane  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2920",
    doi = "10.18653/v1/W16-2920",
    pages = "146--155",
}
@inproceedings{cotik-etal-2016-syntactic,
    title = "Syntactic methods for negation detection in radiology reports in {S}panish",
    author = "Cotik, Viviana  and
      Stricker, Vanesa  and
      Vivaldi, Jorge  and
      Rodriguez, Horacio",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2921",
    doi = "10.18653/v1/W16-2921",
    pages = "156--165",
}
@inproceedings{chiu-etal-2016-train,
    title = "How to Train good Word Embeddings for Biomedical {NLP}",
    author = "Chiu, Billy  and
      Crichton, Gamal  and
      Korhonen, Anna  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2922",
    doi = "10.18653/v1/W16-2922",
    pages = "166--174",
}
@inproceedings{connolly-etal-2016-information,
    title = "An Information Foraging Approach to Determining the Number of Relevant Features",
    author = "Connolly, Brian  and
      Glass, Benjamin  and
      Pestian, John",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2923",
    doi = "10.18653/v1/W16-2923",
    pages = "175--180",
}
@inproceedings{chapman-etal-2016-assessing,
    title = "Assessing the Feasibility of an Automated Suggestion System for Communicating Critical Findings from Chest Radiology Reports to Referring Physicians",
    author = "Chapman, Brian E.  and
      Mowery, Danielle L.  and
      Narasimhan, Evan  and
      Patel, Neel  and
      Chapman, Wendy  and
      Heilbrun, Marta",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2924",
    doi = "10.18653/v1/W16-2924",
    pages = "181--185",
}
@inproceedings{kocbek-groza-2016-building,
    title = "Building a dictionary of lexical variants for phenotype descriptors",
    author = "Kocbek, Simon  and
      Groza, Tudor",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2925",
    doi = "10.18653/v1/W16-2925",
    pages = "186--190",
}
@inproceedings{jacobson-dalianis-2016-applying,
    title = "Applying deep learning on electronic health records in {S}wedish to predict healthcare-associated infections",
    author = "Jacobson, Olof  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2926",
    doi = "10.18653/v1/W16-2926",
    pages = "191--195",
}
@inproceedings{gorrell-etal-2016-identifying,
    title = "Identifying First Episodes of Psychosis in Psychiatric Patient Records using Machine Learning",
    author = "Gorrell, Genevieve  and
      Oduola, Sherifat  and
      Roberts, Angus  and
      Craig, Tom  and
      Morgan, Craig  and
      Stewart, Rob",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2927",
    doi = "10.18653/v1/W16-2927",
    pages = "196--205",
}
@inproceedings{sahu-etal-2016-relation,
    title = "Relation extraction from clinical texts using domain invariant convolutional neural network",
    author = "Sahu, Sunil  and
      Anand, Ashish  and
      Oruganty, Krishnadev  and
      Gattu, Mahanandeeshwar",
    booktitle = "Proceedings of the 15th Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2928",
    doi = "10.18653/v1/W16-2928",
    pages = "206--215",
}
@proceedings{ws-2016-argument,
    title = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    editor = "Reed, Chris",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2800",
    doi = "10.18653/v1/W16-28",
}
@inproceedings{addawood-bashir-2016-evidence,
    title = "{``}What Is Your Evidence?{''} A Study of Controversial Topics on Social Media",
    author = "Addawood, Aseel  and
      Bashir, Masooda",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2801",
    doi = "10.18653/v1/W16-2801",
    pages = "1--11",
}
@inproceedings{barker-gaizauskas-2016-summarizing,
    title = "Summarizing Multi-Party Argumentative Conversations in Reader Comment on News",
    author = "Barker, Emma  and
      Gaizauskas, Robert",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2802",
    doi = "10.18653/v1/W16-2802",
    pages = "12--20",
}
@inproceedings{becker-etal-2016-argumentative,
    title = "Argumentative texts and clause types",
    author = "Becker, Maria  and
      Palmer, Alexis  and
      Frank, Anette",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2803",
    doi = "10.18653/v1/W16-2803",
    pages = "21--30",
}
@inproceedings{rajendran-etal-2016-contextual,
    title = "Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews",
    author = "Rajendran, Pavithra  and
      Bollegala, Danushka  and
      Parsons, Simon",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2804",
    doi = "10.18653/v1/W16-2804",
    pages = "31--39",
}
@inproceedings{duthie-etal-2016-cass,
    title = "The {CASS} Technique for Evaluating the Performance of Argument Mining",
    author = "Duthie, Rory  and
      Lawrence, John  and
      Budzynska, Katarzyna  and
      Reed, Chris",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2805",
    doi = "10.18653/v1/W16-2805",
    pages = "40--49",
}
@inproceedings{savelka-ashley-2016-extracting,
    title = "Extracting Case Law Sentences for Argumentation about the Meaning of Statutory Terms",
    author = "{\v{S}}avelka, Jarom{\'\i}r  and
      Ashley, Kevin D.",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2806",
    doi = "10.18653/v1/W16-2806",
    pages = "50--59",
}
@inproceedings{mandya-etal-2016-scrutable,
    title = "Scrutable Feature Sets for Stance Classification",
    author = "Mandya, Angrosh  and
      Siddharthan, Advaith  and
      Wyner, Adam",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2807",
    doi = "10.18653/v1/W16-2807",
    pages = "60--69",
}
@inproceedings{beigman-klebanov-etal-2016-argumentation,
    title = "{A}rgumentation: Content, Structure, and Relationship with Essay Quality",
    author = "Beigman Klebanov, Beata  and
      Stab, Christian  and
      Burstein, Jill  and
      Song, Yi  and
      Gyawali, Binod  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2808",
    doi = "10.18653/v1/W16-2808",
    pages = "70--75",
}
@inproceedings{koreeda-etal-2016-neural,
    title = "Neural Attention Model for Classification of Sentences that Support Promoting/Suppressing Relationship",
    author = "Koreeda, Yuta  and
      Yanase, Toshihiko  and
      Yanai, Kohsuke  and
      Sato, Misa  and
      Niwa, Yoshiki",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2809",
    doi = "10.18653/v1/W16-2809",
    pages = "76--81",
}
@inproceedings{musi-etal-2016-towards,
    title = "Towards Feasible Guidelines for the Annotation of Argument Schemes",
    author = "Musi, Elena  and
      Ghosh, Debanjan  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2810",
    doi = "10.18653/v1/W16-2810",
    pages = "82--93",
}
@inproceedings{petasis-karkaletsis-2016-identifying,
    title = "Identifying Argument Components through {T}ext{R}ank",
    author = "Petasis, Georgios  and
      Karkaletsis, Vangelis",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2811",
    doi = "10.18653/v1/W16-2811",
    pages = "94--102",
}
@inproceedings{peldszus-stede-2016-rhetorical,
    title = "Rhetorical structure and argumentation structure in monologue text",
    author = "Peldszus, Andreas  and
      Stede, Manfred",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2812",
    doi = "10.18653/v1/W16-2812",
    pages = "103--112",
}
@inproceedings{stab-gurevych-2016-recognizing,
    title = "Recognizing the Absence of Opposing Arguments in Persuasive Essays",
    author = "Stab, Christian  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2813",
    doi = "10.18653/v1/W16-2813",
    pages = "113--118",
}
@inproceedings{toledo-ronen-etal-2016-expert,
    title = "Expert Stance Graphs for Computational Argumentation",
    author = "Toledo-Ronen, Orith  and
      Bar-Haim, Roy  and
      Slonim, Noam",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2814",
    doi = "10.18653/v1/W16-2814",
    pages = "119--123",
}
@inproceedings{boltuzic-snajder-2016-fill,
    title = "Fill the Gap! Analyzing Implicit Premises between Claims from Online Debates",
    author = "Boltu{\v{z}}i{\'c}, Filip  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2815",
    doi = "10.18653/v1/W16-2815",
    pages = "124--133",
}
@inproceedings{egan-etal-2016-summarising,
    title = "Summarising the points made in online political debates",
    author = "Egan, Charlie  and
      Siddharthan, Advaith  and
      Wyner, Adam",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2816",
    doi = "10.18653/v1/W16-2816",
    pages = "134--143",
}
@inproceedings{liebeck-etal-2016-airport,
    title = "What to Do with an Airport? Mining Arguments in the {G}erman Online Participation Project Tempelhofer Feld",
    author = "Liebeck, Matthias  and
      Esau, Katharina  and
      Conrad, Stefan",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2817",
    doi = "10.18653/v1/W16-2817",
    pages = "144--153",
}
@inproceedings{skeppstedt-etal-2016-unshared,
    title = "Unshared task: (Dis)agreement in online debates",
    author = "Skeppstedt, Maria  and
      Sahlgren, Magnus  and
      Paradis, Carita  and
      Kerren, Andreas",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2818",
    doi = "10.18653/v1/W16-2818",
    pages = "154--159",
}
@inproceedings{van-son-etal-2016-unshared,
    title = "Unshared Task at the 3rd Workshop on Argument Mining: Perspective Based Local Agreement and Disagreement in Online Debate",
    author = "van Son, Chantal  and
      Caselli, Tommaso  and
      Fokkens, Antske  and
      Maks, Isa  and
      Morante, Roser  and
      Aroyo, Lora  and
      Vossen, Piek",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2819",
    doi = "10.18653/v1/W16-2819",
    pages = "160--165",
}
@inproceedings{wei-etal-2016-preliminary,
    title = "A Preliminary Study of Disputation Behavior in Online Debating Forum",
    author = "Wei, Zhongyu  and
      Xia, Yandi  and
      Li, Chen  and
      Liu, Yang  and
      Stallbohm, Zachary  and
      Li, Yi  and
      Jin, Yang",
    booktitle = "Proceedings of the Third Workshop on Argument Mining ({A}rg{M}ining2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2820",
    doi = "10.18653/v1/W16-2820",
    pages = "166--171",
}
@proceedings{ws-2016-named,
    title = "Proceedings of the Sixth Named Entity Workshop",
    editor = "Duan, Xiangyu  and
      Banchs, Rafael E.  and
      Zhang, Min  and
      Li, Haizhou  and
      Kumaran, A",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2700",
    doi = "10.18653/v1/W16-27",
}
@inproceedings{lin-etal-2016-leveraging,
    title = "Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration",
    author = "Lin, Ying  and
      Pan, Xiaoman  and
      Deri, Aliya  and
      Ji, Heng  and
      Knight, Kevin",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2701",
    doi = "10.18653/v1/W16-2701",
    pages = "1--10",
}
@inproceedings{vexler-minkov-2016-multi,
    title = "Multi-source named entity typing for social media",
    author = "Vexler, Reuth  and
      Minkov, Einat",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2702",
    doi = "10.18653/v1/W16-2702",
    pages = "11--20",
}
@inproceedings{jiang-etal-2016-evaluating,
    title = "Evaluating and Combining Name Entity Recognition Systems",
    author = "Jiang, Ridong  and
      Banchs, Rafael E.  and
      Li, Haizhou",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2703",
    doi = "10.18653/v1/W16-2703",
    pages = "21--27",
}
@inproceedings{druzhkina-etal-2016-german,
    title = "{G}erman {NER} with a Multilingual Rule Based Information Extraction System: Analysis and Issues",
    author = "Druzhkina, Anna  and
      Leontyev, Alexey  and
      Stepanova, Maria",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2704",
    doi = "10.18653/v1/W16-2704",
    pages = "28--33",
}
@inproceedings{copara-zea-etal-2016-spanish,
    title = "{S}panish {NER} with Word Representations and Conditional Random Fields",
    author = "Copara Zea, Jenny Linet  and
      Ochoa Luna, Jose Eduardo  and
      Thorne, Camilo  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2705",
    doi = "10.18653/v1/W16-2705",
    pages = "34--40",
}
@inproceedings{iwakura-etal-2016-constructing,
    title = "Constructing a {J}apanese Basic Named Entity Corpus of Various Genres",
    author = "Iwakura, Tomoya  and
      Komiya, Kanako  and
      Tachibana, Ryuichi",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2706",
    doi = "10.18653/v1/W16-2706",
    pages = "41--46",
}
@inproceedings{halpern-2016-linguistic,
    title = "Linguistic Issues in the Machine Transliteration of {C}hinese, {J}apanese and {A}rabic Names",
    author = "Halpern, Jack",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2707",
    doi = "10.18653/v1/W16-2707",
    pages = "47--48",
}
@inproceedings{duan-etal-2016-whitepaper,
    title = "Whitepaper of {NEWS} 2016 Shared Task on Machine Transliteration",
    author = "Duan, Xiangyu  and
      Zhang, Min  and
      Li, Haizhou  and
      Banchs, Rafael  and
      Kumaran, A",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2708",
    doi = "10.18653/v1/W16-2708",
    pages = "49--57",
}
@inproceedings{duan-etal-2016-report,
    title = "Report of {NEWS} 2016 Machine Transliteration Shared Task",
    author = "Duan, Xiangyu  and
      Banchs, Rafael  and
      Zhang, Min  and
      Li, Haizhou  and
      Kumaran, A.",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2709",
    doi = "10.18653/v1/W16-2709",
    pages = "58--72",
}
@inproceedings{shao-nivre-2016-applying,
    title = "Applying Neural Networks to {E}nglish-{C}hinese Named Entity Transliteration",
    author = "Shao, Yan  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2710",
    doi = "10.18653/v1/W16-2710",
    pages = "73--77",
}
@inproceedings{finch-etal-2016-target,
    title = "Target-Bidirectional Neural Models for Machine Transliteration",
    author = "Finch, Andrew  and
      Liu, Lemao  and
      Wang, Xiaolin  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2711",
    doi = "10.18653/v1/W16-2711",
    pages = "78--82",
}
@inproceedings{nguyen-etal-2016-regulating,
    title = "Regulating Orthography-Phonology Relationship for {E}nglish to {T}hai Transliteration",
    author = "Nguyen, Binh Minh  and
      Ngo, Hoang Gia  and
      Chen, Nancy F.",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2712",
    doi = "10.18653/v1/W16-2712",
    pages = "83--87",
}
@inproceedings{costa-jussa-2016-moses,
    title = "{M}oses-based official baseline for {NEWS} 2016",
    author = "Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Sixth Named Entity Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2713",
    doi = "10.18653/v1/W16-2713",
    pages = "88--90",
}
@proceedings{ws-2016-web,
    title = "Proceedings of the 10th Web as Corpus Workshop",
    editor = {Cook, Paul  and
      Evert, Stefan  and
      Sch{\"a}fer, Roland  and
      Stemle, Egon},
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2600",
    doi = "10.18653/v1/W16-26",
}
@inproceedings{schafer-bildhauer-2016-automatic,
    title = "Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison",
    author = {Sch{\"a}fer, Roland  and
      Bildhauer, Felix},
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2601",
    doi = "10.18653/v1/W16-2601",
    pages = "1--6",
}
@inproceedings{barbaresi-2016-efficient,
    title = "Efficient construction of metadata-enhanced web corpora",
    author = "Barbaresi, Adrien",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2602",
    doi = "10.18653/v1/W16-2602",
    pages = "7--16",
}
@inproceedings{salway-etal-2016-topically,
    title = "Topically-focused Blog Corpora for Multiple Languages",
    author = "Salway, Andrew  and
      Elgesem, Dag  and
      Hofland, Knut  and
      Reigem, {\O}ystein  and
      Steskal, Lubos",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2603",
    doi = "10.18653/v1/W16-2603",
    pages = "17--26",
}
@inproceedings{krause-2016-challenges,
    title = "The Challenges and Joys of Analysing Ongoing Language Change in Web-based Corpora: a Case Study",
    author = "Krause, Anne",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2604",
    doi = "10.18653/v1/W16-2604",
    pages = "27--34",
}
@inproceedings{wurschinger-etal-2016-using,
    title = "Using the Web and Social Media as Corpora for Monitoring the Spread of Neologisms. The case of {`}rapefugee{'}, {`}rapeugee{'}, and {`}rapugee{'}.",
    author = {W{\"u}rschinger, Quirin  and
      Elahi, Mohammad Fazleh  and
      Zhekova, Desislava  and
      Schmid, Hans-J{\"o}rg},
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2605",
    doi = "10.18653/v1/W16-2605",
    pages = "35--43",
}
@inproceedings{beisswenger-etal-2016-empirist,
    title = "{E}mpiri{ST} 2015: A Shared Task on the Automatic Linguistic Annotation of Computer-Mediated Communication and Web Corpora",
    author = {Bei{\ss}wenger, Michael  and
      Bartsch, Sabine  and
      Evert, Stefan  and
      W{\"u}rzner, Kay-Michael},
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2606",
    doi = "10.18653/v1/W16-2606",
    pages = "44--56",
}
@inproceedings{proisl-uhrig-2016-somajo,
    title = "{S}o{M}a{J}o: State-of-the-art tokenization for {G}erman web and social media texts",
    author = "Proisl, Thomas  and
      Uhrig, Peter",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2607",
    doi = "10.18653/v1/W16-2607",
    pages = "57--62",
}
@inproceedings{prange-etal-2016-uds,
    title = "{U}d{S}-(retrain|distributional|surface): Improving {POS} Tagging for {OOV} Words in {G}erman {CMC} and Web Data",
    author = "Prange, Jakob  and
      Horbach, Andrea  and
      Thater, Stefan",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2608",
    doi = "10.18653/v1/W16-2608",
    pages = "63--71",
}
@inproceedings{mendels-etal-2016-babler,
    title = "Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search",
    author = "Mendels, Gideon  and
      Cooper, Erica  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2609",
    doi = "10.18653/v1/W16-2609",
    pages = "72--81",
}
@inproceedings{ljubesic-fiser-2016-global,
    title = "A Global Analysis of Emoji Usage",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Fi{\v{s}}er, Darja",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2610",
    doi = "10.18653/v1/W16-2610",
    pages = "82--89",
}
@inproceedings{dalan-sharoff-2016-genre,
    title = "Genre classification for a corpus of academic webpages",
    author = "Dalan, Erika  and
      Sharoff, Serge",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2611",
    doi = "10.18653/v1/W16-2611",
    pages = "90--98",
}
@inproceedings{schafer-2016-bias,
    title = "On Bias-free Crawling and Representative Web Corpora",
    author = {Sch{\"a}fer, Roland},
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2612",
    doi = "10.18653/v1/W16-2612",
    pages = "99--105",
}
@inproceedings{remus-etal-2016-empirist,
    title = "{E}mpiri{ST}: {AIPHES} - Robust Tokenization and {POS}-Tagging for Different Genres",
    author = "Remus, Steffen  and
      Hintz, Gerold  and
      Biemann, Chris  and
      Meyer, Christian M.  and
      Benikova, Darina  and
      Eckle-Kohler, Judith  and
      Mieskes, Margot  and
      Arnold, Thomas",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2613",
    doi = "10.18653/v1/W16-2613",
    pages = "106--114",
}
@inproceedings{stemle-2016-bot,
    title = "bot.zen @ {E}mpiri{ST} 2015 - A minimally-deep learning {P}o{S}-tagger (trained for {G}erman {CMC} and Web data)",
    author = "Stemle, Egon",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2614",
    doi = "10.18653/v1/W16-2614",
    pages = "115--119",
}
@inproceedings{horsmann-zesch-2016-ltl,
    title = "{LTL}-{UDE} @ {E}mpiri{ST} 2015: Tokenization and {P}o{S} Tagging of Social Media Text",
    author = "Horsmann, Tobias  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 10th Web as Corpus Workshop",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2615",
    doi = "10.18653/v1/W16-2615",
    pages = "120--126",
}
@proceedings{ws-2016-evaluating,
    title = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2500",
    doi = "10.18653/v1/W16-25",
}
@inproceedings{chiu-etal-2016-intrinsic,
    title = "Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance",
    author = "Chiu, Billy  and
      Korhonen, Anna  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2501",
    doi = "10.18653/v1/W16-2501",
    pages = "1--6",
}
@inproceedings{batchkarov-etal-2016-critique,
    title = "A critique of word similarity as a method for evaluating distributional semantic models",
    author = "Batchkarov, Miroslav  and
      Kober, Thomas  and
      Reffin, Jeremy  and
      Weeds, Julie  and
      Weir, David",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2502",
    doi = "10.18653/v1/W16-2502",
    pages = "7--12",
}
@inproceedings{linzen-2016-issues,
    title = "Issues in evaluating semantic spaces using word analogies",
    author = "Linzen, Tal",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2503",
    doi = "10.18653/v1/W16-2503",
    pages = "13--18",
}
@inproceedings{nayak-etal-2016-evaluating,
    title = "Evaluating Word Embeddings Using a Representative Suite of Practical Tasks",
    author = "Nayak, Neha  and
      Angeli, Gabor  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2504",
    doi = "10.18653/v1/W16-2504",
    pages = "19--23",
}
@inproceedings{mostafazadeh-etal-2016-story,
    title = "Story Cloze Evaluator: Vector Space Representation Evaluation by Predicting What Happens Next",
    author = "Mostafazadeh, Nasrin  and
      Vanderwende, Lucy  and
      Yih, Wen-tau  and
      Kohli, Pushmeet  and
      Allen, James",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2505",
    doi = "10.18653/v1/W16-2505",
    pages = "24--29",
}
@inproceedings{faruqui-etal-2016-problems,
    title = "Problems With Evaluation of Word Embeddings Using Word Similarity Tasks",
    author = "Faruqui, Manaal  and
      Tsvetkov, Yulia  and
      Rastogi, Pushpendre  and
      Dyer, Chris",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2506",
    doi = "10.18653/v1/W16-2506",
    pages = "30--35",
}
@inproceedings{gladkova-drozd-2016-intrinsic,
    title = "Intrinsic Evaluations of Word Embeddings: What Can We Do Better?",
    author = "Gladkova, Anna  and
      Drozd, Aleksandr",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2507",
    doi = "10.18653/v1/W16-2507",
    pages = "36--42",
}
@inproceedings{camacho-collados-navigli-2016-find,
    title = "Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations",
    author = "Camacho-Collados, Jos{\'e}  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2508",
    doi = "10.18653/v1/W16-2508",
    pages = "43--50",
}
@inproceedings{krebs-paperno-2016-capturing,
    title = "Capturing Discriminative Attributes in a Distributional Space: Task Proposal",
    author = "Krebs, Alicia  and
      Paperno, Denis",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2509",
    doi = "10.18653/v1/W16-2509",
    pages = "51--54",
}
@inproceedings{liza-grzes-2016-improved,
    title = "An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods",
    author = "Liza, Farhana Ferdousi  and
      Grze{\'s}, Marek",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2510",
    doi = "10.18653/v1/W16-2510",
    pages = "55--61",
}
@inproceedings{ghannay-etal-2016-evaluation,
    title = "Evaluation of acoustic word embeddings",
    author = "Ghannay, Sahar  and
      Est{\`e}ve, Yannick  and
      Camelin, Nathalie  and
      Deleglise, Paul",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2511",
    doi = "10.18653/v1/W16-2511",
    pages = "62--66",
}
@inproceedings{kohn-2016-evaluating,
    title = "Evaluating Embeddings using Syntax-based Classification Tasks as a Proxy for Parser Performance",
    author = {K{\"o}hn, Arne},
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2512",
    doi = "10.18653/v1/W16-2512",
    pages = "67--71",
}
@inproceedings{ettinger-linzen-2016-evaluating,
    title = "Evaluating vector space models using human semantic priming results",
    author = "Ettinger, Allyson  and
      Linzen, Tal",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2513",
    doi = "10.18653/v1/W16-2513",
    pages = "72--77",
}
@inproceedings{acs-kornai-2016-evaluating,
    title = "Evaluating embeddings on dictionary-based similarity",
    author = "{\'A}cs, Judit  and
      Kornai, Andr{\'a}s",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2514",
    doi = "10.18653/v1/W16-2514",
    pages = "78--82",
}
@inproceedings{borbely-etal-2016-evaluating,
    title = "Evaluating multi-sense embeddings for semantic resolution monolingually and in word translation",
    author = "Borb{\'e}ly, G{\'a}bor  and
      Makrai, M{\'a}rton  and
      Nemeskey, D{\'a}vid M{\'a}rk  and
      Kornai, Andr{\'a}s",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2515",
    doi = "10.18653/v1/W16-2515",
    pages = "83--89",
}
@inproceedings{seyed-2016-subsumption,
    title = "Subsumption Preservation as a Comparative Measure for Evaluating Sense-Directed Embeddings",
    author = "Seyed, Ali",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2516",
    doi = "10.18653/v1/W16-2516",
    pages = "90--93",
}
@inproceedings{saphra-lopez-2016-evaluating,
    title = "Evaluating Informal-Domain Word Representations With {U}rban{D}ictionary",
    author = "Saphra, Naomi  and
      Lopez, Adam",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2517",
    doi = "10.18653/v1/W16-2517",
    pages = "94--98",
}
@inproceedings{sayeed-etal-2016-thematic,
    title = "Thematic fit evaluation: an aspect of selectional preferences",
    author = "Sayeed, Asad  and
      Greenberg, Clayton  and
      Demberg, Vera",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2518",
    doi = "10.18653/v1/W16-2518",
    pages = "99--105",
}
@inproceedings{avraham-goldberg-2016-improving,
    title = "Improving Reliability of Word Similarity Evaluation by Redesigning Annotation Task and Performance Measure",
    author = "Avraham, Oded  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2519",
    doi = "10.18653/v1/W16-2519",
    pages = "106--110",
}
@inproceedings{tsvetkov-etal-2016-correlation,
    title = "Correlation-based Intrinsic Evaluation of Word Vector Representations",
    author = "Tsvetkov, Yulia  and
      Faruqui, Manaal  and
      Dyer, Chris",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2520",
    doi = "10.18653/v1/W16-2520",
    pages = "111--115",
}
@inproceedings{sogaard-2016-evaluating,
    title = "Evaluating word embeddings with f{MRI} and eye-tracking",
    author = "S{\o}gaard, Anders",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2521",
    doi = "10.18653/v1/W16-2521",
    pages = "116--121",
}
@inproceedings{parasca-etal-2016-defining,
    title = "Defining Words with Words: Beyond the Distributional Hypothesis",
    author = "Parasca, Iuliana-Elena  and
      Rauter, Andreas Lukas  and
      Roper, Jack  and
      Rusinov, Aleksandar  and
      Bouchard, Guillaume  and
      Riedel, Sebastian  and
      Stenetorp, Pontus",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2522",
    doi = "10.18653/v1/W16-2522",
    pages = "122--126",
}
@inproceedings{milajevs-griffiths-2016-proposal,
    title = "A Proposal for Linguistic Similarity Datasets Based on Commonality Lists",
    author = "Milajevs, Dmitrijs  and
      Griffiths, Sascha",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2523",
    doi = "10.18653/v1/W16-2523",
    pages = "127--133",
}
@inproceedings{ettinger-etal-2016-probing,
    title = "Probing for semantic evidence of composition by means of simple classification tasks",
    author = "Ettinger, Allyson  and
      Elgohary, Ahmed  and
      Resnik, Philip",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2524",
    doi = "10.18653/v1/W16-2524",
    pages = "134--139",
}
@inproceedings{rimell-vecchi-2016-sledded,
    title = "{SLEDDED}: A Proposed Dataset of Event Descriptions for Evaluating Phrase Representations",
    author = "Rimell, Laura  and
      Vecchi, Eva Maria",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2525",
    doi = "10.18653/v1/W16-2525",
    pages = "140--144",
}
@inproceedings{baumel-etal-2016-sentence,
    title = "Sentence Embedding Evaluation Using Pyramid Annotation",
    author = "Baumel, Tal  and
      Cohen, Raphael  and
      Elhadad, Michael",
    booktitle = "Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2526",
    doi = "10.18653/v1/W16-2526",
    pages = "145--149",
}
@proceedings{ws-2016-sigfsm,
    title = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    editor = {Jurish, Bryan  and
      Maletti, Andreas  and
      W{\"u}rzner, Kay-Michael  and
      Springmann, Uwe},
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2400",
    doi = "10.18653/v1/W16-24",
}
@inproceedings{dietze-2016-equivalences,
    title = "Equivalences between Ranked and Unranked Weighted Tree Automata via Binarization",
    author = "Dietze, Toni",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2401",
    doi = "10.18653/v1/W16-2401",
    pages = "1--10",
}
@inproceedings{teichmann-etal-2016-adaptive,
    title = "Adaptive Importance Sampling from Finite State Automata",
    author = "Teichmann, Christoph  and
      Wansing, Kasimir  and
      Koller, Alexander",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2402",
    doi = "10.18653/v1/W16-2402",
    pages = "11--20",
}
@inproceedings{nederhof-2016-transition,
    title = "Transition-based dependency parsing as latent-variable constituent parsing",
    author = "Nederhof, Mark-Jan",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2403",
    doi = "10.18653/v1/W16-2403",
    pages = "21--31",
}
@inproceedings{allauzen-etal-2016-distributed,
    title = "Distributed representation and estimation of {WFST}-based n-gram models",
    author = "Allauzen, Cyril  and
      Riley, Michael  and
      Roark, Brian",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2404",
    doi = "10.18653/v1/W16-2404",
    pages = "32--41",
}
@inproceedings{forsberg-hulden-2016-learning,
    title = "Learning Transducer Models for Morphological Analysis from Example Inflections",
    author = "Forsberg, Markus  and
      Hulden, Mans",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2405",
    doi = "10.18653/v1/W16-2405",
    pages = "42--50",
}
@inproceedings{silfverberg-etal-2016-data,
    title = "Data-Driven Spelling Correction using Weighted Finite-State Methods",
    author = "Silfverberg, Miikka  and
      Kauppinen, Pekka  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2406",
    doi = "10.18653/v1/W16-2406",
    pages = "51--59",
}
@inproceedings{drewes-etal-2016-em,
    title = "{EM}-Training for Weighted Aligned Hypergraph Bimorphisms",
    author = "Drewes, Frank  and
      Gebhardt, Kilian  and
      Vogler, Heiko",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2407",
    doi = "10.18653/v1/W16-2407",
    pages = "60--69",
}
@inproceedings{asaadi-rudolph-2016-correspondence,
    title = "On the Correspondence between Compositional Matrix-Space Models of Language and Weighted Automata",
    author = "Asaadi, Shima  and
      Rudolph, Sebastian",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2408",
    doi = "10.18653/v1/W16-2408",
    pages = "70--74",
}
@inproceedings{gorman-2016-pynini,
    title = "{P}ynini: A Python library for weighted finite-state grammar compilation",
    author = "Gorman, Kyle",
    booktitle = "Proceedings of the {SIGFSM} Workshop on Statistical {NLP} and Weighted Automata",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2409",
    doi = "10.18653/v1/W16-2409",
    pages = "75--80",
}
@proceedings{ws-2016-machine-translation,
    title = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    editor = {Bojar, Ond{\v{r}}ej  and
      Buck, Christian  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Guillou, Liane  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Pecina, Pavel  and
      Popel, Martin  and
      Koehn, Philipp  and
      Monz, Christof  and
      Negri, Matteo  and
      Post, Matt  and
      Specia, Lucia  and
      Verspoor, Karin  and
      Tiedemann, J{\"o}rg  and
      Turchi, Marco},
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2300",
    doi = "10.18653/v1/W16-2300",
}
@inproceedings{bojar-etal-2016-findings,
    title = "Findings of the 2016 Conference on Machine Translation",
    author = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Graham, Yvette  and
      Haddow, Barry  and
      Huck, Matthias  and
      Jimeno Yepes, Antonio  and
      Koehn, Philipp  and
      Logacheva, Varvara  and
      Monz, Christof  and
      Negri, Matteo  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Popel, Martin  and
      Post, Matt  and
      Rubino, Raphael  and
      Scarton, Carolina  and
      Specia, Lucia  and
      Turchi, Marco  and
      Verspoor, Karin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2301",
    doi = "10.18653/v1/W16-2301",
    pages = "131--198",
}
@inproceedings{bojar-etal-2016-results,
    title = "Results of the {WMT}16 Metrics Shared Task",
    author = "Bojar, Ond{\v{r}}ej  and
      Graham, Yvette  and
      Kamran, Amir  and
      Stanojevi{\'c}, Milo{\v{s}}",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2302",
    doi = "10.18653/v1/W16-2302",
    pages = "199--231",
}
@inproceedings{jawaid-etal-2016-results,
    title = "Results of the {WMT}16 Tuning Shared Task",
    author = "Jawaid, Bushra  and
      Kamran, Amir  and
      Stanojevi{\'c}, Milo{\v{s}}  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2303",
    doi = "10.18653/v1/W16-2303",
    pages = "232--238",
}
@inproceedings{allauzen-etal-2016-limsi,
    title = "{LIMSI}@{WMT}{'}16: Machine Translation of News",
    author = "Allauzen, Alexandre  and
      Aufrant, Lauriane  and
      Burlot, Franck  and
      Lacroix, Oph{\'e}lie  and
      Knyazeva, Elena  and
      Lavergne, Thomas  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2304",
    doi = "10.18653/v1/W16-2304",
    pages = "239--245",
}
@inproceedings{bektas-etal-2016-tubitak,
    title = {{T{\"U}B{\.I}TAK} {SMT} System Submission for {WMT}2016},
    author = "Bekta{\c{s}}, Emre  and
      Yilmaz, Ertu{\u{g}}rul  and
      Mermer, Co{\c{s}}kun  and
      Durgar El-Kahlout, {\.I}lknur",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2305",
    doi = "10.18653/v1/W16-2305",
    pages = "246--251",
}
@inproceedings{bicici-2016-parfda,
    title = "{P}ar{FDA} for Instance Selection for Statistical Machine Translation",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2306",
    doi = "10.18653/v1/W16-2306",
    pages = "252--258",
}
@inproceedings{blain-etal-2016-sheffield,
    title = "{S}heffield Systems for the {E}nglish-{R}omanian {WMT} Translation Task",
    author = "Blain, Fr{\'e}d{\'e}ric  and
      Song, Xingyi  and
      Specia, Lucia",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2307",
    doi = "10.18653/v1/W16-2307",
    pages = "259--263",
}
@inproceedings{bradbury-socher-2016-metamind,
    title = "{M}eta{M}ind Neural Machine Translation System for {WMT} 2016",
    author = "Bradbury, James  and
      Socher, Richard",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2308",
    doi = "10.18653/v1/W16-2308",
    pages = "264--267",
}
@inproceedings{chung-etal-2016-nyu,
    title = "{NYU}-{MILA} Neural Machine Translation Systems for {WMT}{'}16",
    author = "Chung, Junyoung  and
      Cho, Kyunghyun  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2309",
    doi = "10.18653/v1/W16-2309",
    pages = "268--271",
}
@inproceedings{ding-etal-2016-jhu,
    title = "The {JHU} Machine Translation Systems for {WMT} 2016",
    author = "Ding, Shuoyang  and
      Duh, Kevin  and
      Khayrallah, Huda  and
      Koehn, Philipp  and
      Post, Matt",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2310",
    doi = "10.18653/v1/W16-2310",
    pages = "272--280",
}
@inproceedings{dvorkovich-etal-2016-yandex,
    title = "Yandex School of Data Analysis approach to {E}nglish-{T}urkish translation at {WMT}16 News Translation Task",
    author = "Dvorkovich, Anton  and
      Gubanov, Sergey  and
      Galinskaya, Irina",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2311",
    doi = "10.18653/v1/W16-2311",
    pages = "281--288",
}
@inproceedings{gronroos-etal-2016-hybrid,
    title = "Hybrid Morphological Segmentation for Phrase-Based Machine Translation",
    author = {Gr{\"o}nroos, Stig-Arne  and
      Virpioja, Sami  and
      Kurimo, Mikko},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2312",
    doi = "10.18653/v1/W16-2312",
    pages = "289--295",
}
@inproceedings{gwinnup-etal-2016-afrl,
    title = "The {AFRL}-{MITLL} {WMT}16 News-Translation Task Systems",
    author = "Gwinnup, Jeremy  and
      Anderson, Tim  and
      Erdmann, Grant  and
      Young, Katherine  and
      Kazi, Michaeel  and
      Salesky, Elizabeth  and
      Thompson, Brian",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2313",
    doi = "10.18653/v1/W16-2313",
    pages = "296--302",
}
@inproceedings{ha-etal-2016-karlsruhe,
    title = "The Karlsruhe Institute of Technology Systems for the News Translation Task in {WMT} 2016",
    author = "Ha, Thanh-Le  and
      Cho, Eunah  and
      Niehues, Jan  and
      Mediani, Mohammed  and
      Sperber, Matthias  and
      Allauzen, Alexandre  and
      Waibel, Alexander",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2314",
    doi = "10.18653/v1/W16-2314",
    pages = "303--310",
}
@inproceedings{huck-etal-2016-edinburgh,
    title = "The {E}dinburgh/{LMU} Hierarchical Machine Translation System for {WMT} 2016",
    author = "Huck, Matthias  and
      Fraser, Alexander  and
      Haddow, Barry",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2315",
    doi = "10.18653/v1/W16-2315",
    pages = "311--318",
}
@inproceedings{junczys-dowmunt-etal-2016-amu,
    title = "The {AMU}-{UEDIN} Submission to the {WMT}16 News Translation Task: Attention-based {NMT} Models as Feature Functions in Phrase-based {SMT}",
    author = "Junczys-Dowmunt, Marcin  and
      Dwojak, Tomasz  and
      Sennrich, Rico",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2316",
    doi = "10.18653/v1/W16-2316",
    pages = "319--325",
}
@inproceedings{lo-etal-2016-nrc,
    title = "{NRC} {R}ussian-{E}nglish Machine Translation System for {WMT} 2016",
    author = "Lo, Chi-kiu  and
      Cherry, Colin  and
      Foster, George  and
      Stewart, Darlene  and
      Islam, Rabib  and
      Kazantseva, Anna  and
      Kuhn, Roland",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2317",
    doi = "10.18653/v1/W16-2317",
    pages = "326--332",
}
@inproceedings{marecek-2016-merged,
    title = "Merged bilingual trees based on {U}niversal {D}ependencies in Machine Translation",
    author = "Mare{\v{c}}ek, David",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2318",
    doi = "10.18653/v1/W16-2318",
    pages = "333--338",
}
@inproceedings{molchanov-bykov-2016-promt,
    title = "{PROMT} Translation Systems for {WMT} 2016 Translation Tasks",
    author = "Molchanov, Alexander  and
      Bykov, Fedor",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2319",
    doi = "10.18653/v1/W16-2319",
    pages = "339--343",
}
@inproceedings{peter-etal-2016-qt21,
    title = "The {QT}21/{H}im{L} Combined Machine Translation System",
    author = "Peter, Jan-Thorsten  and
      Alkhouli, Tamer  and
      Ney, Hermann  and
      Huck, Matthias  and
      Braune, Fabienne  and
      Fraser, Alexander  and
      Tamchyna, Ale{\v{s}}  and
      Bojar, Ond{\v{r}}ej  and
      Haddow, Barry  and
      Sennrich, Rico  and
      Blain, Fr{\'e}d{\'e}ric  and
      Specia, Lucia  and
      Niehues, Jan  and
      Waibel, Alex  and
      Allauzen, Alexandre  and
      Aufrant, Lauriane  and
      Burlot, Franck  and
      Knyazeva, Elena  and
      Lavergne, Thomas  and
      Yvon, Fran{\c{c}}ois  and
      Pinnis, M{\=a}rcis  and
      Frank, Stella",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2320",
    doi = "10.18653/v1/W16-2320",
    pages = "344--355",
}
@inproceedings{peter-etal-2016-rwth,
    title = "The {RWTH} {A}achen {U}niversity {E}nglish-{R}omanian Machine Translation System for {WMT} 2016",
    author = "Peter, Jan-Thorsten  and
      Alkhouli, Tamer  and
      Guta, Andreas  and
      Ney, Hermann",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2321",
    doi = "10.18653/v1/W16-2321",
    pages = "356--361",
}
@inproceedings{sanchez-cartagena-toral-2016-abu,
    title = "{A}bu-{M}a{T}ran at {WMT} 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences",
    author = "S{\'a}nchez-Cartagena, V{\'\i}ctor M.  and
      Toral, Antonio",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2322",
    doi = "10.18653/v1/W16-2322",
    pages = "362--370",
}
@inproceedings{sennrich-etal-2016-edinburgh,
    title = "{E}dinburgh Neural Machine Translation Systems for {WMT} 16",
    author = "Sennrich, Rico  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2323",
    doi = "10.18653/v1/W16-2323",
    pages = "371--376",
}
@inproceedings{stahlberg-etal-2016-edit,
    title = "The Edit Distance Transducer in Action: The {U}niversity of {C}ambridge {E}nglish-{G}erman System at {WMT}16",
    author = "Stahlberg, Felix  and
      Hasler, Eva  and
      Byrne, Bill",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2324",
    doi = "10.18653/v1/W16-2324",
    pages = "377--384",
}
@inproceedings{tamchyna-etal-2016-cuni,
    title = "{CUNI}-{LMU} Submissions in {WMT}2016: Chimera Constrained and Beaten",
    author = "Tamchyna, Ale{\v{s}}  and
      Sudarikov, Roman  and
      Bojar, Ond{\v{r}}ej  and
      Fraser, Alexander",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2325",
    doi = "10.18653/v1/W16-2325",
    pages = "385--390",
}
@inproceedings{tiedemann-etal-2016-phrase,
    title = "Phrase-Based {SMT} for {F}innish with More Data, Better Models and Alternative Alignment and Translation Tools",
    author = {Tiedemann, J{\"o}rg  and
      Cap, Fabienne  and
      Kanerva, Jenna  and
      Ginter, Filip  and
      Stymne, Sara  and
      {\"O}stling, Robert  and
      Weller-Di Marco, Marion},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2326",
    doi = "10.18653/v1/W16-2326",
    pages = "391--398",
}
@inproceedings{williams-etal-2016-edinburghs,
    title = "{E}dinburgh{'}s Statistical Machine Translation Systems for {WMT}16",
    author = "Williams, Philip  and
      Sennrich, Rico  and
      N{\u{a}}dejde, Maria  and
      Huck, Matthias  and
      Haddow, Barry  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2327",
    doi = "10.18653/v1/W16-2327",
    pages = "399--410",
}
@inproceedings{wolk-marasek-2016-pjait,
    title = "{PJAIT} Systems for the {WMT} 2016",
    author = "Wolk, Krzysztof  and
      Marasek, Krzysztof",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2328",
    doi = "10.18653/v1/W16-2328",
    pages = "411--414",
}
@inproceedings{avramidis-etal-2016-dfkis,
    title = "{DFKI}{'}s system for {WMT}16 {IT}-domain task, including analysis of systematic errors",
    author = "Avramidis, Eleftherios  and
      Burchardt, Aljoscha  and
      Macketanz, Vivien  and
      Srivastava, Ankit",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2329",
    doi = "10.18653/v1/W16-2329",
    pages = "415--422",
}
@inproceedings{cuong-etal-2016-illc,
    title = "{ILLC}-{U}v{A} Adaptation System (Scorpio) at {WMT}{'}16 {IT}-{DOMAIN} Task",
    author = "Cuong, Hoang  and
      Frank, Stella  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2330",
    doi = "10.18653/v1/W16-2330",
    pages = "423--427",
}
@inproceedings{duma-menzel-2016-data,
    title = "Data Selection for {IT} Texts using Paragraph Vector",
    author = "Duma, Mirela-Stefania  and
      Menzel, Wolfgang",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2331",
    doi = "10.18653/v1/W16-2331",
    pages = "428--434",
}
@inproceedings{gaudio-etal-2016-smt,
    title = "{SMT} and Hybrid systems of the {QTL}eap project in the {WMT}16 {IT}-task",
    author = "Gaudio, Rosa  and
      Labaka, Gorka  and
      Agirre, Eneko  and
      Osenova, Petya  and
      Simov, Kiril  and
      Popel, Martin  and
      Oele, Dieke  and
      van Noord, Gertjan  and
      Gomes, Lu{\'\i}s  and
      Ant{\'o}nio Rodrigues, Jo{\~a}o  and
      Neale, Steven  and
      Silva, Jo{\~a}o  and
      Querido, Andreia  and
      Rendeiro, Nuno  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2332",
    doi = "10.18653/v1/W16-2332",
    pages = "435--441",
}
@inproceedings{pahari-etal-2016-ju,
    title = "{JU-USAAR}: A Domain Adaptive {MT} System",
    author = "Pahari, Koushik  and
      Kuila, Alapan  and
      Pal, Santanu  and
      Naskar, Sudip Kumar  and
      Bandyopadhyay, Sivaji  and
      van Genabith, Josef",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2333",
    doi = "10.18653/v1/W16-2333",
    pages = "442--448",
}
@inproceedings{rosa-etal-2016-dictionary,
    title = "Dictionary-based Domain Adaptation of {MT} Systems without Retraining",
    author = "Rosa, Rudolf  and
      Sudarikov, Roman  and
      Nov{\'a}k, Michal  and
      Popel, Martin  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2334",
    doi = "10.18653/v1/W16-2334",
    pages = "449--455",
}
@inproceedings{aires-etal-2016-english,
    title = "{E}nglish-{P}ortuguese Biomedical Translation Task Using a Genuine Phrase-Based Statistical Machine Translation Approach",
    author = "Aires, Jos{\'e}  and
      Lopes, Gabriel  and
      Gomes, Lu{\'\i}s",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2335",
    doi = "10.18653/v1/W16-2335",
    pages = "456--462",
}
@inproceedings{costa-jussa-etal-2016-talp,
    title = "The {TALP}{--}{UPC} {S}panish{--}{E}nglish {WMT} Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System",
    author = "Costa-juss{\`a}, Marta R.  and
      Espa{\~n}a-Bonet, Cristina  and
      Madhyastha, Pranava  and
      Escolano, Carlos  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2336",
    doi = "10.18653/v1/W16-2336",
    pages = "463--468",
}
@inproceedings{ive-etal-2016-limsis,
    title = "{LIMSI}{'}s Contribution to the {WMT}{'}16 Biomedical Translation Task",
    author = "Ive, Julia  and
      Max, Aur{\'e}lien  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2337",
    doi = "10.18653/v1/W16-2337",
    pages = "469--476",
}
@inproceedings{perez-de-vinaspre-labaka-2016-ixa,
    title = "{IXA} Biomedical Translation System at {WMT}16 Biomedical Translation Task",
    author = "Perez-de-Vi{\~n}aspre, Olatz  and
      Labaka, Gorka",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2338",
    doi = "10.18653/v1/W16-2338",
    pages = "477--482",
}
@inproceedings{fomicheva-etal-2016-cobaltf,
    title = "{C}obalt{F}: A Fluent Metric for {MT} Evaluation",
    author = "Fomicheva, Marina  and
      Bel, N{\'u}ria  and
      Specia, Lucia  and
      da Cunha, Iria  and
      Malinovskiy, Anton",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2339",
    doi = "10.18653/v1/W16-2339",
    pages = "483--490",
}
@inproceedings{mccaffery-nederhof-2016-dted,
    title = "{DTED}: Evaluation of Machine Translation Structure Using Dependency Parsing and Tree Edit Distance",
    author = "McCaffery, Martin  and
      Nederhof, Mark-Jan",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2340",
    doi = "10.18653/v1/W16-2340",
    pages = "491--498",
}
@inproceedings{popovic-2016-chrf,
    title = "chr{F} deconstructed: beta parameters and n-gram weights",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2341",
    doi = "10.18653/v1/W16-2341",
    pages = "499--504",
}
@inproceedings{wang-etal-2016-character,
    title = "{C}harac{T}er: Translation Edit Rate on Character Level",
    author = "Wang, Weiyue  and
      Peter, Jan-Thorsten  and
      Rosendahl, Hendrik  and
      Ney, Hermann",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2342",
    doi = "10.18653/v1/W16-2342",
    pages = "505--510",
}
@inproceedings{zhang-etal-2016-extract,
    title = "Extract Domain-specific Paraphrase from Monolingual Corpus for Automatic Evaluation of Machine Translation",
    author = "Zhang, Lilin  and
      Weng, Zhen  and
      Xiao, Wenyan  and
      Wan, Jianyi  and
      Chen, Zhiming  and
      Tan, Yiming  and
      Li, Maoxi  and
      Wang, Mingwen",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2343",
    doi = "10.18653/v1/W16-2343",
    pages = "511--517",
}
@inproceedings{kocur-bojar-2016-particle,
    title = "Particle Swarm Optimization Submission for {WMT}16 Tuning Task",
    author = "Kocur, Viktor  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2344",
    doi = "10.18653/v1/W16-2344",
    pages = "518--524",
}
@inproceedings{guillou-etal-2016-findings,
    title = "Findings of the 2016 {WMT} Shared Task on Cross-lingual Pronoun Prediction",
    author = {Guillou, Liane  and
      Hardmeier, Christian  and
      Nakov, Preslav  and
      Stymne, Sara  and
      Tiedemann, J{\"o}rg  and
      Versley, Yannick  and
      Cettolo, Mauro  and
      Webber, Bonnie  and
      Popescu-Belis, Andrei},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2345",
    doi = "10.18653/v1/W16-2345",
    pages = "525--542",
}
@inproceedings{specia-etal-2016-shared,
    title = "A Shared Task on Multimodal Machine Translation and Crosslingual Image Description",
    author = "Specia, Lucia  and
      Frank, Stella  and
      Sima{'}an, Khalil  and
      Elliott, Desmond",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2346",
    doi = "10.18653/v1/W16-2346",
    pages = "543--553",
}
@inproceedings{buck-koehn-2016-findings,
    title = "Findings of the {WMT} 2016 Bilingual Document Alignment Shared Task",
    author = "Buck, Christian  and
      Koehn, Philipp",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2347",
    doi = "10.18653/v1/W16-2347",
    pages = "554--563",
}
@inproceedings{bawden-2016-cross,
    title = "Cross-lingual Pronoun Prediction with Linguistically Informed Features",
    author = "Bawden, Rachel",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2348",
    doi = "10.18653/v1/W16-2348",
    pages = "564--570",
}
@inproceedings{dabre-etal-2016-kyoto,
    title = "The {K}yoto {U}niversity Cross-Lingual Pronoun Translation System",
    author = "Dabre, Raj  and
      Puzikov, Yevgeniy  and
      Cromieres, Fabien  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2349",
    doi = "10.18653/v1/W16-2349",
    pages = "571--575",
}
@inproceedings{hardmeier-2016-pronoun,
    title = "Pronoun Prediction with Latent Anaphora Resolution",
    author = "Hardmeier, Christian",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2350",
    doi = "10.18653/v1/W16-2350",
    pages = "576--580",
}
@inproceedings{loaiciga-etal-2016-disambiguation,
    title = "It-disambiguation and source-aware language models for cross-lingual pronoun prediction",
    author = "Lo{\'a}iciga, Sharid  and
      Guillou, Liane  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2351",
    doi = "10.18653/v1/W16-2351",
    pages = "581--588",
}
@inproceedings{luong-popescu-belis-2016-pronoun,
    title = "Pronoun Language Model and Grammatical Heuristics for Aiding Pronoun Prediction",
    author = "Luong, Ngoc Quang  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2352",
    doi = "10.18653/v1/W16-2352",
    pages = "589--595",
}
@inproceedings{luotolahti-etal-2016-cross,
    title = "Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks",
    author = "Luotolahti, Juhani  and
      Kanerva, Jenna  and
      Ginter, Filip",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2353",
    doi = "10.18653/v1/W16-2353",
    pages = "596--601",
}
@inproceedings{novak-2016-pronoun,
    title = "Pronoun Prediction with Linguistic Features and Example Weighing",
    author = "Nov{\'a}k, Michal",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2354",
    doi = "10.18653/v1/W16-2354",
    pages = "602--608",
}
@inproceedings{stymne-2016-feature,
    title = "Feature Exploration for Cross-Lingual Pronoun Prediction",
    author = "Stymne, Sara",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2355",
    doi = "10.18653/v1/W16-2355",
    pages = "609--615",
}
@inproceedings{tiedemann-2016-linear,
    title = "A Linear Baseline Classifier for Cross-Lingual Pronoun Prediction",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2356",
    doi = "10.18653/v1/W16-2356",
    pages = "616--619",
}
@inproceedings{wetzel-2016-cross,
    title = "Cross-lingual Pronoun Prediction for {E}nglish, {F}rench and {G}erman with Maximum Entropy Classification",
    author = "Wetzel, Dominikus",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2357",
    doi = "10.18653/v1/W16-2357",
    pages = "620--626",
}
@inproceedings{caglayan-etal-2016-multimodality,
    title = "Does Multimodality Help Human and Machine for Translation and Image Captioning?",
    author = {Caglayan, Ozan  and
      Aransa, Walid  and
      Wang, Yaxing  and
      Masana, Marc  and
      Garc{\'\i}a-Mart{\'\i}nez, Mercedes  and
      Bougares, Fethi  and
      Barrault, Lo{\"\i}c  and
      van de Weijer, Joost},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2358",
    doi = "10.18653/v1/W16-2358",
    pages = "627--633",
}
@inproceedings{calixto-etal-2016-dcu,
    title = "{DCU}-{U}v{A} Multimodal {MT} System Report",
    author = "Calixto, Iacer  and
      Elliott, Desmond  and
      Frank, Stella",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2359",
    doi = "10.18653/v1/W16-2359",
    pages = "634--638",
}
@inproceedings{huang-etal-2016-attention,
    title = "Attention-based Multimodal Neural Machine Translation",
    author = "Huang, Po-Yao  and
      Liu, Frederick  and
      Shiang, Sz-Rung  and
      Oh, Jean  and
      Dyer, Chris",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2360",
    doi = "10.18653/v1/W16-2360",
    pages = "639--645",
}
@inproceedings{libovicky-etal-2016-cuni,
    title = "{CUNI} System for {WMT}16 Automatic Post-Editing and Multimodal Translation Tasks",
    author = "Libovick{\'y}, Jind{\v{r}}ich  and
      Helcl, Jind{\v{r}}ich  and
      Tlust{\'y}, Marek  and
      Bojar, Ond{\v{r}}ej  and
      Pecina, Pavel",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2361",
    doi = "10.18653/v1/W16-2361",
    pages = "646--654",
}
@inproceedings{rodriguez-guasch-costa-jussa-2016-wmt,
    title = "{WMT} 2016 Multimodal Translation System Description based on Bidirectional Recurrent Neural Networks with Double-Embeddings",
    author = "Rodr{\'\i}guez Guasch, Sergio  and
      Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2362",
    doi = "10.18653/v1/W16-2362",
    pages = "655--659",
}
@inproceedings{shah-etal-2016-shef,
    title = "{SHEF}-Multimodal: Grounding Machine Translation on Images",
    author = "Shah, Kashif  and
      Wang, Josiah  and
      Specia, Lucia",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2363",
    doi = "10.18653/v1/W16-2363",
    pages = "660--665",
}
@inproceedings{azpeitia-etchegoyhen-2016-docal,
    title = "{DOCAL} - Vicomtech{'}s Participation in the {WMT}16 Shared Task on Bilingual Document Alignment",
    author = "Azpeitia, Andoni  and
      Etchegoyhen, Thierry",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2364",
    doi = "10.18653/v1/W16-2364",
    pages = "666--671",
}
@inproceedings{buck-koehn-2016-quick,
    title = "Quick and Reliable Document Alignment via {TF}/{IDF}-weighted Cosine Distance",
    author = "Buck, Christian  and
      Koehn, Philipp",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2365",
    doi = "10.18653/v1/W16-2365",
    pages = "672--678",
}
@inproceedings{dara-lin-2016-yoda,
    title = "{YODA} System for {WMT}16 Shared Task: Bilingual Document Alignment",
    author = "Dara, Aswarth Abhilash  and
      Lin, Yiu-Chang",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2366",
    doi = "10.18653/v1/W16-2366",
    pages = "679--684",
}
@inproceedings{espla-gomis-etal-2016-bitextors,
    title = "Bitextor{'}s participation in {WMT}{'}16: shared task on document alignment",
    author = "Espl{\`a}-Gomis, Miquel  and
      Forcada, Mikel  and
      Ortiz-Rojas, Sergio  and
      Ferr{\'a}ndez-Tordera, Jorge",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2367",
    doi = "10.18653/v1/W16-2367",
    pages = "685--691",
}
@inproceedings{germann-2016-bilingual,
    title = "Bilingual Document Alignment with Latent Semantic Indexing",
    author = "Germann, Ulrich",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2368",
    doi = "10.18653/v1/W16-2368",
    pages = "692--696",
}
@inproceedings{gomes-pereira-lopes-2016-first,
    title = "First Steps Towards Coverage-Based Document Alignment",
    author = "Gomes, Lu{\'\i}s  and
      Pereira Lopes, Gabriel",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2369",
    doi = "10.18653/v1/W16-2369",
    pages = "697--702",
}
@inproceedings{jakubina-langlais-2016-bad,
    title = "{BAD} {LUC}@{WMT} 2016: a Bilingual Document Alignment Platform Based on Lucene",
    author = "Jakubina, Laurent  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2370",
    doi = "10.18653/v1/W16-2370",
    pages = "703--709",
}
@inproceedings{le-etal-2016-using,
    title = "Using Term Position Similarity and Language Modeling for Bilingual Document Alignment",
    author = {Le, Thanh C.  and
      Vu, Hoa Trong  and
      Oberl{\"a}nder, Jonathan  and
      Bojar, Ond{\v{r}}ej},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2371",
    doi = "10.18653/v1/W16-2371",
    pages = "710--716",
}
@inproceedings{lohar-etal-2016-adapt,
    title = "The {ADAPT} Bilingual Document Alignment system at {WMT}16",
    author = "Lohar, Pintu  and
      Afli, Haithem  and
      Liu, Chao-Hong  and
      Way, Andy",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2372",
    doi = "10.18653/v1/W16-2372",
    pages = "717--723",
}
@inproceedings{mahata-etal-2016-wmt2016,
    title = "{WMT}2016: A Hybrid Approach to Bilingual Document Alignment",
    author = "Mahata, Sainik  and
      Das, Dipankar  and
      Pal, Santanu",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2373",
    doi = "10.18653/v1/W16-2373",
    pages = "724--727",
}
@inproceedings{medved-etal-2016-english,
    title = "{E}nglish-{F}rench Document Alignment Based on Keywords and Statistical Translation",
    author = "Medve{\v{d}}, Marek  and
      Jakub{\'\i}{\v{c}}ek, Milo{\v{s}}  and
      Kov{\'a}{\v{r}}, Vojtech",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2374",
    doi = "10.18653/v1/W16-2374",
    pages = "728--732",
}
@inproceedings{papavassiliou-etal-2016-ilsp,
    title = "The {ILSP}/{ARC} submission to the {WMT} 2016 Bilingual Document Alignment Shared Task",
    author = "Papavassiliou, Vassilis  and
      Prokopidis, Prokopis  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2375",
    doi = "10.18653/v1/W16-2375",
    pages = "733--739",
}
@inproceedings{shchukin-etal-2016-word,
    title = "Word Clustering Approach to Bilingual Document Alignment ({WMT} 2016 Shared Task)",
    author = "Shchukin, Vadim  and
      Khristich, Dmitry  and
      Galinskaya, Irina",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2376",
    doi = "10.18653/v1/W16-2376",
    pages = "740--744",
}
@inproceedings{chatterjee-etal-2016-fbk,
    title = "The {FBK} Participation in the {WMT} 2016 Automatic Post-editing Shared Task",
    author = "Chatterjee, Rajen  and
      C. de Souza, Jos{\'e} G.  and
      Negri, Matteo  and
      Turchi, Marco",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2377",
    doi = "10.18653/v1/W16-2377",
    pages = "745--750",
}
@inproceedings{junczys-dowmunt-grundkiewicz-2016-log,
    title = "Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing",
    author = "Junczys-Dowmunt, Marcin  and
      Grundkiewicz, Roman",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2378",
    doi = "10.18653/v1/W16-2378",
    pages = "751--758",
}
@inproceedings{pal-etal-2016-usaar,
    title = "{USAAR}: An Operation Sequential Model for Automatic Statistical Post-Editing",
    author = "Pal, Santanu  and
      Zampieri, Marcos  and
      van Genabith, Josef",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2379",
    doi = "10.18653/v1/W16-2379",
    pages = "759--763",
}
@inproceedings{abdelsalam-etal-2016-bilingual,
    title = "Bilingual Embeddings and Word Alignments for Translation Quality Estimation",
    author = "Abdelsalam, Amal  and
      Bojar, Ond{\v{r}}ej  and
      El-Beltagy, Samhaa",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2380",
    doi = "10.18653/v1/W16-2380",
    pages = "764--771",
}
@inproceedings{beck-etal-2016-shef,
    title = "{SHEF}-{MIME}: Word-level Quality Estimation Using Imitation Learning",
    author = "Beck, Daniel  and
      Vlachos, Andreas  and
      Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2381",
    doi = "10.18653/v1/W16-2381",
    pages = "772--776",
}
@inproceedings{bicici-2016-referential,
    title = "Referential Translation Machines for Predicting Translation Performance",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2382",
    doi = "10.18653/v1/W16-2382",
    pages = "777--781",
}
@inproceedings{espla-gomis-etal-2016-ualacant,
    title = "{UA}lacant word-level and phrase-level machine translation quality estimation systems at {WMT} 2016",
    author = "Espl{\`a}-Gomis, Miquel  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Forcada, Mikel",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2383",
    doi = "10.18653/v1/W16-2383",
    pages = "782--786",
}
@inproceedings{kim-lee-2016-recurrent,
    title = "Recurrent Neural Network based Translation Quality Estimation",
    author = "Kim, Hyun  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2384",
    doi = "10.18653/v1/W16-2384",
    pages = "787--792",
}
@inproceedings{kozlova-etal-2016-ysda,
    title = "{YSDA} Participation in the {WMT}{'}16 Quality Estimation Shared Task",
    author = "Kozlova, Anna  and
      Shmatova, Mariya  and
      Frolov, Anton",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2385",
    doi = "10.18653/v1/W16-2385",
    pages = "793--799",
}
@inproceedings{logacheva-etal-2016-usfds,
    title = "{USFD}{'}s Phrase-level Quality Estimation Systems",
    author = "Logacheva, Varvara  and
      Blain, Fr{\'e}d{\'e}ric  and
      Specia, Lucia",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2386",
    doi = "10.18653/v1/W16-2386",
    pages = "800--805",
}
@inproceedings{martins-etal-2016-unbabels,
    title = "Unbabel{'}s Participation in the {WMT}16 Word-Level Translation Quality Estimation Shared Task",
    author = "Martins, Andr{\'e} F. T.  and
      Astudillo, Ram{\'o}n  and
      Hokamp, Chris  and
      Kepler, Fabio",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2387",
    doi = "10.18653/v1/W16-2387",
    pages = "806--811",
}
@inproceedings{paetzold-specia-2016-simplenets,
    title = "{S}imple{N}ets: Quality Estimation with Resource-Light Neural Networks",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2388",
    doi = "10.18653/v1/W16-2388",
    pages = "812--818",
}
@inproceedings{patel-m-2016-translation,
    title = "Translation Quality Estimation using Recurrent Neural Network",
    author = "Patel, Raj Nath  and
      M, Sasikumar",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2389",
    doi = "10.18653/v1/W16-2389",
    pages = "819--824",
}
@inproceedings{sagemo-stymne-2016-uu,
    title = "The {UU} Submission to the Machine Translation Quality Estimation Task",
    author = "Sagemo, Oscar  and
      Stymne, Sara",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2390",
    doi = "10.18653/v1/W16-2390",
    pages = "825--830",
}
@inproceedings{scarton-etal-2016-word,
    title = "Word embeddings and discourse information for Quality Estimation",
    author = "Scarton, Carolina  and
      Beck, Daniel  and
      Shah, Kashif  and
      Sim Smith, Karin  and
      Specia, Lucia",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2391",
    doi = "10.18653/v1/W16-2391",
    pages = "831--837",
}
@inproceedings{shah-etal-2016-shef-lium,
    title = "{SHEF}-{LIUM}-{NN}: Sentence level Quality Estimation with Neural Network Features",
    author = {Shah, Kashif  and
      Bougares, Fethi  and
      Barrault, Lo{\"\i}c  and
      Specia, Lucia},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2392",
    doi = "10.18653/v1/W16-2392",
    pages = "838--842",
}
@inproceedings{tezcan-etal-2016-ugent,
    title = "{UGENT}-{LT}3 {SCATE} Submission for {WMT}16 Shared Task on Quality Estimation",
    author = "Tezcan, Arda  and
      Hoste, V{\'e}ronique  and
      Macken, Lieve",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2393",
    doi = "10.18653/v1/W16-2393",
    pages = "843--850",
}
@proceedings{ws-2016-machine,
    title = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    editor = {Bojar, Ond{\v{r}}ej  and
      Buck, Christian  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Guillou, Liane  and
      Haddow, Barry  and
      Huck, Matthias  and
      Yepes, Antonio Jimeno  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Neves, Mariana  and
      Pecina, Pavel  and
      Popel, Martin  and
      Koehn, Philipp  and
      Monz, Christof  and
      Negri, Matteo  and
      Post, Matt  and
      Specia, Lucia  and
      Verspoor, Karin  and
      Tiedemann, J{\"o}rg  and
      Turchi, Marco},
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2200",
    doi = "10.18653/v1/W16-2200",
}
@inproceedings{shen-etal-2016-cross,
    title = "Cross-language Projection of Dependency Trees with Constrained Partial Parsing for Tree-to-Tree Machine Translation",
    author = "Shen, Yu  and
      Chu, Chenhui  and
      Cromieres, Fabien  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2201",
    doi = "10.18653/v1/W16-2201",
    pages = "1--11",
}
@inproceedings{luong-popescu-belis-2016-improving,
    title = "Improving Pronoun Translation by Modeling Coreference Uncertainty",
    author = "Luong, Ngoc Quang  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2202",
    doi = "10.18653/v1/W16-2202",
    pages = "12--20",
}
@inproceedings{ramm-fraser-2016-modeling,
    title = "Modeling verbal inflection for {E}nglish to {G}erman {SMT}",
    author = "Ramm, Anita  and
      Fraser, Alexander",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2203",
    doi = "10.18653/v1/W16-2203",
    pages = "21--31",
}
@inproceedings{nadejde-etal-2016-modeling,
    title = "Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation",
    author = "N{\u{a}}dejde, Maria  and
      Birch, Alexandra  and
      Koehn, Philipp",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2204",
    doi = "10.18653/v1/W16-2204",
    pages = "32--42",
}
@inproceedings{weller-di-marco-etal-2016-modeling,
    title = "Modeling Complement Types in Phrase-Based {SMT}",
    author = "Weller-Di Marco, Marion  and
      Fraser, Alexander  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2205",
    doi = "10.18653/v1/W16-2205",
    pages = "43--53",
}
@inproceedings{alkhouli-etal-2016-alignment,
    title = "Alignment-Based Neural Machine Translation",
    author = "Alkhouli, Tamer  and
      Bretschner, Gabriel  and
      Peter, Jan-Thorsten  and
      Hethnawi, Mohammed  and
      Guta, Andreas  and
      Ney, Hermann",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2206",
    doi = "10.18653/v1/W16-2206",
    pages = "54--65",
}
@inproceedings{legrand-etal-2016-neural,
    title = "Neural Network-based Word Alignment through Score Aggregation",
    author = {Legrand, Jo{\"e}l  and
      Auli, Michael  and
      Collobert, Ronan},
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2207",
    doi = "10.18653/v1/W16-2207",
    pages = "66--73",
}
@inproceedings{niehues-etal-2016-using,
    title = "Using Factored Word Representation in Neural Network Language Models",
    author = "Niehues, Jan  and
      Ha, Thanh-Le  and
      Cho, Eunah  and
      Waibel, Alex",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2208",
    doi = "10.18653/v1/W16-2208",
    pages = "74--82",
}
@inproceedings{sennrich-haddow-2016-linguistic,
    title = "Linguistic Input Features Improve Neural Machine Translation",
    author = "Sennrich, Rico  and
      Haddow, Barry",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2209",
    doi = "10.18653/v1/W16-2209",
    pages = "83--91",
}
@inproceedings{braune-etal-2016-framework,
    title = "A Framework for Discriminative Rule Selection in Hierarchical {M}oses",
    author = "Braune, Fabienne  and
      Fraser, Alexander  and
      Daum{\'e} III, Hal  and
      Tamchyna, Ale{\v{s}}",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2210",
    doi = "10.18653/v1/W16-2210",
    pages = "92--101",
}
@inproceedings{bogoychev-hoang-2016-fast,
    title = "Fast and highly parallelizable phrase table for statistical machine translation",
    author = "Bogoychev, Nikolay  and
      Hoang, Hieu",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2211",
    doi = "10.18653/v1/W16-2211",
    pages = "102--109",
}
@inproceedings{kim-etal-2016-comparative,
    title = "A Comparative Study on Vocabulary Reduction for Phrase Table Smoothing",
    author = "Kim, Yunsu  and
      Guta, Andreas  and
      Wuebker, Joern  and
      Ney, Hermann",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2212",
    doi = "10.18653/v1/W16-2212",
    pages = "110--117",
}
@inproceedings{daiber-etal-2016-examining,
    title = "Examining the Relationship between Preordering and Word Order Freedom in Machine Translation",
    author = "Daiber, Joachim  and
      Stanojevi{\'c}, Milo{\v{s}}  and
      Aziz, Wilker  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2213",
    doi = "10.18653/v1/W16-2213",
    pages = "118--130",
}
@proceedings{ws-2016-sighum,
    title = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    editor = "Reiter, Nils  and
      Alex, Beatrice  and
      Zervanou, Kalliopi A.",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2100",
    doi = "10.18653/v1/W16-21",
}
@inproceedings{schumann-2016-brave,
    title = "Brave New World: Uncovering Topical Dynamics in the {ACL} {A}nthology Reference Corpus Using Term Life Cycle Information",
    author = "Schumann, Anne-Kathrin",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2101",
    doi = "10.18653/v1/W16-2101",
    pages = "1--11",
}
@inproceedings{karan-etal-2016-analysis,
    title = "Analysis of Policy Agendas: Lessons Learned from Automatic Topic Classification of {C}roatian Political Texts",
    author = "Karan, Mladen  and
      {\v{S}}najder, Jan  and
      {\v{S}}irini{\'c}, Daniela  and
      Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2102",
    doi = "10.18653/v1/W16-2102",
    pages = "12--21",
}
@inproceedings{iglesias-franjo-vilares-2016-searching,
    title = "Searching Four-Millenia-Old Digitized Documents: A Text Retrieval System for Egyptologists",
    author = "Iglesias-Franjo, Est{\'\i}baliz  and
      Vilares, Jes{\'u}s",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2103",
    doi = "10.18653/v1/W16-2103",
    pages = "22--31",
}
@inproceedings{adesam-bouma-2016-old,
    title = "Old {S}wedish Part-of-Speech Tagging between Variation and External Knowledge",
    author = "Adesam, Yvonne  and
      Bouma, Gerlof",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2104",
    doi = "10.18653/v1/W16-2104",
    pages = "32--42",
}
@inproceedings{schulz-keller-2016-code,
    title = "Code-Switching Ubique Est - Language Identification and Part-of-Speech Tagging for Historical Mixed Text",
    author = "Schulz, Sarah  and
      Keller, Mareike",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2105",
    doi = "10.18653/v1/W16-2105",
    pages = "43--51",
}
@inproceedings{barteld-etal-2016-dealing,
    title = "Dealing with word-internal modification and spelling variation in data-driven lemmatization",
    author = {Barteld, Fabian  and
      Schr{\"o}der, Ingrid  and
      Zinsmeister, Heike},
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2106",
    doi = "10.18653/v1/W16-2106",
    pages = "52--62",
}
@inproceedings{coll-ardanuy-etal-2016-shall,
    title = "You Shall Know People by the Company They Keep: Person Name Disambiguation for Social Network Construction",
    author = "Coll Ardanuy, Mariona  and
      van den Bos, Maarten  and
      Sporleder, Caroline",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2107",
    doi = "10.18653/v1/W16-2107",
    pages = "63--73",
}
@inproceedings{opitz-frank-2016-deriving,
    title = "Deriving Players {\&} Themes in the {R}egesta {I}mperii using {SVM}s and Neural Networks",
    author = "Opitz, Juri  and
      Frank, Anette",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2108",
    doi = "10.18653/v1/W16-2108",
    pages = "74--83",
}
@inproceedings{hiippala-2016-semi,
    title = "Semi-automated annotation of page-based documents within the Genre and Multimodality framework",
    author = "Hiippala, Tuomo",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2109",
    doi = "10.18653/v1/W16-2109",
    pages = "84--89",
}
@inproceedings{budassi-passarotti-2016-nomen,
    title = "Nomen Omen. Enhancing the {L}atin Morphological Analyser Lemlat with an Onomasticon",
    author = "Budassi, Marco  and
      Passarotti, Marco",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2110",
    doi = "10.18653/v1/W16-2110",
    pages = "90--94",
}
@inproceedings{joshi-etal-2016-cultural,
    title = "How Do Cultural Differences Impact the Quality of Sarcasm Annotation?: A Case Study of {I}ndian Annotators and {A}merican Text",
    author = "Joshi, Aditya  and
      Bhattacharyya, Pushpak  and
      Carman, Mark  and
      Saraswati, Jaya  and
      Shukla, Rajita",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2111",
    doi = "10.18653/v1/W16-2111",
    pages = "95--99",
}
@inproceedings{etxeberria-etal-2016-combining,
    title = "Combining Phonology and Morphology for the Normalization of Historical Texts",
    author = "Etxeberria, Izaskun  and
      Alegria, I{\~n}aki  and
      Uria, Larraitz  and
      Hulden, Mans",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2112",
    doi = "10.18653/v1/W16-2112",
    pages = "100--105",
}
@inproceedings{sonmez-etal-2016-towards,
    title = "Towards Building a Political Protest Database to Explain Changes in the Welfare State",
    author = {S{\"o}nmez, {\c{C}}a{\u{g}}{\i}l  and
      {\"O}zg{\"u}r, Arzucan  and
      Y{\"o}r{\"u}k, Erdem},
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2113",
    doi = "10.18653/v1/W16-2113",
    pages = "106--110",
}
@inproceedings{hellrich-hahn-2016-assessment,
    title = "An Assessment of Experimental Protocols for Tracing Changes in Word Semantics Relative to Accuracy and Reliability",
    author = "Hellrich, Johannes  and
      Hahn, Udo",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2114",
    doi = "10.18653/v1/W16-2114",
    pages = "111--117",
}
@inproceedings{simon-vincze-2016-universal,
    title = "{U}niversal {M}orphology for {O}ld {H}ungarian",
    author = "Simon, Eszter  and
      Vincze, Veronika",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2115",
    doi = "10.18653/v1/W16-2115",
    pages = "118--127",
}
@inproceedings{schoene-dethlefs-2016-automatic,
    title = "Automatic Identification of Suicide Notes from Linguistic and Sentiment Features",
    author = "Schoene, Annika Marie  and
      Dethlefs, Nina",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2116",
    doi = "10.18653/v1/W16-2116",
    pages = "128--133",
}
@inproceedings{le-etal-2016-towards,
    title = "Towards a text analysis system for political debates",
    author = "Le, Dieu-Thu  and
      Vu, Ngoc Thang  and
      Blessing, Andre",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2117",
    doi = "10.18653/v1/W16-2117",
    pages = "134--139",
}
@inproceedings{hulden-2016-whodunit,
    title = "Whodunit... and to Whom? Subjects, Objects, and Actions in Research Articles on {A}merican Labor Unions",
    author = "Hulden, Vilja",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2118",
    doi = "10.18653/v1/W16-2118",
    pages = "140--145",
}
@inproceedings{zeldes-schroeder-2016-nlp,
    title = "An {NLP} Pipeline for {C}optic",
    author = "Zeldes, Amir  and
      Schroeder, Caroline T.",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2119",
    doi = "10.18653/v1/W16-2119",
    pages = "146--155",
}
@inproceedings{elsner-lane-2016-automatic,
    title = "Automatic discovery of {L}atin syntactic changes",
    author = "Elsner, Micha  and
      Lane, Emily",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2120",
    doi = "10.18653/v1/W16-2120",
    pages = "156--164",
}
@inproceedings{degaetano-ortlieb-teich-2016-information,
    title = "Information-based Modeling of Diachronic Linguistic Change: from Typicality to Productivity",
    author = "Degaetano-Ortlieb, Stefania  and
      Teich, Elke",
    booktitle = "Proceedings of the 10th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2121",
    doi = "10.18653/v1/W16-2121",
    pages = "165--173",
}
@proceedings{ws-2016-sigmorphon,
    title = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    editor = "Elsner, Micha  and
      Kuebler, Sandra",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2000",
    doi = "10.18653/v1/W16-20",
}
@inproceedings{zhang-2016-mining,
    title = "Mining linguistic tone patterns with symbolic representation",
    author = "Zhang, Shuo",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2001",
    doi = "10.18653/v1/W16-2001",
    pages = "1--9",
}
@inproceedings{cotterell-etal-2016-sigmorphon,
    title = "The {SIGMORPHON} 2016 Shared {T}ask{---}{M}orphological Reinflection",
    author = "Cotterell, Ryan  and
      Kirov, Christo  and
      Sylak-Glassman, John  and
      Yarowsky, David  and
      Eisner, Jason  and
      Hulden, Mans",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2002",
    doi = "10.18653/v1/W16-2002",
    pages = "10--22",
}
@inproceedings{ostling-2016-morphological,
    title = "Morphological reinflection with convolutional neural networks",
    author = {{\"O}stling, Robert},
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2003",
    doi = "10.18653/v1/W16-2003",
    pages = "23--26",
}
@inproceedings{alegria-etxeberria-2016-ehu,
    title = "{EHU} at the {SIGMORPHON} 2016 Shared Task. A Simple Proposal: Grapheme-to-Phoneme for Inflection",
    author = "Alegria, I{\~n}aki  and
      Etxeberria, Izaskun",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2004",
    doi = "10.18653/v1/W16-2004",
    pages = "27--30",
}
@inproceedings{nicolai-etal-2016-morphological,
    title = "Morphological Reinflection via Discriminative String Transduction",
    author = "Nicolai, Garrett  and
      Hauer, Bradley  and
      St Arnaud, Adam  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2005",
    doi = "10.18653/v1/W16-2005",
    pages = "31--35",
}
@inproceedings{liu-mao-2016-morphological,
    title = "Morphological reinflection with conditional random fields and unsupervised features",
    author = "Liu, Ling  and
      Mao, Lingshuang Jack",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2006",
    doi = "10.18653/v1/W16-2006",
    pages = "36--40",
}
@inproceedings{aharoni-etal-2016-improving,
    title = "Improving Sequence to Sequence Learning for Morphological Inflection Generation: The {BIU}-{MIT} Systems for the {SIGMORPHON} 2016 Shared Task for Morphological Reinflection",
    author = "Aharoni, Roee  and
      Goldberg, Yoav  and
      Belinkov, Yonatan",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2007",
    doi = "10.18653/v1/W16-2007",
    pages = "41--48",
}
@inproceedings{king-2016-evaluating,
    title = "Evaluating Sequence Alignment for Learning Inflectional Morphology",
    author = "King, David",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2008",
    doi = "10.18653/v1/W16-2008",
    pages = "49--53",
}
@inproceedings{sorokin-2016-using,
    title = "Using longest common subsequence and character models to predict word forms",
    author = "Sorokin, Alexey",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2009",
    doi = "10.18653/v1/W16-2009",
    pages = "54--61",
}
@inproceedings{kann-schutze-2016-med,
    title = "{MED}: The {LMU} System for the {SIGMORPHON} 2016 Shared Task on Morphological Reinflection",
    author = {Kann, Katharina  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2010",
    doi = "10.18653/v1/W16-2010",
    pages = "62--70",
}
@inproceedings{taji-etal-2016-columbia,
    title = "The {C}olumbia {U}niversity - {N}ew {Y}ork {U}niversity {A}bu {D}habi {SIGMORPHON} 2016 Morphological Reinflection Shared Task Submission",
    author = "Taji, Dima  and
      Eskander, Ramy  and
      Habash, Nizar  and
      Rambow, Owen",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2011",
    doi = "10.18653/v1/W16-2011",
    pages = "71--75",
}
@inproceedings{ma-etal-2016-letter,
    title = "Letter Sequence Labeling for Compound Splitting",
    author = "Ma, Jianqiang  and
      Henrich, Verena  and
      Hinrichs, Erhard",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2012",
    doi = "10.18653/v1/W16-2012",
    pages = "76--81",
}
@inproceedings{nguyen-cornips-2016-automatic,
    title = "Automatic Detection of Intra-Word Code-Switching",
    author = "Nguyen, Dong  and
      Cornips, Leonie",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2013",
    doi = "10.18653/v1/W16-2013",
    pages = "82--86",
}
@inproceedings{james-wieling-2016-read,
    title = "Read my points: Effect of animation type when speech-reading from {EMA} data",
    author = "James, Kristy  and
      Wieling, Martijn",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2014",
    doi = "10.18653/v1/W16-2014",
    pages = "87--92",
}
@inproceedings{kisselew-etal-2016-predicting,
    title = "Predicting the Direction of Derivation in {E}nglish Conversion",
    author = "Kisselew, Max  and
      Rimell, Laura  and
      Palmer, Alexis  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2015",
    doi = "10.18653/v1/W16-2015",
    pages = "93--98",
}
@inproceedings{nicolai-etal-2016-morphological-segmentation,
    title = "Morphological Segmentation Can Improve Syllabification",
    author = "Nicolai, Garrett  and
      Yao, Lei  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2016",
    doi = "10.18653/v1/W16-2016",
    pages = "99--103",
}
@inproceedings{declerck-lendvai-2016-towards,
    title = "Towards a Formal Representation of Components of {G}erman Compounds",
    author = "Declerck, Thierry  and
      Lendvai, Piroska",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2017",
    doi = "10.18653/v1/W16-2017",
    pages = "104--109",
}
@inproceedings{shoemark-etal-2016-towards,
    title = "Towards robust cross-linguistic comparisons of phonological networks",
    author = "Shoemark, Philippa  and
      Goldwater, Sharon  and
      Kirby, James  and
      Sarkar, Rik",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2018",
    doi = "10.18653/v1/W16-2018",
    pages = "110--120",
}
@inproceedings{aksenova-etal-2016-morphotactics,
    title = "Morphotactics as Tier-Based Strictly Local Dependencies",
    author = {Aks{\"e}nova, Al{\"e}na  and
      Graf, Thomas  and
      Moradi, Sedigheh},
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2019",
    doi = "10.18653/v1/W16-2019",
    pages = "121--130",
}
@inproceedings{meyer-dickinson-2016-multilinear,
    title = "A Multilinear Approach to the Unsupervised Learning of Morphology",
    author = "Meyer, Anthony  and
      Dickinson, Markus",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2020",
    doi = "10.18653/v1/W16-2020",
    pages = "131--140",
}
@inproceedings{zamaraeva-2016-inferring,
    title = "Inferring Morphotactics from Interlinear Glossed Text: Combining Clustering and Precision Grammars",
    author = "Zamaraeva, Olga",
    booktitle = "Proceedings of the 14th {SIGMORPHON} Workshop on Computational Research in Phonetics, Phonology, and Morphology",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-2021",
    doi = "10.18653/v1/W16-2021",
    pages = "141--150",
}
@proceedings{ws-2016-cognitive,
    title = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    editor = "Korhonen, Anna  and
      Lenci, Alessandro  and
      Murphy, Brian  and
      Poibeau, Thierry  and
      Villavicencio, Aline",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1900",
    doi = "10.18653/v1/W16-19",
}
@inproceedings{regneri-king-2016-automated,
    title = "Automated Discourse Analysis of Narrations by Adolescents with Autistic Spectrum Disorder",
    author = "Regneri, Michaela  and
      King, Diane",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1901",
    doi = "10.18653/v1/W16-1901",
    pages = "1--9",
}
@inproceedings{hernandez-dominguez-etal-2016-detection,
    title = "Detection of {A}lzheimer{'}s disease based on automatic analysis of common objects descriptions",
    author = "Hern{\'a}ndez-Dom{\'\i}nguez, Laura  and
      Garc{\'\i}a-Cano, Edgar  and
      Ratt{\'e}, Sylvie  and
      Sierra-Mart{\'\i}nez, Gerardo",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1902",
    doi = "10.18653/v1/W16-1902",
    pages = "10--15",
}
@inproceedings{hernandez-dominguez-etal-2016-conversing,
    title = "Conversing with the elderly in {L}atin {A}merica: a new cohort for multimodal, multilingual longitudinal studies on aging",
    author = "Hern{\'a}ndez-Dom{\'\i}nguez, Laura  and
      Ratt{\'e}, Sylvie  and
      Davis, Boyd  and
      Pope, Charlene",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1903",
    doi = "10.18653/v1/W16-1903",
    pages = "16--21",
}
@inproceedings{cheri-etal-2016-leveraging,
    title = "Leveraging Annotators{'} Gaze Behaviour for Coreference Resolution",
    author = "Cheri, Joe  and
      Mishra, Abhijit  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1904",
    doi = "10.18653/v1/W16-1904",
    pages = "22--26",
}
@inproceedings{nouri-yangarber-2016-alignment,
    title = "From alignment of etymological data to phylogenetic inference via population genetics",
    author = "Nouri, Javad  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1905",
    doi = "10.18653/v1/W16-1905",
    pages = "27--37",
}
@inproceedings{christodoulopoulos-etal-2016-incremental,
    title = "An incremental model of syntactic bootstrapping",
    author = "Christodoulopoulos, Christos  and
      Roth, Dan  and
      Fisher, Cynthia",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1906",
    doi = "10.18653/v1/W16-1906",
    pages = "38--43",
}
@inproceedings{wiren-etal-2016-longitudinal,
    title = "Longitudinal Studies of Variation Sets in Child-directed Speech",
    author = {Wir{\'e}n, Mats  and
      Nilsson Bj{\"o}rkenstam, Kristina  and
      Grigonyt{\.e}, Gintar{\.e}  and
      Cortes, Elisabet Eir},
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1907",
    doi = "10.18653/v1/W16-1907",
    pages = "44--52",
}
@inproceedings{ma-etal-2016-learning,
    title = "Learning Phone Embeddings for Word Segmentation of Child-Directed Speech",
    author = {Ma, Jianqiang  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Hinrichs, Erhard},
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1908",
    doi = "10.18653/v1/W16-1908",
    pages = "53--63",
}
@inproceedings{alhama-zuidema-2016-generalization,
    title = "Generalization in Artificial Language Learning: Modelling the Propensity to Generalize",
    author = "Alhama, Raquel G.  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1909",
    doi = "10.18653/v1/W16-1909",
    pages = "64--72",
}
@inproceedings{spokoyny-etal-2016-explicit,
    title = "Explicit Causal Connections between the Acquisition of Linguistic Tiers: Evidence from Dynamical Systems Modeling",
    author = "Spokoyny, Daniel  and
      Irvin, Jeremy  and
      Moscoso del Prado Martin, Fermin",
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1910",
    doi = "10.18653/v1/W16-1910",
    pages = "73--81",
}
@inproceedings{nilsson-bjorkenstam-etal-2016-modelling,
    title = "Modelling the informativeness and timing of non-verbal cues in parent-child interaction",
    author = {Nilsson Bj{\"o}rkenstam, Kristina  and
      Wir{\'e}n, Mats  and
      {\"O}stling, Robert},
    booktitle = "Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1911",
    doi = "10.18653/v1/W16-1911",
    pages = "82--90",
}
@proceedings{ws-2016-multiword,
    title = "Proceedings of the 12th Workshop on Multiword Expressions",
    editor = "Kordoni, Valia  and
      Cholakov, Kostadin  and
      Egg, Markus  and
      Markantonatou, Stella  and
      Nakov, Preslav",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1800",
    doi = "10.18653/v1/W16-18",
}
@inproceedings{yimam-etal-2016-learning,
    title = "Learning Paraphrasing for Multiword Expressions",
    author = "Yimam, Seid Muhie  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1801",
    doi = "10.18653/v1/W16-1801",
    pages = "1--10",
}
@inproceedings{daniel-last-2016-exploring,
    title = "Exploring Long-Term Temporal Trends in the Use of Multiword Expressions",
    author = "Daniel, Tal  and
      Last, Mark",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1802",
    doi = "10.18653/v1/W16-1802",
    pages = "11--20",
}
@inproceedings{senaldi-etal-2016-lexical,
    title = "Lexical Variability and Compositionality: Investigating Idiomaticity with Distributional Semantic Models",
    author = "Senaldi, Marco Silvio Giuseppe  and
      Lebani, Gianluca E.  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1803",
    doi = "10.18653/v1/W16-1803",
    pages = "21--31",
}
@inproceedings{ramisch-etal-2016-filtering,
    title = "Filtering and Measuring the Intrinsic Quality of Human Compositionality Judgments",
    author = "Ramisch, Carlos  and
      Cordeiro, Silvio  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1804",
    doi = "10.18653/v1/W16-1804",
    pages = "32--37",
}
@inproceedings{wittmann-etal-2016-graph,
    title = "Graph-based Clustering of Synonym Senses for {G}erman Particle Verbs",
    author = "Wittmann, Moritz  and
      Weller-Di Marco, Marion  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1805",
    doi = "10.18653/v1/W16-1805",
    pages = "38--43",
}
@inproceedings{nokel-loukachevitch-2016-accounting,
    title = "Accounting ngrams and multi-word terms can improve topic models",
    author = "Nokel, Michael  and
      Loukachevitch, Natalia",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1806",
    doi = "10.18653/v1/W16-1806",
    pages = "44--49",
}
@inproceedings{ziering-etal-2016-top,
    title = "Top a Splitter: Using Distributional Semantics for Improving Compound Splitting",
    author = {Ziering, Patrick  and
      M{\"u}ller, Stefan  and
      van der Plas, Lonneke},
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1807",
    doi = "10.18653/v1/W16-1807",
    pages = "50--55",
}
@inproceedings{cholakov-kordoni-2016-using,
    title = "Using Word Embeddings for Improving Statistical Machine Translation of Phrasal Verbs",
    author = "Cholakov, Kostadin  and
      Kordoni, Valia",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1808",
    doi = "10.18653/v1/W16-1808",
    pages = "56--60",
}
@inproceedings{farahmand-henderson-2016-modeling,
    title = "Modeling the Non-Substitutability of Multiword Expressions with Distributional Semantics and a Log-Linear Model",
    author = "Farahmand, Meghdad  and
      Henderson, James",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1809",
    doi = "10.18653/v1/W16-1809",
    pages = "61--66",
}
@inproceedings{legrand-collobert-2016-phrase,
    title = "Phrase Representations for Multiword Expressions",
    author = {Legrand, Jo{\"e}l  and
      Collobert, Ronan},
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1810",
    doi = "10.18653/v1/W16-1810",
    pages = "67--71",
}
@inproceedings{petruck-ellsworth-2016-representing,
    title = "Representing Support Verbs in {F}rame{N}et",
    author = "Petruck, Miriam R. L.  and
      Ellsworth, Michael",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1811",
    doi = "10.18653/v1/W16-1811",
    pages = "72--77",
}
@inproceedings{uresova-etal-2016-inherently,
    title = "Inherently Pronominal Verbs in {C}zech: Description and Conversion Based on Treebank Annotation",
    author = "Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Bej{\v{c}}ek, Eduard  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1812",
    doi = "10.18653/v1/W16-1812",
    pages = "78--83",
}
@inproceedings{bestgen-2016-using,
    title = "Using collocational features to improve automated scoring of {EFL} texts",
    author = "Bestgen, Yves",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1813",
    doi = "10.18653/v1/W16-1813",
    pages = "84--90",
}
@inproceedings{costa-etal-2016-study,
    title = "A study on the production of collocations by {E}uropean {P}ortuguese learners",
    author = "Costa, {\^A}ngela  and
      Coheur, Lu{\'\i}sa  and
      Lino, Teresa",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1814",
    doi = "10.18653/v1/W16-1814",
    pages = "91--95",
}
@inproceedings{chrzaszcz-2016-extraction,
    title = "Extraction and Recognition of {P}olish Multiword Expressions using {W}ikipedia and Finite-State Automata",
    author = "Chrz{\k{a}}szcz, Pawe{\l}",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1815",
    doi = "10.18653/v1/W16-1815",
    pages = "96--106",
}
@inproceedings{riedl-biemann-2016-impact,
    title = "Impact of {MWE} Resources on Multiword Recognition",
    author = "Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1816",
    doi = "10.18653/v1/W16-1816",
    pages = "107--111",
}
@inproceedings{gharbieh-etal-2016-word,
    title = "A Word Embedding Approach to Identifying Verb-Noun Idiomatic Combinations",
    author = "Gharbieh, Waseem  and
      Bhavsar, Virendra  and
      Cook, Paul",
    booktitle = "Proceedings of the 12th Workshop on Multiword Expressions",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1817",
    doi = "10.18653/v1/W16-1817",
    pages = "112--118",
}
@proceedings{ws-2016-linguistic,
    title = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    editor = "Friedrich, Annemarie  and
      Tomanek, Katrin",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1700",
    doi = "10.18653/v1/W16-17",
}
@inproceedings{hong-etal-2016-building,
    title = "Building a Cross-document Event-Event Relation Corpus",
    author = "Hong, Yu  and
      Zhang, Tongtao  and
      O{'}Gorman, Tim  and
      Horowit-Hendler, Sharone  and
      Ji, Heng  and
      Palmer, Martha",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1701",
    doi = "10.18653/v1/W16-1701",
    pages = "1--6",
}
@inproceedings{li-etal-2016-annotating,
    title = "Annotating the Little Prince with {C}hinese {AMR}s",
    author = "Li, Bin  and
      Wen, Yuan  and
      Qu, Weiguang  and
      Bu, Lijun  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1702",
    doi = "10.18653/v1/W16-1702",
    pages = "7--15",
}
@inproceedings{luu-etal-2016-converting,
    title = "Converting {S}yn{T}ag{R}us Dependency Treebank into {P}enn {T}reebank Style",
    author = "Luu, Alex  and
      Malamud, Sophia A.  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1703",
    doi = "10.18653/v1/W16-1703",
    pages = "16--21",
}
@inproceedings{webber-etal-2016-discourse,
    title = "A Discourse-Annotated Corpus of Conjoined {VP}s",
    author = "Webber, Bonnie  and
      Prasad, Rashmi  and
      Lee, Alan  and
      Joshi, Aravind",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1704",
    doi = "10.18653/v1/W16-1704",
    pages = "22--31",
}
@inproceedings{laarmann-quante-etal-2016-annotating,
    title = "Annotating Spelling Errors in {G}erman Texts Produced by Primary School Children",
    author = "Laarmann-Quante, Ronja  and
      Knichel, Lukas  and
      Dipper, Stefanie  and
      Betken, Carina",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1705",
    doi = "10.18653/v1/W16-1705",
    pages = "32--42",
}
@inproceedings{martinez-alonso-etal-2016-supersense,
    title = "Supersense tagging with inter-annotator disagreement",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Johannsen, Anders  and
      Plank, Barbara",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1706",
    doi = "10.18653/v1/W16-1706",
    pages = "43--48",
}
@inproceedings{rohde-etal-2016-filling,
    title = "Filling in the Blanks in Understanding Discourse Adverbials: Consistency, Conflict, and Context-Dependence in a Crowdsourced Elicitation Task",
    author = "Rohde, Hannah  and
      Dickinson, Anna  and
      Schneider, Nathan  and
      Clark, Christopher N. L.  and
      Louis, Annie  and
      Webber, Bonnie",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1707",
    doi = "10.18653/v1/W16-1707",
    pages = "49--58",
}
@inproceedings{komiya-etal-2016-comparison,
    title = "Comparison of Annotating Methods for Named Entity Corpora",
    author = "Komiya, Kanako  and
      Suzuki, Masaya  and
      Iwakura, Tomoya  and
      Sasaki, Minoru  and
      Shinnou, Hiroyuki",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1708",
    doi = "10.18653/v1/W16-1708",
    pages = "59--67",
}
@inproceedings{zeldes-simonson-2016-different,
    title = "Different Flavors of {GUM}: Evaluating Genre and Sentence Type Effects on Multilayer Corpus Annotation Quality",
    author = "Zeldes, Amir  and
      Simonson, Dan",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1709",
    doi = "10.18653/v1/W16-1709",
    pages = "68--78",
}
@inproceedings{elfardy-diab-2016-addressing,
    title = "Addressing Annotation Complexity: The Case of Annotating Ideological Perspective in {E}gyptian Social Media",
    author = "Elfardy, Heba  and
      Diab, Mona",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1710",
    doi = "10.18653/v1/W16-1710",
    pages = "79--88",
}
@inproceedings{bollmann-etal-2016-evaluating,
    title = "Evaluating Inter-Annotator Agreement on Historical Spelling Normalization",
    author = "Bollmann, Marcel  and
      Dipper, Stefanie  and
      Petran, Florian",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1711",
    doi = "10.18653/v1/W16-1711",
    pages = "89--98",
}
@inproceedings{schneider-etal-2016-corpus,
    title = "A Corpus of Preposition Supersenses",
    author = "Schneider, Nathan  and
      Hwang, Jena D.  and
      Srikumar, Vivek  and
      Green, Meredith  and
      Suresh, Abhijit  and
      Conger, Kathryn  and
      O{'}Gorman, Tim  and
      Palmer, Martha",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1712",
    doi = "10.18653/v1/W16-1712",
    pages = "99--109",
}
@inproceedings{de-kuthy-etal-2016-focus,
    title = "Focus Annotation of Task-based Data: Establishing the Quality of Crowd Annotation",
    author = "De Kuthy, Kordula  and
      Ziai, Ramon  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1713",
    doi = "10.18653/v1/W16-1713",
    pages = "110--119",
}
@inproceedings{cetinoglu-coltekin-2016-part,
    title = "Part of Speech Annotation of a {T}urkish-{G}erman Code-Switching Corpus",
    author = {{\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1714",
    doi = "10.18653/v1/W16-1714",
    pages = "120--130",
}
@inproceedings{gerdes-kahane-2016-dependency,
    title = "Dependency Annotation Choices: Assessing Theoretical and Practical Issues of {U}niversal {D}ependencies",
    author = "Gerdes, Kim  and
      Kahane, Sylvain",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1715",
    doi = "10.18653/v1/W16-1715",
    pages = "131--140",
}
@inproceedings{tandon-etal-2016-conversion,
    title = "Conversion from Paninian Karakas to {U}niversal {D}ependencies for {H}indi Dependency Treebank",
    author = "Tandon, Juhi  and
      Chaudhry, Himani  and
      Bhat, Riyaz Ahmad  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1716",
    doi = "10.18653/v1/W16-1716",
    pages = "141--150",
}
@inproceedings{di-felippo-nenkova-2016-phrase,
    title = "Phrase Generalization: a Corpus Study in Multi-Document Abstracts and Original News Alignments",
    author = "Di-Felippo, Ariani  and
      Nenkova, Ani",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1717",
    doi = "10.18653/v1/W16-1717",
    pages = "151--159",
}
@inproceedings{duan-etal-2016-generating,
    title = "Generating Disambiguating Paraphrases for Structurally Ambiguous Sentences",
    author = "Duan, Manjuan  and
      Hill, Ethan  and
      White, Michael",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1718",
    doi = "10.18653/v1/W16-1718",
    pages = "160--170",
}
@inproceedings{wagner-etal-2016-applying,
    title = "Applying {U}niversal {D}ependency to the {A}rapaho Language",
    author = "Wagner, Irina  and
      Cowell, Andrew  and
      Hwang, Jena D.",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1719",
    doi = "10.18653/v1/W16-1719",
    pages = "171--179",
}
@inproceedings{xue-etal-2016-annotating,
    title = "Annotating the discourse and dialogue structure of {SMS} message conversations",
    author = "Xue, Nianwen  and
      Su, Qishen  and
      Jeong, Sooyoung",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1720",
    doi = "10.18653/v1/W16-1720",
    pages = "180--187",
}
@inproceedings{delozier-etal-2016-creating,
    title = "Creating a Novel Geolocation Corpus from Historical Texts",
    author = "DeLozier, Grant  and
      Wing, Ben  and
      Baldridge, Jason  and
      Nesbit, Scott",
    booktitle = "Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with {ACL} 2016 ({LAW}-X 2016)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1721",
    doi = "10.18653/v1/W16-1721",
    pages = "188--198",
}
@proceedings{ws-2016-representation,
    title = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    editor = "Blunsom, Phil  and
      Cho, Kyunghyun  and
      Cohen, Shay  and
      Grefenstette, Edward  and
      Hermann, Karl Moritz  and
      Rimell, Laura  and
      Weston, Jason  and
      Yih, Scott Wen-tau",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1600",
    doi = "10.18653/v1/W16-16",
}
@inproceedings{arras-etal-2016-explaining,
    title = "Explaining Predictions of Non-Linear Classifiers in {NLP}",
    author = {Arras, Leila  and
      Horn, Franziska  and
      Montavon, Gr{\'e}goire  and
      M{\"u}ller, Klaus-Robert  and
      Samek, Wojciech},
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1601",
    doi = "10.18653/v1/W16-1601",
    pages = "1--7",
}
@inproceedings{baudis-etal-2016-joint,
    title = "Joint Learning of Sentence Embeddings for Relevance and Entailment",
    author = "Baudi{\v{s}}, Petr  and
      Stanko, Silvestr  and
      {\v{S}}ediv{\'y}, Jan",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1602",
    doi = "10.18653/v1/W16-1602",
    pages = "8--17",
}
@inproceedings{cao-rei-2016-joint,
    title = "A Joint Model for Word Embedding and Word Morphology",
    author = "Cao, Kris  and
      Rei, Marek",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1603",
    doi = "10.18653/v1/W16-1603",
    pages = "18--26",
}
@inproceedings{dima-2016-compositionality,
    title = "On the Compositionality and Semantic Interpretation of {E}nglish Noun Compounds",
    author = "Dima, Corina",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1604",
    doi = "10.18653/v1/W16-1604",
    pages = "27--39",
}
@inproceedings{emerson-copestake-2016-functional,
    title = "Functional Distributional Semantics",
    author = "Emerson, Guy  and
      Copestake, Ann",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1605",
    doi = "10.18653/v1/W16-1605",
    pages = "40--52",
}
@inproceedings{hagstedt-p-suorra-mogren-2016-assisting,
    title = "Assisting Discussion Forum Users using Deep Recurrent Neural Networks",
    author = "Hagstedt P Suorra, Jacob  and
      Mogren, Olof",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1606",
    doi = "10.18653/v1/W16-1606",
    pages = "53--61",
}
@inproceedings{kim-etal-2016-adjusting,
    title = "Adjusting Word Embeddings with Semantic Intensity Orders",
    author = "Kim, Joo-Kyung  and
      de Marneffe, Marie-Catherine  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1607",
    doi = "10.18653/v1/W16-1607",
    pages = "62--69",
}
@inproceedings{kim-etal-2016-towards,
    title = "Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization",
    author = "Kim, Minsoo  and
      Moirangthem, Dennis Singh  and
      Lee, Minho",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1608",
    doi = "10.18653/v1/W16-1608",
    pages = "70--77",
}
@inproceedings{lau-baldwin-2016-empirical,
    title = "An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation",
    author = "Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1609",
    doi = "10.18653/v1/W16-1609",
    pages = "78--86",
}
@inproceedings{le-zuidema-2016-quantifying,
    title = "Quantifying the Vanishing Gradient and Long Distance Dependency Problem in Recursive Neural Networks and Recursive {LSTM}s",
    author = "Le, Phong  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1610",
    doi = "10.18653/v1/W16-1610",
    pages = "87--93",
}
@inproceedings{le-etal-2016-lstm,
    title = "{LSTM}-Based Mixture-of-Experts for Knowledge-Aware Dialogues",
    author = "Le, Phong  and
      Dymetman, Marc  and
      Renders, Jean-Michel",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1611",
    doi = "10.18653/v1/W16-1611",
    pages = "94--99",
}
@inproceedings{madhyastha-etal-2016-mapping,
    title = "Mapping Unseen Words to Task-Trained Embedding Spaces",
    author = "Madhyastha, Pranava Swaroop  and
      Bansal, Mohit  and
      Gimpel, Kevin  and
      Livescu, Karen",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1612",
    doi = "10.18653/v1/W16-1612",
    pages = "100--110",
}
@inproceedings{marasovic-frank-2016-multilingual,
    title = "Multilingual Modal Sense Classification using a Convolutional Neural Network",
    author = "Marasovi{\'c}, Ana  and
      Frank, Anette",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1613",
    doi = "10.18653/v1/W16-1613",
    pages = "111--120",
}
@inproceedings{miceli-barone-2016-towards,
    title = "Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders",
    author = "Miceli Barone, Antonio Valerio",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1614",
    doi = "10.18653/v1/W16-1614",
    pages = "121--126",
}
@inproceedings{mitchell-2016-decomposing,
    title = "Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors",
    author = "Mitchell, Jeff",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1615",
    doi = "10.18653/v1/W16-1615",
    pages = "127--136",
}
@inproceedings{nassif-etal-2016-learning,
    title = "Learning Semantic Relatedness in Community Question Answering Using Neural Models",
    author = "Nassif, Henry  and
      Mohtarami, Mitra  and
      Glass, James",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1616",
    doi = "10.18653/v1/W16-1616",
    pages = "137--147",
}
@inproceedings{neculoiu-etal-2016-learning,
    title = "Learning Text Similarity with {S}iamese Recurrent Networks",
    author = "Neculoiu, Paul  and
      Versteegh, Maarten  and
      Rotaru, Mihai",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1617",
    doi = "10.18653/v1/W16-1617",
    pages = "148--157",
}
@inproceedings{nguyen-etal-2016-two,
    title = "A Two-stage Approach for Extending Event Detection to New Types via Neural Networks",
    author = "Nguyen, Thien Huu  and
      Fu, Lisheng  and
      Cho, Kyunghyun  and
      Grishman, Ralph",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1618",
    doi = "10.18653/v1/W16-1618",
    pages = "158--165",
}
@inproceedings{norlund-etal-2016-parameterized,
    title = "Parameterized context windows in Random Indexing",
    author = "Norlund, Tobias  and
      Nilsson, David  and
      Sahlgren, Magnus",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1619",
    doi = "10.18653/v1/W16-1619",
    pages = "166--173",
}
@inproceedings{pelevina-etal-2016-making,
    title = "Making Sense of Word Embeddings",
    author = "Pelevina, Maria  and
      Arefiev, Nikolay  and
      Biemann, Chris  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1620",
    doi = "10.18653/v1/W16-1620",
    pages = "174--183",
}
@inproceedings{ramni-etal-2016-pair,
    title = "Pair Distance Distribution: A Model of Semantic Representation",
    author = "Ramni, Yonatan  and
      Maimon, Oded  and
      Khmelnitsky, Evgeni",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1621",
    doi = "10.18653/v1/W16-1621",
    pages = "184--192",
}
@inproceedings{recski-etal-2016-measuring,
    title = "Measuring Semantic Similarity of Words Using Concept Networks",
    author = "Recski, G{\'a}bor  and
      Ikl{\'o}di, Eszter  and
      Pajkossy, Katalin  and
      Kornai, Andr{\'a}s",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1622",
    doi = "10.18653/v1/W16-1622",
    pages = "193--200",
}
@inproceedings{ruseti-etal-2016-using,
    title = "Using Embedding Masks for Word Categorization",
    author = "Ruseti, Stefan  and
      Rebedea, Traian  and
      Trausan-Matu, Stefan",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1623",
    doi = "10.18653/v1/W16-1623",
    pages = "201--205",
}
@inproceedings{sattigeri-j-thiagarajan-2016-sparsifying,
    title = "Sparsifying Word Representations for Deep Unordered Sentence Modeling",
    author = "Sattigeri, Prasanna  and
      J. Thiagarajan, Jayaraman",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1624",
    doi = "10.18653/v1/W16-1624",
    pages = "206--214",
}
@inproceedings{searle-bingham-walker-2016-blow,
    title = "Why {``}Blow Out{''}? A Structural Analysis of the Movie Dialog Dataset",
    author = "Searle, Richard  and
      Bingham-Walker, Megan",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1625",
    doi = "10.18653/v1/W16-1625",
    pages = "215--221",
}
@inproceedings{sheikh-etal-2016-learning,
    title = "Learning Word Importance with the Neural Bag-of-Words Model",
    author = "Sheikh, Imran  and
      Illina, Irina  and
      Fohr, Dominique  and
      Linar{\`e}s, Georges",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1626",
    doi = "10.18653/v1/W16-1626",
    pages = "222--229",
}
@inproceedings{sokolov-2016-vector,
    title = "A Vector Model for Type-Theoretical Semantics",
    author = "Sokolov, Konstantin",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1627",
    doi = "10.18653/v1/W16-1627",
    pages = "230--238",
}
@inproceedings{triantafillou-etal-2016-towards,
    title = "Towards Generalizable Sentence Embeddings",
    author = "Triantafillou, Eleni  and
      Kiros, Jamie Ryan  and
      Urtasun, Raquel  and
      Zemel, Richard",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1628",
    doi = "10.18653/v1/W16-1628",
    pages = "239--248",
}
@inproceedings{watanabe-etal-2016-domain,
    title = "Domain Adaptation for Neural Networks by Parameter Augmentation",
    author = "Watanabe, Yusuke  and
      Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1629",
    doi = "10.18653/v1/W16-1629",
    pages = "249--257",
}
@inproceedings{weissenborn-2016-neural,
    title = "Neural Associative Memory for Dual-Sequence Modeling",
    author = "Weissenborn, Dirk",
    booktitle = "Proceedings of the 1st Workshop on Representation Learning for {NLP}",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1630",
    doi = "10.18653/v1/W16-1630",
    pages = "258--266",
}
@proceedings{ws-2016-joint,
    title = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    editor = "Cabanac, Guillaume  and
      Chandrasekaran, Muthu Kumar  and
      Frommholz, Ingo  and
      Jaidka, Kokil  and
      Kan, Min-Yen  and
      Mayr, Philipp  and
      Wolfram, Dietmar",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1500",
    pages = "1--5",
}
@inproceedings{wolfram-2016-bibliometrics,
    title = "Bibliometrics, Information Retrieval and Natural Language Processing: Natural Synergies to Support Digital Library Research",
    author = "Wolfram, Dietmar",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1501",
    pages = "6--13",
}
@inproceedings{bertin-atanassova-2016-multiple,
    title = "Multiple In-text Reference Aggregation Phenomenon",
    author = "Bertin, Marc  and
      Atanassova, Iana",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1502",
    pages = "14--22",
}
@inproceedings{halevi-bar-ilan-2016-post,
    title = "Post Retraction Citations in Context",
    author = "Halevi, Gali  and
      Bar-Ilan, Judit",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1503",
    pages = "23--29",
}
@inproceedings{eto-2016-incorporating,
    title = "Incorporating Satellite Documents into Co-citation Networks for Scientific Paper Searches",
    author = "Eto, Masaki",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1504",
    pages = "30--35",
}
@inproceedings{ronzano-etal-2016-making,
    title = "Making Sense of Massive Amounts of Scientific Publications: the Scientific Knowledge Miner Project",
    author = "Ronzano, Francesco  and
      Freire, Ana  and
      Saez-Trumper, Diego  and
      Saggion, Horacio",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1505",
    pages = "36--41",
}
@inproceedings{kim-etal-2016-exploring,
    title = "Exploring the Leading Authors and Journals in Major Topics by Citation Sentences and Topic Modeling",
    author = "Kim, Ha Jin  and
      An, Juyoung  and
      Jeong, Yoo Kyung  and
      Song, Min",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1506",
    pages = "42--50",
}
@inproceedings{raamkumar-etal-2016-papers,
    title = "What Papers Should {I} Cite from my Reading List? User Evaluation of a Manuscript Preparatory Assistive Task",
    author = "Raamkumar, Aravind Sesagiri  and
      Foo, Schubert  and
      Pang, Natalie",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1507",
    pages = "51--62",
}
@inproceedings{west-portenoy-2016-delineating,
    title = "Delineating Fields Using Mathematical Jargon",
    author = "West, Jevin  and
      Portenoy, Jason",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1508",
    pages = "63--71",
}
@inproceedings{mariani-etal-2016-study,
    title = "A Study of Reuse and Plagiarism in Speech and Natural Language Processing papers",
    author = "Mariani, Joseph  and
      Francopoulo, Gil  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1509",
    pages = "72--83",
}
@inproceedings{mayr-2016-practitioners,
    title = "How do Practitioners, {P}h{D} Students and Postdocs in the Social Sciences Assess Topic-specific Recommendations?",
    author = "Mayr, Philipp",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1510",
    pages = "84--92",
}
@inproceedings{jaidka-etal-2016-overview,
    title = "Overview of the {CL}-{S}ci{S}umm 2016 Shared Task",
    author = "Jaidka, Kokil  and
      Kumar Chandrasekaran, Muthu  and
      Rustagi, Sajal  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1511",
    pages = "93--102",
}
@inproceedings{aggarwal-sharma-2016-lexical,
    title = "Lexical and Syntactic cues to identify Reference Scope of Citance",
    author = "Aggarwal, Peeyush  and
      Sharma, Richa",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1512",
    pages = "103--112",
}
@inproceedings{moraes-etal-2016-university,
    title = "{U}niversity of {H}ouston at {CL}-{S}ci{S}umm 2016: {SVM}s with tree kernels and Sentence Similarity",
    author = "Moraes, Luis  and
      Baki, Shahryar  and
      Verma, Rakesh  and
      Lee, Daniel",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1513",
    pages = "113--121",
}
@inproceedings{klampfl-etal-2016-identifying,
    title = "Identifying Referenced Text in Scientific Publications by Summarisation and Classification Techniques",
    author = "Klampfl, Stefan  and
      Rexha, Andi  and
      Kern, Roman",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1514",
    pages = "122--131",
}
@inproceedings{cao-etal-2016-polyu,
    title = "{P}oly{U} at {CL}-{S}ci{S}umm 2016",
    author = "Cao, Ziqiang  and
      Li, Wenjie  and
      Wu, Dapeng",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1515",
    pages = "132--138",
}
@inproceedings{lu-etal-2016-recognizing,
    title = "Recognizing Reference Spans and Classifying their Discourse Facets",
    author = "Lu, Kun  and
      Mao, Jin  and
      Li, Gang  and
      Xu, Jian",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1516",
    pages = "139--145",
}
@inproceedings{malenfant-lapalme-2016-rali,
    title = "{RALI} System Description for {CL}-{S}ci{S}umm 2016 Shared Task",
    author = "Malenfant, Bruno  and
      Lapalme, Guy",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1517",
    pages = "146--155",
}
@inproceedings{li-etal-2016-cist,
    title = "{CIST} System for {CL}-{S}ci{S}umm 2016 Shared Task",
    author = "Li, Lei  and
      Mao, Liyuan  and
      Zhang, Yazhao  and
      Chi, Junqi  and
      Huang, Taiwen  and
      Cong, Xiaoyue  and
      Peng, Heng",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1518",
    pages = "156--167",
}
@inproceedings{nomoto-2016-neal,
    title = "{NEAL}: A Neurally Enhanced Approach to Linking Citation and Reference",
    author = "Nomoto, Tadashi",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1519",
    pages = "168--174",
}
@inproceedings{saggion-etal-2016-trainable,
    title = "Trainable Citation-enhanced Summarization of Scientific Articles",
    author = "Saggion, Horacio  and
      AbuRa{'}ed, Ahmed  and
      Ronzano, Francesco",
    booktitle = "Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries ({BIRNDL})",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/W16-1520",
    pages = "175--186",
}
@proceedings{ws-2016-textgraphs,
    title = "Proceedings of {T}ext{G}raphs-10: the Workshop on Graph-based Methods for Natural Language Processing",
    editor = "Chakraborty, Tanmoy  and
      Riedl, Martin  and
      Vydiswaran, V.G.Vinod",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1400",
    doi = "10.18653/v1/W16-14",
}
@inproceedings{nieto-pina-johansson-2016-embedding,
    title = "Embedding Senses for Efficient Graph-based Word Sense Disambiguation",
    author = "Nieto Pi{\~n}a, Luis  and
      Johansson, Richard",
    booktitle = "Proceedings of {T}ext{G}raphs-10: the Workshop on Graph-based Methods for Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1401",
    doi = "10.18653/v1/W16-1401",
    pages = "1--5",
}
@inproceedings{demir-2016-context,
    title = "Context Tailoring for Text Normalization",
    author = "Demir, Seniz",
    booktitle = "Proceedings of {T}ext{G}raphs-10: the Workshop on Graph-based Methods for Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1402",
    doi = "10.18653/v1/W16-1402",
    pages = "6--14",
}
@inproceedings{pouran-ben-veyseh-2016-cross,
    title = "Cross-Lingual Question Answering Using Common Semantic Space",
    author = "Pouran Ben Veyseh, Amir",
    booktitle = "Proceedings of {T}ext{G}raphs-10: the Workshop on Graph-based Methods for Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1403",
    doi = "10.18653/v1/W16-1403",
    pages = "15--19",
}
@inproceedings{arnold-weihe-2016-network,
    title = "Network Motifs May Improve Quality Assessment of Text Documents",
    author = "Arnold, Thomas  and
      Weihe, Karsten",
    booktitle = "Proceedings of {T}ext{G}raphs-10: the Workshop on Graph-based Methods for Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1404",
    doi = "10.18653/v1/W16-1404",
    pages = "20--28",
}
@inproceedings{lai-etal-2016-better,
    title = "Better Together: Combining Language and Social Interactions into a Shared Representation",
    author = "Lai, Yi-Yu  and
      Li, Chang  and
      Goldwasser, Dan  and
      Neville, Jennifer",
    booktitle = "Proceedings of {T}ext{G}raphs-10: the Workshop on Graph-based Methods for Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1405",
    doi = "10.18653/v1/W16-1405",
    pages = "29--33",
}
@inproceedings{rodin-etal-2016-visualization,
    title = "Visualization of Dynamic Reference Graphs",
    author = "Rodin, Ivan  and
      Chernyak, Ekaterina  and
      Dubov, Mikhail  and
      Mirkin, Boris",
    booktitle = "Proceedings of {T}ext{G}raphs-10: the Workshop on Graph-based Methods for Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1406",
    doi = "10.18653/v1/W16-1406",
    pages = "34--38",
}
@proceedings{ws-2016-automated,
    title = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    editor = "Pujara, Jay  and
      Rocktaschel, Tim  and
      Chen, Danqi  and
      Singh, Sameer",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1300",
    doi = "10.18653/v1/W16-13",
}
@inproceedings{bing-etal-2016-using,
    title = "Using Graphs of Classifiers to Impose Constraints on Semi-supervised Relation Extraction",
    author = "Bing, Lidong  and
      Cohen, William  and
      Dhingra, Bhuwan  and
      Wang, Richard",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1301",
    doi = "10.18653/v1/W16-1301",
    pages = "1--6",
}
@inproceedings{chisholm-etal-2016-discovering,
    title = "Discovering Entity Knowledge Bases on the Web",
    author = "Chisholm, Andrew  and
      Radford, Will  and
      Hachey, Ben",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1302",
    doi = "10.18653/v1/W16-1302",
    pages = "7--11",
}
@inproceedings{dalvi-etal-2016-ike,
    title = "{IKE} - An Interactive Tool for Knowledge Extraction",
    author = "Dalvi, Bhavana  and
      Bhakthavatsalam, Sumithra  and
      Clark, Chris  and
      Clark, Peter  and
      Etzioni, Oren  and
      Fader, Anthony  and
      Groeneveld, Dirk",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1303",
    doi = "10.18653/v1/W16-1303",
    pages = "12--17",
}
@inproceedings{das-etal-2016-incorporating,
    title = "Incorporating Selectional Preferences in Multi-hop Relation Extraction",
    author = "Das, Rajarshi  and
      Neelakantan, Arvind  and
      Belanger, David  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1304",
    doi = "10.18653/v1/W16-1304",
    pages = "18--23",
}
@inproceedings{gao-etal-2016-knowledge,
    title = "Knowledge Base Population for Organization Mentions in Email",
    author = "Gao, Ning  and
      Dredze, Mark  and
      Oard, Douglas",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1305",
    doi = "10.18653/v1/W16-1305",
    pages = "24--28",
}
@inproceedings{mousselly-sergieh-gurevych-2016-enriching,
    title = "Enriching {W}ikidata with Frame Semantics",
    author = "Mousselly-Sergieh, Hatem  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1306",
    doi = "10.18653/v1/W16-1306",
    pages = "29--34",
}
@inproceedings{pal-mausam-2016-demonyms,
    title = "Demonyms and Compound Relational Nouns in Nominal Open {IE}",
    author = "Pal, Harinder  and
      {Mausam}",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1307",
    doi = "10.18653/v1/W16-1307",
    pages = "35--39",
}
@inproceedings{razniewski-etal-2016-actually,
    title = "But What Do We Actually Know?",
    author = "Razniewski, Simon  and
      Suchanek, Fabian  and
      Nutt, Werner",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1308",
    doi = "10.18653/v1/W16-1308",
    pages = "40--44",
}
@inproceedings{rocktaschel-riedel-2016-learning,
    title = "Learning Knowledge Base Inference with Neural Theorem Provers",
    author = {Rockt{\"a}schel, Tim  and
      Riedel, Sebastian},
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1309",
    doi = "10.18653/v1/W16-1309",
    pages = "45--50",
}
@inproceedings{russell-etal-2016-physics,
    title = "The Physics of Text: Ontological Realism in Information Extraction",
    author = "Russell, Stuart  and
      Lassen, Ole Torp  and
      Uang, Justin  and
      Wang, Wei",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1310",
    doi = "10.18653/v1/W16-1310",
    pages = "51--56",
}
@inproceedings{nag-chowdhury-etal-2016-know2look,
    title = "{K}now2{L}ook: Commonsense Knowledge for Visual Search",
    author = "Nag Chowdhury, Sreyasi  and
      Tandon, Niket  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1311",
    doi = "10.18653/v1/W16-1311",
    pages = "57--62",
}
@inproceedings{verga-mccallum-2016-row,
    title = "Row-less Universal Schema",
    author = "Verga, Patrick  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1312",
    doi = "10.18653/v1/W16-1312",
    pages = "63--68",
}
@inproceedings{shimaoka-etal-2016-attentive,
    title = "An Attentive Neural Architecture for Fine-grained Entity Type Classification",
    author = "Shimaoka, Sonse  and
      Stenetorp, Pontus  and
      Inui, Kentaro  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1313",
    doi = "10.18653/v1/W16-1313",
    pages = "69--74",
}
@inproceedings{demeester-etal-2016-regularizing,
    title = "Regularizing Relation Representations by First-order Implications",
    author = {Demeester, Thomas  and
      Rockt{\"a}schel, Tim  and
      Riedel, Sebastian},
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1314",
    doi = "10.18653/v1/W16-1314",
    pages = "75--80",
}
@inproceedings{groth-etal-2016-applying,
    title = "Applying Universal Schemas for Domain Specific Ontology Expansion",
    author = "Groth, Paul  and
      Pal, Sujit  and
      McBeath, Darin  and
      Allen, Brad  and
      Daniel, Ron",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1315",
    doi = "10.18653/v1/W16-1315",
    pages = "81--85",
}
@inproceedings{machida-etal-2016-design,
    title = "Design of Word Association Games using Dialog Systems for Acquisition of Word Association Knowledge",
    author = "Machida, Yuichiro  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao  and
      Sassano, Manabu",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1316",
    doi = "10.18653/v1/W16-1316",
    pages = "86--91",
}
@inproceedings{martin-etal-2016-call,
    title = "Call for Discussion: Building a New Standard Dataset for Relation Extraction Tasks",
    author = "Martin, Teresa  and
      Botschen, Fiete  and
      Nagesh, Ajay  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1317",
    doi = "10.18653/v1/W16-1317",
    pages = "92--96",
}
@inproceedings{soni-etal-2016-comparison,
    title = "A Comparison of Weak Supervision methods for Knowledge Base Construction",
    author = "Soni, Ameet  and
      Viswanathan, Dileep  and
      Pachaiyappan, Niranjan  and
      Natarajan, Sriraam",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1318",
    doi = "10.18653/v1/W16-1318",
    pages = "97--102",
}
@inproceedings{welbl-etal-2016-factorization,
    title = "A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion",
    author = "Welbl, Johannes  and
      Bouchard, Guillaume  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 5th Workshop on Automated Knowledge Base Construction",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1319",
    doi = "10.18653/v1/W16-1319",
    pages = "103--107",
}
@proceedings{ws-2016-multilingual,
    title = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    editor = "Das, Dipanjan  and
      Dyer, Chris  and
      Faruqui, Manaal  and
      Tsvetkov, Yulia",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1200",
    doi = "10.18653/v1/W16-12",
}
@inproceedings{aldarmaki-diab-2016-learning,
    title = "Learning Cross-lingual Representations with Matrix Factorization",
    author = "Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1201",
    doi = "10.18653/v1/W16-1201",
    pages = "1--9",
}
@inproceedings{de-lhoneux-nivre-2016-investigating,
    title = "Should Have, Would Have, Could Have. Investigating Verb Group Representations for Parsing with {U}niversal {D}ependencies.",
    author = "de Lhoneux, Miryam  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1202",
    doi = "10.18653/v1/W16-1202",
    pages = "10--19",
}
@inproceedings{lacroix-etal-2016-cross,
    title = "Cross-lingual Dependency Transfer : What Matters? Assessing the Impact of Pre- and Post-processing",
    author = "Lacroix, Oph{\'e}lie  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1203",
    doi = "10.18653/v1/W16-1203",
    pages = "20--29",
}
@inproceedings{al-tarouti-kalita-2016-enhancing,
    title = "Enhancing Automatic {W}ordnet Construction Using Word Embeddings",
    author = "Al Tarouti, Feras  and
      Kalita, Jugal",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1204",
    doi = "10.18653/v1/W16-1204",
    pages = "30--34",
}
@inproceedings{aufrant-etal-2016-cross,
    title = "Cross-lingual alignment transfer: a chicken-and-egg story?",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1205",
    doi = "10.18653/v1/W16-1205",
    pages = "35--44",
}
@inproceedings{wan-2016-leveraging,
    title = "Leveraging Data-Driven Methods in Word-Level Language Identification for a Multilingual Alpine Heritage Corpus",
    author = "Wan, Ada",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1206",
    doi = "10.18653/v1/W16-1206",
    pages = "45--54",
}
@inproceedings{saers-wu-2016-learning,
    title = "Learning Translations for Tagged Words: Extending the Translation Lexicon of an {ITG} for Low Resource Languages",
    author = "Saers, Markus  and
      Wu, Dekai",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1207",
    doi = "10.18653/v1/W16-1207",
    pages = "55--64",
}
@inproceedings{asgari-mofrad-2016-comparing,
    title = "Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence ({WELD}) as a Quantitative Measure of Language Distance",
    author = "Asgari, Ehsaneddin  and
      Mofrad, Mohammad R.K.",
    booktitle = "Proceedings of the Workshop on Multilingual and Cross-lingual Methods in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1208",
    doi = "10.18653/v1/W16-1208",
    pages = "65--74",
}
@proceedings{ws-2016-metaphor,
    title = "Proceedings of the Fourth Workshop on Metaphor in {NLP}",
    editor = "Beigman Klebanov, Beata  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1100",
    doi = "10.18653/v1/W16-11",
}
@inproceedings{lederer-2016-finding,
    title = "Finding metaphorical triggers through source (not target) domain lexicalization patterns",
    author = "Lederer, Jenny",
    booktitle = "Proceedings of the Fourth Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1101",
    doi = "10.18653/v1/W16-1101",
    pages = "1--9",
}
@inproceedings{haagsma-bjerva-2016-detecting,
    title = "Detecting novel metaphor using selectional preference information",
    author = "Haagsma, Hessel  and
      Bjerva, Johannes",
    booktitle = "Proceedings of the Fourth Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1102",
    doi = "10.18653/v1/W16-1102",
    pages = "10--17",
}
@inproceedings{rai-etal-2016-supervised,
    title = "Supervised Metaphor Detection using Conditional Random Fields",
    author = "Rai, Sunny  and
      Chakraverty, Shampa  and
      Tayal, Devendra K.",
    booktitle = "Proceedings of the Fourth Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1103",
    doi = "10.18653/v1/W16-1103",
    pages = "18--27",
}
@inproceedings{do-dinh-gurevych-2016-token,
    title = "Token-Level Metaphor Detection using Neural Networks",
    author = "Do Dinh, Erik-L{\^a}n  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Fourth Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1104",
    doi = "10.18653/v1/W16-1104",
    pages = "28--33",
}
@inproceedings{veale-2016-round,
    title = "Round Up The Usual Suspects: Knowledge-Based Metaphor Generation",
    author = "Veale, Tony",
    booktitle = "Proceedings of the Fourth Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1105",
    doi = "10.18653/v1/W16-1105",
    pages = "34--41",
}
@proceedings{ws-2016-events,
    title = "Proceedings of the Fourth Workshop on Events",
    editor = "Palmer, Martha  and
      Hovy, Ed  and
      Mitamura, Teruko  and
      O{'}Gorman, Tim",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1000",
    doi = "10.18653/v1/W16-10",
}
@inproceedings{upadhyay-etal-2016-making,
    title = "{``}Making the News{''}: Identifying Noteworthy Events in News Articles",
    author = "Upadhyay, Shyam  and
      Christodoulopoulos, Christos  and
      Roth, Dan",
    booktitle = "Proceedings of the Fourth Workshop on Events",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1001",
    doi = "10.18653/v1/W16-1001",
    pages = "1--7",
}
@inproceedings{croft-etal-2016-annotation,
    title = "Annotation of causal and aspectual structure of events in {RED}: a preliminary report",
    author = "Croft, William  and
      Pe{\v{s}}kov{\'a}, Pavlina  and
      Regan, Michael",
    booktitle = "Proceedings of the Fourth Workshop on Events",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1002",
    doi = "10.18653/v1/W16-1002",
    pages = "8--17",
}
@inproceedings{bonial-etal-2016-multimodal,
    title = "Multimodal Use of an Upper-Level Event Ontology",
    author = "Bonial, Claire  and
      Tahmoush, David  and
      Windisch Brown, Susan  and
      Palmer, Martha",
    booktitle = "Proceedings of the Fourth Workshop on Events",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1003",
    doi = "10.18653/v1/W16-1003",
    pages = "18--26",
}
@inproceedings{bies-etal-2016-comparison,
    title = "A Comparison of Event Representations in {DEFT}",
    author = "Bies, Ann  and
      Song, Zhiyi  and
      Getman, Jeremy  and
      Ellis, Joe  and
      Mott, Justin  and
      Strassel, Stephanie  and
      Palmer, Martha  and
      Mitamura, Teruko  and
      Freedman, Marjorie  and
      Ji, Heng  and
      O{'}Gorman, Tim",
    booktitle = "Proceedings of the Fourth Workshop on Events",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1004",
    doi = "10.18653/v1/W16-1004",
    pages = "27--36",
}
@inproceedings{song-etal-2016-event,
    title = "Event Nugget and Event Coreference Annotation",
    author = "Song, Zhiyi  and
      Bies, Ann  and
      Strassel, Stephanie  and
      Ellis, Joe  and
      Mitamura, Teruko  and
      Dang, Hoa Trang  and
      Yamakawa, Yukari  and
      Holm, Sue",
    booktitle = "Proceedings of the Fourth Workshop on Events",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1005",
    doi = "10.18653/v1/W16-1005",
    pages = "37--45",
}
@inproceedings{nakamura-kawahara-2016-constructing,
    title = "Constructing a Dictionary Describing Feature Changes of Arguments in Event Sentences",
    author = "Nakamura, Tetsuaki  and
      Kawahara, Daisuke",
    booktitle = "Proceedings of the Fourth Workshop on Events",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1006",
    doi = "10.18653/v1/W16-1006",
    pages = "46--50",
}
@inproceedings{mostafazadeh-etal-2016-caters,
    title = "{C}a{T}e{RS}: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures",
    author = "Mostafazadeh, Nasrin  and
      Grealish, Alyson  and
      Chambers, Nathanael  and
      Allen, James  and
      Vanderwende, Lucy",
    booktitle = "Proceedings of the Fourth Workshop on Events",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-1007",
    doi = "10.18653/v1/W16-1007",
    pages = "51--61",
}
@proceedings{ws-2016-discontinuous,
    title = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    editor = {Maier, Wolfgang  and
      K{\"u}bler, Sandra  and
      Orasan, Constantin},
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0900",
    doi = "10.18653/v1/W16-09",
}
@inproceedings{snijders-2016-lfg,
    title = "An {LFG} Account of Discontinuous Nominal Expressions",
    author = "Snijders, Liselotte",
    booktitle = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0901",
    doi = "10.18653/v1/W16-0901",
    pages = "1--11",
}
@inproceedings{uresova-etal-2016-non,
    title = "Non-projectivity and valency",
    author = "Uresova, Zdenka  and
      Fucikova, Eva  and
      Hajic, Jan",
    booktitle = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0902",
    doi = "10.18653/v1/W16-0902",
    pages = "12--21",
}
@inproceedings{barreiro-batista-2016-machine,
    title = "Machine Translation of Non-Contiguous Multiword Units",
    author = "Barreiro, Anabela  and
      Batista, Fernando",
    booktitle = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0903",
    doi = "10.18653/v1/W16-0903",
    pages = "22--30",
}
@inproceedings{balabanova-2016-discontinuous,
    title = "Discontinuous {VP} in {B}ulgarian",
    author = "Balabanova, Elisaveta",
    booktitle = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0904",
    doi = "10.18653/v1/W16-0904",
    pages = "31--36",
}
@inproceedings{sulger-2016-discontinuous,
    title = "Discontinuous Genitives in {H}indi/{U}rdu",
    author = "Sulger, Sebastian",
    booktitle = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0905",
    doi = "10.18653/v1/W16-0905",
    pages = "37--46",
}
@inproceedings{maier-lichte-2016-discontinuous,
    title = "Discontinuous parsing with continuous trees",
    author = "Maier, Wolfgang  and
      Lichte, Timm",
    booktitle = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0906",
    doi = "10.18653/v1/W16-0906",
    pages = "47--57",
}
@inproceedings{versley-2016-discontinuity,
    title = "Discontinuity (Re){\mbox{$^2$}}-visited: A Minimalist Approach to Pseudoprojective Constituent Parsing",
    author = "Versley, Yannick",
    booktitle = "Proceedings of the Workshop on Discontinuous Structures in Natural Language Processing",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0907",
    doi = "10.18653/v1/W16-0907",
    pages = "58--69",
}
@proceedings{ws-2016-approaches-deception,
    title = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    editor = "Fornaciari, Tommaso  and
      Fitzpatrick, Eileen  and
      Bachenko, Joan",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0800",
    doi = "10.18653/v1/W16-08",
}
@inproceedings{volkova-bell-2016-account,
    title = "Account Deletion Prediction on {R}u{N}et: A Case Study of Suspicious {T}witter Accounts Active During the {R}ussian-{U}krainian Crisis",
    author = "Volkova, Svitlana  and
      Bell, Eric",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0801",
    doi = "10.18653/v1/W16-0801",
    pages = "1--6",
}
@inproceedings{rubin-etal-2016-fake,
    title = "Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News",
    author = "Rubin, Victoria  and
      Conroy, Niall  and
      Chen, Yimin  and
      Cornwell, Sarah",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0802",
    doi = "10.18653/v1/W16-0802",
    pages = "7--17",
}
@inproceedings{kleinberg-etal-2016-using,
    title = "Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach",
    author = "Kleinberg, Bennett  and
      Nahari, Galit  and
      Verschuere, Bruno",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0803",
    doi = "10.18653/v1/W16-0803",
    pages = "18--25",
}
@inproceedings{fitzpatrick-bachenko-2016-estimating,
    title = "Estimating the amenibility of new domains for deception detection",
    author = "Fitzpatrick, Eileen  and
      Bachenko, Joan",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0804",
    doi = "10.18653/v1/W16-0804",
    pages = "26--31",
}
@inproceedings{kunath-mccabe-2016-use,
    title = "The Use of Second Life for Deception Detection Research",
    author = "Kunath, Stephen  and
      McCabe, Kevin",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0805",
    doi = "10.18653/v1/W16-0805",
    pages = "32--39",
}
@inproceedings{levitan-etal-2016-identifying,
    title = "Identifying Individual Differences in Gender, Ethnicity, and Personality from Dialogue for Deception Detection",
    author = "Levitan, Sarah Ita  and
      Levitan, Yocheved  and
      An, Guozhen  and
      Levine, Michelle  and
      Levitan, Rivka  and
      Rosenberg, Andrew  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0806",
    doi = "10.18653/v1/W16-0806",
    pages = "40--44",
}
@inproceedings{appling-briscoe-2016-individual,
    title = "Individual Differences in Strategic Deception",
    author = "Appling, Scott  and
      Briscoe, Erica",
    booktitle = "Proceedings of the Second Workshop on Computational Approaches to Deception Detection",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0807",
    doi = "10.18653/v1/W16-0807",
    pages = "45--52",
}
@proceedings{ws-2016-coreference,
    title = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    editor = "Ogrodniczuk, Maciej  and
      Ng, Vincent",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0700",
    doi = "10.18653/v1/W16-07",
}
@inproceedings{recasens-etal-2016-sense,
    title = "Sense Anaphoric Pronouns: Am {I} One?",
    author = "Recasens, Marta  and
      Hu, Zhichao  and
      Rhinehart, Olivia",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0701",
    doi = "10.18653/v1/W16-0701",
    pages = "1--6",
}
@inproceedings{grishina-2016-experiments,
    title = "Experiments on bridging across languages and genres",
    author = "Grishina, Yulia",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0702",
    doi = "10.18653/v1/W16-0702",
    pages = "7--15",
}
@inproceedings{ogrodniczuk-zawislawska-2016-bridging,
    title = "Bridging Relations in {P}olish: Adaptation of Existing Typologies",
    author = "Ogrodniczuk, Maciej  and
      Zawis{\l}awska, Magdalena",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0703",
    doi = "10.18653/v1/W16-0703",
    pages = "16--22",
}
@inproceedings{kunz-etal-2016-beyond,
    title = "Beyond Identity Coreference: Contrasting Indicators of Textual Coherence in {E}nglish and {G}erman",
    author = "Kunz, Kerstin  and
      Lapshinova-Koltunski, Ekaterina  and
      Mart{\'\i}nez, Jos{\'e} Manuel",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0704",
    doi = "10.18653/v1/W16-0704",
    pages = "23--31",
}
@inproceedings{liu-etal-2016-exploring,
    title = "Exploring the steps of Verb Phrase Ellipsis",
    author = "Liu, Zhengzhong  and
      Gonz{\`a}lez Pellicer, Edgar  and
      Gillick, Daniel",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0705",
    doi = "10.18653/v1/W16-0705",
    pages = "32--40",
}
@inproceedings{stede-grishina-2016-anaphoricity,
    title = "Anaphoricity in Connectives: A Case Study on {G}erman",
    author = "Stede, Manfred  and
      Grishina, Yulia",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0706",
    doi = "10.18653/v1/W16-0706",
    pages = "41--46",
}
@inproceedings{nedoluzhko-lapshinova-koltunski-2016-abstract,
    title = "Abstract Coreference in a Multilingual Perspective: a View on {C}zech and {G}erman",
    author = "Nedoluzhko, Anna  and
      Lapshinova-Koltunski, Ekaterina",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0707",
    doi = "10.18653/v1/W16-0707",
    pages = "47--52",
}
@inproceedings{wiseman-etal-2016-antecedent,
    title = "Antecedent Prediction Without a Pipeline",
    author = "Wiseman, Sam  and
      Rush, Alexander M.  and
      Shieber, Stuart",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0708",
    doi = "10.18653/v1/W16-0708",
    pages = "53--58",
}
@inproceedings{roitberg-nedoluzhko-2016-bridging,
    title = "Bridging Corpus for {R}ussian in comparison with {C}zech",
    author = "Roitberg, Anna  and
      Nedoluzhko, Anna",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0709",
    doi = "10.18653/v1/W16-0709",
    pages = "59--66",
}
@inproceedings{soraluze-etal-2016-coreference,
    title = "Coreference Resolution for the {B}asque Language with {BART}",
    author = "Soraluze, Ander  and
      Arregi, Olatz  and
      Arregi, Xabier  and
      D{\'\i}az de Ilarraza, Arantza  and
      Kabadjov, Mijail  and
      Poesio, Massimo",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0710",
    doi = "10.18653/v1/W16-0710",
    pages = "67--73",
}
@inproceedings{toldova-etal-2016-error,
    title = "Error analysis for anaphora resolution in {R}ussian: new challenging issues for anaphora resolution task in a morphologically rich language",
    author = "Toldova, Svetlana  and
      Azerkovich, Ilya  and
      Ladygina, Alina  and
      Roitberg, Anna  and
      Vasilyeva, Maria",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0711",
    doi = "10.18653/v1/W16-0711",
    pages = "74--83",
}
@inproceedings{sundar-ram-lalitha-devi-2016-handle,
    title = "How to Handle Split Antecedents in {T}amil?",
    author = "Sundar Ram, Vijay  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0712",
    doi = "10.18653/v1/W16-0712",
    pages = "84--91",
}
@inproceedings{zeldes-zhang-2016-annotation,
    title = "When Annotation Schemes Change Rules Help: A Configurable Approach to Coreference Resolution beyond {O}nto{N}otes",
    author = "Zeldes, Amir  and
      Zhang, Shuo",
    booktitle = "Proceedings of the Workshop on Coreference Resolution Beyond {O}nto{N}otes ({CORBON} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0713",
    doi = "10.18653/v1/W16-0713",
    pages = "92--101",
}
@proceedings{ws-2016-semantics,
    title = "Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation ({S}ed{MT} 2016)",
    editor = "Xiong, Deyi  and
      Duh, Kevin  and
      Agirre, Eneko  and
      Aranberri, Nora  and
      Wang, Houfeng",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0600",
    doi = "10.18653/v1/W16-06",
}
@inproceedings{butler-2016-deterministic,
    title = "Deterministic natural language generation from meaning representations for machine translation",
    author = "Butler, Alastair",
    booktitle = "Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation ({S}ed{MT} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0601",
    doi = "10.18653/v1/W16-0601",
    pages = "1--9",
}
@inproceedings{li-etal-2016-extending,
    title = "Extending Phrase-Based Translation with Dependencies by Using Graphs",
    author = "Li, Liangyou  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation ({S}ed{MT} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0602",
    doi = "10.18653/v1/W16-0602",
    pages = "10--12",
}
@inproceedings{ge-song-2016-naming,
    title = "The Naming Sharing Structure and its Cognitive Meaning in {C}hinese and {E}nglish",
    author = "Ge, Shili  and
      Song, Rou",
    booktitle = "Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation ({S}ed{MT} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0603",
    doi = "10.18653/v1/W16-0603",
    pages = "13--21",
}
@inproceedings{simov-etal-2016-towards,
    title = "Towards Semantic-based Hybrid Machine Translation between {B}ulgarian and {E}nglish",
    author = "Simov, Kiril  and
      Osenova, Petya  and
      Popov, Alexander",
    booktitle = "Proceedings of the 2nd Workshop on Semantics-Driven Machine Translation ({S}ed{MT} 2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0604",
    doi = "10.18653/v1/W16-0604",
    pages = "22--26",
}
@proceedings{ws-2016-innovative,
    title = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    editor = "Tetreault, Joel  and
      Burstein, Jill  and
      Leacock, Claudia  and
      Yannakoudakis, Helen",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0500",
    doi = "10.18653/v1/W16-05",
}
@inproceedings{napoles-etal-2016-effect,
    title = "The Effect of Multiple Grammatical Errors on Processing Non-Native Writing",
    author = "Napoles, Courtney  and
      Cahill, Aoife  and
      Madnani, Nitin",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0501",
    doi = "10.18653/v1/W16-0501",
    pages = "1--11",
}
@inproceedings{xia-etal-2016-text,
    title = "Text Readability Assessment for Second Language Learners",
    author = "Xia, Menglin  and
      Kochmar, Ekaterina  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0502",
    doi = "10.18653/v1/W16-0502",
    pages = "12--22",
}
@inproceedings{hill-simha-2016-automatic,
    title = "Automatic Generation of Context-Based Fill-in-the-Blank Exercises Using Co-occurrence Likelihoods and {G}oogle n-grams",
    author = "Hill, Jennifer  and
      Simha, Rahul",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0503",
    doi = "10.18653/v1/W16-0503",
    pages = "23--30",
}
@inproceedings{flor-etal-2016-automated,
    title = "Automated classification of collaborative problem solving interactions in simulated science tasks",
    author = "Flor, Michael  and
      Yoon, Su-Youn  and
      Hao, Jiangang  and
      Liu, Lei  and
      von Davier, Alina",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0504",
    doi = "10.18653/v1/W16-0504",
    pages = "31--41",
}
@inproceedings{meyer-koch-2016-computer,
    title = "Computer-assisted stylistic revision with incomplete and noisy feedback. A pilot study",
    author = "Meyer, Christian M.  and
      Koch, Johann Frerik",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0505",
    doi = "10.18653/v1/W16-0505",
    pages = "42--52",
}
@inproceedings{daudaravicius-etal-2016-report,
    title = "A Report on the Automatic Evaluation of Scientific Writing Shared Task",
    author = "Daudaravicius, Vidas  and
      Banchs, Rafael E.  and
      Volodina, Elena  and
      Napoles, Courtney",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0506",
    doi = "10.18653/v1/W16-0506",
    pages = "53--62",
}
@inproceedings{beigman-klebanov-etal-2016-topicality,
    title = "Topicality-Based Indices for Essay Scoring",
    author = "Beigman Klebanov, Beata  and
      Flor, Michael  and
      Gyawali, Binod",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0507",
    doi = "10.18653/v1/W16-0507",
    pages = "63--72",
}
@inproceedings{beinborn-etal-2016-predicting,
    title = "Predicting the Spelling Difficulty of Words for Language Learners",
    author = "Beinborn, Lisa  and
      Zesch, Torsten  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0508",
    doi = "10.18653/v1/W16-0508",
    pages = "73--83",
}
@inproceedings{chen-meurers-2016-characterizing,
    title = "Characterizing Text Difficulty with Word Frequencies",
    author = "Chen, Xiaobin  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0509",
    doi = "10.18653/v1/W16-0509",
    pages = "84--94",
}
@inproceedings{cummins-etal-2016-unsupervised,
    title = "Unsupervised Modeling of Topical Relevance in {L}2 Learner Text",
    author = "Cummins, Ronan  and
      Yannakoudakis, Helen  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0510",
    doi = "10.18653/v1/W16-0510",
    pages = "95--104",
}
@inproceedings{flickinger-etal-2016-uw,
    title = "{UW}-{S}tanford System Description for {AESW} 2016 Shared Task on Grammatical Error Detection",
    author = "Flickinger, Dan  and
      Goodman, Michael  and
      Packard, Woodley",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0511",
    doi = "10.18653/v1/W16-0511",
    pages = "105--111",
}
@inproceedings{king-dickinson-2016-shallow,
    title = "Shallow Semantic Reasoning from an Incomplete Gold Standard for Learner Language",
    author = "King, Levi  and
      Dickinson, Markus",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0512",
    doi = "10.18653/v1/W16-0512",
    pages = "112--121",
}
@inproceedings{lee-etal-2016-ntnu,
    title = "The {NTNU}-{YZU} System in the {AESW} Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network",
    author = "Lee, Lung-Hao  and
      Lin, Bo-Lin  and
      Yu, Liang-Chih  and
      Tseng, Yuen-Hsien",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0513",
    doi = "10.18653/v1/W16-0513",
    pages = "122--129",
}
@inproceedings{loukina-cahill-2016-automated,
    title = "Automated scoring across different modalities",
    author = "Loukina, Anastassia  and
      Cahill, Aoife",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0514",
    doi = "10.18653/v1/W16-0514",
    pages = "130--135",
}
@inproceedings{madnani-etal-2016-model,
    title = "Model Combination for Correcting Preposition Selection Errors",
    author = "Madnani, Nitin  and
      Heilman, Michael  and
      Cahill, Aoife",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0515",
    doi = "10.18653/v1/W16-0515",
    pages = "136--141",
}
@inproceedings{martinez-santiago-etal-2016-pictogrammar,
    title = "{P}ictogrammar: an {AAC} device based on a semantic grammar",
    author = "Mart{\'\i}nez-Santiago, Fernando  and
      Garc{\'\i}a-Cumbreras, Miguel {\'A}ngel  and
      Montejo-R{\'a}ez, Arturo  and
      D{\'\i}az-Galiano, Manuel Carlos",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0516",
    doi = "10.18653/v1/W16-0516",
    pages = "142--150",
}
@inproceedings{pilan-2016-detecting,
    title = "Detecting Context Dependence in Exercise Item Candidates Selected from Corpora",
    author = "Pil{\'a}n, Ildik{\'o}",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0517",
    doi = "10.18653/v1/W16-0517",
    pages = "151--161",
}
@inproceedings{remse-etal-2016-feature,
    title = "Feature-Rich Error Detection in Scientific Writing Using Logistic Regression",
    author = "Remse, Madeline  and
      Mesgar, Mohsen  and
      Strube, Michael",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0518",
    doi = "10.18653/v1/W16-0518",
    pages = "162--171",
}
@inproceedings{wojatzki-etal-2016-bundled,
    title = "Bundled Gap Filling: A New Paradigm for Unambiguous Cloze Exercises",
    author = "Wojatzki, Michael  and
      Melamud, Oren  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0519",
    doi = "10.18653/v1/W16-0519",
    pages = "172--181",
}
@inproceedings{banjade-etal-2016-evaluation,
    title = "Evaluation Dataset ({DT}-Grade) and Word Weighting Approach towards Constructed Short Answers Assessment in Tutorial Dialogue Context",
    author = "Banjade, Rajendra  and
      Maharjan, Nabin  and
      Niraula, Nobal Bikram  and
      Gautam, Dipesh  and
      Samei, Borhan  and
      Rus, Vasile",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0520",
    doi = "10.18653/v1/W16-0520",
    pages = "182--187",
}
@inproceedings{chinkina-meurers-2016-linguistically,
    title = "Linguistically Aware Information Retrieval: Providing Input Enrichment for Second Language Learners",
    author = "Chinkina, Maria  and
      Meurers, Detmar",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0521",
    doi = "10.18653/v1/W16-0521",
    pages = "188--198",
}
@inproceedings{beigman-klebanov-etal-2016-enhancing,
    title = "Enhancing {STEM} Motivation through Personal and Communal Values: {NLP} for Assessment of Utility Value in Student Writing",
    author = "Beigman Klebanov, Beata  and
      Burstein, Jill  and
      Harackiewicz, Judith  and
      Priniski, Stacy  and
      Mulholland, Matthew",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0522",
    doi = "10.18653/v1/W16-0522",
    pages = "199--205",
}
@inproceedings{ledbetter-dickinson-2016-cost,
    title = "Cost-Effectiveness in Building a Low-Resource Morphological Analyzer for Learner Language",
    author = "Ledbetter, Scott  and
      Dickinson, Markus",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0523",
    doi = "10.18653/v1/W16-0523",
    pages = "206--216",
}
@inproceedings{madnani-etal-2016-automatically,
    title = "Automatically Scoring Tests of Proficiency in Music Instruction",
    author = "Madnani, Nitin  and
      Cahill, Aoife  and
      Riordan, Brian",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0524",
    doi = "10.18653/v1/W16-0524",
    pages = "217--222",
}
@inproceedings{mamani-sanchez-franco-penya-2016-combined,
    title = "Combined Tree Kernel-based classifiers for Assessing Quality of Scientific Text",
    author = "Mamani Sanchez, Liliana  and
      Franco-Penya, Hector-Hugo",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0525",
    doi = "10.18653/v1/W16-0525",
    pages = "223--228",
}
@inproceedings{milli-hearst-2016-augmenting,
    title = "Augmenting Course Material with Open Access Textbooks",
    author = "Milli, Smitha  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0526",
    doi = "10.18653/v1/W16-0526",
    pages = "229--234",
}
@inproceedings{rudzewitz-2016-exploring,
    title = "Exploring the Intersection of Short Answer Assessment, Authorship Attribution, and Plagiarism Detection",
    author = {Rudzewitz, Bj{\"o}rn},
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0527",
    doi = "10.18653/v1/W16-0527",
    pages = "235--241",
}
@inproceedings{schmaltz-etal-2016-sentence,
    title = "Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction",
    author = "Schmaltz, Allen  and
      Kim, Yoon  and
      Rush, Alexander M.  and
      Shieber, Stuart",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0528",
    doi = "10.18653/v1/W16-0528",
    pages = "242--251",
}
@inproceedings{witte-sateli-2016-combining,
    title = "Combining Off-the-shelf Grammar and Spelling Tools for the Automatic Evaluation of Scientific Writing ({AESW}) Shared Task 2016",
    author = "Witte, Ren{\'e}  and
      Sateli, Bahar",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0529",
    doi = "10.18653/v1/W16-0529",
    pages = "252--255",
}
@inproceedings{yuan-etal-2016-candidate,
    title = "Candidate re-ranking for {SMT}-based grammatical error correction",
    author = "Yuan, Zheng  and
      Briscoe, Ted  and
      Felice, Mariano",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0530",
    doi = "10.18653/v1/W16-0530",
    pages = "256--266",
}
@inproceedings{yoon-etal-2016-spoken,
    title = "Spoken Text Difficulty Estimation Using Linguistic Features",
    author = "Yoon, Su-Youn  and
      Cho, Yeonsuk  and
      Napolitano, Diane",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0531",
    doi = "10.18653/v1/W16-0531",
    pages = "267--276",
}
@inproceedings{rahimi-litman-2016-automatically,
    title = "Automatically Extracting Topical Components for a Response-to-Text Writing Assessment",
    author = "Rahimi, Zahra  and
      Litman, Diane",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0532",
    doi = "10.18653/v1/W16-0532",
    pages = "277--282",
}
@inproceedings{rei-cummins-2016-sentence,
    title = "Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays",
    author = "Rei, Marek  and
      Cummins, Ronan",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0533",
    doi = "10.18653/v1/W16-0533",
    pages = "283--288",
}
@inproceedings{reynolds-2016-insights,
    title = "Insights from {R}ussian second language readability classification: complexity-dependent training requirements, and feature evaluation of multiple categories",
    author = "Reynolds, Robert",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0534",
    doi = "10.18653/v1/W16-0534",
    pages = "289--300",
}
@inproceedings{horbach-palmer-2016-investigating,
    title = "Investigating Active Learning for Short-Answer Scoring",
    author = "Horbach, Andrea  and
      Palmer, Alexis",
    booktitle = "Proceedings of the 11th Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2016",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0535",
    doi = "10.18653/v1/W16-0535",
    pages = "301--311",
}
@proceedings{ws-2016-approaches,
    title = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    editor = "Balahur, Alexandra  and
      van der Goot, Erik  and
      Vossen, Piek  and
      Montoyo, Andres",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0400",
    doi = "10.18653/v1/W16-04",
}
@inproceedings{balahur-2016-sentiment,
    title = "Sentiment Analysis - What are we talking about?",
    author = "Balahur, Alexandra",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0401",
    doi = "10.18653/v1/W16-0401",
    pages = "1",
}
@inproceedings{grimes-2016-sentiment,
    title = "Sentiment, Subjectivity, and Social Analysis Go {T}o{W}ork: An Industry View - Invited Talk",
    author = "Grimes, Seth",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0402",
    doi = "10.18653/v1/W16-0402",
    pages = "2",
}
@inproceedings{hamidian-diab-2016-rumor,
    title = "Rumor Identification and Belief Investigation on {T}witter",
    author = "Hamidian, Sardar  and
      Diab, Mona",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0403",
    doi = "10.18653/v1/W16-0403",
    pages = "3--8",
}
@inproceedings{preotiuc-pietro-etal-2016-modelling,
    title = "Modelling Valence and Arousal in {F}acebook posts",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Schwartz, H. Andrew  and
      Park, Gregory  and
      Eichstaedt, Johannes  and
      Kern, Margaret  and
      Ungar, Lyle  and
      Shulman, Elisabeth",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0404",
    doi = "10.18653/v1/W16-0404",
    pages = "9--15",
}
@inproceedings{dehghani-2016-purity,
    title = "Purity Homophily in Social Networks - Invited Talk",
    author = "Dehghani, Morteza",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0405",
    doi = "10.18653/v1/W16-0405",
    pages = "16",
}
@inproceedings{harsley-etal-2016-hit,
    title = "Hit Songs{'} Sentiments Harness Public Mood {\&} Predict Stock Market",
    author = "Harsley, Rachel  and
      Gupta, Bhavesh  and
      Di Eugenio, Barbara  and
      Li, Huayi",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0406",
    doi = "10.18653/v1/W16-0406",
    pages = "17--25",
}
@inproceedings{chakraborty-etal-2016-fashioning,
    title = "Fashioning Data - A Social Media Perspective on Fast Fashion Brands",
    author = "Chakraborty, Rupak  and
      Kundu, Senjuti  and
      Agarwal, Prakul",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0407",
    doi = "10.18653/v1/W16-0407",
    pages = "26--35",
}
@inproceedings{socher-2016-deep,
    title = "Deep Learning for Sentiment Analysis - Invited Talk",
    author = "Socher, Richard",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0408",
    doi = "10.18653/v1/W16-0408",
    pages = "36",
}
@inproceedings{dias-cardoso-roy-2016-sentiment,
    title = "Sentiment Lexicon Creation using Continuous Latent Space and Neural Networks",
    author = "Dias Cardoso, Pedro  and
      Roy, Anindya",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0409",
    doi = "10.18653/v1/W16-0409",
    pages = "37--42",
}
@inproceedings{kiritchenko-mohammad-2016-effect,
    title = "The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition",
    author = "Kiritchenko, Svetlana  and
      Mohammad, Saif",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0410",
    doi = "10.18653/v1/W16-0410",
    pages = "43--52",
}
@inproceedings{deng-wiebe-2016-nlp,
    title = "How can {NLP} Tasks Mutually Benefit Sentiment Analysis? A Holistic Approach to Sentiment Analysis",
    author = "Deng, Lingjia  and
      Wiebe, Janyce",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0411",
    doi = "10.18653/v1/W16-0411",
    pages = "53--59",
}
@inproceedings{higgins-etal-2016-unsupervised,
    title = "An Unsupervised System for Visual Exploration of {T}witter Conversations",
    author = "Higgins, Derrick  and
      Heilman, Michael  and
      Jelesnianska, Adrianna  and
      Ingersoll, Keith",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0412",
    doi = "10.18653/v1/W16-0412",
    pages = "60--65",
}
@inproceedings{wester-etal-2016-threat,
    title = "Threat detection in online discussions",
    author = "Wester, Aksel  and
      {\O}vrelid, Lilja  and
      Velldal, Erik  and
      Hammer, Hugo Lewi",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0413",
    doi = "10.18653/v1/W16-0413",
    pages = "66--71",
}
@inproceedings{menard-barriere-2016-classification,
    title = "Classification of comment helpfulness to improve knowledge sharing among medical practitioners.",
    author = "M{\'e}nard, Pierre Andr{\'e}  and
      Barri{\`e}re, Caroline",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0414",
    doi = "10.18653/v1/W16-0414",
    pages = "72--81",
}
@inproceedings{joshi-etal-2016-political,
    title = "Political Issue Extraction Model: A Novel Hierarchical Topic Model That Uses Tweets By Political And Non-Political Authors",
    author = "Joshi, Aditya  and
      Bhattacharyya, Pushpak  and
      Carman, Mark",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0415",
    doi = "10.18653/v1/W16-0415",
    pages = "82--90",
}
@inproceedings{escalante-etal-2016-early,
    title = {Early text classification: a Na{\"\i}ve solution},
    author = "Escalante, Hugo Jair  and
      Montes y Gomez, Manuel  and
      Villasenor, Luis  and
      Errecalde, Marcelo Luis",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0416",
    doi = "10.18653/v1/W16-0416",
    pages = "91--99",
}
@inproceedings{perumal-hirst-2016-semi,
    title = "Semi-supervised and unsupervised categorization of posts in Web discussion forums using part-of-speech information and minimal features",
    author = "Perumal, Krish  and
      Hirst, Graeme",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0417",
    doi = "10.18653/v1/W16-0417",
    pages = "100--108",
}
@inproceedings{zhou-ganesan-2016-linguistic,
    title = "Linguistic Understanding of Complaints and Praises in User Reviews",
    author = "Zhou, Guangyu  and
      Ganesan, Kavita",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0418",
    doi = "10.18653/v1/W16-0418",
    pages = "109--114",
}
@inproceedings{jha-etal-2016-reputation,
    title = "Reputation System: Evaluating Reputation among All Good Sellers",
    author = "Jha, Vandana  and
      R, Savitha  and
      Shenoy, P Deepa  and
      K R, Venugopal",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0419",
    doi = "10.18653/v1/W16-0419",
    pages = "115--121",
}
@inproceedings{ma-etal-2016-improve,
    title = "Improve Sentiment Analysis of Citations with Author Modelling",
    author = "Ma, Zheng  and
      Nam, Jinseok  and
      Weihe, Karsten",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0420",
    doi = "10.18653/v1/W16-0420",
    pages = "122--127",
}
@inproceedings{panchendrarajan-etal-2016-implicit,
    title = "Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words",
    author = "Panchendrarajan, Rrubaa  and
      Ahamed, Nazick  and
      Murugaiah, Brunthavan  and
      Sivakumar, Prakhash  and
      Ranathunga, Surangika  and
      Pemasiri, Akila",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0421",
    doi = "10.18653/v1/W16-0421",
    pages = "128--136",
}
@inproceedings{jimenez-zafra-etal-2016-domain,
    title = "Domain Adaptation of Polarity Lexicon combining Term Frequency and Bootstrapping",
    author = "Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Martin, Maite  and
      Molina-Gonzalez, M. Dolores  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0422",
    doi = "10.18653/v1/W16-0422",
    pages = "137--146",
}
@inproceedings{buechel-etal-2016-enterprises,
    title = "Do Enterprises Have Emotions?",
    author = {Buechel, Sven  and
      Hahn, Udo  and
      Goldenstein, Jan  and
      H{\"a}ndschke, Sebastian G. M.  and
      Walgenbach, Peter},
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0423",
    doi = "10.18653/v1/W16-0423",
    pages = "147--153",
}
@inproceedings{palogiannidi-etal-2016-semantic,
    title = "A semantic-affective compositional approach for the affective labelling of adjective-noun and noun-noun pairs",
    author = "Palogiannidi, Elisavet  and
      Iosif, Elias  and
      Koutsakis, Polychronis  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0424",
    doi = "10.18653/v1/W16-0424",
    pages = "154--160",
}
@inproceedings{ghosh-veale-2016-fracking,
    title = "Fracking Sarcasm using Neural Network",
    author = "Ghosh, Aniruddha  and
      Veale, Tony",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0425",
    doi = "10.18653/v1/W16-0425",
    pages = "161--169",
}
@inproceedings{klenner-2016-hymn,
    title = "An Hymn of an even Deeper Sentiment Analysis",
    author = "Klenner, Manfred",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0426",
    doi = "10.18653/v1/W16-0426",
    pages = "170",
}
@inproceedings{nakov-2016-sentiment,
    title = "Sentiment Analysis in {T}witter: A {S}em{E}val Perspective",
    author = "Nakov, Preslav",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0427",
    doi = "10.18653/v1/W16-0427",
    pages = "171--172",
}
@inproceedings{sebastiani-2016-challenge,
    title = "The Challenge of Sentiment Quantification",
    author = "Sebastiani, Fabrizio",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0428",
    doi = "10.18653/v1/W16-0428",
    pages = "173",
}
@inproceedings{mohammad-2016-practical,
    title = "A Practical Guide to Sentiment Annotation: Challenges and Solutions",
    author = "Mohammad, Saif",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0429",
    doi = "10.18653/v1/W16-0429",
    pages = "174--179",
}
@inproceedings{strapparava-2016-emotions,
    title = "Emotions and {NLP}: Future Directions",
    author = "Strapparava, Carlo",
    booktitle = "Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0430",
    doi = "10.18653/v1/W16-0430",
    pages = "180",
}
@proceedings{ws-2016-linguistics-clinical,
    title = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    editor = "Hollingshead, Kristy  and
      Ungar, Lyle",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0300",
    doi = "10.18653/v1/W16-03",
}
@inproceedings{fraser-etal-2016-detecting,
    title = "Detecting late-life depression in {A}lzheimer{'}s disease through analysis of speech and language",
    author = "Fraser, Kathleen C.  and
      Rudzicz, Frank  and
      Hirst, Graeme",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0301",
    doi = "10.18653/v1/W16-0301",
    pages = "1--11",
}
@inproceedings{bullard-etal-2016-towards,
    title = "Towards Early Dementia Detection: Fusing Linguistic and Non-Linguistic Clinical Data",
    author = "Bullard, Joseph  and
      Ovesdotter Alm, Cecilia  and
      Liu, Xumin  and
      Yu, Qi  and
      Proa{\~n}o, Rub{\'e}n",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0302",
    doi = "10.18653/v1/W16-0302",
    pages = "12--22",
}
@inproceedings{shickel-etal-2016-self,
    title = "Self-Reflective Sentiment Analysis",
    author = "Shickel, Benjamin  and
      Heesacker, Martin  and
      Benton, Sherry  and
      Ebadi, Ashkan  and
      Nickerson, Paul  and
      Rashidi, Parisa",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0303",
    doi = "10.18653/v1/W16-0303",
    pages = "23--32",
}
@inproceedings{tanana-etal-2016-sentiment,
    title = "Is Sentiment in Movies the Same as Sentiment in Psychotherapy? Comparisons Using a New Psychotherapy Sentiment Database",
    author = "Tanana, Michael  and
      Dembe, Aaron  and
      Soma, Christina S.  and
      Imel, Zac  and
      Atkins, David  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0304",
    doi = "10.18653/v1/W16-0304",
    pages = "33--41",
}
@inproceedings{perez-rosas-etal-2016-building,
    title = "Building a Motivational Interviewing Dataset",
    author = "P{\'e}rez-Rosas, Ver{\'o}nica  and
      Mihalcea, Rada  and
      Resnicow, Kenneth  and
      Singh, Satinder  and
      An, Lawrence",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0305",
    doi = "10.18653/v1/W16-0305",
    pages = "42--51",
}
@inproceedings{hwang-hollingshead-2016-crazy,
    title = "Crazy Mad Nutters: The Language of Mental Health",
    author = "Hwang, Jena D.  and
      Hollingshead, Kristy",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0306",
    doi = "10.18653/v1/W16-0306",
    pages = "52--62",
}
@inproceedings{gkotsis-etal-2016-language,
    title = "The language of mental health problems in social media",
    author = "Gkotsis, George  and
      Oellrich, Anika  and
      Hubbard, Tim  and
      Dobson, Richard  and
      Liakata, Maria  and
      Velupillai, Sumithra  and
      Dutta, Rina",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0307",
    doi = "10.18653/v1/W16-0307",
    pages = "63--73",
}
@inproceedings{parish-morris-etal-2016-exploring,
    title = "Exploring Autism Spectrum Disorders Using {HLT}",
    author = "Parish-Morris, Julia  and
      Liberman, Mark  and
      Ryant, Neville  and
      Cieri, Christopher  and
      Bateman, Leila  and
      Ferguson, Emily  and
      Schultz, Robert",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0308",
    doi = "10.18653/v1/W16-0308",
    pages = "74--84",
}
@inproceedings{oak-etal-2016-generating,
    title = "Generating Clinically Relevant Texts: A Case Study on Life-Changing Events",
    author = "Oak, Mayuresh  and
      Behera, Anil  and
      Thomas, Titus  and
      Ovesdotter Alm, Cecilia  and
      Prud{'}hommeaux, Emily  and
      Homan, Christopher  and
      Ptucha, Raymond",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0309",
    doi = "10.18653/v1/W16-0309",
    pages = "85--94",
}
@inproceedings{gkotsis-etal-2016-dont,
    title = "Don{'}t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records",
    author = "Gkotsis, George  and
      Velupillai, Sumithra  and
      Oellrich, Anika  and
      Dean, Harry  and
      Liakata, Maria  and
      Dutta, Rina",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0310",
    doi = "10.18653/v1/W16-0310",
    pages = "95--105",
}
@inproceedings{coppersmith-etal-2016-exploratory,
    title = "Exploratory Analysis of Social Media Prior to a Suicide Attempt",
    author = "Coppersmith, Glen  and
      Ngo, Kim  and
      Leary, Ryan  and
      Wood, Anthony",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0311",
    doi = "10.18653/v1/W16-0311",
    pages = "106--117",
}
@inproceedings{milne-etal-2016-clpsych,
    title = "{CLP}sych 2016 Shared Task: Triaging content in online peer-support forums",
    author = "Milne, David N.  and
      Pink, Glen  and
      Hachey, Ben  and
      Calvo, Rafael A.",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0312",
    doi = "10.18653/v1/W16-0312",
    pages = "118--127",
}
@inproceedings{kim-etal-2016-data61,
    title = "{D}ata61-{CSIRO} systems at the {CLP}sych 2016 Shared Task",
    author = "Kim, Sunghwan Mac  and
      Wang, Yufei  and
      Wan, Stephen  and
      Paris, C{\'e}cile",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0313",
    doi = "10.18653/v1/W16-0313",
    pages = "128--132",
}
@inproceedings{malmasi-etal-2016-predicting,
    title = "Predicting Post Severity in Mental Health Forums",
    author = "Malmasi, Shervin  and
      Zampieri, Marcos  and
      Dras, Mark",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0314",
    doi = "10.18653/v1/W16-0314",
    pages = "133--137",
}
@inproceedings{brew-2016-classifying,
    title = "Classifying {R}each{O}ut posts with a radial basis function {SVM}",
    author = "Brew, Chris",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0315",
    doi = "10.18653/v1/W16-0315",
    pages = "138--142",
}
@inproceedings{cohan-etal-2016-triaging,
    title = "Triaging Mental Health Forum Posts",
    author = "Cohan, Arman  and
      Young, Sydney  and
      Goharian, Nazli",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0316",
    doi = "10.18653/v1/W16-0316",
    pages = "143--147",
}
@inproceedings{desmet-etal-2016-mental,
    title = "Mental Distress Detection and Triage in Forum Posts: The {LT}3 {CLP}sych 2016 Shared Task System",
    author = "Desmet, Bart  and
      Jacobs, Gilles  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0317",
    doi = "10.18653/v1/W16-0317",
    pages = "148--152",
}
@inproceedings{asgari-etal-2016-text,
    title = "Text Analysis and Automatic Triage of Posts in a Mental Health Forum",
    author = "Asgari, Ehsaneddin  and
      Nasiriany, Soroush  and
      Mofrad, Mohammad R.K.",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0318",
    doi = "10.18653/v1/W16-0318",
    pages = "153--157",
}
@inproceedings{friedenberg-etal-2016-umd,
    title = "The {UMD} {CLP}sych 2016 Shared Task System: Text Representation for Predicting Triage of Forum Posts about Mental Health",
    author = "Friedenberg, Meir  and
      Amiri, Hadi  and
      Daum{\'e} III, Hal  and
      Resnik, Philip",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0319",
    doi = "10.18653/v1/W16-0319",
    pages = "158--161",
}
@inproceedings{opitz-2016-using,
    title = "Using Linear Classifiers for the Automatic Triage of Posts in the 2016 {CLP}sych Shared Task",
    author = "Opitz, Juri",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0320",
    doi = "10.18653/v1/W16-0320",
    pages = "162--165",
}
@inproceedings{zirikly-etal-2016-gw,
    title = "The {GW}/{UMD} {CLP}sych 2016 Shared Task System",
    author = "Zirikly, Ayah  and
      Kumar, Varun  and
      Resnik, Philip",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0321",
    doi = "10.18653/v1/W16-0321",
    pages = "166--170",
}
@inproceedings{rey-villamizar-etal-2016-semi,
    title = "Semi-supervised {CLP}sych 2016 Shared Task System Submission",
    author = "Rey-Villamizar, Nicolas  and
      Shrestha, Prasha  and
      Solorio, Thamar  and
      Sadeque, Farig  and
      Bethard, Steven  and
      Pedersen, Ted",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0322",
    doi = "10.18653/v1/W16-0322",
    pages = "171--175",
}
@inproceedings{wang-etal-2016-combining,
    title = "Combining Multiple Classifiers Using Global Ranking for {R}each{O}ut.com Post Triage",
    author = "Wang, Chen-Kai  and
      Dai, Hong-Jie  and
      Chen, Chih-Wei  and
      Jonnagaddala, Jitendra  and
      Chang, Nai-Wen",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0323",
    doi = "10.18653/v1/W16-0323",
    pages = "176--179",
}
@inproceedings{pink-etal-2016-classification,
    title = "Classification of mental health forum posts",
    author = "Pink, Glen  and
      Radford, Will  and
      Hachey, Ben",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0324",
    doi = "10.18653/v1/W16-0324",
    pages = "180--182",
}
@inproceedings{almeida-etal-2016-automatic,
    title = "Automatic Triage of Mental Health Online Forum Posts: {CLP}sych 2016 System Description",
    author = "Almeida, Hayda  and
      Queudot, Marc  and
      Meurs, Marie-Jean",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0325",
    doi = "10.18653/v1/W16-0325",
    pages = "183--187",
}
@inproceedings{shickel-rashidi-2016-automatic,
    title = "Automatic Triage of Mental Health Forum Posts",
    author = "Shickel, Benjamin  and
      Rashidi, Parisa",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0326",
    doi = "10.18653/v1/W16-0326",
    pages = "188--192",
}
@inproceedings{franco-penya-mamani-sanchez-2016-text,
    title = "Text-based experiments for Predicting mental health emergencies in online web forum posts",
    author = "Franco-Penya, Hector-Hugo  and
      Mamani Sanchez, Liliana",
    booktitle = "Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology",
    month = jun,
    year = "2016",
    address = "San Diego, CA, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0327",
    doi = "10.18653/v1/W16-0327",
    pages = "193--197",
}
@proceedings{ws-2016-linguistics,
    title = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    editor = "Feldman, Anna  and
      Kazantseva, Anna  and
      Szpakowicz, Stan",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0200",
    doi = "10.18653/v1/W16-02",
}
@inproceedings{estes-hench-2016-supervised,
    title = "Supervised Machine Learning for Hybrid Meter",
    author = "Estes, Alex  and
      Hench, Christopher",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0201",
    doi = "10.18653/v1/W16-0201",
    pages = "1--8",
}
@inproceedings{daza-etal-2016-automatic,
    title = "Automatic Text Generation by Learning from Literary Structures",
    author = "Daza, Angel  and
      Calvo, Hiram  and
      Figueroa-Nazuno, Jes{\'u}s",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0202",
    doi = "10.18653/v1/W16-0202",
    pages = "9--19",
}
@inproceedings{gagliano-etal-2016-intersecting,
    title = "Intersecting Word Vectors to Take Figurative Language to New Heights",
    author = "Gagliano, Andrea  and
      Paul, Emily  and
      Booten, Kyle  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0203",
    doi = "10.18653/v1/W16-0203",
    pages = "20--31",
}
@inproceedings{schofield-mehr-2016-gender,
    title = "Gender-Distinguishing Features in Film Dialogue",
    author = "Schofield, Alexandra  and
      Mehr, Leo",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0204",
    doi = "10.18653/v1/W16-0204",
    pages = "32--39",
}
@inproceedings{koppel-etal-2016-reconstructing,
    title = "Reconstructing Ancient Literary Texts from Noisy Manuscripts",
    author = "Koppel, Moshe  and
      Michaely, Moty  and
      Tal, Alex",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0205",
    doi = "10.18653/v1/W16-0205",
    pages = "40--46",
}
@inproceedings{dubremetz-nivre-2016-syntax,
    title = "Syntax Matters for Rhetorical Structure: The Case of Chiasmus",
    author = "Dubremetz, Marie  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0206",
    doi = "10.18653/v1/W16-0206",
    pages = "47--53",
}
@inproceedings{rahgozar-inkpen-2016-bilingual,
    title = "Bilingual Chronological Classification of Hafez{'}s Poems",
    author = "Rahgozar, Arya  and
      Inkpen, Diana",
    booktitle = "Proceedings of the Fifth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2016",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0207",
    doi = "10.18653/v1/W16-0207",
    pages = "54--62",
}
@proceedings{ws-2016-human,
    title = "Proceedings of the Workshop on Human-Computer Question Answering",
    editor = "Iyyer, Mohit  and
      He, He  and
      Boyd-Graber, Jordan  and
      Daum{\'e} III, Hal",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0100",
    doi = "10.18653/v1/W16-01",
}
@inproceedings{beller-etal-2016-watson,
    title = "{W}atson Discovery Advisor: Question-answering in an industrial setting",
    author = "Beller, Charley  and
      Katz, Graham  and
      Ginsberg, Allen  and
      Phipps, Chris  and
      Bethard, Sean  and
      Chase, Paul  and
      Shek, Elinna  and
      Summers, Kristen",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0101",
    doi = "10.18653/v1/W16-0101",
    pages = "1--7",
}
@inproceedings{savenkov-etal-2016-crowdsourcing,
    title = "Crowdsourcing for (almost) Real-time Question Answering",
    author = "Savenkov, Denis  and
      Weitzner, Scott  and
      Agichtein, Eugene",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0102",
    doi = "10.18653/v1/W16-0102",
    pages = "8--14",
}
@inproceedings{yin-etal-2016-attention,
    title = "Attention-Based Convolutional Neural Network for Machine Comprehension",
    author = {Yin, Wenpeng  and
      Ebert, Sebastian  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0103",
    doi = "10.18653/v1/W16-0103",
    pages = "15--21",
}
@inproceedings{aghaebrahimian-jurcicek-2016-open,
    title = "Open-domain Factoid Question Answering via Knowledge Graph Search",
    author = "Aghaebrahimian, Ahmad  and
      Jur{\v{c}}{\'\i}{\v{c}}ek, Filip",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0104",
    doi = "10.18653/v1/W16-0104",
    pages = "22--28",
}
@inproceedings{yin-etal-2016-neural,
    title = "Neural Enquirer: Learning to Query Tables in Natural Language",
    author = "Yin, Pengcheng  and
      Lu, Zhengdong  and
      Li, Hang  and
      Ben, Kao",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0105",
    doi = "10.18653/v1/W16-0105",
    pages = "29--35",
}
@inproceedings{yin-etal-2016-neural-generative,
    title = "Neural Generative Question Answering",
    author = "Yin, Jun  and
      Jiang, Xin  and
      Lu, Zhengdong  and
      Shang, Lifeng  and
      Li, Hang  and
      Li, Xiaoming",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0106",
    doi = "10.18653/v1/W16-0106",
    pages = "36--42",
}
@inproceedings{guha-etal-2016-distorted,
    title = "{``}A Distorted Skull Lies in the Bottom Center...{''} Identifying Paintings from Text Descriptions",
    author = "Guha, Anupam  and
      Iyyer, Mohit  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0107",
    doi = "10.18653/v1/W16-0107",
    pages = "43--47",
}
@inproceedings{yoshida-boyd-graber-2016-using,
    title = "Using Confusion Graphs to Understand Classifier Error",
    author = "Yoshida, Davis  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0108",
    doi = "10.18653/v1/W16-0108",
    pages = "48--52",
}
@inproceedings{xu-etal-2016-paraphrase,
    title = "Paraphrase for Open Question Answering: New Dataset and Methods",
    author = "Xu, Ying  and
      Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Miyao, Yusuke  and
      Goebel, Randy",
    booktitle = "Proceedings of the Workshop on Human-Computer Question Answering",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W16-0109",
    doi = "10.18653/v1/W16-0109",
    pages = "53--61",
}
@proceedings{alta-2016-australasian,
    title = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    editor = "Cohn, Trevor",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1000",
}
@inproceedings{hoang-etal-2016-improving,
    title = "Improving Neural Translation Models with Linguistic Factors",
    author = "Hoang, Cong Duy Vu  and
      Haffari, Gholamreza  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1001",
    pages = "7--14",
}
@inproceedings{hassanzadeh-etal-2016-evaluation,
    title = "Evaluation of Medical Concept Annotation Systems on Clinical Records",
    author = "Hassanzadeh, Hamed  and
      Nguyen, Anthony  and
      Koopman, Bevan",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1002",
    pages = "15--24",
}
@inproceedings{kholghi-etal-2016-benefits,
    title = "The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction",
    author = "Kholghi, Mahnoosh  and
      De Vine, Lance  and
      Sitbon, Laurianne  and
      Zuccon, Guido  and
      Nguyen, Anthony",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1003",
    pages = "25--34",
}
@inproceedings{huang-etal-2016-syndromic,
    title = "Syndromic Surveillance using Generic Medical Entities on {T}witter",
    author = "Huang, Pin  and
      MacKinlay, Andrew  and
      Yepes, Antonio Jimeno",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1004",
    pages = "35--44",
}
@inproceedings{aamer-etal-2016-syndromic,
    title = "Syndromic Surveillance through Measuring Lexical Shift in Emergency Department Chief Complaint Texts",
    author = "Aamer, Hafsah  and
      Ofoghi, Bahadorreza  and
      Verspoor, Karin",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1005",
    pages = "45--53",
}
@inproceedings{li-etal-2016-unsupervised,
    title = "Unsupervised Pre-training With {S}eq2{S}eq Reconstruction Loss for Deep Relation Extraction Models",
    author = "Li, Zhuang  and
      Qu, Lizhen  and
      Xu, Qiongkai  and
      Johnson, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1006",
    pages = "54--64",
}
@inproceedings{panyam-etal-2016-asm,
    title = "{ASM} Kernel: Graph Kernel using Approximate Subgraph Matching for Relation Extraction",
    author = "Panyam, Nagesh C.  and
      Verspoor, Karin  and
      Cohn, Trevor  and
      Kotagiri, Rao",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1007",
    pages = "65--73",
}
@inproceedings{khirbat-etal-2016-n,
    title = "N-ary Biographical Relation Extraction using Shortest Path Dependencies",
    author = "Khirbat, Gitansh  and
      Qi, Jianzhong  and
      Zhang, Rui",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1008",
    pages = "74--83",
}
@inproceedings{shcherbakov-etal-2016-phonotactic,
    title = "Phonotactic Modeling of Extremely Low Resource Languages",
    author = "Shcherbakov, Andrei  and
      Vylomova, Ekaterina  and
      Thieberger, Nick",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1009",
    pages = "84--93",
}
@inproceedings{wang-etal-2016-role,
    title = "The Role of Features and Context on Suicide Ideation Detection",
    author = "Wang, Yufei  and
      Wan, Stephen  and
      Paris, C{\'e}cile",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1010",
    pages = "94--102",
}
@inproceedings{wang-etal-2016-featureless,
    title = "Featureless Domain-Specific Term Extraction with Minimal Labelled Data",
    author = "Wang, Rui  and
      Liu, Wei  and
      McDonald, Chris",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1011",
    pages = "103--112",
}
@inproceedings{ishihara-2016-effect,
    title = "An Effect of Background Population Sample Size on the Performance of a Likelihood Ratio-based Forensic Text Comparison System: A {M}onte {C}arlo Simulation with {G}aussian Mixture Model",
    author = "Ishihara, Shunichi",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1012",
    pages = "113--121",
}
@inproceedings{joshi-etal-2016-challenging,
    title = "How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from {E}nglish Literature",
    author = "Joshi, Aditya  and
      Tripathi, Vaibhav  and
      Bhattacharyya, Pushpak  and
      Carman, Mark  and
      Singh, Meghna  and
      Saraswati, Jaya  and
      Shukla, Rajita",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1013",
    pages = "123--127",
}
@inproceedings{liu-etal-2016-learning,
    title = "Learning cascaded latent variable models for biomedical text classification",
    author = "Liu, Ming  and
      Haffari, Gholamreza  and
      Buntine, Wray",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1014",
    pages = "128--132",
}
@inproceedings{han-etal-2016-temporal,
    title = "Temporal Modelling of Geospatial Words in {T}witter",
    author = "Han, Bo  and
      Yepes, Antonio Jimeno  and
      MacKinlay, Andrew  and
      Chi, Lianhua",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1015",
    pages = "133--137",
}
@inproceedings{yepes-mackinlay-2016-ner,
    title = "{NER} for Medical Entities in {T}witter using Sequence to Sequence Neural Networks",
    author = "Yepes, Antonio Jimeno  and
      MacKinlay, Andrew",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1016",
    pages = "138--142",
}
@inproceedings{nguyen-etal-2016-empirical,
    title = "An empirical study for {V}ietnamese dependency parsing",
    author = "Nguyen, Dat Quoc  and
      Dras, Mark  and
      Johnson, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1017",
    pages = "143--149",
}
@inproceedings{radford-etal-2016-telephone,
    title = ":telephone::person::sailboat::whale::okhand: ; or {``}Call me Ishmael{''} {--} How do you translate emoji?",
    author = "Radford, Will  and
      Hachey, Ben  and
      Han, Bo  and
      Chisholm, Andy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1018",
    pages = "150--154",
}
@inproceedings{holt-etal-2016-presenting,
    title = "Presenting a New Dataset for the Timeline Generation Problem",
    author = "Holt, Xavier  and
      Radford, Will  and
      Hachey, Ben",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1019",
    pages = "155--159",
}
@inproceedings{chisholm-etal-2016-overview,
    title = "Overview of the 2016 {ALTA} Shared Task: Cross-{KB} Coreference",
    author = "Chisholm, Andrew  and
      Hachey, Ben  and
      Moll{\'a}, Diego",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1020",
    pages = "161--164",
}
@inproceedings{khirbat-etal-2016-disambiguating,
    title = "Disambiguating Entities Referred by Web Endpoints using Tree Ensembles",
    author = "Khirbat, Gitansh  and
      Qi, Jianzhong  and
      Zhang, Rui",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1021",
    pages = "165--169",
}
@inproceedings{shivashankar-etal-2016-filter,
    title = "Filter and Match Approach to Pair-wise Web {URI} Linking",
    author = "Shivashankar, S.  and
      Li, Yitong  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1022",
    pages = "170--174",
}
@inproceedings{yu-etal-2016-pairwise,
    title = "Pairwise {F}ast{T}ext Classifier for Entity Disambiguation",
    author = "Yu, Cheng  and
      Chu, Bing  and
      Ram, Rohit  and
      Aichinger, James  and
      Qu, Lizhen  and
      Suominen, Hanna",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2016",
    month = dec,
    year = "2016",
    address = "Melbourne, Australia",
    url = "https://www.aclweb.org/anthology/U16-1023",
    pages = "175--179",
}
@proceedings{semeval-2016-joint,
    title = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    editor = "Gardent, Claire  and
      Bernardi, Raffaella  and
      Titov, Ivan",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2000",
    doi = "10.18653/v1/S16-2",
}
@inproceedings{linzen-etal-2016-quantificational,
    title = "Quantificational features in distributional word representations",
    author = "Linzen, Tal  and
      Dupoux, Emmanuel  and
      Spector, Benjamin",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2001",
    doi = "10.18653/v1/S16-2001",
    pages = "1--11",
}
@inproceedings{falk-martin-2016-automatic,
    title = "Automatic Identification of Aspectual Classes across Verbal Readings",
    author = "Falk, Ingrid  and
      Martin, Fabienne",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2002",
    doi = "10.18653/v1/S16-2002",
    pages = "12--22",
}
@inproceedings{mohammad-etal-2016-metaphor,
    title = "Metaphor as a Medium for Emotion: An Empirical Study",
    author = "Mohammad, Saif  and
      Shutova, Ekaterina  and
      Turney, Peter",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2003",
    doi = "10.18653/v1/S16-2003",
    pages = "23--33",
}
@inproceedings{kim-schubert-2016-high,
    title = "High-Fidelity Lexical Axiom Construction from Verb Glosses",
    author = "Kim, Gene  and
      Schubert, Lenhart",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2004",
    doi = "10.18653/v1/S16-2004",
    pages = "34--44",
}
@inproceedings{sikos-etal-2016-implicit,
    title = "Implicit Semantic Roles in a Multilingual Setting",
    author = "Sikos, Jennifer  and
      Versley, Yannick  and
      Frank, Anette",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2005",
    doi = "10.18653/v1/S16-2005",
    pages = "45--54",
}
@inproceedings{beloucif-wu-2016-driving,
    title = "Driving inversion transduction grammar induction with semantic evaluation",
    author = "Beloucif, Meriem  and
      Wu, Dekai",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2006",
    doi = "10.18653/v1/S16-2006",
    pages = "55--63",
}
@inproceedings{abzianidze-2016-natural,
    title = "Natural Solution to {F}ra{C}a{S} Entailment Problems",
    author = "Abzianidze, Lasha",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2007",
    doi = "10.18653/v1/S16-2007",
    pages = "64--74",
}
@inproceedings{klenner-clematide-2016-factuality,
    title = "How Factuality Determines Sentiment Inferences",
    author = "Klenner, Manfred  and
      Clematide, Simon",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2008",
    doi = "10.18653/v1/S16-2008",
    pages = "75--84",
}
@inproceedings{song-etal-2016-sense,
    title = "Sense Embedding Learning for Word Sense Induction",
    author = "Song, Linfeng  and
      Wang, Zhiguo  and
      Mi, Haitao  and
      Gildea, Daniel",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2009",
    doi = "10.18653/v1/S16-2009",
    pages = "85--90",
}
@inproceedings{koper-etal-2016-improving,
    title = "Improving Zero-Shot-Learning for {G}erman Particle Verbs by using Training-Space Restrictions and Local Scaling",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine  and
      Kisselew, Max  and
      Pad{\'o}, Sebastian},
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2010",
    doi = "10.18653/v1/S16-2010",
    pages = "91--96",
}
@inproceedings{krebs-paperno-2016-hyperparameters,
    title = "When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models",
    author = "Krebs, Alicia  and
      Paperno, Denis",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2011",
    doi = "10.18653/v1/S16-2011",
    pages = "97--101",
}
@inproceedings{peterson-etal-2016-leveraging,
    title = "Leveraging {V}erb{N}et to build Corpus-Specific Verb Clusters",
    author = "Peterson, Daniel  and
      Boyd-Graber, Jordan  and
      Palmer, Martha  and
      Kawahara, Daisuke",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2012",
    doi = "10.18653/v1/S16-2012",
    pages = "102--107",
}
@inproceedings{shwartz-dagan-2016-adding,
    title = "Adding Context to Semantic Data-Driven Paraphrasing",
    author = "Shwartz, Vered  and
      Dagan, Ido",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2013",
    doi = "10.18653/v1/S16-2013",
    pages = "108--113",
}
@inproceedings{pavlick-callison-burch-2016-called,
    title = "So-Called Non-Subsective Adjectives",
    author = "Pavlick, Ellie  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2014",
    doi = "10.18653/v1/S16-2014",
    pages = "114--119",
}
@inproceedings{van-der-pol-etal-2016-linguistic,
    title = "Linguistic Style Accommodation in Disagreements",
    author = "van der Pol, Elise  and
      Gieske, Sharon  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2015",
    doi = "10.18653/v1/S16-2015",
    pages = "120--124",
}
@inproceedings{glavas-etal-2016-unsupervised,
    title = "Unsupervised Text Segmentation Using Semantic Relatedness Graphs",
    author = "Glava{\v{s}}, Goran  and
      Nanni, Federico  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2016",
    doi = "10.18653/v1/S16-2016",
    pages = "125--130",
}
@inproceedings{sevens-etal-2016-improving,
    title = "Improving Text-to-Pictograph Translation Through Word Sense Disambiguation",
    author = "Sevens, Leen  and
      Jacobs, Gilles  and
      Vandeghinste, Vincent  and
      Schuurman, Ineke  and
      Van Eynde, Frank",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2017",
    doi = "10.18653/v1/S16-2017",
    pages = "131--135",
}
@inproceedings{abad-moschitti-2016-taking,
    title = "Taking the best from the Crowd:Learning Question Passage Classification from Noisy Data",
    author = "Abad, Azad  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2018",
    doi = "10.18653/v1/S16-2018",
    pages = "136--141",
}
@inproceedings{xiao-etal-2016-orthogonality,
    title = "Orthogonality regularizer for question answering",
    author = "Xiao, Chunyang  and
      Bouchard, Guillaume  and
      Dymetman, Marc  and
      Gardent, Claire",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2019",
    doi = "10.18653/v1/S16-2019",
    pages = "142--147",
}
@inproceedings{schulte-im-walde-etal-2016-role,
    title = "The Role of Modifier and Head Properties in Predicting the Compositionality of {E}nglish and {G}erman Noun-Noun Compounds: A Vector-Space Perspective",
    author = {Schulte im Walde, Sabine  and
      H{\"a}tty, Anna  and
      Bott, Stefan},
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2020",
    doi = "10.18653/v1/S16-2020",
    pages = "148--158",
}
@inproceedings{sobhani-etal-2016-detecting,
    title = "Detecting Stance in Tweets And Analyzing its Interaction with Sentiment",
    author = "Sobhani, Parinaz  and
      Mohammad, Saif  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2021",
    doi = "10.18653/v1/S16-2021",
    pages = "159--169",
}
@inproceedings{negi-etal-2016-study,
    title = "A Study of Suggestions in Opinionated Texts and their Automatic Detection",
    author = "Negi, Sapna  and
      Asooja, Kartik  and
      Mehrotra, Shubham  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2022",
    doi = "10.18653/v1/S16-2022",
    pages = "170--178",
}
@inproceedings{herbelot-qasemizadeh-2016-vector,
    title = "You and me... in a vector space: modelling individual speakers with distributional semantics",
    author = "Herbelot, Aur{\'e}lie  and
      QasemiZadeh, Behrang",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2023",
    doi = "10.18653/v1/S16-2023",
    pages = "179--188",
}
@inproceedings{qasemizadeh-kallmeyer-2016-random,
    title = "Random Positive-Only Projections: {PPMI}-Enabled Incremental Semantic Space Construction",
    author = "QasemiZadeh, Behrang  and
      Kallmeyer, Laura",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2024",
    doi = "10.18653/v1/S16-2024",
    pages = "189--198",
}
@inproceedings{sales-etal-2016-compositional,
    title = "A Compositional-Distributional Semantic Model for Searching Complex Entity Categories",
    author = "Sales, Juliano Efson  and
      Freitas, Andr{\'e}  and
      Davis, Brian  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2025",
    doi = "10.18653/v1/S16-2025",
    pages = "199--208",
}
@inproceedings{ziai-etal-2016-approximating,
    title = "Approximating Givenness in Content Assessment through Distributional Semantics",
    author = "Ziai, Ramon  and
      De Kuthy, Kordula  and
      Meurers, Detmar",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2026",
    doi = "10.18653/v1/S16-2026",
    pages = "209--218",
}
@inproceedings{perez-beltrachini-gardent-2016-learning,
    title = "Learning Embeddings to lexicalise {RDF} Properties",
    author = "Perez-Beltrachini, Laura  and
      Gardent, Claire",
    booktitle = "Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-2027",
    doi = "10.18653/v1/S16-2027",
    pages = "219--228",
}
@proceedings{semeval-2016-international,
    title = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Cer, Daniel  and
      Jurgens, David  and
      Nakov, Preslav  and
      Zesch, Torsten",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1000",
    doi = "10.18653/v1/S16-1",
}
@inproceedings{nakov-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 4: Sentiment Analysis in {T}witter",
    author = "Nakov, Preslav  and
      Ritter, Alan  and
      Rosenthal, Sara  and
      Sebastiani, Fabrizio  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1001",
    doi = "10.18653/v1/S16-1001",
    pages = "1--18",
}
@inproceedings{pontiki-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 5: Aspect Based Sentiment Analysis",
    author = {Pontiki, Maria  and
      Galanis, Dimitris  and
      Papageorgiou, Haris  and
      Androutsopoulos, Ion  and
      Manandhar, Suresh  and
      AL-Smadi, Mohammad  and
      Al-Ayyoub, Mahmoud  and
      Zhao, Yanyan  and
      Qin, Bing  and
      De Clercq, Orph{\'e}e  and
      Hoste, V{\'e}ronique  and
      Apidianaki, Marianna  and
      Tannier, Xavier  and
      Loukachevitch, Natalia  and
      Kotelnikov, Evgeniy  and
      Bel, Nuria  and
      Jim{\'e}nez-Zafra, Salud Mar{\'\i}a  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1002",
    doi = "10.18653/v1/S16-1002",
    pages = "19--30",
}
@inproceedings{mohammad-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 6: Detecting Stance in Tweets",
    author = "Mohammad, Saif  and
      Kiritchenko, Svetlana  and
      Sobhani, Parinaz  and
      Zhu, Xiaodan  and
      Cherry, Colin",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1003",
    doi = "10.18653/v1/S16-1003",
    pages = "31--41",
}
@inproceedings{kiritchenko-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 7: Determining Sentiment Intensity of {E}nglish and {A}rabic Phrases",
    author = "Kiritchenko, Svetlana  and
      Mohammad, Saif  and
      Salameh, Mohammad",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1004",
    doi = "10.18653/v1/S16-1004",
    pages = "42--51",
}
@inproceedings{nabil-etal-2016-cufe,
    title = "{CUFE} at {S}em{E}val-2016 Task 4: A Gated Recurrent Model for Sentiment Classification",
    author = "Nabil, Mahmoud  and
      Atyia, Amir  and
      Aly, Mohamed",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1005",
    doi = "10.18653/v1/S16-1005",
    pages = "52--57",
}
@inproceedings{da-san-martino-etal-2016-qcri,
    title = "{QCRI} at {S}em{E}val-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification",
    author = "Da San Martino, Giovanni  and
      Gao, Wei  and
      Sebastiani, Fabrizio",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1006",
    doi = "10.18653/v1/S16-1006",
    pages = "58--63",
}
@inproceedings{rabiger-etal-2016-stem,
    title = "{S}te{M} at {S}em{E}val-2016 Task 4: Applying Active Learning to Improve Sentiment Classification",
    author = {R{\"a}biger, Stefan  and
      Kazmi, Mishal  and
      Sayg{\i}n, Y{\"u}cel  and
      Sch{\"u}ller, Peter  and
      Spiliopoulou, Myra},
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1007",
    doi = "10.18653/v1/S16-1007",
    pages = "64--70",
}
@inproceedings{zhang-etal-2016-i2rntu,
    title = "{I}2{RNTU} at {S}em{E}val-2016 Task 4: Classifier Fusion for Polarity Classification in {T}witter",
    author = "Zhang, Zhengchen  and
      Zhang, Chen  and
      Wu, Fuxiang  and
      Huang, Dong-Yan  and
      Lin, Weisi  and
      Dong, Minghui",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1008",
    doi = "10.18653/v1/S16-1008",
    pages = "71--78",
}
@inproceedings{vilares-etal-2016-lys,
    title = "{L}y{S} at {S}em{E}val-2016 Task 4: Exploiting Neural Activation Values for {T}witter Sentiment Classification and Quantification",
    author = "Vilares, David  and
      Doval, Yerai  and
      Alonso, Miguel A.  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1009",
    doi = "10.18653/v1/S16-1009",
    pages = "79--84",
}
@inproceedings{balikas-amini-2016-twise,
    title = "{T}wi{SE} at {S}em{E}val-2016 Task 4: {T}witter Sentiment Classification",
    author = "Balikas, Georgios  and
      Amini, Massih-Reza",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1010",
    doi = "10.18653/v1/S16-1010",
    pages = "85--91",
}
@inproceedings{esuli-2016-isti,
    title = "{ISTI}-{CNR} at {S}em{E}val-2016 Task 4: Quantification on an Ordinal Scale",
    author = "Esuli, Andrea",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1011",
    doi = "10.18653/v1/S16-1011",
    pages = "92--95",
}
@inproceedings{giorgis-etal-2016-aueb,
    title = "aueb.twitter.sentiment at {S}em{E}val-2016 Task 4: A Weighted Ensemble of {SVM}s for {T}witter Sentiment Analysis",
    author = "Giorgis, Stavros  and
      Rousas, Apostolos  and
      Pavlopoulos, John  and
      Malakasiotis, Prodromos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1012",
    doi = "10.18653/v1/S16-1012",
    pages = "96--99",
}
@inproceedings{yadav-2016-thecerealkiller,
    title = "thecerealkiller at {S}em{E}val-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale",
    author = "Yadav, Vikrant",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1013",
    doi = "10.18653/v1/S16-1013",
    pages = "100--102",
}
@inproceedings{jahren-etal-2016-ntnusenteval,
    title = "{NTNUS}ent{E}val at {S}em{E}val-2016 Task 4: Combining General Classifiers for Fast {T}witter Sentiment Analysis",
    author = {Jahren, Brage Ekroll  and
      Fredriksen, Valerij  and
      Gamb{\"a}ck, Bj{\"o}rn  and
      Bungum, Lars},
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1014",
    doi = "10.18653/v1/S16-1014",
    pages = "103--108",
}
@inproceedings{castillo-etal-2016-udlap,
    title = "{UDLAP} at {S}em{E}val-2016 Task 4: Sentiment Quantification Using a Graph Based Representation",
    author = "Castillo, Esteban  and
      Cervantes, Ofelia  and
      Vilari{\~n}o, Darnes  and
      B{\'a}ez, David",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1015",
    doi = "10.18653/v1/S16-1015",
    pages = "109--114",
}
@inproceedings{juncal-martinez-etal-2016-gti,
    title = "{GTI} at {S}em{E}val-2016 Task 4: Training a Naive {B}ayes Classifier using Features of an Unsupervised System",
    author = "Juncal-Mart{\'\i}nez, Jonathan  and
      {\'A}lvarez-L{\'o}pez, Tamara  and
      Fern{\'a}ndez-Gavilanes, Milagros  and
      Costa-Montenegro, Enrique  and
      Gonz{\'a}lez-Casta{\~n}o, Francisco Javier",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1016",
    doi = "10.18653/v1/S16-1016",
    pages = "115--119",
}
@inproceedings{du-zhang-2016-aicyber,
    title = "Aicyber at {S}em{E}val-2016 Task 4: i-vector based sentence representation",
    author = "Du, Steven  and
      Zhang, Xi",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1017",
    doi = "10.18653/v1/S16-1017",
    pages = "120--125",
}
@inproceedings{lango-etal-2016-put,
    title = "{PUT} at {S}em{E}val-2016 Task 4: The {ABC} of {T}witter Sentiment Analysis",
    author = "Lango, Mateusz  and
      Brzezinski, Dariusz  and
      Stefanowski, Jerzy",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1018",
    doi = "10.18653/v1/S16-1018",
    pages = "126--132",
}
@inproceedings{cozza-petrocchi-2016-mib,
    title = "mib at {S}em{E}val-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in {T}witter",
    author = "Cozza, Vittoria  and
      Petrocchi, Marinella",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1019",
    doi = "10.18653/v1/S16-1019",
    pages = "133--138",
}
@inproceedings{gao-oates-2016-mdsent,
    title = "{MDSENT} at {S}em{E}val-2016 Task 4: A Supervised System for Message Polarity Classification",
    author = "Gao, Hang  and
      Oates, Tim",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1020",
    doi = "10.18653/v1/S16-1020",
    pages = "139--144",
}
@inproceedings{gomez-etal-2016-cicbuapnlp,
    title = "{CICBUAP}nlp at {S}em{E}val-2016 Task 4-A: Discovering {T}witter Polarity using Enhanced Embeddings",
    author = "Gomez, Helena  and
      Vilari{\~n}o, Darnes  and
      Sidorov, Grigori  and
      Pinto Avenda{\~n}o, David",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1021",
    doi = "10.18653/v1/S16-1021",
    pages = "145--148",
}
@inproceedings{stojanovski-etal-2016-finki,
    title = "Finki at {S}em{E}val-2016 Task 4: Deep Learning Architecture for {T}witter Sentiment Analysis",
    author = "Stojanovski, Dario  and
      Strezoski, Gjorgji  and
      Madjarov, Gjorgji  and
      Dimitrovski, Ivica",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1022",
    doi = "10.18653/v1/S16-1022",
    pages = "149--154",
}
@inproceedings{palogiannidi-etal-2016-tweester,
    title = "Tweester at {S}em{E}val-2016 Task 4: Sentiment Analysis in {T}witter Using Semantic-Affective Model Adaptation",
    author = "Palogiannidi, Elisavet  and
      Kolovou, Athanasia  and
      Christopoulou, Fenia  and
      Kokkinos, Filippos  and
      Iosif, Elias  and
      Malandrakis, Nikolaos  and
      Papageorgiou, Haris  and
      Narayanan, Shrikanth  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1023",
    doi = "10.18653/v1/S16-1023",
    pages = "155--163",
}
@inproceedings{abdelwahab-elmaghraby-2016-uofl,
    title = "{U}of{L} at {S}em{E}val-2016 Task 4: Multi Domain word2vec for {T}witter Sentiment Classification",
    author = "Abdelwahab, Omar  and
      Elmaghraby, Adel",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1024",
    doi = "10.18653/v1/S16-1024",
    pages = "164--170",
}
@inproceedings{karpov-etal-2016-nru,
    title = "{NRU}-{HSE} at {S}em{E}val-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library",
    author = "Karpov, Nikolay  and
      Porshnev, Alexander  and
      Rudakov, Kirill",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1025",
    doi = "10.18653/v1/S16-1025",
    pages = "171--177",
}
@inproceedings{ruder-etal-2016-insight,
    title = "{INSIGHT}-1 at {S}em{E}val-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification",
    author = "Ruder, Sebastian  and
      Ghaffari, Parsa  and
      Breslin, John G.",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1026",
    doi = "10.18653/v1/S16-1026",
    pages = "178--182",
}
@inproceedings{xu-etal-2016-unimelb,
    title = "{UNIMELB} at {S}em{E}val-2016 Tasks 4{A} and 4{B}: An Ensemble of Neural Networks and a {W}ord2{V}ec Based Model for Sentiment Classification",
    author = "Xu, Steven  and
      Liang, HuiZhi  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1027",
    doi = "10.18653/v1/S16-1027",
    pages = "183--189",
}
@inproceedings{hamdan-2016-sentisys,
    title = "{S}enti{S}ys at {S}em{E}val-2016 Task 4: Feature-Based System for Sentiment Analysis in {T}witter",
    author = "Hamdan, Hussam",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1028",
    doi = "10.18653/v1/S16-1028",
    pages = "190--197",
}
@inproceedings{martinez-morant-etal-2016-dsic,
    title = "{DSIC}-{ELIRF} at {S}em{E}val-2016 Task 4: Message Polarity Classification in {T}witter using a Support Vector Machine Approach",
    author = "Martinez Morant, V{\'\i}ctor  and
      Hurtado, LLu{\'\i}s-F.  and
      Pla, Ferran",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1029",
    doi = "10.18653/v1/S16-1029",
    pages = "198--201",
}
@inproceedings{rouvier-favre-2016-sensei,
    title = "{SENSEI}-{LIF} at {S}em{E}val-2016 Task 4: Polarity embedding fusion for robust sentiment analysis",
    author = "Rouvier, Mickael  and
      Favre, Benoit",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1030",
    doi = "10.18653/v1/S16-1030",
    pages = "202--208",
}
@inproceedings{sarker-gonzalez-2016-diegolab16,
    title = "{D}iego{L}ab16 at {S}em{E}val-2016 Task 4: Sentiment Analysis in {T}witter using Centroids, Clusters, and Sentiment Lexicons",
    author = "Sarker, Abeed  and
      Gonzalez, Graciela",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1031",
    doi = "10.18653/v1/S16-1031",
    pages = "209--214",
}
@inproceedings{briones-etal-2016-vcu,
    title = "{VCU}-{TSA} at {S}emeval-2016 Task 4: Sentiment Analysis in {T}witter",
    author = "Briones, Gerard  and
      Amarasinghe, Kasun  and
      McInnes, Bridget",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1032",
    doi = "10.18653/v1/S16-1032",
    pages = "215--219",
}
@inproceedings{attardi-sartiano-2016-unipi,
    title = "{U}ni{PI} at {S}em{E}val-2016 Task 4: Convolutional Neural Networks for Sentiment Classification",
    author = "Attardi, Giuseppe  and
      Sartiano, Daniele",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1033",
    doi = "10.18653/v1/S16-1033",
    pages = "220--224",
}
@inproceedings{friedrichs-2016-iip,
    title = "{IIP} at {S}em{E}val-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets",
    author = "Friedrichs, Jasper",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1034",
    doi = "10.18653/v1/S16-1034",
    pages = "225--229",
}
@inproceedings{sidarenka-2016-potts,
    title = "{P}ot{TS} at {S}em{E}val-2016 Task 4: Sentiment Analysis of {T}witter Using Character-level Convolutional Neural Networks.",
    author = "Sidarenka, Uladzimir",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1035",
    doi = "10.18653/v1/S16-1035",
    pages = "230--237",
}
@inproceedings{amir-etal-2016-inesc,
    title = "{INESC}-{ID} at {S}em{E}val-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words",
    author = "Amir, Silvio  and
      F. Astudillo, Ramon  and
      Ling, Wang  and
      Silva, M{\'a}rio J.  and
      Trancoso, Isabel",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1036",
    doi = "10.18653/v1/S16-1036",
    pages = "238--242",
}
@inproceedings{florean-etal-2016-sentimentalitsts,
    title = "{S}entimental{IT}sts at {S}em{E}val-2016 Task 4: building a {T}witter sentiment analyzer in your backyard",
    author = "Florean, Cosmin  and
      Bejenaru, Oana  and
      Apostol, Eduard  and
      Ciobanu, Octavian  and
      Iftene, Adrian  and
      Trandab{\u{a}}{\c{t}}, Diana",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1037",
    doi = "10.18653/v1/S16-1037",
    pages = "243--246",
}
@inproceedings{ciubotariu-etal-2016-minions,
    title = "Minions at {S}em{E}val-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources?",
    author = "Ciubotariu, C{\u{a}}lin-Cristian  and
      Hri{\c{s}}ca, Marius-Valentin  and
      Gliga, Mihail  and
      Daraban{\u{a}}, Diana  and
      Trandab{\u{a}}{\c{t}}, Diana  and
      Iftene, Adrian",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1038",
    doi = "10.18653/v1/S16-1038",
    pages = "247--250",
}
@inproceedings{he-etal-2016-yzu,
    title = "{YZU}-{NLP} Team at {S}em{E}val-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network",
    author = "He, Yunchao  and
      Yu, Liang-Chih  and
      Yang, Chin-Sheng  and
      Lai, K. Robert  and
      Liu, Weiyi",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1039",
    doi = "10.18653/v1/S16-1039",
    pages = "251--255",
}
@inproceedings{zhou-etal-2016-ecnu,
    title = "{ECNU} at {S}em{E}val-2016 Task 4: An Empirical Investigation of Traditional {NLP} Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in {T}witter",
    author = "Zhou, Yunxiao  and
      Zhang, Zhihua  and
      Lan, Man",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1040",
    doi = "10.18653/v1/S16-1040",
    pages = "256--261",
}
@inproceedings{balahur-2016-opal,
    title = "{OPAL} at {S}em{E}val-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the {``}Real{''} World",
    author = "Balahur, Alexandra",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1041",
    doi = "10.18653/v1/S16-1041",
    pages = "262--265",
}
@inproceedings{falk-etal-2016-know,
    title = "Know-Center at {S}em{E}val-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction",
    author = "Falk, Stefan  and
      Rexha, Andi  and
      Kern, Roman",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1042",
    doi = "10.18653/v1/S16-1042",
    pages = "266--270",
}
@inproceedings{khalil-el-beltagy-2016-niletmrg,
    title = "{N}ile{TMRG} at {S}em{E}val-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction",
    author = "Khalil, Talaat  and
      El-Beltagy, Samhaa R.",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1043",
    doi = "10.18653/v1/S16-1043",
    pages = "271--276",
}
@inproceedings{brun-etal-2016-xrce,
    title = "{XRCE} at {S}em{E}val-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis",
    author = "Brun, Caroline  and
      Perez, Julien  and
      Roux, Claude",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1044",
    doi = "10.18653/v1/S16-1044",
    pages = "277--281",
}
@inproceedings{toh-su-2016-nlangp,
    title = "{NLANGP} at {S}em{E}val-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features",
    author = "Toh, Zhiqiang  and
      Su, Jian",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1045",
    doi = "10.18653/v1/S16-1045",
    pages = "282--288",
}
@inproceedings{yanase-etal-2016-bunji,
    title = "bunji at {S}em{E}val-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis",
    author = "Yanase, Toshihiko  and
      Yanai, Kohsuke  and
      Sato, Misa  and
      Miyoshi, Toshinori  and
      Niwa, Yoshiki",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1046",
    doi = "10.18653/v1/S16-1046",
    pages = "289--295",
}
@inproceedings{chernyshevich-2016-ihs,
    title = "{IHS}-{RD}-{B}elarus at {S}em{E}val-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence",
    author = "Chernyshevich, Maryna",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1047",
    doi = "10.18653/v1/S16-1047",
    pages = "296--300",
}
@inproceedings{machacek-2016-butknot,
    title = "{BUT}knot at {S}em{E}val-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection",
    author = "Mach{\'a}{\v{c}}ek, Jakub",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1048",
    doi = "10.18653/v1/S16-1048",
    pages = "301--305",
}
@inproceedings{alvarez-lopez-etal-2016-gti,
    title = "{GTI} at {S}em{E}val-2016 Task 5: {SVM} and {CRF} for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis",
    author = "{\'A}lvarez-L{\'o}pez, Tamara  and
      Juncal-Mart{\'\i}nez, Jonathan  and
      Fern{\'a}ndez-Gavilanes, Milagros  and
      Costa-Montenegro, Enrique  and
      Gonz{\'a}lez-Casta{\~n}o, Francisco Javier",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1049",
    doi = "10.18653/v1/S16-1049",
    pages = "306--311",
}
@inproceedings{xenos-etal-2016-aueb,
    title = "{AUEB}-{ABSA} at {S}em{E}val-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis",
    author = "Xenos, Dionysios  and
      Theodorakakos, Panagiotis  and
      Pavlopoulos, John  and
      Malakasiotis, Prodromos  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1050",
    doi = "10.18653/v1/S16-1050",
    pages = "312--317",
}
@inproceedings{pateria-choubey-2016-aktski,
    title = "{AKTSKI} at {S}em{E}val-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews",
    author = "Pateria, Shubham  and
      Choubey, Prafulla",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1051",
    doi = "10.18653/v1/S16-1051",
    pages = "318--324",
}
@inproceedings{mayorov-andrianov-2016-mayand,
    title = "{M}ay{A}nd at {S}em{E}val-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in {R}ussian",
    author = "Mayorov, Vladimir  and
      Andrianov, Ivan",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1052",
    doi = "10.18653/v1/S16-1052",
    pages = "325--329",
}
@inproceedings{ruder-etal-2016-insight-1,
    title = "{INSIGHT}-1 at {S}em{E}val-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis",
    author = "Ruder, Sebastian  and
      Ghaffari, Parsa  and
      Breslin, John G.",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1053",
    doi = "10.18653/v1/S16-1053",
    pages = "330--336",
}
@inproceedings{cetin-etal-2016-tgb,
    title = "{TGB} at {S}em{E}val-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis",
    author = {{\c{C}}etin, Fatih Samet  and
      Y{\i}ld{\i}r{\i}m, Ezgi  and
      {\"O}zbey, Can  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1054",
    doi = "10.18653/v1/S16-1054",
    pages = "337--341",
}
@inproceedings{hercig-etal-2016-uwb,
    title = "{UWB} at {S}em{E}val-2016 Task 5: Aspect Based Sentiment Analysis",
    author = "Hercig, Tom{\'a}{\v{s}}  and
      Brychc{\'\i}n, Tom{\'a}{\v{s}}  and
      Svoboda, Luk{\'a}{\v{s}}  and
      Konkol, Michal",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1055",
    doi = "10.18653/v1/S16-1055",
    pages = "342--349",
}
@inproceedings{hamdan-2016-sentisys-semeval,
    title = "{S}enti{S}ys at {S}em{E}val-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection",
    author = "Hamdan, Hussam",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1056",
    doi = "10.18653/v1/S16-1056",
    pages = "350--355",
}
@inproceedings{schouten-frasincar-2016-commit,
    title = "{COMMIT} at {S}em{E}val-2016 Task 5: Sentiment Analysis with {R}hetorical {S}tructure {T}heory",
    author = "Schouten, Kim  and
      Frasincar, Flavius",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1057",
    doi = "10.18653/v1/S16-1057",
    pages = "356--360",
}
@inproceedings{jiang-etal-2016-ecnu,
    title = "{ECNU} at {S}em{E}val-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews",
    author = "Jiang, Mengxiao  and
      Zhang, Zhihua  and
      Lan, Man",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1058",
    doi = "10.18653/v1/S16-1058",
    pages = "361--366",
}
@inproceedings{tamchyna-veselovska-2016-ufal,
    title = "{UFAL} at {S}em{E}val-2016 Task 5: Recurrent Neural Networks for Sentence Classification",
    author = "Tamchyna, Ale{\v{s}}  and
      Veselovsk{\'a}, Kate{\v{r}}ina",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1059",
    doi = "10.18653/v1/S16-1059",
    pages = "367--371",
}
@inproceedings{vechtomova-he-2016-uwaterloo,
    title = "{UW}aterloo at {S}em{E}val-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis",
    author = "Vechtomova, Olga  and
      He, Anni",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1060",
    doi = "10.18653/v1/S16-1060",
    pages = "372--377",
}
@inproceedings{dias-becker-2016-inf,
    title = "{INF}-{UFRGS}-{OPINION}-{MINING} at {S}em{E}val-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets",
    author = "Dias, Marcelo  and
      Becker, Karin",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1061",
    doi = "10.18653/v1/S16-1061",
    pages = "378--383",
}
@inproceedings{wei-etal-2016-pkudblab,
    title = "pkudblab at {S}em{E}val-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection",
    author = "Wei, Wan  and
      Zhang, Xiao  and
      Liu, Xuqin  and
      Chen, Wei  and
      Wang, Tengjiao",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1062",
    doi = "10.18653/v1/S16-1062",
    pages = "384--388",
}
@inproceedings{augenstein-etal-2016-usfd,
    title = "{USFD} at {S}em{E}val-2016 Task 6: Any-Target Stance Detection on {T}witter with Autoencoders",
    author = "Augenstein, Isabelle  and
      Vlachos, Andreas  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1063",
    doi = "10.18653/v1/S16-1063",
    pages = "389--393",
}
@inproceedings{liu-etal-2016-iucl,
    title = "{IUCL} at {S}em{E}val-2016 Task 6: An Ensemble Model for Stance Detection in {T}witter",
    author = {Liu, Can  and
      Li, Wen  and
      Demarest, Bradford  and
      Chen, Yue  and
      Couture, Sara  and
      Dakota, Daniel  and
      Haduong, Nikita  and
      Kaufman, Noah  and
      Lamont, Andrew  and
      Pancholi, Manan  and
      Steimel, Kenneth  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1064",
    doi = "10.18653/v1/S16-1064",
    pages = "394--400",
}
@inproceedings{igarashi-etal-2016-tohoku,
    title = "Tohoku at {S}em{E}val-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection",
    author = "Igarashi, Yuki  and
      Komatsu, Hiroya  and
      Kobayashi, Sosuke  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1065",
    doi = "10.18653/v1/S16-1065",
    pages = "401--407",
}
@inproceedings{krejzl-steinberger-2016-uwb,
    title = "{UWB} at {S}em{E}val-2016 Task 6: Stance Detection",
    author = "Krejzl, Peter  and
      Steinberger, Josef",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1066",
    doi = "10.18653/v1/S16-1066",
    pages = "408--412",
}
@inproceedings{vijayaraghavan-etal-2016-deepstance,
    title = "{D}eep{S}tance at {S}em{E}val-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level {CNN}s",
    author = "Vijayaraghavan, Prashanth  and
      Sysoev, Ivan  and
      Vosoughi, Soroush  and
      Roy, Deb",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1067",
    doi = "10.18653/v1/S16-1067",
    pages = "413--419",
}
@inproceedings{misra-etal-2016-nlds,
    title = "{NLDS}-{UCSC} at {S}em{E}val-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets",
    author = "Misra, Amita  and
      Ecker, Brian  and
      Handleman, Theodore  and
      Hahn, Nicolas  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1068",
    doi = "10.18653/v1/S16-1068",
    pages = "420--427",
}
@inproceedings{wojatzki-zesch-2016-ltl,
    title = "ltl.uni-due at {S}em{E}val-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers",
    author = "Wojatzki, Michael  and
      Zesch, Torsten",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1069",
    doi = "10.18653/v1/S16-1069",
    pages = "428--433",
}
@inproceedings{elfardy-diab-2016-cu,
    title = "{CU}-{GWU} Perspective at {S}em{E}val-2016 Task 6: Ideological Stance Detection in Informal Text",
    author = "Elfardy, Heba  and
      Diab, Mona",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1070",
    doi = "10.18653/v1/S16-1070",
    pages = "434--439",
}
@inproceedings{patra-etal-2016-ju,
    title = "{JU}{\_}{NLP} at {S}em{E}val-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines",
    author = "Patra, Braja Gopal  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1071",
    doi = "10.18653/v1/S16-1071",
    pages = "440--444",
}
@inproceedings{bohler-etal-2016-idi,
    title = "{IDI}@{NTNU} at {S}em{E}val-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and {G}lo{V}e Vectors for Word Representation",
    author = "B{\o}hler, Henrik  and
      Asla, Petter  and
      Marsi, Erwin  and
      S{\ae}tre, Rune",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1072",
    doi = "10.18653/v1/S16-1072",
    pages = "445--450",
}
@inproceedings{zhang-lan-2016-ecnu,
    title = "{ECNU} at {S}em{E}val 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets",
    author = "Zhang, Zhihua  and
      Lan, Man",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1073",
    doi = "10.18653/v1/S16-1073",
    pages = "451--457",
}
@inproceedings{zarrella-marsh-2016-mitre,
    title = "{MITRE} at {S}em{E}val-2016 Task 6: Transfer Learning for Stance Detection",
    author = "Zarrella, Guido  and
      Marsh, Amy",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1074",
    doi = "10.18653/v1/S16-1074",
    pages = "458--463",
}
@inproceedings{tutek-etal-2016-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble",
    author = "Tutek, Martin  and
      Sekuli{\'c}, Ivan  and
      Gombar, Paula  and
      Paljak, Ivan  and
      {\v{C}}ulinovi{\'c}, Filip  and
      Boltu{\v{z}}i{\'c}, Filip  and
      Karan, Mladen  and
      Alagi{\'c}, Domagoj  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1075",
    doi = "10.18653/v1/S16-1075",
    pages = "464--468",
}
@inproceedings{htait-etal-2016-lsis,
    title = "{LSIS} at {S}em{E}val-2016 Task 7: Using Web Search Engines for {E}nglish and {A}rabic Unsupervised Sentiment Intensity Prediction",
    author = "Htait, Amal  and
      Fournier, Sebastien  and
      Bellot, Patrice",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1076",
    doi = "10.18653/v1/S16-1076",
    pages = "469--473",
}
@inproceedings{refaee-rieser-2016-ilab,
    title = "i{L}ab-{E}dinburgh at {S}em{E}val-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of {A}rabic {T}witter Phrases",
    author = "Refaee, Eshrag  and
      Rieser, Verena",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1077",
    doi = "10.18653/v1/S16-1077",
    pages = "474--480",
}
@inproceedings{lenc-etal-2016-uwb,
    title = "{UWB} at {S}em{E}val-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination",
    author = "Lenc, Ladislav  and
      Kr{\'a}l, Pavel  and
      Rajtmajer, V{\'a}clav",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1078",
    doi = "10.18653/v1/S16-1078",
    pages = "481--485",
}
@inproceedings{el-beltagy-2016-niletmrg,
    title = "{N}ile{TMRG} at {S}em{E}val-2016 Task 7: Deriving Prior Polarities for {A}rabic Sentiment Terms",
    author = "El-Beltagy, Samhaa R.",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1079",
    doi = "10.18653/v1/S16-1079",
    pages = "486--490",
}
@inproceedings{wang-etal-2016-ecnu,
    title = "{ECNU} at {S}em{E}val-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking",
    author = "Wang, Feixiang  and
      Zhang, Zhihua  and
      Lan, Man",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1080",
    doi = "10.18653/v1/S16-1080",
    pages = "491--496",
}
@inproceedings{agirre-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation",
    author = "Agirre, Eneko  and
      Banea, Carmen  and
      Cer, Daniel  and
      Diab, Mona  and
      Gonzalez-Agirre, Aitor  and
      Mihalcea, Rada  and
      Rigau, German  and
      Wiebe, Janyce",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1081",
    doi = "10.18653/v1/S16-1081",
    pages = "497--511",
}
@inproceedings{agirre-etal-2016-semeval-2016,
    title = "{S}em{E}val-2016 Task 2: Interpretable Semantic Textual Similarity",
    author = "Agirre, Eneko  and
      Gonzalez-Agirre, Aitor  and
      Lopez-Gazpio, I{\~n}igo  and
      Maritxalar, Montse  and
      Rigau, German  and
      Uria, Larraitz",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1082",
    doi = "10.18653/v1/S16-1082",
    pages = "512--524",
}
@inproceedings{nakov-etal-2016-semeval-2016,
    title = "{S}em{E}val-2016 Task 3: Community Question Answering",
    author = "Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s  and
      Moschitti, Alessandro  and
      Magdy, Walid  and
      Mubarak, Hamdy  and
      Freihat, Abed Alhakim  and
      Glass, Jim  and
      Randeree, Bilal",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1083",
    doi = "10.18653/v1/S16-1083",
    pages = "525--545",
}
@inproceedings{schneider-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 10: Detecting Minimal Semantic Units and their Meanings ({D}i{MSUM})",
    author = "Schneider, Nathan  and
      Hovy, Dirk  and
      Johannsen, Anders  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1084",
    doi = "10.18653/v1/S16-1084",
    pages = "546--559",
}
@inproceedings{paetzold-specia-2016-semeval,
    title = "{S}em{E}val 2016 Task 11: Complex Word Identification",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1085",
    doi = "10.18653/v1/S16-1085",
    pages = "560--569",
}
@inproceedings{ataman-etal-2016-fbk,
    title = "{FBK} {HLT}-{MT} at {S}em{E}val-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings",
    author = "Ataman, Duygu  and
      C. de Souza, Jos{\'e} G.  and
      Turchi, Marco  and
      Negri, Matteo",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1086",
    doi = "10.18653/v1/S16-1086",
    pages = "570--576",
}
@inproceedings{henry-sands-2016-vrep,
    title = "{VR}ep at {S}em{E}val-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity",
    author = "Henry, Sam  and
      Sands, Allison",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1087",
    doi = "10.18653/v1/S16-1087",
    pages = "577--583",
}
@inproceedings{li-huang-2016-uta,
    title = "{UTA} {DLNLP} at {S}em{E}val-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation",
    author = "Li, Peng  and
      Huang, Heng",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1088",
    doi = "10.18653/v1/S16-1088",
    pages = "584--587",
}
@inproceedings{brychcin-svoboda-2016-uwb,
    title = "{UWB} at {S}em{E}val-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information",
    author = "Brychc{\'\i}n, Tom{\'a}{\v{s}}  and
      Svoboda, Luk{\'a}{\v{s}}",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1089",
    doi = "10.18653/v1/S16-1089",
    pages = "588--594",
}
@inproceedings{liebeck-etal-2016-hhu,
    title = "{HHU} at {S}em{E}val-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity",
    author = "Liebeck, Matthias  and
      Pollack, Philipp  and
      Modaresi, Pashutan  and
      Conrad, Stefan",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1090",
    doi = "10.18653/v1/S16-1090",
    pages = "595--601",
}
@inproceedings{rychalska-etal-2016-samsung,
    title = "{S}amsung {P}oland {NLP} Team at {S}em{E}val-2016 Task 1: Necessity for diversity; combining recursive autoencoders, {W}ord{N}et and ensemble methods to measure semantic similarity.",
    author = "Rychalska, Barbara  and
      Pakulska, Katarzyna  and
      Chodorowska, Krystyna  and
      Walczak, Wojciech  and
      Andruszkiewicz, Piotr",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1091",
    doi = "10.18653/v1/S16-1091",
    pages = "602--608",
}
@inproceedings{aker-etal-2016-usfd,
    title = "{USFD} at {S}em{E}val-2016 Task 1: Putting different State-of-the-Arts into a Box",
    author = "Aker, Ahmet  and
      Blain, Frederic  and
      Duque, Andres  and
      Fomicheva, Marina  and
      Seva, Jurica  and
      Shah, Kashif  and
      Beck, Daniel",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1092",
    doi = "10.18653/v1/S16-1092",
    pages = "609--613",
}
@inproceedings{przybyla-etal-2016-nactem,
    title = "{N}a{CT}e{M} at {S}em{E}val-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features",
    author = "Przyby{\l}a, Piotr  and
      Nguyen, Nhung T. H.  and
      Shardlow, Matthew  and
      Kontonatsios, Georgios  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1093",
    doi = "10.18653/v1/S16-1093",
    pages = "614--620",
}
@inproceedings{tian-lan-2016-ecnu,
    title = "{ECNU} at {S}em{E}val-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity",
    author = "Tian, Junfeng  and
      Lan, Man",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1094",
    doi = "10.18653/v1/S16-1094",
    pages = "621--627",
}
@inproceedings{tan-etal-2016-saarsheff,
    title = "{SAARSHEFF} at {S}em{E}val-2016 Task 1: Semantic Textual Similarity with Machine Translation Evaluation Metrics and (e{X}treme) Boosted Tree Ensembles",
    author = "Tan, Liling  and
      Scarton, Carolina  and
      Specia, Lucia  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1095",
    doi = "10.18653/v1/S16-1095",
    pages = "628--633",
}
@inproceedings{bechara-etal-2016-wolvesaar,
    title = "{WOLVESAAR} at {S}em{E}val-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity",
    author = "Bechara, Hannah  and
      Gupta, Rohit  and
      Tan, Liling  and
      Or{\u{a}}san, Constantin  and
      Mitkov, Ruslan  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1096",
    doi = "10.18653/v1/S16-1096",
    pages = "634--639",
}
@inproceedings{banjade-etal-2016-dtsim,
    title = "{DTS}im at {S}em{E}val-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics",
    author = "Banjade, Rajendra  and
      Maharjan, Nabin  and
      Gautam, Dipesh  and
      Rus, Vasile",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1097",
    doi = "10.18653/v1/S16-1097",
    pages = "640--644",
}
@inproceedings{fu-etal-2016-iscas,
    title = "{ISCAS}{\_}{NLP} at {S}em{E}val-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features",
    author = "Fu, Cheng  and
      An, Bo  and
      Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1098",
    doi = "10.18653/v1/S16-1098",
    pages = "645--649",
}
@inproceedings{sultan-etal-2016-dls,
    title = "{DLS}@{CU} at {S}em{E}val-2016 Task 1: Supervised Models of Sentence Similarity",
    author = "Sultan, Md Arafat  and
      Bethard, Steven  and
      Sumner, Tamara",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1099",
    doi = "10.18653/v1/S16-1099",
    pages = "650--655",
}
@inproceedings{hokamp-arora-2016-dcu,
    title = "{DCU}-{SEM}aniacs at {S}em{E}val-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity",
    author = "Hokamp, Chris  and
      Arora, Piyush",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1100",
    doi = "10.18653/v1/S16-1100",
    pages = "656--662",
}
@inproceedings{aldarmaki-diab-2016-gwu,
    title = "{GWU} {NLP} at {S}em{E}val-2016 Shared Task 1: Matrix Factorization for Crosslingual {STS}",
    author = "Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1101",
    doi = "10.18653/v1/S16-1101",
    pages = "663--667",
}
@inproceedings{lo-etal-2016-cnrc,
    title = "{CNRC} at {S}em{E}val-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity",
    author = "Lo, Chi-kiu  and
      Goutte, Cyril  and
      Simard, Michel",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1102",
    doi = "10.18653/v1/S16-1102",
    pages = "668--673",
}
@inproceedings{afzal-etal-2016-mayonlp,
    title = "{M}ayo{NLP} at {S}em{E}val-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model",
    author = "Afzal, Naveed  and
      Wang, Yanshan  and
      Liu, Hongfang",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1103",
    doi = "10.18653/v1/S16-1103",
    pages = "674--679",
}
@inproceedings{tayyar-madabushi-etal-2016-uob,
    title = "{U}o{B}-{UK} at {S}em{E}val-2016 Task 1: A Flexible and Extendable System for Semantic Text Similarity using Types, Surprise and Phrase Linking",
    author = "Tayyar Madabushi, Harish  and
      Buhagiar, Mark  and
      Lee, Mark",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1104",
    doi = "10.18653/v1/S16-1104",
    pages = "680--685",
}
@inproceedings{wu-etal-2016-bit,
    title = "{BIT} at {S}em{E}val-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content",
    author = "Wu, Hao  and
      Huang, Heyan  and
      Lu, Wenpeng",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1105",
    doi = "10.18653/v1/S16-1105",
    pages = "686--690",
}
@inproceedings{itoh-2016-ricoh,
    title = "{RICOH} at {S}em{E}val-2016 Task 1: {IR}-based Semantic Textual Similarity Estimation",
    author = "Itoh, Hideo",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1106",
    doi = "10.18653/v1/S16-1106",
    pages = "691--695",
}
@inproceedings{beliuha-chernyshevich-2016-ihs,
    title = "{IHS}-{RD}-{B}elarus at {S}em{E}val-2016 Task 1: Multistage Approach for Measuring Semantic Similarity",
    author = "Beliuha, Maryna  and
      Chernyshevich, Maryna",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1107",
    doi = "10.18653/v1/S16-1107",
    pages = "696--701",
}
@inproceedings{sarkar-etal-2016-junitmz,
    title = "{JUNITMZ} at {S}em{E}val-2016 Task 1: Identifying Semantic Similarity Using {L}evenshtein Ratio",
    author = "Sarkar, Sandip  and
      Das, Dipankar  and
      Pakray, Partha  and
      Gelbukh, Alexander",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1108",
    doi = "10.18653/v1/S16-1108",
    pages = "702--705",
}
@inproceedings{ganesh-hb-etal-2016-amrita,
    title = "{A}mrita{\_}{CEN} at {S}em{E}val-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension",
    author = "Ganesh HB, Barathi  and
      M, Anand Kumar  and
      KP, Soman",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1109",
    doi = "10.18653/v1/S16-1109",
    pages = "706--711",
}
@inproceedings{mccrae-etal-2016-nuig,
    title = "{NUIG}-{UNLP} at {S}em{E}val-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity",
    author = "McCrae, John Philip  and
      Asooja, Kartik  and
      Aggarwal, Nitish  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1110",
    doi = "10.18653/v1/S16-1110",
    pages = "712--717",
}
@inproceedings{adebayo-etal-2016-normas,
    title = "{NORMAS} at {S}em{E}val-2016 Task 1: {SEMSIM}: A Multi-Feature Approach to Semantic Text Similarity",
    author = "Adebayo, Kolawole  and
      Di Caro, Luigi  and
      Boella, Guido",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1111",
    doi = "10.18653/v1/S16-1111",
    pages = "718--725",
}
@inproceedings{lightgow-serrano-etal-2016-lipn,
    title = "{LIPN}-{IIMAS} at {S}em{E}val-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies",
    author = "Lightgow Serrano, Oscar William  and
      Meza Ruiz, Ivan Vladimir  and
      Orozco Camacho, Albert Manuel  and
      Garcia Flores, Jorge  and
      Buscaldi, Davide",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1112",
    doi = "10.18653/v1/S16-1112",
    pages = "726--731",
}
@inproceedings{king-etal-2016-unbnlp,
    title = "{UNBNLP} at {S}em{E}val-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation",
    author = "King, Milton  and
      Gharbieh, Waseem  and
      Park, SoHyun  and
      Cook, Paul",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1113",
    doi = "10.18653/v1/S16-1113",
    pages = "732--735",
}
@inproceedings{eyecioglu-keller-2016-asobek,
    title = "{ASOBEK} at {S}em{E}val-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity",
    author = "Eyecioglu, Asli  and
      Keller, Bill",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1114",
    doi = "10.18653/v1/S16-1114",
    pages = "736--740",
}
@inproceedings{potash-etal-2016-simihawk,
    title = "{S}imi{H}awk at {S}em{E}val-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity",
    author = "Potash, Peter  and
      Boag, William  and
      Romanov, Alexey  and
      Ramanishka, Vasili  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1115",
    doi = "10.18653/v1/S16-1115",
    pages = "741--748",
}
@inproceedings{jimenez-2016-sergiojimenez,
    title = "{SERGIOJIMENEZ} at {S}em{E}val-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, {W}ord{N}et, and Word Embedding for Semantic Textual Similarity",
    author = "Jimenez, Sergio",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1116",
    doi = "10.18653/v1/S16-1116",
    pages = "749--757",
}
@inproceedings{bicici-2016-rtm,
    title = "{RTM} at {S}em{E}val-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1117",
    doi = "10.18653/v1/S16-1117",
    pages = "758--764",
}
@inproceedings{mei-etal-2016-dalgtm,
    title = "{D}al{GTM} at {S}em{E}val-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity",
    author = "Mei, Jie  and
      Islam, Aminul  and
      Milios, Evangelos",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1118",
    doi = "10.18653/v1/S16-1118",
    pages = "765--770",
}
@inproceedings{lopez-gazpio-etal-2016-iubc,
    title = "i{UBC} at {S}em{E}val-2016 Task 2: {RNN}s and {LSTM}s for interpretable {STS}",
    author = "Lopez-Gazpio, I{\~n}igo  and
      Agirre, Eneko  and
      Maritxalar, Montse",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1119",
    doi = "10.18653/v1/S16-1119",
    pages = "771--776",
}
@inproceedings{tan-etal-2016-rev,
    title = "Rev at {S}em{E}val-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence",
    author = "Tan, Ping  and
      Verspoor, Karin  and
      Miller, Timothy",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1120",
    doi = "10.18653/v1/S16-1120",
    pages = "777--782",
}
@inproceedings{magnolini-etal-2016-fbk,
    title = "{FBK}-{HLT}-{NLP} at {S}em{E}val-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity",
    author = "Magnolini, Simone  and
      Feltracco, Anna  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1121",
    doi = "10.18653/v1/S16-1121",
    pages = "783--789",
}
@inproceedings{tekumalla-jat-2016-iiscnlp,
    title = "{IISCNLP} at {S}em{E}val-2016 Task 2: Interpretable {STS} with {ILP} based Multiple Chunk Aligner",
    author = "Tekumalla, Lavanya  and
      Jat, Sharmistha",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1122",
    doi = "10.18653/v1/S16-1122",
    pages = "790--795",
}
@inproceedings{delmonte-2016-venseseval,
    title = "{VENSESEVAL} at {S}emeval-2016 Task 2 i{STS} - with a full-fledged rule-based approach",
    author = "Delmonte, Rodolfo",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1123",
    doi = "10.18653/v1/S16-1123",
    pages = "796--802",
}
@inproceedings{konopik-etal-2016-uwb,
    title = "{UWB} at {S}em{E}val-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks",
    author = "Konop{\'\i}k, Miloslav  and
      Pra{\v{z}}{\'a}k, Ond{\v{r}}ej  and
      Steinberger, David  and
      Brychc{\'\i}n, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1124",
    doi = "10.18653/v1/S16-1124",
    pages = "803--808",
}
@inproceedings{banjade-etal-2016-dtsim-semeval,
    title = "{DTS}im at {S}em{E}val-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction",
    author = "Banjade, Rajendra  and
      Maharjan, Nabin  and
      Niraula, Nobal Bikram  and
      Rus, Vasile",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1125",
    doi = "10.18653/v1/S16-1125",
    pages = "809--813",
}
@inproceedings{franco-salvador-etal-2016-uh,
    title = "{UH}-{PRHLT} at {S}em{E}val-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering",
    author = "Franco-Salvador, Marc  and
      Kar, Sudipta  and
      Solorio, Thamar  and
      Rosso, Paolo",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1126",
    doi = "10.18653/v1/S16-1126",
    pages = "814--821",
}
@inproceedings{magooda-etal-2016-rdi,
    title = "{RDI}{\_}{T}eam at {S}em{E}val-2016 Task 3: {RDI} Unsupervised Framework for Text Ranking",
    author = "Magooda, Ahmed  and
      Gomaa, Amr  and
      Mahgoub, Ashraf  and
      Ahmed, Hany  and
      Rashwan, Mohsen  and
      Raafat, Hazem  and
      Kamal, Eslam  and
      Al Sallab, Ahmad",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1127",
    doi = "10.18653/v1/S16-1127",
    pages = "822--827",
}
@inproceedings{mohtarami-etal-2016-sls,
    title = "{SLS} at {S}em{E}val-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering",
    author = "Mohtarami, Mitra  and
      Belinkov, Yonatan  and
      Hsu, Wei-Ning  and
      Zhang, Yu  and
      Lei, Tao  and
      Bar, Kfir  and
      Cyphers, Scott  and
      Glass, Jim",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1128",
    doi = "10.18653/v1/S16-1128",
    pages = "828--835",
}
@inproceedings{mihaylova-etal-2016-super,
    title = "{SU}per Team at {S}em{E}val-2016 Task 3: Building a Feature-Rich System for Community Question Answering",
    author = "Mihaylova, Tsvetomila  and
      Gencheva, Pepa  and
      Boyanov, Martin  and
      Yovcheva, Ivana  and
      Mihaylov, Todor  and
      Hardalov, Momchil  and
      Kiprov, Yasen  and
      Balchev, Daniel  and
      Koychev, Ivan  and
      Nakov, Preslav  and
      Nikolova, Ivelina  and
      Angelova, Galia",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1129",
    doi = "10.18653/v1/S16-1129",
    pages = "836--843",
}
@inproceedings{balchev-etal-2016-pmi,
    title = "{PMI}-cool at {S}em{E}val-2016 Task 3: Experiments with {PMI} and Goodness Polarity Lexicons for Community Question Answering",
    author = "Balchev, Daniel  and
      Kiprov, Yasen  and
      Koychev, Ivan  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1130",
    doi = "10.18653/v1/S16-1130",
    pages = "844--850",
}
@inproceedings{baldwin-etal-2016-unimelb,
    title = "{U}ni{M}elb at {S}em{E}val-2016 Task 3: Identifying Similar Questions by combining a {CNN} with String Similarity Measures",
    author = "Baldwin, Timothy  and
      Liang, Huizhi  and
      Salehi, Bahar  and
      Hoogeveen, Doris  and
      Li, Yitong  and
      Duong, Long",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1131",
    doi = "10.18653/v1/S16-1131",
    pages = "851--856",
}
@inproceedings{wu-zhang-2016-icl00,
    title = "{ICL}00 at {S}em{E}val-2016 Task 3: Translation-Based Method for {CQA} System",
    author = "Wu, Yunfang  and
      Zhang, Minghua",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1132",
    doi = "10.18653/v1/S16-1132",
    pages = "857--860",
}
@inproceedings{wang-poupart-2016-overfitting,
    title = "Overfitting at {S}em{E}val-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings",
    author = "Wang, Hujie  and
      Poupart, Pascal",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1133",
    doi = "10.18653/v1/S16-1133",
    pages = "861--865",
}
@inproceedings{malhas-etal-2016-qu,
    title = "{QU}-{IR} at {S}em{E}val 2016 Task 3: Learning to Rank on {A}rabic Community Question Answering Forums with Word Embedding",
    author = "Malhas, Rana  and
      Torki, Marwan  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1134",
    doi = "10.18653/v1/S16-1134",
    pages = "866--871",
}
@inproceedings{wu-lan-2016-ecnu,
    title = "{ECNU} at {S}em{E}val-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering",
    author = "Wu, Guoshun  and
      Lan, Man",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1135",
    doi = "10.18653/v1/S16-1135",
    pages = "872--878",
}
@inproceedings{mihaylov-nakov-2016-semanticz,
    title = "{S}emantic{Z} at {S}em{E}val-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings",
    author = "Mihaylov, Todor  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1136",
    doi = "10.18653/v1/S16-1136",
    pages = "879--886",
}
@inproceedings{guzman-etal-2016-mte,
    title = "{MTE}-{NN} at {S}em{E}val-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?",
    author = "Guzm{\'a}n, Francisco  and
      Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1137",
    doi = "10.18653/v1/S16-1137",
    pages = "887--895",
}
@inproceedings{barron-cedeno-etal-2016-convkn,
    title = "{C}onv{KN} at {S}em{E}val-2016 Task 3: Answer and Question Selection for Question Answering on {A}rabic and {E}nglish Fora",
    author = "Barr{\'o}n-Cede{\~n}o, Alberto  and
      Bonadiman, Daniele  and
      Da San Martino, Giovanni  and
      Joty, Shafiq  and
      Moschitti, Alessandro  and
      Al Obaidli, Fahad  and
      Romeo, Salvatore  and
      Tymoshenko, Kateryna  and
      Uva, Antonio",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1138",
    doi = "10.18653/v1/S16-1138",
    pages = "896--903",
}
@inproceedings{jia-2016-itnlp,
    title = "{ITNLP}-{A}i{KF} at {S}em{E}val-2016 Task 3 a quesiton answering system using community {QA} repository",
    author = "Jia, Chang{'}e",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1139",
    doi = "10.18653/v1/S16-1139",
    pages = "904--909",
}
@inproceedings{cordeiro-etal-2016-ufrgs,
    title = "{UFRGS}{\&}{LIF} at {S}em{E}val-2016 Task 10: Rule-Based {MWE} Identification and Predominant-Supersense Tagging",
    author = "Cordeiro, Silvio  and
      Ramisch, Carlos  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1140",
    doi = "10.18653/v1/S16-1140",
    pages = "910--917",
}
@inproceedings{tang-etal-2016-whunlp,
    title = "{WHUN}lp at {S}em{E}val-2016 Task {D}i{MSUM}: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models",
    author = "Tang, Xin  and
      Li, Fei  and
      Ji, Donghong",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1141",
    doi = "10.18653/v1/S16-1141",
    pages = "918--924",
}
@inproceedings{bjorne-salakoski-2016-utu,
    title = "{UTU} at {S}em{E}val-2016 Task 10: Binary Classification for Expression Detection ({BCED})",
    author = {Bj{\"o}rne, Jari  and
      Salakoski, Tapio},
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1142",
    doi = "10.18653/v1/S16-1142",
    pages = "925--930",
}
@inproceedings{hosseini-etal-2016-uw,
    title = "{UW}-{CSE} at {S}em{E}val-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields",
    author = "Hosseini, Mohammad Javad  and
      Smith, Noah A.  and
      Lee, Su-In",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1143",
    doi = "10.18653/v1/S16-1143",
    pages = "931--936",
}
@inproceedings{kirilin-etal-2016-icl,
    title = "{ICL}-{HD} at {S}em{E}val-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings",
    author = "Kirilin, Angelika  and
      Krauss, Felix  and
      Versley, Yannick",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1144",
    doi = "10.18653/v1/S16-1144",
    pages = "937--945",
}
@inproceedings{scherbakov-etal-2016-vectorweavers,
    title = "{V}ector{W}eavers at {S}em{E}val-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)",
    author = "Scherbakov, Andreas  and
      Vylomova, Ekaterina  and
      Liu, Fei  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1145",
    doi = "10.18653/v1/S16-1145",
    pages = "946--952",
}
@inproceedings{wrobel-2016-plujagh,
    title = "{PLUJAGH} at {S}em{E}val-2016 Task 11: Simple System for Complex Word Identification",
    author = "Wr{\'o}bel, Krzysztof",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1146",
    doi = "10.18653/v1/S16-1146",
    pages = "953--957",
}
@inproceedings{martinez-martinez-tan-2016-usaar,
    title = "{USAAR} at {S}em{E}val-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity",
    author = "Mart{\'\i}nez Mart{\'\i}nez, Jos{\'e} Manuel  and
      Tan, Liling",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1147",
    doi = "10.18653/v1/S16-1147",
    pages = "958--962",
}
@inproceedings{nat-2016-sensible,
    title = "Sensible at {S}em{E}val-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess",
    author = "Nat, Gillin",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1148",
    doi = "10.18653/v1/S16-1148",
    pages = "963--968",
}
@inproceedings{paetzold-specia-2016-sv000gg,
    title = "{SV}000gg at {S}em{E}val-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1149",
    doi = "10.18653/v1/S16-1149",
    pages = "969--974",
}
@inproceedings{brooke-etal-2016-melbourne,
    title = "{M}elbourne at {S}em{E}val 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features",
    author = "Brooke, Julian  and
      Uitdenbogerd, Alexandra  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1150",
    doi = "10.18653/v1/S16-1150",
    pages = "975--981",
}
@inproceedings{davoodi-kosseim-2016-clac,
    title = "{CL}a{C} at {S}em{E}val-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification",
    author = "Davoodi, Elnaz  and
      Kosseim, Leila",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1151",
    doi = "10.18653/v1/S16-1151",
    pages = "982--985",
}
@inproceedings{mukherjee-etal-2016-ju,
    title = "{JU}{\_}{NLP} at {S}em{E}val-2016 Task 11: Identifying Complex Words in a Sentence",
    author = "Mukherjee, Niloy  and
      Patra, Braja Gopal  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1152",
    doi = "10.18653/v1/S16-1152",
    pages = "986--990",
}
@inproceedings{malmasi-zampieri-2016-maza,
    title = "{MAZA} at {S}em{E}val-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier",
    author = "Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1153",
    doi = "10.18653/v1/S16-1153",
    pages = "991--995",
}
@inproceedings{malmasi-etal-2016-ltg,
    title = "{LTG} at {S}em{E}val-2016 Task 11: Complex Word Identification with Classifier Ensembles",
    author = "Malmasi, Shervin  and
      Dras, Mark  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1154",
    doi = "10.18653/v1/S16-1154",
    pages = "996--1000",
}
@inproceedings{zampieri-etal-2016-macsaar,
    title = "{M}ac{S}aar at {S}em{E}val-2016 Task 11: {Z}ipfian and Character Features for {C}omplex{W}ord Identification",
    author = "Zampieri, Marcos  and
      Tan, Liling  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1155",
    doi = "10.18653/v1/S16-1155",
    pages = "1001--1005",
}
@inproceedings{choubey-pateria-2016-garuda,
    title = "Garuda {\&} {B}hasha at {S}em{E}val-2016 Task 11: Complex Word Identification Using Aggregated Learning Models",
    author = "Choubey, Prafulla  and
      Pateria, Shubham",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1156",
    doi = "10.18653/v1/S16-1156",
    pages = "1006--1010",
}
@inproceedings{ronzano-etal-2016-taln,
    title = "{TALN} at {S}em{E}val-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features",
    author = "Ronzano, Francesco  and
      Abura{'}ed, Ahmed  and
      Espinosa-Anke, Luis  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1157",
    doi = "10.18653/v1/S16-1157",
    pages = "1011--1016",
}
@inproceedings{palakurthi-mamidi-2016-iiit,
    title = "{IIIT} at {S}em{E}val-2016 Task 11: Complex Word Identification using Nearest Centroid Classification",
    author = "Palakurthi, Ashish  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1158",
    doi = "10.18653/v1/S16-1158",
    pages = "1017--1021",
}
@inproceedings{s-p-etal-2016-amritacen,
    title = "{A}mrita{CEN} at {S}em{E}val-2016 Task 11: Complex Word Identification using Word Embedding",
    author = "S.P, Sanjay  and
      Kumar M, Anand  and
      K P, Soman",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1159",
    doi = "10.18653/v1/S16-1159",
    pages = "1022--1027",
}
@inproceedings{bingel-etal-2016-coastalcph,
    title = "{C}oastal{CPH} at {S}em{E}val-2016 Task 11: The importance of designing your Neural Networks right",
    author = "Bingel, Joachim  and
      Schluter, Natalie  and
      Mart{\'\i}nez Alonso, H{\'e}ctor",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1160",
    doi = "10.18653/v1/S16-1160",
    pages = "1028--1033",
}
@inproceedings{quijada-medero-2016-hmc,
    title = "{HMC} at {S}em{E}val-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees",
    author = "Quijada, Maury  and
      Medero, Julie",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1161",
    doi = "10.18653/v1/S16-1161",
    pages = "1034--1037",
}
@inproceedings{konkol-2016-uwb,
    title = "{UWB} at {S}em{E}val-2016 Task 11: Exploring Features for Complex Word Identification",
    author = "Konkol, Michal",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1162",
    doi = "10.18653/v1/S16-1162",
    pages = "1038--1041",
}
@inproceedings{kuru-2016-ai,
    title = "{AI}-{KU} at {S}em{E}val-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification",
    author = "Kuru, Onur",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1163",
    doi = "10.18653/v1/S16-1163",
    pages = "1042--1046",
}
@inproceedings{kauchak-2016-pomona,
    title = "Pomona at {S}em{E}val-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency",
    author = "Kauchak, David",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1164",
    doi = "10.18653/v1/S16-1164",
    pages = "1047--1051",
}
@inproceedings{bethard-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 12: Clinical {T}emp{E}val",
    author = "Bethard, Steven  and
      Savova, Guergana  and
      Chen, Wei-Te  and
      Derczynski, Leon  and
      Pustejovsky, James  and
      Verhagen, Marc",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1165",
    doi = "10.18653/v1/S16-1165",
    pages = "1052--1062",
}
@inproceedings{may-2016-semeval,
    title = "{S}em{E}val-2016 Task 8: Meaning Representation Parsing",
    author = "May, Jonathan",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1166",
    doi = "10.18653/v1/S16-1166",
    pages = "1063--1073",
}
@inproceedings{che-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 9: {C}hinese Semantic Dependency Parsing",
    author = "Che, Wanxiang  and
      Shao, Yanqiu  and
      Liu, Ting  and
      Ding, Yu",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1167",
    doi = "10.18653/v1/S16-1167",
    pages = "1074--1080",
}
@inproceedings{bordea-etal-2016-semeval,
    title = "{S}em{E}val-2016 Task 13: Taxonomy Extraction Evaluation ({TE}x{E}val-2)",
    author = "Bordea, Georgeta  and
      Lefever, Els  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1168",
    doi = "10.18653/v1/S16-1168",
    pages = "1081--1091",
}
@inproceedings{jurgens-pilehvar-2016-semeval,
    title = "{S}em{E}val-2016 Task 14: Semantic Taxonomy Enrichment",
    author = "Jurgens, David  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1169",
    doi = "10.18653/v1/S16-1169",
    pages = "1092--1102",
}
@inproceedings{he-etal-2016-umd,
    title = "{UMD}-{TTIC}-{UW} at {S}em{E}val-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement",
    author = "He, Hua  and
      Wieting, John  and
      Gimpel, Kevin  and
      Rao, Jinfeng  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1170",
    doi = "10.18653/v1/S16-1170",
    pages = "1103--1108",
}
@inproceedings{kazmi-schuller-2016-inspire,
    title = "Inspire at {S}em{E}val-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming",
    author = {Kazmi, Mishal  and
      Sch{\"u}ller, Peter},
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1171",
    doi = "10.18653/v1/S16-1171",
    pages = "1109--1115",
}
@inproceedings{filice-etal-2016-kelp,
    title = "{K}e{LP} at {S}em{E}val-2016 Task 3: Learning Semantic Relations between Questions and Answers",
    author = "Filice, Simone  and
      Croce, Danilo  and
      Moschitti, Alessandro  and
      Basili, Roberto",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1172",
    doi = "10.18653/v1/S16-1172",
    pages = "1116--1123",
}
@inproceedings{deriu-etal-2016-swisscheese,
    title = "{S}wiss{C}heese at {S}em{E}val-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision",
    author = "Deriu, Jan  and
      Gonzenbach, Maurice  and
      Uzdilli, Fatih  and
      Lucchi, Aurelien  and
      De Luca, Valeria  and
      Jaggi, Martin",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1173",
    doi = "10.18653/v1/S16-1173",
    pages = "1124--1128",
}
@inproceedings{kumar-etal-2016-iit,
    title = "{IIT}-{TUDA} at {S}em{E}val-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis",
    author = "Kumar, Ayush  and
      Kohail, Sarah  and
      Kumar, Amit  and
      Ekbal, Asif  and
      Biemann, Chris",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1174",
    doi = "10.18653/v1/S16-1174",
    pages = "1129--1135",
}
@inproceedings{tourille-etal-2016-limsi,
    title = "{LIMSI}-{COT} at {S}em{E}val-2016 Task 12: Temporal relation identification using a pipeline of classifiers",
    author = "Tourille, Julien  and
      Ferret, Olivier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Tannier, Xavier",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1175",
    doi = "10.18653/v1/S16-1175",
    pages = "1136--1142",
}
@inproceedings{barzdins-gosko-2016-riga,
    title = "{RIGA} at {S}em{E}val-2016 Task 8: Impact of {S}match Extensions and Character-Level Neural Translation on {AMR} Parsing Accuracy",
    author = "Barzdins, Guntis  and
      Gosko, Didzis",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1176",
    doi = "10.18653/v1/S16-1176",
    pages = "1143--1147",
}
@inproceedings{butler-2016-dynamicpower,
    title = "{D}ynamic{P}ower at {S}em{E}val-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core",
    author = "Butler, Alastair",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1177",
    doi = "10.18653/v1/S16-1177",
    pages = "1148--1153",
}
@inproceedings{puzikov-etal-2016-m2l,
    title = "{M}2{L} at {S}em{E}val-2016 Task 8: {AMR} Parsing with Neural Networks",
    author = "Puzikov, Yevgeniy  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1178",
    doi = "10.18653/v1/S16-1178",
    pages = "1154--1159",
}
@inproceedings{brandt-etal-2016-icl,
    title = "{ICL}-{HD} at {S}em{E}val-2016 Task 8: Meaning Representation Parsing - Augmenting {AMR} Parsing with a Preposition Semantic Role Labeling Neural Network",
    author = "Brandt, Lauritz  and
      Grimm, David  and
      Zhou, Mengfei  and
      Versley, Yannick",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1179",
    doi = "10.18653/v1/S16-1179",
    pages = "1160--1166",
}
@inproceedings{goodman-etal-2016-ucl,
    title = "{UCL}+{S}heffield at {S}em{E}val-2016 Task 8: Imitation learning for {AMR} parsing with an alpha-bound",
    author = "Goodman, James  and
      Vlachos, Andreas  and
      Naradowsky, Jason",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1180",
    doi = "10.18653/v1/S16-1180",
    pages = "1167--1172",
}
@inproceedings{wang-etal-2016-camr,
    title = "{CAMR} at {S}em{E}val-2016 Task 8: An Extended Transition-based {AMR} Parser",
    author = "Wang, Chuan  and
      Pradhan, Sameer  and
      Pan, Xiaoman  and
      Ji, Heng  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1181",
    doi = "10.18653/v1/S16-1181",
    pages = "1173--1178",
}
@inproceedings{bjerva-etal-2016-meaning,
    title = "The Meaning Factory at {S}em{E}val-2016 Task 8: Producing {AMR}s with Boxer",
    author = "Bjerva, Johannes  and
      Bos, Johan  and
      Haagsma, Hessel",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1182",
    doi = "10.18653/v1/S16-1182",
    pages = "1179--1184",
}
@inproceedings{peng-gildea-2016-uofr,
    title = "{U}of{R} at {S}em{E}val-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for {AMR} Parsing",
    author = "Peng, Xiaochang  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1183",
    doi = "10.18653/v1/S16-1183",
    pages = "1185--1189",
}
@inproceedings{rao-etal-2016-clip,
    title = "{CLIP}@{UMD} at {S}em{E}val-2016 Task 8: Parser for {A}bstract {M}eaning {R}epresentation using Learning to Search",
    author = "Rao, Sudha  and
      Vyas, Yogarshi  and
      Daum{\'e} III, Hal  and
      Resnik, Philip",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1184",
    doi = "10.18653/v1/S16-1184",
    pages = "1190--1196",
}
@inproceedings{foland-martin-2016-cu,
    title = "{CU}-{NLP} at {S}em{E}val-2016 Task 8: {AMR} Parsing using {LSTM}-based Recurrent Neural Networks",
    author = "Foland, William  and
      Martin, James H.",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1185",
    doi = "10.18653/v1/S16-1185",
    pages = "1197--1201",
}
@inproceedings{flanigan-etal-2016-cmu,
    title = "{CMU} at {S}em{E}val-2016 Task 8: Graph-based {AMR} Parsing with Infinite Ramp Loss",
    author = "Flanigan, Jeffrey  and
      Dyer, Chris  and
      Smith, Noah A.  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1186",
    doi = "10.18653/v1/S16-1186",
    pages = "1202--1206",
}
@inproceedings{artsymenia-etal-2016-ihs,
    title = "{IHS}-{RD}-{B}elarus at {S}em{E}val-2016 Task 9: Transition-based {C}hinese Semantic Dependency Parsing with Online Reordering and Bootstrapping.",
    author = "Artsymenia, Artsiom  and
      Dounar, Palina  and
      Yermakovich, Maria",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1187",
    doi = "10.18653/v1/S16-1187",
    pages = "1207--1211",
}
@inproceedings{jin-etal-2016-oclsp,
    title = "{OCLSP} at {S}em{E}val-2016 Task 9: Multilayered {LSTM} as a Neural Semantic Dependency Parser",
    author = "Jin, Lifeng  and
      Duan, Manjuan  and
      Schuler, William",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1188",
    doi = "10.18653/v1/S16-1188",
    pages = "1212--1217",
}
@inproceedings{duan-etal-2016-osu,
    title = "{OSU}{\_}{CHGCG} at {S}em{E}val-2016 Task 9 : {C}hinese Semantic Dependency Parsing with Generalized Categorial Grammar",
    author = "Duan, Manjuan  and
      Jin, Lifeng  and
      Schuler, William",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1189",
    doi = "10.18653/v1/S16-1189",
    pages = "1218--1224",
}
@inproceedings{grouin-moriceau-2016-limsi,
    title = "{LIMSI} at {S}em{E}val-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions",
    author = "Grouin, Cyril  and
      Moriceau, V{\'e}ronique",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1190",
    doi = "10.18653/v1/S16-1190",
    pages = "1225--1230",
}
@inproceedings{p-r-etal-2016-hitachi,
    title = "Hitachi at {S}em{E}val-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes",
    author = "P R, Sarath  and
      R, Manikandan  and
      Niwa, Yoshiki",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1191",
    doi = "10.18653/v1/S16-1191",
    pages = "1231--1236",
}
@inproceedings{chikka-2016-cde,
    title = "{CDE}-{IIITH} at {S}em{E}val-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques",
    author = "Chikka, Veera Raghavendra",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1192",
    doi = "10.18653/v1/S16-1192",
    pages = "1237--1240",
}
@inproceedings{caselli-morante-2016-vuacltl,
    title = "{VUACLTL} at {S}em{E}val 2016 Task 12: A {CRF} Pipeline to Clinical {T}emp{E}val",
    author = "Caselli, Tommaso  and
      Morante, Roser",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1193",
    doi = "10.18653/v1/S16-1193",
    pages = "1241--1247",
}
@inproceedings{cohan-etal-2016-guir,
    title = "{GUIR} at {S}em{E}val-2016 task 12: Temporal Information Processing for Clinical Narratives",
    author = "Cohan, Arman  and
      Meurer, Kevin  and
      Goharian, Nazli",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1194",
    doi = "10.18653/v1/S16-1194",
    pages = "1248--1255",
}
@inproceedings{khalifa-etal-2016-utahbmi,
    title = "{U}tah{BMI} at {S}em{E}val-2016 Task 12: Extracting Temporal Information from Clinical Text",
    author = "Khalifa, Abdulrahman  and
      Velupillai, Sumithra  and
      Meystre, Stephane",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1195",
    doi = "10.18653/v1/S16-1195",
    pages = "1256--1262",
}
@inproceedings{barros-etal-2016-ulisboa,
    title = "{ULISBOA} at {S}em{E}val-2016 Task 12: Extraction of temporal expressions, clinical events and relations using {IBE}nt",
    author = "Barros, Marcia  and
      Lamurias, Andre  and
      Figueiro, Gon{\c{c}}alo  and
      Antunes, Marta  and
      Teixeira, Joana  and
      Pinheiro, Alexandre  and
      Couto, Francisco M.",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1196",
    doi = "10.18653/v1/S16-1196",
    pages = "1263--1267",
}
@inproceedings{li-huang-2016-uta-dlnlp,
    title = "{UTA} {DLNLP} at {S}em{E}val-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports",
    author = "Li, Peng  and
      Huang, Heng",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1197",
    doi = "10.18653/v1/S16-1197",
    pages = "1268--1273",
}
@inproceedings{fries-2016-brundlefly,
    title = "Brundlefly at {S}em{E}val-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction",
    author = "Fries, Jason",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1198",
    doi = "10.18653/v1/S16-1198",
    pages = "1274--1279",
}
@inproceedings{leeuwenberg-moens-2016-kuleuven,
    title = "{KUL}euven-{LIIR} at {S}em{E}val 2016 Task 12: Detecting Narrative Containment in Clinical Records",
    author = "Leeuwenberg, Artuur  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1199",
    doi = "10.18653/v1/S16-1199",
    pages = "1280--1285",
}
@inproceedings{hansart-etal-2016-cental,
    title = "{CENTAL} at {S}em{E}val-2016 Task 12: a linguistically fed {CRF} model for medical and temporal information extraction",
    author = "Hansart, Charlotte  and
      De Meyere, Damien  and
      Watrin, Patrick  and
      Bittar, Andr{\'e}  and
      Fairon, C{\'e}drick",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1200",
    doi = "10.18653/v1/S16-1200",
    pages = "1286--1291",
}
@inproceedings{lee-etal-2016-uthealth,
    title = "{UTH}ealth at {S}em{E}val-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes",
    author = "Lee, Hee-Jin  and
      Xu, Hua  and
      Wang, Jingqi  and
      Zhang, Yaoyun  and
      Moon, Sungrim  and
      Xu, Jun  and
      Wu, Yonghui",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1201",
    doi = "10.18653/v1/S16-1201",
    pages = "1292--1297",
}
@inproceedings{pocostales-2016-nuig,
    title = "{NUIG}-{UNLP} at {S}em{E}val-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy Extraction",
    author = "Pocostales, Joel",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1202",
    doi = "10.18653/v1/S16-1202",
    pages = "1298--1302",
}
@inproceedings{tan-etal-2016-usaar,
    title = "{USAAR} at {S}em{E}val-2016 Task 13: Hyponym Endocentricity",
    author = "Tan, Liling  and
      Bond, Francis  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1203",
    doi = "10.18653/v1/S16-1203",
    pages = "1303--1309",
}
@inproceedings{maitra-das-2016-junlp,
    title = "{JUNLP} at {S}em{E}val-2016 Task 13: A Language Independent Approach for Hypernym Identification",
    author = "Maitra, Promita  and
      Das, Dipankar",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1204",
    doi = "10.18653/v1/S16-1204",
    pages = "1310--1314",
}
@inproceedings{cleuziou-moreno-2016-qassit,
    title = "{QASSIT} at {S}em{E}val-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition",
    author = "Cleuziou, Guillaume  and
      Moreno, Jose G.",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1205",
    doi = "10.18653/v1/S16-1205",
    pages = "1315--1319",
}
@inproceedings{panchenko-etal-2016-taxi,
    title = "{TAXI} at {S}em{E}val-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling",
    author = "Panchenko, Alexander  and
      Faralli, Stefano  and
      Ruppert, Eugen  and
      Remus, Steffen  and
      Naets, Hubert  and
      Fairon, C{\'e}drick  and
      Ponzetto, Simone Paolo  and
      Biemann, Chris",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1206",
    doi = "10.18653/v1/S16-1206",
    pages = "1320--1327",
}
@inproceedings{pedersen-2016-duluth,
    title = "{D}uluth at {S}em{E}val 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies",
    author = "Pedersen, Ted",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1207",
    doi = "10.18653/v1/S16-1207",
    pages = "1328--1331",
}
@inproceedings{espinosa-anke-etal-2016-taln,
    title = "{TALN} at {S}em{E}val-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings",
    author = "Espinosa-Anke, Luis  and
      Ronzano, Francesco  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1208",
    doi = "10.18653/v1/S16-1208",
    pages = "1332--1336",
}
@inproceedings{schlichtkrull-martinez-alonso-2016-msejrku,
    title = "{MS}ejr{K}u at {S}em{E}val-2016 Task 14: Taxonomy Enrichment by Evidence Ranking",
    author = "Schlichtkrull, Michael  and
      Mart{\'\i}nez Alonso, H{\'e}ctor",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1209",
    doi = "10.18653/v1/S16-1209",
    pages = "1337--1341",
}
@inproceedings{tanev-rotondi-2016-deftor,
    title = "Deftor at {S}em{E}val-2016 Task 14: Taxonomy enrichment using definition vectors",
    author = "Tanev, Hristo  and
      Rotondi, Agata",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1210",
    doi = "10.18653/v1/S16-1210",
    pages = "1342--1345",
}
@inproceedings{rusert-pedersen-2016-umnduluth,
    title = "{UMND}uluth at {S}em{E}val-2016 Task 14: {W}ord{N}et{'}s Missing Lemmas",
    author = "Rusert, Jon  and
      Pedersen, Ted",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1211",
    doi = "10.18653/v1/S16-1211",
    pages = "1346--1350",
}
@inproceedings{mcinnes-2016-vcu,
    title = "{VCU} at {S}emeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment",
    author = "McInnes, Bridget",
    booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation ({S}em{E}val-2016)",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S16-1212",
    doi = "10.18653/v1/S16-1212",
    pages = "1351--1355",
}
@article{tacl-2016-transactions,
    title = "Transactions of the Association for Computational Linguistics, Volume 4",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1000",
}
@article{faruqui-etal-2016-morpho,
    title = "Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning",
    author = "Faruqui, Manaal  and
      McDonald, Ryan  and
      Soricut, Radu",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1001",
    doi = "10.1162/tacl_a_00079",
    pages = "1--16",
    abstract = "Morpho-syntactic lexicons provide information about the morphological and syntactic roles of words in a language. Such lexicons are not available for all languages and even when available, their coverage can be limited. We present a graph-based semi-supervised learning method that uses the morphological, syntactic and semantic relations between words to automatically construct wide coverage lexicons from small seed sets. Our method is language-independent, and we show that we can expand a 1000 word seed lexicon to more than 100 times its size with high quality for 11 languages. In addition, the automatically created lexicons provide features that improve performance in two downstream tasks: morphological tagging and dependency parsing.",
}
@article{hill-etal-2016-learning-understand,
    title = "Learning to Understand Phrases by Embedding the Dictionary",
    author = "Hill, Felix  and
      Cho, Kyunghyun  and
      Korhonen, Anna  and
      Bengio, Yoshua",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1002",
    doi = "10.1162/tacl_a_00080",
    pages = "17--30",
    abstract = "Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: reverse dictionaries that return the name of a concept given a definition or description and general-knowledge crossword question answerers. On both tasks, neural language embedding models trained on definitions from a handful of freely-available lexical resources perform as well or better than existing commercial systems that rely on significant task-specific engineering. The results highlight the effectiveness of both neural embedding architectures and definition-based training for developing models that understand phrases and sentences.",
}
@article{frermann-lapata-2016-bayesian,
    title = "A {B}ayesian Model of Diachronic Meaning Change",
    author = "Frermann, Lea  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1003",
    doi = "10.1162/tacl_a_00081",
    pages = "31--45",
    abstract = "Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems.",
}
@article{gutierrez-etal-2016-detecting,
    title = "Detecting Cross-Cultural Differences Using a Multilingual Topic Model",
    author = "Guti{\'e}rrez, E.D.  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      de Melo, Gerard  and
      Gilardi, Luca",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1004",
    doi = "10.1162/tacl_a_00082",
    pages = "47--60",
    abstract = "Understanding cross-cultural differences has important implications for world affairs and many aspects of the life of society. Yet, the majority of text-mining methods to date focus on the analysis of monolingual texts. In contrast, we present a statistical model that simultaneously learns a set of common topics from multilingual, non-parallel data and automatically discovers the differences in perspectives on these topics across linguistic communities. We perform a behavioural evaluation of a subset of the differences identified by our model in English and Spanish to investigate their psychological validity.",
}
@article{pavlick-tetreault-2016-empirical,
    title = "An Empirical Analysis of Formality in Online Communication",
    author = "Pavlick, Ellie  and
      Tetreault, Joel",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1005",
    doi = "10.1162/tacl_a_00083",
    pages = "61--74",
    abstract = "This paper presents an empirical study of linguistic formality. We perform an analysis of humans{'} perceptions of formality in four different genres. These findings are used to develop a statistical model for predicting formality, which is evaluated under different feature settings and genres. We apply our model to an investigation of formality in online discussion forums, and present findings consistent with theories of formality and linguistic coordination.",
}
@article{hauer-kondrak-2016-decoding,
    title = "Decoding Anagrammed Texts Written in an Unknown Language and Script",
    author = "Hauer, Bradley  and
      Kondrak, Grzegorz",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1006",
    doi = "10.1162/tacl_a_00084",
    pages = "75--86",
    abstract = "Algorithmic decipherment is a prime example of a truly unsupervised problem. The first step in the decipherment process is the identification of the encrypted language. We propose three methods for determining the source language of a document enciphered with a monoalphabetic substitution cipher. The best method achieves 97{\%} accuracy on 380 languages. We then present an approach to decoding anagrammed substitution ciphers, in which the letters within words have been arbitrarily transposed. It obtains the average decryption word accuracy of 93{\%} on a set of 50 ciphertexts in 5 languages. Finally, we report the results on the Voynich manuscript, an unsolved fifteenth century cipher, which suggest Hebrew as the language of the document.",
}
@article{jardine-heinz-2016-learning,
    title = "Learning Tier-based Strictly 2-Local Languages",
    author = "Jardine, Adam  and
      Heinz, Jeffrey",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1007",
    doi = "10.1162/tacl_a_00085",
    pages = "87--98",
    abstract = "The Tier-based Strictly 2-Local (TSL2) languages are a class of formal languages which have been shown to model long-distance phonotactic generalizations in natural language (Heinz et al., 2011). This paper introduces the Tier-based Strictly 2-Local Inference Algorithm (2TSLIA), the first nonenumerative learner for the TSL2 languages. We prove the 2TSLIA is guaranteed to converge in polynomial time on a data sample whose size is bounded by a constant.",
}
@article{cuong-etal-2016-adapting,
    title = "Adapting to All Domains at Once: Rewarding Domain Invariance in {SMT}",
    author = "Cuong, Hoang  and
      Sima{'}an, Khalil  and
      Titov, Ivan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1008",
    doi = "10.1162/tacl_a_00086",
    pages = "99--112",
    abstract = "Existing work on domain adaptation for statistical machine translation has consistently assumed access to a small sample from the test distribution (target domain) at training time. In practice, however, the target domain may not be known at training time or it may change to match user needs. In such situations, it is natural to push the system to make safer choices, giving higher preference to domain-invariant translations, which work well across domains, over risky domain-specific alternatives. We encode this intuition by (1) inducing latent subdomains from the training data only; (2) introducing features which measure how specialized phrases are to individual induced sub-domains; (3) estimating feature weights on out-of-domain data (rather than on the target domain). We conduct experiments on three language pairs and a number of different domains. We observe consistent improvements over a baseline which does not explicitly reward domain invariance.",
}
@article{sultan-etal-2016-joint,
    title = "A Joint Model for Answer Sentence Ranking and Answer Extraction",
    author = "Sultan, Md Arafat  and
      Castelli, Vittorio  and
      Florian, Radu",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1009",
    doi = "10.1162/tacl_a_00087",
    pages = "113--125",
    abstract = "Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.",
}
@article{reddy-etal-2016-transforming,
    title = "Transforming Dependency Structures to Logical Forms for Semantic Parsing",
    author = {Reddy, Siva  and
      T{\"a}ckstr{\"o}m, Oscar  and
      Collins, Michael  and
      Kwiatkowski, Tom  and
      Das, Dipanjan  and
      Steedman, Mark  and
      Lapata, Mirella},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1010",
    doi = "10.1162/tacl_a_00088",
    pages = "127--140",
    abstract = "The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast{---}partly due to the lack of a strong type system{---}dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.",
}
@article{tsai-roth-2016-concept,
    title = "Concept Grounding to Multiple Knowledge Bases via Indirect Supervision",
    author = "Tsai, Chen-Tse  and
      Roth, Dan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1011",
    doi = "10.1162/tacl_a_00089",
    pages = "141--154",
    abstract = "We consider the problem of disambiguating concept mentions appearing in documents and grounding them in multiple knowledge bases, where each knowledge base addresses some aspects of the domain. This problem poses a few additional challenges beyond those addressed in the popular Wikification problem. Key among them is that most knowledge bases do not contain the rich textual and structural information Wikipedia does; consequently, the main supervision signal used to train Wikification rankers does not exist anymore. In this work we develop an algorithmic approach that, by carefully examining the relations between various related knowledge bases, generates an indirect supervision signal it uses to train a ranking model that accurately chooses knowledge base entries for a given mention; moreover, it also induces prior knowledge that can be used to support a global coherent mapping of all the concepts in a given document to the knowledge bases. Using the biomedical domain as our application, we show that our indirectly supervised ranking model outperforms other unsupervised baselines and that the quality of this indirect supervision scheme is very close to a supervised model. We also show that considering multiple knowledge bases together has an advantage over grounding concepts to each knowledge base individually.",
}
@article{richardson-kuhn-2016-learning,
    title = "Learning to Make Inferences in a Semantic Parsing Task",
    author = "Richardson, Kyle  and
      Kuhn, Jonas",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1012",
    doi = "10.1162/tacl_a_00090",
    pages = "155--168",
    abstract = "We introduce a new approach to training a semantic parser that uses textual entailment judgements as supervision. These judgements are based on high-level inferences about whether the meaning of one sentence follows from another. When applied to an existing semantic parsing task, they prove to be a useful tool for revealing semantic distinctions and background knowledge not captured in the target representations. This information is used to improve the quality of the semantic representations being learned and to acquire generic knowledge for reasoning. Experiments are done on the benchmark Sportscaster corpus (Chen and Mooney, 2008), and a novel RTE-inspired inference dataset is introduced. On this new dataset our method strongly outperforms several strong baselines. Separately, we obtain state-of-the-art results on the original Sportscaster semantic parsing task.",
}
@article{sakaguchi-etal-2016-reassessing,
    title = "Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality",
    author = "Sakaguchi, Keisuke  and
      Napoles, Courtney  and
      Post, Matt  and
      Tetreault, Joel",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1013",
    doi = "10.1162/tacl_a_00091",
    pages = "169--182",
    abstract = "The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, however, is the reliance of GEC evaluation on error-coded corpora, which contain specific labeled corrections. We examine current practices and show that GEC{'}s reliance on such corpora unnaturally constrains annotation and automatic evaluation, resulting in (a) sentences that do not sound acceptable to native speakers and (b) system rankings that do not correlate with human judgments. In light of this, we propose an alternate approach that jettisons costly error coding in favor of unannotated, whole-sentence rewrites. We compare the performance of existing metrics over different gold-standard annotations, and show that automatic evaluation with our new annotation scheme has very strong correlation with expert rankings (ρ = 0.82). As a result, we advocate for a fundamental and necessary shift in the goal of GEC, from correcting small, labeled error types, to producing text that has native fluency.",
}
@article{vaswani-sagae-2016-efficient,
    title = "Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States",
    author = "Vaswani, Ashish  and
      Sagae, Kenji",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1014",
    doi = "10.1162/tacl_a_00092",
    pages = "183--196",
    abstract = "Transition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art. In this paper, we propose a new approach for approximate structured inference for transition-based parsing that produces scores suitable for global scoring using local models. This is accomplished with the introduction of error states in local training, which add information about incorrect derivation paths typically left out completely in locally-trained models. Using neural networks for our local classifiers, our approach achieves 93.61{\%} accuracy for transition-based dependency parsing in English.",
}
@article{hartmann-etal-2016-generating,
    title = "Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources",
    author = "Hartmann, Silvana  and
      Eckle-Kohler, Judith  and
      Gurevych, Iryna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1015",
    doi = "10.1162/tacl_a_00093",
    pages = "197--213",
    abstract = "We present a new approach for generating role-labeled training data using Linked Lexical Resources, i.e., integrated lexical resources that combine several resources (e.g., Word-Net, FrameNet, Wiktionary) by linking them on the sense or on the role level. Unlike resource-based supervision in relation extraction, we focus on complex linguistic annotations, more specifically FrameNet senses and roles. The automatically labeled training data (www.ukp.tu-darmstadt.de/knowledge-based-srl/) are evaluated on four corpora from different domains for the tasks of word sense disambiguation and semantic role classification. Results show that classifiers trained on our generated data equal those resulting from a standard supervised setting.",
}
@article{nguyen-etal-2016-j,
    title = "{J}-{NERD}: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features",
    author = "Nguyen, Dat Ba  and
      Theobald, Martin  and
      Weikum, Gerhard",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1016",
    doi = "10.1162/tacl_a_00094",
    pages = "215--229",
    abstract = "Methods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages. Therefore, NED may be penalized with respect to precision by NER false positives, and suffers in recall from NER false negatives. Conversely, NED does not fully exploit information computed by NER such as types of mentions. This paper presents J-NERD, a new approach to perform NER and NED jointly, by means of a probabilistic graphical model that captures mention spans, mention types, and the mapping of mentions to entities in a knowledge base. We present experiments with different kinds of texts from the CoNLL{'}03, ACE{'}05, and ClueWeb{'}09-FACC1 corpora. J-NERD consistently outperforms state-of-the-art competitors in end-to-end NERD precision, recall, and F1.",
}
@article{marcheggiani-titov-2016-discrete,
    title = "Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations",
    author = "Marcheggiani, Diego  and
      Titov, Ivan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1017",
    doi = "10.1162/tacl_a_00095",
    pages = "231--244",
    abstract = "We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.",
}
@article{stratos-etal-2016-unsupervised,
    title = "Unsupervised Part-Of-Speech Tagging with Anchor Hidden {M}arkov Models",
    author = "Stratos, Karl  and
      Collins, Michael  and
      Hsu, Daniel",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1018",
    doi = "10.1162/tacl_a_00096",
    pages = "245--257",
    abstract = "We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS tagging (e.g., {``}the{''} is a word that appears only under the determiner tag). We exploit this assumption and extend the non-negative matrix factorization framework of Arora et al. (2013) to design a consistent estimator for anchor HMMs. In experiments, our algorithm is competitive with strong baselines such as the clustering method of Brown et al. (1992) and the log-linear model of Berg-Kirkpatrick et al. (2010). Furthermore, it produces an interpretable model in which hidden states are automatically lexicalized by words.",
}
@article{yin-etal-2016-abcnn,
    title = "{ABCNN}: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs",
    author = {Yin, Wenpeng  and
      Sch{\"u}tze, Hinrich  and
      Xiang, Bing  and
      Zhou, Bowen},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1019",
    doi = "10.1162/tacl_a_00097",
    pages = "259--272",
    abstract = "How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence{'}s representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer{\_}Selection.",
}
@article{hashimoto-etal-2016-word,
    title = "Word Embeddings as Metric Recovery in Semantic Spaces",
    author = "Hashimoto, Tatsunori B.  and
      Alvarez-Melis, David  and
      Jaakkola, Tommi S.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1020",
    doi = "10.1162/tacl_a_00098",
    pages = "273--286",
    abstract = "Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and demonstrates that existing algorithms are consistent metric recovery methods given co-occurrence counts from random walks. Furthermore, we propose a simple, principled, direct metric recovery algorithm that performs on par with the state-of-the-art word embedding and manifold learning methods. Finally, we complement recent focus on analogies by constructing two new inductive reasoning datasets{---}series completion and classification{---}and demonstrate that word embeddings can be used to solve them as well.",
}
@article{schofield-mimno-2016-comparing,
    title = "Comparing Apples to Apple: The Effects of Stemmers on Topic Models",
    author = "Schofield, Alexandra  and
      Mimno, David",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1021",
    doi = "10.1162/tacl_a_00099",
    pages = "287--300",
    abstract = "Rule-based stemmers such as the Porter stemmer are frequently used to preprocess English corpora for topic modeling. In this work, we train and evaluate topic models on a variety of corpora using several different stemming algorithms. We examine several different quantitative measures of the resulting models, including likelihood, coherence, model stability, and entropy. Despite their frequent use in topic modeling, we find that stemmers produce no meaningful improvement in likelihood and coherence and in fact can degrade topic stability.",
}
@article{agic-etal-2016-multilingual,
    title = "Multilingual Projection for Parsing Truly Low-Resource Languages",
    author = "Agi{\'c}, {\v{Z}}eljko  and
      Johannsen, Anders  and
      Plank, Barbara  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Schluter, Natalie  and
      S{\o}gaard, Anders",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1022",
    doi = "10.1162/tacl_a_00100",
    pages = "301--312",
    abstract = "We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.",
}
@article{kiperwasser-goldberg-2016-simple,
    title = "Simple and Accurate Dependency Parsing Using Bidirectional {LSTM} Feature Representations",
    author = "Kiperwasser, Eliyahu  and
      Goldberg, Yoav",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1023",
    doi = "10.1162/tacl_a_00101",
    pages = "313--327",
    abstract = "We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.",
}
@article{pelemans-etal-2016-sparse,
    title = "Sparse Non-negative Matrix Language Modeling",
    author = "Pelemans, Joris  and
      Shazeer, Noam  and
      Chelba, Ciprian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1024",
    doi = "10.1162/tacl_a_00102",
    pages = "329--342",
    abstract = "We present Sparse Non-negative Matrix (SNM) estimation, a novel probability estimation technique for language modeling that can efficiently incorporate arbitrary features. We evaluate SNM language models on two corpora: the One Billion Word Benchmark and a subset of the LDC English Gigaword corpus. Results show that SNM language models trained with n-gram features are a close match for the well-established Kneser-Ney models. The addition of skip-gram features yields a model that is in the same league as the state-of-the-art recurrent neural network language models, as well as complementary: combining the two modeling techniques yields the best known result on the One Billion Word Benchmark. On the Gigaword corpus further improvements are observed using features that cross sentence boundaries. The computational advantages of SNM estimation over both maximum entropy and neural network estimation are probably its main strength, promising an approach that has large flexibility in combining arbitrary features and yet scales gracefully to large amounts of data.",
}
@article{gulordava-merlo-2016-multi,
    title = "Multi-lingual Dependency Parsing Evaluation: a Large-scale Analysis of Word Order Properties using Artificial Data",
    author = "Gulordava, Kristina  and
      Merlo, Paola",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1025",
    doi = "10.1162/tacl_a_00103",
    pages = "343--356",
    abstract = "The growing work in multi-lingual parsing faces the challenge of fair comparative evaluation and performance analysis across languages and their treebanks. The difficulty lies in teasing apart the properties of treebanks, such as their size or average sentence length, from those of the annotation scheme, and from the linguistic properties of languages. We propose a method to evaluate the effects of word order of a language on dependency parsing performance, while controlling for confounding treebank properties. The method uses artificially-generated treebanks that are minimal permutations of actual treebanks with respect to two word order properties: word order variation and dependency lengths. Based on these artificial data on twelve languages, we show that longer dependencies and higher word order variability degrade parsing performance. Our method also extends to minimal pairs of individual sentences, leading to a finer-grained understanding of parsing errors.",
}
@article{chiu-nichols-2016-named,
    title = "Named Entity Recognition with Bidirectional {LSTM}-{CNN}s",
    author = "Chiu, Jason P.C.  and
      Nichols, Eric",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1026",
    doi = "10.1162/tacl_a_00104",
    pages = "357--370",
    abstract = "Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.",
}
@article{zhou-etal-2016-deep,
    title = "Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation",
    author = "Zhou, Jie  and
      Cao, Ying  and
      Wang, Xuguang  and
      Li, Peng  and
      Xu, Wei",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1027",
    doi = "10.1162/tacl_a_00105",
    pages = "371--383",
    abstract = "Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT{'}14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT{'}14 English-to-German task.",
}
@article{arora-etal-2016-latent,
    title = "A Latent Variable Model Approach to {PMI}-based Word Embeddings",
    author = "Arora, Sanjeev  and
      Li, Yuanzhi  and
      Liang, Yingyu  and
      Ma, Tengyu  and
      Risteski, Andrej",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1028",
    doi = "10.1162/tacl_a_00106",
    pages = "385--399",
    abstract = "Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper proposes a new generative model, a dynamic version of the log-linear topic model of Mnih and Hinton (2007). The methodological novelty is to use the prior to compute closed form expressions for word statistics. This provides a theoretical justification for nonlinear models like PMI, word2vec, and GloVe, as well as some hyperparameter choices. It also helps explain why low-dimensional semantic embeddings contain linear algebraic structure that allows solution of word analogies, as shown by Mikolov et al. (2013a) and many subsequent papers. Experimental support is provided for the generative model assumptions, the most important of which is that latent word vectors are fairly uniformly dispersed in space.",
}
@article{xu-etal-2016-optimizing,
    title = "Optimizing Statistical Machine Translation for Text Simplification",
    author = "Xu, Wei  and
      Napoles, Courtney  and
      Pavlick, Ellie  and
      Chen, Quanze  and
      Callison-Burch, Chris",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1029",
    doi = "10.1162/tacl_a_00107",
    pages = "401--415",
    abstract = "Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.",
}
@article{osborne-etal-2016-encoding,
    title = "Encoding Prior Knowledge with Eigenword Embeddings",
    author = "Osborne, Dominique  and
      Narayan, Shashi  and
      Cohen, Shay B.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1030",
    doi = "10.1162/tacl_a_00108",
    pages = "417--430",
    abstract = "Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets.",
}
@article{ammar-etal-2016-many,
    title = "Many Languages, One Parser",
    author = "Ammar, Waleed  and
      Mulcaire, George  and
      Ballesteros, Miguel  and
      Dyer, Chris  and
      Smith, Noah A.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1031",
    doi = "10.1162/tacl_a_00109",
    pages = "431--444",
    abstract = "We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser{'}s performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.",
}
@article{kiperwasser-goldberg-2016-easy,
    title = "Easy-First Dependency Parsing with Hierarchical Tree {LSTM}s",
    author = "Kiperwasser, Eliyahu  and
      Goldberg, Yoav",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1032",
    doi = "10.1162/tacl_a_00110",
    pages = "445--461",
    abstract = "We suggest a compositional vector representation of parse trees that relies on a recursive combination of recurrent-neural network encoders. To demonstrate its effectiveness, we use the representation as the backbone of a greedy, bottom-up dependency parser, achieving very strong accuracies for English and Chinese, without relying on external word embeddings. The parser{'}s implementation is available for download at the first author{'}s webpage.",
}
@article{althoff-etal-2016-large,
    title = "Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health",
    author = "Althoff, Tim  and
      Clark, Kevin  and
      Leskovec, Jure",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1033",
    doi = "10.1162/tacl_a_00111",
    pages = "463--476",
    abstract = "Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.",
}
@article{shareghi-etal-2016-fast,
    title = "Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees",
    author = "Shareghi, Ehsan  and
      Petri, Matthias  and
      Haffari, Gholamreza  and
      Cohn, Trevor",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1034",
    doi = "10.1162/tacl_a_00112",
    pages = "477--490",
    abstract = "Efficient methods for storing and querying are critical for scaling high-order m-gram language models to large corpora. We propose a language model based on compressed suffix trees, a representation that is highly compact and can be easily held in memory, while supporting queries needed in computing language model probabilities on-the-fly. We present several optimisations which improve query runtimes up to 2500{\mbox{$\times$}}, despite only incurring a modest increase in construction time and memory usage. For large corpora and high Markov orders, our method is highly competitive with the state-of-the-art KenLM package. It imposes much lower memory requirements, often by orders of magnitude, and has runtimes that are either similar (for training) or comparable (for querying).",
}
@article{wang-eisner-2016-galactic,
    title = "The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages",
    author = "Wang, Dingquan  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1035",
    doi = "10.1162/tacl_a_00113",
    pages = "491--505",
    abstract = "We release Galactic Dependencies 1.0{---}a large set of synthetic languages not found on Earth, but annotated in Universal Dependencies format. This new resource aims to provide training and development data for NLP methods that aim to adapt to unfamiliar languages. Each synthetic treebank is produced from a real treebank by stochastically permuting the dependents of nouns and/or verbs to match the word order of other real languages. We discuss the usefulness, realism, parsability, perplexity, and diversity of the synthetic languages. As a simple demonstration of the use of Galactic Dependencies, we consider single-source transfer, which attempts to parse a real target language using a parser trained on a {``}nearby{''} source language. We find that including synthetic source languages somewhat increases the diversity of the source pool, which significantly improves results for most target languages.",
}
@article{gorman-sproat-2016-minimally,
    title = "Minimally Supervised Number Normalization",
    author = "Gorman, Kyle  and
      Sproat, Richard",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1036",
    doi = "10.1162/tacl_a_00114",
    pages = "507--519",
    abstract = "We propose two models for verbalizing numbers, a key component in speech recognition and synthesis systems. The first model uses an end-to-end recurrent neural network. The second model, drawing inspiration from the linguistics literature, uses finite-state transducers constructed with a minimal amount of training data. While both models achieve near-perfect performance, the latter model can be trained using several orders of magnitude less data than the former, making it particularly useful for low-resource languages.",
}
@article{linzen-etal-2016-assessing,
    title = "Assessing the Ability of {LSTM}s to Learn Syntax-Sensitive Dependencies",
    author = "Linzen, Tal  and
      Dupoux, Emmanuel  and
      Goldberg, Yoav",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1037",
    doi = "10.1162/tacl_a_00115",
    pages = "521--535",
    abstract = "The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic structure; can such dependencies be captured by LSTMs, which do not have explicit structural representations? We begin addressing this question using number agreement in English subject-verb dependencies. We probe the architecture{'}s grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models. In the strongly supervised settings, the LSTM achieved very high overall accuracy (less than 1{\%} errors), but errors increased when sequential and structural information conflicted. The frequency of such errors rose sharply in the language-modeling setting. We conclude that LSTMs can capture a non-trivial amount of grammatical structure given targeted supervision, but stronger architectures may be required to further reduce errors; furthermore, the language modeling signal is insufficient for capturing syntax-sensitive dependencies, and should be supplemented with more direct supervision if such dependencies need to be captured.",
}
@article{goldwasser-zhang-2016-understanding,
    title = "Understanding Satirical Articles Using Common-Sense",
    author = "Goldwasser, Dan  and
      Zhang, Xiao",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1038",
    doi = "10.1162/tacl_a_00116",
    pages = "537--549",
    abstract = "Automatic satire detection is a subtle text classification task, for machines and at times, even for humans. In this paper we argue that satire detection should be approached using common-sense inferences, rather than traditional text classification methods. We present a highly structured latent variable model capturing the required inferences. The model abstracts over the specific entities appearing in the articles, grouping them into generalized categories, thus allowing the model to adapt to previously unseen situations.",
}
@article{tuan-etal-2016-utilizing,
    title = "Utilizing Temporal Information for Taxonomy Construction",
    author = "Tuan, Luu Anh  and
      Hui, Siu Cheung  and
      Ng, See Kiong",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "4",
    year = "2016",
    url = "https://www.aclweb.org/anthology/Q16-1039",
    doi = "10.1162/tacl_a_00117",
    pages = "551--564",
    abstract = "Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of {`}is-a{'} relations between terms. Previous work on automatic construction of taxonomies from text documents either ignored temporal information or used fixed time periods to discretize the time series of documents. In this paper, we propose a time-aware method to automatically construct and effectively maintain a taxonomy from a given series of documents preclustered for a domain of interest. The method extracts temporal information from the documents and uses a timestamp contribution function to score the temporal relevance of the evidence from source texts when identifying the taxonomic relations for constructing the taxonomy. Experimental results show that our proposed method outperforms the state-of-the-art methods by increasing F-measure up to 7{\%}{--}20{\%}. Furthermore, the proposed method can incrementally update the taxonomy by adding fresh relations from new data and removing outdated relations using an information decay function. It thus avoids rebuilding the whole taxonomy from scratch for every update and keeps the taxonomy effectively up-to-date in order to track the latest information trends in the rapidly evolving domain.",
}
@proceedings{acl-2016-,
    title = "",
    editor = "Birch, Alexandra  and
      Zuidema, Willem",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
}
@inproceedings{elliott-etal-2016-multimodal,
    title = "Multimodal Learning and Reasoning",
    author = "Elliott, Desmond  and
      Kiela, Douwe  and
      Lazaridou, Angeliki",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5001",
    abstract = "Natural Language Processing has broadened in scope to tackle more and more challenging language understanding and reasoning tasks. The core NLP tasks remain predominantly unimodal, focusing on linguistic input, despite the fact that we, humans, acquire and use language while communicating in perceptually rich environments. Moving towards human-level AI will require the integration and modeling of multiple modalities beyond language. With this tutorial, our aim is to introduce researchers to the areas of NLP that have dealt with multimodal signals. The key advantage of using multimodal signals in NLP tasks is the complementarity of the data in different modalities. For example, we are less likely to nd descriptions of yellow bananas or wooden chairs in text corpora, but these visual attributes can be readily extracted directly from images. Multimodal signals, such as visual, auditory or olfactory data, have proven useful for models of word similarity and relatedness, automatic image and video description, and even predicting the associated smells of words. Finally, multimodality offers a practical opportunity to study and apply multitask learning, a general machine learning paradigm that improves generalization performance of a task by using training signals of other related tasks.All material associated to the tutorial will be available at http://multimodalnlp.github.io/",
}
@inproceedings{slonim-etal-2016-nlp,
    title = "{NLP} Approaches to Computational Argumentation",
    author = "Slonim, Noam  and
      Gurevych, Iryna  and
      Reed, Chris  and
      Stein, Benno",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5002",
    abstract = "Argumentation and debating represent primary intellectual activities of the human mind. People in all societies argue and debate, not only to convince others of their own opinions but also in order to explore the differences between multiple perspectives and conceptualizations, and to learn from this exploration. The process of reaching a resolution on controversial topics typically does not follow a simple sequence of purely logical steps. Rather it involves a wide variety of complex and interwoven actions. Presumably, pros and cons are identified, considered, and weighed, via cognitive processes that often involve persuasion and emotions, which are inherently harder to formalize from a computational perspective.This wide range of conceptual capabilities and activities, have only in part been studied in fields like CL and NLP, and typically within relatively small sub-communities that overlap the ACL audience. The new field of Computational Argumentation has very recently seen significant expansion within the CL and NLP community as new techniques and datasets start to become available, allowing for the first time investigation of the computational aspects of human argumentation in a holistic manner.The main goal of this tutorial would be to introduce this rapidly evolving field to the CL community. Specifically, we will aim to review recent advances in the field and to outline the challenging research questions - that are most relevant to the ACL audience - that naturally arise when trying to model human argumentation.We will further emphasize the practical value of this line of study, by considering real-world CL and NLP applications that are expected to emerge from this research, and to impact various industries, including legal, finance, healthcare, media, and education, to name just a few examples.The first part of the tutorial will provide introduction to the basics of argumentation and rhetoric. Next, we will cover fundamental analysis tasks in Computational Argumentation, including argumentation mining, revealing argument relations, assessing arguments quality, stance classification, polarity analysis, and more. After the coffee break, we will first review existing resources and recently introduced benchmark data. In the following part we will cover basic synthesis tasks in Computational Argumentation, including the relation to NLG and dialogue systems, and the evolving area of Debate Technologies, defined as technologies developed directly to enhance, support, and engage with human debating. Finally, we will present relevant demos, review potential applications, and discuss the future of this emerging field.",
}
@inproceedings{koehn-2016-computer,
    title = "Computer Aided Translation",
    author = "Koehn, Philipp",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5003",
    abstract = "Moving beyond post-editing machine translation, a number of recent research efforts have advanced computer aided translation methods that allow for more interactivity, richer information such as confidence scores, and the completed feedback loop of instant adaptation of machine translation models to user translations.This tutorial will explain the main techniques for several aspects of computer aided translation: confidence measures;interactive machine translation (interactive translation prediction);bilingual concordancers;translation option display;paraphrasing (alternative translation suggestions);visualization of word alignment;online adaptation;automatic reviewing;integration of translation memory;eye tracking, logging, and cognitive user models;For each of these, the state of the art and open challenges are presented. The tutorial will also look under the hood of the open source CASMACAT toolkit that is based on MATECAT, and available as a ``Home Edition'' to be installed on a desktop machine. The target audience of this tutorials are researchers interested in computer aided machine translation and practitioners who want to use or deploy advanced CAT technology.",
}
@inproceedings{camacho-collados-etal-2016-semantic,
    title = "Semantic Representations of Word Senses and Concepts",
    author = "Camacho-Collados, Jos{\'e}  and
      Iacobacci, Ignacio  and
      Navigli, Chris  and
      Taher Pilehvar, Roberto",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5004",
    abstract = "Representing the semantics of linguistic items in a machine {\-}interpretable form has been a major goal of Natural Language Processing since its earliest days. Among the range of different linguistic items, words have attracted the most research attention. However, word representations have an important limitation: they conflate different meanings of a word into a single vector. Representations of word senses have the potential to overcome this inherent limitation. Indeed, the representation of individual word senses and concepts has recently gained in popularity with several experimental results showing that a considerable performance improvement can be achieved across different NLP applications upon moving from word level to the deeper sense and concept levels. Another interesting point regarding the representation of concepts and word senses is that these models can be seamlessly applied to other linguistic items, such as words, phrases, sentences, etc.This tutorial will first provide a brief overview of the recent literature concerning word representation (both count based and neural network based). It will then describe the advantages of moving from the word level to the deeper level of word senses and concepts, providing an extensive review of state {\-}of {\-}the {\-}art systems. Approaches covered will not only include those which draw upon knowledge resources such as WordNet, Wikipedia, BabelNet or FreeBase as reference, but also the so {\-}called multi {\-}prototype approaches which learn sense distinctions by using different clustering techniques. Our tutorial will discuss the advantages and potential limitations of all approaches, showing their most successful applications to date. We will conclude by presenting current open problems and lines of future work.",
}
@inproceedings{luong-etal-2016-neural,
    title = "Neural Machine Translation",
    author = "Luong, Thang  and
      Cho, Kyunghyun  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5005",
    abstract = "Neural Machine Translation (NMT) is a simple new architecture for getting machines to learn to translate. Despite being relatively new (Kalchbrenner and Blunsom, 2013; Cho et al., 2014; Sutskever et al., 2014), NMT has already shown promising results, achieving state-of-the-art performances for various language pairs (Luong et al, 2015a; Jean et al, 2015; Luong et al, 2015b; Sennrich et al., 2016; Luong and Manning, 2016). While many of these NMT papers were presented to the ACL community, research and practice of NMT are only at their beginning stage. This tutorial would be a great opportunity for the whole community of machine translation and natural language processing to learn more about a very promising new approach to MT. This tutorial has four parts.In the first part, we start with an overview of MT approaches, including: (a) traditional methods that have been dominant over the past twenty years and (b) recent hybrid models with the use of neural network components. From these, we motivate why an end-to-end approach like neural machine translation is needed. The second part introduces a basic instance of NMT. We start out with a discussion of recurrent neural networks, including the back-propagation-through-time algorithm and stochastic gradient descent optimizers, as these are the foundation on which NMT builds. We then describe in detail the basic sequence-to-sequence architecture of NMT (Cho et al., 2014; Sutskever et al., 2014), the maximum likelihood training approach, and a simple beam-search decoder to produce translations.The third part of our tutorial describes techniques to build state-of-the-art NMT. We start with approaches to extend the vocabulary coverage of NMT (Luong et al., 2015a; Jean et al., 2015; Chitnis and DeNero, 2015). We then introduce the idea of jointly learning both translations and alignments through an attention mechanism (Bahdanau et al., 2015); other variants of attention (Luong et al., 2015b; Tu et al., 2016) are discussed too. We describe a recent trend in NMT, that is to translate at the sub-word level (Chung et al., 2016; Luong and Manning, 2016; Sennrich et al., 2016), so that language variations can be effectively handled. We then give tips on training and testing NMT systems such as batching and ensembling. In the final part of the tutorial, we briefly describe promising approaches, such as (a) how to combine multiple tasks to help translation (Dong et al., 2015; Luong et al., 2016; Firat et al., 2016; Zoph and Knight, 2016) and (b) how to utilize monolingual corpora (Sennrich et al., 2016). Lastly, we conclude with challenges remained to be solved for future NMT.PS: we would also like to acknowledge the very first paper by Forcada and {\~N}eco (1997) on sequence-to-sequence models for translation!",
}
@inproceedings{tripodi-pelillo-2016-game,
    title = "Game Theory and Natural Language: Origin, Evolution and Processing",
    author = "Tripodi, Rocco  and
      Pelillo, Marcello",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5006",
    abstract = "The development of game theory in the early 1940's by John von Neumann was a reaction against the then dominant view that problems in economic theory can be formulated using standard methods from optimization theory. Indeed, most real-world economic problems involve conflicting interactions among decision-making agents that cannot be adequately captured by a single (global) objective function. The main idea behind game theory is to shift the emphasis from optimality criteria to equilibrium conditions. Game theory provides a framework to model complex scenarios, with applications in economics and social science but also in different fields of information technology. With the recent development of algorithmic game theory, it has been used to solve problems in computer vision, pattern recognition, machine learning and natural language processing.Game-theoretic frameworks have been used in different ways to study language origin and evolution. Furthermore, the so-called game metaphor has been used by philosophers and linguists to explain how language evolved and how it works. Ludwig Wittgenstein, for example, famously introduced the concept of a language game to explain the conventional nature of language, and put forward the idea of the spontaneous formation of a common language that gradually emerges from the interactions among the speakers within a population.This concept opens the way to the interpretation of language as a complex adaptive system composed of linguistic units and their interactions, which gives rise to the emergence of structural properties. It is the core part of many computational models of language that are based on classical game theory and evolutionary game theory. With the former it is possible to model how speakers form a signaling system in which the ambiguity of the symbols is minimized; with the latter it is possible to model how speakers coordinate their linguistic choices according to the satisfaction that they have about the outcome of a communication act, converging to a common language. In the same vein, many other attempts have been proposed to explain how other characteristics of language follow similar dynamics.Game theory, and in particular evolutionary game theory, thanks to their ability to model interactive situations and to integrate information from multiple sources, have also been used to solve specific problems in natural language processing and information retrieval, such as language generation, word sense disambiguation and document and text clustering.The goal of this tutorial is to offer an introduction to the basic concepts of game theory and to show its main applications in the study of language, from different perspectives. We shall assume no pre-existing knowledge of game theory by the audience, thereby making the tutorial self-contained and understandable by a non-expert.",
}
@inproceedings{wang-wang-2016-understanding,
    title = "Understanding Short Texts",
    author = "Wang, Zhongyuan  and
      Wang, Haixun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5007",
    abstract = "Billions of short texts are produced every day, in the form of search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Unlike documents, short texts have some unique characteristics which make them difficult to handle. First, short texts, especially search queries, do not always observe the syntax of a written language. This means traditional NLP techniques, such as syntactic parsing, do not always apply to short texts. Second, short texts contain limited context. The majority of search queries contain less than 5 words, and tweets can have no more than 140 characters. Because of the above reasons, short texts give rise to a significant amount of ambiguity, which makes them extremely difficult to handle. On the other hand, many applications, including search engines, ads, automatic question answering, online advertising, recommendation systems, etc., rely on short text understanding. In all these applications, the necessary first step is to transform an input text into a machine-interpretable representation, namely to ``understand'' the short text. A growing number of approaches leverage external knowledge to address the issue of inadequate contextual information that accompanies the short texts. These approaches can be classified into two categories: Explicit Representation Model (ERM) and Implicit Representation Model (IRM). In this tutorial, we will present a comprehensive overview of short text understanding based on explicit semantics (knowledge graph representation, acquisition, and reasoning) and implicit semantics (embedding and deep learning). Specifically, we will go over various techniques in knowledge acquisition, representation, and inferencing has been proposed for text understanding, and we will describe massive structured and semi-structured data that have been made available in the recent decade that directly or indirectly encode human knowledge, turning the knowledge representation problems into a computational grand challenge with feasible solutions insight.",
}
@inproceedings{petruck-dodge-2016-metanet,
    title = "{M}eta{N}et: Repository, Identification System, and Applications",
    author = "Petruck, Miriam R L  and
      Dodge, Ellen K",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-5008",
    abstract = "The ubiquity of metaphor in language (Lakoff and Johnson 1980) has served as impetus for cognitive linguistic approaches to the study of language, mind, and the study of mind (e.g. Thibodeau {\&} Boroditsky 2011). While native speakers use metaphor naturally and easily, the treatment and interpretation of metaphor in computational systems remains challenging because such systems have not succeeded in developing ways to recognize the semantic elements that define metaphor. This tutorial demonstrates MetaNet's frame-based semantic analyses, and their informing of MetaNet's automatic metaphor identification system. Participants will gain a complete understanding of the theoretical basis and the practical workings of MetaNet, and acquire relevant information about the Frame Semantics basis of that knowledge base and the way that FrameNet handles the widespread phenomenon of metaphor in language. The tutorial is geared to researchers and practitioners of language technology, not necessarily experts in metaphor analysis or knowledgeable about either FrameNet or MetaNet, but who are interested in natural language processing tasks that involve automatic metaphor processing, or could benefit from exposure to tools and resources that support frame-based deep semantic, analyses of language, including metaphor as a widespread phenomenon in human language.",
}
@proceedings{acl-2016-acl-2016,
    title = "Proceedings of {ACL}-2016 System Demonstrations",
    editor = "Pradhan, Sameer  and
      Apidianaki, Marianna",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4000",
    doi = "10.18653/v1/P16-4",
}
@inproceedings{akbik-li-2016-polyglot,
    title = "{POLYGLOT}: Multilingual Semantic Role Labeling with Unified Labels",
    author = "Akbik, Alan  and
      Li, Yunyao",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4001",
    doi = "10.18653/v1/P16-4001",
    pages = "1--6",
}
@inproceedings{chinkina-etal-2016-online,
    title = "Online Information Retrieval for Language Learning",
    author = "Chinkina, Maria  and
      Kannan, Madeeswaran  and
      Meurers, Detmar",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4002",
    doi = "10.18653/v1/P16-4002",
    pages = "7--12",
}
@inproceedings{cram-daille-2016-terminology,
    title = "Terminology Extraction with Term Variant Detection",
    author = "Cram, Damien  and
      Daille, B{\'e}atrice",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4003",
    doi = "10.18653/v1/P16-4003",
    pages = "13--18",
}
@inproceedings{ernst-etal-2016-deeplife,
    title = "{D}eep{L}ife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences",
    author = "Ernst, Patrick  and
      Siu, Amy  and
      Milchevski, Dragan  and
      Hoffart, Johannes  and
      Weikum, Gerhard",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4004",
    doi = "10.18653/v1/P16-4004",
    pages = "19--24",
}
@inproceedings{ge-etal-2016-visualizing,
    title = "Visualizing and Curating Knowledge Graphs over Time and Space",
    author = "Ge, Tong  and
      Wang, Yafang  and
      de Melo, Gerard  and
      Li, Haofeng  and
      Chen, Baoquan",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4005",
    doi = "10.18653/v1/P16-4005",
    pages = "25--30",
}
@inproceedings{georgi-etal-2016-web,
    title = "A Web-framework for {ODIN} Annotation",
    author = "Georgi, Ryan  and
      Goodman, Michael Wayne  and
      Xia, Fei",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4006",
    doi = "10.18653/v1/P16-4006",
    pages = "31--36",
}
@inproceedings{hennig-etal-2016-real,
    title = "Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams",
    author = "Hennig, Leonhard  and
      Thomas, Philippe  and
      Ai, Renlong  and
      Kirschnick, Johannes  and
      Wang, He  and
      Pannier, Jakob  and
      Zimmermann, Nora  and
      Schmeier, Sven  and
      Xu, Feiyu  and
      Ostwald, Jan  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4007",
    doi = "10.18653/v1/P16-4007",
    pages = "37--42",
}
@inproceedings{jalalvand-etal-2016-transcrater,
    title = "{T}ransc{R}ater: a Tool for Automatic Speech Recognition Quality Estimation",
    author = "Jalalvand, Shahab  and
      Negri, Matteo  and
      Turchi, Marco  and
      C. de Souza, Jos{\'e} G.  and
      Falavigna, Daniele  and
      Qwaider, Mohammed R. H.",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4008",
    doi = "10.18653/v1/P16-4008",
    pages = "43--48",
}
@inproceedings{jalili-sabet-etal-2016-tmop,
    title = "{TM}op: a Tool for Unsupervised Translation Memory Cleaning",
    author = "Jalili Sabet, Masoud  and
      Negri, Matteo  and
      Turchi, Marco  and
      C. de Souza, Jos{\'e} G.  and
      Federico, Marcello",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4009",
    doi = "10.18653/v1/P16-4009",
    pages = "49--54",
}
@inproceedings{kiela-2016-mmfeat,
    title = "{MMF}eat: A Toolkit for Extracting Multi-Modal Features",
    author = "Kiela, Douwe",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4010",
    doi = "10.18653/v1/P16-4010",
    pages = "55--60",
}
@inproceedings{kirschnick-etal-2016-jedi,
    title = "{JEDI}: Joint Entity and Relation Detection using Type Inference",
    author = "Kirschnick, Johannes  and
      Hemsen, Holmer  and
      Markl, Volker",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4011",
    doi = "10.18653/v1/P16-4011",
    pages = "61--66",
}
@inproceedings{lison-kennington-2016-opendial,
    title = "{O}pen{D}ial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules",
    author = "Lison, Pierre  and
      Kennington, Casey",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4012",
    doi = "10.18653/v1/P16-4012",
    pages = "67--72",
}
@inproceedings{litvak-etal-2016-museec,
    title = "{MUSEEC}: A Multilingual Text Summarization Tool",
    author = "Litvak, Marina  and
      Vanetik, Natalia  and
      Last, Mark  and
      Churkin, Elena",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4013",
    doi = "10.18653/v1/P16-4013",
    pages = "73--78",
}
@inproceedings{madnani-etal-2016-language,
    title = "Language Muse: Automated Linguistic Activity Generation for {E}nglish Language Learners",
    author = "Madnani, Nitin  and
      Burstein, Jill  and
      Sabatini, John  and
      Biggers, Kietha  and
      Andreyev, Slava",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4014",
    doi = "10.18653/v1/P16-4014",
    pages = "79--84",
}
@inproceedings{martinez-gomez-etal-2016-ccg2lambda,
    title = "ccg2lambda: A Compositional Semantics System",
    author = "Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Mineshima, Koji  and
      Miyao, Yusuke  and
      Bekki, Daisuke",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4015",
    doi = "10.18653/v1/P16-4015",
    pages = "85--90",
}
@inproceedings{massung-etal-2016-meta,
    title = "{M}e{TA}: A Unified Toolkit for Text Retrieval and Analysis",
    author = "Massung, Sean  and
      Geigle, Chase  and
      Zhai, ChengXiang",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4016",
    doi = "10.18653/v1/P16-4016",
    pages = "91--96",
}
@inproceedings{meyer-etal-2016-mdswriter,
    title = "{MDSW}riter: Annotation Tool for Creating High-Quality Multi-Document Summarization Corpora",
    author = "Meyer, Christian M.  and
      Benikova, Darina  and
      Mieskes, Margot  and
      Gurevych, Iryna",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4017",
    doi = "10.18653/v1/P16-4017",
    pages = "97--102",
}
@inproceedings{noji-miyao-2016-jigg,
    title = "{J}igg: A Framework for an Easy Natural Language Processing Pipeline",
    author = "Noji, Hiroshi  and
      Miyao, Yusuke",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4018",
    doi = "10.18653/v1/P16-4018",
    pages = "103--108",
}
@inproceedings{ploch-etal-2016-advanced,
    title = "An Advanced Press Review System Combining Deep News Analysis and Machine Learning Algorithms",
    author = "Ploch, Danuta  and
      Lommatzsch, Andreas  and
      Schultze, Florian",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4019",
    doi = "10.18653/v1/P16-4019",
    pages = "109--114",
}
@inproceedings{lee-luo-2016-personalized,
    title = "Personalized Exercises for Preposition Learning",
    author = "Lee, John  and
      Luo, Mengqi",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4020",
    doi = "10.18653/v1/P16-4020",
    pages = "115--120",
}
@inproceedings{pradhan-etal-2016-science,
    title = "My Science {T}utor{---}{L}earning Science with a Conversational Virtual Tutor",
    author = "Pradhan, Sameer  and
      Cole, Ron  and
      Ward, Wayne",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4021",
    doi = "10.18653/v1/P16-4021",
    pages = "121--126",
}
@inproceedings{rahimi-etal-2016-pigeo,
    title = "pigeo: A Python Geotagging Tool",
    author = "Rahimi, Afshin  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4022",
    doi = "10.18653/v1/P16-4022",
    pages = "127--132",
}
@inproceedings{renduchintala-etal-2016-creating,
    title = "Creating Interactive Macaronic Interfaces for Language Learning",
    author = "Renduchintala, Adithya  and
      Knowles, Rebecca  and
      Koehn, Philipp  and
      Eisner, Jason",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4023",
    doi = "10.18653/v1/P16-4023",
    pages = "133--138",
}
@inproceedings{sayeed-etal-2016-roleo,
    title = "{R}oleo: Visualising Thematic Fit Spaces on the Web",
    author = "Sayeed, Asad  and
      Hong, Xudong  and
      Demberg, Vera",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4024",
    doi = "10.18653/v1/P16-4024",
    pages = "139--144",
}
@inproceedings{steinberger-2016-mediagist,
    title = "{M}edia{G}ist: A Cross-lingual Analyser of Aggregated News and Commentaries",
    author = "Steinberger, Josef",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4025",
    doi = "10.18653/v1/P16-4025",
    pages = "145--150",
}
@inproceedings{tixier-etal-2016-gowvis,
    title = "{G}o{W}vis: A Web Application for Graph-of-Words-based Text Visualization and Summarization",
    author = "Tixier, Antoine  and
      Skianis, Konstantinos  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4026",
    doi = "10.18653/v1/P16-4026",
    pages = "151--156",
}
@inproceedings{uryupina-etal-2016-limosine,
    title = "{L}i{M}o{SIN}e Pipeline: Multilingual {UIMA}-based {NLP} Platform",
    author = "Uryupina, Olga  and
      Plank, Barbara  and
      Barlacchi, Gianni  and
      Valverde Albacete, Francisco J.  and
      Tsagkias, Manos  and
      Uva, Antonio  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4027",
    doi = "10.18653/v1/P16-4027",
    pages = "157--162",
}
@inproceedings{yimam-etal-2016-new,
    title = "new/s/leak {--} Information Extraction and Visualization for Investigative Data Journalists",
    author = "Yimam, Seid Muhie  and
      Ulrich, Heiner  and
      von Landesberger, Tatiana  and
      Rosenbach, Marcel  and
      Regneri, Michaela  and
      Panchenko, Alexander  and
      Lehmann, Franziska  and
      Fahrer, Uli  and
      Biemann, Chris  and
      Ballweg, Kathrin",
    booktitle = "Proceedings of {ACL}-2016 System Demonstrations",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-4028",
    doi = "10.18653/v1/P16-4028",
    pages = "163--168",
}
@proceedings{acl-2016-acl,
    title = "Proceedings of the {ACL} 2016 Student Research Workshop",
    editor = "He, He  and
      Lei, Tao  and
      Roberts, Will",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3000",
    doi = "10.18653/v1/P16-3",
}
@inproceedings{kodaira-etal-2016-controlled,
    title = "Controlled and Balanced Dataset for {J}apanese Lexical Simplification",
    author = "Kodaira, Tomonori  and
      Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3001",
    doi = "10.18653/v1/P16-3001",
    pages = "1--7",
}
@inproceedings{otsuki-etal-2016-dependency,
    title = "Dependency Forest based Word Alignment",
    author = "Otsuki, Hitoshi  and
      Chu, Chenhui  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3002",
    doi = "10.18653/v1/P16-3002",
    pages = "8--14",
}
@inproceedings{benzschawel-2016-identifying,
    title = "Identifying Potential Adverse Drug Events in Tweets Using Bootstrapped Lexicons",
    author = "Benzschawel, Eric",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3003",
    doi = "10.18653/v1/P16-3003",
    pages = "15--21",
}
@inproceedings{matsuo-etal-2016-generating,
    title = "Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity",
    author = "Matsuo, Eri  and
      Kobayashi, Ichiro  and
      Nishimoto, Shinji  and
      Nishida, Satoshi  and
      Asoh, Hideki",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3004",
    doi = "10.18653/v1/P16-3004",
    pages = "22--29",
}
@inproceedings{lam-2016-improving,
    title = "Improving {T}witter Community Detection through Contextual Sentiment Analysis",
    author = "Lam, Alron Jan",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3005",
    doi = "10.18653/v1/P16-3005",
    pages = "30--36",
}
@inproceedings{v-v-sharma-2016-significance,
    title = "Significance of an Accurate Sandhi-Splitter in Shallow Parsing of {D}ravidian Languages",
    author = "V V, Devadath  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3006",
    doi = "10.18653/v1/P16-3006",
    pages = "37--42",
}
@inproceedings{llewellyn-etal-2016-improving,
    title = "Improving Topic Model Clustering of Newspaper Comments for Summarisation",
    author = "Llewellyn, Clare  and
      Grover, Claire  and
      Oberlander, Jon",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3007",
    doi = "10.18653/v1/P16-3007",
    pages = "43--50",
}
@inproceedings{tobaili-2016-arabizi,
    title = "{A}rabizi Identification in {T}witter Data",
    author = "Tobaili, Taha",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3008",
    doi = "10.18653/v1/P16-3008",
    pages = "51--57",
}
@inproceedings{milajevs-etal-2016-robust,
    title = "Robust Co-occurrence Quantification for Lexical Distributional Semantics",
    author = "Milajevs, Dmitrijs  and
      Sadrzadeh, Mehrnoosh  and
      Purver, Matthew",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3009",
    doi = "10.18653/v1/P16-3009",
    pages = "58--64",
}
@inproceedings{haagsma-2016-singleton,
    title = "Singleton Detection using Word Embeddings and Neural Networks",
    author = "Haagsma, Hessel",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3010",
    doi = "10.18653/v1/P16-3010",
    pages = "65--71",
}
@inproceedings{fares-2016-dataset,
    title = "A Dataset for Joint Noun-Noun Compound Bracketing and Interpretation",
    author = "Fares, Murhaf",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3011",
    doi = "10.18653/v1/P16-3011",
    pages = "72--79",
}
@inproceedings{moradiannasab-2016-investigation,
    title = "An Investigation on The Effectiveness of Employing Topic Modeling Techniques to Provide Topic Awareness For Conversational Agents",
    author = "Moradiannasab, Omid",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3012",
    doi = "10.18653/v1/P16-3012",
    pages = "80--85",
}
@inproceedings{kriz-hladka-2016-improving,
    title = "Improving Dependency Parsing Using Sentence Clause Charts",
    author = "Kr{\'\i}{\v{z}}, Vincent  and
      Hladk{\'a}, Barbora",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3013",
    doi = "10.18653/v1/P16-3013",
    pages = "86--92",
}
@inproceedings{muszynska-2016-graph,
    title = "Graph- and surface-level sentence chunking",
    author = "Muszy{\'n}ska, Ewa",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3014",
    doi = "10.18653/v1/P16-3014",
    pages = "93--99",
}
@inproceedings{mehta-2016-extractive,
    title = "From Extractive to Abstractive Summarization: A Journey",
    author = "Mehta, Parth",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3015",
    doi = "10.18653/v1/P16-3015",
    pages = "100--106",
}
@inproceedings{abercrombie-hovy-2016-putting,
    title = "Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of {T}witter Conversations",
    author = "Abercrombie, Gavin  and
      Hovy, Dirk",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3016",
    doi = "10.18653/v1/P16-3016",
    pages = "107--113",
}
@inproceedings{daks-clark-2016-unsupervised,
    title = "Unsupervised Authorial Clustering Based on Syntactic Structure",
    author = "Daks, Alon  and
      Clark, Aidan",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3017",
    doi = "10.18653/v1/P16-3017",
    pages = "114--118",
}
@inproceedings{negi-2016-suggestion,
    title = "Suggestion Mining from Opinionated Text",
    author = "Negi, Sapna",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3018",
    doi = "10.18653/v1/P16-3018",
    pages = "119--125",
}
@inproceedings{xia-etal-2016-efficient,
    title = "An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network",
    author = "Xia, Yandi  and
      Wei, Zhongyu  and
      Liu, Yang",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3019",
    doi = "10.18653/v1/P16-3019",
    pages = "126--131",
}
@inproceedings{lee-etal-2016-qa,
    title = "{QA}-It: Classifying Non-Referential It for Question Answer Pairs",
    author = "Lee, Timothy  and
      Lutz, Alex  and
      Choi, Jinho D.",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3020",
    doi = "10.18653/v1/P16-3020",
    pages = "132--137",
}
@inproceedings{jargalsaikhan-etal-2016-building,
    title = "Building a Corpus for {J}apanese Wikification with Fine-Grained Entity Classes",
    author = "Jargalsaikhan, Davaajav  and
      Okazaki, Naoaki  and
      Matsuda, Koji  and
      Inui, Kentaro",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3021",
    doi = "10.18653/v1/P16-3021",
    pages = "138--144",
}
@inproceedings{jindal-2016-personalized,
    title = "A Personalized {M}arkov Clustering and Deep Learning Approach for {A}rabic Text Categorization",
    author = "Jindal, Vasu",
    booktitle = "Proceedings of the {ACL} 2016 Student Research Workshop",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-3022",
    doi = "10.18653/v1/P16-3022",
    pages = "145--151",
}
@proceedings{acl-2016-association-linguistics,
    title = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    editor = "Erk, Katrin  and
      Smith, Noah A.",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2000",
    doi = "10.18653/v1/P16-2",
}
@inproceedings{de-kok-hinrichs-2016-transition,
    title = "Transition-based dependency parsing with topological fields",
    author = {de Kok, Dani{\"e}l  and
      Hinrichs, Erhard},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2001",
    doi = "10.18653/v1/P16-2001",
    pages = "1--7",
}
@inproceedings{kim-etal-2016-scalable,
    title = "Scalable Semi-Supervised Query Classification Using Matrix Sketching",
    author = "Kim, Young-Bum  and
      Stratos, Karl  and
      Sarikaya, Ruhi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2002",
    doi = "10.18653/v1/P16-2002",
    pages = "8--13",
}
@inproceedings{benton-etal-2016-learning,
    title = "Learning Multiview Embeddings of {T}witter Users",
    author = "Benton, Adrian  and
      Arora, Raman  and
      Dredze, Mark",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2003",
    doi = "10.18653/v1/P16-2003",
    pages = "14--19",
}
@inproceedings{chen-chen-2016-implicit,
    title = "Implicit Polarity and Implicit Aspect Recognition in Opinion Mining",
    author = "Chen, Huan-Yuan  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2004",
    doi = "10.18653/v1/P16-2004",
    pages = "20--25",
}
@inproceedings{clinchant-etal-2016-domain,
    title = "A Domain Adaptation Regularization for Denoising Autoencoders",
    author = "Clinchant, St{\'e}phane  and
      Csurka, Gabriela  and
      Chidlovskii, Boris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2005",
    doi = "10.18653/v1/P16-2005",
    pages = "26--31",
}
@inproceedings{cross-huang-2016-incremental,
    title = "Incremental Parsing with Minimal Features Using Bi-Directional {LSTM}",
    author = "Cross, James  and
      Huang, Liang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2006",
    doi = "10.18653/v1/P16-2006",
    pages = "32--37",
}
@inproceedings{dakwale-monz-2016-improving,
    title = "Improving Statistical Machine Translation Performance by Oracle-{BLEU} Model Re-estimation",
    author = "Dakwale, Praveen  and
      Monz, Christof",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2007",
    doi = "10.18653/v1/P16-2007",
    pages = "38--44",
}
@inproceedings{dusek-jurcicek-2016-sequence,
    title = "Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Jur{\v{c}}{\'\i}{\v{c}}ek, Filip",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2008",
    doi = "10.18653/v1/P16-2008",
    pages = "45--51",
}
@inproceedings{eger-mehler-2016-linearity,
    title = "On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models",
    author = "Eger, Steffen  and
      Mehler, Alexander",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2009",
    doi = "10.18653/v1/P16-2009",
    pages = "52--58",
}
@inproceedings{elsner-etal-2016-joint,
    title = "Joint Word Segmentation and Phonetic Category Induction",
    author = "Elsner, Micha  and
      Antetomaso, Stephanie  and
      Feldman, Naomi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2010",
    doi = "10.18653/v1/P16-2010",
    pages = "59--65",
}
@inproceedings{feng-etal-2016-language,
    title = "A Language-Independent Neural Network for Event Detection",
    author = "Feng, Xiaocheng  and
      Huang, Lifu  and
      Tang, Duyu  and
      Ji, Heng  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2011",
    doi = "10.18653/v1/P16-2011",
    pages = "66--71",
}
@inproceedings{ficler-goldberg-2016-improved,
    title = "Improved Parsing for Argument-Clusters Coordination",
    author = "Ficler, Jessica  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2012",
    doi = "10.18653/v1/P16-2012",
    pages = "72--76",
}
@inproceedings{fomicheva-specia-2016-reference,
    title = "Reference Bias in Monolingual Machine Translation Evaluation",
    author = "Fomicheva, Marina  and
      Specia, Lucia",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2013",
    doi = "10.18653/v1/P16-2013",
    pages = "77--82",
}
@inproceedings{gfeller-etal-2016-cross,
    title = "Cross-lingual projection for class-based language models",
    author = "Gfeller, Beat  and
      Schogol, Vlad  and
      Hall, Keith",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2014",
    doi = "10.18653/v1/P16-2014",
    pages = "83--88",
}
@inproceedings{gu-etal-2016-fast,
    title = "A Fast Approach for Semantic Similar Short Texts Retrieval",
    author = "Gu, Yanhui  and
      Yang, Zhenglu  and
      Zhou, Junsheng  and
      Qu, Weiguang  and
      Wei, Jinmao  and
      Shi, Xingtian",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2015",
    doi = "10.18653/v1/P16-2015",
    pages = "89--94",
}
@inproceedings{hayashi-nagata-2016-empty,
    title = "Empty element recovery by spinal parser operations",
    author = "Hayashi, Katsuhiko  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2016",
    doi = "10.18653/v1/P16-2016",
    pages = "95--100",
}
@inproceedings{beigman-klebanov-etal-2016-semantic,
    title = "Semantic classifications for detection of verb metaphors",
    author = "Beigman Klebanov, Beata  and
      Leong, Chee Wee  and
      Gutierrez, E. Dario  and
      Shutova, Ekaterina  and
      Flor, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2017",
    doi = "10.18653/v1/P16-2017",
    pages = "101--106",
}
@inproceedings{kozareva-etal-2016-recognizing,
    title = "Recognizing Salient Entities in Shopping Queries",
    author = "Kozareva, Zornitsa  and
      Li, Qi  and
      Zhai, Ke  and
      Guo, Weiwei",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2018",
    doi = "10.18653/v1/P16-2018",
    pages = "107--111",
}
@inproceedings{long-etal-2016-leveraging,
    title = "Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data",
    author = "Long, Teng  and
      Lowe, Ryan  and
      Cheung, Jackie Chi Kit  and
      Precup, Doina",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2019",
    doi = "10.18653/v1/P16-2019",
    pages = "112--117",
}
@inproceedings{luan-etal-2016-multiplicative,
    title = "Multiplicative Representations for Unsupervised Semantic Role Induction",
    author = "Luan, Yi  and
      Ji, Yangfeng  and
      Hajishirzi, Hannaneh  and
      Li, Boyang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2020",
    doi = "10.18653/v1/P16-2020",
    pages = "118--123",
}
@inproceedings{mi-etal-2016-vocabulary,
    title = "Vocabulary Manipulation for Neural Machine Translation",
    author = "Mi, Haitao  and
      Wang, Zhiguo  and
      Ittycheriah, Abe",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2021",
    doi = "10.18653/v1/P16-2021",
    pages = "124--129",
}
@inproceedings{mou-etal-2016-natural,
    title = "Natural Language Inference by Tree-Based Convolution and Heuristic Matching",
    author = "Mou, Lili  and
      Men, Rui  and
      Li, Ge  and
      Xu, Yan  and
      Zhang, Lu  and
      Yan, Rui  and
      Jin, Zhi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2022",
    doi = "10.18653/v1/P16-2022",
    pages = "130--136",
}
@inproceedings{onrust-etal-2016-improving,
    title = "Improving cross-domain n-gram language modelling with skipgrams",
    author = "Onrust, Louis  and
      van den Bosch, Antal  and
      Van hamme, Hugo",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2023",
    doi = "10.18653/v1/P16-2023",
    pages = "137--142",
}
@inproceedings{pavlick-callison-burch-2016-simple,
    title = "Simple {PPDB}: A Paraphrase Database for Simplification",
    author = "Pavlick, Ellie  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2024",
    doi = "10.18653/v1/P16-2024",
    pages = "143--148",
}
@inproceedings{peng-dredze-2016-improving,
    title = "Improving Named Entity Recognition for {C}hinese Social Media with Word Segmentation Representation Learning",
    author = "Peng, Nanyun  and
      Dredze, Mark",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2025",
    doi = "10.18653/v1/P16-2025",
    pages = "149--155",
}
@inproceedings{ramisch-etal-2016-naked,
    title = "How Naked is the Naked Truth? A Multilingual Lexicon of Nominal Compound Compositionality",
    author = "Ramisch, Carlos  and
      Cordeiro, Silvio  and
      Zilio, Leonardo  and
      Idiart, Marco  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2026",
    doi = "10.18653/v1/P16-2026",
    pages = "156--161",
}
@inproceedings{rayner-etal-2016-open,
    title = "An Open Web Platform for Rule-Based Speech-to-Sign Translation",
    author = "Rayner, Manny  and
      Bouillon, Pierrette  and
      Ebling, Sarah  and
      Gerlach, Johanna  and
      Strasly, Irene  and
      Tsourakis, Nikos",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2027",
    doi = "10.18653/v1/P16-2027",
    pages = "162--168",
}
@inproceedings{schulz-etal-2016-word,
    title = "Word Alignment without {NULL} Words",
    author = "Schulz, Philip  and
      Aziz, Wilker  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2028",
    doi = "10.18653/v1/P16-2028",
    pages = "169--174",
}
@inproceedings{varjokallio-klakow-2016-unsupervised,
    title = "Unsupervised morph segmentation and statistical language models for vocabulary expansion",
    author = "Varjokallio, Matti  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2029",
    doi = "10.18653/v1/P16-2029",
    pages = "175--180",
}
@inproceedings{vincze-etal-2016-detecting,
    title = "Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts",
    author = "Vincze, Veronika  and
      Gosztolya, G{\'a}bor  and
      T{\'o}th, L{\'a}szl{\'o}  and
      Hoffmann, Ildik{\'o}  and
      Szatl{\'o}czki, Gr{\'e}ta  and
      B{\'a}nr{\'e}ti, Zolt{\'a}n  and
      P{\'a}k{\'a}ski, Magdolna  and
      K{\'a}lm{\'a}n, J{\'a}nos",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2030",
    doi = "10.18653/v1/P16-2030",
    pages = "181--187",
}
@inproceedings{vulic-etal-2016-multi,
    title = "Multi-Modal Representations for Improved Bilingual Lexicon Learning",
    author = "Vuli{\'c}, Ivan  and
      Kiela, Douwe  and
      Clark, Stephen  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2031",
    doi = "10.18653/v1/P16-2031",
    pages = "188--194",
}
@inproceedings{wei-etal-2016-post,
    title = "Is This Post Persuasive? Ranking Argumentative Comments in Online Forum",
    author = "Wei, Zhongyu  and
      Liu, Yang  and
      Li, Yi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2032",
    doi = "10.18653/v1/P16-2032",
    pages = "195--200",
}
@inproceedings{yih-etal-2016-value,
    title = "The Value of Semantic Parse Labeling for Knowledge Base Question Answering",
    author = "Yih, Wen-tau  and
      Richardson, Matthew  and
      Meek, Chris  and
      Chang, Ming-Wei  and
      Suh, Jina",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2033",
    doi = "10.18653/v1/P16-2033",
    pages = "201--206",
}
@inproceedings{zhou-etal-2016-attention-based,
    title = "Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification",
    author = "Zhou, Peng  and
      Shi, Wei  and
      Tian, Jun  and
      Qi, Zhenyu  and
      Li, Bingchen  and
      Hao, Hongwei  and
      Xu, Bo",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2034",
    doi = "10.18653/v1/P16-2034",
    pages = "207--212",
}
@inproceedings{lazaridou-etal-2016-red,
    title = "The red one!: On learning to refer to things based on discriminative properties",
    author = "Lazaridou, Angeliki  and
      Pham, Nghia The  and
      Baroni, Marco",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2035",
    doi = "10.18653/v1/P16-2035",
    pages = "213--218",
}
@inproceedings{vo-zhang-2016-dont,
    title = "Don{'}t Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short Text",
    author = "Vo, Duy Tin  and
      Zhang, Yue",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2036",
    doi = "10.18653/v1/P16-2036",
    pages = "219--224",
}
@inproceedings{wang-etal-2016-dimensional,
    title = "Dimensional Sentiment Analysis Using a Regional {CNN}-{LSTM} Model",
    author = "Wang, Jin  and
      Yu, Liang-Chih  and
      Lai, K. Robert  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2037",
    doi = "10.18653/v1/P16-2037",
    pages = "225--230",
}
@inproceedings{sogaard-goldberg-2016-deep,
    title = "Deep multi-task learning with low level tasks supervised at lower layers",
    author = "S{\o}gaard, Anders  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2038",
    doi = "10.18653/v1/P16-2038",
    pages = "231--235",
}
@inproceedings{tomori-etal-2016-domain,
    title = "Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks",
    author = "Tomori, Suzushi  and
      Ninomiya, Takashi  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2039",
    doi = "10.18653/v1/P16-2039",
    pages = "236--242",
}
@inproceedings{saldanha-etal-2016-entity,
    title = "An Entity-Focused Approach to Generating Company Descriptions",
    author = "Saldanha, Gavin  and
      Biran, Or  and
      McKeown, Kathleen  and
      Gliozzo, Alfio",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2040",
    doi = "10.18653/v1/P16-2040",
    pages = "243--248",
}
@inproceedings{levy-dagan-2016-annotating,
    title = "Annotating Relation Inference in Context via Question Answering",
    author = "Levy, Omer  and
      Dagan, Ido",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2041",
    doi = "10.18653/v1/P16-2041",
    pages = "249--255",
}
@inproceedings{koper-schulte-im-walde-2016-automatic,
    title = "Automatic Semantic Classification of {G}erman Preposition Types: Comparing Hard and Soft Clustering Approaches across Features",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2042",
    doi = "10.18653/v1/P16-2042",
    pages = "256--263",
}
@inproceedings{gkatzia-etal-2016-natural,
    title = "Natural Language Generation enhances human decision-making with uncertain information",
    author = "Gkatzia, Dimitra  and
      Lemon, Oliver  and
      Rieser, Verena",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2043",
    doi = "10.18653/v1/P16-2043",
    pages = "264--268",
}
@inproceedings{dhingra-etal-2016-tweet2vec,
    title = "{T}weet2{V}ec: Character-Based Distributed Representations for Social Media",
    author = "Dhingra, Bhuwan  and
      Zhou, Zhong  and
      Fitzpatrick, Dylan  and
      Muehl, Michael  and
      Cohen, William",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2044",
    doi = "10.18653/v1/P16-2044",
    pages = "269--274",
}
@inproceedings{li-etal-2016-phrase,
    title = "Phrase-Level Combination of {SMT} and {TM} Using Constrained Word Lattice",
    author = "Li, Liangyou  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2045",
    doi = "10.18653/v1/P16-2045",
    pages = "275--280",
}
@inproceedings{pal-etal-2016-neural,
    title = "A Neural Network based Approach to Automatic Post-Editing",
    author = "Pal, Santanu  and
      Naskar, Sudip Kumar  and
      Vela, Mihaela  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2046",
    doi = "10.18653/v1/P16-2046",
    pages = "281--286",
}
@inproceedings{jalili-sabet-etal-2016-unsupervised,
    title = "An Unsupervised Method for Automatic Translation Memory Cleaning",
    author = "Jalili Sabet, Masoud  and
      Negri, Matteo  and
      Turchi, Marco  and
      Barbu, Eduard",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2047",
    doi = "10.18653/v1/P16-2047",
    pages = "287--292",
}
@inproceedings{peter-etal-2016-exponentially,
    title = "Exponentially Decaying Bag-of-Words Input Features for Feed-Forward Neural Network in Statistical Machine Translation",
    author = "Peter, Jan-Thorsten  and
      Wang, Weiyue  and
      Ney, Hermann",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2048",
    doi = "10.18653/v1/P16-2048",
    pages = "293--298",
}
@inproceedings{stahlberg-etal-2016-syntactically,
    title = "Syntactically Guided Neural Machine Translation",
    author = "Stahlberg, Felix  and
      Hasler, Eva  and
      Waite, Aurelien  and
      Byrne, Bill",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2049",
    doi = "10.18653/v1/P16-2049",
    pages = "299--305",
}
@inproceedings{hendrickx-etal-2016-quaffable,
    title = "Very quaffable and great fun: Applying {NLP} to wine reviews",
    author = "Hendrickx, Iris  and
      Lefever, Els  and
      Croijmans, Ilja  and
      Majid, Asifa  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2050",
    doi = "10.18653/v1/P16-2050",
    pages = "306--312",
}
@inproceedings{flekova-etal-2016-exploring,
    title = "Exploring Stylistic Variation with Age and Income on {T}witter",
    author = "Flekova, Lucie  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2051",
    doi = "10.18653/v1/P16-2051",
    pages = "313--319",
}
@inproceedings{ruan-etal-2016-finding,
    title = "Finding Optimists and Pessimists on {T}witter",
    author = "Ruan, Xianzhi  and
      Wilson, Steven  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2052",
    doi = "10.18653/v1/P16-2052",
    pages = "320--325",
}
@inproceedings{clinchant-etal-2016-transductive,
    title = "Transductive Adaptation of Black Box Predictions",
    author = "Clinchant, St{\'e}phane  and
      Chidlovskii, Boris  and
      Csurka, Gabriela",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2053",
    doi = "10.18653/v1/P16-2053",
    pages = "326--331",
}
@inproceedings{kozareva-yamada-2016-tumblr,
    title = "Which Tumblr Post Should {I} Read Next?",
    author = "Kozareva, Zornitsa  and
      Yamada, Makoto",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2054",
    doi = "10.18653/v1/P16-2054",
    pages = "332--336",
}
@inproceedings{bingel-sogaard-2016-text,
    title = "Text Simplification as Tree Labeling",
    author = "Bingel, Joachim  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2055",
    doi = "10.18653/v1/P16-2055",
    pages = "337--343",
}
@inproceedings{brooke-etal-2016-bootstrapped,
    title = "Bootstrapped Text-level Named Entity Recognition for Literature",
    author = "Brooke, Julian  and
      Hammond, Adam  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2056",
    doi = "10.18653/v1/P16-2056",
    pages = "344--350",
}
@inproceedings{hovy-2016-enemy,
    title = "The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews {--} An Adversarial Study",
    author = "Hovy, Dirk",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2057",
    doi = "10.18653/v1/P16-2057",
    pages = "351--356",
}
@inproceedings{costa-jussa-fonollosa-2016-character,
    title = "Character-based Neural Machine Translation",
    author = "Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2058",
    doi = "10.18653/v1/P16-2058",
    pages = "357--361",
}
@inproceedings{elgohary-carpuat-2016-learning,
    title = "Learning Monolingual Compositional Representations via Bilingual Supervision",
    author = "Elgohary, Ahmed  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2059",
    doi = "10.18653/v1/P16-2059",
    pages = "362--368",
}
@inproceedings{ghaeini-etal-2016-event,
    title = "Event Nugget Detection with Forward-Backward Recurrent Neural Networks",
    author = "Ghaeini, Reza  and
      Fern, Xiaoli  and
      Huang, Liang  and
      Tadepalli, Prasad",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2060",
    doi = "10.18653/v1/P16-2060",
    pages = "369--373",
}
@inproceedings{zukov-gregoric-etal-2016-ibc,
    title = "{IBC}-{C}: A Dataset for Armed Conflict Analysis",
    author = "{\v{Z}}ukov-Gregori{\v{c}}, Andrej  and
      Luo, Zhiyuan  and
      Veyhe, Bartal",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2061",
    doi = "10.18653/v1/P16-2061",
    pages = "374--379",
}
@inproceedings{hu-tsujii-2016-latent,
    title = "A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings",
    author = "Hu, Weihua  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2062",
    doi = "10.18653/v1/P16-2062",
    pages = "380--386",
}
@inproceedings{ling-etal-2016-word,
    title = "Word Embeddings with Limited Memory",
    author = "Ling, Shaoshi  and
      Song, Yangqiu  and
      Roth, Dan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2063",
    doi = "10.18653/v1/P16-2063",
    pages = "387--392",
}
@inproceedings{lukasik-etal-2016-hawkes,
    title = "{H}awkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in {T}witter",
    author = "Lukasik, Michal  and
      Srijith, P. K.  and
      Vu, Duy  and
      Bontcheva, Kalina  and
      Zubiaga, Arkaitz  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2064",
    doi = "10.18653/v1/P16-2064",
    pages = "393--398",
}
@inproceedings{mihaylov-nakov-2016-hunting,
    title = "Hunting for Troll Comments in News Community Forums",
    author = "Mihaylov, Todor  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2065",
    doi = "10.18653/v1/P16-2065",
    pages = "399--405",
}
@inproceedings{nishino-etal-2016-phrase,
    title = "Phrase Table Pruning via Submodular Function Maximization",
    author = "Nishino, Masaaki  and
      Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2066",
    doi = "10.18653/v1/P16-2066",
    pages = "406--411",
}
@inproceedings{plank-etal-2016-multilingual,
    title = "Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss",
    author = "Plank, Barbara  and
      S{\o}gaard, Anders  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2067",
    doi = "10.18653/v1/P16-2067",
    pages = "412--418",
}
@inproceedings{salle-etal-2016-matrix,
    title = "Matrix Factorization using Window Sampling and Negative Sampling for Improved Word Representations",
    author = "Salle, Alexandre  and
      Villavicencio, Aline  and
      Idiart, Marco",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2068",
    doi = "10.18653/v1/P16-2068",
    pages = "419--424",
}
@inproceedings{vilares-etal-2016-one,
    title = "One model, two languages: training bilingual parsers with harmonized treebanks",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos  and
      Alonso, Miguel A.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2069",
    doi = "10.18653/v1/P16-2069",
    pages = "425--431",
}
@inproceedings{webster-nothman-2016-using,
    title = "Using mention accessibility to improve coreference resolution",
    author = "Webster, Kellie  and
      Nothman, Joel",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2070",
    doi = "10.18653/v1/P16-2070",
    pages = "432--437",
}
@inproceedings{woods-2016-exploiting,
    title = "Exploiting Linguistic Features for Sentence Completion",
    author = "Woods, Aubrie",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2071",
    doi = "10.18653/v1/P16-2071",
    pages = "438--442",
}
@inproceedings{xu-reitter-2016-convergence,
    title = "Convergence of Syntactic Complexity in Conversation",
    author = "Xu, Yang  and
      Reitter, David",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2072",
    doi = "10.18653/v1/P16-2072",
    pages = "443--448",
}
@inproceedings{yu-etal-2016-user,
    title = "User Embedding for Scholarly Microblog Recommendation",
    author = "Yu, Yang  and
      Wan, Xiaojun  and
      Zhou, Xinjie",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2073",
    doi = "10.18653/v1/P16-2073",
    pages = "449--453",
}
@inproceedings{nguyen-etal-2016-integrating,
    title = "Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction",
    author = "Nguyen, Kim Anh  and
      Schulte im Walde, Sabine  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2074",
    doi = "10.18653/v1/P16-2074",
    pages = "454--459",
}
@inproceedings{guzman-etal-2016-machine-translation,
    title = "Machine Translation Evaluation Meets Community Question Answering",
    author = "Guzm{\'a}n, Francisco  and
      M{\`a}rquez, Llu{\'\i}s  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2075",
    doi = "10.18653/v1/P16-2075",
    pages = "460--466",
}
@inproceedings{sachan-etal-2016-science,
    title = "Science Question Answering using Instructional Materials",
    author = "Sachan, Mrinmaya  and
      Dubey, Kumar  and
      Xing, Eric",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2076",
    doi = "10.18653/v1/P16-2076",
    pages = "467--473",
}
@inproceedings{stanovsky-etal-2016-specifying,
    title = "Specifying and Annotating Reduced Argument Span Via {QA}-{SRL}",
    author = "Stanovsky, Gabriel  and
      Dagan, Ido  and
      Adler, Meni",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2077",
    doi = "10.18653/v1/P16-2077",
    pages = "474--478",
}
@inproceedings{fang-teufel-2016-improving,
    title = "Improving Argument Overlap for Proposition-Based Summarisation",
    author = "Fang, Yimai  and
      Teufel, Simone",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2078",
    doi = "10.18653/v1/P16-2078",
    pages = "479--485",
}
@inproceedings{sachan-xing-2016-machine,
    title = "Machine Comprehension using Rich Semantic Representations",
    author = "Sachan, Mrinmaya  and
      Xing, Eric",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2079",
    doi = "10.18653/v1/P16-2079",
    pages = "486--492",
}
@inproceedings{oshikiri-etal-2016-cross,
    title = "Cross-Lingual Word Representations via Spectral Graph Embeddings",
    author = "Oshikiri, Takamasa  and
      Fukui, Kazuki  and
      Shimodaira, Hidetoshi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2080",
    doi = "10.18653/v1/P16-2080",
    pages = "493--498",
}
@inproceedings{rodriguez-fernandez-etal-2016-semantics,
    title = "Semantics-Driven Recognition of Collocations Using Word Embeddings",
    author = "Rodr{\'\i}guez-Fern{\'a}ndez, Sara  and
      Espinosa-Anke, Luis  and
      Carlini, Roberto  and
      Wanner, Leo",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2081",
    doi = "10.18653/v1/P16-2081",
    pages = "499--505",
}
@inproceedings{kumar-araki-2016-incorporating,
    title = "Incorporating Relational Knowledge into Word Representations using Subspace Regularization",
    author = "Kumar, Abhishek  and
      Araki, Jun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2082",
    doi = "10.18653/v1/P16-2082",
    pages = "506--511",
}
@inproceedings{rothe-schutze-2016-word,
    title = "Word Embedding Calculus in Meaningful Ultradense Subspaces",
    author = {Rothe, Sascha  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2083",
    doi = "10.18653/v1/P16-2083",
    pages = "512--517",
}
@inproceedings{vulic-korhonen-2016-universal,
    title = "Is {``}Universal Syntax{''} Universally Useful for Learning Distributed Word Representations?",
    author = "Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2084",
    doi = "10.18653/v1/P16-2084",
    pages = "518--524",
}
@inproceedings{bilu-slonim-2016-claim,
    title = "Claim Synthesis via Predicate Recycling",
    author = "Bilu, Yonatan  and
      Slonim, Noam",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2085",
    doi = "10.18653/v1/P16-2085",
    pages = "525--530",
}
@inproceedings{yung-etal-2016-modelling-interpretation,
    title = "Modelling the Interpretation of Discourse Connectives by {B}ayesian Pragmatics",
    author = "Yung, Frances  and
      Duh, Kevin  and
      Komura, Taku  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2086",
    doi = "10.18653/v1/P16-2086",
    pages = "531--536",
}
@inproceedings{batmanghelich-etal-2016-nonparametric,
    title = "Nonparametric Spherical Topic Modeling with Word Embeddings",
    author = "Batmanghelich, Kayhan  and
      Saeedi, Ardavan  and
      Narasimhan, Karthik  and
      Gershman, Sam",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2087",
    doi = "10.18653/v1/P16-2087",
    pages = "537--542",
}
@inproceedings{morstatter-liu-2016-novel,
    title = "A Novel Measure for Coherence in Statistical Topic Models",
    author = "Morstatter, Fred  and
      Liu, Huan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2088",
    doi = "10.18653/v1/P16-2088",
    pages = "543--548",
}
@inproceedings{ghosh-etal-2016-coarse,
    title = "Coarse-grained Argumentation Features for Scoring Persuasive Essays",
    author = "Ghosh, Debanjan  and
      Khanam, Aquila  and
      Han, Yubo  and
      Muresan, Smaranda",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2089",
    doi = "10.18653/v1/P16-2089",
    pages = "549--554",
}
@inproceedings{kann-schutze-2016-single,
    title = "Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection",
    author = {Kann, Katharina  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2090",
    doi = "10.18653/v1/P16-2090",
    pages = "555--560",
}
@inproceedings{johannsen-etal-2016-joint,
    title = "Joint part-of-speech and dependency projection from multiple sources",
    author = "Johannsen, Anders  and
      Agi{\'c}, {\v{Z}}eljko  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2091",
    doi = "10.18653/v1/P16-2091",
    pages = "561--566",
}
@inproceedings{xu-sun-2016-dependency,
    title = "Dependency-based Gated Recursive Neural Network for {C}hinese Word Segmentation",
    author = "Xu, Jingjing  and
      Sun, Xu",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2092",
    doi = "10.18653/v1/P16-2092",
    pages = "567--572",
}
@inproceedings{legrand-collobert-2016-deep,
    title = "Deep Neural Networks for Syntactic Parsing of Morphologically Rich Languages",
    author = {Legrand, Jo{\"e}l  and
      Collobert, Ronan},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2093",
    doi = "10.18653/v1/P16-2093",
    pages = "573--578",
}
@inproceedings{barrett-etal-2016-weakly,
    title = "Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data",
    author = "Barrett, Maria  and
      Bingel, Joachim  and
      Keller, Frank  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2094",
    doi = "10.18653/v1/P16-2094",
    pages = "579--584",
}
@inproceedings{logacheva-etal-2016-metrics,
    title = "Metrics for Evaluation of Word-level Machine Translation Quality Estimation",
    author = "Logacheva, Varvara  and
      Lukasik, Michal  and
      Specia, Lucia",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2095",
    doi = "10.18653/v1/P16-2095",
    pages = "585--590",
}
@inproceedings{hovy-spruit-2016-social,
    title = "The Social Impact of Natural Language Processing",
    author = "Hovy, Dirk  and
      Spruit, Shannon L.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2096",
    doi = "10.18653/v1/P16-2096",
    pages = "591--598",
}
@inproceedings{list-etal-2016-using,
    title = "Using Sequence Similarity Networks to Identify Partial Cognates in Multilingual Wordlists",
    author = "List, Johann-Mattis  and
      Lopez, Philippe  and
      Bapteste, Eric",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-2097",
    doi = "10.18653/v1/P16-2097",
    pages = "599--605",
}
@proceedings{acl-2016-association,
    title = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    editor = "Erk, Katrin  and
      Smith, Noah A.",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1000",
    doi = "10.18653/v1/P16-1",
}
@inproceedings{goodman-etal-2016-noise,
    title = "Noise reduction and targeted exploration in imitation learning for {A}bstract {M}eaning {R}epresentation parsing",
    author = "Goodman, James  and
      Vlachos, Andreas  and
      Naradowsky, Jason",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1001",
    doi = "10.18653/v1/P16-1001",
    pages = "1--11",
}
@inproceedings{jia-liang-2016-data,
    title = "Data Recombination for Neural Semantic Parsing",
    author = "Jia, Robin  and
      Liang, Percy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1002",
    doi = "10.18653/v1/P16-1002",
    pages = "12--22",
}
@inproceedings{pasupat-liang-2016-inferring,
    title = "Inferring Logical Forms From Denotations",
    author = "Pasupat, Panupong  and
      Liang, Percy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1003",
    doi = "10.18653/v1/P16-1003",
    pages = "23--32",
}
@inproceedings{dong-lapata-2016-language,
    title = "Language to Logical Form with Neural Attention",
    author = "Dong, Li  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1004",
    doi = "10.18653/v1/P16-1004",
    pages = "33--43",
}
@inproceedings{yu-ji-2016-unsupervised,
    title = "Unsupervised Person Slot Filling based on Graph Mining",
    author = "Yu, Dian  and
      Ji, Heng",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1005",
    doi = "10.18653/v1/P16-1005",
    pages = "44--53",
}
@inproceedings{lu-etal-2016-multi-media,
    title = "A Multi-media Approach to Cross-lingual Entity Knowledge Transfer",
    author = "Lu, Di  and
      Pan, Xiaoman  and
      Pourdamghani, Nima  and
      Chang, Shih-Fu  and
      Ji, Heng  and
      Knight, Kevin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1006",
    doi = "10.18653/v1/P16-1006",
    pages = "54--65",
}
@inproceedings{wuebker-etal-2016-models,
    title = "Models and Inference for Prefix-Constrained Machine Translation",
    author = "Wuebker, Joern  and
      Green, Spence  and
      DeNero, John  and
      Hasan, Sa{\v{s}}a  and
      Luong, Minh-Thang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1007",
    doi = "10.18653/v1/P16-1007",
    pages = "66--75",
}
@inproceedings{tu-etal-2016-modeling,
    title = "Modeling Coverage for Neural Machine Translation",
    author = "Tu, Zhaopeng  and
      Lu, Zhengdong  and
      Liu, Yang  and
      Liu, Xiaohua  and
      Li, Hang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1008",
    doi = "10.18653/v1/P16-1008",
    pages = "76--85",
}
@inproceedings{sennrich-etal-2016-improving,
    title = "Improving Neural Machine Translation Models with Monolingual Data",
    author = "Sennrich, Rico  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1009",
    doi = "10.18653/v1/P16-1009",
    pages = "86--96",
}
@inproceedings{li-etal-2016-graph,
    title = "Graph-Based Translation Via Graph Segmentation",
    author = "Li, Liangyou  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1010",
    doi = "10.18653/v1/P16-1010",
    pages = "97--107",
}
@inproceedings{she-chai-2016-incremental,
    title = "Incremental Acquisition of Verb Hypothesis Space towards Physical World Interaction",
    author = "She, Lanbo  and
      Chai, Joyce",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1011",
    doi = "10.18653/v1/P16-1011",
    pages = "108--117",
}
@inproceedings{hintz-biemann-2016-language,
    title = "Language Transfer Learning for Supervised Lexical Substitution",
    author = "Hintz, Gerold  and
      Biemann, Chris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1012",
    doi = "10.18653/v1/P16-1012",
    pages = "118--129",
}
@inproceedings{tsvetkov-etal-2016-learning,
    title = "Learning the Curriculum with {B}ayesian Optimization for Task-Specific Word Representation Learning",
    author = "Tsvetkov, Yulia  and
      Faruqui, Manaal  and
      Ling, Wang  and
      MacWhinney, Brian  and
      Dyer, Chris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1013",
    doi = "10.18653/v1/P16-1013",
    pages = "130--139",
}
@inproceedings{gulcehre-etal-2016-pointing,
    title = "Pointing the Unknown Words",
    author = "Gulcehre, Caglar  and
      Ahn, Sungjin  and
      Nallapati, Ramesh  and
      Zhou, Bowen  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1014",
    doi = "10.18653/v1/P16-1014",
    pages = "140--149",
}
@inproceedings{bohnet-etal-2016-generalized,
    title = "Generalized Transition-based Dependency Parsing via Control Parameters",
    author = "Bohnet, Bernd  and
      McDonald, Ryan  and
      Pitler, Emily  and
      Ma, Ji",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1015",
    doi = "10.18653/v1/P16-1015",
    pages = "150--160",
}
@inproceedings{constant-nivre-2016-transition,
    title = "A Transition-Based System for Joint Lexical and Syntactic Analysis",
    author = "Constant, Matthieu  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1016",
    doi = "10.18653/v1/P16-1016",
    pages = "161--171",
}
@inproceedings{coavoux-crabbe-2016-neural,
    title = "Neural Greedy Constituent Parsing with Dynamic Oracles",
    author = "Coavoux, Maximin  and
      Crabb{\'e}, Beno{\^\i}t",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1017",
    doi = "10.18653/v1/P16-1017",
    pages = "172--182",
}
@inproceedings{gutierrez-etal-2016-literal,
    title = "Literal and Metaphorical Senses in Compositional Distributional Semantic Models",
    author = "Guti{\'e}rrez, E. Dario  and
      Shutova, Ekaterina  and
      Marghetis, Tyler  and
      Bergen, Benjamin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1018",
    doi = "10.18653/v1/P16-1018",
    pages = "183--193",
}
@inproceedings{salton-etal-2016-idiom,
    title = "Idiom Token Classification using Sentential Distributed Semantics",
    author = "Salton, Giancarlo  and
      Ross, Robert  and
      Kelleher, John",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1019",
    doi = "10.18653/v1/P16-1019",
    pages = "194--204",
}
@inproceedings{hashimoto-tsuruoka-2016-adaptive,
    title = "Adaptive Joint Learning of Compositional and Non-Compositional Phrase Embeddings",
    author = "Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1020",
    doi = "10.18653/v1/P16-1020",
    pages = "205--215",
}
@inproceedings{jang-etal-2016-metaphor,
    title = "Metaphor Detection with Topic Transition, Emotion and Cognition in Context",
    author = "Jang, Hyeju  and
      Jo, Yohan  and
      Shen, Qinlan  and
      Miller, Michael  and
      Moon, Seungwhan  and
      Ros{\'e}, Carolyn",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1021",
    doi = "10.18653/v1/P16-1021",
    pages = "216--225",
}
@inproceedings{chen-etal-2016-compressing,
    title = "Compressing Neural Language Models by Sparse Word Representations",
    author = "Chen, Yunchuan  and
      Mou, Lili  and
      Xu, Yan  and
      Li, Ge  and
      Jin, Zhi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1022",
    doi = "10.18653/v1/P16-1022",
    pages = "226--235",
}
@inproceedings{yaghoobzadeh-schutze-2016-intrinsic,
    title = "Intrinsic Subspace Evaluation of Word Embedding Representations",
    author = {Yaghoobzadeh, Yadollah  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1023",
    doi = "10.18653/v1/P16-1023",
    pages = "236--246",
}
@inproceedings{vulic-korhonen-2016-role,
    title = "On the Role of Seed Lexicons in Learning Bilingual Word Embeddings",
    author = "Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1024",
    doi = "10.18653/v1/P16-1024",
    pages = "247--257",
}
@inproceedings{huang-etal-2016-liberal,
    title = "Liberal Event Extraction and Event Schema Induction",
    author = "Huang, Lifu  and
      Cassidy, Taylor  and
      Feng, Xiaocheng  and
      Ji, Heng  and
      Voss, Clare R.  and
      Han, Jiawei  and
      Sil, Avirup",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1025",
    doi = "10.18653/v1/P16-1025",
    pages = "258--268",
}
@inproceedings{zhou-etal-2016-jointly,
    title = "Jointly Event Extraction and Visualization on {T}witter via Probabilistic Modelling",
    author = "Zhou, Deyu  and
      Gao, Tianmeng  and
      He, Yulan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1026",
    doi = "10.18653/v1/P16-1026",
    pages = "269--278",
}
@inproceedings{pichotta-mooney-2016-using,
    title = "Using Sentence-Level {LSTM} Language Models for Script Inference",
    author = "Pichotta, Karl  and
      Mooney, Raymond J.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1027",
    doi = "10.18653/v1/P16-1027",
    pages = "279--289",
}
@inproceedings{peng-roth-2016-two,
    title = "Two Discourse Driven Language Models for Semantics",
    author = "Peng, Haoruo  and
      Roth, Dan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1028",
    doi = "10.18653/v1/P16-1028",
    pages = "290--300",
}
@inproceedings{wu-huang-2016-sentiment,
    title = "Sentiment Domain Adaptation with Multiple Sources",
    author = "Wu, Fangzhao  and
      Huang, Yongfeng",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1029",
    doi = "10.18653/v1/P16-1029",
    pages = "301--310",
}
@inproceedings{rashkin-etal-2016-connotation,
    title = "Connotation Frames: A Data-Driven Investigation",
    author = "Rashkin, Hannah  and
      Singh, Sameer  and
      Choi, Yejin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1030",
    doi = "10.18653/v1/P16-1030",
    pages = "311--321",
}
@inproceedings{zhou-etal-2016-bi,
    title = "Bi-Transferring Deep Neural Networks for Domain Adaptation",
    author = "Zhou, Guangyou  and
      Xie, Zhiwen  and
      Huang, Jimmy Xiangji  and
      He, Tingting",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1031",
    doi = "10.18653/v1/P16-1031",
    pages = "322--332",
}
@inproceedings{choi-etal-2016-document,
    title = "Document-level Sentiment Inference with Social, Faction, and Discourse Context",
    author = "Choi, Eunsol  and
      Rashkin, Hannah  and
      Zettlemoyer, Luke  and
      Choi, Yejin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1032",
    doi = "10.18653/v1/P16-1032",
    pages = "333--343",
}
@inproceedings{li-etal-2016-active,
    title = "Active Learning for Dependency Parsing with Partial Annotation",
    author = "Li, Zhenghua  and
      Zhang, Min  and
      Zhang, Yue  and
      Liu, Zhanyi  and
      Chen, Wenliang  and
      Wu, Hua  and
      Wang, Haifeng",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1033",
    doi = "10.18653/v1/P16-1033",
    pages = "344--354",
}
@inproceedings{corro-etal-2016-dependency,
    title = "Dependency Parsing with Bounded Block Degree and Well-nestedness via {L}agrangian Relaxation and Branch-and-Bound",
    author = "Corro, Caio  and
      Le Roux, Joseph  and
      Lacroix, Mathieu  and
      Rozenknop, Antoine  and
      Wolfler Calvo, Roberto",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1034",
    doi = "10.18653/v1/P16-1034",
    pages = "355--366",
}
@inproceedings{diaz-etal-2016-query,
    title = "Query Expansion with Locally-Trained Word Embeddings",
    author = "Diaz, Fernando  and
      Mitra, Bhaskar  and
      Craswell, Nick",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1035",
    doi = "10.18653/v1/P16-1035",
    pages = "367--377",
}
@inproceedings{das-etal-2016-together,
    title = "Together we stand: {S}iamese Networks for Similar Question Retrieval",
    author = "Das, Arpita  and
      Yenala, Harish  and
      Chinnakotla, Manoj  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1036",
    doi = "10.18653/v1/P16-1036",
    pages = "378--387",
}
@inproceedings{peng-etal-2016-news,
    title = "News Citation Recommendation with Implicit and Explicit Semantics",
    author = "Peng, Hao  and
      Liu, Jing  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1037",
    doi = "10.18653/v1/P16-1037",
    pages = "388--398",
}
@inproceedings{deri-knight-2016-grapheme,
    title = "Grapheme-to-Phoneme Models for (Almost) Any Language",
    author = "Deri, Aliya  and
      Knight, Kevin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1038",
    doi = "10.18653/v1/P16-1038",
    pages = "399--408",
}
@inproceedings{cai-zhao-2016-neural,
    title = "Neural Word Segmentation Learning for {C}hinese",
    author = "Cai, Deng  and
      Zhao, Hai",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1039",
    doi = "10.18653/v1/P16-1039",
    pages = "409--420",
}
@inproceedings{zhang-etal-2016-transition,
    title = "Transition-Based Neural Word Segmentation",
    author = "Zhang, Meishan  and
      Zhang, Yue  and
      Fu, Guohong",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1040",
    doi = "10.18653/v1/P16-1040",
    pages = "421--431",
}
@inproceedings{trischler-etal-2016-parallel,
    title = "A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data",
    author = "Trischler, Adam  and
      Ye, Zheng  and
      Yuan, Xingdi  and
      He, Jing  and
      Bachman, Philip",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1041",
    doi = "10.18653/v1/P16-1041",
    pages = "432--441",
}
@inproceedings{angeli-etal-2016-combining,
    title = "Combining Natural Logic and Shallow Reasoning for Question Answering",
    author = "Angeli, Gabor  and
      Nayak, Neha  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1042",
    doi = "10.18653/v1/P16-1042",
    pages = "442--452",
}
@inproceedings{sachan-xing-2016-easy,
    title = "Easy Questions First? A Case Study on Curriculum Learning for Question Answering",
    author = "Sachan, Mrinmaya  and
      Xing, Eric",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1043",
    doi = "10.18653/v1/P16-1043",
    pages = "453--463",
}
@inproceedings{tan-etal-2016-improved,
    title = "Improved Representation Learning for Question Answer Matching",
    author = "Tan, Ming  and
      dos Santos, Cicero  and
      Xiang, Bing  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1044",
    doi = "10.18653/v1/P16-1044",
    pages = "464--473",
}
@inproceedings{jauhar-etal-2016-tables,
    title = "Tables as Semi-structured Knowledge for Question Answering",
    author = "Jauhar, Sujay Kumar  and
      Turney, Peter  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1045",
    doi = "10.18653/v1/P16-1045",
    pages = "474--483",
}
@inproceedings{cheng-lapata-2016-neural,
    title = "Neural Summarization by Extracting Sentences and Words",
    author = "Cheng, Jianpeng  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1046",
    doi = "10.18653/v1/P16-1046",
    pages = "484--494",
}
@inproceedings{fancellu-etal-2016-neural,
    title = "Neural Networks For Negation Scope Detection",
    author = "Fancellu, Federico  and
      Lopez, Adam  and
      Webber, Bonnie",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1047",
    doi = "10.18653/v1/P16-1047",
    pages = "495--504",
}
@inproceedings{wang-etal-2016-cse,
    title = "{CSE}: Conceptual Sentence Embeddings based on Attention Model",
    author = "Wang, Yashen  and
      Huang, Heyan  and
      Feng, Chong  and
      Zhou, Qiang  and
      Gu, Jiahui  and
      Gao, Xiong",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1048",
    doi = "10.18653/v1/P16-1048",
    pages = "505--515",
}
@inproceedings{yan-etal-2016-docchat,
    title = "{D}oc{C}hat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents",
    author = "Yan, Zhao  and
      Duan, Nan  and
      Bao, Junwei  and
      Chen, Peng  and
      Zhou, Ming  and
      Li, Zhoujun  and
      Zhou, Jianshe",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1049",
    doi = "10.18653/v1/P16-1049",
    pages = "516--525",
}
@inproceedings{doyle-frank-2016-investigating,
    title = "Investigating the Sources of Linguistic Alignment in Conversation",
    author = "Doyle, Gabriel  and
      Frank, Michael C.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1050",
    doi = "10.18653/v1/P16-1050",
    pages = "526--536",
}
@inproceedings{xu-reitter-2016-entropy,
    title = "Entropy Converges Between Dialogue Participants: Explanations from an Information-Theoretic Perspective",
    author = "Xu, Yang  and
      Reitter, David",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1051",
    doi = "10.18653/v1/P16-1051",
    pages = "537--546",
}
@inproceedings{janzen-etal-2016-finding,
    title = "Finding the Middle Ground - A Model for Planning Satisficing Answers",
    author = "Janzen, Sabine  and
      Maa{\ss}, Wolfgang  and
      Kowatsch, Tobias",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1052",
    doi = "10.18653/v1/P16-1052",
    pages = "547--557",
}
@inproceedings{liu-huang-2016-sentence,
    title = "A Sentence Interaction Network for Modeling Dependence between Sentences",
    author = "Liu, Biao  and
      Huang, Minlie",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1053",
    doi = "10.18653/v1/P16-1053",
    pages = "558--567",
}
@inproceedings{castro-ferreira-etal-2016-towards-variation,
    title = "Towards more variation in text generation: Developing and evaluating variation models for choice of referential form",
    author = "Castro Ferreira, Thiago  and
      Krahmer, Emiel  and
      Wubben, Sander",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1054",
    doi = "10.18653/v1/P16-1054",
    pages = "568--577",
}
@inproceedings{chaganty-liang-2016-much,
    title = "How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions",
    author = "Chaganty, Arun  and
      Liang, Percy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1055",
    doi = "10.18653/v1/P16-1055",
    pages = "578--587",
}
@inproceedings{serban-etal-2016-generating,
    title = "Generating Factoid Questions With Recurrent Neural Networks: The 30{M} Factoid Question-Answer Corpus",
    author = "Serban, Iulian Vlad  and
      Garc{\'\i}a-Dur{\'a}n, Alberto  and
      Gulcehre, Caglar  and
      Ahn, Sungjin  and
      Chandar, Sarath  and
      Courville, Aaron  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1056",
    doi = "10.18653/v1/P16-1056",
    pages = "588--598",
}
@inproceedings{ling-etal-2016-latent,
    title = "Latent Predictor Networks for Code Generation",
    author = "Ling, Wang  and
      Blunsom, Phil  and
      Grefenstette, Edward  and
      Hermann, Karl Moritz  and
      Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}}  and
      Wang, Fumin  and
      Senior, Andrew",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1057",
    doi = "10.18653/v1/P16-1057",
    pages = "599--609",
}
@inproceedings{zarriess-schlangen-2016-easy,
    title = "Easy Things First: Installments Improve Referring Expression Generation for Objects in Photographs",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1058",
    doi = "10.18653/v1/P16-1058",
    pages = "610--620",
}
@inproceedings{globerson-etal-2016-collective,
    title = "Collective Entity Resolution with Multi-Focal Attention",
    author = "Globerson, Amir  and
      Lazic, Nevena  and
      Chakrabarti, Soumen  and
      Subramanya, Amarnag  and
      Ringgaard, Michael  and
      Pereira, Fernando",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1059",
    doi = "10.18653/v1/P16-1059",
    pages = "621--631",
}
@inproceedings{moosavi-strube-2016-coreference,
    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1060",
    doi = "10.18653/v1/P16-1060",
    pages = "632--642",
}
@inproceedings{clark-manning-2016-improving,
    title = "Improving Coreference Resolution by Learning Entity-Level Distributed Representations",
    author = "Clark, Kevin  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1061",
    doi = "10.18653/v1/P16-1061",
    pages = "643--653",
}
@inproceedings{finegan-dollak-etal-2016-effects,
    title = "Effects of Creativity and Cluster Tightness on Short Text Clustering Performance",
    author = "Finegan-Dollak, Catherine  and
      Coke, Reed  and
      Zhang, Rui  and
      Ye, Xiangyi  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1062",
    doi = "10.18653/v1/P16-1062",
    pages = "654--665",
}
@inproceedings{li-etal-2016-generative,
    title = "Generative Topic Embedding: a Continuous Representation of Documents",
    author = "Li, Shaohua  and
      Chua, Tat-Seng  and
      Zhu, Jun  and
      Miao, Chunyan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1063",
    doi = "10.18653/v1/P16-1063",
    pages = "666--675",
}
@inproceedings{shi-etal-2016-detecting,
    title = "Detecting Common Discussion Topics Across Culture From News Reader Comments",
    author = "Shi, Bei  and
      Lam, Wai  and
      Bing, Lidong  and
      Xu, Yinqing",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1064",
    doi = "10.18653/v1/P16-1064",
    pages = "676--685",
}
@inproceedings{yang-etal-2016-discriminative,
    title = "A Discriminative Topic Model using Document Network Structure",
    author = "Yang, Weiwei  and
      Boyd-Graber, Jordan  and
      Resnik, Philip",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1065",
    doi = "10.18653/v1/P16-1065",
    pages = "686--696",
}
@inproceedings{al-twairesh-etal-2016-arasenti,
    title = "{A}ra{S}en{T}i: Large-Scale {T}witter-Specific {A}rabic Sentiment Lexicons",
    author = "Al-Twairesh, Nora  and
      Al-Khalifa, Hend  and
      Al-Salman, Abdulmalik",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1066",
    doi = "10.18653/v1/P16-1066",
    pages = "697--705",
}
@inproceedings{aldebei-etal-2016-unsupervised,
    title = "Unsupervised Multi-Author Document Decomposition Based on Hidden {M}arkov Model",
    author = "Aldebei, Khaled  and
      He, Xiangjian  and
      Jia, Wenjing  and
      Yang, Jie",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1067",
    doi = "10.18653/v1/P16-1067",
    pages = "706--714",
}
@inproceedings{alikaniotis-etal-2016-automatic,
    title = "Automatic Text Scoring Using Neural Networks",
    author = "Alikaniotis, Dimitrios  and
      Yannakoudakis, Helen  and
      Rei, Marek",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1068",
    doi = "10.18653/v1/P16-1068",
    pages = "715--725",
}
@inproceedings{beltagy-quirk-2016-improved,
    title = "Improved Semantic Parsers For If-Then Statements",
    author = "Beltagy, I.  and
      Quirk, Chris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1069",
    doi = "10.18653/v1/P16-1069",
    pages = "726--736",
}
@inproceedings{berzak-etal-2016-universal,
    title = "{U}niversal {D}ependencies for Learner {E}nglish",
    author = "Berzak, Yevgeni  and
      Kenney, Jessica  and
      Spadine, Carolyn  and
      Wang, Jing Xian  and
      Lam, Lucia  and
      Mori, Keiko Sophie  and
      Garza, Sebastian  and
      Katz, Boris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1070",
    doi = "10.18653/v1/P16-1070",
    pages = "737--746",
}
@inproceedings{bingel-etal-2016-extracting,
    title = "Extracting token-level signals of syntactic processing from f{MRI} - with an application to {P}o{S} induction",
    author = "Bingel, Joachim  and
      Barrett, Maria  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1071",
    doi = "10.18653/v1/P16-1071",
    pages = "747--755",
}
@inproceedings{cai-etal-2016-bidirectional,
    title = "Bidirectional Recurrent Convolutional Neural Network for Relation Classification",
    author = "Cai, Rui  and
      Zhang, Xiaodong  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1072",
    doi = "10.18653/v1/P16-1072",
    pages = "756--765",
}
@inproceedings{chen-etal-2016-sentence,
    title = "Sentence Rewriting for Semantic Parsing",
    author = "Chen, Bo  and
      Sun, Le  and
      Han, Xianpei  and
      An, Bo",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1073",
    doi = "10.18653/v1/P16-1073",
    pages = "766--777",
}
@inproceedings{chen-ng-2016-chinese,
    title = "{C}hinese Zero Pronoun Resolution with Deep Neural Networks",
    author = "Chen, Chen  and
      Ng, Vincent",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1074",
    doi = "10.18653/v1/P16-1074",
    pages = "778--788",
}
@inproceedings{cummins-etal-2016-constrained,
    title = "Constrained Multi-Task Learning for Automated Essay Scoring",
    author = "Cummins, Ronan  and
      Zhang, Meng  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1075",
    doi = "10.18653/v1/P16-1075",
    pages = "789--799",
}
@inproceedings{dai-etal-2016-cfo,
    title = "{CFO}: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases",
    author = "Dai, Zihang  and
      Li, Lei  and
      Xu, Wei",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1076",
    doi = "10.18653/v1/P16-1076",
    pages = "800--810",
}
@inproceedings{eckle-kohler-2016-verbs,
    title = "Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality {--} Revisiting the Issue of Meaning Grounded in Syntax",
    author = "Eckle-Kohler, Judith",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1077",
    doi = "10.18653/v1/P16-1077",
    pages = "811--822",
}
@inproceedings{eriguchi-etal-2016-tree,
    title = "Tree-to-Sequence Attentional Neural Machine Translation",
    author = "Eriguchi, Akiko  and
      Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1078",
    doi = "10.18653/v1/P16-1078",
    pages = "823--833",
}
@inproceedings{ficler-goldberg-2016-coordination,
    title = "Coordination Annotation Extension in the {P}enn {T}ree {B}ank",
    author = "Ficler, Jessica  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1079",
    doi = "10.18653/v1/P16-1079",
    pages = "834--842",
}
@inproceedings{flekova-etal-2016-analyzing,
    title = "Analyzing Biases in Human Perception of User Age and Gender from Text",
    author = "Flekova, Lucie  and
      Carpenter, Jordan  and
      Giorgi, Salvatore  and
      Ungar, Lyle  and
      Preo{\c{t}}iuc-Pietro, Daniel",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1080",
    doi = "10.18653/v1/P16-1080",
    pages = "843--854",
}
@inproceedings{gong-etal-2016-modeling,
    title = "Modeling Social Norms Evolution for Personalized Sentiment Classification",
    author = "Gong, Lin  and
      Al Boni, Mohammad  and
      Wang, Hongning",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1081",
    doi = "10.18653/v1/P16-1081",
    pages = "855--865",
}
@inproceedings{gordon-etal-2016-modeling,
    title = "Modeling Concept Dependencies in a Scientific Corpus",
    author = "Gordon, Jonathan  and
      Zhu, Linhong  and
      Galstyan, Aram  and
      Natarajan, Prem  and
      Burns, Gully",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1082",
    doi = "10.18653/v1/P16-1082",
    pages = "866--875",
}
@inproceedings{heafield-etal-2016-normalized,
    title = "Normalized Log-Linear Interpolation of Backoff Language Models is Efficient",
    author = "Heafield, Kenneth  and
      Geigle, Chase  and
      Massung, Sean  and
      Schwartz, Lane",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1083",
    doi = "10.18653/v1/P16-1083",
    pages = "876--886",
}
@inproceedings{huang-etal-2016-well,
    title = "How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation",
    author = "Huang, Danqing  and
      Shi, Shuming  and
      Lin, Chin-Yew  and
      Yin, Jian  and
      Ma, Wei-Ying",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1084",
    doi = "10.18653/v1/P16-1084",
    pages = "887--896",
}
@inproceedings{iacobacci-etal-2016-embeddings,
    title = "Embeddings for Word Sense Disambiguation: An Evaluation Study",
    author = "Iacobacci, Ignacio  and
      Pilehvar, Mohammad Taher  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1085",
    doi = "10.18653/v1/P16-1085",
    pages = "897--907",
}
@inproceedings{kadlec-etal-2016-text,
    title = "Text Understanding with the Attention Sum Reader Network",
    author = "Kadlec, Rudolf  and
      Schmid, Martin  and
      Bajgar, Ondrej  and
      Kleindienst, Jan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1086",
    doi = "10.18653/v1/P16-1086",
    pages = "908--918",
}
@inproceedings{katiyar-cardie-2016-investigating,
    title = "Investigating {LSTM}s for Joint Extraction of Opinion Entities and Relations",
    author = "Katiyar, Arzoo  and
      Cardie, Claire",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1087",
    doi = "10.18653/v1/P16-1087",
    pages = "919--929",
}
@inproceedings{kato-matsubara-2016-transition,
    title = "Transition-Based Left-Corner Parsing for Identifying {PTB}-Style Nonlocal Dependencies",
    author = "Kato, Yoshihide  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1088",
    doi = "10.18653/v1/P16-1088",
    pages = "930--940",
}
@inproceedings{kenter-etal-2016-siamese,
    title = "{S}iamese {CBOW}: Optimizing Word Embeddings for Sentence Representations",
    author = "Kenter, Tom  and
      Borisov, Alexey  and
      de Rijke, Maarten",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1089",
    doi = "10.18653/v1/P16-1089",
    pages = "941--951",
}
@inproceedings{khani-etal-2016-unanimous,
    title = "Unanimous Prediction for 100{\%} Precision with Application to Learning Semantic Mappings",
    author = "Khani, Fereshte  and
      Rinard, Martin  and
      Liang, Percy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1090",
    doi = "10.18653/v1/P16-1090",
    pages = "952--962",
}
@inproceedings{kim-etal-2016-exploring-convolutional,
    title = "Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking",
    author = "Kim, Seokhwan  and
      Banchs, Rafael  and
      Li, Haizhou",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1091",
    doi = "10.18653/v1/P16-1091",
    pages = "963--973",
}
@inproceedings{kochmar-shutova-2016-cross,
    title = "Cross-Lingual Lexico-Semantic Transfer in Language Learning",
    author = "Kochmar, Ekaterina  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1092",
    doi = "10.18653/v1/P16-1092",
    pages = "974--983",
}
@inproceedings{lee-etal-2016-call,
    title = "A {CALL} System for Learning Preposition Usage",
    author = "Lee, John  and
      Sturgeon, Donald  and
      Luo, Mengqi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1093",
    doi = "10.18653/v1/P16-1093",
    pages = "984--993",
}
@inproceedings{li-etal-2016-persona,
    title = "A Persona-Based Neural Conversation Model",
    author = "Li, Jiwei  and
      Galley, Michel  and
      Brockett, Chris  and
      Spithourakis, Georgios  and
      Gao, Jianfeng  and
      Dolan, Bill",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1094",
    doi = "10.18653/v1/P16-1094",
    pages = "994--1003",
}
@inproceedings{li-etal-2016-discriminative,
    title = "Discriminative Deep Random Walk for Network Classification",
    author = "Li, Juzheng  and
      Zhu, Jun  and
      Zhang, Bo",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1095",
    doi = "10.18653/v1/P16-1095",
    pages = "1004--1013",
}
@inproceedings{limsopatham-collier-2016-normalising,
    title = "Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation",
    author = "Limsopatham, Nut  and
      Collier, Nigel",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1096",
    doi = "10.18653/v1/P16-1096",
    pages = "1014--1023",
}
@inproceedings{liu-etal-2016-agreement-based,
    title = "Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora",
    author = "Liu, Chunyang  and
      Liu, Yang  and
      Sun, Maosong  and
      Luan, Huanbo  and
      Yu, Heng",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1097",
    doi = "10.18653/v1/P16-1097",
    pages = "1024--1033",
}
@inproceedings{liu-etal-2016-deep-fusion,
    title = "Deep Fusion {LSTM}s for Text Semantic Matching",
    author = "Liu, Pengfei  and
      Qiu, Xipeng  and
      Chen, Jifan  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1098",
    doi = "10.18653/v1/P16-1098",
    pages = "1034--1043",
}
@inproceedings{liu-etal-2016-understanding,
    title = "Understanding Discourse on Work and Job-Related Well-Being in Public Social Media",
    author = "Liu, Tong  and
      Homan, Christopher  and
      Ovesdotter Alm, Cecilia  and
      Lytle, Megan  and
      Marie White, Ann  and
      Kautz, Henry",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1099",
    doi = "10.18653/v1/P16-1099",
    pages = "1044--1053",
}
@inproceedings{luong-manning-2016-achieving,
    title = "Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models",
    author = "Luong, Minh-Thang  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1100",
    doi = "10.18653/v1/P16-1100",
    pages = "1054--1063",
}
@inproceedings{ma-hovy-2016-end,
    title = "End-to-end Sequence Labeling via Bi-directional {LSTM}-{CNN}s-{CRF}",
    author = "Ma, Xuezhe  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1101",
    doi = "10.18653/v1/P16-1101",
    pages = "1064--1074",
}
@inproceedings{malinin-etal-2016-topic,
    title = "Off-topic Response Detection for Spontaneous Spoken {E}nglish Assessment",
    author = "Malinin, Andrey  and
      Van Dalen, Rogier  and
      Knill, Kate  and
      Wang, Yu  and
      Gales, Mark",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1102",
    doi = "10.18653/v1/P16-1102",
    pages = "1075--1084",
}
@inproceedings{matthews-etal-2016-synthesizing,
    title = "Synthesizing Compound Words for Machine Translation",
    author = "Matthews, Austin  and
      Schlinger, Eva  and
      Lavie, Alon  and
      Dyer, Chris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1103",
    doi = "10.18653/v1/P16-1103",
    pages = "1085--1094",
}
@inproceedings{mishra-etal-2016-harnessing,
    title = "Harnessing Cognitive Features for Sarcasm Detection",
    author = "Mishra, Abhijit  and
      Kanojia, Diptesh  and
      Nagar, Seema  and
      Dey, Kuntal  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1104",
    doi = "10.18653/v1/P16-1104",
    pages = "1095--1104",
}
@inproceedings{miwa-bansal-2016-end,
    title = "End-to-End Relation Extraction using {LSTM}s on Sequences and Tree Structures",
    author = "Miwa, Makoto  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1105",
    doi = "10.18653/v1/P16-1105",
    pages = "1105--1116",
}
@inproceedings{nederhof-2016-short,
    title = "A short proof that O{\_}2 is an {MCFL}",
    author = "Nederhof, Mark-Jan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1106",
    doi = "10.18653/v1/P16-1106",
    pages = "1117--1126",
}
@inproceedings{nguyen-litman-2016-context,
    title = "Context-aware Argumentative Relation Mining",
    author = "Nguyen, Huy  and
      Litman, Diane",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1107",
    doi = "10.18653/v1/P16-1107",
    pages = "1127--1137",
}
@inproceedings{nicolai-kondrak-2016-leveraging,
    title = "Leveraging Inflection Tables for Stemming and Lemmatization.",
    author = "Nicolai, Garrett  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1108",
    doi = "10.18653/v1/P16-1108",
    pages = "1138--1147",
}
@inproceedings{pfeil-ray-2016-scaling,
    title = "Scaling a Natural Language Generation System",
    author = "Pfeil, Jonathan  and
      Ray, Soumya",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1109",
    doi = "10.18653/v1/P16-1109",
    pages = "1148--1157",
}
@inproceedings{poursabzi-sangdeh-etal-2016-alto,
    title = "{ALTO}: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling",
    author = "Poursabzi-Sangdeh, Forough  and
      Boyd-Graber, Jordan  and
      Findlater, Leah  and
      Seppi, Kevin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1110",
    doi = "10.18653/v1/P16-1110",
    pages = "1158--1169",
}
@inproceedings{prabhakaran-etal-2016-predicting,
    title = "Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing",
    author = "Prabhakaran, Vinodkumar  and
      Hamilton, William L.  and
      McFarland, Dan  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1111",
    doi = "10.18653/v1/P16-1111",
    pages = "1170--1180",
}
@inproceedings{rei-yannakoudakis-2016-compositional,
    title = "Compositional Sequence Labeling Models for Error Detection in Learner Writing",
    author = "Rei, Marek  and
      Yannakoudakis, Helen",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1112",
    doi = "10.18653/v1/P16-1112",
    pages = "1181--1191",
}
@inproceedings{roth-lapata-2016-neural,
    title = "Neural Semantic Role Labeling with Dependency Path Embeddings",
    author = "Roth, Michael  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1113",
    doi = "10.18653/v1/P16-1113",
    pages = "1192--1202",
}
@inproceedings{sano-etal-2016-prediction,
    title = "Prediction of Prospective User Engagement with Intelligent Assistants",
    author = "Sano, Shumpei  and
      Kaji, Nobuhiro  and
      Sassano, Manabu",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1114",
    doi = "10.18653/v1/P16-1114",
    pages = "1203--1212",
}
@inproceedings{schlangen-etal-2016-resolving,
    title = "Resolving References to Objects in Photographs using the Words-As-Classifiers Model",
    author = "Schlangen, David  and
      Zarrie{\ss}, Sina  and
      Kennington, Casey",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1115",
    doi = "10.18653/v1/P16-1115",
    pages = "1213--1223",
}
@inproceedings{sha-etal-2016-rbpb,
    title = "{RBPB}: Regularization-Based Pattern Balancing Method for Event Extraction",
    author = "Sha, Lei  and
      Liu, Jing  and
      Lin, Chin-Yew  and
      Li, Sujian  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1116",
    doi = "10.18653/v1/P16-1116",
    pages = "1224--1234",
}
@inproceedings{shibata-etal-2016-neural,
    title = "Neural Network-Based Model for {J}apanese Predicate Argument Structure Analysis",
    author = "Shibata, Tomohide  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1117",
    doi = "10.18653/v1/P16-1117",
    pages = "1235--1244",
}
@inproceedings{shivade-etal-2016-addressing,
    title = "Addressing Limited Data for Textual Entailment Across Domains",
    author = "Shivade, Chaitanya  and
      Raghavan, Preethi  and
      Patwardhan, Siddharth",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1118",
    doi = "10.18653/v1/P16-1118",
    pages = "1245--1255",
}
@inproceedings{stanovsky-dagan-2016-annotating,
    title = "Annotating and Predicting Non-Restrictive Noun Phrase Modifications",
    author = "Stanovsky, Gabriel  and
      Dagan, Ido",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1119",
    doi = "10.18653/v1/P16-1119",
    pages = "1256--1265",
}
@inproceedings{tamura-sumita-2016-bilingual,
    title = "Bilingual Segmented Topic Model",
    author = "Tamura, Akihiro  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1120",
    doi = "10.18653/v1/P16-1120",
    pages = "1266--1276",
}
@inproceedings{tian-etal-2016-learning,
    title = "Learning Semantically and Additively Compositional Distributional Representations",
    author = "Tian, Ran  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1121",
    doi = "10.18653/v1/P16-1121",
    pages = "1277--1287",
}
@inproceedings{wang-etal-2016-inner,
    title = "Inner Attention based Recurrent Neural Networks for Answer Selection",
    author = "Wang, Bingning  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1122",
    doi = "10.18653/v1/P16-1122",
    pages = "1288--1297",
}
@inproceedings{wang-etal-2016-relation,
    title = "Relation Classification via Multi-Level Attention {CNN}s",
    author = "Wang, Linlin  and
      Cao, Zhu  and
      de Melo, Gerard  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1123",
    doi = "10.18653/v1/P16-1123",
    pages = "1298--1307",
}
@inproceedings{wang-etal-2016-knowledge,
    title = "Knowledge Base Completion via Coupled Path Ranking",
    author = "Wang, Quan  and
      Liu, Jing  and
      Luo, Yuanfei  and
      Wang, Bin  and
      Lin, Chin-Yew",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1124",
    doi = "10.18653/v1/P16-1124",
    pages = "1308--1318",
}
@inproceedings{wang-cho-2016-larger,
    title = "Larger-Context Language Modelling with Recurrent Neural Network",
    author = "Wang, Tian  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1125",
    doi = "10.18653/v1/P16-1125",
    pages = "1319--1329",
}
@inproceedings{wilson-etal-2016-creation,
    title = "The Creation and Analysis of a Website Privacy Policy Corpus",
    author = "Wilson, Shomir  and
      Schaub, Florian  and
      Dara, Aswarth Abhilash  and
      Liu, Frederick  and
      Cherivirala, Sushain  and
      Giovanni Leon, Pedro  and
      Schaarup Andersen, Mads  and
      Zimmeck, Sebastian  and
      Sathyendra, Kanthashree Mysore  and
      Russell, N. Cameron  and
      Norton, Thomas B.  and
      Hovy, Eduard  and
      Reidenberg, Joel  and
      Sadeh, Norman",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1126",
    doi = "10.18653/v1/P16-1126",
    pages = "1330--1340",
}
@inproceedings{xiao-etal-2016-sequence,
    title = "Sequence-based Structured Prediction for Semantic Parsing",
    author = "Xiao, Chunyang  and
      Dymetman, Marc  and
      Gardent, Claire",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1127",
    doi = "10.18653/v1/P16-1127",
    pages = "1341--1350",
}
@inproceedings{yin-schutze-2016-learning,
    title = "Learning Word Meta-Embeddings",
    author = {Yin, Wenpeng  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1128",
    doi = "10.18653/v1/P16-1128",
    pages = "1351--1360",
}
@inproceedings{zhang-etal-2016-towards,
    title = "Towards Constructing Sports News from Live Text Commentary",
    author = "Zhang, Jianmin  and
      Yao, Jin-ge  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1129",
    doi = "10.18653/v1/P16-1129",
    pages = "1361--1371",
}
@inproceedings{zhang-etal-2016-continuous,
    title = "A Continuous Space Rule Selection Model for Syntax-based Statistical Machine Translation",
    author = "Zhang, Jingyi  and
      Utiyama, Masao  and
      Sumita, Eiichro  and
      Neubig, Graham  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1130",
    doi = "10.18653/v1/P16-1130",
    pages = "1372--1381",
}
@inproceedings{zhang-etal-2016-probabilistic,
    title = "Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network",
    author = "Zhang, Zhisong  and
      Zhao, Hai  and
      Qin, Lianhui",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1131",
    doi = "10.18653/v1/P16-1131",
    pages = "1382--1392",
}
@inproceedings{zhou-etal-2016-search,
    title = "A Search-Based Dynamic Reranking Model for Dependency Parsing",
    author = "Zhou, Hao  and
      Zhang, Yue  and
      Huang, Shujian  and
      Zhou, Junsheng  and
      Dai, Xin-Yu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1132",
    doi = "10.18653/v1/P16-1132",
    pages = "1393--1402",
}
@inproceedings{zhou-etal-2016-cross,
    title = "Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning",
    author = "Zhou, Xinjie  and
      Wan, Xiaojun  and
      Xiao, Jianguo",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1133",
    doi = "10.18653/v1/P16-1133",
    pages = "1403--1412",
}
@inproceedings{zhuo-etal-2016-segment,
    title = "Segment-Level Sequence Modeling using Gated Recursive Semi-{M}arkov Conditional Random Fields",
    author = "Zhuo, Jingwei  and
      Cao, Yong  and
      Zhu, Jun  and
      Zhang, Bo  and
      Nie, Zaiqing",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1134",
    doi = "10.18653/v1/P16-1134",
    pages = "1413--1423",
}
@inproceedings{hidey-mckeown-2016-identifying,
    title = "Identifying Causal Relations Using Parallel {W}ikipedia Articles",
    author = "Hidey, Christopher  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1135",
    doi = "10.18653/v1/P16-1135",
    pages = "1424--1433",
}
@inproceedings{toutanova-etal-2016-compositional,
    title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
    author = "Toutanova, Kristina  and
      Lin, Victoria  and
      Yih, Wen-tau  and
      Poon, Hoifung  and
      Quirk, Chris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1136",
    doi = "10.18653/v1/P16-1136",
    pages = "1434--1444",
}
@inproceedings{li-etal-2016-commonsense,
    title = "Commonsense Knowledge Base Completion",
    author = "Li, Xiang  and
      Taheri, Aynaz  and
      Tu, Lifu  and
      Gimpel, Kevin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1137",
    doi = "10.18653/v1/P16-1137",
    pages = "1445--1455",
}
@inproceedings{long-etal-2016-simpler,
    title = "Simpler Context-Dependent Logical Forms via Model Projections",
    author = "Long, Reginald  and
      Pasupat, Panupong  and
      Liang, Percy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1138",
    doi = "10.18653/v1/P16-1138",
    pages = "1456--1465",
}
@inproceedings{bowman-etal-2016-fast,
    title = "A Fast Unified Model for Parsing and Sentence Understanding",
    author = "Bowman, Samuel R.  and
      Gauthier, Jon  and
      Rastogi, Abhinav  and
      Gupta, Raghav  and
      Manning, Christopher D.  and
      Potts, Christopher",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1139",
    doi = "10.18653/v1/P16-1139",
    pages = "1466--1477",
}
@inproceedings{qian-etal-2016-investigating,
    title = "Investigating Language Universal and Specific Properties in Word Embeddings",
    author = "Qian, Peng  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1140",
    doi = "10.18653/v1/P16-1140",
    pages = "1478--1488",
}
@inproceedings{hamilton-etal-2016-diachronic,
    title = "Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change",
    author = "Hamilton, William L.  and
      Leskovec, Jure  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1141",
    doi = "10.18653/v1/P16-1141",
    pages = "1489--1501",
}
@inproceedings{vempala-blanco-2016-beyond,
    title = "Beyond Plain Spatial Knowledge: Determining Where Entities Are and Are Not Located, and For How Long",
    author = "Vempala, Alakananda  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1142",
    doi = "10.18653/v1/P16-1142",
    pages = "1502--1512",
}
@inproceedings{bennett-etal-2016-lexsemtm,
    title = "{L}ex{S}em{T}m: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning",
    author = "Bennett, Andrew  and
      Baldwin, Timothy  and
      Lau, Jey Han  and
      McCarthy, Diana  and
      Bond, Francis",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1143",
    doi = "10.18653/v1/P16-1143",
    pages = "1513--1524",
}
@inproceedings{paperno-etal-2016-lambada,
    title = "The {LAMBADA} dataset: Word prediction requiring a broad discourse context",
    author = "Paperno, Denis  and
      Kruszewski, Germ{\'a}n  and
      Lazaridou, Angeliki  and
      Pham, Ngoc Quan  and
      Bernardi, Raffaella  and
      Pezzelle, Sandro  and
      Baroni, Marco  and
      Boleda, Gemma  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1144",
    doi = "10.18653/v1/P16-1144",
    pages = "1525--1534",
}
@inproceedings{hewlett-etal-2016-wikireading,
    title = "{W}iki{R}eading: A Novel Large-scale Language Understanding Task over {W}ikipedia",
    author = "Hewlett, Daniel  and
      Lacoste, Alexandre  and
      Jones, Llion  and
      Polosukhin, Illia  and
      Fandrianto, Andrew  and
      Han, Jay  and
      Kelcey, Matthew  and
      Berthelot, David",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1145",
    doi = "10.18653/v1/P16-1145",
    pages = "1535--1545",
}
@inproceedings{narayan-cohen-2016-optimizing,
    title = "Optimizing Spectral Learning for Parsing",
    author = "Narayan, Shashi  and
      Cohen, Shay B.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1146",
    doi = "10.18653/v1/P16-1146",
    pages = "1546--1556",
}
@inproceedings{zhang-weiss-2016-stack,
    title = "Stack-propagation: Improved Representation Learning for Syntax",
    author = "Zhang, Yuan  and
      Weiss, David",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1147",
    doi = "10.18653/v1/P16-1147",
    pages = "1557--1566",
}
@inproceedings{volkova-bachrach-2016-inferring,
    title = "Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast",
    author = "Volkova, Svitlana  and
      Bachrach, Yoram",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1148",
    doi = "10.18653/v1/P16-1148",
    pages = "1567--1578",
}
@inproceedings{burgess-etal-2016-prototype,
    title = "Prototype Synthesis for Model Laws",
    author = "Burgess, Matthew  and
      Giraudy, Eugenia  and
      Adar, Eytan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1149",
    doi = "10.18653/v1/P16-1149",
    pages = "1579--1588",
}
@inproceedings{habernal-gurevych-2016-argument,
    title = "Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional {LSTM}",
    author = "Habernal, Ivan  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1150",
    doi = "10.18653/v1/P16-1150",
    pages = "1589--1599",
}
@inproceedings{fong-grimmer-2016-discovery,
    title = "Discovery of Treatments from Text Corpora",
    author = "Fong, Christian  and
      Grimmer, Justin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1151",
    doi = "10.18653/v1/P16-1151",
    pages = "1600--1609",
}
@inproceedings{sokolov-etal-2016-learning,
    title = "Learning Structured Predictors from Bandit Feedback for Interactive {NLP}",
    author = "Sokolov, Artem  and
      Kreutzer, Julia  and
      Lo, Christopher  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1152",
    doi = "10.18653/v1/P16-1152",
    pages = "1610--1620",
}
@inproceedings{he-etal-2016-deep-reinforcement,
    title = "Deep Reinforcement Learning with a Natural Language Action Space",
    author = "He, Ji  and
      Chen, Jianshu  and
      He, Xiaodong  and
      Gao, Jianfeng  and
      Li, Lihong  and
      Deng, Li  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1153",
    doi = "10.18653/v1/P16-1153",
    pages = "1621--1630",
}
@inproceedings{gu-etal-2016-incorporating,
    title = "Incorporating Copying Mechanism in Sequence-to-Sequence Learning",
    author = "Gu, Jiatao  and
      Lu, Zhengdong  and
      Li, Hang  and
      Li, Victor O.K.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1154",
    doi = "10.18653/v1/P16-1154",
    pages = "1631--1640",
}
@inproceedings{bhatt-etal-2016-cross,
    title = "Cross-domain Text Classification with Multiple Domains and Disparate Label Sets",
    author = "Bhatt, Himanshu Sharad  and
      Sinha, Manjira  and
      Roy, Shourya",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1155",
    doi = "10.18653/v1/P16-1155",
    pages = "1641--1650",
}
@inproceedings{cotterell-etal-2016-morphological-smoothing,
    title = "Morphological Smoothing and Extrapolation of Word Embeddings",
    author = {Cotterell, Ryan  and
      Sch{\"u}tze, Hinrich  and
      Eisner, Jason},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1156",
    doi = "10.18653/v1/P16-1156",
    pages = "1651--1660",
}
@inproceedings{upadhyay-etal-2016-cross,
    title = "Cross-lingual Models of Word Embeddings: An Empirical Comparison",
    author = "Upadhyay, Shyam  and
      Faruqui, Manaal  and
      Dyer, Chris  and
      Roth, Dan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1157",
    doi = "10.18653/v1/P16-1157",
    pages = "1661--1670",
}
@inproceedings{vylomova-etal-2016-take,
    title = "Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning",
    author = "Vylomova, Ekaterina  and
      Rimell, Laura  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1158",
    doi = "10.18653/v1/P16-1158",
    pages = "1671--1682",
}
@inproceedings{shen-etal-2016-minimum,
    title = "Minimum Risk Training for Neural Machine Translation",
    author = "Shen, Shiqi  and
      Cheng, Yong  and
      He, Zhongjun  and
      He, Wei  and
      Wu, Hua  and
      Sun, Maosong  and
      Liu, Yang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1159",
    doi = "10.18653/v1/P16-1159",
    pages = "1683--1692",
}
@inproceedings{chung-etal-2016-character,
    title = "A Character-level Decoder without Explicit Segmentation for Neural Machine Translation",
    author = "Chung, Junyoung  and
      Cho, Kyunghyun  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1160",
    doi = "10.18653/v1/P16-1160",
    pages = "1693--1703",
}
@inproceedings{tamchyna-etal-2016-target,
    title = "Target-Side Context for Discriminative Models in Statistical Machine Translation",
    author = "Tamchyna, Ale{\v{s}}  and
      Fraser, Alexander  and
      Bojar, Ond{\v{r}}ej  and
      Junczys-Dowmunt, Marcin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1161",
    doi = "10.18653/v1/P16-1161",
    pages = "1704--1714",
}
@inproceedings{sennrich-etal-2016-neural,
    title = "Neural Machine Translation of Rare Words with Subword Units",
    author = "Sennrich, Rico  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1162",
    doi = "10.18653/v1/P16-1162",
    pages = "1715--1725",
}
@inproceedings{chen-etal-2016-implicit,
    title = "Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network",
    author = "Chen, Jifan  and
      Zhang, Qi  and
      Liu, Pengfei  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1163",
    doi = "10.18653/v1/P16-1163",
    pages = "1726--1735",
}
@inproceedings{scheible-etal-2016-model,
    title = "Model Architectures for Quotation Detection",
    author = "Scheible, Christian  and
      Klinger, Roman  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1164",
    doi = "10.18653/v1/P16-1164",
    pages = "1736--1745",
}
@inproceedings{joty-hoque-2016-speech,
    title = "Speech Act Modeling of Written Asynchronous Conversations with Task-Specific Embeddings and Conditional Structured Models",
    author = "Joty, Shafiq  and
      Hoque, Enamul",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1165",
    doi = "10.18653/v1/P16-1165",
    pages = "1746--1756",
}
@inproceedings{friedrich-etal-2016-situation,
    title = "Situation entity types: automatic classification of clause-level aspect",
    author = "Friedrich, Annemarie  and
      Palmer, Alexis  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1166",
    doi = "10.18653/v1/P16-1166",
    pages = "1757--1768",
}
@inproceedings{bosselut-etal-2016-learning,
    title = "Learning Prototypical Event Structure from Photo Albums",
    author = "Bosselut, Antoine  and
      Chen, Jianfu  and
      Warren, David  and
      Hajishirzi, Hannaneh  and
      Choi, Yejin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1167",
    doi = "10.18653/v1/P16-1167",
    pages = "1769--1779",
}
@inproceedings{miyazaki-shimizu-2016-cross,
    title = "Cross-Lingual Image Caption Generation",
    author = "Miyazaki, Takashi  and
      Shimizu, Nobuyuki",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1168",
    doi = "10.18653/v1/P16-1168",
    pages = "1780--1790",
}
@inproceedings{zhang-etal-2016-learning,
    title = "Learning Concept Taxonomies from Multi-modal Data",
    author = "Zhang, Hao  and
      Hu, Zhiting  and
      Deng, Yuntian  and
      Sachan, Mrinmaya  and
      Yan, Zhicheng  and
      Xing, Eric",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1169",
    doi = "10.18653/v1/P16-1169",
    pages = "1791--1801",
}
@inproceedings{mostafazadeh-etal-2016-generating,
    title = "Generating Natural Questions About an Image",
    author = "Mostafazadeh, Nasrin  and
      Misra, Ishan  and
      Devlin, Jacob  and
      Mitchell, Margaret  and
      He, Xiaodong  and
      Vanderwende, Lucy",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1170",
    doi = "10.18653/v1/P16-1170",
    pages = "1802--1813",
}
@inproceedings{gao-etal-2016-physical,
    title = "Physical Causality of Action Verbs in Grounded Language Understanding",
    author = "Gao, Qiaozi  and
      Doering, Malcolm  and
      Yang, Shaohua  and
      Chai, Joyce",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1171",
    doi = "10.18653/v1/P16-1171",
    pages = "1814--1824",
}
@inproceedings{peyrard-eckle-kohler-2016-optimizing,
    title = "Optimizing an Approximation of {ROUGE} - a Problem-Reduction Approach to Extractive Multi-Document Summarization",
    author = "Peyrard, Maxime  and
      Eckle-Kohler, Judith",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1172",
    doi = "10.18653/v1/P16-1172",
    pages = "1825--1836",
}
@inproceedings{nagata-sakaguchi-2016-phrase,
    title = "Phrase Structure Annotation and Parsing for Learner {E}nglish",
    author = "Nagata, Ryo  and
      Sakaguchi, Keisuke",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1173",
    doi = "10.18653/v1/P16-1173",
    pages = "1837--1847",
}
@inproceedings{settles-meeder-2016-trainable,
    title = "A Trainable Spaced Repetition Model for Language Learning",
    author = "Settles, Burr  and
      Meeder, Brendan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1174",
    doi = "10.18653/v1/P16-1174",
    pages = "1848--1858",
}
@inproceedings{renduchintala-etal-2016-user,
    title = "User Modeling in Language Learning with Macaronic Texts",
    author = "Renduchintala, Adithya  and
      Knowles, Rebecca  and
      Koehn, Philipp  and
      Eisner, Jason",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1175",
    doi = "10.18653/v1/P16-1175",
    pages = "1859--1869",
}
@inproceedings{rabinovich-etal-2016-similarities,
    title = "On the Similarities Between Native, Non-native and Translated Texts",
    author = "Rabinovich, Ella  and
      Nisioi, Sergiu  and
      Ordan, Noam  and
      Wintner, Shuly",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1176",
    doi = "10.18653/v1/P16-1176",
    pages = "1870--1881",
}
@inproceedings{amiri-etal-2016-learning,
    title = "Learning Text Pair Similarity with Context-sensitive Autoencoders",
    author = "Amiri, Hadi  and
      Resnik, Philip  and
      Boyd-Graber, Jordan  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1177",
    doi = "10.18653/v1/P16-1177",
    pages = "1882--1892",
}
@inproceedings{arapakis-etal-2016-linguistic,
    title = "Linguistic Benchmarks of Online News Article Quality",
    author = "Arapakis, Ioannis  and
      Peleja, Filipa  and
      Berkant, Barla  and
      Magalhaes, Joao",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1178",
    doi = "10.18653/v1/P16-1178",
    pages = "1893--1902",
}
@inproceedings{barrena-etal-2016-alleviating,
    title = "Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation",
    author = "Barrena, Ander  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1179",
    doi = "10.18653/v1/P16-1179",
    pages = "1903--1912",
}
@inproceedings{biran-etal-2016-mining,
    title = "Mining Paraphrasal Typed Templates from a Plain Text Corpus",
    author = "Biran, Or  and
      Blevins, Terra  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1180",
    doi = "10.18653/v1/P16-1180",
    pages = "1913--1923",
}
@inproceedings{bjorkelund-etal-2016-train,
    title = "How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents",
    author = {Bj{\"o}rkelund, Anders  and
      Fale{\'n}ska, Agnieszka  and
      Seeker, Wolfgang  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1181",
    doi = "10.18653/v1/P16-1181",
    pages = "1924--1934",
}
@inproceedings{boag-etal-2016-mutt,
    title = "{MUTT}: Metric Unit {T}es{T}ing for Language Generation Tasks",
    author = "Boag, William  and
      Campos, Renan  and
      Saenko, Kate  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1182",
    doi = "10.18653/v1/P16-1182",
    pages = "1935--1943",
}
@inproceedings{bogoychev-lopez-2016-n,
    title = "N-gram language models for massively parallel devices",
    author = "Bogoychev, Nikolay  and
      Lopez, Adam",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1183",
    doi = "10.18653/v1/P16-1183",
    pages = "1944--1953",
}
@inproceedings{buys-botha-2016-cross,
    title = "Cross-Lingual Morphological Tagging for Low-Resource Languages",
    author = "Buys, Jan  and
      Botha, Jan A.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1184",
    doi = "10.18653/v1/P16-1184",
    pages = "1954--1964",
}
@inproceedings{cheng-etal-2016-semi,
    title = "Semi-Supervised Learning for Neural Machine Translation",
    author = "Cheng, Yong  and
      Xu, Wei  and
      He, Zhongjun  and
      He, Wei  and
      Wu, Hua  and
      Sun, Maosong  and
      Liu, Yang",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1185",
    doi = "10.18653/v1/P16-1185",
    pages = "1965--1974",
}
@inproceedings{chen-etal-2016-strategies,
    title = "Strategies for Training Large Vocabulary Neural Language Models",
    author = "Chen, Wenlin  and
      Grangier, David  and
      Auli, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1186",
    doi = "10.18653/v1/P16-1186",
    pages = "1975--1985",
}
@inproceedings{cordeiro-etal-2016-predicting,
    title = "Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time",
    author = "Cordeiro, Silvio  and
      Ramisch, Carlos  and
      Idiart, Marco  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1187",
    doi = "10.18653/v1/P16-1187",
    pages = "1986--1997",
}
@inproceedings{durrett-etal-2016-learning,
    title = "Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints",
    author = "Durrett, Greg  and
      Berg-Kirkpatrick, Taylor  and
      Klein, Dan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1188",
    doi = "10.18653/v1/P16-1188",
    pages = "1998--2008",
}
@inproceedings{etchegoyhen-azpeitia-2016-set,
    title = "Set-Theoretic Alignment for Comparable Corpora",
    author = "Etchegoyhen, Thierry  and
      Azpeitia, Andoni",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1189",
    doi = "10.18653/v1/P16-1189",
    pages = "2009--2018",
}
@inproceedings{ferreira-etal-2016-jointly,
    title = "Jointly Learning to Embed and Predict with Multiple Languages",
    author = "Ferreira, Daniel C.  and
      Martins, Andr{\'e} F. T.  and
      Almeida, Mariana S. C.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1190",
    doi = "10.18653/v1/P16-1190",
    pages = "2019--2028",
}
@inproceedings{flekova-gurevych-2016-supersense,
    title = "Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization",
    author = "Flekova, Lucie  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1191",
    doi = "10.18653/v1/P16-1191",
    pages = "2029--2041",
}
@inproceedings{groschwitz-etal-2016-efficient,
    title = "Efficient techniques for parsing with tree automata",
    author = "Groschwitz, Jonas  and
      Koller, Alexander  and
      Johnson, Mark",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1192",
    doi = "10.18653/v1/P16-1192",
    pages = "2042--2051",
}
@inproceedings{henderson-popa-2016-vector,
    title = "A Vector Space for Distributional Semantics for Entailment",
    author = "Henderson, James  and
      Popa, Diana",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1193",
    doi = "10.18653/v1/P16-1193",
    pages = "2052--2062",
}
@inproceedings{he-etal-2016-hidden,
    title = "Hidden Softmax Sequence Model for Dialogue Structure Analysis",
    author = "He, Zhiyang  and
      Liu, Xien  and
      Lv, Ping  and
      Wu, Ji",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1194",
    doi = "10.18653/v1/P16-1194",
    pages = "2063--2072",
}
@inproceedings{iyer-etal-2016-summarizing,
    title = "Summarizing Source Code using a Neural Attention Model",
    author = "Iyer, Srinivasan  and
      Konstas, Ioannis  and
      Cheung, Alvin  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1195",
    doi = "10.18653/v1/P16-1195",
    pages = "2073--2083",
}
@inproceedings{kacorri-huenerfauth-2016-continuous,
    title = "Continuous Profile Models in {ASL} Syntactic Facial Expression Synthesis",
    author = "Kacorri, Hernisa  and
      Huenerfauth, Matt",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1196",
    doi = "10.18653/v1/P16-1196",
    pages = "2084--2093",
}
@inproceedings{kazemian-etal-2016-evaluating,
    title = "Evaluating Sentiment Analysis in the Context of Securities Trading",
    author = "Kazemian, Siavash  and
      Zhao, Shunan  and
      Penn, Gerald",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1197",
    doi = "10.18653/v1/P16-1197",
    pages = "2094--2103",
}
@inproceedings{levi-etal-2016-edge,
    title = "Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference",
    author = "Levi, Effi  and
      Reichart, Roi  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1198",
    doi = "10.18653/v1/P16-1198",
    pages = "2104--2113",
}
@inproceedings{li-etal-2016-topic,
    title = "Topic Extraction from Microblog Posts Using Conversation Structures",
    author = "Li, Jing  and
      Liao, Ming  and
      Gao, Wei  and
      He, Yulan  and
      Wong, Kam-Fai",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1199",
    doi = "10.18653/v1/P16-1199",
    pages = "2114--2123",
}
@inproceedings{lin-etal-2016-neural,
    title = "Neural Relation Extraction with Selective Attention over Instances",
    author = "Lin, Yankai  and
      Shen, Shiqi  and
      Liu, Zhiyuan  and
      Luan, Huanbo  and
      Sun, Maosong",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1200",
    doi = "10.18653/v1/P16-1200",
    pages = "2124--2133",
}
@inproceedings{liu-etal-2016-leveraging,
    title = "Leveraging {F}rame{N}et to Improve Automatic Event Detection",
    author = "Liu, Shulin  and
      Chen, Yubo  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1201",
    doi = "10.18653/v1/P16-1201",
    pages = "2134--2143",
}
@inproceedings{mitra-baral-2016-learning,
    title = "Learning To Use Formulas To Solve Simple Arithmetic Problems",
    author = "Mitra, Arindam  and
      Baral, Chitta",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1202",
    doi = "10.18653/v1/P16-1202",
    pages = "2144--2153",
}
@inproceedings{papantoniou-konstantopoulos-2016-unravelling,
    title = "Unravelling Names of Fictional Characters",
    author = "Papantoniou, Katerina  and
      Konstantopoulos, Stasinos",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1203",
    doi = "10.18653/v1/P16-1203",
    pages = "2154--2163",
}
@inproceedings{pavlick-callison-burch-2016-babies,
    title = "Most {``}babies{''} are {``}little{''} and most {``}problems{''} are {``}huge{''}: Compositional Entailment in Adjective-Nouns",
    author = "Pavlick, Ellie  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1204",
    doi = "10.18653/v1/P16-1204",
    pages = "2164--2173",
}
@inproceedings{persing-ng-2016-modeling,
    title = "Modeling Stance in Student Essays",
    author = "Persing, Isaac  and
      Ng, Vincent",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1205",
    doi = "10.18653/v1/P16-1205",
    pages = "2174--2184",
}
@inproceedings{qian-etal-2016-new,
    title = "A New Psychometric-inspired Evaluation Metric for {C}hinese Word Segmentation",
    author = "Qian, Peng  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1206",
    doi = "10.18653/v1/P16-1206",
    pages = "2185--2194",
}
@inproceedings{reimers-etal-2016-temporal,
    title = "Temporal Anchoring of Events for the {T}ime{B}ank Corpus",
    author = "Reimers, Nils  and
      Dehghani, Nazanin  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1207",
    doi = "10.18653/v1/P16-1207",
    pages = "2195--2204",
}
@inproceedings{rozovskaya-roth-2016-grammatical,
    title = "Grammatical Error Correction: Machine Translation and Classifiers",
    author = "Rozovskaya, Alla  and
      Roth, Dan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1208",
    doi = "10.18653/v1/P16-1208",
    pages = "2205--2215",
}
@inproceedings{sahu-anand-2016-recurrent,
    title = "Recurrent neural network models for disease name recognition using domain invariant features",
    author = "Sahu, Sunil  and
      Anand, Ashish",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1209",
    doi = "10.18653/v1/P16-1209",
    pages = "2216--2225",
}
@inproceedings{sapkota-etal-2016-domain,
    title = "Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning",
    author = "Sapkota, Upendra  and
      Solorio, Thamar  and
      Montes, Manuel  and
      Bethard, Steven",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1210",
    doi = "10.18653/v1/P16-1210",
    pages = "2226--2235",
}
@inproceedings{sasano-okumura-2016-corpus,
    title = "A Corpus-Based Analysis of Canonical Word Order of {J}apanese Double Object Constructions",
    author = "Sasano, Ryohei  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1211",
    doi = "10.18653/v1/P16-1211",
    pages = "2236--2244",
}
@inproceedings{shi-etal-2016-knowledge,
    title = "Knowledge-Based Semantic Embedding for Machine Translation",
    author = "Shi, Chen  and
      Liu, Shujie  and
      Ren, Shuo  and
      Feng, Shi  and
      Li, Mu  and
      Zhou, Ming  and
      Sun, Xu  and
      Wang, Houfeng",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1212",
    doi = "10.18653/v1/P16-1212",
    pages = "2245--2254",
}
@inproceedings{sil-florian-2016-one,
    title = "One for All: Towards Language Independent Named Entity Linking",
    author = "Sil, Avirup  and
      Florian, Radu",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1213",
    doi = "10.18653/v1/P16-1213",
    pages = "2255--2264",
}
@inproceedings{sugawara-etal-2016-approximately,
    title = "On Approximately Searching for Similar Word Embeddings",
    author = "Sugawara, Kohei  and
      Kobayashi, Hayato  and
      Iwasaki, Masajiro",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1214",
    doi = "10.18653/v1/P16-1214",
    pages = "2265--2275",
}
@inproceedings{takase-etal-2016-composing,
    title = "Composing Distributed Representations of Relational Patterns",
    author = "Takase, Sho  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1215",
    doi = "10.18653/v1/P16-1215",
    pages = "2276--2286",
}
@inproceedings{vala-etal-2016-antecedents,
    title = "The More Antecedents, the Merrier: Resolving Multi-Antecedent Anaphors",
    author = "Vala, Hardik  and
      Piper, Andrew  and
      Ruths, Derek",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1216",
    doi = "10.18653/v1/P16-1216",
    pages = "2287--2296",
}
@inproceedings{wan-wang-2016-automatic,
    title = "Automatic Labeling of Topic Models Using Text Summaries",
    author = "Wan, Xiaojun  and
      Wang, Tianming",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1217",
    doi = "10.18653/v1/P16-1217",
    pages = "2297--2305",
}
@inproceedings{wang-chang-2016-graph,
    title = "Graph-based Dependency Parsing with Bidirectional {LSTM}",
    author = "Wang, Wenhui  and
      Chang, Baobao",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1218",
    doi = "10.18653/v1/P16-1218",
    pages = "2306--2315",
}
@inproceedings{xiao-etal-2016-transg,
    title = "{T}rans{G} : A Generative Model for Knowledge Graph Embedding",
    author = "Xiao, Han  and
      Huang, Minlie  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1219",
    doi = "10.18653/v1/P16-1219",
    pages = "2316--2325",
}
@inproceedings{xu-etal-2016-question,
    title = "Question Answering on {F}reebase via Relation Extraction and Textual Evidence",
    author = "Xu, Kun  and
      Reddy, Siva  and
      Feng, Yansong  and
      Huang, Songfang  and
      Zhao, Dongyan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1220",
    doi = "10.18653/v1/P16-1220",
    pages = "2326--2336",
}
@inproceedings{yancheva-rudzicz-2016-vector,
    title = "Vector-space topic models for detecting {A}lzheimer{'}s disease",
    author = "Yancheva, Maria  and
      Rudzicz, Frank",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1221",
    doi = "10.18653/v1/P16-1221",
    pages = "2337--2346",
}
@inproceedings{yan-etal-2016-chinese,
    title = "{C}hinese Couplet Generation with Neural Network Structures",
    author = "Yan, Rui  and
      Li, Cheng-Te  and
      Hu, Xiaohua  and
      Zhang, Ming",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1222",
    doi = "10.18653/v1/P16-1222",
    pages = "2347--2357",
}
@inproceedings{chen-etal-2016-thorough,
    title = "A Thorough Examination of the {CNN}/{D}aily {M}ail Reading Comprehension Task",
    author = "Chen, Danqi  and
      Bolton, Jason  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1223",
    doi = "10.18653/v1/P16-1223",
    pages = "2358--2367",
}
@inproceedings{wang-etal-2016-learning-language,
    title = "Learning Language Games through Interaction",
    author = "Wang, Sida I.  and
      Liang, Percy  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1224",
    doi = "10.18653/v1/P16-1224",
    pages = "2368--2378",
}
@inproceedings{gutierrez-etal-2016-finding,
    title = "Finding Non-Arbitrary Form-Meaning Systematicity Using String-Metric Learning for Kernel Regression",
    author = "Guti{\'e}rrez, E. Dario  and
      Levy, Roger  and
      Bergen, Benjamin",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1225",
    doi = "10.18653/v1/P16-1225",
    pages = "2379--2388",
}
@inproceedings{shwartz-etal-2016-improving,
    title = "Improving Hypernymy Detection with an Integrated Path-based and Distributional Method",
    author = "Shwartz, Vered  and
      Goldberg, Yoav  and
      Dagan, Ido",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1226",
    doi = "10.18653/v1/P16-1226",
    pages = "2389--2398",
}
@inproceedings{hitschler-etal-2016-multimodal,
    title = "Multimodal Pivots for Image Caption Translation",
    author = "Hitschler, Julian  and
      Schamoni, Shigehiko  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1227",
    doi = "10.18653/v1/P16-1227",
    pages = "2399--2409",
}
@inproceedings{hu-etal-2016-harnessing,
    title = "Harnessing Deep Neural Networks with Logic Rules",
    author = "Hu, Zhiting  and
      Ma, Xuezhe  and
      Liu, Zhengzhong  and
      Hovy, Eduard  and
      Xing, Eric",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1228",
    doi = "10.18653/v1/P16-1228",
    pages = "2410--2420",
}
@inproceedings{moscoso-del-prado-martin-brendel-2016-case,
    title = "Case and Cause in {I}celandic: Reconstructing Causal Networks of Cascaded Language Changes",
    author = "Moscoso del Prado Mart{\'\i}n, Ferm{\'\i}n  and
      Brendel, Christian",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1229",
    doi = "10.18653/v1/P16-1229",
    pages = "2421--2430",
}
@inproceedings{su-etal-2016-line,
    title = "On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems",
    author = "Su, Pei-Hao  and
      Ga{\v{s}}i{\'c}, Milica  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Rojas-Barahona, Lina M.  and
      Ultes, Stefan  and
      Vandyke, David  and
      Wen, Tsung-Hsien  and
      Young, Steve",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1230",
    doi = "10.18653/v1/P16-1230",
    pages = "2431--2441",
}
@inproceedings{andor-etal-2016-globally,
    title = "Globally Normalized Transition-Based Neural Networks",
    author = "Andor, Daniel  and
      Alberti, Chris  and
      Weiss, David  and
      Severyn, Aliaksei  and
      Presta, Alessandro  and
      Ganchev, Kuzman  and
      Petrov, Slav  and
      Collins, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1231",
    doi = "10.18653/v1/P16-1231",
    pages = "2442--2452",
}
@proceedings{rocling-ijclclp-2016-international-journal,
    title = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 2, {D}ecember 2016",
    editor = "Tseng, Yuen-Hsien  and
      Chien, Jen-Tzung",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-3000",
}
@inproceedings{bai-etal-2016-ji-yu,
    title = "基於詞語分布均勻度的核心詞彙選擇 (A Study on Dispersion Measures for Core Vocabulary Compilation) [In {C}hinese]",
    author = "Bai, Ming-Hong  and
      Wu, Jian-Cheng  and
      Chien, Ying-Ni  and
      Huang, Shu-Ling  and
      Lin, Ching-Lung",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 2, {D}ecember 2016",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-3001",
}
@inproceedings{hsieh-ma-2016-n,
    title = "N-best Rescoring for Parsing Based on Dependency-Based Word Embeddings",
    author = "Hsieh, Yu-Ming  and
      Ma, Wei-Yun",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 2, {D}ecember 2016",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-3002",
}
@inproceedings{yan-etal-2016-shi-yong,
    title = "使用字典學習法於強健性語音辨識 (The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In {C}hinese]",
    author = "Yan, Bi-Cheng  and
      Shih, Chin-Hong  and
      Liu, Shih-Hung  and
      Chen, Berlin",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 2, {D}ecember 2016",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-3003",
}
@inproceedings{hsu-etal-2016-ping-gu,
    title = "評估尺度相關最佳化方法於華語錯誤發音檢測之研究 (Evaluation Metric-related Optimization Methods for {M}andarin Mispronunciation Detection) [In {C}hinese]",
    author = "Hsu, Yao-Chi  and
      Yang, Ming-Han  and
      Hung, Hsiao-Tsung  and
      Lin, Yi-Ju  and
      Chen, Kuan-Yu  and
      Chen, Berlin",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 2, {D}ecember 2016",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-3004",
}
@inproceedings{chen-etal-2016-ji-yu-zi,
    title = "基於字元階層之語音合成用文脈訊息擷取 (Character-Level Linguistic Features Extraction for Text-to-Speech System) [In {C}hinese]",
    author = "Chen, Kuan-Hung  and
      Liao, Shu-Han  and
      Liao, Yuan-Fu  and
      Wang, Yih-Ru",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 2, {D}ecember 2016",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-3005",
}
@inproceedings{yang-etal-2016-rong-duo,
    title = "融合多任務學習類神經網路聲學模型訓練於會議語音辨識之研究 (Leveraging Multi-Task Learning with Neural Network Based Acoustic Modeling for Improved Meeting Speech Recognition) [In {C}hinese]",
    author = "Yang, Ming-Han  and
      Hsu, Yao-Chi  and
      Hung, Hsiao-Tsung  and
      Chen, Ying-Wen  and
      Chen, Kuan-Yu  and
      Chen, Berlin",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 2, {D}ecember 2016",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-3006",
}
@proceedings{rocling-ijclclp-2016-international,
    title = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 1, June 2016",
    editor = "Tseng, Yuen-Hsien  and
      Chen, Kuang-Hua",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-2000",
}
@inproceedings{chen-etal-2016-segmentation,
    title = "A Segmentation Matrix Method for {C}hinese Segmentation Ambiguity Analysis",
    author = "Chen, Yanping  and
      Zheng, Qinghua  and
      Tian, Feng  and
      Zheng, Deli",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 1, June 2016",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-2001",
}
@inproceedings{chang-etal-2016-linguistic,
    title = "Linguistic Template Extraction for Recognizing Reader-Emotion",
    author = "Chang, Yung-Chun  and
      Chu, Chun-Han  and
      Chen, Chien Chin  and
      Hsu, Wen-Lian",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 1, June 2016",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-2002",
}
@inproceedings{huang-etal-2016-enriching,
    title = "Enriching Cold Start Personalized Language Model Using Social Network Information",
    author = "Huang, Yu-Yang  and
      Yan, Rui  and
      Kuo, Tsung-Ting  and
      Lin, Shou-De",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 1, June 2016",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-2003",
}
@inproceedings{wang-lu-2016-identifying,
    title = "Identifying the Names of Complex Search Tasks with Task-Related Entities",
    author = "Wang, Ting-Xuan  and
      Lu, Wen-Hsiang",
    booktitle = "International Journal of Computational Linguistics {\&} {C}hinese Language Processing, Volume 21, Number 1, June 2016",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/O16-2004",
}
@proceedings{rocling-ijclclp-2016-linguistics,
    title = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    editor = "Wu, Chung-Hsien  and
      Tseng, Yuen-Hsien  and
      Kao, Hung-Yu",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1000",
}
@inproceedings{hsu-etal-2016-ping,
    title = "評估尺度相關最佳化方法於華語錯誤發音檢測之研究(Evaluation Metric-related Optimization Methods for {M}andarin Mispronunciation Detection) [In {C}hinese]",
    author = "Hsu, Yao-Chi  and
      Yang, Ming-Han  and
      Hung, Hsiao-Tsung  and
      Lin, Yi-Ju  and
      Chen, Berlin",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1001",
    pages = "1--3",
}
@inproceedings{yang-etal-2016-rong,
    title = "融合多任務學習類神經網路聲學模型訓練於會議語音辨識之研究(Leveraging Multi-task Learning with Neural Network Based Acoustic Modeling for Improved Meeting Speech Recognition) [In {C}hinese]",
    author = "Yang, Ming-Han  and
      Hsu, Yao-Chi  and
      Hung, Hsiao-Tsung  and
      Chen, Ying-Wen  and
      Chen, Berlin  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1002",
    pages = "4--6",
}
@inproceedings{yan-etal-2016-shi,
    title = "使用字典學習法於強健性語音辨識(The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In {C}hinese]",
    author = "Yan, Bi-Cheng  and
      Shih, Chin-Hong  and
      Liu, Shih-Hung  and
      Chen, Berlin",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1003",
    pages = "7--9",
}
@inproceedings{chan-chen-2016-yi,
    title = "以多層感知器辨識情緒於國台客語料庫 (Use Multilayer Perceptron To Recognize Emotion in {M}andarin,{T}aiwanese and {H}akka Database) [In {C}hinese]",
    author = "Chan, Chia-Hsien  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1004",
    pages = "10--21",
}
@inproceedings{huang-etal-2016-vdao,
    title = "「{V}到」結構的合分詞及語意區分(Word segmentation and sense representation for {V}-dao structure in {C}hinese)[In {C}hinese]",
    author = "Huang, Shu-Ling  and
      Li, Shi-Min  and
      Bai, Ming-Hong  and
      Wu, Jian-Cheng  and
      Wang, Ying-Ni  and
      Lin, Qing-Long",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1005",
    pages = "22--34",
}
@inproceedings{kung-etal-2016-ge,
    title = "歌詞演唱錯誤偵測(Automatic Sung Lyrics Verification)[In {C}hinese]",
    author = "Kung, Shiang-Shiun  and
      Ma, Cin-Hao  and
      Shen, Sin-Fu  and
      Hsiao, Po-Yuan  and
      Tsai, Wei-Ho",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1006",
    pages = "35--48",
}
@inproceedings{bai-etal-2016-ji,
    title = "基於詞語分布均勻度的核心詞彙選擇之研究(A Study on Dispersion Measures for Core Vocabulary Compilation )[In {C}hinese]",
    author = "Bai, Ming-Hong  and
      Wu, Jian-Cheng  and
      Chien, Ying-Ni  and
      Huang, Shu-Ling  and
      Lin, Ching-Lung",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1007",
    pages = "49--51",
}
@inproceedings{chen-chung-2016-shi,
    title = "什麼時候「認真就輸了」？{---}{---}語料庫中「認真」一詞的語意變化(Do We Lose When Being Serious? {---}{C}hange in Meaning of the Word {``}Renzen(認真){''} in Corpora)",
    author = "Chen, Pei-Yi  and
      Chung, Siaw-Fong",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1008",
    pages = "52--81",
}
@inproceedings{huang-etal-2016-crowdsourcing,
    title = "Crowdsourcing Experiment Designs for {C}hinese Word Sense Annotation",
    author = "Huang, Tzu-Yun  and
      Wu, Hsiao-Han  and
      Lee, Chia-Chen  and
      Lee, Shao-Man  and
      Li, Guan-Wei  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1009",
    pages = "82--99",
}
@inproceedings{hsieh-ma-2016-ji,
    title = "基於相依詞向量的剖析結果重估與排序(N-best Parse Rescoring Based on Dependency-Based Word Embeddings)",
    author = "Hsieh, Yu-Ming  and
      Ma, Wei-Yun",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1010",
    pages = "100--102",
}
@inproceedings{pu-etal-2016-yi,
    title = "以語言模型評估學習者文句修改前後之流暢度(Using language model to assess the fluency of learners sentences edited by teachers)[In {C}hinese]",
    author = "Pu, Guan-Ying  and
      Chen, Po-Lin  and
      Wu, Shih-Hung",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1011",
    pages = "103--114",
}
@inproceedings{hsieh-etal-2016-yun,
    title = "運用序列到序列生成架構於重寫式自動摘要(Exploiting Sequence-to-Sequence Generation Framework for Automatic Abstractive Summarization)[In {C}hinese]",
    author = "Hsieh, Yu-Lun  and
      Liu, Shih-Hung  and
      Chen, Kuan-Yu  and
      Wang, Hsin-Min  and
      Hsu, Wen-Lian  and
      Chen, Berlin",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1012",
    pages = "115--128",
}
@inproceedings{chen-etal-2016-ji,
    title = "基於字元階層之語音合成用文脈訊息擷取(Character-Level Linguistic Features Extraction for Text-to-Speech System) [In {C}hinese]",
    author = "Chen, Kuan-Hung  and
      Liao, Shu-Han  and
      Liao, Yuan-Fu  and
      Wang, Yih-Ru",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1013",
    pages = "129--130",
}
@inproceedings{kuan-etal-2016-duo,
    title = "多通道之多重音頻串流方法之研究(Multi-channel Source Clustering of Polyphonic Music) [In {C}hinese]",
    author = "Kuan, Chih Yi  and
      Su, Li  and
      Chin, Yu Hao  and
      Wang, Jia-Ching",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1014",
    pages = "131--141",
}
@inproceedings{chen-chen-2016-support,
    title = "Support Super-Vector Machines in Automatic Speech Emotion Recognition",
    author = "Chen, Chia-Ying  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1015",
    pages = "142--152",
}
@inproceedings{liu-etal-2016-speech,
    title = "Speech Intelligibility and the Production of Fricative and Affricate among {M}andarin-speaking Children with Cerebral Palsy",
    author = "Liu, Chin-Ting  and
      Chen, Li-mei  and
      Lin, Yu-Ching  and
      Cheng, Chia-Fang  and
      Chang, Hui-chen",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1016",
    pages = "153--163",
}
@inproceedings{hu-chung-2016-wang,
    title = "網路新興語言{\&}耍{'}之語意辨析：以批踢踢語料庫為本(On the semantic analysis of the verb shua3 in {T}aiwan {M}andarin: The {PTT} corpus-based study)[In {C}hinese]",
    author = "Hu, Hsueh-ying  and
      Chung, Siaw-Fong",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1017",
    pages = "164--180",
}
@inproceedings{wang-etal-2016-fei,
    title = "非負矩陣分解法於語音調變頻譜強化之研究(A study of enhancing the modulation spectrum of speech signals via nonnegative matrix factorization)[In {C}hinese]",
    author = "Wang, Xu-Xiang  and
      Zheng, Zhi-Hao  and
      Tsao, Yu  and
      Hong, Jhih-Wei",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1018",
    pages = "181--193",
}
@inproceedings{chen-wang-2016-yi,
    title = "以多重表示選擇文章分類的樣本(Using Multiple Representations to Select Instances for Text Classification)[In {C}hinese]",
    author = "Chen, Yao-Hui  and
      Wang, Jhih-Wei",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1019",
    pages = "194--203",
}
@inproceedings{tran-phan-2016-computing,
    title = "Computing Sentiment Scores of Verb Phrases for {V}ietnamese",
    author = "Tran, Thien Khai  and
      Phan, Tuoi Thi",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1020",
    pages = "204--213",
}
@inproceedings{rysova-etal-2016-automatic,
    title = "Automatic evaluation of surface coherence in {L}2 texts in {C}zech",
    author = "Rysov{\'a}, Kate{\v{r}}ina  and
      Rysov{\'a}, Magdal{\'e}na  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1021",
    pages = "214--228",
}
@inproceedings{lin-chang-2016-facebook,
    title = "{F}acebook 活動事件擷取系統({F}acebook Activity Event Extraction System)[In {C}hinese]",
    author = "Lin, Yuan-Hao  and
      Chang, Chia-Hui",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1022",
    pages = "229--243",
}
@inproceedings{wu-chen-2016-biao,
    title = "標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In {C}hinese]",
    author = "Wu, Tung-Han  and
      Chen, Chia-Ping",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1023",
    pages = "244--254",
}
@inproceedings{tseng-etal-2016-ji,
    title = "基於深層類神經網路及表示學習技術之文件可讀性分類(Classification of Text Readability Based on Deep Neural Network and Representation Learning Techniques)[In {C}hinese]",
    author = "Tseng, Hou-Chiang  and
      Hung, Hsiao-Tsung  and
      Sung, Yao-Ting  and
      Chen, Berlin",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1024",
    pages = "255--270",
}
@inproceedings{hung-etal-2016-ming,
    title = "命名實體識別運用於產品同義詞擴增(Using Named Entity Recognition Increases the Synonym of Products)[In {C}hinese]",
    author = "Hung, Chihli  and
      Huang, Jheng-Hua  and
      Zhong, Rui-Jia  and
      Chen, Liang-Pu  and
      Yang, Ping-Che",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1025",
    pages = "271--283",
}
@inproceedings{magistry-2016-design,
    title = "Design of an Input Method for {T}aiwanese Hokkien using Unsupervized Word Segmentation for Language Modeling",
    author = "Magistry, Pierre",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1026",
    pages = "284--298",
}
@inproceedings{lin-hsieh-2016-sarcasm,
    title = "Sarcasm Detection in {C}hinese Using a Crowdsourced Corpus",
    author = "Lin, Shih-Kai  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1027",
    pages = "299--310",
}
@inproceedings{chen-etal-2016-ji-yu,
    title = "基於深層類神經網路之音訊事件偵測系統(Deep Neural Networks for Audio Event Detection)[In {C}hinese]",
    author = "Chen, Jhih-wei  and
      Liu, Chia-Hsin  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1028",
    pages = "311--324",
}
@inproceedings{hsiao-etal-2016-ji,
    title = "基於增強式深層類神經網路之語言辨認系統(Reinforcement Training for Deep Neural Networks-based Language Recognition)[In {C}hinese]",
    author = "Hsiao, Yen-Wen  and
      Liu, Hung-Jui  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1029",
    pages = "325--341",
}
@inproceedings{li-etal-2016-zhong,
    title = "中文近義詞的偵測與判別(Detection and Discrimination of {C}hinese Near-synonyms)[In {C}hinese]",
    author = "Li, Shih-Min  and
      Bai, Ming-Hong  and
      Wu, Jian-Cheng  and
      Huang, Shu-Ling  and
      Lin, Ching-Lung",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1030",
    pages = "342--351",
}
@inproceedings{miao-etal-2016-gou,
    title = "構建一個中文國小數學文字問題語料庫(Building a Corpus for Developing the {C}hinese Elementary School Math Word Problem Solver)[In {C}hinese]",
    author = "Miao, Shen-Yun  and
      Lin, Su-Chu  and
      Ma, Wei-Yun  and
      Su, Keh-Yih",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1031",
    pages = "352--371",
}
@inproceedings{huang-etal-2016-ji,
    title = "基於深層遞迴類神經網路之多通道電視回聲消除系統(Multi-Channel Television Echo Cancellation based on Deep Recurrent Neural Networks)[In {C}hinese]",
    author = "Huang, Hung  and
      Hung, Wei-Jung  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1032",
    pages = "372--386",
}
@inproceedings{sun-lee-2016-ji,
    title = "基於多模態主動式學習法進行需備標記樣本之挑選用於候用校長評鑑之自動化評分系統建置(A Multimodal Active Learning Approach toward Identifying Samples to Label during the Development of Automatic Oral Presentation Assessment System for Pre-service Principals Certification Program)[In {C}hinese]",
    author = "Sun, Hung-Ching  and
      Lee, Chi-Chun",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1033",
    pages = "387--401",
}
@inproceedings{lin-etal-2016-generation,
    title = "Generation of Conceptual-Level Text Cloud with Graph Diffusion",
    author = "Lin, Ying-Chun  and
      Yang, Po-An  and
      Lee, Yen-Kuan  and
      Chuang, Kun-Ta",
    booktitle = "Proceedings of the 28th Conference on Computational Linguistics and Speech Processing ({ROCLING} 2016)",
    month = oct,
    year = "2016",
    address = "Tainan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://www.aclweb.org/anthology/O16-1034",
    pages = "402--411",
}
@proceedings{naacl-2016-2016-north-american,
    title = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    editor = "Bansal, Mohit  and
      Rush, Alexander M.",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-4000",
    doi = "10.18653/v1/N16-4",
}
@inproceedings{flickinger-etal-2016-english,
    title = "{E}nglish {R}esource {S}emantics",
    author = "Flickinger, Dan  and
      Bender, Emily M.  and
      Packard, Woodley",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-4001",
    doi = "10.18653/v1/N16-4001",
    pages = "1--5",
}
@inproceedings{khapra-chandar-2016-multilingual,
    title = "Multilingual Multimodal Language Processing Using Neural Networks",
    author = "Khapra, Mitesh M  and
      Chandar, Sarath",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-4002",
    doi = "10.18653/v1/N16-4002",
    pages = "6--7",
}
@inproceedings{yih-ma-2016-question,
    title = "Question Answering with Knowledge Base, Web and Beyond",
    author = "Yih, Wen-tau  and
      Ma, Hao",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-4003",
    doi = "10.18653/v1/N16-4003",
    pages = "8--10",
}
@inproceedings{lu-li-2016-recent,
    title = "Recent Progress in Deep Learning for {NLP}",
    author = "Lu, Zhengdong  and
      Li, Hang",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-4004",
    doi = "10.18653/v1/N16-4004",
    pages = "11--13",
}
@inproceedings{wang-cohen-2016-scalable,
    title = "Scalable Statistical Relational Learning for {NLP}",
    author = "Wang, William Yang  and
      Cohen, William",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-4005",
    doi = "10.18653/v1/N16-4005",
    pages = "14--16",
}
@inproceedings{bhattacharyya-etal-2016-statistical,
    title = "Statistical Machine Translation between Related Languages",
    author = "Bhattacharyya, Pushpak  and
      Khapra, Mitesh M.  and
      Kunchukuttan, Anoop",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-4006",
    doi = "10.18653/v1/N16-4006",
    pages = "17--20",
}
@proceedings{naacl-2016-2016-north,
    title = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    editor = "DeNero, John  and
      Finlayson, Mark  and
      Reddy, Sravana",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3000",
    doi = "10.18653/v1/N16-3",
}
@inproceedings{zeldes-2016-rstweb,
    title = "rst{W}eb - A Browser-based Annotation Interface for {R}hetorical {S}tructure {T}heory and Discourse Relations",
    author = "Zeldes, Amir",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3001",
    doi = "10.18653/v1/N16-3001",
    pages = "1--5",
}
@inproceedings{nguyen-etal-2016-instant,
    title = "Instant Feedback for Increasing the Presence of Solutions in Peer Reviews",
    author = "Nguyen, Huy  and
      Xiong, Wenting  and
      Litman, Diane",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3002",
    doi = "10.18653/v1/N16-3002",
    pages = "6--10",
}
@inproceedings{abdelali-etal-2016-farasa,
    title = "{F}arasa: A Fast and Furious Segmenter for {A}rabic",
    author = "Abdelali, Ahmed  and
      Darwish, Kareem  and
      Durrani, Nadir  and
      Mubarak, Hamdy",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3003",
    doi = "10.18653/v1/N16-3003",
    pages = "11--16",
}
@inproceedings{abdelali-etal-2016-iappraise,
    title = "i{A}ppraise: A Manual Machine Translation Evaluation Environment Supporting Eye-tracking",
    author = "Abdelali, Ahmed  and
      Durrani, Nadir  and
      Guzm{\'a}n, Francisco",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3004",
    doi = "10.18653/v1/N16-3004",
    pages = "17--21",
}
@inproceedings{lee-goldsmith-2016-linguistica,
    title = "Linguistica 5: Unsupervised Learning of Linguistic Structure",
    author = "Lee, Jackson  and
      Goldsmith, John",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3005",
    doi = "10.18653/v1/N16-3005",
    pages = "22--26",
}
@inproceedings{yvon-etal-2016-transread,
    title = "{T}rans{R}ead: Designing a Bilingual Reading Experience with Machine Translation Technologies",
    author = "Yvon, Fran{\c{c}}ois  and
      Xu, Yong  and
      Apidianaki, Marianna  and
      Pillias, Cl{\'e}ment  and
      Cubaud, Pierre",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3006",
    doi = "10.18653/v1/N16-3006",
    pages = "27--31",
}
@inproceedings{artstein-etal-2016-new,
    title = "New Dimensions in Testimony Demonstration",
    author = "Artstein, Ron  and
      Gainer, Alesia  and
      Georgila, Kallirroi  and
      Leuski, Anton  and
      Shapiro, Ari  and
      Traum, David",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3007",
    doi = "10.18653/v1/N16-3007",
    pages = "32--36",
}
@inproceedings{zhang-etal-2016-argrewrite,
    title = "{A}rg{R}ewrite: A Web-based Revision Assistant for Argumentative Writings",
    author = "Zhang, Fan  and
      Hwa, Rebecca  and
      Litman, Diane  and
      Hashemi, Homa B.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3008",
    doi = "10.18653/v1/N16-3008",
    pages = "37--41",
}
@inproceedings{dehdari-etal-2016-scaling,
    title = "Scaling Up Word Clustering",
    author = "Dehdari, Jon  and
      Tan, Liling  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3009",
    doi = "10.18653/v1/N16-3009",
    pages = "42--46",
}
@inproceedings{crook-etal-2016-task,
    title = "Task Completion Platform: A self-serve multi-domain goal oriented dialogue platform",
    author = "Crook, Paul  and
      Marin, Alex  and
      Agarwal, Vipul  and
      Aggarwal, Khushboo  and
      Anastasakos, Tasos  and
      Bikkula, Ravi  and
      Boies, Daniel  and
      Celikyilmaz, Asli  and
      Chandramohan, Senthilkumar  and
      Feizollahi, Zhaleh  and
      Holenstein, Roman  and
      Jeong, Minwoo  and
      Khan, Omar  and
      Kim, Young-Bum  and
      Krawczyk, Elizabeth  and
      Liu, Xiaohu  and
      Panic, Danko  and
      Radostev, Vasiliy  and
      Ramesh, Nikhil  and
      Robichaud, Jean-Phillipe  and
      Rochette, Alexandre  and
      Stromberg, Logan  and
      Sarikaya, Ruhi",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3010",
    doi = "10.18653/v1/N16-3010",
    pages = "47--51",
}
@inproceedings{roy-roth-2016-illinois,
    title = "{I}llinois Math Solver: Math Reasoning on the Web",
    author = "Roy, Subhro  and
      Roth, Dan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3011",
    doi = "10.18653/v1/N16-3011",
    pages = "52--56",
}
@inproceedings{pusse-etal-2016-lingoturk,
    title = "{L}ingo{T}urk: managing crowdsourced tasks for psycholinguistics",
    author = "Pusse, Florian  and
      Sayeed, Asad  and
      Demberg, Vera",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3012",
    doi = "10.18653/v1/N16-3012",
    pages = "57--61",
}
@inproceedings{napoles-etal-2016-sentential,
    title = "Sentential Paraphrasing as Black-Box Machine Translation",
    author = "Napoles, Courtney  and
      Callison-Burch, Chris  and
      Post, Matt",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3013",
    doi = "10.18653/v1/N16-3013",
    pages = "62--66",
}
@inproceedings{liang-etal-2016-tag,
    title = "A Tag-based {E}nglish Math Word Problem Solver with Understanding, Reasoning and Explanation",
    author = "Liang, Chao-Chun  and
      Hsu, Kuang-Yi  and
      Huang, Chien-Tsung  and
      Li, Chung-Min  and
      Miao, Shen-Yu  and
      Su, Keh-Yih",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3014",
    doi = "10.18653/v1/N16-3014",
    pages = "67--71",
}
@inproceedings{lu-etal-2016-cross,
    title = "Cross-media Event Extraction and Recommendation",
    author = "Lu, Di  and
      Voss, Clare  and
      Tao, Fangbo  and
      Ren, Xiang  and
      Guan, Rachel  and
      Korolov, Rostyslav  and
      Zhang, Tongtao  and
      Wang, Dongang  and
      Li, Hongzhi  and
      Cassidy, Taylor  and
      Ji, Heng  and
      Chang, Shih-fu  and
      Han, Jiawei  and
      Wallace, William  and
      Hendler, James  and
      Si, Mei  and
      Kaplan, Lance",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3015",
    doi = "10.18653/v1/N16-3015",
    pages = "72--76",
}
@inproceedings{bhatt-etal-2016-soda,
    title = "{SODA}:Service Oriented Domain Adaptation Architecture for Microblog Categorization",
    author = "Bhatt, Himanshu Sharad  and
      Dandapat, Sandipan  and
      Balaji, Peddamuthu  and
      Roy, Shourya  and
      Jat, Sharmistha  and
      Semwal, Deepali",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3016",
    doi = "10.18653/v1/N16-3016",
    pages = "77--81",
}
@inproceedings{muller-etal-2016-lecture,
    title = "Lecture Translator - Speech translation framework for simultaneous lecture translation",
    author = {M{\"u}ller, Markus  and
      Nguyen, Thai Son  and
      Niehues, Jan  and
      Cho, Eunah  and
      Kr{\"u}ger, Bastian  and
      Ha, Thanh-Le  and
      Kilgour, Kevin  and
      Sperber, Matthias  and
      Mediani, Mohammed  and
      St{\"u}ker, Sebastian  and
      Waibel, Alex},
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3017",
    doi = "10.18653/v1/N16-3017",
    pages = "82--86",
}
@inproceedings{fung-etal-2016-zara,
    title = "{Z}ara The {S}upergirl: An Empathetic Personality Recognition System",
    author = "Fung, Pascale  and
      Dey, Anik  and
      Siddique, Farhad Bin  and
      Lin, Ruixi  and
      Yang, Yang  and
      Wan, Yan  and
      Chan, Ho Yin Ricky",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3018",
    doi = "10.18653/v1/N16-3018",
    pages = "87--91",
}
@inproceedings{mohanty-etal-2016-kathaa-visual,
    title = "{K}athaa: A Visual Programming Framework for {NLP} Applications",
    author = "Mohanty, Sharada Prasanna  and
      Wani, Nehal J  and
      Srivastava, Manish  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3019",
    doi = "10.18653/v1/N16-3019",
    pages = "92--96",
}
@inproceedings{ribeiro-etal-2016-trust,
    title = "{``}Why Should {I} Trust You?{''}: Explaining the Predictions of Any Classifier",
    author = "Ribeiro, Marco  and
      Singh, Sameer  and
      Guestrin, Carlos",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-3020",
    doi = "10.18653/v1/N16-3020",
    pages = "97--101",
}
@proceedings{naacl-2016-naacl,
    title = "Proceedings of the {NAACL} Student Research Workshop",
    editor = "Andreas, Jacob  and
      Choi, Eunsol  and
      Lazaridou, Angeliki",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2000",
    doi = "10.18653/v1/N16-2",
}
@inproceedings{feng-etal-2016-end,
    title = "An End-to-end Approach to Learning Semantic Frames with Feedforward Neural Network",
    author = "Feng, Yukun  and
      Xu, Yipei  and
      Yu, Dong",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2001",
    doi = "10.18653/v1/N16-2001",
    pages = "1--7",
}
@inproceedings{gladkova-etal-2016-analogy,
    title = "Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn{'}t.",
    author = "Gladkova, Anna  and
      Drozd, Aleksandr  and
      Matsuoka, Satoshi",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2002",
    doi = "10.18653/v1/N16-2002",
    pages = "8--15",
}
@inproceedings{chow-2016-argument,
    title = "Argument Identification in {C}hinese Editorials",
    author = "Chow, Marisa",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2003",
    doi = "10.18653/v1/N16-2003",
    pages = "16--21",
}
@inproceedings{sharma-karnick-2016-automatic,
    title = "Automatic tagging and retrieval of {E}-Commerce products based on visual features",
    author = "Sharma, Vasu  and
      Karnick, Harish",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2004",
    doi = "10.18653/v1/N16-2004",
    pages = "22--28",
}
@inproceedings{gemechu-etal-2016-combining,
    title = "Combining syntactic patterns and {W}ikipedia{'}s hierarchy of hyperlinks to extract meronym relations",
    author = "Gemechu, Debela Tesfaye  and
      Zock, Michael  and
      Teferra, Solomon",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2005",
    doi = "10.18653/v1/N16-2005",
    pages = "29--36",
}
@inproceedings{hintz-2016-data,
    title = "Data-driven Paraphrasing and Stylistic Harmonization",
    author = "Hintz, Gerold",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2006",
    doi = "10.18653/v1/N16-2006",
    pages = "37--44",
}
@inproceedings{liu-etal-2016-detecting,
    title = "Detecting {``}Smart{''} Spammers on Social Network: A Topic Model Approach",
    author = "Liu, Linqing  and
      Lu, Yao  and
      Luo, Ye  and
      Zhang, Renxian  and
      Itti, Laurent  and
      Lu, Jianwei",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2007",
    doi = "10.18653/v1/N16-2007",
    pages = "45--50",
}
@inproceedings{motlani-2016-developing,
    title = "Developing language technology tools and resources for a resource-poor language: {S}indhi",
    author = "Motlani, Raveesh",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2008",
    doi = "10.18653/v1/N16-2008",
    pages = "51--58",
}
@inproceedings{binoun-etal-2016-effects,
    title = "Effects of Communicative Pressures on Novice {L}2 Learners{'} Use of Optional Formal Devices",
    author = "Binoun, Yoav  and
      Delogu, Francesca  and
      Greenberg, Clayton  and
      Mozuraitis, Mindaugas  and
      Crocker, Matthew",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2009",
    doi = "10.18653/v1/N16-2009",
    pages = "59--65",
}
@inproceedings{jain-sharma-2016-explicit,
    title = "Explicit Argument Identification for Discourse Parsing In {H}indi: A Hybrid Pipeline",
    author = "Jain, Rohit  and
      Sharma, Dipti",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2010",
    doi = "10.18653/v1/N16-2010",
    pages = "66--72",
}
@inproceedings{liew-turtle-2016-exploring,
    title = "Exploring Fine-Grained Emotion Detection in Tweets",
    author = "Liew, Jasy Suet Yan  and
      Turtle, Howard R.",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2011",
    doi = "10.18653/v1/N16-2011",
    pages = "73--80",
}
@inproceedings{yang-etal-2016-extraction,
    title = "Extraction of Bilingual Technical Terms for {C}hinese-{J}apanese Patent Translation",
    author = "Yang, Wei  and
      Yan, Jinghui  and
      Lepage, Yves",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2012",
    doi = "10.18653/v1/N16-2012",
    pages = "81--87",
}
@inproceedings{waseem-hovy-2016-hateful,
    title = "Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on {T}witter",
    author = "Waseem, Zeerak  and
      Hovy, Dirk",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2013",
    doi = "10.18653/v1/N16-2013",
    pages = "88--93",
}
@inproceedings{j-kurisinkel-etal-2016-non,
    title = "Non-decreasing Sub-modular Function for Comprehensible Summarization",
    author = "J Kurisinkel, Litton  and
      Mishra, Pruthwik  and
      Muralidaran, Vigneshwaran  and
      Varma, Vasudeva  and
      Misra Sharma, Dipti",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2014",
    doi = "10.18653/v1/N16-2014",
    pages = "94--101",
}
@inproceedings{lamont-washington-2016-phylogenetic,
    title = "Phylogenetic simulations over constraint-based grammar formalisms",
    author = "Lamont, Andrew  and
      Washington, Jonathan",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2015",
    doi = "10.18653/v1/N16-2015",
    pages = "102--108",
}
@inproceedings{jain-2016-question,
    title = "Question Answering over Knowledge Base using Factual Memory Networks",
    author = "Jain, Sarthak",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2016",
    doi = "10.18653/v1/N16-2016",
    pages = "109--115",
}
@inproceedings{currey-etal-2016-using,
    title = "Using Related Languages to Enhance Statistical Language Models",
    author = "Currey, Anna  and
      Karakanta, Alina  and
      Dehdari, Jon",
    booktitle = "Proceedings of the {NAACL} Student Research Workshop",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-2017",
    doi = "10.18653/v1/N16-2017",
    pages = "116--123",
}
@proceedings{naacl-2016-2016,
    title = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    editor = "Knight, Kevin  and
      Nenkova, Ani  and
      Rambow, Owen",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1000",
    doi = "10.18653/v1/N16-1",
}
@inproceedings{graham-liu-2016-achieving,
    title = "Achieving Accurate Conclusions in Evaluation of Automatic Machine Translation Metrics",
    author = "Graham, Yvette  and
      Liu, Qun",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1001",
    doi = "10.18653/v1/N16-1001",
    pages = "1--10",
}
@inproceedings{richardson-etal-2016-flexible,
    title = "Flexible Non-Terminals for Dependency Tree-to-Tree Reordering",
    author = "Richardson, John  and
      Cromi{\`e}res, Fabien  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1002",
    doi = "10.18653/v1/N16-1002",
    pages = "11--19",
}
@inproceedings{miura-etal-2016-selecting,
    title = "Selecting Syntactic, Non-redundant Segments in Active Learning for Machine Translation",
    author = "Miura, Akiva  and
      Neubig, Graham  and
      Paul, Michael  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1003",
    doi = "10.18653/v1/N16-1003",
    pages = "20--29",
}
@inproceedings{zoph-knight-2016-multi,
    title = "Multi-Source Neural Translation",
    author = "Zoph, Barret  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1004",
    doi = "10.18653/v1/N16-1004",
    pages = "30--34",
}
@inproceedings{sennrich-etal-2016-controlling,
    title = "Controlling Politeness in Neural Machine Translation via Side Constraints",
    author = "Sennrich, Rico  and
      Haddow, Barry  and
      Birch, Alexandra",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1005",
    doi = "10.18653/v1/N16-1005",
    pages = "35--40",
}
@inproceedings{cherry-2016-empirical,
    title = "An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation",
    author = "Cherry, Colin",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1006",
    doi = "10.18653/v1/N16-1006",
    pages = "41--46",
}
@inproceedings{wang-ling-2016-neural,
    title = "Neural Network-Based Abstract Generation for Opinions and Arguments",
    author = "Wang, Lu  and
      Ling, Wang",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1007",
    doi = "10.18653/v1/N16-1007",
    pages = "47--57",
}
@inproceedings{wang-etal-2016-low,
    title = "A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization",
    author = "Wang, William Yang  and
      Mehdad, Yashar  and
      Radev, Dragomir R.  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1008",
    doi = "10.18653/v1/N16-1008",
    pages = "58--68",
}
@inproceedings{contractor-etal-2016-entity,
    title = "Entity-balanced {G}aussian p{LSA} for Automated Comparison",
    author = "Contractor, Danish  and
      Singla, Parag  and
      {Mausam}",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1009",
    doi = "10.18653/v1/N16-1009",
    pages = "69--79",
}
@inproceedings{luo-etal-2016-automatic,
    title = "Automatic Summarization of Student Course Feedback",
    author = "Luo, Wencan  and
      Liu, Fei  and
      Liu, Zitao  and
      Litman, Diane",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1010",
    doi = "10.18653/v1/N16-1010",
    pages = "80--85",
}
@inproceedings{jain-mausam-2016-knowledge,
    title = "Knowledge-Guided Linguistic Rewrites for Inference Rule Verification",
    author = "Jain, Prachi  and
      {Mausam}",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1011",
    doi = "10.18653/v1/N16-1011",
    pages = "86--92",
}
@inproceedings{chopra-etal-2016-abstractive,
    title = "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks",
    author = "Chopra, Sumit  and
      Auli, Michael  and
      Rush, Alexander M.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1012",
    doi = "10.18653/v1/N16-1012",
    pages = "93--98",
}
@inproceedings{perret-etal-2016-integer,
    title = "Integer Linear Programming for Discourse Parsing",
    author = "Perret, J{\'e}r{\'e}my  and
      Afantenos, Stergos  and
      Asher, Nicholas  and
      Morey, Mathieu",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1013",
    doi = "10.18653/v1/N16-1013",
    pages = "99--109",
}
@inproceedings{li-etal-2016-diversity,
    title = "A Diversity-Promoting Objective Function for Neural Conversation Models",
    author = "Li, Jiwei  and
      Galley, Michel  and
      Brockett, Chris  and
      Gao, Jianfeng  and
      Dolan, Bill",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1014",
    doi = "10.18653/v1/N16-1014",
    pages = "110--119",
}
@inproceedings{wen-etal-2016-multi,
    title = "Multi-domain Neural Network Language Generation for Spoken Dialogue Systems",
    author = "Wen, Tsung-Hsien  and
      Ga{\v{s}}i{\'c}, Milica  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Rojas-Barahona, Lina M.  and
      Su, Pei-Hao  and
      Vandyke, David  and
      Young, Steve",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1015",
    doi = "10.18653/v1/N16-1015",
    pages = "120--129",
}
@inproceedings{bertero-fung-2016-long,
    title = "A Long Short-Term Memory Framework for Predicting Humor in Dialogues",
    author = "Bertero, Dario  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1016",
    doi = "10.18653/v1/N16-1016",
    pages = "130--135",
}
@inproceedings{zhang-etal-2016-conversational,
    title = "Conversational Flow in {O}xford-style Debates",
    author = "Zhang, Justine  and
      Kumar, Ravi  and
      Ravi, Sujith  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1017",
    doi = "10.18653/v1/N16-1017",
    pages = "136--141",
}
@inproceedings{mrksic-etal-2016-counter,
    title = "Counter-fitting Word Vectors to Linguistic Constraints",
    author = "Mrk{\v{s}}i{\'c}, Nikola  and
      {\'O} S{\'e}aghdha, Diarmuid  and
      Thomson, Blaise  and
      Ga{\v{s}}i{\'c}, Milica  and
      Rojas-Barahona, Lina M.  and
      Su, Pei-Hao  and
      Vandyke, David  and
      Wen, Tsung-Hsien  and
      Young, Steve",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1018",
    doi = "10.18653/v1/N16-1018",
    pages = "142--148",
}
@inproceedings{yang-etal-2016-grounded,
    title = "Grounded Semantic Role Labeling",
    author = "Yang, Shaohua  and
      Gao, Qiaozi  and
      Liu, Changsong  and
      Xiong, Caiming  and
      Zhu, Song-Chun  and
      Chai, Joyce Y.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1019",
    doi = "10.18653/v1/N16-1019",
    pages = "149--159",
}
@inproceedings{shutova-etal-2016-black,
    title = "Black Holes and White Rabbits: Metaphor Identification with Visual Features",
    author = "Shutova, Ekaterina  and
      Kiela, Douwe  and
      Maillard, Jean",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1020",
    doi = "10.18653/v1/N16-1020",
    pages = "160--170",
}
@inproceedings{rajendran-etal-2016-bridge,
    title = "Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning",
    author = "Rajendran, Janarthanan  and
      Khapra, Mitesh M.  and
      Chandar, Sarath  and
      Ravindran, Balaraman",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1021",
    doi = "10.18653/v1/N16-1021",
    pages = "171--181",
}
@inproceedings{gella-etal-2016-unsupervised,
    title = "Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings",
    author = "Gella, Spandana  and
      Lapata, Mirella  and
      Keller, Frank",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1022",
    doi = "10.18653/v1/N16-1022",
    pages = "182--192",
}
@inproceedings{yatskar-etal-2016-stating,
    title = "Stating the Obvious: Extracting Visual Common Sense Knowledge",
    author = "Yatskar, Mark  and
      Ordonez, Vicente  and
      Farhadi, Ali",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1023",
    doi = "10.18653/v1/N16-1023",
    pages = "193--198",
}
@inproceedings{dyer-etal-2016-recurrent,
    title = "Recurrent Neural Network Grammars",
    author = "Dyer, Chris  and
      Kuncoro, Adhiguna  and
      Ballesteros, Miguel  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1024",
    doi = "10.18653/v1/N16-1024",
    pages = "199--209",
}
@inproceedings{xu-etal-2016-expected,
    title = "Expected {F}-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks",
    author = "Xu, Wenduan  and
      Auli, Michael  and
      Clark, Stephen",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1025",
    doi = "10.18653/v1/N16-1025",
    pages = "210--220",
}
@inproceedings{lewis-etal-2016-lstm,
    title = "{LSTM} {CCG} Parsing",
    author = "Lewis, Mike  and
      Lee, Kenton  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1026",
    doi = "10.18653/v1/N16-1026",
    pages = "221--231",
}
@inproceedings{vaswani-etal-2016-supertagging,
    title = "Supertagging With {LSTM}s",
    author = "Vaswani, Ashish  and
      Bisk, Yonatan  and
      Sagae, Kenji  and
      Musa, Ryan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1027",
    doi = "10.18653/v1/N16-1027",
    pages = "232--237",
}
@inproceedings{luo-yang-2016-empirical,
    title = "An Empirical Study of Automatic {C}hinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition",
    author = "Luo, Wencan  and
      Yang, Fan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1028",
    doi = "10.18653/v1/N16-1028",
    pages = "238--248",
}
@inproceedings{zhang-etal-2016-name,
    title = "Name Tagging for Low-resource Incident Languages based on Expectation-driven Learning",
    author = "Zhang, Boliang  and
      Pan, Xiaoman  and
      Wang, Tianlu  and
      Vaswani, Ashish  and
      Ji, Heng  and
      Knight, Kevin  and
      Marcu, Daniel",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1029",
    doi = "10.18653/v1/N16-1029",
    pages = "249--259",
}
@inproceedings{lample-etal-2016-neural,
    title = "Neural Architectures for Named Entity Recognition",
    author = "Lample, Guillaume  and
      Ballesteros, Miguel  and
      Subramanian, Sandeep  and
      Kawakami, Kazuya  and
      Dyer, Chris",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1030",
    doi = "10.18653/v1/N16-1030",
    pages = "260--270",
}
@inproceedings{choi-2016-dynamic,
    title = "Dynamic Feature Induction: The Last Gist to the State-of-the-Art",
    author = "Choi, Jinho D.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1031",
    doi = "10.18653/v1/N16-1031",
    pages = "271--281",
}
@inproceedings{yang-etal-2016-drop,
    title = "Drop-out Conditional Random Fields for {T}witter with Huge Mined Gazetteer",
    author = "Yang, Eunsuk  and
      Kim, Young-Bum  and
      Sarikaya, Ruhi  and
      Kim, Yu-Seop",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1032",
    doi = "10.18653/v1/N16-1032",
    pages = "282--288",
}
@inproceedings{yang-mitchell-2016-joint,
    title = "Joint Extraction of Events and Entities within a Document Context",
    author = "Yang, Bishan  and
      Mitchell, Tom M.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1033",
    doi = "10.18653/v1/N16-1033",
    pages = "289--299",
}
@inproceedings{nguyen-etal-2016-joint,
    title = "Joint Event Extraction via Recurrent Neural Networks",
    author = "Nguyen, Thien Huu  and
      Cho, Kyunghyun  and
      Grishman, Ralph",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1034",
    doi = "10.18653/v1/N16-1034",
    pages = "300--309",
}
@inproceedings{zhang-etal-2016-top,
    title = "Top-down Tree Long Short-Term Memory Networks",
    author = "Zhang, Xingxing  and
      Lu, Liang  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1035",
    doi = "10.18653/v1/N16-1035",
    pages = "310--320",
}
@inproceedings{tran-etal-2016-recurrent,
    title = "Recurrent Memory Networks for Language Modeling",
    author = "Tran, Ke  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1036",
    doi = "10.18653/v1/N16-1036",
    pages = "321--331",
}
@inproceedings{ji-etal-2016-latent,
    title = "A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models",
    author = "Ji, Yangfeng  and
      Haffari, Gholamreza  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1037",
    doi = "10.18653/v1/N16-1037",
    pages = "332--342",
}
@inproceedings{abramova-fernandez-2016-questioning,
    title = "Questioning Arbitrariness in Language: a Data-Driven Study of Conventional Iconicity",
    author = "Abramova, Ekaterina  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1038",
    doi = "10.18653/v1/N16-1038",
    pages = "343--352",
}
@inproceedings{koper-schulte-im-walde-2016-distinguishing,
    title = "Distinguishing Literal and Non-Literal Usage of {G}erman Particle Verbs",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1039",
    doi = "10.18653/v1/N16-1039",
    pages = "353--362",
}
@inproceedings{liu-hwa-2016-phrasal,
    title = "Phrasal Substitution of Idiomatic Expressions",
    author = "Liu, Changsheng  and
      Hwa, Rebecca",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1040",
    doi = "10.18653/v1/N16-1040",
    pages = "363--373",
}
@inproceedings{peng-jiang-2016-leverage,
    title = "Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks",
    author = "Peng, Yangtuo  and
      Jiang, Hui",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1041",
    doi = "10.18653/v1/N16-1041",
    pages = "374--379",
}
@inproceedings{yuan-briscoe-2016-grammatical,
    title = "Grammatical error correction using neural machine translation",
    author = "Yuan, Zheng  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1042",
    doi = "10.18653/v1/N16-1042",
    pages = "380--386",
}
@inproceedings{lazaridou-etal-2016-multimodal,
    title = "Multimodal Semantic Learning from Child-Directed Input",
    author = "Lazaridou, Angeliki  and
      Chrupa{\l}a, Grzegorz  and
      Fern{\'a}ndez, Raquel  and
      Baroni, Marco",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1043",
    doi = "10.18653/v1/N16-1043",
    pages = "387--392",
}
@inproceedings{shi-etal-2016-recurrent,
    title = "Recurrent Support Vector Machines For Slot Tagging In Spoken Language Understanding",
    author = "Shi, Yangyang  and
      Yao, Kaisheng  and
      Chen, Hu  and
      Yu, Dong  and
      Pan, Yi-Cheng  and
      Hwang, Mei-Yuh",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1044",
    doi = "10.18653/v1/N16-1044",
    pages = "393--399",
}
@inproceedings{chang-etal-2016-expectation,
    title = "Expectation-Regulated Neural Model for Event Mention Extraction",
    author = "Chang, Ching-Yun  and
      Teng, Zhiyang  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1045",
    doi = "10.18653/v1/N16-1045",
    pages = "400--410",
}
@inproceedings{liu-etal-2016-agreement,
    title = "Agreement on Target-bidirectional Neural Machine Translation",
    author = "Liu, Lemao  and
      Utiyama, Masao  and
      Finch, Andrew  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1046",
    doi = "10.18653/v1/N16-1046",
    pages = "411--416",
}
@inproceedings{girlea-etal-2016-psycholinguistic,
    title = "Psycholinguistic Features for Deceptive Role Detection in Werewolf",
    author = "Girlea, Codruta  and
      Girju, Roxana  and
      Amir, Eyal",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1047",
    doi = "10.18653/v1/N16-1047",
    pages = "417--422",
}
@inproceedings{castro-ferreira-etal-2016-individual,
    title = "Individual Variation in the Choice of Referential Form",
    author = "Castro Ferreira, Thiago  and
      Krahmer, Emiel  and
      Wubben, Sander",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1048",
    doi = "10.18653/v1/N16-1048",
    pages = "423--427",
}
@inproceedings{sha-etal-2016-joint,
    title = "Joint Learning Templates and Slots for Event Schema Induction",
    author = "Sha, Lei  and
      Li, Sujian  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1049",
    doi = "10.18653/v1/N16-1049",
    pages = "428--434",
}
@inproceedings{paetzold-specia-2016-inferring,
    title = "Inferring Psycholinguistic Properties of Words",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1050",
    doi = "10.18653/v1/N16-1050",
    pages = "435--440",
}
@inproceedings{liu-etal-2016-intra,
    title = "Intra-Topic Variability Normalization based on Linear Projection for Topic Classification",
    author = "Liu, Quan  and
      Guo, Wu  and
      Ling, Zhen-Hua  and
      Jiang, Hui  and
      Hu, Yu",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1051",
    doi = "10.18653/v1/N16-1051",
    pages = "441--446",
}
@inproceedings{ambati-etal-2016-shift,
    title = "Shift-Reduce {CCG} Parsing using Neural Network Models",
    author = "Ambati, Bharat Ram  and
      Deoskar, Tejaswini  and
      Steedman, Mark",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1052",
    doi = "10.18653/v1/N16-1052",
    pages = "447--453",
}
@inproceedings{krstovski-etal-2016-online,
    title = "Online Multilingual Topic Models with Multi-Level Hyperpriors",
    author = "Krstovski, Kriste  and
      Smith, David  and
      Kurtz, Michael J.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1053",
    doi = "10.18653/v1/N16-1053",
    pages = "454--459",
}
@inproceedings{nguyen-etal-2016-stranse,
    title = "{ST}rans{E}: a novel embedding model of entities and relationships in knowledge bases",
    author = "Nguyen, Dat Quoc  and
      Sirts, Kairit  and
      Qu, Lizhen  and
      Johnson, Mark",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1054",
    doi = "10.18653/v1/N16-1054",
    pages = "460--466",
}
@inproceedings{garrette-alpert-abrams-2016-unsupervised,
    title = "An Unsupervised Model of Orthographic Variation for Historical Document Transcription",
    author = "Garrette, Dan  and
      Alpert-Abrams, Hannah",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1055",
    doi = "10.18653/v1/N16-1055",
    pages = "467--472",
}
@inproceedings{jagannatha-yu-2016-bidirectional,
    title = "Bidirectional {RNN} for Medical Event Detection in Electronic Health Records",
    author = "Jagannatha, Abhyuday N  and
      Yu, Hong",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1056",
    doi = "10.18653/v1/N16-1056",
    pages = "473--482",
}
@inproceedings{lau-baldwin-2016-sensitivity,
    title = "The Sensitivity of Topic Coherence Evaluation to Topic Cardinality",
    author = "Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1057",
    doi = "10.18653/v1/N16-1057",
    pages = "483--487",
}
@inproceedings{puduppully-etal-2016-transition,
    title = "Transition-Based Syntactic Linearization with Lookahead Features",
    author = "Puduppully, Ratish  and
      Zhang, Yue  and
      Shrivastava, Manish",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1058",
    doi = "10.18653/v1/N16-1058",
    pages = "488--493",
}
@inproceedings{kim-lee-2016-recurrent-neural,
    title = "A Recurrent Neural Networks Approach for Estimating the Quality of Machine Translation Output",
    author = "Kim, Hyun  and
      Lee, Jong-Hyeok",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1059",
    doi = "10.18653/v1/N16-1059",
    pages = "494--498",
}
@inproceedings{schwartz-etal-2016-symmetric,
    title = "Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives",
    author = "Schwartz, Roy  and
      Reichart, Roi  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1060",
    doi = "10.18653/v1/N16-1060",
    pages = "499--505",
}
@inproceedings{fei-liu-2016-breaking,
    title = "Breaking the Closed World Assumption in Text Classification",
    author = "Fei, Geli  and
      Liu, Bing",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1061",
    doi = "10.18653/v1/N16-1061",
    pages = "506--514",
}
@inproceedings{lee-dernoncourt-2016-sequential,
    title = "Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks",
    author = "Lee, Ji Young  and
      Dernoncourt, Franck",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1062",
    doi = "10.18653/v1/N16-1062",
    pages = "515--520",
}
@inproceedings{kurata-etal-2016-improved,
    title = "Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence",
    author = "Kurata, Gakuto  and
      Xiang, Bing  and
      Zhou, Bowen",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1063",
    doi = "10.18653/v1/N16-1063",
    pages = "521--526",
}
@inproceedings{wang-etal-2016-learning-distributed,
    title = "Learning Distributed Word Representations For Bidirectional {LSTM} Recurrent Neural Network",
    author = "Wang, Peilu  and
      Qian, Yao  and
      Soong, Frank K.  and
      He, Lei  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1064",
    doi = "10.18653/v1/N16-1064",
    pages = "527--533",
}
@inproceedings{vu-etal-2016-combining,
    title = "Combining Recurrent and Convolutional Neural Networks for Relation Classification",
    author = {Vu, Ngoc Thang  and
      Adel, Heike  and
      Gupta, Pankaj  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1065",
    doi = "10.18653/v1/N16-1065",
    pages = "534--539",
}
@inproceedings{yu-etal-2016-building,
    title = "Building {C}hinese Affective Resources in Valence-Arousal Dimensions",
    author = "Yu, Liang-Chih  and
      Lee, Lung-Hao  and
      Hao, Shuai  and
      Wang, Jin  and
      He, Yunchao  and
      Hu, Jun  and
      Lai, K. Robert  and
      Zhang, Xuejie",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1066",
    doi = "10.18653/v1/N16-1066",
    pages = "540--545",
}
@inproceedings{ahrendt-demberg-2016-improving,
    title = "Improving event prediction by representing script participants",
    author = "Ahrendt, Simon  and
      Demberg, Vera",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1067",
    doi = "10.18653/v1/N16-1067",
    pages = "546--551",
}
@inproceedings{quattoni-etal-2016-structured,
    title = "Structured Prediction with Output Embeddings for Semantic Image Annotation",
    author = "Quattoni, Ariadna  and
      Ramisa, Arnau  and
      Madhyastha, Pranava Swaroop  and
      Simo-Serra, Edgar  and
      Moreno-Noguer, Francesc",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1068",
    doi = "10.18653/v1/N16-1068",
    pages = "552--557",
}
@inproceedings{shah-specia-2016-large,
    title = "Large-scale Multitask Learning for Machine Translation Quality Estimation",
    author = "Shah, Kashif  and
      Specia, Lucia",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1069",
    doi = "10.18653/v1/N16-1069",
    pages = "558--567",
}
@inproceedings{niculae-danescu-niculescu-mizil-2016-conversational,
    title = "Conversational Markers of Constructive Discussions",
    author = "Niculae, Vlad  and
      Danescu-Niculescu-Mizil, Cristian",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1070",
    doi = "10.18653/v1/N16-1070",
    pages = "568--578",
}
@inproceedings{bulat-etal-2016-vision,
    title = "Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps",
    author = "Bulat, Luana  and
      Kiela, Douwe  and
      Clark, Stephen",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1071",
    doi = "10.18653/v1/N16-1071",
    pages = "579--588",
}
@inproceedings{tsai-roth-2016-cross,
    title = "Cross-lingual Wikification Using Multilingual Embeddings",
    author = "Tsai, Chen-Tse  and
      Roth, Dan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1072",
    doi = "10.18653/v1/N16-1072",
    pages = "589--598",
}
@inproceedings{mehrotra-etal-2016-deconstructing,
    title = "Deconstructing Complex Search Tasks: a {B}ayesian Nonparametric Approach for Extracting Sub-tasks",
    author = "Mehrotra, Rishabh  and
      Bhattacharya, Prasanta  and
      Yilmaz, Emine",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1073",
    doi = "10.18653/v1/N16-1073",
    pages = "599--605",
}
@inproceedings{krishnamurthy-2016-probabilistic,
    title = "Probabilistic Models for Learning a Semantic Parser Lexicon",
    author = "Krishnamurthy, Jayant",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1074",
    doi = "10.18653/v1/N16-1074",
    pages = "606--616",
}
@inproceedings{riedl-biemann-2016-unsupervised,
    title = "Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised Methods",
    author = "Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1075",
    doi = "10.18653/v1/N16-1075",
    pages = "617--622",
}
@inproceedings{rastogi-etal-2016-weighting,
    title = "Weighting Finite-State Transductions With Neural Context",
    author = "Rastogi, Pushpendre  and
      Cotterell, Ryan  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1076",
    doi = "10.18653/v1/N16-1076",
    pages = "623--633",
}
@inproceedings{faruqui-etal-2016-morphological,
    title = "Morphological Inflection Generation Using Character Sequence to Sequence Learning",
    author = "Faruqui, Manaal  and
      Tsvetkov, Yulia  and
      Neubig, Graham  and
      Dyer, Chris",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1077",
    doi = "10.18653/v1/N16-1077",
    pages = "634--643",
}
@inproceedings{ziering-van-der-plas-2016-towards,
    title = "Towards Unsupervised and Language-independent Compound Splitting using Inflectional Morphological Transformations",
    author = "Ziering, Patrick  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1078",
    doi = "10.18653/v1/N16-1078",
    pages = "644--653",
}
@inproceedings{jaech-etal-2016-phonological,
    title = "Phonological Pun-derstanding",
    author = "Jaech, Aaron  and
      Koncel-Kedziorski, Rik  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1079",
    doi = "10.18653/v1/N16-1079",
    pages = "654--663",
}
@inproceedings{cotterell-etal-2016-joint,
    title = "A Joint Model of Orthography and Morphological Segmentation",
    author = {Cotterell, Ryan  and
      Vieira, Tim  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1080",
    doi = "10.18653/v1/N16-1080",
    pages = "664--669",
}
@inproceedings{pinter-etal-2016-syntactic,
    title = "Syntactic Parsing of Web Queries with Question Intent",
    author = "Pinter, Yuval  and
      Reichart, Roi  and
      Szpektor, Idan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1081",
    doi = "10.18653/v1/N16-1081",
    pages = "670--680",
}
@inproceedings{li-etal-2016-visualizing,
    title = "Visualizing and Understanding Neural Models in {NLP}",
    author = "Li, Jiwei  and
      Chen, Xinlei  and
      Hovy, Eduard  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1082",
    doi = "10.18653/v1/N16-1082",
    pages = "681--691",
}
@inproceedings{mogadala-rettinger-2016-bilingual,
    title = "Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification",
    author = "Mogadala, Aditya  and
      Rettinger, Achim",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1083",
    doi = "10.18653/v1/N16-1083",
    pages = "692--702",
}
@inproceedings{joty-etal-2016-joint,
    title = "Joint Learning with Global Inference for Comment Classification in Community Question Answering",
    author = "Joty, Shafiq  and
      M{\`a}rquez, Llu{\'\i}s  and
      Nakov, Preslav",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1084",
    doi = "10.18653/v1/N16-1084",
    pages = "703--713",
}
@inproceedings{muis-lu-2016-weak,
    title = "Weak Semi-{M}arkov {CRF}s for Noun Phrase Chunking in Informal Text",
    author = "Muis, Aldrian Obaja  and
      Lu, Wei",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1085",
    doi = "10.18653/v1/N16-1085",
    pages = "714--719",
}
@inproceedings{mei-etal-2016-talk,
    title = "What to talk about and how? Selective Generation using {LSTM}s with Coarse-to-Fine Alignment",
    author = "Mei, Hongyuan  and
      Bansal, Mohit  and
      Walter, Matthew R.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1086",
    doi = "10.18653/v1/N16-1086",
    pages = "720--730",
}
@inproceedings{flanigan-etal-2016-generation,
    title = "Generation from {A}bstract {M}eaning {R}epresentation using Tree Transducers",
    author = "Flanigan, Jeffrey  and
      Dyer, Chris  and
      Smith, Noah A.  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1087",
    doi = "10.18653/v1/N16-1087",
    pages = "731--739",
}
@inproceedings{haas-riezler-2016-corpus,
    title = "A Corpus and Semantic Parser for Multilingual Natural Language Querying of {O}pen{S}treet{M}ap",
    author = "Haas, Carolin  and
      Riezler, Stefan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1088",
    doi = "10.18653/v1/N16-1088",
    pages = "740--750",
}
@inproceedings{bisk-etal-2016-natural,
    title = "Natural Language Communication with Robots",
    author = "Bisk, Yonatan  and
      Yuret, Deniz  and
      Marcu, Daniel",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1089",
    doi = "10.18653/v1/N16-1089",
    pages = "751--761",
}
@inproceedings{tran-etal-2016-inter,
    title = "Inter-document Contextual Language model",
    author = "Tran, Quan Hung  and
      Zukerman, Ingrid  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1090",
    doi = "10.18653/v1/N16-1090",
    pages = "762--766",
}
@inproceedings{rothe-etal-2016-ultradense,
    title = "Ultradense Word Embeddings by Orthogonal Transformation",
    author = {Rothe, Sascha  and
      Ebert, Sebastian  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1091",
    doi = "10.18653/v1/N16-1091",
    pages = "767--777",
}
@inproceedings{wiegand-etal-2016-separating,
    title = "Separating Actor-View from Speaker-View Opinion Expressions using Linguistic Features",
    author = "Wiegand, Michael  and
      Schulder, Marc  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1092",
    doi = "10.18653/v1/N16-1092",
    pages = "778--788",
}
@inproceedings{chen-etal-2016-clustering-simultaneous,
    title = "Clustering for Simultaneous Extraction of Aspects and Features from Reviews",
    author = "Chen, Lu  and
      Martineau, Justin  and
      Cheng, Doreen  and
      Sheth, Amit",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1093",
    doi = "10.18653/v1/N16-1093",
    pages = "789--799",
}
@inproceedings{wiegand-etal-2016-opinion,
    title = "Opinion Holder and Target Extraction on Opinion Compounds {--} A Linguistic Approach",
    author = "Wiegand, Michael  and
      Bocionek, Christine  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1094",
    doi = "10.18653/v1/N16-1094",
    pages = "800--810",
}
@inproceedings{kiritchenko-mohammad-2016-capturing,
    title = "Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best{--}Worst Scaling",
    author = "Kiritchenko, Svetlana  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1095",
    doi = "10.18653/v1/N16-1095",
    pages = "811--817",
}
@inproceedings{wijaya-mitchell-2016-mapping,
    title = "Mapping Verbs in Different Languages to Knowledge Base Relations using Web Text as Interlingua",
    author = "Wijaya, Derry Tanti  and
      Mitchell, Tom M.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1096",
    doi = "10.18653/v1/N16-1096",
    pages = "818--827",
}
@inproceedings{adel-etal-2016-comparing,
    title = "Comparing Convolutional Neural Networks to Traditional Models for Slot Filling",
    author = {Adel, Heike  and
      Roth, Benjamin  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1097",
    doi = "10.18653/v1/N16-1097",
    pages = "828--838",
}
@inproceedings{mostafazadeh-etal-2016-corpus,
    title = "A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories",
    author = "Mostafazadeh, Nasrin  and
      Chambers, Nathanael  and
      He, Xiaodong  and
      Parikh, Devi  and
      Batra, Dhruv  and
      Vanderwende, Lucy  and
      Kohli, Pushmeet  and
      Allen, James",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1098",
    doi = "10.18653/v1/N16-1098",
    pages = "839--849",
}
@inproceedings{kobayashi-etal-2016-dynamic,
    title = "Dynamic Entity Representation with Max-pooling Improves Machine Reading",
    author = "Kobayashi, Sosuke  and
      Tian, Ran  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1099",
    doi = "10.18653/v1/N16-1099",
    pages = "850--855",
}
@inproceedings{beck-etal-2016-speed,
    title = "Speed-Constrained Tuning for Statistical Machine Translation Using {B}ayesian Optimization",
    author = "Beck, Daniel  and
      de Gispert, Adri{\`a}  and
      Iglesias, Gonzalo  and
      Waite, Aurelien  and
      Byrne, Bill",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1100",
    doi = "10.18653/v1/N16-1100",
    pages = "856--865",
}
@inproceedings{firat-etal-2016-multi,
    title = "Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism",
    author = "Firat, Orhan  and
      Cho, Kyunghyun  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1101",
    doi = "10.18653/v1/N16-1101",
    pages = "866--875",
}
@inproceedings{cohn-etal-2016-incorporating,
    title = "Incorporating Structural Alignment Biases into an Attentional Neural Translation Model",
    author = "Cohn, Trevor  and
      Hoang, Cong Duy Vu  and
      Vymolova, Ekaterina  and
      Yao, Kaisheng  and
      Dyer, Chris  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1102",
    doi = "10.18653/v1/N16-1102",
    pages = "876--885",
}
@inproceedings{verga-etal-2016-multilingual,
    title = "Multilingual Relation Extraction using Compositional Universal Schema",
    author = "Verga, Patrick  and
      Belanger, David  and
      Strubell, Emma  and
      Roth, Benjamin  and
      McCallum, Andrew",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1103",
    doi = "10.18653/v1/N16-1103",
    pages = "886--896",
}
@inproceedings{liu-etal-2016-effective,
    title = "Effective Crowd Annotation for Relation Extraction",
    author = "Liu, Angli  and
      Soderland, Stephen  and
      Bragg, Jonathan  and
      Lin, Christopher H.  and
      Ling, Xiao  and
      Weld, Daniel S.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1104",
    doi = "10.18653/v1/N16-1104",
    pages = "897--906",
}
@inproceedings{yoon-etal-2016-translation,
    title = "A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations",
    author = "Yoon, Hee-Geun  and
      Song, Hyun-Je  and
      Park, Seong-Bae  and
      Park, Se-Young",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1105",
    doi = "10.18653/v1/N16-1105",
    pages = "907--916",
}
@inproceedings{zhu-etal-2016-dag,
    title = "{DAG}-Structured Long Short-Term Memory for Semantic Compositionality",
    author = "Zhu, Xiaodan  and
      Sobhani, Parinaz  and
      Guo, Hongyu",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1106",
    doi = "10.18653/v1/N16-1106",
    pages = "917--926",
}
@inproceedings{sultan-etal-2016-bayesian,
    title = "{B}ayesian Supervised Domain Adaptation for Short Text Similarity",
    author = "Sultan, Md Arafat  and
      Boyd-Graber, Jordan  and
      Sumner, Tamara",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1107",
    doi = "10.18653/v1/N16-1107",
    pages = "927--936",
}
@inproceedings{he-lin-2016-pairwise,
    title = "Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement",
    author = "He, Hua  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1108",
    doi = "10.18653/v1/N16-1108",
    pages = "937--948",
}
@inproceedings{duong-etal-2016-attentional,
    title = "An Attentional Model for Speech Translation Without Transcription",
    author = "Duong, Long  and
      Anastasopoulos, Antonios  and
      Chiang, David  and
      Bird, Steven  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1109",
    doi = "10.18653/v1/N16-1109",
    pages = "949--959",
}
@inproceedings{rubino-etal-2016-information,
    title = "Information Density and Quality Estimation Features as Translationese Indicators for Human Translation Classification",
    author = "Rubino, Raphael  and
      Lapshinova-Koltunski, Ekaterina  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1110",
    doi = "10.18653/v1/N16-1110",
    pages = "960--970",
}
@inproceedings{he-etal-2016-interpretese,
    title = "Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation",
    author = "He, He  and
      Boyd-Graber, Jordan  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1111",
    doi = "10.18653/v1/N16-1111",
    pages = "971--976",
}
@inproceedings{cui-etal-2016-lstm,
    title = "{LSTM} Neural Reordering Feature for Statistical Machine Translation",
    author = "Cui, Yiming  and
      Wang, Shijin  and
      Li, Jianfeng",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1112",
    doi = "10.18653/v1/N16-1112",
    pages = "977--982",
}
@inproceedings{wang-etal-2016-novel,
    title = "A Novel Approach to Dropped Pronoun Translation",
    author = "Wang, Longyue  and
      Tu, Zhaopeng  and
      Zhang, Xiaojun  and
      Li, Hang  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1113",
    doi = "10.18653/v1/N16-1113",
    pages = "983--993",
}
@inproceedings{wiseman-etal-2016-learning,
    title = "Learning Global Features for Coreference Resolution",
    author = "Wiseman, Sam  and
      Rush, Alexander M.  and
      Shieber, Stuart M.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1114",
    doi = "10.18653/v1/N16-1114",
    pages = "994--1004",
}
@inproceedings{moosavi-strube-2016-search,
    title = "Search Space Pruning: A Simple Solution for Better Coreference Resolvers",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1115",
    doi = "10.18653/v1/N16-1115",
    pages = "1005--1011",
}
@inproceedings{ma-etal-2016-unsupervised,
    title = "Unsupervised Ranking Model for Entity Coreference Resolution",
    author = "Ma, Xuezhe  and
      Liu, Zhengzhong  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1116",
    doi = "10.18653/v1/N16-1116",
    pages = "1012--1018",
}
@inproceedings{yu-etal-2016-embedding,
    title = "Embedding Lexical Features via Low-Rank Tensors",
    author = "Yu, Mo  and
      Dredze, Mark  and
      Arora, Raman  and
      Gormley, Matthew R.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1117",
    doi = "10.18653/v1/N16-1117",
    pages = "1019--1029",
}
@inproceedings{melamud-etal-2016-role,
    title = "The Role of Context Types and Dimensionality in Learning Word Embeddings",
    author = "Melamud, Oren  and
      McClosky, David  and
      Patwardhan, Siddharth  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1118",
    doi = "10.18653/v1/N16-1118",
    pages = "1030--1040",
}
@inproceedings{xu-etal-2016-improve,
    title = "Improve {C}hinese Word Embeddings by Exploiting Internal Structure",
    author = "Xu, Jian  and
      Liu, Jiawei  and
      Zhang, Liangang  and
      Li, Zhengyu  and
      Chen, Huanhuan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1119",
    doi = "10.18653/v1/N16-1119",
    pages = "1041--1050",
}
@inproceedings{ambati-etal-2016-assessing,
    title = "Assessing Relative Sentence Complexity using an Incremental {CCG} Parser",
    author = "Ambati, Bharat Ram  and
      Reddy, Siva  and
      Steedman, Mark",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1120",
    doi = "10.18653/v1/N16-1120",
    pages = "1051--1057",
}
@inproceedings{lacroix-etal-2016-frustratingly,
    title = "Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing",
    author = "Lacroix, Oph{\'e}lie  and
      Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1121",
    doi = "10.18653/v1/N16-1121",
    pages = "1058--1063",
}
@inproceedings{dredze-etal-2016-geolocation,
    title = "Geolocation for {T}witter: Timing Matters",
    author = "Dredze, Mark  and
      Osborne, Miles  and
      Kambadur, Prabhanjan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1122",
    doi = "10.18653/v1/N16-1122",
    pages = "1064--1069",
}
@inproceedings{sultan-etal-2016-fast,
    title = "Fast and Easy Short Answer Grading with High Accuracy",
    author = "Sultan, Md Arafat  and
      Salazar, Cristobal  and
      Sumner, Tamara",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1123",
    doi = "10.18653/v1/N16-1123",
    pages = "1070--1075",
}
@inproceedings{kyaw-thu-etal-2016-interlocking,
    title = "Interlocking Phrases in Phrase-based Statistical Machine Translation",
    author = "Kyaw Thu, Ye  and
      Finch, Andrew  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1124",
    doi = "10.18653/v1/N16-1124",
    pages = "1076--1081",
}
@inproceedings{sajjad-etal-2016-eyes,
    title = "Eyes Don{'}t Lie: Predicting Machine Translation Quality Using Eye Movement",
    author = "Sajjad, Hassan  and
      Guzm{\'a}n, Francisco  and
      Durrani, Nadir  and
      Abdelali, Ahmed  and
      Bouamor, Houda  and
      Temnikova, Irina  and
      Vogel, Stephan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1125",
    doi = "10.18653/v1/N16-1125",
    pages = "1082--1088",
}
@inproceedings{shen-etal-2016-making,
    title = "Making Dependency Labeling Simple, Fast and Accurate",
    author = "Shen, Tianxiao  and
      Lei, Tao  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1126",
    doi = "10.18653/v1/N16-1126",
    pages = "1089--1094",
}
@inproceedings{constant-etal-2016-deep,
    title = "Deep Lexical Segmentation and Syntactic Parsing in the Easy-First Dependency Framework",
    author = "Constant, Matthieu  and
      Le Roux, Joseph  and
      Tomeh, Nadi",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1127",
    doi = "10.18653/v1/N16-1127",
    pages = "1095--1101",
}
@inproceedings{kiritchenko-mohammad-2016-sentiment,
    title = "Sentiment Composition of Words with Opposing Polarities",
    author = "Kiritchenko, Svetlana  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1128",
    doi = "10.18653/v1/N16-1128",
    pages = "1102--1108",
}
@inproceedings{filice-moschitti-2016-learning,
    title = "Learning to Recognize Ancillary Information for Automatic Paraphrase Identification",
    author = "Filice, Simone  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1129",
    doi = "10.18653/v1/N16-1129",
    pages = "1109--1114",
}
@inproceedings{jorgensen-etal-2016-learning,
    title = "Learning a {POS} tagger for {AAVE}-like language",
    author = "J{\o}rgensen, Anna  and
      Hovy, Dirk  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1130",
    doi = "10.18653/v1/N16-1130",
    pages = "1115--1120",
}
@inproceedings{roller-erk-2016-pic,
    title = "{PIC} a Different Word: A Simple Model for Lexical Substitution in Context",
    author = "Roller, Stephen  and
      Erk, Katrin",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1131",
    doi = "10.18653/v1/N16-1131",
    pages = "1121--1126",
}
@inproceedings{krstovski-smith-2016-bootstrapping,
    title = "Bootstrapping Translation Detection and Sentence Extraction from Comparable Corpora",
    author = "Krstovski, Kriste  and
      Smith, David",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1132",
    doi = "10.18653/v1/N16-1132",
    pages = "1127--1132",
}
@inproceedings{mizumoto-matsumoto-2016-discriminative,
    title = "Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation",
    author = "Mizumoto, Tomoya  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1133",
    doi = "10.18653/v1/N16-1133",
    pages = "1133--1138",
}
@inproceedings{booten-hearst-2016-patterns,
    title = "Patterns of Wisdom: Discourse-Level Style in Multi-Sentence Quotations",
    author = "Booten, Kyle  and
      Hearst, Marti A.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1134",
    doi = "10.18653/v1/N16-1134",
    pages = "1139--1144",
}
@inproceedings{suzuki-nagata-2016-right,
    title = "Right-truncatable Neural Word Embeddings",
    author = "Suzuki, Jun  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1135",
    doi = "10.18653/v1/N16-1135",
    pages = "1145--1151",
}
@inproceedings{koncel-kedziorski-etal-2016-mawps,
    title = "{MAWPS}: A Math Word Problem Repository",
    author = "Koncel-Kedziorski, Rik  and
      Roy, Subhro  and
      Amini, Aida  and
      Kushman, Nate  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1136",
    doi = "10.18653/v1/N16-1136",
    pages = "1152--1157",
}
@inproceedings{li-ji-2016-cross,
    title = "Cross-genre Event Extraction with Knowledge Enrichment",
    author = "Li, Hao  and
      Ji, Heng",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1137",
    doi = "10.18653/v1/N16-1137",
    pages = "1158--1162",
}
@inproceedings{ferreira-vlachos-2016-emergent,
    title = "{E}mergent: a novel data-set for stance classification",
    author = "Ferreira, William  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1138",
    doi = "10.18653/v1/N16-1138",
    pages = "1163--1168",
}
@inproceedings{dehdari-etal-2016-bira,
    title = "{BIRA}: Improved Predictive Exchange Word Clustering",
    author = "Dehdari, Jon  and
      Tan, Liling  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1139",
    doi = "10.18653/v1/N16-1139",
    pages = "1169--1174",
}
@inproceedings{salameh-etal-2016-integrating,
    title = "Integrating Morphological Desegmentation into Phrase-based Decoding",
    author = "Salameh, Mohammad  and
      Cherry, Colin  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1140",
    doi = "10.18653/v1/N16-1140",
    pages = "1175--1180",
}
@inproceedings{li-nenkova-2016-instantiation,
    title = "The Instantiation Discourse Relation: A Corpus Analysis of Its Properties and Improved Detection",
    author = "Li, Junyi Jessy  and
      Nenkova, Ani",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1141",
    doi = "10.18653/v1/N16-1141",
    pages = "1181--1186",
}
@inproceedings{vyas-carpuat-2016-sparse,
    title = "Sparse Bilingual Word Representations for Cross-lingual Lexical Entailment",
    author = "Vyas, Yogarshi  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1142",
    doi = "10.18653/v1/N16-1142",
    pages = "1187--1197",
}
@inproceedings{weissenbacher-etal-2016-automatic,
    title = "Automatic Prediction of Linguistic Decline in Writings of Subjects with Degenerative Dementia",
    author = "Weissenbacher, Davy  and
      Johnson, Travis A.  and
      Wojtulewicz, Laura  and
      Dueck, Amylou  and
      Locke, Dona  and
      Caselli, Richard  and
      Gonzalez, Graciela",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1143",
    doi = "10.18653/v1/N16-1143",
    pages = "1198--1207",
}
@inproceedings{rodriguez-etal-2016-consensus,
    title = "Consensus Maximization Fusion of Probabilistic Information Extractors",
    author = "Rodr{\'\i}guez, Miguel  and
      Goldberg, Sean  and
      Wang, Daisy Zhe",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1144",
    doi = "10.18653/v1/N16-1144",
    pages = "1208--1216",
}
@inproceedings{zoph-etal-2016-simple,
    title = "Simple, Fast Noise-Contrastive Estimation for Large {RNN} Vocabularies",
    author = "Zoph, Barret  and
      Vaswani, Ashish  and
      May, Jonathan  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1145",
    doi = "10.18653/v1/N16-1145",
    pages = "1217--1222",
}
@inproceedings{qadir-etal-2016-automatically,
    title = "Automatically Inferring Implicit Properties in Similes",
    author = "Qadir, Ashequl  and
      Riloff, Ellen  and
      Walker, Marilyn A.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1146",
    doi = "10.18653/v1/N16-1146",
    pages = "1223--1232",
}
@inproceedings{huang-etal-2016-visual,
    title = "Visual Storytelling",
    author = "Huang, Ting-Hao Kenneth  and
      Ferraro, Francis  and
      Mostafazadeh, Nasrin  and
      Misra, Ishan  and
      Agrawal, Aishwarya  and
      Devlin, Jacob  and
      Girshick, Ross  and
      He, Xiaodong  and
      Kohli, Pushmeet  and
      Batra, Dhruv  and
      Zitnick, C. Lawrence  and
      Parikh, Devi  and
      Vanderwende, Lucy  and
      Galley, Michel  and
      Mitchell, Margaret",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1147",
    doi = "10.18653/v1/N16-1147",
    pages = "1233--1239",
}
@inproceedings{cheng-etal-2016-primt,
    title = "{PRIMT}: A Pick-Revise Framework for Interactive Machine Translation",
    author = "Cheng, Shanbo  and
      Huang, Shujian  and
      Chen, Huadong  and
      Dai, Xin-Yu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1148",
    doi = "10.18653/v1/N16-1148",
    pages = "1240--1249",
}
@inproceedings{hoang-etal-2016-incorporating,
    title = "Incorporating Side Information into Recurrent Neural Network Language Models",
    author = "Hoang, Cong Duy Vu  and
      Cohn, Trevor  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1149",
    doi = "10.18653/v1/N16-1149",
    pages = "1250--1255",
}
@inproceedings{francis-landau-etal-2016-capturing,
    title = "Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks",
    author = "Francis-Landau, Matthew  and
      Durrett, Greg  and
      Klein, Dan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1150",
    doi = "10.18653/v1/N16-1150",
    pages = "1256--1261",
}
@inproceedings{vu-parker-2016-k,
    title = "$K$-Embeddings: Learning Conceptual Embeddings for Words using Context",
    author = "Vu, Thuy  and
      Parker, D. Stott",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1151",
    doi = "10.18653/v1/N16-1151",
    pages = "1262--1267",
}
@inproceedings{tymoshenko-etal-2016-convolutional,
    title = "Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking",
    author = "Tymoshenko, Kateryna  and
      Bonadiman, Daniele  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1152",
    doi = "10.18653/v1/N16-1152",
    pages = "1268--1278",
}
@inproceedings{lei-etal-2016-semi,
    title = "Semi-supervised Question Retrieval with Gated Convolutions",
    author = "Lei, Tao  and
      Joshi, Hrishikesh  and
      Barzilay, Regina  and
      Jaakkola, Tommi  and
      Tymoshenko, Kateryna  and
      Moschitti, Alessandro  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1153",
    doi = "10.18653/v1/N16-1153",
    pages = "1279--1289",
}
@inproceedings{bogdanova-foster-2016-answer,
    title = "This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering",
    author = "Bogdanova, Dasha  and
      Foster, Jennifer",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1154",
    doi = "10.18653/v1/N16-1154",
    pages = "1290--1295",
}
@inproceedings{gillick-etal-2016-multilingual,
    title = "Multilingual Language Processing From Bytes",
    author = "Gillick, Dan  and
      Brunk, Cliff  and
      Vinyals, Oriol  and
      Subramanya, Amarnag",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1155",
    doi = "10.18653/v1/N16-1155",
    pages = "1296--1306",
}
@inproceedings{zhang-etal-2016-ten,
    title = "Ten Pairs to Tag {--} Multilingual {POS} Tagging via Coarse Mapping between Embeddings",
    author = "Zhang, Yuan  and
      Gaddy, David  and
      Barzilay, Regina  and
      Jaakkola, Tommi",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1156",
    doi = "10.18653/v1/N16-1156",
    pages = "1307--1317",
}
@inproceedings{yang-eisenstein-2016-part,
    title = "Part-of-Speech Tagging for Historical {E}nglish",
    author = "Yang, Yi  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1157",
    doi = "10.18653/v1/N16-1157",
    pages = "1318--1328",
}
@inproceedings{murawaki-2016-statistical,
    title = "Statistical Modeling of Creole Genesis",
    author = "Murawaki, Yugo",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1158",
    doi = "10.18653/v1/N16-1158",
    pages = "1329--1339",
}
@inproceedings{sharma-etal-2016-shallow,
    title = "Shallow Parsing Pipeline - {H}indi-{E}nglish Code-Mixed Social Media Text",
    author = "Sharma, Arnav  and
      Gupta, Sakshi  and
      Motlani, Raveesh  and
      Bansal, Piyush  and
      Shrivastava, Manish  and
      Mamidi, Radhika  and
      Sharma, Dipti M.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1159",
    doi = "10.18653/v1/N16-1159",
    pages = "1340--1345",
}
@inproceedings{suster-etal-2016-bilingual,
    title = "Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders",
    author = "{\v{S}}uster, Simon  and
      Titov, Ivan  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1160",
    doi = "10.18653/v1/N16-1160",
    pages = "1346--1356",
}
@inproceedings{tsvetkov-etal-2016-polyglot,
    title = "Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning",
    author = "Tsvetkov, Yulia  and
      Sitaram, Sunayana  and
      Faruqui, Manaal  and
      Lample, Guillaume  and
      Littell, Patrick  and
      Mortensen, David  and
      Black, Alan W  and
      Levin, Lori  and
      Dyer, Chris",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1161",
    doi = "10.18653/v1/N16-1161",
    pages = "1357--1366",
}
@inproceedings{hill-etal-2016-learning,
    title = "Learning Distributed Representations of Sentences from Unlabelled Data",
    author = "Hill, Felix  and
      Cho, Kyunghyun  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1162",
    doi = "10.18653/v1/N16-1162",
    pages = "1367--1377",
}
@inproceedings{ettinger-etal-2016-retrofitting,
    title = "Retrofitting Sense-Specific Word Vectors Using Parallel Text",
    author = "Ettinger, Allyson  and
      Resnik, Philip  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1163",
    doi = "10.18653/v1/N16-1163",
    pages = "1378--1383",
}
@inproceedings{persing-ng-2016-end,
    title = "End-to-End Argumentation Mining in Student Essays",
    author = "Persing, Isaac  and
      Ng, Vincent",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1164",
    doi = "10.18653/v1/N16-1164",
    pages = "1384--1394",
}
@inproceedings{al-khatib-etal-2016-cross,
    title = "Cross-Domain Mining of Argumentative Text through Distant Supervision",
    author = {Al-Khatib, Khalid  and
      Wachsmuth, Henning  and
      Hagen, Matthias  and
      K{\"o}hler, Jonas  and
      Stein, Benno},
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1165",
    doi = "10.18653/v1/N16-1165",
    pages = "1395--1404",
}
@inproceedings{cano-basave-he-2016-study,
    title = "A Study of the Impact of Persuasive Argumentation in Political Debates",
    author = "Cano-Basave, Amparo Elizabeth  and
      He, Yulan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1166",
    doi = "10.18653/v1/N16-1166",
    pages = "1405--1413",
}
@inproceedings{mesgar-strube-2016-lexical,
    title = "Lexical Coherence Graph Modeling Using Word Embeddings",
    author = "Mesgar, Mohsen  and
      Strube, Michael",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1167",
    doi = "10.18653/v1/N16-1167",
    pages = "1414--1423",
}
@inproceedings{zhang-litman-2016-using,
    title = "Using Context to Predict the Purpose of Argumentative Writing Revisions",
    author = "Zhang, Fan  and
      Litman, Diane",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1168",
    doi = "10.18653/v1/N16-1168",
    pages = "1424--1430",
}
@inproceedings{blanco-sarabi-2016-automatic,
    title = "Automatic Generation and Scoring of Positive Interpretations from Negated Statements",
    author = "Blanco, Eduardo  and
      Sarabi, Zahra",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1169",
    doi = "10.18653/v1/N16-1169",
    pages = "1431--1441",
}
@inproceedings{wang-jiang-2016-learning,
    title = "Learning Natural Language Inference with {LSTM}",
    author = "Wang, Shuohang  and
      Jiang, Jing",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1170",
    doi = "10.18653/v1/N16-1170",
    pages = "1442--1451",
}
@inproceedings{qadir-etal-2016-activity,
    title = "Activity Modeling in Email",
    author = "Qadir, Ashequl  and
      Gamon, Michael  and
      Pantel, Patrick  and
      Awadallah, Ahmed Hassan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1171",
    doi = "10.18653/v1/N16-1171",
    pages = "1452--1462",
}
@inproceedings{cocos-callison-burch-2016-clustering,
    title = "Clustering Paraphrases by Word Sense",
    author = "Cocos, Anne  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1172",
    doi = "10.18653/v1/N16-1172",
    pages = "1463--1472",
}
@inproceedings{schenk-chiarcos-2016-unsupervised,
    title = "Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling",
    author = "Schenk, Niko  and
      Chiarcos, Christian",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1173",
    doi = "10.18653/v1/N16-1173",
    pages = "1473--1479",
}
@inproceedings{yang-etal-2016-hierarchical,
    title = "Hierarchical Attention Networks for Document Classification",
    author = "Yang, Zichao  and
      Yang, Diyi  and
      Dyer, Chris  and
      He, Xiaodong  and
      Smola, Alex  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1174",
    doi = "10.18653/v1/N16-1174",
    pages = "1480--1489",
}
@inproceedings{komninos-manandhar-2016-dependency,
    title = "Dependency Based Embeddings for Sentence Classification Tasks",
    author = "Komninos, Alexandros  and
      Manandhar, Suresh",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1175",
    doi = "10.18653/v1/N16-1175",
    pages = "1490--1500",
}
@inproceedings{shi-etal-2016-deep,
    title = "Deep {LSTM} based Feature Mapping for Query Classification",
    author = "Shi, Yangyang  and
      Yao, Kaisheng  and
      Tian, Le  and
      Jiang, Daxin",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1176",
    doi = "10.18653/v1/N16-1176",
    pages = "1501--1511",
}
@inproceedings{zhang-etal-2016-dependency,
    title = "Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents",
    author = "Zhang, Rui  and
      Lee, Honglak  and
      Radev, Dragomir R.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1177",
    doi = "10.18653/v1/N16-1177",
    pages = "1512--1521",
}
@inproceedings{zhang-etal-2016-mgnc,
    title = "{MGNC}-{CNN}: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification",
    author = "Zhang, Ye  and
      Roller, Stephen  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1178",
    doi = "10.18653/v1/N16-1178",
    pages = "1522--1527",
}
@inproceedings{klerke-etal-2016-improving,
    title = "Improving sentence compression by learning to predict gaze",
    author = "Klerke, Sigrid  and
      Goldberg, Yoav  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1179",
    doi = "10.18653/v1/N16-1179",
    pages = "1528--1533",
}
@inproceedings{iyyer-etal-2016-feuding,
    title = "Feuding Families and Former {F}riends: Unsupervised Learning for Dynamic Fictional Relationships",
    author = "Iyyer, Mohit  and
      Guha, Anupam  and
      Chaturvedi, Snigdha  and
      Boyd-Graber, Jordan  and
      Daum{\'e} III, Hal",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1180",
    doi = "10.18653/v1/N16-1180",
    pages = "1534--1544",
}
@inproceedings{andreas-etal-2016-learning,
    title = "Learning to Compose Neural Networks for Question Answering",
    author = "Andreas, Jacob  and
      Rohrbach, Marcus  and
      Darrell, Trevor  and
      Klein, Dan",
    booktitle = "Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2016",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N16-1181",
    doi = "10.18653/v1/N16-1181",
    pages = "1545--1554",
}
@proceedings{lrec-2016-international,
    title = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Grobelnik, Marko  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, Helene  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1000",
}
@inproceedings{gaudio-etal-2016-evaluating,
    title = "Evaluating Machine Translation in a Usage Scenario",
    author = "Gaudio, Rosa  and
      Burchardt, Aljoscha  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1001",
    pages = "1--8",
    abstract = "In this document we report on a user-scenario-based evaluation aiming at assessing the performance of machine translation (MT) systems in a real context of use. We describe a sequel of experiments that has been performed to estimate the usefulness of MT and to test if improvements of MT technology lead to better performance in the usage scenario. One goal is to find the best methodology for evaluating the eventual benefit of a machine translation system in an application. The evaluation is based on the QTLeap corpus, a novel multilingual language resource that was collected through a real-life support service via chat. It is composed of naturally occurring utterances produced by users while interacting with a human technician providing answers. The corpus is available in eight different languages: Basque, Bulgarian, Czech, Dutch, English, German, Portuguese and Spanish.",
}
@inproceedings{du-etal-2016-using,
    title = "Using {B}abel{N}et to Improve {OOV} Coverage in {SMT}",
    author = "Du, Jinhua  and
      Way, Andy  and
      Zydron, Andrzej",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1002",
    pages = "9--15",
    abstract = "Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods ― using direct training data, domain-adaptation techniques and the BabelNet API ― are proposed in this paper to obtain translations for OOVs to improve system performance. Experimental results on English―Polish and English―Chinese language pairs show that domain adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems.",
}
@inproceedings{kordoni-etal-2016-enhancing,
    title = "Enhancing Access to Online Education: Quality Machine Translation of {MOOC} Content",
    author = "Kordoni, Valia  and
      van den Bosch, Antal  and
      Kermanidis, Katia Lida  and
      Sosoni, Vilelmini  and
      Cholakov, Kostadin  and
      Hendrickx, Iris  and
      Huck, Matthias  and
      Way, Andy",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1003",
    pages = "16--22",
    abstract = "The present work is an overview of the TraMOOC (Translation for Massive Open Online Courses) research and innovation project, a machine translation approach for online educational content. More specifically, videolectures, assignments, and MOOC forum text is automatically translated from English into eleven European and BRIC languages. Unlike previous approaches to machine translation, the output quality in TraMOOC relies on a multimodal evaluation schema that involves crowdsourcing, error type markup, an error taxonomy for translation model comparison, and implicit evaluation via text mining, i.e. entity recognition and its performance comparison between the source and the translated text, and sentiment analysis on the students{'} forum posts. Finally, the evaluation output will result in more and better quality in-domain parallel data that will be fed back to the translation engine for higher quality output. The translation service will be incorporated into the Iversity MOOC platform and into the VideoLectures.net digital library portal.",
}
@inproceedings{marg-2016-trials,
    title = "The Trials and Tribulations of Predicting Post-Editing Productivity",
    author = "Marg, Lena",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1004",
    pages = "23--26",
    abstract = "While an increasing number of (automatic) metrics is available to assess the linguistic quality of machine translations, their interpretation remains cryptic to many users, specifically in the translation community. They are clearly useful for indicating certain overarching trends, but say little about actual improvements for translation buyers or post-editors. However, these metrics are commonly referenced when discussing pricing and models, both with translation buyers and service providers. With the aim of focusing on automatic metrics that are easier to understand for non-research users, we identified Edit Distance (or Post-Edit Distance) as a good fit. While Edit Distance as such does not express cognitive effort or time spent editing machine translation suggestions, we found that it correlates strongly with the productivity tests we performed, for various language pairs and domains. This paper aims to analyse Edit Distance and productivity data on a segment level based on data gathered over some years. Drawing from these findings, we want to then explore how Edit Distance could help in predicting productivity on new content. Some further analysis is proposed, with findings to be presented at the conference.",
}
@inproceedings{popovic-arcan-2016-pe2rr,
    title = "{PE}2rr Corpus: Manual Error Annotation of Automatically Pre-annotated {MT} Post-edits",
    author = "Popovi{\'c}, Maja  and
      Ar{\v{c}}an, Mihael",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1005",
    pages = "27--32",
    abstract = "We present a freely available corpus containing source language texts from different domains along with their automatically generated translations into several distinct morphologically rich languages, their post-edited versions, and error annotations of the performed post-edit operations. We believe that the corpus will be useful for many different applications. The main advantage of the approach used for creation of the corpus is the fusion of post-editing and error classification tasks, which have usually been seen as two independent tasks, although naturally they are not. We also show benefits of coupling automatic and manual error classification which facilitates the complex manual error annotation task as well as the development of automatic error classification tools. In addition, the approach facilitates annotation of language pair related issues.",
}
@inproceedings{mohammad-etal-2016-sentiment,
    title = "Sentiment Lexicons for {A}rabic Social Media",
    author = "Mohammad, Saif  and
      Salameh, Mohammad  and
      Kiritchenko, Svetlana",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1006",
    pages = "33--37",
    abstract = "Existing Arabic sentiment lexicons have low coverage―with only a few thousand entries. In this paper, we present several large sentiment lexicons that were automatically generated using two different methods: (1) by using distant supervision techniques on Arabic tweets, and (2) by translating English sentiment lexicons into Arabic using a freely available statistical machine translation system. We compare the usefulness of new and old sentiment lexicons in the downstream application of sentence-level sentiment analysis. Our baseline sentiment analysis system uses numerous surface form features. Nonetheless, the system benefits from using additional features drawn from sentiment lexicons. The best result is obtained using the automatically generated Dialectal Hashtag Lexicon and the Arabic translations of the NRC Emotion Lexicon (accuracy of 66.6{\%}). Finally, we describe a qualitative study of the automatic translations of English sentiment lexicons into Arabic, which shows that about 88{\%} of the automatically translated entries are valid for English as well. Close to 10{\%} of the invalid entries are caused by gross mistranslations, close to 40{\%} by translations into a related word, and about 50{\%} by differences in how the word is used in Arabic.",
}
@inproceedings{castellucci-etal-2016-language,
    title = "A Language Independent Method for Generating Large Scale Polarity Lexicons",
    author = "Castellucci, Giuseppe  and
      Croce, Danilo  and
      Basili, Roberto",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1007",
    pages = "38--45",
    abstract = "Sentiment Analysis systems aims at detecting opinions and sentiments that are expressed in texts. Many approaches in literature are based on resources that model the prior polarity of words or multi-word expressions, i.e. a polarity lexicon. Such resources are defined by teams of annotators, i.e. a manual annotation is provided to associate emotional or sentiment facets to the lexicon entries. The development of such lexicons is an expensive and language dependent process, making them often not covering all the linguistic sentiment phenomena. Moreover, once a lexicon is defined it can hardly be adopted in a different language or even a different domain. In this paper, we present several Distributional Polarity Lexicons (DPLs), i.e. large-scale polarity lexicons acquired with an unsupervised methodology based on Distributional Models of Lexical Semantics. Given a set of heuristically annotated sentences from Twitter, we transfer the sentiment information from sentences to words. The approach is mostly unsupervised, and experimental evaluations on Sentiment Analysis tasks in two languages show the benefits of the generated resources. The generated DPLs are publicly available in English and Italian.",
}
@inproceedings{naskar-etal-2016-sentiment,
    title = "Sentiment Analysis in Social Networks through Topic modeling",
    author = "Naskar, Debashis  and
      Mokaddem, Sidahmed  and
      Rebollo, Miguel  and
      Onaindia, Eva",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1008",
    pages = "46--53",
    abstract = "In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network through their conversations. We conduct a study to determine whether users of social networks (twitter in particular) tend to gather together according to the likeness of their sentiments. In our proposed framework, (1) we use ANEW, a lexical dictionary to identify affective emotional feelings associated to a message according to the Russell{'}s model of affection; (2) we design a topic modeling mechanism called Sent{\_}LDA, based on the Latent Dirichlet Allocation (LDA) generative model, which allows us to find the topic distribution in a general conversation and we associate topics with emotions; (3) we detect communities in the network according to the density and frequency of the messages among the users; and (4) we compare the sentiments of the communities by using the Russell{'}s model of affect versus polarity and we measure the extent to which topic distribution strengthen likeness in the sentiments of the users of a community. This works contributes with a topic modeling methodology to analyze the sentiments in conversations that take place in social networks.",
}
@inproceedings{garcia-pablos-etal-2016-comparison,
    title = "A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings",
    author = "Garc{\'\i}a Pablos, Aitor  and
      Cuadros, Montse  and
      Rigau, German",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1009",
    pages = "54--60",
    abstract = "A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity. Currently words are also modelled as continuous dense vectors, known as word embeddings, which seem to encode interesting semantic knowledge. With regard to Sentiment Analysis, word embeddings are used as features to more complex supervised classification systems to obtain sentiment classifiers. In this paper we compare a set of existing sentiment lexicons and sentiment lexicon generation techniques. We also show a simple but effective technique to calculate a word polarity value for each word in a domain using existing continuous word embeddings generation methods. Further, we also show that word embeddings calculated on in-domain corpus capture the polarity better than the ones calculated on general-domain corpus.",
}
@inproceedings{sidorov-etal-2016-speaker,
    title = "Could Speaker, Gender or Age Awareness be beneficial in Speech-based Emotion Recognition?",
    author = "Sidorov, Maxim  and
      Schmitt, Alexander  and
      Semenkin, Eugene  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1010",
    pages = "61--68",
    abstract = "Emotion Recognition (ER) is an important part of dialogue analysis which can be used in order to improve the quality of Spoken Dialogue Systems (SDSs). The emotional hypothesis of the current response of an end-user might be utilised by the dialogue manager component in order to change the SDS strategy which could result in a quality enhancement. In this study additional speaker-related information is used to improve the performance of the speech-based ER process. The analysed information is the speaker identity, gender and age of a user. Two schemes are described here, namely, using additional information as an independent variable within the feature vector and creating separate emotional models for each speaker, gender or age-cluster independently. The performances of the proposed approaches were compared against the baseline ER system, where no additional information has been used, on a number of emotional speech corpora of German, English, Japanese and Russian. The study revealed that for some of the corpora the proposed approach significantly outperforms the baseline methods with a relative difference of up to 11.9{\%}.",
}
@inproceedings{takamura-etal-2016-discriminative,
    title = "Discriminative Analysis of Linguistic Features for Typological Study",
    author = "Takamura, Hiroya  and
      Nagata, Ryo  and
      Kawasaki, Yoshifumi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1011",
    pages = "69--76",
    abstract = "We address the task of automatically estimating the missing values of linguistic features by making use of the fact that some linguistic features in typological databases are informative to each other. The questions to address in this work are (i) how much predictive power do features have on the value of another feature? (ii) to what extent can we attribute this predictive power to genealogical or areal factors, as opposed to being provided by tendencies or implicational universals? To address these questions, we conduct a discriminative or predictive analysis on the typological database. Specifically, we use a machine-learning classifier to estimate the value of each feature of each language using the values of the other features, under different choices of training data: all the other languages, or all the other languages except for the ones having the same origin or area with the target language.",
}
@inproceedings{hupkes-bod-2016-pos,
    title = "{POS}-tagging of Historical {D}utch",
    author = "Hupkes, Dieuwke  and
      Bod, Rens",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1012",
    pages = "77--82",
    abstract = "We present a study of the adequacy of current methods that are used for POS-tagging historical Dutch texts, as well as an exploration of the influence of employing different techniques to improve upon the current practice. The main focus of this paper is on (unsupervised) methods that are easily adaptable for different domains without requiring extensive manual input. It was found that modernising the spelling of corpora prior to tagging them with a tagger trained on contemporary Dutch results in a large increase in accuracy, but that spelling normalisation alone is not sufficient to obtain state-of-the-art results. The best results were achieved by training a POS-tagger on a corpus automatically annotated by projecting (automatically assigned) POS-tags via word alignments from a contemporary corpus. This result is promising, as it was reached without including any domain knowledge or context dependencies. We argue that the insights of this study combined with semi-supervised learning techniques for domain adaptation can be used to develop a general-purpose diachronic tagger for Dutch.",
}
@inproceedings{rauschenberger-etal-2016-language,
    title = "A Language Resource of {G}erman Errors Written by Children with Dyslexia",
    author = {Rauschenberger, Maria  and
      Rello, Luz  and
      F{\"u}chsel, Silke  and
      Thomaschewski, J{\"o}rg},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1013",
    pages = "83--87",
    abstract = "In this paper we present a language resource for German, composed of a list of 1,021 unique errors extracted from a collection of texts written by people with dyslexia. The errors were annotated with a set of linguistic characteristics as well as visual and phonetic features. We present the compilation and the annotation criteria for the different types of dyslexic errors. This language resource has many potential uses since errors written by people with dyslexia reflect their difficulties. For instance, it has already been used to design language exercises to treat dyslexia in German. To the best of our knowledge, this is first resource of this kind in German.",
}
@inproceedings{barbagli-etal-2016-cita,
    title = "{CI}t{A}: an {L}1 {I}talian Learners Corpus to Study the Development of Writing Competence",
    author = "Barbagli, Alessia  and
      Lucisano, Pietro  and
      Dell{'}Orletta, Felice  and
      Montemagni, Simonetta  and
      Venturi, Giulia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1014",
    pages = "88--95",
    abstract = "In this paper, we present the CItA corpus (Corpus Italiano di Apprendenti L1), a collection of essays written by Italian L1 learners collected during the first and second year of lower secondary school. The corpus was built in the framework of an interdisciplinary study jointly carried out by computational linguistics and experimental pedagogists and aimed at tracking the development of written language competence over the years and students{'} background information.",
}
@inproceedings{yu-etal-2016-even,
    title = "If You {E}ven Don{'}t Have a Bit of {B}ible: Learning Delexicalized {POS} Taggers",
    author = "Yu, Zhiwei  and
      Mare{\v{c}}ek, David  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1015",
    pages = "96--103",
    abstract = "Part-of-speech (POS) induction is one of the most popular tasks in research on unsupervised NLP. Various unsupervised and semi-supervised methods have been proposed to tag an unseen language. However, many of them require some partial understanding of the target language because they rely on dictionaries or parallel corpora such as the Bible. In this paper, we propose a different method named delexicalized tagging, for which we only need a raw corpus of the target language. We transfer tagging models trained on annotated corpora of one or more resource-rich languages. We employ language-independent features such as word length, frequency, neighborhood entropy, character classes (alphabetic vs. numeric vs. punctuation) etc. We demonstrate that such features can, to certain extent, serve as predictors of the part of speech, represented by the universal POS tag.",
}
@inproceedings{lopes-etal-2016-spedial,
    title = "The {S}pe{D}ial datasets: datasets for Spoken Dialogue Systems analytics",
    author = "Lopes, Jos{\'e}  and
      Chorianopoulou, Arodami  and
      Palogiannidi, Elisavet  and
      Moniz, Helena  and
      Abad, Alberto  and
      Louka, Katerina  and
      Iosif, Elias  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1016",
    pages = "104--110",
    abstract = "The SpeDial consortium is sharing two datasets that were used during the SpeDial project. By sharing them with the community we are providing a resource to reduce the duration of cycle of development of new Spoken Dialogue Systems (SDSs). The datasets include audios and several manual annotations, i.e., miscommunication, anger, satisfaction, repetition, gender and task success. The datasets were created with data from real users and cover two different languages: English and Greek. Detectors for miscommunication, anger and gender were trained for both systems. The detectors were particularly accurate in tasks where humans have high annotator agreement such as miscommunication and gender. As expected due to the subjectivity of the task, the anger detector had a less satisfactory performance. Nevertheless, we proved that the automatic detection of situations that can lead to problems in SDSs is possible and can be a promising direction to reduce the duration of SDS{'}s development cycle.",
}
@inproceedings{amanova-etal-2016-creating,
    title = "Creating Annotated Dialogue Resources: Cross-domain Dialogue Act Classification",
    author = "Amanova, Dilafruz  and
      Petukhova, Volha  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1017",
    pages = "111--117",
    abstract = "This paper describes a method to automatically create dialogue resources annotated with dialogue act information by reusing existing dialogue corpora. Numerous dialogue corpora are available for research purposes and many of them are annotated with dialogue act information that captures the intentions encoded in user utterances. Annotated dialogue resources, however, differ in various respects: data collection settings and modalities used, dialogue task domains and scenarios (if any) underlying the collection, number and roles of dialogue participants involved and dialogue act annotation schemes applied. The presented study encompasses three phases of data-driven investigation. We, first, assess the importance of various types of features and their combinations for effective cross-domain dialogue act classification. Second, we establish the best predictive model comparing various cross-corpora training settings. Finally, we specify models adaptation procedures and explore late fusion approaches to optimize the overall classification decision taking process. The proposed methodology accounts for empirically motivated and technically sound classification procedures that may reduce annotation and training costs significantly.",
}
@inproceedings{collins-traum-2016-towards,
    title = "Towards a Multi-dimensional Taxonomy of Stories in Dialogue",
    author = "Collins, Kathryn J.  and
      Traum, David",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1018",
    pages = "118--124",
    abstract = "In this paper, we present a taxonomy of stories told in dialogue. We based our scheme on prior work analyzing narrative structure and method of telling, relation to storyteller identity, as well as some categories particular to dialogue, such as how the story gets introduced. Our taxonomy currently has 5 major dimensions, with most having sub-dimensions - each dimension has an associated set of dimension-specific labels. We adapted an annotation tool for this taxonomy and have annotated portions of two different dialogue corpora, Switchboard and the Distress Analysis Interview Corpus. We present examples of some of the tags and concepts with stories from Switchboard, and some initial statistics of frequencies of the tags.",
}
@inproceedings{zarriess-etal-2016-pentoref,
    title = "{P}ento{R}ef: A Corpus of Spoken References in Task-oriented Dialogues",
    author = "Zarrie{\ss}, Sina  and
      Hough, Julian  and
      Kennington, Casey  and
      Manuvinakurike, Ramesh  and
      DeVault, David  and
      Fern{\'a}ndez, Raquel  and
      Schlangen, David",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1019",
    pages = "125--131",
    abstract = "PentoRef is a corpus of task-oriented dialogues collected in systematically manipulated settings. The corpus is multilingual, with English and German sections, and overall comprises more than 20000 utterances. The dialogues are fully transcribed and annotated with referring expressions mapped to objects in corresponding visual scenes, which makes the corpus a rich resource for research on spoken referring expressions in generation and resolution. The corpus includes several sub-corpora that correspond to different dialogue situations where parameters related to interactivity, visual access, and verbal channel have been manipulated in systematic ways. The corpus thus lends itself to very targeted studies of reference in spontaneous dialogue.",
}
@inproceedings{chowdhury-etal-2016-transfer,
    title = "Transfer of Corpus-Specific Dialogue Act Annotation to {ISO} Standard: Is it worth it?",
    author = "Chowdhury, Shammur Absar  and
      Stepanov, Evgeny  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1020",
    pages = "132--135",
    abstract = "Spoken conversation corpora often adapt existing Dialogue Act (DA) annotation specifications, such as DAMSL, DIT++, etc., to task specific needs, yielding incompatible annotations; thus, limiting corpora re-usability. Recently accepted ISO standard for DA annotation {--} Dialogue Act Markup Language (DiAML) {--} is designed as domain and application independent. Moreover, the clear separation of dialogue dimensions and communicative functions, coupled with the hierarchical organization of the latter, allows for classification at different levels of granularity. However, re-annotating existing corpora with the new scheme might require significant effort. In this paper we test the utility of the ISO standard through comparative evaluation of the corpus-specific legacy and the semi-automatically transferred DiAML DA annotations on supervised dialogue act classification task. To test the domain independence of the resulting annotations, we perform cross-domain and data aggregation evaluation. Compared to the legacy annotation scheme, on the Italian LUNA Human-Human corpus, the DiAML annotation scheme exhibits better cross-domain and data aggregation classification performance, while maintaining comparable in-domain performance.",
}
@inproceedings{ghaddar-langlais-2016-wikicoref,
    title = "{W}iki{C}oref: An {E}nglish Coreference-annotated Corpus of {W}ikipedia Articles",
    author = "Ghaddar, Abbas  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1021",
    pages = "136--142",
    abstract = "This paper presents WikiCoref, an English corpus annotated for anaphoric relations, where all documents are from the English version of Wikipedia. Our annotation scheme follows the one of OntoNotes with a few disparities. We annotated each markable with coreference type, mention type and the equivalent Freebase topic. Since most similar annotation efforts concentrate on very specific types of written text, mainly newswire, there is a lack of resources for otherwise over-used Wikipedia texts. The corpus described in this paper addresses this issue. We present a freely available resource we initially devised for improving coreference resolution algorithms dedicated to Wikipedia texts. Our corpus has no restriction on the topics of the documents being annotated, and documents of various sizes have been considered for annotation.",
}
@inproceedings{schlechtweg-2016-exploitation,
    title = "Exploitation of Co-reference in Distributional Semantics",
    author = "Schlechtweg, Dominik",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1022",
    pages = "143--149",
    abstract = "The aim of distributional semantics is to model the similarity of the meaning of words via the words they occur with. Thereby, it relies on the distributional hypothesis implying that similar words have similar contexts. Deducing meaning from the distribution of words is interesting as it can be done automatically on large amounts of freely available raw text. It is because of this convenience that most current state-of-the-art-models of distributional semantics operate on raw text, although there have been successful attempts to integrate other kinds of―e.g., syntactic―information to improve distributional semantic models. In contrast, less attention has been paid to semantic information in the research community. One reason for this is that the extraction of semantic information from raw text is a complex, elaborate matter and in great parts not yet satisfyingly solved. Recently, however, there have been successful attempts to integrate a certain kind of semantic information, i.e., co-reference. Two basically different kinds of information contributed by co-reference with respect to the distribution of words will be identified. We will then focus on one of these and examine its general potential to improve distributional semantic models as well as certain more specific hypotheses.",
}
@inproceedings{fonseca-etal-2016-adapting,
    title = "Adapting an Entity Centric Model for {P}ortuguese Coreference Resolution",
    author = "Fonseca, Evandro  and
      Vieira, Renata  and
      Vanin, Aline",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1023",
    pages = "150--154",
    abstract = "This paper presents the adaptation of an Entity Centric Model for Portuguese coreference resolution, considering 10 named entity categories. The model was evaluated on named e using the HAREM Portuguese corpus and the results are 81.0{\%} of precision and 58.3{\%} of recall overall, the resulting system is freely available",
}
@inproceedings{roesiger-kuhn-2016-ims,
    title = "{IMS} {H}ot{C}oref {DE}: A Data-driven Co-reference Resolver for {G}erman",
    author = "Roesiger, Ina  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1024",
    pages = "155--160",
    abstract = {This paper presents a data-driven co-reference resolution system for German that has been adapted from IMS HotCoref, a co-reference resolver for English. It describes the difficulties when resolving co-reference in German text, the adaptation process and the features designed to address linguistic challenges brought forth by German. We report performance on the reference dataset T{\"u}Ba-D/Z and include a post-task SemEval 2010 evaluation, showing that the resolver achieves state-of-the-art performance. We also include ablation experiments that indicate that integrating linguistic features increases results. The paper also describes the steps and the format necessary to use the resolver on new texts. The tool is freely available for download.},
}
@inproceedings{mujadia-etal-2016-coreference,
    title = "Coreference Annotation Scheme and Relation Types for {H}indi",
    author = "Mujadia, Vandan  and
      Gupta, Palash  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1025",
    pages = "161--168",
    abstract = "This paper describes a coreference annotation scheme, coreference annotation specific issues and their solutions through our proposed annotation scheme for Hindi. We introduce different co-reference relation types between continuous mentions of the same coreference chain such as {``}Part-of{''}, {``}Function-value pair{''} etc. We used Jaccard similarity based Krippendorff{`}s{'} alpha to demonstrate consistency in annotation scheme, annotation and corpora. To ease the coreference annotation process, we built a semi-automatic Coreference Annotation Tool (CAT). We also provide statistics of coreference annotation on Hindi Dependency Treebank (HDTB).",
}
@inproceedings{nedoluzhko-etal-2016-coreference,
    title = "Coreference in {P}rague {C}zech-{E}nglish {D}ependency {T}reebank",
    author = "Nedoluzhko, Anna  and
      Nov{\'a}k, Michal  and
      Cinkov{\'a}, Silvie  and
      Mikulov{\'a}, Marie  and
      M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1026",
    pages = "169--176",
    abstract = "We present coreference annotation on parallel Czech-English texts of the Prague Czech-English Dependency Treebank (PCEDT). The paper describes innovations made to PCEDT 2.0 concerning coreference, as well as coreference information already present there. We characterize the coreference annotation scheme, give the statistics and compare our annotation with the coreference annotation in Ontonotes and Prague Dependency Treebank for Czech. We also present the experiments made using this corpus to improve the alignment of coreferential expressions, which helps us to collect better statistics of correspondences between types of coreferential relations in Czech and English. The corpus released as PCEDT 2.0 Coref is publicly available.",
}
@inproceedings{bell-etal-2016-sieve,
    title = "Sieve-based Coreference Resolution in the Biomedical Domain",
    author = "Bell, Dane  and
      Hahn-Powell, Gus  and
      Valenzuela-Esc{\'a}rcega, Marco A.  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1027",
    pages = "177--183",
    abstract = "We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution. Domain-general coreference resolution algorithms perform poorly on biomedical documents, because the cues they rely on such as gender are largely absent in this domain, and because they do not encode domain-specific knowledge such as the number and type of participants required in chemical reactions. Moreover, it is difficult to directly encode this knowledge into most coreference resolution algorithms because they are not rule-based. Our rule-based architecture uses sequentially applied hand-designed {``}sieves{''}, with the output of each sieve informing and constraining subsequent sieves. This architecture provides a 3.2{\%} increase in throughput to our Reach event extraction system with precision parallel to that of the stricter system that relies solely on syntactic patterns for extraction.",
}
@inproceedings{vala-etal-2016-annotating,
    title = "Annotating Characters in Literary Corpora: A Scheme, the {CHARLES} Tool, and an Annotated Novel",
    author = "Vala, Hardik  and
      Dimitrov, Stefan  and
      Jurgens, David  and
      Piper, Andrew  and
      Ruths, Derek",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1028",
    pages = "184--189",
    abstract = "Characters form the focus of various studies of literary works, including social network analysis, archetype induction, and plot comparison. The recent rise in the computational modelling of literary works has produced a proportional rise in the demand for character-annotated literary corpora. However, automatically identifying characters is an open problem and there is low availability of literary texts with manually labelled characters. To address the latter problem, this work presents three contributions: (1) a comprehensive scheme for manually resolving mentions to characters in texts. (2) A novel collaborative annotation tool, CHARLES (CHAracter Resolution Label-Entry System) for character annotation and similiar cross-document tagging tasks. (3) The character annotations resulting from a pilot study on the novel Pride and Prejudice, demonstrating the scheme and tool facilitate the efficient production of high-quality annotations. We expect this work to motivate the further production of annotated literary corpora to help meet the demand of the community.",
}
@inproceedings{garnier-saint-dizier-2016-error,
    title = "Error Typology and Remediation Strategies for Requirements Written in {E}nglish by Non-Native Speakers",
    author = "Garnier, Marie  and
      Saint-Dizier, Patrick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1029",
    pages = "190--197",
    abstract = "In most international industries, English is the main language of communication for technical documents. These documents are designed to be as unambiguous as possible for their users. For international industries based in non-English speaking countries, the professionals in charge of writing requirements are often non-native speakers of English, who rarely receive adequate training in the use of English for this task. As a result, requirements can contain a relatively large diversity of lexical and grammatical errors, which are not eliminated by the use of guidelines from controlled languages. This article investigates the distribution of errors in a corpus of requirements written in English by native speakers of French. Errors are defined on the basis of grammaticality and acceptability principles, and classified using comparable categories. Results show a high proportion of errors in the Noun Phrase, notably through modifier stacking, and errors consistent with simplification strategies. Comparisons with similar corpora in other genres reveal the specificity of the distribution of errors in requirements. This research also introduces possible applied uses, in the form of strategies for the automatic detection of errors, and in-person training provided by certification boards in requirements authoring.",
}
@inproceedings{keiper-etal-2016-improving,
    title = "Improving {POS} Tagging of {G}erman Learner Language in a Reading Comprehension Scenario",
    author = "Keiper, Lena  and
      Horbach, Andrea  and
      Thater, Stefan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1030",
    pages = "198--205",
    abstract = "We present a novel method to automatically improve the accurracy of part-of-speech taggers on learner language. The key idea underlying our approach is to exploit the structure of a typical language learner task and automatically induce POS information for out-of-vocabulary (OOV) words. To evaluate the effectiveness of our approach, we add manual POS and normalization information to an existing language learner corpus. Our evaluation shows an increase in accurracy from 72.4{\%} to 81.5{\%} on OOV words.",
}
@inproceedings{volodina-etal-2016-swell,
    title = "{S}we{LL} on the rise: {S}wedish Learner Language corpus for {E}uropean Reference Level studies",
    author = {Volodina, Elena  and
      Pil{\'a}n, Ildik{\'o}  and
      Enstr{\"o}m, Ingegerd  and
      Llozhi, Lorena  and
      Lundkvist, Peter  and
      Sundberg, Gunl{\"o}g  and
      Sandell, Monica},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1031",
    pages = "206--212",
    abstract = "We present a new resource for Swedish, SweLL, a corpus of Swedish Learner essays linked to learners{'} performance according to the Common European Framework of Reference (CEFR). SweLL consists of three subcorpora ― SpIn, SW1203 and Tisus, collected from three different educational establishments. The common metadata for all subcorpora includes age, gender, native languages, time of residence in Sweden, type of written task. Depending on the subcorpus, learner texts may contain additional information, such as text genres, topics, grades. Five of the six CEFR levels are represented in the corpus: A1, A2, B1, B2 and C1 comprising in total 339 essays. C2 level is not included since courses at C2 level are not offered. The work flow consists of collection of essays and permits, essay digitization and registration, meta-data annotation, automatic linguistic annotation. Inter-rater agreement is presented on the basis of SW1203 subcorpus. The work on SweLL is still ongoing with more that 100 essays waiting in the pipeline. This article both describes the resource and the {``}how-to{''} behind the compilation of SweLL.",
}
@inproceedings{francois-etal-2016-svalex,
    title = "{SVAL}ex: a {CEFR}-graded Lexical Resource for {S}wedish Foreign and Second Language Learners",
    author = {Fran{\c{c}}ois, Thomas  and
      Volodina, Elena  and
      Pil{\'a}n, Ildik{\'o}  and
      Tack, Ana{\"\i}s},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1032",
    pages = "213--219",
    abstract = "The paper introduces SVALex, a lexical resource primarily aimed at learners and teachers of Swedish as a foreign and second language that describes the distribution of 15,681 words and expressions across the Common European Framework of Reference (CEFR). The resource is based on a corpus of coursebook texts, and thus describes receptive vocabulary learners are exposed to during reading activities, as opposed to productive vocabulary they use when speaking or writing. The paper describes the methodology applied to create the list and to estimate the frequency distribution. It also discusses some characteristics of the resulting resource and compares it to other lexical resources for Swedish. An interesting feature of this resource is the possibility to separate the wheat from the chaff, identifying the core vocabulary at each level, i.e. vocabulary shared by several coursebook writers at each level, from peripheral vocabulary which is used by the minority of the coursebook writers.",
}
@inproceedings{shiue-chen-2016-detecting,
    title = "Detecting Word Usage Errors in {C}hinese Sentences for Learning {C}hinese as a Foreign Language",
    author = "Shiue, Yow-Ting  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1033",
    pages = "220--224",
    abstract = "Automated grammatical error detection, which helps users improve their writing, is an important application in NLP. Recently more and more people are learning Chinese, and an automated error detection system can be helpful for the learners. This paper proposes n-gram features, dependency count features, dependency bigram features, and single-character features to determine if a Chinese sentence contains word usage errors, in which a word is written as a wrong form or the word selection is inappropriate. With marking potential errors on the level of sentence segments, typically delimited by punctuation marks, the learner can try to correct the problems without the assistant of a language teacher. Experiments on the HSK corpus show that the classifier combining all sets of features achieves an accuracy of 0.8423. By utilizing certain combination of the sets of features, we can construct a system that favors precision or recall. The best precision we achieve is 0.9536, indicating that our system is reliable and seldom produces misleading results.",
}
@inproceedings{zhang-etal-2016-libn3l,
    title = "{L}ib{N}3{L}:A Lightweight Package for Neural {NLP}",
    author = "Zhang, Meishan  and
      Yang, Jie  and
      Teng, Zhiyang  and
      Zhang, Yue",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1034",
    pages = "225--229",
    abstract = "We present a light-weight machine learning tool for NLP research. The package supports operations on both discrete and dense vectors, facilitating implementation of linear models as well as neural models. It provides several basic layers which mainly aims for single-layer linear and non-linear transformations. By using these layers, we can conveniently implement linear models and simple neural models. Besides, this package also integrates several complex layers by composing those basic layers, such as RNN, Attention Pooling, LSTM and gated RNN. Those complex layers can be used to implement deep neural models directly.",
}
@inproceedings{tack-etal-2016-evaluating,
    title = "Evaluating Lexical Simplification and Vocabulary Knowledge for Learners of {F}rench: Possibilities of Using the {FLEL}ex Resource",
    author = {Tack, Ana{\"\i}s  and
      Fran{\c{c}}ois, Thomas  and
      Ligozat, Anne-Laure  and
      Fairon, C{\'e}drick},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1035",
    pages = "230--236",
    abstract = "This study examines two possibilities of using the FLELex graded lexicon for the automated assessment of text complexity in French as a foreign language learning. From the lexical frequency distributions described in FLELex, we derive a single level of difficulty for each word in a parallel corpus of original and simplified texts. We then use this data to automatically address the lexical complexity of texts in two ways. On the one hand, we evaluate the degree of lexical simplification in manually simplified texts with respect to their original version. Our results show a significant simplification effect, both in the case of French narratives simplified for non-native readers and in the case of simplified Wikipedia texts. On the other hand, we define a predictive model which identifies the number of words in a text that are expected to be known at a particular learning level. We assess the accuracy with which these predictions are able to capture actual word knowledge as reported by Dutch-speaking learners of French. Our study shows that although the predictions seem relatively accurate in general (87.4{\%} to 92.3{\%}), they do not yet seem to cover the learners{'} lack of knowledge very well.",
}
@inproceedings{baur-etal-2016-shared,
    title = "A Shared Task for Spoken {CALL}?",
    author = "Baur, Claudia  and
      Gerlach, Johanna  and
      Rayner, Manny  and
      Russell, Martin  and
      Strik, Helmer",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1036",
    pages = "237--244",
    abstract = "We argue that the field of spoken CALL needs a shared task in order to facilitate comparisons between different groups and methodologies, and describe a concrete example of such a task, based on data collected from a speech-enabled online tool which has been used to help young Swiss German teens practise skills in English conversation. Items are prompt-response pairs, where the prompt is a piece of German text and the response is a recorded English audio file. The task is to label pairs as {``}accept{''} or {``}reject{''}, accepting responses which are grammatically and linguistically correct to match a set of hidden gold standard answers as closely as possible. Initial resources are provided so that a scratch system can be constructed with a minimal investment of effort, and in particular without necessarily using a speech recogniser. Training data for the task will be released in June 2016, and test data in January 2017.",
}
@inproceedings{khalifa-etal-2016-joining,
    title = "Joining-in-type Humanoid Robot Assisted Language Learning System",
    author = "Khalifa, AlBara  and
      Kato, Tsuneo  and
      Yamamoto, Seiichi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1037",
    pages = "245--249",
    abstract = "Dialogue robots are attractive to people, and in language learning systems, they motivate learners and let them practice conversational skills in more realistic environment. However, automatic speech recognition (ASR) of the second language (L2) learners is still a challenge, because their speech contains not just pronouncing, lexical, grammatical errors, but is sometimes totally disordered. Hence, we propose a novel robot assisted language learning (RALL) system using two robots, one as a teacher and the other as an advanced learner. The system is designed to simulate multiparty conversation, expecting implicit learning and enhancement of predictability of learners{'} utterance through an alignment similar to {``}interactive alignment{''}, which is observed in human-human conversation. We collected a database with the prototypes, and measured how much the alignment phenomenon observed in the database with initial analysis.",
}
@inproceedings{el-haj-rayson-2016-osman,
    title = "{OSMAN} ― A Novel {A}rabic Readability Metric",
    author = "El-Haj, Mahmoud  and
      Rayson, Paul",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1038",
    pages = "250--255",
    abstract = "We present OSMAN (Open Source Metric for Measuring Arabic Narratives) - a novel open source Arabic readability metric and tool. It allows researchers to calculate readability for Arabic text with and without diacritics. OSMAN is a modified version of the conventional readability formulas such as Flesch and Fog. In our work we introduce a novel approach towards counting short, long and stress syllables in Arabic which is essential for judging readability of Arabic narratives. We also introduce an additional factor called {``}Faseeh{''} which considers aspects of script usually dropped in informal Arabic writing. To evaluate our methods we used Spearman{'}s correlation metric to compare text readability for 73,000 parallel sentences from English and Arabic UN documents. The Arabic sentences were written with the absence of diacritics and in order to count the number of syllables we added the diacritics in using an open source tool called Mishkal. The results show that OSMAN readability formula correlates well with the English ones making it a useful tool for researchers and educators working with Arabic text.",
}
@inproceedings{geoffrois-2016-evaluating,
    title = "Evaluating Interactive System Adaptation",
    author = "Geoffrois, Edouard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1039",
    pages = "256--260",
    abstract = "Enabling users of intelligent systems to enhance the system performance by providing feedback on their errors is an important need. However, the ability of systems to learn from user feedback is difficult to evaluate in an objective and comparative way. Indeed, the involvement of real users in the adaptation process is an impediment to objective evaluation. This issue can be solved by using an oracle approach, where users are simulated by oracles having access to the reference test data. Another difficulty is to find a meaningful metric despite the fact that system improvements depend on the feedback provided and on the system itself. A solution is to measure the minimal amount of information needed to correct all system errors. It can be shown that for any well defined non interactive task, the interactively supervised version of the task can be evaluated by combining such an oracle-based approach and a minimum supervision rate metric. This new evaluation protocol for adaptive systems is not only expected to drive progress for such systems, but also to pave the way for a specialisation of actors along the value chain of their technological development.",
}
@inproceedings{derczynski-2016-complementarity,
    title = "Complementarity, {F}-score, and {NLP} Evaluation",
    author = "Derczynski, Leon",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1040",
    pages = "261--266",
    abstract = "This paper addresses the problem of quantifying the differences between entity extraction systems, where in general only a small proportion a document should be selected. Comparing overall accuracy is not very useful in these cases, as small differences in accuracy may correspond to huge differences in selections over the target minority class. Conventionally, one may use per-token complementarity to describe these differences, but it is not very useful when the set is heavily skewed. In such situations, which are common in information retrieval and entity recognition, metrics like precision and recall are typically used to describe performance. However, precision and recall fail to describe the differences between sets of objects selected by different decision strategies, instead just describing the proportional amount of correct and incorrect objects selected. This paper presents a method for measuring complementarity for precision, recall and F-score, quantifying the difference between entity extraction approaches.",
}
@inproceedings{dragoni-etal-2016-dranziera,
    title = "{DRANZIERA}: An Evaluation Protocol For Multi-Domain Opinion Mining",
    author = "Dragoni, Mauro  and
      Tettamanzi, Andrea  and
      da Costa Pereira, C{\'e}lia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1041",
    pages = "267--272",
    abstract = "Opinion Mining is a topic which attracted a lot of interest in the last years. By observing the literature, it is often hard to replicate system evaluation due to the unavailability of the data used for the evaluation or to the lack of details about the protocol used in the campaign. In this paper, we propose an evaluation protocol, called DRANZIERA, composed of a multi-domain dataset and guidelines allowing both to evaluate opinion mining systems in different contexts (Closed, Semi-Open, and Open) and to compare them to each other and to a number of baselines.",
}
@inproceedings{fothergill-etal-2016-evaluating,
    title = "Evaluating a Topic Modelling Approach to Measuring Corpus Similarity",
    author = "Fothergill, Richard  and
      Cook, Paul  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1042",
    pages = "273--279",
    abstract = "Web corpora are often constructed automatically, and their contents are therefore often not well understood. One technique for assessing the composition of such a web corpus is to empirically measure its similarity to a reference corpus whose composition is known. In this paper we evaluate a number of measures of corpus similarity, including a method based on topic modelling which has not been previously evaluated for this task. To evaluate these methods we use known-similarity corpora that have been previously used for this purpose, as well as a number of newly-constructed known-similarity corpora targeting differences in genre, topic, time, and region. Our findings indicate that, overall, the topic modelling approach did not improve on a chi-square method that had previously been found to work well for measuring corpus similarity.",
}
@inproceedings{fandrych-etal-2016-user,
    title = "User, who art thou? User Profiling for Oral Corpus Platforms",
    author = "Fandrych, Christian  and
      Frick, Elena  and
      Hedeland, Hanna  and
      Iliash, Anna  and
      Jettka, Daniel  and
      Mei{\ss}ner, Cordula  and
      Schmidt, Thomas  and
      Wallner, Franziska  and
      Weigert, Kathrin  and
      Westpfahl, Swantje",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1043",
    pages = "280--287",
    abstract = "This contribution presents the background, design and results of a study of users of three oral corpus platforms in Germany. Roughly 5.000 registered users of the Database for Spoken German (DGD), the GeWiss corpus and the corpora of the Hamburg Centre for Language Corpora (HZSK) were asked to participate in a user survey. This quantitative approach was complemented by qualitative interviews with selected users. We briefly introduce the corpus resources involved in the study in section 2. Section 3 describes the methods employed in the user studies. Section 4 summarizes results of the studies focusing on selected key topics. Section 5 attempts a generalization of these results to larger contexts.",
}
@inproceedings{costa-etal-2016-building,
    title = "Building a Corpus of Errors and Quality in Machine Translation: Experiments on Error Impact",
    author = "Costa, {\^A}ngela  and
      Correia, Rui  and
      Coheur, Lu{\'\i}sa",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1044",
    pages = "288--292",
    abstract = "In this paper we describe a corpus of automatic translations annotated with both error type and quality. The 300 sentences that we have selected were generated by Google Translate, Systran and two in-house Machine Translation systems that use Moses technology. The errors present on the translations were annotated with an error taxonomy that divides errors in five main linguistic categories (Orthography, Lexis, Grammar, Semantics and Discourse), reflecting the language level where the error is located. After the error annotation process, we accessed the translation quality of each sentence using a four point comprehension scale from 1 to 5. Both tasks of error and quality annotation were performed by two different annotators, achieving good levels of inter-annotator agreement. The creation of this corpus allowed us to use it as training data for a translation quality classifier. We concluded on error severity by observing the outputs of two machine learning classifiers: a decision tree and a regression model.",
}
@inproceedings{yaneva-etal-2016-evaluating,
    title = "Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities",
    author = "Yaneva, Victoria  and
      Temnikova, Irina  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1045",
    pages = "293--299",
    abstract = "This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities. First, we present our work towards the development of the EasyRead corpus, which contains easy-to-read documents created especially for people with cognitive disabilities. We then compare the EasyRead corpus to the simplified output contained in the LocalNews corpus (Feng, 2009), the accessibility of which has been evaluated through reading comprehension experiments including 20 adults with mild intellectual disability. This comparison is made on the basis of 13 disability-specific linguistic features. The comparison reveals that there are no major differences between the two corpora, which shows that the EasyRead corpus is to a similar reading level as the user-evaluated texts. We also discuss the role of Simple Wikipedia (Zhu et al., 2010) as a widely-used accessibility benchmark, in light of our finding that it is significantly more complex than both the EasyRead and the LocalNews corpora.",
}
@inproceedings{ghannay-etal-2016-word,
    title = "Word Embedding Evaluation and Combination",
    author = "Ghannay, Sahar  and
      Favre, Benoit  and
      Est{\`e}ve, Yannick  and
      Camelin, Nathalie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1046",
    pages = "300--305",
    abstract = "Word embeddings have been successfully used in several natural language processing tasks (NLP) and speech processing. Different approaches have been introduced to calculate word embeddings through neural networks. In the literature, many studies focused on word embedding evaluation, but for our knowledge, there are still some gaps. This paper presents a study focusing on a rigorous comparison of the performances of different kinds of word embeddings. These performances are evaluated on different NLP and linguistic tasks, while all the word embeddings are estimated on the same training data using the same vocabulary, the same number of dimensions, and other similar characteristics. The evaluation results reported in this paper match those in the literature, since they point out that the improvements achieved by a word embedding in one task are not consistently observed across all tasks. For that reason, this paper investigates and evaluates approaches to combine word embeddings in order to take advantage of their complementarity, and to look for the effective word embeddings that can achieve good performances on all tasks. As a conclusion, this paper provides new perceptions of intrinsic qualities of the famous word embedding families, which can be different from the ones provided by works previously published in the scientific literature.",
}
@inproceedings{poignant-etal-2016-benchmarking,
    title = "Benchmarking multimedia technologies with the {CAMOMILE} platform: the case of Multimodal Person Discovery at {M}edia{E}val 2015",
    author = "Poignant, Johann  and
      Bredin, Herv{\'e}  and
      Barras, Claude  and
      Stefas, Mickael  and
      Bruneau, Pierrick  and
      Tamisier, Thomas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1047",
    pages = "306--309",
    abstract = "In this paper, we claim that the CAMOMILE collaborative annotation platform (developed in the framework of the eponymous CHIST-ERA project) eases the organization of multimedia technology benchmarks, automating most of the campaign technical workflow and enabling collaborative (hence faster and cheaper) annotation of the evaluation data. This is demonstrated through the successful organization of a new multimedia task at MediaEval 2015, Multimodal Person Discovery in Broadcast TV.",
}
@inproceedings{castilho-obrien-2016-evaluating,
    title = "Evaluating the Impact of Light Post-Editing on Usability",
    author = "Castilho, Sheila  and
      O{'}Brien, Sharon",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1048",
    pages = "310--316",
    abstract = "This paper discusses a methodology to measure the usability of machine translated content by end users, comparing lightly post-edited content with raw output and with the usability of source language content. The content selected consists of Online Help articles from a software company for a spreadsheet application, translated from English into German. Three groups of five users each used either the source text - the English version (EN) -, the raw MT version (DE{\_}MT), or the light PE version (DE{\_}PE), and were asked to carry out six tasks. Usability was measured using an eye tracker and cognitive, temporal and pragmatic measures of usability. Satisfaction was measured via a post-task questionnaire presented after the participants had completed the tasks.",
}
@inproceedings{salesky-etal-2016-operational,
    title = "Operational Assessment of Keyword Search on Oral History",
    author = "Salesky, Elizabeth  and
      Ray, Jessica  and
      Shen, Wade",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1049",
    pages = "317--321",
    abstract = "This project assesses the resources necessary to make oral history searchable by means of automatic speech recognition (ASR). There are many inherent challenges in applying ASR to conversational speech: smaller training set sizes and varying demographics, among others. We assess the impact of dataset size, word error rate and term-weighted value on human search capability through an information retrieval task on Mechanical Turk. We use English oral history data collected by StoryCorps, a national organization that provides all people with the opportunity to record, share and preserve their stories, and control for a variety of demographics including age, gender, birthplace, and dialect on four different training set sizes. We show comparable search performance using a standard speech recognition system as with hand-transcribed data, which is promising for increased accessibility of conversational speech and oral history archives.",
}
@inproceedings{valenzuela-escarcega-etal-2016-odins,
    title = "Odin{'}s Runes: A Rule Language for Information Extraction",
    author = "Valenzuela-Esc{\'a}rcega, Marco A.  and
      Hahn-Powell, Gus  and
      Surdeanu, Mihai",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1050",
    pages = "322--329",
    abstract = "Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs. Support for syntactic patterns allow us to concisely define relations that are otherwise difficult to express in languages such as Common Pattern Specification Language (CPSL), which are currently limited to shallow linguistic features. The interaction of lexical and syntactic automata provides robustness and flexibility when writing extraction rules. This paper describes Odin{'}s declarative language for writing these cascaded automata.",
}
@inproceedings{lefever-hoste-2016-classification,
    title = "A Classification-based Approach to Economic Event Detection in {D}utch News Text",
    author = "Lefever, Els  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1051",
    pages = "330--335",
    abstract = "Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text.",
}
@inproceedings{francopoulo-etal-2016-predictive,
    title = "Predictive Modeling: Guessing the {NLP} Terms of Tomorrow",
    author = "Francopoulo, Gil  and
      Mariani, Joseph  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1052",
    pages = "336--343",
    abstract = "Predictive modeling, often called {``}predictive analytics{''} in a commercial context, encompasses a variety of statistical techniques that analyze historical and present facts to make predictions about unknown events. Often the unknown events are in the future, but prediction can be applied to any type of unknown whether it be in the past or future. In our case, we present some experiments applying predictive modeling to the usage of technical terms within the NLP domain.",
}
@inproceedings{sahlgren-etal-2016-gavagai,
    title = "The Gavagai Living Lexicon",
    author = "Sahlgren, Magnus  and
      Gyllensten, Amaru Cuba  and
      Espinoza, Fredrik  and
      Hamfors, Ola  and
      Karlgren, Jussi  and
      Olsson, Fredrik  and
      Persson, Per  and
      Viswanathan, Akshay  and
      Holst, Anders",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1053",
    pages = "344--350",
    abstract = "This paper presents the Gavagai Living Lexicon, which is an online distributional semantic model currently available in 20 different languages. We describe the underlying distributional semantic model, and how we have solved some of the challenges in applying such a model to large amounts of streaming data. We also describe the architecture of our implementation, and discuss how we deal with continuous quality assurance of the lexicon.",
}
@inproceedings{mubarak-abdelali-2016-arabic,
    title = "{A}rabic to {E}nglish Person Name Transliteration using {T}witter",
    author = "Mubarak, Hamdy  and
      Abdelali, Ahmed",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1054",
    pages = "351--355",
    abstract = "Social media outlets are providing new opportunities for harvesting valuable resources. We present a novel approach for mining data from Twitter for the purpose of building transliteration resources and systems. Such resources are crucial in translation and retrieval tasks. We demonstrate the benefits of the approach on Arabic to English transliteration. The contribution of this approach includes the size of data that can be collected and exploited within the span of a limited time; the approach is very generic and can be adopted to other languages and the ability of the approach to cope with new transliteration phenomena and trends. A statistical transliteration system built using this data improved a comparable system built from Wikipedia wikilinks data.",
}
@inproceedings{jeong-etal-2016-korean,
    title = "{K}orean {T}ime{ML} and {K}orean {T}ime{B}ank",
    author = "Jeong, Young-Seob  and
      Joo, Won-Tae  and
      Do, Hyun-Woo  and
      Lim, Chae-Gyun  and
      Choi, Key-Sun  and
      Choi, Ho-Jin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1055",
    pages = "356--359",
    abstract = "Many emerging documents usually contain temporal information. Because the temporal information is useful for various applications, it became important to develop a system of extracting the temporal information from the documents. Before developing the system, it first necessary to define or design the structure of temporal information. In other words, it is necessary to design a language which defines how to annotate the temporal information. There have been some studies about the annotation languages, but most of them was applicable to only a specific target language (e.g., English). Thus, it is necessary to design an individual annotation language for each language. In this paper, we propose a revised version of Koreain Time Mark-up Language (K-TimeML), and also introduce a dataset, named Korean TimeBank, that is constructed basd on the K-TimeML. We believe that the new K-TimeML and Korean TimeBank will be used in many further researches about extraction of temporal information.",
}
@inproceedings{seitner-etal-2016-large,
    title = "A Large {D}ata{B}ase of Hypernymy Relations Extracted from the Web.",
    author = "Seitner, Julian  and
      Bizer, Christian  and
      Eckert, Kai  and
      Faralli, Stefano  and
      Meusel, Robert  and
      Paulheim, Heiko  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1056",
    pages = "360--367",
    abstract = "Hypernymy relations (those where an hyponym term shares a {``}isa{''} relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e.g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction. In fact, such relations may provide the basis for the construction of more complex structures such as taxonomies, or be used as effective background knowledge for many word understanding applications. We present a publicly available database containing more than 400 million hypernymy relations we extracted from the CommonCrawl web corpus. We describe the infrastructure we developed to iterate over the web corpus for extracting the hypernymy relations and store them effectively into a large database. This collection of relations represents a rich source of knowledge and may be useful for many researchers. We offer the tuple dataset for public download and an Application Programming Interface (API) to help other researchers programmatically query the database.",
}
@inproceedings{katris-etal-2016-using,
    title = "Using a Cross-Language Information Retrieval System based on {OHSUMED} to Evaluate the {M}oses and {K}antan{MT} Statistical Machine Translation Systems",
    author = "Katris, Nikolaos  and
      Sutcliffe, Richard  and
      Kalamboukis, Theodore",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1057",
    pages = "368--372",
    abstract = "The objective of this paper was to evaluate the performance of two statistical machine translation (SMT) systems within a cross-language information retrieval (CLIR) architecture and examine if there is a correlation between translation quality and CLIR performance. The SMT systems were KantanMT, a cloud-based machine translation (MT) platform, and Moses, an open-source MT application. First we trained both systems using the same language resources: the EMEA corpus for the translation model and language model and the QTLP corpus for tuning. Then we translated the 63 queries of the OHSUMED test collection from Greek into English using both MT systems. Next, we ran the queries on the document collection using Apache Solr to get a list of the top ten matches. The results were compared to the OHSUMED gold standard. KantanMT achieved higher average precision and F-measure than Moses, while both systems produced the same recall score. We also calculated the BLEU score for each system using the ECDC corpus. Moses achieved a higher BLEU score than KantanMT. Finally, we also tested the IR performance of the original English queries. This work overall showed that CLIR performance can be better even when BLEU score is worse.",
}
@inproceedings{pardelli-etal-2016-two,
    title = "Two Decades of Terminology: {E}uropean Framework Programmes Titles",
    author = "Pardelli, Gabriella  and
      Goggi, Sara  and
      Giannini, Silvia  and
      Biagioni, Stefania",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1058",
    pages = "373--378",
    abstract = "This work analyses a corpus made of the titles of research projects belonging to the last four European Commission Framework Programmes (FP4, FP5, FP6, FP7) during a time span of nearly two decades (1994-2012). The starting point is the idea of creating a corpus of titles which would constitute a terminological niche, a sort of {``}cluster map{''} offering an overall vision on the terms used and the links between them. Moreover, by performing a terminological comparison over a period of time it is possible to trace the presence of obsolete words in outdated research areas as well as of neologisms in the most recent fields. Within this scenario, the minimal purpose is to build a corpus of titles of European projects belonging to the several Framework Programmes in order to obtain a terminological mapping of relevant words in the various research areas: particularly significant would be those terms spread across different domains or those extremely tied to a specific domain. A term could actually be found in many fields and being able to acknowledge and retrieve this cross-presence means being able to linking those different domains by means of a process of terminological mapping.",
}
@inproceedings{peters-wyner-2016-legal,
    title = "Legal Text Interpretation: Identifying Hohfeldian Relations from Text",
    author = "Peters, Wim  and
      Wyner, Adam",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1059",
    pages = "379--384",
    abstract = "The paper investigates the extent of the support semi-automatic analysis can provide for the specific task of assigning Hohfeldian relations of Duty, using the General Architecture for Text Engineering tool for the automated extraction of Duty instances and the bearers of associated roles. The outcome of the analysis supports scholars in identifying Hohfeldian structures in legal text when performing close reading of the texts. A cyclic workflow involving automated annotation and expert feedback will incrementally increase the quality and coverage of the automatic extraction process, and increasingly reduce the amount of manual work required of the scholar.",
}
@inproceedings{tachibana-komachi-2016-analysis,
    title = "Analysis of {E}nglish Spelling Errors in a Word-Typing Game",
    author = "Tachibana, Ryuichi  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1060",
    pages = "385--390",
    abstract = "The emergence of the web has necessitated the need to detect and correct noisy consumer-generated texts. Most of the previous studies on English spelling-error extraction collected English spelling errors from web services such as Twitter by using the edit distance or from input logs utilizing crowdsourcing. However, in the former approach, it is not clear which word corresponds to the spelling error, and the latter approach requires an annotation cost for the crowdsourcing. One notable exception is Rodrigues and Rytting (2012), who proposed to extract English spelling errors by using a word-typing game. Their approach saves the cost of crowdsourcing, and guarantees an exact alignment between the word and the spelling error. However, they did not assert whether the extracted spelling error corpora reflect the usual writing process such as writing a document. Therefore, we propose a new correctable word-typing game that is more similar to the actual writing process. Experimental results showed that we can regard typing-game logs as a source of spelling errors.",
}
@inproceedings{kovar-etal-2016-finding,
    title = "Finding Definitions in Large Corpora with {S}ketch {E}ngine",
    author = "Kov{\'a}{\v{r}}, Vojt{\v{e}}ch  and
      Mo{\v{c}}iarikov{\'a}, Monika  and
      Rychl{\'y}, Pavel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1061",
    pages = "391--394",
    abstract = "The paper describes automatic definition finding implemented within the leading corpus query and management tool, Sketch Engine. The implementation exploits complex pattern-matching queries in the corpus query language (CQL) and the indexing mechanism of word sketches for finding and storing definition candidates throughout the corpus. The approach is evaluated for Czech and English corpora, showing that the results are usable in practice: precision of the tool ranges between 30 and 75 percent (depending on the major corpus text types) and we were able to extract nearly 2 million definition candidates from an English corpus with 1.4 billion words. The feature is embedded into the interface as a concordance filter, so that users can search for definitions of any query to the corpus, including very specific multi-word queries. The results also indicate that ordinary texts (unlike explanatory texts) contain rather low number of definitions, which is perhaps the most important problem with automatic definition finding in general.",
}
@inproceedings{rodriguez-ferreira-etal-2016-improving,
    title = "Improving Information Extraction from {W}ikipedia Texts using Basic {E}nglish",
    author = "Rodr{\'\i}guez-Ferreira, Teresa  and
      Rabad{\'a}n, Adri{\'a}n  and
      Herv{\'a}s, Raquel  and
      D{\'\i}az, Alberto",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1062",
    pages = "395--400",
    abstract = "The aim of this paper is to study the effect that the use of Basic English versus common English has on information extraction from online resources. The amount of online information available to the public grows exponentially, and is potentially an excellent resource for information extraction. The problem is that this information often comes in an unstructured format, such as plain text. In order to retrieve knowledge from this type of text, it must first be analysed to find the relevant details, and the nature of the language used can greatly impact the quality of the extracted information. In this paper, we compare triplets that represent definitions or properties of concepts obtained from three online collaborative resources (English Wikipedia, Simple English Wikipedia and Simple English Wiktionary) and study the differences in the results when Basic English is used instead of common English. The results show that resources written in Basic English produce less quantity of triplets, but with higher quality.",
}
@inproceedings{caselli-etal-2016-nlp,
    title = "{NLP} and Public Engagement: The Case of the {I}talian School Reform",
    author = "Caselli, Tommaso  and
      Moretti, Giovanni  and
      Sprugnoli, Rachele  and
      Tonelli, Sara  and
      Lanfrey, Damien  and
      Kutzmann, Donatella Solda",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1063",
    pages = "401--406",
    abstract = "In this paper we present PIERINO (PIattaforma per l{'}Estrazione e il Recupero di INformazione Online), a system that was implemented in collaboration with the Italian Ministry of Education, University and Research to analyse the citizens{'} comments given in {\#}labuonascuola survey. The platform includes various levels of automatic analysis such as key-concept extraction and word co-occurrences. Each analysis is displayed through an intuitive view using different types of visualizations, for example radar charts and sunburst. PIERINO was effectively used to support shaping the last Italian school reform, proving the potential of NLP in the context of policy making.",
}
@inproceedings{saralegi-etal-2016-evaluating,
    title = "Evaluating Translation Quality and {CLIR} Performance of Query Sessions",
    author = "Saralegi, Xabier  and
      Agirre, Eneko  and
      Alegria, I{\~n}aki",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1064",
    pages = "407--411",
    abstract = "This paper presents the evaluation of the translation quality and Cross-Lingual Information Retrieval (CLIR) performance when using session information as the context of queries. The hypothesis is that previous queries provide context that helps to solve ambiguous translations in the current query. We tested several strategies on the TREC 2010 Session track dataset, which includes query reformulations grouped by generalization, specification, and drifting types. We study the Basque to English direction, evaluating both the translation quality and CLIR performance, with positive results in both cases. The results show that the quality of translation improved, reducing error rate by 12{\%} (HTER) when using session information, which improved CLIR results 5{\%} (nDCG). We also provide an analysis of the improvements across the three kinds of sessions: generalization, specification, and drifting. Translation quality improved in all three types (generalization, specification, and drifting), and CLIR improved for generalization and specification sessions, preserving the performance in drifting sessions.",
}
@inproceedings{le-quasthoff-2016-construction,
    title = "Construction and Analysis of a Large {V}ietnamese Text Corpus",
    author = "Le, Dieu-Thu  and
      Quasthoff, Uwe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1065",
    pages = "412--416",
    abstract = "This paper presents a new Vietnamese text corpus which contains around 4.05 billion words. It is a collection of Wikipedia texts, newspaper articles and random web texts. The paper describes the process of collecting, cleaning and creating the corpus. Processing Vietnamese texts faced several challenges, for example, different from many Latin languages, Vietnamese language does not use blanks for separating words, hence using common tokenizers such as replacing blanks with word boundary does not work. A short review about different approaches of Vietnamese tokenization is presented together with how the corpus has been processed and created. After that, some statistical analysis on this data is reported including the number of syllable, average word length, sentence length and topic analysis. The corpus is integrated into a framework which allows searching and browsing. Using this web interface, users can find out how many times a particular word appears in the corpus, sample sentences where this word occurs, its left and right neighbors.",
}
@inproceedings{asooja-etal-2016-forecasting,
    title = "Forecasting Emerging Trends from Scientific Literature",
    author = "Asooja, Kartik  and
      Bordea, Georgeta  and
      Vulcu, Gabriela  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1066",
    pages = "417--420",
    abstract = "Text analysis methods for the automatic identification of emerging technologies by analyzing the scientific publications, are gaining attention because of their socio-economic impact. The approaches so far have been mainly focused on retrospective analysis by mapping scientific topic evolution over time. We propose regression based approaches to predict future keyword distribution. The prediction is based on historical data of the keywords, which in our case, are LREC conference proceedings. Considering the insufficient number of data points available from LREC proceedings, we do not employ standard time series forecasting methods. We form a dataset by extracting the keywords from previous year proceedings and quantify their yearly relevance using tf-idf scores. This dataset additionally contains ranked lists of related keywords and experts for each keyword.",
}
@inproceedings{choi-etal-2016-extracting,
    title = "Extracting Structured Scholarly Information from the Machine Translation Literature",
    author = "Choi, Eunsol  and
      Horvat, Matic  and
      May, Jonathan  and
      Knight, Kevin  and
      Marcu, Daniel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1067",
    pages = "421--425",
    abstract = "Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work. We introduce a structured, queryable representation for experimental results and a baseline system that automatically populates this representation. The representation can answer compositional questions such as: {``}Which are the best published results reported on the NIST 09 Chinese to English dataset?{''} and {``}What are the most important methods for speeding up phrase-based decoding?{''} Answering such questions usually involves lengthy literature surveys. Current machine reading for academic papers does not usually consider the actual experiments, but mostly focuses on understanding abstracts. We describe annotation work to create an initial hscientific paper; experimental results representationi corpus. The corpus is composed of 67 papers which were manually annotated with a structured representation of experimental results by domain experts. Additionally, we present a baseline algorithm that characterizes the difficulty of the inference task.",
}
@inproceedings{wu-etal-2016-staggered,
    title = "Staggered {NLP}-assisted refinement for Clinical Annotations of Chronic Disease Events",
    author = "Wu, Stephen  and
      Wi, Chung-Il  and
      Sohn, Sunghwan  and
      Liu, Hongfang  and
      Juhn, Young",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1068",
    pages = "426--429",
    abstract = "Domain-specific annotations for NLP are often centered on real-world applications of text, and incorrect annotations may be particularly unacceptable. In medical text, the process of manual chart review (of a patient{'}s medical record) is error-prone due to its complexity. We propose a staggered NLP-assisted approach to the refinement of clinical annotations, an interactive process that allows initial human judgments to be verified or falsified by means of comparison with an improving NLP system. We show on our internal Asthma Timelines dataset that this approach improves the quality of the human-produced clinical annotations.",
}
@inproceedings{menini-etal-2016-pietro,
    title = "{``}Who was Pietro Badoglio?{''} Towards a {QA} system for {I}talian History",
    author = "Menini, Stefano  and
      Sprugnoli, Rachele  and
      Uva, Antonio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1069",
    pages = "430--435",
    abstract = "This paper presents QUANDHO (QUestion ANswering Data for italian HistOry), an Italian question answering dataset created to cover a specific domain, i.e. the history of Italy in the first half of the XX century. The dataset includes questions manually classified and annotated with Lexical Answer Types, and a set of question-answer pairs. This resource, freely available for research purposes, has been used to retrain a domain independent question answering system so to improve its performances in the domain of interest. Ongoing experiments on the development of a question classifier and an automatic tagger of Lexical Answer Types are also presented.",
}
@inproceedings{funk-etal-2016-document,
    title = "A Document Repository for Social Media and Speech Conversations",
    author = "Funk, Adam  and
      Gaizauskas, Robert  and
      Favre, Benoit",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1070",
    pages = "436--440",
    abstract = "We present a successfully implemented document repository REST service for flexible SCRUD (search, crate, read, update, delete) storage of social media conversations, using a GATE/TIPSTER-like document object model and providing a query language for document features. This software is currently being used in the SENSEI research project and will be published as open-source software before the project ends. It is, to the best of our knowledge, the first freely available, general purpose data repository to support large-scale multimodal (i.e., speech or text) conversation analytics.",
}
@inproceedings{parvizi-etal-2016-towards,
    title = "Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse",
    author = "Parvizi, Artemis  and
      Kohl, Matt  and
      Gonz{\`a}lez, Meritxell  and
      Saur{\'\i}, Roser",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1071",
    pages = "441--448",
    abstract = "The Dictionaries division at Oxford University Press (OUP) is aiming to model, integrate, and publish lexical content for 100 languages focussing on digitally under-represented languages. While there are multiple ontologies designed for linguistic resources, none had adequate features for meeting our requirements, chief of which was the capability to losslessly capture diverse features of many different languages in a dictionary format, while supplying a framework for inferring relations like translation, derivation, etc., between the data. Building on valuable features of existing models, and working with OUP monolingual and bilingual dictionary datasets, we have designed and implemented a new linguistic ontology. The ontology has been reviewed by a number of computational linguists, and we are working to move more dictionary data into it. We have also developed APIs to surface the linked data to dictionary websites.",
}
@inproceedings{maegaard-etal-2016-providing,
    title = "Providing a Catalogue of Language Resources for Commercial Users",
    author = "Maegaard, Bente  and
      Henriksen, Lina  and
      Joscelyne, Andrew  and
      Lusicky, Vesna  and
      Mazura, Margaretha  and
      Olsen, Sussi  and
      Povlsen, Claus  and
      Wacker, Philippe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1072",
    pages = "449--456",
    abstract = "Language resources (LR) are indispensable for the development of tools for machine translation (MT) or various kinds of computer-assisted translation (CAT). In particular language corpora, both parallel and monolingual are considered most important for instance for MT, not only SMT but also hybrid MT. The Language Technology Observatory will provide easy access to information about LRs deemed to be useful for MT and other translation tools through its LR Catalogue. In order to determine what aspects of an LR are useful for MT practitioners, a user study was made, providing a guide to the most relevant metadata and the most relevant quality criteria. We have seen that many resources exist which are useful for MT and similar work, but the majority are for (academic) research or educational use only, and as such not available for commercial use. Our work has revealed a list of gaps: coverage gap, awareness gap, quality gap, quantity gap. The paper ends with recommendations for a forward-looking strategy.",
}
@inproceedings{ide-etal-2016-language,
    title = "The Language Application Grid and Galaxy",
    author = "Ide, Nancy  and
      Suderman, Keith  and
      Pustejovsky, James  and
      Verhagen, Marc  and
      Cieri, Christopher",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1073",
    pages = "457--462",
    abstract = "The NSF-SI2-funded LAPPS Grid project is a collaborative effort among Brandeis University, Vassar College, Carnegie-Mellon University (CMU), and the Linguistic Data Consortium (LDC), which has developed an open, web-based infrastructure through which resources can be easily accessed and within which tailored language services can be efficiently composed, evaluated, disseminated and consumed by researchers, developers, and students across a wide variety of disciplines. The LAPPS Grid project recently adopted Galaxy (Giardine et al., 2005), a robust, well-developed, and well-supported front end for workflow configuration, management, and persistence. Galaxy allows data inputs and processing steps to be selected from graphical menus, and results are displayed in intuitive plots and summaries that encourage interactive workflows and the exploration of hypotheses. The Galaxy workflow engine provides significant advantages for deploying pipelines of LAPPS Grid web services, including not only means to create and deploy locally-run and even customized versions of the LAPPS Grid as well as running the LAPPS Grid in the cloud, but also access to a huge array of statistical and visualization tools that have been developed for use in genomics research.",
}
@inproceedings{choukri-etal-2016-elra,
    title = "{ELRA} Activities and Services",
    author = "Choukri, Khalid  and
      Mapelli, Val{\'e}rie  and
      Mazo, H{\'e}l{\`e}ne  and
      Popescu, Vladimir",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1074",
    pages = "463--468",
    abstract = "After celebrating its 20th anniversary in 2015, ELRA is carrying on its strong involvement in the HLT field. To share ELRA{'}s expertise of those 21 past years, this article begins with a presentation of ELRA{'}s strategic Data and LR Management Plan for a wide use by the language communities. Then, we further report on ELRA{'}s activities and services provided since LREC 2014. When looking at the cataloguing and licensing activities, we can see that ELRA has been active at making the Meta-Share repository move toward new developments steps, supporting Europe to obtain accurate LRs within the Connecting Europe Facility programme, promoting the use of LR citation, creating the ELRA License Wizard web portal.The article further elaborates on the recent LR production activities of various written, speech and video resources, commissioned by public and private customers. In parallel, ELDA has also worked on several EU-funded projects centred on strategic issues related to the European Digital Single Market. The last part gives an overview of the latest dissemination activities, with a special focus on the celebration of its 20th anniversary organised in Dubrovnik (Croatia) and the following up of LREC, as well as the launching of the new ELRA portal.",
}
@inproceedings{navarretta-2016-mirroring,
    title = "Mirroring Facial Expressions and Emotions in Dyadic Conversations",
    author = "Navarretta, Costanza",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1075",
    pages = "469--474",
    abstract = "This paper presents an investigation of mirroring facial expressions and the emotions which they convey in dyadic naturally occurring first encounters. Mirroring facial expressions are a common phenomenon in face-to-face interactions, and they are due to the mirror neuron system which has been found in both animals and humans. Researchers have proposed that the mirror neuron system is an important component behind many cognitive processes such as action learning and understanding the emotions of others. Preceding studies of the first encounters have shown that overlapping speech and overlapping facial expressions are very frequent. In this study, we want to determine whether the overlapping facial expressions are mirrored or are otherwise correlated in the encounters, and to what extent mirroring facial expressions convey the same emotion. The results of our study show that the majority of smiles and laughs, and one fifth of the occurrences of raised eyebrows are mirrored in the data. Moreover some facial traits in co-occurring expressions co-occur more often than it would be expected by chance. Finally, amusement, and to a lesser extent friendliness, are often emotions shared by both participants, while other emotions indicating individual affective states such as uncertainty and hesitancy are never showed by both participants, but co-occur with complementary emotions such as friendliness and support. Whether these tendencies are specific to this type of conversations or are more common should be investigated further.",
}
@inproceedings{radev-etal-2016-humor,
    title = "Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest",
    author = "Radev, Dragomir  and
      Stent, Amanda  and
      Tetreault, Joel  and
      Pappu, Aasish  and
      Iliakopoulou, Aikaterini  and
      Chanfreau, Agustin  and
      de Juan, Paloma  and
      Vallmitjana, Jordi  and
      Jaimes, Alejandro  and
      Jha, Rahul  and
      Mankoff, Robert",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1076",
    pages = "475--479",
    abstract = "The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.",
}
@inproceedings{yaneva-etal-2016-corpus,
    title = "A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults",
    author = "Yaneva, Victoria  and
      Temnikova, Irina  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1077",
    pages = "480--487",
    abstract = "The paper presents a corpus of text data and its corresponding gaze fixations obtained from autistic and non-autistic readers. The data was elicited through reading comprehension testing combined with eye-tracking recording. The corpus consists of 1034 content words tagged with their POS, syntactic role and three gaze-based measures corresponding to the autistic and control participants. The reading skills of the participants were measured through multiple-choice questions and, based on the answers given, they were divided into groups of skillful and less-skillful readers. This division of the groups informs researchers on whether particular fixations were elicited from skillful or less-skillful readers and allows a fair between-group comparison for two levels of reading ability. In addition to describing the process of data collection and corpus development, we present a study on the effect that word length has on reading in autism. The corpus is intended as a resource for investigating the particular linguistic constructions which pose reading difficulties for people with autism and hopefully, as a way to inform future text simplification research intended for this population.",
}
@inproceedings{chollet-etal-2016-multimodal,
    title = "A Multimodal Corpus for the Assessment of Public Speaking Ability and Anxiety",
    author = {Chollet, Mathieu  and
      W{\"o}rtwein, Torsten  and
      Morency, Louis-Philippe  and
      Scherer, Stefan},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1078",
    pages = "488--495",
    abstract = "The ability to efficiently speak in public is an essential asset for many professions and is used in everyday life. As such, tools enabling the improvement of public speaking performance and the assessment and mitigation of anxiety related to public speaking would be very useful. Multimodal interaction technologies, such as computer vision and embodied conversational agents, have recently been investigated for the training and assessment of interpersonal skills. Once central requirement for these technologies is multimodal corpora for training machine learning models. This paper addresses the need of these technologies by presenting and sharing a multimodal corpus of public speaking presentations. These presentations were collected in an experimental study investigating the potential of interactive virtual audiences for public speaking training. This corpus includes audio-visual data and automatically extracted features, measures of public speaking anxiety and personality, annotations of participants{'} behaviors and expert ratings of behavioral aspects and overall performance of the presenters. We hope this corpus will help other research teams in developing tools for supporting public speaking training.",
}
@inproceedings{bertero-fung-2016-deep,
    title = "Deep Learning of Audio and Language Features for Humor Prediction",
    author = "Bertero, Dario  and
      Fung, Pascale",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1079",
    pages = "496--501",
    abstract = "We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues. We retrieve our humorous dialogues from a very popular TV sitcom: {``}The Big Bang Theory{''}. We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track. Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network. Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5{\%} over 66.5{\%} by CRF and 52.9{\%} by RNN. Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.",
}
@inproceedings{alghamdi-etal-2016-empirical,
    title = "An Empirical Study of {A}rabic Formulaic Sequence Extraction Methods",
    author = "Alghamdi, Ayman  and
      Atwell, Eric  and
      Brierley, Claire",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1080",
    pages = "502--506",
    abstract = "This paper aims to implement what is referred to as the collocation of the Arabic keywords approach for extracting formulaic sequences (FSs) in the form of high frequency but semantically regular formulas that are not restricted to any syntactic construction or semantic domain. The study applies several distributional semantic models in order to automatically extract relevant FSs related to Arabic keywords. The data sets used in this experiment are rendered from a new developed corpus-based Arabic wordlist consisting of 5,189 lexical items which represent a variety of modern standard Arabic (MSA) genres and regions, the new wordlist being based on an overlapping frequency based on a comprehensive comparison of four large Arabic corpora with a total size of over 8 billion running words. Empirical n-best precision evaluation methods are used to determine the best association measures (AMs) for extracting high frequency and meaningful FSs. The gold standard reference FSs list was developed in previous studies and manually evaluated against well-established quantitative and qualitative criteria. The results demonstrate that the MI.log{\_}f AM achieved the highest results in extracting significant FSs from the large MSA corpus, while the T-score association measure achieved the worst results.",
}
@inproceedings{stankovic-etal-2016-rule,
    title = "Rule-based Automatic Multi-word Term Extraction and Lemmatization",
    author = "Stankovi{\'c}, Ranka  and
      Krstev, Cvetana  and
      Obradovi{\'c}, Ivan  and
      Lazi{\'c}, Biljana  and
      Trtovac, Aleksandra",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1081",
    pages = "507--514",
    abstract = "In this paper we present a rule-based method for multi-word term extraction that relies on extensive lexical resources in the form of electronic dictionaries and finite-state transducers for modelling various syntactic structures of multi-word terms. The same technology is used for lemmatization of extracted multi-word terms, which is unavoidable for highly inflected languages in order to pass extracted data to evaluators and subsequently to terminological e-dictionaries and databases. The approach is illustrated on a corpus of Serbian texts from the mining domain containing more than 600,000 simple word forms. Extracted and lemmatized multi-word terms are filtered in order to reject falsely offered lemmas and then ranked by introducing measures that combine linguistic and statistical information (C-Value, T-Score, LLR, and Keyness). Mean average precision for retrieval of MWU forms ranges from 0.789 to 0.804, while mean average precision of lemma production ranges from 0.956 to 0.960. The evaluation showed that 94{\%} of distinct multi-word forms were evaluated as proper multi-word units, and among them 97{\%} were associated with correct lemmas.",
}
@inproceedings{kettnerova-bejcek-2016-distribution,
    title = "Distribution of Valency Complements in {C}zech Complex Predicates: Between Verb and Noun",
    author = "Kettnerov{\'a}, V{\'a}clava  and
      Bej{\v{c}}ek, Eduard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1082",
    pages = "515--521",
    abstract = "In this paper, we focus on Czech complex predicates formed by a light verb and a predicative noun expressed as the direct object. Although Czech ― as an inflectional language encoding syntactic relations via morphological cases ― provides an excellent opportunity to study the distribution of valency complements in the syntactic structure with complex predicates, this distribution has not been described so far. On the basis of a manual analysis of the richly annotated data from the Prague Dependency Treebank, we thus formulate principles governing this distribution. In an automatic experiment, we verify these principles on well-formed syntactic structures from the Prague Dependency Treebank and the Prague Czech-English Dependency Treebank with very satisfactory results: the distribution of 97{\%} of valency complements in the surface structure is governed by the proposed principles. These results corroborate that the surface structure formation of complex predicates is a regular process.",
}
@inproceedings{liebeskind-hacohen-kerner-2016-lexical,
    title = "A Lexical Resource of {H}ebrew Verb-Noun Multi-Word Expressions",
    author = "Liebeskind, Chaya  and
      HaCohen-Kerner, Yaakov",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1083",
    pages = "522--527",
    abstract = "A verb-noun Multi-Word Expression (MWE) is a combination of a verb and a noun with or without other words, in which the combination has a meaning different from the meaning of the words considered separately. In this paper, we present a new lexical resource of Hebrew Verb-Noun MWEs (VN-MWEs). The VN-MWEs of this resource were manually collected and annotated from five different web resources. In addition, we analyze the lexical properties of Hebrew VN-MWEs by classifying them to three types: morphological, syntactic, and semantic. These two contributions are essential for designing algorithms for automatic VN-MWEs extraction. The analysis suggests some interesting features of VN-MWEs for exploration. The lexical resource enables to sample a set of positive examples for Hebrew VN-MWEs. This set of examples can either be used for training supervised algorithms or as seeds in unsupervised bootstrapping algorithms. Thus, this resource is a first step towards automatic identification of Hebrew VN-MWEs, which is important for natural language understanding, generation and translation systems.",
}
@inproceedings{jacquet-etal-2016-cross,
    title = "Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms",
    author = {Jacquet, Guillaume  and
      Ehrmann, Maud  and
      Steinberger, Ralf  and
      V{\"a}yrynen, Jaakko},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1084",
    pages = "528--535",
    abstract = "This paper reports on an approach and experiments to automatically build a cross-lingual multi-word entity resource. Starting from a collection of millions of acronym/expansion pairs for 22 languages where expansion variants were grouped into monolingual clusters, we experiment with several aggregation strategies to link these clusters across languages. Aggregation strategies make use of string similarity distances and translation probabilities and they are based on vector space and graph representations. The accuracy of the approach is evaluated against Wikipedia{'}s redirection and cross-lingual linking tables. The resulting multi-word entity resource contains 64,000 multi-word entities with unique identifiers and their 600,000 multilingual lexical variants. We intend to make this new resource publicly available.",
}
@inproceedings{meurs-etal-2016-semlinker,
    title = "{S}em{L}inker, a Modular and Open Source Framework for Named Entity Discovery and Linking",
    author = "Meurs, Marie-Jean  and
      Almeida, Hayda  and
      Jean-Louis, Ludovic  and
      Charton, Eric",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1085",
    pages = "536--540",
    abstract = "This paper presents SemLinker, an open source system that discovers named entities, connects them to a reference knowledge base, and clusters them semantically. SemLinker relies on several modules that perform surface form generation, mutual disambiguation, entity clustering, and make use of two annotation engines. SemLinker was evaluated in the English Entity Discovery and Linking track of the Text Analysis Conference on Knowledge Base Population, organized by the US National Institute of Standards and Technology. Along with the SemLinker source code, we release our annotation files containing the discovered named entities, their types, and position across processed documents.",
}
@inproceedings{ilievski-etal-2016-context,
    title = "Context-enhanced Adaptive Entity Linking",
    author = {Ilievski, Filip  and
      Rizzo, Giuseppe  and
      van Erp, Marieke  and
      Plu, Julien  and
      Troncy, Rapha{\"e}l},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1086",
    pages = "541--548",
    abstract = "More and more knowledge bases are publicly available as linked data. Since these knowledge bases contain structured descriptions of real-world entities, they can be exploited by entity linking systems that anchor entity mentions from text to the most relevant resources describing those entities. In this paper, we investigate adaptation of the entity linking task using contextual knowledge. The key intuition is that entity linking can be customized depending on the textual content, as well as on the application that would make use of the extracted information. We present an adaptive approach that relies on contextual knowledge from text to enhance the performance of ADEL, a hybrid linguistic and graph-based entity linking system. We evaluate our approach on a domain-specific corpus consisting of annotated WikiNews articles.",
}
@inproceedings{okur-etal-2016-named,
    title = "Named Entity Recognition on {T}witter for {T}urkish using Semi-supervised Learning with Word Embeddings",
    author = {Okur, Eda  and
      Demir, Hakan  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1087",
    pages = "549--555",
    abstract = "Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural networks. We applied a fast unsupervised method for learning continuous representations of words in vector space. We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts. We evaluated our Turkish NER system on Twitter messages and achieved better F-score performances than the published results of previously proposed NER systems on Turkish tweets. Since we did not employ any language dependent features, we believe that our method can be easily adapted to microblog texts in other morphologically rich languages.",
}
@inproceedings{pershina-etal-2016-entity,
    title = "Entity Linking with a Paraphrase Flavor",
    author = "Pershina, Maria  and
      He, Yifan  and
      Grishman, Ralph",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1088",
    pages = "556--560",
    abstract = "The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models ― for entity disambiguation and for paraphrase detection ― to overcome these challenges. We consider name variations as paraphrases of the same entity mention and adopt a paraphrase model for this task. Our approach utilizes a graph-based disambiguation model based on Personalized Page Rank, and then refines and clusters its output using the paraphrase similarity between entity mention strings. It achieves a competitive performance of 80.5{\%} in B3+F clustering score on diagnostic TAC EDL 2014 data.",
}
@inproceedings{tian-etal-2016-domain,
    title = "Domain Adaptation for Named Entity Recognition Using {CRF}s",
    author = "Tian, Tian  and
      Dinarelli, Marco  and
      Tellier, Isabelle  and
      Cardoso, Pedro Dias",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1089",
    pages = "561--565",
    abstract = "In this paper we explain how we created a labelled corpus in English for a Named Entity Recognition (NER) task from multi-source and multi-domain data, for an industrial partner. We explain the specificities of this corpus with examples and describe some baseline experiments. We present some results of domain adaptation on this corpus using a labelled Twitter corpus (Ritter et al., 2011). We tested a semi-supervised method from (Garcia-Fernandez et al., 2014) combined with a supervised domain adaptation approach proposed in (Raymond and Fayolle, 2010) for machine learning experiments with CRFs (Conditional Random Fields). We use the same technique to improve the NER results on the Twitter corpus (Ritter et al., 2011). Our contributions thus consist in an industrial corpus creation and NER performance improvements.",
}
@inproceedings{arcan-etal-2016-iris,
    title = "{IRIS}: {E}nglish-{I}rish Machine Translation System",
    author = "Arcan, Mihael  and
      Lane, Caoilfhionn  and
      Droighne{\'a}in, Eoin {\'O}  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1090",
    pages = "566--572",
    abstract = "We describe IRIS, a statistical machine translation (SMT) system for translating from English into Irish and vice versa. Since Irish is considered an under-resourced language with a limited amount of machine-readable text, building a machine translation system that produces reasonable translations is rather challenging. As translation is a difficult task, current research in SMT focuses on obtaining statistics either from a large amount of parallel, monolingual or other multilingual resources. Nevertheless, we collected available English-Irish data and developed an SMT system aimed at supporting human translators and enabling cross-lingual language technology tasks.",
}
@inproceedings{tambouratzis-pouli-2016-linguistically,
    title = "Linguistically Inspired Language Model Augmentation for {MT}",
    author = "Tambouratzis, George  and
      Pouli, Vasiliki",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1091",
    pages = "573--577",
    abstract = "The present article reports on efforts to improve the translation accuracy of a corpus―based Machine Translation (MT) system. In order to achieve that, an error analysis performed on past translation outputs has indicated the likelihood of improving the translation accuracy by augmenting the coverage of the Target-Language (TL) side language model. The method adopted for improving the language model is initially presented, based on the concatenation of consecutive phrases. The algorithmic steps are then described that form the process for augmenting the language model. The key idea is to only augment the language model to cover the most frequent cases of phrase sequences, as counted over a TL-side corpus, in order to maximize the cases covered by the new language model entries. Experiments presented in the article show that substantial improvements in translation accuracy are achieved via the proposed method, when integrating the grown language model to the corpus-based MT system.",
}
@inproceedings{abercrombie-2016-rule,
    title = "A Rule-based Shallow-transfer Machine Translation System for {S}cots and {E}nglish",
    author = "Abercrombie, Gavin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1092",
    pages = "578--584",
    abstract = "An open-source rule-based machine translation system is developed for Scots, a low-resourced minor language closely related to English and spoken in Scotland and Ireland. By concentrating on translation for assimilation (gist comprehension) from Scots to English, it is proposed that the development of dictionaries designed to be used with in the Apertium platform will be sufficient to produce translations that improve non-Scots speakers understanding of the language. Mono- and bilingual Scots dictionaries are constructed using lexical items gathered from a variety of resources across several domains. Although the primary goal of this project is translation for gisting, the system is evaluated for both assimilation and dissemination (publication-ready translations). A variety of evaluation methods are used, including a cloze test undertaken by human volunteers. While evaluation results are comparable to, and in some cases superior to, those of other language pairs within the Apertium platform, room for improvement is identified in several areas of the system.",
}
@inproceedings{rikters-skadina-2016-syntax,
    title = "Syntax-based Multi-system Machine Translation",
    author = "Rikters, Mat{\=\i}ss  and
      Skadi{\c{n}}a, Inguna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1093",
    pages = "585--591",
    abstract = "This paper describes a hybrid machine translation system that explores a parser to acquire syntactic chunks of a source sentence, translates the chunks with multiple online machine translation (MT) system application program interfaces (APIs) and creates output by combining translated chunks to obtain the best possible translation. The selection of the best translation hypothesis is performed by calculating the perplexity for each translated chunk. The goal of this approach is to enhance the baseline multi-system hybrid translation (MHyT) system that uses only a language model to select best translation from translations obtained with different APIs and to improve overall English ― Latvian machine translation quality over each of the individual MT APIs. The presented syntax-based multi-system translation (SyMHyT) system demonstrates an improvement in terms of BLEU and NIST scores compared to the baseline system. Improvements reach from 1.74 up to 2.54 BLEU points.",
}
@inproceedings{stajner-etal-2016-use,
    title = "Use of Domain-Specific Language Resources in Machine Translation",
    author = "{\v{S}}tajner, Sanja  and
      Querido, Andreia  and
      Rendeiro, Nuno  and
      Rodrigues, Jo{\~a}o Ant{\'o}nio  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1094",
    pages = "592--598",
    abstract = "In this paper, we address the problem of Machine Translation (MT) for a specialised domain in a language pair for which only a very small domain-specific parallel corpus is available. We conduct a series of experiments using a purely phrase-based SMT (PBSMT) system and a hybrid MT system (TectoMT), testing three different strategies to overcome the problem of the small amount of in-domain training data. Our results show that adding a small size in-domain bilingual terminology to the small in-domain training corpus leads to the best improvements of a hybrid MT system, while the PBSMT system achieves the best results by adding a combination of in-domain bilingual terminology and a larger out-of-domain corpus. We focus on qualitative human evaluation of the output of two best systems (one for each approach) and perform a systematic in-depth error analysis which revealed advantages of the hybrid MT system over the pure PBSMT system for this specific task.",
}
@inproceedings{pal-etal-2016-catalog,
    title = "{CAT}a{L}og Online: Porting a Post-editing Tool to the Web",
    author = "Pal, Santanu  and
      Zampieri, Marcos  and
      Naskar, Sudip Kumar  and
      Nayak, Tapas  and
      Vela, Mihaela  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1095",
    pages = "599--604",
    abstract = "This paper presents CATaLog online, a new web-based MT and TM post-editing tool. CATaLog online is a freeware software that can be used through a web browser and it requires only a simple registration. The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper. CATaLog online is designed to allow users to post-edit both translation memory segments as well as machine translation output. The tool provides a complete set of log information currently not available in most commercial CAT tools. Log information can be used both for project management purposes as well as for the study of the translation process and translator{'}s productivity.",
}
@inproceedings{hayakawa-etal-2016-ilmt,
    title = "The {ILMT}-s2s Corpus ― A Multimodal Interlingual Map Task Corpus",
    author = "Hayakawa, Akira  and
      Luz, Saturnino  and
      Cerrato, Loredana  and
      Campbell, Nick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1096",
    pages = "605--612",
    abstract = "This paper presents the multimodal Interlingual Map Task Corpus (ILMT-s2s corpus) collected at Trinity College Dublin, and discuss some of the issues related to the collection and analysis of the data. The corpus design is inspired by the HCRC Map Task Corpus which was initially designed to support the investigation of linguistic phenomena, and has been the focus of a variety of studies of communicative behaviour. The simplicity of the task, and the complexity of phenomena it can elicit, make the map task an ideal object of study. Although there are studies that used replications of the map task to investigate communication in computer mediated tasks, this ILMT-s2s corpus is, to the best of our knowledge, the first investigation of communicative behaviour in the presence of three additional {``}filters{''}: Automatic Speech Recognition (ASR), Machine Translation (MT) and Text To Speech (TTS) synthesis, where the instruction giver and the instruction follower speak different languages. This paper details the data collection setup and completed annotation of the ILMT-s2s corpus, and outlines preliminary results obtained from the data.",
}
@inproceedings{sadamitsu-etal-2016-name,
    title = "Name Translation based on Fine-grained Named Entity Recognition in a Single Language",
    author = "Sadamitsu, Kugatsu  and
      Saito, Itsumi  and
      Katayama, Taichi  and
      Asano, Hisako  and
      Matsuo, Yoshihiro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1097",
    pages = "613--619",
    abstract = "We propose named entity abstraction methods with fine-grained named entity labels for improving statistical machine translation (SMT). The methods are based on a bilingual named entity recognizer that uses a monolingual named entity recognizer with transliteration. Through experiments, we demonstrate that incorporating fine-grained named entities into statistical machine translation improves the accuracy of SMT with more adequate granularity compared with the standard SMT, which is a non-named entity abstraction method.",
}
@inproceedings{s-bhattacharyya-2016-lexical,
    title = "Lexical Resources to Enrich {E}nglish {M}alayalam Machine Translation",
    author = "S, Sreelekha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1098",
    pages = "620--627",
    abstract = "In this paper we present our work on the usage of lexical resources for the Machine Translation English and Malayalam. We describe a comparative performance between different Statistical Machine Translation (SMT) systems on top of phrase based SMT system as baseline. We explore different ways of utilizing lexical resources to improve the quality of English Malayalam statistical machine translation. In order to enrich the training corpus we have augmented the lexical resources in two ways (a) additional vocabulary and (b) inflected verbal forms. Lexical resources include IndoWordnet semantic relation set, lexical words and verb phrases etc. We have described case studies, evaluations and have given detailed error analysis for both Malayalam to English and English to Malayalam machine translation systems. We observed significant improvement in evaluations of translation quality. Lexical resources do help uplift performance when parallel corpora are scanty.",
}
@inproceedings{xu-yvon-2016-novel,
    title = "Novel elicitation and annotation schemes for sentential and sub-sentential alignments of bitexts",
    author = "Xu, Yong  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1099",
    pages = "628--635",
    abstract = "Resources for evaluating sentence-level and word-level alignment algorithms are unsatisfactory. Regarding sentence alignments, the existing data is too scarce, especially when it comes to difficult bitexts, containing instances of non-literal translations. Regarding word-level alignments, most available hand-aligned data provide a complete annotation at the level of words that is difficult to exploit, for lack of a clear semantics for alignment links. In this study, we propose new methodologies for collecting human judgements on alignment links, which have been used to annotate 4 new data sets, at the sentence and at the word level. These will be released online, with the hope that they will prove useful to evaluate alignment software and quality estimation tools for automatic alignment. Keywords: Parallel corpora, Sentence Alignments, Word Alignments, Confidence Estimation",
}
@inproceedings{guillou-hardmeier-2016-protest,
    title = "{PROTEST}: A Test Suite for Evaluating Pronouns in Machine Translation",
    author = "Guillou, Liane  and
      Hardmeier, Christian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1100",
    pages = "636--643",
    abstract = "We present PROTEST, a test suite for the evaluation of pronoun translation by MT systems. The test suite comprises 250 hand-selected pronoun tokens and an automatic evaluation method which compares the translations of pronouns in MT output with those in the reference translation. Pronoun translations that do not match the reference are referred for manual evaluation. PROTEST is designed to support analysis of system performance at the level of individual pronoun groups, rather than to provide a single aggregate measure over all pronouns. We wish to encourage detailed analyses to highlight issues in the handling of specific linguistic mechanisms by MT systems, thereby contributing to a better understanding of those problems involved in translating pronouns. We present two use cases for PROTEST: a) for measuring improvement/degradation of an incremental system change, and b) for comparing the performance of a group of systems whose design may be largely unrelated. Following the latter use case, we demonstrate the application of PROTEST to the evaluation of the systems submitted to the DiscoMT 2015 shared task on pronoun translation.",
}
@inproceedings{chu-kurohashi-2016-paraphrasing,
    title = "Paraphrasing Out-of-Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation",
    author = "Chu, Chenhui  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1101",
    pages = "644--648",
    abstract = "Out-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources. OOV paraphrasing that augments the translation model for the OOV words by using the translation knowledge of their paraphrases has been proposed to address the OOV problem. In this paper, we propose using word embeddings and semantic lexicons for OOV paraphrasing. Experiments conducted on a low resource setting of the OLYMPICS task of IWSLT 2012 verify the effectiveness of our proposed method.",
}
@inproceedings{daiber-van-der-goot-2016-denoised,
    title = "The Denoised Web Treebank: Evaluating Dependency Parsing under Noisy Input Conditions",
    author = "Daiber, Joachim  and
      van der Goot, Rob",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1102",
    pages = "649--653",
    abstract = "We introduce the Denoised Web Treebank: a treebank including a normalization layer and a corresponding evaluation metric for dependency parsing of noisy text, such as Tweets. This benchmark enables the evaluation of parser robustness as well as text normalization methods, including normalization as machine translation and unsupervised lexical normalization, directly on syntactic trees. Experiments show that text normalization together with a combination of domain-specific and generic part-of-speech taggers can lead to a significant improvement in parsing accuracy on this test set.",
}
@inproceedings{che-etal-2016-punctuation,
    title = "Punctuation Prediction for Unsegmented Transcript Based on Word Vector",
    author = "Che, Xiaoyin  and
      Wang, Cheng  and
      Yang, Haojin  and
      Meinel, Christoph",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1103",
    pages = "654--658",
    abstract = "In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only.",
}
@inproceedings{zhou-etal-2016-evaluating,
    title = "Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing",
    author = "Zhou, Hao  and
      Zhang, Yue  and
      Huang, Shujian  and
      Dai, Xin-Yu  and
      Chen, Jiajun",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1104",
    pages = "659--663",
    abstract = "Greedy transition-based parsers are appealing for their very fast speed, with reasonably high accuracies. In this paper, we build a fast shift-reduce neural constituent parser by using a neural network to make local decisions. One challenge to the parsing speed is the large hidden and output layer sizes caused by the number of constituent labels and branching options. We speed up the parser by using a hierarchical output layer, inspired by the hierarchical log-bilinear neural language model. In standard WSJ experiments, the neural parser achieves an almost 2.4 time speed up (320 sen/sec) compared to a non-hierarchical baseline without significant accuracy loss (89.06 vs 89.13 F-score).",
}
@inproceedings{ushiku-etal-2016-language,
    title = "Language Resource Addition Strategies for Raw Text Parsing",
    author = "Ushiku, Atsushi  and
      Sasada, Tetsuro  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1105",
    pages = "664--671",
    abstract = "We focus on the improvement of accuracy of raw text parsing, from the viewpoint of language resource addition. In Japanese, the raw text parsing is divided into three steps: word segmentation, part-of-speech tagging, and dependency parsing. We investigate the contribution of language resource addition in each of three steps to the improvement in accuracy for two domain corpora. The experimental results show that this improvement depends on the target domain. For example, when we handle well-written texts of limited vocabulary, white paper, an effective language resource is a word-POS pair sequence corpus for the parsing accuracy. So we conclude that it is important to check out the characteristics of the target domain and to choose a suitable language resource addition strategy for the parsing accuracy improvement.",
}
@inproceedings{marton-toutanova-2016-e,
    title = "{E}-{TIPSY}: Search Query Corpus Annotated with Entities, Term Importance, {POS} Tags, and Syntactic Parses",
    author = "Marton, Yuval  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1106",
    pages = "672--676",
    abstract = "We present E-TIPSY, a search query corpus annotated with named Entities, Term Importance, POS tags, and SYntactic parses. This corpus contains crowdsourced (gold) annotations of the three most important terms in each query. In addition, it contains automatically produced annotations of named entities, part-of-speech tags, and syntactic parses for the same queries. This corpus comes in two formats: (1) Sober Subset: annotations that two or more crowd workers agreed upon, and (2) Full Glass: all annotations. We analyze the strikingly low correlation between term importance and syntactic headedness, which invites research into effective ways of combining these different signals. Our corpus can serve as a benchmark for term importance methods aimed at improving search engine quality and as an initial step toward developing a dataset of gold linguistic analysis of web search queries. In addition, it can be used as a basis for linguistic inquiries into the kind of expressions used in search.",
}
@inproceedings{augustinus-etal-2016-afribooms,
    title = "{A}fri{B}ooms: An Online Treebank for {A}frikaans",
    author = "Augustinus, Liesbeth  and
      Dirix, Peter  and
      van Niekerk, Daniel  and
      Schuurman, Ineke  and
      Vandeghinste, Vincent  and
      Van Eynde, Frank  and
      van Huyssteen, Gerhard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1107",
    pages = "677--682",
    abstract = "Compared to well-resourced languages such as English and Dutch, natural language processing (NLP) tools for Afrikaans are still not abundant. In the context of the AfriBooms project, KU Leuven and the North-West University collaborated to develop a first, small treebank, a dependency parser, and an easy to use online linguistic search engine for Afrikaans for use by researchers and students in the humanities and social sciences. The search tool is based on a similar development for Dutch, i.e. GrETEL, a user-friendly search engine which allows users to query a treebank by means of a natural language example instead of a formal search instruction.",
}
@inproceedings{ponti-passarotti-2016-differentia,
    title = "Differentia compositionem facit. A Slower-Paced and Reliable Parser for {L}atin",
    author = "Ponti, Edoardo Maria  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1108",
    pages = "683--688",
    abstract = "The Index Thomisticus Treebank is the largest available treebank for Latin; it contains Medieval Latin texts by Thomas Aquinas. After experimenting on its data with a number of dependency parsers based on different supervised machine learning techniques, we found that DeSR with a multilayer perceptron algorithm, a right-to-left transition, and a tailor-made feature model is the parser providing the highest accuracy rates. We improved the results further by using a technique that combines the output parses of DeSR with those provided by other parsers, outperforming the previous state of the art in parsing the Index Thomisticus Treebank. The key idea behind such improvement is to ensure a sufficient diversity and accuracy of the outputs to be combined; for this reason, we performed an in-depth evaluation of the results provided by the different parsers that we combined. Finally, we assessed that, although the general architecture of the parser is portable to Classical Latin, yet the model trained on Medieval Latin is inadequate for such purpose.",
}
@inproceedings{eiselen-2016-south,
    title = "{S}outh {A}frican Language Resources: Phrase Chunking",
    author = "Eiselen, Roald",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1109",
    pages = "689--693",
    abstract = "Phrase chunking remains an important natural language processing (NLP) technique for intermediate syntactic processing. This paper describes the development of protocols, annotated phrase chunking data sets and automatic phrase chunkers for ten South African languages. Various problems with adapting the existing annotation protocols of English are discussed as well as an overview of the annotated data sets. Based on the annotated sets, CRF-based phrase chunkers are created and tested with a combination of different features, including part of speech tags and character n-grams. The results of the phrase chunking evaluation show that disjunctively written languages can achieve notably better results for phrase chunking with a limited data set than conjunctive languages, but that the addition of character n-grams improve the results for conjunctive languages.",
}
@inproceedings{libovicky-2016-neural,
    title = "Neural Scoring Function for {MST} Parser",
    author = "Libovick{\'y}, Jind{\v{r}}ich",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1110",
    pages = "694--698",
    abstract = "Continuous word representations appeared to be a useful feature in many natural language processing tasks. Using fixed-dimension pre-trained word embeddings allows avoiding sparse bag-of-words representation and to train models with fewer parameters. In this paper, we use fixed pre-trained word embeddings as additional features for a neural scoring function in the MST parser. With the multi-layer architecture of the scoring function we can avoid handcrafting feature conjunctions. The continuous word representations on the input also allow us to reduce the number of lexical features, make the parser more robust to out-of-vocabulary words, and reduce the total number of parameters of the model. Although its accuracy stays below the state of the art, the model size is substantially smaller than with the standard features set. Moreover, it performs well for languages where only a smaller treebank is available and the results promise to be useful in cross-lingual parsing.",
}
@inproceedings{listenmaa-claessen-2016-analysing,
    title = "Analysing Constraint Grammars with a {SAT}-solver",
    author = "Listenmaa, Inari  and
      Claessen, Koen",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1111",
    pages = "699--706",
    abstract = "We describe a method for analysing Constraint Grammars (CG) that can detect internal conflicts and redundancies in a given grammar, without the need for a corpus. The aim is for grammar writers to be able to automatically diagnose, and then manually improve their grammars. Our method works by translating the given grammar into logical constraints that are analysed by a SAT-solver. We have evaluated our analysis on a number of non-trivial grammars and found inconsistencies.",
}
@inproceedings{stein-2016-old,
    title = "{O}ld {F}rench Dependency Parsing: Results of Two Parsers Analysed from a Linguistic Point of View",
    author = "Stein, Achim",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1112",
    pages = "707--713",
    abstract = "The treatment of medieval texts is a particular challenge for parsers. I compare how two dependency parsers, one graph-based, the other transition-based, perform on Old French, facing some typical problems of medieval texts: graphical variation, relatively free word order, and syntactic variation of several parameters over a diachronic period of about 300 years. Both parsers were trained and evaluated on the {``}Syntactic Reference Corpus of Medieval French{''} (SRCMF), a manually annotated dependency treebank. I discuss the relation between types of parsers and types of language, as well as the differences of the analyses from a linguistic point of view.",
}
@inproceedings{di-buono-2016-semi,
    title = "Semi-automatic Parsing for Web Knowledge Extraction through Semantic Annotation",
    author = "di Buono, Maria Pia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1113",
    pages = "714--717",
    abstract = "Parsing Web information, namely parsing content to find relevant documents on the basis of a user{'}s query, represents a crucial step to guarantee fast and accurate Information Retrieval (IR). Generally, an automated approach to such task is considered faster and cheaper than manual systems. Nevertheless, results do not seem have a high level of accuracy, indeed, as also Hjorland (2007) states, using stochastic algorithms entails: {\mbox{$\bullet$}} Low precision due to the indexing of common Atomic Linguistic Units (ALUs) or sentences. {\mbox{$\bullet$}} Low recall caused by the presence of synonyms. {\mbox{$\bullet$}} Generic results arising from the use of too broad or too narrow terms. Usually IR systems are based on invert text index, namely an index data structure storing a mapping from content to its locations in a database file, or in a document or a set of documents. In this paper we propose a system, by means of which we will develop a search engine able to process online documents, starting from a natural language query, and to return information to users. The proposed approach, based on the Lexicon-Grammar (LG) framework and its language formalization methodologies, aims at integrating a semantic annotation process for both query analysis and document retrieval.",
}
@inproceedings{weiner-etal-2016-towards,
    title = "Towards Automatic Transcription of {ILSE} ― an Interdisciplinary Longitudinal Study of Adult Development and Aging",
    author = {Weiner, Jochen  and
      Frankenberg, Claudia  and
      Telaar, Dominic  and
      Wendelstein, Britta  and
      Schr{\"o}der, Johannes  and
      Schultz, Tanja},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1114",
    pages = "718--725",
    abstract = "The Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE) was created to facilitate the study of challenges posed by rapidly aging societies in developed countries such as Germany. ILSE contains over 8,000 hours of biographic interviews recorded from more than 1,000 participants over the course of 20 years. Investigations on various aspects of aging, such as cognitive decline, often rely on the analysis of linguistic features which can be derived from spoken content like these interviews. However, transcribing speech is a time and cost consuming manual process and so far only 380 hours of ILSE interviews have been transcribed. Thus, it is the aim of our work to establish technical systems to fully automatically transcribe the ILSE interview data. The joint occurrence of poor recording quality, long audio segments, erroneous transcriptions, varying speaking styles {\&} crosstalk, and emotional {\&} dialectal speech in these interviews presents challenges for automatic speech recognition (ASR). We describe our ongoing work towards the fully automatic transcription of all ILSE interviews and the steps we implemented in preparing the transcriptions to meet the interviews{'} challenges. Using a recursive long audio alignment procedure 96 hours of the transcribed data have been made accessible for ASR training.",
}
@inproceedings{ajili-etal-2016-fabiole,
    title = "{FABIOLE}, a Speech Database for Forensic Speaker Comparison",
    author = "Ajili, Moez  and
      Bonastre, Jean-Fran{\c{c}}ois  and
      Kahn, Juliette  and
      Rossato, Solange  and
      Bernard, Guillaume",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1115",
    pages = "726--733",
    abstract = "A speech database has been collected for use to highlight the importance of {``}speaker factor{''} in forensic voice comparison. FABIOLE has been created during the FABIOLE project funded by the French Research Agency (ANR) from 2013 to 2016. This corpus consists in more than 3 thousands excerpts spoken by 130 French native male speakers. The speakers are divided into two categories: 30 target speakers who everyone has 100 excerpts and 100 {``}impostors{''} who everyone has only one excerpt. The data were collected from 10 different French radio and television shows where each utterance turns with a minimum duration of 30s and has a good speech quality. The data set is mainly used for investigating speaker factor in forensic voice comparison and interpreting some unsolved issue such as the relationship between speaker characteristics and system behavior. In this paper, we present FABIOLE database. Then, preliminary experiments are performed to evaluate the effect of the {``}speaker factor{''} and the show on a voice comparison system behavior.",
}
@inproceedings{halabi-wald-2016-phonetic,
    title = "Phonetic Inventory for an {A}rabic Speech Corpus",
    author = "Halabi, Nawar  and
      Wald, Mike",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1116",
    pages = "734--738",
    abstract = "Corpus design for speech synthesis is a well-researched topic in languages such as English compared to Modern Standard Arabic, and there is a tendency to focus on methods to automatically generate the orthographic transcript to be recorded (usually greedy methods). In this work, a study of Modern Standard Arabic (MSA) phonetics and phonology is conducted in order to create criteria for a greedy method to create a speech corpus transcript for recording. The size of the dataset is reduced a number of times using these optimisation methods with different parameters to yield a much smaller dataset with identical phonetic coverage than before the reduction, and this output transcript is chosen for recording. This is part of a larger work to create a completely annotated and segmented speech corpus for MSA.",
}
@inproceedings{chen-hakkani-tur-2016-aimu,
    title = "{AIMU}: Actionable Items for Meeting Understanding",
    author = {Chen, Yun-Nung  and
      Hakkani-T{\"u}r, Dilek},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1117",
    pages = "739--743",
    abstract = "With emerging conversational data, automated content analysis is needed for better data interpretation, so that it is accurately understood and can be effectively integrated and utilized in various applications. ICSI meeting corpus is a publicly released data set of multi-party meetings in an organization that has been released over a decade ago, and has been fostering meeting understanding research since then. The original data collection includes transcription of participant turns as well as meta-data annotations, such as disfluencies and dialog act tags. This paper presents an extended set of annotations for the ICSI meeting corpus with a goal of deeply understanding meeting conversations, where participant turns are annotated by actionable items that could be performed by an automated meeting assistant. In addition to the user utterances that contain an actionable item, annotations also include the arguments associated with the actionable item. The set of actionable items are determined by aligning human-human interactions to human-machine interactions, where a data annotation schema designed for a virtual personal assistant (human-machine genre) is adapted to the meetings domain (human-human genre). The data set is formed by annotating participants{'} utterances in meetings with potential intents/actions considering their contexts. The set of actions target what could be accomplished by an automated meeting assistant, such as taking a note of action items that a participant commits to, or finding emails or topic related documents that were mentioned during the meeting. A total of 10 defined intents/actions are considered as actionable items in meetings. Turns that include actionable intents were annotated for 22 public ICSI meetings, that include a total of 21K utterances, segmented by speaker turns. Participants{'} spoken turns, possible actions along with associated arguments and their vector representations as computed by convolutional deep structured semantic models are included in the data set for future research. We present a detailed statistical analysis of the data set and analyze the performance of applying convolutional deep structured semantic models for an actionable item detection task. The data is available at http://research.microsoft.com/ projects/meetingunderstanding/.",
}
@inproceedings{burkhardt-reichel-2016-taxonomy,
    title = "A Taxonomy of Specific Problem Classes in Text-to-Speech Synthesis: Comparing Commercial and Open Source Performance",
    author = "Burkhardt, Felix  and
      Reichel, Uwe D.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1118",
    pages = "744--749",
    abstract = "Current state-of-the-art speech synthesizers for domain-independent systems still struggle with the challenge of generating understandable and natural-sounding speech. This is mainly because the pronunciation of words of foreign origin, inflections and compound words often cannot be handled by rules. Furthermore there are too many of these for inclusion in exception dictionaries. We describe an approach to evaluating text-to-speech synthesizers with a subjective listening experiment. The focus is to differentiate between known problem classes for speech synthesizers. The target language is German but we believe that many of the described phenomena are not language specific. We distinguish the following problem categories: Normalization, Foreign linguistics, Natural writing, Language specific and General. Each of them is divided into five to three problem classes. Word lists for each of the above mentioned categories were compiled and synthesized by both a commercial and an open source synthesizer, both being based on the non-uniform unit-selection approach. The synthesized speech was evaluated by human judges using the Speechalyzer toolkit and the results are discussed. It shows that, as expected, the commercial synthesizer performs much better than the open-source one, and especially words of foreign origin were pronounced badly by both systems.",
}
@inproceedings{braunger-etal-2016-comparative,
    title = "A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems",
    author = {Braunger, Patricia  and
      Hofmann, Hansj{\"o}rg  and
      Werner, Steffen  and
      Schmidt, Maria},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1119",
    pages = "750--755",
    abstract = "Recent spoken dialog systems have been able to recognize freely spoken user input in restricted domains thanks to statistical methods in the automatic speech recognition. These methods require a high number of natural language utterances to train the speech recognition engine and to assess the quality of the system. Since human speech offers many variants associated with a single intent, a high number of user utterances have to be elicited. Developers are therefore turning to crowdsourcing to collect this data. This paper compares three different methods to elicit multiple utterances for given semantics via crowd sourcing, namely with pictures, with text and with semantic entities. Specifically, we compare the methods with regard to the number of valid data and linguistic variance, whereby a quantitative and qualitative approach is proposed. In our study, the method with text led to a high variance in the utterances and a relatively low rate of invalid data.",
}
@inproceedings{sarasola-etal-2016-singing,
    title = "A Singing Voice Database in {B}asque for Statistical Singing Synthesis of Bertsolaritza",
    author = "Sarasola, Xabier  and
      Navas, Eva  and
      Tavarez, David  and
      Erro, Daniel  and
      Saratxaga, Ibon  and
      Hernaez, Inma",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1120",
    pages = "756--759",
    abstract = "This paper describes the characteristics and structure of a Basque singing voice database of bertsolaritza. Bertsolaritza is a popular singing style from Basque Country sung exclusively in Basque that is improvised and a capella. The database is designed to be used in statistical singing voice synthesis for bertsolaritza style. Starting from the recordings and transcriptions of numerous singers, diarization and phoneme alignment experiments have been made to extract the singing voice from the recordings and create phoneme alignments. This labelling processes have been performed applying standard speech processing techniques and the results prove that these techniques can be used in this specific singing style.",
}
@inproceedings{pessentheiner-etal-2016-amisco,
    title = "{AMISCO}: The {A}ustrian {G}erman Multi-Sensor Corpus",
    author = {Pessentheiner, Hannes  and
      Pichler, Thomas  and
      Hagm{\"u}ller, Martin},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1121",
    pages = "760--766",
    abstract = "We introduce a unique, comprehensive Austrian German multi-sensor corpus with moving and non-moving speakers to facilitate the evaluation of estimators and detectors that jointly detect a speaker{'}s spatial and temporal parameters. The corpus is suitable for various machine learning and signal processing tasks, linguistic studies, and studies related to a speaker{'}s fundamental frequency (due to recorded glottograms). Available corpora are limited to (synthetically generated/spatialized) speech data or recordings of musical instruments that lack moving speakers, glottograms, and/or multi-channel distant speech recordings. That is why we recorded 24 spatially non-moving and moving speakers, balanced male and female, to set up a two-room and 43-channel Austrian German multi-sensor speech corpus. It contains 8.2 hours of read speech based on phonetically balanced sentences, commands, and digits. The orthographic transcriptions include around 53,000 word tokens and 2,070 word types. Special features of this corpus are the laryngograph recordings (representing glottograms required to detect a speaker{'}s instantaneous fundamental frequency and pitch), corresponding clean-speech recordings, and spatial information and video data provided by four Kinects and a camera.",
}
@inproceedings{aichinger-etal-2016-database,
    title = "A Database of Laryngeal High-Speed Videos with Simultaneous High-Quality Audio Recordings of Pathological and Non-Pathological Voices",
    author = "Aichinger, Philipp  and
      Roesner, Immer  and
      Leonhard, Matthias  and
      Denk-Linnert, Doris-Maria  and
      Bigenzahn, Wolfgang  and
      Schneider-Stickler, Berit",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1122",
    pages = "767--770",
    abstract = "Auditory voice quality judgements are used intensively for the clinical assessment of pathological voice. Voice quality concepts are fuzzily defined and poorly standardized however, which hinders scientific and clinical communication. The described database documents a wide variety of pathologies and is used to investigate auditory voice quality concepts with regard to phonation mechanisms. The database contains 375 laryngeal high-speed videos and simultaneous high-quality audio recordings of sustained phonations of 80 pathological and 40 non-pathological subjects. Interval wise annotations regarding video and audio quality, as well as voice quality ratings are provided. Video quality is annotated for the visibility of anatomical structures and artefacts such as blurring or reduced contrast. Voice quality annotations include ratings on the presence of dysphonia and diplophonia. The purpose of the database is to aid the formulation of observationally well-founded models of phonation and the development of model-based automatic detectors for distinct types of phonation, especially for clinically relevant nonmodal voice phenomena. Another application is the training of audio-based fundamental frequency extractors on video-based reference fundamental frequencies.",
}
@inproceedings{hateva-etal-2016-bulphonc,
    title = "{B}ul{P}hon{C}: {B}ulgarian Speech Corpus for the Development of {ASR} Technology",
    author = "Hateva, Neli  and
      Mitankin, Petar  and
      Mihov, Stoyan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1123",
    pages = "771--774",
    abstract = "In this paper we introduce a Bulgarian speech database, which was created for the purpose of ASR technology development. The paper describes the design and the content of the speech database. We present also an empirical evaluation of the performance of a LVCSR system for Bulgarian trained on the BulPhonC data. The resource is available free for scientific usage.",
}
@inproceedings{pinnis-etal-2016-designing,
    title = "Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in {L}atvian",
    author = "Pinnis, M{\=a}rcis  and
      Salimbajevs, Askars  and
      Auzi{\c{n}}a, Ilze",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1124",
    pages = "775--780",
    abstract = "In this paper the authors present a speech corpus designed and created for the development and evaluation of dictation systems in Latvian. The corpus consists of over nine hours of orthographically annotated speech from 30 different speakers. The corpus features spoken commands that are common for dictation systems for text editors. The corpus is evaluated in an automatic speech recognition scenario. Evaluation results in an ASR dictation scenario show that the addition of the corpus to the acoustic model training data in combination with language model adaptation allows to decrease the WER by up to relative 41.36{\%} (or 16.83{\%} in absolute numbers) compared to a baseline system without language model adaptation. Contribution of acoustic data augmentation is at relative 12.57{\%} (or 3.43{\%} absolute).",
}
@inproceedings{proenca-etal-2016-letsread,
    title = "The {L}ets{R}ead Corpus of {P}ortuguese Children Reading Aloud for Performance Evaluation",
    author = "Proen{\c{c}}a, Jorge  and
      Celorico, Dirce  and
      Candeias, Sara  and
      Lopes, Carla  and
      Perdig{\~a}o, Fernando",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1125",
    pages = "781--785",
    abstract = "This paper introduces the LetsRead Corpus of European Portuguese read speech from 6 to 10 years old children. The motivation for the creation of this corpus stems from the inexistence of databases with recordings of reading tasks of Portuguese children with different performance levels and including all the common reading aloud disfluencies. It is also essential to develop techniques to fulfill the main objective of the LetsRead project: to automatically evaluate the reading performance of children through the analysis of reading tasks. The collected data amounts to 20 hours of speech from 284 children from private and public Portuguese schools, with each child carrying out two tasks: reading sentences and reading a list of pseudowords, both with varying levels of difficulty throughout the school grades. In this paper, the design of the reading tasks presented to children is described, as well as the collection procedure. Manually annotated data is analyzed according to disfluencies and reading performance. The considered word difficulty parameter is also confirmed to be suitable for the pseudoword reading tasks.",
}
@inproceedings{reichel-etal-2016-bas,
    title = "The {BAS} Speech Data Repository",
    author = {Reichel, Uwe  and
      Schiel, Florian  and
      Kisler, Thomas  and
      Draxler, Christoph  and
      P{\"o}rner, Nina},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1126",
    pages = "786--791",
    abstract = "The BAS CLARIN speech data repository is introduced. At the current state it comprises 31 pre-dominantly German corpora of spoken language. It is compliant to the CLARIN-D as well as the OLAC requirements. This enables its embedding into several infrastructures. We give an overview over its structure, its implementation as well as the corpora it contains.",
}
@inproceedings{yilmaz-etal-2016-dutch,
    title = "A {D}utch Dysarthric Speech Database for Individualized Speech Therapy Research",
    author = "Yilmaz, Emre  and
      Ganzeboom, Mario  and
      Beijer, Lilian  and
      Cucchiarini, Catia  and
      Strik, Helmer",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1127",
    pages = "792--795",
    abstract = "We present a new Dutch dysarthric speech database containing utterances of neurological patients with Parkinson{'}s disease, traumatic brain injury and cerebrovascular accident. The speech content is phonetically and linguistically diversified by using numerous structured sentence and word lists. Containing more than 6 hours of mildly to moderately dysarthric speech, this database can be used for research on dysarthria and for developing and testing speech-to-text systems designed for medical applications. Current activities aimed at extending this database are also discussed.",
}
@inproceedings{humayoun-etal-2016-urdu,
    title = "{U}rdu Summary Corpus",
    author = "Humayoun, Muhammad  and
      Nawab, Rao Muhammad Adeel  and
      Uzair, Muhammad  and
      Aslam, Saba  and
      Farzand, Omer",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1128",
    pages = "796--800",
    abstract = "Language resources, such as corpora, are important for various natural language processing tasks. Urdu has millions of speakers around the world but it is under-resourced in terms of standard evaluation resources. This paper reports the construction of a benchmark corpus for Urdu summaries (abstracts) to facilitate the development and evaluation of single document summarization systems for Urdu language. In Urdu, space does not always mark word boundary. Therefore, we created two versions of the same corpus. In the first version, words are separated by space. In contrast, proper word boundaries are manually tagged in the second version. We further apply normalization, part-of-speech tagging, morphological analysis, lemmatization, and stemming for the articles and their summaries in both versions. In order to apply these annotations, we re-implemented some NLP tools for Urdu. We provide Urdu Summary Corpus, all these annotations and the needed software tools (as open-source) for researchers to run experiments and to evaluate their work including but not limited to single-document summarization task.",
}
@inproceedings{koto-2016-publicly,
    title = "A Publicly Available {I}ndonesian Corpora for Automatic Abstractive and Extractive Chat Summarization",
    author = "Koto, Fajri",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1129",
    pages = "801--805",
    abstract = "In this paper we report our effort to construct the first ever Indonesian corpora for chat summarization. Specifically, we utilized documents of multi-participant chat from a well known online instant messaging application, WhatsApp. We construct the gold standard by asking three native speakers to manually summarize 300 chat sections (152 of them contain images). As result, three reference summaries in extractive and either abstractive form are produced for each chat sections. The corpus is still in its early stage of investigation, yielding exciting possibilities of future works.",
}
@inproceedings{cohan-goharian-2016-revisiting,
    title = "Revisiting Summarization Evaluation for Scientific Articles",
    author = "Cohan, Arman  and
      Goharian, Nazli",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1130",
    pages = "806--813",
    abstract = "Evaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology variations and paraphrasing, ROUGE is not as effective. Scientific article summarization is one such case that is different from general domain summarization (e.g. newswire data). We provide an extensive analysis of ROUGE{'}s effectiveness as an evaluation metric for scientific summarization; we show that, contrary to the common belief, ROUGE is not much reliable in evaluating scientific summaries. We furthermore show how different variants of ROUGE result in very different correlations with the manual Pyramid scores. Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries. We call our metric SERA (Summarization Evaluation by Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.",
}
@inproceedings{kabadjov-etal-2016-onforums,
    title = "The {O}n{F}orum{S} corpus from the Shared Task on Online Forum Summarisation at {M}ulti{L}ing 2015",
    author = "Kabadjov, Mijail  and
      Kruschwitz, Udo  and
      Poesio, Massimo  and
      Steinberger, Josef  and
      Valderrama, Jorge  and
      Zaragoza, Hugo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1131",
    pages = "814--818",
    abstract = "In this paper we present the OnForumS corpus developed for the shared task of the same name on Online Forum Summarisation (OnForumS at MultiLing{'}15). The corpus consists of a set of news articles with associated readers{'} comments from The Guardian (English) and La Repubblica (Italian). It comes with four levels of annotation: argument structure, comment-article linking, sentiment and coreference. The former three were produced through crowdsourcing, whereas the latter, by an experienced annotator using a mature annotation scheme. Given its annotation breadth, we believe the corpus will prove a useful resource in stimulating and furthering research in the areas of Argumentation Mining, Summarisation, Sentiment, Coreference and the interlinks therein.",
}
@inproceedings{di-caro-boella-2016-automatic,
    title = "Automatic Enrichment of {W}ord{N}et with Common-Sense Knowledge",
    author = "Di Caro, Luigi  and
      Boella, Guido",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1132",
    pages = "819--822",
    abstract = "WordNet represents a cornerstone in the Computational Linguistics field, linking words to meanings (or senses) through a taxonomical representation of synsets, i.e., clusters of words with an equivalent meaning in a specific context often described by few definitions (or glosses) and examples. Most of the approaches to the Word Sense Disambiguation task fully rely on these short texts as a source of contextual information to match with the input text to disambiguate. This paper presents the first attempt to enrich synsets data with common-sense definitions, automatically retrieved from ConceptNet 5, and disambiguated accordingly to WordNet. The aim was to exploit the shared- and immediate-thinking nature of common-sense knowledge to extend the short but incredibly useful contextual information of the synsets. A manual evaluation on a subset of the entire result (which counts a total of almost 600K synset enrichments) shows a very high precision with an estimated good recall.",
}
@inproceedings{baisa-etal-2016-vps,
    title = "{VPS}-{G}rade{U}p: Graded Decisions on Usage Patterns",
    author = "Baisa, V{\'\i}t  and
      Cinkov{\'a}, Silvie  and
      Krej{\v{c}}ov{\'a}, Ema  and
      Vernerov{\'a}, Anna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1133",
    pages = "823--827",
    abstract = "We present VPS-GradeUp ― a set of 11,400 graded human decisions on usage patterns of 29 English lexical verbs from the Pattern Dictionary of English Verbs by Patrick Hanks. The annotation contains, for each verb lemma, a batch of 50 concordances with the given lemma as KWIC, and for each of these concordances we provide a graded human decision on how well the individual PDEV patterns for this particular lemma illustrate the given concordance, indicated on a 7-point Likert scale for each PDEV pattern. With our annotation, we were pursuing a pilot investigation of the foundations of human clustering and disambiguation decisions with respect to usage patterns of verbs in context. The data set is publicly available at http://hdl.handle.net/11234/1-1585.",
}
@inproceedings{miller-etal-2016-sense,
    title = "Sense-annotating a Lexical Substitution Data Set with Ubyline",
    author = "Miller, Tristan  and
      Khemakhem, Mohamed  and
      de Castilho, Richard Eckart  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1134",
    pages = "828--835",
    abstract = "We describe the construction of GLASS, a newly sense-annotated version of the German lexical substitution data set used at the GermEval 2015: LexSub shared task. Using the two annotation layers, we conduct the first known empirical study of the relationship between manually applied word senses and lexical substitutions. We find that synonymy and hypernymy/hyponymy are the only semantic relations directly linking targets to their substitutes, and that substitutes in the target{'}s hypernymy/hyponymy taxonomy closely align with the synonyms of a single GermaNet synset. Despite this, these substitutes account for a minority of those provided by the annotators. The results of our analysis accord with those of a previous study on English-language data (albeit with automatically induced word senses), leading us to suspect that the sense―substitution relations we discovered may be of a universal nature. We also tentatively conclude that relatively cheap lexical substitution annotations can be used as a knowledge source for automatic WSD. Also introduced in this paper is Ubyline, the web application used to produce the sense annotations. Ubyline presents an intuitive user interface optimized for annotating lexical sample data, and is readily adaptable to sense inventories other than GermaNet.",
}
@inproceedings{horbach-etal-2016-corpus,
    title = "A Corpus of Literal and Idiomatic Uses of {G}erman Infinitive-Verb Compounds",
    author = "Horbach, Andrea  and
      Hensler, Andrea  and
      Krome, Sabine  and
      Prange, Jakob  and
      Scholze-Stubenrecht, Werner  and
      Steffen, Diana  and
      Thater, Stefan  and
      Wellner, Christian  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1135",
    pages = "836--841",
    abstract = "We present an annotation study on a representative dataset of literal and idiomatic uses of German infinitive-verb compounds in newspaper and journal texts. Infinitive-verb compounds form a challenge for writers of German, because spelling regulations are different for literal and idiomatic uses. Through the participation of expert lexicographers we were able to obtain a high-quality corpus resource which offers itself as a testbed for automatic idiomaticity detection and coarse-grained word-sense disambiguation. We trained a classifier on the corpus which was able to distinguish literal and idiomatic uses with an accuracy of 85 {\%}.",
}
@inproceedings{pedersen-etal-2016-semdax,
    title = "The {S}em{D}a{X} Corpus ― Sense Annotations with Scalable Sense Inventories",
    author = "Pedersen, Bolette  and
      Braasch, Anna  and
      Johannsen, Anders  and
      Alonso, H{\'e}ctor Mart{\'\i}nez  and
      Nimb, Sanni  and
      Olsen, Sussi  and
      S{\o}gaard, Anders  and
      S{\o}rensen, Nicolai Hartvig",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1136",
    pages = "842--847",
    abstract = "We launch the SemDaX corpus which is a recently completed Danish human-annotated corpus available through a CLARIN academic license. The corpus includes approx. 90,000 words, comprises six textual domains, and is annotated with sense inventories of different granularity. The aim of the developed corpus is twofold: i) to assess the reliability of the different sense annotation schemes for Danish measured by qualitative analyses and annotation agreement scores, and ii) to serve as training and test data for machine learning algorithms with the practical purpose of developing sense taggers for Danish. To these aims, we take a new approach to human-annotated corpus resources by double annotating a much larger part of the corpus than what is normally seen: for the all-words task we double annotated 60{\%} of the material and for the lexical sample task 100{\%}. We include in the corpus not only the adjucated files, but also the diverging annotations. In other words, we consider not all disagreement to be noise, but rather to contain valuable linguistic information that can help us improve our annotation schemes and our learning algorithms.",
}
@inproceedings{cinkova-etal-2016-graded,
    title = "Graded and Word-Sense-Disambiguation Decisions in Corpus Pattern Analysis: a Pilot Study",
    author = "Cinkov{\'a}, Silvie  and
      Krej{\v{c}}ov{\'a}, Ema  and
      Vernerov{\'a}, Anna  and
      Baisa, V{\'\i}t",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1137",
    pages = "848--854",
    abstract = "We present a pilot analysis of a new linguistic resource, VPS-GradeUp (available at http://hdl.handle.net/11234/1-1585). The resource contains 11,400 graded human decisions on usage patterns of 29 English lexical verbs, randomly selected from the Pattern Dictionary of English Verbs (Hanks, 2000 2014) based on their frequency and the number of senses their lemmas have in PDEV. This data set has been created to observe the interannotator agreement on PDEV patterns produced using the Corpus Pattern Analysis (Hanks, 2013). Apart from the graded decisions, the data set also contains traditional Word-Sense-Disambiguation (WSD) labels. We analyze the associations between the graded annotation and WSD annotation. The results of the respective annotations do not correlate with the size of the usage pattern inventory for the respective verbs lemmas, which makes the data set worth further linguistic analysis.",
}
@inproceedings{lu-etal-2016-multi,
    title = "Multi-prototype {C}hinese Character Embedding",
    author = "Lu, Yanan  and
      Zhang, Yue  and
      Ji, Donghong",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1138",
    pages = "855--859",
    abstract = "Chinese sentences are written as sequences of characters, which are elementary units of syntax and semantics. Characters are highly polysemous in forming words. We present a position-sensitive skip-gram model to learn multi-prototype Chinese character embeddings, and explore the usefulness of such character embeddings to Chinese NLP tasks. Evaluation on character similarity shows that multi-prototype embeddings are significantly better than a single-prototype baseline. In addition, used as features in the Chinese NER task, the embeddings result in a 1.74{\%} F-score improvement over a state-of-the-art baseline.",
}
@inproceedings{chang-etal-2016-comparison,
    title = "A comparison of Named-Entity Disambiguation and Word Sense Disambiguation",
    author = "Chang, Angel  and
      Spitkovsky, Valentin I.  and
      Manning, Christopher D.  and
      Agirre, Eneko",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1139",
    pages = "860--867",
    abstract = "Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia. This task is closely related to word-sense disambiguation (WSD), where the mention of an open-class word is linked to a concept in a knowledge-base, typically WordNet. This paper analyzes the relation between two annotated datasets on NED and WSD, highlighting the commonalities and differences. We detail the methods to construct a NED system following the WSD word-expert approach, where we need a dictionary and one classifier is built for each target entity mention string. Constructing a dictionary for NED proved challenging, and although similarity and ambiguity are higher for NED, the results are also higher due to the larger number of training data, and the more crisp and skewed meaning differences.",
}
@inproceedings{villegas-etal-2016-leveraging,
    title = "Leveraging {RDF} Graphs for Crossing Multiple Bilingual Dictionaries",
    author = "Villegas, Marta  and
      Melero, Maite  and
      Bel, N{\'u}ria  and
      Gracia, Jorge",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1140",
    pages = "868--876",
    abstract = "The experiments presented here exploit the properties of the Apertium RDF Graph, principally cycle density and nodes{'} degree, to automatically generate new translation relations between words, and therefore to enrich existing bilingual dictionaries with new entries. Currently, the Apertium RDF Graph includes data from 22 Apertium bilingual dictionaries and constitutes a large unified array of linked lexical entries and translations that are available and accessible on the Web (http://linguistic.linkeddata.es/apertium/). In particular, its graph structure allows for interesting exploitation opportunities, some of which are addressed in this paper. Two {`}massive{'} experiments are reported: in the first one, the original EN-ES translation set was removed from the Apertium RDF Graph and a new EN-ES version was generated. The results were compared against the previously removed EN-ES data and against the Concise Oxford Spanish Dictionary. In the second experiment, a new non-existent EN-FR translation set was generated. In this case the results were compared against a converted wiktionary English-French file. The results we got are really good and perform well for the extreme case of correlated polysemy. This lead us to address the possibility to use cycles and nodes degree to identify potential oddities in the source data. If cycle density proves efficient when considering potential targets, we can assume that in dense graphs nodes with low degree may indicate potential errors.",
}
@inproceedings{corcoglioniti-etal-2016-premon,
    title = "{P}re{MO}n: a Lemon Extension for Exposing Predicate Models as Linked Data",
    author = "Corcoglioniti, Francesco  and
      Rospocher, Marco  and
      Aprosio, Alessio Palmero  and
      Tonelli, Sara",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1141",
    pages = "877--884",
    abstract = "We introduce PreMOn (predicate model for ontologies), a linguistic resource for exposing predicate models (PropBank, NomBank, VerbNet, and FrameNet) and mappings between them (e.g, SemLink) as Linked Open Data. It consists of two components: (i) the PreMOn Ontology, an extension of the lemon model by the W3C Ontology-Lexica Community Group, that enables to homogeneously represent data from the various predicate models; and, (ii) the PreMOn Dataset, a collection of RDF datasets integrating various versions of the aforementioned predicate models and mapping resources. PreMOn is freely available and accessible online in different ways, including through a dedicated SPARQL endpoint.",
}
@inproceedings{chalub-etal-2016-semantic,
    title = "Semantic Links for {P}ortuguese",
    author = "Chalub, Fabricio  and
      Real, Livy  and
      Rademaker, Alexandre  and
      de Paiva, Valeria",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1142",
    pages = "885--891",
    abstract = "This paper describes work on incorporating Princenton{'}s WordNet morphosemantics links to the fabric of the Portuguese OpenWordNet-PT. Morphosemantic links are relations between verbs and derivationally related nouns that are semantically typed (such as for tune-tuner ― in Portuguese {``}afinar-afinador{''} {--} linked through an {``}agent{''} link). Morphosemantic links have been discussed for Princeton{'}s WordNet for a while, but have not been added to the official database. These links are very useful, they help us to improve our Portuguese WordNet. Thus we discuss the integration of these links in our base and the issues we encountered with the integration.",
}
@inproceedings{klimek-etal-2016-creating,
    title = "Creating Linked Data Morphological Language Resources with {MM}o{O}n - The {H}ebrew Morpheme Inventory",
    author = "Klimek, Bettina  and
      Arndt, Natanael  and
      Krause, Sebastian  and
      Arndt, Timotheus",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1143",
    pages = "892--899",
    abstract = "The development of standard models for describing general lexical resources has led to the emergence of numerous lexical datasets of various languages in the Semantic Web. However, equivalent models covering the linguistic domain of morphology do not exist. As a result, there are hardly any language resources of morphemic data available in RDF to date. This paper presents the creation of the Hebrew Morpheme Inventory from a manually compiled tabular dataset comprising around 52.000 entries. It is an ongoing effort of representing the lexemes, word-forms and morphologigal patterns together with their underlying relations based on the newly created Multilingual Morpheme Ontology (MMoOn). It will be shown how segmented Hebrew language data can be granularly described in a Linked Data format, thus, serving as an exemplary case for creating morpheme inventories of any inflectional language with MMoOn. The resulting dataset is described a) according to the structure of the underlying data format, b) with respect to the Hebrew language characteristic of building word-forms directly from roots, c) by exemplifying how inflectional information is realized and d) with regard to its enrichment with external links to sense resources.",
}
@inproceedings{prokopidis-etal-2016-parallel,
    title = "Parallel {G}lobal {V}oices: a Collection of Multilingual Corpora with Citizen Media Stories",
    author = "Prokopidis, Prokopis  and
      Papavassiliou, Vassilis  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1144",
    pages = "900--905",
    abstract = "We present a new collection of multilingual corpora automatically created from the content available in the Global Voices websites, where volunteers have been posting and translating citizen media stories since 2004. We describe how we crawled and processed this content to generate parallel resources comprising 302.6K document pairs and 8.36M segment alignments in 756 language pairs. For some language pairs, the segment alignments in this resource are the first open examples of their kind. In an initial use of this resource, we discuss how a set of document pair detection algorithms performs on the Greek-English corpus.",
}
@inproceedings{li-etal-2016-large,
    title = "Large Multi-lingual, Multi-level and Multi-genre Annotation Corpus",
    author = "Li, Xuansong  and
      Palmer, Martha  and
      Xue, Nianwen  and
      Ramshaw, Lance  and
      Maamouri, Mohamed  and
      Bies, Ann  and
      Conger, Kathryn  and
      Grimes, Stephen  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1145",
    pages = "906--913",
    abstract = "High accuracy for automated translation and information retrieval calls for linguistic annotations at various language levels. The plethora of informal internet content sparked the demand for porting state-of-art natural language processing (NLP) applications to new social media as well as diverse language adaptation. Effort launched by the BOLT (Broad Operational Language Translation) program at DARPA (Defense Advanced Research Projects Agency) successfully addressed the internet information with enhanced NLP systems. BOLT aims for automated translation and linguistic analysis for informal genres of text and speech in online and in-person communication. As a part of this program, the Linguistic Data Consortium (LDC) developed valuable linguistic resources in support of the training and evaluation of such new technologies. This paper focuses on methodologies, infrastructure, and procedure for developing linguistic annotation at various language levels, including Treebank (TB), word alignment (WA), PropBank (PB), and co-reference (CoRef). Inspired by the OntoNotes approach with adaptations to the tasks to reflect the goals and scope of the BOLT project, this effort has introduced more annotation types of informal and free-style genres in English, Chinese and Egyptian Arabic. The corpus produced is by far the largest multi-lingual, multi-level and multi-genre annotation corpus of informal text and speech.",
}
@inproceedings{habernal-etal-2016-c4corpus,
    title = "{C}4{C}orpus: Multilingual Web-size Corpus with Free License",
    author = "Habernal, Ivan  and
      Zayed, Omnia  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1146",
    pages = "914--922",
    abstract = "Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks. In this article we present the construction of 12 million-pages Web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs. Our highly-scalable Hadoop-based framework is able to process the full CommonCrawl corpus on 2000+ CPU cluster on the Amazon Elastic Map/Reduce infrastructure. The processing pipeline includes license identification, state-of-the-art boilerplate removal, exact duplicate and near-duplicate document removal, and language detection. The construction of the corpus is highly configurable and fully reproducible, and we provide both the framework (DKPro C4CorpusTools) and the resulting data (C4Corpus) to the research community.",
}
@inproceedings{lison-tiedemann-2016-opensubtitles2016,
    title = "{O}pen{S}ubtitles2016: Extracting Large Parallel Corpora from Movie and {TV} Subtitles",
    author = {Lison, Pierre  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1147",
    pages = "923--929",
    abstract = "We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.",
}
@inproceedings{rambelli-etal-2016-lexfr,
    title = "{L}ex{F}r: Adapting the {L}ex{I}t Framework to Build a Corpus-based {F}rench Subcategorization Lexicon",
    author = "Rambelli, Giulia  and
      Lebani, Gianluca  and
      Pr{\'e}vot, Laurent  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1148",
    pages = "930--937",
    abstract = "This paper introduces LexFr, a corpus-based French lexical resource built by adapting the framework LexIt, originally developed to describe the combinatorial potential of Italian predicates. As in the original framework, the behavior of a group of target predicates is characterized by a series of syntactic (i.e., subcategorization frames) and semantic (i.e., selectional preferences) statistical information (a.k.a. distributional profiles) whose extraction process is mostly unsupervised. The first release of LexFr includes information for 2,493 verbs, 7,939 nouns and 2,628 adjectives. In these pages we describe the adaptation process and evaluated the final resource by comparing the information collected for 20 test verbs against the information available in a gold standard dictionary. In the best performing setting, we obtained 0.74 precision, 0.66 recall and 0.70 F-measure.",
}
@inproceedings{vicente-saralegi-2016-polarity,
    title = "Polarity Lexicon Building: to what Extent Is the Manual Effort Worth?",
    author = "Vicente, I{\~n}aki San  and
      Saralegi, Xabier",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1149",
    pages = "938--942",
    abstract = "Polarity lexicons are a basic resource for analyzing the sentiments and opinions expressed in texts in an automated way. This paper explores three methods to construct polarity lexicons: translating existing lexicons from other languages, extracting polarity lexicons from corpora, and annotating sentiments Lexical Knowledge Bases. Each of these methods require a different degree of human effort. We evaluate how much manual effort is needed and to what extent that effort pays in terms of performance improvement. Experiment setup includes generating lexicons for Basque, and evaluating them against gold standard datasets in different domains. Results show that extracting polarity lexicons from corpora is the best solution for achieving a good performance with reasonable human effort.",
}
@inproceedings{nahli-etal-2016-al,
    title = "Al Qamus al Muhit, a Medieval {A}rabic Lexicon in {LMF}",
    author = "Nahli, Ouafae  and
      Frontini, Francesca  and
      Monachini, Monica  and
      Khan, Fahad  and
      Zarghili, Arsalan  and
      Khalfi, Mustapha",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1150",
    pages = "943--950",
    abstract = "This paper describes the conversion into LMF, a standard lexicographic digital format of {`}al-q{\=a}m{\=u}s al-muḥ{\=\i}ṭ, a Medieval Arabic lexicon. The lexicon is first described, then all the steps required for the conversion are illustrated. The work is will produce a useful lexicographic resource for Arabic NLP, but is also interesting per se, to study the implications of adapting the LMF model to the Arabic language. Some reflections are offered as to the status of roots with respect to previously suggested representations. In particular, roots are, in our opinion are to be not treated as lexical entries, but modeled as lexical metadata for classifying and identifying lexical entries. In this manner, each root connects all entries that are derived from it.",
}
@inproceedings{baeza-yates-etal-2016-cassaurus,
    title = "{CASSA}urus: A Resource of Simpler {S}panish Synonyms",
    author = "Baeza-Yates, Ricardo  and
      Rello, Luz  and
      Dembowski, Julia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1151",
    pages = "951--955",
    abstract = "In this work we introduce and describe a language resource composed of lists of simpler synonyms for Spanish. The synonyms are divided in different senses taken from the Spanish OpenThesaurus, where context disambiguation was performed by using statistical information from the Web and Google Books Ngrams. This resource is freely available online and can be used for different NLP tasks such as lexical simplification. Indeed, so far it has been already integrated into four tools.",
}
@inproceedings{kettunen-paakkonen-2016-measuring,
    title = "Measuring Lexical Quality of a Historical {F}innish Newspaper Collection ― Analysis of Garbled {OCR} Data with Basic Language Technology Tools and Means",
    author = {Kettunen, Kimmo  and
      P{\"a}{\"a}kk{\"o}nen, Tuula},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1152",
    pages = "956--961",
    abstract = {The National Library of Finland has digitized a large proportion of the historical newspapers published in Finland between 1771 and 1910 (Bremer-Laamanen 2001). This collection contains approximately 1.95 million pages in Finnish and Swedish. Finnish part of the collection consists of about 2.39 billion words. The National Library{'}s Digital Collections are offered via the digi.kansalliskirjasto.fi web service, also known as Digi. Part of this material is also available freely downloadable in The Language Bank of Finland provided by the Fin-CLARIN consortium . The collection can also be accessed through the Korp environment that has been developed by Spr{\"a}kbanken at the University of Gothenburg and extended by FIN-CLARIN team at the University of Helsinki to provide concordances of text resources. A Cranfield-style information retrieval test collection has been produced out of a small part of the Digi newspaper material at the University of Tampere (J{\"a}rvelin et al., 2015). The quality of the OCRed collections is an important topic in digital humanities, as it affects general usability and searchability of collections. There is no single available method to assess the quality of large collections, but different methods can be used to approximate the quality. This paper discusses different corpus analysis style ways to approximate the overall lexical quality of the Finnish part of the Digi collection.},
}
@inproceedings{afli-etal-2016-using,
    title = "Using {SMT} for {OCR} Error Correction of Historical Texts",
    author = "Afli, Haithem  and
      Qiu, Zhengwei  and
      Way, Andy  and
      Sheridan, P{\'a}raic",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1153",
    pages = "962--966",
    abstract = "A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13{\%} relative improvement compared to the initial baseline.",
}
@inproceedings{reynaert-2016-ocr,
    title = "{OCR} Post-Correction Evaluation of Early {D}utch Books Online - Revisited",
    author = "Reynaert, Martin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1154",
    pages = "967--974",
    abstract = "We present further work on evaluation of the fully automatic post-correction of Early Dutch Books Online, a collection of 10,333 18th century books. In prior work we evaluated the new implementation of Text-Induced Corpus Clean-up (TICCL) on the basis of a single book Gold Standard derived from this collection. In the current paper we revisit the same collection on the basis of a sizeable 1020 item random sample of OCR post-corrected strings from the full collection. Both evaluations have their own stories to tell and lessons to teach.",
}
@inproceedings{clematide-etal-2016-crowdsourcing,
    title = "Crowdsourcing an {OCR} Gold Standard for a {G}erman and {F}rench Heritage Corpus",
    author = "Clematide, Simon  and
      Furrer, Lenz  and
      Volk, Martin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1155",
    pages = "975--982",
    abstract = "Crowdsourcing approaches for post-correction of OCR output (Optical Character Recognition) have been successfully applied to several historic text collections. We report on our crowd-correction platform Kokos, which we built to improve the OCR quality of the digitized yearbooks of the Swiss Alpine Club (SAC) from the 19th century. This multilingual heritage corpus consists of Alpine texts mainly written in German and French, all typeset in Antiqua font. Finding and engaging volunteers for correcting large amounts of pages into high quality text requires a carefully designed user interface, an easy-to-use workflow, and continuous efforts for keeping the participants motivated. More than 180,000 characters on about 21,000 pages were corrected by volunteers in about 7 month, achieving an OCR gold standard with a systematically evaluated accuracy of 99.7{\%} on the word level. The crowdsourced OCR gold standard and the corresponding original OCR recognition results from Abby FineReader 7 for each page are available as a resource. Additionally, the scanned images (300dpi) of all pages are included in order to facilitate tests with other OCR software.",
}
@inproceedings{saint-dizier-2016-argument,
    title = "Argument Mining: the Bottleneck of Knowledge and Language Resources",
    author = "Saint-Dizier, Patrick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1156",
    pages = "983--990",
    abstract = "Given a controversial issue, argument mining from natural language texts (news papers, and any form of text on the Internet) is extremely challenging: domain knowledge is often required together with appropriate forms of inferences to identify arguments. This contribution explores the types of knowledge that are required and how they can be paired with reasoning schemes, language processing and language resources to accurately mine arguments. We show via corpus analysis that the Generative Lexicon, enhanced in different manners and viewed as both a lexicon and a domain knowledge representation, is a relevant approach. In this paper, corpus annotation for argument mining is first developed, then we show how the generative lexicon approach must be adapted and how it can be paired with language processing patterns to extract and specify the nature of arguments. Our approach to argument mining is thus knowledge driven.",
}
@inproceedings{lapshinova-koltunski-etal-2016-interoperable,
    title = "From Interoperable Annotations towards Interoperable Resources: A Multilingual Approach to the Analysis of Discourse",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Kunz, Kerstin Anna  and
      Nedoluzhko, Anna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1157",
    pages = "991--997",
    abstract = "In the present paper, we analyse variation of discourse phenomena in two typologically different languages, i.e. in German and Czech. The novelty of our approach lies in the nature of the resources we are using. Advantage is taken of existing resources, which are, however, annotated on the basis of two different frameworks. We use an interoperable scheme unifying discourse phenomena in both frameworks into more abstract categories and considering only those phenomena that have a direct match in German and Czech. The discourse properties we focus on are relations of identity, semantic similarity, ellipsis and discourse relations. Our study shows that the application of interoperable schemes allows an exploitation of discourse-related phenomena analysed in different projects and on the basis of different frameworks. As corpus compilation and annotation is a time-consuming task, positive results of this experiment open up new paths for contrastive linguistics, translation studies and NLP, including machine translation.",
}
@inproceedings{van-den-heuvel-oostdijk-2016-falling,
    title = "Falling silent, lost for words ... Tracing personal involvement in interviews with {D}utch war veterans",
    author = "van den Heuvel, Henk  and
      Oostdijk, Nelleke",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1158",
    pages = "998--1001",
    abstract = "In sources used in oral history research (such as interviews with eye witnesses), passages where the degree of personal emotional involvement is found to be high can be of particular interest, as these may give insight into how historical events were experienced, and what moral dilemmas and psychological or religious struggles were encountered. In a pilot study involving a large corpus of interview recordings with Dutch war veterans, we have investigated if it is possible to develop a method for automatically identifying those passages where the degree of personal emotional involvement is high. The method is based on the automatic detection of exceptionally large silences and filled pause segments (using Automatic Speech Recognition), and cues taken from specific n-grams. The first results appear to be encouraging enough for further elaboration of the method.",
}
@inproceedings{liu-etal-2016-bilingual,
    title = "A Bilingual Discourse Corpus and Its Applications",
    author = "Liu, Yang  and
      Zhang, Jiajun  and
      Zong, Chengqing  and
      Yang, Yating  and
      Zhou, Xi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1159",
    pages = "1002--1007",
    abstract = "Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation. To address this issue, we design and build a bilingual discource corpus in which we are currently defining and annotating the bilingual elementary discourse units (BEDUs). The BEDUs are then organized into hierarchical structures. Using this discourse style, we have annotated nearly 20K LDC sentences. Finally, we design a bilingual discourse based method for machine translation evaluation and show the effectiveness of our bilingual discourse annotations.",
}
@inproceedings{scheffler-stede-2016-adding,
    title = "Adding Semantic Relations to a Large-Coverage Connective Lexicon of {G}erman",
    author = "Scheffler, Tatjana  and
      Stede, Manfred",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1160",
    pages = "1008--1013",
    abstract = "DiMLex is a lexicon of German connectives that can be used for various language understanding purposes. We enhanced the coverage to 275 connectives, which we regard as covering all known German discourse connectives in current use. In this paper, we consider the task of adding the semantic relations that can be expressed by each connective. After discussing different approaches to retrieving semantic information, we settle on annotating each connective with senses from the new PDTB 3.0 sense hierarchy. We describe our new implementation in the extended DiMLex, which will be available for research purposes.",
}
@inproceedings{janier-reed-2016-corpus,
    title = "Corpus Resources for Dispute Mediation Discourse",
    author = "Janier, Mathilde  and
      Reed, Chris",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1161",
    pages = "1014--1021",
    abstract = "Dispute mediation is a growing activity in the resolution of conflicts, and more and more research emerge to enhance and better understand this (until recently) understudied practice. Corpus analyses are necessary to study discourse in this context; yet, little data is available, mainly because of its confidentiality principle. After proposing hints and avenues to acquire transcripts of mediation sessions, this paper presents the Dispute Mediation Corpus, which gathers annotated excerpts of mediation dialogues. Although developed as part of a project on argumentation, it is freely available and the text data can be used by anyone. This first-ever open corpus of mediation interactions can be of interest to scholars studying discourse, but also conflict resolution, argumentation, linguistics, communication, etc. We advocate for using and extending this resource that may be valuable to a large variety of domains of research, particularly those striving to enhance the study of the rapidly growing activity of dispute mediation.",
}
@inproceedings{valmaseda-etal-2016-tagged,
    title = "A Tagged Corpus for Automatic Labeling of Disabilities in Medical Scientific Papers",
    author = "Valmaseda, Carlos  and
      Martinez-Romo, Juan  and
      Araujo, Lourdes",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1162",
    pages = "1022--1025",
    abstract = "This paper presents the creation of a corpus of labeled disabilities in scientific papers. The identification of medical concepts in documents and, especially, the identification of disabilities, is a complex task mainly due to the variety of expressions that can make reference to the same problem. Currently there is not a set of documents manually annotated with disabilities with which to evaluate an automatic detection system of such concepts. This is the reason why this corpus arises, aiming to facilitate the evaluation of systems that implement an automatic annotation tool for extracting biomedical concepts such as disabilities. The result is a set of scientific papers manually annotated. For the selection of these scientific papers has been conducted a search using a list of rare diseases, since they generally have associated several disabilities of different kinds.",
}
@inproceedings{lukin-etal-2016-personabank,
    title = "{P}ersona{B}ank: A Corpus of Personal Narratives and Their Story Intention Graphs",
    author = "Lukin, Stephanie  and
      Bowden, Kevin  and
      Barackman, Casey  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1163",
    pages = "1026--1033",
    abstract = "We present a new corpus, PersonaBank, consisting of 108 personal stories from weblogs that have been annotated with their Story Intention Graphs, a deep representation of the content of a story. We describe the topics of the stories and the basis of the Story Intention Graph representation, as well as the process of annotating the stories to produce the Story Intention Graphs and the challenges of adapting the tool to this new personal narrative domain. We also discuss how the corpus can be used in applications that retell the story using different styles of tellings, co-tellings, or as a content planner.",
}
@inproceedings{chen-etal-2016-fine,
    title = "Fine-Grained {C}hinese Discourse Relation Labelling",
    author = "Chen, Huan-Yuan  and
      Liao, Wan-Shan  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1164",
    pages = "1034--1038",
    abstract = "This paper explores several aspects together for a fine-grained Chinese discourse analysis. We deal with the issues of ambiguous discourse markers, ambiguous marker linkings, and more than one discourse marker. A universal feature representation is proposed. The pair-once postulation, cross-discourse-unit-first rule and word-pair-marker-first rule select a set of discourse markers from ambiguous linkings. Marker-Sum feature considers total contribution of markers and Marker-Preference feature captures the probability distribution of discourse functions of a representative marker by using preference rule. The HIT Chinese discourse relation treebank (HIT-CDTB) is used to evaluate the proposed models. The 25-way classifier achieves 0.57 micro-averaged F-score.",
}
@inproceedings{rehbein-etal-2016-annotating,
    title = "Annotating Discourse Relations in Spoken Language: A Comparison of the {PDTB} and {CCR} Frameworks",
    author = "Rehbein, Ines  and
      Scholman, Merel  and
      Demberg, Vera",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1165",
    pages = "1039--1046",
    abstract = "In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.",
}
@inproceedings{lailler-etal-2016-enhancing,
    title = "Enhancing The {RATP}-{DECODA} Corpus With Linguistic Annotations For Performing A Large Range Of {NLP} Tasks",
    author = {Lailler, Carole  and
      Landeau, Ana{\"\i}s  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Est{\`e}ve, Yannick  and
      Del{\'e}glise, Paul},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1166",
    pages = "1047--1050",
    abstract = "In this article, we present the RATP-DECODA Corpus which is composed by a set of 67 hours of speech from telephone conversations of a Customer Care Service (CCS). This corpus is already available on line at http://sldr.org/sldr000847/fr in its first version. However, many enhancements have been made in order to allow the development of automatic techniques to transcript conversations and to capture their meaning. These enhancements fall into two categories: firstly, we have increased the size of the corpus with manual transcriptions from a new operational day; secondly we have added new linguistic annotations to the whole corpus (either manually or through an automatic processing) in order to perform various linguistic tasks from syntactic and semantic parsing to dialog act tagging and dialog summarization.",
}
@inproceedings{stede-etal-2016-parallel,
    title = "Parallel Discourse Annotations on a Corpus of Short Texts",
    author = "Stede, Manfred  and
      Afantenos, Stergos  and
      Peldszus, Andreas  and
      Asher, Nicholas  and
      Perret, J{\'e}r{\'e}my",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1167",
    pages = "1051--1058",
    abstract = "We present the first corpus of texts annotated with two alternative approaches to discourse structure, Rhetorical Structure Theory (Mann and Thompson, 1988) and Segmented Discourse Representation Theory (Asher and Lascarides, 2003). 112 short argumentative texts have been analyzed according to these two theories. Furthermore, in previous work, the same texts have already been annotated for their argumentation structure, according to the scheme of Peldszus and Stede (2013). This corpus therefore enables studies of correlations between the two accounts of discourse structure, and between discourse and argumentation. We converted the three annotation formats to a common dependency tree format that enables to compare the structures, and we describe some initial findings.",
}
@inproceedings{lee-yeung-2016-annotated,
    title = "An Annotated Corpus of Direct Speech",
    author = "Lee, John  and
      Yeung, Chak Yan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1168",
    pages = "1059--1063",
    abstract = "We propose a scheme for annotating direct speech in literary texts, based on the Text Encoding Initiative (TEI) and the coreference annotation guidelines from the Message Understanding Conference (MUC). The scheme encodes the speakers and listeners of utterances in a text, as well as the quotative verbs that reports the utterances. We measure inter-annotator agreement on this annotation task. We then present statistics on a manually annotated corpus that consists of books from the New Testament. Finally, we visualize the corpus as a conversational network.",
}
@inproceedings{etxeberria-etal-2016-evaluating,
    title = "Evaluating the Noisy Channel Model for the Normalization of Historical Texts: {B}asque, {S}panish and {S}lovene",
    author = "Etxeberria, Izaskun  and
      Alegria, I{\~n}aki  and
      Uria, Larraitz  and
      Hulden, Mans",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1169",
    pages = "1064--1069",
    abstract = "This paper presents a method for the normalization of historical texts using a combination of weighted finite-state transducers and language models. We have extended our previous work on the normalization of dialectal texts and tested the method against a 17th century literary work in Basque. This preprocessed corpus is made available in the LREC repository. The performance of this method for learning relations between historical and contemporary word forms is evaluated against resources in three languages. The method we present learns to map phonological changes using a noisy channel model. The model is based on techniques commonly used for phonological inference and producing Grapheme-to-Grapheme conversion systems encoded as weighted transducers and produces F-scores above 80{\%} in the task for Basque. A wider evaluation shows that the approach performs equally well with all the languages in our evaluation suite: Basque, Spanish and Slovene. A comparison against other methods that address the same task is also provided.",
}
@inproceedings{darwish-mubarak-2016-farasa,
    title = "{F}arasa: A New Fast and Accurate {A}rabic Word Segmenter",
    author = "Darwish, Kareem  and
      Mubarak, Hamdy",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1170",
    pages = "1070--1074",
    abstract = "In this paper, we present Farasa (meaning insight in Arabic), which is a fast and accurate Arabic segmenter. Segmentation involves breaking Arabic words into their constituent clitics. Our approach is based on SVMrank using linear kernels. The features that we utilized account for: likelihood of stems, prefixes, suffixes, and their combination; presence in lexicons containing valid stems and named entities; and underlying stem templates. Farasa outperforms or equalizes state-of-the-art Arabic segmenters, namely QATARA and MADAMIRA. Meanwhile, Farasa is nearly one order of magnitude faster than QATARA and two orders of magnitude faster than MADAMIRA. The segmenter should be able to process one billion words in less than 5 hours. Farasa is written entirely in native Java, with no external dependencies, and is open-source.",
}
@inproceedings{bick-2016-morphological,
    title = "A Morphological Lexicon of {E}speranto with Morpheme Frequencies",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1171",
    pages = "1075--1078",
    abstract = "This paper discusses the internal structure of complex Esperanto words (CWs). Using a morphological analyzer, possible affixation and compounding is checked for over 50,000 Esperanto lexemes against a list of 17,000 root words. Morpheme boundaries in the resulting analyses were then checked manually, creating a CW dictionary of 28,000 words, representing 56.4{\%} of the lexicon, or 19.4{\%} of corpus tokens. The error percentage of the EspGram morphological analyzer for new corpus CWs was 4.3{\%} for types and 6.4{\%} for tokens, with a recall of almost 100{\%}, and wrong/spurious boundaries being more common than missing ones. For pedagogical purposes a morpheme frequency dictionary was constructed for a 16 million word corpus, confirming the importance of agglutinative derivational morphemes in the Esperanto lexicon. Finally, as a means to reduce the morphological ambiguity of CWs, we provide POS likelihoods for Esperanto suffixes.",
}
@inproceedings{liu-wang-2016-dictionary,
    title = "How does Dictionary Size Influence Performance of {V}ietnamese Word Segmentation?",
    author = "Liu, Wuying  and
      Wang, Lin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1172",
    pages = "1079--1083",
    abstract = "Vietnamese word segmentation (VWS) is a challenging basic issue for natural language processing. This paper addresses the problem of how does dictionary size influence VWS performance, proposes two novel measures: square overlap ratio (SOR) and relaxed square overlap ratio (RSOR), and validates their effectiveness. The SOR measure is the product of dictionary overlap ratio and corpus overlap ratio, and the RSOR measure is the relaxed version of SOR measure under an unsupervised condition. The two measures both indicate the suitable degree between segmentation dictionary and object corpus waiting for segmentation. The experimental results show that the more suitable, neither smaller nor larger, dictionary size is better to achieve the state-of-the-art performance for dictionary-based Vietnamese word segmenters.",
}
@inproceedings{hathout-namer-2016-giving,
    title = "Giving Lexical Resources a Second Life: D{\'e}monette, a Multi-sourced Morpho-semantic Network for {F}rench",
    author = "Hathout, Nabil  and
      Namer, Fiammetta",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1173",
    pages = "1084--1091",
    abstract = "D{\'e}monette is a derivational morphological network designed for the description of French. Its original architecture enables its use as a formal framework for the description of morphological analyses and as a repository for existing lexicons. It is fed with a variety of resources, which all are already validated. The harmonization of their content into a unified format provides them a second life, in which they are enriched with new properties, provided these are deductible from their contents. D{\'e}monette is released under a Creative Commons license. It is usable for theoretical and descriptive research in morphology, as a source of experimental material for psycholinguistics, natural language processing (NLP) and information retrieval (IR), where it fills a gap, since French lacks a large-coverage derivational resources database. The article presents the integration of two existing lexicons into D{\'e}monette. The first is Verbaction, a lexicon of deverbal action nouns. The second is Lexeur, a database of agent nouns in -eur derived from verbs or from nouns.",
}
@inproceedings{trips-2016-syntactic,
    title = "Syntactic Analysis of Phrasal Compounds in Corpora: a Challenge for {NLP} Tools",
    author = "Trips, Carola",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1174",
    pages = "1092--1097",
    abstract = "The paper introduces a {``}train once, use many{''} approach for the syntactic analysis of phrasal compounds (PC) of the type XP+N like {``}Would you like to sit on my knee?{''} nonsense. PCs are a challenge for NLP tools since they require the identification of a syntactic phrase within a morphological complex. We propose a method which uses a state-of-the-art dependency parser not only to analyse sentences (the environment of PCs) but also to compound the non-head of PCs in a well-defined particular condition which is the analysis of the non-head spanning from the left boundary (mostly marked by a determiner) to the nominal head of the PC. This method contains the following steps: (a) the use an English state-of-the-art dependency parser with data comprising sentences with PCs from the British National Corpus (BNC), (b) the detection of parsing errors of PCs, (c) the separate treatment of the non-head structure using the same model, and (d) the attachment of the non-head to the compound head. The evaluation of the method showed that the accuracy of 76{\%} could be improved by adding a step in the PC compounder module which specified user-defined contexts being sensitive to the part of speech of the non-head parts and by using TreeTagger, in line with our approach.",
}
@inproceedings{khalifa-etal-2016-dalila,
    title = "{DALILA}: The Dialectal {A}rabic Linguistic Learning Assistant",
    author = "Khalifa, Salam  and
      Bouamor, Houda  and
      Habash, Nizar",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1175",
    pages = "1098--1102",
    abstract = "Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of tools to support learning Arabic as a living language on the web, where authentic material can be found in both MSA and DA. In this paper, we present the Dialectal Arabic Linguistic Learning Assistant (DALILA), a Chrome extension that utilizes cutting-edge Arabic dialect NLP research to assist learners and non-native speakers in understanding text written in either MSA or DA. DALILA provides dialectal word analysis and English gloss corresponding to each word.",
}
@inproceedings{steiner-2016-refurbishing,
    title = "Refurbishing a Morphological Database for {G}erman",
    author = "Steiner, Petra",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1176",
    pages = "1103--1108",
    abstract = "The CELEX database is one of the standard lexical resources for German. It yields a wealth of data especially for phonological and morphological applications. The morphological part comprises deep-structure morphological analyses of German. However, as it was developed in the Nineties, both encoding and spelling are outdated. About one fifth of over 50,000 datasets contain umlauts and signs such as {\ss}. Changes to a modern version cannot be obtained by simple substitution. In this paper, we shortly describe the original content and form of the orthographic and morphological database for German in CELEX. Then we present our work on modernizing the linguistic data. Lemmas and morphological analyses are transferred to a modern standard of encoding by first merging orthographic and morphological information of the lemmas and their entries and then performing a second substitution for the morphs within their morphological analyses. Changes to modern German spelling are performed by substitution rules according to orthographical standards. We show an example of the use of the data for the disambiguation of morphological structures. The discussion describes prospects of future work on this or similar lexicons. The Perl script is publicly available on our website.",
}
@inproceedings{lopez-etal-2016-encoding,
    title = "Encoding Adjective Scales for Fine-grained Resources",
    author = "Lopez, C{\'e}dric  and
      Segond, Fr{\'e}d{\'e}rique  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1177",
    pages = "1109--1113",
    abstract = "We propose an automatic approach towards determining the relative location of adjectives on a common scale based on their strength. We focus on adjectives expressing different degrees of goodness occurring in French product (perfumes) reviews. Using morphosyntactic patterns, we extract from the reviews short phrases consisting of a noun that encodes a particular aspect of the perfume and an adjective modifying that noun. We then associate each such n-gram with the corresponding product aspect and its related star rating. Next, based on the star scores, we generate adjective scales reflecting the relative strength of specific adjectives associated with a shared attribute of the product. An automatic ordering of the adjectives {``}correct{''} (correct), {``}sympa{''} (nice), {``}bon{''} (good) and {``}excellent{''} (excellent) according to their score in our resource is consistent with an intuitive scale based on human judgments. Our long-term objective is to generate different adjective scales in an empirical manner, which could allow the enrichment of lexical resources.",
}
@inproceedings{sanger-etal-2016-scare,
    title = "{SCARE} ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in {G}erman",
    author = {S{\"a}nger, Mario  and
      Leser, Ulf  and
      Kemmerer, Steffen  and
      Adolphs, Peter  and
      Klinger, Roman},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1178",
    pages = "1114--1121",
    abstract = "The automatic analysis of texts containing opinions of users about, e.g., products or political views has gained attention within the last decades. However, previous work on the task of analyzing user reviews about mobile applications in app stores is limited. Publicly available corpora do not exist, such that a comparison of different methods and models is difficult. We fill this gap by contributing the Sentiment Corpus of App Reviews (SCARE), which contains fine-grained annotations of application aspects, subjective (evaluative) phrases and relations between both. This corpus consists of 1,760 annotated application reviews from the Google Play Store with 2,487 aspects and 3,959 subjective phrases. We describe the process and methodology how the corpus was created. The Fleiss Kappa between four annotators reveals an agreement of 0.72. We provide a strong baseline with a linear-chain conditional random field and word-embedding features with a performance of 0.62 for aspect detection and 0.63 for the extraction of subjective phrases. The corpus is available to the research community to support the development of sentiment analysis methods on mobile application reviews.",
}
@inproceedings{apidianaki-etal-2016-datasets,
    title = "Datasets for Aspect-Based Sentiment Analysis in {F}rench",
    author = "Apidianaki, Marianna  and
      Tannier, Xavier  and
      Richart, C{\'e}cile",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1179",
    pages = "1122--1126",
    abstract = "Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects. This article describes two datasets for the development and testing of ABSA systems for French which comprise user reviews annotated with relevant entities, aspects and polarity values. The first dataset contains 457 restaurant reviews (2365 sentences) for training and testing ABSA systems, while the second contains 162 museum reviews (655 sentences) dedicated to out-of-domain evaluation. Both datasets were built as part of SemEval-2016 Task 5 {``}Aspect-Based Sentiment Analysis{''} where seven different languages were represented, and are publicly available for research purposes.",
}
@inproceedings{shaikh-etal-2016-anew,
    title = "{ANEW}+: Automatic Expansion and Validation of Affective Norms of Words Lexicons in Multiple Languages",
    author = "Shaikh, Samira  and
      Cho, Kit  and
      Strzalkowski, Tomek  and
      Feldman, Laurie  and
      Lien, John  and
      Liu, Ting  and
      Broadwell, George Aaron",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1180",
    pages = "1127--1132",
    abstract = "In this article we describe our method of automatically expanding an existing lexicon of words with affective valence scores. The automatic expansion process was done in English. In addition, we describe our procedure for automatically creating lexicons in languages where such resources may not previously exist. The foreign languages we discuss in this paper are Spanish, Russian and Farsi. We also describe the procedures to systematically validate our newly created resources. The main contributions of this work are: 1) A general method for expansion and creation of lexicons with scores of words on psychological constructs such as valence, arousal or dominance; and 2) a procedure for ensuring validity of the newly constructed resources.",
}
@inproceedings{sidarenka-2016-potts-potsdam,
    title = "{P}ot{TS}: The {P}otsdam {T}witter Sentiment Corpus",
    author = "Sidarenka, Uladzimir",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1181",
    pages = "1133--1141",
    abstract = "In this paper, we introduce a novel comprehensive dataset of 7,992 German tweets, which were manually annotated by two human experts with fine-grained opinion relations. A rich annotation scheme used for this corpus includes such sentiment-relevant elements as opinion spans, their respective sources and targets, emotionally laden terms with their possible contextual negations and modifiers. Various inter-annotator agreement studies, which were carried out at different stages of work on these data (at the initial training phase, upon an adjudication step, and after the final annotation run), reveal that labeling evaluative judgements in microblogs is an inherently difficult task even for professional coders. These difficulties, however, can be alleviated by letting the annotators revise each other{'}s decisions. Once rechecked, the experts can proceed with the annotation of further messages, staying at a fairly high level of agreement.",
}
@inproceedings{maynard-bontcheva-2016-challenges,
    title = "Challenges of Evaluating Sentiment Analysis Tools on Social Media",
    author = "Maynard, Diana  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1182",
    pages = "1142--1148",
    abstract = "This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems. Firstly, these are due to differences in corpus annotation guidelines and sentiment class distribution. Secondly, different systems often make different assumptions about how to interpret certain statements, e.g. tweets with URLs. In order to study the impact of these on evaluation results, this paper focuses on tweet sentiment analysis in particular. One existing and two newly created corpora are used, and the performance of four different sentiment analysis systems is reported; we make our annotated datasets and sentiment analysis applications publicly available. We see considerable variations in results across the different corpora, which calls into question the validity of many existing annotated datasets and evaluations, and we make some observations about both the systems and the datasets as a result.",
}
@inproceedings{liew-etal-2016-emotweet,
    title = "{E}mo{T}weet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis",
    author = "Liew, Jasy Suet Yan  and
      Turtle, Howard R.  and
      Liddy, Elizabeth D.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1183",
    pages = "1149--1156",
    abstract = "This paper describes EmoTweet-28, a carefully curated corpus of 15,553 tweets annotated with 28 emotion categories for the purpose of training and evaluating machine learning models for emotion classification. EmoTweet-28 is, to date, the largest tweet corpus annotated with fine-grained emotion categories. The corpus contains annotations for four facets of emotion: valence, arousal, emotion category and emotion cues. We first used small-scale content analysis to inductively identify a set of emotion categories that characterize the emotions expressed in microblog text. We then expanded the size of the corpus using crowdsourcing. The corpus encompasses a variety of examples including explicit and implicit expressions of emotions as well as tweets containing multiple emotions. EmoTweet-28 represents an important resource to advance the development and evaluation of more emotion-sensitive systems.",
}
@inproceedings{kiritchenko-mohammad-2016-happy,
    title = "Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases",
    author = "Kiritchenko, Svetlana  and
      Mohammad, Saif",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1184",
    pages = "1157--1164",
    abstract = "Sentiment composition is the determining of sentiment of a multi-word linguistic unit, such as a phrase or a sentence, based on its constituents. We focus on sentiment composition in phrases formed by at least one positive and at least one negative word ― phrases like {`}happy accident{'} and {`}best winter break{'}. We refer to such phrases as opposing polarity phrases. We manually annotate a collection of opposing polarity phrases and their constituent single words with real-valued sentiment intensity scores using a method known as Best―Worst Scaling. We show that the obtained annotations are consistent. We explore the entries in the lexicon for linguistic regularities that govern sentiment composition in opposing polarity phrases. Finally, we list the current and possible future applications of the lexicon.",
}
@inproceedings{balahur-tanev-2016-detecting,
    title = "Detecting Implicit Expressions of Affect from Text using Semantic Knowledge on Common Concept Properties",
    author = "Balahur, Alexandra  and
      Tanev, Hristo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1185",
    pages = "1165--1170",
    abstract = "Emotions are an important part of the human experience. They are responsible for the adaptation and integration in the environment, offering, most of the time together with the cognitive system, the appropriate responses to stimuli in the environment. As such, they are an important component in decision-making processes. In today{'}s society, the avalanche of stimuli present in the environment (physical or virtual) makes people more prone to respond to stronger affective stimuli (i.e., those that are related to their basic needs and motivations ― survival, food, shelter, etc.). In media reporting, this is translated in the use of arguments (factual data) that are known to trigger specific (strong, affective) behavioural reactions from the readers. This paper describes initial efforts to detect such arguments from text, based on the properties of concepts. The final system able to retrieve and label this type of data from the news in traditional and social platforms is intended to be integrated Europe Media Monitor family of applications to detect texts that trigger certain (especially negative) reactions from the public, with consequences on citizen safety and security.",
}
@inproceedings{loukachevitch-levchik-2016-creating,
    title = "Creating a General {R}ussian Sentiment Lexicon",
    author = "Loukachevitch, Natalia  and
      Levchik, Anatolii",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1186",
    pages = "1171--1176",
    abstract = "The paper describes the new Russian sentiment lexicon - RuSentiLex. The lexicon was gathered from several sources: opinionated words from domain-oriented Russian sentiment vocabularies, slang and curse words extracted from Twitter, objective words with positive or negative connotations from a news collection. The words in the lexicon having different sentiment orientations in specific senses are linked to appropriate concepts of the thesaurus of Russian language RuThes. All lexicon entries are classified according to four sentiment categories and three sources of sentiment (opinion, emotion, or fact). The lexicon can serve as the first version for the construction of domain-specific sentiment lexicons or can be used for feature generation in machine-learning approaches. In this role, the RuSentiLex lexicon was utilized by the participants of the SentiRuEval-2016 Twitter reputation monitoring shared task and allowed them to achieve high results.",
}
@inproceedings{van-son-etal-2016-grasp,
    title = "{GR}a{SP}: A Multilayered Annotation Scheme for Perspectives",
    author = "van Son, Chantal  and
      Caselli, Tommaso  and
      Fokkens, Antske  and
      Maks, Isa  and
      Morante, Roser  and
      Aroyo, Lora  and
      Vossen, Piek",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1187",
    pages = "1177--1184",
    abstract = "This paper presents a framework and methodology for the annotation of perspectives in text. In the last decade, different aspects of linguistic encoding of perspectives have been targeted as separated phenomena through different annotation initiatives. We propose an annotation scheme that integrates these different phenomena. We use a multilayered annotation approach, splitting the annotation of different aspects of perspectives into small subsequent subtasks in order to reduce the complexity of the task and to better monitor interactions between layers. Currently, we have included four layers of perspective annotation: events, attribution, factuality and opinion. The annotations are integrated in a formal model called GRaSP, which provides the means to represent instances (e.g. events, entities) and propositions in the (real or assumed) world in relation to their mentions in text. Then, the relation between the source and target of a perspective is characterized by means of perspective annotations. This enables us to place alternative perspectives on the same entity, event or proposition next to each other.",
}
@inproceedings{khiari-etal-2016-integration,
    title = "Integration of Lexical and Semantic Knowledge for Sentiment Analysis in {SMS}",
    author = "Khiari, Wejdene  and
      Roche, Mathieu  and
      Hafsia, Asma Bouhafs",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1188",
    pages = "1185--1189",
    abstract = "With the explosive growth of online social media (forums, blogs, and social networks), exploitation of these new information sources has become essential. Our work is based on the sud4science project. The goal of this project is to perform multidisciplinary work on a corpus of authentic SMS, in French, collected in 2011 and anonymised (88milSMS corpus: http://88milsms.huma-num.fr). This paper highlights a new method to integrate opinion detection knowledge from an SMS corpus by combining lexical and semantic information. More precisely, our approach gives more weight to words with a sentiment (i.e. presence of words in a dedicated dictionary) for a classification task based on three classes: positive, negative, and neutral. The experiments were conducted on two corpora: an elongated SMS corpus (i.e. repetitions of characters in messages) and a non-elongated SMS corpus. We noted that non-elongated SMS were much better classified than elongated SMS. Overall, this study highlighted that the integration of semantic knowledge always improves classification.",
}
@inproceedings{tamburini-2016-specialising,
    title = "Specialising Paragraph Vectors for Text Polarity Detection",
    author = "Tamburini, Fabio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1189",
    pages = "1190--1195",
    abstract = "This paper presents some experiments for specialising Paragraph Vectors, a new technique for creating text fragment (phrase, sentence, paragraph, text, ...) embedding vectors, for text polarity detection. The first extension regards the injection of polarity information extracted from a polarity lexicon into embeddings and the second extension aimed at inserting word order information into Paragraph Vectors. These two extensions, when training a logistic-regression classifier on the combined embeddings, were able to produce a relevant gain in performance when compared to the standard Paragraph Vector methods proposed by Le and Mikolov (2014).",
}
@inproceedings{jadi-etal-2016-evaluating,
    title = "Evaluating Lexical Similarity to build Sentiment Similarity",
    author = "Jadi, Gr{\'e}goire  and
      Claveau, Vincent  and
      Daille, B{\'e}atrice  and
      Monceaux, Laura",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1190",
    pages = "1196--1201",
    abstract = "In this article, we propose to evaluate the lexical similarity information provided by word representations against several opinion resources using traditional Information Retrieval tools. Word representation have been used to build and to extend opinion resources such as lexicon, and ontology and their performance have been evaluated on sentiment analysis tasks. We question this method by measuring the correlation between the sentiment proximity provided by opinion resources and the semantic similarity provided by word representations using different correlation coefficients. We also compare the neighbors found in word representations and list of similar opinion words. Our results show that the proximity of words in state-of-the-art word representations is not very effective to build sentiment similarity.",
}
@inproceedings{koper-etal-2016-visualisation,
    title = "Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification",
    author = {K{\"o}per, Maximilian  and
      Zai{\ss}, Melanie  and
      Han, Qi  and
      Koch, Steffen  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1191",
    pages = "1202--1206",
    abstract = "Vector space models and distributional information are widely used in NLP. The models typically rely on complex, high-dimensional objects. We present an interactive visualisation tool to explore salient lexical-semantic features of high-dimensional word objects and word similarities. Most visualisation tools provide only one low-dimensional map of the underlying data, so they are not capable of retaining the local and the global structure. We overcome this limitation by providing an additional trust-view to obtain a more realistic picture of the actual object distances. Additional tool options include the reference to a gold standard classification, the reference to a cluster analysis as well as listing the most salient (common) features for a selected subset of the words.",
}
@inproceedings{maharjan-etal-2016-semaligner,
    title = "{S}em{A}ligner: A Method and Tool for Aligning Chunks with Semantic Relation Types and Semantic Similarity Scores",
    author = "Maharjan, Nabin  and
      Banjade, Rajendra  and
      Niraula, Nobal Bikram  and
      Rus, Vasile",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1192",
    pages = "1207--1211",
    abstract = "This paper introduces a ruled-based method and software tool, called SemAligner, for aligning chunks across texts in a given pair of short English texts. The tool, based on the top performing method at the Interpretable Short Text Similarity shared task at SemEval 2015, where it was used with human annotated (gold) chunks, can now additionally process plain text-pairs using two powerful chunkers we developed, e.g. using Conditional Random Fields. Besides aligning chunks, the tool automatically assigns semantic relations to the aligned chunks (such as EQUI for equivalent and OPPO for opposite) and semantic similarity scores that measure the strength of the semantic relation between the aligned chunks. Experiments show that SemAligner performs competitively for system generated chunks and that these results are also comparable to results obtained on gold chunks. SemAligner has other capabilities such as handling various input formats and chunkers as well as extending lookup resources.",
}
@inproceedings{falk-martin-2016-aspectual,
    title = "Aspectual Flexibility Increases with Agentivity and {C}oncreteness{A} Computational Classification Experiment on Polysemous Verbs",
    author = "Falk, Ingrid  and
      Martin, Fabienne",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1193",
    pages = "1212--1220",
    abstract = "We present an experimental study making use of a machine learning approach to identify the factors that affect the aspectual value that characterizes verbs under each of their readings. The study is based on various morpho-syntactic and semantic features collected from a French lexical resource and on a gold standard aspectual classification of verb readings designed by an expert. Our results support the tested hypothesis, namely that agentivity and abstractness influence lexical aspect.",
}
@inproceedings{cordeiro-etal-2016-mwetoolkit,
    title = "mwetoolkit+sem: Integrating Word Embeddings in the mwetoolkit for Semantic {MWE} Processing",
    author = "Cordeiro, Silvio  and
      Ramisch, Carlos  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1194",
    pages = "1221--1225",
    abstract = "This paper presents mwetoolkit+sem: an extension of the mwetoolkit that estimates semantic compositionality scores for multiword expressions (MWEs) based on word embeddings. First, we describe our implementation of vector-space operations working on distributional vectors. The compositionality score is based on the cosine distance between the MWE vector and the composition of the vectors of its member words. Our generic system can handle several types of word embeddings and MWE lists, and may combine individual word representations using several composition techniques. We evaluate our implementation on a dataset of 1042 English noun compounds, comparing different configurations of the underlying word embeddings and word-composition models. We show that our vector-based scores model non-compositionality better than standard association measures such as log-likelihood.",
}
@inproceedings{iosif-etal-2016-cognitively,
    title = "Cognitively Motivated Distributional Representations of Meaning",
    author = "Iosif, Elias  and
      Georgiladakis, Spiros  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1195",
    pages = "1226--1232",
    abstract = "Although meaning is at the core of human cognition, state-of-the-art distributional semantic models (DSMs) are often agnostic to the findings in the area of semantic cognition. In this work, we present a novel type of DSMs motivated by the dual-processing cognitive perspective that is triggered by lexico-semantic activations in the short-term human memory. The proposed model is shown to perform better than state-of-the-art models for computing semantic similarity between words. The fusion of different types of DSMs is also investigated achieving results that are comparable or better than the state-of-the-art. The used corpora along with a set of tools, as well as large repositories of vectorial word representations are made publicly available for four languages (English, German, Italian, and Greek).",
}
@inproceedings{hayashi-luo-2016-extending,
    title = "Extending Monolingual Semantic Textual Similarity Task to Multiple Cross-lingual Settings",
    author = "Hayashi, Yoshihiko  and
      Luo, Wentao",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1196",
    pages = "1233--1239",
    abstract = "This paper describes our independent effort for extending the monolingual semantic textual similarity (STS) task setting to multiple cross-lingual settings involving English, Japanese, and Chinese. So far, we have adopted a {``}monolingual similarity after translation{''} strategy to predict the semantic similarity between a pair of sentences in different languages. With this strategy, a monolingual similarity method is applied after having (one of) the target sentences translated into a pivot language. Therefore, this paper specifically details the required and developed resources to implement this framework, while presenting our current results for English-Japanese-Chinese cross-lingual STS tasks that may exemplify the validity of the framework.",
}
@inproceedings{copestake-etal-2016-resources,
    title = "Resources for building applications with Dependency {M}inimal {R}ecursion {S}emantics",
    author = "Copestake, Ann  and
      Emerson, Guy  and
      Goodman, Michael Wayne  and
      Horvat, Matic  and
      Kuhnle, Alexander  and
      Muszy{\'n}ska, Ewa",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1197",
    pages = "1240--1247",
    abstract = "We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium. We concentrate in particular on the Dependency Minimal Recursion Semantics (DMRS) formalism, a graph-based representation designed for compositional semantic representation with deep grammars. Our main focus is on English, and specifically English Resource Semantics (ERS) as used in the English Resource Grammar. We first give an introduction to ERS and DMRS and a brief overview of some existing resources and then describe in detail a new repository which has been developed to simplify the use of ERS/DMRS. We explain a number of operations on DMRS graphs which our repository supports, with sketches of the algorithms, and illustrate how these operations can be exploited in application building. We believe that this work will aid researchers to exploit the rich and effective but complex DELPH-IN resources.",
}
@inproceedings{kuo-chen-2016-subtask,
    title = "Subtask Mining from Search Query Logs for How-Knowledge Acceleration",
    author = "Kuo, Chung-Lun  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1198",
    pages = "1248--1252",
    abstract = "How-knowledge is indispensable in daily life, but has relatively less quantity and poorer quality than what-knowledge in publicly available knowledge bases. This paper first extracts task-subtask pairs from wikiHow, then mines linguistic patterns from search query logs, and finally applies the mined patterns to extract subtasks to complete given how-to tasks. To evaluate the proposed methodology, we group tasks and the corresponding recommended subtasks into pairs, and evaluate the results automatically and manually. The automatic evaluation shows the accuracy of 0.4494. We also classify the mined patterns based on prepositions and find that the prepositions like {``}on{''}, {``}to{''}, and {``}with{''} have the better performance. The results can be used to accelerate how-knowledge base construction.",
}
@inproceedings{ryzhova-etal-2016-typology,
    title = "Typology of Adjectives Benchmark for Compositional Distributional Models",
    author = "Ryzhova, Daria  and
      Kyuseva, Maria  and
      Paperno, Denis",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1199",
    pages = "1253--1257",
    abstract = "In this paper we present a novel application of compositional distributional semantic models (CDSMs): prediction of lexical typology. The paper introduces the notion of typological closeness, which is a novel rigorous formalization of semantic similarity based on comparison of multilingual data. Starting from the Moscow Database of Qualitative Features for adjective typology, we create four datasets of typological closeness, on which we test a range of distributional semantic models. We show that, on the one hand, vector representations of phrases based on data from one language can be used to predict how words within the phrase translate into different languages, and, on the other hand, that typological data can serve as a semantic benchmark for distributional models. We find that compositional distributional models, especially parametric ones, perform way above non-compositional alternatives on the task.",
}
@inproceedings{bosc-etal-2016-dart,
    title = "{DART}: a Dataset of Arguments and their Relations on {T}witter",
    author = "Bosc, Tom  and
      Cabrio, Elena  and
      Villata, Serena",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1200",
    pages = "1258--1263",
    abstract = "The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.",
}
@inproceedings{mota-etal-2016-port4nooj,
    title = "{P}ort4{N}oo{J} v3.0: Integrated Linguistic Resources for {P}ortuguese {NLP}",
    author = "Mota, Cristina  and
      Carvalho, Paula  and
      Barreiro, Anabela",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1201",
    pages = "1264--1269",
    abstract = "This paper introduces Port4NooJ v3.0, the latest version of the Portuguese module for NooJ, highlights its main features, and details its three main new components: (i) a lexicon-grammar based dictionary of 5,177 human intransitive adjectives, and a set of local grammars that use the distributional properties of those adjectives for paraphrasing (ii) a polarity dictionary with 9,031 entries for sentiment analysis, and (iii) a set of priority dictionaries and local grammars for named entity recognition. These new components were derived and/or adapted from publicly available resources. The Port4NooJ v3.0 resource is innovative in terms of the specificity of the linguistic knowledge it incorporates. The dictionary is bilingual Portuguese-English, and the semantico-syntactic information assigned to each entry validates the linguistic relation between the terms in both languages. These characteristics, which cannot be found in any other public resource for Portuguese, make it a valuable resource for translation and paraphrasing. The paper presents the current statistics and describes the different complementary and synergic components and integration efforts.",
}
@inproceedings{rozis-etal-2016-collecting,
    title = "Collecting Language Resources for the {L}atvian e-Government Machine Translation Platform",
    author = "Rozis, Roberts  and
      Vasi{\c{l}}jevs, Andrejs  and
      Skadi{\c{n}}{\v{s}}, Raivis",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1202",
    pages = "1270--1276",
    abstract = "This paper describes corpora collection activity for building large machine translation systems for Latvian e-Government platform. We describe requirements for corpora, selection and assessment of data sources, collection of the public corpora and creation of new corpora from miscellaneous sources. Methodology, tools and assessment methods are also presented along with the results achieved, challenges faced and conclusions made. Several approaches to address the data scarceness are discussed. We summarize the volume of obtained corpora and provide quality metrics of MT systems trained on this data. Resulting MT systems for English-Latvian, Latvian English and Latvian Russian are integrated in the Latvian e-service portal and are freely available on website HUGO.LV. This paper can serve as a guidance for similar activities initiated in other countries, particularly in the context of European Language Resource Coordination action.",
}
@inproceedings{brugman-etal-2016-nederlab,
    title = "{N}ederlab: Towards a Single Portal and Research Environment for Diachronic {D}utch Text Corpora",
    author = "Brugman, Hennie  and
      Reynaert, Martin  and
      van der Sijs, Nicoline  and
      van Stipriaan, Ren{\'e}  and
      Tjong Kim Sang, Erik  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1203",
    pages = "1277--1281",
    abstract = "The Nederlab project aims to bring together all digitized texts relevant to the Dutch national heritage, the history of the Dutch language and culture (circa 800 {--} present) in one user friendly and tool enriched open access web interface. This paper describes Nederlab halfway through the project period and discusses the collections incorporated, back-office processes, system back-end as well as the Nederlab Research Portal end-user web application.",
}
@inproceedings{soler-wanner-2016-semi,
    title = "A Semi-Supervised Approach for Gender Identification",
    author = "Soler, Juan  and
      Wanner, Leo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1204",
    pages = "1282--1287",
    abstract = "In most of the research studies on Author Profiling, large quantities of correctly labeled data are used to train the models. However, this does not reflect the reality in forensic scenarios: in practical linguistic forensic investigations, the resources that are available to profile the author of a text are usually scarce. To pay tribute to this fact, we implemented a Semi-Supervised Learning variant of the k nearest neighbors algorithm that uses small sets of labeled data and a larger amount of unlabeled data to classify the authors of texts by gender (man vs woman). We describe the enriched KNN algorithm and show that the use of unlabeled instances improves the accuracy of our gender identification model. We also present a feature set that facilitates the use of a very small number of instances, reaching accuracies higher than 70{\%} with only 113 instances to train the model. It is also shown that the algorithm also performs well using publicly available data.",
}
@inproceedings{korkontzelos-etal-2016-ensemble,
    title = "Ensemble Classification of Grants using {LDA}-based Features",
    author = "Korkontzelos, Yannis  and
      Thomas, Beverley  and
      Miwa, Makoto  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1205",
    pages = "1288--1294",
    abstract = "Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making. Automating this classification process would save time and effort, providing the accuracy of the classifications is maintained. We employ five classification models to classify a set of BBSRC-funded research grants in 21 research topics based on unigrams, technical terms and Latent Dirichlet Allocation models. To boost precision, we investigate methods for combining their predictions into five aggregate classifiers. Evaluation confirmed that ensemble classification models lead to higher precision.It was observed that there is not a single best-performing aggregate method for all research topics. Instead, the best-performing method for a research topic depends on the number of positive training instances available for this topic. Subject matter experts considered the predictions of aggregate models to correct erroneous or incomplete manual assignments.",
}
@inproceedings{yang-etal-2016-edit,
    title = "Edit Categories and Editor Role Identification in {W}ikipedia",
    author = "Yang, Diyi  and
      Halfaker, Aaron  and
      Kraut, Robert  and
      Hovy, Eduard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1206",
    pages = "1295--1299",
    abstract = "In this work, we introduced a corpus for categorizing edit types in Wikipedia. This fine-grained taxonomy of edit types enables us to differentiate editing actions and find editor roles in Wikipedia based on their low-level edit types. To do this, we first created an annotated corpus based on 1,996 edits obtained from 953 article revisions and built machine-learning models to automatically identify the edit categories associated with edits. Building on this automated measurement of edit types, we then applied a graphical model analogous to Latent Dirichlet Allocation to uncover the latent roles in editors{'} edit histories. Applying this technique revealed eight different roles editors play, such as Social Networker, Substantive Expert, etc.",
}
@inproceedings{al-shargi-etal-2016-morphologically,
    title = "Morphologically Annotated Corpora and Morphological Analyzers for {M}oroccan and Sanaani Yemeni {A}rabic",
    author = "Al-Shargi, Faisal  and
      Kaplan, Aidan  and
      Eskander, Ramy  and
      Habash, Nizar  and
      Rambow, Owen",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1207",
    pages = "1300--1306",
    abstract = "We present new language resources for Moroccan and Sanaani Yemeni Arabic. The resources include corpora for each dialect which have been morphologically annotated, and morphological analyzers for each dialect which are derived from these corpora. These are the first sets of resources for Moroccan and Yemeni Arabic. The resources will be made available to the public.",
}
@inproceedings{zabokrtsky-etal-2016-merging,
    title = "Merging Data Resources for Inflectional and Derivational Morphology in {C}zech",
    author = "{\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k  and
      {\v{S}}ev{\v{c}}{\'\i}kov{\'a}, Magda  and
      Straka, Milan  and
      Vidra, Jon{\'a}{\v{s}}  and
      Limbursk{\'a}, Ad{\'e}la",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1208",
    pages = "1307--1314",
    abstract = "The paper deals with merging two complementary resources of morphological data previously existing for Czech, namely the inflectional dictionary MorfFlex CZ and the recently developed lexical network DeriNet. The MorfFlex CZ dictionary has been used by a morphological analyzer capable of analyzing/generating several million Czech word forms according to the rules of Czech inflection. The DeriNet network contains several hundred thousand Czech lemmas interconnected with links corresponding to derivational relations (relations between base words and words derived from them). After summarizing basic characteristics of both resources, the process of merging is described, focusing on both rather technical aspects (growth of the data, measuring the quality of newly added derivational relations) and linguistic issues (treating lexical homonymy and vowel/consonant alternations). The resulting resource contains 970 thousand lemmas connected with 715 thousand derivational relations and is publicly available on the web under the CC-BY-NC-SA license. The data were incorporated in the MorphoDiTa library version 2.0 (which provides morphological analysis, generation, tagging and lemmatization for Czech) and can be browsed and searched by two web tools (DeriNet Viewer and DeriNet Search tool).",
}
@inproceedings{novak-etal-2016-new,
    title = "A New Integrated Open-source Morphological Analyzer for {H}ungarian",
    author = "Nov{\'a}k, Attila  and
      Sikl{\'o}si, Borb{\'a}la  and
      Oravecz, Csaba",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1209",
    pages = "1315--1322",
    abstract = "The goal of a Hungarian research project has been to create an integrated Hungarian natural language processing framework. This infrastructure includes tools for analyzing Hungarian texts, integrated into a standardized environment. The morphological analyzer is one of the core components of the framework. The goal of this paper is to describe a fast and customizable morphological analyzer and its development framework, which synthesizes and further enriches the morphological knowledge implemented in previous tools existing for Hungarian. In addition, we present the method we applied to add semantic knowledge to the lexical database of the morphology. The method utilizes neural word embedding models and morphological and shallow syntactic knowledge.",
}
@inproceedings{chodroff-etal-2016-new,
    title = "New release of Mixer-6: Improved validity for phonetic study of speaker variation and identification",
    author = "Chodroff, Eleanor  and
      Maciejewski, Matthew  and
      Trmal, Jan  and
      Khudanpur, Sanjeev  and
      Godfrey, John",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1210",
    pages = "1323--1327",
    abstract = "The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies. These evaluations focused on conversational speech over a variety of channels and recording conditions. One of the series, Mixer-6, added a new condition, read speech, to support basic scientific research on speaker characteristics, as well as technology evaluation. With read speech it is possible to make relatively precise measurements of phonetic events and features, which can be correlated with the performance of speaker recognition algorithms, or directly used in phonetic analysis of speaker variability. The read speech, as originally recorded, was adequate for large-scale evaluations (e.g., fixed-text speaker ID algorithms) but only marginally suitable for acoustic-phonetic studies. Numerous errors due largely to speaker behavior remained in the corpus, with no record of their locations or rate of occurrence. We undertook the effort to correct this situation with automatic methods supplemented by human listening and annotation. The present paper describes the tools and methods, resulting corrections, and some examples of the kinds of research studies enabled by these enhancements.",
}
@inproceedings{coutinho-etal-2016-assessing,
    title = "Assessing the Prosody of Non-Native Speakers of {E}nglish: Measures and Feature Sets",
    author = {Coutinho, Eduardo  and
      H{\"o}nig, Florian  and
      Zhang, Yue  and
      Hantke, Simone  and
      Batliner, Anton  and
      N{\"o}th, Elmar  and
      Schuller, Bj{\"o}rn},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1211",
    pages = "1328--1332",
    abstract = "In this paper, we describe a new database with audio recordings of non-native (L2) speakers of English, and the perceptual evaluation experiment conducted with native English speakers for assessing the prosody of each recording. These annotations are then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of naturalness of L2 speech. Further, we compare the relevance of different feature groups modelling prosody in general (without speech tempo), speech rate and pauses modelling speech tempo (fluency), voice quality, and a variety of spectral features. We also discuss the impact of various fusion strategies on performance.Overall, our results demonstrate that the prosody of non-native speakers of English as L2 can be reliably assessed using supra-segmental audio features; prosodic features seem to be the most important ones.",
}
@inproceedings{trouvain-etal-2016-ifcasl,
    title = "The {IFCASL} Corpus of {F}rench and {G}erman Non-native and Native Read Speech",
    author = {Trouvain, Juergen  and
      Bonneau, Anne  and
      Colotte, Vincent  and
      Fauth, Camille  and
      Fohr, Dominique  and
      Jouvet, Denis  and
      J{\"u}gler, Jeanin  and
      Laprie, Yves  and
      Mella, Odile  and
      M{\"o}bius, Bernd  and
      Zimmerer, Frank},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1212",
    pages = "1333--1338",
    abstract = "The IFCASL corpus is a French-German bilingual phonetic learner corpus designed, recorded and annotated in a project on individualized feedback in computer-assisted spoken language learning. The motivation for setting up this corpus was that there is no phonetically annotated and segmented corpus for this language pair of comparable of size and coverage. In contrast to most learner corpora, the IFCASL corpus incorporate data for a language pair in both directions, i.e. in our case French learners of German, and German learners of French. In addition, the corpus is complemented by two sub-corpora of native speech by the same speakers. The corpus provides spoken data by about 100 speakers with comparable productions, annotated and segmented on the word and the phone level, with more than 50{\%} manually corrected data. The paper reports on inter-annotator agreement and the optimization of the acoustic models for forced speech-text alignment in exercises for computer-assisted pronunciation training. Example studies based on the corpus data with a phonetic focus include topics such as the realization of /h/ and glottal stop, final devoicing of obstruents, vowel quantity and quality, pitch range, and tempo.",
}
@inproceedings{saint-dizier-2016-lelio,
    title = "{LELIO}: An Auto-Adaptative System to Acquire Domain Lexical Knowledge in Technical Texts",
    author = "Saint-Dizier, Patrick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1213",
    pages = "1339--1345",
    abstract = "In this paper, we investigate some language acquisition facets of an auto-adaptative system that can automatically acquire most of the relevant lexical knowledge and authoring practices for an application in a given domain. This is the LELIO project: producing customized LELIE solutions. Our goal, within the framework of LELIE (a system that tags language uses that do not follow the Constrained Natural Language principles), is to automate the long, costly and error prone lexical customization of LELIE to a given application domain. Technical texts being relatively restricted in terms of syntax and lexicon, results obtained show that this approach is feasible and relatively reliable. By auto-adaptative, we mean that the system learns from a sample of the application corpus the various lexical terms and uses crucial for LELIE to work properly (e.g. verb uses, fuzzy terms, business terms, stylistic patterns). A technical writer validation method is developed at each step of the acquisition.",
}
@inproceedings{murawaki-mori-2016-wikification,
    title = "Wikification for Scriptio Continua",
    author = "Murawaki, Yugo  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1214",
    pages = "1346--1351",
    abstract = "The fact that Japanese employs scriptio continua, or a writing system without spaces, complicates the first step of an NLP pipeline. Word segmentation is widely used in Japanese language processing, and lexical knowledge is crucial for reliable identification of words in text. Although external lexical resources like Wikipedia are potentially useful, segmentation mismatch prevents them from being straightforwardly incorporated into the word segmentation task. If we intentionally violate segmentation standards with the direct incorporation, quantitative evaluation will be no longer feasible. To address this problem, we propose to define a separate task that directly links given texts to an external resource, that is, wikification in the case of Wikipedia. By doing so, we can circumvent segmentation mismatch that may not necessarily be important for downstream applications. As the first step to realize the idea, we design the task of Japanese wikification and construct wikification corpora. We annotated subsets of the Balanced Corpus of Contemporary Written Japanese plus Twitter short messages. We also implement a simple wikifier and investigate its performance on these corpora.",
}
@inproceedings{niton-etal-2016-accessing,
    title = "Accessing and Elaborating Walenty - a Valence Dictionary of {P}olish - via {I}nternet Browser",
    author = "Nito{\'n}, Bart{\l}omiej  and
      Bartosiak, Tomasz  and
      Hajnicz, El{\.z}bieta",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1215",
    pages = "1352--1359",
    abstract = "This article presents Walenty - a new valence dictionary of Polish predicates, concentrating on its creation process and access via Internet browser. The dictionary contains two layers, syntactic and semantic. The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants. The semantic layer shows how predicates and their arguments are involved in a situation described in an utterance. These two layers are connected, representing how semantic arguments can be realised on the surface. Walenty also contains a powerful phraseological (idiomatic) component. Walenty has been created and can be accessed remotely with a dedicated tool called Slowal. In this article, we focus on most important functionalities of this system. First, we will depict how to access the dictionary and how built-in filtering system (covering both syntactic and semantic phenomena) works. Later, we will describe the process of creating dictionary by Slowal tool that both supports and controls the work of lexicographers.",
}
@inproceedings{santos-etal-2016-ceplexicon,
    title = "{CEPLEX}icon ― A Lexicon of Child {E}uropean {P}ortuguese",
    author = "Santos, Ana L{\'u}cia  and
      Freitas, Maria Jo{\~a}o  and
      Cardoso, Aida",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1216",
    pages = "1360--1364",
    abstract = "CEPLEXicon (version 1.1) is a child lexicon resulting from the automatic tagging of two child corpora: the corpus Santos (Santos, 2006; Santos et al. 2014) and the corpus Child ― Adult Interaction (Freitas et al. 2012), which integrates information from the corpus Freitas (Freitas, 1997). This lexicon includes spontaneous speech produced by seven children (1;02.00 to 3;11.12) during approximately 86h of child-adult interaction. The automatic tagging comprised the lemmatization and morphosyntactic classification of the speech produced by the seven children included in the two child corpora; the lexicon contains information pertaining to lemmas and syntactic categories as well as absolute number of occurrences and frequencies in three age intervals: {\textless} 2 years; {\mbox{$\geq$}} 2 years and {\textless} 3 years; {\mbox{$\geq$}} 3 years. The information included in this lexicon and the format in which it is presented enables research in different areas and allows researchers to obtain measures of lexical growth. CEPLEXicon is available through the ELRA catalogue.",
}
@inproceedings{grefenstette-2016-extracting,
    title = "Extracting Weighted Language Lexicons from {W}ikipedia",
    author = "Grefenstette, Gregory",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1217",
    pages = "1365--1368",
    abstract = "Language models are used in applications as diverse as speech recognition, optical character recognition and information retrieval. They are used to predict word appearance, and to weight the importance of words in these applications. One basic element of language models is the list of words in a language. Another is the unigram frequency of each word. But this basic information is not available for most languages in the world. Since the multilingual Wikipedia project encourages the production of encyclopedic-like articles in many world languages, we can find there an ever-growing source of text from which to extract these two language modelling elements: word list and frequency. Here we present a simple technique for converting this Wikipedia text into lexicons of weighted unigrams for the more than 280 languages present currently present in Wikipedia. The lexicons produced, and the source code for producing them in a Linux-based system are here made available for free on the Web.",
}
@inproceedings{hathout-sajous-2016-wiktionnaires,
    title = "Wiktionnaire{'}s Wikicode {GLAWI}fied: a Workable {F}rench Machine-Readable Dictionary",
    author = "Hathout, Nabil  and
      Sajous, Franck",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1218",
    pages = "1369--1376",
    abstract = "GLAWI is a free, large-scale and versatile Machine-Readable Dictionary (MRD) that has been extracted from the French language edition of Wiktionary, called Wiktionnaire. In (Sajous and Hathout, 2015), we introduced GLAWI, gave the rationale behind the creation of this lexicographic resource and described the extraction process, focusing on the conversion and standardization of the heterogeneous data provided by this collaborative dictionary. In the current article, we describe the content of GLAWI and illustrate how it is structured. We also suggest various applications, ranging from linguistic studies, NLP applications to psycholinguistic experimentation. They all can take advantage of the diversity of the lexical knowledge available in GLAWI. Besides this diversity and extensive lexical coverage, GLAWI is also remarkable because it is the only free lexical resource of contemporary French that contains definitions. This unique material opens way to the renewal of MRD-based methods, notably the automated extraction and acquisition of semantic relations.",
}
@inproceedings{hollink-etal-2016-corpus,
    title = "A Corpus of Images and Text in Online News",
    author = "Hollink, Laura  and
      Bedjeti, Adriatik  and
      van Harmelen, Martin  and
      Elliott, Desmond",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1219",
    pages = "1377--1382",
    abstract = "In recent years, several datasets have been released that include images and text, giving impulse to new methods that combine natural language processing and computer vision. However, there is a need for datasets of images in their natural textual context. The ION corpus contains 300K news articles published between August 2014 - 2015 in five online newspapers from two countries. The 1-year coverage over multiple publishers ensures a broad scope in terms of topics, image quality and editorial viewpoints. The corpus consists of JSON-LD files with the following data about each article: the original URL of the article on the news publisher{'}s website, the date of publication, the headline of the article, the URL of the image displayed with the article (if any), and the caption of that image. Neither the article text nor the images themselves are included in the corpus. Instead, the images are distributed as high-dimensional feature vectors extracted from a Convolutional Neural Network, anticipating their use in computer vision tasks. The article text is represented as a list of automatically generated entity and topic annotations in the form of Wikipedia/DBpedia pages. This facilitates the selection of subsets of the corpus for separate analysis or evaluation.",
}
@inproceedings{camgoz-etal-2016-bosphorussign,
    title = "{B}osphorus{S}ign: A {T}urkish {S}ign {L}anguage Recognition Corpus in Health and Finance Domains",
    author = {Camg{\"o}z, Necati Cihan  and
      K{\i}nd{\i}ro{\u{g}}lu, Ahmet Alp  and
      Karab{\"u}kl{\"u}, Serpil  and
      Kelepir, Meltem  and
      {\"O}zsoy, Ay{\c{s}}e Sumru  and
      Akarun, Lale},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1220",
    pages = "1383--1388",
    abstract = "There are as many sign languages as there are deaf communities in the world. Linguists have been collecting corpora of different sign languages and annotating them extensively in order to study and understand their properties. On the other hand, the field of computer vision has approached the sign language recognition problem as a grand challenge and research efforts have intensified in the last 20 years. However, corpora collected for studying linguistic properties are often not suitable for sign language recognition as the statistical methods used in the field require large amounts of data. Recently, with the availability of inexpensive depth cameras, groups from the computer vision community have started collecting corpora with large number of repetitions for sign language recognition research. In this paper, we present the BosphorusSign Turkish Sign Language corpus, which consists of 855 sign and phrase samples from the health, finance and everyday life domains. The corpus is collected using the state-of-the-art Microsoft Kinect v2 depth sensor, and will be the first in this sign language research field. Furthermore, there will be annotations rendered by linguists so that the corpus will appeal both to the linguistic and sign language recognition research communities.",
}
@inproceedings{vacher-etal-2016-cirdo,
    title = "The {CIRDO} Corpus: Comprehensive Audio/Video Database of Domestic Falls of Elderly People",
    author = {Vacher, Michel  and
      Bouakaz, Sa{\"\i}da  and
      Chaumon, Marc-Eric Bobillier  and
      Aman, Fr{\'e}d{\'e}ric  and
      Khan, R. A.  and
      Bekkadja, Slima  and
      Portet, Fran{\c{c}}ois  and
      Guillou, Erwan  and
      Rossato, Solange  and
      Lecouteux, Benjamin},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1221",
    pages = "1389--1396",
    abstract = "Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes. In particular, regarding elderly living alone at home, the detection of distress situation after a fall is very important to reassure this kind of population. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The C IRDO corpus is a dataset recorded in realistic conditions in D OMUS , a fully equipped Smart Home with microphones and home automation sensors, in which participants performed scenarios including real falls on a carpet and calls for help. These scenarios were elaborated thanks to a field study involving elderly persons. Experiments related in a first part to distress detection in real-time using audio and speech analysis and in a second part to fall detection using video analysis are presented. Results show the difficulty of the task. The database can be used as standardized database by researchers to evaluate and compare their systems for elderly person{'}s assistance.",
}
@inproceedings{shrestha-moens-2016-semi,
    title = "Semi-automatically Alignment of Predicates between Speech and {O}nto{N}otes data",
    author = "Shrestha, Niraj  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1222",
    pages = "1397--1401",
    abstract = "Speech data currently receives a growing attention and is an important source of information. We still lack suitable corpora of transcribed speech annotated with semantic roles that can be used for semantic role labeling (SRL), which is not the case for written data. Semantic role labeling in speech data is a challenging and complex task due to the lack of sentence boundaries and the many transcription errors such as insertion, deletion and misspellings of words. In written data, SRL evaluation is performed at the sentence level, but in speech data sentence boundaries identification is still a bottleneck which makes evaluation more complex. In this work, we semi-automatically align the predicates found in transcribed speech obtained with an automatic speech recognizer (ASR) with the predicates found in the corresponding written documents of the OntoNotes corpus and manually align the semantic roles of these predicates thus obtaining annotated semantic frames in the speech data. This data can serve as gold standard alignments for future research in semantic role labeling of speech data.",
}
@inproceedings{del-carmen-cabeza-pereiro-etal-2016-corilse,
    title = "{CORILSE}: a {S}panish {S}ign {L}anguage Repository for Linguistic Analysis",
    author = "del Carmen Cabeza-Pereiro, Mar{\'\i}a  and
      Garcia-Miguel, Jos{\'e} Mª  and
      Mateo, Carmen Garc{\'\i}a  and
      Castro, Jos{\'e} Luis Alba",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1223",
    pages = "1402--1407",
    abstract = "CORILSE is a computerized corpus of Spanish Sign Language (Lengua de Signos Espa{\~n}ola, LSE). It consists of a set of recordings from different discourse genres by Galician signers living in the city of Vigo. In this paper we describe its annotation system, developed on the basis of pre-existing ones (mostly the model of Auslan corpus). This includes primary annotation of id-glosses for manual signs, annotation of non-manual component, and secondary annotation of grammatical categories and relations, because this corpus is been built for grammatical analysis, in particular argument structures in LSE. Up until this moment the annotation has been basically made by hand, which is a slow and time-consuming task. The need to facilitate this process leads us to engage in the development of automatic or semi-automatic tools for manual and facial recognition. Finally, we also present the web repository that will make the corpus available to different types of users, and will allow its exploitation for research purposes and other applications (e.g. teaching of LSE or design of tasks for signed language assessment).",
}
@inproceedings{schreitter-krenn-2016-ofai,
    title = "The {OFAI} Multi-Modal Task Description Corpus",
    author = "Schreitter, Stephanie  and
      Krenn, Brigitte",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1224",
    pages = "1408--1414",
    abstract = "The OFAI Multimodal Task Description Corpus (OFAI-MMTD Corpus) is a collection of dyadic teacher-learner (human-human and human-robot) interactions. The corpus is multimodal and tracks the communication signals exchanged between interlocutors in task-oriented scenarios including speech, gaze and gestures. The focus of interest lies on the communicative signals conveyed by the teacher and which objects are salient at which time. Data are collected from four different task description setups which involve spatial utterances, navigation instructions and more complex descriptions of joint tasks.",
}
@inproceedings{mori-etal-2016-japanese,
    title = "A {J}apanese Chess Commentary Corpus",
    author = "Mori, Shinsuke  and
      Richardson, John  and
      Ushiku, Atsushi  and
      Sasada, Tetsuro  and
      Kameko, Hirotaka  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1225",
    pages = "1415--1420",
    abstract = "In recent years there has been a surge of interest in the natural language prosessing related to the real world, such as symbol grounding, language generation, and nonlinguistic data search by natural language queries. In order to concentrate on language ambiguities, we propose to use a well-defined {``}real world,{''} that is game states. We built a corpus consisting of pairs of sentences and a game state. The game we focus on is shogi (Japanese chess). We collected 742,286 commentary sentences in Japanese. They are spontaneously generated contrary to natural language annotations in many image datasets provided by human workers on Amazon Mechanical Turk. We defined domain specific named entities and we segmented 2,508 sentences into words manually and annotated each word with a named entity tag. We describe a detailed definition of named entities and show some statistics of our game commentary corpus. We also show the results of the experiments of word segmentation and named entity recognition. The accuracies are as high as those on general domain texts indicating that we are ready to tackle various new problems related to the real world.",
}
@inproceedings{poignant-etal-2016-camomile,
    title = "The {CAMOMILE} Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents",
    author = "Poignant, Johann  and
      Budnik, Mateusz  and
      Bredin, Herv{\'e}  and
      Barras, Claude  and
      Stefas, Mickael  and
      Bruneau, Pierrick  and
      Adda, Gilles  and
      Besacier, Laurent  and
      Ekenel, Hazim  and
      Francopoulo, Gil  and
      Hernando, Javier  and
      Mariani, Joseph  and
      Morros, Ramon  and
      Qu{\'e}not, Georges  and
      Rosset, Sophie  and
      Tamisier, Thomas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1226",
    pages = "1421--1425",
    abstract = "In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.",
}
@inproceedings{luecking-etal-2016-finding,
    title = "Finding Recurrent Features of Image Schema Gestures: the {FIGURE} corpus",
    author = {Luecking, Andy  and
      Mehler, Alexander  and
      Walther, D{\'e}sir{\'e}e  and
      Mauri, Marcel  and
      Kurf{\"u}rst, Dennis},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1227",
    pages = "1426--1431",
    abstract = "The Frankfurt Image GestURE corpus (FIGURE) is introduced. The corpus data is collected in an experimental setting where 50 naive participants spontaneously produced gestures in response to five to six terms from a total of 27 stimulus terms. The stimulus terms have been compiled mainly from image schemata from psycholinguistics, since such schemata provide a panoply of abstract contents derived from natural language use. The gestures have been annotated for kinetic features. FIGURE aims at finding (sets of) stable kinetic feature configurations associated with the stimulus terms. Given such configurations, they can be used for designing HCI gestures that go beyond pre-defined gesture vocabularies or touchpad gestures. It is found, for instance, that movement trajectories are far more informative than handshapes, speaking against purely handshape-based HCI vocabularies. Furthermore, the mean temporal duration of hand and arm movements associated vary with the stimulus terms, indicating a dynamic dimension not covered by vocabulary-based approaches. Descriptive results are presented and related to findings from gesture studies and natural language dialogue.",
}
@inproceedings{engelmann-etal-2016-interaction,
    title = "An Interaction-Centric Dataset for Learning Automation Rules in Smart Homes",
    author = "Engelmann, Kai Frederic  and
      Holthaus, Patrick  and
      Wrede, Britta  and
      Wrede, Sebastian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1228",
    pages = "1432--1437",
    abstract = "The term smart home refers to a living environment that by its connected sensors and actuators is capable of providing intelligent and contextualised support to its user. This may result in automated behaviors that blends into the user{'}s daily life. However, currently most smart homes do not provide such intelligent support. A first step towards such intelligent capabilities lies in learning automation rules by observing the user{'}s behavior. We present a new type of corpus for learning such rules from user behavior as observed from the events in a smart homes sensor and actuator network. The data contains information about intended tasks by the users and synchronized events from this network. It is derived from interactions of 59 users with the smart home in order to solve five tasks. The corpus contains recordings of more than 40 different types of data streams and has been segmented and pre-processed to increase signal quality. Overall, the data shows a high noise level on specific data types that can be filtered out by a simple smoothing approach. The resulting data provides insights into event patterns resulting from task specific user behavior and thus constitutes a basis for machine learning approaches to learn automation rules.",
}
@inproceedings{becker-etal-2016-web,
    title = "A Web Tool for Building Parallel Corpora of Spoken and Sign Languages",
    author = "Becker, Alex  and
      Kepler, Fabio  and
      Candeias, Sara",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1229",
    pages = "1438--1445",
    abstract = "In this paper we describe our work in building an online tool for manually annotating texts in any spoken language with SignWriting in any sign language. The existence of such tool will allow the creation of parallel corpora between spoken and sign languages that can be used to bootstrap the creation of efficient tools for the Deaf community. As an example, a parallel corpus between English and American Sign Language could be used for training Machine Learning models for automatic translation between the two languages. Clearly, this kind of tool must be designed in a way that it eases the task of human annotators, not only by being easy to use, but also by giving smart suggestions as the annotation progresses, in order to save time and effort. By building a collaborative, online, easy to use annotation tool for building parallel corpora between spoken and sign languages we aim at helping the development of proper resources for sign languages that can then be used in state-of-the-art models currently used in tools for spoken languages. There are several issues and difficulties in creating this kind of resource, and our presented tool already deals with some of them, like adequate text representation of a sign and many to many alignments between words and signs.",
}
@inproceedings{muzaffar-etal-2016-issues,
    title = "Issues and Challenges in Annotating {U}rdu Action Verbs on the {IMAGACT}4{ALL} Platform",
    author = "Muzaffar, Sharmin  and
      Behera, Pitambar  and
      Jha, Girish",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1230",
    pages = "1446--1451",
    abstract = "In South-Asian languages such as Hindi and Urdu, action verbs having compound constructions and serial verbs constructions pose serious problems for natural language processing and other linguistic tasks. Urdu is an Indo-Aryan language spoken by 51, 500, 0001 speakers in India. Action verbs that occur spontaneously in day-to-day communication are highly ambiguous in nature semantically and as a consequence cause disambiguation issues that are relevant and applicable to Language Technologies (LT) like Machine Translation (MT) and Natural Language Processing (NLP). IMAGACT4ALL is an ontology-driven web-based platform developed by the University of Florence for storing action verbs and their inter-relations. This group is currently collaborating with Jawaharlal Nehru University (JNU) in India to connect Indian languages on this platform. Action verbs are frequently used in both written and spoken discourses and refer to various meanings because of their polysemic nature. The IMAGACT4ALL platform stores each 3d animation image, each one of them referring to a variety of possible ontological types, which in turn makes the annotation task for the annotator quite challenging with regard to selecting verb argument structure having a range of probability distribution. The authors, in this paper, discuss the issues and challenges such as complex predicates (compound and conjunct verbs), ambiguously animated video illustrations, semantic discrepancies, and the factors of verb-selection preferences that have produced significant problems in annotating Urdu verbs on the IMAGACT ontology.",
}
@inproceedings{xiao-etal-2016-domain,
    title = "Domain Ontology Learning Enhanced by Optimized Relation Instance in {DB}pedia",
    author = "Xiao, Liumingjing  and
      Ruan, Chong  and
      Yang, An  and
      Zhang, Junhao  and
      Hu, Junfeng",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1231",
    pages = "1452--1456",
    abstract = "Ontologies are powerful to support semantic based applications and intelligent systems. While ontology learning are challenging due to its bottleneck in handcrafting structured knowledge sources and training data. To address this difficulty, many researchers turn to ontology enrichment and population using external knowledge sources such as DBpedia. In this paper, we propose a method using DBpedia in a different manner. We utilize relation instances in DBpedia to supervise the ontology learning procedure from unstructured text, rather than populate the ontology structure as a post-processing step. We construct three language resources in areas of computer science: enriched Wikipedia concept tree, domain ontology, and gold standard from NSFC taxonomy. Experiment shows that the result of ontology learning from corpus of computer science can be improved via the relation instances extracted from DBpedia in the same field. Furthermore, making distinction between the relation instances and applying a proper weighting scheme in the learning procedure lead to even better result.",
}
@inproceedings{johannessen-etal-2016-constructing,
    title = "Constructing a {N}orwegian Academic Wordlist",
    author = "Johannessen, Janne M  and
      Saidi, Arash  and
      Hagen, Kristin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1232",
    pages = "1457--1462",
    abstract = {We present the development of a Norwegian Academic Wordlist (AKA list) for the Norwegian Bokm{\"a}l variety. To identify specific academic vocabulary we developed a 100-million-word academic corpus based on the University of Oslo archive of digital publications. Other corpora were used for testing and developing general word lists. We tried two different methods, those of Carlund et al. (2012) and Gardner {\&} Davies (2013), and compared them. The resulting list is presented on a web site, where the words can be inspected in different ways, and freely downloaded.},
}
@inproceedings{segers-etal-2016-event,
    title = "The Event and Implied Situation Ontology ({ESO}): Application and Evaluation",
    author = "Segers, Roxane  and
      Rospocher, Marco  and
      Vossen, Piek  and
      Laparra, Egoitz  and
      Rigau, German  and
      Minard, Anne-Lyse",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1233",
    pages = "1463--1470",
    abstract = "This paper presents the Event and Implied Situation Ontology (ESO), a manually constructed resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology is built on top of existing resources such as WordNet, SUMO and FrameNet. The ontology is injected to the Predicate Matrix, a resource that integrates predicate and role information from amongst others FrameNet, VerbNet, PropBank, NomBank and WordNet. We illustrate how these resources are used on large document collections to detect information that otherwise would have remained implicit. The ontology is evaluated on two aspects: recall and precision based on a manually annotated corpus and secondly, on the quality of the knowledge inferred by the situation assertions in the ontology. Evaluation results on the quality of the system show that 50{\%} of the events typed and enriched with ESO assertions are correct.",
}
@inproceedings{sukhareva-chiarcos-2016-combining,
    title = "Combining Ontologies and Neural Networks for Analyzing Historical Language Varieties. A Case Study in {M}iddle {L}ow {G}erman",
    author = "Sukhareva, Maria  and
      Chiarcos, Christian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1234",
    pages = "1471--1480",
    abstract = "In this paper, we describe experiments on the morphosyntactic annotation of historical language varieties for the example of Middle Low German (MLG), the official language of the German Hanse during the Middle Ages and a dominant language around the Baltic Sea by the time. To our best knowledge, this is the first experiment in automatically producing morphosyntactic annotations for Middle Low German, and accordingly, no part-of-speech (POS) tagset is currently agreed upon. In our experiment, we illustrate how ontology-based specifications of projected annotations can be employed to circumvent this issue: Instead of training and evaluating against a given tagset, we decomponse it into independent features which are predicted independently by a neural network. Using consistency constraints (axioms) from an ontology, then, the predicted feature probabilities are decoded into a sound ontological representation. Using these representations, we can finally bootstrap a POS tagset capturing only morphosyntactic features which could be reliably predicted. In this way, our approach is capable to optimize precision and recall of morphosyntactic annotations simultaneously with bootstrapping a tagset rather than performing iterative cycles.",
}
@inproceedings{girard-rivier-etal-2016-ecological,
    title = "Ecological Gestures for {HRI}: the {GEE} Corpus",
    author = "Girard-Rivier, Maxence  and
      Magnani, Romain  and
      Auberg{\'e}, V{\'e}ronique  and
      Sasa, Yuko  and
      Tsvetanova, Liliya  and
      Aman, Fr{\'e}d{\'e}ric  and
      Bayol, Clarisse",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1235",
    pages = "1481--1484",
    abstract = "As part of a human-robot interaction project, we are interested by gestural modality as one of many ways to communicate. In order to develop a relevant gesture recognition system associated to a smart home butler robot. Our methodology is based on an IQ game-like Wizard of Oz experiment to collect spontaneous and implicitly produced gestures in an ecological context. During the experiment, the subject has to use non-verbal cues (i.e. gestures) to interact with a robot that is the referee. The subject is unaware that his gestures will be the focus of our study. In the second part of the experiment, we asked the subjects to do the gestures he had produced in the experiment, those are the explicit gestures. The implicit gestures are compared with explicitly produced ones to determine a relevant ontology. This preliminary qualitative analysis will be the base to build a big data corpus in order to optimize acceptance of the gesture dictionary in coherence with the {``}socio-affective glue{''} dynamics.",
}
@inproceedings{nazar-renau-2016-taxonomy,
    title = "A Taxonomy of {S}panish Nouns, a Statistical Algorithm to Generate it and its Implementation in Open Source Code",
    author = "Nazar, Rogelio  and
      Renau, Irene",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1236",
    pages = "1485--1492",
    abstract = "In this paper we describe our work in progress in the automatic development of a taxonomy of Spanish nouns, we offer the Perl implementation we have so far, and we discuss the different problems that still need to be addressed. We designed a statistically-based taxonomy induction algorithm consisting of a combination of different strategies not involving explicit linguistic knowledge. Being all quantitative, the strategies we present are however of different nature. Some of them are based on the computation of distributional similarity coefficients which identify pairs of sibling words or co-hyponyms, while others are based on asymmetric co-occurrence and identify pairs of parent-child words or hypernym-hyponym relations. A decision making process is then applied to combine the results of the previous steps, and finally connect lexical units to a basic structure containing the most general categories of the language. We evaluate the quality of the taxonomy both manually and also using Spanish Wordnet as a gold-standard. We estimate an average of 89.07{\%} precision and 25.49{\%} recall considering only the results which the algorithm presents with high degree of certainty, or 77.86{\%} precision and 33.72{\%} recall considering all results.",
}
@inproceedings{westpfahl-schmidt-2016-folk,
    title = "{FOLK}-Gold ― A Gold Standard for Part-of-Speech-Tagging of Spoken {G}erman",
    author = "Westpfahl, Swantje  and
      Schmidt, Thomas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1237",
    pages = "1493--1499",
    abstract = {In this paper, we present a GOLD standard of part-of-speech tagged transcripts of spoken German. The GOLD standard data consists of four annotation layers ― transcription (modified orthography), normalization (standard orthography), lemmatization and POS tags ― all of which have undergone careful manual quality control. It comes with guidelines for the manual POS annotation of transcripts of German spoken data and an extended version of the STTS (Stuttgart T{\"u}bingen Tagset) which accounts for phenomena typically found in spontaneous spoken German. The GOLD standard was developed on the basis of the Research and Teaching Corpus of Spoken German, FOLK, and is, to our knowledge, the first such dataset based on a wide variety of spontaneous and authentic interaction types. It can be used as a basis for further development of language technology and corpus linguistic applications for German spoken language.},
}
@inproceedings{albogamy-ramsay-2016-fast,
    title = "Fast and Robust {POS} tagger for {A}rabic Tweets Using Agreement-based Bootstrapping",
    author = "Albogamy, Fahad  and
      Ramsay, Allan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1238",
    pages = "1500--1506",
    abstract = "Part-of-Speech(POS) tagging is a key step in many NLP algorithms. However, tweets are difficult to POS tag because they are short, are not always written maintaining formal grammar and proper spelling, and abbreviations are often used to overcome their restricted lengths. Arabic tweets also show a further range of linguistic phenomena such as usage of different dialects, romanised Arabic and borrowing foreign words. In this paper, we present an evaluation and a detailed error analysis of state-of-the-art POS taggers for Arabic when applied to Arabic tweets. On the basis of this analysis, we combine normalisation and external knowledge to handle the domain noisiness and exploit bootstrapping to construct extra training data in order to improve POS tagging for Arabic tweets. Our results show significant improvements over the performance of a number of well-known taggers for Arabic.",
}
@inproceedings{eger-etal-2016-lemmatization,
    title = "Lemmatization and Morphological Tagging in {G}erman and {L}atin: A Comparison and a Survey of the State-of-the-art",
    author = {Eger, Steffen  and
      Gleim, R{\"u}diger  and
      Mehler, Alexander},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1239",
    pages = "1507--1513",
    abstract = "This paper relates to the challenge of morphological tagging and lemmatization in morphologically rich languages by example of German and Latin. We focus on the question what a practitioner can expect when using state-of-the-art solutions out of the box. Moreover, we contrast these with old(er) methods and implementations for POS tagging. We examine to what degree recent efforts in tagger development are reflected by improved accuracies ― and at what cost, in terms of training and processing time. We also conduct in-domain vs. out-domain evaluation. Out-domain evaluations are particularly insightful because the distribution of the data which is being tagged by a user will typically differ from the distribution on which the tagger has been trained. Furthermore, two lemmatization techniques are evaluated. Finally, we compare pipeline tagging vs. a tagging approach that acknowledges dependencies between inflectional categories.",
}
@inproceedings{vor-der-bruck-mehler-2016-tlt,
    title = "{TLT}-{CRF}: A Lexicon-supported Morphological Tagger for {L}atin Based on Conditional Random Fields",
    author = {vor der Br{\"u}ck, Tim  and
      Mehler, Alexander},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1240",
    pages = "1514--1519",
    abstract = "We present a morphological tagger for Latin, called TTLab Latin Tagger based on Conditional Random Fields (TLT-CRF) which uses a large Latin lexicon. Beyond Part of Speech (PoS), TLT-CRF tags eight inflectional categories of verbs, adjectives or nouns. It utilizes a statistical model based on CRFs together with a rule interpreter that addresses scenarios of sparse training data. We present results of evaluating TLT-CRF to answer the question what can be learnt following the paradigm of 1st order CRFs in conjunction with a large lexical resource and a rule interpreter. Furthermore, we investigate the contigency of representational features and targeted parts of speech to learn about selective features.",
}
@inproceedings{aufrant-etal-2016-cross-lingual,
    title = "Cross-lingual and Supervised Models for Morphosyntactic Annotation: a Comparison on {R}omanian",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1241",
    pages = "1520--1526",
    abstract = "Because of the small size of Romanian corpora, the performance of a PoS tagger or a dependency parser trained with the standard supervised methods fall far short from the performance achieved in most languages. That is why, we apply state-of-the-art methods for cross-lingual transfer on Romanian tagging and parsing, from English and several Romance languages. We compare the performance with monolingual systems trained with sets of different sizes and establish that training on a few sentences in target language yields better results than transferring from large datasets in other languages.",
}
@inproceedings{ljubesic-erjavec-2016-corpus,
    title = "Corpus vs. Lexicon Supervision in Morphosyntactic Tagging: the Case of {S}lovene",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Erjavec, Toma{\v{z}}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1242",
    pages = "1527--1531",
    abstract = "In this paper we present a tagger developed for inflectionally rich languages for which both a training corpus and a lexicon are available. We do not constrain the tagger by the lexicon entries, allowing both for lexicon incompleteness and noisiness. By using the lexicon indirectly through features we allow for known and unknown words to be tagged in the same manner. We test our tagger on Slovene data, obtaining a 25{\%} error reduction of the best previous results both on known and unknown words. Given that Slovene is, in comparison to some other Slavic languages, a well-resourced language, we perform experiments on the impact of token (corpus) vs. type (lexicon) supervision, obtaining useful insights in how to balance the effort of extending resources to yield better tagging results.",
}
@inproceedings{nguyen-etal-2016-challenges,
    title = "Challenges and Solutions for Consistent Annotation of {V}ietnamese Treebank",
    author = "Nguyen, Quy  and
      Miyao, Yusuke  and
      Le, Ha  and
      Nguyen, Ngan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1243",
    pages = "1532--1539",
    abstract = "Treebanks are important resources for researchers in natural language processing, speech recognition, theoretical linguistics, etc. To strengthen the automatic processing of the Vietnamese language, a Vietnamese treebank has been built. However, the quality of this treebank is not satisfactory and is a possible source for the low performance of Vietnamese language processing. We have been building a new treebank for Vietnamese with about 40,000 sentences annotated with three layers: word segmentation, part-of-speech tagging, and bracketing. In this paper, we describe several challenges of Vietnamese language and how we solve them in developing annotation guidelines. We also present our methods to improve the quality of the annotation guidelines and ensure annotation accuracy and consistency. Experiment results show that inter-annotator agreement ratios and accuracy are higher than 90{\%} which is satisfactory.",
}
@inproceedings{suzuki-etal-2016-correcting,
    title = "Correcting Errors in a Treebank Based on Tree Mining",
    author = "Suzuki, Kanta  and
      Kato, Yoshihide  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1244",
    pages = "1540--1545",
    abstract = "This paper provides a new method to correct annotation errors in a treebank. The previous error correction method constructs a pseudo parallel corpus where incorrect partial parse trees are paired with correct ones, and extracts error correction rules from the parallel corpus. By applying these rules to a treebank, the method corrects errors. However, this method does not achieve wide coverage of error correction. To achieve wide coverage, our method adopts a different approach. In our method, we consider that an infrequent pattern which can be transformed to a frequent one is an annotation error pattern. Based on a tree mining technique, our method seeks such infrequent tree patterns, and constructs error correction rules each of which consists of an infrequent pattern and a corresponding frequent pattern. We conducted an experiment using the Penn Treebank. We obtained 1,987 rules which are not constructed by the previous method, and the rules achieved good precision.",
}
@inproceedings{blache-etal-2016-4couv,
    title = "4{C}ouv: A New Treebank for {F}rench",
    author = "Blache, Philippe  and
      de Montcheuil, Gr{\'e}goire  and
      Pr{\'e}vot, Laurent  and
      Rauzy, St{\'e}phane",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1245",
    pages = "1546--1551",
    abstract = "The question of the type of text used as primary data in treebanks is of certain importance. First, it has an influence at the discourse level: an article is not organized in the same way as a novel or a technical document. Moreover, it also has consequences in terms of semantic interpretation: some types of texts can be easier to interpret than others. We present in this paper a new type of treebank which presents the particularity to answer to specific needs of experimental linguistic. It is made of short texts (book backcovers) that presents a strong coherence in their organization and can be rapidly interpreted. This type of text is adapted to short reading sessions, making it easy to acquire physiological data (e.g. eye movement, electroencepholagraphy). Such a resource offers reliable data when looking for correlations between computational models and human language processing.",
}
@inproceedings{de-carvalho-etal-2016-cintil,
    title = "{CINTIL} {D}ependency{B}ank {PREMIUM} - A Corpus of Grammatical Dependencies for {P}ortuguese",
    author = "de Carvalho, Rita  and
      Querido, Andreia  and
      Campos, Marisa  and
      Pereira, Rita Valadas  and
      Silva, Jo{\~a}o  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1246",
    pages = "1552--1557",
    abstract = "This paper presents a new linguistic resource for the study and computational processing of Portuguese. CINTIL DependencyBank PREMIUM is a corpus of Portuguese news text, accurately manually annotated with a wide range of linguistic information (morpho-syntax, named-entities, syntactic function and semantic roles), making it an invaluable resource specially for the development and evaluation of data-driven natural language processing tools. The corpus is under active development, reaching 4,000 sentences in its current version. The paper also reports on the training and evaluation of a dependency parser over this corpus. CINTIL DependencyBank PREMIUM is freely-available for research purposes through META-SHARE.",
}
@inproceedings{muischnek-etal-2016-estonian,
    title = "{E}stonian Dependency Treebank: from Constraint Grammar tagset to {U}niversal {D}ependencies",
    author = {Muischnek, Kadri  and
      M{\"u}{\"u}risep, Kaili  and
      Puolakainen, Tiina},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1247",
    pages = "1558--1565",
    abstract = "This paper presents the first version of Estonian Universal Dependencies Treebank which has been semi-automatically acquired from Estonian Dependency Treebank and comprises ca 400,000 words (ca 30,000 sentences) representing the genres of fiction, newspapers and scientific writing. Article analyses the differences between two annotation schemes and the conversion procedure to Universal Dependencies format. The conversion has been conducted by manually created Constraint Grammar transfer rules. As the rules enable to consider unbounded context, include lexical information and both flat and tree structure features at the same time, the method has proved to be reliable and flexible enough to handle most of transformations. The automatic conversion procedure achieved LAS 95.2{\%}, UAS 96.3{\%} and LA 98.4{\%}. If punctuation marks were excluded from the calculations, we observed LAS 96.4{\%}, UAS 97.7{\%} and LA 98.2{\%}. Still the refinement of the guidelines and methodology is needed in order to re-annotate some syntactic phenomena, e.g. inter-clausal relations. Although automatic rules usually make quite a good guess even in obscure conditions, some relations should be checked and annotated manually after the main conversion.",
}
@inproceedings{dobrovoljc-nivre-2016-universal,
    title = "The {U}niversal {D}ependencies Treebank of Spoken {S}lovenian",
    author = "Dobrovoljc, Kaja  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1248",
    pages = "1566--1573",
    abstract = "This paper presents the construction of an open-source dependency treebank of spoken Slovenian, the first syntactically annotated collection of spontaneous speech in Slovenian. The treebank has been manually annotated using the Universal Dependencies annotation scheme, a one-layer syntactic annotation scheme with a high degree of cross-modality, cross-framework and cross-language interoperability. In this original application of the scheme to spoken language transcripts, we address a wide spectrum of syntactic particularities in speech, either by extending the scope of application of existing universal labels or by proposing new speech-specific extensions. The initial analysis of the resulting treebank and its comparison with the written Slovenian UD treebank confirms significant syntactic differences between the two language modalities, with spoken data consisting of shorter and more elliptic sentences, less and simpler nominal phrases, and more relations marking disfluencies, interaction, deixis and modality.",
}
@inproceedings{thu-etal-2016-introducing,
    title = "Introducing the {A}sian Language Treebank ({ALT})",
    author = "Thu, Ye Kyaw  and
      Pa, Win Pa  and
      Utiyama, Masao  and
      Finch, Andrew  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1249",
    pages = "1574--1578",
    abstract = "This paper introduces the ALT project initiated by the Advanced Speech Translation Research and Development Promotion Center (ASTREC), NICT, Kyoto, Japan. The aim of this project is to accelerate NLP research for Asian languages such as Indonesian, Japanese, Khmer, Laos, Malay, Myanmar, Philippine, Thai and Vietnamese. The original resource for this project was English articles that were randomly selected from Wikinews. The project has so far created a corpus for Myanmar and will extend in scope to include other languages in the near future. A 20000-sentence corpus of Myanmar that has been manually translated from an English corpus has been word segmented, word aligned, part-of-speech tagged and constituency parsed by human annotators. In this paper, we present the implementation steps for creating the treebank in detail, including a description of the ALT web-based treebanking tool. Moreover, we report statistics on the annotation quality of the Myanmar treebank created so far.",
}
@inproceedings{ovrelid-hohle-2016-universal,
    title = "{U}niversal {D}ependencies for {N}orwegian",
    author = "{\O}vrelid, Lilja  and
      Hohle, Petter",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1250",
    pages = "1579--1585",
    abstract = "This article describes the conversion of the Norwegian Dependency Treebank to the Universal Dependencies scheme. This paper details the mapping of PoS tags, morphological features and dependency relations and provides a description of the structural changes made to NDT analyses in order to make it compliant with the UD guidelines. We further present PoS tagging and dependency parsing experiments which report first results for the processing of the converted treebank. The full converted treebank was made available with the 1.2 release of the UD treebanks.",
}
@inproceedings{rehm-etal-2016-fostering,
    title = "Fostering the Next Generation of {E}uropean Language Technology: Recent Developments ― Emerging Initiatives ― Challenges and Opportunities",
    author = "Rehm, Georg  and
      Haji{\v{c}}, Jan  and
      van Genabith, Josef  and
      Vasiljevs, Andrejs",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1251",
    pages = "1586--1592",
    abstract = "META-NET is a European network of excellence, founded in 2010, that consists of 60 research centres in 34 European countries. One of the key visions and goals of META-NET is a truly multilingual Europe, which is substantially supported and realised through language technologies. In this article we provide an overview of recent developments around the multilingual Europe topic, we also describe recent and upcoming events as well as recent and upcoming strategy papers. Furthermore, we provide overviews of two new emerging initiatives, the CEF.AT and ELRC activity on the one hand and the Cracking the Language Barrier federation on the other. The paper closes with several suggested next steps in order to address the current challenges and to open up new opportunities.",
}
@inproceedings{fort-couillault-2016-yes,
    title = "Yes, We Care! Results of the Ethics and Natural Language Processing Surveys",
    author = {Fort, Kar{\"e}n  and
      Couillault, Alain},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1252",
    pages = "1593--1600",
    abstract = "We present here the context and results of two surveys (a French one and an international one) concerning Ethics and NLP, which we designed and conducted between June and September 2015. These surveys follow other actions related to raising concern for ethics in our community, including a Journ{\'e}e d{'}{\'e}tudes, a workshop and the Ethics and Big Data Charter. The concern for ethics shows to be quite similar in both surveys, despite a few differences which we present and discuss. The surveys also lead to think there is a growing awareness in the field concerning ethical issues, which translates into a willingness to get involved in ethics-related actions, to debate about the topic and to see ethics be included in major conferences themes. We finally discuss the limits of the surveys and the means of action we consider for the future. The raw data from the two surveys are freely available online.",
}
@inproceedings{lewis-etal-2016-open,
    title = "Open Data Vocabularies for Assigning Usage Rights to Data Resources from Translation Projects",
    author = "Lewis, David  and
      Fatema, Kaniz  and
      Maldonado, Alfredo  and
      Walshe, Brian  and
      Calvo, Arturo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1253",
    pages = "1601--1609",
    abstract = "An assessment of the intellectual property requirements for data used in machine-aided translation is provided based on a recent EC-funded legal review. This is compared against the capabilities offered by current linked open data standards from the W3C for publishing and sharing translation memories from translation projects, and proposals for adequately addressing the intellectual property needs of stakeholders in translation projects using open data vocabularies are suggested.",
}
@inproceedings{mapelli-etal-2016-language,
    title = "Language Resource Citation: the {ISLRN} Dissemination and Further Developments",
    author = "Mapelli, Val{\'e}rie  and
      Popescu, Vladimir  and
      Liu, Lin  and
      Choukri, Khalid",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1254",
    pages = "1610--1613",
    abstract = "This article presents the latest dissemination activities and technical developments that were carried out for the International Standard Language Resource Number (ISLRN) service. It also recalls the main principle and submission process for providers to obtain their 13-digit ISLRN identifier. Up to March 2016, 2100 Language Resources were allocated an ISLRN number, not only ELRA{'}s and LDC{'}s catalogued Language Resources, but also the ones from other important organisations like the Joint Research Centre (JRC) and the Resource Management Agency (RMA) who expressed their strong support to this initiative.In the research field, not only assigning a unique identification number is important, but also referring to a Language Resource as an object \textit{per se} (like publications) has now become an obvious requirement. The ISLRN could also become an important parameter to be considered to compute a Language Resource Impact Factor (LRIF) in order to recognize the merits of the producers of Language Resources. Integrating the ISLRN number into a LR-oriented bibliographical reference is thus part of the objective. The idea is to make use of a BibTeX entry that would take into account Language Resources items, including ISLRN.The ISLRN being a requested field within the LREC 2016 submission, we expect that several other LRs will be allocated an ISLRN number by the conference date. With this expansion, this number aims to be a spreadly-used LR citation instrument within works referring to LRs.",
}
@inproceedings{dipersio-cieri-2016-trends,
    title = "Trends in {HLT} Research: A Survey of {LDC}{'}s Data Scholarship Program",
    author = "DiPersio, Denise  and
      Cieri, Christopher",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1255",
    pages = "1614--1618",
    abstract = "Since its inception in 2010, the Linguistic Data Consortium{'}s data scholarship program has awarded no cost grants in data to 64 recipients from 26 countries. A survey of the twelve cycles to date ― two awards each in the Fall and Spring semesters from Fall 2010 through Spring 2016 ― yields an interesting view into graduate program research trends in human language technology and related fields and the particular data sets deemed important to support that research. The survey also reveals regions in which such activity appears to be on a rise, including in Arabic-speaking regions and portions of the Americas and Asia.",
}
@inproceedings{bosco-etal-2016-tweeting,
    title = "Tweeting and Being Ironic in the Debate about a Political Reform: the {F}rench Annotated Corpus {TW}itter-{M}ariage{P}our{T}ous",
    author = "Bosco, Cristina  and
      Lai, Mirko  and
      Patti, Viviana  and
      Virone, Daniela",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1256",
    pages = "1619--1626",
    abstract = "The paper introduces a new annotated French data set for Sentiment Analysis, which is a currently missing resource. It focuses on the collection from Twitter of data related to the socio-political debate about the reform of the bill for wedding in France. The design of the annotation scheme is described, which extends a polarity label set by making available tags for marking target semantic areas and figurative language devices. The annotation process is presented and the disagreement discussed, in particular, in the perspective of figurative language use and in that of the semantic oriented annotation, which are open challenges for NLP systems.",
}
@inproceedings{alhelbawy-etal-2016-towards,
    title = "Towards a Corpus of Violence Acts in {A}rabic Social Media",
    author = "Alhelbawy, Ayman  and
      Massimo, Poesio  and
      Kruschwitz, Udo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1257",
    pages = "1627--1631",
    abstract = "In this paper we present a new corpus of Arabic tweets that mention some form of violent event, developed to support the automatic identification of Human Rights Abuse. The dataset was manually labelled for seven classes of violence using crowdsourcing.",
}
@inproceedings{verhoeven-etal-2016-twisty,
    title = "{T}wi{S}ty: A Multilingual {T}witter Stylometry Corpus for Gender and Personality Profiling",
    author = "Verhoeven, Ben  and
      Daelemans, Walter  and
      Plank, Barbara",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1258",
    pages = "1632--1637",
    abstract = "Personality profiling is the task of detecting personality traits of authors based on writing style. Several personality typologies exist, however, the Briggs-Myer Type Indicator (MBTI) is particularly popular in the non-scientific community, and many people use it to analyse their own personality and talk about the results online. Therefore, large amounts of self-assessed data on MBTI are readily available on social-media platforms such as Twitter. We present a novel corpus of tweets annotated with the MBTI personality type and gender of their author for six Western European languages (Dutch, German, French, Italian, Portuguese and Spanish). We outline the corpus creation and annotation, show statistics of the obtained data distributions and present first baselines on Myers-Briggs personality profiling and gender prediction for all six languages.",
}
@inproceedings{imran-etal-2016-twitter,
    title = "{T}witter as a Lifeline: Human-annotated {T}witter Corpora for {NLP} of Crisis-related Messages",
    author = "Imran, Muhammad  and
      Mitra, Prasenjit  and
      Castillo, Carlos",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1259",
    pages = "1638--1643",
    abstract = "Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.",
}
@inproceedings{begum-etal-2016-functions,
    title = "Functions of Code-Switching in Tweets: An Annotation Framework and Some Initial Experiments",
    author = "Begum, Rafiya  and
      Bali, Kalika  and
      Choudhury, Monojit  and
      Rudra, Koustav  and
      Ganguly, Niloy",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1260",
    pages = "1644--1650",
    abstract = "Code-Switching (CS) between two languages is extremely common in communities with societal multilingualism where speakers switch between two or more languages when interacting with each other. CS has been extensively studied in spoken language by linguists for several decades but with the popularity of social-media and less formal Computer Mediated Communication, we now see a big rise in the use of CS in the text form. This poses interesting challenges and a need for computational processing of such code-switched data. As with any Computational Linguistic analysis and Natural Language Processing tools and applications, we need annotated data for understanding, processing, and generation of code-switched language. In this study, we focus on CS between English and Hindi Tweets extracted from the Twitter stream of Hindi-English bilinguals. We present an annotation scheme for annotating the pragmatic functions of CS in Hindi-English (Hi-En) code-switched tweets based on a linguistic analysis and some initial experiments.",
}
@inproceedings{tanaka-etal-2016-universal,
    title = "{U}niversal {D}ependencies for {J}apanese",
    author = "Tanaka, Takaaki  and
      Miyao, Yusuke  and
      Asahara, Masayuki  and
      Uematsu, Sumire  and
      Kanayama, Hiroshi  and
      Mori, Shinsuke  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1261",
    pages = "1651--1658",
    abstract = "We present an attempt to port the international syntactic annotation scheme, Universal Dependencies, to the Japanese language in this paper. Since the Japanese syntactic structure is usually annotated on the basis of unique chunk-based dependencies, we first introduce word-based dependencies by using a word unit called the Short Unit Word, which usually corresponds to an entry in the lexicon UniDic. Porting is done by mapping the part-of-speech tagset in UniDic to the universal part-of-speech tagset, and converting a constituent-based treebank to a typed dependency tree. The conversion is not straightforward, and we discuss the problems that arose in the conversion and the current solutions. A treebank consisting of 10,000 sentences was built by converting the existent resources and currently released to the public.",
}
@inproceedings{nivre-etal-2016-universal,
    title = "{U}niversal {D}ependencies v1: A Multilingual Treebank Collection",
    author = "Nivre, Joakim  and
      de Marneffe, Marie-Catherine  and
      Ginter, Filip  and
      Goldberg, Yoav  and
      Haji{\v{c}}, Jan  and
      Manning, Christopher D.  and
      McDonald, Ryan  and
      Petrov, Slav  and
      Pyysalo, Sampo  and
      Silveira, Natalia  and
      Tsarfaty, Reut  and
      Zeman, Daniel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1262",
    pages = "1659--1666",
    abstract = "Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.",
}
@inproceedings{kato-etal-2016-construction,
    title = "Construction of an {E}nglish Dependency Corpus incorporating Compound Function Words",
    author = "Kato, Akihiko  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1263",
    pages = "1667--1671",
    abstract = "The recognition of multiword expressions (MWEs) in a sentence is important for such linguistic analyses as syntactic and semantic parsing, because it is known that combining an MWE into a single token improves accuracy for various NLP tasks, such as dependency parsing and constituency parsing. However, MWEs are not annotated in Penn Treebank. Furthermore, when converting word-based dependency to MWE-aware dependency directly, one could combine nodes in an MWE into a single node. Nevertheless, this method often leads to the following problem: A node derived from an MWE could have multiple heads and the whole dependency structure including MWE might be cyclic. Therefore we converted a phrase structure to a dependency structure after establishing an MWE as a single subtree. This approach can avoid an occurrence of multiple heads and/or cycles. In this way, we constructed an English dependency corpus taking into account compound function words, which are one type of MWEs that serve as functional expressions. In addition, we report experimental results of dependency parsing using a constructed corpus.",
}
@inproceedings{simi-attardi-2016-adapting,
    title = "Adapting the {TANL} tool suite to {U}niversal {D}ependencies",
    author = "Simi, Maria  and
      Attardi, Giuseppe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1264",
    pages = "1672--1678",
    abstract = "TANL is a suite of tools for text analytics based on the software architecture paradigm of data driven pipelines. The strategies for upgrading TANL to the use of Universal Dependencies range from a minimalistic approach consisting of introducing pre/post-processing steps into the native pipeline to revising the whole pipeline. We explore the issue in the context of the Italian Treebank, considering both the efforts involved, how to avoid losing linguistically relevant information and the loss of accuracy in the process. In particular we compare different strategies for parsing and discuss the implications of simplifying the pipeline when detailed part-of-speech and morphological annotations are not available, as it is the case for less resourceful languages. The experiments are relative to the Italian linguistic pipeline, but the use of different parsers in our evaluations and the avoidance of language specific tagging make the results general enough to be useful in helping the transition to UD for other languages.",
}
@inproceedings{wong-lee-2016-dependency,
    title = "A Dependency Treebank of the {C}hinese Buddhist Canon",
    author = "Wong, Tak-sum  and
      Lee, John",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1265",
    pages = "1679--1683",
    abstract = "We present a dependency treebank of the Chinese Buddhist Canon, which contains 1,514 texts with about 50 million Chinese characters. The treebank was created by an automatic parser trained on a smaller treebank, containing four manually annotated sutras (Lee and Kong, 2014). We report results on word segmentation, part-of-speech tagging and dependency parsing, and discuss challenges posed by the processing of medieval Chinese. In a case study, we exploit the treebank to examine verbs frequently associated with Buddha, and to analyze usage patterns of quotative verbs in direct speech. Our results suggest that certain quotative verbs imply status differences between the speaker and the listener.",
}
@inproceedings{lossio-ventura-etal-2016-automatic,
    title = "Automatic Biomedical Term Polysemy Detection",
    author = "Lossio-Ventura, Juan Antonio  and
      Jonquet, Clement  and
      Roche, Mathieu  and
      Teisseire, Maguelonne",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1266",
    pages = "1684--1688",
    abstract = "Polysemy is the capacity for a word to have multiple meanings. Polysemy detection is a first step for Word Sense Induction (WSI), which allows to find different meanings for a term. The polysemy detection is also important for information extraction (IE) systems. In addition, the polysemy detection is important for building/enriching terminologies and ontologies. In this paper, we present a novel approach to detect if a biomedical term is polysemic, with the long term goal of enriching biomedical ontologies. This approach is based on the extraction of new features. In this context we propose to extract features following two manners: (i) extracted directly from the text dataset, and (ii) from an induced graph. Our method obtains an Accuracy and F-Measure of 0.978.",
}
@inproceedings{alagic-snajder-2016-cro36wsd,
    title = "{C}ro36{WSD}: A Lexical Sample for {C}roatian Word Sense Disambiguation",
    author = "Alagi{\'c}, Domagoj  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1267",
    pages = "1689--1694",
    abstract = "We introduce Cro36WSD, a freely-available medium-sized lexical sample for Croatian word sense disambiguation (WSD).Cro36WSD comprises 36 words: 12 adjectives, 12 nouns, and 12 verbs, balanced across both frequency bands and polysemy levels. We adopt the multi-label annotation scheme in the hope of lessening the drawbacks of discrete sense inventories and obtaining more realistic annotations from human experts. Sense-annotated data is collected through multiple annotation rounds to ensure high-quality annotations: with a 115 person-hours effort we reached an inter-annotator agreement score of 0.877. We analyze the obtained data and perform a correlation analysis between several relevant variables, including word frequency, number of senses, sense distribution skewness, average annotation time, and the observed inter-annotator agreement (IAA). Using the obtained data, we compile multi- and single-labeled dataset variants using different label aggregation schemes. Finally, we evaluate three different baseline WSD models on both dataset variants and report on the insights gained. We make both dataset variants freely available.",
}
@inproceedings{postma-etal-2016-addressing,
    title = "Addressing the {MFS} Bias in {WSD} systems",
    author = "Postma, Marten  and
      Izquierdo, Ruben  and
      Agirre, Eneko  and
      Rigau, German  and
      Vossen, Piek",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1268",
    pages = "1695--1700",
    abstract = "Word Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very low performance on the less frequent senses. We addressed the MFS bias in WSD systems by combining the output from a WSD system with a set of mostly static features to create a MFS classifier to decide when to and not to choose the MFS. The output from this MFS classifier, which is based on the Random Forest algorithm, is then used to modify the output from the original WSD system. We applied our classifier to one of the state-of-the-art supervised WSD systems, i.e. IMS, and to of the best state-of-the-art unsupervised WSD systems, i.e. UKB. Our main finding is that we are able to improve the system output in terms of choosing between the MFS and the less frequent senses. When we apply the MFS classifier to fine-grained WSD, we observe an improvement on the less frequent sense cases, whereas we maintain the overall recall.",
}
@inproceedings{camacho-collados-etal-2016-large,
    title = "A Large-Scale Multilingual Disambiguation of Glosses",
    author = "Camacho-Collados, Jos{\'e}  and
      Delli Bovi, Claudio  and
      Raganato, Alessandro  and
      Navigli, Roberto",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1269",
    pages = "1701--1708",
    abstract = "Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task. In this respect, recent works have shown the key advantage of exploiting textual definitions in various Natural Language Processing applications. However, to date there are no reliable large-scale corpora of sense-annotated textual definitions available to the research community. In this paper we present a large-scale high-quality corpus of disambiguated glosses in multiple languages, comprising sense annotations of both concepts and named entities from a unified sense inventory. Our approach for the construction and disambiguation of the corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system; first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation, and then we combine it with a semantic similarity-based refinement. As a result we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we make it freely available at http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information Extraction and Sense Clustering show how two state-of-the-art approaches improve their performance by integrating our disambiguated corpus into their pipeline.",
}
@inproceedings{ecker-etal-2016-unsupervised,
    title = "Unsupervised Ranked Cross-Lingual Lexical Substitution for Low-Resource Languages",
    author = "Ecker, Stefan  and
      Horbach, Andrea  and
      Thater, Stefan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1270",
    pages = "1709--1717",
    abstract = "We propose an unsupervised system for a variant of cross-lingual lexical substitution (CLLS) to be used in a reading scenario in computer-assisted language learning (CALL), in which single-word translations provided by a dictionary are ranked according to their appropriateness in context. In contrast to most alternative systems, ours does not rely on either parallel corpora or machine translation systems, making it suitable for low-resource languages as the language to be learned. This is achieved by a graph-based scoring mechanism which can deal with ambiguous translations of context words provided by a dictionary. Due to this decoupling from the source language, we need monolingual corpus resources only for the target language, i.e. the language of the translation candidates. We evaluate our approach for the language pair Norwegian Nynorsk-English on an exploratory manually annotated gold standard and report promising results. When running our system on the original SemEval CLLS task, we rank 6th out of 18 (including 2 baselines and our 2 system variants) in the best evaluation.",
}
@inproceedings{stede-mamprin-2016-information,
    title = "Information structure in the {P}otsdam Commentary Corpus: Topics",
    author = "Stede, Manfred  and
      Mamprin, Sara",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1271",
    pages = "1718--1723",
    abstract = "The Potsdam Commentary Corpus is a collection of 175 German newspaper commentaries annotated on a variety of different layers. This paper introduces a new layer that covers the linguistic notion of information-structural topic (not to be confused with {`}topic{'} as applied to documents in information retrieval). To our knowledge, this is the first larger topic-annotated resource for German (and one of the first for any language). We describe the annotation guidelines and the annotation process, and the results of an inter-annotator agreement study, which compare favourably to the related work. The annotated corpus is freely available for research.",
}
@inproceedings{read-etal-2016-corpus,
    title = "A Corpus of Clinical Practice Guidelines Annotated with the Importance of Recommendations",
    author = "Read, Jonathon  and
      Velldal, Erik  and
      Cavazza, Marc  and
      Georg, Gersende",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1272",
    pages = "1724--1731",
    abstract = "In this paper we present the Corpus of REcommendation STrength (CREST), a collection of HTML-formatted clinical guidelines annotated with the location of recommendations. Recommendations are labelled with an author-provided indicator of their strength of importance. As data was drawn from many disparate authors, we define a unified scheme of importance labels, and provide a mapping for each guideline. We demonstrate the utility of the corpus and its annotations in some initial measurements investigating the type of language constructions associated with strong and weak recommendations, and experiments into promising features for recommendation classification, both with respect to strong and weak labels, and to all labels of the unified scheme. An error analysis indicates that, while there is a strong relationship between lexical choices and strength labels, there can be substantial variance in the choices made by different authors.",
}
@inproceedings{isard-2016-methodius,
    title = "The Methodius Corpus of Rhetorical Discourse Structures and Generated Texts",
    author = "Isard, Amy",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1273",
    pages = "1732--1736",
    abstract = "Using the Methodius Natural Language Generation (NLG) System, we have created a corpus which consists of a collection of generated texts which describe ancient Greek artefacts. Each text is linked to two representations created as part of the NLG process. The first is a content plan, which uses rhetorical relations to describe the high-level discourse structure of the text, and the second is a logical form describing the syntactic structure, which is sent to the OpenCCG surface realization module to produce the final text output. In recent work, White and Howcroft (2015) have used the SPaRKy restaurant corpus, which contains similar combination of texts and representations, for their research on the induction of rules for the combination of clauses. In the first instance this corpus will be used to test their algorithms on an additional domain, and extend their work to include the learning of referring expression generation rules. As far as we know, the SPaRKy restaurant corpus is the only existing corpus of this type, and we hope that the creation of this new corpus in a different domain will provide a useful resource to the Natural Language Generation community.",
}
@inproceedings{duma-etal-2016-applying,
    title = "Applying Core Scientific Concepts to Context-Based Citation Recommendation",
    author = "Duma, Daniel  and
      Liakata, Maria  and
      Clare, Amanda  and
      Ravenscroft, James  and
      Klein, Ewan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1274",
    pages = "1737--1742",
    abstract = "The task of recommending relevant scientific literature for a draft academic paper has recently received significant interest. In our effort to ease the discovery of scientific literature and augment scientific writing, we aim to improve the relevance of results based on a shallow semantic analysis of the source document and the potential documents to recommend. We investigate the utility of automatic argumentative and rhetorical annotation of documents for this purpose. Specifically, we integrate automatic Core Scientific Concepts (CoreSC) classification into a prototype context-based citation recommendation system and investigate its usefulness to the task. We frame citation recommendation as an information retrieval task and we use the categories of the annotation schemes to apply different weights to the similarity formula. Our results show interesting and consistent correlations between the type of citation and the type of sentence containing the relevant information.",
}
@inproceedings{roesiger-2016-scicorp,
    title = "{S}ci{C}orp: A Corpus of {E}nglish Scientific Articles Annotated for Information Status Analysis",
    author = "Roesiger, Ina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1275",
    pages = "1743--1749",
    abstract = "This paper presents SciCorp, a corpus of full-text English scientific papers of two disciplines, genetics and computational linguistics. The corpus comprises co-reference and bridging information as well as information status labels. Since SciCorp is annotated with both labels and the respective co-referent and bridging links, we believe it is a valuable resource for NLP researchers working on scientific articles or on applications such as co-reference resolution, bridging resolution or information status classification. The corpus has been reliably annotated by independent human coders with moderate inter-annotator agreement (average kappa = 0.71). In total, we have annotated 14 full papers containing 61,045 tokens and marked 8,708 definite noun phrases. The paper describes in detail the annotation scheme as well as the resulting corpus. The corpus is available for download in two different formats: in an offset-based format and for the co-reference annotations in the widely-used, tabular CoNLL-2012 format.",
}
@inproceedings{jain-etal-2016-using,
    title = "Using lexical and Dependency Features to Disambiguate Discourse Connectives in {H}indi",
    author = "Jain, Rohit  and
      Sharma, Himanshu  and
      Sharma, Dipti",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1276",
    pages = "1750--1754",
    abstract = "Discourse parsing is a challenging task in NLP and plays a crucial role in discourse analysis. To enable discourse analysis for Hindi, Hindi Discourse Relations Bank was created on a subset of Hindi TreeBank. The benefits of a discourse analyzer in automated discourse analysis, question summarization and question answering domains has motivated us to begin work on a discourse analyzer for Hindi. In this paper, we focus on discourse connective identification for Hindi. We explore various available syntactic features for this task. We also explore the use of dependency tree parses present in the Hindi TreeBank and study the impact of the same on the performance of the system. We report that the novel dependency features introduced have a higher impact on precision, in comparison to the syntactic features previously used for this task. In addition, we report a high accuracy of 96{\%} for this task.",
}
@inproceedings{andersson-etal-2016-annotating,
    title = "Annotating Topic Development in Information Seeking Queries",
    author = {Andersson, Marta  and
      {\"O}zt{\"u}rel, Adnan  and
      Pareti, Silvia},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1277",
    pages = "1755--1761",
    abstract = "This paper contributes to the limited body of empirical research in the domain of discourse structure of information seeking queries. We describe the development of an annotation schema for coding topic development in information seeking queries and the initial observations from a pilot sample of query sessions. The main idea that we explore is the relationship between constant and variable discourse entities and their role in tracking changes in the topic progression. We argue that the topicalized entities remain stable across development of the discourse and can be identified by a simple mechanism where anaphora resolution is a precursor. We also claim that a corpus annotated in this framework can be used as training data for dialogue management and computational semantics systems.",
}
@inproceedings{mirovsky-etal-2016-searching,
    title = "Searching in the {P}enn {D}iscourse {T}reebank Using the {PML}-Tree Query",
    author = "M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i}  and
      Pol{\'a}kov{\'a}, Lucie  and
      {\v{S}}t{\v{e}}p{\'a}nek, Jan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1278",
    pages = "1762--1769",
    abstract = "The PML-Tree Query is a general, powerful and user-friendly system for querying richly linguistically annotated treebanks. The paper shows how the PML-Tree Query can be used for searching for discourse relations in the Penn Discourse Treebank 2.0 mapped onto the syntactic annotation of the Penn Treebank.",
}
@inproceedings{alharbi-hain-2016-opencourseware,
    title = "The {O}pen{C}ourse{W}are Metadiscourse ({OCWMD}) Corpus",
    author = "Alharbi, Ghada  and
      Hain, Thomas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1279",
    pages = "1770--1776",
    abstract = "This study describes a new corpus of over 60,000 hand-annotated metadiscourse acts from 106 OpenCourseWare lectures, from two different disciplines: Physics and Economics. Metadiscourse is a set of linguistic expressions that signal different functions in the discourse. This type of language is hypothesised to be helpful in finding a structure in unstructured text, such as lectures discourse. A brief summary is provided about the annotation scheme and labelling procedures, inter-annotator reliability statistics, overall distributional statistics, a description of auxiliary data that will be distributed with the corpus, and information relating to how to obtain the data. The results provide a deeper understanding of lecture structure and confirm the reliable coding of metadiscursive acts in academic lectures across different disciplines. The next stage of our research will be to build a classification model to automate the tagging process, instead of manual annotation, which take time and efforts. This is in addition to the use of these tags as indicators of the higher level structure of lecture discourse.",
}
@inproceedings{hernandez-etal-2016-ubuntu,
    title = "{U}buntu-fr: A Large and Open Corpus for Multi-modal Analysis of Online Written Conversations",
    author = "Hernandez, Nicolas  and
      Salim, Soufian  and
      Clouet, Elizaveta Loginova",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1280",
    pages = "1777--1783",
    abstract = "We present a large, free, French corpus of online written conversations extracted from the Ubuntu platform{'}s forums, mailing lists and IRC channels. The corpus is meant to support multi-modality and diachronic studies of online written conversations. We choose to build the corpus around a robust metadata model based upon strong principles, such as the {``}stand off{''} annotation principle. We detail the model, we explain how the data was collected and processed - in terms of meta-data, text and conversation - and we detail the corpus{'}contents through a series of meaningful statistics. A portion of the corpus - about 4,700 sentences from emails, forum posts and chat messages sent in November 2014 - is annotated in terms of dialogue acts and sentiment. We discuss how we adapted our dialogue act taxonomy from the DIT++ annotation scheme and how the data was annotated, before presenting our results as well as a brief qualitative analysis of the annotated data.",
}
@inproceedings{hough-etal-2016-duel,
    title = "{DUEL}: A Multi-lingual Multimodal Dialogue Corpus for Disfluency, Exclamations and Laughter",
    author = "Hough, Julian  and
      Tian, Ye  and
      de Ruiter, Laura  and
      Betz, Simon  and
      Kousidis, Spyros  and
      Schlangen, David  and
      Ginzburg, Jonathan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1281",
    pages = "1784--1788",
    abstract = "We present the DUEL corpus, consisting of 24 hours of natural, face-to-face, loosely task-directed dialogue in German, French and Mandarin Chinese. The corpus is uniquely positioned as a cross-linguistic, multimodal dialogue resource controlled for domain. DUEL includes audio, video and body tracking data and is transcribed and annotated for disfluency, laughter and exclamations.",
}
@inproceedings{barzdins-etal-2016-character,
    title = "Character-Level Neural Translation for Multilingual Media Monitoring in the {SUMMA} Project",
    author = "Barzdins, Guntis  and
      Renals, Steve  and
      Gosko, Didzis",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1282",
    pages = "1789--1793",
    abstract = "The paper steps outside the comfort-zone of the traditional NLP tasks like automatic speech recognition (ASR) and machine translation (MT) to addresses two novel problems arising in the automated multilingual news monitoring: segmentation of the TV and radio program ASR transcripts into individual stories, and clustering of the individual stories coming from various sources and languages into storylines. Storyline clustering of stories covering the same events is an essential task for inquisitorial media monitoring. We address these two problems jointly by engaging the low-dimensional semantic representation capabilities of the sequence to sequence neural translation models. To enable joint multi-task learning for multilingual neural translation of morphologically rich languages we replace the attention mechanism with the sliding-window mechanism and operate the sequence to sequence neural translation model on the character-level rather than on the word-level. The story segmentation and storyline clustering problem is tackled by examining the low-dimensional vectors produced as a side-product of the neural translation process. The results of this paper describe a novel approach to the automatic story segmentation and storyline clustering problem.",
}
@inproceedings{van-hee-etal-2016-exploring,
    title = "Exploring the Realization of Irony in {T}witter Data",
    author = "Van Hee, Cynthia  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1283",
    pages = "1794--1799",
    abstract = "Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in tasks such as sentiment analysis. In this research, we present the construction of a Twitter dataset for two languages, being English and Dutch, and the development of new guidelines for the annotation of verbal irony in social media texts. Furthermore, we present some statistics on the annotated corpora, from which we can conclude that the detection of contrasting evaluations might be a good indicator for recognizing irony.",
}
@inproceedings{goutte-etal-2016-discriminating,
    title = "Discriminating Similar Languages: Evaluations and Explorations",
    author = "Goutte, Cyril  and
      L{\'e}ger, Serge  and
      Malmasi, Shervin  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1284",
    pages = "1800--1807",
    abstract = "We present an analysis of the performance of machine learning classifiers on discriminating between similar languages and language varieties. We carried out a number of experiments using the results of the two editions of the Discriminating between Similar Languages (DSL) shared task. We investigate the progress made between the two tasks, estimate an upper bound on possible performance using ensemble and oracle combination, and provide learning curves to help us understand which languages are more challenging. A number of difficult sentences are identified and investigated further with human annotation",
}
@inproceedings{al-sulaiti-etal-2016-compilation,
    title = "Compilation of an {A}rabic Children{'}s Corpus",
    author = "Al-Sulaiti, Latifa  and
      Abbas, Noorhan  and
      Brierley, Claire  and
      Atwell, Eric  and
      Alghamdi, Ayman",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1285",
    pages = "1808--1812",
    abstract = "Inspired by the Oxford Children{'}s Corpus, we have developed a prototype corpus of Arabic texts written and/or selected for children. Our Arabic Children{'}s Corpus of 2950 documents and nearly 2 million words has been collected manually from the web during a 3-month project. It is of high quality, and contains a range of different children{'}s genres based on sources located, including classic tales from The Arabian Nights, and popular fictional characters such as Goha. We anticipate that the current and subsequent versions of our corpus will lead to interesting studies in text classification, language use, and ideology in children{'}s texts.",
}
@inproceedings{eriksson-2016-quality,
    title = "Quality Assessment of the {R}euters Vol. 2 Multilingual Corpus",
    author = "Eriksson, Robin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1286",
    pages = "1813--1819",
    abstract = "We introduce a framework for quality assurance of corpora, and apply it to the Reuters Multilingual Corpus (RCV2). The results of this quality assessment of this standard newsprint corpus reveal a significant duplication problem and, to a lesser extent, a problem with corrupted articles. From the raw collection of some 487,000 articles, almost one tenth are trivial duplicates. A smaller fraction of articles appear to be corrupted and should be excluded for that reason. The detailed results are being made available as on-line appendices to this article. This effort also demonstrates the beginnings of a constraint-based methodological framework for quality assessment and quality assurance for corpora. As a first implementation of this framework, we have investigated constraints to verify sample integrity, and to diagnose sample duplication, entropy aberrations, and tagging inconsistencies. To help identify near-duplicates in the corpus, we have employed both entropy measurements and a simple byte bigram incidence digest.",
}
@inproceedings{el-haj-etal-2016-learning,
    title = "Learning Tone and Attribution for Financial Text Mining",
    author = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Young, Steve  and
      Moore, Andrew  and
      Walker, Martin  and
      Schleicher, Thomas  and
      Athanasakou, Vasiliki",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1287",
    pages = "1820--1825",
    abstract = "Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds of attributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale. Our best machine learning algorithm correctly classified performance sentences with 70{\%} accuracy and detected tone and attribution in financial PEAs with accuracy of 79{\%}.",
}
@inproceedings{sergienko-etal-2016-comparative,
    title = "A Comparative Study of Text Preprocessing Approaches for Topic Detection of User Utterances",
    author = "Sergienko, Roman  and
      Shan, Muhammad  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1288",
    pages = "1826--1831",
    abstract = "The paper describes a comparative study of existing and novel text preprocessing and classification techniques for domain detection of user utterances. Two corpora are considered. The first one contains customer calls to a call centre for further call routing; the second one contains answers of call centre employees with different kinds of customer orientation behaviour. Seven different unsupervised and supervised term weighting methods were applied. The collective use of term weighting methods is proposed for classification effectiveness improvement. Four different dimensionality reduction methods were applied: stop-words filtering with stemming, feature selection based on term weights, feature transformation based on term clustering, and a novel feature transformation method based on terms belonging to classes. As classification algorithms we used k-NN and a SVM-based algorithm. The numerical experiments have shown that the simultaneous use of the novel proposed approaches (collectives of term weighting methods and the novel feature transformation method) allows reaching the high classification results with very small number of features.",
}
@inproceedings{sharjeel-etal-2016-uppc,
    title = "{UPPC} - {U}rdu Paraphrase Plagiarism Corpus",
    author = "Sharjeel, Muhammad  and
      Rayson, Paul  and
      Nawab, Rao Muhammad Adeel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1289",
    pages = "1832--1836",
    abstract = "Paraphrase plagiarism is a significant and widespread problem and research shows that it is hard to detect. Several methods and automatic systems have been proposed to deal with it. However, evaluation and comparison of such solutions is not possible because of the unavailability of benchmark corpora with manual examples of paraphrase plagiarism. To deal with this issue, we present the novel development of a paraphrase plagiarism corpus containing simulated (manually created) examples in the Urdu language - a language widely spoken around the world. This resource is the first of its kind developed for the Urdu language and we believe that it will be a valuable contribution to the evaluation of paraphrase plagiarism detection systems.",
}
@inproceedings{korkontzelos-etal-2016-identifying,
    title = "Identifying Content Types of Messages Related to Open Source Software Projects",
    author = "Korkontzelos, Yannis  and
      Thompson, Paul  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1290",
    pages = "1837--1844",
    abstract = "Assessing the suitability of an Open Source Software project for adoption requires not only an analysis of aspects related to the code, such as code quality, frequency of updates and new version releases, but also an evaluation of the quality of support offered in related online forums and issue trackers. Understanding the content types of forum messages and issue trackers can provide information about the extent to which requests are being addressed and issues are being resolved, the percentage of issues that are not being fixed, the cases where the user acknowledged that the issue was successfully resolved, etc. These indicators can provide potential adopters of the OSS with estimates about the level of available support. We present a detailed hierarchy of content types of online forum messages and issue tracker comments and a corpus of messages annotated accordingly. We discuss our experiments to classify forum messages and issue tracker comments into content-related classes, i.e.{\textasciitilde}to assign them to nodes of the hierarchy. The results are very encouraging.",
}
@inproceedings{li-etal-2016-emotion,
    title = "Emotion Corpus Construction Based on Selection from Hashtags",
    author = "Li, Minglei  and
      Long, Yunfei  and
      Qin, Lu  and
      Li, Wenjie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1291",
    pages = "1845--1849",
    abstract = "The availability of labelled corpus is of great importance for supervised learning in emotion classification tasks. Because it is time-consuming to manually label text, hashtags have been used as naturally annotated labels to obtain a large amount of labelled training data from microblog. However, natural hashtags contain too much noise for it to be used directly in learning algorithms. In this paper, we design a three-stage semi-automatic method to construct an emotion corpus from microblogs. Firstly, a lexicon based voting approach is used to verify the hashtag automatically. Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels. Finally, the remaining data will be manually examined to filter out the noisy data. Out of about 48K filtered Chinese microblogs, 39k microblogs are selected to form the final corpus with the Kappa value reaching over 0.92 for the automatic parts and over 0.81 for the manual part. The proportion of automatic selection reaches 54.1{\%}. Thus, the method can reduce about 44.5{\%} of manual workload for acquiring quality data. Experiment on a classifier trained on this corpus shows that it achieves comparable results compared to the manually annotated NLP{\&}CC2013 corpus.",
}
@inproceedings{gamback-das-2016-comparing,
    title = "Comparing the Level of Code-Switching in Corpora",
    author = {Gamb{\"a}ck, Bj{\"o}rn  and
      Das, Amitava},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1292",
    pages = "1850--1855",
    abstract = "Social media texts are often fairly informal and conversational, and when produced by bilinguals tend to be written in several different languages simultaneously, in the same way as conversational speech. The recent availability of large social media corpora has thus also made large-scale code-switched resources available for research. The paper addresses the issues of evaluation and comparison these new corpora entail, by defining an objective measure of corpus level complexity of code-switched texts. It is also shown how this formal measure can be used in practice, by applying it to several code-switched corpora.",
}
@inproceedings{muller-etal-2016-evaluation,
    title = "Evaluation of the {KIT} Lecture Translation System",
    author = {M{\"u}ller, Markus  and
      F{\"u}nfer, Sarah  and
      St{\"u}ker, Sebastian  and
      Waibel, Alex},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1293",
    pages = "1856--1861",
    abstract = "To attract foreign students is among the goals of the Karlsruhe Institute of Technology (KIT). One obstacle to achieving this goal is that lectures at KIT are usually held in German which many foreign students are not sufficiently proficient in, as, e.g., opposed to English. While the students from abroad are learning German during their stay at KIT, it is challenging to become proficient enough in it in order to follow a lecture. As a solution to this problem we offer our automatic simultaneous lecture translation. It translates German lectures into English in real time. While not as good as human interpreters, the system is available at a price that KIT can afford in order to offer it in potentially all lectures. In order to assess whether the quality of the system we have conducted a user study. In this paper we present this study, the way it was conducted and its results. The results indicate that the quality of the system has passed a threshold as to be able to support students in their studies. The study has helped to identify the most crucial weaknesses of the systems and has guided us which steps to take next.",
}
@inproceedings{qasemizadeh-schumann-2016-acl,
    title = "The {ACL} {RD}-{TEC} 2.0: A Language Resource for Evaluating Term Extraction and Entity Recognition Methods",
    author = "QasemiZadeh, Behrang  and
      Schumann, Anne-Kathrin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1294",
    pages = "1862--1868",
    abstract = "This paper introduces the ACL Reference Dataset for Terminology Extraction and Classification, version 2.0 (ACL RD-TEC 2.0). The ACL RD-TEC 2.0 has been developed with the aim of providing a benchmark for the evaluation of term and entity recognition tasks based on specialised text from the computational linguistics domain. This release of the corpus consists of 300 abstracts from articles in the ACL Anthology Reference Corpus, published between 1978{--}2006. In these abstracts, terms (i.e., single or multi-word lexical units with a specialised meaning) are manually annotated. In addition to their boundaries in running text, annotated terms are classified into one of the seven categories method, tool, language resource (LR), LR product, model, measures and measurements, and other. To assess the quality of the annotations and to determine the difficulty of this annotation task, more than 171 of the abstracts are annotated twice, independently, by each of the two annotators. In total, 6,818 terms are identified and annotated in more than 1300 sentences, resulting in a specialised vocabulary made of 3,318 lexical forms, mapped to 3,471 concepts. We explain the development of the annotation guidelines and discuss some of the challenges we encountered in this annotation task.",
}
@inproceedings{zaghouani-etal-2016-building,
    title = "Building an {A}rabic Machine Translation Post-Edited Corpus: Guidelines and Annotation",
    author = "Zaghouani, Wajdi  and
      Habash, Nizar  and
      Obeid, Ossama  and
      Mohit, Behrang  and
      Bouamor, Houda  and
      Oflazer, Kemal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1295",
    pages = "1869--1876",
    abstract = "We present our guidelines and annotation procedure to create a human corrected machine translated post-edited corpus for the Modern Standard Arabic. Our overarching goal is to use the annotated corpus to develop automatic machine translation post-editing systems for Arabic that can be used to help accelerate the human revision process of translated texts. The creation of any manually annotated corpus usually presents many challenges. In order to address these challenges, we created comprehensive and simplified annotation guidelines which were used by a team of five annotators and one lead annotator. In order to ensure a high annotation agreement between the annotators, multiple training sessions were held and regular inter-annotator agreement measures were performed to check the annotation quality. The created corpus of manual post-edited translations of English to Arabic articles is the largest to date for this language pair.",
}
@inproceedings{aranberri-etal-2016-tools,
    title = "Tools and Guidelines for Principled Machine Translation Development",
    author = "Aranberri, Nora  and
      Avramidis, Eleftherios  and
      Burchardt, Aljoscha  and
      Klejch, Ond{\v{r}}ej  and
      Popel, Martin  and
      Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1296",
    pages = "1877--1882",
    abstract = "This work addresses the need to aid Machine Translation (MT) development cycles with a complete workflow of MT evaluation methods. Our aim is to assess, compare and improve MT system variants. We hereby report on novel tools and practices that support various measures, developed in order to support a principled and informed approach of MT development. Our toolkit for automatic evaluation showcases quick and detailed comparison of MT system variants through automatic metrics and n-gram feedback, along with manual evaluation via edit-distance, error annotation and task-based feedback.",
}
@inproceedings{galibert-etal-2016-generating,
    title = "Generating Task-Pertinent sorted Error Lists for Speech Recognition",
    author = "Galibert, Olivier  and
      Jannet, Mohamed Ameur Ben  and
      Kahn, Juliette  and
      Rosset, Sophie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1297",
    pages = "1883--1889",
    abstract = "Automatic Speech recognition (ASR) is one of the most widely used components in spoken language processing applications. ASR errors are of varying importance with respect to the application, making error analysis keys to improving speech processing applications. Knowing the most serious errors for the applicative case is critical to build better systems. In the context of Automatic Speech Recognition (ASR) used as a first step towards Named Entity Recognition (NER) in speech, error seriousness is usually determined by their frequency, due to the use of the WER as metric to evaluate the ASR output, despite the emergence of more relevant measures in the literature. We propose to use a different evaluation metric form the literature in order to classify ASR errors according to their seriousness for NER. Our results show that the ASR errors importance is ranked differently depending on the used evaluation metric. A more detailed analysis shows that the estimation of the error impact given by the ATENE metric is more adapted to the NER task than the estimation based only on the most used frequency metric WER.",
}
@inproceedings{francopoulo-etal-2016-study,
    title = "A Study of Reuse and Plagiarism in {LREC} papers",
    author = "Francopoulo, Gil  and
      Mariani, Joseph  and
      Paroubek, Patrick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1298",
    pages = "1890--1897",
    abstract = "The aim of this experiment is to present an easy way to compare fragments of texts in order to detect (supposed) results of copy {\&} paste operations between articles in the domain of Natural Language Processing (NLP). The search space of the comparisons is a corpus labeled as NLP4NLP gathering a large part of the NLP field. The study is centered on LREC papers in both directions, first with an LREC paper borrowing a fragment of text from the collection, and secondly in the reverse direction with fragments of LREC documents borrowed and inserted in the collection.",
}
@inproceedings{ganguly-etal-2016-developing,
    title = "Developing a Dataset for Evaluating Approaches for Document Expansion with Images",
    author = "Ganguly, Debasis  and
      Calixto, Iacer  and
      Jones, Gareth",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1299",
    pages = "1898--1901",
    abstract = "Motivated by the adage that a {``}picture is worth a thousand words{''} it can be reasoned that automatically enriching the textual content of a document with relevant images can increase the readability of a document. Moreover, features extracted from the additional image data inserted into the textual content of a document may, in principle, be also be used by a retrieval engine to better match the topic of a document with that of a given query. In this paper, we describe our approach of building a ground truth dataset to enable further research into automatic addition of relevant images to text documents. The dataset is comprised of the official ImageCLEF 2010 collection (a collection of images with textual metadata) to serve as the images available for automatic enrichment of text, a set of 25 benchmark documents that are to be enriched, which in this case are children{'}s short stories, and a set of manually judged relevant images for each query story obtained by the standard procedure of depth pooling. We use this benchmark dataset to evaluate the effectiveness of standard information retrieval methods as simple baselines for this task. The results indicate that using the whole story as a weighted query, where the weight of each query term is its tf-idf value, achieves an precision of 0:1714 within the top 5 retrieved images on an average.",
}
@inproceedings{ruiz-etal-2016-word,
    title = "More than Word Cooccurrence: Exploring Support and Opposition in International Climate Negotiations with Semantic Parsing",
    author = "Ruiz, Pablo  and
      Plancq, Cl{\'e}ment  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1300",
    pages = "1902--1907",
    abstract = "Text analysis methods widely used in digital humanities often involve word co-occurrence, e.g. concept co-occurrence networks. These methods provide a useful corpus overview, but cannot determine the predicates that relate co-occurring concepts. Our goal was identifying propositions expressing the points supported or opposed by participants in international climate negotiations. Word co-occurrence methods were not sufficient, and an analysis based on open relation extraction had limited coverage for nominal predicates. We present a pipeline which identifies the points that different actors support and oppose, via a domain model with support/opposition predicates, and analysis rules that exploit the output of semantic role labelling, syntactic dependencies and anaphora resolution. Entity linking and keyphrase extraction are also performed on the propositions related to each actor. A user interface allows examining the main concepts in points supported or opposed by each participant, which participants agree or disagree with each other, and about which issues. The system is an example of tools that digital humanities scholars are asking for, to render rich textual information (beyond word co-occurrence) more amenable to quantitative treatment. An evaluation of the tool was satisfactory.",
}
@inproceedings{collovini-etal-2016-sequence,
    title = "A Sequence Model Approach to Relation Extraction in {P}ortuguese",
    author = "Collovini, Sandra  and
      Machado, Gabriel  and
      Vieira, Renata",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1301",
    pages = "1908--1912",
    abstract = "The task of Relation Extraction from texts is one of the main challenges in the area of Information Extraction, considering the required linguistic knowledge and the sophistication of the language processing techniques employed. This task aims at identifying and classifying semantic relations that occur between entities recognized in a given text. In this paper, we evaluated a Conditional Random Fields classifier for the extraction of any relation descriptor occurring between named entities (Organisation, Person and Place categories), as well as pre-defined relation types between these entities in Portuguese texts.",
}
@inproceedings{hladek-etal-2016-evaluation,
    title = "Evaluation Set for {S}lovak News Information Retrieval",
    author = "Hl{\'a}dek, Daniel  and
      Sta{\v{s}}, Jan  and
      Juh{\'a}r, Jozef",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1302",
    pages = "1913--1916",
    abstract = "This work proposes an information retrieval evaluation set for the Slovak language. A set of 80 queries written in the natural language is given together with the set of relevant documents. The document set contains 3980 newspaper articles sorted into 6 categories. Each document in the result set is manually annotated for relevancy with its corresponding query. The evaluation set is mostly compatible with the Cranfield test collection using the same methodology for queries and annotation of relevancy. In addition to that it provides annotation for document title, author, publication date and category that can be used for evaluation of automatic document clustering and categorization.",
}
@inproceedings{imada-etal-2016-analyzing,
    title = "Analyzing Time Series Changes of Correlation between Market Share and Concerns on Companies measured through Search Engine Suggests",
    author = "Imada, Takakazu  and
      Inoue, Yusuke  and
      Chen, Lei  and
      Doi, Syunya  and
      Nie, Tian  and
      Zhao, Chen  and
      Utsuro, Takehito  and
      Kawada, Yasuhide",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1303",
    pages = "1917--1923",
    abstract = "This paper proposes how to utilize a search engine in order to predict market shares. We propose to compare rates of concerns of those who search for Web pages among several companies which supply products, given a specific products domain. We measure concerns of those who search for Web pages through search engine suggests. Then, we analyze whether rates of concerns of those who search for Web pages have certain correlation with actual market share. We show that those statistics have certain correlations. We finally propose how to predict the market share of a specific product genre based on the rates of concerns of those who search for Web pages.",
}
@inproceedings{bougouin-etal-2016-termith,
    title = "{T}erm{ITH}-Eval: a {F}rench Standard-Based Resource for Keyphrase Extraction Evaluation",
    author = "Bougouin, Adrien  and
      Barreaux, Sabine  and
      Romary, Laurent  and
      Boudin, Florian  and
      Daille, B{\'e}atrice",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1304",
    pages = "1924--1927",
    abstract = "Keyphrase extraction is the task of finding phrases that represent the important content of a document. The main aim of keyphrase extraction is to propose textual units that represent the most important topics developed in a document. The output keyphrases of automatic keyphrase extraction methods for test documents are typically evaluated by comparing them to manually assigned reference keyphrases. Each output keyphrase is considered correct if it matches one of the reference keyphrases. However, the choice of the appropriate textual unit (keyphrase) for a topic is sometimes subjective and evaluating by exact matching underestimates the performance. This paper presents a dataset of evaluation scores assigned to automatically extracted keyphrases by human evaluators. Along with the reference keyphrases, the manual evaluations can be used to validate new evaluation measures. Indeed, an evaluation measure that is highly correlated to the manual evaluation is appropriate for the evaluation of automatic keyphrase extraction methods.",
}
@inproceedings{kermes-etal-2016-royal,
    title = "The Royal Society Corpus: From Uncharted Data to Corpus",
    author = {Kermes, Hannah  and
      Degaetano-Ortlieb, Stefania  and
      Khamis, Ashraf  and
      Knappen, J{\"o}rg  and
      Teich, Elke},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1305",
    pages = "1928--1931",
    abstract = "We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665―1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.",
}
@inproceedings{goeuriot-etal-2016-building,
    title = "Building Evaluation Datasets for Consumer-Oriented Information Retrieval",
    author = "Goeuriot, Lorraine  and
      Kelly, Liadh  and
      Zuccon, Guido  and
      Palotti, Joao",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1306",
    pages = "1932--1938",
    abstract = "Common people often experience difficulties in accessing relevant, correct, accurate and understandable health information online. Developing search techniques that aid these information needs is challenging. In this paper we present the datasets created by CLEF eHealth Lab from 2013-2015 for evaluation of search solutions to support common people finding health information online. Specifically, the CLEF eHealth information retrieval (IR) task of this Lab has provided the research community with benchmarks for evaluating consumer-centered health information retrieval, thus fostering research and development aimed to address this challenging problem. Given consumer queries, the goal of the task is to retrieve relevant documents from the provided collection of web pages. The shared datasets provide a large health web crawl, queries representing people{'}s real world information needs, and relevance assessment judgements for the queries.",
}
@inproceedings{nguyen-etal-2016-dataset,
    title = "A Dataset for Open Event Extraction in {E}nglish",
    author = "Nguyen, Kiem-Hieu  and
      Tannier, Xavier  and
      Ferret, Olivier  and
      Besan{\c{c}}on, Romaric",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1307",
    pages = "1939--1943",
    abstract = "This article presents a corpus for development and testing of event schema induction systems in English. Schema induction is the task of learning templates with no supervision from unlabeled texts, and to group together entities corresponding to the same role in a template. Most of the previous work on this subject relies on the MUC-4 corpus. We describe the limits of using this corpus (size, non-representativeness, similarity of roles across templates) and propose a new, partially-annotated corpus in English which remedies some of these shortcomings. We make use of Wikinews to select the data inside the category Laws {\&} Justice, and query Google search engine to retrieve different documents on the same events. Only Wikinews documents are manually annotated and can be used for evaluation, while the others can be used for unsupervised learning. We detail the methodology used for building the corpus and evaluate some existing systems on this new data.",
}
@inproceedings{kocharov-2016-phoneme,
    title = "Phoneme Alignment Using the Information on Phonological Processes in Continuous Speech",
    author = "Kocharov, Daniil",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1308",
    pages = "1944--1948",
    abstract = "The current study focuses on optimization of Levenshtein algorithm for the purpose of computing the optimal alignment between two phoneme transcriptions of spoken utterance containing sequences of phonetic symbols. The alignment is computed with the help of a confusion matrix in which costs for phonetic symbol deletion, insertion and substitution are defined taking into account various phonological processes that occur in fluent speech, such as anticipatory assimilation, phone elision and epenthesis. The corpus containing about 30 hours of Russian read speech was used to evaluate the presented algorithms. The experimental results have shown significant reduction of misalignment rate in comparison with the baseline Levenshtein algorithm: the number of errors has been reduced from 1.1 {\%} to 0.28 {\%}",
}
@inproceedings{kachkovskaia-etal-2016-coruss,
    title = "{C}o{R}u{SS} - a New Prosodically Annotated Corpus of {R}ussian Spontaneous Speech",
    author = "Kachkovskaia, Tatiana  and
      Kocharov, Daniil  and
      Skrelin, Pavel  and
      Volskaya, Nina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1309",
    pages = "1949--1954",
    abstract = "This paper describes speech data recording, processing and annotation of a new speech corpus CoRuSS (Corpus of Russian Spontaneous Speech), which is based on connected communicative speech recorded from 60 native Russian male and female speakers of different age groups (from 16 to 77). Some Russian speech corpora available at the moment contain plain orthographic texts and provide some kind of limited annotation, but there are no corpora providing detailed prosodic annotation of spontaneous conversational speech. This corpus contains 30 hours of high quality recorded spontaneous Russian speech, half of it has been transcribed and prosodically labeled. The recordings consist of dialogues between two speakers, monologues (speakers{'} self-presentations) and reading of a short phonetically balanced text. Since the corpus is labeled for a wide range of linguistic - phonetic and prosodic - information, it provides basis for empirical studies of various spontaneous speech phenomena as well as for comparison with those we observe in prepared read speech. Since the corpus is designed as a open-access resource of speech data, it will also make possible to advance corpus-based analysis of spontaneous speech data across languages and speech technology development as well.",
}
@inproceedings{dediu-moisik-2016-defining,
    title = "Defining and Counting Phonological Classes in Cross-linguistic Segment Databases",
    author = "Dediu, Dan  and
      Moisik, Scott",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1310",
    pages = "1955--1962",
    abstract = "Recently, there has been an explosion in the availability of large, good-quality cross-linguistic databases such as WALS (Dryer {\&} Haspelmath, 2013), Glottolog (Hammarstrom et al., 2015) and Phoible (Moran {\&} McCloy, 2014). Databases such as Phoible contain the actual segments used by various languages as they are given in the primary language descriptions. However, this segment-level representation cannot be used directly for analyses that require generalizations over classes of segments that share theoretically interesting features. Here we present a method and the associated R (R Core Team, 2014) code that allows the flexible definition of such meaningful classes and that can identify the sets of segments falling into such a class for any language inventory. The method and its results are important for those interested in exploring cross-linguistic patterns of phonetic and phonological diversity and their relationship to extra-linguistic factors and processes such as climate, economics, history or human genetics.",
}
@inproceedings{martinez-etal-2016-introducing,
    title = "Introducing the {SEA}{\_}{AP}: an Enhanced Tool for Automatic Prosodic Analysis",
    author = "Mart{\'\i}nez, Marta  and
      Varela, Roc{\'\i}o  and
      Mateo, Carmen Garc{\'\i}a  and
      Rei, Elisa Fern{\'a}ndez  and
      Calvo, Adela Mart{\'\i}nez",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1311",
    pages = "1963--1969",
    abstract = "SEA{\_}AP (Segmentador e Etiquetador Autom{\'a}tico para An{\'a}lise Pros{\'o}dica, Automatic Segmentation and Labelling for Prosodic Analysis) toolkit is an application that performs audio segmentation and labelling to create a TextGrid file which will be used to launch a prosodic analysis using Praat. In this paper, we want to describe the improved functionality of the tool achieved by adding a dialectometric analysis module using R scripts. The dialectometric analysis includes computing correlations among F0 curves and it obtains prosodic distances among the different variables of interest (location, speaker, structure, etc.). The dialectometric analysis requires large databases in order to be adequately computed, and automatic segmentation and labelling can create them thanks to a procedure less costly than the manual alternative. Thus, the integration of these tools into the SEA{\_}AP allows to propose a distribution of geoprosodic areas by means of a quantitative method, which completes the traditional dialectological point of view. The current version of the SEA{\_}AP toolkit is capable of analysing Galician, Spanish and Brazilian Portuguese data, and hence the distances between several prosodic linguistic varieties can be measured at present.",
}
@inproceedings{mostafa-etal-2016-machine,
    title = "A Machine Learning based Music Retrieval and Recommendation System",
    author = "Mostafa, Naziba  and
      Wan, Yan  and
      Amitabh, Unnayan  and
      Fung, Pascale",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1312",
    pages = "1970--1977",
    abstract = "In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by humming system for music retrieval that uses deep neural networks for note transcription and a note-based retrieval system for retrieving the correct song from the database. We evaluate our query by humming system using the standard MIREX QBSH dataset. We also propose a similar artist recommendation system which recommends similar artists based on acoustic features of the artists{'} music, online text descriptions of the artists and social media data. We use supervised machine learning techniques over all our features and compare our recommendation results to those produced by a popular similar artist recommendation website.",
}
@inproceedings{aman-etal-2016-cirdox,
    title = "{C}irdo{X}: an on/off-line multisource speech and sound analysis software",
    author = "Aman, Fr{\'e}d{\'e}ric  and
      Vacher, Michel  and
      Portet, Fran{\c{c}}ois  and
      Duclot, William  and
      Lecouteux, Benjamin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1313",
    pages = "1978--1985",
    abstract = "Vocal User Interfaces in domestic environments recently gained interest in the speech processing community. This interest is due to the opportunity of using it in the framework of Ambient Assisted Living both for home automation (vocal command) and for call for help in case of distress situations, i.e. after a fall. C IRDO X, which is a modular software, is able to analyse online the audio environment in a home, to extract the uttered sentences and then to process them thanks to an ASR module. Moreover, this system perfoms non-speech audio event classification; in this case, specific models must be trained. The software is designed to be modular and to process on-line the audio multichannel stream. Some exemples of studies in which C IRDO X was involved are described. They were operated in real environment, namely a Living lab environment.",
}
@inproceedings{sperber-etal-2016-optimizing,
    title = "Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces",
    author = "Sperber, Matthias  and
      Neubig, Graham  and
      Nakamura, Satoshi  and
      Waibel, Alex",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1314",
    pages = "1986--1992",
    abstract = "Computer-assisted transcription promises high-quality speech transcription at reduced costs. This is achieved by limiting human effort to transcribing parts for which automatic transcription quality is insufficient. Our goal is to improve the human transcription quality via appropriate user interface design. We focus on iterative interfaces that allow humans to solve tasks based on an initially given suggestion, in this case an automatic transcription. We conduct a user study that reveals considerable quality gains for three variations of iterative interfaces over a non-iterative from-scratch transcription interface. Our iterative interfaces included post-editing, confidence-enhanced post-editing, and a novel retyping interface. All three yielded similar quality on average, but we found that the proposed retyping interface was less sensitive to the difficulty of the segment, and superior when the automatic transcription of the segment contained relatively many errors. An analysis using mixed-effects models allows us to quantify these and other factors and draw conclusions over which interface design should be chosen in which circumstance.",
}
@inproceedings{nicolao-etal-2016-framework,
    title = "A Framework for Collecting Realistic Recordings of Dysarthric Speech - the home{S}ervice Corpus",
    author = "Nicolao, Mauro  and
      Christensen, Heidi  and
      Cunningham, Stuart  and
      Green, Phil  and
      Hain, Thomas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1315",
    pages = "1993--1997",
    abstract = "This paper introduces a new British English speech database, named the homeService corpus, which has been gathered as part of the homeService project. This project aims to help users with speech and motor disabilities to operate their home appliances using voice commands. The audio recorded during such interactions consists of realistic data of speakers with severe dysarthria. The majority of the homeService corpus is recorded in real home environments where voice control is often the normal means by which users interact with their devices. The collection of the corpus is motivated by the shortage of realistic dysarthric speech corpora available to the scientific community. Along with the details on how the data is organised and how it can be accessed, a brief description of the framework used to make the recordings is provided. Finally, the performance of the homeService automatic recogniser for dysarthric speech trained with single-speaker data from the corpus is provided as an initial baseline. Access to the homeService corpus is provided through the dedicated web page at http://mini.dcs.shef.ac.uk/resources/homeservice-corpus/. This will also have the most updated description of the data. At the time of writing the collection process is still ongoing.",
}
@inproceedings{laaridh-etal-2016-automatic,
    title = "Automatic Anomaly Detection for Dysarthria across Two Speech Styles: Read vs Spontaneous Speech",
    author = "Laaridh, Imed  and
      Fredouille, Corinne  and
      Meunier, Christine",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1316",
    pages = "1998--2004",
    abstract = "Perceptive evaluation of speech disorders is still the standard method in clinical practice for the diagnosing and the following of the condition progression of patients. Such methods include different tasks such as read speech, spontaneous speech, isolated words, sustained vowels, etc. In this context, automatic speech processing tools have proven pertinence in speech quality evaluation and assistive technology-based applications. Though, a very few studies have investigated the use of automatic tools on spontaneous speech. This paper investigates the behavior of an automatic phone-based anomaly detection system when applied on read and spontaneous French dysarthric speech. The behavior of the automatic tool reveals interesting inter-pathology differences across speech styles.",
}
@inproceedings{gutkin-etal-2016-tts,
    title = "{TTS} for Low Resource Languages: A {B}angla Synthesizer",
    author = "Gutkin, Alexander  and
      Ha, Linne  and
      Jansche, Martin  and
      Pipatsrisawat, Knot  and
      Sproat, Richard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1317",
    pages = "2005--2010",
    abstract = "We present a text-to-speech (TTS) system designed for the dialect of Bengali spoken in Bangladesh. This work is part of an ongoing effort to address the needs of under-resourced languages. We propose a process for streamlining the bootstrapping of TTS systems for under-resourced languages. First, we use crowdsourcing to collect the data from multiple ordinary speakers, each speaker recording small amount of sentences. Second, we leverage an existing text normalization system for a related language (Hindi) to bootstrap a linguistic front-end for Bangla. Third, we employ statistical techniques to construct multi-speaker acoustic models using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Hidden Markov Model (HMM) approaches. We then describe our experiments that show that the resulting TTS voices score well in terms of their perceived quality as measured by Mean Opinion Score (MOS) evaluations.",
}
@inproceedings{vallet-etal-2016-speech,
    title = "Speech Trax: A Bottom to the Top Approach for Speaker Tracking and Indexing in an Archiving Context",
    author = "Vallet, F{\'e}licien  and
      Uro, Jim  and
      Andriamakaoly, J{\'e}r{\'e}my  and
      Nabi, Hakim  and
      Derval, Mathieu  and
      Carrive, Jean",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1318",
    pages = "2011--2016",
    abstract = "With the increasing amount of audiovisual and digital data deriving from televisual and radiophonic sources, professional archives such as INA, France{'}s national audiovisual institute, acknowledge a growing need for efficient indexing tools. In this paper, we describe the Speech Trax system that aims at analyzing the audio content of TV and radio documents. In particular, we focus on the speaker tracking task that is very valuable for indexing purposes. First, we detail the overall architecture of the system and show the results obtained on a large-scale experiment, the largest to our knowledge for this type of content (about 1,300 speakers). Then, we present the Speech Trax demonstrator that gathers the results of various automatic speech processing techniques on top of our speaker tracking system (speaker diarization, speech transcription, etc.). Finally, we provide insight on the obtained performances and suggest hints for future improvements.",
}
@inproceedings{damnati-etal-2016-web,
    title = "Web Chat Conversations from Contact Centers: a Descriptive Study",
    author = "Damnati, G{\'e}raldine  and
      Guerraz, Aleksandra  and
      Charlet, Delphine",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1319",
    pages = "2017--2021",
    abstract = "In this article we propose a descriptive study of a chat conversations corpus from an assistance contact center. Conversations are described from several view points, including interaction analysis, language deviation analysis and typographic expressivity marks analysis. We provide in particular a detailed analysis of language deviations that are encountered in our corpus of 230 conversations, corresponding to 6879 messages and 76839 words. These deviations may be challenging for further syntactic and semantic parsing. Analysis is performed with a distinction between Customer messages and Agent messages. On the overall only 4{\%} of the observed words are misspelled but 26{\%} of the messages contain at least one erroneous word (rising to 40{\%} when focused on Customer messages). Transcriptions of telephone conversations from an assistance call center are also studied, allowing comparisons between these two interaction modes to be drawn. The study reveals significant differences in terms of conversation flow, with an increased efficiency for chat conversations in spite of longer temporal span.",
}
@inproceedings{morlane-hondere-etal-2016-identification,
    title = "Identification of Drug-Related Medical Conditions in Social Media",
    author = "Morlane-Hond{\`e}re, Fran{\c{c}}ois  and
      Grouin, Cyril  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1320",
    pages = "2022--2028",
    abstract = "Monitoring social media has been shown to be an interesting approach for the early detection of drug adverse effects. In this paper, we describe a system which extracts medical entities in French drug reviews written by users. We focus on the identification of medical conditions, which is based on the concept of post-coordination: we first extract minimal medical-related entities (pain, stomach) then we combine them to identify complex ones (It was the worst [pain I ever felt in my stomach]). These two steps are respectively performed by two classifiers, the first being based on Conditional Random Fields and the second one on Support Vector Machines. The overall results of the minimal entity classifier are the following: P=0.926; R=0.849; F1=0.886. A thourough analysis of the feature set shows that, when combined with word lemmas, clusters generated by word2vec are the most valuable features. When trained on the output of the first classifier, the second classifier{'}s performances are the following: p=0.683;r=0.956;f1=0.797. The addition of post-processing rules did not add any significant global improvement but was found to modify the precision/recall ratio.",
}
@inproceedings{burghardt-etal-2016-creating,
    title = "Creating a Lexicon of {B}avarian Dialect by Means of {F}acebook Language Data and Crowdsourcing",
    author = "Burghardt, Manuel  and
      Granvogl, Daniel  and
      Wolff, Christian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1321",
    pages = "2029--2033",
    abstract = "Data acquisition in dialectology is typically a tedious task, as dialect samples of spoken language have to be collected via questionnaires or interviews. In this article, we suggest to use the {``}web as a corpus{''} approach for dialectology. We present a case study that demonstrates how authentic language data for the Bavarian dialect (ISO 639-3:bar) can be collected automatically from the social network Facebook. We also show that Facebook can be used effectively as a crowdsourcing platform, where users are willing to translate dialect words collaboratively in order to create a common lexicon of their Bavarian dialect. Key insights from the case study are summarized as {``}lessons learned{''}, together with suggestions for future enhancements of the lexicon creation approach.",
}
@inproceedings{prabhakaran-rambow-2016-corpus,
    title = "A Corpus of {W}ikipedia Discussions: Over the Years, with Topic, Power and Gender Labels",
    author = "Prabhakaran, Vinodkumar  and
      Rambow, Owen",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1322",
    pages = "2034--2038",
    abstract = "In order to gain a deep understanding of how social context manifests in interactions, we need data that represents interactions from a large community of people over a long period of time, capturing different aspects of social context. In this paper, we present a large corpus of Wikipedia Talk page discussions that are collected from a broad range of topics, containing discussions that happened over a period of 15 years. The dataset contains 166,322 discussion threads, across 1236 articles/topics that span 15 different topic categories or domains. The dataset also captures whether the post is made by an registered user or not, and whether he/she was an administrator at the time of making the post. It also captures the Wikipedia age of editors in terms of number of months spent as an editor, as well as their gender. This corpus will be a valuable resource to investigate a variety of computational sociolinguistics research questions regarding online social interactions.",
}
@inproceedings{chamberlain-etal-2016-phrase,
    title = "Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.",
    author = "Chamberlain, Jon  and
      Poesio, Massimo  and
      Kruschwitz, Udo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1323",
    pages = "2039--2046",
    abstract = "Natural Language Engineering tasks require large and complex annotated datasets to build more advanced models of language. Corpora are typically annotated by several experts to create a gold standard; however, there are now compelling reasons to use a non-expert crowd to annotate text, driven by cost, speed and scalability. Phrase Detectives Corpus 1.0 is an anaphorically-annotated corpus of encyclopedic and narrative text that contains a gold standard created by multiple experts, as well as a set of annotations created by a large non-expert crowd. Analysis shows very good inter-expert agreement (kappa=.88-.93) but a more variable baseline crowd agreement (kappa=.52-.96). Encyclopedic texts show less agreement (and by implication are harder to annotate) than narrative texts. The release of this corpus is intended to encourage research into the use of crowds for text annotation and the development of more advanced, probabilistic language models, in particular for anaphoric coreference.",
}
@inproceedings{fonseca-etal-2016-summ,
    title = "Summ-it++: an Enriched Version of the Summ-it Corpus",
    author = "Fonseca, Evandro  and
      Antonitsch, Andr{\'e}  and
      Collovini, Sandra  and
      Amaral, Daniela  and
      Vieira, Renata  and
      Figueira, Anny",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1324",
    pages = "2047--2051",
    abstract = "This paper presents Summ-it++, an enriched version the Summ-it corpus. In this new version, the corpus has received new semantic layers, named entity categories and relations between named entities, adding to the previous coreference annotation. In addition, we change the original Summ-it format to SemEval",
}
@inproceedings{burga-etal-2016-towards,
    title = "Towards Multiple Antecedent Coreference Resolution in Specialized Discourse",
    author = "Burga, Alicia  and
      Cajal, Sergio  and
      Codina-Filb{\`a}, Joan  and
      Wanner, Leo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1325",
    pages = "2052--2057",
    abstract = "Despite the popularity of coreference resolution as a research topic, the overwhelming majority of the work in this area focused so far on single antecedence coreference only. Multiple antecedent coreference (MAC) has been largely neglected. This can be explained by the scarcity of the phenomenon of MAC in generic discourse. However, in specialized discourse such as patents, MAC is very dominant. It seems thus unavoidable to address the problem of MAC resolution in the context of tasks related to automatic patent material processing, among them abstractive summarization, deep parsing of patents, construction of concept maps of the inventions, etc. We present the first version of an operational rule-based MAC resolution strategy for patent material that covers the three major types of MAC: (i) nominal MAC, (ii) MAC with personal / relative pronouns, and MAC with reflexive / reciprocal pronouns. The evaluation shows that our strategy performs well in terms of precision and recall.",
}
@inproceedings{uryupina-etal-2016-arrau,
    title = "{ARRAU}: Linguistically-Motivated Annotation of Anaphoric Descriptions",
    author = "Uryupina, Olga  and
      Artstein, Ron  and
      Bristot, Antonella  and
      Cavicchio, Federica  and
      Rodriguez, Kepa  and
      Poesio, Massimo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1326",
    pages = "2058--2062",
    abstract = "This paper presents a second release of the ARRAU dataset: a multi-domain corpus with thorough linguistically motivated annotation of anaphora and related phenomena. Building upon the first release almost a decade ago, a considerable effort had been invested in improving the data both quantitatively and qualitatively. Thus, we have doubled the corpus size, expanded the selection of covered phenomena to include referentiality and genericity and designed and implemented a methodology for enforcing the consistency of the manual annotation. We believe that the new release of ARRAU provides a valuable material for ongoing research in complex cases of coreference as well as for a variety of related tasks. The corpus is publicly available through LDC.",
}
@inproceedings{yeh-etal-2016-annotated,
    title = "An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text",
    author = "Yeh, Eric  and
      Niekrasz, John  and
      Freitag, Dayne  and
      Rohwer, Richard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1327",
    pages = "2063--2070",
    abstract = "We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of {``}naturally{''} occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work.",
}
@inproceedings{aga-etal-2016-learning,
    title = "Learning Thesaurus Relations from Distributional Features",
    author = "Aga, Rosa Tsegaye  and
      Wartena, Christian  and
      Drumond, Lucas  and
      Schmidt-Thieme, Lars",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1328",
    pages = "2071--2075",
    abstract = "In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics.",
}
@inproceedings{wonsever-etal-2016-factuality,
    title = "Factuality Annotation and Learning in {S}panish Texts",
    author = "Wonsever, Dina  and
      Ros{\'a}, Aiala  and
      Malcuori, Marisa",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1329",
    pages = "2076--2080",
    abstract = "We present a proposal for the annotation of factuality of event mentions in Spanish texts and a free available annotated corpus. Our factuality model aims to capture a pragmatic notion of factuality, trying to reflect a casual reader judgements about the realis / irrealis status of mentioned events. Also, some learning experiments (SVM and CRF) have been held, showing encouraging results.",
}
@inproceedings{caroli-etal-2016-nnblocks,
    title = "{NNB}locks: A Deep Learning Framework for Computational Linguistics Neural Network Models",
    author = "Caroli, Frederico Tommasi  and
      Freitas, Andr{\'e}  and
      da Silva, Jo{\~a}o Carlos Pereira  and
      Handschuh, Siegfried",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1330",
    pages = "2081--2085",
    abstract = "Lately, with the success of Deep Learning techniques in some computational linguistics tasks, many researchers want to explore new models for their linguistics applications. These models tend to be very different from what standard Neural Networks look like, limiting the possibility to use standard Neural Networks frameworks. This work presents NNBlocks, a new framework written in Python to build and train Neural Networks that are not constrained by a specific kind of architecture, making it possible to use it in computational linguistics.",
}
@inproceedings{beltrami-etal-2016-automatic,
    title = "Automatic identification of Mild Cognitive Impairment through the analysis of {I}talian spontaneous speech productions",
    author = "Beltrami, Daniela  and
      Calz{\`a}, Laura  and
      Gagliardi, Gloria  and
      Ghidoni, Enrico  and
      Marcello, Norina  and
      Favretti, Rema Rossini  and
      Tamburini, Fabio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1331",
    pages = "2086--2093",
    abstract = "This paper presents some preliminary results of the OPLON project. It aimed at identifying early linguistic symptoms of cognitive decline in the elderly. This pilot study was conducted on a corpus composed of spontaneous speech sample collected from 39 subjects, who underwent a neuropsychological screening for visuo-spatial abilities, memory, language, executive functions and attention. A rich set of linguistic features was extracted from the digitalised utterances (at phonetic, suprasegmental, lexical, morphological and syntactic levels) and the statistical significance in pinpointing the pathological process was measured. Our results show remarkable trends for what concerns both the linguistic traits selection and the automatic classifiers building.",
}
@inproceedings{corrales-astorgano-etal-2016-use,
    title = "On the Use of a Serious Game for Recording a Speech Corpus of People with Intellectual Disabilities",
    author = "Corrales-Astorgano, Mario  and
      Escudero-Mancebo, David  and
      Guti{\'e}rrez-Gonz{\'a}lez, Yurena  and
      Flores-Lucas, Valle  and
      Gonz{\'a}lez-Ferreras, C{\'e}sar  and
      Carde{\~n}oso-Payo, Valent{\'\i}n",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1332",
    pages = "2094--2099",
    abstract = "This paper describes the recording of a speech corpus focused on prosody of people with intellectual disabilities. To do this, a video game is used with the aim of improving the user{'}s motivation. Moreover, the player{'}s profiles and the sentences recorded during the game sessions are described. With the purpose of identifying the main prosodic troubles of people with intellectual disabilities, some prosodic features are extracted from recordings, like fundamental frequency, energy and pauses. After that, a comparison is made between the recordings of people with intellectual disabilities and people without intellectual disabilities. This comparison shows that pauses are the best discriminative feature between these groups. To check this, a study has been done using machine learning techniques, with a classification rate superior to 80{\%}.",
}
@inproceedings{parish-morris-etal-2016-building,
    title = "Building Language Resources for Exploring Autism Spectrum Disorders",
    author = "Parish-Morris, Julia  and
      Cieri, Christopher  and
      Liberman, Mark  and
      Bateman, Leila  and
      Ferguson, Emily  and
      Schultz, Robert T.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1333",
    pages = "2100--2107",
    abstract = "Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that would benefit from low-cost and reliable improvements to screening and diagnosis. Human language technologies (HLTs) provide one possible route to automating a series of subjective decisions that currently inform {``}Gold Standard{''} diagnosis based on clinical judgment. In this paper, we describe a new resource to support this goal, comprised of 100 20-minute semi-structured English language samples labeled with child age, sex, IQ, autism symptom severity, and diagnostic classification. We assess the feasibility of digitizing and processing sensitive clinical samples for data sharing, and identify areas of difficulty. Using the methods described here, we propose to join forces with researchers and clinicians throughout the world to establish an international repository of annotated language samples from individuals with ASD and related disorders. This project has the potential to improve the lives of individuals with ASD and their families by identifying linguistic features that could improve remote screening, inform personalized intervention, and promote advancements in clinically-oriented HLTs.",
}
@inproceedings{terbeh-zrigui-2016-vocal,
    title = "Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of {A}rabic Continuous Speech",
    author = "Terbeh, Naim  and
      Zrigui, Mounir",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1334",
    pages = "2108--2113",
    abstract = "We propose in this work a novel acoustic phonetic study for Arabic people suffering from language disabilities and non-native learners of Arabic language to classify Arabic continuous speech to pathological or healthy and to identify phonemes that pose pronunciation problems (case of pathological speeches). The main idea can be summarized in comparing between the phonetic model reference to Arabic spoken language and that proper to concerned speaker. For this task, we use techniques of automatic speech processing like forced alignment and artificial neural network (ANN) (Basheer, 2000). Based on a test corpus containing 100 speech sequences, recorded by different speakers (healthy/pathological speeches and native/foreign speakers), we attain 97{\%} as classification rate. Algorithms used in identifying phonemes that pose pronunciation problems show high efficiency: we attain an identification rate of 100{\%}.",
}
@inproceedings{hoenen-2016-wikipedia,
    title = "{W}ikipedia Titles As Noun Tag Predictors",
    author = "Hoenen, Armin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1335",
    pages = "2114--2118",
    abstract = "In this paper, we investigate a covert labeling cue, namely the probability that a title (by example of the Wikipedia titles) is a noun. If this probability is very large, any list such as or comparable to the Wikipedia titles can be used as a reliable word-class (or part-of-speech tag) predictor or noun lexicon. This may be especially useful in the case of Low Resource Languages (LRL) where labeled data is lacking and putatively for Natural Language Processing (NLP) tasks such as Word Sense Disambiguation, Sentiment Analysis and Machine Translation. Profitting from the ease of digital publication on the web as opposed to print, LRL speaker communities produce resources such as Wikipedia and Wiktionary, which can be used for an assessment. We provide statistical evidence for a strong noun bias for the Wikipedia titles from 2 corpora (English, Persian) and a dictionary (Japanese) and for a typologically balanced set of 17 languages including LRLs. Additionally, we conduct a small experiment on predicting noun tags for out-of-vocabulary items in part-of-speech tagging for English.",
}
@inproceedings{harashima-2016-japanese,
    title = "{J}apanese {W}ord―{C}olor Associations with and without Contexts",
    author = "Harashima, Jun",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1336",
    pages = "2119--2123",
    abstract = "Although some words carry strong associations with specific colors (e.g., the word danger is associated with the color red), few studies have investigated these relationships. This may be due to the relative rarity of databases that contain large quantities of such information. Additionally, these resources are often limited to particular languages, such as English. Moreover, the existing resources often do not consider the possible contexts of words in assessing the associations between a word and a color. As a result, the influence of context on word―color associations is not fully understood. In this study, we constructed a novel language resource for word―color associations. The resource has two characteristics: First, our resource is the first to include Japanese word―color associations, which were collected via crowdsourcing. Second, the word―color associations in the resource are linked to contexts. We show that word―color associations depend on language and that associations with certain colors are affected by context information.",
}
@inproceedings{van-miltenburg-etal-2016-vu,
    title = "The {VU} Sound Corpus: Adding More Fine-grained Annotations to the Freesound Database",
    author = "van Miltenburg, Emiel  and
      Timmermans, Benjamin  and
      Aroyo, Lora",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1337",
    pages = "2124--2130",
    abstract = "This paper presents a collection of annotations (tags or keywords) for a set of 2,133 environmental sounds taken from the Freesound database (www.freesound.org). The annotations are acquired through an open-ended crowd-labeling task, in which participants were asked to provide keywords for each of three sounds. The main goal of this study is to find out (i) whether it is feasible to collect keywords for a large collection of sounds through crowdsourcing, and (ii) how people talk about sounds, and what information they can infer from hearing a sound in isolation. Our main finding is that it is not only feasible to perform crowd-labeling for a large collection of sounds, it is also very useful to highlight different aspects of the sounds that authors may fail to mention. Our data is freely available, and can be used to ground semantic models, improve search in audio databases, and to study the language of sound.",
}
@inproceedings{sukhareva-etal-2016-crowdsourcing,
    title = "Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations",
    author = "Sukhareva, Maria  and
      Eckle-Kohler, Judith  and
      Habernal, Ivan  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1338",
    pages = "2131--2137",
    abstract = "We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license at www.ukp.tu-darmstadt.de/data/verb-relations/. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization.",
}
@inproceedings{feltracco-etal-2016-acquiring,
    title = "Acquiring Opposition Relations among {I}talian Verb Senses using Crowdsourcing",
    author = "Feltracco, Anna  and
      Magnolini, Simone  and
      Jezek, Elisabetta  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1339",
    pages = "2138--2144",
    abstract = "We describe an experiment for the acquisition of opposition relations among Italian verb senses, based on a crowdsourcing methodology. The goal of the experiment is to discuss whether the types of opposition we distinguish (i.e. complementarity, antonymy, converseness and reversiveness) are actually perceived by the crowd. In particular, we collect data for Italian by using the crowdsourcing platform CrowdFlower. We ask annotators to judge the type of opposition existing among pairs of sentences -previously judged as opposite- that differ only for a verb: the verb in the first sentence is opposite of the verb in second sentence. Data corroborate the hypothesis that some opposition relations exclude each other, while others interact, being recognized as compatible by the contributors.",
}
@inproceedings{caines-etal-2016-crowdsourcing,
    title = "Crowdsourcing a Multi-lingual Speech Corpus: Recording, Transcription and Annotation of the {C}rowd{IS} Corpora",
    author = "Caines, Andrew  and
      Bentz, Christian  and
      Graham, Calbert  and
      Polzehl, Tim  and
      Buttery, Paula",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1340",
    pages = "2145--2152",
    abstract = "We announce the release of the CROWDED CORPUS: a pair of speech corpora collected via crowdsourcing, containing a native speaker corpus of English (CROWDED{\_}ENGLISH), and a corpus of German/English bilinguals (CROWDED{\_}BILINGUAL). Release 1 of the CROWDED CORPUS contains 1000 recordings amounting to 33,400 tokens collected from 80 speakers and is freely available to other researchers. We recruited participants via the Crowdee application for Android. Recruits were prompted to respond to business-topic questions of the type found in language learning oral tests. We then used the CrowdFlower web application to pass these recordings to crowdworkers for transcription and annotation of errors and sentence boundaries. Finally, the sentences were tagged and parsed using standard natural language processing tools. We propose that crowdsourcing is a valid and economical method for corpus collection, and discuss the advantages and disadvantages of this approach.",
}
@inproceedings{bartie-etal-2016-real,
    title = "The {REAL} Corpus: A Crowd-Sourced Corpus of Human Generated and Evaluated Spatial References to Real-World Urban Scenes",
    author = "Bartie, Phil  and
      Mackaness, William  and
      Gkatzia, Dimitra  and
      Rieser, Verena",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1341",
    pages = "2153--2155",
    abstract = "Our interest is in people{'}s capacity to efficiently and effectively describe geographic objects in urban scenes. The broader ambition is to develop spatial models capable of equivalent functionality able to construct such referring expressions. To that end we present a newly crowd-sourced data set of natural language references to objects anchored in complex urban scenes (In short: The REAL Corpus ― Referring Expressions Anchored Language). The REAL corpus contains a collection of images of real-world urban scenes together with verbal descriptions of target objects generated by humans, paired with data on how successful other people were able to identify the same object based on these descriptions. In total, the corpus contains 32 images with on average 27 descriptions per image and 3 verifications for each description. In addition, the corpus is annotated with a variety of linguistically motivated features. The paper highlights issues posed by collecting data using crowd-sourcing with an unrestricted input format, as well as using real-world urban scenes.",
}
@inproceedings{hantke-etal-2016-introducing,
    title = "Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification",
    author = {Hantke, Simone  and
      Marchi, Erik  and
      Schuller, Bj{\"o}rn},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1342",
    pages = "2156--2161",
    abstract = "Crowdsourcing is an arising collaborative approach applicable among many other applications to the area of language and speech processing. In fact, the use of crowdsourcing was already applied in the field of speech processing with promising results. However, only few studies investigated the use of crowdsourcing in computational paralinguistics. In this contribution, we propose a novel evaluator for crowdsourced-based ratings termed Weighted Trustability Evaluator (WTE) which is computed from the rater-dependent consistency over the test questions. We further investigate the reliability of crowdsourced annotations as compared to the ones obtained with traditional labelling procedures, such as constrained listening experiments in laboratories or in controlled environments. This comparison includes an in-depth analysis of obtainable classification performances. The experiments were conducted on the Speaker Likability Database (SLD) already used in the INTERSPEECH Challenge 2012, and the results lend further weight to the assumption that crowdsourcing can be applied as a reliable annotation source for computational paralinguistics given a sufficient number of raters and suited measurements of their reliability.",
}
@inproceedings{arimoto-okanoya-2016-comparison,
    title = "Comparison of Emotional Understanding in Modality-Controlled Environments using Multimodal Online Emotional Communication Corpus",
    author = "Arimoto, Yoshiko  and
      Okanoya, Kazuo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1343",
    pages = "2162--2167",
    abstract = "In online computer-mediated communication, speakers were considered to have experienced difficulties in catching their partner{'}s emotions and in conveying their own emotions. To explain why online emotional communication is so difficult and to investigate how this problem should be solved, multimodal online emotional communication corpus was constructed by recording approximately 100 speakers{'} emotional expressions and reactions in a modality-controlled environment. Speakers communicated over the Internet using video chat, voice chat or text chat; their face-to-face conversations were used for comparison purposes. The corpora incorporated emotional labels by evaluating the speaker{'}s dynamic emotional states and the measurements of the speaker{'}s facial expression, vocal expression and autonomic nervous system activity. For the initial study of this project, which used a large-scale emotional communication corpus, the accuracy of online emotional understanding was assessed to demonstrate the emotional labels evaluated by the speakers and to summarize the speaker{'}s answers on the questionnaire regarding the difference between an online chat and face-to-face conversations in which they actually participated. The results revealed that speakers have difficulty communicating their emotions in online communication environments, regardless of the type of communication modality and that inaccurate emotional understanding occurs more frequently in online computer-mediated communication than in face-to-face communication.",
}
@inproceedings{bigi-bertrand-2016-laughter,
    title = "Laughter in {F}rench Spontaneous Conversational Dialogs",
    author = "Bigi, Brigitte  and
      Bertrand, Roxane",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1344",
    pages = "2168--2174",
    abstract = "This paper presents a quantitative description of laughter in height 1-hour French spontaneous conversations. The paper includes the raw figures for laughter as well as more details concerning inter-individual variability. It firstly describes to what extent the amount of laughter and their durations varies from speaker to speaker in all dialogs. In a second suite of analyses, this paper compares our corpus with previous analyzed corpora. In a final set of experiments, it presents some facts about overlapping laughs. This paper have quantified these all effects in free-style conversations, for the first time.",
}
@inproceedings{haddad-etal-2016-avab,
    title = "{AVAB}-{DBS}: an Audio-Visual Affect Bursts Database for Synthesis",
    author = {Haddad, Kevin El  and
      {\c{C}}akmak, H{\"u}seyin  and
      Dupont, St{\'e}phane  and
      Dutoit, Thierry},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1345",
    pages = "2175--2179",
    abstract = "It has been shown that adding expressivity and emotional expressions to an agent{'}s communication systems would improve the interaction quality between this agent and a human user. In this paper we present a multimodal database of affect bursts, which are very short non-verbal expressions with facial, vocal, and gestural components that are highly synchronized and triggered by an identifiable event. This database contains motion capture and audio data of affect bursts representing disgust, startle and surprise recorded at three different levels of arousal each. This database is to be used for synthesis purposes in order to generate affect bursts of these emotions on a continuous arousal level scale.",
}
@inproceedings{lubis-etal-2016-construction,
    title = "Construction of {J}apanese Audio-Visual Emotion Database and Its Application in Emotion Recognition",
    author = "Lubis, Nurul  and
      Gomez, Randy  and
      Sakti, Sakriani  and
      Nakamura, Keisuke  and
      Yoshino, Koichiro  and
      Nakamura, Satoshi  and
      Nakadai, Kazuhiro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1346",
    pages = "2180--2184",
    abstract = "Emotional aspects play a vital role in making human communication a rich and dynamic experience. As we introduce more automated system in our daily lives, it becomes increasingly important to incorporate emotion to provide as natural an interaction as possible. To achieve said incorporation, rich sets of labeled emotional data is prerequisite. However, in Japanese, existing emotion database is still limited to unimodal and bimodal corpora. Since emotion is not only expressed through speech, but also visually at the same time, it is essential to include multiple modalities in an observation. In this paper, we present the first audio-visual emotion corpora in Japanese, collected from 14 native speakers. The corpus contains 100 minutes of annotated and transcribed material. We performed preliminary emotion recognition experiments on the corpus and achieved an accuracy of 61.42{\%} for five classes of emotion.",
}
@inproceedings{passaro-lenci-2016-evaluating,
    title = "Evaluating Context Selection Strategies to Build Emotive Vector Space Models",
    author = "Passaro, Lucia C.  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1347",
    pages = "2185--2191",
    abstract = "In this paper we compare different context selection approaches to improve the creation of Emotive Vector Space Models (VSMs). The system is based on the results of an existing approach that showed the possibility to create and update VSMs by exploiting crowdsourcing and human annotation. Here, we introduce a method to manipulate the contexts of the VSMs under the assumption that the emotive connotation of a target word is a function of both its syntagmatic and paradigmatic association with the various emotions. To study the differences among the proposed spaces and to confirm the reliability of the system, we report on two experiments: in the first one we validated the best candidates extracted from each model, and in the second one we compared the models{'} performance on a random sample of target words. Both experiments have been implemented as crowdsourcing tasks.",
}
@inproceedings{bourlon-etal-2016-simultaneous,
    title = "Simultaneous Sentence Boundary Detection and Alignment with Pivot-based Machine Translation Generated Lexicons",
    author = "Bourlon, Antoine  and
      Chu, Chenhui  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1348",
    pages = "2192--2198",
    abstract = "Sentence alignment is a task that consists in aligning the parallel sentences in a translated article pair. This paper describes a method to perform sentence boundary detection and alignment simultaneously, which significantly improves the alignment accuracy on languages like Chinese with uncertain sentence boundaries. It relies on the definition of hard (certain) and soft (uncertain) punctuation delimiters, the latter being possibly ignored to optimize the alignment result. The alignment method is used in combination with lexicons automatically generated from the input article pairs using pivot-based MT, achieving better coverage of the input words with fewer entries than pre-existing dictionaries. Pivot-based MT makes it possible to build dictionaries for language pairs that have scarce parallel data. The alignment method is implemented in a tool that will be freely available in the near future.",
}
@inproceedings{kanojia-etal-2016-thatll,
    title = "That{'}ll Do Fine!: A Coarse Lexical Resource for {E}nglish-{H}indi {MT}, Using Polylingual Topic Models",
    author = "Kanojia, Diptesh  and
      Joshi, Aditya  and
      Bhattacharyya, Pushpak  and
      Carman, Mark James",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1349",
    pages = "2199--2203",
    abstract = "Parallel corpora are often injected with bilingual lexical resources for improved Indian language machine translation (MT). In absence of such lexical resources, multilingual topic models have been used to create coarse lexical resources in the past, using a Cartesian product approach. Our results show that for morphologically rich languages like Hindi, the Cartesian product approach is detrimental for MT. We then present a novel {`}sentential{'} approach to use this coarse lexical resource from a multilingual topic model. Our coarse lexical resource when injected with a parallel corpus outperforms a system trained using parallel corpus and a good quality lexical resource. As demonstrated by the quality of our coarse lexical resource and its benefit to MT, we believe that our sentential approach to create such a resource will help MT for resource-constrained languages.",
}
@inproceedings{nakazawa-etal-2016-aspec,
    title = "{ASPEC}: {A}sian Scientific Paper Excerpt Corpus",
    author = "Nakazawa, Toshiaki  and
      Yaguchi, Manabu  and
      Uchimoto, Kiyotaka  and
      Utiyama, Masao  and
      Sumita, Eiichiro  and
      Kurohashi, Sadao  and
      Isahara, Hitoshi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1350",
    pages = "2204--2208",
    abstract = "In this paper, we describe the details of the ASPEC (Asian Scientific Paper Excerpt Corpus), which is the first large-size parallel corpus of scientific paper domain. ASPEC was constructed in the Japanese-Chinese machine translation project conducted between 2006 and 2010 using the Special Coordination Funds for Promoting Science and Technology. It consists of a Japanese-English scientific paper abstract corpus of approximately 3 million parallel sentences (ASPEC-JE) and a Chinese-Japanese scientific paper excerpt corpus of approximately 0.68 million parallel sentences (ASPEC-JC). ASPEC is used as the official dataset for the machine translation evaluation workshop WAT (Workshop on Asian Translation).",
}
@inproceedings{labaka-etal-2016-domain,
    title = "Domain Adaptation in {MT} Using Titles in {W}ikipedia as a Parallel Corpus: Resources and Evaluation",
    author = "Labaka, Gorka  and
      Alegria, I{\~n}aki  and
      Sarasola, Kepa",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1351",
    pages = "2209--2213",
    abstract = "This paper presents how an state-of-the-art SMT system is enriched by using an extra in-domain parallel corpora extracted from Wikipedia. We collect corpora from parallel titles and from parallel fragments in comparable articles from Wikipedia. We carried out an evaluation with a double objective: evaluating the quality of the extracted data and evaluating the improvement due to the domain-adaptation. We think this can be very useful for languages with limited amount of parallel corpora, where in-domain data is crucial to improve the performance of MT sytems. The experiments on the Spanish-English language pair improve a baseline trained with the Europarl corpus in more than 2 points of BLEU when translating in the Computer Science domain.",
}
@inproceedings{wu-etal-2016-prophetmt,
    title = "{P}rophet{MT}: A Tree-based {SMT}-driven Controlled Language Authoring/Post-Editing Tool",
    author = "Wu, Xiaofeng  and
      Du, Jinhua  and
      Liu, Qun  and
      Way, Andy",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1352",
    pages = "2214--2221",
    abstract = "This paper presents ProphetMT, a tree-based SMT-driven Controlled Language (CL) authoring and post-editing tool. ProphetMT employs the source-side rules in a translation model and provides them as auto-suggestions to users. Accordingly, one might say that users are writing in a Controlled Language that is understood by the computer. ProphetMT also allows users to easily attach structural information as they compose content. When a specific rule is selected, a partial translation is promptly generated on-the-fly with the help of the structural information. Our experiments conducted on English-to-Chinese show that our proposed ProphetMT system can not only better regularise an author{'}s writing behaviour, but also significantly improve translation fluency which is vital to reduce the post-editing time. Additionally, when the writing and translation process is over, ProphetMT can provide an effective colour scheme to further improve the productivity of post-editors by explicitly featuring the relations between the source and target rules.",
}
@inproceedings{han-bel-2016-towards,
    title = "Towards producing bilingual lexica from monolingual corpora",
    author = "Han, Jingyi  and
      Bel, N{\'u}ria",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1353",
    pages = "2222--2227",
    abstract = "Bilingual lexica are the basis for many cross-lingual natural language processing tasks. Recent works have shown success in learning bilingual dictionary by taking advantages of comparable corpora and a diverse set of signals derived from monolingual corpora. In the present work, we describe an approach to automatically learn bilingual lexica by training a supervised classifier using word embedding-based vectors of only a few hundred translation equivalent word pairs. The word embedding representations of translation pairs were obtained from source and target monolingual corpora, which are not necessarily related. Our classifier is able to predict whether a new word pair is under a translation relation or not. We tested it on two quite distinct language pairs Chinese-Spanish and English-Spanish. The classifiers achieved more than 0.90 precision and recall for both language pairs in different evaluation scenarios. These results show a high potential for this method to be used in bilingual lexica production for language pairs with reduced amount of parallel or comparable corpora, in particular for phrase table expansion in Statistical Machine Translation systems.",
}
@inproceedings{gomes-lopes-2016-first,
    title = "First Steps Towards Coverage-Based Sentence Alignment",
    author = "Gomes, Lu{\'\i}s  and
      Lopes, Gabriel Pereira",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1354",
    pages = "2228--2231",
    abstract = "In this paper, we introduce a coverage-based scoring function that discriminates between parallel and non-parallel sentences. When plugged into Bleualign, a state-of-the-art sentence aligner, our function improves both precision and recall of alignments over the originally proposed BLEU score. Furthermore, since our scoring function uses Moses phrase tables directly we avoid the need to translate the texts to be aligned, which is time-consuming and a potential source of alignment errors.",
}
@inproceedings{liyanapathirana-popescu-belis-2016-using,
    title = "Using the {TED} Talks to Evaluate Spoken Post-editing of Machine Translation",
    author = "Liyanapathirana, Jeevanthi  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1355",
    pages = "2232--2239",
    abstract = "This paper presents a solution to evaluate spoken post-editing of imperfect machine translation output by a human translator. We compare two approaches to the combination of machine translation (MT) and automatic speech recognition (ASR): a heuristic algorithm and a machine learning method. To obtain a data set with spoken post-editing information, we use the French version of TED talks as the source texts submitted to MT, and the spoken English counterparts as their corrections, which are submitted to an ASR system. We experiment with various levels of artificial ASR noise and also with a state-of-the-art ASR system. The results show that the combination of MT with ASR improves over both individual outputs of MT and ASR in terms of BLEU scores, especially when ASR performance is low.",
}
@inproceedings{blain-etal-2016-phrase,
    title = "Phrase Level Segmentation and Labelling of Machine Translation Errors",
    author = "Blain, Fr{\'e}d{\'e}ric  and
      Logacheva, Varvara  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1356",
    pages = "2240--2245",
    abstract = "This paper presents our work towards a novel approach for Quality Estimation (QE) of machine translation based on sequences of adjacent words, the so-called phrases. This new level of QE aims to provide a natural balance between QE at word and sentence-level, which are either too fine grained or too coarse levels for some applications. However, phrase-level QE implies an intrinsic challenge: how to segment a machine translation into sequence of words (contiguous or not) that represent an error. We discuss three possible segmentation strategies to automatically extract erroneous phrases. We evaluate these strategies against annotations at phrase-level produced by humans, using a new dataset collected for this purpose.",
}
@inproceedings{martinez-vela-2016-subco,
    title = "{S}ub{C}o: A Learner Translation Corpus of Human and Machine Subtitles",
    author = "Mart{\'\i}nez, Jos{\'e} Manuel Mart{\'\i}nez  and
      Vela, Mihaela",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1357",
    pages = "2246--2254",
    abstract = "In this paper, we present a freely available corpus of human and automatic translations of subtitles. The corpus comprises, the original English subtitles (SRC), both human (HT) and machine translations (MT) into German, as well as post-editions (PE) of the MT output. HT and MT are annotated with errors. Moreover, human evaluation is included in HT, MT, and PE. Such a corpus is a valuable resource for both human and machine translation communities, enabling the direct comparison {--} in terms of errors and evaluation {--} between human and machine translations and post-edited machine translations.",
}
@inproceedings{bogantes-etal-2016-towards,
    title = "Towards Lexical Encoding of Multi-Word Expressions in {S}panish Dialects",
    author = "Bogantes, Diana  and
      Rodr{\'\i}guez, Eric  and
      Arauco, Alejandro  and
      Rodr{\'\i}guez, Alejandro  and
      Savary, Agata",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1358",
    pages = "2255--2261",
    abstract = "This paper describes a pilot study in lexical encoding of multi-word expressions (MWEs) in 4 Latin American dialects of Spanish: Costa Rican, Colombian, Mexican and Peruvian. We describe the variability of MWE usage across dialects. We adapt an existing data model to a dialect-aware encoding, so as to represent dialect-related specificities, while avoiding redundancy of the data common for all dialects. A dozen of linguistic properties of MWEs can be expressed in this model, both on the level of a whole MWE and of its individual components. We describe the resulting lexical resource containing several dozens of MWEs in four dialects and we propose a method for constructing a web corpus as a support for crowdsourcing examples of MWE occurrences. The resource is available under an open license and paves the way towards a large-scale dialect-aware language resource construction, which should prove useful in both traditional and novel NLP applications.",
}
@inproceedings{zhang-etal-2016-jate,
    title = "{JATE} 2.0: {J}ava Automatic Term Extraction with {A}pache {S}olr",
    author = "Zhang, Ziqi  and
      Gao, Jie  and
      Ciravegna, Fabio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1359",
    pages = "2262--2269",
    abstract = "Automatic Term Extraction (ATE) or Recognition (ATR) is a fundamental processing step preceding many complex knowledge engineering tasks. However, few methods have been implemented as public tools and in particular, available as open-source freeware. Further, little effort is made to develop an adaptable and scalable framework that enables customization, development, and comparison of algorithms under a uniform environment. This paper introduces JATE 2.0, a complete remake of the free Java Automatic Term Extraction Toolkit (Zhang et al., 2008) delivering new features including: (1) highly modular, adaptable and scalable ATE thanks to integration with Apache Solr, the open source free-text indexing and search platform; (2) an extended collection of state-of-the-art algorithms. We carry out experiments on two well-known benchmarking datasets and compare the algorithms along the dimensions of effectiveness (precision) and efficiency (speed and memory consumption). To the best of our knowledge, this is by far the only free ATE library offering a flexible architecture and the most comprehensive collection of algorithms.",
}
@inproceedings{lievers-huang-2016-lexicon,
    title = "A lexicon of perception for the identification of synaesthetic metaphors in corpora",
    author = "Lievers, Francesca Strik  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1360",
    pages = "2270--2277",
    abstract = "Synaesthesia is a type of metaphor associating linguistic expressions that refer to two different sensory modalities. Previous studies, based on the analysis of poetic texts, have shown that synaesthetic transfers tend to go from the lower toward the higher senses (e.g., sweet music vs. musical sweetness). In non-literary language synaesthesia is rare, and finding a sufficient number of examples manually would be too time-consuming. In order to verify whether the directionality also holds for conventional synaesthesia found in non-literary texts, an automatic procedure for the identification of instances of synaesthesia is therefore highly desirable. In this paper, we first focus on the preliminary step of this procedure, that is, the creation of a controlled lexicon of perception. Next, we present the results of a small pilot study that applies the extraction procedure to English and Italian corpus data.",
}
@inproceedings{marciniak-etal-2016-termopl,
    title = "{T}ermo{PL} - a Flexible Tool for Terminology Extraction",
    author = "Marciniak, Malgorzata  and
      Mykowiecka, Agnieszka  and
      Rychlik, Piotr",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1361",
    pages = "2278--2284",
    abstract = "The purpose of this paper is to introduce the TermoPL tool created to extract terminology from domain corpora in Polish. The program extracts noun phrases, term candidates, with the help of a simple grammar that can be adapted for user{'}s needs. It applies the C-value method to rank term candidates being either the longest identified nominal phrases or their nested subphrases. The method operates on simplified base forms in order to unify morphological variants of terms and to recognize their contexts. We support the recognition of nested terms by word connection strength which allows us to eliminate truncated phrases from the top part of the term list. The program has an option to convert simplified forms of phrases into correct phrases in the nominal case. TermoPL accepts as input morphologically annotated and disambiguated domain texts and creates a list of terms, the top part of which comprises domain terminology. It can also compare two candidate term lists using three different coefficients showing asymmetry of term occurrences in this data.",
}
@inproceedings{schulte-im-walde-etal-2016-ghost,
    title = "{G}ho{S}t-{NN}: A Representative Gold Standard of {G}erman Noun-Noun Compounds",
    author = {Schulte im Walde, Sabine  and
      H{\"a}tty, Anna  and
      Bott, Stefan  and
      Khvtisavrishvili, Nana},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1362",
    pages = "2285--2292",
    abstract = "This paper presents a novel gold standard of German noun-noun compounds (Ghost-NN) including 868 compounds annotated with corpus frequencies of the compounds and their constituents, productivity and ambiguity of the constituents, semantic relations between the constituents, and compositionality ratings of compound-constituent pairs. Moreover, a subset of the compounds containing 180 compounds is balanced for the productivity of the modifiers (distinguishing low/mid/high productivity) and the ambiguity of the heads (distinguishing between heads with 1, 2 and {\textgreater}2 senses",
}
@inproceedings{ramisch-etal-2016-deque,
    title = "{D}e{Q}ue: A Lexicon of Complex Prepositions and Conjunctions in {F}rench",
    author = "Ramisch, Carlos  and
      Nasr, Alexis  and
      Valli, Andr{\'e}  and
      Deulofeu, Jos{\'e}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1363",
    pages = "2293--2298",
    abstract = "We introduce DeQue, a lexicon covering French complex prepositions (CPRE) like {``}{\`a} partir de{''} (from) and complex conjunctions (CCONJ) like {``}bien que{''} (although). The lexicon includes fine-grained linguistic description based on empirical evidence. We describe the general characteristics of CPRE and CCONJ in French, with special focus on syntactic ambiguity. Then, we list the selection criteria used to build the lexicon and the corpus-based methodology employed to collect entries. Finally, we quantify the ambiguity of each construction by annotating around 100 sentences randomly taken from the FRWaC. In addition to its theoretical value, the resource has many potential practical applications. We intend to employ DeQue for treebank annotation and to train a dependency parser that can takes complex constructions into account.",
}
@inproceedings{losnegaard-etal-2016-parseme,
    title = "{PARSEME} Survey on {MWE} Resources",
    author = "Losnegaard, Gyri Sm{\o}rdal  and
      Sangati, Federico  and
      Escart{\'\i}n, Carla Parra  and
      Savary, Agata  and
      Bargmann, Sascha  and
      Monti, Johanna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1364",
    pages = "2299--2306",
    abstract = "This paper summarizes the preliminary results of an ongoing survey on multiword resources carried out within the IC1207 Cost Action PARSEME (PARSing and Multi-word Expressions). Despite the availability of language resource catalogs and the inventory of multiword datasets on the SIGLEX-MWE website, multiword resources are scattered and difficult to find. In many cases, language resources such as corpora, treebanks, or lexical databases include multiwords as part of their data or take them into account in their annotations. However, these resources need to be centralized to make them accessible. The aim of this survey is to create a portal where researchers can easily find multiword(-aware) language resources for their research. We report on the design of the survey and analyze the data gathered so far. We also discuss the problems we have detected upon examination of the data as well as possible ways of enhancing the survey.",
}
@inproceedings{wilkens-etal-2016-multiword,
    title = "Multiword Expressions in Child Language",
    author = "Wilkens, Rodrigo  and
      Idiart, Marco  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1365",
    pages = "2307--2311",
    abstract = "The goal of this work is to introduce CHILDES-MWE, which contains English CHILDES corpora automatically annotated with Multiword Expressions (MWEs) information. The result is a resource with almost 350,000 sentences annotated with more than 70,000 distinct MWEs of various types from both longitudinal and latitudinal corpora. This resource can be used for large scale language acquisition studies of how MWEs feature in child language. Focusing on compound nouns (CN), we then verify in a longitudinal study if there are differences in the distribution and compositionality of CNs in child-directed and child-produced sentences across ages. Moreover, using additional latitudinal data, we investigate if there are further differences in CN usage and in compositionality preferences. The results obtained for the child-produced sentences reflect CN distribution and compositionality in child-directed sentences.",
}
@inproceedings{bouamor-etal-2016-transfer,
    title = "Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality",
    author = "Bouamor, Dhouha  and
      Llanos, Leonardo Campillos  and
      Ligozat, Anne-Laure  and
      Rosset, Sophie  and
      Zweigenbaum, Pierre",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1366",
    pages = "2312--2316",
    abstract = "While measuring the readability of texts has been a long-standing research topic, assessing the technicality of terms has only been addressed more recently and mostly for the English language. In this paper, we train a learning-to-rank model to determine a specialization degree for each term found in a given list. Since no training data for this task exist for French, we train our system with non-lexical features on English data, namely, the Consumer Health Vocabulary, then apply it to French. The features include the likelihood ratio of the term based on specialized and lay language models, and tests for containing morphologically complex words. The evaluation of this approach is conducted on 134 terms from the UMLS Metathesaurus and 868 terms from the Eugloss thesaurus. The Normalized Discounted Cumulative Gain obtained by our system is over 0.8 on both test sets. Besides, thanks to the learning-to-rank approach, adding morphological features to the language model features improves the results on the Eugloss thesaurus.",
}
@inproceedings{rodriguez-fernandez-etal-2016-example,
    title = "Example-based Acquisition of Fine-grained Collocation Resources",
    author = "Rodr{\'\i}guez-Fern{\'a}ndez, Sara  and
      Carlini, Roberto  and
      Anke, Luis Espinosa  and
      Wanner, Leo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1367",
    pages = "2317--2322",
    abstract = "Collocations such as {``}heavy rain{''} or {``}make [a] decision{''}, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base. Collocations present difficulties even to advanced language learners, who usually struggle to find the right collocate to express a particular meaning, e.g., both {``}heavy{''} and {``}strong{''} express the meaning {`}intense{'}, but while {``}rain{''} selects {``}heavy{''}, {``}wind{''} selects {``}strong{''}. Lexical Functions (LFs) describe the meanings that hold between the elements of collocations, such as {`}intense{'}, {`}perform{'}, {`}create{'}, {`}increase{'}, etc. Language resources with semantically classified collocations would be of great help for students, however they are expensive to build, since they are manually constructed, and scarce. We present an unsupervised approach to the acquisition and semantic classification of collocations according to LFs, based on word embeddings in which, given an example of a collocation for each of the target LFs and a set of bases, the system retrieves a list of collocates for each base and LF.",
}
@inproceedings{rosen-etal-2016-mwes,
    title = "{MWE}s in Treebanks: From Survey to Guidelines",
    author = "Ros{\'e}n, Victoria  and
      De Smedt, Koenraad  and
      Losnegaard, Gyri Sm{\o}rdal  and
      Bej{\v{c}}ek, Eduard  and
      Savary, Agata  and
      Osenova, Petya",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1368",
    pages = "2323--2330",
    abstract = "By means of an online survey, we have investigated ways in which various types of multiword expressions are annotated in existing treebanks. The results indicate that there is considerable variation in treatments across treebanks and thereby also, to some extent, across languages and across theoretical frameworks. The comparison is focused on the annotation of light verb constructions and verbal idioms. The survey shows that the light verb constructions either get special annotations as such, or are treated as ordinary verbs, while VP idioms are handled through different strategies. Based on insights from our investigation, we propose some general guidelines for annotating multiword expressions in treebanks. The recommendations address the following application-based needs: distinguishing MWEs from similar but compositional constructions; searching distinct types of MWEs in treebanks; awareness of literal and nonliteral meanings; and normalization of the MWE representation. The cross-lingually and cross-theoretically focused survey is intended as an aid to accessing treebanks and an aid for further work on treebank annotation.",
}
@inproceedings{singh-etal-2016-multiword,
    title = "Multiword Expressions Dataset for {I}ndian Languages",
    author = "Singh, Dhirendra  and
      Bhingardive, Sudha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1369",
    pages = "2331--2335",
    abstract = "Multiword Expressions (MWEs) are used frequently in natural languages, but understanding the diversity in MWEs is one of the open problem in the area of Natural Language Processing. In the context of Indian languages, MWEs play an important role. In this paper, we present MWEs annotation dataset created for Indian languages viz., Hindi and Marathi. We extract possible MWE candidates using two repositories: 1) the POS-tagged corpus and 2) the IndoWordNet synsets. Annotation is done for two types of MWEs: compound nouns and light verb constructions. In the process of annotation, human annotators tag valid MWEs from these candidates based on the standard guidelines provided to them. We obtained 3178 compound nouns and 2556 light verb constructions in Hindi and 1003 compound nouns and 2416 light verb constructions in Marathi using two repositories mentioned before. This created resource is made available publicly and can be used as a gold standard for Hindi and Marathi MWE systems.",
}
@inproceedings{blache-etal-2016-marsagram,
    title = "{M}arsa{G}ram: an excursion in the forests of parsing trees",
    author = "Blache, Philippe  and
      Rauzy, St{\'e}phane  and
      Montcheuil, Gr{\'e}goire",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1370",
    pages = "2336--2342",
    abstract = "The question of how to compare languages and more generally the domain of linguistic typology, relies on the study of different linguistic properties or phenomena. Classically, such a comparison is done semi-manually, for example by extracting information from databases such as the WALS. However, it remains difficult to identify precisely regular parameters, available for different languages, that can be used as a basis towards modeling. We propose in this paper, focusing on the question of syntactic typology, a method for automatically extracting such parameters from treebanks, bringing them into a typology perspective. We present the method and the tools for inferring such information and navigating through the treebanks. The approach has been applied to 10 languages of the Universal Dependencies Treebank. We approach is evaluated by showing how automatic classification correlates with language families.",
}
@inproceedings{little-tratz-2016-easytree,
    title = "{E}asy{T}ree: A Graphical Tool for Dependency Tree Annotation",
    author = "Little, Alexa  and
      Tratz, Stephen",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1371",
    pages = "2343--2347",
    abstract = "This paper introduces EasyTree, a dynamic graphical tool for dependency tree annotation. Built in JavaScript using the popular D3 data visualization library, EasyTree allows annotators to construct and label trees entirely by manipulating graphics, and then export the corresponding data in JSON format. Human users are thus able to annotate in an intuitive way without compromising the machine-compatibility of the output. EasyTree has a number of features to assist annotators, including color-coded part-of-speech indicators and optional translation displays. It can also be customized to suit a wide range of projects; part-of-speech categories, edge labels, and many other settings can be edited from within the GUI. The system also utilizes UTF-8 encoding and properly handles both left-to-right and right-to-left scripts. By providing a user-friendly annotation tool, we aim to reduce time spent transforming data or learning to use the software, to improve the user experience for annotators, and to make annotation approachable even for inexperienced users. Unlike existing solutions, EasyTree is built entirely with standard web technologies{--}JavaScript, HTML, and CSS{--}making it ideal for web-based annotation efforts, including crowdsourcing efforts.",
}
@inproceedings{morales-etal-2016-hypergraph,
    title = "Hypergraph Modelization of a Syntactically Annotated {E}nglish {W}ikipedia Dump",
    author = "Morales, Edmundo Pavel Soriano  and
      Ah-Pine, Julien  and
      Loudcher, Sabine",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1372",
    pages = "2348--2353",
    abstract = "Wikipedia, the well known internet encyclopedia, is nowadays a widely used source of information. To leverage its rich information, already parsed versions of Wikipedia have been proposed. We present an annotated dump of the English Wikipedia. This dump draws upon previously released Wikipedia parsed dumps. Still, we head in a different direction. In this parse we focus more into the syntactical characteristics of words: aside from the classical Part-of-Speech (PoS) tags and dependency parsing relations, we provide the full constituent parse branch for each word in a succinct way. Additionally, we propose a hypergraph network representation of the extracted linguistic information. The proposed modelization aims to take advantage of the information stocked within our parsed Wikipedia dump. We hope that by releasing these resources, researchers from the concerned communities will have a ready-to-experiment Wikipedia corpus to compare and distribute their work. We render public our parsed Wikipedia dump as well as the tool (and its source code) used to perform the parse. The hypergraph network and its related metadata is also distributed.",
}
@inproceedings{versley-steen-2016-detecting,
    title = "Detecting Annotation Scheme Variation in Out-of-Domain Treebanks",
    author = "Versley, Yannick  and
      Steen, Julius",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1373",
    pages = "2354--2360",
    abstract = "To ensure portability of NLP systems across multiple domains, existing treebanks are often extended by adding trees from interesting domains that were not part of the initial annotation effort. In this paper, we will argue that it is both useful from an application viewpoint and enlightening from a linguistic viewpoint to detect and reduce divergence in annotation schemes between extant and new parts in a set of treebanks that is to be used in evaluation experiments. The results of our correction and harmonization efforts will be made available to the public as a test suite for the evaluation of constituent parsing.",
}
@inproceedings{seraji-etal-2016-universal,
    title = "{U}niversal {D}ependencies for {P}ersian",
    author = "Seraji, Mojgan  and
      Ginter, Filip  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1374",
    pages = "2361--2365",
    abstract = "The Persian Universal Dependency Treebank (Persian UD) is a recent effort of treebanking Persian with Universal Dependencies (UD), an ongoing project that designs unified and cross-linguistically valid grammatical representations including part-of-speech tags, morphological features, and dependency relations. The Persian UD is the converted version of the Uppsala Persian Dependency Treebank (UPDT) to the universal dependencies framework and consists of nearly 6,000 sentences and 152,871 word tokens with an average sentence length of 25 words. In addition to the universal dependencies syntactic annotation guidelines, the two treebanks differ in tokenization. All words containing unsegmented clitics (pronominal and copula clitics) annotated with complex labels in the UPDT have been separated from the clitics and appear with distinct labels in the Persian UD. The treebank has its original syntactic annotation scheme based on Stanford Typed Dependencies. In this paper, we present the approaches taken in the development of the Persian UD.",
}
@inproceedings{seddah-candito-2016-hard,
    title = "Hard Time Parsing Questions: Building a {Q}uestion{B}ank for {F}rench",
    author = "Seddah, Djam{\'e}  and
      Candito, Marie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1375",
    pages = "2366--2370",
    abstract = "We present the French Question Bank, a treebank of 2600 questions. We show that classical parsing model performance drop while the inclusion of this data set is highly beneficial without harming the parsing of non-question data. when facing out-of- domain data with strong structural diver- gences. Two thirds being aligned with the QB (Judge et al., 2006) and being freely available, this treebank will prove useful to build robust NLP systems.",
}
@inproceedings{schuster-manning-2016-enhanced,
    title = "Enhanced {E}nglish {U}niversal {D}ependencies: An Improved Representation for Natural Language Understanding Tasks",
    author = "Schuster, Sebastian  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1376",
    pages = "2371--2378",
    abstract = "Many shallow natural language understanding tasks use dependency trees to extract relations between content words. However, strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words. To mitigate this problem, the original Stanford Dependencies representation also defines two dependency graph representations which contain additional and augmented relations that explicitly capture otherwise implicit relations between content words. In this paper, we revisit and extend these dependency graph representations in light of the recent Universal Dependencies (UD) initiative and provide a detailed account of an enhanced and an enhanced++ English UD representation. We further present a converter from constituency to basic, i.e., strict surface structure, UD trees, and a converter from basic UD trees to enhanced and enhanced++ English UD graphs. We release both converters as part of Stanford CoreNLP and the Stanford Parser.",
}
@inproceedings{anwar-etal-2016-proposition,
    title = "A {P}roposition {B}ank of {U}rdu",
    author = "Anwar, Maaz  and
      Bhat, Riyaz Ahmad  and
      Sharma, Dipti  and
      Vaidya, Ashwini  and
      Palmer, Martha  and
      Khan, Tafseer Ahmed",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1377",
    pages = "2379--2386",
    abstract = "This paper describes our efforts for the development of a Proposition Bank for Urdu, an Indo-Aryan language. Our primary goal is the labeling of syntactic nodes in the existing Urdu dependency Treebank with specific argument labels. In essence, it involves annotation of predicate argument structures of both simple and complex predicates in the Treebank corpus. We describe the overall process of building the PropBank of Urdu. We discuss various statistics pertaining to the Urdu PropBank and the issues which the annotators encountered while developing the PropBank. We also discuss how these challenges were addressed to successfully expand the PropBank corpus. While reporting the Inter-annotator agreement between the two annotators, we show that the annotators share similar understanding of the annotation guidelines and of the linguistic phenomena present in the language. The present size of this Propbank is around 180,000 tokens which is double-propbanked by the two annotators for simple predicates. Another 100,000 tokens have been annotated for complex predicates of Urdu.",
}
@inproceedings{kriz-etal-2016-czech,
    title = "{C}zech Legal Text Treebank 1.0",
    author = "Kr{\'\i}{\v{z}}, Vincent  and
      Hladk{\'a}, Barbora  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1378",
    pages = "2387--2392",
    abstract = "We introduce a new member of the family of Prague dependency treebanks. The Czech Legal Text Treebank 1.0 is a morphologically and syntactically annotated corpus of 1,128 sentences. The treebank contains texts from the legal domain, namely the documents from the Collection of Laws of the Czech Republic. Legal texts differ from other domains in several language phenomena influenced by rather high frequency of very long sentences. A manual annotation of such sentences presents a new challenge. We describe a strategy and tools for this task. The resulting treebank can be explored in various ways. It can be downloaded from the LINDAT/CLARIN repository and viewed locally using the TrEd editor or it can be accessed on-line using the KonText and TreeQuery tools.",
}
@inproceedings{list-etal-2016-concepticon,
    title = "{C}oncepticon: A Resource for the Linking of Concept Lists",
    author = "List, Johann-Mattis  and
      Cysouw, Michael  and
      Forkel, Robert",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1379",
    pages = "2393--2400",
    abstract = "We present an attempt to link the large amount of different concept lists which are used in the linguistic literature, ranging from Swadesh lists in historical linguistics to naming tests in clinical studies and psycholinguistics. This resource, our Concepticon, links 30 222 concept labels from 160 conceptlists to 2495 concept sets. Each concept set is given a unique identifier, a unique label, and a human-readable definition. Concept sets are further structured by defining different relations between the concepts. The resource can be used for various purposes. Serving as a rich reference for new and existing databases in diachronic and synchronic linguistics, it allows researchers a quick access to studies on semantic change, cross-linguistic polysemies, and semantic associations.",
}
@inproceedings{falk-stein-2016-lvf,
    title = "{LVF}-lemon ― Towards a Linked Data Representation of {``}Les Verbes fran{\c{c}}ais{''}",
    author = "Falk, Ingrid  and
      Stein, Achim",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1380",
    pages = "2401--2406",
    abstract = "In this study we elaborate a road map for the conversion of a traditional lexical syntactico-semantic resource for French into a linguistic linked open data (LLOD) model. Our approach uses current best-practices and the analyses of earlier similar undertakings (lemonUBY and PDEV-lemon) to tease out the most appropriate representation for our resource.",
}
@inproceedings{galvan-etal-2016-riddle,
    title = "Riddle Generation using Word Associations",
    author = "Galv{\'a}n, Paloma  and
      Francisco, Virginia  and
      Herv{\'a}s, Raquel  and
      M{\'e}ndez, Gonzalo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1381",
    pages = "2407--2412",
    abstract = "In knowledge bases where concepts have associated properties, there is a large amount of comparative information that is implicitly encoded in the values of the properties these concepts share. Although there have been previous approaches to generating riddles, none of them seem to take advantage of structured information stored in knowledge bases such as Thesaurus Rex, which organizes concepts according to the fine grained ad-hoc categories they are placed into by speakers in everyday language, along with associated properties or modifiers. Taking advantage of these shared properties, we have developed a riddle generator that creates riddles about concepts represented as common nouns. The base of these riddles are comparisons between the target concept and other entities that share some of its properties. In this paper, we describe the process we have followed to generate the riddles starting from the target concept and we show the results of the first evaluation we have carried out to test the quality of the resulting riddles.",
}
@inproceedings{rudnicka-etal-2016-challenges,
    title = "Challenges of Adjective Mapping between pl{W}ord{N}et and {P}rinceton {W}ord{N}et",
    author = "Rudnicka, Ewa  and
      Witkowski, Wojciech  and
      Podlaska, Katarzyna",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1382",
    pages = "2413--2418",
    abstract = "The paper presents the strategy and results of mapping adjective synsets between plWordNet (the wordnet of Polish, cf. Piasecki et al. 2009, Maziarz et al. 2013) and Princeton WordNet (cf. Fellbaum 1998). The main challenge of this enterprise has been very different synset relation structures in the two networks: horizontal, dumbbell-model based in PWN and vertical, hyponymy-based in plWN. Moreover, the two wordnets display differences in the grouping of adjectives into semantic domains and in the size of the adjective category. The handle the above contrasts, a series of automatic prompt algorithms and a manual mapping procedure relying on corresponding synset and lexical unit relations as well as on inter-lingual relations between noun synsets were proposed in the pilot stage of mapping (Rudnicka et al. 2015). In the paper we discuss the final results of the mapping process as well as explain example mapping choices. Suggestions for further development of mapping are also given.",
}
@inproceedings{gabryszak-etal-2016-relation,
    title = "Relation- and Phrase-level Linking of {F}rame{N}et with Sar-graphs",
    author = "Gabryszak, Aleksandra  and
      Krause, Sebastian  and
      Hennig, Leonhard  and
      Xu, Feiyu  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1383",
    pages = "2419--2424",
    abstract = "Recent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data. We describe our approach for combining two English resources, FrameNet and sar-graphs, and illustrate the benefits of the linked data in a relation extraction setting. While FrameNet consists of schematic representations of situations, linked to lexemes and their valency patterns, sar-graphs are knowledge resources that connect semantic relations from factual knowledge graphs to the linguistic phrases used to express instances of these relations. We analyze the conceptual similarities and differences of both resources and propose to link sar-graphs and FrameNet on the levels of relations/frames as well as phrases. The former alignment involves a manual ontology mapping step, which allows us to extend sar-graphs with new phrase patterns from FrameNet. The phrase-level linking, on the other hand, is fully automatic. We investigate the quality of the automatically constructed links and identify two main classes of errors.",
}
@inproceedings{indig-etal-2016-mapping,
    title = "Mapping Ontologies Using Ontologies: Cross-lingual Semantic Role Information Transfer",
    author = "Indig, Bal{\'a}zs  and
      Mih{\'a}ltz, M{\'a}rton  and
      Simonyi, Andr{\'a}s",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1384",
    pages = "2425--2430",
    abstract = "This paper presents the process of enriching the verb frame database of a Hungarian natural language parser to enable the assignment of semantic roles. We accomplished this by linking the parser{'}s verb frame database to existing linguistic resources such as VerbNet and WordNet, and automatically transferring back semantic knowledge. We developed OWL ontologies that map the various constraint description formalisms of the linked resources and employed a logical reasoning device to facilitate the linking procedure. We present results and discuss the challenges and pitfalls that arose from this undertaking.",
}
@inproceedings{harige-buitelaar-2016-generating,
    title = "Generating a Large-Scale Entity Linking Dictionary from {W}ikipedia Link Structure and Article Text",
    author = "Harige, Ravindra  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1385",
    pages = "2431--2434",
    abstract = "Wikipedia has been increasingly used as a knowledge base for open-domain Named Entity Linking and Disambiguation. In this task, a dictionary with entity surface forms plays an important role in finding a set of candidate entities for the mentions in text. Existing dictionaries mostly rely on the Wikipedia link structure, like anchor texts, redirect links and disambiguation links. In this paper, we introduce a dictionary for Entity Linking that includes name variations extracted from Wikipedia article text, in addition to name variations derived from the Wikipedia link structure. With this approach, we show an increase in the coverage of entities and their mentions in the dictionary in comparison to other Wikipedia based dictionaries.",
}
@inproceedings{mccrae-etal-2016-open,
    title = "The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud",
    author = "McCrae, John Philip  and
      Chiarcos, Christian  and
      Bond, Francis  and
      Cimiano, Philipp  and
      Declerck, Thierry  and
      de Melo, Gerard  and
      Gracia, Jorge  and
      Hellmann, Sebastian  and
      Klimek, Bettina  and
      Moran, Steven  and
      Osenova, Petya  and
      Pareja-Lora, Antonio  and
      Pool, Jonathan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1386",
    pages = "2435--2441",
    abstract = "The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections. A major outcome of our work is the Linguistic Linked Open Data (LLOD) cloud, an LOD (sub-)cloud of linguistic resources, which covers various linguistic databases, lexicons, corpora, terminologies, and metadata repositories. We present and summarize five years of progress on the development of the cloud and of advancements in open data in linguistics, and we describe recent community activities. The paper aims to serve as a guideline to orient and involve researchers with the community and/or Linguistic Linked Open Data.",
}
@inproceedings{lesnikova-etal-2016-cross,
    title = "Cross-lingual {RDF} Thesauri Interlinking",
    author = "Lesnikova, Tatiana  and
      David, J{\'e}r{\^o}me  and
      Euzenat, J{\'e}r{\^o}me",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1387",
    pages = "2442--2449",
    abstract = "Various lexical resources are being published in RDF. To enhance the usability of these resources, identical resources in different data sets should be linked. If lexical resources are described in different natural languages, then techniques to deal with multilinguality are required for interlinking. In this paper, we evaluate machine translation for interlinking concepts, i.e., generic entities named with a common noun or term. In our previous work, the evaluated method has been applied on named entities. We conduct two experiments involving different thesauri in different languages. The first experiment involves concepts from the TheSoz multilingual thesaurus in three languages: English, French and German. The second experiment involves concepts from the EuroVoc and AGROVOC thesauri in English and Chinese respectively. Our results demonstrate that machine translation can be beneficial for cross-lingual thesauri interlinking independently of a dataset structure.",
}
@inproceedings{rehm-2016-language,
    title = "The Language Resource Life Cycle: Towards a Generic Model for Creating, Maintaining, Using and Distributing Language Resources",
    author = "Rehm, Georg",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1388",
    pages = "2450--2454",
    abstract = "Language Resources (LRs) are an essential ingredient of current approaches in Linguistics, Computational Linguistics, Language Technology and related fields. LRs are collections of spoken or written language data, typically annotated with linguistic analysis information. Different types of LRs exist, for example, corpora, ontologies, lexicons, collections of spoken language data (audio), or collections that also include video (multimedia, multimodal). Often, LRs are distributed with specific tools, documentation, manuals or research publications. The different phases that involve creating and distributing an LR can be conceptualised as a life cycle. While the idea of handling the LR production and maintenance process in terms of a life cycle has been brought up quite some time ago, a best practice model or common approach can still be considered a research gap. This article wants to help fill this gap by proposing an initial version of a generic Language Resource Life Cycle that can be used to inform, direct, control and evaluate LR research and development activities (including description, management, production, validation and evaluation workflows).",
}
@inproceedings{harashima-etal-2016-large,
    title = "A Large-scale Recipe and Meal Data Collection as Infrastructure for Food Research",
    author = "Harashima, Jun  and
      Ariga, Michiaki  and
      Murata, Kenta  and
      Ioki, Masayuki",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1389",
    pages = "2455--2459",
    abstract = "Everyday meals are an important part of our daily lives and, currently, there are many Internet sites that help us plan these meals. Allied to the growth in the amount of food data such as recipes available on the Internet is an increase in the number of studies on these data, such as recipe analysis and recipe search. However, there are few publicly available resources for food research; those that do exist do not include a wide range of food data or any meal data (that is, likely combinations of recipes). In this study, we construct a large-scale recipe and meal data collection as the underlying infrastructure to promote food research. Our corpus consists of approximately 1.7 million recipes and 36000 meals in cookpad, one of the largest recipe sites in the world. We made the corpus available to researchers in February 2015 and as of February 2016, 82 research groups at 56 universities have made use of it to enhance their studies.",
}
@inproceedings{orasmaa-etal-2016-estnltk,
    title = "{E}st{NLTK} - {NLP} Toolkit for {E}stonian",
    author = "Orasmaa, Siim  and
      Petmanson, Timo  and
      Tkachenko, Alexander  and
      Laur, Sven  and
      Kaalep, Heiki-Jaan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1390",
    pages = "2460--2466",
    abstract = "Although there are many tools for natural language processing tasks in Estonian, these tools are very loosely interoperable, and it is not easy to build practical applications on top of them. In this paper, we introduce a new Python library for natural language processing in Estonian, which provides unified programming interface for various NLP components. The EstNLTK toolkit provides utilities for basic NLP tasks including tokenization, morphological analysis, lemmatisation and named entity recognition as well as offers more advanced features such as a clause segmentation, temporal expression extraction and normalization, verb chain detection, Estonian Wordnet integration and rule-based information extraction. Accompanied by a detailed API documentation and comprehensive tutorials, EstNLTK is suitable for a wide range of audience. We believe EstNLTK is mature enough to be used for developing NLP-backed systems both in industry and research. EstNLTK is freely available under the GNU GPL version 2+ license, which is standard for academic software.",
}
@inproceedings{roux-2016-south,
    title = "{S}outh {A}frican National Centre for Digital Language Resources",
    author = "Roux, Justus",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1391",
    pages = "2467--2470",
    abstract = "This presentation introduces the imminent establishment of a new language resource infrastructure focusing on languages spoken in Southern Africa, with an eventual aim to become a hub for digital language resources within Sub-Saharan Africa. The Constitution of South Africa makes provision for 11 official languages all with equal status. The current language Resource Management Agency will be merged with the new Centre, which will have a wider focus than that of data acquisition, management and distribution. The Centre will entertain two main programs: Digitisation and Digital Humanities. The digitisation program will focus on the systematic digitisation of relevant text, speech and multi-modal data across the official languages. Relevancy will be determined by a Scientific Advisory Board. This will take place on a continuous basis through specified projects allocated to national members of the Centre, as well as through open-calls aimed at the academic as well as local communities. The digital resources will be managed and distributed through a dedicated web-based portal. The development of the Digital Humanities program will entail extensive academic support for projects implementing digital language based data. The Centre will function as an enabling research infrastructure primarily supported by national government and hosted by the North-West University.",
}
@inproceedings{lyding-schone-2016-design,
    title = "Design and Development of the {MERLIN} Learner Corpus Platform",
    author = {Lyding, Verena  and
      Sch{\"o}ne, Karin},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1392",
    pages = "2471--2477",
    abstract = "In this paper, we report on the design and development of an online search platform for the MERLIN corpus of learner texts in Czech, German and Italian. It was created in the context of the MERLIN project, which aims at empirically illustrating features of the Common European Framework of Reference (CEFR) for evaluating language competences based on authentic learner text productions compiled into a learner corpus. Furthermore, the project aims at providing access to the corpus through a search interface adapted to the needs of multifaceted target groups involved with language learning and teaching. This article starts by providing a brief overview on the project ambition, the data resource and its intended target groups. Subsequently, the main focus of the article is on the design and development process of the platform, which is carried out in a user-centred fashion. The paper presents the user studies carried out to collect requirements, details the resulting decisions concerning the platform design and its implementation, and reports on the evaluation of the platform prototype and final adjustments.",
}
@inproceedings{windhouwer-etal-2016-flat,
    title = "{FLAT}: Constructing a {CLARIN} Compatible Home for Language Resources",
    author = "Windhouwer, Menzo  and
      Kemps-Snijders, Marc  and
      Trilsbeek, Paul  and
      Moreira, Andr{\'e}  and
      van der Veen, Bas  and
      Silva, Guilherme  and
      von Reihn, Daniel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1393",
    pages = "2478--2483",
    abstract = "Language resources are valuable assets, both for institutions and researchers. To safeguard these resources requirements for repository systems and data management have been specified by various branch organizations, e.g., CLARIN and the Data Seal of Approval. This paper describes these and some additional ones posed by the authors{'} home institutions. And it shows how they are met by FLAT, to provide a new home for language resources. The basis of FLAT is formed by the Fedora Commons repository system. This repository system can meet many of the requirements out-of-the box, but still additional configuration and some development work is needed to meet the remaining ones, e.g., to add support for Handles and Component Metadata. This paper describes design decisions taken in the construction of FLAT{'}s system architecture via a mix-and-match strategy, with a preference for the reuse of existing solutions. FLAT is developed and used by the Meertens Institute and The Language Archive, but is also freely available for anyone in need of a CLARIN-compliant repository for their language resources.",
}
@inproceedings{odijk-2016-clariah,
    title = "{CLARIAH} in the {N}etherlands",
    author = "Odijk, Jan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1394",
    pages = "2484--2488",
    abstract = "I introduce CLARIAH in the Netherlands, which aims to contribute the Netherlands part of a Europe-wide humanities research infrastructure. I describe the digital turn in the humanities, the background and context of CLARIAH, both nationally and internationally, its relation to the CLARIN and DARIAH infrastructures, and the rationale for joining forces between CLARIN and DARIAH in the Netherlands. I also describe the first results of joining forces as achieved in the CLARIAH-SEED project, and the plans of the CLARIAH-CORE project, which is currently running",
}
@inproceedings{zinn-etal-2016-crosswalking,
    title = "Crosswalking from {CMDI} to {D}ublin {C}ore and {MARC} 21",
    author = "Zinn, Claus  and
      Trippel, Thorsten  and
      Kaminski, Steve  and
      Dima, Emanuel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1395",
    pages = "2489--2495",
    abstract = "The Component MetaData Infrastructure (CMDI) is a framework for the creation and usage of metadata formats to describe all kinds of resources in the CLARIN world. To better connect to the library world, and to allow librarians to enter metadata for linguistic resources into their catalogues, a crosswalk from CMDI-based formats to bibliographic standards is required. The general and rather fluid nature of CMDI, however, makes it hard to map arbitrary CMDI schemas to metadata standards such as Dublin Core (DC) or MARC 21, which have a mature, well-defined and fixed set of field descriptors. In this paper, we address the issue and propose crosswalks between CMDI-based profiles originating from the NaLiDa project and DC and MARC 21, respectively.",
}
@inproceedings{dipersio-etal-2016-data,
    title = "Data Management Plans and Data Centers",
    author = "DiPersio, Denise  and
      Cieri, Christopher  and
      Jaquette, Daniel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1396",
    pages = "2496--2501",
    abstract = "Data management plans, data sharing plans and the like are now required by funders worldwide as part of research proposals. Concerned with promoting the notion of open scientific data, funders view such plans as the framework for satisfying the generally accepted requirements for data generated in funded research projects, among them that it be accessible, usable, standardized to the degree possible, secure and stable. This paper examines the origins of data management plans, their requirements and issues they raise for data centers and HLT resource development in general.",
}
@inproceedings{hahn-etal-2016-uima,
    title = "{UIMA}-Based {JC}o{R}e 2.0 Goes {G}it{H}ub and Maven Central ― State-of-the-Art Software Resource Engineering and Distribution of {NLP} Pipelines",
    author = "Hahn, Udo  and
      Matthies, Franz  and
      Faessler, Erik  and
      Hellrich, Johannes",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1397",
    pages = "2502--2509",
    abstract = "We introduce JCoRe 2.0, the relaunch of a UIMA-based open software repository for full-scale natural language processing originating from the Jena University Language {\&} Information Engineering (JULIE) Lab. In an attempt to put the new release of JCoRe on firm software engineering ground, we uploaded it to GitHub, a social coding platform, with an underlying source code versioning system and various means to support collaboration for software development and code modification management. In order to automate the builds of complex NLP pipelines and properly represent and track dependencies of the underlying Java code, we incorporated Maven as part of our software configuration management efforts. In the meantime, we have deployed our artifacts on Maven Central, as well. JCoRe 2.0 offers a broad range of text analytics functionality (mostly) for English-language scientific abstracts and full-text articles, especially from the life sciences domain.",
}
@inproceedings{offersgaard-hansen-2016-facilitating,
    title = "Facilitating Metadata Interoperability in {CLARIN}-{DK}",
    author = "Offersgaard, Lene  and
      Hansen, Dorte Haltrup",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1398",
    pages = "2510--2515",
    abstract = "The issue for CLARIN archives at the metadata level is to facilitate the user{'}s possibility to describe their data, even with their own standard, and at the same time make these metadata meaningful for a variety of users with a variety of resource types, and ensure that the metadata are useful for search across all resources both at the national and at the European level. We see that different people from different research communities fill in the metadata in different ways even though the metadata was defined and documented. This has impacted when the metadata are harvested and displayed in different environments. A loss of information is at stake. In this paper we view the challenges of ensuring metadata interoperability through examples of propagation of metadata values from the CLARIN-DK archive to the VLO. We see that the CLARIN Community in many ways support interoperability, but argue that agreeing upon standards, making clear definitions of the semantics of the metadata and their content is inevitable for the interoperability to work successfully. The key points are clear and freely available definitions, accessible documentation and easily usable facilities and guidelines for the metadata creators.",
}
@inproceedings{tufis-etal-2016-ipr,
    title = "The {IPR}-cleared Corpus of Contemporary Written and Spoken {R}omanian Language",
    author = "Tufi{\textcommabelow{s}}, Dan  and
      Mititelu, Verginica Barbu  and
      Irimia, Elena  and
      Dumitrescu, {\textcommabelow{S}}tefan Daniel  and
      Boro{\textcommabelow{s}}, Tiberiu",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1399",
    pages = "2516--2521",
    abstract = "The article describes the current status of a large national project, CoRoLa, aiming at building a reference corpus for the contemporary Romanian language. Unlike many other national corpora, CoRoLa contains only - IPR cleared texts and speech data, obtained from some of the country{'}s most representative publishing houses, broadcasting agencies, editorial offices, newspapers and popular bloggers. For the written component 500 million tokens are targeted and for the oral one 300 hours of recordings. The choice of texts is done according to their functional style, domain and subdomain, also with an eye to the international practice. A metadata file (following the CMDI model) is associated to each text file. Collected texts are cleaned and transformed in a format compatible with the tools for automatic processing (segmentation, tokenization, lemmatization, part-of-speech tagging). The paper also presents up-to-date statistics about the structure of the corpus almost two years before its official launching. The corpus will be freely available for searching. Users will be able to download the results of their searches and those original files when not against stipulations in the protocols we have with text providers.",
}
@inproceedings{kren-etal-2016-syn2015,
    title = "{SYN}2015: Representative Corpus of Contemporary Written {C}zech",
    author = "K{\v{r}}en, Michal  and
      Cvr{\v{c}}ek, V{\'a}clav  and
      {\v{C}}apka, Tom{\'a}{\v{s}}  and
      {\v{C}}erm{\'a}kov{\'a}, Anna  and
      Hn{\'a}tkov{\'a}, Milena  and
      Chlumsk{\'a}, Lucie  and
      Jel{\'\i}nek, Tom{\'a}{\v{s}}  and
      Kov{\'a}{\v{r}}{\'\i}kov{\'a}, Dominika  and
      Petkevi{\v{c}}, Vladim{\'\i}r  and
      Proch{\'a}zka, Pavel  and
      Skoumalov{\'a}, Hana  and
      {\v{S}}krabal, Michal  and
      Trune{\v{c}}ek, Petr  and
      Vond{\v{r}}i{\v{c}}ka, Pavel  and
      Zasina, Adrian Jan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1400",
    pages = "2522--2528",
    abstract = "The paper concentrates on the design, composition and annotation of SYN2015, a new 100-million representative corpus of contemporary written Czech. SYN2015 is a sequel of the representative corpora of the SYN series that can be described as traditional (as opposed to the web-crawled corpora), featuring cleared copyright issues, well-defined composition, reliability of annotation and high-quality text processing. At the same time, SYN2015 is designed as a reflection of the variety of written Czech text production with necessary methodological and technological enhancements that include a detailed bibliographic annotation and text classification based on an updated scheme. The corpus has been produced using a completely rebuilt text processing toolchain called SynKorp. SYN2015 is lemmatized, morphologically and syntactically annotated with state-of-the-art tools. It has been published within the framework of the Czech National Corpus and it is available via the standard corpus query interface KonText at http://kontext.korpus.cz as well as a dataset in shuffled format.",
}
@inproceedings{del-gratta-etal-2016-lrec,
    title = "{LREC} as a Graph: People and Resources in a Network",
    author = "Del Gratta, Riccardo  and
      Frontini, Francesca  and
      Monachini, Monica  and
      Pardelli, Gabriella  and
      Russo, Irene  and
      Bartolini, Roberto  and
      Khan, Fahad  and
      Soria, Claudia  and
      Calzolari, Nicoletta",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1401",
    pages = "2529--2532",
    abstract = "This proposal describes a new way to visualise resources in the LREMap, a community-built repository of language resource descriptions and uses. The LREMap is represented as a force-directed graph, where resources, papers and authors are nodes. The analysis of the visual representation of the underlying graph is used to study how the community gathers around LRs and how LRs are used in research.",
}
@inproceedings{kamocki-etal-2016-public,
    title = "The Public License Selector: Making Open Licensing Easier",
    author = "Kamocki, Pawel  and
      Stra{\v{n}}{\'a}k, Pavel  and
      Sedl{\'a}k, Michal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1402",
    pages = "2533--2538",
    abstract = "Researchers in Natural Language Processing rely on availability of data and software, ideally under open licenses, but little is done to actively encourage it. In fact, the current Copyright framework grants exclusive rights to authors to copy their works, make them available to the public and make derivative works (such as annotated language corpora). Moreover, in the EU databases are protected against unauthorized extraction and re-utilization of their contents. Therefore, proper public licensing plays a crucial role in providing access to research data. A public license is a license that grants certain rights not to one particular user, but to the general public (everybody). Our article presents a tool that we developed and whose purpose is to assist the user in the licensing process. As software and data should be licensed under different licenses, the tool is composed of two separate parts: Data and Software. The underlying logic as well as elements of the graphic interface are presented below.",
}
@inproceedings{vitkute-adzgauskiene-etal-2016-nlp,
    title = "{NLP} Infrastructure for the {L}ithuanian Language",
    author = "Vitkut{\.e}-Ad{\v{z}}gauskien{\.e}, Daiva  and
      Utka, Andrius  and
      Amilevi{\v{c}}ius, Darius  and
      Krilavi{\v{c}}ius, Tomas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1403",
    pages = "2539--2542",
    abstract = "The Information System for Syntactic and Semantic Analysis of the Lithuanian language (lith. Lietuvi{\k{u}} kalbos sintaksin{\.e}s ir semantin{\.e}s analiz{\.e}s informacin{\.e} sistema, LKSSAIS) is the first infrastructure for the Lithuanian language combining Lithuanian language tools and resources for diverse linguistic research and applications tasks. It provides access to the basic as well as advanced natural language processing tools and resources, including tools for corpus creation and management, text preprocessing and annotation, ontology building, named entity recognition, morphosyntactic and semantic analysis, sentiment analysis, etc. It is an important platform for researchers and developers in the field of natural language technology.",
}
@inproceedings{krieg-holz-etal-2016-code,
    title = "{C}od{E} Alltag: A {G}erman-Language {E}-Mail Corpus",
    author = "Krieg-Holz, Ulrike  and
      Schuschnig, Christian  and
      Matthies, Franz  and
      Redling, Benjamin  and
      Hahn, Udo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1404",
    pages = "2543--2550",
    abstract = "We introduce CODE ALLTAG, a text corpus composed of German-language e-mails. It is divided into two partitions: the first of these portions, CODE ALLTAG{\_}XL, consists of a bulk-size collection drawn from an openly accessible e-mail archive (roughly 1.5M e-mails), whereas the second portion, CODE ALLTAG{\_}S+d, is much smaller in size (less than thousand e-mails), yet excels with demographic data from each author of an e-mail. CODE ALLTAG, thus, currently constitutes the largest E-Mail corpus ever built. In this paper, we describe, for both parts, the solicitation process for gathering e-mails, present descriptive statistical properties of the corpus, and, for CODE ALLTAG{\_}S+d, reveal a compilation of demographic features of the donors of e-mails.",
}
@inproceedings{kulick-bies-2016-rapid,
    title = "Rapid Development of Morphological Analyzers for Typologically Diverse Languages",
    author = "Kulick, Seth  and
      Bies, Ann",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1405",
    pages = "2551--2557",
    abstract = "The Low Resource Language research conducted under DARPA{'}s Broad Operational Language Translation (BOLT) program required the rapid creation of text corpora of typologically diverse languages (Turkish, Hausa, and Uzbek) which were annotated with morphological information, along with other types of annotation. Since the output of morphological analyzers is a significant aid to morphological annotation, we developed a morphological analyzer for each language in order to support the annotation task, and also as a deliverable by itself. Our framework for analyzer creation results in tables similar to those used in the successful SAMA analyzer for Arabic, but with a more abstract linguistic level, from which the tables are derived. A lexicon was developed from available resources for integration with the analyzer, and given the speed of development and uncertain coverage of the lexicon, we assumed that the analyzer would necessarily be lacking in some coverage for the project annotation. Our analyzer framework was therefore focused on rapid implementation of the key structures of the language, together with accepting {``}wildcard{''} solutions as possible analyses for a word with an unknown stem, building upon our similar experiences with morphological annotation with Modern Standard Arabic and Egyptian Arabic.",
}
@inproceedings{chakrabarty-etal-2016-neural,
    title = "A Neural Lemmatizer for {B}engali",
    author = "Chakrabarty, Abhisek  and
      Chaturvedi, Akshay  and
      Garain, Utpal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1406",
    pages = "2558--2561",
    abstract = "We propose a novel neural lemmatization model which is language independent and supervised in nature. To handle the words in a neural framework, word embedding technique is used to represent words as vectors. The proposed lemmatizer makes use of contextual information of the surface word to be lemmatized. Given a word along with its contextual neighbours as input, the model is designed to produce the lemma of the concerned word as output. We introduce a new network architecture that permits only dimension specific connections between the input and the output layer of the model. For the present work, Bengali is taken as the reference language. Two datasets are prepared for training and testing purpose consisting of 19,159 and 2,126 instances respectively. As Bengali is a resource scarce language, these datasets would be beneficial for the respective research community. Evaluation method shows that the neural lemmatizer achieves 69.57{\%} accuracy on the test dataset and outperforms the simple cosine similarity based baseline strategy by a margin of 1.37{\%}.",
}
@inproceedings{tyers-etal-2016-finite,
    title = "A Finite-state Morphological Analyser for Tuvan",
    author = "Tyers, Francis  and
      Bayyr-ool, Aziyana  and
      Salchak, Aelita  and
      Washington, Jonathan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1407",
    pages = "2562--2567",
    abstract = "{\textasciitilde}This paper describes the development of free/open-source finite-state morphological transducers for Tuvan, a Turkic language spoken in and around the Tuvan Republic in Russia. The finite-state toolkit used for the work is the Helsinki Finite-State Toolkit (HFST), we use the lexc formalism for modelling the morphotactics and twol formalism for modelling morphophonological alternations. We present a novel description of the morphological combinatorics of pseudo-derivational morphemes in Tuvan. An evaluation is presented which shows that the transducer has a reasonable coverage―around 93{\%}―on freely-available corpora of the languages, and high precision―over 99{\%}―on a manually verified test set.",
}
@inproceedings{spektors-etal-2016-tezaurs,
    title = "{T}{\=e}zaurs.lv: the Largest Open Lexical Database for {L}atvian",
    author = "Spektors, Andrejs  and
      Auzina, Ilze  and
      Dargis, Roberts  and
      Gruzitis, Normunds  and
      Paikens, Peteris  and
      Pretkalnina, Lauma  and
      Rituma, Laura  and
      Saulite, Baiba",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1408",
    pages = "2568--2571",
    abstract = "We describe an extensive and versatile lexical resource for Latvian, an under-resourced Indo-European language, which we call Tezaurs (Latvian for {`}thesaurus{'}). It comprises a large explanatory dictionary of more than 250,000 entries that are derived from more than 280 external sources. The dictionary is enriched with phonetic, morphological, semantic and other annotations, as well as augmented by various language processing tools allowing for the generation of inflectional forms and pronunciation, for on-the-fly selection of corpus examples, for suggesting synonyms, etc. Tezaurs is available as a public and widely used web application for end-users, as an open data set for the use in language technology (LT), and as an API ― a set of web services for the integration into third-party applications. The ultimate goal of Tezaurs is to be the central computational lexicon for Latvian, bringing together all Latvian words and frequently used multi-word units and allowing for the integration of other LT resources and tools.",
}
@inproceedings{motlani-etal-2016-finite,
    title = "A Finite-State Morphological Analyser for {S}indhi",
    author = "Motlani, Raveesh  and
      Tyers, Francis  and
      Sharma, Dipti",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1409",
    pages = "2572--2577",
    abstract = "Morphological analysis is a fundamental task in natural-language processing, which is used in other NLP applications such as part-of-speech tagging, syntactic parsing, information retrieval, machine translation, etc. In this paper, we present our work on the development of free/open-source finite-state morphological analyser for Sindhi. We have used Apertium{'}s lttoolbox as our finite-state toolkit to implement the transducer. The system is developed using a paradigm-based approach, wherein a paradigm defines all the word forms and their morphological features for a given stem (lemma). We have evaluated our system on the Sindhi Wikipedia corpus and achieved a reasonable coverage of 81{\%} and a precision of over 97{\%}.",
}
@inproceedings{forsberg-hulden-2016-deriving,
    title = "Deriving Morphological Analyzers from Example Inflections",
    author = "Forsberg, Markus  and
      Hulden, Mans",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1410",
    pages = "2578--2583",
    abstract = "This paper presents a semi-automatic method to derive morphological analyzers from a limited number of example inflections suitable for languages with alphabetic writing systems. The system we present learns the inflectional behavior of morphological paradigms from examples and converts the learned paradigms into a finite-state transducer that is able to map inflected forms of previously unseen words into lemmas and corresponding morphosyntactic descriptions. We evaluate the system when provided with inflection tables for several languages collected from the Wiktionary.",
}
@inproceedings{smith-hulden-2016-morphological,
    title = "Morphological Analysis of Sahidic {C}optic for Automatic Glossing",
    author = "Smith, Daniel  and
      Hulden, Mans",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1411",
    pages = "2584--2588",
    abstract = "We report on the implementation of a morphological analyzer for the Sahidic dialect of Coptic, a now extinct Afro-Asiatic language. The system is developed in the finite-state paradigm. The main purpose of the project is provide a method by which scholars and linguists can semi-automatically gloss extant texts written in Sahidic. Since a complete lexicon containing all attested forms in different manuscripts requires significant expertise in Coptic spanning almost 1,000 years, we have equipped the analyzer with a core lexicon and extended it with a {``}guesser{''} ability to capture out-of-vocabulary items in any inflection. We also suggest an ASCII transliteration for the language. A brief evaluation is provided.",
}
@inproceedings{wolinski-kieras-2016-line,
    title = "The on-line version of Grammatical Dictionary of {P}olish",
    author = "Woli{\'n}ski, Marcin  and
      Kiera{\'s}, Witold",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1412",
    pages = "2589--2594",
    abstract = "We present the new online edition of a dictionary of Polish inflection ― the Grammatical Dictionary of Polish (http://sgjp.pl). The dictionary is interesting for several reasons: it is comprehensive (over 330,000 lexemes corresponding to almost 4,300,000 different textual words; 1116 handcrafted inflectional patterns), the inflection is presented in an explicit manner in the form of carefully designed tables, the user interface facilitates advanced queries by several features (lemmas, forms, applicable grammatical categories, types of inflection). Moreover, the data of the dictionary is used in morphological analysers, including our product Morfeusz (http://sgjp. pl/morfeusz). From the start, the dictionary was meant to be comfortable for the human reader as well as to be ready for use in NLP applications. In the paper we briefly discuss both aspects of the resource.",
}
@inproceedings{koper-schulte-im-walde-2016-automatically,
    title = "Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 {G}erman Lemmas",
    author = {K{\"o}per, Maximilian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1413",
    pages = "2595--2598",
    abstract = "This paper presents a collection of 350,000 German lemmatised words, rated on four psycholinguistic affective attributes. All ratings were obtained via a supervised learning algorithm that can automatically calculate a numerical rating of a word. We applied this algorithm to abstractness, arousal, imageability and valence. Comparison with human ratings reveals high correlation across all rating types. The full resource is publically available at: http://www.ims.uni-stuttgart.de/data/affective{\_}norms/",
}
@inproceedings{passarotti-etal-2016-latin,
    title = "{L}atin {V}allex. A Treebank-based Semantic Valency Lexicon for {L}atin",
    author = "Passarotti, Marco  and
      Saavedra, Berta Gonz{\'a}lez  and
      Onambele, Christophe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1414",
    pages = "2599--2606",
    abstract = "Despite a centuries-long tradition in lexicography, Latin lacks state-of-the-art computational lexical resources. This situation is strictly related to the still quite limited amount of linguistically annotated textual data for Latin, which can help the building of new lexical resources by supporting them with empirical evidence. However, projects for creating new language resources for Latin have been launched over the last decade to fill this gap. In this paper, we present Latin Vallex, a valency lexicon for Latin built in mutual connection with the semantic and pragmatic annotation of two Latin treebanks featuring texts of different eras. On the one hand, such a connection between the empirical evidence provided by the treebanks and the lexicon allows to enhance each frame entry in the lexicon with its frequency in real data. On the other hand, each valency-capable word in the treebanks is linked to a frame entry in the lexicon.",
}
@inproceedings{hayashi-2016-framework,
    title = "A Framework for Cross-lingual/Node-wise Alignment of Lexical-Semantic Resources",
    author = "Hayashi, Yoshihiko",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1415",
    pages = "2607--2613",
    abstract = "Given lexical-semantic resources in different languages, it is useful to establish cross-lingual correspondences, preferably with semantic relation labels, between the concept nodes in these resources. This paper presents a framework for enabling a cross-lingual/node-wise alignment of lexical-semantic resources, where cross-lingual correspondence candidates are first discovered and ranked, and then classified by a succeeding module. Indeed, we propose that a two-tier classifier configuration is feasible for the second module: the first classifier filters out possibly irrelevant correspondence candidates and the second classifier assigns a relatively fine-grained semantic relation label to each of the surviving candidates. The results of Japanese-to-English alignment experiments using EDR Electronic Dictionary and Princeton WordNet are described to exemplify the validity of the proposal.",
}
@inproceedings{piao-etal-2016-lexical,
    title = "Lexical Coverage Evaluation of Large-scale Multilingual Semantic Lexicons for Twelve Languages",
    author = {Piao, Scott  and
      Rayson, Paul  and
      Archer, Dawn  and
      Bianchi, Francesca  and
      Dayrell, Carmen  and
      El-Haj, Mahmoud  and
      Jim{\'e}nez, Ricardo-Mar{\'\i}a  and
      Knight, Dawn  and
      K{\v{r}}en, Michal  and
      L{\"o}fberg, Laura  and
      Nawab, Rao Muhammad Adeel  and
      Shafi, Jawad  and
      Teh, Phoey Lee  and
      Mudraya, Olga},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1416",
    pages = "2614--2619",
    abstract = "The last two decades have seen the development of various semantic lexical resources such as WordNet (Miller, 1995) and the USAS semantic lexicon (Rayson et al., 2004), which have played an important role in the areas of natural language processing and corpus-based studies. Recently, increasing efforts have been devoted to extending the semantic frameworks of existing lexical knowledge resources to cover more languages, such as EuroWordNet and Global WordNet. In this paper, we report on the construction of large-scale multilingual semantic lexicons for twelve languages, which employ the unified Lancaster semantic taxonomy and provide a multilingual lexical knowledge base for the automatic UCREL semantic annotation system (USAS). Our work contributes towards the goal of constructing larger-scale and higher-quality multilingual semantic lexical resources and developing corpus annotation tools based on them. Lexical coverage is an important factor concerning the quality of the lexicons and the performance of the corpus annotation tools, and in this experiment we focus on evaluating the lexical coverage achieved by the multilingual lexicons and semantic annotation tools based on them. Our evaluation shows that some semantic lexicons such as those for Finnish and Italian have achieved lexical coverage of over 90{\%} while others need further expansion.",
}
@inproceedings{recski-2016-building,
    title = "Building Concept Graphs from Monolingual Dictionary Entries",
    author = "Recski, G{\'a}bor",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1417",
    pages = "2620--2624",
    abstract = "We present the dict{\_}to{\_}4lang tool for processing entries of three monolingual dictionaries of English and mapping definitions to concept graphs following the 4lang principles of semantic representation introduced by (Kornai, 2010). 4lang representations are domain- and language-independent, and make use of only a very limited set of primitives to encode the meaning of all utterances. Our pipeline relies on the Stanford Dependency Parser for syntactic analysis, the dep to 4lang module then builds directed graphs of concepts based on dependency relations between words in each definition. Several issues are handled by construction-specific rules that are applied to the output of dep{\_}to{\_}4lang. Manual evaluation suggests that ca. 75{\%} of graphs built from the Longman Dictionary are either entirely correct or contain only minor errors. dict{\_}to{\_}4lang is available under an MIT license as part of the 4lang library and has been used successfully in measuring Semantic Textual Similarity (Recski and {\'A}cs, 2015). An interactive demo of core 4lang functionalities is available at http://4lang.hlt.bme.hu.",
}
@inproceedings{hajnicz-etal-2016-semantic,
    title = "Semantic Layer of the Valence Dictionary of {P}olish Walenty",
    author = "Hajnicz, El{\.z}bieta  and
      Andrzejczuk, Anna  and
      Bartosiak, Tomasz",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1418",
    pages = "2625--2632",
    abstract = "This article presents the semantic layer of Walenty―a new valence dictionary of Polish predicates, with a number of novel features, as compared to other such dictionaries. The dictionary contains two layers, syntactic and semantic. The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants. In particular, it includes a comprehensive and powerful phraseological component. The semantic layer shows how predicates and their arguments are involved in a described situation in an utterance. These two layers are connected, representing how semantic arguments can be realised on the surface. Each syntactic schema and each semantic frame are illustrated by at least one exemplary sentence attested in linguistic reality. The semantic layer consists of semantic frames represented as lists of pairs and connected with PlWordNet lexical units. Semantic roles have a two-level representation (basic roles are provided with an attribute) enabling representation of arguments in a flexible way. Selectional preferences are based on PlWordNet structure as well.",
}
@inproceedings{busso-lenci-2016-italian,
    title = "{I}talian {V}erb{N}et: A Construction-based Approach to {I}talian Verb Classification",
    author = "Busso, Lucia  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1419",
    pages = "2633--2642",
    abstract = "This paper proposes a new method for Italian verb classification -and a preliminary example of resulting classes- inspired by Levin (1993) and VerbNet (Kipper-Schuler, 2005), yet partially independent from these resources; we achieved such a result by integrating Levin and VerbNet{'}s models of classification with other theoretic frameworks and resources. The classification is rooted in the constructionist framework (Goldberg, 1995; 2006) and is distribution-based. It is also semantically characterized by a link to FrameNet{'}ssemanticframesto represent the event expressed by a class. However, the new Italian classes maintain the hierarchic {``}tree{''} structure and monotonic nature of VerbNet{'}s classes, and, where possible, the original names (e.g.: Verbs of Killing, Verbs of Putting, etc.). We therefore propose here a taxonomy compatible with VerbNet but at the same time adapted to Italian syntax and semantics. It also addresses a number of problems intrinsic to the original classifications, such as the role of argument alternations, here regarded simply as epiphenomena, consistently with the constructionist approach.",
}
@inproceedings{grabar-hamon-2016-large,
    title = "A Large Rated Lexicon with {F}rench Medical Words",
    author = "Grabar, Natalia  and
      Hamon, Thierry",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1420",
    pages = "2643--2648",
    abstract = "Patients are often exposed to medical terms, such as anosognosia, myelodysplastic, or hepatojejunostomy, that can be semantically complex and hardly understandable by non-experts in medicine. Hence, it is important to assess which words are potentially non-understandable and require further explanations. The purpose of our work is to build specific lexicon in which the words are rated according to whether they are understandable or non-understandable. We propose to work with medical words in French such as provided by an international medical terminology. The terms are segmented in single words and then each word is manually processed by three annotators. The objective is to assign each word into one of the three categories: I can understand, I am not sure, I cannot understand. The annotators do not have medical training nor they present specific medical problems. They are supposed to represent an average patient. The inter-annotator agreement is then computed. The content of the categories is analyzed. Possible applications in which this lexicon can be helpful are proposed and discussed. The rated lexicon is freely available for the research purposes. It is accessible online at http://natalia.grabar.perso.sfr.fr/rated-lexicon.html",
}
@inproceedings{panchenko-2016-best,
    title = "Best of Both Worlds: Making Word Sense Embeddings Interpretable",
    author = "Panchenko, Alexander",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1421",
    pages = "2649--2655",
    abstract = "Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show that it is possible to retrieve synsets that correspond to automatically learned sense vectors with Precision of 0.87, Recall of 0.42 and AUC of 0.78.",
}
@inproceedings{zilio-etal-2016-verblexpor,
    title = "{V}erb{L}ex{P}or: a lexical resource with semantic roles for {P}ortuguese",
    author = "Zilio, Leonardo  and
      Finatto, Maria Jos{\'e} Bocorny  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1422",
    pages = "2656--2661",
    abstract = "This paper presents a lexical resource developed for Portuguese. The resource contains sentences annotated with semantic roles. The sentences were extracted from two domains: Cardiology research papers and newspaper articles. Both corpora were analyzed with the PALAVRAS parser and subsequently processed with a subcategorization frames extractor, so that each sentence that contained at least one main verb was stored in a database together with its syntactic organization. The annotation was manually carried out by a linguist using an annotation interface. Both the annotated and non-annotated data were exported to an XML format, which is readily available for download. The reason behind exporting non-annotated data is that there is syntactic information collected from the parser annotation in the non-annotated data, and this could be useful for other researchers. The sentences from both corpora were annotated separately, so that it is possible to access sentences either from the Cardiology or from the newspaper corpus. The full resource presents more than seven thousand semantically annotated sentences, containing 192 different verbs and more than 15 thousand individual arguments and adjuncts.",
}
@inproceedings{lopez-de-lacalle-etal-2016-multilingual,
    title = "A Multilingual Predicate Matrix",
    author = "Lopez de Lacalle, Maddalen  and
      Laparra, Egoitz  and
      Aldabe, Itziar  and
      Rigau, German",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1423",
    pages = "2662--2668",
    abstract = "This paper presents the Predicate Matrix 1.3, a lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet. This new version of the Predicate Matrix has been extended to cover nominal predicates by adding mappings to NomBank. Similarly, we have integrated resources in Spanish, Catalan and Basque. As a result, the Predicate Matrix 1.3 provides a multilingual lexicon to allow interoperable semantic analysis in multiple languages.",
}
@inproceedings{wilkinson-tim-2016-gold,
    title = "A Gold Standard for Scalar Adjectives",
    author = "Wilkinson, Bryan  and
      Tim, Oates",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1424",
    pages = "2669--2675",
    abstract = "We present a gold standard for evaluating scale membership and the order of scalar adjectives. In addition to evaluating existing methods of ordering adjectives, this knowledge will aid in studying the organization of adjectives in the lexicon. This resource is the result of two elicitation tasks conducted with informants from Amazon Mechanical Turk. The first task is notable for gathering open-ended lexical data from informants. The data is analyzed using Cultural Consensus Theory, a framework from anthropology, to not only determine scale membership but also the level of consensus among the informants (Romney et al., 1986). The second task gathers a culturally salient ordering of the words determined to be members. We use this method to produce 12 scales of adjectives for use in evaluation.",
}
@inproceedings{sekulic-snajder-2016-verbcrocean,
    title = "{V}erb{CRO}cean: A Repository of Fine-Grained Semantic Verb Relations for {C}roatian",
    author = "Sekuli{\'c}, Ivan  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1425",
    pages = "2676--2681",
    abstract = "In this paper we describe VerbCROcean, a broad-coverage repository of fine-grained semantic relations between Croatian verbs. Adopting the methodology of Chklovski and Pantel (2004) used for acquiring the English VerbOcean, we first acquire semantically related verb pairs from a web corpus hrWaC by relying on distributional similarity of subject-verb-object paths in the dependency trees. We then classify the semantic relations between each pair of verbs as similarity, intensity, antonymy, or happens-before, using a number of manually-constructed lexico-syntatic patterns. We evaluate the quality of the resulting resource on a manually annotated sample of 1000 semantic verb relations. The evaluation revealed that the predictions are most accurate for the similarity relation, and least accurate for the intensity relation. We make available two variants of VerbCROcean: a coverage-oriented version, containing about 36k verb pairs at a precision of 41{\%}, and a precision-oriented version containing about 5k verb pairs, at a precision of 56{\%}.",
}
@inproceedings{simoes-etal-2016-enriching,
    title = "Enriching a {P}ortuguese {W}ord{N}et using Synonyms from a Monolingual Dictionary",
    author = "Sim{\~o}es, Alberto  and
      G{\'o}mez Guinovart, Xavier  and
      Almeida, Jos{\'e} Jo{\~a}o",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1426",
    pages = "2682--2687",
    abstract = "In this article we present an exploratory approach to enrich a WordNet-like lexical ontology with the synonyms present in a standard monolingual Portuguese dictionary. The dictionary was converted from PDF into XML and senses were automatically identified and annotated. This allowed us to extract them, independently of definitions, and to create sets of synonyms (synsets). These synsets were then aligned with WordNet synsets, both in the same language (Portuguese) and projecting the Portuguese terms into English, Spanish and Galician. This process allowed both the addition of new term variants to existing synsets, as to create new synsets for Portuguese.",
}
@inproceedings{batanovic-etal-2016-reliable,
    title = "Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The {S}erbian Movie Review Dataset",
    author = "Batanovi{\'c}, Vuk  and
      Nikoli{\'c}, Bo{\v{s}}ko  and
      Milosavljevi{\'c}, Milan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1427",
    pages = "2688--2696",
    abstract = "Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset ― SerbMR ― the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.",
}
@inproceedings{wang-ku-2016-antusd,
    title = "{ANTUSD}: A Large {C}hinese Sentiment Dictionary",
    author = "Wang, Shih-Ming  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1428",
    pages = "2697--2702",
    abstract = "This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work. A total of 26,021 words were collected in ANTUSD. For each word, the CopeOpi numerical sentiment score and the number of positive annotation, neutral annotation, negative annotation, non-opinionated annotation, and not-a-word annotation are provided. Words and their sentiment information in ANTUSD have been linked to the Chinese ontology E-HowNet to provide rich semantic information. We demonstrate the usage of ANTUSD in polarity classification of words, and the results show that a superior f-score 98.21 is achieved, which supports the usefulness of the ANTUSD. ANTUSD can be freely obtained through application from NLPSA lab, Academia Sinica: http://academiasinicanlplab.github.io/",
}
@inproceedings{akhtar-etal-2016-aspect,
    title = "Aspect based Sentiment Analysis in {H}indi: Resource Creation and Evaluation",
    author = "Akhtar, Md Shad  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1429",
    pages = "2703--2709",
    abstract = "Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers. A number of benchmark datasets for a wide range of domains have been made available for sentiment analysis, especially in resource-rich languages. In this paper we assess the challenges of SA in Hindi by providing a benchmark setup, where we create an annotated dataset of high quality, build machine learning models for sentiment analysis in order to show the effective usage of the dataset, and finally make the resource available to the community for further advancement of research. The dataset comprises of Hindi product reviews crawled from various online sources. Each sentence of the review is annotated with aspect term and its associated sentiment. As classification algorithms we use Conditional Random Filed (CRF) and Support Vector Machine (SVM) for aspect term extraction and sentiment analysis, respectively. Evaluation results show the average F-measure of 41.07{\%} for aspect term extraction and accuracy of 54.05{\%} for sentiment classification.",
}
@inproceedings{adouane-johansson-2016-gulf,
    title = "{G}ulf {A}rabic Linguistic Resource Building for Sentiment Analysis",
    author = "Adouane, Wafia  and
      Johansson, Richard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1430",
    pages = "2710--2715",
    abstract = "This paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning. To our knowledge, no previous works were done for Gulf Arabic sentiment analysis despite the fact that it is present in different online platforms. Hence, the first challenge is the absence of annotated data and sentiment lexicons. To fill this gap, we created these two main linguistic resources. Then we conducted different experiments: use Naive Bayes classifier without any lexicon; add a sentiment lexicon designed basically for MSA; use only the compiled Gulf Arabic sentiment lexicon and finally use both MSA and Gulf Arabic sentiment lexicons. The Gulf Arabic lexicon gives a good improvement of the classifier accuracy (90.54 {\%}) over a baseline that does not use the lexicon (82.81{\%}), while the MSA lexicon causes the accuracy to drop to (76.83{\%}). Moreover, mixing MSA and Gulf Arabic lexicons causes the accuracy to drop to (84.94{\%}) compared to using only Gulf Arabic lexicon. This indicates that it is useless to use MSA resources to deal with Gulf Arabic due to the considerable differences and conflicting structures between these two languages.",
}
@inproceedings{noferesti-shamsfard-2016-using,
    title = "Using Data Mining Techniques for Sentiment Shifter Identification",
    author = "Noferesti, Samira  and
      Shamsfard, Mehrnoush",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1431",
    pages = "2716--2720",
    abstract = "Sentiment shifters, i.e., words and expressions that can affect text polarity, play an important role in opinion mining. However, the limited ability of current automated opinion mining systems to handle shifters represents a major challenge. The majority of existing approaches rely on a manual list of shifters; few attempts have been made to automatically identify shifters in text. Most of them just focus on negating shifters. This paper presents a novel and efficient semi-automatic method for identifying sentiment shifters in drug reviews, aiming at improving the overall accuracy of opinion mining systems. To this end, we use weighted association rule mining (WARM), a well-known data mining technique, for finding frequent dependency patterns representing sentiment shifters from a domain-specific corpus. These patterns that include different kinds of shifter words such as shifter verbs and quantifiers are able to handle both local and long-distance shifters. We also combine these patterns with a lexicon-based approach for the polarity classification task. Experiments on drug reviews demonstrate that extracted shifters can improve the precision of the lexicon-based approach for polarity classification 9.25 percent.",
}
@inproceedings{asher-etal-2016-discourse,
    title = "Discourse Structure and Dialogue Acts in Multiparty Dialogue: the {STAC} Corpus",
    author = "Asher, Nicholas  and
      Hunter, Julie  and
      Morey, Mathieu  and
      Farah, Benamara  and
      Afantenos, Stergos",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1432",
    pages = "2721--2727",
    abstract = "This paper describes the STAC resource, a corpus of multi-party chats annotated for discourse structure in the style of SDRT (Asher and Lascarides, 2003; Lascarides and Asher, 2009). The main goal of the STAC project is to study the discourse structure of multi-party dialogues in order to understand the linguistic strategies adopted by interlocutors to achieve their conversational goals, especially when these goals are opposed. The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides full discourse structures for multi-party dialogues. It has other remarkable features that make it an interesting resource for other topics: interleaved threads, creative language, and interactions between linguistic and extra-linguistic contexts.",
}
@inproceedings{dubuisson-duplessis-etal-2016-purely,
    title = "Purely Corpus-based Automatic Conversation Authoring",
    author = "Dubuisson Duplessis, Guillaume  and
      Letard, Vincent  and
      Ligozat, Anne-Laure  and
      Rosset, Sophie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1433",
    pages = "2728--2735",
    abstract = "This paper presents an automatic corpus-based process to author an open-domain conversational strategy usable both in chatterbot systems and as a fallback strategy for out-of-domain human utterances. Our approach is implemented on a corpus of television drama subtitles. This system is used as a chatterbot system to collect a corpus of 41 open-domain textual dialogues with 27 human participants. The general capabilities of the system are studied through objective measures and subjective self-reports in terms of understandability, repetition and coherence of the system responses selected in reaction to human utterances. Subjective evaluations of the collected dialogues are presented with respect to amusement, engagement and enjoyability. The main factors influencing those dimensions in our chatterbot experiment are discussed.",
}
@inproceedings{inoue-ueno-2016-dialogue,
    title = "Dialogue System Characterisation by Back-channelling Patterns Extracted from Dialogue Corpus",
    author = "Inoue, Masashi  and
      Ueno, Hiroshi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1434",
    pages = "2736--2740",
    abstract = "In this study, we describe the use of back-channelling patterns extracted from a dialogue corpus as a mean to characterising text-based dialogue systems. Our goal was to provide system users with the feeling that they are interacting with distinct individuals rather than artificially created characters. An analysis of the corpus revealed that substantial difference exists among speakers regarding the usage patterns of back-channelling. The patterns consist of back-channelling frequency, types, and expressions. They were used for system characterisation. Implemented system characters were tested by asking users of the dialogue system to identify the source speakers in the corpus. Experimental results suggest that possibility of using back-channelling patterns alone to characterize the dialogue system in some cases even among the same age and gender groups.",
}
@inproceedings{pincus-traum-2016-towards,
    title = "Towards Automatic Identification of Effective Clues for Team Word-Guessing Games",
    author = "Pincus, Eli  and
      Traum, David",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1435",
    pages = "2741--2747",
    abstract = "Team word-guessing games where one player, the clue-giver, gives clues attempting to elicit a target-word from another player, the receiver, are a popular form of entertainment and also used for educational purposes. Creating an engaging computational agent capable of emulating a talented human clue-giver in a timed word-guessing game depends on the ability to provide effective clues (clues able to elicit a correct guess from a human receiver). There are many available web resources and databases that can be mined for the raw material for clues for target-words; however, a large number of those clues are unlikely to be able to elicit a correct guess from a human guesser. In this paper, we propose a method for automatically filtering a clue corpus for effective clues for an arbitrary target-word from a larger set of potential clues, using machine learning on a set of features of the clues, including point-wise mutual information between a clue{'}s constituent words and a clue{'}s target-word. The results of the experiments significantly improve the average clue quality over previous approaches, and bring quality rates in-line with measures of human clue quality derived from a corpus of human-human interactions. The paper also introduces the data used to develop this method; audio recordings of people making guesses after having heard the clues being spoken by a synthesized voice.",
}
@inproceedings{wang-etal-2016-automatic,
    title = "Automatic Construction of Discourse Corpora for Dialogue Translation",
    author = "Wang, Longyue  and
      Zhang, Xiaojun  and
      Tu, Zhaopeng  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1436",
    pages = "2748--2754",
    abstract = "In this paper, a novel approach is proposed to automatically construct parallel discourse corpus for dialogue machine translation. Firstly, the parallel subtitle data and its corresponding monolingual movie script data are crawled and collected from Internet. Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts. We not only evaluate the mapping results, but also integrate speaker information into the translation. Experiments show our proposed method can achieve 81.79{\%} and 98.64{\%} accuracy on speaker and dialogue boundary annotation, and speaker-based language model adaptation can obtain around 0.5 BLEU points improvement in translation qualities. Finally, we publicly release around 100K parallel discourse data with manual speaker and dialogue boundary annotation.",
}
@inproceedings{fomicheva-bel-2016-using,
    title = "Using Contextual Information for Machine Translation Evaluation",
    author = "Fomicheva, Marina  and
      Bel, N{\'u}ria",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1437",
    pages = "2755--2761",
    abstract = "Automatic evaluation of Machine Translation (MT) is typically approached by measuring similarity between the candidate MT and a human reference translation. An important limitation of existing evaluation systems is that they are unable to distinguish candidate-reference differences that arise due to acceptable linguistic variation from the differences induced by MT errors. In this paper we present a new metric, UPF-Cobalt, that addresses this issue by taking into consideration the syntactic contexts of candidate and reference words. The metric applies a penalty when the words are similar but the contexts in which they occur are not equivalent. In this way, Machine Translations (MTs) that are different from the human translation but still essentially correct are distinguished from those that share high number of words with the reference but alter the meaning of the sentence due to translation errors. The results show that the method proposed is indeed beneficial for automatic MT evaluation. We report experiments based on two different evaluation tasks with various types of manual quality assessment. The metric significantly outperforms state-of-the-art evaluation systems in varying evaluation settings.",
}
@inproceedings{rodrigues-etal-2016-bootstrapping,
    title = "Bootstrapping a Hybrid {MT} System to a New Language Pair",
    author = "Rodrigues, Jo{\~a}o Ant{\'o}nio  and
      Rendeiro, Nuno  and
      Querido, Andreia  and
      {\v{S}}tajner, Sanja  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1438",
    pages = "2762--2765",
    abstract = "The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain. In this paper, we describe a way of adapting an existing hybrid MT system to a new language pair, and show that such a system can outperform a standard phrase-based statistical machine translation system with an average of 10 persons/month of work. This is specifically important in the case of domain-specific MT for which there is not enough parallel data for training a statistical machine translation system.",
}
@inproceedings{makrai-2016-filtering,
    title = "Filtering {W}iktionary Triangles by Linear Mbetween Distributed Word Models",
    author = "Makrai, M{\'a}rton",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1439",
    pages = "2766--2770",
    abstract = "Word translations arise in dictionary-like organization as well as via machine learning from corpora. The former is exemplified by Wiktionary, a crowd-sourced dictionary with editions in many languages. {\'A}cs et al. (2013) obtain word translations from Wiktionary with the pivot-based method, also called triangulation, that infers word translations in a pair of languages based on translations to other, typically better resourced ones called pivots. Triangulation may introduce noise if words in the pivot are polysemous. The reliability of each triangulated translation is basically estimated by the number of pivot languages (Tanaka et al 1994). Mikolov et al (2013) introduce a method for generating or scoring word translations. Translation is formalized as a linear mapping between distributed vector space models (VSM) of the two languages. VSMs are trained on monolingual data, while the mapping is learned in a supervised fashion, using a seed dictionary of some thousand word pairs. The mapping can be used to associate existing translations with a real-valued similarity score. This paper exploits human labor in Wiktionary combined with distributional information in VSMs. We train VSMs on gigaword corpora, and the linear translation mapping on direct (non-triangulated) Wiktionary pairs. This mapping is used to filter triangulated translations based on scores. The motivation is that scores by the mapping may be a smoother measure of merit than considering only the number of pivot for the triangle. We evaluate the scores against dictionaries extracted from parallel corpora (Tiedemann 2012). We show that linear translation really provides a more reliable method for triangle scoring than pivot count. The methods we use are language-independent, and the training data is easy to obtain for many languages. We chose the German-Hungarian pair for evaluation, in which the filtered triangles resulting from our experiments are the greatest freely available list of word translations we are aware of.",
}
@inproceedings{matsuzaki-etal-2016-translation,
    title = "Translation Errors and Incomprehensibility: a Case Study using Machine-Translated Second Language Proficiency Tests",
    author = "Matsuzaki, Takuya  and
      Fujita, Akira  and
      Todo, Naoya  and
      Arai, Noriko H.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1440",
    pages = "2771--2776",
    abstract = "This paper reports on an experiment where 795 human participants answered to the questions taken from second language proficiency tests that were translated to their native language. The output of three machine translation systems and two different human translations were used as the test material. We classified the translation errors in the questions according to an error taxonomy and analyzed the participants{'} response on the basis of the type and frequency of the translation errors. Through the analysis, we identified several types of errors that deteriorated most the accuracy of the participants{'} answers, their confidence on the answers, and their overall evaluation of the translation quality.",
}
@inproceedings{neale-etal-2016-word,
    title = "Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models",
    author = "Neale, Steven  and
      Gomes, Lu{\'\i}s  and
      Agirre, Eneko  and
      de Lacalle, Oier Lopez  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1441",
    pages = "2777--2783",
    abstract = "Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question. Some successful approaches have involved reformulating either WSD or the word senses it produces, but work on using traditional word senses to improve machine translation have met with limited success. In this paper, we build upon previous work that experimented on including word senses as contextual features in maxent-based translation models. Training on a large, open-domain corpus (Europarl), we demonstrate that this aproach yields significant improvements in machine translation from English to Portuguese.",
}
@inproceedings{cohen-etal-2016-supercat,
    title = "{S}uper{CAT}: The (New and Improved) Corpus Analysis Toolkit",
    author = "Cohen, K. Bretonnel  and
      Baumgartner Jr., William A.  and
      Temnikova, Irina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1442",
    pages = "2784--2788",
    abstract = "This paper reports SuperCAT, a corpus analysis toolkit. It is a radical extension of SubCAT, the Sublanguage Corpus Analysis Toolkit, from sublanguage analysis to corpus analysis in general. The idea behind SuperCAT is that representative corpora have no tendency towards closure―that is, they tend towards infinity. In contrast, non-representative corpora have a tendency towards closure―roughly, finiteness. SuperCAT focuses on general techniques for the quantitative description of the characteristics of any corpus (or other language sample), particularly concerning the characteristics of lexical distributions. Additionally, SuperCAT features a complete re-engineering of the previous SubCAT architecture.",
}
@inproceedings{roziewski-stokowiec-2016-languagecrawl,
    title = "{L}anguage{C}rawl: A Generic Tool for Building Language Models Upon {C}ommon-{C}rawl",
    author = "Roziewski, Szymon  and
      Stokowiec, Wojciech",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1443",
    pages = "2789--2793",
    abstract = "The web data contains immense amount of data, hundreds of billion words are waiting to be extracted and used for language research. In this work we introduce our tool LanguageCrawl which allows NLP researchers to easily construct web-scale corpus from Common Crawl Archive: a petabyte scale, open repository of web crawl information. Three use-cases are presented: filtering Polish websites, building an N-gram corpora and training continuous skip-gram language model with hierarchical softmax. Each of them has been implemented within the LanguageCrawl toolkit, with the possibility to adjust specified language and N-gram ranks. Special effort has been put on high computing efficiency, by applying highly concurrent multitasking. We make our tool publicly available to enrich NLP resources. We strongly believe that our work will help to facilitate NLP research, especially in under-resourced languages, where the lack of appropriately sized corpora is a serious hindrance to applying data-intensive methods, such as deep neural networks.",
}
@inproceedings{eckart-etal-2016-features,
    title = "Features for Generic Corpus Querying",
    author = "Eckart, Thomas  and
      Kuras, Christoph  and
      Quasthoff, Uwe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1444",
    pages = "2794--2798",
    abstract = "The availability of large corpora for more and more languages enforces generic querying and standard interfaces. This development is especially relevant in the context of integrated research environments like CLARIN or DARIAH. The paper focuses on several applications and implementation details on the basis of a unified corpus format, a unique POS tag set, and prepared data for word similarities. All described data or applications are already or will be in the near future accessible via well-documented RESTful Web services. The target group are all kinds of interested persons with varying level of experience in programming or corpus query languages.",
}
@inproceedings{baisa-etal-2016-european,
    title = "{E}uropean {U}nion Language Resources in {S}ketch {E}ngine",
    author = "Baisa, V{\'\i}t  and
      Michelfeit, Jan  and
      Medve{\v{d}}, Marek  and
      Jakub{\'\i}{\v{c}}ek, Milo{\v{s}}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1445",
    pages = "2799--2803",
    abstract = "Several parallel corpora built from European Union language resources are presented here. They were processed by state-of-the-art tools and made available for researchers in the corpus manager Sketch Engine. A completely new resource is introduced: EUR-Lex Corpus, being one of the largest parallel corpus available at the moment, containing 840 million English tokens and the largest language pair English-French has more than 25 million aligned segments (paragraphs).",
}
@inproceedings{banski-etal-2016-corpus,
    title = "{C}orpus {Q}uery {L}ingua {F}ranca ({CQLF})",
    author = "Ba{\'n}ski, Piotr  and
      Frick, Elena  and
      Witt, Andreas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1446",
    pages = "2804--2809",
    abstract = "The present paper describes Corpus Query Lingua Franca (ISO CQLF), a specification designed at ISO Technical Committee 37 Subcommittee 4 {``}Language resource management{''} for the purpose of facilitating the comparison of properties of corpus query languages. We overview the motivation for this endeavour and present its aims and its general architecture. CQLF is intended as a multi-part specification; here, we concentrate on the basic metamodel that provides a frame that the other parts fit in.",
}
@inproceedings{kiss-etal-2016-sense,
    title = "A sense-based lexicon of count and mass expressions: The Bochum {E}nglish Countability Lexicon",
    author = "Kiss, Tibor  and
      Pelletier, Francis Jeffry  and
      Husic, Halima  and
      Simunic, Roman Nino  and
      Poppek, Johanna Marie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1447",
    pages = "2810--2814",
    abstract = "The present paper describes the current release of the Bochum English Countability Lexicon (BECL 2.1), a large empirical database consisting of lemmata from Open ANC (http://www.anc.org) with added senses from WordNet (Fellbaum 1998). BECL 2.1 contains {\mbox{$\approx$}} 11,800 annotated noun-sense pairs, divided in four major countability classes and 18 fine-grained subclasses. In the current version, BECL also provides information on nouns whose senses occur in more than one class allowing a closer look on polysemy and homonymy with regard to countability. Further included are sets of similar senses using the Leacock and Chodorow (LCH) score for semantic similarity (Leacock {\&} Chodorow 1998), information on orthographic variation, on the completeness of all WordNet senses in the database and an annotated representation of different types of proper names. The further development of BECL will investigate the different countability classes of proper names and the general relation between semantic similarity and countability as well as recurring syntactic patterns for noun-sense pairs. The BECL 2.1 database is also publicly available via http://count-and-mass.org.",
}
@inproceedings{kornai-etal-2016-detecting,
    title = "Detecting Optional Arguments of Verbs",
    author = "Kornai, Andr{\'a}s  and
      Nemeskey, D{\'a}vid M{\'a}rk  and
      Recski, G{\'a}bor",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1448",
    pages = "2815--2818",
    abstract = "We propose a novel method for detecting optional arguments of Hungarian verbs using only positive data. We introduce a custom variant of collexeme analysis that explicitly models the noise in verb frames. Our method is, for the most part, unsupervised: we use the spectral clustering algorithm described in Brew and Schulte in Walde (2002) to build a noise model from a short, manually verified seed list of verbs. We experimented with both raw count- and context-based clusterings and found their performance almost identical. The code for our algorithm and the frame list are freely available at http://hlt.bme.hu/en/resources/tade.",
}
@inproceedings{kloppenburg-nissim-2016-leveraging,
    title = "Leveraging Native Data to Correct Preposition Errors in Learners{'} {D}utch",
    author = "Kloppenburg, Lennart  and
      Nissim, Malvina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1449",
    pages = "2819--2824",
    abstract = "We address the task of automatically correcting preposition errors in learners{'} Dutch by modelling preposition usage in native language. Specifically, we build two models exploiting a large corpus of Dutch. The first is a binary model for detecting whether a preposition should be used at all in a given position or not. The second is a multiclass model for selecting the appropriate preposition in case one should be used. The models are tested on native as well as learners data. For the latter we exploit a crowdsourcing strategy to elicit native judgements. On native test data the models perform very well, showing that we can model preposition usage appropriately. However, the evaluation on learners{'} data shows that while detecting that a given preposition is wrong is doable reasonably well, detecting the absence of a preposition is a lot more difficult. Observing such results and the data we deal with, we envisage various ways of improving performance, and report them in the final section of this article.",
}
@inproceedings{basile-sangati-2016-h,
    title = "{D}({H})ante: A New Set of Tools for {XIII} Century {I}talian",
    author = "Basile, Angelo  and
      Sangati, Federico",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1450",
    pages = "2825--2828",
    abstract = "In this paper we describe 1) the process of converting a corpus of Dante Alighieri from a TEI XML format in to a pseudo-CoNLL format; 2) how a pos-tagger trained on modern Italian performs on Dante{'}s Italian 3) the performances of two different pos-taggers trained on the given corpus. We are making our conversion scripts and models available to the community. The two other models trained on the corpus performs reasonably well. The tool used for the conversion process might turn useful for bridging the gap between traditional digital humanities and modern NLP applications since the TEI original format is not usually suitable for being processed with standard NLP tools. We believe our work will serve both communities: the DH community will be able to tag new documents and the NLP world will have an easier way in converting existing documents to a standardized machine-readable format.",
}
@inproceedings{celli-etal-2016-multilevel,
    title = "Multilevel Annotation of Agreement and Disagreement in {I}talian News Blogs",
    author = "Celli, Fabio  and
      Riccardi, Giuseppe  and
      Alam, Firoj",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1451",
    pages = "2829--2832",
    abstract = "In this paper, we present a corpus of news blog conversations in Italian annotated with gold standard agreement/disagreement relations at message and sentence levels. This is the first resource of this kind in Italian. From the analysis of ADRs at the two levels emerged that agreement annotated at message level is consistent and generally reflected at sentence level, moreover, the argumentation structure of disagreement is more complex than agreement. The manual error analysis revealed that this resource is useful not only for the analysis of argumentation, but also for the detection of irony/sarcasm in online debates. The corpus and annotation tool are available for research purposes on request.",
}
@inproceedings{ozates-etal-2016-sentence,
    title = "Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization",
    author = {{\"O}zate{\c{s}}, {\c{S}}aziye Bet{\"u}l  and
      {\"O}zg{\"u}r, Arzucan  and
      Radev, Dragomir},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1452",
    pages = "2833--2838",
    abstract = "We introduce an approach based on using the dependency grammar representations of sentences to compute sentence similarity for extractive multi-document summarization. We adapt and investigate the effects of two untyped dependency tree kernels, which have originally been proposed for relation extraction, to the multi-document summarization problem. In addition, we propose a series of novel dependency grammar based kernels to better represent the syntactic and semantic similarities among the sentences. The proposed methods incorporate the type information of the dependency relations for sentence similarity calculation. To our knowledge, this is the first study that investigates using dependency tree based sentence similarity for multi-document summarization.",
}
@inproceedings{loaiciga-gulordava-2016-discontinuous,
    title = "Discontinuous Verb Phrases in Parsing and Machine Translation of {E}nglish and {G}erman",
    author = "Lo{\'a}iciga, Sharid  and
      Gulordava, Kristina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1453",
    pages = "2839--2845",
    abstract = "In this paper, we focus on the verb-particle (V-Prt) split construction in English and German and its difficulty for parsing and Machine Translation (MT). For German, we use an existing test suite of V-Prt split constructions, while for English, we build a new and comparable test suite from raw data. These two data sets are then used to perform an analysis of errors in dependency parsing, word-level alignment and MT, which arise from the discontinuous order in V-Prt split constructions. In the automatic alignments of parallel corpora, most of the particles align to NULL. These mis-alignments and the inability of phrase-based MT system to recover discontinuous phrases result in low quality translations of V-Prt split constructions both in English and German. However, our results show that the V-Prt split phrases are correctly parsed in 90{\%} of cases, suggesting that syntactic-based MT should perform better on these constructions. We evaluate a syntactic-based MT system on German and compare its performance to the phrase-based system.",
}
@inproceedings{krisch-etal-2016-lexical,
    title = "A Lexical Resource for the Identification of {``}Weak Words{''} in {G}erman Specification Documents",
    author = "Krisch, Jennifer  and
      Dick, Melanie  and
      Jauch, Ronny  and
      Heid, Ulrich",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1454",
    pages = "2846--2850",
    abstract = "We report on the creation of a lexical resource for the identification of potentially unspecific or imprecise constructions in German requirements documentation from the car manufacturing industry. In requirements engineering, such expressions are called {``}weak words{''}: they are not sufficiently precise to ensure an unambiguous interpretation by the contractual partners, who for the definition of their cooperation, typically rely on specification documents (Melchisedech, 2000); an example are dimension adjectives, such as kurz or lang ({`}short{'}, {`}long{'}) which need to be modified by adverbials indicating the exact duration, size etc. Contrary to standard practice in requirements engineering, where the identification of such weak words is merely based on stopword lists, we identify weak uses in context, by querying annotated text. The queries are part of the resource, as they define the conditions when a word use is weak. We evaluate the recognition of weak uses on our development corpus and on an unseen evaluation corpus, reaching stable F1-scores above 0.95.",
}
@inproceedings{vetulani-etal-2016-recent,
    title = "Recent Advances in Development of a Lexicon-Grammar of {P}olish: {P}ol{N}et 3.0",
    author = "Vetulani, Zygmunt  and
      Vetulani, Gra{\.z}yna  and
      Kochanowski, Bart{\l}omiej",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1455",
    pages = "2851--2854",
    abstract = "The granularity of PolNet (Polish Wordnet) is the main theoretical issue discussed in the paper. We describe the latest extension of PolNet including valency information of simple verbs and noun-verb collocations using manual and machine-assisted methods. Valency is defined to include both semantic and syntactic selectional restrictions. We assume the valency structure of a verb to be an index of meaning. Consistently we consider it an attribute of a synset. Strict application of this principle results in fine granularity of the verb section of the wordnet. Considering valency as a distinctive feature of synsets was an essential step to transform the initial PolNet (first intended as a lexical ontology) into a lexicon-grammar. For the present refinement of PolNet we assume that the category of language register is a part of meaning. The totality of PolNet 2.0 synsets is being revised in order to split the PolNet 2.0 synsets that contain different register words into register-uniform sub-synsets. We completed this operation for synsets that were used as values of semantic roles. The operation augmented the number of considered synsets by 29{\%}. In the paper we report an extension of the class of collocation-based verb synsets.",
}
@inproceedings{mahlow-2016-c,
    title = "{C}-{WEP}―{R}ich Annotated Collection of Writing Errors by Professionals",
    author = "Mahlow, Cerstin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1456",
    pages = "2855--2861",
    abstract = "This paper presents C-WEP, the Collection of Writing Errors by Professionals Writers of German. It currently consists of 245 sentences with grammatical errors. All sentences are taken from published texts. All authors are professional writers with high skill levels with respect to German, the genres, and the topics. The purpose of this collection is to provide seeds for more sophisticated writing support tools as only a very small proportion of those errors can be detected by state-of-the-art checkers. C-WEP is annotated on various levels and freely available.",
}
@inproceedings{klyueva-stranak-2016-improving,
    title = "Improving corpus search via parsing",
    author = "Klyueva, Natalia  and
      Stra{\v{n}}{\'a}k, Pavel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1457",
    pages = "2862--2866",
    abstract = "In this paper, we describe an addition to the corpus query system Kontext that enables to enhance the search using syntactic attributes in addition to the existing features, mainly lemmas and morphological categories. We present the enhancements of the corpus query system itself, the attributes we use to represent syntactic structures in data, and some examples of querying the syntactically annotated corpora, such as treebanks in various languages as well as an automatically parsed large corpus.",
}
@inproceedings{palogiannidi-etal-2016-affective,
    title = "Affective Lexicon Creation for the {G}reek Language",
    author = "Palogiannidi, Elisavet  and
      Koutsakis, Polychronis  and
      Iosif, Elias  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1458",
    pages = "2867--2872",
    abstract = "Starting from the English affective lexicon ANEW (Bradley and Lang, 1999a) we have created the first Greek affective lexicon. It contains human ratings for the three continuous affective dimensions of valence, arousal and dominance for 1034 words. The Greek affective lexicon is compared with affective lexica in English, Spanish and Portuguese. The lexicon is automatically expanded by selecting a small number of manually annotated words to bootstrap the process of estimating affective ratings of unknown words. We experimented with the parameters of the semantic-affective model in order to investigate their impact to its performance, which reaches 85{\%} binary classification accuracy (positive vs. negative ratings). We share the Greek affective lexicon that consists of 1034 words and the automatically expanded Greek affective lexicon that contains 407K words.",
}
@inproceedings{szabo-etal-2016-hungarian,
    title = "A {H}ungarian Sentiment Corpus Manually Annotated at Aspect Level",
    author = "Szab{\'o}, Martina Katalin  and
      Vincze, Veronika  and
      Simk{\'o}, Katalin Ilona  and
      Varga, Viktor  and
      Hangya, Viktor",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1459",
    pages = "2873--2878",
    abstract = "In this paper we present a Hungarian sentiment corpus manually annotated at aspect level. Our corpus consists of Hungarian opinion texts written about different types of products. The main aim of creating the corpus was to produce an appropriate database providing possibilities for developing text mining software tools. The corpus is a unique Hungarian database: to the best of our knowledge, no digitized Hungarian sentiment corpus that is annotated on the level of fragments and targets has been made so far. In addition, many language elements of the corpus, relevant from the point of view of sentiment analysis, got distinct types of tags in the annotation. In this paper, on the one hand, we present the method of annotation, and we discuss the difficulties concerning text annotation process. On the other hand, we provide some quantitative and qualitative data on the corpus. We conclude with a description of the applicability of the corpus.",
}
@inproceedings{ruppenhofer-brandes-2016-effect,
    title = "Effect Functors for Opinion Inference",
    author = "Ruppenhofer, Josef  and
      Brandes, Jasper",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1460",
    pages = "2879--2887",
    abstract = "Sentiment analysis has so far focused on the detection of explicit opinions. However, of late implicit opinions have received broader attention, the key idea being that the evaluation of an event type by a speaker depends on how the participants in the event are valued and how the event itself affects the participants. We present an annotation scheme for adding relevant information, couched in terms of so-called effect functors, to German lexical items. Our scheme synthesizes and extends previous proposals. We report on an inter-annotator agreement study. We also present results of a crowdsourcing experiment to test the utility of some known and some new functors for opinion inference where, unlike in previous work, subjects are asked to reason from event evaluation to participant evaluation.",
}
@inproceedings{klenner-amsler-2016-sentiframes,
    title = "{S}entiframes: A Resource for Verb-centered {G}erman Sentiment Inference",
    author = "Klenner, Manfred  and
      Amsler, Michael",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1461",
    pages = "2888--2891",
    abstract = "In this paper, a German verb resource for verb-centered sentiment inference is introduced and evaluated. Our model specifies verb polarity frames that capture the polarity effects on the fillers of the verb{'}s arguments given a sentence with that verb frame. Verb signatures and selectional restrictions are also part of the model. An algorithm to apply the verb resource to treebank sentences and the results of our first evaluation are discussed.",
}
@inproceedings{stranisci-etal-2016-annotating,
    title = "Annotating Sentiment and Irony in the Online {I}talian Political Debate on {\#}labuonascuola",
    author = "Stranisci, Marco  and
      Bosco, Cristina  and
      Hern{\'a}ndez Far{\'\i}as, Delia Iraz{\'u}  and
      Patti, Viviana",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1462",
    pages = "2892--2899",
    abstract = "In this paper we present the TWitterBuonaScuola corpus (TW-BS), a novel Italian linguistic resource for Sentiment Analysis, developed with the main aim of analyzing the online debate on the controversial Italian political reform {``}Buona Scuola{''} (Good school), aimed at reorganizing the national educational and training systems. We describe the methodologies applied in the collection and annotation of data. The collection has been driven by the detection of the hashtags mainly used by the participants to the debate, while the annotation has been focused on sentiment polarity and irony, but also extended to mark the aspects of the reform that were mainly discussed in the debate. An in-depth study of the disagreement among annotators is included. We describe the collection and annotation stages, and the in-depth analysis of disagreement made with Crowdflower, a crowdsourcing annotation platform.",
}
@inproceedings{el-beltagy-2016-nileulex,
    title = "{N}ile{UL}ex: A Phrase and Word Level Sentiment Lexicon for {E}gyptian and {M}odern {S}tandard {A}rabic",
    author = "El-Beltagy, Samhaa R.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1463",
    pages = "2900--2905",
    abstract = "This paper presents NileULex, which is an Arabic sentiment lexicon containing close to six thousands Arabic words and compound phrases. Forty five percent of the terms and expressions in the lexicon are Egyptian or colloquial while fifty five percent are Modern Standard Arabic. While the collection of many of the terms included in the lexicon was done automatically, the actual addition of any term was done manually. One of the important criterions for adding terms to the lexicon, was that they be as unambiguous as possible. The result is a lexicon with a much higher quality than any translated variant or automatically constructed one. To demonstrate that a lexicon such as this can directly impact the task of sentiment analysis, a very basic machine learning based sentiment analyser that uses unigrams, bigrams, and lexicon based features was applied on two different Twitter datasets. The obtained results were compared to a baseline system that only uses unigrams and bigrams. The same lexicon based features were also generated using a publicly available translation of a popular sentiment lexicon. The experiments show that usage of the developed lexicon improves the results over both the baseline and the publicly available lexicon.",
}
@inproceedings{wawer-2016-opfi,
    title = "{OPFI}: A Tool for Opinion Finding in {P}olish",
    author = "Wawer, Aleksander",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1464",
    pages = "2906--2909",
    abstract = "The paper contains a description of OPFI: Opinion Finder for the Polish Language, a freely available tool for opinion target extraction. The goal of the tool is opinion finding: a task of identifying tuples composed of sentiment (positive or negative) and its target (about what or whom is the sentiment expressed). OPFI is not dependent on any particular method of sentiment identification and provides a built-in sentiment dictionary as a convenient option. Technically, it contains implementations of three different modes of opinion tuple generation: one hybrid based on dependency parsing and CRF, the second based on shallow parsing and the third on deep learning, namely GRU neural network. The paper also contains a description of related language resources: two annotated treebanks and one set of tweets.",
}
@inproceedings{de-clercq-hoste-2016-rude,
    title = "Rude waiter but mouthwatering pastries! An exploratory study into {D}utch Aspect-Based Sentiment Analysis",
    author = "De Clercq, Orph{\'e}e  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1465",
    pages = "2910--2917",
    abstract = "The fine-grained task of automatically detecting all sentiment expressions within a given document and the aspects to which they refer is known as aspect-based sentiment analysis. In this paper we present the first full aspect-based sentiment analysis pipeline for Dutch and apply it to customer reviews. To this purpose, we collected reviews from two different domains, i.e. restaurant and smartphone reviews. Both corpora have been manually annotated using newly developed guidelines that comply to standard practices in the field. For our experimental pipeline we perceive aspect-based sentiment analysis as a task consisting of three main subtasks which have to be tackled incrementally: aspect term extraction, aspect category classification and polarity classification. First experiments on our Dutch restaurant corpus reveal that this is indeed a feasible approach that yields promising results.",
}
@inproceedings{shi-etal-2016-building,
    title = "Building A Case-based Semantic {E}nglish-{C}hinese Parallel Treebank",
    author = "Shi, Huaxing  and
      Zhao, Tiejun  and
      Su, Keh-Yih",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1466",
    pages = "2918--2924",
    abstract = "We construct a case-based English-to-Chinese semantic constituent parallel Treebank for a Statistical Machine Translation (SMT) task by labelling each node of the Deep Syntactic Tree (DST) with our refined semantic cases. Since subtree span-crossing is harmful in tree-based SMT, DST is adopted to alleviate this problem. At the same time, we tailor an existing case set to represent bilingual shallow semantic relations more precisely. This Treebank is a part of a semantic corpus building project, which aims to build a semantic bilingual corpus annotated with syntactic, semantic cases and word senses. Data in our Treebank is from the news domain of Datum corpus. 4,000 sentence pairs are selected to cover various lexicons and part-of-speech (POS) n-gram patterns as much as possible. This paper presents the construction of this case Treebank. Also, we have tested the effect of adopting DST structure in alleviating subtree span-crossing. Our preliminary analysis shows that the compatibility between Chinese and English trees can be significantly increased by transforming the parse-tree into the DST. Furthermore, the human agreement rate in annotation is found to be acceptable (90{\%} in English nodes, 75{\%} in Chinese nodes).",
}
@inproceedings{li-etal-2016-uzbek,
    title = "{U}zbek-{E}nglish and {T}urkish-{E}nglish Morpheme Alignment Corpora",
    author = "Li, Xuansong  and
      Tracey, Jennifer  and
      Grimes, Stephen  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1467",
    pages = "2925--2930",
    abstract = "Morphologically-rich languages pose problems for machine translation (MT) systems, including word-alignment errors, data sparsity and multiple affixes. Current alignment models at word-level do not distinguish words and morphemes, thus yielding low-quality alignment and subsequently affecting end translation quality. Models using morpheme-level alignment can reduce the vocabulary size of morphologically-rich languages and overcomes data sparsity. The alignment data based on smallest units reveals subtle language features and enhances translation quality. Recent research proves such morpheme-level alignment (MA) data to be valuable linguistic resources for SMT, particularly for languages with rich morphology. In support of this research trend, the Linguistic Data Consortium (LDC) created Uzbek-English and Turkish-English alignment data which are manually aligned at the morpheme level. This paper describes the creation of MA corpora, including alignment and tagging process and approaches, highlighting annotation challenges and specific features of languages with rich morphology. The light tagging annotation on the alignment layer adds extra value to the MA data, facilitating users in flexibly tailoring the data for various MT model training.",
}
@inproceedings{chu-etal-2016-parallel,
    title = "Parallel Sentence Extraction from Comparable Corpora with Neural Network Features",
    author = "Chu, Chenhui  and
      Dabre, Raj  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1468",
    pages = "2931--2935",
    abstract = "Parallel corpora are crucial for machine translation (MT), however they are quite scarce for most language pairs and domains. As comparable corpora are far more available, many studies have been conducted to extract parallel sentences from them for MT. In this paper, we exploit the neural network features acquired from neural MT for parallel sentence extraction. We observe significant improvements for both accuracy in sentence extraction and MT performance.",
}
@inproceedings{vicente-etal-2016-tweetmt,
    title = "{T}weet{MT}: A Parallel Microblog Corpus",
    author = "Vicente, I{\~n}aki San  and
      Alegr{\'\i}a, I{\~n}aki  and
      Espa{\~n}a-Bonet, Cristina  and
      Gamallo, Pablo  and
      Oliveira, Hugo Gon{\c{c}}alo  and
      Garcia, Eva Mart{\'\i}nez  and
      Toral, Antonio  and
      Zubiaga, Arkaitz  and
      Aranberri, Nora",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1469",
    pages = "2936--2941",
    abstract = "We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese), all of which have an official status in the Iberian Peninsula. The corpus has been created by combining automatic collection and crowdsourcing approaches, and it is publicly available. It is intended for the development and testing of microtext machine translation systems. In this paper we describe the methodology followed to build the corpus, and present the results of the shared task in which it was tested.",
}
@inproceedings{neves-etal-2016-scielo,
    title = "The Scielo Corpus: a Parallel Corpus of Scientific Publications for Biomedicine",
    author = "Neves, Mariana  and
      Yepes, Antonio Jimeno  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1470",
    pages = "2942--2948",
    abstract = "The biomedical scientific literature is a rich source of information not only in the English language, for which it is more abundant, but also in other languages, such as Portuguese, Spanish and French. We present the first freely available parallel corpus of scientific publications for the biomedical domain. Documents from the {''}Biological Sciences{''} and {''}Health Sciences{''} categories were retrieved from the Scielo database and parallel titles and abstracts are available for the following language pairs: Portuguese/English (about 86,000 documents in total), Spanish/English (about 95,000 documents) and French/English (about 2,000 documents). Additionally, monolingual data was also collected for all four languages. Sentences in the parallel corpus were automatically aligned and a manual analysis of 200 documents by native experts found that a minimum of 79{\%} of sentences were correctly aligned in all language pairs. We demonstrate the utility of the corpus by running baseline machine translation experiments. We show that for all language pairs, a statistical machine translation system trained on the parallel corpora achieves performance that rivals or exceeds the state of the art in the biomedical domain. Furthermore, the corpora are currently being used in the biomedical task in the First Conference on Machine Translation (WMT{'}16).",
}
@inproceedings{ljubesic-etal-2016-producing,
    title = "Producing Monolingual and Parallel Web Corpora at the Same Time - {S}pider{L}ing and Bitextor{'}s Love Affair",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Espl{\`a}-Gomis, Miquel  and
      Toral, Antonio  and
      Rojas, Sergio Ortiz  and
      Klubi{\v{c}}ka, Filip",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1471",
    pages = "2949--2956",
    abstract = "This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For gathering linguistically relevant data from top-level domains we use the SpiderLing crawler, modified to crawl data written in multiple languages. The output of this process is then fed to Bitextor, a tool for harvesting parallel data from a collection of documents. We call the system combining these two tools Spidextor, a blend of the names of its two crucial parts. We evaluate the described approach intrinsically by measuring the accuracy of the extracted bitexts from the Croatian top-level domain {``}.hr{''} and the Slovene top-level domain {``}.si{''}, and extrinsically on the English-Croatian language pair by comparing an SMT system built from the crawled data with third-party systems. We finally present parallel datasets collected with our approach for the English-Croatian, English-Finnish, English-Serbian and English-Slovene language pairs.",
}
@inproceedings{bell-etal-2016-towards,
    title = "Towards Using Social Media to Identify Individuals at Risk for Preventable Chronic Illness",
    author = "Bell, Dane  and
      Fried, Daniel  and
      Huangfu, Luwen  and
      Surdeanu, Mihai  and
      Kobourov, Stephen",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1472",
    pages = "2957--2964",
    abstract = "We describe a strategy for the acquisition of training data necessary to build a social-media-driven early detection system for individuals at risk for (preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter. The questions are designed to inspire participant engagement and collect relevant data to train a public-health model applied to individuals. Prior systems designed to use social media such as Twitter to predict obesity (a risk factor for T2DM) operate on entire communities such as states, counties, or cities, based on statistics gathered by government agencies. Because there is considerable variation among individuals within these groups, training data on the individual level would be more effective, but this data is difficult to acquire. The approach proposed here aims to address this issue. Our strategy has two steps. First, we trained a random forest classifier on data gathered from (public) Twitter statuses and state-level statistics with state-of-the-art accuracy. We then converted this classifier into a 20-questions-style quiz and made it available online. In doing so, we achieved high engagement with individuals that took the quiz, while also building a training set of voluntarily supplied individual-level data for future classification.",
}
@inproceedings{sommerdijk-etal-2016-tweets,
    title = "Can Tweets Predict {TV} Ratings?",
    author = "Sommerdijk, Bridget  and
      Sanders, Eric  and
      van den Bosch, Antal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1473",
    pages = "2965--2970",
    abstract = "We set out to investigate whether TV ratings and mentions of TV programmes on the Twitter social media platform are correlated. If such a correlation exists, Twitter may be used as an alternative source for estimating viewer popularity. Moreover, the Twitter-based rating estimates may be generated during the programme, or even before. We count the occurrences of programme-specific hashtags in an archive of Dutch tweets of eleven popular TV shows broadcast in the Netherlands in one season, and perform correlation tests. Overall we find a strong correlation of 0.82; the correlation remains strong, 0.79, if tweets are counted a half hour before broadcast time. However, the two most popular TV shows account for most of the positive effect; if we leave out the single and second most popular TV shows, the correlation drops to being moderate to weak. Also, within a TV show, correlations between ratings and tweet counts are mostly weak, while correlations between TV ratings of the previous and next shows are strong. In absence of information on previous shows, Twitter-based counts may be a viable alternative to classic estimation methods for TV ratings. Estimates are more reliable with more popular TV shows.",
}
@inproceedings{park-etal-2016-classifying,
    title = "Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus",
    author = "Park, SoHyun  and
      Fazly, Afsaneh  and
      Lee, Annie  and
      Seibel, Brandon  and
      Zi, Wenjie  and
      Cook, Paul",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1474",
    pages = "2971--2975",
    abstract = "In this paper we consider the problem of out-of-vocabulary term classification in web forum text from the automotive domain. We develop a set of nine domain- and application-specific categories for out-of-vocabulary terms. We then propose a supervised approach to classify out-of-vocabulary terms according to these categories, drawing on features based on word embeddings, and linguistic knowledge of common properties of out-of-vocabulary terms. We show that the features based on word embeddings are particularly informative for this task. The categories that we predict could serve as a preliminary, automatically-generated source of lexical knowledge about out-of-vocabulary terms. Furthermore, we show that this approach can be adapted to give a semi-automated method for identifying out-of-vocabulary terms of a particular category, automotive named entities, that is of particular interest to us.",
}
@inproceedings{sakaki-etal-2016-corpus,
    title = "Corpus for Customer Purchase Behavior Prediction in Social Media",
    author = "Sakaki, Shigeyuki  and
      Chen, Francine  and
      Korpusik, Mandy  and
      Chen, Yan-Ying",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1475",
    pages = "2976--2980",
    abstract = "Many people post about their daily life on social media. These posts may include information about the purchase activity of people, and insights useful to companies can be derived from them: e.g. profile information of a user who mentioned something about their product. As a further advanced analysis, we consider extracting users who are likely to buy a product from the set of users who mentioned that the product is attractive. In this paper, we report our methodology for building a corpus for Twitter user purchase behavior prediction. First, we collected Twitter users who posted a want phrase + product name: e.g. {``}want a Xperia{''} as candidate want users, and also candidate bought users in the same way. Then, we asked an annotator to judge whether a candidate user actually bought a product. We also annotated whether tweets randomly sampled from want/bought user timelines are relevant or not to purchase. In this annotation, 58{\%} of want user tweets and 35{\%} of bought user tweets were annotated as relevant. Our data indicate that information embedded in timeline tweets can be used to predict purchase behavior of tweeted products.",
}
@inproceedings{celebi-ozgur-2016-segmenting,
    title = "Segmenting Hashtags using Automatically Created Training Data",
    author = {{\c{C}}elebi, Arda  and
      {\"O}zg{\"u}r, Arzucan},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1476",
    pages = "2981--2985",
    abstract = "Hashtags, which are commonly composed of multiple words, are increasingly used to convey the actual messages in tweets. Understanding what tweets are saying is getting more dependent on understanding hashtags. Therefore, identifying the individual words that constitute a hashtag is an important, yet a challenging task due to the abrupt nature of the language used in tweets. In this study, we introduce a feature-rich approach based on using supervised machine learning methods to segment hashtags. Our approach is unsupervised in the sense that instead of using manually segmented hashtags for training the machine learning classifiers, we automatically create our training data by using tweets as well as by automatically extracting hashtag segmentations from a large corpus. We achieve promising results with such automatically created noisy training data.",
}
@inproceedings{hovy-johannsen-2016-exploring,
    title = "Exploring Language Variation Across {E}urope - A Web-based Tool for Computational Sociolinguistics",
    author = "Hovy, Dirk  and
      Johannsen, Anders",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1477",
    pages = "2986--2989",
    abstract = "Language varies not only between countries, but also along regional and socio-demographic lines. This variation is one of the driving factors behind language change. However, investigating language variation is a complex undertaking: the more factors we want to consider, the more data we need. Traditional qualitative methods are not well-suited to do this, an therefore restricted to isolated factors. This reduction limits the potential insights, and risks attributing undue importance to easily observed factors. While there is a large interest in linguistics to increase the quantitative aspect of such studies, it requires training in both variational linguistics and computational methods, a combination that is still not common. We take a first step here to alleviating the problem by providing an interface, www.languagevariation.com, to explore large-scale language variation along multiple socio-demographic factors {--} without programming knowledge. It makes use of large amounts of data and provides statistical analyses, maps, and interactive features that will enable scholars to explore language variation in a data-driven way.",
}
@inproceedings{zhang-etal-2016-predicting,
    title = "Predicting Author Age from {W}eibo Microblog Posts",
    author = "Zhang, Wanru  and
      Caines, Andrew  and
      Alikaniotis, Dimitrios  and
      Buttery, Paula",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1478",
    pages = "2990--2997",
    abstract = "Binary file summaries/958.html matches",
}
@inproceedings{yates-etal-2016-effects,
    title = "Effects of Sampling on {T}witter Trend Detection",
    author = "Yates, Andrew  and
      Kolcz, Alek  and
      Goharian, Nazli  and
      Frieder, Ophir",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1479",
    pages = "2998--3005",
    abstract = "Much research has focused on detecting trends on Twitter, including health-related trends such as mentions of Influenza-like illnesses or their symptoms. The majority of this research has been conducted using Twitter{'}s public feed, which includes only about 1{\%} of all public tweets. It is unclear if, when, and how using Twitter{'}s 1{\%} feed has affected the evaluation of trend detection methods. In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection. We focus on using health-related trends to estimate the prevalence of Influenza-like illnesses based on tweets. We use ground truth obtained from the CDC and Google Flu Trends to explore how the prevalence estimates degrade when moving from a 100{\%} to a 1{\%} sample. We find that using the 1{\%} sample is unlikely to substantially harm ILI estimates made at the national level, but can cause poor performance when estimates are made at the city level.",
}
@inproceedings{foucault-courtin-2016-automatic,
    title = "Automatic Classification of Tweets for Analyzing Communication Behavior of Museums",
    author = "Foucault, Nicolas  and
      Courtin, Antoine",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1480",
    pages = "3006--3013",
    abstract = "In this paper, we present a study on tweet classification which aims to define the communication behavior of the 103 French museums that participated in 2014 in the Twitter operation: MuseumWeek. The tweets were automatically classified in four communication categories: sharing experience, promoting participation, interacting with the community, and promoting-informing about the institution. Our classification is multi-class. It combines Support Vector Machines and Naive Bayes methods and is supported by a selection of eighteen subtypes of features of four different kinds: metadata information, punctuation marks, tweet-specific and lexical features. It was tested against a corpus of 1,095 tweets manually annotated by two experts in Natural Language Processing and Information Communication and twelve Community Managers of French museums. We obtained an state-of-the-art result of F1-score of 72{\%} by 10-fold cross-validation. This result is very encouraging since is even better than some state-of-the-art results found in the tweet classification literature.",
}
@inproceedings{bekavac-snajder-2016-graph,
    title = "Graph-Based Induction of Word Senses in {C}roatian",
    author = "Bekavac, Marko  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1481",
    pages = "3014--3018",
    abstract = "Word sense induction (WSI) seeks to induce senses of words from unannotated corpora. In this paper, we address the WSI task for the Croatian language. We adopt the word clustering approach based on co-occurrence graphs, in which senses are taken to correspond to strongly inter-connected components of co-occurring words. We experiment with a number of graph construction techniques and clustering algorithms, and evaluate the sense inventories both as a clustering problem and extrinsically on a word sense disambiguation (WSD) task. In the cluster-based evaluation, Chinese Whispers algorithm outperformed Markov Clustering, yielding a normalized mutual information score of 64.3. In contrast, in WSD evaluation Markov Clustering performed better, yielding an accuracy of about 75{\%}. We are making available two induced sense inventories of 10,000 most frequent Croatian words: one coarse-grained and one fine-grained inventory, both obtained using the Markov Clustering algorithm.",
}
@inproceedings{johansson-etal-2016-multi,
    title = "A Multi-domain Corpus of {S}wedish Word Sense Annotation",
    author = "Johansson, Richard  and
      Adesam, Yvonne  and
      Bouma, Gerlof  and
      Hedberg, Karin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1482",
    pages = "3019--3022",
    abstract = "We describe the word sense annotation layer in \textit{Eukalyptus}, a freely available five-domain corpus of contemporary Swedish with several annotation layers. The annotation uses the SALDO lexicon to define the sense inventory, and allows word sense annotation of compound segments and multiword units. We give an overview of the new annotation tool developed for this project, and finally present an analysis of the inter-annotator agreement between two annotators.",
}
@inproceedings{otegi-etal-2016-qtleap,
    title = "{QTL}eap {WSD}/{NED} Corpora: Semantic Annotation of Parallel Corpora in Six Languages",
    author = "Otegi, Arantxa  and
      Aranberri, Nora  and
      Branco, Antonio  and
      Haji{\v{c}}, Jan  and
      Popel, Martin  and
      Simov, Kiril  and
      Agirre, Eneko  and
      Osenova, Petya  and
      Pereira, Rita  and
      Silva, Jo{\~a}o  and
      Neale, Steven",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1483",
    pages = "3023--3030",
    abstract = "This work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference. The corpora comprise both the well-known Europarl corpus and a domain-specific question-answer troubleshooting corpus on the IT domain. English is common in all parallel corpora, with translations in five languages, namely, Basque, Bulgarian, Czech, Portuguese and Spanish. We describe the annotated corpora and the tools used for annotation, as well as annotation statistics for each language. These new resources are freely available and will help research on semantic processing for machine translation and cross-lingual transfer.",
}
@inproceedings{mujdricza-maydt-etal-2016-combining,
    title = "Combining Semantic Annotation of Word Sense {\&} Semantic Roles: A Novel Annotation Scheme for {V}erb{N}et Roles on {G}erman Language Data",
    author = "M{\'u}jdricza-Maydt, {\'E}va  and
      Hartmann, Silvana  and
      Gurevych, Iryna  and
      Frank, Anette",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1484",
    pages = "3031--3038",
    abstract = "We present a VerbNet-based annotation scheme for semantic roles that we explore in an annotation study on German language data that combines word sense and semantic role annotation. We reannotate a substantial portion of the SALSA corpus with GermaNet senses and a revised scheme of VerbNet roles. We provide a detailed evaluation of the interaction between sense and role annotation. The resulting corpus will allow us to compare VerbNet role annotation for German to FrameNet and PropBank annotation by mapping to existing role annotations on the SALSA corpus. We publish the annotated corpus and detailed guidelines for the new role annotation scheme.",
}
@inproceedings{bhingardive-etal-2016-synset,
    title = "Synset Ranking of {H}indi {W}ord{N}et",
    author = "Bhingardive, Sudha  and
      Shukla, Rajita  and
      Saraswati, Jaya  and
      Kashyap, Laxmi  and
      Singh, Dhirendra  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1485",
    pages = "3039--3043",
    abstract = "Word Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context. It has been observed that such approaches often find it difficult to beat the WordNet First Sense (WFS) baseline which assigns the sense irrespective of context. In this paper, we present our work on creating the WFS baseline for Hindi language by manually ranking the synsets of Hindi WordNet. A ranking tool is developed where human experts can see the frequency of the word senses in the sense-tagged corpora and have been asked to rank the senses of a word by using this information and also his/her intuition. The accuracy of WFS baseline is tested on several standard datasets. F-score is found to be 60{\%}, 65{\%} and 55{\%} on Health, Tourism and News datasets respectively. The created rankings can also be used in other NLP applications viz., Machine Translation, Information Retrieval, Text Summarization, etc.",
}
@inproceedings{kutuzov-kuzmenko-2016-neural,
    title = "Neural Embedding Language Models in Semantic Clustering of Web Search Results",
    author = "Kutuzov, Andrey  and
      Kuzmenko, Elizaveta",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1486",
    pages = "3044--3048",
    abstract = "In this paper, a new approach towards semantic clustering of the results of ambiguous search queries is presented. We propose using distributed vector representations of words trained with the help of prediction-based neural embedding models to detect senses of search queries and to cluster search engine results page according to these senses. The words from titles and snippets together with semantic relationships between them form a graph, which is further partitioned into components related to different query senses. This approach to search engine results clustering is evaluated against a new manually annotated evaluation data set of Russian search queries. We show that in the task of semantically clustering search results, prediction-based models slightly but stably outperform traditional count-based ones, with the same training corpora.",
}
@inproceedings{alvarez-etal-2016-impact,
    title = "Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles",
    author = "{\'A}lvarez, Aitor  and
      Balenciaga, Marina  and
      del Pozo, Arantza  and
      Arzelus, Haritz  and
      Matamala, Anna  and
      Mart{\'\i}nez-Hinarejos, Carlos-D.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1487",
    pages = "3049--3053",
    abstract = "This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.",
}
@inproceedings{elliott-kleppe-2016-1,
    title = "1 Million Captioned {D}utch Newspaper Images",
    author = "Elliott, Desmond  and
      Kleppe, Martijn",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1488",
    pages = "3054--3058",
    abstract = "Images naturally appear alongside text in a wide variety of media, such as books, magazines, newspapers, and in online articles. This type of multi-modal data offers an interesting basis for vision and language research but most existing datasets use crowdsourced text, which removes the images from their original context. In this paper, we introduce the KBK-1M dataset of 1.6 million images in their original context, with co-occurring texts found in Dutch newspapers from 1922 - 1994. The images are digitally scanned photographs, cartoons, sketches, and weather forecasts; the text is generated from OCR scanned blocks. The dataset is suitable for experiments in automatic image captioning, image―article matching, object recognition, and data-to-text generation for weather forecasting. It can also be used by humanities scholars to analyse photographic style changes, the representation of people and societal issues, and new tools for exploring photograph reuse via image-similarity-based search.",
}
@inproceedings{wang-gaizauskas-2016-cross,
    title = "Cross-validating Image Description Datasets and Evaluation Metrics",
    author = "Wang, Josiah  and
      Gaizauskas, Robert",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1489",
    pages = "3059--3066",
    abstract = "The task of automatically generating sentential descriptions of image content has become increasingly popular in recent years, resulting in the development of large-scale image description datasets and the proposal of various metrics for evaluating image description generation systems. However, not much work has been done to analyse and understand both datasets and the metrics. In this paper, we propose using a leave-one-out cross validation (LOOCV) process as a means to analyse multiply annotated, human-authored image description datasets and the various evaluation metrics, i.e. evaluating one image description against other human-authored descriptions of the same image. Such an evaluation process affords various insights into the image description datasets and evaluation metrics, such as the variations of image descriptions within and across datasets and also what the metrics capture. We compute and analyse (i) human upper-bound performance; (ii) ranked correlation between metric pairs across datasets; (iii) lower-bound performance by comparing a set of descriptions describing one image to another sentence not describing that image. Interesting observations are made about the evaluation metrics and image description datasets, and we conclude that such cross-validation methods are extremely useful for assessing and gaining insights into image description datasets and evaluation metrics for image descriptions.",
}
@inproceedings{yanovich-etal-2016-detection,
    title = "Detection of Major {ASL} Sign Types in Continuous Signing For {ASL} Recognition",
    author = "Yanovich, Polina  and
      Neidle, Carol  and
      Metaxas, Dimitris",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1490",
    pages = "3067--3073",
    abstract = "In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27{\%} of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker.",
}
@inproceedings{paetzold-specia-2016-benchmarking,
    title = "Benchmarking Lexical Simplification Systems",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1491",
    pages = "3074--3080",
    abstract = "Lexical Simplification is the task of replacing complex words in a text with simpler alternatives. A variety of strategies have been devised for this challenge, yet there has been little effort in comparing their performance. In this contribution, we present a benchmarking of several Lexical Simplification systems. By combining resources created in previous work with automatic spelling and inflection correction techniques, we introduce BenchLS: a new evaluation dataset for the task. Using BenchLS, we evaluate the performance of solutions for various steps in the typical Lexical Simplification pipeline, both individually and jointly. This is the first time Lexical Simplification systems are compared in such fashion on the same data, and the findings introduce many contributions to the field, revealing several interesting properties of the systems evaluated.",
}
@inproceedings{fisas-etal-2016-multi,
    title = "A Multi-Layered Annotated Corpus of Scientific Papers",
    author = "Fisas, Beatriz  and
      Ronzano, Francesco  and
      Saggion, Horacio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1492",
    pages = "3081--3088",
    abstract = "Scientific literature records the research process with a standardized structure and provides the clues to track the progress in a scientific field. Understanding its internal structure and content is of paramount importance for natural language processing (NLP) technologies. To meet this requirement, we have developed a multi-layered annotated corpus of scientific papers in the domain of Computer Graphics. Sentences are annotated with respect to their role in the argumentative structure of the discourse. The purpose of each citation is specified. Special features of the scientific discourse such as advantages and disadvantages are identified. In addition, a grade is allocated to each sentence according to its relevance for being included in a summary.To the best of our knowledge, this complex, multi-layered collection of annotations and metadata characterizing a set of research papers had never been grouped together before in one corpus and therefore constitutes a newer, richer resource with respect to those currently available in the field.",
}
@inproceedings{mehdad-etal-2016-extractive,
    title = "Extractive Summarization under Strict Length Constraints",
    author = "Mehdad, Yashar  and
      Stent, Amanda  and
      Thadani, Kapil  and
      Radev, Dragomir  and
      Billawala, Youssef  and
      Buchner, Karolina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1493",
    pages = "3089--3093",
    abstract = "In this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e.g. summarization of news articles by news aggregators). We show that, evaluated using ROUGE, numerous algorithms from the literature fail to beat a simple lead-based baseline for this task. However, a supervised approach with lightweight and efficient features improves over the lead-based baseline. Additional human evaluation demonstrates that the supervised approach also performs competitively with a commercial system that uses more sophisticated features.",
}
@inproceedings{barker-etal-2016-whats,
    title = "What{'}s the Issue Here?: Task-based Evaluation of Reader Comment Summarization Systems",
    author = "Barker, Emma  and
      Paramita, Monica  and
      Funk, Adam  and
      Kurtic, Emina  and
      Aker, Ahmet  and
      Foster, Jonathan  and
      Hepple, Mark  and
      Gaizauskas, Robert",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1494",
    pages = "3094--3101",
    abstract = "Automatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known techniques imported from other areas of language processing. But are extractive summaries of comments what users really want? Do they support users in performing the sorts of tasks they are likely to want to perform with reader comments? In this paper we address these questions by doing three things. First, we offer a specification of one possible summary type for reader comment, based on an analysis of reader comment in terms of issues and viewpoints. Second, we define a task-based evaluation framework for reader comment summarization that allows summarization systems to be assessed in terms of how well they support users in a time-limited task of identifying issues and characterising opinion on issues in comments. Third, we describe a pilot evaluation in which we used the task-based evaluation framework to evaluate a prototype reader comment clustering and summarization system, demonstrating the viability of the evaluation framework and illustrating the sorts of insight such an evaluation affords.",
}
@inproceedings{nouri-yangarber-2016-novel,
    title = "A Novel Evaluation Method for Morphological Segmentation",
    author = "Nouri, Javad  and
      Yangarber, Roman",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1495",
    pages = "3102--3109",
    abstract = "Unsupervised learning of morphological segmentation of words in a language, based only on a large corpus of words, is a challenging task. Evaluation of the learned segmentations is a challenge in itself, due to the inherent ambiguity of the segmentation task. There is no way to posit unique {``}correct{''} segmentation for a set of data in an objective way. Two models may arrive at different ways of segmenting the data, which may nonetheless both be valid. Several evaluation methods have been proposed to date, but they do not insist on consistency of the evaluated model. We introduce a new evaluation methodology, which enforces correctness of segmentation boundaries while also assuring consistency of segmentation decisions across the corpus.",
}
@inproceedings{hazem-daille-2016-bilingual,
    title = "Bilingual Lexicon Extraction at the Morpheme Level Using Distributional Analysis",
    author = "Hazem, Amir  and
      Daille, B{\'e}atrice",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1496",
    pages = "3110--3115",
    abstract = "Bilingual lexicon extraction from comparable corpora is usually based on distributional methods when dealing with single word terms (SWT). These methods often treat SWT as single tokens without considering their compositional property. However, many SWT are compositional (composed of roots and affixes) and this information, if taken into account can be very useful to match translational pairs, especially for infrequent terms where distributional methods often fail. For instance, the English compound \textit{xenograft} which is composed of the root \textit{xeno} and the lexeme \textit{graft} can be translated into French compositionally by aligning each of its elements (\textit{xeno} with \textit{x{\'e}no} and \textit{graft} with \textit{greffe}) resulting in the translation: \textit{x{\'e}nogreffe}. In this paper, we experiment several distributional modellings at the morpheme level that we apply to perform compositional translation to a subset of French and English compounds. We show promising results using distributional analysis at the root and affix levels. We also show that the adapted approach significantly improve bilingual lexicon extraction from comparable corpora compared to the approach at the word level.",
}
@inproceedings{sylak-glassman-etal-2016-remote,
    title = "Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages",
    author = "Sylak-Glassman, John  and
      Kirov, Christo  and
      Yarowsky, David",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1497",
    pages = "3116--3120",
    abstract = "Structured, complete inflectional paradigm data exists for very few of the world{'}s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages.",
}
@inproceedings{kirov-etal-2016-large,
    title = "Very-large Scale Parsing and Normalization of {W}iktionary Morphological Paradigms",
    author = "Kirov, Christo  and
      Sylak-Glassman, John  and
      Que, Roger  and
      Yarowsky, David",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1498",
    pages = "3121--3126",
    abstract = "Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches.",
}
@inproceedings{sun-etal-2016-appdialogue,
    title = "{A}pp{D}ialogue: Multi-App Dialogues for Intelligent Assistants",
    author = "Sun, Ming  and
      Chen, Yun-Nung  and
      Hua, Zhenhao  and
      Tamres-Rudnicky, Yulian  and
      Dash, Arnab  and
      Rudnicky, Alexander",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1499",
    pages = "3127--3132",
    abstract = "Users will interact with an individual app on smart devices (e.g., phone, TV, car) to fulfill a specific goal (e.g. find a photographer), but users may also pursue more complex tasks that will span multiple domains and apps (e.g. plan a wedding ceremony). Planning and executing such multi-app tasks are typically managed by users, considering the required global context awareness. To investigate how users arrange domains/apps to fulfill complex tasks in their daily life, we conducted a user study on 14 participants to collect such data from their Android smart phones. This document 1) summarizes the techniques used in the data collection and 2) provides a brief statistical description of the data. This data guilds the future direction for researchers in the fields of conversational agent and personal assistant, etc. This data is available at http://AppDialogue.com.",
}
@inproceedings{petukhova-etal-2016-modelling,
    title = "Modelling Multi-issue Bargaining Dialogues: Data Collection, Annotation Design and Corpus",
    author = "Petukhova, Volha  and
      Stevens, Christopher  and
      de Weerd, Harmen  and
      Taatgen, Niels  and
      Cnossen, Fokie  and
      Malchanau, Andrei",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1500",
    pages = "3133--3140",
    abstract = "The paper describes experimental dialogue data collection activities, as well semantically annotated corpus creation undertaken within EU-funded METALOGUE project(www.metalogue.eu). The project aims to develop a dialogue system with flexible dialogue management to enable system{'}s adaptive, reactive, interactive and proactive dialogue behavior in setting goals, choosing appropriate strategies and monitoring numerous parallel interpretation and management processes. To achieve these goals negotiation (or more precisely multi-issue bargaining) scenario has been considered as the specific setting and application domain. The dialogue corpus forms the basis for the design of task and interaction models of participants negotiation behavior, and subsequently for dialogue system development which would be capable to replace one of the negotiators. The METALOGUE corpus will be released to the community for research purposes.",
}
@inproceedings{konovalov-etal-2016-negochat,
    title = "The Negochat Corpus of Human-agent Negotiation Dialogues",
    author = "Konovalov, Vasily  and
      Artstein, Ron  and
      Melamud, Oren  and
      Dagan, Ido",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1501",
    pages = "3141--3145",
    abstract = "Annotated in-domain corpora are crucial to the successful development of dialogue systems of automated agents, and in particular for developing natural language understanding (NLU) components of such systems. Unfortunately, such important resources are scarce. In this work, we introduce an annotated natural language human-agent dialogue corpus in the negotiation domain. The corpus was collected using Amazon Mechanical Turk following the {`}Wizard-Of-Oz{'} approach, where a {`}wizard{'} human translates the participants{'} natural language utterances in real time into a semantic language. Once dialogue collection was completed, utterances were annotated with intent labels by two independent annotators, achieving high inter-annotator agreement. Our initial experiments with an SVM classifier show that automatically inferring such labels from the utterances is far from trivial. We make our corpus publicly available to serve as an aid in the development of dialogue systems for negotiation agents, and suggest that analogous corpora can be created following our methodology and using our available source code. To the best of our knowledge this is the first publicly available negotiation dialogue corpus.",
}
@inproceedings{higashinaka-etal-2016-dialogue,
    title = "The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics",
    author = "Higashinaka, Ryuichiro  and
      Funakoshi, Kotaro  and
      Kobayashi, Yuka  and
      Inaba, Michimasa",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1502",
    pages = "3146--3150",
    abstract = "Dialogue breakdown detection is a promising technique in dialogue systems. To promote the research and development of such a technique, we organized a dialogue breakdown detection challenge where the task is to detect a system{'}s inappropriate utterances that lead to dialogue breakdowns in chat. This paper describes the design, datasets, and evaluation metrics for the challenge as well as the methods and results of the submitted runs of the participants.",
}
@inproceedings{bunt-etal-2016-dialogbank,
    title = "The {D}ialog{B}ank",
    author = "Bunt, Harry  and
      Petukhova, Volha  and
      Malchanau, Andrei  and
      Wijnhoven, Kars  and
      Fang, Alex",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1503",
    pages = "3151--3158",
    abstract = "This paper presents the DialogBank, a new language resource consisting of dialogues with gold standard annotations according to the ISO 24617-2 standard. Some of these dialogues have been taken from existing corpora and have been re-annotated according to the ISO standard; others have been annotated directly according to the standard. The ISO 24617-2 annotations have been designed according to the ISO principles for semantic annotation, as formulated in ISO 24617-6. The DialogBank makes use of three alternative representation formats, which are shown to be interoperable.",
}
@inproceedings{liu-etal-2016-coordinating,
    title = "Coordinating Communication in the Wild: The Artwalk Dialogue Corpus of Pedestrian Navigation and Mobile Referential Communication",
    author = "Liu, Kris  and
      Fox Tree, Jean  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1504",
    pages = "3159--3166",
    abstract = "The Artwalk Corpus is a collection of 48 mobile phone conversations between 24 pairs of friends and 24 pairs of strangers performing a novel, naturalistically-situated referential communication task. This task produced dialogues which, on average, are just under 40 minutes. The task requires the identification of public art while walking around and navigating pedestrian routes in the downtown area of Santa Cruz, California. The task involves a Director on the UCSC campus with access to maps providing verbal instructions to a Follower executing the task. The task provides a setting for real-world situated dialogic language and is designed to: (1) elicit entrainment and coordination of referring expressions between the dialogue participants, (2) examine the effect of friendship on dialogue strategies, and (3) examine how the need to complete the task while negotiating myriad, unanticipated events in the real world ― such as avoiding cars and other pedestrians ― affects linguistic coordination and other dialogue behaviors. Previous work on entrainment and coordinating communication has primarily focused on similar tasks in laboratory settings where there are no interruptions and no need to navigate from one point to another in a complex space. The corpus provides a general resource for studies on how coordinated task-oriented dialogue changes when we move outside the laboratory and into the world. It can also be used for studies of entrainment in dialogue, and the form and style of pedestrian instruction dialogues, as well as the effect of friendship on dialogic behaviors.",
}
@inproceedings{llanos-etal-2016-managing,
    title = "Managing Linguistic and Terminological Variation in a Medical Dialogue System",
    author = "Llanos, Leonardo Campillos  and
      Bouamor, Dhouha  and
      Zweigenbaum, Pierre  and
      Rosset, Sophie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1505",
    pages = "3167--3173",
    abstract = "We introduce a dialogue task between a virtual patient and a doctor where the dialogue system, playing the patient part in a simulated consultation, must reconcile a specialized level, to understand what the doctor says, and a lay level, to output realistic patient-language utterances. This increases the challenges in the analysis and generation phases of the dialogue. This paper proposes methods to manage linguistic and terminological variation in that situation and illustrates how they help produce realistic dialogues. Our system makes use of lexical resources for processing synonyms, inflectional and derivational variants, or pronoun/verb agreement. In addition, specialized knowledge is used for processing medical roots and affixes, ontological relations and concept mapping, and for generating lay variants of terms according to the patient{'}s non-expert discourse. We also report the results of a first evaluation carried out by 11 users interacting with the system. We evaluated the non-contextual analysis module, which supports the Spoken Language Understanding step. The annotation of task domain entities obtained 91.8{\%} of Precision, 82.5{\%} of Recall, 86.9{\%} of F-measure, 19.0{\%} of Slot Error Rate, and 32.9{\%} of Sentence Error Rate.",
}
@inproceedings{gokcen-etal-2016-corpus,
    title = "A Corpus of Word-Aligned Asked and Anticipated Questions in a Virtual Patient Dialogue System",
    author = "Gokcen, Ajda  and
      Jaffe, Evan  and
      Erdmann, Johnsey  and
      White, Michael  and
      Danforth, Douglas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1506",
    pages = "3174--3179",
    abstract = "We present a corpus of virtual patient dialogues to which we have added manually annotated gold standard word alignments. Since each question asked by a medical student in the dialogues is mapped to a canonical, anticipated version of the question, the corpus implicitly defines a large set of paraphrase (and non-paraphrase) pairs. We also present a novel process for selecting the most useful data to annotate with word alignments and for ensuring consistent paraphrase status decisions. In support of this process, we have enhanced the earlier Edinburgh alignment tool (Cohn et al., 2008) and revised and extended the Edinburgh guidelines, in particular adding guidance intended to ensure that the word alignments are consistent with the overall paraphrase status decision. The finished corpus and the enhanced alignment tool are made freely available.",
}
@inproceedings{prevot-etal-2016-cup,
    title = "A {CUP} of {C}o{F}ee: A large Collection of feedback Utterances Provided with communicative function annotations",
    author = "Pr{\'e}vot, Laurent  and
      Gorisch, Jan  and
      Bertrand, Roxane",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1507",
    pages = "3180--3185",
    abstract = "There have been several attempts to annotate communicative functions to utterances of verbal feedback in English previously. Here, we suggest an annotation scheme for verbal and non-verbal feedback utterances in French including the categories base, attitude, previous and visual. The data comprises conversations, maptasks and negotiations from which we extracted ca. 13,000 candidate feedback utterances and gestures. 12 students were recruited for the annotation campaign of ca. 9,500 instances. Each instance was annotated by between 2 and 7 raters. The evaluation of the annotation agreement resulted in an average best-pair kappa of 0.6. While the base category with the values acknowledgement, evaluation, answer, elicit achieve good agreement, this is not the case for the other main categories. The data sets, which also include automatic extractions of lexical, positional and acoustic features, are freely available and will further be used for machine learning classification experiments to analyse the form-function relationship of feedback.",
}
@inproceedings{sanders-etal-2016-palabras,
    title = "{P}alabras: Crowdsourcing Transcriptions of {L}2 Speech",
    author = "Sanders, Eric  and
      Burgos, Pepi  and
      Cucchiarini, Catia  and
      van Hout, Roeland",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1508",
    pages = "3186--3191",
    abstract = "We developed a web application for crowdsourcing transcriptions of Dutch words spoken by Spanish L2 learners. In this paper we discuss the design of the application and the influence of metadata and various forms of feedback. Useful data were obtained from 159 participants, with an average of over 20 transcriptions per item, which seems a satisfactory result for this type of research. Informing participants about how many items they still had to complete, and not how many they had already completed, turned to be an incentive to do more items. Assigning participants a score for their performance made it more attractive for them to carry out the transcription task, but this seemed to influence their performance. We discuss possible advantages and disadvantages in connection with the aim of the research and consider possible lessons for designing future experiments.",
}
@inproceedings{megyesi-etal-2016-uppsala,
    title = "The {U}ppsala Corpus of Student Writings: Corpus Creation, Annotation, and Analysis",
    author = {Megyesi, Be{\'a}ta  and
      N{\"a}sman, Jesper  and
      Palm{\'e}r, Anne},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1509",
    pages = "3192--3199",
    abstract = "The Uppsala Corpus of Student Writings consists of Swedish texts produced as part of a national test of students ranging in age from nine (in year three of primary school) to nineteen (the last year of upper secondary school) who are studying either Swedish or Swedish as a second language. National tests have been collected since 1996. The corpus currently consists of 2,500 texts containing over 1.5 million tokens. Parts of the texts have been annotated on several linguistic levels using existing state-of-the-art natural language processing tools. In order to make the corpus easy to interpret for scholars in the humanities, we chose the CoNLL format instead of an XML-based representation. Since spelling and grammatical errors are common in student writings, the texts are automatically corrected while keeping the original tokens in the corpus. Each token is annotated with part-of-speech and morphological features as well as syntactic structure. The main purpose of the corpus is to facilitate the systematic and quantitative empirical study of the writings of various student groups based on gender, geographic area, age, grade awarded or a combination of these, synchronically or diachronically. The intention is for this to be a monitor corpus, currently under development.",
}
@inproceedings{berkling-2016-corpus,
    title = "Corpus for Children{'}s Writing with Enhanced Output for Specific Spelling Patterns (2nd and 3rd Grade)",
    author = "Berkling, Kay",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1510",
    pages = "3200--3206",
    abstract = "This paper describes the collection of the H1 Corpus of children{'}s weekly writing over the course of 3 months in 2nd and 3rd grades, aged 7-11. The texts were collected within the normal classroom setting by the teacher. Texts of children whose parents signed the permission to donate the texts to science were collected and transcribed. The corpus consists of the elicitation techniques, an overview of the data collected and the transcriptions of the texts both with and without spelling errors, aligned on a word by word basis, as well as the scanned in texts. The corpus is available for research via Linguistic Data Consortium (LDC). Researchers are strongly encouraged to make additional annotations and improvements and return it to the public domain via LDC.",
}
@inproceedings{mendes-etal-2016-cople2,
    title = "The {COPLE}2 corpus: a learner corpus for {P}ortuguese",
    author = "Mendes, Am{\'a}lia  and
      Antunes, Sandra  and
      Janssen, Maarten  and
      Gon{\c{c}}alves, Anabela",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1511",
    pages = "3207--3214",
    abstract = "We present the COPLE2 corpus, a learner corpus of Portuguese that includes written and spoken texts produced by learners of Portuguese as a second or foreign language. The corpus includes at the moment a total of 182,474 tokens and 978 texts, classified according to the CEFR scales. The original handwritten productions are transcribed in TEI compliant XML format and keep record of all the original information, such as reformulations, insertions and corrections made by the teacher, while the recordings are transcribed and aligned with EXMARaLDA. The TEITOK environment enables different views of the same document (XML, student version, corrected version), a CQP-based search interface, the POS, lemmatization and normalization of the tokens, and will soon be used for error annotation in stand-off format. The corpus has already been a source of data for phonological, lexical and syntactic interlanguage studies and will be used for a data-informed selection of language features for each proficiency level.",
}
@inproceedings{wottawa-adda-decker-2016-french,
    title = "{F}rench Learners Audio Corpus of {G}erman Speech ({FLACGS})",
    author = "Wottawa, Jane  and
      Adda-Decker, Martine",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1512",
    pages = "3215--3219",
    abstract = "The French Learners Audio Corpus of German Speech (FLACGS) was created to compare German speech production of German native speakers (GG) and French learners of German (FG) across three speech production tasks of increasing production complexity: repetition, reading and picture description. 40 speakers, 20 GG and 20 FG performed each of the three tasks, which in total leads to approximately 7h of speech. The corpus was manually transcribed and automatically aligned. Analysis that can be performed on this type of corpus are for instance segmental differences in the speech production of L2 learners compared to native speakers. We chose the realization of the velar nasal consonant engma. In spoken French, engma does not appear in a VCV context which leads to production difficulties in FG. With increasing speech production complexity (reading and picture description), engma is realized as engma + plosive by FG in over 50{\%} of the cases. The results of a two way ANOVA with unequal sample sizes on the durations of the different realizations of engma indicate that duration is a reliable factor to distinguish between engma and engma + plosive in FG productions compared to the engma productions in GG in a VCV context. The FLACGS corpus allows to study L2 production and perception.",
}
@inproceedings{stefanec-etal-2016-croatian,
    title = "{C}roatian Error-Annotated Corpus of Non-Professional Written Language",
    author = "{\v{S}}tefanec, Vanja  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Kraljevi{\'c}, Jelena Kuva{\v{c}}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1513",
    pages = "3220--3226",
    abstract = "In the paper authors present the Croatian corpus of non-professional written language. Consisting of two subcorpora, i.e. the clinical subcorpus, consisting of written texts produced by speakers with various types of language disorders, and the healthy speakers subcorpus, as well as by the levels of its annotation, it offers an opportunity for different lines of research. The authors present the corpus structure, describe the sampling methodology, explain the levels of annotation, and give some very basic statistics. On the basis of data from the corpus, existing language technologies for Croatian are adapted in order to be implemented in a platform facilitating text production to speakers with language disorders. In this respect, several analyses of the corpus data and a basic evaluation of the developed technologies are presented.",
}
@inproceedings{hubert-etal-2016-training,
    title = "Training {\&} Quality Assessment of an Optical Character Recognition Model for {N}orthern {H}aida",
    author = "Hubert, Isabell  and
      Arppe, Antti  and
      Lachler, Jordan  and
      Santos, Eddie Antonio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1514",
    pages = "3227--3234",
    abstract = "We are presenting our work on the creation of the first optical character recognition (OCR) model for Northern Haida, also known as Masset or Xaad Kil, a nearly extinct First Nations language spoken in the Haida Gwaii archipelago in British Columbia, Canada. We are addressing the challenges of training an OCR model for a language with an extensive, non-standard Latin character set as follows: (1) We have compared various training approaches and present the results of practical analyses to maximize recognition accuracy and minimize manual labor. An approach using just one or two pages of Source Images directly performed better than the Image Generation approach, and better than models based on three or more pages. Analyses also suggest that a character{'}s frequency is directly correlated with its recognition accuracy. (2) We present an overview of current OCR accuracy analysis tools available. (3) We have ported the once de-facto standardized OCR accuracy tools to be able to cope with Unicode input. Our work adds to a growing body of research on OCR for particularly challenging character sets, and contributes to creating the largest electronic corpus for this severely endangered language.",
}
@inproceedings{gibbon-2016-legacy,
    title = "Legacy language atlas data mining: mapping Kru languages",
    author = "Gibbon, Dafydd",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1515",
    pages = "3235--3242",
    abstract = "An online tool based on dialectometric methods, DistGraph, is applied to a group of Kru languages of C{\^o}te d{'}Ivoire, Liberia and Burkina Faso. The inputs to this resource consist of tables of languages x linguistic features (e.g. phonological, lexical or grammatical), and statistical and graphical outputs are generated which show similarities and differences between the languages in terms of the features as virtual distances. In the present contribution, attention is focussed on the consonant systems of the languages, a traditional starting point for language comparison. The data are harvested from a legacy language data resource based on fieldwork in the 1970s and 1980s, a language atlas of the Kru languages. The method on which the online tool is based extends beyond documentation of individual languages to the documentation of language groups, and supports difference-based prioritisation in education programmes, decisions on language policy and documentation and conservation funding, as well as research on language typology and heritage documentation of history and migration.",
}
@inproceedings{ohya-2016-data,
    title = "Data Formats and Management Strategies from the Perspective of Language Resource Producers ― Personal Diachronic and Social Synchronic Data Sharing ―",
    author = "Ohya, Kazushi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1516",
    pages = "3243--3248",
    abstract = "This is a report of findings from on-going language documentation research based on three consecutive projects from 2008 to 2016. In the light of this research, we propose that (1) we should stand on the side of language resource producers to enhance the research of language processing. We support personal data management in addition to social data sharing. (2) This support leads to adopting simple data formats instead of the multi-link-path data models proposed as international standards up to the present. (3) We should set up a framework for total language resource study that includes not only pivotal data formats such as standard formats, but also the surroundings of data formation to capture a wider range of language activities, e.g. annotation, hesitant language formation, and reference-referent relations. A study of this framework is expected to be a foundation of rebuilding man-machine interface studies in which we seek to observe generative processes of informational symbols in order to establish a high affinity interface in regard to documentation.",
}
@inproceedings{van-den-heuvel-etal-2016-curation,
    title = "Curation of {D}utch Regional Dictionaries",
    author = "van den Heuvel, Henk  and
      Sanders, Eric  and
      van der Sijs, Nicoline",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1517",
    pages = "3249--3255",
    abstract = "This paper describes the process of semi-automatically converting dictionaries from paper to structured text (database) and the integration of these into the CLARIN infrastructure in order to make the dictionaries accessible and retrievable for the research community. The case study at hand is that of the curation of 42 fascicles of the Dictionaries of the Brabantic and Limburgian dialects, and 6 fascicles of the Dictionary of dialects in Gelderland.",
}
@inproceedings{soria-etal-2016-fostering,
    title = "Fostering digital representation of {EU} regional and minority languages: the Digital Language Diversity Project",
    author = "Soria, Claudia  and
      Russo, Irene  and
      Quochi, Valeria  and
      Hicks, Davyth  and
      Gurrutxaga, Antton  and
      Sarhimaa, Anneli  and
      Tuomisto, Matti",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1518",
    pages = "3256--3260",
    abstract = "Poor digital representation of minority languages further prevents their usability on digital media and devices. The Digital Language Diversity Project, a three-year project funded under the Erasmus+ programme, aims at addressing the problem of low digital representation of EU regional and minority languages by giving their speakers the intellectual an practical skills to create, share, and reuse online digital content. Availability of digital content and technical support to use it are essential prerequisites for the development of language-based digital applications, which in turn can boost digital usage of these languages. In this paper we introduce the project, its aims, objectives and current activities for sustaining digital usability of minority languages through adult education.",
}
@inproceedings{prys-etal-2016-cysill,
    title = "Cysill Ar-lein: A Corpus of Written Contemporary {W}elsh Compiled from an On-line Spelling and Grammar Checker",
    author = "Prys, Delyth  and
      Prys, Gruffudd  and
      Jones, Dewi Bryn",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1519",
    pages = "3261--3264",
    abstract = "This paper describes the use of a free, on-line language spelling and grammar checking aid as a vehicle for the collection of a significant (31 million words and rising) corpus of text for academic research in the context of less resourced languages where such data in sufficient quantities are often unavailable. It describes two versions of the corpus: the texts as submitted, prior to the correction process, and the texts following the user{'}s incorporation of any suggested changes. An overview of the corpus{'} contents is given and an analysis of use including usage statistics is also provided. Issues surrounding privacy and the anonymization of data are explored as is the data{'}s potential use for linguistic analysis, lexical research and language modelling. The method used for gathering this corpus is believed to be unique, and is a valuable addition to corpus studies in a minority language.",
}
@inproceedings{wieling-etal-2016-alt,
    title = "{ALT} Explored: Integrating an Online Dialectometric Tool and an Online Dialect Atlas",
    author = "Wieling, Martijn  and
      Sassolini, Eva  and
      Cucurullo, Sebastiana  and
      Montemagni, Simonetta",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1520",
    pages = "3265--3272",
    abstract = "In this paper, we illustrate the integration of an online dialectometric tool, Gabmap, together with an online dialect atlas, the Atlante Lessicale Toscano (ALT-Web). By using a newly created url-based interface to Gabmap, ALT-Web is able to take advantage of the sophisticated dialect visualization and exploration options incorporated in Gabmap. For example, distribution maps showing the distribution in the Tuscan dialect area of a specific dialectal form (selected via the ALT-Web website) are easily obtainable. Furthermore, the complete ALT-Web dataset as well as subsets of the data (selected via the ALT-Web website) can be automatically uploaded and explored in Gabmap. By combining these two online applications, macro- and micro-analyses of dialectal data (respectively offered by Gabmap and ALT-Web) are effectively and dynamically combined.",
}
@inproceedings{strassel-tracey-2016-lorelei,
    title = "{LORELEI} Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages",
    author = "Strassel, Stephanie  and
      Tracey, Jennifer",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1521",
    pages = "3273--3280",
    abstract = "In this paper, we describe the textual linguistic resources in nearly 3 dozen languages being produced by Linguistic Data Consortium for DARPA{'}s LORELEI (Low Resource Languages for Emergent Incidents) Program. The goal of LORELEI is to improve the performance of human language technologies for low-resource languages and enable rapid re-training of such technologies for new languages, with a focus on the use case of deployment of resources in sudden emergencies such as natural disasters. Representative languages have been selected to provide broad typological coverage for training, and surprise incident languages for testing will be selected over the course of the program. Our approach treats the full set of language packs as a coherent whole, maintaining LORELEI-wide specifications, tagsets, and guidelines, while allowing for adaptation to the specific needs created by each language. Each representative language corpus, therefore, both stands on its own as a resource for the specific language and forms part of a large multilingual resource for broader cross-language technology development.",
}
@inproceedings{ciobanu-dinu-2016-computational,
    title = "A Computational Perspective on the {R}omanian Dialects",
    author = "Ciobanu, Alina Maria  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1522",
    pages = "3281--3285",
    abstract = "In this paper we conduct an initial study on the dialects of Romanian. We analyze the differences between Romanian and its dialects using the Swadesh list. We analyze the predictive power of the orthographic and phonetic features of the words, building a classification problem for dialect identification.",
}
@inproceedings{nordhoff-etal-2016-alaskan,
    title = "The Alaskan Athabascan Grammar Database",
    author = "Nordhoff, Sebastian  and
      Tuttle, Siri  and
      Lovick, Olga",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1523",
    pages = "3286--3290",
    abstract = "This paper describes a repository of example sentences in three endangered Athabascan languages: Koyukon, Upper Tanana, Lower Tanana. The repository allows researchers or language teachers to browse the example sentence corpus to either investigate the languages or to prepare teaching materials. The originally heterogeneous text collection was imported into a SOLR store via the POIO bridge. This paper describes the requirements, implementation, advantages and drawbacks of this approach and discusses the potential to apply it for other languages of the Athabascan family or beyond.",
}
@inproceedings{nasution-etal-2016-constraint,
    title = "Constraint-Based Bilingual Lexicon Induction for Closely Related Languages",
    author = "Nasution, Arbi Haza  and
      Murakami, Yohei  and
      Ishida, Toru",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1524",
    pages = "3291--3298",
    abstract = "The lack or absence of parallel and comparable corpora makes bilingual lexicon extraction becomes a difficult task for low-resource languages. Pivot language and cognate recognition approach have been proven useful to induce bilingual lexicons for such languages. We analyze the features of closely related languages and define a semantic constraint assumption. Based on the assumption, we propose a constraint-based bilingual lexicon induction for closely related languages by extending constraints and translation pair candidates from recent pivot language approach. We further define three constraint sets based on language characteristics. In this paper, two controlled experiments are conducted. The former involves four closely related language pairs with different language pair similarities, and the latter focuses on sense connectivity between non-pivot words and pivot words. We evaluate our result with F-measure. The result indicates that our method works better on voluminous input dictionaries and high similarity languages. Finally, we introduce a strategy to use proper constraint sets for different goals and language characteristics.",
}
@inproceedings{otrusina-smrz-2016-wtf,
    title = "{WTF}-{LOD} - A New Resource for Large-Scale {NER} Evaluation",
    author = "Otrusina, Lubomir  and
      Smrz, Pavel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1525",
    pages = "3299--3302",
    abstract = "This paper introduces the Web TextFull linkage to Linked Open Data (WTF-LOD) dataset intended for large-scale evaluation of named entity recognition (NER) systems. First, we present the process of collecting data from the largest publically-available textual corpora, including Wikipedia dumps, monthly runs of the CommonCrawl, and ClueWeb09/12. We discuss similarities and differences of related initiatives such as WikiLinks and WikiReverse. Our work primarily focuses on links from {``}textfull{''} documents (links surrounded by a text that provides a useful context for entity linking), de-duplication of the data and advanced cleaning procedures. Presented statistics demonstrate that the collected data forms one of the largest available resource of its kind. They also prove suitability of the result for complex NER evaluation campaigns, including an analysis of the most ambiguous name mentions appearing in the data.",
}
@inproceedings{bleicken-etal-2016-using,
    title = "Using a Language Technology Infrastructure for {G}erman in order to Anonymize {G}erman {S}ign {L}anguage Corpus Data",
    author = "Bleicken, Julian  and
      Hanke, Thomas  and
      Salden, Uta  and
      Wagner, Sven",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1526",
    pages = "3303--3306",
    abstract = "For publishing sign language corpus data on the web, anonymization is crucial even if it is impossible to hide the visual appearance of the signers: In a small community, even vague references to third persons may be enough to identify those persons. In the case of the DGS Korpus (German Sign Language corpus) project, we want to publish data as a contribution to the cultural heritage of the sign language community while annotation of the data is still ongoing. This poses the question how well anonymization can be achieved given that no full linguistic analysis of the data is available. Basically, we combine analysis of all data that we have, including named entity recognition on translations into German. For this, we use the WebLicht language technology infrastructure. We report on the reliability of these methods in this special context and also illustrate how the anonymization of the video data is technically achieved in order to minimally disturb the viewer.",
}
@inproceedings{dojchinovski-etal-2016-crowdsourced,
    title = "Crowdsourced Corpus with Entity Salience Annotations",
    author = "Dojchinovski, Milan  and
      Reddy, Dinesh  and
      Kliegr, Tom{\'a}{\v{s}}  and
      Vitvar, Tom{\'a}{\v{s}}  and
      Sack, Harald",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1527",
    pages = "3307--3311",
    abstract = "In this paper, we present a crowdsourced dataset which adds entity salience (importance) annotations to the Reuters-128 dataset, which is subset of Reuters-21578. The dataset is distributed under a free license and publish in the NLP Interchange Format, which fosters interoperability and re-use. We show the potential of the dataset on the task of learning an entity salience classifier and report on the results from several experiments.",
}
@inproceedings{oramas-etal-2016-elmd,
    title = "{ELMD}: An Automatically Generated Entity Linking Gold Standard Dataset in the Music Domain",
    author = "Oramas, Sergio  and
      Anke, Luis Espinosa  and
      Sordo, Mohamed  and
      Saggion, Horacio  and
      Serra, Xavier",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1528",
    pages = "3312--3317",
    abstract = "In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain. It contains thousands of musical named entities such as Artist, Song or Record Label, which have been automatically annotated on a set of artist biographies coming from the Music website and social network Last.fm. The annotation process relies on the analysis of the hyperlinks present in the source texts and in a voting-based algorithm for EL, which considers, for each entity mention in text, the degree of agreement across three state-of-the-art EL systems. Manual evaluation shows that EL Precision is at least 94{\%}, and due to its tunable nature, it is possible to derive annotations favouring higher Precision or Recall, at will. We make available the annotated dataset along with evaluation data and the code.",
}
@inproceedings{littell-etal-2016-bridge,
    title = "Bridge-Language Capitalization Inference in {W}estern {I}ranian: {S}orani, {K}urmanji, Zazaki, and {T}ajik",
    author = "Littell, Patrick  and
      Mortensen, David R.  and
      Goyal, Kartik  and
      Dyer, Chris  and
      Levin, Lori",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1529",
    pages = "3318--3324",
    abstract = "In Sorani Kurdish, one of the most useful orthographic features in named-entity recognition {--} capitalization {--} is absent, as the language{'}s Perso-Arabic script does not make a distinction between uppercase and lowercase letters. We describe a system for deriving an inferred capitalization value from closely related languages by phonological similarity, and illustrate the system using several related Western Iranian languages.",
}
@inproceedings{kilicoglu-etal-2016-annotating,
    title = "Annotating Named Entities in Consumer Health Questions",
    author = "Kilicoglu, Halil  and
      Abacha, Asma Ben  and
      Mrabet, Yassine  and
      Roberts, Kirk  and
      Rodriguez, Laritza  and
      Shooshan, Sonya  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1530",
    pages = "3325--3332",
    abstract = "We describe a corpus of consumer health questions annotated with named entities. The corpus consists of 1548 de-identified questions about diseases and drugs, written in English. We defined 15 broad categories of biomedical named entities for annotation. A pilot annotation phase in which a small portion of the corpus was double-annotated by four annotators was followed by a main phase in which double annotation was carried out by six annotators, and a reconciliation phase in which all annotations were reconciled by an expert. We conducted the annotation in two modes, manual and assisted, to assess the effect of automatic pre-annotation and calculated inter-annotator agreement. We obtained moderate inter-annotator agreement; assisted annotation yielded slightly better agreement and fewer missed annotations than manual annotation. Due to complex nature of biomedical entities, we paid particular attention to nested entities for which we obtained slightly lower inter-annotator agreement, confirming that annotating nested entities is somewhat more challenging. To our knowledge, the corpus is the first of its kind for consumer health text and is publicly available.",
}
@inproceedings{brasoveanu-etal-2016-regional,
    title = "A Regional News Corpora for Contextualized Entity Discovery and Linking",
    author = "Bra{\c{s}}oveanu, Adrian  and
      Nixon, Lyndon J.B.  and
      Weichselbraun, Albert  and
      Scharl, Arno",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1531",
    pages = "3333--3338",
    abstract = "This paper presents a German corpus for Named Entity Linking (NEL) and Knowledge Base Population (KBP) tasks. We describe the annotation guideline, the annotation process, NIL clustering techniques and conversion to popular NEL formats such as NIF and TAC that have been used to construct this corpus based on news transcripts from the German regional broadcaster RBB (Rundfunk Berlin Brandenburg). Since creating such language resources requires significant effort, the paper also discusses how to derive additional evaluation resources for tasks like named entity contextualization or ontology enrichment by exploiting the links between named entities from the annotated corpus. The paper concludes with an evaluation that shows how several well-known NEL tools perform on the corpus, a discussion of the evaluation results, and with suggestions on how to keep evaluation corpora and datasets up to date.",
}
@inproceedings{brummer-etal-2016-dbpedia,
    title = "{DB}pedia Abstracts: A Large-Scale, Open, Multilingual {NLP} Training Corpus",
    author = {Br{\"u}mmer, Martin  and
      Dojchinovski, Milan  and
      Hellmann, Sebastian},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1532",
    pages = "3339--3343",
    abstract = "The ever increasing importance of machine learning in Natural Language Processing is accompanied by an equally increasing need in large-scale training and evaluation corpora. Due to its size, its openness and relative quality, the Wikipedia has already been a source of such data, but on a limited scale. This paper introduces the DBpedia Abstract Corpus, a large-scale, open corpus of annotated Wikipedia texts in six languages, featuring over 11 million texts and over 97 million entity links. The properties of the Wikipedia texts are being described, as well as the corpus creation process, its format and interesting use-cases, like Named Entity Linking training and evaluation.",
}
@inproceedings{eiselen-2016-government,
    title = "Government Domain Named Entity Recognition for {S}outh {A}frican Languages",
    author = "Eiselen, Roald",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1533",
    pages = "3344--3348",
    abstract = "This paper describes the named entity language resources developed as part of a development project for the South African languages. The development efforts focused on creating protocols and annotated data sets with at least 15,000 annotated named entity tokens for ten of the official South African languages. The description of the protocols and annotated data sets provide an overview of the problems encountered during the annotation of the data sets. Based on these annotated data sets, CRF named entity recognition systems are developed that leverage existing linguistic resources. The newly created named entity recognisers are evaluated, with F-scores of between 0.64 and 0.77, and error analysis is performed to identify possible avenues for improving the quality of the systems.",
}
@inproceedings{ehrmann-etal-2016-named,
    title = "Named Entity Resources - Overview and Outlook",
    author = "Ehrmann, Maud  and
      Nouvel, Damien  and
      Rosset, Sophie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1534",
    pages = "3349--3356",
    abstract = "Recognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition of new tasks (e.g. entity linking) and the emergence of new types of data (e.g. speech transcriptions, micro-blogging). These pose certainly new challenges which affect not only methods and algorithms but especially linguistic resources. Where do we stand with respect to named entity resources? This paper aims at providing a systematic overview of named entity resources, accounting for qualities such as multilingualism, dynamicity and interoperability, and to identify shortfalls in order to guide future developments.",
}
@inproceedings{garcia-2016-incorporating,
    title = "Incorporating Lexico-semantic Heuristics into Coreference Resolution Sieves for Named Entity Recognition at Document-level",
    author = "Garcia, Marcos",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1535",
    pages = "3357--3361",
    abstract = "This paper explores the incorporation of lexico-semantic heuristics into a deterministic Coreference Resolution (CR) system for classifying named entities at document-level. The highest precise sieves of a CR tool are enriched with both a set of heuristics for merging named entities labeled with different classes and also with some constraints that avoid the incorrect merging of similar mentions. Several tests show that this strategy improves both NER labeling and CR. The CR tool can be applied in combination with any system for named entity recognition using the CoNLL format, and brings benefits to text analytics tasks such as Information Extraction. Experiments were carried out in Spanish, using three different NER tools.",
}
@inproceedings{sulea-etal-2016-using,
    title = "Using Word Embeddings to Translate Named Entities",
    author = "{\c{S}}ulea, Octavia-Maria  and
      Nisioi, Sergiu  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1536",
    pages = "3362--3366",
    abstract = "In this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant to the task at hand, introducing a novel, yet simple way of obtaining bilingual word vectors. Inspired by observations in (Mikolov et al., 2013b), which show that training their word vector model on comparable corpora yields comparable vector space representations of those corpora, reducing the problem of translating words to finding a rotation matrix, and results in (Zou et al., 2013), which showed that bilingual word embeddings can improve Chinese Named Entity Recognition (NER) and English to Chinese phrase translation, we use the sentence-aligned English-French EuroParl corpora and show that word embeddings extracted from a merged corpus (corpus resulted from the merger of the two aligned corpora) can be used to NE translation. We extrapolate that word embeddings trained on merged parallel corpora are useful in Named Entity Recognition and Translation tasks for resource-poor languages.",
}
@inproceedings{eichler-etal-2016-teg,
    title = "{TEG}-{REP}: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns",
    author = "Eichler, Kathrin  and
      Xu, Feiyu  and
      Uszkoreit, Hans  and
      Hennig, Leonhard  and
      Krause, Sebastian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1537",
    pages = "3367--3372",
    abstract = "The task of relation extraction is to recognize and extract relations between entities or concepts in texts. Dependency parse trees have become a popular source for discovering extraction patterns, which encode the grammatical relations among the phrases that jointly express relation instances. State-of-the-art weakly supervised approaches to relation extraction typically extract thousands of unique patterns only potentially expressing the target relation. Among these patterns, some are semantically equivalent, but differ in their morphological, lexical-semantic or syntactic form. Some express a relation that entails the target relation. We propose a new approach to structuring extraction patterns by utilizing entailment graphs, hierarchical structures representing entailment relations, and present a novel resource of gold-standard entailment graphs based on a set of patterns automatically acquired using distant supervision. We describe the methodology used for creating the dataset and present statistics of the resource as well as an analysis of inference types underlying the entailment decisions.",
}
@inproceedings{fawei-etal-2016-passing,
    title = "Passing a {USA} National Bar Exam: a First Corpus for Experimentation",
    author = "Fawei, Biralatei  and
      Wyner, Adam  and
      Pan, Jeff",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1538",
    pages = "3373--3378",
    abstract = "Bar exams provide a key watershed by which legal professionals demonstrate their knowledge of the law and its application. Passing the bar entitles one to practice the law in a given jurisdiction. The bar provides an excellent benchmark for the performance of legal information systems since passing the bar would arguably signal that the system has acquired key aspects of legal reason on a par with a human lawyer. The paper provides a corpus and experimental results with material derived from a real bar exam, treating the problem as a form of textual entailment from the question to an answer. The providers of the bar exam material set the Gold Standard, which is the answer key. The experiments carried out using the {`}out of the box{'} the Excitement Open Platform for textual entailment. The results and evaluation show that the tool can identify wrong answers (non-entailment) with a high F1 score, but it performs poorly in identifying the correct answer (entailment). The results provide a baseline performance measure against which to evaluate future improvements. The reasons for the poor performance are examined, and proposals are made to augment the tool in the future. The corpus facilitates experimentation by other researchers.",
}
@inproceedings{vo-popescu-2016-corpora,
    title = "Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment",
    author = "Vo, Ngoc Phuoc An  and
      Popescu, Octavian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1539",
    pages = "3379--3386",
    abstract = "In this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, if any, the relationship between these two tasks for the mutual benefit of resolving one of them by relying on the insights gained from the other. We considered a corpora already annotated with TE judgments and we proceed to the manual annotation with SR scores. The RTE 1-4 corpora used in the PASCAL competition fit our need. The annotators worked independently of one each other and they did not have access to the TE judgment during annotation. The intuition that the two annotations are correlated received major support from this experiment and this finding led to a system that uses this information to revise the initial estimates of SR scores. As semantic relatedness is one of the most general and difficult task in natural language processing we expect that future systems will combine different sources of information in order to solve it. Our work suggests that textual entailment plays a quantifiable role in addressing it.",
}
@inproceedings{ferrugento-etal-2016-topic,
    title = "Can Topic Modelling benefit from Word Sense Information?",
    author = "Ferrugento, Adriana  and
      Oliveira, Hugo Gon{\c{c}}alo  and
      Alves, Ana  and
      Rodrigues, Filipe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1540",
    pages = "3387--3393",
    abstract = "This paper proposes a new topic model that exploits word sense information in order to discover less redundant and more informative topics. Word sense information is obtained from WordNet and the discovered topics are groups of synsets, instead of mere surface words. A key feature is that all the known senses of a word are considered, with their probabilities. Alternative configurations of the model are described and compared to each other and to LDA, the most popular topic model. However, the obtained results suggest that there are no benefits of enriching LDA with word sense information.",
}
@inproceedings{shrestha-etal-2016-age,
    title = "Age and Gender Prediction on Health Forum Data",
    author = "Shrestha, Prasha  and
      Rey-Villamizar, Nicolas  and
      Sadeque, Farig  and
      Pedersen, Ted  and
      Bethard, Steven  and
      Solorio, Thamar",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1541",
    pages = "3394--3401",
    abstract = "Health support forums have become a rich source of data that can be used to improve health care outcomes. A user profile, including information such as age and gender, can support targeted analysis of forum data. But users might not always disclose their age and gender. It is desirable then to be able to automatically extract this information from users{'} content. However, to the best of our knowledge there is no such resource for author profiling of health forum data. Here we present a large corpus, with close to 85,000 users, for profiling and also outline our approach and benchmark results to automatically detect a user{'}s age and gender from their forum posts. We use a mix of features from a user{'}s text as well as forum specific features to obtain accuracy well above the baseline, thus showing that both our dataset and our method are useful and valid.",
}
@inproceedings{nisioi-2016-comparing,
    title = "Comparing Speech and Text Classification on {ICNALE}",
    author = "Nisioi, Sergiu",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1542",
    pages = "3402--3406",
    abstract = "In this paper we explore and compare a speech and text classification approach on a corpus of native and non-native English speakers. We experiment on a subset of the International Corpus Network of Asian Learners of English containing the recorded speeches and the equivalent text transcriptions. Our results suggest a high correlation between the spoken and written classification results, showing that native accent is highly correlated with grammatical structures found in text.",
}
@inproceedings{arsevska-etal-2016-monitoring,
    title = "Monitoring Disease Outbreak Events on the Web Using Text-mining Approach and Domain Expert Knowledge",
    author = "Arsevska, Elena  and
      Roche, Mathieu  and
      Falala, Sylvain  and
      Lancelot, Renaud  and
      Chavernac, David  and
      Hendrikx, Pascal  and
      Dufour, Barbara",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1543",
    pages = "3407--3411",
    abstract = "Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. We propose a generic method to enrich and extend the use of different expressions as queries in order to improve the acquisition of relevant disease related pages on the web. Our method combines a text mining approach to extract terms from corpora of relevant disease outbreak documents, and domain expert elicitation (Delphi method) to propose expressions and to select relevant combinations between terms obtained with text mining. In this paper we evaluated the performance as queries of a number of expressions obtained with text mining and validated by a domain expert and expressions proposed by a panel of 21 domain experts. We used African swine fever as an infectious animal disease model. The expressions obtained with text mining outperformed as queries the expressions proposed by domain experts. However, domain experts proposed expressions not extracted automatically. Our method is simple to conduct and flexible to adapt to any other animal infectious disease and even in the public health domain.",
}
@inproceedings{wu-etal-2016-developing,
    title = "On Developing Resources for Patient-level Information Retrieval",
    author = "Wu, Stephen  and
      Timmons, Tamara  and
      Yates, Amy  and
      Wang, Meikun  and
      Bedrick, Steven  and
      Hersh, William  and
      Liu, Hongfang",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1544",
    pages = "3412--3416",
    abstract = "Privacy concerns have often served as an insurmountable barrier for the production of research and resources in clinical information retrieval (IR). We believe that both clinical IR research innovation and legitimate privacy concerns can be served by the creation of intra-institutional, fully protected resources. In this paper, we provide some principles and tools for IR resource-building in the unique problem setting of patient-level IR, following the tradition of the Cranfield paradigm.",
}
@inproceedings{klassen-etal-2016-annotating,
    title = "Annotating and Detecting Medical Events in Clinical Notes",
    author = "Klassen, Prescott  and
      Xia, Fei  and
      Yetisgen, Meliha",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1545",
    pages = "3417--3421",
    abstract = "Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7{\%} and 91.8{\%}, respectively.",
}
@inproceedings{sitaram-black-2016-speech,
    title = "Speech Synthesis of Code-Mixed Text",
    author = "Sitaram, Sunayana  and
      Black, Alan W",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1546",
    pages = "3422--3428",
    abstract = "Most Text to Speech (TTS) systems today assume that the input text is in a single language and is written in the same language that the text needs to be synthesized in. However, in bilingual and multilingual communities, code mixing or code switching occurs in speech, in which speakers switch between languages in the same utterance. Due to the popularity of social media, we now see code-mixing even in text in these multilingual communities. TTS systems capable of synthesizing such text need to be able to handle text that is written in multiple languages and scripts. Code-mixed text poses many challenges to TTS systems, such as language identification, spelling normalization and pronunciation modeling. In this work, we describe a preliminary framework for synthesizing code-mixed text. We carry out experiments on synthesizing code-mixed Hindi and English text. We find that there is a significant user preference for TTS systems that can correctly identify and pronounce words in different languages.",
}
@inproceedings{chiarain-chasaide-2016-chatbot,
    title = "Chatbot Technology with Synthetic Voices in the Acquisition of an Endangered Language: Motivation, Development and Evaluation of a Platform for {I}rish",
    author = "Chiar{\'a}in, Neasa N{\'\i}  and
      Chasaide, Ailbhe N{\'\i}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1547",
    pages = "3429--3435",
    abstract = "This paper describes the development and evaluation of a chatbot platform designed for the teaching/learning of Irish. The chatbot uses synthetic voices developed for the dialects of Irish. Speech-enabled chatbot technology offers a potentially powerful tool for dealing with the challenges of teaching/learning an endangered language where learners have limited access to native speaker models of the language and limited exposure to the language in a truly communicative setting. The sociolinguistic context that motivates the present development is explained. The evaluation of the chatbot was carried out in 13 schools by 228 pupils and consisted of two parts. Firstly, learners{'} opinions of the overall chatbot platform as a learning environment were elicited. Secondly, learners evaluated the intelligibility, quality, and attractiveness of the synthetic voices used in this platform. Results were overwhelmingly positive to both the learning platform and the synthetic voices and indicate that the time may now be ripe for language learning applications which exploit speech and language technologies. It is further argued that these technologies have a particularly vital role to play in the maintenance of the endangered language.",
}
@inproceedings{campbell-2016-chatr,
    title = "{CHATR} the Corpus; a 20-year-old archive of Concatenative Speech Synthesis",
    author = "Campbell, Nick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1548",
    pages = "3436--3439",
    abstract = "This paper reports the preservation of an old speech synthesis website as a corpus. CHATR was a revolutionary technique developed in the mid nineties for concatenative speech synthesis. The method has since become the standard for high quality speech output by computer although much of the current research is devoted to parametric or hybrid methods that employ smaller amounts of data and can be more easily tunable to individual voices. The system was first reported in 1994 and the website was functional in 1996. The ATR labs where this system was invented no longer exist, but the website has been preserved as a corpus containing 1537 samples of synthesised speech from that period (118 MB in aiff format) in 211 pages under various finely interrelated themes The corpus can be accessed from www.speech-data.jp as well as www.tcd-fastnet.com, where the original code and samples are now being maintained.",
}
@inproceedings{holthaus-etal-2016-address,
    title = "How to Address Smart Homes with a Social Robot? A Multi-modal Corpus of User Interactions with an Intelligent Environment",
    author = {Holthaus, Patrick  and
      Leichsenring, Christian  and
      Bernotat, Jasmin  and
      Richter, Viktor  and
      Pohling, Marian  and
      Carlmeyer, Birte  and
      K{\"o}ster, Norman  and
      zu Borgsen, Sebastian Meyer  and
      Zorn, Ren{\'e}  and
      Schiffhauer, Birte  and
      Engelmann, Kai Frederic  and
      Lier, Florian  and
      Schulz, Simon  and
      Cimiano, Philipp  and
      Eyssel, Friederike  and
      Hermann, Thomas  and
      Kummert, Franz  and
      Schlangen, David  and
      Wachsmuth, Sven  and
      Wagner, Petra  and
      Wrede, Britta  and
      Wrede, Sebastian},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1549",
    pages = "3440--3446",
    abstract = "In order to explore intuitive verbal and non-verbal interfaces in smart environments we recorded user interactions with an intelligent apartment. Besides offering various interactive capabilities itself, the apartment is also inhabited by a social robot that is available as a humanoid interface. This paper presents a multi-modal corpus that contains goal-directed actions of naive users in attempts to solve a number of predefined tasks. Alongside audio and video recordings, our data-set consists of large amount of temporally aligned sensory data and system behavior provided by the environment and its interactive components. Non-verbal system responses such as changes in light or display contents, as well as robot and apartment utterances and gestures serve as a rich basis for later in-depth analysis. Manual annotations provide further information about meta data like the current course of study and user behavior including the incorporated modality, all literal utterances, language features, emotional expressions, foci of attention, and addressees.",
}
@inproceedings{hu-etal-2016-corpus,
    title = "A Corpus of Gesture-Annotated Dialogues for Monologue-to-Dialogue Generation from Personal Narratives",
    author = "Hu, Zhichao  and
      Dick, Michelle  and
      Chang, Chung-Ning  and
      Bowden, Kevin  and
      Neff, Michael  and
      Fox Tree, Jean  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1550",
    pages = "3447--3454",
    abstract = "Story-telling is a fundamental and prevalent aspect of human social behavior. In the wild, stories are told conversationally in social settings, often as a dialogue and with accompanying gestures and other nonverbal behavior. This paper presents a new corpus, the Story Dialogue with Gestures (SDG) corpus, consisting of 50 personal narratives regenerated as dialogues, complete with annotations of gesture placement and accompanying gesture forms. The corpus includes dialogues generated by human annotators, gesture annotations on the human generated dialogues, videos of story dialogues generated from this representation, video clips of each gesture used in the gesture annotations, and annotations of the original personal narratives with a deep representation of story called a Story Intention Graph. Our long term goal is the automatic generation of story co-tellings as animated dialogues from the Story Intention Graph. We expect this corpus to be a useful resource for researchers interested in natural language generation, intelligent virtual agents, generation of nonverbal behavior, and story and narrative representations.",
}
@inproceedings{fotinea-etal-2016-multimodal,
    title = "Multimodal Resources for Human-Robot Communication Modelling",
    author = "Fotinea, Stavroula{--}Evita  and
      Efthimiou, Eleni  and
      Koutsombogera, Maria  and
      Dimou, Athanasia-Lida  and
      Goulas, Theodore  and
      Vasilaki, Kyriaki",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1551",
    pages = "3455--3460",
    abstract = "This paper reports on work related to the modelling of Human-Robot Communication on the basis of multimodal and multisensory human behaviour analysis. A primary focus in this framework of analysis is the definition of semantics of human actions in interaction, their capture and their representation in terms of behavioural patterns that, in turn, feed a multimodal human-robot communication system. Semantic analysis encompasses both oral and sign languages, as well as both verbal and non-verbal communicative signals to achieve an effective, natural interaction between elderly users with slight walking and cognitive inability and an assistive robotic platform.",
}
@inproceedings{tolins-etal-2016-verbal,
    title = "A Verbal and Gestural Corpus of Story Retellings to an Expressive Embodied Virtual Character",
    author = "Tolins, Jackson  and
      Liu, Kris  and
      Neff, Michael  and
      Walker, Marilyn  and
      Fox Tree, Jean",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1552",
    pages = "3461--3468",
    abstract = "We present a corpus of 44 human-agent verbal and gestural story retellings designed to explore whether humans would gesturally entrain to an embodied intelligent virtual agent. We used a novel data collection method where an agent presented story components in installments, which the human would then retell to the agent. At the end of the installments, the human would then retell the embodied animated agent the story as a whole. This method was designed to allow us to observe whether changes in the agent{'}s gestural behavior would result in human gestural changes. The agent modified its gestures over the course of the story, by starting out the first installment with gestural behaviors designed to manifest extraversion, and slowly modifying gestures to express introversion over time, or the reverse. The corpus contains the verbal and gestural transcripts of the human story retellings. The gestures were coded for type, handedness, temporal structure, spatial extent, and the degree to which the participants{'} gestures match those produced by the agent. The corpus illustrates the variation in expressive behaviors produced by users interacting with embodied virtual characters, and the degree to which their gestures were influenced by the agent{'}s dynamic changes in personality-based expressive style.",
}
@inproceedings{tolins-etal-2016-multimodal,
    title = "A Multimodal Motion-Captured Corpus of Matched and Mismatched Extravert-Introvert Conversational Pairs",
    author = "Tolins, Jackson  and
      Liu, Kris  and
      Wang, Yingying  and
      Fox Tree, Jean E.  and
      Walker, Marilyn  and
      Neff, Michael",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1553",
    pages = "3469--3476",
    abstract = "This paper presents a new corpus, the Personality Dyads Corpus, consisting of multimodal data for three conversations between three personality-matched, two-person dyads (a total of 9 separate dialogues). Participants were selected from a larger sample to be 0.8 of a standard deviation above or below the mean on the Big-Five Personality extraversion scale, to produce an Extravert-Extravert dyad, an Introvert-Introvert dyad, and an Extravert-Introvert dyad. Each pair carried out conversations for three different tasks. The conversations were recorded using optical motion capture for the body and data gloves for the hands. Dyads{'} speech was transcribed and the gestural and postural behavior was annotated with ANVIL. The released corpus includes personality profiles, ANVIL files containing speech transcriptions and the gestural annotations, and BVH files containing body and hand motion in 3D.",
}
@inproceedings{lanser-etal-2016-crowdsourcing,
    title = "Crowdsourcing Ontology Lexicons",
    author = "Lanser, Bettina  and
      Unger, Christina  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1554",
    pages = "3477--3484",
    abstract = "In order to make the growing amount of conceptual knowledge available through ontologies and datasets accessible to humans, NLP applications need access to information on how this knowledge can be verbalized in natural language. One way to provide this kind of information are ontology lexicons, which apart from the actual verbalizations in a given target language can provide further, rich linguistic information about them. Compiling such lexicons manually is a very time-consuming task and requires expertise both in Semantic Web technologies and lexicon engineering, as well as a very good knowledge of the target language at hand. In this paper we present an alternative approach to generating ontology lexicons by means of crowdsourcing: We use CrowdFlower to generate a small Japanese ontology lexicon for ten exemplary ontology elements from the DBpedia ontology according to a two-stage workflow, the main underlying idea of which is to turn the task of generating lexicon entries into a translation task; the starting point of this translation task is a manually created English lexicon for DBpedia. Comparison of the results to a manually created Japanese lexicon shows that the presented workflow is a viable option if an English seed lexicon is already available.",
}
@inproceedings{modi-etal-2016-inscript,
    title = "{I}n{S}cript: Narrative texts annotated with script information",
    author = "Modi, Ashutosh  and
      Anikina, Tatjana  and
      Ostermann, Simon  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1555",
    pages = "3485--3493",
    abstract = "This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.",
}
@inproceedings{wanzare-etal-2016-crowdsourced,
    title = "A Crowdsourced Database of Event Sequence Descriptions for the Acquisition of High-quality Script Knowledge",
    author = "Wanzare, Lilian D. A.  and
      Zarcone, Alessandra  and
      Thater, Stefan  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1556",
    pages = "3494--3501",
    abstract = "Scripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing). We present a large-scale crowdsourced collection of explicit linguistic descriptions of script-specific event sequences (40 scenarios with 100 sequences each). The corpus is enriched with crowdsourced alignment annotation on a subset of the event descriptions, to be used in future work as seed data for automatic alignment of event descriptions (for example via clustering). The event descriptions to be aligned were chosen among those expected to have the strongest corrective effect on the clustering algorithm. The alignment annotation was evaluated against a gold standard of expert annotators. The resulting database of partially-aligned script-event descriptions provides a sound empirical basis for inducing high-quality script knowledge, as well as for any task involving alignment and paraphrase detection of events.",
}
@inproceedings{caselli-etal-2016-temporal,
    title = "Temporal Information Annotation: Crowd vs. Experts",
    author = "Caselli, Tommaso  and
      Sprugnoli, Rachele  and
      Inel, Oana",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1557",
    pages = "3502--3509",
    abstract = "This paper describes two sets of crowdsourcing experiments on temporal information annotation conducted on two languages, i.e., English and Italian. The first experiment, launched on the CrowdFlower platform, was aimed at classifying temporal relations given target entities. The second one, relying on the CrowdTruth metric, consisted in two subtasks: one devoted to the recognition of events and temporal expressions and one to the detection and classification of temporal relations. The outcomes of the experiments suggest a valuable use of crowdsourcing annotations also for a complex task like Temporal Processing.",
}
@inproceedings{salvetti-etal-2016-tangled,
    title = "A Tangled Web: The Faint Signals of Deception in Text - Boulder Lies and Truth Corpus ({BLT}-{C})",
    author = "Salvetti, Franco  and
      Lowe, John B.  and
      Martin, James H.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1558",
    pages = "3510--3517",
    abstract = "We present an approach to creating corpora for use in detecting deception in text, including a discussion of the challenges peculiar to this task. Our approach is based on soliciting several types of reviews from writers and was implemented using Amazon Mechanical Turk. We describe the multi-dimensional corpus of reviews built using this approach, available free of charge from LDC as the Boulder Lies and Truth Corpus (BLT-C). Challenges for both corpus creation and the deception detection include the fact that human performance on the task is typically at chance, that the signal is faint, that paid writers such as turkers are sometimes deceptive, and that deception is a complex human behavior; manifestations of deception depend on details of domain, intrinsic properties of the deceiver (such as education, linguistic competence, and the nature of the intention), and specifics of the deceptive act (e.g., lying vs. fabricating.) To overcome the inherent lack of ground truth, we have developed a set of semi-automatic techniques to ensure corpus validity. We present some preliminary results on the task of deception detection which suggest that the BLT-C is an improvement in the quality of resources available for this task.",
}
@inproceedings{tiedemann-2016-finding,
    title = "Finding Alternative Translations in a Large Corpus of Movie Subtitle",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1559",
    pages = "3518--3522",
    abstract = "OpenSubtitles.org provides a large collection of user contributed subtitles in various languages for movies and TV programs. Subtitle translations are valuable resources for cross-lingual studies and machine translation research. A less explored feature of the collection is the inclusion of alternative translations, which can be very useful for training paraphrase systems or collecting multi-reference test suites for machine translation. However, differences in translation may also be due to misspellings, incomplete or corrupt data files, or wrongly aligned subtitles. This paper reports our efforts in recognising and classifying alternative subtitle translations with language independent techniques. We use time-based alignment with lexical re-synchronisation techniques and BLEU score filters and sort alternative translations into categories using edit distance metrics and heuristic rules. Our approach produces large numbers of sentence-aligned translation alternatives for over 50 languages provided via the OPUS corpus collection.",
}
@inproceedings{etchegoyhen-etal-2016-exploiting,
    title = "Exploiting a Large Strongly Comparable Corpus",
    author = "Etchegoyhen, Thierry  and
      Azpeitia, Andoni  and
      P{\'e}rez, Naiara",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1560",
    pages = "3523--3529",
    abstract = "This article describes a large comparable corpus for Basque and Spanish and the methods employed to build a parallel resource from the original data. The EITB corpus, a strongly comparable corpus in the news domain, is to be shared with the research community, as an aid for the development and testing of methods in comparable corpora exploitation, and as basis for the improvement of data-driven machine translation systems for this language pair. Competing approaches were explored for the alignment of comparable segments in the corpus, resulting in the design of a simple method which outperformed a state-of-the-art method on the corpus test sets. The method we present is highly portable, computationally efficient, and significantly reduces deployment work, a welcome result for the exploitation of comparable corpora.",
}
@inproceedings{ziemski-etal-2016-united,
    title = "The {U}nited {N}ations Parallel Corpus v1.0",
    author = "Ziemski, Micha{\l}  and
      Junczys-Dowmunt, Marcin  and
      Pouliquen, Bruno",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1561",
    pages = "3530--3534",
    abstract = "This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator. The parallel corpus presented consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. The corpus is freely available for download under a liberal license. Apart from the pairwise aligned documents, a fully aligned subcorpus for the six official UN languages is distributed. We provide baseline BLEU scores of our Moses-based SMT systems trained with the full data of language pairs involving English and for all possible translation directions of the six-way subcorpus.",
}
@inproceedings{bentivogli-etal-2016-wags,
    title = "{WAGS}: A Beautiful {E}nglish-{I}talian Benchmark Supporting Word Alignment Evaluation on Rare Words",
    author = "Bentivogli, Luisa  and
      Cettolo, Mauro  and
      Farajian, M. Amin  and
      Federico, Marcello",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1562",
    pages = "3535--3542",
    abstract = "This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words. WAGS is a subset of the Common Test section of the Europarl English-Italian parallel corpus, and is specifically tailored to OOV and rare words. WAGS is composed of 6,715 sentence pairs containing 11,958 occurrences of OOV and rare words up to frequency 15 in the Europarl Training set (5,080 English words and 6,878 Italian words), representing almost 3{\%} of the whole text. Since WAGS is focused on OOV/rare words, manual alignments are provided for these words only, and not for the whole sentences. Two off-the-shelf word aligners have been evaluated on WAGS, and results have been compared to those obtained on an existing benchmark tailored to full text alignment. The results obtained confirm that WAGS is a valuable resource, which allows a statistically sound evaluation of WA systems{'} performance on OOV and rare words, as well as extensive data analyses. WAGS is publicly released under a Creative Commons Attribution license.",
}
@inproceedings{tamchyna-barancikova-2016-manual,
    title = "Manual and Automatic Paraphrases for {MT} Evaluation",
    author = "Tamchyna, Ale{\v{s}}  and
      Baran{\v{c}}{\'\i}kov{\'a}, Petra",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1563",
    pages = "3543--3548",
    abstract = "Paraphrasing of reference translations has been shown to improve the correlation with human judgements in automatic evaluation of machine translation (MT) outputs. In this work, we present a new dataset for evaluating English-Czech translation based on automatic paraphrases. We compare this dataset with an existing set of manually created paraphrases and find that even automatic paraphrases can improve MT evaluation. We have also propose and evaluate several criteria for selecting suitable reference translations from a larger set.",
}
@inproceedings{augustinus-etal-2016-poly,
    title = "Poly-{G}r{ETEL}: Cross-Lingual Example-based Querying of Syntactic Constructions",
    author = "Augustinus, Liesbeth  and
      Vandeghinste, Vincent  and
      Vanallemeersch, Tom",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1564",
    pages = "3549--3554",
    abstract = "We present Poly-GrETEL, an online tool which enables syntactic querying in parallel treebanks, based on the monolingual GrETEL environment. We provide online access to the Europarl parallel treebank for Dutch and English, allowing users to query the treebank using either an XPath expression or an example sentence in order to look for similar constructions. We provide automatic alignments between the nodes. By combining example-based query functionality with node alignments, we limit the need for users to be familiar with the query language and the structure of the trees in the source and target language, thus facilitating the use of parallel corpora for comparative linguistics and translation studies.",
}
@inproceedings{dyvik-etal-2016-norgrambank,
    title = "{N}or{G}ram{B}ank: A {`}Deep{'} Treebank for {N}orwegian",
    author = "Dyvik, Helge  and
      Meurer, Paul  and
      Ros{\'e}n, Victoria  and
      De Smedt, Koenraad  and
      Haugereid, Petter  and
      Losnegaard, Gyri Sm{\o}rdal  and
      Lyse, Gunn Inger  and
      Thunes, Martha",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1565",
    pages = "3555--3562",
    abstract = "We present NorGramBank, a treebank for Norwegian with highly detailed LFG analyses. It is one of many treebanks made available through the INESS treebanking infrastructure. NorGramBank was constructed as a parsebank, i.e. by automatically parsing a corpus, using the wide coverage grammar NorGram. One part consisting of 350,000 words has been manually disambiguated using computer-generated discriminants. A larger part of 50 M words has been stochastically disambiguated. The treebank is dynamic: by global reparsing at certain intervals it is kept compatible with the latest versions of the grammar and the lexicon, which are continually further developed in interaction with the annotators. A powerful query language, INESS Search, has been developed for search across formalisms in the INESS treebanks, including LFG c- and f-structures. Evaluation shows that the grammar provides about 85{\%} of randomly selected sentences with good analyses. Agreement among the annotators responsible for manual disambiguation is satisfactory, but also suggests desirable simplifications of the grammar.",
}
@inproceedings{ribeyre-etal-2016-accurate,
    title = "Accurate Deep Syntactic Parsing of Graphs: The Case of {F}rench",
    author = "Ribeyre, Corentin  and
      Villemonte de la Clergerie, Eric  and
      Seddah, Djam{\'e}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1566",
    pages = "3563--3568",
    abstract = "Parsing predicate-argument structures in a deep syntax framework requires graphs to be predicted. Argument structures represent a higher level of abstraction than the syntactic ones and are thus more difficult to predict even for highly accurate parsing models on surfacic syntax. In this paper we investigate deep syntax parsing, using a French data set (Ribeyre et al., 2014a). We demonstrate that the use of topologically different types of syntactic features, such as dependencies, tree fragments, spines or syntactic paths, brings a much needed context to the parser. Our higher-order parsing model, gaining thus up to 4 points, establishes the state of the art for parsing French deep syntactic structures.",
}
@inproceedings{hawwari-etal-2016-explicit,
    title = "Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in {A}rabic",
    author = "Hawwari, Abdelati  and
      Attia, Mohammed  and
      Ghoneim, Mahmoud  and
      Diab, Mona",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1567",
    pages = "3569--3577",
    abstract = "Idafa in traditional Arabic grammar is an umbrella construction that covers several phenomena including what is expressed in English as noun-noun compounds and Saxon and Norman genitives. Additionally, Idafa participates in some other constructions, such as quantifiers, quasi-prepositions, and adjectives. Identifying the various types of the Idafa construction (IC) is of importance to Natural Language processing (NLP) applications. Noun-Noun compounds exhibit special behavior in most languages impacting their semantic interpretation. Hence distinguishing them could have an impact on downstream NLP applications. The most comprehensive syntactic representation of the Arabic language is the LDC Arabic Treebank (ATB). In the ATB, ICs are not explicitly labeled and furthermore, there is no distinction between ICs of noun-noun relations and other traditional ICs. Hence, we devise a detailed syntactic and semantic typification process of the IC phenomenon in Arabic. We target the ATB as a platform for this classification. We render the ATB annotated with explicit IC labels but with the further semantic characterization which is useful for syntactic, semantic and cross language processing. Our typification of IC comprises 3 main syntactic IC types: FIC, GIC, and TIC, and they are further divided into 10 syntactic subclasses. The TIC group is further classified into semantic relations. We devise a method for automatic IC labeling and compare its yield against the CATiB treebank. Our evaluation shows that we achieve the same level of accuracy, but with the additional fine-grained classification into the various syntactic and semantic types.",
}
@inproceedings{schumann-fischer-2016-compasses,
    title = "Compasses, Magnets, Water Microscopes: Annotation of Terminology in a Diachronic Corpus of Scientific Texts",
    author = "Schumann, Anne-Kathrin  and
      Fischer, Stefan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1568",
    pages = "3578--3585",
    abstract = "The specialised lexicon belongs to the most prominent attributes of specialised writing: Terms function as semantically dense encodings of specialised concepts, which, in the absence of terms, would require lengthy explanations and descriptions. In this paper, we argue that terms are the result of diachronic processes on both the semantic and the morpho-syntactic level. Very little is known about these processes. We therefore present a corpus annotation project aiming at revealing how terms are coined and how they evolve to fit their function as semantically and morpho-syntactically dense encodings of specialised knowledge. The scope of this paper is two-fold: Firstly, we outline our methodology for annotating terminology in a diachronic corpus of scientific publications. Moreover, we provide a detailed analysis of our annotation results and suggest methods for improving the accuracy of annotations in a setting as difficult as ours. Secondly, we present results of a pilot study based on the annotated terms. The results suggest that terms in older texts are linguistically relatively simple units that are hard to distinguish from the lexicon of general language. We believe that this supports our hypothesis that terminology undergoes diachronic processes of densification and specialisation.",
}
@inproceedings{diewald-etal-2016-korap,
    title = "{K}or{AP} Architecture ― Diving in the Deep Sea of Corpus Data",
    author = "Diewald, Nils  and
      Hanl, Michael  and
      Margaretha, Eliza  and
      Bingel, Joachim  and
      Kupietz, Marc  and
      Ba{\'n}ski, Piotr  and
      Witt, Andreas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1569",
    pages = "3586--3591",
    abstract = "KorAP is a corpus search and analysis platform, developed at the Institute for the German Language (IDS). It supports very large corpora with multiple annotation layers, multiple query languages, and complex licensing scenarios. KorAP{'}s design aims to be scalable, flexible, and sustainable to serve the German Reference Corpus DeReKo for at least the next decade. To meet these requirements, we have adopted a highly modular microservice-based architecture. This paper outlines our approach: An architecture consisting of small components that are easy to extend, replace, and maintain. The components include a search backend, a user and corpus license management system, and a web-based user frontend. We also describe a general corpus query protocol used by all microservices for internal communications. KorAP is open source, licensed under BSD-2, and available on GitHub.",
}
@inproceedings{grouin-2016-text,
    title = "Text Segmentation of Digitized Clinical Texts",
    author = "Grouin, Cyril",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1570",
    pages = "3592--3599",
    abstract = "In this paper, we present the experiments we made to recover the original page layout structure into two columns from layout damaged digitized files. We designed several CRF-based approaches, either to identify column separator or to classify each token from each line into left or right columns. We achieved our best results with a model trained on homogeneous corpora (only files composed of 2 columns) when classifying each token into left or right columns (overall F-measure of 0.968). Our experiments show it is possible to recover the original layout in columns of digitized documents with results of quality.",
}
@inproceedings{acar-etal-2016-turkish,
    title = "A {T}urkish Database for Psycholinguistic Studies Based on Frequency, Age of Acquisition, and Imageability",
    author = "Acar, Elif Ahsen  and
      Zeyrek, Deniz  and
      Kurfal{\i}, Murathan  and
      Boz{\c{s}}ahin, Cem",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1571",
    pages = "3600--3606",
    abstract = "This study primarily aims to build a Turkish psycholinguistic database including three variables: word frequency, age of acquisition (AoA), and imageability, where AoA and imageability information are limited to nouns. We used a corpus-based approach to obtain information about the AoA variable. We built two corpora: a child literature corpus (CLC) including 535 books written for 3-12 years old children, and a corpus of transcribed children{'}s speech (CSC) at ages 1;4-4;8. A comparison between the word frequencies of CLC and CSC gave positive correlation results, suggesting the usability of the CLC to extract AoA information. We assumed that frequent words of the CLC would correspond to early acquired words whereas frequent words of a corpus of adult language would correspond to late acquired words. To validate AoA results from our corpus-based approach, a rated AoA questionnaire was conducted on adults. Imageability values were collected via a different questionnaire conducted on adults. We conclude that it is possible to deduce AoA information for high frequency words with the corpus-based approach. The results about low frequency words were inconclusive, which is attributed to the fact that corpus-based AoA information is affected by the strong negative correlation between corpus frequency and rated AoA.",
}
@inproceedings{remus-biemann-2016-domain,
    title = "Domain-Specific Corpus Expansion with Focused Webcrawling",
    author = "Remus, Steffen  and
      Biemann, Chris",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1572",
    pages = "3607--3611",
    abstract = "This work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling. The focused webcrawler uses statistical N-gram language models to estimate the relatedness of documents and weblinks and needs as input only N-grams or plain texts of a predefined domain and seed URLs as starting points. Two experiments demonstrate that our focused crawler is able to stay focused in domain and language. The first experiment shows that the crawler stays in a focused domain, the second experiment demonstrates that language models trained on focused crawls obtain better perplexity scores on in-domain corpora. We distribute the focused crawler as open source software.",
}
@inproceedings{ljubesic-etal-2016-corpus,
    title = "Corpus-Based Diacritic Restoration for {S}outh {S}lavic Languages",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Erjavec, Toma{\v{z}}  and
      Fi{\v{s}}er, Darja",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1573",
    pages = "3612--3616",
    abstract = "In computer-mediated communication, Latin-based scripts users often omit diacritics when writing. Such text is typically easily understandable to humans but very difficult for computational processing because many words become ambiguous or unknown. Letter-level approaches to diacritic restoration generalise better and do not require a lot of training data but word-level approaches tend to yield better results. However, they typically rely on a lexicon which is an expensive resource, not covering non-standard forms, and often not available for less-resourced languages. In this paper we present diacritic restoration models that are trained on easy-to-acquire corpora. We test three different types of corpora (Wikipedia, general web, Twitter) for three South Slavic languages (Croatian, Serbian and Slovene) and evaluate them on two types of text: standard (Wikipedia) and non-standard (Twitter). The proposed approach considerably outperforms charlifter, so far the only open source tool available for this task. We make the best performing systems freely available.",
}
@inproceedings{couto-vale-etal-2016-automatic,
    title = "Automatic Recognition of Linguistic Replacements in Text Series Generated from Keystroke Logs",
    author = "Couto-Vale, Daniel  and
      Neumann, Stella  and
      Niemietz, Paula",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1574",
    pages = "3617--3623",
    abstract = "This paper introduces a toolkit used for the purpose of detecting replacements of different grammatical and semantic structures in ongoing text production logged as a chronological series of computer interaction events (so-called keystroke logs). The specific case we use involves human translations where replacements can be indicative of translator behaviour that leads to specific features of translations that distinguish them from non-translated texts. The toolkit uses a novel CCG chart parser customised so as to recognise grammatical words independently of space and punctuation boundaries. On the basis of the linguistic analysis, structures in different versions of the target text are compared and classified as potential equivalents of the same source text segment by {`}equivalence judges{'}. In that way, replacements of grammatical and semantic structures can be detected. Beyond the specific task at hand the approach will also be useful for the analysis of other types of spaceless text such as Twitter hashtags and texts in agglutinative or spaceless languages like Finnish or Chinese.",
}
@inproceedings{manishina-etal-2016-automatic,
    title = "Automatic Corpus Extension for Data-driven Natural Language Generation",
    author = "Manishina, Elena  and
      Jabaian, Bassam  and
      Huet, St{\'e}phane  and
      Lef{\`e}vre, Fabrice",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1575",
    pages = "3624--3631",
    abstract = "As data-driven approaches started to make their way into the Natural Language Generation (NLG) domain, the need for automation of corpus building and extension became apparent. Corpus creation and extension in data-driven NLG domain traditionally involved manual paraphrasing performed by either a group of experts or with resort to crowd-sourcing. Building the training corpora manually is a costly enterprise which requires a lot of time and human resources. We propose to automate the process of corpus extension by integrating automatically obtained synonyms and paraphrases. Our methodology allowed us to significantly increase the size of the training corpus and its level of variability (the number of distinct tokens and specific syntactic structures). Our extension solutions are fully automatic and require only some initial validation. The human evaluation results confirm that in many cases native speakers favor the outputs of the model built on the extended corpus.",
}
@inproceedings{htait-etal-2016-bilbo,
    title = "Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers",
    author = "Htait, Amal  and
      Fournier, Sebastien  and
      Bellot, Patrice",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1576",
    pages = "3632--3636",
    abstract = "In this paper, we present the automatic annotation of bibliographical references{'} zone in papers and articles of XML/TEI format. Our work is applied through two phases: first, we use machine learning technology to classify bibliographical and non-bibliographical paragraphs in papers, by means of a model that was initially created to differentiate between the footnotes containing or not containing bibliographical references. The previous description is one of BILBO{'}s features, which is an open source software for automatic annotation of bibliographic reference. Also, we suggest some methods to minimize the margin of error. Second, we propose an algorithm to find the largest list of bibliographical references in the article. The improvement applied on our model results an increase in the model{'}s efficiency with an Accuracy equal to 85.89. And by testing our work, we are able to achieve 72.23{\%} as an average for the percentage of success in detecting bibliographical references{'} zone.",
}
@inproceedings{zaghouani-etal-2016-guidelines,
    title = "Guidelines and Framework for a Large Scale {A}rabic Diacritized Corpus",
    author = "Zaghouani, Wajdi  and
      Bouamor, Houda  and
      Hawwari, Abdelati  and
      Diab, Mona  and
      Obeid, Ossama  and
      Ghoneim, Mahmoud  and
      Alqahtani, Sawsan  and
      Oflazer, Kemal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1577",
    pages = "3637--3643",
    abstract = "This paper presents the annotation guidelines developed as part of an effort to create a large scale manually diacritized corpus for various Arabic text genres. The target size of the annotated corpus is 2 million words. We summarize the guidelines and describe issues encountered during the training of the annotators. We also discuss the challenges posed by the complexity of the Arabic language and how they are addressed. Finally, we present the diacritization annotation procedure and detail the quality of the resulting annotations.",
}
@inproceedings{temnikova-etal-2016-applying,
    title = "Applying the Cognitive Machine Translation Evaluation Approach to {A}rabic",
    author = "Temnikova, Irina  and
      Zaghouani, Wajdi  and
      Vogel, Stephan  and
      Habash, Nizar",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1578",
    pages = "3644--3651",
    abstract = "The goal of the cognitive machine translation (MT) evaluation approach is to build classifiers which assign post-editing effort scores to new texts. The approach helps estimate fair compensation for post-editors in the translation industry by evaluating the cognitive difficulty of post-editing MT output. The approach counts the number of errors classified in different categories on the basis of how much cognitive effort they require in order to be corrected. In this paper, we present the results of applying an existing cognitive evaluation approach to Modern Standard Arabic (MSA). We provide a comparison of the number of errors and categories of errors in three MSA texts of different MT quality (without any language-specific adaptation), as well as a comparison between MSA texts and texts from three Indo-European languages (Russian, Spanish, and Bulgarian), taken from a previous experiment. The results show how the error distributions change passing from the MSA texts of worse MT quality to MSA texts of better MT quality, as well as a similarity in distinguishing the texts of better MT quality for all four languages.",
}
@inproceedings{scarton-specia-2016-reading,
    title = "A Reading Comprehension Corpus for Machine Translation Evaluation",
    author = "Scarton, Carolina  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1579",
    pages = "3652--3658",
    abstract = "Effectively assessing Natural Language Processing output tasks is a challenge for research in the area. In the case of Machine Translation (MT), automatic metrics are usually preferred over human evaluation, given time and budget constraints.However, traditional automatic metrics (such as BLEU) are not reliable for absolute quality assessment of documents, often producing similar scores for documents translated by the same MT system.For scenarios where absolute labels are necessary for building models, such as document-level Quality Estimation, these metrics can not be fully trusted. In this paper, we introduce a corpus of reading comprehension tests based on machine translated documents, where we evaluate documents based on answers to questions by fluent speakers of the target language. We describe the process of creating such a resource, the experiment design and agreement between the test takers. Finally, we discuss ways to convert the reading comprehension test into document-level quality scores.",
}
@inproceedings{wilkens-etal-2016-b2sg,
    title = "{B}2{SG}: a {TOEFL}-like Task for {P}ortuguese",
    author = "Wilkens, Rodrigo  and
      Zilio, Leonardo  and
      Ferreira, Eduardo  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1580",
    pages = "3659--3662",
    abstract = "Resources such as WordNet are useful for NLP applications, but their manual construction consumes time and personnel, and frequently results in low coverage. One alternative is the automatic construction of large resources from corpora like distributional thesauri, containing semantically associated words. However, as they may contain noise, there is a strong need for automatic ways of evaluating the quality of the resulting resource. This paper introduces a gold standard that can aid in this task. The BabelNet-Based Semantic Gold Standard (B2SG) was automatically constructed based on BabelNet and partly evaluated by human judges. It consists of sets of tests that present one target word, one related word and three unrelated words. B2SG contains 2,875 validated relations: 800 for verbs and 2,075 for nouns; these relations are divided among synonymy, antonymy and hypernymy. They can be used as the basis for evaluating the accuracy of the similarity relations on distributional thesauri by comparing the proximity of the target word with the related and unrelated options and observing if the related word has the highest similarity value among them. As a case study two distributional thesauri were also developed: one using surface forms from a large (1.5 billion word) corpus and the other using lemmatized forms from a smaller (409 million word) corpus. Both distributional thesauri were then evaluated against B2SG, and the one using lemmatized forms performed slightly better.",
}
@inproceedings{yuan-etal-2016-mobil,
    title = "{M}o{B}i{L}: A Hybrid Feature Set for Automatic Human Translation Quality Assessment",
    author = "Yuan, Yu  and
      Sharoff, Serge  and
      Babych, Bogdan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1581",
    pages = "3663--3670",
    abstract = "In this paper we introduce MoBiL, a hybrid Monolingual, Bilingual and Language modelling feature set and feature selection and evaluation framework. The set includes translation quality indicators that can be utilized to automatically predict the quality of human translations in terms of content adequacy and language fluency. We compare MoBiL with the QuEst baseline set by using them in classifiers trained with support vector machine and relevance vector machine learning algorithms on the same data set. We also report an experiment on feature selection to opt for fewer but more informative features from MoBiL. Our experiments show that classifiers trained on our feature set perform consistently better in predicting both adequacy and fluency than the classifiers trained on the baseline feature set. MoBiL also performs well when used with both support vector machine and relevance vector machine algorithms.",
}
@inproceedings{logacheva-etal-2016-marmot,
    title = "{MARMOT}: A Toolkit for Translation Quality Estimation at the Word Level",
    author = "Logacheva, Varvara  and
      Hokamp, Chris  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1582",
    pages = "3671--3674",
    abstract = "We present Marmot{\textasciitilde}― a new toolkit for quality estimation (QE) of machine translation output. Marmot contains utilities targeted at quality estimation at the word and phrase level. However, due to its flexibility and modularity, it can also be extended to work at the sentence level. In addition, it can be used as a framework for extracting features and learning models for many common natural language processing tasks. The tool has a set of state-of-the-art features for QE, and new features can easily be added. The tool is open-source and can be downloaded from https://github.com/qe-team/marmot/",
}
@inproceedings{katerenchuk-rosenberg-2016-rankdcg,
    title = "{R}ank{DCG}: Rank-Ordering Evaluation Measure",
    author = "Katerenchuk, Denys  and
      Rosenberg, Andrew",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1583",
    pages = "3675--3680",
    abstract = "Ranking is used for a wide array of problems, most notably information retrieval (search). Kendall{'}s τ, Average Precision, and nDCG are a few popular approaches to the evaluation of ranking. When dealing with problems such as user ranking or recommendation systems, all these measures suffer from various problems, including the inability to deal with elements of the same rank, inconsistent and ambiguous lower bound scores, and an inappropriate cost function. We propose a new measure, a modification of the popular nDCG algorithm, named rankDCG, that addresses these problems. We provide a number of criteria for any effective ranking algorithm and show that only rankDCG satisfies them all. Results are presented on constructed and real data sets. We release a publicly available rankDCG evaluation package.",
}
@inproceedings{etcheverry-wonsever-2016-spanish,
    title = "{S}panish Word Vectors from {W}ikipedia",
    author = "Etcheverry, Mathias  and
      Wonsever, Dina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1584",
    pages = "3681--3685",
    abstract = "Contents analisys from text data requires semantic representations that are difficult to obtain automatically, as they may require large handcrafted knowledge bases or manually annotated examples. Unsupervised autonomous methods for generating semantic representations are of greatest interest in face of huge volumes of text to be exploited in all kinds of applications. In this work we describe the generation and validation of semantic representations in the vector space paradigm for Spanish. The method used is GloVe (Pennington, 2014), one of the best performing reported methods , and vectors were trained over Spanish Wikipedia. The learned vectors evaluation is done in terms of word analogy and similarity tasks (Pennington, 2014; Baroni, 2014; Mikolov, 2013a). The vector set and a Spanish version for some widely used semantic relatedness tests are made publicly available.",
}
@inproceedings{humayoun-yu-2016-analyzing,
    title = "Analyzing Pre-processing Settings for {U}rdu Single-document Extractive Summarization",
    author = "Humayoun, Muhammad  and
      Yu, Hwanjo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1585",
    pages = "3686--3693",
    abstract = "Preprocessing is a preliminary step in many fields including IR and NLP. The effect of basic preprocessing settings on English for text summarization is well-studied. However, there is no such effort found for the Urdu language (with the best of our knowledge). In this study, we analyze the effect of basic preprocessing settings for single-document text summarization for Urdu, on a benchmark corpus using various experiments. The analysis is performed using the state-of-the-art algorithms for extractive summarization and the effect of stopword removal, lemmatization, and stemming is analyzed. Results showed that these pre-processing settings improve the results.",
}
@inproceedings{gabor-etal-2016-semantic,
    title = "Semantic Annotation of the {ACL} {A}nthology Corpus for the Automatic Analysis of Scientific Literature",
    author = {G{\'a}bor, Kata  and
      Zargayouna, Ha{\"\i}fa  and
      Buscaldi, Davide  and
      Tellier, Isabelle  and
      Charnois, Thierry},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1586",
    pages = "3694--3701",
    abstract = "This paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, but the annotation process itself is not specific to the computational linguistics domain and could be applied to any scientific corpora. Concepts were identified and annotated fully automatically, based on a combination of terminology extraction and available ontological resources. A typology of semantic relations between concepts is also proposed. This typology, consisting of 18 domain-specific and 3 generic relations, is the result of a corpus-based investigation of the text sequences occurring between concepts in sentences. A sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations. Only explicit relations are taken into account, so that the data could serve to train or evaluate pattern-based semantic relation classification systems.",
}
@inproceedings{derczynski-etal-2016-gate,
    title = "{GATE}-Time: Extraction of Temporal Expressions and Events",
    author = {Derczynski, Leon  and
      Str{\"o}tgen, Jannik  and
      Maynard, Diana  and
      Greenwood, Mark A.  and
      Jung, Manuel},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1587",
    pages = "3702--3708",
    abstract = "GATE is a widely used open-source solution for text processing with a large user community. It contains components for several natural language processing tasks. However, temporal information extraction functionality within GATE has been rather limited so far, despite being a prerequisite for many application scenarios in the areas of natural language processing and information retrieval. This paper presents an integrated approach to temporal information processing. We take state-of-the-art tools in temporal expression and event recognition and bring them together to form an openly-available resource within the GATE infrastructure. GATE-Time provides annotation in the form of TimeML events and temporal expressions complying with this mature ISO standard for temporal semantic annotation of documents. Major advantages of GATE-Time are (i) that it relies on HeidelTime for temporal tagging, so that temporal expressions can be extracted and normalized in multiple languages and across different domains, (ii) it includes a modern, fast event recognition and classification tool, and (iii) that it can be combined with different linguistic pre-processing annotations, and is thus not bound to license restricted preprocessing components.",
}
@inproceedings{claveau-kijak-2016-distributional,
    title = "Distributional Thesauri for Information Retrieval and vice versa",
    author = "Claveau, Vincent  and
      Kijak, Ewa",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1588",
    pages = "3709--3716",
    abstract = "Distributional thesauri are useful in many tasks of Natural Language Processing. In this paper, we address the problem of building and evaluating such thesauri with the help of Information Retrieval (IR) concepts. Two main contributions are proposed. First, following the work of [8], we show how IR tools and concepts can be used with success to build a thesaurus. Through several experiments and by evaluating directly the results with reference lexicons, we show that some IR models outperform state-of-the-art systems. Secondly, we use IR as an applicative framework to indirectly evaluate the generated thesaurus. Here again, this task-based evaluation validates the IR approach used to build the thesaurus. Moreover, it allows us to compare these results with those from the direct evaluation framework used in the literature. The observed differences bring these evaluation habits into question.",
}
@inproceedings{mott-etal-2016-parallel,
    title = "Parallel {C}hinese-{E}nglish Entities, Relations and Events Corpora",
    author = "Mott, Justin  and
      Bies, Ann  and
      Song, Zhiyi  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1589",
    pages = "3717--3722",
    abstract = "This paper introduces the parallel Chinese-English Entities, Relations and Events (ERE) corpora developed by Linguistic Data Consortium under the DARPA Deep Exploration and Filtering of Text (DEFT) Program. Original Chinese newswire and discussion forum documents are annotated for two versions of the ERE task. The texts are manually translated into English and then annotated for the same ERE tasks on the English translation, resulting in a rich parallel resource that has utility for performers within the DEFT program, for participants in NIST{'}s Knowledge Base Population evaluations, and for cross-language projection research more generally.",
}
@inproceedings{ellendorff-etal-2016-psymine,
    title = "The {P}sy{M}ine Corpus - A Corpus annotated with Psychiatric Disorders and their Etiological Factors",
    author = "Ellendorff, Tilia  and
      Foster, Simon  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1590",
    pages = "3723--3729",
    abstract = "We present the first version of a corpus annotated for psychiatric disorders and their etiological factors. The paper describes the choice of text, annotated entities and events/relations as well as the annotation scheme and procedure applied. The corpus is featuring a selection of focus psychiatric disorders including depressive disorder, anxiety disorder, obsessive-compulsive disorder, phobic disorders and panic disorder. Etiological factors for these focus disorders are widespread and include genetic, physiological, sociological and environmental factors among others. Etiological events, including annotated evidence text, represent the interactions between their focus disorders and their etiological factors. Additionally to these core events, symptomatic and treatment events have been annotated. The current version of the corpus includes 175 scientific abstracts. All entities and events/relations have been manually annotated by domain experts and scores of inter-annotator agreement are presented. The aim of the corpus is to provide a first gold standard to support the development of biomedical text mining applications for the specific area of mental disorders which belong to the main contributors to the contemporary burden of disease.",
}
@inproceedings{fulgoni-etal-2016-empirical,
    title = "An Empirical Exploration of Moral Foundations Theory in Partisan News Sources",
    author = "Fulgoni, Dean  and
      Carpenter, Jordan  and
      Ungar, Lyle  and
      Preo{\c{t}}iuc-Pietro, Daniel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1591",
    pages = "3730--3736",
    abstract = "News sources frame issues in different ways in order to appeal or control the perception of their readers. We present a large scale study of news articles from partisan sources in the US across a variety of different issues. We first highlight that differences between sides exist by predicting the political leaning of articles of unseen political bias. Framing can be driven by different types of morality that each group values. We emphasize differences in framing of different news building on the moral foundations theory quantified using hand crafted lexicons. Our results show that partisan sources frame political issues differently both in terms of words usage and through the moral foundations they relate to.",
}
@inproceedings{banea-etal-2016-building,
    title = "Building a Dataset for Possessions Identification in Text",
    author = "Banea, Carmen  and
      Chen, Xi  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1592",
    pages = "3737--3740",
    abstract = "Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one{'}s most intimately held thoughts, opinions and beliefs. This relatively new type of data is able to represent finer and more narrowly defined demographic slices. If until now researchers have primarily focused on leveraging personalized content to identify latent information such as gender, nationality, location, or age of the author, this study seeks to establish a structured way of extracting possessions, or items that people own or are entitled to, as a way to ultimately provide insights into people{'}s behaviors and characteristics. In order to promote more research in this area, we are releasing a set of 798 possessions extracted from blog genre, where possessions are marked at different confidence levels, as well as a detailed set of guidelines to help in future annotation studies.",
}
@inproceedings{griffitt-strassel-2016-query,
    title = "The Query of Everything: Developing Open-Domain, Natural-Language Queries for {BOLT} Information Retrieval",
    author = "Griffitt, Kira  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1593",
    pages = "3741--3747",
    abstract = "The DARPA BOLT Information Retrieval evaluations target open-domain natural-language queries over a large corpus of informal text in English, Chinese and Egyptian Arabic. We outline the goals of BOLT IR, comparing it with the prior GALE Distillation task. After discussing the properties of the BOLT IR corpus, we provide a detailed description of the query creation process, contrasting the summary query format presented to systems at run time with the full query format created by annotators. We describe the relevance criteria used to assess BOLT system responses, highlighting the evolution of the procedures used over the three evaluation phases. We provide a detailed review of the decision points model for relevance assessment introduced during Phase 2, and conclude with information about inter-assessor consistency achieved with the decision points assessment model.",
}
@inproceedings{liu-etal-2016-validation,
    title = "The Validation of {MRCPD} Cross-language Expansions on Imageability Ratings",
    author = "Liu, Ting  and
      Cho, Kit  and
      Strzalkowski, Tomek  and
      Shaikh, Samira  and
      Mirzaei, Mehrdad",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1594",
    pages = "3748--3751",
    abstract = "In this article, we present a method to validate a multi-lingual (English, Spanish, Russian, and Farsi) corpus on imageability ratings automatically expanded from MRCPD (Liu et al., 2014). We employed the corpus (Brysbaert et al., 2014) on concreteness ratings for our English MRCPD+ validation because of lacking human assessed imageability ratings and high correlation between concreteness ratings and imageability ratings (e.g. r = .83). For the same reason, we built a small corpus with human imageability assessment for the other language corpus validation. The results show that the automatically expanded imageability ratings are highly correlated with human assessment in all four languages, which demonstrate our automatic expansion method is valid and robust. We believe these new resources can be of significant interest to the research community, particularly in natural language processing and computational sociolinguistics.",
}
@inproceedings{pawar-etal-2016-building,
    title = "Building Tempo-{H}indi{W}ord{N}et: A resource for effective temporal information access in {H}indi",
    author = "Pawar, Dipawesh  and
      Hasanuzzaman, Mohammed  and
      Ekbal, Asif",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1595",
    pages = "3752--3759",
    abstract = "In this paper, we put forward a strategy that supplements Hindi WordNet entries with information on the temporality of its word senses. Each synset of Hindi WordNet is automatically annotated to one of the five dimensions: past, present, future, neutral and atemporal. We use semi-supervised learning strategy to build temporal classifiers over the glosses of manually selected initial seed synsets. The classification process is iterated based on the repetitive confidence based expansion strategy of the initial seed list until cross-validation accuracy drops. The resource is unique in its nature as, to the best of our knowledge, still no such resource is available for Hindi.",
}
@inproceedings{grabar-eshkol-taravela-2016-detection,
    title = "Detection of Reformulations in Spoken {F}rench",
    author = "Grabar, Natalia  and
      Eshkol-Taravela, Iris",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1596",
    pages = "3760--3767",
    abstract = "Our work addresses automatic detection of enunciations and segments with reformulations in French spoken corpora. The proposed approach is syntagmatic. It is based on reformulation markers and specificities of spoken language. The reference data are built manually and have gone through consensus. Automatic methods, based on rules and CRF machine learning, are proposed in order to detect the enunciations and segments that contain reformulations. With the CRF models, different features are exploited within a window of various sizes. Detection of enunciations with reformulations shows up to 0.66 precision. The tests performed for the detection of reformulated segments indicate that the task remains difficult. The best average performance values reach up to 0.65 F-measure, 0.75 precision, and 0.63 recall. We have several perspectives to this work for improving the detection of reformulated segments and for studying the data from other points of view.",
}
@inproceedings{banjade-rus-2016-dt,
    title = "{DT}-Neg: Tutorial Dialogues Annotated for Negation Scope and Focus in Context",
    author = "Banjade, Rajendra  and
      Rus, Vasile",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1597",
    pages = "3768--3771",
    abstract = "Negation is often found more frequent in dialogue than commonly written texts, such as literary texts. Furthermore, the scope and focus of negation depends on context in dialogues than other forms of texts. Existing negation datasets have focused on non-dialogue texts such as literary texts where the scope and focus of negation is normally present within the same sentence where the negation is located and therefore are not the most appropriate to inform the development of negation handling algorithms for dialogue-based systems. In this paper, we present DT -Neg corpus (DeepTutor Negation corpus) which contains texts extracted from tutorial dialogues where students interacted with an Intelligent Tutoring System (ITS) to solve conceptual physics problems. The DT -Neg corpus contains annotated negations in student responses with scope and focus marked based on the context of the dialogue. Our dataset contains 1,088 instances and is available for research purposes at http://language.memphis.edu/dt-neg.",
}
@inproceedings{roberts-demner-fushman-2016-annotating,
    title = "Annotating Logical Forms for {EHR} Questions",
    author = "Roberts, Kirk  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1598",
    pages = "3772--3778",
    abstract = "This paper discusses the creation of a semantically annotated corpus of questions about patient data in electronic health records (EHRs). The goal is provide the training data necessary for semantic parsers to automatically convert EHR questions into a structured query. A layered annotation strategy is used which mirrors a typical natural language processing (NLP) pipeline. First, questions are syntactically analyzed to identify multi-part questions. Second, medical concepts are recognized and normalized to a clinical ontology. Finally, logical forms are created using a lambda calculus representation. We use a corpus of 446 questions asking for patient-specific information. From these, 468 specific questions are found containing 259 unique medical concepts and requiring 53 unique predicates to represent the logical forms. We further present detailed characteristics of the corpus, including inter-annotator agreement results, and describe the challenges automatic NLP systems will face on this task.",
}
@inproceedings{bethard-parker-2016-semantically,
    title = "A Semantically Compositional Annotation Scheme for Time Normalization",
    author = "Bethard, Steven  and
      Parker, Jonathan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1599",
    pages = "3779--3786",
    abstract = "We present a new annotation scheme for normalizing time expressions, such as {``}three days ago{''}, to computer-readable forms, such as 2016-03-07. The annotation scheme addresses several weaknesses of the existing TimeML standard, allowing the representation of time expressions that align to more than one calendar unit (e.g., {``}the past three summers{''}), that are defined relative to events (e.g., {``}three weeks postoperative{''}), and that are unions or intersections of smaller time expressions (e.g., {``}Tuesdays and Thursdays{''}). It achieves this by modeling time expression interpretation as the semantic composition of temporal operators like UNION, NEXT, and AFTER. We have applied the annotation scheme to 34 documents so far, producing 1104 annotations, and achieving inter-annotator agreement of 0.821.",
}
@inproceedings{ozbal-etal-2016-prometheus,
    title = "{PROMETHEUS}: A Corpus of Proverbs Annotated with Metaphors",
    author = {{\"O}zbal, G{\"o}zde  and
      Strapparava, Carlo  and
      Tekiro{\u{g}}lu, Serra Sinem},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1600",
    pages = "3787--3793",
    abstract = "Proverbs are commonly metaphoric in nature and the mapping across domains is commonly established in proverbs. The abundance of proverbs in terms of metaphors makes them an extremely valuable linguistic resource since they can be utilized as a gold standard for various metaphor related linguistic tasks such as metaphor identification or interpretation. Besides, a collection of proverbs fromvarious languages annotated with metaphors would also be essential for social scientists to explore the cultural differences betweenthose languages. In this paper, we introduce PROMETHEUS, a dataset consisting of English proverbs and their equivalents in Italian.In addition to the word-level metaphor annotations for each proverb, PROMETHEUS contains other types of information such as the metaphoricity degree of the overall proverb, its meaning, the century that it was first recorded in and a pair of subjective questions responded by the annotators. To the best of our knowledge, this is the first multi-lingual and open-domain corpus of proverbs annotated with word-level metaphors.",
}
@inproceedings{djemaa-etal-2016-corpus,
    title = "Corpus Annotation within the {F}rench {F}rame{N}et: a Domain-by-domain Methodology",
    author = "Djemaa, Marianne  and
      Candito, Marie  and
      Muller, Philippe  and
      Vieu, Laure",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1601",
    pages = "3794--3801",
    abstract = "This paper reports on the development of a French FrameNet, within the ASFALDA project. While the first phase of the project focused on the development of a French set of frames and corresponding lexicon (Candito et al., 2014), this paper concentrates on the subsequent corpus annotation phase, which focused on four notional domains (commercial transactions, cognitive stances, causality and verbal communication). Given full coverage is not reachable for a relatively {``}new{''} FrameNet project, we advocate that focusing on specific notional domains allowed us to obtain full lexical coverage for the frames of these domains, while partially reflecting word sense ambiguities. Furthermore, as frames and roles were annotated on two French Treebanks (the French Treebank (Abeill{\'e} and Barrier, 2004) and the Sequoia Treebank (Candito and Seddah, 2012), we were able to extract a syntactico-semantic lexicon from the annotated frames. In the resource{'}s current status, there are 98 frames, 662 frame evoking words, 872 senses, and about 13000 annotated frames, with their semantic roles assigned to portions of text. The French FrameNet is freely available at alpage.inria.fr/asfalda.",
}
@inproceedings{lefeuvre-halftermeyer-etal-2016-covering,
    title = "Covering various Needs in Temporal Annotation: a Proposal of Extension of {ISO} {T}ime{ML} that Preserves Upward Compatibility",
    author = {Lefeuvre-Halftermeyer, Ana{\"\i}s  and
      Antoine, Jean-Yves  and
      Couillault, Alain  and
      Schang, Emmanuel  and
      Abouda, Lotfi  and
      Savary, Agata  and
      Maurel, Denis  and
      Eshkol, Iris  and
      Battistelli, Delphine},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1602",
    pages = "3802--3806",
    abstract = "This paper reports a critical analysis of the ISO TimeML standard, in the light of several experiences of temporal annotation that were conducted on spoken French. It shows that the norm suffers from weaknesses that should be corrected to fit a larger variety of needs inNLP and in corpus linguistics. We present our proposition of some improvements of the norm before it will be revised by the ISO Committee in 2017. These modifications concern mainly (1) Enrichments of well identified features of the norm: temporal function of TIMEX time expressions, additional types for TLINK temporal relations; (2) Deeper modifications concerning the units or features annotated: clarification between time and tense for EVENT units, coherence of representation between temporal signals (the SIGNAL unit) and TIMEX modifiers (the MOD feature); (3) A recommendation to perform temporal annotation on top of a syntactic (rather than lexical) layer (temporal annotation on a treebank).",
}
@inproceedings{vieu-etal-2016-general,
    title = "A General Framework for the Annotation of Causality Based on {F}rame{N}et",
    author = "Vieu, Laure  and
      Muller, Philippe  and
      Candito, Marie  and
      Djemaa, Marianne",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1603",
    pages = "3807--3813",
    abstract = "We present here a general set of semantic frames to annotate causal expressions, with a rich lexicon in French and an annotated corpus of about 5000 instances of causal lexical items with their corresponding semantic frames. The aim of our project is to have both the largest possible coverage of causal phenomena in French, across all parts of speech, and have it linked to a general semantic framework such as FN, to benefit in particular from the relations between other semantic frames, e.g., temporal ones or intentional ones, and the underlying upper lexical ontology that enable some forms of reasoning. This is part of the larger ASFALDA French FrameNet project, which focuses on a few different notional domains which are interesting in their own right (Djemma et al., 2016), including cognitive positions and communication frames. In the process of building the French lexicon and preparing the annotation of the corpus, we had to remodel some of the frames proposed in FN based on English data, with hopefully more precise frame definitions to facilitate human annotation. This includes semantic clarifications of frames and frame elements, redundancy elimination, and added coverage. The result is arguably a significant improvement of the treatment of causality in FN itself.",
}
@inproceedings{vempala-blanco-2016-annotating,
    title = "Annotating Temporally-Anchored Spatial Knowledge on Top of {O}nto{N}otes Semantic Roles",
    author = "Vempala, Alakananda  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1604",
    pages = "3814--3821",
    abstract = "This paper presents a two-step methodology to annotate spatial knowledge on top of OntoNotes semantic roles. First, we manipulate semantic roles to automatically generate potential additional spatial knowledge. Second, we crowdsource annotations with Amazon Mechanical Turk to either validate or discard the potential additional spatial knowledge. The resulting annotations indicate whether entities are or are not located somewhere with a degree of certainty, and temporally anchor this spatial information. Crowdsourcing experiments show that the additional spatial knowledge is ubiquitous and intuitive to humans, and experimental results show that it can be inferred automatically using standard supervised machine learning techniques.",
}
@inproceedings{gotze-boye-2016-spaceref,
    title = "{S}pace{R}ef: A corpus of street-level geographic descriptions",
    author = {G{\"o}tze, Jana  and
      Boye, Johan},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1605",
    pages = "3822--3827",
    abstract = "This article describes SPACEREF, a corpus of street-level geographic descriptions. Pedestrians are walking a route in a (real) urban environment, describing their actions. Their position is automatically logged, their speech is manually transcribed, and their references to objects are manually annotated with respect to a crowdsourced geographic database. We describe how the data was collected and annotated, and how it has been used in the context of creating resources for an automatic pedestrian navigation system.",
}
@inproceedings{mirzaei-moloodi-2016-persian,
    title = "{P}ersian {P}roposition {B}ank",
    author = "Mirzaei, Azadeh  and
      Moloodi, Amirsaeid",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1606",
    pages = "3828--3835",
    abstract = "This paper describes the procedure of semantic role labeling and the development of the first manually annotated Persian Proposition Bank (PerPB) which added a layer of predicate-argument information to the syntactic structures of Persian Dependency Treebank (known as PerDT). Through the process of annotating, the annotators could see the syntactic information of all the sentences and so they annotated 29982 sentences with more than 9200 unique verbs. In the annotation procedure, the direct syntactic dependents of the verbs were the first candidates for being annotated. So we did not annotate the other indirect dependents unless their phrasal heads were propositional and had their own arguments or adjuncts. Hence besides the semantic role labeling of verbs, the argument structure of 1300 unique propositional nouns and 300 unique propositional adjectives were annotated in the sentences, too. The accuracy of annotation process was measured by double annotation of the data at two separate stages and finally the data was prepared in the CoNLL dependency format.",
}
@inproceedings{tateisi-etal-2016-typed,
    title = "Typed Entity and Relation Annotation on Computer Science Papers",
    author = "Tateisi, Yuka  and
      Ohta, Tomoko  and
      Pyysalo, Sampo  and
      Miyao, Yusuke  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1607",
    pages = "3836--3843",
    abstract = "We describe our ongoing effort to establish an annotation scheme for describing the semantic structures of research articles in the computer science domain, with the intended use of developing search systems that can refine their results by the roles of the entities denoted by the query keys. In our scheme, mentions of entities are annotated with ontology-based types, and the roles of the entities are annotated as relations with other entities described in the text. So far, we have annotated 400 abstracts from the ACL anthology and the ACM digital library. In this paper, the scheme and the annotated dataset are described, along with the problems found in the course of annotation. We also show the results of automatic annotation and evaluate the corpus in a practical setting in application to topic extraction.",
}
@inproceedings{gast-etal-2016-enriching,
    title = "Enriching {T}ime{B}ank: Towards a more precise annotation of temporal relations in a text",
    author = "Gast, Volker  and
      Bierkandt, Lennart  and
      Druskat, Stephan  and
      Rzymski, Christoph",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1608",
    pages = "3844--3850",
    abstract = "We propose a way of enriching the TimeML annotations of TimeBank by adding information about the Topic Time in terms of Klein (1994). The annotations are partly automatic, partly inferential and partly manual. The corpus was converted into the native format of the annotation software GraphAnno and POS-tagged using the Stanford bidirectional dependency network tagger. On top of each finite verb, a FIN-node with tense information was created, and on top of any FIN-node, a TOPICTIME-node, in accordance with Klein{'}s (1994) treatment of finiteness as the linguistic correlate of the Topic Time. Each TOPICTIME-node is linked to a MAKEINSTANCE-node representing an (instantiated) event in TimeML (Pustejovsky et al. 2005), the markup language used for the annotation of TimeBank. For such links we introduce a new category, ELINK. ELINKs capture the relationship between the Topic Time (TT) and the Time of Situation (TSit) and have an aspectual interpretation in Klein{'}s (1994) theory. In addition to these automatic and inferential annotations, some TLINKs were added manually. Using an example from the corpus, we show that the inclusion of the Topic Time in the annotations allows for a richer representation of the temporal structure than does TimeML. A way of representing this structure in a diagrammatic form similar to the T-Box format (Verhagen, 2007) is proposed.",
}
@inproceedings{sheikh-etal-2016-diachronic,
    title = "How Diachronic Text Corpora Affect Context based Retrieval of {OOV} Proper Names for Audio News",
    author = "Sheikh, Imran  and
      Illina, Irina  and
      Fohr, Dominique",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1609",
    pages = "3851--3855",
    abstract = "Out-Of-Vocabulary (OOV) words missed by Large Vocabulary Continuous Speech Recognition (LVCSR) systems can be recovered with the help of topic and semantic context of the OOV words captured from a diachronic text corpus. In this paper we investigate how the choice of documents for the diachronic text corpora affects the retrieval of OOV Proper Names (PNs) relevant to an audio document. We first present our diachronic French broadcast news datasets, which highlight the motivation of our study on OOV PNs. Then the effect of using diachronic text data from different sources and a different time span is analysed. With OOV PN retrieval experiments on French broadcast news videos, we conclude that a diachronic corpus with text from different sources leads to better retrieval performance than one relying on text from single source or from a longer time span.",
}
@inproceedings{wong-etal-2016-syllable,
    title = "Syllable based {DNN}-{HMM} {C}antonese Speech to Text System",
    author = "Wong, Timothy  and
      Li, Claire  and
      Lam, Sam  and
      Chiu, Billy  and
      Lu, Qin  and
      Li, Minglei  and
      Xiong, Dan  and
      Yu, Roy Shing  and
      Ng, Vincent T.Y.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1610",
    pages = "3856--3862",
    abstract = "This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model. This is a part of an effort in building a STT system to aid dyslexic students who have cognitive deficiency in writing skills but have no problem expressing their ideas through speech. For Cantonese speech recognition, the basic unit of acoustic models can either be the conventional Initial-Final (IF) syllables, or the Onset-Nucleus-Coda (ONC) syllables where finals are further split into nucleus and coda to reflect the intra-syllable variations in Cantonese. By using the Kaldi toolkit, our system is trained using the stochastic gradient descent optimization model with the aid of GPUs for the hybrid Deep Neural Network and Hidden Markov Model (DNN-HMM) with and without I-vector based speaker adaptive training technique. The input features of the same Gaussian Mixture Model with speaker adaptive training (GMM-SAT) to DNN are used in all cases. Experiments show that the ONC-based syllable acoustic modeling with I-vector based DNN-HMM achieves the best performance with the word error rate (WER) of 9.66{\%} and the real time factor (RTF) of 1.38812.",
}
@inproceedings{gauthier-etal-2016-collecting,
    title = "Collecting Resources in Sub-{S}aharan {A}frican Languages for Automatic Speech Recognition: a Case Study of {W}olof",
    author = "Gauthier, Elodie  and
      Besacier, Laurent  and
      Voisin, Sylvie  and
      Melese, Michael  and
      Elingui, Uriel Pascal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1611",
    pages = "3863--3867",
    abstract = "This article presents the data collected and ASR systems developped for 4 sub-saharan african languages (Swahili, Hausa, Amharic and Wolof). To illustrate our methodology, the focus is made on Wolof (a very under-resourced language) for which we designed the first ASR system ever built in this language. All data and scripts are available online on our github repository.",
}
@inproceedings{pelemans-etal-2016-scale,
    title = "{SCALE}: A Scalable Language Engineering Toolkit",
    author = "Pelemans, Joris  and
      Verwimp, Lyan  and
      Demuynck, Kris  and
      Van hamme, Hugo  and
      Wambacq, Patrick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1612",
    pages = "3868--3871",
    abstract = "In this paper we present SCALE, a new Python toolkit that contains two extensions to n-gram language models. The first extension is a novel technique to model compound words called Semantic Head Mapping (SHM). The second extension, Bag-of-Words Language Modeling (BagLM), bundles popular models such as Latent Semantic Analysis and Continuous Skip-grams. Both extensions scale to large data and allow the integration into first-pass ASR decoding. The toolkit is open source, includes working examples and can be found on http://github.com/jorispelemans/scale.",
}
@inproceedings{brognaux-etal-2016-combining,
    title = "Combining Manual and Automatic Prosodic Annotation for Expressive Speech Synthesis",
    author = "Brognaux, Sandrine  and
      Fran{\c{c}}ois, Thomas  and
      Saerens, Marco",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1613",
    pages = "3872--3879",
    abstract = "Text-to-speech has long been centered on the production of an intelligible message of good quality. More recently, interest has shifted to the generation of more natural and expressive speech. A major issue of existing approaches is that they usually rely on a manual annotation in expressive styles, which tends to be rather subjective. A typical related issue is that the annotation is strongly influenced ― and possibly biased ― by the semantic content of the text (e.g. a shot or a fault may incite the annotator to tag that sequence as expressing a high degree of excitation, independently of its acoustic realization). This paper investigates the assumption that human annotation of basketball commentaries in excitation levels can be automatically improved on the basis of acoustic features. It presents two techniques for label correction exploiting a Gaussian mixture and a proportional-odds logistic regression. The automatically re-annotated corpus is then used to train HMM-based expressive speech synthesizers, the performance of which is assessed through subjective evaluations. The results indicate that the automatic correction of the annotation with Gaussian mixture helps to synthesize more contrasted excitation levels, while preserving naturalness.",
}
@inproceedings{kisler-etal-2016-bas,
    title = "{BAS} Speech Science Web Services - an Update of Current Developments",
    author = {Kisler, Thomas  and
      Reichel, Uwe  and
      Schiel, Florian  and
      Draxler, Christoph  and
      Jackl, Bernhard  and
      P{\"o}rner, Nina},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1614",
    pages = "3880--3885",
    abstract = "In 2012 the Bavarian Archive for Speech Signals started providing some of its tools from the field of spoken language in the form of Software as a Service (SaaS). This means users access the processing functionality over a web browser and therefore do not have to install complex software packages on a local computer. Amongst others, these tools include segmentation {\&} labeling, grapheme-to-phoneme conversion, text alignment, syllabification and metadata generation, where all but the last are available for a variety of languages. Since its creation the number of available services and the web interface have changed considerably. We give an overview and a detailed description of the system architecture, the available web services and their functionality. Furthermore, we show how the number of files processed over the system developed in the last four years.",
}
@inproceedings{batista-etal-2016-spa,
    title = "{SPA}: Web-based Platform for easy Access to Speech Processing Modules",
    author = "Batista, Fernando  and
      Curto, Pedro  and
      Trancoso, Isabel  and
      Abad, Alberto  and
      Ferreira, Jaime  and
      Ribeiro, Eug{\'e}nio  and
      Moniz, Helena  and
      de Matos, David Martins  and
      Ribeiro, Ricardo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1615",
    pages = "3886--3892",
    abstract = "This paper presents SPA, a web-based Speech Analytics platform that integrates several speech processing modules and that makes it possible to use them through the web. It was developed with the aim of facilitating the usage of the modules, without the need to know about software dependencies and specific configurations. Apart from being accessed by a web-browser, the platform also provides a REST API for easy integration with other applications. The platform is flexible, scalable, provides authentication for access restrictions, and was developed taking into consideration the time and effort of providing new services. The platform is still being improved, but it already integrates a considerable number of audio and text processing modules, including: Automatic transcription, speech disfluency classification, emotion detection, dialog act recognition, age and gender classification, non-nativeness detection, hyper-articulation detection, dialog act recognition, and two external modules for feature extraction and DTMF detection. This paper describes the SPA architecture, presents the already integrated modules, and provides a detailed description for the ones most recently integrated.",
}
@inproceedings{seara-etal-2016-enhanced,
    title = "Enhanced {CORILGA}: Introducing the Automatic Phonetic Alignment Tool for Continuous Speech",
    author = "Seara, Roberto  and
      Martinez, Marta  and
      Varela, Roc{\'\i}o  and
      Mateo, Carmen Garc{\'\i}a  and
      Rei, Elisa Fernandez  and
      Regueira, Xos{\'e} Luis",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1616",
    pages = "3893--3898",
    abstract = "The {``}Corpus Oral Informatizado da Lingua Galega (CORILGA){''} project aims at building a corpus of oral language for Galician, primarily designed to study the linguistic variation and change. This project is currently under development and it is periodically enriched with new contributions. The long-term goal is that all the speech recordings will be enriched with phonetic, syllabic, morphosyntactic, lexical and sentence ELAN-complaint annotations. A way to speed up the process of annotation is to use automatic speech-recognition-based tools tailored to the application. Therefore, CORILGA repository has been enhanced with an automatic alignment tool, available to the administrator of the repository, that aligns speech with an orthographic transcription. In the event that no transcription, or just a partial one, were available, a speech recognizer for Galician is used to generate word and phonetic segmentations. These recognized outputs may contain errors that will have to be manually corrected by the administrator. For assisting this task, the tool also provides an ELAN tier with the confidence measure of each recognized word. In this paper, after the description of the main facts of the CORILGA corpus, the speech alignment and recognition tools are described. Both have been developed using the Kaldi toolkit.",
}
@inproceedings{konat-etal-2016-corpus,
    title = "A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues",
    author = "Konat, Barbara  and
      Lawrence, John  and
      Park, Joonsuk  and
      Budzynska, Katarzyna  and
      Reed, Chris",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1617",
    pages = "3899--3906",
    abstract = "Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. This creates a demand for tools and technologies which will enable governments to quickly and thoroughly digest the points being made and to respond accordingly. By determining the argumentative and dialogical structures contained within a debate, we are able to determine the issues which are divisive and those which attract agreement. This paper proposes a method of graph-based analytics which uses properties of graphs representing networks of arguments pro- {\&} con- in order to automatically analyse issues which divide citizens about new regulations. By future application of the most recent advances in argument mining, the results reported here will have a chance to scale up to enable sense-making of the vast amount of feedback received from citizens on directions that policy should take.",
}
@inproceedings{correia-etal-2016-metated,
    title = "meta{TED}: a Corpus of Metadiscourse for Spoken Language",
    author = "Correia, Rui  and
      Mamede, Nuno  and
      Baptista, Jorge  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1618",
    pages = "3907--3913",
    abstract = "This paper describes metaTED ― a freely available corpus of metadiscursive acts in spoken language collected via crowdsourcing. Metadiscursive acts were annotated on a set of 180 randomly chosen TED talks in English, spanning over different speakers and topics. The taxonomy used for annotation is composed of 16 categories, adapted from Adel(2010). This adaptation takes into account both the material to annotate and the setting in which the annotation task is performed. The crowdsourcing setup is described, including considerations regarding training and quality control. The collected data is evaluated in terms of quantity of occurrences, inter-annotator agreement, and annotation related measures (such as average time on task and self-reported confidence). Results show different levels of agreement among metadiscourse acts (α ∈ [0.15; 0.49]). To further assess the collected material, a subset of the annotations was submitted to expert appreciation, who validated which of the marked occurrences truly correspond to instances of the metadiscursive act at hand. Similarly to what happened with the crowd, experts revealed different levels of agreement between categories (α ∈ [0.18; 0.72]). The paper concludes with a discussion on the applicability of metaTED with respect to each of the 16 categories of metadiscourse.",
}
@inproceedings{pareti-2016-parc,
    title = "{PARC} 3.0: A Corpus of Attribution Relations",
    author = "Pareti, Silvia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1619",
    pages = "3914--3920",
    abstract = "Quotation and opinion extraction, discourse and factuality have all partly addressed the annotation and identification of Attribution Relations. However, disjoint efforts have provided a partial and partly inaccurate picture of attribution and generated small or incomplete resources, thus limiting the applicability of machine learning approaches. This paper presents PARC 3.0, a large corpus fully annotated with Attribution Relations (ARs). The annotation scheme was tested with an inter-annotator agreement study showing satisfactory results for the identification of ARs and high agreement on the selection of the text spans corresponding to its constitutive elements: source, cue and content. The corpus, which comprises around 20k ARs, was used to investigate the range of structures that can express attribution. The results show a complex and varied relation of which the literature has addressed only a portion. PARC 3.0 is available for research use and can be used in a range of different studies to analyse attribution and validate assumptions as well as to develop supervised attribution extraction models.",
}
@inproceedings{li-etal-2016-improving,
    title = "Improving the Annotation of Sentence Specificity",
    author = "Li, Junyi Jessy  and
      O{'}Daniel, Bridget  and
      Wu, Yi  and
      Zhao, Wenli  and
      Nenkova, Ani",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1620",
    pages = "3921--3927",
    abstract = "We introduce improved guidelines for annotation of sentence specificity, addressing the issues encountered in prior work. Our annotation provides judgements of sentences in context. Rather than binary judgements, we introduce a specificity scale which accommodates nuanced judgements. Our augmented annotation procedure also allows us to define where in the discourse context the lack of specificity can be resolved. In addition, the cause of the underspecification is annotated in the form of free text questions. We present results from a pilot annotation with this new scheme and demonstrate good inter-annotator agreement. We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context. We find that missing details that are not resolved in the the prior context are more likely to trigger questions about the reason behind events, {``}why{''} and {``}how{''}. Our data is accessible at http://www.cis.upenn.edu/{\textasciitilde}nlp/corpora/lrec16spec.html",
}
@inproceedings{de-kuthy-etal-2016-focus-annotation,
    title = "Focus Annotation of Task-based Data: A Comparison of Expert and Crowd-Sourced Annotation in a Reading Comprehension Corpus",
    author = "De Kuthy, Kordula  and
      Ziai, Ramon  and
      Meurers, Detmar",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1621",
    pages = "3928--3935",
    abstract = "While the formal pragmatic concepts in information structure, such as the focus of an utterance, are precisely defined in theoretical linguistics and potentially very useful in conceptual and practical terms, it has turned out to be difficult to reliably annotate such notions in corpus data. We present a large-scale focus annotation effort designed to overcome this problem. Our annotation study is based on the tasked-based corpus CREG, which consists of answers to explicitly given reading comprehension questions. We compare focus annotation by trained annotators with a crowd-sourcing setup making use of untrained native speakers. Given the task context and an annotation process incrementally making the question form and answer type explicit, the trained annotators reach substantial agreement for focus annotation. Interestingly, the crowd-sourcing setup also supports high-quality annotation ― for specific subtypes of data. Finally, we turn to the question whether the relevance of focus annotation can be extrinsically evaluated. We show that automatic short-answer assessment significantly improves for focus annotated data. The focus annotated CREG corpus is freely available and constitutes the largest such resource for German.",
}
@inproceedings{alex-etal-2016-homing,
    title = "Homing in on {T}witter Users: Evaluating an Enhanced Geoparser for User Profile Locations",
    author = "Alex, Beatrice  and
      Llewellyn, Clare  and
      Grover, Claire  and
      Oberlander, Jon  and
      Tobin, Richard",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1622",
    pages = "3936--3944",
    abstract = "Twitter-related studies often need to geo-locate Tweets or Twitter users, identifying their real-world geographic locations. As tweet-level geotagging remains rare, most prior work exploited tweet content, timezone and network information to inform geolocation, or else relied on off-the-shelf tools to geolocate users from location information in their user profiles. However, such user location metadata is not consistently structured, causing such tools to fail regularly, especially if a string contains multiple locations, or if locations are very fine-grained. We argue that user profile location (UPL) and tweet location need to be treated as distinct types of information from which differing inferences can be drawn. Here, we apply geoparsing to UPLs, and demonstrate how task performance can be improved by adapting our Edinburgh Geoparser, which was originally developed for processing English text. We present a detailed evaluation method and results, including inter-coder agreement. We demonstrate that the optimised geoparser can effectively extract and geo-reference multiple locations at different levels of granularity with an F1-score of around 0.90. We also illustrate how geoparsed UPLs can be exploited for international information trade studies and country-level sentiment analysis.",
}
@inproceedings{mohammad-etal-2016-dataset,
    title = "A Dataset for Detecting Stance in Tweets",
    author = "Mohammad, Saif  and
      Kiritchenko, Svetlana  and
      Sobhani, Parinaz  and
      Zhu, Xiaodan  and
      Cherry, Colin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1623",
    pages = "3945--3952",
    abstract = "We can often detect from a person{'}s utterances whether he/she is in favor of or against a given target entity (a product, topic, another person, etc.). Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets of interest―their stance. The targets of interest may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. The data pertains to six targets of interest commonly known and debated in the United States. Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet. The annotations were performed by crowdsourcing. Several techniques were employed to encourage high-quality annotations (for example, providing clear and simple instructions) and to identify and discard poor annotations (for example, using a small set of check questions annotated by the authors). This Stance Dataset, which was subsequently also annotated for sentiment, can be used to better understand the relationship between stance, sentiment, entity relationships, and textual inference.",
}
@inproceedings{dini-bittar-2016-emotion,
    title = "Emotion Analysis on {T}witter: The Hidden Challenge",
    author = "Dini, Luca  and
      Bittar, Andr{\'e}",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1624",
    pages = "3953--3958",
    abstract = "In this paper, we present an experiment to detect emotions in tweets. Unlike much previous research, we draw the important distinction between the tasks of emotion detection in a closed world assumption (i.e. every tweet is emotional) and the complicated task of identifying emotional versus non-emotional tweets. Given an apparent lack of appropriately annotated data, we created two corpora for these tasks. We describe two systems, one symbolic and one based on machine learning, which we evaluated on our datasets. Our evaluation shows that a machine learning classifier performs best on emotion detection, while a symbolic approach is better for identifying relevant (i.e. emotional) tweets.",
}
@inproceedings{inel-etal-2016-crowdsourcing,
    title = "Crowdsourcing Salient Information from News and Tweets",
    author = "Inel, Oana  and
      Caselli, Tommaso  and
      Aroyo, Lora",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1625",
    pages = "3959--3966",
    abstract = "The increasing streams of information pose challenges to both humans and machines. On the one hand, humans need to identify relevant information and consume only the information that lies at their interests. On the other hand, machines need to understand the information that is published in online data streams and generate concise and meaningful overviews. We consider events as prime factors to query for information and generate meaningful context. The focus of this paper is to acquire empirical insights for identifying salience features in tweets and news about a target event, i.e., the event of {``}whaling{''}. We first derive a methodology to identify such features by building up a knowledge space of the event enriched with relevant phrases, sentiments and ranked by their novelty. We applied this methodology on tweets and we have performed preliminary work towards adapting it to news articles. Our results show that crowdsourcing text relevance, sentiments and novelty (1) can be a main step in identifying salient information, and (2) provides a deeper and more precise understanding of the data at hand compared to state-of-the-art approaches.",
}
@inproceedings{barbieri-etal-2016-emoji,
    title = "What does this Emoji Mean? A Vector Space Skip-Gram Model for {T}witter Emojis",
    author = "Barbieri, Francesco  and
      Ronzano, Francesco  and
      Saggion, Horacio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1626",
    pages = "3967--3972",
    abstract = "Emojis allow us to describe objects, situations and even feelings with small images, providing a visual and quick way to communicate. In this paper, we analyse emojis used in Twitter with distributional semantic models. We retrieve 10 millions tweets posted by USA users, and we build several skip gram word embedding models by mapping in the same vectorial space both words and emojis. We test our models with semantic similarity experiments, comparing the output of our models with human assessment. We also carry out an exhaustive qualitative evaluation, showing interesting results.",
}
@inproceedings{iosif-potamianos-2016-crossmodal,
    title = "Crossmodal Network-Based Distributional Semantic Models",
    author = "Iosif, Elias  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1627",
    pages = "3973--3979",
    abstract = "Despite the recent success of distributional semantic models (DSMs) in various semantic tasks they remain disconnected with real-world perceptual cues since they typically rely on linguistic features. Text data constitute the dominant source of features for the majority of such models, although there is evidence from cognitive science that cues from other modalities contribute to the acquisition and representation of semantic knowledge. In this work, we propose the crossmodal extension of a two-tier text-based model, where semantic representations are encoded in the first layer, while the second layer is used for computing similarity between words. We exploit text- and image-derived features for performing computations at each layer, as well as various approaches for their crossmodal fusion. It is shown that the crossmodal model performs better (from 0.68 to 0.71 correlation coefficient) than the unimodal one for the task of similarity computation between words.",
}
@inproceedings{bonial-palmer-2016-comprehensive,
    title = "Comprehensive and Consistent {P}rop{B}ank Light Verb Annotation",
    author = "Bonial, Claire  and
      Palmer, Martha",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1628",
    pages = "3980--3985",
    abstract = "Recent efforts have focused on expanding the annotation coverage of PropBank from verb relations to adjective and noun relations, as well as light verb constructions (e.g., make an offer, take a bath). While each new relation type has presented unique annotation challenges, ensuring consistent and comprehensive annotation of light verb constructions has proved particularly challenging, given that light verb constructions are semi-productive, difficult to define, and there are often borderline cases. This research describes the iterative process of developing PropBank annotation guidelines for light verb constructions, the current guidelines, and a comparison to related resources.",
}
@inproceedings{hollenstein-etal-2016-inconsistency,
    title = "Inconsistency Detection in Semantic Annotation",
    author = "Hollenstein, Nora  and
      Schneider, Nathan  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1629",
    pages = "3986--3990",
    abstract = "Inconsistencies are part of any manually annotated corpus. Automatically finding these inconsistencies and correcting them (even manually) can increase the quality of the data. Past research has focused mainly on detecting inconsistency in syntactic annotation. This work explores new approaches to detecting inconsistency in semantic annotation. Two ranking methods are presented in this paper: a discrepancy ranking and an entropy ranking. Those methods are then tested and evaluated on multiple corpora annotated with multiword expressions and supersense labels. The results show considerable improvements in detecting inconsistency candidates over a random baseline. Possible applications of methods for inconsistency detection are improving the annotation procedure as well as the guidelines and correcting errors in completed annotations.",
}
@inproceedings{oepen-etal-2016-towards,
    title = "Towards Comparability of Linguistic Graph {B}anks for Semantic Parsing",
    author = "Oepen, Stephan  and
      Kuhlmann, Marco  and
      Miyao, Yusuke  and
      Zeman, Daniel  and
      Cinkov{\'a}, Silvie  and
      Flickinger, Dan  and
      Haji{\v{c}}, Jan  and
      Ivanova, Angelina  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1630",
    pages = "3991--3995",
    abstract = "We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP{\textasciitilde}2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises. For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium in May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations.",
}
@inproceedings{lu-ng-2016-event,
    title = "Event Coreference Resolution with Multi-Pass Sieves",
    author = "Lu, Jing  and
      Ng, Vincent",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1631",
    pages = "3996--4003",
    abstract = "Multi-pass sieve approaches have been successfully applied to entity coreference resolution and many other tasks in natural language processing (NLP), owing in part to the ease of designing high-precision rules for these tasks. However, the same is not true for event coreference resolution: typically lying towards the end of the standard information extraction pipeline, an event coreference resolver assumes as input the noisy outputs of its upstream components such as the trigger identification component and the entity coreference resolution component. The difficulty in designing high-precision rules makes it challenging to successfully apply a multi-pass sieve approach to event coreference resolution. In this paper, we investigate this challenge, proposing the first multi-pass sieve approach to event coreference resolution. When evaluated on the version of the KBP 2015 corpus available to the participants of EN Task 2 (Event Nugget Detection and Coreference), our approach achieves an Avg F-score of 40.32{\%}, outperforming the best participating system by 0.67{\%} in Avg F-score.",
}
@inproceedings{cavar-etal-2016-endangered,
    title = "Endangered Language Documentation: Bootstrapping a Chatino Speech Corpus, Forced Aligner, {ASR}",
    author = "{\'C}avar, Malgorzata  and
      {\'C}avar, Damir  and
      Cruz, Hilaria",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1632",
    pages = "4004--4011",
    abstract = "This project approaches the problem of language documentation and revitalization from a rather untraditional angle. To improve and facilitate language documentation of endangered languages, we attempt to use corpus linguistic methods and speech and language technologies to reduce the time needed for transcription and annotation of audio and video language recordings. The paper demonstrates this approach on the example of the endangered and seriously under-resourced variety of Eastern Chatino (CTP). We show how initial speech corpora can be created that can facilitate the development of speech and language technologies for under-resourced languages by utilizing Forced Alignment tools to time align transcriptions. Time-aligned transcriptions can be used to train speech corpora and utilize automatic speech recognition tools for the transcription and annotation of untranscribed data. Speech technologies can be used to reduce the time and effort necessary for transcription and annotation of large collections of audio and video recordings in digital language archives, addressing the transcription bottleneck problem that most language archives and many under-documented languages are confronted with. This approach can increase the availability of language resources from low-resourced and endangered languages to speech and language technology research and development.",
}
@inproceedings{matos-etal-2016-dirha,
    title = "The {DIRHA} {P}ortuguese Corpus: A Comparison of Home Automation Command Detection and Recognition in Simulated and Real Data.",
    author = "Matos, Miguel  and
      Abad, Alberto  and
      Serralheiro, Ant{\'o}nio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1633",
    pages = "4012--4018",
    abstract = "In this paper, we describe a new corpus -named DIRHA-L2F RealCorpus- composed of typical home automation speech interactions in European Portuguese that has been recorded by the INESC-ID{'}s Spoken Language Systems Laboratory (L2F) to support the activities of the Distant-speech Interaction for Robust Home Applications (DIRHA) EU-funded project. The corpus is a multi-microphone and multi-room database of real continuous audio sequences containing read phonetically rich sentences, read and spontaneous keyword activation sentences, and read and spontaneous home automation commands. The background noise conditions are controlled and randomly recreated with noises typically found in home environments. Experimental validation on this corpus is reported in comparison with the results obtained on a simulated corpus using a fully automated speech processing pipeline for two fundamental automatic speech recognition tasks of typical {`}always-listening{'} home-automation scenarios: system activation and voice command recognition. Attending to results on both corpora, the presence of overlapping voice-like noise is shown as the main problem: simulated sequences contain concurrent speakers that result in general in a more challenging corpus, while real sequences performance drops drastically when TV or radio is on.",
}
@inproceedings{mori-etal-2016-accuracy,
    title = "Accuracy of Automatic Cross-Corpus Emotion Labeling for Conversational Speech Corpus Commonization",
    author = "Mori, Hiroki  and
      Nagaoka, Atsushi  and
      Arimoto, Yoshiko",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1634",
    pages = "4019--4023",
    abstract = "There exists a major incompatibility in emotion labeling framework among emotional speech corpora, that is, category-based and dimension-based. Commonizing these requires inter-corpus emotion labeling according to both frameworks, but doing this by human annotators is too costly for most cases. This paper examines the possibility of automatic cross-corpus emotion labeling. In order to evaluate the effectiveness of the automatic labeling, a comprehensive emotion annotation for two conversational corpora, UUDB and OGVC, was performed. With a state-of-the-art machine learning technique, dimensional and categorical emotion estimation models were trained and tested against the two corpora. For the emotion dimension estimation, the automatic cross-corpus emotion labeling for the different corpus was effective for the dimensions of aroused-sleepy, dominant-submissive and interested-indifferent, showing only slight performance degradation against the result for the same corpus. On the other hand, the performance for the emotion category estimation was not sufficient.",
}
@inproceedings{carl-etal-2016-english,
    title = "{E}nglish-to-{J}apanese Translation vs. Dictation vs. Post-editing: Comparing Translation Modes in a Multilingual Setting",
    author = "Carl, Michael  and
      Aizawa, Akiko  and
      Yamada, Masaru",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1635",
    pages = "4024--4031",
    abstract = "Speech-enabled interfaces have the potential to become one of the most efficient and ergonomic environments for human-computer interaction and for text production. However, not much research has been carried out to investigate in detail the processes and strategies involved in the different modes of text production. This paper introduces and evaluates a corpus of more than 55 hours of English-to-Japanese user activity data that were collected within the ENJA15 project, in which translators were observed while writing and speaking translations (translation dictation) and during machine translation post-editing. The transcription of the spoken data, keyboard logging and eye-tracking data were recorded with Translog-II, post-processed and integrated into the CRITT Translation Process Research-DB (TPR-DB), which is publicly available under a creative commons license. The paper presents the ENJA15 data as part of a large multilingual Chinese, Danish, German, Hindi and Spanish translation process data collection of more than 760 translation sessions. It compares the ENJA15 data with the other language pairs and reviews some of its particularities.",
}
@inproceedings{neergaard-etal-2016-database,
    title = "Database of {M}andarin Neighborhood Statistics",
    author = "Neergaard, Karl  and
      Xu, Hongzhi  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1636",
    pages = "4032--4036",
    abstract = "In the design of controlled experiments with language stimuli, researchers from psycholinguistic, neurolinguistic, and related fields, require language resources that isolate variables known to affect language processing. This article describes a freely available database that provides word level statistics for words and nonwords of Mandarin, Chinese. The featured lexical statistics include subtitle corpus frequency, phonological neighborhood density, neighborhood frequency, and homophone density. The accompanying word descriptors include pinyin, ascii phonetic transcription (sampa), lexical tone, syllable structure, dominant PoS, and syllable, segment and pinyin lengths for each phonological word. It is designed for researchers particularly concerned with language processing of isolated words and made to accommodate multiple existing hypotheses concerning the structure of the Mandarin syllable. The database is divided into multiple files according to the desired search criteria: 1) the syllable segmentation schema used to calculate density measures, and 2) whether the search is for words or nonwords. The database is open to the research community at https://github.com/karlneergaard/Mandarin-Neighborhood-Statistics.",
}
@inproceedings{janssen-2016-teitok,
    title = "{TEITOK}: Text-Faithful Annotated Corpora",
    author = "Janssen, Maarten",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1637",
    pages = "4037--4043",
    abstract = "TEITOK is a web-based framework for corpus creation, annotation, and distribution, that combines textual and linguistic annotation within a single TEI based XML document. TEITOK provides several built-in NLP tools to automatically (pre)process texts, and is highly customizable. It features multiple orthographic transcription layers, and a wide range of user-defined token-based annotations. For searching, TEITOK interfaces with a local CQP server. TEITOK can handle various types of additional resources including Facsimile images and linked audio files, making it possible to have a combined written/spoken corpus. It also has additional modules for PSDX syntactic annotation and several types of stand-off annotation.",
}
@inproceedings{schenner-nordhoff-2016-extracting,
    title = "Extracting Interlinear Glossed Text from {L}a{T}e{X} Documents",
    author = "Schenner, Mathias  and
      Nordhoff, Sebastian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1638",
    pages = "4044--4048",
    abstract = "We present texigt, a command-line tool for the extraction of structured linguistic data from LaTeX source documents, and a language resource that has been generated using this tool: a corpus of interlinear glossed text (IGT) extracted from open access books published by Language Science Press. Extracted examples are represented in a simple XML format that is easy to process and can be used to validate certain aspects of interlinear glossed text. The main challenge involved is the parsing of TeX and LaTeX documents. We review why this task is impossible in general and how the texhs Haskell library uses a layered architecture and selective early evaluation (expansion) during lexing and parsing in order to provide access to structured representations of LaTeX documents at several levels. In particular, its parsing modules generate an abstract syntax tree for LaTeX documents after expansion of all user-defined macros and lexer-level commands that serves as an ideal interface for the extraction of interlinear glossed text by texigt. This architecture can easily be adapted to extract other types of linguistic data structures from LaTeX source documents.",
}
@inproceedings{carlotto-etal-2016-interoperability,
    title = "Interoperability of Annotation Schemes: Using the Pepper Framework to Display {AWA} Documents in the {ANNIS} Interface",
    author = "Carlotto, Talvany  and
      Beloki, Zuhaitz  and
      Artola, Xabier  and
      Soroa, Aitor",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1639",
    pages = "4049--4054",
    abstract = "Natural language processing applications are frequently integrated to solve complex linguistic problems, but the lack of interoperability between these tools tends to be one of the main issues found in that process. That is often caused by the different linguistic formats used across the applications, which leads to attempts to both establish standard formats to represent linguistic information and to create conversion tools to facilitate this integration. Pepper is an example of the latter, as a framework that helps the conversion between different linguistic annotation formats. In this paper, we describe the use of Pepper to convert a corpus linguistically annotated by the annotation scheme AWA into the relANNIS format, with the ultimate goal of interacting with AWA documents through the ANNIS interface. The experiment converted 40 megabytes of AWA documents, allowed their use on the ANNIS interface, and involved making architectural decisions during the mapping from AWA into relANNIS using Pepper. The main issues faced during this process were due to technical issues mainly caused by the integration of the different systems and projects, namely AWA, Pepper and ANNIS.",
}
@inproceedings{al-badrashiny-etal-2016-split,
    title = "{SPLIT}: Smart Preprocessing (Quasi) Language Independent Tool",
    author = "Al-Badrashiny, Mohamed  and
      Pasha, Arfath  and
      Diab, Mona  and
      Habash, Nizar  and
      Rambow, Owen  and
      Salloum, Wael  and
      Eskander, Ramy",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1640",
    pages = "4055--4060",
    abstract = "Text preprocessing is an important and necessary task for all NLP applications. A simple variation in any preprocessing step may drastically affect the final results. Moreover replicability and comparability, as much as feasible, is one of the goals of our scientific enterprise, thus building systems that can ensure the consistency in our various pipelines would contribute significantly to our goals. The problem has become quite pronounced with the abundance of NLP tools becoming more and more available yet with different levels of specifications. In this paper, we present a dynamic unified preprocessing framework and tool, SPLIT, that is highly configurable based on user requirements which serves as a preprocessing tool for several tools at once. SPLIT aims to standardize the implementations of the most important preprocessing steps by allowing for a unified API that could be exchanged across different researchers to ensure complete transparency in replication. The user is able to select the required preprocessing tasks among a long list of preprocessing steps. The user is also able to specify the order of execution which in turn affects the final preprocessing output.",
}
@inproceedings{samardzic-etal-2016-archimob,
    title = "{A}rchi{M}ob - A Corpus of Spoken {S}wiss {G}erman",
    author = "Samard{\v{z}}i{\'c}, Tanja  and
      Scherrer, Yves  and
      Glaser, Elvira",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1641",
    pages = "4061--4066",
    abstract = "Swiss dialects of German are, unlike most dialects of well standardised languages, widely used in everyday communication. Despite this fact, automatic processing of Swiss German is still a considerable challenge due to the fact that it is mostly a spoken variety rarely recorded and that it is subject to considerable regional variation. This paper presents a freely available general-purpose corpus of spoken Swiss German suitable for linguistic research, but also for training automatic tools. The corpus is a result of a long design process, intensive manual work and specially adapted computational processing. We first describe how the documents were transcribed, segmented and aligned with the sound source, and how inconsistent transcriptions were unified through an additional normalisation layer. We then present a bootstrapping approach to automatic normalisation using different machine-translation-inspired methods. Furthermore, we evaluate the performance of part-of-speech taggers on our data and show how the same bootstrapping approach improves part-of-speech tagging by 10{\%} over four rounds. Finally, we present the modalities of access of the corpus as well as the data format.",
}
@inproceedings{homburg-chiarcos-2016-word,
    title = "Word Segmentation for {A}kkadian Cuneiform",
    author = "Homburg, Timo  and
      Chiarcos, Christian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1642",
    pages = "4067--4074",
    abstract = "We present experiments on word segmentation for Akkadian cuneiform, an ancient writing system and a language used for about 3 millennia in the ancient Near East. To our best knowledge, this is the first study of this kind applied to either the Akkadian language or the cuneiform writing system. As a logosyllabic writing system, cuneiform structurally resembles Eastern Asian writing systems, so, we employ word segmentation algorithms originally developed for Chinese and Japanese. We describe results of rule-based algorithms, dictionary-based algorithms, statistical and machine learning approaches. Our results may indicate possible promising steps in cuneiform word segmentation that can create and improve natural language processing in this area.",
}
@inproceedings{grouin-2016-controlled,
    title = "Controlled Propagation of Concept Annotations in Textual Corpora",
    author = "Grouin, Cyril",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1643",
    pages = "4075--4079",
    abstract = "In this paper, we presented the annotation propagation tool we designed to be used in conjunction with the BRAT rapid annotation tool. We designed two experiments to annotate a corpus of 60 files, first not using our tool, second using our propagation tool. We evaluated the annotation time and the quality of annotations. We shown that using the annotation propagation tool reduces by 31.7{\%} the time spent to annotate the corpus with a better quality of results.",
}
@inproceedings{hasida-2016-graphical,
    title = "Graphical Annotation for Syntax-Semantics Mapping",
    author = "Hasida, K{\^o}iti",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1644",
    pages = "4080--4084",
    abstract = "A potential work item (PWI) for ISO standard (MAP) about linguistic annotation concerning syntax-semantics mapping is discussed. MAP is a framework for graphical linguistic annotation to specify a mapping (set of combinations) between possible syntactic and semantic structures of the annotated linguistic data. Just like a UML diagram, a MAP diagram is formal, in the sense that it accurately specifies such a mapping. MAP provides a diagrammatic sort of concrete syntax for linguistic annotation far easier to understand than textual concrete syntax such as in XML, so that it could better facilitate collaborations among people involved in research, standardization, and practical use of linguistic data. MAP deals with syntactic structures including dependencies, coordinations, ellipses, transsentential constructions, and so on. Semantic structures treated by MAP are argument structures, scopes, coreferences, anaphora, discourse relations, dialogue acts, and so forth. In order to simplify explicit annotations, MAP allows partial descriptions, and assumes a few general rules on correspondence between syntactic and semantic compositions.",
}
@inproceedings{sammons-etal-2016-edison,
    title = "{EDISON}: Feature Extraction for {NLP}, Simplified",
    author = "Sammons, Mark  and
      Christodoulopoulos, Christos  and
      Kordjamshidi, Parisa  and
      Khashabi, Daniel  and
      Srikumar, Vivek  and
      Roth, Dan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1645",
    pages = "4085--4092",
    abstract = "When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures. These feature extractors populate simple data structures encoding the extracted features, which the package can also serialize to an intuitive JSON file format that can be easily mapped to formats used by ML packages. EDISON can also be used programmatically with JVM-based (Java/Scala) NLP software to provide the feature extractor input. The collection of feature extractors is organised hierarchically and a simple search interface is provided. In this paper we include examples that demonstrate the versatility and ease-of-use of the EDISON feature extraction suite to show that this can significantly reduce the time spent by developers on feature extraction design for NLP systems. The library is publicly hosted at https://github.com/IllinoisCogComp/illinois-cogcomp-nlp/, and we hope that other NLP researchers will contribute to the set of feature extractors. In this way, the community can help simplify reproduction of published results and the integration of ideas from diverse sources when developing new and improved NLP applications.",
}
@inproceedings{al-twairesh-etal-2016-madad,
    title = "{MADAD}: A Readability Annotation Tool for {A}rabic Text",
    author = "Al-Twairesh, Nora  and
      Al-Dayel, Abeer  and
      Al-Khalifa, Hend  and
      Al-Yahya, Maha  and
      Alageel, Sinaa  and
      Abanmy, Nora  and
      Al-Shenaifi, Nouf",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1646",
    pages = "4093--4097",
    abstract = "This paper introduces MADAD, a general-purpose annotation tool for Arabic text with focus on readability annotation. This tool will help in overcoming the problem of lack of Arabic readability training data by providing an online environment to collect readability assessments on various kinds of corpora. Also the tool supports a broad range of annotation tasks for various linguistic and semantic phenomena by allowing users to create their customized annotation schemes. MADAD is a web-based tool, accessible through any web browser; the main features that distinguish MADAD are its flexibility, portability, customizability and its bilingual interface (Arabic/English).",
}
@inproceedings{zampieri-etal-2016-modeling,
    title = "Modeling Language Change in Historical Corpora: The Case of {P}ortuguese",
    author = "Zampieri, Marcos  and
      Malmasi, Shervin  and
      Dras, Mark",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1647",
    pages = "4098--4104",
    abstract = "This paper presents a number of experiments to model changes in a historical Portuguese corpus composed of literary texts for the purpose of temporal text classification. Algorithms were trained to classify texts with respect to their publication date taking into account lexical variation represented as word n-grams, and morphosyntactic variation represented by part-of-speech (POS) distribution. We report results of 99.8{\%} accuracy using word unigram features with a Support Vector Machines classifier to predict the publication date of documents in time intervals of both one century and half a century. A feature analysis is performed to investigate the most informative features for this task and how they are linked to language change.",
}
@inproceedings{gralinski-etal-2016-said,
    title = "{``}He Said She Said{''} ― a Male/Female Corpus of {P}olish",
    author = "Grali{\'n}ski, Filip  and
      Borchmann, {\L}ukasz  and
      Wierzcho{\'n}, Piotr",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1648",
    pages = "4105--4110",
    abstract = "Gender differences in language use have long been of interest in linguistics. The task of automatic gender attribution has been considered in computational linguistics as well. Most research of this type is done using (usually English) texts with authorship metadata. In this paper, we propose a new method of male/female corpus creation based on gender-specific first-person expressions. The method was applied on CommonCrawl Web corpus for Polish (language, in which gender-revealing first-person expressions are particularly frequent) to yield a large (780M words) and varied collection of men{'}s and women{'}s texts. The whole procedure for building the corpus and filtering out unwanted texts is described in the present paper. The quality check was done on a random sample of the corpus to make sure that the majority (84{\%}) of texts are correctly attributed, natural texts. Some preliminary (socio)linguistic insights (websites and words frequently occurring in male/female fragments) are given as well.",
}
@inproceedings{smith-etal-2016-cohere,
    title = "{C}ohere: A Toolkit for Local Coherence",
    author = "Smith, Karin Sim  and
      Aziz, Wilker  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1649",
    pages = "4111--4114",
    abstract = "We describe COHERE, our coherence toolkit which incorporates various complementary models for capturing and measuring different aspects of text coherence. In addition to the traditional entity grid model (Lapata, 2005) and graph-based metric (Guinaudeau and Strube, 2013), we provide an implementation of a state-of-the-art syntax-based model (Louis and Nenkova, 2012), as well as an adaptation of this model which shows significant performance improvements in our experiments. We benchmark these models using the standard setting for text coherence: original documents and versions of the document with sentences in shuffled order.",
}
@inproceedings{ravenscroft-etal-2016-multi,
    title = "Multi-label Annotation in Scientific Articles - The Multi-label Cancer Risk Assessment Corpus",
    author = "Ravenscroft, James  and
      Oellrich, Anika  and
      Saha, Shyamasree  and
      Liakata, Maria",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1650",
    pages = "4115--4123",
    abstract = "With the constant growth of the scientific literature, automated processes to enable access to its contents are increasingly in demand. Several functional discourse annotation schemes have been proposed to facilitate information extraction and summarisation from scientific articles, the most well known being argumentative zoning. Core Scientific concepts (CoreSC) is a three layered fine-grained annotation scheme providing content-based annotations at the sentence level and has been used to index, extract and summarise scientific publications in the biomedical literature. A previously developed CoreSC corpus on which existing automated tools have been trained contains a single annotation for each sentence. However, it is the case that more than one CoreSC concept can appear in the same sentence. Here, we present the Multi-CoreSC CRA corpus, a text corpus specific to the domain of cancer risk assessment (CRA), consisting of 50 full text papers, each of which contains sentences annotated with one or more CoreSCs. The full text papers have been annotated by three biology experts. We present several inter-annotator agreement measures appropriate for multi-label annotation assessment. Employing several inter-annotator agreement measures, we were able to identify the most reliable annotator and we built a harmonised consensus (gold standard) from the three different annotators, while also taking concept priority (as specified in the guidelines) into account. We also show that the new Multi-CoreSC CRA corpus allows us to improve performance in the recognition of CoreSCs. The updated guidelines, the multi-label CoreSC CRA corpus and other relevant, related materials are available at the time of publication at http://www.sapientaproject.com/.",
}
@inproceedings{orizu-he-2016-detecting,
    title = "Detecting Expressions of Blame or Praise in Text",
    author = "Orizu, Udochukwu  and
      He, Yulan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1651",
    pages = "4124--4129",
    abstract = "The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities{'} social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.",
}
@inproceedings{tulkens-etal-2016-evaluating,
    title = "Evaluating Unsupervised {D}utch Word Embeddings as a Linguistic Resource",
    author = "Tulkens, St{\'e}phan  and
      Emmery, Chris  and
      Daelemans, Walter",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1652",
    pages = "4130--4136",
    abstract = "Word embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the importance of benchmark datasets, and the difficulty of constructing these for a variety of language-specific tasks. Still, many of the datasets used in these tasks could prove to be fruitful linguistic resources, allowing for unique observations into language use and variability. In this paper we demonstrate the performance of multiple types of embeddings, created with both count and prediction-based architectures on a variety of corpora, in two language-specific tasks: relation evaluation, and dialect identification. For the latter, we compare unsupervised methods with a traditional, hand-crafted dictionary. With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.",
}
@inproceedings{frain-wubben-2016-satiriclr,
    title = "{S}atiric{LR}: a Language Resource of Satirical News Articles",
    author = "Frain, Alice  and
      Wubben, Sander",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1653",
    pages = "4137--4140",
    abstract = "In this paper we introduce the Satirical Language Resource: a dataset containing a balanced collection of satirical and non satirical news texts from various domains. This is the first dataset of this magnitude and scope in the domain of satire. We envision this dataset will facilitate studies on various aspects of of sat- ire in news articles. We test the viability of our data on the task of classification of satire.",
}
@inproceedings{klang-nugues-2016-wikiparq,
    title = "{WIKIPARQ}: A Tabulated {W}ikipedia Resource Using the Parquet Format",
    author = "Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1654",
    pages = "4141--4148",
    abstract = "Wikipedia has become one of the most popular resources in natural language processing and it is used in quantities of applications. However, Wikipedia requires a substantial pre-processing step before it can be used. For instance, its set of nonstandardized annotations, referred to as the wiki markup, is language-dependent and needs specific parsers from language to language, for English, French, Italian, etc. In addition, the intricacies of the different Wikipedia resources: main article text, categories, wikidata, infoboxes, scattered into the article document or in different files make it difficult to have global view of this outstanding resource. In this paper, we describe WikiParq, a unified format based on the Parquet standard to tabulate and package the Wikipedia corpora. In combination with Spark, a map-reduce computing framework, and the SQL query language, WikiParq makes it much easier to write database queries to extract specific information or subcorpora from Wikipedia, such as all the first paragraphs of the articles in French, or all the articles on persons in Spanish, or all the articles on persons that have versions in French, English, and Spanish. WikiParq is available in six language versions and is potentially extendible to all the languages of Wikipedia. The WikiParq files are downloadable as tarball archives from this location: http://semantica.cs.lth.se/wikiparq/.",
}
@inproceedings{vilares-etal-2016-en,
    title = "{EN}-{ES}-{CS}: An {E}nglish-{S}panish Code-Switching {T}witter Corpus for Multilingual Sentiment Analysis",
    author = "Vilares, David  and
      Alonso, Miguel A.  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1655",
    pages = "4149--4153",
    abstract = "Code-switching texts are those that contain terms in two or more different languages, and they appear increasingly often in social media. The aim of this paper is to provide a resource to the research community to evaluate the performance of sentiment classification techniques on this complex multilingual environment, proposing an English-Spanish corpus of tweets with code-switching (EN-ES-CS CORPUS). The tweets are labeled according to two well-known criteria used for this purpose: SentiStrength and a trinary scale (positive, neutral and negative categories). Preliminary work on the resource is already done, providing a set of baselines for the research community.",
}
@inproceedings{benikova-biemann-2016-semreldata,
    title = "{S}em{R}el{D}ata ― Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines",
    author = "Benikova, Darina  and
      Biemann, Chris",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1656",
    pages = "4154--4161",
    abstract = "Semantic relations play an important role in linguistic knowledge representation. Although their role is relevant in the context of written text, there is no approach or dataset that makes use of contextuality of classic semantic relations beyond the boundary of one sentence. We present the SemRelData dataset that contains annotations of semantic relations between nominals in the context of one paragraph. To be able to analyse the universality of this context notion, the annotation was performed on a multi-lingual and multi-genre corpus. To evaluate the dataset, it is compared to large, manually created knowledge resources in the respective languages. The comparison shows that knowledge bases not only have coverage gaps; they also do not account for semantic relations that are manifested in particular contexts only, yet still play an important role for text cohesion.",
}
@inproceedings{ferrero-etal-2016-multilingual,
    title = "A Multilingual, Multi-style and Multi-granularity Dataset for Cross-language Textual Similarity Detection",
    author = "Ferrero, J{\'e}r{\'e}my  and
      Agn{\`e}s, Fr{\'e}d{\'e}ric  and
      Besacier, Laurent  and
      Schwab, Didier",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1657",
    pages = "4162--4169",
    abstract = "In this paper we describe our effort to create a dataset for the evaluation of cross-language textual similarity detection. We present preexisting corpora and their limits and we explain the various gathered resources to overcome these limits and build our enriched dataset. The proposed dataset is multilingual, includes cross-language alignment for different granularities (from chunk to document), is based on both parallel and comparable corpora and contains human and machine translated texts. Moreover, it includes texts written by multiple types of authors (from average to professionals). With the obtained dataset, we conduct a systematic and rigorous evaluation of several state-of-the-art cross-language textual similarity detection methods. The evaluation results are reviewed and discussed. Finally, dataset and scripts are made publicly available on GitHub: http://github.com/FerreroJeremy/Cross-Language-Dataset.",
}
@inproceedings{samih-maier-2016-arabic,
    title = "An {A}rabic-{M}oroccan {D}arija Code-Switched Corpus",
    author = "Samih, Younes  and
      Maier, Wolfgang",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1658",
    pages = "4170--4175",
    abstract = "In this paper, we describe our effort in the development and annotation of a large scale corpus containing code-switched data. Until recently, very limited effort has been devoted to develop computational approaches or even basic linguistic resources to support research into the processing of Moroccan Darija.",
}
@inproceedings{rekabsaz-etal-2016-standard,
    title = "Standard Test Collection for {E}nglish-{P}ersian Cross-Lingual Word Sense Disambiguation",
    author = "Rekabsaz, Navid  and
      Sabetghadam, Serwah  and
      Lupu, Mihai  and
      Andersson, Linda  and
      Hanbury, Allan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1659",
    pages = "4176--4179",
    abstract = "In this paper, we address the shortage of evaluation benchmarks on Persian (Farsi) language by creating and making available a new benchmark for English to Persian Cross Lingual Word Sense Disambiguation (CL-WSD). In creating the benchmark, we follow the format of the SemEval 2013 CL-WSD task, such that the introduced tools of the task can also be applied on the benchmark. In fact, the new benchmark extends the SemEval-2013 CL-WSD task to Persian language.",
}
@inproceedings{dojchinovski-etal-2016-freme,
    title = "{FREME}: Multilingual Semantic Enrichment with Linked Data and Language Technologies",
    author = "Dojchinovski, Milan  and
      Sasaki, Felix  and
      Gornostaja, Tatjana  and
      Hellmann, Sebastian  and
      Mannens, Erik  and
      Salliau, Frank  and
      Osella, Michele  and
      Ritchie, Phil  and
      Stoitsis, Giannis  and
      Koidl, Kevin  and
      Ackermann, Markus  and
      Chakraborty, Nilesh",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1660",
    pages = "4180--4183",
    abstract = "In the recent years, Linked Data and Language Technology solutions gained popularity. Nevertheless, their coupling in real-world business is limited due to several issues. Existing products and services are developed for a particular domain, can be used only in combination with already integrated datasets or their language coverage is limited. In this paper, we present an innovative solution FREME - an open framework of e-Services for multilingual and semantic enrichment of digital content. The framework integrates six interoperable e-Services. We describe the core features of each e-Service and illustrate their usage in the context of four business cases: i) authoring and publishing; ii) translation and localisation; iii) cross-lingual access to data; and iv) personalised Web content recommendations. Business cases drive the design and development of the framework.",
}
@inproceedings{hazem-morin-2016-improving,
    title = "Improving Bilingual Terminology Extraction from Comparable Corpora via Multiple Word-Space Models",
    author = "Hazem, Amir  and
      Morin, Emmanuel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1661",
    pages = "4184--4187",
    abstract = "There is a rich flora of word space models that have proven their efficiency in many different applications including information retrieval (Dumais, 1988), word sense disambiguation (Schutze, 1992), various semantic knowledge tests (Lund et al., 1995; Karlgren, 2001), and text categorization (Sahlgren, 2005). Based on the assumption that each model captures some aspects of word meanings and provides its own empirical evidence, we present in this paper a systematic exploration of the principal corpus-based word space models for bilingual terminology extraction from comparable corpora. We find that, once we have identified the best procedures, a very simple combination approach leads to significant improvements compared to individual models.",
}
@inproceedings{berard-etal-2016-multivec,
    title = "{M}ulti{V}ec: a Multilingual and Multilevel Representation Learning Toolkit for {NLP}",
    author = "B{\'e}rard, Alexandre  and
      Servan, Christophe  and
      Pietquin, Olivier  and
      Besacier, Laurent",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1662",
    pages = "4188--4192",
    abstract = "We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes word2vec{'}s features, paragraph vector (batch and online) and bivec for bilingual distributed representations. MultiVec also includes different distance measures between words and sequences of words. The toolkit is written in C++ and is aimed at being fast (in the same order of magnitude as word2vec), easy to use, and easy to extend. It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification.",
}
@inproceedings{abouammoh-etal-2016-creation,
    title = "Creation of comparable corpora for {E}nglish-{Urdu, Arabic, Persian}",
    author = "Abouammoh, Murad  and
      Shah, Kashif  and
      Aker, Ahmet",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1663",
    pages = "4193--4196",
    abstract = "Statistical Machine Translation (SMT) relies on the availability of rich parallel corpora. However, in the case of under-resourced languages or some specific domains, parallel corpora are not readily available. This leads to under-performing machine translation systems in those sparse data settings. To overcome the low availability of parallel resources the machine translation community has recognized the potential of using comparable resources as training data. However, most efforts have been related to European languages and less in middle-east languages. In this study, we report comparable corpora created from news articles for the pair English ―{Arabic, Persian, Urdu} languages. The data has been collected over a period of a year, entails Arabic, Persian and Urdu languages. Furthermore using the English as a pivot language, comparable corpora that involve more than one language can be created, e.g. English- Arabic - Persian, English - Arabic - Urdu, English ― Urdu - Persian, etc. Upon request the data can be provided for research purposes.",
}
@inproceedings{nisioi-etal-2016-corpus,
    title = "A Corpus of Native, Non-native and Translated Texts",
    author = "Nisioi, Sergiu  and
      Rabinovich, Ella  and
      Dinu, Liviu P.  and
      Wintner, Shuly",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1664",
    pages = "4197--4201",
    abstract = "We describe a monolingual English corpus of original and (human) translated texts, with an accurate annotation of speaker properties, including the original language of the utterances and the speaker{'}s country of origin. We thus obtain three sub-corpora of texts reflecting native English, non-native English, and English translated from a variety of European languages. This dataset will facilitate the investigation of similarities and differences between these kinds of sub-languages. Moreover, it will facilitate a unified comparative study of translations and language produced by (highly fluent) non-native speakers, two closely-related phenomena that have only been studied in isolation so far.",
}
@inproceedings{fischer-etal-2016-orthographic,
    title = "Orthographic and Morphological Correspondences between Related {S}lavic Languages as a Base for Modeling of Mutual Intelligibility",
    author = "Fischer, Andrea  and
      J{\'a}grov{\'a}, Kl{\'a}ra  and
      Stenger, Irina  and
      Avgustinova, Tania  and
      Klakow, Dietrich  and
      Marti, Roland",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1665",
    pages = "4202--4209",
    abstract = "In an intercomprehension scenario, typically a native speaker of language L1 is confronted with output from an unknown, but related language L2. In this setting, the degree to which the receiver recognizes the unfamiliar words greatly determines communicative success. Despite exhibiting great string-level differences, cognates may be recognized very successfully if the receiver is aware of regular correspondences which allow to transform the unknown word into its familiar form. Modeling L1-L2 intercomprehension then requires the identification of all the regular correspondences between languages L1 and L2. We here present a set of linguistic orthographic correspondences manually compiled from comparative linguistics literature along with a set of statistically-inferred suggestions for correspondence rules. In order to do statistical inference, we followed the Minimum Description Length principle, which proposes to choose those rules which are most effective at describing the data. Our statistical model was able to reproduce most of our linguistic correspondences (88.5{\%} for Czech-Polish and 75.7{\%} for Bulgarian-Russian) and furthermore allowed to easily identify many more non-trivial correspondences which also cover aspects of morphology.",
}
@inproceedings{gutierrez-vasques-etal-2016-axolotl,
    title = "{A}xolotl: a Web Accessible Parallel Corpus for {S}panish-{N}ahuatl",
    author = "Gutierrez-Vasques, Ximena  and
      Sierra, Gerardo  and
      Pompa, Isaac Hernandez",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1666",
    pages = "4210--4214",
    abstract = "This paper describes the project called Axolotl which comprises a Spanish-Nahuatl parallel corpus and its search interface. Spanish and Nahuatl are distant languages spoken in the same country. Due to the scarcity of digital resources, we describe the several problems that arose when compiling this corpus: most of our sources were non-digital books, we faced errors when digitizing the sources and there were difficulties in the sentence alignment process, just to mention some. The documents of the parallel corpus are not homogeneous, they were extracted from different sources, there is dialectal, diachronical, and orthographical variation. Additionally, we present a web search interface that allows to make queries through the whole parallel corpus, the system is capable to retrieve the parallel fragments that contain a word or phrase searched by a user in any of the languages. To our knowledge, this is the first Spanish-Nahuatl public available digital parallel corpus. We think that this resource can be useful to develop language technologies and linguistic studies for this language pair.",
}
@inproceedings{cetinoglu-2016-turkish,
    title = "A {T}urkish-{G}erman Code-Switching Corpus",
    author = {{\c{C}}etino{\u{g}}lu, {\"O}zlem},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1667",
    pages = "4215--4220",
    abstract = "Bilingual communities often alternate between languages both in spoken and written communication. One such community, Germany residents of Turkish origin produce Turkish-German code-switching, by heavily mixing two languages at discourse, sentence, or word level. Code-switching in general, and Turkish-German code-switching in particular, has been studied for a long time from a linguistic perspective. Yet resources to study them from a more computational perspective are limited due to either small size or licence issues. In this work we contribute the solution of this problem with a corpus. We present a Turkish-German code-switching corpus which consists of 1029 tweets, with a majority of intra-sentential switches. We share different type of code-switching we have observed in our collection and describe our processing steps. The first step is data collection and filtering. This is followed by manual tokenisation and normalisation. And finally, we annotate data with word-level language identification information. The resulting corpus is available for research purposes.",
}
@inproceedings{mohler-etal-2016-introducing,
    title = "Introducing the {LCC} Metaphor Datasets",
    author = "Mohler, Michael  and
      Brunson, Mary  and
      Rink, Bryan  and
      Tomlinson, Marc",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1668",
    pages = "4221--4227",
    abstract = "In this work, we present the Language Computer Corporation (LCC) annotated metaphor datasets, which represent the largest and most comprehensive resource for metaphor research to date. These datasets were produced over the course of three years by a staff of nine annotators working in four languages (English, Spanish, Russian, and Farsi). As part of these datasets, we provide (1) metaphoricity ratings for within-sentence word pairs on a four-point scale, (2) scored links to our repository of 114 source concept domains and 32 target concept domains, and (3) ratings for the affective polarity and intensity of each pair. Altogether, we provide 188,741 annotations in English (for 80,100 pairs), 159,915 annotations in Spanish (for 63,188 pairs), 99,740 annotations in Russian (for 44,632 pairs), and 137,186 annotations in Farsi (for 57,239 pairs). In addition, we are providing a large set of likely metaphors which have been independently extracted by our two state-of-the-art metaphor detection systems but which have not been analyzed by our team of annotators.",
}
@inproceedings{diab-etal-2016-creating,
    title = "Creating a Large Multi-Layered Representational Repository of Linguistic Code Switched {A}rabic Data",
    author = "Diab, Mona  and
      Ghoneim, Mahmoud  and
      Hawwari, Abdelati  and
      AlGhamdi, Fahad  and
      AlMarwani, Nada  and
      Al-Badrashiny, Mohamed",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1669",
    pages = "4228--4235",
    abstract = "We present our effort to create a large Multi-Layered representational repository of Linguistic Code-Switched Arabic data. The process involves developing clear annotation standards and Guidelines, streamlining the annotation process, and implementing quality control measures. We used two main protocols for annotation: in-lab gold annotations and crowd sourcing annotations. We developed a web-based annotation tool to facilitate the management of the annotation process. The current version of the repository contains a total of 886,252 tokens that are tagged into one of sixteen code-switching tags. The data exhibits code switching between Modern Standard Arabic and Egyptian Dialectal Arabic representing three data genres: Tweets, commentaries, and discussion fora. The overall Inter-Annotator Agreement is 93.1{\%}.",
}
@inproceedings{meurant-etal-2016-modelling,
    title = "Modelling a Parallel Corpus of {F}rench and {F}rench {B}elgian {S}ign {L}anguage",
    author = "Meurant, Laurence  and
      Gobert, Maxime  and
      Cleve, Anthony",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1670",
    pages = "4236--4240",
    abstract = "The overarching objective underlying this research is to develop an online tool, based on a parallel corpus of French Belgian Sign Language (LSFB) and written Belgian French. This tool is aimed to assist various set of tasks related to the comparison of LSFB and French, to the benefit of general users as well as teachers in bilingual schools, translators and interpreters, as well as linguists. These tasks include (1) the comprehension of LSFB or French texts, (2) the production of LSFB or French texts, (3) the translation between LSFB and French in both directions and (4) the contrastive analysis of these languages. The first step of investigation aims at creating an unidirectional French-LSFB concordancer, able to align a one- or multiple-word expression from the French translated text with its corresponding expressions in the videotaped LSFB productions. We aim at testing the efficiency of this concordancer for the extraction of a dictionary of meanings in context. In this paper, we will present the modelling of the different data sources at our disposal and specifically the way they interact with one another.",
}
@inproceedings{cebovic-tadic-2016-building,
    title = "Building the {M}acedonian-{C}roatian Parallel Corpus",
    author = "Cebovi{\'c}, Ines  and
      Tadi{\'c}, Marko",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1671",
    pages = "4241--4244",
    abstract = "In this paper we present the newly created parallel corpus of two under-resourced languages, namely, Macedonian-Croatian Parallel Corpus (mk-hr{\_}pcorp) that has been collected during 2015 at the Faculty of Humanities and Social Sciences, University of Zagreb. The mk-hr{\_}pcorp is a unidirectional (mk→hr) parallel corpus composed of synchronic fictional prose texts received already in digital form with over 500 Kw in each language. The corpus was sentence segmented and provides 39,735 aligned sentences. The alignment was done automatically and then post-corrected manually. The alignments order was shuffled and this enabled the corpus to be available under CC-BY license through META-SHARE. However, this prevents the research in language units over the sentence level.",
}
@inproceedings{benko-2016-two,
    title = "Two Years of Aranea: Increasing Counts and Tuning the Pipeline",
    author = "Benko, Vladim{\'\i}r",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1672",
    pages = "4245--4248",
    abstract = "The Aranea Project is targeted at creation of a family of Gigaword web-corpora for a dozen of languages that could be used for teaching language- and linguistics-related subjects at Slovak universities, as well as for research purposes in various areas of linguistics. All corpora are being built according to a standard methodology and using the same set of tools for processing and annotation, which ― together with their standard size and― makes them also a valuable resource for translators and contrastive studies. All our corpora are freely available either via a web interface or in a source form in an annotated vertical format.",
}
@inproceedings{umata-etal-2016-quantitative,
    title = "Quantitative Analysis of Gazes and Grounding Acts in {L}1 and {L}2 Conversations",
    author = "Umata, Ichiro  and
      Ijuin, Koki  and
      Ishida, Mitsuru  and
      Takeuchi, Moe  and
      Yamamoto, Seiichi",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1673",
    pages = "4249--4252",
    abstract = "The listener{'}s gazing activities during utterances were analyzed in a face-to-face three-party conversation setting. The function of each utterance was categorized according to the Grounding Acts defined by Traum (Traum, 1994) so that gazes during utterances could be analyzed from the viewpoint of grounding in communication (Clark, 1996). Quantitative analysis showed that the listeners were gazing at the speakers more in the second language (L2) conversation than in the native language (L1) conversation during the utterances that added new pieces of information, suggesting that they are using visual information to compensate for their lack of linguistic proficiency in L2 conversation.",
}
@inproceedings{jones-etal-2016-multi,
    title = "Multi-language Speech Collection for {NIST} {LRE}",
    author = "Jones, Karen  and
      Strassel, Stephanie  and
      Walker, Kevin  and
      Graff, David  and
      Wright, Jonathan",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1674",
    pages = "4253--4258",
    abstract = "The Multi-language Speech (MLS) Corpus supports NIST{'}s Language Recognition Evaluation series by providing new conversational telephone speech and broadcast narrowband data in 20 languages/dialects. The corpus was built with the intention of testing system performance in the matter of distinguishing closely related or confusable linguistic varieties, and careful manual auditing of collected data was an important aspect of this work. This paper lists the specific data requirements for the collection and provides both a commentary on the rationale for those requirements as well as an outline of the various steps taken to ensure all goals were met as specified. LDC conducted a large-scale recruitment effort involving the implementation of candidate assessment and interview techniques suitable for hiring a large contingent of telecommuting workers, and this recruitment effort is discussed in detail. We also describe the telephone and broadcast collection infrastructure and protocols, and provide details of the steps taken to pre-process collected data prior to auditing. Finally, annotation training, procedures and outcomes are presented in detail.",
}
@inproceedings{zesch-horsmann-2016-flextag,
    title = "{F}lex{T}ag: A Highly Flexible {P}o{S} Tagging Framework",
    author = "Zesch, Torsten  and
      Horsmann, Tobias",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1675",
    pages = "4259--4263",
    abstract = "We present FlexTag, a highly flexible PoS tagging framework. In contrast to monolithic implementations that can only be retrained but not adapted otherwise, FlexTag enables users to modify the feature space and the classification algorithm. Thus, FlexTag makes it easy to quickly develop custom-made taggers exactly fitting the research problem.",
}
@inproceedings{ljubesic-etal-2016-new,
    title = "New Inflectional Lexicons and Training Corpora for Improved Morphosyntactic Annotation of {C}roatian and {S}erbian",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Klubi{\v{c}}ka, Filip  and
      Agi{\'c}, {\v{Z}}eljko  and
      Jazbec, Ivo-Pavao",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1676",
    pages = "4264--4270",
    abstract = "In this paper we present newly developed inflectional lexcions and manually annotated corpora of Croatian and Serbian. We introduce hrLex and srLex - two freely available inflectional lexicons of Croatian and Serbian - and describe the process of building these lexicons, supported by supervised machine learning techniques for lemma and paradigm prediction. Furthermore, we introduce hr500k, a manually annotated corpus of Croatian, 500 thousand tokens in size. We showcase the three newly developed resources on the task of morphosyntactic annotation of both languages by using a recently developed CRF tagger. We achieve best results yet reported on the task for both languages, beating the HunPos baseline trained on the same datasets by a wide margin.",
}
@inproceedings{luecking-etal-2016-tgermacorp,
    title = "{TG}erma{C}orp {--} A (Digital) Humanities Resource for (Computational) Linguistics",
    author = "Luecking, Andy  and
      Hoenen, Armin  and
      Mehler, Alexander",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1677",
    pages = "4271--4277",
    abstract = "TGermaCorp is a German text corpus whose primary sources are collected from German literature texts which date from the sixteenth century to the present. The corpus is intended to represent its target language (German) in syntactic, lexical, stylistic and chronological diversity. For this purpose, it is hand-annotated on several linguistic layers, including POS, lemma, named entities, multiword expressions, clauses, sentences and paragraphs. In order to introduce TGermaCorp in comparison to more homogeneous corpora of contemporary everyday language, quantitative assessments of syntactic and lexical diversity are provided. In this respect, TGermaCorp contributes to establishing characterising features for resource descriptions, which is needed for keeping track of a meaningful comparison of the ever-growing number of natural language resources. The assessments confirm the special role of proper names, whose propagation in text may influence lexical and syntactic diversity measures in rather trivial ways. TGermaCorp will be made available via hucompute.org.",
}
@inproceedings{pajkossy-zseder-2016-hunvec,
    title = "The hunvec framework for {NN}-{CRF}-based sequential tagging",
    author = "Pajkossy, Katalin  and
      Zs{\'e}der, Attila",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1678",
    pages = "4278--4281",
    abstract = "In this work we present the open source hunvec framework for sequential tagging, built upon Theano and Pylearn2. The underlying statistical model, which connects linear CRF-s with neural networks, was used by Collobert and co-workers, and several other researchers. For demonstrating the flexibility of our tool, we describe a set of experiments on part-of-speech and named-entity-recognition tasks, using English and Hungarian datasets, where we modify both model and training parameters, and illustrate the usage of custom features. Model parameters we experiment with affect the vectorial word representations used by the model; we apply different word vector initializations, defined by Word2vec and GloVe embeddings and enrich the representation of words by vectors assigned trigram features. We extend training methods by using their regularized (l2 and dropout) version. When testing our framework on a Hungarian named entity corpus, we find that its performance reaches the best published results on this dataset, with no need for language-specific feature engineering. Our code is available at http://github.com/zseder/hunvec",
}
@inproceedings{khalifa-etal-2016-large,
    title = "A Large Scale Corpus of {G}ulf {A}rabic",
    author = "Khalifa, Salam  and
      Habash, Nizar  and
      Abdulrahim, Dana  and
      Hassan, Sara",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1679",
    pages = "4282--4289",
    abstract = "Most Arabic natural language processing tools and resources are developed to serve Modern Standard Arabic (MSA), which is the official written language in the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have received some attention lately and have a growing collection of resources that include annotated corpora and morphological analyzers and taggers. Gulf Arabic, however, lags behind in that respect. In this paper, we present the Gumar Corpus, a large-scale corpus of Gulf Arabic consisting of 110 million words from 1,200 forum novels. We annotate the corpus for sub-dialect information at the document level. We also present results of a preliminary study in the morphological annotation of Gulf Arabic which includes developing guidelines for a conventional orthography. The text of the corpus is publicly browsable through a web interface we developed for it.",
}
@inproceedings{straka-etal-2016-udpipe,
    title = "{UDP}ipe: Trainable Pipeline for Processing {C}o{NLL}-{U} Files Performing Tokenization, Morphological Analysis, {POS} Tagging and Parsing",
    author = "Straka, Milan  and
      Haji{\v{c}}, Jan  and
      Strakov{\'a}, Jana",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1680",
    pages = "4290--4297",
    abstract = "Automatic natural language processing of large texts often presents recurring challenges in multiple languages: even for most advanced tasks, the texts are first processed by basic processing steps {--} from tokenization to parsing. We present an extremely simple-to-use tool consisting of one binary and one model (per language), which performs these tasks for multiple languages without the need for any other external data. UDPipe, a pipeline processing CoNLL-U-formatted files, performs tokenization, morphological analysis, part-of-speech tagging, lemmatization and dependency parsing for nearly all treebanks of Universal Dependencies 1.2 (namely, the whole pipeline is currently available for 32 out of 37 treebanks). In addition, the pipeline is easily trainable with training data in CoNLL-U format (and in some cases also with additional raw corpora) and requires minimal linguistic knowledge on the users{'} part. The training code is also released.",
}
@inproceedings{habash-etal-2016-exploiting,
    title = "Exploiting {A}rabic Diacritization for High Quality Automatic Annotation",
    author = "Habash, Nizar  and
      Shahrour, Anas  and
      Al-Khalil, Muhamed",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1681",
    pages = "4298--4304",
    abstract = "We present a novel technique for Arabic morphological annotation. The technique utilizes diacritization to produce morphological annotations of quality comparable to human annotators. Although Arabic text is generally written without diacritics, diacritization is already available for large corpora of Arabic text in several genres. Furthermore, diacritization can be generated at a low cost for new text as it does not require specialized training beyond what educated Arabic typists know. The basic approach is to enrich the input to a state-of-the-art Arabic morphological analyzer with word diacritics (full or partial) to enhance its performance. When applied to fully diacritized text, our approach produces annotations with an accuracy of over 97{\%} on lemma, part-of-speech, and tokenization combined.",
}
@inproceedings{kabashi-proisl-2016-proposal,
    title = "A Proposal for a Part-of-Speech Tagset for the {A}lbanian Language",
    author = "Kabashi, Besim  and
      Proisl, Thomas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1682",
    pages = "4305--4310",
    abstract = "Part-of-speech tagging is a basic step in Natural Language Processing that is often essential. Labeling the word forms of a text with fine-grained word-class information adds new value to it and can be a prerequisite for downstream processes like a dependency parser. Corpus linguists and lexicographers also benefit greatly from the improved search options that are available with tagged data. The Albanian language has some properties that pose difficulties for the creation of a part-of-speech tagset. In this paper, we discuss those difficulties and present a proposal for a part-of-speech tagset that can adequately represent the underlying linguistic phenomena.",
}
@inproceedings{outahajala-rosso-2016-using,
    title = "Using a Small Lexicon with {CRF}s Confidence Measure to Improve {POS} Tagging Accuracy",
    author = "Outahajala, Mohamed  and
      Rosso, Paolo",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1683",
    pages = "4311--4315",
    abstract = "Like most of the languages which have only recently started being investigated for the Natural Language Processing (NLP) tasks, Amazigh lacks annotated corpora and tools and still suffers from the scarcity of linguistic tools and resources. The main aim of this paper is to present a new part-of-speech (POS) tagger based on a new Amazigh tag set (AMTS) composed of 28 tags. In line with our goal we have trained Conditional Random Fields (CRFs) to build a POS tagger for the Amazigh language. We have used the 10-fold technique to evaluate and validate our approach. The CRFs 10 folds average level is 87.95{\%} and the best fold level result is 91.18{\%}. In order to improve this result, we have gathered a set of about 8k words with their POS tags. The collected lexicon was used with CRFs confidence measure in order to have a more accurate POS-tagger. Hence, we have obtained a better performance of 93.82{\%}.",
}
@inproceedings{schulz-kuhn-2016-learning,
    title = "Learning from Within? Comparing {P}o{S} Tagging Approaches for Historical Text",
    author = "Schulz, Sarah  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1684",
    pages = "4316--4322",
    abstract = "In this paper, we investigate unsupervised and semi-supervised methods for part-of-speech (PoS) tagging in the context of historical German text. We locate our research in the context of Digital Humanities where the non-canonical nature of text causes issues facing an Natural Language Processing world in which tools are mainly trained on standard data. Data deviating from the norm requires tools adjusted to this data. We explore to which extend the availability of such training material and resources related to it influences the accuracy of PoS tagging. We investigate a variety of algorithms including neural nets, conditional random fields and self-learning techniques in order to find the best-fitted approach to tackle data sparsity. Although methods using resources from related languages outperform weakly supervised methods using just a few training examples, we can still reach a promising accuracy with methods abstaining additional resources.",
}
@inproceedings{da-costa-bond-2016-wow,
    title = "Wow! What a Useful Extension! Introducing Non-Referential Concepts to {W}ordnet",
    author = "Da Costa, Luis Morgado  and
      Bond, Francis",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1685",
    pages = "4323--4328",
    abstract = "In this paper we present the ongoing efforts to expand the depth and breath of the Open Multilingual Wordnet coverage by introducing two new classes of non-referential concepts to wordnet hierarchies: interjections and numeral classifiers. The lexical semantic hierarchy pioneered by Princeton Wordnet has traditionally restricted its coverage to referential and contentful classes of words: such as nouns, verbs, adjectives and adverbs. Previous efforts have been employed to enrich wordnet resources including, for example, the inclusion of pronouns, determiners and quantifiers within their hierarchies. Following similar efforts, and motivated by the ongoing semantic annotation of the NTU-Multilingual Corpus, we decided that the four traditional classes of words present in wordnets were too restrictive. Though non-referential, interjections and classifiers possess interesting semantics features that can be well captured by lexical resources like wordnets. In this paper, we will further motivate our decision to include non-referential concepts in wordnets and give an account of the current state of this expansion.",
}
@inproceedings{dhuliawala-etal-2016-slangnet,
    title = "{S}lang{N}et: A {W}ord{N}et like resource for {E}nglish Slang",
    author = "Dhuliawala, Shehzaad  and
      Kanojia, Diptesh  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1686",
    pages = "4329--4332",
    abstract = "We present a WordNet like structured resource for slang words and neologisms on the internet. The dynamism of language is often an indication that current language technology tools trained on today{'}s data, may not be able to process the language in the future. Our resource could be (1) used to augment the WordNet, (2) used in several Natural Language Processing (NLP) applications which make use of noisy data on the internet like Information Retrieval and Web Mining. Such a resource can also be used to distinguish slang word senses from conventional word senses. To stimulate similar innovations widely in the NLP community, we test the efficacy of our resource for detecting slang using standard bag of words Word Sense Disambiguation (WSD) algorithms (Lesk and Extended Lesk) for English data on the internet.",
}
@inproceedings{oliveira-santos-2016-discovering,
    title = "Discovering Fuzzy Synsets from the Redundancy in Different Lexical-Semantic Resources",
    author = "Oliveira, Hugo Gon{\c{c}}alo  and
      Santos, F{\'a}bio",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1687",
    pages = "4333--4340",
    abstract = "Although represented as such in wordnets, word senses are not discrete. To handle word senses as fuzzy objects, we exploit the graph structure of synonymy pairs acquired from different sources to discover synsets where words have different membership degrees that reflect confidence. Following this approach, a wide-coverage fuzzy thesaurus was discovered from a synonymy network compiled from seven Portuguese lexical-semantic resources. Based on a crowdsourcing evaluation, we can say that the quality of the obtained synsets is far from perfect but, as expected in a confidence measure, it increases significantly for higher cut-points on the membership and, at a certain point, reaches 100{\%} correction rate.",
}
@inproceedings{hayoun-elhadad-2016-hebrew,
    title = "The {H}ebrew {F}rame{N}et Project",
    author = "Hayoun, Avi  and
      Elhadad, Michael",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1688",
    pages = "4341--4347",
    abstract = "We present the Hebrew FrameNet project, describe the development and annotation processes and enumerate the challenges we faced along the way. We have developed semi-automatic tools to help speed the annotation and data collection process. The resource currently covers 167 frames, 3,000 lexical units and about 500 fully annotated sentences. We have started training and testing automatic SRL tools on the seed data.",
}
@inproceedings{neudecker-2016-open,
    title = "An Open Corpus for Named Entity Recognition in Historic Newspapers",
    author = "Neudecker, Clemens",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1689",
    pages = "4348--4352",
    abstract = "The availability of openly available textual datasets ({``}corpora{''}) with highly accurate manual annotations ({``}gold standard{''}) of named entities (e.g. persons, locations, organizations, etc.) is crucial in the training and evaluation of named entity recognition systems. Currently there are only few such datasets available on the web, and even less for texts containing historical spelling variation. The production and subsequent release into the public domain of four such datasets with 100 pages each for the languages Dutch, French, German (including Austrian) as part of the Europeana Newspapers project is expected to contribute to the further development and improvement of named entity recognition systems with a focus on historical content. This paper describes how these datasets were produced, what challenges were encountered in their creation and informs about their final quality and availability.",
}
@inproceedings{daille-etal-2016-ambiguity,
    title = "Ambiguity Diagnosis for Terms in Digital Humanities",
    author = {Daille, B{\'e}atrice  and
      Jacquey, Evelyne  and
      Lejeune, Ga{\"e}l  and
      Melo, Luis Felipe  and
      Toussaint, Yannick},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1690",
    pages = "4353--4359",
    abstract = "Among all researches dedicating to terminology and word sense disambiguation, little attention has been devoted to the ambiguity of term occurrences. If a lexical unit is indeed a term of the domain, it is not true, even in a specialised corpus, that all its occurrences are terminological. Some occurrences are terminological and other are not. Thus, a global decision at the corpus level about the terminological status of all occurrences of a lexical unit would then be erroneous. In this paper, we propose three original methods to characterise the ambiguity of term occurrences in the domain of social sciences for French. These methods differently model the context of the term occurrences: one is relying on text mining, the second is based on textometry, and the last one focuses on text genre properties. The experimental results show the potential of the proposed approaches and give an opportunity to discuss about their hybridisation.",
}
@inproceedings{navarro-etal-2016-metrical,
    title = "Metrical Annotation of a Large Corpus of {S}panish Sonnets: Representation, Scansion and Evaluation",
    author = "Navarro, Borja  and
      Ribes Lafoz, Mar{\'\i}a  and
      S{\'a}nchez, Noelia",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1691",
    pages = "4360--4364",
    abstract = "In order to analyze metrical and semantics aspects of poetry in Spanish with computational techniques, we have developed a large corpus annotated with metrical information. In this paper we will present and discuss the development of this corpus: the formal representation of metrical patterns, the semi-automatic annotation process based on a new automatic scansion system, the main annotation problems, and the evaluation, in which an inter-annotator agreement of 96{\%} has been obtained. The corpus is open and available.",
}
@inproceedings{haaf-2016-corpus,
    title = "Corpus Analysis based on Structural Phenomena in Texts: Exploiting {TEI} Encoding for Linguistic Research",
    author = "Haaf, Susanne",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1692",
    pages = "4365--4372",
    abstract = "This paper poses the question, how linguistic corpus-based research may be enriched by the exploitation of conceptual text structures and layout as provided via TEI annotation. Examples for possible areas of research and usage scenarios are provided based on the German historical corpus of the Deutsches Textarchiv (DTA) project, which has been consistently tagged accordant to the TEI Guidelines, more specifically to the DTA ›Base Format‹ (DTABf). The paper shows that by including TEI-XML structuring in corpus-based analyses significances can be observed for different linguistic phenomena, as e.g. the development of conceptual text structures themselves, the syntactic embedding of terms in certain conceptual text structures, and phenomena of language change which become obvious via the layout of a text. The exemplary study carried out here shows some of the potential for the exploitation of TEI annotation for linguistic research, which might be kept in mind when making design decisions for new corpora as well when working with existing TEI corpora.",
}
@inproceedings{van-erp-etal-2016-evaluating,
    title = "Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job",
    author = "van Erp, Marieke  and
      Mendes, Pablo  and
      Paulheim, Heiko  and
      Ilievski, Filip  and
      Plu, Julien  and
      Rizzo, Giuseppe  and
      Waitelonis, Joerg",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1693",
    pages = "4373--4379",
    abstract = "Entity linking has become a popular task in both natural language processing and semantic web communities. However, we find that the benchmark datasets for entity linking tasks do not accurately evaluate entity linking systems. In this paper, we aim to chart the strengths and weaknesses of current benchmark datasets and sketch a roadmap for the community to devise better benchmark datasets.",
}
@inproceedings{preotiuc-pietro-etal-2016-studying,
    title = "Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Srijith, P. K.  and
      Hepple, Mark  and
      Cohn, Trevor",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1694",
    pages = "4380--4387",
    abstract = "Streaming media provides a number of unique challenges for computational linguistics. This paper studies the temporal variation in word co-occurrence statistics, with application to event detection. We develop a spectral clustering approach to find groups of mutually informative terms occurring in discrete time frames. Experiments on large datasets of tweets show that these groups identify key real world events as they occur in time, despite no explicit supervision. The performance of our method rivals state-of-the-art methods for event detection on F-score, obtaining higher recall at the expense of precision.",
}
@inproceedings{mojica-de-la-vega-ng-2016-markov,
    title = "{M}arkov {L}ogic {N}etworks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming",
    author = "Mojica de la Vega, Luis Gerardo  and
      Ng, Vincent",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1695",
    pages = "4388--4395",
    abstract = "Joint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline counterparts. However, MLNs are arguably much less popular among NLP researchers than ILP. While NLP researchers who desire to employ these joint inference frameworks do not necessarily have to understand their theoretical underpinnings, it is imperative that they understand which of them should be applied under what circumstances. With the goal of helping NLP researchers better understand the relative strengths and weaknesses of MLNs and ILP; we will compare them along different dimensions of interest, such as expressiveness, ease of use, scalability, and performance. To our knowledge, this is the first systematic comparison of ILP and MLNs on an NLP task.",
}
@inproceedings{zaatari-etal-2016-arabic,
    title = "{A}rabic Corpora for Credibility Analysis",
    author = "Zaatari, Ayman Al  and
      Ballouli, Rim El  and
      ELbassouni, Shady  and
      El-Hajj, Wassim  and
      Hajj, Hazem  and
      Shaban, Khaled  and
      Habash, Nizar  and
      Yahya, Emad",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1696",
    pages = "4396--4401",
    abstract = "A significant portion of data generated on blogging and microblogging websites is non-credible as shown in many recent studies. To filter out such non-credible information, machine learning can be deployed to build automatic credibility classifiers. However, as in the case with most supervised machine learning approaches, a sufficiently large and accurate training data must be available. In this paper, we focus on building a public Arabic corpus of blogs and microblogs that can be used for credibility classification. We focus on Arabic due to the recent popularity of blogs and microblogs in the Arab World and due to the lack of any such public corpora in Arabic. We discuss our data acquisition approach and annotation process, provide rigid analysis on the annotated data and finally report some results on the effectiveness of our data for credibility classification.",
}
@inproceedings{kaplan-etal-2016-solving,
    title = "Solving the {AL} Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction",
    author = "Kaplan, Dain  and
      Rubens, Neil  and
      Teufel, Simone  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1697",
    pages = "4402--4409",
    abstract = "Active learning (AL) is often used in corpus construction (CC) for selecting {``}informative{''} documents for annotation. This is ideal for focusing annotation efforts when all documents cannot be annotated, but has the limitation that it is carried out in a closed-loop, selecting points that will improve an existing model. For phenomena-driven and exploratory CC, the lack of existing-models and specific task(s) for using it make traditional AL inapplicable. In this paper we propose a novel method for model-free AL utilising characteristics of phenomena for applying AL to select documents for annotation. The method can also supplement traditional closed-loop AL-based CC to extend the utility of the corpus created beyond a single task. We introduce our tool, MOVE, and show its potential with a real world case-study.",
}
@inproceedings{freitas-etal-2016-quemdisse,
    title = "{QUEMDISSE}? Reported speech in {P}ortuguese",
    author = "Freitas, Cl{\'a}udia  and
      Freitas, Bianca  and
      Santos, Diana",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1698",
    pages = "4410--4416",
    abstract = "This paper presents some work on direct and indirect speech in Portuguese using corpus-based methods: we report on a study whose aim was to identify (i) Portuguese verbs used to introduce reported speech and (ii) syntactic patterns used to convey reported speech, in order to enhance the performance of a quotation extraction system, dubbed QUEMDISSE?. In addition, (iii) we present a Portuguese corpus annotated with reported speech, using the lexicon and rules provided by (i) and (ii), and discuss the process of their annotation and what was learned.",
}
@inproceedings{minard-etal-2016-meantime,
    title = "{MEANTIME}, the {N}ews{R}eader Multilingual Event and Time Corpus",
    author = "Minard, Anne-Lyse  and
      Speranza, Manuela  and
      Urizar, Ruben  and
      Altuna, Bego{\~n}a  and
      van Erp, Marieke  and
      Schoen, Anneleen  and
      van Son, Chantal",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1699",
    pages = "4417--4422",
    abstract = "In this paper, we present the NewsReader MEANTIME corpus, a semantically annotated corpus of Wikinews articles. The corpus consists of 480 news articles, i.e. 120 English news articles and their translations in Spanish, Italian, and Dutch. MEANTIME contains annotations at different levels. The document-level annotation includes markables (e.g. entity mentions, event mentions, time expressions, and numerical expressions), relations between markables (modeling, for example, temporal information and semantic role labeling), and entity and event intra-document coreference. The corpus-level annotation includes entity and event cross-document coreference. Semantic annotation on the English section was performed manually; for the annotation in Italian, Spanish, and (partially) Dutch, a procedure was devised to automatically project the annotations on the English texts onto the translated texts, based on the manual alignment of the annotated elements; this enabled us not only to speed up the annotation process but also provided cross-lingual coreference. The English section of the corpus was extended with timeline annotations for the SemEval 2015 TimeLine shared task. The {``}First CLIN Dutch Shared Task{''} at CLIN26 was based on the Dutch section, while the EVALITA 2016 FactA (Event Factuality Annotation) shared task, based on the Italian section, is currently being organized.",
}
@inproceedings{moran-2016-acqdiv,
    title = "The {ACQDIV} Database: {M}in(d)ing the Ambient Language",
    author = "Moran, Steven",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1700",
    pages = "4423--4429",
    abstract = "One of the most pressing questions in cognitive science remains unanswered: what cognitive mechanisms enable children to learn any of the world{'}s 7000 or so languages? Much discovery has been made with regard to specific learning mechanisms in specific languages, however, given the remarkable diversity of language structures (Evans and Levinson 2009, Bickel 2014) the burning question remains: what are the underlying processes that make language acquisition possible, despite substantial cross-linguistic variation in phonology, morphology, syntax, etc.? To investigate these questions, a comprehensive cross-linguistic database of longitudinal child language acquisition corpora from maximally diverse languages has been built.",
}
@inproceedings{danieli-etal-2016-summarizing,
    title = "Summarizing Behaviours: An Experiment on the Annotation of Call-Centre Conversations",
    author = "Danieli, Morena  and
      R, Balamurali A  and
      Stepanov, Evgeny  and
      Favre, Benoit  and
      Bechet, Frederic  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1701",
    pages = "4430--4433",
    abstract = "Annotating and predicting behavioural aspects in conversations is becoming critical in the conversational analytics industry. In this paper we look into inter-annotator agreement of agent behaviour dimensions on two call center corpora. We find that the task can be annotated consistently over time, but that subjectivity issues impacts the quality of the annotation. The reformulation of some of the annotated dimensions is suggested in order to improve agreement.",
}
@inproceedings{koiso-etal-2016-survey,
    title = "Survey of Conversational Behavior: Towards the Design of a Balanced Corpus of Everyday {J}apanese Conversation",
    author = "Koiso, Hanae  and
      Tsuchiya, Tomoyuki  and
      Watanabe, Ryoko  and
      Yokomori, Daisuke  and
      Aizawa, Masao  and
      Den, Yasuharu",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1702",
    pages = "4434--4439",
    abstract = "In 2016, we set about building a large-scale corpus of everyday Japanese conversation―a collection of conversations embedded in naturally occurring activities in daily life. We will collect more than 200 hours of recordings over six years,publishing the corpus in 2022. To construct such a huge corpus, we have conducted a pilot project, one of whose purposes is to establish a corpus design for collecting various kinds of everyday conversations in a balanced manner. For this purpose, we conducted a survey of everyday conversational behavior, with about 250 adults, in order to reveal how diverse our everyday conversational behavior is and to build an empirical foundation for corpus design. The questionnaire included when, where, how long,with whom, and in what kind of activity informants were engaged in conversations. We found that ordinary conversations show the following tendencies: i) they mainly consist of chats, business talks, and consultations; ii) in general, the number of participants is small and the duration of the conversation is short; iii) many conversations are conducted in private places such as homes, as well as in public places such as offices and schools; and iv) some questionnaire items are related to each other. This paper describes an overview of this survey study, and then discusses how to design a large-scale corpus of everyday Japanese conversation on this basis.",
}
@inproceedings{stefanov-beskow-2016-multi,
    title = "A Multi-party Multi-modal Dataset for Focus of Visual Attention in Human-human and Human-robot Interaction",
    author = "Stefanov, Kalin  and
      Beskow, Jonas",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1703",
    pages = "4440--4444",
    abstract = "This papers describes a data collection setup and a newly recorded dataset. The main purpose of this dataset is to explore patterns in the focus of visual attention of humans under three different conditions - two humans involved in task-based interaction with a robot; same two humans involved in task-based interaction where the robot is replaced by a third human, and a free three-party human interaction. The dataset contains two parts - 6 sessions with duration of approximately 3 hours and 9 sessions with duration of approximately 4.5 hours. Both parts of the dataset are rich in modalities and recorded data streams - they include the streams of three Kinect v2 devices (color, depth, infrared, body and face data), three high quality audio streams, three high resolution GoPro video streams, touch data for the task-based interactions and the system state of the robot. In addition, the second part of the dataset introduces the data streams from three Tobii Pro Glasses 2 eye trackers. The language of all interactions is English and all data streams are spatially and temporally aligned.",
}
@inproceedings{abbott-etal-2016-internet,
    title = "{I}nternet Argument Corpus 2.0: An {SQL} schema for Dialogic Social Media and the Corpora to go with it",
    author = "Abbott, Rob  and
      Ecker, Brian  and
      Anand, Pranav  and
      Walker, Marilyn",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1704",
    pages = "4445--4452",
    abstract = "Large scale corpora have benefited many areas of research in natural language processing, but until recently, resources for dialogue have lagged behind. Now, with the emergence of large scale social media websites incorporating a threaded dialogue structure, content feedback, and self-annotation (such as stance labeling), there are valuable new corpora available to researchers. In previous work, we released the INTERNET ARGUMENT CORPUS, one of the first larger scale resources available for opinion sharing dialogue. We now release the INTERNET ARGUMENT CORPUS 2.0 (IAC 2.0) in the hope that others will find it as useful as we have. The IAC 2.0 provides more data than IAC 1.0 and organizes it using an extensible, repurposable SQL schema. The database structure in conjunction with the associated code facilitates querying from and combining multiple dialogically structured data sources. The IAC 2.0 schema provides support for forum posts, quotations, markup (bold, italic, etc), and various annotations, including Stanford CoreNLP annotations. We demonstrate the generalizablity of the schema by providing code to import the ConVote corpus.",
}
@inproceedings{gilmartin-campbell-2016-capturing,
    title = "Capturing Chat: Annotation and Tools for Multiparty Casual Conversation.",
    author = "Gilmartin, Emer  and
      Campbell, Nick",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1705",
    pages = "4453--4457",
    abstract = "Casual multiparty conversation is an understudied but very common genre of spoken interaction, whose analysis presents a number of challenges in terms of data scarcity and annotation. We describe the annotation process used on the d64 and DANS multimodal corpora of multiparty casual talk, which have been manually segmented, transcribed, annotated for laughter and disfluencies, and aligned using the Penn Aligner. We also describe a visualization tool, STAVE, developed during the annotation process, which allows long stretches of talk or indeed entire conversations to be viewed, aiding preliminary identification of features and patterns worthy of analysis. It is hoped that this tool will be of use to other researchers working in this field.",
}
@inproceedings{kamocki-oregan-2016-privacy,
    title = "Privacy Issues in Online Machine Translation Services - {E}uropean Perspective",
    author = "Kamocki, Pawel  and
      O{'}Regan, Jim",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1706",
    pages = "4458--4462",
    abstract = "In order to develop its full potential, global communication needs linguistic support systems such as Machine Translation (MT). In the past decade, free online MT tools have become available to the general public, and the quality of their output is increasing. However, the use of such tools may entail various legal implications, especially as far as processing of personal data is concerned. This is even more evident if we take into account that their business model is largely based on providing translation in exchange for data, which can subsequently be used to improve the translation model, but also for commercial purposes. The purpose of this paper is to examine how free online MT tools fit in the European data protection framework, harmonised by the EU Data Protection Directive. The perspectives of both the user and the MT service provider are taken into account.",
}
@inproceedings{chiarcos-etal-2016-lin,
    title = "{L}in|gu|is|tik: Building the Linguist{'}s Pathway to Bibliographies, Libraries, Language Resources and Linked Open Data",
    author = {Chiarcos, Christian  and
      F{\"a}th, Christian  and
      Renner-Westermann, Heike  and
      Abromeit, Frank  and
      Dimitrova, Vanya},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1707",
    pages = "4463--4471",
    abstract = "This paper introduces a novel research tool for the field of linguistics: The Lin|gu|is|tik web portal provides a virtual library which offers scientific information on every linguistic subject. It comprises selected internet sources and databases as well as catalogues for linguistic literature, and addresses an interdisciplinary audience. The virtual library is the most recent outcome of the Special Subject Collection Linguistics of the German Research Foundation (DFG), and also integrates the knowledge accumulated in the Bibliography of Linguistic Literature. In addition to the portal, we describe long-term goals and prospects with a special focus on ongoing efforts regarding an extension towards integrating language resources and Linguistic Linked Open Data.",
}
@inproceedings{nguyen-etal-2016-towards,
    title = "Towards a Language Service Infrastructure for Mobile Environments",
    author = "Nguyen, Ngoc  and
      Lin, Donghui  and
      Nakaguchi, Takao  and
      Ishida, Toru",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1708",
    pages = "4472--4478",
    abstract = "Since mobile devices have feature-rich configurations and provide diverse functions, the use of mobile devices combined with the language resources of cloud environments is high promising for achieving a wide range communication that goes beyond the current language barrier. However, there are mismatches between using resources of mobile devices and services in the cloud such as the different communication protocol and different input and output methods. In this paper, we propose a language service infrastructure for mobile environments to combine these services. The proposed language service infrastructure allows users to use and mashup existing language resources on both cloud environments and their mobile devices. Furthermore, it allows users to flexibly use services in the cloud or services on mobile devices in their composite service without implementing several different composite services that have the same functionality. A case study of Mobile Shopping Translation System using both a service in the cloud (translation service) and services on mobile devices (Bluetooth low energy (BLE) service and text-to-speech service) is introduced.",
}
@inproceedings{agosti-etal-2016-designing,
    title = "Designing A Long Lasting Linguistic Project: The Case Study of {ASI}t",
    author = "Agosti, Maristella  and
      Di Buccio, Emanuele  and
      Di Nunzio, Giorgio Maria  and
      Poletto, Cecilia  and
      Rinke, Esther",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1709",
    pages = "4479--4483",
    abstract = "In this paper, we discuss the requirements that a long lasting linguistic database should have in order to meet the needs of the linguists together with the aim of durability and sharing of data. In particular, we discuss the generalizability of the Syntactic Atlas of Italy, a linguistic project that builds on a long standing tradition of collecting and analyzing linguistic corpora, on a more recent project that focuses on the synchronic and diachronic analysis of the syntax of Italian and Portuguese relative clauses. The results that are presented are in line with the FLaReNet Strategic Agenda that highlighted the most pressing needs for research areas, such as Natural Language Processing, and presented a set of recommendations for the development and progress of Language resources in Europe.",
}
@inproceedings{cavar-etal-2016-global,
    title = "Global Open Resources and Information for Language and Linguistic Analysis ({GORILLA})",
    author = "Cavar, Damir  and
      Cavar, Malgorzata  and
      Moe, Lwin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1710",
    pages = "4484--4491",
    abstract = "The infrastructure Global Open Resources and Information for Language and Linguistic Analysis (GORILLA) was created as a resource that provides a bridge between disciplines such as documentary, theoretical, and corpus linguistics, speech and language technologies, and digital language archiving services. GORILLA is designed as an interface between digital language archive services and language data producers. It addresses various problems of common digital language archive infrastructures. At the same time it serves the speech and language technology communities by providing a platform to create and share speech and language data from low-resourced and endangered languages. It hosts an initial collection of language models for speech and natural language processing (NLP), and technologies or software tools for corpus creation and annotation. GORILLA is designed to address the Transcription Bottleneck in language documentation, and, at the same time to provide solutions to the general Language Resource Bottleneck in speech and language technologies. It does so by facilitating the cooperation between documentary and theoretical linguistics, and speech and language technologies research and development, in particular for low-resourced and endangered languages.",
}
@inproceedings{druskat-etal-2016-corpus,
    title = "corpus-tools.org: An Interoperable Generic Software Tool Set for Multi-layer Linguistic Corpora",
    author = "Druskat, Stephan  and
      Gast, Volker  and
      Krause, Thomas  and
      Zipser, Florian",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1711",
    pages = "4492--4499",
    abstract = "This paper introduces an open source, interoperable generic software tool set catering for the entire workflow of creation, migration, annotation, query and analysis of multi-layer linguistic corpora. It consists of four components: Salt, a graph-based meta model and API for linguistic data, the common data model for the rest of the tool set; Pepper, a conversion tool and platform for linguistic data that can be used to convert many different linguistic formats into each other; Atomic, an extensible, platform-independent multi-layer desktop annotation software for linguistic corpora; ANNIS, a search and visualization architecture for multi-layer linguistic corpora with many different visualizations and a powerful native query language. The set was designed to solve the following issues in a multi-layer corpus workflow: Lossless data transition between tools through a common data model generic enough to allow for a potentially unlimited number of different types of annotation, conversion capabilities for different linguistic formats to cater for the processing of data from different sources and/or with existing annotations, a high level of extensibility to enhance the sustainability of the whole tool set, analysis capabilities encompassing corpus and annotation query alongside multi-faceted visualizations of all annotation layers.",
}
@inproceedings{schafer-2016-commoncow,
    title = "{C}ommon{COW}: Massively Huge Web Corpora from {C}ommon{C}rawl Data and a Method to Distribute them Freely under Restrictive {EU} Copyright Laws",
    author = {Sch{\"a}fer, Roland},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1712",
    pages = "4500--4504",
    abstract = "In this paper, I describe a method of creating massively huge web corpora from the CommonCrawl data sets and redistributing the resulting annotations in a stand-off format. Current EU (and especially German) copyright legislation categorically forbids the redistribution of downloaded material without express prior permission by the authors. Therefore, such stand-off annotations (or other derivates) are the only format in which European researchers (like myself) are allowed to re-distribute the respective corpora. In order to make the full corpora available to the public despite such restrictions, the stand-off format presented here allows anybody to locally reconstruct the full corpora with the least possible computational effort.",
}
@inproceedings{katakis-etal-2016-clarin,
    title = "{CLARIN}-{EL} Web-based Annotation Tool",
    author = "Katakis, Ioannis Manousos  and
      Petasis, Georgios  and
      Karkaletsis, Vangelis",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1713",
    pages = "4505--4512",
    abstract = "This paper presents a new Web-based annotation tool, the {``}CLARIN-EL Web-based Annotation Tool{''}. Based on an existing annotation infrastructure offered by the {``}Ellogon{''} language enginneering platform, this new tool transfers a large part of Ellogon{'}s features and functionalities to a Web environment, by exploiting the capabilities of cloud computing. This new annotation tool is able to support a wide range of annotation tasks, through user provided annotation schemas in XML. The new annotation tool has already been employed in several annotation tasks, including the anotation of arguments, which is presented as a use case. The CLARIN-EL annotation tool is compared to existing solutions along several dimensions and features. Finally, future work includes the improvement of integration with the CLARIN-EL infrastructure, and the inclusion of features not currently supported, such as the annotation of aligned documents.",
}
@inproceedings{kattenberg-etal-2016-two,
    title = "Two Architectures for Parallel Processing of Huge Amounts of Text",
    author = "Kattenberg, Mathijs  and
      Beloki, Zuhaitz  and
      Soroa, Aitor  and
      Artola, Xabier  and
      Fokkens, Antske  and
      Huygen, Paul  and
      Verstoep, Kees",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1714",
    pages = "4513--4519",
    abstract = "This paper presents two alternative NLP architectures to analyze massive amounts of documents, using parallel processing. The two architectures focus on different processing scenarios, namely batch-processing and streaming processing. The batch-processing scenario aims at optimizing the overall throughput of the system, i.e., minimizing the overall time spent on processing all documents. The streaming architecture aims to minimize the time to process real-time incoming documents and is therefore especially suitable for live feeds. The paper presents experiments with both architectures, and reports the overall gain when they are used for batch as well as for streaming processing. All the software described in the paper is publicly available under free licenses.",
}
@inproceedings{cassidy-2016-publishing,
    title = "Publishing the Trove Newspaper Corpus",
    author = "Cassidy, Steve",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1715",
    pages = "4520--4525",
    abstract = "The Trove Newspaper Corpus is derived from the National Library of Australia{'}s digital archive of newspaper text. The corpus is a snapshot of the NLA collection taken in 2015 to be made available for language research as part of the Alveo Virtual Laboratory and contains 143 million articles dating from 1806 to 2007. This paper describes the work we have done to make this large corpus available as a research collection, facilitating access to individual documents and enabling large scale processing of the newspaper text in a cloud-based environment.",
}
@inproceedings{popescu-etal-2016-new,
    title = "New Developments in the {LRE} Map",
    author = "Popescu, Vladimir  and
      Liu, Lin  and
      Del Gratta, Riccardo  and
      Choukri, Khalid  and
      Calzolari, Nicoletta",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1716",
    pages = "4526--4530",
    abstract = "In this paper we describe the new developments brought to LRE Map, especially in terms of the user interface of the Web application, of the searching of the information therein, and of the data model updates.",
}
@inproceedings{edlund-gustafson-2016-hidden,
    title = "Hidden Resources ― Strategies to Acquire and Exploit Potential Spoken Language Resources in National Archives",
    author = "Edlund, Jens  and
      Gustafson, Joakim",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1717",
    pages = "4531--4534",
    abstract = "In 2014, the Swedish government tasked a Swedish agency, The Swedish Post and Telecom Authority (PTS), with investigating how to best create and populate an infrastructure for spoken language resources (Ref N2014/2840/ITP). As a part of this work, the department of Speech, Music and Hearing at KTH Royal Institute of Technology have taken inventory of existing potential spoken language resources, mainly in Swedish national archives and other governmental or public institutions. In this position paper, key priorities, perspectives, and strategies that may be of general, rather than Swedish, interest are presented. We discuss broad types of potential spoken language resources available; to what extent these resources are free to use; and thirdly the main contribution: strategies to ensure the continuous acquisition of spoken language resources in a manner that facilitates speech and speech technology research.",
}
@inproceedings{mapelli-etal-2016-elra,
    title = "The {ELRA} License Wizard",
    author = "Mapelli, Val{\'e}rie  and
      Popescu, Vladimir  and
      Liu, Lin  and
      Barrera, Meritxell Fern{\'a}ndez  and
      Choukri, Khalid",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1718",
    pages = "4535--4538",
    abstract = "To allow an easy understanding of the various licenses that exist for the use of Language Resources (ELRA{'}s, META-SHARE{'}s, Creative Commons{'}, etc.), ELRA has developed a License Wizardto help the right-holders share/distribute their resources under the appropriate license. It also aims to be exploited by users to better understand the legal obligations that apply in various licensing situations. The present paper elaborates on the License Wizard functionalities of this web configurator, which enables to select a number of legal features and obtain the user license adapted to the users selection, to define which user licenses they would like to select in order to distribute their Language Resources, to integrate the user license terms into a Distribution Agreement that could be proposed to ELRA or META-SHARE for further distribution through the ELRA Catalogue of Language Resources. Thanks to a flexible back office, the structure of the legal feature selection can easily be reviewed to include other features that may be relevant for other licenses. Integrating contributions from other initiatives thus aim to be one of the obvious next steps, with a special focus on CLARIN and Linked Data experiences.",
}
@inproceedings{grouas-etal-2016-review,
    title = "Review on the Existing Language Resources for Languages of {F}rance",
    author = "Grouas, Thibault  and
      Mapelli, Val{\'e}rie  and
      Samier, Quentin",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1719",
    pages = "4539--4542",
    abstract = "With the support of the DGLFLF, ELDA conducted an inventory of existing language resources for the regional languages of France. The main aim of this inventory was to assess the exploitability of the identified resources within technologies. A total of 2,299 Language Resources were identified. As a second step, a deeper analysis of a set of three language groups (Breton, Occitan, overseas languages) was carried out along with a focus of their exploitability within three technologies: automatic translation, voice recognition/synthesis and spell checkers. The survey was followed by the organisation of the TLRF2015 Conference which aimed to present the state of the art in the field of the Technologies for Regional Languages of France. The next step will be to activate the network of specialists built up during the TLRF conference and to begin the organisation of a second TLRF conference. Meanwhile, the French Ministry of Culture continues its actions related to linguistic diversity and technology, in particular through a project with Wikimedia France related to contributions to Wikipedia in regional languages, the upcoming new version of the {``}Corpus de la Parole{''} and the reinforcement of the DGLFLF{'}s Observatory of Linguistic Practices.",
}
@inproceedings{cieri-etal-2016-selection,
    title = "Selection Criteria for Low Resource Language Programs",
    author = "Cieri, Christopher  and
      Maxwell, Mike  and
      Strassel, Stephanie  and
      Tracey, Jennifer",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1720",
    pages = "4543--4549",
    abstract = "This paper documents and describes the criteria used to select languages for study within programs that include low resource languages whether given that label or another similar one. It focuses on five US common task, Human Language Technology research and development programs in which the authors have provided information or consulting related to the choice of language. The paper does not describe the actual selection process which is the responsibility of program management and highly specific to a program{'}s individual goals and context. Instead it concentrates on the data and criteria that have been considered relevant previously with the thought that future program managers and their consultants may adapt these and apply them with different prioritization to future programs.",
}
@inproceedings{barrera-etal-2016-enhancing,
    title = "Enhancing Cross-border {EU} {E}-commerce through Machine Translation: Needed Language Resources, Challenges and Opportunities",
    author = "Barrera, Meritxell Fern{\'a}ndez  and
      Popescu, Vladimir  and
      Toral, Antonio  and
      Gaspari, Federico  and
      Choukri, Khalid",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1721",
    pages = "4550--4556",
    abstract = "This paper discusses the role that statistical machine translation (SMT) can play in the development of cross-border EU e-commerce,by highlighting extant obstacles and identifying relevant technologies to overcome them. In this sense, it firstly proposes a typology of e-commerce static and dynamic textual genres and it identifies those that may be more successfully targeted by SMT. The specific challenges concerning the automatic translation of user-generated content are discussed in detail. Secondly, the paper highlights the risk of data sparsity inherent to e-commerce and it explores the state-of-the-art strategies to achieve domain adequacy via adaptation. Thirdly, it proposes a robust workflow for the development of SMT systems adapted to the e-commerce domain by relying on inexpensive methods. Given the scarcity of user-generated language corpora for most language pairs, the paper proposes to obtain monolingual target-language data to train language models and aligned parallel corpora to tune and evaluate MT systems by means of crowdsourcing.",
}
@inproceedings{santus-etal-2016-nine,
    title = "Nine Features in a Random Forest to Learn Taxonomical Semantic Relations",
    author = "Santus, Enrico  and
      Lenci, Alessandro  and
      Chiu, Tin-Shing  and
      Lu, Qin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1722",
    pages = "4557--4564",
    abstract = "ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies on a Random Forest algorithm and nine unsupervised corpus-based features. We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are present, ROOT9 achieves an F1 score of 90.7{\%}, against a baseline of 57.2{\%} (vector cosine). When the classification is binary, ROOT9 achieves the following results against the baseline. hypernyms-co-hyponyms 95.7{\%} vs. 69.8{\%}, hypernyms-random 91.8{\%} vs. 64.1{\%} and co-hyponyms-random 97.8{\%} vs. 79.4{\%}. In order to compare the performance with the state-of-the-art, we have also evaluated ROOT9 in subsets of the Weeds et al. (2014) datasets, proving that it is in fact competitive. Finally, we investigated whether the system learns the semantic relation or it simply learns the prototypical hypernyms, as claimed by Levy et al. (2015). The second possibility seems to be the most likely, even though ROOT9 can be trained on negative examples (i.e., switched hypernyms) to drastically reduce this bias.",
}
@inproceedings{santus-etal-2016-nerd,
    title = "What a Nerd! Beating Students and Vector Cosine in the {ESL} and {TOEFL} Datasets",
    author = "Santus, Enrico  and
      Lenci, Alessandro  and
      Chiu, Tin-Shing  and
      Lu, Qin  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1723",
    pages = "4565--4572",
    abstract = "In this paper, we claim that Vector Cosine ― which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models ― can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists. This claim comes from the hypothesis that similar words do not simply occur in similar contexts, but they share a larger portion of their most relevant contexts compared to other related words. To prove it, we describe and evaluate APSyn, a variant of Average Precision that ― independently of the adopted parameters ― outperforms the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets. In the best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy in the TOEFL dataset, beating therefore the non-English US college applicants (whose average, as reported in the literature, is 64.50{\%}) and several state-of-the-art approaches.",
}
@inproceedings{del-tredici-bel-2016-assessing,
    title = "Assessing the Potential of Metaphoricity of verbs using corpus data",
    author = "Del Tredici, Marco  and
      Bel, N{\'u}ria",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1724",
    pages = "4573--4577",
    abstract = "The paper investigates the relation between metaphoricity and distributional characteristics of verbs, introducing POM, a corpus-derived index that can be used to define the upper bound of metaphoricity of any expression in which a given verb occurs. The work moves from the observation that while some verbs can be used to create highly metaphoric expressions, others can not. We conjecture that this fact is related to the number of contexts in which a verb occurs and to the frequency of each context. This intuition is modelled by introducing a method in which each context of a verb in a corpus is assigned a vector representation, and a clustering algorithm is employed to identify similar contexts. Eventually, the Standard Deviation of the relative frequency values of the clusters is computed and taken as the POM of the target verb. We tested POM in two experimental settings obtaining values of accuracy of 84{\%} and 92{\%}. Since we are convinced, along with (Shutoff, 2015), that metaphor detection systems should be concerned only with the identification of highly metaphoric expressions, we believe that POM could be profitably employed by these systems to a priori exclude expressions that, due to the verb they include, can only have low degrees of metaphoricity",
}
@inproceedings{lafourcade-ramadier-2016-semantic,
    title = "Semantic Relation Extraction with Semantic Patterns Experiment on Radiology Reports",
    author = "Lafourcade, Mathieu  and
      Ramadier, Lionel",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1725",
    pages = "4578--4582",
    abstract = "This work presents a practical system for indexing terms and relations from French radiology reports, called IMAIOS. In this paper, we present how semantic relations (causes, consequences, symptoms, locations, parts...) between medical terms can be extracted. For this purpose, we handcrafted some linguistic patterns from on a subset of our radiology report corpora. As semantic patterns (de (of)) may be too general or ambiguous, semantic constraints have been added. For instance, in the sentence n{\'e}oplasie du sein (neoplasm of breast) the system knowing neoplasm as a disease and breast as an anatomical location, identify the relation as being a location: neoplasm r-lieu breast. An evaluation of the effect of semantic constraints is proposed.",
}
@inproceedings{hongchao-etal-2016-evalution,
    title = "{EVAL}ution-{MAN}: A {C}hinese Dataset for the Training and Evaluation of {DSM}s",
    author = "Hongchao, Liu  and
      Neergaard, Karl  and
      Santus, Enrico  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1726",
    pages = "4583--4587",
    abstract = "Distributional semantic models (DSMs) are currently being used in the measurement of word relatedness and word similarity. One shortcoming of DSMs is that they do not provide a principled way to discriminate different semantic relations. Several approaches have been adopted that rely on annotated data either in the training of the model or later in its evaluation. In this paper, we introduce a dataset for training and evaluating DSMs on semantic relations discrimination between words, in Mandarin, Chinese. The construction of the dataset followed EVALution 1.0, which is an English dataset for the training and evaluating of DSMs. The dataset contains 360 relation pairs, distributed in five different semantic relations, including antonymy, synonymy, hypernymy, meronymy and nearsynonymy. All relation pairs were checked manually to estimate their quality. In the 360 word relation pairs, there are 373 relata. They were all extracted and subsequently manually tagged according to their semantic type. The relatas{'} frequency was calculated in a combined corpus of Sinica and Chinese Gigaword. To the best of our knowledge, EVALution-MAN is the first of its kind for Mandarin, Chinese.",
}
@inproceedings{anwar-sharma-2016-towards,
    title = "Towards Building Semantic Role Labeler for {I}ndian Languages",
    author = "Anwar, Maaz  and
      Sharma, Dipti",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1727",
    pages = "4588--4595",
    abstract = "We present a statistical system for identifying the semantic relationships or semantic roles for two major Indian Languages, Hindi and Urdu. Given an input sentence and a predicate/verb, the system first identifies the arguments pertaining to that verb and then classifies it into one of the semantic labels which can either be a DOER, THEME, LOCATIVE, CAUSE, PURPOSE etc. The system is based on 2 statistical classifiers trained on roughly 130,000 words for Urdu and 100,000 words for Hindi that were hand-annotated with semantic roles under the PropBank project for these two languages. Our system achieves an accuracy of 86{\%} in identifying the arguments of a verb for Hindi and 75{\%} for Urdu. At the subsequent task of classifying the constituents into their semantic roles, the Hindi system achieved 58{\%} precision and 42{\%} recall whereas Urdu system performed better and achieved 83{\%} precision and 80{\%} recall. Our study also allowed us to compare the usefulness of different linguistic features and feature combinations in the semantic role labeling task. We also examine the use of statistical syntactic parsing as feature in the role labeling task.",
}
@inproceedings{samardzic-milicevic-2016-framework,
    title = "A Framework for Automatic Acquisition of {C}roatian and {S}erbian Verb Aspect from Corpora",
    author = "Samard{\v{z}}i{\'c}, Tanja  and
      Mili{\v{c}}evi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1728",
    pages = "4596--4601",
    abstract = "Verb aspect is a grammatical and lexical category that encodes temporal unfolding and duration of events described by verbs. It is a potentially interesting source of information for various computational tasks, but has so far not been studied in much depth from the perspective of automatic processing. Slavic languages are particularly interesting in this respect, as they encode aspect through complex and not entirely consistent lexical derivations involving prefixation and suffixation. Focusing on Croatian and Serbian, in this paper we propose a novel framework for automatic classification of their verb types into a number of fine-grained aspectual classes based on the observable morphology of verb forms. In addition, we provide a set of around 2000 verbs classified based on our framework. This set can be used for linguistic research as well as for testing automatic classification on a larger scale. With minor adjustments the approach is also applicable to other Slavic languages",
}
@inproceedings{lendvai-etal-2016-monolingual,
    title = "Monolingual Social Media Datasets for Detecting Contradiction and Entailment",
    author = "Lendvai, Piroska  and
      Augenstein, Isabelle  and
      Bontcheva, Kalina  and
      Declerck, Thierry",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1729",
    pages = "4602--4605",
    abstract = "Entailment recognition approaches are useful for application domains such as information extraction, question answering or summarisation, for which evidence from multiple sentences needs to be combined. We report on a new 3-way judgement Recognizing Textual Entailment (RTE) resource that originates in the Social Media domain, and explain our semi-automatic creation method for the special purpose of information verification, which draws on manually established rumourous claims reported during crisis events. From about 500 English tweets related to 70 unique claims we compile and evaluate 5.4k RTE pairs, while continue automatizing the workflow to generate similar-sized datasets in other languages.",
}
@inproceedings{pustejovsky-krishnaswamy-2016-voxml,
    title = "{V}ox{ML}: A Visualization Modeling Language",
    author = "Pustejovsky, James  and
      Krishnaswamy, Nikhil",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1730",
    pages = "4606--4613",
    abstract = "We present the specification for a modeling language, VoxML, which encodes semantic knowledge of real-world objects represented as three-dimensional models, and of events and attributes related to and enacted over these objects.VoxML is intended to overcome the limitations of existing 3D visual markup languages by allowing for the encoding of a broad range of semantic knowledge that can be exploited by a variety of systems and platforms, leading to multimodal simulations of real-world scenarios using conceptual objects that represent their semantic values",
}
@inproceedings{teraoka-2016-metonymy,
    title = "Metonymy Analysis Using Associative Relations between Words",
    author = "Teraoka, Takehiro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1731",
    pages = "4614--4620",
    abstract = "Metonymy is a figure of speech in which one item{'}s name represents another item that usually has a close relation with the first one. Metonymic expressions need to be correctly detected and interpreted because sentences including such expressions have different mean- ings from literal ones; computer systems may output inappropriate results in natural language processing. In this paper, an associative approach for analyzing metonymic expressions is proposed. By using associative information and two conceptual distances between words in a sentence, a previous method is enhanced and a decision tree is trained to detect metonymic expressions. After detecting these expressions, they are interpreted as metonymic understanding words by using associative information. This method was evaluated by comparing it with two baseline methods based on previous studies on the Japanese language that used case frames and co-occurrence information. As a result, the proposed method exhibited significantly better accuracy (0.85) of determining words as metonymic or literal expressions than the baselines. It also exhibited better accuracy (0.74) of interpreting the detected metonymic expressions than the baselines.",
}
@inproceedings{goodwin-harabagiu-2016-embedding,
    title = "Embedding Open-domain Common-sense Knowledge from Text",
    author = "Goodwin, Travis  and
      Harabagiu, Sanda",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1732",
    pages = "4621--4628",
    abstract = "Our ability to understand language often relies on common-sense knowledge ― background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can be achieved by taking advantage of recent advances in open information extraction (IE) techniques and, more importantly, of knowledge embeddings, which are multi-dimensional representations of concepts and relations. Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge. Common-sense knowledge is acquired from a vast collection of blogs and books as well as from WordNet. Similarly, medical knowledge is learned from two large sets of electronic health records. The evaluation results of these two forms of knowledge are promising: the same knowledge acquisition methodology based on learning knowledge embeddings works well both for common-sense knowledge and for medical knowledge Interestingly, the common-sense knowledge that we have acquired was evaluated as being less neutral than than the medical knowledge, as it often reflected the opinion of the knowledge utterer. In addition, the acquired medical knowledge was evaluated as more plausible than the common-sense knowledge, reflecting the complexity of acquiring common-sense knowledge due to the pragmatics and economicity of language.",
}
@inproceedings{mencia-etal-2016-medical,
    title = "Medical Concept Embeddings via Labeled Background Corpora",
    author = "Menc{\'\i}a, Eneldo Loza  and
      de Melo, Gerard  and
      Nam, Jinseok",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1733",
    pages = "4629--4636",
    abstract = "In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.",
}
@inproceedings{dumont-etal-2016-question,
    title = "Question-Answering with Logic Specific to Video Games",
    author = "Dumont, Corentin  and
      Tian, Ran  and
      Inui, Kentaro",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1734",
    pages = "4637--4643",
    abstract = "We present a corpus and a knowledge database aiming at developing Question-Answering in a new context, the open world of a video game. We chose a popular game called {`}Minecraft{'}, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database. We are interested in the logic rules specific to the game, which may not exist in the real world. The ultimate goal of this research is to build a QA system that can answer natural language questions from players by using inference on these game-specific logic rules. The QA corpus is partially composed of online quiz questions and partially composed of manually written variations of the most relevant ones. The knowledge database is extracted from several wiki-like websites about Minecraft. It is composed of unstructured data, such as text, that will be structured using the meaning representation we defined, and already structured data such as infoboxes. A preliminary examination of the data shows that players are asking creative questions about the game, and that the QA corpus can be used for clustering verbs and linking them to predefined actions in the game.",
}
@inproceedings{kohn-etal-2016-mining,
    title = "Mining the Spoken {W}ikipedia for Speech Data and Beyond",
    author = {K{\"o}hn, Arne  and
      Stegen, Florian  and
      Baumann, Timo},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1735",
    pages = "4644--4647",
    abstract = "We present a corpus of time-aligned spoken data of Wikipedia articles as well as the pipeline that allows to generate such corpora for many languages. There are initiatives to create and sustain spoken Wikipedia versions in many languages and hence the data is freely available, grows over time, and can be used for automatic corpus creation. Our pipeline automatically downloads and aligns this data. The resulting German corpus currently totals 293h of audio, of which we align 71h in full sentences and another 86h of sentences with some missing words. The English corpus consists of 287h, for which we align 27h in full sentence and 157h with some missing words. Results are publically available.",
}
@inproceedings{herms-etal-2016-corpus,
    title = "A Corpus of Read and Spontaneous {U}pper {S}axon {G}erman Speech for {ASR} Evaluation",
    author = {Herms, Robert  and
      Seelig, Laura  and
      M{\"u}nch, Stefanie  and
      Eibl, Maximilian},
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1736",
    pages = "4648--4651",
    abstract = "In this Paper we present a corpus named SXUCorpus which contains read and spontaneous speech of the Upper Saxon German dialect. The data has been collected from eight archives of local television stations located in the Free State of Saxony. The recordings include broadcasted topics of news, economy, weather, sport, and documentation from the years 1992 to 1996 and have been manually transcribed and labeled. In the paper, we report the methodology of collecting and processing analog audiovisual material, constructing the corpus and describe the properties of the data. In its current version, the corpus is available to the scientific community and is designed for automatic speech recognition (ASR) evaluation with a development set and a test set. We performed ASR experiments with the open-source framework sphinx-4 including a configuration for Standard German on the dataset. Additionally, we show the influence of acoustic model and language model adaptation by the utilization of the development set.",
}
@inproceedings{yoshino-etal-2016-parallel,
    title = "Parallel Speech Corpora of {J}apanese Dialects",
    author = "Yoshino, Koichiro  and
      Hirayama, Naoki  and
      Mori, Shinsuke  and
      Takahashi, Fumihiko  and
      Itoyama, Katsutoshi  and
      Okuno, Hiroshi G.",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1737",
    pages = "4652--4657",
    abstract = "Binary file summaries/549.html matches",
}
@inproceedings{meunier-etal-2016-typaloc,
    title = "The {TYPALOC} Corpus: A Collection of Various Dysarthric Speech Recordings in Read and Spontaneous Styles",
    author = "Meunier, Christine  and
      Fougeron, Cecile  and
      Fredouille, Corinne  and
      Bigi, Brigitte  and
      Crevier-Buchman, Lise  and
      Delais-Roussarie, Elisabeth  and
      Georgeton, Laurianne  and
      Ghio, Alain  and
      Laaridh, Imed  and
      Legou, Thierry  and
      Pillot-Loiseau, Claire  and
      Pouchoulin, Gilles",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1738",
    pages = "4658--4665",
    abstract = "This paper presents the TYPALOC corpus of French Dysarthric and Healthy speech and the rationale underlying its constitution. The objective is to compare phonetic variation in the speech of dysarthric vs. healthy speakers in different speech conditions (read and unprepared speech). More precisely, we aim to compare the extent, types and location of phonetic variation within these different populations and speech conditions. The TYPALOC corpus is constituted of a selection of 28 dysarthric patients (three different pathologies) and of 12 healthy control speakers recorded while reading the same text and in a more natural continuous speech condition. Each audio signal has been segmented into Inter-Pausal Units. Then, the corpus has been manually transcribed and automatically aligned. The alignment has been corrected by an expert phonetician. Moreover, the corpus benefits from an automatic syllabification and an Automatic Detection of Acoustic Phone-Based Anomalies. Finally, in order to interpret phonetic variations due to pathologies, a perceptual evaluation of each patient has been conducted. Quantitative data are provided at the end of the paper.",
}
@inproceedings{yilmaz-etal-2016-longitudinal,
    title = "A Longitudinal Bilingual {F}risian-{D}utch Radio Broadcast Database Designed for Code-Switching Research",
    author = "Yilmaz, Emre  and
      Andringa, Maaike  and
      Kingma, Sigrid  and
      Dijkstra, Jelske  and
      van der Kuip, Frits  and
      Van de Velde, Hans  and
      Kampstra, Frederik  and
      Algra, Jouke  and
      van den Heuvel, Henk  and
      van Leeuwen, David",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1739",
    pages = "4666--4669",
    abstract = "We present a new speech database containing 18.5 hours of annotated radio broadcasts in the Frisian language. Frisian is mostly spoken in the province Fryslan and it is the second official language of the Netherlands. The recordings are collected from the archives of Omrop Fryslan, the regional public broadcaster of the province Fryslan. The database covers almost a 50-year time span. The native speakers of Frisian are mostly bilingual and often code-switch in daily conversations due to the extensive influence of the Dutch language. Considering the longitudinal and code-switching nature of the data, an appropriate annotation protocol has been designed and the data is manually annotated with the orthographic transcription, speaker identities, dialect information, code-switching details and background noise/music information.",
}
@inproceedings{zgank-etal-2016-si,
    title = "The {SI} {TED}x-{UM} speech database: a new {S}lovenian Spoken Language Resource",
    author = "{\v{Z}}gank, Andrej  and
      Mau{\v{c}}ec, Mirjam Sepesy  and
      Verdonik, Darinka",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1740",
    pages = "4670--4673",
    abstract = "This paper presents a new Slovenian spoken language resource built from TEDx Talks. The speech database contains 242 talks in total duration of 54 hours. The annotation and transcription of acquired spoken material was generated automatically, applying acoustic segmentation and automatic speech recognition. The development and evaluation subset was also manually transcribed using the guidelines specified for the Slovenian GOS corpus. The manual transcriptions were used to evaluate the quality of unsupervised transcriptions. The average word error rate for the SI TEDx-UM evaluation subset was 50.7{\%}, with out of vocabulary rate of 24{\%} and language model perplexity of 390. The unsupervised transcriptions contain 372k tokens, where 32k of them were different.",
}
@inproceedings{iribe-etal-2016-speech,
    title = "Speech Corpus Spoken by Young-old, Old-old and Oldest-old {J}apanese",
    author = "Iribe, Yurie  and
      Kitaoka, Norihide  and
      Segawa, Shuhei",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1741",
    pages = "4674--4677",
    abstract = "We have constructed a new speech data corpus, using the utterances of 100 elderly Japanese people, to improve speech recognition accuracy of the speech of older people. Humanoid robots are being developed for use in elder care nursing homes. Interaction with such robots is expected to help maintain the cognitive abilities of nursing home residents, as well as providing them with companionship. In order for these robots to interact with elderly people through spoken dialogue, a high performance speech recognition system for speech of elderly people is needed. To develop such a system, we recorded speech uttered by 100 elderly Japanese, most of them are living in nursing homes, with an average age of 77.2. Previously, a seniors{'} speech corpus named S-JNAS was developed, but the average age of the participants was 67.6 years, but the target age for nursing home care is around 75 years old, much higher than that of the S-JNAS samples. In this paper we compare our new corpus with an existing Japanese read speech corpus, JNAS, which consists of adult speech, and with the above mentioned S-JNAS, the senior version of JNAS.",
}
@inproceedings{wagner-etal-2016-polish,
    title = "{P}olish Rhythmic Database ― New Resources for Speech Timing and Rhythm Analysis",
    author = "Wagner, Agnieszka  and
      Klessa, Katarzyna  and
      Bachan, Jolanta",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1742",
    pages = "4678--4683",
    abstract = "This paper reports on a new database ― Polish rhythmic database and tools developed with the aim of investigating timing phenomena and rhythmic structure in Polish including topics such as, inter alia, the effect of speaking style and tempo on timing patterns, phonotactic and phrasal properties of speech rhythm and stability of rhythm metrics. So far, 19 native and 12 non-native speakers with different first languages have been recorded. The collected speech data (5 h 14 min.) represents five different speaking styles and five different tempi. For the needs of speech corpus management, annotation and analysis, a database was developed and integrated with Annotation Pro (Klessa et al., 2013, Klessa, 2016). Currently, the database is the only resource for Polish which allows for a systematic study of a broad range of phenomena related to speech timing and rhythm. The paper also introduces new tools and methods developed to facilitate the database annotation and analysis with respect to various timing and rhythm measures. In the end, the results of an ongoing research and first experimental results using the new resources are reported and future work is sketched.",
}
@inproceedings{viszlay-etal-2016-extension,
    title = "An Extension of the {S}lovak Broadcast News Corpus based on Semi-Automatic Annotation",
    author = "Viszlay, Peter  and
      Sta{\v{s}}, J{\'a}n  and
      Koct{\'u}r, Tom{\'a}{\v{s}}  and
      Lojka, Martin  and
      Juh{\'a}r, Jozef",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1743",
    pages = "4684--4687",
    abstract = "In this paper, we introduce an extension of our previously released TUKE-BNews-SK corpus based on a semi-automatic annotation scheme. It firstly relies on the automatic transcription of the BN data performed by our Slovak large vocabulary continuous speech recognition system. The generated hypotheses are then manually corrected and completed by trained human annotators. The corpus is composed of 25 hours of fully-annotated spontaneous and prepared speech. In addition, we have acquired 900 hours of another BN data, part of which we plan to annotate semi-automatically. We present a preliminary corpus evaluation that gives very promising results.",
}
@inproceedings{cavar-etal-2016-generating,
    title = "Generating a {Y}iddish Speech Corpus, Forced Aligner and Basic {ASR} System for the {AHEYM} Project",
    author = "{\'C}avar, Malgorzata  and
      {\'C}avar, Damir  and
      Kerler, Dov-Ber  and
      Quilitzsch, Anya",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1744",
    pages = "4688--4693",
    abstract = "To create automatic transcription and annotation tools for the AHEYM corpus of recorded interviews with Yiddish speakers in Eastern Europe we develop initial Yiddish language resources that are used for adaptations of speech and language technologies. Our project aims at the development of resources and technologies that can make the entire AHEYM corpus and other Yiddish resources more accessible to not only the community of Yiddish speakers or linguists with language expertise, but also historians and experts from other disciplines or the general public. In this paper we describe the rationale behind our approach, the procedures and methods, and challenges that are not specific to the AHEYM corpus, but apply to all documentary language data that is collected in the field. To the best of our knowledge, this is the first attempt to create a speech corpus and speech technologies for Yiddish. This is also the first attempt to work out speech and language technologies to transcribe and translate a large collection of Yiddish spoken language resources.",
}
@inproceedings{quispesaravia-etal-2016-coh,
    title = "{C}oh-{M}etrix-{E}sp: A Complexity Analysis Tool for Documents Written in {S}panish",
    author = "Quispesaravia, Andre  and
      Perez, Walter  and
      Sobrevilla Cabezudo, Marco  and
      Alva-Manchego, Fernando",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://www.aclweb.org/anthology/L16-1745",
    pages = "4694--4698",
    abstract = "Text Complexity Analysis is an useful task in Education. For example, it can help teachers select appropriate texts for their students according to their educational level. This task requires the analysis of several text features that people do mostly manually (e.g. syntactic complexity, words variety, etc.). In this paper, we present a tool useful for Complexity Analysis, called Coh-Metrix-Esp. This is the Spanish version of Coh-Metrix and is able to calculate 45 readability indices. We analyse how these indices behave in a corpus of {``}simple{''} and {``}complex{''} documents, and also use them as features in a complexity binary classifier for texts in Spanish. After some experiments with machine learning algorithms, we got 0.9 F-measure for a corpus that contains tales for kids and adults and 0.82 F-measure for a corpus with texts written for students of Spanish as a foreign language.",
}
@proceedings{conll-2016-conll,
    title = "Proceedings of the {C}o{NLL}-16 shared task",
    editor = "Xue, Nianwen",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2000",
    doi = "10.18653/v1/K16-2",
}
@inproceedings{xue-etal-2016-conll,
    title = "{C}o{NLL} 2016 Shared Task on Multilingual Shallow Discourse Parsing",
    author = "Xue, Nianwen  and
      Ng, Hwee Tou  and
      Pradhan, Sameer  and
      Rutherford, Attapol  and
      Webber, Bonnie  and
      Wang, Chuan  and
      Wang, Hongmin",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2001",
    doi = "10.18653/v1/K16-2001",
    pages = "1--19",
}
@inproceedings{oepen-etal-2016-opt,
    title = "{OPT}: {O}slo{--}{P}otsdam{--}{T}eesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing",
    author = "Oepen, Stephan  and
      Read, Jonathon  and
      Scheffler, Tatjana  and
      Sidarenka, Uladzimir  and
      Stede, Manfred  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2002",
    doi = "10.18653/v1/K16-2002",
    pages = "20--26",
}
@inproceedings{kang-etal-2016-end,
    title = "An End-to-End {C}hinese Discourse Parser with Adaptation to Explicit and Non-explicit Relation Recognition",
    author = "Kang, Xiaomian  and
      Li, Haoran  and
      Zhou, Long  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2003",
    doi = "10.18653/v1/K16-2003",
    pages = "27--32",
}
@inproceedings{wang-lan-2016-two,
    title = "Two End-to-end Shallow Discourse Parsers for {E}nglish and {C}hinese in {C}o{NLL}-2016 Shared Task",
    author = "Wang, Jianxiang  and
      Lan, Man",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2004",
    doi = "10.18653/v1/K16-2004",
    pages = "33--40",
}
@inproceedings{schenk-etal-2016-really,
    title = "Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense Labeling",
    author = {Schenk, Niko  and
      Chiarcos, Christian  and
      Donandt, Kathrin  and
      R{\"o}nnqvist, Samuel  and
      Stepanov, Evgeny  and
      Riccardi, Giuseppe},
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2005",
    doi = "10.18653/v1/K16-2005",
    pages = "41--49",
}
@inproceedings{weiss-bajec-2016-discourse,
    title = "Discourse Sense Classification from Scratch using Focused {RNN}s",
    author = "Weiss, Gregor  and
      Bajec, Marko",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2006",
    doi = "10.18653/v1/K16-2006",
    pages = "50--54",
}
@inproceedings{rutherford-xue-2016-robust,
    title = "Robust Non-Explicit Neural Discourse Parser in {E}nglish and {C}hinese",
    author = "Rutherford, Attapol  and
      Xue, Nianwen",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2007",
    doi = "10.18653/v1/K16-2007",
    pages = "55--59",
}
@inproceedings{li-etal-2016-constituent,
    title = "A Constituent Syntactic Parse Tree Based Discourse Parser",
    author = "Li, Zhongyi  and
      Zhao, Hai  and
      Pang, Chenxi  and
      Wang, Lili  and
      Wang, Huan",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2008",
    doi = "10.18653/v1/K16-2008",
    pages = "60--64",
}
@inproceedings{kong-etal-2016-sonlp,
    title = "{S}o{NLP}-{DP} System for {C}on{LL}-2016 {E}nglish Shallow Discourse Parsing",
    author = "Kong, Fang  and
      Li, Sheng  and
      Li, Junhui  and
      Zhu, Muhua  and
      Zhou, Guodong",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2009",
    doi = "10.18653/v1/K16-2009",
    pages = "65--69",
}
@inproceedings{qin-etal-2016-shallow,
    title = "Shallow Discourse Parsing Using Convolutional Neural Network",
    author = "Qin, Lianhui  and
      Zhang, Zhisong  and
      Zhao, Hai",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2010",
    doi = "10.18653/v1/K16-2010",
    pages = "70--77",
}
@inproceedings{li-etal-2016-sonlp,
    title = "{S}o{NLP}-{DP} System for {C}on{LL}-2016 {C}hinese Shallow Discourse Parsing",
    author = "Li, Junhui  and
      Kong, Fang  and
      Li, Sheng  and
      Zhu, Muhua  and
      Zhou, Guodong",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2011",
    doi = "10.18653/v1/K16-2011",
    pages = "78--84",
}
@inproceedings{stepanov-riccardi-2016-unitn,
    title = "{U}ni{TN} End-to-End Discourse Parser for {C}o{NLL} 2016 Shared Task",
    author = "Stepanov, Evgeny  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2012",
    doi = "10.18653/v1/K16-2012",
    pages = "85--91",
}
@inproceedings{laali-etal-2016-clac,
    title = "The {CL}a{C} Discourse Parser at {C}o{NLL}-2016",
    author = "Laali, Majid  and
      Cianflone, Andre  and
      Kosseim, Leila",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2013",
    doi = "10.18653/v1/K16-2013",
    pages = "92--99",
}
@inproceedings{mihaylov-frank-2016-discourse,
    title = "Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings",
    author = "Mihaylov, Todor  and
      Frank, Anette",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2014",
    doi = "10.18653/v1/K16-2014",
    pages = "100--107",
}
@inproceedings{kaur-etal-2016-iit,
    title = "{IIT} ({BHU}) Submission on the {C}o{NLL}-2016 Shared Task: Shallow Discourse Parsing using Semantic Lexicons",
    author = "Kaur, Manpreet  and
      Kumari, Nishu  and
      Singh, Anil Kumar  and
      Sangal, Rajeev",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2015",
    doi = "10.18653/v1/K16-2015",
    pages = "108--114",
}
@inproceedings{chandrasekar-etal-2016-virginia,
    title = "The {V}irginia Tech System at {C}o{NLL}-2016 Shared Task on Shallow Discourse Parsing",
    author = "Chandrasekar, Prashant  and
      Zhang, Xuan  and
      Chakravarty, Saurabh  and
      Ray, Arijit  and
      Krulick, John  and
      Rozovskaya, Alla",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2016",
    doi = "10.18653/v1/K16-2016",
    pages = "115--121",
}
@inproceedings{jain-majumder-2016-da,
    title = "{DA}-{IICT} Submission for {PDTB}-styled Discourse Parser",
    author = "Jain, Devanshu  and
      Majumder, Prasenjit",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2017",
    doi = "10.18653/v1/K16-2017",
    pages = "122--128",
}
@inproceedings{kido-aizawa-2016-discourse,
    title = "Discourse Relation Sense Classification with Two-Step Classifiers",
    author = "Kido, Yusuke  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2018",
    doi = "10.18653/v1/K16-2018",
    pages = "129--135",
}
@inproceedings{pacheco-etal-2016-adapting,
    title = "Adapting Event Embedding for Implicit Discourse Relation Recognition",
    author = "Pacheco, Maria Leonor  and
      Lee, I-Ta  and
      Zhang, Xiao  and
      Zehady, Abdullah Khan  and
      Daga, Pranjal  and
      Jin, Di  and
      Parolia, Ayush  and
      Goldwasser, Dan",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2019",
    doi = "10.18653/v1/K16-2019",
    pages = "136--142",
}
@inproceedings{nguyen-2016-sdp,
    title = "{SDP}-{JAIST}: A Shallow Discourse Parsing system @ {C}o{NLL} 2016 Shared Task",
    author = "Nguyen, Minh",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2020",
    doi = "10.18653/v1/K16-2020",
    pages = "143--149",
}
@inproceedings{fan-etal-2016-finding,
    title = "Finding Arguments as Sequence Labeling in Discourse Parsing",
    author = "Fan, Ziwei  and
      Li, Zhenghua  and
      Zhang, Min",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2021",
    doi = "10.18653/v1/K16-2021",
    pages = "150--157",
}
@inproceedings{jian-etal-2016-discourse,
    title = "Discourse Relation Sense Classification Systems for {C}o{NLL}-2016 Shared Task",
    author = "Jian, Ping  and
      She, Xiaohan  and
      Zhang, Chenwei  and
      Zhang, Pengcheng  and
      Feng, Jian",
    booktitle = "Proceedings of the {C}o{NLL}-16 shared task",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-2022",
    doi = "10.18653/v1/K16-2022",
    pages = "158--163",
}
@proceedings{conll-2016-signll,
    title = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    editor = "Riezler, Stefan  and
      Goldberg, Yoav",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1000",
    doi = "10.18653/v1/K16-1",
}
@inproceedings{powers-2016-computational,
    title = "Computational Natural Language Learning: +-20years +-Data +-Features +-Multimodal +-Bioplausible",
    author = "Powers, David",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1001",
    doi = "10.18653/v1/K16-1001",
    pages = "1--9",
}
@inproceedings{bowman-etal-2016-generating,
    title = "Generating Sentences from a Continuous Space",
    author = "Bowman, Samuel R.  and
      Vilnis, Luke  and
      Vinyals, Oriol  and
      Dai, Andrew  and
      Jozefowicz, Rafal  and
      Bengio, Samy",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1002",
    doi = "10.18653/v1/K16-1002",
    pages = "10--21",
}
@inproceedings{hasanuzzaman-etal-2016-identifying,
    title = "Identifying Temporal Orientation of Word Senses",
    author = {Hasanuzzaman, Mohammed  and
      Dias, Ga{\"e}l  and
      Ferrari, St{\'e}phane  and
      Mathet, Yann  and
      Way, Andy},
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1003",
    doi = "10.18653/v1/K16-1003",
    pages = "22--30",
}
@inproceedings{wang-etal-2016-semi,
    title = "Semi-supervised Clustering for Short Text via Deep Representation Learning",
    author = "Wang, Zhiguo  and
      Mi, Haitao  and
      Ittycheriah, Abraham",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1004",
    doi = "10.18653/v1/K16-1004",
    pages = "31--39",
}
@inproceedings{nguyen-etal-2016-neighborhood,
    title = "Neighborhood Mixture Model for Knowledge Base Completion",
    author = "Nguyen, Dat Quoc  and
      Sirts, Kairit  and
      Qu, Lizhen  and
      Johnson, Mark",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1005",
    doi = "10.18653/v1/K16-1005",
    pages = "40--50",
}
@inproceedings{melamud-etal-2016-context2vec,
    title = "context2vec: Learning Generic Context Embedding with Bidirectional {LSTM}",
    author = "Melamud, Oren  and
      Goldberger, Jacob  and
      Dagan, Ido",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1006",
    doi = "10.18653/v1/K16-1006",
    pages = "51--61",
}
@inproceedings{bakhshandeh-etal-2016-learning,
    title = "Learning to Jointly Predict Ellipsis and Comparison Structures",
    author = "Bakhshandeh, Omid  and
      Cornelia Wellwood, Alexis  and
      Allen, James",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1007",
    doi = "10.18653/v1/K16-1007",
    pages = "62--74",
}
@inproceedings{modi-2016-event,
    title = "Event Embeddings for Semantic Script Modeling",
    author = "Modi, Ashutosh",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1008",
    doi = "10.18653/v1/K16-1008",
    pages = "75--83",
}
@inproceedings{zopf-etal-2016-beyond,
    title = "Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization",
    author = {Zopf, Markus  and
      Loza Menc{\'\i}a, Eneldo  and
      F{\"u}rnkranz, Johannes},
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1009",
    doi = "10.18653/v1/K16-1009",
    pages = "84--94",
}
@inproceedings{grissom-ii-etal-2016-incremental,
    title = "Incremental Prediction of Sentence-final Verbs: Humans versus Machines",
    author = "Grissom II, Alvin  and
      Orita, Naho  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1010",
    doi = "10.18653/v1/K16-1010",
    pages = "95--104",
}
@inproceedings{hiller-fernandez-2016-data,
    title = "A Data-driven Investigation of Corrective Feedback on Subject Omission Errors in First Language Acquisition",
    author = "Hiller, Sarah  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1011",
    doi = "10.18653/v1/K16-1011",
    pages = "105--114",
}
@inproceedings{kutuzov-etal-2016-redefining,
    title = "Redefining part-of-speech classes with distributional semantic models",
    author = "Kutuzov, Andrey  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1012",
    doi = "10.18653/v1/K16-1012",
    pages = "115--125",
}
@inproceedings{knowles-etal-2016-analyzing,
    title = "Analyzing Learner Understanding of Novel {L}2 Vocabulary",
    author = "Knowles, Rebecca  and
      Renduchintala, Adithya  and
      Koehn, Philipp  and
      Eisner, Jason",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1013",
    doi = "10.18653/v1/K16-1013",
    pages = "126--135",
}
@inproceedings{nouri-yangarber-2016-modeling,
    title = "Modeling language evolution with codes that utilize context and phonetic features",
    author = "Nouri, Javad  and
      Yangarber, Roman",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1014",
    doi = "10.18653/v1/K16-1014",
    pages = "136--145",
}
@inproceedings{joshi-etal-2016-harnessing,
    title = "Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from {TV} Series {`}{F}riends{'}",
    author = "Joshi, Aditya  and
      Tripathi, Vaibhav  and
      Bhattacharyya, Pushpak  and
      Carman, Mark J.",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1015",
    doi = "10.18653/v1/K16-1015",
    pages = "146--155",
}
@inproceedings{mishra-etal-2016-leveraging,
    title = "Leveraging Cognitive Features for Sentiment Analysis",
    author = "Mishra, Abhijit  and
      Kanojia, Diptesh  and
      Nagar, Seema  and
      Dey, Kuntal  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1016",
    doi = "10.18653/v1/K16-1016",
    pages = "156--166",
}
@inproceedings{amir-etal-2016-modelling,
    title = "Modelling Context with User Embeddings for Sarcasm Detection in Social Media",
    author = "Amir, Silvio  and
      Wallace, Byron C.  and
      Lyu, Hao  and
      Carvalho, Paula  and
      Silva, M{\'a}rio J.",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1017",
    doi = "10.18653/v1/K16-1017",
    pages = "167--177",
}
@inproceedings{fang-cohn-2016-learning,
    title = "Learning when to trust distant supervision: An application to low-resource {POS} tagging using cross-lingual projection",
    author = "Fang, Meng  and
      Cohn, Trevor",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1018",
    doi = "10.18653/v1/K16-1018",
    pages = "178--186",
}
@inproceedings{swayamdipta-etal-2016-greedy,
    title = "Greedy, Joint Syntactic-Semantic Parsing with Stack {LSTM}s",
    author = "Swayamdipta, Swabha  and
      Ballesteros, Miguel  and
      Dyer, Chris  and
      Smith, Noah A.",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1019",
    doi = "10.18653/v1/K16-1019",
    pages = "187--197",
}
@inproceedings{gonzalez-rubio-etal-2016-beyond,
    title = "Beyond Prefix-Based Interactive Translation Prediction",
    author = "Gonz{\'a}lez-Rubio, Jes{\'u}s  and
      Ortiz-Mart{\'\i}nez, Daniel  and
      Casacuberta, Francisco  and
      Benedi Ruiz, Jos{\'e} Miguel",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1020",
    doi = "10.18653/v1/K16-1020",
    pages = "198--207",
}
@inproceedings{beck-etal-2016-exploring,
    title = "Exploring Prediction Uncertainty in Machine Translation Quality Estimation",
    author = "Beck, Daniel  and
      Specia, Lucia  and
      Cohn, Trevor",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1021",
    doi = "10.18653/v1/K16-1021",
    pages = "208--218",
}
@inproceedings{tsai-etal-2016-cross,
    title = "Cross-Lingual Named Entity Recognition via Wikification",
    author = "Tsai, Chen-Tse  and
      Mayhew, Stephen  and
      Roth, Dan",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1022",
    doi = "10.18653/v1/K16-1022",
    pages = "219--228",
}
@inproceedings{ghaddar-langlais-2016-coreference,
    title = "Coreference in {W}ikipedia: Main Concept Resolution",
    author = "Ghaddar, Abbas  and
      Langlais, Phillippe",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1023",
    doi = "10.18653/v1/K16-1023",
    pages = "229--238",
}
@inproceedings{krause-etal-2016-event,
    title = "Event Linking with Sentential Features from Convolutional Neural Networks",
    author = "Krause, Sebastian  and
      Xu, Feiyu  and
      Uszkoreit, Hans  and
      Weissenborn, Dirk",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1024",
    doi = "10.18653/v1/K16-1024",
    pages = "239--249",
}
@inproceedings{yamada-etal-2016-joint,
    title = "Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation",
    author = "Yamada, Ikuya  and
      Shindo, Hiroyuki  and
      Takeda, Hideaki  and
      Takefuji, Yoshiyasu",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1025",
    doi = "10.18653/v1/K16-1025",
    pages = "250--259",
}
@inproceedings{fang-etal-2016-entity,
    title = "Entity Disambiguation by Knowledge and Text Jointly Embedding",
    author = "Fang, Wei  and
      Zhang, Jianwen  and
      Wang, Dilin  and
      Chen, Zheng  and
      Li, Ming",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1026",
    doi = "10.18653/v1/K16-1026",
    pages = "260--269",
}
@inproceedings{kunchukuttan-etal-2016-substring,
    title = "Substring-based unsupervised transliteration with phonetic and contextual knowledge",
    author = "Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak  and
      Khapra, Mitesh M.",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1027",
    doi = "10.18653/v1/K16-1027",
    pages = "270--279",
}
@inproceedings{nallapati-etal-2016-abstractive,
    title = "Abstractive Text Summarization using Sequence-to-sequence {RNN}s and Beyond",
    author = "Nallapati, Ramesh  and
      Zhou, Bowen  and
      dos Santos, Cicero  and
      Gu̇l{\c{c}}ehre, {\c{C}}a{\u{g}}lar  and
      Xiang, Bing",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1028",
    doi = "10.18653/v1/K16-1028",
    pages = "280--290",
}
@inproceedings{see-etal-2016-compression,
    title = "Compression of Neural Machine Translation Models via Pruning",
    author = "See, Abigail  and
      Luong, Minh-Thang  and
      Manning, Christopher D.",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1029",
    doi = "10.18653/v1/K16-1029",
    pages = "291--301",
}
@inproceedings{yung-etal-2016-modelling,
    title = "Modelling the Usage of Discourse Connectives as Rational Speech Acts",
    author = "Yung, Frances  and
      Duh, Kevin  and
      Komura, Taku  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1030",
    doi = "10.18653/v1/K16-1030",
    pages = "302--313",
}
@inproceedings{chen-huang-2016-semi,
    title = "Semi-supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data",
    author = "Chen, Boxing  and
      Huang, Fei",
    booktitle = "Proceedings of The 20th {SIGNLL} Conference on Computational Natural Language Learning",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/K16-1031",
    doi = "10.18653/v1/K16-1031",
    pages = "314--323",
}
@article{cl-2016-linguistics-42-issue-4,
    title = "Computational Linguistics, Volume 42, Issue 4 - {D}ecember 2016",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4000",
}
@article{bresnan-2016-lifetime,
    title = "Lifetime Achievement Award: Linguistics: The Garden and the Bush",
    author = "Bresnan, Joan",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4001",
    doi = "10.1162/COLI_a_00260",
    pages = "599--617",
}
@article{boleda-herbelot-2016-formal,
    title = "Formal Distributional Semantics: Introduction to the Special Issue",
    author = "Boleda, Gemma  and
      Herbelot, Aur{\'e}lie",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4002",
    doi = "10.1162/COLI_a_00261",
    pages = "619--635",
}
@article{kruszewski-etal-2016-logical,
    title = "There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics",
    author = "Kruszewski, Germ{\'a}n  and
      Paperno, Denis  and
      Bernardi, Raffaella  and
      Baroni, Marco",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4003",
    doi = "10.1162/COLI_a_00262",
    pages = "637--660",
}
@article{rimell-etal-2016-relpron,
    title = "{RELPRON}: A Relative Clause Evaluation Data Set for Compositional Distributional Semantics",
    author = "Rimell, Laura  and
      Maillard, Jean  and
      Polajnar, Tamara  and
      Clark, Stephen",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4004",
    doi = "10.1162/COLI_a_00263",
    pages = "661--701",
}
@article{asher-etal-2016-integrating,
    title = "Integrating Type Theory and Distributional Semantics: A Case Study on Adjective{--}Noun Compositions",
    author = "Asher, Nicholas  and
      Van de Cruys, Tim  and
      Bride, Antoine  and
      Abrus{\'a}n, M{\'a}rta",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4005",
    doi = "10.1162/COLI_a_00264",
    pages = "703--725",
}
@article{weir-etal-2016-aligning,
    title = "Aligning Packed Dependency Trees: A Theory of Composition for Distributional Semantics",
    author = "Weir, David  and
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      Reffin, Jeremy  and
      Kober, Thomas",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4006",
    doi = "10.1162/COLI_a_00265",
    pages = "727--761",
}
@article{beltagy-etal-2016-representing,
    title = "Representing Meaning with a Combination of Logical and Distributional Models",
    author = "Beltagy, I.  and
      Roller, Stephen  and
      Cheng, Pengxiang  and
      Erk, Katrin  and
      Mooney, Raymond J.",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4007",
    doi = "10.1162/COLI_a_00266",
    pages = "763--808",
}
@article{gomez-rodriguez-2016-squibs,
    title = "{S}quibs: Restricted Non-Projectivity: Coverage vs. Efficiency",
    author = "G{\'o}mez-Rodr{\'\i}guez, Carlos",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4008",
    doi = "10.1162/COLI_a_00267",
    pages = "809--817",
}
@article{kuhlmann-oepen-2016-squibs,
    title = "{S}quibs: Towards a Catalogue of Linguistic Graph {B}anks",
    author = "Kuhlmann, Marco  and
      Oepen, Stephan",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4009",
    doi = "10.1162/COLI_a_00268",
    pages = "819--827",
}
@article{xiong-2016-book,
    title = "Book Reviews: Semantic Similarity from Natural Language and Ontology Analysis by S{\'e}bastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain",
    author = "Xiong, Deyi",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4010",
    doi = "10.1162/COLI_r_00269",
    pages = "829--831",
}
@article{louis-2016-book,
    title = "Book Reviews: Natural Language Processing for Social Media by Atefeh Farzindar and Diana Inkpen",
    author = "Louis, Annie",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4011",
    doi = "10.1162/COLI_r_00270",
    pages = "833--836",
}
@article{erlewine-2016-book,
    title = "Book Reviews: Elements of Formal Semantics: An Introduction to the Mathematical Theory of Meaning in Natural Language by Yoad Winter",
    author = "Erlewine, Michael Yoshitaka",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4012",
    doi = "10.1162/COLI_r_00271",
    pages = "837--839",
}
@article{nn-2016-reviewers,
    title = "Reviewers for Volume 42",
    journal = "Computational Linguistics",
    volume = "42",
    number = "4",
    month = dec,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-4013",
    doi = "10.1162/COLI_x_00272",
    pages = "841--841",
}
@article{cl-2016-linguistics-42-issue,
    title = "Computational Linguistics, Volume 42, Issue 3 - September 2016",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3000",
}
@article{zhang-etal-2016-transition-based,
    title = "Transition-Based Parsing for Deep Dependency Structures",
    author = "Zhang, Xun  and
      Du, Yantao  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3001",
    doi = "10.1162/COLI_a_00252",
    pages = "353--389",
}
@article{sun-wan-2016-towards,
    title = "Towards Accurate and Efficient {C}hinese Part-of-Speech Tagging",
    author = "Sun, Weiwei  and
      Wan, Xiaojun",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3002",
    doi = "10.1162/COLI_a_00253",
    pages = "391--419",
}
@article{cohen-gildea-2016-parsing,
    title = "Parsing Linear Context-Free Rewriting Systems with Fast Matrix Multiplication",
    author = "Cohen, Shay B.  and
      Gildea, Daniel",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3003",
    doi = "10.1162/COLI_a_00254",
    pages = "421--455",
}
@article{de-clercq-hoste-2016-mixed,
    title = "All Mixed Up? Finding the Optimal Feature Set for General Readability Prediction and Its Application to {E}nglish and {D}utch",
    author = "De Clercq, Orph{\'e}e  and
      Hoste, V{\'e}ronique",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3004",
    doi = "10.1162/COLI_a_00255",
    pages = "457--490",
}
@article{ionescu-etal-2016-string,
    title = "String Kernels for Native Language Identification: Insights from Behind the Curtains",
    author = "Ionescu, Radu Tudor  and
      Popescu, Marius  and
      Cahill, Aoife",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3005",
    doi = "10.1162/COLI_a_00256",
    pages = "491--525",
}
@article{bos-2016-squib,
    title = "{S}quib: Expressive Power of {A}bstract {M}eaning {R}epresentations",
    author = "Bos, Johan",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3006",
    doi = "10.1162/COLI_a_00257",
    pages = "527--535",
}
@article{nguyen-etal-2016-survey,
    title = "{S}urvey: Computational Sociolinguistics: A {S}urvey",
    author = {Nguyen, Dong  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Ros{\'e}, Carolyn P.  and
      de Jong, Franciska},
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3007",
    doi = "10.1162/COLI_a_00258",
    pages = "537--593",
}
@article{zhao-etal-2016-book,
    title = "Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing {L}iu",
    author = "Zhao, Jun  and
      Liu, Kang  and
      Xu, Liheng",
    journal = "Computational Linguistics",
    volume = "42",
    number = "3",
    month = sep,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-3008",
    doi = "10.1162/COLI_r_00259",
    pages = "595--598",
}
@article{cl-2016-linguistics-42,
    title = "Computational Linguistics, Volume 42, Issue 2 - June 2016",
    journal = "Computational Linguistics",
    volume = "42",
    number = "2",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2000",
}
@article{bisazza-federico-2016-surveys,
    title = "{S}urveys: A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena",
    author = "Bisazza, Arianna  and
      Federico, Marcello",
    journal = "Computational Linguistics",
    volume = "42",
    number = "2",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2001",
    doi = "10.1162/COLI_a_00245",
    pages = "163--205",
}
@article{gildea-satta-2016-synchronous,
    title = "Synchronous Context-Free Grammars and Optimal Parsing Strategies",
    author = "Gildea, Daniel  and
      Satta, Giorgio",
    journal = "Computational Linguistics",
    volume = "42",
    number = "2",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2002",
    doi = "10.1162/COLI_a_00246",
    pages = "207--243",
}
@article{mccarthy-etal-2016-word,
    title = "Word Sense Clustering and Clusterability",
    author = "McCarthy, Diana  and
      Apidianaki, Marianna  and
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    volume = "42",
    number = "2",
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    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2003",
    doi = "10.1162/COLI_a_00247",
    pages = "245--275",
}
@article{wang-etal-2016-source,
    title = "Source Language Adaptation Approaches for Resource-Poor Machine Translation",
    author = "Wang, Pidong  and
      Nakov, Preslav  and
      Ng, Hwee Tou",
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    volume = "42",
    number = "2",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2004",
    doi = "10.1162/COLI_a_00248",
    pages = "277--306",
}
@article{ling-etal-2016-mining,
    title = "Mining Parallel Corpora from {S}ina {W}eibo and {T}witter",
    author = "Ling, Wang  and
      Marujo, Lu{\'\i}s  and
      Dyer, Chris  and
      Black, Alan W.  and
      Trancoso, Isabel",
    journal = "Computational Linguistics",
    volume = "42",
    number = "2",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2005",
    doi = "10.1162/COLI_a_00249",
    pages = "307--343",
}
@article{paperno-baroni-2016-squibs,
    title = "{S}quibs: When the Whole Is Less Than the Sum of Its Parts: How Composition Affects {PMI} Values in Distributional Semantic Vectors",
    author = "Paperno, Denis  and
      Baroni, Marco",
    journal = "Computational Linguistics",
    volume = "42",
    number = "2",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2006",
    doi = "10.1162/COLI_a_00250",
    pages = "345--350",
}
@article{bouillon-etal-2016-obituary,
    title = "{O}bituary: In Memoriam: Susan Armstrong",
    author = "Bouillon, Pierrette  and
      Merlo, Paola  and
      van Noord, Gertjan  and
      Rosner, Mike",
    journal = "Computational Linguistics",
    volume = "42",
    number = "2",
    month = jun,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-2007",
    doi = "10.1162/COLI_a_00251",
    pages = "351--352",
}
@article{cl-2016-linguistics,
    title = "Computational Linguistics, Volume 42, Issue 1 - March 2016",
    journal = "Computational Linguistics",
    volume = "42",
    number = "1",
    month = mar,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-1000",
}
@article{neubig-watanabe-2016-optimization,
    title = "Optimization for Statistical Machine Translation: A Survey",
    author = "Neubig, Graham  and
      Watanabe, Taro",
    journal = "Computational Linguistics",
    volume = "42",
    number = "1",
    month = mar,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-1001",
    doi = "10.1162/COLI_a_00241",
    pages = "1--54",
}
@article{mirroshandel-nasr-2016-integrating,
    title = "Integrating Selectional Constraints and Subcategorization Frames in a Dependency Parser",
    author = "Mirroshandel, Seyed Abolghasem  and
      Nasr, Alexis",
    journal = "Computational Linguistics",
    volume = "42",
    number = "1",
    month = mar,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-1002",
    doi = "10.1162/COLI_a_00242",
    pages = "55--90",
}
@article{ruokolainen-etal-2016-comparative,
    title = "A Comparative Study of Minimally Supervised Morphological Segmentation",
    author = {Ruokolainen, Teemu  and
      Kohonen, Oskar  and
      Sirts, Kairit  and
      Gr{\"o}nroos, Stig-Arne  and
      Kurimo, Mikko  and
      Virpioja, Sami},
    journal = "Computational Linguistics",
    volume = "42",
    number = "1",
    month = mar,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-1003",
    doi = "10.1162/COLI_a_00243",
    pages = "91--120",
}
@article{ortiz-martinez-2016-online,
    title = "Online Learning for Statistical Machine Translation",
    author = "Ortiz-Mart{\'\i}nez, Daniel",
    journal = "Computational Linguistics",
    volume = "42",
    number = "1",
    month = mar,
    year = "2016",
    url = "https://www.aclweb.org/anthology/J16-1004",
    doi = "10.1162/COLI_a_00244",
    pages = "121--161",
}
@proceedings{emnlp-2016-,
    title = "",
    editor = "Yang, Bishan  and
      Hwa, Rebecca",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
}
@inproceedings{dyer-etal-2016-practical,
    title = "Practical Neural Networks for {NLP}: From Theory to Code",
    author = "Dyer, Chris  and
      Goldberg, Yoav  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-2001",
    abstract = "This tutorial aims to bring NLP researchers up to speed with the current techniques in deep learning and neural networks, and show them how they can turn their ideas into practical implementations. We will start with simple classification models (logistic regression and multilayer perceptrons) and cover more advanced patterns that come up in NLP such as recurrent networks for sequence tagging and prediction problems, structured networks (e.g., compositional architectures based on syntax trees), structured output spaces (sequences and trees), attention for sequence-to-sequence transduction, and feature induction for complex algorithm states. A particular emphasis will be on learning to represent complex objects as recursive compositions of simpler objects. This representation will reflect characterize standard objects in NLP, such as the composition of characters and morphemes into words, and words into sentences and documents. In addition, new opportunities such as learning to embed ``algorithm states'' such as those used in transition-based parsing and other sequential structured prediction models (for which effective features may be difficult to engineer by hand) will be covered.Everything in the tutorial will be grounded in code {---} we will show how to program seemingly complex neural-net models using toolkits based on the computation-graph formalism. Computation graphs decompose complex computations into a DAG, with nodes representing inputs, target outputs, parameters, or (sub)differentiable functions (e.g., ``tanh'', ``matrix multiply'', and ``softmax''), and edges represent data dependencies. These graphs can be run ``forward'' to make predictions and compute errors (e.g., log loss, squared error) and then ``backward'' to compute derivatives with respect to model parameters. In particular we'll cover the Python bindings of the CNN library. CNN has been designed from the ground up for NLP applications, dynamically structured NNs, rapid prototyping, and a transparent data and execution model.",
}
@inproceedings{venugopal-etal-2016-advanced,
    title = "Advanced {M}arkov {L}ogic Techniques for Scalable Joint Inference in {NLP}",
    author = "Venugopal, Deepak  and
      Gogate, Vibhav  and
      Ng, Vincent",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-2002",
    abstract = "In the early days of the statistical NLP era, many language processing tasks were tackled using the so-called pipeline architecture: the given task is broken into a series of sub-tasks such that the output of one sub-task is an input to the next sub-task in the sequence. The pipeline architecture is appealing for various reasons, including modularity, modeling convenience, and manageable computational complexity. However, it suffers from the error propagation problem: errors made in one sub-task are propagated to the next sub-task in the sequence, leading to poor accuracy on that sub-task, which in turn leads to more errors downstream. Another disadvantage associated with it is lack of feedback: errors made in a sub-task are often not corrected using knowledge uncovered while solving another sub-task down the pipeline.Realizing these weaknesses, researchers have turned to joint inference approaches in recent years. One such approach involves the use of Markov logic, which is defined as a set of weighted first-order logic formulas and, at a high level, unifies first-order logic with probabilistic graphical models. It is an ideal modeling language (knowledge representation) for compactly representing relational and uncertain knowledge in NLP. In a typical use case of MLNs in NLP, the application designer describes the background knowledge using a few first-order logic sentences and then uses software packages such as Alchemy, Tuffy, and Markov the beast to perform learning and inference (prediction) over the MLN. However, despite its obvious advantages, over the years, researchers and practitioners have found it difficult to use MLNs effectively in many NLP applications. The main reason for this is that it is hard to scale inference and learning algorithms for MLNs to large datasets and complex models, that are typical in NLP.In this tutorial, we will introduce the audience to recent advances in scaling up inference and learning in MLNs as well as new approaches to make MLNs a ``black-box'' for NLP applications (with only minor tuning required on the part of the user). Specifically, we will introduce attendees to a key idea that has emerged in the MLN research community over the last few years, lifted inference , which refers to inference techniques that take advantage of symmetries (e.g., synonyms), both exact and approximate, in the MLN . We will describe how these next-generation inference techniques can be used to perform effective joint inference. We will also present our new software package for inference and learning in MLNs, Alchemy 2.0, which is based on lifted inference, focusing primarily on how it can be used to scale up inference and learning in large models and datasets for applications such as semantic similarity determination, information extraction and question answering.",
}
@inproceedings{chen-liu-2016-lifelong,
    title = "Lifelong Machine Learning for Natural Language Processing",
    author = "Chen, Zhiyuan  and
      Liu, Bing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-2003",
    abstract = "Machine learning (ML) has been successfully used as a prevalent approach to solving numerous NLP problems. However, the classic ML paradigm learns in isolation. That is, given a dataset, an ML algorithm is executed on the dataset to produce a model without using any related or prior knowledge. Although this type of isolated learning is very useful, it also has serious limitations as it does not accumulate knowledge learned in the past and use the knowledge to help future learning, which is the hallmark of human learning and human intelligence. Lifelong machine learning (LML) aims to achieve this capability. Specifically, it aims to design and develop computational learning systems and algorithms that learn as humans do, i.e., retaining the results learned in the past, abstracting knowledge from them, and using the knowledge to help future learning. In this tutorial, we will introduce the existing research of LML and to show that LML is very suitable for NLP tasks and has potential to help NLP make major progresses.",
}
@inproceedings{zhang-vo-2016-neural,
    title = "Neural Networks for Sentiment Analysis",
    author = "Zhang, Yue  and
      Vo, Duy Tin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-2004",
    abstract = "Sentiment analysis has been a major research topic in natural language processing (NLP). Traditionally, the problem has been attacked using discrete models and manually-defined sparse features. Over the past few years, neural network models have received increased research efforts in most sub areas of sentiment analysis, giving highly promising results. A main reason is the capability of neural models to automatically learn dense features that capture subtle semantic information over words, sentences and documents, which are difficult to model using traditional discrete features based on words and ngram patterns. This tutorial gives an introduction to neural network models for sentiment analysis, discussing the mathematics of word embeddings, sequence models and tree structured models and their use in sentiment analysis on the word, sentence and document levels, and fine-grained sentiment analysis. The tutorial covers a range of neural network models (e.g. CNN, RNN, RecNN, LSTM) and their extensions, which are employed in four main subtasks of sentiment analysis:Sentiment-oriented embeddings;Sentence-level sentiment;Document-level sentiment;Fine-grained sentiment.The content of the tutorial is divided into 3 sections of 1 hour each. We assume that the audience is familiar with linear algebra and basic neural network structures, introduce the mathematical details of the most typical models. First, we will introduce the sentiment analysis task, basic concepts related to neural network models for sentiment analysis, and show detail approaches to integrate sentiment information into embeddings. Sentence-level models will be described in the second section. Finally, we will discuss neural network models use for document-level and fine-grained sentiment.",
}
@inproceedings{banchs-2016-continuous,
    title = "Continuous Vector Spaces for Cross-language {NLP} Applications",
    author = "Banchs, Rafael E.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-2005",
    abstract = "The mathematical metaphor offered by the geometric concept of distance in vector spaces with respect to semantics and meaning has been proven to be useful in many monolingual natural language processing applications. There is also some recent and strong evidence that this paradigm can also be useful in the cross-language setting. In this tutorial, we present and discuss some of the most recent advances on exploiting the vector space model paradigm in specific cross-language natural language processing applications, along with a comprehensive review of the theoretical background behind them.First, the tutorial introduces some fundamental concepts of distributional semantics and vector space models. More specifically, the concepts of distributional hypothesis and term-document matrices are revised, followed by a brief discussion on linear and non-linear dimensionality reduction techniques and their implications to the parallel distributed approach to semantic cognition. Next, some classical examples of using vector space models in monolingual natural language processing applications are presented. Specific examples in the areas of information retrieval, related term identification and semantic compositionality are described.Then, the tutorial focuses its attention on the use of the vector space model paradigm in cross-language applications. To this end, some recent examples are presented and discussed in detail, addressing the specific problems of cross-language information retrieval, cross-language sentence matching, and machine translation. Some of the most recent developments in the area of Neural Machine Translation are also discussed.Finally, the tutorial concludes with a discussion about current and future research problems related to the use of vector space models in cross-language settings. Future avenues for scientific research are described, with major emphasis on the extension from vector and matrix representations to tensors, as well as the problem of encoding word position information into the vector-based representations.",
}
@inproceedings{sun-feng-2016-methods,
    title = "Methods and Theories for Large-scale Structured Prediction",
    author = "Sun, Xu  and
      Feng, Yansong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-2006",
    abstract = "Many important NLP tasks are casted as structured prediction problems, and try to predict certain forms of structured output from the input. Examples of structured prediction include POS tagging, named entity recognition, PCFG parsing, dependency parsing, machine translation, and many others. When apply structured prediction to a specific NLP task, there are the following challenges:1. Model selection: Among various models/algorithms with different characteristics, which one should we choose for a specific NLP task?2. Training: How to train the model parameters effectively and efficiently?3. Overfitting: To achieve good accuracy on test data, it is important to control the overfitting from the training data. How to control the overfitting risk for structured prediction?This tutorial will provide a clear overview of recent advances in structured prediction methods and theories, and address the above issues when we apply structured prediction to NLP tasks. We will introduce large margin methods (e.g., perceptrons, MIRA), graphical models (e.g., CRFs), and deep learning methods (e.g., RNN, LSTM), and show the respective advantages and disadvantages for NLP applications. For the training algorithms, we will introduce online/ stochastic training methods, and we will introduce parallel online/stochastic learning algorithms and theories to speed up the training (e.g., the Hogwild algorithm). For controlling the overfitting from training data, we will introduce the weight regularization methods, structure regularization, and implicit regularization methods.",
}
@proceedings{emnlp-2016-2016,
    title = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    editor = "Su, Jian  and
      Duh, Kevin  and
      Carreras, Xavier",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1000",
    doi = "10.18653/v1/D16-1",
}
@inproceedings{cross-huang-2016-span,
    title = "Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles",
    author = "Cross, James  and
      Huang, Liang",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1001",
    doi = "10.18653/v1/D16-1001",
    pages = "1--11",
}
@inproceedings{martinez-gomez-miyao-2016-rule,
    title = "Rule Extraction for Tree-to-Tree Transducers by Cost Minimization",
    author = "Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1002",
    doi = "10.18653/v1/D16-1002",
    pages = "12--22",
}
@inproceedings{ficler-goldberg-2016-neural,
    title = "A Neural Network for Coordination Boundary Prediction",
    author = "Ficler, Jessica  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1003",
    doi = "10.18653/v1/D16-1003",
    pages = "23--32",
}
@inproceedings{noji-etal-2016-using,
    title = "Using Left-corner Parsing to Encode Universal Structural Constraints in Grammar Induction",
    author = "Noji, Hiroshi  and
      Miyao, Yusuke  and
      Johnson, Mark",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1004",
    doi = "10.18653/v1/D16-1004",
    pages = "33--43",
}
@inproceedings{huang-etal-2016-distinguishing,
    title = "Distinguishing Past, On-going, and Future Events: The {E}vent{S}tatus Corpus",
    author = "Huang, Ruihong  and
      Cases, Ignacio  and
      Jurafsky, Dan  and
      Condoravdi, Cleo  and
      Riloff, Ellen",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1005",
    doi = "10.18653/v1/D16-1005",
    pages = "44--54",
}
@inproceedings{bhutani-etal-2016-nested,
    title = "Nested Propositions in Open Information Extraction",
    author = "Bhutani, Nikita  and
      Jagadish, H. V.  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1006",
    doi = "10.18653/v1/D16-1006",
    pages = "55--64",
}
@inproceedings{yang-etal-2016-position,
    title = "A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification",
    author = "Yang, Yunlun  and
      Tong, Yunhai  and
      Ma, Shulei  and
      Deng, Zhi-Hong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1007",
    doi = "10.18653/v1/D16-1007",
    pages = "65--74",
}
@inproceedings{muis-lu-2016-learning,
    title = "Learning to Recognize Discontiguous Entities",
    author = "Muis, Aldrian Obaja  and
      Lu, Wei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1008",
    doi = "10.18653/v1/D16-1008",
    pages = "75--84",
}
@inproceedings{hahn-keller-2016-modeling,
    title = "Modeling Human Reading with Neural Attention",
    author = "Hahn, Michael  and
      Keller, Frank",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1009",
    doi = "10.18653/v1/D16-1009",
    pages = "85--95",
}
@inproceedings{barak-etal-2016-comparing,
    title = "Comparing Computational Cognitive Models of Generalization in a Language Acquisition Task",
    author = "Barak, Libby  and
      Goldberg, Adele E.  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1010",
    doi = "10.18653/v1/D16-1010",
    pages = "96--106",
}
@inproceedings{lei-etal-2016-rationalizing,
    title = "Rationalizing Neural Predictions",
    author = "Lei, Tao  and
      Barzilay, Regina  and
      Jaakkola, Tommi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1011",
    doi = "10.18653/v1/D16-1011",
    pages = "107--117",
}
@inproceedings{liu-etal-2016-deep,
    title = "Deep Multi-Task Learning with Shared Memory for Text Classification",
    author = "Liu, Pengfei  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1012",
    doi = "10.18653/v1/D16-1012",
    pages = "118--127",
}
@inproceedings{trischler-etal-2016-natural,
    title = "Natural Language Comprehension with the {E}pi{R}eader",
    author = "Trischler, Adam  and
      Ye, Zheng  and
      Yuan, Xingdi  and
      Bachman, Philip  and
      Sordoni, Alessandro  and
      Suleman, Kaheer",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1013",
    doi = "10.18653/v1/D16-1013",
    pages = "128--137",
}
@inproceedings{sharp-etal-2016-creating,
    title = "Creating Causal Embeddings for Question Answering with Minimal Supervision",
    author = "Sharp, Rebecca  and
      Surdeanu, Mihai  and
      Jansen, Peter  and
      Clark, Peter  and
      Hammond, Michael",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1014",
    doi = "10.18653/v1/D16-1014",
    pages = "138--148",
}
@inproceedings{yavuz-etal-2016-improving,
    title = "Improving Semantic Parsing via Answer Type Inference",
    author = "Yavuz, Semih  and
      Gur, Izzeddin  and
      Su, Yu  and
      Srivatsa, Mudhakar  and
      Yan, Xifeng",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1015",
    doi = "10.18653/v1/D16-1015",
    pages = "149--159",
}
@inproceedings{krishnamurthy-etal-2016-semantic,
    title = "Semantic Parsing to Probabilistic Programs for Situated Question Answering",
    author = "Krishnamurthy, Jayant  and
      Tafjord, Oyvind  and
      Kembhavi, Aniruddha",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1016",
    doi = "10.18653/v1/D16-1016",
    pages = "160--170",
}
@inproceedings{tilk-etal-2016-event,
    title = "Event participant modelling with neural networks",
    author = "Tilk, Ottokar  and
      Demberg, Vera  and
      Sayeed, Asad  and
      Klakow, Dietrich  and
      Thater, Stefan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1017",
    doi = "10.18653/v1/D16-1017",
    pages = "171--182",
}
@inproceedings{qiu-etal-2016-context,
    title = "Context-Dependent Sense Embedding",
    author = "Qiu, Lin  and
      Tu, Kewei  and
      Yu, Yong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1018",
    doi = "10.18653/v1/D16-1018",
    pages = "183--191",
}
@inproceedings{guo-etal-2016-jointly,
    title = "Jointly Embedding Knowledge Graphs and Logical Rules",
    author = "Guo, Shu  and
      Wang, Quan  and
      Wang, Lihong  and
      Wang, Bin  and
      Guo, Li",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1019",
    doi = "10.18653/v1/D16-1019",
    pages = "192--202",
}
@inproceedings{braud-denis-2016-learning,
    title = "Learning Connective-based Word Representations for Implicit Discourse Relation Identification",
    author = "Braud, Chlo{\'e}  and
      Denis, Pascal",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1020",
    doi = "10.18653/v1/D16-1020",
    pages = "203--213",
}
@inproceedings{tang-etal-2016-aspect,
    title = "Aspect Level Sentiment Classification with Deep Memory Network",
    author = "Tang, Duyu  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1021",
    doi = "10.18653/v1/D16-1021",
    pages = "214--224",
}
@inproceedings{shu-etal-2016-lifelong,
    title = "Lifelong-{RL}: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets",
    author = "Shu, Lei  and
      Liu, Bing  and
      Xu, Hu  and
      Kim, Annice",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1022",
    doi = "10.18653/v1/D16-1022",
    pages = "225--235",
}
@inproceedings{yu-jiang-2016-learning,
    title = "Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification",
    author = "Yu, Jianfei  and
      Jiang, Jing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1023",
    doi = "10.18653/v1/D16-1023",
    pages = "236--246",
}
@inproceedings{zhou-etal-2016-attention,
    title = "Attention-based {LSTM} Network for Cross-Lingual Sentiment Classification",
    author = "Zhou, Xinjie  and
      Wan, Xiaojun  and
      Xiao, Jianguo",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1024",
    doi = "10.18653/v1/D16-1024",
    pages = "247--256",
}
@inproceedings{bentivogli-etal-2016-neural,
    title = "Neural versus Phrase-Based Machine Translation Quality: a Case Study",
    author = "Bentivogli, Luisa  and
      Bisazza, Arianna  and
      Cettolo, Mauro  and
      Federico, Marcello",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1025",
    doi = "10.18653/v1/D16-1025",
    pages = "257--267",
}
@inproceedings{firat-etal-2016-zero,
    title = "Zero-Resource Translation with Multi-Lingual Neural Machine Translation",
    author = "Firat, Orhan  and
      Sankaran, Baskaran  and
      Al-onaizan, Yaser  and
      Yarman Vural, Fatos T.  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1026",
    doi = "10.18653/v1/D16-1026",
    pages = "268--277",
}
@inproceedings{wang-etal-2016-memory,
    title = "Memory-enhanced Decoder for Neural Machine Translation",
    author = "Wang, Mingxuan  and
      Lu, Zhengdong  and
      Li, Hang  and
      Liu, Qun",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1027",
    doi = "10.18653/v1/D16-1027",
    pages = "278--286",
}
@inproceedings{marinho-etal-2016-semi,
    title = "Semi-Supervised Learning of Sequence Models with Method of Moments",
    author = "Marinho, Zita  and
      Martins, Andr{\'e} F. T.  and
      Cohen, Shay B.  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1028",
    doi = "10.18653/v1/D16-1028",
    pages = "287--296",
}
@inproceedings{upadhyay-etal-2016-learning,
    title = "Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems",
    author = "Upadhyay, Shyam  and
      Chang, Ming-Wei  and
      Chang, Kai-Wei  and
      Yih, Wen-tau",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1029",
    doi = "10.18653/v1/D16-1029",
    pages = "297--306",
}
@inproceedings{tabassum-etal-2016-tweetime,
    title = "{T}wee{T}ime : A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in {T}witter",
    author = "Tabassum, Jeniya  and
      Ritter, Alan  and
      Xu, Wei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1030",
    doi = "10.18653/v1/D16-1030",
    pages = "307--318",
}
@inproceedings{miao-blunsom-2016-language,
    title = "Language as a Latent Variable: Discrete Generative Models for Sentence Compression",
    author = "Miao, Yishu  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1031",
    doi = "10.18653/v1/D16-1031",
    pages = "319--328",
}
@inproceedings{kiddon-etal-2016-globally,
    title = "Globally Coherent Text Generation with Neural Checklist Models",
    author = "Kiddon, Chlo{\'e}  and
      Zettlemoyer, Luke  and
      Choi, Yejin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1032",
    doi = "10.18653/v1/D16-1032",
    pages = "329--339",
}
@inproceedings{toutanova-etal-2016-dataset,
    title = "A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs",
    author = "Toutanova, Kristina  and
      Brockett, Chris  and
      Tran, Ke M.  and
      Amershi, Saleema",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1033",
    doi = "10.18653/v1/D16-1033",
    pages = "340--350",
}
@inproceedings{brunato-etal-2016-paccss,
    title = "{P}a{CCSS}-{IT}: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification",
    author = "Brunato, Dominique  and
      Cimino, Andrea  and
      Dell{'}Orletta, Felice  and
      Venturi, Giulia",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1034",
    doi = "10.18653/v1/D16-1034",
    pages = "351--361",
}
@inproceedings{li-etal-2016-discourse,
    title = "Discourse Parsing with Attention-based Hierarchical Neural Networks",
    author = "Li, Qi  and
      Li, Tianshi  and
      Chang, Baobao",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1035",
    doi = "10.18653/v1/D16-1035",
    pages = "362--371",
}
@inproceedings{zhou-etal-2016-multi,
    title = "Multi-view Response Selection for Human-Computer Conversation",
    author = "Zhou, Xiangyang  and
      Dong, Daxiang  and
      Wu, Hua  and
      Zhao, Shiqi  and
      Yu, Dianhai  and
      Tian, Hao  and
      Liu, Xuan  and
      Yan, Rui",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1036",
    doi = "10.18653/v1/D16-1036",
    pages = "372--381",
}
@inproceedings{zhang-etal-2016-variational,
    title = "Variational Neural Discourse Relation Recognizer",
    author = "Zhang, Biao  and
      Xiong, Deyi  and
      Su, Jinsong  and
      Liu, Qun  and
      Ji, Rongrong  and
      Duan, Hong  and
      Zhang, Min",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1037",
    doi = "10.18653/v1/D16-1037",
    pages = "382--391",
}
@inproceedings{peng-etal-2016-event,
    title = "Event Detection and Co-reference with Minimal Supervision",
    author = "Peng, Haoruo  and
      Song, Yangqiu  and
      Roth, Dan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1038",
    doi = "10.18653/v1/D16-1038",
    pages = "392--402",
}
@inproceedings{luu-etal-2016-learning,
    title = "Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network",
    author = "Luu, Anh Tuan  and
      Tay, Yi  and
      Hui, Siu Cheung  and
      Ng, See Kiong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1039",
    doi = "10.18653/v1/D16-1039",
    pages = "403--413",
}
@inproceedings{nimishakavi-etal-2016-relation,
    title = "Relation Schema Induction using Tensor Factorization with Side Information",
    author = "Nimishakavi, Madhav  and
      Saini, Uday Singh  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1040",
    doi = "10.18653/v1/D16-1040",
    pages = "414--423",
}
@inproceedings{espinosa-anke-etal-2016-supervised,
    title = "Supervised Distributional Hypernym Discovery via Domain Adaptation",
    author = "Espinosa-Anke, Luis  and
      Camacho-Collados, Jose  and
      Delli Bovi, Claudio  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1041",
    doi = "10.18653/v1/D16-1041",
    pages = "424--435",
}
@inproceedings{brychcin-2016-latent,
    title = "Latent Tree Language Model",
    author = "Brychc{\'\i}n, Tom{\'a}{\v{s}}",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1042",
    doi = "10.18653/v1/D16-1042",
    pages = "436--446",
}
@inproceedings{kiela-etal-2016-comparing,
    title = "Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics",
    author = "Kiela, Douwe  and
      Ver{\H{o}}, Anita Lilla  and
      Clark, Stephen",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1043",
    doi = "10.18653/v1/D16-1043",
    pages = "447--456",
}
@inproceedings{fukui-etal-2016-multimodal,
    title = "Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding",
    author = "Fukui, Akira  and
      Park, Dong Huk  and
      Yang, Daylen  and
      Rohrbach, Anna  and
      Darrell, Trevor  and
      Rohrbach, Marcus",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1044",
    doi = "10.18653/v1/D16-1044",
    pages = "457--468",
}
@inproceedings{dror-reichart-2016-structured,
    title = "The Structured Weighted Violations Perceptron Algorithm",
    author = "Dror, Rotem  and
      Reichart, Roi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1045",
    doi = "10.18653/v1/D16-1045",
    pages = "469--478",
}
@inproceedings{mou-etal-2016-transferable,
    title = "How Transferable are Neural Networks in {NLP} Applications?",
    author = "Mou, Lili  and
      Meng, Zhao  and
      Yan, Rui  and
      Li, Ge  and
      Xu, Yan  and
      Zhang, Lu  and
      Jin, Zhi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1046",
    doi = "10.18653/v1/D16-1046",
    pages = "479--489",
}
@inproceedings{bhatia-etal-2016-morphological,
    title = "Morphological Priors for Probabilistic Neural Word Embeddings",
    author = "Bhatia, Parminder  and
      Guthrie, Robert  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1047",
    doi = "10.18653/v1/D16-1047",
    pages = "490--500",
}
@inproceedings{jiang-etal-2016-automatic,
    title = "Automatic Cross-Lingual Similarization of Dependency Grammars for Tree-based Machine Translation",
    author = "Jiang, Wenbin  and
      Zhang, Wen  and
      Xu, Jinan  and
      Cai, Rangjia",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1048",
    doi = "10.18653/v1/D16-1048",
    pages = "501--510",
}
@inproceedings{otani-etal-2016-irt,
    title = "{IRT}-based Aggregation Model of Crowdsourced Pairwise Comparison for Evaluating Machine Translations",
    author = "Otani, Naoki  and
      Nakazawa, Toshiaki  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1049",
    doi = "10.18653/v1/D16-1049",
    pages = "511--520",
}
@inproceedings{zhang-etal-2016-variational-neural,
    title = "Variational Neural Machine Translation",
    author = "Zhang, Biao  and
      Xiong, Deyi  and
      Su, Jinsong  and
      Duan, Hong  and
      Zhang, Min",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1050",
    doi = "10.18653/v1/D16-1050",
    pages = "521--530",
}
@inproceedings{simion-etal-2016-towards,
    title = "Towards a Convex {HMM} Surrogate for Word Alignment",
    author = "Simion, Andrei  and
      Collins, Michael  and
      Stein, Cliff",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1051",
    doi = "10.18653/v1/D16-1051",
    pages = "531--540",
}
@inproceedings{wang-etal-2016-solving,
    title = "Solving Verbal Questions in {IQ} Test by Knowledge-Powered Word Embedding",
    author = "Wang, Huazheng  and
      Tian, Fei  and
      Gao, Bin  and
      Zhu, Chengjieren  and
      Bian, Jiang  and
      Liu, Tie-Yan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1052",
    doi = "10.18653/v1/D16-1052",
    pages = "541--550",
}
@inproceedings{cheng-etal-2016-long,
    title = "Long Short-Term Memory-Networks for Machine Reading",
    author = "Cheng, Jianpeng  and
      Dong, Li  and
      Lapata, Mirella",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1053",
    doi = "10.18653/v1/D16-1053",
    pages = "551--561",
}
@inproceedings{su-etal-2016-generating,
    title = "On Generating Characteristic-rich Question Sets for {QA} Evaluation",
    author = {Su, Yu  and
      Sun, Huan  and
      Sadler, Brian  and
      Srivatsa, Mudhakar  and
      G{\"u}r, Izzeddin  and
      Yan, Zenghui  and
      Yan, Xifeng},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1054",
    doi = "10.18653/v1/D16-1054",
    pages = "562--572",
}
@inproceedings{ture-boschee-2016-learning,
    title = "Learning to Translate for Multilingual Question Answering",
    author = "Ture, Ferhan  and
      Boschee, Elizabeth",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1055",
    doi = "10.18653/v1/D16-1055",
    pages = "573--584",
}
@inproceedings{abdelwahab-etal-2016-semiparametric,
    title = "A Semiparametric Model for {B}ayesian Reader Identification",
    author = "Abdelwahab, Ahmed  and
      Kliegl, Reinhold  and
      Landwehr, Niels",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1056",
    doi = "10.18653/v1/D16-1056",
    pages = "585--594",
}
@inproceedings{hamilton-etal-2016-inducing,
    title = "Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora",
    author = "Hamilton, William L.  and
      Clark, Kevin  and
      Leskovec, Jure  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1057",
    doi = "10.18653/v1/D16-1057",
    pages = "595--605",
}
@inproceedings{wang-etal-2016-attention,
    title = "Attention-based {LSTM} for Aspect-level Sentiment Classification",
    author = "Wang, Yequan  and
      Huang, Minlie  and
      Zhu, Xiaoyan  and
      Zhao, Li",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1058",
    doi = "10.18653/v1/D16-1058",
    pages = "606--615",
}
@inproceedings{wang-etal-2016-recursive,
    title = "Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis",
    author = "Wang, Wenya  and
      Pan, Sinno Jialin  and
      Dahlmeier, Daniel  and
      Xiao, Xiaokui",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1059",
    doi = "10.18653/v1/D16-1059",
    pages = "616--626",
}
@inproceedings{laddha-mukherjee-2016-extracting,
    title = "Extracting Aspect Specific Opinion Expressions",
    author = "Laddha, Abhishek  and
      Mukherjee, Arjun",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1060",
    doi = "10.18653/v1/D16-1060",
    pages = "627--637",
}
@inproceedings{zhou-etal-2016-emotion,
    title = "Emotion Distribution Learning from Texts",
    author = "Zhou, Deyu  and
      Zhang, Xuan  and
      Zhou, Yin  and
      Zhao, Quan  and
      Geng, Xin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1061",
    doi = "10.18653/v1/D16-1061",
    pages = "638--647",
}
@inproceedings{lalor-etal-2016-building,
    title = "Building an Evaluation Scale using Item Response Theory",
    author = "Lalor, John P.  and
      Wu, Hao  and
      Yu, Hong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1062",
    doi = "10.18653/v1/D16-1062",
    pages = "648--657",
}
@inproceedings{ji-etal-2016-wordrank,
    title = "{W}ord{R}ank: Learning Word Embeddings via Robust Ranking",
    author = "Ji, Shihao  and
      Yun, Hyokun  and
      Yanardag, Pinar  and
      Matsushima, Shin  and
      Vishwanathan, S. V. N.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1063",
    doi = "10.18653/v1/D16-1063",
    pages = "658--668",
}
@inproceedings{ruan-etal-2016-exploring,
    title = "Exploring Semantic Representation in Brain Activity Using Word Embeddings",
    author = "Ruan, Yu-Ping  and
      Ling, Zhen-Hua  and
      Hu, Yu",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1064",
    doi = "10.18653/v1/D16-1064",
    pages = "669--679",
}
@inproceedings{zhou-etal-2016-amr,
    title = "{AMR} Parsing with an Incremental Joint Model",
    author = "Zhou, Junsheng  and
      Xu, Feiyu  and
      Uszkoreit, Hans  and
      Qu, Weiguang  and
      Li, Ran  and
      Gu, Yanhui",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1065",
    doi = "10.18653/v1/D16-1065",
    pages = "680--689",
}
@inproceedings{fast-horvitz-2016-identifying,
    title = "Identifying Dogmatism in Social Media: Signals and Models",
    author = "Fast, Ethan  and
      Horvitz, Eric",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1066",
    doi = "10.18653/v1/D16-1066",
    pages = "690--699",
}
@inproceedings{zhou-etal-2016-enhanced,
    title = "Enhanced Personalized Search using Social Data",
    author = "Zhou, Dong  and
      Lawless, S{\'e}amus  and
      Wu, Xuan  and
      Zhao, Wenyu  and
      Liu, Jianxun",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1067",
    doi = "10.18653/v1/D16-1067",
    pages = "700--710",
}
@inproceedings{tchernowitz-etal-2016-effective,
    title = "Effective Greedy Inference for Graph-based Non-Projective Dependency Parsing",
    author = "Tchernowitz, Ilan  and
      Yedidsion, Liron  and
      Reichart, Roi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1068",
    doi = "10.18653/v1/D16-1068",
    pages = "711--720",
}
@inproceedings{zhang-etal-2016-generating,
    title = "Generating Abbreviations for {C}hinese Named Entities Using Recurrent Neural Network with Dynamic Dictionary",
    author = "Zhang, Qi  and
      Qian, Jin  and
      Guo, Ya  and
      Zhou, Yaqian  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1069",
    doi = "10.18653/v1/D16-1069",
    pages = "721--730",
}
@inproceedings{chen-etal-2016-neural,
    title = "Neural Network for Heterogeneous Annotations",
    author = "Chen, Hongshen  and
      Zhang, Yue  and
      Liu, Qun",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1070",
    doi = "10.18653/v1/D16-1070",
    pages = "731--741",
}
@inproceedings{ebert-etal-2016-lamb,
    title = "{LAMB}: A Good Shepherd of Morphologically Rich Languages",
    author = {Ebert, Sebastian  and
      M{\"u}ller, Thomas  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1071",
    doi = "10.18653/v1/D16-1071",
    pages = "742--752",
}
@inproceedings{li-etal-2016-fast,
    title = "Fast Coupled Sequence Labeling on Heterogeneous Annotations via Context-aware Pruning",
    author = "Li, Zhenghua  and
      Chao, Jiayuan  and
      Zhang, Min  and
      Yang, Jiwen",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1072",
    doi = "10.18653/v1/D16-1072",
    pages = "753--762",
}
@inproceedings{jiang-etal-2016-unsupervised,
    title = "Unsupervised Neural Dependency Parsing",
    author = "Jiang, Yong  and
      Han, Wenjuan  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1073",
    doi = "10.18653/v1/D16-1073",
    pages = "763--771",
}
@inproceedings{parveen-etal-2016-generating,
    title = "Generating Coherent Summaries of Scientific Articles Using Coherence Patterns",
    author = "Parveen, Daraksha  and
      Mesgar, Mohsen  and
      Strube, Michael",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1074",
    doi = "10.18653/v1/D16-1074",
    pages = "772--783",
}
@inproceedings{ge-etal-2016-news,
    title = "News Stream Summarization using Burst Information Networks",
    author = "Ge, Tao  and
      Cui, Lei  and
      Chang, Baobao  and
      Li, Sujian  and
      Zhou, Ming  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1075",
    doi = "10.18653/v1/D16-1075",
    pages = "784--794",
}
@inproceedings{zhang-etal-2016-rationale,
    title = "Rationale-Augmented Convolutional Neural Networks for Text Classification",
    author = "Zhang, Ye  and
      Marshall, Iain  and
      Wallace, Byron C.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1076",
    doi = "10.18653/v1/D16-1076",
    pages = "795--804",
}
@inproceedings{huang-lin-2016-transferring,
    title = "Transferring User Interests Across Websites with Unstructured Text for Cold-Start Recommendation",
    author = "Huang, Yu-Yang  and
      Lin, Shou-De",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1077",
    doi = "10.18653/v1/D16-1077",
    pages = "805--814",
}
@inproceedings{qian-etal-2016-speculation,
    title = "Speculation and Negation Scope Detection via Convolutional Neural Networks",
    author = "Qian, Zhong  and
      Li, Peifeng  and
      Zhu, Qiaoming  and
      Zhou, Guodong  and
      Luo, Zhunchen  and
      Luo, Wei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1078",
    doi = "10.18653/v1/D16-1078",
    pages = "815--825",
}
@inproceedings{qian-etal-2016-analyzing,
    title = "Analyzing Linguistic Knowledge in Sequential Model of Sentence",
    author = "Qian, Peng  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1079",
    doi = "10.18653/v1/D16-1079",
    pages = "826--835",
}
@inproceedings{zhang-etal-2016-keyphrase,
    title = "Keyphrase Extraction Using Deep Recurrent Neural Networks on {T}witter",
    author = "Zhang, Qi  and
      Wang, Yang  and
      Gong, Yeyun  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1080",
    doi = "10.18653/v1/D16-1080",
    pages = "836--845",
}
@inproceedings{tan-etal-2016-solving,
    title = "Solving and Generating {C}hinese Character Riddles",
    author = "Tan, Chuanqi  and
      Wei, Furu  and
      Dong, Li  and
      Lv, Weifeng  and
      Zhou, Ming",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1081",
    doi = "10.18653/v1/D16-1081",
    pages = "846--855",
}
@inproceedings{jagannatha-yu-2016-structured,
    title = "Structured prediction models for {RNN} based sequence labeling in clinical text",
    author = "Jagannatha, Abhyuday  and
      Yu, Hong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1082",
    doi = "10.18653/v1/D16-1082",
    pages = "856--865",
}
@inproceedings{wang-etal-2016-learning-represent,
    title = "Learning to Represent Review with Tensor Decomposition for Spam Detection",
    author = "Wang, Xuepeng  and
      Liu, Kang  and
      He, Shizhu  and
      Zhao, Jun",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1083",
    doi = "10.18653/v1/D16-1083",
    pages = "866--875",
}
@inproceedings{augenstein-etal-2016-stance,
    title = "Stance Detection with Bidirectional Conditional Encoding",
    author = {Augenstein, Isabelle  and
      Rockt{\"a}schel, Tim  and
      Vlachos, Andreas  and
      Bontcheva, Kalina},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1084",
    doi = "10.18653/v1/D16-1084",
    pages = "876--885",
}
@inproceedings{nguyen-grishman-2016-modeling,
    title = "Modeling Skip-Grams for Event Detection with Convolutional Neural Networks",
    author = "Nguyen, Thien Huu  and
      Grishman, Ralph",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1085",
    doi = "10.18653/v1/D16-1085",
    pages = "886--891",
}
@inproceedings{falke-etal-2016-porting,
    title = "Porting an Open Information Extraction System from {E}nglish to {G}erman",
    author = "Falke, Tobias  and
      Stanovsky, Gabriel  and
      Gurevych, Iryna  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1086",
    doi = "10.18653/v1/D16-1086",
    pages = "892--898",
}
@inproceedings{qu-etal-2016-named,
    title = "Named Entity Recognition for Novel Types by Transfer Learning",
    author = "Qu, Lizhen  and
      Ferraro, Gabriela  and
      Zhou, Liyuan  and
      Hou, Weiwei  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1087",
    doi = "10.18653/v1/D16-1087",
    pages = "899--905",
}
@inproceedings{badgett-huang-2016-extracting,
    title = "Extracting Subevents via an Effective Two-phase Approach",
    author = "Badgett, Allison  and
      Huang, Ruihong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1088",
    doi = "10.18653/v1/D16-1088",
    pages = "906--911",
}
@inproceedings{mukherjee-hospedales-2016-gaussian,
    title = "{G}aussian Visual-Linguistic Embedding for Zero-Shot Recognition",
    author = "Mukherjee, Tanmoy  and
      Hospedales, Timothy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1089",
    doi = "10.18653/v1/D16-1089",
    pages = "912--918",
}
@inproceedings{ray-etal-2016-question,
    title = "Question Relevance in {VQA}: Identifying Non-Visual And False-Premise Questions",
    author = "Ray, Arijit  and
      Christie, Gordon  and
      Bansal, Mohit  and
      Batra, Dhruv  and
      Parikh, Devi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1090",
    doi = "10.18653/v1/D16-1090",
    pages = "919--924",
}
@inproceedings{agrawal-etal-2016-sort,
    title = "Sort Story: Sorting Jumbled Images and Captions into Stories",
    author = "Agrawal, Harsh  and
      Chandrasekaran, Arjun  and
      Batra, Dhruv  and
      Parikh, Devi  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1091",
    doi = "10.18653/v1/D16-1091",
    pages = "925--931",
}
@inproceedings{das-etal-2016-human,
    title = "Human Attention in Visual Question Answering: Do Humans and Deep Networks look at the same regions?",
    author = "Das, Abhishek  and
      Agrawal, Harsh  and
      Zitnick, Larry  and
      Parikh, Devi  and
      Batra, Dhruv",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1092",
    doi = "10.18653/v1/D16-1092",
    pages = "932--937",
}
@inproceedings{wang-tian-2016-recurrent,
    title = "Recurrent Residual Learning for Sequence Classification",
    author = "Wang, Yiren  and
      Tian, Fei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1093",
    doi = "10.18653/v1/D16-1093",
    pages = "938--943",
}
@inproceedings{shareghi-etal-2016-richer,
    title = "Richer Interpolative Smoothing Based on Modified {K}neser-{N}ey Language Modeling",
    author = "Shareghi, Ehsan  and
      Cohn, Trevor  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1094",
    doi = "10.18653/v1/D16-1094",
    pages = "944--949",
}
@inproceedings{lu-etal-2016-general,
    title = "A General Regularization Framework for Domain Adaptation",
    author = {Lu, Wei  and
      Chieu, Hai Leong  and
      L{\"o}fgren, Jonathan},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1095",
    doi = "10.18653/v1/D16-1095",
    pages = "950--954",
}
@inproceedings{mi-etal-2016-coverage,
    title = "Coverage Embedding Models for Neural Machine Translation",
    author = "Mi, Haitao  and
      Sankaran, Baskaran  and
      Wang, Zhiguo  and
      Ittycheriah, Abe",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1096",
    doi = "10.18653/v1/D16-1096",
    pages = "955--960",
}
@inproceedings{kann-etal-2016-neural,
    title = "Neural Morphological Analysis: Encoding-Decoding Canonical Segments",
    author = {Kann, Katharina  and
      Cotterell, Ryan  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1097",
    doi = "10.18653/v1/D16-1097",
    pages = "961--967",
}
@inproceedings{shi-etal-2016-exploiting,
    title = "Exploiting Mutual Benefits between Syntax and Semantic Roles using Neural Network",
    author = "Shi, Peng  and
      Teng, Zhiyang  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1098",
    doi = "10.18653/v1/D16-1098",
    pages = "968--974",
}
@inproceedings{sahlgren-lenci-2016-effects,
    title = "The Effects of Data Size and Frequency Range on Distributional Semantic Models",
    author = "Sahlgren, Magnus  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1099",
    doi = "10.18653/v1/D16-1099",
    pages = "975--980",
}
@inproceedings{yin-etal-2016-multi,
    title = "Multi-Granularity {C}hinese Word Embedding",
    author = "Yin, Rongchao  and
      Wang, Quan  and
      Li, Peng  and
      Li, Rui  and
      Wang, Bin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1100",
    doi = "10.18653/v1/D16-1100",
    pages = "981--986",
}
@inproceedings{spithourakis-etal-2016-numerically,
    title = "Numerically Grounded Language Models for Semantic Error Correction",
    author = "Spithourakis, Georgios  and
      Augenstein, Isabelle  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1101",
    doi = "10.18653/v1/D16-1101",
    pages = "987--992",
}
@inproceedings{akbik-etal-2016-towards,
    title = "Towards Semi-Automatic Generation of Proposition {B}anks for Low-Resource Languages",
    author = "Akbik, Alan  and
      Kumar, Vishwajeet  and
      Li, Yunyao",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1102",
    doi = "10.18653/v1/D16-1102",
    pages = "993--998",
}
@inproceedings{ruder-etal-2016-hierarchical,
    title = "A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis",
    author = "Ruder, Sebastian  and
      Ghaffari, Parsa  and
      Breslin, John G.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1103",
    doi = "10.18653/v1/D16-1103",
    pages = "999--1005",
}
@inproceedings{joshi-etal-2016-word,
    title = "Are Word Embedding-based Features Useful for Sarcasm Detection?",
    author = "Joshi, Aditya  and
      Tripathi, Vaibhav  and
      Patel, Kevin  and
      Bhattacharyya, Pushpak  and
      Carman, Mark",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1104",
    doi = "10.18653/v1/D16-1104",
    pages = "1006--1011",
}
@inproceedings{ebrahimi-etal-2016-weakly,
    title = "Weakly Supervised Tweet Stance Classification by Relational Bootstrapping",
    author = "Ebrahimi, Javid  and
      Dou, Dejing  and
      Lowd, Daniel",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1105",
    doi = "10.18653/v1/D16-1105",
    pages = "1012--1017",
}
@inproceedings{pavlick-etal-2016-gun,
    title = "The Gun Violence Database: A new task and data set for {NLP}",
    author = "Pavlick, Ellie  and
      Ji, Heng  and
      Pan, Xiaoman  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1106",
    doi = "10.18653/v1/D16-1106",
    pages = "1018--1024",
}
@inproceedings{lippincott-van-durme-2016-fluency,
    title = "Fluency detection on communication networks",
    author = "Lippincott, Tom  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1107",
    doi = "10.18653/v1/D16-1107",
    pages = "1025--1029",
}
@inproceedings{tran-ostendorf-2016-characterizing,
    title = "Characterizing the Language of Online Communities and its Relation to Community Reception",
    author = "Tran, Trang  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1108",
    doi = "10.18653/v1/D16-1108",
    pages = "1030--1035",
}
@inproceedings{yoshikawa-etal-2016-joint,
    title = "Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts",
    author = "Yoshikawa, Masashi  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1109",
    doi = "10.18653/v1/D16-1109",
    pages = "1036--1041",
}
@inproceedings{bertero-etal-2016-real,
    title = "Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems",
    author = "Bertero, Dario  and
      Siddique, Farhad Bin  and
      Wu, Chien-Sheng  and
      Wan, Yan  and
      Chan, Ricky Ho Yin  and
      Fung, Pascale",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1110",
    doi = "10.18653/v1/D16-1110",
    pages = "1042--1047",
}
@inproceedings{ballesteros-wanner-2016-neural,
    title = "A Neural Network Architecture for Multilingual Punctuation Generation",
    author = "Ballesteros, Miguel  and
      Wanner, Leo",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1111",
    doi = "10.18653/v1/D16-1111",
    pages = "1048--1053",
}
@inproceedings{takase-etal-2016-neural,
    title = "Neural Headline Generation on {A}bstract {M}eaning {R}epresentation",
    author = "Takase, Sho  and
      Suzuki, Jun  and
      Okazaki, Naoaki  and
      Hirao, Tsutomu  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1112",
    doi = "10.18653/v1/D16-1112",
    pages = "1054--1059",
}
@inproceedings{kekec-tax-2016-robust,
    title = "Robust Gram Embeddings",
    author = "Keke{\c{c}}, Taygun  and
      Tax, David M. J.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1113",
    doi = "10.18653/v1/D16-1113",
    pages = "1060--1065",
}
@inproceedings{kim-etal-2016-simplescience,
    title = "{S}imple{S}cience: Lexical Simplification of Scientific Terminology",
    author = "Kim, Yea-Seul  and
      Hullman, Jessica  and
      Burgess, Matthew  and
      Adar, Eytan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1114",
    doi = "10.18653/v1/D16-1114",
    pages = "1066--1071",
}
@inproceedings{dong-zhang-2016-automatic,
    title = "Automatic Features for Essay Scoring {--} An Empirical Study",
    author = "Dong, Fei  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1115",
    doi = "10.18653/v1/D16-1115",
    pages = "1072--1077",
}
@inproceedings{kocisky-etal-2016-semantic,
    title = "Semantic Parsing with Semi-Supervised Sequential Autoencoders",
    author = "Ko{\v{c}}isk{\'y}, Tom{\'a}{\v{s}}  and
      Melis, G{\'a}bor  and
      Grefenstette, Edward  and
      Dyer, Chris  and
      Ling, Wang  and
      Blunsom, Phil  and
      Hermann, Karl Moritz",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1116",
    doi = "10.18653/v1/D16-1116",
    pages = "1078--1087",
}
@inproceedings{roy-etal-2016-equation,
    title = "Equation Parsing : Mapping Sentences to Grounded Equations",
    author = "Roy, Subhro  and
      Upadhyay, Shyam  and
      Roth, Dan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1117",
    doi = "10.18653/v1/D16-1117",
    pages = "1088--1097",
}
@inproceedings{sanders-blanco-2016-automatic,
    title = "Automatic Extraction of Implicit Interpretations from Modal Constructions",
    author = "Sanders, Jordan  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1118",
    doi = "10.18653/v1/D16-1118",
    pages = "1098--1107",
}
@inproceedings{sarabi-blanco-2016-understanding,
    title = "Understanding Negation in Positive Terms Using Syntactic Dependencies",
    author = "Sarabi, Zahra  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1119",
    doi = "10.18653/v1/D16-1119",
    pages = "1108--1118",
}
@inproceedings{blodgett-etal-2016-demographic,
    title = "Demographic Dialectal Variation in Social Media: A Case Study of {A}frican-{A}merican {E}nglish",
    author = "Blodgett, Su Lin  and
      Green, Lisa  and
      O{'}Connor, Brendan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1120",
    doi = "10.18653/v1/D16-1120",
    pages = "1119--1130",
}
@inproceedings{rudra-etal-2016-understanding,
    title = "Understanding Language Preference for Expression of Opinion and Sentiment: What do {H}indi-{E}nglish Speakers do on {T}witter?",
    author = "Rudra, Koustav  and
      Rijhwani, Shruti  and
      Begum, Rafiya  and
      Bali, Kalika  and
      Choudhury, Monojit  and
      Ganguly, Niloy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1121",
    doi = "10.18653/v1/D16-1121",
    pages = "1131--1141",
}
@inproceedings{chaney-etal-2016-detecting,
    title = "Detecting and Characterizing Events",
    author = "Chaney, Allison  and
      Wallach, Hanna  and
      Connelly, Matthew  and
      Blei, David",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1122",
    doi = "10.18653/v1/D16-1122",
    pages = "1142--1152",
}
@inproceedings{pham-etal-2016-convolutional,
    title = "Convolutional Neural Network Language Models",
    author = "Pham, Ngoc-Quan  and
      Kruszewski, German  and
      Boleda, Gemma",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1123",
    doi = "10.18653/v1/D16-1123",
    pages = "1153--1162",
}
@inproceedings{neubig-dyer-2016-generalizing,
    title = "Generalizing and Hybridizing Count-based and Neural Language Models",
    author = "Neubig, Graham  and
      Dyer, Chris",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1124",
    doi = "10.18653/v1/D16-1124",
    pages = "1163--1172",
}
@inproceedings{andreas-klein-2016-reasoning,
    title = "Reasoning about Pragmatics with Neural Listeners and Speakers",
    author = "Andreas, Jacob  and
      Klein, Dan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1125",
    doi = "10.18653/v1/D16-1125",
    pages = "1173--1182",
}
@inproceedings{ghazvininejad-etal-2016-generating,
    title = "Generating Topical Poetry",
    author = "Ghazvininejad, Marjan  and
      Shi, Xing  and
      Choi, Yejin  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1126",
    doi = "10.18653/v1/D16-1126",
    pages = "1183--1191",
}
@inproceedings{li-etal-2016-deep,
    title = "Deep Reinforcement Learning for Dialogue Generation",
    author = "Li, Jiwei  and
      Monroe, Will  and
      Ritter, Alan  and
      Jurafsky, Dan  and
      Galley, Michel  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1127",
    doi = "10.18653/v1/D16-1127",
    pages = "1192--1202",
}
@inproceedings{lebret-etal-2016-neural,
    title = "Neural Text Generation from Structured Data with Application to the Biography Domain",
    author = "Lebret, R{\'e}mi  and
      Grangier, David  and
      Auli, Michael",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1128",
    doi = "10.18653/v1/D16-1128",
    pages = "1203--1213",
}
@inproceedings{habernal-gurevych-2016-makes,
    title = "What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in Web argumentation",
    author = "Habernal, Ivan  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1129",
    doi = "10.18653/v1/D16-1129",
    pages = "1214--1223",
}
@inproceedings{liu-li-2016-recognizing,
    title = "Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention",
    author = "Liu, Yang  and
      Li, Sujian",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1130",
    doi = "10.18653/v1/D16-1130",
    pages = "1224--1233",
}
@inproceedings{anand-hardt-2016-antecedent,
    title = "Antecedent Selection for Sluicing: Structure and Content",
    author = "Anand, Pranav  and
      Hardt, Daniel",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1131",
    doi = "10.18653/v1/D16-1131",
    pages = "1234--1243",
}
@inproceedings{iida-etal-2016-intra,
    title = "Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network",
    author = "Iida, Ryu  and
      Torisawa, Kentaro  and
      Oh, Jong-Hoon  and
      Kruengkrai, Canasai  and
      Kloetzer, Julien",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1132",
    doi = "10.18653/v1/D16-1132",
    pages = "1244--1254",
}
@inproceedings{anastasopoulos-etal-2016-unsupervised,
    title = "An Unsupervised Probability Model for Speech-to-Translation Alignment of Low-Resource Languages",
    author = "Anastasopoulos, Antonios  and
      Chiang, David  and
      Duong, Long",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1133",
    doi = "10.18653/v1/D16-1133",
    pages = "1255--1263",
}
@inproceedings{birch-etal-2016-hume,
    title = "{HUME}: Human {UCCA}-Based Evaluation of Machine Translation",
    author = "Birch, Alexandra  and
      Abend, Omri  and
      Bojar, Ond{\v{r}}ej  and
      Haddow, Barry",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1134",
    doi = "10.18653/v1/D16-1134",
    pages = "1264--1274",
}
@inproceedings{ni-florian-2016-improving,
    title = "Improving Multilingual Named Entity Recognition with {W}ikipedia Entity Type Mapping",
    author = "Ni, Jian  and
      Florian, Radu",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1135",
    doi = "10.18653/v1/D16-1135",
    pages = "1275--1284",
}
@inproceedings{duong-etal-2016-learning,
    title = "Learning Crosslingual Word Embeddings without Bilingual Corpora",
    author = "Duong, Long  and
      Kanayama, Hiroshi  and
      Ma, Tengfei  and
      Bird, Steven  and
      Cohn, Trevor",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1136",
    doi = "10.18653/v1/D16-1136",
    pages = "1285--1295",
}
@inproceedings{wiseman-rush-2016-sequence,
    title = "Sequence-to-Sequence Learning as Beam-Search Optimization",
    author = "Wiseman, Sam  and
      Rush, Alexander M.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1137",
    doi = "10.18653/v1/D16-1137",
    pages = "1296--1306",
}
@inproceedings{yu-etal-2016-online,
    title = "Online Segment to Segment Neural Transduction",
    author = "Yu, Lei  and
      Buys, Jan  and
      Blunsom, Phil",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1138",
    doi = "10.18653/v1/D16-1138",
    pages = "1307--1316",
}
@inproceedings{kim-rush-2016-sequence,
    title = "Sequence-Level Knowledge Distillation",
    author = "Kim, Yoon  and
      Rush, Alexander M.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1139",
    doi = "10.18653/v1/D16-1139",
    pages = "1317--1327",
}
@inproceedings{kikuchi-etal-2016-controlling,
    title = "Controlling Output Length in Neural Encoder-Decoders",
    author = "Kikuchi, Yuta  and
      Neubig, Graham  and
      Sasano, Ryohei  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1140",
    doi = "10.18653/v1/D16-1140",
    pages = "1328--1338",
}
@inproceedings{peterson-fyshe-2016-poet,
    title = "Poet Admits // Mute Cypher: Beam Search to find Mutually Enciphering Poetic Texts",
    author = "Peterson, Cole  and
      Fyshe, Alona",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1141",
    doi = "10.18653/v1/D16-1141",
    pages = "1339--1347",
}
@inproceedings{chakraborty-narayanam-2016-fingers,
    title = "All Fingers are not Equal: Intensity of References in Scientific Articles",
    author = "Chakraborty, Tanmoy  and
      Narayanam, Ramasuri",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1142",
    doi = "10.18653/v1/D16-1142",
    pages = "1348--1358",
}
@inproceedings{wang-etal-2016-improving,
    title = "Improving Users{'} Demographic Prediction via the Videos They Talk about",
    author = "Wang, Yuan  and
      Xiao, Yang  and
      Ma, Chao  and
      Xiao, Zhen",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1143",
    doi = "10.18653/v1/D16-1143",
    pages = "1359--1368",
}
@inproceedings{ren-etal-2016-afet,
    title = "{AFET}: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding",
    author = "Ren, Xiang  and
      He, Wenqi  and
      Qu, Meng  and
      Huang, Lifu  and
      Ji, Heng  and
      Han, Jiawei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1144",
    doi = "10.18653/v1/D16-1144",
    pages = "1369--1378",
}
@inproceedings{wei-etal-2016-mining,
    title = "Mining Inference Formulas by Goal-Directed Random Walks",
    author = "Wei, Zhuoyu  and
      Zhao, Jun  and
      Liu, Kang",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1145",
    doi = "10.18653/v1/D16-1145",
    pages = "1379--1388",
}
@inproceedings{demeester-etal-2016-lifted,
    title = "Lifted Rule Injection for Relation Embeddings",
    author = {Demeester, Thomas  and
      Rockt{\"a}schel, Tim  and
      Riedel, Sebastian},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1146",
    doi = "10.18653/v1/D16-1146",
    pages = "1389--1399",
}
@inproceedings{miller-etal-2016-key,
    title = "Key-Value Memory Networks for Directly Reading Documents",
    author = "Miller, Alexander  and
      Fisch, Adam  and
      Dodge, Jesse  and
      Karimi, Amir-Hossein  and
      Bordes, Antoine  and
      Weston, Jason",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1147",
    doi = "10.18653/v1/D16-1147",
    pages = "1400--1409",
}
@inproceedings{card-etal-2016-analyzing,
    title = "Analyzing Framing through the Casts of Characters in the News",
    author = "Card, Dallas  and
      Gross, Justin  and
      Boydstun, Amber  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1148",
    doi = "10.18653/v1/D16-1148",
    pages = "1410--1420",
}
@inproceedings{litman-etal-2016-teams,
    title = "The Teams Corpus and Entrainment in Multi-Party Spoken Dialogues",
    author = "Litman, Diane  and
      Paletz, Susannah  and
      Rahimi, Zahra  and
      Allegretti, Stefani  and
      Rice, Caitlin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1149",
    doi = "10.18653/v1/D16-1149",
    pages = "1421--1431",
}
@inproceedings{ding-pan-2016-personalized,
    title = "Personalized Emphasis Framing for Persuasive Message Generation",
    author = "Ding, Tao  and
      Pan, Shimei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1150",
    doi = "10.18653/v1/D16-1150",
    pages = "1432--1441",
}
@inproceedings{louvan-etal-2016-cross,
    title = "Cross Sentence Inference for Process Knowledge",
    author = "Louvan, Samuel  and
      Naik, Chetan  and
      Kumaravel, Sadhana  and
      Kwon, Heeyoung  and
      Balasubramanian, Niranjan  and
      Clark, Peter",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1151",
    doi = "10.18653/v1/D16-1151",
    pages = "1442--1451",
}
@inproceedings{yang-etal-2016-toward,
    title = "Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities",
    author = "Yang, Yi  and
      Chang, Ming-Wei  and
      Eisenstein, Jacob",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1152",
    doi = "10.18653/v1/D16-1152",
    pages = "1452--1461",
}
@inproceedings{bharadwaj-etal-2016-phonologically,
    title = "Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings",
    author = "Bharadwaj, Akash  and
      Mortensen, David  and
      Dyer, Chris  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1153",
    doi = "10.18653/v1/D16-1153",
    pages = "1462--1472",
}
@inproceedings{oualil-etal-2016-long,
    title = "Long-Short Range Context Neural Networks for Language Modeling",
    author = "Oualil, Youssef  and
      Singh, Mittul  and
      Greenberg, Clayton  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1154",
    doi = "10.18653/v1/D16-1154",
    pages = "1473--1481",
}
@inproceedings{liu-etal-2016-jointly,
    title = "Jointly Learning Grounded Task Structures from Language Instruction and Visual Demonstration",
    author = "Liu, Changsong  and
      Yang, Shaohua  and
      Saba-Sadiya, Sari  and
      Shukla, Nishant  and
      He, Yunzhong  and
      Zhu, Song-Chun  and
      Chai, Joyce",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1155",
    doi = "10.18653/v1/D16-1155",
    pages = "1482--1492",
}
@inproceedings{christie-etal-2016-resolving,
    title = "Resolving Language and Vision Ambiguities Together: Joint Segmentation {\&} Prepositional Attachment Resolution in Captioned Scenes",
    author = "Christie, Gordon  and
      Laddha, Ankit  and
      Agrawal, Aishwarya  and
      Antol, Stanislaw  and
      Goyal, Yash  and
      Kochersberger, Kevin  and
      Batra, Dhruv",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1156",
    doi = "10.18653/v1/D16-1156",
    pages = "1493--1503",
}
@inproceedings{wieting-etal-2016-charagram,
    title = "{C}haragram: Embedding Words and Sentences via Character n-grams",
    author = "Wieting, John  and
      Bansal, Mohit  and
      Gimpel, Kevin  and
      Livescu, Karen",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1157",
    doi = "10.18653/v1/D16-1157",
    pages = "1504--1515",
}
@inproceedings{sountsov-sarawagi-2016-length,
    title = "Length bias in Encoder Decoder Models and a Case for Global Conditioning",
    author = "Sountsov, Pavel  and
      Sarawagi, Sunita",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1158",
    doi = "10.18653/v1/D16-1158",
    pages = "1516--1525",
}
@inproceedings{shi-etal-2016-string,
    title = "Does String-Based Neural {MT} Learn Source Syntax?",
    author = "Shi, Xing  and
      Padhi, Inkit  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1159",
    doi = "10.18653/v1/D16-1159",
    pages = "1526--1534",
}
@inproceedings{zhang-zong-2016-exploiting,
    title = "Exploiting Source-side Monolingual Data in Neural Machine Translation",
    author = "Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1160",
    doi = "10.18653/v1/D16-1160",
    pages = "1535--1545",
}
@inproceedings{junczys-dowmunt-grundkiewicz-2016-phrase,
    title = "Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction",
    author = "Junczys-Dowmunt, Marcin  and
      Grundkiewicz, Roman",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1161",
    doi = "10.18653/v1/D16-1161",
    pages = "1546--1556",
}
@inproceedings{arthur-etal-2016-incorporating,
    title = "Incorporating Discrete Translation Lexicons into Neural Machine Translation",
    author = "Arthur, Philip  and
      Neubig, Graham  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1162",
    doi = "10.18653/v1/D16-1162",
    pages = "1557--1567",
}
@inproceedings{zoph-etal-2016-transfer,
    title = "Transfer Learning for Low-Resource Neural Machine Translation",
    author = "Zoph, Barret  and
      Yuret, Deniz  and
      May, Jonathan  and
      Knight, Kevin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1163",
    doi = "10.18653/v1/D16-1163",
    pages = "1568--1575",
}
@inproceedings{p-2016-mixkmeans,
    title = "{M}ix{KM}eans: Clustering Question-Answer Archives",
    author = "P, Deepak",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1164",
    doi = "10.18653/v1/D16-1164",
    pages = "1576--1585",
}
@inproceedings{nakov-etal-2016-takes,
    title = "It Takes Three to Tango: Triangulation Approach to Answer Ranking in Community Question Answering",
    author = "Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s  and
      Guzm{\'a}n, Francisco",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1165",
    doi = "10.18653/v1/D16-1165",
    pages = "1586--1597",
}
@inproceedings{he-golub-2016-character,
    title = "Character-Level Question Answering with Attention",
    author = "He, Xiaodong  and
      Golub, David",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1166",
    doi = "10.18653/v1/D16-1166",
    pages = "1598--1607",
}
@inproceedings{bouchard-etal-2016-learning,
    title = "Learning to Generate Textual Data",
    author = "Bouchard, Guillaume  and
      Stenetorp, Pontus  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1167",
    doi = "10.18653/v1/D16-1167",
    pages = "1608--1616",
}
@inproceedings{koncel-kedziorski-etal-2016-theme,
    title = "A Theme-Rewriting Approach for Generating Algebra Word Problems",
    author = "Koncel-Kedziorski, Rik  and
      Konstas, Ioannis  and
      Zettlemoyer, Luke  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1168",
    doi = "10.18653/v1/D16-1168",
    pages = "1617--1628",
}
@inproceedings{teng-etal-2016-context,
    title = "Context-Sensitive Lexicon Features for Neural Sentiment Analysis",
    author = "Teng, Zhiyang  and
      Vo, Duy-Tin  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1169",
    doi = "10.18653/v1/D16-1169",
    pages = "1629--1638",
}
@inproceedings{gui-etal-2016-event,
    title = "Event-Driven Emotion Cause Extraction with Corpus Construction",
    author = "Gui, Lin  and
      Wu, Dongyin  and
      Xu, Ruifeng  and
      Lu, Qin  and
      Zhou, Yu",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1170",
    doi = "10.18653/v1/D16-1170",
    pages = "1639--1649",
}
@inproceedings{chen-etal-2016-neural-sentiment,
    title = "Neural Sentiment Classification with User and Product Attention",
    author = "Chen, Huimin  and
      Sun, Maosong  and
      Tu, Cunchao  and
      Lin, Yankai  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1171",
    doi = "10.18653/v1/D16-1171",
    pages = "1650--1659",
}
@inproceedings{xu-etal-2016-cached,
    title = "Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification",
    author = "Xu, Jiacheng  and
      Chen, Danlu  and
      Qiu, Xipeng  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1172",
    doi = "10.18653/v1/D16-1172",
    pages = "1660--1669",
}
@inproceedings{hu-etal-2016-deep,
    title = "Deep Neural Networks with Massive Learned Knowledge",
    author = "Hu, Zhiting  and
      Yang, Zichao  and
      Salakhutdinov, Ruslan  and
      Xing, Eric",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1173",
    doi = "10.18653/v1/D16-1173",
    pages = "1670--1679",
}
@inproceedings{pilehvar-collier-2016-de,
    title = "De-Conflated Semantic Representations",
    author = "Pilehvar, Mohammad Taher  and
      Collier, Nigel",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1174",
    doi = "10.18653/v1/D16-1174",
    pages = "1680--1690",
}
@inproceedings{kober-etal-2016-improving,
    title = "Improving Sparse Word Representations with Distributional Inference for Semantic Composition",
    author = "Kober, Thomas  and
      Weeds, Julie  and
      Reffin, Jeremy  and
      Weir, David",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1175",
    doi = "10.18653/v1/D16-1175",
    pages = "1691--1702",
}
@inproceedings{liu-etal-2016-modelling,
    title = "Modelling Interaction of Sentence Pair with Coupled-{LSTM}s",
    author = "Liu, Pengfei  and
      Qiu, Xipeng  and
      Zhou, Yaqian  and
      Chen, Jifan  and
      Huang, Xuanjing",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1176",
    doi = "10.18653/v1/D16-1176",
    pages = "1703--1712",
}
@inproceedings{white-etal-2016-universal,
    title = "Universal Decompositional Semantics on {U}niversal {D}ependencies",
    author = "White, Aaron Steven  and
      Reisinger, Drew  and
      Sakaguchi, Keisuke  and
      Vieira, Tim  and
      Zhang, Sheng  and
      Rudinger, Rachel  and
      Rawlins, Kyle  and
      Van Durme, Benjamin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1177",
    doi = "10.18653/v1/D16-1177",
    pages = "1713--1723",
}
@inproceedings{sim-etal-2016-friends,
    title = "{F}riends with Motives: Using Text to Infer Influence on {SCOTUS}",
    author = "Sim, Yanchuan  and
      Routledge, Bryan  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1178",
    doi = "10.18653/v1/D16-1178",
    pages = "1724--1733",
}
@inproceedings{kenyon-dean-etal-2016-verb,
    title = "Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms",
    author = "Kenyon-Dean, Kian  and
      Cheung, Jackie Chi Kit  and
      Precup, Doina",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1179",
    doi = "10.18653/v1/D16-1179",
    pages = "1734--1743",
}
@inproceedings{kuncoro-etal-2016-distilling,
    title = "Distilling an Ensemble of Greedy Dependency Parsers into One {MST} Parser",
    author = "Kuncoro, Adhiguna  and
      Ballesteros, Miguel  and
      Kong, Lingpeng  and
      Dyer, Chris  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1180",
    doi = "10.18653/v1/D16-1180",
    pages = "1744--1753",
}
@inproceedings{xu-2016-lstm,
    title = "{LSTM} Shift-Reduce {CCG} Parsing",
    author = "Xu, Wenduan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1181",
    doi = "10.18653/v1/D16-1181",
    pages = "1754--1764",
}
@inproceedings{hashemi-hwa-2016-evaluation,
    title = "An Evaluation of Parser Robustness for Ungrammatical Sentences",
    author = "Hashemi, Homa B.  and
      Hwa, Rebecca",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1182",
    doi = "10.18653/v1/D16-1182",
    pages = "1765--1774",
}
@inproceedings{misra-artzi-2016-neural,
    title = "Neural Shift-Reduce {CCG} Semantic Parsing",
    author = "Misra, Dipendra Kumar  and
      Artzi, Yoav",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1183",
    doi = "10.18653/v1/D16-1183",
    pages = "1775--1786",
}
@inproceedings{sun-etal-2016-syntactic,
    title = "Syntactic Parsing of Web Queries",
    author = "Sun, Xiangyan  and
      Wang, Haixun  and
      Xiao, Yanghua  and
      Wang, Zhongyuan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1184",
    doi = "10.18653/v1/D16-1184",
    pages = "1787--1796",
}
@inproceedings{yu-etal-2016-unsupervised,
    title = "Unsupervised Text Recap Extraction for {TV} Series",
    author = "Yu, Hongliang  and
      Zhang, Shikun  and
      Morency, Louis-Philippe",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1185",
    doi = "10.18653/v1/D16-1185",
    pages = "1797--1806",
}
@inproceedings{dollmann-geierhos-2016-topic,
    title = "On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements",
    author = "Dollmann, Markus  and
      Geierhos, Michaela",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1186",
    doi = "10.18653/v1/D16-1186",
    pages = "1807--1816",
}
@inproceedings{hai-etal-2016-deceptive,
    title = "Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data",
    author = "Hai, Zhen  and
      Zhao, Peilin  and
      Cheng, Peng  and
      Yang, Peng  and
      Li, Xiao-Li  and
      Li, Guangxia",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1187",
    doi = "10.18653/v1/D16-1187",
    pages = "1817--1826",
}
@inproceedings{skianis-etal-2016-regularizing,
    title = "Regularizing Text Categorization with Clusters of Words",
    author = "Skianis, Konstantinos  and
      Rousseau, Fran{\c{c}}ois  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1188",
    doi = "10.18653/v1/D16-1188",
    pages = "1827--1837",
}
@inproceedings{he-etal-2016-deep,
    title = "Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular {R}eddit Threads",
    author = "He, Ji  and
      Ostendorf, Mari  and
      He, Xiaodong  and
      Chen, Jianshu  and
      Gao, Jianfeng  and
      Li, Lihong  and
      Deng, Li",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1189",
    doi = "10.18653/v1/D16-1189",
    pages = "1838--1848",
}
@inproceedings{moritz-etal-2016-non,
    title = "Non-Literal Text Reuse in Historical Texts: An Approach to Identify Reuse Transformations and its Application to {B}ible Reuse",
    author = {Moritz, Maria  and
      Wiederhold, Andreas  and
      Pavlek, Barbara  and
      Bizzoni, Yuri  and
      B{\"u}chler, Marco},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1190",
    doi = "10.18653/v1/D16-1190",
    pages = "1849--1859",
}
@inproceedings{tixier-etal-2016-graph,
    title = "A Graph Degeneracy-based Approach to Keyword Extraction",
    author = "Tixier, Antoine  and
      Malliaros, Fragkiskos  and
      Vazirgiannis, Michalis",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1191",
    doi = "10.18653/v1/D16-1191",
    pages = "1860--1870",
}
@inproceedings{schumacher-etal-2016-predicting,
    title = "Predicting the Relative Difficulty of Single Sentences With and Without Surrounding Context",
    author = "Schumacher, Elliot  and
      Eskenazi, Maxine  and
      Frishkoff, Gwen  and
      Collins-Thompson, Kevyn",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1192",
    doi = "10.18653/v1/D16-1192",
    pages = "1871--1881",
}
@inproceedings{taghipour-ng-2016-neural,
    title = "A Neural Approach to Automated Essay Scoring",
    author = "Taghipour, Kaveh  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1193",
    doi = "10.18653/v1/D16-1193",
    pages = "1882--1891",
}
@inproceedings{wang-etal-2016-non,
    title = "Non-uniform Language Detection in Technical Writing",
    author = "Wang, Weibo  and
      Moh{'}d, Abidalrahman  and
      Islam, Aminul  and
      Soto, Axel  and
      Milios, Evangelos",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1194",
    doi = "10.18653/v1/D16-1194",
    pages = "1892--1900",
}
@inproceedings{chollampatt-etal-2016-adapting,
    title = "Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models",
    author = "Chollampatt, Shamil  and
      Hoang, Duc Tam  and
      Ng, Hwee Tou",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1195",
    doi = "10.18653/v1/D16-1195",
    pages = "1901--1911",
}
@inproceedings{kunchukuttan-bhattacharyya-2016-orthographic,
    title = "Orthographic Syllable as basic unit for {SMT} between Related Languages",
    author = "Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1196",
    doi = "10.18653/v1/D16-1196",
    pages = "1912--1917",
}
@inproceedings{locascio-etal-2016-neural,
    title = "Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge",
    author = "Locascio, Nicholas  and
      Narasimhan, Karthik  and
      DeLeon, Eduardo  and
      Kushman, Nate  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1197",
    doi = "10.18653/v1/D16-1197",
    pages = "1918--1923",
}
@inproceedings{sterckx-etal-2016-supervised,
    title = "Supervised Keyphrase Extraction as Positive Unlabeled Learning",
    author = "Sterckx, Lucas  and
      Caragea, Cornelia  and
      Demeester, Thomas  and
      Develder, Chris",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1198",
    doi = "10.18653/v1/D16-1198",
    pages = "1924--1929",
}
@inproceedings{morales-etal-2016-learning,
    title = "Learning to Answer Questions from {W}ikipedia Infoboxes",
    author = "Morales, Alvaro  and
      Premtoon, Varot  and
      Avery, Cordelia  and
      Felshin, Sue  and
      Katz, Boris",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1199",
    doi = "10.18653/v1/D16-1199",
    pages = "1930--1935",
}
@inproceedings{cornegruta-vlachos-2016-timeline,
    title = "Timeline extraction using distant supervision and joint inference",
    author = "Cornegruta, Savelie  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1200",
    doi = "10.18653/v1/D16-1200",
    pages = "1936--1942",
}
@inproceedings{rajani-mooney-2016-combining,
    title = "Combining Supervised and Unsupervised Enembles for Knowledge Base Population",
    author = "Rajani, Nazneen Fatema  and
      Mooney, Raymond",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1201",
    doi = "10.18653/v1/D16-1201",
    pages = "1943--1948",
}
@inproceedings{kawakami-etal-2016-character,
    title = "Character Sequence Models for Colorful Words",
    author = "Kawakami, Kazuya  and
      Dyer, Chris  and
      Routledge, Bryan  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1202",
    doi = "10.18653/v1/D16-1202",
    pages = "1949--1954",
}
@inproceedings{agrawal-etal-2016-analyzing,
    title = "Analyzing the Behavior of Visual Question Answering Models",
    author = "Agrawal, Aishwarya  and
      Batra, Dhruv  and
      Parikh, Devi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1203",
    doi = "10.18653/v1/D16-1203",
    pages = "1955--1960",
}
@inproceedings{venugopalan-etal-2016-improving,
    title = "Improving {LSTM}-based Video Description with Linguistic Knowledge Mined from Text",
    author = "Venugopalan, Subhashini  and
      Hendricks, Lisa Anne  and
      Mooney, Raymond  and
      Saenko, Kate",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1204",
    doi = "10.18653/v1/D16-1204",
    pages = "1961--1966",
}
@inproceedings{chersoni-etal-2016-representing,
    title = "Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity",
    author = "Chersoni, Emmanuele  and
      Santus, Enrico  and
      Lenci, Alessandro  and
      Blache, Philippe  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1205",
    doi = "10.18653/v1/D16-1205",
    pages = "1967--1972",
}
@inproceedings{vieira-etal-2016-speed,
    title = "Speed-Accuracy Tradeoffs in Tagging with Variable-Order {CRF}s and Structured Sparsity",
    author = "Vieira, Tim  and
      Cotterell, Ryan  and
      Eisner, Jason",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1206",
    doi = "10.18653/v1/D16-1206",
    pages = "1973--1978",
}
@inproceedings{li-etal-2016-learning,
    title = "Learning Robust Representations of Text",
    author = "Li, Yitong  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1207",
    doi = "10.18653/v1/D16-1207",
    pages = "1979--1985",
}
@inproceedings{tu-2016-modified,
    title = "Modified {D}irichlet Distribution: Allowing Negative Parameters to Induce Stronger Sparsity",
    author = "Tu, Kewei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1208",
    doi = "10.18653/v1/D16-1208",
    pages = "1986--1991",
}
@inproceedings{miyamoto-cho-2016-gated,
    title = "Gated Word-Character Recurrent Language Model",
    author = "Miyamoto, Yasumasa  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1209",
    doi = "10.18653/v1/D16-1209",
    pages = "1992--1997",
}
@inproceedings{kamigaito-etal-2016-unsupervised,
    title = "Unsupervised Word Alignment by Agreement Under {ITG} Constraint",
    author = "Kamigaito, Hidetaka  and
      Tamura, Akihiro  and
      Takamura, Hiroya  and
      Okumura, Manabu  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1210",
    doi = "10.18653/v1/D16-1210",
    pages = "1998--2004",
}
@inproceedings{ballesteros-etal-2016-training,
    title = "Training with Exploration Improves a Greedy Stack {LSTM} Parser",
    author = "Ballesteros, Miguel  and
      Goldberg, Yoav  and
      Dyer, Chris  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1211",
    doi = "10.18653/v1/D16-1211",
    pages = "2005--2010",
}
@inproceedings{sha-etal-2016-capturing,
    title = "Capturing Argument Relationship for {C}hinese Semantic Role Labeling",
    author = "Sha, Lei  and
      Li, Sujian  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Jiang, Tingsong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1212",
    doi = "10.18653/v1/D16-1212",
    pages = "2011--2016",
}
@inproceedings{xu-etal-2016-brainbench,
    title = "{B}rain{B}ench: A Brain-Image Test Suite for Distributional Semantic Models",
    author = "Xu, Haoyan  and
      Murphy, Brian  and
      Fyshe, Alona",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1213",
    doi = "10.18653/v1/D16-1213",
    pages = "2017--2021",
}
@inproceedings{bisk-etal-2016-evaluating,
    title = "Evaluating Induced {CCG} Parsers on Grounded Semantic Parsing",
    author = "Bisk, Yonatan  and
      Reddy, Siva  and
      Blitzer, John  and
      Hockenmaier, Julia  and
      Steedman, Mark",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1214",
    doi = "10.18653/v1/D16-1214",
    pages = "2022--2027",
}
@inproceedings{apidianaki-2016-vector,
    title = "Vector-space models for {PPDB} paraphrase ranking in context",
    author = "Apidianaki, Marianna",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1215",
    doi = "10.18653/v1/D16-1215",
    pages = "2028--2034",
}
@inproceedings{aubakirova-bansal-2016-interpreting,
    title = "Interpreting Neural Networks to Improve Politeness Comprehension",
    author = "Aubakirova, Malika  and
      Bansal, Mohit",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1216",
    doi = "10.18653/v1/D16-1216",
    pages = "2035--2041",
}
@inproceedings{smith-etal-2016-well,
    title = "Does {`}well-being{'} translate on {T}witter?",
    author = "Smith, Laura  and
      Giorgi, Salvatore  and
      Solanki, Rishi  and
      Eichstaedt, Johannes  and
      Schwartz, H. Andrew  and
      Abdul-Mageed, Muhammad  and
      Buffone, Anneke  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1217",
    doi = "10.18653/v1/D16-1217",
    pages = "2042--2047",
}
@inproceedings{milli-bamman-2016-beyond,
    title = "Beyond Canonical Texts: A Computational Analysis of Fanfiction",
    author = "Milli, Smitha  and
      Bamman, David",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1218",
    doi = "10.18653/v1/D16-1218",
    pages = "2048--2053",
}
@inproceedings{rouhizadeh-etal-2016-using,
    title = "Using Syntactic and Semantic Context to Explore Psychodemographic Differences in Self-reference",
    author = "Rouhizadeh, Masoud  and
      Ungar, Lyle  and
      Buffone, Anneke  and
      Schwartz, H Andrew",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1219",
    doi = "10.18653/v1/D16-1219",
    pages = "2054--2059",
}
@inproceedings{ozbal-etal-2016-learning,
    title = "Learning to Identify Metaphors from a Corpus of Proverbs",
    author = {{\"O}zbal, G{\"o}zde  and
      Strapparava, Carlo  and
      Tekiro{\u{g}}lu, Serra Sinem  and
      Pighin, Daniele},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1220",
    doi = "10.18653/v1/D16-1220",
    pages = "2060--2065",
}
@inproceedings{kraft-etal-2016-embedding,
    title = "An Embedding Model for Predicting Roll-Call Votes",
    author = "Kraft, Peter  and
      Jain, Hirsh  and
      Rush, Alexander M.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1221",
    doi = "10.18653/v1/D16-1221",
    pages = "2066--2070",
}
@inproceedings{kim-etal-2016-natural,
    title = "Natural Language Model Re-usability for Scaling to Different Domains",
    author = "Kim, Young-Bum  and
      Rochette, Alexandre  and
      Sarikaya, Ruhi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1222",
    doi = "10.18653/v1/D16-1222",
    pages = "2071--2076",
}
@inproceedings{kurata-etal-2016-leveraging,
    title = "Leveraging Sentence-level Information with Encoder {LSTM} for Semantic Slot Filling",
    author = "Kurata, Gakuto  and
      Xiang, Bing  and
      Zhou, Bowen  and
      Yu, Mo",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1223",
    doi = "10.18653/v1/D16-1223",
    pages = "2077--2083",
}
@inproceedings{song-etal-2016-amr,
    title = "{AMR}-to-text generation as a Traveling Salesman Problem",
    author = "Song, Linfeng  and
      Zhang, Yue  and
      Peng, Xiaochang  and
      Wang, Zhiguo  and
      Gildea, Daniel",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1224",
    doi = "10.18653/v1/D16-1224",
    pages = "2084--2089",
}
@inproceedings{susanto-etal-2016-learning,
    title = "Learning to Capitalize with Character-Level Recurrent Neural Networks: An Empirical Study",
    author = "Susanto, Raymond Hendy  and
      Chieu, Hai Leong  and
      Lu, Wei",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1225",
    doi = "10.18653/v1/D16-1225",
    pages = "2090--2095",
}
@inproceedings{rohlfs-etal-2016-effects,
    title = "The Effects of the Content of {FOMC} Communications on {US} Treasury Rates",
    author = "Rohlfs, Christopher  and
      Chakraborty, Sunandan  and
      Subramanian, Lakshminarayanan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1226",
    doi = "10.18653/v1/D16-1226",
    pages = "2096--2102",
}
@inproceedings{gu-etal-2016-learning,
    title = "Learning to refine text based recommendations",
    author = "Gu, Youyang  and
      Lei, Tao  and
      Barzilay, Regina  and
      Jaakkola, Tommi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1227",
    doi = "10.18653/v1/D16-1227",
    pages = "2103--2108",
}
@inproceedings{napoles-etal-2016-theres,
    title = "There{'}s No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction",
    author = "Napoles, Courtney  and
      Sakaguchi, Keisuke  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1228",
    doi = "10.18653/v1/D16-1228",
    pages = "2109--2115",
}
@inproceedings{hamilton-etal-2016-cultural,
    title = "Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change",
    author = "Hamilton, William L.  and
      Leskovec, Jure  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1229",
    doi = "10.18653/v1/D16-1229",
    pages = "2116--2121",
}
@inproceedings{liu-etal-2016-evaluate,
    title = "How {NOT} To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation",
    author = "Liu, Chia-Wei  and
      Lowe, Ryan  and
      Serban, Iulian  and
      Noseworthy, Mike  and
      Charlin, Laurent  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1230",
    doi = "10.18653/v1/D16-1230",
    pages = "2122--2132",
}
@inproceedings{ouchi-tsuboi-2016-addressee,
    title = "Addressee and Response Selection for Multi-Party Conversation",
    author = "Ouchi, Hiroki  and
      Tsuboi, Yuta",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1231",
    doi = "10.18653/v1/D16-1231",
    pages = "2133--2143",
}
@inproceedings{wakabayashi-etal-2016-nonparametric,
    title = "Nonparametric {B}ayesian Models for Spoken Language Understanding",
    author = "Wakabayashi, Kei  and
      Takeuchi, Johane  and
      Funakoshi, Kotaro  and
      Nakano, Mikio",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1232",
    doi = "10.18653/v1/D16-1232",
    pages = "2144--2152",
}
@inproceedings{wen-etal-2016-conditional,
    title = "Conditional Generation and Snapshot Learning in Neural Dialogue Systems",
    author = "Wen, Tsung-Hsien  and
      Ga{\v{s}}i{\'c}, Milica  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Rojas-Barahona, Lina M.  and
      Su, Pei-Hao  and
      Ultes, Stefan  and
      Vandyke, David  and
      Young, Steve",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1233",
    doi = "10.18653/v1/D16-1233",
    pages = "2153--2162",
}
@inproceedings{roller-erk-2016-relations,
    title = "Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment",
    author = "Roller, Stephen  and
      Erk, Katrin",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1234",
    doi = "10.18653/v1/D16-1234",
    pages = "2163--2172",
}
@inproceedings{gerz-etal-2016-simverb,
    title = "{S}im{V}erb-3500: A Large-Scale Evaluation Set of Verb Similarity",
    author = "Gerz, Daniela  and
      Vuli{\'c}, Ivan  and
      Hill, Felix  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1235",
    doi = "10.18653/v1/D16-1235",
    pages = "2173--2182",
}
@inproceedings{grycner-weikum-2016-poly,
    title = "{POLY}: Mining Relational Paraphrases from Multilingual Sentences",
    author = "Grycner, Adam  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1236",
    doi = "10.18653/v1/D16-1236",
    pages = "2183--2192",
}
@inproceedings{li-srikumar-2016-exploiting,
    title = "Exploiting Sentence Similarities for Better Alignments",
    author = "Li, Tao  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1237",
    doi = "10.18653/v1/D16-1237",
    pages = "2193--2203",
}
@inproceedings{cheng-etal-2016-bi,
    title = "Bi-directional Attention with Agreement for Dependency Parsing",
    author = "Cheng, Hao  and
      Fang, Hao  and
      He, Xiaodong  and
      Gao, Jianfeng  and
      Deng, Li",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1238",
    doi = "10.18653/v1/D16-1238",
    pages = "2204--2214",
}
@inproceedings{berzak-etal-2016-anchoring,
    title = "Anchoring and Agreement in Syntactic Annotations",
    author = "Berzak, Yevgeni  and
      Huang, Yan  and
      Barbu, Andrei  and
      Korhonen, Anna  and
      Katz, Boris",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1239",
    doi = "10.18653/v1/D16-1239",
    pages = "2215--2224",
}
@inproceedings{pavlick-callison-burch-2016-tense,
    title = "Tense Manages to Predict Implicative Behavior in Verbs",
    author = "Pavlick, Ellie  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1240",
    doi = "10.18653/v1/D16-1240",
    pages = "2225--2229",
}
@inproceedings{onishi-etal-2016-large,
    title = "Who did What: A Large-Scale Person-Centered Cloze Dataset",
    author = "Onishi, Takeshi  and
      Wang, Hai  and
      Bansal, Mohit  and
      Gimpel, Kevin  and
      McAllester, David",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1241",
    doi = "10.18653/v1/D16-1241",
    pages = "2230--2235",
}
@inproceedings{mineshima-etal-2016-building,
    title = "Building compositional semantics and higher-order inference system for a wide-coverage {J}apanese {CCG} parser",
    author = "Mineshima, Koji  and
      Tanaka, Ribeka  and
      Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Miyao, Yusuke  and
      Bekki, Daisuke",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1242",
    doi = "10.18653/v1/D16-1242",
    pages = "2236--2242",
}
@inproceedings{monroe-etal-2016-learning,
    title = "Learning to Generate Compositional Color Descriptions",
    author = "Monroe, Will  and
      Goodman, Noah D.  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1243",
    doi = "10.18653/v1/D16-1243",
    pages = "2243--2248",
}
@inproceedings{parikh-etal-2016-decomposable,
    title = "A Decomposable Attention Model for Natural Language Inference",
    author = {Parikh, Ankur  and
      T{\"a}ckstr{\"o}m, Oscar  and
      Das, Dipanjan  and
      Uszkoreit, Jakob},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1244",
    doi = "10.18653/v1/D16-1244",
    pages = "2249--2255",
}
@inproceedings{clark-manning-2016-deep,
    title = "Deep Reinforcement Learning for Mention-Ranking Coreference Models",
    author = "Clark, Kevin  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1245",
    doi = "10.18653/v1/D16-1245",
    pages = "2256--2262",
}
@inproceedings{qin-etal-2016-stacking,
    title = "A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification",
    author = "Qin, Lianhui  and
      Zhang, Zhisong  and
      Zhao, Hai",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1246",
    doi = "10.18653/v1/D16-1246",
    pages = "2263--2270",
}
@inproceedings{nakazawa-etal-2016-insertion,
    title = "Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation",
    author = "Nakazawa, Toshiaki  and
      Richardson, John  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1247",
    doi = "10.18653/v1/D16-1247",
    pages = "2271--2277",
}
@inproceedings{shi-etal-2016-neural,
    title = "Why Neural Translations are the Right Length",
    author = "Shi, Xing  and
      Knight, Kevin  and
      Yuret, Deniz",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1248",
    doi = "10.18653/v1/D16-1248",
    pages = "2278--2282",
}
@inproceedings{mi-etal-2016-supervised,
    title = "Supervised Attentions for Neural Machine Translation",
    author = "Mi, Haitao  and
      Wang, Zhiguo  and
      Ittycheriah, Abe",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1249",
    doi = "10.18653/v1/D16-1249",
    pages = "2283--2288",
}
@inproceedings{artetxe-etal-2016-learning,
    title = "Learning principled bilingual mappings of word embeddings while preserving monolingual invariance",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1250",
    doi = "10.18653/v1/D16-1250",
    pages = "2289--2294",
}
@inproceedings{russell-gillespie-2016-measuring,
    title = "Measuring the behavioral impact of machine translation quality improvements with A/{B} testing",
    author = "Russell, Ben  and
      Gillespie, Duncan",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1251",
    doi = "10.18653/v1/D16-1251",
    pages = "2295--2299",
}
@inproceedings{stanovsky-dagan-2016-creating,
    title = "Creating a Large Benchmark for Open Information Extraction",
    author = "Stanovsky, Gabriel  and
      Dagan, Ido",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1252",
    doi = "10.18653/v1/D16-1252",
    pages = "2300--2305",
}
@inproceedings{wu-etal-2016-bilingually,
    title = "Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition",
    author = "Wu, Changxing  and
      Shi, Xiaodong  and
      Chen, Yidong  and
      Huang, Yanzhou  and
      Su, Jinsong",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1253",
    doi = "10.18653/v1/D16-1253",
    pages = "2306--2312",
}
@inproceedings{buckman-etal-2016-transition,
    title = "Transition-Based Dependency Parsing with Heuristic Backtracking",
    author = "Buckman, Jacob  and
      Ballesteros, Miguel  and
      Dyer, Chris",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1254",
    doi = "10.18653/v1/D16-1254",
    pages = "2313--2318",
}
@inproceedings{schmaltz-etal-2016-word,
    title = "Word Ordering Without Syntax",
    author = "Schmaltz, Allen  and
      Rush, Alexander M.  and
      Shieber, Stuart",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1255",
    doi = "10.18653/v1/D16-1255",
    pages = "2319--2324",
}
@inproceedings{cotterell-etal-2016-morphological,
    title = "Morphological Segmentation Inside-Out",
    author = {Cotterell, Ryan  and
      Kumar, Arun  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1256",
    doi = "10.18653/v1/D16-1256",
    pages = "2325--2330",
}
@inproceedings{choe-charniak-2016-parsing,
    title = "Parsing as Language Modeling",
    author = "Choe, Do Kook  and
      Charniak, Eugene",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1257",
    doi = "10.18653/v1/D16-1257",
    pages = "2331--2336",
}
@inproceedings{he-etal-2016-human,
    title = "Human-in-the-Loop Parsing",
    author = "He, Luheng  and
      Michael, Julian  and
      Lewis, Mike  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1258",
    doi = "10.18653/v1/D16-1258",
    pages = "2337--2342",
}
@inproceedings{bauer-teufel-2016-unsupervised,
    title = "Unsupervised Timeline Generation for {W}ikipedia History Articles",
    author = "Bauer, Sandro  and
      Teufel, Simone",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1259",
    doi = "10.18653/v1/D16-1259",
    pages = "2343--2349",
}
@inproceedings{jiang-etal-2016-encoding,
    title = "Encoding Temporal Information for Time-Aware Link Prediction",
    author = "Jiang, Tingsong  and
      Liu, Tianyu  and
      Ge, Tao  and
      Sha, Lei  and
      Li, Sujian  and
      Chang, Baobao  and
      Sui, Zhifang",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1260",
    doi = "10.18653/v1/D16-1260",
    pages = "2350--2354",
}
@inproceedings{narasimhan-etal-2016-improving,
    title = "Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning",
    author = "Narasimhan, Karthik  and
      Yala, Adam  and
      Barzilay, Regina",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1261",
    doi = "10.18653/v1/D16-1261",
    pages = "2355--2365",
}
@inproceedings{lee-etal-2016-global,
    title = "Global Neural {CCG} Parsing with Optimality Guarantees",
    author = "Lee, Kenton  and
      Lewis, Mike  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1262",
    doi = "10.18653/v1/D16-1262",
    pages = "2366--2376",
}
@inproceedings{adams-etal-2016-learning,
    title = "Learning a Lexicon and Translation Model from Phoneme Lattices",
    author = "Adams, Oliver  and
      Neubig, Graham  and
      Cohn, Trevor  and
      Bird, Steven  and
      Do, Quoc Truong  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1263",
    doi = "10.18653/v1/D16-1263",
    pages = "2377--2382",
}
@inproceedings{rajpurkar-etal-2016-squad,
    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
    author = "Rajpurkar, Pranav  and
      Zhang, Jian  and
      Lopyrev, Konstantin  and
      Liang, Percy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D16-1264",
    doi = "10.18653/v1/D16-1264",
    pages = "2383--2392",
}
@proceedings{coling-2016-coling-2016-international,
    title = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    editor = "Federico, Marcello  and
      Aizawa, Akiko",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3000",
}
@inproceedings{sadrzadeh-kartsaklis-2016-compositional,
    title = "Compositional Distributional Models of Meaning",
    author = "Sadrzadeh, Mehrnoosh  and
      Kartsaklis, Dimitri",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3001",
    pages = "1--4",
    abstract = "Compositional distributional models of meaning (CDMs) provide a function that produces a vectorial representation for a phrase or a sentence by composing the vectors of its words. Being the natural evolution of the traditional and well-studied distributional models at the word level, CDMs are steadily evolving to a popular and active area of NLP. This COLING 2016 tutorial aims at providing a concise introduction to this emerging field, presenting the different classes of CDMs and the various issues related to them in sufficient detail.",
}
@inproceedings{ku-chen-2016-chinese,
    title = "{C}hinese Textual Sentiment Analysis: Datasets, Resources and Tools",
    author = "Ku, Lun-Wei  and
      Chen, Wei-Fan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3002",
    pages = "5--8",
    abstract = "The rapid accumulation of data in social media (in million and billion scales) has imposed great challenges in information extraction, knowledge discovery, and data mining, and texts bearing sentiment and opinions are one of the major categories of user generated data in social media. Sentiment analysis is the main technology to quickly capture what people think from these text data, and is a research direction with immediate practical value in {`}big data{'} era. Learning such techniques will allow data miners to perform advanced mining tasks considering real sentiment and opinions expressed by users in additional to the statistics calculated from the physical actions (such as viewing or purchasing records) user perform, which facilitates the development of real-world applications. However, the situation that most tools are limited to the English language might stop academic or industrial people from doing research or products which cover a wider scope of data, retrieving information from people who speak different languages, or developing applications for worldwide users. More specifically, sentiment analysis determines the polarities and strength of the sentiment-bearing expressions, and it has been an important and attractive research area. In the past decade, resources and tools have been developed for sentiment analysis in order to provide subsequent vital applications, such as product reviews, reputation management, call center robots, automatic public survey, etc. However, most of these resources are for the English language. Being the key to the understanding of business and government issues, sentiment analysis resources and tools are required for other major languages, e.g., Chinese. In this tutorial, audience can learn the skills for retrieving sentiment from texts in another major language, Chinese, to overcome this obstacle. The goal of this tutorial is to introduce the proposed sentiment analysis technologies and datasets in the literature, and give the audience the opportunities to use resources and tools to process Chinese texts from the very basic preprocessing, i.e., word segmentation and part of speech tagging, to sentiment analysis, i.e., applying sentiment dictionaries and obtaining sentiment scores, through step-by-step instructions and a hand-on practice. The basic processing tools are from CKIP Participants can download these resources, use them and solve the problems they encounter in this tutorial. This tutorial will begin from some background knowledge of sentiment analysis, such as how sentiment are categorized, where to find available corpora and which models are commonly applied, especially for the Chinese language. Then a set of basic Chinese text processing tools for word segmentation, tagging and parsing will be introduced for the preparation of mining sentiment and opinions. After bringing the idea of how to pre-process the Chinese language to the audience, I will describe our work on compositional Chinese sentiment analysis from words to sentences, and an application on social media text (Facebook) as an example. All our involved and recently developed related resources, including Chinese Morphological Dataset, Augmented NTU Sentiment Dictionary (aug-NTUSD), E-hownet with sentiment information, Chinese Opinion Treebank, and the CopeOpi Sentiment Scorer, will also be introduced and distributed in this tutorial. The tutorial will end by a hands-on session of how to use these materials and tools to process Chinese sentiment. Content Details, Materials, and Program please refer to the tutorial URL: \url{http://www.lunweiku.com/}",
}
@inproceedings{saggion-ronzano-2016-natural,
    title = "Natural Language Processing for Intelligent Access to Scientific Information",
    author = "Saggion, Horacio  and
      Ronzano, Francesco",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3003",
    pages = "9--13",
    abstract = "During the last decade the amount of scientific information available on-line increased at an unprecedented rate. As a consequence, nowadays researchers are overwhelmed by an enormous and continuously growing number of articles to consider when they perform research activities like the exploration of advances in specific topics, peer reviewing, writing and evaluation of proposals. Natural Language Processing Technology represents a key enabling factor in providing scientists with intelligent patterns to access to scientific information. Extracting information from scientific papers, for example, can contribute to the development of rich scientific knowledge bases which can be leveraged to support intelligent knowledge access and question answering. Summarization techniques can reduce the size of long papers to their essential content or automatically generate state-of-the-art-reviews. Paraphrase or textual entailment techniques can contribute to the identification of relations across different scientific textual sources. This tutorial provides an overview of the most relevant tasks related to the processing of scientific documents, including but not limited to the in-depth analysis of the structure of the scientific articles, their semantic interpretation, content extraction and summarization.",
}
@inproceedings{scarton-etal-2016-quality,
    title = "Quality Estimation for Language Output Applications",
    author = "Scarton, Carolina  and
      Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3004",
    pages = "14--17",
    abstract = "Quality Estimation (QE) of language output applications is a research area that has been attracting significant attention. The goal of QE is to estimate the quality of language output applications without the need of human references. Instead, machine learning algorithms are used to build supervised models based on a few labelled training instances. Such models are able to generalise over unseen data and thus QE is a robust method applicable to scenarios where human input is not available or possible. One such a scenario where QE is particularly appealing is that of Machine Translation, where a score for predicted quality can help decide whether or not a translation is useful (e.g. for post-editing) or reliable (e.g. for gisting). Other potential applications within Natural Language Processing (NLP) include Text Summarisation and Text Simplification. In this tutorial we present the task of QE and its application in NLP, focusing on Machine Translation. We also introduce QuEst++, a toolkit for QE that encompasses feature extraction and machine learning, and propose a practical activity to extend this toolkit in various ways.",
}
@inproceedings{wintner-2016-translationese,
    title = "{T}ranslationese: Between Human and Machine Translation",
    author = "Wintner, Shuly",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3005",
    pages = "18--19",
    abstract = "Translated texts, in any language, have unique characteristics that set them apart from texts originally written in the same language. Translation Studies is a research field that focuses on investigating these characteristics. Until recently, research in machine translation (MT) has been entirely divorced from translation studies. The main goal of this tutorial is to introduce some of the findings of translation studies to researchers interested mainly in machine translation, and to demonstrate that awareness to these findings can result in better, more accurate MT systems.",
}
@inproceedings{petri-cohn-2016-succinct,
    title = "Succinct Data Structures for {NLP}-at-Scale",
    author = "Petri, Matthias  and
      Cohn, Trevor",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3006",
    pages = "20--21",
    abstract = "Succinct data structures involve the use of novel data structures, compression technologies, and other mechanisms to allow data to be stored in extremely small memory or disk footprints, while still allowing for efficient access to the underlying data. They have successfully been applied in areas such as Information Retrieval and Bioinformatics to create highly compressible in-memory search indexes which provide efficient search functionality over datasets which traditionally could only be processed using external memory data structures. Modern technologies in this space are not well known within the NLP community, but have the potential to revolutionise NLP, particularly the application to {`}big data{'} in the form of terabyte and larger corpora. This tutorial will present a practical introduction to the most important succinct data structures, tools, and applications with the intent of providing the researchers with a jump-start into this domain. The focus of this tutorial will be efficient text processing utilising space efficient representations of suffix arrays, suffix trees and searchable integer compression schemes with specific applications of succinct data structures to common NLP tasks such as $n$-gram language modelling.",
}
@inproceedings{pasca-2016-role,
    title = "The Role of {W}ikipedia in Text Analysis and Retrieval",
    author = "Pa{\c{s}}ca, Marius",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-3007",
    pages = "22",
    abstract = "This tutorial examines the characteristics, advantages and limitations of Wikipedia relative to other existing, human-curated resources of knowledge; derivative resources, created by converting semi-structured content in Wikipedia into structured data; the role of Wikipedia and its derivatives in text analysis; and the role of Wikipedia and its derivatives in enhancing information retrieval.",
}
@proceedings{coling-2016-coling-2016,
    title = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    editor = "Watanabe, Hideo",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2000",
}
@inproceedings{hoque-etal-2016-interactive,
    title = "An Interactive System for Exploring Community Question Answering Forums",
    author = "Hoque, Enamul  and
      Joty, Shafiq  and
      M{\`a}rquez, Llu{\'\i}s  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Da San Martino, Giovanni  and
      Moschitti, Alessandro  and
      Nakov, Preslav  and
      Romeo, Salvatore  and
      Carenini, Giuseppe",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2001",
    pages = "1--5",
    abstract = "We present an interactive system to provide effective and efficient search capabilities in Community Question Answering (cQA) forums. The system integrates state-of-the-art technology for answer search with a Web-based user interface specifically tailored to support the cQA forum readers. The answer search module automatically finds relevant answers for a new question by exploring related questions and the comments within their threads. The graphical user interface presents the search results and supports the exploration of related information. The system is running live at \url{http://www.qatarliving.com/betasearch/}.",
}
@inproceedings{lawrence-riezler-2016-nlmaps,
    title = "{NL}maps: A Natural Language Interface to Query {O}pen{S}treet{M}ap",
    author = "Lawrence, Carolin  and
      Riezler, Stefan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2002",
    pages = "6--10",
    abstract = "We present a Natural Language Interface (nlmaps.cl.uni-heidelberg.de) to query OpenStreetMap. Natural language questions about geographical facts are parsed into database queries that can be executed against the OpenStreetMap (OSM) database. After parsing the question, the system provides a text based answer as well as an interactive map with all points of interest and their relevant information marked. Additionally, we provide several options for users to give feedback after a question has been parsed.",
}
@inproceedings{lee-etal-2016-reading,
    title = "A Reading Environment for Learners of {C}hinese as a Foreign Language",
    author = "Lee, John  and
      Lam, Chun Yin  and
      Jiang, Shu",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2003",
    pages = "11--15",
    abstract = "We present a mobile app that provides a reading environment for learners of Chinese as a foreign language. The app includes a text database that offers over 500K articles from Chinese Wikipedia. These articles have been word-segmented; each word is linked to its entry in a Chinese-English dictionary, and to automatically-generated review exercises. The app estimates the reading proficiency of the user based on a {``}to-learn{''} list of vocabulary items. It automatically constructs and maintains this list by tracking the user{'}s dictionary lookup behavior and performance in review exercises. When a user searches for articles to read, search results are filtered such that the proportion of unknown words does not exceed a user-specified threshold.",
}
@inproceedings{simianer-etal-2016-post,
    title = "A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation",
    author = "Simianer, Patrick  and
      Karimova, Sariya  and
      Riezler, Stefan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2004",
    pages = "16--20",
    abstract = "Adaptive machine translation (MT) systems are a promising approach for improving the effectiveness of computer-aided translation (CAT) environments. There is, however, virtually only theoretical work that examines how such a system could be implemented. We present an open source post-editing interface for adaptive statistical MT, which has in-depth monitoring capabilities and excellent expandability, and can facilitate practical studies. To this end, we designed text-based and graphical post-editing interfaces. The graphical interface offers means for displaying and editing a rich view of the MT output. Our translation systems may learn from post-edits using several weight, language model and novel translation model adaptation techniques, in part by exploiting the output of the graphical interface. In a user study we show that using the proposed interface and adaptation methods, reductions in technical effort and time can be achieved.",
}
@inproceedings{wible-tsao-2016-word,
    title = "Word {M}idas Powered by {S}tring{N}et: Discovering Lexicogrammatical Constructions in Situ",
    author = "Wible, David  and
      Tsao, Nai-Lung",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2005",
    pages = "21--24",
    abstract = "Adult second language learners face the daunting but underappreciated task of mastering patterns of language use that are neither products of fully productive grammar rules nor frozen items to be memorized. Word Midas, a web browser extention, targets this uncharted territory of lexicogrammar by detecting multiword tokens of lexicogrammatical patterning in real time in situ within the noisy digital texts from the user{'}s unscripted web browsing or other digital venues. The language model powering Word Midas is StringNet, a densely cross-indexed navigable network of one billion lexicogrammatical patterns of English. These resources are described and their functionality is illustrated with a detailed scenario.",
}
@inproceedings{asahara-etal-2016-bonten,
    title = "{`}{B}on{T}en{'} {--} Corpus Concordance System for {`}{NINJAL} Web {J}apanese Corpus{'}",
    author = "Asahara, Masayuki  and
      Kawahara, Kazuya  and
      Takei, Yuya  and
      Masuoka, Hideto  and
      Ohba, Yasuko  and
      Torii, Yuki  and
      Morii, Toru  and
      Tanaka, Yuki  and
      Maekawa, Kikuo  and
      Kato, Sachi  and
      Konishi, Hikari",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2006",
    pages = "25--29",
    abstract = "The National Institute for Japanese Language and Linguistics, Japan (NINJAL) has undertaken a corpus compilation project to construct a web corpus for linguistic research comprising ten billion words. The project is divided into four parts: page collection, linguistic analysis, development of the corpus concordance system, and preservation. This article presents the corpus concordance system named {`}BonTen{'} which enables the ten-billion-scaled corpus to be queried by string, a sequence of morphological information or a subtree of the syntactic dependency structure.",
}
@inproceedings{wang-etal-2016-prototype,
    title = "A Prototype Automatic Simultaneous Interpretation System",
    author = "Wang, Xiaolin  and
      Finch, Andrew  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2007",
    pages = "30--34",
    abstract = "Simultaneous interpretation allows people to communicate spontaneously across language boundaries, but such services are prohibitively expensive for the general public. This paper presents a fully automatic simultaneous interpretation system to address this problem. Though the development is still at an early stage, the system is capable of keeping up with the fastest of the TED speakers while at the same time delivering high-quality translations. We believe that the system will become an effective tool for facilitating cross-lingual communication in the future.",
}
@inproceedings{miyata-etal-2016-mutual,
    title = "{M}u{TUAL}: A Controlled Authoring Support System Enabling Contextual Machine Translation",
    author = "Miyata, Rei  and
      Hartley, Anthony  and
      Kageura, Kyo  and
      Paris, C{\'e}cile  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2008",
    pages = "35--39",
    abstract = "The paper introduces a web-based authoring support system, MuTUAL, which aims to help writers create multilingual texts. The highlighted feature of the system is that it enables machine translation (MT) to generate outputs appropriate to their functional context within the target document. Our system is operational online, implementing core mechanisms for document structuring and controlled writing. These include a topic template and a controlled language authoring assistant, linked to our statistical MT system.",
}
@inproceedings{fucikova-etal-2016-joint,
    title = "Joint search in a bilingual valency lexicon and an annotated corpus",
    author = "Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}, Jan  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2009",
    pages = "40--44",
    abstract = "In this paper and the associated system demo, we present an advanced search system that allows to perform a joint search over a (bilingual) valency lexicon and a correspondingly annotated linked parallel corpus. This search tool has been developed on the basis of the Prague Czech-English Dependency Treebank, but its ideas are applicable in principle to any bilingual parallel corpus that is annotated for dependencies and valency (i.e., predicate-argument structure), and where verbs are linked to appropriate entries in an associated valency lexicon. Our online search tool consolidates more search interfaces into one, providing expanded structured search capability and a more efficient advanced way to search, allowing users to search for verb pairs, verbal argument pairs, their surface realization as recorded in the lexicon, or for their surface form actually appearing in the linked parallel corpus. The search system is currently under development, and is replacing our current search tool available at \url{http://lindat.mff.cuni.cz/services/CzEngVallex}, which could search the lexicon but the queries cannot take advantage of the underlying corpus nor use the additional surface form information from the lexicon(s). The system is available as open source.",
}
@inproceedings{kumar-etal-2016-experiments,
    title = "Experiments in Candidate Phrase Selection for Financial Named Entity Extraction - A Demo",
    author = "Kumar, Aman  and
      Alam, Hassan  and
      Werner, Tina  and
      Vyas, Manan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2010",
    pages = "45--48",
    abstract = "In this study we develop a system that tags and extracts financial concepts called financial named entities (FNE) along with corresponding numeric values {--} monetary and temporal. We employ machine learning and natural language processing methods to identify financial concepts and dates, and link them to numerical entities.",
}
@inproceedings{asahara-etal-2016-demonstration,
    title = "Demonstration of {C}ha{K}i.{NET} {--} beyond the corpus search system",
    author = "Asahara, Masayuki  and
      Matsumoto, Yuji  and
      Morita, Toshio",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2011",
    pages = "49--53",
    abstract = "ChaKi.NET is a corpus management system for dependency structure annotated corpora. After more than 10 years of continuous development, the system is now usable not only for corpus search, but also for visualization, annotation, labelling, and formatting for statistical analysis. This paper describes the various functions included in the current ChaKi.NET system.",
}
@inproceedings{krishnaswamy-pustejovsky-2016-voxsim,
    title = "{V}ox{S}im: A Visual Platform for Modeling Motion Language",
    author = "Krishnaswamy, Nikhil  and
      Pustejovsky, James",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2012",
    pages = "54--58",
    abstract = "Much existing work in text-to-scene generation focuses on generating static scenes. By introducing a focus on motion verbs, we integrate dynamic semantics into a rich formal model of events to generate animations in real time that correlate with human conceptions of the event described. This paper presents a working system that generates these animated scenes over a test set, discussing challenges encountered and describing the solutions implemented.",
}
@inproceedings{hemati-etal-2016-textimager,
    title = "{T}ext{I}mager: a Distributed {UIMA}-based System for {NLP}",
    author = "Hemati, Wahed  and
      Uslu, Tolga  and
      Mehler, Alexander",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2013",
    pages = "59--63",
    abstract = "More and more disciplines require NLP tools for performing automatic text analyses on various levels of linguistic resolution. However, the usage of established NLP frameworks is often hampered for several reasons: in most cases, they require basic to sophisticated programming skills, interfere with interoperability due to using non-standard I/O-formats and often lack tools for visualizing computational results. This makes it difficult especially for humanities scholars to use such frameworks. In order to cope with these challenges, we present TextImager, a UIMA-based framework that offers a range of NLP and visualization tools by means of a user-friendly GUI. Using TextImager requires no programming skills.",
}
@inproceedings{vo-etal-2016-disco,
    title = "{DISCO}: A System Leveraging Semantic Search in Document Review",
    author = "Vo, Ngoc Phuoc An  and
      Guillot, Fabien  and
      Privault, Caroline",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2014",
    pages = "64--68",
    abstract = "This paper presents Disco, a prototype for supporting knowledge workers in exploring, reviewing and sorting collections of textual data. The goal is to facilitate, accelerate and improve the discovery of information. To this end, it combines Semantic Relatedness techniques with a review workflow developed in a tangible environment. Disco uses a semantic model that is leveraged on-line in the course of search sessions, and accessed through natural hand-gesture, in a simple and intuitive way.",
}
@inproceedings{boudin-2016-pke,
    title = "pke: an open source python-based keyphrase extraction toolkit",
    author = "Boudin, Florian",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2015",
    pages = "69--73",
    abstract = "We describe pke, an open source python-based keyphrase extraction toolkit. It provides an end-to-end keyphrase extraction pipeline in which each component can be easily modified or extented to develop new approaches. pke also allows for easy benchmarking of state-of-the-art keyphrase extraction approaches, and ships with supervised models trained on the SemEval-2010 dataset.",
}
@inproceedings{klang-nugues-2016-langforia,
    title = "{L}angforia: Language Pipelines for Annotating Large Collections of Documents",
    author = "Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2016",
    pages = "74--78",
    abstract = "In this paper, we describe \textbf{Langforia}, a multilingual processing pipeline to annotate texts with multiple layers: formatting, parts of speech, named entities, dependencies, semantic roles, and entity links. Langforia works as a web service, where the server hosts the language processing components and the client, the input and result visualization. To annotate a text or a Wikipedia page, the user chooses an NLP pipeline and enters the text in the interface or selects the page URL. Once processed, the results are returned to the client, where the user can select the annotation layers s/he wants to visualize. We designed Langforia with a specific focus for Wikipedia, although it can process any type of text. Wikipedia has become an essential encyclopedic corpus used in many NLP projects. However, processing articles and visualizing the annotations are nontrivial tasks that require dealing with multiple markup variants, encodings issues, and tool incompatibilities across the language versions. This motivated the development of a new architecture. A demonstration of Langforia is available for six languages: English, French, German, Spanish, Russian, and Swedish at \url{http://vilde.cs.lth.se:9000/} as well as a web API: \url{http://vilde.cs.lth.se:9000/api}. Langforia is also provided as a standalone library and is compatible with cluster computing.",
}
@inproceedings{paetzold-specia-2016-anita,
    title = "{A}nita: An Intelligent Text Adaptation Tool",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2017",
    pages = "79--83",
    abstract = "We introduce Anita: a flexible and intelligent Text Adaptation tool for web content that provides Text Simplification and Text Enhancement modules. Anita{'}s simplification module features a state-of-the-art system that adapts texts according to the needs of individual users, and its enhancement module allows the user to search for a word{'}s definitions, synonyms, translations, and visual cues through related images. These utilities are brought together in an easy-to-use interface of a freely available web browser extension.",
}
@inproceedings{jatowt-bron-2016-historycomparator,
    title = "{H}istory{C}omparator: Interactive Across-Time Comparison in Document Archives",
    author = "Jatowt, Adam  and
      Bron, Marc",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2018",
    pages = "84--88",
    abstract = "Recent years have witnessed significant increase in the number of large scale digital collections of archival documents such as news articles, books, etc. Typically, users access these collections through searching or browsing. In this paper we investigate another way of accessing temporal collections - across-time comparison, i.e., comparing query-relevant information at different periods in the past. We propose an interactive framework called HistoryComparator for contrastively analyzing concepts in archival document collections at different time periods.",
}
@inproceedings{wehrli-etal-2016-line,
    title = "On-line Multilingual Linguistic Services",
    author = "Wehrli, Eric  and
      Scherrer, Yves  and
      Nerima, Luka",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2019",
    pages = "89--92",
    abstract = "In this demo, we present our free on-line multilingual linguistic services which allow to analyze sentences or to extract collocations from a corpus directly on-line, or by uploading a corpus. They are available for 8 European languages (English, French, German, Greek, Italian, Portuguese, Romanian, Spanish) and can also be accessed as web services by programs. While several open systems are available for POS-tagging and dependency parsing or terminology extraction, their integration into an application requires some computational competence. Furthermore, none of the parsers/taggers handles MWEs very satisfactorily, in particular when the two terms of the collocation are distant from each other or in reverse order. Our tools, on the other hand, are specifically designed for users with no particular computational literacy. They do not require from the user any download, installation or adaptation if used on-line, and their integration in an application, using one the scripts described below is quite easy. Furthermore, by default, the parser handles collocations and other MWEs, as well as anaphora resolution (limited to 3rd person personal pronouns). When used in the tagger mode, it can be set to display grammatical functions and collocations.",
}
@inproceedings{lee-etal-2016-customizable,
    title = "A Customizable Editor for Text Simplification",
    author = "Lee, John  and
      Zhao, Wenlong  and
      Xie, Wenxiu",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2020",
    pages = "93--97",
    abstract = "We present a browser-based editor for simplifying English text. Given an input sentence, the editor performs both syntactic and lexical simplification. It splits a complex sentence into shorter ones, and suggests word substitutions in drop-down lists. The user can choose the best substitution from the list, undo any inappropriate splitting, and further edit the sentence as necessary. A significant novelty is that the system accepts a customized vocabulary list for a target reader population. It identifies all words in the text that do not belong to the list, and attempts to substitute them with words from the list, thus producing a text tailored for the targeted readers.",
}
@inproceedings{pal-etal-2016-catalog-online,
    title = "{CAT}a{L}og Online: A Web-based {CAT} Tool for Distributed Translation with Data Capture for {APE} and Translation Process Research",
    author = "Pal, Santanu  and
      Naskar, Sudip Kumar  and
      Zampieri, Marcos  and
      Nayak, Tapas  and
      van Genabith, Josef",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2021",
    pages = "98--102",
    abstract = "We present a free web-based CAT tool called CATaLog Online which provides a novel and user-friendly online CAT environment for post-editors/translators. The goal is to support distributed translation, reduce post-editing time and effort, improve the post-editing experience and capture data for incremental MT/APE (automatic post-editing) and translation process research. The tool supports individual as well as batch mode file translation and provides translations from three engines {--} translation memory (TM), MT and APE. TM suggestions are color coded to accelerate the post-editing task. The users can integrate their personal TM/MT outputs. The tool remotely monitors and records post-editing activities generating an extensive range of post-editing logs.",
}
@inproceedings{schneider-etal-2016-interactive,
    title = "Interactive Relation Extraction in Main Memory Database Systems",
    author = {Schneider, Rudolf  and
      Guder, Cordula  and
      Kilias, Torsten  and
      L{\"o}ser, Alexander  and
      Graupmann, Jens  and
      Kozachuk, Oleksandr},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2022",
    pages = "103--106",
    abstract = "We present INDREX-MM, a main memory database system for interactively executing two interwoven tasks, declarative relation extraction from text and their exploitation with SQL. INDREX-MM simplifies these tasks for the user with powerful SQL extensions for gathering statistical semantics, for executing open information extraction and for integrating relation candidates with domain specific data. We demonstrate these functions on 800k documents from Reuters RCV1 with more than a billion linguistic annotations and report execution times in the order of seconds.",
}
@inproceedings{merlo-pasin-2016-open,
    title = "An Open Source Library for Semantic-Based Datetime Resolution",
    author = "Merlo, Aur{\'e}lie  and
      Pasin, Denis",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2023",
    pages = "107--110",
    abstract = "In this paper, we introduce an original Python implementation of datetime resolution in french, which we make available as open-source library. Our approach is based on Frame Semantics and Corpus Pattern Analysis in order to provide a precise semantic interpretation of datetime expressions. This interpretation facilitates the contextual resolution of datetime expressions in timestamp format.",
}
@inproceedings{arnold-etal-2016-tasty,
    title = "{TASTY}: Interactive Entity Linking As-You-Type",
    author = {Arnold, Sebastian  and
      Dziuba, Robert  and
      L{\"o}ser, Alexander},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2024",
    pages = "111--115",
    abstract = "We introduce TASTY (Tag-as-you-type), a novel text editor for interactive entity linking as part of the writing process. Tasty supports the author of a text with complementary information about the mentioned entities shown in a {`}live{'} exploration view. The system is automatically triggered by keystrokes, recognizes mention boundaries and disambiguates the mentioned entities to Wikipedia articles. The author can use seven operators to interact with the editor and refine the results according to his specific intention while writing. Our implementation captures syntactic and semantic context using a robust end-to-end LSTM sequence learner and word embeddings. We demonstrate the applicability of our system in English and German language for encyclopedic or medical text. Tasty is currently being tested in interactive applications for text production, such as scientific research, news editorial, medical anamnesis, help desks and product reviews.",
}
@inproceedings{wilcock-etal-2016-topic,
    title = "What topic do you want to hear about? A bilingual talking robot using {E}nglish and {J}apanese {W}ikipedias",
    author = "Wilcock, Graham  and
      Jokinen, Kristiina  and
      Yamamoto, Seiichi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2025",
    pages = "116--120",
    abstract = "We demonstrate a bilingual robot application, WikiTalk, that can talk fluently in both English and Japanese about almost any topic using information from English and Japanese Wikipedias. The English version of the system has been demonstrated previously, but we now present a live demo with a Nao robot that speaks English and Japanese and switches language on request. The robot supports the verbal interaction with face-tracking, nodding and communicative gesturing. One of the key features of the WikiTalk system is that the robot can switch from the current topic to related topics during the interaction in order to navigate around Wikipedia following the user{'}s individual interests.",
}
@inproceedings{lee-etal-2016-annotating,
    title = "Annotating Discourse Relations with the {PDTB} Annotator",
    author = "Lee, Alan  and
      Prasad, Rashmi  and
      Webber, Bonnie  and
      Joshi, Aravind K.",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2026",
    pages = "121--125",
    abstract = "The PDTB Annotator is a tool for annotating and adjudicating discourse relations based on the annotation framework of the Penn Discourse TreeBank (PDTB). This demo describes the benefits of using the PDTB Annotator, gives an overview of the PDTB Framework and discusses the tool{'}s features, setup requirements and how it can also be used for adjudication.",
}
@inproceedings{kim-etal-2016-opinion,
    title = "Opinion Retrieval Systems using Tweet-external Factors",
    author = "Kim, Yoon-Sung  and
      Song, Young-In  and
      Rim, Hae-Chang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2027",
    pages = "126--130",
    abstract = "Opinion mining is a natural language processing technique which extracts subjective information from natural language text. To estimate an opinion about a query in large data collection, an opinion retrieval system that retrieves subjective and relevant information about the query can be useful. We present an opinion retrieval system that retrieves subjective and query-relevant tweets from Twitter, which is a useful source of obtaining real-time opinions. Our system outperforms previous opinion retrieval systems, and it further provides subjective information about Twitter authors and hashtags to describe their subjective tendencies.",
}
@inproceedings{magnini-etal-2016-textpro,
    title = "{T}ext{P}ro-{AL}: An Active Learning Platform for Flexible and Efficient Production of Training Data for {NLP} Tasks",
    author = "Magnini, Bernardo  and
      Minard, Anne-Lyse  and
      Qwaider, Mohammed R. H.  and
      Speranza, Manuela",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2028",
    pages = "131--135",
    abstract = "This paper presents TextPro-AL (Active Learning for Text Processing), a platform where human annotators can efficiently work to produce high quality training data for new domains and new languages exploiting Active Learning methodologies. TextPro-AL is a web-based application integrating four components: a machine learning based NLP pipeline, an annotation editor for task definition and text annotations, an incremental re-training procedure based on active learning selection from a large pool of unannotated data, and a graphical visualization of the learning status of the system.",
}
@inproceedings{abekawa-aizawa-2016-sidenoter,
    title = "{S}ide{N}oter: Scholarly Paper Browsing System based on {PDF} Restructuring and Text Annotation",
    author = "Abekawa, Takeshi  and
      Aizawa, Akiko",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2029",
    pages = "136--140",
    abstract = "In this paper, we discuss our ongoing efforts to construct a scientific paper browsing system that helps users to read and understand advanced technical content distributed in PDF. Since PDF is a format specifically designed for printing, layout and logical structures of documents are indistinguishably embedded in the file. It requires much effort to extract natural language text from PDF files, and reversely, display semantic annotations produced by NLP tools on the original page layout. In our browsing system, we tackle these issues caused by the gap between printable document and plain text. Our system provides ways to extract natural language sentences from PDF files together with their logical structures, and also to map arbitrary textual spans to their corresponding regions on page images. We setup a demonstration system using papers published in ACL anthology and demonstrate the enhanced search and refined recommendation functions which we plan to make widely available to NLP researchers.",
}
@inproceedings{wang-etal-2016-sensing,
    title = "{S}ensing Emotions in Text Messages: An Application and Deployment Study of {E}motion{P}ush",
    author = "Wang, Shih-Ming  and
      Lee, Chun-Hui Scott  and
      Lo, Yu-Chun  and
      Huang, Ting-Hao  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2030",
    pages = "141--145",
    abstract = "Instant messaging and push notifications play important roles in modern digital life. To enable robust sense-making and rich context awareness in computer mediated communications, we introduce EmotionPush, a system that automatically conveys the emotion of received text with a colored push notification on mobile devices. EmotionPush is powered by state-of-the-art emotion classifiers and is deployed for Facebook Messenger clients on Android. The study showed that the system is able to help users prioritize interactions.",
}
@inproceedings{tsai-roth-2016-illinois,
    title = "{I}llinois Cross-Lingual Wikifier: Grounding Entities in Many Languages to the {E}nglish {W}ikipedia",
    author = "Tsai, Chen-Tse  and
      Roth, Dan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2031",
    pages = "146--150",
    abstract = "We release a cross-lingual wikification system for all languages in Wikipedia. Given a piece of text in any supported language, the system identifies names of people, locations, organizations, and grounds these names to the corresponding English Wikipedia entries. The system is based on two components: a cross-lingual named entity recognition (NER) model and a cross-lingual mention grounding model. The cross-lingual NER model is a language-independent model which can extract named entity mentions in the text of any language in Wikipedia. The extracted mentions are then grounded to the English Wikipedia using the cross-lingual mention grounding model. The only resources required to train the proposed system are the multilingual Wikipedia dump and existing training data for English NER. The system is online at \url{http://cogcomp.cs.illinois.edu/page/demo_view/xl_wikifier}",
}
@inproceedings{liang-etal-2016-meaning,
    title = "A Meaning-based {E}nglish Math Word Problem Solver with Understanding, Reasoning and Explanation",
    author = "Liang, Chao-Chun  and
      Tsai, Shih-Hong  and
      Chang, Ting-Yun  and
      Lin, Yi-Chung  and
      Su, Keh-Yih",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2032",
    pages = "151--155",
    abstract = "This paper presents a meaning-based statistical math word problem (MWP) solver with understanding, reasoning and explanation. It comprises a web user interface and pipelined modules for analysing the text, transforming both body and question parts into their logic forms, and then performing inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating the extracted math quantity with its associated syntactic and semantic information (which specifies the physical meaning of that quantity). Those role-tags are then used to identify the desired operands and filter out irrelevant quantities (so that the answer can be obtained precisely). Since the physical meaning of each quantity is explicitly represented with those role-tags and used in the inference process, the proposed approach could explain how the answer is obtained in a human comprehensible way.",
}
@inproceedings{kabbach-ribeyre-2016-valencer,
    title = "{V}alencer: an {API} to Query Valence Patterns in {F}rame{N}et",
    author = "Kabbach, Alexandre  and
      Ribeyre, Corentin",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2033",
    pages = "156--160",
    abstract = "This paper introduces Valencer: a RESTful API to search for annotated sentences matching a given combination of syntactic realizations of the arguments of a predicate {--} also called {`}valence pattern{'} {--} in the FrameNet database. The API takes as input an HTTP GET request specifying a valence pattern and outputs a list of exemplifying annotated sentences in JSON format. The API is designed to be modular and language-independent, and can therefore be easily integrated to other (NLP) server-side or client-side applications, as well as non-English FrameNet projects. Valencer is free, open-source, and licensed under the MIT license.",
}
@inproceedings{kim-etal-2016-open,
    title = "The Open Framework for Developing Knowledge Base And Question Answering System",
    author = "Kim, Jiseong  and
      Choi, GyuHyeon  and
      Kim, Jung-Uk  and
      Kim, Eun-Kyung  and
      Choi, Key-Sun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2034",
    pages = "161--165",
    abstract = "Developing a question answering (QA) system is a task of implementing and integrating modules of different technologies and evaluating an integrated whole system, which inevitably goes with a collaboration among experts of different domains. For supporting a easy collaboration, this demonstration presents the open framework that aims to support developing a QA system in collaborative and intuitive ways. The demonstration also shows the QA system developed by our novel framework.",
}
@inproceedings{chen-etal-2016-linggle,
    title = "Linggle Knows: A Search Engine Tells How People Write",
    author = "Chen, Jhih-Jie  and
      Peng, Hao-Chun  and
      Yeh, Mei-Cih  and
      Chen, Peng-Yu  and
      Chang, Jason",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2035",
    pages = "166--169",
    abstract = "This paper shows the great potential of incorporating different approaches to help writing. Not only did they solve different kinds of writing problems, but also they complement and reinforce each other to be a complete and effective solution. Despite the extensive and multifaceted feedback and suggestion, writing is not all about syntactically or lexically well-written. It involves contents, structure, the certain understanding of the background, and many other factors to compose a rich, organized and sophisticated text. (e.g., conventional structure and idioms in academic writing). There is still a long way to go to accomplish the ultimate goal. We envision the future of writing to be a joyful experience with the help of instantaneous suggestion and constructive feedback.",
}
@inproceedings{niklaus-etal-2016-sentence,
    title = "A Sentence Simplification System for Improving Relation Extraction",
    author = "Niklaus, Christina  and
      Bermeitinger, Bernhard  and
      Handschuh, Siegfried  and
      Freitas, Andr{\'e}",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2036",
    pages = "170--174",
    abstract = "We present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.",
}
@inproceedings{kim-etal-2016-korean,
    title = "{K}orean {F}rame{N}et Expansion Based on Projection of {J}apanese {F}rame{N}et",
    author = "Kim, Jeong-uk  and
      Hahm, Younggyun  and
      Choi, Key-Sun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2037",
    pages = "175--179",
    abstract = "FrameNet project has begun from Berkeley in 1997, and is now supported in several countries reflecting characteristics of each language. The work for generating Korean FrameNet was already done by converting annotated English sentences into Korean with trained translators. However, high cost of frame-preservation and error revision was a huge burden on further expansion of FrameNet. This study makes use of linguistic similarity between Japanese and Korean to increase Korean FrameNet corpus with low cost. We also suggest adapting PubAnnotation and Korean-friendly valence patterns to FrameNet for increased accessibility.",
}
@inproceedings{dasgupta-etal-2016-framework,
    title = "A Framework for Mining Enterprise Risk and Risk Factors from News Documents",
    author = "Dasgupta, Tirthankar  and
      Dey, Lipika  and
      Dey, Prasenjit  and
      Saha, Rupsa",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2038",
    pages = "180--184",
    abstract = "Any real world events or trends that can affect the company{'}s growth trajectory can be considered as risk. There has been a growing need to automatically identify, extract and analyze risk related statements from news events. In this demonstration, we will present a risk analytics framework that processes enterprise project management reports in the form of textual data and news documents and classify them into valid and invalid risk categories. The framework also extracts information from the text pertaining to the different categories of risks like their possible cause and impacts. Accordingly, we have used machine learning based techniques and studied different linguistic features like n-gram, POS, dependency, future timing, uncertainty factors in texts and their various combinations. A manual annotation study from management experts using risk descriptions collected for a specific organization was conducted to evaluate the framework. The evaluation showed promising results for automated risk analysis and identification.",
}
@inproceedings{lee-etal-2016-papago,
    title = "papago: A Machine Translation Service with Word Sense Disambiguation and Currency Conversion",
    author = "Lee, Hyoung-Gyu  and
      Kim, Jun-Seok  and
      Shin, Joong-Hwi  and
      Lee, Jaesong  and
      Quan, Ying-Xiu  and
      Jeong, Young-Seob",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2039",
    pages = "185--188",
    abstract = "In this paper, we introduce papago - a translator for mobile device which is equipped with new features that can provide convenience for users. The first feature is word sense disambiguation based on user feedback. By using the feature, users can select one among multiple meanings of a homograph and obtain the corrected translation with the user-selected sense. The second feature is the instant currency conversion of money expressions contained in a translation result with current exchange rate. Users can be quickly and precisely provided the amount of money converted as local currency when they travel abroad.",
}
@inproceedings{el-khatib-etal-2016-topotext,
    title = "{T}opo{T}ext: Interactive Digital Mapping of Literary Text",
    author = "El Khatib, Randa  and
      El Zini, Julia  and
      Wrisley, David  and
      Jaber, Mohamad  and
      Elbassuoni, Shady",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2040",
    pages = "189--193",
    abstract = "We demonstrate TopoText, an interactive tool for digital mapping of literary text. TopoText takes as input a literary piece of text such as a novel or a biography article and automatically extracts all place names in the text. The identified places are then geoparsed and displayed on an interactive map. TopoText calculates the number of times a place was mentioned in the text, which is then reflected on the map allowing the end-user to grasp the importance of the different places within the text. It also displays the most frequent words mentioned within a specified proximity of a place name in context or across the entire text. This can also be faceted according to part of speech tags. Finally, TopoText keeps the human in the loop by allowing the end-user to disambiguate places and to provide specific place annotations. All extracted information such as geolocations, place frequencies, as well as all user-provided annotations can be automatically exported as a CSV file that can be imported later by the same user or other users.",
}
@inproceedings{dong-etal-2016-ace,
    title = "{ACE}: Automatic Colloquialism, Typographical and Orthographic Errors Detection for {C}hinese Language",
    author = "Dong, Shichao  and
      Fung, Gabriel Pui Cheong  and
      Li, Binyang  and
      Peng, Baolin  and
      Liao, Ming  and
      Zhu, Jia  and
      Wong, Kam-fai",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2041",
    pages = "194--197",
    abstract = "We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese. ACE is based on the combination of N-gram model and rule-base model. Although it focuses on detecting colloquial Cantonese (a dialect of Chinese) at the current stage, it can be extended to detect other dialects. We chose Cantonese becauase it has many interesting properties, such as unique grammar system and huge colloquial terms, that turn the detection task extremely challenging. We conducted experiments using real data and synthetic data. The results indicated that ACE is highly reliable and effective.",
}
@inproceedings{galitsky-2016-tool,
    title = "A Tool for Efficient Content Compilation",
    author = "Galitsky, Boris",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2042",
    pages = "198--202",
    abstract = "We build a tool to assist in content creation by mining the web for information relevant to a given topic. This tool imitates the process of essay writing by humans: searching for topics on the web, selecting content frag-ments from the found document, and then compiling these fragments to obtain a coherent text. The process of writing starts with automated building of a table of content by obtaining the list of key entities for the given topic extracted from web resources such as Wikipedia. Once a table of content is formed, each item forms a seed for web mining. The tool builds a full-featured structured Word document with table of content, section structure, images and captions and web references for all mined text fragments. Two linguistic technologies are employed: for relevance verification, we use similarity computed as a tree similarity between parse trees for a seed and candidate text fragment. For text coherence, we use a measure of agreement between a given and consecutive paragraph by tree kernel learning of their discourse trees. The tool is available at \url{http://animatronica.io/submit.html}.",
}
@inproceedings{kim-choi-2016-mages,
    title = "{MAGES}: A Multilingual Angle-integrated Grouping-based Entity Summarization System",
    author = "Kim, Eun-kyung  and
      Choi, Key-Sun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2043",
    pages = "203--207",
    abstract = "This demo presents MAGES (multilingual angle-integrated grouping-based entity summarization), an entity summarization system for a large knowledge base such as DBpedia based on a entity-group-bound ranking in a single integrated entity space across multiple language-specific editions. MAGES offers a multilingual angle-integrated space model, which has the advantage of overcoming missing semantic tags (i.e., categories) caused by biases in different language communities, and can contribute to the creation of entity groups that are well-formed and more stable than the monolingual condition within it. MAGES can help people quickly identify the essential points of the entities when they search or browse a large volume of entity-centric data. Evaluation results on the same experimental data demonstrate that our system produces a better summary compared with other representative DBpedia entity summarization methods.",
}
@inproceedings{abu-ali-habash-2016-botta,
    title = "{B}otta: An {A}rabic Dialect Chatbot",
    author = "Abu Ali, Dana  and
      Habash, Nizar",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2044",
    pages = "208--212",
    abstract = "This paper presents BOTTA, the first Arabic dialect chatbot. We explore the challenges of creating a conversational agent that aims to simulate friendly conversations using the Egyptian Arabic dialect. We present a number of solutions and describe the different components of the BOTTA chatbot. The BOTTA database files are publicly available for researchers working on Arabic chatbot technologies. The BOTTA chatbot is also publicly available for any users who want to chat with it online.",
}
@inproceedings{litvak-etal-2016-whats,
    title = "What{'}s up on {T}witter? Catch up with {TWIST}!",
    author = "Litvak, Marina  and
      Vanetik, Natalia  and
      Levi, Efi  and
      Roistacher, Michael",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2045",
    pages = "213--217",
    abstract = "Event detection and analysis with respect to public opinions and sentiments in social media is a broad and well-addressed research topic. However, the characteristics and sheer volume of noisy Twitter messages make this a difficult task. This demonstration paper describes a TWItter event Summarizer and Trend detector (TWIST) system for event detection, visualization, textual description, and geo-sentiment analysis of real-life events reported in Twitter.",
}
@inproceedings{dominguez-etal-2016-praat,
    title = "{P}raat on the Web: An Upgrade of {P}raat for Semi-Automatic Speech Annotation",
    author = "Dom{\'\i}nguez, M{\'o}nica  and
      Latorre, Iv{\'a}n  and
      Farr{\'u}s, Mireia  and
      Codina-Filb{\`a}, Joan  and
      Wanner, Leo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2046",
    pages = "218--222",
    abstract = "This paper presents an implementation of the widely used speech analysis tool Praat as a web application with an extended functionality for feature annotation. In particular, Praat on the Web addresses some of the central limitations of the original Praat tool and provides (i) enhanced visualization of annotations in a dedicated window for feature annotation at interval and point segments, (ii) a dynamic scripting composition exemplified with a modular prosody tagger, and (iii) portability and an operational web interface. Speech annotation tools with such a functionality are key for exploring large corpora and designing modular pipelines.",
}
@inproceedings{khalifa-etal-2016-yamama,
    title = "{YAMAMA}: Yet Another Multi-Dialect {A}rabic Morphological Analyzer",
    author = "Khalifa, Salam  and
      Zalmout, Nasser  and
      Habash, Nizar",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2047",
    pages = "223--227",
    abstract = "In this paper, we present YAMAMA, a multi-dialect Arabic morphological analyzer and disambiguator. Our system is almost five times faster than the state-of-art MADAMIRA system with a slightly lower quality. In addition to speed, YAMAMA outputs a rich representation which allows for a wider spectrum of use. In this regard, YAMAMA transcends other systems, such as FARASA, which is faster but provides specific outputs catering to specific applications.",
}
@inproceedings{shahrour-etal-2016-camelparser,
    title = "{C}amel{P}arser: A system for {A}rabic Syntactic Analysis and Morphological Disambiguation",
    author = "Shahrour, Anas  and
      Khalifa, Salam  and
      Taji, Dima  and
      Habash, Nizar",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2048",
    pages = "228--232",
    abstract = "In this paper, we present CamelParser, a state-of-the-art system for Arabic syntactic dependency analysis aligned with contextually disambiguated morphological features. CamelParser uses a state-of-the-art morphological disambiguator and improves its results using syntactically driven features. The system offers a number of output formats that include basic dependency with morphological features, two tree visualization modes, and traditional Arabic grammatical analysis.",
}
@inproceedings{milde-etal-2016-demonstrating,
    title = "Demonstrating Ambient Search: Implicit Document Retrieval for Speech Streams",
    author = {Milde, Benjamin  and
      Wacker, Jonas  and
      Radomski, Stefan  and
      M{\"u}hlh{\"a}user, Max  and
      Biemann, Chris},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2049",
    pages = "233--237",
    abstract = "In this demonstration paper we describe Ambient Search, a system that displays and retrieves documents in real time based on speech input. The system operates continuously in ambient mode, i.e. it generates speech transcriptions and identifies main keywords and keyphrases, while also querying its index to display relevant documents without explicit query. Without user intervention, the results are dynamically updated; users can choose to interact with the system at any time, employing a conversation protocol that is enriched with the ambient information gathered continuously. Our evaluation shows that Ambient Search outperforms another implicit speech-based information retrieval system. Ambient search is available as open source software.",
}
@inproceedings{mediankin-2016-confarm,
    title = "{C}on{F}arm: Extracting Surface Representations of Verb and Noun Constructions from Dependency Annotated Corpora of {R}ussian",
    author = "Mediankin, Nikita",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2050",
    pages = "238--242",
    abstract = "ConFarm is a web service dedicated to extraction of surface representations of verb and noun constructions from dependency annotated corpora of Russian texts. Currently, the extraction of constructions with a specific lemma from SynTagRus and Russian National Corpus is available. The system provides flexible interface that allows users to fine-tune the output. Extracted constructions are grouped by their contents to allow for compact representation, and the groups are visualised as a graph in order to help navigating the extraction results. ConFarm differs from similar existing tools for Russian language in that it offers full constructions, as opposed to extracting separate dependents of search word or working with collocations, and allows users to discover unexpected constructions as opposed to searching for examples of a user-defined construction.",
}
@inproceedings{jaworski-kozakoszczak-2016-eniam,
    title = "{ENIAM}: Categorial Syntactic-Semantic Parser for {P}olish",
    author = "Jaworski, Wojciech  and
      Kozakoszczak, Jakub",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2051",
    pages = "243--247",
    abstract = "This paper presents ENIAM, the first syntactic and semantic parser that generates semantic representations for sentences in Polish. The parser processes non-annotated data and performs tokenization, lemmatization, dependency recognition, word sense annotation, thematic role annotation, partial disambiguation and computes the semantic representation.",
}
@inproceedings{fang-etal-2016-towards,
    title = "Towards Non-projective High-Order Dependency Parser",
    author = "Fang, Wenjing  and
      Zhu, Kenny  and
      Wang, Yizhong  and
      Tan, Jia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2052",
    pages = "248--252",
    abstract = "This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks. It imitates how a human parses sentences in an intuitive way. At every step of the parse, it determines which word is the easiest to process among all the remaining words, identifies its head word and then folds it under the head word. Further, this work is flexible enough to be augmented with other parsing techniques.",
}
@inproceedings{ogata-etal-2016-using,
    title = "Using Synthetically Collected Scripts for Story Generation",
    author = "Ogata, Takashi  and
      Arai, Tatsuya  and
      Ono, Jumpei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2053",
    pages = "253--257",
    abstract = "A script is a type of knowledge representation in artificial intelligence (AI). This paper presents two methods for synthetically using collected scripts for story generation. The first method recursively generates long sequences of events and the second creates script networks. Although related studies generally use one or more scripts for story generation, this research synthetically uses many scripts to flexibly generate a diverse narrative.",
}
@inproceedings{polsley-etal-2016-casesummarizer,
    title = "{C}ase{S}ummarizer: A System for Automated Summarization of Legal Texts",
    author = "Polsley, Seth  and
      Jhunjhunwala, Pooja  and
      Huang, Ruihong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2054",
    pages = "258--262",
    abstract = "Attorneys, judges, and others in the justice system are constantly surrounded by large amounts of legal text, which can be difficult to manage across many cases. We present CaseSummarizer, a tool for automated text summarization of legal documents which uses standard summary methods based on word frequency augmented with additional domain-specific knowledge. Summaries are then provided through an informative interface with abbreviations, significance heat maps, and other flexible controls. It is evaluated using ROUGE and human scoring against several other summarization systems, including summary text and feedback provided by domain experts.",
}
@inproceedings{mizuno-etal-2016-wisdom,
    title = "{WISDOM} {X}, {DISAANA} and {D}-{SUMM}: Large-scale {NLP} Systems for Analyzing Textual Big Data",
    author = "Mizuno, Junta  and
      Tanaka, Masahiro  and
      Ohtake, Kiyonori  and
      Oh, Jong-Hoon  and
      Kloetzer, Julien  and
      Hashimoto, Chikara  and
      Torisawa, Kentaro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2055",
    pages = "263--267",
    abstract = "We demonstrate our large-scale NLP systems: WISDOM X, DISAANA, and D-SUMM. WISDOM X provides numerous possible answers including unpredictable ones to widely diverse natural language questions to provide deep insights about a broad range of issues. DISAANA and D-SUMM enable us to assess the damage caused by large-scale disasters in real time using Twitter as an information source.",
}
@inproceedings{akbik-etal-2016-multilingual-information,
    title = "Multilingual Information Extraction with {P}olyglot{IE}",
    author = "Akbik, Alan  and
      Chiticariu, Laura  and
      Danilevsky, Marina  and
      Kbrom, Yonas  and
      Li, Yunyao  and
      Zhu, Huaiyu",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2056",
    pages = "268--272",
    abstract = "We present PolyglotIE, a web-based tool for developing extractors that perform Information Extraction (IE) over multilingual data. Our tool has two core features: First, it allows users to develop extractors against a unified abstraction that is shared across a large set of natural languages. This means that an extractor needs only be created once for one language, but will then run on multilingual data without any additional effort or language-specific knowledge on part of the user. Second, it embeds this abstraction as a set of views within a declarative IE system, allowing users to quickly create extractors using a mature IE query language. We present PolyglotIE as a hands-on demo in which users can experiment with creating extractors, execute them on multilingual text and inspect extraction results. Using the UI, we discuss the challenges and potential of using unified, crosslingual semantic abstractions as basis for downstream applications. We demonstrate multilingual IE for 9 languages from 4 different language groups: English, German, French, Spanish, Japanese, Chinese, Arabic, Russian and Hindi.",
}
@inproceedings{chen-etal-2016-wordforce,
    title = "{W}ord{F}orce: Visualizing Controversial Words in Debates",
    author = "Chen, Wei-Fan  and
      Lin, Fang-Yu  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2057",
    pages = "273--277",
    abstract = "This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users. It generates the scatter plots to show the force of a word, i.e., whether the semantics of word embeddings from posts of different stances are clearly separated from the aspect of this controversial word. In addition, WordForce provides the dispersion and the distance of word embeddings from posts of different stance groups, and proposes the most controversial words accordingly to show clues to what people argue about in a debate.",
}
@inproceedings{fung-etal-2016-zara-virtual,
    title = "{Z}ara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition",
    author = "Fung, Pascale  and
      Dey, Anik  and
      Siddique, Farhad Bin  and
      Lin, Ruixi  and
      Yang, Yang  and
      Bertero, Dario  and
      Wan, Yan  and
      Chan, Ricky Ho Yin  and
      Wu, Chien-Sheng",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2058",
    pages = "278--281",
    abstract = "Zara, or {`}Zara the Supergirl{'} is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module. At the end of the 5-10 minute conversation, Zara can give a personality analysis of the user based on all the user utterances. We have also implemented a real-time emotion recognition, using a CNN model that detects emotion from raw audio without feature extraction, and have achieved an average of 65.7{\%} accuracy on six different emotion classes, which is an impressive 4.5{\%} improvement from the conventional feature based SVM classification. Also, we have described a CNN based sentiment analysis module trained using out-of-domain data, that recognizes sentiment from the speech recognition transcript, which has a 74.8 F-measure when tested on human-machine dialogues.",
}
@inproceedings{wei-etal-2016-nl2kb,
    title = "{NL}2{KB}: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval",
    author = "Wei, Sheng-Lun  and
      Chiu, Yen-Pin  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2059",
    pages = "282--286",
    abstract = "Words to express relations in natural language (NL) statements may be different from those to represent properties in knowledge bases (KB). The vocabulary gap becomes barriers for knowledge base construction and retrieval. With the demo system called NL2KB in this paper, users can browse which properties in KB side may be mapped to for a given relational pattern in NL side. Besides, they can retrieve the sets of relational patterns in NL side for a given property in KB side. We describe how the mapping is established in detail. Although the mined patterns are used for Chinese knowledge base applications, the methodology can be extended to other languages.",
}
@inproceedings{zhang-etal-2016-pkusumsum,
    title = "{PKUSUMSUM} : A {J}ava Platform for Multilingual Document Summarization",
    author = "Zhang, Jianmin  and
      Wang, Tianming  and
      Wan, Xiaojun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2060",
    pages = "287--291",
    abstract = "PKUSUMSUM is a Java platform for multilingual document summarization, and it sup-ports multiple languages, integrates 10 automatic summarization methods, and tackles three typical summarization tasks. The summarization platform has been released and users can easily use and update it. In this paper, we make a brief description of the char-acteristics, the summarization methods, and the evaluation results of the platform, and al-so compare PKUSUMSUM with other summarization toolkits.",
}
@inproceedings{iwanari-etal-2016-kotonush,
    title = "{K}otonush: Understanding Concepts Based on Values behind Social Media",
    author = "Iwanari, Tatsuya  and
      Ohara, Kohei  and
      Yoshinaga, Naoki  and
      Kaji, Nobuhiro  and
      Toyoda, Masashi  and
      Kitsuregawa, Masaru",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2061",
    pages = "292--296",
    abstract = "Kotonush, a system that clarifies people{'}s values on various concepts on the basis of what they write about on social media, is presented. The values are represented by ordering sets of concepts (e.g., London, Berlin, and Rome) in accordance with a common attribute intensity expressed by an adjective (e.g., entertaining). We exploit social media text written by different demographics and at different times in order to induce specific orderings for comparison. The system combines a text-to-ordering module with an interactive querying interface enabled by massive hyponymy relations and provides mechanisms to compare the induced orderings from various viewpoints. We empirically evaluate Kotonush and present some case studies, featuring real-world concept orderings with different domains on Twitter, to demonstrate the usefulness of our system.",
}
@inproceedings{marchal-poibeau-2016-exploring,
    title = "Exploring a Continuous and Flexible Representation of the Lexicon",
    author = "Marchal, Pierre  and
      Poibeau, Thierry",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2062",
    pages = "297--301",
    abstract = "We aim at showing that lexical descriptions based on multifactorial and continuous models can be used by linguists and lexicographers (and not only by machines) so long as they are provided with a way to efficiently navigate data collections. We propose to demonstrate such a system.",
}
@inproceedings{huang-etal-2016-automatically,
    title = "Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners",
    author = "Huang, Chieh-Yang  and
      Peinelt, Nicole  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2063",
    pages = "302--306",
    abstract = "In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners. The difficulty of the example sentences is automatically detected. Furthermore, the usage models of the near-synonyms are built by the GMM and Bi-LSTM models to suggest the best elaborative sentences. Experiments show the good performance both in the fill-in-the-blank test and on the manually labeled gold data, that is, the built models can select the appropriate words for the given context and vice versa.",
}
@inproceedings{cromieres-2016-kyoto,
    title = "{K}yoto-{NMT}: a Neural Machine Translation implementation in Chainer",
    author = "Cromi{\`e}res, Fabien",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-2064",
    pages = "307--311",
    abstract = "We present Kyoto-NMT, an open-source implementation of the Neural Machine Translation paradigm. This implementation is done in Python and Chainer, an easy-to-use Deep Learning Framework.",
}
@proceedings{coling-2016-coling,
    title = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    editor = "Matsumoto, Yuji  and
      Prasad, Rashmi",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1000",
}
@inproceedings{bawden-crabbe-2016-boosting,
    title = "Boosting for Efficient Model Selection for Syntactic Parsing",
    author = "Bawden, Rachel  and
      Crabb{\'e}, Beno{\^\i}t",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1001",
    pages = "1--11",
    abstract = "We present an efficient model selection method using boosting for transition-based constituency parsing. It is designed for exploring a high-dimensional search space, defined by a large set of feature templates, as for example is typically the case when parsing morphologically rich languages. Our method removes the need to manually define heuristic constraints, which are often imposed in current state-of-the-art selection methods. Our experiments for French show that the method is more efficient and is also capable of producing compact, state-of-the-art models.",
}
@inproceedings{guo-etal-2016-universal,
    title = "A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks",
    author = "Guo, Jiang  and
      Che, Wanxiang  and
      Wang, Haifeng  and
      Liu, Ting",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1002",
    pages = "12--22",
    abstract = "Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain common syntactic knowledge that is potential to benefit each other. This paper presents a universal framework for transfer parsing across multi-typed treebanks with deep multi-task learning. We consider two kinds of treebanks as source: the multilingual universal treebanks and the monolingual heterogeneous treebanks. Knowledge across the source and target treebanks are effectively transferred through multi-level parameter sharing. Experiments on several benchmark datasets in various languages demonstrate that our approach can make effective use of arbitrary source treebanks to improve target parsing models.",
}
@inproceedings{pate-johnson-2016-grammar,
    title = "Grammar induction from (lots of) words alone",
    author = "Pate, John K  and
      Johnson, Mark",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1003",
    pages = "23--32",
    abstract = "Grammar induction is the task of learning syntactic structure in a setting where that structure is hidden. Grammar induction from words alone is interesting because it is similiar to the problem that a child learning a language faces. Previous work has typically assumed richer but cognitively implausible input, such as POS tag annotated data, which makes that work less relevant to human language acquisition. We show that grammar induction from words alone is in fact feasible when the model is provided with sufficient training data, and present two new streaming or mini-batch algorithms for PCFG inference that can learn from millions of words of training data. We compare the performance of these algorithms to a batch algorithm that learns from less data. The minibatch algorithms outperform the batch algorithm, showing that cheap inference with more data is better than intensive inference with less data. Additionally, we show that the harmonic initialiser, which previous work identified as essential when learning from small POS-tag annotated corpora (Klein and Manning, 2004), is not superior to a uniform initialisation.",
}
@inproceedings{ren-etal-2016-redundancy,
    title = "A Redundancy-Aware Sentence Regression Framework for Extractive Summarization",
    author = "Ren, Pengjie  and
      Wei, Furu  and
      Chen, Zhumin  and
      Ma, Jun  and
      Zhou, Ming",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1004",
    pages = "33--43",
    abstract = "Existing sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes. They first evaluate the importance f(s) of each sentence s and then select sentences to generate a summary based on both the importance scores and redundancy among sentences. In this paper, we propose to model importance and redundancy simultaneously by directly evaluating the relative importance f(s|S) of a sentence s given a set of selected sentences S. Specifically, we present a new framework to conduct regression with respect to the relative gain of s given S calculated by the ROUGE metric. Besides the single sentence features, additional features derived from the sentence relations are incorporated. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that the proposed method outperforms state-of-the-art extractive summarization approaches.",
}
@inproceedings{laokulrat-etal-2016-generating,
    title = "Generating Video Description using Sequence-to-sequence Model with Temporal Attention",
    author = "Laokulrat, Natsuda  and
      Phan, Sang  and
      Nishida, Noriki  and
      Shu, Raphael  and
      Ehara, Yo  and
      Okazaki, Naoaki  and
      Miyao, Yusuke  and
      Nakayama, Hideki",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1005",
    pages = "44--52",
    abstract = "Automatic video description generation has recently been getting attention after rapid advancement in image caption generation. Automatically generating description for a video is more challenging than for an image due to its temporal dynamics of frames. Most of the work relied on Recurrent Neural Network (RNN) and recently attentional mechanisms have also been applied to make the model learn to focus on some frames of the video while generating each word in a describing sentence. In this paper, we focus on a sequence-to-sequence approach with temporal attention mechanism. We analyze and compare the results from different attention model configuration. By applying the temporal attention mechanism to the system, we can achieve a METEOR score of 0.310 on Microsoft Video Description dataset, which outperformed the state-of-the-art system so far.",
}
@inproceedings{luo-etal-2016-improved,
    title = "An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses",
    author = "Luo, Wencan  and
      Liu, Fei  and
      Litman, Diane",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1006",
    pages = "53--63",
    abstract = "Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner. Automatic text summarization systems can be leveraged to summarize the student feedback, submitted immediately after each lecture, but it is left to be discovered what makes a good summary for student responses. In this work we explore a new methodology that effectively extracts summary phrases from the student responses. Each phrase is tagged with the number of students who raise the issue. The phrases are evaluated along two dimensions: with respect to text content, they should be informative and well-formed, measured by the ROUGE metric; additionally, they shall attend to the most pressing student needs, measured by a newly proposed metric. This work is enabled by a phrase-based annotation and highlighting scheme, which is new to the summarization task. The phrase-based framework allows us to summarize the student responses into a set of bullet points and present to the instructor promptly.",
}
@inproceedings{mirza-tonelli-2016-catena,
    title = "{CATENA}: {CA}usal and {TE}mporal relation extraction from {NA}tural language texts",
    author = "Mirza, Paramita  and
      Tonelli, Sara",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1007",
    pages = "64--75",
    abstract = "We present CATENA, a sieve-based system to perform temporal and causal relation extraction and classification from English texts, exploiting the interaction between the temporal and the causal model. We evaluate the performance of each sieve, showing that the rule-based, the machine-learned and the reasoning components all contribute to achieving state-of-the-art performance on TempEval-3 and TimeBank-Dense data. Although causal relations are much sparser than temporal ones, the architecture and the selected features are mostly suitable to serve both tasks. The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.",
}
@inproceedings{iso-etal-2016-forecasting,
    title = "Forecasting Word Model: {T}witter-based Influenza Surveillance and Prediction",
    author = "Iso, Hayate  and
      Wakamiya, Shoko  and
      Aramaki, Eiji",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1008",
    pages = "76--86",
    abstract = "Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events. Particularly, social media-based infectious disease surveillance has attracted increasing attention. In this work, we specifically examine influenza: a common topic of communication on social media. The fundamental theory of this work is that several words, such as symptom words (fever, headache, etc.), appear in advance of flu epidemic occurrence. Consequently, past word occurrence can contribute to estimation of the number of current patients. To employ such forecasting words, one can first estimate the optimal time lag for each word based on their cross correlation. Then one can build a linear model consisting of word frequencies at different time points for nowcasting and for forecasting influenza epidemics. Experimentally obtained results (using 7.7 million tweets of August 2012 {--} January 2016), the proposed model achieved the best nowcasting performance to date (correlation ratio 0.93) and practically sufficient forecasting performance (correlation ratio 0.91 in 1-week future prediction, and correlation ratio 0.77 in 3-weeks future prediction). This report is the first of the relevant literature to describe a model enabling prediction of future epidemics using Twitter.",
}
@inproceedings{reimers-etal-2016-task,
    title = "Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity",
    author = "Reimers, Nils  and
      Beyer, Philip  and
      Gurevych, Iryna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1009",
    pages = "87--96",
    abstract = "Semantic Textual Similarity (STS) is a foundational NLP task and can be used in a wide range of tasks. To determine the STS of two texts, hundreds of different STS systems exist, however, for an NLP system designer, it is hard to decide which system is the best one. To answer this question, an intrinsic evaluation of the STS systems is conducted by comparing the output of the system to human judgments on semantic similarity. The comparison is usually done using Pearson correlation. In this work, we show that relying on intrinsic evaluations with Pearson correlation can be misleading. In three common STS based tasks we could observe that the Pearson correlation was especially ill-suited to detect the best STS system for the task and other evaluation measures were much better suited. In this work we define how the validity of an intrinsic evaluation can be assessed and compare different intrinsic evaluation methods. Understanding of the properties of the targeted task is crucial and we propose a framework for conducting the intrinsic evaluation which takes the properties of the targeted task into account.",
}
@inproceedings{arcan-etal-2016-expanding,
    title = "Expanding wordnets to new languages with multilingual sense disambiguation",
    author = "Arcan, Mihael  and
      McCrae, John Philip  and
      Buitelaar, Paul",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1010",
    pages = "97--108",
    abstract = "Princeton WordNet is one of the most important resources for natural language processing, but is only available for English. While it has been translated using the expand approach to many other languages, this is an expensive manual process. Therefore it would be beneficial to have a high-quality automatic translation approach that would support NLP techniques, which rely on WordNet in new languages. The translation of wordnets is fundamentally complex because of the need to translate all senses of a word including low frequency senses, which is very challenging for current machine translation approaches. For this reason we leverage existing translations of WordNet in other languages to identify contextual information for wordnet senses from a large set of generic parallel corpora. We evaluate our approach using 10 translated wordnets for European languages. Our experiment shows a significant improvement over translation without any contextual information. Furthermore, we evaluate how the choice of pivot languages affects performance of multilingual word sense disambiguation.",
}
@inproceedings{saha-etal-2016-correlational,
    title = "A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation",
    author = "Saha, Amrita  and
      Khapra, Mitesh M.  and
      Chandar, Sarath  and
      Rajendran, Janarthanan  and
      Cho, Kyunghyun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1011",
    pages = "109--118",
    abstract = "Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X. However, there is no parallel training data available between X and Y but, training data is available between X {\&} Z and Z {\&} Y (as is often the case in many real world applications). Z thus acts as a pivot/bridge. An obvious solution, which is perhaps less elegant but works very well in practice is to train a two stage model which first converts from X to Z and then from Z to Y. Instead we explore an interlingua inspired solution which jointly learns to do the following (i) encode X and Z to a common representation and (ii) decode Y from this common representation. We evaluate our model on two tasks: (i) bridge transliteration and (ii) bridge captioning. We report promising results in both these applications and believe that this is a right step towards truly interlingua inspired encoder decoder architectures.",
}
@inproceedings{aufrant-etal-2016-zero,
    title = "Zero-resource Dependency Parsing: Boosting Delexicalized Cross-lingual Transfer with Linguistic Knowledge",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1012",
    pages = "119--130",
    abstract = "This paper studies cross-lingual transfer for dependency parsing, focusing on very low-resource settings where delexicalized transfer is the only fully automatic option. We show how to boost parsing performance by rewriting the source sentences so as to better match the linguistic regularities of the target language. We contrast a data-driven approach with an approach relying on linguistically motivated rules automatically extracted from the World Atlas of Language Structures. Our findings are backed up by experiments involving 40 languages. They show that both approaches greatly outperform the baseline, the knowledge-driven method yielding the best accuracies, with average improvements of +2.9 UAS, and up to +90 UAS (absolute) on some frequent PoS configurations.",
}
@inproceedings{bollmann-sogaard-2016-improving,
    title = "Improving historical spelling normalization with bi-directional {LSTM}s and multi-task learning",
    author = "Bollmann, Marcel  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1013",
    pages = "131--139",
    abstract = "Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to modern forms. We explore the suitability of a deep neural network architecture for this task, particularly a deep bi-LSTM network applied on a character level. Our model compares well to previously established normalization algorithms when evaluated on a diverse set of texts from Early New High German. We show that multi-task learning with additional normalization data can improve our model{'}s performance further.",
}
@inproceedings{ren-zhang-2016-deceptive,
    title = "Deceptive Opinion Spam Detection Using Neural Network",
    author = "Ren, Yafeng  and
      Zhang, Yue",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1014",
    pages = "140--150",
    abstract = "Deceptive opinion spam detection has attracted significant attention from both business and research communities. Existing approaches are based on manual discrete features, which can capture linguistic and psychological cues. However, such features fail to encode the semantic meaning of a document from the discourse perspective, which limits the performance. In this paper, we empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam. In particular, given a document, the model learns sentence representations with a convolutional neural network, which are combined using a gated recurrent neural network with attention mechanism to model discourse information and yield a document vector. Finally, the document representation is used directly as features to identify deceptive opinion spam. Experimental results on three domains (Hotel, Restaurant, and Doctor) show that our proposed method outperforms state-of-the-art methods.",
}
@inproceedings{li-etal-2016-integrating,
    title = "Integrating Topic Modeling with Word Embeddings by Mixtures of v{MF}s",
    author = "Li, Ximing  and
      Chi, Jinjin  and
      Li, Changchun  and
      Ouyang, Jihong  and
      Fu, Bo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1015",
    pages = "151--160",
    abstract = "Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space. This can take semantic information of words into account. However, the Euclidean similarity used in Gaussian topics is not an optimal semantic measure for word embeddings. Acknowledgedly, the cosine similarity better describes the semantic relatedness between word embeddings. To employ the cosine measure and capture complex topic structure, we use von Mises-Fisher (vMF) mixture models to represent topics, and then develop a novel mix-vMF topic model (MvTM). Using public pre-trained word embeddings, we evaluate MvTM on three real-world data sets. Experimental results show that our model can discover more coherent topics than the state-of-the-art baseline models, and achieve competitive classification performance.",
}
@inproceedings{takeda-komatani-2016-bayesian,
    title = "{B}ayesian Language Model based on Mixture of Segmental Contexts for Spontaneous Utterances with Unexpected Words",
    author = "Takeda, Ryu  and
      Komatani, Kazunori",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1016",
    pages = "161--170",
    abstract = "This paper describes a Bayesian language model for predicting spontaneous utterances. People sometimes say unexpected words, such as fillers or hesitations, that cause the miss-prediction of words in normal N-gram models. Our proposed model considers mixtures of possible segmental contexts, that is, a kind of context-word selection. It can reduce negative effects caused by unexpected words because it represents conditional occurrence probabilities of a word as weighted mixtures of possible segmental contexts. The tuning of mixture weights is the key issue in this approach as the segment patterns becomes numerous, thus we resolve it by using Bayesian model. The generative process is achieved by combining the stick-breaking process and the process used in the variable order Pitman-Yor language model. Experimental evaluations revealed that our model outperformed contiguous N-gram models in terms of perplexity for noisy text including hesitations.",
}
@inproceedings{ma-etal-2016-label,
    title = "Label Embedding for Zero-shot Fine-grained Named Entity Typing",
    author = "Ma, Yukun  and
      Cambria, Erik  and
      Gao, Sa",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1017",
    pages = "171--180",
    abstract = "Named entity typing is the task of detecting the types of a named entity in context. For instance, given {``}Eric is giving a presentation{''}, our goal is to infer that {`}Eric{'} is a speaker or a presenter and a person. Existing approaches to named entity typing cannot work with a growing type set and fails to recognize entity mentions of unseen types. In this paper, we present a label embedding method that incorporates prototypical and hierarchical information to learn pre-trained label embeddings. In addition, we adapt a zero-shot learning framework that can predict both seen and previously unseen entity types. We perform evaluation on three benchmark datasets with two settings: 1) few-shots recognition where all types are covered by the training set; and 2) zero-shot recognition where fine-grained types are assumed absent from training set. Results show that prior knowledge encoded using our label embedding methods can significantly boost the performance of classification for both cases.",
}
@inproceedings{shen-etal-2016-role,
    title = "The Role of Context in Neural Morphological Disambiguation",
    author = "Shen, Qinlan  and
      Clothiaux, Daniel  and
      Tagtow, Emily  and
      Littell, Patrick  and
      Dyer, Chris",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1018",
    pages = "181--191",
    abstract = "Languages with rich morphology often introduce sparsity in language processing tasks. While morphological analyzers can reduce this sparsity by providing morpheme-level analyses for words, they will often introduce ambiguity by returning multiple analyses for the same surface form. The problem of disambiguating between these morphological parses is further complicated by the fact that a correct parse for a word is not only be dependent on the surface form but also on other words in its context. In this paper, we present a language-agnostic approach to morphological disambiguation. We address the problem of using context in morphological disambiguation by presenting several LSTM-based neural architectures that encode long-range surface-level and analysis-level contextual dependencies. We applied our approach to Turkish, Russian, and Arabic to compare effectiveness across languages, matching state-of-the-art results in two of the three languages. Our results also demonstrate that while context plays a role in learning how to disambiguate, the type and amount of context needed varies between languages.",
}
@inproceedings{sun-2016-asynchronous,
    title = "Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features",
    author = "Sun, Xu",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1019",
    pages = "192--202",
    abstract = "Existing asynchronous parallel learning methods are only for the sparse feature models, and they face new challenges for the dense feature models like neural networks (e.g., LSTM, RNN). The problem for dense features is that asynchronous parallel learning brings gradient errors derived from overwrite actions. We show that gradient errors are very common and inevitable. Nevertheless, our theoretical analysis shows that the learning process with gradient errors can still be convergent towards the optimum of objective functions for many practical applications. Thus, we propose a simple method \textit{AsynGrad} for asynchronous parallel learning with gradient error. Base on various dense feature models (LSTM, dense-CRF) and various NLP tasks, experiments show that \textit{AsynGrad} achieves substantial improvement on training speed, and without any loss on accuracy.",
}
@inproceedings{wu-etal-2016-empirical,
    title = "An Empirical Exploration of Skip Connections for Sequential Tagging",
    author = "Wu, Huijia  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1020",
    pages = "203--212",
    abstract = "In this paper, we empirically explore the effects of various kinds of skip connections in stacked bidirectional LSTMs for sequential tagging. We investigate three kinds of skip connections connecting to LSTM cells: (a) skip connections to the gates, (b) skip connections to the internal states and (c) skip connections to the cell outputs. We present comprehensive experiments showing that skip connections to cell outputs outperform the remaining two. Furthermore, we observe that using gated identity functions as skip mappings works pretty well. Based on this novel skip connections, we successfully train deep stacked bidirectional LSTM models and obtain state-of-the-art results on CCG supertagging and comparable results on POS tagging.",
}
@inproceedings{wang-etal-2016-exploring,
    title = "Exploring Text Links for Coherent Multi-Document Summarization",
    author = "Wang, Xun  and
      Nishino, Masaaki  and
      Hirao, Tsutomu  and
      Sudoh, Katsuhito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1021",
    pages = "213--223",
    abstract = "Summarization aims to represent source documents by a shortened passage. Existing methods focus on the extraction of key information, but often neglect coherence. Hence the generated summaries suffer from a lack of readability. To address this problem, we have developed a graph-based method by exploring the links between text to produce coherent summaries. Our approach involves finding a sequence of sentences that best represent the key information in a coherent way. In contrast to the previous methods that focus only on salience, the proposed method addresses both coherence and informativeness based on textual linkages. We conduct experiments on the DUC2004 summarization task data set. A performance comparison reveals that the summaries generated by the proposed system achieve comparable results in terms of the ROUGE metric, and show improvements in readability by human evaluation.",
}
@inproceedings{mcmahan-stone-2016-syntactic,
    title = "Syntactic realization with data-driven neural tree grammars",
    author = "McMahan, Brian  and
      Stone, Matthew",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1022",
    pages = "224--235",
    abstract = "A key component in surface realization in natural language generation is to choose concrete syntactic relationships to express a target meaning. We develop a new method for syntactic choice based on learning a stochastic tree grammar in a neural architecture. This framework can exploit state-of-the-art methods for modeling word sequences and generalizing across vocabulary. We also induce embeddings to generalize over elementary tree structures and exploit a tree recurrence over the input structure to model long-distance influences between NLG choices. We evaluate the models on the task of linearizing unannotated dependency trees, documenting the contribution of our modeling techniques to improvements in both accuracy and run time.",
}
@inproceedings{li-etal-2016-abstractive,
    title = "Abstractive News Summarization based on Event Semantic Link Network",
    author = "Li, Wei  and
      He, Lei  and
      Zhuge, Hai",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1023",
    pages = "236--246",
    abstract = "This paper studies the abstractive multi-document summarization for event-oriented news texts through event information extraction and abstract representation. Fine-grained event mentions and semantic relations between them are extracted to build a unified and connected event semantic link network, an abstract representation of source texts. A network reduction algorithm is proposed to summarize the most salient and coherent event information. New sentences with good linguistic quality are automatically generated and selected through sentences over-generation and greedy-selection processes. Experimental results on DUC 2006 and DUC 2007 datasets show that our system significantly outperforms the state-of-the-art extractive and abstractive baselines under both pyramid and ROUGE evaluation metrics.",
}
@inproceedings{peyrard-eckle-kohler-2016-general,
    title = "A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence",
    author = "Peyrard, Maxime  and
      Eckle-Kohler, Judith",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1024",
    pages = "247--257",
    abstract = "Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers {--} one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.",
}
@inproceedings{rojas-barahona-etal-2016-exploiting,
    title = "Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding",
    author = "Rojas-Barahona, Lina M.  and
      Ga{\v{s}}i{\'c}, Milica  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Su, Pei-Hao  and
      Ultes, Stefan  and
      Wen, Tsung-Hsien  and
      Young, Steve",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1025",
    pages = "258--267",
    abstract = "This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic annotations as in sequence taggers and (ii) delexicalisation, or a mapping of input words to domain-specific concepts using heuristics that try to capture morphological variation but that do not scale to other domains nor to language variation (e.g., morphology, synonyms, paraphrasing ). In this work the semantic decoder is trained using unaligned semantic annotations and it uses distributed semantic representation learning to overcome the limitations of explicit delexicalisation. The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the context representation. Results are presented for the publicly available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a significantly higher word error rate (WER).",
}
@inproceedings{kohn-baumann-2016-predictive,
    title = "Predictive Incremental Parsing Helps Language Modeling",
    author = {K{\"o}hn, Arne  and
      Baumann, Timo},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1026",
    pages = "268--277",
    abstract = "Predictive incremental parsing produces syntactic representations of sentences as they are produced, e.g. by typing or speaking. In order to generate connected parses for such unfinished sentences, upcoming word types can be hypothesized and structurally integrated with already realized words. For example, the presence of a determiner as the last word of a sentence prefix may indicate that a noun will appear somewhere in the completion of that sentence, and the determiner can be attached to the predicted noun. We combine the forward-looking parser predictions with backward-looking N-gram histories and analyze in a set of experiments the impact on language models, i.e. stronger discriminative power but also higher data sparsity. Conditioning N-gram models, MaxEnt models or RNN-LMs on parser predictions yields perplexity reductions of about 6{\%}. Our method (a) retains online decoding capabilities and (b) incurs relatively little computational overhead which sets it apart from previous approaches that use syntax for language modeling. Our method is particularly attractive for modular systems that make use of a syntax parser anyway, e.g. as part of an understanding pipeline where predictive parsing improves language modeling at no additional cost.",
}
@inproceedings{wang-etal-2016-neural,
    title = "A Neural Attention Model for Disfluency Detection",
    author = "Wang, Shaolei  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1027",
    pages = "278--287",
    abstract = "In this paper, we study the problem of disfluency detection using the encoder-decoder framework. We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can efficiently model the long-range dependencies between words and make the resulting sentence more likely to be grammatically correct. Our model firstly encode the source sentence with a bidirectional Long Short-Term Memory (BI-LSTM) and then use the neural attention as a pointer to select an ordered sub sequence of the input as the output. Experiments show that our model achieves the state-of-the-art f-score of 86.7{\%} on the commonly used English Switchboard test set. We also evaluate the performance of our model on the in-house annotated Chinese data and achieve a significantly higher f-score compared to the baseline of CRF-based approach.",
}
@inproceedings{hellwig-2016-detecting,
    title = "Detecting Sentence Boundaries in {S}anskrit Texts",
    author = "Hellwig, Oliver",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1028",
    pages = "288--297",
    abstract = "The paper applies a deep recurrent neural network to the task of sentence boundary detection in Sanskrit, an important, yet underresourced ancient Indian language. The deep learning approach improves the F scores set by a metrical baseline and by a Conditional Random Field classifier by more than 10{\%}.",
}
@inproceedings{shen-etal-2016-consistent,
    title = "Consistent Word Segmentation, Part-of-Speech Tagging and Dependency Labelling Annotation for {C}hinese Language",
    author = "Shen, Mo  and
      Li, Wingmui  and
      Choe, HyunJeong  and
      Chu, Chenhui  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1029",
    pages = "298--308",
    abstract = "In this paper, we propose a new annotation approach to Chinese word segmentation, part-of-speech (POS) tagging and dependency labelling that aims to overcome the two major issues in traditional morphology-based annotation: Inconsistency and data sparsity. We re-annotate the Penn Chinese Treebank 5.0 (CTB5) and demonstrate the advantages of this approach compared to the original CTB5 annotation through word segmentation, POS tagging and machine translation experiments.",
}
@inproceedings{rei-etal-2016-attending,
    title = "Attending to Characters in Neural Sequence Labeling Models",
    author = "Rei, Marek  and
      Crichton, Gamal  and
      Pyysalo, Sampo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1030",
    pages = "309--318",
    abstract = "Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture for combining alternative word representations. By using an attention mechanism, the model is able to dynamically decide how much information to use from a word- or character-level component. We evaluated different architectures on a range of sequence labeling datasets, and character-level extensions were found to improve performance on every benchmark. In addition, the proposed attention-based architecture delivered the best results even with a smaller number of trainable parameters.",
}
@inproceedings{zhou-etal-2016-word,
    title = "A Word Labeling Approach to {T}hai Sentence Boundary Detection and {POS} Tagging",
    author = "Zhou, Nina  and
      Aw, AiTi  and
      Lertcheva, Nattadaporn  and
      Wang, Xuancong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1031",
    pages = "319--327",
    abstract = "Previous studies on Thai Sentence Boundary Detection (SBD) mostly assumed sentence ends at a space disambiguation problem, which classified space either as an indicator for Sentence Boundary (SB) or non-Sentence Boundary (nSB). In this paper, we propose a word labeling approach which treats space as a normal word, and detects SB between any two words. This removes the restriction for SB to be oc-curred only at space and makes our system more robust for modern Thai writing. It is because in modern Thai writing, space is not consistently used to indicate SB. As syntactic information contributes to better SBD, we further propose a joint Part-Of-Speech (POS) tagging and SBD framework based on Factorial Conditional Random Field (FCRF) model. We compare the performance of our proposed ap-proach with reported methods on ORCHID corpus. We also performed experiments of FCRF model on the TaLAPi corpus. The results show that the word labelling approach has better performance than pre-vious space-based classification approaches and FCRF joint model outperforms LCRF model in terms of SBD in all experiments.",
}
@inproceedings{horsmann-zesch-2016-assigning,
    title = "Assigning Fine-grained {P}o{S} Tags based on High-precision Coarse-grained Tagging",
    author = "Horsmann, Tobias  and
      Zesch, Torsten",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1032",
    pages = "328--336",
    abstract = "We propose a new approach to PoS tagging where in a first step, we assign a coarse-grained tag corresponding to the main syntactic category. Based on this high-precision decision, in the second step we utilize specially trained fine-grained models with heavily reduced decision complexity. By analyzing the system under oracle conditions, we show that there is a quite large potential for significantly outperforming a competitive baseline. When we take error-propagation from the coarse-grained tagging into account, our approach is on par with the state of the art. Our approach also allows tailoring the tagger towards recognizing single word classes which are of interest e.g. for researchers searching for specific phenomena in large corpora. In a case study, we significantly outperform a standard model that also makes use of the same optimizations.",
}
@inproceedings{more-tsarfaty-2016-data,
    title = "Data-Driven Morphological Analysis and Disambiguation for Morphologically Rich Languages and {U}niversal {D}ependencies",
    author = "More, Amir  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1033",
    pages = "337--348",
    abstract = "Parsing texts into universal dependencies (UD) in realistic scenarios requires infrastructure for the morphological analysis and disambiguation (MA{\&}D) of typologically different languages as a first tier. MA{\&}D is particularly challenging in morphologically rich languages (MRLs), where the ambiguous space-delimited tokens ought to be disambiguated with respect to their constituent morphemes, each morpheme carrying its own tag and a rich set features. Here we present a novel, language-agnostic, framework for MA{\&}D, based on a transition system with two variants {---} word-based and morpheme-based {---} and a dedicated transition to mitigate the biases of variable-length morpheme sequences. Our experiments on a Modern Hebrew case study show state of the art results, and we show that the morpheme-based MD consistently outperforms our word-based variant. We further illustrate the utility and multilingual coverage of our framework by morphologically analyzing and disambiguating the large set of languages in the UD treebanks.",
}
@inproceedings{gyanendro-singh-etal-2016-automatic,
    title = "Automatic Syllabification for {M}anipuri language",
    author = "Gyanendro Singh, Loitongbam  and
      Laitonjam, Lenin  and
      Ranbir Singh, Sanasam",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1034",
    pages = "349--357",
    abstract = "Development of hand crafted rule for syllabifying words of a language is an expensive task. This paper proposes several data-driven methods for automatic syllabification of words written in Manipuri language. Manipuri is one of the scheduled Indian languages. First, we propose a language-independent rule-based approach formulated using entropy based phonotactic segmentation. Second, we project the syllabification problem as a sequence labeling problem and investigate its effect using various sequence labeling approaches. Third, we combine the effect of sequence labeling and rule-based method and investigate the performance of the hybrid approach. From various experimental observations, it is evident that the proposed methods outperform the baseline rule-based method. The entropy based phonotactic segmentation provides a word accuracy of 96{\%}, CRF (sequence labeling approach) provides 97{\%} and hybrid approach provides 98{\%} word accuracy.",
}
@inproceedings{chen-etal-2016-learning,
    title = "Learning to Distill: The Essence Vector Modeling Framework",
    author = "Chen, Kuan-Yu  and
      Liu, Shih-Hung  and
      Chen, Berlin  and
      Wang, Hsin-Min",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1035",
    pages = "358--368",
    abstract = "In the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Learning representations of words is a pioneering study in this school of research. However, paragraph (or sentence and document) embedding learning is more suitable/reasonable for some tasks, such as sentiment classification and document summarization. Nevertheless, as far as we are aware, there is only a dearth of research focusing on launching unsupervised paragraph embedding methods. Classic paragraph embedding methods infer the representation of a given paragraph by considering all of the words occurring in the paragraph. Consequently, those stop or function words that occur frequently may mislead the embedding learning process to produce a misty paragraph representation. Motivated by these observations, our major contributions are twofold. First, we propose a novel unsupervised paragraph embedding method, named the essence vector (EV) model, which aims at not only distilling the most representative information from a paragraph but also excluding the general background information to produce a more informative low-dimensional vector representation for the paragraph. We evaluate the proposed EV model on benchmark sentiment classification and multi-document summarization tasks. The experimental results demonstrate the effectiveness and applicability of the proposed embedding method. Second, in view of the increasing importance of spoken content processing, an extension of the EV model, named the denoising essence vector (D-EV) model, is proposed. The D-EV model not only inherits the advantages of the EV model but also can infer a more robust representation for a given spoken paragraph against imperfect speech recognition. The utility of the D-EV model is evaluated on a spoken document summarization task, confirming the effectiveness of the proposed embedding method in relation to several well-practiced and state-of-the-art summarization methods.",
}
@inproceedings{lorenzo-trueba-etal-2016-continuous,
    title = "Continuous Expressive Speaking Styles Synthesis based on {CVSM} and {MR}-{HMM}",
    author = "Lorenzo-Trueba, Jaime  and
      Barra-Chicote, Roberto  and
      Gallardo-Antolin, Ascension  and
      Yamagishi, Junichi  and
      Montero, Juan M.",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1036",
    pages = "369--376",
    abstract = "This paper introduces a continuous system capable of automatically producing the most adequate speaking style to synthesize a desired target text. This is done thanks to a joint modeling of the acoustic and lexical parameters of the speaker models by adapting the CVSM projection of the training texts using MR-HMM techniques. As such, we consider that as long as sufficient variety in the training data is available, we should be able to model a continuous lexical space into a continuous acoustic space. The proposed continuous automatic text to speech system was evaluated by means of a perceptual evaluation in order to compare them with traditional approaches to the task. The system proved to be capable of conveying the correct expressiveness (average adequacy of 3.6) with an expressive strength comparable to oracle traditional expressive speech synthesis (average of 3.6) although with a drop in speech quality mainly due to the semi-continuous nature of the data (average quality of 2.9). This means that the proposed system is capable of improving traditional neutral systems without requiring any additional user interaction.",
}
@inproceedings{dominguez-etal-2016-automatic,
    title = "An Automatic Prosody Tagger for Spontaneous Speech",
    author = "Dom{\'\i}nguez, M{\'o}nica  and
      Farr{\'u}s, Mireia  and
      Wanner, Leo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1037",
    pages = "377--386",
    abstract = "Speech prosody is known to be central in advanced communication technologies. However, despite the advances of theoretical studies in speech prosody, so far, no large scale prosody annotated resources that would facilitate empirical research and the development of empirical computational approaches are available. This is to a large extent due to the fact that current common prosody annotation conventions offer a descriptive framework of intonation contours and phrasing based on labels. This makes it difficult to reach a satisfactory inter-annotator agreement during the annotation of gold standard annotations and, subsequently, to create consistent large scale annotations. To address this problem, we present an annotation schema for prominence and boundary labeling of prosodic phrases based upon acoustic parameters and a tagger for prosody annotation at the prosodic phrase level. Evaluation proves that inter-annotator agreement reaches satisfactory values, from 0.60 to 0.80 Cohen{'}s kappa, while the prosody tagger achieves acceptable recall and f-measure figures for five spontaneous samples used in the evaluation of monologue and dialogue formats in English and Spanish. The work presented in this paper is a first step towards a semi-automatic acquisition of large corpora for empirical prosodic analysis.",
}
@inproceedings{kim-etal-2016-frustratingly,
    title = "Frustratingly Easy Neural Domain Adaptation",
    author = "Kim, Young-Bum  and
      Stratos, Karl  and
      Sarikaya, Ruhi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1038",
    pages = "387--396",
    abstract = "Popular techniques for domain adaptation such as the feature augmentation method of Daum{\'e} III (2009) have mostly been considered for sparse binary-valued features, but not for dense real-valued features such as those used in neural networks. In this paper, we describe simple neural extensions of these techniques. First, we propose a natural generalization of the feature augmentation method that uses K + 1 LSTMs where one model captures global patterns across all K domains and the remaining K models capture domain-specific information. Second, we propose a novel application of the framework for learning shared structures by Ando and Zhang (2005) to domain adaptation, and also provide a neural extension of their approach. In experiments on slot tagging over 17 domains, our methods give clear performance improvement over Daum{\'e} III (2009) applied on feature-rich CRFs.",
}
@inproceedings{bhat-etal-2016-house,
    title = "A House United: Bridging the Script and Lexical Barrier between {H}indi and {U}rdu",
    author = "Bhat, Riyaz A.  and
      Bhat, Irshad A.  and
      Jain, Naman  and
      Sharma, Dipti Misra",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1039",
    pages = "397--408",
    abstract = "In Computational Linguistics, Hindi and Urdu are not viewed as a monolithic entity and have received separate attention with respect to their text processing. From part-of-speech tagging to machine translation, models are separately trained for both Hindi and Urdu despite the fact that they represent the same language. The reasons mainly are their divergent literary vocabularies and separate orthographies, and probably also their political status and the social perception that they are two separate languages. In this article, we propose a simple but efficient approach to bridge the lexical and orthographic differences between Hindi and Urdu texts. With respect to text processing, addressing the differences between the Hindi and Urdu texts would be beneficial in the following ways: (a) instead of training separate models, their individual resources can be augmented to train single, unified models for better generalization, and (b) their individual text processing applications can be used interchangeably under varied resource conditions. To remove the script barrier, we learn accurate statistical transliteration models which use sentence-level decoding to resolve word ambiguity. Similarly, we learn cross-register word embeddings from the harmonized Hindi and Urdu corpora to nullify their lexical divergences. As a proof of the concept, we evaluate our approach on the Hindi and Urdu dependency parsing under two scenarios: (a) resource sharing, and (b) resource augmentation. We demonstrate that a neural network-based dependency parser trained on augmented, harmonized Hindi and Urdu resources performs significantly better than the parsing models trained separately on the individual resources. We also show that we can achieve near state-of-the-art results when the parsers are used interchangeably.",
}
@inproceedings{michalon-etal-2016-deeper,
    title = "Deeper syntax for better semantic parsing",
    author = "Michalon, Olivier  and
      Ribeyre, Corentin  and
      Candito, Marie  and
      Nasr, Alexis",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1040",
    pages = "409--420",
    abstract = "Syntax plays an important role in the task of predicting the semantic structure of a sentence. But syntactic phenomena such as alternations, control and raising tend to obfuscate the relation between syntax and semantics. In this paper we predict the semantic structure of a sentence using a deeper syntax than what is usually done. This deep syntactic representation abstracts away from purely syntactic phenomena and proposes a structural organization of the sentence that is closer to the semantic representation. Experiments conducted on a French corpus annotated with semantic frames showed that a semantic parser reaches better performances with such a deep syntactic input.",
}
@inproceedings{lee-wang-2016-language,
    title = "Language Independent Dependency to Constituent Tree Conversion",
    author = "Lee, Young-Suk  and
      Wang, Zhiguo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1041",
    pages = "421--428",
    abstract = "We present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages. The technique works in two steps. First, a partial constituent tree is derived from a dependency tree with a very simple deterministic algorithm that is both language and dependency type independent. Second, a complete high accuracy constituent tree is derived with a constraint-based parser, which uses the partial constituent tree as external constraints. Evaluated on Section 22 of the WSJ Treebank, the technique achieves the state-of-the-art conversion F-score 95.6. When applied to English Universal Dependency treebank and German CoNLL2006 treebank, the converted treebanks added to the human-annotated constituent parser training corpus improve parsing F-scores significantly for both languages.",
}
@inproceedings{waszczuk-etal-2016-promoting,
    title = "Promoting multiword expressions in {A}* {TAG} parsing",
    author = "Waszczuk, Jakub  and
      Savary, Agata  and
      Parmentier, Yannick",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1042",
    pages = "429--439",
    abstract = "Multiword expressions (MWEs) are pervasive in natural languages and often have both idiomatic and compositional readings, which leads to high syntactic ambiguity. We show that for some MWE types idiomatic readings are usually the correct ones. We propose a heuristic for an A* parser for Tree Adjoining Grammars which benefits from this knowledge by promoting MWE-oriented analyses. This strategy leads to a substantial reduction in the parsing search space in case of true positive MWE occurrences, while avoiding parsing failures in case of false positives.",
}
@inproceedings{ulinski-etal-2016-incrementally,
    title = "Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics",
    author = "Ulinski, Morgan  and
      Hirschberg, Julia  and
      Rambow, Owen",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1043",
    pages = "440--449",
    abstract = "We present experiments in incrementally learning a dependency parser. The parser will be used in the WordsEye Linguistics Tools (WELT) (Ulinski et al., 2014) which supports field linguists documenting a language{'}s syntax and semantics. Our goal is to make syntactic annotation faster for field linguists. We have created a new parallel corpus of descriptions of spatial relations and motion events, based on pictures and video clips used by field linguists for elicitation of language from native speaker informants. We collected descriptions for each picture and video from native speakers in English, Spanish, German, and Egyptian Arabic. We compare the performance of MSTParser (McDonald et al., 2006) and MaltParser (Nivre et al., 2006) when trained on small amounts of this data. We find that MaltParser achieves the best performance. We also present the results of experiments using the parser to assist with annotation. We find that even when the parser is trained on a single sentence from the corpus, annotation time significantly decreases.",
}
@inproceedings{zennaki-etal-2016-inducing,
    title = "Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks",
    author = "Zennaki, Othman  and
      Semmar, Nasredine  and
      Besacier, Laurent",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1044",
    pages = "450--460",
    abstract = "This work focuses on the development of linguistic analysis tools for resource-poor languages. We use a parallel corpus to produce a multilingual word representation based only on sentence level alignment. This representation is combined with the annotated source side (resource-rich language) of the parallel corpus to train text analysis tools for resource-poor languages. Our approach is based on Recurrent Neural Networks (RNN) and has the following advantages: (a) it does not use word alignment information, (b) it does not assume any knowledge about foreign languages, which makes it applicable to a wide range of resource-poor languages, (c) it provides truly multilingual taggers. In a previous study, we proposed a method based on Simple RNN to automatically induce a Part-Of-Speech (POS) tagger. In this paper, we propose an improvement of our neural model. We investigate the Bidirectional RNN and the inclusion of external information (for instance low level information from Part-Of-Speech tags) in the RNN to train a more complex tagger (for instance, a multilingual super sense tagger). We demonstrate the validity and genericity of our method by using parallel corpora (obtained by manual or automatic translation). Our experiments are conducted to induce cross-lingual POS and super sense taggers.",
}
@inproceedings{zhang-etal-2016-bitext,
    title = "Bitext Name Tagging for Cross-lingual Entity Annotation Projection",
    author = "Zhang, Dongxu  and
      Zhang, Boliang  and
      Pan, Xiaoman  and
      Feng, Xiaocheng  and
      Ji, Heng  and
      Xu, Weiran",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1045",
    pages = "461--470",
    abstract = "Annotation projection is a practical method to deal with the low resource problem in incident languages (IL) processing. Previous methods on annotation projection mainly relied on word alignment results without any training process, which led to noise propagation caused by word alignment errors. In this paper, we focus on the named entity recognition (NER) task and propose a weakly-supervised framework to project entity annotations from English to IL through bitexts. Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training. The model is finally used to accomplish the projecting process. Experimental results on two low-resource ILs show that the proposed method can generate better annotations projected from English-IL parallel corpora. The performance of IL name tagger can also be improved significantly by training on the newly projected IL annotation set.",
}
@inproceedings{salehi-etal-2016-determining,
    title = "Determining the Multiword Expression Inventory of a Surprise Language",
    author = "Salehi, Bahar  and
      Cook, Paul  and
      Baldwin, Timothy",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1046",
    pages = "471--481",
    abstract = "Much previous research on multiword expressions (MWEs) has focused on the token- and type-level tasks of MWE identification and extraction, respectively. Such studies typically target known prevalent MWE types in a given language. This paper describes the first attempt to learn the MWE inventory of a {``}surprise{''} language for which we have no explicit prior knowledge of MWE patterns, certainly no annotated MWE data, and not even a parallel corpus. Our proposed model is trained on a treebank with MWE relations of a source language, and can be applied to the monolingual corpus of the surprise language to identify its MWE construction types.",
}
@inproceedings{akhtar-etal-2016-hybrid,
    title = "A Hybrid Deep Learning Architecture for Sentiment Analysis",
    author = "Akhtar, Md Shad  and
      Kumar, Ayush  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1047",
    pages = "482--493",
    abstract = "In this paper, we propose a novel hybrid deep learning archtecture which is highly efficient for sentiment analysis in resource-poor languages. We learn sentiment embedded vectors from the Convolutional Neural Network (CNN). These are augmented to a set of optimized features selected through a multi-objective optimization (MOO) framework. The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification. We evaluate our proposed approach for coarse-grained (i.e. sentence level) as well as fine-grained (i.e. aspect level) sentiment analysis on four Hindi datasets covering varying domains. In order to show that our proposed method is generic in nature we also evaluate it on two benchmark English datasets. Evaluation shows that the results of the proposed method are consistent across all the datasets and often outperforms the state-of-art systems. To the best of our knowledge, this is the very first attempt where such a deep learning model is used for less-resourced languages such as Hindi.",
}
@inproceedings{krishna-etal-2016-word,
    title = "Word Segmentation in {S}anskrit Using Path Constrained Random Walks",
    author = "Krishna, Amrith  and
      Santra, Bishal  and
      Satuluri, Pavankumar  and
      Bandaru, Sasi Prasanth  and
      Faldu, Bhumi  and
      Singh, Yajuvendra  and
      Goyal, Pawan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1048",
    pages = "494--504",
    abstract = "In Sanskrit, the phonemes at the word boundaries undergo changes to form new phonemes through a process called as sandhi. A fused sentence can be segmented into multiple possible segmentations. We propose a word segmentation approach that predicts the most semantically valid segmentation for a given sentence. We treat the problem as a query expansion problem and use the path-constrained random walks framework to predict the correct segments.",
}
@inproceedings{wang-etal-2016-mongolian,
    title = "{M}ongolian Named Entity Recognition System with Rich Features",
    author = "Wang, Weihua  and
      Bao, Feilong  and
      Gao, Guanglai",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1049",
    pages = "505--512",
    abstract = "In this paper, we first build a manually annotated named entity corpus of Mongolian. Then, we propose three morphological processing methods and study comprehensive features, including syllable features, lexical features, context features, morphological features and semantic features in Mongolian named entity recognition. Moreover, we also evaluate the influence of word cluster features on the system and combine all features together eventually. The experimental result shows that segmenting each suffix into an individual token achieves better results than deleting suffixes or using the suffixes as feature. The system based on segmenting suffixes with all proposed features yields benchmark result of F-measure=84.65 on this corpus.",
}
@inproceedings{shafieibavani-etal-2016-appraising,
    title = "Appraising {UMLS} Coverage for Summarizing Medical Evidence",
    author = "ShafieiBavani, Elaheh  and
      Ebrahimi, Mohammad  and
      Wong, Raymond  and
      Chen, Fang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1050",
    pages = "513--524",
    abstract = "When making clinical decisions, practitioners need to rely on the most relevant evidence available. However, accessing a vast body of medical evidence and confronting with the issue of information overload can be challenging and time consuming. This paper proposes an effective summarizer for medical evidence by utilizing both UMLS and WordNet. Given a clinical query and a set of relevant abstracts, our aim is to generate a fluent, well-organized, and compact summary that answers the query. Analysis via ROUGE metrics shows that using WordNet as a general-purpose lexicon helps to capture the concepts not covered by the UMLS Metathesaurus, and hence significantly increases the performance. The effectiveness of our proposed approach is demonstrated by conducting a set of experiments over a specialized evidence-based medicine (EBM) corpus - which has been gathered and annotated for the purpose of biomedical text summarization.",
}
@inproceedings{cevahir-murakami-2016-large,
    title = "Large-scale Multi-class and Hierarchical Product Categorization for an {E}-commerce Giant",
    author = "Cevahir, Ali  and
      Murakami, Koji",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1051",
    pages = "525--535",
    abstract = "In order to organize the large number of products listed in e-commerce sites, each product is usually assigned to one of the multi-level categories in the taxonomy tree. It is a time-consuming and difficult task for merchants to select proper categories within thousands of options for the products they sell. In this work, we propose an automatic classification tool to predict the matching category for a given product title and description. We used a combination of two different neural models, i.e., deep belief nets and deep autoencoders, for both titles and descriptions. We implemented a selective reconstruction approach for the input layer during the training of the deep neural networks, in order to scale-out for large-sized sparse feature vectors. GPUs are utilized in order to train neural networks in a reasonable time. We have trained our models for around 150 million products with a taxonomy tree with at most 5 levels that contains 28,338 leaf categories. Tests with millions of products show that our first predictions matches 81{\%} of merchants{'} assignments, when {``}others{''} categories are excluded.",
}
@inproceedings{gupta-etal-2016-product,
    title = "Product Classification in {E}-Commerce using Distributional Semantics",
    author = "Gupta, Vivek  and
      Karnick, Harish  and
      Bansal, Ashendra  and
      Jhala, Pradhuman",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1052",
    pages = "536--546",
    abstract = "Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction.To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilising (with respect to the taxonomy tree) path-wise, node-wise and depth-wise classifiers to reduce error in the final product classification task. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve improved results on various evaluation metrics compared to earlier approaches.",
}
@inproceedings{cao-etal-2016-attsum,
    title = "{A}tt{S}um: Joint Learning of Focusing and Summarization with Neural Attention",
    author = "Cao, Ziqiang  and
      Li, Wenjie  and
      Li, Sujian  and
      Wei, Furu  and
      Li, Yanran",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1053",
    pages = "547--556",
    abstract = "Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the two tasks in isolation. However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well. This paper proposes a novel summarization system called AttSum, which tackles the two tasks jointly. It automatically learns distributed representations for sentences as well as the document cluster. Meanwhile, it applies the attention mechanism to simulate the attentive reading of human behavior when a query is given. Extensive experiments are conducted on DUC query-focused summarization benchmark datasets. Without using any hand-crafted features, AttSum achieves competitive performance. We also observe that the sentences recognized to focus on the query indeed meet the query need.",
}
@inproceedings{li-etal-2016-using,
    title = "Using Relevant Public Posts to Enhance News Article Summarization",
    author = "Li, Chen  and
      Wei, Zhongyu  and
      Liu, Yang  and
      Jin, Yang  and
      Huang, Fei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1054",
    pages = "557--566",
    abstract = "A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article. In this paper we explore using public posts following a new article to improve automatic summary generation for the news article. We propose different approaches to incorporate information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge{'}s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments on data collected from Facebook show that relevant public posts provide useful information and can be effectively leveraged to improve news article summarization results.",
}
@inproceedings{fang-etal-2016-proposition,
    title = "A Proposition-Based Abstractive Summariser",
    author = "Fang, Yimai  and
      Zhu, Haoyue  and
      Muszy{\'n}ska, Ewa  and
      Kuhnle, Alexander  and
      Teufel, Simone",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1055",
    pages = "567--578",
    abstract = "Abstractive summarisation is not yet common amongst today{'}s deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middle-man of sentence extraction can lead to better abstractive summaries.",
}
@inproceedings{evang-bos-2016-cross,
    title = "Cross-lingual Learning of an Open-domain Semantic Parser",
    author = "Evang, Kilian  and
      Bos, Johan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1056",
    pages = "579--588",
    abstract = "We propose a method for learning semantic CCG parsers by projecting annotations via a parallel corpus. The method opens an avenue towards cheaply creating multilingual semantic parsers mapping open-domain text to formal meaning representations. A first cross-lingually learned Dutch (from English) semantic parser obtains f-scores ranging from 42.99{\%} to 69.22{\%} depending on the level of label informativity taken into account, compared to 58.40{\%} to 78.88{\%} for the underlying source-language system. These are promising numbers compared to state-of-the-art semantic parsing in open domains.",
}
@inproceedings{zhao-liu-2016-subtree,
    title = "A subtree-based factorization of dependency parsing",
    author = "Zhao, Qiuye  and
      Liu, Qun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1057",
    pages = "589--598",
    abstract = "We propose a dependency parsing pipeline, in which the parsing of long-distance projections and localized dependencies are explicitly decomposed at the input level. A chosen baseline dependency parsing model performs only on {`}carved{'} sequences at the second stage, which are transformed from coarse constituent parsing outputs at the first stage. When k-best constituent parsing outputs are kept, a third-stage is required to search for an optimal combination of the overlapped dependency subtrees. In this sense, our dependency model is subtree-factored. We explore alternative approaches for scoring subtrees, including feature-based models as well as continuous representations. The search for optimal subset to combine is formulated as an ILP problem. This framework especially benefits the models poor on long sentences, generally improving baselines by 0.75-1.28 (UAS) on English, achieving comparable performance with high-order models but faster. For Chinese, the most notable increase is as high as 3.63 (UAS) when the proposed framework is applied to first-order parsing models.",
}
@inproceedings{akbik-li-2016-k,
    title = "K-{SRL}: Instance-based Learning for Semantic Role Labeling",
    author = "Akbik, Alan  and
      Li, Yunyao",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1058",
    pages = "599--608",
    abstract = "Semantic role labeling (SRL) is the task of identifying and labeling predicate-argument structures in sentences with semantic frame and role labels. A known challenge in SRL is the large number of low-frequency exceptions in training data, which are highly context-specific and difficult to generalize. To overcome this challenge, we propose the use of instance-based learning that performs no explicit generalization, but rather extrapolates predictions from the most similar instances in the training data. We present a variant of k-nearest neighbors (kNN) classification with composite features to identify nearest neighbors for SRL. We show that high-quality predictions can be derived from a very small number of similar instances. In a comparative evaluation we experimentally demonstrate that our instance-based learning approach significantly outperforms current state-of-the-art systems on both in-domain and out-of-domain data, reaching F1-scores of 89,28{\%} and 79.91{\%} respectively.",
}
@inproceedings{plank-2016-keystroke,
    title = "Keystroke dynamics as signal for shallow syntactic parsing",
    author = "Plank, Barbara",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1059",
    pages = "609--619",
    abstract = "Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn better natural language processing models? We postulate that keystroke dynamics contain information about syntactic structure that can inform shallow syntactic parsing. To test this hypothesis, we explore labels derived from keystroke logs as auxiliary task in a multi-task bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising results on two shallow syntactic parsing tasks, chunking and CCG supertagging. Our model is simple, has the advantage that data can come from distinct sources, and produces models that are significantly better than models trained on the text annotations alone.",
}
@inproceedings{ostling-2016-bayesian,
    title = "A {B}ayesian model for joint word alignment and part-of-speech transfer",
    author = {{\"O}stling, Robert},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1060",
    pages = "620--629",
    abstract = "Current methods for word alignment require considerable amounts of parallel text to deliver accurate results, a requirement which is met only for a small minority of the world{'}s approximately 7,000 languages. We show that by jointly performing word alignment and annotation transfer in a novel Bayesian model, alignment accuracy can be improved for language pairs where annotations are available for only one of the languages{---}a finding which could facilitate the study and processing of a vast number of low-resource languages. We also present an evaluation where our method is used to perform single-source and multi-source part-of-speech transfer with 22 translations of the same text in four different languages. This allows us to quantify the considerable variation in accuracy depending on the specific source text(s) used, even with different translations into the same language.",
}
@inproceedings{shapiro-2016-splitting,
    title = "Splitting compounds with ngrams",
    author = "Shapiro, Naomi Tachikawa",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1061",
    pages = "630--640",
    abstract = "Compound words with unmarked word boundaries are problematic for many tasks in NLP and computational linguistics, including information extraction, machine translation, and syllabification. This paper introduces a simple, proof-of-concept language modeling approach to automatic compound segmentation, as applied to Finnish. This approach utilizes an off-the-shelf morphological analyzer to split training words into their constituent morphemes. A language model is subsequently trained on ngrams composed of morphemes, morpheme boundaries, and word boundaries. Linguistic constraints are then used to weed out phonotactically ill-formed segmentations, thereby allowing the language model to select the best grammatical segmentation. This approach achieves an accuracy of {\textasciitilde}97{\%}.",
}
@inproceedings{feng-etal-2016-gake,
    title = "{GAKE}: Graph Aware Knowledge Embedding",
    author = "Feng, Jun  and
      Huang, Minlie  and
      Yang, Yang  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1062",
    pages = "641--651",
    abstract = "Knowledge embedding, which projects triples in a given knowledge base to d-dimensional vectors, has attracted considerable research efforts recently. Most existing approaches treat the given knowledge base as a set of triplets, each of whose representation is then learned separately. However, as a fact, triples are connected and depend on each other. In this paper, we propose a graph aware knowledge embedding method (GAKE), which formulates knowledge base as a directed graph, and learns representations for any vertices or edges by leveraging the graph{'}s structural information. We introduce three types of graph context for embedding: neighbor context, path context, and edge context, each reflects properties of knowledge from different perspectives. We also design an attention mechanism to learn representative power of different vertices or edges. To validate our method, we conduct several experiments on two tasks. Experimental results suggest that our method outperforms several state-of-art knowledge embedding models.",
}
@inproceedings{wu-etal-2016-ranking,
    title = "Ranking Responses Oriented to Conversational Relevance in Chat-bots",
    author = "Wu, Bowen  and
      Wang, Baoxun  and
      Xue, Hui",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1063",
    pages = "652--662",
    abstract = "For automatic chatting systems, it is indeed a great challenge to reply the given query considering the conversation history, rather than based on the query only. This paper proposes a deep neural network to address the context-aware response ranking problem by end-to-end learning, so as to help to select conversationally relevant candidate. By combining the multi-column convolutional layer and the recurrent layer, our model is able to model the semantics of the utterance sequence by grasping the semantic clue within the conversation, on the basis of the effective representation for each sentence. Especially, the network utilizes attention pooling to further emphasis the importance of essential words in conversations, thus the representations of contexts tend to be more meaningful and the performance of candidate ranking is notably improved. Meanwhile, due to the adoption of attention pooling, it is possible to visualize the semantic clues. The experimental results on the large amount of conversation data from social media have shown that our approach is promising for quantifying the conversational relevance of responses, and indicated its good potential for building practical IR based chat-bots.",
}
@inproceedings{lee-etal-2016-probabilistic,
    title = "Probabilistic Prototype Model for Serendipitous Property Mining",
    author = "Lee, Taesung  and
      Hwang, Seung-won  and
      Wang, Zhongyuan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1064",
    pages = "663--673",
    abstract = "Besides providing the relevant information, amusing users has been an important role of the web. Many web sites provide serendipitous (unexpected but relevant) information to draw user traffic. In this paper, we study the representative scenario of mining an amusing quiz. An existing approach leverages a knowledge base to mine an unexpected property then find quiz questions on such property, based on prototype theory in cognitive science. However, existing deterministic model is vulnerable to noise in the knowledge base. Therefore, we instead propose to leverage probabilistic approach to build a prototype that can overcome noise. Our extensive empirical study shows that our approach not only significantly outperforms baselines by 0.06 in accuracy, and 0.11 in serendipity but also shows higher relevance than the traditional relevance-pursuing baseline using TF-IDF.",
}
@inproceedings{garimella-etal-2016-identifying,
    title = "Identifying Cross-Cultural Differences in Word Usage",
    author = "Garimella, Aparna  and
      Mihalcea, Rada  and
      Pennebaker, James",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1065",
    pages = "674--683",
    abstract = "Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures. In this paper, we explore this relation by developing cross-cultural word models to identify words with cultural bias {--} i.e., words that are used in significantly different ways by speakers from different cultures. Focusing specifically on two cultures: United States and Australia, we identify a set of words with significant usage differences, and further investigate these words through feature analysis and topic modeling, shedding light on the attributes of language that contribute to these differences.",
}
@inproceedings{asahara-etal-2016-reading,
    title = "Reading-Time Annotations for {``}{B}alanced {C}orpus of {C}ontemporary {W}ritten {J}apanese{''}",
    author = "Asahara, Masayuki  and
      Ono, Hajime  and
      Miyamoto, Edson T.",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1066",
    pages = "684--694",
    abstract = "The Dundee Eyetracking Corpus contains eyetracking data collected while native speakers of English and French read newspaper editorial articles. Similar resources for other languages are still rare, especially for languages in which words are not overtly delimited with spaces. This is a report on a project to build an eyetracking corpus for Japanese. Measurements were collected while 24 native speakers of Japanese read excerpts from the Balanced Corpus of Contemporary Written Japanese Texts were presented with or without segmentation (i.e. with or without space at the boundaries between bunsetsu segmentations) and with two types of methodologies (eyetracking and self-paced reading presentation). Readers{'} background information including vocabulary-size estimation and Japanese reading-span score were also collected. As an example of the possible uses for the corpus, we also report analyses investigating the phenomena of anti-locality.",
}
@inproceedings{nand-etal-2016-bullying,
    title = "{``}How Bullying is this Message?{''}: A Psychometric Thermometer for Bullying",
    author = "Nand, Parma  and
      Perera, Rivindu  and
      Kasture, Abhijeet",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1067",
    pages = "695--706",
    abstract = "Cyberbullying statistics are shocking, the number of affected young people is increasing dramatically with the affordability of mobile technology devices combined with a growing number of social networks. This paper proposes a framework to analyse Tweets with the goal to identify cyberharassment in social networks as an important step to protect people from cyberbullying. The proposed framework incorporates latent or hidden variables with supervised learning to determine potential bullying cases resembling short blogging type texts such as Tweets. It uses the LIWC2007 - tool that translates Tweet messages into 67 numeric values, representing 67 word categories. The output vectors are then used as features for four different classifiers implemented in Weka. Tests on all four classifiers delivered encouraging predictive capability of Tweet messages. Overall it was found that the use of numeric psychometric values outperformed the same algorithms using both filtered and unfiltered words as features. The best performing algorithms was Random Forest with an F1-value of 0.947 using psychometric features compared to a value of 0.847 for the same algorithm using words as features.",
}
@inproceedings{yatbaz-etal-2016-learning,
    title = "Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition",
    author = {Yatbaz, Mehmet Ali  and
      Cirik, Volkan  and
      K{\"u}ntay, Aylin  and
      Yuret, Deniz},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1068",
    pages = "707--716",
    abstract = "Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.",
}
@inproceedings{paetzold-specia-2016-understanding,
    title = "Understanding the Lexical Simplification Needs of Non-Native Speakers of {E}nglish",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1069",
    pages = "717--727",
    abstract = "We report three user studies in which the Lexical Simplification needs of non-native English speakers are investigated. Our analyses feature valuable new insight on the relationship between the non-natives{'} notion of complexity and various morphological, semantic and lexical word properties. Some of our findings contradict long-standing misconceptions about word simplicity. The data produced in our studies consists of 211,564 annotations made by 1,100 volunteers, which we hope will guide forthcoming research on Text Simplification for non-native speakers of English.",
}
@inproceedings{alam-etal-2016-interlocutors,
    title = "How Interlocutors Coordinate with each other within Emotional Segments?",
    author = "Alam, Firoj  and
      Chowdhury, Shammur Absar  and
      Danieli, Morena  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1070",
    pages = "728--738",
    abstract = "In this paper, we aim to investigate the coordination of interlocutors behavior in different emotional segments. Conversational coordination between the interlocutors is the tendency of speakers to predict and adjust each other accordingly on an ongoing conversation. In order to find such a coordination, we investigated 1) lexical similarities between the speakers in each emotional segments, 2) correlation between the interlocutors using psycholinguistic features, such as linguistic styles, psychological process, personal concerns among others, and 3) relation of interlocutors turn-taking behaviors such as competitiveness. To study the degree of coordination in different emotional segments, we conducted our experiments using real dyadic conversations collected from call centers in which agent{'}s emotional state include empathy and customer{'}s emotional states include anger and frustration. Our findings suggest that the most coordination occurs between the interlocutors inside anger segments, where as, a little coordination was observed when the agent was empathic, even though an increase in the amount of non-competitive overlaps was observed. We found no significant difference between anger and frustration segment in terms of turn-taking behaviors. However, the length of pause significantly decreases in the preceding segment of anger where as it increases in the preceding segment of frustration.",
}
@inproceedings{bykh-meurers-2016-advancing,
    title = "Advancing Linguistic Features and Insights by Label-informed Feature Grouping: An Exploration in the Context of Native Language Identification",
    author = "Bykh, Serhiy  and
      Meurers, Detmar",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1071",
    pages = "739--749",
    abstract = "We propose a hierarchical clustering approach designed to group linguistic features for supervised machine learning that is inspired by variationist linguistics. The method makes it possible to abstract away from the individual feature occurrences by grouping features together that behave alike with respect to the target class, thus providing a new, more general perspective on the data. On the one hand, it reduces data sparsity, leading to quantitative performance gains. On the other, it supports the formation and evaluation of hypotheses about individual choices of linguistic structures. We explore the method using features based on verb subcategorization information and evaluate the approach in the context of the Native Language Identification (NLI) task.",
}
@inproceedings{rubino-etal-2016-modeling,
    title = "Modeling Diachronic Change in Scientific Writing with Information Density",
    author = "Rubino, Raphael  and
      Degaetano-Ortlieb, Stefania  and
      Teich, Elke  and
      van Genabith, Josef",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1072",
    pages = "750--761",
    abstract = "Previous linguistic research on scientific writing has shown that language use in the scientific domain varies considerably in register and style over time. In this paper we investigate the introduction of information theory inspired features to study long term diachronic change on three levels: lexis, part-of-speech and syntax. Our approach is based on distinguishing between sentences from 19th and 20th century scientific abstracts using supervised classification models. To the best of our knowledge, the introduction of information theoretic features to this task is novel. We show that these features outperform more traditional features, such as token or character n-grams, while leading to more compact models. We present a detailed analysis of feature informativeness in order to gain a better understanding of diachronic change on different linguistic levels.",
}
@inproceedings{hu-etal-2016-different,
    title = "Different Contexts Lead to Different Word Embeddings",
    author = "Hu, Wenpeng  and
      Zhang, Jiajun  and
      Zheng, Nan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1073",
    pages = "762--771",
    abstract = "Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per word type without considering polysemy and homonymy. In this paper, we present an extension to the CBOW model which not only improves the quality of embeddings but also makes embeddings suitable for polysemy. It differs from most of the related work in that it learns one semantic center embedding and one context bias instead of training multiple embeddings per word type. Different context leads to different bias which is defined as the weighted average embeddings of local context. Experimental results on similarity task and analogy task show that the word representations learned by the proposed method outperform the competitive baselines.",
}
@inproceedings{agirrezabal-etal-2016-machine,
    title = "Machine Learning for Metrical Analysis of {E}nglish Poetry",
    author = "Agirrezabal, Manex  and
      Alegria, I{\~n}aki  and
      Hulden, Mans",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1074",
    pages = "772--781",
    abstract = "In this work we tackle the challenge of identifying rhythmic patterns in poetry written in English. Although poetry is a literary form that makes use standard meters usually repeated among different authors, we will see in this paper how performing such analyses is a difficult task in machine learning due to the unexpected deviations from such standard patterns. After breaking down some examples of classical poetry, we apply a number of NLP techniques for the scansion of poetry, training and testing our systems against a human-annotated corpus. With these experiments, our purpose is establish a baseline of automatic scansion of poetry using NLP tools in a straightforward manner and to raise awareness of the difficulties of this task.",
}
@inproceedings{moore-etal-2016-automated,
    title = "Automated speech-unit delimitation in spoken learner {E}nglish",
    author = "Moore, Russell  and
      Caines, Andrew  and
      Graham, Calbert  and
      Buttery, Paula",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1075",
    pages = "782--793",
    abstract = "In order to apply computational linguistic analyses and pass information to downstream applications, transcriptions of speech obtained via automatic speech recognition (ASR) need to be divided into smaller meaningful units, in a task we refer to as {`}speech-unit (SU) delimitation{'}. We closely recreate the automatic delimitation system described by Lee and Glass (2012), {`}Sentence detection using multiple annotations{'}, Proceedings of INTERSPEECH, which combines a prosodic model, language model and speech-unit length model in log-linear fashion. Since state-of-the-art natural language processing (NLP) tools have been developed to deal with written text and its characteristic sentence-like units, SU delimitation helps bridge the gap between ASR and NLP, by normalising spoken data into a more canonical format. Previous work has focused on native speaker recordings; we test the system of Lee and Glass (2012) on non-native speaker (or {`}learner{'}) data, achieving performance above the state-of-the-art. We also consider alternative evaluation metrics which move away from the idea of a single {`}truth{'} in SU delimitation, and frame this work in the context of downstream NLP applications.",
}
@inproceedings{song-etal-2016-learning,
    title = "Learning to Identify Sentence Parallelism in Student Essays",
    author = "Song, Wei  and
      Liu, Tong  and
      Fu, Ruiji  and
      Liu, Lizhen  and
      Wang, Hanshi  and
      Liu, Ting",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1076",
    pages = "794--803",
    abstract = "Parallelism is an important rhetorical device. We propose a machine learning approach for automated sentence parallelism identification in student essays. We build an essay dataset with sentence level parallelism annotated. We derive features by combining generalized word alignment strategies and the alignment measures between word sequences. The experimental results show that sentence parallelism can be effectively identified with a F1 score of 82{\%} at pair-wise level and 72{\%} at parallelism chunk level.Based on this approach, we automatically identify sentence parallelism in more than 2000 student essays and study the correlation between the use of sentence parallelism and the types and quality of essays.",
}
@inproceedings{basaldella-etal-2016-evaluating,
    title = "Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction",
    author = "Basaldella, Marco  and
      Chiaradia, Giorgia  and
      Tasso, Carlo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1077",
    pages = "804--814",
    abstract = "In this paper we analyze the effectiveness of using linguistic knowledge from coreference and anaphora resolution for improving the performance for supervised keyphrase extraction. In order to verify the impact of these features, we define a baseline keyphrase extraction system and evaluate its performance on a standard dataset using different machine learning algorithms. Then, we consider new sets of features by adding combinations of the linguistic features we propose and we evaluate the new performance of the system. We also use anaphora and coreference resolution to transform the documents, trying to simulate the cohesion process performed by the human mind. We found that our approach has a slightly positive impact on the performance of automatic keyphrase extraction, in particular when considering the ranking of the results.",
}
@inproceedings{ehrlemark-etal-2016-retrieving,
    title = "Retrieving Occurrences of Grammatical Constructions",
    author = "Ehrlemark, Anna  and
      Johansson, Richard  and
      Lyngfelt, Benjamin",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1078",
    pages = "815--824",
    abstract = "Finding authentic examples of grammatical constructions is central in constructionist approaches to linguistics, language processing, and second language learning. In this paper, we address this problem as an information retrieval (IR) task. To facilitate research in this area, we built a benchmark collection by annotating the occurrences of six constructions in a Swedish corpus. Furthermore, we implemented a simple and flexible retrieval system for finding construction occurrences, in which the user specifies a ranking function using lexical-semantic similarities (lexicon-based or distributional). The system was evaluated using standard IR metrics on the new benchmark, and we saw that lexical-semantical rerankers improve significantly over a purely surface-oriented system, but must be carefully tailored for each individual construction.",
}
@inproceedings{felice-etal-2016-automatic,
    title = "Automatic Extraction of Learner Errors in {ESL} Sentences Using Linguistically Enhanced Alignments",
    author = "Felice, Mariano  and
      Bryant, Christopher  and
      Briscoe, Ted",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1079",
    pages = "825--835",
    abstract = "We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely mappings between tokens, and secondly employs a rule-based function to decide which alignments should be merged. Our method beats all previous approaches on the tested datasets, achieving state-of-the-art results for automatic error extraction.",
}
@inproceedings{yamauchi-murawaki-2016-contrasting,
    title = "Contrasting Vertical and Horizontal Transmission of Typological Features",
    author = "Yamauchi, Kenji  and
      Murawaki, Yugo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1080",
    pages = "836--846",
    abstract = "Linguistic typology provides features that have a potential of uncovering deep phylogenetic relations among the world{'}s languages. One of the key challenges in using typological features for phylogenetic inference is that horizontal (spatial) transmission obscures vertical (phylogenetic) signals. In this paper, we characterize typological features with respect to the relative strength of vertical and horizontal transmission. To do this, we first construct (1) a spatial neighbor graph of languages and (2) a phylogenetic neighbor graph by collapsing known language families. We then develop an autologistic model that predicts a feature{'}s distribution from these two graphs. In the experiments, we managed to separate vertically and/or horizontally stable features from unstable ones, and the results are largely consistent with previous findings.",
}
@inproceedings{mao-hulden-2016-regular,
    title = "How Regular is {J}apanese Loanword Adaptation? A Computational Study",
    author = "Mao, Lingshuang  and
      Hulden, Mans",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1081",
    pages = "847--856",
    abstract = "The modifications that foreign loanwords undergo when adapted into Japanese have been the subject of much study in linguistics. The scholarly interest of the topic can be attributed to the fact that Japanese loanwords undergo a complex series of phonological adaptations, something which has been puzzling scholars for decades. While previous studies of Japanese loanword accommodation have focused on specific phonological phenomena of limited scope, the current study leverages computational methods to provide a more complete description of all the sound changes that occur when adopting English words into Japanese. To investigate this, we have developed a parallel corpus of 250 English transcriptions and their respective Japanese equivalents. These words were then used to develop a wide-coverage finite state transducer based phonological grammar that mimics the behavior of the Japanese adaption process. By developing rules with the goal of accounting completely for a large number of borrowing and analyzing forms mistakenly generated by the system, we discovered an internal inconsistency inside the loanword phonology of the Japanese language, something arguably underestimated by previous studies. The result of the investigation suggests that there are multiple {`}dimensions{'} that shape the output form of the current Japanese loanwords. These dimensions include orthography, phonetics, and historical changes.",
}
@inproceedings{inurrieta-etal-2016-using,
    title = "Using Linguistic Data for {E}nglish and {S}panish Verb-Noun Combination Identification",
    author = "I{\~n}urrieta, Uxoa  and
      D{\'\i}az de Ilarraza, Arantza  and
      Labaka, Gorka  and
      Sarasola, Kepa  and
      Aduriz, Itziar  and
      Carroll, John",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1082",
    pages = "857--867",
    abstract = "We present a linguistic analysis of a set of English and Spanish verb+noun combinations (VNCs), and a method to use this information to improve VNC identification. Firstly, a sample of frequent VNCs are analysed in-depth and tagged along lexico-semantic and morphosyntactic dimensions, obtaining satisfactory inter-annotator agreement scores. Then, a VNC identification experiment is undertaken, where the analysed linguistic data is combined with chunking information and syntactic dependencies. A comparison between the results of the experiment and the results obtained by a basic detection method shows that VNC identification can be greatly improved by using linguistic information, as a large number of additional occurrences are detected with high precision.",
}
@inproceedings{terkik-etal-2016-analyzing,
    title = "Analyzing Gender Bias in Student Evaluations",
    author = "Terkik, Andamlak  and
      Prud{'}hommeaux, Emily  and
      Ovesdotter Alm, Cecilia  and
      Homan, Christopher  and
      Franklin, Scott",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1083",
    pages = "868--876",
    abstract = "University students in the United States are routinely asked to provide feedback on the quality of the instruction they have received. Such feedback is widely used by university administrators to evaluate teaching ability, despite growing evidence that students assign lower numerical scores to women and people of color, regardless of the actual quality of instruction. In this paper, we analyze students{'} written comments on faculty evaluation forms spanning eight years and five STEM disciplines in order to determine whether open-ended comments reflect these same biases. First, we apply sentiment analysis techniques to the corpus of comments to determine the overall affect of each comment. We then use this information, in combination with other features, to explore whether there is bias in how students describe their instructors. We show that while the gender of the evaluated instructor does not seem to affect students{'} expressed level of overall satisfaction with their instruction, it does strongly influence the language that they use to describe their instructors and their experience in class.",
}
@inproceedings{huynh-etal-2016-adverse,
    title = "Adverse Drug Reaction Classification With Deep Neural Networks",
    author = "Huynh, Trung  and
      He, Yulan  and
      Willis, Alistair  and
      Rueger, Stefan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1084",
    pages = "877--887",
    abstract = "We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.",
}
@inproceedings{huang-etal-2016-chinese,
    title = "{C}hinese Preposition Selection for Grammatical Error Diagnosis",
    author = "Huang, Hen-Hsen  and
      Shao, Yen-Chi  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1085",
    pages = "888--899",
    abstract = "Misuse of Chinese prepositions is one of common word usage errors in grammatical error diagnosis. In this paper, we adopt the Chinese Gigaword corpus and HSK corpus as L1 and L2 corpora, respectively. We explore gated recurrent neural network model (GRU), and an ensemble of GRU model and maximum entropy language model (GRU-ME) to select the best preposition from 43 candidates for each test sentence. The experimental results show the advantage of the GRU models over simple RNN and n-gram models. We further analyze the effectiveness of linguistic information such as word boundary and part-of-speech tag in this task.",
}
@inproceedings{eskander-etal-2016-extending,
    title = "Extending the Use of {A}daptor {G}rammars for Unsupervised Morphological Segmentation of Unseen Languages",
    author = "Eskander, Ramy  and
      Rambow, Owen  and
      Yang, Tianchun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1086",
    pages = "900--910",
    abstract = "We investigate using Adaptor Grammars for unsupervised morphological segmentation. Using six development languages, we investigate in detail different grammars, the use of morphological knowledge from outside sources, and the use of a cascaded architecture. Using cross-validation on our development languages, we propose a system which is language-independent. We show that it outperforms two state-of-the-art systems on 5 out of 6 languages.",
}
@inproceedings{kuru-etal-2016-charner,
    title = "{C}har{NER}: Character-Level Named Entity Recognition",
    author = "Kuru, Onur  and
      Can, Ozan Arkan  and
      Yuret, Deniz",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1087",
    pages = "911--921",
    abstract = "We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consists of stacked bidirectional LSTMs which inputs characters and outputs tag probabilities for each character. These probabilities are then converted to consistent word level named entity tags using a Viterbi decoder. We are able to achieve close to state-of-the-art NER performance in seven languages with the same basic model using only labeled NER data and no hand-engineered features or other external resources like syntactic taggers or Gazetteers.",
}
@inproceedings{hardmeier-2016-neural,
    title = "A Neural Model for Part-of-Speech Tagging in Historical Texts",
    author = "Hardmeier, Christian",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1088",
    pages = "922--931",
    abstract = "Historical texts are challenging for natural language processing because they differ linguistically from modern texts and because of their lack of orthographical and grammatical standardisation. We use a character-level neural network to build a part-of-speech (POS) tagger that can process historical data directly without requiring a separate spelling normalisation stage. Its performance in a Swedish verb identification and a German POS tagging task is similar to that of a two-stage model. We analyse the performance of this tagger and a more traditional baseline system, discuss some of the remaining problems for tagging historical data and suggest how the flexibility of our neural tagger could be exploited to address diachronic divergences in morphology and syntax in early modern Swedish with the help of data from closely related languages.",
}
@inproceedings{wang-etal-2016-extracting,
    title = "Extracting Discriminative Keyphrases with Learned Semantic Hierarchies",
    author = "Wang, Yunli  and
      Jin, Yong  and
      Zhu, Xiaodan  and
      Goutte, Cyril",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1089",
    pages = "932--942",
    abstract = "The goal of keyphrase extraction is to automatically identify the most salient phrases from documents. The technique has a wide range of applications such as rendering a quick glimpse of a document, or extracting key content for further use. While previous work often assumes keyphrases are a static property of a given documents, in many applications, the appropriate set of keyphrases that should be extracted depends on the set of documents that are being considered together. In particular, good keyphrases should not only accurately describe the content of a document, but also reveal what discriminates it from the other documents. In this paper, we study this problem of extracting discriminative keyphrases. In particularly, we propose to use the hierarchical semantic structure between candidate keyphrases to promote keyphrases that have the right level of specificity to clearly distinguish the target document from others. We show that such knowledge can be used to construct better discriminative keyphrase extraction systems that do not assume a static, fixed set of keyphrases for a document. We show how this helps identify key expertise of authors from their papers, as well as competencies covered by online courses within different domains.",
}
@inproceedings{huang-etal-2016-hashtag,
    title = "Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention",
    author = "Huang, Haoran  and
      Zhang, Qi  and
      Gong, Yeyun  and
      Huang, Xuanjing",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1090",
    pages = "943--952",
    abstract = "On microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags for microblogs has received much attention in recent years. Previous deep neural network-based hashtag recommendation approaches converted the task into a multi-class classification problem. However, most of these methods only took the microblog itself into consideration. Motivated by the intuition that the history of users should impact the recommendation procedure, in this work, we extend end-to-end memory networks to perform this task. We incorporate the histories of users into the external memory and introduce a hierarchical attention mechanism to select more appropriate histories. To train and evaluate the proposed method, we also construct a dataset based on microblogs collected from Twitter. Experimental results demonstrate that the proposed methods can significantly outperform state-of-the-art methods. By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67.9{\%} in the F1-score.",
}
@inproceedings{bhatia-etal-2016-automatic,
    title = "Automatic Labelling of Topics with Neural Embeddings",
    author = "Bhatia, Shraey  and
      Lau, Jey Han  and
      Baldwin, Timothy",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1091",
    pages = "953--963",
    abstract = "Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Comparing to a state-of-the-art topic labelling system, our methodology is simpler, more efficient and finds better topic labels.",
}
@inproceedings{shain-etal-2016-memory-bounded,
    title = "Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input",
    author = "Shain, Cory  and
      Bryce, William  and
      Jin, Lifeng  and
      Krakovna, Victoria  and
      Doshi-Velez, Finale  and
      Miller, Timothy  and
      Schuler, William  and
      Schwartz, Lane",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1092",
    pages = "964--975",
    abstract = "This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM). We deploy this algorithm to shed light on the extent to which human language learners can discover hierarchical syntax through distributional statistics alone, by modeling two widely-accepted features of human language acquisition and sentence processing that have not been simultaneously modeled by any existing grammar induction algorithm: (1) a left-corner parsing strategy and (2) limited working memory capacity. To model realistic input to human language learners, we evaluate our system on a corpus of child-directed speech rather than typical newswire corpora. Results beat or closely match those of three competing systems.",
}
@inproceedings{herbelot-kochmar-2016-calling,
    title = "{`}Calling on the classical phone{'}: a distributional model of adjective-noun errors in learners{'} {E}nglish",
    author = "Herbelot, Aur{\'e}lie  and
      Kochmar, Ekaterina",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1093",
    pages = "976--986",
    abstract = "In this paper we discuss three key points related to error detection (ED) in learners{'} English. We focus on content word ED as one of the most challenging tasks in this area, illustrating our claims on adjective{--}noun (AN) combinations. In particular, we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system{'}s performance across individual adjective classes, concluding that a class-dependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words.",
}
@inproceedings{todirascu-etal-2016-cohesive,
    title = "Are Cohesive Features Relevant for Text Readability Evaluation?",
    author = "Todirascu, Amalia  and
      Fran{\c{c}}ois, Thomas  and
      Bernhard, Delphine  and
      Gala, N{\'u}ria  and
      Ligozat, Anne-Laure",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1094",
    pages = "987--997",
    abstract = "This paper investigates the effectiveness of 65 cohesion-based variables that are commonly used in the literature as predictive features to assess text readability. We evaluate the efficiency of these variables across narrative and informative texts intended for an audience of L2 French learners. In our experiments, we use a French corpus that has been both manually and automatically annotated as regards to co-reference and anaphoric chains. The efficiency of the 65 variables for readability is analyzed through a correlational analysis and some modelling experiments.",
}
@inproceedings{littell-etal-2016-named,
    title = "Named Entity Recognition for Linguistic Rapid Response in Low-Resource Languages: {S}orani {K}urdish and {T}ajik",
    author = "Littell, Patrick  and
      Goyal, Kartik  and
      Mortensen, David R.  and
      Little, Alexa  and
      Dyer, Chris  and
      Levin, Lori",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1095",
    pages = "998--1006",
    abstract = "This paper describes our construction of named-entity recognition (NER) systems in two Western Iranian languages, Sorani Kurdish and Tajik, as a part of a pilot study of {``}Linguistic Rapid Response{''} to potential emergency humanitarian relief situations. In the absence of large annotated corpora, parallel corpora, treebanks, bilingual lexica, etc., we found the following to be effective: exploiting distributional regularities in monolingual data, projecting information across closely related languages, and utilizing human linguist judgments. We show promising results on both a four-month exercise in Sorani and a two-day exercise in Tajik, achieved with minimal annotation costs.",
}
@inproceedings{exner-etal-2016-multilingual,
    title = "Multilingual Supervision of Semantic Annotation",
    author = "Exner, Peter  and
      Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1096",
    pages = "1007--1017",
    abstract = "In this paper, we investigate the annotation projection of semantic units in a practical setting. Previous approaches have focused on using parallel corpora for semantic transfer. We evaluate an alternative approach using loosely parallel corpora that does not require the corpora to be exact translations of each other. We developed a method that transfers semantic annotations from one language to another using sentences aligned by entities, and we extended it to include alignments by entity-like linguistic units. We conducted our experiments on a large scale using the English, Swedish, and French language editions of Wikipedia. Our results show that the annotation projection using entities in combination with loosely parallel corpora provides a viable approach to extending previous attempts. In addition, it allows the generation of proposition banks upon which semantic parsers can be trained.",
}
@inproceedings{rama-2016-siamese,
    title = "{S}iamese Convolutional Networks for Cognate Identification",
    author = "Rama, Taraka",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1097",
    pages = "1018--1027",
    abstract = "In this paper, we present phoneme level Siamese convolutional networks for the task of pair-wise cognate identification. We represent a word as a two-dimensional matrix and employ a siamese convolutional network for learning deep representations. We present siamese architectures that jointly learn phoneme level feature representations and language relatedness from raw words for cognate identification. Compared to previous works, we train and test on larger and realistic datasets; and, show that siamese architectures consistently perform better than traditional linear classifier approach.",
}
@inproceedings{he-etal-2016-exploring,
    title = "Exploring Differential Topic Models for Comparative Summarization of Scientific Papers",
    author = "He, Lei  and
      Li, Wei  and
      Zhuge, Hai",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1098",
    pages = "1028--1038",
    abstract = "This paper investigates differential topic models (dTM) for summarizing the differences among document groups. Starting from a simple probabilistic generative model, we propose dTM-SAGE that explicitly models the deviations on group-specific word distributions to indicate how words are used differen-tially across different document groups from a background word distribution. It is more effective to capture unique characteristics for comparing document groups. To generate dTM-based comparative summaries, we propose two sentence scoring methods for measuring the sentence discriminative capacity. Experimental results on scientific papers dataset show that our dTM-based comparative summari-zation methods significantly outperform the generic baselines and the state-of-the-art comparative summarization methods under ROUGE metrics.",
}
@inproceedings{benikova-etal-2016-bridging,
    title = "Bridging the gap between extractive and abstractive summaries: Creation and evaluation of coherent extracts from heterogeneous sources",
    author = "Benikova, Darina  and
      Mieskes, Margot  and
      Meyer, Christian M.  and
      Gurevych, Iryna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1099",
    pages = "1039--1050",
    abstract = "Coherent extracts are a novel type of summary combining the advantages of manually created abstractive summaries, which are fluent but difficult to evaluate, and low-quality automatically created extractive summaries, which lack coherence and structure. We use a corpus of heterogeneous documents to address the issue that information seekers usually face {--} a variety of different types of information sources. We directly extract information from these, but minimally redact and meaningfully order it to form a coherent text. Our qualitative and quantitative evaluations show that quantitative results are not sufficient to judge the quality of a summary and that other quality criteria, such as coherence, should also be taken into account. We find that our manually created corpus is of high quality and that it has the potential to bridge the gap between reference corpora of abstracts and automatic methods producing extracts. Our corpus is available to the research community for further development.",
}
@inproceedings{wang-etal-2016-chinese,
    title = "{C}hinese Poetry Generation with Planning based Neural Network",
    author = "Wang, Zhe  and
      He, Wei  and
      Wu, Hua  and
      Wu, Haiyang  and
      Li, Wei  and
      Wang, Haifeng  and
      Chen, Enhong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1100",
    pages = "1051--1060",
    abstract = "Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user{'}s writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning-based method can ensure that the generated poem is coherent and semantically consistent with the user{'}s intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets.",
}
@inproceedings{chenal-cheung-2016-predicting,
    title = "Predicting sentential semantic compatibility for aggregation in text-to-text generation",
    author = "Chenal, Victor  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1101",
    pages = "1061--1070",
    abstract = "We examine the task of aggregation in the context of text-to-text generation. We introduce a new aggregation task which frames the process as grouping input sentence fragments into clusters that are to be expressed as a single output sentence. We extract datasets for this task from a corpus using an automatic extraction process. Based on the results of a user study, we develop two gold-standard clusterings and corresponding evaluation methods for each dataset. We present a hierarchical clustering framework for predicting aggregation decisions on this task, which outperforms several baselines and can serve as a reference in future work.",
}
@inproceedings{zopf-etal-2016-sequential,
    title = "Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization",
    author = {Zopf, Markus  and
      Loza Menc{\'\i}a, Eneldo  and
      F{\"u}rnkranz, Johannes},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1102",
    pages = "1071--1082",
    abstract = "Unexpected events such as accidents, natural disasters and terrorist attacks represent an information situation where it is crucial to give users access to important and non-redundant information as early as possible. Incremental update summarization (IUS) aims at summarizing events which develop over time. In this paper, we propose a combination of sequential clustering and contextual importance measures to identify important sentences in a stream of documents in a timely manner. Sequential clustering is used to cluster similar sentences. The created clusters are scored by a contextual importance measure which identifies important information as well as redundant information. Experiments on the TREC Temporal Summarization 2015 shared task dataset show that our system achieves superior results compared to the best participating systems.",
}
@inproceedings{goyal-etal-2016-natural,
    title = "Natural Language Generation through Character-based {RNN}s with Finite-state Prior Knowledge",
    author = "Goyal, Raghav  and
      Dymetman, Marc  and
      Gaussier, Eric",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1103",
    pages = "1083--1092",
    abstract = "Recently Wen et al. (2015) have proposed a Recurrent Neural Network (RNN) approach to the generation of utterances from dialog acts, and shown that although their model requires less effort to develop than a rule-based system, it is able to improve certain aspects of the utterances, in particular their naturalness. However their system employs generation at the word-level, which requires one to pre-process the data by substituting named entities with placeholders. This pre-processing prevents the model from handling some contextual effects and from managing multiple occurrences of the same attribute. Our approach uses a character-level model, which unlike the word-level model makes it possible to learn to {``}copy{''} information from the dialog act to the target without having to pre-process the input. In order to avoid generating non-words and inventing information not present in the input, we propose a method for incorporating prior knowledge into the RNN in the form of a weighted finite-state automaton over character sequences. Automatic and human evaluations show improved performance over baselines on several evaluation criteria.",
}
@inproceedings{chachra-etal-2016-hybrid,
    title = "A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature",
    author = "Chachra, Suchet  and
      Ben Abacha, Asma  and
      Shooshan, Sonya  and
      Rodriguez, Laritza  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1104",
    pages = "1093--1100",
    abstract = "Readers usually rely on abstracts to identify relevant medical information from scientific articles. Abstracts are also essential to advanced information retrieval methods. More than 50 thousand scientific publications in PubMed lack author-generated abstracts, and the relevancy judgements for these papers have to be based on their titles alone. In this paper, we propose a hybrid summarization technique that aims to select the most pertinent sentences from articles to generate an extractive summary in lieu of a missing abstract. We combine i) health outcome detection, ii) keyphrase extraction, and iii) textual entailment recognition between sentences. We evaluate our hybrid approach and analyze the improvements of multi-factor summarization over techniques that rely on a single method, using a collection of 295 manually generated reference summaries. The obtained results show that the hybrid approach outperforms the baseline techniques with an improvement of 13{\%} in recall and 4{\%} in F1 score.",
}
@inproceedings{lampouras-vlachos-2016-imitation,
    title = "Imitation learning for language generation from unaligned data",
    author = "Lampouras, Gerasimos  and
      Vlachos, Andreas",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1105",
    pages = "1101--1112",
    abstract = "Natural language generation (NLG) is the task of generating natural language from a meaning representation. Current rule-based approaches require domain-specific and manually constructed linguistic resources, while most machine-learning based approaches rely on aligned training data and/or phrase templates. The latter are needed to restrict the search space for the structured prediction task defined by the unaligned datasets. In this work we propose the use of imitation learning for structured prediction which learns an incremental model that handles the large search space by avoiding explicit enumeration of the outputs. We focus on the Locally Optimal Learning to Search framework which allows us to train against non-decomposable loss functions such as the BLEU or ROUGE scores while not assuming gold standard alignments. We evaluate our approach on three datasets using both automatic measures and human judgements and achieve results comparable to the state-of-the-art approaches developed for each of them.",
}
@inproceedings{yu-etal-2016-product,
    title = "Product Review Summarization by Exploiting Phrase Properties",
    author = "Yu, Naitong  and
      Huang, Minlie  and
      Shi, Yuanyuan  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1106",
    pages = "1113--1124",
    abstract = "We propose a phrase-based approach for generating product review summaries. The main idea of our method is to leverage phrase properties to choose a subset of optimal phrases for generating the final summary. Specifically, we exploit two phrase properties, popularity and specificity. Popularity describes how popular the phrase is in the original reviews. Specificity describes how descriptive a phrase is in comparison to generic comments. We formalize the phrase selection procedure as an optimization problem and solve it using integer linear programming (ILP). An aspect-based bigram language model is used for generating the final summary with the selected phrases. Experiments show that our summarizer outperforms the other baselines.",
}
@inproceedings{araki-etal-2016-generating,
    title = "Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts",
    author = "Araki, Jun  and
      Rajagopal, Dheeraj  and
      Sankaranarayanan, Sreecharan  and
      Holm, Susan  and
      Yamakawa, Yukari  and
      Mitamura, Teruko",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1107",
    pages = "1125--1136",
    abstract = "We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. Our system is aimed at engaging language learners by generating multiple-choice questions which utilize specific inference steps over multiple sentences, namely coreference resolution and paraphrase detection. The system also generates correct answers and semantically-motivated phrase-level distractors as answer choices. Evaluation by human annotators indicates that our approach requires a larger number of inference steps, which necessitate deeper semantic understanding of texts than a traditional single-sentence approach.",
}
@inproceedings{marques-beuls-2016-evaluation,
    title = "Evaluation Strategies for Computational Construction Grammars",
    author = "Marques, T{\^a}nia  and
      Beuls, Katrien",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1108",
    pages = "1137--1146",
    abstract = "Despite the growing number of Computational Construction Grammar implementations, the field is still lacking evaluation methods to compare grammar fragments across different platforms. Moreover, the hand-crafted nature of most grammars requires profiling tools to understand the complex interactions between constructions of different types. This paper presents a number of evaluation measures, partially based on existing measures in the field of semantic parsing, that are especially relevant for reversible grammar formalisms. The measures are tested on a grammar fragment for European Portuguese clitic placement that is currently under development.",
}
@inproceedings{kajiwara-komachi-2016-building,
    title = "Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings",
    author = "Kajiwara, Tomoyuki  and
      Komachi, Mamoru",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1109",
    pages = "1147--1158",
    abstract = "Methods for text simplification using the framework of statistical machine translation have been extensively studied in recent years. However, building the monolingual parallel corpus necessary for training the model requires costly human annotation. Monolingual parallel corpora for text simplification have therefore been built only for a limited number of languages, such as English and Portuguese. To obviate the need for human annotation, we propose an unsupervised method that automatically builds the monolingual parallel corpus for text simplification using sentence similarity based on word embeddings. For any sentence pair comprising a complex sentence and its simple counterpart, we employ a many-to-one method of aligning each word in the complex sentence with the most similar word in the simple sentence and compute sentence similarity by averaging these word similarities. The experimental results demonstrate the excellent performance of the proposed method in a monolingual parallel corpus construction task for English text simplification. The results also demonstrated the superior accuracy in text simplification that use the framework of statistical machine translation trained using the corpus built by the proposed method to that using the existing corpora.",
}
@inproceedings{servan-etal-2016-word2vec-vs,
    title = "{W}ord2{V}ec vs {DB}nary: Augmenting {METEOR} using Vector Representations or Lexical Resources?",
    author = "Servan, Christophe  and
      B{\'e}rard, Alexandre  and
      Elloumi, Zied  and
      Blanchon, Herv{\'e}  and
      Besacier, Laurent",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1110",
    pages = "1159--1168",
    abstract = "This paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT). This study is made through the enrichment of a well-known MT evaluation metric: METEOR. METEOR enables an approximate match (synonymy or morphological similarity) between an automatic and a reference translation. Our experiments are made in the framework of the Metrics task of WMT 2014. We show that distributed representations are a good alternative to lexico-semanticresources for MT evaluation and they can even bring interesting additional information. The augmented versions of METEOR, using vector representations, are made available on our Github page.",
}
@inproceedings{derczynski-etal-2016-broad,
    title = "Broad {T}witter Corpus: A Diverse Named Entity Recognition Resource",
    author = "Derczynski, Leon  and
      Bontcheva, Kalina  and
      Roberts, Ian",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1111",
    pages = "1169--1179",
    abstract = "One of the main obstacles, hampering method development and comparative evaluation of named entity recognition in social media, is the lack of a sizeable, diverse, high quality annotated corpus, analogous to the CoNLL{'}2003 news dataset. For instance, the biggest Ritter tweet corpus is only 45,000 tokens {--} a mere 15{\%} the size of CoNLL{'}2003. Another major shortcoming is the lack of temporal, geographic, and author diversity. This paper introduces the Broad Twitter Corpus (BTC), which is not only significantly bigger, but sampled across different regions, temporal periods, and types of Twitter users. The gold-standard named entity annotations are made by a combination of NLP experts and crowd workers, which enables us to harness crowd recall while maintaining high quality. We also measure the entity drift observed in our dataset (i.e. how entity representation varies over time), and compare to newswire. The corpus is released openly, including source text and intermediate annotations.",
}
@inproceedings{ilievski-etal-2016-semantic,
    title = "Semantic overfitting: what {`}world{'} do we consider when evaluating disambiguation of text?",
    author = "Ilievski, Filip  and
      Postma, Marten  and
      Vossen, Piek",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1112",
    pages = "1180--1191",
    abstract = "Semantic text processing faces the challenge of defining the relation between lexical expressions and the world to which they make reference within a period of time. It is unclear whether the current test sets used to evaluate disambiguation tasks are representative for the full complexity considering this time-anchored relation, resulting in semantic overfitting to a specific period and the frequent phenomena within. We conceptualize and formalize a set of metrics which evaluate this complexity of datasets. We provide evidence for their applicability on five different disambiguation tasks. To challenge semantic overfitting of disambiguation systems, we propose a time-based, metric-aware method for developing datasets in a systematic and semi-automated manner, as well as an event-based QA task.",
}
@inproceedings{suzuki-takatsuka-2016-extraction,
    title = "Extraction of Keywords of Novelties From Patent Claims",
    author = "Suzuki, Shoko  and
      Takatsuka, Hiromichi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1113",
    pages = "1192--1200",
    abstract = "There are growing needs for patent analysis using Natural Language Processing (NLP)-based approaches. Although NLP-based approaches can extract various information from patents, there are very few approaches proposed to extract those parts what inventors regard as novel or having an inventive step compared to all existing works ever. To extract such parts is difficult even for human annotators except for well-trained experts. This causes many difficulties in analyzing patents. We propose a novel approach to automatically extract such keywords that relate to novelties or inventive steps from patent claims using the structure of the claims. In addition, we also propose a new framework of evaluating our approach. The experiments show that our approach outperforms the existing keyword extraction methods significantly in many technical fields.",
}
@inproceedings{hsi-etal-2016-leveraging,
    title = "Leveraging Multilingual Training for Limited Resource Event Extraction",
    author = "Hsi, Andrew  and
      Yang, Yiming  and
      Carbonell, Jaime  and
      Xu, Ruochen",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1114",
    pages = "1201--1210",
    abstract = "Event extraction has become one of the most important topics in information extraction, but to date, there is very limited work on leveraging cross-lingual training to boost performance. We propose a new event extraction approach that trains on multiple languages using a combination of both language-dependent and language-independent features, with particular focus on the case where target domain training data is of very limited size. We show empirically that multilingual training can boost performance for the tasks of event trigger extraction and event argument extraction on the Chinese ACE 2005 dataset.",
}
@inproceedings{al-badrashiny-diab-2016-lili,
    title = "{LILI}: A Simple Language Independent Approach for Language Identification",
    author = "Al-Badrashiny, Mohamed  and
      Diab, Mona",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1115",
    pages = "1211--1219",
    abstract = "We introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection. The system uses characters level 5-grams and word level unigram language models to train a conditional random fields (CRF) model for classifying input words into various languages. We test our proposed framework and compare it to the state-of-the-art published systems on standard data sets from several language pairs: English-Spanish, Nepali-English, English-Hindi, Arabizi (Refers to Arabic written using the Latin/Roman script)-English, Arabic-Engari (Refers to English written using Arabic script), Modern Standard Arabic(MSA)-Egyptian, Levantine-MSA, Gulf-MSA, one more English-Spanish, and one more MSA-EGY. The overall weighted average F-score of each language pair are 96.4{\%}, 97.3{\%}, 98.0{\%}, 97.0{\%}, 98.9{\%}, 86.3{\%}, 88.2{\%}, 90.6{\%}, 95.2{\%}, and 85.0{\%} respectively. The results show that our approach despite its simplicity, either outperforms or performs at comparable levels to state-of-the-art published systems.",
}
@inproceedings{tayyar-madabushi-lee-2016-high,
    title = "High Accuracy Rule-based Question Classification using Question Syntax and Semantics",
    author = "Tayyar Madabushi, Harish  and
      Lee, Mark",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1116",
    pages = "1220--1230",
    abstract = "We present in this paper a purely rule-based system for Question Classification which we divide into two parts: The first is the extraction of relevant words from a question by use of its structure, and the second is the classification of questions based on rules that associate these words to Concepts. We achieve an accuracy of 97.2{\%}, close to a 6 point improvement over the previous State of the Art of 91.6{\%}. Additionally, we believe that machine learning algorithms can be applied on top of this method to further improve accuracy.",
}
@inproceedings{xiang-etal-2016-incorporating,
    title = "Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via {CNN}-{LSTM}-{CRF}",
    author = "Xiang, Yang  and
      Zhou, Xiaoqiang  and
      Chen, Qingcai  and
      Zheng, Zhihui  and
      Tang, Buzhou  and
      Wang, Xiaolong  and
      Qin, Yang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1117",
    pages = "1231--1241",
    abstract = "In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that the dependency between the answer quality labels also plays a pivotal role. To validate the effectiveness of label dependency, we propose two neural network-based models, with different combination modes of Convolutional Neural Net-works, Long Short Term Memory and Conditional Random Fields. Extensive experi-ments are taken on the dataset released by the SemEval-2015 cQA shared task. The first model is a stacked ensemble of the networks. It achieves 58.96{\%} on macro averaged F1, which improves the state-of-the-art neural network-based method by 2.82{\%} and outper-forms the Top-1 system in the shared task by 1.77{\%}. The second is a simple attention-based model whose input is the connection of the question and its corresponding answers. It produces promising results with 58.29{\%} on overall F1 and gains the best performance on the Good and Bad categories.",
}
@inproceedings{liebeskind-hacohen-kerner-2016-semantically,
    title = "Semantically Motivated {H}ebrew Verb-Noun Multi-Word Expressions Identification",
    author = "Liebeskind, Chaya  and
      HaCohen-Kerner, Yaakov",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1118",
    pages = "1242--1253",
    abstract = "Identification of Multi-Word Expressions (MWEs) lies at the heart of many natural language processing applications. In this research, we deal with a particular type of Hebrew MWEs, Verb-Noun MWEs (VN-MWEs), which combine a verb and a noun with or without other words. Most prior work on MWEs classification focused on linguistic and statistical information. In this paper, we claim that it is essential to utilize semantic information. To this end, we propose a semantically motivated indicator for classifying VN-MWE and define features that are related to various semantic spaces and combine them as features in a supervised classification framework. We empirically demonstrate that our semantic feature set yields better performance than the common linguistic and statistical feature sets and that combining semantic features contributes to the VN-MWEs identification task.",
}
@inproceedings{xiao-liu-2016-semantic,
    title = "Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention",
    author = "Xiao, Minguang  and
      Liu, Cong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1119",
    pages = "1254--1263",
    abstract = "Semantic relation classification remains a challenge in natural language processing. In this paper, we introduce a hierarchical recurrent neural network that is capable of extracting information from raw sentences for relation classification. Our model has several distinctive features: (1) Each sentence is divided into three context subsequences according to two annotated nominals, which allows the model to encode each context subsequence independently so as to selectively focus as on the important context information; (2) The hierarchical model consists of two recurrent neural networks (RNNs): the first one learns context representations of the three context subsequences respectively, and the second one computes semantic composition of these three representations and produces a sentence representation for the relationship classification of the two nominals. (3) The attention mechanism is adopted in both RNNs to encourage the model to concentrate on the important information when learning the sentence representations. Experimental results on the SemEval-2010 Task 8 dataset demonstrate that our model is comparable to the state-of-the-art without using any hand-crafted features.",
}
@inproceedings{guo-etal-2016-unified,
    title = "A Unified Architecture for Semantic Role Labeling and Relation Classification",
    author = "Guo, Jiang  and
      Che, Wanxiang  and
      Wang, Haifeng  and
      Liu, Ting  and
      Xu, Jun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1120",
    pages = "1264--1274",
    abstract = "This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities or nominals. While mostly studied separately in prior work, we show that the two tasks can be effectively connected and modeled using a general architecture. Experiments on CoNLL-2009 benchmark datasets show that our SRL models significantly outperform state-of-the-art approaches. Our RC models also yield competitive performance with the best published records. Furthermore, we show that the two tasks can be trained jointly with multi-task learning, resulting in additive significant improvements for SRL.",
}
@inproceedings{do-etal-2016-facing,
    title = "Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training",
    author = "Do, Quynh Ngoc Thi  and
      Bethard, Steven  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1121",
    pages = "1275--1284",
    abstract = "We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results.",
}
@inproceedings{pado-etal-2016-predictability,
    title = "Predictability of Distributional Semantics in Derivational Word Formation",
    author = "Pad{\'o}, Sebastian  and
      Herbelot, Aur{\'e}lie  and
      Kisselew, Max  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1122",
    pages = "1285--1296",
    abstract = "Compositional distributional semantic models (CDSMs) have successfully been applied to the task of predicting the meaning of a range of linguistic constructions. Their performance on semi-compositional word formation process of (morphological) derivation, however, has been extremely variable, with no large-scale empirical investigation to date. This paper fills that gap, performing an analysis of CDSM predictions on a large dataset (over 30,000 German derivationally related word pairs). We use linear regression models to analyze CDSM performance and obtain insights into the linguistic factors that influence how predictable the distributional context of a derived word is going to be. We identify various such factors, notably part of speech, argument structure, and semantic regularity.",
}
@inproceedings{ohoran-etal-2016-survey,
    title = "Survey on the Use of Typological Information in Natural Language Processing",
    author = "O{'}Horan, Helen  and
      Berzak, Yevgeni  and
      Vuli{\'c}, Ivan  and
      Reichart, Roi  and
      Korhonen, Anna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1123",
    pages = "1297--1308",
    abstract = "In recent years linguistic typologies, which classify the world{'}s languages according to their functional and structural properties, have been widely used to support multilingual NLP. While the growing importance of typologies in supporting multilingual tasks has been recognised, no systematic survey of existing typological resources and their use in NLP has been published. This paper provides such a survey as well as discussion which we hope will both inform and inspire future work in the area.",
}
@inproceedings{gelderloos-chrupala-2016-phonemes,
    title = "From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning",
    author = "Gelderloos, Lieke  and
      Chrupa{\l}a, Grzegorz",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1124",
    pages = "1309--1319",
    abstract = "We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our model indeed learns to predict features of the visual context given phonetically transcribed image descriptions, and show that it represents linguistic information in a hierarchy of levels: lower layers in the stack are comparatively more sensitive to form, whereas higher layers are more sensitive to meaning.",
}
@inproceedings{vaidya-etal-2016-linguistic,
    title = "Linguistic features for {H}indi light verb construction identification",
    author = "Vaidya, Ashwini  and
      Agarwal, Sumeet  and
      Palmer, Martha",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1125",
    pages = "1320--1329",
    abstract = "Light verb constructions (LVC) in Hindi are highly productive. If we can distinguish a case such as nirnay lenaa {`}decision take; decide{'} from an ordinary verb-argument combination kaagaz lenaa {`}paper take; take (a) paper{'},it has been shown to aid NLP applications such as parsing (Begum et al., 2011) and machine translation (Pal et al., 2011). In this paper, we propose an LVC identification system using language specific features for Hindi which shows an improvement over previous work(Begum et al., 2011). To build our system, we carry out a linguistic analysis of Hindi LVCs using Hindi Treebank annotations and propose two new features that are aimed at capturing the diversity of Hindi LVCs in the corpus. We find that our model performs robustly across a diverse range of LVCs and our results underscore the importance of semantic features, which is in keeping with the findings for English. Our error analysis also demonstrates that our classifier can be used to further refine LVC annotations in the Hindi Treebank and make them more consistent across the board.",
}
@inproceedings{barrett-etal-2016-cross,
    title = "Cross-lingual Transfer of Correlations between Parts of Speech and Gaze Features",
    author = "Barrett, Maria  and
      Keller, Frank  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1126",
    pages = "1330--1339",
    abstract = "Several recent studies have shown that eye movements during reading provide information about grammatical and syntactic processing, which can assist the induction of NLP models. All these studies have been limited to English, however. This study shows that gaze and part of speech (PoS) correlations largely transfer across English and French. This means that we can replicate previous studies on gaze-based PoS tagging for French, but also that we can use English gaze data to assist the induction of French NLP models.",
}
@inproceedings{wang-etal-2016-sentence,
    title = "Sentence Similarity Learning by Lexical Decomposition and Composition",
    author = "Wang, Zhiguo  and
      Mi, Haitao  and
      Ittycheriah, Abraham",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1127",
    pages = "1340--1349",
    abstract = "Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.",
}
@inproceedings{wang-he-2016-chinese,
    title = "{C}hinese Hypernym-Hyponym Extraction from User Generated Categories",
    author = "Wang, Chengyu  and
      He, Xiaofeng",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1128",
    pages = "1350--1361",
    abstract = "Hypernym-hyponym ({``}is-a{''}) relations are key components in taxonomies, object hierarchies and knowledge graphs. While there is abundant research on is-a relation extraction in English, it still remains a challenge to identify such relations from Chinese knowledge sources accurately due to the flexibility of language expression. In this paper, we introduce a weakly supervised framework to extract Chinese is-a relations from user generated categories. It employs piecewise linear projection models trained on a Chinese taxonomy and an iterative learning algorithm to update models incrementally. A pattern-based relation selection method is proposed to prevent {``}semantic drift{''} in the learning process using bi-criteria optimization. Experimental results illustrate that the proposed approach outperforms state-of-the-art methods.",
}
@inproceedings{emms-jayapal-2016-dynamic,
    title = "Dynamic Generative model for Diachronic Sense Emergence Detection",
    author = "Emms, Martin  and
      Jayapal, Arun Kumar",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1129",
    pages = "1362--1373",
    abstract = "As time passes words can acquire meanings they did not previously have, such as the {`}twitter post{'} usage of {`}tweet{'}. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the {`}pseudo-word{'} technique (Schutze, 1992) to give a novel further evaluation via {`}pseudo-neologisms{'}. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.",
}
@inproceedings{yuan-etal-2016-semi,
    title = "Semi-supervised Word Sense Disambiguation with Neural Models",
    author = "Yuan, Dayu  and
      Richardson, Julian  and
      Doherty, Ryan  and
      Evans, Colin  and
      Altendorf, Eric",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1130",
    pages = "1374--1385",
    abstract = "Determining the intended sense of words in text {--} word sense disambiguation (WSD) {--} is a long-standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms. However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text. In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text. To alleviate the lack of training data in all-words WSD, we employ the same LSTM in a semi-supervised label propagation classifier. We demonstrate state-of-the-art results, especially on verbs.",
}
@inproceedings{zhang-etal-2016-fast,
    title = "Fast Gated Neural Domain Adaptation: Language Model as a Case Study",
    author = "Zhang, Jian  and
      Wu, Xiaofeng  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1131",
    pages = "1386--1397",
    abstract = "Neural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a novel domain adaptation mechanism in neural network training. Instead of learning and adapting the neural network on millions of training sentences {--} which can be very time-consuming or even infeasible in some cases {--} we design a domain adaptation gating mechanism which can be used in recurrent neural networks and quickly learn the out-of-domain knowledge directly from the word vector representations with little speed overhead. In our experiments, we use the recurrent neural network language model (LM) as a case study. We show that the neural LM perplexity can be reduced by 7.395 and 12.011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively. Furthermore, we show that using the domain-adapted neural LM to re-rank the statistical machine translation n-best list on the French-to-English language pair can significantly improve translation quality.",
}
@inproceedings{guzman-etal-2016-machine,
    title = "Machine Translation Evaluation for {A}rabic using Morphologically-enriched Embeddings",
    author = "Guzm{\'a}n, Francisco  and
      Bouamor, Houda  and
      Baly, Ramy  and
      Habash, Nizar",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1132",
    pages = "1398--1408",
    abstract = "Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges. In this paper, we explore the use of embeddings obtained from different levels of lexical and morpho-syntactic linguistic analysis and show that they improve MT evaluation into an MRL. Specifically we report on Arabic, a language with complex and rich morphology. Our results show that using a neural-network model with different input representations produces results that clearly outperform the state-of-the-art for MT evaluation into Arabic, by almost over 75{\%} increase in correlation with human judgments on pairwise MT evaluation quality task. More importantly, we demonstrate the usefulness of morpho-syntactic representations to model sentence similarity for MT evaluation and address complex linguistic phenomena of Arabic.",
}
@inproceedings{garmash-monz-2016-ensemble,
    title = "Ensemble Learning for Multi-Source Neural Machine Translation",
    author = "Garmash, Ekaterina  and
      Monz, Christof",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1133",
    pages = "1409--1418",
    abstract = "In this paper we describe and evaluate methods to perform ensemble prediction in neural machine translation (NMT). We compare two methods of ensemble set induction: sampling parameter initializations for an NMT system, which is a relatively established method in NMT (Sutskever et al., 2014), and NMT systems translating from different source languages into the same target language, i.e., multi-source ensembles, a method recently introduced by Firat et al. (2016). We are motivated by the observation that for different language pairs systems make different types of mistakes. We propose several methods with different degrees of parameterization to combine individual predictions of NMT systems so that they mutually compensate for each other{'}s mistakes and improve overall performance. We find that the biggest improvements can be obtained from a context-dependent weighting scheme for multi-source ensembles. This result offers stronger support for the linguistic motivation of using multi-source ensembles than previous approaches. Evaluation is carried out for German and French into English translation. The best multi-source ensemble method achieves an improvement of up to 2.2 BLEU points over the strongest single-source ensemble baseline, and a 2 BLEU improvement over a multi-source ensemble baseline.",
}
@inproceedings{oda-etal-2016-phrase,
    title = "Phrase-based Machine Translation using Multiple Preordering Candidates",
    author = "Oda, Yusuke  and
      Kudo, Taku  and
      Nakagawa, Tetsuji  and
      Watanabe, Taro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1134",
    pages = "1419--1428",
    abstract = "In this paper, we propose a new decoding method for phrase-based statistical machine translation which directly uses multiple preordering candidates as a graph structure. Compared with previous phrase-based decoding methods, our method is based on a simple left-to-right dynamic programming in which no decoding-time reordering is performed. As a result, its runtime is very fast and implementing the algorithm becomes easy. Our system does not depend on specific preordering methods as long as they output multiple preordering candidates, and it is trivial to employ existing preordering methods into our system. In our experiments for translating diverse 11 languages into English, the proposed method outperforms conventional phrase-based decoder in terms of translation qualities under comparable or faster decoding time.",
}
@inproceedings{suggu-etal-2016-hand,
    title = "Hand in Glove: Deep Feature Fusion Network Architectures for Answer Quality Prediction in Community Question Answering",
    author = "Suggu, Sai Praneeth  and
      Naga Goutham, Kushwanth  and
      Chinnakotla, Manoj K.  and
      Shrivastava, Manish",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1135",
    pages = "1429--1440",
    abstract = "Community Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this, the problem of Answer Quality Prediction (AQP) aims to predict the quality of an answer posted in response to a forum question. Current AQP systems either learn models using - a) various hand-crafted features (HCF) or b) Deep Learning (DL) techniques which automatically learn the required feature representations. In this paper, we propose a novel approach for AQP known as - {``}Deep Feature Fusion Network (DFFN){''} which combines the advantages of both hand-crafted features and deep learning based systems. Given a question-answer pair along with its metadata, the DFFN architecture independently - a) learns features from the Deep Neural Network (DNN) and b) computes hand-crafted features using various external resources and then combines them using a fully connected neural network trained to predict the final answer quality. DFFN is end-end differentiable and trained as a single system. We propose two different DFFN architectures which vary mainly in the way they model the input question/answer pair - DFFN-CNN uses a Convolutional Neural Network (CNN) and DFFN-BLNA uses a Bi-directional LSTM with Neural Attention (BLNA). Both these proposed variants of DFFN (DFFN-CNN and DFFN-BLNA) achieve state-of-the-art performance on the standard SemEval-2015 and SemEval-2016 benchmark datasets and outperforms baseline approaches which individually employ either HCF or DL based techniques alone.",
}
@inproceedings{li-etal-2016-learning-event,
    title = "Learning Event Expressions via Bilingual Structure Projection",
    author = "Li, Fangyuan  and
      Huang, Ruihong  and
      Xiong, Deyi  and
      Zhang, Min",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1136",
    pages = "1441--1450",
    abstract = "Identifying events of a specific type is a challenging task as events in texts are described in numerous and diverse ways. Aiming to resolve high complexities of event descriptions, previous work (Huang and Riloff, 2013) proposes multi-faceted event recognition and a bootstrapping method to automatically acquire both event facet phrases and event expressions from unannotated texts. However, to ensure high quality of learned phrases, this method is constrained to only learn phrases that match certain syntactic structures. In this paper, we propose a bilingual structure projection algorithm that explores linguistic divergences between two languages (Chinese and English) and mines new phrases with new syntactic structures, which have been ignored in the previous work. Experiments show that our approach can successfully find novel event phrases and structures, e.g., phrases headed by nouns. Furthermore, the newly mined phrases are capable of recognizing additional event descriptions and increasing the recall of event recognition.",
}
@inproceedings{li-etal-2016-global,
    title = "Global Inference to {C}hinese Temporal Relation Extraction",
    author = "Li, Peifeng  and
      Zhu, Qiaoming  and
      Zhou, Guodong  and
      Wang, Hongling",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1137",
    pages = "1451--1460",
    abstract = "Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences. In this paper, we propose a discourse-level global inference model to mine those temporal relations between event mentions in document-level, especially in nonadjacent sentences. Moreover, we provide various kinds of discourse-level constraints, which derived from event semantics, to further improve our global inference model. Evaluation on a Chinese corpus justifies the effectiveness of our discourse-level global inference model over two strong baselines.",
}
@inproceedings{xu-etal-2016-improved,
    title = "Improved relation classification by deep recurrent neural networks with data augmentation",
    author = "Xu, Yan  and
      Jia, Ran  and
      Mou, Lili  and
      Li, Ge  and
      Chen, Yunchuan  and
      Lu, Yangyang  and
      Jin, Zhi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1138",
    pages = "1461--1470",
    abstract = "Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluated our DRNNs on the SemEval-2010 Task 8, and achieve an F1-score of 86.1{\%}, outperforming previous state-of-the-art recorded results.",
}
@inproceedings{jiang-etal-2016-relation,
    title = "Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks",
    author = "Jiang, Xiaotian  and
      Wang, Quan  and
      Li, Peng  and
      Wang, Bin",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1139",
    pages = "1471--1480",
    abstract = "Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error propagation. Recently, a neural network architecture has been proposed to automatically extract features for relation classification. However, this approach follows the traditional expressed-at-least-once assumption, and fails to make full use of information across different sentences. Moreover, it ignores the fact that there can be multiple relations holding between the same entity pair. In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE. It first relaxes the expressed-at-least-once assumption, and employs cross-sentence max-pooling so as to enable information sharing across different sentences. Then it handles overlapping relations by multi-label learning with a neural network classifier. Experimental results show that our approach performs significantly and consistently better than state-of-the-art methods.",
}
@inproceedings{glaser-kuhn-2016-named,
    title = "Named Entity Disambiguation for little known referents: a topic-based approach",
    author = "Glaser, Andrea  and
      Kuhn, Jonas",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1140",
    pages = "1481--1492",
    abstract = "We propose an approach to Named Entity Disambiguation that avoids a problem of standard work on the task (likewise affecting fully supervised, weakly supervised, or distantly supervised machine learning techniques): the treatment of name mentions referring to people with no (or very little) coverage in the textual training data is systematically incorrect. We propose to indirectly take into account the property information for the {``}non-prominent{''} name bearers, such as nationality and profession (e.g., for a Canadian law professor named Michael Jackson, with no Wikipedia article, it is very hard to obtain reliable textual training data). The target property information for the entities is directly available from name authority files, or inferrable, e.g., from listings of sportspeople etc. Our proposed approach employs topic modeling to exploit textual training data based on entities sharing the relevant properties. In experiments with a pilot implementation of the general approach, we show that the approach does indeed work well for name/referent pairs with limited textual coverage in the training data.",
}
@inproceedings{perez-beltrachini-etal-2016-building,
    title = "Building {RDF} Content for Data-to-Text Generation",
    author = "Perez-Beltrachini, Laura  and
      Sayed, Rania  and
      Gardent, Claire",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1141",
    pages = "1493--1502",
    abstract = "In Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators. In this paper, we make one step in that direction and introduce a method for automatically creating an arbitrary large repertoire of data units that could serve as input for generation. Using both automated metrics and a human evaluation, we show that the data units produced by our method are both diverse and coherent.",
}
@inproceedings{ive-yvon-2016-parallel,
    title = "Parallel Sentence Compression",
    author = "Ive, Julia  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1142",
    pages = "1503--1513",
    abstract = "Sentence compression is a way to perform text simplification and is usually handled in a monolingual setting. In this paper, we study ways to extend sentence compression in a bilingual context, where the goal is to obtain parallel compressions of parallel sentences. This can be beneficial for a series of multilingual natural language processing (NLP) tasks. We compare two ways to take bilingual information into account when compressing parallel sentences. Their efficiency is contrasted on a parallel corpus of News articles.",
}
@inproceedings{ma-etal-2016-unsupervised-multi,
    title = "An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model",
    author = "Ma, Shulei  and
      Deng, Zhi-Hong  and
      Yang, Yunlun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1143",
    pages = "1514--1523",
    abstract = "In the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply treat the document set as a group of sentences while ignoring the global semantics of the documents. Meanwhile, neural document model is effective on representing the semantic content of documents in low-dimensional vectors. In this paper, we propose a document-level reconstruction framework named DocRebuild, which reconstructs the documents with summary sentences through a neural document model and selects summary sentences to minimize the reconstruction error. We also apply two strategies, sentence filtering and beamsearch, to improve the performance of our method. Experimental results on the benchmark datasets DUC 2006 and DUC 2007 show that DocRebuild is effective and outperforms the state-of-the-art unsupervised algorithms.",
}
@inproceedings{schwenger-etal-2016-openccg,
    title = "From {O}pen{CCG} to {AI} Planning: Detecting Infeasible Edges in Sentence Generation",
    author = "Schwenger, Maximilian  and
      Torralba, {\'A}lvaro  and
      Hoffmann, Joerg  and
      Howcroft, David M.  and
      Demberg, Vera",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1144",
    pages = "1524--1534",
    abstract = "The search space in grammar-based natural language generation tasks can get very large, which is particularly problematic when generating long utterances or paragraphs. Using surface realization with OpenCCG as an example, we show that we can effectively detect partial solutions (edges) which cannot ultimately be part of a complete sentence because of their syntactic category. Formulating the completion of an edge into a sentence as finding a solution path in a large state-transition system, we demonstrate a connection to AI Planning which is concerned with this kind of problem. We design a compilation from OpenCCG into AI Planning allowing the detection of infeasible edges via AI Planning dead-end detection methods (proving the absence of a solution to the compilation). Our experiments show that this can filter out large fractions of infeasible edges in, and thus benefit the performance of, complex realization processes.",
}
@inproceedings{zopf-etal-2016-next,
    title = "The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach",
    author = "Zopf, Markus  and
      Peyrard, Maxime  and
      Eckle-Kohler, Judith",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1145",
    pages = "1535--1545",
    abstract = "Research in multi-document summarization has focused on newswire corpora since the early beginnings. However, the newswire genre provides genre-specific features such as sentence position which are easy to exploit in summarization systems. Such easy to exploit genre-specific features are available in other genres as well. We therefore present the new hMDS corpus for multi-document summarization, which contains heterogeneous source documents from multiple text genres, as well as summaries with different lengths. For the construction of the corpus, we developed a novel construction approach which is suited to build large and heterogeneous summarization corpora with little effort. The method reverses the usual process of writing summaries for given source documents: it combines already available summaries with appropriate source documents. In a detailed analysis, we show that our new corpus is significantly different from the homogeneous corpora commonly used, and that it is heterogeneous along several dimensions. Our experimental evaluation using well-known state-of-the-art summarization systems shows that our corpus poses new challenges in the field of multi-document summarization. Last but not least, we make our corpus publicly available to the research community at the corpus web page \url{https://github.com/AIPHES/hMDS}.",
}
@inproceedings{saeidi-etal-2016-sentihood,
    title = "{S}enti{H}ood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods",
    author = "Saeidi, Marzieh  and
      Bouchard, Guillaume  and
      Liakata, Maria  and
      Riedel, Sebastian",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1146",
    pages = "1546--1556",
    abstract = "In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis {--} that assumes a single entity per document {---} and targeted sentiment analysis {---} that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighbourhoods are discussed by users. In this context units of text often mention several aspects of one or more neighbourhoods. This is the first time that a generic social media platform,i.e. QA, is used for fine-grained opinion mining. Text coming from QA platforms are far less constrained compared to text from review specific platforms which current datasets are based on. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks",
}
@inproceedings{jovanoski-etal-2016-impact,
    title = "On the Impact of Seed Words on Sentiment Polarity Lexicon Induction",
    author = "Jovanoski, Dame  and
      Pachovski, Veno  and
      Nakov, Preslav",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1147",
    pages = "1557--1567",
    abstract = "Sentiment polarity lexicons are key resources for sentiment analysis, and researchers have invested a lot of efforts in their manual creation. However, there has been a recent shift towards automatically extracted lexicons, which are orders of magnitude larger and perform much better. These lexicons are typically mined using bootstrapping, starting from very few seed words whose polarity is given, e.g., 50-60 words, and sometimes even just 5-6. Here we demonstrate that much higher-quality lexicons can be built by starting with hundreds of words and phrases as seeds, especially when they are in-domain. Thus, we combine (i) mid-sized high-quality manually crafted lexicons as seeds and (ii) bootstrapping, in order to build large-scale lexicons.",
}
@inproceedings{somasundaran-etal-2016-evaluating,
    title = "Evaluating Argumentative and Narrative Essays using Graphs",
    author = "Somasundaran, Swapna  and
      Riordan, Brian  and
      Gyawali, Binod  and
      Yoon, Su-Youn",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1148",
    pages = "1568--1578",
    abstract = "This work investigates whether the development of ideas in writing can be captured by graph properties derived from the text. Focusing on student essays, we represent the essay as a graph, and encode a variety of graph properties including PageRank as features for modeling essay scores related to quality of development. We demonstrate that our approach improves on a state-of-the-art system on the task of holistic scoring of persuasive essays and on the task of scoring narrative essays along the development dimension.",
}
@inproceedings{agrawal-an-2016-selective,
    title = "Selective Co-occurrences for Word-Emotion Association",
    author = "Agrawal, Ameeta  and
      An, Aijun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1149",
    pages = "1579--1590",
    abstract = "Emotion classification from text typically requires some degree of word-emotion association, either gathered from pre-existing emotion lexicons or calculated using some measure of semantic relatedness. Most emotion lexicons contain a fixed number of emotion categories and provide a rather limited coverage. Current measures of computing semantic relatedness, on the other hand, do not adapt well to the specific task of word-emotion association and therefore, yield average results. In this work, we propose an unsupervised method of learning word-emotion association from large text corpora, called Selective Co-occurrences (SECO), by leveraging the property of mutual exclusivity generally exhibited by emotions. Extensive evaluation, using just one seed word per emotion category, indicates the effectiveness of the proposed approach over three emotion lexicons and two state-of-the-art models of word embeddings on three datasets from different domains.",
}
@inproceedings{li-etal-2016-weighted,
    title = "Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification",
    author = "Li, Bofang  and
      Zhao, Zhe  and
      Liu, Tao  and
      Wang, Puwei  and
      Du, Xiaoyong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1150",
    pages = "1591--1600",
    abstract = "NBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to weight sparse bag-of-n-grams representation. N-gram captures word order in short context and NB feature assigns more weights to those important words. However, NBSVM suffers from sparsity problem and is reported to be exceeded by newly proposed distributed (dense) text representations learned by neural networks. In this paper, we transfer the n-grams and NB weighting to neural models. We train n-gram embeddings and use NB weighting to guide the neural models to focus on important words. In fact, our methods can be viewed as distributed (dense) counterparts of sparse bag-of-n-grams in NBSVM. We discover that n-grams and NB weighting are also effective in distributed representations. As a result, our models achieve new strong baselines on 9 text classification datasets, e.g. on IMDB dataset, we reach performance of 93.5{\%} accuracy, which exceeds previous state-of-the-art results obtained by deep neural models. All source codes are publicly available at \url{https://github.com/zhezhaoa/neural_BOW_toolkit}.",
}
@inproceedings{poria-etal-2016-deeper,
    title = "A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks",
    author = "Poria, Soujanya  and
      Cambria, Erik  and
      Hazarika, Devamanyu  and
      Vij, Prateek",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1151",
    pages = "1601--1612",
    abstract = "Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an {``}apparently positive{''} sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network{'}s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.",
}
@inproceedings{barnes-etal-2016-exploring,
    title = "Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.",
    author = "Barnes, Jeremy  and
      Lambert, Patrik  and
      Badia, Toni",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1152",
    pages = "1613--1623",
    abstract = "Cross-lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language. Almost all research into CLSC has been carried out at sentence and document level, although this level of granularity is often less useful. This paper explores methods for performing aspect-based cross-lingual sentiment classification (aspect-based CLSC) for under-resourced languages. Given the limited nature of parallel data for many languages, we would like to make the most of this resource for our task. We compare zero-shot learning, bilingual word embeddings, stacked denoising autoencoder representations and machine translation techniques for aspect-based CLSC. Each of these approaches requires differing amounts of parallel data. We show that models based on distributed semantics can achieve comparable results to machine translation on aspect-based CLSC and give an analysis of the errors found for each method.",
}
@inproceedings{wang-etal-2016-bilingual,
    title = "A Bilingual Attention Network for Code-switched Emotion Prediction",
    author = "Wang, Zhongqing  and
      Zhang, Yue  and
      Lee, Sophia  and
      Li, Shoushan  and
      Zhou, Guodong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1153",
    pages = "1624--1634",
    abstract = "Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words.",
}
@inproceedings{chen-ku-2016-utcnn,
    title = "{UTCNN}: a Deep Learning Model of Stance Classification on Social Media Text",
    author = "Chen, Wei-Fan  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1154",
    pages = "1635--1645",
    abstract = "Most neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work, showing that UTCNN performs well regardless of language or platform.",
}
@inproceedings{cornudella-etal-2016-role,
    title = "The Role of Intrinsic Motivation in Artificial Language Emergence: a Case Study on Colour",
    author = "Cornudella, Miquel  and
      Poibeau, Thierry  and
      van Trijp, Remi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1155",
    pages = "1646--1656",
    abstract = "Human languages have multiple strategies that allow us to discriminate objects in a vast variety of contexts. Colours have been extensively studied from this point of view. In particular, previous research in artificial language evolution has shown how artificial languages may emerge based on specific strategies to distinguish colours. Still, it has not been shown how several strategies of diverse complexity can be autonomously managed by artificial agents . We propose an intrinsic motivation system that allows agents in a population to create a shared artificial language and progressively increase its expressive power. Our results show that with such a system agents successfully regulate their language development, which indicates a relation between population size and consistency in the emergent communicative systems.",
}
@inproceedings{hayashi-2016-predicting,
    title = "Predicting the Evocation Relation between Lexicalized Concepts",
    author = "Hayashi, Yoshihiko",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1156",
    pages = "1657--1668",
    abstract = "Evocation is a directed yet weighted semantic relationship between lexicalized concepts. Although evocation relations are considered potentially useful in several semantic NLP tasks, the prediction of the evocation relation between an arbitrary pair of concepts remains difficult, since evocation relationships cover a broader range of semantic relations rooted in human perception and experience. This paper presents a supervised learning approach to predict the strength (by regression) and to determine the directionality (by classification) of the evocation relation that might hold between a pair of lexicalized concepts. Empirical results that were obtained by investigating useful features are shown, indicating that a combination of the proposed features largely outperformed individual baselines, and also suggesting that semantic relational vectors computed from existing semantic vectors for lexicalized concepts were indeed effective for both the prediction of strength and the determination of directionality.",
}
@inproceedings{paetzold-specia-2016-collecting,
    title = "Collecting and Exploring Everyday Language for Predicting Psycholinguistic Properties of Words",
    author = "Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1157",
    pages = "1669--1679",
    abstract = "Exploring language usage through frequency analysis in large corpora is a defining feature in most recent work in corpus and computational linguistics. From a psycholinguistic perspective, however, the corpora used in these contributions are often not representative of language usage: they are either domain-specific, limited in size, or extracted from unreliable sources. In an effort to address this limitation, we introduce SubIMDB, a corpus of everyday language spoken text we created which contains over 225 million words. The corpus was extracted from 38,102 subtitles of family, comedy and children movies and series, and is the first sizeable structured corpus of subtitles made available. Our experiments show that word frequency norms extracted from this corpus are more effective than those from well-known norms such as Kucera-Francis, HAL and SUBTLEXus in predicting various psycholinguistic properties of words, such as lexical decision times, familiarity, age of acquisition and simplicity. We also provide evidence that contradict the long-standing assumption that the ideal size for a corpus can be determined solely based on how well its word frequencies correlate with lexical decision times.",
}
@inproceedings{wachsmuth-etal-2016-using,
    title = "Using Argument Mining to Assess the Argumentation Quality of Essays",
    author = "Wachsmuth, Henning  and
      Al-Khatib, Khalid  and
      Stein, Benno",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1158",
    pages = "1680--1691",
    abstract = "Argument mining aims to determine the argumentative structure of texts. Although it is said to be crucial for future applications such as writing support systems, the benefit of its output has rarely been evaluated. This paper puts the analysis of the output into the focus. In particular, we investigate to what extent the mined structure can be leveraged to assess the argumentation quality of persuasive essays. We find insightful statistical patterns in the structure of essays. From these, we derive novel features that we evaluate in four argumentation-related essay scoring tasks. Our results reveal the benefit of argument mining for assessing argumentation quality. Among others, we improve the state of the art in scoring an essay{'}s organization and its argument strength.",
}
@inproceedings{wang-andersen-2016-grammatical,
    title = "Grammatical Templates: Improving Text Difficulty Evaluation for Language Learners",
    author = "Wang, Shuhan  and
      Andersen, Erik",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1159",
    pages = "1692--1702",
    abstract = "Language students are most engaged while reading texts at an appropriate difficulty level. However, existing methods of evaluating text difficulty focus mainly on vocabulary and do not prioritize grammatical features, hence they do not work well for language learners with limited knowledge of grammar. In this paper, we introduce grammatical templates, the expert-identified units of grammar that students learn from class, as an important feature of text difficulty evaluation. Experimental classification results show that grammatical template features significantly improve text difficulty prediction accuracy over baseline readability features by 7.4{\%}. Moreover,we build a simple and human-understandable text difficulty evaluation approach with 87.7{\%} accuracy, using only 5 grammatical template features.",
}
@inproceedings{schnober-etal-2016-still,
    title = "Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks",
    author = "Schnober, Carsten  and
      Eger, Steffen  and
      Do Dinh, Erik-L{\^a}n  and
      Gurevych, Iryna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1160",
    pages = "1703--1714",
    abstract = "We analyze the performance of encoder-decoder neural models and compare them with well-known established methods. The latter represent different classes of traditional approaches that are applied to the monotone sequence-to-sequence tasks OCR post-correction, spelling correction, grapheme-to-phoneme conversion, and lemmatization. Such tasks are of practical relevance for various higher-level research fields including digital humanities, automatic text correction, and speech recognition. We investigate how well generic deep-learning approaches adapt to these tasks, and how they perform in comparison with established and more specialized methods, including our own adaptation of pruned CRFs.",
}
@inproceedings{jiang-etal-2016-towards,
    title = "Towards Time-Aware Knowledge Graph Completion",
    author = "Jiang, Tingsong  and
      Liu, Tianyu  and
      Ge, Tao  and
      Sha, Lei  and
      Chang, Baobao  and
      Li, Sujian  and
      Sui, Zhifang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1161",
    pages = "1715--1724",
    abstract = "Knowledge graph (KG) completion adds new facts to a KG by making inferences from existing facts. Most existing methods ignore the time information and only learn from time-unknown fact triples. In dynamic environments that evolve over time, it is important and challenging for knowledge graph completion models to take into account the temporal aspects of facts. In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts. To incorporate the happening time of facts, we propose a time-aware KG embedding model using temporal order information among facts. To incorporate the valid time of facts, we propose a joint time-aware inference model based on Integer Linear Programming (ILP) using temporal consistencyinformationasconstraints. Wefurtherintegratetwomodelstomakefulluseofglobal temporal information. We empirically evaluate our models on time-aware KG completion task. Experimental results show that our time-aware models achieve the state-of-the-art on temporal facts consistently.",
}
@inproceedings{dadashkarimi-etal-2016-learning,
    title = "Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries",
    author = "Dadashkarimi, Javid  and
      Jalili Sabet, Masoud  and
      Shakery, Azadeh",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1162",
    pages = "1725--1733",
    abstract = "Ordinal regression which is known with learning to rank has long been used in information retrieval (IR). Learning to rank algorithms, have been tailored in document ranking, information filtering, and building large aligned corpora successfully. In this paper, we propose to use this algorithm for query modeling in cross-language environments. To this end, first we build a query-generated training data using pseudo-relevant documents to the query and all translation candidates. The pseudo-relevant documents are obtained by top-ranked documents in response to a translation of the original query. The class of each candidate in the training data is determined based on presence/absence of the candidate in the pseudo-relevant documents. We learn an ordinal regression model to score the candidates based on their relevance to the context of the query, and after that, we construct a query-dependent translation model using a softmax function. Finally, we re-weight the query based on the obtained model. Experimental results on French, German, Spanish, and Italian CLEF collections demonstrate that the proposed method achieves better results compared to state-of-the-art cross-language information retrieval methods, particularly in long queries with large training data.",
}
@inproceedings{romeo-etal-2016-neural,
    title = "Neural Attention for Learning to Rank Questions in Community Question Answering",
    author = "Romeo, Salvatore  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Moschitti, Alessandro  and
      Belinkov, Yonatan  and
      Hsu, Wei-Ning  and
      Zhang, Yu  and
      Mohtarami, Mitra  and
      Glass, James",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1163",
    pages = "1734--1745",
    abstract = "In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.",
}
@inproceedings{yin-etal-2016-simple,
    title = "Simple Question Answering by Attentive Convolutional Neural Network",
    author = {Yin, Wenpeng  and
      Yu, Mo  and
      Xiang, Bing  and
      Zhou, Bowen  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1164",
    pages = "1746--1756",
    abstract = "This work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA), can be addressed via a two-step pipeline: entity linking and fact selection. In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character-level convolutional neural network (char-CNN), and match the predicate in that fact with the question by a word-level CNN (word-CNN). This work makes two main contributions. (i) A simple and effective entity linker over Freebase is proposed. Our entity linker outperforms the state-of-the-art entity linker over SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so that the predicate representation can be matched with the predicate-focused question representation more effectively. Experiments show that our system sets new state-of-the-art in this task.",
}
@inproceedings{semeniuta-etal-2016-recurrent,
    title = "Recurrent Dropout without Memory Loss",
    author = "Semeniuta, Stanislau  and
      Severyn, Aliaksei  and
      Barth, Erhardt",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1165",
    pages = "1757--1766",
    abstract = "This paper presents a novel approach to recurrent neural network (RNN) regularization. Differently from the widely adopted dropout method, which is applied to forward connections of feedforward architectures or RNNs, we propose to drop neurons directly in recurrent connections in a way that does not cause loss of long-term memory. Our approach is as easy to implement and apply as the regular feed-forward dropout and we demonstrate its effectiveness for the most effective modern recurrent network {--} Long Short-Term Memory network. Our experiments on three NLP benchmarks show consistent improvements even when combined with conventional feed-forward dropout.",
}
@inproceedings{balikas-etal-2016-modeling,
    title = "Modeling topic dependencies in semantically coherent text spans with copulas",
    author = "Balikas, Georgios  and
      Amoualian, Hesam  and
      Clausel, Marianne  and
      Gaussier, Eric  and
      Amini, Massih R.",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1166",
    pages = "1767--1776",
    abstract = "The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent. We propose copulaLDA, that extends LDA by integrating part of the text structure to the model and relaxes the conditional independence assumption between the word-specific latent topics given the per-document topic distributions. To this end, we assume that the words of text spans like noun-phrases are topically bound and we model this dependence with copulas. We demonstrate empirically the effectiveness of copulaLDA on both intrinsic and extrinsic evaluation tasks on several publicly available corpora.",
}
@inproceedings{cui-etal-2016-consensus,
    title = "Consensus Attention-based Neural Networks for {C}hinese Reading Comprehension",
    author = "Cui, Yiming  and
      Liu, Ting  and
      Chen, Zhipeng  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1167",
    pages = "1777--1786",
    abstract = "Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children{'}s Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query. Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets. Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.",
}
@inproceedings{felt-etal-2016-semantic,
    title = "Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings",
    author = "Felt, Paul  and
      Ringger, Eric  and
      Seppi, Kevin",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1168",
    pages = "1787--1796",
    abstract = "In modern text annotation projects, crowdsourced annotations are often aggregated using item response models or by majority vote. Recently, item response models enhanced with generative data models have been shown to yield substantial benefits over those with conditional or no data models. However, suitable generative data models do not exist for many tasks, such as semantic labeling tasks. When no generative data model exists, we demonstrate that similar benefits may be derived by conditionally modeling documents that have been previously embedded in a semantic space using recent work in vector space models. We use this approach to show state-of-the-art results on a variety of semantic annotation aggregation tasks.",
}
@inproceedings{ye-etal-2016-interactive,
    title = "Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix",
    author = "Ye, Na  and
      Zhang, Guiping  and
      Cai, Dongfeng",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1169",
    pages = "1797--1806",
    abstract = "Interactive-predictive machine translation (IPMT) is a translation mode which combines machine translation technology and human behaviours. In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. However, state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of the prefix is not fully developed. Focusing on this problem, this paper mines the deep constraints of prefix on syntactic level to improve the performance of IPMT systems. Two syntactic subtree matching rules based on phrase structure grammar are proposed to filter the translation hypotheses more strictly. Experimental results on LDC Chinese-English corpora show that the proposed method outperforms state-of-the-art phrase-based IPMT system while keeping comparable decoding speed.",
}
@inproceedings{zhang-etal-2016-topic,
    title = "Topic-Informed Neural Machine Translation",
    author = "Zhang, Jian  and
      Li, Liangyou  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1170",
    pages = "1807--1817",
    abstract = "In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters to maximize the conditional probability of target sentences given source sentences. In this paper, we present a novel approach to improve the translation performance in NMT by conveying topic knowledge during translation. The proposed topic-informed NMT can increase the likelihood of selecting words from the same topic and domain for translation. Experimentally, we demonstrate that topic-informed NMT can achieve a 1.15 (3.3{\%} relative) and 1.67 (5.4{\%} relative) absolute improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005 test sets, respectively, compared to NMT without topic information.",
}
@inproceedings{cao-etal-2016-distribution,
    title = "A Distribution-based Model to Learn Bilingual Word Embeddings",
    author = "Cao, Hailong  and
      Zhao, Tiejun  and
      Zhang, Shu  and
      Meng, Yao",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1171",
    pages = "1818--1827",
    abstract = "We introduce a distribution based model to learn bilingual word embeddings from monolingual data. It is simple, effective and does not require any parallel data or any seed lexicon. We take advantage of the fact that word embeddings are usually in form of dense real-valued low-dimensional vector and therefore the distribution of them can be accurately estimated. A novel cross-lingual learning objective is proposed which directly matches the distributions of word embeddings in one language with that in the other language. During the joint learning process, we dynamically estimate the distributions of word embeddings in two languages respectively and minimize the dissimilarity between them through standard back propagation algorithm. Our learned bilingual word embeddings allow to group each word and its translations together in the shared vector space. We demonstrate the utility of the learned embeddings on the task of finding word-to-word translations from monolingual corpora. Our model achieved encouraging performance on data in both related languages and substantially different languages.",
}
@inproceedings{niehues-etal-2016-pre,
    title = "Pre-Translation for Neural Machine Translation",
    author = "Niehues, Jan  and
      Cho, Eunah  and
      Ha, Thanh-Le  and
      Waibel, Alex",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1172",
    pages = "1828--1836",
    abstract = "Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur. When using statistical machine translation, it has already been shown that significant gains can be achieved by simplifying the input in a preprocessing step. A commonly used example is the pre-reordering approach. In this work, we used phrase-based machine translation to pre-translate the input into the target language. Then a neural machine translation system generates the final hypothesis using the pre-translation. Thereby, we use either only the output of the phrase-based machine translation (PBMT) system or a combination of the PBMT output and the source sentence. We evaluate the technique on the English to German translation task. Using this approach we are able to outperform the PBMT system as well as the baseline neural MT system by up to 2 BLEU points. We analyzed the influence of the quality of the initial system on the final result.",
}
@inproceedings{claveau-kijak-2016-direct,
    title = "Direct vs. indirect evaluation of distributional thesauri",
    author = "Claveau, Vincent  and
      Kijak, Ewa",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1173",
    pages = "1837--1848",
    abstract = "With the success of word embedding methods in various Natural Language Processing tasks, all the field of distributional semantics has experienced a renewed interest. Beside the famous word2vec, recent studies have presented efficient techniques to build distributional thesaurus; in particular, Claveau et al. (2014) have already shown that Information Retrieval (IR) tools and concepts can be successfully used to build a thesaurus. In this paper, we address the problem of the evaluation of such thesauri or embedding models and compare their results. Through several experiments and by evaluating directly the results with reference lexicons, we show that the recent IR-based distributional models outperform state-of-the-art systems such as word2vec. Following the work of Claveau and Kijak (2016), we use IR as an applicative framework to indirectly evaluate the generated thesaurus. Here again, this task-based evaluation validates the IR approach used to build the thesaurus. Moreover, it allows us to compare these results with those from the direct evaluation framework used in the literature. The observed differences bring these evaluation habits into question.",
}
@inproceedings{jameel-schockaert-2016-glove,
    title = "{D}-{G}lo{V}e: A Feasible Least Squares Model for Estimating Word Embedding Densities",
    author = "Jameel, Shoaib  and
      Schockaert, Steven",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1174",
    pages = "1849--1860",
    abstract = "We propose a new word embedding model, inspired by GloVe, which is formulated as a feasible least squares optimization problem. In contrast to existing models, we explicitly represent the uncertainty about the exact definition of each word vector. To this end, we estimate the error that results from using noisy co-occurrence counts in the formulation of the model, and we model the imprecision that results from including uninformative context words. Our experimental results demonstrate that this model compares favourably with existing word embedding models.",
}
@inproceedings{de-deyne-etal-2016-predicting,
    title = "Predicting human similarity judgments with distributional models: The value of word associations.",
    author = "De Deyne, Simon  and
      Perfors, Amy  and
      Navarro, Daniel J",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1175",
    pages = "1861--1870",
    abstract = "Most distributional lexico-semantic models derive their representations based on external language resources such as text corpora. In this study, we propose that internal language models, that are more closely aligned to the mental representations of words could provide important insights into cognitive science, including linguistics. Doing so allows us to reflect upon theoretical questions regarding the structure of the mental lexicon, and also puts into perspective a number of assumptions underlying recently proposed distributional text-based models. In particular, we focus on word-embedding models which have been proposed to learn aspects of word meaning in a manner similar to humans. These are contrasted with internal language models derived from a new extensive data set of word associations. Using relatedness and similarity judgments we evaluate these models and find that the word-association-based internal language models consistently outperform current state-of-the art text-based external language models, often with a large margin. These results are not just a performance improvement; they also have implications for our understanding of how distributional knowledge is used by people.",
}
@inproceedings{yamane-etal-2016-distributional,
    title = "Distributional Hypernym Generation by Jointly Learning Clusters and Projections",
    author = "Yamane, Josuke  and
      Takatani, Tomoya  and
      Yamada, Hitoshi  and
      Miwa, Makoto  and
      Sasaki, Yutaka",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1176",
    pages = "1871--1879",
    abstract = "We propose a novel word embedding-based hypernym generation model that jointly learns clusters of hyponym-hypernym relations, i.e., hypernymy, and projections from hyponym to hypernym embeddings. Most of the recent hypernym detection models focus on a hypernymy classification problem that determines whether a pair of words is in hypernymy or not. These models do not directly deal with a hypernym generation problem in that a model generates hypernyms for a given word. Differently from previous studies, our model jointly learns the clusters and projections with adjusting the number of clusters so that the number of clusters can be determined depending on the learned projections and vice versa. Our model also boosts the performance by incorporating inner product-based similarity measures and negative examples, i.e., sampled non-hypernyms, into our objectives in learning. We evaluated our joint learning models on the task of Japanese and English hypernym generation and showed a significant improvement over an existing pipeline model. Our model also compared favorably to existing distributed hypernym detection models on the English hypernym classification task.",
}
@inproceedings{hou-2016-incremental,
    title = "Incremental Fine-grained Information Status Classification Using Attention-based {LSTM}s",
    author = "Hou, Yufang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1177",
    pages = "1880--1890",
    abstract = "Information status plays an important role in discourse processing. According to the hearer{'}s common sense knowledge and his comprehension of the preceding text, a discourse entity could be old, mediated or new. In this paper, we propose an attention-based LSTM model to address the problem of fine-grained information status classification in an incremental manner. Our approach resembles how human beings process the task, i.e., decide the information status of the current discourse entity based on its preceding context. Experimental results on the ISNotes corpus (Markert et al., 2012) reveal that (1) despite its moderate result, our model with only word embedding features captures the necessary semantic knowledge needed for the task by a large extent; and (2) when incorporating with additional several simple features, our model achieves the competitive results compared to the state-of-the-art approach (Hou et al., 2013) which heavily depends on lots of hand-crafted semantic features.",
}
@inproceedings{shih-chen-2016-detection,
    title = "Detection, Disambiguation and Argument Identification of Discourse Connectives in {C}hinese Discourse Parsing",
    author = "Shih, Yong-Siang  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1178",
    pages = "1891--1902",
    abstract = "In this paper, we investigate four important issues together for explicit discourse relation labelling in Chinese texts: (1) discourse connective extraction, (2) linking ambiguity resolution, (3) relation type disambiguation, and (4) argument boundary identification. In a pipelined Chinese discourse parser, we identify potential connective candidates by string matching, eliminate non-discourse usages from them with a binary classifier, resolve linking ambiguities among connective components by ranking, disambiguate relation types by a multiway classifier, and determine the argument boundaries by conditional random fields. The experiments on Chinese Discourse Treebank show that the F1 scores of 0.7506, 0.7693, 0.7458, and 0.3134 are achieved for discourse usage disambiguation, linking disambiguation, relation type disambiguation, and argument boundary identification, respectively, in a pipelined Chinese discourse parser.",
}
@inproceedings{braud-etal-2016-multi,
    title = "Multi-view and multi-task training of {RST} discourse parsers",
    author = "Braud, Chlo{\'e}  and
      Plank, Barbara  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1179",
    pages = "1903--1913",
    abstract = "We experiment with different ways of training LSTM networks to predict RST discourse trees. The main challenge for RST discourse parsing is the limited amounts of training data. We combat this by regularizing our models using task supervision from related tasks as well as alternative views on discourse structures. We show that a simple LSTM sequential discourse parser takes advantage of this multi-view and multi-task framework with 12-15{\%} error reductions over our baseline (depending on the metric) and results that rival more complex state-of-the-art parsers.",
}
@inproceedings{qin-etal-2016-implicit,
    title = "Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings",
    author = "Qin, Lianhui  and
      Zhang, Zhisong  and
      Zhao, Hai",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1180",
    pages = "1914--1924",
    abstract = "For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode the latent semantic information, and are suitable for recognizing semantic relations between argument pairs. However, conventional vector representations usually adopt embeddings at the word level and cannot well handle the rare word problem without carefully considering morphological information at character level. Moreover, embeddings are assigned to individual words independently, which lacks of the crucial contextual information. This paper proposes a neural model utilizing context-aware character-enhanced embeddings to alleviate the drawbacks of the current word level representation. Our experiments show that the enhanced embeddings work well and the proposed model obtains state-of-the-art results.",
}
@inproceedings{pluss-piwek-2016-measuring,
    title = "Measuring Non-cooperation in Dialogue",
    author = {Pl{\"u}ss, Brian  and
      Piwek, Paul},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1181",
    pages = "1925--1936",
    abstract = "This paper introduces a novel method for measuring non-cooperation in dialogue. The key idea is that linguistic non-cooperation can be measured in terms of the extent to which dialogue participants deviate from conventions regarding the proper introduction and discharging of conversational obligations (e.g., the obligation to respond to a question). Previous work on non cooperation has focused mainly on non-linguistic task-related non-cooperation or modelled non-cooperation in terms of special rules describing non-cooperative behaviours. In contrast, we start from rules for normal/correct dialogue behaviour - i.e., a dialogue game - which in principle can be derived from a corpus of cooperative dialogues, and provide a quantitative measure for the degree to which participants comply with these rules. We evaluated the model on a corpus of political interviews, with encouraging results. The model predicts accurately the degree of cooperation for one of the two dialogue game roles (interviewer) and also the relative cooperation for both roles (i.e., which interlocutor in the conversation was most cooperative). Being able to measure cooperation has applications in many areas from the analysis - manual, semi and fully automatic - of natural language interactions to human-like virtual personal assistants, tutoring agents, sophisticated dialogue systems, and role-playing virtual humans.",
}
@inproceedings{derczynski-2016-representation,
    title = "Representation and Learning of Temporal Relations",
    author = "Derczynski, Leon",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1182",
    pages = "1937--1948",
    abstract = "Determining the relative order of events and times described in text is an important problem in natural language processing. It is also a difficult one: general state-of-the-art performance has been stuck at a relatively low ceiling for years. We investigate the representation of temporal relations, and empirically evaluate the effect that various temporal relation representations have on machine learning performance. While machine learning performance decreases with increased representational expressiveness, not all representation simplifications have equal impact.",
}
@inproceedings{upadhyay-etal-2016-revisiting,
    title = "Revisiting the Evaluation for Cross Document Event Coreference",
    author = "Upadhyay, Shyam  and
      Gupta, Nitish  and
      Christodoulopoulos, Christos  and
      Roth, Dan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1183",
    pages = "1949--1958",
    abstract = "Cross document event coreference (CDEC) is an important task that aims at aggregating event-related information across multiple documents. We revisit the evaluation for CDEC, and discover that past works have adopted different, often inconsistent, evaluation settings, which either overlook certain mistakes in coreference decisions, or make assumptions that simplify the coreference task considerably. We suggest a new evaluation methodology which overcomes these limitations, and allows for an accurate assessment of CDEC systems. Our new evaluation setting better reflects the corpus-wide information aggregation ability of CDEC systems by separating event-coreference decisions made across documents from those made within a document. In addition, we suggest a better baseline for the task and semi-automatically identify several inconsistent annotations in the evaluation dataset.",
}
@inproceedings{watanabe-etal-2016-modeling,
    title = "Modeling Discourse Segments in Lyrics Using Repeated Patterns",
    author = "Watanabe, Kento  and
      Matsubayashi, Yuichiroh  and
      Orita, Naho  and
      Okazaki, Naoaki  and
      Inui, Kentaro  and
      Fukayama, Satoru  and
      Nakano, Tomoyasu  and
      Smith, Jordan  and
      Goto, Masataka",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1184",
    pages = "1959--1969",
    abstract = "This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics. To test our hypothesis that discourse segmentations in lyrics strongly correlate with repeated patterns, we conduct the first large-scale corpus study on discourse segments in lyrics. Next, we propose the task to automatically identify segment boundaries in lyrics and train a logistic regression model for the task with the repeated pattern and textual features. The results of our empirical experiments illustrate the significance of capturing repeated patterns in predicting the boundaries of discourse segments in lyrics.",
}
@inproceedings{li-wu-2016-multi,
    title = "Multi-level Gated Recurrent Neural Network for dialog act classification",
    author = "Li, Wei  and
      Wu, Yunfang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1185",
    pages = "1970--1979",
    abstract = "In this paper we focus on the problem of dialog act (DA) labelling. This problem has recently attracted a lot of attention as it is an important sub-part of an automatic question answering system, which is currently in great demand. Traditional methods tend to see this problem as a sequence labelling task and deals with it by applying classifiers with rich features. Most of the current neural network models still omit the sequential information in the conversation. Henceforth, we apply a novel multi-level gated recurrent neural network (GRNN) with non-textual information to predict the DA tag. Our model not only utilizes textual information, but also makes use of non-textual and contextual information. In comparison, our model has shown significant improvement over previous works on Switchboard Dialog Act (SWDA) task by over 6{\%}.",
}
@inproceedings{patra-etal-2016-multimodal,
    title = "Multimodal Mood Classification - A Case Study of Differences in {H}indi and Western Songs",
    author = "Patra, Braja Gopal  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1186",
    pages = "1980--1989",
    abstract = "Music information retrieval has emerged as a mainstream research area in the past two decades. Experiments on music mood classification have been performed mainly on Western music based on audio, lyrics and a combination of both. Unfortunately, due to the scarcity of digitalized resources, Indian music fares poorly in music mood retrieval research. In this paper, we identified the mood taxonomy and prepared multimodal mood annotated datasets for Hindi and Western songs. We identified important audio and lyric features using correlation based feature selection technique. Finally, we developed mood classification systems using Support Vector Machines and Feed Forward Neural Networks based on the features collected from audio, lyrics, and a combination of both. The best performing multimodal systems achieved F-measures of 75.1 and 83.5 for classifying the moods of the Hindi and Western songs respectively using Feed Forward Neural Networks. A comparative analysis indicates that the selected features work well for mood classification of the Western songs and produces better results as compared to the mood classification systems for Hindi songs.",
}
@inproceedings{li-etal-2016-detecting,
    title = "Detecting Context Dependent Messages in a Conversational Environment",
    author = "Li, Chaozhuo  and
      Wu, Yu  and
      Wu, Wei  and
      Xing, Chen  and
      Li, Zhoujun  and
      Zhou, Ming",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1187",
    pages = "1990--1999",
    abstract = "While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an input text in the generation process. The task is challenging, as messages in a conversational environment are short and informal, and evidence that can indicate a message is context dependent is scarce. After a study of social conversation data crawled from the web, we observed that some characteristics estimated from the responses of messages are discriminative for identifying context dependent messages. With the characteristics as weak supervision, we propose using a Long Short Term Memory (LSTM) network to learn a classifier. Our method carries out text representation and classifier learning in a unified framework. Experimental results show that the proposed method can significantly outperform baseline methods on accuracy of classification.",
}
@inproceedings{venugopal-rus-2016-joint,
    title = "Joint Inference for Mode Identification in Tutorial Dialogues",
    author = "Venugopal, Deepak  and
      Rus, Vasile",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1188",
    pages = "2000--2011",
    abstract = "Identifying dialogue acts and dialogue modes during tutorial interactions is an extremely crucial sub-step in understanding patterns of effective tutor-tutee interactions. In this work, we develop a novel joint inference method that labels each utterance in a tutoring dialogue session with a dialogue act and a specific mode from a set of pre-defined dialogue acts and modes, respectively. Specifically, we develop our joint model using Markov Logic Networks (MLNs), a framework that combines first-order logic with probabilities, and is thus capable of representing complex, uncertain knowledge. We define first-order formulas in our MLN that encode the inter-dependencies between dialogue modes and more fine-grained dialogue actions. We then use a joint inference to jointly label the modes as well as the dialogue acts in an utterance. We compare our system against a pipeline system based on SVMs on a real-world dataset with tutoring sessions of over 500 students. Our results show that the joint inference system is far more effective than the pipeline system in mode detection, and improves over the performance of the pipeline system by about 6 points in F1 score. The joint inference system also performs much better than the pipeline system in the context of labeling modes that highlight important pedagogical steps in tutoring.",
}
@inproceedings{khanpour-etal-2016-dialogue,
    title = "Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network",
    author = "Khanpour, Hamed  and
      Guntakandla, Nishitha  and
      Nielsen, Rodney",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1189",
    pages = "2012--2021",
    abstract = "In this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy. Using the findings of these experiments, we trained a deep LSTM network that outperforms the state-of-the-art on the Switchboard corpus by 3.11{\%}, and MRDA by 2.2{\%}.",
}
@inproceedings{kumar-joshi-2016-non,
    title = "Non-sentential Question Resolution using Sequence to Sequence Learning",
    author = "Kumar, Vineet  and
      Joshi, Sachindra",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1190",
    pages = "2022--2031",
    abstract = "An interactive Question Answering (QA) system frequently encounters non-sentential (incomplete) questions. These non-sentential questions may not make sense to the system when a user asks them without the context of conversation. The system thus needs to take into account the conversation context to process the question. In this work, we present a recurrent neural network (RNN) based encoder decoder network that can generate a complete (intended) question, given an incomplete question and conversation context. RNN encoder decoder networks have been show to work well when trained on a parallel corpus with millions of sentences, however it is extremely hard to obtain conversation data of this magnitude. We therefore propose to decompose the original problem into two separate simplified problems where each problem focuses on an abstraction. Specifically, we train a semantic sequence model to learn semantic patterns, and a syntactic sequence model to learn linguistic patterns. We further combine syntactic and semantic sequence models to generate an ensemble model. Our model achieves a BLEU score of 30.15 as compared to 18.54 using a standard RNN encoder decoder model.",
}
@inproceedings{zhou-etal-2016-context,
    title = "Context-aware Natural Language Generation for Spoken Dialogue Systems",
    author = "Zhou, Hao  and
      Huang, Minlie  and
      Zhu, Xiaoyan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1191",
    pages = "2032--2041",
    abstract = "Natural language generation (NLG) is an important component of question answering(QA) systems which has a significant impact on system quality. Most tranditional QA systems based on templates or rules tend to generate rigid and stylised responses without the natural variation of human language. Furthermore, such methods need an amount of work to generate the templates or rules. To address this problem, we propose a Context-Aware LSTM model for NLG. The model is completely driven by data without manual designed templates or rules. In addition, the context information, including the question to be answered, semantic values to be addressed in the response, and the dialogue act type during interaction, are well approached in the neural network model, which enables the model to produce variant and informative responses. The quantitative evaluation and human evaluation show that CA-LSTM obtains state-of-the-art performance.",
}
@inproceedings{serriere-etal-2016-weakly,
    title = "Weakly-supervised text-to-speech alignment confidence measure",
    author = "Serri{\`e}re, Guillaume  and
      Cerisara, Christophe  and
      Fohr, Dominique  and
      Mella, Odile",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1192",
    pages = "2042--2050",
    abstract = "This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.",
}
@inproceedings{kim-etal-2016-domainless,
    title = "Domainless Adaptation by Constrained Decoding on a Schema Lattice",
    author = "Kim, Young-Bum  and
      Stratos, Karl  and
      Sarikaya, Ruhi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1193",
    pages = "2051--2060",
    abstract = "In many applications such as personal digital assistants, there is a constant need for new domains to increase the system{'}s coverage of user queries. A conventional approach is to learn a separate model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck for scaling to a large number of domains. In this paper, we introduce a framework that allows us to have a single model that can handle all domains: including unknown domains that may be created in the future as long as they are covered in the master schema. The key idea is to remove the need for distinguishing domains by explicitly predicting the schema of queries. Given permitted schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under the schema. The proposed model achieves competitive and often superior performance over the conventional model trained separately per domain.",
}
@inproceedings{singh-etal-2016-sub,
    title = "Sub-Word Similarity based Search for Embeddings: Inducing Rare-Word Embeddings for Word Similarity Tasks and Language Modelling",
    author = "Singh, Mittul  and
      Greenberg, Clayton  and
      Oualil, Youssef  and
      Klakow, Dietrich",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1194",
    pages = "2061--2070",
    abstract = "Training good word embeddings requires large amounts of data. Out-of-vocabulary words will still be encountered at test-time, leaving these words without embeddings. To overcome this lack of embeddings for rare words, existing methods leverage morphological features to generate embeddings. While the existing methods use computationally-intensive rule-based (Soricut and Och, 2015) or tool-based (Botha and Blunsom, 2014) morphological analysis to generate embeddings, our system applies a computationally-simpler sub-word search on words that have existing embeddings. Embeddings of the sub-word search results are then combined using string similarity functions to generate rare word embeddings. We augmented pre-trained word embeddings with these novel embeddings and evaluated on a rare word similarity task, obtaining up to 3 times improvement in correlation over the original set of embeddings. Applying our technique to embeddings trained on larger datasets led to on-par performance with the existing state-of-the-art for this task. Additionally, while analysing augmented embeddings in a log-bilinear language model, we observed up to 50{\%} reduction in rare word perplexity in comparison to other more complex language models.",
}
@inproceedings{meyer-etal-2016-semi,
    title = "Semi-automatic Detection of Cross-lingual Marketing Blunders based on Pragmatic Label Propagation in {W}iktionary",
    author = "Meyer, Christian M.  and
      Eckle-Kohler, Judith  and
      Gurevych, Iryna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1195",
    pages = "2071--2081",
    abstract = "We introduce the task of detecting cross-lingual marketing blunders, which occur if a trade name resembles an inappropriate or negatively connotated word in a target language. To this end, we suggest a formal task definition and a semi-automatic method based the propagation of pragmatic labels from Wiktionary across sense-disambiguated translations. Our final tool assists users by providing clues for problematic names in any language, which we simulate in two experiments on detecting previously occurred marketing blunders and identifying relevant clues for established international brands. We conclude the paper with a suggested research roadmap for this new task. To initiate further research, we publish our online demo along with the source code and data at \url{http://uby.ukp.informatik.tu-darmstadt.de/blunder/}.",
}
@inproceedings{milde-etal-2016-ambient,
    title = "Ambient Search: A Document Retrieval System for Speech Streams",
    author = {Milde, Benjamin  and
      Wacker, Jonas  and
      Radomski, Stefan  and
      M{\"u}hlh{\"a}user, Max  and
      Biemann, Chris},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1196",
    pages = "2082--2091",
    abstract = "We present Ambient Search, an open source system for displaying and retrieving relevant documents in real time for speech input. The system works ambiently, that is, it unobstructively listens to speech streams in the background, identifies keywords and keyphrases for query construction and continuously serves relevant documents from its index. Query terms are ranked with Word2Vec and TF-IDF and are continuously updated to allow for ongoing querying of a document collection. The retrieved documents, in our case Wikipedia articles, are visualized in real time in a browser interface. Our evaluation shows that Ambient Search compares favorably to another implicit information retrieval system on speech streams. Furthermore, we extrinsically evaluate multiword keyphrase generation, showing positive impact for manual transcriptions.",
}
@inproceedings{li-etal-2016-semi,
    title = "Semi-supervised Gender Classification with Joint Textual and Social Modeling",
    author = "Li, Shoushan  and
      Dai, Bin  and
      Gong, Zhengxian  and
      Zhou, Guodong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1197",
    pages = "2092--2100",
    abstract = "In gender classification, labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for gender classification to improve the performance by exploring the knowledge in both labeled and unlabeled data. In this paper, we propose a semi-supervised approach to gender classification by leveraging textual features and a specific kind of indirect links among the users which we call {``}same-interest{''} links. Specifically, we propose a factor graph, namely Textual and Social Factor Graph (TSFG), to model both the textual and the {``}same-interest{''} link information. Empirical studies demonstrate the effectiveness of the proposed approach to semi-supervised gender classification.",
}
@inproceedings{pilan-etal-2016-predicting,
    title = "Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks",
    author = "Pil{\'a}n, Ildik{\'o}  and
      Volodina, Elena  and
      Zesch, Torsten",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1198",
    pages = "2101--2111",
    abstract = "The lack of a sufficient amount of data tailored for a task is a well-recognized problem for many statistical NLP methods. In this paper, we explore whether data sparsity can be successfully tackled when classifying language proficiency levels in the domain of learner-written output texts. We aim at overcoming data sparsity by incorporating knowledge in the trained model from another domain consisting of input texts written by teaching professionals for learners. We compare different domain adaptation techniques and find that a weighted combination of the two types of data performs best, which can even rival systems based on considerably larger amounts of in-domain data. Moreover, we show that normalizing errors in learners{'} texts can substantially improve classification when level-annotated in-domain data is not available.",
}
@inproceedings{zhang-etal-2016-user,
    title = "User Classification with Multiple Textual Perspectives",
    author = "Zhang, Dong  and
      Li, Shoushan  and
      Wang, Hongling  and
      Zhou, Guodong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1199",
    pages = "2112--2121",
    abstract = "Textual information is of critical importance for automatic user classification in social media. However, most previous studies model textual features in a single perspective while the text in a user homepage typically possesses different styles of text, such as original message and comment from others. In this paper, we propose a novel approach, namely ensemble LSTM, to user classification by incorporating multiple textual perspectives. Specifically, our approach first learns a LSTM representation with a LSTM recurrent neural network and then presents a joint learning method to integrating all naturally-divided textual perspectives. Empirical studies on two basic user classification tasks, i.e., gender classification and age classification, demonstrate the effectiveness of the proposed approach to user classification with multiple textual perspectives.",
}
@inproceedings{jiang-diesner-2016-says,
    title = "Says Who{\ldots}? Identification of Expert versus Layman Critics{'} Reviews of Documentary Films",
    author = "Jiang, Ming  and
      Diesner, Jana",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1200",
    pages = "2122--2132",
    abstract = "We extend classic review mining work by building a binary classifier that predicts whether a review of a documentary film was written by an expert or a layman with 90.70{\%} accuracy (F1 score), and compare the characteristics of the predicted classes. A variety of standard lexical and syntactic features was used for this supervised learning task. Our results suggest that experts write comparatively lengthier and more detailed reviews that feature more complex grammar and a higher diversity in their vocabulary. Layman reviews are more subjective and contextualized in peoples{'} everyday lives. Our error analysis shows that laymen are about twice as likely to be mistaken as experts than vice versa. We argue that the type of author might be a useful new feature for improving the accuracy of predicting the rating, helpfulness and authenticity of reviews. Finally, the outcomes of this work might help researchers and practitioners in the field of impact assessment to gain a more fine-grained understanding of the perception of different types of media consumers and reviewers of a topic, genre or information product.",
}
@inproceedings{ding-etal-2016-knowledge,
    title = "Knowledge-Driven Event Embedding for Stock Prediction",
    author = "Ding, Xiao  and
      Zhang, Yue  and
      Liu, Ting  and
      Duan, Junwen",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1201",
    pages = "2133--2142",
    abstract = "Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as event-driven stock prediction. On the other hand, events extracted from raw texts do not contain background knowledge on entities and relations that they are mentioned. To address this issue, this paper proposes to leverage extra information from knowledge graph, which provides ground truth such as attributes and properties of entities and encodes valuable relations between entities. Specifically, we propose a joint model to combine knowledge graph information into the objective function of an event embedding learning model. Experiments on event similarity and stock market prediction show that our model is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities.",
}
@inproceedings{chen-etal-2016-distributed,
    title = "Distributed Representations for Building Profiles of Users and Items from Text Reviews",
    author = "Chen, Wenliang  and
      Zhang, Zhenjie  and
      Li, Zhenghua  and
      Zhang, Min",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1202",
    pages = "2143--2153",
    abstract = "In this paper, we propose an approach to learn distributed representations of users and items from text comments for recommendation systems. Traditional recommendation algorithms, e.g. collaborative filtering and matrix completion, are not designed to exploit the key information hidden in the text comments, while existing opinion mining methods do not provide direct support to recommendation systems with useful features on users and items. Our approach attempts to construct vectors to represent profiles of users and items under a unified framework to maximize word appearance likelihood. Then, the vector representations are used for a recommendation task in which we predict scores on unobserved user-item pairs without given texts. The recommendation-aware distributed representation approach is fully supported by effective and efficient learning algorithms over massive text archive. Our empirical evaluations on real datasets show that our system outperforms the state-of-the-art baseline systems.",
}
@inproceedings{tang-etal-2016-improving,
    title = "Improving Statistical Machine Translation with Selectional Preferences",
    author = "Tang, Haiqing  and
      Xiong, Deyi  and
      Zhang, Min  and
      Gong, Zhengxian",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1203",
    pages = "2154--2163",
    abstract = "Long-distance semantic dependencies are crucial for lexical choice in statistical machine translation. In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the context of machine translation. We incorporate preferences that verbs impose on subjects and objects into translation. In addition, bilingual selectional preferences between source-side verbs and target-side arguments are also investigated. Our experiments on Chinese-to-English translation tasks with large-scale training data demonstrate that statistical machine translation using verbal selectional preferences can achieve statistically significant improvements over a state-of-the-art baseline.",
}
@inproceedings{stanojevic-simaan-2016-hierarchical,
    title = "Hierarchical Permutation Complexity for Word Order Evaluation",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1204",
    pages = "2164--2173",
    abstract = "Existing approaches for evaluating word order in machine translation work with metrics computed directly over a permutation of word positions in system output relative to a reference translation. However, every permutation factorizes into a permutation tree (PET) built of primal permutations, i.e., atomic units that do not factorize any further. In this paper we explore the idea that permutations factorizing into (on average) shorter primal permutations should represent simpler ordering as well. Consequently, we contribute Permutation Complexity, a class of metrics over PETs and their extension to forests, and define tight metrics, a sub-class of metrics implementing this idea. Subsequently we define example tight metrics and empirically test them in word order evaluation. Experiments on the WMT13 data sets for ten language pairs show that a tight metric is more often than not better than the baselines.",
}
@inproceedings{meng-etal-2016-interactive,
    title = "Interactive Attention for Neural Machine Translation",
    author = "Meng, Fandong  and
      Lu, Zhengdong  and
      Li, Hang  and
      Liu, Qun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1205",
    pages = "2174--2185",
    abstract = "Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence. By repeatedly reading the representation of source sentence, which keeps fixed after generated by the encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced state-of-the-art NMT. In this paper, we propose a new attention mechanism, called INTERACTIVE ATTENTION, which models the interaction between the decoder and the representation of source sentence during translation by both reading and writing operations. INTERACTIVE ATTENTION can keep track of the interaction history and therefore improve the translation performance. Experiments on NIST Chinese-English translation task show that INTERACTIVE ATTENTION can achieve significant improvements over both the previous attention-based NMT baseline and some state-of-the-art variants of attention-based NMT (i.e., coverage models (Tu et al., 2016)). And neural machine translator with our INTERACTIVE ATTENTION can outperform the open source attention-based NMT system Groundhog by 4.22 BLEU points and the open source phrase-based system Moses by 3.94 BLEU points averagely on multiple test sets.",
}
@inproceedings{pado-2016-get,
    title = "Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading",
    author = "Pad{\'o}, Ulrike",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1206",
    pages = "2186--2195",
    abstract = "Automated short-answer grading is key to help close the automation loop for large-scale, computerised testing in education. A wide range of features on different levels of linguistic processing has been proposed so far. We investigate the relative importance of the different types of features across a range of standard corpora (both from a language skill and content assessment context, in English and in German). We find that features on the lexical, text similarity and dependency level often suffice to approximate full-model performance. Features derived from semantic processing particularly benefit the linguistically more varied answers in content assessment corpora.",
}
@inproceedings{blevins-etal-2016-automatically,
    title = "Automatically Processing Tweets from Gang-Involved Youth: Towards Detecting Loss and Aggression",
    author = "Blevins, Terra  and
      Kwiatkowski, Robert  and
      MacBeth, Jamie  and
      McKeown, Kathleen  and
      Patton, Desmond  and
      Rambow, Owen",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1207",
    pages = "2196--2206",
    abstract = "Violence is a serious problems for cities like Chicago and has been exacerbated by the use of social media by gang-involved youths for taunting rival gangs. We present a corpus of tweets from a young and powerful female gang member and her communicators, which we have annotated with discourse intention, using a deep read to understand how and what triggered conversations to escalate into aggression. We use this corpus to develop a part-of-speech tagger and phrase table for the variant of English that is used and a classifier for identifying tweets that express grieving and aggression.",
}
@inproceedings{chen-etal-2016-content,
    title = "Content-based Influence Modeling for Opinion Behavior Prediction",
    author = "Chen, Chengyao  and
      Wang, Zhitao  and
      Lei, Yu  and
      Li, Wenjie",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1208",
    pages = "2207--2216",
    abstract = "Nowadays, social media has become a popular platform for companies to understand their customers. It provides valuable opportunities to gain new insights into how a person{'}s opinion about a product is influenced by his friends. Though various approaches have been proposed to study the opinion formation problem, they all formulate opinions as the derived sentiment values either discrete or continuous without considering the semantic information. In this paper, we propose a Content-based Social Influence Model to study the implicit mechanism underlying the change of opinions. We then apply the learned model to predict users{'} future opinions. The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors. In the experiments conducted on Twitter datasets, our model significantly outperforms other popular opinion formation models.",
}
@inproceedings{doyle-levy-2016-data,
    title = "Data-driven learning of symbolic constraints for a log-linear model in a phonological setting",
    author = "Doyle, Gabriel  and
      Levy, Roger",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1209",
    pages = "2217--2226",
    abstract = "We propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints, combining observations of the output classes with a (potentially infinite) grammar over potential constraint definitions. We present results on a small morphophonological system, English regular plurals, as a test case. The inferred constraints, based on a grammar of articulatory features, perform as well as theoretically-defined constraints on both observed and novel forms of English regular plurals. The learned constraint values and definitions also closely resemble standard constraints defined within phonological theory.",
}
@inproceedings{huang-etal-2016-chinese-tense,
    title = "{C}hinese Tense Labelling and Causal Analysis",
    author = "Huang, Hen-Hsen  and
      Yang, Chang-Rui  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1210",
    pages = "2227--2237",
    abstract = "This paper explores the role of tense information in Chinese causal analysis. Both tasks of causal type classification and causal directionality identification are experimented to show the significant improvement gained from tense features. To automatically extract the tense features, a Chinese tense predictor is proposed. Based on large amount of parallel data, our semi-supervised approach improves the dependency-based convolutional neural network (DCNN) models for Chinese tense labelling and thus the causal analysis.",
}
@inproceedings{yang-etal-2016-exploring,
    title = "Exploring Topic Discriminating Power of Words in {L}atent {D}irichlet {A}llocation",
    author = "Yang, Kai  and
      Cai, Yi  and
      Chen, Zhenhong  and
      Leung, Ho-fung  and
      Lau, Raymond",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1211",
    pages = "2238--2247",
    abstract = "Latent Dirichlet Allocation (LDA) and its variants have been widely used to discover latent topics in textual documents. However, some of topics generated by LDA may be noisy with irrelevant words scattering across these topics. We name this kind of words as topic-indiscriminate words, which tend to make topics more ambiguous and less interpretable by humans. In our work, we propose a new topic model named TWLDA, which assigns low weights to words with low topic discriminating power (ability). Our experimental results show that the proposed approach, which effectively reduces the number of topic-indiscriminate words in discovered topics, improves the effectiveness of LDA.",
}
@inproceedings{zhao-etal-2016-textual,
    title = "Textual Entailment with Structured Attentions and Composition",
    author = "Zhao, Kai  and
      Huang, Liang  and
      Ma, Mingbo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1212",
    pages = "2248--2258",
    abstract = {Deep learning techniques are increasingly popular in the textual entailment task, overcoming the fragility of traditional discrete models with hard alignments and logics. In particular, the recently proposed attention models (Rockt{\"a}schel et al., 2015; Wang and Jiang, 2015) achieves state-of-the-art accuracy by computing soft word alignments between the premise and hypothesis sentences. However, there remains a major limitation: this line of work completely ignores syntax and recursion, which is helpful in many traditional efforts. We show that it is beneficial to extend the attention model to tree nodes between premise and hypothesis. More importantly, this subtree-level attention reveals information about entailment relation. We study the recursive composition of this subtree-level entailment relation, which can be viewed as a soft version of the Natural Logic framework (MacCartney and Manning, 2009). Experiments show that our structured attention and entailment composition model can correctly identify and infer entailment relations from the bottom up, and bring significant improvements in accuracy.},
}
@inproceedings{maziarz-etal-2016-plwordnet,
    title = "pl{W}ord{N}et 3.0 {--} a Comprehensive Lexical-Semantic Resource",
    author = "Maziarz, Marek  and
      Piasecki, Maciej  and
      Rudnicka, Ewa  and
      Szpakowicz, Stan  and
      K{\k{e}}dzia, Pawe{\l}",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1213",
    pages = "2259--2268",
    abstract = "We have released plWordNet 3.0, a very large wordnet for Polish. In addition to what is expected in wordnets {--} richly interrelated synsets {--} it contains sentiment and emotion annotations, a large set of multi-word expressions, and a mapping onto WordNet 3.1. Part of the release is enWordNet 1.0, a substantially enlarged copy of WordNet 3.1, with material added to allow for a more complete mapping. The paper discusses the design principles of plWordNet, its content, its statistical portrait, a comparison with similar resources, and a partial list of applications.",
}
@inproceedings{londhe-etal-2016-time,
    title = "Time-Independent and Language-Independent Extraction of Multiword Expressions From {T}witter",
    author = "Londhe, Nikhil  and
      Srihari, Rohini  and
      Gopalakrishnan, Vishrawas",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1214",
    pages = "2269--2278",
    abstract = "Multiword Expressions (MWEs) are crucial lexico-semantic units in any language. However, most work on MWEs has been focused on standard monolingual corpora. In this work, we examine MWE usage on Twitter - an inherently multilingual medium with an extremely short average text length that is often replete with grammatical errors. In this work we present a new graph based, language agnostic method for automatically extracting MWEs from tweets. We show how our method outperforms standard Association Measures. We also present a novel unsupervised evaluation technique to ascertain the accuracy of MWE extraction.",
}
@inproceedings{judea-strube-2016-incremental,
    title = "Incremental Global Event Extraction",
    author = "Judea, Alex  and
      Strube, Michael",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1215",
    pages = "2279--2289",
    abstract = "Event extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This joint system achieves state-of-the-art performance in all tasks. However, as a system operating only at the sentence level, it misses valuable information from other parts of the document. In this paper, we present an incremental easy-first approach to make the global context of the entire document available to the intra-sentential, state-of-the-art event extractor. We show that our method robustly increases performance on two datasets, namely ACE 2005 and TAC 2015.",
}
@inproceedings{xu-etal-2016-hierarchical,
    title = "Hierarchical Memory Networks for Answer Selection on Unknown Words",
    author = "Xu, Jiaming  and
      Shi, Jing  and
      Yao, Yiqun  and
      Zheng, Suncong  and
      Xu, Bo  and
      Xu, Bo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1216",
    pages = "2290--2299",
    abstract = "Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory. However, memory networks conduct the reasoning on sentence-level memory to output coarse semantic vectors and do not further take any attention mechanism to focus on words, which may lead to the model lose some detail information, especially when the answers are rare or unknown words. In this paper, we propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the past facts into sentence-level memory and word-level memory respectively. Then, $k$-max pooling is exploited following reasoning module on the sentence-level memory to sample the $k$ most relevant sentences to a question and feed these sentences into attention mechanism on the word-level memory to focus the words in the selected sentences. Finally, the prediction is jointly learned over the outputs of the sentence-level reasoning module and the word-level attention mechanism. The experimental results demonstrate that our approach successfully conducts answer selection on unknown words and achieves a better performance than memory networks.",
}
@inproceedings{gupta-etal-2016-revisiting,
    title = "Revisiting Taxonomy Induction over {W}ikipedia",
    author = "Gupta, Amit  and
      Piccinno, Francesco  and
      Kozhevnikov, Mikhail  and
      Pa{\c{s}}ca, Marius  and
      Pighin, Daniele",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1217",
    pages = "2300--2309",
    abstract = "Guided by multiple heuristics, a unified taxonomy of entities and categories is distilled from the Wikipedia category network. A comprehensive evaluation, based on the analysis of upward generalization paths, demonstrates that the taxonomy supports generalizations which are more than twice as accurate as the state of the art. The taxonomy is available at \url{http://headstaxonomy.com}.",
}
@inproceedings{nguyen-etal-2016-joint-learning,
    title = "Joint Learning of Local and Global Features for Entity Linking via Neural Networks",
    author = "Nguyen, Thien Huu  and
      Fauceglia, Nicolas  and
      Rodriguez Muro, Mariano  and
      Hassanzadeh, Oktie  and
      Massimiliano Gliozzo, Alfio  and
      Sadoghi, Mohammad",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1218",
    pages = "2310--2320",
    abstract = "Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence. We introduce a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking. The proposed model benefits from the capacity of convolutional neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. Our evaluation on multiple datasets demonstrates the effectiveness of the model and yields the state-of-the-art performance on such datasets. In addition, we examine the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.",
}
@inproceedings{gunes-etal-2016-structured,
    title = "Structured Aspect Extraction",
    author = "Gunes, Omer  and
      Furche, Tim  and
      Orsi, Giorgio",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1219",
    pages = "2321--2332",
    abstract = "Aspect extraction identifies relevant features from a textual description of an entity, e.g., a phone, and is typically targeted to product descriptions, reviews, and other short texts as an enabling task for, e.g., opinion mining and information retrieval. Current aspect extraction methods mostly focus on aspect terms and often neglect interesting modifiers of the term or embed them in the aspect term without proper distinction. Moreover, flat syntactic structures are often assumed, resulting in inaccurate extractions of complex aspects. This paper studies the problem of structured aspect extraction, a variant of traditional aspect extraction aiming at a fine-grained extraction of complex (i.e., hierarchical) aspects. We propose an unsupervised and scalable method for structured aspect extraction consisting of statistical noun phrase clustering, cPMI-based noun phrase segmentation, and hierarchical pattern induction. Our evaluation shows a substantial improvement over existing methods in terms of both quality and computational efficiency.",
}
@inproceedings{baker-etal-2016-robust,
    title = "Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings",
    author = "Baker, Simon  and
      Kiela, Douwe  and
      Korhonen, Anna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1220",
    pages = "2333--2343",
    abstract = "The conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features that aims to alleviate this problem. Our approach jointly learns embeddings at different levels of granularity (word, sentence and document) along with the class labels. The intuition is that topic semantics represented by embeddings at multiple levels results in better classification. We evaluate this approach in unsupervised and semi-supervised settings on two sparsely labelled classification tasks, outperforming the handcrafted models and several embedding baselines.",
}
@inproceedings{stathopoulos-teufel-2016-mathematical,
    title = "Mathematical Information Retrieval based on Type Embeddings and Query Expansion",
    author = "Stathopoulos, Yiannos  and
      Teufel, Simone",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1221",
    pages = "2344--2355",
    abstract = "We present an approach to mathematical information retrieval (MIR) that exploits a special kind of technical terminology, referred to as a mathematical type. In this paper, we present and evaluate a type detection mechanism and show its positive effect on the retrieval of research-level mathematics. Our best model, which performs query expansion with a type-aware embedding space, strongly outperforms standard IR models with state-of-the-art query expansion (vector space-based and language modelling-based), on a relatively new corpus of research-level queries.",
}
@inproceedings{sneiders-2016-text,
    title = "Text Retrieval by Term Co-occurrences in a Query-based Vector Space",
    author = "Sneiders, Eriks",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1222",
    pages = "2356--2365",
    abstract = "Term co-occurrence in a sentence or paragraph is a powerful and often overlooked feature for text matching in document retrieval. In our experiments with matching email-style query messages to webpages, such term co-occurrence helped greatly to filter and rank documents, compared to matching document-size bags-of-words. The paper presents the results of the experiments as well as a text-matching model where the query shapes the vector space, a document is modelled by two or three vectors in this vector space, and the query-document similarity score depends on the length of the vectors and the relationships between them.",
}
@inproceedings{yu-jiang-2016-pairwise,
    title = "Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network",
    author = "Yu, Jianfei  and
      Jiang, Jing",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1223",
    pages = "2366--2377",
    abstract = "Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on the SemEval-2010 Task 8 dataset. The empirical results show that with two additional features, our model achieves the state-of-the-art result of F1 score of 85.7.",
}
@inproceedings{wang-etal-2016-fasthybrid,
    title = "{F}ast{H}ybrid: A Hybrid Model for Efficient Answer Selection",
    author = "Wang, Lidan  and
      Tan, Ming  and
      Han, Jiawei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1224",
    pages = "2378--2388",
    abstract = "Answer selection is a core component in any question-answering systems. It aims to select correct answer sentences for a given question from a pool of candidate sentences. In recent years, many deep learning methods have been proposed and shown excellent results for this task. However, these methods typically require extensive parameter (and hyper-parameter) tuning, which give rise to efficiency issues for large-scale datasets, and potentially make them less portable across new datasets and domains (as re-tuning is usually required). In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view. FastHybrid is a light-weight model that requires little tuning and adaptation across different domains. It combines a fast deep model (which will be introduced in the method section) with an initial information retrieval model to effectively and efficiently handle answer selection. We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets. Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.",
}
@inproceedings{kim-lee-2016-extracting,
    title = "Extracting Spatial Entities and Relations in {K}orean Text",
    author = "Kim, Bogyum  and
      Lee, Jae Sung",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1225",
    pages = "2389--2396",
    abstract = "A spatial information extraction system retrieves spatial entities and their relationships for geological searches and reasoning. Spatial information systems have been developed mainly for English text, e.g., through the SpaceEval competition. Some of the techniques are useful but not directly applicable to Korean text, because of linguistic differences and the lack of language resources. In this paper, we propose a Korean spatial entity extraction model and a spatial relation extraction model; the spatial entity extraction model uses word vectors to alleviate the over generation and the spatial relation extraction mod-el uses dependency parse labels to find the proper arguments in relations. Experiments with Korean text show that the two models are effective for spatial information extraction.",
}
@inproceedings{xu-etal-2016-hybrid,
    title = "Hybrid Question Answering over Knowledge Base and Free Text",
    author = "Xu, Kun  and
      Feng, Yansong  and
      Huang, Songfang  and
      Zhao, Dongyan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1226",
    pages = "2397--2407",
    abstract = "Recent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer. In this paper, we present a hybrid question answering (hybrid-QA) system which exploits both structured knowledge base and free text to answer a question. The main challenge is to recognize the meaning of a question using these two resources, i.e., structured KB and free text. To address this, we map relational phrases to KB predicates and textual relations simultaneously, and further develop an integer linear program (ILP) model to infer on these candidates and provide a globally optimal solution. Experiments on benchmark datasets show that our system can benefit from both structured KB and free text, outperforming the state-of-the-art systems.",
}
@inproceedings{shen-liu-2016-improved,
    title = "Improved Word Embeddings with Implicit Structure Information",
    author = "Shen, Jie  and
      Liu, Cong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1227",
    pages = "2408--2417",
    abstract = "Distributed word representation is an efficient method for capturing semantic and syntactic word relations. In this work, we introduce an extension to the continuous bag-of-words model for learning word representations efficiently by using implicit structure information. Instead of relying on a syntactic parser which might be noisy and slow to build, we compute weights representing probabilities of syntactic relations based on the Huffman softmax tree in an efficient heuristic. The constructed {``}implicit graphs{''} from these weights show that these weights contain useful implicit structure information. Extensive experiments performed on several word similarity and word analogy tasks show gains compared to the basic continuous bag-of-words model.",
}
@inproceedings{dahou-etal-2016-word,
    title = "Word Embeddings and Convolutional Neural Network for {A}rabic Sentiment Classification",
    author = "Dahou, Abdelghani  and
      Xiong, Shengwu  and
      Zhou, Junwei  and
      Haddoud, Mohamed Houcine  and
      Duan, Pengfei",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1228",
    pages = "2418--2427",
    abstract = "With the development and the advancement of social networks, forums, blogs and online sales, a growing number of Arabs are expressing their opinions on the web. In this paper, a scheme of Arabic sentiment classification, which evaluates and detects the sentiment polarity from Arabic reviews and Arabic social media, is studied. We investigated in several architectures to build a quality neural word embeddings using a 3.4 billion words corpus from a collected 10 billion words web-crawled corpus. Moreover, a convolutional neural network trained on top of pre-trained Arabic word embeddings is used for sentiment classification to evaluate the quality of these word embeddings. The simulation results show that the proposed scheme outperforms the existed methods on 4 out of 5 balanced and unbalanced datasets.",
}
@inproceedings{wang-etal-2016-combination,
    title = "Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts",
    author = "Wang, Xingyou  and
      Jiang, Weijie  and
      Luo, Zhiyong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1229",
    pages = "2428--2437",
    abstract = "Sentiment analysis of short texts is challenging because of the limited contextual information they usually contain. In recent years, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to text sentiment analysis with comparatively remarkable results. In this paper, we describe a jointed CNN and RNN architecture, taking advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via RNN for sentiment analysis of short texts. Experimental results show an obvious improvement upon the state-of-the-art on three benchmark corpora, MR, SST1 and SST2, with 82.28{\%}, 51.50{\%} and 89.95{\%} accuracy, respectively.",
}
@inproceedings{zubiaga-etal-2016-stance,
    title = "Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations",
    author = "Zubiaga, Arkaitz  and
      Kochkina, Elena  and
      Liakata, Maria  and
      Procter, Rob  and
      Lukasik, Michal",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1230",
    pages = "2438--2448",
    abstract = "Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users{'} replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.",
}
@inproceedings{zhang-etal-2016-tweet,
    title = "Tweet Sarcasm Detection Using Deep Neural Network",
    author = "Zhang, Meishan  and
      Zhang, Yue  and
      Fu, Guohong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1231",
    pages = "2449--2460",
    abstract = "Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.",
}
@inproceedings{menini-tonelli-2016-agreement,
    title = "Agreement and Disagreement: Comparison of Points of View in the Political Domain",
    author = "Menini, Stefano  and
      Tonelli, Sara",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1232",
    pages = "2461--2470",
    abstract = "The automated comparison of points of view between two politicians is a very challenging task, due not only to the lack of annotated resources, but also to the different dimensions participating to the definition of agreement and disagreement. In order to shed light on this complex task, we first carry out a pilot study to manually annotate the components involved in detecting agreement and disagreement. Then, based on these findings, we implement different features to capture them automatically via supervised classification. We do not focus on debates in dialogical form, but we rather consider sets of documents, in which politicians may express their position with respect to different topics in an implicit or explicit way, like during an electoral campaign. We create and make available three different datasets.",
}
@inproceedings{welch-mihalcea-2016-targeted,
    title = "Targeted Sentiment to Understand Student Comments",
    author = "Welch, Charles  and
      Mihalcea, Rada",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1233",
    pages = "2471--2481",
    abstract = "We address the task of targeted sentiment as a means of understanding the sentiment that students hold toward courses and instructors, as expressed by students in their comments. We introduce a new dataset consisting of student comments annotated for targeted sentiment and describe a system that can both identify the courses and instructors mentioned in student comments, as well as label the students{'} sentiment toward those entities. Through several comparative evaluations, we show that our system outperforms previous work on a similar task.",
}
@inproceedings{joshi-etal-2016-towards,
    title = "Towards Sub-Word Level Compositions for Sentiment Analysis of {H}indi-{E}nglish Code Mixed Text",
    author = "Joshi, Aditya  and
      Prabhu, Ameya  and
      Shrivastava, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1234",
    pages = "2482--2491",
    abstract = "Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. In this paper, we introduce learning sub-word level representations in our LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in our architecture enables us to learn the information about sentiment value of important morphemes. This also seems to work well in highly noisy text containing misspellings as shown in our experiments which is demonstrated in morpheme-level feature maps learned by our model. Also, we hypothesize that encoding this linguistic prior in the Subword-LSTM architecture leads to the superior performance. Our system attains accuracy 4-5{\%} greater than traditional approaches on our dataset, and also outperforms the available system for sentiment analysis in Hi-En code-mixed text by 18{\%}.",
}
@inproceedings{xiong-etal-2016-distance,
    title = "Distance Metric Learning for Aspect Phrase Grouping",
    author = "Xiong, Shufeng  and
      Zhang, Yue  and
      Ji, Donghong  and
      Lou, Yinxia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1235",
    pages = "2492--2502",
    abstract = "Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phrase sample pairs for distant supervision. Second, we feed word embeddings of aspect phrases and their contexts into an attention-based neural network to learn feature representation of contexts. Both aspect phrase embedding and context embedding are used to learn a deep feature subspace for measure the distances between aspect phrases for K-means clustering. Experiments on four review datasets show that the proposed method outperforms state-of-the-art strong baseline methods.",
}
@inproceedings{bao-etal-2016-constraint,
    title = "Constraint-Based Question Answering with Knowledge Graph",
    author = "Bao, Junwei  and
      Duan, Nan  and
      Yan, Zhao  and
      Zhou, Ming  and
      Zhao, Tiejun",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1236",
    pages = "2503--2514",
    abstract = "WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are {`}simple{'} questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on complicated questions. Motivated by this issue, we release a new data-set, namely ComplexQuestions, aiming to measure the quality of KBQA systems on {`}multi-constraint{'} questions which require multiple knowledge base relations to get the answer. Beside, we propose a novel systematic KBQA approach to solve multi-constraint questions. Compared to state-of-the-art methods, our approach not only obtains comparable results on the two existing benchmark data-sets, but also achieves significant improvements on the ComplexQuestions.",
}
@inproceedings{barron-cedeno-etal-2016-selecting,
    title = "Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking",
    author = "Barr{\'o}n-Cede{\~n}o, Alberto  and
      Da San Martino, Giovanni  and
      Romeo, Salvatore  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1237",
    pages = "2515--2525",
    abstract = "Community question answering (cQA) websites are focused on users who query questions onto an online forum, expecting for other users to provide them answers or suggestions. Unlike other social media, the length of the posted queries has no limits and queries tend to be multi-sentence elaborations combining context, actual questions, and irrelevant information. We approach the problem of question ranking: given a user{'}s new question, to retrieve those previously-posted questions which could be equivalent, or highly relevant. This could prevent the posting of nearly-duplicate questions and provide the user with instantaneous answers. For the first time in cQA, we address the selection of relevant text {---}both at sentence- and at constituent-level{---} for parse tree-based representations. Our supervised models for text selection boost the performance of a tree kernel-based machine learning model, allowing it to overtake the current state of the art on a recently released cQA evaluation framework.",
}
@inproceedings{shen-huang-2016-attention,
    title = "Attention-Based Convolutional Neural Network for Semantic Relation Extraction",
    author = "Shen, Yatian  and
      Huang, Xuanjing",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1238",
    pages = "2526--2536",
    abstract = "Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several state-of-the-art neural network models and can achieve a competitive performance just with minimal feature engineering.",
}
@inproceedings{gupta-etal-2016-table,
    title = "Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction",
    author = {Gupta, Pankaj  and
      Sch{\"u}tze, Hinrich  and
      Andrassy, Bernt},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1239",
    pages = "2537--2547",
    abstract = "This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies. The proposed neural network architecture is capable of modeling multiple relation instances without knowing the corresponding relation arguments in a sentence. The experimental results show that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance. We present state-of-the-art results with improvements of 2.0{\%} and 2.7{\%} for entity recognition and relation classification, respectively on CoNLL04 dataset.",
}
@inproceedings{zhang-etal-2016-bilingual,
    title = "Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences",
    author = "Zhang, Biao  and
      Xiong, Deyi  and
      Su, Jinsong  and
      Duan, Hong  and
      Zhang, Min",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1240",
    pages = "2548--2558",
    abstract = "Parallel sentence representations are important for bilingual and cross-lingual tasks in natural language processing. In this paper, we explore a bilingual autoencoder approach to model parallel sentences. We extract sentence-level global descriptors (e.g. min, max) from word embeddings, and construct two monolingual autoencoders over these descriptors on the source and target language. In order to tightly connect the two autoencoders with bilingual correspondences, we force them to share the same decoding parameters and minimize a corpus-level semantic distance between the two languages. Being optimized towards a joint objective function of reconstruction and semantic errors, our bilingual antoencoder is able to learn continuous-valued latent representations for parallel sentences. Experiments on both intrinsic and extrinsic evaluations on statistical machine translation tasks show that our autoencoder achieves substantial improvements over the baselines.",
}
@inproceedings{pal-etal-2016-multi,
    title = "Multi-Engine and Multi-Alignment Based Automatic Post-Editing and its Impact on Translation Productivity",
    author = "Pal, Santanu  and
      Naskar, Sudip Kumar  and
      van Genabith, Josef",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1241",
    pages = "2559--2570",
    abstract = "In this paper we combine two strands of machine translation (MT) research: automatic post-editing (APE) and multi-engine (system combination) MT. APE systems learn a target-language-side second stage MT system from the data produced by human corrected output of a first stage MT system, to improve the output of the first stage MT in what is essentially a sequential MT system combination architecture. At the same time, there is a rich research literature on parallel MT system combination where the same input is fed to multiple engines and the best output is selected or smaller sections of the outputs are combined to obtain improved translation output. In the paper we show that parallel system combination in the APE stage of a sequential MT-APE combination yields substantial translation improvements both measured in terms of automatic evaluation metrics as well as in terms of productivity improvements measured in a post-editing experiment. We also show that system combination on the level of APE alignments yields further improvements. Overall our APE system yields statistically significant improvement of 5.9{\%} relative BLEU over a strong baseline (English{--}Italian Google MT) and 21.76{\%} productivity increase in a human post-editing experiment with professional translators.",
}
@inproceedings{van-der-wees-etal-2016-measuring,
    title = "Measuring the Effect of Conversational Aspects on Machine Translation Quality",
    author = "van der Wees, Marlies  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1242",
    pages = "2571--2581",
    abstract = "Research in statistical machine translation (SMT) is largely driven by formal translation tasks, while translating informal text is much more challenging. In this paper we focus on SMT for the informal genre of dialogues, which has rarely been addressed to date. Concretely, we investigate the effect of dialogue acts, speakers, gender, and text register on SMT quality when translating fictional dialogues. We first create and release a corpus of multilingual movie dialogues annotated with these four dialogue-specific aspects. When measuring translation performance for each of these variables, we find that BLEU fluctuations between their categories are often significantly larger than randomly expected. Following this finding, we hypothesize and show that SMT of fictional dialogues benefits from adaptation towards dialogue acts and registers. Finally, we find that male speakers are harder to translate and use more vulgar language than female speakers, and that vulgarity is often not preserved during translation.",
}
@inproceedings{passban-etal-2016-enriching,
    title = "Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings",
    author = "Passban, Peyman  and
      Liu, Qun  and
      Way, Andy",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1243",
    pages = "2582--2591",
    abstract = "The phrase table is considered to be the main bilingual resource for the phrase-based statistical machine translation (PBSMT) model. During translation, a source sentence is decomposed into several phrases. The best match of each source phrase is selected among several target-side counterparts within the phrase table, and processed by the decoder to generate a sentence-level translation. The best match is chosen according to several factors, including a set of bilingual features. PBSMT engines by default provide four probability scores in phrase tables which are considered as the main set of bilingual features. Our goal is to enrich that set of features, as a better feature set should yield better translations. We propose new scores generated by a Convolutional Neural Network (CNN) which indicate the semantic relatedness of phrase pairs. We evaluate our model in different experimental settings with different language pairs. We observe significant improvements when the proposed features are incorporated into the PBSMT pipeline.",
}
@inproceedings{song-etal-2016-anecdote,
    title = "Anecdote Recognition and Recommendation",
    author = "Song, Wei  and
      Fu, Ruiji  and
      Liu, Lizhen  and
      Wang, Hanshi  and
      Liu, Ting",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1244",
    pages = "2592--2602",
    abstract = "We introduce a novel task Anecdote Recognition and Recommendation. An anecdote is a story with a point revealing account of an individual person. Recommending proper anecdotes can be used as evidence to support argumentative writing or as a clue for further reading. We represent an anecdote as a structured tuple {---} {\textless} person, story, implication {\textgreater}. Anecdote recognition runs on archived argumentative essays. We extract narratives containing events of a person as the anecdote story. More importantly, we uncover the anecdote implication, which reveals the meaning and topic of an anecdote. Our approach depends on discourse role identification. Discourse roles such as thesis, main ideas and support help us locate stories and their implications in essays. The experiments show that informative and interpretable anecdotes can be recognized. These anecdotes are used for anecdote recommendation. The anecdote recommender can recommend proper anecdotes in response to given topics. The anecdote implication contributes most for bridging user interested topics and relevant anecdotes.",
}
@inproceedings{jiang-etal-2016-training,
    title = "Training Data Enrichment for Infrequent Discourse Relations",
    author = "Jiang, Kailang  and
      Carenini, Giuseppe  and
      Ng, Raymond",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1245",
    pages = "2603--2614",
    abstract = "Discourse parsing is a popular technique widely used in text understanding, sentiment analysis and other NLP tasks. However, for most discourse parsers, the performance varies significantly across different discourse relations. In this paper, we first validate the underfitting hypothesis, i.e., the less frequent a relation is in the training data, the poorer the performance on that relation. We then explore how to increase the number of positive training instances, without resorting to manually creating additional labeled data. We propose a training data enrichment framework that relies on co-training of two different discourse parsers on unlabeled documents. Importantly, we show that co-training alone is not sufficient. The framework requires a filtering step to ensure that only {``}good quality{''} unlabeled documents can be used for enrichment and re-training. We propose and evaluate two ways to perform the filtering. The first is to use an agreement score between the two parsers. The second is to use only the confidence score of the faster parser. Our empirical results show that agreement score can help to boost the performance on infrequent relations, and that the confidence score is a viable approximation of the agreement score for infrequent relations.",
}
@inproceedings{zhang-etal-2016-inferring,
    title = "Inferring Discourse Relations from {PDTB}-style Discourse Labels for Argumentative Revision Classification",
    author = "Zhang, Fan  and
      Litman, Diane  and
      Forbes Riley, Katherine",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1246",
    pages = "2615--2624",
    abstract = "Penn Discourse Treebank (PDTB)-style annotation focuses on labeling local discourse relations between text spans and typically ignores larger discourse contexts. In this paper we propose two approaches to infer discourse relations in a paragraph-level context from annotated PDTB labels. We investigate the utility of inferring such discourse information using the task of revision classification. Experimental results demonstrate that the inferred information can significantly improve classification performance compared to baselines, not only when PDTB annotation comes from humans but also from automatic parsers.",
}
@inproceedings{kabbara-etal-2016-capturing,
    title = "Capturing Pragmatic Knowledge in Article Usage Prediction using {LSTM}s",
    author = "Kabbara, Jad  and
      Feng, Yulan  and
      Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1247",
    pages = "2625--2634",
    abstract = "We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction. We train and compare several variants of Long Short-Term Memory (LSTM) networks with an attention mechanism. Our model outperforms a previous state-of-the-art system, achieving up to 96.63{\%} accuracy on the WSJ/PTB corpus. In addition, we perform a series of analyses to understand the impact of various model choices. We find that the gain in performance can be attributed to the ability of LSTMs to pick up on contextual cues, both local and further away in distance, and that the model is able to solve cases involving reasoning about coreference and synonymy. We also show how the attention mechanism contributes to the interpretability of the model{'}s effectiveness.",
}
@inproceedings{pateria-2016-aspect,
    title = "Aspect Based Sentiment Analysis using Sentiment Flow with Local and Non-local Neighbor Information",
    author = "Pateria, Shubham",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1248",
    pages = "2635--2646",
    abstract = "Aspect-level analysis of sentiments contained in a review text is important to reveal a detailed picture of consumer opinions. While a plethora of methods have been traditionally employed for this task, majority focus has been on analyzing only aspect-centered local information. However, incorporating context information from non-local aspect neighbors may capture richer structure in review text and enhance prediction. This may especially be helpful to resolve ambiguous predictions. The context around an aspect can be incorporated using semantic relations within text and inter-label dependencies in the output. On the output side, this becomes a structured prediction task. However, non-local label correlations are computationally heavy and intractable to infer for structured prediction models like Conditional Random Fields (CRF). Moreover, some prior intuition is required to incorporate non-local context. Thus, inspired by previous research on multi-stage prediction, we propose a two-level model for aspect-based analysis. The proposed model uses predicted probability estimates from first level to incorporate neighbor information in the second level. The model is evaluated on data taken from SemEval Workshops and Bing Liu{'}s review collection. It shows comparatively better performance against few existing methods. Overall, we get prediction accuracy in a range of 83-88{\%} and almost 3-4 point increment against baseline (first level only) scores.",
}
@inproceedings{li-etal-2016-two,
    title = "Two-View Label Propagation to Semi-supervised Reader Emotion Classification",
    author = "Li, Shoushan  and
      Xu, Jian  and
      Zhang, Dong  and
      Zhou, Guodong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1249",
    pages = "2647--2655",
    abstract = "In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data is limited. In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm. Specifically, our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively. Besides, the two bipartite graphs are integrated by linking each source text sample with its corresponding response text sample via a length-sensitive transition probability. In this way, our two-view label propagation approach to semi-supervised reader emotion classification largely alleviates the reliance on the strong sufficiency and independence assumptions of the two views, as required in co-training. Empirical evaluation demonstrates the effectiveness of our two-view label propagation approach to semi-supervised reader emotion classification.",
}
@inproceedings{ebrahimi-etal-2016-joint,
    title = "A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets",
    author = "Ebrahimi, Javid  and
      Dou, Dejing  and
      Lowd, Daniel",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1250",
    pages = "2656--2665",
    abstract = "Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables as extra variables to the feature space, we use a novel formulation to incorporate three-way interactions among sentiment-stance-input variables and three-way interactions among target-stance-input variables. The proposed specification intuitively aims to discriminate sentiment features from target features for stance classification. In addition, regularizing a single stance classifier, which handles all targets, acts as a soft weight-sharing among them. We demonstrate that discriminative training of this model achieves the state-of-the-art results in supervised stance classification, and its generative training obtains competitive results in the weakly supervised setting.",
}
@inproceedings{cambria-etal-2016-senticnet,
    title = "{S}entic{N}et 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives",
    author = "Cambria, Erik  and
      Poria, Soujanya  and
      Bajpai, Rajiv  and
      Schuller, Bjoern",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1251",
    pages = "2666--2677",
    abstract = "An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation-specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.",
}
@inproceedings{li-etal-2016-joint,
    title = "Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification",
    author = "Li, Yuezhang  and
      Zheng, Ronghuo  and
      Tian, Tian  and
      Hu, Zhiting  and
      Iyer, Rahul  and
      Sycara, Katia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1252",
    pages = "2678--2688",
    abstract = "Existing work learning distributed representations of knowledge base entities has largely failed to incorporate rich categorical structure, and is unable to induce category representations. We propose a new framework that embeds entities and categories jointly into a semantic space, by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. Our framework enables to compute meaningful semantic relatedness between entities and categories in a principled way, and can handle both single-word and multiple-word concepts. Our method shows significant improvement on the tasks of concept categorization and dataless hierarchical classification.",
}
@inproceedings{jiang-etal-2016-latent,
    title = "Latent Topic Embedding",
    author = "Jiang, Di  and
      Shi, Lei  and
      Lian, Rongzhong  and
      Wu, Hua",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1253",
    pages = "2689--2698",
    abstract = "Topic modeling and word embedding are two important techniques for deriving latent semantics from data. General-purpose topic models typically work in coarse granularity by capturing word co-occurrence at the document/sentence level. In contrast, word embedding models usually work in much finer granularity by modeling word co-occurrence within small sliding windows. With the aim of deriving latent semantics by considering word co-occurrence at different levels of granularity, we propose a novel model named \textit{Latent Topic Embedding} (LTE), which seamlessly integrates topic generation and embedding learning in one unified framework. We further propose an efficient Monte Carlo EM algorithm to estimate the parameters of interest. By retaining the individual advantages of topic modeling and word embedding, LTE results in better latent topics and word embedding. Extensive experiments verify the superiority of LTE over the state-of-the-arts.",
}
@inproceedings{nguyen-etal-2016-neural,
    title = "Neural-based Noise Filtering from Word Embeddings",
    author = "Nguyen, Kim Anh  and
      Schulte im Walde, Sabine  and
      Vu, Ngoc Thang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1254",
    pages = "2699--2707",
    abstract = "Word embeddings have been demonstrated to benefit NLP tasks impressively. Yet, there is room for improvements in the vector representations, because current word embeddings typically contain unnecessary information, i.e., noise. We propose two novel models to improve word embeddings by unsupervised learning, in order to yield word denoising embeddings. The word denoising embeddings are obtained by strengthening salient information and weakening noise in the original word embeddings, based on a deep feed-forward neural network filter. Results from benchmark tasks show that the filtered word denoising embeddings outperform the original word embeddings.",
}
@inproceedings{aga-etal-2016-integrating,
    title = "Integrating Distributional and Lexical Information for Semantic Classification of Words using {MRMF}",
    author = "Aga, Rosa Tsegaye  and
      Drumond, Lucas  and
      Wartena, Christian  and
      Schmidt-Thieme, Lars",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1255",
    pages = "2708--2717",
    abstract = "Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multi-relational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.",
}
@inproceedings{gonen-goldberg-2016-semi,
    title = "Semi Supervised Preposition-Sense Disambiguation using Multilingual Data",
    author = "Gonen, Hila  and
      Goldberg, Yoav",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1256",
    pages = "2718--2729",
    abstract = "Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence. Supervised corpora for the preposition-sense disambiguation task are small, suggesting a semi-supervised approach to the task. We show that signals from unannotated multilingual data can be used to improve supervised preposition-sense disambiguation. Our approach pre-trains an LSTM encoder for predicting the translation of a preposition, and then incorporates the pre-trained encoder as a component in a supervised classification system, and fine-tunes it for the task. The multilingual signals consistently improve results on two preposition-sense datasets.",
}
@inproceedings{van-hee-etal-2016-monday,
    title = "{M}onday mornings are my fave :) {\#}not Exploring the Automatic Recognition of Irony in {E}nglish tweets",
    author = "Van Hee, Cynthia  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1257",
    pages = "2730--2739",
    abstract = "Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66{\%}. When explicit hashtag information like {\#}irony is included in the data, the system even obtains an F1-score of 92.77{\%}. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).",
}
@inproceedings{guggilla-etal-2016-cnn,
    title = "{CNN}- and {LSTM}-based Claim Classification in Online User Comments",
    author = "Guggilla, Chinnappa  and
      Miller, Tristan  and
      Gurevych, Iryna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1258",
    pages = "2740--2751",
    abstract = "When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.",
}
@inproceedings{peng-feldman-2016-experiments,
    title = "Experiments in Idiom Recognition",
    author = "Peng, Jing  and
      Feldman, Anna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1259",
    pages = "2752--2761",
    abstract = "Some expressions can be ambiguous between idiomatic and literal interpretations depending on the context they occur in, e.g., {`}sales hit the roof{'} vs. {`}hit the roof of the car{'}. We present a novel method of classifying whether a given instance is literal or idiomatic, focusing on verb-noun constructions. We report state-of-the-art results on this task using an approach based on the hypothesis that the distributions of the contexts of the idiomatic phrases will be different from the contexts of the literal usages. We measure contexts by using projections of the words into vector space. For comparison, we implement Fazly et al. (2009){'}s, Sporleder and Li (2009){'}s, and Li and Sporleder (2010b){'}s methods and apply them to our data. We provide experimental results validating the proposed techniques.",
}
@inproceedings{laha-raykar-2016-empirical,
    title = "An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks",
    author = "Laha, Anirban  and
      Raykar, Vikas",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1260",
    pages = "2762--2773",
    abstract = "Several tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification). For several single sequence classification tasks, the current state-of-the-art approaches are based on recurrent and convolutional neural networks. On the other hand, for bi-sequence classification problems, there is not much understanding as to the best deep learning architecture. In this paper, we attempt to get an understanding of this category of problems by extensive empirical evaluation of 19 different deep learning architectures (specifically on different ways of handling context) for various problems originating in natural language processing like debating, textual entailment and question-answering. Following the empirical evaluation, we offer our insights and conclusions regarding the architectures we have considered. We also establish the first deep learning baselines for three argumentation mining tasks.",
}
@inproceedings{senuma-aizawa-2016-learning,
    title = "Learning Succinct Models: Pipelined Compression with {L}1-Regularization, Hashing, {E}lias-{F}ano Indices, and Quantization",
    author = "Senuma, Hajime  and
      Aizawa, Akiko",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1261",
    pages = "2774--2784",
    abstract = "The recent proliferation of smart devices necessitates methods to learn small-sized models. This paper demonstrates that if there are $m$ features in total but only $n = o(\sqrt{m})$ features are required to distinguish examples, with $\Omega(\log m)$ training examples and reasonable settings, it is possible to obtain a good model in a \textit{succinct} representation using $n \log_2 \frac{m}{n} + o(m)$ bits, by using a pipeline of existing compression methods: L1-regularized logistic regression, feature hashing, Elias{--}Fano indices, and randomized quantization. An experiment shows that a noun phrase chunking task for which an existing library requires 27 megabytes can be compressed to less than 13 \textit{kilo}bytes without notable loss of accuracy.",
}
@inproceedings{hellrich-hahn-2016-bad,
    title = "Bad {C}ompany{---}{N}eighborhoods in Neural Embedding Spaces Considered Harmful",
    author = "Hellrich, Johannes  and
      Hahn, Udo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1262",
    pages = "2785--2796",
    abstract = "We assess the reliability and accuracy of (neural) word embeddings for both modern and historical English and German. Our research provides deeper insights into the empirically justified choice of optimal training methods and parameters. The overall low reliability we observe, nevertheless, casts doubt on the suitability of word neighborhoods in embedding spaces as a basis for qualitative conclusions on synchronic and diachronic lexico-semantic matters, an issue currently high up in the agenda of Digital Humanities.",
}
@inproceedings{thorat-choudhari-2016-implementing,
    title = "Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture",
    author = "Thorat, Sushrut  and
      Choudhari, Varad",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1263",
    pages = "2797--2806",
    abstract = "In this paper, we outline an approach to build graph-based reverse dictionaries using word definitions. A reverse dictionary takes a phrase as an input and outputs a list of words semantically similar to that phrase. It is a solution to the Tip-of-the-Tongue problem. We use a distance-based similarity measure, computed on a graph, to assess the similarity between a word and the input phrase. We compare the performance of our approach with the Onelook Reverse Dictionary and a distributional semantics method based on word2vec, and show that our approach is much better than the distributional semantics method, and as good as Onelook, on a 3k lexicon. This simple approach sets a new performance baseline for reverse dictionaries.",
}
@inproceedings{collell-moens-2016-image,
    title = "Is an Image Worth More than a Thousand Words? On the Fine-Grain Semantic Differences between Visual and Linguistic Representations",
    author = "Collell, Guillem  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1264",
    pages = "2807--2817",
    abstract = "Human concept representations are often grounded with visual information, yet some aspects of meaning cannot be visually represented or are better described with language. Thus, vision and language provide complementary information that, properly combined, can potentially yield more complete concept representations. Recently, state-of-the-art distributional semantic models and convolutional neural networks have achieved great success in representing linguistic and visual knowledge respectively. In this paper, we compare both, visual and linguistic representations in their ability to capture different types of fine-grain semantic knowledge{---}or attributes{---}of concepts. Humans often describe objects using attributes, that is, properties such as shape, color or functionality, which often transcend the linguistic and visual modalities. In our setting, we evaluate how well attributes can be predicted by using the unimodal representations as inputs. We are interested in first, finding out whether attributes are generally better captured by either the vision or by the language modality; and second, if none of them is clearly superior (as we hypothesize), what type of attributes or semantic knowledge are better encoded from each modality. Ultimately, our study sheds light on the potential of combining visual and textual representations.",
}
@inproceedings{mirza-tonelli-2016-contribution,
    title = "On the contribution of word embeddings to temporal relation classification",
    author = "Mirza, Paramita  and
      Tonelli, Sara",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1265",
    pages = "2818--2828",
    abstract = "Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose entities are not connected via direct syntactic relations making classification even more difficult. In these cases, resorting to features that focus on the semantic content of the event words may be very beneficial for inferring implicit relations. Specifically, while morpho-syntactic and context features are considered sufficient for classifying event-timex pairs, we believe that exploiting distributional semantic information about event words can benefit supervised classification of other types of pairs. In this work, we assess the impact of using word embeddings as features for event words in classifying temporal relations of event-event pairs and event-DCT (document creation time) pairs.",
}
@inproceedings{inoue-etal-2016-modeling,
    title = "Modeling Context-sensitive Selectional Preference with Distributed Representations",
    author = "Inoue, Naoya  and
      Matsubayashi, Yuichiroh  and
      Ono, Masayuki  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1266",
    pages = "2829--2838",
    abstract = "This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, we present a novel CSP model that extends the neural SP model (Van de Cruys, 2014) to incorporate contextual information into the distributed representations of arguments. Experimental results demonstrate that the proposed CSP model successfully learns CSP and outperforms the conventional SP model in coreference cluster ranking.",
}
@inproceedings{petersen-hellwig-2016-exploring,
    title = "Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in {G}erman",
    author = "Petersen, Wiebke  and
      Hellwig, Oliver",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1267",
    pages = "2839--2848",
    abstract = "The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix. The clustering method combines a Vector Space Models (VSM) and the results of a Latent Dirichlet Allocation (LDA), whose results are merged in each iterative step. The aim is to derive a clustering of German adjectives that reflects latent semantic classes of adjectives, and that can be used to induce frame-based representations of nouns in a later step. We are able to show that the method induces meaningful groups of adjectives, and that it outperforms a baseline k-means algorithm.",
}
@inproceedings{kartsaklis-sadrzadeh-2016-distributional,
    title = "Distributional Inclusion Hypothesis for Tensor-based Composition",
    author = "Kartsaklis, Dimitri  and
      Sadrzadeh, Mehrnoosh",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1268",
    pages = "2849--2860",
    abstract = "According to the distributional inclusion hypothesis, entailment between words can be measured via the feature inclusions of their distributional vectors. In recent work, we showed how this hypothesis can be extended from words to phrases and sentences in the setting of compositional distributional semantics. This paper focuses on inclusion properties of tensors; its main contribution is a theoretical and experimental analysis of how feature inclusion works in different concrete models of verb tensors. We present results for relational, Frobenius, projective, and holistic methods and compare them to the simple vector addition, multiplication, min, and max models. The degrees of entailment thus obtained are evaluated via a variety of existing word-based measures, such as Weed{'}s and Clarke{'}s, KL-divergence, APinc, balAPinc, and two of our previously proposed metrics at the phrase/sentence level. We perform experiments on three entailment datasets, investigating which version of tensor-based composition achieves the highest performance when combined with the sentence-level measures.",
}
@inproceedings{maki-etal-2016-parameter,
    title = "Parameter estimation of {J}apanese predicate argument structure analysis model using eye gaze information",
    author = "Maki, Ryosuke  and
      Nishikawa, Hitoshi  and
      Tokunaga, Takenobu",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1269",
    pages = "2861--2869",
    abstract = "In this paper, we propose utilising eye gaze information for estimating parameters of a Japanese predicate argument structure (PAS) analysis model. We employ not only linguistic information in the text, but also the information of annotator eye gaze during their annotation process. We hypothesise that annotator{'}s frequent looks at certain candidates imply their plausibility of being the argument of the predicate. Based on this hypothesis, we consider annotator eye gaze for estimating the model parameters of the PAS analysis. The evaluation experiment showed that introducing eye gaze information increased the accuracy of the PAS analysis by 0.05 compared with the conventional methods.",
}
@inproceedings{sha-etal-2016-reading,
    title = "Reading and Thinking: Re-read {LSTM} Unit for Textual Entailment Recognition",
    author = "Sha, Lei  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Li, Sujian",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1270",
    pages = "2870--2879",
    abstract = "Recognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate deep neural network methods for the RTE task. Previous neural network based methods usually try to encode the two sentences (premise and hypothesis) and send them together into a multi-layer perceptron to get their entailment type, or use LSTM-RNN to link two sentences together while using attention mechanic to enhance the model{'}s ability. In this paper, we propose to use the re-read mechanic, which means to read the premise again and again while reading the hypothesis. After read the premise again, the model can get a better understanding of the premise, which can also affect the understanding of the hypothesis. On the contrary, a better understanding of the hypothesis can also affect the understanding of the premise. With the alternative re-read process, the model can {``}think{''} of a better decision of entailment type. We designed a new LSTM unit called re-read LSTM (rLSTM) to implement this {``}thinking{''} process. Experiments show that we achieve results better than current state-of-the-art equivalents.",
}
@inproceedings{dey-etal-2016-paraphrase,
    title = "A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs",
    author = "Dey, Kuntal  and
      Shrivastava, Ritvik  and
      Kaushik, Saroj",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1271",
    pages = "2880--2890",
    abstract = "Existing systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain an accuracy as high as 0:8596. However, existing systems for detecting paraphrases and semantic similarity on user-generated short-text content on microblogs such as Twitter, comprising of noisy and ad hoc short-text, needs significant research attention. In this paper, we propose a machine learning based approach towards this. We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter. We apply support vector machine (SVM) based learning. We use the benchmark Twitter paraphrase data, released as a part of SemEval 2015, for experiments. Our system delivers a paraphrase detection F1-score of 0.717 and semantic similarity detection F1-score of 0.741, thereby significantly outperforming the existing systems, that deliver F1-scores of 0.696 and 0.724 for the two problems respectively. Our features also allow us to obtain a rank among the top-10, when trained on the Microsoft Paraphrase corpus and tested on the corresponding test data, thereby empirically establishing our approach as ubiquitous across the different paraphrase detection databases.",
}
@inproceedings{levy-etal-2016-modeling,
    title = "Modeling Extractive Sentence Intersection via Subtree Entailment",
    author = "Levy, Omer  and
      Dagan, Ido  and
      Stanovsky, Gabriel  and
      Eckle-Kohler, Judith  and
      Gurevych, Iryna",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1272",
    pages = "2891--2901",
    abstract = "Sentence intersection captures the semantic overlap of two texts, generalizing over paradigms such as textual entailment and semantic text similarity. Despite its modeling power, it has received little attention because it is difficult for non-experts to annotate. We analyze 200 pairs of similar sentences and identify several underlying properties of sentence intersection. We leverage these insights to design an algorithm that decomposes the sentence intersection task into several simpler annotation tasks, facilitating the construction of a high quality dataset via crowdsourcing. We implement this approach and provide an annotated dataset of 1,764 sentence intersections.",
}
@inproceedings{han-sun-2016-context,
    title = "Context-Sensitive Inference Rule Discovery: A Graph-Based Method",
    author = "Han, Xianpei  and
      Sun, Le",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1273",
    pages = "2902--2911",
    abstract = "Inference rule discovery aims to identify entailment relations between predicates, e.g., {`}X acquire Y {--}{\textgreater} X purchase Y{'} and {`}X is author of Y {--}{\textgreater} X write Y{'}. Traditional methods dis-cover inference rules by computing distributional similarities between predicates, with each predicate is represented as one or more feature vectors of its instantiations. These methods, however, have two main drawbacks. Firstly, these methods are mostly context-insensitive, cannot accurately measure the similarity between two predicates in a specific context. Secondly, traditional methods usually model predicates independently, ignore the rich inter-dependencies between predicates. To address the above two issues, this pa-per proposes a graph-based method, which can discover inference rules by effectively modelling and exploiting both the context and the inter-dependencies between predicates. Specifically, we propose a graph-based representation{---}Predicate Graph, which can capture the semantic relevance between predicates using both the predicate-feature co-occurrence statistics and the inter-dependencies between predicates. Based on the predicate graph, we propose a context-sensitive random walk algorithm, which can learn con-text-specific predicate representations by distinguishing context-relevant information from context-irrelevant information. Experimental results show that our method significantly outperforms traditional inference rule discovery methods.",
}
@inproceedings{zhou-etal-2016-modelling,
    title = "Modelling Sentence Pairs with Tree-structured Attentive Encoder",
    author = "Zhou, Yao  and
      Liu, Cong  and
      Pan, Yan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1274",
    pages = "2912--2922",
    abstract = "We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs. Since existing attentive models exert attention on the sequential structure, we propose a way to incorporate attention into the tree topology. Specially, given a pair of sentences, our attentive encoder uses the representation of one sentence, which generated via an RNN, to guide the structural encoding of the other sentence on the dependency parse tree. We evaluate the proposed attentive encoder on three tasks: semantic similarity, paraphrase identification and true-false question selection. Experimental results show that our encoder outperforms all baselines and achieves state-of-the-art results on two tasks.",
}
@inproceedings{prakash-etal-2016-neural,
    title = "Neural Paraphrase Generation with Stacked Residual {LSTM} Networks",
    author = "Prakash, Aaditya  and
      Hasan, Sadid A.  and
      Lee, Kathy  and
      Datla, Vivek  and
      Qadir, Ashequl  and
      Liu, Joey  and
      Farri, Oladimeji",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1275",
    pages = "2923--2934",
    abstract = "In this paper, we propose a novel neural approach for paraphrase generation. Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation. Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers. This allows for efficient training of deep LSTMs. We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers, and MSCOCO. Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based, and bi-directional LSTM models on BLEU, METEOR, TER, and an embedding-based sentence similarity metric.",
}
@inproceedings{feng-etal-2016-english,
    title = "{E}nglish-{C}hinese Knowledge Base Translation with Neural Network",
    author = "Feng, Xiaocheng  and
      Tang, Duyu  and
      Qin, Bing  and
      Liu, Ting",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1276",
    pages = "2935--2944",
    abstract = "Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language like Chinese. To expand Chinese KB by leveraging English KB resources, an effective way is to translate English KB (source) into Chinese (target). In this direction, two major challenges are to model triple semantics and to build a robust KB translator. We address these challenges by presenting a neural network approach, which learns continuous triple representation with a gated neural network. Accordingly, source triples and target triples are mapped in the same semantic vector space. We build a new dataset for English-Chinese KB translation from Freebase, and compare with several baselines on it. Experimental results show that the proposed method improves translation accuracy compared with baseline methods. We show that adaptive composition model improves standard solution such as neural tensor network in terms of translation accuracy.",
}
@inproceedings{bougouin-etal-2016-keyphrase,
    title = "Keyphrase Annotation with Graph Co-Ranking",
    author = "Bougouin, Adrien  and
      Boudin, Florian  and
      Daille, B{\'e}atrice",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1277",
    pages = "2945--2955",
    abstract = "Keyphrase annotation is the task of identifying textual units that represent the main content of a document. Keyphrase annotation is either carried out by extracting the most important phrases from a document, keyphrase extraction, or by assigning entries from a controlled domain-specific vocabulary, keyphrase assignment. Assignment methods are generally more reliable. They provide better-formed keyphrases, as well as keyphrases that do not occur in the document. But they are often silent on the contrary of extraction methods that do not depend on manually built resources. This paper proposes a new method to perform both keyphrase extraction and keyphrase assignment in an integrated and mutual reinforcing manner. Experiments have been carried out on datasets covering different domains of humanities and social sciences. They show statistically significant improvements compared to both keyphrase extraction and keyphrase assignment state-of-the art methods.",
}
@inproceedings{jansen-etal-2016-whats,
    title = "What{'}s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams",
    author = "Jansen, Peter  and
      Balasubramanian, Niranjan  and
      Surdeanu, Mihai  and
      Clark, Peter",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1278",
    pages = "2956--2965",
    abstract = "QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges. In particular, we model the requirements based on appropriate sources of evidence to be used for the QA task. We create requirements by first identifying suitable sentences in a knowledge base that support the correct answer, then use these to build explanations, filling in any necessary missing information. These explanations are used to create a fine-grained categorization of the requirements. Using these requirements, we compare a retrieval and an inference solver on 212 questions. The analysis validates the gains of the inference solver, demonstrating that it answers more questions requiring complex inference, while also providing insights into the relative strengths of the solvers and knowledge sources. We release the annotated questions and explanations as a resource with broad utility for science exam QA, including determining knowledge base construction targets, as well as supporting information aggregation in automated inference.",
}
@inproceedings{johnson-goldwasser-2016-know,
    title = "{``}All {I} know about politics is what {I} read in {T}witter{''}: Weakly Supervised Models for Extracting Politicians{'} Stances From {T}witter",
    author = "Johnson, Kristen  and
      Goldwasser, Dan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1279",
    pages = "2966--2977",
    abstract = "During the 2016 United States presidential election, politicians have increasingly used Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events. Given the limited length of tweets and the scrutiny politicians face for what they choose or neglect to say, they must craft and time their tweets carefully. The content and delivery of these tweets is therefore highly indicative of a politician{'}s stances. We present a weakly supervised method for extracting how issues are framed and temporal activity patterns on Twitter for popular politicians and issues of the 2016 election. These behavioral components are combined into a global model which collectively infers the most likely stance and agreement patterns among politicians, with respective accuracies of 86.44{\%} and 84.6{\%} on average.",
}
@inproceedings{yang-etal-2016-leveraging,
    title = "Leveraging Multiple Domains for Sentiment Classification",
    author = "Yang, Fan  and
      Mukherjee, Arjun  and
      Zhang, Yifan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1280",
    pages = "2978--2988",
    abstract = "Sentiment classification becomes more and more important with the rapid growth of user generated content. However, sentiment classification task usually comes with two challenges: first, sentiment classification is highly domain-dependent and training sentiment classifier for every domain is inefficient and often impractical; second, since the quantity of labeled data is important for assessing the quality of classifier, it is hard to evaluate classifiers when labeled data is limited for certain domains. To address the challenges mentioned above, we focus on learning high-level features that are able to generalize across domains, so a global classifier can benefit with a simple combination of documents from multiple domains. In this paper, the proposed model incorporates both sentiment polarity and unlabeled data from multiple domains and learns new feature representations. Our model doesn{'}t require labels from every domain, which means the learned feature representation can be generalized for sentiment domain adaptation. In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features. Empirical evaluations demonstrate our model outperforms baselines and yields competitive results to other state-of-the-art works on benchmark datasets.",
}
@inproceedings{bakken-etal-2016-political,
    title = "Political News Sentiment Analysis for Under-resourced Languages",
    author = "Bakken, Patrik F.  and
      Bratlie, Terje A.  and
      Marco, Cristina  and
      Gulla, Jon Atle",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1281",
    pages = "2989--2996",
    abstract = "This paper presents classification results for the analysis of sentiment in political news articles. The domain of political news is particularly challenging, as journalists are presumably objective, whilst at the same time opinions can be subtly expressed. To deal with this challenge, in this work we conduct a two-step classification model, distinguishing first subjective and second positive and negative sentiment texts. More specifically, we propose a shallow machine learning approach where only minimal features are needed to train the classifier, including sentiment-bearing Co-Occurring Terms (COTs) and negation words. This approach yields close to state-of-the-art results. Contrary to results in other domains, the use of negations as features does not have a positive impact in the evaluation results. This method is particularly suited for languages that suffer from a lack of resources, such as sentiment lexicons or parsers, and for those systems that need to function in real-time.",
}
@inproceedings{lund-etal-2016-fast,
    title = "Fast Inference for Interactive Models of Text",
    author = "Lund, Jeffrey  and
      Felt, Paul  and
      Seppi, Kevin  and
      Ringger, Eric",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1282",
    pages = "2997--3006",
    abstract = "Probabilistic models are a useful means for analyzing large text corpora. Integrating such models with human interaction enables many new use cases. However, adding human interaction to probabilistic models requires inference algorithms which are both fast and accurate. We explore the use of Iterated Conditional Modes as a fast alternative to Gibbs sampling or variational EM. We demonstrate superior performance both in run time and model quality on three different models of text including a DP Mixture of Multinomials for web search result clustering, the Interactive Topic Model, and M OM R ESP , a multinomial crowdsourcing model.",
}
@inproceedings{tsakalidis-etal-2016-combining,
    title = "Combining Heterogeneous User Generated Data to Sense Well-being",
    author = "Tsakalidis, Adam  and
      Liakata, Maria  and
      Damoulas, Theo  and
      Jellinek, Brigitte  and
      Guo, Weisi  and
      Cristea, Alexandra",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1283",
    pages = "3007--3018",
    abstract = "In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile phones. We describe the method for collecting the heterogeneous longitudinal data, how features are extracted to address missing information and differences in temporal alignment, and how the latter are combined to yield promising predictions of affect and well-being on the basis of widely used psychological scales. We achieve a coefficient of determination ($R^2$) of 0.71-0.76 and a correlation coefficient of 0.68-0.87 which is higher than the state-of-the art in equivalent multi-modal tasks for affect.",
}
@inproceedings{li-etal-2016-hashtag,
    title = "Hashtag Recommendation with Topical Attention-Based {LSTM}",
    author = "Li, Yang  and
      Liu, Ting  and
      Jiang, Jing  and
      Zhang, Liang",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1284",
    pages = "3019--3029",
    abstract = "Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn the representation of a microblog post. Observing that hashtags indicate the primary topics of microblog posts, we propose a novel attention-based LSTM model which incorporates topic modeling into the LSTM architecture through an attention mechanism. We evaluate our model using a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical attention mechanism gives more than 7.4{\%} improvement in F1 score compared with standard LSTM method.",
}
@inproceedings{kordjamshidi-etal-2016-better,
    title = "Better call {S}aul: Flexible Programming for Learning and Inference in {NLP}",
    author = "Kordjamshidi, Parisa  and
      Khashabi, Daniel  and
      Christodoulopoulos, Christos  and
      Mangipudi, Bhargav  and
      Singh, Sameer  and
      Roth, Dan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1285",
    pages = "3030--3040",
    abstract = "We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with components that are necessary for a broad range of learning based Natural Language Processing tasks at various levels of granularity. We illustrate these advances using three different, well-known NLP problems, and show how these generic learning and inference modules can directly exploit Saul{'}s graph-based data representation. These properties allow the programmer to easily switch between different model formulations and configurations, and consider various kinds of dependencies and correlations among variables of interest with minimal programming effort. We argue that Saul provides an extremely useful paradigm both for the design of advanced NLP systems and for supporting advanced research in NLP.",
}
@inproceedings{guillaume-etal-2016-crowdsourcing,
    title = "Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax",
    author = {Guillaume, Bruno  and
      Fort, Kar{\"e}n  and
      Lefebvre, Nicolas},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1286",
    pages = "3041--3052",
    abstract = "This article presents the results we obtained on a complex annotation task (that of dependency syntax) using a specifically designed Game with a Purpose, ZombiLingo. We show that with suitable mechanisms (decomposition of the task, training of the players and regular control of the annotation quality during the game), it is possible to obtain annotations whose quality is significantly higher than that obtainable with a parser, provided that enough players participate. The source code of the game and the resulting annotated corpora (for French) are freely available.",
}
@inproceedings{singhal-bhattacharyya-2016-borrow,
    title = "Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of {E}nglish Words for Multilingual Sentiment Classification",
    author = "Singhal, Prerana  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1287",
    pages = "3053--3062",
    abstract = "In this paper, we provide a solution to multilingual sentiment classification using deep learning. Given input text in a language, we use word translation into English and then the embeddings of these English words to train a classifier. This projection into the English space plus word embeddings gives a simple and uniform framework for multilingual sentiment analysis. A novel idea is augmentation of the training data with polar words, appearing in these sentences, along with their polarities. This approach leads to a performance gain of 7-10{\%} over traditional classifiers on many languages, irrespective of text genre, despite the scarcity of resources in most languages.",
}
@inproceedings{yang-etal-2016-character,
    title = "A Character-Aware Encoder for Neural Machine Translation",
    author = "Yang, Zhen  and
      Chen, Wei  and
      Wang, Feng  and
      Xu, Bo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1288",
    pages = "3063--3070",
    abstract = "This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn{'}t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.",
}
@inproceedings{su-etal-2016-convolution,
    title = "Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling",
    author = "Su, Jinsong  and
      Zhang, Biao  and
      Xiong, Deyi  and
      Li, Ruochen  and
      Yin, Jianmin",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1289",
    pages = "3071--3081",
    abstract = "Estimating similarities at different levels of linguistic units, such as words, sub-phrases and phrases, is helpful for measuring semantic similarity of an entire bilingual phrase. In this paper, we propose a convolution-enhanced bilingual recursive neural network (ConvBRNN), which not only exploits word alignments to guide the generation of phrase structures but also integrates multiple-level information of the generated phrase structures into bilingual semantic modeling. In order to accurately learn the semantic hierarchy of a bilingual phrase, we develop a recursive neural network to constrain the learned bilingual phrase structures to be consistent with word alignments. Upon the generated source and target phrase structures, we stack a convolutional neural network to integrate vector representations of linguistic units on the structures into bilingual phrase embeddings. After that, we fully incorporate information of different linguistic units into a bilinear semantic similarity model. We introduce two max-margin losses to train the ConvBRNN model: one for the phrase structure inference and the other for the semantic similarity model. Experiments on NIST Chinese-English translation tasks demonstrate the high quality of the generated bilingual phrase structures with respect to word alignments and the effectiveness of learned semantic similarities on machine translation.",
}
@inproceedings{feng-etal-2016-improving,
    title = "Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation",
    author = "Feng, Shi  and
      Liu, Shujie  and
      Yang, Nan  and
      Li, Mu  and
      Zhou, Ming  and
      Zhu, Kenny Q.",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1290",
    pages = "3082--3092",
    abstract = "In neural machine translation, the attention mechanism facilitates the translation process by producing a soft alignment between the source sentence and the target sentence. However, without dedicated distortion and fertility models seen in traditional SMT systems, the learned alignment may not be accurate, which can lead to low translation quality. In this paper, we propose two novel models to improve attention-based neural machine translation. We propose a recurrent attention mechanism as an implicit distortion model, and a fertility conditioned decoder as an implicit fertility model. We conduct experiments on large-scale Chinese{--}English translation tasks. The results show that our models significantly improve both the alignment and translation quality compared to the original attention mechanism and several other variations.",
}
@inproceedings{liu-etal-2016-neural,
    title = "Neural Machine Translation with Supervised Attention",
    author = "Liu, Lemao  and
      Utiyama, Masao  and
      Finch, Andrew  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1291",
    pages = "3093--3102",
    abstract = "The attention mechanism is appealing for neural machine translation, since it is able to dynamically encode a source sentence by generating a alignment between a target word and source words. Unfortunately, it has been proved to be worse than conventional alignment models in alignment accuracy. In this paper, we analyze and explain this issue from the point view of reordering, and propose a supervised attention which is learned with guidance from conventional alignment models. Experiments on two Chinese-to-English translation tasks show that the supervised attention mechanism yields better alignments leading to substantial gains over the standard attention based NMT.",
}
@inproceedings{sperber-etal-2016-lightly,
    title = "Lightly Supervised Quality Estimation",
    author = {Sperber, Matthias  and
      Neubig, Graham  and
      Niehues, Jan  and
      St{\"u}ker, Sebastian  and
      Waibel, Alex},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1292",
    pages = "3103--3113",
    abstract = "Evaluating the quality of output from language processing systems such as machine translation or speech recognition is an essential step in ensuring that they are sufficient for practical use. However, depending on the practical requirements, evaluation approaches can differ strongly. Often, reference-based evaluation measures (such as BLEU or WER) are appealing because they are cheap and allow rapid quantitative comparison. On the other hand, practitioners often focus on manual evaluation because they must deal with frequently changing domains and quality standards requested by customers, for which reference-based evaluation is insufficient or not possible due to missing in-domain reference data (Harris et al., 2016). In this paper, we attempt to bridge this gap by proposing a framework for lightly supervised quality estimation. We collect manually annotated scores for a small number of segments in a test corpus or document, and combine them with automatically predicted quality scores for the remaining segments to predict an overall quality estimate. An evaluation shows that our framework estimates quality more reliably than using fully automatic quality estimation approaches, while keeping annotation effort low by not requiring full references to be available for the particular domain.",
}
@inproceedings{tang-etal-2016-improving-translation,
    title = "Improving Translation Selection with Supersenses",
    author = "Tang, Haiqing  and
      Xiong, Deyi  and
      Lopez de Lacalle, Oier  and
      Agirre, Eneko",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1293",
    pages = "3114--3123",
    abstract = "Selecting appropriate translations for source words with multiple meanings still remains a challenge for statistical machine translation (SMT). One reason for this is that most SMT systems are not good at detecting the proper sense for a polysemic word when it appears in different contexts. In this paper, we adopt a supersense tagging method to annotate source words with coarse-grained ontological concepts. In order to enable the system to choose an appropriate translation for a word or phrase according to the annotated supersense of the word or phrase, we propose two translation models with supersense knowledge: a maximum entropy based model and a supersense embedding model. The effectiveness of our proposed models is validated on a large-scale English-to-Spanish translation task. Results indicate that our method can significantly improve translation quality via correctly conveying the meaning of the source language to the target language.",
}
@inproceedings{graham-etal-2016-glitters,
    title = "Is all that Glitters in Machine Translation Quality Estimation really Gold?",
    author = "Graham, Yvette  and
      Baldwin, Timothy  and
      Dowling, Meghan  and
      Eskevich, Maria  and
      Lynn, Teresa  and
      Tounsi, Lamia",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1294",
    pages = "3124--3134",
    abstract = "Human-targeted metrics provide a compromise between human evaluation of machine translation, where high inter-annotator agreement is difficult to achieve, and fully automatic metrics, such as BLEU or TER, that lack the validity of human assessment. Human-targeted translation edit rate (HTER) is by far the most widely employed human-targeted metric in machine translation, commonly employed, for example, as a gold standard in evaluation of quality estimation. Original experiments justifying the design of HTER, as opposed to other possible formulations, were limited to a small sample of translations and a single language pair, however, and this motivates our re-evaluation of a range of human-targeted metrics on a substantially larger scale. Results show significantly stronger correlation with human judgment for HBLEU over HTER for two of the nine language pairs we include and no significant difference between correlations achieved by HTER and HBLEU for the remaining language pairs. Finally, we evaluate a range of quality estimation systems employing HTER and direct assessment (DA) of translation adequacy as gold labels, resulting in a divergence in system rankings, and propose employment of DA for future quality estimation evaluations.",
}
@inproceedings{wang-etal-2016-connecting,
    title = "Connecting Phrase based Statistical Machine Translation Adaptation",
    author = "Wang, Rui  and
      Zhao, Hai  and
      Lu, Bao-Liang  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1295",
    pages = "3135--3145",
    abstract = "Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains of the original corpus can indeed enhance SMT performance directly. A series of SMT adaptation methods have been proposed to select these similar-domain data, and most of them focus on sentence selection. In comparison, phrase is a smaller and more fine grained unit for data selection, therefore we propose a straightforward and efficient connecting phrase based adaptation method, which is applied to both bilingual phrase pair and monolingual n-gram adaptation. The proposed method is evaluated on IWSLT/NIST data sets, and the results show that phrase based SMT performances are significantly improved (up to +1.6 in comparison with phrase based SMT baseline system and +0.9 in comparison with existing methods).",
}
@inproceedings{schulz-aziz-2016-fast,
    title = "Fast Collocation-Based {B}ayesian {HMM} Word Alignment",
    author = "Schulz, Philip  and
      Aziz, Wilker",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1296",
    pages = "3146--3155",
    abstract = "We present a new Bayesian HMM word alignment model for statistical machine translation. The model is a mixture of an alignment model and a language model. The alignment component is a Bayesian extension of the standard HMM. The language model component is responsible for the generation of words needed for source fluency reasons from source language context. This allows for untranslatable source words to remain unaligned and at the same time avoids the introduction of artificial NULL words which introduces unusually long alignment jumps. Existing Bayesian word alignment models are unpractically slow because they consider each target position when resampling a given alignment link. The sampling complexity therefore grows linearly in the target sentence length. In order to make our model useful in practice, we devise an auxiliary variable Gibbs sampler that allows us to resample alignment links in constant time independently of the target sentence length. This leads to considerable speed improvements. Experimental results show that our model performs as well as existing word alignment toolkits in terms of resulting BLEU score.",
}
@inproceedings{jehl-riezler-2016-learning,
    title = "Learning to translate from graded and negative relevance information",
    author = "Jehl, Laura  and
      Riezler, Stefan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1297",
    pages = "3156--3166",
    abstract = "We present an approach for learning to translate by exploiting cross-lingual link structure in multilingual document collections. We propose a new learning objective based on structured ramp loss, which learns from graded relevance, explicitly including negative relevance information. Our results on English German translation of Wikipedia entries show small, but significant, improvements of our method over an unadapted baseline, even when only a weak relevance signal is used. We also compare our method to monolingual language model adaptation and automatic pseudo-parallel data extraction and find small improvements even over these strong baselines.",
}
@inproceedings{daiber-etal-2016-universal,
    title = "Universal Reordering via Linguistic Typology",
    author = "Daiber, Joachim  and
      Stanojevi{\'c}, Milo{\v{s}}  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1298",
    pages = "3167--3176",
    abstract = "In this paper we explore the novel idea of building a single universal reordering model from English to a large number of target languages. To build this model we exploit typological features of word order for a large number of target languages together with source (English) syntactic features and we train this model on a single combined parallel corpus representing all (22) involved language pairs. We contribute experimental evidence for the usefulness of linguistically defined typological features for building such a model. When the universal reordering model is used for preordering followed by monotone translation (no reordering inside the decoder), our experiments show that this pipeline gives comparable or improved translation performance with a phrase-based baseline for a large number of language pairs (12 out of 22) from diverse language families.",
}
@inproceedings{durrani-etal-2016-deep,
    title = "A Deep Fusion Model for Domain Adaptation in Phrase-based {MT}",
    author = "Durrani, Nadir  and
      Sajjad, Hassan  and
      Joty, Shafiq  and
      Abdelali, Ahmed",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1299",
    pages = "3177--3187",
    abstract = "We present a novel fusion model for domain adaptation in Statistical Machine Translation. Our model is based on the joint source-target neural network Devlin et al., 2014, and is learned by fusing in- and out-domain models. The adaptation is performed by backpropagating errors from the output layer to the word embedding layer of each model, subsequently adjusting parameters of the composite model towards the in-domain data. On the standard tasks of translating English-to-German and Arabic-to-English TED talks, we observed average improvements of +0.9 and +0.7 BLEU points, respectively over a competition grade phrase-based system. We also demonstrate improvements over existing adaptation methods.",
}
@inproceedings{zhang-etal-2016-inducing,
    title = "Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover{'}s Distance Regularization",
    author = "Zhang, Meng  and
      Liu, Yang  and
      Luan, Huanbo  and
      Liu, Yiqun  and
      Sun, Maosong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1300",
    pages = "3188--3198",
    abstract = "Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains. Previous endeavors typically simplify this task by imposing the one-to-one translation assumption, which is too strong to hold for natural languages. We remove this constraint by introducing the Earth Mover{'}s Distance into the training of bilingual word embeddings. In this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization. Our approach shows significant and consistent improvements across four language pairs. We also demonstrate that our approach is particularly preferable in resource-scarce settings as it only requires a minimal seed lexicon.",
}
@inproceedings{hirschmann-etal-2016-makes,
    title = "What Makes Word-level Neural Machine Translation Hard: A Case Study on {E}nglish-{G}erman Translation",
    author = {Hirschmann, Fabian  and
      Nam, Jinseok  and
      F{\"u}rnkranz, Johannes},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1301",
    pages = "3199--3208",
    abstract = "Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems. In recent years, several end-to-end learning architectures based on recurrent neural networks have been proposed. Unlike traditional systems, Neural Machine Translation (NMT) systems learn the parameters of the model and require only minimal preprocessing. Memory and time constraints allow to take only a fixed number of words into account, which leads to the out-of-vocabulary (OOV) problem. In this work, we analyze why the OOV problem arises and why it is considered a serious problem in German. We study the effectiveness of compound word splitters for alleviating the OOV problem, resulting in a 2.5+ BLEU points improvement over a baseline on the WMT{'}14 German-to-English translation task. For English-to-German translation, we use target-side compound splitting through a special syntax during training that allows the model to merge compound words and gain 0.2 BLEU points.",
}
@inproceedings{jalili-sabet-etal-2016-improving,
    title = "Improving Word Alignment of Rare Words with Word Embeddings",
    author = "Jalili Sabet, Masoud  and
      Faili, Heshaam  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1302",
    pages = "3209--3215",
    abstract = "We address the problem of inducing word alignment for language pairs by developing an unsupervised model with the capability of getting applied to other generative alignment models. We approach the task by: i)proposing a new alignment model based on the IBM alignment model 1 that uses vector representation of words, and ii)examining the use of similar source words to overcome the problem of rare source words and improving the alignments. We apply our method to English-French corpora and run the experiments with different sizes of sentence pairs. Our results show competitive performance against the baseline and in some cases improve the results up to 6.9{\%} in terms of precision.",
}
@inproceedings{chang-etal-2016-measuring,
    title = "Measuring the Information Content of Financial News",
    author = "Chang, Ching-Yun  and
      Zhang, Yue  and
      Teng, Zhiyang  and
      Bozanic, Zahn  and
      Ke, Bin",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1303",
    pages = "3216--3225",
    abstract = "Measuring the information content of news text is useful for decision makers in their investments since news information can influence the intrinsic values of companies. We propose a model to automatically measure the information content given news text, trained using news and corresponding cumulative abnormal returns of listed companies. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a novel tree-structured LSTM is used to find target-specific representations of news text given syntax structures. Empirical results show that the neural models can outperform sentiment-based models, demonstrating the effectiveness of recent NLP technology advances for computational finance.",
}
@inproceedings{godea-etal-2016-automatic,
    title = "Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems",
    author = "Godea, Andreea  and
      Bulgarov, Florin  and
      Nielsen, Rodney",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1304",
    pages = "3226--3236",
    abstract = "Truly effective and practical educational systems will only be achievable when they have the ability to fully recognize deep relationships between a learner{'}s interpretation of a subject and the desired conceptual understanding. In this paper, we take important steps in this direction by introducing a new representation of sentences {--} Minimal Meaningful Propositions (MMPs), which will allow us to significantly improve the mapping between a learner{'}s answer and the ideal response. Using this technique, we make significant progress towards highly scalable and domain independent educational systems, that will be able to operate without human intervention. Even though this is a new task, we show very good results both for the extraction of MMPs and for classification with respect to their importance.",
}
@inproceedings{panagiotou-etal-2016-first,
    title = "First Story Detection using Entities and Relations",
    author = "Panagiotou, Nikolaos  and
      Akkaya, Cem  and
      Tsioutsiouliklis, Kostas  and
      Kalogeraki, Vana  and
      Gunopulos, Dimitrios",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1305",
    pages = "3237--3244",
    abstract = "News portals, such as Yahoo News or Google News, collect large amounts of documents from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable and accurate First Story Detection (FSD) pipeline that identifies fresh news. In comparison to other FSD systems, our method relies on relation extraction methods exploiting entities and their relations. We evaluate our pipeline using two distinct datasets from Yahoo News and Google News. Experimental results demonstrate that our method improves over the state-of-the-art systems on both datasets with constant space and time requirements.",
}
@inproceedings{loukina-etal-2016-textual,
    title = "Textual complexity as a predictor of difficulty of listening items in language proficiency tests",
    author = "Loukina, Anastassia  and
      Yoon, Su-Youn  and
      Sakano, Jennifer  and
      Wei, Youhua  and
      Sheehan, Kathy",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1306",
    pages = "3245--3253",
    abstract = "In this paper we explore to what extent the difficulty of listening items in an English language proficiency test can be predicted by the textual properties of the prompt. We show that a system based on multiple text complexity features can predict item difficulty for several different item types and for some items achieves higher accuracy than human estimates of item difficulty.",
}
@inproceedings{hu-etal-2016-construction,
    title = "The Construction of a {C}hinese Collocational Knowledge Resource and Its Application for Second Language Acquisition",
    author = "Hu, Renfen  and
      Chen, Jiayong  and
      Chen, Kuang-hua",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1307",
    pages = "3254--3263",
    abstract = "The appropriate use of collocations is a challenge for second language acquisition. However, high quality and easily accessible Chinese collocation resources are not available for both teachers and students. This paper presents the design and construction of a large scale resource of Chinese collocational knowledge, and a web-based application (OCCA, Online Chinese Collocation Assistant) which offers free and convenient collocation search service to end users. We define and classify collocations based on practical language acquisition needs and utilize a syntax based method to extract nine types of collocations. Totally 37 extraction rules are compiled with word, POS and dependency relation features, 1,750,000 collocations are extracted from a corpus for L2 learning and complementary Wikipedia data, and OCCA is implemented based on these extracted collocations. By comparing OCCA with two traditional collocation dictionaries, we find OCCA has higher entry coverage and collocation quantity, and our method achieves quite low error rate at less than 5{\%}. We also discuss how to apply collocational knowledge to grammatical error detection and demonstrate comparable performance to the best results in 2015 NLP-TEA CGED shared task. The preliminary experiment shows that the collocation knowledge is helpful in detecting all the four types of grammatical errors.",
}
@inproceedings{lu-etal-2016-joint,
    title = "Joint Inference for Event Coreference Resolution",
    author = "Lu, Jing  and
      Venugopal, Deepak  and
      Gogate, Vibhav  and
      Ng, Vincent",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1308",
    pages = "3264--3275",
    abstract = "Event coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs. We hypothesize that exploiting the inter-dependencies between these components can significantly improve the performance of an event coreference resolver, and subsequently propose a novel joint inference based event coreference resolver using Markov Logic Networks (MLNs). However, the rich features that are important for this task are typically very hard to explicitly encode as MLN formulas since they significantly increase the size of the MLN, thereby making joint inference and learning infeasible. To address this problem, we propose a novel solution where we implicitly encode rich features into our model by augmenting the MLN distribution with low dimensional unit clauses. Our approach achieves state-of-the-art results on two standard evaluation corpora.",
}
@inproceedings{ge-etal-2016-event,
    title = "Event Detection with Burst Information Networks",
    author = "Ge, Tao  and
      Cui, Lei  and
      Chang, Baobao  and
      Sui, Zhifang  and
      Zhou, Ming",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1309",
    pages = "3276--3286",
    abstract = "Retrospective event detection is an important task for discovering previously unidentified events in a text stream. In this paper, we propose two fast centroid-aware event detection models based on a novel text stream representation {--} Burst Information Networks (BINets) for addressing the challenge. The BINets are time-aware, efficient and can be easily analyzed for identifying key information (centroids). These advantages allow the BINet-based approaches to achieve the state-of-the-art performance on multiple datasets, demonstrating the efficacy of BINets for the task of event detection.",
}
@inproceedings{zhu-etal-2016-corpus,
    title = "Corpus Fusion for Emotion Classification",
    author = "Zhu, Suyang  and
      Li, Shoushan  and
      Chen, Ying  and
      Zhou, Guodong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1310",
    pages = "3287--3297",
    abstract = "Machine learning-based methods have obtained great progress on emotion classification. However, in most previous studies, the models are learned based on a single corpus which often suffers from insufficient labeled data. In this paper, we propose a corpus fusion approach to address emotion classification across two corpora which use different emotion taxonomies. The objective of this approach is to utilize the annotated data from one corpus to help the emotion classification on another corpus. An Integer Linear Programming (ILP) optimization is proposed to refine the classification results. Empirical studies show the effectiveness of the proposed approach to corpus fusion for emotion classification.",
}
@inproceedings{tang-etal-2016-effective,
    title = "Effective {LSTM}s for Target-Dependent Sentiment Classification",
    author = "Tang, Duyu  and
      Qin, Bing  and
      Feng, Xiaocheng  and
      Liu, Ting",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1311",
    pages = "3298--3307",
    abstract = "Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.",
}
@inproceedings{stede-2016-towards,
    title = "Towards assessing depth of argumentation",
    author = "Stede, Manfred",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1312",
    pages = "3308--3317",
    abstract = "For analyzing argumentative text, we propose to study the {`}depth{'} of argumentation as one important component, which we distinguish from argument quality. In a pilot study with German newspaper commentary texts, we asked students to rate the degree of argumentativeness, and then looked for correlations with features of the annotated argumentation structure and the rhetorical structure (in terms of RST). The results indicate that the human judgements correlate with our operationalization of depth and with certain structural features of RST trees.",
}
@inproceedings{phan-etal-2016-video,
    title = "Video Event Detection by Exploiting Word Dependencies from Image Captions",
    author = "Phan, Sang  and
      Miyao, Yusuke  and
      Le, Duy-Dinh  and
      Satoh, Shin{'}ichi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1313",
    pages = "3318--3327",
    abstract = "Video event detection is a challenging problem in information and multimedia retrieval. Different from single action detection, event detection requires a richer level of semantic information from video. In order to overcome this challenge, existing solutions often represent videos using high level features such as concepts. However, concept-based representation can be confusing because it does not encode the relationship between concepts. This issue can be addressed by exploiting the co-occurrences of the concepts, however, it often leads to a very huge number of possible combinations. In this paper, we propose a new approach to obtain the relationship between concepts by exploiting the syntactic dependencies between words in the image captions. The main advantage of this approach is that it significantly reduces the number of informative combinations between concepts. We conduct extensive experiments to analyze the effectiveness of using the new dependency representation for event detection on two large-scale TRECVID Multimedia Event Detection 2013 and 2014 datasets. Experimental results show that i) Dependency features are more discriminative than concept-based features. ii) Dependency features can be combined with our current event detection system to further improve the performance. For instance, the relative improvement can be as far as 8.6{\%} on the MEDTEST14 10Ex setting.",
}
@inproceedings{xiao-etal-2016-predicting,
    title = "Predicting Restaurant Consumption Level through Social Media Footprints",
    author = "Xiao, Yang  and
      Wang, Yuan  and
      Mao, Hangyu  and
      Xiao, Zhen",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1314",
    pages = "3328--3338",
    abstract = "Accurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting. In this paper, we propose a systematic method to leverage user online social media content for predicting offline restaurant consumption level. We utilize the social login as a bridge and construct a dataset of 8,844 users who have been linked across Dianping (similar to Yelp) and Sina Weibo. More specifically, we construct consumption level ground truth based on user self report spending. We build predictive models using both raw features and, especially, latent features, such as topic distributions and celebrities clusters. The employed methods demonstrate that online social media content has strong predictive power for offline spending. Finally, combined with qualitative feature analysis, we present the differences in words usage, topic interests and following behavior between different consumption level groups.",
}
@inproceedings{mao-etal-2016-novel,
    title = "A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing",
    author = "Mao, Xian-Ling  and
      Hao, Yi-Jing  and
      Zhou, Qiang  and
      Yuan, Wen-Qing  and
      Yang, Liner  and
      Huang, Heyan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1315",
    pages = "3339--3348",
    abstract = "Recently, topic modeling has been widely applied in data mining due to its powerful ability. A common, major challenge in applying such topic models to other tasks is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, most of previous works only focus on the effectiveness of topic labeling, and less attention has been paid to quickly creating good topic descriptors; meanwhile, it{'}s hard to assign labels for new emerging topics by using most of existing methods. To solve the problems above, in this paper, we propose a novel fast topic labeling framework that casts the labeling problem as a k-nearest neighbor (KNN) search problem in a probability vector set. Our experimental results show that the proposed sequential interleaving method based on locality sensitive hashing (LSH) technology is efficient in boosting the comparison speed among probability distributions, and the proposed framework can generate meaningful labels to interpret topics, including new emerging topics.",
}
@inproceedings{mou-etal-2016-sequence,
    title = "Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation",
    author = "Mou, Lili  and
      Song, Yiping  and
      Yan, Rui  and
      Li, Ge  and
      Zhang, Lu  and
      Jin, Zhi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1316",
    pages = "3349--3358",
    abstract = "Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a {``}sequence to backward and forward sequences{''} model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.",
}
@inproceedings{gervits-etal-2016-disfluent,
    title = "Disfluent but effective? A quantitative study of disfluencies and conversational moves in team discourse",
    author = "Gervits, Felix  and
      Eberhard, Kathleen  and
      Scheutz, Matthias",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1317",
    pages = "3359--3369",
    abstract = "Situated dialogue systems that interact with humans as part of a team (e.g., robot teammates) need to be able to use information from communication channels to gauge the coordination level and effectiveness of the team. Currently, the feasibility of this end goal is limited by several gaps in both the empirical and computational literature. The purpose of this paper is to address those gaps in the following ways: (1) investigate which properties of task-oriented discourse correspond with effective performance in human teams, and (2) discuss how and to what extent these properties can be utilized in spoken dialogue systems. To this end, we analyzed natural language data from a unique corpus of spontaneous, task-oriented dialogue (CReST corpus), which was annotated for disfluencies and conversational moves. We found that effective teams made more self-repair disfluencies and used specific communication strategies to facilitate grounding and coordination. Our results indicate that truly robust and natural dialogue systems will need to interpret highly disfluent utterances and also utilize specific collaborative mechanisms to facilitate grounding. These data shed light on effective communication in performance scenarios and directly inform the development of robust dialogue systems for situated artificial agents.",
}
@inproceedings{vougiouklis-etal-2016-neural,
    title = "A Neural Network Approach for Knowledge-Driven Response Generation",
    author = "Vougiouklis, Pavlos  and
      Hare, Jonathon  and
      Simperl, Elena",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1318",
    pages = "3370--3380",
    abstract = "We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55{\%} improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.",
}
@inproceedings{poostchi-etal-2016-personer,
    title = "{P}erso{NER}: {P}ersian Named-Entity Recognition",
    author = "Poostchi, Hanieh  and
      Zare Borzeshi, Ehsan  and
      Abdous, Mohammad  and
      Piccardi, Massimo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1319",
    pages = "3381--3389",
    abstract = "Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.",
}
@inproceedings{singh-etal-2016-ocr,
    title = "{OCR}++: A Robust Framework For Information Extraction from Scholarly Articles",
    author = "Singh, Mayank  and
      Barua, Barnopriyo  and
      Palod, Priyank  and
      Garg, Manvi  and
      Satapathy, Sidhartha  and
      Bushi, Samuel  and
      Ayush, Kumar  and
      Sai Rohith, Krishna  and
      Gamidi, Tulasi  and
      Goyal, Pawan  and
      Mukherjee, Animesh",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1320",
    pages = "3390--3400",
    abstract = "This paper proposes OCR++, an open-source framework designed for a variety of information extraction tasks from scholarly articles including metadata (title, author names, affiliation and e-mail), structure (section headings and body text, table and figure headings, URLs and footnotes) and bibliography (citation instances and references). We analyze a diverse set of scientific articles written in English to understand generic writing patterns and formulate rules to develop this hybrid framework. Extensive evaluations show that the proposed framework outperforms the existing state-of-the-art tools by a large margin in structural information extraction along with improved performance in metadata and bibliography extraction tasks, both in terms of accuracy (around 50{\%} improvement) and processing time (around 52{\%} improvement). A user experience study conducted with the help of 30 researchers reveals that the researchers found this system to be very helpful. As an additional objective, we discuss two novel use cases including automatically extracting links to public datasets from the proceedings, which would further accelerate the advancement in digital libraries. The result of the framework can be exported as a whole into structured TEI-encoded documents. Our framework is accessible online at \url{http://www.cnergres.iitkgp.ac.in/OCR++/home/}.",
}
@inproceedings{hazem-morin-2016-efficient,
    title = "Efficient Data Selection for Bilingual Terminology Extraction from Comparable Corpora",
    author = "Hazem, Amir  and
      Morin, Emmanuel",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1321",
    pages = "3401--3411",
    abstract = "Comparable corpora are the main alternative to the use of parallel corpora to extract bilingual lexicons. Although it is easier to build comparable corpora, specialized comparable corpora are often of modest size in comparison with corpora issued from the general domain. Consequently, the observations of word co-occurrences which are the basis of context-based methods are unreliable. We propose in this article to improve word co-occurrences of specialized comparable corpora and thus context representation by using general-domain data. This idea, which has been already used in machine translation task for more than a decade, is not straightforward for the task of bilingual lexicon extraction from specific-domain comparable corpora. We go against the mainstream of this task where many studies support the idea that adding out-of-domain documents decreases the quality of lexicons. Our empirical evaluation shows the advantages of this approach which induces a significant gain in the accuracy of extracted lexicons.",
}
@inproceedings{ljubesic-etal-2016-tweetgeo,
    title = "{T}weet{G}eo - A Tool for Collecting, Processing and Analysing Geo-encoded Linguistic Data",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Samard{\v{z}}i{\'c}, Tanja  and
      Derungs, Curdin",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1322",
    pages = "3412--3421",
    abstract = "In this paper we present a newly developed tool that enables researchers interested in spatial variation of language to define a geographic perimeter of interest, collect data from the Twitter streaming API published in that perimeter, filter the obtained data by language and country, define and extract variables of interest and analyse the extracted variables by one spatial statistic and two spatial visualisations. We showcase the tool on the area and a selection of languages spoken in former Yugoslavia. By defining the perimeter, languages and a series of linguistic variables of interest we demonstrate the data collection, processing and analysis capabilities of the tool.",
}
@inproceedings{espinosa-anke-etal-2016-extending,
    title = "Extending {W}ord{N}et with Fine-Grained Collocational Information via Supervised Distributional Learning",
    author = "Espinosa-Anke, Luis  and
      Camacho-Collados, Jose  and
      Rodr{\'\i}guez-Fern{\'a}ndez, Sara  and
      Saggion, Horacio  and
      Wanner, Leo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1323",
    pages = "3422--3432",
    abstract = "WordNet is probably the best known lexical resource in Natural Language Processing. While it is widely regarded as a high quality repository of concepts and semantic relations, updating and extending it manually is costly. One important type of relation which could potentially add enormous value to WordNet is the inclusion of collocational information, which is paramount in tasks such as Machine Translation, Natural Language Generation and Second Language Learning. In this paper, we present ColWordNet (CWN), an extended WordNet version with fine-grained collocational information, automatically introduced thanks to a method exploiting linear relations between analogous sense-level embeddings spaces. We perform both intrinsic and extrinsic evaluations, and release CWN for the use and scrutiny of the community.",
}
@inproceedings{al-khatib-etal-2016-news,
    title = "A News Editorial Corpus for Mining Argumentation Strategies",
    author = "Al-Khatib, Khalid  and
      Wachsmuth, Henning  and
      Kiesel, Johannes  and
      Hagen, Matthias  and
      Stein, Benno",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1324",
    pages = "3433--3443",
    abstract = "Many argumentative texts, and news editorials in particular, follow a specific strategy to persuade their readers of some opinion or attitude. This includes decisions such as when to tell an anecdote or where to support an assumption with statistics, which is reflected by the composition of different types of argumentative discourse units in a text. While several argument mining corpora have recently been published, they do not allow the study of argumentation strategies due to incomplete or coarse-grained unit annotations. This paper presents a novel corpus with 300 editorials from three diverse news portals that provides the basis for mining argumentation strategies. Each unit in all editorials has been assigned one of six types by three annotators with a high Fleiss{'} Kappa agreement of 0.56. We investigate various challenges of the annotation process and we conduct a first corpus analysis. Our results reveal different strategies across the news portals, exemplifying the benefit of studying editorials{---}a so far underresourced text genre in argument mining.",
}
@inproceedings{sulubacak-etal-2016-universal,
    title = "{U}niversal {D}ependencies for {T}urkish",
    author = {Sulubacak, Umut  and
      Gokirmak, Memduh  and
      Tyers, Francis  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}  and
      Nivre, Joakim  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1325",
    pages = "3444--3454",
    abstract = "The Universal Dependencies (UD) project was conceived after the substantial recent interest in unifying annotation schemes across languages. With its own annotation principles and abstract inventory for parts of speech, morphosyntactic features and dependency relations, UD aims to facilitate multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. This paper presents the Turkish IMST-UD Treebank, the first Turkish treebank to be in a UD release. The IMST-UD Treebank was automatically converted from the IMST Treebank, which was also recently released. We describe this conversion procedure in detail, complete with mapping tables. We also present our evaluation of the parsing performances of both versions of the IMST Treebank. Our findings suggest that the UD framework is at least as viable for Turkish as the original annotation framework of the IMST Treebank.",
}
@inproceedings{eskander-etal-2016-creating,
    title = "Creating Resources for Dialectal {A}rabic from a Single Annotation: A Case Study on {E}gyptian and {L}evantine",
    author = "Eskander, Ramy  and
      Habash, Nizar  and
      Rambow, Owen  and
      Pasha, Arfath",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1326",
    pages = "3455--3465",
    abstract = "Arabic dialects present a special problem for natural language processing because there are few resources, they have no standard orthography, and have not been studied much. However, as more and more written dialectal Arabic is found in social media, NLP for Arabic dialects becomes an important goal. We present a methodology for creating a morphological analyzer and a morphological tagger for dialectal Arabic, and we illustrate it on Egyptian and Levantine Arabic. To our knowledge, these are the first analyzer and tagger for Levantine.",
}
@inproceedings{akbik-etal-2016-multilingual,
    title = "Multilingual Aliasing for Auto-Generating Proposition {B}anks",
    author = "Akbik, Alan  and
      Guan, Xinyu  and
      Li, Yunyao",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1327",
    pages = "3466--3474",
    abstract = "Semantic Role Labeling (SRL) is the task of identifying the predicate-argument structure in sentences with semantic frame and role labels. For the English language, the Proposition Bank provides both a lexicon of all possible semantic frames and large amounts of labeled training data. In order to expand SRL beyond English, previous work investigated automatic approaches based on parallel corpora to automatically generate Proposition Banks for new target languages (TLs). However, this approach heuristically produces the frame lexicon from word alignments, leading to a range of lexicon-level errors and inconsistencies. To address these issues, we propose to manually alias TL verbs to existing English frames. For instance, the German verb drehen may evoke several meanings, including {``}turn something{''} and {``}film something{''}. Accordingly, we alias the former to the frame TURN.01 and the latter to a group of frames that includes FILM.01 and SHOOT.03. We execute a large-scale manual aliasing effort for three target languages and apply the new lexicons to automatically generate large Proposition Banks for Chinese, French and German with manually curated frames. We present a detailed evaluation in which we find that our proposed approach significantly increases the quality and consistency of the generated Proposition Banks. We release these resources to the research community.",
}
@inproceedings{mortensen-etal-2016-panphon,
    title = "{P}an{P}hon: A Resource for Mapping {IPA} Segments to Articulatory Feature Vectors",
    author = "Mortensen, David R.  and
      Littell, Patrick  and
      Bharadwaj, Akash  and
      Goyal, Kartik  and
      Dyer, Chris  and
      Levin, Lori",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1328",
    pages = "3475--3484",
    abstract = "This paper contributes to a growing body of evidence that{---}when coupled with appropriate machine-learning techniques{--}linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data. In particular, we show that phonological features outperform character-based models. PanPhon is a database relating over 5,000 IPA segments to 21 subsegmental articulatory features. We show that this database boosts performance in various NER-related tasks. Phonologically aware, neural CRF models built on PanPhon features are able to perform better on monolingual Spanish and Turkish NER tasks that character-based models. They have also been shown to work well in transfer models (as between Uzbek and Turkish). PanPhon features also contribute measurably to Orthography-to-IPA conversion tasks.",
}
@inproceedings{zhou-etal-2016-text,
    title = "Text Classification Improved by Integrating Bidirectional {LSTM} with Two-dimensional Max Pooling",
    author = "Zhou, Peng  and
      Qi, Zhenyu  and
      Zheng, Suncong  and
      Xu, Jiaming  and
      Bao, Hongyun  and
      Xu, Bo",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1329",
    pages = "3485--3495",
    abstract = "Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimensions: the time-step dimension and the feature vector dimension. Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector. However, the features on the feature vector dimension are not mutually independent, and simply applying 1D pooling operation over the time-step dimension independently may destroy the structure of the feature representation. On the other hand, applying two-dimensional (2D) pooling operation over the two dimensions may sample more meaningful features for sequence modeling tasks. To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text. This paper also utilizes 2D convolution to sample more meaningful information of the matrix. Experiments are conducted on six text classification tasks, including sentiment analysis, question classification, subjectivity classification and newsgroup classification. Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks. Specifically, one of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.",
}
@inproceedings{postma-etal-2016-always,
    title = "More is not always better: balancing sense distributions for all-words Word Sense Disambiguation",
    author = "Postma, Marten  and
      Izquierdo Bevia, Ruben  and
      Vossen, Piek",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1330",
    pages = "3496--3506",
    abstract = "Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses, which is mainly caused by the difference in sense distributions between training and test data. The main focus in tackling this problem has been on acquiring more data or selecting a single predominant sense and not necessarily on the meta properties of the data itself. We demonstrate that these properties, such as the volume, provenance, and balancing, play an important role with respect to system performance. In this paper, we describe a set of experiments to analyze these meta properties in the framework of a state-of-the-art WSD system when evaluated on the SemEval-2013 English all-words dataset. We show that volume and provenance are indeed important, but that approximating the perfect balancing of the selected training data leads to an improvement of 21 points and exceeds state-of-the-art systems by 14 points while using only simple features. We therefore conclude that unsupervised acquisition of training data should be guided by strategies aimed at matching meta properties.",
}
@inproceedings{eger-etal-2016-language,
    title = "Language classification from bilingual word embedding graphs",
    author = "Eger, Steffen  and
      Hoenen, Armin  and
      Mehler, Alexander",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1331",
    pages = "3507--3518",
    abstract = "We study the role of the second language in bilingual word embeddings in monolingual semantic evaluation tasks. We find strongly and weakly positive correlations between down-stream task performance and second language similarity to the target language. Additionally, we show how bilingual word embeddings can be employed for the task of semantic language classification and that joint semantic spaces vary in meaningful ways across second languages. Our results support the hypothesis that semantic language similarity is influenced by both structural similarity as well as geography/contact.",
}
@inproceedings{drozd-etal-2016-word,
    title = "Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen",
    author = "Drozd, Aleksandr  and
      Gladkova, Anna  and
      Matsuoka, Satoshi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1332",
    pages = "3519--3530",
    abstract = "Solving word analogies became one of the most popular benchmarks for word embeddings on the assumption that linear relations between word pairs (such as \textit{king}:\textit{man} :: \textit{woman}:\textit{queen}) are indicative of the quality of the embedding. We question this assumption by showing that the information not detected by linear offset may still be recoverable by a more sophisticated search method, and thus is actually encoded in the embedding. The general problem with linear offset is its sensitivity to the idiosyncrasies of individual words. We show that simple averaging over multiple word pairs improves over the state-of-the-art. A further improvement in accuracy (up to 30{\%} for some embeddings and relations) is achieved by combining cosine similarity with an estimation of the extent to which a candidate answer belongs to the correct word class. In addition to this practical contribution, this work highlights the problem of the interaction between word embeddings and analogy retrieval algorithms, and its implications for the evaluation of word embeddings and the use of analogies in extrinsic tasks.",
}
@inproceedings{bjerva-etal-2016-semantic,
    title = "Semantic Tagging with Deep Residual Networks",
    author = "Bjerva, Johannes  and
      Plank, Barbara  and
      Bos, Johan",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1333",
    pages = "3531--3541",
    abstract = "We propose a novel semantic tagging task, semtagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both word and character representations, and includes a novel residual bypass architecture. We evaluate the tagset both intrinsically on the new task of semantic tagging, as well as on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an auxiliary loss function predicting our semantic tags, significantly outperforms prior results on English Universal Dependencies POS tagging (95.71{\%} accuracy on UD v1.2 and 95.67{\%} accuracy on UD v1.3).",
}
@inproceedings{virk-etal-2016-supervised,
    title = "A Supervised Approach for Enriching the Relational Structure of Frame Semantics in {F}rame{N}et",
    author = "Virk, Shafqat Mumtaz  and
      Muller, Philippe  and
      Conrath, Juliette",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1334",
    pages = "3542--3552",
    abstract = "Frame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, is a popular lexical semantic resource. In addition to providing a set of core semantic frames and their frame elements, FrameNet also provides relations between those frames (hence providing a network of frames i.e. FrameNet). We address here the limited coverage of the network of conceptual relations between frames in FrameNet, which has previously been pointed out by others. We present a supervised model using rich features from three different sources: structural features from the existing FrameNet network, information from the WordNet relations between synsets projected into semantic frames, and corpus-collected lexical associations. We show large improvements over baselines consisting of each of the three groups of features in isolation. We then use this model to select frame pairs as candidate relations, and perform evaluation on a sample with good precision.",
}
@inproceedings{dang-etal-2016-reddit,
    title = "{R}eddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis",
    author = "Dang, Anh  and
      Moh{'}d, Abidalrahman  and
      Islam, Aminul  and
      Minghim, Rosane  and
      Smit, Michael  and
      Milios, Evangelos",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1335",
    pages = "3553--3564",
    abstract = "This paper introduces a new large-scale n-gram corpus that is created specifically from social media text. Two distinguishing characteristics of this corpus are its monthly temporal attribute and that it is created from 1.65 billion comments of user-generated text in Reddit. The usefulness of this corpus is exemplified and evaluated by a novel Topic-based Latent Semantic Analysis (TLSA) algorithm. The experimental results show that unsupervised TLSA outperforms all the state-of-the-art unsupervised and semi-supervised methods in SEMEVAL 2015: paraphrase and semantic similarity in Twitter tasks.",
}
@inproceedings{garrido-gutierrez-2016-dictionaries,
    title = "Dictionaries as Networks: Identifying the graph structure of Ogden{'}s Basic {E}nglish",
    author = "Garrido, Camilo  and
      Gutierrez, Claudio",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1336",
    pages = "3565--3576",
    abstract = "We study the network structure underlying dictionaries. We systematize the properties of such networks and show their relevance for linguistics. As case of study, we apply this technique to identify the graph structure of Ogden{'}s Basic English. We show that it constitutes a strong core of the English language network and that classic centrality measures fail to capture this set of words.",
}
@inproceedings{komninos-manandhar-2016-structured,
    title = "Structured Generative Models of Continuous Features for Word Sense Induction",
    author = "Komninos, Alexandros  and
      Manandhar, Suresh",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://www.aclweb.org/anthology/C16-1337",
    pages = "3577--3587",
    abstract = "We propose a structured generative latent variable model that integrates information from multiple contextual representations for Word Sense Induction. Our approach jointly models global lexical, local lexical and dependency syntactic context. Each context type is associated with a latent variable and the three types of variables share a hierarchical structure. We use skip-gram based word and dependency context embeddings to construct all three types of representations, reducing the total number of parameters to be estimated and enabling better generalization. We describe an EM algorithm to efficiently estimate model parameters and use the Integrated Complete Likelihood criterion to automatically estimate the number of senses. Our model achieves state-of-the-art results on the SemEval-2010 and SemEval-2013 Word Sense Induction datasets.",
}
@proceedings{lilt-2016--2,
    title = "",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
}
@inproceedings{zaenen-2016-modality,
    title = "Modality: logic, semantics, annotation and machine learning",
    author = "Zaenen, Annie",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-14.1",
    abstract = "Up to rather recently Natural Language Processing has not given much attention to modality. As long as the main task was to determined what a text was about (Information Retrieval) or who the participants in an eventuality were (Information Extraction), this neglect was understandable. With the focus moving to questions of natural language understanding and inferencing as well as to sentiment and opinion analysis, it becomes necessary to distinguish between actual and envisioned eventualities and to draw conclusions about the attitude of the writer or speaker towards the eventualities referred to. This means, i.a., to be able to distinguish {`}John went to Paris{'} and {`}John wanted to go to Paris{'}. To do this one has to calculate the effect of different linguistic operators on the eventuality predication.",
}
@inproceedings{qian-etal-2016-modal,
    title = "Modal Subordination in Type Theoretic Dynamic Logic",
    author = "Qian, Sai  and
      de Groote, Philippe  and
      Amblard, Maxime",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-14.2",
    abstract = "Classical theories of discourse semantics, such as Discourse Representation Theory (DRT), Dynamic Predicate Logic (DPL), predict that an indefinite noun phrase cannot serve as antecedent for an anaphor if the noun phrase is, but the anaphor is not, in the scope of a modal expression. However, this prediction meets with counterexamples. The phenomenon modal subordination is one of them. In general, modal subordination is concerned with more than two modalities, where the modality in subsequent sentences is interpreted in a context {`}subordinate{'} to the one created by the first modal expression. In other words, subsequent sentences are interpreted as being conditional on the scenario introduced in the first sentence. One consequence is that the anaphoric potential of indefinites may extend beyond the standard limits of accessibility constraints. This paper aims to give a formal interpretation on modal subordination. The theoretical backbone of the current work is Type Theoretic Dynamic Logic (TTDL), which is a Montagovian account of discourse semantics. Different from other dynamic theories, TTDL was built on classical mathematical and logical tools, such as λ-calculus and Church{'}s theory of types. Hence it is completely compositional and does not suffer from the destructive assignment problem. We will review the basic set-up of TTDL and then present Kratzer{'}s theory on natural language modality. After that, by integrating the notion of conversation background, in particular, the modal base usage, we offer an extension of TTDL (called Modal-TTDL, or M-TTDL in short) which properly deals with anaphora across modality. The formal relation between Modal-TTDL and TTDL will be discussed as well. We uncover the difficulty of specific sense distinctions by investigating distributional bias and reducing the sparsity of existing small-scale corpora used in prior work. We build a semantically enriched model for modal sense classification by designing novel features related to lexical, proposition-level and discourse-level semantic factors. Besides improved classification performance, closer examination of interpretable feature sets unveils relevant semantic and contextual factors in modal sense classification. Finally, we investigate genre effects on modal sense distribution and how they affect classification performance. Our investigations uncover the difficulty of specific sense distinctions and how they are affected by training set size and distributional bias. Our large-scale experiments confirm that semantically enriched models outperform models built on shallow feature sets. Cross-genre experiments shed light on differences in sense distributions across genres and confirm that semantically enriched models have high generalization capacity, especially in unstable distributional settings.",
}
@inproceedings{marasovic-etal-2016-modal,
    title = "Modal Sense Classification At Large: Paraphrase-Driven Sense Projection, Semantically Enriched Classification Models and Cross-Genre Evaluations",
    author = "Marasovi{\'c}, Ana  and
      Zhou, Mengfei  and
      Palmer, Alexis  and
      Frank, Anette",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-14.3",
    abstract = "Modal verbs have different interpretations depending on their context. Their sense categories {--} epistemic, deontic and dynamic {--} provide important dimensions of meaning for the interpretation of discourse. Previous work on modal sense classification achieved relatively high performance using shallow lexical and syntactic features drawn from small-size annotated corpora. Due to the restricted empirical basis, it is difficult to assess the particular difficulties of modal sense classification and the generalization capacity of the proposed models. In this work we create large-scale, high-quality annotated corpora for modal sense classification using an automatic paraphrase-driven projection approach. Using the acquired corpora, we investigate the modal sense classification task from different perspectives.",
}
@inproceedings{lavid-etal-2016-linguistically,
    title = "A linguistically-motivated annotation model of modality in {E}nglish and {S}panish: Insights from {MULTINOT}",
    author = "Lavid, Julia  and
      Carretrero, Marta  and
      Zamorano-Mansilla, Juan Rafael",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-14.4",
    abstract = "In this paper we present current work on the design and validation of a linguistically-motivated annotation model of modality in English and Spanish in the context of the MULTINOT project. Our annotation model captures four basic modal meanings and their subtypes, on the one hand, and provides a fine-grained characterisation of the syntactic realisations of those meanings in English and Spanish, on the other. We validate the modal tagset proposed through an agreement study performed on a bilingual sample of four hundred sentences extracted from original texts of the MULTINOT corpus, and discuss the difficult cases encountered in the annotation experiment. We also describe current steps in the implementation of the proposed scheme for the large-scale annotation of the bilingual corpus using both automatic and manual procedures.",
}
@inproceedings{mendes-etal-2016-modality,
    title = "Modality annotation for {P}ortuguese: from manual annotation to automatic labeling",
    author = "Mendes, Am{\'a}lia  and
      Hendrickx, Iris  and
      {\'A}vila, Liciana  and
      Quaresma, Paulo  and
      Gonҫalves, Teresa  and
      Sequeira, Jo{\~a}o",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-14.5",
    abstract = "We investigate modality in Portuguese and we combine a linguistic perspective with an application-oriented perspective on modality. We design an annotation scheme reflecting theoretical linguistic concepts and apply this schema to a small corpus sample to show how the scheme deals with real world language usage. We present two schemas for Portuguese, one for spoken Brazilian Portuguese and one for written European Portuguese. Furthermore, we use the annotated data not only to study the linguistic phenomena of modality, but also to train a practical text mining tool to detect modality in text automatically. The modality tagger uses a machine learning classifier trained on automatically extracted features from a syntactic parser. As we only have a small annotated sample available, the tagger was evaluated on 11 modal verbs that are frequent in our corpus and that denote more than one modal meaning. Finally, we discuss several valuable insights into the complexity of the semantic concept of modality that derive from the process of manual annotation of the corpus and from the analysis of the results of the automatic labeling: ambiguity and the semantic and syntactic properties typically associated to one modal meaning in context, and also the interaction of modality with negation and focus. The knowledge gained from the manual annotation task leads us to propose a new unified scheme for modality that applies to the two Portuguese varieties and covers both written and spoken data.",
}
@inproceedings{moon-etal-2016-selective,
    title = "Selective Annotation of Modal Readings: Delving into the Difficult Data",
    author = "Moon, Lori  and
      Kirvaitis, Patricija  and
      Madden, Noreen",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-14.6",
    abstract = "Modal auxiliaries have different readings, depending on the context in which they occur (Kratzer, 1981). Several projects have attempted to classify uses of modal auxiliaries in corpora according to their reading using supervised machine learning techniques (e.g., Rubinstein et al., 2013, Ruppenhofer {\&} Rehbein, 2012). In each study, traditional taxonomic labels, such as {`}epistemic{'} and {`}deontic{'} are used by human annotators to label instances of modal auxiliaries in a corpus. In order to achieve higher agreement among annotators, results in these previous studies are reported after collapsing some of the initial categories. The results show that human annotators have fairly good agreement on some of the categories, such as whether or not a use is epistemic, but poor agreement on others. They also show that annotators agree more on modals such as might than on modals such as could. In this study, we used traditional taxonomic categories on sentences containing modal auxiliary verbs that were randomly extracted from the English Gigaword 4th edition corpus (Parker et al., 2009). The lowest inner-annotator agreement using traditional taxonomic labels occurred with uses of could, with raw agreements of 42{\%}−48{\%} (κ = 0.196−0.259), compared to might, for instance, with raw agreement of 98{\%}. In response to the low numbers, rather than collapsing traditional categories, we tried a new method of classifying uses of could with respect to where the reading situates the eventuality being described relative to the speech time. For example, the sentence {`}Jess could swim.{'} is about a swimming eventuality in the past leading up to the time of speech, if it is read as being an ability. The sentence is about a swimming eventuality in the future, if it is read as being a statement about a possibility. The classification labels we propose are crucial in separating uses of could that have actuality inferences (Bhatt, 1999, Hacquard, 2006) from uses that do not. For the temporal location of the event described by a use of could, using four category labels, we achieved 73{\%}−90{\%} raw agreement (κ = 0.614−0.744). Sequence of tense contexts (Abusch, 1997) present a major factor in the difficulty of determining the temporal properties present in uses of could. Among three annotators, we achieved raw agreement scores of 89{\%}−96{\%}(κ =0.779−0.919{\%}) on identification of sequence of tense contexts. We discuss the role of our findings with respect to textual entailment.",
}
@inproceedings{hallmann-etal-2016-sarcastic,
    title = "Sarcastic Soulmates: Intimacy and irony markers in social media messaging",
    author = "Hallmann, Koen  and
      Kunneman, Florian  and
      Liebrecht, Christine  and
      van den Bosch, Antal  and
      van Mulken, Margot",
    booktitle = "Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning",
    month = "sept",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-14.7",
    abstract = "Verbal irony, or sarcasm, presents a significant technical and conceptual challenge when it comes to automatic detection. Moreover, it can be a disruptive factor in sentiment analysis and opinion mining, because it changes the polarity of a message implicitly. Extant methods for automatic detection are mostly based on overt clues to ironic intent such as hashtags, also known as irony markers. In this paper, we investigate whether people who know each other make use of irony markers less often than people who do not know each other. We trained a machinelearning classifier to detect sarcasm in Twitter messages (tweets) that were addressed to specific users, and in tweets that were not addressed to a particular user. Human coders analyzed the top-1000 features found to be most discriminative into ten categories of irony markers. The classifier was also tested within and across the two categories. We find that tweets with a user mention contain fewer irony markers than tweets not addressed to a particular user. Classification experiments confirm that the irony in the two types of tweets is signaled differently. The within-category performance of the classifier is about 91{\%} for both categories, while cross-category experiments yield substantially lower generalization performance scores of 75{\%} and 71{\%}. We conclude that irony markers are used more often when there is less mutual knowledge between sender and receiver. Senders addressing other Twitter users less often use irony markers, relying on mutual knowledge which should lead the receiver to infer ironic intent from more implicit clues. With regard to automatic detection, we conclude that our classifier is able to detect ironic tweets addressed at another user as reliably as tweets that are not addressed at at a particular person.",
}
@proceedings{lilt-2016-,
    title = "",
    booktitle = "Linguistic Issues in Language Technology, Volume 13, 2016",
    year = "2016",
    publisher = "CSLI Publications",
}
@inproceedings{mcshane-babkin-2016-detection,
    title = "Detection and Resolution of Verb Phrase Ellipsis",
    author = "McShane, Marjorie  and
      Babkin, Petr",
    booktitle = "Linguistic Issues in Language Technology, Volume 13, 2016",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-13.1",
    abstract = "Verb phrase (VP) ellipsis is the omission of a verb phrase whose meaning can be reconstructed from the linguistic or real-world context. It is licensed in English by auxiliary verbs, often modal auxiliaries: She can go to Hawaii but he can{'}t [e]. This paper describes a system called ViPER (VP Ellipsis Resolver) that detects and resolves VP ellipsis, relying on linguistic principles such as syntactic parallelism, modality correlations, and the delineation of core vs. peripheral sentence constituents. The key insight guiding the work is that not all cases of ellipsis are equally difficult: some can be detected and resolved with high confidence even before we are able to build systems with human-level semantic and pragmatic understanding of text.",
}
@inproceedings{herbelot-vecchi-2016-many,
    title = "Many speakers, many worlds: Interannotator variations in the quantification of feature norms",
    author = "Herbelot, Aur{\'e}lie  and
      Vecchi, Eva Maria",
    booktitle = "Linguistic Issues in Language Technology, Volume 13, 2016",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-13.2",
    abstract = "Quantification (see e.g. Peters and Westerst ̊ahl, 2006) is probably one of the most extensively studied phenomena in formal semantics. But because of the specific representation of meaning assumed by modeltheoretic semantics (one where a true model of the world is a priori available), research in the area has primarily focused on one question: what is the relation of a quantifier to the truth value of a sentence? In contrast, relatively little has been said about the way the underlying model comes about, and its relation to individual speakers{'} conceptual knowledge. In this paper, we make a first step in investigating how native speakers of English model relations between non-grounded sets, by observing how they quantify simple statements. We first give some motivation for our task, from both a theoretical linguistic and computational semantic point of view ({\S}2). We then describe our annotation setup ({\S}3) and follow on with an analysis of the produced dataset, conducting a quantitative evaluation which includes inter-annotator agreement for different classes of predicates ({\S}4). We observe that there is significant agreement between speakers but also noticeable variations. We posit that in settheoretic terms, there are as many worlds as there are speakers ({\S}5), but the overwhelming use of underspecified quantification in ordinary language covers up the individual differences that might otherwise be observed.",
}
@inproceedings{loaiciga-grisot-2016-predicting,
    title = "Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving {E}nglish-to-{F}rench Machine Translation",
    author = "Lo{\'a}iciga, Sharid  and
      Grisot, Cristina",
    booktitle = "Linguistic Issues in Language Technology, Volume 13, 2016",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-13.3",
    abstract = "This paper proposes a method for improving the results of a statistical Machine Translation system using boundedness, a pragmatic component of the verbal phrase{'}s lexical aspect. First, the paper presents manual and automatic annotation experiments for lexical aspect in EnglishFrench parallel corpora. It will be shown that this aspectual property is identified and classified with ease both by humans and by automatic systems. Second, Statistical Machine Translation experiments using the boundedness annotations are presented. These experiments show that the information regarding lexical aspect is useful to improve the output of a Machine Translation system in terms of better choices of verbal tenses in the target language, as well as better lexical choices. Ultimately, this work aims at providing a method for the automatic annotation of data with boundedness information and at contributing to Machine Translation by taking into account linguistic data.",
}
@inproceedings{kolachina-ranta-2016-abstract,
    title = "From Abstract Syntax to {U}niversal {D}ependencies",
    author = "Kolachina, Prasanth  and
      Ranta, Aarnte",
    booktitle = "Linguistic Issues in Language Technology, Volume 13, 2016",
    year = "2016",
    publisher = "CSLI Publications",
    url = "https://www.aclweb.org/anthology/2016.lilt-13.4",
    abstract = "Abstract syntax is a semantic tree representation that lies between parse trees and logical forms. It abstracts away from word order and lexical items, but contains enough information to generate both surface strings and logical forms. Abstract syntax is commonly used in compilers as an intermediate between source and target languages. Grammatical Framework (GF) is a grammar formalism that generalizes the idea to natural languages, to capture cross-lingual generalizations and perform interlingual translation. As one of the main results, the GF Resource Grammar Library (GF-RGL) has implemented a shared abstract syntax for over 30 languages. Each language has its own set of concrete syntax rules (morphology and syntax), by which it can be generated from the abstract syntax and parsed into it. This paper presents a conversion method from abstract syntax trees to dependency trees. The method is applied for converting GF-RGL trees to Universal Dependencies (UD), which uses a common set of labels for different languages. The correspondence between GF-RGL and UD turns out to be good, and the relatively few discrepancies give rise to interesting questions about universality. The conversion also has potential for practical applications: (1) it makes the GF parser usable as a rule-based dependency parser; (2) it enables bootstrapping UD treebanks from GF treebanks; (3) it defines formal criteria to assess the informal annotation schemes of UD; (4) it gives a method to check the consistency of manually annotated UD trees with respect to the annotation schemes; (5) it makes information from UD treebanks available.",
}
@proceedings{jep-taln-recital-2016-actes-de-la-conjointe,
    title = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    editor = "Danlos, Laurence  and
      Hamon, Thierry",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.0",
}
@inproceedings{neifar-ltaief-2016-acquisition,
    title = "Acquisition terminologique en arabe: {\'E}tat de l{'}art (Terminological acquisition on {MSA} : State of the art)",
    author = "Neifar, Wafa  and
      Ltaief, Ahmed Ben",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.1",
    pages = "1--12",
    abstract = "L{'}acquisition terminologique est une t{\^a}che indispensable pour l{'}acc{\`e}s aux informations pr{\'e}sentes dans les corpus de sp{\'e}cialit{\'e}. Il s{'}agit d{'}une part, d{'}identifier et d{'}extraire des termes, et d{'}autre part, de structurer ces termes {\`a} l{'}aide de m{\'e}thodes d{'}acquisition de relations s{\'e}mantiques. Dans cet article, nous nous int{\'e}ressons l{'}acquisition terminologique sur des textes arabe standard moderne (MSA). Nous r{\'e}alisons tout d{'}abord, un {\'e}tat de l{'}art d{\'e}crivant les m{\'e}thodes d{'}extraction de termes sur cette langue ainsi que les approches propos{\'e}es pour la reconnaissance de relations s{\'e}mantiques entre termes issus. Apr{\`e}s avoir pr{\'e}senter quelques corpus de sp{\'e}cialit{\'e} et ressources terminologiques disponibles en MSA que nous avons identifi{\'e}s, nous d{\'e}crivons nos premi{\`e}res pistes de travail.",
    language = "French",
}
@inproceedings{hamlat-2016-apprentissage,
    title = "Apprentissage bay{\'e}sien incr{\'e}mental pour la d{\'e}termination de l{'}{\^a}ge et du genre d{'}utilisateurs de plateformes du web social ({UGC} text-based age {\&} gender author profiling through incrementally semi-supervised bayesian learning)",
    author = {Hamlat, Jugurtha A{\"\i}t},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.2",
    pages = "13--26",
    abstract = "Les m{\'e}thodes de classification textuelles bas{\'e}es sur l{'}apprentissage automatique ont l{'}avantage, en plus d{'}{\^e}tre robustes, de fournir des r{\'e}sultats satisfaisants, sous r{\'e}serve de disposer d{'}une base d{'}entra{\^\i}nement de qualit{\'e} et en quantit{\'e} suffisante. Les corpus d{'}apprentissage {\'e}tant co{\^u}teux {\`a} construire, leur carence {\`a} grande {\'e}chelle se r{\'e}v{\`e}le {\^e}tre l{'}une des principales causes d{'}erreurs. Dans un contexte industriel {\`a} forte volum{\'e}trie de donn{\'e}es, nous pr{\'e}sentons une approche de pr{\'e}diction des deux plus importants indicateurs socio-d{\'e}mographiques « {\^a}ge » et « genre » appliqu{\'e}e {\`a} des utilisateurs de forums, blogs et r{\'e}seaux sociaux et ce, {\`a} partir de leurs seules productions textuelles. Le mod{\`e}le bay{\'e}sien multinomial est construit {\`a} partir d{'}un processus d{'}apprentissage incr{\'e}mental et it{\'e}ratif sur une vaste base d{'}entra{\^\i}nement semi-supervis{\'e}e. Le caract{\`e}re incr{\'e}mental permet de s{'}affranchir des contraintes de volum{\'e}trie. L{'}aspect it{\'e}ratif a pour objectif d{'}affiner le mod{\`e}le et d{'}augmenter ainsi les niveaux de rappel {\&} pr{\'e}cision.",
    language = "French",
}
@inproceedings{bernard-2016-conjonctions,
    title = "Conjonctions de subordination, verbes de dire et d{'}attitude propositionnelle : une mod{\'e}lisation {STAG} pour le discours (Modelling Subordinate Conjunctions, Attitude Verbs and Reporting Verbs in {STAG}: a Discourse Perspective)",
    author = "Bernard, Timoth{\'e}e",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.3",
    pages = "27--39",
    abstract = "Nous proposons une nouvelle mod{\'e}lisation en grammaire d{'}arbres adjoints synchrone (STAG) syntaxe/s{\'e}mantique pour les conjonctions de subordination (ConjSub) et les verbes de dire et d{'}attitude propositionnelle (VAP ; dire, penser, croire, etc.). Cette mod{\'e}lisation, plus riche que les mod{\'e}lisations traditionnelles, est con{\c{c}}ue pour l{'}analyse du discours et fond{\'e}e sur l{'}observation que ces deux cat{\'e}gories sont loin d{'}{\^e}tre homog{\`e}nes. En effet, des travaux ant{\'e}rieurs ont montr{\'e} d{'}une part que les occurrences de ConjSub pouvaient {\^e}tre divis{\'e}es en deux classes aux propri{\'e}t{\'e}s syntaxiques et s{\'e}mantiques diff{\'e}rentes, d{'}autre part que les VAP pr{\'e}sentaient en discours deux usages distincts : {\'e}videntiel et intentionnel. Notre proposition vise donc {\`a} rendre compte pr{\'e}cis{\'e}ment de ces diff{\'e}rences tout en mod{\'e}lisant les interactions entre VAP et ConjSub.",
    language = "French",
}
@inproceedings{ghamnia-2016-hypernym,
    title = "Hypernym extraction from {W}ikipedia",
    author = "Ghamnia, Adel",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.4",
    pages = "40--51",
    abstract = "Hypernym extraction from Wikip{\'e}dia The volume of available documents on the Web continues to increase, the texts contained in these documents are rich information describing concepts and relationships between concepts specific to a particular field. In this paper, we propose and exploit an hypernymy extractor based on lexico-syntactic patterns designed for Wikipedia semi-structured pages, especially the disambiguation pages, to enrich a knowledge base as BabelNet and DBPedia. The results show a precision of 0.68 and a recall of 0.75 for the patterns that we have defined, and an enrichment rate up to 33{\%} for both BabelNet and DBP{\'e}dia semantic resources.",
}
@inproceedings{merdy-2016-identifier,
    title = {Identifier et cat{\'e}goriser l{'}ambigu{\"\i}t{\'e} dans les sp{\'e}cifications techniques de conceptions de syst{\`e}mes (Identifying and classifying ambiguity in requirements)},
    author = "Merdy, {\'E}milie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.5",
    pages = "52--65",
    abstract = {Cette {\'e}tude s{'}inscrit dans le cadre d{'}une th{\`e}se Cifre avec Prometil 1 , une soci{\'e}t{\'e} qui commercialise un outil de d{\'e}tection automatique des erreurs dans les exigences, i.e. le contenu textuel des sp{\'e}cifications techniques. Il s{'}agit d{'}un travail de recherche dans la lign{\'e}e des travaux en analyse de corpus men{\'e}s par le laboratoire CLLE-ERSS qui s{'}int{\'e}resse aux corpus sp{\'e}cialis{\'e}s. Dans le cadre de l{'}adaptation automatique des analyses s{\'e}mantiques {\`a} de nouveaux domaines, nous {\'e}tudions la d{\'e}tection automatique de l{'}ambigu{\"\i}t{\'e} - qu{'}elle soit syntaxique, s{\'e}mantique ou lexicale - dans les exigences {\`a} partir de ressources lexicales sp{\'e}cifiques mais incompl{\`e}tes. En parall{\`e}le, l{'}exploration des exigences, qui sont des donn{\'e}es non-massives et porteuses de peu de vari{\'e}t{\'e}s lexicale et syntaxique, doit permettre de mieux appr{\'e}hender la sp{\'e}cificit{\'e} linguistique de corpus techniques sp{\'e}cialis{\'e}s pour enrichir semi-automatiquement des ressources lexicales adapt{\'e}es.},
    language = "French",
}
@inproceedings{seminck-2016-un,
    title = "Un mod{\`e}le simple de co{\^u}t cognitif de la r{\'e}solution d{'}anaphores (A Simple Model of Cognitive Cost of Anaphora Resolution)",
    author = "Seminck, Olga",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.6",
    pages = "66--79",
    abstract = "Nous pr{\'e}sentons un travail en cours sur un projet de recherche en TAL et en psycholinguistique. Le but de notre projet est de mod{\'e}liser le co{\^u}t cognitif que repr{\'e}sente la r{\'e}solution d{'}anaphores. Nous voulons obtenir une mesure du co{\^u}t cognitif continue et incr{\'e}mentale qui peut, {\`a} un stade de recherche plus avanc{\'e}, {\^e}tre corr{\'e}l{\'e}e avec des mesures d{'}occulom{\'e}trie sur corpus. Pour cela, nous proposons une mod{\'e}lisation inspir{\'e}e par des techniques venues du TAL. Nous utilisons un solveur d{'}anaphores probabiliste bas{\'e} sur l{'}algorithme couples de mentions et la notion d{'}entropie pour {\'e}tablir une mesure du co{\^u}t cognitif des anaphores. Ensuite, nous montrons par des visualisations quelles sont les pr{\'e}dictions de cette premi{\`e}re mod{\'e}lisation pour les pronoms personnels de troisi{\`e}me personne dans le corpus ANCOR Centre.",
    language = "French",
}
@inproceedings{petkovic-rabiet-2016-la,
    title = "La polys{\'e}mie lexicale et syntaxique de l{'}alternance modale indicatif/subjonctif {--} perspectives {TAL} (Lexical and syntactic polysemy of the modal alternation indicative/subjunctive {--} {NLP} perspectives)",
    author = "Petkovic, Divna  and
      Rabiet, Victor",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.7",
    pages = "80--93",
    abstract = {Certains verbes ont une double commande modale : ils admettent une construction permettant l{'}usage du subjonctif et de l{'}indicatif dans la compl{\'e}tive qui leur est adjointe. Ainsi, ces verbes se trouvent dans des contextes polys{\'e}miques {\`a} tous les niveaux d{'}analyse (lexicale, syntaxique, grammaticale et pragmatique). Dans un tel cas, le mode peut repr{\'e}senter une marque formelle d{\'e}sambigu{\"\i}sante. Plus pr{\'e}cis{\'e}ment, parfois le verbe r{\'e}gissant (dans la principale) est polys{\'e}mique en lui-m{\^e}me, et selon son sens il commande soit l{'}indicatif, soit le subjonctif dans la subordonn{\'e}e compl{\'e}tive : il s{'}agit de la polys{\'e}mie lexicale, qui peut {\^e}tre forte ou faible. D{'}un autre point de vue, certains verbes {\`a} l{'}indicatif/subjonctif dans la subordonn{\'e}e modifient plus ou moins l{\'e}g{\`e}rement le sens de la phrase enti{\`e}re : on consid{\`e}re ici cela comme un cas de polys{\'e}mie syntaxique, {\'e}tant donn{\'e} que ces verbes apparaissent dans diverses structures et modalit{\'e}s.},
    language = "French",
}
@inproceedings{maslowski-2016-quelles,
    title = "Quelles sont les caract{\'e}ristiques des interactions probl{\'e}matiques entre des utilisateurs et un conseiller virtuel ? (How to characterize problematic interactions between users and a web virtual advisor?)",
    author = "Maslowski, Irina",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 3 : RECITAL",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-recital.8",
    pages = "94--107",
    abstract = "L{'}utilisation d{'}un conseiller virtuel pour la gestion de la relation client sur les sites des entreprises est une solution num{\'e}rique de plus en plus adopt{\'e}e. Le d{\'e}fi pour les entreprises est de mieux r{\'e}pondre aux attentes des clients en leur fournissant des interactions fluides entre le client et l{'}agent. Pour faire face {\`a} ce probl{\`e}me, cet article met l{'}accent sur la d{\'e}tection des probl{\`e}mes d{'}interactions dans un corpus de tchat {\'e}crit entre un conseiller virtuel et ses utilisateurs. Il fournit une analyse de corpus en d{\'e}crivant non seulement les sp{\'e}cificit{\'e}s linguistiques et les marqueurs d{'}opinion contenus dans le corpus du tchat humain-agent, mais aussi les indices linguistiques et dialogiques qui peuvent {\^e}tre pertinents pour caract{\'e}riser une interaction probl{\'e}matique. Le mod{\`e}le de r{\`e}gles propos{\'e}, utilisant les indices trouv{\'e}s, est appliqu{\'e} {\`a} un corpus avec des retours client n{\'e}gatifs et positifs pour r{\'e}v{\'e}ler les tendances.",
    language = "French",
}
@proceedings{jep-taln-recital-2016-actes-de-la,
    title = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    editor = "Danlos, Laurence  and
      Hamon, Thierry",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.0",
}
@inproceedings{salah-etal-2016-amelioration,
    title = "Am{\'e}lioration de la traduction automatique d{'}un corpus annot{\'e} (Improvement of the automatic translation of an annotated corpus)",
    author = "Salah, Marwa Hadj  and
      Blanchon, Herv{\'e}  and
      Zrigui, Mounir  and
      Schwab, Didier",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.1",
    pages = "318--324",
    abstract = {Dans cet article, nous pr{\'e}sentons une m{\'e}thode pour am{\'e}liorer la traduction automatique d{'}un corpus annot{\'e} et porter ses annotations de l{'}anglais vers une langue cible. Il s{'}agit d{'}am{\'e}liorer la m{\'e}thode de (Nasiruddin et al., 2015) qui donnait de nombreux segments non traduits, des duplications et des d{\'e}sordres. Nous proposons un processus de pr{\'e}-traitement du SemCor anglais, pour qu{'}il soit adapt{\'e} au syst{\`e}me de traduction automatique statistique utilis{\'e}, ainsi qu{'}un processus de post-traitement pour la sortie. Nous montrons une augmentation de 2,9 points en terme de score F1 sur une t{\^a}che de d{\'e}sambigu{\"\i}sation lexicale ce qui prouve l{'}efficacit{\'e} de notre m{\'e}thode.},
    language = "French",
}
@inproceedings{tanguy-etal-2016-analyse,
    title = "Analyse d{'}une t{\^a}che de substitution lexicale : quelles sont les sources de difficult{\'e} ? (Difficulty analysis for a lexical substitution task)",
    author = "Tanguy, Ludovic  and
      Fabre, C{\'e}cile  and
      Mercier, Camille",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.2",
    pages = "325--332",
    abstract = "Nous proposons dans cet article une analyse des r{\'e}sultats de la campagne SemDis 2014 qui proposait une t{\^a}che de substitution lexicale en fran{\c{c}}ais. Pour les 300 phrases du jeu de test, des annotateurs ont propos{\'e} des substituts {\`a} un mot cible, permettant ainsi d{'}{\'e}tablir un gold standard sur lequel les syst{\`e}mes participants ont {\'e}t{\'e} {\'e}valu{\'e}s. Nous cherchons {\`a} identifier les principales caract{\'e}ristiques des items du jeu de test qui peuvent expliquer les variations de performance pour les humains comme pour les syst{\`e}mes, en nous basant sur l{'}accord inter-annotateurs des premiers et les scores de rappel des seconds. Nous montrons que si plusieurs caract{\'e}ristiques communes sont associ{\'e}es aux deux types de difficult{\'e} (raret{\'e} du sens dans lequel le mot-cible est employ{\'e}, fr{\'e}quence d{'}emploi du mot-cible), d{'}autres sont sp{\'e}cifiques aux syst{\`e}mes (degr{\'e} de polys{\'e}mie du mot-cible, complexit{\'e} syntaxique).",
    language = "French",
}
@inproceedings{ho-dac-etal-2016-lanti,
    title = "L{'}anti-correcteur : outil d{'}{\'e}valuation positive de l{'}orthographe et de la grammaire (The {''}anticorrecteur{''}: a positive evaluation module for spell and grammar checking)",
    author = "Ho-Dac, Lydia-Mai  and
      Muller, Sophie  and
      Delbar, Valentine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.3",
    pages = "333--341",
    abstract = "L{'}objectif de cette {\'e}tude est d{'}exp{\'e}rimenter l{'}int{\'e}gration d{'}une nouvelle forme d{'}{\'e}valuation dans un correcteur orthographique et grammatical. L{'}« anticorrecteur » a pour objet de mesurer le taux de r{\'e}ussites orthographiques et grammaticales d{'}un texte sur certains points jug{\'e}s difficiles selon la litt{\'e}rature et une observation d{'}erreurs en corpus. L{'}{\'e}valuation du niveau d{'}{\'e}criture ne se base plus uniquement sur les erreurs commises, mais {\'e}galement sur les r{\'e}ussites r{\'e}alis{\'e}es. Une version b{\^e}ta de ce nouveau mode d{'}{\'e}valuation positive a {\'e}t{\'e} int{\'e}gr{\'e} dans le correcteur Cordial. Cet article a pour but de discuter de l{'}int{\'e}r{\^e}t de ce nouveau rapport {\`a} l{'}orthographe et de pr{\'e}senter quelques premiers {\'e}l{\'e}ments d{'}analyse r{\'e}sultant de l{'}application de l{'}anticorrecteur sur un corpus de productions vari{\'e}es en mati{\`e}re de niveau d{'}{\'e}criture et genre discursif. Ici, un r{\'e}sum{\'e} en fran{\c{c}}ais (max. 150 mots). Times, 10pt.",
    language = "French",
}
@inproceedings{desoyer-etal-2016-appariement,
    title = "Appariement d{'}articles en ligne et de vid{\'e}os : strat{\'e}gies de s{\'e}lection et m{\'e}thodes d{'}{\'e}valuation (Pairing On-line News Articles to Videos : Selection Strategies and Evaluation Methods)",
    author = "D{\'e}soyer, Ad{\`e}le  and
      Battistelli, Delphine  and
      Minel, Jean-Luc",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.4",
    pages = "342--348",
    abstract = "Dans cet article, nous proposons une m{\'e}thode d{'}appariement de contenus d{'}actualit{\'e} multim{\'e}dias, consid{\'e}rant les exigences {\`a} la fois s{\'e}mantiques et temporelles du besoin d{'}information. La pertinence d{'}une vid{\'e}o pour un article de presse est mesur{\'e}e par deux indices, l{'}un saisissant la similarit{\'e} de leurs contenus, l{'}autre la coh{\'e}rence de leurs dates d{'}{\'e}dition. Nous pr{\'e}sentons {\'e}galement une m{\'e}thodologie d{'}{\'e}valuation s{'}affranchissant des standards comparant les r{\'e}sultats du syst{\`e}me {\`a} des r{\'e}sultats de r{\'e}f{\'e}rence, en soumettant les paires de documents propos{\'e}es automatiquement {\`a} un panel d{'}utilisateurs charg{\'e} de juger de leur pertinence.",
    language = "French",
}
@inproceedings{schluter-martinez-alonso-2016-approximate,
    title = "Approximate unsupervised summary optimisation for selections of {ROUGE}",
    author = "Schluter, Natalie  and
      Mart{\'\i}nez Alonso, H{\'e}ctor",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.5",
    pages = "349--354",
    abstract = "Approximate summary optimisation for selections of ROUGE It is standard to measure automatic summariser performance using the ROUGE metric. Unfortunately, ROUGE is not appropriate for unsupervised summarisation approaches. On the other hand, we show that it is possible to optimise approximately for ROUGE-n by using a document-weighted ROUGE objective. Doing so results in state-of-the-art summariser performance for single and multiple document summaries for both English and French. This is despite a non-correlation of the documentweighted ROUGE metric with human judgments, unlike the original ROUGE metric. These findings suggest a theoretical approximation link between the two metrics.",
}
@inproceedings{kogkitsidou-antoniadis-2016-larchitecture,
    title = "L{'}architecture d{'}un mod{\`e}le hybride pour la normalisation de {SMS} (A hybrid model architecture for {SMS} normalization)",
    author = "Kogkitsidou, Eleni  and
      Antoniadis, Georges",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.6",
    pages = "355--363",
    abstract = "La communication par SMS (Short Message Service), aussi bien que tout autre type de communication virtuelle sous forme de textes courts (mails, microblogs, tweets, etc.), pr{\'e}sente certaines particularit{\'e}s sp{\'e}cifiques (syntaxe irr{\'e}guli{\`e}re, fusionnement et phon{\'e}tisation de mots, formes abr{\'e}g{\'e}es, etc.). A cause de ces caract{\'e}ristiques, l{'}application d{'}outils en Traitement Automatique du Langage (TAL) rend difficile l{'}exploitation d{'}informations utiles contenues dans des messages bruit{\'e}s. Nous proposons un mod{\`e}le de normalisation en deux {\'e}tapes fond{\'e} sur une approche symbolique et statistique. La premi{\`e}re partie vise {\`a} produire une repr{\'e}sentation interm{\'e}diaire du message SMS par l{'}application des grammaires locales, tandis que la deuxi{\`e}me utilise un syst{\`e}me de traduction automatique {\`a} base de r{\`e}gles pour convertir la repr{\'e}sentation interm{\'e}diaire vers une forme standard.",
    language = "French",
}
@inproceedings{zweigenbaum-etal-2016-une,
    title = "Une cat{\'e}gorisation de fins de lignes non-supervis{\'e}e (End-of-line classification with no supervision)",
    author = "Zweigenbaum, Pierre  and
      Grouin, Cyril  and
      Lavergne, Thomas",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.7",
    pages = "364--371",
    abstract = "Dans certains textes bruts, les marques de fin de ligne peuvent marquer ou pas la fronti{\`e}re d{'}une unit{\'e} textuelle (typiquement un paragraphe). Ce probl{\`e}me risque d{'}influencer les traitements subs{\'e}quents, mais est rarement trait{\'e} dans la litt{\'e}rature. Nous proposons une m{\'e}thode enti{\`e}rement non-supervis{\'e}e pour d{\'e}terminer si une fin de ligne doit {\^e}tre vue comme un simple espace ou comme une v{\'e}ritable fronti{\`e}re d{'}unit{\'e} textuelle, et la testons sur un corpus de comptes rendus m{\'e}dicaux. Cette m{\'e}thode obtient une F-mesure de 0,926 sur un {\'e}chantillon de 24 textes contenant des lignes repli{\'e}es. Appliqu{\'e}e sur un {\'e}chantillon plus grand de textes contenant ou pas des lignes repli{\'e}es, notre m{\'e}thode la plus prudente obtient une F-mesure de 0,898, valeur {\'e}lev{\'e}e pour une m{\'e}thode enti{\`e}rement non-supervis{\'e}e.",
    language = "French",
}
@inproceedings{muller-etal-2016-classification,
    title = "Classification automatique de dict{\'e}es selon leur niveau de difficult{\'e} de compr{\'e}hension et orthographique (Automatic classification of dictations according to their complexity for comprehension and writing production)",
    author = {M{\"u}ller, Adeline  and
      Francois, Thomas  and
      Roekhaut, Sophie  and
      Fairon, Cedrick},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.8",
    pages = "372--380",
    abstract = "Cet article pr{\'e}sente une approche visant {\`a} {\'e}valuer automatiquement la difficult{\'e} de dict{\'e}es en vue de les int{\'e}grer dans une plateforme d{'}apprentissage de l{'}orthographe. La particularit{\'e} de l{'}exercice de la dict{\'e}e est de devoir percevoir du code oral et de le retranscrire via le code {\'e}crit. Nous envisageons ce double niveau de difficult{\'e} {\`a} l{'}aide de 375 variables mesurant la difficult{\'e} de compr{\'e}hension d{'}un texte ainsi que les ph{\'e}nom{\`e}nes orthographiques et grammaticaux complexes qu{'}il contient. Un sous-ensemble optimal de ces variables est combin{\'e} {\`a} l{'}aide d{'}un mod{\`e}le par machines {\`a} vecteurs de support (SVM) qui classe correctement 56{\%} des textes. Les variables lexicales bas{\'e}es sur la liste orthographique de Catach (1984) se r{\'e}v{\`e}lent les plus informatives pour le mod{\`e}le.",
    language = "French",
}
@inproceedings{bernier-colborne-drouin-2016-combiner,
    title = "Combiner des mod{\`e}les s{\'e}mantiques distributionnels pour mieux d{\'e}tecter les termes {\'e}voquant le m{\^e}me cadre s{\'e}mantique (Combining distributional semantic models to improve the identification of terms that evoke the same semantic frame)",
    author = "Bernier-Colborne, Gabriel  and
      Drouin, Patrick",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.9",
    pages = "381--388",
    abstract = "Nous utilisons des mod{\`e}les s{\'e}mantiques distributionnels pour d{\'e}tecter des termes qui {\'e}voquent le m{\^e}me cadre s{\'e}mantique. Dans cet article, nous v{\'e}rifions si une combinaison de diff{\'e}rents mod{\`e}les permet d{'}obtenir une pr{\'e}cision plus {\'e}lev{\'e}e qu{'}un mod{\`e}le unique. Nous mettons {\`a} l{'}{\'e}preuve plusieurs m{\'e}thodes simples pour combiner les mesures de similarit{\'e} calcul{\'e}es {\`a} partir de chaque mod{\`e}le. Les r{\'e}sultats indiquent qu{'}on obtient syst{\'e}matiquement une augmentation de la pr{\'e}cision par rapport au meilleur mod{\`e}le unique en combinant des mod{\`e}les diff{\'e}rents.",
    language = "French",
}
@inproceedings{partalas-etal-2016-comparing,
    title = "Comparing Named-Entity Recognizers in a Targeted Domain: Handcrafted Rules vs Machine Learning",
    author = "Partalas, Ioannis  and
      Lopez, C{\'e}dric  and
      Segond, Fr{\'e}d{\'e}rique",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.10",
    pages = "389--395",
    abstract = "Comparing Named-Entity Recognizers in a Targeted Domain : Handcrafted Rules vs. Machine Learning Named-Entity Recognition concerns the classification of textual objects in a predefined set of categories such as persons, organizations, and localizations. While Named-Entity Recognition is well studied since 20 years, the application to specialized domains still poses challenges for current systems. We developed a rule-based system and two machine learning approaches to tackle the same task : recognition of product names, brand names, etc., in the domain of Cosmetics, for French. Our systems can thus be compared under ideal conditions. In this paper, we introduce both systems and we compare them.",
}
@inproceedings{prost-etal-2016-compilation,
    title = "Compilation de grammaire de propri{\'e}t{\'e}s pour l{'}analyse syntaxique par optimisation de contraintes (Compilation of a Property Grammar for Syntactic Parsing through Constraint Optimisation)",
    author = "Prost, Jean-Philippe  and
      Coletta, R{\'e}mi  and
      Lecoutre, Christophe",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.11",
    pages = "396--402",
    abstract = "Cet article pr{\'e}sente un processus de compilation d{'}une grammaire de propri{\'e}t{\'e}s en une contrainte en extension. Le processus s{'}ins{\`e}re dans le cadre d{'}un analyseur syntaxique robuste par r{\'e}solution d{'}un probl{\`e}me d{'}optimisation de contraintes. La grammaire compil{\'e}e est une {\'e}num{\'e}ration de tous les constituants imm{\'e}diats uniques de l{'}espace de recherche. L{'}int{\'e}r{\^e}t de ce travail encore pr{\'e}liminaire tient principalement dans l{'}exploration d{'}une mod{\'e}lisation computationnelle de la langue {\`a} base de Syntaxe par Mod{\`e}les (MTS, Model-Theoretic Syntax), qui int{\`e}gre la repr{\'e}sentation indiff{\'e}renci{\'e}e des {\'e}nonc{\'e}s canoniques et non-canoniques. L{'}objectif plus particulier du travail pr{\'e}sent{\'e} ici est d{'}explorer la possibilit{\'e} de construire l{'}ensemble des structures candidat-mod{\`e}les {\`a} partir de l{'}ensemble des structures syntagmatiques observ{\'e}es sur corpus. Cet article discute notamment le potentiel en mati{\`e}re d{'}int{\'e}gration de pr{\'e}dictions probabilistes dans un raisonnement exact pour contribuer {\`a} la discrimination entre analyses grammaticales et agrammaticales.",
    language = "French",
}
@inproceedings{zenasni-etal-2016-decouverte,
    title = "D{\'e}couverte de nouvelles entit{\'e}s et relations spatiales {\`a} partir d{'}un corpus de {SMS} (Discovering of new Spatial Entities and Relations from {SMS} Within the context of the currently available data masses, many works related to the analysis of spatial information are based on the exploitation of textual data)",
    author = "Zenasni, Sarah  and
      Teisseire, Maguelonne  and
      Roche, Mathieu  and
      Kergosien, Eric",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.12",
    pages = "403--410",
    abstract = "Dans le contexte des masses de donn{\'e}es aujourd{'}hui disponibles, de nombreux travaux li{\'e}s {\`a} l{'}analyse de l{'}information spatiale s{'}appuient sur l{'}exploitation des donn{\'e}es textuelles. La communication m{\'e}di{\'e}e (SMS, tweets, etc.) v{\'e}hiculant des informations spatiales prend une place pr{\'e}pond{\'e}rante. L{'}objectif du travail pr{\'e}sent{\'e} dans cet article consiste {\`a} extraire ces informations spatiales {\`a} partir d{'}un corpus authentique de SMS en fran{\c{c}}ais. Nous proposons un processus dans lequel, dans un premier temps, nous extrayons de nouvelles entit{\'e}s spatiales (par exemple, motpellier, montpeul {\`a} associer au toponyme Montpellier). Dans un second temps, nous identifions de nouvelles relations spatiales qui pr{\'e}c{\`e}dent les entit{\'e}s spatiales (par exemple, sur, par, pres, etc.). La t{\^a}che est difficile et complexe en raison de la sp{\'e}cificit{\'e} du langage SMS qui repose sur une {\'e}criture peu standardis{\'e}e (apparition de nombreux lexiques, utilisation massive d{'}abr{\'e}viations, variation par rapport {\`a} l{'}{\'e}crit classique, etc.). Les exp{\'e}rimentations qui ont {\'e}t{\'e} r{\'e}alis{\'e}es {\`a} partir du corpus 88milSMS mettent en relief la robustesse de notre syst{\`e}me pour identifier de nouvelles entit{\'e}s et relations spatiales.",
    language = "French",
}
@inproceedings{hadjadj-filhol-2016-description,
    title = "Description de la juxtaposition en Langue des Signes Fran{\c{c}}aise {\`a} partir d{'}une grammaire r{\'e}cursive (The present communication tackles formal grammar developpement of {F}rench {S}ign {L}anguage ({LSF}))",
    author = "Hadjadj, Mohamed Nassime  and
      Filhol, Michael",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.13",
    pages = "411--418",
    abstract = "La pr{\'e}sente communication s{'}inscrit dans le cadre du d{\'e}veloppement d{'}une grammaire formelle pour la langue des signes fran{\c{c}}aise (LSF). G{\'e}n{\'e}rer automatiquement des {\'e}nonc{\'e}s en LSF implique la d{\'e}finition de certaines r{\`e}gles de production pour synchroniser les diff{\'e}rents articulateurs du corps, signes, mouvements, etc. Cet article pr{\'e}sente dans sa premi{\`e}re partie notre m{\'e}thodologie pour d{\'e}finir des r{\`e}gles de production {\`a} partir d{'}une {\'e}tude de corpus. Dans la deuxi{\`e}me partie nous pr{\'e}senterons notre {\'e}tude qui portera sur deux r{\`e}gles de production pour juxtaposer quelques types de structures en LSF. Nous finissons par une discussion sur la nature et l{'}apport de notre d{\'e}marche par rapport aux approches existantes.",
    language = "French",
}
@inproceedings{efraim-moreau-2016-detecter,
    title = "D{\'e}tecter le besoin d{'}information dans des requ{\^e}tes d{'}usagers d{'}agents virtuels : s{\'e}lection de donn{\'e}es pertinentes (Selecting relevant data for information need detection in virtual agent user queries)",
    author = "Efraim, Octavia  and
      Moreau, Fabienne",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.14",
    pages = "419--427",
    abstract = "Pour orienter efficacement les messages re{\c{c}}us par diff{\'e}rents canaux de communication, dont l{'}agent virtuel (AV), un syst{\`e}me de gestion de la relation client doit prendre en compte le besoin d{'}information de l{'}usager. En vue d{'}une t{\^a}che de classification par type de besoin d{'}information, il est utile de pouvoir en amont s{\'e}lectionner dans les messages des utilisateurs, souvent de mauvaise qualit{\'e}, les unit{\'e}s textuelles qui seront pertinentes pour repr{\'e}senter ce besoin d{'}information. Apr{\`e}s avoir d{\'e}crit les sp{\'e}cificit{\'e}s d{'}un corpus de requ{\^e}tes d{'}AV nous exp{\'e}rimentons deux m{\'e}thodes de s{\'e}lection de segments informatifs : par extraction et par filtrage. Les r{\'e}sultats sont encourageants, mais des am{\'e}liorations et une {\'e}valuation extrins{\`e}que restent {\`a} faire.",
    language = "French",
}
@inproceedings{elashter-maurel-2016-estimer,
    title = "Estimer la notori{\'e}t{\'e} d{'}un nom propre via {W}ikipedia (Estimate the notoriety of a Proper name using {W}ikipedia)",
    author = "Elashter, Mouna  and
      Maurel, Denis",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.15",
    pages = "428--434",
    abstract = "Cet article propose de calculer, via Wikipedia, un indice de notori{\'e}t{\'e} pour les entr{\'e}es du dictionnaire relationnel multilingue de noms propres Prolexbase. Cet indice de notori{\'e}t{\'e} d{\'e}pend de la langue et participera, d{'}une part, {\`a} la construction d{'}un module de Prolexbase pour la langue arabe et, d{'}autre part, {\`a} la r{\'e}vision de la notori{\'e}t{\'e} actuellement pr{\'e}sente pour les autres langues de la base. Pour calculer la notori{\'e}t{\'e}, nous utilisons la m{\'e}thode SAW (pr{\'e}c{\'e}d{\'e}e du calcul de l{'}entropie de Shannon) {\`a} partir de cinq valeurs num{\'e}riques d{\'e}duites de Wikipedia.",
    language = "French",
}
@inproceedings{sagot-2016-etiquetage,
    title = "{\'E}tiquetage multilingue en parties du discours avec {ME}lt (Multilingual part-of-speech tagging with {ME}lt)",
    author = "Sagot, Beno{\^\i}t",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.16",
    pages = "435--442",
    abstract = "Nous pr{\'e}sentons des travaux r{\'e}cents r{\'e}alis{\'e}s autour de MElt, syst{\`e}me discriminant d{'}{\'e}tiquetage en parties du discours. MElt met l{'}accent sur l{'}exploitation optimale d{'}informations lexicales externes pour am{\'e}liorer les performances des {\'e}tiqueteurs par rapport aux mod{\`e}les entra{\^\i}n{\'e}s seulement sur des corpus annot{\'e}s. Nous avons entra{\^\i}n{\'e} MElt sur plus d{'}une quarantaine de jeux de donn{\'e}es couvrant plus d{'}une trentaine de langues. Compar{\'e} au syst{\`e}me {\'e}tat-de-l{'}art MarMoT, MElt obtient en moyenne des r{\'e}sultats l{\'e}g{\`e}rement moins bons en l{'}absence de lexique externe, mais meilleurs lorsque de telles ressources sont disponibles, produisant ainsi des {\'e}tiqueteurs {\'e}tat-de-l{'}art pour plusieurs langues.",
    language = "French",
}
@inproceedings{jadi-etal-2016-extraction,
    title = "Extraction d{'}expressions-cibles de l{'}opinion : de l{'}anglais au fran{\c{c}}ais (Opinion Target Expression extraction : from {E}nglish to {F}rench)",
    author = "Jadi, Gr{\'e}goire  and
      Monceaux, Laura  and
      Claveau, Vincent  and
      Daille, B{\'e}atrice",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.17",
    pages = "443--450",
    abstract = "Dans cet article, nous pr{\'e}sentons le d{\'e}veloppement d{'}un syst{\`e}me d{'}extraction d{'}expressions-cibles pour l{'}anglais et sa transposition au fran{\c{c}}ais. En compl{\'e}ment, nous avons r{\'e}alis{\'e} une {\'e}tude de l{'}efficacit{\'e} des traits en anglais et en fran{\c{c}}ais qui tend {\`a} montrer qu{'}il est possible de r{\'e}aliser un syst{\`e}me d{'}extraction d{'}expressions-cibles ind{\'e}pendant du domaine. Pour finir, nous proposons une analyse comparative des erreurs commises par nos syst{\`e}mes en anglais et fran{\c{c}}ais et envisageons diff{\'e}rentes solutions {\`a} ces probl{\`e}mes.",
    language = "French",
}
@inproceedings{lark-etal-2016-extraction,
    title = {Extraction d{'}opinions ambig{\"u}es dans des corpus d{'}avis clients (Ambiguous opinion extraction in user feedbacks)},
    author = "Lark, Joseph  and
      Morin, Emmanuel  and
      Saldarriaga, Sebasti{\'a}n Pe{\~n}a",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.18",
    pages = "451--458",
    abstract = "Nous d{\'e}tectons dans des corpus d{'}avis clients en fran{\c{c}}ais des expressions d{'}opinion ne contenant pas de marqueur d{'}opinion explicitement positif ou n{\'e}gatif. Nous proc{\'e}dons pour cela en deux {\'e}tapes en nous appuyant sur des m{\'e}thodes existantes : nous identifions ces expressions {\`a} l{'}aide de fen{\^e}tres de mots puis nous les classifions en polarit{\'e}. Le processus global pr{\'e}sente des r{\'e}sultats satisfaisants pour notre cadre applicatif demandant une haute pr{\'e}cision.",
    language = "French",
}
@inproceedings{tourille-etal-2016-extraction,
    title = "Extraction de relations temporelles dans des dossiers {\'e}lectroniques patient (Extracting Temporal Relations from Electronic Health Records)",
    author = "Tourille, Julien  and
      Ferret, Olivier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Tannier, Xavier",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.19",
    pages = "459--466",
    abstract = "L{'}analyse temporelle des documents cliniques permet d{'}obtenir des repr{\'e}sentations riches des informations contenues dans les dossiers {\'e}lectroniques patient. Cette analyse repose sur l{'}extraction d{'}{\'e}v{\'e}nements, d{'}expressions temporelles et des relations entre eux. Dans ce travail, nous consid{\'e}rons que nous disposons des {\'e}v{\'e}nements et des expressions temporelles pertinents et nous nous int{\'e}ressons aux relations temporelles entre deux {\'e}v{\'e}nements ou entre un {\'e}v{\'e}nement et une expression temporelle. Nous pr{\'e}sentons des mod{\`e}les de classification supervis{\'e}e pour l{'}extraction de des relations en fran{\c{c}}ais et en anglais. Les performances obtenues sont comparables dans les deux langues, sugg{\'e}rant ainsi que diff{\'e}rents domaines cliniques et diff{\'e}rentes langues pourraient {\^e}tre abord{\'e}s de mani{\`e}re similaire.",
    language = "French",
}
@inproceedings{neifar-etal-2016-impact,
    title = "Impact de l{'}agglutination dans l{'}extraction de termes en arabe standard moderne (Adaptation of a term extractor to the {M}odern {S}tandard {A}rabic language)",
    author = "Neifar, Wafa  and
      Hamon, Thierry  and
      Zweigenbaum, Pierre  and
      Ellouze, Mariem  and
      Belguith, Lamia Hadrich",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.20",
    pages = "467--474",
    abstract = "Nous pr{\'e}sentons, dans cet article, une adaptation {\`a} l{'}arabe standard moderne d{'}un extracteur de termes pour le fran{\c{c}}ais et l{'}anglais. L{'}adaptation a d{'}abord consist{\'e} {\`a} d{\'e}crire le processus d{'}extraction des termes de mani{\`e}re similaire {\`a} celui d{\'e}fini pour l{'}anglais et le fran{\c{c}}ais en prenant en compte certains particularit{\'e}s morpho-syntaxiques de la langue arabe. Puis, nous avons consid{\'e}r{\'e} le ph{\'e}nom{\`e}ne de l{'}agglutination de la langue arabe. L{'}{\'e}valuation a {\'e}t{\'e} r{\'e}alis{\'e}e sur un corpus de textes m{\'e}dicaux. Les r{\'e}sultats montrent que parmi 400 termes candidats maximaux analys{\'e}s, 288 sont jug{\'e}s corrects par rapport au domaine (72,1{\%}). Les erreurs d{'}extraction sont dues {\`a} l{'}{\'e}tiquetage morpho-syntaxique et {\`a} la non-voyellation des textes mais aussi {\`a} des ph{\'e}nom{\`e}nes d{'}agglutination.",
    language = "French",
}
@inproceedings{quintana-planes-2016-inbenta,
    title = "Inbenta Semantic Clustering : un outil de classification non-supervis{\'e}e hybride (Inbenta Semantic Clustering : a hybrid unsupervised classification tool)",
    author = "Quintana, Manon  and
      Planes, Laurie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.21",
    pages = "475--481",
    abstract = "Inbenta d{\'e}veloppe un outil de classification non-supervis{\'e}e hybride qui allie {\`a} la fois les statistiques et la puissance de notre lexique inspir{\'e} de la Th{\'e}orie Sens-Texte. Nous pr{\'e}senterons ici le contexte qui a amen{\'e} {\`a} la n{\'e}cessit{\'e} de d{\'e}velopper un tel outil. Apr{\`e}s un rapide {\'e}tat de l{'}art sur la classification non-supervis{\'e}e en TAL, nous d{\'e}crirons le fonctionnement de notre clustering s{\'e}mantique.",
    language = "French",
}
@inproceedings{mnasri-etal-2016-integration,
    title = "Int{\'e}gration de la similarit{\'e} entre phrases comme crit{\`e}re pour le r{\'e}sum{\'e} multi-document (Integrating sentence similarity as a constraint for multi-document summarization)",
    author = {Mnasri, Ma{\^a}li  and
      de Chalendar, Ga{\"e}l  and
      Ferret, Olivier},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.22",
    pages = "482--489",
    abstract = {multi-document Ma{\^a}li Mnasri1, 2 Ga{\"e}l de Chalendar1 Olivier Ferret1 (1) CEA, LIST, Laboratoire Vision et Ing{\'e}nierie des Contenus, Gif-sur-Yvette, F-91191, France. (2) Universit{\'e} Paris-Sud, Universit{\'e} Paris-Saclay, F-91405 Orsay, France. maali.mnasri@cea.fr, gael.de-chalendar@cea.fr, olivier.ferret@cea.fr R {\'E}SUM{\'E} {\`A} la suite des travaux de Gillick {\&} Favre (2009), beaucoup de travaux portant sur le r{\'e}sum{\'e} par extraction se sont appuy{\'e}s sur une mod{\'e}lisation de cette t{\^a}che sous la forme de deux contraintes antagonistes : l{'}une vise {\`a} maximiser la couverture du r{\'e}sum{\'e} produit par rapport au contenu des textes d{'}origine tandis que l{'}autre repr{\'e}sente la limite du r{\'e}sum{\'e} en termes de taille. Dans cette approche, la notion de redondance n{'}est prise en compte que de fa{\c{c}}on implicite. Dans cet article, nous reprenons le cadre d{\'e}fini par Gillick {\&} Favre (2009) mais nous examinons comment et dans quelle mesure la prise en compte explicite de la similarit{\'e} s{\'e}mantique des phrases peut am{\'e}liorer les performances d{'}un syst{\`e}me de r{\'e}sum{\'e} multi-document. Nous v{\'e}rifions cet impact par des {\'e}valuations men{\'e}es sur les corpus DUC 2003 et 2004.},
    language = "French",
}
@inproceedings{bawden-etal-2016-investigating,
    title = "Investigating gender adaptation for speech translation",
    author = "Bawden, Rachel  and
      Wisniewski, Guillaume  and
      Maynard, H{\'e}l{\`e}ne",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.23",
    pages = "490--497",
    abstract = "In this paper we investigate the impact of the integration of context into dialogue translation. We present a new contextual parallel corpus of television subtitles and show how taking into account speaker gender can significantly improve machine translation quality in terms of B LEU and M ETEOR scores. We perform a manual analysis, which suggests that these improvements are not necessary related to the morphological consequences of speaker gender, but to more general linguistic divergences.",
}
@inproceedings{maigrot-etal-2016-medias,
    title = "M{\'e}dias traditionnels, m{\'e}dias sociaux : caract{\'e}riser la r{\'e}information (Traditional medias, social medias : characterizing reinformation)",
    author = "Maigrot, C{\'e}dric  and
      Kijak, Ewa  and
      Claveau, Vincent",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.24",
    pages = "498--505",
    abstract = "Les m{\'e}dias traditionnels sont de plus en plus pr{\'e}sents sur les r{\'e}seaux sociaux, mais ces sources d{'}informations sont confront{\'e}es {\`a} d{'}autres sources dites de r{\'e}information. Ces derni{\`e}res ont parfois tendance {\`a} d{\'e}former les informations relay{\'e}es pour correspondre aux id{\'e}ologies qu{'}elles souhaitent d{\'e}fendre, les rendant partiellement ou totalement fausses. Le but de cet article est, d{'}une part, de pr{\'e}senter un corpus que nous avons constitu{\'e} {\`a} partir de groupes Facebook de ces deux types de m{\'e}dias. Nous pr{\'e}sentons d{'}autre part quelques exp{\'e}riences de d{\'e}tection automatique des messages issus des m{\'e}dias de r{\'e}information, en {\'e}tudiant notamment l{'}influence d{'}attributs de surface et d{'}attributs portant plus sp{\'e}cifiquement sur le contenu de ces messages.",
    language = "French",
}
@inproceedings{miletic-etal-2016-mise,
    title = "Mise au point d{'}une m{\'e}thode d{'}annotation morphosyntaxique fine du serbe (Developping a method for detailed morphosyntactic tagging of {S}erbian)",
    author = "Miletic, Aleksandra  and
      Fabre, C{\'e}cile  and
      Stosic, Dejan",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.25",
    pages = "506--513",
    abstract = "Cet article pr{\'e}sente une exp{\'e}rience d{'}annotation morphosyntaxique fine du volet serbe du corpus parall{\`e}le ParCoLab (corpus serbe-fran{\c{c}}ais-anglais). Elle a consist{\'e} {\`a} enrichir une annotation existante en parties du discours avec des traits morphosyntaxiques fins, afin de pr{\'e}parer une {\'e}tape ult{\'e}rieure de parsing. Nous avons compar{\'e} trois approches : 1) annotation manuelle ; 2) pr{\'e}annotation avec un {\'e}tiqueteur entra{\^\i}n{\'e} sur le croate suivie d{'}une correction manuelle ; 3) r{\'e}entra{\^\i}nement de l{'}outil sur un petit {\'e}chantillon valid{\'e} du corpus, suivi de l{'}annotation automatique et de la correction manuelle. Le mod{\`e}le croate maintient une stabilit{\'e} globale en passant au serbe, mais les diff{\'e}rences entre les deux jeux d{'}{\'e}tiquettes exigent des interventions manuelles importantes. Le mod{\`e}le r{\'e}-entra{\^\i}n{\'e} sur un {\'e}chantillon de taille limit{\'e} (20K tokens) atteint la m{\^e}me exactitude que le mod{\`e}le existant et le gain de temps observ{\'e} montre que cette m{\'e}thode optimise la phase de correction.",
    language = "French",
}
@inproceedings{ramadier-lafourcade-2016-patrons,
    title = "Patrons s{\'e}mantiques pour l{'}extraction de relations entre termes - Application aux comptes rendus radiologiques (Here the title in {E}nglish)",
    author = "Ramadier, Lionel  and
      Lafourcade, Mathieu",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.26",
    pages = "514--521",
    abstract = "Dans cet article nous nous int{\'e}ressons {\`a} la t{\^a}che d{'}extraction de relations s{\'e}mantiques dans les textes m{\'e}dicaux et plus particuli{\`e}rement dans les comptes rendus radiologiques. L{'}identification de relations s{\'e}mantiques est une t{\^a}che importante pour plusieurs applications (recherche d{'}information, g{\'e}n{\'e}ration de r{\'e}sum{\'e}, etc). Nous proposons une approche fond{\'e}e sur l{'}utilisation de patrons s{\'e}mantiques v{\'e}rifiant des contraintes dans une base de connaissances.",
    language = "French",
}
@inproceedings{pulizzotto-etal-2016-recherche,
    title = "Recherche de « p{\'e}risegments » dans un contexte d{'}analyse conceptuelle assist{\'e}e par ordinateur : le concept d{'}« esprit » chez Peirce (Search of {``}perisegments{''} in computer-assisted conceptual analysis : the concept of {``}mind{''} in Peirce)",
    author = "Pulizzotto, Davide  and
      Lopez Gonzalez, Jos{\'e} Alejandro  and
      Chartier, Jean-Fran{\c{c}}ois  and
      Meunier, Jean-Guy  and
      Chartrand, Louis  and
      Lareau, Francis  and
      Ngoc, Tan Le",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.27",
    pages = "522--530",
    abstract = "En sciences humaines et plus particuli{\`e}rement en philosophie, l{'}analyse conceptuelle (AC) est une pratique fondamentale qui permet de d{\'e}cortiquer les propri{\'e}t{\'e}s d{'}un concept. Lors de l{'}analyse d{'}un un corpus textuel, le principal d{\'e}fi est l{'}identification des segments de texte qui expriment le concept. Parfois, ces segments sont facilement reconnaissables gr{\^a}ce {\`a} une unit{\'e} lexicale attendue, appel{\'e}e forme canonique. Toutefois, ce n{'}est pas toujours le cas. Cet article propose une cha{\^\i}ne de traitement pour la d{\'e}couverte d{'}un certain nombre de segments p{\'e}riph{\'e}riques, dits p{\'e}risegments. Pour illustrer le processus, nous r{\'e}alisons des exp{\'e}rimentations sur le concept d{'}« esprit » dans les Collected Papers de Ch. S. Peirce, en obtenant une pr{\'e}cision moyenne sup{\'e}rieure {\`a} 83{\%}.",
    language = "French",
}
@inproceedings{pineau-etal-2016-segmentation,
    title = "Segmentation automatique d{'}un texte en rh{\`e}ses (Automatic segmentation of a text into rhesis)",
    author = "Pineau, Victor  and
      Nin, Constance  and
      Quiniou, Solen  and
      Daille, B{\'e}atrice",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.28",
    pages = "531--538",
    abstract = "La segmentation d{'}un texte en rh{\`e}ses, unit{\'e}s-membres signifiantes de la phrase, permet de fournir des adaptations de celui-ci pour faciliter la lecture aux personnes dyslexiques. Dans cet article, nous proposons une m{\'e}thode d{'}identification automatique des rh{\`e}ses bas{\'e}e sur un apprentissage supervis{\'e} {\`a} partir d{'}un corpus que nous avons annot{\'e}. Nous comparons celle-ci {\`a} l{'}identification manuelle ainsi qu{'}{\`a} l{'}utilisation d{'}outils et de concepts proches, tels que la segmentation d{'}un texte en chunks.",
    language = "French",
}
@inproceedings{hkiri-etal-2016-systeme,
    title = "Syst{\`e}me hybride pour la reconnaissance des entit{\'e}s nomm{\'e}es arabes {\`a} base des {CRF} (Hybrid arabic {NER} system using {CRF} Model)",
    author = "Hkiri, Emna  and
      Mallat, Souheyl  and
      Zrigui, Mounir",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.29",
    pages = "539--546",
    abstract = "La reconnaissance d{'}entit{\'e}s nomm{\'e}es (REN) pour les langues naturelles telles que l{'}arabe est une t{\^a}che essentielle et difficile. Dans cet article, nous d{\'e}crivons notre syst{\`e}me hybride afin d{'}am{\'e}liorer la performance du syst{\`e}me de REN et de combler le manque de ressources pour le TAL arabe. Notre syst{\`e}me applique un mod{\`e}le CRF, un lexique bilingue d{'}ENs et des r{\`e}gles linguistiques sp{\'e}cifiques {\`a} la t{\^a}che de reconnaissance d{'}entit{\'e}s nomm{\'e}es dans les textes arabes. Les r{\'e}sultats empiriques indiquent que notre syst{\`e}me surpasse l{'}{\'e}tat-de l{'}art de la REN arabe lorsqu{'}il est appliqu{\'e} au corpus d{'}{\'e}valuation standard ANERcorp.",
    language = "French",
}
@inproceedings{steible-bernhard-2016-vers,
    title = "Vers un lexique ouvert des formes fl{\'e}chies de l{'}alsacien : g{\'e}n{\'e}ration de flexions pour les verbes (Towards an Open Lexicon of Inflected Word Forms for {A}lsatian: Generation of Verbal Inflection)",
    author = "Steibl{\'e}, Lucie  and
      Bernhard, Delphine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.30",
    pages = "547--554",
    abstract = "Cet article pr{\'e}sente les m{\'e}thodes mises en {\oe}uvre et les r{\'e}sultats obtenus pour la cr{\'e}ation d{'}un lexique de formes fl{\'e}chies de l{'}alsacien. Les dialectes d{'}Alsace font partie des langues peu dot{\'e}es : rares sont les outils et ressources informatis{\'e}es les concernant. Plusieurs difficult{\'e}s doivent {\^e}tre prises en compte afin de g{\'e}n{\'e}rer des ressources pour ces langues, g{\'e}n{\'e}ralement li{\'e}es {\`a} la variabilit{\'e} en l{'}absence de norme graphique, et au manque de formes fl{\'e}chies dans les quelques ressources existantes. Nous avons pour ce faire utilis{\'e} plusieurs outils permettant la g{\'e}n{\'e}ration automatique de variantes graphiques et la cr{\'e}ation de formes fl{\'e}chies (graphes morphologiques et de flexion d{'}Unitex). Les r{\'e}sultats en termes de couverture des formes rencontr{\'e}es dans des textes ont permis l{'}{\'e}valuation de la m{\'e}thode.",
    language = "French",
}
@inproceedings{lefer-etal-2016-vers,
    title = "Vers une analyse des diff{\'e}rences interlinguistiques entre les genres textuels : {\'e}tude de cas bas{\'e}e sur les n-grammes et l{'}analyse factorielle des correspondances (Towards a cross-linguistic analysis of genres: A case study based on n-grams and Correspondence Analysis)",
    author = "Lefer, Marie-Aude  and
      Bestgen, Yves  and
      Grabar, Natalia",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-poster.31",
    pages = "555--563",
    abstract = "L{'}objectif de notre travail est d{'}{\'e}valuer l{'}int{\'e}r{\^e}t d{'}employer les n-grammes et l{'}analyse factorielle des correspondances (AFC) pour comparer les genres textuels dans les {\'e}tudes contrastives interlinguistiques. Nous exploitons un corpus bilingue anglais-fran{\c{c}}ais constitu{\'e} de textes originaux comparables. Le corpus r{\'e}unit trois genres : les d{\'e}bats parlementaires europ{\'e}ens, les {\'e}ditoriaux de presse et les articles scientifiques. Dans un premier temps, les n-grammes d{'}une longueur de 2 {\`a} 4 mots sont extraits dans chaque langue. Ensuite, pour chaque longueur, les 1 000 n-grammes les plus fr{\'e}quents dans chaque langue sont trait{\'e}s par l{'}AFC pour d{\'e}terminer quels n-grammes sont particuli{\`e}rement saillants dans les genres {\'e}tudi{\'e}s. Enfin, les n-grammes sont cat{\'e}goris{\'e}s manuellement en distinguant les expressions d{'}opinion et de certitude, les marqueurs discursifs et les expressions r{\'e}f{\'e}rentielles. Les r{\'e}sultats montrent que les n-grammes permettent de mettre au jour des caract{\'e}ristiques typiques des genres {\'e}tudi{\'e}s, de m{\^e}me que des contrastes interlangues int{\'e}ressants.",
    language = "French",
}
@proceedings{jep-taln-recital-2016-actes-de,
    title = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    editor = "Danlos, Laurence  and
      Hamon, Thierry",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.0",
}
@inproceedings{lacroix-etal-2016-apprentissage,
    title = "Apprentissage d{'}analyseur en d{\'e}pendances cross-lingue par projection partielle de d{\'e}pendances (Cross-lingual learning of dependency parsers from partially projected dependencies )",
    author = "Lacroix, Oph{\'e}lie  and
      Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.1",
    pages = "1--14",
    abstract = "Cet article pr{\'e}sente une m{\'e}thode simple de transfert cross-lingue de d{\'e}pendances. Nous montrons tout d{'}abord qu{'}il est possible d{'}apprendre un analyseur en d{\'e}pendances par transition {\`a} partir de donn{\'e}es partiellement annot{\'e}es. Nous proposons ensuite de construire de grands ensembles de donn{\'e}es partiellement annot{\'e}s pour plusieurs langues cibles en projetant les d{\'e}pendances via les liens d{'}alignement les plus s{\^u}rs. En apprenant des analyseurs pour les langues cibles {\`a} partir de ces donn{\'e}es partielles, nous montrons que cette m{\'e}thode simple obtient des performances qui rivalisent avec celles de m{\'e}thodes {\'e}tat-de-l{'}art r{\'e}centes, tout en ayant un co{\^u}t algorithmique moindre.",
    language = "French",
}
@inproceedings{francois-etal-2016-bleu,
    title = "{B}leu, contusion, ecchymose : tri automatique de synonymes en fonction de leur difficult{\'e} de lecture et compr{\'e}hension (Automatic ranking of synonyms according to their reading and comprehension difficulty)",
    author = "Francois, Thomas  and
      Billami, Mokhtar B.  and
      Gala, N{\'u}ria  and
      Bernhard, Delphine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.2",
    pages = "15--28",
    abstract = "La lisibilit{\'e} d{'}un texte d{\'e}pend fortement de la difficult{\'e} des unit{\'e}s lexicales qui le composent. La simplification lexicale vise ainsi {\`a} remplacer les termes complexes par des {\'e}quivalents s{\'e}mantiques plus simples {\`a} comprendre : par exemple, BLEU ({`}r{\'e}sultat d{'}un choc{'}) est plus simple que CONTUSION ou ECCHYMOSE. Il est pour cela n{\'e}cessaire de disposer de ressources qui listent des synonymes pour des sens donn{\'e}s et les trient par ordre de difficult{\'e}. Cet article d{\'e}crit une m{\'e}thode pour constituer une ressource de ce type pour le fran{\c{c}}ais. Les listes de synonymes sont extraites de BabelNet et de JeuxDeMots, puis tri{\'e}es gr{\^a}ce {\`a} un algorithme statistique d{'}ordonnancement. Les r{\'e}sultats du tri sont {\'e}valu{\'e}s par rapport {\`a} 36 listes de synonymes ordonn{\'e}es manuellement par quarante annotateurs.",
    language = "French",
}
@inproceedings{salim-etal-2016-comparaison,
    title = "Comparaison d{'}approches de classification automatique des actes de dialogue dans un corpus de conversations {\'e}crites en ligne sur diff{\'e}rentes modalit{\'e}s (A comparison of automatic dialog act recognition approaches in a multimodal corpus of online written conversations)",
    author = "Salim, Soufian  and
      Hernandez, Nicolas  and
      Morin, Emmanuel",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.3",
    pages = "29--42",
    abstract = "L{'}analyse des conversations {\'e}crites porteuses de demandes d{'}assistance est un enjeu important pour le d{\'e}veloppement de nouvelles technologies li{\'e}es au support client. Dans cet article, nous nous int{\'e}ressons {\`a} l{'}analyse d{'}un m{\^e}me type d{'}{\'e}change sur un canal diff{\'e}rent : les conversations se d{\'e}roulant sur les plate-formes d{'}entraide entre utilisateurs. Nous comparons des approches de classification supervis{\'e}es sur trois modalit{\'e}s des CMR 1 diff{\'e}rentes {\`a} m{\^e}me th{\'e}matique : des courriels, forums et chats issus de la communaut{\'e} Ubuntu. Le syst{\`e}me emploie une taxonomie fine bas{\'e}e sur le sch{\'e}ma DIT++. D{'}autres exp{\'e}riences sont d{\'e}taill{\'e}es, et nous rapportons les r{\'e}sultats obtenus avec diff{\'e}rentes approches et diff{\'e}rents traits sur les diff{\'e}rentes parties de notre corpus multimodal.",
    language = "French",
}
@inproceedings{lafourcade-etal-2016-construire,
    title = "Construire un lexique de sentiments par crowdsourcing et propagation (Building a sentiment lexicon through crowdsourcing and spreading)",
    author = "Lafourcade, Mathieu  and
      Brun, Nathalie Le  and
      Joubert, Alain",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.4",
    pages = "43--56",
    abstract = "Cet article pr{\'e}sente une m{\'e}thode de construction d{'}une ressource lexicale de sentiments/{\'e}motions. Son originalit{\'e} est d{'}associer le crowdsourcing via un GWAP (Game With A Purpose) {\`a} un algorithme de propagation, les deux ayant pour support et source de donn{\'e}es le r{\'e}seau lexical JeuxDeMots. Nous d{\'e}crivons le jeu permettant de collecter des informations de sentiments, ainsi que les principes et hypoth{\`e}ses qui sous-tendent le fonctionnement de l{'}algorithme qui les propage au sein du r{\'e}seau. Enfin, nous donnons les r{\'e}sultats quantitatifs et expliquons les m{\'e}thodes d{'}{\'e}valuation qualitative des donn{\'e}es obtenues, {\`a} la fois par le jeu et par la propagation par l{'}algorithme. Ces m{\'e}thodes incluent une comparaison avec Emolex, une autre ressource de sentiments/{\'e}motions.",
    language = "French",
}
@inproceedings{trione-etal-2016-detection,
    title = "D{\'e}tection de concepts pertinents pour le r{\'e}sum{\'e} automatique de conversations par recombinaison de patrons (Relevant concepts detection for the automatic summary of conversations using patterns recombination )",
    author = "Trione, J{\'e}r{\'e}my  and
      Favre, Benoit  and
      Bechet, Frederic",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.5",
    pages = "57--69",
    abstract = "automatique de conversations par recombinaison de patrons J{\'e}r{\'e}my Trione Benoit Favre Fr{\'e}d{\'e}ric B{\'e}chet Aix-Marseille Universit{\'e}, CNRS, LIF UMR 7279, 13000, Marseille, France pr{\'e}nom.nom@lif.univ-mrs.fr R {\'E}SUM{\'E} Ce papier d{\'e}crit une approche pour cr{\'e}er des r{\'e}sum{\'e}s de conversations parl{\'e}es par remplissage de patrons. Les patrons sont g{\'e}n{\'e}r{\'e}s automatiquement {\`a} partir de fragments g{\'e}n{\'e}ralis{\'e}s depuis un corpus de r{\'e}sum{\'e}s d{'}apprentissage. Les informations n{\'e}cessaires pour remplir les patrons sont d{\'e}tect{\'e}es dans les transcriptions des conversations et utilis{\'e}es pour s{\'e}lectionner les fragments candidats. L{'}approche obtient un score ROUGE-2 de 0.116 sur le corpus RATP-DECODA. Les r{\'e}sultats obtenus montrent que cette approche abstractive est plus performante que les approches extractives utilis{\'e}es habituellement dans le domaine du r{\'e}sum{\'e} automatique.",
    language = "French",
}
@inproceedings{gabor-etal-2016-detection,
    title = "D{\'e}tection et classification non supervis{\'e}es de relations s{\'e}mantiques dans des articles scientifiques (Unsupervised Classification of Semantic Relations in Scientific Papers)",
    author = {G{\'a}bor, Kata  and
      Tellier, Isabelle  and
      Charnois, Thierry  and
      Zargayouna, Ha{\"\i}fa  and
      Buscaldi, Davide},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.6",
    pages = "70--83",
    abstract = "Dans cet article, nous abordons une t{\^a}che encore peu explor{\'e}e, consistant {\`a} extraire automatiquement l{'}{\'e}tat de l{'}art d{'}un domaine scientifique {\`a} partir de l{'}analyse d{'}articles de ce domaine. Nous la ramenons {\`a} deux sous-t{\^a}ches {\'e}l{\'e}mentaires : l{'}identification de concepts et la reconnaissance de relations entre ces concepts. Une extraction terminologique permet d{'}identifier les concepts candidats, qui sont ensuite align{\'e}s {\`a} des ressources externes. Dans un deuxi{\`e}me temps, nous cherchons {\`a} reconna{\^\i}tre et classifier automatiquement les relations s{\'e}mantiques entre concepts de mani{\`e}re nonsupervis{\'e}e, en nous appuyant sur diff{\'e}rentes techniques de clustering et de biclustering. Nous mettons en {\oe}uvre ces deux {\'e}tapes dans un corpus extrait de l{'}archive de l{'}ACL Anthology. Une analyse manuelle nous a permis de proposer une typologie des relations s{\'e}mantiques, et de classifier un {\'e}chantillon d{'}instances de relations. Les premi{\`e}res {\'e}valuations sugg{\`e}rent l{'}int{\'e}r{\^e}t du biclustering pour d{\'e}tecter de nouveaux types de relations dans le corpus.",
    language = "French",
}
@inproceedings{semmar-etal-2016-etude,
    title = "Etude de l{'}impact d{'}un lexique bilingue sp{\'e}cialis{\'e} sur la performance d{'}un moteur de traduction {\`a} base d{'}exemples (Studying the impact of a specialized bilingual lexicon on the performance of an example-based machine translation engine)",
    author = "Semmar, Nasredine  and
      Zennaki, Othman  and
      Laib, Meriama",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.7",
    pages = "84--97",
    abstract = "La traduction automatique statistique bien que performante est aujourd{'}hui limit{\'e}e parce qu{'}elle n{\'e}cessite de gros volumes de corpus parall{\`e}les qui n{'}existent pas pour tous les couples de langues et toutes les sp{\'e}cialit{\'e}s et que leur production est lente et co{\^u}teuse. Nous pr{\'e}sentons, dans cet article, un prototype d{'}un moteur de traduction {\`a} base d{'}exemples utilisant la recherche d{'}information interlingue et ne n{\'e}cessitant qu{'}un corpus de textes en langue cible. Plus particuli{\`e}rement, nous proposons d{'}{\'e}tudier l{'}impact d{'}un lexique bilingue de sp{\'e}cialit{\'e} sur la performance de ce prototype. Nous {\'e}valuons ce prototype de traduction et comparons ses r{\'e}sultats {\`a} ceux du syst{\`e}me de traduction statistique Moses en utilisant les corpus parall{\`e}les anglais-fran{\c{c}}ais Europarl (European Parliament Proceedings) et Emea (European Medicines Agency Documents). Les r{\'e}sultats obtenus montrent que le score BLEU du prototype du moteur de traduction {\`a} base d{'}exemples est proche de celui du syst{\`e}me Moses sur des documents issus du corpus Europarl et meilleur sur des documents extraits du corpus Emea.",
    language = "French",
}
@inproceedings{dinarelli-tellier-2016-etude,
    title = "{\'E}tude des r{\'e}seaux de neurones r{\'e}currents pour {\'e}tiquetage de s{\'e}quences (A study of Recurrent Neural Networks for Sequence Labelling)",
    author = "Dinarelli, Marco  and
      Tellier, Isabelle",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.8",
    pages = "98--111",
    abstract = "Dans cet article nous {\'e}tudions plusieurs types de r{\'e}seaux neuronaux r{\'e}currents (RNN) pour l{'}{\'e}tiquetage de s{\'e}quences. Nous proposons deux nouvelles variantes de RNN et nous les comparons aux variantes plus classiques de type Jordan et Elman. Nous expliquons en d{\'e}tails quels sont les avantages de nos nouvelles variantes par rapport aux autres RNN. Nous {\'e}valuons tous les mod{\`e}les, les nouvelles variantes ainsi que les RNN existants, sur deux t{\^a}ches de compr{\'e}hension de la parole : ATIS et MEDIA. Les r{\'e}sultats montrent que nos nouvelles variantes de RNN sont plus efficaces que les autres.",
    language = "French",
}
@inproceedings{letard-etal-2016-evaluation,
    title = "{\'E}valuation de l{'}apprentissage incr{\'e}mental par analogie (Incremental Learning From Scratch Using Analogical Reasoning )",
    author = "Letard, Vincent  and
      Illouz, Gabriel  and
      Rosset, Sophie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.9",
    pages = "112--124",
    abstract = "Cet article examine l{'}utilisation du raisonnement analogique dans le contexte de l{'}apprentissage incr{\'e}mental. Le probl{\`e}me d{'}apprentissage sous-jacent d{\'e}velopp{\'e} est le transfert de requ{\^e}tes formul{\'e}es en langue naturelle vers des commandes dans un langage de programmation. Nous y explorons deux questions principales : Comment se comporte le raisonnement par analogie dans le contexte de l{'}apprentissage incr{\'e}mental ? De quelle mani{\`e}re la s{\'e}quence d{'}apprentissage influence-t-elle la performance globale ? Pour y r{\'e}pondre, nous proposons un protocole exp{\'e}rimental simulant deux utilisateurs et diff{\'e}rentes s{\'e}quences d{'}apprentissage. Nous montrons que l{'}ordre dans la s{\'e}quence d{'}apprentissage incr{\'e}mental n{'}a d{'}influence notable que sous des conditions sp{\'e}cifiques. Nous constatons {\'e}galement la compl{\'e}mentarit{\'e} de l{'}apprentissage incr{\'e}mental avec l{'}analogie pour un nombre d{'}exemples d{'}apprentissage minimal.",
    language = "French",
}
@inproceedings{bernier-colborne-drouin-2016-evaluation-des,
    title = "{\'E}valuation des mod{\`e}les s{\'e}mantiques distributionnels : le cas de la d{\'e}rivation syntaxique (Evaluation of distributional semantic models : The case of syntactic derivation )",
    author = "Bernier-Colborne, Gabriel  and
      Drouin, Patrick",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.10",
    pages = "125--138",
    abstract = "Nous {\'e}valuons deux mod{\`e}les s{\'e}mantiques distributionnels au moyen d{'}un jeu de donn{\'e}es repr{\'e}sentant quatre types de relations lexicales et analysons l{'}influence des param{\`e}tres des deux mod{\`e}les. Les r{\'e}sultats indiquent que le mod{\`e}le qui offre les meilleurs r{\'e}sultats d{\'e}pend des relations cibl{\'e}es, et que l{'}influence des param{\`e}tres des deux mod{\`e}les varie consid{\'e}rablement en fonction de ce facteur. Ils montrent {\'e}galement que ces mod{\`e}les captent aussi bien la d{\'e}rivation syntaxique que la synonymie, mais que les configurations qui captent le mieux ces deux types de relations sont tr{\`e}s diff{\'e}rentes.",
    language = "French",
}
@inproceedings{simon-etal-2016-evaluation,
    title = "{\'E}valuation dune nouvelle structuration th{\'e}matique hi{\'e}rarchique des textes dans un cadre de r{\'e}sum{\'e} automatique et de d{\'e}tection d{'}ancres au sein de vid{\'e}os (Evaluation of a novel hierarchical thematic structuring of texts in the framework of text summarization and anchor detection for video hyperlinking )",
    author = "Simon, Anca  and
      Gravier, Guillaume  and
      S{\'e}billot, Pascale",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.11",
    pages = "139--152",
    abstract = "automatique et de d{\'e}tection d{'}ancres au sein de vid{\'e}os Anca Simon1 Guillaume Gravier2 Pascale S{\'e}billot3 (1) Universit{\'e} de Rennes 1, IRISA {\&} INRIA Rennes, Campus de Beaulieu, 35042 Rennes, France (2) CNRS, IRISA {\&} INRIA Rennes, Campus de Beaulieu, 35042 Rennes, France (3) INSA, IRISA {\&} INRIA Rennes, Campus de Beaulieu, 35042 Rennes, France anca.simon@irisa.fr, guillaume.gravier@irisa.fr, pascale.sebillot@irisa.fr R {\'E}SUM{\'E} Dans cet article, nous {\'e}valuons, {\`a} travers son int{\'e}r{\^e}t pour le r{\'e}sum{\'e} automatique et la d{\'e}tection d{'}ancres dans des vid{\'e}os, le potentiel d{'}une nouvelle structure th{\'e}matique extraite de donn{\'e}es textuelles, compos{\'e}e d{'}une hi{\'e}rarchie de fragments th{\'e}matiquement focalis{\'e}s. Cette structure est produite par un algorithme exploitant les distributions temporelles d{'}apparition des mots dans les textes en se fondant sur une analyse de salves lexicales. La hi{\'e}rarchie obtenue a pour objet de filtrer le contenu non crucial et de ne conserver que l{'}information saillante des textes, {\`a} diff{\'e}rents niveaux de d{\'e}tail. Nous montrons qu{'}elle permet d{'}am{\'e}liorer la production de r{\'e}sum{\'e}s ou au moins de maintenir les r{\'e}sultats de l{'}{\'e}tat de l{'}art, tandis que pour la d{\'e}tection d{'}ancres, elle nous conduit {\`a} la meilleure pr{\'e}cision dans le contexte de la t{\^a}che Search and Anchoring in Video Archives {\`a} MediaEval. Les exp{\'e}riences sont r{\'e}alis{\'e}es sur du texte {\'e}crit et sur un corpus de transcriptions automatiques d{'}{\'e}missions de t{\'e}l{\'e}vision.",
    language = "French",
}
@inproceedings{antoine-grabar-2016-exploitation,
    title = "Exploitation de reformulations pour l{'}acquisition d{'}un vocabulaire expert/non expert (Exploitation of reformulations for the acquisition of expert/non-expert vocabulary)",
    author = "Antoine, Edwige  and
      Grabar, Natalia",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.12",
    pages = "153--166",
    abstract = "Les notions de domaines techniques, comme les notions m{\'e}dicales, pr{\'e}sentent souvent des difficult{\'e}s de compr{\'e}hension par les non experts. Un vocabulaire qui associe les termes techniques aux expressions grand public peut aider {\`a} rendre les textes techniques mieux compr{\'e}hensibles. L{'}objectif de notre travail est de construire un tel vocabulaire. Nous proposons d{'}exploiter la notion de reformulation gr{\^a}ce {\`a} trois m{\'e}thodes : extraction d{'}abr{\'e}viations, exploitation de marqueurs de reformulation et de parenth{\`e}ses. Les segments associ{\'e}s gr{\^a}ce {\`a} ces m{\'e}thodes sont align{\'e}s avec les terminologies m{\'e}dicales. Nos r{\'e}sultats permettent de couvrir un grand nombre de termes m{\'e}dicaux et montrent une pr{\'e}cision d{'}extraction entre 0,68 et 0,98. Au total, plusieurs dizaines de milliers de paires sont propos{\'e}s. Ces r{\'e}sultats sont analys{\'e}s et compar{\'e}s avec les travaux existants.",
    language = "French",
}
@inproceedings{vial-etal-2016-extension,
    title = "Extension lexicale de d{\'e}finitions gr{\^a}ce {\`a} des corpus annot{\'e}s en sens (Lexical Expansion of definitions based on sense-annotated corpus )",
    author = {Vial, Lo{\"\i}c  and
      Tchechmedjiev, Andon  and
      Schwab, Didier},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.13",
    pages = "167--179",
    abstract = {Pour un certain nombre de t{\^a}ches ou d{'}applications du TALN, il est n{\'e}cessaire de d{\'e}terminer la proximit{\'e} s{\'e}mantique entre des sens, des mots ou des segments textuels. Dans cet article, nous nous int{\'e}ressons {\`a} une mesure bas{\'e}e sur des savoirs, la mesure de Lesk. La proximit{\'e} s{\'e}mantique de deux d{\'e}finitions est {\'e}valu{\'e}e en comptant le nombre de mots communs dans les d{\'e}finitions correspondantes dans un dictionnaire. Dans cet article, nous {\'e}tudions plus particuli{\`e}rement l{'}extension de d{\'e}finitions gr{\^a}ce {\`a} des corpus annot{\'e}s en sens. Il s{'}agit de prendre en compte les mots qui sont utilis{\'e}s dans le voisinage d{'}un certain sens et d{'}{\'e}tendre lexicalement la d{\'e}finition correspondante. Nous montrons une am{\'e}lioration certaine des performances obtenues en d{\'e}sambigu{\"\i}sation lexicale qui d{\'e}passent l{'}{\'e}tat de l{'}art.},
    language = "French",
}
@inproceedings{linard-etal-2016-extraction,
    title = "Extraction de lexiques bilingues {\`a} partir de corpus comparables sp{\'e}cialis{\'e}s {\`a} travers une langue pivot (Bilingual lexicon extraction from specialized comparable corpora using a pivot language)",
    author = "Linard, Alexis  and
      Morin, Emmanuel  and
      Daille, B{\'e}atrice",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.14",
    pages = "180--193",
    abstract = "L{'}extraction de lexiques bilingues {\`a} partir de corpus comparables se r{\'e}alise traditionnellement en s{'}appuyant sur deux langues. Des travaux pr{\'e}c{\'e}dents en extraction de lexiques bilingues {\`a} partir de corpus parall{\`e}les ont d{\'e}montr{\'e} que l{'}utilisation de plus de deux langues peut {\^e}tre utile pour am{\'e}liorer la qualit{\'e} des alignements extraits. Nos travaux montrent qu{'}il est possible d{'}utiliser la m{\^e}me strat{\'e}gie pour des corpus comparables. Nous avons d{\'e}fini deux m{\'e}thodes originales impliquant des langues pivots et nous les avons {\'e}valu{\'e}es sur quatre langues et deux langues pivots en particulier. Nos exp{\'e}rimentations ont montr{\'e} que lorsque l{'}alignement entre la langue source et la langue pivot est de bonne qualit{\'e}, l{'}extraction du lexique en langue cible s{'}en trouve am{\'e}lior{\'e}e.",
    language = "French",
}
@inproceedings{holat-etal-2016-fouille,
    title = "Fouille de motifs et {CRF} pour la reconnaissance de sympt{\^o}mes dans les textes biom{\'e}dicaux (Pattern mining and {CRF} for symptoms recognition in biomedical texts)",
    author = "Holat, Pierre  and
      Tomeh, Nadi  and
      Charnois, Thierry  and
      Battistelli, Delphine  and
      Jaulent, Marie-Christine  and
      M{\'e}tivier, Jean-Philippe",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.15",
    pages = "194--206",
    abstract = "Dans cet article, nous nous int{\'e}ressons {\`a} l{'}extraction d{'}entit{\'e}s m{\'e}dicales de type sympt{\^o}me dans les textes biom{\'e}dicaux. Cette t{\^a}che est peu explor{\'e}e dans la litt{\'e}rature et il n{'}existe pas {\`a} notre connaissance de corpus annot{\'e} pour entra{\^\i}ner un mod{\`e}le d{'}apprentissage. Nous proposons deux approches faiblement supervis{\'e}es pour extraire ces entit{\'e}s. Une premi{\`e}re est fond{\'e}e sur la fouille de motifs et introduit une nouvelle contrainte de similarit{\'e} s{\'e}mantique. La seconde formule la tache comme une tache d{'}{\'e}tiquetage de s{\'e}quences en utilisant les CRF (champs conditionnels al{\'e}atoires). Nous d{\'e}crivons les exp{\'e}rimentations men{\'e}es qui montrent que les deux approches sont compl{\'e}mentaires en termes d{'}{\'e}valuation quantitative (rappel et pr{\'e}cision). Nous montrons en outre que leur combinaison am{\'e}liore sensiblement les r{\'e}sultats.",
    language = "French",
}
@inproceedings{loser-allauzen-2016-une,
    title = "Une m{\'e}thode non-supervis{\'e}e pour la segmentation morphologique et l{'}apprentissage de morphotactique {\`a} l{'}aide de processus de {P}itman-{Y}or (An unsupervised method for joint morphological segmentation and morphotactics learning using {P}itman-{Y}or processes)",
    author = {L{\"o}ser, Kevin  and
      Allauzen, Alexandre},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.16",
    pages = "207--220",
    abstract = "Cet article pr{\'e}sente un mod{\`e}le bay{\'e}sien non-param{\'e}trique pour la segmentation morphologique non supervis{\'e}e. Ce mod{\`e}le semi-markovien s{'}appuie sur des classes latentes de morph{\`e}mes afin de mod{\'e}liser les caract{\'e}ristiques morphotactiques du lexique, et son caract{\`e}re non-param{\'e}trique lui permet de s{'}adapter aux donn{\'e}es sans avoir {\`a} sp{\'e}cifier {\`a} l{'}avance l{'}inventaire des morph{\`e}mes ainsi que leurs classes. Un processus de Pitman-Yor est utilis{\'e} comme a priori sur les param{\`e}tres afin d{'}{\'e}viter une convergence vers des solutions d{\'e}g{\'e}n{\'e}r{\'e}es et inadapt{\'e}es au traitemement automatique des langues. Les r{\'e}sultats exp{\'e}rimentaux montrent la pertinence des segmentations obtenues pour le turc et l{'}anglais. Une {\'e}tude qualitative montre {\'e}galement que le mod{\`e}le inf{\`e}re une morphotactique linguistiquement pertinente, sans le recours {\`a} des connaissances expertes quant {\`a} la structure morphologique des formes de mots.",
    language = "French",
}
@inproceedings{tack-etal-2016-modeles,
    title = "Mod{\`e}les adaptatifs pour pr{\'e}dire automatiquement la comp{\'e}tence lexicale d{'}un apprenant de fran{\c{c}}ais langue {\'e}trang{\`e}re (Adaptive models for automatically predicting the lexical competence of {F}rench as a foreign language learners)",
    author = {Tack, Ana{\"\i}s  and
      Fran{\c{c}}ois, Thomas  and
      Ligozat, Anne-Laure  and
      Fairon, C{\'e}drick},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.17",
    pages = "221--234",
    abstract = "Cette {\'e}tude examine l{'}utilisation de m{\'e}thodes d{'}apprentissage incr{\'e}mental supervis{\'e} afin de pr{\'e}dire la comp{\'e}tence lexicale d{'}apprenants de fran{\c{c}}ais langue {\'e}trang{\`e}re (FLE). Les apprenants cibl{\'e}s sont des n{\'e}erlandophones ayant un niveau A2/B1 selon le Cadre europ{\'e}en commun de r{\'e}f{\'e}rence pour les langues (CECR). {\`A} l{'}instar des travaux r{\'e}cents portant sur la pr{\'e}diction de la ma{\^\i}trise lexicale {\`a} l{'}aide d{'}indices de complexit{\'e}, nous {\'e}laborons deux types de mod{\`e}les qui s{'}adaptent en fonction d{'}un retour d{'}exp{\'e}rience, r{\'e}v{\'e}lant les connaissances de l{'}apprenant. En particulier, nous d{\'e}finissons (i) un mod{\`e}le qui pr{\'e}dit la comp{\'e}tence lexicale de tous les apprenants du m{\^e}me niveau de ma{\^\i}trise et (ii) un mod{\`e}le qui pr{\'e}dit la comp{\'e}tence lexicale d{'}un apprenant individuel. Les mod{\`e}les obtenus sont ensuite {\'e}valu{\'e}s par rapport {\`a} un mod{\`e}le de r{\'e}f{\'e}rence d{\'e}terminant la comp{\'e}tence lexicale {\`a} partir d{'}un lexique sp{\'e}cialis{\'e} pour le FLE et s{'}av{\`e}rent gagner significativement en exactitude (9{\%}-17{\%}).",
    language = "French",
}
@inproceedings{bougouin-etal-2016-modelisation,
    title = "Mod{\'e}lisation unifi{\'e}e du document et de son domaine pour une indexation par termes-cl{\'e}s libre et contr{\^o}l{\'e}e (Unified document and domain-specific model for keyphrase extraction and assignment )",
    author = "Bougouin, Adrien  and
      Boudin, Florian  and
      Daille, Beatrice",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.18",
    pages = "235--247",
    abstract = "Dans cet article, nous nous int{\'e}ressons {\`a} l{'}indexation de documents de domaines de sp{\'e}cialit{\'e} par l{'}interm{\'e}diaire de leurs termes-cl{\'e}s. Plus particuli{\`e}rement, nous nous int{\'e}ressons {\`a} l{'}indexation telle qu{'}elle est r{\'e}alis{\'e}e par les documentalistes de biblioth{\`e}ques num{\'e}riques. Apr{\`e}s analyse de la m{\'e}thodologie de ces indexeurs professionnels, nous proposons une m{\'e}thode {\`a} base de graphe combinant les informations pr{\'e}sentes dans le document et la connaissance du domaine pour r{\'e}aliser une indexation (hybride) libre et contr{\^o}l{\'e}e. Notre m{\'e}thode permet de proposer des termes-cl{\'e}s ne se trouvant pas n{\'e}cessairement dans le document. Nos exp{\'e}riences montrent aussi que notre m{\'e}thode surpasse significativement l{'}approche {\`a} base de graphe {\'e}tat de l{'}art.",
    language = "French",
}
@inproceedings{aufrant-etal-2016-ne,
    title = "Ne nous arr{\^e}tons pas en si bon chemin : am{\'e}liorations de l{'}apprentissage global d{'}analyseurs en d{\'e}pendances par transition (Don{'}t Stop Me Now ! Improved Update Strategies for Global Training of Transition-Based)",
    author = "Aufrant, Lauriane  and
      Wisniewski, Guillaume  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.19",
    pages = "248--261",
    abstract = "Dans cet article, nous proposons trois am{\'e}liorations simples pour l{'}apprentissage global d{'}analyseurs en d{\'e}pendances par transition de type A RC E AGER : un oracle non d{\'e}terministe, la reprise sur le m{\^e}me exemple apr{\`e}s une mise {\`a} jour et l{'}entra{\^\i}nement en configurations sous-optimales. Leur combinaison apporte un gain moyen de 0,2 UAS sur le corpus SPMRL. Nous introduisons {\'e}galement un cadre g{\'e}n{\'e}ral permettant la comparaison syst{\'e}matique de ces strat{\'e}gies et de la plupart des variantes connues. Nous montrons que la litt{\'e}rature n{'}a {\'e}tudi{\'e} que quelques strat{\'e}gies parmi les nombreuses variations possibles, n{\'e}gligeant ainsi plusieurs pistes d{'}am{\'e}liorations potentielles.",
    language = "French",
}
@inproceedings{grabar-eshkol-taravella-2016-prediction,
    title = "Pr{\'e}diction automatique de fonctions pragmatiques dans les reformulations (Automatic prediction of pragmatic functions in reformulations)",
    author = "Grabar, Natalia  and
      Eshkol-Taravella, Iris",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.20",
    pages = "262--275",
    abstract = "La reformulation participe {\`a} la structuration du discours, notamment dans le cas des dialogues, et contribue {\'e}galement {\`a} la dynamique du discours. Reformuler est un acte significatif qui poursuit des objectifs pr{\'e}cis. L{'}objectif de notre travail est de pr{\'e}dire automatiquement la raison pour laquelle un locuteur effectue une reformulation. Nous utilisons une classification de onze fonctions pragmatiques inspir{\'e}es des travaux existants et des donn{\'e}es analys{\'e}es. Les donn{\'e}es de r{\'e}f{\'e}rence sont issues d{'}annotations manuelles et consensuelles des reformulations spontan{\'e}es form{\'e}es autour de trois marqueurs (c{'}est-{\`a}-dire, je veux dire, disons). Les donn{\'e}es proviennent d{'}un corpus oral et d{'}un corpus de discussions sur les forums de sant{\'e}. Nous exploitons des algorithmes de cat{\'e}gorisation supervis{\'e}e et un ensemble de plusieurs descripteurs (syntaxiques, formels, s{\'e}mantiques et discursifs) pour pr{\'e}dire les cat{\'e}gories de reformulation. La distribution des {\'e}nonc{\'e}s et phrases selon les cat{\'e}gories n{'}est pas homog{\`e}ne. Les exp{\'e}riences sont positionn{\'e}es {\`a} deux niveaux : g{\'e}n{\'e}rique et sp{\'e}cifique. Nos r{\'e}sultats indiquent qu{'}il est plus facile de pr{\'e}dire les types de fonctions au niveau g{\'e}n{\'e}rique (la moyenne des F-mesures est autour de 0,80), qu{'}au niveau des cat{\'e}gories individuelles (la moyenne des F-mesures est autour de 0,40). L{'}influence de diff{\'e}rents param{\`e}tres est {\'e}tudi{\'e}e.",
    language = "French",
}
@inproceedings{zennaki-etal-2016-projection,
    title = "Projection Interlingue d{'}{\'E}tiquettes pour l{'}Annotation S{\'e}mantique Non Supervis{\'e}e (Cross-lingual Annotation Projection for Unsupervised Semantic Tagging)",
    author = "Zennaki, Othman  and
      Semmar, Nasredine  and
      Besacier, Laurent",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.21",
    pages = "276--289",
    abstract = "Nos travaux portent sur la construction rapide d{'}outils d{'}analyse linguistique pour des langues peu dot{\'e}es en ressources. Dans une pr{\'e}c{\'e}dente contribution, nous avons propos{\'e} une m{\'e}thode pour la construction automatique d{'}un analyseur morpho-syntaxique via une projection interlingue d{'}annotations linguistiques {\`a} partir de corpus parall{\`e}les (m{\'e}thode fond{\'e}e sur les r{\'e}seaux de neurones r{\'e}currents). Nous pr{\'e}sentons, dans cet article, une am{\'e}lioration de notre mod{\`e}le neuronal, avec la prise en compte d{'}informations linguistiques externes pour un annotateur plus complexe. En particulier, nous proposons d{'}int{\'e}grer des annotations morpho-syntaxiques dans notre architecture neuronale pour l{'}apprentissage non supervis{\'e} d{'}annotateurs s{\'e}mantiques multilingues {\`a} gros grain (annotation en SuperSenses). Nous montrons la validit{\'e} de notre m{\'e}thode et sa g{\'e}n{\'e}ricit{\'e} sur l{'}italien et le fran{\c{c}}ais et {\'e}tudions aussi l{'}impact de la qualit{\'e} du corpus parall{\`e}le sur notre approche (g{\'e}n{\'e}r{\'e} par traduction manuelle ou automatique). Nos exp{\'e}riences portent sur la projection d{'}annotations de l{'}anglais vers le fran{\c{c}}ais et l{'}italien.",
    language = "French",
}
@inproceedings{besancon-etal-2016-utilisation,
    title = {Utilisation des relations d{'}une base de connaissances pour la d{\'e}sambigu{\"\i}sation d{'}entit{\'e}s nomm{\'e}es (Using the Relations of a Knowledge Base to Improve Entity Linking )},
    author = "Besan{\c{c}}on, Romaric  and
      Daher, Hani  and
      Ferret, Olivier  and
      Le Borgne, Herv{\'e}",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.22",
    pages = "290--303",
    abstract = {L{'}identification des entit{\'e}s nomm{\'e}es dans un texte est une t{\^a}che essentielle des outils d{'}extraction d{'}information dans de nombreuses applications. Cette identification passe par la reconnaissance d{'}une mention d{'}entit{\'e} dans le texte, ce qui a {\'e}t{\'e} tr{\`e}s largement {\'e}tudi{\'e}, et par l{'}association des entit{\'e}s reconnues {\`a} des entit{\'e}s connues, pr{\'e}sentes dans une base de connaissances. Cette association repose souvent sur une mesure de similarit{\'e} entre le contexte textuel de la mention de l{'}entit{\'e} et un contexte textuel de description des entit{\'e}s de la base de connaissances. Or, ce contexte de description n{'}est en g{\'e}n{\'e}ral pas pr{\'e}sent pour toutes les entit{\'e}s. Nous proposons d{'}exploiter les relations de la base de connaissances pour ajouter un indice de d{\'e}sambigu{\"\i}sation pour ces entit{\'e}s. Nous {\'e}valuons notre travail sur des corpus d{'}{\'e}valuation standards en anglais issus de la t{\^a}che de d{\'e}sambigu{\"\i}sation d{'}entit{\'e}s de la campagne TAC-KBP.},
    language = "French",
}
@inproceedings{servan-etal-2016-word2vec,
    title = "{W}ord2{V}ec vs {DB}nary ou comment (r{\'e})concilier repr{\'e}sentations distribu{\'e}es et r{\'e}seaux lexico-s{\'e}mantiques ? Le cas de l{'}{\'e}valuation en traduction automatique ({W}ord2{V}ec vs {DB}nary or how to bring back together vector representations and lexical resources ? A case study for machine translation evaluation)",
    author = "Servan, Christophe  and
      Elloumi, Zied  and
      Blanchon, Herv{\'e}  and
      Besacier, Laurent",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-long.23",
    pages = "304--317",
    abstract = "Cet article pr{\'e}sente une approche associant r{\'e}seaux lexico-s{\'e}mantiques et repr{\'e}sentations distribu{\'e}es de mots appliqu{\'e}e {\`a} l{'}{\'e}valuation de la traduction automatique. Cette {\'e}tude est faite {\`a} travers l{'}enrichissement d{'}une m{\'e}trique bien connue pour {\'e}valuer la traduction automatique (TA) : METEOR. METEOR permet un appariement approch{\'e} (similarit{\'e} morphologique ou synonymie) entre une sortie de syst{\`e}me automatique et une traduction de r{\'e}f{\'e}rence. Nos exp{\'e}rimentations s{'}appuient sur la t{\^a}che Metrics de la campagne d{'}{\'e}valuation WMT 2014 et montrent que les repr{\'e}sentations distribu{\'e}es restent moins performantes que les ressources lexico-s{\'e}mantiques pour l{'}{\'e}valuation en TA mais peuvent n{\'e}ammoins apporter un compl{\'e}ment d{'}information int{\'e}ressant {\`a} ces derni{\`e}res.",
    language = "French",
}
@proceedings{jep-taln-recital-2016-actes,
    title = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    editor = "Danlos, Laurence  and
      Hamon, Thierry",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.0",
}
@inproceedings{bayraktar-ridouane-2016-le,
    title = "Le {VOT} des {\'e}jectives : le cas du maya yucat{\`e}que (The {VOT} of ejective stops in Maya Yucatec)",
    author = "Bayraktar, Emre  and
      Ridouane, Rachid",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.1",
    pages = "1--9",
    abstract = "Cet article pr{\'e}sente une {\'e}tude acoustique des occlusives {\'e}jectives du maya yucat{\`e}que. S{'}int{\'e}ressant sp{\'e}cifiquement au voice onset time (VOT), l{'}{\'e}tude examine d{'}une part si le VOT est un corr{\'e}lat acoustique fiable de l{'}{\'e}jectivit{\'e} dans cette langue et d{'}autre part si le VOT varie selon le lieu d{'}articulation et la hauteur vocalique. Les r{\'e}sultats, obtenus {\`a} partir des productions de deux locuteurs natifs, montrent que les {\'e}jectives ont un VOT plus long compar{\'e}es {\`a} leurs contreparties pulmonaires. Parmi les {\'e}jectives, le VOT varie en fonction du lieu d{'}articulation, les v{\'e}laires pr{\'e}sentant le VOT le plus long. De m{\^e}me une tendance pour un VOT plus court devant les voyelles hautes a {\'e}t{\'e} observ{\'e}e. Ces r{\'e}sultats soul{\`e}vent un ensemble de questions concernant les m{\'e}canismes qui sous-tendent les variations du VOT, notamment en lien avec les contraintes a{\'e}rodynamiques en jeu lors de la production des occlusives {\'e}jectives.",
    language = "French",
}
@inproceedings{hidalgo-etal-2016-accommodation,
    title = "Accommodation temporelle chez l{'}enfant dans une t{\^a}che de parole altern{\'e}e (Children{'}s temporal accommodation in an alternated naming task)",
    author = {Hidalgo, C{\'e}line  and
      Falk, Simone  and
      Sch{\"o}n, Daniele},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.2",
    pages = "10--18",
    abstract = "L{'}accommodation temporelle entre deux interlocuteurs est un ph{\'e}nom{\`e}ne qui {\'e}merge lors d{'}une interaction et qui jouerait un r{\^o}le important dans la fluidit{\'e} des {\'e}changes. Cette {\'e}tude examine cette capacit{\'e} temporelle chez l{'}enfant {\^a}g{\'e} de 5 {\`a} 6 ans gr{\^a}ce au d{\'e}veloppement d{'}une nouvelle t{\^a}che de d{\'e}nomination en alternance avec un partenaire virtuel. Les variables temporelles analys{\'e}es sont le tempo de l{'}alternance (lent versus rapide) et la rythmicit{\'e} des mots {\'e}chang{\'e}s (constante versus al{\'e}atoire). Les enfants sont plus pr{\'e}cis dans la condition de tempo rapide et plus r{\'e}guliers lorsque la rythmicit{\'e} des listes de mots est maintenue constante. Ces r{\'e}sultats montrent 1) que la d{\'e}nomination en alternance est un paradigme permettant de mesurer les capacit{\'e}s d{'}accommodation temporelle des enfants et que 2) d{\`e}s 5 ans, les enfants peuvent ajuster leur parole {\`a} celle d{'}un agent. Ces donn{\'e}es constituent une base pour mesurer les capacit{\'e}s linguistiques d{'}accommodation temporelle chez des populations cliniques.",
    language = "French",
}
@inproceedings{meynadier-dufour-2016-acces,
    title = "Acc{\`e}s lexical et reconnaissance du voisement en voix chuchot{\'e}e (Lexical acces and recognition of voicing in whisper)",
    author = "Meynadier, Yohann  and
      Dufour, Sophie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.3",
    pages = "19--27",
    abstract = {La reconnaissance du trait de voisement de consonnes obstruantes chuchot{\'e}es en fran{\c{c}}ais a {\'e}t{\'e} examin{\'e}e via un paradigme d{'}amor{\c{c}}age s{\'e}mantique auditif-visuel. Un effet d{'}amor{\c{c}}age d{'}amplitude similaire {\`a} celui mesur{\'e} en voix modale a {\'e}t{\'e} observ{\'e} uniquement lorsque l{'}obstruante du mot amorce chuchot{\'e} est sourde (dessert-CHOCOLAT). Aucun effet d{'}amor{\c{c}}age n{'}a {\'e}t{\'e} not{\'e} quand l{'}obstruante du mot amorce est vois{\'e}e (d{\'e}sert) que ce soit sur le mot cible SABLE associ{\'e} s{\'e}mantique de d{\'e}sert ou sur le mot cible CHOCOLAT associ{\'e} s{\'e}mantique de dessert. Ainsi, m{\^e}me si certaines travaux ont mis en {\'e}vidence qu{'}en voix chuchot{\'e}e les consonnes obstruantes vois{\'e}es maintiennent des traces phon{\'e}tiques de leur identit{\'e} sous-jacente, notre {\'e}tude montre que ces consonnes sont ambig{\"u}es pour l{'}auditeur et que leur reconnaissance n{'}est pas imm{\'e}diate.},
    language = "French",
}
@inproceedings{vacher-etal-2016-acquisition,
    title = "Acquisition et reconnaissance automatique d{'}expressions et d{'}appels vocaux dans un habitat. (Acquisition and recognition of expressions and vocal calls in a smart home)",
    author = "Vacher, Michel  and
      Lecouteux, Benjamin  and
      Aman, Fr{\'e}d{\'e}ric  and
      Portet, Fran{\c{c}}ois  and
      Rossato, Solange",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.4",
    pages = "28--36",
    abstract = "Cet article pr{\'e}sente un syst{\`e}me capable de reconna{\^\i}tre les appels {\`a} l{'}aide de personnes {\^a}g{\'e}es vivant {\`a} domicile afin de leur fournir une assistance. Le syst{\`e}me utilise une technologie de Reconnaissance Automatique de la Parole (RAP) qui doit fonctionner en conditions de parole distante et avec de la parole expressive. Pour garantir l{'}intimit{\'e}, le syst{\`e}me s{'}ex{\'e}cute localement et ne reconna{\^\i}t que des phrases pr{\'e}d{\'e}finies. Le syst{\`e}me a {\'e}t{\'e} {\'e}valu{\'e} par 17 participants jouant des sc{\'e}narios incluant des chutes dans un Living lab reproduisant un salon. Le taux d{'}erreur de d{\'e}tection obtenu, 29{\%}, est encourageant et souligne les d{\'e}fis {\`a} surmonter pour cette t{\^a}che.",
    language = "French",
}
@inproceedings{qader-etal-2016-adaptation,
    title = "Adaptation de la prononciation pour la synth{\`e}se de la parole spontan{\'e}e en utilisant des informations linguistiques (Pronunciation adaptation for spontaneous speech synthesis using linguistic information)",
    author = "Qader, Raheel  and
      Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Lolive, Damien  and
      S{\'e}billot, Pascale",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.5",
    pages = "37--45",
    abstract = "Cet article pr{\'e}sente une nouvelle m{\'e}thode d{'}adaptation de la prononciation dont le but est de reproduire le style spontan{\'e}. Il s{'}agit d{'}une t{\^a}che-cl{\'e} en synth{\`e}se de la parole car elle permet d{'}apporter de l{'}expressivit{\'e} aux signaux produits, ouvrant ainsi la voie {\`a} de nouvelles applications. La force de la m{\'e}thode propos{\'e}e est de ne s{'}appuyer que sur des informations linguistiques et de consid{\'e}rer un cadre probabiliste pour ce faire, pr{\'e}cis{\'e}ment les champs al{\'e}atoires conditionnels. Dans cet article, nous {\'e}tudions tout d{'}abord la pertinence d{'}un ensemble d{'}informations pour l{'}adaptation, puis nous combinons les informations les plus pertinentes lors d{'}exp{\'e}riences finales. Les {\'e}valuations de la m{\'e}thode sur un corpus de parole conversationnelle en anglais montrent que les prononciations adapt{\'e}es refl{\`e}tent significativement mieux un style spontan{\'e} que les prononciations canoniques.",
    language = "French",
}
@inproceedings{dutrey-etal-2016-alignement,
    title = "Alignement de s{\'e}quences phon{\'e}tiques pour une analyse phonologique des erreurs de transcription automatique (Phonetic sequences alignment for a phonemic analysis of automatic speech transcription errors )",
    author = "Dutrey, Camille  and
      Adda-Decker, Martine  and
      Yamaguchi, Naomi",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.6",
    pages = "46--54",
    abstract = "La transcription automatique de la parole obtient aujourd{'}hui des performances {\'e}lev{\'e}es avec des taux d{'}erreur qui tombent facilement en dessous de 10{\%} pour une parole journalistique. Cependant, pour des conversations plus libres, ils stagnent souvent autour de 20{--}30{\%}. En fran{\c{c}}ais, une grande partie des erreurs sont dues {\`a} des confusions entre homophones n{'}impliquant pas les niveaux acousticophon{\'e}tique et phonologique. Cependant, de nombreuses erreurs peuvent s{'}expliquer par des variantes de productions non pr{\'e}vues par le syst{\`e}me. Afin de mieux comprendre quels processus phonologiques pourraient expliquer ces variantes sp{\'e}cifiques de la parole spontan{\'e}e, nous proposons une analyse des erreurs en comparant prononciations attendue (r{\'e}f{\'e}rence) et reconnue (hypoth{\`e}se) via un alignement phon{\'e}tique par programmation dynamique. Les distances locales entre paires de phon{\`e}mes appari{\'e}s correspondent au nombre de traits phon{\'e}tiques disjoints. Nos analyses permettent d{'}identifier les traits phon{\'e}tiques les plus fr{\'e}quemment impliqu{\'e}s dans les erreurs et donnent des pistes pour des interpr{\'e}tations phonologiques.",
    language = "French",
}
@inproceedings{popescu-chitoran-2016-allophonie,
    title = "Allophonie et position dans la syllabe: Indices acoustiques pour les consonnes laterales (Acoustics of syllable position allophony: The case of lateral consonants)",
    author = "Popescu, Anisia  and
      Chitoran, Ioana",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.7",
    pages = "55--63",
    abstract = "L‟article traite de la manifestation acoustique des consonnes lat{\'e}rales en anglais am{\'e}ricain et en roumain en fonction de la position syllabique et de la complexit{\'e} phonotactique. Nous avons consid{\'e}r{\'e} quatre types de mesures: valeurs formantiques, {\'e}quations locus, ratio d‟intensit{\'e} et pr{\'e}sence/absence de rel{\^a}chements. Notre but est, d‟une part, de classifier les allophones des deux langues consid{\'e}r{\'e}es et d‟autre part de d{\'e}terminer les indices acoustiques des gestes articulatoires des consonnes lat{\'e}rales. Les r{\'e}sultats indiquent des diff{\'e}rences importantes entre les deux langues. On montre que la distribution des allophones n‟est pas binaire, mais gradu{\'e}e et que le statut du geste dorsal peut {\^e}tre consid{\'e}r{\'e} comme un marqueur de « degr{\'e} de clart{\'e} ». On montre aussi que l‟allophonie d{\'e}pend de la position syllabique mais pas forc{\'e}ment de la complexit{\'e} syllabique.",
    language = "French",
}
@inproceedings{thill-2016-analyses,
    title = "Analyses acoustiques des monophtongues du luxembourgeois produites dans la parole lue (Acoustic analyses of {L}uxembourgish monophthongs produced in reading speech)",
    author = "Thill, Tina",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.8",
    pages = "64--72",
    abstract = "Cet article pr{\'e}sente une analyse acoustique de 12 monophtongues du luxembourgeois produites par des locuteurs de la r{\'e}gion centrale du Grand-Duch{\'e} de Luxembourg. Cette analyse fait partie du travail empirique de notre th{\`e}se de doctorat sur les productions natives et non natives des voyelles du luxembourgeois. A partir des donn{\'e}es de 10 locuteurs natifs, nous analysons les valeurs de la dur{\'e}e et des trois premiers formants des paires de voyelles longues et br{\`e}ves oppos{\'e}es [iː]-[i], [eː][e], [aː]-[ɑ], [oː]-[ɔ], [uː]-[u] et de l{'}allophone [ɛː] r{\'e}alis{\'e}e lorsqu{'}elle est suivie d{'}un /r/. Les analyses montrent que (i) les voyelles longues et br{\`e}ves se distinguent tant par la dur{\'e}e acoustique que par le timbre, (ii) la voyelle semi-ouverte [ɛː] suivie d{'}un /r/ vocalis{\'e} tend {\`a} se diphtonguer.",
    language = "French",
}
@inproceedings{janod-etal-2016-auto,
    title = "Auto-encodeurs pour la compr{\'e}hension de documents parl{\'e}s (Auto-encoders for Spoken Document Understanding)",
    author = "Janod, Killian  and
      Morchid, Mohamed  and
      Dufour, Richard  and
      Linar{\`e}s, Georges  and
      De Mori, Renato",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.9",
    pages = "73--81",
    abstract = "Les repr{\'e}sentations de documents au moyen d{'}approches {\`a} base de r{\'e}seaux de neurones ont montr{\'e} des am{\'e}liorations significatives dans de nombreuses t{\^a}ches du traitement du langage naturel. Dans le cadre d{'}applications r{\'e}elles, o{\`u} des conditions d{'}enregistrement difficiles peuvent {\^e}tre rencontr{\'e}es, la transcription automatique de documents parl{\'e}s peut g{\'e}n{\'e}rer un nombre de mots mal transcrits important. Cet article propose une repr{\'e}sentation des documents parl{\'e}s tr{\`e}s bruit{\'e}s utilisant des caract{\'e}ristiques apprises par un auto-encodeur profond supervis{\'e}. La m{\'e}thode propos{\'e}e s{'}appuie {\`a} la fois sur les documents bruit{\'e}s et leur {\'e}quivalent propre annot{\'e} manuellement pour estimer une repr{\'e}sentation plus robuste des documents bruit{\'e}s. Cette repr{\'e}sentation est {\'e}valu{\'e}e sur le corpus DECODA sur une t{\^a}che de classification th{\'e}matique de conversations t{\'e}l{\'e}phoniques atteignant une pr{\'e}cision de 83{\%} avec un gain d{'}environ 6{\%}.",
    language = "French",
}
@inproceedings{le-lan-etal-2016-autoapprentissage,
    title = "Autoapprentissage pour le regroupement en locuteurs : premi{\`e}res investigations (First investigations on self trained speaker diarization )",
    author = {Le Lan, Ga{\"e}l  and
      Meignier, Sylvain  and
      Charlet, Delphine  and
      Larcher, Anthony},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.10",
    pages = "82--90",
    abstract = "This paper investigates self trained cross-show speaker diarization applied to collections of French TV archives, based on an \textit{i-vector/PLDA} framework. The parameters used for i-vectors extraction and PLDA scoring are trained in a unsupervised way, using the data of the collection itself. Performances are compared, using combinations of target data and external data for training. The experimental results on two distinct target corpora show that using data from the corpora themselves to perform unsupervised iterative training and domain adaptation of PLDA parameters can improve an existing system, trained on external annotated data. Such results indicate that performing speaker indexation on small collections of unlabeled audio archives should only rely on the availability of a sufficient external corpus, which can be specifically adapted to every target collection. We show that a minimum collection size is required to exclude the use of such an external bootstrap.",
    language = "French",
}
@inproceedings{philippart-de-foy-etal-2016-bilinguismes,
    title = "Bilinguismes et compliance phonique ({BILINGUALISMS} {AND} {PHONETIC} {COMPLIANCE})",
    author = "Philippart de Foy, Marie  and
      Delvaux, V{\'e}ronique  and
      Huet, Kathy  and
      Piccaluga, Myriam  and
      Rabeh, Rima  and
      Harmegnies, Bernard",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.11",
    pages = "91--100",
    abstract = "BILINGUISMES ET COMPLIANCE PHONIQUE Certains types de bilinguisme pourraient avoir un impact positif sur l{'}apprentissage phonique et faciliter l{'}acquisition d{'}une L3. Certains bilingues pourraient donc pr{\'e}senter une meilleure compliance phonique (aptitude {\`a} produire des sons de parole non familiers) que les monolingues. Les donn{\'e}es de quatre sujets bilingues ont {\'e}t{\'e} recueillies lors d{'}une t{\^a}che de reproduction de voyelles synth{\'e}tiques pr{\'e}c{\'e}d{\'e}e d{'}une phase de production de voyelles en langue maternelle (paradigme d{\'e}velopp{\'e} par Huet et al., 2012). Trois indices ont {\'e}t{\'e} calcul{\'e}s et compar{\'e}s {\`a} ceux obtenus par des monolingues francophones lors d{'}une {\'e}tude pr{\'e}c{\'e}dente (Delvaux et al., 2014). Les r{\'e}sultats n{'}ont pas r{\'e}v{\'e}l{\'e} de diff{\'e}rence significative entre monolingues et bilingues. Toutefois, le classement des bilingues variait d{'}un indice {\`a} l{'}autre, sugg{\'e}rant des profils plus diversifi{\'e}s que chez les monolingues. En conclusion, ces r{\'e}sultats confirment la complexit{\'e} de la compliance phonique, en particulier chez des locuteurs bilingues, et soulignent l{'}int{\'e}r{\^e}t d{'}une approche multi-componentielle ainsi que le besoin d{'}ajustements ult{\'e}rieurs de la r{\'e}flexion th{\'e}orique sous-jacente.",
    language = "French",
}
@inproceedings{caudrelier-etal-2016-de,
    title = "De b{\'e} {\`a} b{\'e}b{\'e} : le transfert d{'}apprentissage auditori-moteur pour interroger l{'}unit{\'e} de production de la parole (From sensorimotor experience to speech unit)",
    author = "Caudrelier, Tiphaine  and
      Perrier, Pascal  and
      Schwartz, Jean-Luc  and
      Savariaux, Christophe  and
      Rochet-Capellan, Am{\'e}lie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.12",
    pages = "101--109",
    abstract = "La parole est souvent d{\'e}crite comme une mise en s{\'e}quence d{'}unit{\'e}s associant des repr{\'e}sentations linguistiques, sensorielles et motrices. Le lien entre ces repr{\'e}sentations se fait-il de mani{\`e}re privil{\'e}gi{\'e}e sur une unit{\'e} sp{\'e}cifique ? Par exemple, est-ce la syllabe ou le mot ? Dans cette {\'e}tude, nous voulons contraster ces deux hypoth{\`e}ses. Pour cela, nous avons modifi{\'e} chez des locuteurs du fran{\c{c}}ais la production de la syllabe « b{\'e} », selon un paradigme d{'}adaptation auditori-motrice, consistant {\`a} perturber le retour auditif. Nous avons {\'e}tudi{\'e} comment cette modification se transf{\`e}re ensuite {\`a} la production du mot « b{\'e}b{\'e} ». Les r{\'e}sultats sugg{\`e}rent un lien entre repr{\'e}sentations linguistiques et motrices {\`a} plusieurs niveaux, {\`a} la fois celui du mot et de la syllabe. Ils montrent {\'e}galement une influence de la position de la syllabe dans le mot sur le transfert, qui soul{\`e}ve de nouvelles questions sur le contr{\^o}le s{\'e}riel de la parole.",
    language = "French",
}
@inproceedings{millasseau-crouzet-2016-caracterisation,
    title = "Caract{\'e}risation statique et dynamique des voyelles dans des transitions {VV} (Static and dynamic characterization of vowels in {VV} sequences)",
    author = "Millasseau, Julien  and
      Crouzet, Olivier",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.13",
    pages = "110--118",
    abstract = "Nous {\'e}tudions les indices acoustiques li{\'e}s {\`a} la caract{\'e}risation statique et / ou dynamique des voyelles du fran{\c{c}}ais. Nous avons analys{\'e} les caract{\'e}ristiques formantiques de six r{\'e}alisations vocaliques ainsi que les transitions formantiques de seize combinaisons V1 V2 impliquant ces 6 voyelles afin d{'}{\'e}valuer les contributions des indices dynamiques li{\'e}s aux transitions entre voyelles et des indices statiques de fr{\'e}quence. Les mesures correspondantes sont issues d{'}un protocole dans lequel le d{\'e}bit de parole {\'e}tait influenc{\'e} exp{\'e}rimentalement afin de provoquer d{'}{\'e}ventuelles variations de vitesse de transition. Les r{\'e}sultats ne permettent pas de d{\'e}partager ces deux hypoth{\`e}ses mais montrent que les indices dynamiques pourraient {\^e}tre aussi fiables que les mesures statiques. Des pistes d{'}extension de ce travail sont propos{\'e}es qui pourraient contribuer de mani{\`e}re plus informative {\`a} cette probl{\'e}matique.",
    language = "French",
}
@inproceedings{boula-de-mareuil-etal-2016-cartopho,
    title = "Cartopho : un site web de cartographie de variantes de prononciation en fran{\c{c}}ais (Cartopho: a website for mapping pronunciation variants in {F}rench)",
    author = {Boula de Mare{\"u}il, Philippe  and
      Goldman, Jean-Philippe  and
      Rilliard, Albert  and
      Scherrer, Yves  and
      Vernier, Fr{\'e}d{\'e}ric},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.14",
    pages = "119--127",
    abstract = "Le pr{\'e}sent travail se propose de renouveler les traditionnels atlas dialectologiques pour cartographier les variantes de prononciation en fran{\c{c}}ais, {\`a} travers un site internet. La toile est utilis{\'e}e non seulement pour collecter des donn{\'e}es, mais encore pour diss{\'e}miner les r{\'e}sultats aupr{\`e}s des chercheurs et du grand public. La m{\'e}thodologie utilis{\'e}e, {\`a} base de crowdsourcing (ou « production participative »), nous a permis de recueillir des informations aupr{\`e}s de 2500 francophones d{'}Europe (France, Belgique, Suisse). Une plateforme dynamique {\`a} l{'}interface conviviale a ensuite {\'e}t{\'e} d{\'e}velopp{\'e}e pour cartographier la prononciation de 70 mots dans les diff{\'e}rentes r{\'e}gions des pays concern{\'e}s (des mots notamment {\`a} voyelle moyenne ou dont la consonne finale peut {\^e}tre prononc{\'e}e ou non). Les options de visualisation par d{\'e}partement/canton/province ou par r{\'e}gion, combinant plusieurs traits de prononciation et ensembles de mots, sous forme de pastilles color{\'e}es, de hachures, etc. sont pr{\'e}sent{\'e}es dans cet article. On peut ainsi observer imm{\'e}diatement un /E/ plus ferm{\'e} (ainsi qu{'}un /O/ plus ouvert) dans le Nord-Pas-de-Calais et le sud de la France, pour des mots comme parfait ou rose, un /{\OE}/ plus ferm{\'e} en Suisse pour un mot comme gueule, par exemple.",
    language = "French",
}
@inproceedings{galibert-etal-2016-comparaison,
    title = "Comparaison de listes d{'}erreurs de transcription automatique de la parole : quelle compl{\'e}mentarit{\'e} entre les diff{\'e}rentes m{\'e}triques ? (Comparing error lists for {ASR} systems : contribution of different metrics)",
    author = "Galibert, Olivier  and
      Kahn, Juliette  and
      Rosset, Sophie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.15",
    pages = "128--136",
    abstract = "Le travail que nous pr{\'e}sentons ici s{'}inscrit dans le domaine de l{'}{\'e}valuation des syst{\`e}mes de reconnaissance automatique de la parole en vue de leur utilisation dans une t{\^a}che aval, ici la reconnaissance des entit{\'e}s nomm{\'e}es. Plus largement, la question que nous nous posons est {``}que peut apporter une m{\'e}trique d{'}{\'e}valuation en dehors d{'}un score ?''. Nous nous int{\'e}ressons particuli{\`e}rement aux erreurs des syst{\`e}mes et {\`a} leur analyse et {\'e}ventuellement {\`a} l{'}utilisation de ce que nous connaissons de ces erreurs. Nous {\'e}tudions dans ce travail les listes ordonn{\'e}es d{'}erreurs g{\'e}n{\'e}r{\'e}es {\`a} partir de diff{\'e}rentes m{\'e}triques et analysons ce qui en ressort. Nous avons appliqu{\'e} la m{\^e}me m{\'e}thode sur les sorties de diff{\'e}rents syst{\`e}mes de reconnaissance de la parole. Nos exp{\'e}riences mettent en {\'e}vidence que certaines m{\'e}triques apportent une information plus pertinente {\'e}tant donn{\'e} une t{\^a}che et transverse {\`a} diff{\'e}rents syst{\`e}mes.",
    language = "French",
}
@inproceedings{chevelu-etal-2016-se,
    title = "Se concentrer sur les diff{\'e}rences : une m{\'e}thode d{'}{\'e}valuation subjective efficace pour la comparaison de syst{\`e}mes de synth{\`e}se (Focus on differences : a subjective evaluation method to efficiently compare {TTS} systems * )",
    author = "Chevelu, Jonathan  and
      Lolive, Damien  and
      Maguer, S{\'e}bastien Le  and
      Guennec, David",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.16",
    pages = "137--145",
    abstract = "En proposant une nouvelle approche de synth{\`e}se de la parole, les {\'e}tudes comportent g{\'e}n{\'e}ralement une {\'e}valuation subjective d{'}{\'e}chantillons acoustiques produits par un syst{\`e}me de r{\'e}f{\'e}rence et un nouveau syst{\`e}me. Ces {\'e}chantillons sont produits {\`a} partir d{'}un petit ensemble de phrases choisies al{\'e}atoirement dans un unique domaine. Ainsi, statistiquement, des {\'e}chantillons pratiquement identiques sont pr{\'e}sent{\'e}s et r{\'e}duisent les {\'e}carts de mesure entre les syst{\`e}mes, au risque de les consid{\'e}rer comme non significatifs. Pour {\'e}viter cette probl{\'e}matique m{\'e}thodologique, nous comparons deux syst{\`e}mes sur des milliers d{'}{\'e}chantillons de diff{\'e}rents domaines. L{'}{\'e}valuation est r{\'e}alis{\'e}e uniquement sur les paires d{'}{\'e}chantillons les plus pertinentes, c{'}est-{\`a}-dire les plus diff{\'e}rentes acoustiquement. Cette m{\'e}thode est appliqu{\'e}e sur un syst{\`e}me de synth{\`e}se de type HTS et un second par s{\'e}lection d{'}unit{\'e}s. La comparaison avec l{'}approche classique montre que cette m{\'e}thode r{\'e}v{\`e}le des {\'e}carts qui jusqu{'}alors n{'}{\'e}taient pas significatifs.",
    language = "French",
}
@inproceedings{garnier-etal-2016-constituance,
    title = "Constituance et phras{\'e} prosodique en fran{\c{c}}ais : une {\'e}tude perceptive. (Prosodic constituency and phrasing in {F}rench: a perception study)",
    author = "Garnier, Laury  and
      Ast{\'e}sano, Corine  and
      Baqu{\'e}, Lorraine  and
      Dagnac, Anne",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.17",
    pages = "146--154",
    abstract = {L{'}objectif de cette {\'e}tude est d{'}explorer l{'}organisation du phras{\'e} prosodique en fran{\c{c}}ais. Il n{'}existe pas de consensus clair sur le nombre de niveaux n{\'e}cessaires pour refl{\'e}ter la hi{\'e}rarchie prosodique de la langue. Dans ce cadre, nous proposons une {\'e}tude perceptive, via un corpus de parole contr{\^o}l{\'e}e manipulant des structures syntaxiques ambigu{\"e}s, o{\`u} 27 participants ont effectu{\'e} 3 t{\^a}ches de perception : pro{\'e}minence, fronti{\`e}re et groupement. Nos r{\'e}sultats montrent une utilisation privil{\'e}gi{\'e}e des indices de fronti{\`e}res dans le marquage des groupes prosodiques. Plus pr{\'e}cis{\'e}ment, on observe que les auditeurs sont capables de percevoir des niveaux de granularit{\'e} de fronti{\`e}res plus fins que ce que les descriptions traditionnelles du fran{\c{c}}ais pr{\'e}disent. Par ailleurs, les r{\'e}sultats de la t{\^a}che de pro{\'e}minence montrent que l{'}accent initial est toujours per{\c{c}}u plus fort que l{'}accent final, et ce d{\`e}s les niveaux les plus bas de la hi{\'e}rarchie.},
    language = "French",
}
@inproceedings{michaud-etal-2016-contribuer,
    title = "Contribuer au progr{\`e}s solidaire des recherches et de la documentation : la Collection Pangloss et la Collection {A}u{C}o (Contributing to joint progress in documentation and research: some achievements and future perspectives of the Pangloss Collection and the {A}u{C}o Collection)",
    author = "Michaud, Alexis  and
      Guillaume, S{\'e}verine  and
      Jacques, Guillaume  and
      Mạc, {\DJ}{\u{a}}ng-Khoa  and
      Jacobson, Michel  and
      Phạm, Thu-H{\`a}  and
      Deo, Matthew",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.18",
    pages = "155--163",
    abstract = "La pr{\'e}sente communication pr{\'e}sente les projets scientifiques et les r{\'e}alisations de deux collections h{\'e}berg{\'e}es par la plateforme de ressources orales Cocoon : la Collection Pangloss, qui concerne principalement des langues de tradition orale (sans {\'e}criture), du monde entier ; et la Collection AuCo, d{\'e}di{\'e}e aux langues du Vietnam et de pays voisins. L{'}objectif est un progr{\`e}s solidaire des recherches et de la documentation linguistique. L{'}accent est mis sur les perspectives ouvertes pour la recherche en phon{\'e}tique/phonologie par certaines r{\'e}alisations r{\'e}centes dans le cadre de ces deux Collections.",
    language = "French",
}
@inproceedings{godement-berline-2016-contribution,
    title = "Contribution {\`a} l{'}{\'e}tude de la focalisation prosodique en fran{\c{c}}ais (Contribution to the study of prosodic highlighting in {F}rench)",
    author = "Godement-Berline, R{\'e}mi",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.19",
    pages = "164--172",
    abstract = "Cette {\'e}tude porte sur la focalisation prosodique en fran{\c{c}}ais dans plusieurs styles de parole (parole spontan{\'e}e et lecture ou interpr{\'e}tation par des acteurs). Nous attribuons {\`a} la focalisation des fonctions s{\'e}mantico-pragmatiques ou emphatiques. Un groupe de dix experts en prosodie a relev{\'e} les occurrences de focalisation dans le corpus d{'}{\'e}tude. Les r{\'e}sultats confirment que la focalisation est r{\'e}alis{\'e}e par une augmentation de hauteur et de dur{\'e}e. Ils diff{\`e}rent de la litt{\'e}rature pr{\'e}c{\'e}dente du point de vue du type de contour prosodique employ{\'e} sur les occurrences de focalisation et de la pr{\'e}sence d{'}accent initial. Des probl{\`e}mes m{\'e}thodologiques sont soulev{\'e}s concernant l{'}analyse des contours terminaux et de la d{\'e}saccentuation.",
    language = "French",
}
@inproceedings{bouaziz-etal-2016-un,
    title = "Un Corpus de Flux {TV} Annot{\'e}s pour la Pr{\'e}diction de Genres (A Genre Annotated Corpus of {F}rench Multi-channel {TV} Streams for Genre Prediction)",
    author = "Bouaziz, Mohamed  and
      Morchid, Mohamed  and
      Dufour, Richard  and
      Linar{\`e}s, Georges  and
      Correa, Prosper",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.20",
    pages = "173--181",
    abstract = "Cet article pr{\'e}sente une m{\'e}thode de pr{\'e}diction de genres d{'}{\'e}missions t{\'e}l{\'e}vis{\'e}es couvrant 2 jours de diffusion de 4 cha{\^\i}nes TV fran{\c{c}}aises structur{\'e}s en {\'e}missions annot{\'e}es en genres. Ce travail traite des m{\'e}dias de masse de flux de cha{\^\i}nes t{\'e}l{\'e}vis{\'e}es et rejoint l{'}effort global d{'}extraction de connaissance {\`a} partir de cette grande quantit{\'e} de donn{\'e}es produites continuellement. Le corpus employ{\'e} est fourni par l{'}entreprise EDD, anciennement appel{\'e}e {``}L{'}Europ{\'e}enne de Donn{\'e}es{''}, une entreprise sp{\'e}cialis{\'e}e dans la gestion des flux multim{\'e}dias. Les exp{\'e}riences d{\'e}taill{\'e}es dans cet article montrent qu{'}une approche simple fond{\'e}e sur un mod{\`e}le de n-grammes permet de pr{\'e}dire le genre d{'}une {\'e}mission selon un historique avec une pr{\'e}cision avoisinant les 50 {\%}.",
    language = "French",
}
@inproceedings{caussade-etal-2016-disfluences,
    title = "Disfluences dans le vieillissement « normal » et la maladie d{'}{A}lzheimer : indices segmentaux, suprasegmentaux et gestuels (Disfluencies in {``}normal{''} aging and {A}lzheimer{'}s disease: segmental, suprasegmental and gestural markers)",
    author = "Caussade, Diane  and
      Vall{\'e}e, Nathalie  and
      Henrich Bernardoni, Nathalie  and
      Colletta, Jean-Marc  and
      Gerber, Silvain  and
      Letu{\'e}, Fr{\'e}d{\'e}rique  and
      Martinez, Marie-Jos{\'e}",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.21",
    pages = "182--190",
    abstract = "L{'}objectif de cette {\'e}tude est d{'}analyser et comparer les productions langagi{\`e}res dans leur multimodalit{\'e} de 10 personnes atteintes de la maladie d{'}Alzheimer (MA) appari{\'e}es {\`a} 10 contr{\^o}les. Diff{\'e}rentes mesures aux niveaux segmental et suprasegmental {--} erreurs, pauses et allongements vocaliques {--} ont {\'e}t{\'e} r{\'e}alis{\'e}es dans une t{\^a}che de r{\'e}p{\'e}tition avec ou sans gestes impos{\'e}s pour caract{\'e}riser une disfluence, typique de la MA, puis observ{\'e}es en lien avec les gestes manuels produits. Les r{\'e}sultats montrent la diminution significative de la fluence chez les personnes atteintes de la MA, avec davantage d{'}erreurs produites au niveau lexical par le groupe Patient et au niveau phon{\'e}tique par les patients au stade mod{\'e}r{\'e} de la maladie, ainsi que de nombreuses pauses silencieuses pr{\'e}c{\'e}dant ou suivant souvent les erreurs produites au niveau segmental. De plus, dans la t{\^a}che avec gestes impos{\'e}s, la r{\'e}p{\'e}tition de ceux-ci a impact{\'e} la fluence des groupes Contr{\^o}le et Patient avec une augmentation significative des disfluences au niveau suprasegmental et des erreurs phon{\'e}tiques au niveau segmental.",
    language = "French",
}
@inproceedings{didirkova-etal-2016-disfluences,
    title = "Disfluences normales vs. Disfluences s{\'e}v{\`e}res : une {\'e}tude acoustique (Normal disfluences vs)",
    author = "Didirkova, Ivana  and
      Fauth, Camille  and
      Hirsch, Fabrice  and
      Luxardo, Giancarlo  and
      Diwersy, Sascha",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.22",
    pages = "191--199",
    abstract = "L{'}objectif de cette recherche est d{'}{\'e}tudier les caract{\'e}ristiques acoustiques et perceptives des disfluences normales et s{\'e}v{\`e}res. Pour ce faire, un jury d{'}auditeurs experts a relev{\'e} les disfluences s{\'e}v{\`e}res et normales de 4 locuteurs qui b{\'e}gaient ainsi que les accidents de parole de 4 sujets normofluents. Une analyse acoustique portant sur des param{\`e}tres tels que la dur{\'e}e de la disfluence, le nombre de disfluences ou encore sur la pr{\'e}sence d{'}{\'e}l{\'e}ments prosodiques particuliers a {\'e}t{\'e} men{\'e}e sur les seules disfluences ayant {\'e}t{\'e} relev{\'e}s par l{'}ensemble du jury. Nos r{\'e}sultats montrent que si les prolongations et les r{\'e}p{\'e}titions sont bien {\'e}videmment cat{\'e}goris{\'e}es comme s{\'e}v{\`e}res respectivement en fonction de leur dur{\'e}e et du nombre d{'}{\'e}l{\'e}ments r{\'e}it{\'e}r{\'e}s, d{'}autres param{\`e}tres sont {\'e}galement significatifs, tels que la pr{\'e}sence ou non d{'}une tension audible, le type d{'}{\'e}l{\'e}ments r{\'e}p{\'e}t{\'e}s ou encore le fait que la syllabe soit ou non cliv{\'e}e.",
    language = "French",
}
@inproceedings{verhaegen-etal-2016-la,
    title = "La distinction entre les paraphasies phon{\'e}tiques et phonologiques dans l{'}aphasie : Etude de cas de deux patients aphasiques (The distinction between phonetic and phonological paraphasias in aphasia: A multiple casestudy of aphasic patients)",
    author = "Verhaegen, Cl{\'e}mence  and
      Delvaux, V{\'e}ronique  and
      Huet, Kathy  and
      Sophie, Fagniart  and
      Piccaluga, Myriam  and
      Harmegnies, Bernard",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.23",
    pages = "200--210",
    abstract = "La sp{\'e}cificit{\'e} phonologique ou phon{\'e}tique des erreurs de production orale observ{\'e}es chez les patients aphasiques reste d{\'e}battue. Cependant, la distinction entre ces deux types d{'}erreurs est fr{\'e}quemment bas{\'e}e sur des analyses perceptives qui peuvent {\^e}tre influenc{\'e}es par le syst{\`e}me perceptif de l{'}exp{\'e}rimentateur. Afin de pallier ce biais, nous avons r{\'e}alis{\'e} des analyses acoustiques des productions de deux patients aphasiques, dans une t{\^a}che de r{\'e}p{\'e}tition de non-mots. Nous nous sommes centr{\'e}s sur l{'}analyse de consonnes occlusives. Les r{\'e}sultats ont montr{\'e} la pr{\'e}sence de difficult{\'e}s de gestion du voisement chez les deux patients, indiquant la pr{\'e}sence de troubles phon{\'e}tiques. En outre, les r{\'e}sultats montrent une grande diversit{\'e} des manifestations des troubles langagiers des patients ainsi que l{'}intervention potentielle de strat{\'e}gies de compensation de leurs difficult{\'e}s. L{'}int{\'e}r{\^e}t de proc{\'e}der {\`a} des analyses acoustiques pr{\'e}cises utilisant des indices multiples est discut{\'e}.",
    language = "French",
}
@inproceedings{roland-etal-2016-dynamique,
    title = "Dynamique phon{\'e}tique et contr{\^o}le moteur dans la maladie de {P}arkinson: analyse du contr{\^o}le de la production des glides (Speech dynamics and motion control in people with {P}arkinson{'}s disease: analysis of glides{'} production)",
    author = "Roland, Virginie  and
      Delvaux, V{\'e}ronique  and
      Huet, Kathy  and
      Piccaluga, Myriam  and
      Haelewyck, Marie-Claire  and
      Harmegnies, Bernard",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.24",
    pages = "211--219",
    abstract = "Nous nous interrogeons quant {\`a} la possibilit{\'e} d{'}identifier les difficult{\'e}s de contr{\^o}le du mouvement chez les personnes atteintes de la maladie de Parkinson (MP) {\`a} partir de l{'}{\'e}tude de leurs comportements dans la production de sons de parole n{\'e}cessitant des mouvements continus des articulateurs supralaryng{\'e}s (logatomes VCV, o{\`u} C est un glide). Notre hypoth{\`e}se est que les parkinsoniens pr{\'e}sentent des modifications dans leur dynamique de mouvement par rapport {\`a} des personnes sans pathologie lors de la production. A cette fin, sont {\'e}tudi{\'e}s des sons de parole recueillis hors contexte communicationnel aupr{\`e}s de neuf personnes porteuses de la MP et de dix sujets sains. Les analyses r{\'e}v{\`e}lent des diff{\'e}rences entre les deux groupes, notamment en ce qui concerne l{'}espace articulatoire, l{'}amplitude des mouvements et leur localisation dans le plan F 1-F2. On note par ailleurs qu{'}un point-cible est pr{\'e}serv{\'e} lors de l{'}{\'e}mission de logatomes : le centre du glide.",
    language = "French",
}
@inproceedings{halle-etal-2016-denomination,
    title = "D{\'e}nomination d{'}image versus d{\'e}tection interne de phon{\`e}me : deux m{\'e}thodes pour {\'e}tudier la planification de la production de parole (Picture naming versus internal phoneme monitoring: two methods for exploring speech production planning)",
    author = "Hall{\'e}, Pierre  and
      Manoiloff, Laura  and
      Segui, Juan",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.25",
    pages = "220--228",
    abstract = "Cette {\'e}tude est motiv{\'e}e initialement par une question m{\'e}thodologique : la validit{\'e} des mesures de temps de d{\'e}nomination d{'}image, tr{\`e}s utilis{\'e}s pour explorer les processus de planification de production de parole. Le temps de d{\'e}nomination est le temps {\'e}coul{\'e} entre affichage de l{'}image et d{\'e}but acoustique de la r{\'e}ponse verbale. Dans cet article, nous r{\'e}sumons la litt{\'e}rature sur les inconv{\'e}nients de cette mesure. Nous pr{\'e}sentons ensuite notre {\'e}tude, qui compare directement temps de d{\'e}nomination d{'}image et temps de d{\'e}tection interne de phon{\`e}me initial. Les participants sont hispanophones. Les noms d{'}image sont contrast{\'e}s en fr{\'e}quence lexicale et phon{\`e}me initial. Les temps de r{\'e}ponse pour les deux mesures sont assez proches. Cependant, ceux de d{\'e}tection de phon{\`e}me sont relativement insensibles au type de phon{\`e}me initial, contrairement aux temps de d{\'e}nomination. Au del{\`a} de l{'}avantage m{\'e}thodologique de la d{\'e}tection interne de phon{\`e}me, nos donn{\'e}es sugg{\`e}rent que celle-ci op{\`e}re sur des repr{\'e}sentations relativement abstraites.",
    language = "French",
}
@inproceedings{laaridh-etal-2016-detection,
    title = "D{\'e}tection automatique d{'}anomalies sur deux styles de parole dysarthrique: parole lue vs spontan{\'e}e (Automatic anomaly detection for dysarthria across two speech styles : read vs spontaneous speech)",
    author = "Laaridh, Imed  and
      Fredouille, Corinne  and
      Christine, Meunier",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.26",
    pages = "229--237",
    abstract = "L{'}{\'e}valuation perceptive de la parole pathologique reste le standard dans la pratique clinique pour le diagnostic et le suivi des patients. De telles m{\'e}thodes incluent plusieurs t{\^a}ches telles que la lecture, la parole spontan{\'e}e, le chant, les mots isol{\'e}s, la voyelle tenue, etc. Dans ce contexte, les outils de traitement automatique de la parole ont montr{\'e} leur pertinence dans l{'}{\'e}valuation de la qualit{\'e} de parole ainsi que dans le cadre de la communication am{\'e}lior{\'e}e et alternative (CAA) pour les patients atteints de troubles de parole. Cependant, peu de travaux ont {\'e}tudi{\'e} l{'}utilisation de ces outils sur la parole spontan{\'e}e. Ce papier examine le comportement d{'}un syst{\`e}me de d{\'e}tection automatique d{'}anomalies au niveau phon{\`e}me face {\`a} la parole dysarthrique lue et spontan{\'e}e. Le comportement du syst{\`e}me r{\'e}v{\`e}le une variabilit{\'e} inter-pathologique {\`a} travers les styles de parole.",
    language = "French",
}
@inproceedings{grandon-vilain-2016-effet,
    title = "Effet de l{'}input auditif sur la production de voyelles orales : {\'e}tude acoustique chez des enfants normo-entendants et des enfants porteurs d{'}implants cochl{\'e}aires {\^a}g{\'e}s de 5 {\`a} 11 ans (Effect of audio input on vowel production: an acoustic study in 5- to 11-year old normalhearing and cochlear implanted children)",
    author = "Grandon, Benedicte  and
      Vilain, Anne",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.27",
    pages = "238--246",
    abstract = "Treize enfants porteurs d{'}implants cochl{\'e}aires (CI) et vingt enfants normo-entendants (NH) ont {\'e}t{\'e} enregistr{\'e}s dans deux conditions : r{\'e}p{\'e}tition de mots avec un mod{\`e}le audio et production des m{\^e}mes mots sans mod{\`e}le audio. Notre but {\'e}tait d{'}{\'e}tudier l{'}effet de l{'}input audio sur la hauteur, l{'}ant{\'e}riorit{\'e} et la dispersion des dix voyelles orales du fran{\c{c}}ais chez ces deux populations d{'}enfants. Les r{\'e}sultats de notre {\'e}tude acoustique indiquent que : (1) l{'}input imm{\'e}diat n{'}influence que la hauteur du /a/ chez les enfants NH, (2) les enfants CI produisent des voyelles /y/, /{\o}/, /{\oe}/ plus post{\'e}rieures que les enfants NH mais que cette diff{\'e}rence diminue {\`a} mesure que la dur{\'e}e d{'}utilisation de l{'}implant augmente, et (3) la dispersion de /y/, /{\o}/, /{\oe}/ est plus grande chez les enfants CI que chez les enfants NH.",
    language = "French",
}
@inproceedings{liegeois-2016-effet,
    title = "Effet de la fr{\'e}quence d{'}usage sur l{'}{\'e}lision du schwa des clitiques : {\'e}tude d{'}un corpus d{'}interactions naturelles (Frequency effect on schwa elision in clitics: a corpus based study)",
    author = {Li{\'e}geois, Lo{\"\i}c},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.28",
    pages = "247--255",
    abstract = "Cette {\'e}tude s{'}int{\'e}resse {\`a} l{'}influence d{'}un facteur d{'}usage, {\`a} savoir la fr{\'e}quence des formes, sur la (non) production des schwas des clitiques. Dans cet objectif, nous nous appuyons sur un corpus d{'}interactions entre adultes recueillies en situation naturelle : les enregistrements, r{\'e}alis{\'e}s au domicile de nos six sujets, ont {\'e}t{\'e} r{\'e}colt{\'e}s au cours de sc{\`e}nes de vie quotidienne. Les donn{\'e}es pr{\'e}sent{\'e}es au cours de nos analyses corroborent les r{\'e}sultats expos{\'e}s dans de pr{\'e}c{\'e}dents travaux au sujet des schwas initiaux de polysyllabes. En effet, il s{'}av{\`e}re que la fr{\'e}quence d{'}emploi des collocations {``}Clitique + X{''} a un effet significatif sur les taux d{'}{\'e}lision relev{\'e}s dans les productions de nos sujets.",
    language = "French",
}
@inproceedings{robieux-etal-2016-effort,
    title = "Effort produit et ressenti selon le voisement en fran{\c{c}}ais (Produced and perceived effort according to the voicing in {F}rench)",
    author = "Robieux, Camille  and
      Legou, Thierry  and
      Meynadier, Yohann  and
      Christine, Meunier",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.29",
    pages = "256--264",
    abstract = "Les muscles laryng{\'e}s et articulatoires sont impliqu{\'e}s dans la r{\'e}alisation des traits qui distinguent les phon{\`e}mes. Cette {\'e}tude porte sur l{'}auto-perception par les locuteurs et la r{\'e}partition de l{'}effort vocal et articulatoire en fonction du trait de voisement en parole modale compar{\'e}e {\`a} la parole chuchot{\'e}e en fran{\c{c}}ais. Pour les 12 obstruantes du fran{\c{c}}ais, l{'}effort est ressenti plus important pour les vois{\'e}es que les non vois{\'e}es correspondantes, except{\'e} dans le cas des fricatives labiodentales. Les analyses de la production des occlusives bilabiales montrent que l{'}effort laryng{\'e} est sup{\'e}rieur pour les consonnes vois{\'e}es et l{'}effort articulatoire sup{\'e}rieur pour les non vois{\'e}es, mais l{'}inverse pour les fricatives. Ces r{\'e}sultats indiquent que l{'}effort ressenti lors de sa propre production repose sur une perception pr{\'e}dominante de l{'}effort laryng{\'e} sur l{'}effort articulatoire en voix modale comme en voix chuchot{\'e}e ; mais qu{'}il est cependant modul{\'e} selon le lieu et le mode d{'}articulation des consonnes.",
    language = "French",
}
@inproceedings{guyot-talbot-etal-2016-entrainements,
    title = "Entra{\^\i}nements {\`a} la prosodie des questions ouvertes et ferm{\'e}es de l{'}anglais chez des apprenants francophones (Prosodic training for {F}rench students of {E}nglish on Wh- and yes-no questions)",
    author = "Guyot-Talbot, Anne  and
      Heidlmayr, Karin  and
      Ferragne, Emmanuel",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.30",
    pages = "265--273",
    abstract = "Des {\'e}tudiants en anglais {\'e}taient invit{\'e}s {\`a} lire trois types de phrases : assertions, questions ferm{\'e}es et ouvertes. Ils {\'e}taient ensuite soumis {\`a} 3 sessions d{'}entra{\^\i}nements o{\`u} ils devaient r{\'e}p{\'e}ter des phrases interrogatives prononc{\'e}es par une anglophone. Apr{\`e}s chaque phrase, leur contour de F0 sur la syllabe portant le noyau intonatif ainsi que celui de la locutrice anglaise {\'e}taient affich{\'e}s {\`a} l{'}{\'e}cran. Ces sessions devaient leur permettre d{'}inf{\'e}rer une r{\`e}gle du syst{\`e}me intonatif de l{'}anglais qui induit, par d{\'e}faut, un contour montant pour les questions ferm{\'e}es et un contour descendant pour les questions ouvertes. Puis, une nouvelle s{\'e}ance d{'}enregistrements permettait de collecter des phrases {\`a} comparer au pr{\'e}-test pour juger l{'}efficacit{\'e} de l{'}entra{\^\i}nement. Les r{\'e}sultats montrent une r{\'e}duction significative de la distance entre les contours m{\'e}lodiques des apprenants du groupe test et ceux de la locutrice mod{\`e}le entre pr{\'e}-test et post-test, ce qui sugg{\`e}re un effet b{\'e}n{\'e}fique de nos entra{\^\i}nements.",
    language = "French",
}
@inproceedings{galibert-etal-2016-estimation,
    title = "Estimation de la qualit{\'e} d{'}un syst{\`e}me de reconnaissance de la parole pour une t{\^a}che de compr{\'e}hension (Quality estimation of a Speech Recognition System for a Spoken Language Understanding task)",
    author = "Galibert, Olivier  and
      Camelin, Nathalie  and
      Del{\'e}glise, Paul  and
      Rosset, Sophie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.31",
    pages = "274--282",
    abstract = "Nous nous int{\'e}ressons {\`a} l{'}{\'e}valuation de la qualit{\'e} des syst{\`e}mes de reconnaissance de la parole {\'e}tant donn{\'e} une t{\^a}che de compr{\'e}hension. L{'}objectif de ce travail est de fournir un outil permettant la s{\'e}lection d{'}un syst{\`e}me de reconnaissance automatique de la parole le plus adapt{\'e} pour un syst{\`e}me de dialogue donn{\'e}. Nous comparons ici diff{\'e}rentes m{\'e}triques, notamment le WER, NE-WER et ATENE m{\'e}trique propos{\'e}e r{\'e}cemment pour l{'}{\'e}valuation des syst{\`e}mes de reconnaissance de la parole {\'e}tant donn{\'e} une t{\^a}che de reconnaissance d{'}entit{\'e}s nomm{\'e}es. Cette derni{\`e}re m{\'e}trique montrait une meilleure corr{\'e}lation avec les r{\'e}sultats de la t{\^a}che globale que toutes les autres m{\'e}triques test{\'e}es. Nos mesures indiquent une tr{\`e}s forte corr{\'e}lation avec la mesure ATENE et une moins forte avec le WER.",
    language = "French",
}
@inproceedings{perez-2016-etude,
    title = "Etude acoustique du discours politique d{'}hispanophones : le cas de Hugo Ch{\'a}vez et de Jos{\'e} Zapatero (Politicians{'} speech styles can be distinguished thanks to their prosodic realizations)",
    author = "P{\'e}rez, Carmen Patricia",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.32",
    pages = "283--291",
    abstract = "Les styles de discours des hommes politiques peuvent {\^e}tre identifi{\'e}s gr{\^a}ce {\`a} leurs r{\'e}alisations prosodiques. On peut reconna{\^\i}tre un homme politique {`}r{\'e}volutionnaire{'} ou {`}traditionnel{'} en {\'e}coutant quelques minutes de discours. Je me propose de montrer quels sont les param{\`e}tres prosodiques pertinents dans cette distinction en comparant les phonostyles de Hugo Ch{\'a}vez et Jos{\'e} Zapatero. Je pr{\'e}sente {\'e}galement le changement de phonostyle de Ch{\'a}vez dans deux situations diff{\'e}rentes (c.-{\`a}d. deux phono-genres), en interview et en public. Le mod{\`e}le de Ph. Martin Contraste de Pente M{\'e}lodique est utilis{\'e} pour d{\'e}crire la structure prosodique. Les analyses acoustiques montrent que les phonostyles de ces personnalit{\'e}s se diff{\'e}rencient, dans le m{\^e}me phono-genre, dans la r{\'e}alisation des contours de continuation, l{'}{\'e}tendue du registre et le d{\'e}bit, alors que la construction des groupes intonatifs est semblable. Une br{\`e}ve {\'e}tude sur les imitateurs de Ch{\'a}vez et de Zapatero est rajout{\'e}e pour montrer qu{'}ils reproduisent avec efficacit{\'e} les param{\`e}tres acoustiques pertinents de ces leaders.",
    language = "French",
}
@inproceedings{liu-2016-etude,
    title = "Etude acoustique et repr{\'e}sentation phonologique sur /ə˞/ suffixe rhotique en mandarin (Acoustic study and phonological representation of the rhotic suffix /ə˞/ in mandarin)",
    author = "Liu, Anqi",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.33",
    pages = "292--300",
    abstract = "Historiquement, le suffixe /ə˞/ est un suffixe diminutif correspondant au mot 儿 ({\textless}er{\textgreater} en pinyin) qui signifie {''}petitesse{''}. Il rel{\`e}ve d{'}une particularit{\'e} du style plut{\^o}t que de la grammaire. Il apparait souvent dans la parole des locuteurs du nord de la Chine. Pour mieux comprendre le ph{\'e}nom{\`e}ne et son comportement phonologique, on pr{\'e}sente les r{\'e}sultats d{'}une {\'e}tude acoustique qui v{\'e}rifie les effets de la rhoticit{\'e} sur les voyelles adjacentes. Sur la base de ces r{\'e}sultats, on propose une repr{\'e}sentation gestuelle du suffixe et des processus qui l{'}impliquent dans le cadre de la phonologie articulatoire (Browman {\&} Goldstein1992).",
    language = "French",
}
@inproceedings{xuelu-sock-2016-etude,
    title = "{\'E}tude de la contribution acoustique de la structure formantique {\`a} la perception du ton chuchot{\'e} (A study of the acoustic contri bution of formant structure to tone i dentificati on in whis pered speech)",
    author = "Xuelu, Zhang  and
      Sock, Rudolph",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.34",
    pages = "301--309",
    abstract = "Cette {\'e}tude examine la contribution de la structure formantique du segment vocalique {\`a} l{'}identification du ton que ce segment porte, et cela en voix chuchot{\'e}e. Le mandarin a {\'e}t{\'e} choisi en tant que langue cible parce que les traits tonals (tone features) en mandarin s{'}appuient acoustiquement sur deux dimensions : le registre et le contour. Nous supposons qu{'}en l{'}absence d e F0, la structure formantique subirait n{\'e}anmo ins une modification, en fonction du ton et fournirait des indices acoustiques des traits tonals {\`a} l{'}auditeur. Nous nous int{\'e}ressons aux rapports entre les deux dimensions de traits tonals et {\`a} la modification de la structure formantique. {\`A} travers l{'}analyse des donn{\'e}es acoustiques issues de 13 sujets locutrices, nous avons observ{\'e} une divergence d{'}importance dans les intervalles F2-F3 et F3-F4, en fonction du ton. Cette divergence semble li{\'e}e aux contrastes tonals en registre et non au x contours m{\'e}lodiques. Cette d istinction semble d{\'e}pendre d{'}ailleurs de la nature de voyelle.",
    language = "French",
}
@inproceedings{xiu-etal-2016-etude,
    title = {{\'E}tude de la qualit{\'e} vocale post-thyro{\"\i}dectomie chez des patients souffrants ou non de paralysie r{\'e}currentielle (A post-thyroidectomy voice quality study in patients suffering or not from laryngeal paralysis)},
    author = "Xiu, Ming  and
      Fauth, Camille  and
      Vaxelaire, B{\'e}atrice  and
      Rodier, Jean-Fran{\c{c}}ois  and
      Volkmar, Pierre-Philippe  and
      Sock, Rudolph",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.35",
    pages = "310--318",
    abstract = {L{'}objet principal de cette {\'e}tude est la qualit{\'e} vocale apr{\`e}s une thyro{\"\i}dectomie. Cette op{\'e}ration provoque souvent une d{\'e}gradation de la qualit{\'e} vocale de fa{\c{c}}on permanente ou temporaire. La qualit{\'e} vocale sera {\'e}tudi{\'e}e {\`a} l{'}aide d{'}indices a{\'e}rodynamiques et acoustiques. Deux groupes de patients sont suivis et {\'e}tudi{\'e}s : un premier groupe de patients pour lesquels l{'}examen post-op{\'e}ratoire a r{\'e}v{\'e}l{\'e} un d{\'e}faut de mobilit{\'e} de l{'}un des plis vocaux ; Un second groupe de patients pour lesquels l{'}examen post-op{\'e}ratoire n{'}a pas r{\'e}v{\'e}l{\'e} de perturbation de la mobilit{\'e} laryng{\'e}e. Il s{'}agit d{'}une {\'e}tude longitudinale dans laquelle la r{\'e}f{\'e}rence est constitu{\'e}e par la voix du locuteur en pr{\'e}op{\'e}ratoire. Les r{\'e}sultats pr{\'e}liminaires indiquent que l{'}ablation de la glande thyro{\"\i}de modifie la voix des patients alors m{\^e}me que la mobilit{\'e} laryng{\'e}e est pr{\'e}serv{\'e}e. Tous les param{\`e}tres {\'e}tudi{\'e}s ont {\'e}t{\'e} modifi{\'e}s. Le temps a toutefois un effet positif pour tous les locuteurs suivis, puisque leurs productions s{'}approchent, un mois apr{\`e}s l{'}op{\'e}ration, des valeurs mesur{\'e}es en pr{\'e}op{\'e}ratoire.},
    language = "French",
}
@inproceedings{zeroual-etal-2016-etude,
    title = "Etude par {EMA} des mouvements de la m{\^a}choire inf{\'e}rieure durant les consonnes de l{'}arabe marocain ({EMA} study of jaw movements during {M}oroccan {A}rabic consonants)",
    author = "Zeroual, Chakir  and
      Hoole, Philip  and
      Gafos, Adamantios",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.36",
    pages = "319--327",
    abstract = "Cette {\'e}tude est bas{\'e}e sur des donn{\'e}es obtenues {\`a} l{'}aide d{'}EMA (AG500) enregistrant les mouvements de la m{\^a}choire inf{\'e}rieure (Minf) durant les consonnes labiales, coronales, v{\'e}laires, uvulaires, pharyngales et laryngales de l{'}arabe marocain dans les contextes aCa et iCi. Nous avons montr{\'e} que l{'}implication de la Minf est cruciale durant /s S t T/ (S T : consonnes emphatiques). Le recul de la racine de la langue n{'}est pas n{\'e}cessairement corr{\'e}l{\'e} {\`a} la baisse de la Minf. Les consonnes apicales ne sont pas toujours associ{\'e}es {\`a} l{'}abaissement de la Minf. La Minf ne semble pas impliqu{\'e}e durant les laryngales et les pharyngales, ce qui est en accord avec les d{\'e}ductions de Goldstein (1995). Les mouvements verticaux et horizontaux de la Minf sont relativement ind{\'e}pendants.",
    language = "French",
}
@inproceedings{michardiere-etal-2016-etude,
    title = "{\'E}tude transversale du rythme de l{'}anglais chez des apprenants francophones (A cross-sectional study of rhythm in {F}rench students of {E}nglish)",
    author = "Michardi{\`e}re, Quentin  and
      Guyot-Talbot, Anne  and
      Ferragne, Emmanuel  and
      Pellegrino, Fran{\c{c}}ois",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.37",
    pages = "328--336",
    abstract = "Dans cette {\'e}tude, nous avons demand{\'e} {\`a} 3 groupes d{'}{\'e}tudiants en anglais (niveaux L1, L2 et L3) de lire un dialogue en anglais afin d{'}{\'e}valuer la possibilit{\'e} d{'}une am{\'e}lioration de leur production sur le plan du rythme en fonction de leur niveau universitaire. Le dialogue a {\'e}galement {\'e}t{\'e} lu par des anglophones, et une traduction du dialogue en fran{\c{c}}ais a {\'e}t{\'e} enregistr{\'e}e par des francophones dans le but d{'}{\'e}tablir un espace de r{\'e}f{\'e}rence pour l{'}anglais L1 et le fran{\c{c}}ais L1. Nous avons employ{\'e} des mesures classiques du rythme s{'}appuyant sur la dur{\'e}e, et avons {\'e}galement explor{\'e} la pertinence de mesures bas{\'e}es sur l{'}enveloppe d{'}amplitude et le spectre de cette enveloppe. Nous constatons un manque de fiabilit{\'e} des mesures classiques du rythme, et proposons quelques pistes.",
    language = "French",
}
@inproceedings{tomashenko-etal-2016-exploration,
    title = "Exploration de param{\`e}tres acoustiques d{\'e}riv{\'e}s de {GMM} pour l{'}adaptation non supervis{\'e}e de mod{\`e}les acoustiques {\`a} base de r{\'e}seaux de neurones profonds (Exploring {GMM}-derived features for unsupervised adaptation of deep neural network acoustic models)",
    author = "Tomashenko, Natalia  and
      Khokhlov, Yuri  and
      Larcher, Anthony  and
      Est{\`e}ve, Yannick",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.38",
    pages = "337--345",
    abstract = "L{'}{\'e}tude pr{\'e}sent{\'e}e dans cet article am{\'e}liore une m{\'e}thode r{\'e}cemment propos{\'e}e pour l{'}adaptation de mod{\`e}les acoustiques markoviens coupl{\'e}s {\`a} un r{\'e}seau de neurones profond (DNN-HMM). Cette m{\'e}thode d{'}adaptation utilise des param{\`e}tres acoustiques d{\'e}riv{\'e}s de mixtures de mod{\`e}les Gaussiens (GMM-derived features, GMMD ). L{'}am{\'e}lioration provient de l{'}emploi de scores et de mesures de confiance calcul{\'e}s {\`a} partir de graphes construits dans le cadre d{'}un algorithme d{'}adaptation conventionnel dit de maximum a posteriori (MAP). Une version modifi{\'e}e de l{'}adaptation MAP est appliqu{\'e}e sur le mod{\`e}le GMM auxiliaire utilis{\'e} dans une proc{\'e}dure d{'}apprentissage adaptatif au locuteur (speaker adaptative training, SAT) lors de l{'}apprentissage du DNN. Des exp{\'e}riences men{\'e}es sur le corpus Wall Street Journal (WSJ0) montrent que la technique d{'}adaptation non supervis{\'e}e propos{\'e}e dans cet article permet une r{\'e}duction relative de 8, 4{\%} du taux d{'}erreurs sur les mots (WER), par rapport aux r{\'e}sultats obtenus avec des mod{\`e}les DNN-HMM ind{\'e}pendants du locuteur utilisant des param{\`e}tres acoustiques plus conventionnels.",
    language = "French",
}
@inproceedings{liu-etal-2016-extraction,
    title = "Extraction automatique de contour de l{\`e}vre {\`a} partir du mod{\`e}le {CLNF} (Automatic lip contour extraction using {CLNF} model)",
    author = "Liu, Li  and
      Feng, Gang  and
      Beautemps, Denis",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.39",
    pages = "346--354",
    abstract = "Dans cet article nous proposons une nouvelle solution pour extraire le contour interne des l{\`e}vres d{'}un locuteur sans utiliser d{'}artifices. La m{\'e}thode s{'}appuie sur un algorithme r{\'e}cent d{'}extraction du contour de visage d{\'e}velopp{\'e} en vision par ordinateur, CLNF pour Constrained Local Neural Field. Cet algorithme fournit en particulier 8 points caract{\'e}ristiques d{\'e}limitant le contour interne des l{\`e}vres. Appliqu{\'e} directement {\`a} nos donn{\'e}es audio-visuelles du locuteur, le CLNF donne de tr{\`e}s bons r{\'e}sultats dans environ 70{\%} des cas. Des erreurs subsistent cependant pour le reste des cas. Nous proposons des solutions pour estimer un contour raisonnable des l{\`e}vres {\`a} partir des points fournis par CLNF utilisant l{'}interpolation par spline permettant de corriger ses erreurs et d{'}extraire correctement les param{\`e}tres labiaux classiques. Les {\'e}valuations sur une base de donn{\'e}es de 179 images confirment les performances de notre algorithme.",
    language = "French",
}
@inproceedings{lewy-2016-fn5,
    title = "{FN}5, un mod{\`e}le psycholinguistique informatique de la reconnaissance des mots parl{\'e}s chez l{'}auditeur fran{\c{c}}ais, mis {\`a} la disposition des chercheurs et enseignants ({FN}5, a computational psycholinguistic model of spoken word recognition in {F}rench, made available to researchers and teachers)",
    author = "L{\'e}wy, Nicolas",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.40",
    pages = "355--363",
    abstract = "Voici un mod{\`e}le psycholinguistique informatique pour le fran{\c{c}}ais. Il s{'}appelle FN5 et simule la reconnaissance humaine de mots parl{\'e}s, pr{\'e}sent{\'e}s seuls (d{\'e}terminant, adjectif ant{\'e}pos{\'e}, substantif) ou en suites de deux mots (d{\'e}terminant et substantif, adjectif ant{\'e}pos{\'e} et substantif). Le mod{\`e}le contient un lexique de 17 668 mots et cela dans deux versions, fran{\c{c}}aise et Suisse romande. Gr{\^a}ce {\`a} une architecture connexionniste localiste {\`a} trois niveaux (traits distinctifs, phon{\`e}mes, mots) qui est enrichie de plusieurs innovations cl{\'e}s (processeur de position, groupements de connexions, et point d{'}isolation), le mod{\`e}le peut reconna{\^\i}tre la plupart des mots et des suites qu{'}on lui pr{\'e}sente (taux de succ{\`e}s entre 83.6{\%} et 99.7{\%}), et en plus, il est capable de reproduire un grand nombre d{'}effets trouv{\'e}s lors d{'}{\'e}tudes exp{\'e}rimentales (ex. fr{\'e}quence, longueur, effacement du schwa, liaison, etc.). Le mod{\`e}le, qui poss{\`e}de une interface graphique, est t{\'e}l{\'e}chargeable, et utilisable {\`a} la fois pour la recherche et pour l{'}enseignement.",
    language = "French",
}
@inproceedings{delecraz-etal-2016-fusion,
    title = "Fusion d{'}espaces de repr{\'e}sentations multimodaux pour la reconnaissance du r{\^o}le du locuteur dans des documents t{\'e}l{\'e}visuels (Multimodal embedding fusion for robust speaker role recognition in video broadcast )",
    author = "Delecraz, Sebastien  and
      Bechet, Frederic  and
      Favre, Benoit  and
      Rouvier, Mickael",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.41",
    pages = "364--372",
    abstract = "L{'}identification du r{\^o}le d{'}un locuteur dans des {\'e}missions de t{\'e}l{\'e}vision est un probl{\`e}me de classification de personne selon une liste de r{\^o}les comme pr{\'e}sentateur, journaliste, invit{\'e}, etc. {\`A} cause de la nonsynchronie entre les modalit{\'e}s, ainsi que par le manque de corpus de vid{\'e}os annot{\'e}es dans toutes les modalit{\'e}s, seulement une des modalit{\'e}s est souvent utilis{\'e}e. Nous pr{\'e}sentons dans cet article une fusion multimodale des espaces de repr{\'e}sentations de l{'}audio, du texte et de l{'}image pour la reconnaissance du r{\^o}le du locuteur pour des donn{\'e}es asynchrones. Les espaces de repr{\'e}sentations monomodaux sont entra{\^\i}n{\'e}s sur des corpus de donn{\'e}es exog{\`e}nes puis ajust{\'e}s en utilisant des r{\'e}seaux de neurones profonds sur un corpus d{'}{\'e}missions fran{\c{c}}aises pour notre t{\^a}che de classification. Les exp{\'e}riences r{\'e}alis{\'e}es sur le corpus de donn{\'e}es REPERE ont mis en {\'e}vidence les gains d{'}une fusion au niveau des espaces de repr{\'e}sentations par rapport aux m{\'e}thodes de fusion tardive standard.",
    language = "French",
}
@inproceedings{abuoudeh-crouzet-2016-limpact,
    title = "L{'}impact des variations temporelles intrins{\`e}ques et extrins{\`e}ques de la voyelle sur la relation consonne-voyelle : {\'E}tude translinguistique sur l{'}arabe jordanien et le fran{\c{c}}ais (The impact of extrinsic and intrisic vowel temporal variations on the consonant-vowel relationship : A trans-linguistic investigation on Jordanian arabic and {F}rench)",
    author = "Abuoudeh, Mohammad  and
      Crouzet, Olivier",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.42",
    pages = "373--381",
    abstract = "Cette {\'e}tude permet d{'}explorer les variations spectrales engendr{\'e}es par deux types de variations temporelles qui r{\'e}sultent respectivement de l{'}opposition de longueur vocalique et des variations de d{\'e}bit de parole. Deux protocoles exp{\'e}rimentaux ont {\'e}t{\'e} con{\c{c}}us, l{'}un en arabe jordanien et l{'}autre en fran{\c{c}}ais, pour examiner ce ph{\'e}nom{\`e}ne. Un int{\'e}r{\^e}t particulier a {\'e}t{\'e} port{\'e} aux occlusives produites dans des s{\'e}quences CVC dans le but d{'}{\'e}tudier la consonne en position initiale et la coarticulation anticipatoire. La dur{\'e}e des voyelles et la fr{\'e}quence des trois premiers formants au d{\'e}but et au milieu de chaque s{\'e}quence ont {\'e}t{\'e} mesur{\'e}es dans chaque condition de longueur / d{\'e}bit. Les {\'e}quations de locus ont {\'e}t{\'e} utilis{\'e}es afin de d{\'e}crire la relation CV quand elle subit ces deux types de variations. Selon les r{\'e}sultats, la qualit{\'e} de la voyelle et de la consonne est influenc{\'e}e dans l{'}opposition de dur{\'e}e et dans le d{\'e}bit de parole. Ce changement g{\'e}n{\'e}r{\'e} par les variations temporelles est d{\'e}tect{\'e} {\`a} l{'}aide des {\'e}quations de locus. Ces derni{\`e}res r{\'e}v{\`e}lent qu{'}il existe un chevauchement coarticulatoire plus important quand la dur{\'e}e de la voyelle d{\'e}cro{\^\i}t.",
    language = "French",
}
@inproceedings{buchman-etal-2016-incidence,
    title = "Incidence de la chirurgie naso-sinusienne sur la qualit{\'e} vocale : {\'e}tude d{'}un cas clinique (Impact of Sinus Surgery on Voice Quality: Case Study)",
    author = "Buchman, Lise Crevier  and
      Amelot, Angelique  and
      Mas, Benedicte  and
      Giron, Mathilde  and
      Bonfils, Pierre",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.43",
    pages = "382--391",
    abstract = "Les fosses nasales participent {\`a} la r{\'e}sonance vocale et toute modification de ces structures peut alt{\'e}rer la qualit{\'e} vocale. Le r{\^o}le des sinus comme r{\'e}sonateurs dans la production vocale reste plus controvers{\'e}. Le but de notre {\'e}tude prospective {\'e}tait d{'}explorer d{'}{\'e}ventuelles modifications acoustiques chez un chanteur professionnel en pr{\'e} et post-op{\'e}ratoire apr{\`e}s chirurgie naso-sinusienne unilat{\'e}rale. A partir de la lecture d{'}un texte, nous avons extrait les voyelles /a,i,u/ pour mesurer les param{\`e}tres acoustiques de fr{\'e}quence (F0), des formants F1 et F2, de leur largeur de bande, et de qualit{\'e} vocale (LTAS et H1*-H2*). L{'}{\'e}tude a {\'e}t{\'e} compl{\'e}t{\'e}e par une auto-{\'e}valuation de la qualit{\'e} de voix. Nos r{\'e}sultats n{'}ont pas permis de mettre en {\'e}vidence de diff{\'e}rence statistiquement significative des param{\`e}tres acoustiques bien que le patient ait signal{\'e} une impression d{'}am{\'e}lioration vocale chant{\'e}e. Ces r{\'e}sultats pour le fran{\c{c}}ais confirment ceux de la litt{\'e}rature et peuvent servir {\`a} informer les patients.",
    language = "French",
}
@inproceedings{manenti-etal-2016-influence,
    title = "Influence de la quantit{\'e} de donn{\'e}es sur une t{\^a}che de segmentation de phones fond{\'e}e sur les r{\'e}seaux de neurones (Phone-level speech segmentation with neural networks : influence of the amount of data )",
    author = "Manenti, C{\'e}line  and
      Pellegrini, Thomas  and
      Pinquier, Julien",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.44",
    pages = "392--400",
    abstract = "Dans cet article, nous d{\'e}crivons une {\'e}tude exp{\'e}rimentale de segmentation de parole en unit{\'e}s acoustiques sous-lexicales (phones) {\`a} l{'}aide de r{\'e}seaux de neurones. Sur le corpus de parole spontan{\'e}e d{'}anglais am{\'e}ricain BUCKEYE, une F-mesure de 68{\%} a {\'e}t{\'e} obtenue {\`a} l{'}aide d{'}un r{\'e}seau convolutif, en consid{\'e}rant une marge d{'}erreur de 10 ms. Cette performance est sup{\'e}rieure {\`a} celle d{'}un annotateur manuel, l{'}accord inter-annotateurs {\'e}tant de 62{\%}. Restreindre les donn{\'e}es d{'}apprentissage {\`a} celles d{'}un unique locuteur, 30 minutes environ, a eu pour cons{\'e}quence moins de 10{\%} de perte et utiliser celles de 5 locuteurs a permis d{'}atteindre des r{\'e}sultats similaires {\`a} utiliser plus de donn{\'e}es. Utiliser le mod{\`e}le entra{\^\i}n{\'e} avec le corpus anglais sur un petit corpus d{'}une langue peu dot{\'e}e a donn{\'e} des r{\'e}sultats comparables {\`a} estimer un mod{\`e}le avec des donn{\'e}es de cette langue.",
    language = "French",
}
@inproceedings{dubeda-2016-linvasivite,
    title = "L{'}invasivit{\'e} phonologique dans le traitement des anglicismes : une {\'e}tude quantitative de trois langues (Phonological invasiveness in the treatment of loanwords)",
    author = "Dub{\v{e}}da, Tom{\'a}{\v{s}}",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.45",
    pages = "401--409",
    abstract = "Dans la pr{\'e}sente {\'e}tude, nous analysons, dans une perspective typologique, l{'}adaptation phonologique des anglicismes dans trois langues (fran{\c{c}}ais, allemand et tch{\`e}que). La classification des formes phonologiques, qui s{'}appuie sur un syst{\`e}me de huit principes d{'}adaptation, a pour but d{'}{\'e}tablir le degr{\'e} d{'}« invasivit{\'e} phonologique » propre {\`a} chaque langue. L{'}approximation phonologique (substitution de phon{\`e}mes natifs aux phon{\`e}mes {\'e}trangers) semble {\^e}tre le principe fondamental dans les trois langues analys{\'e}es, alors que la prononciation orthographique (phon{\'e}tisation des graph{\`e}mes) intervient avant tout en fran{\c{c}}ais. La prononciation authentique (imitation phonologique de la langue source) n{'}est active qu{'}en allemand. Les m{\'e}canismes d{'}approximation phonologique sont plus invasifs en fran{\c{c}}ais que dans les deux autres langues, et ce notamment en ce qui concerne le syst{\`e}me vocalique. Globalement, l{'}invasivit{\'e} phonologique semble augmenter dans l{'}ordre allemand {--} tch{\`e}que {--} fran{\c{c}}ais.",
    language = "French",
}
@inproceedings{givois-etal-2016-investigation,
    title = "Investigation glottographique et laryngoscopique de la transition entre les deux principaux m{\'e}canismes laryng{\'e}s (Glottographic and laryngoscopic investigation of the transition between the two main laryngeal mechanisms)",
    author = "Givois, Arthur  and
      Demolin, Didier  and
      Crevier-Buchman, Lise  and
      Amelot, Ang{\'e}lique",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.46",
    pages = "410--418",
    abstract = "Cet article {\'e}tudie par une approche descriptive la transition entre le premier et le second m{\'e}canisme laryng{\'e}. Des mesures {\'e}lectroglottographiques ont {\'e}t{\'e} r{\'e}alis{\'e}es simultan{\'e}ment {\`a} des captures d{'}images par laryngoscopie sur deux sujets : une femme et un homme. Des diff{\'e}rences de comportement entre les deux sujets ont {\'e}t{\'e} observ{\'e}es. Un mouvement vertical de grande amplitude du larynx est syst{\'e}matiquement observ{\'e} au moment de la transition chez le sujet masculin, tandis que des modifications de petite amplitude de la distance entre paroi pharyngale et {\'e}piglotte, ou de la compression des plis ary{\'e}piglottiques sont remarqu{\'e}es chez le sujet f{\'e}minin. Ces changements de configurations s{'}effectuent de fa{\c{c}}on continue chez cette derni{\`e}re alors qu{'}un changement soudain de l{'}activit{\'e} des plis vocaux a lieu {\`a} un instant pr{\'e}cis{\'e}ment localis{\'e} pour les productions des deux sujets. Ces diff{\'e}rences d{'}ajustements laryng{\'e}s sont li{\'e}es {\`a} des modifications des param{\`e}tres m{\'e}caniques dont d{\'e}pendent la fr{\'e}quence fondamentale et qui restent {\`a} estimer.",
    language = "French",
}
@inproceedings{patri-etal-2016-modelisation,
    title = "Mod{\'e}lisation bay{\'e}sienne de la planification motrice des gestes de parole: {\'E}valuation du r{\^o}le des diff{\'e}rentes modalit{\'e}s sensorielles ({B}ayesian modeling of speech gesture motor planning: Evaluating the role of different sensory modalities )",
    author = "Patri, Jean-Fran{\c{c}}ois  and
      Diard, Julien  and
      Perrier, Pascal",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.47",
    pages = "419--427",
    abstract = "La prise en compte des informations auditives et proprioceptives dans le contr{\^o}le de la parole est mise en {\'e}vidence par un nombre croissant de r{\'e}sultats exp{\'e}rimentaux. Cependant, les mod{\`e}les de production imposent le plus souvent l{'}une ou l{'}autre des modalit{\'e}s, ou n{'}offrent pas de cadre formel pour {\'e}valuer leurs contributions respectives. Nous proposons d{'}explorer le r{\^o}le de ces modalit{\'e}s sensorielles dans la planification des gestes de parole {\`a} partir d{'}un mod{\`e}le bay{\'e}sien repr{\'e}sentant la structure des connaissances mises en jeu dans cette t{\^a}che. Le mod{\`e}le permet d{'}envisager trois m{\'e}canismes de planification, reposant sur la modalit{\'e} auditive, proprioceptive ou sur les deux conjointement. Nous comparons des simulations obtenues par les deux premiers m{\'e}canismes de planification. Les r{\'e}sultats indiquent des r{\'e}alisations articulatoires diff{\'e}rentes mais donnant n{\'e}anmoins des r{\'e}alisations auditives qualitativement similaires dans leur variabilit{\'e}.",
    language = "French",
}
@inproceedings{magnen-etal-2016-une,
    title = "Une m{\'e}thode d{'}{\'e}valuation de la compr{\'e}hension orale par choix d{'}image : application {\`a} de la parole d{\'e}grad{\'e}e par simulation de la presbyacousie (A method for assessing listening comprehension using image selection : application to speech degraded by presbycusis simulation)",
    author = "Magnen, Cynthia  and
      Tardieu, Julien  and
      Fontan, Lionel  and
      Gaillard, Pascal  and
      Spanghero-Gaillard, Nathalie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.48",
    pages = "428--436",
    abstract = "Nous pr{\'e}sentons une m{\'e}thode permettant d{'}{\'e}valuer la compr{\'e}hension de la parole d{\'e}grad{\'e}e par simulation des effets de la presbyacousie, dans le calme et dans le bruit. Cette m{\'e}thode int{\`e}gre des phrases signifiantes et implique pour l{'}auditeur de s{\'e}lectionner, parmi un ensemble de quatre images, celle qui correspond {\`a} l{'}{\'e}nonc{\'e} qu{'}il entend. Le test pr{\'e}sente de nombreux avantages m{\'e}thodologiques comme l{'}imm{\'e}diatet{\'e} du score et le fait qu{'}il ne n{\'e}cessite pas de faire r{\'e}p{\'e}ter la phrase entendue. Les r{\'e}sultats obtenus montrent un effet significatif de la d{\'e}gradation et du bruit du fond. La coh{\'e}rence de ces effets avec les {\'e}tudes pr{\'e}c{\'e}dentes sur la presbyacousie permet de valider cette m{\'e}thode. Par ailleurs, la nature exacte du score mesur{\'e} dans ce test est discut{\'e}e en le comparant avec le score d{'}intelligibilit{\'e} obtenu par r{\'e}p{\'e}tition d{'}items dans une pr{\'e}c{\'e}dente {\'e}tude.",
    language = "French",
}
@inproceedings{ferreira-etal-2016-optimiser,
    title = "Optimiser l{'}adaptation en ligne d{'}un module de compr{\'e}hension de la parole avec un algorithme de bandit contre un adversaire (Adversarial bandit for optimising online active learning of spoken language understanding)",
    author = "Ferreira, Emmanuel  and
      Reiffers-Masson, Alexandre  and
      Jabaian, Bassam  and
      Lef{\`e}vre, Fabrice",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.49",
    pages = "437--445",
    abstract = "De nombreux modules de compr{\'e}hension de la parole ont en commun d{'}{\^e}tre probabilistes et bas{\'e}s sur des algorithmes d{'}apprentissage automatique. Deux difficult{\'e}s majeures, rencontr{\'e}es par toutes les m{\'e}thodes existantes sont : le co{\^u}t de la collecte des donn{\'e}es et l{'}adaptation d{'}un module existant {\`a} un nouveau domaine. Dans cet article, nous proposons un processus d{'}adaptation en ligne avec une politique apprise en utilisant un algorithme de type bandit contre un adversaire. Nous montrons que cette proposition peut permettre d{'}optimiser un {\'e}quilibre entre le co{\^u}t de la collecte des retours demand{\'e}s aux utilisateurs et la performance globale de la compr{\'e}hension du langage parl{\'e} apr{\`e}s sa mise {\`a} jour.",
    language = "French",
}
@inproceedings{delais-roussarie-etal-2016-patrons,
    title = "Patrons Rythmiques et Genres Litt{\'e}raires en Synth{\`e}se de la Parole (How to improve rhythmic patterns according to literary genre in synthesized speech ⇤ )",
    author = "Delais-Roussarie, Elisabeth  and
      Lolive, Damien  and
      Yoo, Hiyon  and
      Guennec, David",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.50",
    pages = "446--454",
    abstract = "Ces vingt derni{\`e}res ann{\'e}es, la qualit{\'e} de la parole synth{\'e}tique s{'}est am{\'e}lior{\'e}e gr{\^a}ce notamment {\`a} l{'}{\'e}mergence de nouvelles techniques comme la synth{\`e}se par corpus. Mais les patrons rythmiques obtenus ne sont pas toujours per{\c{c}}us comme tr{\`e}s naturels. Dans ce papier, nous comparons les patrons rythmiques observ{\'e}s en parole naturelle et synth{\'e}tique pour trois genres litt{\'e}raires. Le but de ce travail est d{'}{\'e}tudier comment le rythme pourrait {\^e}tre am{\'e}lior{\'e} en synth{\`e}se de parole. La comparaison des patrons rythmiques est r{\'e}alis{\'e}e gr{\^a}ce {\`a} une analyse de la dur{\'e}e relativement {\`a} la structure prosodique, les donn{\'e}es audio provenant de six comptines, quatre po{\`e}mes et deux extraits de conte. Les r{\'e}sultats obtenus laissent penser que les diff{\'e}rences rythmiques entre parole naturelle et synth{\'e}tique sont principalement dues au marquage de la structure prosodique, particuli{\`e}rement au niveau des groupes intonatifs. De fait, le taux d{'}allongement des syllabes accentu{\'e}es en fin de groupes intonatifs est beaucoup plus important en synth{\`e}se que dans la parole naturelle.",
    language = "French",
}
@inproceedings{guennec-lolive-2016-une,
    title = "Une p{\'e}nalit{\'e} floue fond{\'e}e phonologiquement pour am{\'e}liorer la S{\'e}lection d{'}Unit{\'e} (A Phonologically Motivated Penalty To Improve Unit Selection)",
    author = "Guennec, David  and
      Lolive, Damien",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.51",
    pages = "455--463",
    abstract = "Les syst{\`e}mes de synth{\`e}se par corpus reposent, sauf de rares exceptions, sur des co{\^u}ts cibles et des co{\^u}ts de concat{\'e}nation pour s{\'e}lectionner la meilleure s{\'e}quence d{'}unit{\'e}s. Le r{\^o}le du co{\^u}t de concat{\'e}nation est de s{'}assurer que l{'}assemblage de deux segments de parole ne causera l{'}apparition d{'}aucun artefact acoustique. Pour cette t{\^a}che, des distances acoustiques (MFCC, F0) sont g{\'e}n{\'e}ralement utilis{\'e}es, mais dans de nombreux cas cela ne suffit pas. Dans cet article, nous introduisons une p{\'e}nalit{\'e} h{\'e}rit{\'e}e du domaine de la couverture de corpus dans le co{\^u}t de concat{\'e}nation afin de bloquer certaines concat{\'e}nations en fonction de la classe phonologique des diphones {\`a} concat{\'e}ner. En outre, une seconde version faisant appel {\`a} une fonction floue est propos{\'e}e pour rel{\^a}cher la p{\'e}nalit{\'e} en fonction du positionnement du co{\^u}t de concat{\'e}nation par rapport {\`a} sa distribution. Une {\'e}valuation objective montre que la p{\'e}nalit{\'e} est efficace et am{\`e}ne {\`a} un meilleur classement des s{\'e}quences d{'}unit{\'e}s candidates au cours de la s{\'e}lection. Une {\'e}valuation subjective r{\'e}v{\`e}le une performance sup{\'e}rieure de l{'}approche floue.",
    language = "French",
}
@inproceedings{hennequin-etal-2016-perception,
    title = "Perception audio-visuelle de s{\'e}quences {VCV} produites par des personnes porteuses de Trisomie 21 : une {\'e}tude pr{\'e}liminaire (Auditory-visual Perception of {VCV}s Produced by People with Down Syndrome: a Preliminary Study)",
    author = "Hennequin, Alexandre  and
      Rochet-Capellan, Am{\'e}lie  and
      Dohen, Marion",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.52",
    pages = "464--472",
    abstract = "La parole des personnes avec trisomie 21 (T21) pr{\'e}sente une alt{\'e}ration syst{\'e}matique de l{'}intelligibilit{\'e} qui n{'}a {\'e}t{\'e} quantifi{\'e}e qu{'}auditivement. Or la modalit{\'e} visuelle pourrait am{\'e}liorer l{'}intelligibilit{\'e} comme c{'}est le cas pour les personnes « ordinaires ». Cette {\'e}tude compare la mani{\`e}re dont 24 participants ordinaires per{\c{c}}oivent des s{\'e}quences VCV voyelle-consonne-voyelle) produites par quatre adultes (2 avec T21 et 2 ordinaires) et pr{\'e}sent{\'e}es dans le bruit en modalit{\'e}s auditive, visuelle et audiovisuelle. Les r{\'e}sultats confirment la perte d{'}intelligibilit{\'e} en modalit{\'e} auditive dans le cas de locuteurs porteurs de T21. Pour les deux locuteurs impliqu{\'e}s, l{'}intelligibilit{\'e} visuelle est n{\'e}anmoins {\'e}quivalente {\`a} celle des deux locuteurs ordinaires et compensent le d{\'e}ficit d{'}intelligibilit{\'e} auditive. Ces r{\'e}sultats sugg{\`e}rent l{'}apport de la modalit{\'e} visuelle vers une meilleure intelligibilit{\'e} des personnes porteuses de T21.",
    language = "French",
}
@inproceedings{takemura-kamiyama-2016-perception,
    title = "Perception des consonnes g{\'e}min{\'e}es en japonais langue {\'e}trang{\`e}re par des apprenants francophones (Perception of geminate consonants in {J}apanese as a foreign language by {F}rench-speaking learners)",
    author = "Takemura, Akiko  and
      Kamiyama, Takeki",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.53",
    pages = "473--481",
    abstract = "Le japonais pr{\'e}sente une opposition phon{\'e}mique entre les obstruantes simples et g{\'e}min{\'e}es, qui pose des difficult{\'e}s aux apprenants non-natifs tant au niveau de la perception que de la production, notamment quand une opposition similaire est absente dans la langue des apprenants. La discrimination perceptive de cette opposition a {\'e}t{\'e} {\'e}tudi{\'e}e chez 19 apprenants francophones de deux niveaux diff{\'e}rents de comp{\'e}tence et chez 6 auditeurs natifs {\`a} l{'}aide d{'}une exp{\'e}rience AXB avec des non-mots dysyllabiques prononc{\'e}s par 2 locuteurs natifs du japonais de Tokyo. Les r{\'e}sultats montrent une diff{\'e}rence significative entre les apprenants (10,91{\%} d{'}erreurs en moyenne) et les natifs (3,86{\%} en moyenne). Le taux d{'}erreurs {\'e}tait plus {\'e}lev{\'e} quand l{'}accent lexical du mot test{\'e} {\'e}tait du type HB (haut-bas) que BH. Les auditeurs natifs ont {\'e}galement montr{\'e} un taux d{'}erreur plus {\'e}lev{\'e} pour la fricative /s/, et aussi quand la consonne est entour{\'e}e des voyelles ferm{\'e}es /i/ et /u/.",
    language = "French",
}
@inproceedings{guan-kwon-2016-la,
    title = "La perception des s{\'e}quences consonantiques non-natives par les locuteurs monolingues de mandarin (Perception of non-native consonant sequences by {M}andarin monolingual speakers)",
    author = "Guan, Qianwen  and
      Kwon, Harim",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.54",
    pages = "482--490",
    abstract = "Cette {\'e}tude examine le r{\^o}le de la structure phonotactique native et des facteurs phon{\'e}tiques dans la perception des s{\'e}quences consonantiques non-natives. Des locuteurs monolingues de mandarin ont {\'e}t{\'e} test{\'e}s dans les deux exp{\'e}riences suivantes: dans la premi{\`e}re exp{\'e}rience, les locuteurs ont du d{\'e}cider s{'}ils entendaient une voyelle entre deux consonnes en {\'e}coutant des s{\'e}quences intervocaliques-CC (akta) et leurs contr{\^o}les CVC (akata). Les participants mandarins monolingues ont tendance {\`a} percevoir une voyelle entre deux consonnes dans les deux s{\'e}quences CC et CVC. Mais le pourcentage de la voyelle per{\c{c}}ue varie selon les diff{\'e}rentes s{\'e}quences. Dans la deuxi{\`e}me exp{\'e}rience, les m{\^e}mes participants ont {\'e}cout{\'e} des s{\'e}quences CC initiales et intervocaliques (ktapa, akta) ainsi que CVC (katapa, akata) et les ont transcrites en Pinyin. Les strat{\'e}gies observ{\'e}es dans la transcription: l{'}{\'e}penth{\`e}se, la m{\'e}tath{\`e}se, l{'}omission de C1 et celle de C2, montrent que les participants sont sensibles aux facteurs phon{\'e}tiques. Les r{\'e}sultats des deux exp{\'e}riences sugg{\`e}rent que la phonotactique native ainsi que des facteurs phon{\'e}tiques affectent la perception des s{\'e}quences non-natives.",
    language = "French",
}
@inproceedings{krzonowski-etal-2016-perception,
    title = "Perception et production de voyelles de l{'}anglais par des apprenants francophones : effet d{'}entra{\^\i}nements en perception et en production (Perception and production of {E}nglish vowels by {F}rench learners: effect of perception and production trainings)",
    author = "Krzonowski, Jennifer  and
      Ferragne, Emmanuel  and
      Pellegrino, Fran{\c{c}}ois",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.55",
    pages = "491--499",
    abstract = "Cette {\'e}tude propose de tester l{'}effet de deux entra{\^\i}nements, en perception et en production, sur l{'}acquisition de voyelles de l{'}anglais britannique par des francophones. L{'}{\'e}tude se focalise sur deux r{\'e}gions de l{'}espace acoustique pour lesquelles plusieurs cat{\'e}gories phonologiques existent en anglais alors qu{'}une seule existe en fran{\c{c}}ais. Trois groupes ont {\'e}t{\'e} constitu{\'e}s : l{'}un recevant un entra{\^\i}nement de type High Variability Perceptual Training, un second recevant un entra{\^\i}nement en production et le troisi{\`e}me constituait un groupe contr{\^o}le ne recevant pas d{'}entra{\^\i}nement. Les performances des participants ont {\'e}t{\'e} {\'e}valu{\'e}es avant et apr{\`e}s entra{\^\i}nement en perception et en production. Les r{\'e}sultats semblent montrer un effet de l{'}entra{\^\i}nement en perception sur les performances en perception et en production et un effet plus restreint de l{'}entra{\^\i}nement en production. Mais leur interpr{\'e}tation reste difficile du fait d{'}un effet test/re-test observ{\'e} sur le groupe contr{\^o}le.",
    language = "French",
}
@inproceedings{magnen-etal-2016-perception,
    title = "Perception native des voyelles catalanes produites par des locutrices multilingues (Native perception of {C}atalan vowels uttered by female multilingual speakers)",
    author = "Magnen, Cynthia  and
      Carrera-Sabat{\'e}, Josefina  and
      Gaillard, Pascal",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.56",
    pages = "500--508",
    abstract = "Cette {\'e}tude porte sur les voyelles catalanes produites par des adolescentes multilingues en CatalanCastillan ayant pour langue maternelle soit le Catalan, soit le Roumain, soit l{'}Arabe du Maghreb. Nous proposons {\`a} vingt-et-un auditeurs catalanophones natifs un Test de Cat{\'e}gorisation Libre des voyelles produites dans ce contexte multilingue. Ce faisant, nous testons le mod{\`e}le Automatic Selective Perception (ASP - Strange, 2011) qui stipule qu{'}en fonction de la variabilit{\'e} des stimuli et de la t{\^a}che propos{\'e}e, les auditeurs r{\'e}alisent un traitement des stimuli selon un mode phon{\'e}tique ou phonologique. Les r{\'e}sultats indiquent que le traitement des stimuli est double : les voyelles moyennes sont trait{\'e}es selon un mode phon{\'e}tique, tandis que les voyelles extr{\^e}mes sont trait{\'e}es selon un mode phonologique. L{'}assimilation de voyelles d{'}une cat{\'e}gorie vocalique {\`a} une autre informe sur la qualit{\'e} des r{\'e}alisations non natives et t{\'e}moigne de l{'}influence de la L1.",
    language = "French",
}
@inproceedings{tsvetanova-etal-2016-peut,
    title = "Peut-on caract{\'e}riser globalement une « qualit{\'e} d{'}acte expressif » : de « breathy voice » {\`a} « breathy turn taking » dans la glu socio-affective de l{'}interaction humain-robot ? (Multidimensional prosodic style, as characteristics of the {``}gluing{''} relation process: extension of {``}breathiness{''} from voice quality to {``}turn talk quality{''})",
    author = "Tsvetanova, Liliya  and
      Auberg{\'e}, V{\'e}ronique  and
      Sasa, Yuko",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.57",
    pages = "509--517",
    abstract = "L{'}interaction face-{\`a}-face est consid{\'e}r{\'e}e ici comme un syst{\`e}me {\'e}mergeant, englobant les soussyst{\`e}mes en synchronie des interactants inscrits, {\`a} travers leur personnalit{\'e}, dans leur r{\^o}le social, leurs motivations, leurs intentions, leurs {\'e}tats socio-affectifs. L{'}interaction est instanci{\'e}e par une « glu » socio-affective pour laquelle nous testons une dimension altruiste, orthogonale {\`a} la dimension de dominance, exp{\'e}riment{\'e}e dans le sc{\'e}nario {\'e}cologique Emoz (Sasa et Auberg{\'e}, 2014) pour des personnes {\^a}g{\'e}es donnant des commandes domotiques de forme impos{\'e}e {\`a} un robot. Le dialogue est conduit par des feedbacks socio-affectifs primitifs du robot suppos{\'e}s « gluer » progressivement. Nous montrons que la variation faite par les sujets autour des commandes r{\'e}f{\'e}rentes, non seulement suit un d{\'e}cours dynamique de « glu » progressive, mais que le comportement communicatif des sujets est globalement inscrit dans des caract{\'e}ristiques d{'}« intimit{\'e}-care» d{'}une production breathy de toutes les modalit{\'e}s (voix, prosodie, paraphrasage lexico-morpho-syntaxique, timing, posture, direction du regard, prox{\'e}mie, d{\'e}placement).",
    language = "French",
}
@inproceedings{lecorve-lolive-2016-phonetisation,
    title = "Phon{\'e}tisation statistique adaptable d{'}{\'e}nonc{\'e}s pour le fran{\c{c}}ais (Adaptive statistical utterance phonetization for {F}rench ⇤ )",
    author = "Lecorv{\'e}, Gw{\'e}nol{\'e}  and
      Lolive, Damien",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.58",
    pages = "518--526",
    abstract = "Les m{\'e}thodes classiques de phon{\'e}tisation d{'}{\'e}nonc{\'e}s concat{\`e}nent les prononciations hors-contexte des mots. Ce type d{'}approches est trop faible pour certaines langues, comme le fran{\c{c}}ais, o{\`u} les transitions entre les mots impliquent des modifications de prononciation. De plus, cela rend difficile la mod{\'e}lisation de strat{\'e}gies de prononciation globales, par exemple pour mod{\'e}liser un locuteur ou un accent particulier. Pour palier ces probl{\`e}mes, ce papier pr{\'e}sente une approche originale pour la phon{\'e}tisation du fran{\c{c}}ais afin de g{\'e}n{\'e}rer des variantes de prononciation dans le cas d{'}{\'e}nonc{\'e}s. Par l{'}emploi de champs al{\'e}atoires conditionnels et de transducteurs finis pond{\'e}r{\'e}s, cette approche propose un cadre statistique particuli{\`e}rement souple et adaptable. Cette approche est {\'e}valu{\'e}e sur un corpus de mots isol{\'e}s et sur un corpus d{'}{\'e}nonc{\'e}s prononc{\'e}s.",
    language = "French",
}
@inproceedings{barbosa-de-mareuil-2016-pics,
    title = "Pics m{\'e}lodiques pr{\'e}toniques en portugais br{\'e}silien : une {\'e}tude quantitative (Pre-stress pitch peaks in {B}razilian {P}ortuguese: a quantitative study)",
    author = {Barbosa, Pl{\'\i}nio  and
      de Mare{\"u}il, Philippe Boula},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.59",
    pages = "527--535",
    abstract = "Le pr{\'e}sent travail porte sur un trait prosodique assez typique du portugais br{\'e}silien : un pic m{\'e}lodique en position pr{\'e}tonique en fin d{'}{\'e}nonc{\'e} d{\'e}claratif. Il vise {\`a} quantifier le ph{\'e}nom{\`e}ne, {\`a} partir d{'}enregistrements de cinq hommes et cinq femmes de l{'}{\'e}tat de S{\~a}o Paulo, en lecture et en narration. Il en r{\'e}sulte que des mont{\'e}es sur les pr{\'e}toniques de 4 demi-tons suivies de descentes de 8 demi-tons, en moyenne, s{'}observent dans les deux styles de parole, chez les femmes. Chez les hommes, ces valeurs sont respectivement de 3 et 7 demi-tons. Ces mont{\'e}es-descentes d{'}une tierce et d{'}une quinte, respectivement, peuvent donner au portugais br{\'e}silien cette musicalit{\'e} particuli{\`e}re et, puisque les descentes sont plus rapides chez les femmes, elles ouvrent des perspectives sociolinguistiques int{\'e}ressantes.",
    language = "French",
}
@inproceedings{georgeton-christine-2016-preservation,
    title = "Pr{\'e}servation du pattern syllabique iambique dans la production des locuteurs dysarthriques (The preservation of iambic syllabic pattern in the production of dysarthric speakers)",
    author = "Georgeton, Laurianne  and
      Christine, Meunier",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.60",
    pages = "536--544",
    abstract = "Ce travail vise {\`a} {\'e}valuer une {\'e}ventuelle d{\'e}gradation du pattern rythmique iambique dans la production de locuteurs atteints de diff{\'e}rents types de dysarthrie. Ce pattern se traduit par une structure court-long dans les mots dissyllabiques. Cette structure est tr{\`e}s robuste en fran{\c{c}}ais aussi bien en production qu{\P}en perception. Par ailleurs, chez des locuteurs dysarthriques, des perturbations prosodiques et donc rythmiques sont souvent observ{\'e}es. Ainsi, ces patients peuventils maintenir ce pattern iambique dans leurs productions? Les r{\'e}sultats montrent que le pattern rythmique iambique est bien conserv{\'e} chez toutes les populations dysarthriques aussi bien en lecture qu{\P}en parole spontan{\'e}e. Ce pattern est en g{\'e}n{\'e}ral plus marqu{\'e} en spontan{\'e} qu{\P}en lecture et la population contr{\^o}le se d{\'e}marque des populations dysarthriques par un pattern plus marqu{\'e} en lecture, mais plus encore en spontan{\'e}. Ce pattern rythmique semble donc robuste m{\^e}me s{\P}il semble {\^e}tre affect{\'e} quand la s{\'e}v{\'e}rit{\'e} de la maladie augmente.",
    language = "French",
}
@inproceedings{pillot-loiseau-etal-2016-production,
    title = "Production des voyelles parl{\'e}es et chant{\'e}es dans le Cantu in Paghjella (Production of spoken and sung vowels in Cantu in Paghjella)",
    author = "Pillot-Loiseau, Claire  and
      Chawah, Patrick  and
      Amelot, Ang{\'e}lique  and
      Bachman, Gr{\'e}goire  and
      Herrgott, Catherine  and
      Adda-Decker, Martine  and
      Crevier-Buchman, Lise",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.61",
    pages = "545--553",
    abstract = {Quelles sont les caract{\'e}ristiques acoustiques et articulatoires des voyelles parl{\'e}es et chant{\'e}es du Cantu in Paghjella (polyphonie corse {\`a} trois voix), en fonction du chanteur, de la voyelle et de la fr{\'e}quence fondamentale ? L{'}analyse acoustique des quatre premiers formants de la parole au chant et celle des mouvements articulatoires lingual et labial, montrent g{\'e}n{\'e}ralement (i) une significative augmentation de F1 avec abaissement lingual mais fermeture labiale, en lien avec une corr{\'e}lation entre F0 et F1 ; (ii) une baisse de F2 pour les voyelles ant{\'e}rieures, une post{\'e}riorisation linguale et un recul de l{'}ombre hyo{\"\i}dienne uniquement pour le bassu ; (iii) une nette augmentation de F3 et F4 surtout chez le bassu ; (iv) une augmentation du Singing Power Ratio surtout chez les bassu et secunda. Ses valeurs sont toutefois inf{\'e}rieures {\`a} celles de chanteurs lyriques, et ne correspondant pas comme ces derniers {\`a} un rapprochement de F3 et F4.},
    language = "French",
}
@inproceedings{benali-2016-la,
    title = "La prosodie du focus dans les parlers alg{\'e}rois et oranais (The prosody of focus in {A}lgiers and {O}ran dialects)",
    author = {Benali, Isma{\"e}l},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.62",
    pages = "554--562",
    abstract = "Le but de cette {\'e}tude est d{'}{\'e}tudier les caract{\'e}ristiques prosodiques de diff{\'e}rents types de focus dans les parlers alg{\'e}rois et oranais. Il ressort de l{'}analyse acoustique des productions des locuteurs que les r{\'e}currences des sch{\`e}mes prosodiques qui distinguent les deux parlers sont observ{\'e}es dans deux types de focus : le focus {\'e}troit d{'}insistance quand il est plac{\'e} {\`a} la fronti{\`e}re d{'}un groupe intonatif et le focus interrogatif. Le premier est r{\'e}alis{\'e} dans le parler alg{\'e}rois par un contour montant descendant. Dans le parler oranais, il est produit par un contour plat ou l{\'e}g{\`e}rement montant ou descendant. On retrouve, dans le focus interrogatif, le m{\^e}mes contour intonatif plus amplifi{\'e} du focus d{'}insistance chez les Alg{\'e}rois alors que chez les Oranais la derni{\`e}re syllabe est toujours montante pr{\'e}c{\'e}d{\'e}e d{'}une descente. Le focus de contraste est produit diff{\'e}remment dans le m{\^e}me dialecte avec plus d{'}allongement en oranais. La r{\'e}alisation du focus large n{'}est pas distinctive.",
    language = "French",
}
@inproceedings{muriel-etal-2016-que,
    title = "Que disents nos silences? Apport des donn{\'e}es acoustiques, articulatoires et physiologiques pour l{'}{\'e}tude des pauses silencieuses (What do our silences say? Contribution of acoustic, articulatory and physiological data to the study on silent pauses)",
    author = "Muriel, Lalain  and
      Thierry, Legou  and
      Camille, Fauth  and
      Fabrice, Hirsch  and
      Ivana, Didirkova",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.63",
    pages = "563--570",
    abstract = "Si la rh{\'e}torique s{'}est int{\'e}ress{\'e}e tr{\`e}s t{\^o}t {\`a} la pause, il a fallu attendre le XX{\`e}me si{\`e}cle pour que d{'}autres disciplines {--} la psycholinguistique, le traitement automatique des langues, la phon{\'e}tique {--} accordent {\`a} ces moments de silence l{'}int{\'e}r{\^e}t qu{'}ils m{\'e}ritent. Il a ainsi {\'e}t{\'e} montr{\'e} que ces ruptures dans le signal acoustique, loin de signer une absence d{'}activit{\'e}, constituaient en r{\'e}alit{\'e} le lieu d{'}une activit{\'e} physiologique (la respiration) et/ou cognitive (planification du discours) qui participent tout autant au message que la parole elle-m{\^e}me. Dans cette {\'e}tude pilote, nous proposons des observations et des pistes de r{\'e}flexions {\`a} partir de l{'}analyse des pauses silencieuses dans un corpus de parole lue et semi dirig{\'e}e. Nous mettons notamment en {\'e}vidence l{'}apport de l{'}analyse conjointe de donn{\'e}es acoustiques, articulatoires (EMA) et physiologiques (respiratoires) pour l{'}identification, parmi les pauses silencieuses, des pauses respiratoires, syntaxiques et d{'}h{\'e}sitation.",
    language = "French",
}
@inproceedings{turco-etal-2016-que,
    title = "Que nous apprennent les gros corpus sur l{'}harmonie vocalique en fran{\c{c}}ais ? (What can we learn from big speech corpora about {F}rench vowel harmony?)",
    author = "Turco, Giuseppina  and
      Fougeron, C{\'e}cile  and
      Audibert, Nicolas",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.64",
    pages = "571--579",
    abstract = "Afin de mieux identifier le poids relatif des diff{\'e}rents facteurs d{\'e}crits dans la litt{\'e}rature comme influen{\c{c}}ant le ph{\'e}nom{\`e}ne d{'}harmonie vocalique (HV) en fran{\c{c}}ais, 33k mots extraits de deux corpus de parole continue et pr{\'e}sentant un contexte d{'}HV possible V1C(C)V2 (V1∈e,ɛ,o,ɔ) sont analys{\'e}s. Le degr{\'e} d{'}HV est mesur{\'e} en termes d{'}abaissement du F1 de V1 induit par la pr{\'e}sence d{'}une V2 /+haut/ (ferm{\'e}e ou mi-ferm{\'e}e) par rapport {\`a} une V2 /-haut/ (ouverte ou mi-ouverte). Les r{\'e}sultats montrent une HV plus importante pour les voyelles moyennes post{\'e}rieures que pour les ant{\'e}rieures, et plus faible lorsque l{'}orthographe favorise une prononciation mi-ferm{\'e}e de V1. Comme attendu, l{'}HV est plus forte quand V1 est s{\'e}par{\'e} de V2 par une consonne labiale vs. linguale ou par un cluster consonantique sous-jacent vs. un cluster r{\'e}sultant de la chute d{'}un schwa. En revanche, le style de parole (conversationnelle vs. journalistique) a un effet plus nuanc{\'e} que celui attendu.",
    language = "French",
}
@inproceedings{nocaudie-astesano-2016-quelle,
    title = "Quelle(s) mesure(s) de similarit{\'e} prosodique comme {\'e}valuation de l{'}imitation ? (Which measure(s) of prosodic similarity as an evaluation of imitation?)",
    author = "Nocaudie, Olivier  and
      Ast{\'e}sano, Corine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.65",
    pages = "580--588",
    abstract = {La performance imitative des locuteurs varie de celle du professionnel, expert, {\`a} celle du na{\"\i}f, plus ou moins talentueux. L{'}{\'e}tude de l{'}imitation souligne la difficult{\'e} pour trouver des indices mesurables de la r{\'e}ussite d{'}une imitation. Dans cette {\'e}tude exploratoire, des contours de f0 recueillis au fil de t{\^a}ches d{'}imitation sont test{\'e}s au moyen d{'}une double approche : mesure objective par le biais de deux mesures de la similarit{\'e} prosodique report{\'e}es dans la litt{\'e}rature et {\'e}valuation perceptive par un panel de 15 auditeurs na{\"\i}fs. Nos premiers r{\'e}sultats indiquent une bonne corr{\'e}lation entre les deux approches et soul{\`e}vent la question du choix de l{'}indice mesurable qui rendrait le mieux compte d{'}une imitation au niveau tonal. Ils soulignent {\'e}galement la variabilit{\'e} interindividuelle des comportements imitatifs en parole tout en ouvrant des perspectives int{\'e}ressantes dans le domaine de la formation {\`a} la phon{\'e}tique corrective par la M{\'e}thode Verbotonale.},
    language = "French",
}
@inproceedings{ghio-etal-2016-quels,
    title = "Quels tests d{'}intelligibilit{\'e} pour {\'e}valuer les troubles de production de la parole ? (What kind of intelligibility test to assess speech production disorders?)",
    author = "Ghio, Alain  and
      Giusti, Laurence  and
      Blanc, Emilie  and
      Pinto, Serge  and
      Muriel, Lalain  and
      Robert, Dani{\`e}le  and
      Fredouille, Corine  and
      Woisard, Virginie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.66",
    pages = "589--596",
    abstract = "L{'}intelligibilit{\'e} de la parole se d{\'e}finit comme le degr{\'e} de pr{\'e}cision avec lequel un message est compris par un auditeur. A ce titre, la perte d{'}intelligibilit{\'e} repr{\'e}sente souvent une plainte importante pour les patients atteints de troubles de production de la parole, puisqu{'}elle participe {\`a} la diminution de la qualit{\'e} de vie au niveau communicationnel. Plusieurs outils existent actuellement pour {\'e}valuer l{'}intelligibilit{\'e} mais aucun ne satisfait pleinement les contraintes cliniques. Dans une premi{\`e}re {\'e}tude, nous avons adapt{\'e} au fran{\c{c}}ais la version 2 du Frenchay Dysarthria Assessment, un test reconnu dans le milieu anglo-saxon pour l{'}{\'e}valuation de locuteurs dysarthriques. Nous avons cr{\'e}{\'e} le corpus de mots fran{\c{c}}ais en nous appuyant sur les crit{\`e}res d{\'e}finis dans le FDA-2 puis nous avons test{\'e} le protocole sur une cinquantaine de locuteurs. Les r{\'e}sultats sont satisfaisants mais divers biais m{\'e}thodologiques nous ont conduits {\`a} poursuivre notre d{\'e}marche en proposant des listes de pseudo-mots apparentant le test {\`a} du d{\'e}codage acoustico-phon{\'e}tique.",
    language = "French",
}
@inproceedings{vasilescu-etal-2016-realisation,
    title = "R{\'e}alisation phon{\'e}tique et contraste phonologique marginal : une {\'e}tude automatique des voyelles du roumain (Phonetic realization and marginal phonemic contrast : an automatic study of the {R}omanian vowels)",
    author = "Vasilescu, Ioana  and
      Renwick, Margaret  and
      Dutrey, Camille  and
      Lamel, Lori  and
      Vieru, Biana",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.67",
    pages = "597--606",
    abstract = "Cet article est d{\'e}di{\'e} {\`a} l{'}analyse acoustique des voyelles du roumain : des productions en parole continue sont compar{\'e}es {\`a} des prononciations {``}de laboratoire{''}. Les objectifs sont : (1) d{\'e}crire les traits acoustiques des voyelles en fonction du style de parole ; (2) estimer la relation entre traits acoustiques et contrastes phon{\'e}miques de la langue ; (3) estimer dans quelle mesure l{'}{\'e}tude de l{'}oral apporte des {\'e}clairages au sujet des attributs phon{\'e}miques des voyelles centrales [2] et [1], dont le statut (phon{\`e}mes vs allophones) est controvers{\'e}. Nous montrons que les traits acoustiques sont comparables pour la parole journalistique vs contr{\^o}l{\'e}e pour l{'}ensemble de l{'}inventaire sauf [2] et [1]. Dans la parole contr{\^o}l{\'e}e [2] et [1] sont distinctes, mais confondues en faveur du timbre [2] {\`a} l{'}oral. La confusion de timbres n{'}est pas source d{'}inintelligibilit{\'e} car [2] et [1] sont en distribution quasicompl{\'e}mentaire. Ce r{\'e}sultat apporte des {\'e}clairages sur la question du contraste phon{\'e}mique graduel et marginal (Goldsmith, 1995; Scobbie {\&} Stuart-Smith, 2008; Hall, 2013).",
    language = "French",
}
@inproceedings{dei-etal-2016-la,
    title = "La reconnaissance des mots dans la parole accentu{\'e}e : Une {\'e}tude en laboratoire et {\`a} l{'}ext{\'e}rieur. (Mispronunciations slow down word recognition: A study using touchscreens in the lab and the real world)",
    author = {De{\"\i}, Delphine  and
      Piccinini, Page  and
      Dautriche, Isabelle  and
      Heugten, Marieke Van  and
      Cristia, Alejandrina},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.68",
    pages = "607--614",
    abstract = "Des travaux r{\'e}cents sugg{\`e}rent que les enfants et les adultes sont initialement ralentis dans leur compr{\'e}hension des mots qui n{'}ont pas {\'e}t{\'e} prononc{\'e}s de fa{\c{c}}on standard. N{\'e}anmoins, quand ils font face {\`a} un interlocuteur qui {\`a} un discours accentu{\'e}, ils d{\'e}veloppent rapidement des strat{\'e}gies sp{\'e}cifiques qui leur permettent de comprendre m{\^e}me des prononciations atypiques. Cependant, ces r{\'e}sultats sont typiquement issus de recherches en laboratoire, o{\`u} l{'}attention des participants se concentre sur une t{\^a}che unique qui leur demande peu de ressources. Afin de d{\'e}passer ces limitations, nous avons men{\'e} une exp{\'e}rience de reconnaissance de mots sur tablette tactile, en {\'e}valuant des enfants et des adultes, en laboratoire et dans l{'}environnement naturel de chaque groupe. Nous avons constat{\'e} que des d{\'e}viations de prononciation dans la parole accentu{\'e}e ralentissent la reconnaissance des mots, chez des enfants et adultes, tant dans le laboratoire que dans des environnements naturels.",
    language = "French",
}
@inproceedings{christine-bigi-2016-repartition,
    title = "R{\'e}partition des phon{\`e}mes r{\'e}duits en parole conversationnelle. Approche quantitative par extraction automatique (The distribution of reduced phoneme in conversational speech)",
    author = "Christine, Meunier  and
      Bigi, Brigitte",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.69",
    pages = "615--623",
    abstract = "Cette {\'e}tude vise {\`a} mieux comprendre la r{\'e}partition des r{\'e}ductions phon{\'e}tiques pr{\'e}sentes dans la production de parole. Nous avons s{\'e}lectionn{\'e} l! ensemble des phon{\`e}mes les plus courts (30ms) {\`a} partir de l! alignement d! un corpus de parole conversationnelle. Cette version contenant uniquement les phon{\`e}mes courts (V1) est compar{\'e}e {\`a} la version contenant l! alignement de tous les phon{\`e}mes du corpus (V0). Les deux versions sont mises en relation avec l! annotation des mots et de leur cat{\'e}gorie syntaxique. Les r{\'e}sultats montrent que les liquides, les glissantes et les voyelles ferm{\'e}es sont plus repr{\'e}sent{\'e}es dans V1 que dans V0. Par ailleurs, la nature et la cat{\'e}gorie syntaxique des mots modulent la distribution des phon{\`e}mes en V1. Ainsi, la nature instable du /l/, ainsi que sa pr{\'e}sence dans de tr{\`e}s nombreux pronoms et d{\'e}terminants, en fait le phon{\`e}me le plus marqu{\'e} par la r{\'e}duction. Enfin, la fr{\'e}quence des mots semble montrer des effets contradictoires.",
    language = "French",
}
@inproceedings{pellegrini-etal-2016-reseau,
    title = "R{\'e}seau de neurones convolutif pour l{'}{\'e}valuation automatique de la prononciation ({CNN}-based automatic pronunciation assessment of {J}apanese speakers learning {F}rench )",
    author = "Pellegrini, Thomas  and
      Fontan, Lionel  and
      Sahraoui, Halima",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.70",
    pages = "624--632",
    abstract = "Dans cet article, nous comparons deux approches d{'}{\'e}valuation automatique de la prononciation de locuteurs japonophones apprenant le fran{\c{c}}ais. La premi{\`e}re, l{'}algorithme standard appel{\'e} Goodness Of Pronunciation (GOP), compare les vraisemblances obtenues lors d{'}un alignement forc{\'e} et lors d{'}une reconnaissance de phones sans contrainte. La deuxi{\`e}me, n{\'e}cessitant {\'e}galement un alignement pr{\'e}alable, fait appel {\`a} un r{\'e}seau de neurones convolutif (CNN) comme classifieur binaire, avec comme entr{\'e}e des trames de coefficients spectraux. Les deux approches sont {\'e}valu{\'e}es sur deux phon{\`e}mes cibles /R/ et /v/ du fran{\c{c}}ais, particuli{\`e}rement difficiles {\`a} prononcer pour des Japonophones. Les param{\`e}tres du GOP (seuils) et du CNN sont estim{\'e}s sur un corpus de parole lue par des locuteurs natifs du fran{\c{c}}ais, dans lequel des erreurs de prononciation artificielles sont introduites. Un gain de performance relatif de 13,4{\%} a {\'e}t{\'e} obtenu avec le CNN, avec une pr{\'e}cision globale de 72,6{\%}, sur un corpus d{'}{\'e}valuation enregistr{\'e} par 23 locuteurs japonophones.",
    language = "French",
}
@inproceedings{wu-etal-2016-role,
    title = "R{\^o}le des contextes lexical et post-lexical dans la r{\'e}alisation du schwa : apports du traitement automatique de grands corpus (Role of lexical and post-lexical contexts in {F}rench schwa realisations : benefits of automatic processing of large corpora )",
    author = "Wu, Yaru  and
      Adda-Decker, Martine  and
      Fougeron, C{\'e}cile",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.71",
    pages = "633--641",
    abstract = "Le r{\^o}le du contexte est connu dans la r{\'e}alisation ou non du schwa en fran{\c{c}}ais. Deux grands corpus oraux de parole journalistique (ETAPE) et de parole famili{\`e}re (NCCFr), dans lesquels la realisation de schwa est d{\'e}termin{\'e}e {\`a} partir d{'}un alignement automatique, ont {\'e}t{\'e} utilis{\'e}s pour examiner la contribution du contexte au sein du mot contenant schwa (lexical) vs. au travers de la fronti{\`e}re avec le mot pr{\'e}c{\'e}dent (post-lexical). Nos r{\'e}sultats montrent l{'}importance du contexte pr{\'e}-fronti{\`e}re dans l{'}explication de la chute du schwa dans la premi{\`e}re syllabe d{'}un mot polysyllabique en parole spontan{\'e}e. Si le mot pr{\'e}c{\'e}dant se termine par une consonne, nous pouvons faire appel {\`a} la loi des trois consonnes et au principe de sonorit{\'e} pour expliquer des diff{\'e}rences de comportement en fonction de la nature des consonnes en contact.",
    language = "French",
}
@inproceedings{simonnet-etal-2016-des,
    title = "Des R{\'e}seaux de Neurones avec M{\'e}canisme d{'}Attention pour la Compr{\'e}hension de la Parole (Exploring the use of Attention-Based Recurrent Neural Networks For Spoken Language Understanding )",
    author = "Simonnet, Edwin  and
      Del{\'e}glise, Paul  and
      Camelin, Nathalie  and
      Est{\`e}ve, Yannick",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.72",
    pages = "642--650",
    abstract = "L{'}{\'e}tude porte sur l{'}apport d{'}un r{\'e}seau de neurones r{\'e}current (Recurrent Neural Network RNN) bidirectionnel encodeur/d{\'e}codeur avec m{\'e}canisme d{'}attention pour une t{\^a}che de compr{\'e}hension de la parole. Les premi{\`e}res exp{\'e}riences faites sur le corpus ATIS confirment la qualit{\'e} du syst{\`e}me RNN {\'e}tat de l{'}art utilis{\'e} pour cet article, en comparant les r{\'e}sultats obtenus {\`a} ceux r{\'e}cemment publi{\'e}s dans la litt{\'e}rature. Des exp{\'e}riences suppl{\'e}mentaires montrent que les RNNs avec m{\'e}canisme d{'}attention obtiennent de meilleures performances que les RNNs r{\'e}cemment propos{\'e}s pour la t{\^a}che d{'}{\'e}tiquetage en concepts s{\'e}mantiques. Sur le corpus MEDIA, un corpus fran{\c{c}}ais {\'e}tat de l{'}art pour la compr{\'e}hension d{\'e}di{\'e} {\`a} la r{\'e}servation d{'}h{\^o}tel et aux informations touristiques, les exp{\'e}riences montrent qu{'}un RNN bidirectionnel atteint une f-mesure de 79,51 tandis que le m{\^e}me syst{\`e}me int{\'e}grant le m{\'e}canisme d{'}attention permet d{'}atteindre une f-mesure de 80,27.",
    language = "French",
}
@inproceedings{bouaziz-etal-2016-un-sous,
    title = "Un Sous-espace Th{\'e}matique Latent pour la Compr{\'e}hension du Langage Parl{\'e} (A Latent Topic-based Subspace for Spoken Language Understanding)",
    author = "Bouaziz, Mohamed  and
      Morchid, Mohamed  and
      Bousquet, Pierre-Michel  and
      Dufour, Richard  and
      Janod, Killian  and
      Kheder, Waad Ben  and
      Linar{\`e}s, Georges",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.73",
    pages = "651--659",
    abstract = "Les applications de compr{\'e}hension du langage parl{\'e} sont moins performantes si les documents transcrits automatiquement contiennent un taux d{'}erreur-mot {\'e}lev{\'e}. Des solutions r{\'e}centes proposent de projeter ces transcriptions dans un espace de th{\`e}mes, comme par exemple l{'}allocation latente de Dirichlet (LDA), la LDA supervis{\'e}e ainsi que le mod{\`e}le author-topic (AT). Une repr{\'e}sentation compacte originale, appel{\'e}e c-vector, a {\'e}t{\'e} r{\'e}cemment introduite afin de surmonter la difficult{\'e} li{\'e}e au choix de la taille de ces espaces th{\'e}matiques. Cette repr{\'e}sentation am{\'e}liore la robustesse aux erreurs de transcription, en compactant les diff{\'e}rentes repr{\'e}sentations LDA d{'}un document parl{\'e} dans un espace r{\'e}duit. Le d{\'e}faut majeur de cette m{\'e}thode est le nombre {\'e}lev{\'e} de sous-t{\^a}ches n{\'e}cessaires {\`a} la construction de l{'}espace c-vector. Cet article propose de corriger ce d{\'e}faut en utilisant un cadre original fond{\'e} sur un espace de caract{\'e}ristiques robustes de faible dimension provenant d{'}un ensemble de mod{\`e}les AT consid{\'e}rant {\`a} la fois le contenu du dialogue parl{\'e} (les mots) et la classe du document. Les exp{\'e}rimentations, conduites sur le corpus DECODA, montrent que la repr{\'e}sentation propos{\'e}e permet un gain de plus de 2.5 points en termes de conversations correctement classifi{\'e}es.",
    language = "French",
}
@inproceedings{kuhnert-antolik-2016-strategies,
    title = "Strat{\'e}gies d{'}adaptation de la vitesse d{'}articulation lors de conversations spontan{\'e}es entre locuteurs natifs et non-natifs (Adaptation of articulation rate in spontaneous speech between native speakers and {L}2 learners)",
    author = {K{\"u}hnert, Barbara  and
      Antol{\'\i}k, Tanja Kocjan{\v{c}}i{\v{c}}},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.74",
    pages = "660--668",
    abstract = "Cet article examine la vitesse d{'}articulation dans un corpus de conversations spontan{\'e}es entre locuteurs natifs et non-natifs. L{'}objectif est d{'}{\'e}tudier (i) dans quelle mesure les locuteurs natifs adaptent dans leur L1 leur vitesse d{'}articulation aux apprenants L2 et (ii) dans quelle mesure les deux locuteurs en interaction ont tendance {\`a} rapprocher ou {\`a} dissocier leurs caract{\'e}ristiques temporelles au cours d{'}une conversation. Les donn{\'e}es proviennent du corpus SITAF d{'}interactions tandem en anglais-fran{\c{c}}ais. A ce jour, 10 sujets ont {\'e}t{\'e} analys{\'e}s, chacun ayant {\'e}t{\'e} enregistr{\'e} dans trois conditions diff{\'e}rentes : en utilisant sa L1 avec un autre locuteur natif, en utilisant sa L1 avec un apprenant L2, et en utilisant sa L2 avec un interlocuteur parlant sa propre L1. Les r{\'e}sultats indiquent que les propri{\'e}t{\'e}s rythmiques de la L1 ont une nette influence sur les variations de la vitesse d{'}articulation des locuteurs non seulement lorsqu{'}ils interagissent dans leur L2 mais {\'e}galement dans leurs strat{\'e}gies d{'}adaptation lorsqu{'}ils interagissent avec des apprenants.",
    language = "French",
}
@inproceedings{luc-etal-2016-stress,
    title = "Stress, charge cognitive et signal de parole : {\'e}tude exploratoire aupr{\`e}s de pilotes de chasse. (Stress, cognitive load and speech signal : an exploratory study among fighter pilots)",
    author = "Luc, Stavaux  and
      Albart, Margaux  and
      Delvaux, V{\'e}ronique  and
      Huet, Kathy  and
      Piccaluga, Myriam  and
      Harmegnies, Bernard",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.75",
    pages = "669--677",
    abstract = "Cet article traite des effets de la charge cognitive sur la fr{\'e}quence fondamentale de pilotes de F-16 plac{\'e}s dans un sc{\'e}nario de vol de nuit. La charge cognitive a {\'e}t{\'e} estim{\'e}e {\`a} l{'}aide de param{\`e}tres li{\'e}s {\`a} la t{\^a}che (h{\'e}t{\'e}ro-{\'e}valuation), {\`a} l{'}individu (anxi{\'e}t{\'e}, auto-{\'e}valuation du stress ressenti) et {\`a} la situation (simulation contr{\^o}l{\'e}e). Nos r{\'e}sultats montrent que l{'}{\'e}cart m{\'e}lodique est un bon candidat pour {\'e}valuer le niveau de la charge cognitive, m{\^e}me si la relation entre eux pr{\'e}sente des profils individuels sp{\'e}cifiques. La cr{\'e}ation d{'}une typologie des situations de communication, l{'}adjonction d{'}autres indices acoustiques et le croisement avec des donn{\'e}es physiologiques constituent les perspectives de cette {\'e}tude.",
    language = "French",
}
@inproceedings{martin-2016-structure,
    title = "Structure prosodique des langues romanes (Prosodic Structures of {R}omance Languages)",
    author = "Martin, Philippe",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.76",
    pages = "678--686",
    abstract = "La description phonologique de la structure prosodique des langues romanes apparait similaire lorsque les interactions entre les accents m{\'e}lodiques est prise en compte (ce qui n{'}est pas le cas dans la th{\'e}orie autosegmentale-m{\'e}trique). L{'}analyse acoustique de plus de 2600 {\'e}nonc{\'e}s lus et spontan{\'e}s sugg{\`e}re que la r{\'e}alisation des accents m{\'e}lodiques, d{\'e}crits en termes de contours m{\'e}lodiques plut{\^o}t que de cibles tonales, indiquent avec les contours de fronti{\`e}re, des relations de d{\'e}pendance « vers la droite » entre groupes accentuels. Ces relations permettent par incr{\'e}mentation successive dans l{'}axe du temps la reconstitution par l{'}auditeur de la structure prosodique voulue par le locuteur. Dans ce cadre th{\'e}orique, les langues romanes (italien, espagnol, catalan, portugais, roumain) utilisent les m{\^e}mes contours phonologiques pour indiquer les relations de d{\'e}pendance menant au codage de la structure prosodique. Le fran{\c{c}}ais, d{\'e}pourvu d{'}accent lexical, utilise un syst{\`e}me de contours diff{\'e}rent.",
    language = "French",
}
@inproceedings{labrunie-etal-2016-suivi,
    title = "Suivi de contours d{'}articulateurs orofaciaux {\`a} partir d{'}{IRM} dynamique (Orofacial articulators tracking from dynamic {MRI})",
    author = {Labrunie, Mathieu  and
      Badin, Pierre  and
      Lamalle, Laurent  and
      Vilain, Coriandre  and
      Bo{\"e}, Louis-Jean  and
      Frahm, Jens  and
      Birkholz, Peter},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.77",
    pages = "687--695",
    abstract = "Nous pr{\'e}sentons une m{\'e}thode de pr{\'e}diction de contours m{\'e}diosagittaux des organes orofaciaux de la parole et la d{\'e}glutition {\`a} partir d{'}images IRM dynamiques. Pour chaque locuteur, un ensemble de 60 images repr{\'e}sentatives pour lesquelles les contours ont {\'e}t{\'e} trac{\'e}s manuellement permet d{'}entra{\^\i}ner des mod{\`e}les ACP d{'}images et de contours articulatoires, ainsi qu{'}un mod{\`e}le multilin{\'e}aire qui pr{\'e}dit les param{\`e}tres des contours {\`a} partir des param{\`e}tres des images. Les contours obtenus sont ensuite corrig{\'e}s par des mod{\`e}les de forme actifs (ASM) modifi{\'e}s utilisant les informations locales de profils d{'}intensit{\'e} de pixels le long des normales aux contours. Les performances de cette m{\'e}thode (erreurs moyennes « points {\`a} contour » entre 0,57 et 0,70 mm) sont insensibles au type de s{\'e}quence IRM ({\'e}cho de gradient avec {\'e}chantillonnage synchronis{\'e} ou {\'e}cho de gradient radial hautement sous-{\'e}chantillonn{\'e}), sont meilleures que celles de la litt{\'e}rature, et rendent possible le traitement de volumineux corpus d{'}images IRM dynamiques.",
    language = "French",
}
@inproceedings{wottawa-adda-decker-2016-sur,
    title = "Sur les traces acoustiques de /ʃ/ et /{\c{c}}/ en allemand {L}2 (Acoustic tracing of /{S}/ and /{\c{c}}/ in {G}erman {L}2)",
    author = "Wottawa, Jane  and
      Adda-Decker, Martine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.78",
    pages = "696--704",
    abstract = "Les apprenants fran{\c{c}}ais de l{'}allemand ont des difficult{\'e}s {\`a} produire la fricative palatale sourde allemande /{\c{c}}/ (Ich-Laut) et ont tendance {\`a} la remplacer par la fricative post-alv{\'e}olaire /S/. Nous nous demandons si avec des mesures acoustiques ces impr{\'e}cisions de production peuvent {\^e}tre quantifi{\'e}es d{'}une mani{\`e}re plus objective. Deux mesures acoustiques ont {\'e}t{\'e} examin{\'e}es afin de distinguer au mieux /S/ et /{\c{c}}/ dans un contexte VC en position finale de mot dans des productions de locuteurs germanophones natifs. Elles servent ensuite {\`a} quantifier les difficult{\'e}s de production des apprenants fran{\c{c}}ais. 285 tokens de 20 locuteurs natifs et 20 locuteurs L2 ont {\'e}t{\'e} analys{\'e}s. Les mesures appliqu{\'e}es sont le centre de gravit{\'e} spectral et des rapports d{'}intensit{\'e} par bande de fr{\'e}quence. Sur les productions de locuteurs natifs, les r{\'e}sultats montrent que la mesure la plus fiable pour distinguer acoustiquement /S/ et /{\c{c}}/ est le ratio d{'}intensit{\'e} entre fr{\'e}quences hautes (4-7 kHz) et basses (1-4 kHz). Les mesures confirment {\'e}galement les difficult{\'e}s de production des locuteurs natifs fran{\c{c}}ais.",
    language = "French",
}
@inproceedings{tran-etal-2016-syllabe,
    title = "Syllabe {CVC} et cycle mandibulaire : une {\'e}tude articulatoire des asym{\'e}tries. Le cas du vietnamien ({CVC} syllable and jaw cycle: an articulatory study of asymmetries)",
    author = "Tran, Thi Thuy Hien  and
      Vall{\'e}e, Nathalie  and
      Gerber, Silvain",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.79",
    pages = "705--713",
    abstract = "Cette {\'e}tude se situe dans le cadre d{'}un projet qui tente d{'}{\'e}tablir le lien entre asym{\'e}tries phon{\'e}tique et phonologique de la syllabe, plus sp{\'e}cifiquement le lien entre caract{\'e}ristiques du geste mandibulaire et MOP, Maximum Onset Principle, principe phonologique empirique qui affecte les segments consonantiques {\`a} la position initiale de syllabe plut{\^o}t que finale. Plusieurs travaux ant{\'e}rieurs sur l{'}anglais am{\'e}ricain ont montr{\'e} l{'}existence d{'}asym{\'e}tries au niveau des phases du cycle mandibulaire qui pourraient expliquer certaines tendances des structures syllabiques et notamment la structure canonique CV (plut{\^o}t que VC). Dans ce projet, une premi{\`e}re {\'e}tude sur le fran{\c{c}}ais a confirm{\'e} un patron d{'}asym{\'e}tries mais inverse {\`a} celui trouv{\'e} pour l{'}anglais. Nous pr{\'e}sentons les premiers r{\'e}sultats obtenus pour le vietnamien. Les r{\'e}sultats sont discut{\'e}s dans deux cadres th{\'e}oriques, Frame/Content Theory et Articulatory Phonology, le premier attribuant un r{\^o}le fondamental au geste mandibulaire dans la phonologie de la syllabe, le second ne lui conc{\'e}dant qu{'}un r{\^o}le secondaire.",
    language = "French",
}
@inproceedings{maguer-etal-2016-de,
    title = "De l{'}utilisation de descripteurs issus de la linguistique computationnelle dans le cadre de la synth{\`e}se par {HMM} (Toward the use of information density based descriptive features in {HMM} based speech synthesis)",
    author = "Maguer, S{\'e}bastien Le  and
      Moebius, Bernd  and
      Steiner, Ingmar  and
      Lolive, Damien",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.80",
    pages = "714--722",
    abstract = "Durant les derni{\`e}res d{\'e}cennies, la mod{\'e}lisation acoustique effectu{\'e}e par les syst{\`e}mes de synth{\`e}se de parole param{\'e}trique a fait l{'}objet d{'}une attention particuli{\`e}re. Toutefois, dans la plupart des syst{\`e}mes connus, l{'}ensemble des descripteurs linguistiques utilis{\'e}s pour repr{\'e}senter le texte reste identique. Plus specifiquement, la mod{\'e}lisation de la prosodie reste guid{\'e}e par des descripteurs de bas niveau comme l{'}information d{'}accentuation de la syllabe ou bien l{'}{\'e}tiquette grammaticale du mot. Dans cet article, nous proposons d{'}int{\'e}grer des informations bas{\'e}es sur la pr{\'e}dictibilit{\'e} d{'}un {\'e}v{\`e}nement (la syllabe ou le mot). Plusieurs {\'e}tudes indiquent une corr{\'e}lation forte entre cette mesure, fortement pr{\'e}sente dans la linguistique computationnelle, et certaines sp{\'e}cificit{\'e}s lors de la production humaine de la parole. Notre hypoth{\`e}se est donc que l{'}ajout de ces descripteurs am{\'e}liore la mod{\'e}lisation de la prosodie. Cet article se focalise sur une analyse objective de l{'}apport de ces descripteurs sur la synth{\`e}se HMM pour la langue anglaise et fran{\c{c}}aise.",
    language = "French",
}
@inproceedings{ghannay-etal-2016-utilisation,
    title = "Utilisation des repr{\'e}sentations continues des mots et des param{\`e}tres prosodiques pour la d{\'e}tection d{'}erreurs dans les transcriptions automatiques de la parole (Combining continuous word representation and prosodic features for {ASR} error detection)",
    author = "Ghannay, Sahar  and
      Est{\`e}ve, Yannick  and
      Camelin, Nathalie  and
      Dutrey, Camille  and
      Santiago, Fabian  and
      Adda-Decker, Martine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.81",
    pages = "723--731",
    abstract = "R{\'e}cemment, l{'}utilisation des repr{\'e}sentations continues de mots a connu beaucoup de succ{\`e}s dans plusieurs t{\^a}ches de traitement du langage naturel. Dans cet article, nous proposons d{'}{\'e}tudier leur utilisation dans une architecture neuronale pour la t{\^a}che de d{\'e}tection des erreurs au sein de transcriptions automatiques de la parole. Nous avons {\'e}galement exp{\'e}riment{\'e} et {\'e}valu{\'e} l{'}utilisation de param{\`e}tres prosodiques en suppl{\'e}ments des param{\`e}tres classiques (lexicaux, syntaxiques, . . .). La principale contribution de cet article porte sur la combinaison de diff{\'e}rentes repr{\'e}sentations continues de mots : plusieurs approches de combinaison sont propos{\'e}es et {\'e}valu{\'e}es afin de tirer profit de leurs compl{\'e}mentarit{\'e}s. Les exp{\'e}riences sont effectu{\'e}es sur des transcriptions automatiques du corpus ETAPE g{\'e}n{\'e}r{\'e}es par le syst{\`e}me de reconnaissance automatique du LIUM. Les r{\'e}sultats obtenus sont meilleurs que ceux d{'}un syst{\`e}me {\'e}tat de l{'}art bas{\'e} sur les champs al{\'e}atoires conditionnels. Pour terminer, nous montrons que la mesure de confiance produite est particuli{\`e}rement bien calibr{\'e}e selon une {\'e}valuation en terme d{'}Entropie Crois{\'e}e Normalis{\'e}e (NCE).",
    language = "French",
}
@inproceedings{ballier-etal-2016-variabilite,
    title = "Variabilit{\'e} des syllabes r{\'e}alis{\'e}es par des apprenants de l{'}anglais (Analysing syllable variability in a {F}rench learner corpus of {E}nglish)",
    author = "Ballier, Nicolas  and
      Martin, Philippe  and
      Amand, Maelle",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.82",
    pages = "732--740",
    abstract = "Cette contribution analyse la segmentation syllabique des francophones du corpus d{'}apprenant d{'}anglais ANGLISH (Tortel 2009). A partir d{'}une m{\'e}thode d{'}alignement par alignement forc{\'e}, on montre la pertinence d{'}une analyse de l{'}interlangue fond{\'e}e sur la comparaison des dur{\'e}es des syllabes. La comparaison des r{\'e}alisations est ici centr{\'e}e sur une typologie des syllabes fond{\'e}e sur des propri{\'e}t{\'e}s distributionnelles, accentuelles et o{\`u} l{'}interlangue tient sa place (risques d{'}isosyllabicit{\'e} les plus manifestes pour les r{\'e}alisations des francophones). La variabilit{\'e} des r{\'e}alisations des syllabes est appr{\'e}ci{\'e}e en fonction des propri{\'e}t{\'e}s positionnelles, accentuelles et structurelles des syllabes. L{'}{\'e}tude d{\'e}montre l{'}int{\'e}r{\^e}t d{'}une approche fonctionnelle des syllabes, plus pertinente que les intervalles interconsonantiques et intervocaliques inspir{\'e}s de Ramus et al. (1999) pour la discrimination du niveau des locuteurs.",
    language = "French",
}
@inproceedings{lecocq-etal-2016-variabilite,
    title = "Variabilit{\'e} du geste palatal : effet du locuteur, de la structure syllabique et de l{'}accent sur diff{\'e}rents types de consonnes en russe (Palatal gesture variability: speaker, stress and syllabic structure effects in {R}ussian)",
    author = "Lecocq, Ekaterina Biteeva  and
      Vall{\'e}e, Nathalie  and
      Gerber, Silvain  and
      Savariaux, Christophe",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.83",
    pages = "741--749",
    abstract = "Les linguistes se sont r{\'e}guli{\`e}rement pench{\'e}s sur la description du trait consonantique [+palatal] ; pourtant, le manque de donn{\'e}es exp{\'e}rimentales constitue un obstacle au classement des consonnes concern{\'e}es. Peu de travaux ont abord{\'e} la question du contr{\^o}le du geste lingual dans l{'}articulation palatale. Cependant, ils montrent que celui-ci semble bien plus complexe que dans d{'}autres consonnes. En russe, la plupart des consonnes poss{\`e}dent une contrepartie palatalis{\'e}e ce qui permet d{'}{\'e}tudier les diff{\'e}rences de r{\'e}alisation du trait palatal au sein du m{\^e}me syst{\`e}me. Nous proposons ici, {\`a} partir de donn{\'e}es acquises avec un articulographe {\'e}lectromagn{\'e}tique, de caract{\'e}riser la variabilit{\'e} du geste palatal impliqu{\'e} dans la r{\'e}alisation de diff{\'e}rents types de consonnes palatalis{\'e}es et pr{\'e}palatales du russe en fonction des facteurs locuteur, accent et structure syllabique.",
    language = "French",
}
@inproceedings{catteau-etal-2016-variation,
    title = "Variation prosodique et traduction po{\'e}tique ({LSF}/fran{\c{c}}ais) : Que devient la prosodie lorsqu{'}elle change de canal ? (Prosodic variation and poetic translation ({LSF}/{F}rench): What happens to prosody with a channel change?)",
    author = "Catteau, Fanny  and
      Blondel, Marion  and
      Vincent, Coralie  and
      Guyot, Patrice  and
      Boutet, Dominique",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.84",
    pages = "750--758",
    abstract = "L{'}{\'e}tude de la prosodie des langues vocales repose en partie sur la mesure des param{\`e}tres de dur{\'e}e, d{'}intensit{\'e} et de fr{\'e}quence sonores. Les langues des signes, quant {\`a} elles, empruntent le canal visuogestuel et mobilisent des articulateurs manuels et non manuels (buste, t{\^e}te, {\'e}l{\'e}ments du visage). Notre {\'e}tude a pour objectif d{'}{\'e}tablir des outils permettant de comparer, au niveau prosodique, la traduction en fran{\c{c}}ais de s{\'e}quences po{\'e}tiques et la version originale en langue des signes fran{\c{c}}aise (LSF). Nous avons recueilli des donn{\'e}es vid{\'e}o augment{\'e}es de capture de mouvement {--} qui offrent plusieurs pistes d{'}exploration des param{\`e}tres prosodiques pour la LSF {--} ainsi que des donn{\'e}es audio des traductions en fran{\c{c}}ais {--} qui r{\'e}v{\`e}lent les strat{\'e}gies des interpr{\`e}tes pour interpr{\'e}ter la variation prosodique.",
    language = "French",
}
@inproceedings{pepiot-2016-voix,
    title = "Voix de femmes, voix d{'}hommes: une {\'e}tude du voice onset time, de la r{\'e}partition consonnes/voyelles et du d{\'e}bit de parole chez des locuteurs francophones et anglophones am{\'e}ricains (Female and male speech: a study of {VOT}, {C}/{V} temporal distribution and speech rate in Parisian {F}rench and {A}merican {E}nglish speakers)",
    author = "P{\'e}piot, Erwan",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.85",
    pages = "759--767",
    abstract = "{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_} La pr{\'e}sente {\'e}tude est une analyse acoustique de mots et pseudo-mots de type /CVCV/ produits par des locuteurs anglophones du nord-est des {\'E}tats-Unis (5 femmes, 5 hommes) et des francophones parisiens (5 femmes, 5 hommes). Le VOT des consonnes occlusives initiales, la dur{\'e}e des {\'e}nonc{\'e}s, ainsi que la r{\'e}partition temporelle consonnes/voyelles ont {\'e}t{\'e} mesur{\'e}s. Des diff{\'e}rences inter-genres significatives ont {\'e}t{\'e} observ{\'e}es dans les deux langues sur chacun des param{\`e}tres test{\'e}s : le contraste de VOT entre les occlusives sourdes et vois{\'e}es s{'}est r{\'e}v{\'e}l{\'e} plus important chez les locutrices, le d{\'e}bit de parole plus {\'e}lev{\'e} chez les locuteurs masculins, et la proportion occup{\'e}e par les consonnes plus importantes chez les femmes. Ces r{\'e}sultats sugg{\`e}rent une tendance {\`a} la recherche d{'}une plus grande intelligibilit{\'e} chez les locutrices. Les diff{\'e}rences acoustiques femmes-hommes seraient donc en partie construites socialement.",
    language = "French",
}
@inproceedings{lewis-2016-voyelles,
    title = "Voyelles moyennes en fran{\c{c}}ais cal{\'e}donien : propri{\'e}t{\'e}s phon{\'e}tiques acoustiques (Mid vowels in New Caledonian {F}rench: Acoustic phonetic properties)",
    author = "Lewis, Eleanor",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 1 : JEP",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-jep.86",
    pages = "768--776",
    abstract = "Cette {\'e}tude examine la r{\'e}alisation des voyelles moyennes /e, ɛ, {\o}, {\oe}, o, ɔ/ par dix locuteurs du fran{\c{c}}ais cal{\'e}donien. Les propri{\'e}t{\'e}s formantiques de ces voyelles sont analys{\'e}es en ce qui concerne le genre de syllabe dans lesquelles elles se produisent. La dur{\'e}e des voyelles mi-ferm{\'e}es et miouvertes produites en paires minimales est statistiquement compar{\'e}e. Les r{\'e}sultats indiquent que les locuteurs de cette vari{\'e}t{\'e} ont tendance {\`a} respecter cat{\'e}goriquement la loi de position, tel que les variantes mi-ferm{\'e}es se pr{\'e}sentent dans les syllabes ouvertes et les variantes mi-ouvertes se pr{\'e}sentent dans les syllabes ferm{\'e}es. Il existe pourtant une certaine variation individuelle concernant le niveau de conformit{\'e} {\`a} cette loi. Cette {\'e}tude met {\'e}galement en avant des indices de l{'}ant{\'e}riorisation du /ɔ/ (et du /o/ en syllabe ferm{\'e}e), une caract{\'e}ristique qui a {\'e}t{\'e} document{\'e}e dans d{'}autres vari{\'e}t{\'e}s du fran{\c{c}}ais.",
    language = "French",
}
@proceedings{jep-taln-recital-2016-actes-de-la-conjointe-jep,
    title = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. Volume 4 : Conf{\'e}rences invit{\'e}es",
    editor = "Danlos, Laurence  and
      Hamon, Thierry",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-invite.0",
}
@inproceedings{chiarcos-2016-corpora,
    title = "Corpora and Linguistic Linked Open Data: Motivations, Applications, Limitations",
    author = "Chiarcos, Christian",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. Volume 4 : Conf{\'e}rences invit{\'e}es",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-invite.1",
    pages = "1--2",
    abstract = "Linguistic Linked Open Data (LLOD) is a technology and a movement in several disciplines working with language resources, including Natural Language Processing, general linguistics, computational lexicography and the localization industry. This talk describes basic principles of Linguistic Linked Open Data and their application to linguistically annotated corpora, it summarizes the current status of the Linguistic Linked Open Data cloud and gives an overview over selected LLOD vocabularies and their uses.",
}
@inproceedings{liberman-2016-human,
    title = "From Human Language Technology to Human Language Science",
    author = "Liberman, Mark",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. Volume 4 : Conf{\'e}rences invit{\'e}es",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-invite.2",
    pages = "3--3",
    abstract = "Thirty years ago, in order to get past roadblocks in Machine Translation and Automatic Speech Recognition, DARPA invented a new way to organize and manage technological R{\&}D : a {``}common task{''} is defined by a formal quantitative evaluation metric and a body of shared training data, and researchers join an open competition to compare approaches. Over the past three decades, this method has produced steadily improving technologies, with many practical applications now possible. And Moore{'}s law has created a sort of digital shadow universe, which increasingly mirrors the real world in flows and stores of bits, while the same improvements in digital hardware and software make it increasingly easy to pull content out of the these rivers and oceans of information. It{'}s natural to be excited about these technologies, where we can see an open road to rapid improvements beyond the current state of the art, and an explosion of near-term commercial applications. But there are some important opportunities in a less obvious direction. Several areas of scientific and humanistic research are being revolutionized by the application of Human Language Technology. At a minimum, orders of magnitude more data can be addressed with orders of magnitude less effort - but this change also transforms old theoretical questions, and poses new ones. And eventually, new modes of research organization and funding are likely to emerge..",
}
@proceedings{jep-taln-recital-2016-actes-de-la-conjointe-jep-taln,
    title = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    editor = "Danlos, Laurence  and
      Hamon, Thierry",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.0",
}
@inproceedings{pogodalla-2016-acgtk,
    title = "{ACG}tk : un outil de d{\'e}veloppement et de test pour les grammaires cat{\'e}gorielles abstraites ({ACG} {TK} : a Toolkit to Develop and Test Abstract Categorial Grammars )",
    author = "Pogodalla, Sylvain",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.1",
    pages = "1--2",
    abstract = "Nous pr{\'e}sentons un outil, ACG TK, offrant un environnement de d{\'e}veloppement et d{'}utilisation des grammaires cat{\'e}gorielles abstraites pour l{'}analyse et la g{\'e}n{\'e}ration.",
    language = "French",
}
@inproceedings{de-mazancourt-etal-2016-un,
    title = "Un analyseur de conversations pour la relation client (Parsing email and chat conversations for customer support softwares)",
    author = {de Mazancourt, Hugues  and
      Recourc{\'e}, Ga{\"e}lle  and
      Salim, Soufian},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.2",
    pages = "3--5",
    abstract = "Cette d{\'e}monstration a pour objet de pr{\'e}senter l{'}utilisation d{'}un analyseur de conversations par email ou chat dans le cadre d{'}une application de support client : mise en valeur des demandes d{'}action, rep{\'e}rage des th{\`e}mes dangereux, tableau de bord pour le superviseur, alertes pour l{'}agent ...",
    language = "French",
}
@inproceedings{bui-quang-etal-2016-appfm,
    title = "{A}pp{FM}, une plate-forme de gestion de modules de {TAL} ({A}pp{FM}, a tool for managing {NLP} modules)",
    author = "Bui-Quang, Paul  and
      Grau, Brigitte  and
      Paroubek, Patrick",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.3",
    pages = "6--8",
    abstract = "AppFM 1 est un outil {\`a} mi-chemin entre un environnement de cr{\'e}ation de cha{\^\i}nes modulaires de TAL et un gestionnaire de services syst{\`e}mes. Il permet l{'}int{\'e}gration d{'}applications ayant des d{\'e}pendances complexes en des cha{\^\i}nes de traitements r{\'e}utilisables facilement par le biais de multiples interfaces.",
    language = "French",
}
@inproceedings{roze-2016-communicotool,
    title = "{C}ommunico{T}ool Advance, un prototype d{'}application d{'}aide {\`a} la communication ({C}ommunico{T}ool Advance: an assistive communication app prototype)",
    author = "Roze, Charlotte",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.4",
    pages = "9--11",
    abstract = "CommunicoTool Advance est un prototype d{'}application mobile d{'}aide {\`a} la communication destin{\'e}e {\`a} des personnes qui pr{\'e}sentent des troubles moteurs et des troubles de la parole.",
    language = "French",
}
@inproceedings{mesnard-etal-2016-construction,
    title = "Construction automatis{\'e}e d{'}une base de connaissances (Automated Building a Knowledge Base)",
    author = "Mesnard, Olivier  and
      Dupont, Yoann  and
      Guillemot, J{\'e}r{\'e}my  and
      Rahman, Rashedur",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.5",
    pages = "12--14",
    abstract = "Le syst{\`e}me pr{\'e}sent{\'e} permet la construction automatis{\'e}e d{'}une base de connaissances sur des personnes et des organisations {\`a} partir d{'}une collection de documents. Il s{'}appuie sur de l{'}apprentissage distant pour l{'}extraction d{'}hypoth{\`e}ses de relations entre mentions d{'}entit{\'e}s qu{'}il consolide avec des informations orient{\'e}es graphe.",
    language = "French",
}
@inproceedings{alrahabi-2016-e,
    title = "{E}-Quotes : un outil de navigation textuelle guid{\'e}e par les annotations s{\'e}mantiques ({E}-Quotes : A semantic annotations-driven tool for textual navigation)",
    author = "Alrahabi, Motasem",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.6",
    pages = "15--17",
    abstract = "Nous pr{\'e}sentons E-Quotes, un outil de navigation textuelle guid{\'e}e par les annotations s{\'e}mantiques. Le syst{\`e}me permet de localiser les mots cl{\'e}s et leurs variantes dans les citations s{\'e}mantiquement cat{\'e}goris{\'e}s dans corpus annot{\'e}, et de naviguer entre ces citations. Nous avons exp{\'e}riment{\'e} ce syst{\`e}me sur un corpus de litt{\'e}rature fran{\c{c}}aise automatiquement annot{\'e} selon des cat{\'e}gories s{\'e}mantiques pr{\'e}sentes dans le contexte des citations, comme par exemple la d{\'e}finition, l{'}argumentation, l{'}opinion, l{'}ironie ou la rumeur rapport{\'e}es.",
    language = "French",
}
@inproceedings{damnati-etal-2016-exploration,
    title = "Exploration de collections d{'}archives multim{\'e}dia dans le contexte des Humanit{\'e}s Num{\'e}riques : revisiter {TALN}{'}2015 ? (Exploring multimedia archives in the context of Digital Humanities: browsing {TALN}{'}2015?)",
    author = "Damnati, G{\'e}raldine  and
      Denjean, Marc  and
      Charlet, Delphine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.7",
    pages = "18--20",
    abstract = "Cette d{\'e}monstration pr{\'e}sente un prototype d{'}exploration de contenus multim{\'e}dias d{\'e}velopp{\'e} dans le but de faciliter l{'}acc{\`e}s aux contenus de la Connaissance. Apr{\`e}s une extraction automatique de m{\'e}tadonn{\'e}es, les contenus sont index{\'e}s et accessibles via un moteur de recherche sp{\'e}cifique. Des fonctionnalit{\'e}s innovantes de navigation {\`a} l{'}int{\'e}rieur des contenus sont {\'e}galement pr{\'e}sent{\'e}es. La collection des enregistrements vid{\'e}o de TALN{'}2015 sert de support privil{\'e}gi{\'e} {\`a} cette d{\'e}monstration.",
    language = "French",
}
@inproceedings{kostov-2016-fleximac,
    title = "{F}lexi{M}ac 1.1. {--} Conjugueur automatique des verbes mac{\'e}doniens ({F}lexi{M}ac 1)",
    author = "Kostov, Jovan",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.8",
    pages = "21--23",
    abstract = "Cette d{\'e}monstration pr{\'e}sente la plateforme FlexiMac 1.1., g{\'e}n{\'e}rateur automatique des verbes mac{\'e}doniens qui permet de conjuguer un verbe dans la plupart des modes et des temps, sans faire appel {\`a} une base de donn{\'e}es. Apr{\`e}s un bref expos{\'e} du fonctionnement de la plateforme, nous allons {\'e}galement {\'e}voquer les travaux actuels qui en ont d{\'e}coul{\'e}, et ceux qui sont en train d{'}{\'e}merger dans une perspective de traitement automatique du mac{\'e}donien en tant que langue europ{\'e}enne peu-dot{\'e}e.",
    language = "French",
}
@inproceedings{berment-etal-2016-heloise,
    title = {H{\'e}lo{\"\i}se, une plate-forme pour d{\'e}velopper des syst{\`e}mes de {TA} compatibles Ariane en r{\'e}seau (Heloise, a platform for collaborative development of Ariane-compatible {MT} systems)},
    author = "Berment, Vincent  and
      Boitet, Christian  and
      de Mal{\'e}zieux, Guillaume",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.9",
    pages = "24--26",
    abstract = {Dans cette d{\'e}mo, nous montrons comment utiliser H{\'e}lo{\"\i}se pour d{\'e}velopper des syst{\`e}mes de TA.},
    language = "French",
}
@inproceedings{doumouro-etal-2016-identification,
    title = "Identification de lieux dans les messageries mobiles (Place extraction from smartphone messaging applications)",
    author = "Doumouro, Cl{\'e}ment  and
      Ball, Adrien  and
      Dureau, Joseph  and
      Raybaud, Sylvain  and
      Yahya, Ramzi Ben",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.10",
    pages = "27--28",
    abstract = "Nous pr{\'e}sentons un syst{\`e}me d{'}identification de lieux dans les messageries typiquement utilis{\'e}es sur smartphone. L{'}impl{\'e}mentation sur mobile et son cort{\`e}ge de contraintes, ainsi que la faible quantit{\'e} de ressources disponibles pour le type de langage utilis{\'e} rendent la t{\^a}che particuli{\`e}rement d{\'e}licate. Ce syst{\`e}me, impl{\'e}ment{\'e} sur Android, atteint une pr{\'e}cision de 30{\%} et un rappel de 72{\%}.",
    language = "French",
}
@inproceedings{hamon-2016-interface,
    title = "Interface Web pour l{'}annotation morpho-syntaxique de textes (Web interface for the morpho-syntactic annotation of texts)",
    author = "Hamon, Thierry",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.11",
    pages = "29--31",
    abstract = "Nous pr{\'e}sentons une interface Web pour la visualisation etl{'}annotation de textes avec des {\'e}tiquettes morphosyntaxiques etdes lemmes. Celle-ci est actuellement utilis{\'e}e pour annoter destextes ukrainiens avec le jeu d{'}{\'e}tiquettes Multext-East.Les utilisateurs peuvent rapidement visualiser les annotationsassoci{\'e}es aux mots d{'}un texte, modifier les annotationsexistantes ou en ajouter de nouvelles. Les annotations peuvent charg{\'e}eset export{\'e}es en XML au format TEI, mais aussi sous forme tabul{\'e}e.Des scripts de conversion de format et de chargement dans une basede donn{\'e}es sont {\'e}galement mis {\`a} disposition.",
    language = "French",
}
@inproceedings{yvon-etal-2016-lecture,
    title = "Lecture bilingue augment{\'e}e par des alignements multi-niveaux (Augmenting bilingual reading with alignment information)",
    author = "Yvon, Fran{\c{c}}ois  and
      Xu, Yong  and
      Apidianaki, Marianna  and
      Pillias, Cl{\'e}ment  and
      Pierre, Cubaud",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.12",
    pages = "32--33",
    abstract = "Le travail qui a conduit {\`a} cette d{\'e}monstration combine des outils de traitement des langues multilingues, en particulier l{'}alignement automatique, avec des techniques de visualisation et d{'}interaction. Il vise {\`a} proposer des pistes pour le d{\'e}veloppement d{'}outils permettant de lire simultan{\'e}ment les diff{\'e}rentes versions d{'}un texte disponible en plusieurs langues, avec des applications en lecture de loisir ou en lecture professionnelle.",
    language = "French",
}
@inproceedings{bernard-etal-2016-lne,
    title = "{LNE}-Visu : a tool to explore and visualize multimedia data",
    author = "Bernard, Guillaume  and
      Kahn, Juliette  and
      Galibert, Olivier  and
      Regnier, R{\'e}mi  and
      Demeyer, S{\'e}verine",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.13",
    pages = "34--36",
    abstract = "LNE-Visu : a tool to explore and visualize multimedia data LNE-Visu is a tool to explore and visualize multimedia data created for the LNE evaluation campaigns. 3 functionalities are available: explore and select data, visualize and listen data, apply significance tests",
}
@inproceedings{nerima-etal-2016-un,
    title = "Un outil multilingue d{'}extraction de collocations en ligne (This demo shows the web version of a multilingual collocation extraction tool)",
    author = "Nerima, Luka  and
      Seretan, Violeta  and
      Wehrli, Eric",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.14",
    pages = "37--39",
    abstract = "Cette d{\'e}monstration pr{\'e}sente la version web d{'}un outil multilingue d{'}extraction de collocations. Elle est destin{\'e}e aux lexicographes, aux traducteurs, aux enseignants et apprenants L2 et, plus g{\'e}n{\'e}ralement, aux linguistes d{\'e}sireux d{'}analyser et d{'}exploiter leurs propres corpus.",
    language = "French",
}
@inproceedings{copet-etal-2016-radarly,
    title = "Radarly : {\'e}couter et analyser le web conversationnel en temps r{\'e}el (Real time listening and analysis of the social web using Radarly)",
    author = "Copet, Jade  and
      de Carvalho, Christine  and
      Mouilleron, Virginie  and
      Tabutiaux, Benoit  and
      Zanghi, Hugo",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.15",
    pages = "40--42",
    abstract = "De par le contexte conversationnel digital, l{'}outil Radarly a {\'e}t{\'e} con{\c{c}}u pour permettre de traiter de grands volumes de donn{\'e}es h{\'e}t{\'e}rog{\`e}nes en temps r{\'e}el, de g{\'e}n{\'e}rer de nouveaux indicateurs et de les visualiser sur une interface coh{\'e}rente et confortable afin d{'}en tirer des analyses et {\'e}tudes pertinentes. Ce document expose les techniques et processus utilis{\'e}s pour extraire et traiter toutes ces donn{\'e}es.",
    language = "French",
}
@inproceedings{guibon-2016-sofa,
    title = "{SOFA} : Une plateforme d{'}analyse syntaxique en ligne pour l{'}ancien fran{\c{c}}ais ({SOFA} : An online Syntactic {O}ld {F}rench Annotator)",
    author = {Guibon, Ga{\"e}l},
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.16",
    pages = "43--45",
    abstract = "SOFA une application web d{\'e}di{\'e}e {\`a} l{'}{\'e}tiquetage syntaxique de l{'}ancien fran{\c{c}}ais. Cette plateforme est une d{\'e}monstration permettant d{'}appliquer sur n{'}importe quel texte, ou sur un des textes d{'}ancien fran{\c{c}}ais, des mod{\`e}les de lemmatisation, d{'}annotation morpho-syntaxique, et d{'}analyse syntaxique, en plus d{'}en visualiser les performances.",
    language = "French",
}
@inproceedings{embarek-embarek-2016-stam,
    title = "{STAM} : traduction des textes non structur{\'e}s (dialectes du Maghreb) ({STAM}: Translation of unstructured text (Maghreb dialects) The use of communication platforms (social networks, discussion forums)",
    author = "Embarek, Mehdi  and
      Embarek, Soumya",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.17",
    pages = "46--48",
    abstract = "L{'}utilisation des plateformes de communication (r{\'e}seaux sociaux, forums de discussions, ...) a pris une ampleur consid{\'e}rable. Ces plateformes permettent aux internautes d{'}exprimer leur avis concernant un sujet, demander ou {\'e}changer des informations, commenter un {\'e}v{\'e}nement, etc. Ainsi, nous retrouvons dans ces diff{\'e}rentes sources d{'}informations une quantit{\'e} importante de textes r{\'e}dig{\'e}s dans des dialectes locaux dont sont originaires les r{\'e}dacteurs. Cependant, ces textes non structur{\'e}s rendent l{'}exploitation des outils de traitement automatique des langues tr{\`e}s difficile. Le syst{\`e}me STAM aborde cette probl{\'e}matique en proposant un syst{\`e}me capable de transcrire automatiquement des textes {\'e}crits dans un dialecte parl{\'e} dans les pays du Maghreb en un texte facilement interpr{\'e}table et compr{\'e}hensible (fran{\c{c}}ais ou anglais).",
    language = "French",
}
@inproceedings{charras-etal-2016-un,
    title = "Un syst{\`e}me automatique de s{\'e}lection de r{\'e}ponse en domaine ouvert int{\'e}grable {\`a} un syst{\`e}me de dialogue social (An automatic open-domain response selection system integrable to a social dialogue system)",
    author = "Charras, Franck  and
      Dubuisson Duplessis, Guillaume  and
      Letard, Vincent  and
      Ligozat, Anne-Laure  and
      Rosset, Sophie",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.18",
    pages = "49--51",
    abstract = "Cette d{\'e}monstration pr{\'e}sente un syst{\`e}me de dialogue en domaine ouvert qui utilise une base d{'}exemples de dialogue automatiquement constitu{\'e}e depuis un corpus de sous-titres afin de g{\'e}rer un dialogue social de type « chatbot ».",
    language = "French",
}
@inproceedings{lecarpentier-etal-2016-tag,
    title = "Tag Thunder : plateforme de d{\'e}monstration et d{'}exp{\'e}rimentation (Tag Thunder : demonstration and experimentation platform)",
    author = "Lecarpentier, Jean-Marc  and
      Manishina, Elena  and
      Busson, Maxence  and
      Maurel, Fabrice  and
      Ferrari, Stephane",
    booktitle = "Actes de la conf{\'e}rence conjointe JEP-TALN-RECITAL 2016. volume 5 : D{\'e}monstrations",
    month = "7",
    year = "2016",
    address = "Paris, France",
    publisher = "AFCP - ATALA",
    url = "https://www.aclweb.org/anthology/2016.jeptalnrecital-demo.19",
    pages = "52--54",
    abstract = "Dans cette d{\'e}monstration, nous proposons un syst{\`e}me qui permettrait aux utilisateurs non-voyants d{'}obtenir le first glance d{'}une page web. L{'}objectif est de r{\'e}duire le temps d{'}acc{\`e}s {\`a} la structure logico-th{\'e}matique de la page et de favoriser le d{\'e}veloppement de strat{\'e}gies de lecture de haut niveau. Notre concept, appel{\'e} Tag Thunder, s{'}appuie sur une phase de segmentation de la page en zones, suivie d{'}une {\'e}tape de repr{\'e}sentation des zones par un mot ou groupe de mots, puis une vocalisation simultan{\'e}e de ces repr{\'e}sentants.",
    language = "French",
}
@proceedings{gwc-2016-global,
    title = "Proceedings of the 8th Global WordNet Conference (GWC)",
    editor = "Fellbaum, Christiane  and
      Vossen, Piek  and
      Mititelu, Verginica Barbu  and
      Forascu, Corina",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.0",
}
@inproceedings{ajotikar-kulkarni-2016-adverbs,
    title = "Adverbs in {S}anskrit {W}ordnet",
    author = "Ajotikar, Tanuja  and
      Kulkarni, Malhar",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.1",
    pages = "1--8",
    abstract = "The wordnet contains part-of-speech categories such as noun, verb, adjective and adverb. In Sanskrit, there is no formal distinction among nouns, adjectives and adverbs. This poses the question, is an adverb a separate category in Sanskrit? If not, then how do we accommodate it in a lexical resource? To investigate the issue, we attempt to study the complex nature of adverbs in Sanskrit and the policies adopted by Sanskrit lexicographers that would guide us in storing them in the Sanskrit wordnet.",
}
@inproceedings{alexeyevsky-temchenko-2016-word,
    title = "Word Sense Disambiguation in Monolingual Dictionaries for Building {R}ussian {W}ord{N}et",
    author = "Alexeyevsky, Daniil  and
      Temchenko, Anastasiya V.",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.2",
    pages = "9--14",
    abstract = "Russian Language is currently poorly supported with WordNet-like resources. One of the new efforts for building Russian WordNet involves mining the monolingual dictionaries. While most steps of the building process are straightforward, word sense disambiguation (WSD) is a source of problems. Due to limited word context specific WSD mechanism is required for each kind of relations mined. This paper describes the WSD method used for mining hypernym relations. First part of the paper explains the main reasons for choosing monolingual dictionaries as the primary source of information for Russian language WordNet and states some problems faced during the information extraction. The second part defines algorithm used to extract hyponym-hypernym pair. The third part describes the algorithm used for WSD.",
}
@inproceedings{aller-etal-2016-playing,
    title = "Playing Alias - efficiency for wordnet(s)",
    author = "Aller, Sven  and
      Orav, Heili  and
      Vare, Kadri  and
      Zupping, Sirli",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.3",
    pages = "15--20",
    abstract = "This paper describes an electronic variant of popular word game Alias where people have to guess words according to their associations via synonyms, opposites, hyperonyms etc. Lexical data comes from the Estonian Wordnet. The computer game Alias which draws information from Estonian Wordnet is useful at least for two reasons: it creates an opportunity to learn language through play, and it helps to evaluate and improve the quality of Estonian Wordnet.",
}
@inproceedings{arora-etal-2016-detecting,
    title = "Detecting Most Frequent Sense using Word Embeddings and {B}abel{N}et",
    author = "Arora, Harpreet Singh  and
      Bhingardive, Sudha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.4",
    pages = "21--25",
    abstract = "Since the inception of the SENSEVAL evaluation exercises there has been a great deal of recent research into Word Sense Disambiguation (WSD). Over the years, various supervised, unsupervised and knowledge based WSD systems have been proposed. Beating the first sense heuristics is a challenging task for these systems. In this paper, we present our work on Most Frequent Sense (MFS) detection using Word Embeddings and BabelNet features. The semantic features from BabelNet viz., synsets, gloss, relations, etc. are used for generating sense embeddings. We compare word embedding of a word with its sense embeddings to obtain the MFS with the highest similarity. The MFS is detected for six languages viz., English, Spanish, Russian, German, French and Italian. However, this approach can be applied to any language provided that word embeddings are available for that language.",
}
@inproceedings{benjamin-2016-problems,
    title = "Problems and Procedures to Make {W}ordnet Data (Retro)Fit for a Multilingual Dictionary",
    author = "Benjamin, Martin",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.5",
    pages = "26--33",
    abstract = "The data compiled through many Wordnet projects can be a rich source of seed information for a multilingual dictionary. However, the original Princeton WordNet was not intended as a dictionary per se, and spawning other languages from it introduces inherent ambiguity that confounds precise inter-lingual linking. This paper discusses a new presentation of existing Wordnet data that displays joints (distance between predicted links) and substitution (degree of equivalence between confirmed pairs) as a two-tiered horizontal ontology. Improvements to make Wordnet data function as lexicography include term-specific English definitions where the topical synset glosses are inadequate, validation of mappings between each member of an English synset and each member of the synsets from other languages, removal of erroneous translation terms, creation of own-language definitions for the many languages where those are absent, and validation of predicted links between non-English pairs. The paper describes the current state and future directions of a system to crowdsource human review and expansion of Wordnet data, using gamification to build consensus validated, dictionary caliber data for languages now in the Global WordNet as well as new languages that do not have formal Wordnet projects of their own.",
}
@inproceedings{berti-etal-2016-ancient,
    title = "{A}ncient {G}reek {W}ord{N}et Meets the Dynamic Lexicon: the Example of the Fragments of the {G}reek Historians",
    author = "Berti, Monica  and
      Bizzoni, Yuri  and
      Boschetti, Federico  and
      Crane, Gregory R.  and
      Gratta, Riccardo Del  and
      Yousef, Tariq",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.6",
    pages = "34--38",
    abstract = "The Ancient Greek WordNet (AGWN) and the Dynamic Lexicon (DL) are multilingual resources to study the lexicon of Ancient Greek texts and their translations. Both AGWN and DL are works in progress that need accuracy improvement and manual validation. After a detailed description of the current state of each work, this paper illustrates a methodology to cross AGWN and DL data, in order to mutually score the items of each resource according to the evidence provided by the other resource. The training data is based on the corpus of the Digital Fragmenta Historicorum Graecorum (DFHG), which includes ancient Greek texts with Latin translations.",
}
@inproceedings{bhingardive-etal-2016-indowordnet,
    title = "{I}ndo{W}ord{N}et::{S}imilarity- Computing Semantic Similarity and Relatedness using {I}ndo{W}ord{N}et",
    author = "Bhingardive, Sudha  and
      Redkar, Hanumant  and
      Sappadla, Prateek  and
      Singh, Dhirendra  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.7",
    pages = "39--43",
    abstract = "Semantic similarity and relatedness measures play an important role in natural language processing applications. In this paper, we present the IndoWordNet::Similarity tool and interface, designed for computing the semantic similarity and relatedness between two words in IndoWordNet. A java based tool and a web interface have been developed to compute this semantic similarity and relatedness. Also, Java API has been developed for this purpose. This tool, web interface and the API are made available for the research purpose.",
}
@inproceedings{bonansinga-bond-2016-multilingual,
    title = "Multilingual Sense Intersection in a Parallel Corpus with Diverse Language Families",
    author = "Bonansinga, Giulia  and
      Bond, Francis",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.8",
    pages = "44--49",
    abstract = "Supervised methods for Word Sense Disambiguation (WSD) benefit from high-quality sense-annotated resources, which are lacking for many languages less common than English. There are, however, several multilingual parallel corpora that can be inexpensively annotated with senses through cross-lingual methods. We test the effectiveness of such an approach by attempting to disambiguate English texts through their translations in Italian, Romanian and Japanese. Specifically, we try to find the appropriate word senses for the English words by comparison with all the word senses associated to their translations. The main advantage of this approach is in that it can be applied to any parallel corpus, as long as large, high-quality inter-linked sense inventories exist for all the languages considered.",
}
@inproceedings{bond-etal-2016-cili,
    title = "{CILI}: the Collaborative Interlingual Index",
    author = "Bond, Francis  and
      Vossen, Piek  and
      McCrae, John  and
      Fellbaum, Christiane",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.9",
    pages = "50--57",
    abstract = "This paper introduces the motivation for and design of the Collaborative InterLingual Index (CILI). It is designed to make possible coordination between multiple loosely coupled wordnet projects. The structure of the CILI is based on the Interlingual index first proposed in the EuroWordNet project with several pragmatic extensions: an explicit open license, definitions in English and links to wordnets in the Global Wordnet Grid.",
}
@inproceedings{braslavski-etal-2016-yarn,
    title = "{YARN}: Spinning-in-Progress",
    author = "Braslavski, Pavel  and
      Ustalov, Dmitry  and
      Mukhin, Mikhail  and
      Kiselev, Yuri",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.10",
    pages = "58--65",
    abstract = "YARN (Yet Another RussNet), a project started in 2013, aims at creating a large open WordNet-like thesaurus for Russian by means of crowdsourcing. The first stage of the project was to create noun synsets. Currently, the resource comprises 48K+ word entries and 44K+ synsets. More than 200 people have taken part in assembling synsets throughout the project. The paper describes the linguistic, technical, and organizational principles of the project, as well as the evaluation results, lessons learned, and the future plans.",
}
@inproceedings{cai-etal-2016-word,
    title = "Word Substitution in Short Answer Extraction: A {W}ord{N}et-based Approach",
    author = "Cai, Qingqing  and
      Gung, James  and
      Guan, Maochen  and
      Kurlandski, Gerald  and
      Pease, Adam",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.11",
    pages = "66--73",
    abstract = "We describe the implementation of a short answer extraction system. It consists of a simple sentence selection front-end and a two phase approach to answer extraction from a sentence. In the first phase sentence classification is performed with a classifier trained with the passive aggressive algorithm utilizing the UIUC dataset and taxonomy and a feature set including word vectors. This phase outperforms the current best published results on that dataset. In the second phase, a sieve algorithm consisting of a series of increasingly general extraction rules is applied, using WordNet to find word types aligned with the UIUC classifications determined in the first phase. Some very preliminary performance metrics are presented.",
}
@inproceedings{de-paiva-etal-2016-overview,
    title = "An overview of {P}ortuguese {W}ord{N}ets",
    author = "de Paiva, Valeria  and
      Real, Livy  and
      Oliveira, Hugo Gon{\c{c}}alo  and
      Rademaker, Alexandre  and
      Freitas, Cl{\'a}udia  and
      Sim{\~o}es, Alberto",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.12",
    pages = "74--82",
    abstract = "Semantic relations between words are key to building systems that aim to understand and manipulate language. For English, the {``}de facto{''} standard for representing this kind of knowledge is Princeton{'}s WordNet. Here, we describe the wordnet-like resources currently available for Portuguese: their origins, methods of creation, sizes, and usage restrictions. We start tackling the problem of comparing them, but only in quantitative terms. Finally, we sketch ideas for potential collaboration between some of the projects that produce Portuguese wordnets.",
}
@inproceedings{declerck-etal-2016-towards,
    title = "Towards a {W}ord{N}et based Classification of Actors in Folktales",
    author = "Declerck, Thierry  and
      Klement, Tyler  and
      Kostova, Antonia",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.13",
    pages = "83--87",
    abstract = "In the context of a student software project we are investigating the use of WordNet for improving the automatic detection and classification of actors (or characters) mentioned in folktales. Our starting point is the book {``}Classification of International Folktales{''}, out of which we extract text segments that name the different actors involved in tales, taking advantage of patterns used by its author, Hans-Jo ̈rg Uther. We apply on those text segments functions that are implemented in the NLTK interface to WordNet in order to obtain lexical semantic information to enrich the original naming of characters proposed in the {``}Classification of International Folktales{''} and to support their translation in other languages.",
}
@inproceedings{dziob-wendelberger-2016-extraction,
    title = "Extraction and description of multi-word lexical units in pl{W}ord{N}et 3.0",
    author = "Dziob, Agnieszka  and
      Wendelberger, Micha{\l}",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.14",
    pages = "88--92",
    abstract = "In this paper, we present methods of extraction of multi-word lexical units (MWLUs) from large text corpora and their description in plWordNet 3.0. MWLUs are filtered from collocations of the structural type Noun+Adjective (NA).",
}
@inproceedings{fakoornia-ardakani-2016-establishing,
    title = "Establishing Morpho-semantic Relations in {F}ars{N}et (a focus on derived nouns)",
    author = "Fakoornia, Nasim  and
      Ardakani, Negar Davari",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.15",
    pages = "93--100",
    abstract = "This paper aims at a morpho-semantic analysis of 2461 Persian derived nouns, documented in FarsNet addressing computational codification via formulating specific morpho-semantic relations between classes of derived nouns and their bases. Considering the ultimate aim of the study, FarsNet derived nouns included 12 most productive suffixes have been analysed and as a consequence 45 morpho-semantic patterns were distinguished leading to creation of 17 morpho-semantic relations. The approach includes a close examination of beginners, grammatical category and part of speech shifts of bases undergoing the derivation process. In this research the morpho-semantic relations are considered at the word level and not at the synset level which will represent a cross-lingual validity, even if the morphological aspect of the relation is not the same in the studied languages. The resulting morpho-semantic formulations notably increase linguistic and operative competence and performance of FarsNet while is considered an achievement in Persian descriptive morphology and its codification.",
}
@inproceedings{feltracco-etal-2016-using,
    title = "Using {W}ord{N}et to Build Lexical Sets for {I}talian Verbs",
    author = "Feltracco, Anna  and
      Gatti, Lorenzo  and
      Jezek, Elisabetta  and
      Magnini, Bernardo  and
      Magnolini, Simone",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.16",
    pages = "101--105",
    abstract = "We present a methodology for building lexical sets for argument slots of Italian verbs. We start from an inventory of semantically typed Italian verb frames and through a mapping to WordNet we automatically annotate the sets of fillers for the argument positions in a corpus of sentences. We evaluate both a baseline algorithm and a syntax driven algorithm and show that the latter performs significantly better in terms of precision.",
}
@inproceedings{freihat-etal-2016-taxonomic,
    title = "A Taxonomic Classification of {W}ord{N}et Polysemy Types",
    author = "Freihat, Abed Alhakim  and
      Giunchiglia, Fausto  and
      Dutta, Biswanath",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.17",
    pages = "106--114",
    abstract = "WordNet represents polysemous terms by capturing the different meanings of these terms at the lexical level, but without giving emphasis on the polysemy types such terms belong to. The state of the art polysemy approaches identify several polysemy types in WordNet but they do not explain how to classify and organize them. In this paper, we present a novel approach for classifying the polysemy types which exploits taxonomic principles which in turn, allow us to discover a set of polysemy structural patterns.",
}
@inproceedings{herrera-etal-2016-strategies,
    title = "Some strategies for the improvement of a {S}panish {W}ord{N}et",
    author = "Herrera, Matias  and
      Gonzalez, Javier  and
      Chiruzzo, Luis  and
      Wonsever, Dina",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.18",
    pages = "115--122",
    abstract = "Although there are currently several versions of Princeton WordNet for different languages, the lack of development of some of these versions does not make it possible to use them in different Natural Language Processing applications. So is the case of the Spanish Wordnet contained in the Multilingual Central Repository (MCR), which we tried unsuccessfully to incorporate into an anaphora resolution application and also in search terms expansion. In this situation, different strategies to improve MCR Spanish WordNet coverage were put forward and tested, obtaining encouraging results. A specific process was conducted to increase the number of adverbs, and a few simple processes were applied which made it possible to increase, at a very low cost, the number of terms in the Spanish WordNet. Finally, a more complex method based on distributional semantics was proposed, using the relations between English Wordnet synsets, also returning positive results.",
}
@inproceedings{hicks-etal-2016-analysis,
    title = "An Analysis of {W}ord{N}et{'}s Coverage of Gender Identity Using {T}witter and The National Transgender Discrimination Survey",
    author = "Hicks, Amanda  and
      Rutherford, Michael  and
      Fellbaum, Christiane  and
      Bian, Jiang",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.19",
    pages = "123--130",
    abstract = "While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender. There is also a growing need to lexically represent this variety of gender identities. In our previous work, we developed a set of tools and approaches for analyzing Twitter data as a basis for generating hypotheses on language used to identify gender and discuss gender-related issues across geographic regions and population groups in the U.S.A. In this paper we analyze the coverage and relative frequency of the word forms in our Twitter analysis with respect to the National Transgender Discrimination Survey data set, one of the most comprehensive data sets on transgender, gender non-conforming, and gender variant people in the U.S.A. We then analyze the coverage of WordNet, a widely used lexical database, with respect to these identities and discuss some key considerations and next steps for adding gender identity words and their meanings to WordNet.",
}
@inproceedings{horvath-etal-2016-bears,
    title = "Where Bears Have the Eyes of Currant: Towards a {M}ansi {W}ord{N}et",
    author = "Horv{\'a}th, Csilla  and
      Nagy, {\'A}goston  and
      Szil{\'a}gyi, Norbert  and
      Vincze, Veronika",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.20",
    pages = "131--135",
    abstract = "Here we report the construction of a wordnet for Mansi, an endangered minority language spoken in Russia. We will pay special attention to challenges that we encountered during the building process, among which the most important ones are the low number of native speakers, the lack of thesauri and the bear language. We will discuss our solutions to these issues, which might have some theoretical implications for the methodology of wordnet building in general.",
}
@inproceedings{huang-2016-wnspell,
    title = "{WNS}pell: a {W}ord{N}et-Based Spell Corrector",
    author = "Huang, Bill",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.21",
    pages = "136--143",
    abstract = "This paper presents a standalone spell corrector, WNSpell, based on and written for WordNet. It is aimed at generating the best possible suggestion for a mistyped query but can also serve as an all-purpose spell corrector. The spell corrector consists of a standard initial correction system, which evaluates word entries using a multifaceted approach to achieve the best results, and a semantic recognition system, wherein given a related word input, the system will adjust the spelling suggestions accordingly. Both feature significant performance improvements over current context-free spell correctors.",
}
@inproceedings{kanojia-etal-2016-sophisticated,
    title = "Sophisticated Lexical Databases - Simplified Usage: Mobile Applications and Browser Plugins For Wordnets",
    author = "Kanojia, Diptesh  and
      Dabre, Raj  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.22",
    pages = "144--149",
    abstract = "India is a country with 22 officially recognized languages and 17 of these have WordNets, a crucial resource. Web browser based interfaces are available for these WordNets, but are not suited for mobile devices which deters people from effectively using this resource. We present our initial work on developing mobile applications and browser extensions to access WordNets for Indian Languages. Our contribution is two fold: (1) We develop mobile applications for the Android, iOS and Windows Phone OS platforms for Hindi, Marathi and Sanskrit WordNets which allow users to search for words and obtain more information along with their translations in English and other Indian languages. (2) We also develop browser extensions for English, Hindi, Marathi, and Sanskrit WordNets, for both Mozilla Firefox, and Google Chrome. We believe that such applications can be quite helpful in a classroom scenario, where students would be able to access the WordNets as dictionaries as well as lexical knowledge bases. This can help in overcoming the language barrier along with furthering language understanding.",
}
@inproceedings{kanojia-etal-2016-picture,
    title = "A picture is worth a thousand words: Using {O}pen{C}lip{A}rt library for enriching {I}ndo{W}ord{N}et",
    author = "Kanojia, Diptesh  and
      Dhuliawala, Shehzaad  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.23",
    pages = "150--154",
    abstract = "WordNet has proved to be immensely useful for Word Sense Disambiguation, and thence Machine translation, Information Retrieval and Question Answering. It can also be used as a dictionary for educational purposes. The semantic nature of concepts in a WordNet motivates one to try to express this meaning in a more visual way. In this paper, we describe our work of enriching IndoWordNet with image acquisitions from the OpenClipArt library. We describe an approach used to enrich WordNets for eighteen Indian languages. Our contribution is three fold: (1) We develop a system, which, given a synset in English, finds an appropriate image for the synset. The system uses the OpenclipArt library (OCAL) to retrieve images and ranks them. (2) After retrieving the images, we map the results along with the linkages between Princeton WordNet and Hindi WordNet, to link several synsets to corresponding images. We choose and sort top three images based on our ranking heuristic per synset. (3) We develop a tool that allows a lexicographer to manually evaluate these images. The top images are shown to a lexicographer by the evaluation tool for the task of choosing the best image representation. The lexicographer also selects the number of relevant images. Using our system, we obtain an Average Precision (P @ 3) score of 0.30.",
}
@inproceedings{kazemi-etal-2016-using,
    title = "Using {W}ordnet to Improve Reordering in Hierarchical Phrase-Based Statistical Machine Translation",
    author = "Kazemi, Arefeh  and
      Toral, Antonio  and
      Way, Andy",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.24",
    pages = "155--161",
    abstract = "We propose the use of WordNet synsets in a syntax-based reordering model for hierarchical statistical machine translation (HPB-SMT) to enable the model to generalize to phrases not seen in the training data but that have equivalent meaning. We detail our methodology to incorporate synsets{'} knowledge in the reordering model and evaluate the resulting WordNet-enhanced SMT systems on the English-to-Farsi language direction. The inclusion of synsets leads to the best BLEU score, outperforming the baseline (standard HPB-SMT) by 0.6 points absolute.",
}
@inproceedings{kiselev-etal-2016-eliminating,
    title = "Eliminating Fuzzy Duplicates in Crowdsourced Lexical Resources",
    author = "Kiselev, Yuri  and
      Ustalov, Dmitry  and
      Porshnev, Sergey",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.25",
    pages = "162--168",
    abstract = "Collaboratively created lexical resources is a trending approach to creating high quality thesauri in a short time span at a remarkably low price. The key idea is to invite non-expert participants to express and share their knowledge with the aim of constructing a resource. However, this approach tends to be noisy and error-prone, thus making data cleansing a highly topical task to perform. In this paper, we study different techniques for synset deduplication including machine- and crowd-based ones. Eventually, we put forward an approach that can solve the deduplication problem fully automatically, with the quality comparable to the expert-based approach.",
}
@inproceedings{koeva-etal-2016-automatic,
    title = "Automatic Prediction of Morphosemantic Relations",
    author = "Koeva, Svetla  and
      Leseva, Svetlozara  and
      Stoyanova, Ivelina  and
      Dimitrova, Tsvetana  and
      Todorova, Maria",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.26",
    pages = "169--177",
    abstract = "This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb{--}noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.",
}
@inproceedings{lohk-etal-2016-tuning,
    title = "Tuning Hierarchies in {P}rinceton {W}ord{N}et",
    author = "Lohk, Ahti  and
      Fellbaum, Christiane  and
      Vohandu, Leo",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.27",
    pages = "178--184",
    abstract = "Many new wordnets in the world are constantly created and most take the original Princeton WordNet (PWN) as their starting point. This arguably central position imposes a responsibility on PWN to ensure that its structure is clean and consistent. To validate PWN hierarchical structures we propose the application of a system of test patterns. In this paper, we report on how to validate the PWN hierarchies using the system of test patterns. In sum, test patterns provide lexicographers with a very powerful tool, which we hope will be adopted by the global wordnet community.",
}
@inproceedings{lohk-etal-2016-experiences,
    title = "Experiences of Lexicographers and Computer Scientists in Validating {E}stonian {W}ordnet with Test Patterns",
    author = "Lohk, Ahti  and
      Orav, Heili  and
      Vare, Kadri  and
      Vohandu, Leo",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.28",
    pages = "185--192",
    abstract = "New concepts and semantic relations are constantly added to Estonian Wordnet (EstWN) to increase its size. In addition to this, with the use of test patterns, the validation of EstWN hierarchies is also performed. This parallel work was carried out over the past four years (2011-2014) with 10 different EstWN versions (60-70). This has been a collaboration between the creators of test patterns and the lexicographers currently working on EstWN. This paper describes the usage of test patterns from the points of views of information scientists (the creators of test patterns) as well as the users (lexicographers). Using EstWN as an example, we illustrate how the continuous use of test patterns has led to significant improvement of the semantic hierarchies in EstWN.",
}
@inproceedings{madonsela-etal-2016-african,
    title = "{A}frican {W}ord{N}et: A Viable Tool for Sense Discrimination in the Indigenous {A}frican Languages of {S}outh {A}frica",
    author = "Madonsela, Stanley  and
      Mojapelo, Mampaka Lydia  and
      Masubelele, Rose  and
      Mafela, James",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.29",
    pages = "193--198",
    abstract = "In promoting a multilingual South Africa, the government is encouraging people to speak more than one language. In order to comply with this initiative, people choose to learn the languages which they do not speak as home language. The African languages are mostly chosen because they are spoken by the majority of the country{'}s population. Most words in these languages have many possible senses. This phenomenon tends to pose problems to people who want to learn these languages. This article argues that the African WordNet may the best tool to address the problem of sense discrimination. The focus of the argument will be on the primary sense of the word {`}hand{'}, which is part of the body, as lexicalized in three indigenous languages spoken in South Africa, namely, Tshivenḓa, Sesotho sa Leboa and isiZulu. A brief historical background of the African WordNet will be provided, followed by the definition of the word {`}hand{'} in the three languages and the analysis of the word in context. Lastly, the primary sense of the word {`}hand{'} across the three languages will be discussed.",
}
@inproceedings{alonso-etal-2016-empirically,
    title = "An empirically grounded expansion of the supersense inventory",
    author = "Alonso, Hector Martinez  and
      Johannsen, Anders  and
      Nimb, Sanni  and
      Olsen, Sussi  and
      Pedersen, Bolette",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.30",
    pages = "199--208",
    abstract = "In this article we present an expansion of the supersense inventory. All new super-senses are extensions of members of the current inventory, which we postulate by identifying semantically coherent groups of synsets. We cover the expansion of the already-established supernsense inventory for nouns and verbs, the addition of coarse supersenses for adjectives in absence of a canonical supersense inventory, and super-senses for verbal satellites. We evaluate the viability of the new senses examining the annotation agreement, frequency and co-ocurrence patterns.",
}
@inproceedings{maziarz-etal-2016-adverbs,
    title = "Adverbs in pl{W}ord{N}et: Theory and Implementation",
    author = "Maziarz, Marek  and
      Szpakowicz, Stan  and
      Kalinski, Michal",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.31",
    pages = "209--217",
    abstract = "Adverbs are seldom well represented in wordnets. Princeton WordNet, for example, derives from adjectives practically all its adverbs and whatever involvement they have. GermaNet stays away from this part of speech. Adverbs in plWordNet will be emphatically present in all their semantic and syntactic distinctness. We briefly discuss the linguistic background of the lexical system of Polish adverbs. We describe an automated generator of accurate candidate adverbs, and introduce the lexicographic procedures which will ensure high consistency of wordnet editors{'} decisions about adverbs.",
}
@inproceedings{mladenovic-etal-2016-language,
    title = "A Language-independent Model for Introducing a New Semantic Relation Between Adjectives and Nouns in a {W}ord{N}et",
    author = "Mladenovi{\'c}, Miljana  and
      Mitrovi{\'c}, Jelena  and
      Krstev, Cvetana",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.32",
    pages = "218--226",
    abstract = "The aim of this paper is to show a language-independent process of creating a new semantic relation between adjectives and nouns in wordnets. The existence of such a relation is expected to improve the detection of figurative language and sentiment analysis (SA). The proposed method uses an annotated corpus to explore the semantic knowledge contained in linguistic constructs performing as the rhetorical figure Simile. Based on the frequency of occurrence of similes in an annotated corpus, we propose a new relation, which connects the noun synset with the synset of an adjective representing that noun{'}s specific attribute. We elaborate on adding this new relation in the case of the Serbian WordNet (SWN). The proposed method is evaluated by human judgement in order to determine the relevance of automatically selected relation items. The evaluation has shown that 84{\%} of the automatically selected and the most frequent linguistic constructs, whose frequency threshold was equal to 3, were also selected by humans.",
}
@inproceedings{moeljadi-bond-2016-identifying,
    title = "Identifying and Exploiting Definitions in {W}ordnet {B}ahasa",
    author = "Moeljadi, David  and
      Bond, Francis",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.33",
    pages = "227--233",
    abstract = "This paper describes our attempts to add Indonesian definitions to synsets in the Wordnet Bahasa (Nurril Hirfana Mohamed Noor et al., 2011; Bond et al., 2014), to extract semantic relations between lemmas and definitions for nouns and verbs, such as synonym, hyponym, hypernym and instance hypernym, and to generally improve Wordnet. The original, somewhat noisy, definitions for Indonesian came from the Asian Wordnet project (Riza et al., 2010). The basic method of extracting the relations is based on Bond et al. (2004). Before the relations can be extracted, the definitions were cleaned up and tokenized. We found that the definitions cannot be completely cleaned up because of many misspellings and bad translations. However, we could identify four semantic relations in 57.10{\%} of noun and verb definitions. For the remaining 42.90{\%}, we propose to add 149 new Indonesian lemmas and make some improvements to Wordnet Bahasa and Wordnet in general.",
}
@inproceedings{mojapelo-2016-semantics,
    title = "Semantics of body parts in {A}frican {W}ord{N}et: a case of {N}orthern {S}otho",
    author = "Mojapelo, Mampaka Lydia",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.34",
    pages = "234--242",
    abstract = "This paper presents a linguistic account of the lexical semantics of body parts in African WordNet, with special reference to Northern Sotho. It focuses on external human body parts synsets in Northern Sotho. The paper seeks to support the effectiveness of African WordNet as a resource for services such as in the healthcare and medical field in South Africa. It transpired from this exploration that there is either a one-to-one correspondence or some form of misalignment of lexicalisation with regard to the sample of examined synsets. The paper concludes by making suggestions on how African WordNet can deal with such semantic misalignments in order to improve its efficiency as a resource for the targeted purpose.",
}
@inproceedings{mondal-etal-2016-wme,
    title = "{WME}: Sense, Polarity and Affinity based Concept Resource for Medical Events",
    author = "Mondal, Anupam  and
      Das, Dipankar  and
      Cambria, Erik  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.35",
    pages = "243--248",
    abstract = "In order to overcome the lack of medical corpora, we have developed a WordNet for Medical Events (WME) for identifying medical terms and their sense related information using a seed list. The initial WME resource contains 1654 medical terms or concepts. In the present research, we have reported the enhancement of WME with 6415 number of medical concepts along with their conceptual features viz. Parts-of-Speech (POS), gloss, semantics, polarity, sense and affinity. Several polarity lexicons viz. SentiWordNet, SenticNet, Bing Liu{'}s subjectivity list and Taboda{'}s adjective list were introduced with WordNet synonyms and hyponyms for expansion. The semantics feature guided us to build a semantic co-reference relation based network between the related medical concepts. These features help to prepare a medical concept network for better sense relation based visualization. Finally, we evaluated with respect to Adaptive Lesk Algorithm and conducted an agreement analysis for validating the expanded WME resource.",
}
@inproceedings{costa-etal-2016-mapping,
    title = "Mapping and Generating Classifiers using an Open {C}hinese Ontology",
    author = "Costa, Luis Morgado Da  and
      Bond, Francis  and
      Gao, Helena",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.36",
    pages = "249--256",
    abstract = "In languages such as Chinese, classifiers (CLs) play a central role in the quantification of noun-phrases. This can be a problem when generating text from input that does not specify the classifier, as in machine translation (MT) from English to Chinese. Many solutions to this problem rely on dictionaries of noun-CL pairs. However, there is no open large-scale machine-tractable dictionary of noun-CL associations. Many published resources exist, but they tend to focus on how a CL is used (e.g. what kinds of nouns can be used with it, or what features seem to be selected by each CL). In fact, since nouns are open class words, producing an exhaustive definite list of noun-CL associations is not possible, since it would quickly get out of date. Our work tries to address this problem by providing an algorithm for automatic building of a frequency based dictionary of noun-CL pairs, mapped to concepts in the Chinese Open Wordnet (Wang and Bond, 2013), an open machine-tractable dictionary for Chinese. All results will released under an open license.",
}
@inproceedings{nagvenkar-etal-2016-indowordnet,
    title = "{I}ndo{W}ord{N}et Conversion to Web Ontology Language ({OWL})",
    author = "Nagvenkar, Apurva  and
      Pawar, Jyoti  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.37",
    pages = "257--260",
    abstract = "WordNet plays a significant role in Linked Open Data (LOD) cloud. It has numerous application ranging from ontology annotation to ontology mapping. IndoWordNet is a linked WordNet connecting 18 Indian language WordNets with Hindi as a source WordNet. The Hindi WordNet was initially developed by linking it to English WordNet. In this paper, we present a data representation of IndoWordNet in Web Ontology Language (OWL). The schema of Princeton WordNet has been enhanced to support the representation of IndoWordNet. This IndoWordNet representation in OWL format is now available to link other web resources. This representation is implemented for eight Indian languages.",
}
@inproceedings{nguyen-etal-2016-two-phase,
    title = "A Two-Phase Approach for Building {V}ietnamese {W}ord{N}et",
    author = "Nguyen, Thai Phuong  and
      Pham, Van-Lam  and
      Nguyen, Hoang-An  and
      Vu, Huy-Hien  and
      Tran, Ngoc-Anh  and
      Truong, Thi-Thu-Ha",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.38",
    pages = "261--266",
    abstract = "Wordnets play an important role not only in linguistics but also in natural language processing (NLP). This paper reports major results of a project which aims to construct a wordnet for Vietnamese language. We propose a two-phase approach to the construction of Vietnamese WordNet employing available language resources and ensuring Vietnamese specific linguistic and cultural characteristics. We also give statistical results and analyses to show characteristics of the wordnet.",
}
@inproceedings{oliver-2016-extending,
    title = "Extending the {WN}-Toolkit: dealing with polysemous words in the dictionary-based strategy",
    author = "Oliver, Antoni",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.39",
    pages = "267--274",
    abstract = "In this paper we present an extension of the dictionary-based strategy for wordnet construction implemented in the WN-Toolkit. This strategy allows the extraction of information for polysemous English words if definitions and/or semantic relations are present in the dictionary. The WN-Toolkit is a freely available set of programs for the creation and expansion of wordnets using dictionary-based and parallel-corpus based strategies. In previous versions of the toolkit the dictionary-based strategy was only used for translating monosemous English variants. In the experiments we have used Omegawiki and Wiktionary and we present automatic evaluation results for 24 languages that have wordnets in the Open Multilingual Wordnet project. We have used these existing versions of the wordnet to perform an automatic evaluation.",
}
@inproceedings{petrolito-2016-language,
    title = "A language-independent {LESK} based approach to Word Sense Disambiguation",
    author = "Petrolito, Tommaso",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.40",
    pages = "275--281",
    abstract = "This paper describes a language- independent LESK based approach to Word Sense Disambiguation (WSD), involving also Vector Space Models applied to the Distributional Semantics Hypotesis. In particular this approach tries to solve some issues that come up with less-resourced languages. The approach also addresses the inadequacy of the Most Frequent Sense (MFS) heuristics to fit specific domain corpora.",
}
@inproceedings{piasecki-etal-2016-plwordnet,
    title = "pl{W}ord{N}et in Word Sense Disambiguation task",
    author = "Piasecki, Maciej  and
      K{\k{e}}dzia, Pawe{\l}  and
      Orli{\'n}ska, Marlena",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.41",
    pages = "282--291",
    abstract = "The paper explores the application of plWordNet, a very large wordnet of Polish, in weakly supervised Word Sense Disambiguation (WSD). Because plWordNet provides only partial descriptions by glosses and usage examples, and does not include sense-disambiguated glosses, PageRank-based WSD methods perform slightly worse than for English. However, we show that the use of weights for the relation types and the order in which lexical units have been added for sense re-ranking can significantly improve WSD precision. The evaluation was done on two Polish corpora (KPWr and Sk{\l}adnica) including manual WSD. We discuss the fundamental difference in the construction of both corpora and very different test results.",
}
@inproceedings{piasecki-etal-2016-plwordnet-3,
    title = "pl{W}ord{N}et 3.0 {--} Almost There",
    author = "Piasecki, Maciej  and
      Szpakowicz, Stan  and
      Maziarz, Marek  and
      Rudnicka, Ewa",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.42",
    pages = "292--301",
    abstract = "It took us nearly ten years to get from no wordnet for Polish to the largest wordnet ever built. We started small but quickly learned to dream big. Now we are about to release plWordNet 3.0-emo {--} complete with sentiment and emotions annotated {--} and a domestic version of Princeton WordNet, larger than WordNet 3.1 by nearly ten thousand newly added words. The paper retraces the road we travelled and talks a little about the future.",
}
@inproceedings{postma-etal-2016-open,
    title = "Open {D}utch {W}ord{N}et",
    author = "Postma, Marten  and
      van Miltenburg, Emiel  and
      Segers, Roxane  and
      Schoen, Anneleen  and
      Vossen, Piek",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.43",
    pages = "302--310",
    abstract = "We describe Open Dutch WordNet, which has been derived from the Cornetto database, the Princeton WordNet and open source resources. We exploited existing equivalence relations between Cornetto synsets and WordNet synsets in order to move the open source content from Cornetto into WordNet synsets. Currently, Open Dutch Wordnet contains 117,914 synsets, of which 51,588 synsets contain at least one Dutch synonym, which leaves 66,326 synsets still to obtain a Dutch synonym. The average polysemy is 1.5. The resource is currently delivered in XML under the CC BY-SA 4.0 license1 and it has been linked to the Global Wordnet Grid. In order to use the resource, we refer to: https: //github.com/MartenPostma/OpenDutchWordnet.",
}
@inproceedings{rademaker-chalub-2016-verifying,
    title = "Verifying Integrity Constraints of a {RDF}-based {W}ord{N}et",
    author = "Rademaker, Alexandre  and
      Chalub, Fabricio",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.44",
    pages = "311--319",
    abstract = "This paper presents our first attempt at verifying integrity constraints of our openWordnet-PT against the ontology for Wordnets encoding. Our wordnet is distributed in Resource Description Format (RDF) and we want to guarantee not only the syntax correctness but also its semantics soundness.",
}
@inproceedings{rambousek-horak-2016-debvisdic,
    title = "{DEBV}is{D}ic: Instant {W}ordnet Building",
    author = "Rambousek, Adam  and
      Horak, Ales",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.45",
    pages = "320--324",
    abstract = "The semantic network editor DEBVisDic has been used by different development teams to create more than 20 national wordnets. The editor was recently re-developed as a multi-platform web-based application for general semantic networks editing. One of the main advantages, when compared to the previous implementation, lies in the fact that no client-side installation is needed now. Following the successful first phase in building the Open Dutch Wordnet, DEBVisDic was extended with features that allow users to easily create, edit, and share a new (usually national) wordnet without the need of any complicated configuration or advanced technical skills. The DEBVisDic editor provides advanced features for wordnet browsing, editing, and visualization. Apart from the user-friendly web-based application, DEBVisDic also provides an API interface to integrate the semantic network data into external applications.",
}
@inproceedings{redkar-etal-2016-samasa,
    title = "Sam{\=a}sa-Kart{\=a}: An Online Tool for Producing Compound Words using {I}ndo{W}ord{N}et",
    author = "Redkar, Hanumant  and
      Joshi, Nilesh  and
      Singh, Sandhya  and
      Kulkarni, Irawati  and
      Kulkarni, Malhar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.46",
    pages = "325--332",
    abstract = "Sam{\=a}sa or compounds are a regular feature of Indian Languages. They are also found in other languages like German, Italian, French, Russian, Spanish, etc. Compound word is constructed from two or more words to form a single word. The meaning of this word is derived from each of the individual words of the compound. To develop a system to generate, identify and interpret compounds, is an important task in Natural Language Processing. This paper introduces a web based tool - Sam{\=a}sa-Kart{\=a} for producing compound words. Here, the focus is on Sanskrit language due to its richness in usage of compounds; however, this approach can be applied to any Indian language as well as other languages. IndoWordNet is used as a resource for words to be compounded. The motivation behind creating compound words is to create, to improve the vocabulary, to reduce sense ambiguity, etc. in order to enrich the WordNet. The Sam{\=a}sa-Kart{\=a} can be used for various applications viz., compound categorization, sandhi creation, morphological analysis, paraphrasing, synset creation, etc.",
}
@inproceedings{regragui-etal-2016-arabic,
    title = "{A}rabic {W}ord{N}et: New Content and New Applications",
    author = "Regragui, Yasser  and
      Abouenour, Lahsen  and
      Krieche, Fettoum  and
      Bouzoubaa, Karim  and
      Rosso, Paolo",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.47",
    pages = "333--341",
    abstract = "The Arabic WordNet project has provided the Arabic Natural Language Processing (NLP) community with the first WordNet-compliant resource. It allowed new possibilities in terms of building sophisticated NLP applications related to this Semitic language. In this paper, we present the new content added to this resource, using semi-automatic techniques, and validated by Arabic native-speaker lexicographers. We also present how this content helps in the implementation of new Arabic NLP applications, especially for Question Answering (QA) systems. The obtained results show the contribution of the added content. The resource, fully transformed into the standard Lexical Markup Framework (LMF), is made available for the community.",
}
@inproceedings{rizov-dimitrova-2016-hydra,
    title = "Hydra for Web: A Browser for Easy Access to Wordnets",
    author = "Rizov, Borislav  and
      Dimitrova, Tsvetana",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.48",
    pages = "342--346",
    abstract = "This paper presents a web interface for wordnets named Hydra for Web which is built on top of Hydra {--} an open source tool for wordnet development {--} by means of modern web technologies. It is a Single Page Application with simple but powerful and convenient GUI. It has two modes for visualisation of the language correspondences of searched (and found) wordnet synsets {--} single and parallel modes. Hydra for web is available at: http://dcl.bas.bg/bulnet/.",
}
@inproceedings{rudnicka-etal-2016-towards,
    title = "Towards a methodology for filtering out gaps and mismatches across wordnets: the case of pl{W}ord{N}et and {P}rinceton {W}ord{N}et",
    author = "Rudnicka, Ewa  and
      Witkowski, Wojciech  and
      Grabowski, {\L}ukasz",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.49",
    pages = "347--354",
    abstract = "This paper presents the results of large-scale noun synset mapping between plWordNet, the wordnet of Polish, and Princeton WordNet, the wordnet of English, which have shown high predominance of inter-lingual hyponymy relation over inter-synonymy relation. Two main sources of such effect are identified in the paper: differences in the methodologies of construction of plWN and PWN and cross-linguistic differences in lexicalization of concepts and grammatical categories between English and Polish. Next, we propose a typology of specific gaps and mismatches across wordnets and a rule-based system of filters developed specifically to scan all I(inter-lingual)-hyponymy links between plWN and PWN. The proposed system, it should be stressed, also enables one to pinpoint the frequencies of the identified gaps and mismatches.",
}
@inproceedings{schraagen-2016-folktale,
    title = "Folktale similarity based on ontological abstraction",
    author = "Schraagen, Marijn",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.50",
    pages = "355--363",
    abstract = "This paper presents a method to compute similarity of folktales based on conceptual overlap at various levels of abstraction as defined in Dutch WordNet. The method is applied on a corpus of Dutch folktales and evaluated using a comparison to traditional folktale similarity analysis based on the Aarne{--}Thompson{--}Uther (ATU) classification system. Document similarity computed by the presented method is in agreement with traditional analysis for a certain amount of folktale pairs, but differs for other pairs. However, it can be argued that the current approach computes an alternative, data-driven type of similarity. Using WordNet instead of a domain-specific ontology or classification system ensures applicability of the method outside of the folktale domain.",
}
@inproceedings{segers-etal-2016-predicate,
    title = "The Predicate Matrix and the Event and Implied Situation Ontology: Making More of Events",
    author = "Segers, Roxane  and
      Laparra, Egoitz  and
      Rospocher, Marco  and
      Vossen, Piek  and
      Rigau, German  and
      Ilievski, Filip",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.51",
    pages = "364--372",
    abstract = "This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology reuses and maps across existing resources such as WordNet, SUMO, VerbNet, PropBank and FrameNet. We describe how ESO is injected into a new version of the Predicate Matrix and illustrate how these resources are used to detect information in large document collections that otherwise would have remained implicit. The model targets interpretations of situations rather than the semantics of verbs per se. The event is interpreted as a situation using RDF taking all event components into account. Hence, the ontology and the linked resources need to be considered from the perspective of this interpretation model.",
}
@inproceedings{seppala-etal-2016-semi,
    title = "Semi-Automatic Mapping of {W}ord{N}et to Basic Formal Ontology",
    author = {Sepp{\"a}l{\"a}, Selja  and
      Hicks, Amanda  and
      Ruttenberg, Alan},
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.52",
    pages = "373--381",
    abstract = "We present preliminary work on the mapping of WordNet 3.0 to the Basic Formal Ontology (BFO 2.0). WordNet is a large, widely used semantic network. BFO is a domain-neutral upper-level ontology that represents the types of things that exist in the world and relations between them. BFO serves as an integration hub for more specific ontologies, such as the Ontology for Biomedical Investigations (OBI) and Ontology for Biobanking (OBIB). This work aims at creating a lexico-semantic resource that can be used in NLP tools to perform ontology-related text manipulation tasks. Our semi-automatic mapping method consists in using existing mappings between WordNet and the KYOTO Ontology. The latter allows machines to reason over texts by providing interpretations of the words in ontological terms. Our working hypothesis is that a large portion of WordNet synsets can be semi-automatically mapped to BFO using simple mapping rules from KYOTO to BFO. We evaluate the method on a randomized subset of synsets, examine preliminary results, and discuss challenges related to the method. We conclude with suggestions for future work.",
}
@inproceedings{shamsfard-ghazanfari-2016-augmenting,
    title = "Augmenting {F}ars{N}et with New Relations and Structures for verbs",
    author = "Shamsfard, Mehrnoush  and
      Ghazanfari, Yasaman",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.53",
    pages = "382--388",
    abstract = "This paper discusses the semantic augmentation of FarsNet - the Persian WordNet - with new relations and structures for verbs. FarsNet1.0, the first Persian WordNet obeys the Structure of Princeton WordNet 2.1. In this paper we discuss FarsNet 2.0 in which new inter-POS relations and verb frames are added. In fact FarsNet2.0 is a combination of WordNet and VerbNet for Persian. It includes more than 30,000 lexical entries arranged in about 20,000 synsets with about 18000 mappings to Princeton WordNet synsets. There ae about 43000 relations between synsets and senses in FarsNet 2.0. It includes verb frames in two levels (syntactic and thematic) for about 200 simple Persian verbs.",
}
@inproceedings{sharma-bhattacharyya-2016-high,
    title = "High, Medium or Low? Detecting Intensity Variation Among polar synonyms in {W}ord{N}et",
    author = "Sharma, Raksha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.54",
    pages = "389--395",
    abstract = "For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives (synonyms) that share the same sense. Choice of a word from a set of synonyms, provides a way to select the exact polarity-intensity. For example, choosing to describe a person as benevolent rather than kind1 changes the intensity of the expression. In this paper, we present a sense based lexical resource, where synonyms are assigned intensity levels, viz., high, medium and low. We show that the measure P (s|w) (probability of a sense s given the word w) can derive the intensity of a word within the sense. We observe a statistically significant positive correlation between P(s|w) and intensity of synonyms for three languages, viz., English, Marathi and Hindi. The average correlation scores are 0.47 for English, 0.56 for Marathi and 0.58 for Hindi.",
}
@inproceedings{simov-etal-2016-role,
    title = "The Role of the {W}ord{N}et Relations in the Knowledge-based Word Sense Disambiguation Task",
    author = "Simov, Kiril  and
      Popov, Alexander  and
      Osenova, Petya",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.55",
    pages = "396--403",
    abstract = "In this paper we present an analysis of different semantic relations extracted from WordNet, Extended WordNet and SemCor, with respect to their role in the task of knowledge-based word sense disambiguation. The experiments use the same algorithm and the same test sets, but different variants of the knowledge graph. The results show that different sets of relations have different impact on the results: positive or negative. The beneficial ones are discussed with respect to the combination of relations and with respect to the test set. The inclusion of inference has only a modest impact on accuracy, while the addition of syntactic relations produces stable improvement over the baselines.",
}
@inproceedings{singh-etal-2016-detection,
    title = "Detection of Compound Nouns and Light Verb Constructions using {I}ndo{W}ord{N}et",
    author = "Singh, Dhirendra  and
      Bhingardive, Sudha  and
      Bhattacharyyaa, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.56",
    pages = "404--410",
    abstract = "Detection of MultiWord Expressions (MWEs) is one of the fundamental problems in Natural Language Processing. In this paper, we focus on two categories of MWEs - Compound Nouns and Light Verb Constructions. These two categories can be tackled using knowledge bases, rather than pure statistics. We investigate usability of IndoWordNet for the detection of MWEs. Our IndoWordNet based approach uses semantic and ontological features of words that can be extracted from IndoWordNet. This approach has been tested on Indian languages viz., Assamese, Bengali, Hindi, Konkani, Marathi, Odia and Punjabi. Results show that ontological features are found to be very useful for the detection of light verb constructions, while use of semantic properties for the detection of compound nouns is found to be satisfactory. This approach can be easily adapted by other Indian languages. Detected MWEs can be interpolated into WordNets as they help in representing semantic knowledge.",
}
@inproceedings{singh-etal-2016-mapping,
    title = "Mapping it differently: A solution to the linking challenges",
    author = "Singh, Meghna  and
      Shukla, Rajita  and
      Saraswati, Jaya  and
      Kashyap, Laxmi  and
      Kanojia, Diptesh  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.57",
    pages = "411--418",
    abstract = "This paper reports the work of creating bilingual mappings in English for certain synsets of Hindi wordnet, the need for doing this, the methods adopted and the tools created for the task. Hindi wordnet, which forms the foundation for other Indian language wordnets, has been linked to the English WordNet. To maximize linkages, an important strategy of using direct and hypernymy linkages has been followed. However, the hypernymy linkages were found to be inadequate in certain cases and posed a challenge due to sense granularity of language. Thus, the idea of creating bilingual mappings was adopted as a solution. A bilingual mapping means a linkage between a concept in two different languages, with the help of translation and/or transliteration. Such mappings retain meaningful representations, while capturing semantic similarity at the same time. This has also proven to be a great enhancement of Hindi wordnet and can be a crucial resource for multilingual applications in natural language processing, including machine translation and cross language information retrieval.",
}
@inproceedings{van-miltenburg-2016-wordnet,
    title = "{W}ord{N}et-based similarity metrics for adjectives",
    author = "van Miltenburg, Emiel",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.58",
    pages = "419--423",
    abstract = "Le and Fokkens (2015) recently showed that taxonomy-based approaches are more reliable than corpus-based approaches in estimating human similarity ratings. On the other hand, distributional models provide much better coverage. The lack of an established similarity metric for adjectives in WordNet is a case in point. I present initial work to establish such a metric, and propose ways to move forward by looking at extensions to WordNet. I show that the shortest path distance between derivationally related forms provides a reliable estimate of adjective similarity. Furthermore, I find that a hybrid method combining this measure with vector-based similarity estimations gives us the best of both worlds: more reliable similarity estimations than vectors alone, but with the same coverage as corpus-based methods.",
}
@inproceedings{vossen-etal-2016-toward,
    title = "Toward a truly multilingual {G}lobal{W}ordnet Grid",
    author = "Vossen, Piek  and
      Bond, Francis  and
      McCrae, John",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.59",
    pages = "424--431",
    abstract = "In this paper, we describe a new and improved Global Wordnet Grid that takes advantage of the Collaborative InterLingual Index (CILI). Currently, the Open Multilingal Wordnet has made many wordnets accessible as a single linked wordnet, but as it used the Princeton Wordnet of English (PWN) as a pivot, it loses concepts that are not part of PWN. The technical solution to this, a central registry of concepts, as proposed in the EuroWordnet project through the InterLingual Index, has been known for many years. However, the practical issues of how to host this index and who decides what goes in remained unsolved. Inspired by current practice in the Semantic Web and the Linked Open Data community, we propose a way to solve this issue. In this paper we define the principles and protocols for contributing to the Grid. We tested them on two use cases, adding version 3.1 of the Princeton WordNet to a CILI based on 3.0 and adding the Open Dutch Wordnet, to validate the current set up. This paper aims to be a call for action that we hope will be further discussed and ultimately taken up by the whole wordnet community.",
}
@inproceedings{wilson-etal-2016-table,
    title = "This Table is Different: A {W}ord{N}et-Based Approach to Identifying References to Document Entities",
    author = "Wilson, Shomir  and
      Black, Alan  and
      Oberlander, Jon",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.60",
    pages = "432--440",
    abstract = "Writing intended to inform frequently contains references to document entities (DEs), a mixed class that includes orthographically structured items (e.g., illustrations, sections, lists) and discourse entities (arguments, suggestions, points). Such references are vital to the interpretation of documents, but they often eschew identifiers such as {``}Figure 1{''} for inexplicit phrases like {``}in this figure{''} or {``}from these premises{''}. We examine inexplicit references to DEs, termed DE references, and recast the problem of their automatic detection into the determination of relevant word senses. We then show the feasibility of machine learning for the detection of DE-relevant word senses, using a corpus of human-labeled synsets from WordNet. We test cross-domain performance by gathering lemmas and synsets from three corpora: website privacy policies, Wikipedia articles, and Wikibooks textbooks. Identifying DE references will enable language technologies to use the information encoded by them, permitting the automatic generation of finely-tuned descriptions of DEs and the presentation of richly-structured information to readers.",
}
@inproceedings{zock-schwab-2016-wordnet,
    title = "{W}ord{N}et and beyond: the case of lexical access",
    author = "Zock, Michael  and
      Schwab, Didier",
    booktitle = "Proceedings of the 8th Global WordNet Conference (GWC)",
    month = "27--30 " # jan,
    year = "2016",
    address = "Bucharest, Romania",
    publisher = "Global Wordnet Association",
    url = "https://www.aclweb.org/anthology/2016.gwc-1.61",
    pages = "441--449",
    abstract = "For humans the main functions of a dictionary is to store information concerning words and to reveal it when needed. While readers are interested in the meaning of words, writers look for answers concerning usage, spelling, grammar or word forms (lemma). We will focus here on this latter task: help authors to find the word they are looking for, word they may know but whose form is eluding them. Put differently, we try to build a resource helping authors to overcome the tip-of-the-tongue problem (ToT). Obviously, in order to access a word, it must be stored somewhere (brain, resource). Yet this is by no means sufficient. We will illustrate this here by comparing WordNet (WN) to an equivalent lexical resource bootstrapped from Wikipedia (WiPi). Both may contain a given word, but ease and success of access may be different depending on other factors like quality of the query, proximity, type of connections, etc. Next we will show under what conditions WN is suitable for word access, and finally we will present a roadmap showing the obstacles to be overcome to build a resource allowing the text producer to find the word s/he is looking for.",
}
@proceedings{eamt-2016-,
    title = "",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
}
@inproceedings{popel-etal-2016-tectomt,
    title = "{T}ecto{MT} {--} a deep linguistic core of the combined Cimera {MT} system",
    author = "Popel, Martin  and
      Sudarikov, Roman  and
      Bojar, Ond{\v{r}}ej  and
      Rosa, Rudolf  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.1",
}
@inproceedings{barczewska-etal-2016-witkom,
    title = "{W}i{TK}o{M} {--} virtual sign language translator project",
    author = "Barczewska, Katarzyna  and
      Galka, Jakub  and
      Malawski, Filip  and
      M{\k{a}}sior, Mariusz  and
      Szulc, Dorota  and
      Wilczy{\'n}ski, Tomasz  and
      Wr{\'o}bel, Krzysztof",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.2",
}
@inproceedings{h-paetzold-specia-2016-multi,
    title = "Multi-level quality prediction with {Q}u{E}st++",
    author = "H.Paetzold, Gustavo  and
      Specia, Lucia",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.3",
}
@inproceedings{forcada-tyers-2016-apertium,
    title = "Apertium: a free/open source platform for machine translation and basic language technology",
    author = "Forcada, Mikel L.  and
      Tyers, Francis M.",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.4",
}
@inproceedings{bouillon-spechbach-2016-babeldr,
    title = "{B}abel{D}r: a web platform for rapid construction of phrasebook-style medical speech translation applications",
    author = "Bouillon, Pierrette  and
      Spechbach, Herv{\'e}",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.5",
}
@inproceedings{vandeghinste-etal-2016-scate,
    title = "{SCATE} {--} smart computer aided translation environment",
    author = "Vandeghinste, V.  and
      Vanallemeersch, T.  and
      Augustinus, L.  and
      Pelemans, J.  and
      Heymans, G.  and
      Van der Lek-Ciudin, I.  and
      Tezcan, A.  and
      Degraen, D.  and
      Van den Bergh, J.  and
      Macken, L.  and
      Lefever, E.  and
      Moens, M.  and
      Wambacq, P.  and
      Steurs, F.  and
      Coninx, K.  and
      Van Eynde, F.",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.6",
}
@inproceedings{haddow-fraser-2016-himl,
    title = "{H}im{L}: Health in my language",
    author = "Haddow, Barry  and
      Fraser, Alex",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.7",
}
@inproceedings{tiedemann-2016-opus,
    title = "{OPUS} {--} parallel corpora for everyone",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.8",
}
@inproceedings{costa-jussa-2016-integration,
    title = "Integration of machine translation paradigms",
    author = "Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.9",
}
@inproceedings{hofmann-2016-star,
    title = "{STAR} Transit {\&} {STAR} {MT}: morphologically generated additional information for improving {MT} quality",
    author = "Hofmann, Nadira",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.10",
}
@inproceedings{ramirez-sanchez-2016-altlang,
    title = "{A}lt{L}ang: an automatic converter between varieties of {E}nglish, {S}panish, {F}rench and {P}ortuguese",
    author = "Ram{\'\i}rez-S{\`a}nchez, Gema",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.11",
}
@inproceedings{nikiforovs-2016-iems,
    title = "i{EMS} {--} interactive experiment management system for machine translation",
    author = "Ṇikiforovs, P{\=e}teris",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.12",
}
@inproceedings{luri-2016-kantanlqr,
    title = "{K}antan{LQR}: a platform for human evaluation of machine translation output to drive engine rapid improvement",
    author = "Luri, Laura Casanellas",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.13",
}
@inproceedings{helle-herranz-2016-pangeamt,
    title = "{P}angea{MT} v 3 {--} customise your own machine translation environment",
    author = "Helle, Alexandre  and
      Herranz, Manuel",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.14",
}
@inproceedings{ranta-etal-2016-international,
    title = "International translation in the Grammatical Framework ({GF})",
    author = "Ranta, Aarne  and
      Angelov, Kasimir  and
      Hallgren, Thomas  and
      Kolachina, Prasanth  and
      Listenmaa, Inari",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.15",
}
@inproceedings{ranta-etal-2016-domain,
    title = "Domain-specific multilingual translation for producers of information",
    author = "Ranta, Aarne  and
      Angelov, Kasimir  and
      Forsberg, Markus  and
      Hallgren, Thomas",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.16",
}
@inproceedings{orasan-2016-expert,
    title = "The {EXPERT} project: training the future experts in translation technology",
    author = "Ora{\v{s}}an, Constantin",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.17",
}
@inproceedings{ceausu-etal-2016-amplexor,
    title = "Amplexor {MTE}xpert {--} machine translation adapted to the translation workflow",
    author = "Ceausu, Alexandru  and
      Hunsicker, Sabine  and
      Droumaguet, Tudy",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.18",
}
@inproceedings{toral-etal-2016-abu,
    title = "{A}bu-{M}a{T}ran: automatic building of machine translation",
    author = "Toral, Antonio  and
      Ortiz Rojas, Sergio  and
      Forcada, Mikel  and
      Lubesic, Nikola  and
      Prokopidis, Prokopis",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.19",
}
@inproceedings{kordoni-etal-2016-tramooc,
    title = "{T}ra{MOOC} (Translation for Massive Open Online Courses): providing reliable {MT} for {MOOC}s",
    author = "Kordoni, Valia  and
      Birch, Lexi  and
      Buliga, Ioana  and
      Cholakov, Kostadin  and
      Egg, Markus  and
      Gaspari, Federico  and
      Georgakopolou, Yota  and
      Gialama, Maria  and
      Hendrickx, Iris  and
      Jermol, Mitja  and
      Kermanidis, Katia  and
      Moorkens, Joss  and
      Orlic, Davor  and
      Papadopoulos, Michael  and
      Popovi{\'c}, Maja  and
      Sennrich, Rico  and
      Sosoni, Vilelmini  and
      Tsoumakos, Dimitrios  and
      van den Bosch, Antal  and
      van Zaanen, Menno  and
      Way, Andy",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.20",
}
@inproceedings{germann-etal-2016-modern,
    title = "{M}odern {MT}: a new open-source machine translation platform for the translation industry",
    author = "Germann, U.  and
      Barbu, E.  and
      Bentivogli, L.  and
      Bertoldi, N.  and
      Bogoychev, N.  and
      Buck, C.  and
      Caroselli, D.  and
      Carvalho, L.  and
      Cattelan, A.  and
      Cettolo, R.  and
      Federico, M.  and
      Haddow, B.  and
      Madl, D.  and
      Mastrostefano, L.  and
      Mathur, P.  and
      Ruopp, A.  and
      Samiotou, A.  and
      Sudharshan, V.  and
      Trombetti, M.  and
      van der Meer, Jan",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.21",
}
@inproceedings{popescu-belis-etal-2016-modern,
    title = "{MODERN}: modelling discourse entities and relations for coherent machine translation",
    author = "Popescu-Belis, A.  and
      Evers-Vermeul, J.  and
      Fishel, M.  and
      Grisot, C.  and
      Groen, M.  and
      Hoeck, J.  and
      Loaiciga, S.  and
      Luong, N. Q.  and
      Mascarelli, L.  and
      Meyer, T.  and
      Miculicich, L.  and
      Moeschler, J.  and
      Pu, X.  and
      Rios, A.  and
      Sanders, T.  and
      Volk, M.  and
      Zufferey, S.",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.22",
}
@inproceedings{rehm-sasaki-2016-digital,
    title = "Digital curation technologies ({DKT})",
    author = "Rehm, Georg  and
      Sasaki, Felix",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.23",
}
@inproceedings{rehm-2016-cracker,
    title = "{CRACKER} {--} cracking the language barrier. Selected results 2015/2016",
    author = "Rehm, Georg",
    booktitle = "Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products",
    month = may # " 30{--}" # jun # " 1",
    year = "2016",
    address = "Riga, Latvia",
    publisher = "Baltic Journal of Modern Computing",
    url = "https://www.aclweb.org/anthology/2016.eamt-2.24",
}
@proceedings{paclic-2015-pacific-asia,
    title = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    editor = "Zhao, Hai",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2000",
}
@inproceedings{fukuoka-shirai-2015-identification,
    title = "Identification of Sympathy in Free Conversation",
    author = "Fukuoka, Tomotaka  and
      Shirai, Kiyoaki",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2001",
    pages = "1--9",
}
@inproceedings{kwong-2015-toward,
    title = "Toward a Corpus of {C}antonese Verbal Comments and their Classification by Multi-dimensional Analysis",
    author = "Kwong, Oi Yee",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2002",
    pages = "10--18",
}
@inproceedings{jeong-etal-2015-improved,
    title = "Improved Entity Linking with User History and News Articles",
    author = "Jeong, Soyun  and
      Park, Youngmin  and
      Kang, Sangwoo  and
      Seo, Jungyun",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2003",
    pages = "19--26",
}
@inproceedings{chuang-hsieh-2015-arguing,
    title = "An Arguing Lexicon for Stance Classification on Short Text Comments in {C}hinese",
    author = "Chuang, Ju-han  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2004",
    pages = "27--36",
}
@inproceedings{chang-etal-2015-learning,
    title = "Learning Sentential Patterns of Various Rhetoric Moves for Assisted Academic Writing",
    author = "Chang, Jim  and
      Hsu, Hsiang-Ling  and
      Boisson, Joanne  and
      Peng, Hao-Chun  and
      Wu, Yu-Hsuan  and
      Chang, Jason S.",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2005",
    pages = "37--45",
}
@inproceedings{chae-2015-idioms,
    title = "{I}dioms: Formally Flexible but Semantically Non-transparent",
    author = "Chae, Hee-Rahk",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2006",
    pages = "46--54",
}
@inproceedings{son-2015-asymmetries,
    title = "Asymmetries in Scrambling and Distinctness of Copies",
    author = "Son, Gwangrak",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2007",
    pages = "55--63",
}
@inproceedings{uehara-etal-2015-detecting,
    title = "Detecting an Infant{'}s Developmental Reactions in Reviews on Picture Books",
    author = "Uehara, Hiroshi  and
      Baba, Mizuho  and
      Utsuro, Takehito",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2008",
    pages = "64--71",
}
@inproceedings{choi-etal-2015-semi,
    title = "Semi-automatic Filtering of Translation Errors in Triangle Corpus",
    author = "Choi, Sung-Kwon  and
      Shin, Jong-Hun  and
      Kim, Young-Gil",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2009",
    pages = "72--79",
}
@inproceedings{shen-etal-2015-cross,
    title = "Cross-language Projection of Dependency Trees for Tree-to-tree Machine Translation",
    author = "Shen, Yu  and
      Chu, Chenhui  and
      Cromieres, Fabien  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2010",
    pages = "80--88",
}
@inproceedings{wu-etal-2015-realignment,
    title = "Realignment from Finer-grained Alignment to Coarser-grained Alignment to Enhance {M}ongolian-{C}hinese {SMT}",
    author = "Wu, Jing  and
      Hou, Hongxu  and
      Xie, Congjiao",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2011",
    pages = "89--95",
}
@inproceedings{islam-dundua-2015-finding,
    title = "Finding the Origin of a Translated Historical Document",
    author = "Islam, Zahrul  and
      Dundua, Natia",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2012",
    pages = "96--105",
}
@inproceedings{semmar-etal-2015-improving,
    title = "Improving the Performance of an Example-Based Machine Translation System Using a Domain-specific Bilingual Lexicon",
    author = "Semmar, Nasredine  and
      Zennaki, Othman  and
      Laib, Meriama",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2013",
    pages = "106--115",
}
@inproceedings{lee-etal-2015-multifactorial,
    title = "A Multifactorial Analysis of {E}nglish Particle Movement in {K}orean {EFL} Learners{'} Writings",
    author = "Lee, Gyu-Hyeong  and
      Kim, Ha-Eung  and
      Lee, Yong-hun",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2014",
    pages = "116--124",
}
@inproceedings{komai-etal-2015-efficient,
    title = "An Efficient Annotation for Phrasal Verbs using Dependency Information",
    author = "Komai, Masayuki  and
      Shindo, Hiroyuki  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2015",
    pages = "125--131",
}
@inproceedings{liu-etal-2015-color,
    title = "Color Aesthetics and Social Networks in Complete Tang Poems: Explorations and Discoveries",
    author = "Liu, Chao-Lin  and
      Wang, Hongsu  and
      Cheng, Wen-Huei  and
      Hsu, Chu-Ting  and
      Chiu, Wei-Yun",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2016",
    pages = "132--141",
}
@inproceedings{do-choi-2015-korean,
    title = "{K}orean {T}witter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features",
    author = "Do, Hyo Jin  and
      Choi, Ho-Jin",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2017",
    pages = "142--150",
}
@inproceedings{zheng-etal-2015-chinese,
    title = "{C}hinese Word Segmentation based on analogy and majority voting",
    author = "Zheng, Zongrong  and
      Wang, Yi  and
      Lepage, Yves",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2018",
    pages = "151--156",
}
@inproceedings{el-defrawy-etal-2015-enhancing,
    title = "Enhancing Root Extractors Using Light Stemmers",
    author = "El-Defrawy, Mahmoud  and
      El-Sonbaty, Yasser  and
      Belal, Nahla",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2019",
    pages = "157--166",
}
@inproceedings{takehisa-2015-morphological,
    title = "Where Morphological Complexity Matters",
    author = "Takehisa, Tomokazu",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2020",
    pages = "167--175",
}
@inproceedings{hacohen-kerner-etal-2015-distinguishing,
    title = "Distinguishing between True and False Stories using various Linguistic Features",
    author = "Hacohen-Kerner, Yaakov  and
      Dilmon, Rakefet  and
      Friedlich, Shimon  and
      Cohen, Daniel Nisim",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2021",
    pages = "176--186",
}
@inproceedings{mahesh-etal-2015-bilingually,
    title = "Bilingually motivated segmentation and generation of word translations using relatively small translation data sets",
    author = "Mahesh, Kavitha Karimbi  and
      Gomes, Lu{\'\i}s  and
      Lopes, Jos{\'e}",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2022",
    pages = "187--196",
}
@inproceedings{perera-nand-2015-selecting,
    title = "Selecting Contextual Peripheral Information for Answer Presentation: The Need for Pragmatic Models",
    author = "Perera, Rivindu  and
      Nand, Parma",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2023",
    pages = "197--205",
}
@inproceedings{perera-nand-2015-realtext,
    title = "{R}eal{T}ext-asg: A Model to Present Answers Utilizing the Linguistic Structure of Source Question",
    author = "Perera, Rivindu  and
      Nand, Parma",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2024",
    pages = "206--214",
}
@inproceedings{shinnou-etal-2015-learning,
    title = "Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation",
    author = "Shinnou, Hiroyuki  and
      Sasaki, Minoru  and
      Komiya, Kanako",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2025",
    pages = "215--223",
}
@inproceedings{kouno-etal-2015-unsupervised,
    title = "Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder",
    author = "Kouno, Kazuhei  and
      Shinnou, Hiroyuki  and
      Sasaki, Minoru  and
      Komiya, Kanako",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2026",
    pages = "224--231",
}
@inproceedings{liu-etal-2015-construction,
    title = "Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing",
    author = "Liu, Shijun  and
      Shao, Yanqiu  and
      Ding, Yu  and
      Zheng, Lijuan",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2027",
    pages = "232--240",
}
@inproceedings{wu-2015-dynamic,
    title = "Dynamic Semantics for Intensification and Epistemic Necessity: The Case of Y{\'\i}d{\`\i}ng and Sh{\`\i}b{\`\i} in {M}andarin {C}hinese",
    author = "Wu, Jiun-Shiung",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2028",
    pages = "241--248",
}
@inproceedings{zhang-liu-2015-corpus,
    title = "A Corpus-based Comparatively Study on the Semantic Features and Syntactic patterns of Y{\`o}u/H{\'a}i in {M}andarin {C}hinese",
    author = "Zhang, Yuncui  and
      Liu, Pengyuan",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2029",
    pages = "249--257",
}
@inproceedings{lin-etal-2015-empirical,
    title = "An Empirical Study on Sentiment Classification of {C}hinese Review using Word Embedding",
    author = "Lin, Yiou  and
      Lei, Hang  and
      Wu, Jia  and
      Li, Xiaoyu",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2030",
    pages = "258--266",
}
@inproceedings{song-etal-2015-polarity,
    title = "Polarity Classification of Short Product Reviews via Multiple Cluster-based {SVM} Classifiers",
    author = "Song, Jiaying  and
      He, Yu  and
      Fu, Guohong",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2031",
    pages = "267--274",
}
@inproceedings{hacohen-kerner-hagege-2015-automatic,
    title = "Automatic Classification of Spoken Languages using Diverse Acoustic Features",
    author = "Hacohen-Kerner, Yaakov  and
      Hagege, Ruben",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2032",
    pages = "275--285",
}
@inproceedings{chen-etal-2015-syntactic,
    title = "The Syntactic and Semantic Analysis of H{\v{e}}n {X} Constructions in Spoken Corpora",
    author = "Chen, Yen-ju  and
      Lai, Huei-ling  and
      Hsu, Shao-chun",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2033",
    pages = "286--292",
}
@inproceedings{munthuli-etal-2015-methods,
    title = "Methods and Tool for Constructing Phonetically-Balanced Materials for Speech Perception Testing: A Development of {T}hai Sentence-Length Materials",
    author = "Munthuli, Adirek  and
      Tantibundhit, Charturong  and
      Onsuwan, Chutamanee  and
      Kosawat, Krit",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2034",
    pages = "293--301",
}
@inproceedings{neergaard-huang-2015-graph,
    title = "Graph Theoretic Features of the Adult Mental lexicon Predict Language Production in {M}andarin: Clustering Coefficient",
    author = "Neergaard, Karl  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2035",
    pages = "302--308",
}
@inproceedings{xia-etal-2015-feature,
    title = "Feature Reduction Using Ensemble Approach",
    author = "Xia, Yingju  and
      Hou, Cuiqin  and
      Xu, Zhuoran  and
      Sun, Jun",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2036",
    pages = "309--318",
}
@inproceedings{kim-etal-2015-measuring,
    title = "Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model",
    author = "Kim, Jong Myoung  and
      Park, Hancheol  and
      Jeong, Young-Seob  and
      Choi, Ho-Jin  and
      Gweon, Gahgene  and
      Hur, Jeong",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2037",
    pages = "319--327",
}
@inproceedings{iwai-etal-2015-acquiring,
    title = "Acquiring distributed representations for verb-object pairs by using word2vec",
    author = "Iwai, Miki  and
      Ninomiya, Takashi  and
      Kageura, Kyo",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2038",
    pages = "328--336",
}
@inproceedings{li-etal-2015-dependency-parsing,
    title = "Dependency parsing for {C}hinese long sentence: A second-stage main structure parsing method",
    author = "Li, Bo  and
      Long, Yunfei  and
      Qu, Weiguang",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2039",
    pages = "337--344",
}
@inproceedings{wang-zhao-2015-light,
    title = "A Light Rule-based Approach to {E}nglish Subject-Verb Agreement Errors on the Third Person Singular Forms",
    author = "Wang, Yuzhu  and
      Zhao, Hai",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2040",
    pages = "345--353",
}
@inproceedings{li-etal-2015-machine,
    title = "A Machine Learning Method to Distinguish Machine Translation from Human Translation",
    author = "Li, Yitong  and
      Wang, Rui  and
      Zhao, Hai",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-2041",
    pages = "354--360",
}
@proceedings{paclic-2015-pacific,
    title = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    editor = "Zhao, Hai",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1000",
}
@inproceedings{wang-huang-2015-two,
    title = "Two-level Word Class Categorization Model in Analytic Languages and Its Implications for {POS} Tagging in {M}odern {C}hinese Corpora",
    author = "Wang, Renqiang  and
      Huang, Changning",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1001",
    pages = "1--10",
}
@inproceedings{yao-2015-review,
    title = "A Review of Corpus-based Statistical Models of Language Variation",
    author = "Yao, Yao",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1002",
    pages = "11--15",
}
@inproceedings{wang-etal-2015-translation,
    title = "Translation of Unseen Bigrams by Analogy Using an {SVM} Classifier",
    author = "Wang, Hao  and
      Lyu, Lu  and
      Lepage, Yves",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1003",
    pages = "16--25",
}
@inproceedings{meftouh-etal-2015-machine,
    title = "Machine Translation Experiments on {PADIC}: A Parallel {A}rabic {DI}alect Corpus",
    author = "Meftouh, Karima  and
      Harrat, Salima  and
      Jamoussi, Salma  and
      Abbas, Mourad  and
      Smaili, Kamel",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1004",
    pages = "26--34",
}
@inproceedings{komiya-etal-2015-surrounding,
    title = "Surrounding Word Sense Model for {J}apanese All-words Word Sense Disambiguation",
    author = "Komiya, Kanako  and
      Sasaki, Yuto  and
      Morita, Hajime  and
      Sasaki, Minoru  and
      Shinnou, Hiroyuki  and
      Kotani, Yoshiyuki",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1005",
    pages = "35--43",
}
@inproceedings{liu-etal-2015-computing,
    title = "Computing Semantic Text Similarity Using Rich Features",
    author = "Liu, Yang  and
      Sun, Chengjie  and
      Lin, Lei  and
      Wang, Xiaolong  and
      Zhao, Yuming",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1006",
    pages = "44--52",
}
@inproceedings{wang-etal-2015-mechanical,
    title = "{M}echanical {T}urk-based Experiment vs Laboratory-based Experiment: A Case Study on the Comparison of Semantic Transparency Rating Data",
    author = "Wang, Shichang  and
      Huang, Chu-Ren  and
      Yao, Yao  and
      Chan, Angel",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1007",
    pages = "53--62",
}
@inproceedings{otsuka-etal-2015-discourse,
    title = "Discourse Relation Recognition by Comparing Various Units of Sentence Expression with Recursive Neural Network",
    author = "Otsuka, Atsushi  and
      Hirano, Toru  and
      Miyazaki, Chiaki  and
      Masumura, Ryo  and
      Higashinaka, Ryuichiro  and
      Makino, Toshiro  and
      Matsuo, Yoshihiro",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1008",
    pages = "63--72",
}
@inproceedings{zhang-etal-2015-bidirectional,
    title = "Bidirectional Long Short-Term Memory Networks for Relation Classification",
    author = "Zhang, Shu  and
      Zheng, Dequan  and
      Hu, Xinchen  and
      Yang, Ming",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1009",
    pages = "73--78",
}
@inproceedings{fan-etal-2015-distant,
    title = "Distant Supervision for Entity Linking",
    author = "Fan, Miao  and
      Zhou, Qiang  and
      Zheng, Thomas Fang",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1010",
    pages = "79--86",
}
@inproceedings{liu-etal-2015-toward-algorithmic,
    title = "Toward Algorithmic Discovery of Biographical Information in Local Gazetteers of {A}ncient {C}hina",
    author = "Liu, Chao-Lin  and
      Huang, Chih-Kai  and
      Wang, Hongsu  and
      Bol, Peter K.",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1011",
    pages = "87--95",
}
@inproceedings{takase-etal-2015-fast,
    title = "Fast and Large-scale Unsupervised Relation Extraction",
    author = "Takase, Sho  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1012",
    pages = "96--105",
}
@inproceedings{komatsu-etal-2015-reducing,
    title = "Reducing Lexical Features in Parsing by Word Embeddings",
    author = "Komatsu, Hiroya  and
      Tian, Ran  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1013",
    pages = "106--113",
}
@inproceedings{zhang-zhao-2015-high,
    title = "High-order Graph-based Neural Dependency Parsing",
    author = "Zhang, Zhisong  and
      Zhao, Hai",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1014",
    pages = "114--123",
}
@inproceedings{seraku-2015-dynamic,
    title = "A Dynamic Syntax Modelling of Postposing in {J}apanese Narratives",
    author = "Seraku, Tohru",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1015",
    pages = "124--132",
}
@inproceedings{zennaki-etal-2015-unsupervised,
    title = "Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks",
    author = "Zennaki, Othman  and
      Semmar, Nasredine  and
      Besacier, Laurent",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1016",
    pages = "133--142",
}
@inproceedings{li-jin-2015-identifying,
    title = "Identifying Prepositional Phrases in {C}hinese Patent Texts with Rule-based and {CRF} Methods",
    author = "Li, Hongzheng  and
      Jin, Yaohong",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1017",
    pages = "143--149",
}
@inproceedings{zhang-komachi-2015-japanese,
    title = "{J}apanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation",
    author = "Zhang, Peinan  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1018",
    pages = "150--159",
}
@inproceedings{zhou-etal-2015-wikipedia,
    title = "Is {W}ikipedia Really Neutral? A Sentiment Perspective Study of War-related {W}ikipedia Articles since 1945",
    author = "Zhou, Yiwei  and
      Cristea, Alexandra  and
      Roberts, Zachary",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1019",
    pages = "160--168",
}
@inproceedings{le-ho-2015-comprehensive,
    title = "A Comprehensive Filter Feature Selection for Improving Document Classification",
    author = "Le, Nguyen Hoai Nam  and
      Ho, Bao Quoc",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1020",
    pages = "169--177",
}
@inproceedings{tungthamthiti-etal-2015-sentiment,
    title = "Sentiment Analyzer with Rich Features for Ironic and Sarcastic Tweets",
    author = "Tungthamthiti, Piyoros  and
      Santus, Enrico  and
      Xu, Hongzhi  and
      Huang, Chu-Ren  and
      Shirai, Kiyoaki",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1021",
    pages = "178--187",
}
@inproceedings{netisopakul-chattupan-2015-thai,
    title = "{T}hai Stock News Sentiment Classification using Wordpair Features",
    author = "Netisopakul, Ponrudee  and
      Chattupan, Apinan",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1022",
    pages = "188--195",
}
@inproceedings{bayoudhi-etal-2015-sentiment,
    title = "Sentiment Classification of {A}rabic Documents: Experiments with multi-type features and ensemble algorithms",
    author = "Bayoudhi, Amine  and
      Ghorbel, Hatem  and
      Belguith, Lamia Hadrich",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1023",
    pages = "196--205",
}
@inproceedings{cong-etal-2015-invertible,
    title = "The Invertible Construction in {C}hinese",
    author = "Cong, Yan  and
      Huang, Chu-Ren  and
      Wee, Lian-Hee",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1024",
    pages = "206--211",
}
@inproceedings{park-2015-pans,
    title = "Pan{'}s (2001) puzzle revisited",
    author = "Park, Hyunjun",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1025",
    pages = "212--220",
}
@inproceedings{kamada-2015-english,
    title = "{E}nglish Right Dislocation",
    author = "Kamada, Kohji",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1026",
    pages = "221--230",
}
@inproceedings{lau-lee-2015-comparative,
    title = "A Comparative Study on {M}andarin and {C}antonese Resultative Verb Compounds",
    author = "Lau, Helena Yan Ping  and
      Lee, Sophia Yat Mei",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1027",
    pages = "231--239",
}
@inproceedings{lee-park-2015-complex,
    title = "Complex-{NP} Islands in {K}orean: An Experimental Approach",
    author = "Lee, Yong-hun  and
      Park, Yeonkyung",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1028",
    pages = "240--249",
}
@inproceedings{kim-etal-2015-two,
    title = "Two Types of Multiple Subject Constructions ({MSC}s) in {K}orean",
    author = "Kim, Ji-Hye  and
      Kim, Eunah  and
      Yoon, James",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1029",
    pages = "250--258",
}
@inproceedings{kyaw-thu-etal-2015-large,
    title = "A Large-scale Study of Statistical Machine Translation Methods for {K}hmer Language",
    author = "Kyaw Thu, Ye  and
      Chea, Vichet  and
      Finch, Andrew  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1030",
    pages = "259--269",
}
@inproceedings{wang-etal-2015-english,
    title = "{E}nglish to {C}hinese Translation: How {C}hinese Character Matters",
    author = "Wang, Rui  and
      Zhao, Hai  and
      Lu, Bao-Liang",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1031",
    pages = "270--280",
}
@inproceedings{li-etal-2015-well,
    title = "Well-Formed Dependency to String translation with {BTG} Grammar",
    author = "Li, Xiaoqing  and
      Wang, Kun  and
      Zhang, Dakun  and
      Hao, Jie",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1032",
    pages = "281--288",
}
@inproceedings{dabre-etal-2015-large,
    title = "Large-scale Dictionary Construction via Pivot-based Statistical Machine Translation with Significance Pruning and Neural Network Features",
    author = "Dabre, Raj  and
      Chu, Chenhui  and
      Cromieres, Fabien  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1033",
    pages = "289--297",
}
@inproceedings{prevot-etal-2015-annotation,
    title = "Annotation and Classification of {F}rench Feedback Communicative Functions",
    author = "Pr{\'e}vot, Laurent  and
      Gorisch, Jan  and
      Mukherjee, Sankar",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1034",
    pages = "298--306",
}
@inproceedings{miyazaki-etal-2015-automatic,
    title = "Automatic conversion of sentence-end expressions for utterance characterization of dialogue systems",
    author = "Miyazaki, Chiaki  and
      Hirano, Toru  and
      Higashinaka, Ryuichiro  and
      Makino, Toshiro  and
      Matsuo, Yoshihiro",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1035",
    pages = "307--314",
}
@inproceedings{zhao-etal-2015-auditory,
    title = "Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in {M}andarin {C}hinese",
    author = "Zhao, Qingqing  and
      Huang, Chu-Ren  and
      Xu, Hongzhi",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1036",
    pages = "315--322",
}
@inproceedings{tsukahara-uchiumi-2015-system,
    title = "System Utterance Generation by Label Propagation over Association Graph of Words and Utterance Patterns for Open-Domain Dialogue Systems",
    author = "Tsukahara, Hiroshi  and
      Uchiumi, Kei",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1037",
    pages = "323--331",
}
@inproceedings{chang-chui-2015-cross,
    title = "The Cross-modal Representation of Metaphors",
    author = "Chang, Yutung  and
      Chui, Kawai",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1038",
    pages = "332--340",
}
@inproceedings{zhang-reilly-2015-writing,
    title = "Writing to Read: the Case of {C}hinese",
    author = "Zhang, Qi  and
      Reilly, Ronan",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1039",
    pages = "341--350",
}
@inproceedings{kotani-yoshimi-2015-design,
    title = "Design of a Learner Corpus for Listening and Speaking Performance",
    author = "Kotani, Katsunori  and
      Yoshimi, Takehiko",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1040",
    pages = "351--358",
}
@inproceedings{gao-guo-2015-understanding,
    title = "Understanding Infants{'} Language Development in Relation to Levels of Consciousness: An Approach in Building up an Agent-based Model",
    author = "Gao, Helena Hong  and
      Guo, Can",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1041",
    pages = "359--368",
}
@inproceedings{richardson-etal-2015-pivot,
    title = "Pivot-Based Topic Models for Low-Resource Lexicon Extraction",
    author = "Richardson, John  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1042",
    pages = "369--377",
}
@inproceedings{liu-2015-corpus,
    title = "A Corpus-Based Study of zunshou and Its {E}nglish Equivalents",
    author = "Liu, Ying",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1043",
    pages = "378--386",
}
@inproceedings{elleuch-etal-2015-self,
    title = "Self Syntactico-Semantic Enrichment of {LMF} Normalized Dictionaries",
    author = "Elleuch, Imen  and
      Gargouri, Bilel  and
      Ben Hamadou, Abdelmajid",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1044",
    pages = "387--395",
}
@inproceedings{duann-huang-2015-embodiment,
    title = "When Embodiment Meets {G}enerative {L}exicon: The Human Body Part Metaphors in Sinica Corpus",
    author = "Duann, Ren-feng  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1045",
    pages = "396--403",
}
@inproceedings{oda-2015-degree,
    title = "Degree Variables by Choose Degree in Izyooni {`}than{'}-Clauses",
    author = "Oda, Toshiko",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1046",
    pages = "404--412",
}
@inproceedings{park-choi-2015-voice,
    title = "Not Voice but Case Identity in {VP} Ellipsis of {E}nglish",
    author = "Park, Myungkwan  and
      Choi, Sunjoo",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1047",
    pages = "413--421",
}
@inproceedings{kim-park-2015-statistical,
    title = "A Statistical Modeling of the Correlation between Island Effects and Working-memory Capacity for {L}2 Learners",
    author = "Kim, Euhee  and
      Park, Myungkwan",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1048",
    pages = "422--430",
}
@inproceedings{xiong-huang-2015-de,
    title = "De-verbalization and Nominal Categories in {M}andarin {C}hinese: A corpus-driven study in both Mainland {M}andarin and {T}aiwan {M}andarin",
    author = "Xiong, Jiajuan  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1049",
    pages = "431--438",
}
@inproceedings{park-etal-2015-zero,
    title = "Zero Object Resolution in {K}orean",
    author = "Park, Arum  and
      Lim, Seunghee  and
      Hong, Munpyo",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1050",
    pages = "439--448",
}
@inproceedings{wu-matsumoto-2015-improved,
    title = "An Improved Hierarchical Word Sequence Language Model Using Directional Information",
    author = "Wu, Xiaoyi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1051",
    pages = "449--454",
}
@inproceedings{chen-etal-2015-neural,
    title = "Neural Network Language Model for {C}hinese {P}inyin Input Method Engine",
    author = "Chen, Shenyuan  and
      Zhao, Hai  and
      Wang, Rui",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1052",
    pages = "455--461",
}
@inproceedings{tu-etal-2015-real,
    title = "Real-time Detection and Sorting of News on Microblogging Platforms",
    author = "Tu, Wenting  and
      Cheung, David  and
      Mamoulis, Nikos  and
      Yang, Min  and
      Lu, Ziyu",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1053",
    pages = "462--470",
}
@inproceedings{kurihara-shimada-2015-trouble,
    title = "Trouble information extraction based on a bootstrap approach from {T}witter",
    author = "Kurihara, Kohei  and
      Shimada, Kazutaka",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1054",
    pages = "471--479",
}
@inproceedings{hu-tanida-2015-using,
    title = "Using {T}witter Data to Infer Personal Values of {J}apanese Consumers",
    author = "Hu, Yinjun  and
      Tanida, Yasuo",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1055",
    pages = "480--487",
}
@inproceedings{fujinuma-etal-2015-distant,
    title = "Distant-supervised Language Model for Detecting Emotional Upsurge on {T}witter",
    author = "Fujinuma, Yoshinari  and
      Yokono, Hikaru  and
      Mart{\'\i}nez-G{\'o}mez, Pascual  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1056",
    pages = "488--495",
}
@inproceedings{shinnou-etal-2015-hybrid,
    title = "Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification",
    author = "Shinnou, Hiroyuki  and
      Xiao, Liying  and
      Sasaki, Minoru  and
      Komiya, Kanako",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1057",
    pages = "496--503",
}
@inproceedings{nguyen-son-etal-2015-paraphrase,
    title = "Paraphrase Detection Based on Identical Phrase and Similar Word Matching",
    author = "Nguyen-Son, Hoang-Quoc  and
      Miyao, Yusuke  and
      Echizen, Isao",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1058",
    pages = "504--512",
}
@inproceedings{nakamuta-shimada-2015-multi,
    title = "Multi-aspects Rating Prediction Using Aspect Words and Sentences",
    author = "Nakamuta, Takuto  and
      Shimada, Kazutaka",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1059",
    pages = "513--521",
}
@inproceedings{kamath-etal-2015-understanding,
    title = "Understanding Rating Behaviour and Predicting Ratings by Identifying Representative Users",
    author = "Kamath, Rahul  and
      Ochi, Masanao  and
      Matsuo, Yutaka",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1060",
    pages = "522--528",
}
@inproceedings{wang-etal-2015-cross,
    title = "Cross-lingual Pseudo Relevance Feedback Based on Weak Relevant Topic Alignment",
    author = "Wang, Xuwen  and
      Zhang, Qiang  and
      Wang, Xiaojie  and
      Li, Junlian",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1061",
    pages = "529--534",
}
@inproceedings{chung-etal-2015-corpus,
    title = "Corpus annotation with a linguistic analysis of the associations between event mentions and spatial expressions",
    author = "Chung, Jin-Woo  and
      You, Jinseon  and
      Park, Jong C.",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1062",
    pages = "535--543",
}
@inproceedings{mizuno-etal-2015-recognizing,
    title = "Recognizing Complex Negation on {T}witter",
    author = "Mizuno, Junta  and
      Kruengkrai, Canasai  and
      Ohtake, Kiyonori  and
      Hashimoto, Chikara  and
      Torisawa, Kentaro  and
      Kloetzer, Julien  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1063",
    pages = "544--552",
}
@inproceedings{huang-etal-2015-topic,
    title = "Topic Model for Identifying Suicidal Ideation in {C}hinese Microblog",
    author = "Huang, Xiaolei  and
      Li, Xin  and
      Liu, Tianli  and
      Chiu, David  and
      Zhu, Tingshao  and
      Zhang, Lei",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1064",
    pages = "553--562",
}
@inproceedings{lu-etal-2015-predicting,
    title = "Predicting Sector Index Movement with Microblogging Public Mood Time Series on Social Issues",
    author = "Lu, Yujie  and
      Guo, Jinlong  and
      Sakamoto, Kotaro  and
      Shibuki, Hideyuki  and
      Mori, Tatsunori",
    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation",
    month = oct,
    year = "2015",
    address = "Shanghai, China",
    url = "https://www.aclweb.org/anthology/Y15-1065",
    pages = "563--571",
}
@proceedings{ws-2015-international-natural-language,
    title = "Proceedings of the 12th International Conference on Natural Language Processing",
    editor = "Sharma, Dipti Misra  and
      Sangal, Rajeev  and
      Sherly, Elizabeth",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5900",
}
@inproceedings{matsumoto-2015-keynote,
    title = "Keynote Lecture 1: Scientific Paper Analysis",
    author = "Matsumoto, Yuji",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5901",
    pages = "1",
}
@inproceedings{kunchukuttan-bhattacharyya-2015-addressing,
    title = "Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization",
    author = "Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5902",
    pages = "2--10",
}
@inproceedings{singh-mukerjee-2015-words,
    title = "Words are not Equal: Graded Weighting Model for Building Composite Document Vectors",
    author = "Singh, Pranjal  and
      Mukerjee, Amitabha",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5903",
    pages = "11--19",
}
@inproceedings{joharapurkar-etal-2015-online,
    title = "Online Adspace Posts{'} Category Classification",
    author = "Joharapurkar, Dhawal  and
      Salin, Vaishak  and
      Krishna, Vishal",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5904",
    pages = "20--28",
}
@inproceedings{pawar-etal-2015-noun,
    title = "Noun Phrase Chunking for {M}arathi using Distant Supervision",
    author = "Pawar, Sachin  and
      Ramrakhiyani, Nitin  and
      Palshikar, Girish K.  and
      Bhattacharyya, Pushpak  and
      Hingmire, Swapnil",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5905",
    pages = "29--38",
}
@inproceedings{bungum-gamback-2015-self,
    title = "Self-Organizing Maps for Classification of a Multi-Labeled Corpus",
    author = {Bungum, Lars  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5906",
    pages = "39--48",
}
@inproceedings{tayal-etal-2015-word,
    title = "Word Sense Disambiguation in {H}indi Language Using Hyperspace Analogue to Language and Fuzzy {C}-Means Clustering",
    author = "Tayal, Devendra K.  and
      Ahuja, Leena  and
      Chhabra, Shreya",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5907",
    pages = "49--58",
}
@inproceedings{bhingardive-etal-2015-using,
    title = "Using Word Embeddings for Bilingual Unsupervised {WSD}",
    author = "Bhingardive, Sudha  and
      Singh, Dhirendra  and
      V, Rudramurthy  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5908",
    pages = "59--64",
}
@inproceedings{dasgupta-etal-2015-compositionality,
    title = "Compositionality in {B}angla Compound Verbs and their Processing in the Mental Lexicon",
    author = "Dasgupta, Tirthankar  and
      Sinha, Manjira  and
      Basu, Anupam",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5909",
    pages = "65--70",
}
@inproceedings{redkar-etal-2015-indowordnet,
    title = "{I}ndo{W}ord{N}et Dictionary: An Online Multilingual Dictionary using {I}ndo{W}ord{N}et",
    author = "Redkar, Hanumant  and
      Singh, Sandhya  and
      Joshi, Nilesh  and
      Ghosh, Anupam  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5910",
    pages = "71--78",
}
@inproceedings{nagvenkar-etal-2015-sense,
    title = "Let Sense Bags Do Talking: Cross Lingual Word Semantic Similarity for {E}nglish and {H}indi",
    author = "Nagvenkar, Apurva  and
      Pawar, Jyoti  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5911",
    pages = "79--83",
}
@inproceedings{gupta-etal-2015-temporal,
    title = "A temporal expression recognition system for medical documents by",
    author = "Gupta, Naman  and
      Joshi, Aditya  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5912",
    pages = "84--88",
}
@inproceedings{emms-jayapal-2015-unsupervised,
    title = "An unsupervised {EM} method to infer time variation in sense probabilities",
    author = "Emms, Martin  and
      Jayapal, Arun",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5913",
    pages = "89--94",
}
@inproceedings{s-etal-2015-solving,
    title = "Solving Data Sparsity by Morphology Injection in Factored {SMT}",
    author = "S, Sreelekha  and
      Dungarwal, Piyush  and
      Bhattacharyya, Pushpak  and
      D, Malathi",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5914",
    pages = "95--99",
}
@inproceedings{phani-etal-2015-authorship,
    title = "Authorship Attribution in {B}engali Language",
    author = "Phani, Shanta  and
      Lahiri, Shibamouli  and
      Biswas, Arindam",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5915",
    pages = "100--105",
}
@inproceedings{kanojia-etal-2015-transchat,
    title = "{T}rans{C}hat: Cross-Lingual Instant Messaging for {I}ndian Languages",
    author = "Kanojia, Diptesh  and
      Dhuliawala, Shehzaad  and
      Mishra, Abhijit  and
      Gupta, Naman  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5916",
    pages = "106--111",
}
@inproceedings{sharma-etal-2015-database,
    title = "A Database of Infant Cry Sounds to Study the Likely Cause of Cry",
    author = "Sharma, Shivam  and
      Asthana, Shubham  and
      Mittal, V. K.",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5917",
    pages = "112--117",
}
@inproceedings{carlos-2015-perplexed,
    title = "Perplexed {B}ayes Classifier",
    author = "Carlos, Cohan Sujay",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5918",
    pages = "118--123",
}
@inproceedings{singh-2015-empirical,
    title = "An Empirical Study of Diversity of Word Alignment and its Symmetrization Techniques for System Combination",
    author = "Singh, Thoudam Doren",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5919",
    pages = "124--129",
}
@inproceedings{sharma-bhattacharyya-2015-domain,
    title = "Domain Sentiment Matters: A Two Stage Sentiment Analyzer",
    author = "Sharma, Raksha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5920",
    pages = "130--137",
}
@inproceedings{gulati-2015-extracting,
    title = "Extracting Information from {I}ndian First Names",
    author = "Gulati, Akshay",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5921",
    pages = "138--143",
}
@inproceedings{rajan-2015-punct,
    title = "punct-An Alternative Verb Semantic Ontology Representation",
    author = "Rajan, Kavitha",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5922",
    pages = "144--151",
}
@inproceedings{alam-2015-smt,
    title = "{SMT} Errors Requiring Grammatical Knowledge for Prevention",
    author = "Alam, Yukiko Sasaki",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5923",
    pages = "152--157",
}
@inproceedings{moneykumar-etal-2015-isolated,
    title = "Isolated Word Recognition System for {M}alayalam using Machine Learning",
    author = "Moneykumar, Maya  and
      Sherly, Elizabeth  and
      Varghese, Win Sam",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5924",
    pages = "158--165",
}
@inproceedings{dhuliawala-etal-2015-judge,
    title = "Judge a Book by its Cover: Conservative Focused Crawling under Resource Constraints",
    author = "Dhuliawala, Shehzaad  and
      V, Arjun Atreya  and
      Yadav, Ravi Kumar  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5925",
    pages = "166--171",
}
@inproceedings{p-etal-2015-text,
    title = "Text Normalization and Unit Selection for a Memory Based Non Uniform Unit Selection {TTS} in {M}alayalam",
    author = "P., Gokul  and
      Thomas, Neethu  and
      Thomas, Crisil  and
      Gopinath, Deepa P.",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5926",
    pages = "172--177",
}
@inproceedings{baxi-etal-2015-morphological,
    title = "Morphological Analyzer for {G}ujarati using Paradigm based approach with Knowledge based and Statistical Methods",
    author = "Baxi, Jatayu  and
      Patel, Pooja  and
      Bhatt, Brijesh",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5927",
    pages = "178--182",
}
@inproceedings{jonnalagadda-mamidi-2015-resolution,
    title = "Resolution of Pronominal Anaphora for {T}elugu Dialogues",
    author = "Jonnalagadda, Hemanth Reddy  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5928",
    pages = "183--188",
}
@inproceedings{jayan-sherly-2015-study,
    title = "A Study on Divergence in {M}alayalam and {T}amil Language in Machine Translation Perceptive",
    author = "Jayan, Jisha P  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5929",
    pages = "189--196",
}
@inproceedings{grandhi-paul-2015-automatic,
    title = "Automatic conversion of {I}ndian Language Morphological Processors into Grammatical Framework ({GF})",
    author = "Grandhi, Harsha Vardhan  and
      Paul, Soma",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5930",
    pages = "197--202",
}
@inproceedings{desai-etal-2015-logistic,
    title = "Logistic Regression for Automatic Lexical Level Morphological Paradigm Selection for {K}onkani Nouns",
    author = "Desai, Shilpa  and
      Pawar, Jyoti  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5931",
    pages = "203--208",
}
@inproceedings{ahiladas-etal-2015-ruchi,
    title = "{R}uchi: Rating Individual Food Items in Restaurant Reviews",
    author = "Ahiladas, Burusothman  and
      Saravanaperumal, Paraneetharan  and
      Balachandran, Sanjith  and
      Sripalan, Thamayanthy  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5932",
    pages = "209--214",
}
@inproceedings{sharma-mittal-2015-dependency,
    title = "Dependency Extraction for Knowledge-based Domain Classification",
    author = "Sharma, Lokesh Kumar  and
      Mittal, Namita",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5933",
    pages = "215--218",
}
@inproceedings{kulkarni-etal-2015-approach,
    title = "An Approach to Collective Entity Linking",
    author = "Kulkarni, Ashish  and
      Agarwal, Kanika  and
      Shah, Pararth  and
      Rathod, Sunny Raj  and
      Ramakrishnan, Ganesh",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5934",
    pages = "219--228",
}
@inproceedings{kurian-2015-development,
    title = "Development of Speech corpora for different Speech Recognition tasks in {M}alayalam language",
    author = "Kurian, Cini",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5935",
    pages = "229--236",
}
@inproceedings{sequiera-etal-2015-pos,
    title = "{POS} Tagging of {H}indi-{E}nglish Code Mixed Text from Social Media: Some Machine Learning Experiments",
    author = "Sequiera, Royal  and
      Choudhury, Monojit  and
      Bali, Kalika",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5936",
    pages = "237--246",
}
@inproceedings{rakshit-etal-2015-automated,
    title = "Automated Analysis of {B}angla Poetry for Classification and Poet Identification",
    author = "Rakshit, Geetanjali  and
      Ghosh, Anupam  and
      Bhattacharyya, Pushpak  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5937",
    pages = "247--253",
}
@inproceedings{rudrapal-etal-2015-sentence,
    title = "Sentence Boundary Detection for Social Media Text",
    author = {Rudrapal, Dwijen  and
      Jamatia, Anupam  and
      Chakma, Kunal  and
      Das, Amitava  and
      Gamb{\"a}ck, Bj{\"o}rn},
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5938",
    pages = "254--260",
}
@inproceedings{patra-etal-2015-mood,
    title = "Mood Classification of {H}indi Songs based on Lyrics",
    author = "Patra, Braja Gopal  and
      Das, Dipankar  and
      Bandyopadhyay, Sivaji",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5939",
    pages = "261--267",
}
@inproceedings{badr-fatima-2015-using,
    title = "Using Skipgrams, Bigrams, and Part of Speech Features for Sentiment Classification of {T}witter Messages",
    author = "Badr, Badr Mohammed  and
      Fatima, S. Sameen",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5940",
    pages = "268--275",
}
@inproceedings{batra-paul-2015-hybrid,
    title = "A Hybrid Approach for Bracketing Noun Sequence",
    author = "Batra, Arpita  and
      Paul, Soma",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5941",
    pages = "276--284",
}
@inproceedings{khan-etal-2015-simultaneous,
    title = "Simultaneous Feature Selection and Parameter Optimization Using Multi-objective Optimization for Sentiment Analysis",
    author = "Khan, Mohammed Arif  and
      Ekbal, Asif  and
      Menc{\'\i}a, Eneldo Loza",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5942",
    pages = "285--294",
}
@inproceedings{singh-etal-2015-detection,
    title = "Detection of Multiword Expressions for {H}indi Language using Word Embeddings and {W}ord{N}et-based Features",
    author = "Singh, Dhirendra  and
      Bhingardive, Sudha  and
      Patel, Kevin  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5943",
    pages = "295--302",
}
@inproceedings{more-etal-2015-augmenting,
    title = "Augmenting Pivot based {SMT} with word segmentation",
    author = "More, Rohit  and
      Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak  and
      Dabre, Raj",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5944",
    pages = "303--307",
}
@inproceedings{kanojia-etal-2015-using,
    title = "Using Multilingual Topic Models for Improved Alignment in {E}nglish-{H}indi {MT}",
    author = "Kanojia, Diptesh  and
      Joshi, Aditya  and
      Bhattacharyya, Pushpak  and
      Carman, Mark James",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5945",
    pages = "308--315",
}
@inproceedings{patil-etal-2015-triangulation,
    title = "Triangulation of Reordering Tables: An Advancement Over Phrase Table Triangulation in Pivot-Based {SMT}",
    author = "Patil, Deepak  and
      Chavan, Harshad  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5946",
    pages = "316--324",
}
@inproceedings{shah-etal-2015-post,
    title = "Post-editing a chapter of a specialized textbook into 7 languages: importance of terminological proximity with {E}nglish for productivity",
    author = "Shah, Ritesh  and
      Boitet, Christian  and
      Bhattacharyya, Pushpak  and
      Padmakumar, Mithun  and
      Zilio, Leonardo  and
      Kalitvianski, Ruslan  and
      Nasiruddin, Mohammad  and
      Tomokiyo, Mutsuko  and
      P{\'a}ez, Sandra Castellanos",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5947",
    pages = "325--332",
}
@inproceedings{dash-etal-2015-generating,
    title = "Generating Translation Corpora in Indic Languages:Cultivating Bilingual Texts for Cross Lingual Fertilization",
    author = "Dash, Niladri Sekhar  and
      Selvraj, Arulmozi  and
      Hussain, Mazhar",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5948",
    pages = "333--342",
}
@inproceedings{sturgeon-lee-2015-translation,
    title = "Translation Quality and Effort: Options versus Post-editing",
    author = "Sturgeon, Donald  and
      Lee, John S. Y.",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5949",
    pages = "343--350",
}
@inproceedings{mehta-etal-2015-investigating,
    title = "Investigating the potential of post-ordering {SMT} output to improve translation quality",
    author = "Mehta, Pratik  and
      Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5950",
    pages = "351--356",
}
@inproceedings{bharati-etal-2015-applying,
    title = "Applying {S}anskrit Concepts for Reordering in {MT}",
    author = "Bharati, Akshar  and
      {Sukhada}  and
      Jha, Prajna  and
      Paul, Soma  and
      Sharma, Dipti M",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5951",
    pages = "357--366",
}
@inproceedings{palihakkara-etal-2015-dialogue,
    title = "Dialogue Act Recognition for Text-based {S}inhala",
    author = "Palihakkara, Sudheera  and
      Sahabandu, Dammina  and
      Shamsudeen, Ahsan  and
      Bandara, Chamika  and
      Ranathunga, Surangika",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5952",
    pages = "367--375",
}
@inproceedings{dowlagar-mamidi-2015-semi,
    title = "A Semi Supervised Dialog Act Tagging for {T}elugu",
    author = "Dowlagar, Suman  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5953",
    pages = "376--383",
}
@inproceedings{divate-salgaonkar-2015-ranking,
    title = "Ranking Model with a Reduced Feature Set for an Automated Question Generation System",
    author = "Divate, Manisha Satish  and
      Salgaonkar, Ambuja",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5954",
    pages = "384--393",
}
@inproceedings{sundaram-khemani-2015-natural,
    title = "Natural Language Processing for Solving Simple Word Problems",
    author = "Sundaram, Sowmya S  and
      Khemani, Deepak",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5955",
    pages = "394--402",
}
@inproceedings{saha-das-mandal-2015-analysis,
    title = "Analysis of Influence of {L}2 {E}nglish Speakers{'} Fluency on Occurrence and Duration of Sentence-medial Pauses in {E}nglish Readout Speech",
    author = "Saha, Shambhu Nath  and
      Das Mandal, Shyamal Kr.",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5956",
    pages = "403--412",
}
@inproceedings{ghosh-2015-acoustic,
    title = "Acoustic Correlates of Voicing and Gemination in {B}angla",
    author = "Ghosh, Aanusha",
    booktitle = "Proceedings of the 12th International Conference on Natural Language Processing",
    month = dec,
    year = "2015",
    address = "Trivandrum, India",
    publisher = "NLP Association of India",
    url = "https://www.aclweb.org/anthology/W15-5957",
    pages = "413--418",
}
@proceedings{ws-2015-deep,
    title = "Proceedings of the 1st Deep Machine Translation Workshop",
    editor = "Haji{\v{c}}, Jan  and
      Branco, Ant{\'o}nio",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5700",
}
@inproceedings{arnoult-simaan-2015-modelling,
    title = "Modelling the Adjunct/Argument Distinction in Hierarchical Phrase-Based {SMT}",
    author = "Arnoult, Sophie  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5701",
    pages = "2--11",
}
@inproceedings{avramidis-etal-2015-towards,
    title = "Towards Deeper {MT} - A Hybrid System for {G}erman",
    author = "Avramidis, Eleftherios  and
      Burchardt, Aljoscha  and
      Popovi{\'c}, Maja  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5702",
    pages = "12--19",
}
@inproceedings{daiber-etal-2015-splitting,
    title = "Splitting Compounds by Semantic Analogy",
    author = "Daiber, Joachim  and
      Quiroz, Lautaro  and
      Wechsler, Roger  and
      Frank, Stella",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5703",
    pages = "20--28",
}
@inproceedings{daiber-simaan-2015-delimiting,
    title = "Delimiting Morphosyntactic Search Space with Source-Side Reordering Models",
    author = "Daiber, Joachim  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5704",
    pages = "29--38",
}
@inproceedings{del-gaudio-etal-2015-evaluating,
    title = "Evaluating a Machine Translation System in a Technical Support Scenario",
    author = "Del Gaudio, Rosa  and
      Burchardt, Aljoscha  and
      Lommel, Arle",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5705",
    pages = "39--47",
}
@inproceedings{krishnamurthy-2015-development,
    title = "Development of {T}elugu-{T}amil Transfer-Based Machine Translation system: With Special reference to Divergence Index",
    author = "Krishnamurthy, Parameswari",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5706",
    pages = "48--54",
}
@inproceedings{labaka-etal-2015-deep,
    title = "Deep-syntax {T}ecto{MT} for {E}nglish-{S}panish {MT}",
    author = "Labaka, Gorka  and
      Jauregi, Oneka  and
      D{\'\i}az de Ilarraza, Arantza  and
      Ustaszewski, Michael  and
      Aranberri, Nora  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5707",
    pages = "55--63",
}
@inproceedings{neale-etal-2015-first,
    title = "First Steps in Using Word Senses as Contextual Features in Maxent Models for Machine Translation",
    author = "Neale, Steven  and
      Gomes, Lu{\'\i}s  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5708",
    pages = "64--72",
}
@inproceedings{oele-van-noord-2015-lexical,
    title = "Lexical choice in Abstract Dependency Trees",
    author = "Oele, Dieke  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5709",
    pages = "73--80",
}
@inproceedings{gaona-sharoff-2015-large,
    title = "Large Scale Translation Quality Estimation",
    author = "Gaona, Miguel Angel Rios  and
      Sharoff, Serge",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5710",
    pages = "81--88",
}
@inproceedings{rosa-etal-2015-translation,
    title = "Translation Model Interpolation for Domain Adaptation in {T}ecto{MT}",
    author = "Rosa, Rudolf  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Nov{\'a}k, Michal  and
      Popel, Martin",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5711",
    pages = "89--96",
}
@inproceedings{simov-etal-2015-factored,
    title = "Factored models for Deep Machine Translation",
    author = "Simov, Kiril  and
      Simova, Iliana  and
      Todorova, Velislava  and
      Osenova, Petya",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5712",
    pages = "97--105",
}
@inproceedings{stajner-etal-2015-machine,
    title = "Machine Translation for Multilingual Troubleshooting in the {IT} Domain: A Comparison of Different Strategies",
    author = "{\v{S}}tajner, Sanja  and
      Rodrigues, Jo{\~a}o  and
      Gomes, Lu{\'\i}s  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 1st Deep Machine Translation Workshop",
    year = "2015",
    address = "Praha, Czechia",
    publisher = "{\'U}FAL MFF UK",
    url = "https://www.aclweb.org/anthology/W15-5713",
    pages = "106--115",
}
@proceedings{ws-2015-brazilian,
    title = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    editor = "Freitas, Claudia  and
      Rademaker, Alexandre",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5600",
}
@inproceedings{de-alencar-etal-2015-jclext,
    title = "{JCL}ext: A {J}ava Tool for Compiling Finite-State Transducers from Full-Form Lexicons",
    author = "de Alencar, Leonel F.  and
      Costa, Philipp B.  and
      Fran{\c{c}}a, Mardonio J. C.  and
      Ewart, Alexander  and
      Andrade, Katiuscia M.  and
      Andrade, Rossana M. C.",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5601",
    pages = "15--19",
}
@inproceedings{moraes-etal-2015-7x1,
    title = "7x1-{PT}: um Corpus extra{\'\i}do do {T}witter para An{\'a}lise de Sentimentos em L{\'\i}ngua Portuguesa (7x1-{PT}: a Corpus extracted from {T}witter for Sentiment Analysis in {P}ortuguese Language)",
    author = "Moraes, Silvia M. W.  and
      Manssour, Isabel H.  and
      Silveira, Milene S.",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5602",
    pages = "21--25",
}
@inproceedings{do-amaral-etal-2015-comparative,
    title = "Comparative Analysis between Notations to Classify Named Entities using Conditional Random Fields",
    author = "do Amaral, Daniela Oliveira F.  and
      Buffet, Maiki  and
      Vieira, Renata",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5603",
    pages = "27--31",
}
@inproceedings{angelo-etal-2015-extrator,
    title = "Do Extrator de Conhecimento Coletivo {\`a} {\'A}gora Virtual: desenvolvendo uma ferramenta para democracia participativa (From Collective Knowledge Extractor to Virtual Agora: Developing a tool for Participatory Democracy)",
    author = "Angelo, Tiago Novaes  and
      Pagan, Cesar Jos{\'e} Bonjuani  and
      Attux, Romis Ribeiro Faissol  and
      Gudwin, Ricardo Ribeiro",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5604",
    pages = "33--37",
}
@inproceedings{santos-2015-um,
    title = "Um novo corpo e os seus desafios (A new corpus and the challenges it offers)",
    author = "Santos, Diana",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5605",
    pages = "39--43",
}
@inproceedings{da-silva-feltrim-2015-analise,
    title = "An{\'a}lise Autom{\'a}tica de Coer{\^e}ncia Textual em Resumos Cient{\'\i}ficos: Avaliando Quebras de Linearidade (Automatic Analysis of Textual Coherence in Scientific Abstracts: Evaluating Linearity Breaks)",
    author = "da Silva, Leandro Lago  and
      Feltrim, Val{\'e}ria Delisandra",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5606",
    pages = "45--49",
}
@inproceedings{freitas-etal-2015-anotacao,
    title = "Anota{\c{c}}{\~a}o de corpus com a {O}pen{W}ord{N}et-{PT}: um exerc{\'\i}cio de desambigua{\c{c}}{\~a}o (Sense annotation with {O}pen{W}ord{N}et-{PT}: an exercise of word sense disambiguation)",
    author = "Freitas, Cl{\'a}udia  and
      Real, Livy  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5607",
    pages = "51--55",
}
@inproceedings{rassi-etal-2015-integrating,
    title = "Integrating support verb constructions into a parser",
    author = "Rassi, Amanda  and
      Baptista, Jorge  and
      Mamede, Nuno  and
      Vale, Oto",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5608",
    pages = "57--61",
}
@inproceedings{de-oliveira-feltrim-2015-extracao,
    title = "Extra{\c{c}}{\~a}o de Alvos em Coment{\'a}rios de Not{\'\i}cias em Portugu{\^e}s baseada na Teoria da Centraliza{\c{c}}{\~a}o (Target Extraction in News Reviews in {P}ortuguese based on Centering Theory)",
    author = "de Oliveira, Frank Willian Cardoso  and
      Feltrim, Val{\'e}ria Delisandra",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5609",
    pages = "63--67",
}
@inproceedings{candido-junior-etal-2015-portal,
    title = "Portal {M}in@s: Uma Ferramenta Geral de Apoio ao Processamento de C{\'o}rpus de Prop{\'o}sito Geral (Portal {M}in@s: A General Purpose Support Tool for Corpora Processing)",
    author = "Candido Junior, Arnaldo  and
      Vieira, Thiago Lima  and
      Serikawa, Marcel  and
      Silva, Matheus Antonio Ribeiro  and
      Zangirolami, R{\'e}gis  and
      Alu{\'\i}sio, Sandra Maria",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5610",
    pages = "69--73",
}
@inproceedings{garcia-da-silva-2015-prepnet,
    title = "{P}rep{N}et.{B}r: a Semantic Network for Prepositions",
    author = "Garcia, D{\'e}bora D.  and
      da Silva, Bento Carlos Dias",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5611",
    pages = "75--79",
}
@inproceedings{cardoso-pardo-2015-joint,
    title = "Joint semantic discourse models for automatic multi-document summarization",
    author = "Cardoso, Paula C. Figueira  and
      Pardo, Thiago A. S.",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5612",
    pages = "81--90",
}
@inproceedings{lopes-vieira-2015-building,
    title = "Building and Applying Profiles Through Term Extraction",
    author = "Lopes, Lucelene  and
      Vieira, Renata",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5613",
    pages = "91--100",
}
@inproceedings{de-arruda-etal-2015-annotated,
    title = "An Annotated Corpus for Sentiment Analysis in Political News",
    author = "de Arruda, Gabriel Domingos  and
      Roman, Norton Trevisan  and
      Monteiro, Ana Maria",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5614",
    pages = "101--110",
}
@inproceedings{andreani-feltrim-2015-campos,
    title = "Campos Aleat{\'o}rios Condicionais Aplicados {\`a} Detec{\c{c}}{\~a}o de Estrutura Ret{\'o}rica em Resumos de Textos Acad{\^e}micos em Portugu{\^e}s (Conditional Random Fields Applied to Rhetorical Structure Detection in Academic Abstracts in {P}ortuguese)",
    author = "Andreani, Alexandre C.  and
      Feltrim, Val{\'e}ria D.",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5615",
    pages = "111--120",
}
@inproceedings{dosciatti-etal-2015-anotando,
    title = "Anotando um Corpus de Not{\'\i}cias para a An{\'a}lise de Sentimentos: um Relato de Experi{\^e}ncia (Annotating a corpus of News for Sentiment Analysis: An Experience Report)",
    author = "Dosciatti, Mariza Miola  and
      Ferreira, Lohann Paterno Coutinho  and
      Paraiso, Emerson Cabrera",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5616",
    pages = "121--130",
}
@inproceedings{wilkens-etal-2015-distributional,
    title = "Distributional Thesauri for {P}ortuguese: methodology evaluation",
    author = "Wilkens, Rodrigo  and
      Zilio, Leonardo  and
      Ferreira, Eduardo  and
      Gon{\c{c}}alves, Gabriel  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5617",
    pages = "131--140",
}
@inproceedings{de-camargo-etal-2015-strategies,
    title = "On Strategies of Human Multi-Document Summarization",
    author = "de Camargo, Renata Tironi  and
      Di Felippo, Ariani  and
      Pardo, Thiago A. S.",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5618",
    pages = "141--150",
}
@inproceedings{de-s-dias-pardo-2015-enriching,
    title = "Enriching entity grids and graphs with discourse relations: the impact in local coherence evaluation",
    author = "de S. Dias, M{\'a}rcio  and
      Pardo, Thiago A. S.",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5619",
    pages = "151--160",
}
@inproceedings{zilio-etal-2015-verblexpor,
    title = "{V}erb{L}ex{P}or: um recurso l{\'e}xico com anota{\c{c}}{\~a}o de pap{\'e}is sem{\^a}nticos para o portugu{\^e}s ({V}erb{L}ex{P}or: a lexical resource annotated with semantic roles for {P}ortuguese)",
    author = "Zilio, Leonardo  and
      Finatto, Maria Jos{\'e} Bocorny  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5620",
    pages = "161--170",
}
@inproceedings{vale-baptista-2015-novo,
    title = "Novo dicion{\'a}rio de formas flexionadas do Unitex-{PB}: avalia{\c{c}}{\~a}o da flex{\~a}o verbal (New Dictionary of Inflected forms of {UNITEX}-{PB}: Evaluation of Verbal Inflection)",
    author = "Vale, Oto A.  and
      Baptista, Jorge",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5621",
    pages = "171--180",
}
@inproceedings{hamada-neto-2015-desambiguacao,
    title = "Desambigua{\c{c}}{\~a}o de Hom{\'o}grafos-Heter{\'o}fonos por Aprendizado de M{\'a}quina em Portugu{\^e}s Brasileiro (A Machine Learning Approach for Homographic Heterophone Disambiguation in {B}razilian {P}ortuguese)",
    author = "Hamada, Leonardo  and
      Neto, Nelson",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5622",
    pages = "181--190",
}
@inproceedings{pereira-pinheiro-2015-report,
    title = "{R}e{P}ort - Um Sistema de Extra{\c{c}}{\~a}o de Informa{\c{c}}{\~o}es Aberta para L{\'\i}ngua Portuguesa (Report - An Open Information Extraction System for {P}ortuguese Language)",
    author = "Pereira, Victor  and
      Pinheiro, Vl{\'a}dia",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5623",
    pages = "191--200",
}
@inproceedings{fonseca-aluisio-2015-semi,
    title = "Semi-Automatic Construction of a Textual Entailment Dataset: Selecting Candidates with Vector Space Models",
    author = "Fonseca, Erick R.  and
      Alu{\'\i}sio, Sandra Maria",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5624",
    pages = "201--210",
}
@inproceedings{guimaraes-etal-2015-n,
    title = "N-Gramas de Caractere como T{\'e}cnica de Normaliza{\c{c}}{\~a}o Morfol{\'o}gica para L{\'\i}ngua Portuguesa: Um Estudo em Categoriza{\c{c}}{\~a}o de Textos (Character N-grams as a Morphological Normalization Technique for {P}ortuguese Language: A Study in Text Categorization)",
    author = "Guimar{\~a}es, Guilherme T.  and
      Meirose, Marcus V.  and
      Moraes, S{\'\i}lvia M. W.",
    booktitle = "Proceedings of the 10th {B}razilian Symposium in Information and Human Language Technology",
    month = nov,
    year = "2015",
    address = "Natal, Brazil",
    publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
    url = "https://www.aclweb.org/anthology/W15-5625",
    pages = "211--220",
}
@proceedings{ws-2015-natural-language-processing,
    title = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    editor = "Vossen, Piek  and
      Rigau, German  and
      Osenova, Petya  and
      Simov, Kiril",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5500",
}
@inproceedings{rigau-2015-cross,
    title = "Cross-lingual Event Detection in Discourse",
    author = "Rigau, German",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5501",
    pages = "1",
}
@inproceedings{perera-nand-2015-generating,
    title = "Generating Lexicalization Patterns for Linked Open Data",
    author = "Perera, Rivindu  and
      Nand, Parma",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5502",
    pages = "2--5",
}
@inproceedings{neale-etal-2015-small,
    title = "Small in Size, Big in Precision: A Case for Using Language-Specific Lexical Resources for Word Sense Disambiguation",
    author = "Neale, Steven  and
      Silva, Jo{\~a}o  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5503",
    pages = "6--15",
}
@inproceedings{declerck-lendvai-2015-towards,
    title = "Towards the Representation of Hashtags in Linguistic Linked Open Data Format",
    author = "Declerck, Thierry  and
      Lendvai, Piroska",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5504",
    pages = "16--22",
}
@inproceedings{sukhareva-chiarcos-2015-ontology,
    title = "An Ontology-based Approach To Automatic Part-of-Speech Tagging Using Heterogeneously Annotated Corpora",
    author = "Sukhareva, Maria  and
      Chiarcos, Christian",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5505",
    pages = "23--32",
}
@inproceedings{simov-kiryakov-2015-accessing,
    title = "Accessing Linked Open Data via A Common Ontology",
    author = "Simov, Kiril  and
      Kiryakov, Atanas",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5506",
    pages = "33--41",
}
@inproceedings{el-maarouf-etal-2015-guanxi,
    title = "The {G}uan{X}i network: a new multilingual {LLOD} for Language Learning applications",
    author = "El Maarouf, Ismail  and
      Mousselly-Sergieh, Hatem  and
      Alferov, Eugene  and
      Wang, Haofen  and
      Fang, Zhijia  and
      Cooper, Doug",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing and Linked Open Data",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5507",
    pages = "42--51",
}
@proceedings{ws-2015-joint-language,
    title = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    editor = {Nakov, Preslav  and
      Zampieri, Marcos  and
      Osenova, Petya  and
      Tan, Liling  and
      Vertan, Cristina  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg},
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5400",
}
@inproceedings{zampieri-etal-2015-overview,
    title = "Overview of the {DSL} Shared Task 2015",
    author = {Zampieri, Marcos  and
      Tan, Liling  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Nakov, Preslav},
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5401",
    pages = "1--9",
}
@inproceedings{derczynski-2015-handling,
    title = "Handling and Mining Linguistic Variation in {UGC}",
    author = "Derczynski, Leon",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5402",
    pages = "10",
}
@inproceedings{franco-salvador-etal-2015-distributed,
    title = "Distributed Representations of Words and Documents for Discriminating Similar Languages",
    author = "Franco-Salvador, Marc  and
      Rosso, Paolo  and
      Rangel, Francisco",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5403",
    pages = "11--16",
}
@inproceedings{kumar-etal-2015-joint,
    title = "Joint {B}ayesian Morphology Learning for {D}ravidian Languages",
    author = "Kumar, Arun  and
      Padr{\'o}, Llu{\'\i}s  and
      Oliver, Antoni",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5404",
    pages = "17--23",
}
@inproceedings{onyenwe-etal-2015-use,
    title = "Use of Transformation-Based Learning in Annotation Pipeline of {I}gbo, an {A}frican Language",
    author = "Onyenwe, Ikechukwu  and
      Hepple, Mark  and
      Uchechukwu, Chinedu  and
      Ezeani, Ignatius",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5405",
    pages = "24--33",
}
@inproceedings{bick-2015-wikitrans,
    title = "{W}iki{T}rans: {S}wedish-{D}anish Machine Translation in a Constraint Grammar Framework",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5406",
    pages = "34",
}
@inproceedings{malmasi-dras-2015-language,
    title = "Language Identification using Classifier Ensembles",
    author = "Malmasi, Shervin  and
      Dras, Mark",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5407",
    pages = "35--43",
}
@inproceedings{jauhiainen-etal-2015-discriminating,
    title = "Discriminating Similar Languages with Token-Based Backoff",
    author = "Jauhiainen, Tommi  and
      Jauhiainen, Heidi  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5408",
    pages = "44--51",
}
@inproceedings{fabra-boluda-etal-2015-nlel,
    title = "{NLEL} {UPV} Autoritas Participation at Discrimination between Similar Languages ({DSL}) 2015 Shared Task",
    author = {Fabra-Boluda, Ra{\"u}l  and
      Rangel, Francisco  and
      Rosso, Paolo},
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5409",
    pages = "52--58",
}
@inproceedings{bobicev-2015-discriminating,
    title = "Discriminating between Similar Languages Using {PPM}",
    author = "Bobicev, Victoria",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5410",
    pages = "59--65",
}
@inproceedings{zampieri-etal-2015-comparing,
    title = "Comparing Approaches to the Identification of Similar Languages",
    author = "Zampieri, Marcos  and
      Gebre, Binyam Gebrekidan  and
      Costa, Hernani  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5411",
    pages = "66--72",
}
@inproceedings{acs-etal-2015-two,
    title = "A Two-level Classifier for Discriminating Similar Languages",
    author = "{\'A}cs, Judit  and
      Grad-Gyenge, L{\'a}szl{\'o}  and
      Rodrigues de Rezende Oliveira, Thiago Bruno",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5412",
    pages = "73--77",
}
@inproceedings{goutte-leger-2015-experiments,
    title = "Experiments in Discriminating Similar Languages",
    author = "Goutte, Cyril  and
      L{\'e}ger, Serge",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5413",
    pages = "78--84",
}
@inproceedings{xu-etal-2015-building,
    title = "Building Monolingual Word Alignment Corpus for the Greater {C}hina Region",
    author = "Xu, Fan  and
      Xu, Xiongfei  and
      Wang, Mingwen  and
      Li, Maoxi",
    booktitle = "Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5414",
    pages = "85--94",
}
@proceedings{ws-2015-balto,
    title = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    editor = "Piskorski, Jakub  and
      Pivovarova, Lidia  and
      {\v{S}}najder, Jan  and
      Tanev, Hristo  and
      Yangarber, Roman",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5300",
}
@inproceedings{agic-ljubesic-2015-universal,
    title = "{U}niversal {D}ependencies for {C}roatian (that work for {S}erbian, too)",
    author = "Agi{\'c}, {\v{Z}}eljko  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5301",
    pages = "1--8",
}
@inproceedings{petkevic-etal-2015-analytic,
    title = "Analytic Morphology {--} Merging the Paradigmatic and Syntagmatic Perspective in a Treebank",
    author = "Petkevi{\v{c}}, Vladim{\'\i}r  and
      Rosen, Alexandr  and
      Skoumalov{\'a}, Hana  and
      V{\'\i}tovec, P{\v{r}}emysl",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5302",
    pages = "9--16",
}
@inproceedings{glavas-snajder-2015-resolving,
    title = "Resolving Entity Coreference in {C}roatian with a Constrained Mention-Pair Model",
    author = "Glava{\v{s}}, Goran  and
      {\v{S}}najder, Jan",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5303",
    pages = "17--23",
}
@inproceedings{kaczmarek-marcinczuk-2015-evaluation,
    title = "Evaluation of Coreference Resolution Tools for {P}olish from the Information Extraction Perspective",
    author = "Kaczmarek, Adam  and
      Marci{\'n}czuk, Micha{\l}",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5304",
    pages = "24--33",
}
@inproceedings{piskorski-2015-open,
    title = "Open Relation Extraction for {P}olish: Preliminary Experiments",
    author = "Piskorski, Jakub",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5305",
    pages = "34--39",
}
@inproceedings{samardzic-etal-2015-regional,
    title = "Regional Linguistic Data Initiative ({R}e{LDI})",
    author = "Samard{\v{z}}i{\'c}, Tanja  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Mili{\v{c}}evi{\'c}, Maja",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5306",
    pages = "40--42",
}
@inproceedings{kopotev-etal-2015-online,
    title = "Online Extraction of {R}ussian Multiword Expressions",
    author = "Kopotev, Mikhail  and
      Escoter, Lloren{\c{c}}  and
      Kormacheva, Daria  and
      Pierce, Matthew  and
      Pivovarova, Lidia  and
      Yangarber, Roman",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5307",
    pages = "43--45",
}
@inproceedings{kozlowski-etal-2015-e,
    title = "{E}-law Module Supporting Lawyers in the Process of Knowledge Discovery from Legal Documents",
    author = "Koz{\l}owski, Marek  and
      Kowalski, Maciej  and
      Kazula, Maciej",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5308",
    pages = "46--48",
}
@inproceedings{alagic-snajder-2015-experiments,
    title = "Experiments on Active Learning for {C}roatian Word Sense Disambiguation",
    author = "Alagi{\'c}, Domagoj  and
      {\v{S}}najder, Jan",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5309",
    pages = "49--58",
}
@inproceedings{leseva-etal-2015-automatic,
    title = "Automatic Classification of {W}ord{N}et Morphosemantic Relations",
    author = "Leseva, Svetlozara  and
      Stoyanova, Ivelina  and
      Todorova, Maria  and
      Dimitrova, Tsvetana  and
      Rizov, Borislav  and
      Koeva, Svetla",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5310",
    pages = "59--64",
}
@inproceedings{katinskaya-sharoff-2015-applying,
    title = "Applying Multi-Dimensional Analysis to a {R}ussian Webcorpus: Searching for Evidence of Genres",
    author = "Katinskaya, Anisya  and
      Sharoff, Serge",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5311",
    pages = "65--74",
}
@inproceedings{harbusch-krusko-2015-distinctive,
    title = "Distinctive Similarity of Clausal Coordinate Ellipsis in {R}ussian Compared to {D}utch, {E}stonian, {G}erman, and {H}ungarian",
    author = "Harbusch, Karin  and
      Krusko, Denis",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5312",
    pages = "75--80",
}
@inproceedings{osenova-simov-2015-universalizing,
    title = "Universalizing {B}ul{T}ree{B}ank: a Linguistic Tale about Glocalization",
    author = "Osenova, Petya  and
      Simov, Kiril",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5313",
    pages = "81--89",
}
@inproceedings{loukachevitch-etal-2015-types,
    title = "Types of Aspect Terms in Aspect-Oriented Sentiment Labeling",
    author = "Loukachevitch, Natalia  and
      Kotelnikov, Evgeniy  and
      Blinov, Pavel",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5314",
    pages = "90--95",
}
@inproceedings{kapociute-dzikiene-etal-2015-authorship,
    title = "Authorship Attribution and Author Profiling of {L}ithuanian Literary Texts",
    author = "Kapo{\v{c}}i{\=u}t{\.e}-Dzikien{\.e}, Jurgita  and
      Utka, Andrius  and
      {\v{S}}arkut{\.e}, Ligita",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5315",
    pages = "96--105",
}
@inproceedings{mickevicius-etal-2015-classification,
    title = "Classification of Short Legal {L}ithuanian Texts",
    author = "Mickevi{\v{c}}ius, Vytautas  and
      Krilavi{\v{c}}ius, Tomas  and
      Morkevi{\v{c}}ius, Vaidas",
    booktitle = "The 5th Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/W15-5316",
    pages = "106--111",
}
@proceedings{ws-2015-natural-language,
    title = "Proceedings of the Workshop Natural Language Processing for Translation Memories",
    editor = "Orasan, Constantin  and
      Gupta, Rohit",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5200",
}
@inproceedings{parra-escartin-2015-creation,
    title = "Creation of new {TM} segments: Fulfilling translators{'} wishes",
    author = "Parra Escart{\'\i}n, Carla",
    booktitle = "Proceedings of the Workshop Natural Language Processing for Translation Memories",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5201",
    pages = "1--8",
}
@inproceedings{barbu-2015-spotting,
    title = "Spotting false translation segments in translation memories",
    author = "Barbu, Eduard",
    booktitle = "Proceedings of the Workshop Natural Language Processing for Translation Memories",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5202",
    pages = "9--16",
}
@inproceedings{raisa-timonera-mitkov-2015-improving,
    title = "Improving Translation Memory Matching through Clause Splitting",
    author = "Raisa Timonera, Katerina  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the Workshop Natural Language Processing for Translation Memories",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5203",
    pages = "17--23",
}
@inproceedings{chatzitheodorou-2015-improving,
    title = "Improving translation memory fuzzy matching by paraphrasing",
    author = "Chatzitheodorou, Konstantinos",
    booktitle = "Proceedings of the Workshop Natural Language Processing for Translation Memories",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5204",
    pages = "24--30",
}
@inproceedings{baisa-etal-2015-increasing,
    title = "Increasing Coverage of Translation Memories with Linguistically Motivated Segment Combination Methods",
    author = "Baisa, V{\'\i}t  and
      Hor{\'a}k, Ale{\v{s}}  and
      Medve{\v{d}}, Marek",
    booktitle = "Proceedings of the Workshop Natural Language Processing for Translation Memories",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5205",
    pages = "31--35",
}
@inproceedings{nayek-etal-2015-catalog,
    title = "{CAT}a{L}og: New Approaches to {TM} and Post Editing Interfaces",
    author = "Nayek, Tapas  and
      Naskar, Sudip Kumar  and
      Pal, Santanu  and
      Zampieri, Marcos  and
      Vela, Mihaela  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Workshop Natural Language Processing for Translation Memories",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5206",
    pages = "36--42",
}
@proceedings{ws-2015-slpat,
    title = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    editor = {Alexandersson, Jan  and
      Altinsoy, Ercan  and
      Christensen, Heidi  and
      Ljungl{\"o}f, Peter  and
      Portet, Fran{\c{c}}ois  and
      Rudzicz, Frank},
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5100",
    doi = "10.18653/v1/W15-51",
}
@inproceedings{beskow-2015-talking,
    title = "Talking Heads, Signing Avatars and Social Robots",
    author = "Beskow, Jonas",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5101",
    doi = "10.18653/v1/W15-5101",
    pages = "1--1",
}
@inproceedings{ebling-huenerfauth-2015-bridging,
    title = "Bridging the gap between sign language machine translation and sign language animation using sequence classification",
    author = "Ebling, Sarah  and
      Huenerfauth, Matt",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5102",
    doi = "10.18653/v1/W15-5102",
    pages = "2--9",
}
@inproceedings{ebling-etal-2015-synthesizing,
    title = "Synthesizing the finger alphabet of {S}wiss {G}erman Sign Language and evaluating the comprehensibility of the resulting animations",
    author = "Ebling, Sarah  and
      Wolfe, Rosalee  and
      Schnepp, Jerry  and
      Baowidan, Souad  and
      McDonald, John  and
      Moncrief, Robyn  and
      Sidler-Miserez, Sandra  and
      Tissi, Katja",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5103",
    doi = "10.18653/v1/W15-5103",
    pages = "10--16",
}
@inproceedings{hatano-etal-2015-contour,
    title = "Contour-based Hand Pose Recognition for Sign Language Recognition",
    author = "Hatano, Mika  and
      Sako, Shinji  and
      Kitamura, Tadashi",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5104",
    doi = "10.18653/v1/W15-5104",
    pages = "17--21",
}
@inproceedings{huenerfauth-etal-2015-synthesizing,
    title = "Synthesizing and Evaluating Animations of {A}merican {S}ign {L}anguage Verbs Modeled from Motion-Capture Data",
    author = "Huenerfauth, Matt  and
      Lu, Pengfei  and
      Kacorri, Hernisa",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5105",
    doi = "10.18653/v1/W15-5105",
    pages = "22--28",
}
@inproceedings{kacorri-huenerfauth-2015-evaluating,
    title = "Evaluating a Dynamic Time Warping Based Scoring Algorithm for Facial Expressions in {ASL} Animations",
    author = "Kacorri, Hernisa  and
      Huenerfauth, Matt",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5106",
    doi = "10.18653/v1/W15-5106",
    pages = "29--35",
}
@inproceedings{piquard-kipffer-etal-2015-qualitative,
    title = "Qualitative investigation of the display of speech recognition results for communication with deaf people",
    author = "Piquard-Kipffer, Agn{\`e}s  and
      Mella, Odile  and
      Miranda, J{\'e}r{\'e}my  and
      Jouvet, Denis  and
      Orosanu, Luiza",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5107",
    doi = "10.18653/v1/W15-5107",
    pages = "36--41",
}
@inproceedings{fontan-etal-2015-predicting,
    title = "Predicting disordered speech comprehensibility from Goodness of Pronunciation scores",
    author = "Fontan, Lionel  and
      Pellegrini, Thomas  and
      Olcoz, Julia  and
      Abad, Alberto",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5108",
    doi = "10.18653/v1/W15-5108",
    pages = "42--46",
}
@inproceedings{hahm-etal-2015-recognizing,
    title = "Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization",
    author = "Hahm, Seongjun  and
      Heitzman, Daragh  and
      Wang, Jun",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5109",
    doi = "10.18653/v1/W15-5109",
    pages = "47--54",
}
@inproceedings{ishaq-etal-2015-vowel,
    title = "Vowel Enhancement in Early Stage {S}panish Esophageal Speech Using Natural Glottal Flow Pulse and Vocal Tract Frequency Warping",
    author = "Ishaq, Rizwan  and
      Gowda, Dhanananjaya  and
      Alku, Paavo  and
      Zapirain, Begonya Garcia",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5110",
    doi = "10.18653/v1/W15-5110",
    pages = "55--59",
}
@inproceedings{oue-etal-2015-automatic,
    title = "Automatic dysfluency detection in dysarthric speech using deep belief networks",
    author = "Oue, Stacey  and
      Marxer, Ricard  and
      Rudzicz, Frank",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5111",
    doi = "10.18653/v1/W15-5111",
    pages = "60--64",
}
@inproceedings{sehgal-cunningham-2015-model,
    title = "Model adaptation and adaptive training for the recognition of dysarthric speech",
    author = "Sehgal, Siddharth  and
      Cunningham, Stuart",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5112",
    doi = "10.18653/v1/W15-5112",
    pages = "65--71",
}
@inproceedings{sriranjani-etal-2015-pronunciation,
    title = "Pronunciation Adaptation For Disordered Speech Recognition Using State-Specific Vectors of Phone-Cluster Adaptive Training",
    author = "Sriranjani, R  and
      Umesh, S.  and
      Reddy, M. Ramasubba",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5113",
    doi = "10.18653/v1/W15-5113",
    pages = "72--78",
}
@inproceedings{wang-etal-2015-determining,
    title = "Determining an Optimal Set of Flesh Points on Tongue, Lips, and Jaw for Continuous Silent Speech Recognition",
    author = "Wang, Jun  and
      Hahm, Seongjun  and
      Mau, Ted",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5114",
    doi = "10.18653/v1/W15-5114",
    pages = "79--85",
}
@inproceedings{wong-etal-2015-analysis,
    title = "Analysis of Dysarthric Speech using Distinctive Feature Recognition",
    author = "Wong, Ka Ho  and
      Yeung, Yu Ting  and
      Wong, Patrick C. M.  and
      Levow, Gina-Anne  and
      Meng, Helen",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5115",
    doi = "10.18653/v1/W15-5115",
    pages = "86--90",
}
@inproceedings{draffan-etal-2015-generating,
    title = "Generating acceptable {A}rabic Core Vocabularies and Symbols for {AAC} users",
    author = "Draffan, E.A.  and
      Wald, Mike  and
      Halabi, Nawar  and
      Sabia, Ouadie  and
      Zaghouani, Wajdi  and
      Kadous, Amatullah  and
      Idris, Amal  and
      Zeinoun, Nadine  and
      Banes, David  and
      Lawand, Dana",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5116",
    doi = "10.18653/v1/W15-5116",
    pages = "91--96",
}
@inproceedings{green-etal-2015-remote,
    title = "Remote Speech Technology for Speech Professionals - the {C}loud{CAST} initiative",
    author = "Green, Phil  and
      Marxer, Ricard  and
      Cunningham, Stuart  and
      Christensen, Heidi  and
      Rudzicz, Frank  and
      Yancheva, Maria  and
      Coy, Andr{\'e}  and
      Malavasi, Massimuliano  and
      Desideri, Lorenzo",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5117",
    doi = "10.18653/v1/W15-5117",
    pages = "97--102",
}
@inproceedings{pompili-etal-2015-speech,
    title = "Speech and language technologies for the automatic monitoring and training of cognitive functions",
    author = "Pompili, Anna  and
      Amorim, Cristiana  and
      Abad, Alberto  and
      Trancoso, Isabel",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5118",
    doi = "10.18653/v1/W15-5118",
    pages = "103--109",
}
@inproceedings{sevens-etal-2015-extending,
    title = "Extending a {D}utch Text-to-Pictograph Converter to {E}nglish and {S}panish",
    author = "Sevens, Leen  and
      Vandeghinste, Vincent  and
      Schuurman, Ineke  and
      Van Eynde, Frank",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5119",
    doi = "10.18653/v1/W15-5119",
    pages = "110--117",
}
@inproceedings{ueda-etal-2015-individuality,
    title = "Individuality-Preserving Spectrum Modification for Articulation Disorders Using Phone Selective Synthesis",
    author = "Ueda, Reina  and
      Aihara, Ryo  and
      Takiguchi, Tetsuya  and
      Ariki, Yasuo",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5120",
    doi = "10.18653/v1/W15-5120",
    pages = "118--123",
}
@inproceedings{vacher-etal-2015-recognition,
    title = "Recognition of Distress Calls in Distant Speech Setting: a Preliminary Experiment in a Smart Home",
    author = "Vacher, Michel  and
      Lecouteux, Benjamin  and
      Aman, Fr{\'e}d{\'e}ric  and
      Rossato, Solange  and
      Portet, Fran{\c{c}}ois",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5121",
    doi = "10.18653/v1/W15-5121",
    pages = "124--129",
}
@inproceedings{veaux-etal-2015-comparison,
    title = "A Comparison of Manual and Automatic Voice Repair for Individual with Vocal Disabilities",
    author = "Veaux, Christophe  and
      Yamagishi, Junichi  and
      King, Simon",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5122",
    doi = "10.18653/v1/W15-5122",
    pages = "130--133",
}
@inproceedings{yancheva-etal-2015-using,
    title = "Using linguistic features longitudinally to predict clinical scores for {A}lzheimer{'}s disease and related dementias",
    author = "Yancheva, Maria  and
      Fraser, Kathleen  and
      Rudzicz, Frank",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5123",
    doi = "10.18653/v1/W15-5123",
    pages = "134--139",
}
@inproceedings{almeida-etal-2015-european,
    title = "From {E}uropean {P}ortuguese to {P}ortuguese {S}ign {L}anguage",
    author = "Almeida, In{\^e}s  and
      Coheur, Lu{\'\i}sa  and
      Candeias, Sara",
    booktitle = "Proceedings of {SLPAT} 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies",
    month = sep,
    year = "2015",
    address = "Dresden, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-5124",
    doi = "10.18653/v1/W15-5124",
    pages = "140--143",
}
@proceedings{ws-2015-asian,
    title = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    editor = "Nakazawa, Toshiaki  and
      Mino, Hideya  and
      Goto, Isao  and
      Neubig, Graham  and
      Kurohashi, Sadao  and
      Sumita, Eiichiro",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5000",
}
@inproceedings{nakazawa-etal-2015-overview,
    title = "Overview of the 2nd Workshop on {A}sian Translation",
    author = "Nakazawa, Toshiaki  and
      Mino, Hideya  and
      Goto, Isao  and
      Neubig, Graham  and
      Kurohashi, Sadao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5001",
    pages = "1--28",
}
@inproceedings{ehara-2015-system,
    title = "System Combination of {RBMT} plus {SPE} and Preordering plus {SMT}",
    author = "Ehara, Terumasa",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5002",
    pages = "29--34",
}
@inproceedings{neubig-etal-2015-neural,
    title = "Neural Reranking Improves Subjective Quality of Machine Translation: {NAIST} at {WAT}2015",
    author = "Neubig, Graham  and
      Morishita, Makoto  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5003",
    pages = "35--41",
}
@inproceedings{ding-etal-2015-nict,
    title = "{NICT} at {WAT} 2015",
    author = "Ding, Chenchen  and
      Utiyama, Masao  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5004",
    pages = "42--47",
}
@inproceedings{sonoh-kinoshita-2015-toshiba,
    title = "Toshiba {MT} System Description for the {WAT}2015 Workshop",
    author = "Sonoh, Satoshi  and
      Kinoshita, Satoshi",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5005",
    pages = "48--53",
}
@inproceedings{richardson-etal-2015-kyotoebmt,
    title = "{K}yoto{EBMT} System Description for the 2nd Workshop on {A}sian Translation",
    author = "Richardson, John  and
      Dabre, Raj  and
      Chu, Chenhui  and
      Cromi{\`e}res, Fabien  and
      Nakazawa, Toshiaki  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5006",
    pages = "54--60",
}
@inproceedings{zhu-2015-evaluating,
    title = "Evaluating Neural Machine Translation in {E}nglish-{J}apanese Task",
    author = "Zhu, Zhongyuan",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5007",
    pages = "61--68",
}
@inproceedings{lee-etal-2015-naver,
    title = "{NAVER} Machine Translation System for {WAT} 2015",
    author = "Lee, Hyoung-Gyu  and
      Lee, JaeSong  and
      Kim, Jun-Seok  and
      Lee, Chang-Ki",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5008",
    pages = "69--73",
}
@inproceedings{tan-etal-2015-awkward,
    title = "An Awkward Disparity between {BLEU} / {RIBES} Scores and Human Judgements in Machine Translation",
    author = "Tan, Liling  and
      Dehdari, Jon  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5009",
    pages = "74--81",
}
@inproceedings{shan-etal-2015-dependency,
    title = "A Dependency-to-String Model for {C}hinese-{J}apanese {SMT} System",
    author = "Shan, Hua  and
      Zhang, Yujie  and
      Bai, Lu  and
      Luo, Te",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5010",
    pages = "82--86",
}
@inproceedings{yang-etal-2015-sampling,
    title = "Sampling-based Alignment and Hierarchical Sub-sentential Alignment in {C}hinese{--}{J}apanese Translation of Patents",
    author = "Yang, Wei  and
      Zhao, Zhongwen  and
      Yang, Baosong  and
      Lepage, Yves",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5011",
    pages = "87--94",
}
@inproceedings{sudoh-nagata-2015-chinese,
    title = "{C}hinese-to-{J}apanese Patent Machine Translation based on Syntactic Pre-ordering for{WAT} 2015",
    author = "Sudoh, Katsuhito  and
      Nagata, Masaaki",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5012",
    pages = "95--98",
}
@inproceedings{matsuo-etal-2015-source,
    title = "Source Phrase Segmentation and Translation for {J}apanese-{E}nglish Translation Using Dependency Structure",
    author = "Matsuo, Junki  and
      Ohwada, Kenichi  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 2nd Workshop on {A}sian Translation ({WAT}2015)",
    month = oct,
    year = "2015",
    address = "Kyoto, Japan",
    publisher = "Workshop on Asian Translation",
    url = "https://www.aclweb.org/anthology/W15-5013",
    pages = "99--104",
}
@proceedings{ws-2015-european-association,
    title = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    editor = {El-Kahlout, {\.I}lknur Durgar  and
      {\"O}zkan, Mehmed  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Ram{\'\i}rez-S{\'a}nchez, Gema  and
      Hollowood, Fred  and
      Way, Andy},
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4900",
}
@inproceedings{aranberri-etal-2015-exploiting,
    title = "Exploiting portability to build an {RBMT} prototype for a new source language",
    author = "Aranberri, Nora  and
      Labaka, Gorka  and
      D{\'\i}az de Ilarraza, Arantza  and
      Sarasola, Kepa",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4901",
    pages = "3--10",
}
@inproceedings{artetxe-etal-2015-building,
    title = "Building hybrid machine translation systems by using an {EBMT} preprocessor to create partial translations",
    author = "Artetxe, Mikel  and
      Labaka, Gorka  and
      Sarasola, Kepa",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4902",
    pages = "11--18",
}
@inproceedings{espla-gomis-etal-2015-using,
    title = "Using on-line available sources of bilingual information for word-level machine translation quality estimation",
    author = "Espl{\`a}-Gomis, Miquel  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Forcada, Mikel L.",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4903",
    pages = "19--26",
}
@inproceedings{forcada-sanchez-martinez-2015-general,
    title = "A general framework for minimizing translation effort: towards a principled combination of translation technologies in computer-aided translation",
    author = "Forcada, Mikel L.  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4904",
    pages = "27--34",
}
@inproceedings{gupta-etal-2015-translation,
    title = "Can Translation Memories afford not to use paraphrasing?",
    author = "Gupta, Rohit  and
      Or{\u{a}}san, Constantin  and
      Zampieri, Marcos  and
      Vela, Mihaela  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4905",
    pages = "35--42",
}
@inproceedings{kazemi-etal-2015-dependency,
    title = "Dependency-based Reordering Model for Constituent Pairs in Hierarchical {SMT}",
    author = "Kazemi, Arefeh  and
      Toral, Antonio  and
      Way, Andy  and
      Monadjemi, Amirhassan  and
      Nematbakhsh, Mohammadali",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4906",
    pages = "43--50",
}
@inproceedings{logacheva-specia-2015-role,
    title = "The role of artificially generated negative data for quality estimation of machine translation",
    author = "Logacheva, Varvara  and
      Specia, Lucia",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4907",
    pages = "51--58",
}
@inproceedings{garcia-etal-2015-document,
    title = "Document-Level Machine Translation with Word Vector Models",
    author = "Garcia, Eva Mart{\'\i}nez  and
      Espa{\~n}a-Bonet, Cristina  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4908",
    pages = "59--66",
}
@inproceedings{mitchell-2015-potential,
    title = "The potential and limits of lay post-editing in an online community",
    author = "Mitchell, Linda",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4909",
    pages = "67--74",
}
@inproceedings{moorkens-obrien-2015-post,
    title = "Post-Editing Evaluations: Trade-offs between Novice and Professional Participants",
    author = "Moorkens, Joss  and
      O{'}Brien, Sharon",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4910",
    pages = "75--81",
}
@inproceedings{passban-etal-2015-benchmarking,
    title = "Benchmarking {SMT} Performance for {F}arsi Using the {TEP}++ Corpus",
    author = "Passban, Peyman  and
      Way, Andy  and
      Liu, Qun",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4911",
    pages = "82--88",
}
@inproceedings{pinnis-2015-dynamic,
    title = "Dynamic Terminology Integration Methods in Statistical Machine Translation",
    author = "Pinnis, M{\=a}rcis",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4912",
    pages = "89--96",
}
@inproceedings{popovic-arcan-2015-identifying,
    title = "Identifying main obstacles for statistical machine translation of morphologically rich {S}outh {S}lavic languages",
    author = "Popovi{\'c}, Maja  and
      Ar{\v{c}}an, Mihael",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4913",
    pages = "97--104",
}
@inproceedings{popovic-etal-2015-poor,
    title = "Poor man{'}s lemmatisation for automatic error classification",
    author = "Popovi{\'c}, Maja  and
      Ar{\v{c}}an, Mihael  and
      Avramidis, Eleftherios  and
      Burchardt, Aljoscha  and
      Lommel, Arle",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4914",
    pages = "105--112",
}
@inproceedings{qin-specia-2015-truly,
    title = "Truly Exploring Multiple References for Machine Translation Evaluation",
    author = "Qin, Ying  and
      Specia, Lucia",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4915",
    pages = "113--120",
}
@inproceedings{scarton-etal-2015-searching,
    title = "Searching for Context: a Study on Document-Level Labels for Translation Quality Estimation",
    author = "Scarton, Carolina  and
      Zampieri, Marcos  and
      Vela, Mihaela  and
      van Genabith, Josef  and
      Specia, Lucia",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4916",
    pages = "121--128",
}
@inproceedings{slawik-etal-2015-stripping,
    title = "Stripping Adjectives: Integration Techniques for Selective Stemming in {SMT} Systems",
    author = "Slawik, Isabel  and
      Niehues, Jan  and
      Waibel, Alex",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4917",
    pages = "129--136",
}
@inproceedings{ageeva-etal-2015-evaluating,
    title = "Evaluating machine translation for assimilation via a gap-filling task",
    author = "Ageeva, Ekaterina  and
      Forcada, Mikel L.  and
      Tyers, Francis M.  and
      P{\'e}rez-Ortiz, Juan Antonio",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4918",
    pages = "137--144",
}
@inproceedings{tyers-etal-2015-unsupervised,
    title = "Unsupervised training of maximum-entropy models for lexical selection in rule-based machine translation",
    author = "Tyers, Francis M.  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Forcada, Mikel L.",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4919",
    pages = "145--152",
}
@inproceedings{vanallemeersch-vandeghinste-2015-assessing,
    title = "Assessing linguistically aware fuzzy matching in translation memories",
    author = "Vanallemeersch, Tom  and
      Vandeghinste, Vincent",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4920",
    pages = "153--160",
}
@inproceedings{vela-van-genabith-2015-assessing,
    title = "Re-assessing the {WMT}2013 Human Evaluation with Professional Translators Trainees",
    author = "Vela, Mihaela  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4921",
    pages = "161--168",
}
@inproceedings{waschle-riezler-2015-integrating,
    title = "Integrating a Large, Monolingual Corpus as Translation Memory into Statistical Machine Translation",
    author = {W{\"a}schle, Katharina  and
      Riezler, Stefan},
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4922",
    pages = "169--176",
}
@inproceedings{weller-etal-2015-target,
    title = "Target-Side Generation of Prepositions for {SMT}",
    author = "Weller, Marion  and
      Fraser, Alexander  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4923",
    pages = "177--184",
}
@inproceedings{zarina-etal-2015-word,
    title = "Word Alignment Based Parallel Corpora Evaluation and Cleaning Using Machine Learning Techniques",
    author = "Zari{\c{n}}a, Ieva  and
      {\c{N}}ikiforovs, P{\=e}teris  and
      Skadi{\c{n}}{\v{s}}, Raivis",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4924",
    pages = "185--192",
}
@inproceedings{laxstrom-etal-2015-content,
    title = "Content Translation: Computer assisted translation tool for {W}ikipedia articles",
    author = {Laxstr{\"o}m, Niklas  and
      Giner, Pau  and
      Thottingal, Santhosh},
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4925",
    pages = "194--197",
}
@inproceedings{plamada-etal-2015-pre,
    title = "Pre-reordering for Statistical Machine Translation of Non-fictional Subtitles",
    author = {Plamad{\u{a}}, Magdalena  and
      Linder, Gion  and
      Str{\"o}bel, Phillip  and
      Volk, Martin},
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4926",
    pages = "198--201",
}
@inproceedings{pouliquen-etal-2015-smt,
    title = "{SMT} at the International Maritime Organization: experiences with combining in-house corpora with out-of-domain corpora",
    author = "Pouliquen, Bruno  and
      Junczys-Dowmunt, Marcin  and
      Pinero, Blanca  and
      Ziemski, Michal",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4927",
    pages = "202--205",
}
@inproceedings{thurmair-2015-evaluation,
    title = "Evaluation of the domain adaptation of {MT} systems in {ACCURAT}",
    author = "Thurmair, Gregor",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4928",
    pages = "206--209",
}
@inproceedings{arcan-buitelaar-2015-mixedemotions,
    title = "{M}ixed{E}motions: Social Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets",
    author = "Arcan, Mihael  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4929",
    pages = "211",
}
@inproceedings{bouillon-etal-2015-accept,
    title = "The {ACCEPT} Academic Portal: Bringing Together Pre-editing, {MT} and Post-editing into a Learning Environment",
    author = "Bouillon, Pierrette  and
      Gerlach, Johanna  and
      Gulati, Asheesh  and
      Porro, Victoria  and
      Seretan, Violeta",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4930",
    pages = "212",
}
@inproceedings{du-etal-2015-russian,
    title = "{R}ussian-{C}hinese Sentence-level Aligned News Corpus",
    author = "Du, Wenjun  and
      Liu, Wuying  and
      Yu, Junting  and
      Yi, Mianzhu",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4931",
    pages = "213",
}
@inproceedings{haddow-2015-himl,
    title = "{H}im{L} (Health in my Language)",
    author = "Haddow, Barry",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4932",
    pages = "214",
}
@inproceedings{hofmann-2015-mt,
    title = "{MT}-enhanced fuzzy matching with Transit {NXT} and {STAR} {M}oses",
    author = "Hofmann, Nadira",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4933",
    pages = "215",
}
@inproceedings{hokamp-liu-2015-handycat,
    title = "{H}andy{CAT} - An Open-Source Platform for {CAT} Tool Research",
    author = "Hokamp, Christopher  and
      Liu, Qun",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4934",
    pages = "216",
}
@inproceedings{kordoni-etal-2015-tramooc,
    title = "{T}ra{MOOC}: Translation for Massive Open Online Courses",
    author = "Kordoni, Valia  and
      Cholakov, Kostadin  and
      Egg, Markus  and
      Way, Andy  and
      Birch, Lexi  and
      Kermanidis, Katia  and
      Sosoni, Vilelmini  and
      Tsoumakos, Dimitrios  and
      van den Bosch, Antal  and
      Hendrickx, Iris  and
      Papadopoulos, Michael  and
      Georgakopoulou, Panayota  and
      Gialama, Maria  and
      van Zaanen, Menno  and
      Buliga, Ioana  and
      Jermol, Mitja  and
      Orlic, Davor",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4935",
    pages = "217",
}
@inproceedings{landan-beregovaya-2015-streamlining,
    title = "Streamlining Translation Workflows with {S}tyle{S}corer",
    author = "Landan, David  and
      Beregovaya, Olga",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4936",
    pages = "218",
}
@inproceedings{leete-2015-estonian,
    title = "{E}stonian-{E}nglish Reversible Smart Phone Dictionary of Military Terms and Relevant Vocabulary",
    author = "Leete, Epp",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4937",
    pages = "219",
}
@inproceedings{lewis-2015-falcon,
    title = "{FALCON}: Federated Active Linguistic data {C}urati{ON}",
    author = "Lewis, David",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4938",
    pages = "220",
}
@inproceedings{maguire-etal-2015-tapadoir,
    title = "Tapad{\'o}ir",
    author = "Maguire, Eimear  and
      Judge, John  and
      Lynn, Teresa",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4939",
    pages = "221",
}
@inproceedings{paetzold-etal-2015-okapi,
    title = "{O}kapi+{Q}u{E}st: Translation Quality Estimation within Okapi",
    author = "Paetzold, Gustavo Henrique  and
      Specia, Lucia  and
      Savourel, Yves",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4940",
    pages = "222",
}
@inproceedings{rehm-2015-cracker,
    title = "{CRACKER}: Cracking the Language Barrier",
    author = "Rehm, Georg",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4941",
    pages = "223",
}
@inproceedings{rinsche-rinsches-2015-ltc,
    title = "{LTC} {K}now{H}ow: Empowering the Social Enterprise in the Language Industry",
    author = "Rinsche, Adriane  and
      Rinsches, Sabine",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4942",
    pages = "224--225",
}
@inproceedings{sadat-2015-multi,
    title = "Multi-Dialect Machine Translation ({M}u{DM}at)",
    author = "Sadat, Fatiha",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4943",
    pages = "226",
}
@inproceedings{toral-etal-2015-abu,
    title = "{A}bu-{M}a{T}ran: Automatic building of Machine Translation",
    author = "Toral, Antonio  and
      Pirinen, Tommi A.  and
      Way, Andy  and
      Ram{\'\i}rez-S{\'a}nchez, Gema  and
      Rojas, Sergio Ortiz  and
      Rubino, Raphael  and
      Espl{\`a}, Miquel  and
      Forcada, Mikel L.  and
      Papavassiliou, Vassilis  and
      Prokopidis, Prokopis  and
      Ljube{\v{s}}i{\'c}, Nikola",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4944",
    pages = "227",
}
@inproceedings{utiyama-etal-2015-mnh,
    title = "{MNH}-{TT}: A Platform to Support Collaborative Translator Training",
    author = "Utiyama, Masao  and
      Kageura, Kyo  and
      Thomas, Martin  and
      Hartley, Anthony",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4945",
    pages = "228",
}
@inproceedings{vandeghinste-etal-2015-smart,
    title = "Smart Computer Aided Translation Environment - {SCATE}",
    author = "Vandeghinste, Vincent  and
      Vanallemeersch, Tom  and
      Van Eynde, Frank  and
      Heyman, Geert  and
      Moens, Sien  and
      Pelemans, Joris  and
      Wambacq, Patrick  and
      Van der Lek - Ciudin, Iulianna  and
      Tezcan, Arda  and
      Macken, Lieve  and
      Hoste, V{\'e}ronique  and
      Geurts, Eva  and
      Haesen, Mieke",
    booktitle = "Proceedings of the 18th Annual Conference of the {E}uropean Association for Machine Translation",
    month = may,
    year = "2015",
    address = "Antalya, Turkey",
    url = "https://www.aclweb.org/anthology/W15-4946",
    pages = "229",
}
@proceedings{ws-2015-international-finite,
    title = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    editor = "Hanneforth, Thomas  and
      Wurm, Christian",
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4800",
}
@inproceedings{maletti-2015-extended,
    title = "Extended Tree Transducers in Natural Language Processing",
    author = "Maletti, Andreas",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4801",
}
@inproceedings{daciuk-2015-preserving,
    title = "Preserving Trees in Minimal Automata",
    author = "Daciuk, Jan",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4802",
}
@inproceedings{denkinger-2015-chomsky,
    title = {A {C}homsky-Sch{\"u}tzenberger Representation for Weighted Multiple Context-free Languages},
    author = "Denkinger, Tobias",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4803",
}
@inproceedings{dietze-nederhof-2015-count,
    title = "Count-based State Merging for Probabilistic Regular Tree Grammars",
    author = "Dietze, Toni  and
      Nederhof, Mark-Jan",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4804",
}
@inproceedings{gebhardt-osterholzer-2015-direct,
    title = "A Direct Link between Tree-Adjoining and Context-Free Tree Grammars",
    author = "Gebhardt, Kilian  and
      Osterholzer, Johannes",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4805",
}
@inproceedings{drobac-etal-2015-automated,
    title = "Automated Lossless Hyper-Minimization for Morphological Analyzers",
    author = "Drobac, Senka  and
      Silfverberg, Miikka  and
      Lind{\'e}n, Krister",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4806",
}
@inproceedings{hulden-2015-grammar,
    title = "Grammar Design with Multi-tape Automata and Composition",
    author = "Hulden, Mans",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4807",
}
@inproceedings{kelleher-etal-2015-temporal,
    title = "Temporal Forces and Type Coercion in Strings",
    author = "Kelleher, Derek  and
      Fernando, Tim  and
      Vogel, Carl",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4808",
}
@inproceedings{maxwell-2015-accounting,
    title = "Accounting for Allomorphy in Finite-state Transducers",
    author = "Maxwell, Michael",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4809",
}
@inproceedings{nederhof-rahman-2015-probabilistic,
    title = "A Probabilistic Model of {A}ncient {E}gyptian Writing",
    author = "Nederhof, Mark-Jan  and
      Rahman, Fahrurrozi",
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4810",
}
@inproceedings{wurzner-jurish-2015-hybrid,
    title = "A Hybrid Approach to Grapheme-Phoneme Conversion",
    author = {W{\"u}rzner, Kay-Michael  and
      Jurish, Bryan},
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4811",
}
@inproceedings{yli-jyra-2015-three,
    title = "Three Equivalent Codes for Autosegmental Representations",
    author = {Yli-Jyr{\"a}, Anssi},
    booktitle = {Proceedings of the 12th International Conference on Finite-State Methods and Natural Language Processing 2015 ({FSMNLP} 2015 D{\"u}sseldorf)},
    year = "2015",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4812",
}
@proceedings{ws-2015-european,
    title = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    editor = "Belz, Anya  and
      Gatt, Albert  and
      Portet, Fran{\c{c}}ois  and
      Purver, Matthew",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4700",
    doi = "10.18653/v1/W15-47",
}
@inproceedings{dokkara-etal-2015-simple,
    title = "A Simple Surface Realization Engine for {T}elugu",
    author = "Dokkara, Sasi Raja Sekhar  and
      Penumathsa, Suresh Verma  and
      Sripada, Somayajulu Gowri",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4701",
    doi = "10.18653/v1/W15-4701",
    pages = "1--8",
}
@inproceedings{barros-lloret-2015-input,
    title = "Input Seed Features for Guiding the Generation Process: A Statistical Approach for {S}panish",
    author = "Barros, Cristina  and
      Lloret, Elena",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4702",
    doi = "10.18653/v1/W15-4702",
    pages = "9--17",
}
@inproceedings{gyawali-etal-2015-domain,
    title = "A Domain Agnostic Approach to Verbalizing n-ary Events without Parallel Corpora",
    author = "Gyawali, Bikash  and
      Gardent, Claire  and
      Cerisara, Christophe",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4703",
    doi = "10.18653/v1/W15-4703",
    pages = "18--27",
}
@inproceedings{white-howcroft-2015-inducing,
    title = "Inducing Clause-Combining Rules: A Case Study with the {SP}a{RK}y Restaurant Corpus",
    author = "White, Michael  and
      Howcroft, David M.",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4704",
    doi = "10.18653/v1/W15-4704",
    pages = "28--37",
}
@inproceedings{zarriess-etal-2015-reading,
    title = "Reading Times Predict the Quality of Generated Text Above and Beyond Human Ratings",
    author = "Zarrie{\ss}, Sina  and
      Loth, Sebastian  and
      Schlangen, David",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4705",
    doi = "10.18653/v1/W15-4705",
    pages = "38--47",
}
@inproceedings{baltaretu-etal-2015-moving,
    title = "Moving Targets: Human References to Unstable Landmarks",
    author = "Baltaretu, Adriana  and
      Krahmer, Emiel  and
      Maes, Alfons",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4706",
    doi = "10.18653/v1/W15-4706",
    pages = "48--51",
}
@inproceedings{farrell-etal-2015-framework,
    title = "A Framework for the Generation of Computer System Diagnostics in Natural Language using Finite State Methods",
    author = "Farrell, Rachel  and
      Pace, Gordon  and
      Rosner, Michael",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4707",
    doi = "10.18653/v1/W15-4707",
    pages = "52--56",
}
@inproceedings{gkatzia-mahamood-2015-snapshot,
    title = "A Snapshot of {NLG} Evaluation Practices 2005 - 2014",
    author = "Gkatzia, Dimitra  and
      Mahamood, Saad",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4708",
    doi = "10.18653/v1/W15-4708",
    pages = "57--60",
}
@inproceedings{ohno-etal-2015-japanese,
    title = "{J}apanese Word Reordering Executed Concurrently with Dependency Parsing and Its Evaluation",
    author = "Ohno, Tomohiro  and
      Yoshida, Kazushi  and
      Kato, Yoshihide  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4709",
    doi = "10.18653/v1/W15-4709",
    pages = "61--65",
}
@inproceedings{sauvage-vincent-etal-2015-sentence,
    title = "Sentence Ordering in Electronic Navigational Chart Companion Text Generation",
    author = "Sauvage-Vincent, Julie  and
      Haralambous, Yannis  and
      Puentes, John",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4710",
    doi = "10.18653/v1/W15-4710",
    pages = "66--70",
}
@inproceedings{sevens-etal-2015-natural,
    title = "Natural Language Generation from Pictographs",
    author = "Sevens, Leen  and
      Vandeghinste, Vincent  and
      Schuurman, Ineke  and
      Van Eynde, Frank",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4711",
    doi = "10.18653/v1/W15-4711",
    pages = "71--75",
}
@inproceedings{mazzei-2015-translating,
    title = "Translating {I}talian to {LIS} in the Rail Stations",
    author = "Mazzei, Alessandro",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4712",
    doi = "10.18653/v1/W15-4712",
    pages = "76--80",
}
@inproceedings{yuan-etal-2015-response,
    title = "Response Generation in Dialogue Using a Tailored {PCFG} Parser",
    author = "Yuan, Caixia  and
      Wang, Xiaojie  and
      He, Qianhui",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4713",
    doi = "10.18653/v1/W15-4713",
    pages = "81--85",
}
@inproceedings{baez-miranda-etal-2015-generating,
    title = "Generating R{\'e}cit from Sensor Data: Evaluation of a Task Model for Story Planning and Preliminary Experiments with {GPS} Data",
    author = "Baez Miranda, Bel{\'e}n A.  and
      Caffiau, Sybille  and
      Garbay, Catherine  and
      Portet, Fran{\c{c}}ois",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4714",
    doi = "10.18653/v1/W15-4714",
    pages = "86--89",
}
@inproceedings{cercas-curry-etal-2015-generating,
    title = "Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments",
    author = "Cercas Curry, Amanda  and
      Gkatzia, Dimitra  and
      Rieser, Verena",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4715",
    doi = "10.18653/v1/W15-4715",
    pages = "90--94",
}
@inproceedings{inglis-2015-summarising,
    title = "Summarising Unreliable Data",
    author = "Inglis, Stephanie",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4716",
    doi = "10.18653/v1/W15-4716",
    pages = "95--99",
}
@inproceedings{muscat-belz-2015-generating,
    title = "Generating Descriptions of Spatial Relations between Objects in Images",
    author = "Muscat, Adrian  and
      Belz, Anja",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4717",
    doi = "10.18653/v1/W15-4717",
    pages = "100--104",
}
@inproceedings{fischer-etal-2015-towards,
    title = "Towards Flexible, Small-Domain Surface Generation: Combining Data-Driven and Grammatical Approaches",
    author = "Fischer, Andrea  and
      Demberg, Vera  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4718",
    doi = "10.18653/v1/W15-4718",
    pages = "105--108",
}
@inproceedings{molins-lapalme-2015-jsrealb,
    title = "{JS}real{B}: A Bilingual Text Realizer for Web Programming",
    author = "Molins, Paul  and
      Lapalme, Guy",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4719",
    doi = "10.18653/v1/W15-4719",
    pages = "109--111",
}
@inproceedings{gkatzia-etal-2015-game,
    title = "A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation",
    author = "Gkatzia, Dimitra  and
      Cercas Curry, Amanda  and
      Rieser, Verena  and
      Lemon, Oliver",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4720",
    doi = "10.18653/v1/W15-4720",
    pages = "112--113",
}
@inproceedings{haque-horacek-2015-generating,
    title = "Generating Referential Descriptions Involving Relations by a Best-First Searching Procedure {--} A System Demo",
    author = "Haque, Florin  and
      Horacek, Helmut",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4721",
    doi = "10.18653/v1/W15-4721",
    pages = "114--116",
}
@inproceedings{wang-gaizauskas-2015-generating,
    title = "Generating Image Descriptions with Gold Standard Visual Inputs: Motivation, Evaluation and Baselines",
    author = "Wang, Josiah  and
      Gaizauskas, Robert",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4722",
    doi = "10.18653/v1/W15-4722",
    pages = "117--126",
}
@inproceedings{de-oliveira-etal-2015-designing,
    title = "Designing an Algorithm for Generating Named Spatial References",
    author = "de Oliveira, Rodrigo  and
      Sripada, Yaji  and
      Reiter, Ehud",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4723",
    doi = "10.18653/v1/W15-4723",
    pages = "127--135",
}
@inproceedings{vaudry-lapalme-2015-narrative,
    title = "Narrative Generation from Extracted Associations",
    author = "Vaudry, Pierre-Luc  and
      Lapalme, Guy",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4724",
    doi = "10.18653/v1/W15-4724",
    pages = "136--145",
}
@inproceedings{glas-pelachaud-2015-topic,
    title = "Topic Transition Strategies for an Information-Giving Agent",
    author = "Glas, Nadine  and
      Pelachaud, Catherine",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4725",
    doi = "10.18653/v1/W15-4725",
    pages = "146--155",
}
@inproceedings{braun-etal-2015-creating,
    title = "Creating Textual Driver Feedback from Telemetric Data",
    author = "Braun, Daniel  and
      Reiter, Ehud  and
      Siddharthan, Advaith",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4726",
    doi = "10.18653/v1/W15-4726",
    pages = "156--165",
}
@inproceedings{labbe-etal-2015-personal,
    title = "A Personal Storytelling about Your Favorite Data",
    author = "Labb{\'e}, Cyril  and
      Roncancio, Claudia  and
      Bras, Damien",
    booktitle = "Proceedings of the 15th {E}uropean Workshop on Natural Language Generation ({ENLG})",
    month = sep,
    year = "2015",
    address = "Brighton, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4727",
    doi = "10.18653/v1/W15-4727",
    pages = "166--174",
}
@proceedings{ws-2015-special,
    title = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    editor = "Koller, Alexander  and
      Skantze, Gabriel  and
      Jurcicek, Filip  and
      Araki, Masahiro  and
      Rose, Carolyn Penstein",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4600",
    doi = "10.18653/v1/W15-46",
}
@inproceedings{fischer-2015-keynote,
    title = "{K}eynote: The Interplay of Discussion, Cognition and Instruction in Computer-Supported Collaborative Learning Environments",
    author = "Fischer, Frank",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4601",
    doi = "10.18653/v1/W15-4601",
    pages = "1",
}
@inproceedings{barlier-etal-2015-human,
    title = "Human-Machine Dialogue as a Stochastic Game",
    author = "Barlier, Merwan  and
      Perolat, Julien  and
      Laroche, Romain  and
      Pietquin, Olivier",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4602",
    doi = "10.18653/v1/W15-4602",
    pages = "2--11",
}
@inproceedings{casanueva-etal-2015-knowledge,
    title = "Knowledge transfer between speakers for personalised dialogue management",
    author = "Casanueva, I{\~n}igo  and
      Hain, Thomas  and
      Christensen, Heidi  and
      Marxer, Ricard  and
      Green, Phil",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4603",
    doi = "10.18653/v1/W15-4603",
    pages = "12--21",
}
@inproceedings{marge-rudnicky-2015-miscommunication,
    title = "Miscommunication Recovery in Physically Situated Dialogue",
    author = "Marge, Matthew  and
      Rudnicky, Alexander",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4604",
    doi = "10.18653/v1/W15-4604",
    pages = "22--31",
}
@inproceedings{hiraoka-etal-2015-reinforcement,
    title = "Reinforcement Learning in Multi-Party Trading Dialog",
    author = "Hiraoka, Takuya  and
      Georgila, Kallirroi  and
      Nouri, Elnaz  and
      Traum, David  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4605",
    doi = "10.18653/v1/W15-4605",
    pages = "32--41",
}
@inproceedings{zhao-etal-2015-incremental,
    title = "An Incremental Turn-Taking Model with Active System Barge-in for Spoken Dialog Systems",
    author = "Zhao, Tiancheng  and
      Black, Alan W  and
      Eskenazi, Maxine",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4606",
    doi = "10.18653/v1/W15-4606",
    pages = "42--50",
}
@inproceedings{jordan-etal-2015-exploring,
    title = "Exploring the Effects of Redundancy within a Tutorial Dialogue System: Restating Students{'} Responses",
    author = "Jordan, Pamela  and
      Albacete, Patricia  and
      Katz, Sandra",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4607",
    doi = "10.18653/v1/W15-4607",
    pages = "51--59",
}
@inproceedings{dias-pardo-2015-discursive,
    title = "A Discursive Grid Approach to Model Local Coherence in Multi-document Summaries",
    author = "Dias, M{\'a}rcio  and
      Pardo, Thiago",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4608",
    doi = "10.18653/v1/W15-4608",
    pages = "60--67",
}
@inproceedings{ramachandran-ratnaparkhi-2015-belief,
    title = "Belief Tracking with Stacked Relational Trees",
    author = "Ramachandran, Deepak  and
      Ratnaparkhi, Adwait",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4609",
    doi = "10.18653/v1/W15-4609",
    pages = "68--76",
}
@inproceedings{paetzel-etal-2015-one,
    title = "{``}So, which one is it?{''} The effect of alternative incremental architectures in a high-performance game-playing agent",
    author = "Paetzel, Maike  and
      Manuvinakurike, Ramesh  and
      DeVault, David",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4610",
    doi = "10.18653/v1/W15-4610",
    pages = "77--86",
}
@inproceedings{higashinaka-etal-2015-towards,
    title = "Towards Taxonomy of Errors in Chat-oriented Dialogue Systems",
    author = "Higashinaka, Ryuichiro  and
      Funakoshi, Kotaro  and
      Araki, Masahiro  and
      Tsukahara, Hiroshi  and
      Kobayashi, Yuka  and
      Mizukami, Masahiro",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4611",
    doi = "10.18653/v1/W15-4611",
    pages = "87--95",
}
@inproceedings{biran-mckeown-2015-pdtb,
    title = "{PDTB} Discourse Parsing as a Tagging Task: The Two Taggers Approach",
    author = "Biran, Or  and
      McKeown, Kathleen",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4612",
    doi = "10.18653/v1/W15-4612",
    pages = "96--104",
}
@inproceedings{pincus-etal-2015-synthetic,
    title = "Which Synthetic Voice Should {I} Choose for an Evocative Task?",
    author = "Pincus, Eli  and
      Georgila, Kallirroi  and
      Traum, David",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4613",
    doi = "10.18653/v1/W15-4613",
    pages = "105--113",
}
@inproceedings{zarisheva-scheffler-2015-dialog,
    title = "Dialog Act Annotation for {T}witter Conversations",
    author = "Zarisheva, Elina  and
      Scheffler, Tatjana",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4614",
    doi = "10.18653/v1/W15-4614",
    pages = "114--123",
}
@inproceedings{kim-etal-2015-towards,
    title = "Towards Improving Dialogue Topic Tracking Performances with Wikification of Concept Mentions",
    author = "Kim, Seokhwan  and
      Banchs, Rafael E.  and
      Li, Haizhou",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4615",
    doi = "10.18653/v1/W15-4615",
    pages = "124--128",
}
@inproceedings{han-etal-2015-exploiting,
    title = "Exploiting knowledge base to generate responses for natural language dialog listening agents",
    author = "Han, Sangdo  and
      Bang, Jeesoo  and
      Ryu, Seonghan  and
      Lee, Gary Geunbae",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4616",
    doi = "10.18653/v1/W15-4616",
    pages = "129--133",
}
@inproceedings{ivanov-etal-2015-automated,
    title = "Automated Speech Recognition Technology for Dialogue Interaction with Non-Native Interlocutors",
    author = "Ivanov, Alexei V.  and
      Ramanarayanan, Vikram  and
      Suendermann-Oeft, David  and
      Lopez, Melissa  and
      Evanini, Keelan  and
      Tao, Jidong",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4617",
    doi = "10.18653/v1/W15-4617",
    pages = "134--138",
}
@inproceedings{lee-etal-2015-conversational,
    title = "Conversational Knowledge Teaching Agent that uses a Knowledge Base",
    author = "Lee, Kyusong  and
      Seo, Paul Hongsuck  and
      Choi, Junhwi  and
      Koo, Sangjun  and
      Lee, Gary Geunbae",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4618",
    doi = "10.18653/v1/W15-4618",
    pages = "139--143",
}
@inproceedings{rahman-2015-information,
    title = "Information Theoretical and Statistical Features for Intrinsic Plagiarism Detection",
    author = "Rahman, Rashedur",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4619",
    doi = "10.18653/v1/W15-4619",
    pages = "144--148",
}
@inproceedings{prevot-etal-2015-sip,
    title = "A {SIP} of {C}o{F}ee : A Sample of Interesting Productions of Conversational Feedback",
    author = "Pr{\'e}vot, Laurent  and
      Gorisch, Jan  and
      Bertrand, Roxane  and
      Gor{\`e}ne, Emilien  and
      Bigi, Brigitte",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4620",
    doi = "10.18653/v1/W15-4620",
    pages = "149--153",
}
@inproceedings{papangelis-georgila-2015-reinforcement,
    title = "Reinforcement Learning of Multi-Issue Negotiation Dialogue Policies",
    author = "Papangelis, Alexandros  and
      Georgila, Kallirroi",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4621",
    doi = "10.18653/v1/W15-4621",
    pages = "154--158",
}
@inproceedings{williams-etal-2015-fast,
    title = "Fast and easy language understanding for dialog systems with {M}icrosoft Language Understanding Intelligent Service ({LUIS})",
    author = "Williams, Jason D.  and
      Kamal, Eslam  and
      Ashour, Mokhtar  and
      Amr, Hani  and
      Miller, Jessica  and
      Zweig, Geoff",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4622",
    doi = "10.18653/v1/W15-4622",
    pages = "159--161",
}
@inproceedings{wilcock-jokinen-2015-multilingual,
    title = "Multilingual {W}iki{T}alk: {W}ikipedia-based talking robots that switch languages.",
    author = "Wilcock, Graham  and
      Jokinen, Kristiina",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4623",
    doi = "10.18653/v1/W15-4623",
    pages = "162--164",
}
@inproceedings{skantze-johansson-2015-modelling,
    title = "Modelling situated human-robot interaction using {I}ris{TK}",
    author = "Skantze, Gabriel  and
      Johansson, Martin",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4624",
    doi = "10.18653/v1/W15-4624",
    pages = "165--167",
}
@inproceedings{rosenthal-mckeown-2015-couldnt,
    title = "{I} Couldn{'}t Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions",
    author = "Rosenthal, Sara  and
      McKeown, Kathy",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4625",
    doi = "10.18653/v1/W15-4625",
    pages = "168--177",
}
@inproceedings{chiarcos-schenk-2015-memory,
    title = "Memory-Based Acquisition of Argument Structures and its Application to Implicit Role Detection",
    author = "Chiarcos, Christian  and
      Schenk, Niko",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4626",
    doi = "10.18653/v1/W15-4626",
    pages = "178--187",
}
@inproceedings{lukin-etal-2015-generating,
    title = "Generating Sentence Planning Variations for Story Telling",
    author = "Lukin, Stephanie  and
      Reed, Lena  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4627",
    doi = "10.18653/v1/W15-4627",
    pages = "188--197",
}
@inproceedings{hakkani-tur-2015-keynote,
    title = "{K}eynote: Graph-based Approaches for Spoken Language Understanding",
    author = "Hakkani-Tur, Dilek",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4628",
    doi = "10.18653/v1/W15-4628",
    pages = "198",
}
@inproceedings{traum-etal-2015-evaluating,
    title = "Evaluating Spoken Dialogue Processing for Time-Offset Interaction",
    author = "Traum, David  and
      Georgila, Kallirroi  and
      Artstein, Ron  and
      Leuski, Anton",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4629",
    doi = "10.18653/v1/W15-4629",
    pages = "199--208",
}
@inproceedings{eskenazi-etal-2015-real,
    title = "The Real Challenge 2014: Progress and Prospects",
    author = "Eskenazi, Maxine  and
      Black, Alan W  and
      Lee, Sungjin  and
      Traum, David",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4630",
    doi = "10.18653/v1/W15-4630",
    pages = "209--216",
}
@inproceedings{swanson-etal-2015-argument,
    title = "Argument Mining: Extracting Arguments from Online Dialogue",
    author = "Swanson, Reid  and
      Ecker, Brian  and
      Walker, Marilyn",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4631",
    doi = "10.18653/v1/W15-4631",
    pages = "217--226",
}
@inproceedings{vanetik-litvak-2015-multilingual,
    title = "Multilingual Summarization with Polytope Model",
    author = "Vanetik, Natalia  and
      Litvak, Marina",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4632",
    doi = "10.18653/v1/W15-4632",
    pages = "227--231",
}
@inproceedings{favre-etal-2015-call,
    title = "Call Centre Conversation Summarization: A Pilot Task at Multiling 2015",
    author = "Favre, Benoit  and
      Stepanov, Evgeny  and
      Trione, J{\'e}r{\'e}my  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Riccardi, Giuseppe",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4633",
    doi = "10.18653/v1/W15-4633",
    pages = "232--236",
}
@inproceedings{aries-etal-2015-allsummarizer,
    title = "{A}ll{S}ummarizer system at {M}ulti{L}ing 2015: Multilingual single and multi-document summarization",
    author = "Aries, Abdelkrime  and
      Zegour, Djamel Eddine  and
      Hidouci, Khaled Walid",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4634",
    doi = "10.18653/v1/W15-4634",
    pages = "237--244",
}
@inproceedings{aker-etal-2015-comment,
    title = "Comment-to-Article Linking in the Online News Domain",
    author = "Aker, Ahmet  and
      Kurtic, Emina  and
      Hepple, Mark  and
      Gaizauskas, Rob  and
      Di Fabbrizio, Giuseppe",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4635",
    doi = "10.18653/v1/W15-4635",
    pages = "245--249",
}
@inproceedings{vicente-etal-2015-university,
    title = "The {U}niversity of {A}licante at {M}ulti{L}ing 2015: approach, results and further insights",
    author = "Vicente, Marta  and
      Alc{\'o}n, {\'O}scar  and
      Lloret, Elena",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4636",
    doi = "10.18653/v1/W15-4636",
    pages = "250--259",
}
@inproceedings{thomas-etal-2015-exb,
    title = "{E}x{B} Text Summarizer",
    author = {Thomas, Stefan  and
      Beutenm{\"u}ller, Christian  and
      de la Puente, Xose  and
      Remus, Robert  and
      Bordag, Stefan},
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4637",
    doi = "10.18653/v1/W15-4637",
    pages = "260--269",
}
@inproceedings{giannakopoulos-etal-2015-multiling,
    title = "{M}ulti{L}ing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations",
    author = "Giannakopoulos, George  and
      Kubina, Jeff  and
      Conroy, John  and
      Steinberger, Josef  and
      Favre, Benoit  and
      Kabadjov, Mijail  and
      Kruschwitz, Udo  and
      Poesio, Massimo",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4638",
    doi = "10.18653/v1/W15-4638",
    pages = "270--274",
}
@inproceedings{wen-etal-2015-stochastic,
    title = "Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking",
    author = "Wen, Tsung-Hsien  and
      Ga{\v{s}}i{\'c}, Milica  and
      Kim, Dongho  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Su, Pei-Hao  and
      Vandyke, David  and
      Young, Steve",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4639",
    doi = "10.18653/v1/W15-4639",
    pages = "275--284",
}
@inproceedings{lowe-etal-2015-ubuntu,
    title = "The {U}buntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems",
    author = "Lowe, Ryan  and
      Pow, Nissan  and
      Serban, Iulian  and
      Pineau, Joelle",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4640",
    doi = "10.18653/v1/W15-4640",
    pages = "285--294",
}
@inproceedings{xie-etal-2015-recurrent,
    title = "Recurrent Polynomial Network for Dialogue State Tracking with Mismatched Semantic Parsers",
    author = "Xie, Qizhe  and
      Sun, Kai  and
      Zhu, Su  and
      Chen, Lu  and
      Yu, Kai",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4641",
    doi = "10.18653/v1/W15-4641",
    pages = "295--304",
}
@inproceedings{johansson-skantze-2015-opportunities,
    title = "Opportunities and Obligations to Take Turns in Collaborative Multi-Party Human-Robot Interaction",
    author = "Johansson, Martin  and
      Skantze, Gabriel",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4642",
    doi = "10.18653/v1/W15-4642",
    pages = "305--314",
}
@inproceedings{khouzaimi-etal-2015-optimising,
    title = "Optimising Turn-Taking Strategies With Reinforcement Learning",
    author = "Khouzaimi, Hatim  and
      Laroche, Romain  and
      Lef{\`e}vre, Fabrice",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4643",
    doi = "10.18653/v1/W15-4643",
    pages = "315--324",
}
@inproceedings{levitan-etal-2015-acoustic,
    title = "Acoustic-prosodic entrainment in {S}lovak, {S}panish, {E}nglish and {C}hinese: A cross-linguistic comparison",
    author = "Levitan, Rivka  and
      Be{\v{n}}u{\v{s}}, {\v{S}}tefan  and
      Gravano, Agust{\'\i}n  and
      Hirschberg, Julia",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4644",
    doi = "10.18653/v1/W15-4644",
    pages = "325--334",
}
@inproceedings{raghu-etal-2015-statistical,
    title = "A statistical approach for Non-Sentential Utterance Resolution for Interactive {QA} System",
    author = "Raghu, Dinesh  and
      Indurthi, Sathish  and
      Ajmera, Jitendra  and
      Joshi, Sachindra",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4645",
    doi = "10.18653/v1/W15-4645",
    pages = "335--343",
}
@inproceedings{nothdurft-etal-2015-interplay,
    title = "The Interplay of User-Centered Dialog Systems and {AI} Planning",
    author = "Nothdurft, Florian  and
      Behnke, Gregor  and
      Bercher, Pascal  and
      Biundo, Susanne  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4646",
    doi = "10.18653/v1/W15-4646",
    pages = "344--353",
}
@inproceedings{meena-etal-2015-automatic,
    title = "Automatic Detection of Miscommunication in Spoken Dialogue Systems",
    author = "Meena, Raveesh  and
      Lopes, Jos{\'e}  and
      Skantze, Gabriel  and
      Gustafson, Joakim",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4647",
    doi = "10.18653/v1/W15-4647",
    pages = "354--363",
}
@inproceedings{ge-xu-2015-dialogue,
    title = "Dialogue Management based on Multi-domain Corpus",
    author = "Ge, Wendong  and
      Xu, Bo",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4648",
    doi = "10.18653/v1/W15-4648",
    pages = "364--373",
}
@inproceedings{ultes-etal-2015-quality,
    title = "Quality-adaptive Spoken Dialogue Initiative Selection And Implications On Reward Modelling",
    author = "Ultes, Stefan  and
      Kraus, Matthias  and
      Schmitt, Alexander  and
      Minker, Wolfgang",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4649",
    doi = "10.18653/v1/W15-4649",
    pages = "374--383",
}
@inproceedings{jang-etal-2015-metaphor,
    title = "Metaphor Detection in Discourse",
    author = "Jang, Hyeju  and
      Moon, Seungwhan  and
      Jo, Yohan  and
      Ros{\'e}, Carolyn",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4650",
    doi = "10.18653/v1/W15-4650",
    pages = "384--392",
}
@inproceedings{komatani-etal-2015-user,
    title = "User Adaptive Restoration for Incorrectly-Segmented Utterances in Spoken Dialogue Systems",
    author = "Komatani, Kazunori  and
      Hotta, Naoki  and
      Sato, Satoshi  and
      Nakano, Mikio",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4651",
    doi = "10.18653/v1/W15-4651",
    pages = "393--401",
}
@inproceedings{yu-etal-2015-incremental,
    title = "Incremental Coordination: Attention-Centric Speech Production in a Physically Situated Conversational Agent",
    author = "Yu, Zhou  and
      Bohus, Dan  and
      Horvitz, Eric",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4652",
    doi = "10.18653/v1/W15-4652",
    pages = "402--406",
}
@inproceedings{chen-etal-2015-hyper,
    title = "Hyper-parameter Optimisation of {G}aussian Process Reinforcement Learning for Statistical Dialogue Management",
    author = "Chen, Lu  and
      Su, Pei-Hao  and
      Ga{\v{s}}i{\'c}, Milica",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4653",
    doi = "10.18653/v1/W15-4653",
    pages = "407--411",
}
@inproceedings{wang-etal-2015-learning-domain,
    title = "Learning Domain-Independent Dialogue Policies via Ontology Parameterisation",
    author = "Wang, Zhuoran  and
      Wen, Tsung-Hsien  and
      Su, Pei-Hao  and
      Stylianou, Yannis",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4654",
    doi = "10.18653/v1/W15-4654",
    pages = "412--416",
}
@inproceedings{su-etal-2015-reward,
    title = "Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems",
    author = "Su, Pei-Hao  and
      Vandyke, David  and
      Ga{\v{s}}i{\'c}, Milica  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Wen, Tsung-Hsien  and
      Young, Steve",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4655",
    doi = "10.18653/v1/W15-4655",
    pages = "417--421",
}
@inproceedings{kobayashi-etal-2015-effects,
    title = "Effects of Game on User Engagement with Spoken Dialogue System",
    author = "Kobayashi, Hayato  and
      Tanio, Kaori  and
      Sassano, Manabu",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4656",
    doi = "10.18653/v1/W15-4656",
    pages = "422--426",
}
@inproceedings{schmidt-etal-2015-evaluation,
    title = "Evaluation of Crowdsourced User Input Data for Spoken Dialog Systems",
    author = {Schmidt, Maria  and
      M{\"u}ller, Markus  and
      Wagner, Martin  and
      St{\"u}ker, Sebastian  and
      Waibel, Alex  and
      Hofmann, Hansj{\"o}rg  and
      Werner, Steffen},
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4657",
    doi = "10.18653/v1/W15-4657",
    pages = "427--431",
}
@inproceedings{ramanarayanan-etal-2015-distributed,
    title = "A distributed cloud-based dialog system for conversational application development",
    author = "Ramanarayanan, Vikram  and
      Suendermann-Oeft, David  and
      Ivanov, Alexei V.  and
      Evanini, Keelan",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4658",
    doi = "10.18653/v1/W15-4658",
    pages = "432--434",
}
@inproceedings{ramachandran-etal-2015-tv,
    title = "A {TV} Program Discovery Dialog System using recommendations",
    author = "Ramachandran, Deepak  and
      Fanty, Mark  and
      Provine, Ronald  and
      Yeh, Peter  and
      Jarrold, William  and
      Ratnaparkhi, Adwait  and
      Douglas, Benjamin",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4659",
    doi = "10.18653/v1/W15-4659",
    pages = "435--437",
}
@inproceedings{campillos-llanos-etal-2015-description,
    title = "Description of the {P}atient{G}enesys Dialogue System",
    author = "Campillos Llanos, Leonardo  and
      Bouamor, Dhouha  and
      Bilinski, {\'E}ric  and
      Ligozat, Anne-Laure  and
      Zweigenbaum, Pierre  and
      Rosset, Sophie",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4660",
    doi = "10.18653/v1/W15-4660",
    pages = "438--440",
}
@inproceedings{kasturi-etal-2015-cohort,
    title = "The Cohort and Speechify Libraries for Rapid Construction of Speech Enabled Applications for Android",
    author = "Kasturi, Tejaswi  and
      Jin, Haojian  and
      Pappu, Aasish  and
      Lee, Sungjin  and
      Harrison, Beverley  and
      Murthy, Ramana  and
      Stent, Amanda",
    booktitle = "Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2015",
    address = "Prague, Czech Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4661",
    doi = "10.18653/v1/W15-4661",
    pages = "441--443",
}
@proceedings{ws-2015-computing,
    title = "Proceedings of the First Workshop on Computing News Storylines",
    editor = "Caselli, Tommaso  and
      van Erp, Marieke  and
      Minard, Anne-Lyse  and
      Finlayson, Mark  and
      Miller, Ben  and
      Atserias, Jordi  and
      Balahur, Alexandra  and
      Vossen, Piek",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4500",
    doi = "10.18653/v1/W15-45",
}
@inproceedings{simonson-davis-2015-interactions,
    title = "Interactions between Narrative Schemas and Document Categories",
    author = "Simonson, Dan  and
      Davis, Anthony",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4501",
    doi = "10.18653/v1/W15-4501",
    pages = "1--10",
}
@inproceedings{li-etal-2015-improving-event,
    title = "Improving Event Detection with {A}bstract {M}eaning {R}epresentation",
    author = "Li, Xiang  and
      Nguyen, Thien Huu  and
      Cao, Kai  and
      Grishman, Ralph",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4502",
    doi = "10.18653/v1/W15-4502",
    pages = "11--15",
}
@inproceedings{makhalova-etal-2015-news,
    title = "News clustering approach based on discourse text structure",
    author = "Makhalova, Tatyana  and
      Ilvovsky, Dmitry  and
      Galitsky, Boris",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4503",
    doi = "10.18653/v1/W15-4503",
    pages = "16--20",
}
@inproceedings{stalpouskaya-baden-2015-role,
    title = "To Do or Not to Do: the Role of Agendas for Action in Analyzing News Coverage of Violent Conflict",
    author = "Stalpouskaya, Katsiaryna  and
      Baden, Christian",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4504",
    doi = "10.18653/v1/W15-4504",
    pages = "21--29",
}
@inproceedings{nomoto-2015-mediameter,
    title = "{M}edia{M}eter: A Global Monitor for Online News Coverage",
    author = "Nomoto, Tadashi",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4505",
    doi = "10.18653/v1/W15-4505",
    pages = "30--34",
}
@inproceedings{fedorovsky-etal-2015-expanding,
    title = "Expanding the horizons: adding a new language to the news personalization system",
    author = "Fedorovsky, Andrey  and
      Ionov, Maxim  and
      Litvinova, Varvara  and
      Olenina, Tatyana  and
      Trofimova, Darya",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4506",
    doi = "10.18653/v1/W15-4506",
    pages = "35--39",
}
@inproceedings{vossen-etal-2015-storylines,
    title = "Storylines for structuring massive streams of news",
    author = "Vossen, Piek  and
      Caselli, Tommaso  and
      Kontzopoulou, Yiota",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4507",
    doi = "10.18653/v1/W15-4507",
    pages = "40--49",
}
@inproceedings{laparra-etal-2015-timelines,
    title = "From {T}ime{L}ines to {S}tory{L}ines: A preliminary proposal for evaluating narratives",
    author = "Laparra, Egoitz  and
      Aldabe, Itziar  and
      Rigau, German",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4508",
    doi = "10.18653/v1/W15-4508",
    pages = "50--55",
}
@inproceedings{miller-etal-2015-cross,
    title = "Cross-Document Non-Fiction Narrative Alignment",
    author = "Miller, Ben  and
      Olive, Jennifer  and
      Gopavaram, Shakthidhar  and
      Shrestha, Ayush",
    booktitle = "Proceedings of the First Workshop on Computing News Storylines",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4509",
    doi = "10.18653/v1/W15-4509",
    pages = "56--61",
}
@proceedings{ws-2015-natural,
    title = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    editor = "Chen, Hsin-Hsi  and
      Tseng, Yuen-Hsien  and
      Matsumoto, Yuji  and
      Wong, Lung Hsiang",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4400",
    doi = "10.18653/v1/W15-44",
}
@inproceedings{lee-etal-2015-overview,
    title = "Overview of the {NLP}-{TEA} 2015 Shared Task for {C}hinese Grammatical Error Diagnosis",
    author = "Lee, Lung-Hao  and
      Yu, Liang-Chih  and
      Chang, Li-Ping",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4401",
    doi = "10.18653/v1/W15-4401",
    pages = "1--6",
}
@inproceedings{chen-etal-2015-chinese,
    title = "{C}hinese Grammatical Error Diagnosis by Conditional Random Fields",
    author = "Chen, Po-Lin  and
      Wu, Shih-Hung  and
      Chen, Liang-Pu  and
      Yang, Ping-Che  and
      Yang, Ren-Dar",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4402",
    doi = "10.18653/v1/W15-4402",
    pages = "7--14",
}
@inproceedings{lin-chen-2015-ntou,
    title = "{NTOU} {C}hinese Grammar Checker for {CGED} Shared Task",
    author = "Lin, Chuan-Jie  and
      Chen, Shao-Heng",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4403",
    doi = "10.18653/v1/W15-4403",
    pages = "15--19",
}
@inproceedings{pereira-matsumoto-2015-collocation,
    title = "Collocation Assistant for Learners of {J}apanese as a Second Language",
    author = "Pereira, Lis  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4404",
    doi = "10.18653/v1/W15-4404",
    pages = "20--25",
}
@inproceedings{ai-etal-2015-semi,
    title = "Semi-automatic Generation of Multiple-Choice Tests from Mentions of Semantic Relations",
    author = "Ai, Renlong  and
      Krause, Sebastian  and
      Kasper, Walter  and
      Xu, Feiyu  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4405",
    doi = "10.18653/v1/W15-4405",
    pages = "26--33",
}
@inproceedings{chen-etal-2015-interactive,
    title = "Interactive Second Language Learning from News Websites",
    author = "Chen, Tao  and
      Zheng, Naijia  and
      Zhao, Yue  and
      Chandrasekaran, Muthu Kumar  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4406",
    doi = "10.18653/v1/W15-4406",
    pages = "34--42",
}
@inproceedings{huang-etal-2015-bilingual,
    title = "Bilingual Keyword Extraction and its Educational Application",
    author = "Huang, Chung-Chi  and
      Chen, Mei-Hua  and
      Yang, Ping-Che",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4407",
    doi = "10.18653/v1/W15-4407",
    pages = "43--48",
}
@inproceedings{ostermann-etal-2015-annotating,
    title = "Annotating Entailment Relations for Shortanswer Questions",
    author = "Ostermann, Simon  and
      Horbach, Andrea  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4408",
    doi = "10.18653/v1/W15-4408",
    pages = "49--58",
}
@inproceedings{cheon-etal-2015-automated,
    title = "An Automated Scoring Tool for {K}orean Supply-type Items Based on Semi-Supervised Learning",
    author = "Cheon, Minah  and
      Seo, Hyeong-Won  and
      Kim, Jae-Hoon  and
      Noh, Eun-Hee  and
      Sung, Kyung-Hee  and
      Lim, EunYong",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4409",
    doi = "10.18653/v1/W15-4409",
    pages = "59--63",
}
@inproceedings{majumder-saha-2015-system,
    title = "A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection",
    author = "Majumder, Mukta  and
      Saha, Sujan Kumar",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4410",
    doi = "10.18653/v1/W15-4410",
    pages = "64--72",
}
@inproceedings{tanaka-etal-2015-news,
    title = "The {``}News Web Easy{''} news service as a resource for teaching and learning {J}apanese: An assessment of the comprehension difficulty of {J}apanese sentence-end expressions",
    author = "Tanaka, Hideki  and
      Kumano, Tadashi  and
      Goto, Isao",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4411",
    doi = "10.18653/v1/W15-4411",
    pages = "73--81",
}
@inproceedings{mizumoto-etal-2015-grammatical,
    title = "Grammatical Error Correction Considering Multi-word Expressions",
    author = "Mizumoto, Tomoya  and
      Mita, Masato  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4412",
    doi = "10.18653/v1/W15-4412",
    pages = "82--86",
}
@inproceedings{regalado-etal-2015-salinlahi,
    title = "Salinlahi {III}: An Intelligent Tutoring System for {F}ilipino Heritage Language Learners",
    author = "Regalado, Ralph Vincent  and
      Bo{\~n}on, Michael Louie  and
      Chua, Nadine  and
      Pi{\~n}era, Rene Rose  and
      Dela Cruz, Shannen Rose",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4413",
    doi = "10.18653/v1/W15-4413",
    pages = "87--93",
}
@inproceedings{kaya-eryigit-2015-using,
    title = "Using Finite State Transducers for Helping Foreign Language Learning",
    author = {Kaya, Hasan  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4414",
    doi = "10.18653/v1/W15-4414",
    pages = "94--98",
}
@inproceedings{xiang-etal-2015-chinese,
    title = "{C}hinese Grammatical Error Diagnosis Using Ensemble Learning",
    author = "Xiang, Yang  and
      Wang, Xiaolong  and
      Han, Wenying  and
      Hong, Qinghua",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4415",
    doi = "10.18653/v1/W15-4415",
    pages = "99--104",
}
@inproceedings{yeh-etal-2015-condition,
    title = "Condition Random Fields-based Grammatical Error Detection for {C}hinese as Second Language",
    author = "Yeh, Jui-Feng  and
      Yeh, Chan-Kun  and
      Yu, Kai-Hsiang  and
      Li, Ya-Ting  and
      Tsai, Wan-Ling",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4416",
    doi = "10.18653/v1/W15-4416",
    pages = "105--110",
}
@inproceedings{zhao-etal-2015-improving,
    title = "Improving {C}hinese Grammatical Error Correction with Corpus Augmentation and Hierarchical Phrase-based Statistical Machine Translation",
    author = "Zhao, Yinchen  and
      Komachi, Mamoru  and
      Ishikawa, Hiroshi",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4417",
    doi = "10.18653/v1/W15-4417",
    pages = "111--116",
}
@inproceedings{wu-etal-2015-chinese,
    title = "{C}hinese Grammatical Error Diagnosis System Based on Hybrid Model",
    author = "Wu, Xiupeng  and
      Huang, Peijie  and
      Wang, Jundong  and
      Guo, Qingwen  and
      Xu, Yuhong  and
      Chen, Chuping",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4418",
    doi = "10.18653/v1/W15-4418",
    pages = "117--125",
}
@proceedings{ws-2015-noisy,
    title = "Proceedings of the Workshop on Noisy User-generated Text",
    editor = "Xu, Wei  and
      Han, Bo  and
      Ritter, Alan",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4300",
    doi = "10.18653/v1/W15-43",
}
@inproceedings{lynn-etal-2015-minority,
    title = "Minority Language {T}witter: Part-of-Speech Tagging and Analysis of {I}rish Tweets",
    author = "Lynn, Teresa  and
      Scannell, Kevin  and
      Maguire, Eimear",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4301",
    doi = "10.18653/v1/W15-4301",
    pages = "1--8",
}
@inproceedings{jorgensen-etal-2015-challenges,
    title = "Challenges of studying and processing dialects in social media",
    author = "J{\o}rgensen, Anna  and
      Hovy, Dirk  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4302",
    doi = "10.18653/v1/W15-4302",
    pages = "9--18",
}
@inproceedings{arshi-saloot-etal-2015-toward,
    title = "Toward Tweets Normalization Using Maximum Entropy",
    author = "Arshi Saloot, Mohammad  and
      Idris, Norisma  and
      Shuib, Liyana  and
      Gopal Raj, Ram  and
      Aw, AiTi",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4303",
    doi = "10.18653/v1/W15-4303",
    pages = "19--27",
}
@inproceedings{van-der-wees-etal-2015-five,
    title = "Five Shades of Noise: Analyzing Machine Translation Errors in User-Generated Text",
    author = "van der Wees, Marlies  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4304",
    doi = "10.18653/v1/W15-4304",
    pages = "28--37",
}
@inproceedings{sanches-duran-etal-2015-normalizer,
    title = "A Normalizer for {UGC} in {B}razilian {P}ortuguese",
    author = "Sanches Duran, Magali  and
      Volpe Nunes, Maria das Gra{\c{c}}as  and
      Avan{\c{c}}o, Lucas",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4305",
    doi = "10.18653/v1/W15-4305",
    pages = "38--47",
}
@inproceedings{derczynski-etal-2015-usfd,
    title = "{USFD}: {T}witter {NER} with Drift Compensation and Linked Data",
    author = "Derczynski, Leon  and
      Augenstein, Isabelle  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4306",
    doi = "10.18653/v1/W15-4306",
    pages = "48--53",
}
@inproceedings{cherry-etal-2015-nrc,
    title = "{NRC}: Infused Phrase Vectors for Named Entity Recognition in {T}witter",
    author = "Cherry, Colin  and
      Guo, Hongyu  and
      Dai, Chengbi",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4307",
    doi = "10.18653/v1/W15-4307",
    pages = "54--60",
}
@inproceedings{akhtar-etal-2015-iitp,
    title = "{IITP}: Multiobjective Differential Evolution based {T}witter Named Entity Recognition",
    author = "Akhtar, Md Shad  and
      Sikdar, Utpal Kumar  and
      Ekbal, Asif",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4308",
    doi = "10.18653/v1/W15-4308",
    pages = "61--67",
}
@inproceedings{tian-etal-2015-data,
    title = "Data Adaptation for Named Entity Recognition on Tweets with Features-Rich {CRF}",
    author = "Tian, Tian  and
      Dinarelli, Marco  and
      Tellier, Isabelle",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4309",
    doi = "10.18653/v1/W15-4309",
    pages = "68--71",
}
@inproceedings{yang-kim-2015-hallym,
    title = "{H}allym: Named Entity Recognition on {T}witter with Word Representation",
    author = "Yang, Eun-Suk  and
      Kim, Yu-Seop",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4310",
    doi = "10.18653/v1/W15-4310",
    pages = "72--77",
}
@inproceedings{supranovich-patsepnia-2015-ihs,
    title = "{IHS}{\_}{RD}: Lexical Normalization for {E}nglish Tweets",
    author = "Supranovich, Dmitry  and
      Patsepnia, Viachaslau",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4311",
    doi = "10.18653/v1/W15-4311",
    pages = "78--81",
}
@inproceedings{beckley-2015-bekli,
    title = "{B}ekli:A Simple Approach to {T}witter Text Normalization.",
    author = "Beckley, Russell",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4312",
    doi = "10.18653/v1/W15-4312",
    pages = "82--86",
}
@inproceedings{jin-2015-ncsu,
    title = "{NCSU}-{SAS}-Ning: Candidate Generation and Feature Engineering for Supervised Lexical Normalization",
    author = "Jin, Ning",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4313",
    doi = "10.18653/v1/W15-4313",
    pages = "87--92",
}
@inproceedings{wagner-foster-2015-dcu,
    title = "{DCU}-{ADAPT}: Learning Edit Operations for Microblog Normalisation with the Generalised Perceptron",
    author = "Wagner, Joachim  and
      Foster, Jennifer",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4314",
    doi = "10.18653/v1/W15-4314",
    pages = "93--98",
}
@inproceedings{doval-mosquera-etal-2015-lysgroup,
    title = "{LYSGROUP}: Adapting a {S}panish microtext normalization system to {E}nglish.",
    author = "Doval Mosquera, Yerai  and
      Vilares, Jes{\'u}s  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4315",
    doi = "10.18653/v1/W15-4315",
    pages = "99--105",
}
@inproceedings{akhtar-etal-2015-iitp-hybrid,
    title = "{IITP}: Hybrid Approach for Text Normalization in {T}witter",
    author = "Akhtar, Md Shad  and
      Sikdar, Utpal Kumar  and
      Ekbal, Asif",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4316",
    doi = "10.18653/v1/W15-4316",
    pages = "106--110",
}
@inproceedings{min-mott-2015-ncsu,
    title = "{NCSU}{\_}{SAS}{\_}{WOOKHEE}: A Deep Contextual Long-Short Term Memory Model for Text Normalization",
    author = "Min, Wookhee  and
      Mott, Bradford",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4317",
    doi = "10.18653/v1/W15-4317",
    pages = "111--119",
}
@inproceedings{berend-tasnadi-2015-uszeged,
    title = "{USZEGED}: Correction Type-sensitive Normalization of {E}nglish Tweets Using Efficiently Indexed n-gram Statistics",
    author = "Berend, G{\'a}bor  and
      Tasn{\'a}di, Ervin",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4318",
    doi = "10.18653/v1/W15-4318",
    pages = "120--125",
}
@inproceedings{baldwin-etal-2015-shared,
    title = "Shared Tasks of the 2015 Workshop on Noisy User-generated Text: {T}witter Lexical Normalization and Named Entity Recognition",
    author = "Baldwin, Timothy  and
      de Marneffe, Marie Catherine  and
      Han, Bo  and
      Kim, Young-Bum  and
      Ritter, Alan  and
      Xu, Wei",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4319",
    doi = "10.18653/v1/W15-4319",
    pages = "126--135",
}
@inproceedings{yamada-etal-2015-enhancing,
    title = "Enhancing Named Entity Recognition in {T}witter Messages Using Entity Linking",
    author = "Yamada, Ikuya  and
      Takeda, Hideaki  and
      Takefuji, Yoshiyasu",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4320",
    doi = "10.18653/v1/W15-4320",
    pages = "136--140",
}
@inproceedings{toh-etal-2015-improving,
    title = "Improving {T}witter Named Entity Recognition using Word Representations",
    author = "Toh, Zhiqiang  and
      Chen, Bin  and
      Su, Jian",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4321",
    doi = "10.18653/v1/W15-4321",
    pages = "141--145",
}
@inproceedings{godin-etal-2015-multimedia,
    title = "Multimedia Lab @ {ACL} {WNUT} {NER} Shared Task: Named Entity Recognition for {T}witter Microposts using Distributed Word Representations",
    author = "Godin, Fr{\'e}deric  and
      Vandersmissen, Baptist  and
      De Neve, Wesley  and
      Van de Walle, Rik",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4322",
    doi = "10.18653/v1/W15-4322",
    pages = "146--153",
}
@inproceedings{leeman-munk-etal-2015-ncsu,
    title = "{NCSU}{\_}{SAS}{\_}{SAM}: Deep Encoding and Reconstruction for Normalization of Noisy Text",
    author = "Leeman-Munk, Samuel  and
      Lester, James  and
      Cox, James",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4323",
    doi = "10.18653/v1/W15-4323",
    pages = "154--161",
}
@inproceedings{wulff-sogaard-2015-learning,
    title = "Learning finite state word representations for unsupervised {T}witter adaptation of {POS} taggers",
    author = "Wulff, Julie  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4324",
    doi = "10.18653/v1/W15-43",
    pages = "162--166",
}
@inproceedings{albogamy-ramasy-2015-towards,
    title = "Towards {POS} Tagging for {A}rabic Tweets",
    author = "Albogamy, Fahad  and
      Ramasy, Allan",
    booktitle = "Proceedings of the Workshop on Noisy User-generated Text",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4325",
    doi = "10.18653/v1/W15-43",
    pages = "167--171",
}
@proceedings{ws-2015-linked,
    title = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    editor = "Chiarcos, Christian  and
      McCrae, John Philip  and
      Osenova, Petya  and
      Cimiano, Philipp  and
      Ide, Nancy",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4200",
    doi = "10.18653/v1/W15-42",
}
@inproceedings{mcgovern-etal-2015-dbpedia,
    title = "From {DB}pedia and {W}ord{N}et hierarchies to {L}inked{I}n and {T}witter",
    author = "McGovern, Aonghus  and
      O{'}Connor, Alexander  and
      Wade, Vincent",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4201",
    doi = "10.18653/v1/W15-4201",
    pages = "1--10",
}
@inproceedings{sanchez-rada-etal-2015-linked,
    title = "A Linked Data Model for Multimodal Sentiment and Emotion Analysis",
    author = "S{\'a}nchez-Rada, J. Fernando  and
      Iglesias, Carlos A.  and
      Gil, Ronald",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4202",
    doi = "10.18653/v1/W15-4202",
    pages = "11--19",
}
@inproceedings{real-etal-2015-seeing,
    title = "Seeing is Correcting: curating lexical resources using social interfaces",
    author = "Real, Livy  and
      Chalub, Fabricio  and
      de Paiva, Valeria  and
      Freitas, Claudia  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4203",
    doi = "10.18653/v1/W15-4203",
    pages = "20--29",
}
@inproceedings{krause-etal-2015-sar,
    title = "Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with Language",
    author = "Krause, Sebastian  and
      Hennig, Leonhard  and
      Gabryszak, Aleksandra  and
      Xu, Feiyu  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4204",
    doi = "10.18653/v1/W15-4204",
    pages = "30--38",
}
@inproceedings{mccrae-etal-2015-reconciling,
    title = "Reconciling Heterogeneous Descriptions of Language Resources",
    author = "McCrae, John Philip  and
      Cimiano, Philipp  and
      Rodr{\'\i}guez Doncel, Victor  and
      Vila-Suero, Daniel  and
      Gracia, Jorge  and
      Matteis, Luca  and
      Navigli, Roberto  and
      Abele, Andrejs  and
      Vulcu, Gabriela  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4205",
    doi = "10.18653/v1/W15-4205",
    pages = "39--48",
}
@inproceedings{rodriguez-doncel-labropoulou-2015-rdf,
    title = "{RDF} Representation of Licenses for Language Resources",
    author = "Rodriguez-Doncel, Victor  and
      Labropoulou, Penny",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4206",
    doi = "10.18653/v1/W15-4206",
    pages = "49--58",
}
@inproceedings{siemoneit-etal-2015-linking,
    title = "Linking Four Heterogeneous Language Resources as Linked Data",
    author = "Siemoneit, Benjamin  and
      McCrae, John Philip  and
      Cimiano, Philipp",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4207",
    doi = "10.18653/v1/W15-4207",
    pages = "59--63",
}
@inproceedings{santus-etal-2015-evalution,
    title = "{EVAL}ution 1.0: an Evolving Semantic Dataset for Training and Evaluation of Distributional Semantic Models",
    author = "Santus, Enrico  and
      Yung, Frances  and
      Lenci, Alessandro  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4208",
    doi = "10.18653/v1/W15-4208",
    pages = "64--69",
}
@inproceedings{lee-hsieh-2015-linguistic,
    title = "Linguistic Linked Data in {C}hinese: The Case of {C}hinese {W}ordnet",
    author = "Lee, Chih-Yao  and
      Hsieh, Shu-Kai",
    booktitle = "Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4209",
    doi = "10.18653/v1/W15-4209",
    pages = "70--74",
}
@proceedings{ws-2015-hybrid,
    title = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    editor = "Babych, Bogdan  and
      Eberle, Kurt  and
      Lambert, Patrik  and
      Rapp, Reinhard  and
      Banchs, Rafael E.  and
      Costa-juss{\`a}, Marta R.",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4100",
    doi = "10.18653/v1/W15-41",
}
@inproceedings{silva-etal-2015-bootstrapping,
    title = "Bootstrapping a hybrid deep {MT} system",
    author = "Silva, Jo{\~a}o  and
      Rodrigues, Jo{\~a}o  and
      Gomes, Lu{\'\i}s  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4101",
    doi = "10.18653/v1/W15-4101",
    pages = "1--5",
}
@inproceedings{rikters-2015-multi,
    title = "Multi-system machine translation using online {API}s for {E}nglish-{L}atvian",
    author = "Rikters, Mat{\=\i}ss",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4102",
    doi = "10.18653/v1/W15-41",
    pages = "6--10",
}
@inproceedings{tamchyna-bojar-2015-transfer,
    title = "What a Transfer-Based System Brings to the Combination with {PBMT}",
    author = "Tamchyna, Ale{\v{s}}  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4103",
    doi = "10.18653/v1/W15-4103",
    pages = "11--20",
}
@inproceedings{tambouratzis-pouli-2015-establishing,
    title = "Establishing sentential structure via realignments from small parallel corpora",
    author = "Tambouratzis, George  and
      Pouli, Vassiliki",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4104",
    doi = "10.18653/v1/W15-4104",
    pages = "21--29",
}
@inproceedings{tan-etal-2015-passive,
    title = "Passive and Pervasive Use of Bilingual Dictionary in Statistical Machine Translation",
    author = "Tan, Liling  and
      van Genabith, Josef  and
      Bond, Francis",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4105",
    doi = "10.18653/v1/W15-4105",
    pages = "30--34",
}
@inproceedings{banchs-2015-automated,
    title = "Automated Simultaneous Interpretation: Hints of a Cognitive Framework for Machine Translation",
    author = "Banchs, Rafael E.",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4106",
    doi = "10.18653/v1/W15-4106",
    pages = "35--39",
}
@inproceedings{parra-escartin-arcedillo-2015-fuzzier,
    title = "A fuzzier approach to machine translation evaluation: A pilot study on post-editing productivity and automated metrics in commercial settings",
    author = "Parra Escart{\'\i}n, Carla  and
      Arcedillo, Manuel",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4107",
    doi = "10.18653/v1/W15-4107",
    pages = "40--45",
}
@inproceedings{rapp-2015-methodology,
    title = "A Methodology for Bilingual Lexicon Extraction from Comparable Corpora",
    author = "Rapp, Reinhard",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4108",
    doi = "10.18653/v1/W15-4108",
    pages = "46--55",
}
@inproceedings{costa-jussa-2015-ongoing,
    title = "Ongoing Study for Enhancing {C}hinese-{S}panish Translation with Morphology Strategies",
    author = "Costa-juss{\`a}, Marta R.",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4109",
    doi = "10.18653/v1/W15-4109",
    pages = "56--60",
}
@inproceedings{he-2015-baidu,
    title = "{B}aidu Translate: Research and Products",
    author = "He, Zhongjun",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4110",
    doi = "10.18653/v1/W15-4110",
    pages = "61--62",
}
@inproceedings{geng-2015-improving,
    title = "On Improving the Human Translation Process by Using {MT} Technologies under a Cognitive Framework",
    author = "Geng, Xinhui",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4111",
    doi = "10.18653/v1/W15-4111",
    pages = "63",
}
@inproceedings{eberle-2015-towards,
    title = "Towards a shared task for shallow semantics-based translation (in an industrial setting)",
    author = "Eberle, Kurt",
    booktitle = "Proceedings of the Fourth Workshop on Hybrid Approaches to Translation ({H}y{T}ra)",
    month = jul,
    year = "2015",
    address = "Beijing",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4112",
    doi = "10.18653/v1/W15-4112",
    pages = "64",
}
@proceedings{ws-2015-continuous,
    title = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    editor = "Allauzen, Alexandre  and
      Grefenstette, Edward  and
      Hermann, Karl Moritz  and
      Larochelle, Hugo  and
      Yih, Scott Wen-tau",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4000",
    doi = "10.18653/v1/W15-40",
}
@inproceedings{hashimoto-tsuruoka-2015-learning,
    title = "Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization",
    author = "Hashimoto, Kazuma  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4001",
    doi = "10.18653/v1/W15-4001",
    pages = "1--11",
}
@inproceedings{bowman-etal-2015-recursive,
    title = "Recursive Neural Networks Can Learn Logical Semantics",
    author = "Bowman, Samuel R.  and
      Potts, Christopher  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4002",
    doi = "10.18653/v1/W15-4002",
    pages = "12--21",
}
@inproceedings{cheung-2015-concept,
    title = "Concept Extensions as the Basis for Vector-Space Semantics: Combining Distributional and Ontological Information about Entities",
    author = "Cheung, Jackie Chi Kit",
    booktitle = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4003",
    doi = "10.18653/v1/W15-4003",
    pages = "22--31",
}
@inproceedings{zhang-etal-2015-joint,
    title = "Joint Semantic Relevance Learning with Text Data and Graph Knowledge",
    author = "Zhang, Dongxu  and
      Yuan, Bin  and
      Wang, Dong  and
      Liu, Rong",
    booktitle = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4004",
    doi = "10.18653/v1/W15-4004",
    pages = "32--40",
}
@inproceedings{sachdeva-sharma-2015-exploring,
    title = "Exploring the effect of semantic similarity for Phrase-based Machine Translation",
    author = "Sachdeva, Kunal  and
      Sharma, Dipti",
    booktitle = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4005",
    doi = "10.18653/v1/W15-4005",
    pages = "41--47",
}
@inproceedings{ter-sarkisov-etal-2015-incremental,
    title = "Incremental Adaptation Strategies for Neural Network Language Models",
    author = {Ter-Sarkisov, Alex  and
      Schwenk, Holger  and
      Bougares, Fethi  and
      Barrault, Lo{\"\i}c},
    booktitle = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4006",
    doi = "10.18653/v1/W15-4006",
    pages = "48--56",
}
@inproceedings{toutanova-chen-2015-observed,
    title = "Observed versus latent features for knowledge base and text inference",
    author = "Toutanova, Kristina  and
      Chen, Danqi",
    booktitle = "Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-4007",
    doi = "10.18653/v1/W15-4007",
    pages = "57--66",
}
@proceedings{ws-2015-named,
    title = "Proceedings of the Fifth Named Entity Workshop",
    editor = "Duan, Xiangyu  and
      Banchs, Rafael E.  and
      Zhang, Min  and
      Li, Haizhou  and
      Kumaran, A",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3900",
    doi = "10.18653/v1/W15-39",
}
@inproceedings{zhang-etal-2015-whitepaper,
    title = "Whitepaper of {NEWS} 2015 Shared Task on Machine Transliteration",
    author = "Zhang, Min  and
      Li, Haizhou  and
      Banchs, Rafael E.  and
      Kumaran, A",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3901",
    doi = "10.18653/v1/W15-3901",
    pages = "1--9",
}
@inproceedings{banchs-etal-2015-report,
    title = "Report of {NEWS} 2015 Machine Transliteration Shared Task",
    author = "Banchs, Rafael E.  and
      Zhang, Min  and
      Duan, Xiangyu  and
      Li, Haizhou  and
      Kumaran, A.",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3902",
    doi = "10.18653/v1/W15-3902",
    pages = "10--23",
}
@inproceedings{kondrak-2015-spell,
    title = "How do you spell that? A journey through word representations",
    author = "Kondrak, Greg",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3903",
    doi = "10.18653/v1/W15-3903",
    pages = "24",
}
@inproceedings{dos-santos-guimaraes-2015-boosting,
    title = "Boosting Named Entity Recognition with Neural Character Embeddings",
    author = "dos Santos, C{\'\i}cero  and
      Guimar{\~a}es, Victor",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3904",
    doi = "10.18653/v1/W15-3904",
    pages = "25--33",
}
@inproceedings{kwong-2015-regularity,
    title = "Regularity and Flexibility in {E}nglish-{C}hinese Name Transliteration",
    author = "Kwong, Oi Yee",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3905",
    doi = "10.18653/v1/W15-3905",
    pages = "34--42",
}
@inproceedings{real-rademaker-2015-harem,
    title = "{HAREM} and Klue: how to put two tagsets for named entities annotation together",
    author = "Real, Livy  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3906",
    doi = "10.18653/v1/W15-3906",
    pages = "43--49",
}
@inproceedings{pham-etal-2015-semi,
    title = "Semi-supervised Learning for {V}ietnamese Named Entity Recognition using Online Conditional Random Fields",
    author = "Pham, Quang Hong  and
      Nguyen, Minh-Le  and
      Nguyen, Binh Thanh  and
      Cuong, Nguyen Viet",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3907",
    doi = "10.18653/v1/W15-3907",
    pages = "50--55",
}
@inproceedings{shao-etal-2015-boosting,
    title = "Boosting {E}nglish-{C}hinese Machine Transliteration via High Quality Alignment and Multilingual Resources",
    author = {Shao, Yan  and
      Tiedemann, J{\"o}rg  and
      Nivre, Joakim},
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3908",
    doi = "10.18653/v1/W15-3908",
    pages = "56--60",
}
@inproceedings{finch-etal-2015-neural,
    title = "Neural Network Transduction Models in Transliteration Generation",
    author = "Finch, Andrew  and
      Liu, Lemao  and
      Wang, Xiaolin  and
      Sumita, Eiichiro",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3909",
    doi = "10.18653/v1/W15-3909",
    pages = "61--66",
}
@inproceedings{wang-etal-2015-hybrid,
    title = "A Hybrid Transliteration Model for {C}hinese/{E}nglish Named Entities {---}{BJTU}-{NLP} Report for the 5th Named Entities Workshop",
    author = "Wang, Dandan  and
      Yang, Xiaohui  and
      Xu, Jinan  and
      Chen, Yufeng  and
      Wang, Nan  and
      Liu, Bojia  and
      Yang, Jian  and
      Zhang, Yujie",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3910",
    doi = "10.18653/v1/W15-3910",
    pages = "67--71",
}
@inproceedings{nicolai-etal-2015-multiple,
    title = "Multiple System Combination for Transliteration",
    author = "Nicolai, Garrett  and
      Hauer, Bradley  and
      Salameh, Mohammad  and
      St Arnaud, Adam  and
      Xu, Ying  and
      Yao, Lei  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3911",
    doi = "10.18653/v1/W15-3911",
    pages = "72--77",
}
@inproceedings{kunchukuttan-bhattacharyya-2015-data,
    title = "Data representation methods and use of mined corpora for {I}ndian language transliteration",
    author = "Kunchukuttan, Anoop  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3912",
    doi = "10.18653/v1/W15-3912",
    pages = "78--82",
}
@inproceedings{wang-etal-2015-ncu,
    title = "{NCU} {IISR} {E}nglish-{K}orean and {E}nglish-{C}hinese Named Entity Transliteration Using Different Grapheme Segmentation Approaches",
    author = "Wang, Yu-Chun  and
      Wu, Chun-Kai  and
      Tsai, Richard Tzong-Han",
    booktitle = "Proceedings of the Fifth Named Entity Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3913",
    doi = "10.18653/v1/W15-3913",
    pages = "83--87",
}
@proceedings{ws-2015-bionlp,
    title = "Proceedings of {B}io{NLP} 15",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Jun-ichi",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3800",
    doi = "10.18653/v1/W15-38",
}
@inproceedings{allen-etal-2015-complex,
    title = "Complex Event Extraction using {DRUM}",
    author = "Allen, James  and
      de Beaumont, Will  and
      Galescu, Lucian  and
      Teng, Choh Man",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3801",
    doi = "10.18653/v1/W15-3801",
    pages = "1--11",
}
@inproceedings{roller-stevenson-2015-making,
    title = "Making the most of limited training data using distant supervision",
    author = "Roller, Roland  and
      Stevenson, Mark",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3802",
    doi = "10.18653/v1/W15-3802",
    pages = "12--20",
}
@inproceedings{peng-etal-2015-extended,
    title = "An extended dependency graph for relation extraction in biomedical texts",
    author = "Peng, Yifan  and
      Gupta, Samir  and
      Wu, Cathy  and
      Shanker, Vijay",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3803",
    doi = "10.18653/v1/W15-3803",
    pages = "21--30",
}
@inproceedings{zerva-ananiadou-2015-event,
    title = "Event Extraction in pieces:Tackling the partial event identification problem on unseen corpora",
    author = "Zerva, Chrysoula  and
      Ananiadou, Sophia",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3804",
    doi = "10.18653/v1/W15-3804",
    pages = "31--41",
}
@inproceedings{spranger-etal-2015-extracting,
    title = "Extracting Biological Pathway Models From {NLP} Event Representations",
    author = "Spranger, Michael  and
      Palaniappan, Sucheendra  and
      Ghosh, Samik",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3805",
    doi = "10.18653/v1/W15-3805",
    pages = "42--51",
}
@inproceedings{nallapati-florian-2015-shallow,
    title = "Shallow Training is cheap but is it good enough? Experiments with Medical Fact Coding",
    author = "Nallapati, Ramesh  and
      Florian, Radu",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3806",
    doi = "10.18653/v1/W15-3806",
    pages = "52--60",
}
@inproceedings{kim-riloff-2015-stacked,
    title = "Stacked Generalization for Medical Concept Extraction from Clinical Notes",
    author = "Kim, Youngjun  and
      Riloff, Ellen",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3807",
    doi = "10.18653/v1/W15-3807",
    pages = "61--70",
}
@inproceedings{hassan-etal-2015-extracting,
    title = "Extracting Disease-Symptom Relationships by Learning Syntactic Patterns from Dependency Graphs",
    author = "Hassan, Mohsen  and
      Makkaoui, Olfa  and
      Coulet, Adrien  and
      Toussaint, Yannick",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3808",
    doi = "10.18653/v1/W15-3808",
    pages = "71--80",
}
@inproceedings{miller-etal-2015-extracting,
    title = "Extracting Time Expressions from Clinical Text",
    author = "Miller, Timothy  and
      Bethard, Steven  and
      Dligach, Dmitriy  and
      Lin, Chen  and
      Savova, Guergana",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3809",
    doi = "10.18653/v1/W15-3809",
    pages = "81--91",
}
@inproceedings{liu-etal-2015-exploiting,
    title = "Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion",
    author = "Liu, Yue  and
      Ge, Tao  and
      Mathews, Kusum  and
      Ji, Heng  and
      McGuinness, Deborah",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3810",
    doi = "10.18653/v1/W15-3810",
    pages = "92--97",
}
@inproceedings{siu-weikum-2015-semantic,
    title = "Semantic Type Classification of Common Words in Biomedical Noun Phrases",
    author = "Siu, Amy  and
      Weikum, Gerhard",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3811",
    doi = "10.18653/v1/W15-3811",
    pages = "98--103",
}
@inproceedings{jin-etal-2015-comagd,
    title = "{C}o{MAGD}: Annotation of Gene-Depression Relations",
    author = "Jin, Rize  and
      You, Jinseon  and
      Chung, Jin-Woo  and
      Lee, Hee-Jin  and
      Wolters, Maria  and
      Park, Jong",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3812",
    doi = "10.18653/v1/W15-3812",
    pages = "104--113",
}
@inproceedings{ju-etal-2015-lexical,
    title = "Lexical Characteristics Analysis of {C}hinese Clinical Documents",
    author = "Ju, Meizhi  and
      Li, Haomin  and
      Duan, Huilong",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3813",
    doi = "10.18653/v1/W15-3813",
    pages = "114--120",
}
@inproceedings{li-etal-2015-using-word,
    title = "Using word embedding for bio-event extraction",
    author = "Li, Chen  and
      Song, Runqing  and
      Liakata, Maria  and
      Vlachos, Andreas  and
      Seneff, Stephanie  and
      Zhang, Xiangrong",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3814",
    doi = "10.18653/v1/W15-3814",
    pages = "121--126",
}
@inproceedings{severance-cohen-2015-measuring,
    title = "Measuring the readability of medical research journal abstracts",
    author = "Severance, Samuel J.  and
      Cohen, K. Bretonnel",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3815",
    doi = "10.18653/v1/W15-3815",
    pages = "127--133",
}
@inproceedings{liu-cai-2015-translating,
    title = "Translating Electronic Health Record Notes from {E}nglish to {S}panish: A Preliminary Study",
    author = "Liu, Weisong  and
      Cai, Shu",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3816",
    doi = "10.18653/v1/W15-3816",
    pages = "134--140",
}
@inproceedings{alamri-stevenson-2015-automatic,
    title = "Automatic Detection of Answers to Research Questions from {M}edline Abstracts",
    author = "Alamri, Abdulaziz  and
      Stevenson, Mark",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3817",
    doi = "10.18653/v1/W15-3817",
    pages = "141--146",
}
@inproceedings{jonnagaddala-etal-2015-preliminary,
    title = "A preliminary study on automatic identification of patient smoking status in unstructured electronic health records",
    author = "Jonnagaddala, Jitendra  and
      Dai, Hong-Jie  and
      Ray, Pradeep  and
      Liaw, Siaw-Teng",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3818",
    doi = "10.18653/v1/W15-3818",
    pages = "147--151",
}
@inproceedings{siklosi-novak-2015-restoring,
    title = "Restoring the intended structure of {H}ungarian ophthalmology documents",
    author = "Sikl{\'o}si, Borb{\'a}la  and
      Nov{\'a}k, Attila",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3819",
    doi = "10.18653/v1/W15-3819",
    pages = "152--157",
}
@inproceedings{th-etal-2015-evaluating,
    title = "Evaluating distributed word representations for capturing semantics of biomedical concepts",
    author = "TH, Muneeb  and
      Sahu, Sunil  and
      Anand, Ashish",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3820",
    doi = "10.18653/v1/W15-3820",
    pages = "158--163",
}
@inproceedings{jimeno-yepes-etal-2015-investigating,
    title = "Investigating Public Health Surveillance using {T}witter",
    author = "Jimeno Yepes, Antonio  and
      MacKinlay, Andrew  and
      Han, Bo",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3821",
    doi = "10.18653/v1/W15-3821",
    pages = "164--170",
}
@inproceedings{wu-etal-2015-clinical,
    title = "Clinical Abbreviation Disambiguation Using Neural Word Embeddings",
    author = "Wu, Yonghui  and
      Xu, Jun  and
      Zhang, Yaoyun  and
      Xu, Hua",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3822",
    doi = "10.18653/v1/W15-3822",
    pages = "171--176",
}
@inproceedings{hong-etal-2015-representing,
    title = "Representing Clinical Diagnostic Criteria in Quality Data Model Using Natural Language Processing",
    author = "Hong, Na  and
      Li, Dingcheng  and
      Yu, Yue  and
      Liu, Hongfang  and
      Chute, Christopher G.  and
      Jiang, Guoqian",
    booktitle = "Proceedings of {B}io{NLP} 15",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3823",
    doi = "10.18653/v1/W15-3823",
    pages = "177--182",
}
@proceedings{ws-2015-sighum,
    title = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    editor = "Zervanou, Kalliopi  and
      van Erp, Marieke  and
      Alex, Beatrice",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3700",
    doi = "10.18653/v1/W15-37",
}
@inproceedings{luo-etal-2015-catching,
    title = "Catching the Red Priest: Using Historical Editions of Encyclopaedia Britannica to Track the Evolution of Reputations",
    author = "Luo, Yen-Fu  and
      Rumshisky, Anna  and
      Gronas, Mikhail",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3701",
    doi = "10.18653/v1/W15-3701",
    pages = "1--9",
}
@inproceedings{bak-oh-2015-five,
    title = "Five Centuries of Monarchy in {K}orea: Mining the Text of the Annals of the {J}oseon Dynasty",
    author = "Bak, JinYeong  and
      Oh, Alice",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3702",
    doi = "10.18653/v1/W15-3702",
    pages = "10--14",
}
@inproceedings{hou-frank-2015-analyzing,
    title = "Analyzing Sentiment in Classical {C}hinese Poetry",
    author = "Hou, Yufang  and
      Frank, Anette",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3703",
    doi = "10.18653/v1/W15-3703",
    pages = "15--24",
}
@inproceedings{lestari-manurung-2015-measuring,
    title = "Measuring the Structural and Conceptual Similarity of Folktales using Plot Graphs",
    author = "Lestari, Victoria Anugrah  and
      Manurung, Ruli",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3704",
    doi = "10.18653/v1/W15-3704",
    pages = "25--33",
}
@inproceedings{reiter-2015-towards,
    title = "Towards Annotating Narrative Segments",
    author = "Reiter, Nils",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3705",
    doi = "10.18653/v1/W15-3705",
    pages = "34--38",
}
@inproceedings{pettersson-etal-2015-ranking,
    title = "Ranking Relevant Verb Phrases Extracted from Historical Text",
    author = "Pettersson, Eva  and
      Megyesi, Be{\'a}ta  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3706",
    doi = "10.18653/v1/W15-3706",
    pages = "39--47",
}
@inproceedings{wan-paris-2015-ranking,
    title = "Ranking election issues through the lens of social media",
    author = "Wan, Stephen  and
      Paris, C{\'e}cile",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3707",
    doi = "10.18653/v1/W15-3707",
    pages = "48--52",
}
@inproceedings{bjerva-praet-2015-word,
    title = "Word Embeddings Pointing the Way for Late Antiquity",
    author = "Bjerva, Johannes  and
      Praet, Raf",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3708",
    doi = "10.18653/v1/W15-3708",
    pages = "53--57",
}
@inproceedings{georgi-etal-2015-enriching,
    title = "Enriching Interlinear Text using Automatically Constructed Annotators",
    author = "Georgi, Ryan  and
      Xia, Fei  and
      Lewis, William",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3709",
    doi = "10.18653/v1/W15-3709",
    pages = "58--67",
}
@inproceedings{samardzic-etal-2015-automatic,
    title = "Automatic interlinear glossing as two-level sequence classification",
    author = "Samard{\v{z}}i{\'c}, Tanja  and
      Schikowski, Robert  and
      Stoll, Sabine",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3710",
    doi = "10.18653/v1/W15-3710",
    pages = "68--72",
}
@inproceedings{arronte-alvarez-2015-enriching,
    title = "Enriching Digitized Medieval Manuscripts: Linking Image, Text and Lexical Knowledge",
    author = "Arronte {\'A}lvarez, Aitor",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3711",
    doi = "10.18653/v1/W15-3711",
    pages = "73--77",
}
@inproceedings{vladimir-etal-2015-preliminary,
    title = "A preliminary study on similarity-preserving digital book identifiers",
    author = "Vladimir, Klemo  and
      Silic, Marin  and
      Romic, Nenad  and
      Delac, Goran  and
      Srbljic, Sinisa",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3712",
    doi = "10.18653/v1/W15-3712",
    pages = "78--83",
}
@inproceedings{bellandi-etal-2015-translation,
    title = "When Translation Requires Interpretation: Collaborative Computer{--}Assisted Translation of Ancient Texts",
    author = "Bellandi, Andrea  and
      Albanesi, Davide  and
      Benotto, Giulia  and
      Giovannetti, Emiliano  and
      Di Segni, Gianfranco",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3713",
    doi = "10.18653/v1/W15-3713",
    pages = "84--88",
}
@inproceedings{liebeskind-dagan-2015-integrating,
    title = "Integrating Query Performance Prediction in Term Scoring for Diachronic Thesaurus",
    author = "Liebeskind, Chaya  and
      Dagan, Ido",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3714",
    doi = "10.18653/v1/W15-3714",
    pages = "89--94",
}
@inproceedings{petrolito-etal-2015-minoan,
    title = "{M}inoan linguistic resources: The {L}inear {A} Digital Corpus",
    author = "Petrolito, Tommaso  and
      Petrolito, Ruggero  and
      Perono Cacciafoco, Francesco  and
      Winterstein, Gr{\'e}goire",
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3715",
    doi = "10.18653/v1/W15-3715",
    pages = "95--104",
}
@inproceedings{eger-etal-2015-lexicon,
    title = "Lexicon-assisted tagging and lemmatization in {L}atin: A comparison of six taggers and two lemmatization methods",
    author = {Eger, Steffen  and
      vor der Br{\"u}ck, Tim  and
      Mehler, Alexander},
    booktitle = "Proceedings of the 9th {SIGHUM} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities ({L}a{T}e{CH})",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3716",
    doi = "10.18653/v1/W15-3716",
    pages = "105--113",
}
@proceedings{ws-2015-acl,
    title = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    editor = "Gollapalli, Sujatha Das  and
      Caragea, Cornelia  and
      Li, Xiaoli  and
      Giles, C. Lee",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3600",
    doi = "10.18653/v1/W15-36",
}
@inproceedings{kan-2015-keywords,
    title = "Keywords, phrases, clauses and sentences: topicality, indicativeness and informativeness at scales",
    author = "Kan, Min-Yen",
    booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3601",
    doi = "10.18653/v1/W15-3601",
    pages = "1",
}
@inproceedings{norman-aizawa-2015-technical,
    title = "Technical Term Extraction Using Measures of Neology",
    author = "Norman, Christopher  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3602",
    doi = "10.18653/v1/W15-3602",
    pages = "2--9",
}
@inproceedings{erbs-etal-2015-counting,
    title = "Counting What Counts: Decompounding for Keyphrase Extraction",
    author = "Erbs, Nicolai  and
      Santos, Pedro Bispo  and
      Zesch, Torsten  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3603",
    doi = "10.18653/v1/W15-3603",
    pages = "10--17",
}
@inproceedings{nakov-2015-web,
    title = "The Web as an Implicit Training Set: Application to Noun Compounds Syntax and Semantics",
    author = "Nakov, Preslav",
    booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3604",
    doi = "10.18653/v1/W15-3604",
    pages = "18",
}
@inproceedings{boudin-2015-reducing,
    title = "Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming",
    author = "Boudin, Florian",
    booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3605",
    doi = "10.18653/v1/W15-3605",
    pages = "19--24",
}
@inproceedings{tuarob-etal-2015-twittdict,
    title = "{T}witt{D}ict: Extracting Social Oriented Keyphrase Semantics from {T}witter",
    author = "Tuarob, Suppawong  and
      Chu, Wanghuan  and
      Chen, Dong  and
      Tucker, Conrad",
    booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3606",
    doi = "10.18653/v1/W15-3606",
    pages = "25--31",
}
@inproceedings{paul-das-2015-identification,
    title = "Identification and Classification of Emotional Key Phrases from Psychological Texts",
    author = "Paul, Apurba  and
      Das, Dipankar",
    booktitle = "Proceedings of the {ACL} 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3607",
    doi = "10.18653/v1/W15-3607",
    pages = "32--38",
}
@proceedings{ws-2015-semantics,
    title = "Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation ({S}2{MT} 2015)",
    editor = "Xiong, Deyi  and
      Duh, Kevin  and
      Hardmeier, Christian  and
      Navigli, Roberto",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3500",
    doi = "10.18653/v1/W15-35",
}
@inproceedings{butler-2015-round,
    title = "Round trips with meaning stopovers",
    author = "Butler, Alastair",
    booktitle = "Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation ({S}2{MT} 2015)",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3501",
    doi = "10.18653/v1/W15-3501",
    pages = "1--10",
}
@inproceedings{sulem-etal-2015-conceptual,
    title = "Conceptual Annotations Preserve Structure Across Translations: A {F}rench-{E}nglish Case Study",
    author = "Sulem, Elior  and
      Abend, Omri  and
      Rappoport, Ari",
    booktitle = "Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation ({S}2{MT} 2015)",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3502",
    doi = "10.18653/v1/W15-3502",
    pages = "11--22",
}
@inproceedings{xu-etal-2015-integrating,
    title = "Integrating Case Frame into {J}apanese to {C}hinese Hierarchical Phrase-based Translation Model",
    author = "Xu, Jinan  and
      Liu, Jiangming  and
      Chen, Yufeng  and
      Zhang, Yujie  and
      Ming, Fang  and
      Li, Shaotong",
    booktitle = "Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation ({S}2{MT} 2015)",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3503",
    doi = "10.18653/v1/W15-3503",
    pages = "23--29",
}
@inproceedings{tamchyna-etal-2015-discriminative,
    title = "A Discriminative Model for Semantics-to-String Translation",
    author = "Tamchyna, Ale{\v{s}}  and
      Quirk, Chris  and
      Galley, Michel",
    booktitle = "Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation ({S}2{MT} 2015)",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3504",
    doi = "10.18653/v1/W15-3504",
    pages = "30--36",
}
@proceedings{ws-2015-building,
    title = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    editor = "Zweigenbaum, Pierre  and
      Sharoff, Serge  and
      Rapp, Reinhard",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3400",
    doi = "10.18653/v1/W15-34",
}
@inproceedings{tsou-2015-augmented,
    title = "Augmented Comparative Corpora and Monitoring Corpus in {C}hinese: {LIVAC} and Sketch Search Engine Compared",
    author = "Tsou, Benjamin K.",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3401",
    doi = "10.18653/v1/W15-3401",
    pages = "1--2",
}
@inproceedings{barron-cedeno-etal-2015-factory,
    title = "A Factory of Comparable Corpora from {W}ikipedia",
    author = "Barr{\'o}n-Cede{\~n}o, Alberto  and
      Espa{\~n}a-Bonet, Cristina  and
      Boldoba, Josu  and
      M{\`a}rquez, Llu{\'\i}s",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3402",
    doi = "10.18653/v1/W15-3402",
    pages = "3--13",
}
@inproceedings{grishina-stede-2015-knowledge,
    title = "Knowledge-lean projection of coreference chains across languages",
    author = "Grishina, Yulia  and
      Stede, Manfred",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3403",
    doi = "10.18653/v1/W15-3403",
    pages = "14--22",
}
@inproceedings{jakubina-langlais-2015-projective,
    title = "Projective methods for mining missing translations in {DB}pedia",
    author = "Jakubina, Laurent  and
      Langlais, Phillippe",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3404",
    doi = "10.18653/v1/W15-3404",
    pages = "23--31",
}
@inproceedings{linard-etal-2015-attempting,
    title = "Attempting to Bypass Alignment from Comparable Corpora via Pivot Language",
    author = "Linard, Alexis  and
      Daille, B{\'e}atrice  and
      Morin, Emmanuel",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3405",
    doi = "10.18653/v1/W15-3405",
    pages = "32--37",
}
@inproceedings{kotani-yoshimi-2015-application,
    title = "Application of a Corpus to Identify Gaps between {E}nglish Learners and Native Speakers",
    author = "Kotani, Katsunori  and
      Yoshimi, Takehiko",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3406",
    doi = "10.18653/v1/W15-3406",
    pages = "38--42",
}
@inproceedings{bakhshaei-etal-2015-generative,
    title = "A Generative Model for Extracting Parallel Fragments from Comparable Documents",
    author = "Bakhshaei, Somayeh  and
      Khadivi, Shahram  and
      Safabakhsh, Reza",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3407",
    doi = "10.18653/v1/W15-3407",
    pages = "43--51",
}
@inproceedings{long-etal-2015-evaluating,
    title = "Evaluating Features for Identifying {J}apanese-{C}hinese Bilingual Synonymous Technical Terms from Patent Families",
    author = "Long, Zi  and
      Utsuro, Takehito  and
      Mitsuhashi, Tomoharu  and
      Yamamoto, Mikio",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3408",
    doi = "10.18653/v1/W15-3408",
    pages = "52--61",
}
@inproceedings{seo-etal-2015-extracting,
    title = "Extracting Bilingual Lexica from Comparable Corpora Using Self-Organizing Maps",
    author = "Seo, Hyeong-Won  and
      Cheon, Minah  and
      Kim, Jae-Hoon",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3409",
    doi = "10.18653/v1/W15-3409",
    pages = "62--67",
}
@inproceedings{rios-sharoff-2015-obtaining,
    title = "Obtaining {SMT} dictionaries for related languages",
    author = "Rios, Miguel  and
      Sharoff, Serge",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3410",
    doi = "10.18653/v1/W15-3410",
    pages = "68--73",
}
@inproceedings{sharoff-etal-2015-bucc,
    title = "{BUCC} Shared Task: Cross-Language Document Similarity",
    author = "Sharoff, Serge  and
      Zweigenbaum, Pierre  and
      Rapp, Reinhard",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3411",
    doi = "10.18653/v1/W15-3411",
    pages = "74--78",
}
@inproceedings{zafarian-etal-2015-aut,
    title = "{AUT} Document Alignment Framework for {BUCC} Workshop Shared Task",
    author = "Zafarian, Atefeh  and
      Agha Sadeghi, Amir Pouya  and
      Azadi, Fatemeh  and
      Ghiasifard, Sonia  and
      Ali Panahloo, Zeinab  and
      Bakhshaei, Somayeh  and
      Mohammadzadeh Ziabary, Seyyed Mohammad",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3412",
    doi = "10.18653/v1/W15-3412",
    pages = "79--87",
}
@inproceedings{morin-etal-2015-lina,
    title = "{LINA}: Identifying Comparable Documents from {W}ikipedia",
    author = "Morin, Emmanuel  and
      Hazem, Amir  and
      Boudin, Florian  and
      Loginova-Clouet, Elizaveta",
    booktitle = "Proceedings of the Eighth Workshop on Building and Using Comparable Corpora",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3413",
    doi = "10.18653/v1/W15-3413",
    pages = "88--91",
}
@proceedings{ws-2015-grammar,
    title = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    editor = {Bender, Emily M.  and
      Levin, Lori  and
      M{\"u}ller, Stefan  and
      Parmentier, Yannick  and
      Ranta, Aarne},
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3300",
    doi = "10.18653/v1/W15-33",
}
@inproceedings{ranta-etal-2015-grammar,
    title = "Grammar Engineering for a Customer: a Case Study with Five Languages",
    author = "Ranta, Aarne  and
      Unger, Christina  and
      Vidal Hussey, Daniel",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3301",
    doi = "10.18653/v1/W15-3301",
    pages = "1--8",
}
@inproceedings{moeljadi-etal-2015-building,
    title = "Building an {HPSG}-based {I}ndonesian Resource Grammar ({INDRA})",
    author = "Moeljadi, David  and
      Bond, Francis  and
      Song, Sanghoun",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3302",
    doi = "10.18653/v1/W15-3302",
    pages = "9--16",
}
@inproceedings{fan-etal-2015-hpsg,
    title = "An {HPSG}-based Shared-Grammar for the {C}hinese Languages: {ZHONG} [|]",
    author = "Fan, Zhenzhen  and
      Song, Sanghoun  and
      Bond, Francis",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3303",
    doi = "10.18653/v1/W15-3303",
    pages = "17--24",
}
@inproceedings{duan-schuler-2015-parsing,
    title = "Parsing {C}hinese with a Generalized Categorial Grammar",
    author = "Duan, Manjuan  and
      Schuler, William",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3304",
    doi = "10.18653/v1/W15-3304",
    pages = "25--32",
}
@inproceedings{angelov-2015-orthography,
    title = "Orthography Engineering in Grammatical Framework",
    author = "Angelov, Krasimir",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3305",
    doi = "10.18653/v1/W15-3305",
    pages = "33--40",
}
@inproceedings{hallgren-etal-2015-cloud,
    title = "A Cloud-Based Editor for Multilingual Grammars",
    author = "Hallgren, Thomas  and
      Enache, Ramona  and
      Ranta, Aarne",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3306",
    doi = "10.18653/v1/W15-3306",
    pages = "41--48",
}
@inproceedings{gruzitis-etal-2015-formalising,
    title = "Formalising the {S}wedish Constructicon in Grammatical Framework",
    author = "Gruzitis, Normunds  and
      Dann{\'e}lls, Dana  and
      Lyngfelt, Benjamin  and
      Ranta, Aarne",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3307",
    doi = "10.18653/v1/W15-3307",
    pages = "49--56",
}
@inproceedings{song-2015-representing,
    title = "Representing Honorifics via Individual Constraints",
    author = "Song, Sanghoun",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3308",
    doi = "10.18653/v1/W15-3308",
    pages = "57--64",
}
@inproceedings{crysmann-2015-resumption,
    title = "Resumption and Extraction in an Implemented {HPSG} of {H}ausa",
    author = "Crysmann, Berthold",
    booktitle = "Proceedings of the Grammar Engineering Across Frameworks ({GEAF}) 2015 Workshop",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3309",
    doi = "10.18653/v1/W15-3309",
    pages = "65--72",
}
@proceedings{ws-2015-arabic,
    title = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    editor = "Habash, Nizar  and
      Vogel, Stephan  and
      Darwish, Kareem",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3200",
    doi = "10.18653/v1/W15-32",
}
@inproceedings{mubarak-darwish-2015-classifying,
    title = "Classifying {A}rab Names Geographically",
    author = "Mubarak, Hamdy  and
      Darwish, Kareem",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3201",
    doi = "10.18653/v1/W15-3201",
    pages = "1--8",
}
@inproceedings{al-sallab-etal-2015-deep,
    title = "Deep Learning Models for Sentiment Analysis in {A}rabic",
    author = "Al Sallab, Ahmad  and
      Hajj, Hazem  and
      Badaro, Gilbert  and
      Baly, Ramy  and
      El Hajj, Wassim  and
      Bashir Shaban, Khaled",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3202",
    doi = "10.18653/v1/W15-3202",
    pages = "9--17",
}
@inproceedings{badaro-etal-2015-light,
    title = "A Light Lexicon-based Mobile Application for Sentiment Mining of {A}rabic Tweets",
    author = "Badaro, Gilbert  and
      Baly, Ramy  and
      Akel, Rana  and
      Fayad, Linda  and
      Khairallah, Jeffrey  and
      Hajj, Hazem  and
      Shaban, Khaled  and
      El-Hajj, Wassim",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3203",
    doi = "10.18653/v1/W15-3203",
    pages = "18--25",
}
@inproceedings{rozovskaya-etal-2015-second,
    title = "The Second {QALB} Shared Task on Automatic Text Correction for {A}rabic",
    author = "Rozovskaya, Alla  and
      Bouamor, Houda  and
      Habash, Nizar  and
      Zaghouani, Wajdi  and
      Obeid, Ossama  and
      Mohit, Behrang",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3204",
    doi = "10.18653/v1/W15-3204",
    pages = "26--35",
}
@inproceedings{shoufan-alameri-2015-natural,
    title = "Natural Language Processing for Dialectical {A}rabic: A Survey",
    author = "Shoufan, Abdulhadi  and
      Alameri, Sumaya",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3205",
    doi = "10.18653/v1/W15-3205",
    pages = "36--48",
}
@inproceedings{al-shargi-rambow-2015-diwan,
    title = "{DIWAN}: A Dialectal Word Annotation Tool for {A}rabic",
    author = "Al-Shargi, Faisal  and
      Rambow, Owen",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3206",
    doi = "10.18653/v1/W15-3206",
    pages = "49--58",
}
@inproceedings{hamdi-etal-2015-pos,
    title = "{POS}-tagging of {T}unisian Dialect Using {S}tandard {A}rabic Resources and Tools",
    author = "Hamdi, Ahmed  and
      Nasr, Alexis  and
      Habash, Nizar  and
      Gala, N{\'u}ria",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3207",
    doi = "10.18653/v1/W15-3207",
    pages = "59--68",
}
@inproceedings{saadane-habash-2015-conventional,
    title = "A Conventional Orthography for {A}lgerian {A}rabic",
    author = "Saadane, Houda  and
      Habash, Nizar",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3208",
    doi = "10.18653/v1/W15-3208",
    pages = "69--79",
}
@inproceedings{bouamor-etal-2015-pilot,
    title = "A Pilot Study on {A}rabic Multi-Genre Corpus Diacritization",
    author = "Bouamor, Houda  and
      Zaghouani, Wajdi  and
      Diab, Mona  and
      Obeid, Ossama  and
      Oflazer, Kemal  and
      Ghoneim, Mahmoud  and
      Hawwari, Abdelati",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3209",
    doi = "10.18653/v1/W15-3209",
    pages = "80--88",
}
@inproceedings{farra-etal-2015-annotating,
    title = "Annotating Targets of Opinions in {A}rabic using Crowdsourcing",
    author = "Farra, Noura  and
      McKeown, Kathy  and
      Habash, Nizar",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3210",
    doi = "10.18653/v1/W15-3210",
    pages = "89--98",
}
@inproceedings{wray-etal-2015-best,
    title = "Best Practices for Crowdsourcing Dialectal {A}rabic Speech Transcription",
    author = "Wray, Samantha  and
      Mubarak, Hamdy  and
      Ali, Ahmed",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3211",
    doi = "10.18653/v1/W15-3211",
    pages = "99--107",
}
@inproceedings{algahtani-mcnaught-2015-joint,
    title = "Joint {A}rabic Segmentation and Part-Of-Speech Tagging",
    author = "AlGahtani, Shabib  and
      McNaught, John",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3212",
    doi = "10.18653/v1/W15-3212",
    pages = "108--117",
}
@inproceedings{ali-etal-2015-multi,
    title = "Multi-Reference Evaluation for Dialectal Speech Recognition System: A Study for {E}gyptian {ASR}",
    author = "Ali, Ahmed  and
      Magdy, Walid  and
      Renals, Steve",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3213",
    doi = "10.18653/v1/W15-3213",
    pages = "118--126",
}
@inproceedings{alshenaifi-etal-2015-arib,
    title = "Arib@{QALB}-2015 Shared Task: A Hybrid Cascade Model for {A}rabic Spelling Error Detection and Correction",
    author = "AlShenaifi, Nouf  and
      AlNefie, Rehab  and
      Al-Yahya, Maha  and
      Al-Khalifa, Hend",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3214",
    doi = "10.18653/v1/W15-3214",
    pages = "127--132",
}
@inproceedings{nawar-2015-cufe,
    title = "{CUFE}@{QALB}-2015 Shared Task: {A}rabic Error Correction System",
    author = "Nawar, Michael",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3215",
    doi = "10.18653/v1/W15-3215",
    pages = "133--137",
}
@inproceedings{attia-etal-2015-gwu,
    title = "{GWU}-{HASP}-2015@{QALB}-2015 Shared Task: Priming Spelling Candidates with Probability",
    author = "Attia, Mohammed  and
      Al-Badrashiny, Mohamed  and
      Diab, Mona",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3216",
    doi = "10.18653/v1/W15-3216",
    pages = "138--143",
}
@inproceedings{bouamor-etal-2015-qcmuq,
    title = "{QCMUQ}@{QALB}-2015 Shared Task: Combining Character level {MT} and Error-tolerant Finite-State Recognition for {A}rabic Spelling Correction",
    author = "Bouamor, Houda  and
      Sajjad, Hassan  and
      Durrani, Nadir  and
      Oflazer, Kemal",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3217",
    doi = "10.18653/v1/W15-3217",
    pages = "144--149",
}
@inproceedings{mubarak-etal-2015-qcri,
    title = "{QCRI}@{QALB}-2015 Shared Task: Correction of {A}rabic Text for Native and Non-Native Speakers{'} Errors",
    author = "Mubarak, Hamdy  and
      Darwish, Kareem  and
      Abdelali, Ahmed",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3218",
    doi = "10.18653/v1/W15-3218",
    pages = "150--154",
}
@inproceedings{zaghouani-etal-2015-sahsoh,
    title = "{SAHSOH}@{QALB}-2015 Shared Task: A Rule-Based Correction Method of Common {A}rabic Native and Non-Native Speakers{'} Errors",
    author = "Zaghouani, Wajdi  and
      Zerrouki, Taha  and
      Balla, Amar",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3219",
    doi = "10.18653/v1/W15-3219",
    pages = "155--160",
}
@inproceedings{mostefa-etal-2015-techlimed,
    title = "{TECHLIMED}@{QALB}-Shared Task 2015: a hybrid {A}rabic Error Correction System",
    author = "Mostefa, Djamel  and
      Abualasal, Jaber  and
      Asbayou, Omar  and
      Gzawi, Mahmoud  and
      Abbes, Ramzi",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3220",
    doi = "10.18653/v1/W15-3220",
    pages = "161--165",
}
@inproceedings{bougares-bouamor-2015-ummu,
    title = "{UMMU}@{QALB}-2015 Shared Task: Character and Word level {SMT} pipeline for Automatic Error Correction of {A}rabic Text",
    author = "Bougares, Fethi  and
      Bouamor, Houda",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3221",
    doi = "10.18653/v1/W15-3221",
    pages = "166--172",
}
@inproceedings{aldarmaki-diab-2015-robust,
    title = "Robust Part-of-speech Tagging of {A}rabic Text",
    author = "Aldarmaki, Hanan  and
      Diab, Mona",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3222",
    doi = "10.18653/v1/W15-3222",
    pages = "173--182",
}
@inproceedings{belinkov-etal-2015-answer,
    title = "Answer Selection in {A}rabic Community Question Answering: A Feature-Rich Approach",
    author = "Belinkov, Yonatan  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Mubarak, Hamdy",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3223",
    doi = "10.18653/v1/W15-3223",
    pages = "183--190",
}
@inproceedings{gad-elrab-etal-2015-edrak,
    title = "{EDRAK}: Entity-Centric Data Resource for {A}rabic Knowledge",
    author = "Gad-Elrab, Mohamed H.  and
      Yosef, Mohamed Amir  and
      Weikum, Gerhard",
    booktitle = "Proceedings of the Second Workshop on {A}rabic Natural Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3224",
    doi = "10.18653/v1/W15-3224",
    pages = "191--200",
}
@proceedings{ws-2015-sighan,
    title = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    editor = "Yu, Liang-Chih  and
      Sui, Zhifang  and
      Zhang, Yue  and
      Ng, Vincent",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3100",
    doi = "10.18653/v1/W15-31",
}
@inproceedings{yung-etal-2015-sequential,
    title = "Sequential Annotation and Chunking of {C}hinese Discourse Structure",
    author = "Yung, Frances  and
      Duh, Kevin  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3101",
    doi = "10.18653/v1/W15-3101",
    pages = "1--6",
}
@inproceedings{wang-etal-2015-create,
    title = "Create a Manual {C}hinese Word Segmentation Dataset Using Crowdsourcing Method",
    author = "Wang, Shichang  and
      Huang, Chu-Ren  and
      Yao, Yao  and
      Chan, Angel",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3102",
    doi = "10.18653/v1/W15-3102",
    pages = "7--14",
}
@inproceedings{han-etal-2015-chinese,
    title = "{C}hinese Named Entity Recognition with Graph-based Semi-supervised Learning Model",
    author = "Han, Aaron Li-Feng  and
      Zeng, Xiaodong  and
      Wong, Derek F.  and
      Chao, Lidia S.",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3103",
    doi = "10.18653/v1/W15-3103",
    pages = "15--20",
}
@inproceedings{yuan-etal-2015-sentence,
    title = "Sentence selection for automatic scoring of {M}andarin proficiency",
    author = "Yuan, Jiahong  and
      Xu, Xiaoying  and
      Lai, Wei  and
      Ye, Weiping  and
      Zhao, Xinru  and
      Liberman, Mark",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3104",
    doi = "10.18653/v1/W15-3104",
    pages = "21--25",
}
@inproceedings{huang-etal-2015-acbima,
    title = "{ACB}i{MA}: Advanced {C}hinese Bi-Character Word Morphological Analyzer",
    author = "Huang, Ting-Hao  and
      Chen, Yun-Nung  and
      Kong, Lingpeng",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3105",
    doi = "10.18653/v1/W15-3105",
    pages = "26--31",
}
@inproceedings{tseng-etal-2015-introduction,
    title = "Introduction to {SIGHAN} 2015 Bake-off for {C}hinese Spelling Check",
    author = "Tseng, Yuen-Hsien  and
      Lee, Lung-Hao  and
      Chang, Li-Ping  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3106",
    doi = "10.18653/v1/W15-3106",
    pages = "32--37",
}
@inproceedings{zhang-etal-2015-hanspeller,
    title = "{HANS}peller++: A Unified Framework for {C}hinese Spelling Correction",
    author = "Zhang, Shuiyuan  and
      Xiong, Jinhua  and
      Hou, Jianpeng  and
      Zhang, Qiao  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3107",
    doi = "10.18653/v1/W15-3107",
    pages = "38--45",
}
@inproceedings{wang-liao-2015-word,
    title = "Word Vector/Conditional Random Field-based {C}hinese Spelling Error Detection for {SIGHAN}-2015 Evaluation",
    author = "Wang, Yih-Ru  and
      Liao, Yuan-Fu",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3108",
    doi = "10.18653/v1/W15-3108",
    pages = "46--49",
}
@inproceedings{chang-etal-2015-introduction,
    title = "Introduction to a Proofreading Tool for {C}hinese Spelling Check Task of {SIGHAN}-8",
    author = "Chang, Tao-Hsing  and
      Chen, Hsueh-Chih  and
      Yang, Cheng-Han",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3109",
    doi = "10.18653/v1/W15-3109",
    pages = "50--55",
}
@inproceedings{liao-etal-2015-overview,
    title = "Overview of Topic-based {C}hinese Message Polarity Classification in {SIGHAN} 2015",
    author = "Liao, Xiangwen  and
      Li, Binyang  and
      Xu, Liheng",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3110",
    doi = "10.18653/v1/W15-3110",
    pages = "56--60",
}
@inproceedings{cao-etal-2015-joint,
    title = "A Joint Model for {C}hinese Microblog Sentiment Analysis",
    author = "Cao, Yuhui  and
      Chen, Zhao  and
      Xu, Ruifeng  and
      Chen, Tao  and
      Gui, Lin",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3111",
    doi = "10.18653/v1/W15-3111",
    pages = "61--67",
}
@inproceedings{kang-etal-2015-learning,
    title = "Learning Salient Samples and Distributed Representations for Topic-Based {C}hinese Message Polarity Classification",
    author = "Kang, Xin  and
      Wu, Yunong  and
      Zhang, Zhifei",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3112",
    doi = "10.18653/v1/W15-3112",
    pages = "68--73",
}
@inproceedings{li-etal-2015-combined,
    title = "An combined sentiment classification system for {SIGHAN}-8",
    author = "Li, Qiuchi  and
      Zhi, Qiyu  and
      Li, Miao",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3113",
    doi = "10.18653/v1/W15-3113",
    pages = "74--78",
}
@inproceedings{park-yuan-2015-linguistic,
    title = "Linguistic Knowledge-driven Approach to {C}hinese Comparative Elements Extraction",
    author = "Park, MinJun  and
      Yuan, Yulin",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3114",
    doi = "10.18653/v1/W15-3114",
    pages = "79--85",
}
@inproceedings{li-jin-2015-crf,
    title = "A {CRF} Method of Identifying Prepositional Phrases in {C}hinese Patent Texts",
    author = "Li, Hongzheng  and
      Jin, Yaohong",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3115",
    doi = "10.18653/v1/W15-3115",
    pages = "86--90",
}
@inproceedings{lee-wang-2015-emotion,
    title = "Emotion in Code-switching Texts: Corpus Construction and Analysis",
    author = "Lee, Sophia  and
      Wang, Zhongqing",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3116",
    doi = "10.18653/v1/W15-3116",
    pages = "91--99",
}
@inproceedings{ranta-etal-2015-chinese,
    title = "{C}hinese in the Grammatical Framework: Grammar, Translation, and Other Applications Anonymous",
    author = "Ranta, Aarne  and
      Tian, Yan  and
      Qiao, Haiyan",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3117",
    doi = "10.18653/v1/W15-3117",
    pages = "100--109",
}
@inproceedings{bai-etal-2015-kwb,
    title = "{KWB}: An Automated Quick News System for {C}hinese Readers",
    author = "Bai, Yiqi  and
      Yang, Wenjing  and
      Zhang, Hao  and
      Wang, Jingwen  and
      Jia, Ming  and
      Tong, Roland  and
      Wang, Jie",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3118",
    doi = "10.18653/v1/W15-3118",
    pages = "110--119",
}
@inproceedings{jin-etal-2015-chinese,
    title = "{C}hinese Semantic Role Labeling using High-quality Syntactic Knowledge",
    author = "Jin, Gongye  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3119",
    doi = "10.18653/v1/W15-3119",
    pages = "120--127",
}
@inproceedings{xie-etal-2015-chinese,
    title = "{C}hinese Spelling Check System Based on N-gram Model",
    author = "Xie, Weijian  and
      Huang, Peijie  and
      Zhang, Xinrui  and
      Hong, Kaiduo  and
      Huang, Qiang  and
      Chen, Bingzhou  and
      Huang, Lei",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3120",
    doi = "10.18653/v1/W15-3120",
    pages = "128--136",
}
@inproceedings{chu-lin-2015-ntou,
    title = "{NTOU} {C}hinese Spelling Check System in Sighan-8 Bake-off",
    author = "Chu, Wei-Cheng  and
      Lin, Chuan-Jie",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3121",
    doi = "10.18653/v1/W15-3121",
    pages = "137--143",
}
@inproceedings{li-etal-2015-topic,
    title = "Topic-Based {C}hinese Message Sentiment Analysis: A Multilayered Analysis System",
    author = "Li, Hongjie  and
      Sun, Zhongqian  and
      Yang, Wei",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3122",
    doi = "10.18653/v1/W15-3122",
    pages = "144--148",
}
@inproceedings{zhou-etal-2015-rule,
    title = "Rule-Based {W}eibo Messages Sentiment Polarity Classification towards Given Topics",
    author = "Zhou, Hongzhao  and
      Teng, Yonglin  and
      Hou, Min  and
      He, Wei  and
      Zhu, Hongtao  and
      Zhu, Xiaolin  and
      Mu, Yanfei",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3123",
    doi = "10.18653/v1/W15-3123",
    pages = "149--157",
}
@inproceedings{liao-etal-2015-topic,
    title = "Topic-Based {C}hinese Message Polarity Classification System at {SIGHAN}8-Task2",
    author = "Liao, Chun  and
      Feng, Chong  and
      Yang, Sen  and
      Huang, Heyan",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3124",
    doi = "10.18653/v1/W15-3124",
    pages = "158--163",
}
@inproceedings{chang-etal-2015-ct,
    title = "{CT}-{SPA}: Text sentiment polarity prediction model using semi-automatically expanded sentiment lexicon",
    author = "Chang, Tao-Hsing  and
      Lin, Ming-Jhih  and
      Chen, Chun-Hsien  and
      Wang, Shao-Yu",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3125",
    doi = "10.18653/v1/W15-3125",
    pages = "164--170",
}
@inproceedings{ye-etal-2015-chinese,
    title = "{C}hinese Microblogs Sentiment Classification using Maximum Entropy",
    author = "Ye, Dashu  and
      Huang, Peijie  and
      Hong, Kaiduo  and
      Tang, Zhuoying  and
      Xie, Weijian  and
      Zhou, Guilong",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3126",
    doi = "10.18653/v1/W15-3126",
    pages = "171--179",
}
@inproceedings{wang-etal-2015-ndmscs,
    title = "{NDMSCS}: A Topic-Based {C}hinese Microblog Polarity Classification System",
    author = "Wang, Yang  and
      Wang, Yaqi  and
      Feng, Shi  and
      Wang, Daling  and
      Zhang, Yifei",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3127",
    doi = "10.18653/v1/W15-3127",
    pages = "180--184",
}
@inproceedings{wang-etal-2015-neudm,
    title = "{NEUDM}: A System for Topic-Based Message Polarity Classification",
    author = "Wang, Yaqi  and
      Feng, Shi  and
      Wang, Daling  and
      Zhang, Yifei",
    booktitle = "Proceedings of the Eighth {SIGHAN} Workshop on {C}hinese Language Processing",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3128",
    doi = "10.18653/v1/W15-3128",
    pages = "185--189",
}
@proceedings{ws-2015-statistical,
    title = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    editor = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajan  and
      Federmann, Christian  and
      Haddow, Barry  and
      Hokamp, Chris  and
      Huck, Matthias  and
      Logacheva, Varvara  and
      Pecina, Pavel",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3000",
    doi = "10.18653/v1/W15-30",
}
@inproceedings{bojar-etal-2015-findings,
    title = "Findings of the 2015 Workshop on Statistical Machine Translation",
    author = "Bojar, Ond{\v{r}}ej  and
      Chatterjee, Rajen  and
      Federmann, Christian  and
      Haddow, Barry  and
      Huck, Matthias  and
      Hokamp, Chris  and
      Koehn, Philipp  and
      Logacheva, Varvara  and
      Monz, Christof  and
      Negri, Matteo  and
      Post, Matt  and
      Scarton, Carolina  and
      Specia, Lucia  and
      Turchi, Marco",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3001",
    doi = "10.18653/v1/W15-3001",
    pages = "1--46",
}
@inproceedings{twitto-etal-2015-statistical,
    title = "Statistical Machine Translation with Automatic Identification of Translationese",
    author = "Twitto, Naama  and
      Ordan, Noam  and
      Wintner, Shuly",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3002",
    doi = "10.18653/v1/W15-3002",
    pages = "47--57",
}
@inproceedings{axelrod-etal-2015-data,
    title = "Data Selection With Fewer Words",
    author = "Axelrod, Amittai  and
      Resnik, Philip  and
      He, Xiaodong  and
      Ostendorf, Mari",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3003",
    doi = "10.18653/v1/W15-3003",
    pages = "58--65",
}
@inproceedings{avramidis-etal-2015-dfkis,
    title = "{DFKI}{'}s experimental hybrid {MT} system for {WMT} 2015",
    author = "Avramidis, Eleftherios  and
      Popovi{\'c}, Maja  and
      Burchardt, Aljoscha",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3004",
    doi = "10.18653/v1/W15-3004",
    pages = "66--73",
}
@inproceedings{bicici-etal-2015-parfda,
    title = "{P}ar{FDA} for Fast Deployment of Accurate Statistical Machine Translation Systems, Benchmarks, and Statistics",
    author = "Bi{\c{c}}ici, Ergun  and
      Liu, Qun  and
      Way, Andy",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3005",
    doi = "10.18653/v1/W15-3005",
    pages = "74--78",
}
@inproceedings{bojar-tamchyna-2015-cuni,
    title = "{CUNI} in {WMT}15: Chimera Strikes Again",
    author = "Bojar, Ond{\v{r}}ej  and
      Tamchyna, Ale{\v{s}}",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3006",
    doi = "10.18653/v1/W15-3006",
    pages = "79--83",
}
@inproceedings{cap-etal-2015-cims,
    title = "{C}im{S} - The {CIS} and {IMS} Joint Submission to {WMT} 2015 addressing morphological and syntactic differences in {E}nglish to {G}erman {SMT}",
    author = "Cap, Fabienne  and
      Weller, Marion  and
      Ramm, Anita  and
      Fraser, Alexander",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3007",
    doi = "10.18653/v1/W15-3007",
    pages = "84--91",
}
@inproceedings{cho-etal-2015-karlsruhe,
    title = "The Karlsruhe Institute of Technology Translation Systems for the {WMT} 2015",
    author = "Cho, Eunah  and
      Ha, Thanh-Le  and
      Niehues, Jan  and
      Herrmann, Teresa  and
      Mediani, Mohammed  and
      Zhang, Yuqi  and
      Waibel, Alex",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3008",
    doi = "10.18653/v1/W15-3008",
    pages = "92--97",
}
@inproceedings{dusek-etal-2015-new,
    title = "New Language Pairs in {T}ecto{MT}",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Gomes, Lu{\'\i}s  and
      Nov{\'a}k, Michal  and
      Popel, Martin  and
      Rosa, Rudolf",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3009",
    doi = "10.18653/v1/W15-3009",
    pages = "98--104",
}
@inproceedings{gronroos-etal-2015-tuning,
    title = "Tuning Phrase-Based Segmented Translation for a Morphologically Complex Target Language",
    author = {Gr{\"o}nroos, Stig-Arne  and
      Virpioja, Sami  and
      Kurimo, Mikko},
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3010",
    doi = "10.18653/v1/W15-3010",
    pages = "105--111",
}
@inproceedings{gwinnup-etal-2015-afrl,
    title = "The {AFRL}-{MITLL} {WMT}15 System: There{'}s More than One Way to Decode It!",
    author = "Gwinnup, Jeremy  and
      Anderson, Tim  and
      Erdmann, Grant  and
      Young, Katherine  and
      May, Christina  and
      Kazi, Michaeel  and
      Salesky, Elizabeth  and
      Thompson, Brian",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3011",
    doi = "10.18653/v1/W15-3011",
    pages = "112--119",
}
@inproceedings{ha-etal-2015-kit,
    title = "The {KIT}-{LIMSI} Translation System for {WMT} 2015",
    author = "Ha, Thanh-Le  and
      Do, Quoc-Khanh  and
      Cho, Eunah  and
      Niehues, Jan  and
      Allauzen, Alexandre  and
      Yvon, Fran{\c{c}}ois  and
      Waibel, Alex",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3012",
    doi = "10.18653/v1/W15-3012",
    pages = "120--125",
}
@inproceedings{haddow-etal-2015-edinburgh,
    title = "The {E}dinburgh/{JHU} Phrase-based Machine Translation Systems for {WMT} 2015",
    author = "Haddow, Barry  and
      Huck, Matthias  and
      Birch, Alexandra  and
      Bogoychev, Nikolay  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3013",
    doi = "10.18653/v1/W15-3013",
    pages = "126--133",
}
@inproceedings{jean-etal-2015-montreal,
    title = "{M}ontreal Neural Machine Translation Systems for {WMT}{'}15",
    author = "Jean, S{\'e}bastien  and
      Firat, Orhan  and
      Cho, Kyunghyun  and
      Memisevic, Roland  and
      Bengio, Yoshua",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3014",
    doi = "10.18653/v1/W15-3014",
    pages = "134--140",
}
@inproceedings{kolachina-ranta-2015-gf,
    title = "{GF} Wide-coverage {E}nglish-{F}innish {MT} system for {WMT} 2015",
    author = "Kolachina, Prasanth  and
      Ranta, Aarne",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3015",
    doi = "10.18653/v1/W15-3015",
    pages = "141--144",
}
@inproceedings{marie-etal-2015-limsi,
    title = "{LIMSI}@{WMT}{'}15 : Translation Task",
    author = {Marie, Benjamin  and
      Allauzen, Alexandre  and
      Burlot, Franck  and
      Do, Quoc-Khanh  and
      Ive, Julia  and
      Knyazeva, Elena  and
      Labeau, Matthieu  and
      Lavergne, Thomas  and
      L{\"o}ser, Kevin  and
      P{\'e}cheux, Nicolas  and
      Yvon, Fran{\c{c}}ois},
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3016",
    doi = "10.18653/v1/W15-3016",
    pages = "145--151",
}
@inproceedings{pal-etal-2015-uds,
    title = "{U}d{S}-Sant: {E}nglish{--}{G}erman Hybrid Machine Translation System",
    author = "Pal, Santanu  and
      Naskar, Sudip  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3017",
    doi = "10.18653/v1/W15-3017",
    pages = "152--157",
}
@inproceedings{peter-etal-2015-rwth,
    title = "The {RWTH} {A}achen {G}erman-{E}nglish Machine Translation System for {WMT} 2015",
    author = "Peter, Jan-Thorsten  and
      Toutounchi, Farzad  and
      Wuebker, Joern  and
      Ney, Hermann",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3018",
    doi = "10.18653/v1/W15-3018",
    pages = "158--163",
}
@inproceedings{quernheim-2015-exact,
    title = "Exact Decoding with Multi Bottom-Up Tree Transducers",
    author = "Quernheim, Daniel",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3019",
    doi = "10.18653/v1/W15-3019",
    pages = "164--171",
}
@inproceedings{steele-etal-2015-sheffield,
    title = "{S}heffield Systems for the {F}innish-{E}nglish {WMT} Translation Task",
    author = "Steele, David  and
      Sim Smith, Karin  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3020",
    doi = "10.18653/v1/W15-3020",
    pages = "172--176",
}
@inproceedings{tiedemann-etal-2015-morphological,
    title = "Morphological Segmentation and {OPUS} for {F}innish-{E}nglish Machine Translation",
    author = {Tiedemann, J{\"o}rg  and
      Ginter, Filip  and
      Kanerva, Jenna},
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3021",
    doi = "10.18653/v1/W15-3021",
    pages = "177--183",
}
@inproceedings{rubino-etal-2015-abu,
    title = "{A}bu-{M}a{T}ran at {WMT} 2015 Translation Task: Morphological Segmentation and Web Crawling",
    author = "Rubino, Raphael  and
      Pirinen, Tommi  and
      Espl{\`a}-Gomis, Miquel  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Ortiz-Rojas, Sergio  and
      Papavassiliou, Vassilis  and
      Prokopidis, Prokopis  and
      Toral, Antonio",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3022",
    doi = "10.18653/v1/W15-3022",
    pages = "184--191",
}
@inproceedings{schwartz-etal-2015-university,
    title = "The {U}niversity of {I}llinois submission to the {WMT} 2015 Shared Translation Task",
    author = "Schwartz, Lane  and
      Bryce, Bill  and
      Geigle, Chase  and
      Massung, Sean  and
      Liu, Yisi  and
      Peng, Haoruo  and
      Raja, Vignesh  and
      Roy, Subhro  and
      Upadhyay, Shyam",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3023",
    doi = "10.18653/v1/W15-3023",
    pages = "192--198",
}
@inproceedings{williams-etal-2015-edinburghs,
    title = "{E}dinburgh{'}s Syntax-Based Systems at {WMT} 2015",
    author = "Williams, Philip  and
      Sennrich, Rico  and
      Nadejde, Maria  and
      Huck, Matthias  and
      Koehn, Philipp",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3024",
    doi = "10.18653/v1/W15-3024",
    pages = "199--209",
}
@inproceedings{chatterjee-etal-2015-fbk,
    title = "The {FBK} Participation in the {WMT}15 Automatic Post-editing Shared Task",
    author = "Chatterjee, Rajen  and
      Turchi, Marco  and
      Negri, Matteo",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3025",
    doi = "10.18653/v1/W15-3025",
    pages = "210--215",
}
@inproceedings{pal-etal-2015-usaar,
    title = "{USAAR}-{SAPE}: An {E}nglish{--}{S}panish Statistical Automatic Post-Editing System",
    author = "Pal, Santanu  and
      Vela, Mihaela  and
      Naskar, Sudip Kumar  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3026",
    doi = "10.18653/v1/W15-3026",
    pages = "216--221",
}
@inproceedings{wisniewski-etal-2015-predicting,
    title = "Why Predicting Post-Edition is so Hard? Failure Analysis of {LIMSI} Submission to the {APE} Shared Task",
    author = "Wisniewski, Guillaume  and
      P{\'e}cheux, Nicolas  and
      Yvon, Fran{\c{c}}ois",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3027",
    doi = "10.18653/v1/W15-3027",
    pages = "222--227",
}
@inproceedings{kaeshammer-2015-hierarchical,
    title = "Hierarchical Machine Translation With Discontinuous Phrases",
    author = "Kaeshammer, Miriam",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3028",
    doi = "10.18653/v1/W15-3028",
    pages = "228--238",
}
@inproceedings{seemann-maletti-2015-discontinuous,
    title = "Discontinuous Statistical Machine Translation with Target-Side Dependency Syntax",
    author = "Seemann, Nina  and
      Maletti, Andreas",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3029",
    doi = "10.18653/v1/W15-3029",
    pages = "239--247",
}
@inproceedings{niehues-etal-2015-listnet,
    title = "{L}ist{N}et-based {MT} Rescoring",
    author = "Niehues, Jan  and
      Do, Quoc Khanh  and
      Allauzen, Alexandre  and
      Waibel, Alex",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3030",
    doi = "10.18653/v1/W15-3030",
    pages = "248--255",
}
@inproceedings{stanojevic-etal-2015-results,
    title = "Results of the {WMT}15 Metrics Shared Task",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Kamran, Amir  and
      Koehn, Philipp  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3031",
    doi = "10.18653/v1/W15-3031",
    pages = "256--273",
}
@inproceedings{stanojevic-etal-2015-results-wmt15,
    title = "Results of the {WMT}15 Tuning Shared Task",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Kamran, Amir  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3032",
    doi = "10.18653/v1/W15-3032",
    pages = "274--281",
}
@inproceedings{guta-etal-2015-extended,
    title = "Extended Translation Models in Phrase-based Decoding",
    author = "Guta, Andreas  and
      Wuebker, Joern  and
      Gra{\c{c}}a, Miguel  and
      Kim, Yunsu  and
      Ney, Hermann",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3033",
    doi = "10.18653/v1/W15-3033",
    pages = "282--293",
}
@inproceedings{alkhouli-etal-2015-investigations,
    title = "Investigations on Phrase-based Decoding with Recurrent Neural Network Language and Translation Models",
    author = "Alkhouli, Tamer  and
      Rietig, Felix  and
      Ney, Hermann",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3034",
    doi = "10.18653/v1/W15-3034",
    pages = "294--303",
}
@inproceedings{bicici-etal-2015-referential,
    title = "Referential Translation Machines for Predicting Translation Quality and Related Statistics",
    author = "Bi{\c{c}}ici, Ergun  and
      Liu, Qun  and
      Way, Andy",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3035",
    doi = "10.18653/v1/W15-3035",
    pages = "304--308",
}
@inproceedings{espla-gomis-etal-2015-ualacant,
    title = "{UA}lacant word-level machine translation quality estimation system at {WMT} 2015",
    author = "Espl{\`a}-Gomis, Miquel  and
      S{\'a}nchez-Mart{\'\i}nez, Felipe  and
      Forcada, Mikel",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3036",
    doi = "10.18653/v1/W15-3036",
    pages = "309--315",
}
@inproceedings{kreutzer-etal-2015-quality,
    title = "{QU}ality Estimation from {S}cra{TCH} ({QUETCH}): Deep Learning for Word-level Translation Quality Estimation",
    author = "Kreutzer, Julia  and
      Schamoni, Shigehiko  and
      Riezler, Stefan",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3037",
    doi = "10.18653/v1/W15-3037",
    pages = "316--322",
}
@inproceedings{langlois-2015-loria,
    title = "{LORIA} System for the {WMT}15 Quality Estimation Shared Task",
    author = "Langlois, David",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3038",
    doi = "10.18653/v1/W15-3038",
    pages = "323--329",
}
@inproceedings{logacheva-etal-2015-data,
    title = "Data enhancement and selection strategies for the word-level Quality Estimation",
    author = "Logacheva, Varvara  and
      Hokamp, Chris  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3039",
    doi = "10.18653/v1/W15-3039",
    pages = "330--335",
}
@inproceedings{scarton-etal-2015-ushef,
    title = "{USHEF} and {USAAR}-{USHEF} participation in the {WMT}15 {QE} shared task",
    author = "Scarton, Carolina  and
      Tan, Liling  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3040",
    doi = "10.18653/v1/W15-3040",
    pages = "336--341",
}
@inproceedings{shah-etal-2015-shef,
    title = "{SHEF}-{NN}: Translation Quality Estimation with Neural Networks",
    author = "Shah, Kashif  and
      Logacheva, Varvara  and
      Paetzold, Gustavo  and
      Blain, Frederic  and
      Beck, Daniel  and
      Bougares, Fethi  and
      Specia, Lucia",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3041",
    doi = "10.18653/v1/W15-3041",
    pages = "342--347",
}
@inproceedings{shang-etal-2015-strategy,
    title = "Strategy-Based Technology for Estimating {MT} Quality",
    author = "Shang, Liugang  and
      Cai, Dongfeng  and
      Ji, Duo",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3042",
    doi = "10.18653/v1/W15-3042",
    pages = "348--352",
}
@inproceedings{tezcan-etal-2015-ugent,
    title = "{UGENT}-{LT}3 {SCATE} System for Machine Translation Quality Estimation",
    author = "Tezcan, Arda  and
      Hoste, Veronique  and
      Desmet, Bart  and
      Macken, Lieve",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3043",
    doi = "10.18653/v1/W15-3043",
    pages = "353--360",
}
@inproceedings{chen-etal-2015-multi,
    title = "Multi-level Evaluation for Machine Translation",
    author = "Chen, Boxing  and
      Guo, Hongyu  and
      Kuhn, Roland",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3044",
    doi = "10.18653/v1/W15-3044",
    pages = "361--365",
}
@inproceedings{comelles-atserias-2015-verta,
    title = "{VERT}a: a Linguistically-motivated Metric at the {WMT}15 Metrics Task",
    author = "Comelles, Elisabet  and
      Atserias, Jordi",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3045",
    doi = "10.18653/v1/W15-3045",
    pages = "366--372",
}
@inproceedings{fomicheva-etal-2015-upf,
    title = "{UPF}-Cobalt Submission to {WMT}15 Metrics Task",
    author = "Fomicheva, Marina  and
      Bel, N{\'u}ria  and
      da Cunha, Iria  and
      Malinovskiy, Anton",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3046",
    doi = "10.18653/v1/W15-3046",
    pages = "373--379",
}
@inproceedings{gupta-etal-2015-machine,
    title = "Machine Translation Evaluation using Recurrent Neural Networks",
    author = "Gupta, Rohit  and
      Or{\u{a}}san, Constantin  and
      van Genabith, Josef",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3047",
    doi = "10.18653/v1/W15-3047",
    pages = "380--384",
}
@inproceedings{marie-apidianaki-2015-alignment,
    title = "Alignment-based sense selection in {METEOR} and the {RATATOUILLE} recipe",
    author = "Marie, Benjamin  and
      Apidianaki, Marianna",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3048",
    doi = "10.18653/v1/W15-3048",
    pages = "385--391",
}
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{stanojevic-simaan-2015-beer,
    title = "{BEER} 1.1: {ILLC} {U}v{A} submission to metrics and tuning task",
    author = "Stanojevi{\'c}, Milo{\v{s}}  and
      Sima{'}an, Khalil",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3050",
    doi = "10.18653/v1/W15-3050",
    pages = "396--401",
}
@inproceedings{vela-tan-2015-predicting,
    title = "Predicting Machine Translation Adequacy with Document Embeddings",
    author = "Vela, Mihaela  and
      Tan, Liling",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3051",
    doi = "10.18653/v1/W15-3051",
    pages = "402--410",
}
@inproceedings{virpioja-gronroos-2015-lebleu,
    title = "{L}e{BLEU}: N-gram-based Translation Evaluation Score for Morphologically Complex Languages",
    author = {Virpioja, Sami  and
      Gr{\"o}nroos, Stig-Arne},
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3052",
    doi = "10.18653/v1/W15-3052",
    pages = "411--416",
}
@inproceedings{yu-etal-2015-casict,
    title = "{CASICT}-{DCU} Participation in {WMT}2015 Metrics Task",
    author = "Yu, Hui  and
      Ma, Qingsong  and
      Wu, Xiaofeng  and
      Liu, Qun",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3053",
    doi = "10.18653/v1/W15-3053",
    pages = "417--421",
}
@inproceedings{erdmann-gwinnup-2015-drem,
    title = "{D}rem: The {AFRL} Submission to the {WMT}15 Tuning Task",
    author = "Erdmann, Grant  and
      Gwinnup, Jeremy",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3054",
    doi = "10.18653/v1/W15-3054",
    pages = "422--427",
}
@inproceedings{li-etal-2015-mt,
    title = "{MT} Tuning on {RED}: A Dependency-Based Evaluation Metric",
    author = "Li, Liangyou  and
      Yu, Hui  and
      Liu, Qun",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3055",
    doi = "10.18653/v1/W15-3055",
    pages = "428--433",
}
@inproceedings{lo-etal-2015-improving,
    title = "Improving evaluation and optimization of {MT} systems against {MEANT}",
    author = "Lo, Chi-kiu  and
      Dowling, Philipp  and
      Wu, Dekai",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3056",
    doi = "10.18653/v1/W15-3056",
    pages = "434--441",
}
@inproceedings{sugiyama-etal-2015-investigation,
    title = "An Investigation of Machine Translation Evaluation Metrics in Cross-lingual Question Answering",
    author = "Sugiyama, Kyoshiro  and
      Mizukami, Masahiro  and
      Neubig, Graham  and
      Yoshino, Koichiro  and
      Sakti, Sakriani  and
      Toda, Tomoki  and
      Nakamura, Satoshi",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3057",
    doi = "10.18653/v1/W15-3057",
    pages = "442--449",
}
@inproceedings{isozaki-kouchi-2015-dependency,
    title = "Dependency Analysis of Scrambled References for Better Evaluation of {J}apanese Translation",
    author = "Isozaki, Hideki  and
      Kouchi, Natsume",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3058",
    doi = "10.18653/v1/W15-3058",
    pages = "450--456",
}
@inproceedings{guzman-etal-2015-humans,
    title = "How do Humans Evaluate Machine Translation",
    author = "Guzm{\'a}n, Francisco  and
      Abdelali, Ahmed  and
      Temnikova, Irina  and
      Sajjad, Hassan  and
      Vogel, Stephan",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3059",
    doi = "10.18653/v1/W15-3059",
    pages = "457--466",
}
@inproceedings{freitag-etal-2015-local,
    title = "Local System Voting Feature for Machine Translation System Combination",
    author = "Freitag, Markus  and
      Peter, Jan-Thorsten  and
      Peitz, Stephan  and
      Feng, Minwei  and
      Ney, Hermann",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-3060",
    doi = "10.18653/v1/W15-3060",
    pages = "467--476",
}
@proceedings{ws-2015-approaches,
    title = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    editor = "Balahur, Alexandra  and
      van der Goot, Erik  and
      Vossen, Piek  and
      Montoyo, Andres",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2900",
    doi = "10.18653/v1/W15-29",
}
@inproceedings{kozareva-2015-multilingual,
    title = "Multilingual Affect Polarity and Valence Prediction in Metaphors",
    author = "Kozareva, Zornitsa",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2901",
    doi = "10.18653/v1/W15-2901",
    pages = "1",
}
@inproceedings{vilares-etal-2015-sentiment,
    title = "Sentiment Analysis on Monolingual, Multilingual and Code-Switching {T}witter Corpora",
    author = "Vilares, David  and
      Alonso, Miguel A.  and
      G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2902",
    doi = "10.18653/v1/W15-2902",
    pages = "2--8",
}
@inproceedings{carpuat-2015-connotation,
    title = "Connotation in Translation",
    author = "Carpuat, Marine",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2903",
    doi = "10.18653/v1/W15-2903",
    pages = "9--15",
}
@inproceedings{vosoughi-etal-2015-enhanced,
    title = "Enhanced {T}witter Sentiment Classification Using Contextual Information",
    author = "Vosoughi, Soroush  and
      Zhou, Helen  and
      Roy, Deb",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2904",
    doi = "10.18653/v1/W15-2904",
    pages = "16--24",
}
@inproceedings{khattri-etal-2015-sentiment,
    title = "Your Sentiment Precedes You: Using an author{'}s historical tweets to predict sarcasm",
    author = "Khattri, Anupam  and
      Joshi, Aditya  and
      Bhattacharyya, Pushpak  and
      Carman, Mark",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2905",
    doi = "10.18653/v1/W15-2905",
    pages = "25--30",
}
@inproceedings{kober-weir-2015-optimising,
    title = "Optimising Agile Social Media Analysis",
    author = "Kober, Thomas  and
      Weir, David",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2906",
    doi = "10.18653/v1/W15-2906",
    pages = "31--40",
}
@inproceedings{tsunoda-etal-2015-utilizing,
    title = "Utilizing review analysis to suggest product advertisement improvements",
    author = "Tsunoda, Takaaki  and
      Inui, Takashi  and
      Sekine, Satoshi",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2907",
    doi = "10.18653/v1/W15-2907",
    pages = "41--50",
}
@inproceedings{kessler-etal-2015-towards,
    title = "Towards Opinion Mining from Reviews for the Prediction of Product Rankings",
    author = "Kessler, Wiltrud  and
      Klinger, Roman  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2908",
    doi = "10.18653/v1/W15-2908",
    pages = "51--57",
}
@inproceedings{cagnina-rosso-2015-classification,
    title = "Classification of deceptive opinions using a low dimensionality representation",
    author = "Cagnina, Leticia  and
      Rosso, Paolo",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2909",
    doi = "10.18653/v1/W15-2909",
    pages = "58--66",
}
@inproceedings{ruppenhofer-brandes-2015-extending,
    title = "Extending effect annotation with lexical decomposition",
    author = "Ruppenhofer, Josef  and
      Brandes, Jasper",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2910",
    doi = "10.18653/v1/W15-2910",
    pages = "67--76",
}
@inproceedings{flekova-etal-2015-analysing,
    title = "Analysing domain suitability of a sentiment lexicon by identifying distributionally bipolar words",
    author = "Flekova, Lucie  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Ruppert, Eugen",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2911",
    doi = "10.18653/v1/W15-2911",
    pages = "77--84",
}
@inproceedings{mohammad-2015-imagisaurus,
    title = "{I}magisaurus: An Interactive Visualizer of Valence and Emotion in the {R}oget{'}s Thesaurus",
    author = "Mohammad, Saif",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2912",
    doi = "10.18653/v1/W15-2912",
    pages = "85--91",
}
@inproceedings{plank-hovy-2015-personality,
    title = "Personality Traits on {T}witter{---}or{---}{H}ow to Get 1,500 Personality Tests in a Week",
    author = "Plank, Barbara  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2913",
    doi = "10.18653/v1/W15-2913",
    pages = "92--98",
}
@inproceedings{reitan-etal-2015-negation,
    title = "Negation Scope Detection for {T}witter Sentiment Analysis",
    author = {Reitan, Johan  and
      Faret, J{\o}rgen  and
      Gamb{\"a}ck, Bj{\"o}rn  and
      Bungum, Lars},
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2914",
    doi = "10.18653/v1/W15-2914",
    pages = "99--108",
}
@inproceedings{ebert-etal-2015-linguistically,
    title = "A Linguistically Informed Convolutional Neural Network",
    author = {Ebert, Sebastian  and
      Vu, Ngoc Thang  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2915",
    doi = "10.18653/v1/W15-2915",
    pages = "109--114",
}
@inproceedings{sumanth-inkpen-2015-much,
    title = "How much does word sense disambiguation help in sentiment analysis of micropost data?",
    author = "Sumanth, Chiraag  and
      Inkpen, Diana",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2916",
    doi = "10.18653/v1/W15-2916",
    pages = "115--121",
}
@inproceedings{schmit-wubben-2015-predicting,
    title = "Predicting Ratings for New Movie Releases from {T}witter Content",
    author = "Schmit, Wernard  and
      Wubben, Sander",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2917",
    doi = "10.18653/v1/W15-2917",
    pages = "122--126",
}
@inproceedings{mihaltz-etal-2015-beyond,
    title = "Beyond Sentiment: Social Psychological Analysis of Political {F}acebook Comments in {H}ungary",
    author = {Mih{\'a}ltz, M{\'a}rton  and
      V{\'a}radi, Tam{\'a}s  and
      Csert{\H{o}}, Istv{\'a}n  and
      F{\"u}l{\"o}p, {\'E}va  and
      P{\'o}lya, Tibor  and
      K{\H{o}}v{\'a}g{\'o}, P{\'a}l},
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2918",
    doi = "10.18653/v1/W15-2918",
    pages = "127--133",
}
@inproceedings{klenner-2015-verb,
    title = "Verb-centered Sentiment Inference with Description Logics",
    author = "Klenner, Manfred",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2919",
    doi = "10.18653/v1/W15-2919",
    pages = "134--139",
}
@inproceedings{sinha-etal-2015-mining,
    title = "Mining {HEXACO} personality traits from Enterprise Social Media",
    author = "Sinha, Priyanka  and
      Dey, Lipika  and
      Mitra, Pabitra  and
      Basu, Anupam",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2920",
    doi = "10.18653/v1/W15-2920",
    pages = "140--147",
}
@inproceedings{wiegand-etal-2015-opinion,
    title = "Opinion Holder and Target Extraction for Verb-based Opinion Predicates {--} The Problem is Not Solved",
    author = "Wiegand, Michael  and
      Schulder, Marc  and
      Ruppenhofer, Josef",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2921",
    doi = "10.18653/v1/W15-2921",
    pages = "148--155",
}
@inproceedings{maqsud-2015-synthetic,
    title = "Synthetic Text Generation for Sentiment Analysis",
    author = "Maqsud, Umar",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2922",
    doi = "10.18653/v1/W15-2922",
    pages = "156--161",
}
@inproceedings{skeppstedt-etal-2015-detecting,
    title = "Detecting speculations, contrasts and conditionals in consumer reviews",
    author = "Skeppstedt, Maria  and
      Schamp-Bjerede, Teri  and
      Sahlgren, Magnus  and
      Paradis, Carita  and
      Kerren, Andreas",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2923",
    doi = "10.18653/v1/W15-2923",
    pages = "162--168",
}
@inproceedings{simeon-hilderman-2015-using,
    title = "Using Combined Lexical Resources to Identify Hashtag Types",
    author = "Simeon, Credell  and
      Hilderman, Robert",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2924",
    doi = "10.18653/v1/W15-2924",
    pages = "169--174",
}
@inproceedings{smith-lee-2015-sentiment,
    title = "Sentiment Classification via a Response Recalibration Framework",
    author = "Smith, Phillip  and
      Lee, Mark",
    booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2015",
    address = "Lisboa, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2925",
    doi = "10.18653/v1/W15-2925",
    pages = "175--180",
}
@proceedings{ws-2015-vision,
    title = "Proceedings of the Fourth Workshop on Vision and Language",
    editor = "Belz, Anja  and
      Coheur, Luisa  and
      Ferrari, Vittorio  and
      Moens, Marie-Francine  and
      Pastra, Katerina  and
      Vuli{\'c}, Ivan",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2800",
    doi = "10.18653/v1/W15-28",
}
@inproceedings{krishnamurthy-2015-visually,
    title = "Visually-Verifiable Textual Entailment: A Challenge Task for Combining Language and Vision",
    author = "Krishnamurthy, Jayant",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2801",
    doi = "10.18653/v1/W15-2801",
    pages = "1--3",
}
@inproceedings{vaidyanathan-etal-2015-computational,
    title = "Computational Integration of Human Vision and Natural Language through Bitext Alignment",
    author = "Vaidyanathan, Preethi  and
      Prud{'}hommeaux, Emily  and
      O. Alm, Cecilia  and
      Pelz, Jeff B.  and
      Haake, Anne R.",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2802",
    doi = "10.18653/v1/W15-2802",
    pages = "4--5",
}
@inproceedings{lautenbacher-etal-2015-towards,
    title = "Towards Reliable Automatic Multimodal Content Analysis",
    author = "Lautenbacher, Olli-Philippe  and
      Tiittula, Liisa  and
      Hirvonen, Maija  and
      Laaksonen, Jorma  and
      Kurimo, Mikko",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2803",
    doi = "10.18653/v1/W15-2803",
    pages = "6--7",
}
@inproceedings{kadar-etal-2015-lingusitic,
    title = "Lingusitic Analysis of Multi-Modal Recurrent Neural Networks",
    author = "K{\'a}d{\'a}r, {\'A}kos  and
      Chrupa{\l}a, Grzegorz  and
      Alishahi, Afra",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2804",
    doi = "10.18653/v1/W15-2804",
    pages = "8--9",
}
@inproceedings{gaizauskas-etal-2015-defining,
    title = "Defining Visually Descriptive Language",
    author = "Gaizauskas, Robert  and
      Wang, Josiah  and
      Ramisa, Arnau",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2805",
    doi = "10.18653/v1/W15-2805",
    pages = "10--17",
}
@inproceedings{ellebracht-etal-2015-semantic,
    title = "Semantic Tuples for Evaluation of Image to Sentence Generation",
    author = "Ellebracht, Lily D.  and
      Ramisa, Arnau  and
      Madhyastha, Pranava Swaroop  and
      Cordero-Rama, Jose  and
      Moreno-Noguer, Francesc  and
      Quattoni, Ariadna",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2806",
    doi = "10.18653/v1/W15-2806",
    pages = "18--28",
}
@inproceedings{hessel-etal-2015-image,
    title = "Image Representations and New Domains in Neural Image Captioning",
    author = "Hessel, Jack  and
      Savva, Nicolas  and
      Wilber, Michael",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2807",
    doi = "10.18653/v1/W15-2807",
    pages = "29--39",
}
@inproceedings{weiland-etal-2015-image,
    title = "Image with a Message: Towards Detecting Non-Literal Image Usages by Visual Linking",
    author = "Weiland, Lydia  and
      Dietz, Laura  and
      Ponzetto, Simone Paolo",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2808",
    doi = "10.18653/v1/W15-2808",
    pages = "40--47",
}
@inproceedings{elhoseiny-elgammal-2015-visual,
    title = "Visual Classifier Prediction by Distributional Semantic Embedding of Text Descriptions",
    author = "Elhoseiny, Mohamed  and
      Elgammal, Ahmed",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2809",
    doi = "10.18653/v1/W15-2809",
    pages = "48--50",
}
@inproceedings{polisciuc-etal-2015-understanding,
    title = "Understanding Urban Land Use through the Visualization of Points of Interest",
    author = "Polisciuc, Evgheni  and
      Alves, Ana  and
      Machado, Penousal",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2810",
    doi = "10.18653/v1/W15-2810",
    pages = "51--59",
}
@inproceedings{yu-etal-2015-comparing,
    title = "Comparing Attribute Classifiers for Interactive Language Grounding",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Lemon, Oliver",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2811",
    doi = "10.18653/v1/W15-2811",
    pages = "60--69",
}
@inproceedings{schuster-etal-2015-generating,
    title = "Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval",
    author = "Schuster, Sebastian  and
      Krishna, Ranjay  and
      Chang, Angel  and
      Fei-Fei, Li  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2812",
    doi = "10.18653/v1/W15-2812",
    pages = "70--80",
}
@inproceedings{lazaridou-etal-2015-distributed,
    title = "Do Distributed Semantic Models Dream of Electric Sheep? Visualizing Word Representations through Image Synthesis",
    author = "Lazaridou, Angeliki  and
      Tien Nguyen, Dat  and
      Baroni, Marco",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2813",
    doi = "10.18653/v1/W15-2813",
    pages = "81--86",
}
@inproceedings{taniguchi-etal-2015-weighted,
    title = "A Weighted Combination of Text and Image Classifiers for User Gender Inference",
    author = "Taniguchi, Tomoki  and
      Sakaki, Shigeyuki  and
      Shigenaka, Ryosuke  and
      Tsuboshita, Yukihiro  and
      Ohkuma, Tomoko",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2814",
    doi = "10.18653/v1/W15-2814",
    pages = "87--93",
}
@inproceedings{almeida-etal-2015-coupling,
    title = "Coupling Natural Language Processing and Animation Synthesis in {P}ortuguese {S}ign {L}anguage Translation",
    author = "Almeida, In{\^e}s  and
      Coheur, Lu{\'\i}sa  and
      Candeias, Sara",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2815",
    doi = "10.18653/v1/W15-2815",
    pages = "94--103",
}
@inproceedings{belz-etal-2015-describing,
    title = "Describing Spatial Relationships between Objects in Images in {E}nglish and {F}rench",
    author = "Belz, Anja  and
      Muscat, Adrian  and
      Aberton, Maxime  and
      Benjelloun, Sami",
    booktitle = "Proceedings of the Fourth Workshop on Vision and Language",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2816",
    doi = "10.18653/v1/W15-2816",
    pages = "104--113",
}
@proceedings{ws-2015-linking,
    title = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    editor = "Roth, Michael  and
      Louis, Annie  and
      Webber, Bonnie  and
      Baldwin, Tim",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2700",
    doi = "10.18653/v1/W15-27",
}
@inproceedings{polajnar-etal-2015-exploration,
    title = "An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics",
    author = "Polajnar, Tamara  and
      Rimell, Laura  and
      Clark, Stephen",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2701",
    doi = "10.18653/v1/W15-2701",
    pages = "1--11",
}
@inproceedings{mavridou-etal-2015-linking,
    title = "Linking discourse modes and situation entity types in a cross-linguistic corpus study",
    author = "Mavridou, Kleio-Isidora  and
      Friedrich, Annemarie  and
      Peate S{\o}rensen, Melissa  and
      Palmer, Alexis  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2702",
    doi = "10.18653/v1/W15-2702",
    pages = "12--21",
}
@inproceedings{rohde-etal-2015-recovering,
    title = "Recovering discourse relations: Varying influence of discourse adverbials",
    author = "Rohde, Hannah  and
      Dickinson, Anna  and
      Clark, Chris  and
      Louis, Annie  and
      Webber, Bonnie",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2703",
    doi = "10.18653/v1/W15-2703",
    pages = "22--31",
}
@inproceedings{delmonte-tripodi-2015-semantics,
    title = "Semantics and Discourse Processing for Expressive {TTS}",
    author = "Delmonte, Rodolfo  and
      Tripodi, Rocco",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2704",
    doi = "10.18653/v1/W15-2704",
    pages = "32--43",
}
@inproceedings{zhou-etal-2015-semantically,
    title = "Semantically Enriched Models for Modal Sense Classification",
    author = "Zhou, Mengfei  and
      Frank, Anette  and
      Friedrich, Annemarie  and
      Palmer, Alexis",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2705",
    doi = "10.18653/v1/W15-2705",
    pages = "44--53",
}
@inproceedings{khazaei-etal-2015-identification,
    title = "Identification and Disambiguation of Lexical Cues of Rhetorical Relations across Different Text Genres",
    author = "Khazaei, Taraneh  and
      Xiao, Lu  and
      Mercer, Robert",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2706",
    doi = "10.18653/v1/W15-2706",
    pages = "54--63",
}
@inproceedings{prasad-etal-2015-bridging,
    title = "Bridging Sentential and Discourse-level Semantics through Clausal Adjuncts",
    author = "Prasad, Rashmi  and
      Webber, Bonnie  and
      Lee, Alan  and
      Pradhan, Sameer  and
      Joshi, Aravind",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2707",
    doi = "10.18653/v1/W15-2707",
    pages = "64--69",
}
@inproceedings{correia-etal-2015-lexical,
    title = "Lexical Level Distribution of Metadiscourse in Spoken Language",
    author = "Correia, Rui  and
      Eskenazi, Maxine  and
      Mamede, Nuno",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2708",
    doi = "10.18653/v1/W15-2708",
    pages = "70--75",
}
@inproceedings{pershina-etal-2015-idiom,
    title = "Idiom Paraphrases: Seventh Heaven vs Cloud Nine",
    author = "Pershina, Maria  and
      He, Yifan  and
      Grishman, Ralph",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2709",
    doi = "10.18653/v1/W15-2709",
    pages = "76--82",
}
@inproceedings{murakami-tsuruoka-2015-alexander,
    title = "Where Was Alexander the Great in 325 {BC}? Toward Understanding History Text with a World Model",
    author = "Murakami, Yuki  and
      Tsuruoka, Yoshimasa",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2710",
    doi = "10.18653/v1/W15-2710",
    pages = "83--88",
}
@inproceedings{martinez-alonso-etal-2015-predicting,
    title = "Predicting word sense annotation agreement",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Johannsen, Anders  and
      Lopez de Lacalle, Oier  and
      Agirre, Eneko",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2711",
    doi = "10.18653/v1/W15-2711",
    pages = "89--94",
}
@inproceedings{bernardi-etal-2015-distributional,
    title = "Distributional Semantics in Use",
    author = "Bernardi, Raffaella  and
      Boleda, Gemma  and
      Fern{\'a}ndez, Raquel  and
      Paperno, Denis",
    booktitle = "Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2712",
    doi = "10.18653/v1/W15-2712",
    pages = "95--101",
}
@proceedings{ws-2015-international-health,
    title = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    editor = "Grouin, Cyril  and
      Hamon, Thierry  and
      N{\'e}v{\'e}ol, Aur{\'e}lie  and
      Zweigenbaum, Pierre",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2600",
    doi = "10.18653/v1/W15-26",
}
@inproceedings{yim-etal-2015-depth,
    title = "In-depth annotation for patient level liver cancer staging",
    author = "Yim, Wen-wai  and
      Kwan, Sharon  and
      Yetisgen, Meliha",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2601",
    doi = "10.18653/v1/W15-2601",
    pages = "1--11",
}
@inproceedings{sadeque-etal-2015-predicting,
    title = "Predicting Continued Participation in Online Health Forums",
    author = "Sadeque, Farig  and
      Solorio, Thamar  and
      Pedersen, Ted  and
      Shrestha, Prasha  and
      Bethard, Steven",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2602",
    doi = "10.18653/v1/W15-2602",
    pages = "12--20",
}
@inproceedings{dhondt-etal-2015-redundancy,
    title = "Redundancy in {F}rench Electronic Health Records: A preliminary study",
    author = "D{'}hondt, Eva  and
      Tannier, Xavier  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2603",
    doi = "10.18653/v1/W15-2603",
    pages = "21--30",
}
@inproceedings{grouin-etal-2015-possible,
    title = "Is it possible to recover personal health information from an automatically de-identified corpus of {F}rench {EHR}s?",
    author = "Grouin, Cyril  and
      Griffon, Nicolas  and
      N{\'e}v{\'e}ol, Aur{\'e}lie",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2604",
    doi = "10.18653/v1/W15-2604",
    pages = "31--39",
}
@inproceedings{cruz-diaz-mana-lopez-2015-analysis,
    title = "An Analysis of Biomedical Tokenization: Problems and Strategies",
    author = "Cruz D{\'\i}az, Noa P.  and
      Ma{\~n}a L{\'o}pez, Manuel",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2605",
    doi = "10.18653/v1/W15-2605",
    pages = "40--49",
}
@inproceedings{yetisgen-etal-2015-annotation,
    title = "Annotation of Clinically Important Follow-up Recommendations in Radiology Reports",
    author = "Yetisgen, Meliha  and
      Klassen, Prescott  and
      McCarthy, Lucas  and
      Pellicer, Elena  and
      Payne, Tom  and
      Gunn, Martin",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2606",
    doi = "10.18653/v1/W15-2606",
    pages = "50--54",
}
@inproceedings{dwi-prasetyo-etal-2015-impact,
    title = "On the Impact of {T}witter-based Health Campaigns: A Cross-Country Analysis of Movember",
    author = "Dwi Prasetyo, Nugroho  and
      Hauff, Claudia  and
      Nguyen, Dong  and
      van den Broek, Tijs  and
      Hiemstra, Djoerd",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2607",
    doi = "10.18653/v1/W15-2607",
    pages = "55--63",
}
@inproceedings{segura-bedmar-etal-2015-exploring,
    title = "Exploring Word Embedding for Drug Name Recognition",
    author = "Segura-Bedmar, Isabel  and
      Su{\'a}rez-Paniagua, V{\'\i}ctor  and
      Mart{\'\i}nez, Paloma",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2608",
    doi = "10.18653/v1/W15-2608",
    pages = "64--72",
}
@inproceedings{weegar-dalianis-2015-creating,
    title = "Creating a rule based system for text mining of {N}orwegian breast cancer pathology reports",
    author = "Weegar, Rebecka  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2609",
    doi = "10.18653/v1/W15-2609",
    pages = "73--78",
}
@inproceedings{mitchell-steedman-2015-parser,
    title = "Parser Adaptation to the Biomedical Domain without Re-Training",
    author = "Mitchell, Jeff  and
      Steedman, Mark",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2610",
    doi = "10.18653/v1/W15-2610",
    pages = "79--89",
}
@inproceedings{alfalahi-etal-2015-expanding,
    title = "Expanding a dictionary of marker words for uncertainty and negation using distributional semantics",
    author = "Alfalahi, Alyaa  and
      Skeppstedt, Maria  and
      Ahlbom, Rickard  and
      Baskalayci, Roza  and
      Henriksson, Aron  and
      Asker, Lars  and
      Paradis, Carita  and
      Kerren, Andreas",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2611",
    doi = "10.18653/v1/W15-2611",
    pages = "90--96",
}
@inproceedings{roller-stevenson-2015-held,
    title = "Held-out versus Gold Standard: Comparison of Evaluation Strategies for Distantly Supervised Relation Extraction from {M}edline abstracts",
    author = "Roller, Roland  and
      Stevenson, Mark",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2612",
    doi = "10.18653/v1/W15-2612",
    pages = "97--102",
}
@inproceedings{busse-2015-checking,
    title = "Checking a structured pathology report for completeness of content using terminological knowledge",
    author = "Busse, Sebastian",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2613",
    doi = "10.18653/v1/W15-2613",
    pages = "103--108",
}
@inproceedings{cocos-etal-2015-effectively,
    title = "Effectively Crowdsourcing Radiology Report Annotations",
    author = "Cocos, Anne  and
      Masino, Aaron  and
      Qian, Ting  and
      Pavlick, Ellie  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2614",
    doi = "10.18653/v1/W15-2614",
    pages = "109--114",
}
@inproceedings{zheng-yu-2015-identifying,
    title = "Identifying Key Concepts from {EHR} Notes Using Domain Adaptation",
    author = "Zheng, Jiaping  and
      Yu, Hong",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2615",
    doi = "10.18653/v1/W15-2615",
    pages = "115--119",
}
@inproceedings{moens-2015-information,
    title = "Information Extraction from Biomedical Texts: Learning Models with Limited Supervision",
    author = "Moens, Marie-Francine",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2616",
    doi = "10.18653/v1/W15-2616",
    pages = "120",
}
@inproceedings{friedrich-dalianis-2015-adverse,
    title = "Adverse Drug Event classification of health records using dictionary based pre-processing and machine learning",
    author = "Friedrich, Stefanie  and
      Dalianis, Hercules",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2617",
    doi = "10.18653/v1/W15-2617",
    pages = "121--130",
}
@inproceedings{venturi-etal-2015-nlp,
    title = "{NLP}{--}Based Readability Assessment of Health{--}Related Texts: a Case Study on {I}talian Informed Consent Forms",
    author = "Venturi, Giulia  and
      Bellandi, Tommaso  and
      Dell{'}Orletta, Felice  and
      Montemagni, Simonetta",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2618",
    doi = "10.18653/v1/W15-2618",
    pages = "131--141",
}
@inproceedings{jagannatha-etal-2015-mining,
    title = "Mining and Ranking Biomedical Synonym Candidates from {W}ikipedia",
    author = "Jagannatha, Abhyuday  and
      Chen, Jinying  and
      Yu, Hong",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2619",
    doi = "10.18653/v1/W15-2619",
    pages = "142--151",
}
@inproceedings{henriksson-2015-representing,
    title = "Representing Clinical Notes for Adverse Drug Event Detection",
    author = "Henriksson, Aron",
    booktitle = "Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2620",
    doi = "10.18653/v1/W15-2620",
    pages = "152--158",
}
@proceedings{ws-2015-discourse,
    title = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    editor = "Webber, Bonnie  and
      Carpuat, Marine  and
      Popescu-Belis, Andrei  and
      Hardmeier, Christian",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2500",
    doi = "10.18653/v1/W15-25",
}
@inproceedings{hardmeier-etal-2015-pronoun,
    title = "Pronoun-Focused {MT} and Cross-Lingual Pronoun Prediction: Findings of the 2015 {D}isco{MT} Shared Task on Pronoun Translation",
    author = {Hardmeier, Christian  and
      Nakov, Preslav  and
      Stymne, Sara  and
      Tiedemann, J{\"o}rg  and
      Versley, Yannick  and
      Cettolo, Mauro},
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2501",
    doi = "10.18653/v1/W15-2501",
    pages = "1--16",
}
@inproceedings{novak-etal-2015-comparison,
    title = "Comparison of Coreference Resolvers for Deep Syntax Translation",
    author = "Nov{\'a}k, Michal  and
      Oele, Dieke  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2502",
    doi = "10.18653/v1/W15-2502",
    pages = "17--23",
}
@inproceedings{guillou-webber-2015-analysing,
    title = "Analysing {P}ar{C}or and its Translations by State-of-the-art {SMT} Systems",
    author = "Guillou, Liane  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2503",
    doi = "10.18653/v1/W15-2503",
    pages = "24--32",
}
@inproceedings{gong-etal-2015-document,
    title = "Document-Level Machine Translation Evaluation with Gist Consistency and Text Cohesion",
    author = "Gong, Zhengxian  and
      Zhang, Min  and
      Zhou, Guodong",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2504",
    doi = "10.18653/v1/W15-2504",
    pages = "33--40",
}
@inproceedings{hoek-etal-2015-role,
    title = "The Role of Expectedness in the Implicitation and Explicitation of Discourse Relations",
    author = "Hoek, Jet  and
      Evers-Vermeul, Jacqueline  and
      Sanders, Ted J.M.",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2505",
    doi = "10.18653/v1/W15-2505",
    pages = "41--46",
}
@inproceedings{mascarell-etal-2015-detecting,
    title = "Detecting Document-level Context Triggers to Resolve Translation Ambiguity",
    author = "Mascarell, Laura  and
      Fishel, Mark  and
      Volk, Martin",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2506",
    doi = "10.18653/v1/W15-2506",
    pages = "47--51",
}
@inproceedings{sim-smith-etal-2015-proposal,
    title = "A Proposal for a Coherence Corpus in Machine Translation",
    author = "Sim Smith, Karin  and
      Aziz, Wilker  and
      Specia, Lucia",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2507",
    doi = "10.18653/v1/W15-2507",
    pages = "52--58",
}
@inproceedings{callin-etal-2015-part,
    title = "Part-of-Speech Driven Cross-Lingual Pronoun Prediction with Feed-Forward Neural Networks",
    author = {Callin, Jimmy  and
      Hardmeier, Christian  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2508",
    doi = "10.18653/v1/W15-2508",
    pages = "59--64",
}
@inproceedings{guillou-2015-automatic,
    title = "Automatic Post-Editing for the {D}isco{MT} Pronoun Translation Task",
    author = "Guillou, Liane",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2509",
    doi = "10.18653/v1/W15-2509",
    pages = "65--71",
}
@inproceedings{hardmeier-2015-document,
    title = "A Document-Level {SMT} System with Integrated Pronoun Prediction",
    author = "Hardmeier, Christian",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2510",
    doi = "10.18653/v1/W15-2510",
    pages = "72--77",
}
@inproceedings{loaiciga-2015-predicting,
    title = "Predicting Pronoun Translation Using Syntactic, Morphological and Contextual Features from Parallel Data",
    author = "Lo{\'a}iciga, Sharid",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2511",
    doi = "10.18653/v1/W15-2511",
    pages = "78--85",
}
@inproceedings{loaiciga-wehrli-2015-rule,
    title = "Rule-Based Pronominal Anaphora Treatment for Machine Translation",
    author = "Lo{\'a}iciga, Sharid  and
      Wehrli, {\'E}ric",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2512",
    doi = "10.18653/v1/W15-2512",
    pages = "86--93",
}
@inproceedings{luong-etal-2015-pronoun,
    title = "Pronoun Translation and Prediction with or without Coreference Links",
    author = "Luong, Ngoc Quang  and
      Miculicich Werlen, Lesly  and
      Popescu-Belis, Andrei",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2513",
    doi = "10.18653/v1/W15-2513",
    pages = "94--100",
}
@inproceedings{pham-van-der-plas-2015-predicting,
    title = "Predicting Pronouns across Languages with Continuous Word Spaces",
    author = "Pham, Ngoc-Quan  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2514",
    doi = "10.18653/v1/W15-2514",
    pages = "101--107",
}
@inproceedings{tiedemann-2015-baseline,
    title = "Baseline Models for Pronoun Prediction and Pronoun-Aware Translation",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2515",
    doi = "10.18653/v1/W15-2515",
    pages = "108--114",
}
@inproceedings{wetzel-etal-2015-maximum,
    title = "A Maximum Entropy Classifier for Cross-Lingual Pronoun Prediction",
    author = "Wetzel, Dominikus  and
      Lopez, Adam  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2516",
    doi = "10.18653/v1/W15-2516",
    pages = "115--121",
}
@inproceedings{lapshinova-koltunski-vela-2015-measuring,
    title = "Measuring {`}Registerness{'} in Human and Machine Translation: A Text Classification Approach",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Vela, Mihaela",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2517",
    doi = "10.18653/v1/W15-2517",
    pages = "122--131",
}
@inproceedings{van-der-wees-etal-2015-translation,
    title = "Translation Model Adaptation Using Genre-Revealing Text Features",
    author = "van der Wees, Marlies  and
      Bisazza, Arianna  and
      Monz, Christof",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2518",
    doi = "10.18653/v1/W15-2518",
    pages = "132--141",
}
@inproceedings{yung-etal-2015-crosslingual,
    title = "Crosslingual Annotation and Analysis of Implicit Discourse Connectives for Machine Translation",
    author = "Yung, Frances  and
      Duh, Kevin  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2519",
    doi = "10.18653/v1/W15-2519",
    pages = "142--152",
}
@inproceedings{zhang-ittycheriah-2015-novel,
    title = "Novel Document Level Features for Statistical Machine Translation",
    author = "Zhang, Rong  and
      Ittycheriah, Abraham",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2520",
    doi = "10.18653/v1/W15-2520",
    pages = "153--157",
}
@inproceedings{lapshinova-koltunski-2015-exploration,
    title = "Exploration of Inter- and Intralingual Variation of Discourse Phenomena",
    author = "Lapshinova-Koltunski, Ekaterina",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2521",
    doi = "10.18653/v1/W15-2521",
    pages = "158--167",
}
@inproceedings{hardmeier-2015-statistical,
    title = "On Statistical Machine Translation and Translation Theory",
    author = "Hardmeier, Christian",
    booktitle = "Proceedings of the Second Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2522",
    doi = "10.18653/v1/W15-2522",
    pages = "168--172",
}
@proceedings{ws-2015-cognitive-aspects,
    title = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    editor = "Berwick, Robert  and
      Korhonen, Anna  and
      Lenci, Alessandro  and
      Poibeau, Thierry  and
      Villavicencio, Aline",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2400",
    doi = "10.18653/v1/W15-24",
}
@inproceedings{barrett-sogaard-2015-using,
    title = "Using reading behavior to predict grammatical functions",
    author = "Barrett, Maria  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2401",
    doi = "10.18653/v1/W15-2401",
    pages = "1--5",
}
@inproceedings{klerke-etal-2015-reading,
    title = "Reading metrics for estimating task efficiency with {MT} output",
    author = "Klerke, Sigrid  and
      Castilho, Sheila  and
      Barrett, Maria  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2402",
    doi = "10.18653/v1/W15-2402",
    pages = "6--13",
}
@inproceedings{luong-etal-2015-evaluating,
    title = "Evaluating Models of Computation and Storage in Human Sentence Processing",
    author = "Luong, Thang  and
      O{'}Donnell, Timothy  and
      Goodman, Noah",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2403",
    doi = "10.18653/v1/W15-2403",
    pages = "14--21",
}
@inproceedings{bloem-etal-2015-agent,
    title = "An agent-based model of a historical word order change",
    author = "Bloem, Jelke  and
      Versloot, Arjen  and
      Weerman, Fred",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2404",
    doi = "10.18653/v1/W15-2404",
    pages = "22--27",
}
@inproceedings{grimm-etal-2015-towards,
    title = "Towards a Model of Prediction-based Syntactic Category Acquisition: First Steps with Word Embeddings",
    author = "Grimm, Robert  and
      Cassani, Giovanni  and
      Daelemans, Walter  and
      Gillis, Steven",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2405",
    doi = "10.18653/v1/W15-2405",
    pages = "28--32",
}
@inproceedings{cassani-etal-2015-distributional,
    title = "Which distributional cues help the most? Unsupervised contexts selection for lexical category acquisition",
    author = "Cassani, Giovanni  and
      Grimm, Robert  and
      Daelemans, Walter  and
      Gillis, Steven",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2406",
    doi = "10.18653/v1/W15-2406",
    pages = "33--39",
}
@inproceedings{cornudella-poibeau-2015-language,
    title = "Language Emergence in a Population of Artificial Agents Equipped with the Autotelic Principle",
    author = "Cornudella, Miquel  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2407",
    doi = "10.18653/v1/W15-2407",
    pages = "40--44",
}
@inproceedings{faria-2015-computational,
    title = "A Computational Study of Cross-situational Lexical Learning of {B}razilian {P}ortuguese",
    author = "Faria, Pablo",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2408",
    doi = "10.18653/v1/W15-2408",
    pages = "45--54",
}
@inproceedings{coltekin-2015-units,
    title = "Units in segmentation: a computational investigation",
    author = {{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2409",
    doi = "10.18653/v1/W15-2409",
    pages = "55--64",
}
@inproceedings{berdicevskis-2015-estimating,
    title = "Estimating Grammeme Redundancy by Measuring Their Importance for Syntactic Parser Performance",
    author = "Berdicevskis, Aleksandrs",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2410",
    doi = "10.18653/v1/W15-2410",
    pages = "65--73",
}
@inproceedings{brochhagen-2015-improving,
    title = "Improving Coordination on Novel Meaning through Context and Semantic Structure",
    author = "Brochhagen, Thomas",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2411",
    doi = "10.18653/v1/W15-2411",
    pages = "74--82",
}
@inproceedings{beekhuizen-stevenson-2015-perceptual,
    title = "Perceptual, conceptual, and frequency effects on error patterns in {E}nglish color term acquisition",
    author = "Beekhuizen, Barend  and
      Stevenson, Suzanne",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2412",
    doi = "10.18653/v1/W15-2412",
    pages = "83--92",
}
@inproceedings{ludusan-etal-2015-motif,
    title = "Motif discovery in infant- and adult-directed speech",
    author = "Ludusan, Bogdan  and
      Seidl, Amanda  and
      Dupoux, Emmanuel  and
      Cristia, Alex",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2413",
    doi = "10.18653/v1/W15-2413",
    pages = "93--102",
}
@inproceedings{van-den-bosch-bresnan-2015-modeling,
    title = "Modeling dative alternations of individual children",
    author = "van den Bosch, Antal  and
      Bresnan, Joan",
    booktitle = "Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2414",
    doi = "10.18653/v1/W15-2414",
    pages = "103--112",
}
@proceedings{ws-2015-mathematics,
    title = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    editor = "Kuhlmann, Marco  and
      Kanazawa, Makoto  and
      Kobele, Gregory M.",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2300",
    doi = "10.3115/v1/W15-23",
}
@inproceedings{graf-etal-2015-refined,
    title = "A Refined Notion of Memory Usage for Minimalist Parsing",
    author = "Graf, Thomas  and
      Fodor, Brigitta  and
      Monette, James  and
      Rachiele, Gianpaul  and
      Warren, Aunika  and
      Zhang, Chong",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2301",
    doi = "10.3115/v1/W15-2301",
    pages = "1--14",
}
@inproceedings{de-groote-2015-abstract,
    title = "Abstract Categorial Parsing as Linear Logic Programming",
    author = "de Groote, Philippe",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2302",
    doi = "10.3115/v1/W15-2302",
    pages = "15--25",
}
@inproceedings{fulop-kephart-2015-topology,
    title = "Topology of Language Classes",
    author = "Fulop, Sean A.  and
      Kephart, David",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2303",
    doi = "10.3115/v1/W15-2303",
    pages = "26--38",
}
@inproceedings{chatzikyriakidis-luo-2015-individuation,
    title = "Individuation Criteria, Dot-types and Copredication: A View from Modern Type Theories",
    author = "Chatzikyriakidis, Stergios  and
      Luo, Zhaohui",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2304",
    doi = "10.3115/v1/W15-2304",
    pages = "39--50",
}
@inproceedings{kornai-kracht-2015-lexical,
    title = "Lexical Semantics and Model Theory: Together at Last?",
    author = "Kornai, Andr{\'a}s  and
      Kracht, Marcus",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2305",
    doi = "10.3115/v1/W15-2305",
    pages = "51--61",
}
@inproceedings{kartsaklis-sadrzadeh-2015-frobenius,
    title = "A {F}robenius Model of Information Structure in Categorical Compositional Distributional Semantics",
    author = "Kartsaklis, Dimitri  and
      Sadrzadeh, Mehrnoosh",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2306",
    doi = "10.3115/v1/W15-2306",
    pages = "62--74",
}
@inproceedings{mcallester-2015-synopsis,
    title = "A Synopsis of Morphoid Type Theory",
    author = "McAllester, David",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2307",
    doi = "10.3115/v1/W15-2307",
    pages = "75--86",
}
@inproceedings{yoshinaka-2015-general,
    title = "General Perspective on Distributionally Learnable Classes",
    author = "Yoshinaka, Ryo",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2308",
    doi = "10.3115/v1/W15-2308",
    pages = "87--98",
}
@inproceedings{clark-2015-canonical,
    title = "Canonical Context-Free Grammars and Strong Learning: Two Approaches",
    author = "Clark, Alexander",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2309",
    doi = "10.3115/v1/W15-2309",
    pages = "99--111",
}
@inproceedings{chandlee-etal-2015-output,
    title = "Output Strictly Local Functions",
    author = "Chandlee, Jane  and
      Eyraud, R{\'e}mi  and
      Heinz, Jeffrey",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2310",
    doi = "10.3115/v1/W15-2310",
    pages = "112--125",
}
@inproceedings{magri-kager-2015-choose,
    title = "How to Choose Successful Losers in Error-Driven Phonotactic Learning",
    author = "Magri, Giorgio  and
      Kager, Ren{\'e}",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2311",
    doi = "10.3115/v1/W15-2311",
    pages = "126--138",
}
@inproceedings{jardine-heinz-2015-concatenation,
    title = "A Concatenation Operation to Derive Autosegmental Graphs",
    author = "Jardine, Adam  and
      Heinz, Jeffrey",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2312",
    doi = "10.3115/v1/W15-2312",
    pages = "139--151",
}
@inproceedings{kahane-mazziotta-2015-syntactic,
    title = "Syntactic Polygraphs. A Formalism Extending Both Constituency and Dependency",
    author = "Kahane, Sylvain  and
      Mazziotta, Nicolas",
    booktitle = "Proceedings of the 14th Meeting on the Mathematics of Language ({M}o{L} 2015)",
    month = jul,
    year = "2015",
    address = "Chicago, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2313",
    doi = "10.3115/v1/W15-2313",
    pages = "152--164",
}
@proceedings{ws-2015-international-parsing,
    title = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2200",
    doi = "10.18653/v1/W15-22",
}
@inproceedings{yu-etal-2015-domain,
    title = "Domain Adaptation for Dependency Parsing via Self-Training",
    author = "Yu, Juntao  and
      Elkaref, Mohab  and
      Bohnet, Bernd",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2201",
    doi = "10.18653/v1/W15-2201",
    pages = "1--10",
}
@inproceedings{flannery-mori-2015-combining,
    title = "Combining Active Learning and Partial Annotation for Domain Adaptation of a {J}apanese Dependency Parser",
    author = "Flannery, Daniel  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2202",
    doi = "10.18653/v1/W15-2202",
    pages = "11--19",
}
@inproceedings{uematsu-miyao-2015-incorporating,
    title = "Incorporating Complementary Annotation to a {CCG}bank for Improving Derivations for {J}apanese",
    author = "Uematsu, Sumire  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2203",
    doi = "10.18653/v1/W15-2203",
    pages = "20--29",
}
@inproceedings{guillaume-perrier-2015-dependency,
    title = "Dependency Parsing with Graph Rewriting",
    author = "Guillaume, Bruno  and
      Perrier, Guy",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2204",
    doi = "10.18653/v1/W15-2204",
    pages = "30--39",
}
@inproceedings{lien-kouylekov-2015-semantic,
    title = "Semantic Parsing for Textual Entailment",
    author = "Lien, Elisabeth  and
      Kouylekov, Milen",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2205",
    doi = "10.18653/v1/W15-2205",
    pages = "40--49",
}
@inproceedings{maeta-etal-2015-framework,
    title = "A Framework for Procedural Text Understanding",
    author = "Maeta, Hirokuni  and
      Sasada, Tetsuro  and
      Mori, Shinsuke",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2206",
    doi = "10.18653/v1/W15-2206",
    pages = "50--60",
}
@inproceedings{lloberes-etal-2015-suitability,
    title = "Suitability of {P}ar{T}es Test Suite for Parsing Evaluation",
    author = "Lloberes, Marina  and
      Castell{\'o}n, Irene  and
      Padr{\'o}, Llu{\'\i}s",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2207",
    doi = "10.18653/v1/W15-2207",
    pages = "61--65",
}
@inproceedings{yoshimoto-etal-2015-coordination,
    title = "Coordination-Aware Dependency Parsing (Preliminary Report)",
    author = "Yoshimoto, Akifumi  and
      Hara, Kazuo  and
      Shimbo, Masashi  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2208",
    doi = "10.18653/v1/W15-2208",
    pages = "66--70",
}
@inproceedings{rosa-zabokrtsky-2015-mstparser,
    title = "{MSTP}arser Model Interpolation for Multi-Source Delexicalized Transfer",
    author = "Rosa, Rudolf  and
      {\v{Z}}abokrtsk{\'y}, Zden{\v{e}}k",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2209",
    doi = "10.18653/v1/W15-2209",
    pages = "71--75",
}
@inproceedings{bjorkelund-nivre-2015-non,
    title = "Non-Deterministic Oracles for Unrestricted Non-Projective Transition-Based Dependency Parsing",
    author = {Bj{\"o}rkelund, Anders  and
      Nivre, Joakim},
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2210",
    doi = "10.18653/v1/W15-2210",
    pages = "76--86",
}
@inproceedings{le-2015-enhancing,
    title = "Enhancing the Inside-Outside Recursive Neural Network Reranker for Dependency Parsing",
    author = "Le, Phong",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2211",
    doi = "10.18653/v1/W15-2211",
    pages = "87--91",
}
@inproceedings{schluter-2015-maximising,
    title = "Maximising Spanning Subtree Scores for Parsing Tree Approximations of Semantic Dependency Digraphs",
    author = "Schluter, Natalie",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2212",
    doi = "10.18653/v1/W15-2212",
    pages = "92--96",
}
@inproceedings{irwin-matsumoto-2015-cky,
    title = "{CKY} Parsing with Independence Constraints",
    author = "Irwin, Joseph  and
      Matsumoto, Yuji",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2213",
    doi = "10.18653/v1/W15-2213",
    pages = "97--106",
}
@inproceedings{gamallo-2015-dependency,
    title = "Dependency Parsing with Compression Rules",
    author = "Gamallo, Pablo",
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2214",
    doi = "10.18653/v1/W15-2214",
    pages = "107--117",
}
@inproceedings{falenska-etal-2015-stacking,
    title = "Stacking or Supertagging for Dependency Parsing {--} What{'}s the Difference?",
    author = {Fale{\'n}ska, Agnieszka  and
      Bj{\"o}rkelund, Anders  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem  and
      Seeker, Wolfgang},
    booktitle = "Proceedings of the 14th International Conference on Parsing Technologies",
    month = jul,
    year = "2015",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-2215",
    doi = "10.18653/v1/W15-2215",
    pages = "118--129",
}
@proceedings{ws-2015-international-dependency,
    title = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    editor = "Nivre, Joakim  and
      Haji{\v{c}}ov{\'a}, Eva",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2100",
}
@inproceedings{manning-2015-invited,
    title = "Invited Talk: The Case for {U}niversal {D}ependencies",
    author = "Manning, Christopher",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2101",
    pages = "1",
}
@inproceedings{polguere-2015-invited,
    title = "Invited Talk: Lexicon Embedded Syntax",
    author = "Polgu{\`e}re, Alain",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2102",
    pages = "2--9",
}
@inproceedings{ahrenberg-2015-converting,
    title = "Converting an {E}nglish-{S}wedish Parallel Treebank to {U}niversal {D}ependencies",
    author = "Ahrenberg, Lars",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2103",
    pages = "10--19",
}
@inproceedings{barancikova-rosa-2015-targeted,
    title = "Targeted Paraphrasing on Deep Syntactic Layer for {MT} Evaluation",
    author = "Baran{\v{c}}{\'\i}kov{\'a}, Petra  and
      Rosa, Rudolf",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2104",
    pages = "20--27",
}
@inproceedings{barbu-mititelu-etal-2015-universal,
    title = "Universal and Language-specific Dependency Relations for Analysing {R}omanian",
    author = "Barbu Mititelu, Verginica  and
      M{\u{a}}r{\u{a}}nduc, C{\u{a}}t{\u{a}}lina  and
      Irimia, Elena",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2105",
    pages = "28--37",
}
@inproceedings{boguslavskaya-boguslavsky-2015-emotion,
    title = "Emotion and Inner State Adverbials in {R}ussian",
    author = "Boguslavskaya, Olga  and
      Boguslavsky, Igor",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2106",
    pages = "38--47",
}
@inproceedings{burga-etal-2015-towards,
    title = "Towards a multi-layered dependency annotation of {F}innish",
    author = "Burga, Alicia  and
      Mille, Simon  and
      Granvik, Anton  and
      Wanner, Leo",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2107",
    pages = "48--57",
}
@inproceedings{buys-blunsom-2015-bayesian,
    title = "A {B}ayesian Model for Generative Transition-based Dependency Parsing",
    author = "Buys, Jan  and
      Blunsom, Phil",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2108",
    pages = "58--67",
}
@inproceedings{cech-etal-2015-relation,
    title = "On the relation between verb full valency and synonymy",
    author = "{\v{C}}ech, Radek  and
      Ma{\v{c}}utek, J{\'a}n  and
      Ko{\v{s}}{\v{c}}ov{\'a}, Michaela",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2109",
    pages = "68--73",
}
@inproceedings{chen-etal-2015-classifying,
    title = "Classifying Syntactic Categories in the {C}hinese Dependency Network",
    author = "Chen, Xinying  and
      Liu, Haitao  and
      Gerdes, Kim",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2110",
    pages = "74--81",
}
@inproceedings{dusek-etal-2015-using,
    title = "Using Parallel Texts and Lexicons for Verbal Word Sense Disambiguation",
    author = "Du{\v{s}}ek, Ond{\v{r}}ej  and
      Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}, Jan  and
      Popel, Martin  and
      {\v{S}}indlerov{\'a}, Jana  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2111",
    pages = "82--90",
}
@inproceedings{futrell-etal-2015-quantifying,
    title = "Quantifying Word Order Freedom in Dependency Corpora",
    author = "Futrell, Richard  and
      Mahowald, Kyle  and
      Gibson, Edward",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2112",
    pages = "91--100",
}
@inproceedings{gerdes-kahane-2015-non,
    title = "Non-constituent coordination and other coordinative constructions as Dependency Graphs",
    author = "Gerdes, Kim  and
      Kahane, Sylvain",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2113",
    pages = "101--110",
}
@inproceedings{gross-osborne-2015-dependency,
    title = "The Dependency Status of Function Words: Auxiliaries",
    author = "Gro{\ss}, Thomas  and
      Osborne, Timothy",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2114",
    pages = "111--120",
}
@inproceedings{gulordava-merlo-2015-diachronic,
    title = "Diachronic Trends in Word Order Freedom and Dependency Length in Dependency-Annotated Corpora of {L}atin and {A}ncient {G}reek",
    author = "Gulordava, Kristina  and
      Merlo, Paola",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2115",
    pages = "121--130",
}
@inproceedings{hajicova-etal-2015-reconstructions,
    title = "Reconstructions of Deletions in a Dependency-based Description of {C}zech: Selected Issues",
    author = "Haji{\v{c}}ov{\'a}, Eva  and
      Mikulov{\'a}, Marie  and
      Panevov{\'a}, Jarmila",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2116",
    pages = "131--140",
}
@inproceedings{husain-vasishth-2015-non,
    title = "Non-projectivity and processing constraints: Insights from {H}indi",
    author = "Husain, Samar  and
      Vasishth, Shravan",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2117",
    pages = "141--150",
}
@inproceedings{imrenyi-2015-mutual,
    title = "From mutual dependency to multiple dimensions: remarks on the {DG} analysis of {``}functional heads{''} in {H}ungarian",
    author = "Imr{\'e}nyi, Andr{\'a}s",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2118",
    pages = "151--160",
}
@inproceedings{jing-liu-2015-mean,
    title = "Mean Hierarchical Distance Augmenting Mean Dependency Distance",
    author = "Jing, Yingqi  and
      Liu, Haitao",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2119",
    pages = "161--170",
}
@inproceedings{jarvinen-etal-2015-towards,
    title = "Towards Cross-language Application of Dependency Grammar",
    author = {J{\"a}rvinen, Timo  and
      Bertol, Elisabeth  and
      Larasati, Septina  and
      Rizea, Monica-Mihaela  and
      Ruiz Santabalbina, Maria  and
      Sou{\v{c}}ek, Milan},
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2120",
    pages = "171--180",
}
@inproceedings{kahane-mazziotta-2015-dependency,
    title = "Dependency-based analyses for function words {--} Introducing the polygraphic approach",
    author = "Kahane, Sylvain  and
      Mazziotta, Nicolas",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2121",
    pages = "181--190",
}
@inproceedings{kettnerova-lopatkova-2015-lexicon,
    title = "At the Lexicon-Grammar Interface: The Case of Complex Predicates in the Functional Generative Description",
    author = "Kettnerov{\'a}, V{\'a}clava  and
      Lopatkov{\'a}, Mark{\'e}ta",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2122",
    pages = "191--200",
}
@inproceedings{lloberes-etal-2015-enhancing,
    title = "Enhancing {F}ree{L}ing Rule-Based Dependency Grammars with Subcategorization Frames",
    author = "Lloberes, Marina  and
      Castell{\'o}n, Irene  and
      Padr{\'o}, Llu{\'\i}s",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2123",
    pages = "201--210",
}
@inproceedings{luotolahti-etal-2015-towards,
    title = "Towards Universal Web Parsebanks",
    author = "Luotolahti, Juhani  and
      Kanerva, Jenna  and
      Laippala, Veronika  and
      Pyysalo, Sampo  and
      Ginter, Filip",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2124",
    pages = "211--220",
}
@inproceedings{merlo-2015-evaluation,
    title = "Evaluation of Two-level Dependency Representations of Argument Structure in Long-Distance Dependencies",
    author = "Merlo, Paola",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2125",
    pages = "221--230",
}
@inproceedings{milicevic-2015-subjectival,
    title = "The Subjectival Surface-Syntactic Relation in {S}erbian",
    author = "Mili{\'c}evi{\'c}, Jasmina",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2126",
    pages = "231--240",
}
@inproceedings{osborne-maxwell-2015-historical,
    title = "A Historical Overview of the Status of Function Words in Dependency Grammar",
    author = "Osborne, Timothy  and
      Maxwell, Daniel",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2127",
    pages = "241--250",
}
@inproceedings{osborne-2015-diagnostics,
    title = "Diagnostics for Constituents: Dependency, Constituency, and the Status of Function Words",
    author = "Osborne, Timothy",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2128",
    pages = "251--260",
}
@inproceedings{osborne-ma-2015-dg,
    title = "A {DG} Account of the Descriptive and Resultative de-Constructions in {C}hinese",
    author = "Osborne, Timothy  and
      Ma, Shudong",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2129",
    pages = "261--270",
}
@inproceedings{osborne-liang-2015-survey,
    title = "A Survey of Ellipsis in {C}hinese",
    author = "Osborne, Timothy  and
      Liang, Junying",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2130",
    pages = "271--280",
}
@inproceedings{rosa-2015-multi,
    title = "Multi-source Cross-lingual Delexicalized Parser Transfer: {P}rague or {S}tanford?",
    author = "Rosa, Rudolf",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2131",
    pages = "281--290",
}
@inproceedings{rysova-rysova-2015-secondary,
    title = "Secondary Connectives in the {P}rague Dependency Treebank",
    author = "Rysov{\'a}, Magdal{\'e}na  and
      Rysov{\'a}, Kate{\v{r}}ina",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2132",
    pages = "291--299",
}
@inproceedings{seraji-etal-2015-parsper,
    title = "{P}ars{P}er: A Dependency Parser for {P}ersian",
    author = "Seraji, Mojgan  and
      Bohnet, Bernd  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2133",
    pages = "300--309",
}
@inproceedings{silveira-manning-2015-universal,
    title = "Does {U}niversal {D}ependencies need a parsing representation? An investigation of {E}nglish",
    author = "Silveira, Natalia  and
      Manning, Christopher",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2134",
    pages = "310--319",
}
@inproceedings{simov-osenova-2015-catena,
    title = "Catena Operations for Unified Dependency Analysis",
    author = "Simov, Kiril  and
      Osenova, Petya",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2135",
    pages = "320--329",
}
@inproceedings{sindlerova-etal-2015-zero,
    title = "Zero Alignment of Verb Arguments in a Parallel Treebank",
    author = "{\v{S}}indlerov{\'a}, Jana  and
      Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2136",
    pages = "330--339",
}
@inproceedings{tiedemann-2015-cross,
    title = "Cross-Lingual Dependency Parsing with {U}niversal {D}ependencies and Predicted {P}o{S} Labels",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2137",
    pages = "340--349",
}
@inproceedings{yu-bohnet-2015-exploring,
    title = "Exploring Confidence-based Self-training for Multilingual Dependency Parsing in an Under-Resourced Language Scenario",
    author = "Yu, Juntao  and
      Bohnet, Bernd",
    booktitle = "Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)",
    month = aug,
    year = "2015",
    address = "Uppsala, Sweden",
    publisher = "Uppsala University, Uppsala, Sweden",
    url = "https://www.aclweb.org/anthology/W15-2138",
    pages = "350--358",
}
@proceedings{ws-2015-semantic,
    title = "Proceedings of the workshop on Semantic resources and semantic annotation for Natural Language Processing and the Digital Humanities at {NODALIDA} 2015",
    editor = "Pedersen, Bolette Sandford  and
      Olsen, Sussi  and
      Borin, Lars",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "Northern European Association for Language Technology",
    url = "https://www.aclweb.org/anthology/W15-2000",
    pages = "1--11",
}
@inproceedings{borin-etal-2015-dragons,
    title = "Here be dragons? The perils and promises of inter-resource lexical-semantic mapping",
    author = "Borin, Lars  and
      Nieto Pi{\~n}a, Luis  and
      Johansson, Richard",
    booktitle = "Proceedings of the workshop on Semantic resources and semantic annotation for Natural Language Processing and the Digital Humanities at {NODALIDA} 2015",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "Northern European Association for Language Technology",
    url = "https://www.aclweb.org/anthology/W15-2001",
    pages = "1--11",
}
@inproceedings{friberg-heppin-dannells-2015-polysemy,
    title = "Polysemy, underspecification, and aspects {--} Questions of lumping or splitting in the construction of {S}wedish {F}rame{N}et",
    author = "Friberg Heppin, Karin  and
      Dann{\'e}lls, Dana",
    booktitle = "Proceedings of the workshop on Semantic resources and semantic annotation for Natural Language Processing and the Digital Humanities at {NODALIDA} 2015",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "Northern European Association for Language Technology",
    url = "https://www.aclweb.org/anthology/W15-2002",
    pages = "12--20",
}
@inproceedings{loukachevitch-chetviorkin-2015-determining,
    title = "Determining the most frequent senses using {R}ussian linguistic ontology {R}u{T}hes",
    author = "Loukachevitch, Natalia  and
      Chetviorkin, Ilia",
    booktitle = "Proceedings of the workshop on Semantic resources and semantic annotation for Natural Language Processing and the Digital Humanities at {NODALIDA} 2015",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "Northern European Association for Language Technology",
    url = "https://www.aclweb.org/anthology/W15-2003",
    pages = "21--27",
}
@inproceedings{norrby-nugues-2015-extraction,
    title = "Extraction of lethal events from {W}ikipedia and a semantic repository",
    author = "Norrby, Magnus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the workshop on Semantic resources and semantic annotation for Natural Language Processing and the Digital Humanities at {NODALIDA} 2015",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "Northern European Association for Language Technology",
    url = "https://www.aclweb.org/anthology/W15-2004",
    pages = "28--35",
}
@inproceedings{olsen-etal-2015-coarse,
    title = "Coarse-grained sense annotation of {D}anish across textual domains",
    author = "Olsen, Sussi  and
      Pedersen, Bolette S.  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Johannsen, Anders",
    booktitle = "Proceedings of the workshop on Semantic resources and semantic annotation for Natural Language Processing and the Digital Humanities at {NODALIDA} 2015",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "Northern European Association for Language Technology",
    url = "https://www.aclweb.org/anthology/W15-2005",
    pages = "36--43",
}
@proceedings{ws-2015-nlp,
    title = "Proceedings of the fourth workshop on {NLP} for computer-assisted language learning",
    editor = "Volodina, Elena  and
      Borin, Lars  and
      Pil{\'a}n, Ildik{\'o}",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W15-1900",
}
@inproceedings{da-silva-cardoso-wolska-2015-misspellings,
    title = "Misspellings in Responses to Listening Comprehension Questions: Prospects for Scoring based on Phonetic Normalization",
    author = "da Silva Cardoso, Heike  and
      Wolska, Magdalena",
    booktitle = "Proceedings of the fourth workshop on {NLP} for computer-assisted language learning",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W15-1901",
    pages = "1--10",
}
@inproceedings{henrichsen-2015-taking,
    title = "Taking the {D}anish Speech Trainer from {CALL} to {ICALL}",
    author = "Henrichsen, Peter Juel",
    booktitle = "Proceedings of the fourth workshop on {NLP} for computer-assisted language learning",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W15-1902",
    pages = "11--20",
}
@inproceedings{horbach-etal-2015-using,
    title = "Using Shallow Syntactic Features to Measure Influences of {L}1 and Proficiency Level in {EFL} Writings",
    author = "Horbach, Andrea  and
      Poitz, Jonathan  and
      Palmer, Alexis",
    booktitle = "Proceedings of the fourth workshop on {NLP} for computer-assisted language learning",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W15-1903",
    pages = "21--34",
}
@inproceedings{kutuzov-kuzmenko-2015-semi,
    title = "Semi-automated typical error annotation for learner {E}nglish essays: integrating frameworks",
    author = "Kutuzov, Andrey  and
      Kuzmenko, Elizaveta",
    booktitle = "Proceedings of the fourth workshop on {NLP} for computer-assisted language learning",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W15-1904",
    pages = "35--41",
}
@inproceedings{pado-kiefer-2015-short,
    title = "Short Answer Grading: When Sorting Helps and When it Doesn{'}t",
    author = "Pado, Ulrike  and
      Kiefer, Cornelia",
    booktitle = "Proceedings of the fourth workshop on {NLP} for computer-assisted language learning",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W15-1905",
    pages = "42--50",
}
@inproceedings{uibo-etal-2015-oahpa,
    title = "Oahpa! {\~O}pi! Opiq! Developing free online programs for learning {E}stonian and {V}{\~o}ro",
    author = "Uibo, Heli  and
      Pruulmann-Vengerfeldt, Jaak  and
      Rueter, Jack  and
      Iva, Sulev",
    booktitle = "Proceedings of the fourth workshop on {NLP} for computer-assisted language learning",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = "LiU Electronic Press",
    url = "https://www.aclweb.org/anthology/W15-1906",
    pages = "51--64",
}
@proceedings{ws-2015-nordic,
    title = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    editor = "Megyesi, Be{\'a}ta",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1800",
}
@inproceedings{knight-2015-invited,
    title = "Invited Talk: How Much Information Does a Human Translator Add to the Original?",
    author = "Knight, Kevin",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1801",
    pages = "xi--xi",
}
@inproceedings{pelachaud-2015-invited,
    title = "Invited Talk: Modeling Socio-Emotional Humanoid Agent",
    author = "Pelachaud, Catherine",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1802",
    pages = "xiii--xiii",
}
@inproceedings{riedel-2015-invited,
    title = "Invited Talk: Embedding Probabilistic Logic for Machine Reading",
    author = "Riedel, Sebastian",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1803",
    pages = "xv--xv",
}
@inproceedings{adesam-etal-2015-defining,
    title = "Defining the Eukalyptus forest {--} the Koala treebank of {S}wedish",
    author = "Adesam, Yvonne  and
      Bouma, Gerlof  and
      Johansson, Richard",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1804",
    pages = "1--9",
}
@inproceedings{ahlberg-etal-2015-case,
    title = "A case study on supervised classification of {S}wedish pseudo-coordination",
    author = "Ahlberg, Malin  and
      Andersson, Peter  and
      Forsberg, Markus  and
      Tahmasebi, Nina",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1805",
    pages = "11--19",
}
@inproceedings{martinez-alonso-etal-2015-supersense,
    title = "Supersense tagging for {D}anish",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Johannsen, Anders  and
      Olsen, Sussi  and
      Nimb, Sanni  and
      Hartvig S{\o}rensen, Nicolai  and
      Braasch, Anna  and
      S{\o}gaard, Anders  and
      Sandford Pedersen, Bolette",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1806",
    pages = "21--29",
}
@inproceedings{bick-didriksen-2015-cg,
    title = "{CG}-3 {---} Beyond Classical Constraint Grammar",
    author = "Bick, Eckhard  and
      Didriksen, Tino",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1807",
    pages = "31--39",
}
@inproceedings{boizou-etal-2015-automatic,
    title = "Automatic Lemmatisation of {L}ithuanian {MWE}s",
    author = {Boizou, Lo{\"\i}c  and
      Kovalevskait{\.e}, Jolanta  and
      Rimkut{\.e}, Erika},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1808",
    pages = "41--49",
}
@inproceedings{chernov-etal-2015-linguistically,
    title = "Linguistically Motivated Question Classification",
    author = "Chernov, Alexandr  and
      Petukhova, Volha  and
      Klakow, Dietrich",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1809",
    pages = "51--59",
}
@inproceedings{gotze-boye-2015-resolving,
    title = "Resolving Spatial References using Crowdsourced Geographical Data",
    author = {G{\"o}tze, Jana  and
      Boye, Johan},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1810",
    pages = "61--68",
}
@inproceedings{johansson-nieto-pina-2015-combining,
    title = "Combining Relational and Distributional Knowledge for Word Sense Disambiguation",
    author = "Johansson, Richard  and
      Nieto Pi{\~n}a, Luis",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1811",
    pages = "69--78",
}
@inproceedings{henrichsen-2015-talebob,
    title = "Talebob - an Interactive Speech Trainer for {D}anish",
    author = "Henrichsen, Peter Juel",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1812",
    pages = "79--86",
}
@inproceedings{kapociute-dzikiene-etal-2015-effect,
    title = "The Effect of Author Set Size in Authorship Attribution for {L}ithuanian",
    author = "Kapo{\v{c}}i{\=u}t{\.e}-Dzikien{\.e}, Jurgita  and
      {\v{S}}arkut{\.e}, Ligita  and
      Utka, Andrius",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1813",
    pages = "87--96",
}
@inproceedings{klerke-etal-2015-looking,
    title = "Looking hard: Eye tracking for detecting grammaticality of automatically compressed sentences",
    author = "Klerke, Sigrid  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1814",
    pages = "97--105",
}
@inproceedings{laippala-etal-2015-towards,
    title = "Towards the Classification of the {F}innish {I}nternet Parsebank: Detecting Translations and Informality",
    author = {Laippala, Veronika  and
      Kanerva, Jenna  and
      Missil{\"a}, Anna  and
      Pyysalo, Sampo  and
      Salakoski, Tapio  and
      Ginter, Filip},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1815",
    pages = "107--116",
}
@inproceedings{lien-etal-2015-improving,
    title = "Improving cross-domain dependency parsing with dependency-derived clusters",
    author = "Lien, Jostein  and
      Velldal, Erik  and
      {\O}vrelid, Lilja",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1816",
    pages = "117--126",
}
@inproceedings{lintean-rus-2015-optimal,
    title = "An Optimal Quadratic Approach to Monolingual Paraphrase Alignment",
    author = "Lintean, Mihai  and
      Rus, Vasile",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1817",
    pages = "127--134",
}
@inproceedings{luotolahti-ginter-2015-sentence,
    title = "Sentence Compression For Automatic Subtitling",
    author = "Luotolahti, Juhani  and
      Ginter, Filip",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1818",
    pages = "135--143",
}
@inproceedings{nokel-loukachevitch-2015-topic,
    title = "Topic Models: Accounting Component Structure of Bigrams",
    author = "Nokel, Michael  and
      Loukachevitch, Natalia",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1819",
    pages = "145--152",
}
@inproceedings{pettersson-nivre-2015-improving,
    title = "Improving Verb Phrase Extraction from Historical Text by use of Verb Valency Frames",
    author = "Pettersson, Eva  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1820",
    pages = "153--161",
}
@inproceedings{pyysalo-etal-2015-universal,
    title = "{U}niversal {D}ependencies for {F}innish",
    author = {Pyysalo, Sampo  and
      Kanerva, Jenna  and
      Missil{\"a}, Anna  and
      Laippala, Veronika  and
      Ginter, Filip},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1821",
    pages = "163--172",
}
@inproceedings{reynolds-tyers-2015-automatic,
    title = "Automatic word stress annotation of {R}ussian unrestricted text",
    author = "Reynolds, Robert  and
      Tyers, Francis",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1822",
    pages = "173--180",
}
@inproceedings{sacha-etal-2015-self,
    title = "Self Organizing Maps for the Visual Analysis of Pitch Contours",
    author = "Sacha, Dominik  and
      Asano, Yuki  and
      Rohrdantz, Christian  and
      Hamborg, Felix  and
      Keim, Daniel  and
      Braun, Bettina  and
      Butt, Miriam",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1823",
    pages = "181--189",
}
@inproceedings{tiedemann-2015-improving,
    title = "Improving the Cross-Lingual Projection of Syntactic Dependencies",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1824",
    pages = "191--199",
}
@inproceedings{zapata-soeborg-kirkedal-2015-assessing,
    title = "Assessing the Performance of Automatic Speech Recognition Systems When Used by Native and Non-Native Speakers of Three Major Languages in Dictation Workflows",
    author = "Zapata, Juli{\'a}n  and
      S{\o}eborg Kirkedal, Andreas",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1825",
    pages = "201--210",
}
@inproceedings{berggren-etal-2015-inferring,
    title = "Inferring the location of authors from words in their texts",
    author = {Berggren, Max  and
      Karlgren, Jussi  and
      {\"O}stling, Robert  and
      Parkvall, Mikael},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1826",
    pages = "211--218",
}
@inproceedings{listenmaa-tyers-2015-automatic,
    title = "Automatic conversion of colloquial Finnishto standard {F}innish",
    author = "Listenmaa, Inari  and
      Tyers, Francis M.",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1827",
    pages = "219--223",
}
@inproceedings{mickevicius-etal-2015-automatic,
    title = "Automatic Thematic Classification of the Titles of the Seimas Votes",
    author = "Mickevi{\v{c}}ius, Vytautas  and
      Krilavi{\v{c}}ius, Tomas  and
      Morkevi{\v{c}}ius, Vaidas  and
      Mackut{\.e}-Varoneckien{\.e}, Au{\v{s}}ra",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1828",
    pages = "225--231",
}
@inproceedings{ojamaa-etal-2015-sentiment,
    title = "Sentiment analysis on conversational texts",
    author = {Ojamaa, Birgitta  and
      Jokinen, P{\"a}ivi Kristiina  and
      Muischenk, Kadri},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1829",
    pages = "233--237",
}
@inproceedings{katharina-siencnik-2015-adapting,
    title = "Adapting word2vec to Named Entity Recognition",
    author = "Katharina Sien{\v{c}}nik, Scharolta",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1830",
    pages = "239--243",
}
@inproceedings{martinez-alonso-etal-2015-active,
    title = "Active learning for sense annotation",
    author = "Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Plank, Barbara  and
      Johannsen, Anders  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1831",
    pages = "245--249",
}
@inproceedings{bos-nissim-2015-uncovering,
    title = "Uncovering Noun-Noun Compound Relations by Gamification",
    author = "Bos, Johan  and
      Nissim, Malvina",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1832",
    pages = "251--255",
}
@inproceedings{heimann-muhlenbock-etal-2015-multivariate,
    title = "A multivariate model for classifying texts{'} readability",
    author = {Heimann M{\"u}hlenbock, Katarina  and
      Johansson Kokkinakis, Sofie  and
      Liberg, Caroline  and
      af Geijerstam, {\AA}sa  and
      Wiksten Folkeryd, Jenny  and
      J{\"o}nsson, Arne  and
      Kanebrant, Erik  and
      Falkenjack, Johan},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1833",
    pages = "257--261",
}
@inproceedings{ostling-etal-2015-enriching,
    title = "Enriching the {S}wedish {S}ign {L}anguage Corpus with Part of Speech Tags Using Joint {B}ayesian Word Alignment and Annotation Transfer",
    author = {{\"O}stling, Robert  and
      B{\"o}rstell, Carl  and
      Wallin, Lars},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1834",
    pages = "263--268",
}
@inproceedings{henrique-paetzold-2015-using,
    title = "Using Positional Suffix Trees to Perform Agile Tree Kernel Calculation",
    author = "Henrique Paetzold, Gustavo",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1835",
    pages = "269--273",
}
@inproceedings{pilan-2015-helping,
    title = "Helping {S}wedish words come to their senses: word-sense disambiguation based on sense associations from the {SALDO} lexicon",
    author = "Pil{\'a}n, Ildik{\'o}",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1836",
    pages = "275--279",
}
@inproceedings{salimbajevs-strigins-2015-using,
    title = "Using sub-word n-gram models for dealing with {OOV} in large vocabulary speech recognition for {L}atvian",
    author = "Salimbajevs, Askars  and
      Strigins, Jevgenijs",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1837",
    pages = "281--285",
}
@inproceedings{steingrimsson-etal-2015-analysing,
    title = "Analysing Inconsistencies and Errors in {P}o{S} Tagging in two {I}celandic Gold Standards",
    author = {Steingr{\'\i}msson, Stein{\th}{\'o}r  and
      Helgad{\'o}ttir, Sigr{\'u}n  and
      R{\"o}gnvaldsson, Eir{\'\i}kur},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1838",
    pages = "287--291",
}
@inproceedings{breder-birkenes-etal-2015-digital,
    title = "From digital library to n-grams: {NB} N-gram",
    author = "Breder Birkenes, Magnus  and
      Johnsen, Lars G.  and
      Lindstad, Arne Martinus  and
      Ostad, Johanne",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1839",
    pages = "293--295",
}
@inproceedings{johannessen-2015-corpus,
    title = "The Corpus of {A}merican {N}orwegian Speech ({CANS})",
    author = "Johannessen, Janne Bondi",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1840",
    pages = "297--300",
}
@inproceedings{bos-2015-open,
    title = "Open-Domain Semantic Parsing with Boxer",
    author = "Bos, Johan",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1841",
    pages = "301--304",
}
@inproceedings{hardwick-etal-2015-extracting,
    title = "Extracting Semantic Frames using hfst-pmatch",
    author = "Hardwick, Sam  and
      Silfverberg, Miikka  and
      Lind{\'e}n, Krister",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1842",
    pages = "305--308",
}
@inproceedings{neumann-2015-discoursegraphs,
    title = "discoursegraphs: A graph-based merging tool and converter for multilayer annotated corpora",
    author = "Neumann, Arne",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1843",
    pages = "309--312",
}
@inproceedings{pirinen-2015-omorfi,
    title = "{O}morfi {---} Free and open source morphological lexical database for {F}innish",
    author = "Pirinen, Tommi A",
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1844",
    pages = "313--315",
}
@inproceedings{rennes-jonsson-2015-tool,
    title = "A Tool for Automatic Simplification of {S}wedish Texts",
    author = {Rennes, Evelina  and
      J{\"o}nsson, Arne},
    booktitle = "Proceedings of the 20th Nordic Conference of Computational Linguistics ({NODALIDA} 2015)",
    month = may,
    year = "2015",
    address = "Vilnius, Lithuania",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
    url = "https://www.aclweb.org/anthology/W15-1845",
    pages = "317--320",
}
@proceedings{ws-2015-international-natural,
    title = "Proceedings of the third International Workshop on Natural Language Processing for Social Media",
    editor = "Lin, Shou-de  and
      Ku, Lun-Wei  and
      Li, Cheng-Te  and
      Cambria, Erik",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1700",
    doi = "10.3115/v1/W15-17",
}
@inproceedings{awamura-etal-2015-location,
    title = "Location Name Disambiguation Exploiting Spatial Proximity and Temporal Consistency",
    author = "Awamura, Takashi  and
      Kawahara, Daisuke  and
      Aramaki, Eiji  and
      Shibata, Tomohide  and
      Kurohashi, Sadao",
    booktitle = "Proceedings of the third International Workshop on Natural Language Processing for Social Media",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1701",
    doi = "10.3115/v1/W15-1701",
    pages = "1--9",
}
@inproceedings{vo-etal-2015-paraphrase,
    title = "Paraphrase Identification and Semantic Similarity in {T}witter with Simple Features",
    author = "Vo, Ngoc Phuoc An  and
      Magnolini, Simone  and
      Popescu, Octavian",
    booktitle = "Proceedings of the third International Workshop on Natural Language Processing for Social Media",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1702",
    doi = "10.3115/v1/W15-1702",
    pages = "10--19",
}
@inproceedings{wang-etal-2015-language-detection,
    title = "A Language Detection System for Short Chats in Mobile Games",
    author = "Wang, Pidong  and
      Bojja, Nikhil  and
      Kannan, Shivasankari",
    booktitle = "Proceedings of the third International Workshop on Natural Language Processing for Social Media",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1703",
    doi = "10.3115/v1/W15-1703",
    pages = "20--28",
}
@inproceedings{bracewell-2015-long,
    title = "Long Nights, Rainy Days, and Misspent Youth: Automatically Extracting and Categorizing Occasions Associated with Consumer Products.",
    author = "Bracewell, David",
    booktitle = "Proceedings of the third International Workshop on Natural Language Processing for Social Media",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1704",
    doi = "10.3115/v1/W15-1704",
    pages = "29--38",
}
@inproceedings{riemer-etal-2015-deep,
    title = "A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination",
    author = "Riemer, Matthew  and
      Krasikov, Sophia  and
      Srinivasan, Harini",
    booktitle = "Proceedings of the third International Workshop on Natural Language Processing for Social Media",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1705",
    doi = "10.3115/v1/W15-1705",
    pages = "39--47",
}
@proceedings{ws-2015-linguistic,
    title = "Proceedings of The 9th Linguistic Annotation Workshop",
    editor = "Meyers, Adam  and
      Rehbein, Ines  and
      Zinsmeister, Heike",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1600",
    doi = "10.3115/v1/W15-16",
}
@inproceedings{chang-etal-2015-scaling,
    title = "Scaling Semantic Frame Annotation",
    author = "Chang, Nancy  and
      Paritosh, Praveen  and
      Huynh, David  and
      Baker, Collin F.",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1601",
    doi = "10.3115/v1/W15-1601",
    pages = "1--10",
}
@inproceedings{haertel-etal-2015-analytic,
    title = "An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning",
    author = "Haertel, Robbie  and
      Ringger, Eric  and
      Seppi, Kevin  and
      Felt, Paul",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1602",
    doi = "10.3115/v1/W15-1602",
    pages = "11--20",
}
@inproceedings{friedrich-etal-2015-annotating,
    title = "Annotating genericity: a survey, a scheme, and a corpus",
    author = "Friedrich, Annemarie  and
      Palmer, Alexis  and
      Peate S{\o}rensen, Melissa  and
      Pinkal, Manfred",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1603",
    doi = "10.3115/v1/W15-1603",
    pages = "21--30",
}
@inproceedings{brunato-etal-2015-design,
    title = "Design and Annotation of the First {I}talian Corpus for Text Simplification",
    author = "Brunato, Dominique  and
      Dell{'}Orletta, Felice  and
      Venturi, Giulia  and
      Montemagni, Simonetta",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1604",
    doi = "10.3115/v1/W15-1604",
    pages = "31--41",
}
@inproceedings{fisas-etal-2015-discoursive,
    title = "On the Discoursive Structure of Computer Graphics Research Papers",
    author = "Fisas, Beatriz  and
      Saggion, Horacio  and
      Ronzano, Francesco",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1605",
    doi = "10.3115/v1/W15-1605",
    pages = "42--51",
}
@inproceedings{kamioka-etal-2015-semantic,
    title = "Semantic Annotation of {J}apanese Functional Expressions and its Impact on Factuality Analysis",
    author = "Kamioka, Yudai  and
      Narita, Kazuya  and
      Mizuno, Junta  and
      Kanno, Miwa  and
      Inui, Kentaro",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1606",
    doi = "10.3115/v1/W15-1606",
    pages = "52--61",
}
@inproceedings{lopez-etal-2015-qualitative,
    title = "A Qualitative Analysis of a Corpus of Opinion Summaries based on Aspects",
    author = "L{\'o}pez, Roque  and
      Pardo, Thiago  and
      Avan{\c{c}}o, Lucas  and
      Filho, Pedro  and
      Bokan, Alessandro  and
      Cardoso, Paula  and
      Dias, M{\'a}rcio  and
      N{\'o}brega, Fernando  and
      Cabezudo, Marco  and
      Souza, Jackson  and
      Zacarias, Andressa  and
      Seno, Eloize  and
      Di Felippo, Ariani",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1607",
    doi = "10.3115/v1/W15-1607",
    pages = "62--71",
}
@inproceedings{maharjan-etal-2015-developing,
    title = "Developing Language-tagged Corpora for Code-switching Tweets",
    author = "Maharjan, Suraj  and
      Blair, Elizabeth  and
      Bethard, Steven  and
      Solorio, Thamar",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1608",
    doi = "10.3115/v1/W15-1608",
    pages = "72--84",
}
@inproceedings{matsuda-etal-2015-annotating,
    title = "Annotating Geographical Entities on Microblog Text",
    author = "Matsuda, Koji  and
      Sasaki, Akira  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1609",
    doi = "10.3115/v1/W15-1609",
    pages = "85--94",
}
@inproceedings{pamay-etal-2015-annotation,
    title = "The Annotation Process of the {ITU} Web Treebank",
    author = {Pamay, Tu{\u{g}}ba  and
      Sulubacak, Umut  and
      Toruno{\u{g}}lu-Selamet, Dilara  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1610",
    doi = "10.3115/v1/W15-1610",
    pages = "95--101",
}
@inproceedings{serbina-etal-2015-part,
    title = "Part of Speech Annotation of Intermediate Versions in the Keystroke Logged Translation Corpus",
    author = "Serbina, Tatiana  and
      Niemietz, Paula  and
      Fricke, Matthias  and
      Meisen, Philipp  and
      Neumann, Stella",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1611",
    doi = "10.3115/v1/W15-1611",
    pages = "102--111",
}
@inproceedings{schneider-etal-2015-hierarchy,
    title = "A Hierarchy with, of, and for Preposition Supersenses",
    author = "Schneider, Nathan  and
      Srikumar, Vivek  and
      Hwang, Jena D.  and
      Palmer, Martha",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1612",
    doi = "10.3115/v1/W15-1612",
    pages = "112--123",
}
@inproceedings{uresova-etal-2015-bilingual,
    title = "Bilingual {E}nglish-{C}zech Valency Lexicon Linked to a Parallel Corpus",
    author = "Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Fu{\v{c}}{\'\i}kov{\'a}, Eva  and
      Haji{\v{c}}, Jan  and
      {\v{S}}indlerov{\'a}, Jana",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1613",
    doi = "10.3115/v1/W15-1613",
    pages = "124--128",
}
@inproceedings{zaghouani-etal-2015-correction,
    title = "Correction Annotation for Non-Native {A}rabic Texts: Guidelines and Corpus",
    author = "Zaghouani, Wajdi  and
      Habash, Nizar  and
      Bouamor, Houda  and
      Rozovskaya, Alla  and
      Mohit, Behrang  and
      Heider, Abeer  and
      Oflazer, Kemal",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1614",
    doi = "10.3115/v1/W15-1614",
    pages = "129--139",
}
@inproceedings{bies-2015-balancing,
    title = "Balancing the Existing and the New in the Context of Annotating Non-Canonical Language",
    author = "Bies, Ann",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1615",
    doi = "10.3115/v1/W15-1615",
    pages = "140--143",
}
@inproceedings{cahill-2015-parsing,
    title = "Parsing Learner Text: to Shoehorn or not to Shoehorn",
    author = "Cahill, Aoife",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1616",
    doi = "10.3115/v1/W15-1616",
    pages = "144--147",
}
@inproceedings{plank-etal-2015-non,
    title = "Non-canonical language is not harder to annotate than canonical language",
    author = "Plank, Barbara  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1617",
    doi = "10.3115/v1/W15-1617",
    pages = "148--151",
}
@inproceedings{schneider-2015-ive,
    title = "What {I}{'}ve learned about annotating informal text (and why you shouldn{'}t take my word for it)",
    author = "Schneider, Nathan",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1618",
    doi = "10.3115/v1/W15-1618",
    pages = "152--157",
}
@inproceedings{dickinson-ragheb-2015-grammaticality,
    title = "On Grammaticality in the Syntactic Annotation of Learner Language",
    author = "Dickinson, Markus  and
      Ragheb, Marwa",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1619",
    doi = "10.3115/v1/W15-1619",
    pages = "158--167",
}
@inproceedings{lapshinova-koltunski-etal-2015-across,
    title = "Across Languages and Genres: Creating a Universal Annotation Scheme for Textual Relations",
    author = "Lapshinova-Koltunski, Ekaterina  and
      Nedoluzhko, Anna  and
      Kunz, Kerstin Anna",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1620",
    doi = "10.3115/v1/W15-1620",
    pages = "168--177",
}
@inproceedings{anand-mccloskey-2015-annotating,
    title = "Annotating the Implicit Content of Sluices",
    author = "Anand, Pranav  and
      McCloskey, Jim",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1621",
    doi = "10.3115/v1/W15-1621",
    pages = "178--187",
}
@inproceedings{dunietz-etal-2015-annotating,
    title = "Annotating Causal Language Using Corpus Lexicography of Constructions",
    author = "Dunietz, Jesse  and
      Levin, Lori  and
      Carbonell, Jaime",
    booktitle = "Proceedings of The 9th Linguistic Annotation Workshop",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1622",
    doi = "10.3115/v1/W15-1622",
    pages = "188--196",
}
@proceedings{ws-2015-vector,
    title = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    editor = "Blunsom, Phil  and
      Cohen, Shay  and
      Dhillon, Paramveer  and
      Liang, Percy",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1500",
    doi = "10.3115/v1/W15-15",
}
@inproceedings{melamud-etal-2015-simple,
    title = "A Simple Word Embedding Model for Lexical Substitution",
    author = "Melamud, Oren  and
      Levy, Omer  and
      Dagan, Ido",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1501",
    doi = "10.3115/v1/W15-1501",
    pages = "1--7",
}
@inproceedings{rangarajan-sridhar-2015-unsupervised,
    title = "Unsupervised Text Normalization Using Distributed Representations of Words and Phrases",
    author = "Rangarajan Sridhar, Vivek Kumar",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1502",
    doi = "10.3115/v1/W15-1502",
    pages = "8--16",
}
@inproceedings{zelaia-etal-2015-multi,
    title = "A Multi-classifier Approach to support Coreference Resolution in a Vector Space Model",
    author = "Zelaia, Ana  and
      Arregi, Olatz  and
      Sierra, Basilio",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1503",
    doi = "10.3115/v1/W15-1503",
    pages = "17--24",
}
@inproceedings{kageback-etal-2015-neural,
    title = "Neural context embeddings for automatic discovery of word senses",
    author = {K{\aa}geb{\"a}ck, Mikael  and
      Johansson, Fredrik  and
      Johansson, Richard  and
      Dubhashi, Devdatt},
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1504",
    doi = "10.3115/v1/W15-1504",
    pages = "25--32",
}
@inproceedings{ma-etal-2015-distributional,
    title = "Distributional Representations of Words for Short Text Classification",
    author = "Ma, Chenglong  and
      Xu, Weiqun  and
      Li, Peijia  and
      Yan, Yonghong",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1505",
    doi = "10.3115/v1/W15-1505",
    pages = "33--38",
}
@inproceedings{nguyen-grishman-2015-relation,
    title = "Relation Extraction: Perspective from Convolutional Neural Networks",
    author = "Nguyen, Thien Huu  and
      Grishman, Ralph",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1506",
    doi = "10.3115/v1/W15-1506",
    pages = "39--48",
}
@inproceedings{urbain-etal-2015-distributional,
    title = "Distributional Semantic Concept Models for Entity Relation Discovery",
    author = "Urbain, Jay  and
      Bushee, Glenn  and
      Kowalski, George",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1507",
    doi = "10.3115/v1/W15-1507",
    pages = "49--55",
}
@inproceedings{fonseca-aluisio-2015-deep,
    title = "A Deep Architecture for Non-Projective Dependency Parsing",
    author = "Fonseca, Erick  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1508",
    doi = "10.3115/v1/W15-1508",
    pages = "56--61",
}
@inproceedings{xu-etal-2015-short,
    title = "Short Text Clustering via Convolutional Neural Networks",
    author = "Xu, Jiaming  and
      Wang, Peng  and
      Tian, Guanhua  and
      Xu, Bo  and
      Zhao, Jun  and
      Wang, Fangyuan  and
      Hao, Hongwei",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1509",
    doi = "10.3115/v1/W15-1509",
    pages = "62--69",
}
@inproceedings{del-tredici-bel-2015-word,
    title = "A Word-Embedding-based Sense Index for Regular Polysemy Representation",
    author = "Del Tredici, Marco  and
      Bel, N{\'u}ria",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1510",
    doi = "10.3115/v1/W15-1510",
    pages = "70--78",
}
@inproceedings{stratos-collins-2015-simple,
    title = "Simple Semi-Supervised {POS} Tagging",
    author = "Stratos, Karl  and
      Collins, Michael",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1511",
    doi = "10.3115/v1/W15-1511",
    pages = "79--87",
}
@inproceedings{pham-etal-2015-learning,
    title = "Learning Distributed Representations for Multilingual Text Sequences",
    author = "Pham, Hieu  and
      Luong, Thang  and
      Manning, Christopher",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1512",
    doi = "10.3115/v1/W15-1512",
    pages = "88--94",
}
@inproceedings{garten-etal-2015-combining,
    title = "Combining Distributed Vector Representations for Words",
    author = "Garten, Justin  and
      Sagae, Kenji  and
      Ustun, Volkan  and
      Dehghani, Morteza",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1513",
    doi = "10.3115/v1/W15-1513",
    pages = "95--101",
}
@inproceedings{bansal-2015-dependency,
    title = "Dependency Link Embeddings: Continuous Representations of Syntactic Substructures",
    author = "Bansal, Mohit",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1514",
    doi = "10.3115/v1/W15-1514",
    pages = "102--108",
}
@inproceedings{attardi-2015-deepnl,
    title = "{D}eep{NL}: a Deep Learning {NLP} pipeline",
    author = "Attardi, Giuseppe",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1515",
    doi = "10.3115/v1/W15-1515",
    pages = "109--115",
}
@inproceedings{alghunaim-etal-2015-vector,
    title = "A Vector Space Approach for Aspect Based Sentiment Analysis",
    author = "Alghunaim, Abdulaziz  and
      Mohtarami, Mitra  and
      Cyphers, Scott  and
      Glass, Jim",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1516",
    doi = "10.3115/v1/W15-1516",
    pages = "116--122",
}
@inproceedings{tosik-etal-2015-word,
    title = "Word Embeddings vs Word Types for Sequence Labeling: the Curious Case of {CV} Parsing",
    author = "Tosik, Melanie  and
      Lygteskov Hansen, Carsten  and
      Goossen, Gerard  and
      Rotaru, Mihai",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1517",
    doi = "10.3115/v1/W15-1517",
    pages = "123--128",
}
@inproceedings{nicolai-etal-2015-morpho,
    title = "Morpho-syntactic Regularities in Continuous Word Representations: A multilingual study.",
    author = "Nicolai, Garrett  and
      Cherry, Colin  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1518",
    doi = "10.3115/v1/W15-1518",
    pages = "129--134",
}
@inproceedings{singh-etal-2015-towards,
    title = "Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction",
    author = {Singh, Sameer  and
      Rockt{\"a}schel, Tim  and
      Riedel, Sebastian},
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1519",
    doi = "10.3115/v1/W15-1519",
    pages = "135--142",
}
@inproceedings{kim-etal-2015-neural,
    title = "Neural word embeddings with multiplicative feature interactions for tensor-based compositions",
    author = "Kim, Joo-Kyung  and
      de Marneffe, Marie-Catherine  and
      Fosler-Lussier, Eric",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1520",
    doi = "10.3115/v1/W15-1520",
    pages = "143--150",
}
@inproceedings{luong-etal-2015-bilingual,
    title = "Bilingual Word Representations with Monolingual Quality in Mind",
    author = "Luong, Thang  and
      Pham, Hieu  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1521",
    doi = "10.3115/v1/W15-1521",
    pages = "151--159",
}
@inproceedings{joshi-etal-2015-distributed,
    title = "Distributed Word Representations Improve {NER} for e-Commerce",
    author = "Joshi, Mahesh  and
      Hart, Ethan  and
      Vogel, Mirko  and
      Ruvini, Jean-David",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1522",
    doi = "10.3115/v1/W15-1522",
    pages = "160--167",
}
@inproceedings{chen-de-melo-2015-semantic,
    title = "Semantic Information Extraction for Improved Word Embeddings",
    author = "Chen, Jiaqiang  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1523",
    doi = "10.3115/v1/W15-1523",
    pages = "168--175",
}
@inproceedings{zirikly-diab-2015-named,
    title = "Named Entity Recognition for {A}rabic Social Media",
    author = "Zirikly, Ayah  and
      Diab, Mona",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1524",
    doi = "10.3115/v1/W15-1524",
    pages = "176--185",
}
@inproceedings{conroy-davis-2015-vector,
    title = "Vector Space Models for Scientific Document Summarization",
    author = "Conroy, John  and
      Davis, Sashka",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1525",
    doi = "10.3115/v1/W15-1525",
    pages = "186--191",
}
@inproceedings{rangarajan-sridhar-2015-unsupervised-topic,
    title = "Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words",
    author = "Rangarajan Sridhar, Vivek Kumar",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1526",
    doi = "10.3115/v1/W15-1526",
    pages = "192--200",
}
@inproceedings{liu-inkpen-2015-estimating,
    title = "Estimating User Location in Social Media with Stacked Denoising Auto-encoders",
    author = "Liu, Ji  and
      Inkpen, Diana",
    booktitle = "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1527",
    doi = "10.3115/v1/W15-1527",
    pages = "201--210",
}
@proceedings{ws-2015-metaphor,
    title = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    editor = "Shutova, Ekaterina  and
      Beigman Klebanov, Beata  and
      Lichtenstein, Patricia",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1400",
    doi = "10.3115/v1/W15-14",
}
@inproceedings{jang-etal-2015-effects,
    title = "Effects of Situational Factors on Metaphor Detection in an Online Discussion Forum",
    author = "Jang, Hyeju  and
      Wen, Miaomiao  and
      Ros{\'e}, Carolyn",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1401",
    doi = "10.3115/v1/W15-1401",
    pages = "1--10",
}
@inproceedings{beigman-klebanov-etal-2015-supervised,
    title = "Supervised Word-Level Metaphor Detection: Experiments with Concreteness and Reweighting of Examples",
    author = "Beigman Klebanov, Beata  and
      Leong, Chee Wee  and
      Flor, Michael",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1402",
    doi = "10.3115/v1/W15-1402",
    pages = "11--20",
}
@inproceedings{gargett-barnden-2015-modeling,
    title = "Modeling the interaction between sensory and affective meanings for detecting metaphor",
    author = "Gargett, Andrew  and
      Barnden, John",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1403",
    doi = "10.3115/v1/W15-1403",
    pages = "21--30",
}
@inproceedings{tekiroglu-etal-2015-exploring,
    title = "Exploring Sensorial Features for Metaphor Identification",
    author = {Tekiro{\u{g}}lu, Serra Sinem  and
      {\"O}zbal, G{\"o}zde  and
      Strapparava, Carlo},
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1404",
    doi = "10.3115/v1/W15-1404",
    pages = "31--39",
}
@inproceedings{dodge-etal-2015-metanet,
    title = "{M}eta{N}et: Deep semantic automatic metaphor analysis",
    author = "Dodge, Ellen  and
      Hong, Jisup  and
      Stickles, Elise",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1405",
    doi = "10.3115/v1/W15-1405",
    pages = "40--49",
}
@inproceedings{gordon-etal-2015-high,
    title = "High-Precision Abductive Mapping of Multilingual Metaphors",
    author = "Gordon, Jonathan  and
      Hobbs, Jerry  and
      May, Jonathan  and
      Morbini, Fabrizio",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1406",
    doi = "10.3115/v1/W15-1406",
    pages = "50--55",
}
@inproceedings{gordon-etal-2015-corpus,
    title = "A Corpus of Rich Metaphor Annotation",
    author = "Gordon, Jonathan  and
      Hobbs, Jerry  and
      May, Jonathan  and
      Mohler, Michael  and
      Morbini, Fabrizio  and
      Rink, Bryan  and
      Tomlinson, Marc  and
      Wertheim, Suzanne",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1407",
    doi = "10.3115/v1/W15-1407",
    pages = "56--66",
}
@inproceedings{shaikh-etal-2015-understanding,
    title = "Understanding Cultural Conflicts using Metaphors and Sociolinguistic Measures of Influence",
    author = "Shaikh, Samira  and
      Strzalkowski, Tomek  and
      Taylor, Sarah  and
      Lien, John  and
      Liu, Ting  and
      Broadwell, George Aaron  and
      Feldman, Laurie  and
      Yamrom, Boris  and
      Cho, Kit  and
      Peshkova, Yuliya",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1408",
    doi = "10.3115/v1/W15-1408",
    pages = "67--76",
}
@inproceedings{li-etal-2015-chinese,
    title = "{C}hinese {C}og{B}ank: Where to See the Cognitive Features of {C}hinese Words",
    author = "Li, Bin  and
      Bai, Xiaopeng  and
      Yin, Siqi  and
      Xu, Jie",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1409",
    doi = "10.3115/v1/W15-1409",
    pages = "77--86",
}
@inproceedings{veale-2015-fighting,
    title = "Fighting Words and Antagonistic Worlds",
    author = "Veale, Tony",
    booktitle = "Proceedings of the Third Workshop on Metaphor in {NLP}",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1410",
    doi = "10.3115/v1/W15-1410",
    pages = "87--94",
}
@proceedings{ws-2015-extra,
    title = "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics ({E}x{P}ro{M} 2015)",
    editor = "Blanco, Eduardo  and
      Morante, Roser  and
      Sporleder, Caroline",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1300",
    doi = "10.3115/v1/W15-13",
}
@inproceedings{karttunen-2015-limits,
    title = "Limits of Natural Logic",
    author = "Karttunen, Lauri",
    booktitle = "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics ({E}x{P}ro{M} 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1306",
    doi = "10.3115/v1/W15-1306",
    pages = "1--1",
}
@inproceedings{fancellu-webber-2015-translating-negation,
    title = "Translating Negation: A Manual Error Analysis",
    author = "Fancellu, Federico  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics ({E}x{P}ro{M} 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1301",
    doi = "10.3115/v1/W15-1301",
    pages = "2--11",
}
@inproceedings{rehbein-2015-filled,
    title = "Filled Pauses in User-generated Content are Words with Extra-propositional Meaning",
    author = "Rehbein, Ines",
    booktitle = "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics ({E}x{P}ro{M} 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1302",
    doi = "10.3115/v1/W15-1302",
    pages = "12--21",
}
@inproceedings{kilicoglu-etal-2015-compositional,
    title = "A Compositional Interpretation of Biomedical Event Factuality",
    author = "Kilicoglu, Halil  and
      Rosemblat, Graciela  and
      Cairelli, Michael  and
      Rindflesch, Thomas",
    booktitle = "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics ({E}x{P}ro{M} 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1303",
    doi = "10.3115/v1/W15-1303",
    pages = "22--31",
}
@inproceedings{werner-etal-2015-committed,
    title = "Committed Belief Tagging on the Factbank and {LU} Corpora: A Comparative Study",
    author = "Werner, Gregory  and
      Prabhakaran, Vinodkumar  and
      Diab, Mona  and
      Rambow, Owen",
    booktitle = "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics ({E}x{P}ro{M} 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1304",
    doi = "10.3115/v1/W15-1304",
    pages = "32--40",
}
@inproceedings{shivade-etal-2015-extending,
    title = "Extending {N}eg{E}x with Kernel Methods for Negation Detection in Clinical Text",
    author = "Shivade, Chaitanya  and
      de Marneffe, Marie-Catherine  and
      Fosler-Lussier, Eric  and
      Lai, Albert M.",
    booktitle = "Proceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics ({E}x{P}ro{M} 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1305",
    doi = "10.3115/v1/W15-1305",
    pages = "41--46",
}
@proceedings{ws-2015-linguistics-clinical,
    title = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1200",
    doi = "10.3115/v1/W15-12",
}
@inproceedings{coppersmith-etal-2015-adhd,
    title = "From {ADHD} to {SAD}: Analyzing the Language of Mental Health on {T}witter through Self-Reported Diagnoses",
    author = "Coppersmith, Glen  and
      Dredze, Mark  and
      Harman, Craig  and
      Hollingshead, Kristy",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1201",
    doi = "10.3115/v1/W15-1201",
    pages = "1--10",
}
@inproceedings{mitchell-etal-2015-quantifying,
    title = "Quantifying the Language of Schizophrenia in Social Media",
    author = "Mitchell, Margaret  and
      Hollingshead, Kristy  and
      Coppersmith, Glen",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1202",
    doi = "10.3115/v1/W15-1202",
    pages = "11--20",
}
@inproceedings{preotiuc-pietro-etal-2015-role,
    title = "The role of personality, age, and gender in tweeting about mental illness",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Eichstaedt, Johannes  and
      Park, Gregory  and
      Sap, Maarten  and
      Smith, Laura  and
      Tobolsky, Victoria  and
      Schwartz, H. Andrew  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1203",
    doi = "10.3115/v1/W15-1203",
    pages = "21--30",
}
@inproceedings{coppersmith-etal-2015-clpsych,
    title = "{CLP}sych 2015 Shared Task: Depression and {PTSD} on {T}witter",
    author = "Coppersmith, Glen  and
      Dredze, Mark  and
      Harman, Craig  and
      Hollingshead, Kristy  and
      Mitchell, Margaret",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1204",
    doi = "10.3115/v1/W15-1204",
    pages = "31--39",
}
@inproceedings{preotiuc-pietro-etal-2015-mental,
    title = "Mental Illness Detection at the World Well-Being Project for the {CLP}sych 2015 Shared Task",
    author = "Preo{\c{t}}iuc-Pietro, Daniel  and
      Sap, Maarten  and
      Schwartz, H. Andrew  and
      Ungar, Lyle",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1205",
    doi = "10.3115/v1/W15-1205",
    pages = "40--45",
}
@inproceedings{pedersen-2015-screening,
    title = "Screening {T}witter Users for Depression and {PTSD} with Lexical Decision Lists",
    author = "Pedersen, Ted",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1206",
    doi = "10.3115/v1/W15-1206",
    pages = "46--53",
}
@inproceedings{resnik-etal-2015-university,
    title = "The {U}niversity of {M}aryland {CLP}sych 2015 Shared Task System",
    author = "Resnik, Philip  and
      Armstrong, William  and
      Claudino, Leonardo  and
      Nguyen, Thang",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1207",
    doi = "10.3115/v1/W15-1207",
    pages = "54--60",
}
@inproceedings{del-castillo-etal-2015-computational,
    title = "Computational cognitive modeling of inflectional verb morphology in {S}panish-speakers for the characterization and diagnosis of {A}lzheimer{'}s Disease",
    author = "del Castillo, M. Dolores  and
      Serrano, J. Ignacio  and
      Oliva, Jes{\'u}s",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1208",
    doi = "10.3115/v1/W15-1208",
    pages = "61--70",
}
@inproceedings{tanana-etal-2015-recursive,
    title = "Recursive Neural Networks for Coding Therapist and Patient Behavior in Motivational Interviewing",
    author = "Tanana, Michael  and
      Hallgren, Kevin  and
      Imel, Zac  and
      Atkins, David  and
      Smyth, Padhraic  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1209",
    doi = "10.3115/v1/W15-1209",
    pages = "71--79",
}
@inproceedings{murphy-etal-2015-putting,
    title = "Putting Feelings into Words: Cross-Linguistic Markers of the Referential Process",
    author = "Murphy, Sean  and
      Maskit, Bernard  and
      Bucci, Wilma",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1210",
    doi = "10.3115/v1/W15-1210",
    pages = "80--88",
}
@inproceedings{mowery-etal-2015-towards,
    title = "Towards Developing an Annotation Scheme for Depressive Disorder Symptoms: A Preliminary Study using {T}witter Data",
    author = "Mowery, Danielle  and
      Bryan, Craig  and
      Conway, Mike",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1211",
    doi = "10.3115/v1/W15-1211",
    pages = "89--98",
}
@inproceedings{resnik-etal-2015-beyond,
    title = "Beyond {LDA}: Exploring Supervised Topic Modeling for Depression-Related Language in {T}witter",
    author = "Resnik, Philip  and
      Armstrong, William  and
      Claudino, Leonardo  and
      Nguyen, Thang  and
      Nguyen, Viet-An  and
      Boyd-Graber, Jordan",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1212",
    doi = "10.3115/v1/W15-1212",
    pages = "99--107",
}
@inproceedings{gorman-etal-2015-automated,
    title = "Automated morphological analysis of clinical language samples",
    author = "Gorman, Kyle  and
      Bedrick, Steven  and
      Kiss, G{\'e}za  and
      Morley, Eric  and
      Ingham, Rosemary  and
      Mohammed, Metrah  and
      Papadakis, Katina  and
      van Santen, Jan",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1213",
    doi = "10.3115/v1/W15-1213",
    pages = "108--116",
}
@inproceedings{rouhizadeh-etal-2015-similarity,
    title = "Similarity Measures for Quantifying Restrictive and Repetitive Behavior in Conversations of Autistic Children",
    author = "Rouhizadeh, Masoud  and
      Sproat, Richard  and
      van Santen, Jan",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1214",
    doi = "10.3115/v1/W15-1214",
    pages = "117--123",
}
@inproceedings{rosenstein-etal-2015-practical,
    title = "Practical issues in developing semantic frameworks for the analysis of verbal fluency data: A {N}orwegian data case study",
    author = "Rosenstein, Mark  and
      Foltz, Peter  and
      Vaskinn, Anja  and
      Elvev{\aa}g, Brita",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1215",
    doi = "10.3115/v1/W15-1215",
    pages = "124--133",
}
@inproceedings{maskit-etal-2015-computer,
    title = "A Computer Program for Tracking the Evolution of a Psychotherapy Treatment",
    author = "Maskit, Bernard  and
      Bucci, Wilma  and
      Murphy, Sean",
    booktitle = "Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality",
    month = jun # " 5",
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1216",
    doi = "10.3115/v1/W15-1216",
    pages = "134--145",
}
@proceedings{ws-2015-cognitive,
    title = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    editor = "O{'}Donnell, Tim  and
      van Schijndel, Marten",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1100",
    doi = "10.3115/v1/W15-11",
}
@inproceedings{jacobs-2015-predictions,
    title = "Predictions for self-priming from incremental updating models unifying comprehension and production",
    author = "Jacobs, Cassandra L.",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1101",
    doi = "10.3115/v1/W15-1101",
    pages = "1--8",
}
@inproceedings{wang-etal-2015-pragmatic,
    title = "Pragmatic Alignment on Social Support Type in Health Forum Conversations",
    author = "Wang, Yafei  and
      Yen, John  and
      Reitter, David",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1102",
    doi = "10.3115/v1/W15-1102",
    pages = "9--18",
}
@inproceedings{doyle-frank-2015-audience,
    title = "Audience size and contextual effects on information density in {T}witter conversations",
    author = "Doyle, Gabriel  and
      Frank, Michael",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1103",
    doi = "10.3115/v1/W15-1103",
    pages = "19--28",
}
@inproceedings{noble-fernandez-2015-centre,
    title = "Centre Stage: How Social Network Position Shapes Linguistic Coordination",
    author = "Noble, Bill  and
      Fern{\'a}ndez, Raquel",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1104",
    doi = "10.3115/v1/W15-1104",
    pages = "29--38",
}
@inproceedings{georgiladakis-etal-2015-fusion,
    title = "Fusion of Compositional Network-based and Lexical Function Distributional Semantic Models",
    author = "Georgiladakis, Spiros  and
      Iosif, Elias  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1105",
    doi = "10.3115/v1/W15-1105",
    pages = "39--47",
}
@inproceedings{greenberg-etal-2015-verb,
    title = "Verb polysemy and frequency effects in thematic fit modeling",
    author = "Greenberg, Clayton  and
      Demberg, Vera  and
      Sayeed, Asad",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1106",
    doi = "10.3115/v1/W15-1106",
    pages = "48--57",
}
@inproceedings{xu-reitter-2015-evaluation,
    title = "An Evaluation and Comparison of Linguistic Alignment Measures",
    author = "Xu, Yang  and
      Reitter, David",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1107",
    doi = "10.3115/v1/W15-1107",
    pages = "58--67",
}
@inproceedings{phillips-pearl-2015-utility,
    title = "Utility-based evaluation metrics for models of language acquisition: A look at speech segmentation",
    author = "Phillips, Lawrence  and
      Pearl, Lisa",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1108",
    doi = "10.3115/v1/W15-1108",
    pages = "68--78",
}
@inproceedings{van-schijndel-etal-2015-evidence,
    title = "Evidence of syntactic working memory usage in {MEG} data",
    author = "van Schijndel, Marten  and
      Murphy, Brian  and
      Schuler, William",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1109",
    doi = "10.3115/v1/W15-1109",
    pages = "79--88",
}
@inproceedings{hale-etal-2015-modeling,
    title = "Modeling f{MRI} time courses with linguistic structure at various grain sizes",
    author = "Hale, John  and
      Lutz, David  and
      Luh, Wen-Ming  and
      Brennan, Jonathan",
    booktitle = "Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1110",
    doi = "10.3115/v1/W15-1110",
    pages = "89--97",
}
@proceedings{ws-2015-syntax,
    title = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    editor = "Wu, Dekai  and
      Carpuat, Marine  and
      Agirre, Eneko  and
      Aranberri, Nora",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1000",
    doi = "10.3115/v1/W15-10",
}
@inproceedings{deng-etal-2015-harmonizing,
    title = "Harmonizing word alignments and syntactic structures for extracting phrasal translation equivalents",
    author = "Deng, Dun  and
      Xue, Nianwen  and
      Guo, Shiman",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1001",
    doi = "10.3115/v1/W15-1001",
    pages = "1--9",
}
@inproceedings{miceli-barone-attardi-2015-non,
    title = "Non-projective Dependency-based Pre-Reordering with Recurrent Neural Network for Machine Translation",
    author = "Miceli-Barone, Antonio Valerio  and
      Attardi, Giuseppe",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1002",
    doi = "10.3115/v1/W15-1002",
    pages = "10--20",
}
@inproceedings{fancellu-webber-2015-translating,
    title = "Translating Negation: Induction, Search And Model Errors",
    author = "Fancellu, Federico  and
      Webber, Bonnie",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1003",
    doi = "10.3115/v1/W15-1003",
    pages = "21--29",
}
@inproceedings{aranberri-2015-smt,
    title = "{SMT} error analysis and mapping to syntactic, semantic and structural fixes",
    author = "Aranberri, Nora",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1004",
    doi = "10.3115/v1/W15-1004",
    pages = "30--38",
}
@inproceedings{aminian-etal-2015-unsupervised,
    title = "Unsupervised False Friend Disambiguation Using Contextual Word Clusters and Parallel Word Alignments",
    author = "Aminian, Maryam  and
      Ghoneim, Mahmoud  and
      Diab, Mona",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1005",
    doi = "10.3115/v1/W15-1005",
    pages = "39--48",
}
@inproceedings{apidianaki-marie-2015-meteor,
    title = "{METEOR}-{WSD}: Improved Sense Matching in {MT} Evaluation",
    author = "Apidianaki, Marianna  and
      Marie, Benjamin",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1006",
    doi = "10.3115/v1/W15-1006",
    pages = "49--51",
}
@inproceedings{artetxe-etal-2015-analyzing,
    title = "Analyzing {E}nglish-{S}panish Named-Entity enhanced Machine Translation",
    author = "Artetxe, Mikel  and
      Agirre, Eneko  and
      Alegria, Inaki  and
      Labaka, Gorka",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1007",
    doi = "10.3115/v1/W15-1007",
    pages = "52--54",
}
@inproceedings{weller-etal-2015-predicting,
    title = "Predicting Prepositions for {SMT}",
    author = "Weller, Marion  and
      Fraser, Alexander  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1008",
    doi = "10.3115/v1/W15-1008",
    pages = "55--56",
}
@inproceedings{miceli-barone-2015-translation,
    title = "Translation reranking using source phrase dependency features",
    author = "Miceli-Barone, Antonio Valerio",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1009",
    doi = "10.3115/v1/W15-1009",
    pages = "57--60",
}
@inproceedings{vanallemeersch-vandeghinste-2015-semantics,
    title = "Semantics-based pretranslation for {SMT} using fuzzy matches",
    author = "Vanallemeersch, Tom  and
      Vandeghinste, Vincent",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1010",
    doi = "10.3115/v1/W15-1010",
    pages = "61--64",
}
@inproceedings{salameh-etal-2015-matters,
    title = "What Matters Most in Morphologically Segmented {SMT} Models?",
    author = "Salameh, Mohammad  and
      Cherry, Colin  and
      Kondrak, Grzegorz",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1011",
    doi = "10.3115/v1/W15-1011",
    pages = "65--73",
}
@inproceedings{wu-palmer-2015-improving,
    title = "Improving {C}hinese-{E}nglish {P}rop{B}ank Alignment",
    author = "Wu, Shumin  and
      Palmer, Martha",
    booktitle = "Proceedings of the Ninth Workshop on Syntax, Semantics and Structure in Statistical Translation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-1012",
    doi = "10.3115/v1/W15-1012",
    pages = "74--82",
}
@proceedings{ws-2015-multiword,
    title = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0900",
    doi = "10.3115/v1/W15-09",
}
@inproceedings{nokel-loukachevitch-2015-method,
    title = "A Method of Accounting Bigrams in Topic Models",
    author = "Nokel, Michael  and
      Loukachevitch, Natalia",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0901",
    doi = "10.3115/v1/W15-0901",
    pages = "1--9",
}
@inproceedings{sangati-van-cranenburgh-2015-multiword,
    title = "Multiword Expression Identification with Recurring Tree Fragments and Association Measures",
    author = "Sangati, Federico  and
      van Cranenburgh, Andreas",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0902",
    doi = "10.3115/v1/W15-0902",
    pages = "10--18",
}
@inproceedings{cap-etal-2015-account,
    title = "How to Account for Idiomatic {G}erman Support Verb Constructions in Statistical Machine Translation",
    author = "Cap, Fabienne  and
      Nirmal, Manju  and
      Weller, Marion  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0903",
    doi = "10.3115/v1/W15-0903",
    pages = "19--28",
}
@inproceedings{farahmand-etal-2015-multiword,
    title = "A Multiword Expression Data Set: Annotating Non-Compositionality and Conventionalization for {E}nglish Noun Compounds",
    author = "Farahmand, Meghdad  and
      Smith, Aaron  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0904",
    doi = "10.3115/v1/W15-0904",
    pages = "29--33",
}
@inproceedings{farahmand-nivre-2015-modeling,
    title = "Modeling the Statistical Idiosyncrasy of Multiword Expressions",
    author = "Farahmand, Meghdad  and
      Nivre, Joakim",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0905",
    doi = "10.3115/v1/W15-0905",
    pages = "34--38",
}
@inproceedings{tutubalina-2015-clustering,
    title = "Clustering-based Approach to Multiword Expression Extraction and Ranking",
    author = "Tutubalina, Elena",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0906",
    doi = "10.3115/v1/W15-0906",
    pages = "39--43",
}
@inproceedings{kay-michaelis-2015-constructions,
    title = "How Constructions Mean",
    author = "Kay, Paul  and
      Michaelis, Laura A.",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0907",
    doi = "10.3115/v1/W15-0907",
    pages = "44",
}
@inproceedings{rondon-etal-2015-never,
    title = "Never-Ending Multiword Expressions Learning",
    author = "Rondon, Alexandre  and
      Caseli, Helena  and
      Ramisch, Carlos",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0908",
    doi = "10.3115/v1/W15-0908",
    pages = "45--53",
}
@inproceedings{salehi-etal-2015-impact,
    title = "The Impact of Multiword Expression Compositionality on Machine Translation Evaluation",
    author = "Salehi, Bahar  and
      Mathur, Nitika  and
      Cook, Paul  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0909",
    doi = "10.3115/v1/W15-0909",
    pages = "54--59",
}
@inproceedings{boguraev-etal-2015-bare,
    title = "The Bare Necessities: Increasing Lexical Coverage for Multi-Word Domain Terms with Less Lexical Data",
    author = "Boguraev, Branimir  and
      Manandise, Esme  and
      Segal, Benjamin",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0910",
    doi = "10.3115/v1/W15-0910",
    pages = "60--64",
}
@inproceedings{lam-etal-2015-phrase,
    title = "Phrase translation using a bilingual dictionary and n-gram data: A case study from {V}ietnamese to {E}nglish",
    author = "Lam, Khang Nhut  and
      Al Tarouti, Feras  and
      Kalita, Jugal",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0911",
    doi = "10.3115/v1/W15-0911",
    pages = "65--69",
}
@inproceedings{eryigit-etal-2015-annotation,
    title = "Annotation and Extraction of Multiword Expressions in {T}urkish Treebanks",
    author = {Eryi{\v{g}}it, G{\"u}l{\c{s}}en  and
      Adali, K{\"u}bra  and
      Toruno{\u{g}}lu-Selamet, Dilara  and
      Sulubacak, Umut  and
      Pamay, Tu{\u{g}}ba},
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0912",
    doi = "10.3115/v1/W15-0912",
    pages = "70--76",
}
@inproceedings{marvel-koenig-2015-event,
    title = "Event Categorization beyond Verb Senses",
    author = "Marvel, Aron  and
      Koenig, Jean-Pierre",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0913",
    doi = "10.3115/v1/W15-0913",
    pages = "77--86",
}
@inproceedings{goodkind-rosenberg-2015-muddying,
    title = "Muddying The Multiword Expression Waters: How Cognitive Demand Affects Multiword Expression Production",
    author = "Goodkind, Adam  and
      Rosenberg, Andrew",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0914",
    doi = "10.3115/v1/W15-0914",
    pages = "87--95",
}
@inproceedings{brooke-etal-2015-building,
    title = "Building a Lexicon of Formulaic Language for Language Learners",
    author = "Brooke, Julian  and
      Hammond, Adam  and
      Jacob, David  and
      Tsang, Vivian  and
      Hirst, Graeme  and
      Shein, Fraser",
    booktitle = "Proceedings of the 11th Workshop on Multiword Expressions",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0915",
    doi = "10.3115/v1/W15-0915",
    pages = "96--104",
}
@proceedings{ws-2015-events,
    title = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    editor = "Hovy, Eduard  and
      Mitamura, Teruko  and
      Palmer, Martha",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0800",
    doi = "10.3115/v1/W15-08",
}
@inproceedings{cybulska-vossen-2015-translating,
    title = "Translating Granularity of Event Slots into Features for Event Coreference Resolution.",
    author = "Cybulska, Agata  and
      Vossen, Piek",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0801",
    doi = "10.3115/v1/W15-0801",
    pages = "1--10",
}
@inproceedings{fauceglia-etal-2015-word,
    title = "Word Sense Disambiguation via {P}rop{S}tore and {O}nto{N}otes for Event Mention Detection",
    author = "Fauceglia, Nicolas R.  and
      Lin, Yiu-Chang  and
      Ma, Xuezhe  and
      Hovy, Eduard",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0802",
    doi = "10.3115/v1/W15-0802",
    pages = "11--15",
}
@inproceedings{feltracco-etal-2015-opposition,
    title = "Opposition Relations among Verb Frames",
    author = "Feltracco, Anna  and
      Jezek, Elisabetta  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0803",
    doi = "10.3115/v1/W15-0803",
    pages = "16--24",
}
@inproceedings{hautli-janisz-etal-2015-encoding,
    title = "Encoding event structure in {U}rdu/{H}indi {V}erb{N}et",
    author = "Hautli-Janisz, Annette  and
      King, Tracy Holloway  and
      Ramchand, Gilian",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0804",
    doi = "10.3115/v1/W15-0804",
    pages = "25--33",
}
@inproceedings{keane-etal-2015-using,
    title = "Using Topic Modeling and Similarity Thresholds to Detect Events",
    author = "Keane, Nathan  and
      Yee, Connie  and
      Zhou, Liang",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0805",
    doi = "10.3115/v1/W15-0805",
    pages = "34--42",
}
@inproceedings{li-girju-2015-detecting,
    title = "Detecting Causally Embedded Structures Using an Evolutionary Algorithm",
    author = "Li, Chen  and
      Girju, Roxana",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0806",
    doi = "10.3115/v1/W15-0806",
    pages = "43--52",
}
@inproceedings{liu-etal-2015-evaluation,
    title = "Evaluation Algorithms for Event Nugget Detection : A Pilot Study",
    author = "Liu, Zhengzhong  and
      Mitamura, Teruko  and
      Hovy, Eduard",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0807",
    doi = "10.3115/v1/W15-0807",
    pages = "53--57",
}
@inproceedings{manrique-losada-zapata-jaramillo-2015-event,
    title = "Event analysis for information extraction from business-based technical documents",
    author = "Manrique Losada, Bell  and
      Zapata Jaramillo, Carlos Mario",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0808",
    doi = "10.3115/v1/W15-0808",
    pages = "58--65",
}
@inproceedings{mitamura-etal-2015-event,
    title = "Event Nugget Annotation: Processes and Issues",
    author = "Mitamura, Teruko  and
      Yamakawa, Yukari  and
      Holm, Susan  and
      Song, Zhiyi  and
      Bies, Ann  and
      Kulick, Seth  and
      Strassel, Stephanie",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0809",
    doi = "10.3115/v1/W15-0809",
    pages = "66--76",
}
@inproceedings{olshefski-2015-game,
    title = "Game-Changing Event Definition and Detection in an e{S}ports Corpus",
    author = "Olshefski, Emily",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0810",
    doi = "10.3115/v1/W15-0810",
    pages = "77--81",
}
@inproceedings{sharma-etal-2015-identifying,
    title = "Identifying Various Kinds of Event Mentions in K-Parser Output",
    author = "Sharma, Arpit  and
      Vo, Nguyen  and
      Aditya, Somak  and
      Baral, Chitta",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0811",
    doi = "10.3115/v1/W15-0811",
    pages = "82--88",
}
@inproceedings{song-etal-2015-light,
    title = "From Light to Rich {ERE}: Annotation of Entities, Relations, and Events",
    author = "Song, Zhiyi  and
      Bies, Ann  and
      Strassel, Stephanie  and
      Riese, Tom  and
      Mott, Justin  and
      Ellis, Joe  and
      Wright, Jonathan  and
      Kulick, Seth  and
      Ryant, Neville  and
      Ma, Xiaoyi",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0812",
    doi = "10.3115/v1/W15-0812",
    pages = "89--98",
}
@inproceedings{takabatake-etal-2015-classification,
    title = "Classification and Acquisition of Contradictory Event Pairs using Crowdsourcing",
    author = "Takabatake, Yu  and
      Morita, Hajime  and
      Kawahara, Daisuke  and
      Kurohashi, Sadao  and
      Higashinaka, Ryuichiro  and
      Matsuo, Yoshihiro",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0813",
    doi = "10.3115/v1/W15-0813",
    pages = "99--107",
}
@inproceedings{vossen-etal-2015-semantic,
    title = "Semantic Interoperability for Cross-lingual and cross-document Event Detection",
    author = "Vossen, Piek  and
      Laparra, Egoitz  and
      Rigau, German  and
      Aldabe, Itziar",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0814",
    doi = "10.3115/v1/W15-0814",
    pages = "108--116",
}
@inproceedings{yee-etal-2015-modeling,
    title = "Modeling and Characterizing Social Media Topics Using the Gamma Distribution",
    author = "Yee, Connie  and
      Keane, Nathan  and
      Zhou, Liang",
    booktitle = "Proceedings of the The 3rd Workshop on {EVENTS}: Definition, Detection, Coreference, and Representation",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0815",
    doi = "10.3115/v1/W15-0815",
    pages = "117--122",
}
@proceedings{ws-2015-linguistics,
    title = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    editor = "Feldman, Anna  and
      Kazantseva, Anna  and
      Szpakowicz, Stan  and
      Koolen, Corina",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0700",
    doi = "10.3115/v1/W15-07",
}
@inproceedings{scrivner-kubler-2015-tools,
    title = "Tools for Digital Humanities: Enabling Access to the Old {O}ccitan {R}omance of Flamenca",
    author = {Scrivner, Olga  and
      K{\"u}bler, Sandra},
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0701",
    doi = "10.3115/v1/W15-0701",
    pages = "1--11",
}
@inproceedings{mccurdy-etal-2015-rhymedesign,
    title = "{R}hyme{D}esign: A Tool for Analyzing Sonic Devices in Poetry",
    author = "McCurdy, Nina  and
      Srikumar, Vivek  and
      Meyer, Miriah",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0702",
    doi = "10.3115/v1/W15-0702",
    pages = "12--22",
}
@inproceedings{dubremetz-nivre-2015-rhetorical,
    title = "Rhetorical Figure Detection: the Case of Chiasmus",
    author = "Dubremetz, Marie  and
      Nivre, Joakim",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0703",
    doi = "10.3115/v1/W15-0703",
    pages = "23--31",
}
@inproceedings{jayannavar-etal-2015-validating,
    title = "Validating Literary Theories Using Automatic Social Network Extraction",
    author = "Jayannavar, Prashant  and
      Agarwal, Apoorv  and
      Ju, Melody  and
      Rambow, Owen",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0704",
    doi = "10.3115/v1/W15-0704",
    pages = "32--41",
}
@inproceedings{brooke-etal-2015-gutentag,
    title = "{G}uten{T}ag: an {NLP}-driven Tool for Digital Humanities Research in the {P}roject {G}utenberg Corpus",
    author = "Brooke, Julian  and
      Hammond, Adam  and
      Hirst, Graeme",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0705",
    doi = "10.3115/v1/W15-0705",
    pages = "42--47",
}
@inproceedings{cap-etal-2015-pilot,
    title = "A Pilot Experiment on Exploiting Translations for Literary Studies on Kafka{'}s {``}Verwandlung{''}",
    author = {Cap, Fabienne  and
      R{\"o}siger, Ina  and
      Kuhn, Jonas},
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0706",
    doi = "10.3115/v1/W15-0706",
    pages = "48--57",
}
@inproceedings{van-cranenburgh-koolen-2015-identifying,
    title = "Identifying Literary Texts with Bigrams",
    author = "van Cranenburgh, Andreas  and
      Koolen, Corina",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0707",
    doi = "10.3115/v1/W15-0707",
    pages = "58--67",
}
@inproceedings{delmonte-2015-visualizing,
    title = "Visualizing Poetry with {SPARSAR} {--} Visual Maps from Poetic Content",
    author = "Delmonte, Rodolfo",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0708",
    doi = "10.3115/v1/W15-0708",
    pages = "68--78",
}
@inproceedings{evert-etal-2015-towards,
    title = "Towards a better understanding of Burrows{'}s Delta in literary authorship attribution",
    author = {Evert, Stefan  and
      Proisl, Thomas  and
      Vitt, Thorsten  and
      Sch{\"o}ch, Christof  and
      Jannidis, Fotis  and
      Pielstr{\"o}m, Steffen},
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0709",
    doi = "10.3115/v1/W15-0709",
    pages = "79--88",
}
@inproceedings{kokkinakis-etal-2015-gender,
    title = "Gender-Based Vocation Identification in {S}wedish 19th Century Prose Fiction using Linguistic Patterns, {NER} and {CRF} Learning",
    author = "Kokkinakis, Dimitrios  and
      Ighe, Ann  and
      Malm, Mats",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0710",
    doi = "10.3115/v1/W15-0710",
    pages = "89--97",
}
@inproceedings{krug-etal-2015-rule,
    title = "Rule-based Coreference Resolution in {G}erman Historic Novels",
    author = "Krug, Markus  and
      Puppe, Frank  and
      Jannidis, Fotis  and
      Macharowsky, Luisa  and
      Reger, Isabella  and
      Weimar, Lukas",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0711",
    doi = "10.3115/v1/W15-0711",
    pages = "98--104",
}
@inproceedings{navarro-2015-computational,
    title = "A computational linguistic approach to {S}panish Golden Age Sonnets: metrical and semantic aspects",
    author = "Navarro, Borja",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0712",
    doi = "10.3115/v1/W15-0712",
    pages = "105--113",
}
@inproceedings{besacier-schwartz-2015-automated,
    title = "Automated Translation of a Literary Work: A Pilot Study",
    author = "Besacier, Laurent  and
      Schwartz, Lane",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0713",
    doi = "10.3115/v1/W15-0713",
    pages = "114--122",
}
@inproceedings{toral-way-2015-translating,
    title = "Translating Literary Text between Related Languages using {SMT}",
    author = "Toral, Antonio  and
      Way, Andy",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
    month = jun,
    year = "2015",
    address = "Denver, Colorado, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0714",
    doi = "10.3115/v1/W15-0714",
    pages = "123--132",
}
@proceedings{ws-2015-innovative,
    title = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    editor = "Tetreault, Joel  and
      Burstein, Jill  and
      Leacock, Claudia",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0600",
    doi = "10.3115/v1/W15-06",
}
@inproceedings{beinborn-etal-2015-candidate,
    title = "Candidate evaluation strategies for improved difficulty prediction of language tests",
    author = "Beinborn, Lisa  and
      Zesch, Torsten  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0601",
    doi = "10.3115/v1/W15-0601",
    pages = "1--11",
}
@inproceedings{loukina-etal-2015-feature,
    title = "Feature selection for automated speech scoring",
    author = "Loukina, Anastassia  and
      Zechner, Klaus  and
      Chen, Lei  and
      Heilman, Michael",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0602",
    doi = "10.3115/v1/W15-0602",
    pages = "12--19",
}
@inproceedings{rahimi-etal-2015-incorporating,
    title = "Incorporating Coherence of Topics as a Criterion in Automatic Response-to-Text Assessment of the Organization of Writing",
    author = "Rahimi, Zahra  and
      Litman, Diane  and
      Wang, Elaine  and
      Correnti, Richard",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0603",
    doi = "10.3115/v1/W15-0603",
    pages = "20--30",
}
@inproceedings{ledbetter-dickinson-2015-automatic,
    title = "Automatic morphological analysis of learner {H}ungarian",
    author = "Ledbetter, Scott  and
      Dickinson, Markus",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0604",
    doi = "10.3115/v1/W15-0604",
    pages = "31--41",
}
@inproceedings{somasundaran-etal-2015-automated,
    title = "Automated Scoring of Picture-based Story Narration",
    author = "Somasundaran, Swapna  and
      Lee, Chong Min  and
      Chodorow, Martin  and
      Wang, Xinhao",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0605",
    doi = "10.3115/v1/W15-0605",
    pages = "42--48",
}
@inproceedings{malmasi-cahill-2015-measuring,
    title = "Measuring Feature Diversity in Native Language Identification",
    author = "Malmasi, Shervin  and
      Cahill, Aoife",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0606",
    doi = "10.3115/v1/W15-0606",
    pages = "49--55",
}
@inproceedings{daudaravicius-2015-automated,
    title = "Automated Evaluation of Scientific Writing: {AESW} Shared Task Proposal",
    author = "Daudaravi{\v{c}}ius, Vidas",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0607",
    doi = "10.3115/v1/W15-0607",
    pages = "56--63",
}
@inproceedings{farra-etal-2015-scoring,
    title = "Scoring Persuasive Essays Using Opinions and their Targets",
    author = "Farra, Noura  and
      Somasundaran, Swapna  and
      Burstein, Jill",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0608",
    doi = "10.3115/v1/W15-0608",
    pages = "64--74",
}
@inproceedings{goutte-etal-2015-towards,
    title = "Towards Automatic Description of Knowledge Components",
    author = "Goutte, Cyril  and
      Durand, Guillaume  and
      L{\'e}ger, Serge",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0609",
    doi = "10.3115/v1/W15-0609",
    pages = "75--80",
}
@inproceedings{heilman-madnani-2015-impact,
    title = "The Impact of Training Data on Automated Short Answer Scoring Performance",
    author = "Heilman, Michael  and
      Madnani, Nitin",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0610",
    doi = "10.3115/v1/W15-0610",
    pages = "81--85",
}
@inproceedings{jaffe-etal-2015-interpreting,
    title = "Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System",
    author = "Jaffe, Evan  and
      White, Michael  and
      Schuler, William  and
      Fosler-Lussier, Eric  and
      Rosenfeld, Alex  and
      Danforth, Douglas",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0611",
    doi = "10.3115/v1/W15-0611",
    pages = "86--96",
}
@inproceedings{ramachandran-etal-2015-identifying,
    title = "Identifying Patterns For Short Answer Scoring Using Graph-based Lexico-Semantic Text Matching",
    author = "Ramachandran, Lakshmi  and
      Cheng, Jian  and
      Foltz, Peter",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0612",
    doi = "10.3115/v1/W15-0612",
    pages = "97--106",
}
@inproceedings{volodina-pijetlovic-2015-lark,
    title = "Lark Trills for Language Drills: Text-to-speech technology for language learners",
    author = "Volodina, Elena  and
      Pijetlovic, Dijana",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0613",
    doi = "10.3115/v1/W15-0613",
    pages = "107--117",
}
@inproceedings{wang-etal-2015-jinan,
    title = "The Jinan {C}hinese Learner Corpus",
    author = "Wang, Maolin  and
      Malmasi, Shervin  and
      Huang, Mingxuan",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0614",
    doi = "10.3115/v1/W15-0614",
    pages = "118--123",
}
@inproceedings{zesch-etal-2015-reducing,
    title = "Reducing Annotation Efforts in Supervised Short Answer Scoring",
    author = "Zesch, Torsten  and
      Heilman, Michael  and
      Cahill, Aoife",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0615",
    doi = "10.3115/v1/W15-0615",
    pages = "124--132",
}
@inproceedings{zhang-litman-2015-annotation,
    title = "Annotation and Classification of Argumentative Writing Revisions",
    author = "Zhang, Fan  and
      Litman, Diane",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0616",
    doi = "10.3115/v1/W15-0616",
    pages = "133--143",
}
@inproceedings{chen-etal-2015-embarrassed,
    title = "Embarrassed or Awkward? Ranking Emotion Synonyms for {ESL} Learners{'} Appropriate Wording",
    author = "Chen, Wei-Fan  and
      Chen, Mei-Hua  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0617",
    doi = "10.3115/v1/W15-0617",
    pages = "144--153",
}
@inproceedings{kumar-etal-2015-revup,
    title = "{R}ev{UP}: Automatic Gap-Fill Question Generation from Educational Texts",
    author = "Kumar, Girish  and
      Banchs, Rafael  and
      D{'}Haro, Luis Fernando",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0618",
    doi = "10.3115/v1/W15-0618",
    pages = "154--161",
}
@inproceedings{madnani-etal-2015-preliminary,
    title = "Preliminary Experiments on Crowdsourced Evaluation of Feedback Granularity",
    author = "Madnani, Nitin  and
      Chodorow, Martin  and
      Cahill, Aoife  and
      Lopez, Melissa  and
      Futagi, Yoko  and
      Attali, Yigal",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0619",
    doi = "10.3115/v1/W15-0619",
    pages = "162--171",
}
@inproceedings{malmasi-etal-2015-oracle,
    title = "Oracle and Human Baselines for Native Language Identification",
    author = "Malmasi, Shervin  and
      Tetreault, Joel  and
      Dras, Mark",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0620",
    doi = "10.3115/v1/W15-0620",
    pages = "172--178",
}
@inproceedings{wilson-martin-2015-using,
    title = "Using {PEGW}riting® to Support the Writing Motivation and Writing Quality of Eighth-Grade Students: A Quasi-Experimental Study",
    author = "Wilson, Joshua  and
      Martin, Trish",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0621",
    doi = "10.3115/v1/W15-0621",
    pages = "179--189",
}
@inproceedings{mathew-etal-2015-towards,
    title = "Towards Creating Pedagogic Views from Encyclopedic Resources",
    author = "Mathew, Ditty  and
      Eswaran, Dhivya  and
      Chakraborti, Sutanu",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0622",
    doi = "10.3115/v1/W15-0622",
    pages = "190--195",
}
@inproceedings{niraula-rus-2015-judging,
    title = "Judging the Quality of Automatically Generated Gap-fill Question using Active Learning",
    author = "Niraula, Nobal Bikram  and
      Rus, Vasile",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0623",
    doi = "10.3115/v1/W15-0623",
    pages = "196--206",
}
@inproceedings{ramachandran-foltz-2015-generating,
    title = "Generating Reference Texts for Short Answer Scoring Using Graph-based Summarization",
    author = "Ramachandran, Lakshmi  and
      Foltz, Peter",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0624",
    doi = "10.3115/v1/W15-0624",
    pages = "207--212",
}
@inproceedings{yannakoudakis-cummins-2015-evaluating,
    title = "Evaluating the performance of Automated Text Scoring systems",
    author = "Yannakoudakis, Helen  and
      Cummins, Ronan",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0625",
    doi = "10.3115/v1/W15-0625",
    pages = "213--223",
}
@inproceedings{zesch-etal-2015-task,
    title = "Task-Independent Features for Automated Essay Grading",
    author = "Zesch, Torsten  and
      Wojatzki, Michael  and
      Scholten-Akoun, Dirk",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0626",
    doi = "10.3115/v1/W15-0626",
    pages = "224--232",
}
@inproceedings{kochmar-briscoe-2015-using,
    title = "Using Learner Data to Improve Error Correction in Adjective{--}Noun Combinations",
    author = "Kochmar, Ekaterina  and
      Briscoe, Ted",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0627",
    doi = "10.3115/v1/W15-0627",
    pages = "233--242",
}
@inproceedings{willis-2015-using,
    title = "Using {NLP} to Support Scalable Assessment of Short Free Text Responses",
    author = "Willis, Alistair",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0628",
    doi = "10.3115/v1/W15-0628",
    pages = "243--253",
}
@inproceedings{napoles-callison-burch-2015-automatically,
    title = "Automatically Scoring Freshman Writing: A Preliminary Investigation",
    author = "Napoles, Courtney  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the Tenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0629",
    doi = "10.3115/v1/W15-0629",
    pages = "254--263",
}
@proceedings{ws-2015-argumentation,
    title = "Proceedings of the 2nd Workshop on Argumentation Mining",
    editor = "Cardie, Claire",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0500",
    doi = "10.3115/v1/W15-05",
}
@inproceedings{kirschner-etal-2015-linking,
    title = "Linking the Thoughts: Analysis of Argumentation Structures in Scientific Publications",
    author = "Kirschner, Christian  and
      Eckle-Kohler, Judith  and
      Gurevych, Iryna",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0501",
    doi = "10.3115/v1/W15-0501",
    pages = "1--11",
}
@inproceedings{green-2015-identifying,
    title = "Identifying Argumentation Schemes in Genetics Research Articles",
    author = "Green, Nancy",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0502",
    doi = "10.3115/v1/W15-0502",
    pages = "12--21",
}
@inproceedings{nguyen-litman-2015-extracting,
    title = "Extracting Argument and Domain Words for Identifying Argument Components in Texts",
    author = "Nguyen, Huy  and
      Litman, Diane",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0503",
    doi = "10.3115/v1/W15-0503",
    pages = "22--28",
}
@inproceedings{carstens-toni-2015-towards,
    title = "Towards relation based Argumentation Mining",
    author = "Carstens, Lucas  and
      Toni, Francesca",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0504",
    doi = "10.3115/v1/W15-0504",
    pages = "29--34",
}
@inproceedings{kiesel-etal-2015-shared,
    title = "A Shared Task on Argumentation Mining in Newspaper Editorials",
    author = "Kiesel, Johannes  and
      Al-Khatib, Khalid  and
      Hagen, Matthias  and
      Stein, Benno",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0505",
    doi = "10.3115/v1/W15-0505",
    pages = "35--38",
}
@inproceedings{park-etal-2015-conditional,
    title = "Conditional Random Fields for Identifying Appropriate Types of Support for Propositions in Online User Comments",
    author = "Park, Joonsuk  and
      Katiyar, Arzoo  and
      Yang, Bishan",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0506",
    doi = "10.3115/v1/W15-0506",
    pages = "39--44",
}
@inproceedings{reisert-etal-2015-computational,
    title = "A Computational Approach for Generating Toulmin Model Argumentation",
    author = "Reisert, Paul  and
      Inoue, Naoya  and
      Okazaki, Naoaki  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0507",
    doi = "10.3115/v1/W15-0507",
    pages = "45--55",
}
@inproceedings{sardianos-etal-2015-argument,
    title = "Argument Extraction from News",
    author = "Sardianos, Christos  and
      Katakis, Ioannis Manousos  and
      Petasis, Georgios  and
      Karkaletsis, Vangelis",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0508",
    doi = "10.3115/v1/W15-0508",
    pages = "56--66",
}
@inproceedings{sobhani-etal-2015-argumentation,
    title = "From Argumentation Mining to Stance Classification",
    author = "Sobhani, Parinaz  and
      Inkpen, Diana  and
      Matwin, Stan",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0509",
    doi = "10.3115/v1/W15-0509",
    pages = "67--77",
}
@inproceedings{wyner-etal-2015-argument,
    title = "Argument Discovery and Extraction with the Argument Workbench",
    author = "Wyner, Adam  and
      Peters, Wim  and
      Price, David",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0510",
    doi = "10.3115/v1/W15-0510",
    pages = "78--83",
}
@inproceedings{bilu-etal-2015-automatic,
    title = "Automatic Claim Negation: Why, How and When",
    author = "Bilu, Yonatan  and
      Hershcovich, Daniel  and
      Slonim, Noam",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0511",
    doi = "10.3115/v1/W15-0511",
    pages = "84--93",
}
@inproceedings{yanase-etal-2015-learning,
    title = "Learning Sentence Ordering for Opinion Generation of Debate",
    author = "Yanase, Toshihiko  and
      Miyoshi, Toshinori  and
      Yanai, Kohsuke  and
      Sato, Misa  and
      Iwayama, Makoto  and
      Niwa, Yoshiki  and
      Reisert, Paul  and
      Inui, Kentaro",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0512",
    doi = "10.3115/v1/W15-0512",
    pages = "94--103",
}
@inproceedings{peldszus-stede-2015-towards,
    title = "Towards Detecting Counter-considerations in Text",
    author = "Peldszus, Andreas  and
      Stede, Manfred",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0513",
    doi = "10.3115/v1/W15-0513",
    pages = "104--109",
}
@inproceedings{boltuzic-snajder-2015-identifying,
    title = "Identifying Prominent Arguments in Online Debates Using Semantic Textual Similarity",
    author = "Boltu{\v{z}}i{\'c}, Filip  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0514",
    doi = "10.3115/v1/W15-0514",
    pages = "110--115",
}
@inproceedings{oraby-etal-2015-thats,
    title = "And That{'}s A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue",
    author = "Oraby, Shereen  and
      Reed, Lena  and
      Compton, Ryan  and
      Riloff, Ellen  and
      Walker, Marilyn  and
      Whittaker, Steve",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0515",
    doi = "10.3115/v1/W15-0515",
    pages = "116--126",
}
@inproceedings{lawrence-reed-2015-combining,
    title = "Combining Argument Mining Techniques",
    author = "Lawrence, John  and
      Reed, Chris",
    booktitle = "Proceedings of the 2nd Workshop on Argumentation Mining",
    month = jun,
    year = "2015",
    address = "Denver, CO",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0516",
    doi = "10.3115/v1/W15-0516",
    pages = "127--136",
}
@proceedings{ws-2015-language,
    title = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0400",
}
@inproceedings{krieger-willms-2015-extending,
    title = "Extending {OWL} Ontologies by {C}artesian Types to Represent N-ary Relations in Natural Language",
    author = "Krieger, Hans-Ulrich  and
      Willms, Christian",
    booktitle = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0401",
}
@inproceedings{parvizi-etal-2015-ontology,
    title = "Ontology Authoring Inspired By Dialogue",
    author = "Parvizi, Artemis  and
      Ren, Yuan  and
      Vigo, Markel  and
      van Deemter, Kees  and
      Mellish, Chris  and
      Pan, Jeff Z.  and
      Stevens, Robert  and
      Jay, Caroline",
    booktitle = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0402",
}
@inproceedings{roth-klein-2015-parsing,
    title = "Parsing Software Requirements with an Ontology-based Semantic Role Labeler",
    author = "Roth, Michael  and
      Klein, Ewan",
    booktitle = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0403",
}
@inproceedings{khan-frontini-2015-using,
    title = "Using Ontologies to Model Polysemy in Lexical Resources",
    author = "Khan, Fahad  and
      Frontini, Francesca",
    booktitle = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0404",
}
@inproceedings{couto-vale-de-oliveira-2015-modelling,
    title = "Modelling time and space in {B}razilian culture",
    author = "Couto-Vale, Daniel  and
      de Oliveira, Rodrigo",
    booktitle = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0405",
}
@inproceedings{corman-etal-2015-trimming,
    title = "Trimming a consistent {OWL} knowledge base, relying on linguistic evidence",
    author = "Corman, Julien  and
      Aussenac-Gilles, Nathalie  and
      Vieu, Laure",
    booktitle = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0406",
}
@inproceedings{cerutti-etal-2015-lying,
    title = "When is Lying the Right Choice?",
    author = "Cerutti, Federico  and
      Parvizi, Artemis  and
      Toniolo, Alice  and
      Braines, Dave  and
      de Mel, Geeth R.  and
      Norman, Timothy J.  and
      Oren, Nir  and
      Pan, Jeff Z.  and
      Pearson, Gavin  and
      Pipes, Stephen D.  and
      Sullivan, Paul",
    booktitle = "Proceedings of the 1st Workshop on Language and Ontologies",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0407",
}
@proceedings{ws-2015-models,
    title = "Proceedings of the Workshop on Models for Modality Annotation",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0300",
}
@inproceedings{avila-etal-2015-towards,
    title = "Towards a Unified Approach to Modality Annotation in {P}ortuguese",
    author = "{\'A}vila, Luciana Beatriz  and
      Mendes, Am{\'a}lia  and
      Hendrickx, Iris",
    booktitle = "Proceedings of the Workshop on Models for Modality Annotation",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0301",
}
@inproceedings{sanchez-vogel-2015-hedging,
    title = "A hedging annotation scheme focused on epistemic phrases for informal language",
    author = "Sanchez, Liliana Mamani  and
      Vogel, Carl",
    booktitle = "Proceedings of the Workshop on Models for Modality Annotation",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0302",
}
@inproceedings{gast-etal-2015-annotating,
    title = "Annotating modals with {G}raph{A}nno, a configurable lightweight tool for multi-level annotation",
    author = "Gast, Volker  and
      Bierkandt, Lennart  and
      Rzymski, Christoph",
    booktitle = "Proceedings of the Workshop on Models for Modality Annotation",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0303",
}
@proceedings{ws-2015-joint,
    title = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0200",
}
@inproceedings{bunt-2015-principles,
    title = "On the Principles of Semantic Annotation",
    author = "Bunt, Harry",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0201",
}
@inproceedings{crible-zufferey-2015-using,
    title = "Using a unified taxonomy to annotate discourse markers in speech and writing",
    author = "Crible, Ludivine  and
      Zufferey, Sandrine",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0202",
}
@inproceedings{gast-etal-2015-creating,
    title = "Creating and retrieving tense and aspect annotation with {G}raph{A}nno, a lightweight tool for multi-level annotation",
    author = "Gast, Volker  and
      Bierkandt, Lennart  and
      Rzymski, Christoph",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0203",
}
@inproceedings{bosque-gil-pustejovsky-2015-semantics,
    title = "The Semantics of Image Annotation",
    author = "Bosque-Gil, Julia  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0204",
}
@inproceedings{hoek-zufferey-2015-factors,
    title = "Factors Influencing the Implicitation of Discourse Relations across Languages",
    author = "Hoek, Jet  and
      Zufferey, Sandrine",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0205",
}
@inproceedings{jezek-varvara-2015-instrument,
    title = "Instrument subjects without Instrument role",
    author = "Je{\v{z}}ek, Elisabetta  and
      Varvara, Rossella",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0206",
}
@inproceedings{lee-2015-annotation,
    title = "The Annotation of Measure Expressions in {ISO} Standards",
    author = "Lee, Kiyong",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0207",
}
@inproceedings{neale-etal-2015-flexible,
    title = "A Flexible Tool for Manual Word Sense Annotation",
    author = "Neale, Steven  and
      Silva, Jo{\~a}o  and
      Branco, Ant{\'o}nio",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0208",
}
@inproceedings{pareti-2015-annotating,
    title = "Annotating Attribution Relations across Languages and Genres",
    author = "Pareti, Silvia",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0209",
}
@inproceedings{prasad-bunt-2015-semantic,
    title = "Semantic Relations in Discourse: The Current State of {ISO} 24617-8",
    author = "Prasad, Rashmi  and
      Bunt, Harry",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0210",
}
@inproceedings{tissot-etal-2015-analysis,
    title = "Analysis of Temporal Expressions Annotated in Clinical Notes",
    author = "Tissot, Hegler  and
      Roberts, Angus  and
      Derczynski, Leon  and
      Gorrell, Genevieve  and
      Del Fabro, Marcus Didonet",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0211",
}
@inproceedings{trione-etal-2015-rapid,
    title = "Rapid {F}rame{N}et annotation of spoken conversation transcripts",
    author = "Trione, Jeremy  and
      Bechet, Frederic  and
      Favre, Benoit  and
      Nasr, Alexis",
    booktitle = "Proceedings of the 11th Joint {ACL}-{ISO} Workshop on Interoperable Semantic Annotation ({ISA}-11)",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0212",
}
@proceedings{ws-2015-international,
    title = "Proceedings of the 11th International Conference on Computational Semantics",
    editor = "Purver, Matthew  and
      Sadrzadeh, Mehrnoosh  and
      Stone, Matthew",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0100",
}
@inproceedings{emerson-copestake-2015-leveraging,
    title = "Leveraging a Semantically Annotated Corpus to Disambiguate Prepositional Phrase Attachment",
    author = "Emerson, Guy  and
      Copestake, Ann",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0101",
    pages = "1--11",
}
@inproceedings{bailey-etal-2015-prepositional,
    title = "Prepositional Phrase Attachment Problem Revisited: how Verbnet can Help",
    author = "Bailey, Daniel  and
      Lierler, Yuliya  and
      Susman, Benjamin",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0102",
    pages = "12--22",
}
@inproceedings{bakhshandeh-allen-2015-adjective,
    title = "From Adjective Glosses to Attribute Concepts: Learning Different Aspects That an Adjective Can Describe",
    author = "Bakhshandeh, Omid  and
      Allen, James",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0103",
    pages = "23--33",
}
@inproceedings{bott-schulte-im-walde-2015-exploiting,
    title = "Exploiting Fine-grained Syntactic Transfer Features to Predict the Compositionality of {G}erman Particle Verbs",
    author = "Bott, Stefan  and
      Schulte im Walde, Sabine",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0104",
    pages = "34--39",
}
@inproceedings{koper-etal-2015-multilingual,
    title = "Multilingual Reliability and {``}Semantic{''} Structure of Continuous Word Spaces",
    author = {K{\"o}per, Maximilian  and
      Scheible, Christian  and
      Schulte im Walde, Sabine},
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0105",
    pages = "40--45",
}
@inproceedings{schloder-fernandez-2015-clarifying,
    title = "Clarifying Intentions in Dialogue: A Corpus Study",
    author = {Schl{\"o}der, Julian J.  and
      Fern{\'a}ndez, Raquel},
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0106",
    pages = "46--51",
}
@inproceedings{fagarasan-etal-2015-distributional,
    title = "From distributional semantics to feature norms: grounding semantic models in human perceptual data",
    author = "Fagarasan, Luana  and
      Vecchi, Eva Maria  and
      Clark, Stephen",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0107",
    pages = "52--57",
}
@inproceedings{kisselew-etal-2015-obtaining,
    title = "Obtaining a Better Understanding of Distributional Models of {G}erman Derivational Morphology",
    author = "Kisselew, Max  and
      Pad{\'o}, Sebastian  and
      Palmer, Alexis  and
      {\v{S}}najder, Jan",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0108",
    pages = "58--63",
}
@inproceedings{szymanik-thorne-2015-semantic,
    title = "Semantic Complexity of Quantifiers and Their Distribution in Corpora",
    author = "Szymanik, Jakub  and
      Thorne, Camilo",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0109",
    pages = "64--69",
}
@inproceedings{lopopolo-van-miltenburg-2015-sound,
    title = "Sound-based distributional models",
    author = "Lopopolo, Alessandro  and
      van Miltenburg, Emiel",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0110",
    pages = "70--75",
}
@inproceedings{vaidyanathan-etal-2015-alignment,
    title = "Alignment of Eye Movements and Spoken Language for Semantic Image Understanding",
    author = "Vaidyanathan, Preethi  and
      Prud{'}hommeaux, Emily  and
      O. Alm, Cecilia  and
      Pelz, Jeff B.  and
      Haake, Anne R.",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0111",
    pages = "76--81",
}
@inproceedings{ziering-van-der-plas-2015-distance,
    title = "From a Distance: Using Cross-lingual Word Alignments for Noun Compound Bracketing",
    author = "Ziering, Patrick  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0112",
    pages = "82--87",
}
@inproceedings{anaya-sanchez-penas-2015-unsupervised,
    title = "Unsupervised Learning of Coherent and General Semantic Classes for Entity Aggregates",
    author = "Anaya-S{\'a}nchez, Henry  and
      Pe{\~n}as, Anselmo",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0113",
    pages = "88--93",
}
@inproceedings{lopez-de-lacalle-agirre-2015-crowdsourced,
    title = "Crowdsourced Word Sense Annotations and Difficult Words and Examples",
    author = "Lopez de Lacalle, Oier  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0114",
    pages = "94--100",
}
@inproceedings{negi-buitelaar-2015-curse,
    title = "Curse or Boon? Presence of Subjunctive Mood in Opinionated Text",
    author = "Negi, Sapna  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0115",
    pages = "101--106",
}
@inproceedings{horvat-etal-2015-hierarchical,
    title = "Hierarchical Statistical Semantic Realization for {M}inimal {R}ecursion {S}emantics",
    author = "Horvat, Matic  and
      Copestake, Ann  and
      Byrne, Bill",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0116",
    pages = "107--117",
}
@inproceedings{torabi-asr-demberg-2015-uniform,
    title = "Uniform Surprisal at the Level of Discourse Relations: Negation Markers and Discourse Connective Omission",
    author = "Torabi Asr, Fatemeh  and
      Demberg, Vera",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0117",
    pages = "118--128",
}
@inproceedings{clarke-keller-2015-efficiency,
    title = "Efficiency in Ambiguity: Two Models of Probabilistic Semantics for Natural Language",
    author = "Clarke, Daoud  and
      Keller, Bill",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0118",
    pages = "129--139",
}
@inproceedings{beltagy-erk-2015-proper,
    title = "On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics",
    author = "Beltagy, Islam  and
      Erk, Katrin",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0119",
    pages = "140--150",
}
@inproceedings{herbelot-2015-mr,
    title = "Mr Darcy and Mr Toad, gentlemen: distributional names and their kinds",
    author = "Herbelot, Aur{\'e}lie",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0120",
    pages = "151--161",
}
@inproceedings{iosif-potamianos-2015-feeling,
    title = "Feeling is Understanding: From Affective to Semantic Spaces",
    author = "Iosif, Elias  and
      Potamianos, Alexandros",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0121",
    pages = "162--172",
}
@inproceedings{dima-hinrichs-2015-automatic,
    title = "Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings",
    author = "Dima, Corina  and
      Hinrichs, Erhard",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0122",
    pages = "173--183",
}
@inproceedings{hunter-etal-2015-integrating,
    title = "Integrating Non-Linguistic Events into Discourse Structure",
    author = "Hunter, Julie  and
      Asher, Nicholas  and
      Lascarides, Alex",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0123",
    pages = "184--194",
}
@inproceedings{kennington-etal-2015-discriminative,
    title = "A Discriminative Model for Perceptually-Grounded Incremental Reference Resolution",
    author = "Kennington, Casey  and
      Dia, Livia  and
      Schlangen, David",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0124",
    pages = "195--205",
}
@inproceedings{hough-etal-2015-incremental,
    title = "Incremental Semantics for Dialogue Processing: Requirements, and a Comparison of Two Approaches",
    author = "Hough, Julian  and
      Kennington, Casey  and
      Schlangen, David  and
      Ginzburg, Jonathan",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0125",
    pages = "206--216",
}
@inproceedings{agic-etal-2015-semantic,
    title = "Semantic Dependency Graph Parsing Using Tree Approximations",
    author = "Agi{\'c}, {\v{Z}}eljko  and
      Koller, Alexander  and
      Oepen, Stephan",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0126",
    pages = "217--227",
}
@inproceedings{koller-2015-semantic,
    title = "Semantic construction with graph grammars",
    author = "Koller, Alexander",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0127",
    pages = "228--238",
}
@inproceedings{bender-etal-2015-layers,
    title = "Layers of Interpretation: On Grammar and Compositionality",
    author = "Bender, Emily M.  and
      Flickinger, Dan  and
      Oepen, Stephan  and
      Packard, Woodley  and
      Copestake, Ann",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0128",
    pages = "239--249",
}
@inproceedings{schloder-fernandez-2015-pragmatic,
    title = "Pragmatic Rejection",
    author = {Schl{\"o}der, Julian J.  and
      Fern{\'a}ndez, Raquel},
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0129",
    pages = "250--260",
}
@inproceedings{eshghi-etal-2015-feedback,
    title = "Feedback in Conversation as Incremental Semantic Update",
    author = "Eshghi, Arash  and
      Howes, Christine  and
      Gregoromichelaki, Eleni  and
      Hough, Julian  and
      Purver, Matthew",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0130",
    pages = "261--271",
}
@inproceedings{venant-asher-2015-dynamics,
    title = "Dynamics of Public Commitments in Dialogue",
    author = "Venant, Antoine  and
      Asher, Nicholas",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0131",
    pages = "272--282",
}
@inproceedings{alluhaibi-2015-simple,
    title = "Simple Interval Temporal Logic for Natural Language Assertion Descriptions",
    author = "Alluhaibi, Reyadh",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0132",
    pages = "283--293",
}
@inproceedings{freitas-etal-2015-hard,
    title = "How hard is this query? Measuring the Semantic Complexity of Schema-agnostic Queries",
    author = "Freitas, Andr{\'e}  and
      Efson Sales, Juliano  and
      Handschuh, Siegfried  and
      Curry, Edward",
    booktitle = "Proceedings of the 11th International Conference on Computational Semantics",
    month = apr,
    year = "2015",
    address = "London, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W15-0133",
    pages = "294--304",
}
@proceedings{alta-2015-australasian,
    title = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    editor = "Hachey, Ben  and
      Webster, Kellie",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1000",
}
@inproceedings{azar-etal-2015-query,
    title = "Query-Based Single Document Summarization Using an Ensemble Noisy Auto-Encoder",
    author = "Azar, Mahmood Yousefi  and
      Sirts, Kairit  and
      Moll{\'a} Aliod, Diego  and
      Hamey, Len",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1001",
    pages = "2--10",
}
@inproceedings{biggs-2015-comparison,
    title = "Comparison of Visual and Logical Character Segmentation in Tesseract {OCR} Language Data for Indic Writing Scripts",
    author = "Biggs, Jennifer",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1002",
    pages = "11--20",
}
@inproceedings{de-vine-etal-2015-analysis,
    title = "Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction",
    author = "De Vine, Lance  and
      Kholghi, Mahnoosh  and
      Zuccon, Guido  and
      Sitbon, Laurianne  and
      Nguyen, Anthony",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1003",
    pages = "21--30",
}
@inproceedings{du-etal-2015-using,
    title = "Using Entity Information from a Knowledge Base to Improve Relation Extraction",
    author = "Du, Lan  and
      Kumar, Anish  and
      Johnson, Mark  and
      Ciaramita, Massimiliano",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1004",
    pages = "31--38",
}
@inproceedings{frost-ishihara-2015-likelihood,
    title = "Likelihood Ratio-based Forensic Voice Comparison on {L}2 speakers: A Case of {H}ong {K}ong native male production of {E}nglish vowels",
    author = "Frost, Daniel  and
      Ishihara, Shunichi",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1005",
    pages = "39--47",
}
@inproceedings{hassanzadeh-etal-2015-similarity,
    title = "Similarity Metrics for Clustering {P}ub{M}ed Abstracts for Evidence Based Medicine",
    author = "Hassanzadeh, Hamed  and
      Moll{\'a}, Diego  and
      Groza, Tudor  and
      Nguyen, Anthony  and
      Hunter, Jane",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1006",
    pages = "48--56",
}
@inproceedings{kim-cassidy-2015-finding,
    title = "Finding Names in Trove: Named Entity Recognition for {A}ustralian Historical Newspapers",
    author = "Kim, Sunghwan Mac  and
      Cassidy, Steve",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1007",
    pages = "57--65",
}
@inproceedings{malmasi-etal-2015-clinical,
    title = "Clinical Information Extraction Using Word Representations",
    author = "Malmasi, Shervin  and
      Hassanzadeh, Hamed  and
      Dras, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1008",
    pages = "66--74",
}
@inproceedings{mckinnon-etal-2015-determining,
    title = "How few is too few? Determining the minimum acceptable number of {LSA} dimensions to visualise text cohesion with Lex",
    author = "McKinnon, Caroline  and
      Baazeem, Ibtehal  and
      Angus, Daniel",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1009",
    pages = "75--83",
}
@inproceedings{salinas-alvarado-etal-2015-domain,
    title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment",
    author = "Salinas Alvarado, Julio Cesar  and
      Verspoor, Karin  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1010",
    pages = "84--90",
}
@inproceedings{sirts-johnson-2015-pos,
    title = "Do {POS} Tags Help to Learn Better Morphological Segmentations?",
    author = "Sirts, Kairit  and
      Johnson, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1011",
    pages = "91--100",
}
@inproceedings{tan-etal-2015-structural,
    title = "Structural Alignment as the Basis to Improve Significant Change Detection in Versioned Sentences",
    author = "Tan, Ping Ping  and
      Verspoor, Karin  and
      Miller, Tim",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1012",
    pages = "101--109",
}
@inproceedings{martin-johnson-2015-efficient,
    title = "More Efficient Topic Modelling Through a Noun Only Approach",
    author = "Martin, Fiona  and
      Johnson, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1013",
    pages = "111--115",
}
@inproceedings{nguyen-etal-2015-improving-topic,
    title = "Improving Topic Coherence with Latent Feature Word Representations in {MAP} Estimation for Topic Modeling",
    author = "Nguyen, Dat Quoc  and
      Sirts, Kairit  and
      Johnson, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1014",
    pages = "116--121",
}
@inproceedings{nothman-etal-2015-understanding,
    title = "Understanding engagement with insurgents through retweet rhetoric",
    author = "Nothman, Joel  and
      Ahmad, Atif  and
      Breidbach, Christoph  and
      Malet, David  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1015",
    pages = "122--127",
}
@inproceedings{wei-hachey-2015-comparison,
    title = "A comparison and analysis of models for event trigger detection",
    author = "Wei, Sam Shang Chun  and
      Hachey, Ben",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1016",
    pages = "128--132",
}
@inproceedings{sitbon-etal-2015-overview,
    title = "Overview of the 2015 {ALTA} Shared Task: Identifying {F}rench Cognates in {E}nglish Text",
    author = "Sitbon, Laurianne  and
      Molla, Diego  and
      Wang, Haoxing",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1017",
    pages = "134--137",
}
@inproceedings{malmasi-dras-2015-cognate,
    title = "Cognate Identification using Machine Translation",
    author = "Malmasi, Shervin  and
      Dras, Mark",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1018",
    pages = "138--141",
}
@inproceedings{uitdenbogerd-2015-word,
    title = "Word Transformation Heuristics Agains Lexicons for Cognate Detection",
    author = "Uitdenbogerd, Alexandra",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1019",
    pages = "142--144",
}
@inproceedings{xu-etal-2015-detecting,
    title = "Detecting {E}nglish-{F}rench Cognates Using Orthographic Edit Distance",
    author = "Xu, Qiongkai  and
      Chen, Albert  and
      Li, Chang",
    booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
    month = dec,
    year = "2015",
    address = "Parramatta, Australia",
    url = "https://www.aclweb.org/anthology/U15-1020",
    pages = "145--149",
}
@proceedings{semeval-2015-international,
    title = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    editor = "Nakov, Preslav  and
      Zesch, Torsten  and
      Cer, Daniel  and
      Jurgens, David",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2000",
    doi = "10.18653/v1/S15-2",
}
@inproceedings{xu-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 1: Paraphrase and Semantic Similarity in {T}witter ({PIT})",
    author = "Xu, Wei  and
      Callison-Burch, Chris  and
      Dolan, Bill",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2001",
    doi = "10.18653/v1/S15-2001",
    pages = "1--11",
}
@inproceedings{zarrella-etal-2015-mitre,
    title = "{MITRE}: Seven Systems for Semantic Similarity in Tweets",
    author = "Zarrella, Guido  and
      Henderson, John  and
      Merkhofer, Elizabeth M.  and
      Strickhart, Laura",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2002",
    doi = "10.18653/v1/S15-2002",
    pages = "12--17",
}
@inproceedings{gomez-etal-2015-cicbuapnlp,
    title = "{CICBUAP}nlp: Graph-Based Approach for Answer Selection in Community Question Answering Task",
    author = "Gomez, Helena  and
      Vilari{\~n}o, Darnes  and
      Pinto, David  and
      Sidorov, Grigori",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2003",
    doi = "10.18653/v1/S15-2003",
    pages = "18--22",
}
@inproceedings{bertero-fung-2015-hltc,
    title = "{HLTC}-{HKUST}: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features",
    author = "Bertero, Dario  and
      Fung, Pascale",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2004",
    doi = "10.18653/v1/S15-2004",
    pages = "23--28",
}
@inproceedings{vo-etal-2015-fbk,
    title = "{FBK}-{HLT}: An Effective System for Paraphrase Identification and Semantic Similarity in {T}witter",
    author = "Vo, Ngoc Phuoc An  and
      Magnolini, Simone  and
      Popescu, Octavian",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2005",
    doi = "10.18653/v1/S15-2005",
    pages = "29--33",
}
@inproceedings{zhao-lan-2015-ecnu,
    title = "{ECNU}: Leveraging Word Embeddings to Boost Performance for Paraphrase in {T}witter",
    author = "Zhao, Jiang  and
      Lan, Man",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2006",
    doi = "10.18653/v1/S15-2006",
    pages = "34--39",
}
@inproceedings{van-der-goot-van-noord-2015-rob,
    title = "{ROB}: Using Semantic Meaning to Recognize Paraphrases",
    author = "van der Goot, Rob  and
      van Noord, Gertjan",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2007",
    doi = "10.18653/v1/S15-2007",
    pages = "40--44",
}
@inproceedings{shanumuga-sundaram-etal-2015-amrita,
    title = "{AMRITA}{\_}{CEN}@{S}em{E}val-2015: Paraphrase Detection for {T}witter using Unsupervised Feature Learning with Recursive Autoencoders",
    author = "Shanumuga Sundaram, Mahalakshmi  and
      Madasamy, Anand Kumar  and
      Kotti Padannayil, Soman",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2008",
    doi = "10.18653/v1/S15-2008",
    pages = "45--50",
}
@inproceedings{satyapanich-etal-2015-ebiquity,
    title = "{E}biquity: Paraphrase and Semantic Similarity in {T}witter using Skipgrams",
    author = "Satyapanich, Taneeya  and
      Gao, Hang  and
      Finin, Tim",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2009",
    doi = "10.18653/v1/S15-2009",
    pages = "51--55",
}
@inproceedings{bicici-2015-rtm,
    title = "{RTM}-{DCU}: Predicting Semantic Similarity with Referential Translation Machines",
    author = "Bi{\c{c}}ici, Ergun",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2010",
    doi = "10.18653/v1/S15-2010",
    pages = "56--63",
}
@inproceedings{eyecioglu-keller-2015-twitter,
    title = "{T}witter Paraphrase Identification with Simple Overlap Features and {SVM}s",
    author = "Eyecioglu, Asli  and
      Keller, Bill",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2011",
    doi = "10.18653/v1/S15-2011",
    pages = "64--69",
}
@inproceedings{karan-etal-2015-tklbliir,
    title = "{TKLBLIIR}: Detecting {T}witter Paraphrases with {T}weeting{J}ay",
    author = "Karan, Mladen  and
      Glava{\v{s}}, Goran  and
      {\v{S}}najder, Jan  and
      Dalbelo Ba{\v{s}}i{\'c}, Bojana  and
      Vuli{\'c}, Ivan  and
      Moens, Marie-Francine",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2012",
    doi = "10.18653/v1/S15-2012",
    pages = "70--74",
}
@inproceedings{karampatsis-2015-cdtds,
    title = "{CDTDS}: Predicting Paraphrases in {T}witter via Support Vector Regression",
    author = "Karampatsis, Rafael Michael",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2013",
    doi = "10.18653/v1/S15-2013",
    pages = "75--79",
}
@inproceedings{liu-etal-2015-yigou,
    title = "yi{G}ou: A Semantic Text Similarity Computing System Based on {SVM}",
    author = "Liu, Yang  and
      Sun, Chengjie  and
      Lin, Lei  and
      Wang, Xiaolong",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2014",
    doi = "10.18653/v1/S15-2014",
    pages = "80--84",
}
@inproceedings{tan-etal-2015-usaar,
    title = "{USAAR}-{SHEFFIELD}: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics",
    author = "Tan, Liling  and
      Scarton, Carolina  and
      Specia, Lucia  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2015",
    doi = "10.18653/v1/S15-2015",
    pages = "85--89",
}
@inproceedings{rakib-etal-2015-trwp,
    title = "{T}r{WP}: Text Relatedness using Word and Phrase Relatedness",
    author = "Rakib, Md Rashadul Hasan  and
      Islam, Aminul  and
      Milios, Evangelos",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2016",
    doi = "10.18653/v1/S15-2016",
    pages = "90--95",
}
@inproceedings{bechara-etal-2015-miniexperts,
    title = "{M}ini{E}xperts: An {SVM} Approach for Measuring Semantic Textual Similarity",
    author = "B{\'e}chara, Hanna  and
      Costa, Hernani  and
      Taslimipoor, Shiva  and
      Gupta, Rohit  and
      Orasan, Constantin  and
      Corpas Pastor, Gloria  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2017",
    doi = "10.18653/v1/S15-2017",
    pages = "96--101",
}
@inproceedings{vo-etal-2015-fbk-hlt,
    title = "{FBK}-{HLT}: A New Framework for Semantic Textual Similarity",
    author = "Vo, Ngoc Phuoc An  and
      Magnolini, Simone  and
      Popescu, Octavian",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2018",
    doi = "10.18653/v1/S15-2018",
    pages = "102--106",
}
@inproceedings{karumuri-etal-2015-umduluth,
    title = "{UMD}uluth-{B}lue{T}eam: {SVCSTS} - A Multilingual and Chunk Level Semantic Similarity System",
    author = "Karumuri, Sakethram  and
      Vuggumudi, Viswanadh Kumar Reddy  and
      Chitirala, Sai Charan Raj",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2019",
    doi = "10.18653/v1/S15-2019",
    pages = "107--110",
}
@inproceedings{plotnikova-etal-2015-semantiklue,
    title = "{S}emanti{KLUE}: Semantic Textual Similarity with Maximum Weight Matching",
    author = "Plotnikova, Nataliia  and
      Lapesa, Gabriella  and
      Proisl, Thomas  and
      Evert, Stefan",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2020",
    doi = "10.18653/v1/S15-2020",
    pages = "111--116",
}
@inproceedings{zhao-etal-2015-ecnu,
    title = "{ECNU}: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation",
    author = "Zhao, Jiang  and
      Lan, Man  and
      Tian, Jun Feng",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2021",
    doi = "10.18653/v1/S15-2021",
    pages = "117--122",
}
@inproceedings{hassanzadeh-etal-2015-uqeresearch,
    title = "{UQ}e{R}esearch: Semantic Textual Similarity Quantification",
    author = "Hassanzadeh, Hamed  and
      Groza, Tudor  and
      Nguyen, Anthony  and
      Hunter, Jane",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2022",
    doi = "10.18653/v1/S15-2022",
    pages = "123--127",
}
@inproceedings{miura-takagi-2015-wsl,
    title = "{WSL}: Sentence Similarity Using Semantic Distance Between Words",
    author = "Miura, Naoko  and
      Takagi, Tomohiro",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2023",
    doi = "10.18653/v1/S15-2023",
    pages = "128--131",
}
@inproceedings{buscaldi-etal-2015-sopa,
    title = "{SOPA}: Random Forests Regression for the Semantic Textual Similarity task",
    author = "Buscaldi, Davide  and
      Garc{\'\i}a Flores, Jorge  and
      Meza, Ivan V.  and
      Rodr{\'\i}guez, Isaac",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2024",
    doi = "10.18653/v1/S15-2024",
    pages = "132--137",
}
@inproceedings{recski-acs-2015-mathlingbudapest,
    title = "{M}ath{L}ing{B}udapest: Concept Networks for Semantic Similarity",
    author = "Recski, G{\'a}bor  and
      {\'A}cs, Judit",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2025",
    doi = "10.18653/v1/S15-2025",
    pages = "138--142",
}
@inproceedings{arora-etal-2015-dcu,
    title = "{DCU}: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity {S}em{E}val-2015 Task 2",
    author = "Arora, Piyush  and
      Hokamp, Chris  and
      Foster, Jennifer  and
      Jones, Gareth",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2026",
    doi = "10.18653/v1/S15-2026",
    pages = "143--147",
}
@inproceedings{sultan-etal-2015-dls,
    title = "{DLS}@{CU}: Sentence Similarity from Word Alignment and Semantic Vector Composition",
    author = "Sultan, Md Arafat  and
      Bethard, Steven  and
      Sumner, Tamara",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2027",
    doi = "10.18653/v1/S15-2027",
    pages = "148--153",
}
@inproceedings{hassan-etal-2015-fcicu,
    title = "{FCICU}: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual Similarity",
    author = "Hassan, Basma  and
      AbdelRahman, Samir  and
      Bahgat, Reem",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2028",
    doi = "10.18653/v1/S15-2028",
    pages = "154--158",
}
@inproceedings{jaffe-etal-2015-azmat,
    title = "{AZMAT}: Sentence Similarity Using Associative Matrices",
    author = "Jaffe, Evan  and
      Jin, Lifeng  and
      King, David  and
      van Schijndel, Marten",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2029",
    doi = "10.18653/v1/S15-2029",
    pages = "159--163",
}
@inproceedings{banjade-etal-2015-nerosim,
    title = "{N}e{R}o{S}im: A System for Measuring and Interpreting Semantic Textual Similarity",
    author = "Banjade, Rajendra  and
      Niraula, Nobal Bikram  and
      Maharjan, Nabin  and
      Rus, Vasile  and
      Stefanescu, Dan  and
      Lintean, Mihai  and
      Gautam, Dipesh",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2030",
    doi = "10.18653/v1/S15-2030",
    pages = "164--171",
}
@inproceedings{han-etal-2015-samsung,
    title = "{S}amsung: Align-and-Differentiate Approach to Semantic Textual Similarity",
    author = "Han, Lushan  and
      Martineau, Justin  and
      Cheng, Doreen  and
      Thomas, Christopher",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2031",
    doi = "10.18653/v1/S15-2031",
    pages = "172--177",
}
@inproceedings{agirre-etal-2015-ubc,
    title = "{UBC}: Cubes for {E}nglish Semantic Textual Similarity and Supervised Approaches for Interpretable {STS}",
    author = "Agirre, Eneko  and
      Gonzalez-Agirre, Aitor  and
      Lopez-Gazpio, I{\~n}igo  and
      Maritxalar, Montse  and
      Rigau, German  and
      Uria, Larraitz",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2032",
    doi = "10.18653/v1/S15-2032",
    pages = "178--183",
}
@inproceedings{alves-etal-2015-asap,
    title = "{ASAP}-{II}: From the Alignment of Phrases to Textual Similarity",
    author = "Alves, Ana  and
      Sim{\~o}es, David  and
      Oliveira, Hugo Gon{\c{c}}alo  and
      Ferrugento, Adriana",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2033",
    doi = "10.18653/v1/S15-2033",
    pages = "184--189",
}
@inproceedings{vu-etal-2015-tato,
    title = "{TATO}: Leveraging on Multiple Strategies for Semantic Textual Similarity",
    author = "Vu, Tu Thanh  and
      Tran, Quan Hung  and
      Pham, Son Bao",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2034",
    doi = "10.18653/v1/S15-2034",
    pages = "190--195",
}
@inproceedings{hou-etal-2015-hitsz,
    title = "{HITSZ}-{ICRC}: Exploiting Classification Approach for Answer Selection in Community Question Answering",
    author = "Hou, Yongshuai  and
      Tan, Cong  and
      Wang, Xiaolong  and
      Zhang, Yaoyun  and
      Xu, Jun  and
      Chen, Qingcai",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2035",
    doi = "10.18653/v1/S15-2035",
    pages = "196--202",
}
@inproceedings{nicosia-etal-2015-qcri,
    title = "{QCRI}: Answer Selection for Community Question Answering - Experiments for {A}rabic and {E}nglish",
    author = "Nicosia, Massimo  and
      Filice, Simone  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Saleh, Iman  and
      Mubarak, Hamdy  and
      Gao, Wei  and
      Nakov, Preslav  and
      Da San Martino, Giovanni  and
      Moschitti, Alessandro  and
      Darwish, Kareem  and
      M{\`a}rquez, Llu{\'\i}s  and
      Joty, Shafiq  and
      Magdy, Walid",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2036",
    doi = "10.18653/v1/S15-2036",
    pages = "203--209",
}
@inproceedings{zhou-etal-2015-icrc,
    title = "{ICRC}-{HIT}: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge",
    author = "Zhou, Xiaoqiang  and
      Hu, Baotian  and
      Lin, Jiaxin  and
      Xiang, Yang  and
      Wang, Xiaolong",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2037",
    doi = "10.18653/v1/S15-2037",
    pages = "210--214",
}
@inproceedings{tran-etal-2015-jaist,
    title = "{JAIST}: Combining multiple features for Answer Selection in Community Question Answering",
    author = "Tran, Quan Hung  and
      Tran, Vu Duc  and
      Vu, Tu Thanh  and
      Nguyen, Minh Le  and
      Pham, Son Bao",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2038",
    doi = "10.18653/v1/S15-2038",
    pages = "215--219",
}
@inproceedings{heydari-alashty-etal-2015-shiraz,
    title = "{S}hiraz: A Proposed List Wise Approach to Answer Validation",
    author = "Heydari Alashty, Amin  and
      Rahmani, Saeed  and
      Roostaee, Meysam  and
      Fakhrahmad, Mostafa",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2039",
    doi = "10.18653/v1/S15-2039",
    pages = "220--225",
}
@inproceedings{mohamed-etal-2015-al,
    title = "Al-Bayan: A Knowledge-based System for {A}rabic Answer Selection",
    author = "Mohamed, Reham  and
      Ragab, Maha  and
      Abdelnasser, Heba  and
      M. El-Makky, Nagwa  and
      Torki, Marwan",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2040",
    doi = "10.18653/v1/S15-2040",
    pages = "226--230",
}
@inproceedings{vo-etal-2015-fbk-hlt-application,
    title = "{FBK}-{HLT}: An Application of Semantic Textual Similarity for Answer Selection in Community Question Answering",
    author = "Vo, Ngoc Phuoc An  and
      Magnolini, Simone  and
      Popescu, Octavian",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2041",
    doi = "10.18653/v1/S15-2041",
    pages = "231--235",
}
@inproceedings{yi-etal-2015-ecnu,
    title = "{ECNU}: Using Multiple Sources of {CQA}-based Information for Answers Selection and {YES}/{NO} Response Inference",
    author = "Yi, Liang  and
      Wang, JianXiang  and
      Lan, Man",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2042",
    doi = "10.18653/v1/S15-2042",
    pages = "236--241",
}
@inproceedings{zamanov-etal-2015-voltron,
    title = "{V}oltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features",
    author = "Zamanov, Ivan  and
      Kraeva, Marina  and
      Hateva, Nelly  and
      Yovcheva, Ivana  and
      Nikolova, Ivelina  and
      Angelova, Galia",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2043",
    doi = "10.18653/v1/S15-2043",
    pages = "242--246",
}
@inproceedings{rudzewitz-ziai-2015-comic,
    title = "{C}o{M}i{C}: Adapting a Short Answer Assessment System for Answer Selection",
    author = {Rudzewitz, Bj{\"o}rn  and
      Ziai, Ramon},
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2044",
    doi = "10.18653/v1/S15-2044",
    pages = "247--251",
}
@inproceedings{agirre-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 2: Semantic Textual Similarity, {E}nglish, {S}panish and Pilot on Interpretability",
    author = "Agirre, Eneko  and
      Banea, Carmen  and
      Cardie, Claire  and
      Cer, Daniel  and
      Diab, Mona  and
      Gonzalez-Agirre, Aitor  and
      Guo, Weiwei  and
      Lopez-Gazpio, I{\~n}igo  and
      Maritxalar, Montse  and
      Mihalcea, Rada  and
      Rigau, German  and
      Uria, Larraitz  and
      Wiebe, Janyce",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2045",
    doi = "10.18653/v1/S15-2045",
    pages = "252--263",
}
@inproceedings{hanig-etal-2015-exb,
    title = "{E}x{B} Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity",
    author = {H{\"a}nig, Christian  and
      Remus, Robert  and
      De La Puente, Xose},
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2046",
    doi = "10.18653/v1/S15-2046",
    pages = "264--268",
}
@inproceedings{nakov-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 3: Answer Selection in Community Question Answering",
    author = "Nakov, Preslav  and
      M{\`a}rquez, Llu{\'\i}s  and
      Magdy, Walid  and
      Moschitti, Alessandro  and
      Glass, Jim  and
      Randeree, Bilal",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2047",
    doi = "10.18653/v1/S15-2047",
    pages = "269--281",
}
@inproceedings{belinkov-etal-2015-vectorslu,
    title = "{V}ector{SLU}: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems",
    author = "Belinkov, Yonatan  and
      Mohtarami, Mitra  and
      Cyphers, Scott  and
      Glass, James",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2048",
    doi = "10.18653/v1/S15-2048",
    pages = "282--287",
}
@inproceedings{moro-navigli-2015-semeval,
    title = "{S}em{E}val-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking",
    author = "Moro, Andrea  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2049",
    doi = "10.18653/v1/S15-2049",
    pages = "288--297",
}
@inproceedings{apidianaki-gong-2015-limsi,
    title = "{LIMSI}: Translations as Source of Indirect Supervision for Multilingual All-Words Sense Disambiguation and Entity Linking",
    author = "Apidianaki, Marianna  and
      Gong, Li",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2050",
    doi = "10.18653/v1/S15-2050",
    pages = "298--302",
}
@inproceedings{elhadad-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 14: Analysis of Clinical Text",
    author = "Elhadad, No{\'e}mie  and
      Pradhan, Sameer  and
      Gorman, Sharon  and
      Manandhar, Suresh  and
      Chapman, Wendy  and
      Savova, Guergana",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2051",
    doi = "10.18653/v1/S15-2051",
    pages = "303--310",
}
@inproceedings{xu-etal-2015-uth,
    title = "{UTH}-{CCB}: The Participation of the {S}em{E}val 2015 Challenge {--} Task 14",
    author = "Xu, Jun  and
      Zhang, Yaoyun  and
      Wang, Jingqi  and
      Wu, Yonghui  and
      Jiang, Min  and
      Soysal, Ergin  and
      Xu, Hua",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2052",
    doi = "10.18653/v1/S15-2052",
    pages = "311--314",
}
@inproceedings{baisa-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 15: A {CPA} dictionary-entry-building task",
    author = {Baisa, V{\'\i}t  and
      Bradbury, Jane  and
      Cinkov{\'a}, Silvie  and
      El Maarouf, Isma{\"\i}l  and
      Kilgarriff, Adam  and
      Popescu, Octavian},
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2053",
    doi = "10.18653/v1/S15-2053",
    pages = "315--324",
}
@inproceedings{feng-etal-2015-blcunlp,
    title = "{BLCUNLP}: Corpus Pattern Analysis for Verbs Based on Dependency Chain",
    author = "Feng, Yukun  and
      Deng, Qiao  and
      Yu, Dong",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2054",
    doi = "10.18653/v1/S15-2054",
    pages = "325--328",
}
@inproceedings{tripodi-pelillo-2015-wsd,
    title = "{WSD}-games: a Game-Theoretic Algorithm for Unsupervised Word Sense Disambiguation",
    author = "Tripodi, Rocco  and
      Pelillo, Marcello",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2055",
    doi = "10.18653/v1/S15-2055",
    pages = "329--334",
}
@inproceedings{weissenborn-etal-2015-dfki,
    title = "{DFKI}: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns {\&} Deep Verb Sense Disambiguation",
    author = "Weissenborn, Dirk  and
      Xu, Feiyu  and
      Uszkoreit, Hans",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2056",
    doi = "10.18653/v1/S15-2056",
    pages = "335--339",
}
@inproceedings{ayetiran-boella-2015-ebl,
    title = "{EBL}-Hope: Multilingual Word Sense Disambiguation Using a Hybrid Knowledge-Based Technique",
    author = "Ayetiran, Eniafe Festus  and
      Boella, Guido",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2057",
    doi = "10.18653/v1/S15-2057",
    pages = "340--344",
}
@inproceedings{postma-etal-2015-vua,
    title = "{VUA}-background : When to Use Background Information to Perform Word Sense Disambiguation",
    author = "Postma, Marten  and
      Izquierdo, Ruben  and
      Vossen, Piek",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2058",
    doi = "10.18653/v1/S15-2058",
    pages = "345--349",
}
@inproceedings{fanta-etal-2015-teamufal,
    title = "{T}eam{UFAL}: {WSD}+{EL} as Document Retrieval",
    author = "Fanta, Petr  and
      Sudarikov, Roman  and
      Bojar, Ond{\v{r}}ej",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2059",
    doi = "10.18653/v1/S15-2059",
    pages = "350--354",
}
@inproceedings{ruiz-poibeau-2015-el92,
    title = "{EL}92: Entity Linking Combining Open Source Annotators via Weighted Voting",
    author = "Ruiz, Pablo  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2060",
    doi = "10.18653/v1/S15-2060",
    pages = "355--359",
}
@inproceedings{basile-etal-2015-uniba,
    title = "{UNIBA}: Combining Distributional Semantic Models and Sense Distribution for Multilingual All-Words Sense Disambiguation and Entity Linking",
    author = "Basile, Pierpaolo  and
      Caputo, Annalina  and
      Semeraro, Giovanni",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2061",
    doi = "10.18653/v1/S15-2061",
    pages = "360--364",
}
@inproceedings{manion-2015-sudoku,
    title = "{SUDOKU}: Treating Word Sense Disambiguation {\&} Entitiy Linking as a Deterministic Problem - via an Unsupervised {\&} Iterative Approach",
    author = "Manion, Steve L.",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2062",
    doi = "10.18653/v1/S15-2062",
    pages = "365--369",
}
@inproceedings{huynh-ho-2015-teamhcmus,
    title = "{T}eam{HCMUS}: Analysis of Clinical Text",
    author = "Huynh, Nghia  and
      Ho, Quoc",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2063",
    doi = "10.18653/v1/S15-2063",
    pages = "370--374",
}
@inproceedings{hakala-2015-utu,
    title = "{UTU}: Adapting Biomedical Event Extraction System to Disorder Attribute Detection",
    author = "Hakala, Kai",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2064",
    doi = "10.18653/v1/S15-2064",
    pages = "375--379",
}
@inproceedings{chernyshevich-stankevitch-2015-ihs,
    title = "{IHS}-{RD}-{B}elarus: Identification and Normalization of Disorder Concepts in Clinical Notes",
    author = "Chernyshevich, Maryna  and
      Stankevitch, Vadim",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2065",
    doi = "10.18653/v1/S15-2065",
    pages = "380--384",
}
@inproceedings{ghiasvand-kate-2015-uwm,
    title = "{UWM}: A Simple Baseline Method for Identifying Attributes of Disease and Disorder Mentions in Clinical Text",
    author = "Ghiasvand, Omid  and
      Kate, Rohit",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2066",
    doi = "10.18653/v1/S15-2066",
    pages = "385--388",
}
@inproceedings{glavas-2015-takelab,
    title = "{TAKELAB}: Medical Information Extraction and Linking with {MINERAL}",
    author = "Glava{\v{s}}, Goran",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2067",
    doi = "10.18653/v1/S15-2067",
    pages = "389--393",
}
@inproceedings{jonnagaddala-etal-2015-tmunsw,
    title = "{TMUNSW}: Identification of Disorders and Normalization to {SNOMED}-{CT} Terminology in Unstructured Clinical Notes",
    author = "Jonnagaddala, Jitendra  and
      Liaw, Siaw-Teng  and
      Ray, Pradeep  and
      Kumar, Manish  and
      Dai, Hong-Jie",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2068",
    doi = "10.18653/v1/S15-2068",
    pages = "394--398",
}
@inproceedings{doing-harris-etal-2015-utahpoet,
    title = "{U}tah{POET}: Disorder Mention Identification and Context Slot Filling with Cognitive Inspiration",
    author = "Doing-Harris, Kristina  and
      Igo, Sean  and
      Shi, Jianlin  and
      Hurdle, John",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2069",
    doi = "10.18653/v1/S15-2069",
    pages = "399--405",
}
@inproceedings{leal-etal-2015-ulisboa,
    title = "{UL}isboa: Recognition and Normalization of Medical Concepts",
    author = "Leal, Andr{\'e}  and
      Martins, Bruno  and
      Couto, Francisco",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2070",
    doi = "10.18653/v1/S15-2070",
    pages = "406--411",
}
@inproceedings{pathak-etal-2015-ezdi,
    title = "ez{DI}: A Supervised {NLP} System for Clinical Narrative Analysis",
    author = "Pathak, Parth  and
      Patel, Pinal  and
      Panchal, Vishal  and
      Soni, Sagar  and
      Dani, Kinjal  and
      Patel, Amrish  and
      Choudhary, Narayan",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2071",
    doi = "10.18653/v1/S15-2071",
    pages = "412--416",
}
@inproceedings{gung-etal-2015-cuab,
    title = "{CUAB}: Supervised Learning of Disorders and their Attributes using Relations",
    author = "Gung, James  and
      Osborne, John  and
      Bethard, Steven",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2072",
    doi = "10.18653/v1/S15-2072",
    pages = "417--421",
}
@inproceedings{matos-etal-2015-bioinformaticsua,
    title = "{B}ioinformatics{UA}: Machine Learning and Rule-Based Recognition of Disorders and Clinical Attributes from Patient Notes",
    author = "Matos, S{\'e}rgio  and
      Sequeira, Jos{\'e}  and
      Oliveira, Jos{\'e} Lu{\'\i}s",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2073",
    doi = "10.18653/v1/S15-2073",
    pages = "422--426",
}
@inproceedings{ben-abacha-etal-2015-list,
    title = "{LIST}-{LUX}: Disorder Identification from Clinical Texts",
    author = "Ben Abacha, Asma  and
      Karanasiou, Aikaterini  and
      Mrabet, Yassine  and
      Dos Reis, Julio Cesar",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2074",
    doi = "10.18653/v1/S15-2074",
    pages = "427--432",
}
@inproceedings{mills-levow-2015-cmills,
    title = "{CMILLS}: Adapting Semantic Role Labeling Features to Dependency Parsing",
    author = "Mills, Chad  and
      Levow, Gina-Anne",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2075",
    doi = "10.18653/v1/S15-2075",
    pages = "433--437",
}
@inproceedings{pedersen-2015-duluth,
    title = "{D}uluth: Word Sense Discrimination in the Service of Lexicography",
    author = "Pedersen, Ted",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2076",
    doi = "10.18653/v1/S15-2076",
    pages = "438--442",
}
@inproceedings{russo-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 9: {CLIPE}val Implicit Polarity of Events",
    author = "Russo, Irene  and
      Caselli, Tommaso  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2077",
    doi = "10.18653/v1/S15-2077",
    pages = "443--450",
}
@inproceedings{rosenthal-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 10: Sentiment Analysis in {T}witter",
    author = "Rosenthal, Sara  and
      Nakov, Preslav  and
      Kiritchenko, Svetlana  and
      Mohammad, Saif  and
      Ritter, Alan  and
      Stoyanov, Veselin",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2078",
    doi = "10.18653/v1/S15-2078",
    pages = "451--463",
}
@inproceedings{severyn-moschitti-2015-unitn,
    title = "{UNITN}: Training Deep Convolutional Neural Network for {T}witter Sentiment Classification",
    author = "Severyn, Aliaksei  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2079",
    doi = "10.18653/v1/S15-2079",
    pages = "464--469",
}
@inproceedings{ghosh-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 11: Sentiment Analysis of Figurative Language in {T}witter",
    author = "Ghosh, Aniruddha  and
      Li, Guofu  and
      Veale, Tony  and
      Rosso, Paolo  and
      Shutova, Ekaterina  and
      Barnden, John  and
      Reyes, Antonio",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2080",
    doi = "10.18653/v1/S15-2080",
    pages = "470--478",
}
@inproceedings{ozdemir-bergler-2015-clac,
    title = "{CL}a{C}-{S}enti{P}ipe: {S}em{E}val2015 Subtasks 10 {B},{E}, and Task 11",
    author = {{\"O}zdemir, Canberk  and
      Bergler, Sabine},
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2081",
    doi = "10.18653/v1/S15-2081",
    pages = "479--485",
}
@inproceedings{pontiki-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 12: Aspect Based Sentiment Analysis",
    author = "Pontiki, Maria  and
      Galanis, Dimitris  and
      Papageorgiou, Haris  and
      Manandhar, Suresh  and
      Androutsopoulos, Ion",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2082",
    doi = "10.18653/v1/S15-2082",
    pages = "486--495",
}
@inproceedings{toh-su-2015-nlangp,
    title = "{NLANGP}: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction",
    author = "Toh, Zhiqiang  and
      Su, Jian",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2083",
    doi = "10.18653/v1/S15-2083",
    pages = "496--501",
}
@inproceedings{dragoni-2015-shellfbk,
    title = "{SHELLFBK}: An Information Retrieval-based System For Multi-Domain Sentiment Analysis",
    author = "Dragoni, Mauro",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2084",
    doi = "10.18653/v1/S15-2084",
    pages = "502--509",
}
@inproceedings{sarker-etal-2015-diegolab,
    title = "{DIEGOL}ab: An Approach for Message-level Sentiment Classification in {T}witter",
    author = "Sarker, Abeed  and
      Nikfarjam, Azadeh  and
      Weissenbacher, Davy  and
      Gonzalez, Graciela",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2085",
    doi = "10.18653/v1/S15-2085",
    pages = "510--514",
}
@inproceedings{dong-etal-2015-splusplus,
    title = "{S}plusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets",
    author = "Dong, Li  and
      Wei, Furu  and
      Yin, Yichun  and
      Zhou, Ming  and
      Xu, Ke",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2086",
    doi = "10.18653/v1/S15-2086",
    pages = "515--519",
}
@inproceedings{dalmia-etal-2015-iiit,
    title = "{IIIT}-{H} at {S}em{E}val 2015: {T}witter Sentiment Analysis {--} The Good, the Bad and the Neutral!",
    author = "Dalmia, Ayushi  and
      Gupta, Manish  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2087",
    doi = "10.18653/v1/S15-2087",
    pages = "520--526",
}
@inproceedings{ebert-etal-2015-cis,
    title = "{CIS}-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in {T}witter",
    author = {Ebert, Sebastian  and
      Vu, Ngoc Thang  and
      Sch{\"u}tze, Hinrich},
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2088",
    doi = "10.18653/v1/S15-2088",
    pages = "527--532",
}
@inproceedings{fernandez-gavilanes-etal-2015-gti,
    title = "{GTI}: An Unsupervised Approach for Sentiment Analysis in {T}witter",
    author = "Fern{\'a}ndez-Gavilanes, Milagros  and
      {\'A}lvarez-L{\'o}pez, Tamara  and
      Juncal-Mart{\'\i}nez, Jonathan  and
      Costa-Montenegro, Enrique  and
      Gonz{\'a}lez-Casta{\~n}o, Francisco Javier",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2089",
    doi = "10.18653/v1/S15-2089",
    pages = "533--538",
}
@inproceedings{cerezo-costas-celix-salgado-2015-gradiant,
    title = "Gradiant-Analytics: Training Polarity Shifters with {CRF}s for Message Level Polarity Detection",
    author = "Cerezo-Costas, H{\'e}ctor  and
      Celix-Salgado, Diego",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2090",
    doi = "10.18653/v1/S15-2090",
    pages = "539--544",
}
@inproceedings{li-etal-2015-ioa,
    title = "{IOA}: Improving {SVM} Based Sentiment Classification Through Post Processing",
    author = "Li, Peijia  and
      Xu, Weiqun  and
      Ma, Chenglong  and
      Sun, Jia  and
      Yan, Yonghong",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2091",
    doi = "10.18653/v1/S15-2091",
    pages = "545--550",
}
@inproceedings{liang-etal-2015-rosemerry,
    title = "{R}ose{M}erry: A Baseline Message-level Sentiment Classification System",
    author = "Liang, Huizhi  and
      Fothergill, Richard  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2092",
    doi = "10.18653/v1/S15-2092",
    pages = "551--555",
}
@inproceedings{castillo-etal-2015-udlap,
    title = "{UDLAP}: Sentiment Analysis Using a Graph-Based Representation",
    author = "Castillo, Esteban  and
      Cervantes, Ofelia  and
      Vilari{\~n}o, Darnes  and
      B{\'a}ez, David  and
      S{\'a}nchez, Alfredo",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2093",
    doi = "10.18653/v1/S15-2093",
    pages = "556--560",
}
@inproceedings{zhang-etal-2015-ecnu,
    title = "{ECNU}: Multi-level Sentiment Analysis on {T}witter Using Traditional Linguistic Features and Word Embedding Features",
    author = "Zhang, Zhihua  and
      Wu, Guoshun  and
      Lan, Man",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2094",
    doi = "10.18653/v1/S15-2094",
    pages = "561--567",
}
@inproceedings{hamdan-etal-2015-lsislif,
    title = "{L}sislif: Feature Extraction and Label Weighting for Sentiment Analysis in {T}witter",
    author = "Hamdan, Hussam  and
      Bellot, Patrice  and
      Bechet, Frederic",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2095",
    doi = "10.18653/v1/S15-2095",
    pages = "568--573",
}
@inproceedings{gimenez-etal-2015-elirf,
    title = "{EL}i{RF}: A {SVM} Approach for {SA} tasks in {T}witter at {S}em{E}val-2015",
    author = "Gim{\'e}nez, Mayte  and
      Pla, Ferran  and
      Hurtado, Llu{\'\i}s-F.",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2096",
    doi = "10.18653/v1/S15-2096",
    pages = "574--581",
}
@inproceedings{hagen-etal-2015-webis,
    title = "{W}ebis: An Ensemble for {T}witter Sentiment Detection",
    author = {Hagen, Matthias  and
      Potthast, Martin  and
      B{\"u}chner, Michel  and
      Stein, Benno},
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2097",
    doi = "10.18653/v1/S15-2097",
    pages = "582--589",
}
@inproceedings{guha-etal-2015-sentibase,
    title = "{S}entibase: Sentiment Analysis in {T}witter on a Budget",
    author = "Guha, Satarupa  and
      Joshi, Aditya  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2098",
    doi = "10.18653/v1/S15-2098",
    pages = "590--594",
}
@inproceedings{basile-novielli-2015-uniba,
    title = "{UNIBA}: Sentiment Analysis of {E}nglish Tweets Combining Micro-blogging, Lexicon and Semantic Features",
    author = "Basile, Pierpaolo  and
      Novielli, Nicole",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2099",
    doi = "10.18653/v1/S15-2099",
    pages = "595--600",
}
@inproceedings{kumar-etal-2015-iitpsemeval,
    title = "{IITPS}em{E}val: Sentiment Discovery from 140 Characters",
    author = "Kumar, Ayush  and
      Krishna, Vamsi  and
      Ekbal, Asif",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2100",
    doi = "10.18653/v1/S15-2100",
    pages = "601--607",
}
@inproceedings{uzdilli-etal-2015-swiss,
    title = "{S}wiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment",
    author = "Uzdilli, Fatih  and
      Jaggi, Martin  and
      Egger, Dominic  and
      Julmy, Pascal  and
      Derczynski, Leon  and
      Cieliebak, Mark",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2101",
    doi = "10.18653/v1/S15-2101",
    pages = "608--612",
}
@inproceedings{amir-etal-2015-inesc,
    title = "{INESC}-{ID}: A Regression Model for Large Scale {T}witter Sentiment Lexicon Induction",
    author = "Amir, Silvio  and
      F. Astudillo, Ramon  and
      Ling, Wang  and
      Martins, Bruno  and
      Silva, Mario J.  and
      Trancoso, Isabel",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2102",
    doi = "10.18653/v1/S15-2102",
    pages = "613--618",
}
@inproceedings{plotnikova-etal-2015-klueless,
    title = "{KLUE}less: Polarity Classification and Association",
    author = "Plotnikova, Nataliia  and
      Kohl, Micha  and
      Volkert, Kevin  and
      Evert, Stefan  and
      Lerner, Andreas  and
      Dykes, Natalie  and
      Ermer, Heiko",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2103",
    doi = "10.18653/v1/S15-2103",
    pages = "619--625",
}
@inproceedings{talbot-etal-2015-swash,
    title = "{SWASH}: A Naive {B}ayes Classifier for Tweet Sentiment Identification",
    author = "Talbot, Ruth  and
      Acheampong, Chloe  and
      Wicentowski, Richard",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2104",
    doi = "10.18653/v1/S15-2104",
    pages = "626--630",
}
@inproceedings{wicentowski-2015-swatcs65,
    title = "{SWATCS}65: Sentiment Classification Using an Ensemble of Class Projects",
    author = "Wicentowski, Richard",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2105",
    doi = "10.18653/v1/S15-2105",
    pages = "631--635",
}
@inproceedings{alhessi-wicentowski-2015-swatac,
    title = "{SWATAC}: A Sentiment Analyzer using One-Vs-Rest Logistic Regression",
    author = "Alhessi, Yousef  and
      Wicentowski, Richard",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2106",
    doi = "10.18653/v1/S15-2106",
    pages = "636--639",
}
@inproceedings{boag-etal-2015-twitterhawk,
    title = "{T}witter{H}awk: A Feature Bucket Based Approach to Sentiment Analysis",
    author = "Boag, William  and
      Potash, Peter  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2107",
    doi = "10.18653/v1/S15-2107",
    pages = "640--646",
}
@inproceedings{chikersal-etal-2015-sentu,
    title = "{S}e{NTU}: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning",
    author = "Chikersal, Prerna  and
      Poria, Soujanya  and
      Cambria, Erik",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2108",
    doi = "10.18653/v1/S15-2108",
    pages = "647--651",
}
@inproceedings{f-astudillo-etal-2015-inesc,
    title = "{INESC}-{ID}: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces",
    author = "F. Astudillo, Ramon  and
      Amir, Silvio  and
      Ling, Wang  and
      Martins, Bruno  and
      Silva, Mario J.  and
      Trancoso, Isabel",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2109",
    doi = "10.18653/v1/S15-2109",
    pages = "652--656",
}
@inproceedings{townsend-etal-2015-warwickdcs,
    title = "{W}arwick{DCS}: From Phrase-Based to Target-Specific Sentiment Recognition",
    author = "Townsend, Richard  and
      Tsakalidis, Adam  and
      Zhou, Yiwei  and
      Wang, Bo  and
      Liakata, Maria  and
      Zubiaga, Arkaitz  and
      Cristea, Alexandra  and
      Procter, Rob",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2110",
    doi = "10.18653/v1/S15-2110",
    pages = "657--663",
}
@inproceedings{han-etal-2015-uir,
    title = "{UIR}-{PKU}: {T}witter-{O}pin{M}iner System for Sentiment Analysis in {T}witter at {S}em{E}val 2015",
    author = "Han, Xu  and
      Li, Binyang  and
      Ma, Jing  and
      Zhang, Yuxiao  and
      Ou, Gaoyan  and
      Wang, Tengjiao  and
      Wong, Kam-fai",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2111",
    doi = "10.18653/v1/S15-2111",
    pages = "664--668",
}
@inproceedings{collins-etal-2015-swat,
    title = "{SWAT}-{CMW}: Classification of {T}witter Emotional Polarity using a Multiple-Classifier Decision Schema and Enhanced Emotion Tagging",
    author = "Collins, Riley  and
      May, Daniel  and
      Weinthal, Noah  and
      Wicentowski, Richard",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2112",
    doi = "10.18653/v1/S15-2112",
    pages = "669--672",
}
@inproceedings{xu-etal-2015-llt,
    title = "{LLT}-{P}oly{U}: Identifying Sentiment Intensity in Ironic Tweets",
    author = "Xu, Hongzhi  and
      Santus, Enrico  and
      Laszlo, Anna  and
      Huang, Chu-Ren",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2113",
    doi = "10.18653/v1/S15-2113",
    pages = "673--678",
}
@inproceedings{nguyen-etal-2015-kelabteam,
    title = "{KEL}ab{T}eam: A Statistical Approach on Figurative Language Sentiment Analysis in {T}witter",
    author = "Nguyen, Hoang Long  and
      Nguyen, Trung Duc  and
      Hwang, Dosam  and
      Jung, Jason J.",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2114",
    doi = "10.18653/v1/S15-2114",
    pages = "679--683",
}
@inproceedings{van-hee-etal-2015-lt3,
    title = "{LT}3: Sentiment Analysis of Figurative Tweets: piece of cake {\#}{N}ot{R}eally",
    author = "Van Hee, Cynthia  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2115",
    doi = "10.18653/v1/S15-2115",
    pages = "684--688",
}
@inproceedings{gupta-gomez-2015-prhlt,
    title = "{PRHLT}: Combination of Deep Autoencoders with Classification and Regression Techniques for {S}em{E}val-2015 Task 11",
    author = "Gupta, Parth  and
      G{\'o}mez, Jon Ander",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2116",
    doi = "10.18653/v1/S15-2116",
    pages = "689--693",
}
@inproceedings{hernandez-farias-etal-2015-valento,
    title = "{V}alen{T}o: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm",
    author = "Hern{\'a}ndez Far{\'\i}as, Delia Iraz{\'u}  and
      Sulis, Emilio  and
      Patti, Viviana  and
      Ruffo, Giancarlo  and
      Bosco, Cristina",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2117",
    doi = "10.18653/v1/S15-2117",
    pages = "694--698",
}
@inproceedings{mcgillion-etal-2015-cph,
    title = "{CPH}: Sentiment analysis of Figurative Language on {T}witter {\#}easypeasy {\#}not",
    author = "McGillion, Sarah  and
      Mart{\'\i}nez Alonso, H{\'e}ctor  and
      Plank, Barbara",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2118",
    doi = "10.18653/v1/S15-2118",
    pages = "699--703",
}
@inproceedings{barbieri-etal-2015-upf,
    title = "{UPF}-taln: {S}em{E}val 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in {T}witter",
    author = "Barbieri, Francesco  and
      Ronzano, Francesco  and
      Saggion, Horacio",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2119",
    doi = "10.18653/v1/S15-2119",
    pages = "704--708",
}
@inproceedings{karanasou-etal-2015-dsunipi,
    title = "{D}s{U}ni{P}i: An {SVM}-based Approach for Sentiment Analysis of Figurative Language on {T}witter",
    author = "Karanasou, Maria  and
      Doulkeridis, Christos  and
      Halkidi, Maria",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2120",
    doi = "10.18653/v1/S15-2120",
    pages = "709--713",
}
@inproceedings{garcia-pablos-etal-2015-v3,
    title = "{V}3: Unsupervised Aspect Based Sentiment Analysis for {S}em{E}val2015 Task 12",
    author = "Garc{\'\i}a-Pablos, Aitor  and
      Cuadros, Montse  and
      Rigau, German",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2121",
    doi = "10.18653/v1/S15-2121",
    pages = "714--718",
}
@inproceedings{de-clercq-etal-2015-lt3,
    title = "{LT}3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis",
    author = "De Clercq, Orph{\'e}e  and
      Van de Kauter, Marjan  and
      Lefever, Els  and
      Hoste, V{\'e}ronique",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2122",
    doi = "10.18653/v1/S15-2122",
    pages = "719--724",
}
@inproceedings{kauer-moreira-2015-ufrgs,
    title = "{UFRGS}: Identifying Categories and Targets in Customer Reviews",
    author = "Kauer, Anderson  and
      Moreira, Viviane",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2123",
    doi = "10.18653/v1/S15-2123",
    pages = "725--729",
}
@inproceedings{jimenez-zafra-etal-2015-sinai,
    title = "{SINAI}: Syntactic Approach for Aspect-Based Sentiment Analysis",
    author = "Jim{\'e}nez-Zafra, Salud M.  and
      Mart{\'\i}nez-C{\'a}mara, Eugenio  and
      Mart{\'\i}n-Valdivia, M. Teresa  and
      Ure{\~n}a-L{\'o}pez, L. Alfonso",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2124",
    doi = "10.18653/v1/S15-2124",
    pages = "730--735",
}
@inproceedings{zhang-lan-2015-ecnu,
    title = "{ECNU}: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in Reviews",
    author = "Zhang, Zhihua  and
      Lan, Man",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2125",
    doi = "10.18653/v1/S15-2125",
    pages = "736--741",
}
@inproceedings{repaka-etal-2015-umduluth,
    title = "{UMD}uluth-{CS}8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment Analysis",
    author = "Repaka, Ravikanth  and
      Pallelra, Ranga Reddy  and
      Koppula, Akshay Reddy  and
      Movva, Venkata Subhash",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2126",
    doi = "10.18653/v1/S15-2126",
    pages = "742--747",
}
@inproceedings{san-vicente-etal-2015-elixa,
    title = "{E}li{X}a: A Modular and Flexible {ABSA} Platform",
    author = "San Vicente, I{\~n}aki  and
      Saralegi, Xabier  and
      Agerri, Rodrigo",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2127",
    doi = "10.18653/v1/S15-2127",
    pages = "748--752",
}
@inproceedings{hamdan-etal-2015-lsislif-crf,
    title = "{L}sislif: {CRF} and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis",
    author = "Hamdan, Hussam  and
      Bellot, Patrice  and
      Bechet, Frederic",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2128",
    doi = "10.18653/v1/S15-2128",
    pages = "753--758",
}
@inproceedings{guha-etal-2015-siel,
    title = "{SIEL}: Aspect Based Sentiment Analysis in Reviews",
    author = "Guha, Satarupa  and
      Joshi, Aditya  and
      Varma, Vasudeva",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2129",
    doi = "10.18653/v1/S15-2129",
    pages = "759--766",
}
@inproceedings{saias-2015-sentiue,
    title = "{S}entiue: Target and Aspect based Sentiment Analysis in {S}em{E}val-2015 Task 12",
    author = "Saias, Jos{\'e}",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2130",
    doi = "10.18653/v1/S15-2130",
    pages = "767--771",
}
@inproceedings{zhang-etal-2015-tjudem,
    title = "{TJU}de{M}: A Combination Classifier for Aspect Category Detection and Sentiment Polarity Classification",
    author = "Zhang, Zhifei  and
      Nie, Jian-Yun  and
      Wang, Hongling",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2131",
    doi = "10.18653/v1/S15-2131",
    pages = "772--777",
}
@inproceedings{minard-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 4: {T}ime{L}ine: Cross-Document Event Ordering",
    author = "Minard, Anne-Lyse  and
      Speranza, Manuela  and
      Agirre, Eneko  and
      Aldabe, Itziar  and
      van Erp, Marieke  and
      Magnini, Bernardo  and
      Rigau, German  and
      Urizar, Rub{\'e}n",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2132",
    doi = "10.18653/v1/S15-2132",
    pages = "778--786",
}
@inproceedings{caselli-etal-2015-spinoza,
    title = "{SPINOZA}{\_}{VU}: An {NLP} Pipeline for Cross Document {T}ime{L}ines",
    author = "Caselli, Tommaso  and
      Fokkens, Antske  and
      Morante, Roser  and
      Vossen, Piek",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2133",
    doi = "10.18653/v1/S15-2133",
    pages = "787--791",
}
@inproceedings{llorens-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 5: {QA} {T}emp{E}val - Evaluating Temporal Information Understanding with Question Answering",
    author = "Llorens, Hector  and
      Chambers, Nathanael  and
      UzZaman, Naushad  and
      Mostafazadeh, Nasrin  and
      Allen, James  and
      Pustejovsky, James",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2134",
    doi = "10.18653/v1/S15-2134",
    pages = "792--800",
}
@inproceedings{mirza-minard-2015-hlt,
    title = "{HLT}-{FBK}: a Complete Temporal Processing System for {QA} {T}emp{E}val",
    author = "Mirza, Paramita  and
      Minard, Anne-Lyse",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2135",
    doi = "10.18653/v1/S15-2135",
    pages = "801--805",
}
@inproceedings{bethard-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 6: Clinical {T}emp{E}val",
    author = "Bethard, Steven  and
      Derczynski, Leon  and
      Savova, Guergana  and
      Pustejovsky, James  and
      Verhagen, Marc",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2136",
    doi = "10.18653/v1/S15-2136",
    pages = "806--814",
}
@inproceedings{velupillai-etal-2015-blulab,
    title = "{B}lu{L}ab: Temporal Information Extraction for the 2015 Clinical {T}emp{E}val Challenge",
    author = "Velupillai, Sumithra  and
      Mowery, Danielle L  and
      Abdelrahman, Samir  and
      Christensen, Lee  and
      Chapman, Wendy",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2137",
    doi = "10.18653/v1/S15-2137",
    pages = "815--819",
}
@inproceedings{navarro-saquete-2015-gplsiua,
    title = "{GPLSIUA}: Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering",
    author = "Navarro, Borja  and
      Saquete, Estela",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2138",
    doi = "10.18653/v1/S15-2138",
    pages = "820--824",
}
@inproceedings{moulahi-etal-2015-heideltoul,
    title = "{H}eidel{T}oul: A Baseline Approach for Cross-document Event Ordering",
    author = {Moulahi, Bilel  and
      Str{\"o}tgen, Jannik  and
      Gertz, Michael  and
      Tamine, Lynda},
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2139",
    doi = "10.18653/v1/S15-2139",
    pages = "825--829",
}
@inproceedings{hou-etal-2015-hitsz-icrc,
    title = "{HITSZ}-{ICRC}: An Integration Approach for {QA} {T}emp{E}val Challenge",
    author = "Hou, Yongshuai  and
      Tan, Cong  and
      Chen, Qingcai  and
      Wang, Xiaolong",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2140",
    doi = "10.18653/v1/S15-2140",
    pages = "830--834",
}
@inproceedings{tissot-etal-2015-ufprsheffield,
    title = "{UFPRS}heffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical {T}emp{E}val",
    author = "Tissot, Hegler  and
      Gorrell, Genevieve  and
      Roberts, Angus  and
      Derczynski, Leon  and
      Del Fabro, Marcos Didonet",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2141",
    doi = "10.18653/v1/S15-2141",
    pages = "835--839",
}
@inproceedings{salaberri-etal-2015-ixagroupehudiac,
    title = "{IXAG}roup{EHUD}iac: A Multiple Approach System towards the Diachronic Evaluation of Texts",
    author = "Salaberri, Haritz  and
      Salaberri, Iker  and
      Arregi, Olatz  and
      Zapirain, Be{\~n}at",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2142",
    doi = "10.18653/v1/S15-2142",
    pages = "840--845",
}
@inproceedings{tan-ordan-2015-usaar,
    title = "{USAAR}-{CHRONOS}: Crawling the Web for Temporal Annotations",
    author = "Tan, Liling  and
      Ordan, Noam",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2143",
    doi = "10.18653/v1/S15-2143",
    pages = "846--850",
}
@inproceedings{zampieri-etal-2015-ambra,
    title = "{AMBRA}: A Ranking Approach to Temporal Text Classification",
    author = "Zampieri, Marcos  and
      Ciobanu, Alina Maria  and
      Niculae, Vlad  and
      Dinu, Liviu P.",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2144",
    doi = "10.18653/v1/S15-2144",
    pages = "851--855",
}
@inproceedings{salaberri-etal-2015-ixagroupehuspaceeval,
    title = "{IXAG}roup{EHUS}pace{E}val: ({X}-Space) A {W}ord{N}et-based approach towards the Automatic Recognition of Spatial Information following the {ISO}-Space Annotation Scheme",
    author = "Salaberri, Haritz  and
      Arregi, Olatz  and
      Zapirain, Be{\~n}at",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2145",
    doi = "10.18653/v1/S15-2145",
    pages = "856--861",
}
@inproceedings{dsouza-ng-2015-utd,
    title = "{UTD}: Ensemble-Based Spatial Relation Extraction",
    author = "D{'}Souza, Jennifer  and
      Ng, Vincent",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2146",
    doi = "10.18653/v1/S15-2146",
    pages = "862--869",
}
@inproceedings{popescu-strapparava-2015-semeval,
    title = "{S}em{E}val 2015, Task 7: Diachronic Text Evaluation",
    author = "Popescu, Octavian  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2147",
    doi = "10.18653/v1/S15-2147",
    pages = "870--878",
}
@inproceedings{szymanski-lynch-2015-ucd,
    title = "{UCD} : Diachronic Text Classification with Character, Word, and Syntactic N-grams",
    author = "Szymanski, Terrence  and
      Lynch, Gerard",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2148",
    doi = "10.18653/v1/S15-2148",
    pages = "879--883",
}
@inproceedings{pustejovsky-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 8: {S}pace{E}val",
    author = "Pustejovsky, James  and
      Kordjamshidi, Parisa  and
      Moens, Marie-Francine  and
      Levine, Aaron  and
      Dworman, Seth  and
      Yocum, Zachary",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2149",
    doi = "10.18653/v1/S15-2149",
    pages = "884--894",
}
@inproceedings{nichols-botros-2015-sprl,
    title = "{S}p{RL}-{CWW}: Spatial Relation Classification with Independent Multi-class Models",
    author = "Nichols, Eric  and
      Botros, Fadi",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2150",
    doi = "10.18653/v1/S15-2150",
    pages = "895--901",
}
@inproceedings{bordea-etal-2015-semeval,
    title = "{S}em{E}val-2015 Task 17: Taxonomy Extraction Evaluation ({TE}x{E}val)",
    author = "Bordea, Georgeta  and
      Buitelaar, Paul  and
      Faralli, Stefano  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2151",
    doi = "10.18653/v1/S15-2151",
    pages = "902--910",
}
@inproceedings{grefenstette-2015-inriasac,
    title = "{INRIASAC}: Simple Hypernym Extraction Methods",
    author = "Grefenstette, Gregory",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2152",
    doi = "10.18653/v1/S15-2152",
    pages = "911--914",
}
@inproceedings{oepen-etal-2015-semeval,
    title = "{S}em{E}val 2015 Task 18: Broad-Coverage Semantic Dependency Parsing",
    author = "Oepen, Stephan  and
      Kuhlmann, Marco  and
      Miyao, Yusuke  and
      Zeman, Daniel  and
      Cinkov{\'a}, Silvie  and
      Flickinger, Dan  and
      Haji{\v{c}}, Jan  and
      Ure{\v{s}}ov{\'a}, Zde{\v{n}}ka",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2153",
    doi = "10.18653/v1/S15-2153",
    pages = "915--926",
}
@inproceedings{du-etal-2015-peking,
    title = "{P}eking: Building Semantic Dependency Graphs with a Hybrid Parser",
    author = "Du, Yantao  and
      Zhang, Fan  and
      Zhang, Xun  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2154",
    doi = "10.18653/v1/S15-2154",
    pages = "927--931",
}
@inproceedings{tan-etal-2015-usaar-wlv,
    title = "{USAAR}-{WLV}: Hypernym Generation with Deep Neural Nets",
    author = "Tan, Liling  and
      Gupta, Rohit  and
      van Genabith, Josef",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2155",
    doi = "10.18653/v1/S15-2155",
    pages = "932--937",
}
@inproceedings{ceesay-juan-hou-2015-ntnu,
    title = "{NTNU}: An Unsupervised Knowledge Approach for Taxonomy Extraction",
    author = "Ceesay, Bamfa  and
      Juan Hou, Wen",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2156",
    doi = "10.18653/v1/S15-2156",
    pages = "938--943",
}
@inproceedings{lefever-2015-lt3,
    title = "{LT}3: A Multi-modular Approach to Automatic Taxonomy Construction",
    author = "Lefever, Els",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2157",
    doi = "10.18653/v1/S15-2157",
    pages = "944--948",
}
@inproceedings{espinosa-anke-etal-2015-taln,
    title = "{TALN}-{UPF}: Taxonomy Learning Exploiting {CRF}-Based Hypernym Extraction on Encyclopedic Definitions",
    author = "Espinosa-Anke, Luis  and
      Saggion, Horacio  and
      Ronzano, Francesco",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2158",
    doi = "10.18653/v1/S15-2158",
    pages = "949--954",
}
@inproceedings{cleuziou-etal-2015-qassit,
    title = "{QASSIT}: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts",
    author = "Cleuziou, Guillaume  and
      Buscaldi, Davide  and
      Dias, Gael  and
      Levorato, Vincent  and
      Largeron, Christine",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2159",
    doi = "10.18653/v1/S15-2159",
    pages = "955--959",
}
@inproceedings{barzdins-etal-2015-riga,
    title = "{R}iga: from {F}rame{N}et to Semantic Frames with C6.0 Rules",
    author = "Barzdins, Guntis  and
      Paikens, Peteris  and
      Gosko, Didzis",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2160",
    doi = "10.18653/v1/S15-2160",
    pages = "960--964",
}
@inproceedings{kanerva-etal-2015-turku,
    title = "{T}urku: Semantic Dependency Parsing as a Sequence Classification",
    author = "Kanerva, Jenna  and
      Luotolahti, Juhani  and
      Ginter, Filip",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2161",
    doi = "10.18653/v1/S15-2161",
    pages = "965--969",
}
@inproceedings{almeida-martins-2015-lisbon,
    title = "{L}isbon: Evaluating {T}urbo{S}emantic{P}arser on Multiple Languages and Out-of-Domain Data",
    author = "Almeida, Mariana S. C.  and
      Martins, Andr{\'e} F. T.",
    booktitle = "Proceedings of the 9th International Workshop on Semantic Evaluation ({S}em{E}val 2015)",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-2162",
    doi = "10.18653/v1/S15-2162",
    pages = "970--973",
}
@proceedings{semeval-2015-joint,
    title = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    editor = "Palmer, Martha  and
      Boleda, Gemma  and
      Rosso, Paolo",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1000",
    doi = "10.18653/v1/S15-1",
}
@inproceedings{zhu-etal-2015-neural,
    title = "Neural Networks for Integrating Compositional and Non-compositional Sentiment in Sentiment Composition",
    author = "Zhu, Xiaodan  and
      Guo, Hongyu  and
      Sobhani, Parinaz",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1001",
    doi = "10.18653/v1/S15-1001",
    pages = "1--9",
}
@inproceedings{le-zuidema-2015-compositional,
    title = "Compositional Distributional Semantics with Long Short Term Memory",
    author = "Le, Phong  and
      Zuidema, Willem",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1002",
    doi = "10.18653/v1/S15-1002",
    pages = "10--19",
}
@inproceedings{aletras-stevenson-2015-hybrid,
    title = "A Hybrid Distributional and Knowledge-based Model of Lexical Semantics",
    author = "Aletras, Nikolaos  and
      Stevenson, Mark",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1003",
    doi = "10.18653/v1/S15-1003",
    pages = "20--29",
}
@inproceedings{corman-etal-2015-distributional,
    title = "Distributional semantics for ontology verification",
    author = "Corman, Julien  and
      Vieu, Laure  and
      Aussenac-Gilles, Nathalie",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1004",
    doi = "10.18653/v1/S15-1004",
    pages = "30--39",
}
@inproceedings{feizabadi-pado-2015-combining,
    title = "Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling",
    author = "Feizabadi, Parvin Sadat  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1005",
    doi = "10.18653/v1/S15-1005",
    pages = "40--50",
}
@inproceedings{hwang-palmer-2015-identification,
    title = "Identification of Caused Motion Construction",
    author = "Hwang, Jena D.  and
      Palmer, Martha",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1006",
    doi = "10.18653/v1/S15-1006",
    pages = "51--60",
}
@inproceedings{lopez-de-lacalle-agirre-2015-methodology,
    title = "A Methodology for Word Sense Disambiguation at 90{\%} based on large-scale {C}rowd{S}ourcing",
    author = "Lopez de Lacalle, Oier  and
      Agirre, Eneko",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1007",
    doi = "10.18653/v1/S15-1007",
    pages = "61--70",
}
@inproceedings{prabhakaran-boguraev-2015-learning,
    title = "Learning Structures of Negations from Flat Annotations",
    author = "Prabhakaran, Vinodkumar  and
      Boguraev, Branimir",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1008",
    doi = "10.18653/v1/S15-1008",
    pages = "71--81",
}
@inproceedings{prabhakaran-etal-2015-new,
    title = "A New Dataset and Evaluation for Belief/Factuality",
    author = "Prabhakaran, Vinodkumar  and
      By, Tomas  and
      Hirschberg, Julia  and
      Rambow, Owen  and
      Shaikh, Samira  and
      Strzalkowski, Tomek  and
      Tracey, Jennifer  and
      Arrigo, Michael  and
      Basu, Rupayan  and
      Clark, Micah  and
      Dalton, Adam  and
      Diab, Mona  and
      Guthrie, Louise  and
      Prokofieva, Anna  and
      Strassel, Stephanie  and
      Werner, Gregory  and
      Wilks, Yorick  and
      Wiebe, Janyce",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1009",
    doi = "10.18653/v1/S15-1009",
    pages = "82--91",
}
@inproceedings{aggarwal-etal-2015-non,
    title = "Non-Orthogonal Explicit Semantic Analysis",
    author = "Aggarwal, Nitish  and
      Asooja, Kartik  and
      Bordea, Georgeta  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1010",
    doi = "10.18653/v1/S15-1010",
    pages = "92--100",
}
@inproceedings{barrena-etal-2015-combining,
    title = "Combining Mention Context and Hyperlinks from {W}ikipedia for Named Entity Disambiguation",
    author = "Barrena, Ander  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1011",
    doi = "10.18653/v1/S15-1011",
    pages = "101--105",
}
@inproceedings{burford-etal-2015-collective,
    title = "Collective Document Classification with Implicit Inter-document Semantic Relationships",
    author = "Burford, Clint  and
      Bird, Steven  and
      Baldwin, Timothy",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1012",
    doi = "10.18653/v1/S15-1012",
    pages = "106--116",
}
@inproceedings{danesh-etal-2015-sgrank,
    title = "{SGR}ank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction",
    author = "Danesh, Soheil  and
      Sumner, Tamara  and
      Martin, James H.",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1013",
    doi = "10.18653/v1/S15-1013",
    pages = "117--126",
}
@inproceedings{eger-etal-2015-towards,
    title = "Towards Semantic Language Classification: Inducing and Clustering Semantic Association Networks from {E}uroparl",
    author = "Eger, Steffen  and
      Schenk, Niko  and
      Mehler, Alexander",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1014",
    doi = "10.18653/v1/S15-1014",
    pages = "127--136",
}
@inproceedings{elfardy-etal-2015-ideological,
    title = "Ideological Perspective Detection Using Semantic Features",
    author = "Elfardy, Heba  and
      Diab, Mona  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1015",
    doi = "10.18653/v1/S15-1015",
    pages = "137--146",
}
@inproceedings{benamara-taboada-2015-mapping,
    title = "Mapping Different Rhetorical Relation Annotations: A Proposal",
    author = "Benamara, Farah  and
      Taboada, Maite",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1016",
    doi = "10.18653/v1/S15-1016",
    pages = "147--152",
}
@inproceedings{gupta-etal-2015-dissecting,
    title = "Dissecting the Practical Lexical Function Model for Compositional Distributional Semantics",
    author = "Gupta, Abhijeet  and
      Utt, Jason  and
      Pad{\'o}, Sebastian",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1017",
    doi = "10.18653/v1/S15-1017",
    pages = "153--158",
}
@inproceedings{judea-strube-2015-event,
    title = "Event Extraction as Frame-Semantic Parsing",
    author = "Judea, Alex  and
      Strube, Michael",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1018",
    doi = "10.18653/v1/S15-1018",
    pages = "159--164",
}
@inproceedings{kornai-etal-2015-competence,
    title = "Competence in lexical semantics",
    author = "Kornai, Andr{\'a}s  and
      {\'A}cs, Judit  and
      Makrai, M{\'a}rton  and
      Nemeskey, D{\'a}vid M{\'a}rk  and
      Pajkossy, Katalin  and
      Recski, G{\'a}bor",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1019",
    doi = "10.18653/v1/S15-1019",
    pages = "165--175",
}
@inproceedings{marujo-etal-2015-extending,
    title = "Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach",
    author = "Marujo, Lu{\'\i}s  and
      Ribeiro, Ricardo  and
      Martins de Matos, David  and
      Neto, Jo{\~a}o  and
      Gershman, Anatole  and
      Carbonell, Jaime",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1020",
    doi = "10.18653/v1/S15-1020",
    pages = "176--181",
}
@inproceedings{necsulescu-etal-2015-reading,
    title = "Reading Between the Lines: Overcoming Data Sparsity for Accurate Classification of Lexical Relationships",
    author = "Nec{\c{s}}ulescu, Silvia  and
      Mendes, Sara  and
      Jurgens, David  and
      Bel, N{\'u}ria  and
      Navigli, Roberto",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1021",
    doi = "10.18653/v1/S15-1021",
    pages = "182--192",
}
@inproceedings{noh-etal-2015-multi,
    title = "Multi-Level Alignments As An Extensible Representation Basis for Textual Entailment Algorithms",
    author = "Noh, Tae-Gil  and
      Pad{\'o}, Sebastian  and
      Shwartz, Vered  and
      Dagan, Ido  and
      Nastase, Vivi  and
      Eichler, Kathrin  and
      Kotlerman, Lili  and
      Adler, Meni",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1022",
    doi = "10.18653/v1/S15-1022",
    pages = "193--198",
}
@inproceedings{ritter-etal-2015-leveraging,
    title = "Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics",
    author = "Ritter, Samuel  and
      Long, Cotie  and
      Paperno, Denis  and
      Baroni, Marco  and
      Botvinick, Matthew  and
      Goldberg, Adele",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1023",
    doi = "10.18653/v1/S15-1023",
    pages = "199--204",
}
@inproceedings{rudinger-etal-2015-learning,
    title = "Learning to predict script events from domain-specific text",
    author = "Rudinger, Rachel  and
      Demberg, Vera  and
      Modi, Ashutosh  and
      Van Durme, Benjamin  and
      Pinkal, Manfred",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1024",
    doi = "10.18653/v1/S15-1024",
    pages = "205--210",
}
@inproceedings{ruiz-poibeau-2015-combining,
    title = "Combining Open Source Annotators for Entity Linking through Weighted Voting",
    author = "Ruiz, Pablo  and
      Poibeau, Thierry",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1025",
    doi = "10.18653/v1/S15-1025",
    pages = "211--215",
}
@inproceedings{sanches-duran-aluisio-2015-automatic,
    title = "Automatic Generation of a Lexical Resource to support Semantic Role Labeling in {P}ortuguese",
    author = "Sanches Duran, Magali  and
      Alu{\'\i}sio, Sandra",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1026",
    doi = "10.18653/v1/S15-1026",
    pages = "216--221",
}
@inproceedings{wu-palmer-2015-selectional,
    title = "Can Selectional Preferences Help Automatic Semantic Role Labeling?",
    author = "Wu, Shumin  and
      Palmer, Martha",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1027",
    doi = "10.18653/v1/S15-1027",
    pages = "222--227",
}
@inproceedings{perera-etal-2015-implicit,
    title = "Implicit Entity Recognition in Clinical Documents",
    author = "Perera, Sujan  and
      Mendes, Pablo  and
      Sheth, Amit  and
      Thirunarayan, Krishnaprasad  and
      Alex, Adarsh  and
      Heid, Christopher  and
      Mott, Greg",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1028",
    doi = "10.18653/v1/S15-1028",
    pages = "228--238",
}
@inproceedings{exner-etal-2015-distant,
    title = "A Distant Supervision Approach to Semantic Role Labeling",
    author = "Exner, Peter  and
      Klang, Marcus  and
      Nugues, Pierre",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1029",
    doi = "10.18653/v1/S15-1029",
    pages = "239--248",
}
@inproceedings{fauconnier-kamel-2015-discovering,
    title = "Discovering Hypernymy Relations using Text Layout",
    author = "Fauconnier, Jean-Philippe  and
      Kamel, Mouna",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1030",
    doi = "10.18653/v1/S15-1030",
    pages = "249--258",
}
@inproceedings{schluter-2015-complexity,
    title = "The complexity of finding the maximum spanning {DAG} and other restrictions for {DAG} parsing of natural language",
    author = "Schluter, Natalie",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1031",
    doi = "10.18653/v1/S15-1031",
    pages = "259--268",
}
@inproceedings{kato-matsubara-2015-incremental,
    title = "Incremental Semantic Construction Using Normal Form {CCG} Derivation",
    author = "Kato, Yoshihide  and
      Matsubara, Shigeki",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1032",
    doi = "10.18653/v1/S15-1032",
    pages = "269--278",
}
@inproceedings{foland-martin-2015-dependency,
    title = "Dependency-Based Semantic Role Labeling using Convolutional Neural Networks",
    author = "Foland, William  and
      Martin, James",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1033",
    doi = "10.18653/v1/S15-1033",
    pages = "279--288",
}
@inproceedings{uryupina-moschitti-2015-state,
    title = "A State-of-the-Art Mention-Pair Model for Coreference Resolution",
    author = "Uryupina, Olga  and
      Moschitti, Alessandro",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1034",
    doi = "10.18653/v1/S15-1034",
    pages = "289--298",
}
@inproceedings{jauhar-etal-2015-resolving,
    title = "Resolving Discourse-Deictic Pronouns: A Two-Stage Approach to Do It",
    author = "Jauhar, Sujay Kumar  and
      Guerra, Raul  and
      Gonz{\`a}lez Pellicer, Edgar  and
      Recasens, Marta",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1035",
    doi = "10.18653/v1/S15-1035",
    pages = "299--308",
}
@inproceedings{mesgar-strube-2015-graph,
    title = "Graph-based Coherence Modeling For Assessing Readability",
    author = "Mesgar, Mohsen  and
      Strube, Michael",
    booktitle = "Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2015",
    address = "Denver, Colorado",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S15-1036",
    doi = "10.18653/v1/S15-1036",
    pages = "309--318",
}
@proceedings{ranlp-2015-student,
    title = "Proceedings of the Student Research Workshop",
    editor = "Temnikova, Irina  and
      Nikolova, Ivelina  and
      Popov, Alexander",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-2000",
}
@inproceedings{diez-2015-complexity,
    title = "The Complexity of Scrambling in {J}apanese: A {TAG} Approach",
    author = "Diez, Alexander",
    booktitle = "Proceedings of the Student Research Workshop",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-2001",
    pages = "1--7",
}
@inproceedings{franklin-2015-theoretical,
    title = "Some Theoretical Considerations in Off-the-Shelf Text Analysis Software",
    author = "Franklin, Emma",
    booktitle = "Proceedings of the Student Research Workshop",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-2002",
    pages = "8--15",
}
@inproceedings{kohail-2015-unsupervised,
    title = "Unsupervised Topic-Specific Domain Dependency Graphs for Aspect Identification in Sentiment Analysis",
    author = "Kohail, Sarah",
    booktitle = "Proceedings of the Student Research Workshop",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-2003",
    pages = "16--23",
}
@inproceedings{kumar-2015-unsupervised,
    title = "Unsupervised learning of agglutinated morphology using nested {P}itman-{Y}or process based morpheme induction algorithm",
    author = "Kumar, Arun",
    booktitle = "Proceedings of the Student Research Workshop",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-2004",
    pages = "24--29",
}
@inproceedings{yaneva-2015-easy,
    title = "Easy-read Documents as a Gold Standard for Evaluation of Text Simplification Output",
    author = "Yaneva, Victoria",
    booktitle = "Proceedings of the Student Research Workshop",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-2005",
    pages = "30--36",
}
@proceedings{ranlp-2015-international,
    title = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia  and
      Bontcheva, Kalina",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1000",
}
@inproceedings{albogamy-ramsay-2015-pos,
    title = "{POS} Tagging for {A}rabic Tweets",
    author = "Albogamy, Fahad  and
      Ramsay, Allan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1001",
    pages = "1--8",
}
@inproceedings{amiri-etal-2015-lexicon,
    title = "Lexicon-based Sentiment Analysis for {P}ersian Text",
    author = "Amiri, Fatemeh  and
      Scerri, Simon  and
      Khodashahi, Mohammadhassan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1002",
    pages = "9--16",
}
@inproceedings{ammar-etal-2015-automatic,
    title = "Automatic Construction of a {TMF} Terminological Database using a Transducer Cascade",
    author = "Ammar, Chihebeddine  and
      Haddar, Kais  and
      Romary, Laurent",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1003",
    pages = "17--23",
}
@inproceedings{assylbekov-etal-2015-statistical,
    title = "A Statistical Model for Measuring Structural Similarity between Webpages",
    author = "Assylbekov, Zhenisbek  and
      Nurkas, Assulan  and
      Russinho Mouga, In{\^e}s",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1004",
    pages = "24--31",
}
@inproceedings{baltadzhieva-chrupala-2015-predicting,
    title = "Predicting the quality of questions on {S}tackoverflow",
    author = "Baltadzhieva, Antoaneta  and
      Chrupa{\l}a, Grzegorz",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1005",
    pages = "32--40",
}
@inproceedings{barbieri-etal-2015-topic,
    title = "How Topic Biases Your Results? A Case Study of Sentiment Analysis and Irony Detection in {I}talian",
    author = "Barbieri, Francesco  and
      Ronzano, Francesco  and
      Saggion, Horacio",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1006",
    pages = "41--47",
}
@inproceedings{ben-mesmia-etal-2015-arabic,
    title = "{A}rabic Named Entity Recognition Process using Transducer Cascade and {A}rabic {W}ikipedia",
    author = "Ben Mesmia, Fatma  and
      Haddar, Kais  and
      Maurel, Denis  and
      Friburger, Nathalie",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1007",
    pages = "48--54",
}
@inproceedings{bick-2015-danproof,
    title = "{D}an{P}roof: Pedagogical Spell and Grammar Checking for {D}anish",
    author = "Bick, Eckhard",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1008",
    pages = "55--62",
}
@inproceedings{brixtel-2015-maximal,
    title = "Maximal Repeats Enhance Substring-based Authorship Attribution",
    author = "Brixtel, Romain",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1009",
    pages = "63--71",
}
@inproceedings{cao-etal-2015-improving,
    title = "Improving Event Detection with Active Learning",
    author = "Cao, Kai  and
      Li, Xiang  and
      Fan, Miao  and
      Grishman, Ralph",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1010",
    pages = "72--77",
}
@inproceedings{cao-etal-2015-improving-event,
    title = "Improving Event Detection with Dependency Regularization",
    author = "Cao, Kai  and
      Li, Xiang  and
      Grishman, Ralph",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1011",
    pages = "78--83",
}
@inproceedings{castro-castro-etal-2015-authorship,
    title = "Authorship Verification, Average Similarity Analysis",
    author = "Castro Castro, Daniel  and
      Adame Arcia, Yaritza  and
      Pelaez Brioso, Mar{\'\i}a  and
      Mu{\~n}oz Guillena, Rafael",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1012",
    pages = "84--90",
}
@inproceedings{cheri-bhattacharyya-2015-coreference,
    title = "Coreference Resolution to Support {IE} from {I}ndian Classical Music Forums",
    author = "Cheri, Joe  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1013",
    pages = "91--96",
}
@inproceedings{ciobanu-etal-2015-readability,
    title = "Readability Assessment of Translated Texts",
    author = "Ciobanu, Alina Maria  and
      Dinu, Liviu P.  and
      Pepelea, Flaviu",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1014",
    pages = "97--103",
}
@inproceedings{declerck-lendvai-2015-processing,
    title = "Processing and Normalizing Hashtags",
    author = "Declerck, Thierry  and
      Lendvai, Piroska",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1015",
    pages = "104--109",
}
@inproceedings{derczynski-etal-2015-tune,
    title = "Tune Your Brown Clustering, Please",
    author = "Derczynski, Leon  and
      Chester, Sean  and
      B{\o}gh, Kenneth",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1016",
    pages = "110--117",
}
@inproceedings{derczynski-gaizauskas-2015-temporal,
    title = "Temporal Relation Classification using a Model of Tense and Aspect",
    author = "Derczynski, Leon  and
      Gaizauskas, Robert",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1017",
    pages = "118--122",
}
@inproceedings{derczynski-bontcheva-2015-efficient,
    title = "Efficient Named Entity Annotation through Pre-empting",
    author = "Derczynski, Leon  and
      Bontcheva, Kalina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1018",
    pages = "123--130",
}
@inproceedings{ding-jiang-2015-joint,
    title = "A Joint Model of Product Properties, Aspects and Ratings for Online Reviews",
    author = "Ding, Ying  and
      Jiang, Jing",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1019",
    pages = "131--137",
}
@inproceedings{ding-jiang-2015-towards,
    title = "Towards Opinion Summarization from Online Forums",
    author = "Ding, Ying  and
      Jiang, Jing",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1020",
    pages = "138--146",
}
@inproceedings{dinu-etal-2015-cross,
    title = "Cross-lingual Synonymy Overlap",
    author = "Dinu, Anca  and
      Dinu, Liviu P.  and
      Uban, Ana Sabina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1021",
    pages = "147--152",
}
@inproceedings{el-maarouf-etal-2015-barbecued,
    title = "Barbecued Opakapaka: Using Semantic Preferences for Ontology Population",
    author = "El Maarouf, Ismail  and
      Marsic, Georgiana  and
      Or{\u{a}}san, Constantin",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1022",
    pages = "153--159",
}
@inproceedings{elia-etal-2015-towards,
    title = "Towards a Lexicon-grammar based Framework for {NLP}: an Opinion Mining Application",
    author = "Elia, Annibale  and
      Pelosi, Serena  and
      Maisto, Alessandro  and
      Guarasci, Raffaele",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1023",
    pages = "160--167",
}
@inproceedings{elleuch-etal-2015-using,
    title = "Using the Textual Content of the {LMF}-Normalized Dictionaries for Identifying and Linking the Syntactic Behaviors to the Meanings",
    author = "Elleuch, Imen  and
      Gargouri, Bilel  and
      Ben Hamadou, Abdelmajid",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1024",
    pages = "168--175",
}
@inproceedings{espinosa-anke-etal-2015-weakly,
    title = "Weakly Supervised Definition Extraction",
    author = "Espinosa-Anke, Luis  and
      Saggion, Horacio  and
      Ronzano, Francesco",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1025",
    pages = "176--185",
}
@inproceedings{fan-etal-2015-jointly,
    title = "Jointly Embedding Relations and Mentions for Knowledge Population",
    author = "Fan, Miao  and
      Cao, Kai  and
      He, Yifan  and
      Grishman, Ralph",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1026",
    pages = "186--191",
}
@inproceedings{feuerbach-etal-2015-distributional,
    title = "Distributional Semantics for Resolving Bridging Mentions",
    author = "Feuerbach, Tim  and
      Riedl, Martin  and
      Biemann, Chris",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1027",
    pages = "192--199",
}
@inproceedings{galitsky-etal-2015-text,
    title = "Text Classification into Abstract Classes Based on Discourse Structure",
    author = "Galitsky, Boris  and
      Ilvovsky, Dmitry  and
      Kuznetsov, Sergey O.",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1028",
    pages = "200--207",
}
@inproceedings{ghanimifard-johansson-2015-enriching,
    title = "Enriching Word Sense Embeddings with Translational Context",
    author = "Ghanimifard, Mehdi  and
      Johansson, Richard",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1029",
    pages = "208--215",
}
@inproceedings{hu-buvet-2015-automatic,
    title = "Automatic Acquisition of Artifact Nouns in {F}rench",
    author = "Hu, Xiaoqin  and
      Buvet, Pierre-Andr{\'e}",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1030",
    pages = "216--223",
}
@inproceedings{jackov-2015-feature,
    title = "Feature-Rich Part-Of-Speech Tagging Using Deep Syntactic and Semantic Analysis",
    author = "Jackov, Luchezar",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1031",
    pages = "224--231",
}
@inproceedings{jaf-ramsay-2015-application,
    title = "The Application of Constraint Rules to Data-driven Parsing",
    author = "Jaf, Sardar  and
      Ramsay, Allan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1032",
    pages = "232--238",
}
@inproceedings{jamatia-etal-2015-part,
    title = "Part-of-Speech Tagging for Code-Mixed {E}nglish-{H}indi {T}witter and {F}acebook Chat Messages",
    author = {Jamatia, Anupam  and
      Gamb{\"a}ck, Bj{\"o}rn  and
      Das, Amitava},
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1033",
    pages = "239--248",
}
@inproceedings{jovanoski-etal-2015-sentiment,
    title = "Sentiment Analysis in {T}witter for {M}acedonian",
    author = "Jovanoski, Dame  and
      Pachovski, Veno  and
      Nakov, Preslav",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1034",
    pages = "249--257",
}
@inproceedings{kanishcheva-angelova-2015-emotion,
    title = "About Emotion Identification in Visual Sentiment Analysis",
    author = "Kanishcheva, Olga  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1035",
    pages = "258--265",
}
@inproceedings{kapukaranov-nakov-2015-fine,
    title = "Fine-Grained Sentiment Analysis for Movie Reviews in {B}ulgarian",
    author = "Kapukaranov, Borislav  and
      Nakov, Preslav",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1036",
    pages = "266--274",
}
@inproceedings{kessler-kuhn-2015-structural,
    title = "Structural Alignment for Comparison Detection",
    author = "Kessler, Wiltrud  and
      Kuhn, Jonas",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1037",
    pages = "275--281",
}
@inproceedings{kocon-marcinczuk-2015-recognition,
    title = "Recognition of {P}olish Temporal Expressions",
    author = "Koco{\'n}, Jan  and
      Marci{\'n}czuk, Micha{\l}",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1038",
    pages = "282--290",
}
@inproceedings{komiya-etal-2015-domain,
    title = "Domain Adaptation with Filtering for Named Entity Extraction of {J}apanese Anime-Related Words",
    author = "Komiya, Kanako  and
      Edamura, Daichi  and
      Tamura, Ryuta  and
      Sasaki, Minoru  and
      Shinnou, Hiroyuki  and
      Kotani, Yoshiyuki",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1039",
    pages = "291--297",
}
@inproceedings{kriz-etal-2015-feature,
    title = "Feature Extraction for Native Language Identification Using Language Modeling",
    author = "Kr{\'\i}{\v{z}}, Vincent  and
      Holub, Martin  and
      Pecina, Pavel",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1040",
    pages = "298--306",
}
@inproceedings{kumar-etal-2015-learning,
    title = "Learning Agglutinative Morphology of {I}ndian Languages with Linguistically Motivated {A}daptor {G}rammars",
    author = "Kumar, Arun  and
      Padr{\'o}, Llu{\'\i}s  and
      Oliver, Antoni",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1041",
    pages = "307--312",
}
@inproceedings{kuncham-etal-2015-statistical,
    title = "Statistical Sandhi Splitter and its Effect on {NLP} Applications",
    author = "Kuncham, Prathyusha  and
      Nelakuditi, Kovida  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1042",
    pages = "313--319",
}
@inproceedings{kunneman-van-den-bosch-2015-automatically,
    title = "Automatically Identifying Periodic Social Events from {T}witter",
    author = "Kunneman, Florian  and
      Van den Bosch, Antal",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1043",
    pages = "320--328",
}
@inproceedings{lafourcade-etal-2015-collecting,
    title = "Collecting and Evaluating Lexical Polarity with A Game With a Purpose",
    author = "Lafourcade, Mathieu  and
      Joubert, Alain  and
      Le Brun, Nathalie",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1044",
    pages = "329--337",
}
@inproceedings{lafourcade-ramadier-2015-medical,
    title = "Medical imaging report indexing: enrichment of index through an algorithm of spreading over a lexico-semantic network",
    author = "Lafourcade, Mathieu  and
      Ramadier, Lionel",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1045",
    pages = "338--345",
}
@inproceedings{le-fokkens-2015-taxonomy,
    title = "Taxonomy Beats Corpus in Similarity Identification, but Does It Matter?",
    author = "Le, Minh  and
      Fokkens, Antske",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1046",
    pages = "346--354",
}
@inproceedings{liao-etal-2015-semantic,
    title = "Semantic Parsing via $l_{0}$-norm-based Alignment",
    author = "Liao, Zhihua  and
      Zeng, Qixian  and
      Wang, Qiyun",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1047",
    pages = "355--361",
}
@inproceedings{liao-etal-2015-supervised,
    title = "A Supervised Semantic Parsing with Lexicon Extension and Syntactic Constraint",
    author = "Liao, Zhihua  and
      Zeng, Qixian  and
      Wang, Qiyun",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1048",
    pages = "362--370",
}
@inproceedings{ljubesic-etal-2015-predicting,
    title = "Predicting the Level of Text Standardness in User-generated Content",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Fi{\v{s}}er, Darja  and
      Erjavec, Toma{\v{z}}  and
      {\v{C}}ibej, Jaka  and
      Marko, Dafne  and
      Pollak, Senja  and
      {\v{S}}krjanec, Iza",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1049",
    pages = "371--378",
}
@inproceedings{ljubesic-etal-2015-predicting-inflectional,
    title = "Predicting Inflectional Paradigms and Lemmata of Unknown Words for Semi-automatic Expansion of Morphological Lexicons",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Espl{\`a}-Gomis, Miquel  and
      Klubi{\v{c}}ka, Filip  and
      Mikeli{\'c} Preradovi{\'c}, Nives",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1050",
    pages = "379--387",
}
@inproceedings{magnolini-magnini-2015-predicting,
    title = "Predicting Correlations Between Lexical Alignments and Semantic Inferences",
    author = "Magnolini, Simone  and
      Magnini, Bernardo",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1051",
    pages = "388--397",
}
@inproceedings{magnolini-etal-2015-learning,
    title = "Learning the Impact of Machine Translation Evaluation Metrics for Semantic Textual Similarity",
    author = "Magnolini, Simone  and
      Vo, Ngoc Phuoc An  and
      Popescu, Octavian",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1052",
    pages = "398--403",
}
@inproceedings{malmasi-etal-2015-norwegian,
    title = "{N}orwegian Native Language Identification",
    author = "Malmasi, Shervin  and
      Dras, Mark  and
      Temnikova, Irina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1053",
    pages = "404--412",
}
@inproceedings{marcinczuk-2015-automatic,
    title = "Automatic construction of complex features in Conditional Random Fields for Named Entities Recognition",
    author = "Marci{\'n}czuk, Micha{\l}",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1054",
    pages = "413--419",
}
@inproceedings{marrero-garcia-etal-2015-pattern,
    title = "Pattern Construction for Extracting Domain Terminology",
    author = "Marrero Garc{\'\i}a, Yusney  and
      Moreda Pozo, Paloma  and
      Mu{\~n}oz-Guillena, Rafael",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1055",
    pages = "420--426",
}
@inproceedings{maziarz-etal-2015-procedural,
    title = "A Procedural Definition of Multi-word Lexical Units",
    author = "Maziarz, Marek  and
      Szpakowicz, Stan  and
      Piasecki, Maciej",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1056",
    pages = "427--435",
}
@inproceedings{maziero-etal-2015-semi,
    title = "Semi-Supervised Never-Ending Learning in Rhetorical Relation Identification",
    author = "Maziero, Erick Galani  and
      Hirst, Graeme  and
      Pardo, Thiago Alexandre Salgueiro",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1057",
    pages = "436--442",
}
@inproceedings{mihaylov-etal-2015-exposing,
    title = "Exposing Paid Opinion Manipulation Trolls",
    author = "Mihaylov, Todor  and
      Koychev, Ivan  and
      Georgiev, Georgi  and
      Nakov, Preslav",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1058",
    pages = "443--450",
}
@inproceedings{mogren-etal-2015-extractive,
    title = "Extractive Summarization by Aggregating Multiple Similarities",
    author = {Mogren, Olof  and
      K{\aa}geb{\"a}ck, Mikael  and
      Dubhashi, Devdatt},
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1059",
    pages = "451--457",
}
@inproceedings{nasri-etal-2015-statistical,
    title = "Statistical Machine Translation Improvement based on Phrase Selection",
    author = "Nasri, Cyrine  and
      Latiri, Chiraz  and
      Smaili, Kamel",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1060",
    pages = "458--464",
}
@inproceedings{nieto-pina-johansson-2015-simple,
    title = "A Simple and Efficient Method to Generate Word Sense Representations",
    author = "Nieto Pi{\~n}a, Luis  and
      Johansson, Richard",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1061",
    pages = "465--472",
}
@inproceedings{oliver-vazquez-2015-tbxtools,
    title = "{TBXT}ools: A Free, Fast and Flexible Tool for Automatic Terminology Extraction",
    author = "Oliver, Antoni  and
      V{\`a}zquez, Merc{\`e}",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1062",
    pages = "473--479",
}
@inproceedings{oliver-etal-2015-enlarging,
    title = "Enlarging the {C}roatian {W}ord{N}et with {WN}-Toolkit and Cro-Deriv",
    author = "Oliver, Antoni  and
      {\v{S}}ojat, Kre{\v{s}}imir  and
      Sreba{\v{c}}i{\'c}, Matea",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1063",
    pages = "480--487",
}
@inproceedings{ozdemir-bergler-2015-comparative,
    title = "A Comparative Study of Different Sentiment Lexica for Sentiment Analysis of Tweets",
    author = "Ozdemir, Canberk  and
      Bergler, Sabine",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1064",
    pages = "488--496",
}
@inproceedings{palakurthi-etal-2015-classification,
    title = "Classification of Attributes in a Natural Language Query into Different {SQL} Clauses",
    author = "Palakurthi, Ashish  and
      S M, Ruthu  and
      Akula, Arjun  and
      Mamidi, Radhika",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1065",
    pages = "497--506",
}
@inproceedings{peng-etal-2015-classifying,
    title = "Classifying Idiomatic and Literal Expressions Using Vector Space Representations",
    author = "Peng, Jing  and
      Feldman, Anna  and
      Jazmati, Hamza",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1066",
    pages = "507--511",
}
@inproceedings{piasecki-etal-2015-extraction,
    title = "Extraction of the Multi-word Lexical Units in the Perspective of the {W}ordnet Expansion",
    author = "Piasecki, Maciej  and
      Wendelberger, Micha{\l}  and
      Maziarz, Marek",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1067",
    pages = "512--520",
}
@inproceedings{razon-barnden-2015-new,
    title = "A New Approach to Automated Text Readability Classification based on Concept Indexing with Integrated Part-of-Speech n-gram Features",
    author = "Razon, Abigail  and
      Barnden, John",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1068",
    pages = "521--528",
}
@inproceedings{rodriguez-fernandez-etal-2015-classification,
    title = "Classification of Lexical Collocation Errors in the Writings of Learners of {S}panish",
    author = "Rodr{\'\i}guez-Fern{\'a}ndez, Sara  and
      Carlini, Roberto  and
      Wanner, Leo",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1069",
    pages = "529--536",
}
@inproceedings{rudrapal-etal-2015-measuring,
    title = "Measuring Semantic Similarity for {B}engali Tweets Using {W}ord{N}et",
    author = "Rudrapal, Dwijen  and
      Das, Amitava  and
      Bhattacharya, Baby",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1070",
    pages = "537--544",
}
@inproceedings{ruppenhofer-etal-2015-ordering,
    title = "Ordering adverbs by their scaling effect on adjective intensity",
    author = "Ruppenhofer, Josef  and
      Brandes, Jasper  and
      Steiner, Petra  and
      Wiegand, Michael",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1071",
    pages = "545--554",
}
@inproceedings{salaberri-etal-2015-brol,
    title = "b{R}ol: The Parser of Syntactic and Semantic Dependencies for {B}asque",
    author = "Salaberri, Haritz  and
      Arregi, Olatz  and
      Zapirain, Be{\~n}at",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1072",
    pages = "555--562",
}
@inproceedings{salimbajevs-strigins-2015-error,
    title = "Error Analysis and Improving Speech Recognition for {L}atvian Language",
    author = "Salimbajevs, Askars  and
      Strigins, Jevgenijs",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1073",
    pages = "563--569",
}
@inproceedings{schenk-etal-2015-towards,
    title = "Towards the Unsupervised Acquisition of Implicit Semantic Roles",
    author = "Schenk, Niko  and
      Chiarcos, Christian  and
      Sukhareva, Maria",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1074",
    pages = "570--578",
}
@inproceedings{semmar-etal-2015-evaluating,
    title = "Evaluating the Impact of Using a Domain-specific Bilingual Lexicon on the Performance of a Hybrid Machine Translation Approach",
    author = "Semmar, Nasredine  and
      Zennaki, Othman  and
      Laib, Meriama",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1075",
    pages = "579--587",
}
@inproceedings{simon-etal-2015-hierarchical,
    title = "Hierarchical Topic Structuring: From Dense Segmentation to Topically Focused Fragments via Burst Analysis",
    author = "Simon, Anca-Roxana  and
      S{\'e}billot, Pascale  and
      Gravier, Guillaume",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1076",
    pages = "588--595",
}
@inproceedings{simov-etal-2015-improving,
    title = "Improving Word Sense Disambiguation with Linguistic Knowledge from a Sense Annotated Treebank",
    author = "Simov, Kiril  and
      Popov, Alexander  and
      Osenova, Petya",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1077",
    pages = "596--603",
}
@inproceedings{sokolova-bobicev-2015-learning,
    title = "Learning Relationship between Authors{'} Activity and Sentiments: A case study of online medical forums",
    author = "Sokolova, Marina  and
      Bobicev, Victoria",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1078",
    pages = "604--610",
}
@inproceedings{stajner-saggion-2015-translating,
    title = "Translating from Original to Simplified Sentences using {M}oses: When does it Actually Work?",
    author = "{\v{S}}tajner, Sanja  and
      Saggion, Horacio",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1079",
    pages = "611--617",
}
@inproceedings{stajner-etal-2015-automatic,
    title = "Automatic Text Simplification for {S}panish: Comparative Evaluation of Various Simplification Strategies",
    author = "{\v{S}}tajner, Sanja  and
      Calixto, Iacer  and
      Saggion, Horacio",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1080",
    pages = "618--626",
}
@inproceedings{steinberger-tanev-2015-towards,
    title = "Towards Multilingual Event Extraction Evaluation: A Case Study for the {C}zech Language",
    author = "Steinberger, Josef  and
      Tanev, Hristo",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1081",
    pages = "627--635",
}
@inproceedings{surtani-paul-2015-vsm,
    title = "A {VSM}-based Statistical Model for the Semantic Relation Interpretation of Noun-Modifier Pairs",
    author = "Surtani, Nitesh  and
      Paul, Soma",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1082",
    pages = "636--645",
}
@inproceedings{tanha-etal-2015-lda,
    title = "An {LDA}-based Topic Selection Approach to Language Model Adaptation for Handwritten Text Recognition",
    author = "Tanha, Jafar  and
      de Does, Jesse  and
      Depuydt, Katrien",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1083",
    pages = "646--653",
}
@inproceedings{todorova-simov-2015-training,
    title = "Training Automatic Transliteration Models on {DBP}edia Data",
    author = "Todorova, Velislava  and
      Simov, Kiril",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1084",
    pages = "654--662",
}
@inproceedings{turki-khemakhem-etal-2015-arabic,
    title = "{A}rabic-{E}nglish Semantic Word Class Alignment to Improve Statistical Machine Translation",
    author = "Turki Khemakhem, Ines  and
      Jamoussi, Salma  and
      Ben Hamadou, Abdelmajid",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1085",
    pages = "663--671",
}
@inproceedings{van-hee-etal-2015-detection,
    title = "Detection and Fine-Grained Classification of Cyberbullying Events",
    author = "Van Hee, Cynthia  and
      Lefever, Els  and
      Verhoeven, Ben  and
      Mennes, Julie  and
      Desmet, Bart  and
      De Pauw, Guy  and
      Daelemans, Walter  and
      Hoste, Veronique",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1086",
    pages = "672--680",
}
@inproceedings{verma-vuppuluri-2015-new,
    title = "A New Approach for Idiom Identification Using Meanings and the Web",
    author = "Verma, Rakesh  and
      Vuppuluri, Vasanthi",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1087",
    pages = "681--687",
}
@inproceedings{vo-popescu-2015-learning,
    title = "Learning the Impact and Behavior of Syntactic Structure: A Case Study in Semantic Textual Similarity",
    author = "Vo, Ngoc Phuoc An  and
      Popescu, Octavian",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1088",
    pages = "688--696",
}
@inproceedings{yaneva-evans-2015-six,
    title = "Six Good Predictors of Autistic Text Comprehension",
    author = "Yaneva, Victoria  and
      Evans, Richard",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1089",
    pages = "697--706",
}
@inproceedings{yang-manandhar-2015-user,
    title = "User Name Disambiguation in Community Question Answering",
    author = "Yang, Baoguo  and
      Manandhar, Suresh",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1090",
    pages = "707--713",
}
@inproceedings{yordanova-2015-discovering,
    title = "Discovering Causal Relations in Textual Instructions",
    author = "Yordanova, Kristina",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1091",
    pages = "714--720",
}
@inproceedings{zasko-zielinska-etal-2015-large,
    title = "A Large {W}ordnet-based Sentiment Lexicon for {P}olish",
    author = "Za{\'s}ko-Zieli{\'n}ska, Monika  and
      Piasecki, Maciej  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1092",
    pages = "721--730",
}
@inproceedings{zayed-el-beltagy-2015-named,
    title = "Named Entity Recognition of Persons{'} Names in {A}rabic Tweets",
    author = "Zayed, Omnia  and
      El-Beltagy, Samhaa",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1093",
    pages = "731--738",
}
@inproceedings{ziering-van-der-plas-2015-one,
    title = "One Tree is not Enough: Cross-lingual Accumulative Structure Transfer for Semantic Indeterminacy",
    author = "Ziering, Patrick  and
      van der Plas, Lonneke",
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1094",
    pages = "739--746",
}
@inproceedings{zirn-etal-2015-lost,
    title = "Lost in Discussion? Tracking Opinion Groups in Complex Political Discussions by the Example of the {FOMC} Meeting Transcriptions",
    author = {Zirn, C{\"a}cilia  and
      Meusel, Robert  and
      Stuckenschmidt, Heiner},
    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing",
    month = sep,
    year = "2015",
    address = "Hissar, Bulgaria",
    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
    url = "https://www.aclweb.org/anthology/R15-1095",
    pages = "747--753",
}
@article{tacl-2015-transactions,
    title = "Transactions of the Association for Computational Linguistics, Volume 3",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1000",
}
@article{roy-etal-2015-reasoning,
    title = "Reasoning about Quantities in Natural Language",
    author = "Roy, Subhro  and
      Vieira, Tim  and
      Roth, Dan",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1001",
    doi = "10.1162/tacl_a_00118",
    pages = "1--13",
    abstract = "Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.",
}
@article{dutta-weikum-2015-cross,
    title = "Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment",
    author = "Dutta, Sourav  and
      Weikum, Gerhard",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1002",
    doi = "10.1162/tacl_a_00119",
    pages = "15--28",
    abstract = "Identifying and linking named entities across information sources is the basis of knowledge acquisition and at the heart of Web search, recommendations, and analytics. An important problem in this context is cross-document co-reference resolution (CCR): computing equivalence classes of textual mentions denoting the same entity, within and across documents. Prior methods employ ranking, clustering, or probabilistic graphical models using syntactic features and distant features from knowledge bases. However, these methods exhibit limitations regarding run-time and robustness. This paper presents the CROCS framework for unsupervised CCR, improving the state of the art in two ways. First, we extend the way knowledge bases are harnessed, by constructing a notion of semantic summaries for intra-document co-reference chains using co-occurring entity mentions belonging to different chains. Second, we reduce the computational cost by a new algorithm that embeds sample-based bisection, using spectral clustering or graph partitioning, in a hierarchical clustering process. This allows scaling up CCR to large corpora. Experiments with three datasets show significant gains in output quality, compared to the best prior methods, and the run-time efficiency of CROCS.",
}
@article{tackstrom-etal-2015-efficient,
    title = "Efficient Inference and Structured Learning for Semantic Role Labeling",
    author = {T{\"a}ckstr{\"o}m, Oscar  and
      Ganchev, Kuzman  and
      Das, Dipanjan},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1003",
    doi = "10.1162/tacl_a_00120",
    pages = "29--41",
    abstract = "We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-the-shelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with respect to constrained conditional likelihood. We show that the dynamic program is several times faster than an off-the-shelf integer linear programming solver, while reaching the same solution. Furthermore, we show that our structured model results in significant improvements over its local counterpart, achieving state-of-the-art results on both PropBank- and FrameNet-annotated corpora.",
}
@article{paul-dredze-2015-sprite,
    title = "{S}prite: Generalizing Topic Models with Structured Priors",
    author = "Paul, Michael J.  and
      Dredze, Mark",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1004",
    doi = "10.1162/tacl_a_00121",
    pages = "43--57",
    abstract = "We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing Sprite to be tailored to particular settings. We demonstrate this flexibility by constructing a Sprite-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.",
}
@article{wang-etal-2015-sense,
    title = "A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment",
    author = "Wang, Jing  and
      Bansal, Mohit  and
      Gimpel, Kevin  and
      Ziebart, Brian D.  and
      Yu, Clement T.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1005",
    doi = "10.1162/tacl_a_00122",
    pages = "59--71",
    abstract = "Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and topic as two separate latent variables to be inferred jointly. Topics are informed by the entire document, while senses are informed by the local context surrounding the ambiguous word. We also discuss unsupervised ways of enriching the original corpus in order to improve model performance, including using neural word embeddings and external corpora to expand the context of each data instance. We demonstrate significant improvements over the previous state-of-the-art, achieving the best results reported to date on the SemEval-2013 WSI task.",
}
@article{louis-lapata-2015-step,
    title = "Which Step Do {I} Take First? Troubleshooting with {B}ayesian Models",
    author = "Louis, Annie  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1006",
    doi = "10.1162/tacl_a_00123",
    pages = "73--85",
    abstract = "Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information. In this paper, we propose text mining techniques which aid users navigate troubleshooting-oriented data such as questions asked on forums and their suggested solutions. We introduce Bayesian generative models of the troubleshooting data and apply them to two interrelated tasks: (a) predicting the complexity of the solutions (e.g., plugging a keyboard in the computer is easier compared to installing a special driver) and (b) presenting them in a ranked order from least to most complex. Experimental results show that our models are on par with human performance on these tasks, while outperforming baselines based on solution length or readability.",
}
@article{he-etal-2015-gappy,
    title = "Gappy Pattern Matching on {GPU}s for On-Demand Extraction of Hierarchical Translation Grammars",
    author = "He, Hua  and
      Lin, Jimmy  and
      Lopez, Adam",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1007",
    doi = "10.1162/tacl_a_00124",
    pages = "87--100",
    abstract = "Grammars for machine translation can be materialized on demand by finding source phrases in an indexed parallel corpus and extracting their translations. This approach is limited in practical applications by the computational expense of online lookup and extraction. For phrase-based models, recent work has shown that on-demand grammar extraction can be greatly accelerated by parallelization on general purpose graphics processing units (GPUs), but these algorithms do not work for hierarchical models, which require matching patterns that contain gaps. We address this limitation by presenting a novel GPU algorithm for on-demand hierarchical grammar extraction that is at least an order of magnitude faster than a comparable CPU algorithm when processing large batches of sentences. In terms of end-to-end translation, with decoding on the CPU, we increase throughput by roughly two thirds on a standard MT evaluation dataset. The GPU necessary to achieve these improvements increases the cost of a server by about a third. We believe that GPU-based extraction of hierarchical grammars is an attractive proposition, particularly for MT applications that demand high throughput.",
}
@article{mcmahan-stone-2015-bayesian,
    title = "A {B}ayesian Model of Grounded Color Semantics",
    author = "McMahan, Brian  and
      Stone, Matthew",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1008",
    doi = "10.1162/tacl_a_00126",
    pages = "103--115",
    abstract = "Natural language meanings allow speakers to encode important real-world distinctions, but corpora of grounded language use also reveal that speakers categorize the world in different ways and describe situations with different terminology. To learn meanings from data, we therefore need to link underlying representations of meaning to models of speaker judgment and speaker choice. This paper describes a new approach to this problem: we model variability through uncertainty in categorization boundaries and distributions over preferred vocabulary. We apply the approach to a large data set of color descriptions, where statistical evaluation documents its accuracy. The results are available as a Lexicon of Uncertain Color Standards (LUX), which supports future efforts in grounded language understanding and generation by probabilistically mapping 829 English color descriptions to potentially context-sensitive regions in HSV color space.",
}
@article{zhang-etal-2015-exploiting,
    title = "Exploiting Parallel News Streams for Unsupervised Event Extraction",
    author = "Zhang, Congle  and
      Soderland, Stephen  and
      Weld, Daniel S.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1009",
    doi = "10.1162/tacl_a_00127",
    pages = "117--129",
    abstract = "Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations (e.g. {``}person travels to location{''}). Thus, the problem of extracting a wide range of events {---} e.g., from news streams {---} is an important, open challenge. This paper introduces NewsSpike-RE, a novel, unsupervised algorithm that discovers event relations and then learns to extract them. NewsSpike-RE uses a novel probabilistic graphical model to cluster sentences describing similar events from parallel news streams. These clusters then comprise training data for the extractor. Our evaluation shows that NewsSpike-RE generates high quality training sentences and learns extractors that perform much better than rival approaches, more than doubling the area under a precision-recall curve compared to Universal Schemas.",
}
@article{guo-etal-2015-unsupervised,
    title = "Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents",
    author = "Guo, Yufan  and
      Reichart, Roi  and
      Korhonen, Anna",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1010",
    doi = "10.1162/tacl_a_00128",
    pages = "131--143",
    abstract = "Inferring the information structure of scientific documents is useful for many NLP applications. Existing approaches to this task require substantial human effort. We propose a framework for constraint learning that reduces human involvement considerably. Our model uses topic models to identify latent topics and their key linguistic features in input documents, induces constraints from this information and maps sentences to their dominant information structure categories through a constrained unsupervised model. When the induced constraints are combined with a fully unsupervised model, the resulting model challenges existing lightly supervised feature-based models as well as unsupervised models that use manually constructed declarative knowledge. Our results demonstrate that useful declarative knowledge can be learned from data with very limited human involvement.",
}
@article{chisholm-hachey-2015-entity,
    title = "Entity Disambiguation with Web Links",
    author = "Chisholm, Andrew  and
      Hachey, Ben",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1011",
    doi = "10.1162/tacl_a_00129",
    pages = "145--156",
    abstract = "Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia. Experiments compare web link models to Wikipedia models on well-known conll and tac data sets. Results show that using 34 million web links approaches Wikipedia performance. Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.",
}
@article{narasimhan-etal-2015-unsupervised,
    title = "An Unsupervised Method for Uncovering Morphological Chains",
    author = "Narasimhan, Karthik  and
      Barzilay, Regina  and
      Jaakkola, Tommi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1012",
    doi = "10.1162/tacl_a_00130",
    pages = "157--167",
    abstract = "Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations. We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word. The limited set of candidate parents for each word render contrastive estimation feasible. Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.",
}
@article{sennrich-2015-modelling,
    title = "Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation",
    author = "Sennrich, Rico",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1013",
    doi = "10.1162/tacl_a_00131",
    pages = "169--182",
    abstract = "The role of language models in SMT is to promote fluent translation output, but traditional n-gram language models are unable to capture fluency phenomena between distant words, such as some morphological agreement phenomena, subcategorisation, and syntactic collocations with string-level gaps. Syntactic language models have the potential to fill this modelling gap. We propose a language model for dependency structures that is relational rather than configurational and thus particularly suited for languages with a (relatively) free word order. It is trainable with Neural Networks, and not only improves over standard n-gram language models, but also outperforms related syntactic language models. We empirically demonstrate its effectiveness in terms of perplexity and as a feature function in string-to-tree SMT from English to German and Russian. We also show that using a syntactic evaluation metric to tune the log-linear parameters of an SMT system further increases translation quality when coupled with a syntactic language model.",
}
@article{lazaridou-etal-2015-visual,
    title = "From Visual Attributes to Adjectives through Decompositional Distributional Semantics",
    author = "Lazaridou, Angeliki  and
      Dinu, Georgiana  and
      Liska, Adam  and
      Baroni, Marco",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1014",
    doi = "10.1162/tacl_a_00132",
    pages = "183--196",
    abstract = "As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown{\ldots}) attracting most attention. By building on the recent {``}zero-shot learning{''} approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as {``}visual phrases{''}, and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it out-performs various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.",
}
@article{fried-etal-2015-higher,
    title = "Higher-order Lexical Semantic Models for Non-factoid Answer Reranking",
    author = "Fried, Daniel  and
      Jansen, Peter  and
      Hahn-Powell, Gustave  and
      Surdeanu, Mihai  and
      Clark, Peter",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1015",
    doi = "10.1162/tacl_a_00133",
    pages = "197--210",
    abstract = "Lexical semantic models provide robust performance for question answering, but, in general, can only capitalize on direct evidence seen during training. For example, monolingual alignment models acquire term alignment probabilities from semi-structured data such as question-answer pairs; neural network language models learn term embeddings from unstructured text. All this knowledge is then used to estimate the semantic similarity between question and answer candidates. We introduce a higher-order formalism that allows all these lexical semantic models to chain direct evidence to construct indirect associations between question and answer texts, by casting the task as the traversal of graphs that encode direct term associations. Using a corpus of 10,000 questions from Yahoo! Answers, we experimentally demonstrate that higher-order methods are broadly applicable to alignment and language models, across both word and syntactic representations. We show that an important criterion for success is controlling for the semantic drift that accumulates during graph traversal. All in all, the proposed higher-order approach improves five out of the six lexical semantic models investigated, with relative gains of up to +13{\%} over their first-order variants.",
}
@article{levy-etal-2015-improving,
    title = "Improving Distributional Similarity with Lessons Learned from Word Embeddings",
    author = "Levy, Omer  and
      Goldberg, Yoav  and
      Dagan, Ido",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1016",
    doi = "10.1162/tacl_a_00134",
    pages = "211--225",
    abstract = "Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.",
}
@article{yu-dredze-2015-learning,
    title = "Learning Composition Models for Phrase Embeddings",
    author = "Yu, Mo  and
      Dredze, Mark",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1017",
    doi = "10.1162/tacl_a_00135",
    pages = "227--242",
    abstract = "Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets. We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use.",
}
@article{althobaiti-etal-2015-combining,
    title = "Combining Minimally-supervised Methods for {A}rabic Named Entity Recognition",
    author = "Althobaiti, Maha  and
      Kruschwitz, Udo  and
      Poesio, Massimo",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1018",
    doi = "10.1162/tacl_a_00136",
    pages = "243--255",
    abstract = "Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers.",
}
@article{krishnamurthy-mitchell-2015-learning,
    title = "Learning a Compositional Semantics for {F}reebase with an Open Predicate Vocabulary",
    author = "Krishnamurthy, Jayant  and
      Mitchell, Tom M.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1019",
    doi = "10.1162/tacl_a_00137",
    pages = "257--270",
    abstract = "We present an approach to learning a model-theoretic semantics for natural language tied to Freebase. Crucially, our approach uses an open predicate vocabulary, enabling it to produce denotations for phrases such as {``}Republican front-runner from Texas{''} whose semantics cannot be represented using the Freebase schema. Our approach directly converts a sentence{'}s syntactic CCG parse into a logical form containing predicates derived from the words in the sentence, assigning each word a consistent semantics across sentences. This logical form is evaluated against a learned probabilistic database that defines a distribution over denotations for each textual predicate. A training phase produces this probabilistic database using a corpus of entity-linked text and probabilistic matrix factorization with a novel ranking objective function. We evaluate our approach on a compositional question answering task where it outperforms several competitive baselines. We also compare our approach against manually annotated Freebase queries, finding that our open predicate vocabulary enables us to answer many questions that Freebase cannot.",
}
@article{yang-etal-2015-domain,
    title = "Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks",
    author = "Yang, Haitong  and
      Zhuang, Tao  and
      Zong, Chengqing",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1020",
    doi = "10.1162/tacl_a_00138",
    pages = "271--282",
    abstract = "In current systems for syntactic and semantic dependency parsing, people usually define a very high-dimensional feature space to achieve good performance. But these systems often suffer severe performance drops on out-of-domain test data due to the diversity of features of different domains. This paper focuses on how to relieve this domain adaptation problem with the help of unlabeled target domain data. We propose a deep learning method to adapt both syntactic and semantic parsers. With additional unlabeled target domain data, our method can learn a latent feature representation (LFR) that is beneficial to both domains. Experiments on English data in the CoNLL 2009 shared task show that our method largely reduced the performance drop on out-of-domain test data. Moreover, we get a Macro F1 score that is 2.32 points higher than the best system in the CoNLL 2009 shared task in out-of-domain tests.",
}
@article{xu-etal-2015-problems,
    title = "Problems in Current Text Simplification Research: New Data Can Help",
    author = "Xu, Wei  and
      Callison-Burch, Chris  and
      Napoles, Courtney",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1021",
    doi = "10.1162/tacl_a_00139",
    pages = "283--297",
    abstract = "Simple Wikipedia has dominated simplification research in the past 5 years. In this opinion paper, we argue that focusing on Wikipedia limits simplification research. We back up our arguments with corpus analysis and by highlighting statements that other researchers have made in the simplification literature. We introduce a new simplification dataset that is a significant improvement over Simple Wikipedia, and present a novel quantitative-comparative approach to study the quality of simplification data resources.",
}
@article{nguyen-etal-2015-improving,
    title = "Improving Topic Models with Latent Feature Word Representations",
    author = "Nguyen, Dat Quoc  and
      Billingsley, Richard  and
      Du, Lan  and
      Johnson, Mark",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1022",
    doi = "10.1162/tacl_a_00140",
    pages = "299--313",
    abstract = "Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.",
}
@article{ling-etal-2015-design,
    title = "Design Challenges for Entity Linking",
    author = "Ling, Xiao  and
      Singh, Sameer  and
      Weld, Daniel S.",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1023",
    doi = "10.1162/tacl_a_00141",
    pages = "315--328",
    abstract = "Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.",
}
@article{ji-eisenstein-2015-one,
    title = "One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations",
    author = "Ji, Yangfeng  and
      Eisenstein, Jacob",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1024",
    doi = "10.1162/tacl_a_00142",
    pages = "329--344",
    abstract = "Discourse relations bind smaller linguistic units into coherent texts. Automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lowerlevel components, such as entity mentions. Our solution computes distributed meaning representations for each discourse argument by composition up the syntactic parse tree. We also perform a downward compositional pass to capture the meaning of coreferent entity mentions. Implicit discourse relations are then predicted from these two representations, obtaining substantial improvements on the Penn Discourse Treebank.",
}
@article{wieting-etal-2015-paraphrase,
    title = "From Paraphrase Database to Compositional Paraphrase Model and Back",
    author = "Wieting, John  and
      Bansal, Mohit  and
      Gimpel, Kevin  and
      Livescu, Karen",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1025",
    doi = "10.1162/tacl_a_00143",
    pages = "345--358",
    abstract = "The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates. However, it is still unclear how it can best be used, due to the heuristic nature of the confidences and its necessarily incomplete coverage. We propose models to leverage the phrase pairs from the PPDB to build parametric paraphrase models that score paraphrase pairs more accurately than the PPDB{'}s internal scores while simultaneously improving its coverage. They allow for learning phrase embeddings as well as improved word embeddings. Moreover, we introduce two new, manually annotated datasets to evaluate short-phrase paraphrasing models. Using our paraphrase model trained using PPDB, we achieve state-of-the-art results on standard word and bigram similarity tasks and beat strong baselines on our new short phrase paraphrase tasks.",
}
@article{seeker-cetinoglu-2015-graph,
    title = "A Graph-based Lattice Dependency Parser for Joint Morphological Segmentation and Syntactic Analysis",
    author = {Seeker, Wolfgang  and
      {\c{C}}etino{\u{g}}lu, {\"O}zlem},
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1026",
    doi = "10.1162/tacl_a_00144",
    pages = "359--373",
    abstract = "Space-delimited words in Turkish and Hebrew text can be further segmented into meaningful units, but syntactic and semantic context is necessary to predict segmentation. At the same time, predicting correct syntactic structures relies on correct segmentation. We present a graph-based lattice dependency parser that operates on morphological lattices to represent different segmentations and morphological analyses for a given input sentence. The lattice parser predicts a dependency tree over a path in the lattice and thus solves the joint task of segmentation, morphological analysis, and syntactic parsing. We conduct experiments on the Turkish and the Hebrew treebank and show that the joint model outperforms three state-of-the-art pipeline systems on both data sets. Our work corroborates findings from constituency lattice parsing for Hebrew and presents the first results for full lattice parsing on Turkish.",
}
@article{kruszewski-etal-2015-deriving,
    title = "Deriving {B}oolean structures from distributional vectors",
    author = "Kruszewski, German  and
      Paperno, Denis  and
      Baroni, Marco",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1027",
    doi = "10.1162/tacl_a_00145",
    pages = "375--388",
    abstract = "Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the-art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.",
}
@article{lee-etal-2015-unsupervised,
    title = "Unsupervised Lexicon Discovery from Acoustic Input",
    author = "Lee, Chia-ying  and
      O{'}Donnell, Timothy J.  and
      Glass, James",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1028",
    doi = "10.1162/tacl_a_00146",
    pages = "389--403",
    abstract = "We present a model of unsupervised phonological lexicon discovery{---}the problem of simultaneously learning phoneme-like and word-like units from acoustic input. Our model builds on earlier models of unsupervised phone-like unit discovery from acoustic data (Lee and Glass, 2012), and unsupervised symbolic lexicon discovery using the Adaptor Grammar framework (Johnson et al., 2006), integrating these earlier approaches using a probabilistic model of phonological variation. We show that the model is competitive with state-of-the-art spoken term discovery systems, and present analyses exploring the model{'}s behavior and the kinds of linguistic structures it learns.",
}
@article{martschat-strube-2015-latent,
    title = "Latent Structures for Coreference Resolution",
    author = "Martschat, Sebastian  and
      Strube, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1029",
    doi = "10.1162/tacl_a_00147",
    pages = "405--418",
    abstract = "Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecedent trees. We propose a unified representation of different approaches to coreference resolution in terms of the structure they operate on. We represent several coreference resolution approaches proposed in the literature in our framework and evaluate their performance. Finally, we conduct a systematic analysis of the output of these approaches, highlighting differences and similarities.",
}
@article{rabinovich-wintner-2015-unsupervised,
    title = "Unsupervised Identification of Translationese",
    author = "Rabinovich, Ella  and
      Wintner, Shuly",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1030",
    doi = "10.1162/tacl_a_00148",
    pages = "419--432",
    abstract = "Translated texts are distinctively different from original ones, to the extent that supervised text classification methods can distinguish between them with high accuracy. These differences were proven useful for statistical machine translation. However, it has been suggested that the accuracy of translation detection deteriorates when the classifier is evaluated outside the domain it was trained on. We show that this is indeed the case, in a variety of evaluation scenarios. We then show that unsupervised classification is highly accurate on this task. We suggest a method for determining the correct labels of the clustering outcomes, and then use the labels for voting, improving the accuracy even further. Moreover, we suggest a simple method for clustering in the challenging case of mixed-domain datasets, in spite of the dominance of domain-related features over translation-related ones. The result is an effective, fully-unsupervised method for distinguishing between original and translated texts that can be applied to new domains with reasonable accuracy.",
}
@article{cotterell-etal-2015-modeling,
    title = "Modeling Word Forms Using Latent Underlying Morphs and Phonology",
    author = "Cotterell, Ryan  and
      Peng, Nanyun  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1031",
    doi = "10.1162/tacl_a_00149",
    pages = "433--447",
    abstract = "The observed pronunciations or spellings of words are often explained as arising from the {``}underlying forms{''} of their morphemes. These forms are latent strings that linguists try to reconstruct by hand. We propose to reconstruct them automatically at scale, enabling generalization to new words. Given some surface word types of a concatenative language along with the abstract morpheme sequences that they express, we show how to recover consistent underlying forms for these morphemes, together with the (stochastic) phonology that maps each concatenation of underlying forms to a surface form. Our technique involves loopy belief propagation in a natural directed graphical model whose variables are unknown strings and whose conditional distributions are encoded as finite-state machines with trainable weights. We define training and evaluation paradigms for the task of surface word prediction, and report results on subsets of 7 languages.",
}
@article{roth-lapata-2015-context,
    title = "Context-aware Frame-Semantic Role Labeling",
    author = "Roth, Michael  and
      Lapata, Mirella",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1032",
    doi = "10.1162/tacl_a_00150",
    pages = "449--460",
    abstract = "Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.",
}
@article{beck-etal-2015-learning,
    title = "Learning Structural Kernels for Natural Language Processing",
    author = "Beck, Daniel  and
      Cohn, Trevor  and
      Hardmeier, Christian  and
      Specia, Lucia",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1033",
    doi = "10.1162/tacl_a_00151",
    pages = "461--473",
    abstract = "Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.",
}
@article{reisinger-etal-2015-semantic,
    title = "Semantic Proto-Roles",
    author = "Reisinger, Drew  and
      Rudinger, Rachel  and
      Ferraro, Francis  and
      Harman, Craig  and
      Rawlins, Kyle  and
      Van Durme, Benjamin",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1034",
    doi = "10.1162/tacl_a_00152",
    pages = "475--488",
    abstract = "We present the first large-scale, corpus based verification of Dowty{'}s seminal theory of proto-roles. Our results demonstrate both the need for and the feasibility of a property-based annotation scheme of semantic relationships, as opposed to the currently dominant notion of categorical roles.",
}
@article{gormley-etal-2015-approximation,
    title = "Approximation-Aware Dependency Parsing by Belief Propagation",
    author = "Gormley, Matthew R.  and
      Dredze, Mark  and
      Eisner, Jason",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1035",
    doi = "10.1162/tacl_a_00153",
    pages = "489--501",
    abstract = "We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O(n3) runtime. It outputs the parse with maximum expected recall{---}but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this gradient by back-propagation. That is, we treat the entire parser (approximations and all) as a differentiable circuit, as others have done for loopy CRFs (Domke, 2010; Stoyanov et al., 2011; Domke, 2011; Stoyanov and Eisner, 2012). The resulting parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood.",
}
@article{lazic-etal-2015-plato,
    title = "{P}lato: A Selective Context Model for Entity Resolution",
    author = "Lazic, Nevena  and
      Subramanya, Amarnag  and
      Ringgaard, Michael  and
      Pereira, Fernando",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1036",
    doi = "10.1162/tacl_a_00154",
    pages = "503--515",
    abstract = "We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features, and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 107 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets.",
}
@article{yang-etal-2015-hierarchical,
    title = "A Hierarchical Distance-dependent {B}ayesian Model for Event Coreference Resolution",
    author = "Yang, Bishan  and
      Cardie, Claire  and
      Frazier, Peter",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1037",
    doi = "10.1162/tacl_a_00155",
    pages = "517--528",
    abstract = "We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions {---} information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametric clustering. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution.",
}
@article{delli-bovi-etal-2015-large,
    title = "Large-Scale Information Extraction from Textual Definitions through Deep Syntactic and Semantic Analysis",
    author = "Delli Bovi, Claudio  and
      Telesca, Luca  and
      Navigli, Roberto",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1038",
    doi = "10.1162/tacl_a_00156",
    pages = "529--543",
    abstract = "We present DefIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DefIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations.",
}
@article{berant-liang-2015-imitation,
    title = "Imitation Learning of Agenda-based Semantic Parsers",
    author = "Berant, Jonathan  and
      Liang, Percy",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1039",
    doi = "10.1162/tacl_a_00157",
    pages = "545--558",
    abstract = "Semantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning and agenda-based parsing to train a semantic parser that searches partial logical forms in a more strategic order. Empirically, our parser reduces the number of constructed partial logical forms by an order of magnitude, and obtains a 6x-9x speedup over fixed-order parsing, while maintaining comparable accuracy.",
}
@article{kuhlmann-jonsson-2015-parsing,
    title = "Parsing to Noncrossing Dependency Graphs",
    author = "Kuhlmann, Marco  and
      Jonsson, Peter",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1040",
    doi = "10.1162/tacl_a_00158",
    pages = "559--570",
    abstract = "We study the generalization of maximum spanning tree dependency parsing to maximum acyclic subgraphs. Because the underlying optimization problem is intractable even under an arc-factored model, we consider the restriction to noncrossing dependency graphs. Our main contribution is a cubic-time exact inference algorithm for this class. We extend this algorithm into a practical parser and evaluate its performance on four linguistic data sets used in semantic dependency parsing. We also explore a generalization of our parsing framework to dependency graphs with pagenumber at most k and show that the resulting optimization problem is NP-hard for k {\mbox{$\geq$}} 2.",
}
@article{arthur-etal-2015-semantic,
    title = "Semantic Parsing of Ambiguous Input through Paraphrasing and Verification",
    author = "Arthur, Philip  and
      Neubig, Graham  and
      Sakti, Sakriani  and
      Toda, Tomoki  and
      Nakamura, Satoshi",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1041",
    doi = "10.1162/tacl_a_00159",
    pages = "571--584",
    abstract = "We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we construct a grammar that takes an ambiguous input string and jointly maps it into both a meaning representation and a natural language paraphrase that is less ambiguous than the original input. This paraphrase can be used to disambiguate the meaning representation via verification using a language model that calculates the probability of each paraphrase.",
}
@article{koncel-kedziorski-etal-2015-parsing,
    title = "Parsing Algebraic Word Problems into Equations",
    author = "Koncel-Kedziorski, Rik  and
      Hajishirzi, Hannaneh  and
      Sabharwal, Ashish  and
      Etzioni, Oren  and
      Ang, Siena Dumas",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    url = "https://www.aclweb.org/anthology/Q15-1042",
    doi = "10.1162/tacl_a_00160",
    pages = "585--597",
    abstract = "This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges. We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15{\%} to 50{\%} reduction in error.",
}
@proceedings{acl-2015-association-linguistics-international,
    title = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts",
    editor = "Agirre, Eneko  and
      Duh, Kevin",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P15-5000",
    doi = "10.3115/v1/P15-5",
}
@inproceedings{han-etal-2015-successful,
    title = "Successful Data Mining Methods for {NLP}",
    author = "Han, Jiawei  and
      Ji, Heng  and
      Sun, Yizhou",
    booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts",
    month = jul,
    year = "2015",
    address = "Beijing, China",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P15-5001",
    doi = "10.3115/v1/P15-5001",
    pages = "1--4",
}
@inproceedings{gormley-eisner-2015-structured,
    title = "Structured Belief Propagation for {